[
  {
    "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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Lecture-Slides/Course-1-Week-1-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Optional-Labs/Course-1-Week-1-Optional-Labs-1-Python-and-Jupyter-Notebook.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Optional-Labs/Course-1-Week-1-Optional-Labs-3-Model-Representation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"source\": [\n    \"### Plotting the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Optional-Labs/Course-1-Week-1-Optional-Labs-4-Cost-function.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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\": 1,\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_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   \"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\": 2,\n   \"metadata\": {},\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   \"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   \"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\": 3,\n   \"metadata\": {},\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   \"source\": [\n    \"## Cost Function Intuition\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f2385414941e465893543c12c9eb6aad\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"interactive(children=(IntSlider(value=150, description='w', max=400, step=10), Output()), _dom_classes=('widge…\"\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   \"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   \"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   \"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\": 5,\n   \"metadata\": {},\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   \"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\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3220529fafc8489681534a5e969f41b7\",\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    \"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   \"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   \"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\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ccc5618f20524df5bf9dc8737b0e8111\",\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    \"soup_bowl()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Optional-Labs/Course-1-Week-1-Optional-Labs-5-Gradient-Descent.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"source\": [\n    \"<br/>\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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    \"plt_gradients(x_train,y_train, compute_cost, compute_gradient)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"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[: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   \"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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x432 with 1 Axes>\"\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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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DAAObPn09gYCD379/nq6++0qcS5MbQ0JDhw4czefJkQkJCSExM5Pjx4/oxV6xYwbVr14iMjOSzzz7Tj7lt2zbu37+PoaEhtra2+tQZJycnAgMDM7zByE2DBg24evVqhmOenp4sW7ZMv1qa+T6o0oAjR47U38/re3z58mUaNGgAqI/sc/ufe9++fTl48CCrV6/O8D3M6TVn5/vvv8fPz4/g4GAWLFhAv379sr1227Zt9SvVDg4OeHp6snLlSjw8PDAzM8t27II+x8HBgYSEhCKVY/zpp5/4999/iY6O5vPPP9e/nqioKMqWLYuNjQ03b97MsAGvqNcdMGAAs2bNIjIykqtXr7J8+fJ8/U5XqVKF2rVrM2/ePBITE1m/fn2ufzvx8fEkJCTg6OiIpmnMnTtX/2mEsbExPXv25NNPPyU+Pp79+/frU4YyGzJkCJs2beLo0aPodDqioqKyTcXJTl5/33379mXBggU8ePCAmzdv8v333+fruocPH+bixYtomqb/nTUyMqJ79+5cvHgRb29vkpKSCA4O5ty5cxgaGjJs2DDeeustwsPD0el0XLx4MV/VdYrj90yIopCAWTyzPvroIz766COmTJmCnZ0ddevW5fTp07Rt2xZTU1N27tzJqlWrKFeuHN7e3uzcuTPDx/I56du3L+Hh4djZ2dG/f/88x/r6669Zv3499vb2nDp1Sp8HmF81atQgOjoaBwcH5s2bx5YtW7INbBYvXkxkZCQuLi4MHTqUpUuXUq9ePUDtTj9x4gS2trbZlmAbM2YMXbp0oUmTJnh4eNCtWzdGjx6dr/ktWLAAFxcX6tevT/ny5fVBT9euXXnrrbfo2LEjNWrUwM3NjU8++QRQed9NmjTB0tKSWbNm6SsQ1K1bl27dulGxYsUcV1UhbTWqc+fOWXJe27ZtS1RUlH5F2dPTM8N9AH9/f5577jn9/dy+x4GBgYSFhelzjv39/XNcvQWwt7fH09OTU6dO0atXL/3xnF5zdoYMGcJLL71ErVq1aN68ub7ud+Zru7i44OjoqF+FrFu3Lqamprmuphf0ORYWFrz77rs0bNgQW1vbQlUvePnll5kwYQKurq5YWFjo6xBPnjyZO3fuYGtry+DBgzPUJC7qdWfMmEGlSpWoUaMGL7zwAm+//XaGihS52bBhA9u3b8fe3p4DBw7Qpk2bHN+AWFtbM2/ePDp27IiLiwsJCQkZ0h8WL17M9evXcXBw4JtvvtG/ic2sWrVq/PDDD0ybNg17e3vq1KnDjh078jXfvP6+33jjDZo1a0bNmjXp2bMnQ4cOzdd1AwIC6NmzJ1ZWVjz//PPMmjWL6tWrY2Njw2+//cbq1aspV64czZo14/LlywB88803WFtb66u8DB8+PMdKJukVx++ZEEVhoGmZ6goJIZ4Yhw8fZty4cVy6dKm0p/JYiI6Oxtramri4OP0bEnd3d3bu3En16tXzPY6npyd79+7FwsIiz3MXLVqEn5+fvhbz3LlzqVSpEkOGDCnciyiC0rx2YY0cOVJfGvFJVa1aNdasWUO7du1KeypCiBKSdcu9EEI8obZs2YK7u3uGTwI+/vhj5s+fr9+QlB/5bV6TnJzMypUr2bt3r/7YtGnT8j/hYlaa136W/Pnnn7i6ulKhQgWWLVtGbGxsiXe5FEKULgmYhRBPha5du+Ln58fKlSszHB80aBCDBg0qkWsaGRlJ2atn0J07d+jTpw+RkZHUrl2bbdu2SdtmIZ5ykpIhhBBCCCFELmTTnxBCCCGEELl4rFMy8tMIQAghhBBCiOKUuba+rDALIYQQQgiRCwmYhRBCCCGEyMVjnZKRXn7bzoon3EtANLAecC3luQDEAueAUym3zE34ygINgGZAKx6POQshhBCiwHJLBX5iAmbxjLBCBczJpT2RFGWA51JuAOHAP6jg+T/AH/gz5bYYqAS0AFoCDQGTRzxfIYQQQhQ7CZjF48UVuAfcBSqX8lyyYwt0SLkBBAOnUQGzL3An5bYVFWw3S7k1B3Lu5CyEEEKIx5gEzOLxUgX4GxWEti7dqeSLI9Ap5ZYMnAdOptxuAsdSbgA1gTZAW6AqYPCoJyuEEEKIwnisG5ekzyWRHOZnxGVgHGAJbAGe5OZZ91Grzn+n3GLTPeYCeKbc6gFGj3x2QgghhEgnt7hTAmbx+BkPXATeAnqW8lyKSwIq7/l4yi083WN2qLzndkBTwPSRz04IIYR45knALJ4sB4HPUPnC3in/Pk1SUzd8Um730j1mgUrb8ELlPsumQSGEEOKRkIBZPFk04G1UHrMn8H88vfm+GirX+QRwGLie7jEL1OtvjwTPQgghRAmTgFk8ee4Do4AYYBLQt3Sn88jcAY4Ah4Ab6Y5boOo8d0QFz7JdVwghhChWEjCLJ1NqaoYh8CVptZCfFbdRwfNhMgbP1qiUjReA+ki/TiGEEKIYSMAsnlyrUV3/LFCNQdxKdzqlxh+16nwAuJXueHnS6kJX5+lNXRFCCCFKmATM4smlAz4FjqIafywG7Et1RqVLQ602H0CtwAele6wiatX5RVTZOiGEEELkmwTM4skWC0wFLgG1gG9QXfSedTrgHPA7qjlKWLrHGqCaqXihaloLIYQQIlcSMIsnXxgwEQhAtZn+HNn4ll4yqqrIPlTwHJdy3ARVpq4j6vsmNZ6FEEKIbEnALJ4Od1FBcwQq7eB9ZMNbdmJRKSx7gX9RaRygVprbA11R3QUl31kIIYTQk4BZPD0uotIz4oD+qK6AEvjlLBCV6/w7GWs8V0YFzp14tnPChRBCiBQSMIuny9/AB0ASMAx4FQma8+MmatV5H2n5zoaottwvAq0Bs9KZmhBCCFHaJGAWT59DwCzUxrfuwFuAUanO6MmRBPgCe4A/UPnPoEr3paZs1EfehAghhHimSMAsnk4+qKA5HrVKOgOpnlFQYaiUjf3A5XTHq6DeiLwIyJ+eEEKIZ0BucWeJbZkKCAjAw8MDc3NzkpKSSExMpFWrVlhaWnLt2jX9ebVr18bLywsvLy8uXLhQUtMRTyNPYAEqoPsTtcocluszRGZ2QD9gGbAWGJJyzA9V83oAMBM4SdpKtBBCCPGMKbGA2d7ent9//52WLVsCYGxszI4dO+jfv3+G8xwdHTl8+DCHDx+mXr16JTUd8bSqByxCNeq4DExClZ571HS6UrhoMasMjAE2o5rFNEelbxxB5YwPRAXWt0trgkIIIUTpKLGA2dzcHDs7O/19AwMDypcvn+W80NBQ2rVrx9ixY4mLi8vyuBB5qggsBGqiWkhPBK4WcczoaNi4Ef74A/LKWtq/H2xsoFo1WLUKkpIyPn7hAtStC61bw3//FXFij4Ax0A6YA2wCRgGVgNCU+yNQq/kHgYRSmqMQQgjxCJV6FVsfHx+OHj2Km5sbK1asKO3piCeVPfA14IFKy5gCnCrCeKNGwdChKsh1d4fPP4fQ0OzPXblSBdg3b8Lo0eDqCt98k/b41q1w6ZIKvlu3hp9+SnssKgqmT4d58yDhMYw+HYGhqHSNRai8ZnNUfefPUCkbi1DtuoUQQoinVKkHzPb2qghsnz59OHfuXCnPRjzRLIAvgQ7AQ1Rjk1FzoGZN6NNHBa45fYqhabB9O7z+OvTsCfv2pT124QJ89BE4OsKff2Z8XlgY7NqV8VhQELz1Fixdqu4PHgxWVurrhw+hXz+wtoaICBVsz54N06bBmDF5r2aXFgNU5Yx3gK3AZKAGEAlsA14H3gB2ATGlNEchhBCihJRqc+GEhAQ0TcPMzIzjx49TvXr10pyOeFIEBMDx41CvHtSvn/ExE2A64IDKxV0zC5Kj4do12LEDjI2hfXsVEBume794+jT07Zv7dXU6labRokXaMUPDnIPcgAC1arxgQdZAPSoKJkyAgQPTjq1dC7VrwwcfQGQkHDgAbduqQP1xYgH0TrldAX5FNUa5lHJbgmrF/RJQu5TmKIQQQhSjEisrl5iYSNeuXTl16hQeHh7Mnj2b+fPn4+PjQ9WqVZk2bRotW7aka9euWFpaYmdnx/r167FKXYlDysqJHLi7w/nz6uvq1aFNG5gyBZo0yXje0lswoSZoiVnHWL9epVykunxZBeC5bd5zdIRz58DJKePxzp0zrkibm0OvXrBhg8pZbto0+/FGjFCB8c6dWa/bt69K3ShXDv7+G6pUyXlej4M44BiwG5WukaoW0BO16i8l/4QQQjzGpA6zePxoGsTGQpkyYJBHh4zwcPD1hatX1W3FCvXczE6dAg+PtPvHjkG7dlnPMzRUAXKNGhmPnzunUi7s7WH3brWBD9T9L79U6RqG2WQxbdoEr7yicpenTlXnWViox2Ji1Crx6dNq41+bNhASAsOHq5SN9IF2DhJr1+beK6+QHBeHiYMDJo6O6pbytamjI4bm5nmO88jcRqVm7AWiUo6VRdV0fgmQD5KEEEI8hiRgFo+XpCTo1k1Vl0jNL+7ZE1q1yhqQXryoAs6QkLzH3bABXn457b6mwQsvwMGDacesGsK3G+BV99zHCg+HGTNU9Ys33wQHh9zP1+myD6ZTxceDWaa+0zNnwqef5j5uigfALVSsGUXWPXZGlpaYlC+PZaNGWDVvrm5Nm2JsbZ2v8UtEPHAYFTyfT3e8Hipwfh5pxS2EEOKxIQGzeDxomtoQd/o0dO2a9XE7O7h7F8qWTTu2bp1ajc1L585qVThz0Lpjh8oTNjODDvMgYgwYGKrSaMMp/W2vV66ofGYbG7C2RvfeexiuWZPn0+5XrcpdKysSg4NJfPAALTGbtBMDA8rWrYtV8+ZYpwTRFg0bYmhiUvyvIy83UIHzftI2BVoD3YBeQIVHPyUhhBAiPQmYxaOVnAx+fmp1+NKljP+G5dGK79df1epzqrg4mDxZpVBUr67SKM6fV6vJAJUqwZo10KFDzmMGBqpg3MQUtgDLAR3QFtWQ4zHJrY25eJFrL71Ew+vXySNJRb0xOHQIWrRA8/UluVo14sPDifr7b6J8fYny9SX6v/+yBNJGlpbYPv88dp07Y9+5M+bVq2OQV0pMcYpFrTrvQG0YBFWBoxVqE2FTSv9NjBBCiGeSBMyiaPbtgx9/VBUcevaEOnWyzzuOiVGl0bZtUykIBeXmBtevg5FR7ueFhal0BltbVb7N1rZg1/EF/g+10lkN1dWuYsGnW5ySIiI46eZGckQE9jVqUHvwYEwdHSExUVXm+OefrE8yMVEbCk+eVJsC//wzw4ZEXVwc0f/9R5SvL5G+vkT9+SexVzN2dDGrVAlbLy9svLyw9fLCvGrVRxNAa8BFVOB8GEiN612BHkAXoIA/ViGEEKIoJGAWRWNnp3J6U1lbqxSITZsyBs6//ZZ9qkV6lpZq81t0tFpxBlXq7dNP4b338g6Wi8tt4CPgDmqFeSrwwqO5dHYSw8I4YW8Phoa0CQvLmHt8/XrWDYrZ6d5d1YTOJeCNu3OHsP37Cdu7l7DffycpU264WeXKlOvenXK9e2Pr5YWhqWlhX1L+haFK0+0CglKOmQBeQD+kNJ0QQohHQgJmkT+aBv7+qlrE+fNp/546lX25tZUrVUe8VOHh0Ly5qmRRvrwKjFNvdeqof11dVUDn6wuDBoGzM3z3HTRr9uheZ6oYYB5wJOV+V2ASpZai8UfFiiT4+/PclSuUrVkz7QFNUyv3P/ygVu4zt95O7913VVm7339XDVHat8/xVE2nI+b8ecIPHSLi8GHCjxwhKV03QyNra+y7dcOhVy/sOnXCJKXJUIlJRq3+/wz8iVqFBtUwpS8qhaYU0q+FEEI8GyRgFlkFBaUFxam38+dV97n8On5ctXpOT9NUN7vUsmqPOw34BdXeOQFwQ6VouD36qZzt1o3QPXuo8d13uE6alPOJmqYqf7yQx5K4tbWqA53PGs6aTkf0P//wYOdOQnbuJObs2bQHDQ2xbtEC+65dKffSS1g0alSyqRsBwE7UynPqJkE7VLpGD8Aph+cJIYQQhSQB87MsPDxjYJz6dXBw4ce0t4fvv1fNOZ4WN1GB8i3AHHgL6PRopxC8dSsXBgzAuFw5ml+9iomdXc4nnz+vGrjkpXVrlSrz00+qhF/mNzi5iL1xg5CdOwnZtYsIH58MGwjL1KyJ48CBOA4ahIW7e8kFz7HAPibOZs4AACAASURBVFSus1/KMUPUJsE+gAfkvUNSCCGEyJsEzE+Tn35SH7c3aaKqSbi4ZH/enTsweDCcOFGw8W1sVCDm7q7aTru7q1JvqaXd+vVTqRi5BXNPqljga1TpM1Alz97kkdUK1jSN/zp0IOLwYVwmTqTmwoW5P+HECVXLOjFRlabbsQNu3856nqurSrUB2L4devcu8NySoqIIP3iQ0N27ebB9O4np3nCVrVsXh/79cejTB8vGjUsmeNaAM6h0jaNAalZKVVSec0fUGx0hhBCikCRgfloEB6sAOX0Oq4ODCmbnz8947qJFkNvH+mXLpgXE6f9NzTHO7MgRVZWhVau8O/M9yTRUe+dvUZUbqgEzgMqP5vLRZ89yqnFj0DQa7NmDfefO+X/yG2/AsmW5n2NvD2fOqJ9zIWlJSYQfOULw5s0Eb9uWYeOgWeXKOPTujdOQIVi1aFEywXMoKlVjJ5B6aStUDnpPVKUNIYQQooAkYH5SRUer9IkzZ9Tt33/VqmJ2P7LTp6Fx47T7t2+r2sR37qjNdukDY3d3VcItt850z7prqBSNu6iVy0mogOwRvFe4NWsWfh9/jImDA01Pn8asYj5r3v3zD7z2mmqGkpiY8+bAWrXg6FH47DNVM3v27EJ/YqBLTCT80CEebN9OyM6dJNy7p3+sTM2aOL3yCuVfeYUy1aoVavxcJZJW0/lCyjEDoAUqXaMZUtNZCCFEvknA/LhLToYbN9IC4zNn4OxZVU4sPwwNISBAVabILK+WzSJnMcACILWzdlvgbaCEfxU1nY6zXbsStm8f1m3a0OjgwcKVd3vwQH0ikV0XwDJlIDZWfZ2PcnT5nXfUX38RvHkzQRs3knD/vv4x69atcRw0CMf+/THLKY2oKC4D21E/q/Q1nfuhajo/Js1phBBCPL4kYH6chIamBcSpwfG5c6qyRGE4O8PmzeDpWbzzFIqGymn+FngIOADvozrSlaCE4GBONWlCgr8/FV59lVqrVxc8vSEpSW308/PL+9yFC2HiRBVcx8eretlFoCUnE/b77wSuW8eDn35Cl/r7bWCAjacnTkOG4DRkCMYFbTqTl3BUSs3PQGDKMUvgJdSqs2PxXk4IIcTTQwLm0pCQoNo5pw+Mz5xJ23yVH0ZGqn5xw4bq1qCBqok8Y4Z6fORIlav8pJRwe5LdA2YD51LuDwReB0qwr0fUqVP827YtuthYqs6ZQ+Vp0wo+SEyM2iQaG6uC4aNH1RuszOUDDQ1Vref161Ud5+3bi+1NWHJ0NCG//krwpk2E7N6NltIF0tDcHIe+fanw2mvYPv88BsX5SUgy4INqhX4+5ZgR8DzQH2mGIoQQIgsJmEuSpsG9e1kD44sXs/8oPCcVKqiAODU4bthQ5R6bZSrRoNPBzp0q/aIAJcJEMUgGNgBrAR1QHfiEEt0QGPzTT1zo1w+Lxo2pOGUKFUaMKPqgbm7ZV9NIz9VV1XAuV67o10snKSqKkJ07ub9mDeG//64/blapklp1fuUVLBs0KNZrcgEVOB9F/dwA3FErzu0A4+K9nBBCiCeTBMzFTdNg7Vr43/9UcJypvXCuzMzU5rv0gXGDBuAknRieGBeAz1HNNcyBiagSdCW0IfDe999zZdw4SEyk+tdfU3HKlKIN+OWXMHOmSr3ITZcu4O2tOjE2bgwDBhRrhZQ4Pz/ur13LfW9v4m/d0h+3aNAAp1deocLw4ZhWqFBs1+M+8BMqZSO1GYojasW5OyAf1AghxDNNAubidvasCnTz4uaWMTBu2BBq1ABjWdJ64j0EviGtZnNbYCpQzCm5qe7Mm8eNlJSMiu+8Q7U5c4qWwhAXp9I0EhJUucIWLbLPo3dwUJsHQb1BHDas8NfMgabTEXniBIHr1xO8eTNJYWEAGBgbU653b5xHj8auY0cMjIyK54IPUc1QfgLupByzQHUQ7It0ERRCiGeUBMzFzd9f5RZHR6v7VlZZ0ync3VUTEPF0248KnB+iguUpQPuSuVTgunVcfu01tKQknIYMoba3N4aZU3YKq04dlXOfG0tLVdqwYkW4dUuVpytmuoQEQn/7jfve3oTs2qUqyABmFStSfvhwyo8YQdniuq4O+BPYBPyXcswQlaYxipzrOWvA3pSvuxTPVIQQQpQ+CZhLgr8/XLigVoylpvGz7T4wFzidcr8DqkNgCfzKhu7bx4V+/UiOjsbGywv37duLp9LEqVNqA2lYmFp1PndO1fDOrHx5sLaGq1dh3DhYsqTEGtnE+/tz39ub+97exN24oT9u3bo1FUaOxHHQIIytrYvnYpeAzcCRlPsbgJyyQSKAE6ic6ABgAtAcFUg/xT19hBDiaScBsxAlTYfqPLcCiAPsgHeAEtiXGf3vv5zt1o2Ee/coW68eDfbswbxyMe88vHMH8jNmCaVppKfpdET4+BC4Zg1Bmzeji1EJyIYWFpQfNgzn0aOx8vAonosFo974dMrn+amB83uoYDkaVcZOCCHEEye3uLPElkUDAgLw8PDA3NycpKQkEhMTadWqFZaWlly7dk1/3oYNG2jdujU9evQgMjKypKYjRMkyRFVdWAU0BMKA6cB8ILZ4L2XZuDFNTp6kbL16PLxwgX/btCHm/Pm8n1gQLi6QnyB07FjVZfKvv1TnwIKUTcwnA0NDbNu1o/b339P6/n1qr12LTfv26GJiuLdsGf80bcqppk0JWLqUpMzl8grKkfwFywkp//qjNn7eQTVOeQv4v5TjQgghnholtsIcFxdHbGwsffr04cCBAxgZGREUFMR7773HRx99RI0aNUhMTKRDhw4cOnSIbdu2cfv2bd599139GLLCLJ5IOmArKnhOROXCfgjUK97LJIaFca5nTyJ9fDC2s8P9l1+wKc5SgzqdKo8YF6dKJH71FWzblvU8Y2NV/SUmpsTK0WUn5vx57q1YQeC6dfqNgoZlyuA4cCDOo0Zh3aZNwZu95CZzysU14DNUkPw/oAnQEfWztwWGF9+lhRBClLxSWWE2NzfHzs5Of9/AwIDymVo3X7lyhQYNGmBsbMwLL7zAyZMnS2o6Qjw6hqjGJkuBaqjVxknAciCPSm4FYWJnR8N9+yjXqxdJYWGc6diRoB9+KL4LGBqqEohNm0LLltCkSfbnJSWpYBnUCnPXGvBzNzgyCf5ZADd3Qch5SCrepXaL+vWp8e23tAoIoO7Gjdh26IAuNpbAtWv5t21b/q5XD/+FC0kqrk+uUoPlX4EZqDdEY1ANUR4AwwAX4AVUakchm3cKIYR4/JRqfbPw8HCsUzbt2NjYEJaySiTEU6E6KmhejVp1/BHVfe4doFHxXMKoTBnqb93K1fHjubdyJRdffpmwgwep8e23GJUtWzwXSTVliqoIc+mSWnG+cweOH0+rFpPqr3D4Yo9KS7kDDEatvgJYuIB1NXBoDK7twLktWBSt1rKhubm+1Xbs9evc//577nt78/DSJa69+SY3P/yQ8sOH4zJhAhb1CrnMrwGHUHnq1VDl56qgNnZOQqXjpLoNBAHF/O0XQghRekq1tIOtra0+bzkyMhLb4tjtL8TjxBR4A1gIuAF3UaXnviateUYRGRgbU3P5cmouW4aBmRn3V63in+bNiz+v2cIC3nxTVcZYuRJ++w2mjsv+3JPAZdQq63pDiK4MhsYQEwD3fODsIvhtIHg7w/o6cHAMXF4PUbdVY6BCKlO9OlU//5yWt29Tb9s2bLy8SI6OJmDJEv6uX5//OnbkwfbtaElJBRtYh/rZXUNt6qyICpbvAlFkDJg3AZ3TPU8IIcQTr1QD5lq1anHu3DmSk5M5cOAALVu2LM3pCFFy6qEqaIxAfYT/M/AacKp4hjcwMMBl7Fg8fH0pW6cOD8+f55/nniNw/friuUB2Yu5D5GL1enKToIONNtDzBtx9E5LfgxafQaUXwcQCwi/DhZWwfxisdQNvF9jdB86tgOiAQk3NwNgYx759aXzoEM3OnsV53DgMLSwIP3iQ8337crJKFW5+8glx2ZXOy44RKid5K1AJeBtVg/sBqs12Kl/UhsAOKfel2qQQQjwVSmzTX2JiIl27duXUqVN4eHgwe/Zs5s+fj4+PD1WrVmXatGn06tWLdevWsXTpUuzs7Ni4cWOGJGvZ9CeeSjdQdZtT+4T0AsYCZYpn+OSYGK5OmEDg2rUAuL75JtW++gpDE5PiuUCq6LuwphJEGULjH8HYFiIi4MMPVZ3mzGxs1OOgVqnfeAOSE+HBafA/AgFH1epzfHjG5zk2haovQdWeKpWjkBv5kiIiuL92LQGLFxN75Yo6aGSEY79+uE6ZgnXLlgXbJBiKSrv4ANUlsCwqv9kT1dBER1rAnID6tEEIIcRjS+owC/G4SQZ+ANYCSajNYu8CjYtneE3TuLdiBdcmTUJLTMTa05N6mzdj5uxcPBdItaYyRN+BIWehXMpS67BhkNfKtrm5apZSr15aCoaBgfo64poKoP12wZ39GTcLWlaEKj3UzdVLrVAXkKZpRBw5QsDy5TzYulWfnmH13HO4Tp6MY//+BeugeBLV9MQaFTg3y3xB1Bui8sDLQN0CT1kIIcQjIAGzEI+ra8AXqFVngG6o4KqYGthFnjzJ+f79SfD3x7RCBer+8AO2Xl7FMzjA3iFw9UdoNh1azlLHLl+Gjz8GPz+1OTAgAIKCsj7XwQHmzoUPPlDt5Ddvhsz7GJJi4e5BVWnDb5fKgU5lXFatOtcaApU7g1HB24TH372L/5Il3Fu+nKTQUABMHB2p8PrruIwdi3mVKvkfLJbsPyW4BYxGlRgEVad7ENASSdkQQojHiATMQjzOEoGNqHbMiagugeNRNX2LoYxwQmAgFwYPJuLwYTAwoNK0aVT5v//D0LQYcgQCjsFP7cDQBAadhnL1s54TEgJOTqquc2apq8oAgwfDxo05p1xoGgT/o4LnW7sh6K+0x0xtoHpfqDkEKj6vNhgWQPLDhwSuX0/A4sXEnDmjn5t9t25UfOstbDt0KFpN51BU/vPPpG32dAMGAC8i6RpCCPEYkIBZiCfBbWAB8F/K/WbAVKAYsii0pCT8Pv2U27Nng06HZePG1NmwofBl1tI7NA7OL4fyLaCfT9ZgVdPA0xNOnMh7rDVrVL3nn3+G4cNzb88d6QdXN6kV7gf/ph0v4wjV+6sA2vV5MMxrV2L6qWpEnjhBwNKlBG/ZgpagWvpZNGpExSlTcBo8GENz83yPl0UMKs95G6r0HKg3SAOAnkDBM0yEEEIUEwmYhXhSaMAeYBmqXJk5qjlGL4rl4/uI48e5NHw4cTduYGhuTvUFC3AeN65oq6fxEbCxPsT4Q5N3oM28rOdoGly4ALGxkJAAvr5qc2BsNs1MUjcHOjrCmTNQIR91mkMvpgTPP0D4lbTjFq5QZzjUfRVsaxboZSUEB3Nv+XL8Fy0iMTAQABMHByqMHo3LuHGY5xbM5yUJVdd5E3A95ZglqjxdP1TJOiGEEI+UBMxCPGnCgO+Awyn3GwLTUG22iygpKorrkydz39sbgHI9e1J79WpMHBwKP6j/UdjZEXRJ0GmjyivOTUCAaqOdly5d4NdfVdfB/NA0tdp8bStc2wQR19Mec26jAucaA8HUKn/jAbq4OIJ++AH/hQuJPn1aHTQ0xKF3b1wmTsTWy6vwbzg04C9UOk5KJgjmwEuobpFF+JEIIYQomNziTqOZM2fOfMTzybf4+LQ+wuZF+RhUiCdNGcAL1VXuP9TGsV9QOc11yLv2cS4Mzcxw6NWLsnXrErZ/PzFnzhC4fj2WjRtTplq1wg1q7QZm5VRu8a3dUKE1WFfN+XwrK7XpLyxM/WttrVaVM+c5X7umbn//Da+9pp7n4ZHzuAYGYOEMlTpCw0nqX4CIqyp4vvkz/PcthF4AQ1M1xzxSNgyMjbFs0gTnMWOw69SJ5IcPib14kYfnzxO4di0Ptm/HyNKSsnXrYmBUwB+MAepNUFegKSrX+RZwAdgBBJLWJEUIIUSJyi3ulBVmIR53EcBiVKMMgMrAWxRLCbq4W7e4NGwYEceOFX1DoKbBodFwYTUYGEHbb6HB+PzXTV60CCZNyv0cAwM4dAjaty/Y3BKi4doWuLRG1XtOVcYJ6r4G9UeDTf7fLMTfu8e95csJWLZMn65h6upKxcmTcR4zBuOi/PfqKmrF+ShqBdoAVdt5KFC78MMKIYTInaRkCPE0+Af4BkhtTvciqu22XdGG1ZKTufX559z69FPQ6bBo2JDaa9Zg1aRJwQfTJcPJD+Gfuep+vVHQflH+Sr6dPKkC4ZSNdjmqWBH+/Rd+/x3i42Ho0PynbABE3FD5zlc2Qui5lIMGULkTuI9TNZ7zWWVDFx9P4IYN3J0/n4cXLgBgZGlJhdGjqTh5MuZubvmfV2a3UfWd95FWkq4FMAzIphiJEEKIopGAWYinRQLwI7AeFURZoYLmLhS5BF36DYEGxsZU/vBDKk+fXrjV5ssb4eDrkBwHzp7QbQeUKZf384KC4Nw5Vb85OhpWrYLffst6np2dSucAmDIFvv664HPUNLh/Es4tU/nOySkfxZWtALWGqs2CDg3zN5ROR+jevdz96ivCDx5UB42McOjTh4qTJ2Pdpk3h85xDgC3ATiAu5VhjVBOUZhRL6UEhhBASMAvx9PFHrTb/nXK/CfA2Rd4UmBwTw80PP8T/u+8AVU6tztq1WDZqVPDBgk7B7t6qhbZNTXhpN9jWKNgYn38OH32U93m7d0PXruprTSt4++zYELj8Pzi3HMIvpx13aAwNJkCtl8GkbL6Gijp9mrvz5xO8aZO+i6B1q1ZUev99yvXogUFBVsPTi0DVct5OWi3nmsBgoD1FymsXQgghAbMQTycNOAAsAiIBM9TH9QMBk6INHX7kCJdfey1ttfnjj6n8wQcYmhRw4Gh/+KWHqlxh7gDdd4Jz6/w/398f3nxTlZdLTITwcLU5MDMLC1i4ED79FKpUUV0DnZwKNldQwXbgn3Dpf6q+c3zKKraZvcpzdh8H1lXyNVR8QAABS5cSsGSJvotg2Xr1qDRtGk5DhhS+cUw0sAu16pwyPVxQK86dKPLPXgghnlUSMAvxNAtHbQo8kHK/EjAZVXWhCJJjYrjx/vsELFoEgFWzZtRZt46ydeoUbKCEKNg7CG7tUbnMHb6H2i8XfmJ16qj227np3h127Sr4SnN6yfFqo+B/36XrKmgAlV6E+qNUW+585GYnR0dzb9Uq7s6fT/zduwCYOjvjMmECLmPHFr6cXwLwG6qWc2rH8PKoFeduSPdAIYQoICkrJ8TTzBxoBzQALqI2Be5DlSerT6G7xxmamlKuWzds2rcn/PBhHl68yP3VqzGytMTquefyn1pgZAY1B0HsAwg8CTd+guQEcG0PBoVIT/D1VSvOubl6FcqVU/Wely2D+vVVQ5SCMDRWOcz1R0PlriofO/yyaoxybYvKfX4YCNbVwDzn/GxDU1OsW7bEZeJEylSvTuy1a8TduEH4wYP4L1xI/J07lKldG5Ny+cjxTs8IVTWjN6pyyi1Uqs6fqEDaEFWWsGBdwoUQ4pklZeWEeFYkoD6qXwfEo+o5j0R1jytCjmtSZCTXJk8mcM0aAKyaN6fWypVYNszfpjhApTucWQQ+U0DTqUYiL25QNZwLQtPgjz8gJESlaVy8CF9+qTYJZmZoqGo7V6miKmsU9b8jcWFweT1cWAkhZ1MOGkDVl6Dhm1CxQ56r2pqmEXbgAP5ff03onj0pQxjgOGAAlaZNw6ppIT8a0AHHUD/71H4t0nZbCCHyTVIyhHjW3Eelafik3K+B2hRYwGyKzB7s2sXVN94gwd8fA2NjKr33Hm4ffYRhQT4BunsY9g+FmAAwtYHnV0DNgYWfVGSkWk1O2WCXo6FDYd06uHFDNUApTI5zKk1TaRrnlsHlDaBLKYVnWwvc34A6I8A873p/MRcvcnfBAgLXrkVLVLXjbDt2pNK772LXqVPhKmtowAlU4JyauZLadrs/YF3wIYUQ4lkgAbMQz6o/gG9RHeMMUB/fv4oqR1dISZGR3PzgAwKWLgVNo0ytWtRauRLbdu3yP0jsA1V27ubP6n7d11SjE1PLwk1q5Up1e/gQ4uLg1q3sA2gvLzh8GGxtYd8+eO65wl0vvYeBqrrG+RUQ46+OGZeBmkNU4xanvFeM4+/e5e7XX3NvxQqSU1bKLRo1otI77+A4aFDBN1tC9m23y6I+bZDAWQghspCAWYhnWSywFpWqoUO1WR6Dqt1cyApnoOo2Xxk9mocXLwLgPG4c1ebOxdgqn9G4pqkVWp+pKj/YthZ02QwOhShhl9nEibB4ce7nVK8Op0+r1ebioEuCm7vg3FK4sz/tePnm0HgqVO+XZ0OUpPBwApYtw//bb0m4fx8As0qVcJ0yBZcxYzCyLOQbirPA/0grQ1iWtBVn28INKYQQTxvZ9CfEs8wE1eCiLeCH6iB3ArX6WAMoZJEG88qVcR41CgMjIyJPnCDK15egDRso6+5OmerV8x7AwADKPwfV+sC9YxB2ES6uAQtXcCxEl8H0dDrYsSP3NI2wMLV5MCREteS2t4d69Qp/TQNDsK8LdYZBzZfB0ATCL0HEdbi+VeU+GxiBfX0wyr6EhaG5OTaenrhOmoR51arEXrlC3I0bhO3bR8CKFWhJSVg2aoShWT46J6ZXHlVyrikQjNogeBbVDCUCqIrkOAshnnmy6U8IoWjA78AyVAc5A6AH8Dpq5bmQYs6d49LIkUSfOgWA0yuvUP2rrzAtXz5/AyTFwtFJcGG1ul/3NWj3HZgUIYoLD1d1nBMTVVDcqxfExGQ9z8BArXYbG8OJE8WTppEq8SFcWgv/zleBM6iazg0nQIOJUDb3PGpNpyPk11+5PXs2USdPAmBsa4vrm2/iOnkyJvb2hZvXeVS3yJMp902AzsBQoELhhhRCiCedpGQIITKKQaVpbEOlaVij0jS6Uug0DS0piTtffYXfzJlo8fEY29pS9YsvcB4zJv8l6C54w5HxKkXDuhp0WKkqTxRVfDy4uqrAOTc1asBff8GePaqiRrduRb82gC4ZbuyA0/NUYxRIKbc3WAXO5Zvl+nRN0wj//XduffYZEUePqqdbWuI8ZgyuU6ZgXqlS4eZ1DRU4H0W9mTJCpeq8ggTOQohnTqkEzAEBAfTo0YMLFy4QHR2NsbEx8+bNY+fOnbi5ubFmzRpMTEyoXbs2zs7OACxZsoR66T4SlYBZiBJ2E1gInE65Xx/V9KRm4YeMvX6dqxMnEvbbb4BqC11z2bL8l6B7cAb2D4OQlJ1q9V6HNl+BWRGTbc+dgx9+UKvMCQmwfz9cu5b1PEdHCA5WX3/7reo0WFw0De75wD/zwO8XVJSKKrHX9ENw65pnWbrwo0e5PWsWYftVnrSBsTFOQ4ZQ6f33sShsSsltVOD8O+oNlDHqzdNQVDqHEEI8A0olYI6LiyM2NpY+ffpw4MABwsLCGDFiBLt372bOnDlUq1aNAQMG4OnpiY+PT7ZjSMAsxCOQmqaxBNVq2QDVKW4Uhd4QpmkaD7Zu5drkySTcuwdGRlScOpUqM2ZgZJGPNIvkRDg9F3z/T5VsK1sB2i+B6n0KN6HsfPklfPBB7ueYmqoV54YNVbBblM6BmUVch7NL4eJqiA9Xxxwagcf7UKN/nhsEo/75h7tffUXQ5s2QnKyv5ez28cdYuLsXbk7ZBc7dUG23JXAWQjzlcos7i7BHPnfm5ubY2aXVIfX19cXLywuAF154gZMp+XihoaG0a9eOsWPHEhcXV1LTEULkxAB4AVVFoT/qvwq/AsOA7UByIYZMCd6eu3gRl4kTQafj7rx5/O3uTujevXkPYGQCzabD4H+hQmt4eB/29IUDr6pW28Vh8GDw9ARLSzAzyz4YTkiATp1g7lyws4Nhw1ROdHGwqQ6eX8GI22oFvawzPPgP9g2B/1VTq9BxYTk+3crDg7obN9Li2jVcxo/HwMSE4M2b+btBA8716kXkyZM5PjdHlYEPAW/U74QO+Bm10jwP1UlQCCGeQSUWMGcWHh6OtbUq/GljY0NYmPofgY+PD0ePHsXNzY0VK1Y8qukIITKzBCYA36OqakQD36Fym/8r3JDGNjbUXLiQJn/+iWXjxsT5+XG2Sxcuvvwy8QEBeQ9gXxf6HYO234GROVxaAz82hvuFCAYzq1IFjh2DqChVuzkxUQXFmQUGwvvvQ0QErF8Pn35a9GunZ2oFTd6G4TfAa7kqrxd9B05MgzUV4fB4CLuc49PNq1Sh5uLFtLh+HddJkzA0Nyfk55853aoV/z7/PKH79lHgDxIrA9NJC5w1YDcwHBU43y/kaxVCiCfUIwuYbW1tiYyMBCAyMhJbW/VZr33KLu8+ffpw7ty5RzUdIUROKgNzgc9QG79uAFOAWcCDwg1p/dxzNPH1peqXX2Jobk7QDz/wV+3a3Jk3D11eK7YGhtBoEgz6BxwaQ+QN2OYJf34CScX4qZSREdSvn/1j6QPO2bPh0CH45ht47z1VjaM4GJuD+xgYehFe2g2VOkHSQ1XXeUNd+G0gBJ/O8elmFStS47vvaHHrFpU//BAjGxsiDh/mbOfO/NOsGcFbt6LpdAWbU2rgvBa1GRBU4DwM+BoIKswLFUKIJ0+JV8nw8vLiwIEDhIaG8uqrr/Lrr78yd+5cqlSpQu/evdE0DTMzM1atWkVISAjvvfee/rmSwyxEKYsHfgQ2AglAGdQqY18g+1LCeYq9eZPrU6cSsmMHAGXr1aPmkiXYtm+f95OT4+Hkx6raBKi0hnYL1Wa54pCYCAcPqnbbiYlqBXrFClXXOR3NwACDlP90nm7Ygd2r9tPO1ZDm5cHMqHimAkDIefjvW1WaLrX9dqVO0PR9cPXKNac6KSKCgGXLuPv11yQGBgJg0aABbjNn4tC7d/4rl6R3FxU8/45adU7dHPgyUlVDCPHEK5VNf4mJiXTt2pVTp07h4eHB7NmzOXz4MLt27aJy5cqsWbOGsLAwxThz5QAAIABJREFUunbtiqWlJXZ2dqxfvx6rdF23JGAW4jFxD7UpMHV/rgswDvBE5UAXQuhvv3Ft0iRiUypVOL3yCtXnzcO0Qj4ir4BjKlUhNOVTqWp9oO03YFW5cJPJybFjkI+W39N6z2Fep2lUjr5PvUqWNK9mSTsXaFkeLArR1TqLaH84PR8urIDElFrSTs+pwLlqLzDMOUpPjo3lvrc3d774gvi7dwGwbNwYt08/pdxLL2FQmI2MfsA64BAZy9G9jPrdEEKIJ5DUYRZCFA9fVOB8K+V+Y2AikI/GftnRxcVxe+5cbs+ejRYfj5GNDVU//xyXceMwMMpjqTY5Ec58B74zITEajMtCi8+g0eRcA8iCTVAHM2bA3r2QmEjogyis/W9irGVdcd7dajDdT/zAHduKdJhykGtONTE2hFbl4ZVaMKg62BSwQV8WcaFwdjH89x3EpeTH2NZSrbfrjFBpHTm9lPh47q1eze3PPychJX/csmlT3KZPp1yvXoVbcb6NCpwPojYIGqJWnIchVTWEEE8cCZiFEMUnGVU5YQ0QiQqSegGvAlY5Py03sdevc+3NNwndvRsAq2bNqLFoEdYtWuT95Oi74DMVrm1R9yu0ho7eYFercJPJhed2mPd2K1rdzH3T4e06zRj4yXH+ijBFl/Jf2DLG0LcqvFoHnncFw6JUqEt8CBe/h9NfQVTKu5eyztD0A6g/OvfAOe7/2TvzsKjKNoz/2AVZFNldWBQVd1zTFtts1dLcSi1Ny8z0UzMtTVvcxa3MrdwVLU1L08xKM83cV1QEBBERFARFZIeZ+f54BlCDgRkwMd/fdXHReeGc846QPvOc+7nvLOK/+YbYqVPJuSLTe3YBAdQaMwa3117DzNKwnV2RxAJrgN+RwtkK6IS4a5gYRqhQKBT/NobqTovPPvvss395P6XGUKa3QqG4R5gDAUikdhYQDpxFhsHskW6zkc1KK2dn3Hr1onKTJqTu20dGWBhXliwhMyoKxzZtsNQ77BSJtSPU6Q6uLSHuT7h+FkKXSMfZvZUMDZYTNezhp5BUngrfibmBXoNTUjxvVUvko8x9DN34GVl16rPPqianrsGqCFgZDtezwMcBqprSdbawAvfW0Pg9cG4gg5A3zsHFXyB0GZhbi6dzEV7OZpaWOLZpg9d772Ht4UH66dNkRUWR9OOPJH73HZbOzlRu2NC4jrMTIs95ErgBRCG/Ez8BGUgQTlm76wqFQnGXMVR3qg6zQqEoG+cR+7l86zl/RKZRymC/O9GkpXFx6lRiZ81Cl52NuZ0d3uPGUeP99zG3KaHqyroOfw2H8FVyXK0JPDYXqpdioLCU9PgN9p26SlenZGa0zMX68iV45RWxpisOZ2cu7j3JsrQarAiHmFuspJtWE8nG2wFlkGzodBD9k8hTkk7Imp2HyFMaDTKYkqjNySFxzRouTplSoCe3Cwig1rhxuPXsWbI0piiiEEu6v/XHlZFB0W5IDLtCoVBUQJQkQ6FQ3F10yADYIkCfKs2jiIdzDdMumRkdzflRo0jauBEA27p1qfPVVzg/80zJJ0dvhT1DCuUK/q9CuyBwqGnaZm4hPh36/QG/X4IuvrC+zQ0svTwMF8wATzwBmzah3bCRo64NmWvXmh/OQ0aefNneCt4KgP81Bl9Ti8qiCmcrB2j0jhTP9sX/MHR5eVxZtYqYCRPIjpE/N9u6dfEeN850qcZZxNf7iP7YDugK9ECeRigUCkUFQhXMCoXi3yETWI9Y0WUh7gkvI1Z0Jv4vfH3HDs4NGUJmuIR3uHTtSu3Zs6lUqwRHjLxMScs7OhU0WSLRaDEGAj8wqPEtDSeS4InNkJIDnbwh2HoPjj+ug8xM+di5E65e/eeJ1atDXJzYwf38M1kdnue3WPjyFPyhT9EzN4OuflI4P+xhYhq3TgcXf5N48Ut/6C9sDQ0Hyp+BffFWFtqcHBJWr+bi5MlkRUcDYFunDrU+/hi33r0xtzLB9uMUkiSZXzg7Aq8BnQGltlMoFBUEVTArFIp/lySks7gd6T47Av2Al5Ai2ki0OTlc+uILYiZMQJuejrmdHbXGjKHG++9jYWdn+OTUGNg3qnAosEpdSdSr8bjxG7mFA1fg2Z8hNQc87eDr9tDJR//F/v1h+XLDF3Bzg5AQcHeHzExOpNsy5yR8Gwm5ehMOP0fo5Q+v14W6xasqDJNwRArnyA2ADixsoMHb0Hy0wY67NjdXpBqTJxdINSr5+uLz+ee49eplmlTjFLAECNEfOyNWdJ0w2ddboVAoygtVMCsUintDFDAfyA+o80P0zYGmXS770iWiRo7k6vr1gKTb+UyejHufPiUPqV3aBbvfk6FAgID+8PAMqGS6jcP5VHh9J+zTR0X3qQtfPgzOJw/CW29BVJQEoOTlFX2BBg2k67xrlyQIjhpFXBrMPw0rI0T+kU9XPxjXApq5mLjZ5NMi1YgSiQvmVhDwpnScHX2KPU2Xl0fid98RM2lSQZffrmFDfCdNEjs6Y1vgOuAwsBSI0K+5AH0QSzpVOCsUinuEKpgVCsW9Q4cEniwA9IUljwLvANVNu+T1Xbs4P3IkacelErcPDKT2F19QpaSQEU02HJ0ORyZLcp6NM7T5XAbjinCUKA0aLcw9BWMPQpYGPOxg6ePwgvct33T5MtSsCRqN4Yv9+ivoNdoaLeyOh9URsPYc5Oi7zp28YURTeNzLRLlG8ml5/efWATp53fVeh+YfGbTi02k0JAQHc+HTTws0zg6tWuEzaRJVO3QotnDW6XTETJiAQ+vWVHv+lkRGHTIUuBwZHAXxbn4deBZJEVQoFIp/EVUwKxSKe082om9ei+ibLRF98+uYpG/WabUkrF5N9McfkxMnAmC3Xr3wmzEDG68S4uauh0lSYNwuOXZuAI98AbU6GL8RPedSoP8u2HtFwg8ntoaxzfVFbU4O+PqCPjCkWFxdYfduWLgQPDxg1CiwsiI+HWaegEWhkKlvVtd1gkENoW89cDZFB3w9DI5MgYg1oNMCZlCvN7T+HJz8ij1Nm53N5cWLiZk0idyEBOwaNKDlqVPFdvhzk5O5snIlF8aNo9bYsXiPG3fHBYG/kMI5PxCnBtAfaI/RFoUKhUJhKqpgVigUFYeriL75V6TLWBl5HP8KJj2O12RkEBsUROz06WizsrCwt8f700+pPmyY4QE1nQ6iN8PekeJjDODTCR6ZBVX8jd8IoNXBtGMw7pC8tO61YfkT+njsmzfhjz/ETSMnB5YsgT17/nkRGxvI9wL98EOYNq3gS4kZsOAMLD5bKNewsYAetWFkU2hqilwjJRKOTYewlaDNlY5zw4HQchxU9iz2NE16OnHz5mFXvz4uL79s8BbarCxOPPEEPhMm4NyhQ8Ga+a0+pxrgTyQQ55J+rS7itNLChNelUCgURqIKZoVCUfGIBL6m0DnBHSmOnkBatEaSGR1N1IgRJG/eDIBd/fr4zZiB84svGtbZarLh5JdweKJEbJtbQdPh0OoTsDbN++znGOi1QwYCGzvD/Efh0Tub3h9/LLplQ5iZwe+/Q9OmcOKEWNNZWJCnlXssOgO/xkpxDqJzHtscmruasOnUaDj4GYSvBnRgaSvBKIEfgF3Zcq4vTptG6r59NPrpJ3R5eVzfsYOEVavA3ByfiROx9fUt/OY8JARnFZCsX2sODAAalGkbCoVCYRBVMCsUiorLIaRwztexNkQGA+ubdrnkX34h6n//K3B2cHrsMfxmzMCxdWvDJ6ZfgQMfw9nlgA4qV4dH50DtbiaJhcOuw8u/QIT+r7HOvjCtDdSrqv+GxEQpmMPCZDAwLg4iIqTzfSs2NiLVuHQJnn4atm+HWxwqzqfC3BCRa2TrJdLP1IQxgdDeFJ1z8hk4OA7Ob5Jji0rScW4+GuyNF51nx8dzumNH6sydi9MjjxD9ySdknjuH26uvknbsGDcPH6bR1q3/lHRkAT8gEp784ceHkAj28k89VygUClUwKxSKCo4GsaBbClzXrz0JvAUUrwooFm12NvELFxIzcSJ5164B4Nq9O75TpmBbp47hkxMOw+7BkKhvfXs+DO1mgGdbo/eRlguzTkDQCQkosTCDdxrApy3B7U43vKwscHSU4tkQEybA+PH/WI5Ph9knpeucrtc5P+QuhXNHH/F3NoqEI3BkkshWQHycGwyAVuMNSjXuJHrcODIjImiwfj05V69yOCCAwP37sfP3Jy8lhfOjR+P96afYVC+mGE9FtO8bkSIaZGj0LaAEK26FQqEwBkN1p8Vnn3322b+8n1JjKNNboVD8hzBHuoYdkSGwcMSSbjOQBtQDjIiNNrO0xPGhh/B65x0wMyPtyBHSQ0KIX7iQvBs3cGzT5nb97K3YV4eAAVIUXjkIKeFwdilcCwW3VgZjpu/E2gIerw7964s841gSHEqEb85C9crQpNotHWBLS7GhO3nS4DXZvRsqV4a+feHHH+H558HeHgdr6SwPaiia6VPX4NwN+C4S1kVKwdy0GliUdojO3gvqvgp+r0BWsrhrJB6G0wsg5ya4thDZRhFos7O5vGwZlk5OxM6cSe3Zs7F2cyNi0CDsAgLw7N8fgJzERC7NmoXHgAFY2BZ9LWwQSUZHRHtyDogGfkL08P6IDl6hUCjKiKG6U3WYFQpFxeMKMhi4AymSHJDBwM6YNBiYFRvLhU8+IWHlStDpsHJ1xXfyZDz69zccwJGTCseC4MRsSQ60sBFNb/OPTNI3n7kGo/bDLxfl+BVfWNQeXG+tFZOSID1dOs2zZsGiRYYv+txz8PPPcIekIT0XlpyFWSchNk3WajvC1Iegm58JUo1roXBgHJz/UY6tnUSm0XQYWN1esealpnJu0CCSfvwR6+rVaRMZSd6NGxxv25amf/yBtYcHAGF9+2JmbU29xYvRabUle2mDhOKsAH5B3lxZA12QABRTI8UVCoUC1WFWKBT3G/bIY/d2iGNCDDIcuBNJh/PBqMFASycnXDp3plqnTqSHhpIZFkbyli0k/fgjNt7e2NapU/RgoIUN1HgS6r0BmQmQdALi/4KzK6CSC7g0MarydLOV5D5fB9gZByeTYVW4dJsbOusvZWcHVaqAszNcuQJbthi+aGQkVK0KDz1027K1hUgyhjaGxtXgVLJ0nL+PkkHBek7g7VDqrYOtK/j3hFrPi6vI9TCJ3T67FMxtwLVZgZe1uY0Nrl27Uu2ll7i5fz+ZUVFYOjmRm5iIa/fumFlYkBkZyfnRo2m0ZUtBd7lUISh2yO/FE4h85zxwBtiCFND+gAnp3QqFQqE6zAqF4v5FBxwEFlHo09sQGIxJrgk6nY6r69dzfvRosi9Kq7fKE0/gN2MGDi1K8C+7vB/+GibSBAD31vDIHPBsZ/Q+Ym5Cvz/gT70187SH4MM7ExA1Gti2DU6f5nuzOrhdvUj7BeNE73wnw4bBxIngUHQVnKeVjvOnhyExU9Ye8YDRgfCitwka59idcGAsJBySY/sa0GKs6Jwtbn8MkHv9OhZ2dpx6/nk8Bw7EpkYN4r76CruAAHw++6z03eWiCEfitvPdVqoiTyM6oQpnhUJhFGroT6FQ3P9ogJ+Rx/H5g4GPIMNf3sWcYwBtVhZx8+dzcfJk8q7LBd169cJ38mQq+fgUf6JOC+HBsO8jyLgsaz4d4aHJ0nE2Zg86WB4mw3q7XwaXYmS8mXnwZQjMOK6j369fMuv7EUV/Y82acPAgeBY/lHczR0JQ5p6ClBxZq18FRjWTaG9rAwqVf5DvZX1gPFw7LWtOtaHtdKj9yj+679d++42YiROxdnPDtVs3XHv0MCyJMYajSOEcpj/2QqzoTLQpVCgUDx6qYFYoFP8d0oFvgQ1IeqA5EqX8JmCC/3Du9etcnDqVuLlz0WVnY2ZtTfUhQ6g1bhxWVasWf2JOGhzX65tz0wEzqPuaFM6OPkbtQacrnbKjy3ZoF3OQUYvfQ5eejll2ttjN3eqssXo19OlT4rVu5kjHefZJuKS3bfN3ghlt4SUfIzXOOi1E/QAHx4tUA8CjHbSbDl6P/OPb81JSsKwiw5M6ne42KcbFadOw9vLCvU8f47vO+XHbSyh8GlEf8fe+s3uvUCgUd3BPCub4+Hg6duxIaGgoaWlpWFpaMmPGDDZv3oy3tzcrVqzAysqKNWvWMH/+fJydnVm7di2OjoVTG6pgVigUxZKMhFv8jHSfbYAewGtAMZ1aQ2TFxHBh/HgSgoNBp8PS2RnvTz/Fa9AgzK0NTBpmJMKRyXB6EWhz9IOBo6D5hyYHnxTFukiYcxIOdJXjgiJbo4HgYFiwQOQY334rvs2lJFcD66Jg0lEIT5G1Rz1hZltobWxeiTYPQpfAwU8hM1HWaj4DD00U+UoJZMfHc9DPD112NvbNmuE3cyZVn3rKyE0gvw+/IHHb1/RrLZGOs4n+3gqF4r/PPSmYs7KyyMzMpEuXLuzYsYPr16/Tt29ftm3bxvTp0/Hz86Nz5848+eST7Nq1i40bN3Lx4kVGjRpVqo0rFAoFAHHAYmC3/tgZ6A88B5jwtP/m8eOcHzmSlF27AKjk44P355/j3ru3YflAagzsHwPnvpXjyl7SYa3bC8xM1Ofm7ykHOv0CAwOgV13QaI2whysluRoJP/n8CCTrJdI968CUNuBnrPtEzk04Pkvffb8paz4doc0EcC2+1avTaEgIDib644/JiYsDwPmFF/ALCqJyw4bGv6hMxL/5OwrDTx5Ffj98jL+cQqH4b2Oo7iznv3ILqVSpElVveZx56NAhHn/8cQCefvppDhw4QEREBI0bN8bS0rJgrTQsHraTEYErOb07tmBt38YIRgSuZN3EfQVrNxIzGBG4kk87rL/t/Fm9tjAicCWxockFa9sXnWBE4Eq2LzpRsBYbmsyIwJXM6nX7lPqnHdYzInAlNxIzCtbWTdzHiMCV7NsYUbB2encsIwJXsnjYztvOHxG4khGBK9VrUq9JvabyeE0dVzJi80qYCwQA12DxZzsZ0XIlp78uzI0u7WtyCAxkh/sINjVbQVaDx8m6cIHwvn1ZFvguIwJX8svC40W/JkdveHYtdN3Lp+vmMWLuFG5sGgob2kLsTtZNMP3ntCwMEs8lc7in/Jzyi2Vjfk4lYWUhjhpRveCjQKhkIV3t+t/C0L8KrelKhbUDtPkM3ogWCz5LO7iwFdY1h1+6w43zRZ5mZmGBR9++tI6IwGfyZCwcHLi2bRtHmjQh/O23yYqNLfK8YrFFBgDXIk8ebIC/kE7zdMTHWaFQKEpBqQvmv//+m6CgIGbMmMHff/9t9I1SUlIK5BZOTk5cv369yDWFQqEwicbAfGA8hSEnq4GhwIniTioGvaQ2YN066q1YgY23N7lXpbpKWLWKm8eOFX+u58NQSS+JsHUVF4nNT0vBaAQaMzNWhEFiBmyIgheSLsj6XZ46cbIRr+bw1+CNuuKuMe801F4Dg/fAtSIMOorFthq0myqFc7P3JWY7agOsbQD7x0onuggs7OzwHjuW1pGReA0eDGZmXFmyhEP+/kS+/z45V42sdB0RHXMw8BLy890OvI74fWcadzmFQvHgUSpJxqhRozh27BhdunQB4Mcff6RFixYEBQWVeIPHH3+cHTt28Ouvv3LmzBlGjx7NsWPHCA4OZsCAAcyfP58FCxZw7do13nrrLX744YeCc5UkQ6FQmEQOkgQXDOT/NdIScdSoZ/zlioraduvdWxw1vA1YdOSkQchcOD4DsvUC4ZodoM1E8Ghj8J6pOfDWn7A1Brzt4exrRX9faQcGTeVUMkw+BusjpVnvUglmtYPX65pw37Q4ka2Er5ZjO09oOxXqv25QtpIREcGF8eO5ul665haOjtT6+GNq/O9/xSc2GiIO+AbYoz8uo4xHoVD8NyizhjkgIIDQ0NCCSWatVkuTJk04ffp0iTfPL5ivXbvGm2++yc8//0xQUBA+Pj506dKFp556qkDDfOHCBUaPHl2qjSsUCkWJZCBuGusp1LA+jhTO1Y2/XO7161ycMkUcNXJyMLOxofp771Hzww+xdnMr/sTsG3Bizu2aXr8u0HYaVK1r8J5/xcPQvdC9NgxvIrHXt/K/vdIF/qQleNgVfY18ecec431L+1L/wZlr8N5fsFvvG93WXbyjH/My4WJXDoifdb6Hs2tzeGgK1HrGYBV+8/hxoseO5fr27QDYeHvjO2UKbq++apqP8ylgIXBWf+yD/G60Q1nRKRQPIGVO+tu9ezdNmjTBxcUFgLCwMKKjo+ncuXOx5+Tm5vLss89y/Phxdu7cSYsWLdDpdHz00UdkZmby0UcfYW1tjaWlJYMHDyY8PJypU6felqyikv4UCkWZsAKaAi8i7dFzQBTSfU5Fus1G/NViYWuL8zPP4NanD7mJiaSfOEHq/v3EL1yINj0d++bNC1LrbsOyEtR4HBoOlE7q1WOQfArOLILMJHBr+Y946Xy8HWBQQ/nseselkzKh9044kCCyidg0qFcFqt3xmn79+iQAzw1qVvoXewduttC3ngwA7rsijhorwuFwoiQJuhdTrBeJfQ0JOHGqAwkHISUCIoIh7k+oUg8cahZ5mo2nJ+59+uDYrh3pJ06Qee4cST/8QPLWrdj6+2Pr62vci3IHXgBqIQEoccAfiKdzTf3XFQrFA4PJSX9t27bFzMyMrKwsQkJCCAgIAODs2bMEBgZy+PDhu7RlQXWYFQpFuZKIaFZ/QwpoO6C7/qPoetUgN48e5cInn3Bt2zYALJycqPH++9QYMQLLYhL3AEiLh0OfQOgy2YhVZWg8BAI/AFsXo/YQeg3GHYJN0fKSzIDOvpIa2OYuFXw3c8S/eeZJSMuVe/auCxNaga+xjhp5mRAyD45OhWz9HEvtbvDwTBmkLAadRkPCqlVEjxtHTry0vZ2ffx7f6dOxb9zY+BdVlIznUeAdTHoaoVAo7j9MlmTExMQU9yUAvA1p98oBVTArFIq7QiSiYc1/z++IuCm8DBiwXC6OG/v3c2H8eFJ2ituFlasr3p98gufAgYY9nJNOiaY35mc5trKHZiMh8H2wNq7yDL8Os07CynDI0craK74im/CvYvxrKg2JGaJvXngGcrVgZQ5jAmFcC3HdMIrsG3B8JpyYJUW0RSV5A1GCn7UmI4NLX3xB7LRpaG7eBDMz3N94A99Jk7CpUcP4F5WOSHjWA1mAJfJ78Tqg/hlSKP7TqKQ/hUKhKIoQJBXulP7YDegHPINJw18pf/5J9NixpO7fD4BtnTr4TpmCS9euhjW2CYfh0KcQ84scV6oGLcZA48FgaVwKy5UMidH+8pREaluaw6AGonG+U9JRXlxIhU8OQ3CEdLlbusLqp6C+gaDEYrkZC/s+vN3Puu1UqNfH4GBgztWrXJw4kfhFi9Dl5mJuZ0etMWOoMXJk0TKZkkhCnkZsR15UZaRo7oJJb6oUCkXFRxXMCoVCURw64ABSOOfbA3sjXr2PYPTwl06nI3nTJs6PGUNmeDgAlZs0wWfCBKq99NJtMdD/IP4vsVu7vFeOK1eH1p9CwJtgbmnUPuLSpIhdHgadtu/EwgzcRjzF+02h7l3qOP8ZB/12QcxN8XGe3AaGNjKh2wxweR/8NRwS9Y8B3FrBI7OLjNq+lcyoKM5/9BFJGzYAMhjoFxSEa/fuhv/siyMKWAQc0R97IIOBT3AXkwwUCsW9QBXMCoVCURJaYCcSp3xZv9YQKZyLD6crFl1eHpeXLiVm4sSC1DqHVq3wmTSJqh06FF+86XTSaT4wFpJkWI8qdaHlx5IaaGThfCoZlj0tLhlfDOyLGdCttoSTNC99gnapSc2BYXtlIBDA30lkIV18TbCh02khfA3s/wjS9fYcvi9Du2lQ1XDGdcqffxI5bBjpISEAOLRujV9QEFXatzdyE3oOIYVztP64LqJvbm7a5RQKRcVDFcwKhUJRWnKBrcAqQG+dTCDwJhKOYiTarCziv/mGi5Mnk5uYCIBT+/b4Tp6M08MPF3+iTguR38OBj+FGlKw5+kGLj6B+X7AovS7g9O5YYm/Cxso1WRUhemOAZ2qK5ri9V/l7OW+9ACP3QYT+r/GHPWBmW3jIw4SL5aSJl/XxmZCXIW8amr0PLccb1DfrNBp50/Lpp+RcuQKAc8eO+E2bZlrUtgaRaKxAJBsgTyEGoQYDFYr/AKpgVigUCmPJAH4A1gH5sdCtkcfx/sZfTpOeTtxXXxEbFESePtXU+fnn8Z0yBftmBuzeNLkQsQaOTIEb52TNvia0HCdSDQur4s8tgrg0mB0CX5+B9DxZa+sOn7WCDjXKt3DO1cDis/DZYbiqTwjs7Q8LHwMHU3TA6Zfh4CcQuhTQyZ/DwzOgTg+DG9ekp3Np9mxig4LQpKWBuTmeAwbgM3ky1q4mtNmzEH/vNRQOBr6CDI4aMEdRKBQVG1UwKxQKhamkIY4JG5EiGuBJJBnOhK5iXkoKsbNnEzdnjhRvgGuPHniPH0/lRo2KP1Grgcj1cGQSXAuVNSd/aDMB/HsYHIgriuQsmH8a5p6S/wZo5iIDgr38TSxoiyE1B6YfFyu6LA00cobNz4uns0kkHII/3xU/awDPR+DRL8HNsD4iJzGRmAkTuPz11+jy8rCsUgWfSZPwGjQIMwsThNbJiPZ9u/7YARkMNNFtRaFQ3FtUwaxQKBRl5QbSUdyEyDYsgI7AG0i0spHkXL3KxSlTiF+wAF1ODgAu3brh/cknhn2EC6Qa4ws7zi7NoPVn4NupyMJ538YIANq1z4Qb56F2l4KvpeVK4TzzBCTpC2d7K+jjD+80lCK6vIhIgZd/gbAUcLaB9c/AUyY4vwHyBuLsMpGsZF4FzKTj/tBkqGxY95ERHk7k//7H9d9+A8C+WTPqzJtnWCJjiHDga+C4/tgLeRLxOCoxUKG4j1AFs0KhUJQXCYiG9TdkUNAG6Si+BpjgPpF96RIXp03j8uLFBYWza48e+EyYgF29esWfqMmFsBVw6HNIl6FCXj0BLk1qqDHjAAAgAElEQVT/8a0F0djjlkBqNDgHgP+rkraXvw8NbIyCr0Nhz+XCc9t7wfgW8GT18pFr3MiWdMKfY8DCDMY2F99ma1OcNED8mw9PhJC5oM0FKwdoORaajQALm2JPy3cziRw+nOyLFwFwe+01fKdOpZIpGQP5bitfA/kRBg2A9/SfFQpFhafM0dj3ChWNrVAoKhz2yKDXY8gj+WjgDLAZyATqYFTctqWjI9VeeAGP/v3R5eWRduIE6SEhxC9cSPbFi9gHBmJZVMPA3ALcWkDjd8HWVfS8Dd8u8h43rmYQ2DgW36rHoXco2NeCM4vBvY14PiN+zY2rwZv1oUdtOT6bIl3hVRHwWyy4VhIZhUUZ7NQqWULP2qDRSWG++zJsjobW7uBlQtoilpWg1jPyBiD1Alw7BZd2QtQGefPgUHTxa2Zmhl1AAJ7vvAMWFqQePEj6iRPEL1yIJi0Nh1atMDfm3x0zJE67E+AChAGxwDbEdaUBkiypUCgqLCZHY99rVIdZoVBUeCKQjvN+/bEd0A2J2y7ewKFYsi9dImbiRC4vXQoaDWbW1ni9+y41P/wQG09P0/e5vadYs3X9q9Sn3MiGeadhTkihztmlEvSsA2/UlSK3LOy9DG/ugsgb0m3+KBDGtwQbU7vNABd/hz1DIEVkKDQcCG2nQSXDKSpZMTFEjx1L4tq1AFi5uOA7ZQoeAwYYDp0pjgxEwvM9IuGxRfTNXVH6ZoWigqIkGQqFQnG3CUUK5/y4bQdEptEFozrO+WScO0fMp5+S+K0k3pnZ2OD59tvU+vBD0yKfrxwUt43Lf8uAXAkBILeSliuuGkvD4Oz1wvW27vB+U+jsKx1pU8jIhXGH4IsQUTUEusCPz4F3Wdwm8rLg6BQ4Ok1kGpVcJC0w4E3pzBsg9dAhokaOJHWvhMc4tG6N//z5OLRsadpe4oAFwD79sRfwNtAepW9WKCoYqmBWKBSKf4tTSKTyCf2xM1I4d0L0zkaSdvIkMRMmkPTDDwCYWVvjOWAANT/6iEq1apV8AZ2WG3pPNyc3Ozg2E3JuwEMTjd6LTgcnkmB1hASTXNc/vfRxgOFNoH990901/r4Mb/wB51NF9vFXZxMlGreSfAZ2vysJigCuLeCxueDZzuBpOp2Oq+vWETVyJDnx8WBmhufAgfhOnoxVtWqm7eUwMJ9CfXND4F39Z4VCUSFQBbNCoVD8m+iAo4jlmD7xrqBwfgmTHsmnnTrFxcmTubp+Peh0mFlZ4dGvH7U+/tjwkFpSCJ+8tIubmVWYc7wvHJsB2Snw0KQyTfGl5cLKcJhzEqJSZc3RGjp6Q1c/+WzsIF9KNjy9BY5ehQZVYU9nqFbW8RWdDs6tg78/KByOrNcH2gVBZcMSl7ybN4mZMIG4L74QG7pq1fCbOhWP/v1Ns6HTAD8jTyLyO/VPIsEndyF1UaFQGIca+lMoFIp/EzPk0fuLSMhJLPJo/jDwKyLRqA0YIWOwdnfHtVs3XLt3Jy8lhfRTp0g7epT4+fPJSUjAvmlTLB2LMDaO2khTi8lUccrGt3YOxGyTIblqBjyfS7MfC2jtBu81kojtuHQ4dwNOXYP1UbDoDMSng6cdeJRy2K2SJbziKw4aodchPgNe8SvTNuVNQbVG0OgdsdxLOAhXj0PoErDzlMHAYt44mNvY4PzMM7h060ZGWBgZoaEkb9lC8k8/YRcQQCUfH+P2Yg7UQ+wIzZA3U5HAFv3X6yN2hQqF4p6ghv4UCoXiXqID/gaWA+f1a15AX+ApTCqSMsLDiZk4UYbUbuk41xw9Gts6dW7/5vTLcHQqaPPAp6MUzOaWhV8PXQ6pUdB0ONiabrwcfh1+uiCuGqevFa4Huoj7Ri//0nWMo25Ao3UScrLrJXi8PGOnb5yHPf+DmJ/l2PtFeOJrsDd8E51Ox9Xvv+f8qFEFNnSu3btTe84cbKqbuMErwCJgt/7YCxgCtDXtcgqFomwoSYZCoVBUBLTAn8gj+Vj9mjdSOLfHqI5zPulnzhAzYQJXv/9e5Afm5ri9+iren3zyTx9nreafQ2+aXFjtB2mXwKISBPSDwA/Aqbbxm9Gj04msYnkYrD0HKWIvjbU5PFMTHvcSf+dmLsUPC44/BJOOQkBVONOzfCO70ekgfDX8NUzkKTZVRKLRYECJiYmazEwuzZzJxWnT0GZkYGFvj8/EiVQfMgQzS0uD5xbLUeArCvXNbYDBQCkk6gqFovxQBbNCoVBUJDTA78BKpMsIUji/jqTDmdJxjoggdto0ElavRpeXB+bmuPfuTa3x47Hz9zd88uV9cGSyyDVAisY63aH5h+AaaPxmbiErT7rOy8Pgt0ugveVfHAcreNgDHvOCxzyhlZusLzgNnx+RQtvJGi73BVsTa1GDpMXDroGF3Wb3NtB+QYkR2wBZFy8SOWwYyZs2AVC5SRP8Fy7EqZ3hgcJiyUNSJFcA6cibp5eRN1Pqnz+F4l9BFcwKhUJREckFfgHWIgmCIOEXryPDYCYUzlkxMVycMoUry5ZJ4Wxhwd7WX2Dt6cmojV0Nn3wtVIYCI4JFvgFQ61lo/hFUb1/mNm98Ouy4BLvjJbQk8sbtX69kAVVt4HKGHD9dA+a0g0YmGlOUivyhwL3vQ8ZlebPQaLC4iNiUHN2YvHUrkUOHknXhAgAe/fvjO20a1q4mTvFdQ6Q725AnEg5Af8RlRembFYq7iiqYFQqFoiKTi0RtB1PYca6BdBefwKRCKTM6mouTJ3NlxQo2NV4KwMCGv1Pro4+o3KiEgb+bsXBiDoR+A7npsub+kERO+7xYomyhtMSnwx598bwnHs7onSPqOsGsdvCidzlLMQyRkwoHP5OIbZ0G7NzhicXg26nEUzUZGVycOpXYoCB0OTlYVq2Kz6RJeL3zjmluGgBRiH/zMf2xP/A/oGyzmgqFwgAVpmDOy8ujT58+JCQk0KpVK4KCgnByciIwUB75/fDDDzg7Oxd8vyqYFQrFA0UesANYDcTr17yBN4FHMUnjnBkVxZGxX3Dt521USZeJw2qdOlFzzBic2pYwXZZ1DU7Nh5NfQlayrDn5Q9NhUL8vWJsQZWiApEzxYQ50Aat71U1NCoHdgyXgBaDRu/DwTLAq2eojIyKCyCFDuP777wDYN2uG/9df49i6tWl70QF7kMI5Ub/WAXgHuJtdd4XiAaXC2Mpt3LgRa2trlixZwk8//UT16tU5cuQIe/bsoV+/ftja2t72/cpWTqFQPFCYA3WAzoA70mWMQwYF9yCP570xqnC2cnbGu/sL+PTujC4vj/SQEDJCQ7mydCkpf/6JtZcXlfz8MCuqlWtpK1KMxu9BpWqQEgap0aJ1PrUAMpOgSr1SSRdKg50VVLcHi/JpYJu4CXcZfLR2hLhdYkN3/gfwaFeib7NVtWq49elD5caNST1wgMzwcK4sW0be9es4PfII5tZGGnCbAT6IDZ05cBY4B2wFrBCLunv5Z6VQ/McwVHf+qwXz1q1bady4MXXr1uXy5cvExMSwbt06tm7dSkREBE899dRtf2mrglmhUDyQmCOP4F9GOonnkcJ5D1I8O2J04WxZpQrVXngBz4EDMbOyIj0khMyICBKDg7m2fTvWXl7Y1qlTdOFsYQ2ebaHJUKjWBNLj4cY5uLIfTs2DlCioWg9s/yPpG2Zmkgbo0wni98D1MDi7DDQ5UjibFz+BaGZmRuUGDfB85x10eXmk7t/Pzf37SQgOppKfH3b16xu/HysgENG1X0F+H44gvwvV9R8KhaLMVJiCOSkpib1799KhQwdmz56Ng4MDK1euZPDgwWzYsAGdTke9W2yQVMGsUCgeaCyQMIvOgBsQjUmF8/ZFJ4g8coU6LT2wqFyZqk89hdfgwVg6OZF+8iSZ586RuHYtyZs3Y+nkhF1AAGbmRVzUzBycG0CD/uJfnJcOyach6YR0nJNCoLIX2Nf6F8XHd5HKHhDwJuTehCsHpXiO2ijOIQ6GPd/Mrayo2qED1Tp14uaRI2SGhXH1u+9IO3ECx3btsDRFZuiI+HbXR0JPLiESnnNIxHb5KmQUigeOClMw+/v7s3XrVmbNmoWLiwsBAQG0a9cOMzMzLC0tCQkJ4dFHHy3VxhUKheKBwQKoy+1SjXikcN5NYeFcTI06/+1fiTqawHODmhWsmVeqhNMjj+D13ntYVq1KWkgIWVFRJG3cSMLq1ZhZWFC5SRPMrayKvqh9dajdVWKmtTmQfAqunYazy+H8j2BuJcW1gW7sfYGFFXg/DzWfhiv79N3m5ZB5Fbwek+67AWw8PfEcMAArFxdu7N1LekgIlxcvxtzGBodWrUwbCqyBuGZUBkKRjvNW5OcfgHLTUChMpEIm/Q0cOJCPP/6YGjVqYGFhwbhx42jcuDE9e/Ys+B419KdQKBRFkItEbAdTaEdXC+iFdCDvqFG3LzoBcFvBfCfarCwSVq8mduZMMiMiALByd6fmBx/gNWgQFvYltC/TL8uA4JnFkKmfULNzFxlHo3ehkrPh8+8H8rLgyCQ4Nl1s95z84fkN4NKkVKdnx8UROXw4SRs2AFC5cWP8v/665OFLQyQBC4E/9Mc1gaFAK9MvqVA8qFQYl4y4uDh69+6Nubk5b7zxBs2aNaN///5UrlwZPz8/li1bhsUt77ZVwaxQKBQGyAW2A2soLJw9gNeA5wAjZ8wAdFotyZs3EzN5MmlHjwJgWa0aNUaMoPqQISVLCTTZELkBTsyCq8dlzaoyNHgLmo4AR2/jN1XRuHoCdrwhXXWLSvD4QhkULCXJ27YROWQIWdHRYGZG9eHD8Z00CQu7kp04iuUo8CWFCZKPAu8hTyQUCkWpqDAFs7GoglmhUChKQb4d3VoKCyYX4FXEYcHGwLnHgDTgsduXdTod17Zv5+LEiaTu3w+AhaMjngMHUmPYMGxq1DC8J50OLu2UIJTY32TNzALqdBPXDc9H7m+dc14m7B4iw4AgWufHvpI3B6VAk5lJzIQJxM6YARoNtnXqUHfJEqq0b2/6nnKBDcAqIAv5ufcBemDSmyeF4kFDFcwKhULxIKBBdM3BiK4VwB1ixySDFdRscId5bzrwN5IqdxMYhDzK11Ggh9bpdKT88QcxkyZx488/ATCztMStd29qfvBBySEoAEkn4fhMiPhWQkEAnBtB43dFA23tWIYXfY8JXS6+zZosqFIXnvm2VNHa+dw8coTwN98k/fRpADzffhu/oCAsq5TBqu8qItPYpT/2AoYBJtpBKxQPCqpgVigUigcJHbAPWAnUhxGrVwIw53jf4s85jKQNfkCxHenUw4e5NHMmVzdsAK0WAOcXXqDmqFE4tW9ftCXdrdyMhTPfQOhiyNBrSKzspWhu9G6ptcAVjuTT8OtrMvRobgVtp0KzEaVORNRmZ3Nx2jQuTp6MLjcXa09P/OfPx6VLl7Lt6xjwFXBBf/wkItP4D8jJFYq7gSqYFQqF4kFEB2TDrP5bABi5toiY5xzkcf1fSKd5NBAJ/Ag0QGQddwwRZkZFcWnOHK4sW4Y2MxMAhzZtqDV2LNU6dizaku5WNDlwfhOcXghxfxauez0GgSPFss78PrN6yMuEv0fJ4CNAzWegwyoZfCwl6aGhRLz1VoEExqVLF+rMm4eNl1cZ9gV8j7x5ykacNd5CXDbusz9iheJuowpmhUKhUBRyi+QCgBvAGMRl42+kw9wY8XqugUQxF0FuUhJx8+cT99VX5CVLdLZd/fpUHz4c99dfL90Q27VQOL0IwlZCTqqs2deEgP7i91yC33GFI3oL7OwPWUlSLD/3PXg9WvJ5enRaLfGLFhH94Ydo0tKwcHSkzty5uL/xRskdfENcRoYCD+qP6wLDERs6hUIBqIJZoVAoFEWxFziAhKG0QZwVhgPLkBjuMGAnMAAwYIWvSU/n8uLFXJo1i+xLlwCwdHbGa9AgvIYMwcbTcKQ0IMVy6DII+QpS8wXYZlDrWXHY8H1JPJHvB9Li4fc+Eq1tbgVPLIYAA3KYIsiKjeXc4MFc27oVkG6z/9dfY+1ahjRFHfIkYT6QiLxpegF4G1D/xCoUqmBWKBQKxS3sQyQXrsggmD8Sr/wZYks3SP99x4DVwJzSXVabm0vSxo1cmj2bm4cPA2BmbY1H377UGDUKO3//ki+i04pM48xiiPpBQlEAKleXIcGGA++PCG5tHuwdCSFz5bjlOGgzwShnEJ1OR8KqVUQOHYrm5k2sXF3xX7gQ165dy7a3TOTnuh4ZFHVEfubPUWz4jULxIKAKZoVCoXiA+bTDegA+/70HaJFiaQ3wPDAEsEICMIYi3WVb/YkjgRaIVENLiRHc+eh0OlL37ePS7Nkk/fijWMyZmeHStSu1PvwQh5YtS3ehzGSICBbJxvUwWTO3Bv8eUL8vVH+i4mudTy2EPUPkjUDdXvDUMrAw5PP3T7JiYgh/801Sdonthdtrr1Hnq6+wqlathDNLIAaRaejtsmkOvI+8eVIoHkBUwaxQKBQPMCMCi3DJSAHWIbKMgUiRvBP4UP/1SGQA8Iey3TsjPJzYmTNJWLkSXW4uAI5t21J92DBcu3bFzLIU0dk6HcTukG7thZ8RbQFgXwPqvS6SDSe/sm30bnJhG/zaA3LTZbDx+R/A1rhiN1/bfH7UKLQZGVi5u1P3669xefnlsu1Nh3h4zwNSEf16P6Ab/xj2VCj+66iCWaFQKB5gbiRmAODkVsQQXjoy9OcITEAG/MwRV4U6SPCFEd3l4siOi+PSF19wefFiNPq/2228vakxfDgeAwZg6eBQyhdzHsJWQfgqSI0uXK/1LDQaBD4dwbwCVnpXj8OWFyHjMjj6wYuboVopPKzvIDMqivD+/bmxZw8Abr16UWfu3LJ3m1MQbfMO/bEfMAIwfosKxX2LKpgVCoVCYRgdomndhjhjtAFe5J/WY1r9ZxMLaE1aGgmrV3Npzhwyz50DwMLJCa9Bg6g+dCg21UupB9BpIX6vJO2dWyfBIQCVvaDBAOk8VymFZvrf5GYsbOsMV49JIuDTq6G28V7LOq2WuHnziB4zBm1GBtaentQPDqbqk0+WfY+HgC8QVw2Q3wE1FKh4QFAFs0KhUChKhw5JinO75fjWQbDdwHLEn/lpTH5sr9NoSN6yhdhZs0jduxcAMysr3F57jRojRmDfrFnpL5Z1TbrOpxdBSnjhumsg1HsD6vWuOIOCuRmw622IWCvH7YKg+SiTLpUZFUVY376k/v03mJlR88MP8ZkwAXOrMrqJZCEa9+8QH2cnZCjwWdRQoOI/jSqYFQqF4gFm3cR9APQc367sFxuHeDUDuCOF8wtI+ImJpB48yKVZs7i6cWNBgqDjww9TfehQXF55pfQFoE4Hcbul63x+E+TelHVzK5FqBLwJ3s/fe8mGTgfHZ8G+0YAOnt8ItV8x7VJ5ecRMmkTMxImg1eLQqhUBa9diW6dO2fcZg3SbT+iPWyGDoKXPYlEo7itUwaxQKBQPMEUO/ZlKLjIcuBaI1a85A92Bl4BSZJUUR+b588TNncuV5cvRpEqIibWnJ56DBuE1cCDWHh6lv1heFlzYCmdXwMVfRMIBEiZS73WJ467WxCibt3Ln+Cz4+wORZ3Q7YJKmOZ8be/dytndvsi9exMLenjrz5pU97ATkCcNvwAJkKNAO0bl3pMy6doWioqEKZoVCoXiA2bcxAoB2XeuW30U1iMPGGuCcfs0B6Kz/cC7DpdPSSAgOJu6rr8gIDQVukWuMHIl9kybGXTAtHsJXw9nlt0s2HP3A/1X5cGls+oZNRaeTgJOIteBUB149IcWzieSlpBAxaBBX160DwLVHD/wXLsTKuQw/jHyuId3mv/THTRELuvssiFGhMIQqmBUKhUJxd9Ahg2LBwGn9mhXwDNCDMhVUOp2OlD//JP6rr0javLlArlHlySfxGjyYai+9ZJxeV6eDKwckhjt6E2QkFH7NuWFh8VylHOQMpSU3AzY8BMmnoNn78MisMl2uIOxkyBA0aWlYe3lRb8UKnDt0KPtedYiG/UvEVcMK6A28RpkkOQpFRUEVzAqFQqG4+5xCBsX26Y/NgHZAT6CMDdzM6GjivviCy0uXok1PB/RyjbffxnPgwNK7a+Sj1UD8Hjj3HURugOxrhV9zbSGFs8+LULX+3ZdtJByBDW3kv7vtB/fWZb5kZlQUYW+8Qeo++WHUHDMG3wkTSud7XRI3gEXAdv1xLUTbbGTjX6GoaKiCWaFQKB5gTu8WsXGj9jX/nRteRCzqfkM0zyDFVC8kirsM9WdeSgoJq1cTv2ABGWGS/mdmaYlLt27UGD4cxzZtjL+oJhcu7YCIb28fFgSRbfh2At+XwPNRsCijA0Vx/D0ajs+QYJNXdpfLJXUaDRenTePCJ5+AVkuVJ58k4LvvsHYtJ8eQE8BsCrXsLyP6Zttiz1AoKjSqYFYoFIoHmHId+jOGa8Am4EcgTb9WG5FqPIE80jcRnU7Hjd27iV+wgKs//AAaDQAObdpQY9gwXLp2xdzaBJ1AXibE/AJRP8DF7ZCVXPg1ayfwfg58OonbRqVy0Abnk30DljiDmTkMTAXL8qs6U/78k9CePclNTMSmZk0abNyIY6tW5XPxHETHvhaxoPMCPqLMTxQUinuBKpgVCoXiAWbxsJ0AvP3lU/dmAxnAFqTrnK98cAG6IM4a9mW7fFZsLPHz53P5m2/Iu34dACtXV9z79cNr0CBs/UyMzdZqIOEARG+BC1vgWmjh18wswLW5yCc82kH1xySquyx81wySTkKX3XK9ciQ7Lo4z3bpx88ABzKyt8Z8/H48BA8ruopFPJDAVOI88QegO9EeithWK+4QKUzDn5eXRp08fEhISaNWqFUFBQcyYMYPNmzfj7e3NihUrsLplgEMVzAqFQvEfIgeJXv4euKBfs0OK5m5AGdOdNenpJAQHEz9vHumn9ROIZmY4v/gi1YcMoWqHDpiZl8EL7UZUYfEcvwe0ebd/3dFPJBXV28tnR9/S6Z9zM+DYNDgWBJpseHKJpBWWM9rsbKJGjCB+4UIA3Hr3xn/hwtLHkpdELrAa6ThrgerAB4ARGTQKxb2kwhTM33//PZGRkYwZM4ahQ4fSrVs3pk+fzrZt25g+fTp+fn507969VBtXKBQKxX2KDjiMDAge16/lO2v0BMootdbpdNw8eJD4hQtJXLcOXXY2ALb+/ni99x4efftiWaVK2W6SkwoJhyHhEFz+S2K6b9U+A9h5QJV64OQHTrXBsbb8t2PtQjnH+U2wdwTcjJHjeq/D4wvLZC9XEldWreLcu++izcjA1t+fgHXrcAgMLL8bhAFBQLT+uBMwkDI/SVAo7jYVpmCePn06jRo14sUXX2Tp0qVkZWWRnp7O6NGjOXr0KGvXrmXWrEJLHVUwKxQKxX+cMOBbxN83P4a7HfJIvwlljmLOuXqVK0uXEr9gAdmxMp1mbmeHe+/eeLz9Ng4tW5aPLEGbJ3KK+D3yEbfndueNO7F2AlsX6VoDuDSFx+aB1yNl30spyAgLI7RnT9JDQgokGp5vvVV+N8hFdM3BiLbZBfFtblt+t1AoypsKUzBv2rSJffv2ERQURJ8+fQgICKBatWoMGjSIyMhIpkyZwrJly0q1cYVCoVCUjns29GcMRTlr1EMK5/ZAGd3QdHl5JG/ZQty8eaT88UfBeuWmTfF86y3cevfGqmrVst3kthtqITUabpyH1Cj5fCNK/99RkKufgrSpAg9NhobvgLlF+d2/FGgyM4l6/30uL1oEgPfnn+M9fnz56ZpBusxByBsjkITAwSgnDUWFpMIUzBqNhuHDhxMaGoqPjw/Nmzcv6DAfO3aM4OBgZs+eXaqNKxQKhaJ03BcFcz75zhqbkShmADegK/AC5fJYPz00lMtLlpCwahV5yeKCYV6pEi7duuH51ls4PfZY+RaNd6LTQVYSpMZAFX+wubf/vsV/8w3n3n0XtFo8Bw3Cf948zCzKsXjXABuApciboerAWKBB+d1CoSgPKkzBfCsDBw5k3LhxvPvuu/z8888EBQXh4+NDjx49Cr5HFcwKhULxgJKFdJs3UOjza4foYbsC5WAlrM3OJmnzZq4sWcL1338vWLetWxevd9/FvU8frFxcyn6j+4CkTZsIffVVdNnZuHTpQv01a7CwLec28Hlgsv6zOZIS+DplshdUKMqTClMwx8XF0bt3b8zNzXnjjTfo168f06dPZ8uWLdSqVYsVK1ZgfYtvpiqYFQqF4gFHCxxE5Bon9GsWiEyjK+XWpcyMjubK8uVcWbaMnLg4AMysrKj20kt49OtH1WefNS6G+z4k5a+/OPPSS+SlpODYti2Nfvqp/N8w5ADLkJ+nDvADxgD/Yhq5QlEcFaZgNhZVMCsUCoWigHDEWWMPUkgDBCCFcznonKFQ6xz/zTdc/+030MqNrNzdce/TB49+/ajcqFHZb1RBST9zhlPPP092bCy2devS+JdfTPexNkQIMB2IR94AvYEkQZbDz1ChMBVVMCsUCsUDzD0PLilvEhCN81Yg38ktX+f8IlBOjmzZ8fEkBAeTsHx5QQw3gGO7dngOHIhr9+5Y2NmVz80qENnx8Zx64QXST57Eys2Nxtu24dCiRfnfKBP4BtGsA9RFtM3e5X8rhaI0qIJZoVAoHmDuq6E/Y8gEfgc2Ii4bIMVyR6Az4FE+t9HpdNw8fJgry5eTuHYtmlSZRrSwt8ela1fcevem6pNPlu+g3D0mLzWV0G7duP7775hXrkzDjRtxfvbZu3OzY4iTRgKSDDgUGfC8i3OXCkVRqIJZoVAoHmBO75apuUbty5gIUlHJ1zmvA07q18yBh4FXgKaUW/GlSU8ncd06Ln/zDTcPHixYt/bwwPW11/Do1w/7Jk3K52b3GG1ODuEDBpAYHIyZpSUN1q/HpUuXu3OzdOBL5A0QiMRmJFBOIYQKRWlQBbNCoVAoHgzCEGeN3UhgBoA/4uf8BOWqkc04d47ENWtIXLOGzMjIgnX7Fi3w6N8ft79IV5kAABRaSURBVNdeK19v53uATqvl/OjRXJo1CzMbG5ru2oVT27uYPvI7MAd5euCKSDRUtLbiX0IVzAqFQqF4sEgGftJ/pOjXXJCOc0fKtXOp0+m4eegQCatWkbh2LXkpckMzGxucn3sOt549ce7YEUuH+7NdqtPpODd4MJcXLcLKzY3mBw9Sycfn7t0wDrGfO4s8GegJ9EfZzynuOqpgVigUigeYfRsjAGjXte493sk9IBvYAXwPxOjXKgHPIjpnn/K9nTYri6RNm7iybBnXd+yQkBIkGMX5hRdw79MH5xdewNzGpnxvfJfR5uZy+sUXuf7771Ru1Ihm+/bd3TcAecBqJFpbizwl+Bg1EKi4q6iCWaFQKB5g/rNDf8agAw4jhfORW9YDgS5AO8TerBzJjo8naeNGEtevJ3Xv3oJ1y6pVce3ZE/fXX8exbdu7mypYjuSlpHC8bVsywsJw69WL+sHBd3/vp4ApwBVkIPBd4CXUQKDirqAKZoVCoXiAWTdxHwA9x7e7xzupIEQjVma/IYmCILZ0LyG2dFXK/5bZly6R+N13JKxeTXpISMG6Tc2auHTuTLXOnXF69NEKH46SfvYsx1q1Qpuejv/XX+M1cOC/cFNgLvLzAhkI/IByiUlXKG5FFcwKhUKhUNxJGrAdKZ7j9GtWwFNAN6D2XbptSAgJwcEkrl1bkCoIYOnsjEuXLrj26CE2dZYVM8UjYc0awvr0wczGhhbHj1M5IODfufEfwCwgA/AC/t/evUdXWWZ3HP9iEgiCJCSHiwVhICjlIkO8MJAQE0NQBI0XGhmVVa9L7Squjq2iq+0ArjYVhGnVdupd0CqFdIpoZhhuCjIGQoQRQwKigbEowkBIjoeQkOvTP55DEjLxcEKS857L77PWu0LOeUm2D8+SnYf97r0IW6oh0kWUMIuIiPyQJmyZxnvY9nRn/1acANwKpNEtD5yZpiZO7dpF+dq1nHzvvXOGo8S4XLjuuIMBd95pT5579uz6ADrhwIMPcuzNN4nPymLCxo2BKyv5FpsoH8T+mcwDbkElGtIllDCLiESw749XAxA3MPym0nW5b4E12JPnGu9r/bGDNG4BBnXftz5dWsqJvDyOr15NzYEDza9H9e1LfFYWCTfdROLMmfQaOrT7gvBT/cmTFF1+OQ2VlYx77z1ct90WuG9eC/wHdtIjQCa2Z7O2t3SSEmYRkQimh/4uQDW2J/D72JpnsMNQpmJHcF9Jt51qGmM4vXcvJ/LyKF+7lurS0nPe73v11bhuvZXE226jz/jxjj00eOSXv6Rs3jz6jB/PNXv3Bj6AzdgSjTPAUGAhMCrwYUj4UMIsIhLBFk7PA+CZTXc6HEkIMkAJts75Y6DR+/oobFu6TKB394Zw5vBhKtavp2LdOio3baKpurr5vdgRI0iYMYP4668nLj2dngMHdm8wrTTV1fFJ376Y+nqmVlUR1adPwL53s8PAM8AhbInGX2H/XFSiIRdACbOIiEhnlWMHoeTTMgylD5CFrXUe0f0hNNbUULl5Myfff5+T+fnUHz9+zvsXjx1LfEYG8dOm0X/aNKK7+e/OXRMmcHrvXpJ37qTfpEnd+r1+UC3wn9g/G7BdNJ6i23+QkfCjhFlERKSr1AFbsIlz62qJidjTzVS6dAT3DzGNjXiKinBv2YJ7yxY8BQU01dS03BAVRb/Jk+k/fTr9Jk/mkmuvJSYhocu+f3l+Pl/MnUujx8PYX/2KAbNnd9nXviBbgaXYcpqRwD8DlzoZkIQaJcwiIiLd4RC2zrl1T2cXLT2duy4/Pa+mujpOffop7i1bqNy4Ec+OHZiGhnPu6T1qFJdMmtRyJSdzUWxsh76PaWzk64ULOZybC0DirbcydtWqDn+dbnEY+EfgG6AftlxjoqMRSQhRwiwiEsF+cXc+AH+38haHIwljVdikeS02WQN7ypyK7a6RjH1oMIAaPB57+rx1K6c+/ZSq3btpOnPmnHt6REfTZ8IEYkeMIGbAAHu5XPQ8+2vvZerrqdqzh6o9e6hYv55ThYVw0UWMePZZLnvyyeCaVliFPV3eiZ3eOA9bMhNEIUpwUsIsIhLB1CUjgAzwe2xP5x3YHs8AQ4CbgRl0yyRBfzTV11NdWoqnqIhT3ut0aSk0NZ3/N7cRM2AAY1aton9mZjdE2gUagdeA1d7PZwA/w47XFvkBSphFRCLYN/tOAnDZ2ESHI4kwJ4B1wG+8vwbbySENe+r8Yxw/9WysqqJqzx5qjxyhvryc+hMnWi7v53UnToAx9Jkwgb4TJ9I3OZmEG24gxuVyNnh/bMK2nqsFrsCWaAx2NCIJYkqYRUREnNKILQ/I59xJgsOxp87TAf0V133KgAXAUVTXLD4FTcJcXV1NTk4Op0+fJi4ujry8PAYOHEhycjIAa9asIaHVE7xKmEVEJKwcw546rwNOel+LwQ5EmYUjtc4RwQPkAkXY2vKnsO0ARVrxlXdGLVq0aFGgAsnPz6dnz54sX76cQ4cO4fF4KCsrY9u2bdx333307n1u08Ta2trmX8cGw9O3IiIhaP3LeyjbdYxR1+jfoh3XF5sU3wEkYVugfYudJrjRe1UDf4bt8Sxdoxd2yEw1dhDN77B9msfheFmMBA9feWdAf45NSkpqDsbtdpOYmMj+/ftJS0vj6aefJoirQ0REQtaGVz5nwyufOx2GtBaNHbCxBPhv4D5gEPYEejnwU+AfgEJapgtK50QBf42dBgjwMnbgScefeZQIFPCSjOzsbI4ePcrAgQP58MMPcbvd9O/fn0cffZRZs2aRnZ3dfL9KMkREOm/9y3sAmPGoCjeDWhO2w8avgQLgbAtlF7bOeQYwzJnQws6HwGLsGmcCT2NLYySiBU1Jxuuvv86wYcN49913KS8v5+DBg0yePJkePXoQHR1NcXExaWlpzferJENEpPNGXTNY5RihoAe2FCMD20WjH/BH71WC7fH8KTaxHgr0dCTK8DASW47xO+ArYB+2jlxJc0QLmpIMY0zzQ30ul4vKykoaG+2/NRUUFJCUlBTIcERERIJTf+Bu4L+AF4GZ2JrbfcC/An8BPOf9XNWMF+Zq4HnsWu8G/oaWBzFF2ghoSYbb7WbOnDnU1tYSExPDkiVLeOihh+jTpw8jR47kzTffJCoqqvl+lWSIiHSe+jCHiRrsiehvgOJWrw8HbsB2fRjoQFyh7gi2JONbbB35EuyaSsQJmrZyHaWEWUSk8zTpLwwdxram2wC4va/1wPYXngFcB6iS0X9u4O+B/cAl2PrmsY5GJA7wlXeq26OISJgbOiaBoWMSzn+jhI5hwKPA/wD/gq17jgY+A54FZgPLUMmGv+KxpS4pwCnsifMfHI1IgoxOmEVERMJBFbAFWI9NlM8aBkzzXkMciCuUNGKnAm7Hdif5dzRKO4KoJENERCSS/B/wW+wglMpWr/85tt45E43j/iG1wHxsnfhl2KRZaxURlDCLiIhEogZsB4gPgU+wDw6CLd+Ygk2ef4LaqbVVBfwMOIitZf4FqgmPAEqYRUQi2MLpeQA8s+lOhyMRR53BDkTZCOyiZcJdP+zUwQzgx9iJeALlwDxsH+ypwCK0NmHOV94ZHehgREQksDzlNee/ScJfLC21zCexp84bgENAvvdKwJZrTANGYztvRCoXtlvGY9jT+ZewCbREJJ0wi4iEue+PVwMQN/BihyORoHQQ2Ip9YPBIq9eH0JJgR/JI7s+BJ7DlLX+LncIoYUklGSIiIuKbAQ5gT54/AipavTcKSMOWbURi8rweO9AkClgKJDsbjnQPJcwiIiLiv0ZgD7AZO13wdKv3RmAT5wwiK3l+GViNHaX9hvejhBUlzCIiEWz1P20HYM7PUxyOREJSHbbTxjZsLW9Vq/dGYhPndMI/eW4EnsQOh5kC5BLZNd5hSAmziEgE02hs6TL12OR5KzZ5bn3yPAw7KS8N2+85HGcJHwcexP7Q8HPsA5ISNpQwi4hEsO3/+yUAKbOvcDgSCStnT563YtvVtU6eE4FUbDu2iYRXn+dfY/syDweWo1PmMKKEWURERLpPA7AXe+pcgO1dfFYfYDI2gb4a2/c5lDUAc7APRf4b9gcCCQtKmEVERCQwDPAVNnn+BPhDq/cuAsZgE+ifYLtvhOIJ7XLgbWz99kJnQ5Guo4RZRCSClXz8DQDj0y9zOBKJSEewifNO7Cl0Q6v3XNjEeTL29Ll3wKO7MN8AfwkMAPIcjkW6jBJmEZEIpof+JGjUYOueC7EJdHmr92Kwp88Tvdc4oGegA/RTI3Cj9+NvsVMUJeRpNLaISAQbe91Qp0MQsXpjHwScii3dOIhNnguBfUCx93obmyxfiT15vhpbvhEsnTeisKfLx7zXjxyNRgJAJ8wiIiLivFPYZHmP9ypr834/Wk6exwCjce4E+o/AT73ffw32wUYJeTphFhERkeB2CbaTRqr3czfwe++1C5ukbvNeYDOYUcBYbBJ9JfbUNxA+8n5MQclyhAhowlxdXU1OTg6nT58mLi6OvLw8XnzxRd5//32GDx/OihUriIkJp2aNIiIickHisYNBMrHlG0ewDw2WAvux3Te+8F5rvL/nUmwS/aNW12V0TR9oA3yHrcF+2/va9C74uhISAlqSsWbNGkpKSliwYAG5ubkMHTqU1atXs27dOpYsWcLIkSPJyclpvl8lGSIinaeH/iQsncYmy/uAEu9V3c59FwFDgRHYBHoIEOe94r1Xrza/x2BPtA8AX7b6eKrVPTOwo7KDpa5aOi1oSjKSkpLYvXs3AG63m4aGBjIyMgDIyspi5cqV5yTMrbX+jxAREf8t2noboP+PShga5b2yO/E1znivtnrT0rHjh5zy8Z6ElYD+XHT55Zezc+dOxo0bx65duxg1ahT9+tmRP3FxcVRWVgYyHBERERGR8wpowvzWW29x4403UlpayqxZs2hoaMDj8QDg8XiIj48PZDgiIiIiIucV0JIMYwwJCQkAuFwuvv76a4qKipg/fz6bN29m8uTJ59yvumURERERcVpAH/pzu93MmTOH2tpaYmJiWL16Na+99hr5+fkMGzaMFStW0LNnsI71EREREZFIFNCSjPj4eDZs2MDWrVvZtGkTCQkJPPXUU+Tl5fHFF1/Qr18/GhoaaGpqYu7cuaSnp5OVlUV5uZ2d+e6775KSksLNN9/cXMoh7fvuu++46qqriI2N9bmmo0ePJiMjg4yMDPbt2+dw1MGt7ZoCPPbYY2RkZPDAAw/Q2NgIaJ92hL9rqn3qn507d5KSkkJaWhqPP/44AEuXLmXq1Kncc8891NfXA9qjHeHvmmqP+q/tmtbX1zNlyhT69u1LWVnLtBLtU//4u57ao51kgkBNTY2pqKgw6enppr6+3uzevdvcf//9xhhj3nnnHfP888+buro6M3XqVFNfX29WrVplnnvuOYejDm7+rKkxxqSmpjoZZkhpu6ZFRUXm4YcfNsYYs2zZMrN27Vrt0w7yZ02N0T7119GjR01NTY0xxpi7777bbNu2zdx0003GGGMWL15s8vLytEc7yJ81NUZ7tCParmlxcbE5duyYuffee81XX31ljDHapx3gz3oaoz3aWUHRPTA2Npb+/fs3fz5kyBB69OgB2DKOxMREvvzyS6688kqio6PJysqisLDQqXBDgj9rClBRUcF1113HI488wpkz7fXVkbParumhQ4eYMGECABMnTmTHjh3apx3kz5qC9qm/Bg8eTGxsLADR0dEUFxef07qzsLBQe7SD/FlT0B7tiLZrGhUVxaBBg865R/vUf/6sJ2iPdlZQJMxtuVwuamtrGTNmDC+99BJ33HEHbrdbLeg6ob01Bfjkk0/Ytm0bw4cP59VXX3U4ytAyevRoPv74YwA++ugjKisrtU87qb01Be3TjiouLqa8vJz4+Pg/2Y/aoxfG15qC9uiFOLumY8eO/ZP3tE87ztd6gvZoZwVlwrxx40bi4uLYv38/ixYtYtmyZcTHx6sFXSe0t6ZAc9eS22+/nZKSEidDDDkTJ05k/PjxXH/99Xg8HgYNGqR92kntrSlon3ZERUUF8+bN44033mh3P2qPdtz51hS0Rzuq9Zq2R/u0Y863nqA92llBmTCbNu3nvv/+e6644gpKSkpobGxstwWd+NbemtbV1VFbWwtAQUEBSUlJToYYkhYsWMCWLVtITExk1qxZ2qddoO2aap/6r6Ghgblz57J06VIGDx7Mtdde23xif3Y/ao92jD9rqj3aMW3XtD3ap/7zZz21R7uA00XUxtji/mnTppn4+HiTmZlpCgsLzezZs016erpJS0szZWVlxhhj3n77bTNlyhQzc+ZM43a7HY46uPmzpseOHTPJyckmLS3NZGdnG4/H43TYQa29NU1PTzeZmZkmNze3+T7tU//5s6bap/5buXKlcblcJj093aSnp5vt27ebxYsXm9TUVHPXXXeZ2tpaY4z2aEf4s6baox3T3prm5OSYSy+91KSkpDQ/7Kt96h9/1lN7tPMC2odZRERERCTUBGVJhoiIiIhIsFDCLCIiIiLigxJmEREREREflDCLiIiIiPighFlERERExAclzCIiIiIiPihhFhERERHxQQmziEgYyM3NZcWKFQBkZ2fzxBNPADB//nzWrFnjYGQiIqFPCbOISBhITU2loKAAgMrKSj777DMAduzYQUpKipOhiYiEPCXMIiJhYNKkSRQVFXHgwAHGjRtHdHQ0Ho+HiooKBg8e7HR4IiIhLdrpAEREpPMuvvhievXqxQcffEBKSgoul4tXXnmF5ORkp0MTEQl5OmEWEQkTKSkpvPDCC6SmppKamsoLL7ygcgwRkS6ghFlEJEykpqbS0NBAUlISU6ZM4ejRo0qYRUS6QA9jjHE6CBERERGRYKUTZhERERERH5Qwi4iIiIj4oIRZRERERMQHJcwiIiIiIj4oYRYRERER8UEJs4iIiIiID0qYRURERER8UMIsIiIiIuLD/wPfbpeUcyUwmwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x288 with 1 Axes>\"\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   \"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   \"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   \"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   \"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_divergence(p_hist, J_hist,x_train, y_train)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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\": \"3.7.6\"\n  },\n  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Optional-Labs/Course-1-Week-1-Optional-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Optional-Labs/data/Course-1-Week-1-Optional-Labs-data.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Optional-Labs/data/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-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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  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Optional-Labs/images/Course-1-Week-1-Optional-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-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)\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    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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1-Practice-Quiz/Course-1-Week-1-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-1/Course-1-Week-1.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Lecture-Slides/Course-1-Week-2-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/Course-1-Week-2-Optional-Labs-1-Python-Numpy-Vectorization.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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\": 2,\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"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   \"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\": 3,\n   \"metadata\": {},\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 = [0.35056118 0.42794959 0.85728452 0.28565141], 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   \"source\": [\n    \"Some data creation routines do not take a shape tuple:\"\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      \"np.arange(4.):     a = [0. 1. 2. 3.], a shape = (4,), a data type = float64\\n\",\n      \"np.random.rand(4): a = [0.72143878 0.25299613 0.89534197 0.504286  ], 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   \"source\": [\n    \"values can be specified manually as well. \"\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      \"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   \"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   \"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\": 6,\n   \"metadata\": {},\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   \"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\": 7,\n   \"metadata\": {},\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   \"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\": 8,\n   \"metadata\": {},\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   \"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    \"$$ c_i = a_i + b_i $$\"\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      \"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   \"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\": 10,\n   \"metadata\": {},\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   \"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\": 11,\n   \"metadata\": {},\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   \"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   \"source\": [\n    \"<img src=\\\"./images/C1_W2_Lab04_dot_notrans.gif\\\" width=800> \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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\": 12,\n   \"metadata\": {},\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\": 13,\n   \"metadata\": {},\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   \"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\": 14,\n   \"metadata\": {},\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   \"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   \"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\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"np.dot(a, b) =  2501072.5817\\n\",\n      \"Vectorized version duration: 267.5600 ms \\n\",\n      \"my_dot(a, b) =  2501072.5817\\n\",\n      \"loop version duration: 8815.7332 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   \"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   \"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\": 16,\n   \"metadata\": {},\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   \"source\": [\n    \"<a name=\\\"toc_40015_4\\\"></a>\\n\",\n    \"# 4 Matrices\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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\": 17,\n   \"metadata\": {},\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   \"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\": 18,\n   \"metadata\": {},\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   \"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   \"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   \"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\": 19,\n   \"metadata\": {},\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   \"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   \"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   \"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\": 20,\n   \"metadata\": {},\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   \"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   \"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}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/Course-1-Week-2-Optional-Labs-2-Multiple-Linear-Regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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 <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    \"| $\\\\mathbf{A}$ | matrix, bold capital                                         ||\\n\",\n    \"| **Regression** |         |    |     |\\n\",\n    \"|  $\\\\mathbf{X}$ | training example maxtrix                  | `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   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\n   \"source\": [\n    \"**Expected Result**: Cost at optimal w : 1.5578904045996674e-12\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"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 (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   \"metadata\": {},\n   \"source\": [\n    \"In the next cell you will test the implementation. \"\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      \"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   \"metadata\": {},\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   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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iBgYHGuHHjjNOnT1u9Dsi3Te6t8mt/bBh5rXfr1KljuLi42MT37dtnDBkyxKhZs6ZRoUIFo3bt2kb37t2NhQsXWl5z8wad+dmyZYtx7733Gl5eXkbVqlWNYcOGGWfPnrXaIPSGmJgYIyQkxKhQoYJdG4h+/PHHRseOHQ1PT0/DbDYbPXr0sGqpfKf8btdiWEQkPxpnfpvGmcKNM9OmTTP8/Pys2uMbRt7/S7169TJ8fHysNqo2DMN4/fXXjUaNGhkeHh5G/fr1jejoaMt2Ajf/PdvbZv/06dOGq6ur8dZbb9mVsziHyTD0xLyIiIiIiDOdPn2aoKAgFi5cyLBhw4olh+joaBYvXszBgwfx8vIqlhzElp5BExERERFxspo1azJx4kTmzp1LTk6O0z8/PT2d119/naioKBVnJYxm0EREREREREoIzaCJiIiIiIiUECrQRERERERESohy02b/0qVLxZ2CiIgUEV9f3+JOoUhpDBMRKZvyG780gyYiIiIiIlJCqEATEREREREpIcrNEseblfWlMCIi5UF5XfanMUxEpHS70/ilGTQREREREZESQgWaiIiIiIhICaECTUREREREpIRQgSYiIuIAq1evpmnTpnh7e9OwYUO2bdsGwJIlSwgKCsJsNvPII49w8uRJq/ft3r2bzp07YzabqVGjBq+99lpxpC8iIiWECjQREZFC2rx5M5MnT2bp0qVcvnyZuLg4AgMD2bp1K1OnTmXDhg1cuHCBBg0aEBkZaXnfuXPneOSRRxgzZgznz5/n0KFDPPzww8V4JSIiUtxMhmEYxZ2EM9zcLUUdsERESr+S9L3esWNHRo0axahRo6yO/+UvfyEjI4OFCxcCcPLkSerUqcOhQ4do2LAhU6dO5eeff+bf//73b57fUde64wzU8IT6lQp8ChERKaQ7fadrBk1ERKQQcnJy2LlzJykpKQQFBREQEMCzzz5LRkYGhmFw833QG3/eu3cvANu3b6dKlSp07NiR6tWr06dPH44fP14keV7JgoGbIWQNvBoP2blF8jEiIlJITivQrl27xqhRo6hXrx4+Pj6EhYXxySefAJCcnIzJZMJsNlt+Zs2aZXmvYRhMnjwZf39//P39mTRpktWAl5ycTNeuXfHy8iI4OJgtW7Y4PP9HP4agFdDwfz8/pTr8I0REpBQ6c+YMWVlZxMTEsG3bNuLj49mzZw+zZ8+mV69erF27lsTERDIyMpg5cyYmk4mrV68CcOLECZYvX85rr73G8ePHbZZAOtKMH+DYZbiaDX/5D9y7DnalFMlHiYhIITitQMvOzqZu3bps3bqVS5cuMWvWLAYMGEBycrLlNRcvXiQ9PZ309HSmT59uOb548WI+/PBDEhISSExMZOPGjSxatMgSj4yMJCwsjPPnzzNnzhz69+9PSopjR53j6XA4DY787+e67jyKiAjg6ekJwLhx46hVqxZVq1ZlwoQJbNq0iW7duhEdHU14eDj16tWjfv36+Pj4EBAQYHlv3759adeuHRUrVmTGjBl89913Dt+Ee18qzE+0PrbnXF6R9udvIT3LoR8nIiKF4LQCzdvbm6ioKOrXr4+Liwu9e/emQYMG7Nq1647vXb58ORMnTiQgIIA6deowceJEli1bBsCBAwfYvXs30dHReHp6Eh4eTmhoKOvWrSvS6ykXD+6JiMgd+fn5ERAQgMlkyjc+duxYDh48yNmzZwkPDyc7O5vmzZsD0KJFC6v33fizox8Pb1IZFnWByu7Wx3ONvMItZDVsTHboR4qISAEV2zNoZ86c4cCBA4SEhFiO1atXj4CAAEaMGMG5c+csx5OSkmjZsqXl95YtW5KUlGSJBQYG4uPjk2/cUfIfdkVERGDEiBG88cYbnD17ltTUVObPn0/v3r3JzMxk7969GIbB8ePHGT16NOPHj8fPz8/yvvXr1xMfH09WVhazZs3igQceoHLlyg7Nz8UEo5rC/kiIDLKNH0+HPp9AxGdw6opDP1pERO5SsRRoWVlZDBkyhGHDhhEcHEzVqlX54YcfOHbsGLt27eLy5csMGTLE8vr09HSrDie+vr6kp6djGIZN7Eb88uXLRXoN5aP3pYiI2GP69Om0a9eOxo0b07RpU8LCwpg2bRqZmZkMHjwYs9nMvffeS4cOHayesX7ooYeYO3cujz76KNWrV+fQoUOsXLmyyPKs4QUre8Anj0J9H9t4zBEIXg1vJ+XNromIiPO5OfsDc3NzefLJJ3F3d2fBggUAmM1m2rZtC0CNGjVYsGABtWrVIi0tjUqVKmE2m0lLS7OcIy0tDbPZbGkscnPsRvzmGTVHuM3KFRERESpUqMCbb77Jm2++aXW8YsWKJCYm3uZdeZ555hmeeeaZokzPxiP3wN6BEPUD/CMRcm4qxtKuwzNx8O8DsLgLhFRxamoiIuWeU2fQDMNg1KhRnDlzhnXr1lGhQoV8X3frGvyQkBASEhIs8YSEBMvSyJCQEI4cOWI1Y3ZzvKjoxqKIiJRm3hXglY6wsz+0rWYb/+40hL0PL+yAzGzn5yciUl45tUB75pln2LdvH7GxsZauVwA7duzgp59+Ijc3l/Pnz/Pcc8/x4IMPWpYuDh06lHnz5vHLL79w8uRJXn31VYYPHw5A48aNadWqFdHR0WRmZrJ+/XoSExMJDw93aO6aQBMRkbKoVVXY3g/m3w/et6yrycqFObshdC18eaJ48hMRKW+ctsTx2LFjLFq0CA8PD2rWrGk5vmjRIlxcXJg6dSpnz56lUqVK9OjRg1WrVlleM2bMGI4cOUJoaCgAf/jDHxgzZowlvnr1aoYPH46fnx/33HMPMTExVKuWz+1AERERseHqAuNbQN8G8Ow2iD1mHT90CbrFwrAm8H8doKpn/ucREZHCMxmO7uVbQt28p8ytTUXs0WotJJz/9ffd/SFMNaCISLEp7Pd6aeLMazUM+OAIjPsGTl21jVetCPM6whON9Xy2iEhB3Ok7vdja7Jc2GoRERKQ8MJkgvCHsGwR/DLFd4n8uE4Z+CQ9vhMOO3U9bRERQgVZg5WLaUUREyi1fD1jYGb7tC83z6eS45QQ0XwN/2w1ZOc7PT0SkrFKBZidNoImISHnUoSbs6g9z7wMPV+tYZg5M3QFtYmD76eLJT0SkrFGBVkCaQRMRkfLC3RX+2jpv77RudWzj/70AHdfD2Di4dM35+YmIlCUq0OykGTQRESnvgnxhcx9Y/hD4V7SOGcCbSdBsTV6TkfLRgkxExPFUoImIiIjdTCYY2gT2D4KhjW3jJ69A+Gfw+Kfwc7rz8xMRKe1UoBWQ7gyKiEh5VtUTlneDLX3yZtZu9VEyNFsNrydCTq7T0xMRKbVUoNlJbfZFRERsdQuAxAEwtTW43fKvivQsGP8tdFgPCeeKJz8RkdJGBVoBaQJNREQkj6cbzLkP9vSHDjVs4z+czev0OOk/cDXL+fmJiJQmKtDspAk0ERGR39bcH77pC291hkru1rEcA16Jz9s77bPjxZOfiEhpoAKtgPQMmoiIiC0XEzwdAvsGQf9A2/jRy/DIxzBkC5y96vz8RERKOhVodtIzaCIiIvar7Q3v/w4+6gl1zbbxlQcheDX8c59ueoqI3EwFWgFpLBEREbmzPvUhaSCMD82bXbtZ6jX4w9fQ9SP4KbU4shMRKXlUoNlJE2giIiIF4+MO8x+AHf2gVVXb+NaT0GItzNwJ13Kcn5+ISEmiAk1EREScom11+CEcXukAXm7Wseu5MOMHaLUWtp0snvxEREoCFWgFpPXyIiIid8/NBf7SCvYOhEfq2sb3X4TOG+Cpr+FCptPTExEpdirQ7KQljiIiIo7ToBJsehRWdYfqnrbxJfsgeBWsOKCboiJSvqhAKyCNFSIiIoVjMsGgRnkt+f/Q1DaekglPfAEPb4RDl5yfn4hIcXBagXbt2jVGjRpFvXr18PHxISwsjE8++QSA7du306NHD6pUqUK1atWIiIjg1KlTlvdGRUVRoUIFzGaz5efIkSOWeHJyMl27dsXLy4vg4GC2bNni8PzVZl9ERKRoVKkI7zwIWx+D4Mq28S0n8ja4nr1LTUREpOxzWoGWnZ1N3bp12bp1K5cuXWLWrFkMGDCA5ORkUlNTGT16NMnJyRw7dgwfHx9GjBhh9f6BAweSnp5u+QkM/HX3y8jISMLCwjh//jxz5syhf//+pKSkFOn1aAZNRETEsTrXhvgBMOte8HC1jl3LgenfQ9j7EKcmIiJShjmtQPP29iYqKor69evj4uJC7969adCgAbt27aJnz55ERERQqVIlvLy8ePbZZ/n222/tOu+BAwfYvXs30dHReHp6Eh4eTmhoKOvWrXNo/ppAExERKXoervBCG/jvAOhWxza+LxW6bIBRX8F5NRERkTKo2J5BO3PmDAcOHCAkJMQmFhcXZ3M8NjaWKlWqEBISwltvvWU5npSURGBgID4+PpZjLVu2JCkpqeiSRw8si4iIFKVGlWFzH3ivG1SraBt/d39eE5F//6QxWUTKlmIp0LKyshgyZAjDhg0jODjYKpaYmMjMmTN55ZVXLMcGDBjAvn37SElJ4Z133mHmzJmsWrUKgPT0dHx9fa3O4evry+XLl4v+QkRERKTImEwwpDHsj8y/ici5TBj6JXSPhQMXnZ+fiEhRcHqBlpuby5NPPom7uzsLFiywih06dIiePXvy2muv0alTJ8vxZs2aUbt2bVxdXenYsSPjx48nJiYGALPZTFpamtV50tLSrGbUHEFNQkRERIrHjSYicY9BUz/b+Je/QIu1MGunmoiISOnn1ALNMAxGjRrFmTNnWLduHRUqVLDEjh07Rvfu3Zk+fTpPPvnkb57HZDJh/G89Q0hICEeOHLGaMUtISMh36aQjaTWFiIiIc3WqDfERMPs2TURe/AFaroWtaiIiIqWYUwu0Z555hn379hEbG4un56+7Uv7yyy889NBDjB07lqefftrmfRs2bCA1NRXDMPj+++95/fXXeeyxxwBo3LgxrVq1Ijo6mszMTNavX09iYiLh4eEOzV0TaCIiIsXP3RWmtYG9A6FHgG38p4vw4AYY+RWcy3B+fiIihWUyDOc8Wnvs2DHq16+Ph4cHbm5uluOLFi3i0KFDREVF4e3tbfWe9PR0IK+N/ueff861a9cICAjgj3/8I88995zldcnJyQwfPpwdO3Zwzz33sHDhQrp37251rkuXft3h8tZn1uzxwHr49vSvv8c9lncnT0REikdhv9dLk/J0rXfDMGDVQfjzd3A2n2LMvyK82gGGNtGjCiJSctzpO91pBVpxK+zg1mk9fKMCTUSkxChPRUt5utaCuJAJU7bDO/vyjz9YG97uDE3yeX5NRMTZ7vSdXmxt9ku7clHVioiIlAJVKsLiB+GbxyEknyLs65N5TUSif1ATEREp+VSg2UlLI0REREq2+2vB7giYex9UvKWJyPVciNoJLdbA178UT34iIvZQgSYiIiJlhrsr/LV1XhORh+vaxg9cgq4fwfAv1UREREomFWgFpCWOIiJys9WrV9O0aVO8vb1p2LAh27ZtA2DJkiUEBQVhNpt55JFHOHnStgf89evXCQ4OJiAgn7aEUiANfeHTR2FVd6jhaRtf/hMEr4Zl+/OajYiIlBQq0OykFY4iInI7mzdvZvLkySxdupTLly8TFxdHYGAgW7duZerUqWzYsIELFy7QoEEDIiMjbd7/yiuvUL169WLIvGwzmWBQI9gXCWOa2cbPZ8KIr/Jm1PanOj8/EZH8qEArIN1tExGRG2bMmMGLL75I+/btcXFxoU6dOtSpU4fY2FgiIiIICQnB3d2d6dOnExcXx+HDhy3vPXr0KO+99x5//etfi/EKyjY/D3i7C3zbF5pXsY1v/V8TkRnfQ2a28/MTEbmZCjQ7qUmIiIjkJycnh507d5KSkkJQUBABAQE8++yzZGRkYBgGN+9mc+PPe/futRwbN24cc+fOxdMzn3V44lAda8Lu/vBSe/B0s45l5cLMXXmF2pcniic/ERFQgVZgmkATERGAM2fOkJWVRUxMDNu2bSM+Pp49e/Ywe/ZsevXqxdq1a0lMTCQjI4OZM2diMpm4evUqAOvXryc7O5u+ffsW81WUHxVcYXJYXhOR3+XTROTgJegWC0O/gOIEsBAAACAASURBVBQ1ERGRYqACzU6aQBMRkfzcmPkaN24ctWrVomrVqkyYMIFNmzbRrVs3oqOjCQ8Pp169etSvXx8fHx8CAgK4cuUKkyZN4o033ijmKyifAivBJ4/C6h75NxH59wEIXgXv7tNjDSLiXCrQCkhf1iIiAuDn50dAQACm26yFHzt2LAcPHuTs2bOEh4eTnZ1N8+bNOXjwIMnJyXTq1ImaNWvSr18/Tp06Rc2aNUlOTnbuRZRTJhMMDIL9kfB0M9ubsReuwaiv4cENsE9NRETESVSg2UkzaCIicjsjRozgjTfe4OzZs6SmpjJ//nx69+5NZmYme/fuxTAMjh8/zujRoxk/fjx+fn40b96cn3/+mfj4eOLj41myZAk1atQgPj6eunXzWXsnRaayB7z1vyYiofk0EYk7BS3XwotqIiIiTqACTUREpJCmT59Ou3btaNy4MU2bNiUsLIxp06aRmZnJ4MGDMZvN3HvvvXTo0IFZs2YB4ObmRs2aNS0/VapUwcXFhZo1a+Lq6lrMV1Q+dagJu/rD32/TRGTWLghdC1vUREREipDJMMrHYr1Lly5Z/uzr63vX739oA3x1096iW/pAN+0nKiJSbAr7vV6alKdrLSmOpsHYbfDJ8fzjTzSGVztAdS/n5iUipd+dvtM1g2YntdkXEREpPxpUgo97wdqHoWY+Rdh7ByB4NbzzI+SWi1vdIuIsKtAKSN/FIiIiZZvJBBENYf8g+GOI7fPoqddg9Fbo9CH893yxpCgiZZAKNDtpAk1ERKR88vWAhZ3hP/2ghb9t/LvT0DoGJv8HrmQ5Pz8RKVtUoBVQ+XhyT0RERG64rwbsDIdXOoDXLU1EsnPh5Xhothpik4slPREpI1Sg2UnPoImIiEgFV/hLK/hxEPSpZxs/ng6//wT6fQo/pzs/PxEp/ZxWoF27do1Ro0ZRr149fHx8CAsL45NPPrHEv/jiC4KDg/Hy8qJr164cO3bMEjMMg8mTJ+Pv74+/vz+TJk3i5uaTycnJdO3aFS8vL4KDg9myZYuzLktERETKoXo+sKEnrH8EArxt4+uPQtNVMC8hb3ZNRMReTivQsrOzqVu3Llu3buXSpUvMmjWLAQMGkJyczLlz5+jXrx+zZs3iwoULtG3bloEDB1reu3jxYj788EMSEhJITExk48aNLFq0yBKPjIwkLCyM8+fPM2fOHPr3709KSkqRXo9WOIqIiJRvJhM83gD2RcLEluB6y2qbK9kw8TtoGwPbTxdPjiJS+hTrPmgtWrRgxowZnD9/nmXLlvHdd98BcOXKFapWrcqePXsIDg6mY8eODB8+nNGjRwPwz3/+k3feeYft27dz4MABQkNDOXfuHD4+PgB06tSJIUOG8PTTT1s+q7B7yDwcC5tv2pjys97wcN2CXLWIiDhCedobrDxda2mWcA6ejoPtZ2xjJmB0M/hbe/DzcHpqIlKClNh90M6cOcOBAwcICQkhKSmJli1bWmLe3t40bNiQpKQkAJt4y5YtrWKBgYGW4uzWeFFRkxARERG5Wcuq8G1feLszVHa3jhnAoh8heBWsOKB/R4jI7RVLgZaVlcWQIUMYNmwYwcHBpKen21SPvr6+XL58GcAm7uvrS3p6OoZh3PG9jqImISIiInInLiYYEwL7I2FII9v42Qx44gvoHgs/pTo/PxEp+ZxeoOXm5vLkk0/i7u7OggULADCbzaSlpVm9Li0tzTIrdms8LS0Ns9mMyWS643uLim58iYiIyO3U8IL3usOWPtA4n1WpX/4CLdbCjO8hM9v5+YlIyeXUAs0wDEaNGsWZM2dYt24dFSpUACAkJISEhATL665cucLhw4cJCQnJN56QkGAVO3LkiNWM2c1xR9EEmoiIiNytbgGQOBCi24GHq3Xsei7M3AWha2Hzz8WTn4iUPE4t0J555hn27dtHbGwsnp6eluN9+/Zl7969rFu3jszMTGbOnEmLFi0IDg4GYOjQocybN49ffvmFkydP8uqrrzJ8+HAAGjduTKtWrYiOjiYzM5P169eTmJhIeHh4kV6L1o6LiIiIPTxc4cW28N8B0D3ANn7oEjy8EQZvhtNXnZ+fiJQsTivQjh07xqJFi4iPj6dmzZqYzWbMZjMrVqygWrVqrFu3jmnTpuHn58eOHTtYvXq15b1jxoyhT58+hIaG0rx5cx599FHGjBljia9evZqdO3fi5+fHlClTiImJoVq1ag7NXzNoIiIiUhiNKsPnvWFld6jhaRtfdSivicibeyFHe6eJlFvF2mbfmQrborjnRvj0puUHm3pBz3qOyExERAqiPLWeL0/XWl5cvAbTdsBbSfk/135v9bxukGGOvd8sIiVAiW2zX9qVi6pWREREikRlD1jYGbb3g1ZVbePfn4W26+DP38Ll687PT0SKjwo0O6nNvoiIiDjavTXgh3D4R0cwV7CO5RowPxGaroZ1h/X8u0h5oQKtgPQdKSIiIo7g5gJ/agn7BkF4oG38lyvQ/3PovQmOptnGRaRsUYFmJ02giYiISFEKMEPM72BjL6ifz3aum45DyBp4aTdcz3F+fiLiHCrQCkjLDERERKQoPFoPkgbCX8PyZtdulpENf90Brd+HbSeLJz8RKVoq0OykZ9BERETEWbwqwNz2EB8BD9S0jSelQucNMOorOJfh/PxEpOioQCsgTaCJiIhIUQupAlsfh3e7gn9F2/i7+yF4NSzdr9U9ImWFCjQ7aQJNREREioOLCUYEw/5BMDLYNn4+E0Z+BV02QNIF5+cnIo6lAk1ERESkFKjqCf/sClsfg2Z+tvFtp6DV+zB1O1zNcn5+IuIYKtAKSMsIREREpDh0rg17IuBv94Gnm3UsOxf+tiev2+OmY8WTn4gUjgo0O2mJo4iIiJQU7q4wpXVet8de99jGky/Do5ug/2dwIt35+YlIwalAKyBNoImIiEhxa1Apb9+0mIehtrdtfN0RaLoaXkvMm10TkZJPBZqd1GZfRERESiKTCcIb5jUR+VOLvKYiN0vPgj99C+3WwfbTxZOjiNhPBVoBaQZNREREShIfd/jH/bAzHNpVt43Hn4OO62HMVriQ6fz8RMQ+KtDspAk0ERERKQ3CqsF/+sLCTlDJ3TpmAIt/hOBVsFx7p4mUSCrQRERERMoYVxf4Y3P4KRIGN7KNp2TCcO2dJlIiqUArIN1xEhERkZKuphes6A5b+kBjX9v4jb3TJv8HrmjvNJESQQWandQkREREREqrbgGQOBBm3QsVXa1j2bnwcnxet8cPj+omtEhxc2qBtmDBAtq2bYuHhwfDhw+3HF+xYgVms9ny4+XlhclkYteuXQBERUVRoUIFq9ccOXLE8v7k5GS6du2Kl5cXwcHBbNmypcivRd9dIiIiUpp4uMILbSBpUP57p/2cDn0/hT6fwNE05+cnInmcWqDVrl2bF154gZEjR1odHzJkCOnp6ZafN998k8DAQFq3bm15zcCBA61eExgYaIlFRkYSFhbG+fPnmTNnDv379yclJcWhuWsCTUREfsvq1atp2rQp3t7eNGzYkG3btgGwZMkSgoKCMJvNPPLII5w8edLynldeeYXmzZvj4+NDgwYNeOWVV4orfSlHAv+3d9oHv4OAfPZO+/gYhKyBubvgeo7z8xMp75xaoPXr14/HH38cf3//33zd8uXLGTp0KCY71hUeOHCA3bt3Ex0djaenJ+Hh4YSGhrJu3TpHpZ0vTf+LiMgNmzdvZvLkySxdupTLly8TFxdHYGAgW7duZerUqWzYsIELFy7QoEEDIiMjLe8zDIN//etfpKam8umnn7JgwQJWr15djFci5YXJBH0DYV8k/KUluN7yT66MbJj2PbRcC1+eKJ4cRcqrEvcM2rFjx4iLi2Po0KFWx2NjY6lSpQohISG89dZbluNJSUkEBgbi4+NjOdayZUuSkpIcmpdm0ERE5HZmzJjBiy++SPv27XFxcaFOnTrUqVOH2NhYIiIiCAkJwd3dnenTpxMXF8fhw4cBmDRpEq1bt8bNzY0mTZrw2GOP8e233xbz1Uh5Yq4Ar3SEPRFwf03b+P6L0C0WntgCp686Pz+R8qjEFWj/+te/6NSpEw0aNLAcGzBgAPv27SMlJYV33nmHmTNnsmrVKgDS09Px9bVuS+Tr68vly5eLNE9NoImICEBOTg47d+4kJSWFoKAgAgICePbZZ8nIyMAwDIybllzc+PPevXttzmMYBtu2bSMkJMRpuYvcEOoPcY/Du13Bv6JtfMXBvL3TFu6FnFzn5ydSnpTIAm3YsGFWx5o1a0bt2rVxdXWlY8eOjB8/npiYGADMZjNpadZPsqalpVnNqDmCujiKiEh+zpw5Q1ZWFjExMWzbto34+Hj27NnD7Nmz6dWrF2vXriUxMZGMjAxmzpyJyWTi6lXbqYioqChyc3MZMWJEMVyFCLiYYERw3t5pTzW1jV+6Ds9ug/YfwM6zzs9PpLywu0AbOXJkvrNSV65csWn6UVDffvstJ0+epH///r/5OpPJZLkLGRISwpEjR6xyS0hI0B1IERGxS2HHN09PTwDGjRtHrVq1qFq1KhMmTGDTpk1069aN6OhowsPDqVevHvXr18fHx4eAgACrcyxYsIB//etffPzxx3h4eDjmwkQKyL8iLH4QvusLLfNpG7AzBe5dB2Pj4OI1p6cnUubZXaAtX76cjIwMm+MZGRn861//susc2dnZZGZmkpOTQ05ODpmZmWRnZ1t9Rnh4uM3s14YNG0hNTcUwDL7//ntef/11HnvsMQAaN25Mq1atiI6OJjMzk/Xr15OYmEh4eLi9l1YgWuIoIlI2FHZ88/PzIyAg4LaNrcaOHcvBgwc5e/Ys4eHhZGdn07x5c0v83Xff5aWXXuKLL76wKdxEilOHmrCzP8zrmPes2s0M4M0kaLIK3jug5mkijmR3gWYYhs3gYxgG33zzDdWqVbPrHLNnz8bT05OXXnqJ9957D09PT2bPng1AZmYma9eutVneCHmti4OCgvDx8WHo0KFMnjzZ6nWrV69m586d+Pn5MWXKFGJiYuzOyV5a4SgiUjY5YnwbMWIEb7zxBmfPniU1NZX58+fTu3dvMjMz2bt3L4ZhcPz4cUaPHs348ePx8/MD8vYBnTp1Kps3b7baPkakpHBzgT+3hP2DIKKhbfxsBjz5BXT7CPanOj8/kbLIZBi/fc/DxcXlju3ux48fz7x58xyamKNdunTJ8udbm4rYI+IziPl1b2zW9IABQY7ITERECqKw3+uOHN+ysrIYP348K1eupGLFigwYMICXX36ZzMxMOnfuzOHDh/Hx8WHEiBHMnj0bV1dXABo0aMCJEyesljU+8cQTvP3221bnL+y1ijjKZ8dh7DY4nM9G1hVc4PlWMK01eFWwjYtInjt9p9+xQFuxYgWGYTB06FAWLFhgdRJ3d3caNGhA27ZtHZhy0Sjs4Dbgc3j/8K+/q0ATESlehf1eL03jmwo0KUkys+GlPfC33XA9n46O9X3gjQegd32npyZSKtzpO93tTicYMmQIAHXr1uX+++/Hze2ObykXtNRaRKR00/gmUjAV3SCqHQxplDebtvmWjayTL0OfT+DxBvDa/XCPYxtri5R5dj+D1qhRI86e/bWn6p49e3j++edZtmxZUeRV4ugZNBGRsqm8j28iBdWoMnzWO29VUS0v2/iHR6Hpanh5D2TlOD8/kdLKNSoqKsqeF/bp0wdvb2/CwsI4f/48bdq04dSpU6xZswY3Nzfuv//+Ik61cK5d+7UPbMWK+ezAeAcxhyHppodfwwOheT6tZ0VExDkK+71+Q2kY3xx1rSKOZjJBSBV4qhlk5sD3Z61XGWXlwpYTsO4IhFaBeppNE7njd7rdM2h79+7lvvvuA+CDDz4gMDCQH3/8keXLl7NkyRIHpFqyaaNqEZGyqbyPbyKOUMkd/nE/7OoP7WvYxn9MhS4bYPiXkGK7q4WI3MTuAu3KlStUqlQJgC+//JI+ffoA0LZtW37++eeiyU5ERKSIaXwTcZxWVeHbvrC4C/jls+f68p/y9k5blAS5eqBfJF92F2gNGjQgLi6O9PR0Nm/eTPfu3QFISUmx2Vi6PNB3iohI2aDxTcSxXEx5Sx5/ioThTWzjqdfg6Tjo8AHsSXF+fiIlnd0F2oQJExg+fDh169blnnvusazJj4uLo3nz5kWWYEmhFY4iImVTeR/fRIpKNU9Y+hDEPQYhfrbx789C23Uw/htIu+78/ERKqjvug3az3bt3c/z4cXr06IG3tzcAH330EX5+fnTq1KnIknSEwu4hE7kZVh/69fcV3WBwY0dkJiIiBeHIvcFK+vimfdCktMvKgfmJELUTrmbbxmt55T3DNqChnvuXsq/QG1WXFYUd3AZvhlUq0ERESozyVLSUp2uVsu34ZfjTt7D+aP7x7gGwsBM0ruzcvESc6U7f6XYvcQT46quv6N69O7Vq1aJ27dr06NGDr7/+utBJlkbloqoVESknNL6JOMc9PvDBI7CxF9TP5xHPLScgdA1M/x6uZjk/P5GSwO4CbdWqVXTv3p1KlSoxZcoUJk2ahNlspnv37qxZs6YocywRNN0uIlI2lffxTaQ4PFoPkgbCtNZQ4ZZ/jV7Phdm7IGQNxCYXS3oixcruJY4hISE88cQT/PWvf7U6PnfuXFauXMnevXuLJEFHKezykCFbYOXBX39/rxsM0RJHEZFi46hlf6VhfNMSRynL9qfCH+Pgq5P5x/vUg9cegAaVnJuXSFFx2BLHQ4cOERERYXN8wIABHDp0KJ93lG1a4igiUjZofBMpXsF+8MXv857vr+llG489Bs1Ww5xdcC3H+fmJOJvdBVq1atVITEy0OR4fH0+1atUcmlRJpBWOIiJlU3kf30RKApMpr/na/kHwXGjeXmo3y8yBF76HFmtgs/aPlzLOzd4XPvHEE4wZM4aUlBQ6deqEyWRi69atTJ8+naeeeqoocyyRykfvSxGRsk/jm0jJ4euRt5xxRHDessf/nLGOH7gED2+EiIYwryMEmIsnT5GiZHeBNnv2bHJychg/fjxZWVkYhoGHhwfPPfccM2fOLMocSwQ1CRERKZvK+/gmUhK1qgrf9IVl+2HydjiXaR1//zBsOgZR7WB8KFRwLZ48RYrCHZuE5ObmsnfvXho1aoSnpycZGRmWNfkNGzbk0KFDNG/eHBeXu+rY73SFfcD6yS/gvQO//r78IRjaxBGZiYhIQRT2e700jW9qEiLl2YVMmLoDFv+Yfw+AED94szN0ru301EQKpNBNQlasWMHQoUNxd3cHwNPTk9DQUEJDQ3F3d2fo0KF2tyFesGABbdu2xcPDg+HDh1uOJycnYzKZMJvNlp9Zs2ZZ4oZhMHnyZPz9/fH392fSpEncXFcmJyfTtWtXvLy8CA4OZsuWLXblczc0gSYiUrY4cnwTkaJTpSK83QW294M2+TwWmpQKXTbk3Uw/fdX5+Yk42h0LtH/+859MnDgRV1fbuWM3Nzf+8pe/sHjxYrs+rHbt2rzwwguMHDky3/jFixdJT08nPT2d6dOnW44vXryYDz/8kISEBBITE9m4cSOLFi2yxCMjIwkLC+P8+fPMmTOH/v37k5KSYldOBaVn0ERESjdHjm8iUvTurQE7+sGbnaCyu238vQPQZBUs+C/k5Do/PxFHuWOBtn//fjp27HjbeIcOHdi3b59dH9avXz8ef/xx/P397c8QWL58ORMnTiQgIIA6deowceJEli1bBsCBAwfYvXs30dHReHp6Eh4eTmhoKOvWrburz7gTzaCJiJQtjhzfRMQ5XF3gmebwUyQMy+dRk7TrMO4baLcOtp92fn4ijnDHAu3SpUtkZWXdNn79+nXS0tIckky9evUICAhgxIgRnDt3znI8KSmJli1bWn5v2bIlSUlJllhgYCA+Pj75xkVERPLjzPFNRByruhcsewi2PQ6hVWzje85Bh/Xw1NdwPtM2LlKS3bFAq1evHvHx8beNx8fHc8899xQqiapVq/LDDz9w7Ngxdu3axeXLlxkyZIglnp6ebvUAna+vL+np6RiGYRO7Eb98+XKhcroTrXAUESndnDG+iUjReqAW7Oqf13LfXME2vmQfNF4J7/wIufrHm5QSdyzQfv/73zN9+nTS09NtYmlpacyYMYM+ffoUKgmz2Uzbtm1xc3OjRo0aLFiwgM8//9xy59JsNlvdxUxLS8NsNlsai9x6hzMtLc1qRs0R1GZfRKRsccb4JiJFr4Ir/Lll3rLHQUG28QvXYPRW6PgB7C7aFgUiDnHHAm3KlClcv36dxo0b87e//Y0PP/yQDRs2MHfuXIKDg7l27RpTpkxxaFKm/1VDNzo1hoSEkJCQYIknJCQQEhJiiR05csRqxuzmeFHRTRgRkdKtOMY3ESk6tb1hVQ/Y0geaVLaN7zib92zauG1w8Zrz8xOx1x03qq5SpQrfffcdTz/9NNOnTyc3N68tjouLCz179uTNN9+0u+lHdnY22dnZ5OTkkJOTQ2ZmJm5ubuzatYvKlSvTqFEjUlNTee6553jwwQctSxeHDh3KvHnz6NWrFyaTiVdffZVx48YB0LhxY1q1akV0dDSzZ8/mk08+ITExUU1CRETkNzlyfBORkqNbACQOgHkJMGsXXM3+NZZrwIK9sPYw/F8HeKKxVklJyXPHAg2gTp06xMbGkpqayqFDhzAMg0aNGuHn53dXHzZ79myio6Mtv7/33nvMmDGDJk2aMHXqVM6ePUulSpXo0aMHq1atsrxuzJgxHDlyhNDQUAD+8Ic/MGbMGEt89erVDB8+HD8/P+655x5iYmKoVi2fjTIcSG32RURKP0eNbyJSsri7wpTWENkI/vQtfHjUOn42A4Z+mfeM2sJO0Fz3YqQEMRlG+Sg17rRj952M/AqW7v/1938+CCObOiAxEREpkMJ+r5cm5elaRYrCpmN57feP5NOY1dUEf2oBM9qCTz77q4k42p2+0+/4DJrkr1xUtSIiIiJlQK96sHdgXhHmccve9DkGvJoATVfD2kNaJSXFTwWaiIiIiJR5nm4Q1S6vUOuZzw4av1yBgZvhdxvhwEXn5ydygwo0O+n5UREREZHSL8gXPu4FH/wO6ppt45tPQOgaeGEHXL39XvYiRUYFWgFp+ltERESkdDKZoG8g7BsEk8PA7ZZ/EV/PhTm7odka+Oho/ucQKSoq0OykGTQRERGRssW7ArzUPq8tf9fatvFjl+GxT6HPJjiaT4MRkaKgAq2ANIEmIiIiUjY09YMvfg8ru0MtL9v4xmPQbDXM2gnXcpyfn5QvKtDspE0MRURERMoukylv37T9kXlt911v+bdfZg68+AM0XwOfHS+eHKV8UIFWQJpBExERESl7KrnDP+6HXf3h/pq28UOX4JGPIeIzOJHu/Pyk7FOBZidNoImIyG9ZvXo1TZs2xdvbm4YNG7Jt2zYAlixZQlBQEGazmUceeYSTJ09a3mMYBpMnT8bf3x9/f38mTZqEoS5UIiVCy6oQ9zgs7QpVK9rGY45A8Cp4eQ9c17JHcSAVaCIiIoW0efNmJk+ezNKlS7l8+TJxcXEEBgaydetWpk6dyoYNG7hw4QINGjQgMjLS8r7Fixfz4YcfkpCQQGJiIhs3bmTRokXFeCUicjMXEwwPhp8i4elmtjfsr2TD5O3Qci1sOVEsKUoZpAKtgHSDU0REbpgxYwYvvvgi7du3x8XFhTp16lCnTh1iY2OJiIggJCQEd3d3pk+fTlxcHIcPHwZg+fLlTJw4kYCAAOrUqcPEiRNZtmxZ8V6MiNioUhHe6gI7wqFtNdv4/ovQIxYGfq5lj1J4KtDspCYhIiKSn5ycHHbu3ElKSgpBQUEEBATw7LPPkpGRgWEYVksWb/x57969ACQlJdGyZUtLvGXLliQlJTn3AkTEbu2qw/Z+8FZn8POwja89rGWPUngq0ApIE2giIgJw5swZsrKyiImJYdu2bcTHx7Nnzx5mz55Nr169WLt2LYmJiWRkZDBz5kxMJhNXr14FID09HV9fX8u5fH19SU9P13NoIiWYqws8HQIHIuEPTW3jWvYohaUCzU6aQBMRkfx4enoCMG7cOGrVqkXVqlWZMGECmzZtolu3bkRHRxMeHk69evWoX78+Pj4+BAQEAGA2m0lL+3X327S0NMxmMyYt2xAp8ap6wjsP5s2otfmNZY8DtOxR7pIKtALSzU0REQHw8/MjICDgtkXV2LFjOXjwIGfPniU8PJzs7GyaN28OQEhICAkJCZbXJiQkEBIS4pS8RcQx7qsBO/rB27dZ9vj+/5Y9/l3LHsVOKtDspHuZIiJyOyNGjOCNN97g7NmzpKamMn/+fHr37k1mZiZ79+7FMAyOHz/O6NGjGT9+PH5+fgAMHTqUefPm8csvv3Dy5EleffVVhg8fXrwXIyJ3zdUFxtxh2eOU7dBCyx7FDirQCkgTaCIicsP06dNp164djRs3pmnTpoSFhTFt2jQyMzMZPHgwZrOZe++9lw4dOjBr1izL+8aMGUOfPn0IDQ2lefPmPProo4wZM6YYr0RECuNOyx5/0rJHsYPJKCdPIl+6dMny55sfyLbXH+PgrZsaay3sBH9s7ojMRESkIAr7vV6alKdrFSkrcnJhyT746w5IvWYb93aD6W3hzy3A3dX5+UnxudN3ulNn0BYsWEDbtm3x8PCwWsKxfft2evToQZUqVahWrRoRERGcOnXKEo+KiqJChQqYzWbLz5EjRyzx5ORkunbtipeXF8HBwWzZssWZlyUiIiIiYuXmZY9PNc1/k2ste5T8OLVAq127Ni+88AIjR460Op6amsro0aNJTk7m2LFj+Pj4MGLECKvXDBw4kPT0dMtPYGCgJRYZGUlYWBjnz59nzpw59O/fn5SUlCK9lnIx7SgiIiIihVLVExY/mLfsMb9NrrXsUW7l1AKtX79+PP744/j7+1sd79mzJxEREVSqVAkvLy+effZZvv32W7vOeeDA1CyugAAAIABJREFUAXbv3k10dDSenp6Eh4cTGhrKunXrHJq7moSIiIiISEHdWyOvSFO3R7mTEtkkJC4uzqbNcGxsLFWqVPn/9u49LOpq7Rv4d0TOhwFRQcBMVARRwTamtik8oan5pqDlKdEtO+1N9ElM3CqSSlf2mG7d2i7LXtNoy6Pg4dJtT4nbjLbnUnziMU0BC3KDB2IYBDnd7x9sRocBGWCYGZjv57q84rfumfndvzU6q5u1fmsQGBiIDz74QNOemZkJX19fODs7a9qCgoKQmZmJ1mQZd+4RERERkaFw2SPpw+wKtMuXL2Pt2rXYsGGDpu2ll17ClStXcPv2bXz88cdYu3Yt9uzZAwBQq9U6N9cplUoUFxcbNC9+ZygRERERGUJTlj3+wmWPFsesCrTr169j3Lhx2LJlC5599llNe79+/eDl5QUrKys888wzWLx4MVJSUgAATk5OUKlUWq+jUqm0ZtRaAyfQiIiIiKglHl322InLHunfzKZAu3nzJkaPHo34+Hi88sorj32sQqFA7bcDBAYGIisrS2vGLCMjQ2eJZEtxAo2IiIiIDK122ePVBpY93ueyR4tj1AKtsrISZWVlqKqqQlVVFcrKylBZWYm8vDyMHDkSr7/+OhYsWKDzvEOHDqGwsBAignPnzuEvf/kLXnzxRQCAn58fgoODsWbNGpSVleHAgQO4fPkyIiMjW/VaeA8aERERERkKlz1SLaMWaImJibC3t8f69euRlJQEe3t7JCYmYseOHcjKysKaNWu0vuusVnJyMnr37g1nZ2fMnj0bcXFxiIqK0opfuHABbm5uWL58OVJSUtClSz1/s4mIiIiIzBiXPZJCxDLmghr7xu7GLPoW2Po/D4+3/B5YNNAQmRERUXO09HO9LbGkayWih+6UAivOAjuu1L//QV9XYGsoEN7d6KlRCzT2mW4296C1NRZR1RIRERGRyeiz7HHMEWDql1z22J6wQNMTNwkhIiIiIlOoXfa4Paz+ZY8pWVz22J6wQGsmy1gYSkRERETmwKoD8Go/4NqMmv8+brfHY7+YJEUyEBZoeuIMGhERERGZmrtdzUwalz22XyzQmokTaERERERkKvoue1z/PZc9tjUs0PSk4BQaEREREZkRfZY9/ukslz22NSzQiIiIiIjasNplj2cjH7/sccqXwM1i4+dHTcMCrZm4xJGIiIiIzMngro9f9piaBQQkA4nfAWWVxs+P9MMCTU9c4UhERERE5q6xZY+llUD8OSDwv4AjOabIkBrDAq2ZuM0+EREREZmrR5c9Du6qG89SARO/AF44ClwvMn5+1DAWaHriJiFERERE1NbULnvcMRzobKcb//tNIDAZWHUWKKkwenpUDxZozcQJNCIiIiJqCzoogHkBNcseF/avOX5UeTXw9vc196el3OBKMVNjgaYnTqARERERUVvmZgtsfRb4fgoQ6qkb/0UNTP0KCD8MXCk0fn5UgwVaM/E3C0RERETUFgV1Br6ZBCSNAjwddOPH82q+O23pKUBVbvz8LB0LND1xBo2IiIiI2guFApjpB1ydDiwNAjrWqQoqq4GNGYD/HuDza5ycMCYWaEREREREFsrFBtjwDHD5JWCUt2781n1g1nHguYNAxh3j52eJWKA1E3+JQERERETtRYAbcGwikDIG6O6kG//2X8BTKUBMOlD4wPj5WRIWaHriNvtERERE1J4pFEBkL+DKNGDV7wCbOpVCtQDbfgD8/gZ8cqXmmAzPqAXatm3bEBISAltbW8yZM0crdvz4cfj7+8PBwQEjRozAzZs3NTERQVxcHNzd3eHu7o5ly5ZBHlkIm5OTgxEjRsDBwQH+/v5IS0tr9Wvh30ciIiIiao8crYF1TwOZ04AJPXTjd8qA6K+BofuBc/lGT6/dM2qB5uXlhVWrVuEPf/iDVvudO3cQERGBdevW4d69ewgJCcHLL7+siX/00Uc4ePAgMjIycPnyZRw5cgTbt2/XxKdPn45Bgwbh7t27ePvttzFlyhTcvn3boLlzAo2IiIiILElvJXBkPHB4HODrohs/XwAM2Q9EnwBulxo/v/bKqAVaREQEJk2aBHd3d632/fv3IzAwEFOnToWdnR3eeustZGRk4McffwQA7Nq1C7GxsfDx8YG3tzdiY2Px6aefAgCuXbuG77//HmvWrIG9vT0iIyMxYMAApKamtuq1cCcbIiIiIrIELzwJZL5cM6tm31E3/smPNcse3/+hZvdHahmzuActMzMTQUFBmmNHR0f06tULmZmZ9caDgoK0Yr6+vnB2dq43bii8B42IiIiILJVdx5r70q5MAyJ9deO/lQML04GQFODbW8bPrz0xiwJNrVZDqVRqtSmVShQXF9cbVyqVUKvVEJFGn0tERERERIbRwxlIGQt89QLg76obz7gLPHsQeOU4cKvE+Pm1B2ZRoDk5OUGlUmm1qVQqzaxY3bhKpYKTkxMUCkWjz20tXOFIRERERJYqvDuQ8RKwYRjgZK0bT7oG+O0BNl4CKqqMn19bZhYFWmBgIDIyMjTHJSUluHHjBgIDA+uNZ2RkaMWysrK0ZswejRsKVzgSEdHjJCcnIyAgQLNMPz09HQCwd+9eBAQEwNnZGf369cPBgwc1z3nw4AEWLFgADw8PdOrUCRMnTkReXp6pLoGIqElsrIClwcDV6cDMPrpxdQWw9DQQtA9IyzV+fm2VUQu0yspKlJWVoaqqClVVVSgrK0NlZSUmT56MH374AampqSgrK8PatWsxcOBA+Pv7AwBmz56NTZs2IS8vD7/++is2btyo2abfz88PwcHBWLNmDcrKynDgwAFcvnwZkZGRrXot3CSEiIhqHTt2DHFxcdi5cyeKi4vxzTffwNfXF3l5eZg1axY2bdoElUqFDRs2YMaMGSgoKAAAbNmyBadPn8bly5fx66+/wtXVFTExMSa+GiKipvFyBJJGAydfBAa668avFALhh4GpXwI/8y6kRhm1QEtMTIS9vT3Wr1+PpKQk2NvbIzExEV26dEFqaipWrlwJNzc3nD17FsnJyZrnzZ8/HxMnTsSAAQPQv39/TJgwAfPnz9fEk5OTceHCBbi5uWH58uVISUlBly5dDJo7Z9CIiKghCQkJWL16NYYOHYoOHTrA29sb3t7eyM3NhaurK8aNGweFQoEJEybA0dERN27cAABkZ2dj7Nix8PDwgJ2dHaZNm2bwTa6IiIzlOS/guynA1lBAaaMbT8kC/JOBt78DyiqNn19boRCxjLmgoqIizc91NxXRx4ozwDsXHx4nPg2s/J0hMiMiouZo6ee6oVRVVcHe3h5r167Fjh07UFZWhkmTJmHDhg2wsbHByJEjERsbiwkTJuDw4cNYuHAhrl69CkdHR1y4cAGLFy/Gvn374OrqiujoaHTt2hWbN2/WOoe5XCsRkb4K7gMrztZswV+fXi7AltD6vwi7vWvsM90s7kFrC7jNPhER1Sc/Px8VFRVISUlBeno6Ll26hIsXLyIxMRFWVlaYPXs2ZsyYAVtbW8yYMQPbt2+Ho6MjgJpl+k888QS8vb3h4uKCK1euYPXq1Sa+IiKiluvqAOwYAZyJAELqWdh2QwW8cBSYeBS4UaQbt2Qs0JrJIqYdiYioUfb29gCAmJgYdOvWDZ07d8aSJUtw9OhRpKWlYdmyZfj6669RXl6OkydPIjo6GpcuXQIAvPbaaygrK8Pdu3dRUlKCiIgIjBs3zpSXQ0RkUEM8gLORwEdhgLudbvzITSDwv4D4c8D9CuPnZ45YoOmJE2hERFQfNzc3+Pj4QFHPUotLly7hueeeQ0hICDp06IDBgwdjyJAhSEtLA1Cz6/CcOXPQqVMn2NraIiYmBufOncOdO3eMfRlERK2mgwL4Yz/g2nTg/wbWHD/qQRWQ+B0QkAzsz+JmfCzQiIiIWmju3LnYunUrCgoKUFhYiM2bN+OFF17A4MGDNcseAeDixYtIT0/HwIEDAQCDBw/G7t27UVRUhIqKCvz1r3+Fl5cXOnfubMrLISJqFZ3sgPefAy5EAs946sZ/VgORXwJjjtTs/GipWKA1k6VX9kRE9FB8fDwGDx4MPz8/BAQEYNCgQVi5ciXCwsLw1ltvYcqUKXB2dkZkZCRWrFiBMWPGAADee+892NnZoU+fPujSpQuOHj2KAwcOmPhqiIha16AuwLeTgN0jAU8H3XhaLjBwLxB7ClCVGz8/U+MujnqKP1cz9Vpr7WAgPsQQmRERUXNY0s6GlnStRGRZVOXA2gvAlv8BKqt14x72wPqhwOy+uksj2yru4thKLKKqJSIiIiJqRS42wHvPABlTgZHeuvH8UmDuCeCZ/cD5AuPnZwos0PTUTgp2IiIiIiKz068TkDYR2DsG6O6kGz9bAAxJBaJP1HzHWnvGAq2ZLGNhKBERERGRcSgUwNRewI/TgNW/A2yttOOCmi++9tsDbLkMVFSZJM1WxwJNT5xBIyIiIiJqfQ7WwJqngSvTgEk9deNF5cB//BMYtA/4R67x82ttLNCaiRNoREREREStp6cLcOB54MsXAH9X3XhmITDqMDD1S+BmsfHzay0s0IiIiIiIyGyN6Q5kvAS8NwxwttaNp2TVfMn12gtAaaXx8zM0Fmh6UnCNIxERERGRSdhYAbHBwLUZwJy+uvHSSiDhPNAvGTiQ1bb3i2CB1kxt+U0nIiIiImqLPB2AnSOB05OBkC668ZxiIOJLYOwR4Eqh8fMzBBZoeuIEGhERERGReRjqCZyNBHYMB7rY6caP5QID9wKxp4CiB0ZPr0VYoDUTJ9CIiIiIiEyngwKYF1Cz7HHRAMCqzoxKZTWwKQPouwf49Eeguo38DzwLND3xHjQiIiIiIvPjagtsCQUuTQVGeOnG80uBuSeAZ/YD5wuMn19TsUBrpjZSgBMRERERWYT+7sDx/wPsGwM84aQbP1sAPJ0KzDsBFNw3fn76MpsCzcnJSeuPlZUVYmJiAAA5OTlQKBRa8XXr1mmeKyKIi4uDu7s73N3dsWzZMoiBd/HgBBoRERERkXlTKIApvWq+5Hr17wBbK93H/L8fAb89wJbLQEWV8XNsTEdTJ1BLrVZrfi4pKYGHhwemTp2q9ZjffvsNHTvqpvzRRx/h4MGDyMjIgEKhQHh4OHx9fbFgwYJWz5uIiIiIiMyLgzWw5mlgjj+w5BRwMFs7XlQO/Mc/gY//F/hLKDDSxzR51sdsZtAelZKSgq5du+LZZ5/V6/G7du1CbGwsfHx84O3tjdjYWHz66acGzcm6Tk89MMNqm4iIiIiIHurpAhx4HvjyBcDfVTeeWQiMOgxM+RK4WWz8/OpjlgXarl27MHv2bCjq7MzRo0cP+Pj4YO7cubhz546mPTMzE0FBQZrjoKAgZGZmGjQnpzrfWl5SYdCXJyIiIiKiVjKmO3D5JWDjM4CztW48NQvw3wOsOV/zpdemZHYF2s8//4yTJ08iKipK09a5c2ecP38eN2/exHfffYfi4mLMnDlTE1er1VAqlZpjpVIJtVpt0PvQHOu8kWoWaEREREREbYa1FbAkqGZb/jl9deNlVcBbF4B+ycCBLMDAW1rozewKtN27dyM0NBQ9e/bUtDk5OSEkJAQdO3aEh4cHtm3bhq+++goqlUoTr/0ZAFQqFZycnHRm4FpCZwbNxJU1ERERERE1nacDsHMkcHoyENJFN55TDER8CYw5AlwpNH5+ZlmgPTp7Vp/awqt2hiwwMBAZGRmaeEZGBgIDAw2al1OdvUk4g0ZERERE1HYN9QTORgI7hgNd7HTjabnAwL3Akn8CRQ+Ml5fZ7OIIAKdOnUJeXp7O7o1nz56Fq6sr+vTpg8LCQixatAjDhw/XLGucPXs2Nm3ahPHjx0OhUGDjxo2aLfoNpe4M2ok84LvbNRVu7USd4t8/K+r+/Gi8Trte8aaeozk5/Pu6GsqNiIiIiKi96aAA5gUAkb7AW+eBbT8AVY8sbaysBv58Gfj8J2D9UCCqb81zWpNZFWi7du1CREQEnJ2dtdqzsrKwYsUKFBQUwMXFBeHh4dizZ48mPn/+fGRlZWHAgAEAgOjoaMyfP9+gudW9B628GghJMegpzFqDBRwMUAQ+8nOT4vU8Fo85t7HO0eR4U87R3Bya8j4Z6RyPjevxPrXaLyXq9kEL/q40+BrNzaGh96mxeCtfZ4PneEy8owKwM6sRiIiILJmrLbA5FIgOABZ9C5z4VTteUAr84QSwPRPY+iwwuGvr5aIQQ3+js5kqKirS/PzohiL6ylIBvT43ZEZERJZrbHfgv19o2Wu09HO9LbGkayUiMjWRml0dY08BP6t14w4dgdzZgJtt816/sc90s7sHzVz1dAaC3E2dBRFR+6Bo/CFEREQmoVAAU3oBV6YBq38H2Fppx+MGNb840wcXmOhJoaj5be+mDODkrzVrU0UerlEV1Bxr/RcPt+esr10n3pTXMPA5UE87EVFr4f2tRERk7hysgTVPA3P8a2bTDmQDTzoDbwa37nlZoDWBpwPwn8NMnYVx1VvkGbAIbDQO7YKxoXO3KIeGztGMHHT6qBWus9k56Pk+tso5mphDY++jqX5pYdBzNDeHJrxPxjiHXvF62u3q/DaSiIjIXPV0AfY/Dxz7pebYvpUrKBZo9Fiam/z5224iIiIismDh3Y1zHt6DRkREREREZCZYoBEREREREZkJFmhERERERERmggUaERERERGRmWCBRkREREREZCZYoBEREREREZkJi9xmv6ioyNQpEBERNQvHMCKi9o0zaERERERERGaCBRoREREREZGZUIiImDoJIiIiIiIi4gwaERERERGR2WCBRkREREREZCZYoOnp3r17mDx5MhwdHdGjRw/87W9/M3VKrerBgweYN28eevToAWdnZwwaNAhffPGFJn78+HH4+/vDwcEBI0aMwM2bNzUxEUFcXBzc3d3h7u6OZcuWob2spP3pp59gZ2eHWbNmadostS8AIDk5GQEBAXB0dESvXr2Qnp4OwDL7JCcnB+PHj4ebmxs8PT2xcOFCVFZWAmj//bFt2zaEhITA1tYWc+bM0Yq15NpzcnIwYsQIODg4wN/fH2lpaca6JDIAjiPNw3FGfxyD9GfJY1RjzHIME9LLtGnT5KWXXpLi4mJJT08XFxcX+eGHH0ydVqtRq9WSkJAg2dnZUlVVJYcPHxYnJyfJzs6W27dvi4uLi+zdu1dKS0tl6dKlMmTIEM1zP/zwQ/Hz85NffvlFcnNzJSAgQD744AMTXo3hhIeHS2hoqMycOVNExKL74quvvpInnnhCTp8+LVVVVZKbmyu5ubkW2yfjxo2TqKgoKS0tlVu3bkn//v1ly5YtFtEfqampcuDAAVmwYIFERUVp2lt67UOHDpU33nhD7t+/LykpKaJUKqWgoMCYl0YtwHGkeTjO6IdjUNNY8hjVGHMcw1ig6UGtVou1tbVcvXpV0zZr1iyJi4szYVbGN2DAAElJSZHt27fLsGHDNO1qtVrs7OzkypUrIiIybNgw2b59uya+Y8cOrb/QbdWePXtk6tSpkpCQoBk4LbUvRGqubceOHTrtlton/v7+8ve//11zvHTpUnn11Vctqj9WrlypNbi15NqvXr0qNjY2olKpNPHQ0NB29z8GlsbSx5HGcJzRH8egpuEY1ThzGsO4xFEP165dg5WVFfz8/DRtQUFByMzMNGFWxpWfn49r164hMDAQmZmZCAoK0sRqlxbU9kfdeHvoK5VKhdWrV2Pjxo1a7ZbYFwBQVVWFCxcu4Pbt2+jduzd8fHywcOFClJaWWmyfLF68GMnJybh//z7y8vLwxRdf4Pnnn7fY/gBa9u8jMzMTvr6+cHZ2rjdObY+ljyON4TijP45BTccxqulMOYaxQNODWq2GUqnUalMqlSguLjZRRsZVUVGBmTNnIioqCv7+/o32R924UqmEWq1u02uW4+PjMW/ePHTv3l2r3RL7Aqj5H62KigqkpKQgPT0dly5dwsWLF5GYmGixfRIWFobMzEy4uLjAx8cHISEhmDRpksX2B9Cyfx+W/rnb3nAcaRzHGf1xDGo6jlFNZ8oxjAWaHpycnKBSqbTaVCqVVlXcXlVXV+OVV16BjY0Ntm3bBqDx/qgbV6lUcHJygkKhMF7iBnTp0iWkpaXhjTfe0IlZWl/Usre3BwDExMSgW7du6Ny5M5YsWYKjR49aZJ9UV1dj7NixiIiIQElJCe7cuYPCwkLExcVZZH/Uasm1W/LnbnvDcaRxHGeahmNQ03CMah5TjmEs0PTg5+eHyspK/PTTT5q2jIwMBAYGmjCr1icimDdvHvLz85Gamgpra2sAQGBgIDIyMjSPKykpwY0bNzT9UTfe1vvq66+/Rk5ODp544gl4enrivffeQ2pqKp566imL64tabm5u8PHxqfcD2hL75N69e/jll1+wcOFC2Nrawt3dHXPnzsXRo0ctsj9qteTaAwMDkZWVpfXbxvbUN5aC44h+OM40DcegpuEY1TwmHcOaegOdpXr55Zdl2rRpolar5dtvv233uziKiMyfP1+GDBkixcXFWu0FBQXi4uIiKSkpUlpaKsuWLdO6YfSDDz4Qf39/yc3Nlby8POnXr1+bvrG/pKREbt26pfkTGxsrkZGRUlBQYHF98aj4+HgJCQmR/Px8uXfvnoSGhsqqVasstk969uwp77zzjlRUVEhhYaFMmjRJZsyYYRH9UVFRIaWlpbJ8+XKZNWuWlJaWSkVFRYuvfciQIRIbGyulpaWyf/9+7uLYBnEc0Q/HmabjGNQ0ljxGNcYcxzAWaHq6e/euvPjii+Lg4CDdu3eXzz//3NQptaqcnBwBILa2tuLo6Kj5k5SUJCIix44dk759+4qdnZ2EhYVJdna25rnV1dXy5ptvipubm7i5ucmbb74p1dXVJroSw3t0dy0Ry+2L8vJyee2110SpVIqHh4fExMRIaWmpiFhmn1y8eFHCwsLE1dVV3N3dZcqUKZKfny8i7b8/EhISBIDWn4SEBBFp2bVnZ2dLWFiY2NnZiZ+fnxw7dszIV0YtwXGk+TjONI5jUNNY8hjVGHMcwxQiFnCXHxERERERURvAe9CIiIiIiIjMBAs0IiIiIiIiM8ECjYiIiIiIyEywQCMiIiIiIjITLNCIiIiIiIjMBAs0IiIiIiIiM8ECjagBc+bMwejRo02dRr2GDx+O6OhoU6dBRERmiOMXUdvG70EjakBRURGqq6vh5uYGAIiOjsb169fx9ddfGy2HxMRE7NixAzk5OVrt9+7dQ8eOHeHi4mK0XOpjij4hIqLH4/jVOI5fZM46mjoBInOlVCpb7bXLy8thY2PT7Od36tTJgNkQEVF7wvGLqI0TIqpXVFSUjBo1SkREEhISBIDWn507d4qISHFxsSxatEi8vLzE3t5egoODJTU1VfM62dnZAkCSkpJk3Lhx4uDgILGxsVJdXS3R0dHi6+srdnZ20rNnT/nTn/4kZWVlIiKyc+dOnXMmJCSIiEhYWJjMmzdPc47y8nKJi4sTLy8vsba2loCAAPn888+1rgeAvP/++zJr1ixxcnISHx8feffddx/bB+Xl5fLGG2+It7e32NjYiKenp7z88ssG65Pdu3fLyJEjxc7OTp588klJSkpq+htFRERaOH5x/KK2jQUaUQMeHeCKi4tlxowZMmzYMLl165bcunVL7t+/L9XV1TJ8+HAJCwuT9PR0uXHjhmzfvl2sra0lLS1NRB5+mHt7e8tnn30mN27ckKysLKmqqpKVK1fKmTNnJDs7Ww4dOiSenp6yevVqERG5f/++xMXFiY+Pj+acxcXFIqI7wC1dulQ6deoke/fulatXr8rbb78tCoVCk4NIzQDXtWtX+eijj+T69euyZcsWASD/+Mc/GuyDjRs3ire3t5w4cUJu3rwp586dkz//+c8G65Nu3bpJUlKS/Pjjj7Jy5UpRKBRy/vx5A76LRESWh+MXxy9q21igETXg0QFORGTevHkSFham9ZgTJ06Ira2t/Pbbb1rtc+fOlRdffFFEHn6Yr127ttFzbtq0SXr37q05XrdunfTo0UPncY8OcCUlJWJjYyPvv/++1mMmTZokI0aM0BwDkJiYGK3H9O3bV5YvX95gPosWLZIRI0ZIdXV1vfGW9smqVau0HjNs2DCZOXNmg/kQEVHjOH5x/KK2jfegEbXA+fPnUV5eDm9vb6328vJy9OnTR6vt6aef1nn+xx9/rLmJuqSkBJWVlaiurm5SDtevX0d5eTmee+45rfawsDC88847Wm3BwcFax97e3sjPz2/wtefOnYvw8HD07t0b4eHhCA8Px8SJEx97/0FT+mTYsGFax7///e9x/PjxBl+biIgMg+OXLo5fZC5YoBG1QHV1NZRKJc6fP68TqzsIODo6ah3v27cPr7/+OtavX4+wsDC4uLhg3759WLlyZbNyUSgUWsciotNWNyeFQvHYATU4OBjZ2dk4duwYTpw4gcWLFyM+Ph5nzpxpcAeupvRJXcJNZYmIjILjly6OX2QuWKAR6cnGxgZVVVVabSEhIfjtt99QVlaG/v37N+n1vvnmGwwaNAhLlizRtNXdjri+c9bVu3dv2Nra4uTJkwgMDNR6/UePm8vJyQmTJ0/G5MmTsWLFCnTr1g0nT57U/CayJX1y5swZjB8/XnN8+vRpBAQEtDhnIiJ6iOMXxy9qW1igEempZ8+e2LdvHzIzM+Hh4QFnZ2eMHDkSo0ePRkREBN59910EBQWhsLAQp06dgp2dHf74xz82+Hp9+/bFJ598gkOHDqF///44cuQI9u/fr3POf/3rXzh9+jT69OkDBwcHODg4aD3GwcEBixYtQnx8PLp06YLg4GDs27cPhw4dwrFjx1p0zRs2bICXlxeCg4Ph4OCAPXv2wMrKCn5+fgbpk08++QT+/v4ICQlBUlISTp8+jc2bN7coZyIi0sbxi+MXtTGmvQWOyHzVvcn67t27Mm7cOHFxcdHakrd2t6onn3y1JRTgAAABCklEQVRSrK2txcPDQ8aOHSvHjx8XkYc3FKenp2u9fnl5ubz66qvi5uYmzs7OMn36dNm6das8+s+yvLxcpk+fLm5ubgbZpvizzz7Tahs1apRERUU12AcffvihPPXUU+Ls7CyOjo4SEhIiBw8eNFif7N69W8LCwsTW1lZ69Oghu3fvfsw7QkRE+uD4xfGL2jaFCBfNEpFx5eTkoGfPnkhPT0doaKip0yEiItILxy8yhg6mToCIiIiIiIhqsEAjIiIiIiIyE1ziSEREREREZCY4g0ZERERERGQmWKARERERERGZCRZoREREREREZoIFGhERERERkZlggUZERERERGQmWKARERERERGZif8P5D0X/FKMxAcAAAAASUVORK5CYII=\\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"dl_toc_settings\": {\n   \"rndtag\": \"15456\"\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\": 5\n}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/Course-1-Week-2-Optional-Labs-3-Feature-Scaling-and-Learning-Rate.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Feature scaling and Learning Rate (Multi-variable)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\n   \"source\": [\n    \"## Notation\\n\",\n    \"\\n\",\n    \"|General <br />  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 maxtrix                  | `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 a 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 a parameter $b$| `dj_db`|\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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EhmDRpEl577TWPVTCQyaUNMjHdhrKXnzMhCth4r/yqaCItcJQ6yxvmHwCqbZaCVwupvF8L79VDK7Sycx95h9bOQ3JTnKoF0DYSSIi0rn9rg+/HJ1IvhwF248aNMXfuXLz66qv4+eefcebMGQDSRjM333wzIiMjPV7JQCG38n7dEODWFUBxZd3nO3MbKiHKfhDO1f6kZvZSZ3kLpzy4Ris795H32U1ZphJK/b2ZHvj+XusyR+MTz6+BzemNZgwGAzIyMjxZl4Bmb+X9oFbS7SpbDb0NxdX+pAWOviR6mtIgGeTVWmgHp4iQJa2dZ5SmpeVclN6LbZ2VxietvW9yP4dzsAHg6tWr+M9//oMJEyZgyJAhuOeeezBhwgR89NFHuHr1qqfrGBDsrbyfnSbddrLkjttQXO1P5NgvJa6VBzpOESFLWjvPzE6T5ljbKq92rc5ae9/kfg4D7Pz8fKSkpGDChAk4ePAgwsLCoNfrcejQITz++OPo1KkT8vPzvVFXv3ZE4WR9uuL6bagRHYCMFtJ/3fEtmLe+SQssV/SPXO9cBh13Kr8mX16hUB7oOEWELGntPJMQBXS6Qf7Y6QrnxyOtvW9yP4dTRCZPnoykpCTs3r27Tjq+S5cu4ZFHHsFTTz2Fr7/+2mOV9HcFpUDOBfljWwqBhI+BJbdb34YydfKGzO1SQ4YGInvUcJs1JhQokQkOo0O98/u1JipEvjxSoZz8mxbPM4lG+Y1wNhcCSZ9YL3q2HY9M864PXJR/bTW/b3IvhwH2pk2bsHXrVtlc19HR0Xj99dfRt29fj1QuUNjbKeqakNIEpa8CBrQEFvSXym2Djs2FQLdYoOSa8wG3GjI0ENlj7zart+Zlj+wAvPo/+XKq65zCFTqlcvJvE1KAzw5bB6WNdVK5Will8LLNJgRIz7ntv8DmodJj23OzJZ5fA4vDAFuv11vtt26rtLQUer3erZUKNPZ2irK04ZTUeTvF1O3Ax8ulfybOXOVTQ4YGInvUcJv1b9nK5bN7eq8eWrHrvGvl5N/m7JVPczlnr3rTXFqeG7PygSs19p9/6jKQvhLoFicfXMeHAQNb8fwaaBzOwR46dCjGjh2LDRs2QIjrvUQIgQ0bNmD8+PEYNmyYRyvp75Ruock5Ugpsk7l1Jfc8ZxZTmFZAf3+v9F92flITNdxevlrrWnmgu6bwd1EqJ/+2vUi+fIdCuVqYzo2hTqYLOlGh/F5TYnh+DUQOA+y3334bnTp1wqBBgxAWFoZWrVqhVatWCAsLwx133IHU1FS8/fbb3qir35LLEmKPs4vxLa/y+XqhGFF9eCqDDnlOsMJZRamcSM1iXFhrcfGKfDnnXQcmh1NEDAYDVq5ciby8PGzduhWFhYUApI1m+vTpgw4dOBGxoWynagQB2FgoP98LADrHAudPKR83MXVqNSwUI6oPNUxjigwCymRuEUcyEbas25pL09nkyinwJEbKb5TWTiN71C25HRiw2vH5FpDWTNniBYHA5fRGMx06dGAw7QUCQHwE8NEA4Llt0hxUyz6bGAVEBDnu7JadWg0LxYjqy9cbzUSEAGUyAUIEs2LIWtAf6LkcOGuxRUJc6PUF2hRYTiisl1AqV5t+LaTz8fiNQIVCMgI5nHdNTgfYSq5du4bCwkK0adPGHfUJSEpXmDfdJ/2/7dW7sT/Iv058mDTXy/YqnxoWihFpFfNguy4sGMBVm8cUkMoU+olSv1KbglLgpZ2uBdfA9XnXFLgaHGAfOHAA3bt3R02Ng2W2pMjRFWbbTqq08GtgK/kOrYaFYkRapXSziGv25GXutM5oBEiPeccsMGk9j7zc+dkZPL8Sl52ogL0rzHKLE11d+MWFYkT1l2R0rTzQ8Y4ZWVpyOxBkszI/SCeVq43c+dZRGt3WEUAbg3WZv5xfmRyhYRxewU5KSrJ7vKqK+982REEpcFghzXhQI+XFiR9kAKO/By5dla4EfJChPM9LDQvFiOrLtDNaQ3YtbYjYMNfKAx13ciRLrQ3ADSHWc/JvCJHK1URpqmaqwrbplnOsAcfnV1+PY65icoSGcxhgHzt2DA899BDatWsne7ywsBALFixwe8UCgakBn1T4hrzvHFBsk/bnSCnwzFYg94K0wyMAXKqS5mXba/i+XihGVB9qGOSzz7pWHujOySwItVdO/u2ZrdbBNSA9fmYrsGqwb+okR2mqZrzCF+m5fYCHLPI+2Du/qmEccxWTIzScwwA7NTUVvXv3xsSJE2WP7927lwF2PTma23Xxqnz5jiKgyOZkxYZP/kgNg/x5hX6oVB7odit88VAqJ/+mlY1mlKaCKLXb53dYB9j2qGEccxWnejWcwwD7d7/7HbKzFfYKBhASEsIMIjYsbwUZgwGhA0qr6t4WcjS3K6gRcE1m7ajSois2fPI3HOS1R2nxJxeFkpopJQOoUmi45124I6PFcYzJERrOYYD9/vvv280Q0rFjRxQUFLi1UlohN6cKqHsryJLlbSFHW6T3jQcKyq1fKzEK6BQDrDpW9/ls+ORvbBdHmTRydjtTN4gLBYplrlbHaSQLgrdFNgYuyCzNiWxwzirSol5N5c9Xtzb1fl3smZ0mnZ9tz91KF7Qqa6QYwJkpHloMVuX+Hv6yeNNbHA55oaE8i8hRmlPVKcb+tA/L20JKHRqQVibPz5D+33bxBADkXGTDJ/+Xc961ck/44i4gfZV8OdWl9OXHm1+KSD3e7gv875z1xjKtI6RyNbFMBrD+ZN1pmLaqhfNTPLQYrDI5QsPZDbArKysRFub8UnlXn69lSnOqlJLqWzLdFrJswEdKgTOXgWZ6IDHauiHLdWA2fAoEcldC7ZV7Qr8WwI/3WmftWXK7VE51lSiMgUrl5N8SooAf79PG+cqUDCBjleMAG3B+iodWg1UmR2gYu3mwExMT8c477+DSpUt2X2T79u0YNmwY3nrrLad+aV5eHvR6PUaOHGku27BhA5KTkxEeHo6MjAwcO3b9npIQAtOnT0dsbCxiY2Mxbdo0COFgr3APU5pT5cxFGsvbQqYGvG0YUDAS2Ha/9Niy48nlojT93Pf31n0+kb9QGqB8lcDft6OONqjtMyP10Er/cTR908R2ioe9vNE8Zwceu1ewV69ejT/96U+YMWMG+vXrh7S0NLRs2RJ6vR4XLlxAbm4uNm3ahOLiYjz33HN49tlnnfqlTz75JG655Rbz43PnzmHYsGFYuHAhhgwZgszMTAwfPhzbt28HAMyfPx8rV67E3r17odPpMGjQILRr1w6TJk1qwFtvGKUO2DkWOH9Kun0kx9XbQnJTUVYVAKkxQPtobXwLJqqv21pIt2vlyr1l02lgwOrrfbqkSnq84R5exZbToymw9Yx8OQWeglKg/yrr3T03FwIb71XvuWtCCvBJnv0vBLbncm+m4tNaTu1AZTfA7tGjBzZt2oRdu3Zh6dKl+Oqrr1BQUIDKykrExcWhe/fumDp1KkaMGAGDwbms8cuWLUN0dDR69+6Nw4cPAwBWrFiB1NRUPPDAAwCAV155BU2aNMHBgweRnJyMJUuWYOrUqWjVqhUAYOrUqViwYIFPA+zZadIgYTlotDEAEUHywbVlUnpXOsKEH+pORSmvBnaclf7VpwNvOi3d7r54VdrGlre7Sa3mpwO9Vljfro0Pk8q95ZH1dft0tZDKT47yXj200m+bKCzbUSon//bMVuvzJCA9VlsebEvzD8gH1411QI8mQEQIcOgS0O2L631x/gHvpOKTC+Q3nACCgoDya+oeGwKNU+u6e/TogR49ejT4l5WWluLll1/Ghg0bsGjRInN5bm4uunbtan4cERGBxMRE5ObmIjk5uc7xrl27Ijc3t8H1aSjbWSpnLwPHFHYrS4mROpnpFpIz3zw3nQbWn7ZfB1c7MK/GkdbU1tp/7GlK8yy9mWJLS/32zGX58iKFcvJva2UyiADAOoVyNTisMCu2QxTwZm/5vpgaLf8z7h4n5NZ/nbHYkE7NY0Og8eq0uMzMTIwbNw6tW7e2Ki8vL4fRaLQqMxqNKCsrkz1uNBpRXl7u03nYmTutV0UDQGWtlN1DTouI67fKluYBG09L/+2/ynqelqXR3ztXF1c68Ojv5a/GOfu7iLzp8Y3yu8A9vtF7dVAaZbw5+mip3xaUyZfnK5STf7ui0FEqVTwhu+iKfHleqfIdrRyFoNzdqfgc7Z9hqo8ax4ZA47UAOzs7G+vXr8eUKVPqHDMYDCgttY4yS0tLERkZKXu8tLQUBoMBOp3v8j4pNfJqARhs7guY5mrZu1UmR2knR1uudGCl17zEXelIhbbIzOUF5Of4eorSIBnkvSpoqt8q1dXZ8YzI15opJEOrFkCxwp2YGpkvDJ5IxefsAkw1jg2Bxmup/zdu3IijR4+ad30sLy9HTU0NDhw4gEmTJmHJkiXm51ZUVODIkSNITU0FIG3XvnfvXqSlSS1179695mO+Yq+Rd4qRUu3ZpuNR2jJ2a6F8eUyodLvHHlc7sNJrRnN+JKmQ0nSQGi9OEwkNkjaVsBXixQhbS/1WaYG3Ujn5tyCdfPCptImUGiQage3F8seUt92z1jbSMwscZ6cBWfnAFQcVUePYEGi8dgV7woQJOHLkCLKzs5GdnY1Jkybh7rvvxnfffYehQ4ciJycHWVlZuHLlCmbNmoUuXbogOTkZADBq1Ci89dZbOHXqFE6fPo05c+ZgzJgx3qq6rNlpda9UmyRGu5aO51KVNMfSNr3PktulRRWWGuuAgS2AjBbAiA7WHdheiiATpddccrtz75vIm+LDXSv3hLYKX6bbOreu2y201G+DFc4qSuXk357v5lq5Gtg7v9c6+UUxIdIzmT0SooA7Wtt/Tn3GBmfiB3KN165gh4eHIzz8+lnRYDBAr9cjLi4OAJCVlYXJkydj5MiR6NmzJ5YtW2Z+7sSJE5Gfn4/OnTsDAMaPH4+JEyd6q+qyEqKABf2BRzdYX5mxd0W5fRRQLJO8vkYAd38tZQcxMWUH2XCPcxtcOJsiqF8L51+TyNeWDgRuX2V91Sjot3JvsZ0Dbi5XmKfpCVrqtz3igJ9k7tb1iPN+Xcj3tincuVUqV4OEKODru+uel13hyW3Q5/YBci9Yn+8b64CwICA2zPWxwZspBgOJTri4UnDfvn3Iy8vD4MGDER4ejqtXryI4OBiNGvn+8kRJSYn5/20XTbqbXIM0NJY6pVIAnPqptBDSWSM6OJ8dZOR6adFkQ16DvNuGfPk7taKgFOizAii0+GLaPAzYOsx7A3+j9+UXNOoA1P4/79TBGWppu4NWyWc/GtgCWHevV6pFKhI6D6iSOe+FNAKuTvTd+OfM7y0oBTouA646Oy/kN4lRng9OTbmw3bEzJOOH+nHUhpy+gn3hwgUMGzYMmzZtgk6nQ15eHtq1a4cnn3wSUVFRTu/i6C/kUuWUV0u5MOUC7MydrgXXgGvZQZQWXXozlRiRu2XutA6uAemxu3PL2qOGLCJask8hk9J+hXLyb9cUzntK5WqSEAUYQ+TvPMuJDgHuvtE7G7+4cxtzxg+e4fRl52effRZBQUE4evSo1VSP+++/H999951HKqdmSg3yiEWqHss5TetkdqMzsZ1baeLKLSalRZeevE1F5GlqGPiV1mL5/p4dkfppfU7+rfHOPzc6VJu7KjJ+8Aynr2CvXbsWq1atMmcBMUlKSsLx48fdXjG1U2qQORevLw6wnUKi5JY44NRl6xR+rmYHmZ0mzZmy/H2eSBFE5E1qGPg7RgEHZPpxssZOot6SGCl/xa9dpPfrQr7Xr5n8lKF+zbxfl/qY2wfIPlc3xa6co2VA+kqgWxxQWqWdbcwZP3iGS1NEYmJi6pSXlZWpYv61t81OA1YV1F0AUV4t3b4GnAuuAWBbMdA6ArinLVBWBUSGADoBjP1BuYOa5l9Z7gi5boj75mQRqYEaBv7z11wrD3THFe4uKJWTf8u8BVi/Sr5cCxKigI33Xp8Wuu88cNnOwscTFdab0K0quL42S+68rYZzdEKUeuMHtf7NnOF0gN29e3d8++23eOKJJ6zKlyxZgp49e7q9YmqXEAV0ukE+V+bpCuX5mUGQz6N5okLqgHP7OF7Na2/FLxckkD9Rw8B/VmH+pVJ5oCtX+OJRwS8kAen5n08EFxIAACAASURBVJTLt97v3bo0lD4IGNhKSqt7ycEeFSbl1VI2kq/vli6aqTVThzvndLuL1rObOB1gz5w5E/fddx9OnjyJmpoafPTRR8jNzcWqVavw/feBuSenUjJ6e7ev7S1GPnJJfvHkkVLrRV3OPIfIX/h64Fdai6WBNVo+oaVNccjztp2VL9+uUK42ShnDXFFeLaXYPFpmXc7ztn1aj3WcntsxaNAgfPnll9i2bRsaNWqEN998E2fOnMF3332HPn36eLKOPqeUgH12mnS72pLp9rXcMb2Dnd/OXHFuUZcaFn4ReQs3QNAWLW2KQ56ndDdXK19QlTKGuRpkK21dbnne5lhnTeuxjktN5Pbbb8fttwfWKOnoFoW929e2x85cBjacUv5dMSHOLepSw8IvIm/Q+i3CQNTaAEQHA+csrmJHB0vlRFqjFOR1igGaRgDfHQeuWnxb0EH+S0V0qPy0EtN529djnRrnOms91nH6CvbPP/+MHTt21CnfsWMHdu3a5dZKqYm9WxTA9dvXctuim44typAeZ5+z/7vySoAJKcpXxU3kro4bGks/S+RPHPU/b4hRmNqgVO4pWrm6NWWLdXANSI+nbPFNfci3muhdK1cbpSAvMRqIDLYOrgEpuLa9g5MYJd3BsXdu98RY5+yYYQrul+YBG09L/+3yGdArq+7PeXMcsjdLQAucvoL91FNPYcqUKXUWNJ44cQJz5szBtm3b3F45NXB0i2LTaWlu1cWr0gnXdotSuW+lSkwb1Tha1JUQBXyQYb2Na3m1tICCV/bIn6jhFmGNwsIJpXJP8PXVLVf8IJOSDZBO3BR4WkcA567Il6uN3PncXiajsT/Iv06PptJzbM/h9s7t7h7rXBkzlKbBbC/+7d9vPwd4dxxSwyL3hnA6wM7NzUWPHj3qlHfv3h0HDhxwa6XUxN4tik2ngQGrgerf7geVVEmPN9xzPciWa7j2nK5wblHX/AN1UwRqafI/kTOUBigHyxncqlwhkFYq9wQtLfaxHZdMyuykNiP/pRQ4KpX7ir3zuVKQp3h1W+Ecbu/c7u7pEK6MGY4+C8sr6d4eh3y9yL0hnJ4i0qhRI5SW1o0UL168iNparSxXcJ29WxSjv7/eGU2qhVRu4uog4mxnUsOVPSJP+6XEtXJP0Cms0lIq9wQt9Xelk0rg7ZZAgHI6O2fT3HmLvfO50lRQd05hcPd0CFfGDKXg3vbntDQOqYHTY17v3r0xZ86cOuVz5sxBr1693FopNTHdohjRAchoIf33gwzpG9sJhZ2dLFcLO9NwTVzpTFqf/E/kDDXkVG6m0KeUyj1BS/09Pty1cvJvQuGLqFK5r1xUyPKhlP0DkI8P6jtdwp2vBSiPGVHBdedQywX3tlpEaGscUgOnp4i8+uqrSE9PR7du3TBgwADodDqsX78eeXl52Lhxower6D71XSVreYvCmTnVlvle5eZvWTI0llYjJ0a7NrdIDTvckf/z9cpyNeRU/nsv4OH18uXeoqX+roa/F6lHfDhwUuYKp9q+cDkaa5TGQndOYXDna8mNGa0jgD3nrbd9N82hNk2DOXIJyLloPdXLcqzRyjikBk4H2DfffDN27NiBN954A2vWrDGXffTRR+jUqZPHKugu7lok5GhOdZBNvlfbSfpRwYDQSVuiN2TCvtYn/5P6qWFh3ZLbredFAt7PqfzVMeXyhzp4pw5a6u+fHlYu99bfi9Rj6UDg9tVAjUUfDtJJ5Wpib6xRw1joKrkxo+wasPqo9fMs51BbXki0HWsAqSxOL32WzfSuXxgMNDoh1Hajpv5KSq5PzDQajVbHRq6XUs/YGtFB/huj5WpiQzDQ0QhUAzhwESi2s0Vy8zBg6zA2OK2y14b86Xc6w9U+4ymmvnjpqnQ1yTZTj6f1ypLfsfXWeGDbMO/VwxG1tN0mHwDnZW6rx4YC58Z6pVqkMvb6sK/GP7nfq1TPhsQPctnFnOGJu4cZq+Sz+WS0kOaX26uL7ReMxCh1f8HwBkdt18W9iLTLlcn5cquJnV2sWFgJPLMVWDW4fvUkUgu1LGhpbQD6NLt+ovH2hiWHFe5YHfbiQkstkbvNbq+c/J+v+7Cz+rUACkbWLW9o/GCbXcwRV66YuxKI13cOtVJGkttXA9/fE9hBtj12A+yQkBCcOnUKcXFxCA4Ohk6nU3xuVZW6R09nG1ZBKXDXV3VXE7ti7QnpddjoSMvUsKCloBRIXwmcsDiRbToN/Hif9/qXXA5fe+WBTmnsbMiYStqlxekVtqJC5MsjZcrtZSORC97lOJtiz9VAvLwK0AcBVyxSjDozh1rpC8bRMun3a+mz9Ca7AfaCBQsQFSX91RYuXOiVCnmKM4uETI210on8traN1NKVGnXmpyVyhRoW1k3ZYh1cA9LjKVuAlf/nvXoQUf1oKYe7EldSddYnG4ktZ66YF/x2BflomfVznA3E9UHAHa2BuX0cB8f2sqFp7bP0JrsB9ujRowEA1dXViIuLQ8+ePREbG+uVirmbM4uEXNkUJryxcoANMC8kaZ8aFtZtk5n7DMjPiSYi9VHLVLOGOHNZvrxUJl2oUjaSyhppLrczY6iju4emgNk2uDax/dvKxTZXaqSt3p0Zzx1lQ9PSZ+lNTs3Bbty4MYYNG4aDBw9qNsAGHKfAcWVTmEoHu5IxLyT5A1/volWtsIeVUrknNAIg9+u4cQqRY8Zg+fIohXK1KSgFci/KH5M7z8tlIwGAqzXSQklnpsc4unvo6GKgbb0a+iXHdLFF7oq53O8jidPniJSUFBw9etSDVfE9VzaFqayRUvjIYV5IIvcIU7gEoPfi8myjwvxLpfJAF6JwVlEqJ/8mFM6TSuVqk7nTOie0iaGx/Hm+XwtpQWPbSCBY5j1abjuuxNGmM/YuBsrFH0qxTUGZ9AXCEdNCymZhQHiQ9bHWEYx3lDg95L311luYPn06fvrpJ9UvaKyv2WlSp3FWtZCC7I5GqTPdGtfw3ZdcUVBad0cmIn/SVO9auSfUKMy/rPXyoj2t9HelIVQjFyzJzUoVwoUyjYQRSsFsp1jl87wpG0mf5vLHnblyrLQ9O6AcMLeNlI8/lHZqNC1StDeWmKajLM2TpuZdtpkaayf3hV9oyLjrdDg5aNAg1NbW4rbbbgMABAVZf43xh6A7IQr4+m7g7q/lv7HKqRZAXqlrKXjcwR9WZhM5clFhWFEq94QqhekoV704TUVL/f2KwhePSmYRCUiuZOBQI6Vg1tHW4vZ+tqFTKpSmkCiNBwlRwAcZ8lNXHC1SdDQd5Xi5thY5upLWsKHjrtMB9oIFC+ym6fMX/VoA+4YDt66wv6GMpWohBeX7hnvvZOcPK7OJHDEqjFDRXpwiooZ54Frq70pX9r19xZ/UwZUMHGrUkGxKnsrEVJ8F6PMPKKfKtHdF3Zm1aVpZ5OhqwNzQcdfp09TDDz+M6upqRET4/2z2hCigV1NglcIWyXLKq717svOHldlEjpQo3Em65OQdJncIagRUy2QMauzFOcXs76RVJTKZNgD5DBxq1JBsSp7MxOTqAnR7gbK9K+rOrE3TyiJHVwPmho67DgPsCxcuYPTo0fj2229RW1uLW2+9FUuXLkXbtm2d+w0aYXnbwBgM/HzW9dfw5slODZuAEHlaszD5VevNwr1Xh5tvAH6SGQ+63+C9Omipv0cEARUyX0giguqWkf/TUttV0pBsSr7OxGSi9DkoLdY0mZACfHZY+eq3oyvyntjyvb5cDZgb2nYdXoN58cUXsWPHDvz5z3/G3/72NxQVFWHSpEnOvbpGmHaLW5oHbDwtXbk+LZP30hgMDGwBBCnMlPHmgCG3aIHZS8jfNFUIpJuGea8OTRT6tVK5J2ipvyvNrdXKnFtyLy21XX82IaVuEgdDY2ndWX2mluiDpHE41c6FBssFkhtPS/91tKjSk1wNmBvadh1ewV6zZg0WLVqEIUOGAADuuusudOnSBdeuXUNwsH+sC5fbLU5OVS0wPwM4UV53IaS3Bww1bAJC5GmXFRYzKpV7QrHCJhPOrtFwBy319wqFW/+XNTIlgNxLS23XXxWUAmN/sI5ZTMG1o+QMSld9r9QAVyqB1UeB3Avy85jVtnbE1TnxDW27DgPsU6dO4eabbzY/TklJQUhICAoLC9GmTRvnfovKKe0WZ6vSYgv0fcN9P2Co5dYTkafsU9jgYb9CuSecUQiklXZ38xSt9He56SEAUG5n51vyb1ppu/5KLtAtr5auTjsKsJ2Zg60UNKtt7Uh9AuaGtF2HAXZNTU2dK9VBQUGoqdHWaOmueUCmhsEBgygwxOsV5oF7MRe3lnDnSyL3amj80pBA19E26fZeS43z770ZuzmVReSBBx5ASMj1CXRXrlzBqFGjEBZ2fSLk2rVr3V87N3GUmuXWeOk2hzO0tDCDSOu6xALrT9Yt7xzrvTq0jwZ2yCxyTIz2Xh20pIle/qp/E34hIXKZO3LgNyTQtb3qW1Dm/HbpnkpTqBUOA+zRo0fXKRs5cqRHKuMpjuYBze0DZJ+TEqabNA8DgoOsywKpYRCpgVLmCW9mpAj0k4SrOsXIB9idYrxfFyKtc8c85oaOYZZXfeUCfqXXCvT59w4D7MWLF3ujHh7l6PZIQhSw8d7rjSAyREqCf+Yy0EgnpQQzNSDbhqGmFDRE/kYNOXRNu6CN/h64dBWIDpUes5/LU0pRrq1JheROPE/WnzvmMdcn0FX6zFx9LVNwbnq9sT8EThvw4n5ovuPM7RHLRmD77SxIB3wyUD641sr2xURapIZtlk0r8E23RS9VSY/Zz+Wp4TMj9eB5smHcNY/ZlbnHjj4zV+cxB2obCIh1J3K5DPVBQHlV3XyM9m7H2HLluUTkOjVss8x+7ho1fGakHuw/DeOLPOLOfGYFpcDI9UDGKum/9nJbB2obCIgr2KZbGs9sBdae+C1/Y420ocyGU9a5IF25HaO2FDRE/qZIIUVe0RXv1YH93DVq+MxIPdh/GqYh85jrOzXH0Wfm6hXpQG0DARFgm+w7LwXWlsqrpU1j9g2XGoYrt2PUmIKGyJ+oIQe1UWE/rSj/2GfL7U6UK5TLZB4g/8fzZMPVJ7VcfaZlmALyAwr7DJg+M1cXXgZqGwiIKSKmhiaXWgaQguzbV0vPk7sdowPwaR5gWAAsy7tezi1giTzLqHAJINqLlwaEzrXyQFehsMrxstLqR/JrPE/6hqvTPO77Bui/StrOXG6XWsvPzNUr0mpsA8t+i+ka/7tubOcuAXEFW66h2TpaBqQsA+5oLWUImH8A2FMMHCgBBKR/FdXAw+ul5z/UgSloiDztlMKV6pNevIKthq3StaRMYRv7Ui9ub0/qwfOkbygFwUcuSUH1kRIg54L19uly4sOAga2sPzNXr0irrQ0sy7seywF1Yzt3CYgAW6mh2bpSI204k3tBagydP5N/3viN1z8E7uhI5DkXrrpW7gmc8uAapXR8TNMXuHie9D6lIDjnIrBdZuMsJSkxdT+7+uTVVlMbGL9RudydAXZATBFRamhKTLdRbOdrmyiVE5F7yW25ba/cEzgOEJHWyE3LMDR2fMXaltxVadMV6REdgIwW0n+1lHLPW2N6QFzBlvu21VgHVNtJG3W6QkrlJzefUO/FXeSIAllYEFApM+iFebEPBilchmgcEJcniEiL5KZlHCkBthc7/xr2rkqr6Yq0q7wV23ntFHH16lWMGzcON954IyIjI9GtWzesWbPGfHzDhg1ITk5GeHg4MjIycOzYMfMxIQSmT5+O2NhYxMbGYtq0aRDCuaSqplWxTUKBtpHArfHSt60N99T9dmepRQSwsL/8MaVyInKvDzJcK/eEW+Ply3sqlAe6m5u4Vk5EnmEKgr+/V/pvotH+81tHAPe09c1VaVfyajeUt2I7r13Brq6uRuvWrfHjjz+iTZs2+Oabb/Dggw9i//79MBgMGDZsGBYuXIghQ4YgMzMTw4cPx/bt2wEA8+fPx8qVK7F3717odDoMGjQI7dq1w6RJk+z+Tke7Mq4bAkzZAqw9aX2VzHZb9PEbpVsH+iDpA3DnHB0iUtYzHmgSApyzWCDXJMS7we3cPkD2OeC4xVzsNgapnOp6q4+UjcDyEojut3Ii8h25u/mGxkCnGCAx2ncLD72906MphvN0bKcTzl4K9oAuXbpg5syZOH/+PD788EP89NNPAICKigo0adIEe/bsQXJyMnr37o0xY8ZgwoQJAIBFixZhwYIF5gDcpKSkxPz/RqMRI9dLKWdsjehgfWvDdJVbDatbybds25C//k6tcLYPe5oWxgi1tF21fGakDb4a/wJ13FXjWKbVMcNRG/LZHOyioiL8+uuvSE1Nxfvvv4+uXbuaj0VERCAxMRG5ublITk5Gbm6u1fGuXbsiNzfX4e9wNlejlucSEfkztewAxjHCeWr5zIioLjWOZf46Zvhkmc61a9cwYsQIjB49GsnJySgvL68T/RuNRpSVSXmwbI8bjUaUl5c7nIcdqLsHEfkL9mHt4WdGRK7w1zHD6wF2bW0tHn30UYSEhODdd98FABgMBpSWWs9oLy0tRWRkpOzx0tJSGAwG6HT2t1JT4+5BROQ89mHt4WdGRK7w1zHDqwG2EALjxo1DUVERsrKyEBwcDABITU3F3r17zc+rqKjAkSNHkJqaKnt879695mP2aD1XI1GgYx/WHn5mROQKfx0zvBpg/7//9//wyy+/4Msvv0RYWJi5fOjQocjJyUFWVhauXLmCWbNmoUuXLkhOTgYAjBo1Cm+99RZOnTqF06dPY86cORgzZoxTvzPht29BLSKkeT6ZOz2b/oWIPMNnq7Gp3viZEZEr/GnM8Noix2PHjmHevHkIDQ1Fs2bNzOXz5s3DiBEjkJWVhcmTJ2PkyJHo2bMnli1bZn7OxIkTkZ+fj86dOwMAxo8fj4kTJzr1e72d/oWI3If9V3v4mRGRK/x1zPBpmj53Y8ooaii1pDojCfuv89TSdvmZkSuYpo+0OmY4akN+v9mvv6Z/IQoE7L/aw8+MiFzhr2OGz/Jge4s70r+YErOfqpBeTw2J2YkCgb+mb/Jn/MzIFs+hZI83xgxftEG/D7DltgZ1Jf2Lv84NItKCCSnAZ4eBaouJbI11UjmpU0PHXPIvPIeSI54eM3zVBv1+ikhD079k7rT+UADpceZO99eViKzNP2AdXAPS4/kHfFMfcsxfU25R/fAcSo54eszwVRv0+yvYQMO2BvXXuUFEWsD+p01q3I6ZfIN9mJzhyTHDV23Q769gNxTnExL5DvsfkbaxD5Ov+aoNMsB2wF+38CTSAvY/Im1jHyZf81UbDIgpIg1hmhuUuVO6ndCCK6CJvIb9j0jb2IfJ13zVBv1+oxkiV6hlsw4iV7HtkhZxoxnSqoDfaIaIiIiIyJv8doqI5TcLIq1guyWtYtslrWLbJU/gFWwiIiIiIjdigE1ERERE5EZ+tciRiIiIiMjXeAWbiIiIiMiNGGATEREREbkRA2wiIiIiIjfy+wD73XffRY8ePRAaGooxY8ZYHduwYQOSk5MRHh6OjIwMHDt2zHxMCIHp06cjNjYWsbGxmDZtGiynqx89ehQZGRkIDw9HcnIy1q9f7623BAC4evUqxo0bhxtvvBGRkZHo1q0b1qxZ4xfvDQBGjhyJ5s2bIyoqCklJSVi4cKH5mNbfm7MuXLiAoUOHIiIiAjfeeCM++eQTX1fJ6xy180CUl5cHvV6PkSNH+rQe9sZWW2+//TaaNWsGo9GIsWPH4urVq96ppJc4+7f48MMPERQUBIPBYP63ceNGr9XTk1ztq/7eJnylf//+0Ov15vZ10003mY/ZO3f6G0/Ffi4Rfi4rK0v897//FZMmTRKjR482l589e1ZERUWJzz//XFRWVopnn31W9OzZ03z83//+t0hKShInTpwQJ0+eFB07dhTvv/+++fitt94qpkyZIi5fviyWL18ujEajKC4u9tr7Ki8vFzNnzhQFBQWipqZGfPnll8JgMIiCggLNvzchhMjJyRFXrlwRQgjxyy+/iPj4eLFr1y6/eG/Oeuihh8SDDz4oysrKxObNm0VUVJTIycnxdbW8yl47D1SDBg0Sffv2FSNGjPBpPZTGVlvffvutaNq0qcjJyREXLlwQ6enpYvr06d6rqBc4+7dYvHix6NOnj/cq5kWu9NVAaBO+kp6eLhYsWFCn3NG50994KvZzhd8H2CYvvvii1R953rx5olevXubH5eXlQq/Xi19++UUIIUSvXr3EvHnzzMcXLlxo/hAOHTokQkJCRGlpqfl437596/0huEvnzp3F8uXL/e69HTx4UDRr1kx89tlnfvfelJSXl4vg4GBx6NAhc9nIkSN5EhLX23kg+vTTT8UDDzwgZs6c6fMA28R2bLX18MMPi+eff978eP369SI+Pt4LNfM+R38Lfw6w5Sj11UBqE96mFGA7Onf6K3fGfq7y+ykiSnJzc9G1a1fz44iICCQmJiI3N1f2eNeuXa2OtWvXDpGRkbLHfaGoqAi//vorUlNT/ea9PfHEE+apHM2bN8f//d//+c17c+TXX39FUFAQkpKSzGVqras3WbbzQFNaWoqXX34Zc+bM8XVVXCLXJ4uKinD+/Hkf1sp39uzZgyZNmiApKQmzZ89GdXW1r6vkEfb6KtuEZz3//PNo0qQJ+vTpY56C5OjcGSgaEkO4KmAD7PLychiNRqsyo9GIsrIy2eNGoxHl5eUQQjj8WW+7du0aRowYgdGjRyM5Odlv3tt7772HsrIybN68GcOGDUNoaKjfvDdHtFRXb7Ft54EmMzMT48aNQ+vWrX1dFZfI9UkAAdmW+/Xrh5ycHBQXFyMrKwuffvop/va3v/m6Wm7nqK+yTXjOm2++ifz8fJw6dQoTJkzAkCFDcOTIEZ5TftOQGMJVARtgGwwGlJaWWpWVlpaar27aHi8tLYXBYIBOp3P4s95UW1uLRx99FCEhIXj33XcB+M97A4CgoCD07dsXJ0+exPvvv+9X780eLdXVG+TaeSDJzs7G+vXrMWXKFF9XxWVyfRJAQLbldu3aISEhAY0aNULnzp3x8ssvY/ny5b6ulls501fZJjynZ8+eiIyMRGhoKEaPHo0+ffrgm2++4TnlNw2JIVwVsAF2amoq9u7da35cUVGBI0eOmG9n2R7fu3ev1bH8/Hyrb36Wx71FCIFx48ahqKgIWVlZCA4Olq27Ft+brerqavN78Lf3JicpKQnV1dXIy8szl6m1rp6m1M4DycaNG3H06FG0adMGzZo1w9///ndkZWWhe/fuvq6aQ3J9Mj4+HrGxsT6slTrodLr6ZyhQIWf7KtuE95jamKNzZ6BoSAzhsnrN3NaQa9euicrKSjFjxgwxcuRIUVlZKa5duyaKi4tFVFSUWL58uaisrBTTpk2zmsj+/vvvi+TkZHHy5Elx6tQpkZKSYrUYrmfPnmLq1KmisrJSrFixwifZKCZOnCh69uwpysrKrMq1/t6KiorEp59+KsrKykR1dbX49ttvRXh4uFi5cqXm35srhg8fLh566CFRXl4utmzZEpBZRIRQbueBpKKiQhQWFpr/TZ06VfzhD3/wadtVGlttrVmzRsTHx4vc3Fxx4cIFkZGR4XeLdZ39W3zzzTfizJkzQggpO1Jqaqp45ZVXvF1dj3G2rwZCm/CFixcvim+//dbc/j7++GMRHh4uDh486PDc6W88Ffu5wu8D7JkzZwoAVv9mzpwphBBi3bp14qabbhJ6vV6kp6dbpROqra0Vzz33nIiJiRExMTHiueeeE7W1tebjBQUFIj09Xej1epGUlCTWrVvn1fd19OhRAUCEhoaKiIgI87+PP/5Y8++tuLhY9OvXTxiNRhEZGSk6deok5s+fbz6u5ffmivPnz4t7771XhIeHi9atW4ulS5f6ukpe56idByo1ZBFRGluPHTsmIiIixLFjx8zPnTNnjmjatKmIjIwUY8aMMafg9BfO/i2mTp0qmjZtKsLDw0VCQoLIzMwUVVVVPq69e9jrq4HYJnyhuLhY9OjRQxgMBmE0GkXPnj3F2rVrzcftnTv9jadiP1fohPCj+1NERERERD4WsHOwiYiIiIg8gQE2EREREZEbMcAmIiIiInIjBthERERERG7EAJuIiIiIyI0YYBMRERERuREDbJXR6XT4+OOP3fqatbW16Natm9u35N2/fz/S0tKg1+vRtm1bxedNnDgRzz77rFt/N3lP//79MX78eLe/7pgxYzBw4EC3vy6Ru7zwwguIj4+HTqdD27Zt0b59e19XiYg0ggG2yhQWFuL+++9362suXrwYQgj84Q9/cOvrTps2DVFRUTh48CB+/vlnfPzxx9DpdHWe9/LLL+P9999Hfn6+W38/EZGn7NixA3/5y18wf/58FBYWYvjw4b6uEhFpCANslWnWrBn0er1bX/Ptt9/GhAkTZIPfhsjLy0N6ejratm2LuLg4xee1bNkSAwYMwHvvvefW30/+raqqytdVoACWl5eHRo0a4d5770WzZs0QFhbmld/Ldk/kHxhg+8CWLVvQp08fREZGIjIyEl27dsV3330HwHqKyCuvvAKdTlfn35gxY8yvtW7dOvTp0wdhYWFo2bIlHnvsMZw/f958PDs7G7m5ubjvvvus6rBw4UJ07NgRer0esbGx6NevH06ePGk+/vnnn6N9+/bQ6/Xo3bs3Vq9eDZ1Ohy1btuDo0aPQ6XQ4cuQIXn75Zeh0OvTv3x+PPvqo+T3Y1nPo0KFun/pC3lNbW4sZM2agSZMmiIqKwvjx41FZWWk+/s9//hPJycnQ6/Xo0KEDXnvtNVRXV5uPX7x4EcOHD0dERATi4+Px0ksvwXYT2f79+2PcuHHIzMxE8+bN0bJlSwDA9u3b0a9fP4SFhSEmJgaPPPIIiouLrX52yZIlSElJQWhoKFq1aoWXXnrJ6vebXvull15C06ZNER0djRdffBG1tbWYNWsW4uPjERcXnhoXjAAADbNJREFUhxdffNHqdVetWoVu3bohPDwc0dHRSEtLw549e9z2dyV1GjNmDB599FHU1taaxzM5jtrdtWvXMGPGDLRs2RIhISFISUnBJ598YvUaOp0O77zzDh555BEYjUaMGDECAPD666+jXbt2CA0NRVxcHO68806rPkdUH+vWrUP//v1xww03wGg0Ij09HTt37jQfLygowB133AG9Xo82bdrgX//6V51pgtXV1XjllVeQkJAAvV6P1NRUzJs3zxdvR90auN07uai6ulrExMSIKVOmiF9//VX8+uuvYsWKFWLTpk1CCCEAiI8++kgIIURZWZkoLCw0/1u9erVo3LixWLx4sRBCiA0bNoiwsDDxzjvviF9//VXs3LlT9O/fX9x2222itrZWCCHE3LlzRcuWLa3qsGvXLhEUFCSWLFkijh49Kvbt2ycWLFggTpw4IYQQ4n//+5/Q6XRixowZ4uDBgyIrK0u0bdtWABCbN28W1dXVorCwULRq1UpMnz5dFBYWipKSEvHuu+8KAOb6Xrp0yfw7c3NzBQBx4MABT/+Jyc3S09NFZGSkGD9+vDhw4IBYvXq1iIuLE0899ZQQQoiZM2eKNm3aiBUrVoj8/Hzx9ddfi9atW4uXXnrJ/Br33XefSExMFBs2bBA5OTlixIgRIjIyUgwYMMDq9xgMBjFx4kSRm5sr9u3bJwoLC0VkZKR4+OGHxb59+8TmzZtF586dRd++fc0/99VXX4lGjRqJ119/XRw6dEgsW7ZMREdHW/3+9PR0ERUVJaZNmyYOHTokFi1aJACIwYMHi+eee04cOnRIfPjhhwKA+Oabb4QQQhQWForg4GDx5ptvivz8fHHgwAGxdOlSsW/fPk//ycnHLl26JObOnSuCgoLM49nMmTNFYmKi+TnOtLtnn31W3HDDDeLzzz8Xhw4dEq+99prQ6XRi/fr15ucAEDfccIN45513xOHDh8WhQ4dEVlaWiIyMFKtXrxbHjh0Te/bsEW+//ba4fPmyV/8O5H9WrFhhbo85OTli3LhxIiYmRpw7d07U1taKrl27irS0NLFjxw6xZ88eMXjwYBEVFSXGjRtnfo3Ro0eLzp07i++++07k5+eLZcuWCaPRKBYuXOjDd6Y+DLC97MKFCwKA+OGHH2SPWwbYlo4fPy6aNWsmnnvuOXNZenq6mD59utXzjh07JgCIPXv2CCGEePrpp0VaWprVc1asWCGioqJESUmJbB1GjBghevXqZVX2z3/+0xxgm9x4441i9uzZ5scfffSRUPrOVlJSIgCIr776SvY4qVd6erq48cYbRXV1tbls3rx5IiQkRJSXl4uwsDCxZs0aq59ZsmSJMBqNQggh8vLyBACxdu1a8/GrV6+KFi1a1AmwO3ToIGpqasxlL730kmjZsqW4evWquSw7O1sAED/++KMQQoi+ffuKBx54wOr3z507V+j1evPPpaeni65du1o9JyUlRXTq1MmqrEuXLmLq1KlCCOmLJgBRUFDg3B+K/MrixYtFUFCQ+bFtgO2o3VVUVIiQkBDxr3/9y+o59913n8jIyDA/BiDGjh1r9Zy33npLdOjQQVRVVbnzLRHVUVNTI6Kjo8XHH38s1q5dKwCIvLw88/Hz58+LsLAwc4Cdn58vdDqd+OWXX6xe589//nOdMTbQcYqIl8XExGD8+PG48847MXjwYLzxxhs4dOiQ3Z8pLy/HkCFD0KtXL7zxxhvm8p9//hlz586FwWAw/0tJSQEgzR8EgMrKyjpzugcNGoR27dohISEBDz30EObPn49z586Zjx84cAB9+vSx+pm+ffs26H2b6sBbnNqUlpaGoKAg8+M+ffqgqqoKu3btQmVlJf7whz9YtcOJEyeipKQEZ8+exYEDBwAAvXv3Nv98SEgIbrnlljq/5+abb0ajRteHpdzcXNx6660ICQkxl3Xt2hVGoxG5ubnm5/Tr18/qddLT03HlyhUcOXLE6ucsNWvWDF26dKlTZpp+0qVLF9x5553o1KkThg4din/84x84ceKEc38w8nuO2t3hw4dRVVUl+xxT2zVJS0uzevzggw/i2rVruPHGGzFmzBh89NFHKCsr88wboYBSUFCARx99FO3bt0dUVBSioqJQUlKCY8eO4cCBA2jSpIlVtpwbbrgBN910k/nxrl27IIRAjx49rMb8119/3Rx3kIQBtg8sWLAAu3fvxqBBg/Djjz+iU6dOivOXamtr8cgjjyA4OBgff/yxVfBRW1uL6dOnIzs72+pfXl4eBg8eDACIi4vDhQsXrF7TYDBg165d+O9//4ukpCT8+9//Rvv27bF7924AgBDC7QsiTXWwtxiStEPYzJ/+4osvrNrg/v37kZeXhxtuuKHOc+2JiIioU6bUFi3LbZ9j+p2W5cHBwXV+Xq6strYWABAUFIQ1a9bg+++/xy233IKsrCwkJSXhq6++cvr9kH9zpt3JPce2zLbdt2zZEgcPHsQHH3yApk2bYvbs2bjpppv4BY8a7Pe//z2OHz+Of/3rX9i+fTuys7PRtGlT8+JaR+d+0/j4008/WY35OTk52Ldvn8frryUMsH2kU6dO+NOf/oQ1a9Zg3LhxmD9/vuzznn32WWRnZ+PLL79EeHi41bEePXogNzcX7du3r/PPYDAAALp37468vLw6K9ODgoLQr18/zJo1C7t370bz5s3Ni29SU1OxdetWq+fbPpZjuspYU1NT59j+/fsRFBSEbt26OXwdUp+ff/7Z6nPdtm0bQkJC8Lvf/Q56vR75+fmy7TAoKAipqakApAHZpKqqCj///LPD35uamopt27ZZtd+9e/eipKTE/Lqpqan48ccfrX5u06ZNCAsLQ7t27Rr0vnU6HdLS0vDCCy9g06ZNSE9Px+LFixv0muQfHLW79u3bIzQ0VPY5prZrT2hoKO666y789a9/xf79+3H58mWsXLnSre+BAsv58+dx4MABzJgxA3feeSdSUlKg1+vNd+1SUlJw9uxZHD582PwzFy9exK+//mp+fPPNNwMAjh8/Xme8T0xM9O4bUrnGvq5AoDl8+DAWLFiAIUOGoHXr1jh9+jQ2b96M7t2713nuhx9+iPfeew+rV68GAJw5cwYAEBYWBqPRiFmzZuGOO+7AlClTMHr0aERGRiIvLw9ffPEF3n33XYSFhSEjIwM6nQ47duzAbbfdBkDKjJCfn49+/fohLi4Ou3fvxokTJ8zTS6ZMmYJbbrkFL774IkaPHo3c3FzMmTPH4XtLSEgAAKxevRp9+/ZFWFiYOdDfuHEj+vbti6ioqIb/Ecnrzp8/jyeffBJPP/008vPzkZmZiccffxxGoxEvvPACXnjhBQDS9KPq6mrs378fe/bswZtvvon27dvjnnvuwZNPPol58+YhPj4eb7zxhlO3vCdPnox//OMfGDNmDF544QVcunQJTzzxBPr27Wtuz88//zyGDBmCN954A8OGDUN2djZeeeUVTJ061Wpqiat++uknbNiwAXfccQeaN2+OvLw87Nu3D+PGjav3a5L/cNTuQkJC8Mc//hGZmZmIi4vD7373O3zxxRdYtWoV1q1bZ/e1Fy1ahNraWqSlpSE6OhobNmxAWVmZeYwmqo+YmBjExcVhwYIFSExMxPnz5zFt2jRzCsqBAweia9euGDVqFP7xj38gJCQEL774Iho3bmy+st2+fXuMHTsWjz/+OP7617+iV69eqKiowO7du3H27FlMnz7dl29RXXw4/zsgnT59WgwdOlS0bNlShISEiObNm4vx48ebM27AYpHj6NGjBYA6/0aPHm1+vU2bNokBAwYIg8EgwsPDRXJysnj66afFtWvXzM8ZM2aMePzxx82Pf/zxR5GRkSGaNGkiQkNDRfv27cVf/vIXc+YRIYT49NNPRbt27URISIhIS0sTK1eudLjIUQhpUWXTpk2FTqcz17O2tla0bdtWfPLJJ277O5L3pKeni8cee8ycEcFgMIjHHntMVFRUmJ+zcOFC0bVrVxEaGiqio6NFWlqaeO+998zHz507Jx544AERHh4umjRpImbMmCFGjRpVZ5Gj5Up1k23btonbbrtN6PV6YTQaxcMPPyyKioqsnvPhhx+K5ORkERwcLFq0aCFeeOEFqz4g99oDBgyw6ktCCHHnnXeKESNGCCGEyMnJEYMHDxbx8fEiJCREtGnTRjz77LNWCy7Jfzla5CiE43ZXVVUlpk+fLlq0aCGCg4NFx44dxdKlS61eAzIL27OyskSvXr1EdHS0CAsLE6mpqczQQG6xceNG0aVLFxEaGiqSkpLE8uXLRWJiopg5c6YQQlrEOHDgQBEaGipatWol3n33XXHLLbeIyZMnm1+jurpavPnmm+Kmm24SwcHBIjY2VvTr1098/vnnPnpX6qQTwoUJkqRJR44cMU8nadGiRb1e4+jRo0hISMDmzZtdXvD4+eefY/bs2cjOzrZaKEdERETqVVZWhlatWuHVV1/FU0895evqaAqniASAxMREzJs3DwUFBfUOsBvi6tWrWLx4MYNrIiIiFVu9ejUaN26Mjh07ori4GH/+85+h0+nw4IMP+rpqmsMAO0D4snOYdngkIiIi9bp8+TJmzZqFo0ePIiIiAjfffDO2bNmC+Ph4X1dNczhFhIiIiIjIjZimj4iIiIjIjRhgExERERG5EQNsIiIiIiI3YoBNRERERORGDLCJiIiIiNzo/wMESA5DJFqhgQAAAABJRU5ErkJggg==\\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\n   \"source\": [\n    \"### $\\\\alpha$ = 9.9e-7\"\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      \"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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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 -2.6e-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 -2.7e-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  7.0e-13  7.6e-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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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tLr169ePnll7WSVVvKknnu3Lls3ryZAQMGMGnSJLy8vNi+fTurV6+mS5cujBo1StXOkCFDmDlzJi+++CLJyck0bNiQDRs2lPjgXRHCw8P54osvVP8XzS8MQ66pQtW+fXuio6OZN28eTZs2RaFQMHjwYL3IaWlpyfLlyxkwYAC+vr6MHTsWLy8vsrKySE5O5tdff2XdunWEhYVhaGjIggULGDRoEMHBwbzwwgsYGRmxZMkSGjZsyNmzZ9Xa7t69O35+fsyaNYuMjAzc3d3ZuXMne/bsUQtqAdC7d28sLCwYMWIEU6ZMoX79+uzatYv169fj6elZpktmWbzwwgucP3+eHj164OrqSn5+PqtXryY3N5eRI0eqyn311Vd07NiR3r17M2rUKNq0aUN+fj579+7Fzc2NefPm0axZMwICApg/fz63bt3C19eXlJQUFi5cSGBgIAcPHixXng8++IBdu3YxaNAgBg0aRPv27TExMeHMmTOsX7+eNm3aqO3lJZFIJNVO1QYVlEgkEs3Jy8sTn332mQgNDRX16tUTRkZGwsHBQfTp00csX75c3L9/X638r7/+Kjp06CAsLCyEhYWF6NChg1i3bp1amW3btolBgwYJV1dXYWZmJurXry+Cg4PFd999JwoKCtTKzps3T7i7uwsjIyMBiNmzZ5crc3Jyshg2bJhwdHQUxsbGwsnJSTz11FPiwIEDWssqRMmhswvHwSMhuMuT+dSpU2L48OHC3t5eGBsbC3d3d/HWW2+JvLy8Yu3v2bNHdOzYUZiamoqGDRuKF154QWRmZpYaNl2Tc/Mof/zxhwCEh4eHWn5KSooAhLGxcYmyPSpDYZ2IiAhhbW0tALWw7yWVF0KIqKgotZDn5XH06FExbNgw0bhxY2FsbCwcHBxEhw4dxJw5c0RGRoZa2bVr14qWLVsKExMT0aRJE/HOO++ITZs2lXjNTpw4IXr27CnMzc2Fra2teO6558T58+dLvPaxsbEiNDRUWFlZCVtbW9GnTx9x9OjREkOkaxo2fe3ateLJJ58Uzs7OwsTERNjZ2YkuXbqINWvWFCt7/vx5MWHCBOHi4qI6BxERESI6OlpVJj09XTz77LPCzs5OmJubi3bt2olff/21xBDppYWNz8vLE3PmzBGBgYHCzMxMWFlZCT8/PzF+/HixZ8+ecsckkUgkVYlCCB1WSEskEolEItGKmJgYunXrRlRUlMoyKZFIJJLHH7mGSiKRSCQSiUQikUh0RCpUEolEIpFIJBKJRKIjUqGSSCQSiUQikUgkEh2Ra6gkEolEIpFIJBKJREekhUoikUgkEolEIpFIdEQqVBKJRCKRSCQSiUSiI1KhkkgkEolEIpFIJBIdkQqVRCKRSCQSiUQikeiIVKgkEolEIpFIJBKJREekQiWp00RGRhIYGFjq58pgypQphIWFVbide/fu4ePjw/bt2ysuFJCeno5CoWD//v2lljl69CjOzs7k5eXppc+qIiYmBoVCgUKhoFevXlrVHT16tKrumjVraqU8EolEP7i5ufHJJ5+U+rkysLKyYunSpRVuJzY2Fh8fHx48eFBxodBsPp0+fTpTp07VS39VSdHf4Z9++kmruoX1rKysaq08dRGpUNVxwsLCmDJlSrH8pUuX1skv1/Tp04mNja1uMYoxevRo+vXrp5a3aNEinJ2d6dKlS5XJ0bx5c9q3b89nn31WZX3qk8TERH788Ue1vLVr1+Lv74+pqSn+/v6sW7dO7fgXX3zBpUuXqkSexMREnn32WTw8PFAoFERGRharU5nySCQ1gcKHw/fff18tv/BFxPXr16tJMu2Jj49n0qRJ1S1GMUp6IfP666/z9ttvY2hoWGVyzJgxg6VLl3Lq1Kkq61NfdO/enUuXLjFgwABV3p07d3j55Zexs7PD0tKS/v37c/78ebV6ly5d4vPPP68SeRYtWkS3bt2oV68eCoWC9PT0YvUqS566hlSoJDWagoICvb0t0wQrKysaNmxYZf1VhC+//JJx48ZVeb9jxozhm2++4f79+1Xed0VxcHCgfv36qs9xcXE8//zzDBs2jISEBIYNG8Zzzz3H3r17VWVsbW1p1KhRlchz69Yt3NzceP/993F3dy+xTmXKI5HUFMzMzJg/fz7Xrl3Ta7t3797Va3vlYW9vj4WFRZX2qQu7d+8mOTmZ5557rkr7tbe3p0ePHnzzzTdV2q8+MDU1pVGjRpiZmanypk2bxtq1a/nxxx/ZsWMHOTk59OvXT+05plGjRtja2laJPLdu3aJHjx4lvpyrbHnqGlKhkmhEoYXkiy++wNnZmfr16zNmzBhu3bqlKhMWFsakSZOYOXMmdnZ2ODg4MH36dAoKClRlMjMzGTVqFPXr18fc3Jzu3buTmJioOl5oGVu/fj2BgYGYmJiQlJSEm5sbc+bMYfTo0VhbW+Pi4sLq1avJyspi8ODBWFlZ4e3tzaZNm1RtPXjwgHHjxuHu7o65uTne3t7Mnz9fTZ5HKeqiUOgC92hyc3NTlT9+/Dh9+/bF2toaBwcHhgwZwuXLl9VkmD59OvXr16d+/fpMmzZNLwri/v37SUlJUbNaPf/880ycOFH1+e2330ahUKgpB02aNGHlypVltp2SkkKnTp0wMzPDz89P7ZwC9OjRgxs3bhATE6OxvG+99RZt2rQplt+xY0deeeUVQOlO+MQTT2BjY4O1tTUtW7Zk27ZtJba3YcMGrK2tVUpdamoqCoWi2PgjIiLKlOvzzz+nW7duvP322zRr1oy3336bsLAwrd/W6Uuedu3a8cknnzB06NDH4iFMIqksunXrhpubG//973/LLLd9+3ZCQkIwMzPD0dGRV199VU1pCgsLY+LEiUyfPh17e3tCQ0NVlq4NGzbQpk0bzM3N6dy5M+fPnyc2NpaWLVtiZWVFv379yMjIULUVHx9Pjx49sLOzw8bGhk6dOhEXF1emfEVd/iIjI0ucU4o+7EZFReHv74+ZmRk+Pj4sWLBAbc46efIkYWFhmJmZ4evry19//aXNaS2VVatW0b17d9Xvzs2bNzE2Ni42fzRr1kz1efPmzVhaWnLv3r0y2168eDFNmzbF3NycAQMGFLMw9u/fv5jnQFkUFBTQpEkTvvzyS7X8lJQUFAoFhw4dAmDhwoX4+PhgZmaGvb09PXv2LPVFoD7mz+zsbL7//ns+/vhjIiIiaN26NT/88ANHjhwhOjpa4/HpSx5QKnhvvfUWnTp10qp/ifZIhUqiMTt27ODYsWNER0ezevVq1q1bxxdffKFWZuXKlRgZGbF7926++uorPv/8c1avXq06Pnr0aPbu3cvvv//Ovn37sLCwoFevXuTn56vK3L59m/fff5+FCxdy/PhxXF1dAeXDb3BwMAcPHmTQoEGMGjWKoUOH0qdPHxISEujSpQvDhw/n9u3bgPJH19nZmZ9//pmkpCQ++OAD5s6dS1RUlEbjdXFx4dKlS6qUkpKCq6urav3TpUuX6NKlC4GBgezbt4/o6Ghu3rxJ//79VRPgp59+ynfffcfChQuJi4vjwYMH5f4AasKOHTvw8vKiXr16qrywsDA1BSQmJgY7OztVXmpqKhcuXCh3/dYbb7zB1KlTSUhIICIigqeeeooLFy6ojpuYmNCqVSutXCNHjBjBwYMHSU5OVuWdPn2auLg4hg8fDsDQoUNxcnJi3759HDp0iMjISLU3bUXp3Lkzt2/fVq33enSshXnljTUuLo4ePXqo5fXs2ZPdu3drPDZ9yiORSJQYGBjw0Ucf8e2335KWllZimQsXLtC7d2+CgoI4dOgQ33//PT/++CNvvfWWWrkVK1YghGDHjh0sX75clT979mw+//xz9u7dS2ZmJs8//zxz5sxh0aJFxMTEkJiYqKbs5ObmMmLECHbs2MG+ffto1aoVffr00dgFcfr06WpzyvLlyzEyMlI97H733XfMnDmTOXPmkJSUxKeffsq8efP4+uuvAeWcNnDgQAoKCoiLi2PJkiVERkZy584dbU5tiezYsYO2bduqPltZWdG6dWu1+SM7O5v09HSVy3FMTAwdO3bE2Ni41HbT09NZsWIFv//+O9HR0aSmpjJ27Fi1MsHBwVy4cKHU6/woBgYGDBkypNhcunLlSvz9/QkKCmL//v1MnjyZ2bNnc+LECaKjo8tcr6qP+fPAgQPcu3dPbU5xcXGhWbNmWs8p+prPJVWIkNRpunbtKiZPnlwsPyoqSlhaWqo+jxo1SjRp0kTcu3dPlTd+/HjxxBNPqLXVvn17tXa6d+8uxo0bJ4QQIiUlRQAiNjZWdTwrK0vY2NiI7777TtUvIPbv36/Wjqurqxg8eLDqc25urgDEyy+/rMo7ffq0AER8fHyp450xY4aazLNnzxYBAQGlfi7kwYMHom/fviIkJETk5+cLIYR49913RXh4uFq5GzduCEDs3btXCCGEk5OTeP/999Xa8fb2Fl27di1VxpIYNWqU6Nu3r+rzK6+8Irp06aJW5vjx4wIQFy9eFHl5ecLExER8+OGHokePHkIIIRYtWiS8vLxK7aPw/JUk79tvv61WduDAgWL48OFajaFVq1binXfeUX3+73//K3x8fFSfra2txdKlSzVuLzg4WMydO1cIIcTQoUNFZGSkMDMzU43f2NhY7Ny5UwghxLZt2wQgrl27ptaGsbGxWLZsmVresmXLhImJSbH+APHLL79UqjxFCQgIELNnzy71eHnySCSPK0V/78LCwsTzzz8vhCj+vZk5c6bw9PQUDx48UNWNiooSJiYmIi8vTwihnJeaN2+u1n5hOxs3blTlffnllwIQBw4cUOWVNh8UUlBQIBo1aiR++OEHVZ6rq6v4+OOPS/1cSHJysqhXr55YsGCBKs/FxUUsX75crdyCBQtEs2bNhBBC/PPPP8LAwECcOXNGdXzHjh0CEFFRUaXKWRKP/n7Y2tqKJUuWqJV544031OaPXr16iS5duohVq1YJIYTo2LGj2nzxKLNnzy5V3pSUFFVedna2AER0dLTG8h8+fFgAIjU1VZXn5eWl+g1eu3atsLGxETk5ORq1p+38+eicLIQQK1euFIaGhqKgoEAtv1u3buLFF19Uy3v0Gasy5ClKfHy8AMTp06dLPF6ePJLykRYqicb4+/tjZGSk+ty4cWOuXr2qVqZFixZqn4uWSUpKwsDAgA4dOqiO29ra0rx5c44fP67KMzIyolWrVsX6L9q2lZUVFhYWNG/eXJXn6OgIoCbTt99+S9u2bbG3t8fKyooFCxZw9uxZrcYNyoWzR44c4bffflNZTQ4cOMD27duxsrJSJRcXFwDS0tLIzs7m0qVLauM1MDAgJCRE6/4fJT8/v5j1plmzZjg6OhITE8OuXbvw9PRk8ODB7Nq1i3v37mlsISlJ3qLXB8Dc3FzNqqgJw4cPZ9WqVarPK1euVFmnAP7zn/8wfvx4wsPD+eCDD9SsWSURFhamcjuMjY2ld+/eBAcHq8ZvbGxMcHBwuXIpFAq1z0KIYnmaoC95JBLJv8yfP59ffvmlxOijSUlJdOjQAQODfx9lOnXqxN27dzl58qQqryR3Y1CfUwrnj0fnlKLzydWrV5kwYQI+Pj7Y2tpibW3N1atXtZ5TsrKy6N+/P8899xzTpk0D4Nq1a5w7d44JEyaozSlvvvmmynKTlJSEs7MzTZs2VbUVEhKiNn5dKWlOCQsLU5s/unXrpvqdu3XrFvHx8eXOKaXJm5SUpMozNzdXyaApLVq0oHnz5qo5Ze/evaSlpTF06FAAIiIicHV1xd3dnWHDhrFs2TJyc3NLbU9f82dJ6DKnVKY8kspBKlR1HBsbG7Kzs4vlZ2VlFVuk+KhZX6FQFFuPVFYZIUSpchT9sTE1NS0xylBJbRfNK2yjsL/Vq1czbdo0Ro8ezT///ENCQgKTJk3SelHysmXL+Pbbb/nzzz/VggEUFBTQt29fEhIS1FJqamqxiHz6xs7OjszMzGL5Xbt2Zdu2barJz83NDTs7O+Lj44mNjdXbD/CNGzewt7fXqs7QoUNJT08nLi5O5f43bNgw1fHIyEiOHz/OgAED2L17Ny1atGDJkiWltlc42R8/fpzc3FzatGmjcpPQxBUFlItxi655A+VDU+HDlTboQx6JRKJOu3bteOaZZ5gxYzNTioAAACAASURBVEaxY2U9qBbNt7S0LLFMSfPHo3lF57hRo0YRHx/PggUL2L17NwkJCTRp0kSrOeX+/fsMGjQIZ2dn/u///k+VX9jPt99+qzafHDt2TLXOuKw5tKKUNKd07tyZO3fuqOaPQoVq27Zten1JdOPGDQCt55Rhw4ap3P5WrlxJ586dVUsErK2tOXjwID///DNNmzblww8/xM/Pj4sXL5baXkXnz0aNGvHgwYNiLqC6zilVNZ9L9INUqOo4vr6+HDx4sNgP9cGDB/H19dVrX/7+/irf70JycnI4evQo/v7+eu0LYOfOnYSEhDBlyhRat26Nl5eXxj7ahezevZuJEyeyYsUKWrZsqXasdevWJCYm4urqipeXl1qytrbG1tYWJycn9uzZo6ojhGDfvn0VHltQUBAnTpwoptAWfYAv/LHt2rUrixYt0tjfuiR5iy5EBjh27BitW7fWSmYnJyfCw8NZuXIlK1eupGPHjnh4eKiV8fb2ZurUqfz999+MGzeOxYsXl9pe4WQ/f/58OnXqhKGhYYnjL4sOHTqwefNmtbzNmzfTsWNHrcamL3kkEklx5s6dy44dO9i4caNavr+/P3FxcWq/gzt37sTExARPT0+9y7Fz505efvll+vbtS0BAANbW1lpvYTBt2jROnz7NmjVr1JQ3R0dHnJ2dSUtLKzafeHl5AcrxXrhwgXPnzqnq7du3r8xAS5oSFBRUzBOhcB3VokWLyM3NpXXr1nTo0IGzZ8+qfsPLe0lUmrxF55Rjx45hbGysZh3UhGHDhnHy5En27NnD6tWr1TweQOntEh4ezocffsiRI0fIy8srM4hHRefPNm3aYGxsrDannD9/nqSkJJ3mFH3M55KqQypUdZyJEydy6tQpXn75ZQ4fPsyJEydYsGABP/74I9OnT9drX97e3jz11FNMmDCBHTt2cPToUYYPH46NjY3KTK9PfHx8OHjwIBs2bCA1NZX//ve/WgVSuHz5MgMHDmTSpEmEhIRw+fJlLl++rArjO3nyZLKzs3n++efZu3cvp06dIjo6mhdffFHlWvDKK68wf/581qxZw4kTJ5g2bZpe9hDq1q0bt2/f5siRI2r5YWFhnDx5kn379ql+bMPCwlixYgVeXl44OzuX2/Y333yjJu+ZM2fUog2lp6dz4cKFYsEcNGH48OGsXr2an376SW3yy8/PZ/LkycTExJCens7evXvZuXNnmYp24WS/YsUKunXrBigVpHPnzrF3716NJptXXnmFrVu38uGHH5KcnMyHH37Itm3bVG442qAPee7evat6M3379m0uX75MQkKCmvuSRFLX8PLy4sUXXywWBGnSpElcvHiRSZMmkZSUxN9//82bb77JlClTKiVKpo+PDytWrOD48ePEx8czePBgTExMNK4fFRXFkiVLWLx4MXfv3lXNKTdv3gSUVvr58+ezYMECTpw4wbFjx1i+fDkffvghoNxnyM/Pj5EjR5KQkEBcXByvvvqqmiu+rvTs2ZOdO3cWyy+cPzp37oyhoSFmZmaEhISwYsUKjX7TzM3NGTVqlErel156ib59++Lt7a0qs2PHDjp37qz1NWvSpAldunThpZdeIjs7Wy3k+19//cUXX3zBoUOHOHPmDKtWrSI3N7fYy8FHx1qR+dPW1pZx48bx+uuvEx0dzaFDhxgxYgQtWrSge/fuWo1NH/IAqjkkJSUFUEYmTkhIUFkFJfpDKlR1HA8PD7Zv305qaio9evQgODiYn376iV9++YU+ffrovb+oqCiCg4Pp378/wcHB3Lp1i40bN6p8qPXJhAkTGDRoEEOHDqVdu3akp6fz2muvaVw/OTmZq1ev8umnn+Lk5KRK7dq1A5Trw3bt2oWBgQG9evUiICCAyZMnY2pqiqmpKQCvvfYaY8aMYfz48YSEhFBQUKDm5gbKUPGlbbhXSEFBgdqk2bBhQ55++uliUY6aNWtGo0aN8PX1VblPdOvWjQcPHhSb/ApDBz8a/vyjjz7is88+o2XLlmzcuJF169bRpEkT1fEff/yRHj16qFwr4N9wwOXxzDPPcOvWLa5du8agQYNU+YaGhqqQ+r6+vgwcOJAOHTqUu4Hwo2MzMzOjffv2mJqaauSK0rFjR3766SeWLVtGixYtWL58OatXr9ZonZubmxujR4/WqzwXL14kKCiIoKAg0tLSWLhwIUFBQYwfP77cuhJJbWbWrFnFFAdnZ2c2bNjAoUOHaNWqFWPHjmXIkCHMnTu3UmRYsmQJN2/epE2bNgwePJixY8eqbaNRHrGxseTn5xMWFqY2pxSGVR8/fjxLlizhhx9+oGXLlnTu3JlFixap9qQzMDBg3bp1FBQUEBISwsiRI3nnnXdU800hYWFhZSo7hRatoudz+PDhpKSkqG1jAiXPH6XNKaNHjy52Ptzc3Bg8eDBPPvkk4eHheHh4FIu0++OPP/LCCy8Uq/fo72tJjBgxgsOHD9O3b1+1qLf16tXjt99+Uymhn3zyCYsXL6Zz586ltqXN/FkaCxYs4Omnn+b5558nNDQUKysr/vzzz3I3Sy5pPtaHPN9++y1BQUGq546+ffsSFBTEH3/8oVF9iRZUXzwMiUQihBCzZs0S/v7+ahEUHyUiIkK89NJLannHjh0T9vb2Ijs7W6d+lyxZIhwcHERmZqbGdW7fvi1cXFxU0eoKGTlypIiIiNBJjqpAk6h65UGRqFh5eXnCzMxMFe2quuWRSCSSQpo2baqKdlcSFy5cEIDYs2ePWv6MGTPE2LFjde63S5cuxaLZlcdff/0lmjVrpjb/VfT3tSooL6peeTwaVU+X+bgy5ZFoj7RQSSTVzPr16/nqq69KdNu4fv06v//+O7GxscU2hQ0ICOCTTz7h9OnTOvc7b948tbd65XHmzBnefvttQkNDVXlCCLZu3cpXX32lkxxViZubGwMHDtSqzksvvYSVlZVa3rZt2wgJCWHIkCE1Qh6JRCIBSExMxNTUtERvjAcPHnD69GnmzJmDg4NDsai8M2fOxMPDQ6fN57Ozszlx4oTW1sG8vDyioqLU5j99/b5WNhs3bsTKyoq1a9dqVc/KyoqXXnpJLU+X+bgy5ZFoj0KISgwbI5FIKkR4eDipqamMHj2aOXPm6BTOW6Jco1W4ObGlpSVOTk4a17169So5OTmAMrBGaRHDHmd5JBJJ7Sc9PR0/Pz/8/f1ZsGABXbt2rW6RHluK/g43atRIq5dchWtiDQwMigVmqi3y1EWkQiWRSCQSiUQikUgkOiJd/iQSiUQikUgkEolERyoea7OWU9KmtxKJRCKpGh7dYLy2I+cciUQiqV50mXekhUoikUgkEolEIpFIdEQqVBKJRCKRSCQSiUSiI9LlTwtqg+vJE3/A1gsQ/SQ80aT88oUsGP4XZxMzmPZDH1wD7bXv+CkgB/gN0OI05h48yME2bbAKCqLNwYMlF/qpFVw/DM8fAvtWJRb54AC8sw/eCoK57cvu85+Fh9n4bQI9XmhB70lBmgn6AOiO8hXFFs2qlEVeUhL7/f2x8POjXVJSxRusJm6lphLv44O5lxfBqakVbzAdGAM0BZZpXu1/Yzdw+tBVpnzfC8/WjmWWdYiCa7fhyihwsCil0N5IiH8P2s2GkMhih+9dv85ue3uMGjQgNCNDc0EBtgFzgK5A8abLZcPXh9j03RF6vdSKnhNaalzvtd3w2WH4pAO8VvLXqMqQbm9KasOcIymdW/dg20XYcBb+PgPpuf8eMzKAzo3gKXfo7wbuNtUmpkRSJ6jovCMVqjqKzqEdqysmpEbBKEsvoyi3hI5dai6CTtSWIJx6G4cuF1JdEM2LVqRUYXj7arx+QseTVDvuOImk5mNhDH1dlenLTnAiS6lY/X0Gtl9SKlvbLsK0XdCiITztDs94QECDf39iJBJJzUAqVHUMnX+Dq+vXW6N+yy+jjfg6DVXPp6e27Del93Ho2Jw21TS75copVJFxV/SU6dh37bjjJJLHE4UC/Oor02utIPMObDgDf5xRWrCOZChT5H7wtoWnPeBZD2hjL5UriaQmIBWqOoquL84r/MJd5341qKhBGe2610FYfb/eryUWqpoyDm3EqJCFSpcOdRNA7/VryKWSSOo09U1hqI8y3X2gdNVfewp+Ow2p2TDvkDK5WcNznsrUVipXEkm1IRWqOobKE0nHejo/benoqqXQxHVKUX7jWomvi7eWviexGuAyphcqaxy63kealC3sosw+yhnXw/50cnWsoFujrqe8ot6UEomuFBQI7t95wP17Dyh4IBAFQvlXCBQGCgwNDTA0NsDAUIGhsQGGRga1xoqvCSaG0KupMn3TBXZdhjVpSgUrPRc+TlAmd2sY4g1DvZVugRKJpOqQClUdQ3e9qPABsYIda11Pk6dDfbv86aB1VpZC9bijb4VK16d+lRjlV9To8pejxFfrw56ubpE6vmyRSIpSUCDIy7xN1tVbZF/JI+vqLXKu5ZOXdZu8rDvkZd/hVtYd8nPvcvf2fe7dfsDd2/e16sPAUIGJuRGm5kaYmBtjammMhY0JFrammFubYGlrimV9U2wammNtZ451A+VfCxuTx14RMzKAro2V6YtOsOsS/JwGa07B6VyYe1CZWjRUKlZDvKCpdXVLLZHUfqRCVcfQdS7R2xyks2lMk7b15PJX0bEKPbShausxf7ytKWuodLBQ6UWQilioKoqWfT/ej5mSqiY/9y6XTmZy5XQ2187mcO1MDtfO5pBxPpf7dwu0bs/Y1PChFcoAAwMFCkMFBgooEFBwv4AH9wsouC+4f/8BBfcFt2/e4/bNe0C+5n2YGVK/kSX1HC2p18iS+k6WNHS2xq6JNQ2bWGPd0OyxUrgMFNC5sTJ9Hgo7LsGPJ+GXtH/XXL25R6l8jfBRrrmyNa1uqSWS2olUqOooOgdJ07XDCispFbRQ6dRlNSozj9Gkrgl6P5dVcANrFliynCh/FUFn79qK9f246/AS/XM77x7ph69y5uh1LqTc4MKJG9y4cLPU8ha2ptRzsMDW0QJbBwts7S2wqm+Gha0plvVMVZYkpYXJCGMzIwwMNL9v7997wN38+6qUf/Mu+Tl3uZVzl1s5d7iVfYebmXfIzcgn93o+uTfyyb6Wz528e1xNz+Fqek6J7ZqYG2HnYo2jmy0O7rY4utvi6FEPB1cbjEwMtT5vVYmhAYQ5K9P/OsE/Z2FlKvyRDrEXlWnKDmUI9pG+0NNFae2SSCT6QSpUdQydXf4quoZKV7Rai6KBhUqDZhQVdW/Uo4XqcQ+brtEaOK0arFg9ja6/Hlz+VFTDGipdu5Yuf5JC8nPvkrrvEmkHr3Dq0BUunMhEFKjfGUYmBjTyrEcjz/rYN7XG3tUW+6bW2LnYYGZpXKnyGRkbYmRsiIWNduaW2zfvknklj6xLeWReuUXmxZtknM/l+vlcMs7ncivnLhdTMrmYkqlWz8BIgaObLU7e9WnsXZ/GPg1o4tcA64bm+hyW3jA1hP7uypRzV7nW6ocTEHNR6R74cxo4WcAoXxjjBz71qltiieTxRypUdQzdlzJVj8VEo341KKOV9BV5aNeX3lBbLFSVNQ5dlQVN1lBp1qKmHWrUmg4ClNu1tiepltxxEh0QQnAxJZOknRc4vus8Z45co+DBv/ePgZECF3873Fs64OLfkMY+9XFwtcXQ+PEycZhZmeBkZYKTZ/0Sj+dl3+H62RyunM7myulsrp7O5vKpLDLO53LpZBaXTmZxcMNpVXlbBwtcmjXExV+ZmgbaYVnPrKqGoxE2JkqlaYwfnLsJK1JgaTKkZMNHh5QptBGMawaDPKGSdWGJpNYiFao6ShXsi1oNHes7bLqO6LOTx9xCpaK6LVQ6VNRI4hro8ldRzai23HKSsikoEJw5co2Ezekcjj5D9tVbqmMGRgo82zji1a4RnkGONG1uh6l57X/StrQ1xbK5Pa7N7dXy7+Tf43JaFpdSMrmYmsmFE0q3x+yrt8i+eotjsedUZe1crHFtbk/TQDvcWtjj7NsAwxriW+diBW+1hjeDYPdlWJIMq08qowbuugyv7FQGshjfTO5vJZFoi1So6hg6/0BWeLNRXes9Jhv7Foqhr4fR2jKT1RALlTboMyhFdbps6ho2XVK7uXI6m/g/T3Jgw2myLuep8m3tzfELbYJ/J2e8g50wtzapRilrFqbmxrgG2uMa+K+iVVAguH42h3NJGZw/nsHZxOucS8rg+rlcrp/L5cD6U4ByXZZbC3s8ghzxaO2Aa3N7TMyq99FLoYBQJ2X6ohP8fBIWJ0HcFVh4XJla2cHEAKWCZVX7dWmJpMJIhaqOovNjXkUfECvTQqXnjX1rxPqlmiCDPqjmsOnauPwVolnRcsKmV8P10ynsfxFqyR0nKcKD+wUkxp5j58/JpO67rMqv18iSVhGutOrhRtMAu9rjalwFGBgocHCzxcHNlja9PQB4cK+AS2mZnDl6nTNHr5F++BrXzuaQsvcSKXsvAWBobIBbC3u82znh1a4Rrs3tMDKuvoAXVsYwtpkyJd5QKlbLT0DCdZgQC9N3K4NYTAyQe1tJJGUhFao6hq6xJSock0Ln/YOqY2PfCq7O18cTaS3b2FdvymlFLaxVFZRCH2uoKvhd0/acy6AUtY+bN26ze+0J4tamkHVF6dJnYmZEUE832j3piXuQo1bR9SRlY2hsQBO/hjTxa0joc74A5FzP59ShK5w6dJW0A5e5lJpJ2oErpB24At8qr4dHa0d82zvh26ExjTzrVZtiG9AAFoTCR+1hbRp8kwg7L8P/HVOmzk4wJRAGukM16oASSY1EKlR1DN33oaroSnld6z1mLn/6opa8Ka4pb7yrfB+qioy7wl813RqoGVdKog+yruaxdWkie9alcO/2AwDsXW0IHeRL8JNe0p2vCrGxM6dVhButItwAZeCLtAOXSd13mZP7L3M5LYvk3RdI3n1BVd6nfWP8Oznj19G5Wq6VqSEM9VGmoxlKxeqHFOU+VzsuKSMETvCHCQHQyKLKxZNIaiRSoaqjPG5voTV6215TXP70eHJrhNuhPqgh+1BpI0aF9qHSpcNidXWvWpGua8stVxfJvZHPliXH2PVLsmpzXf/OTegytBnewU7SGlUDsLQ1pUW4Ky3CXQGlBStl70VO7LnEiT0Xybmez/6/0tj/VxoGhgrcWzrQrHMTAjo3wdHDtspfUjVvCF93gXntlUrVV8cgKRMi98MHB5WRAae1gLYOVSqWRFLjkApVHaMKg6TpBc0mD/2GTa8JFqqaYtmpMDVkH6p/PfQ0CJuuR5c/nRRiPbn8aV2tltxydZF7dx4QsyKR6O+Pcjf/PgAtI1zpMb4FjX3kwpeajI2dOW37etK2rydCCC6dVFqsknZe4FTCFdIOKtNfXxzAzsWawK4uBIa54NbSoUqjB1qbwKRA5VqqbRfgy2PKTYNXpipTp0ZKxeopd7lhsKRuIhWqOorub69rcFAKPW3sq0Vz+q1XYluPubmgstaCad2c5ps1a6bPlD2u6gxKUVEeP4nrLkIIEmPP8dun8WScvwkoLVK9J7WiiV/DapZOoi0KhUK5cbB3fcJHBZKfe5cTey5yfMd5ju84z/VzucSsOE7MiuNY1jMloEsTWjzhim/7xhiZVM2iJoUCwpsoU3qOcm3Vd0nKtVY7L4OrNbzSXBl6XXqWSuoSUqGqY1Tbxr6VuYZK3xv76vyKX7dqJbdVS8wF+h5HFVhfHvs1VDXg9pVUPhkXclkzd69q7U0jz3o8/UYw3sFO1SyZRF+YW5uo1l8VPCjg9OFrHIs5y7GYc1w/l8u+P9LY90cappbGBHRuQsvurviFOldZWHY3G/i4I8xqC8tOwBdH4WQ2/Ge30iVwgr9SuXK2qhJxJJJqRSpUdRTdw6ZXU8fVsLFvjTAu1AghKo7e14JVwfK2ioRN17IRnZout7qOfdeSW67WIoRg3+8n+XX+Pu7m38fc2oReE1sR+pxvjdlAVqJ/DAwN8GztiGdrR/q/2pbLp7I4uuUsR7ae5cKJGxzceJqDG09jamFEYFhTgnq64duhcZWEZLc2gSnNlS6Bf6XDp4dh+yX4OAEWHIGhXvB6KwiURlNJLUYqVHUMnUMj62tjX13DpmvUePnNaBM1vVqpEUJUHL27vlXB+jbNbrlyxqUPC5XOa6h0+5JXtFtJ5XMr5w6/fLCHhE3pgHKd1DMzQrBuaF69gkmqFIVCgZNnfZw869PjxZZcP5fDka1nSdiUzrnjGRxYf4oD609hYWNC83BX2vR2x7ONIwaGlatwGyigv7syxV+FTxJgzSlYnqJM/VxhRhB0kkZUSS1EKlR1jIo+pld4H6rK7LiMMrp0L2rCzqiPu7mgpimG2mzsW+ZRDcdVkaAUOqL71ggV61dSuaQdvMLKt3eQeTkPUwsjnnmzPW37edSeADYSnbFzsSF8VCDhowK5fi6HQ5vSSfgnnYupmez9LZW9v6Vi62BBUE832vb1pLFP/Uq/b9o5wOoecCoHPjsM3yfBX2eUqWMjeDNIqWDJ21dSW5AKVR1F6419ddwstMJoEi1Ng419C9FsY1+Nm9NPvRLb0vOGuNVNNbv8KVTnU4OyhV1UxOVPH9dPuvzphQcPHrBlyxa2bdvGqVOnyM/Px97enrZt29K7d2/c3NyqW8QyEUKwfVUSv3+2H1EgaBrQkOFzu2Df1Ka6RZPUQOxcbIgY14KIcS24nJbFwY2nObDhFDcu3CTmh+PE/HAcJ+/6BD/pSZs+HpVu3fSwga86w+y28OVRZdj13Zeh/wZo0RDeaQNPu0MlG88kkkpH3sJ1DJ1d/vS1ZF3HB+FySmnQjubd6/zmToZNL05lufzpquxqUlQbl79SBKnO66fF+wX1erpVq7Hcvn2buXPn4uLiQv/+/dm6dSv379/H2tqaixcv8sEHH+Dl5UXv3r2Jj4+vbnFLpOBBAb/O28dvn8QjCgThowOZGtVHKlMSjWjkWY8+k4N458+nmRrVm9BBvljWM+VSaia/f7afyF6/8P20rRzZeob79x5Uqiz25jAnGM6OgM86QmNLOJIBgzZB4Gr44QTcL6hUESSSSkVaqOoY2r19r4yOdayvJ5c/vW/sWmJFHevpVYgagr4tbVVxH2nUhYaKYnXsQ6VFiHi1Wjq/bKmZ+Pr60rx5c/7v//6PPn36YGpqWqxMamoqK1asoH///nzwwQeMHTu2GiQtnaWvx3B02zkMjQ0YOqcTrXu5V7dINQMB3AZuPUx3gAePJAVgiPIpp/CvGWD+MJlQZ0JbKhQK3Fs54N7KgQHT23F8x3ni/0jj+M7zHIs9x7HYc1jVN6Pdk56EDPDG0d220mSxMoZXWyoDWEQlw0eHIDkLRm5VRgZ8uzWM8IEqiKUhkegVqVDVMSozenm1daxJ2HStdvbVoqw+6pXYVu2Y6fVuqakC46FGZcsblwybXu2sXr2a9u3bl1nG29ub9957jzfeeIPTp09XkWSac3TbOcytTRi3oBuebRpVtziVjwAygUvAxYfpBpABZD08lo1Siaqo5m+AUrGyfZhsHv6tD9g9TPZF/q8lD/hGxoa0CHelRbgruRn5HFh/ir2/n+RyWhbblieybXkibi3taT/Qm6Ae7piYV85joqkhvBQA4/yUGwPPPQip2TAuBv57QKlYjfSFKtpeSyKpMFKhqqPo/oL/Md/YV+99VjI1QQZ9UEPGoX8LZQ0Om65jAzXkUlWY8pSpolhaWhIYGFiJ0uhGfSdLJnzVHUePetUtiv7JBk4BaUXSeSBfw/qmgAVKpcgMpcJTNAHcR2mtuv8w3X7Yfj5wD8h7mC6W05cB0Ahwevi3MeACNH34v7GGMtcwrBuaEzYigK7D/Tl77Dp71qVy6J/TpB++Rvrha/z+6X7aPelJx2d9K81qZWwIo/1guA+sPgnvH1BarF6IhfcPwjutYZSvtFhJaj5Soapj6PzivMKhnHWtp8UaKj1F+VPUhHf1tcRCVVPGoY2lTLNbXUMLVTVE+auKzY8fNy5eVD4xN27cGIBDhw6xatUqAgICGD16dDVKVjZTl/amnoNldYtRcQqAs8Ax4OjDv6UpMVYolZTCZAc0eJjqPUwWVNxidB+lpSsHpXKX/fD/DOD6w3TtYbrBvxazRzF8KGdTwKNIctaDjFWEQqHAtbk9rs3tGTC9HYf+SSfu1xTOHrvO9lVJbF+VhFe7RoQ+50vzsKYYGut/+b2RAQzzgcFe8EsazDkASZlKxeqjQ8rNg4d5y+AVkppLjVGo7ty5w6RJk4iOjubGjRt4eXkxd+5cevfuDUB6ejru7u5YWv47ucyYMYN33323xPasrNS35s7Pz2fSpEl8+eWXavnvvfcekZGRbN68me7du+t5VDUX7beD0m1dht4os2M9R/nToax6RR3r6VWIGkZ1b+yrRZRKrdYSVcY+VKq2q6d+Lbnj1Bg6dCgjR45k7NixZGRkEB4ejpOTEwsXLuTatWu8/vrr1S1iiTzWytQNYN/DdAClslIUM8Ad8CySXFG63lUFRg/7sgGalFP2DnAFpUJV6I54DqWSePnh/+eAXUXqmKAcny/g8zC5U4OeukrG1MKY9gO9aT/Qm3PHM9i95gQHN5zmZPxlTsZfxtbenI7P+tL+aR9s7PQfIdDQAAZ7w3Oe8HMaRMZDSjaM2qp0C4xsC4O8lHteSSQ1iRrz1b5//z4uLi7ExsbStGlT1q9fz6BBgzh69KhaWNusrCyMjMoX++bNm6r/8/LycHR05LnnnlMrk5aWxpo1a3Byqju7zFXbGipd0dcaKq361KawHuqV2FYtmS1qyhoqbaL86aFU9Ub5063vWnLHlcixY8cICQkB4Ndff8XDw4MDBw6wbt063nzzTb0pVOW9GNyyZQuTJ0/m7NmzhISEsHTpUlxdXfXSd43gGrAF2AkcR107twOaF0nuPDYWHExRWqCalnDsDkpXxXSULoynH/69Apx4mAoxduss3AAAIABJREFURqlYBT5M/igtbzUUF/+GPD+rI/1fbcv+v9PY+fMJrp7OZsM3CWz67giterrRZUgzmgbY6b1vQwMY8lCxWpUK7+2HE1kwJBo+PARzQ6BP09ozVUoef2qMQmVpaUlkZKTqc79+/XB3d+fAgQMV3idkzZo1ODg40LlzZ7X8KVOmMG/ePCZNmlSh9h9HdLe+VM8aKs3Wbul5DVUNeMUv96Eqrb3Kr6eXNVQor2F1KFi6nvLacssVJS8vDxsbpelj69atPPnkkwC0bduWc+fO6a2fsl4MWllZ8fTTT7N48WKefPJJ3n33XZ5//nn27Nmjt/6rhdvADmATSktU4f1jDLQGgh8mZ2qn1m7Kvxa2J4rk3wROAin8q1hdABIfptUPyzUGWgAtgVYo12jVMMytTeg8uBmdnvcjdd9ldvyUROL28xz4+xQH/j6FW0t7ug7zp3m3phga6dcnz8hAGZxiiBcsO6FUrI5kQL/1ENoIPmoPnerOO3FJDabGKFSPcuXKFVJSUggICFDLd3V1RaFQEBERwccff4ydXflvRpYtW8bIkSPVHmp++eUXTExM6NOnj95lr8lUdB8qnR+2dN0bR6O1KBqsodJqHyoNuixDDH0oVJqN+zGgpoRN12ZjX03uFW02exJCNxNZhb9r2jVQ2/ahKoq7uzvbt2/nqaeeYvPmzUyePBmAa9euYW1trbd+ynoxmJGRQUBAgMpTIjIyEjs7O5KTk/Hz89ObDFXGaeBXYCvKtUigVKI6At1QKlGVu2dszcYKpYLUqkheLpCEcg3ZsYf/F67N2viwjCNK5ar1w2RfRfJqgEKhwCfECZ8QJ25cvMnO1cnE/ZryMIhFLPWdLOkypBkhA7wxtzbRa9/GhjDeXxm84utEpfvfrsvQ+TelperD9sqNgiWS6qJGKlT37t1j2LBhjBo1SjXR2NnZER8fT6tWrcjIyGDy5MkMGzaMf/75p8y2zp49S2xsLN9//70q7+bNm8ycOZNNmzZV6jhqIo9t2PSyHg717PJXEzb2rS1+DHpXDCt4aTRaQ6VNi+Xdl0LosCHUw786njJdg6rUtn2oivKf//yH0aNHY2Vlhbu7O6GhoQBs3769UqP7FX0x+M0339CyZUvVMUtLSzw9PUlMTHx8FCqB0gq1GthfJN8P6IVSkZJ7DpeONf9a7EAZgfAkcPhhOorSVXDTwwTKdWWtgXZAEMq1ZzWABo2t6P9qW3pOaMm+P06yfVUS18/l8vtn+9m48DAdn/Ghy9Bm1HPU7zpAMyP4T0sY3ww+OwyfHob1Z2HDWeX+VXOCwVV/70gkEo2pMoUqLCyM2NjYEo+Fhoayc+dOAAoKChgxYgQmJiZ89dVXqjJWVla0bdsWAEdHR7766iucnJzIyclRuXKUxPLly+nUqRPu7v9uiDh79mxGjBihllfX0NkdqMIda1leK8VCv0EpasSTZS2xUFU7OshRtlFUw7V91RHl7yG66nG1kfHjx9O6dWvOnj1LRESEStH38PBQsyjpk0dfDN68eRN7e3Vzg62tLbm5uZXSv97ZB0QByQ8/mwE9gQGAWzXJ9LhjiDJohS8wCGU0xFPAIeAgkACceZjWobQABgHtgRCU7oLVjKmFMZ0HNyN0kB/Hd5wn5odE0g5cYdvyRLavSqJ1b3e6jQzAyau+Xvu1MYHIdjApAD44CN8kwvIUWJ0GU5vDW62hfvG9vCWSSqPKFKqYmJhyywghGDduHFeuXGH9+vUYG5e+uYNCQ1ei5cuX8+abb6rlbdmyhfPnz/P1118DSrePQYMGMWPGDGbMmFGunI8zFQ6brnPHFayvUZS/MkpUxca++qSmKCL64jFSDLU78xq6/OlCVW9RUNjt43OptKJ169a0bt1aLa9///6V0ldJLwatrKzIyVEPdZeTk6NXl8NK4RTwDf9apOoBzwL9UVpcJPrDAPB6mJ5DuVdWMspzv+/h/4WRE0FpvQp9mPwe1q8mDAwUBHZ1IbCrC2cTrxOzPJGE6DPE/5lG/J9p+HdpQvcxzXFv5aDXfh0s4ItO8EoLeGcv/HgSPk6A75OUodYnBsjNgSVVQ41y+Zs4cSJJSUlER0djbq7ufL13717q1auHt7c3mZmZTJ06lbCwMGxtS99sbvfu3Vy4cKFYdL8tW7Zw79491ed27drx2WefqSIx1QV0fmaq6qctbdbgaFBGKwNVtZnx1ITQY2PVQGWtBdN6LZ72Ymi0D5UGrqhCiGrS0bVcQ1WLXf4AkpOT2bp1K1evXqWgoEDt2Jw5c/TWT2kvBgMCAli2bJmqXF5eHmlpacXWCdcYbgBLgA0oLSeWwHCUFqka4nZW6zHm36iIY/g3FP0elEpWofVqFVAf5fq1LiitWNW42XDTADtGzutKn/O5xPyQyL7fT3J8+3mObz+PR5AD3cc2xy/UWa/BejxsYFWE0h3w9Tj4f/bOPC6q8vvj75mBYd8XUXZRBFEQN9xNsUXLfV+y8luWWtY3U8tSy9I0bfFnWZqV5b6vqZm7uae4giKICigqguyyzu+PB1G/osxyhxlw3q/XfQ0M9z7Puczlcs9zzvmcPdfgvQMw5wzMaAG9ale/dUoTxoXROFRXrlxh3rx5WFhY4OFxX+Zm3rx5DB48mEuXLjFhwgRu3ryJvb09zz77LMuWLSvbb9q0aezfv5+tW7eWvff777/Tq1evR1YAXVwerlxUKBQ4OTk90ruqOqJ9KZNh7kRqzatBDZVaogQV7yLxgeWNVU3u/EYjSnHvOIn6UKkhSiGTybRzTnT86LX1YavJFVcuP/zwA6NHj8bBwQF3d/eH7isymUxSh+pxC4M9e/Zk7NixrFmzhhdffJEpU6YQGhpqfPVTKmAzIiqVh4h69AReAR6/fmmiMnBG1Kq9gIhenUb0vjqIqL36s3SzRThXzwBNED2xDICrlx19PmrB82+GsX9ZDPuXn+dS1E3mv7MTzyBnnnsjlAbP+CCXsKlUU3fY1Q02XxGO1YU70Ge7UAT8rrX4uQkT+sBoHCpfX98nPnQNHDiQgQMHPvbnEyZMeOS9efPmqTX35cuX1dqvOmEwSWW9yrU/4eFWX1NqZoYWNlTteIHkjrjWfajUP1CKPlQPUdmiFDr+zqv4JVcuX375JVOnTmX8+PF6XRyqaGFwzZo1vP322wwZMoSIiAiWL1+uN1u0IgOYyf0GtS2Btyi//5IJw2KOcJaaAO8A8Yj+X/sQCoz3hC1sgLZAJ4TioAHS3+ycregyqjEdX2nAwTWx7FkcTfL5NH4bs4eadZ147o1QQiN9JXOsZDLo6gcveMOCGJh8TCgCNlsDr9QTPaxqVeGe2SaME6NxqExUDgaLvuhVXlD9wdV6VjQClT9DNobVC0bSh0qjlD+1fHjd1Cf1iilCVUZubi79+vXT+99VRQuDnTp14vz584/9uUE5BkxHpJXZAP/l4b5KJowXGfdrr14FrgJ7gT2IGrhtpZsTImrVCQim0v/oLW2VdHylAW0HBHNk/UV2/naG6xfT+X3cXmrUduC510Np9JwfcoU0xWDmChjRAAYHwtTj8N1p0ctqdTxMaCzSAy1NT8EmJMKAJYwmDInBaqj0GaGSqA+VzphqqO5jZLLpkqX8aRJGquQI1f3yLu0GqOJXXLkMGTKEjRs3GtoM46QQ+AEYh3CmQoEFmJypqowP8DLwC7AQGIporJyOUAscVfreH0BK5ZtnbqGgTf8gPt7Yiz4TWuDkYcONSxksmrCfr/ptJGr7ZUpKpLsT2SthRkuIHgA9/CGnCD4+CsHLYe2lqv9v1oRxYPLNnzK0dSy07BVazgCaHqdBY181Uv40auxqyCKUatbY1+ARKg2ue7WuFXUa++p67lr3odJu2uosSjFr1ix69uzJ7t27CQ0NfURBdtKkSQayzMDkAZMQAgcKhPDBAAySFmZCT/giPtdXgVhgB6IZcxJCBv83RCPhF4D2VGozZjOlgtZ96xHRow7HNsXz94LT3LiUwR/j91KzrhMvvBVGww4+kkWWAxxg3QuwMwn+ewDOpEHvv6CTF/xfGwiWVtndxFOGyaF6yqhqohRq9/uRYBjtdtYTxmCDFFTFGiq1dpXmutR2aH3MW02uuHJZsGAB27Ztw9bWlnPnzj0iSvFUOlR3gI8QUtyOwFSgvkEtMqFPZNzvefUWokHzdmA/9xsLzwE6Ai+W7ldJNwUzcwUtewXSrGsARzcKx+r6xXR+G7MHr2AXuowKJ6hVLcmeQyK94ERfmBcNE4/CjiQIXQnvNhRS6/YGEvEwUbUxOVRPKVUt8KFetEha2XTt0xO1PK7csarYB/UYJBfXqITPRq1d1ZHqr2KfYRUzVy0+++wzPv/8cz766KPqV5+oDdcRKX5JQE3gK8DLoBaZqEwUQPPSLQdRa7UFiEYoPG4GagMvAc8iVAMrATNzBa16B9K8awCH113k7wWnSYq5zfy3d1A73J0ubzcmoHENaeaSw6gG0D9ApP/9HA1fn4IlF+HrljCwbvVZ1zRROZgcqqcMrdN6dKzL0DblT72HH/Vl09WbU4OdtZ1Eb0YYF8ai8qfJceoFqNSJipbKple6yl/lHlcVyM/PZ8CAASZnCuAyMAZRL1UHmIGQ467GFGVlUXDtGkXp6RRlZFB05w5FGRkUZ2ejKip6aAOQK5XIlErkFhbIlUoUtrYoHB0xe2BTursjt7Aw8JlJgA0iIvUiQh1wC/A3Qszi/4B5iKhVd0TUqhIwU4oaq+bd6vDPyvPs/O0sl6Ju8v1/thHUypOXRjfGs540F62rFcxrD28Ew9v74chNGLwTfo6BH9pC/Wr+t2FCOkwO1VOGrvUVuhbKG0qUQpNhtNn34QO1PK5cG6pJuMBIIlSa/D7V29P4aqjKDjeJUpQxaNAgNm7cyHvvvWdoUwzLDeADhDMVDnyOeKCu4qiKi7l75Qq5MTFiO3+eu1euUJCURH5yMsVZWXqZ18zZGaWHh9hq1sTSzw9Lf3/x6ueHhY8PcnMDdtjVFH+EYMVwhAT7JiAK0dx5KxCEaOzcgUrpbaW0MqPjKw1o1TuQPYuj2bM4mvMHk7lwKJkmXWrTeWQ4zrWkCZ81dYeDvWDheRh/WDQGDlsFY8JgYhOwqUIfownDYHKonjK0l03XtduoHufVoLGvWlNqsK80B5YzVHVZSTeSCJVB+lCZaqiMBldXV6ZMmcK+ffsICwt7RJSivD6G1Y5sRM3UbYQIwZdAFQywqFQq7l6+TNbhw2SWbjmnT1Ny9+5jj5FbWWHh6YmZszNmDg4oHBwwc3REYWuLXKkEhQKZmRkyMzNQqVAVFFBSUCBe8/Mpzsoqi2oV3blDUVoahbduUZSWRlFaGrnR0eXOKzMzw7J2bayDgrCqVw/roCBsQkKwadAAhY0Re7LmCKepA5CIcKy2IertpgM/ISJa3YBKaJRraavkhbca0aZ/EH//cpoDKy7w75+XiNp+mbYDguj0n1BsHHS/mOUyGBYslAAnHIH50TAjCpbHwfdt4CU/3c/FRPXF5FA9pei1v67BJpa6hsowkQVJbDA2jOQ8KrUPlTaTPnScdofperyRfFSSsmjRIhwcHIiKiiIqKuqhn8lksurvUBUCkxEpXb6IyFQVcqYKUlJI27aNtL/+4s6uXRTevPnIPspatbAODsY6KAjr4GAsa9fGwssLCy8vzBwdJV+kUpWUUHj7NgUpKRRcv05BcjJ3L1++vyUkkJ+URF5sLHmxsQ8fLJNhVacONqGh2IaFYdu0KfbNmmHu6iqpjZLgDYwE/oNQB1wHXASWAMsQTYP7AA30b4qtkyU9P2hOu4HBbPkhihNbE9izKJoj6+N49vVQ2g4Iwkypu0SlsyX81B5eC4IR+yAqFbpuhZ7+Qg3Qq5JqykxULUwO1VOGEfSs1XBiiWqoKqOIylRD9ShGJpuuznGS9aEqHaiy0za1/ZXrmpVrzCQkJBjaBMPyI3AC0dh1OmBnWHPU4W5iIjeXLCF1/Xqyjhx56Gfmrq7YtWiBfelm16QJZo6OlWqfTC5H6eaG0s0NGjYsd5/ivDzyLl4k78IFcs+fJ/f8eXLOnCE3Jka8f/EiqWvWlO1vGRCAfUSEOK82bbANDUWmMBINewugM0Je/SzCsdqHaCC8F6EQ2RfhYOnZZBdPO16e1o5nXg5h03fHuXj0Ohu//Zd/Vp6n67tNCOvkq5EDfWxzPGZKBeHP+T30fkQNONobvj8DE4/BugT4OwmmNheCFhL1HzZRTTA5VE8p2geKDPS4JVUNlSZTarCv3qjq4QJjSfmTel81RSm0woiza6sDd0tTwywtLQ1sSSWxD/HwawZ8AXgY1pwnUXL3Lqnr15Py22+k//132f1PbmmJY8eOOL/wAk7PPYdVYGCVSItWWFlhGxqKbWjoQ++XFBSQGxNDzunTZEVFkXXsGNnHj3M3Pp678fHcXLpUHG9vj32rVji0bYtju3bYRUQYviZLBjQs3VIR19YmhELgZ4jrqw/QBb33tPIOdmHET89y/kAyG779VzQH/mgfXsE9cfVSb9UgP7cQhULG6qmHSL+eTcdXHg61mcnhvTDoEwCj/xFO1bsHhBrgz89AqIseTsxElcTkUD1laN2gV6oaKi3nfbIjp5/GvgZN+asujX1LMRbZdI1EKap8yp9JlOJBfvvtN6ZOncrly5cB8Pf35+OPP+bVV181qF16JQWYWfr1mxhtn6m8S5dI/u47bixaRNGdOwDIlEpce/TAfcAAnJ57zrhrjjRErlSKVL+wMGq8/DIAqqIics6eJfPIETIPHiRj/37uJiSQvm0b6du2ieNsbHBs1w7Hjh1xjIzENiwMmdyAYRJX4A1gCPAXsBpIBr4H/kAIWPRE9DnTEzKZjOA2XgS2qMWR9RfJuJWntjMFYGFtTr2WtXD2tHtiHZaXLax9ATYkCDXAozehyWoYGwYTm4KV6Wn6qcd0CTxlGExSWZ9iApI39tVgXymOK3cs4199VQeZ1I6hjo65JrtKlfJX6bl39xYhND3s3rTV0KOaPXs2H374ISNGjKB9+/aoVCr27t3LyJEjycrK4p133jG0idJTAkxDiFG0Anob1pzyyImO5sqUKdxatQpKSgCwbdwYj9dew33QIMydnx7NapmZGbaNGmHbqBG13nwTgPzkZDL27ydj/37u7N5NbkwMaVu3krZ1KwDmbm44v/ACzl264PTcc4b7fVkhnKeuwEFgOSJi9Ufp1y8AA9FrdFRhJqdVH8113fPzClnx+SH8G7kT0aNuhft394cOnkK0Yu5Z+DIKVl8S0ar2tbQw3ES1weRQPaWYRCkkmlLXSSq0oYo/3erLodLajIrtUMuxUKePgIEcKm0jrFr3qKsCzJkzh9mzZzN8+PCy93r06EFQUBAzZ86sng7VX8AZRI+pcRiVjGNeQgKXJ03i5pIloFIhMzPDfcgQvP77X2wbNTK0eUaDhacn7gMG4D5gAAD5165xZ/du7uzcSfqOHeQnJnJj0SJuLFoEcjn2rVrh2q0bLj16YF23YudAchSIGqo2iDqr5QgHayPwJ6JJ8CCE0IURUFKiYvv80xQVFNNrXPOy9+TyJ/+x2Cvh+7YwuC68vgei0+GZDTAiBGa0ALtKkJQ3YXyYSuqeMgwmCa7Xwg6pG/saXpSiKtQHqIWRnIcmZhhcNl1XDH/5Gh2JiYlERkY+8n5kZCSJiYkGsEjP5AA/l379FuBgQFseoCQ/nyuff86/9etzc/FiZGZm1Bo5koiEBIJ+/93kTFWARa1a1Bg8mHq//krElSs0PXeO2rNm4dixIzK5nMx//uHSuHEcCwzkWEgIlyZMIPPo0cpfmLtXZzUV+A3hSKkQ0uuvIlQmDagTcz0uHYATWy8Re+QaAz9tDUBJcclDztT1+HRO/n35seO09ICovjC5qai1+vEcNFgB26vhLcVExZgiVE8p2kdfdO02qsd5q2Fj32qTf2UkNVSaHKfWrmpdc4b5DLUu3aoml9yDeHl5sWfPHgICAh56f8+ePXh5eRnIKj2yBEhH1Ex1MrAtpaTv2MHFkSPJu3gRAPfBg/H/4gss/fwMa1gVRSaTYVO/Pjb16+M9ZgxFmZmkb99O6oYNpG3eTG50NLnR0SR++SUWPj649emDW9++2DVvXrl1V37ABOAVhMz6Xwj59V1A+9L3/SvPnPy8QtZ+dRQzcwVZt/MY+Flr7FyshDP1gGxfwd0icjMKiPrrMv9ujmfwF22xKif0pFTAp82gV20YthuO34LnN8OwIPi6FThWofYEJnTD5FA9ZWgfKDLUaru0jX31+qxoqqF6FCNR+bsvbqL2rjrvZDCVP20b+1aTS648RowYwejRo4mLi6Nt27bIZDL27t3LnDlzmDJliqHNk5YUhDgAwNsYPPRYnJ1N3LvvkvLrrwBYBwdTd+5cHJ95xrCG3aO4AHKuQ04y5FyDu6lQkAWFWfdfS4pKbx4P3EDMrMDMGsxtSl/twMoVLF3Byq10cwdF5ajymdnbC6epTx9KCgvJ2LeP1PXrSV23jvyrV0n65huSvvkGC29v3AYMoMaQIY+oD+oVT+AD4GVEKuCf3Jdcr0THysLKnDd/6MQfH+7jdnIWnvVE3Znsf9L8lJZmBDSuQUDjGmz89l/WTD/CoCmtH3K6HiTUBQ73gq9PweRj8Ot52JYIC9pDZ1+9n5YJI8DkUD2lGKyGSlt0rKHSqj7EGJbqjcEGHXjw4V6lUknnmGtZlqTJ71O9PY1X5c8Y+lIbCx988AFWVlbMmDGDGTNmACJqNWvWLEaMGGFg6yRmJaKRb0cg2LCm5MXHc7Z7d3LPnUNmYYHf5Ml4jRmDXGmAIpPsZLh9FtJjIC0a0qPhTizk3dLjpDKwqQm2PmDnLV4dAsAxUGy2niCTPlokNzfHKTISp8hI6syeTebhw9xatYrU1avJT0wkaeZMkmbOxCY0lBpDhuA+cCAWlRWprQG8i6ilWgZsRjhV+4AOwGuAnk0xM1cw7OsObJ9/iiPrLxLUyhMHd+uH9nmwlqplr0C2/3yKwvxiLKwf/3mZyWF8OHT3E9GqQzegyxZ4PVhEq+xNtVXVGpND9ZRhsPKgp6GGSkqMwQZjpBKaLBm8hqpqlV5VGUaNGsWoUaPIysoCwM6uCnS31ZQ7wJbSrwcb0hCR4hfdrx9F6elYBwVRf/VqbEJCKmfykiJIPQXXD4gt5SBkJ5W/r0wB1h7CubGpJaJK5nagtBOv5raguPckLBN/1yoVFOdBYS4U5YjXwkzISxURrrxbpdtNEfXKuQY3Dj86t5k1ONYF5wbgGgoupZtNTcn+B8jkchxatcKhVSsCvv6azIMHubl0KTdXrCDn9GkujRvHpfHjcYyMxOPVV3Ht2ROFtXXFA+uKGzAaof63BBGx2gXsQTQQfhnhfOmR54aHcTs5ixPbLlG/jRc1at/Xd5fLZeTnFXLrSiZ//XQKpbUZFtYi2nh4bSwtegU+dtwgJ9jfQ0SrJh6FBTGiruq3DtCxGmYYmxCYHKqnFK1XoQ1UQ6VWHYrUNVTq7yrRgeWNVY3iBSqVdI5iJURfnto+VNXokvtfEhMTiY6ORiaTERISgqenp6FNkpa1QD7QEqhtODOSvvuO+DFjoKQEl65dCVq0CDMHPStjFGRCwiZI2AhX/4KCjId/rnQA1zBwrg9O9Utf64F1TZAr9GNTSZFwprKulm5XICMO7ly4Hx1LPSW22CX3j7N0hRrNwL05eESIVyvdO8jK5HIc2rTBoU0bAr77jrStW7mxeDG3N23izo4d3NmxA4W9Pe79++MxbBh2ERH6X2B0A94DBiBk1v9COFfbETLsQwAn/U3v4mlH8+51sbQ253p8OknRaeTcucvZPYmUlKhw8rDB2dOWnmOFCuCSif9wasdlov9Jov+kVtg4lt8gXCGHceHwoi+8skvUVkVugrcbCCVAawP3ZzYhPSaH6ilD+8a+pcfpPLGGh6kjPa2GhLVGKX9a9zrS8rhyx1KnoXEV4d6KrhTnorNsugb7qmWIMcqm69iHSrtpjZrc3FxGjBjB4sWLy/6m5HI5Q4YM4ccff8TKysrAFkpAAbCh9OuBhjFBpVKRMGECidOnA+DzySf4ffaZ/kQQSorg0gaIXQxXtkJx/v2fOQSAR2uoWbo5B+slve6JyM3Azkds5XE3XThXt89A6mm4XbrdTRXnc2Xr/X0dA6FWe/BsJ17tdNMelyuVuHbvjmv37hSmp3Nr+XJSFi4k6+hRrv/8M9d//hmb0FBqvfUW7oMHY2Zvr9N8FeKBkPcfCCxERKvWAlsRzlZfRL8rPXCvoW/UtgR2/HKGru81pcfYZti5WOHgZl12z9j9x1luJ2fx1aEh7Pr9LN8N3cIbcyJx9338YkGIMxzqKfpVfX4cvj8LfyfB4kho6q6f8zFhGEwO1VOGtr1mdF6lqlIpfxrsXN4kUvgN1Snl755DZVAbSl8N0IfKIJLF2hxWjftQjR07lj179rBu3Trat28PCIW/0aNHM3bsWL7//nsDWygBh4BMIAAhWW0ALk+aROL06cjMzKj3++/UGDRIPxPl3oTon+HsTw+k8smgVlsI6A1+XcHBgCE6dbF0Ao8WYruHSgVZl+HGMbhxBG4chVvHRUTrTqw4bwB7f/DsAN7PgnekEMHQEnMnJ2qNGEGtESPIOXeOlIULufH77+ScPs3FkSOJHzuWGoMHU/Ott7ALD9ftnCvCG5iIqLFaABxGSK9vQEiud0H0u9IDXUY1xsrOgnP7EvGu74JX0P2oYF6WUPxz8xWOZcdXGuAT4oqrV8Wpw+YKmNQUXvKFl3eKvlUt18GkJvBRY1F7ZaLqY/oYnzJ0fkw35sa+ksuma6t8ICGGdkSkRMpz0TbSqc6+Eu1l8H5mRnD5GgurV69m/vz5dOvWDQcHBxwcHOjevTvz5s1j1apVhjZPGnaWvj5vmOkTv/mGq198AQoF9VfGLHNsAAAgAElEQVSu1I8zlXoa/n4ZFnrD4U+EM+VYD9p8C68mQa99EPZu1XCmHodMJpyluv2gzdfQez+8kQF9DkOrGeD7IijtITMBYn6F7QPhF3dYHg4HxkLSbigu1Hp6m5AQAmbOpEViIsHLluHQvj0lOTlcnz+fE40bE9W6NTeXLaOkoEDCky6HAOBL4FsgCEgDvgGGIZoF6+lfY4ehIXR4pQHLPj3A9bh0iotKkMlkWNkpGf59J2QyGaumHqK4qIQ6TT2QK+SUFJeoNXZjN/i3D7wXCkUlMOkYtF0PcRkVH2vC+DFFqJ5StE35q3SkilBp0tjVGKJDxmCDVEh5LjoOJXkNnRHWUOn6665OPvw9MjIy8Cun35G/vz+ZmZmVb5DUZCNW8mUIdb9K5sbSpVwaMwaAer/+imvPntJOkJ0MB8ffrzOSycG/O4S+DV6R1et+WR4Kc1FL5REBjcdBSTGknoSknZC4A67tF9+nnoSoWcLh8nkB/LuCbxewdNZ4SrmFBe4DBuA+YAA5MTFc/+knUhYuJPPgQTIPHkTp4UHNt96i1ltvoayhR/WIRsBchFjFAuAq8DEQDowE6kg/ZUhbL3wbuGLrZElWWh4WVuYorcywdbKk+/tNWTHlIHdzCstSBe9JqT+oDPg4rMzg29aiturVXXD4BjRaCf/XBl4Lqv6XcnXGFKF6ytA1K81gNT06yqarv4e2O0twXLljVZ+nW0mvHT1GOtVKfdMg5a+ya6juoenvuzqn/DVo0ICff/75kffnz59PgwYNDGCRxBxHSKU3BHTXLtCI7NOniX39dQACvv0Wj6FDpRu8OB+OT4cl9YQzpbCA0NHwcjy8uB68Oz2dT6ByBbg3Ec5V9+3wRhp03wHhY4XYRkEmxK0U0bxf3GF9JzjzI+SkaDWdTXAwdWbPpmVyMnV/+gmbBg0oSEnhyqefctjXlwuvv05OdLTEJ/kAMoSk+kJgFGAHRAHDgVmI6JXE2DoJsYnTO64w982/yLlzF4CYA8koLc2wcbCgML+Yc/sS2bcshrRr2RU6Uw/SyQvO9If+dSCnCP6zB/r/Den5FR5qwkhRO0KVk5PD1atXycvLw83NDW9v3QoiTRgGrbOQNGiMWv4A2h6nRh2KBo191ZtTk50lOK7csarPQ4JMJhMP6cYgSqHBFE82VwOxFAOJUphS/u4zZcoUunfvzj///EO7du3KGvueOHGCjRs3Gto83fm39LVZ5U5blJFBdO/elOTlUePVV/F8913pBr/2D+wcBhkXxfe1e0GbWSIdzsTDmFmJOirvSGj9FWRcgsub4fImSN4jIllJO2HvKKjVBgL6QJ1+YOOh0TQKW1tqvfkmNYcP586ePSTPns3tjRtJ+eUXUn75BefOnfEaMwbHjh31k+lhDvQBnkMoAq5HKALuRsis9wIk7vXUul8QhfnF/PDGX/iEuJKVfpfQDj4UF5WwcNwerGyVNHjGm4Vj99B5ZDjBrdVXDnWygGWdoIsPjNoPq+JFxGpRJLSvJe15mNA/T4xQZWdnM3fuXFq1aoWTkxMNGjSgadOm+Pn54eHhwbBhwzh69Ghl2WpCQjR+VjNUXYdGN2UD11Cpb4YmRkg4mIEwgpQ/jWqo1Moy1WMfKl0xpfw9QpcuXThx4gSBgYHs3LmTHTt2EBgYyPHjx3nhhRcMbZ5uqDCIQ6VSqbjw2mvkxcVhExZG3R9+kOYhurgQDk2Ate2EM+UUDN22Q5c1JmdKXRxqQ9ho6P43DLsBkQvB7yWQm4v0wP3vwkJPWB8J5xYItUENkMlkOHXoQIP162l24QK1RoxAbmVF2tatnO7UiRNNm3Jr1SpUxcX6OT974G3gV0SLgFxgHnqrr3rm5RD6fdKSwIiadHuvKRE96rLlhyhS4u5w60oGLl529BofQfxxzSOAMhkMrQcn+0KEOyRmQ4cNMOmoqLMyUXV4bITqhx9+YPLkyXh6etKzZ08mTpyIl5cXlpaWpKWlcebMGfbs2UNkZCTt2rVjzpw51K5dhQtBnxIMJkqh68RPllyTdHqtTTVFqJ6M1E/qKjT/nUveh0wdJ94wHorGKX96ssPQFBYW0qZNG37//Xf++OMPQ5sjPSmlmz16qSd5HLdWrCB13ToU9vaErF4tTTPYvFTY2geu7RV1Uo0/guaTH2isa0JjLJ0h+BWxFWRCwmaIWyEk2ZN2iW3vKFFvFfwa+Dwv5N7VxLpuXerOnYvflClc++knkufMIfvECaL79cMqMBCfDz/EffBg5Eo9fIY+wDTgKPAD9+urIhAOl4RNdP3C3PELEzrnWbfziDt2nfGru3M7OYsVUw5i52KFuVJBcWEJcjMZKhUPpQCqVKonLjgEOIhmwJ/9C9NOCIn13cmw9FnwtpXuPEzoj8f+1WzatImtW7fSrFn5S14RERG8/vrrZGdn8+OPP7J161ZGjRqlN0NNSIvWWUiV3dhXk7QpdVT+NJnbCGqoqk0fKjCsQ1VmgkSy6Wqk/KnVQ+2JBmh22P8eXhnNj6sC5ubmxMfHY2ZWTTWYYktfg9GbnPT/Upyby6WxYwEImDULqzoSeHKpZ+DPbkIy3LomvLBSpKcZKSUqyCmEzALILITcokf3UcrB1hzszMFOCRaV9Pk8FqU91BsktrvpcGkdxC4VTlX8GrFZ14Sgl4Vz5RSk9tDmrq74fvIJXmPGkPLbbyTNnElebCwXhg3j8uTJeH/4ITWHDUNuWX4jXJ1oDjRGpAAuBI4AJxD9qwYBEk9p62yJq48912LT8Atz5405nVgz7TARPeuiML+X+CXupDEHkglu7alW9NZcAV9EQKQXDN4B/6QIwYrfOkA3U3DW6Hnsf5ht27apNYCtrS1jS2+sJowfrQvPpepDpQ+HSvLGvto1RpVyiV/rh3FjROp+TDLEh6PBcBo19i19raqNfbW9fquzKEX//v1ZunQpkyZNMrQp0nPPoapbeVMmzpxJflIStuHheAwbpvuAyftg80tQmAXuTaHLerBVvxZFHxQUw/k7cOY2XMoUqVhXs8Vrcg5kaKEabi4HV0vwsBZbDSuoaQ1+9lC7dPO2EQ/WesfSCeoPE1t2Mpz/A2J+E2mWJ74SW802EDIc6vQRdVpqoLCywnPkSGoNH87N5cu5+uWX5EZHEzdqFFenTsV73DhqDh+OQupm2maI+qpIYD6wDVgE/I2IVrWWbiqZTEZA4xqsmnaYLqPCCWnnzdAZor/dobWx+IW5UTPAiWOb4jmw6jxeQc7Yuah/vh084VQ/eGUXbL0K3bfB6IbwVUsjcMpNPBatl+yys7OxtTXFIasaeu2vqw+qUmNfKTEKI6ShTJRCsgHRq7dr8BoqXdcuDNz+yhhxdnbmm2++Yf/+/TRv3hwbG5uHfj5hwgQDWSYBpZoNleVQ5SclkThjBgABs2cjU+j4hJe0Gza/CEV5UKcvdPpd7Yd3KbmWIx5eD6TA8VsQkw6FFdSw3Is+2SvB2uzRv6H8YsgqvL8VlsD1XLE9DoUMfO2ggTM0dIZQF2joAnUd9NgA1tYTmn4ETT6ElIPCsbq4Aq7/I7b9o6HeUGjwFjgHqzWkzMyMGkOG4D5oEKnr13NlyhRyTp0i/r33uPrll/iMH0/Nt96S3rFyAsYjGgDPBuKBT4AWwGigpjTTtOpTDxcvOw6vu4jS0oy6zcXAFtbmLPpwH0261Cb++A16jm2ukTN1Dzcr2NwFvjsNHx6G/zsDB1Ng5XPgby/NOZiQFrUcqq+//hovLy/69+8PwNChQ1myZAk+Pj5s2bKF4GD1/sBMGA9a6y0Ycz2IoRv7lh2o3WGPMULCwQyMMZyLAWqoDHXe2l6/xvAxSc3ixYtxcnIiLi6OuLi4h34mk8mqtkN1pfQ1oHKmS/z6a0ry8nDt0wfHtm11G+z2OdjSQzhT9f8Dz8wTkuCVQHGJeEDdclU4UqduP/xzGVDHAcJKnRkfW1HL4m0LXrbgqASFhg7O3SJIvQspufe3a7mQkCmiYAlZkJQtvr6UCRsv3z/W2gyauEFz9/ubr53Ea28yGdRsLbY238LF5XDuZ7h5DE7/n9i8Ownpet8uan1WMrkct169cO3Zk9ubNnFlyhSyjx8n/v33SZw1C99Jk/AYNgy5ubmEJ4JoITAP2IAQrziMkFp/FRHJkiADuF6LWvg3ckdpKQYrKS6h8Qv+2DlbsnDsHgKaeuDb0E38rESFTCbuNwV5Ij9UafVkI+QyeD8M2ngISfV/b0HjVbCwI3Q3pQAaHWpdUj/++CO//PILAPv372ft2rUsXbqUNWvWMG7cODZt2qRXI01Ih/ay6bpOXPqqD5U/dWTTtUj50zxnSsP91bGhOmAE6YsaNXaWai+DqfxpN291uuT+l4SEBEOboB+KgFSEXq97JUx35w4pCxYA4Pvxx7oNlpMiluALMoWMd4f5QohCzyRkwq/nYeF5SMq5/761GXT0hGe9oJm7iArZSvyMb2kmnDGvJyT35BdDXIZINTx9G86kider2bD/utjuUcsG2tUs3WpBsJN4CJcEpR2EvCG2W1Fw9ie4sFg0Ek7cIRQXG74tUgYtHCscTiaT4dqtGy5du5L2559cnjiR7JMnufjWWyR+9RV+n36K+6BBukc8H0SBkFJ/BiFasQvhZO0AxiDqDnWkzJkqUZU1+D296wotegWSejWTPz7cy4BPW6O0NCMnI5+rZ26xf8V5CvKKaN0viPDn/Cqco3kNONEXXtsFGy5Dj23C0ZoeUUnpoSbUQi2H6tq1a/j7C3f4zz//pE+fPvTv358GDRrwzDPP6NM+E3pC60dbXVX+9NiQ9Yk1VBoMo/PzvylC9TD6FKXQ+BD1D1LvktOjyp+uvy4jCLCa0DO3gBLADdGjR89cmz+f4uxsHCMjsW3USPuBCnNEzVTWVajRAp79Q6/OVGExrL4EC2JgV/L992vbQ3c/6OwDbWsKh8fQWCggxFlsAx5I47yVB8duwtHS7cgNkaa4PE5sAC6WEOkJz3mLTTJ1OLdw6DAPWk6HmF/hzA+QmQAHxsDRySK6GPYe2PtVOJRMJsPlpZdw7tKF1DVrSJg4kbwLFzg/dChXZ8yg9vTpOL/4orR9rJyBicDzwHeINMBRQA/gdUACgcp7in5b50aReSuP175uAcCWH05wKeoGXvVc+PfPeK5dTKfZSwG4+tizetphfEJccPG0q3B8JwtY9wJ8cwo+PCJeD5WmAD7JQTdReah1+7CxsSEjIwOAPXv2MGLECACsrKzIzX1CMrAJo0N7SXCJRCn0Mq/EjX21xRShKh+pz0Wb4QzQh0rrBwJTDZUkDB8+XO1958+fr0dL9MiN0tca+p9KVVzMtTlzAPD+4APdBtv9Jtw6Dva14aWNequZUqlg02UYewhixSMMlgroEwCvB4vITlW51bpZQRdfsYFQGTyfDvuuw75r4jU5B1bGiw1ExKqzD3Tzg9YeEtRgWTpB+BjhPF35E07NFgqBp2bD6TmiBi58LLg3qXAomVyOW9++uPbsyY1Fi7j86afknjvH2a5dcWjfntozZ2L/GJVprWmOSP/7HVgJrAMOIKJVzaWZIvx5/4dS+bqMakxJiYoj6y+ScTOXxp39CWopBFdKikvIzSjARU39FZkMxjSCVh7QbzscuiFSAFc8J4QsTBgWtRyq9u3bM2bMGNq0aUNUVFRZI8QLFy7g7e2tVwNN6Afta6h0nVjb4wwhm25a4pcSyevvtBmuKtVQ6dqhwFB/40bCxYsXH/r+xIkTFBYWUq9ePQBiY2MxNzencePGhjBPGkqdBJwqYar9+8lPSsLS3x+n55/XfqAr2yB2iXCiuv4JVm7SGfkAUbdgzEHYfU18X8cB3g+FgXXB0UIvU1YqchnUdxbbWyHi7zY+E/5OhL8SRSQuJl1s35wS0auuvqL25jkvsNYloilXgH83sd06CSe/gYvLhJDFxRWizqrJBPB8pkKPVWZmhsdrr+E+aBDXfvyRK59/TsbevUQ1b45b//74T5uGlZQ9Ti2BNxFqgDMRKpnjEdGrkYh+bjrgEXA//bGkRIVcLiPjZi4pl+7gU9+1zJk6tjmemnWc8K7vAsC+pTHUb+eFq1fF0aqWHiIFcNAO2JEEnTbBlxEwtlHVWSCojqi1XjF79mwsLS1Zu3Yt8+bNo0YNsRy2ZcsWOnXqpFcDTUiL1oEiSa3QZGJNIlQVy6arN6Xhl/glTXcwMJKfixbpo/ezDiuvD5XBZNPvoWVj32riT7F79+6yrXfv3kRERHD16lWioqKIioriypUrtGjRgp49exraVO3JLn21eeJeknBz+XIA3AYM0P5vujAX9ogMF5p/plGfI3W5kw+v74Ymq4Uz5WQB37WGc/1hRIPq4UyVh0wmnMYRDWB9Z7j9GuztLh6y6zrA7buw8AL03AZuC6H/dlgdD7mFOk7s1kikbL58CRqNAXNbUWO1viOsbgUJm0BVgVwiILewwOu994iIj8f7ww+RW1pya8UKjgUHEz9uHEV37uho6P9QB5gLDEeky/6FEKzYL90U99IAE6JukByTRsMOPgBcOnmTa7Fp+Ddyp7hUStJMKee393dzZvdVtcZ2s4JtL8LHjUW0cvxh6POX6ItmwjCo5VB5enqyceNGTp48yauvvlr2/pw5c/j+++/1ZZsJPaJ9DZWBHrfU6kOlxjCaTKnBvvqkWjT3BemuHa1S/jTYtcrLpptEKf6XWbNmMWvWLFxdXcvec3V1Zfr06cycOdOAlunIPVEFPTtUquJiUtetA8C9Xz/tBzo2RTTudQ0TaWMSs/8aNFwBv5wX6W3/DYW4QfBuKCiraPF+cWGJVv8DzBVCqOKrlnBhIEQPgGkRQh0wt0ikBfbdft+52pggas20xs4b2syCV65CxOdg6QI3DotmzcsbQdwqtRwrM0dHan/5Jc1iY6nx8suoCgpImjmTI3XqkDx3Lqqicjooa4sCGAj8glAFTAcmAZ9zP/orAYUFxQQ0qYHSyozE6Nuc25uIla2Sus08UJjLUalUtOpTj/6TW/H3gtMkRt+ueFCEyuQXEbCxMzgoYW0CNF8jopImKh+NM2pVKhUlJSUPbSaqDro29tX6kVjHxr7q/UORVpTCaFL+qrpDZQQqf2VUpZQ/HTEGTRVj4ebNmxQUPLp0W1hYSGpqqgEskoi80lc9t23KPn2awps3sfDxwSYsTMtBkuHkt4BMKPoppFXR+CUGIjcJ5b7m7nC6H3zTGpwtJZ2m0jmw6gKfdFjB8k8PlEUvNHWwZDJRT/VRYzjSGy4PgVktH3auum8Dzz/g/QNCVVBrLJ2g2SfwyhUhvW7jCbfPwLZ+sKIxXNqg1j3R0tuboD/+oPGxYzi0bUvR7dvEjRrF8fBw0nft0sHAcvBGiFWMRqQE7gKGAQelGd4v1J1D6y6y8otD/PHhXuxcLGnY0QdXb3uKCovZ8csZstPv4lzLFs96zlhYa6aO0tUP/u0j+pZduAMRa2BDNRU2NWbUcqiuX7/OwIEDcXd3x8zMDHNz84c2E1UHgzX21VquXYOUvyfcpDWy31jyIqtLyEDq83gV+A8aLQfJuOeYq7MvauyrfspfZav8absgoOs6gjHTrl07Ro0aRXx8fNl78fHxjB49mnbt2hnQMonQ860i86B4snRs3177dL+oWVBSAHX6QA2JFAAQ/aTePwCv7xGNc98LhQM9IagS6soqg9M7r9B1dGPqRtRk96JzRP2VoHMata+dEDc40hsSBsP0FsLhunUXvj0NYSuF2MEPZ0UKpVaY20Cj92BoPLT/QThWqadE37GVzeDKVrVuNnZNmxK2dy/1V6/G0s+PnLNnOR0ZSXS/fty9ql56nFrIgZ6IaFUokAZ8DMzgfmqtltTwd+CjtT2o16IW/Se14pkhIdSsIy5QM3MFNk6WzOi9gfWzjgGgKtH8JlzHAQ71gv51RBPpHtvg02MiHdBE5aCWGzx06FCSkpKYMGECHh4e1aq+42mlWolSyNQPf2mU8mcsS/zV5QlXqvMYpPkh929ZGtRQ6biXzFA1VNpOq230ugowf/58evToQWBgIK6urshkMm7dukVoaCgrVqwwtHlGzz2Hyr5VK+0GyL0J5+aJr5vq2L/qAfKLhdrZxssixe/HtvB6fcmGNzgZt3JJTcykRa9AAJp0vi/OcG+hRiaTkZuZj7W9dsVhfvYwPhzGNRKNYxeeh6UXISoV3t4vFBL71oY36gulQI0f/xQW0HAkBA+Dc/Ph+JdC4XFTF6jZBlpOg1pPbhAtk8lw690blxdfJHHWLK5Om8atVau4vXkzPhMm4P3BB8gtJQpF1gK+BdYCPwPbgBMI4Qod9Gus7JSEdfIt+z7+eAoBTTwAaNU7kItHr9OydyAetR2wcdTuXGzMYVknaOwKHx2Bz/4Vn+OiSLBXam+7CfVQy6E6dOgQBw4cIEzbUL8Jo0FrX9hAESpDyKYbgyiFGE9WPZwpY1iAMdVQ6Xtao8bHx4cTJ06wY8cOYmJiUKlUhISEEBkZaWjTdKOSlER0dqhOz4GiPPB7SdRPSUCJCgb9LZwpJwtY+zw8U82ko28nZWFmoWDum38R1smPiB51MCvt5Hrv7/x2chZb554k+UIateo60WdCC6zsNH96lslEQ+Nm7vB1K9FA9udo2JkMf8SKrb4TvNMQXg4UD+8aYWYJYaOh/utw9kfhWF3/B9a2A98u0PJLcA194hByS0t8P/mEGkOHcmnsWG6tXMnliRNJWbiQunPn4vzccxqfd/kTAX0QUupfAucR0up9gDcAHZ2TxOjbLP/sIN3+25SGHXxIu5aNrZMFljbmWjtT95DJYFw4hLnAgNK/jxZrRZ1VHQfd7DbxZNRKmgkMDCw3/9xE1cVgohSGlk2vDFUKU4SqXIxBXEMTEwxeQ2Woxr6G/5j0RqdOnXjnnXcYPXp01Xem4P6DnbZpWWpQnJvL3cuXkZmbY1Nfi/CPqgTOLxRfh+vYv+oBPjsmivAdlLCrW/VzpgBqh9fgk0296TGmGecPJnMzQSgllBSL2vXkC2nsXRyNi6ctY5a9hLWDkktRN8qOL9JSZcLSTKSO7egmRD0+CgcPa4hOhxH7wHsRjDsEV7O0GNzcWvSyGnoJmk0WqoBXtgjhih2vQnZSxfb5+FB/xQpCd+3COiSEu/HxnHn+eaIHDqQgJUULox6DD/A9Is1cDqwGRgCXdBvWu74LQ2e0Z8cvp/l9/F62/niSrLS7uPnoqNn+AM/7iLqqECchUtF8Deys+FdrQgfUcqi+++47xo8fz8mTJyku1kUGxoSh0bZOwmBpnlWpsa/UGENkRwK0Tn3Tgw2a1FDpvJexRDqNe9pK47fffiM8PBx7e3sSEkTF9ldffcWaNWsMbJkO3FP3y3niXjpx95J4erSsXRuZmWbF8gAk7xMPyXa+FaZ2qcuaeJhyXPRiWvEsNHKt+BhDUlRQTHpKDonRt8lXU6f8wQWoWoHOyOUyUi6VSs+V3ltO/n0ZOxcrWvYOxMxcgZWdknP7xFNz8oU0Ns0+zqfPrWTlF4fITr+rle0BDjCtBVwdItLJWtSA9HyYeRL8l4iUy6M3Kh7nEZT2EPEpvBwPoaNBbgbnf4fFgXD4Eyio2Ftz6tCBJlFR+E+fjtzKilvLl3M0KEioAUr1rKoAXkE4Vp4IZ+otYA06LXp5B7sw8ufnCW7tSbtBwfQc2xyllZmkC48BDnCwl+g/lp4Pz2+G789U70UzQ6KWQ1W3bl2Kiopo0qQJSqUShULx0Gai6qBrhojB/hB1VPlTf48HpzSOu46x2KE1+nCofgYOof4HqsVKgnqXnBGr/Gk5bxW/2spl/vz5jBkzhl69elFYWFj2u3Fzc6v01h9paWn07NkTGxsbfH19Wbp0qfaDVYJDlRcXB4BVnTraDXBR9K8icBDINBYWfoTTt2FoqcjbzJZiJd4YKC4sIflCGkc2XGT9rGP8NPJvvuy5jgntljE2YjFTOq/mm8GbuZGgnh531LaEMlW/W1czsXawoLhIRKbkchnFRSVci03Ht6Eb9m7WACTHphMYUZOSEhVb50Zh62jJp9v7UZBXxIXD13Q6P3MFDKgrhA+O9IJBdYVDuyoeItZCu/Ww6bIWIgjW7tBuNgyOgTp9RWrov1NhUR04Ow9KniyTLjc3x2f8eJqeO4dzly4UZ2QQN2oUUa1bk3P2rNbn+wjBiP87LwKFCAfrQ4R4hZZYWJnTvFsdvINdcCj9DKVevLZXir5kH4VDsQre+UdEGXWSyDdRLmotNw0cOJCUlBRmzJhhEqWo4hjso9NnDZUa+2jW2Ff9fR8+UMvjHjecTFY9Hm71cdHtAHIRzRhHADUqMkF9GwxeQ6UjWgfGqvFtfc6cOcybN4++ffvy1Vdflb3fpEkTxo8fX6m2jBo1CqVSyY0bNzh58iQvvvgiYWFhhISEaD6YbemrNqlXanK3NJpnVbt2BXuWg0oFlzeLr+vo0L+qlIJiUTeVWwRDA0WfKUNy62om0fuTOLcvkUtRN8uatP4vcjMZdk6W2DpbYW6h3iK0Cti3NIatc6NwcLcmoLEH9dt6lf08+UIaNo4WOHnYIJfLyLiZi6pEhZuPPef2JmJhbU6b/qJxso2TBbkZIi+0pERV1nC2pLgEuUJzJ7d5DVhSA75qAXPOwk/nYP91sdUvlWcfUEcIhaiNQwC8sBKuH4QDH0DKIdjzlqi3avt/4PlkNU4rf38abN5M6tq1xI0eTdaRIxxv3Bifjz/G56OPkCslUGWwAj4AIoBZwFHgdWAC0FT34R/Hia2X8A+vgZOHdg3n5DIRZWzgDMP2wLxoiMuAVc+L+kMT0qCWQ3X06FEOHjxIo0aN9G2PiUqiytVQqTW2xBEqre3Q9sDHjVct3Cppz0MBvAvEAzMRxcO9qPCOZogaqsqWTdf18GpytT1EXFwczZs/KtVtY2NDZmZmpdmRk5PDmjVrOHv2LLa2thIvV1kAACAASURBVLRp04Zu3bqxaNEipk+frvmA91Ld9NhKqzBNLMGbuWqRV5d1FXKSwcK5QsEBdfjmFJxLF8X1P7U3zCLAjYQMjmy4yLl9SWU1Tfdw9bbDK9iFWoFOeNR2xNXHDnsXK6zsLcqcGHVp0rk2TTrXJuNmLpm38/AOduH0ziuUlKho2MEHSxtzSorv/7We2nEFB3drLG3NuZGQgUeAI1Z2SvLzCnH1suNutkg1lMtlpCZl4epl91hnSqVSqbUI5WkrJNc/bgwLYsTnE50OL+8Ukt0TGgsBC3NNEplqtoLeByB+NRwYK6TW17WHugOh9Vdg6/XYQ++pATp16sSl8eO5Pm8eVz79lNTVqwn85Rfsy7kHaEVbIAiYCpwCxgIDEO08tMiKfRLxx1NYMvEfrO0teHXWMwQ0rmD18AkMChRpgN23CrGRVmthcxfxngndUeujr1OnDkVSdqc2YTC0r6EqPU7niTWd9/5N/fE3eTUkrMvGUGvSioarYBINj6vQjir+iKtrP6YHOVn6mg8cRzhWQ4A/gTcRPUSeRCX2oTKcbLp212917kNVs2ZN4uLi8PX1fej9Q4cOUVubyIuWxMbGolAoCAwMLHsvLCyMvXv3ajege+nrTcTnrQcHozhDOA1mjo6aH5xySLx6tNA53e9mLkw7Ib6e2xasJH5wfRIlxSVE709i//LzxB65Xva+lZ2SoFa1CGnvTVDLWjortJWHg7s1Du4iHSwwoia3ErNQmMlx9bHnbnYBNxLuYGWv5MiGi3QeGY6NoyWpVzMJjRTXeuatPNKuZeMf5s7t5CxObr9M3L83uJtTQJPOtcuiWA+iaRaSnRL+GwajGsCSi+JzisuA/+wRtW4fhsOwIFCq61jJZCL9z+8lOPEVHJ8OF5fB5Y3Q5GMIf1/IsT8GMwcHAn/6CfcBA4h94w1yzp4lqmVLvN5/H78pU1BYSdAJ2w34GlgC/A4sB84AnwAeug9/D48AR+o09SD2yHXmvvkXvcY2p1XfelpnikXUEP3Hum6BM2kiXXPd89C2lnQ2P62odYf7v//7P8aNG8eZM2eqfj3HU4726UA6elT6XEmspil/1SUHS9IU4VWlWy6iT8i97/OAmk+yQbyqKrEPldYOsY4O1f1pNWzsW437UA0dOpQxY8YQGxuLTCYjLy+PLVu2MH78eIYNG1ZpdmRnZ+Pg8PBysIODA1lZWubs2QCWwF10bj76OIru3AHEQ6rGpAi5dTxa6mzH1BOiYWlnH3jWW+fh1EKlUnF0YxzTeqzjl//uJvbIdZSWZrToVZdRPz/P5zv7M3R6e5p0rq0XZ+p/sbRV4h3sUrq4CBE96rJt3ikWfrCH1v2CaNDeG6Wlgtij1/FvJLztlPg75OcW4dvQjR2/nOFS1E2Gfx/J4C/akpmaS9btPICy+qw7N3P4Z8X5MiVBTVAq4LUgiBkAiyMhyBGuZImancBl8Nt5KNJkWDMraD5Z1FcF9IbCHDg8AZaFQtLuCg93fOYZmpw6hdcHQl0yadYsjoeHk3nkiMbnVi4KYCjwHcLBOgcMBw5KMzyAjaMlw7/vxDMv16ekSMXqL4+w8otDWis4gmjs/E9P6OIDt+9Cp02wJFY6m59W1HKonn32Wfbu3UujRo0wNzdHqVQ+tElBfn4+//nPf/D19cXOzo7w8HC2bt1a9vPLly8jk8mwtbUt2z7//PPHjvfgfra2tigUCt55552yn+fm5jJy5EhcXV1xcHCgXbsn5+dWNzR+aJLqmViXp7UKHxCNRJTClPL3MFJG2qaWbu0e+Hoq8EXp9lgTqlANlc4930x9qP6XiRMn0qhRI4KDg8nOziY0NJSuXbvSuXNnxowZU2l22NraPpJimJmZiZ2dnXYDyri/Gq6b5sBjKc4TD9xybVb1086JV/cmOtmQWQC/xIivp7fQaSi1Sb+ezU8j/mbZ5APcTsrGxcuW7u83ZfJffeg/sRV1mnqgMNddZEMbZDIZMpmMhh18+GBZV16f3ZFWvUXUMz+3iNqNa5AYfZv069nsWxpDnSY1sLAx59y+RKzslXwz+E/+nHOCw+sulolkKEoLnk79fYUrZ26VpQPe+z9YXFjC5dO31BK3MJPD4EA421+oMIY4Ccdq2G5ouEKoNGr078DeDzqvhu5/g1MQ3ImF9R3h76GQd+uJhyqsrQmYOZPwQ4ewDg4m78IFolq1IuHjjynJl6jfQEOEYEULRD3jx8BPgESJXQozOd3fb8aQqW0xt1BweO1F5r65nay0PK3HtFfChs4wuiEUlMCQnbBOAzn41Dy4XHnZ0lUCtYLmCxYs0LcdFBUV4e3tzd69e/Hx8WHLli3069ePM2fO4OfnV7bfnTt3MFNDujU7+/5yXU5ODjVq1KBv375l7w0fPpyioiJiYmJwdnbm5MmT5Q1T7dD1ockgEcoKG9xKLJtuLJEhY7HDmNgIdAM+0vL4qtSHSldMfajKUCgULFy4kMmTJ3P8+HFKSkpo0qQJAQEBlWpHYGAgRUVFXLx4kbp16wJw6tQp7QQp7uELXAauAPUkMPJ/kN1T8i3RPGJBphC0wF633/OSWMgpgva1INRFp6EqRKVScXRDHOu/Psbd7EJsHC3o/n4zmnTx10rAoTKwtL2/sG1lpyT8eX9WTzuMo4cNIe29aNy5NnH/puDkYcOQL9pSUlxCzMFruPnYU6epB+f2J5GdlkezrnVIjL5Nqz7COXtQtGL7z6e4FptO+o0cDqw8T79PWmHr/OSonEIO/epA79qwLA4mHYXzd6DPdmjqBjNaQMfHl0Q9incnGHASTsyEf7+AC4uE6EnrmRA87In/M+2bN6fJiRMkTJpE0qxZXJ02jdubNxO0aBG2oRKomzggFvdWAAtKX6OBSdyvddSRJl1q4+Zrz6//3UVC1E2+HfInr8+OpFZdJ63GM5PD7DZQ2x7WXBLRX3XIK4KfY8Tf5T89wdEkbAGo6VC98sor+rYDGxsbPv3007LvX3rpJfz9/Tl+/PhDDpU2rF69Gnd3d9q2FT0wLly4wMaNG0lKSsLeXjRSa9JEtxW0qobWNVSGfNiqaHJjaewrMVU+zVbKi0fHdEx1fpdS1VDpXDumbcrfvcMru06yCuDv74+bmxsgokWVjY2NDb169WLSpEksWLCAkydPsmHDBg4e1CFH6N5D0BVJTHwEmbw0UqGpQ1VSJEQpkIG9b4W7Pw6VCn4sDXS9pUVfYU3IuJnLis8PEvNPMgANO/rQ9+MW2DlLUHNTiYS09SKkrRd5WQVYWIvHvFqBTtQKdCbqrwTCn/cv2wfA3tWKk38lsHdJDFmpeXiHuFCzjhNWdsJRO7P7KjcSMuj6XhPc/RyYN2oHV87eIqSdN9H7k4j7N4WgVp4ERpSfd62Qw5BA6BcgIo2fH4d/b0HkJvEQP6MFNFTXUVZYQLNPoO4A2DsKErfDrtfh/CLoMB+cAh97qNzSkoCvvsK1e3fOv/IKOadPc6JZM/ynTsXr/ffLrnWtkQMDgRDgc0RN1XCEUyWRpptPiCv/XfISv76/m6tnU/m/V7cwZFo7GrTXPg/23VB4u4H4nNTBygxGhMCcM2KbqEeFw6qE2ldPUVERixcv5qOPPmLChAksXbpUr0IVN27cIDY29pGVO19fX7y8vHjttddITVVP2uj3339n6NChZWk/R44cwdfXl8mTJ+Pq6krDhg2rdnNHDdA+5qFjtESXupCKHsg1qKFSS5NCPaskPPAxw1WXCJWUDlXXB75WPbCpaYJG++o4oKFEKapYP+FK47vvvsPHxwcHBwccHBzw9vbm22+/rfQFi7lz55KXl4e7uzsDBw7kxx9/1C1C5Vf6elkC48rjnkOl6f/77GRQFYNNzScKCFREVKoonne1hJ561A9Jv57Nd69sIeafZKzslAyZ2pbXZj1T5ZypB7GyU5ZFmKztLajXshaH1l7kq74b+PP7E2SniWa/3sEuDP6iLS161sUv1I3k82nM+c82Dq+N5W52AUkxt/Fv5I67nwMlxSVY2ZljYW0OQMHdIswtzdjx62kWvLuzrCarPJQKGNEA4gbB1OZgZw5br0KjVfD6briuST81xzrQbRs8txSs3ODaXlgeJkQsKuhd5dC6NU1PnaLmm2+iKijg0tixnO7UibuJiRoY8ARCgXlAOJAOjAFWItlqlYObNaN+fp7GL/iTn1vEr//dxZ5F53S6l2kSfD2VCn23C2fY5EzdR60IVXx8PJ07dyYpKYn/Z++8w6Mq2ij+291s6qaSkAIhQAgl9N57kY50ARFEkaaiYkHks6Bio4mKoIKAAqKAAkLoTar0GkhCKEkISUghve3e74/Z9OxmGxAw53nyJNncO3dmc/fOvPO+55w6deogSRKLFi3io48+IigoyOIqSTk5OYwZM4Zx48ZRt65QoHF3d+fkyZM0adKE+Ph4pk2bxpgxY9i5c6fetm7fvs3BgwdZvrxA/isyMpJLly4xdOhQ7ty5w7Fjx+jXrx+BgYHUq1fPomMpr3jokuDmrNYMXiGWrfJnDEx+NlVwqB4sTiBIv7GIf6wb0Fb7pXNSMP4OMEyUQt8hjzqyMdHY9wm83WbOnMm3337L9OnTad++PZIkcfToUd5//31iYmJMkyw3EW5ubvz111+WazDPbzfUck0WRp4YRZ44hcHI1kqK27iZdf2d2jXu4BpgoIWT0UiJz+D7ybtJupuGX0MPnp/XJV9Z70lCk57VadKzOgl3UkmKScsv2VPnalBYyQk/E0OXsYH4N/dCo9aQnakm4U4qGSnZ+DcXZL17ESk4e9iTkZINQMMu1WjSszoAP8/YT8yN+zhW0h+E2ithVnOYGCiyVd9fhuVXYf11mN0cXmtk4P9aJoPao8C3FxyZAVdXwdF3IOwP6LYC3BvqPFXh4EDtpUupNGAA1yZMIGn/fk43akTtH37AoxA9xGS4ISw9VgBrge+BYOBthJ+VmbC2teLZuR3xrOlM0JJzbF5withbyQx9p7XFuX056gLp+79vwgcnxebGbG1hl1pjXED2pMKgt+D111+natWq3Lx5k7Nnz3Lu3Dlu3LiBj48Pr7/+ukEX6tKlSz6RsvhXhw4d8o/TaDSMHTsWa2vrIg72KpWKFi1aYGVlhaenJ99++y27du0q00Nk9erVdOjQgRo1auS/Zmdnh1KpZPbs2VhbW9O5c2e6du3Krl27DBrL44xHbvppxmJN9+6L4Z0zZLFYofJnWcjMLX0rjG+AP4HGCLn0Z4GWCNn0rynz/jLo/29Mfwxp8GGLm1SIUpTA8uXLWbZsGZ9++il9+/alX79+fPrppyxbtuyhcIQfKKoi1P7igATLN29VSdRi5cbHG3dijjbdoDTNjDQPu7UB1YNS9ktPzmLplN3E3U6mSh03Jn3X44kMpgrDzUdFzaYFfkYKKzn3Y9NJikmnkq+jMP9VyLF1UKLO0ZCdkYtHNSGccic0EWRQqYoomc1TTr0XmUIlX0dibxmuVOBhB4s7wOWRMMAPUnNg5nFosB623jTi0WlXCXqshAFBoPKF2FPwe3M49WmZ2apK/frR4uJF3Pr1IzcpiSsjRnBt4kTUacaky3RAAUwEPkIEUQeAaUCU+U2DmFt7TWzMc190Rmmj4NjGEH54dU9+sGsJ5Kjhmd3wzx1YGyL+P281KQimNFJFMJUHg96G/fv3M3/+fCpXrpz/mqenJ/PmzWP//rKlKwEOHDiAJEmlfh0+fBgQC64XXniBmJgYNm7ciFKp1NmeoYu01atXl+CANbIEAfExh+nJF3OJHSacamjplD5PIJMuX6HyZ1FYYhwnEMTfbog69UCgMzAX4Uulg+ZhjGx6HvTroDwE2XQzUV5EKssDsrOzSzX2bdmyJTk5OY+gRxaEHMijjVy1fPNKraFvjrEBVW5eQGU6Vy09B47cFR+JblVMbkYncrPV/PjKXu6EJuLh58SkJT3yeUP/JUiShHNleyZ+0x2Xyg7I5bL8tZXSRsHNC3G4eov/4/XTd3H1UuFeTfDPrZQKUhMz2bH0HOpcDbVaGG88W9sFtvSFHf2gnqvwsBoYBL23wbVEIxry6w2jL0H9SaDJgeOzYUNbSAjWe5p15co02LqVWt9+i8zGhrs//cSZFi1IvXDB6LGUik4I1T9f4AYwGThpmaYBmvaqzrQfn0LlZkvI8Wi+mRBEYrRlfBSUChgdAL3+FtYFW/rCM0JPB40Ehf2qNZIIwP6rMDiuLI3PITeXwFcMU6ZMITg4mK1bt2JXTKL1xIkTXLt2DY1GQ3x8PK+++ipdunQp4elRGEePHiUqKqqIuh9Ap06dqFatGp999hm5ubkcOXKEAwcO8NRTT1l0POURJpt3PkrGepkLU0P4LGUdUfJg49+jMrthZHvlQQnEArDkOOyASxTlT2kQC0l9ZRRG3ABG+VCVR1GKCh+qEhgxYgRr1qwp8fq6desYNmzYI+iRhZHnzXrZ8k1bazdSs6OjyziyGNTaXXK57o3RsnAuXkg6N6oElR6AzVPQ9+e4eSEOFy8Hpizt9VjzpcxB3vrOwdmmxGsqV1u8A1zZ+PkJtn93hjshibQaVAtrWyuyM3I5te06K986gKOrLb1ebERlPxP8yrR4qhqcHw5ftwcXa9gVAY1+hw/+hUxDKXzWTtB1qZBYd6wmslXrm8K5hSDpFlaRyWRUmTaNZidPYh8YSPrVq5xp1Yo7y5ZZprqiGrAEaIfwjJuJRXlVfg09eG11XzxrOBMdlsSi57YTdc0yKeuh/iIjJSEUAaFkMJWZCwfvwMSDQgXwvwiDIqJOnTrx1ltvkZhYsFWQkJDA22+/bTH/plu3brFs2TLOnTuHl5dXvn9U3iQYHh5O7969cXR0pEGDBtjY2LBu3br88+fOnUufPn2KtLlq1SqGDBlSwuNDqVSyefNmtm/fjrOzMxMnTmT16tX5fK0nGY+s5O9BcqgMEaWw4OUeGspNR8yEJccxE+H3MQ54E3gLeB5R7vcWosTCzC4YdqwhQiiPyofq0Vy2vGHu3Ln5X15eXixatIjOnTszc+ZMZs6cSZcuXViwYAE+Pj6PuqvmI6/o4rzlm7arUwfHFi2wq61bPa1UWGkjIHWmyde+pqVtBZqmCq0X4edi2b/qEjK5jHGfd8LVy7zSxCcVKjdbBkxvjpW1HDtHG0a+3w5bByVXj0Wx8fPjRFyJp8/Upgya0dIi5sZKBbzaCEJGw4S6IqCecxoa/g57Io1oyLcHjLoo5NTVWXD4DdjcE1L1N6Jq2JBmJ0/i9eKLSFlZhE6eTPCoUeSaar5dpHGE+t9ziI3A74HPAQtV6FWq4sirK/vg39yT5HsZfDMhiOAjlqkvfK85vNEI9mubKxxMpWQLcZHlwXA8Bsbvs8glHzsYJEqxcOFCevbsia+vL4GBgchkMi5fvoy7uzu7d++2SEf8/Pz07gKMGjWKUaNG6fz7rFmzSry2bNkyncfXr1+fY8eOGdfJJwgml/yZu1PzqI19jfEhKieZofLSD7NhiXH4A98CScA9xL/cHTBwwWXU/99SRz2q2rv/uA/Vjz/+WOR3V1dXbt++ze3bt4u8tmbNGr0m8Y8FGiK2R68CGViE9J4Hx6ZNaXbShPokhbYTuaabj4ZoA6o6LiY3USrUuRr++OQYkgQ9nm9A9caVyz7pPww3HxWD3mhZ5LVdP17g1oU42g2rjeMDSB962MHyrjCuDkw+BMGJ0HOrKD9b2A4MorlZO0H35VBzEOybCJH7YF1D6LIMAkboPE1hb0+dH3/EpWtXQidNIm79elLPniVwwwZUDXULXRgEOWITsCYimNoFRCICLfP0WwCh5jh5SU/WfXiEM0E3+Gn6Xkb8ry2tBwWY3faLgcJ7andEAacxRw2Ho2HKIfipC/zaQ3Cu5p4WwiP/JRiUoapduzbXrl3j66+/pmPHjnTo0IHFixdz9erVfHPCCjweeGSS4A9U5c9w2XTDrmfEsZY4T1dzT1iGymKBoRq4g5iEorTfy9rhM6LW1aBDHwaHyuSSP9NKVp+0kr8bN24Y9BUeHv6ou2o+HBA8KjXC+6Y8wCovoEo3uYlQrVBgbQsHVKf+vs7d60m4VVHR66XGlm38P4JXV/ThvS1DULna8fucY/kcKkujkw+cGw5zW4OtAtaGQuB6IZBg8DOuxkAYdQH8+kFWEuwcCXvGQ7Z+npHn6NE0O3UKh4YNyQgJ4Wzr1txdvdrsMQGC+/sN4IkwAJ4CXLdM01bWCsZ80pHuzzdAo5b47cOj7P7pgkXm4CE1wcdBvPe5GpFR7OUrfMSuajdAVnWDGk7/PT6VwSQoW1tbXnjhBebPn8/8+fOZMGECtrYPoKi5Ag8Fj0wS/IGo/OUfYNnLV8imWwQWDQxPA+OBn4FT2t/Xal87rKcPRkS7lir5e/Sy6Y/FZStgKTTTfj/+SHtRAFutU2tGnMlNxGurBb0sLLp3+Heh3tF7UhOUD0qL/T8ANx8VT01qzMvLe9N5TCAKqwcj92atgHebweVnoEdVcV+M2QuDguCOoWJ89p7Qfyt0XiKC/aurYH0ziD2j/7Q6dWh6/Die48ejycjg2rhxhEyZgiYry/yB1ULwqgIRNiCvYLHPr1wuo/+rzRn6bmtkMtj+3Vk2fXECjdq8oNfOCuq7wfowOK39aCvksKKr4FDdTReS96MCCqTW/yswqOQPICQkhC+//JLLly8jk8lo0KABb775JrWNrauuwCOF6ckXCxn7mnKqTKY/RrE4h6qcLISfkAxV83PnQJKwcrUAEeJrRJlEccWvROBVoDVQCge+y3OBtB9RB6Vt2U/4n7rAko6gV+zLqy28EAdy3Y/QxgcOgFqNlYuR2+uOgBk2Rf7NPfl430ijFze9q0HceLF4qcBjiLaIzYXjiIXZA358ZMfGIlMqUer6XNt7iU6kxwjpaj2fFV1I1QowOhh/qk5EXIknMjgBe2cbmvSqbrmG/+MoSx0xMy2HNbP/odu4BtRoYlqJZU0n2NUfVlyFN47C1ltw6DdY2B7G1zGkmEUGDadAlc6wcxTEXxAqgB3mQ8NpOhtQ2NtTZ8UKnNu1I/Tll4leupTUs2epv2EDNlWrmjSWfLgBC4EvgH3Ae8BUYKh5zeahw4i6qFxt+fW9fzi8/hop8Zk8+2lHrMx80Dso4aWDcGgQONsIZcb72VDZrkCwIuiW8Byr52JgieZjDoNm3N27d9OwYUPOnj1LmzZtaNWqFadPn6ZRo0bs3bv3QfexAg8ApnOoHtGFDbq4pTlUhh9rZDeMbO/xzlApK1VC6e6OTGGBlboEFBeRkgAXxAJSx1tlY6dE5WqLjV3ZimNO1uBuV4axpEIJdu5goztYUrq6inFbGbkalCPGaKJYlpVSgcrV1mj5ZxuFGLfTf081+slAPcQ9Ew3csnzzmkyRLspJTOTigAGcbduWsFdfJUuX+p9CKbICSJB+16RrpmnVwlSmCwWWwNGN1wBo2d+/Ijv1EPHPb8FcOhDB4ueDWPHGPmJv3jepHZkMXqgnvKv6+YlF/IT9woLqrqHVpW6BMPwENJwKmmw49AoEDYVM3RrtMpkM74kTaXrkCDbVqpFy4gSnmzcn6Z9/TBpHEVgDsxGVFhoET3gxooTXAmjSszqTl/TEVqXk/J5bLHt5D5mp5ilhDKgOw/1h8A745DSM2wdjAkQgtT8KXj0MM47B72HQ/i9h/vukw6CAatasWUyZMoXTp0+zcOFCFi1axOnTp5k0aRLvvvvug+5jBSwIk2kd+Rs3j2BxbwkOlfb7A+19RYbqwWMA8BrwC7ATQej9DXgJeIpSs1MVqMB/AgqgjfZnPeWvpiDlzBluffwxlwYPJuKLL1C6u9P6+nVsqlYlct483SeqtMz15JsmXTdPKttScY9GI3Fhj4g22wx+zPjfGoTgSCIiaL6B4JAmIjik5XzfrcOIuvR8sRHWtlZc3B/BF8M3s3nBSZMX9lVVsLUPrO4Gztaw7ZYwBP7TUEqklS10/g56/yHEK8L/FGbAMaf0nubYogXNTp3CpVs3cmJjudCtG3eWLjVpDEUgQ6jXvoeYx/4EPgBMF8ksglotvHj5p944utsRdvIu30/eRVqSeY3Pbg6T64OdQpT8TaoPm8JFOWBlOzg1FL7rBG09YcMTQFUtCwYFVJcuXWLKlCklXp86dSoXL5YXBmwFDMEjE6WwBMwx9jWp/+XEGfUxz1BZFCMQrvM2QBhwDfF+zwbGUD7u0wpU4FGhs/b7fss1mXnzJtdffx1NTg5+s2dj5epKmnbed+3Vi9Rz53Sf7Kq1Ikm4YtK17bXJ3XQL+drcvZ5EenI2Ll4OePlbWOnCUsgAziIW1IuA14EhQHegr/bn0cAE4Fnt708BvYDBwCTgQ+AHYKu2Lct4vJoFO0dr+k5ryqzNg2kzOABJLXHglyvMffpP/t0ShkZj/Dwnk8HYOiJb1VPLrRqyE7Ybk6GtNYwb/S6S7t6O1ORY2NgeLv+od9619vCg0c6dVH3jDaTcXEKnTCFk8mQ02RbQP+8BfIWQWD8CvIFQtbUAqtRx49Wf+1Cpqorbl+P55oUdJMUaSkIrHSNqwYwmgtt2KhZWXYPWniLYslcKY+bLCeDzHyj5M6gWxdHRkYiICOrUqVPk9Vu3buHk5PRAOlaBB4uHXvL3iH2o8mBI98uNul556Ud5QxWgP+Ke+m/6cFbABERGRuLj42NxQ/pyheYIxb9w4DbCTNRM5Ny7B5KE/5dfAqBq3Ji7K1ZwbcIEcu7dw33wYN0nV2ogvidcMunaeVWreVwqcxF+NgaAmk3LmUx6InAUsYA+Begarw1gW+grB0jTfuUiFt5JQEgp5/oAAUAdRHloIKLU7CHDubI9I99vR7thddj0xQluXohj3QdHOLrhGkPeaU21+u5Gt1lFBTv6w3eXRKbqKV/DzovPhK/OwbG71ahd+SB3soPZlNwCm/0vwd3j0PnbArXKeM6WpwAAIABJREFUYpBZWeE/fz6qJk24NnEi0cuWkX71KvU3bEDpbvwYiqAxouzvHSAYeBnBsSrOHTYB7lUdeWV5H5ZO3c3d60l8M2EHU5b2wr2qY9kn60G2Guacgs4+oiQTBK9qdyT094OOT4DdX1lQfPjhhx+WdVBYWBhff/01devWxcfHh5ycHPbu3cuUKVMYMGAA/fv3fwhdfTTIKqTk8iSoGipk4O8MXapAdSM+P3K5nMp+TtRq4YWLpwkGiHKEok0jjOaGyJRKXLp0wbljx9J5ODIFuNUDn86gKv1TK5NBJVvo6gNNynjWyeQyVG62BLTwxifACCEFK6AuYowW8IiUKZU4d+6MS8eOxvNwyhFSL15EplCgsC+5RaVOT0euNKJOLw6RjVoNrAROIhYHZWw234tMwUqpQKEUi2lJkpAk8r9KC6Kj04QMbGy6UDayURgR4yYEg50HySdOkHzsGNbe3ijs7MiMiCA3ORkr5zI+BB9TkG0ojIsIhcMO+k9fPfMgjXtUL/F6+NkYgr4/R8Oupa+0R+2GYf4lXz8cDe+fhKdr6L+upfAgnrvVq1dn9OjRODs7c+TIETw9PbEqh58rs8auACIQ2VsXoIn5/bHx8SHy66+xCwhAk5FB1LffYufvj/uQIdj5++PWuzdWjjomk6wkCFkLVg5Qb5zR114fBuHJYhfc30ROYWEc/PUK0WFJtB9ex6SFu8URBsxDiBIcQVhASIigpzUiWzEceBGYjDCEfQYYBjyNEC54Rvv6aO1rnRD/91qAF2Jeuq/9uoVQRt0JbAAuIQIwFeDEQ83wO3vY0/rpWlSq6sjNi3HE3kzmxJ+hpCVlUaOpp9GiCTKZyIyMCQBD9kwiUuG1I3AhHv7qDc/UlvNviieLc19gbPb3EHsSbgVBtd56ubKqxo1x7dWLhG3bSL90ibgNG3Dt2RNrDw+j+l8CLkBXRIbxJkKwoinCd9FM2DooadqrOmGn7nL3+n3O775JvfZVULmZ/qzN1ojP6xtNwNVGzBk7bsOVRBheC3xVBceKOdf8cVga5s47MskAYfrU1FSef/55Nm7cWGThMWzYMJYvX45KpdJz9uON+/cLiJPOZS2CKlCBCpTA6WbNaLxvX77anaRWI1MoUGdkcL5TJ+NMQ2chJpmeiAXG/xBeHnPQu9v61cgtvLKiD7YOSlLiM1g0brtYuMhAk6vhgx3D8489f08QaiNSISINmrnDvUxo7wWL2kP+nJOTDkoddQzrGnMzZAj3NmzAsU0bMsPDqTRgAJELFyJTKPCZOhXfN9/U3eFhCFKynIK0qoQo3XkHUQ6kBx/0/J1Xfu6DXC7LtxuQJMhIyeaHaXv4eN/IUs/zWQWHB5e8bFIW9P4bYp/Xf11LwVLP3c8//5wOHTrQokULvL29uXDhAr6+vjg5OXHu3Dlq1qxpie5aFGaP/STwNiIj8SsWWSTfXbWKexs3YuXqitzWFp+XXzbM4DQ1ClZWBaUjTEwEuXGL5DF7hO/Qyq4wrq6JnS+Ehc/+ze3L8bz6cx+TleYsgizEptBvCG6UFSK72F77ZQGD1yLIRQRToYgy6fMIDlZhVEUEY50QnmYPccGbmZbDzmXnOLQ2GI1awsXLgWHvtqZ+JwNTTUYiSw1P/Q1xGdC9Kjgp4ZPW4m/Lg2GI8yVc9wyC5HCwdYfev0PVrvrbjIri0qBBpJ4+jcLZmfobNuDao4f5nc1AcKlOAvaIzbZmes8wGJlpOSx/fR9hJ+/i4GLDlKW9qFLH9Jtv0Xn4JUTYHHjYgacdTGsAmWpIyRHqfy0rl9+Aytxnb5nbc7m5uRw6dIjvv/+ezz//nCtXRC10/fr1y+VkVIEKVKB8QdJoikiHn27WjBbnz6Ows0PKNZIccQ8x4YNQQPIBkimzllMmk2HrIDJhjpXssHeyYcrSnsjlMr6ZsKPIsS8egLU9IMAFzsbB4ovwczdYdVX8bVNv7YGr/MC3OwQ8I3YxrQrtaMkVxP32G83PnkVua0tuUhLHqlal7Z07KFQqTjVsqD+gSkZk4kqbdCrpHytA2v0sVry+r9RZy8lDd51kfKbwdintsj4WyLo+bFy5coUffviBqKgo1Go13333HSNGjECSpPJT2mtpNEPsYt9BZDQbmd+k17hxVB41itQzZ7CuUgVbX1+k3NyyM+eqKuDoBym3RNmfu3EmujW0ia/wZBM7XgzJ9zIAUXb2yHAGWIAQlJAheE9jAQu4SuiEFeCv/cp7fsUjygtPIRbrkQjZ/bWIzFZnRIas1gPslxa2DkoGvdGS5n1rsn7OMSKD4/lp+j6a9KrOkLdb4VjJsrXdc08LKe8Dg8Tv3bfAlhswsAaMqgX2ygYw4hTsGgW3d8LmntDxa6EKqOO5YVOlCk0OHuTqc89xb9MmLvTuTcB33+EzaZJ5nbUDPkXYhewDZiKEK0qrYDAStg5KJi7uzs8zDnD1aBRLXtrJpCU9Tc7evtYYGrtDYhZ08haZqjyJ++86wrIr8EELYQScJ63+JKHMgMrKyoohQ4Zw9epV/P398fcvpR6kAhWoQAV0QaNBnZaGwsGB3Pv3ybxxg9zkZBT29ki5ucYtbtWIHV0Qi5FLCC5BGadr1Boy03KwdVCSmpCJTCZI0jKZDFmxp3qOBmppN6eaesD5eLGjNq4ufH620O7aczeEMtSlZbBvIlTvDwEjwLcnSBIypRKZtpxRoVKhcHBAbm2NTC5HplDoH7crsMKwt6Q0OFay4+0/Bhl9nqc9XCw9efVYYvXq1QDcuXOHunXrcv36dQYMGEB6ejovv/wyTz31FF26dKFRIwtEHeUFCoRAwVpgBxYJqACS9u9HoVJh6yuyBnnBVPbdu6BQ6C5x8m4vAqroI0YHVDW1FO3wFJO7nQ+NRiJF6xRs6QW6QchC+OgFaX+vDryF4DI9ClRCiFk8hXiuXgQOAoeAu8B67Vct7TE9MdnKwVBUrVuJ11b35fD6q2z/7izndt0k5EQ0Q95uRbM+NSy2CZKlgee0kgD3MqCqQ0FJmr22Al1j44q8/zakY7OQnf0SDr0sfKs6fQOK0sshFA4OBP7xBzdmzSLiiy8InTyZjOvXqfn558jM4W4qEUGUM6I6YQ5C7XaA6U3mwdrWihcWdmXlWwe4fCiS7yfvYtK3Paje2LQMbtdCPK93j8PWm9DUHaxksLwLPLMbmnsICsaTBoP+w4GBgdy8efMBd6UCFajAk4jKo0ZxsU8fbn70EZcHD6bKK69wvls3zrZvj/vw4cZNkt0o8NhRIJSsXqFMcnXzfv4sm7abHcvO8f2UXbQdWjv/ulbWCgpXPtdyhg9Pwj934LXD0MJDBFBqDRTxyrVWQd2xMDAInr0GXm3g9Oewshrcv46qeXOuPf88d1ev5vLw4bj17s3l4cO5MmIEDo0a6R/3eMPfktLQe7JxC9c8fNTSvOuWN2zcuJHY2Fh8fHxQKBTMnz+fyMhI7O3tady4MQcPHqRnz56PupuWx1Pa7/sRggUWgH29euQmFvj0JOzaRcjkyVyfMYPwt97iyqhRxAcFlTzRW0v4izpo9DXzAqpQC6icZaZmo87VYOOgfPj+UxKCKxWEWBxPQDy7HlUwVRwKBO9qOvAHotx4EMJkPAz4DsHl+hgwTbDR8K5Yyek8JpB3NgyiTlsf0u9n8et7/7DyzQOkJlhGQzzAGV4/Ar+FwvrrwsvK3qqAUwvaPTq5gvRWX0DPX0FhC5d/gM29ICNeZ9syuZyan39O7eXLkVlZEfnVVwSPGYOmED/HJMgRc90ExKbiAkTJqAVgZa1g/LwuNO7hR2ZqDkun7ib8XKzZ7SZmwZa+cOhpWHQRLiUI/ypL+sqVJxjEoTpw4ADvvPMOCxcupEWLFlhb/3ecH59EDtXo3bC2lDXE4WhRP/xzt9LP++XdQ4z9rFOJ18PPxnDir1BGfaSHKf8xgu9SHBeB7QheiB4knzhB1u3buHTvjtLNjazISICyXcoTrxZI92pxN13wY5RyUVLibFPytPioFBzd7LC2K0jiFv6o6F0M/wg0ANrq71pZSL14ERtv71IVg9QZGSjsHh+Ju4QdO0i7cgW33r1xCAwk5cwZ0GhwbNHiofXh2vE7RIclUq2BBzX18CeSs+GzM+Lh38wdZjYTohTJ2XAtSdSA60VaNFzfhBQ4iegffyTt8mWc2rXDc/RoEvfvJzsyEo9nntEvxrEFGGjSMAE4uuEa7YbVKfvAYlh2WXiJPGpY6rnbunVrzp8/j6+vL7dv32bBggWMGjUKPz8/zp8/Xy7L1i0250wHLiB2so1PVuqEJieHyPnzSbt4EcdWrXBq0wYpN5fs6GhuffQRLYpbqdy/Dr/UAmtneCFOGP4aiMQscFsBtgpIfgGUZsRBGSnZzOq0DhsHJZ8fHm16Q6ZgE4LraYsIVh4XC6xs4BgiEPyXgtLqQIQgRmdEMPaAIEkSJ/4K46/5J8lKy0HlasuI/7XVKapjDL69CJFpIoAaVAPaeRX87WI8HLwjBCsSs4QEeGP1Sdg2CNKjwdkf+m8DV/3P2ITdu7kydCjqlBScu3ShwZ9/Fil/NxmbEdlOCSFIMh6LcN7UuRrW/u8wZ3bcwNrOipe+7YF/M0+T2srIFeXjkwJhqD8cvwt9t0MXH/i9lxBIK29V1+Y+ew0KqJRKJRpNgc2xopjSWrYltPfLKZ7EgMpnFRwZLD5/hf/5iWUQzz/o+TuvruyDTCYrElykJ+snugOCZP8NJS+aQpkk+5sffVSU3D9wIJELFghy/7Rp+E6fqlPalN+awDPCJ0Wf2MDXHUS9bx70ihioJT4IGqa7wyOBdggp3CmAac8jy4o5PELcWbbM/Dry0hCEKG0yYEI3JsAwOKi4tAwa6B6XWePeilnlHEc3htBuaG2jz/vhCrxUDnbNLfnczczM5MSJE/Tr14+6dety6dIlcnNz81Vq27dvj4ND+SGIWWzse4FPgJrAT5i94MorUb27ciXx27bh+/bbONSrh6KQKNW5zp2pt3YtNlWK6TuvCYTEYHh6X5nk/uKotQauJ8O54YKfYSpystS83eZXrKzlfHVirOkNGYsLCC8hNfA+QlTncUQMYiH/N2LeBqiMCKwG8EAtLBLupLLugyOEnboLQOtBtRj8dits7M1PdeyLhG6F9mW334LvL0NHb/DWUu0+OAlnh4NzTiRsGwhxZ8HGFfpsgqpd9Lafeu4cF/v0IfvuXRwaNKDB9u35ZbNmYRdCSl2D8GecjEWCKo1aw9oPjnB6WzjWdlZM+q4HNZuatojZGynWXL/3gvpuEJIEtQvFk7nFqz4eMR64KAXAjz/++OQSeP+DiM8UVUrGEs/T7mex/DXjie6AINm/h0kk+7jffqPZmTMo7OwKyP1RUSgcHTnVqBG+zp8XiAP49QFFochIVvBpffEArOkhPtBn4uCbwmID+2Fj70KnGSFiUAIKxO7wdYRBXyuE+aKRCs0WFXN4lHhQz44/EYsTQ3ZIjeiD4Ydazh+tBAoHU4U3IAxs0pRgCspHMGVp2Nra0rlzZ5RKJZs2bUKlUuHr60tCQgIzZswgJCSkiFzuE4OOCOnlcATX0ABRPn2QyWRIGg2x69fjN3s2Ti0L6kPvHz7MzY8+wrlDh5LBFAh+YWIw3NhqdEDVsrIIqE7GmhdQKazEh0edo3l4oiT3ESa7asSi93ENpkBsDL6EENDYBWxESPR/D6xBbCQO5oEEVm4+KqYs68U/64L5e/FpTmwO4/qZGMZ+1sls+fuwZLCOhg7eIphadkUIU/T0FUp1AHujQC0Bqqow5B/YNQZubIYtvaD7CqjzrM72VU2a0PT4cS727k3apUuca9eOhrt24VCvnln9phfCl+wT4HcgE7HuMDNAkSvkjP6oPQCnt4WzbNoeJi3pqbeqQxe6V4UZjYXy3/SGYu0VnAg/X4Uv24pg6rdQ8T+Y3dy8fpcHGLTEGz9+/APuRgUeJkwlnptKdAfMItnLlErk2jJThaOjIPfb2gpyv1yONDYc2Y2/iokDjATfHoWNhsjRiNppgGb6xAYoW8Sg1An5HGLBm4VQcpIjnOy3Idzrlxs58MJiDsnJQswhJSU/oHpclMp8Xnop/2eDyyb1QUPBpKEp5bVSUDjAKKsPhYOKwvn7Eoc2eEnvgWaP+wTC7DMWcV+5IcpI21LmpLl+zlG6T2hYwqzxltbvpeWA0sWFvrsEQ2qAt4MwUz0VK9SYZDLxvXsZFbblGdWqVcPKygo3Nzfkcjkff/wxNWvWJD5eNx/isYY10A+x2P0DswMqEPwQaw8P4v74A9vq1bm3eTP3Dx5EnZKC21NPUfWNN0o/scZAOPsVhG+EDvOKbHSVhTae8FsYHIqGF80I+OUKOTb2VmSl55KRko29Uym13pbGZkSlQgNEMPIkwA5RQjoA8YxaA1xGlLr/jvDFehpR3mhByOUyOo8JpHZrb36Z9Q/RoYksHh/EgNea02l0PZPnk5cCIUctfj4eI7z2+vgVVKy8c0yUeivzblmlA/TZCEfehPOLYPdYSImA5jN1bqLZ+vnR5MgRLg0cSPKRI5zv1ImGO3bg2NzMKKIz4n1+H1EmngPMwOwyzOJB1Q8v72Hykp5Ub2S8t9aEeqL6Ke/9rOcqaBdvHoVAV8Fhm/yEbOTpDKju3LljcCM+Pv8BC+QnCKYSz00lugNmkexVzZtzbcIEXLp3596ffwpy/7BhKOzsBLnfxlGIA9QdK8ii1zfA6bmwdwLkpuU/5PLEBnpUhY3hRcUGFMWeg8371mTZtN3UaevDxX236TiybiERA3npD+8/tN/TEaaJeYfIEDK0RsLjmWe42KcPLt27c//gQSHm0LUrMoXCeDGHR4z4oCDit2whKyICZDJsvL1x69+fSv37G6d+NBPhy2GFkB1+FzF5ZCNIunroncGHI7l0MIKkmHSQgZO7HfU7ViWwY1XkiqJ9CLoFW26K0lAZovSjvx/0r15M6vVWENzYIiZUmQzsvaFGfxHUy+Smj/sb7fh6IXaGZQhT420ILsNr6M1WXToQQci/0Yz/qgu+9QpSwF61XFj/8TGa961RYswA318qmNyUchixS5hlWslhdwTcfwFKOe2xwPnz5/N/7tixI3ZaDmKlSgbo0D+uGIxY5B5GZBMsUGlU/ZNPiFm1iksDB2Ln74/H8OHY16+PTZUqyORyMm/exKZataL3t3c7sbufchvuHhPKfwaipzaI3xVhvn+Nm4+K6LAk4qNSH05AlY4QoXieB8o1eiSQIzZ32iA2EH9GBFbLENmr8QiJdguP27uWK6//0o8tC09yeP01/pp3kpB/oxk9pwMOpRGiDYBSATeSBY98Qt2Cxf///hXGtL/1BEdriEkHawW42iig40JhCXD4DTg+C1IjhAKgDq81pZsbjXbt4sqwYSQEBXG+a1cabNmCS5cuJr4TWrQG5iIqgIIQ2dC3sVhQJaklzuy4wbJpu5mytJdJGcG89zNLDTYKWN0davwKe6xhR3/hW/UkQCeHSi7XsWgsBWq12qKdKk94EjlUphLPTSW6A2aR7KXc3AJyf9u2eI4ZU0DuHzkyP3tVAmnRcH0jNHoZKCo20NQd3i1DbCBfxKC+u3E1xJ8hFvoWQHkQczAXYdOnkxEaiudzz2Hj54dMJiMrKoq7K1diU7UqAUuWGB4cxiEmDDsEL+EDwEH7mp6KhE1f/su928m06O+Pm7cDyGTcj03n361huFS2Z9isNvl9mH4YQu/Dc7XBz1HELVFpsPKakNdd0km7qDs0He6HQp3nxMQqkwkj06srQVWVsE1KMsLCTBv3s8AqSp8U9f1Ni/mjtzLs3TasmX2YIe+0om67gjKsr0ZuYcba/qUGVE3/gFNDC4Kmpn/A6WEiiGz2B5wc+nACqifxuWsoLD72LxELrQGIz4yFoMnKQm5jQ3pICCknTpAWHEzS/v3IZDIcW7Sg1uLFRU848hacnQcNp0Hnbw2+jiSB7y/iM3h+BDQyI/79afpeLh+KZPxXQtHsoSARUXr5+Ox/mQYJ4We1HAjRvuaHyMy15YGM/8K+W6z/6Cjpydm4ejkw7qvO+DUwPosCcCcN+m4Tz/fazvDSQXCxEca0jkr4W8utqusiMitf5IlOhW2A3c+COgtqDoZea4t6EhaDJjubq+PGEffbb8hsbAj8/XfcB5qhQJSHc4h1RyZC4v4dLBLMqnM1rJ55iAt7b2HvZM3UH54yy/w3IlVw0nI1QvTjq7aivFIjiVvkUe4TPzAO1f79+/N/vn37Nm+99RZjx46lQweh5Hb48GF+/fVXvvzyS6MvWoFHC5NvWHPudDNOjV6+HJ8pU4q85tq1UDG6LnEAB+/8YApgXSh81qbkYU7WJYOpvOCxThsjsq95QaOFgqk8UQO33gXkLsdmFrJIf4hI2L6dlsHBJUxAPYYO5d+AAFCroSyD0PyTCv1spf3dgJr94MORzNz0NIpiDNjGPfz4dOAmNGopn2Ox/TZcfaZk4DDUHwLWilp6KxlwazuMCQZ5sb7XGgq/BJAQhOnjtkPwXgp7CEmIhYoB45U04BtYiSlLe/Ljq3uJDE6gzZAAIoPj9RK5HaxE2Ut7b9hxG3zstdWMT/pi8EnGSERAtQORNTB9LVQEchsbEvfuJXbNGmRWVjg0akTtH35A1bAhp1u04N6ff+I+eHDBCQGjREAVuh46LNDp5VMcMpkwAv35KgTdNi+gcqsiSmDvRVjIKdgQPEjD3vIEGYIv3AIh178cYXHxHtAUeBXhvWVBNOrmR9W6lVj1zkFuX7rHNxN2MOSd1iZxSH0c4P0WwjvJzkpI9s9pKTJUm8JFwPVbT7EZ23wDDIvVrhtqDQN7TyFWEf4nbO0D/TaDtVOp15FbW1Pv11+xcnEheulSLg8ZQr01a6g80kwDwCYIkYp3gN3a1ywQVCms5Dz3WSdWvn2ASwciWDplNy8v741nDeMDjiw1TDkEjSvBp61FRnDaP7DdFaqoxN9zNYLjXlUFNUp/C8stdK5iOncusGHu06cPn332Gc8/XyD/NmjQIAIDA/nll18YO/YhKuZUwGyYSjw3legOlFQsM2aRVmYgZ1hDRsWDpgSPll50PkYlffqgUKm4f/Qozh07FrwoSaScPl1EIcxo+GDwe25jr+Tm+VhqFpKAlSRKDTBUSjhyV6g85R8LnI4r5p+hVEH0UfApNC4kiD0NShUKlWT6uGcCCxFiLh4UlPzZI8xAy5gkFUo5kgSu3ipeXdmXvxefZv7ov3GqZMuI/7UrNTsFQu1yxC6xW2glhz+fKggsh/s/Mbfkfwt+QAdE2d/vCDUwCyAjPJzwt9/G6/nn8Xr+eRSFlBIdmzcnN6mYeZRHU3BrAAmXRJlsLT1KqcUwwE8EVBuuwztNTe9zlToiuom48oTy5soD5EB3hCjKFmA1cBZ4ESHMMRaLCle4+ah4ZUVvNs8XJYB/fHKMiMv3GPJOa6P9xobUFMF7Zi6420FSFmwIB5WVyKS424lFv4OVoArkw6ejEKvY3AuiDsCf3YTyl13p2TKZQkHAkiVYuboS8dlnBI8ejZSTg+ezusUtDEIjREb6bURQpUDMF2ZWFSiUcsZ90Znlr+/j6tE7LJ2yi1dW9MHNx7j520YByzoJjq4kCSGQ7zqK9/VkLPwTDZtviBLBSwmw8SnzhGgeNgySTXdwcOD8+fPUqlWryOthYWE0btyYtDQLOQeWQzyJpSffXYKhNUXdah7xPO8mkKSiEqKFsX7OUXq+2KjEh+jWxThibyXTsn/pRPd8lEayb4eowTbiA1+mGIOBhfbG1OObJABh4Z39x0WEojhSL14kdMoUcuLihG+YXE52ZCQKR0cCli59KFm3O6GJbJh7nNTETFw87ZHJZCTFpmNrb8Ww99oW4RldiBe7aPcywFcl7pHIVFFDv7STEDQB4N5FODgFMuIEP0Qmh9RIUDpCl6WkxijNH3cScA9xL7nz0Ha7C5OIHwWexOeuoXggY7+GCKRsEWagFmg2evly7h8+TN2ffwYgNzmZjLAwErZvJ+XkSQKWLsXG27voSecXwz/TwbcXDNpp8LUycqHySjFfhY8xfec6JjyJz4duxrmyPR/uHG5aIxUwDskI2f6/Ec8xT+BlRJBvYZzcep0/Pj1GTpaaag3cmTC/K86VTSfojNwluKQrugruVEIm/HVDCFp9XVr/k2/A5p7Ce821LgzcBY66iYuSJHFrzhxuffghyGTUWb4cr0KJC5NxHrEplwn0RQhVWKBUOzsjl2XTdhN+NhZ3X0deXt4bZw/T39+8NdjRu/BnONhaQSdvUQK4PBjO3YNvOpbdjqXwUHyofH19efPNN5k+fXqR1xcvXsxXX31FRESE0Rd+XPAkTuwNfoNzI8QOdJYafFdDK0/x4NgTCUkTSudJ/K/beqztrUoQ3bMycvh6XBBvriudlwHoJtnvROzAl0GyT9ixg3ubN+sm99/aIWRMdYgD5GHHbbEDUqbYABB8JIpLB26XFDHo5Iu8+MF5+Bc4gknKbKWhVFGDAQOo1K+fcWIO5QDZcXFk37mDpNFgU6UK1pWNl2EFIBU4DURrf3dFlJkYUAqUmpjJ/dh0NBoJF097HN10b5XGZYgyD40EVRxA57ycEQdpd0SdnUMVsC86LpPHrUYshPMM6ysBddArvJGH+KgUVG622NiJlJokSUga7aNeJtN9/yJKLk7Fis8IiM9Iq8piQfGw8CQ+dw3FAxv7TMSm1mhgovnNZd68yYVevfD73//QZGaiycwk/do15DY2eL/0EvZ1SuHbZibAzz6Cb/JcODjVMPh6o3YLtb8v2sDbJmapNBqJ2V1+IyMlm/eDhuHqVX78x554XAEWAaHa39shOH0W1oSJvBrPijf2kxidhpO7HS8s6maSkEJGrrBaWdFVZFZupYgMysE7Ios13F/MDSUepWnRsOUpiL8IjtWE95qz/s3mW3PncvNVP/ZfAAAgAElEQVS99wAIWLasiEKsyTiH+MxnIagIZayxDEVGSjZLJu0kMjgB71ouTPupt8liIHmYdVxUf4yoJcTDAKYeEpnAL9qW8h4/IJj77FV8+OGHH5Z1kFKpZMaMGYSGhhITE8Pp06f5+uuvWbhwIXPmzKF169ZGX/hxQWF/EltbC+uAPiL8GCzcq+UyEVStDYODg+CZAFFS8VJg6TfwmR03GPVhe9a89w8e1Z1w9xXbhFZKheAcDa2NTNedvxj4FvBHiAd4IOqpuwPfIWRWdcQHYdOnkxAUhPugQVQaMACXTp1Qenhwd8UKko8cwU21E9ntIKgxCGoMgCqdRKo9eAVEHwG/viCTMf2wqMEfVB0GVBc7IR52sOKqKPHqW60gY7Xpy3+5eiSKBl2q0aBTVfybeaFyteXEljBunoulXocqJbNF3yACqvaIyaIxgowcBFxEZOKMeDCETZ9Oor5x9+372GSspNxc0q9cITM8nNyEBKTsbKw9PUvwi8rEfuBjhDxsXqI0CiHZK0MEHDreEnWuhphwofCVkZyNOkeDYyW7UjcBcjWidj48BRKyIFsj7AZKmBBqciHhCiSHQ1YCqLNFPb2WV2XyuE8jJsMwxA5vNGIxvAqxIVFN/+lLXtpF8741sba1IjUxk3kjt3JwzRUO/36VfSsv0Wl0vVKDqt0R0G87nIsXfnU3UoQ3y4cnoboj1H1IGbIn8blrKAqP3cbGxnKfcR9gO+Ke6ofZstZWLi5Ye3uTuHs3mpwcNJmZuPXujdeECVh76hDxsbKDpGsQfwHkSqjWy/DryWF9mJBcnlLfxKpsmYzrp2O4F5GCT21Xs8j1JaBGfEbDEZtq9oj32h5I037/L8MDkS1xQvBDwxG8Pi/A8Li6TDi529O8T01uX77H3etJnN4eTuXqznjVdCn75EJQyoXq6cUEuJcJOyPgapKo7hlQXcwJ97PAXllgLQGAtaPwxLxzCBIuC2Gs6v3BTnfk6NKxIwqVisTdu0n4+2+sfXzMl1T3Auoh5sxgRLaqOWYHVUobBY26+XHln0juXr/P9dMxNO1dHSulaTtuP10RqsuftREiUJm58GuI8J77oAWorCE2HRzM93AuE+bOOwatZl555RWqVavGvHnz2L59OwCBgYH8/vvvPP3000ZftAKPFoWJ5zu1xHONVFI6vAQkqFa/ENG9dwJthwYQcUU/0R0Qk8llhBeKjIIaw6uUWU+tU9Rg2DBB7m8lwXNXSxEHGAa/BICkBpkV229D8DMlF8XDiosNoEvEwIMmPauXEDHIx3FEvXjx50pnhDKbppS/6UH8tm20unYNmaLoSfnj1mhA8RDTBiYicc8eQqdOxbZ6dWyrVwet2l36lSv4L1iAuzHPkJXAfERQURgvA88hMqCllHVfO36HjZ+dwNXHATcfFTKtyt/d8CSentGShl0LIpQ9kWJ3rLqj+JLJhMLYlQRY2B4G5U3+EXvgwFRwqg6O1QtU/hKvQIeFJIY76B73woW4D9Lj6fY18DlQ3CM1EUHubo2QY9YDW+1nUuVqi52jNRMXd8dWZc2i57YVNQsuhFcOw/Z+BbuEeYhNhw5/QZ9qDzdTZQ6OHj1q8LHt2rV7gD0xHZcPRdKgswW0zgECEffNCWAdMEX/4YbAY9gwPIYJLpQ6NZXEPXsImTiRymPGoGrSBOvKlZE0mqLZ9EbT4dqvcPkHaPm+WIAagL7VwMNWcCtOxEAbE6woAOp1qELwkSiu/BNJq4G1yj7BUCiATYjnTxdgHyKb/hvC1sFwYcMnFwpgKNAJYXp/EpiD4PdNRwRbFoDKzZbJ3/dkw9wTnPgrlJVvHaDfy83oPqGBURsU2/rBjKNC4a+/H7TWFhe03gQDq4sNqOVdxfOySLbK1lWU+/3dTwRWf3aBwftEGaAO+L75JjKFgutvvEHopEnIrKzwnjDB5PcAEAHUR8D/gPWIe9NMmhaIOWXy9z1ZPC6IWxfjWPX2QV5Y0A2F0viqmXuZMDpAiILcThEllTdToIuPUFnMyIUPTwkxkInl3K/K4O3hQYMGMUjfAqACjw0WtYeRuwuI53/1LijxG1ZTd3o178Pi6q3i1Z/78Pc3Z5j3zFac3O30Et0B/ST7MjwTFCoV948dw7lDB2QyWb5JasrJk4Lcby1pvU06iEVtXhVr7EkhHKDdklEp4dhdQYQsfNjJWPG3wsO2sVdy80IcNZtWLnLN25fvYWOvLH131IygsTRYOToKUQNd435MEPbKKzTYtg37gIAir2fHxnKuQwfc+vbVLX1fHDIgt9hrGu1rem6/TV/+y8TF3fHwKzpjpyRk8M3zO6jXoUr+Dtsr/8C2vhBQbEOzRFBx6BUYsA1cio6L9FjY2IGwuXL94+7TR/e4JUryXCQK5JfLKNTWaCQy03NwcLYh7b7YdbNzssZKqdC7oNBIULnYvSpJgjT8eORCC9Ch2Ocmb9zFf4fya/2xZeEp6rWrYtJCpVS8gAio/gKGUVQ10wzErF3LjVmzyE1IwK1PH7Kjori5YQO1f/ih5MGeLcCnk1hoXvkJmrxu0DWsFTC+Lnx1DpZdMT2gqt/Jl01f/MvVY3fIzVGbvLNeBGrEHNYd2IPYRPMCbiCyywGILFVFhaGAB0KRbivwPSL4vIBQpbOQI4iVUsHI99vi4efEtsWn2fbtGe5FpjB8VhuDP082CrFeyttXPXgHxuwRXL7O3qIUevQeUeFjV3w1ba2CAdvh7wEQtR82dRblf5V0e9ZUff11JEkifMYMQl58EblSiae5om9tgVnAJwjlRTtEUGsmXCo7MGlJT76ZEETw4Sh+m3OU0XPaG51R71EV+myDuEzYHyUqh9p6CSGavM276Q3FmtXfSTfHvzzA4IAqOzubHTt2EBoayosvvoizszM3b97ExcUFFxfjUqkVeLRoURmujymdeD5LT5b59V/65f9sq7Jm2LttGPZuKTrkpcEfsUNnAsm+zqpVgtx/7x42vr4gk+WT+2svX46smhUcmAKZ90Dlq80UaMUBui3PN9pb1U232MDyLkV5Y6PntOePucdJS8rCxdMBmYx8EYORH+gIHs0IGvWOOz5eiBrkjdvJidorVpTIXJVXSGp1Cd6QJEkoPTxALi+IbA3BBMT7XJ8Clb9ERHbwGXSW1UhqCZVb0RS+JEmoXG0Fxa5QF9Q6ggoPO7HZkH+oRg12lUseaOcBMrl54x6AqHnvRAHnMB6x8OhFmdmplv39+enVvdRp68OlAxG0HRKQn221slYg6YjIJteHTn+JrG01lbhsdDqsCxOLWUut6x8Gbty4kf/zhQsXmD59OjNmzChi/bFgwQIWLVr0qLpYJuJuJXPkj2t0Gl3PMg0GIBb7BxHlo2+a36SkVpNy8iR1VqzAvl49Ql54Ac/nniNywQLUaWlF1P/y0fRNEVCdWwANp4LCMA7GxHoioFp/Hea1g0omlC26+ajwDnAlOjSRsJN3i3i0mYy8R3FPYC1CaS0d8Tx6HpEdrEBRyBDcnmYI78YrCEW6ZxHy/haY3mQyGd3HN8DD15FfZ//Dib9CSb6Xzvgvu2BdIgIqHXnBlCTBimCY11Zsyg4MgtXd4ClfkUUptTmlA/T/G7Y/DRG74a+u8PR+vUGV7xtvIGVnc+Pdd7k6fjwyGxsqjxhhwugLoRuQAcxDrMMcEKbLZsKzhjMTv+nOkom7OPX3dZzc7Rgw3bhSxRaVhdL85UT4uJXw+PLUzuNXE8V8XN8N3moCiy5AOy8hXlEeYZAoxe3bt+nZsyeRkZFkZWUREhJCzZo1ee2118jMzGTp0qUPo6+PBE8qOVqtEZmZPOK5j73wVNBXzhMflYKjm13+g0iSJLEm1Eq16CO6i4siSPZxiFVpJaAuZS4O85AdF0d2VBSSJJVO7s+IEyVXSKWKA+QhLkOUcElliQ0AqQmZ3I9LR5IknCvrFzHIh4WV2cocdzlHxIIFxKxejcfQoQUGt9HRxK1bh/vw4VR7913jdrUyEaUiMYjslBuilElP5dCBXy9zcut1Gnf3w9VbRAop8RmcCbpB4x5+dJ/QMP/+XXAeVl8TtfJ5xr55QcXwmsIQWiYDzi6Aa6vBf6gw9kUG6dEQsg5qDSfioA0xv/xi+rijEFyMOO04KyH4eQb6kYaejCbqWgK+ge74N9PBaSkFYfeFcEukVpDD216UOdZ7iH46ln7udujQgTfffLNEifqff/7Jl19+ybFjx8y+hqVQeOwfdvkLO0drZm0ejMrVQlyyCMQiXwJ+QGx2mQF1WhrnOnWi4bZtWHt5cb57d6q89hpJ+/dTdfp0bP1KuWElDaxrJDgmXZaW7iOoA73/FnyWj1vBbBNpJjuWnWPn0vM071uTZz+1kIzYXeAfxKZHAvApULiiMC+LVYGSUANrEEG+BuFbNRuLeaYB3LwQx0/T95KWlEX1xh68+HV3o8QU1BqYsB+erS1U6NaHiWqG6k4Q1A/cbPTw+nIzRVB1e6fg2A4+BK76bWhufvQRtz78EJmVFfX/+otK/frpPd4g/AEsQVRzfIrgdVsAwUei+Om1vWhyJYa804qOz5i+AZStLliHHoiCSQdhcQc4fFdUEpljm1AWHorK39ChQ7GysmL16tW4u7tz/vx5atasyf79+5k0aRIhISFlNfHY4kkMqHZHCDO1Gk7gVyhTE5wo0tsDdRBE5z2zlSnLeuHgbENqYiZfj9uOOleDXCEjJ1PN+0HDShin5uMUghfiRVGVv9vANMRCUQ/ydkGzIiNBkrD29saxZUvkNtoHokYtSvxSIwFJqPx5tiyx85kXSEamifWEtzaQLM2uQqPWcPtyPEkxIup08rCnWv1K+ktEzAwayxy3j48Yt6ElcuUEGWFhQqUxMhI0Gqy9vKj09NM41LPQzjuUKVMfdzuZSwciSIpNQ9KAUyU7Gnb1xbMUsrLBQUVSmFCXTI0Ui0R7L6j5NLjVs8y407VjMtG3JTMtBwAbeyujgtaUbPFcUD0EInBpsPRz187OjgsXLhBQrPwyNDSURo0akZGRYfY1LIXCY1878wQhx6NpN6w2w99ra7mLfIPg+zRHcFnMrOe8Om4cqiZNqPr669xdtYqQiRPxnTmTGnPm6D4p7A/YMUJUFYwNNThLtS8Sum8VfKpbY3VkBspAfFQKn/TfhNJWwUe7RmDnaIHn6X2EOE5fhD3IOMRzP5eCWqAKk2z9OIsQHUpEzJ3vU9Tc3EzE3LjPsqm7Sbybhpe/C5OW9MClsuF1mD9dgZXXYM8AkSUJugUNKwkTWigaDEAxa5bcDFH+F7lX2GwM+Ufwb3VAkiTC336byHnzkNnY0DAoCNeuXY0fdHH8iMii2iL4yBbKnv67JYx1HxxBJoPx87rQqJuBO3+FcC0RFlyAZQU2uAzZIdZnnnbQrYru9akl8FACqsqVK7N//37q16+Po6NjfkB18+ZN6tevX+FD9Zih7jr4u29J4nlMOnT8Cy6PhNJihnnPbOX1X/vlB00LxvzNxG+6Y2Ov5OvntvPGmv66a5OfQ6T1i1dXJCDIqD+jswA1YfduwqZNw7ZGDbHbKZORFRlJenAw/osW4d7EDg5OExK8jn4FJX+JwdBxEdQYCJQuNpAXSBYRG6BAxMCtigpXbwfhWxSTRsyN+wx+syUNupQis2Zm0Gj0uAcONK7BR4zclBRkMpl5/K/+CNnnkRTd7b2AWCC+g97gIzMtB5mMskVUMCKoyE4BZKJmvhSYNO44YC4QiVio1UVIDFc37PSk2DTWvHeYuFv3SbufRbX67gx/ry1e/vrLs6NS4dm9EHJfqPy1qiwmt4eZnQLLP3cDAgIYOXIkn3zySZHXZ8+ezfr16wkNDdVx5sNH4bFn3JP4auQWJAlmrO1vOVW6+4jSqlTEfWZmrJZy5gwRX35Jjc8+w65GDTLCw7GrWVP/SYWzVJ2XQEPDVDIkCVpthFNxsKQjTGlgWp+/fXEH10/HMOJ/bWk7xAzT+iKdo6j4UOEAKq8EsAL6EY8QqriAyKRMRvD9LBSIJsWksXTqbmLC7+Pq5cCUpb1KcGv1YdZxuJMusqO1nMVz8tcQmK4N/C7Gwx/XYU6rUk7OSYMtvSH6sFivDPkHVLpLTiVJInTqVKKXLkXu4EDjfftwalVaw0ZAQpSk7kCIgHxDmaqxhmLXj+cJWnIOpY2Cqct6Ub2x8dU0XTcLSfqpDcSadM4peL2xWLPp2q+3FMyedyQDoFKppJCQkPyfr1+/LkmSJP3777+Sm5ubIU08tkhKSsr/elIQsEaSkrOKvqbRSJJaI0m110hSVm7p530xfLOUmpQpSZIkpd3PlOaP3irlZIuDvxq5RcrNVuu+6LOSJKUVe00jSZJakqSxkiRl6z71RJ06Upr2/iuMrJgY6URAgKT+ubYkJZb8u5QWI0mrAyQpVwy2zlpJCkkseVhMmnhPCo977tObpNhb90sc+3/2zjs8quJ745/dkEYSSkgg9BJ6DR2pQWpAEZDyFRUQEBD4ASJiQ8EGFiwogoACNqr0ooRepEPoHZIAKRCSQHqy2T2/PyY9m802FZD3efbJZndm7j13752ZM3Pe98TdTZKPe60xbuuLInLLiAHRouzXGTWvQByuVUuSrlzJ93lqZKSyO83ERXuAkBIWJic7dpQD5crJHmdnOdG2rSScO2ddYyMzXt+LSOY9bMj4+5KI5P/JRETk3u1EmfPynzKtywqZ3OJnmf3SFom4auRmEJFb8SL+60TK/STiPF+k7RqRc9FGCsaHiazpKLKonMhcZ5Hf24pEZ9tlk91viUhgxvtRou6rN3LYXAgWjN8uxzarfnrWcxvlTuh9mT92m+gKergz8NRmkV8vqfdNV4lcuScSsEkkxXQ1u8Pe/e6KFStEq9VKmzZtZMqUKfLGG29I27ZtxcHBQVasWGGXY9gLeW1f8/lhmei3RL4esln0ekMhtS3AShHxl0L7X3OReOGCGNLVjZIcGioxO3aIiIg+1cRNe2WlyLeILCovkpZ3gCgYK6+KMFek8i8Fj1eF4ciGqzLRb4l81n+9GAx2uq5JIrJERFJyfHZNROaLyHci8pmI/GifQz3SSBfVx/tnvGaJxeOnKSTcS5GvB2+WiX5L5L3OK+R2sGX9zIk7uf8ftlNk3F6Rk1EiXTaIzDiu5lRGkXpfZEVzdd//WkckKcrksQx6vZx/4QXZDbK/VClJvHjRonM1Cp2o8cRfRP4nao5iBxgMBln+wV8y0W+JTO24XKJuxlncRkSiGntG7BJ5ZY/IwECRyEQT19OOsHXcMcvfa926NcuWLcv6PzN0ZPbs2bRv395yL+4x/lWMqqt2oj46Dr9cUq/PT0LT300TzzOJ7n/OP8nckYG07J2b6G4ST6Pknn8BAjNey4FRQDdMy6NkhErlhIjg6OWVIdeXEWqVuwC4euUKajZINtkxZzFv1/zCaWIAj1KuecoKbiVdQINxUr+Qny9lgTJbPhgMOObJ5yKZogY5ZQofcFwZPRqfYcN4IiwM9wYNqLVoEdcnT8aQkmJdg1+ghD8+Qa2wZ/7EJu6hVR8fpOUz1ZkeOICy1Uvy3LQ2bPjqGLrU/Opuo/fCiDoQNhgaeMLiJ2HyQZUEOxd2j4K6w+GlMCjVADothr8mq6SlwOVRo/AZPjzL7tqLFyu7c+S6KBB3UYIUoMJIy2XYbOZPfv9OEg2eVMuOYhC8KnqQFJeKwWC6gbBE6JuxsaAXpaoUk6KenYcZAwYM4NixY1SpUoXt27cTGBhIlSpVOHLkCANsJXz/zeg+yg8PL1dCTkVxZJ0dd9J6AxVRnKrVtjdXtHbtLKEc0esJfustALROTqRFRqKLjc1fyfdZ8G4CiWFw+huzj9W3qiKvh8bD4ovWna9f1yp4eLkSfiWWiwfCrWskL1xRkQgCxKM4QQsAZxQvqA0qJHyNfQ73yMIBNTeYhkpkvgklRpRgn+bdijsz+vsuVG/mQ9zdZL57eSu3g+8XXjEDjTPUMTPHhB87wpE70PtPeK2R4tkWCKdi0OtP8Kyvomg2BkBaXIHFNVottRYtwrNHD9KjozndrRup4Tber0VQ17Y2ivv3Fkq0wkZoNBr6vdWK2q3LkRCbwsLxO0iOT7OoDZ+i8Gsn6F4RniijEnmXKWpd3rl/GmZFH8+YMQN/f38uXrxIeno6M2fO5PTp01y4cMGiXB+P8WDgNT+VQ2FDCByPUn2/T1FY2tl0aE/HwfWoUNuTsMsx9Hm9Ob5Ns52YiT/3MH3QAahktweAy6iDeqKIp4WE2pYdPZqT7drh3a9fFsE5k9xfZsgQNI2cYE078O2XIQ5AtjhA7aEqgSS5FcwqZ0RfZYoNDKkFTjkcySeercm3w/6gUecMEQMg7m4yQX8G0+Lp6sa5YplOYwey8yRlKrMV5jQas3vUqALt9hk6FI3jv0RwsRCpt27h1bcvoBLdulavji42FjEYLG9Mi3IwXkdNViaipIpDUfdRAVSIezkcDINe8KrkQVJcGmLEUwhLhD4Z4Z/pGU5FbKri3+UKM0wMg2p91HtDOhT3hZRYxedzgLSwMLz69Mmy28XXV9ltjkS3HhU6BMphPIuKeTdzUDGkG7Js02gh5FQUji5FChWO0RmUIwXqUh+8DUWL/HOZ6v9ONG7cmF9//fXfPg2L4erhRO/XmvPLW3vZOPs49f0r5VOstAqOqPxtb6Dy53VB8VZsgOj1aBwccK1aFefy5QmdMYP0e/eI/fNPKk2dml+tTKOF1p/C+i5w/BOo+7LJBKiZcNDC+82hf6BaGBxa2zgP1hQcnR3oMKgOm745wc4lZ6nTxg5qf5AtRHEQ5VQNRoVUuaDGgBTglH0O9cjDHyiNmiccR92vM4Gytjft7OrIiG+e5IcJO7l6NJLvRm5l7PyuRjm1BbaRcc8tuajyexZzUrxbyFj7kgIcARdPeCYQVreFO8dgS1+VgqMAHqHW0ZG6K1dyqlMn4g8f5kxAAH5791LElnBoV1S47zjUnOx9lFCFjcIpDkW0DPm0A9+89AcRV++xePJuRs3pbFHqh9olcyeRz5Xj6wGGWRY2bdqUw4cP4+zsjK+vL/v376dmzZocPnyYevUKln98jAcXNUrAyLrwUUuY3VatqJjDk6jRoiyt+tSkfO1SWflczEYFFAdmOPB/wPOYpVhWcdIk6q5ahdbVlfjjx4k/dgz0emovXUrlt99G02QydF8FRVwh6rjqoAx66LoUmr2V1aNNagSruoKrg3Ikj0WpyePSzkouPmfH13FwPYZ+5o+jcxFuXYjm5vloRC+8OKMdnYc3ME7wHwBMR61GXkatRBpQg8HzWBwDXvG11wq022JlvH8Rkp6e7URotcQdOICDq6t1S07tyHaahqBWLdNQctCvoyYtRmDQC5LhoGi0EHwqCkcXB6O/Sboh25fRAgci1T2Tr0M3pJOr0cgD6h7UqG7VJrufRDmJoAa4BahnxkzufJPu1bJWXLVaDRtnH6fvGy0K3UkeVENxCkFNWqcchG/bWT5ZfRARHR3NN998w/jx44mOjgbg0KFDhIaGFlLz30fjblWo2aosSXFprP/yqP0aboHaNUkG7CDWq3FwIP3ePaJWryY1LIywb76haK1a1FuzpmDp54qdoWIXSLsPxz42+1h9q0HDUmoCO/+cdefbul8tXNwduXoskpDTUdY1YgxhwGbU9a2BSqhaBCW6sBqL+bT/adRFKdNVQfWJY1Djqx3g7OrIy7M7UaOFD/F3k/lu5FbuhJi/UwXw8XGlCvtzJ9gQAHPOKr62VqPGElD/H72Tp6JbWeVUFS2jhCq2Dc4eT4zAwc2NBps24VqrFomnT3O2d28MaZbt/uRDSVQ+sGKo/HR2Sj7t4u7EiNmd8CjlwpUjEfw+85Dl88UceBicKTBTlOK/jEdRlCIn8TwmRancmUM8z0l0T4pLo2LdUgyY+oR5Kzo5SfZxZJPsLRCCKZTcX4g4QCYsUTCzRMQgCzYqs+WFXcQc/kXc+OQTSnTqRLHmzTnRsiUaR0dqzp+P2z+4GLN90RlqtPChcn1vvnpxM1oHDQOmPkHZ6vlv+k9OQKcK6rlouVqFwM7voHJh5MLxT6BCJ6Umuaql2gn1n5+VY+RBsPthhb373bNnz9KxY0eKFy9OaGgoly5dolq1akydOpUbN27w888/23wMe6Eg26NuxPH5gA3oUvWMmtuZ2k/YaUclApX3Jw1FVm9ufVO66GjO9OiBW8OGePfrR8jUqdRZtgzX6tURgwGNtoA13KggWNFU5Qz83ynwNE96bEMwPPOnykd1ZVD+vIrmYPO3J9i+6Aw1W5XllXldLW/AGBJQE/+FqAW28ygRgBtAR+AZ+xzmP4UE1C7KMVQepc+wm0JdWnI6P0zcyZUjERQvXZTxiwPwLGfeeHs9DopooFJG2o4TUSraxdtVRQHtjYDgOLV427mCkvvPhaiTsLaDCvvzew3azjJ5vJSQEIJatyYtIoIyL75IrZ9+sn1x9QzwGqBDRdn0sa25TISejeK7EVvRperp/XpzOgx6sBOy/SMqfwApKSksXbqU8+fPA1C3bl2ee+45XF3tNGt8QPEoOlRPb4H/VYfna0Kz32F5Fxi/H9Z2N70SvXDCDpp0q0rTHtX4YtAmBn/SnrWfHWHYlx0L51C9hVp174KKjX4PpS7zIYVKiqeGhXHhhRdIvnyZ9JgYPJo3p8b8+dnS0wlhsO0FuHcZUmLUBNd/fpZ0dSbyOpLNvGGBv3FH0irn0Q5Oo0V2P8Z/A3+gnhsrkxkeXn+FZj19C05pUAAWX4QXa/79ykrGYO9+t1u3btSuXZuvv/6aYsWKZSnV7t+/nxdffDFXEuB/G6Zs37H4DJu+OYFnOXemrOpl2UKPKSxD7YKWBX7EpsWg5OBgnMuXR+vkROhHH6G7c4fq3yh+lEmnavcrcPZ7KN8Reu8waydXBDpugD3hMLGhUmu1FIn3U/noqRDRkAUAACAASURBVNWkJOgYu7Ab1Zv5FF7JHMxBqdjeQYXwPgk0QyndOqB2Wypg9q7zY6Am/B+i8n25osL/Gtmn6dRkHfPHbic46A5eFT0YvzggH4+6MGTKpEckKnn16BRo7KXmWml66LEZlnVRzlYu3NyhuFQGHbSbDY3GmzxO/IkTnGzXDkNSElU+/JDKU6daaK0RbEPNX7QofrINCys5EbQ1mJ/f3IvWQcPIOZ2p1aqczW3uvAXB8TDczlMhW8cds4bKoKAgfH19mTBhAnv37mXPnj1MmDCBatWqERQUZPFBH+PfRUHE88Jc64KI7ma55NFkk+wNWESyvzx6NGVHjOCJsDCK1qunRA1eey2b3L9rFNQdocQBPOvBk4vgr9dAn3s7PKfYQL0MsYHXDhgRGwBWfXSIVn1qMD1wAGWqleB/09uw/stjpOtM8F++ROUgWYUKT5gCzEMNAlbg8qhRxu22dZv/X0Tk4sUYdFZeEDvh8Por6HXm8bcWXwRTP3kWzi8GfcF22WT3WlQuGyuxf8VF9OmW89XmnFGcqkcBR48eZdy4cflWcitWrEhkZOS/dFaWw/+FepSv5UlMeAJ/zD1pv4b7A9VQu1U/2daUa9WqWfzOcq+8glsjNeM1pKYW7EwBtPoYXEpB2C64ssKsY2k0KneiBhVqddGI7kVhcCvuTMfBatd485wTNoUm5cIrwEhUiofZqHDlm8CvGd99h9pxeQzz4YgSU+iEClN9A5Xk3Q7IDP8rX8uTuzfjmT92u8WCCpnYEw5RyRBQSTlTAGuuQ7yugAWqip3gyR/V+30T4Zpp1RKPJk2os2wZaDSEvPsud5Yvt+o8c6ELKpWCAXVf2ikSunG3qnQZ0QCDXvj5jT1E3ShYgMMcXLkHXTephL+7w+xzjvaCWQ7V6NGjadKkCTdv3uTIkSMcPXqUmzdv0rx5c0aPHv13n+Nj2Bk6Q7ZqVybx3LVI4QuChnRD1mCTRXR3LmIeFSad3CT7c6hQCDOQFhaWJWqAXq/I/TEx2R5gUjj4Znwv+gxxgJh88ciWKJjdjyrAeTQ1wbTBaTSGtPBwvJ99NuOE9UrMITr6oVH3M4awOXMQWx3CZFT4hzmOjhHsX3HRtGOcA2Y7FWfmgKFgu6yyO+fzos/zmTnVc9zYhgylicJU/iD385Dphz3sKn8igs6IQ3vz5k2KFTM/B82/DQdHLQOntUbroGHv0vOEnMpLzLASRYDJqHttFYr/aQM0Gg2i1+NYqhSlBw4kessWwufN49rkyZzr14+769blr+TiCU98ot7vfxVS75l1LD8veLmuulf/b7913WP7QXVxK+FMyKkozuy8YXkDxuCAyknYDnVd/0AJVbiiIja+QD3PRi7FY5iAA+r69QBSgXdQghV2gKuHE6O+64x3pWKEXYrhx1d3kp5m/kCj0UB4Irx3FNqVVaHjALvCYH+kyl1V0hnupaq5Ry7UflEtKiAQ+DzcPmbyWF69euH75ZcAXBw6lLgjRyywtAC8hBLVSgTeRs1f7IDurzSmfoeKJMWlsWjSrqyE89agRgnFh9cLDAiEm3ZSfrQHzHKoTp8+zcyZMylRIjvcqUSJEnz88cecPn36bzu5x/h7kJN4rtXAGwdhjhnE8ybdq3H7utoS1Wg0bPzmOH3fLJzoDqgVpcxxSosKLxmPWeEOotNBpiKcVkvcwYNoc5L79bo84gAHM8QBcnt6BTmSxgiPVjmPNjiNxiA6XS5Rg/sHDqAt+nBmhsxS9NNokPT03J9ZirEoRUULV9CynQlNoQ5G5scajVL6y/lZLkiOH1z0eT7LY3fGb2mW3W8Cr6Li2m+hBrdJqGfGDL9swbjtzH15K3NHBRJ1I56F/7eduaO28u1LfxiVis9Ej80qsWLnDXDlPvTcAp02QNu1kGLDLtm/jU6dOjF37tys/zUaDampqXz88cd07Won3sw/hIp1StFxcD1EYOm0v0hLttMPUwd4FtWHfYbVO+uZ0Dg4oIuO5tbXXxO7dSv6+HhcKlfGZ9gwrk2aRNJFI3rndYeBT2tIioQDb5p9rI9bgKezIv8vs0JZ3sXNke6v+AGw/stjpNnzZk9COavhwHMZr8wk8p7YrKr2n4QDqm/shbpP30EpodoBHqVcGT2vC8W8XLl2/DZL391v1kJUJqKSlaR/n4zF2zXXYUsoVHBTzn+STu2m9tuq+Ny50PQtlYpDnwJbekNihMljlZ8wgbKjRiGpqZzr3ZvUMBu3bLSosacG6n79CKsXLnM1q9Xw/MftKFO1OJHX7rHsvf027QTPbAldK0JUCvT988EZm8ziUNWvX5/vv/+etm3b5vp83759jBo1KotX9SjiUeRQPWy4MXMmJbt0waNZM040b47GxYWa33+fTe4/NlOpRJVpBiubg4ML+H+fJQ6QiZknFCm0eWlosVoRR42KDQDbf1Qk5Ur1vPjy+U0UcXag/zutKOtrQrnjN1SMfC1Udncn1KS4asFVzLX7eLNmaF1dlahB3Qeb2JkXZ556Cn1cHBonJ+KPHMHdzw+NkxP6xEQa7dyplO8swTAgGMX1qGZelYXjd5CSkIaDowM3zt2lfM2SODg6kJaczpgFXXFyySYo9dysQjOctCq3iJ+XEqZIToedvSCr6ManQBcHWie4fQS8/dR7XSL02cmZ3v2ttzsKNZC5ohyp9wAPlNNuRvL5e7cTMegFJ9cizB0VyJBPO+Di7ohBL5T0cSuw3q0EtfLnVgSe3AAru0JxJ+VUVvwHNVHs3e9eu3aNtm3bUrVqVY4dO0bnzp05d+4cer2egwcPUrFiRZuPkZqaypgxY9i+fTsxMTFUr16dGTNmEBAQkFVmx44djB07lhs3btCyZUuWLFlC5cq5SZbm2K5L1fPFoI3cvn6f9s/Xoc/kvEx3K5GCUmENRwlVDLG+qdRbtzjXvz8ezZpR+n//w6NpU7QuSobzytixuDdpQtnhw/NXjD4HKxorPknffVCubf4yRrDoAgzfDaVd4eJzlgtU6NMNfDloE+FXYun6ckMCxjS2rIGCcBk1NmSG96WjQv+CUItu44HHUwvrYAA+Rwl+uKNCK80cEwpD2KUYvh3+J6mJOjoOrkevV5uZXbfRSmhfVglWVHSH+p7wTNXsPlSnh09PwsFI2NwzT2V9GqzrBBH7oUxL6LMbihScJsGg03G6Sxfu79mDe9Om+O3di4OtC6+RqDnMfWAQ8LJtzWXiTuh9vnphMykJOnqMbUyXEQ2tbis6RWkAhMTDsNrwg7/tuapsHnfMyf67fft2adq0qezatUuSk5MlOTlZdu3aJc2bN5dt27ZZlVH4YYGtmZMfdCy6IKLTW17v0LrLkm5NRRGRLaIyoT/GfwIpN29KckiIpN29K0cbNJCE8+clNSJCkkNDrWvwJVEZ3q+aXyU2MkGiw+IlITZZPu2/XiKvxcr9qCSJCY/PV/ZmvEhInEhUkkiD5SLnY1T29ht5i8bfFLkfojLdL20gEn1eJCFCJO6Gfe0eKSJJllXJiVnPbZSUxDSL6zVdJRJveTW74O/od2/fvi3Tp0+Xnj17SkBAgLz77rty+/Ztu7WfkJAg06ZNk+DgYNHr9bJx40Zxd3eX4OBgERGJioqSYsWKycqVKyU5OVkmT54sLVu2zNeOubbfOHdXJjX9SSb6LZErRyPsZocEiXq+OonIFeubifztN7k8dmyuz1IjIuTml1/KkTp1JOHMmYIrH5wq8i0iv9YW0SWbdTyDQaTdWhHmiozcbd05Xwu6LRP9lshrzX+W2yF2uvcSRaSXiFwWkb9EJFBEvhORr0Qk8xIY7HOo/yTSRWSqqHu2r4jY8VG4eDBMJjVTz9hfqy6aXS8+TWTxBZGvTorcSRK5n6o+NxhEknXZ5Z5cL3I40kgDibdFllRSz0DgYFXRBNKiouRQ1aqyG+T888+LoZDyZuGEiDwp6rpa+TwZw9m9N+XVxkvk1cZL5Pz+Wza1FRQl4rpAPfMLztl+braOOwWG/Dk6OuLk5ISTkxMBAQEEBQXRqVMn3NzccHNzo1OnThw/fpwePQpJ6PoYDzSsJZ5bS3QHbCLZRy5enBUyZhTnF6v8QIVgycVsfkhhOLz+iuW2/oFdtsozUajdDzCcK1TApXJlHEuVQuPkhHOFCjj5+OBSqZJ1DVqxClWijBue5dxxK+GCQxEtxUsXpZiXa1bS5pyo4A6VPcDLFZwcVKiGT1EjOzTuFaBYZXD1UjtT7hXAzQc81G6H3ewuZ53NmfCq6GGVrK5vMTNjwh8SlC5dmmnTprFp0ya2bNnCBx98QOnSZmz3mQk3NzemT59OlSpV0Gq1PPXUU1StWpXjxxXBY82aNdSrV4/+/fvj4uLC9OnTOXXqFBeNhb6ZgYp1S2Wt8C59d7/VBPp88AN6o/qvGZgVYmoMRUqU4P6ePQDcP3CAiEWLCPv2W1Jv3aL2r7/iVr9+wZWbvQMlakHsRTj8nlnH02jg+/ZqN3nBeaUEZimq+ZWmxTPV0esMLH//gEWhXgWiKEpCfQlqJ+UaKvnvGCDzEmiwmmv7n4cD8C7QGKWq+CaKY2sH1GpVjgHvPAHA6k8Pc+3EbbPquTuqZNMTGylFPw9HpfKn0agIh/g0WHoZyhbNncA2C0VLQ4/1UKQoXPoZznxn8niOXl7U37ABrZsbd377jfDvTJc3C41R4imgclXZSaSiXrsKdB/thwj8NnUfsRHW/1h+XjA/g7f+f/uVZP2/iQLHy4ULF+Z6LVq0yOhr4cKF/+T5PoadkDPQU2+KI5KvXnYhMZhPdFcVcrw35PlrJsLmzDGtlnZmjgoVKQTfWuBIWuU82qjMlheF2v2QwNXX17TSlyWwcgLiXdEDjZmZAn2LgYM5RYv7ZiX0NQab7J5GgQmLzcGQTzvg5Gq55vqKrmAvVe5/G9WqVWPEiBH5hCnu3r1LtWp2ihHKg9u3b3P58mXqZYQmnzt3jkaNsjWe3dzc8PX15dw5K7PSAl2GN6RSvVLERiay5tPDNp9zFkaiJL2DUbmUrECpHj3w7NGD440bc/Pzz0m+ehXHMmXw6tcPjyZNSLt9m7sbNhivXMQFOi1Wz1TQLAjfZ9Yx63rCexmRWcN2Q5wVzmCvV5vh4eVKcNAd9q+wztnNh26o5/gDVChVNWAWsAgIzCjzkCQvfSDhhLq2lVET/2nYzAHMRMveNejwfF0M6cKS13cTG5loVr3MqdLAQBU+PWK3CifvvxU6rFciFS3LqKTxRuHtl638t//VQp8Bt/r1qbVoEQDXJk3i/sGDZp2nSTyLypuWjLqmybY3CdB5REPqtClP4r1Ufnpjj9kiUcbwYi0YVVepNfcPVIIf/xYeJ/YtBI8ih6rHZkjUqZU8kxyRPFgwbjtpyeloi2hz8FC06FL0jFnQDUdTqhZvoh7GIsBF1AqdA2r180sKFacQETQaDcebNsVvzx4c3N1z5zTJTACxoin02aOS+4oh3yQ3s1jTVbD7GfBwUo6ksfl15jG/GLSRcT90x7moIwaDoDU1GRfUwDgSFc/tinIarZxL57J7714c3NxM53L5L2A4cB010av+L5/L34kElHpVJi+5JIqjV8q86snxaVw6FE5MuFoB9PB0pdYT5SjmZZqzdj8Vtt1SySgByhRVBGCff1APxd79rlarpVy5clSvXp01a9bg6amIk7dv36ZcuXLo9XbcTgZ0Oh0BAQH4+voyf/58AIYPH463tzeffPJJVrk2bdrw8ssvM3To0KzPLLX9Tsh9Zj23EV2KnsGfdqBx1yr2MeIiSgDGgJr8N7W8CUNKCulxcRQpXpzU8HBcq2YTSu/t28f1SZOoMX8+Hk2aGG/g4DtwfAYUq6oS/jp5FHpMnR6eWAvHoxS34seOlp/3mV03WDRpF04uRXh9VS+8KhR+XLOQjLqWp4F4FA/0KxRXrQE2jRWPgeL+jAFigQDgdeziqOrTDcwfu50rRyKoUKcU/7eoey7erSksuQjvHIEFHaBaMbVTpReoXhyKmZODbP9kOPkFFC0DA4PArazJ4ldffZWwr7/GqXx5mp44gZOtu/DJqJ2qUMAfxee1wzVNvJfCrOc2cS8ykXbP1aHvFOt5oCnp0GYtnLgLvavCmm7W8an+scS+/1U8ig5VJvG8aBGl4LWqq3qwCyOeZxHdXYowd3QgQz/rgLNb4UR3IJtk74JS55mGynaup1CS/ZkePdAnJqJxdMwm9zs6YkhOptHOnWi39VVCAFrHHOIAjpCeDH12gYNiJ1viSJpyHscu7GZc2dBGp9Eiu3ftQutsg4TgP4z0uDhiAwNJCQkBERzLlKFkly44lzU9OBjFCFTYzAKUGpGZSElI49KhCGLC4xEB95Iu1HqiHMW983sKxpyKLhWgbN7bPC0ObgRCXAggatCr2CVr0LPa7t2oyVZ9VCJQLSqc5SDQF5XJ3sTEKygwhC1zTlCtSZmskL/46GTO7b1J+0F1aTuwttGFgZVX1eDfvmxGyJ8GIpJgU6hKnDq2vvHFB3vD3v2ug4MDZ8+eZcyYMYSFhbFlyxaqV69ukUPl7+/PnowQtrxo06YN+/fvB8BgMDBo0CDi4uJYv349jhk5mSZMmIBOp8ulNtigQQOmT5/Os5npEbDO9r9WXuT3mYdx9XDi9RVPGw1ltQq/oHZRvIAfsFo8QZ+YiINb9sNjSE1F6+xM+Lx5xB05Qu3FiwuomAa/t4KoIKjzEnRaZNbxzsdAk9/VqvWabtmKa5bg5zf3ELQ1hGqNSzN2YTe0DnbwdMJQoX/vAN+j8hWWADajEtY+hu24BExASaq/ilICtAMS76Xw5QubiQlLoOUz1fnfdPOzSH97BjaHwoYAFUZuEQzpsL6rys9WroNKeq0tuBGDTsepjh2J++svSnTqRMOtW9E42CgleQPlVCWhRFT62NZcJkLPRPHtsD/RpxsY+rk/jTpXLrxSAbgeB01Wwf00+Kq1Cre0FI8dqr8Zj6JDlRPNflc7Ne4WhvZ8MWgT437ohrM1MUE5d2/MQOqtW4hej7ZoUU536kTdVatwKFYMSU/HpWJFSLgFBj04FlXqON1XgVMx1RF5ZKt3WaJgZpXzaIPTWKjdTz5J3dWr1c6cXq/sfkgQtWoVwe+8Q7E2bXCtUQONVktaZCTRGzdSfsIEyo8da1mH/zJwFYscqpPbQtgyJ4iqft54VSqW28F4ri5tB9bKmjCtugbvHIa2ZaF6hlMRmQQbQ2FCAxjXIMOpuLpKraCXawvFq6vd0KRICN4IjSYQdakMwVPfpXjbtrhUr57f7nHjCt5pHIKSri5j5LvBwFyUqlUBmNl3HaO/65xvYi0izOyzjld/6YmrR34Pv84y2PoUVMqzIC8CtZfBkWeh+D/gx/8dO1SRkZF4enoyevRo1q1bx9q1a6lZs6Zdd6hEhGHDhhESEsKWLVtwzaHkuGDBAn766Sf++usvABITE/H29ubEiRPUrl07q5w1tosIP0zcyfm9t6jauDRjF3TDwWgGUQuhByaiJKlbo2SULXSoQ6ZPJ2rVKsqNHUuxVq3UwlDGfR+9ZQvh331HvXXr0DoWMJbEnFeRB/oU6LYcagw067izT8PEv5Ta36kBlqtUJsSm8NmADcTfTSZgjB9dX7ZihpYXW4H9KOcpFMVN6Y5ahHtMR7cfdqDuVUdgHuBrn2bDL8fw9eAt6FL1DPqwLc2fMr/hqYehhDNM9rPiwEm3YbmfGl+aT4OW000WTw0P57ifH7qoKKp89BGV33nHioPmwS5UWKUjavyxU3TI3qUXWPv5EVzcHZm8/GlKlbd+N3hdMPT5Uy2UH+gDzSycd/0jKn//ZTzqKn/9t4ok6QovlxeLX98lqclWVBQRmSYiKdZVPda0qaTH51dmy8LyJiJpCYW2Y4mC2aznNkhKkoVyZy+LTcpseXGsSRNJTyjcrgcRR+rUkeSQEKPfHa5ZU3SxsZY1OEKU8tAl86vM7LNWosOM3zczeq+RxPvZN2TtpSKhccbbqfmbSGxm0V/rKJU/Y/ilphypXct6u4eISF4BJL2IpGZ8Z+IREBGZ2XetRN24n7u63iC61HSZ2WetJMWlGq1Xd5nI1Txdnd4gkpIuUmeZyD0rn1tLYe9+V6vV5lL0+/TTT8XV1VU+/fRT0Wq1djmGiMioUaOkZcuWEm+kj7pz544UK1ZMfv/9d0lOTpYpU6bYpPKXF/HRyTKtywqZ6LdENs85YbUN+RAhIj1FPXNrLa8ed/y4/OXtLdemTJGTnTvL2T595NqUKXJj1iy58OKLcnvFChER08pkZ+YpxbP5xUTuB5t1XINBpMcmpQDWfq1IuhWitBcPhslEvyUyqelPci3IDoqQOlEqpZnP9lci8qKImC8g9xjm4nNR9+xgsetYfHD1JZnot0SmPPGr3Am5X3iFHMinFGtR5e0i32pE5mhFbu4qtHj01q2yW6OR3VqtxO7da8OBc2CWqGv6otjtmhoMBvnx1R0y0W+JfPnCJklPs1I9OgP/t08989V+zVZXNBd/m8rfY/w3sLKrSm5rKYZ+5m92DHE+TMfqhLeu1aqBqd2MQsQBMlHNAgWzUhWsUEkrh10TNrr6+tq+bf9vQaNRyZlzQAwGDGlpaIoUsTzBX+ZPYUE1jVaDPo8KicEgpOv0aLSaXCItWk1+wRKDqPChXIv+Gg1IHuURMYA+FbRFbLN7GCqE9BPgZ1To1RxUbqC+KOUwE+gxpjELxu1g6Xv7CVxwisCFp1j72RFm9l1Hu+fq4OxmfDfgwxYQsBmG7oQPj8FHx2HCfrVzNb6B4hw+jMh7radMmcLSpUv58EP7xVmFhoYyf/58Tp48iY+PD+7u7ri7u/Pbb78B4O3tzerVq3nnnXcoWbIkhw8fZvny5XY7vrunCy/MaI9GA9t/PM3lw6aTgpoNH1RiWlAr09csq+7RpAnufn6UefFFGm3bRrlx49A4OyN6Pe5NmlCyUycA031svVFQrbcKsQ0cpJK5FwKNBhZ3VNy/vRHw8QnLzhuUytuTQ+tj0Au/vLWXxHspljeSE5k7UWtQHMmxqOe7Vsb39qXy/bcxDiVScQP4xn7NtuxTg8Zdq5CWnM7Pb1kmqGBTLr+KnZT6pRjUM5BsWtLOs2tXKr7xBhgMXBw0CF1MjA0Hz8BYVJjqTZTwlh2g0WgYOK0NJX3cuHH2LlvmBtnU3udPQGMvFQI4ek9uAba/G49D/grBoxryF5cGgTdVUjQRNeh0MYN4npKQxsWD4cRGJCCisorXalU40R2AROAYijgqqCzxzTL+PsYji7tr13L9jTco1ro1rr6+oNGgu3OH6E2bqPj665QdNcoykY2RwBUUB6FWIWUzcGbXDTbOPk7lBt54V/QADSTEpHBu3y06Dq5H6361sjhFa67Dm4egtY/iEWk0cDtJxcBPaQwj62aE/F1bCwfegLKtlSOPBpLvQMgmaPw6d6+U5vqbb1lvdwrZz4sB9Zy0AIqZZ3NacjoXD4YRG5mI6AUPL1fqtClP0WKmVzOSdLD1JtzICJH1cYWAypYnSrUF9u539+zZQ5s2bShSJPci0Llz5zh27BhDhtiQwdbOsNX2P+adJHDBKTxKuTB5eS/z+mZzMAvF9amAevYKoc3mROTixcTu3EmdX37J+sxicZ3kaFjeCBLDwG8StP3CrGrbbkLXTeqZ3fqUSu5uCfQ6A98O/4PQM3ep3bocL3/TyXY+VShqsg9wBjVBfRzyZ38Eo1QV04CPUWGrdkByfBqznttITFgCXUY0oMfYAkRV7A1DOqztqJL+Vn0Geqw1qb5g0Ok42b498YcO4d2/P3VWrLAqhUYuBAN7gRew66Lx9ZN3+G7En4hBeOX7rtRoYQW/OgOX7yk+VWI6/PQkDDZznvCPcajGjRvHBx98kKWO9F/Bo+hQmSKev9oQxhRAPM8kuvs2KUOpDKJ73N1kzu+7SYfn69JmgHGiOwA7UeTmhqjdGy0QDRwC+gHPYHLLKC+538nHh5JduuDk46MK5BMH8MkQB/DJ1U5eR9KUgplVzmMicBS4jV2cxvS4OGK3biUlNNS43Q8J9ElJ6vcLDUX0epzKlMEzIABHa/oTKxwqUA7GpUPhxEQkKAejlCu125THzQgpKEkHgbcgNL4Qp0KXBDcDIT5U8fiKloHKAeDiaZvdmWqRln6XWSRDHdLy7woen019Z288iv2uubDVdoPewLzR27h6LJLqzX14ZV4X+wgqpKBWqK8D7VGRBhbcD+Hz5+MzbBhaR0eT96BJRPwFa/3VxLL7Sqje36xq7x2BD49DKRc43k/lmbMEsREJfDFoE4n3UukyoiE9xja2/NwLQhRKjW4JcA8l/tEOaGm/Q/ynsQq1s1oKWAzYSbDxetBt5gz/EzQaJiwJoHIDb+tO7xpsvQEL/c3sX+NCYXlDNefpuBDqjTBZPPnaNY77+aFPSKD2zz9T5sUXrTpPm5GO2qE1gT/nn2Tr96coXroor6/sZXRsNheLL8KwXUof4GR/8DWjK/1bHSqDwYA2YwXJ09OTkydPUqlSJXr27MkPP/xAWWsUugpAamoqY8aMYfv27cTExFC9enVmzJhBQEAAACEhIVStWhW3HEpBb7zxBu+++67R9tzdc++tJicnM2bMGL799lsAVq5cybRp07h16xYVK1ZkxowZ9O7dO187j+LAXmcZBD6df/s5k3h+tJ9xOc+Zfdcxem6XfKIMkkF0n/RrT1zcC4gJGgJ8Tn5xBsn4bh4FrnjeWbmSkHfeoXj79rhk5PNJi4ggetMmKrz6KuW6lEJz5D0o1z475C8xQu0U+L0KDcaARmuRIxm0NZgt3wVZ5jza6DTms3vFCkKmTi3Y7jFjHnr5dKsmVqOAy1jsUNnjHMx2KgopWOgxJ6HszGufHqUYORaTYX/fvbyVXq82o2Ld3BrrBr2BlR8epM+UFkYFZTquh1mtoWmeuYHeACP3wOy2lgvYWAN7QDa4fwAAIABJREFU9LsjR47kyy+/xN3dnZEjRxZYTqPRZEmbPwiwh+33o5L44rmNxEen0PXlhgSMsZMDcAu14p+Ikqc2z58xG2Y9i6e+gX0TwNEN+h8FzzqFtqs3QM8taue1qTfs711wepCCcPlwBN+P2YYYhGFfdqRBRysTk2cip0T6LCAEJTRTFTVWPFxrZg8u9CjVv3MoAZA37Nf0hq+Osevnc3hXLsbkZU9bnO/vbjL4LlULvT8/qXIqmYVLv8G2F8DRHZ47rdIKmEDEokVcHj4ch2LFaHb6NC6VrVfTsxg64A/UQsxE00X16QbmDP+TkNNRNOpcmSGfdbB6R00EBgTC79ehVRnY+ww4FrKj9reKUri5ucmTTz4p7777rri5ucm1a9dERMTd3T3rvb2QkJAg06ZNk+DgYNHr9bJx40Zxd3eX4OBgEREJDg4WQHQ6y4UQEhISxM3NTfbs2SMiIrdu3RJHR0fZsmWLGAwG2bRpk7i6uuYiLWfiURSlqLNM5HoeLqXBIJKsU98VROSb2Wet3L0Vl6eeQdJSFNE9Od4EA3CIKHJzrsqSTbI3obdwpHZtSb5xI9/nBoNBkfsX1BCJy/+9GAwiv9QUSVXGFiQ2YDAosYGchPsZfdZKTET+kzIYDPLxM2uM2/qiiBjjLeszvks0al6BOFyrlnG79Xpld1wBygkPGPaXKCEhH38shvT0XJ/f27dPzvXvb7kdI0URYy+YX+WtdkslcOEp0edhpl87ESmLX98lyQnZoiPFfxCZcTw/iX1vmBJxyRIzWVBC5OjHIvrcdknYPpE/+ttm98sikvMZ/UyyhShG5vnOCGY9tyGX8MSyafuz7tlZz23MJcKRE01W5hDdEJFhO7P7g6arRGIeIlEKf39/ic0Q/vD39zf5epBgrzHn8uFwebXxEnm18RI5vz+vwokN2CPq+eskIqft12zMtm0S1KGD6O4XcnMbDCJbBymRil9qiqSYd52ik0Wq/qII60N2qGYsxfZFp2Wi3xJ5s+1vEnHVQjEdY4gQkSUiMlZE+opFfdpjWIBQEeki6r614z2blpIuM/uulYl+S2TdrCNWtbHogroni/8gEm6u7pTBIPJHf/UMrO4gYjAt5GAwGORM796yGySoQwcx6G0TfrAYwSLSXURiCi8adTNO3mjzm0z0WyKH11+x6bDRySIVflLXd5oZP8/fKkpx6tQphgwZQmRkJGlpaTRs2JBevXqh0+m4evWq5d6bCbi5uTF9+nSqVKmCVqvlqaeeomrVqhw/ftzmtn///XdKly5Nu3btALh16xYlSpQgICAAjUZDz549cXNz49o1C9m2Dyk+bA7dNsFLOYjn4/dD3eWKeF7QCnTAmMbMH7OdZdOyie5rPj3CJ88qoruTKQn1l1BhDZ+STbL/lmySvakwf40GSc9N/hcRJDU1Q6hBA5KHGCqixAE0DjmbQS/5i6XqwUGbe0NBA4hB8pQV0tMMBYc1alGrjrkqoba6tVgkogCo3ac8cs4igqSlKbsfEvqja40a3F2zhutvvYU+WaVaFxGKt21L0sWLSKqFqc2tEKXwruTB6Z2hbPr2BGkp6VnnUK1xGe6ExJGemn2daxSH1dfhrcMqYaAqC+3KwYVYdb8ASir92ho4+Bakp2QXLNcWYi7gWq1Klt2GlJTcdl+4gKSlmT7pnPfSUVReFTPtFlGrfZm4dCictJTMEy+4AQFyVCPwJiTluAYPE3bt2kWJEiWy3pt6PaiIsUEDoUaLsnQf7YcI/Pr2Xu7eirfPSbUHBqJW/qcDd21vUvR6rk2axP09e7j4wguIIW9HmgMaDXRcAKUawL3LEPi8CrctBJ4usKa7yr/40yX48pTl5/nk0Po06lyZlAQdC8fvID462fJGcmIuSpxiJtAF2Jfx+WNxCvuiEvBcxvv5WDwWFwRHZwcGfdAWjVbDnqUXCLtkufDD0FrQo5LKn/R/+82spNFAh7ngWhrC98C5hYUU11BzwQIcS5fm/p49hOfIhWc3CHA+z2ehqDDL2UBFVEhrIfCq4JGV5Hft50eIjUiw+pQ8XeBnpXnDR8fh6B2rmzILJh0qX19fBg8ezIIFC3Bzc2Pr1q307NkTEaFfv354eXnRt2/fv+XEbt++zeXLl6lXr16uzytXrkyFChV46aWXuHvXvJ78p59+YvDgwVlbh82aNaNOnTps2LABvV7PunXrcHZ2pmHDhna340HEs74Q1B96VQEPRzXAtCoDx/vD6HoFJ+5s1Lkyry1/inodKuLs5oiTSxGqNPTmtaVP0aZ/rYIdDYAOqLxBrVGhSs5AXVTn1guTd2KVDz/kTLduXHzpJUI//JDQjz7i6vjxHK1bl/Ljx+PQ4SOV+G77S3D0Qzj6EewdD0vrQqPxalsc+KiFciSHmuFIBoxpzPevbLPMebTFaSzA7tNduxq3e8IEHNztlLzz74YIDbdtIz02lktDh6KLjc16FjWOjpYTc6yIABCBV77vSnJ8Gsum/UVSXGrWOTgU0eRrc/vTEJsKQ3epv5mn6KjNU/SZbZAaC9uHQEpsdkEHFfqaaffFIUNy2+1UiFxeHdRAdBIV2lgx4//vUbmpCgm7q1zfiz+/P8nVY5Gs//Io3pWL88fcIDZ8dYySZd1xcDT+wLUsDdOOwu4wmHwAapWAd4/A6wcU78Tp4Y4wfejwwg6lMGktOo9oSL32FUiKS2PxpF2kJheujmcWXgYao5JNT0eR/m2AxsGBemvXUqRkSaI3biTkvfdMV3B0g57rwdkTQjfDoalmHcfPS5HUAaYcgj9CLTxPjYZBH7SlUn0vYsITWDh+h23X9HVU6KQH8D8gCLVw8pAKuj7QGAiURIX+/WW/ZivV86LtwNqIQVj9yWGLVWs1GpjXXuXHXH1d5VIyC65e0GGOev/X6yofpwk4eXtTY948AK6/+abio9sTGuBXYANwHJWH833gDmputAAVzmoGmj/tS33/iqQk6Fj+/gHLlYBzoGN5RenQCwzeAcnphdexFiY5VPXr16ddu3a0bduWMWPGcPbsWSpWrIiHhwdBQUEkJSWxd+9exo0bZ9eT0ul0BAQE4OvrmxXbnpCQwMWLF/Hz8yM6OpqxY8cSHx/P1q1bTbZ148YNqlatytWrV6laNfvX/PHHH5kwYQIpKSk4OTmxatUqevbsma/+o8ihspZ4LlYS3VUBbCLZ6xMTs8n9BoMi9/fogWPJkqqALlGJUsSHKlnRoj4Z4gAlc7WTqFOr7qHxagOgjCv0KEDBLDVZx6WD4cRGJCIGM1XSklHKbLfJVmZridVE2Hx2+/hQqkcPimSsvD8MONGyJfU3bsSpdGlCP/yQqN9/p/SgQWp3Ki2NmgsW4OBmgWTYK8BF4DuUU24GvnpxMyNmP4mHpyuBC05xansoTQKqcifkPuk6AwPefQJnV+WltFwNm3qAtyt8cEyp/g2qAedj1e7Ngg5Q1BFY1RKe2gSu3nDkA7i+BmoOUolIDemcmHqJ+ps2W2e3HvgNteLXEngKFYd+D5WlvpD7Sa8zsGPxGULORFG3XQVa9anB4bVXSLyfSrv/1TGa1BdAp4dPguDQbehZGUbUgR8vQHQqjKuvElP+E7BHvztjxgyzy7799ttWHePvQE7bSywtzrtN4YMW1reXHJ/GVy9sJupGHI27VeHFme1tV/oCdS+OQk2YeqKSmdvYbOz27Zzu3h30euosX07pgYUk8b21Uy2miR66/Aa1Bpl1nOlH4f1jiit8qC/UKVl4nZyIj07m6yFbiAlLoH6Hirz0hb9twh+ZfKpolHjCORRP7TpqMaU58JCmLHigsBYloV4JxXe2k+OaHJ/GzD5riY9O4bn329Cil+VZb789oxZ4y7nB+YFmJlAXgT/6wvV1ULknPLWx0AXK8wMGELVqFSU6d6ZhYKB9+oJM7EUtsDRDjVvdyc2N12P2NY+PTubTfutJvJdKv7da0mZA7cIrFYDkdGj6u4owebUhfNnGeLm/lUO1ZcsWmTp1qnTs2FEcHBzE19dXJk6cKK6urnL+/HmLYgs7dOggqKlzvlebNm2yyun1ehk4cKAEBARIWlrByVQjIiIEkPuFxFt/+OGH0r59+1yfbdu2TTw9PeXo0aOi1+vlyJEj4uPjI0FBQfnqP4ocKv91Isfv5P88XS8yfKdIQgGXfc6IP+XG+bv5Pten62XZ9L9MJ7+dKCKXjXyeLoofYmWSOJPJIFUBM9ux4zGNVrK8it3P4V9C6MyZuZISxwcFSfD778vN2bMlPcmKH360qFj4c+ZX2fbjaUlJzL4/b12Mlq3zT8qe387nS1A983juZyAoSuT9oyKzTymeYRaOzcydRPpOkMjh90VOzhbRJdnf7v8Q7NHvVqlSxaxX1apV7XjmtiOn7dp5Kv5/3XXb2gy/GiNTnvhVJvotkR1LztjnREVUQtquop7HNfZp8ubXX8tukL2urnL/iBnEh5PfKC7JXGeR8P1mHUNvEHn2T3VtK/8iEmZFzvTI6/fk7fZLZaLfEln+wV+29cmZNMtQEVklInNE5FsR+Tjj9bn1TT9GDqSJyCBR9+s2+zZ9ZMNVmei3RN7rvML0XKgApOtFWq1W9+RE825jhfgwkfnF1TNwZVWhxVMjI2W/p6fsBon85ReLz7NQ9BKRnPRCfcbLCgQFBstEvyXyRutfJTrMlqzIIkduizjME9HMFdkfbryMreOOSYcqJ0qUKCG//PKLTJo0SYoUKSJOTk7SqlUrmTJlilUHNgaDwSBDhw4Vf39/SSpkwhEZGSlAoYbXqFFDfvzxx1yfff7559K7d+9cnz3zzDPy+ef5e61H0aFqbIR4nslfz0tKz4nP/2eE6J4x88xLgs+HlyWbVC8i8qlkizTk/S4P9hUrJqGffJKP3B+7Z4+cGzBA0me7ixz/ND8x89YekT8GiKSpAxX7QeSTE/nFBvaEiQzYmnsS/Uab32T74jOi1+ceJK8ei5AlU3ZLapIRcZQeIrJU8nceJ0VkuogkF2yjMezz8JAbn32Wj0Aau3u3sjvRQpWLRwWviBoQz/7bJ/IYfxcexX7XXOS0/dMTaoLlvlDkvBmEblM4tT1EJvotkVeb/GRfkYptop7HJ0XkqO3NGQwGuTh8uOwGOeDjY1SYJ08Fkd1j1IRyYSmRWGMrd/mRmJY9gW20omAxJlO4diJSXm/5i0z0WyKb55ywvIGc0IvIfBH5XkR2i0hUju9eFovHj8coABtF3a8vi10XOvV6g3wxaKNM9FsigQtPWdVGUJSIdp6a+J+LtqDimXnq/l9UXiS1cJGniMWLZTfIX97ekhZtyYHMwA7JFv7Q5/krYvE1Xzx5l0z0WyLzXgm0eSH5nUPqea/5m4ixKdzfKkqRF+3bt+eLL77AxcWFvXv38tprr5GUlGT5tlgBeOWVV7hw4QIbN27E1TU34eTw4cNcunQJg8FAdHQ048ePx9/f3+S23IEDBwgLC6N//9zars2bN2ffvn2cPHkSgKCgIPbt2/ef4VBBbnGGrTmI52Car2nQZ5OFLx4MR5eSXVFMxbkKBZPsC4Fr9erc/f13gt95B0OGgIGIUKJ9exLPnUPcq8HVVXDwbSVEoQpA+fYQcw4MKsC/RnGV8+Htw9nCAiLQvpwK50rLcX5eFT04tS2Ezd+eID1Nn3VM36Y+RF67l4vwn4XywB5UHpFMToEAjVCSuBbG7rpWr07UypUEv/12brs7dFB2FyZq8KjiH8qF9BiP8W/jdT8YWB0SdPDMH3DPQv2WnGjYqTLdRjVCDMLPb+7hTuj9wiuZg87A86j+/X1UkloboNFoqDF3LsX9/UmLjOTs00+THm9CUEOjgXazVchTSjRs7AHJhfOrizrCxgCoWRxORUPfPyHNQjGIao3LMPiT9mgdNGz74TR7fsvLyjcTBhTnNhR4GsU59kIJVnyPSgL8kInCPLDoiuJSXUFxVO0ErVbD0xObArBjyVkSrFCU8fOCkXXU/GzSAQuEgOqNhDItVNLrox8UWrzMkCEUb98eXVQUwW+9ZfF5mkRL4EbG+8yxOtPTSMHi8fvZN1tStJgTlw6Gc3SjbcJx7zaDeiXh8n2Vm87eMNuhmjt3Ll5eXln/e3t7069fv6y8TrYiNDSU+fPnc/LkSXx8fHB3d8fd3Z3ffvsNgOvXr9O9e3c8PDyoX78+zs7OLFu2LKt+zpxVmfjpp5/o27cvHh65CQcdOnRg+vTp9OvXDw8PD5599lnefvttunbtahdbHnS0KK1upkziee0cxPNKJojnlep58ce8bKJ76SrF2ZJJdPdxK5DoDiiS/SJUBzYPFcP8I9kke1NxtRoNDbdvRxcVxcWhQ0m/dy8r7lfr5ISIBp7ZDsl3YPtQSL2fHUeszc3e3/40RKfAkJ1qcpJTbCBn56XRaHjl+64kxKaw9L39JMen5RAxKMBODSqfSCxKmCKB7M7Dwpwn6ty1NNyxA92dO1x66SXS79/PttsaMYdHDY8nGI9hAWJjY1m2bBmffPIJH3zwQa7XgwqNBn70h0al4Mp9+N+23CqMlqLryEY06KjI3j9M2ElSnA0eWk4MA9qi+ry3gTjbmtM6OVFv9Wpca9Qg8dQpLgwciEFnQvxBWwS6LQfvxnD/KmzuBemFK/B5ucKfTyku7Y4wNS5YKgJS378SA99rDcC6WUc5uOayZQ2AmoldBF5A5TDM5OKuBpJQycwtFDV6jALgBGSmHF1p36ZrNC9LnTblSU3UEfjDaava+KAFFHdSC91/3Ci8PKByb7b/DtDAqa8h5oLp4hoNNb7/Ho2jIxELFnD/4EGrztUo3FCiSVFkz3+OAHNQwkoLUXxgM+FRypXekxWJdN2so8TdtV5Z09kBFj+phNe+PA2HIq1uyihMilIUhP3799O8eXOcnf8hhvK/iEdRlEKnh5lBcNhC4rleZ2D7ojOEnrWM6A6o3ZnfgAtAKxSJeTMQjyLZmxCsO9GiBQ3+/BNHT09Cpk/n7rp1lHn+eRLPnkX0emp2v4hDv0Bw8YTD0yF4HdR8HqLPKrLykwuhiCstVsMfPaGUC7x/FNYG5xYbWOgPmXn5vnx+E6PndaFoMWf+mHeSs7tv0LRHNSKu3kNEGPhuaxyd83iBo1HJiz1QHcdfqNXbYNTkfzIWEYtPNG9Og8BAHEuWJGTaNO6uX59tt8FArYUL0bq4mN/gv4Q7K1aQEhpK6QEDcKlSxfYGx6FI298C9c2rcjv4Pmd336B0leKFJuTcFw4Hb0PbstC6oOSaaQlwdi4UcYOGY40Wubt+PUmXLuHVqxdFa1tIqF2N2uXsi1LEtADJ8WkcWH0ZFzdH2vQ3P/Px3WRYdFE9H8MLz5X6t8Le/e7Ro0fp3r07IkJcXBze3t7cuXOHokWLUrZsWS5ftmIS/DfBmO0hcdB8NdxNgYkN4asCSNXmIDVJxzdD/yD8Siw1Wvgwak4X04th5iIZ+D/gGiq5+efYLKSQfPUqJ1q1Ij06Gp9hw6j5ww+mSfQJ4fB7K0i4CVV7QcBq5WwVgqAoaL9e7QS+Ug++a2f5etXeZRdY+5la9v7ftNa07F3DsgZ+Bw4C1cgm7xcD/IAGQFjGywaBksfIwH3gWdS4vBqwo8ZT+OUYPh+4EUdnB6ZuepZiXpZ7wl+chMkH1ULKif4FKy/nw65RcG4BVHkantpQaPHrb7/NzZkz8WjWjMaHD6tULfZAOmoRORh1fRNRAlJlUWkWAlGCNo3Ma05EWDBuBxcPhNG4WxUGf9LBptN78xB8GgT1PeF4P3DKmMrZOu44TJ8+fbqllSpVqkSRItYsuT98SM2RI8flIZi8mgMHLXQoB8/XhOal1f/NS6vQN1PZ47UOGqo386Fpj2pUqueF1kFLpXpe+Db1ye9c5KuMGhg6A7VRg0Vt1ANVyKCru3uX4m3bonV0pIS/Px4tW5J47hyuvr5Uef99HAz3oVw7tRtVwR/KtISYs1DcF1q+D0VUh3Y3Bdr6qGzZ/uWVVPy5WKjqAdObZztTAIn3UqnauDQORbTUaO5D5QZeRFy7h1cFD7qP9jNu733UwFcEJSlcBxXqVw4lnW7h5EIXHZ1td8eOyu6zZ5Xd06c/FM4UwNWJE4lcsADPnj1x9fW1vcEtqNWvANTuphm4cjSSVR8fwqAX/LpWMVl24Xl47yhU8VDPiVGkxMDG7nDnGDR53WiRkPfeI2z2bIq3a4dbfTM9v0y8iZpc9QMs/Jnjo5P5YfwOIq7E0uEFM2UQgeB46LtV7YSMtfB07Q1797vPP/887dq1Y+/evcyaNYujR48yefJkDhw4wLvvvkudOv+yB5kDxmwv4QxPlIFfr8CBSCjvBk29rWu/iKMDddqUJ2hrMBFX7xEfk0LddhVsV/tyRC2W7UZNpCJQu1Y2NOvo6UmJ9u25s3Qp8UePAlDC37/gCk4eUKk7XF4Kd09B/E2o+kyh3lFZN3V9l19VCpcpeuhU3jKnqnIDb5yLOnLpUDjn9t6kZFl3ytfyNL+BukBxlNJfQ6AK6vpVRE38T6KiOlqTWzntMSyHC2pR7hbq+ta0X9MepVwJvxxDxNV7aLRQq1VBg0jBaOKlcqVdua8iiBqUMrNi6eZwbr6a/5TrAMVM65QXa9mSyJ9/JvnSJZwrV8ajcWOLz9UotKgFlmWAN2ph0A91T9dFKYNew+zFgf9n77zjmjq/P/5Owt4blaWCiiIq7q2Ie2tbba22tbbVOmq1dmi19evoz047rN1L22qts+6998KBW8GBA1ERAdn8/niCSgskdwQC5v165ZUA9xk33Nw85znnfI5Go6FKPW92Lz5D/Ok7VK7jjVeAi+zptagAf52HU0lgrxM1JkH5906R5qiUgrrp6emcPFm8i9GCBbkEjh+PzsHhwc/OERFUfu89/EaNEkZFw/EPjCZAhH00fh/qjgKrhx+K8fX1ctd66nnBew3htToFjSmA9kPCsXnEuvQP9aTz0Hq0fqZm0cbjsxT0JlQDnkfcTGTs1AaOH4/ukVxC54gIKr///sPzLiuYQWiilCkYdawxByk571J4y0r/v2Q6oqOjGTNmDFqtFq1WS2ZmJv7+/nz44YdmJZleHK0qwbetxevh20XItlw8KjkxZGY7rG117F50hq2/y8z9+Tc+iCK19sBGRJi3QlyaNqXm/Pmg1XLxf//j2o8/Ft/AoyZ0XwlWDnDqV9j1llHJKJF+8HdHsNKK3evph6TPNfK5MLq/Vp+8PJg/eScHVkrM+WiKqPHVEiE97YzIr8pBFFRuh6j1Y0E5rfTP24s9Shbth4h8/F1/n5YVVmtnBe83FK/f2y+iiozCsQLUf1u83jlOlJApBp2TE1U/+giA2AkTyE5WGKv7KPsQ5WMGAP6Ie4IOUWvtPNBJWneefs50GloPgEUz9pKZLr+glL3Vw3vplINw1oiCw8ZQpEHVq1cv+vTpw9q1a8ktomp5fHw8M2bMoFq1auzcqWKlNAsWLJQ/FBTnK4CClb+UKRh3qBFHqXXeEpE7ailN16TodDps9MWUfXx8uHxZKCd4eXlx8aLE6q6lyIs14Y26IkT5ibXKFgJB4d48M6UlAP/MPMDRTSq9DyGIWjRaRJi34cgjg3j17Em12bMBODN0KIlLlxbfoGKzh+F+hz+Bg8bVJOtRGX6PEreYSfvgExmiBVGDw+k6IoK8PPhz0g52LZIYThqLqJcUj8if0vIwB7cqImRKut6BhX/THPGPPoTiwtT/JjDMi+pNK5KRls3uxWdl9fFCqBDSOncX5kq5hOqNBcdKcPMQnP3L4OE+zzyDc9OmZN24wWW9caUKgYg6ahpETuVKxH3hJ0QkT1XpXbZ9thYVQ9xIvHyPTb8eVzS9KH94voYQKHt1mzrfe0UaVKdPn6Z27doMHDgQV1dXWrduzTPPPMPgwYPp1asXISEhBAYGsn79eubNm8dLL72kfDYWLFgodzwIJVJ7pS6nOyPmkG+vFX+oEeekxnnLaKox7gSKbFcO7Snq1KnzQNW1adOmfPDBB6xdu5bx48dTo4bxeWbmwIdNoUcQ3M6ArqtE7ptcIjpWfrD4/33CduKOJKgzycaIQr8AXwA7lHdZaehQgt57D3JzOdG/P3c2bCi+QVBnaD8X0MCeiXDkC6PG6R8CP0eKT/ibu2HmEelz7fBSHbqOFO/r39N2s/GXY8Y3PoHID/0cmA58ijBKFyG8Ke2QHAZsoRA8EOF+WQgjVmVaPyPCiPcuOVu8AnIRWGlFBA2IYus5xorRWDtA4/+J1/smQ27xnhyNRkPwp58CcGXmTDKvq6TUUAWhVjkVIapyDOFlnQT0R1gfEt8WnbWWJyc0BWDTr8e5FV+M+qcRfNIMPGyFIM0CZQKCQDEGlaOjI1OnTuXKlSvMnTuXhg0bkp6ezrVr13BxcWHEiBHExMSwceNGWrZsqXwmFkqEzByhbpdajGBSYWRn5nD/XiaZ92W6WTMQClAydoKy790jOymJvJwi/N45GZCRVKyqU26eOO+7Rnjfc3NyuX8vk/QUiZO9jzhHie9tUTw472z5rm2zQO2QPxndSckPMerIkgr5k2PdyBy3PIf8vfvuu+h0IlR36tSpJCQk0KVLF7Zv386XX35ZyrOThk4Lf3YQIcvn7kKvNaAg+oX2Q8Jp2qcaWRk5/Pj6Jm5eVCnspysidzQXsaiSYFMURdDkyfiNGkVeZibHe/c2rE5W/WmI/F683v46xBgIF9TzQqgQKQIhX/25HKNqSB2eHN8EjQZWfHmI5V8cNG5h3Q0hPf0MMBwIA04jBJwCMTqR34IR5OuGyHMiFUtocz9cve25eSmZ8wdvyOrj6RCR4332Liy8IGXw58E1BJLOwKm5Bg93bd4cz549yU1L4+K0abLmWijDECGsvyA2WCoictd+RhhZ8xH5VBKoGuFLg65VycrIYdmn+xVNz8seZgj7jDEqBNkZlPSwtbWld+/efPbZZyxZsoSHlhFYAAAgAElEQVQ1a9Ywd+5cxowZQ6hU5SoLpc7f58H9Z3hlq7R2e5edY0LreSyVewH/hKivYSBSozCimzdnp7s7qSeKiPM/Ogt+cBe7kEWQcF+cd415RR7ygNvXUpnQeh4fP71c2kQ/RpyjSjHZR1q3Zqe7OynHVFiJmANm4KGS0kStkD85u5NqIHfY8hjy1759e3r3FlrJlStXJiYmhsTERK5fv06rVq0MtDY/nKxhRRfwdxQiFS9sli73nY9Go+HJ8U2p2cKP1KQMvh+1gXu3Fbi9HuU5oDtiI20CIgRIARqNhuDPP8f3+efJTU3leNeupEQbiMsLewlazhSvN78Cp+YYNdaQmvCdXkxszC6hvCaVFv1CGTi9FVorDZt+Pc5fU3YXXsPw37QH/kLUNuwMvIkwTp9G1Kcqiosolqx/rMgXozCByKfOSkvjXsJikyWlj/BSva3XifjgkIR7s84aGk8Wr/f/D3IMbw5X+eAD0Gi49t133L+g8IP6KBV5mFO5GiGuYoNQ+UtByKlLpMfoBtg6WHFs82VO7VKQTIr4nDfxgWsqlNRVSSPRQlmjTC6aVJi0pB7K4ntkjug9JqoZFjK8N1LC4Ixz8BgO51OkmqbAQyV32PLsoSoMDw8P5cp2pYifE6zsBs7W8Nc5IQUsF521luc/aoN/TQ8SL9/j+5EbyUhTwdWuAV7nYY2qNxG5QUq61Gqp8eOPePXtS3ZSEkc7dCh6sy2feq9Ds/8D8kStQiN27QFeqfUweX3cbphyQPrXUP0uVRnyWTus7XTsXXqWn9/YbPi9jUIYU4W9V0WNn18DbAgiL8iCYfIraFwzTfdNeocAcHzzZdkiCi+EQgUHOHoLtkuZZ7WnwaMW3LsIZ+cbPNwxLAzfQYPIy87mkppeqjzgO2Ax0AB4FVFvrQGiHlgawostAVcfBzq8LFy1Sz/db9wmRRFoNfBNawnS9MX1pbwLC2UJueu0h+kgChfFSkKYDI1d3OLW8CGFDCd3i19es6L7K+OWnalyqCTNQfoUjMqhMpUohRrrfJnvdxm/2golOzub7777jn79+tG2bVtat25d4FFWqeMJizqJneyPo2GWAme2rYM1r3zVHk9/J66cvMUv47aQbbS8WDHoEHkTEcBthFGVqKxLjZUVNf/8E/fOnclKTORoVBRpp08X36jBO9B0GsKoet5oT9XQMPg1Uiy43t8Pb+2W/tGq1cqfV7/piIOrLSe2XWHWS2u4m2BgS/xFhFH1b4q6N9xH1FNKRIRXfYPqYgvljnz5eZUcsv/G08+ZgFqeZKZnc3r3VVl92OpErVCAb2MkNNTqIEJf0uPwJ0ZdtEGTJoFOJ6TU1fJSJSNCVr9H5FTZI+pSHQE+QBhWMr502gyoiae/Ezcu3JXtAcwnwhuGGl9hpEgsBtVjhuwNWaU7uUqaGxzbcOdShpe9a632ZncZ3j1/FNW9AI9DDlU+sjYg5A1VTi63QhkxYgRjxowhMzOT0NBQwsLCCjzKMh0C4Ke24vVrO2CJgnWQs6c9Q7/ugJO7Had3X2Xe+zvJlRtL+Cg2wDRE7cFriCLnd4ttYRCtrS1hixfj1q4dmdevcyQykrSzBpJhGr4LTafz0FNlnFH1fCjM7yAM10+OwPBt0kMsq9TzYfRvXfAKcObKydt8/vwqrp69U3SDYordF4o38CUiNFALLEDkYJlAcKHckG9QpZpuiDpRQQAc2ShfRfPlmsKgX3QBbkox/qo/Aw4V4dYxuLTO4OH2ISH4DhoEOTlcmj5d9nwL4Iow8nci8qf2IQr7bkdI1z+D2HSRiJWNjp6vC9WONd9Ec/+est2DqSoUzLYYVI8psr8ilX63KmlvcIfFiHwWSeNJOViFdkX2V058BmaQQyWlTanKpqthi5mJg9Uc+Pvvv1m4cCFLly7l22+/5ZtvvinwKOs8VwOmNRb/uwEbYLu8zXAAvANdePmrKGwdrDi0OpalH+9TJ1zXAZgBBCFyfd5GhKkpQGdvT+1//sG1TRsyr10z0qiaAE0/4IFRdfx7o8Z6KhiWdhYeg29PwLMbhOSyFHyCXBn9a1cq1/Um6XoqX76wipjtV6R1Uhw6RO3DrxAF5c8jclXmoJpYUrkiv8yjiTxUAHWiRFxhzNbLskPTAp2hSyBk5sIcA47YAuhsoe5r4nX0Z0Y1CXr3XeGl+u030uPiJM+1UEYD64HfgD0IIYpmQEf932XeXsLbBRJc35fUpAw2/Kws19xTBeVMi0H1mCFTUVl51JYKqmVFfqlrDHcuRxK61IzOfMwhVE4N1D4PBdeSMQtD464VM5ZN189Nclhv/pBl/HIrDBcXF6pWlVH4pAwxoT4MqwXpOdBjtci5kEtgmBeDP41EZ61l+/xTrPlWhsxdYbgixHsqIcKA3kbkUChA5+hI+IoVuLZqRWZ8PEfatjUc/tdwPDSbAeTBlqFw6GOjxuoWBKu6iry1+eegywrjlGMfxcnDjuHfdSKiY2Uy0rL5afRGNv12XF3xmlrADwjFwCyEytrLqKK0WK7I95JK9QZKwCfIFa8AZ9JTsog/fVt2P0P0GnB/SFUkDBsKVvZweR0kGW5sHxKCz9NPQ04O8V99JX2ihdEAmAh8hLgOByGKVV9BhKVqkJxHBSLypOdY4aXaPu8kSQkmdDUaQZEG1dWrVyU/SkvNyoLxmEs0m6SxSzzkT8LBcgcxqr9yEoNlKlEK6VMwysgwyrCQEPIn67wVhcjqny11qB7wzjvvMGPGDLKyyu82vUYDs1rBE1XhbiZ0WgGxChTfajStxHP/1xqNVsO674+w9Q8Dwg/G4o2oreSLqLn0Doo9BDonJ8JXrRKeqqtXhVF16lTxjRq8Da31EmO73hIqsUZ8Ztr5w7beQihg81VotRTiJXrarG11DJrRms7D6pGXB8s/P8jc8dvIuK/i9emACK2ciai3dBF4DfgEIcFuAfLVzH1NO0xwfTGAXPl0EB4qVxs4nAiniokU/Q927kKgAuD4d0Y18R87FoBrP/xAdrJKspFWiOvuO4QoxSSEumJ+ZKFM905gmBd1ooLIyshh3fdHVZiofIo8BX9/fwICAox6+Pv7ExgYSGysJVi3rCDf+1KKyy0FohQSDpF5sOko8xsVpjIMpbwtUnKopKj8FesVLZ0cKrnjlhPzvVBeeuklbt68iZ+fH61ataJdu3YFHuUFnRb+aA+RleB6GnRYDjcUeIDqRAXx9PvNAVj6yX72LlOpYE8FhFHlhfCavAukK+tS5+RE+MqVuEVGknn9OtFt2hguOVFnBLSfAxodHJgO216DPMNb5fW8YHcfqOEGx25DsyUQI9H5oNFo6DS0Li9+FomtgxWH18bx5QurSbyisrVTD/gR4RWwAlYi5OxXILwEjzMlZVA1qADAhUPyDSo7K7FZAjBP6sew9qvi+eTPxdbrzMe5fn1c27Qh5949rv/8s8TBiiEGYVTNQtQA64GoUZq/VyNTrK/riAg0Wg17l55Vr46eDIq1CRcuXMimTZuMetjY2JTUnC0oQPaiyZxFKYyYm6Tpm4kbryxLOheKORiGqudQGdNRGcuhMoN/k9oMHz6cTZs20bhxY2rUqEFwcHCBR3nCVgdLu0CEF5xPFp6qOxLD0h6lcc8Qeo9rBMBfU3ZzaI1KG6d+CO+JJ3AYIfmt1KhydKT2ihW4d+hAVkICR9q0IXnfvuIbhQ6Czn+D1gaOzYL1g4yq21PZBXb2geYV4HIKtFgCG2SkQ4VHBvL6nG54BThz9cwdPhuwgphtl6V3VBw2CNXAH4E6QBLCoH0FUFYbtWyTHxlamJqiilSt7wPAxeM3FfXztFBhl1bkF8C3EXjXh4w7cMG4YqD+Y8YAcPXrr8nLlS9LXoD1QGNEyG8AsAFoyMP/g0wvlW8VVxr3CCY3J4+1P6gUniwDq6L+4OnpSatWrfDyKq6K3EN8fX0fVKK3YP6UWsJ6GRKlkL2wtIhSFEBjBrlgUqTwJYX8mTqHSgZGpBQW265sX22FM2/ePP7++2+6d+9e2lMpEVxsYE03EY525BZ0WwnreoiCwHJo82wtMlKzWP1NNH9M3I6VtfaBepki/IHPgDE8NKqm81AsQAY6Bwdq//MPJ/r359Y//3A0Koray5fj1rZt0Y2C+0CPVbCqN5z5E+4nQJfFYONc7FiedrChBwzaKBTYOq8QYZfDJApHVgh2Y+wf3flz0g6Ob73Mj6M3ETW4Nl2GR6CzUjHVPQj4HNiMkLG+ALyFWOQOBcp3mmFB0oFd+tfNTTuUe0UnbOysuHcrnfv3MrF3lueAaFNJ5O+duANxycKoN5qag+HmITg9V6j/GcCze3dsAwK4f+4cSZs34x4VJWvOBQhDFPbthKhB9RZQF1FKQSEdX6nLgZUXOLQ6lg5D6uBbxVV5pxIp8pN68+ZNo40pgNjYWIKCVLjBWjApsp0vSnfMVRClMNy5kiMkDGdoELVFKco6ppJNlxPyZ0wdMrUmUUqiFLJl0+U1KxO4ublRrVq10p5GieLjAOt7QIAT7L4BfdaAzNqiAHR4uQ7th4STm5PHnHe2qedFCUR4qtx5aFQpzKnS2tlRa+FCfAYMICclhaOdO3NrxYriGwVEQZ8tYO8DlzfAkjaQet3gWPZWsKAjjI+AnDx4dRuM3gFSBd3snW0Y/Fkk3V+rj1anYeMvx/n65bXcua5yor0GaIdQ/nsFIR2+D3gJIW2vsPBymWEvwqgKBSqadiitVoN3kLB+EuLk1wuw0UHHAPF61SWJjas9DVorIZ9uxHWt0emo8NJLAFz7zrjcK4N0Ai4D54AaiCLUgwBnFK+bPCo50bhXCHm5eaz7vnS8VBaVv8eUEs+hKolCpeU0h8ps5qGU0hSlkDGM4tkqMSRVCfmzFPbN5+2332bGjBlkZyuwKMoggc6wsQf42otwtKfXg9xavRqNhq4jImg7sBY52bn8Mm4Lp3aptPoORHhOPBE72G+juDaQ1tqa0LlzqThsGHkZGcT06cON338vvpFPA3hyF7iGwM3DsLAZ3D5peCwNfNAUfmsH1lr48hh0XyU91FKr1RA1OJzh33fC1ceB2OgEPnl6Occ2S109G4ENogbQ70BfRLzSRkR+1YeUb8MqD5FDBhBZMkP66N1JN2KVFWDrpvdbSDao7L0gqCvk5cDZ+UY1qThkCOh0JC5ZQuYN+flfD3BEhJ7mS5T3QGykgMjnU/jl02FIODprLYfXxnL9fJKyzmSgyKBKTU1l27Ztas3FQgkgP4dKpQmUlodKkotKwrGFYfFQFcQcRCnymxjjoVJJlOLhmKUjmy69Xfll2bJlLFmyBH9/fyIjI+nYsWOBR3mmmpsI93O3hWVxMGgT5MhMiciXKW7ZP5ScrFx+GrOJ03sUFL16lHyjyhshVDEOxWp0Gq2WarNnE/D22+RlZ3Nq0CAuf/xx8Z9J12BhVPk2hntxsKg5xG81arznasCmnuBlB2svQ5NFcEKGUnZwfV/GzetBaHM/0u5m8PPYzfw9fbe6KoD5uAGjEB6rLvrfrUEYVlMRSmzljWXAAcQCX4VINmNw9XEAIOWOskTBDv7iecc16cWlqaYP9buwxKjDbf388OzalbzsbBLmG2eEGaQ+ItQXhKT/TETpBCsKyqfL+O5zr+hEk14h5OXBxl9KvkaAIoPq3LlzREaWkHlvQVXKYsK64YVpKedQmWpFavFQFURGyN9DI8n4RkYdWcw5KcodUzt8VALl5XJ7FH9/f/r06UOXLl2oXLkyfn5+BR7lnTqesLa7yL/46xwM2SJjMaZHo9HQ9+3GtHiqBtmZufz0+iZO7VbJneEPfIFQATwFjEUIKChAo9FQdcYMgj8ThU0vvPUW58eOLT7R3t4bem+GKr0gIwmWdYCTvxk1XsuKcOBJqOsJZ+9C40Ww8Lz0eTt52PHyV1H0eqMhOistuxae4dNnVigWNiiSCoicljlAZ8Q9aBMit2ocQryiPNwbzgOz9a/fQHhFSwBbR5HAmJGqzCj2dxJhvHcz4aQU+XSAoC4i7O/aTkg3ztL3HTQIgBtz50ocrBhyEMaUDeCB8IzGIzyj+R8zmeupdi/URqvTcGhtLHeuKawcLhFLyN9jhtx1Wv7isDQK+xpcmBpT2FfC8HILo5oqh6q8yKaX6nlIuH6l1aEyUQ6VCnWoZBfvVjC0OZKVlUWjRo2YPn06v/zyS6GPx4FGPrCqGzhYwW+nRa6PIqPqnSY0e6I6WRk56hpVFYEvESpg54DXARVsCP8xY6g5bx4aa2viP/+ckwMGkJtRTEyetQN0WQR1X4fcLNj4Aux+1yhZ9SBn2NUHBlSD1Gx4ah28s0d6XpVWq6HtwDDG/N6NCsFu3LyYzJcvrGbNt9HkZKmkvPZv/BAhl38ATyIEQg4ijK2XEN6dkl2nqsd9YApiMd+NEgv3A7BzEAZVukKDCoSyJMAuw6lQBbF1hUqtRdjfxdVGNfHs0QOdiwspBw+SetJw+KtR6ABrxJeNI/AT8C3CS63QY+jp50y9DpXJzc5jy+8q1c4zkmINKhsbm2IfjRo1Kql5WlCJUosiM6VsusqiFLLnainsWyiqy78rKOyr3rHGF/YtacxE9d9ssLa2Zty4ceW6qK+xtKwIy7uAnQ6+PwEjt8vfJNNqNTw5oSnNn6z+wFN1YrsM3fDC8EaE/1XhYUFaFew1n6efJnz1anTOztz86y+OdupE1p1itvm1Omg1E9p8LWpVHfwAVj8JmYYtCgdr+D0KZjYHnQY+PCwk7OXUBfOr4cHYP7rTdmAtIQ393RFmPreSq2dkxBMaiy8wAvgLeBnhSbiA+L88AXyAyHcrKzswtxHG+SWE2uHIkh3e2laoYGdlKC/+1VSosLMvQUbjyj3Es5EGldbODu+nngLgplphfzk8VPfMQlxbfRE5fYHKu2/3Qm0A9iw+S1qygpoREinWoLK2tmb06NH88MMPhT4mTpxYUvO0oDKlVtjXlLLp5iJKYZFNLxy1z0NWDlXJhfxJOkbRJNQdt7xcbo/SoEEDjhkq8vqY0M4flnYW9aq+iYHXdigzqp4Y35QW/UT4389jN6snoOCByK8IBa4j8nzOKe/WPSqKetu2YVOpEne3buVws2bcv2CgsE/4cOi+EmxcRf7J4paQfNHgWBoNvF5XSKv72sOmeIj4G7bIMA6tbXX0eqMRw7/viEclJ+JP3ebTZ1ewevZhsjNNWKHXGRgAzAMmIXJgMhE1hcYAzwI/IELpzPXecRZhQJ1B1ECazkNhhBIiPU0I4tg5yqxd8Ai1PMTzWTn6Fv56F9DVrUZ/8PMNqsQlxuVeGUQHNEUYuKOA7ggpf1Cl2LRfDQ+qN6lIZno2e5aoVIzcCIo1qMLDwwkMDOT5558v9NGrV6+SmqcFlTAqnKmwdmWisG9x+SyGjnj0WPPYqzeXeShG7XpMct4WCeGYqof8yUGFz4vckL/yyPjx43nrrbf4888/OXv2LFevXi3weNzoFAhLOoGNFmYdhzE7FRpV7zSh9YCa5GTn8utbW4heH6fORF0RO9n1gTuIBZgKdrFTvXpE7NmDY3g490+f5nCTJtzdtav4RkGd4Km94FoNEo/AgoYQb5woV1s/OPQUtKoI19IgajlMOygv5LJao4q8uaAnLZ6qQW52Hut+OMonTy8n7ogcd4UEbBBy658iwgEHITyJ14A/EeGAgxE5WBcwD+MqDfgaGIaYZw3gK0xeyLcw7us9JfYu8mpQPUqIvsTSOTkGlWcY2HpAyhUhumIEbpGR6FxcSD12zPDmg7E0ByrrX3fgYa6kSuVs2zxbC4Dt80+RIzXWVibFGlTNmjXj3Lmit4ScnJxo3bq16pOyYDqU5kmYZ2Ffwwt2OYakpbCvSpiqwK0kUQoZOVTGHGWUV7SEVf4kFDFWaUizp3v37pw6dYqBAwcSGhpKQEAAAQEB+Pv7ExAQUNrTKxW6BMHizkLm+4tjyowqjUZD73GNiHw+jNzsPOaO38aBlTKUGArDHvg/oBVCSv1NHhZkVYBdQAD1duzAvXNnshITOdKuHQnz5hXfyL2GMKoCOkJ6IiyLgqOzjHrjKjkKBcAJ9YUhNWmfCAG8LiME0M7RmicnNGXkj53wDnThRuxdvhy8msUf7uX+vUzpHUqlEkL+eh7Ci9gDcEGEZ/6CqC/UH/gIIWyhTClcOqnAAuB5YKH+d0/q5+pRwnPRk5Ys/i8OMov6Pkqgk/jcxqeC5JQsjRYqtRKvjVSv1NrY4NFFSEDe+ucfiQMaIAfhBf0f4jrZDxwFtgEKlNpDW/jhHeRC0vVUjm4yQdmBQijWoJo5cyZffPFFkX8PDg5m8+bNRf7dgvlR6rLpssY21kOl6BBZxxZsKLNdkf2VE5eBGeRQSeremP6NuuZKx0Mld9xycrUVyubNmx88Nm3a9OCR//PjSrcgWNTpoVE1crsyoYoeoxvQ8eU65Obk8eekHexccEqdidoA7yOEBDIQoWfLlXdr5eJC+PLlVHr1VfIyMjg5YACxkyYVrwBo5w49VkLEOMjNhm2jYMPzkGXYMrLSwvQmsKabkFbfcAXq/AWrDEcPFkpwgwqM+6sHUYNro9Fq2D7/FP/XdykHVp4vGREgHVAPoca4CJiBUAh0RwiJrEZIr/dGyLDPAP5BhOCpHaV4Xd/3uwjj6RsgEREy+i0iH8xe5TElcPNSMgCuvo6K+7LSCgMd5BnkVGwunhMOGN3Es1s3AO6sXy9jwGLI90htBeYjikyv0j8+RPwPZaDVamj9TE0Adv6l0n3IAFYlMooFs8NsvC9Shi5h2XSz2aov6x4qPap/wcvqTuUcKmOOKq0cqrIzpMlp06ZNaU/BbOlRWeRU9V0Ls2MgMxe+ayOK1UpFo9HQZXgENg7WrPjiIAv/by/pqVlEDQ5XPlEdQubaA5iLCAW8iQgzU7IBYWVFyNdf41CzJudef51L06aRFhND6Jw56JycCm+ktYIWH4NPQ9j4IpyeC7eOCVVA16oGx+wUCEf7waCNsDEeuq2C4WHwcTMhZiEFGzsrur/WgIhOVVj4f3uIO3KTPybuYPeiMzwxvimVqrkb7kQNrIAm+kcuIuxvv/5xHLisf6x95Hg/hAhBgP7ZBxHm6YrweP37vchDhPFdQ4iUXAOuIjwa/zZK6wBP6+dTynrWubl5XDl5C4CAWurotLvbwsV70otHA+AVIZ4To41u4tauHQB3t20jNysLrbXyXDBAXCvzEP+/moiwzMqAA8KjuBLhaZRBw25VWf7FQc4fusG183eoGGzaz0KRBlVaWhoODg5Gd3T//n3s7UvR/LdgFLKdLwrDiCwqf3L6Kx8+A0X1mNSegzHH6p+Ln64Elb+Slk2Xidz8yrLC7du3mT17NjExMWg0GmrXrs2wYcPw8CilGCAzomuQUP/ruRp+PAlZufBTW9DJXIhGvVAbO0drFv3fHlZ8eYj01Cy6johQJxf3RR6qAM5FGFVvoGh7WKPR4DdqFPbVq3Oif38SlyzhcPPmhC1bhn2VKkU3rNYfPMJgVR+xOP2rPrT/Daoazi+v6CgKLn8aDe/uE8bs5qvwRxREeEs/B78aHoz6uQsHVpxn+ecHuXA4gU+fWU7L/qF0GloXBxdb6Z3KRQuE6B/PIJTczgMngJP656uIRXRx3jl7hBGVo38UlwrjADRAGFCNEdeImXDzYjLpKVm4+jjg6m38uro43PWRg0lyIjy96ornxCOiDIDG8Afd1s8P++rVuX/mDCkHD+LStKmMgQvhNEItcijiesnnFqKwd335Xds52dCga1V2LzrDrr/P8MQ7TZTM1CBFvovOzs4kJBif5Ojr68sFtZLVLJgcuXWoSnzgAm1VUPmTNJx5qPyVlzpU5oBROVSSpmsiUQoVsIhSPOTgwYOEhIQwe/Zs0tPTSUtLY9asWVSrVo3Dhw+X9vTMgg4BBetUDdwIWQrCslo8VYNnp7VCq9Ow4adjLPxgD7k5KiWH9wCmAbbAGmA8qtRG8ujUifr79mFfowapx45xqFEj7mzcWHwjz9rQ7wBU7Q2Zd2FVb9gxDnIMJ7doNfBmBOztCzXdRaHWJoth+kHpNatAhDk17hnChGV9aNk/lLw82PbnSab3WMyWuTGmVQMsDmtE6F1fREjeH4iQru+BiYhwwEigLsI74Y5Ynd4H0hEGWf77YYuQPW+GkG8fhfBkLEXUmOqGWRlTABcOi2SgwDD1qgi76u3jJDkeKgcfcKgIWSmQHGd0M7dIUbgracsWGYMWQRIi5y0E8T8+jwgVnYPIrWqorPsWT9UA4MDK82SmZyvrzABF7unk5eWxePFiXFxcjOooO9u0E7WgDoqLd5pjYV8jOpey+66R6x4wUWHfcuMyUFvlT5IohfQ2aolSlHTIn0ahq6mcXG0FGDduHB07dmTOnDnY2Iit3YyMDJ577jnGjh1ryQXWE+kHa7tD15Uw/xzcz4b5HcBOpvenQdeq2NhbMeedrexaeIbUpAwGTm+FlY0KUl7NEGF/7wIHELWqPgAqKOvWoXp16u/Zw8kBA7i9ejVHO3ak6scf4z9mTNEbi7au0GUxHP4Udr8D0Z/CtZ3QaT64BBkcM8IbDjwBb+8RqosT98GyOPg18qFEthTsnW144p0mNO1TjaWf7ufc/uss++wA2/86RbeR9YnoVLn0VWTtgWr6R2Hkh/dpECtVHcLIKoMbPwdXCmdDWBv1BXDkhOYC4BYCadcgOdaoMFUAl+bNufbdd9zbv1/moIXQDPgbkSOpQRjDOsR1EYW4Tu4jO//Nr4YHgbW9uHQ8kWObL9Ggi3HnKodib5PDhw832cAWSgelohRmGfKnsiiFudywS/0LTy3MSTZdwqHGyaYbPkbWZ8akIbLFD1le7PdH2bt3L/v3739gTAHY2toyadIkmjQxbRhIWaNlRVE3qfNKsajvuRqWdAa55XPCIwMZNrsDP76+iSMbLpJ6N4Mhn/xE2PUAACAASURBVEVi56Rc7YxaCFns8UAsMBxhVIUq69bKzY3ay5cT9957XPrgAy688Qb39u6lxk8/FZ1XpdFA/XEi4X/t03BjD/wVAVE/C++VARys4atW0LMyDNkC+xNEzar/NYJx9YQQgVT8angw/LuOnNwRz/IvDnL9fBJzx29jy9wYuo2qT/UmFc33e0YDKNdvKHVuxd/j/KEbWNvpqBtl2Lg2liy9x85Gbn6Yc2VgO9wzXhHFuaFwF6lqUIHYFDkKuCFEZyrxsMBvCiK8dzTCYyWDRj2CuXQ8kf3Lz5vUoCryX5Gbmyv5UbWq6SZqQV0khwM9aKh0YCVtS1aUwmyEO8r6CleJYVEcMrozZg7SnFnF1T5TwZBU0lRmyF8Zv9oKxdbWluTk5P/8Pjk5GVvbEswtKSM09oWtvUQx2vVXoOMKmaFFeoIbVGDUT51x9rLn3P7rzHp5Lfdu3VdnspWAWUAED2tVGacGXSwanY4q06dTa9EidE5O3FywgENNmpB2+nTxDSs2h/6HoXJ3yLgj8qu2joBs4863QwAc7w8v1xQCIeP3QvMlcPyWzPPQaKjVyp9x83vQf1IzXLzsuXziFt++up7ZQ9dxIdrE9asec/avEOUD6rQLUmcTQU9+9Ka1bINKb9xJMKgcatRA5+RExuXLZN5QoGn+bzwRIZ8RiIK/+cZUKuCEyI9bIb/7iE5V0FlrObPnKkk3UpXNtRiM+lekpKgQnGzBLJAvCV6Ku1glLEphkU1XF9V3QGWqjxl/rEqTKDXZ9BIf0uzp1KkTI0aM4PQji+FTp04xcuRIOnfuXIozM1/CPWFbbwhwgl3Xoe0yuCFHollPpeoejP61C14BzsSfus0XL6wi4aJKBYqcERLLXRA73JOB31Fld8C7b1/q79+PQ2goaSdOcKhhQxIWLCi+kb0ndPsHWs4ErTUcmw1L2xu9y+FiA9+3FeGXAU56b9VCeHevCMOUg85KS9O+1ZnwTx+6jaqPvbMN5/Zf56vBq/luxHrijt6U17GFIkm9m8H2P08C0LhniIGjpaEvayXbc4yTvrpx6jWjm2h0Ohzr1QMg5ehRmQMXQQyw5ZGf9wO/6V93B/bI79rR1Zaw1gHk5UH0ujj5HRnAKIPK3d2d5s2bM3HiRDZt2kRGhoKtKgtmgbk4XySNrYYoRYm4qFTGXOahFDPwUKmXQ5V/kPnlUCltW14ut0f5/PPPsba2platWvj4+ODr60tYWBg2NjbMnDlT9fHOnj2LnZ0dAwcOLPD7jRs3EhoaioODA5GRkVy8KLMAUQlR3Q129IbqrnDkFrRaCnH/dfQZjaefM6/92oWAWp7cupLCly+sJu6ISh4Sa0TR31cQuwM/IYQrVFiuOISGErFvH979+5OTksLJ/v05O2oUucWthTQaqPe6KATsHgp1XzN+tyNL7KJ31HurXg2DnFz44BDUWQAbr8g/F1t7a9q/GM6klU/Q8ZW62Dpac2rXVb54fhWzh67j3IHrZV8IyUxY98MR0pIzqda4ItUaK0zu+xeX9Y6WwCIiUA1irddHyLonqZlDqIinvX/mjMyBi+Amoo5YPg2AdYjC0AeBKohcKplEdKoMwOHSNqjWrVtH+/bt2bJlC126dMHd3Z127doxbdo0du1SoWS5hRKj1BxUauSEFHWTVz2Hyky2+MuJh8qczkNKyF/xBxkRGFhasukPQvekjWs+/yX1qVChAnv37mXdunVMmjSJiRMnsn79evbs2YOvr6/q440YMYJGjRoV+F1iYiJ9+/Zl6tSp3L59m4YNG9K/f3/Vx1abQGfY3hsivODsXWi5FGJuy+/P2cOeET90omZLP1KTMpg9dB3HNl9SZ7IahEz3NEQS+yaEWIUKzhcrZ2dqzptHyFdfobG25uqsWRxu0YL7htSNvSPg6SNCYt0YcjJh5zihFIjwVs1uDTv7QG0POHcX2i+H5zfCTQULTHtnG7q8Wo9JK/rSfkg4dk7WnN13ja9fXstXL67h5M54i2GlgISLd9nx1yk0Gug1tqGqkRqZOXAtVQhSVJKbZ2ajN6gype2QOFSvDsB9Q6GvUmkLJAMJCIn8vYgcyfPAJaATigoz12rpj429FZeOJ3IrXpoRaSxGGVSRkZFMmTKFHTt2cPv2bRYvXkyVKlWYMmUKrVq1MsnELJgW2YrgSm+wJlH5M77zsphDVW6+1MxA5U+9/7+JRSkeTEJ6Ew0yTlbZkGWGqKgoRo0axahRo2inL1SpNvPnz8fNzY2oqKgCv1+8eDFhYWE89dRT2NnZMXnyZI4cOcKpU6dMMg818XGAzT2hdUWITxWeql3X5fdn62DNkJntaNq3GlkZOfzyxma26UOjVKE5QqyiInAGGIYIKVKIRqPBb+RI6u3ciV2VKqQcPMjBiAjDIYA6CbkzOhtoMg0yk2BxW7giFCibVYCDT8IHTcBOB3POQPU/YdYxeRLr+Ti62dFtZH3eW/UkXV6th4OrLbHRCXw/cgMf9/uHPYvPmFxuuryRk53Lwg/2kpudR+Ne1fCroW6tu0sp4j5dyUGeWAkANnqFB4keKvsaQoY8TW0PFUAbhADFz8B2xObIy8AQRNFfBdjYWxHW2h+A6PWmiQww+l+RlpbGmjVr+N///seECROYM2cOtWvXZsyYMSaZmAXTIF/lT4WCjCYb25JDZdaYQQ6VlDkYdahRXtHSyaGSrfpfTi63ojh//jw//PAD06ZNY8qUKQUeapGcnMx7773Hp59++p+/xcTEULdu3Qc/Ozo6EhwcTEyMCiv9EsDVFtZ0h16V4U6G8JKsiJPfn85KS7+JzegyvB55ebDk430s/miferWqqgDfAPWA28AYYKU6Xbs0akSDQ4fw6tuXnORkTvbvz5lXXiEnTUGSWT652SIHq92PQtp6zVNwWdTCstHB+PpwrL8IB0zKhFE7oP7fsCVe2bD2zjZ0fKUuk1Y+QffRDXDxsufauST+mrqbKV0WsvKrQyZN6C8v5OXlsWDqLs7uu4ajmy1dR0SoPsYBfZRsuCplraTd+G0DhPR75tWragxekH4IA8oJUZy5FsJy9FKn+7rtKwMQs/WyOh3+C6OqS7Ru3Zp9+/YREhJC27ZtmThxIpGRkbi7u5tkUhZMT4nXoVKj/WNa2LfcJLWUxxwqEOdVnDVSaip/ljpU+fz+++8MHjwYOzs7fH19Cxi7Go2G9957T5VxJk2axJAhQwgI+G+9mZSUFLy9C1YcdXV15d4904SfmAJ7K1jYCYZthZ9OQe818ENbGCxTplyj0dDx5bp4VHJi/uRdbJ93ktvx9xj0f62xdZCbbf8IrsDHwGxgCfAJcApRDFah4JqVmxu1Fi7k6jffcH7sWK798AN3t2+n5rx5OOkT9yWTmwNaK0i5CvFb4PpuiPwefApWNw1xhTXdhKz9mJ1w7DZE/gP9guHjZiJMUy52jtZEvVCbNs/W5Mj6i2z94wSXT9xiw8/H2PTbcWq3CaD5UzWo1rgiWtlFkMovK748xL5/zmNjZ8XLX0bh4qUgTq0Itul1JFoqScvK0ataSPGeAjb6EGlVVf7ysUVshFRRv2uAGs0qobPWEnckgZTb6Th52Knav1Eeqn379uHi4kKzZs1o2bIlLVu2tBhTZRS50siKFaCVFL01GDqlcmFfpVv1akW2lZfCvmrLpisJ+ZMim268RWVoUGM7KmQSMpo+eL9lDlnGL7fCeP/99xk3bhxJSUmcO3eOs2fPPnicMTJ0pW3btmg0mkIfLVu2JDo6mg0bNhQZteHk5PQf6fbk5GScnRWsfksBK60wot6tDzl58OJmmH5Q2XXTsFswr37bAQdXW2K2XeGrIWtISlDJG2KFyKN6GyFcsQLhrVIhr0qj0eA3fDj19+7FoWZN0k6d4lCTJlz+7DPycmV42rT6gsdbh8O17dBgAgT3fZjvkv8m5+ag0UDvKnDiaVGryk4HC85DjXkwcS/cy1R2blbWOhp0rcqY37vx2i9dqNexMmjg6KZLfPvqemb0WcrmOTGk3E5XNlA5YuOvx9n063G0Vhpe+KQtQeHehhvJYIveOdSmkoJOcvSCKlppBpW1tzdoNGQlJpKXk6NgAgYwwfeQnaM11RtXJC8PTuxQoOxSBEYZVHfv3mX+/Pn4+vry5ZdfEhAQQHh4OKNHj2bZsmWqT8qC6SiT+0klXdhXLpaQv0IxC8NQwk6C8YcqF0sxBbI1VcpxHarr16/z8ssvo9PpZPexZcsW8vLyCn3s2LGDLVu2EBcXR2BgIBUqVOCTTz5h0aJF1K9fH4CwsDCOHDnyoL/U1FTOnz9PWFiY4vMraTQamNYEZrUSn4KJ+2DYNmW5PMENKvD6b10fyKrPfHYll2ISVZsznYGvAB/gBDAUiFana6e6dal/4ACVXn2VvMxMLrzxBkc7diTjisRFW/w22PQyZKdCsxlQ41nx+zz9wjXzLlzZBNtGwdGvAeE1fK8hnHoG+odAeg5MPwQhf8K3Mcr+JyDu31Xq+fD8h20e5Fm5+Tpw81Iy/8w8wPudFvDD6I0cXhdHVoYJF9hmTE52Lotm7GHFFwcBeGZyC2q28DPJWGeT4OQdcLSCRj4KOsrWb1hYOUhqprW2xsrNDXJzyb5zR8EEDGCir8/8PKqYbaVkUNna2hZQ9Tt37hxNmzblm2++oW/fvqpPyoLpkS+4ILOhGh+OEg/5k3CwKTGbicjEDAxDSTl0Rh9o5JGl9f+TW7y7HBIVFcXhw4dNOsYrr7zC+fPniY6OJjo6mmHDhtGtWzfWrl0LQJ8+fTh+/DiLFi0iPT2dKVOmUKdOHUJDZcbLmQEjaosQQDsdfH9ChACmZMnvzzvIhdfndCW4gS/JifeZ9dIaotfHqTZfagDfAfURRYDfAOYBKqRt6RwcqDZ7NmHLlmHt5UXSxo0cCA8nYd4847zzMT/C3omi4GqnBWDrKu4d+WGAWWlw4AOI+QG86sKZP2DPxAfNg5xhfgehBtjUFxLuw6vbIPwvWBqrzm3I1duBjq/UZeKKJ3jxs0hqtfKHPDix7Qpz3t7Ke+3/Yv7/RP5QjlJLroyQeOUe3wxbx46/TqOz1vLstJY07BZssvF+04vrPRkscupkk6I3KBylu7m0diJUrtiyAWZKqN7QPbvvmnr5mnqMyqFKT09n+/btbNy4kU2bNnH48GHs7OyIior6j5KRBfNG/u61SsstJSp/RR9huA9J40k4WO4gRvVXzpa4aof8mXgKqoX8yaEURCnyKev2e2EMGjSIt99+mytXrlC3bl1sbAqGuTRv3lzxGA4ODjg4PNztdXJyws7O7kHelLe3N4sWLWLkyJEMHDiQJk2aMH/+fMXjljZ9q8LGntBjFay8KAoAr+wKvtI2vh/g6GbHsG86sHD6HvYuO8dvb23l+tAkOr5SV52cHTfgI4SS2J/A98Bx4B1EgWCFePXsicuxY5x+6SVur1zJyQEDSFy6lGqzZ2PtWYyKgEYH9d6AKj0f3js0Gh7sex/+GHLSRRigVzj4R+l/l1kgD6Z5BdjVBxZdgLf3wKkk6LMGmvnCjKbQWkmYmB6dlZbwyEDCIwO5d+s+h9bGcmDFBa6cvMXepWfZu/QsTu521IkKpF7HygTX90WrkytHZ57k5uaxc8EpVnxxiMz0bJw97Xjhk0iq1lPiNiqenNyHBpXcvMUH5BtUTv/N9zSERn//zM1UGFdaFH8j5NLHojjX8d94+jnjFeBM4uV7XD5xS9WwTKMMKjc3N7RaLU2aNKFHjx7MnDmTJk2aYGVlVHMLZkiJK4IryaF6MLgKsukl4aKyyKYXxFQhfzJyqCQE8ikP+cvvp7REKaTWoSrHIX/59Z4Ky2/SaDTkmCAXYPLkyf/5Xfv27cuETLpU8hfxXVbCwZvQdLEwqmrJVIu2stbR//3m+FZ1Y/kXB1n73RGunUtiwJQW6ohV6BBqYmHA/wG7EAWB3wdUcBjaVKhA7eXLuf7jj5wfO5abCxZwd9s2qn3/PV49ehTeqNbggj+n3QAHX/HBvLEfEg5CwwngodePjvkeMu4IYyovD5IviILAXnXQaIQHo2dl4TmcehB234A2y6BzAExtDA1VWvc7e9rTZkAt2gyoxfXzSRxcfYHodXEkXr7HroVn2LXwDE7udoQ2r0TNFn7UaFYJRzd1xQBKmnMHrrP8i4NcOi5CUut3qUKfNxvj5G7a81p7Ga6kQlUXUcJAESl6pTsnf8lNtba2AOSZykP1D3AFeAowgbOvepOKJF6+x+ndV0veoFq5ciUtWrTAzq5sfwgsyE8818ht+O+BZbVVL4fKqNlbCvuqixmIUjycg9GHGndgcf2VliiFzAuxnFxthRIbG1vaUyj31HCH3X2hx2rYnwDNlsCiTtBe+noNEIZu5HNhVKjqypzx2zi68SKJl5IZ8nk7PCo5qTPp5ggP1WREvapRiJpVfVH8gdBoNFR8+WXcoqI4/cIL3N2+nZiePfEdNIjgzz/H2qMYazP9NpyaAxWbQ8UWkBgNFZqCa4gI/7sVAzf2QesvIS8X1j4j5NYT9kFIP2jxCWg02OhgZDg8XwNmHoVPomHNZfHoHgTvN1TPsAKoEOxGt5H16Toigqtn7hC9Pu6BcXVg5QUOrLyARqshMMyL0OaVCGlYgaBwb6xtlcSulQyZ97OJXh/HroWnuXhMGFLOXvY8NaEp4ZGBJh8/Nw8m7ROvh9VSYXlw65h4dqsmuWletqhLpjGVUyUAYVBdxSQGVbXGFdm18AznD6mrVGjUu2EJ6ys/lHo4Wyl5qORM31wK+5b1GCw1K8TLn4P0Nsa/64UfWWp1qPIxDwerWRAUFFTaU3gs8HWALT3huU0i5KzzCvimNbxcS36fNVv68/qcbvw0ZhNXz97hs2dX8PxHbajWSOkWvZ6KCLGKbxHS6rMQYhVvAi7Ku7evWpW6mzcT/9VXxI4fz425c7m9bh3Vv/kGrz59Cm9k5wH1xkJGkvg58x5kpYC9viDPgWlQpQfobCF6pjCqui4Sf/unM9w9V2Ch7GwjhCuGh8FH0fD1cVhxUTy66Q0rRQIH/0Kj0eBXwwO/Gh50HRHB9QtJnNp5lZM747lw6AYXj93k4rGbrP3uCNa2OoLqeBPSoAJV6vkQUMsTe2eV47xkkpeXx/ULSexedJYDK85zXy+daOdkTeRzYbR5tpY6HlMjWHgeDiVCJUeRu6iIjCRIOiMU/jzDJTfPSUkBQOek0sbGv8mXg1dQPLw4qujDMuOO3iQnOxed7OrIBbHE7D2mlLQmhSJUyKFSdbgSwhwMEVUxizpUxof8GaYERClklRmQN1Q5u9o4ePAgDRo0MOrY9PR0YmNjqVmzpolnVf5xsIYFHWHCXvjwMLyyFc7eFfk7clOgfKu4MmZuN+aO38bJnfF8++p6eo5pSOsBNdW5T9ogpNXrIupW7UB4rCYC0teb/0Gj0+H/+ut4dOvGmSFDhLeqb1+8+/Uj5KuvsPEpxJrR6oRhBeJL6fYJSEsQBlT6bag+AK7tgjunoP7b4rhbx0XYn33hYUxe9vBRMxhXFz49ArOOi7y3lRdFKOA79UUomZpfPRqNhorB7lQMdifyuTAy0rI4u+8aZ/df59z+61w9e4dz+tf5p+pT2ZXA2l4EhnlRIdgN3yquOHnYlch3YsrtdC5E3+Dsvmuc3BnPrSspD/4WFO5FsyeqE9GxCjb2Jbd8zsoRSpogjF/FNtzNQ+LZu57kOlQAOalCIVDr6KhwIkVgYoPK1dvhQR7V1TN3CKilSoVki0H1uCE/5K/0EywMhowV8/dH78OG6rA+PNC4eanWrsj+yrjPQO0cKjnfqTKmUKp1qFRAdgpgGb/c8unVqxeNGjVi2LBhdOjQAa32v7uQ8fHxzJ07l6+//pr333/fYlCphFYjDKgQF3h1O3wcDWeS4Pf24CRzMWjvbMNLX7Rj1deH2fjLcZZ+sp9LMYn0n9RcvcVtG4QS4BTgJPA68BwwEJF3pRCHatWou2ULV2fP5sI773BzwQLubNhA8Gef4fvcc/81GPJ/rjcG7l2ENf3EIrjtbHCsKIr/etYG34bCS3XnNPg2FoqAtm4P+8nLBc3D69/HAT5sBuPqiTDAr48/DAVs4gNv1hM1rkyhI2HrYE3ttoHUbivC5FKT0jl/8AbnDlzn4vFE4k/f5kbsXW7E3mX/8vMP3zsXG3wqu+Id5IKbryNuFRxx83HArYIjzp522DnaGBU6mJuTy/2ULNKSMrh7M42Ei8kkxN4l4eJdbl5MJvFywULbDq621G0fRPMnq+Mfqs7CWyofRYtNiWqu8KIaoqBXd4hnn0aSm+ZmZZGbmgoaDToHmcozhvDVPyeYpnuAynV9SLx8j7ijCRaDyoI8lGpDyM6DUaGwr+F6P8Z1noeBNfkD27GUdafNoX6TGpjKoJIkSiE9h0qtOlSyPjOKCvvmt328RSlOnz7NjBkzGDhwIOnp6URERODn54ednR23b98mJiaG2NhY2rZty7x582jZsmVpT7nc8VItqOICT66FZXHQYgn800VIfMtBq9PS/bUG+Nf0ZN77Ozm0Opbr55IY/GlbvAJUiM8DsTv+JUIFcD7wK3AIGM/DnXMFaLRa/EaOxKNbN84OHcqd9es5/cIL3PjtN6p9+y0O1asX3rDV55CZAjb6MKvEI3BxJTQ78vDn67vAvSY4VRJhXXdOQ4UmwpgqZCfR214YVm9FwFfHhMdqbwI8uQ5CXGFMHZF/5WjCqDZHNzvqRAVRJ0qE5WZn5nD17B0uHrvJlZO3uBGbzI3YJNKSM4k7epO4o0VXZNZZa7F3ssHOyRqt7pF7fh7k5uZyPzmT+/cyi701WtvpqFzHm6r1fQlt5kdgmGepKhTuvAaTD4jXs1uLwtqKif1HPAd2ltw0Mz4eAJtKldAoqOtXLK7657um6R4goKYnB1acJ/7UbdX6tBhUjxlKi37KH1hBU5VC/jQYt1iUm9RvEaUoArXPw8Rvi2p1qEpNNt0iSgHg6OjI1KlTmThxIqtXr2bbtm3ExsZy584dvL29GTFiBF26dCnTdaDKAlH+sPcJ6Lkajt6CRgthcWdoqSAFql6HylSo6sbPb2zW51Wt5NlpLQlrLV0CulCsEKp/DRAqgEeBl4AxgEop5fZVqhC+di0Jv//O+bFjSdq8mQN16hD07rsEvP02WptCQrFsHslZ0ViBb1OwcYbkOLi4ShQArj4AtrwKjn7CwLLzFuIVtq7/7U+Ppx1MbgRv1YNfTguv1bm7MGK7CDV7pRaMrA3+JkqZeRQrGx2BYSLcL5+8vDzu3UonIe4uiZeSSUpII+l66oPnlDvppKdkkZOVS8qddFLupBc7hr2zDQ6uNji52+NT2QWfIFd8qohnr0BnrKzNQyQjPgWeWCuKM79RV77ASwHuXYabB0VB3wDpF3N6XBwAdqbMS83fG0k23RB+oSKcNv6MxaCyoJBSi2YzpSiFkTvyeQZdVPkHGtWdeu2K6q6se6j0qH4esrqTIDWhNORPekeSuzbFsOXkcnuAra0tvXv3pnfv3qU9lceW6m6wpy/0Xw/rLsP1NOPaxdyGKynQqRARtQrBboyZ240/39vB8S2X+XH0Jjq8FE7nYfXU8yg0AH5C5FXtBKYBe4DRgArGhUajwXfQIDy6duXCm29y/ZdfiHvvPRLmzSNk1izc27UrurFrVchMgr+bCOMqIAoi3oK45XD8O2jwjigOduADOPc3hL303z7+5bVysBaCB0NrweILQhlwzw2RC/fpEXiyKgwLUz/PyhAajQYXL3tcvOwJaVi4mzAvL4+sjBwyUrO4n5JJXs7Dc9NoQKPVYO9sg72zjWoiBKYkORP6roUb96GdnwihVYXYZeI5sBNY2Utunn7xIlAODKrq7gBcO5dEdlaOKka02VxVGRkZDBkyhKCgIJydnYmIiGD16tUP/h4XF4dGo8HJyenBY+rUqUX2FxcXR9euXXF3d6dChQqMHDmSbL3UI8DGjRsJDQ3FwcGByMhILuovkvKO7Hug0runSXfcjfRQGS2HbeRxarUrsr/y4TPQmEHooqSQP/2z0pA/ReetyKOrrF05s6csmAlutqI21ZpuokaSIbJyYF8CvLgFPo0u/Bh7ZxsGfxpJt1H10Wg1rP/xGN8OX8+9W/fVm7grMBV4A7ADNgBDgMPqDWHt6UmNn3+m7ubN2FevTtrJkxyNiuJE//5kXLlSeCMre2EwNZsBLT6CJlOECuCed8XvczJhWQe4nyhk10Hci3L166DcnCJvFlZa6BciZPB394F+waLp/HOicHOt+fD5EbhdvCOoRNFoNNjYWeHsaY9PkCu+VYWYhW8VV5F7FeiCk7tdmTCm7mZApxXi+g9yhvkdVAr1y8uDEz+J18FPyuoi9fhxAOyrSZdbN5r81CwVP8b/xs7JBg8/J3Kycrn1r7w5uZjNlZWdnU1AQABbt27l7t27TJ06lX79+hGndy/mk5SUREpKCikpKUyaNKnI/oYPH46Pjw/Xrl0jOjqarVu3Mnv2bAASExPp27cvU6dO5fbt2zRs2PBB4cfHhVKTBC/lwr5SpiDbo2IRpSiIGRT2lZKTZLRhYSh3T43zlpVD9XCRJOUaVppfacGCIay0hXubCsNaB4NDoZ6nSMgvCq1WQ/sXw3n12w44e9pxdt91Pnl6OecPqigRpgG6I2pWhSKS5cci5NZVNCrc2ral4dGjVJ4+Ha29PTcXLGBfaCiXPvyQ3MzMwhv5R4paVSCk1X2bQEB7aPkJ1BsH17ZDYEf9eWhEHaucLFj7tAgVNEDTCvBXR7jwLExsABUd4FQSjNkFfnNg0EbYHC/qJFlQTlIGdFwhPINBzrC5p8h1U4Wr24RxbecFwX1ldXFvn5AbdG4kXdDCaPKdRerXWy+AT5AIg024qI4rzGwMKkdHRyZPnkzlypXRarV0796dKlWqcPDgQVn9xcbG0q9fP+zsQ8/u8wAAIABJREFU7KhQoQKdO3cmJiYGgMWLFxMWFsZTTz2FnZ0dkydP5siRI+Wygv2/KbUcKiWolKtifL1W8/AMmcs8FGMGOVRSpqCabHoZ+/+VrdlaeBx4c5dYqH/bRvxc3KK9WqOKvDGvB8H1fUlOvM/soevY8NMxctVc6QcgjKgXEIu+xYhcqxPqDaG1tSVowgQanTqF1xNPkJuaSuw773AgPJxbq1YV39jWDTQ62Pgi5GRAUCfotx+q9BSeidO/i+OOzRI5NA7Gq2wEOsPUxnBxICzuBJ0CICMHfj8D7f6BkD9gygG4qM5m/2PJ5RTxXu5LgMrOoqZbFZW0VgA49KF4rjMSrOwkN8/LyeGefk1eLgyqyuLNTYhTR/3CbAyqf3Pjxg3OnDlDWFhYgd8HBQXh7+/P4MGDSUxMLLL96NGjmT9/PmlpacTHx7N69Wo6dxaKJjExMdStW/fBsY6OjgQHBz8wuB4H/p+9M4+Lqu7++Htm2BlAdhAUQURU3Ek0931/THtSS83MsjLtabXVLJ/25dfmkmVPWZZmamXhrmla7qkJ7qhsIiCg7Ovc3x/fAZcEZrnDDHDfr9f3NTBz7/2euVxmvueecz6nzlX+ZKD2uY2ooZJxO4tjM4aYiQ28D2OuX4M3rWVD6/7P1M0+CgpyszAOdlyEr/RlRBW62vtYefi68MiSIQycFoWuQiJ2wV989/Jug+esKNfVvpEdMBVYCIQAycBsYClQTRDJFJyaN6fd6tW037QJ59atKTp9mriRI/l7+HAKjlfjwanUMPhr4Sit6S0cKEn/nvy7w4mv4Oehoqaq9wdiUa0rv/ZPryuHoupV9EBEDseGwcZRkDAJXu4KzbRwPg/mHYAWy6HPT7AoDjIMrJVTgJVnoOMqOHxZKCzuHAMt5HSm0v6ExA3CkW4/y6RDFMTFoSsowLF5cxz8/WvfwVQqHSodFk2Z8AsRJzizoUWorqesrIxJkyYxderUKgUmHx8fDhw4QGJiIocOHSIvL49JkyZVe4y+ffsSHx+Pu7s7wcHBREdHVxUl5+fn4+Fxo+KNh4cHeXkN/9aKUkMl33YmmmHE8RpIzMBSESoTZNMN29bYDWuT8zcBc//dLBzFU1CwBGeuiMcNifBpPCzuA/4uwpm6XmeirALSq1msa+yEtPqMTwai9XSi6/Awg+auKNPx/fw/+eOHU4YZ2xqRAjge8RHwLfAQon+VjHgNGUL0338T9t57aDw8yNm4kYMdOnBm1izKqrup3PMdGLoCAnoIJ0tXDt7t4I6tUJQO+Slw6huxrdpO/POXXIWt98G26bC6J+RfrNW2UHd4tRucnwSbR8HEcHDSwK40oRDY9GsY8gv87wRk2VC9lS2RXQwTt8DdWyGnBEY0FwIuzU1sLXBLdOWwc6b4uePj4Gxa36Ws2FgAPAfKJHVZHZU3JuyxaOqEZ6BQlrkik+dfZw5Vv379UKlUtxzX9//Q6XRMmTIFBwcHFixYUPW8VqslOjoaOzs7/P39WbBgAZs3byY395+epU6nY+jQoYwbN46CggIuX75MTk4Ozz77bNWxbt4vNzcXNzc5r2DbpkHWUBmq8mfwfIZuKNN+1R6vYYQMZIvUmPMBa0xjX4MNsU2VP1PnbhhXG1y8eNHo0VAUNesjhWUwY6dYXL5yEN6MgWg/keZ3vTNVroPdl0QUpDqxCoA2vYJ56ddxtOkZZND8RfmldBwUwo/v7mfv2tOGGe0APAJ8hEgHvADMAj4FSgw7hCGoHRxo9tRTdDtzhsCHHwZJ4uLChewLDyf53XfRFd/CW/FoKQZQ9VkVt0TUWP1rk2juunG8eD7tT5EOplLDqHUQMgyOL70mYFELGjUMbgYrBkP6ffDNQBgZIvy0LSkwfQf4fwWD1onI1cUCM09IA0CS4IcEiPoevj8LrnawpC/8OkLI2MvKsUWiT5lbCES/aPJhsn75BQDv0aPlsuzWVIpRyFU7Vg1N/IX6xZV0eS7IOnOoduzYgSRJtxy7d4uQvCRJTJ8+nfT0dNasWYO9ffXd5K6pdv3zCzA7O5vk5GRmzZqFo6Mj3t7eTJs2jfX6/ON27dpx9OjRqu0LCgpISEj4R3phQ8TUwnMTe4WaPzEGqKUZ2NjXYBPMbewr15rMnMawNoTFVP5MSWczYBuzr/XK48ih8mfy/5vhqob/mLJ+X25VBAcH06xZM4NGcHAwzZs35/z589Y2u9HiYg8/DxdpYhfyYFSLW28Xnw2rzsKIELFYf/KP6o/p6GJ4R1qtpxP2jnb4h3rQukdT44xvD3wOVGpbfQ88CBwz7jC14eDrS8TixXQ9cgTPwYOpuHqVc3PmsD8ykvRvv0XSVZOyqNZAYbpYWEfeC15tYPgPEPNfIVCRsg3U9tD9dbG9s7/oVaTWd9bJTzXYRncHmBwhHIP0qfB5XxgcLD6StqWKyFXQ19B9DbxyAPZeEhHIxoIkwaYk6PUTjN8MaYXQMwCOjhf9vmTPFMi/CPv0Am69PwJ7l5q3r4bSjAzy9u1D5eCA5+DBMhp4CyrvD1jYofLwdwXgqkwRKpvqQ/XII49w4sQJtm7dirPzjWdy3759NGnShFatWpGTk8Njjz1Gv379/pG6ByI9MDQ0lMWLF/P000+Tn5/PsmXLquqmxo4dyzPPPMOaNWsYOXIk8+fPp0OHDo2iwWODFKUworGvYdspjX1lxVLvw6iUPyO2NXbLWh19E5Ah5c/omyYN5HK7ntWrV+Pl5VXrdpIkMWLEiDqwSKEm3B1g+xh4eKcQOxgbCq7X+USXi0RPpJwS2DBKPPfMn7DzIvQ10ge6mcykXH756BCDH+iAZ6AWXYXOuH5WjsDDQB/gHSAReAz4F8K5krEprrZ9e9pv2kTOpk2ce+YZCuLiODl5Minvv0/oW2/hOXjwP9OcXfxhyLfgHSUk09Ua8GwNyduEtHrYGHBrBiVXIDfhmqx24gbY/yo07Q093zXKTi8neKCtGDkl8MsF0d9qUzLsyxDj1YPg6SicrsHB0C8IWro3vM+jsgr46QK89Rf8pc/U9HWC+d3gwTY3RmFlQ1cOm++B0lxoMUqIk5hIxooVIEl4DhqERmvhDs+V1Teulp3Gxd0Bjb2a4vwySosMi8bWhM04VImJiSxZsgRHR0cCAq4pzyxZsoRJkyZx7tw5XnjhBTIyMnB3d2fw4MGsWLGiars33niDXbt2VfWuWrt2LY8//jhvv/02Go2G/v3788EHHwDg6+vLmjVrmDVrFpMnTyYmJoaVK1fW7Ru2MqbfhDbz9rUtpPwZLDZg4HZy7Vft8RpIyMAmUv6MEKWo1Y76kPJX91PaCt7e3vTu3RsfHx+Dtvf390ejMb+5o4L5fNoXjlyGpSdEXY4+MwcfZyGIMP8g/HhO/Pzu7ebPV1JYxq8fHSI82p9Og1sA3OBMlZdWoLZTo65NFQOgLaK2ajmwAliHaAr8GMLZkgmVSoXXsGF4Dh7MpWXLuPDyy+QfPsyxoUPx6NePsDffxL37Td1gvaPEo1ojxCpUarh6FsoLIKi/eC1ps4haaYP1+3SA4Wth/yuw/k4YsFSvJmjcB7GnI9zbWoz8MtieIhyrjclwLhdWJYgBEOwqHKv+TUX0JsL46WwCSRLO09en4LszcFkfefF3hic6wiPtxE0Ei7HvZbi4U4iUDFhq8kmUJIm0zz4DIGD6dDktvDWVuii+lp1GpVLh4u5AXlYxRXml4qaIGdiMQxUSElJjatPdd9/N3XffXe3rL7zwwg2/d+rUiR07dlS7/aBBgxqFTPrNNEhRCkNl0y3d2FdmFNn06o5nWRsM39SGZdNNEaWQ3wqrkplZs1rZzSjpfrZFJx9w1ICzHVwqhAC9U3VXS/BxEk1mx16nN7EjFTKLxV3/foaVTZEUf5nm7XzYveoURflljHlSSEHrdNINztPpfWn8tfE8vca3pkVHv9oP7ADcD/QH3gfigXlAL4RjJeNCUaXREHj//fhNnEjqggUkv/UWV3fs4HCPHniPGUPo66/jeqtyBpXeYcw5BQE9xefVxd2Qvh88wiBQX9vu7Acae+j+GmzXL6bN/GzT2sO/QsUAOHtVOFe/pQplx5QCEaFcri9l83KE7v7QI0A8dvIWzrUtUqGDI1mwMUk4Ucdzrr3W1hNmRsH9keK6tiinlsOhN8XfecgKEaE0kSvbtlF4/DgOgYGWr5+Caw6VYffCzMLZ3ZG8rGIK80pwcTQvTGgzDpVC3WJyz1or3r6WTTbd4PkM3NDS2IwhZmILNVSyilIYdlCryqabcJIayNWm0ABo4ylEKRbFiccXuornVcCeS1BaARlFsD4J3vxLLFQfPQaf9IYJ4TUfu6SojF8/PkRJYTllxeU89uVwQEina+yuLax0FTqCIr0oyi9l9Zv76D6uFb3GG1geEAp8jIhSfQ7sBg4h+liNQ9YVmMbFheZz5tB0xgyS332XlA8/JOvnn8latw7f8eMJmTv31o5V096w6zEovQpxi6HDYxAyXDhRcO0xaZNYlBelg5OnfIYjZMLDPeDRKPF3js8WztXOi7AnXdQZrU8So5JAF+jgDe29oJ0XtPIQw9e5bu9lZRYJew9kwM402J0GV6+Tz/dxgntaichcF586si3hR9GLDKDXhxDcz6zDJb/zDgBNH30UdQ3aBrKRoX+0cIQKwFkrQoRFeWW4+JgXolIcqkaG1WqobEE2Xf9Y2/pWZeiGpplhxPEaSMxAbnENMyTBDbHB4D+/oZFTK4hSiDpAybg6M9OmqtcUFBRw6NAh+vSRMRdLQVbUKnioHdy1Cc7mwpQIoQb4eAdw0MC6C7AvXSjL9QqEjt6wOQXuDAO7Gm44OzrbM3PJUFa9toeDsQlUVIh/Fo2dGkmSqjIE1Bo1Hr4udB0ehmegloO/Jtzweu1vALgD6IloCrwLWAxsAh5HCFrIiF2TJoS+/jpBs2eT+NprpH32GZnff0/mqlX43nWXcKyioq7t0HIsOHpC+l7o/gaE33nttStnIWU7XPpT9KdqPRk8LVtrrlZBe28xHusgPj6T8oUDvSddNL09liWcrLRCEdm6HncHCHeHEDcIcr02mrqKSFcT/XC3r7luSZJE4+KCcuG0XyzQj0JIyhORp/hsERW9mRA3UQ/2rxYwrJno3VVnnPoWtk4FqQI6Pw0dZ5t1uOxNm8jZsgWNuztNH35YJiNrodJxDrb8VPZO4o9TXmJ+F2HFoWqkWK2xrxVrqKrWt7XNY4JC2o12mLhftcer3zEDm1D5M8JBMfg6qcUQ66r8mT51Pb/cjOLs2bP079+figrzv0wVLIenI2z9Fzy+G7alCKfq0SjYmiIW2ZNbCWeqsAy2pkJzbc3O1PWMf6kH4V39id+ZTFBrL4Jae1XrLJUUlJF8PIvi/DKc3RzIzy5G62WgxrUvMB/Yi3CsziHS/4Yh+lc1MewwhuIQEECrBQto9uyzJL/1FmlLl5K5ahWZq1bh8+9/E/Lii2g7dRIbB/e7MYqx5wXISxIfhuXFQtAgZFjNaWNlhVBwEZrUEho0EpVKOCghbjCxlXhOJwklyL+z4OhlOHUFzlwV42qpqFv6q5oWXdfjYieuEzsVaFTi53IdFJaLYchHoZu9iJB18IY+gdA7UOYeUsYQ/zn89hAgCXn0mP+adTipooJzzzwDQPMXX8Te27T+VUZzTv9oWAs5s6iMRutkkJpUHKpGhuk1VNaa2JBaIplV/ky1VYlQ3RobeB/GKDfKtqXS2FdBQVY+7HXj72/9BUOawdDm4vcDmWJx3Elfe1F5c6C2a7vL8DCK80s5tP4cJUXlhHW6VieVmZhLzqUCzh1O58TuFHre1RpnNwfOH8lg6X+2MeC+KAZOMyLM1B3oDHyHEK3YiEgFvB+hCChzNMOpWTNaLVxI8+efJ+mtt0j7/HMur17N5dWr8Ro5kuYvvohHjx5iY0kSinDnfxbKf2M2g0/HaweTpOpPZsJqERlp2hcip0LLceD4TxVmOVCrIMxdjDtCbzQvq1g4Vsn5kFpwbaQVwpUSuFIqHnNLhdNUE44acNaAnzMEukJTFxHpCnKFyCYQ5S0ENKz+2Snp4NBbsFffY6r7GxD9vNmHvfTVVxQcO4ZjSAjBjz1m9vEMohC4iGjq28zy06k14o9XUa44VAom0iAb+8pcQ2UySoTq1thCDZUx28pVbGdFlT9TIsoN5GoDwMHBkhJaCtYkNV9EFOZ0Fr/vSxf9fTwcYJA+VSi3FDz0ZRE1+QIATloHeo6PRKeTOL4rhcObzlNRriP1dA5BrTxpHuXD6MejCY8OoLysgjVv76Pv5Laknsrmi8e3c89/e+HsZuD15ghMAwYjaqwO6B9/AWYjHC6ZcQwOptWCBTR//nmS33uPtCVLyI6NJTs2Fo++fWn27LN4DRuGytED7omHk9/A5skQcTdE60W/ajqBhZfAzlmoyl3cCTsfEZGtiHsgZATYyd2t9p+oVEKswscZetSyrU4SDlWFDsqla49qlWiy62JnISlzuclNhG33QeoO8Xvvj6Cj+c5PycWLnJszB4DQN95A7WT5vx8AlT21Q6gTD0WlF5/RVZj/zac4VI0MUzOJVOamwVViyv5yN/attYbq2peGUbny5qZp/dOQKhvqNXKn/JkTfTGmhsrcLa3491MZn7fY4Br7Atjb2zNz5kyirq8ZuY7ExEReffXVOrZKQQ7cHIQM9ftHhGrc4cuiNuaJjvDOYSHNvSdd1FqNbmF4FEGtVqGxV3N6XxpR/Zrx/No7/rHN6tf34uhsx5AHRfTm148PkXoqm/DogH9sWyPBwNsIWfWFwHngSaAvoqeVkYczBMegIMI/+IDmzz9P6kcfkbpwIVd37uTqzp24RkUR/Mwz+E2ciDpyCjQfKtTi8pJFj6qa6DIH2j0EZ3+A09+JBX7CGjEc3CF0DISPh+aDQWOmPrUMqFXiuqm3SBKc/hZ2Piqiii7+MOALaDFShkNLnH7gAcqzs/EcNgy/GhS2Zeew/rFjjVvJRnmpSPe2dzQ/NFwf/G8FGTFhnXUTJlfJm06tC3KZZdNthIYmmy67KIVRNVSG3xAwXF7fMIfKrBoqUzFHuMPMqW2J9u3b07x5c6ZOnXrLMWbMGGubqGAi7g7w4zDhSP1xCf7dEl7vJqS2X9gnam6+7A+vHBANZY2hdfemPPHtSPKyi/nqmR2UFJVVvSZJEq1iAqko17H1i2MAjHqsq/HOVCUqhJz6V4i0PydgJ3Av8AVQZNpha8PBz4/Q11+ne2IioW+/jUPTphTExXFq6lT2hYWR/N57lJc5Qucna3emKnH0gHYPwNjtcF8S9HwPfLuIBf+pbyB2NHzhD1umwvlfoNxCb66hU5gOmybClini3IbdAXcfk8WZAkj77DOyN2zAztOT1l98UbdrkUqHqlPdTFdWojhUCiZicnmQLfehqsTQxr5GTG0Td+ttwggzsIE+VKaYYHiEqro5ZXjfZmvAGH6ABuK+30CPHj04e/Zsta9rtVpF4a8e00wLywfBF/1Emt+hTHj7MMSOED2AtqbA3GjTPkKb+Lly//v9aeLvyvkjGVXPq1Qqug4P45ElQ0g8lkn6+avAjTeMTCpwdwSmAMuAgUAZojnwFGADYCHdFDsPD5rPmUPM+fO0/vJLXNq1ozQ1lXPPPMPe4GDOzJpFoSk9O7XB0PkpmHAIJp0Sfay8Owh59lNfQ+y/YKk3/PovIaZQkCb/m2toFOeIZr3ftIKzq8BeCwP+JxovO8ujMX71zz85+5//ANBq8WIcmzaV5bgGUQScQHgmdeRQVar72SkOlYKpWK0PlTnlJLWm/NWMMQtGk9bCiihFzdhADZVBKn9GH9MCNVRm379oYNeOiXzwwQd89NFH1b7esmVLfvvttzq0SMESVMpS26thVAgMD4GVg+Gn8zBhsxAiAPGvqDPy3/GOp28jskcQWal57Pgmvur50sJyctILKC0Wyga5l4vIuHCVgivFqM0pvvEDXgIWAJFAFvAOQgnwkOmHrQ21gwMB991H9N9/ExUbS5MBA6jIz+fiwoUcaNOGv4cOJevXX5FMUcT0jBCqc3cfveZc+XYVEaoLv8BvM+DLpvB9V6EwmLIDKkprPWyjoeQq7H8Vvg6FA/+FsjxRnzbxKLSdJttaoSAujriRI5FKSgh8+GH8JkyQ5bgGswcoB9oA2rqZsihPXGdOrubX2yo1VI0MU9N6DCxTquEApu9vuPy0gREqo0NURn5YKaIUN2BLsunGpB0a3ofKAil/VUaYvqux+1//nVxbAb+Cgi3iqIG96bAhUThV60YIoYqhzaGsQjheKkQT1sOXYUZbw4/t4u7I8d0pJMVfZvTj0fy5+hRhnf3xDtKSeCyTte/sp+uIMA78ksDQhzoS1ddMibJ2iLqqbYimwAnA00A3hHNlIUlplVqN94gReI8YQUFcHKmffEL6N9+Qs3kzOZs349SiBYEPP0zA/ffj4GtCVKTSuYp+EfJT4UKscKqSt0LmX2IcelNEX4L6Q7OB4tE7ClSNLAaQFQdxn4p0ydJc8VzwQIh5FQJ7yjpV8YUL/D10KOVXruB9xx20+uQTWY9vEDv0j/3qbsr8HNFIzM3biTLpFk3FjKCRXZ0KVpNNNwe5ZNMtHaJSIlS3xhZS/owRQzf4pkMdyKZboQ9VQ6GwsNCo7YuKlHqO+k5bL/iwJzy1R6T+ZRULZ+pUDkzaBmeuiO1e3AdFtUhm34yzmwMzlwzFp5kba9/aR8GVEqJHhqFWq/ju5d1cTsolPDqAya/3Zvf3Jym4Yt7iDBArtMHAN8ADgAuwH3gQEbXKNH+KmnCNiiJiyRK6p6QQ9s47OIWGUnzhAuefe469wcGcmDKFq7t3m14fqw2CqBkw6hd4MBtGb4SOT4BXWyjLF47WrsdhZUf4wg/W3wlHP4H0gw03glWaJ4RA1vSGFe3h2ELhTAX1g7E74I6tsjtTpenp/D1kCKUXL+LRrx9tV6xAZVfH8ZZCYB/iu6tv3UxZVlJBcX4ZGju14QqdNaBEqBoppqf81d/GvqaYYNLbVSJUt8aa78MI30bulD/rqPyZt78JcVmbw83NjbS0NPz8/GrfGPD39+fIkSOEhdVBN0kFi9G7KWwdDbvSoIl+jdTaEyaGw4QtQhL73y3hPx1E+p9aZVxEdsSjXSjMLcHFXSjV/fx/B2jRwZeB09rzzQu76DCwOR6+zuTnlODa5J9S0zqdhFpt5H+XIzAJGAl8DaxD1FVtA8YCdwOWafkEgL2XF82eeYbgp54ie9MmLi5aRHZsLBnLl5OxfDnOrVoRcP/9+N97r+k1N3bOEDJUDID8FEjaAqm/iZGfAufWigFCKdC3K/jHgP9t4NsZPFqBWuZGXnVBzmlIjIXE9ZC6E3R6ERR7LbSeAlEPg08Hi0xddP48x4YNo+jMGbSdOxP18891J5F+PTuAUqADogl2HZCXJW6iab2cZKl5VhyqRoap14xNi1JYqIbK6GWwEqG6NTYQoarEMil/Jr5eFxjpzKloOCp/kiSxdu1a3N3dDdq+vNzIkIWCzdLUFSaEi59LK8BBA+PCYHUCbE8VkuognCm49q+aViAauNZGpTMF4NvcHZVahV8LDx5eNIhVr+0lP7sI/1Dh4VSU69DYqclMysVJa4+bl7Ppb6wJ8BgwDliKUAP8HtG/ajxwFyKKZSFUajXew4fjPXw4RefPk/bZZ6QvW0bRmTOcf/55zr/4Il7DhxNw3314jx6N2tEMeXRtsKgPajtNfI5dTRCO1cVdkL4PrpyGS3+KUYmdixC+8O0EXlHgGQmebcA10DY+jwF05SKVL32fGBd/F++tCpWIQLWeInp4ObhZzJSc7ds5Pn485VlZuHbsSPuNG7Ez8PNSViTgR/3PI+pu2svJeQB4B8lTsKU4VI0UkxdN1lxtWaOxrw1EqOp9Hyo9sr8PE+qDjJFNN8L1quVlM0QpTE75M69vnCmlg7bIzJkzrW2CgpVx0Acs3vpL/LxlNDy7F2b+Dov6QEkFHM+GeQfEv82lQlg/ErwNvEnv5u3MpiVH8QxwJfL2IKa914+ykgqyL+aTlZpHq9sCAVjxyh90Gx1O97GtzH9TwcArwCngf4g0wK8Qi9K7gTEI+XUL4hwaStibbxL63/+SvWkTl/73P7LWratqFmzn6YnvhAn433sv7t27m3dTVqWCJuFitHtQPFecDekHIH2vvvbqMOQni9/T9964v4OHcK7cw8C9BbiHglsLcAsR/Zsc3OV3uCpKIfe8cPxyTukfTwhby29KR3b0gpBhogFy86Hg7COvLTchSRIXFyzg7BNPQEUFXsOH0+a777Br0sSi81bLMeAs4oZB/7qbNjNJ1KX5NpfHiVQcqkaGues0kxfF5kxcax8jw1bLRjUurVpUW8+ZsZiYQ11jqca+JvShMkblr3Zza35fZv39zPxHNT0SLcyt51ccADqdCdLVCg0OSYLzebAxWdRXtfcWDtPS4+L1H8/BgUyI8oI3usPL++H/jsJ/u12LYNVE+/7NcdLas+6DgyQfz6LXhEic3RyoKNMR+8lhWvdIp7y0grBOfnQZFirvm2uNaAx8FCFcEQ98CqxCOFajEemCFkRlZ4f3yJF4jxxJaUYGGd9+S/rXX5N/5Ahpn35K2qef4hweju+ECfhOmIBrVJQ8LSWcvG5MEQQoyoKso5B5BHKOQ/YJ4cSU5FyLCN0KjRO4+IGzvziuvZtIt3NwEz+r7YUgRuVABRVFQqWwvEg4SKW5UJQJRRlQmAEl2dXb7h4GAd1FumJAd5G6WEepiuW5uZydPZv0r78GoNlzzxH62muoNFZMlVypfxwNmF/KZDCX9Q6VTzPFoVIwAdP7UFlpYgxJN5RflEJlSvKTkvJ3S2yhQbFxf3uZtpRDlKKOsf5fyjLk5+ej1daRDq+CTaFSQZi7aALs6QgVOtCo4YG28Ocl2JMObTxhWqSLOUrRAAAgAElEQVTYvokjHLlsmDNVSavbApn9xXAyk3Kritt9Q9x5/OsRLJi+kfQLV3ngwwE4ONshSVLVZ6JOJ5F7uZAmfgbkGdZER+ATRKTqS0TkaiFioXoPovbKwo4ViGbBwU88QfATT5D/99+kf/MNGcuXU3T2LEmvv07S66/jEhmJ7/jx+N51Fy7t2sn7/eDsDcEDxKhEkoSjk3MS8i6IqFGu/jE/GYrSoawA8pLEkAuVBrTNwLM1NIm4Nnw7y9YzyliyYmM5O2sWxRcuoHZ2pvX//offxIlWsaWKowi5dCdEKmsdcvFsDgB+oYpDpWAGJqcCmT2xOfs2zpS/BoMN9KEyqoZKri2tKJtuakS5vgdFb8bT05PbbruNAQMGMGDAAHr27ImjOfUdCvUOT/2fW6O+5lTtTRfP39VSSK4fz4aTOXBfa7Htnktw+gpMjaz9+A7OdgS19gKgokyHxl5NzqUC7Bw1dBkWyuo399H/3nZVUarMpFx++egQUoVEZnIu97/fH78WZihLqIAYhKz6n4gGwWcQjta3wEREBKCO9Aa0HTqgffddwt58kys7dpC5ahWZa9dSePIkifPnkzh/Ps4REfiMG4fPuHG4RUdb5uabSiWiTy5+QDVNvMsKoDBdjNIrQmmvLF/0eyrLF3VPUgVIumvDzlk/XMSjvRac/YSz5Own0vZsROa96Px5Eh5/nKx16wDQdu5M5Lff4tqmjXUN0wGL9T9PRKT81RGSJJFyPAuAZm3lSbFUHKpGhsmfV9aMMhhc/G9Yyp9hcxqzsRn7GEJ9X91aKuXPKBOMSPkzdFMD+1BZR+XPtItRpQ/M1vMr7h9s3ryZ3377je3bt/Puu++i0Wjo3r17lYN1++23W9tEhTpEo4acEvg0HpYPFPVSlwph3QXwc4YgfcCoqSu8clBEsT41QspZYy8W0t++uItOQ1vQa3wkSfGX2f39SToODOHMgTRO70+jib8r4+Z0449VJ/lz9WlG/acLdvZmpl6pgJ7A7cAfCFXAM8AiYAVCvGIMYIY2hlHm2NnhOWgQnoMGEb5wIVd++43M77/n8s8/U3T6NMlvvUXyW2/hGByM95gxeI0cSZN+/dA415GBAPau4BEmRgNCV1xM8nvvkfT66+iKi9G4udHi1VdpOmsWant7a5snVCpPAT6I67IOyUrJozC3FDdvJ5r4y6PkYhvus0KdYzVRCotEqIxbsBuzvrWqbLoNpMrJghUdi5sxonzO7Boq2R1JEzB2anNrLG2V/v37M3/+fHbv3k12djZr164lNDSU+fPn07t3b2ubp2AFissh3EPUVFXoYFEcZJdAn6ZCZh0gxA02jRL9rF6qpvymOnQVOqJHhtFtdDg6nUTzdj7cM78XedlFnDlwCdcmTgydIaSwNfYaMi5cNd+Zuh4V0AtYArwBRAI5+t8nIFIDr8o3nSGo7e3xGjKE1l98we2XLtFh+3aCZs/GISiIkpQULi5cSNyIEfzp7c2xUaNIXbSIooQEm/juqE+UZWeT+MYb7AsN5cLcueiKi/G75x5uO3mS4CeesA1nKh/4TP/z/dSZg19JYtxlQESn5IqMKhGqRkZ9rKGSTTbdmDoaW4pQ1XdsQDbdIn/7WjY0T9XK9F3F3FaZ1qYpLCzk999/Z/v27WzdupVjx47Rvn17BgwYUPvOCg2OQFfoGQAdV4G/M7Tzgntbw+0B4vXVCVBYLp6L9gNnI30dtUZN93ERVb9XyqcnH89CV66jba8gXJs4cTWjkEsJV7htdEsAkk9ksePreAbe356mrTzNf6MqoAfQHTiAiFjF6x+/R8hUjwcCzJ/KKLPs7PDs3x/P/v1p+eGH5B08SNavv5IdG0v+X39VqQUCOLVogefgwTQZNAjPAQOw97GsCl59pej8eVI//JC0L75AV1AAgLZTJ8L+7//w7F+H8nmGsBC4DLQBhtT99Gf2XwIgrIu/bMdUHKpGitUa+5qDFWqoTHq/SmPfW2MDNVTG2NAQaqhMnbuhXHKV9OnTh/379xMeHk6/fv146aWX6N+/P56eMixYFeotL3aFO8OgVAcdvG987fYAGLAOVpwRKYEDgsybS2MnEoIOxp4juI0XgeGe6HQS8b8no1KDZ6AQTWnWxpuI7oEsf3EX/Sa3pdu/ws2buBIVor6qG0Km+jtgL0Jq/WeEXPV4IKK6A1gOlVqNe7duuHfrRuj8+ZSkpZGzcSNZ69dzZds2ii9cIO3zz0n7/HMAXNu3x6NvX5r07YtHnz44GNi8uyGiKykh65dfuLRsGdnr14Ne3dRzyBCCn34az0GDbEIY6gb2ABsRin7PAXUsMChJEqf2XgQgolugbMdVHKpGhvE9dm7a0eSJzdlXJpU//aNhvYhs4APIFmyQAdnl382QTTfob2+sIZZI+TM7ImxGDVUDZP/+/bi7u9OjRw969epFr169FGdKAYDI6y6DHxJgWDNwcxD1U49GwZUSmBkFHjLIOZeXVuDi7kBEjFjE7VlzmszEXJq186ZFB6H8VlGuI2ZMK7yaavn9uxN0GRaKnYPMK872wJvAOYQS4LbrRmdEg+AYrFYU4hgYSMC0aQRMm4ZUUUH+4cPkbN1KzpYtXP3jDwqOHaPg2DEuLlgAgEtkJG4xMbh3745bTAza9u1R2TXc5a2upISru3aR+cMPZP7wA+U5Qq1OZWeH36RJBD/1FNqOHa1sZTVkA+/pf54ONK97Ey6du8KVSwVovZwIivSS7bgN94pTuCVG9WK6FVasoao1WmRoHyqjJjVmY8tQ7/PHLVVLZEJjX6MiVLVtWheiFGar/FllWpvj6tWr/PHHH2zfvp2PP/6YqVOnEhERUSVKMWbMGGubqGBlynXwzSn49QIsGwhXS8RzgS7XmvxKknk3HewcNIRHB/DZrG206xPMxdM5jJzdhbAuIsKSdjaH9QsP039qFGlnruDu40xpcTkae7VlbvKFAS8gFrZrgF+Bw/rRDCFjPZQ6r2+5HpVGg1t0NG7R0TR/7jl0xcXk7t/PlR07uLpzJ7l79lB48iSFJ0+SvmwZAGpnZ7SdOqHt1AnXTp3Qdu6Ma1RU3QpdyIgkSRTGx5OzZQs5W7ZwZedOdIXXmgNrO3XCf+pU/O65x7ajdSXASwinqgNwp3XMOLZNyOO36RmE2pj+CLWgOFQKBmG2I2ZGtXvtEQ4LlNKb0thXZjMaTGPfeoZNCDPI1djXVFGKBnbJOTo6VjlPAMnJycyfP5/FixezYMECKioqrGyhgrWxU8O6EfDwTuj9I7T0gIwimNv12jZy+DRdR4TRrK03V9IL8ZvlfkMPqsBwTzoMCOGb53+n6/BQmrXxxsW9DuT9/YGZwL1ALLAWSAY+ApYi6qzGAvJlR5mM2smJJn360KSPkEDXlZaSf/gwufv2kbdvH7n79lGckEDunj3k7tlz3Y5qnFu2xKVtW1zatsW1bVtcIiNxatkSexuLVpdmZJB34AB5Bw+KxwMHKMvIuGEb1/bt8f7Xv/CdMAFt+/ZWstQIdIio6AnE9TaPOk/1A+GcHtpwDoDOQ+VttK04VI0MU9dpVk0FklmUwjClN1OUD4zfpebjNZD8K7lV/swQDDEs3dNYQ2xX5c9YGsoldzPFxcXs2rWLbdu2sX37dg4fPoyTkxMDBw5k4MCB1jZPwYb4tC+sTwR/F9DaXVP8kxO/Fh5VPacyk3LJSMylXe9gANr0CuLMgTSGPtQJe0ex4ry+GbBF0SIUAO8Efkc4VvHAD8BqhBT7GKArNqMRrXZwwD0mBveYmKrnyrKyyD9yhPzDh6seC0+epOjMGYrOnCHr559vOIadpydOYWE4t2yJU4sWODRtimNQUNWjvY8PahcX2f4GupISSjMyKL10idKUFArPnKHo9GmKTp+m8PRpytLT/7GPQ0AAnoMHV4lzOAbagHdrDF8AOwFXhGMlX6adUaScyCbjQi5aL6eq1Fu5UByqRobJNVRVmJuDZM6+5olSmHT33RYa+9ajBfktsYEaqkoHWd4/fc3vyxYijEZFWG/Yr2HRpEkT1Go1MTExjB49mg8++ICYmBjsGnCdhYLpjAgxfNsl8aBRwf1twJTsoazUPH56dz/F+aV0HtKC1FPZVJTrKLhSTBN/Eb2q85peO2CAfpxCOFa/Ifpa/QEEIZoEDwPM6EVsKey9vfEcOBDP626W6IqLKTx9msLjxyk4fpzCEycoOnmSonPnKM/JIf/QIfIPHar2mCp7e+w8PbH38kLTpAkaFxfUTk6onZ3Fo4MDkk4nPu8lCUmSkEpKqMjPF6OggIq8PMoyMym/cqVG+9Wurrh17YrbbbdVDafQUNuo7TaFtQgRFDXwCiBvYMgoDsYmANB5SIsqoRi5UL5NGhkm/zs2BFEKY2ywBdn0+vrheROyfwlY+G9jeIBKnsipeUZUN7WJohTmTWuzxMbG0rNnT5ycnCw+18qVK3n11VdJSkoiICCAr776qqrX1bZt23j00UdJSkoiJiaGr776ipAQI1bvCjZFeiE89ScUlMPSE7CwD3T1Ne4YkT2CePDjgfzwxl72rzuLm7czwZHeVc6U1WkNPA88BKwHfgFSgU8RUYc+wCigIzb9AaJ2ckLboQPaDh1ueF6SJMrS0yk6d47ihASKk5IovXiRktRU8XjxIuVZWeiKiynLyPhH6p1JaDQ4+Pnh4O+PQ1AQzq1a4RIRgXOrVjhHROAYHIxKbSMhQHNZC3yi//lJINp6phQXlLF/3VkAuo2RST3zOhSHqpFiumy6vHbIOrmhjX1lnNLsCSxmhA1S32TTDd7UlmXTrTOtrVFXaX1btmzh2Wef5fvvv6dbt26kpaVVvXb58mXGjRvH0qVLGT16NHPnzmXChAns3bu3TmxTkB8/Z1jaD578E/ZlwG2r4aG28HoMeBnhu/u18ODRz4aSfDwLv1B3HJ1rbryq00mseHk3nYaG0rZXUN1ELryAycDdwD5gHbCfa+qAwYhaq6FYLZ3LFFQqFQ4BATgEBOBx++3VbqcrLqYsJ4fy7GzKr1xBV1SErrj42mNZmfg7XDfUTk5oXF3RaLViuLpi7+ODnZdXw3GYqkNCNI/+Rv/7Y8BI65kDsH/dWYrzywjr7EdwpHftOxiJ4lA1Mkxu+GlutbotNPaVcco6wSaMkAEbSH0zZsFheFpsHUSozBSBMfaUN5ArzmrMmzePl19+me7duwMQFHStedHatWtp164dd911FwCvvPIKPj4+nDx5ksjISKvYq2AeKhVMbCVSBOcfhI+OwafH4Ydz8EYMTI8EjRHr5mZtDVvkxf2WxMHYcxyMPUdIex+Gz+xMRExg3ThWGkQt1e3AJUTUagOQAnyGiFr1QDhWMUDNvmG9Qe3khGNgYP2rXbIG5QhBk18RaX5PYnVnSqeT2L3yJAB97mlrkTkauIusUB3G96HS72fLsumGNva1eIhKXhqKbLotvA9j+lAZbK5MkdNbG2H8rnC9D6s09q0rKioqOHjwIJmZmYSHhxMcHMysWbMoKioCID4+no7X9YZxdXWlZcuWxMfHW8tkBZlwd4D3boejd4kGwFnF8NBO6LYG/kirfX9jadMrmDFPRaP1dCLx2GU+fWQLC6Zv5OzBS/JPVhMBwP2IXlZvIJwsCdgNzEX0s/oYUYelfKY0DnKApxHOlAMwH6s7UwBHtyaSmZSLZ4ArUf2aWWQOxaFqZJh6/8rsO1/m3Ky3Sg2V9VX+6m0B6s3YQA2VMSbYRA2VuSiNfeuc9PR0ysrKWL16Nbt27eLIkSMcPnyY1157DYD8/Hw8PG6s4Pfw8CAvL88a5ipYgLZesHU0fD8Ygl3hr8vQ6ye4Zwsk58s3j72jhn6T2/HSr+MYObsLLh6OnDucwcIHN7HgASs4VhpEVOp1YBXwMEJ44Crwo/73qcDXiPorhYZJPOJvfRSR9vl/QE+rWgSIZtkbFx8GYND09rKLUVSiOFSNFKvVUFlS5U+pobJtbKKGyhKb1rylVSNzSg2VbPTr1w+VSnXL0atXL5z1TUNnz55NYGAgPj4+PPnkk6xfvx4ArVZLbm7uDcfMzc3Fzc2tzt+LguVQqWB8OJy8G17uCk4aWHEWWq+AVw9AYZl8czm62DPo/vbM/XUcw2d2wtnNgYRD6cKxmr6RMwfS6v7zxxshvf4FIgXwTsAT0dfqS0Qd1qOIRsKX69Y0BQtRAXyFqJPKANoBS/SPNsCh9efIuJCLd7CWmDGtLDaP4lA1MsxvWmoF2fTaanAM7GJqzHs36U693B1hbaD2SA5klw835TwbkQJndA2VJWTTzU35q9rduAM01Ma+crBjxw4hhXyLsXv3bjw9PQkODq42styuXTuOHj1a9XtBQQEJCQm0a2cjqw4FWXG1h1e7wYmJcFdLKCqHVw5C5Er49jToZPwfc9I6MOTBjsyNvZPhMzvh4u5Awl/pLJqxmU+mb+TEH6l171ipgFbALEQfq7eBwYATcBxYAIwH/oNQgsusW/MUZCIB8Tdehvi+mgB8APhY06hrlBSWsWHxEQCGzuiExt5ybo/iUDUyTBelaACy6WaYYJUJGkr+laVS/kzoQ2XM4c02RA6HykTMFZ9R/CnTmDZtGp988gkZGRnk5OTw4YcfMmrUKADGjh1LXFwca9asobi4mPnz59OhQwdFkKKB08IdVg2BHWOgo7dI/Zu8DbqvhV0X5Z3L2e2aYzXi0c64uDtw/nAGn83ayvv3/MqRLRfQVejkndQQNEA34AWE8/QS0Ashi/Y3QlZ7PGJhvhIhcKFg2xQiIpAPAScBX+A9RMqfDQmRxC44zJVLBQS38abrCMs2wFJU/hoppi6YTL7JJceaWq6UP2PSvkx5w0rK362x5vsw4fqr1dy6qKGq4xTbBuLCW425c+dy+fJlIiIicHJyYvz48bz44osA+Pr6smbNGmbNmsXkyZOJiYlh5cqVVrZYoa7o2xQO/RuWnYKX9sOBDOjzM4wLhbe6Q6sm8s3lpHVg8AMd6H13G/5cfYod38STejKbZXN24hvizsD7oug6Mgw7e418kxqKMzBQPwqAPcDvCCn2eP1YAoQAvRG1WZEot/9thQqE4MQyhACFChgLTAdspHVaJReOZrB75QnUGhUT592O2hjJTRNQHKpGhumNfWUywJyUv+o3kOcwN2xrA0tLW7BBDmzofRik8me0uRZU+TMVM895A3Hh6xx7e3sWLVrEokWLbvn6oEGDOHnyZB1bpWAraNRwfxtRY/XeEXj3CKw9D+sS4eG28HI0+DrLN5+Tqz0DpkbRe2Ib9v98hu3L4slMzGXlq3+yYdFh+k5uS49xEThpHeSb1BhcgUH6UYToa7Ub4WQl6sdyRA1WDNAd0RjWxhbujQIJ+AP4HEjSP9cOmAlYRoXcLMpLK1g5/08kCQbc246g1pZvjqY4VI0Uk4MF5kaobEE23ag5jdjYQtiC3LgcyPY+TEn5M6E4yNwaKlnk4s0tWTQxQtVALjkFBZtEaw+v3AYPtoGXD8CXJ2FBnIhePdsZHu8garDkwt5RQ8/xkXQfG8Ffm87z27I40s5eYd0Hh9j8+d/c/u/W9Lm7DR5+LvJNaizOQF/9KEcoxVU6V+nARv3QIBbwXYHbgNb65xQsQwmwFZGqeU7/XFNgBtAHm01riP3kL9LPXcW3uTtDZnSsfQcZUByqRobZjX2tQK3F/UY29rVMlALZP1hsIkomB5YSpTDBBln7UNUihiK7GIcRmNqIu6FccgoK9YEgLXzRXzhQz+6FDUkiHXBhHMyLhvsjQc6sPI29mttGtSR6ZBgndqeyfVkcCYfS2f5VHDuXH6fz0Bb0m1I3d/NrxA7hMHVFKMddQDhWexEpgcf04ytEtKoz0En/2AIlPVAOMoGfEOl9leKk3sA9wGhsqk7qZuJ2JrNj+XHUdirumd8TB6e6cXUUh6qRYnoNlRVV/sw8uEmLRaWGymws5hiaEqGSdduG14eqkvp9xSko1C/ae8P6kfBbKszZAwcz4eHf4f2j8Fo3+HdLUMv4caJSqWjbO5i2vYNJjMvkt6/j+XtbEgdjz3Ew9hytugXQd3I72vQMQi3nxCYZi+hpFYpYzOcDR4ADwCFEX6vd+gHggXCuOgDtgTCUCJahlCIc1y36x0r9kgiE/H0/RLNeGyb7Yj4rXhYXw8hZXWjR0a/O5lYcqkZGfWzsW0W1joVxBzdosWgDjX0bXLjAmhGqKhPkTPmrOqh5r98Kmf70pqb8KSgo1D39g2D/nbD6HLy4D85chQlboMtheCMGhjST/2shJMqX+97pR1ZqHr+vOMG+H89wZv8lzuy/hG9zd3pOaE230eE4u9nISlqLUAjspf/9EvAXwsk6jOhttVM/AFwQKYJRCHGLSITTpSAoRZy7nQhxkMoG1GqEA3UnolaqHnw5FBeU8b8nf6Mwt5Q2vYLoN6VuW1IoDlUjpc5rqMzB0NSpWl43pYxLaewrA5ZKfTMqQmWCGLqZKX+yvG8z+1CZPG09v+QUFOorKpXoW3VHC/jyFLx6EP66DMNioXegiFj1aSr/vN5Bbox9uhvDHurE3h9Ps2vFSTKTcvnp3QNsWHiY6FEt6T0hEv8wGeUI5SAAGKEfEiJidRiREhgHpAEH9eP6fVrrR0sgHLBylmOdISGaLB/QjyOIOqlKwhH9wgYiUvzqCeWlFfzvye2knsrGp5kbk1/vXefRVcWhamSY22fG5HWWOaIUtRX3G9vY15gaKhOaxyqNfW9CDnEGmTCqhsrQLS0hSmHutWREI+MbdlP6UCko2AT2GpjRFia3EoIVbx+GXWnQ92cYFAz/vQ26B8g/r7ObA/3vjaLPPW2J/z2ZXStPcvbAJf5YdYo/Vp2iVbcAet4VSVTfZhZtkmoSKiBYP0brn7vMNTn2U8BpRFTrEteiWCCUBMMRKYItELLtIYgIV32mDNF8Nw7RUDkeyLhpm5aIiF9fRGplPUNXoeObF3ZxZv8l3HyceWjRYFzcHevcDsWhamSYnvJnpYkxJLogv2y6TVDvDK4GSzX2tZAJctVQmZUma3ZjX9MO0ECuOAWFBoOLPczpDA+3gw//FnVVW1PEGBkCr0RDtAXKRDR2ajoMCKHDgBDSzuawa+VJDsWeq0oH9PB1psedrek+tpV11QFrw4dr6oEg+iglIZrRnkY4GwmInkqVUZvr8UM4aE2BIP1jU/3zbtjOh6YEZCOk5s8hhDzOId5b6U3buiPk57shlBLrcXROkiR+eGMvf29LxElrz8MLB+ET7GYVWxSHqpFi8h1oa4hSGDq3oY19jZnSBu7V20JkRxasmPJnig2ybVmPZNPN3U9BQcEyuDuIPlWzokQPq4+OQWyiGKNChCqgJRwrgMBwT8a/1IPR/+nKgV8T+OOHU2Scv8rGT4+w6bOjtO0VRPdxEbTpGYTGzsaiVjej4ZrIxXD9czpExOoswhFJ1D8mI6I5GYg6rZtxAnwRTpsP0EQ/PPSP7ggVQpfrHo0RyKhA9OcqBvKAq9eNK3q70vUjgxtT966nGaIOqq3+McRIO2wUnU7ix3f2s3ftGewdNTzw0UCaRljPO1QcqkaG6Y19bVixzEjZdMOmtL4oRUORTZddPtykdEzjZdMNPabJr1sSU9sjyGuFgoKCzHg5wRvd4YmOojHwwjj4NVGMEc2F0xXjb5m5nd0c6HN3G3pPjOTsQZEGeGxHEvG/pxD/ewoevs50GxNOtzGtrBYlMAk11yJPfa57vgLhaKUAFxH1WamIuqwMhLOTrB+GokFIjjsgVuB2iO+yyqFD9OEqRqTrGYMb0ByRshiGcBrDaJAiHGUlFXw7dxdHtySisVcz9d1+tOxioQvfQBSHqpFSL+9cy9XY17gQlfEoohQ3YgMOlSk1cYaba0GVP3NPmYnnvJ5fcQoKDR5fZ3inBzzdUaQBLoiD9UliDGkGc7tCr0DLzK1SqWh1WyCtbgskL7uIA78ksPfHM2Qm5rJl6TG2LD1GeHQA3caE03FgCA7O9XSpqUGk+QVV83oBol9TJpCFiBpdP/L12xRe91ihH8UGzK9CRMGcEM6Sx03DF/BHiGz4IaJgjYDC3BL+98RvJPyVjpPWnmnv9ScixkIXuxGopAaTT2QZrl69am0TFBQUFBotHh4N8PZqDSjfOQoKCgrWxZTvHRtPdlVQUFBQUFBQUFBQULBdFIdKQUFBQUFBQUFBQUHBRJSUPwUFBQUFBQUFBQUFBRNRIlQKCgoKCgoKCgoKCgomojhUCgoKCgoKCgoKCgoKJqI4VHVAdnY2Y8eOxdXVlZCQEL777rtbbrds2TK6du2Ku7s7wcHBzJkzh/LycqOPYw3bFixYQHR0NI6Ojtx3331m2yWXbSUlJUyfPp2QkBDc3Nzo3LkzGzZssLpdAJMnTyYwMBB3d3ciIiJYunSpWXbJaVslZ86cwcnJicmTJ9uMbf369cPJyQmtVotWq6V169Y2YxvAypUradOmDa6urrRs2ZJdu3bZhG2V56tyaDQaZs+ebRO2XbhwgREjRuDp6UlAQACzZs265fWo0HCp6RrYtm0bkZGRuLi40L9/fxITE61sreWo6bu0pvMgSRLPPvss3t7eeHt7M2fOnIbTFJ7qz8vevXsZPHgwXl5e+Pr6ctddd5GWllb1emM9L9fz6quvolKp2Lp1a9Vzjfm8FBYWMnPmTHx8fPDw8KBPn2vNx8w6L5KCxZk4caI0fvx4KS8vT9q1a5fk7u4uxcXF/WO7RYsWSb///rtUUlIipaSkSF26dJHefPNNo49jDdvWrFkj/fjjj9LDDz8sTZ061Syb5LQtPz9fmjdvnnT+/HmpoqJC+uWXXyStViudP3/eqnZJkiTFxcVJxcXFkiRJ0okTJyR/f3/p4MGDJtslp22VDB48WOrVq5c0adIks+yS07a+fftKn3/+udn2WMK2zZs3S82bN5f27NkjVVRUSCkpKVJKSopN2HY9+fn5kmFZM5UAABy9SURBVKurq7Rz506bsG348OHS1KlTpaKiIiktLU2KioqSPvroI7NsU6hfVHcNZGZmSu7u7tKqVaukoqIi6emnn5ZiYmKsba7FqO67tLbz8Omnn0oRERFScnKylJKSIrVp00ZavHixFd6BZajuvKxfv15atWqVdPXqVamgoECaNm2aNHTo0KrXG+t5qeTs2bNSVFSUFBgYKG3ZsqXq+cZ8XiZNmiRNmDBBysjIkMrLy29Yd5lzXhSHysLk5+dL9vb20qlTp6qemzx5svTss8/Wuu/7778vjRo1yuzjWNq263nxxRdlcagsYVsl7du3l1avXm1Tdp08eVIKCAiQvv/+e5PssoRtK1askO666y5p3rx5ZjtUctomt0Mlp209evSQli5dapO2Xc9XX30lhYaGSjqdziZsi4yMlGJjY6t+f/rpp6UZM2aYbJtC/aO6a2DJkiVSjx49qp7Pz8+XnJycpBMnTljDzDrj5u/S2s5Djx49pCVLllS9vnTp0gbpeNa2xjh06JCk1Wqrfm/s52XYsGFSbGysFBIScoND1VjPy8mTJyU3Nzfp6tWrt9zenPOipPxZmNOnT6PRaIiIiKh6rmPHjsTHx9e67++//067du3MPo6lbbMElrItPT2d06dPm2y73HbNnDkTFxcXIiMjCQwMZMSIESbZJbdtubm5vPzyy7z//vsm22Mp2wCef/55fHx86NmzJzt27LAJ2yoqKjh48CCZmZmEh4cTHBzMrFmzKCoqsrptN7Ns2TLuvfdeVCqVTdj2n//8h5UrV1JYWEhqaiobNmxg2LBhJtumUP+o7hqIj4+nY8eOVdtVptKa891XH6ntPNz8urnrg/rKzZ8tjfm8/PDDDzg4ONxyXdFYz8u+ffsICQlh3rx5+Pj40L59e9asWVP1ujnnRXGoLEx+fv4/Oi57eHiQl5dX435ffvklBw8e5OmnnzbrOHVhmyWwhG1lZWVMmjSJqVOnEhkZaRN2LVq0iLy8PHbt2sW4ceNwdHQ0yS65bZs7dy7Tp0+nWbNmJttjKdvefvttzp07R2pqKjNmzGD06NEkJCRY3bb09HTKyspYvXo1u3bt4siRIxw+fJjXXnvN6rZdT1JSEjt37mTq1Kkm2yW3bX379iU+Pr6qxio6Opo77rjDLPsU6hfVXQOW+O6rj9R2Hm5+3cPDg/z8/AZVF1Mbf//9N/Pnz+fdd9+teq6xnpf8/HxeeOEFPvzww2pfb4znJSUlhbi4ODw8PLh48SILFixg6tSpnDhxAjDvvCgOlYXRarXk5ube8Fxubi5ubm7V7vPTTz/x3HPPsWHDBnx8fEw+Tl3ZZgnktk2n0zFlyhQcHBxYsGCBzdgFoNFo6NWrFykpKSxevNjqth05coStW7fyxBNPmGyLpWwDiImJwc3NDUdHR6ZOnUrPnj1Zv3691W1zdnYGYPbs2QQGBuLj48OTTz5pE7Zdz9dff02vXr0IDQ012S45bdPpdAwdOpRx48ZRUFDA5cuXycnJ4dlnnzXLPoX6Q03XgCW+++ojtZ2Hm1/Pzc1Fq9WaFYWuT5w9e5bhw4fz0Ucf0bt376rnG+t5mTdvHlOmTKn2c76xnhdnZ2fs7e156aWXcHBwoG/fvvTv35/NmzcD5p0XxaGyMBEREZSXl3PmzJmq544ePVptKs7GjRt58MEH+eWXX2jfvr3Jx6lL2yyBnLZJksT06dNJT09nzZo12Nvb24RdN1NeXm5WpEUu23bs2MGFCxdo3rw5AQEBvPfee6xZs4YuXbpY3bZboVKpzLqrJpdtnp6eBAcHy/qFZInz9vXXX5sdnZLTtuzsbJKTk5k1axaOjo54e3szbdo0sxxRhfpFTddAu3btOHr0aNW2BQUFJCQkWDTl3Bap7Tzc/Lq564P6RGJiIoMGDWLu3LlMmTLlhtca63nZtm0bH3/8MQEBAQQEBJCcnMz48eN5++23gcZ7Xjp06FDj62adFxPrvBSMYMKECdLEiROl/Px8affu3dUqYW3btk3y8vKqVnnL0ONYw7aysjKpqKhIeu6556TJkydLRUVFUllZmU3Y9tBDD0kxMTFSXl6eWfbIaVd6erq0YsUKKS8vTyovL5c2btwoubi4SD/99JPVbSsoKJDS0tKqxlNPPSXdeeedUkZGhtVty8nJkTZu3Fh1fS1fvlxycXGRTp48aXXbJEmS5s6dK0VHR0vp6elSdna21KtXL+mll16yCdskSZL++OMPycXFRcrNzTXLJrltCw0Nld58802prKxMysnJke644w7pnnvukcVGhfpBdddARkaG5O7uLq1evVoqKiqS5syZ0yCL5yup7ru0tvOwePFiKTIyUkpJSZFSU1Oltm3bNijVturOS0pKihQWFia98847t9yvsZ6Xy5cv3/A9HhwcLK1atapqHdRYz0tpaanUsmVLaf78+VJZWZm0e/duSavVVom7mHNeFIeqDsjKypLGjBkjubi4SM2aNZO+/fZbSZIkKTExUXJ1dZUSExMlSZKkfv36SRqNRnJ1da0aw4YNq/U4tmDbvHnzJOCGMW/ePKvbduHCBQmQHB0db3h9+fLlVrUrIyND6tOnj+Th4SG5ublJUVFR0meffWayTXLadjNyqPzJZVtGRoYUHR0tabVaycPDQ4qJiZE2b95sE7ZJkiSVlpZKjzzyiOTh4SH5+/tLs2fPloqKimzCNkmSpBkzZkiTJ082yx5L2Hb48GGpb9++UpMmTSRvb2/p3//+t5Seni6bnQq2T03XwJYtW6TWrVtLTk5OUt++fc1qe2Hr1PRdWtN50Ol00jPPPCN5enpKnp6e0jPPPGOWiqetUd15eeWVVyTghs8WV1fXqv0a63m5mZtV/hrzeYmLi5O6d+8uubi4SG3atJHWrl1btZ8550UlSQ28Ak1BQUFBQUFBQUFBQcFCKDVUCgoKCgoKCgoKCgoKJqI4VAoKCgoKCgoKCgoKCiaiOFQK/9/e3UfVlP1/AH/fQjelbg+3h2GkZFIeU66RTFcNeZiRMCWJkBkNa2KKQYZoEGtkjDGeFoZ7MZ6imWlYQ09KcW8lUlFSMiYMBl0qqf37w6+zHPf2qHnw7fNa66zV3WeffT7n3Nbed5+zzz6EEEIIIYSQFqIOFSGEEEIIIYS0EHWoCCGEEEIIIaSFqENFCCGEEEIIIS1EHSpCmkEgEHCLSqVq1rampqbctr///nuDeVUqFTp37gylUtmksn/44Qe0a9euWfE0RUZGBiwtLfHkyZNWLXfTpk3o0qULtLS0EBERoTFPeXk5LCwseG8tB4ADBw5g4MCBoDc+EEJa6r9al/+XdevWDV999RX3WSqVIigo6G/dZ0lJCQQCAVJTU1tcxqtxt5YPP/wQX3/9dauWefPmTXh4eEBPTw8CgaDefGvWrMHEiRN5aTU1NXBwcMAvv/zSqjGRpqEOFSHN9N1336GsrAx6enpcWnl5OWbNmgUTExPo6elh1KhRKCoq4m2Xl5eHo0ePNmkfa9euhbOzMwYOHNik/L6+vrh161bTD6KJ5s+fj4ULF/KO9XX98ccfmDdvHhYvXoxbt24hLCwM77//PgIDA3n5OnXqhM8//xyhoaG89EmTJuHp06fYt29fq8VECGl7/ot1+ZskJiYG0dHR/3YYjVIqlZg/f36rlhkfHw+lUom5c+e2armrV6/G3bt3kZ2djbKyMqSmpkIgEKCkpISXLyQkBMnJybyOpra2NiIiIhAaGora2tpWjYs0jjpUhDSToaEhLCwseFePAgICEB8fjyNHjiA1NRWMMQwfPhwVFRVcHjMzMxgbGzdafmVlJbZs2YJPPvmkyTHp6urC3Ny8eQfSiIyMDCiVSkybNq1Vy71+/Tpqa2sxduxYWFpaQl9fv968gYGBSE5OxuXLl7k0gUCAmTNn4ptvvmnVuAghbct/sS5/Hc+ePftH9lPH2NgYBgYG/+g+W0IsFrfqRUEAiI6OxtSpUyEUClu13MLCQkgkEvTo0QMWFhb15uvYsSP8/PzU2kFvb2/cv38fv/76a6vGRRpHHSrSZp0+fRo6Ojp4+vQpgBeNn1AohKurK5cnMTER7dq1w+PHj+stp6CgALGxsdi6dSuGDRsGR0dHHDhwALdu3cLBgwebHdfJkydRUVGBESNG8NJXr14NGxsb6OjoQCwWw9PTk2vkXx3y161bN96QlrolKSkJAPD8+XNERETA2toaQqEQvXr1wrZt23j7k8vleO+993g/HB4/fozp06fDwsICOjo6ePvtt/H5559z66uqqhAcHAxDQ0MYGRkhODgYixcvhq2tLQAgIiICQ4cOBQB07doVAoEAgYGBiI+Px549e9TiNDMzg4uLC+RyOS82b29vZGZm4sqVK80+v4SQ/y1vUl1eN4Tt0KFD+PDDD9GxY0fY2NhAJpPxti0rK8OkSZMgEomgq6sLqVSKjIwMbn1SUhIEAgHi4uLg6uoKoVCI7du3IzAwEO+//z43rFpfXx9BQUGorq7G1q1bYWVlBSMjI3z88ce8DtipU6cglUphbGwMQ0NDuLm5QaFQNHh8Lw/5q4vn1aVbt25c/mvXrmHChAkQiUQwMjLCiBEjkJOTwyvz0KFDsLW1hVAohIuLCy5dutToec7NzYWnpydEIhH09PRgb2/PO58vD/n74YcfNMYplUq5/JmZmRgxYgT09fUhFosxfvx43Lhxg1t///59nDx5EuPGjePFERsbC0dHR3Ts2BEikQgSiQQXLlzg1icmJqJv374QCoXo27cvEhMTIRAIuPZNIBAgPj4eu3bt4trGuvbS2tpaLU5vb2/ExsaivLycS2vfvj3GjBmj1maSvx91qEibNWTIEAgEAqSkpAAAzp49i06dOkGhUHBj6hMSEuDs7NzgVbizZ8+iffv28PDw4NKMjIwgkUhaNO47OTkZjo6OvA5STEwMoqKisHHjRhQWFuLUqVMYNWpUvWUolUqUlZVxy7Rp02BhYYGePXsCAIKCghATE4Nt27YhPz8fy5YtwxdffIGdO3fy4pBIJLxyly5diqysLMTGxqKwsBAHDx6Evb09t37RokU4evQo9u7di/T0dOjp6WHz5s3c+rCwMG6oTFZWFsrKyrBx40YMHToUPj4+XLwuLi7cNoMGDUJiYiIvDmtra5iZmamlE0LanjepLq+zaNEiBAQE4NKlS/Dx8cH06dNRWFgIAGCMYdy4cbhy5Qp++eUXKBQKmJubY/jw4bh37x6vnNDQUCxcuBD5+fncD3ylUomMjAycOnUK+/fvh1wuh5eXF9LS0nDixAnIZDLIZDJefa9SqTBnzhycO3cOaWlp6NGjB0aOHIn79+836VhdXFx4bU5ubi7eeustDBs2DABw584duLq6wszMDCkpKTh37hzs7OwglUrx559/AgAuXLiASZMm4aOPPsLFixcRFhaGkJCQRvft5+cHExMTpKWlIScnB9HR0TAyMtKY19fXlxdnWloaOnXqxMWZl5cHNzc3DB48GBkZGUhISIC2tjaGDx+OyspKAOCG4Q0YMIAr9/bt2/joo4/g5+eH3NxcpKenY968edx3/8cff+CDDz6Ak5MTsrKysH79erVjKysrw+DBgzF58mSubYyNjQUAKBQKlJWVISYmhssvkUhQU1Oj9r+pqc0k/wBGSBvm5ubGFixYwBhjbMmSJWzGjBnM3t6excXFMcYYc3FxYYsXL+byA2AymYxXxqpVq5ilpaVa2RMnTmSjR4/mpSUmJjIA7ObNm/XG5OXlxXx8fHhp0dHRrEePHuzZs2cat9m9ezfT1tbWuG7Hjh2sY8eOTKFQMMYYu379OhMIBCw/P5+Xb8WKFaxfv37cZ0NDQ/b999/z8owdO5ZNmzZN435UKhXT0dFh27dv56U7OTmx7t27c581nQMPD496y924cSMzNTVVS3d0dGRhYWEatyGEtC1vSl1eXFzMALD169dzadXV1UxPT49t3bqVMcbY6dOnGQCWm5vL5amsrGQWFhZsxYoVvP3v3buXV/60adOYWCxmVVVVXNro0aOZiYkJq6ys5NLGjh3LJkyYUG/sNTU1TCQSMblczqVZWVmxyMhI7rObmxubOXOm2rbPnj1jUqmUubq6cvtcvnw5GzRoEC9fbW0ts7GxYRs2bGCMMebv788GDx7My7Np0yYGgKWkpNQbq4GBAdu9e3e961+Nu87Dhw+Zg4MD8/HxYbW1tYyxF+fP19eXl6+yspLp6uqyY8eOMcYY27BhAzMzM+PlycrKYgBYcXGxxhjCw8NZ165dWXV1NZf2888/q/0fvnpOU1JSGizXyMiIfffdd7y02NhYBoCpVCqN25C/B92hIm2au7s7EhISALy4gunh4YFhw4YhISEBKpUKSqUS7u7uLS6/oVl66lNRUaE2LtvHxwfV1dWwsrJCYGAgZDIZ7zZ/fRISEjB37lzIZDLuoeiMjAwwxuDs7Ax9fX1uWb16NXeFtL44Pv30Uxw5cgS9e/dGSEgITpw4wT38WlRUhKqqKt7dJQC8YTctIRQKec8vNJZOCGl73pS6vE7//v25v9u1awdzc3PcuXMHwIshbCYmJnBwcODy6OjoYNCgQcjNzeWV8+ooAgCwt7dHhw4duM8WFhaws7ODjo4OL+3u3bvc5+LiYgQEBMDW1hYGBgYwMDDAo0ePeEPdmio4OBg3b97EsWPHuH0qlUpkZmby2pxOnTqhpKSEa3fy8vIwZMgQXllNaT/CwsIQFBQEqVSKiIgIZGVlNbpNTU0NfH19YWBgwA03r4vz2LFjvDhNTExQWVnJxanpe+3bty88PT3Ru3dveHt7Y+PGjbh58ya3Pi8vDxKJhHe38nXbRkBzO1gXG7WP/yzqUJE2zd3dHRcuXEBpaSkyMzPh7u4Od3d3xMfHIyUlBVpaWmoV/KssLS1x79491NTU8NLv3LnT4EOl9RGLxXjw4AEvrXPnzrhy5Qp27doFMzMzREZGws7Ojldhv6qgoAATJ05EZGQkxo8fz6XXdYDS0tKQnZ3NLZcvX+aNV9cUh6enJ0pLSxEeHo7KykpMmTIF7u7uqKmp4aYxb8kPj4Y8ePAAYrG4yemEkLbnTanL67zc4QFe1Jsvz8ymqR5ljKmla5psoX379mpla0p7eX8ffPABSktLsXnzZpw7dw7Z2dkwMzNr9kQX69atQ0xMDOLi4mBqasql19bWwsPDg9fmZGdn4+rVq9yrMzQdX1N8+eWXKCgogI+PDy5fvox3330XS5cubXCbzz77DFevXkVsbCyvc1RbW4uAgAC1OAsKCrjnxTR9r9ra2jhx4gQSEhIwcOBAHD16FO+88w43hbmmY2uNtlJTO/jgwQNoa2s3aeIU0nqoQ0XatEGDBkFXVxcrV67kZtUZNmwYcnJycPjwYbz77rvQ1dVtsIwhQ4agurqauzoKAA8fPsT58+dbdAVqwIABalchgRdXKEeOHIl169YhJycHT58+xfHjxzWW8eDBA4wZMwYTJkzAggULeOucnJwAAKWlpbC1teUt3bt3bzQOY2Nj+Pn5Ydu2bYiLi0NycjLy8vJga2uLDh064OzZs7z8aWlpjR5zhw4d1H7E1MnJyYGzszMvraKiAkVFRWrphJC26U2qyxvTq1cv3Lt3D3l5eVxaVVUVFAoFevXq1ezyGnP//n3k5eVh0aJF8PT0hIODA4RCIe8OVlMcP34cy5YtQ0xMDOzs7HjrnJ2dkZubi86dO6u1O3Udgl69eqm1H69+ro+NjQ03gmLlypXYsmVLvXm//fZb7Nu3D3FxcTAzM1OL89KlS+jevbtanHXPZQ0YMAAqlQqlpaW8bQUCASQSCZYsWYIzZ87Azc0Nu3fv5o7t/PnzvHauKc/l1XW8NbWPhYWFqKqqUmsHc3Jy4OjoCC0t+on/T6KzTdq09u3bw9XVFXv27OGGgxgbG6NPnz6QyWRNGiLyzjvvwMvLC8HBwUhOTkZ2djYmT56Mzp07w9fXt9kxjRo1CsXFxby7Tzt37sSOHTtw8eJF3LhxA/v27UN5eTlvSMjLxo8fD5FIhOXLl+P27dvc8uzZM9ja2mLGjBmYNWsWZDIZrl27hosXL2LXrl1Yu3YtV8bo0aNx5swZXrnh4eGIiYnB1atXUVhYiH379kFfXx9du3aFnp4eZs+ejaVLl+Knn37C1atXsXDhwibNxGdtbY3MzEwUFRXh3r17qK6uBvDiqt6ZM2cwZswYXv7U1FTo6OjAzc2tyeeVEPK/602py5vC3d0dEokEkydPxtmzZ3H58mVMnToVlZWVCA4ObnYcjTEyMoJYLMaOHTtQUFCA9PR0+Pn5NdoBfVlubi6mTJmCiIgI9OzZk2tz6iacmDt3LmpqajBu3DikpKSgpKQEqampCA8P5y66zZ8/H+np6QgPD0dBQQGOHTuG9evXN7jfusk0EhISUFxcjAsXLuDkyZP1to3x8fEIDQ3Fpk2bYGxszMVZd8dpyZIlyM/Px5QpU6BQKFBcXIzExESEhITg+vXrAF4M17S0tERycjJXblpaGiIjI3H+/HmUlpYiPj4ely5d4uIIDg7Gn3/+iY8//hj5+fmIj49HeHh4o+fVysoKWlpa+PXXX3H37l08evSIW5eUlAQrKyu1TnZSUpJam0n+ftShIm2eh4cHnj9/zmtw3d3d1dIaIpPJIJVK4e3tDRcXF9TW1uK3335rVoNUx97eHlKplDftq5GREXbv3g2pVAp7e3tER0dj+/btvNmoXpacnIyMjAy8/fbbsLS05Ja6hmv79u2YP38+Vq1aBQcHB3h4eGDPnj2wsbHhyvD398fdu3d5d5iEQiGWLVsGJycn7kreiRMnYGhoCACIiorCuHHjEBAQAIlEgocPH2LOnDmNHnNoaChMTU3Rr18/iMVi7qpkUlISVCoVfHx8ePnlcjn8/f0bfIcVIaRteRPq8qYQCAQ4fvw4evbsiTFjxmDgwIG4ffs2Tp06xRtG11q0tLRw+PBhFBUVoW/fvggMDMS8efNgaWnZ5DKUSiWePHmCxYsX89qcumd3zc3NkZ6eDlNTU4wfPx52dnbw9/fHjRs3uP04OTlh//79+PHHH9GnTx9ERUVhw4YNDe63Xbt2+OuvvzBz5kzY29vD09MT5ubm2L9/v8b8KSkpeP78OaZOncqLs25YvL29PdLS0qBSqbi7dbNmzUJFRQVEIhF3vj755BPe92poaIj09HR4eXmhR48emDFjBvz9/fHll18CeDFs/+eff4ZCoUD//v0REhLSpJcim5ubY82aNYiKioKlpSW8vLy4dXK5XO0dZ9evX4dCocDMmTMbLZu0sn9zRgxC3jTQMDNUczRlZijGGDtz5gx766232JMnT1q8r9awcuVK5uXl9VplLF++nDfLX3OMGjWKRUVF8dJKS0uZSCSqd9YjQghpTFury0nr+uuvv5hYLGaZmZmvVU5L/w/Pnz/PzM3N2aNHj3jpwcHBbPbs2a8VE2kZukNFSDMFBQVBX18fT548adZ23bp1a/DdUS8bOnQoli9fjuLi4paE2GoWLFgAJyenZh9raygvL8fgwYMxb948XnpJSQl27NjBe2EkIYQ0V1uqy0nrEolEkMvlKCsr+1f2f+fOHcjlct571Wpra9GlSxdERkb+KzG1dQLG/n9qLkJIo65du8b93b1792bN0lNcXMw9WGptbQ1tbe1Wj++/KCIiAnK5nHfuCCHk30R1OfkvEAgEkMlkmDJlyr8dCnlN1KEihBBCCCGEkBaiIX+EEEIIIYQQ0kLUoSKEEEIIIYSQFqIOFSGEEEIIIYS0EHWoCCGEEEIIIaSFqENFCCGEEEIIIS1EHSpCCCGEEEIIaaH/AxGBSzcaRGlvAAAAAElFTkSuQmCC\\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   \"metadata\": {},\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   \"metadata\": {},\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\": 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  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/Course-1-Week-2-Optional-Labs-4-Feature-Engineering-and-Polynomial-Regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"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      \"image/png\": 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RoETdv3mTs2LE89thjpKenc+fOHfr378/w4cOJj49n0KBBOT6M75aVlcU//vEPAgMDOXfuHHFxcQwZMoSmTZvy0Ucf0alTJ5KSkkwf0JMnT+bEiRMcOHCAU6dOERcXx8yZMwH44YcfeOutt9iyZQsnT54sMGcwYsQIli9fbvr9p59+IiMjg969e6OUYurUqVy6dImjR49y4cIFIovxhjcajfTt25eWLVsSFxfH1q1beffdd9m8eXOR9yVEqRYR8XeSesgQiw6kAwkSpUb//v2pUqUK9913H926dePVV181lU2dOpVq1arh6urK4sWLGTt2LB06dMDR0ZHw8HCcnZ3ZtWsXu3btIiMjgxdffJFKlSrxxBNP0K5dO93j7d69m0uXLjF//nzTIENzeQilFIsXL+add96hWrVqeHp68uqrr7L6r8TYmjVrGDVqFPfccw/u7u4FfqgPHz6cbdu2mZaWXL58OcOGDaNSpUo0atSIXr164ezsjJ+fHy+//DLbtm0r8vXcs2cP169fZ8aMGVSuXJkGDRowZswYU5uFEIVTYUZcl3br16/nwQcf1C2rU6eO6f9jY2NZtmwZ77//vmnbnTt3uHTpEgaDgVq1auWYXiIwMFB3nxcuXCAwMBAnp4LfAtevXyclJYV7773XtE0pRVZWFqA91rm7zNwxs9WtW5f777+flStXMn78eNavX8/27dsBuHbtGs8//zzbt28nMTERo9FI1apVC2xjbrGxsVy6dMn0uAy0u6euXbsWeV9CVGQVNkioZ+zdgsK7+0O/Tp06TJs2jWnTpuWpt23bNuLi4lBKmV5z/vx5GjZsmKdunTp1OH/+PJmZmXkCRe45jHx9fXF1dSUmJoZatWrl2VdAQAAXLlww/X7+/PkCzyk8PJw5c+YQEBBA/fr1adOmDaDdNRkMBg4ePIiPjw/r169n/Pjxuvtwd3cnJSXF9PuVK1dynF/9+vU5efJkgW0RQpgnj5vKmDFjxvDRRx/x+++/o5QiOTmZ77//nsTERDp16oSTkxP//ve/yczMZN26dezevVt3P+3btycgIIApU6aQnJxMWloav/32GwD+/v5cvHiRO3fuANoEeGPGjOGll17i2rVrAMTFxZme7z/55JMsXbqUI0eOkJKSQlQhpgN4/PHHuXDhAhEREYSHh5u2JyYmmhLmcXFxzJ8/3+w+WrVqxcaNG4mPj+fKlSu8++67Oc7Py8uLuXPnkpqaSlZWFocPH2bPnj0Ftk0I8TcJEmVM27ZtWbx4MePHj6dq1ao0atSIpUuXAlC5cmXWrVvH0qVLqVq1Kl988QUDBw7U3Y+joyMbNmzg1KlT1K1bl9q1a/PFF18A0LNnT0JCQqhRowa+vr4AzJ07l0aNGtGxY0e8vLx48MEHTWMuevfuzYsvvkjPnj1p1KgRPXv2LPA83N3dTYEiNDTUtD0iIoJ9+/bh7e3No48+arb9oOU2WrZsSb169XjooYcYPHhwnvM7cOAA9evXx9fXl6effrrAVbiEEDkZlFLK3o2wlLs/AHIvX3r9+nX8/Pxs3SRRRsj7Q1RU+X1ugtxJCCGEyIcECSGEsKeoqL/nWVq92mJTfFtKhe3dJIQQdmeDRYNKSoKEEELYg96iQXeXGQwWHz1dHPK4SQgh7MFGiwaVlAQJIYSwBxstGlRSEiSEEMIebLRoUElJkBBCCHuw0aJBJSVBQphERkYSFhZm8f1ac6U7IcosGy0aVFISJEqR7t27U7VqVdLT0wtV31YfvnFxcTg5OXH69Ok8ZQMGDGDixIlWb4MQ5ZINFg0qKQkSpcS5c+fYvn07BoOBb7/91t7NyaFWrVo88MADrFixIsf2+Ph4Nm7cmGOCPiFEEVl50aCSkiBREBuNhly+fDkdO3Zk5MiRLFu2LEdZUZYZ7d69O5988onptbnvNl544QXq1KmDl5cX9957r2kdh4KEh4fnCRKrV68mJCSE5s2bM2fOHBo2bIinpyfNmjXj66+/1t3PuXPnMBgMOdbrzt3mzz77jKZNm1K1alUefvhhYmNjC9VGIYTlSZDIT/ZtX2go9O+v/ddKt4LLly8nNDSU0NBQNm/ezNWrV4GiLzNakHbt2nHgwAHi4+MZNmwYgwYNyrEOtjkDBgzgxo0b/Prrr6ZtK1asYMSIEQA0bNiQ7du3c/v2bSIiIggLC+Py5ctFvg7r16/nzTffZN26dVy/fp2uXbsydOjQIu9HCGEZEiTM0RsNmd0T4e4yC/j111+JjY3lySef5N5776Vhw4Z8/vnnQNGWGS2MsLAwfHx8cHJyYsKECaSnp5um/M6Pq6srgwYNMq1NffLkSfbu3cuwYcMAGDRoEDVr1sTBwYHBgwfTuHFjs2tZ5GfRokVMnTqVpk2b4uTkxKuvvsqBAwfkbkIIO7FpkAgLCyMgIAAvLy+CgoJyPGLYunUrwcHBuLm50aNHjxwfCkopJk+ejI+PDz4+PkyaNAmrz3Buw9GQy5Yt46GHHjKt3TBs2DDTI6eiLDNaGAsWLKBp06Z4e3tTpUoVbt++zY0bNwr12vDwcNasWUNaWhorVqzgkUceoXr16oB2J9SqVSuqVKlClSpVOHz4cKH3e7fY2FheeOEF036qVauGUoq4uLgi70uI8u7PdJi4Az4+Yr1j2HTupqlTp/Lpp5/i7OzMsWPH6N69O61btyYwMJCBAwfyySef0LdvX6ZPn87gwYPZtWsXAB9//DHr168nOjoag8FAr169aNCgAePGjbNeY4cM0XIQd8+nks2CoyFTU1NZs2YNWVlZ1KhRA4D09HRu3bpFdHR0kZYZhfyX9Ny+fTtz585l69athISE4ODgQNWqVQsdcLt27YqPjw/ffPMNK1euZN68eYD2wT5mzBi2bt1Kp06dcHR0pFWrVrr7dXd3ByAlJQUvL688bcxenvXuhYiEEDndyYIPY2DmHxCfDr4uMLgheDtb/lg2vZMICQnB2Vk7C4PBgMFg4PTp06xbt46QkBAGDRqEi4sLkZGRREdHc+zYMUD7pj1hwgRq165NrVq1mDBhgmk1Nqux0WjI9evX4+joyJEjRzhw4AAHDhzg6NGjdO3aleXLlxdpmVHQlvRct24dKSkpnDp1ik8//dRUlpiYiJOTE35+fmRmZjJz5kwSEhKK1N4RI0YwefJkbt26Rd+/phRITk7GYDCYFu1ZsmQJhw8f1n29n58ftWrVYuXKlWRlZfHZZ5/l6Fo7btw4Zs+eTUxMDKAtiPLll18WqY1ClFdKwbozEPIFvPibFiAAbqTB7P3WOabNcxL/+te/cHNzIzg4mICAAPr06UNMTAwtW7Y01XF3d6dhw4amD4rc5S1btjSVWY2NRkMuW7aMUaNGUbduXWrUqGH6GT9+PKtWrUIpVaRlRl966SUqV66Mv78/4eHhOb6RP/zww/Tu3ZugoCACAwNxcXGhTp06RWrviBEjOH/+PIMHDzYF/GbNmjFhwgQ6deqEv78/hw4dokuXLmb3sXjxYubPn4+Pjw8xMTF07tzZVDZgwAAmT57MkCFD8PLy4p577mHTpk1FaqMQ5dHuq3D/enh8M5zSWYV37RnIyLL8ce2yfGlWVhY7d+7k559/ZvLkyYwbNw4/Pz/mzJljqtOlSxfGjBnDyJEjcXR0JCYmhuDgYEBLmgYFBWE0GnM8crH48qV3z/Xet+/fc71HRJSqwS6i5GT5UlFsUVFajjL7EfXx4xYd63AuAV79Hf57Sr/coxJMbQ0vtQTXYiQQClq+1C7rSTg6OnLfffexcuVKPvzwQzw8PPI89khISMDT0xMgT3lCQgIeHh66z+QtKjJSm9Pdim8AIUQZZsVFg26nw5v74L1DkK5zh+BggH82hch24O9WokPly66LDmVmZnL69GlCQkJyDCBLTk42bQctlxEdHU379u0BiI6ONpVZ3d0BoZRM3SuEKAWstGhQRhYsOgJRf2i5Bj196sL8TtCsWjHaXUQ2y0lcu3aN1atXk5SURFZWFps3b+a///0vPXv2ZMCAARw+fJi1a9eSlpbGzJkzadGihenx0ogRI3j77beJi4vj0qVLLFiwgJEjR9qq6UIIkZeFu8krBd+eheZr4Llf9QNESx/Y0he+f9Q2AQJseCdhMBj48MMPGTduHEajkcDAQN5991369esHwNq1axk/fjxhYWF06NCB1Xf1Hho7dixnzpyhefPmADz99NOMHTvWVk0XQoi8LNhNfu91bbzDz5f0y2u6wxvtYXgQONq4u5FdEtfWYvHEtagw5P0himz1am2qHr1ekA4O2oyuBQSKC0kw7XdYcUK/3N0JJrWGCS3BvZIF2qyjVCau7cHBwYG0tDRcXFzs3RRRyqSlpeFg7rGBEOaUoJt84h2Ysx/ejoY0M0np0cEwsx0EuFuovcVUYYJEtWrViI+PJzEx0d5NEaWMg4MD1arZ6AGvKD8iIrREgrlu8jpJ60wjfHIUIvbAtVT93T5cR0tKN/excvsLqcIECYPBgI9PKbnqQojyoZDd5JWCjefhlZ1w9E/9Xd1TTQsOj9S1frOLosLkJIQQwh4O3NCS0lvNzFFZww1mtYNRwbZPSoPkJIQQwi7ikuC13bDsOOh9E3d1gldawiuttVHTpZUECSGEsKCkDJi/H96KhpTMvOUGILwJvN4eannYvHlFJkFCCCEsIMsIS47B9D1wJUW/Ts9asKAztPK1bdtKQoKEEEKU0ObzMHEnHI7XL29aVUtK96mr5bnLEgkSQghRTIduaj2WNl/QL/dzgZnt4emm4FRGh+JIkBBCiCK6kgLTd8Nnx8Cok5V2cYSXWsCUNuBV2fbtsyQJEkKIiq0I60GkZMCCaJi7H5J1ktIAYUHaPEt1Pa3YZhuSICGEqLgKuR5EllGbX2nabriUrL+r+wO0pHTb6jZpuc1IkBBCVEyFXA9i60UtKX3ghv5ugrxhXid4rF7ZS0oXhgQJIUTFlL0ehJlZXI82ascrG+H7WP2X+7hAZFsY2wwqOVq3qfZURvPtQghRQkOGaJPy5XLNw49/TfqO5ol9dANEZQd4pRWcGgbjm5fvAAFyJyGEqKiycxB/Sa3kwrs9X2T2Q1NJdPXSnUtjSCOY3QHqedmwnXYmQUIIUTH9tR6E0WDg83bDePWxN7lQTX8K1i41tKR0B38bt7EUkCAhhKiYIiL4xakuExLv4Y/AdrpVGnrB3I4wsEH5TEoXhgQJIUSFc+IWTN4F66uNAp31pqo6w4x74V/3QOVynnMoiAQJIUSFcSMVZu6FD2O0VeJyq+QAzzWHaW2gmqx0DEiQEEJUAGmZ8P4heGMf3L6jX+eJBjCnIzSU9cpykCAhhCi3lIIvTsHU3+GcmeXtO/prSenONWzbtrJCgoQQolz67TJM2AG/X9Mvr++p3TkMalhxk9KFIUFCCFGunL6tJaXXntEv964M0+/VBsI5V/CkdGFIkBBClAvxaTBrL3xwGDJ0ktJODvCvEJjRVptSQxSOBAkhRJmWHvU6/wnswyxjG/5M168zoL423qFxFdu2rTyQICGEKJOUgrVz1jDZMJQzqQ1167T105LS99e0cePKEQkSQogyZ9cVmPDFeXZUeVK3vO7NWGY7/cGQcY/jIEnpEpFZYIUQZcbZBBiyBTp9DTsq551nySv1NnO+nsyx15sxrF6GBAgLkDsJIUSpdysd3tgL/z4Ed3SS0o5ZmYz9dRER30dRPek69OunTQUuSkyChBCi1MrI0qbQiPoD4s0kpfse/JZ5X08i+Orxvzdu2KBNBS6BosQkSAghSh2l4JtzMGknnLytX6d1xmUWfDCMHid+zltoNGpTgYsSk5yEEKJU+eMadP8GBvygHyBqucOynvDHcwH0GNpN2+jgoD1icvjrIy0iQvsRJSZ3EkKIUiE2Eab9DqtO6pd7VIIpreGlFuBW6a+NkZHanBpNmmiPllav1u4gJEBYjEEppbNIX9l0+/bfXzu8vWUqRyHKgoQ7MHsfvHMQ0rPyljsYYExTiGwHNdxs377yrqDPTbmTEELYRaYRPj4CkXvgepp+nd51YX4nCNFZGEjYhgQJIYRNKQXfx8IrO+HYLf06LXzgrU7Qq45t2ybyslniOj09naeeeorAwEA8PT1p3bo1mzZtMj+2G8wAAB/6SURBVJVv3bqV4OBg3Nzc6NGjB7GxsaYypRSTJ0/Gx8cHHx8fJk2aRDl6SiZEhbH/OjzwLfTdpB8gAtzgsx6w7wkJEKWFzYJEZmYmderUYdu2bdy+fZtZs2bx5JNPcu7cOW7cuMHAgQOZNWsW8fHxtG3blsGDB5te+/HHH7N+/Xqio6M5ePAg3333HYsWLbJV04UQJXQxCUb+H9z7FfzvUt5yNyeIbAsnh8GoYHCUfpelhl0T1y1atCAiIoKbN2+ydOlSduzYAUBycjK+vr7s37+f4OBgOnfuzMiRI/nnP/8JwKeffsrixYvZtWtXjv1J4lqI0iXxDsw7AAuiITUzb7kBGJ26j5k1zlNzWH/pnWQHpTZxffXqVU6cOEFISAgffvghLVu2NJW5u7vTsGFDYmJiCA4OJiYmJkd5y5YtiYmJsUezhRCFkGmEz47BjN1wNVW/Tq/a8Fb0h7SY/i9tfMOavtpIaaNRS1xERtq0zUKfXYJERkYGoaGhhIeHExwcTFJSEn5+fjnqeHt7k5ioLUqblJSUI8J5e3uTlJSEUgqDrDsoRKnyw3mYuANi/tQvD6kKb3WGh5dEYcgOBEYjfPPN35WiorTxD3JHYXc2DxJGo5Hhw4dTuXJlFi5cCICHhwcJCQk56iUkJODp6albnpCQgIeHhwQIIUqRgze1Hks/XtAv93eFWe21nIOTA9oAOAcHLUDk5uCglQu7s2l6SCnFU089xdWrV1m7di2VKmnDJkNCQoiOjjbVS05O5vTp04SEhOiWR0dHm8qEEPZ1ORme/h+0WqMfIFyd4LV7taT0mGZ/BQjQRkj37au/0759ZXK+UsKmQeKZZ57h6NGjbNiwAVdXV9P2AQMGcPjwYdauXUtaWhozZ86kRYsWBAcHAzBixAjefvtt4uLiuHTpEgsWLGDkyJG2bLoQIpfkDIjaA40+h0+PQe4eMAYgvAmcGKrdQXhWzlVh9WotB6EnexZXYXc2e9wUGxvLokWLcHZ2pkaNGqbtixYtIjQ0lLVr1zJ+/HjCwsLo0KEDq+96g4wdO5YzZ87QvHlzAJ5++mnGjh1rq6YLIe6SZYRlx+G13XA5Rb9Oz1raYLjWfvrlgNaLSe9RE8gsrqWIzN0khCi0LRdg4k4t/6AnuIo2jcajgVreuUCRkVqS2sFBe8SU3bspIkJ6N9lIqe0CK4QoO2LitaT0pvP65X4uENUOnm4KlRyLsGOZxbXUkzsJIYRZV1Ngxh745CgYdT4pnB3h5RYwpQ145c45iDJB7iSEEEWWkqFN3T1nPyRl6NcJbQxvdoC6nrZtm7AtCRJCCBOjgpUntMV/Libr17k/ABZ0hrbVbds2YR8SJIQQAPwvDibsgP039Msbe8O8TtCvXq6kdFSU5BTKMQkSQlRwx/6ESTthQ6x+uY8LRLSFcc10ktJ3907KHvcgcy+VKxIkhKigrqdqq8ItOgJZOknpyg7wQgt4tQ1UcdbZQVSU9gMy91I5JkFCiAomLRPePQhv7oNEM0npIY20pHR9r3x2JHMvVQjSBVaICsKoYPVJmPo7nE/Sr9O5BrzdGTr4F3Kn/fvnvIPI1q8frF9f7LYK2ynoc1PWfxKiAth+CTqug9Ct+gGioRd8+RD82r8IAULmXqoQ5HGTEOXYiVswZRd8fVa/vKozTL8Xnr0HKhdlpDTI3EsVhAQJIcqhm2kw8w/4T4y2SlxulRxg/D3aFN7VXIp5kIgIrReTubmXJGldLkiQEKIcSc+C9w/B63vh9h39Ok80gDkdoaEl0nYy91K5V+jEdcOGDRkzZgyjRo3C37+wDy1tSxLXoqJSCr48rT1aOpuoX6dDdW2kdJcA27ZNlG4WS1yPGDGCRYsWUbduXQYOHMjmzZst00IhRInsuAKdv4bBW/QDRD1PWN0Ldg6UACGKrtBBIiIigjNnzvDtt9/i6OjIY489Rr169Xj99de5dOmSNdsohNBx+jY8+SN0+Rp2Xc1b7l1ZW9vh6BAY3KiQ6zsIkUuxx0lcv36djz/+mNdff53MzEz69OnDxIkT6dq1q6XbWGjyuElUBH+mazmH9w9Bhk5S2skBngmBGfeCr2ve8jxk7qUKzSpThR87doxPPvmEZcuW4eHhwfDhw4mLi+PBBx9k8uTJzJw5s/gtFkLoupMF/zkMM/dqgUJP//owtyMEVSnkTmXuJVGAQt9JpKWl8eWXX7J48WJ+++03unTpwtixY3niiSdwdtYmdvn2228ZPnx4jshkS3InIcojpWDdGZi8C04n6Ndp6wdvdYZuNYuw46io/ANBZKTcUVQAFruTCAgIwMHBgbCwMD766COaNWuWp879999PtWrVitlUIURuv1/Vpu/+7Yp+eR0PmN0BhjYGh6LmHGTuJVEIhb6TWLp0KUOGDMHFpbgjb6xP7iREeXEuQZtjafUp/XLPStrsrC+0ANeSjHaSuZcqvII+N2WCPyFKkVvp2uys7x2EOzpf8B0NMLaZtr5DdbcSHmz1aggNNX8nsWqVlswW5ZqscS1EGZCRBR8dgag/tCk19PQNhLmdoGlVCx1U5l4ShSBBQgg7Ugq+PaetDHfCTH+P1r7wVifoWdvCB5e5l0QhSJAQwk7+uAYTd8I2M2NRa7nDG+1heJNiJKULS+ZeEgWQnIQQNnYhCV79HVae0C93d4IpbeDlFuBWybZtExWP5CSEKCUS7sCcffDOQUjLylvuYICnm0JUO6hR0qS0EBYiQUIIK8s0wuIjELEHrptJSveuC/M6wj0+xTiATKshrEiChBBWohRsPA+v7ISjf+rXaV5Nm767V51iHkSm1RBWJkFCCCs4cEMbKf1/cfrlAW7wensIbwKOxV1pPipK+wEtMNw9KC4qSktIyx2FKCEJEkJYUFwSvLYblh0HvR4hbk7wSiuY2Ao8SpqUlmk1hA1I7yYhLCApA+bth7eiITUzb7kBGBUMs9pDTXcLHlim1RAlJL2bhLCiLCN8dgym74arqfp1etXWZmhtUZykdH6ycxB6NmzQymVaDVFCEiSEKKYf/kpKH47XL29WVRsp/UhdK60KJ9NqCBuQICFEER26qY2U/vGCfnl1V+2x0uhgbZU4q5FpNYQNSJAQopAuJ2uPlZYcB6NOJs/FESa0hMmtwbOyjRol02oIK5PEtRAFSM6ABdFaYjrZTFJ6eBC80QFqe9i8eUKUiCSuhSimLCMsP6F1ab2UrF+nR00tKd3GrwQHkhHTohSz5hPTPBYuXEjbtm1xdnZm5MiROcq2bt1KcHAwbm5u9OjRg9jYWFOZUorJkyfj4+ODj48PkyZNohzdAIlS6KeLcO9XMPp/+gGiSRX4tjdsfayEASIyUvsJDdW6s4aG/r1NiFLApkGiZs2avPbaa4wePTrH9hs3bjBw4EBmzZpFfHw8bdu2ZfDgwabyjz/+mPXr1xMdHc3Bgwf57rvvWLRokS2bLiqImHh49HvotQGib+Yt93WBD7rCoSehb70S9lrSGzGd3Vvp7jIh7MimQWLgwIH0798fH5+cHcbXrVtHSEgIgwYNwsXFhcjISKKjozl27BgAy5YtY8KECdSuXZtatWoxYcIEli5dasumi3LuagqM2wYt1mjzLeXm7AhTWsOpYfCve6CSowUOmj1iWo+MmBalhE2DhDkxMTG0bNnS9Lu7uzsNGzYkJiZGt7xly5amMiFKIiUD3twLjT6HRUf0ey2FNobjQ2F2R/B2tuDBhwzRuq3q6dtXBsKJUqFUJK6TkpLw88v5YNfb25vExERT+d1Zd29vb5KSklBKYbDKKCVR3hkVrDqhLf5z0UxSumuANkNru+pWaoSMmBZlQKkIEh4eHiQkJOTYlpCQgKenp255QkICHh4eEiBEsfwcp83Quu+Gfnljb5jXCfrVs9JI6WwyYlqUAaXicVNISAjR0dGm35OTkzl9+jQhISG65dHR0aYyIQrr2J/w2Ebo8a1+gKjmDO91gcODoX99KwcIyDkq2sFBm5QvO0chI6ZFKWHTO4nMzEwyMzPJysoiKyuLtLQ0nJycGDBgAK+88gpr167l0UcfZebMmbRo0YLg4GAARowYwdtvv02fPn0wGAwsWLCA5557zpZNF2XY9VSI+gM+ioEsnZxDZQd4vjlMuxeqWDLnUBgyYlqUcjYdcR0ZGUlUrm59ERERREZG8tNPPzF+/HhiY2Pp0KEDS5cupV69esDf4yQ++eQTAJ5++mnmzp2b53GTjLgWd0vLhPcOwZv7tPWl9QxuBLM7QH2vEhxIBsOJMqygz02ZlkOUO0YFq0/C1N/hfJJ+nc41YEEn6FijhAe7e/nQ3BPsyYA4UQbItByiQtl+CSbshD3X9MsbeMHcjvB4AwvkHGT5UFEBSJAQ5cLJWzB5F3x9Vr+8SmWY3haevUcbGGcRsnyoqADkcZMo026mwaw/4IMYyNT5rK7koAWG6fdCNRcrNECWDxVlnDxuEuVSehYsPASv74VbZpLSjzeAOR2hkbW+L8hgOFEBSJAQZYpS8OVpmLILzibq12lfXRspfV+AlRsjg+FEBSBBQpQZO6/Ayztg11X98kBPrTvr4EbgYIvB+LJ8qKgAJEiIUu9Mgnbn8OVp/XKvyjCtjTYgzsXW72gZDCfKOUlci1Lrz3Qt5/D+IcjQearjaIBnQiCiLfi6luBAMhhOVGCSuBZlzp0s+DAGZv4B8en6dR6rB/M6QpOqJTzY3YPhshPRRqP2GEkGwwkhQUKUHkpp4xwm74JTt/XrtPHVktLda1nggDIYTogCSZAQpcLuq9r03b9e0S+v7a4t+jOssQWT0jIYTogCSU5C2NW5BG3hn/+e0i/3qARTW8NLLcHVGl9pZDCcqOAkJyFKpdvp2uys7x3SBsbl5mCAMU0hqh34u1mpETIYTogCSZAQNpWRpa0lHfUH3EjTr9OnLszvBM2qWbkxMhhOiAKVipXpRPmnFHx7Fpqvged+1Q8QLXxgS1/4/tEiBoioKO1bP2j/zbVmiVmyMpwQBZI7CWF1e6/DxB3w8yX98gA3eKMDjAgCx6J+bSlpF1YZDCdEviRxLazmQpKWlF55Qr/c3QkmtYYJLcG9UjEOEBWVfyCIjJQPeyEKIIlrYXMJd2Dufng7GtLMJKVHB8PMdhDgXoIDSRdWIaxO7iSExWQa4ZOjELEHrqXq13mojpaUbuFjoYNKF1YhSkTuJITVKQUbz8MrO+Hon/p17qkGb3WCh+ta8MDShVUIq5MgIUrkwA0tKb01Tr+8hhvMagcjg8HJXFK6uBPsSRdWIaxOgoQolrgkeG03LDsOes8rXZ1gYkstMe2RX1K6JL2TZD0HIaxOgoQokqQMmL8f3oqGlMy85QYgvAm83h5qeRSwM0tMsCddWIWwKgkSolCyjLDkGEzfA1dS9Ov0rKXN0NrKt5A7tVTvpLsDguQghLAo6d0kCrT5PEzcCYfj9cubVtV6LPWpq32pLxLpnSSEXRX0uSnTcgizDt2ER76DR77XDxB+LvDh/XDw+Ewe3blaCxBFmRajML2ThBB2JY+bRB6Xk2HGHvjsGBh17jNdHOGlFjClDXi9GVn8xLP0ThKi1JMgIUySM2BBNMzbD8k6SWmAsCB4oz3U9aTkiWfpnSREqSdBQpBlhBUnYNpuuJSsX+f+AC0p3bb6XRstkXiW3klClGqSuK7gtl6ECeuuEF2phm55kDfM6wSP1TOTlJbEsxBlmiSuha4j8fCPjfDgBnQDhI8xhffvg8ODoV99MwFCEs9ClHsSJCqYqynwzDZosQa+j81bXjkjnUk/zuXUKzUZvy6KSo757EwSz0KUe5KTqCBSM+GdaJizHxIz9OsM2fNfZn8zlXrxsYXLKUjiWYhyT4JEeZDPBHlGBZ+f1Bb/uZCk//Iup35lwboJdDi3+++NffsWbvSyJJ6FKNckcV3W3T1BXq5v8tv+GcmEHdryoXoaOiQyb9EoBuxfS56Ug4MDrFol01wIUc5J4rosiIr6O8lblBHLeuMUjEaOVw+i35XWdP9GP0BUdYZ3OsORK+8yUC9AZO9PcgpCVHjyuMneSjJVdq5xCjfcfYh6NIKPuo4j0zHv/NyVHOC55vDavVqgoOV0UFmSUxBCmCWPm6D4i96U9PVRUfkHgsjIgvfTvz9p3//A+92f441HpnHbrYputScawJyO0FDvspT0/IUQZVa5Wb40Pj6ep556ih9//BFfX19mz57NsGHDSr7jknyTL+nrSzhiWf13NV9ccmNKxDFiferp1unor42U7qw/Vk4jU20LIcwoM3cSQ4cOxWg08umnn3LgwAEeffRRduzYQUhIiKlOke8kSvpN3kJ3AsUZsfzrZZjw5UV2V6qtW17/xhnmVNrLoMmDij59txCiwigXievk5GTWrl3LrFmz8PDw4L777uOxxx5jxYoVJdtx9jd5PYUZJ1DS1xdjxPKp2/D4D9B1PboBwjvlFm+tncDRrFU8OUUChBCiZMpEkDhx4gSOjo4EBQWZtrVs2ZKYmJiS7XjIEC1Zq6cw4wRK+voijFiOT4OXfoNmq2Hd2bzVnTDyfHM4HfB/TOjqhXPk9PyPLYQQhVAmgkRSUlKe2yBvb28SExNLtuOSzj1U0tff3YPIwUF7xJR9Z/JXWXoWvB0NDVfBuwchQyemDKgPR4Y68N594DNsoCSdhRAWUyYS1x4eHiQkJOTYlpCQgKenZ8l2XNK5hywxd5GZEctqRgRfnYYpu+BMgv5L2/ppSen7axZ8GCGEKI4yESSCgoLIzMzk5MmTNG7cGIDo6OgcSetiKencQ5aauyhX76JdV2DCethxRb96XQ+Y3QGGNAYHyTkIIayozPRuGjJkCAaDgU8++YQDBw7Qp0+fkvduymavcRK5nE3Q7hzWnNYv96oMr7aBF5qDS5kI70KI0q6gz80yEyTi4+MZPXo0W7ZswcfHhzlz5uQZJ1FW5266lQ5v7IV/H4I7Ok+vHA0wLgQi2oKfq+3bJ4Qov8pNkCiMshYk7mTBRzEQ9QfEp+vX6RuorQwXXNW2bRNCVAzlZsR1eaIUrD8Lk3fBydv6dVr7aknpHrVs2zYhhLibBAkb23MNJuyA7Zf1y2u5w5sdICxIktJCCPuTIGEjsYnawj+fn9Qv96gEk1vDyy3ALe8ErkIIYRcSJKzsdjrM3q8NhEvPylvuYIAxTSGyHdRws337hBAiPxIkrCQjCxYfhYg9cCNNv07vujC/E4RUs23bhBCisCRIWJhS8F0svLITjt/Sr9PCB97qBL3q2LZtQghRVBIkLGjfdZi4A/53Sb88wA3e6AAjgsCxTMyaJYSo6CRIWMDFJJj2O6w4AXqDTtycYFIrmNgK3CUpLYQoQyRIlEDiHZi7HxZEQ5pOUtoAjA6GWe0hwN3mzRNCiBKTIFEMmUb49CjM2APXUvXr9KoNb3XW8g9CCFFWSZAoAqXgh/MwcScc+VO/TkhVLTg8Ute2bRNCCGuQIFFI0Te04PDTRf1yf1eY2V57vOQkSWkhRDkhQaIAl5Lhtd2w9Jh+UtrVCSa01BLTnpVt3jwhhLAqCRJmJGXAWwdg/gFIycxbbgBGNIHX20NtD5s3TwghbEKCRC5ZRlh6HKbvhssp+nV61tIGw7X2s23bhBDC1iRI3OXHC9pguEPx+uXBVbRpNB4N1JalFkKI8k6CBJCSAY9vhh8u6Jf7uUBUOxjTTJLSQoiKRYIE2tTceklpZ0dt6u4pbbT1pYUQoqKRIPGX+Z1gy0Uw/hUtwoLgjfZQ19O+7RJCCHuSIPGX5j7aGIcTt7RlQ9tWt3eLhBDC/iRI3OX9+7RHTJKUFkIIjQSJu7jI1RBCiBykr44QQgizJEgIIYQwS4KEEEIIsyRICCGEMEuChBBCCLPKbX+e27dv27sJQghR5smdhBBCCLMkSAghhDDLoJTSm9tOCCGEkDsJIYQQ5kmQEEIIYVaFChLx8fEMGDAAd3d3AgMD+fzzz83Wfeedd6hRowbe3t6MHj2a9PR0q7YtPT2dp556isDAQDw9PWndujWbNm3Srbt06VIcHR3x8PAw/fz8889WbR9A9+7dcXFxMR2zSZMmZuva+vrdfS08PDxwdHTkueee061rq+u3cOFC2rZti7OzMyNHjsxRtnXrVoKDg3Fzc6NHjx7Exsaa3U9R3reWaN+uXbvo1asX1apVw8/Pj0GDBnH58mWz+ynK+8IS7Tt37hwGgyHH32/WrFlm92Pr67dq1aocbXNzc8NgMLB3717d/Vjr+lmMqkCGDBminnzySZWYmKi2b9+uvLy81OHDh/PU++GHH1T16tXV4cOHVXx8vOrWrZuaPHmyVduWlJSkIiIi1NmzZ1VWVpbasGGD8vDwUGfPns1Td8mSJapLly5WbY+ebt26qcWLFxdYzx7X725JSUnK3d1dbdu2TbfcVtdv7dq16uuvv1bjxo1T4eHhpu3Xr19XXl5eas2aNSo1NVVNnDhRdejQwex+Cvu+tVT7Nm7cqNasWaNu376tkpOT1ahRo9TDDz9sdj+FfV9Yqn1nz55VgMrIyCjUfmx9/XJbsmSJatCggTIajbrl1rp+llJhgkRSUpKqVKmSOn78uGlbWFiY7ofX0KFD1dSpU02///TTT8rf398m7bxb8+bN1VdffZVne2kPEva+fkuXLlX169c3+4/S1tdv2rRpOT5EFi1apDp16mT6PSkpSbm4uKijR4/meW1R3reWal9ue/fuVR4eHmbLrf0hl7t9RQkSpeH6de/eXUVGRpotL+1BosI8bjpx4gSOjo4EBQWZtrVs2ZKYmJg8dWNiYmjZsmWOelevXuXmzZs2aSvA1atXOXHiBCEhIbrl+/fvx9fXl6CgIGbNmkVmZqZN2jV16lR8fX3p0qWL2Uc09r5+y5YtY8SIERjyWRjEXtcP8l4fd3d3GjZsqPteLMr71lp++eUXs+/DbIV5X1haYGAgtWvXZtSoUdy4cUO3jr2vX2xsLL/88gsjRozIt549rl9hVZggkZSUhLe3d45t3t7eJCYmFlg3+//16lpDRkYGoaGhhIeHExwcnKf8/vvv5/Dhw1y7do21a9fy3//+l/nz51u9XXPnzuXMmTPExcXxz3/+k759+3L69Ok89ex5/c6fP8+2bdsIDw83W8de1y9bSd6L+dW1hoMHDzJz5sx8r09h3xeW4uvry549e4iNjWXv3r0kJiYSGhqqW9fe12/58uV07dqV+vXrm61j6+tXVBUmSHh4eJCQkJBjW0JCAp6eeRexzl03+//16lqa0Whk+PDhVK5cmYULF+rWadCgAfXr18fBwYHmzZszY8YMvvrqK6u3rUOHDnh6euLs7Ex4eDhdunRh48aNeerZ8/otX76c++67L99/lPa6ftlK8l7Mr66lnTp1it69e/Pee+/RtWtXs/UK+76wFA8PD9q2bYuTkxP+/v4sXLiQH3/8Mc91yq5rr+sH2vsxvy8sYPvrV1QVJkgEBQWRmZnJyZMnTduio6N1b6NDQkKIjo7OUc/f3x8fHx+rtlEpxVNPPcXVq1dZu3YtlSpVKtTrDAYDyg5jIs0d117XDwr3jzI3W1+/3NcnOTmZ06dP674Xi/K+taTY2FgefPBBpk+fzvDhw4v0Wltfz+zHinrHtNf1A/jtt9+4dOkSTzzxRJFeZ69/z2bZMR9ic4MHD1ZDhgxRSUlJ6tdffzXby2HTpk3K399fxcTEqPj4eNWjRw+b9M4ZO3as6tChg0pMTMy33saNG9WVK1eUUkodPXpUhYSE5JsYs4Q///xT/fDDDyo1NVVlZGSolStXKjc3N3Xs2LE8de11/X777Tfl5uamEhIS8q1nq+uXkZGhUlNT1ZQpU1RYWJjp2l27dk15eXmpr776SqWmpqpJkybl27upsO9bS7Xv4sWLqkGDBmrevHkF7qMo7wtLtW/Xrl3q2LFjKisrS924cUM9+eSTqnv37mb3Y+vrl23MmDFq+PDh+e7DmtfPUipUkLh586bq16+fcnNzU3Xq1FGrVq1SSikVGxur3N3dVWxsrKnuggULVPXq1ZWnp6caOXKkSktLs2rbzp07pwDl7Oys3N3dTT8rV67M074JEyao6tWrKzc3N1W/fn01ffp0defOHau279q1a6pt27bKw8NDeXt7qw4dOqgff/xRKVU6rp9SSv3zn/9UYWFhebbb6/pFREQoIMdPRESEUkqpLVu2qCZNmigXFxfVrVu3HF2d33jjDfXII4+Yfjf3vrVW+yIjIxWQ433o7u6u27783hfWat/nn3+u6tWrp9zc3FSNGjXU8OHD1eXLl3Xbp5Ttr59SSqWmpipvb2/1008/5Xmdra6fpcjcTUIIIcyqMDkJIYQQRSdBQgghhFkSJIQQQpglQUIIIYRZEiSEEEKYJUFCCCGEWRIkhBBCmCVBQggrUErxwAMP0KVLF7KysnKU9evXj9atW3Pnzh07tU6IwpMgIYQVGAwGli1bxtGjR3nzzTdN2z/++GN+/PFHVq1aReXKle3YQiEKR0ZcC2FFX3zxBWFhYezYsYOqVavSqlUrZs+ebXZpVSFKGwkSQlhZWFgYe/bsoUqVKlStWpVNmzbluyCSEKWJBAkhrOzWrVvUqlULBwcHTpw4QUBAgL2bJEShSU5CCCtbuXIlRqORlJQU9u7da+/mCFEkcichhBUdO3aMNm3asGDBAs6cOcOKFSs4fPgwvr6+9m6aEIUiQUIIK8nIyKBjx474+/uzceNG0tPTadeuHY0bN2bt2rX2bp4QhSKPm4SwkhkzZnD+/Hk+++wzAJydnVm5ciXff/89y5cvt3PrhCgcuZMQwgp+/fVXunXrxldffcWAAQNylM2fP5833niDgwcPUrduXTu1UIjCkSAhhBDCLHncJIQQwiwJEkIIIcySICGEEMIsCRJCCCHMkiAhhBDCLAkSQgghzJIgIYQQwiwJEkIIIcySICGEEMKs/weZPwqfDWg6AgAAAABJRU5ErkJggg==\\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    \"# 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   \"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   \"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   \"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      \"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 = 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   \"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   \"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   \"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   \"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      \"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    \"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   \"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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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znYA8aNEgPPvig3njjDTU3N4fGDx06pKlTp6q4uFhXX321ioqKNG3aNO3atUuStHbtWm3evFnl5eXyeDyaNGmSzjrrLM2bNy+W5eM0sOk/EhXXPmAtMgU3iekd7KlTp+q6665TRkZGm/FNmzbJ7/frhhtuUHJyspYsWaLy8nLt3btXkvTUU09p4cKFGjJkiAYPHqyFCxdq3bp1sSwdp4lN/5GouPYBa5EpuIkj5mBXVFQoNzc39HlqaqqysrJUUVER9nhubm7oGJyNTf+RqLj2AWuRKbiJIxrsxsbGdiswfT6fGhoawh73+XxqbGxkHrYLsOk/EhXXPmAtMgU3cUSD7fV6FQi0fd8nEAgoLS0t7PFAICCv1yuPxxPTOtF1bPqPRMW1D1iLTMFNHNFg+/1+lZeXhz5vampSZWWl/H5/2OPl5eWhY3C27zf9nzFSmjAo+JEFKUgEXPuAtcgU3CSmu4i0tLSopaVFra2tam1t1dGjR9WzZ09NmTJF9957r0pKSnTllVdq6dKlGjNmjHJyciRJs2bN0sqVKzV58mR5PB6tWLFCCxYsiGXp6IYR6dL6iXZXAcQe1z5gLTIFt4jpHezly5erT58++uMf/6j169erT58+Wr58uc4880yVlJTogQceUN++ffXBBx9ow4YNoe+bO3eurr76ao0ePVrnnXeerrzySs2dOzeWpSMM9iNFIuP6B6KDbCEe8Kh0nJZw+5FmpfN2ndM4ORNOrq0zXP+IBidnIla1kS24BY9KR1SwHykSGdc/EB1kC/GCBhunhf1Ikci4/oHoIFuIFzTYOC3sR4pExvUPRAfZQrygwcZpYT9SJDKufyA6yBbiRUy36UP8+H4/0qLS4Ft3g1KDL4AsQkEi4PoHooNsIV6wiwjCqvpuUUltU/AtO17g3MnJmXBybRIZQOw5ORPRqo2cwa06ywR3sNFOuG2SdtWxTRISBxkAoo+cIZ4xBxvtsE0SEh0ZAKKPnCGe0WCjHbZJQqIjA0D0kTPEMxpstMM2SUh0ZACIPnKGeEaDjXbYJgmJjgwA0UfOEM9Y5JhgIlmxzTZJSASnygIZAKwXLnPkDPGKbfoSSLgV21nprNiOZ07OhJ21kQU4UTznlcwh3nSWCaaIJBBWbANBZAGILTKHREODnUBYsQ0EkQUgtsgcEg0NdgJhxTYQRBaA2CJzSDQ02HGkKiDlb5Mm/D34seqkt+NYsY1EFC4XZAGIje/z9//+I3lP2laBzCGesYtInIjkkbPsjIBEc6pckAUgusLlz9tTOi/jh+aazCFe0WDHiVMtIFk/8YexEeltPwfiWWe5IAtA9ITLX2NLsLkme4h3TBGJEywgAdojF4B9yB8SGQ12nGABCdAeuQDsQ/6QyGiwXYIFjMCpsZgRcAYWNgLMwXYFFjACp8ZiRsAZWNgIBNFguwALGIFTYzEj4AwsbASCaLBtVvVdE1DbFJyvFu63exaKAO39ODuffRv+a8gIEFuVR8KPk0UkGhpsG0Uy9UNioQhwsnDZCYeMALFTFZB2Hw5/jCwi0bDI0Uanelv7x1ioBbQVLjsnIyNAbBWVBqeDnMzbkywi8XAHO4o6m/4R6dQPFjACkU0J6d9HGtWXjAB26Gh6yHl9ySISDw12lEQy/aMrUz9YwIhEFumUkIlDyAlgh1NND8n6SWxrAZyAKSKnobM9qaXIpn8w9QPoXFVA+uXLTAkBnOyu95geAvwYd7BP0tm0jkgXJkYy/YOpH0DHqgLS3e9Kb9RIR1vDfw1TQgD7VQWkN/4v/DGmhyBRuabBPnz4sObMmaOtW7eqX79++sMf/qCbb765Sz/DiuY50j2pI53+wdQPoL2qgHTZ36X/azz11zElBLDfXe9Jx06EP8b0ECQq1zTYv/71r9W7d2/V1dWprKxMV155pXJzc+X3+yP6fqua50gXJi67IPjzf/zzeAsbiMxd73XeXJMnwBne+yr8+Bkio0hcrpiD3dTUpJKSEi1btkxer1d5eXm65ppr9PTTT0f8MyKZEx1J89yVO9NvXi3NGClNGBT8ePI0EgDh7ao79fHhaeQJcIpwc68lqaeHjCJxueIO9r59+9SjRw9lZ2eHxnJzc/X2229H/DOsap67cmea6R+A9bLSaa4BJ0ntKR0Ls04irXfsawGcwhV3sBsbG+Xz+dqM+Xw+NTQ0RPwzIm2eO9vVgzvTQPRd+LPw40NSyRvgNJcMCD+e18E4kAhccQfb6/UqEGg7vyMQCCgtLS3inxHJnedId/XgzjQQXX/Kk/51SPryR+8wDU2V3r6O5hpwmo7y+qc8+2oC7OaKBjs7O1stLS364osvNHLkSElSeXl5xAscJZpnwE1GpAebabawBJyPvALteYwxxu4iIjF9+nR5PB4VFxerrKxMkydP1s6dO9s02UeO/PCc1pOnlACJyMmZcHJtgB2cnAkn1wbYobNMuGIOtiQ9/vjjam5u1s9+9jPddNNNWr16dZfuYAMAAACx4Jo72JH48W8TANpy2l0n8gp0jLwC7uHqO9gAAACAG9BgAwAAABaKqykiAAAAgN24gw0AAABYiAYbAAAAsBANNgAAAGChhGmwDx8+rClTpig1NVWZmZl69tln7S6pSy677DIlJyfL6/XK6/XqnHPOsbuksFatWqWxY8cqKSlJBQUFbY5t375dOTk5SklJ0YQJE1RdXW1PkafQUf0HDhyQx+MJ/f/v9Xq1bNky+wpNAG7I7Ole78YYLVq0SBkZGcrIyFBhYaHsWg5z7NgxzZkzR5mZmUpLS9P555+v1157LXTcLechSfn5+Ro4cKDS09OVnZ2t4uLi0DE3nYcbOT2v0bzODxw4oAkTJiglJUU5OTnatm1bTM/tiy++UHJysvLz811/Phs2bNC5556r1NRUZWVlaceOHe49H5Mgpk+fbm688UbT0NBgduzYYdLT083u3bvtLiti48ePN08++aTdZXSqpKTEvPTSS2bevHlm9uzZofGvv/7apKenmxdeeME0Nzeb3/72t2bcuHH2FdqBjuqvqqoykszx48ftKy7BuCGzp3u9P/HEEyY7O9t8+eWXpqamxpx77rlm9erVNpyBMY2NjWbx4sWmqqrKtLa2mi1bthiv12uqqqpcdR7GGLN7925z9OhRY4wxe/bsMf379zcffvih687DjZye12he57/4xS/M3Xffbf773/+ajRs3Gp/PZ+rr62N2bpMmTTJ5eXlmxowZxpjuv/7YdT5bt241w4YNM++//75pbW01NTU1pqamxrXnkxANdmNjo+nVq5f5/PPPQ2P5+flm0aJFNlbVNW5psL/3wAMPtGk41qxZYy688MLQ542NjSY5Odns2bPHhuo6d3L9NNix5bbMdvV6v/DCC82aNWtCx4uLix31C+fo0aPNxo0bXX0ee/fuNQMGDDDPP/+8q8/DDdyW1+9ZcZ1//vnnpnfv3iYQCISO5+XlxewXtOeee87ccMMNZvHixaEG263nc+GFF5ri4uJ24249n4SYIrJv3z716NFD2dnZobHc3FxVVFTYWFXX3XffferXr58uvvhivfXWW3aX0yUVFRXKzc0Nff792z9u+zvIzMzUkCFDdMstt+jQoUN2lxO33J7Zzq73k4876dzq6uq0b98++f1+V57HnXfeGXoreODAgZo8ebIrz8NN3JhXq67ziooKnXXWWUpLSwt7PJoCgYB+//vfa8WKFW3G3Xg+ra2t+vDDD/X111/r7LPP1pAhQzR//nw1Nze78nykBJmD3djY2O4xlj6fTw0NDTZV1HWPPPKI9u/fr9raWt1+++26+uqrVVlZaXdZEXP730G/fv30z3/+U9XV1froo4/U0NCgGTNm2F1W3HL79dJZ/Scf9/l8amxstH3e7/HjxzVjxgzNnj1bOTk5rjyPxx9/XA0NDdqxY4emTp2qpKQkV56Hm7gtr1Ze53aee1FRkebMmaOhQ4e2GXfj+dTV1en48ePauHGjduzYobKyMn388cdavny5K89HSpAG2+v1KhAItBkLBAJtfqNxunHjxiktLU1JSUmaPXu2Lr74Yr366qt2lxUxt/8deL1ejR07Vj179lT//v21atUqbd26td05wRrxcL2cqv6TjwcCAXm9Xnk8npjW+WMnTpzQzJkz1bt3b61atUqSO89Dknr06KG8vDzV1NRo9erVrj0Pt3BTXq2+zu0697KyMm3btk133313u2NuPJ8+ffpIkhYsWKCBAweqX79+uueee/Tqq6+68nykBGmws7Oz1dLSoi+++CI0Vl5eLr/fb2NV3ePxeFx1d8Xv96u8vDz0eVNTkyorK137d/D9P7xu+jtwE7dntrPr/eTjdp+bMUZz5sxRXV2dSkpK1KtXL0nuO4+TtbS0hOp183k4nVvyGo3r3O/3a//+/W3uiMbi3N966y0dOHBAw4YN04ABA/Too4+qpKREP//5z115Pn379tWQIUPC/lLrxvORlDi7iEybNs1Mnz7dNDY2mnfffddxK5xP5dtvvzWvv/66aW5uNsePHzfr1683KSkpZu/evXaX1s7x48dNc3Oz+d3vfmfy8/NDNdfX15v09HSzceNG09zcbAoLCx25iKij+nft2mX27t1rWltbzaFDh8yNN95oLrvsMrvLjWtuyOzpXu+rV682OTk5pqamxtTW1ppRo0bZumvF3Llzzbhx40xDQ0ObcTedR11dnXnuuedMQ0ODaWlpMa+//rpJSUkxmzdvdtV5uJUb8hqt63zcuHFm4cKFprm52WzatCkmu1Q0NTWZr776KvRn4cKF5vrrrzf19fWuPB9jjCkqKjJjx441dXV15vDhwyYvL888+OCDrj2fhGmwv/nmG3PttdealJQUM3ToUPPMM8/YXVLE6uvrzdixY43X6zU+n8+MGzfObN261e6ywlq8eLGR1ObP4sWLjTHGvPnmm+acc84xycnJZvz48aaqqsrWWsPpqP5nn33WDB8+3KSkpJgBAwaYmTNnmq+++srucuOaGzJ7utf7iRMnzL333mv69u1r+vbta+69915z4sQJW87hwIEDRpJJSkoyqampoT/r16931XnU19ebSy+91Ph8PpOWlmbOO+88s3bt2tBxt5yHWzk9r9G8zquqqsz48eNNcnKyyc7ONm+++WasT6/NLiLGuPN8/ve//5k77rjD+Hw+079/f7NgwQLT3Nzs2vPxGMN73AAAAIBVEmIONgAAABArNNgAAACAhWiwAQAAAAvRYAMAAAAWosEGAAAALESDDQAAAFiIBhsAEpAxRgUFBfJ4PLr++uvV2tpqd0kAOkBe3YcGGwAS0Pz58/Xyyy+ruLhY7733ngoKCnTyYxE+/fRTzZw5U8OHD1dycrJGjBihu+66S//5z39sqhpITJHktbm5WVdddZWGDRum5ORk9e/fX9ddd50+++wzm6pObDTYAJBgCgsLtXnzZr3zzjuaM2eO3n33Xb333nu644472nzdv/71L3m9XhUXF+uzzz7TE088oS1btuimm26yqXIg8USaV4/Ho0mTJumFF17Q559/rn/84x9qaWnR5ZdfrqNHj9pUfQKLyfMiEfcOHTpkhgwZYn7zm9+Exurq6syAAQNMYWGhjZUBiSOSHD700ENm5MiRbR41bIwxBw8eNKNHjzb33HPPKf83Nm7caDwejzly5Ijl9QOJJBZ5LSsrM5JMWVmZ5fXj1HhUOizzzjvv6PLLL9emTZt01VVX6YorrtCRI0e0Y8cO9erVy+7ygIQQ7Rz+7W9/04IFC3TkyBH17NnTgoqBxBXNvDY0NOj+++/XSy+9pH379iklJcWiqhGJHkuWLFlidxGID5mZmTLG6P7771d9fb22bNmiN998UxkZGXaXBiSMaObw3//+t26++WYVFBToiiuusKBaILFFI6+LFi3SlClTtHTpUgUCAW3dulX9+/e3sGpEgjvYsNSJEyeUl5en999/Xxs2bNC0adPsLglIONHIYX19vSZOnKihQ4dq8+bNvCsFWMTqvB46dEjffvutamtr9eijj6q6ulo7d+5UWlqaRRUjEixyhKW++uor7du3Tz169NC+ffvsLgdISFbnsKamRuPHj1dmZqY2bdpEcw1YyOq89uvXTyNHjtRll12mTZs26bsf1UoAAAFwSURBVMCBA3rmmWcsqBRdQYMNy5w4cUL5+fny+/3auHGjli5dqnfffdfusoCEYnUOKysrdckll2jUqFHatGmTkpKSLKwWSGyx+HfTGKNjx45Z+jPROVaowDIPP/ywPv30U5WVlWnIkCGaN2+eZsyYobKyMvXt29fu8oCEYGUOP/vsM02cOFFjxozRX/7yF33zzTehY2eeeaZ69OhhdflAQrEyr2+99Zb27Nmjiy66SH379tWXX36pRx55RGeccYamTJkSpTNAR5iDDUvs3LlT48ePV0lJia655hpJ0rFjx3TBBRfo7LPPVklJic0VAvHP6hwuWbJEDz30UNhjVVVVGj58eHdLBhKW1Xn94IMPVFhYqN27d6uxsVEDBgzQJZdcovvvv1+jRo2KxingFGiwAQAAAAsxBxsAAACwEA02AAAAYCEabAAAAMBCNNgAAACAhWiwAQAAAAvRYAMAAAAWosEGAAAALESDDQAAAFjo/wPM+TMU0vfSzQAAAABJRU5ErkJggg==\\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    \"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   \"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   \"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   \"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   \"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   \"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      \"image/png\": 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/4m1ZSUhI+Pj5oNBq2bNmCjY2NXr2zszN2dnakpaU98oMi+xvZqVOnaNCggV7d6dOn8x3TpUuXGDhwIE2bNqV69epMmzaNdu3a8eqrr+br+G3bthEcHMyQIUPYtWsXfn5+nDhxQnc1VtB++eUXOnbsiJeXF1u2bDG4UnxSAQEBxMfH88UXXzB8+HBGjRql19H7rGVmZpKRkfHE7y03NzddJ/mj3jvr16/HwsKCPXv26H1o79q1y6Dtw76ElC1bVje0N6fz58/nO+4aNWoAWcnjSZKjqcg8iWdIo9EwadIkLl26xOLFiw3qfXx8OHv2rN6ciuzL6Ox6Y8i+0njwW9XMmTOf6CpCq9XSvXt3zpw5Q2hoaK7JztzcnC5durBr165cJ6ClpaXpZuW2bt0aS0tLFixYoDcBa8eOHbkm2dwopfDz8yMjI4NVq1axYMECKleuTM+ePQ1ur+UmLi6Ovn370qBBA2bOnMmaNWu4du0aAwcOzNfjP64//viDd955hxo1arBz506DJPukVqxYwXfffcfUqVMZOnQow4YNY+HChbmuEvC08rp1smzZMu7evcsrr7zyROd1dnbmzTffZMWKFZw9e9agPjExUTeayNzcHI1Go/eNPzMzk1mzZhkcl/0a55a8atasyalTp7h8+bKu7NatW7rRUfnRsGFD3N3d+fLLL3OdrBgXF5fvcxmTXEk8Y++++y6vvPKKbqZqTp988gkbNmzgP//5j24I7Pfff8+OHTv46KOPcr2/+yw0bdoUZ2dnevXqxccff4ydnR0//fQThw8fxtHR8bHP9/XXX7Nt2zbatWtHdHS0wQzjTp06YW1tzfTp0/nll19o2bIl/v7+NGjQgLS0NE6fPs23335LaGgo3t7eODo6Mm7cOCZOnEjLli3p3LkzMTExLFy4kLp16+ZriYe5c+fy008/sWzZMt2VyapVq2jRogXDhg1jyZIlDz2+b9++JCYm8vPPP2NhYUHDhg0JDAzk008/xcfHhx49ejz265SX6Oho2rRpQ2pqKn5+fnz//fd69eXKldPrywoMDCQoKMjgSvBBUVFRDB48GG9vb4YPHw7AtGnT2L17Nx988AEnTpzQ9TkUhGnTprFv3z7atGlDtWrVSElJ4cCBA2zatIlatWoxZMiQJz73okWLaNq0KQ0aNKBfv37Url2bW7duERERQWhoKBEREVStWpUOHTowd+5cWrZsiZ+fH6mpqaxfvz7XLz+NGjVCo9Hw+eefc/PmTUqXLk3jxo2pVq0a/fv3Z86cObRq1YqAgABu377NkiVLqFSpkl7ieBgzMzOWL1/OW2+9RZ06dejbty/Vq1cnPj6ev/76i82bN+uSW6Fiyl7z4uTB0U057dq1Szf8L7fJdL1791ZOTk7KwsJCeXh4qDlz5uQ5mS43edVlj7J4cLhdbiNGjhw5ot544w1la2urypQpozp16qTOnz+vXF1d9SYw5Wd0U14Tk7J/co4EuXHjhho1apSqUaOGsrCwUI6Ojurll19WgYGB6vr163pxz549W7m6uipLS0vl5eWV78l0x48fV5aWlsrX19egbuzYsQpQmzdvzvP44OBg3aiUnDIzM1WzZs2Uvb29io6OfmgMj+NRkxEfHGUzYsQIpdFo1MmTJ/M8Z3asZcqUMRh9lP36dOrUqcCeg1JK/fDDD6pjx46qcuXKytLSUpUuXVrVrVtXjRs3Lt9DPfMaAqtU1t/OgAEDVOXKlVXJkiXVCy+8oJo1a6ZmzpypN9Jv3bp1ql69eqpUqVKqQoUKasiQISoiIsLgfayUUgsXLlTVq1dX5ubmBvWhoaHKw8NDlSxZUrm5uakFCxY8dHTTg3932U6ePKl69Oihypcvr0qWLKkqVKigWrVqpb766qt8vSbGplEql94YIUSR8fLLL1O1alW+/fZbU4ciiiFJEkIUYTdu3KBcuXKEh4frZgkLUZAkSQghhMiTjG4SQgiRJ0kSQggh8lSshsDeunXL1CEIIUSRlduyP3IlIYQQIk+SJIQQQuSpWN1uyimv1VKFEELc96jb9HIlIYQQIk+SJIQQQuRJkoQQQog8Fds+iQcppUhISHjiDXRE8WVmZoaDg0OBbGokhNEFBYG7O3TrBuvWwenTMHFigZ3+uUkSCQkJWFtbF9jGLaL4SEtLIyEh4YmWRRfCpAIDs5KEmVlWgti6FbRaUCqrrgA8N7ebtFqtJAiRq1KlSskVpih6goIgKIj9bs1Ixww2b85KEDnqCsJzkySEEKJYcXfnN7emvDlkD28O2cMVu3L368zMsm5BFQBJEkIIUQRd9unG+x9tIb2EBftrvs5Lnxzh92r39mv38cnqoygAkiSKOX9/f8aPHw/A/v37cS+gbxePotFoct1/+GlVrVqVH3/8scDPK0RRcjcTOq+J43IpB11ZbJmKXLZ3yfpl69asPooCYNQk0bNnT1xcXLCzs6NWrVosXboUyNp7V6PRYGNjo/uZPHmy7jilFGPGjMHR0RFHR0dGjx5NcdoGo2rVqpQuXRobGxvKlStHnz59SE5OLvDHad68OadPn35ku5CQEJo1a1bgjw8QEBBA7969DcqPHz+OpaUlCQkJz+RxhShORvwGv2Y665V9sutzfI9tyvpFq80a5VQAjJokxo4dS1RUFImJiWzZsoXx48dz5MgRXf3NmzdJTk4mOTmZzz77TFe+ePFiwsLCCA8P5/jx42zbto3g4GBjhv7Mbd26leTkZI4ePcrhw4eZMmWKQZuMjAwTRFaw/P392bhxIykpKXrlK1eu5J133sHBwSGPI4UQACtPw4II/bLWJ3czhcNZfRGQNQS2gIbBGjVJeHp6YmlpCWTdjtBoNJw7d+6Rx61YsYIRI0ZQqVIlKlasyIgRIwgJCXmqWDSLnu3Pk6pYsSJt27YlIiLrXaDRaPjqq6+oWbMmNWvWBGDbtm3Ur1+fMmXK8Nprr3H8+HHd8X/99RcNGzbE1taWrl27kpaWpqvbu3cvlSpV0v1+8eJFfH19cXZ2xtHRkUGDBnHy5EkGDBjA77//jo2NDWXKlAHgzp07jBw5kipVqlCuXDkGDBhAamqq7lyzZs3CxcWFChUqsGzZsjyfX5MmTahYsSKhoaG6sszMTL755hv8/Pw4d+4cLVu2xNHREScnJ3r06MHNmzdzPVfOW2m5Pb/Y2Fjee+89nJ2dqVatGvPnz3/4iy9EIXc0DgL26Ze5Zt5k7atJmIdthDVrsoa+FtDwVzBBn8SHH36IlZUVHh4euLi40K5dO12dq6srlSpVok+fPsTHx+vKIyMj8fLy0v3u5eVFZGSkUeM2losXL7J9+3YaNGigKwsLC+PQoUP8/fffHD16lA8++IDg4GCuX79OQEAA7777Lnfu3OHu3bt07NiRXr16kZCQQOfOnfU+jHPKzMzknXfewdXVlaioKGJiYujWrRu1a9fm66+/pkmTJiQnJ+s+oMeMGcOZM2c4duwYZ8+eJSYmhkmTJgGwc+dOZs+eze7du/nnn38e2WfQu3dvVq5cqfv9xx9/JD09nbZt26KUYuzYscTGxnLy5EkuXrxI4BO84bVaLT4+Pnh5eRETE8OePXuYN28eu3bteuxzCVEYXE8D312Qlnm/rJQ5bOpaBsfuvlkF3boV6EQ6MEGSWLhwIUlJSezfvx9fX18sLS1xcnLi8OHDREdHc+TIEZKSkujRo4fumOTkZL1VXe3t7UlOTi5W/RIdO3akTJkyNGvWjBYtWjBu3Dhd3dixY3FwcKB06dIsWbKEgIAAGjdujLm5OX5+flhaWnLw4EEOHjxIeno6Q4cOpWTJkrz//vu8/PLLuT7eH3/8QWxsLLNmzdJNMsyrH0IpxZIlS/jiiy9wcHDA1taWcePGse5ex9iGDRvo06cPdevWxdra+pEf6r169WLfvn1cunQJyLrV1L17d0qWLEmNGjVo3bo1lpaWODs7M3z4cPbt2/fQ8+Xm8OHDxMXFMWHCBCwsLKhevTr9+/fXxSxEUZKphf/shugk/fLFLaCBc+7HFBSTzLg2NzenWbNmrF69mkWLFjF48GAaNWoEQLly5ViwYAEuLi4kJiZiZ2eHjY0NiYmJuuMTExOxsbEpVssohIWF0apVq1zrKleurPv/6OhoVqxYwf/93//pyu7evUtsbCwajYaKFSvqvS6urq65nvPixYu4urpSosSj3wJxcXHcvn2bl156SVemlCIzM+srTWxsrF5dXo+ZrUqVKrz++uusXr2aQYMGERYWxv79+wG4du0agwcPZv/+/SQlJaHVailbtuwjY3xQdHQ0sbGxuttlkHX11Lx588c+lxCmNv4P2H1Jv2xQXehlhMGKJl2WIyMjI9c+iewPuewrBU9PT8LDw3nllVcACA8Px9PT86keWw18qsONKueHfuXKlfn000/59NNPDdrt27ePmJgYlFK6Yy5cuICbm5tB28qVK3PhwgUyMjIMEsWDydfJyYnSpUsTGRlJxYoVDc7l4uLCxYsXdb9fuHDhkc/Jz8+P6dOn4+LiQrVq1WjYsCGQddWk0Wg4fvw4jo6OhIWFMWjQoFzPYW1tze3bt3W/X7lyRe/5VatWjX/++eeRsQhRmIWeg+l/6Zc1LQ9zXjPO4xvtdtO1a9dYt24dycnJZGZmsmvXLtauXUvLli05dOgQp0+fRqvVcv36dQYPHoy3t7fuFlPv3r2ZO3cuMTExxMbGMmfOHPz9/Y0VeqHSv39/vv76aw4dOoRSipSUFL7//nuSkpJo0qQJJUqUYP78+WRkZLBx40b++OOPXM/zyiuv4OLiwieffEJKSgppaWn8+uuvQNbV3KVLl7h79y6QtQBe//79GTZsGNeuXQMgJiZGd3+/S5cuhISE8Pfff3P79m2C8rEcwHvvvcfFixeZOHEifn5+uvKkpCRdh3lMTAyzZs3K8xz169dn+/btJCQkcOXKFebNm6f3/Ozs7JgxYwapqalkZmYSERHB4cOHHxmbEIXF3wng/7N+mYsVfPsWWJgbJwajJQmNRsOiRYuoVKkSZcuWZeTIkcybN48OHTpw/vx52rRpg62tLXXr1sXS0pK1a9fqjg0ICMDHx4d69epRt25d2rdvT0BAgLFCL1QaNWrEkiVLGDRoEGXLlqVGjRq6kV4WFhZs3LiRkJAQypYty/r16/H19c31PObm5mzdupWzZ89SpUoVKlWqxPr16wFo2bIlnp6elC9fHicnJwBmzJhBjRo1ePXVV7Gzs6NVq1a6ORdt27Zl6NChtGzZkho1atCyZctHPg9ra2tdosjZ/zRx4kSOHj2Kvb097du3zzN+yOrb8PLyomrVqrz11lt07drV4PkdO3aMatWq4eTkRL9+/R65C5cQhcWtO9BpJySn3y8raQbfvQ0u1saLQ6OKUe9vzg+AB7cvjYuLw9n5GffwiCJL3h+iMNEq8N0Jm6P0yxc2h4F1C/axHva5CbIshxBCFDqfHzVMEP7uMODpumKfiCQJIYQoRHZEw2cPdCW+5AwLXwdTDOiUJCGEEIXEuVvQ/UfI2QfgWApC34bSJhqLKklCCCEKgdvpWTOqb969X2amgXWtwdXWdHE9N9uXCiFEoRQUhKrlTn/nbhy/rl81vTG0qpT7YcYiVxJCCGEq9xbjm7/0IN88MO+zsxuMrG+SqPRIkhBCCFO4tw/1vpqvM6LTbL2qOrGRLDs4zSQd1Q+SJCGEEKbg7s55Zzfe6x9Kpvn9O/92qbfYtPQ9bGpVN2Fw90mSEDqBgYH07NmzwM/7LHe6E6KoutWpGz6jfuK6jZNe+aqQXtRq7FFge1Q/LUkShYi3tzdly5blzp07+WpvrA/fmJgYSpQoketijJ06dWLkyJHPPAYhipMMLXRbGcvfNlX0ygO3TeTdE1sLdI/qpyVJopCIiopi//79aDQatmzZYupw9FSsWJE333yTVatW6ZUnJCSwfft2vQX6hBCPNup32JlRQa+s2+G1TNietZFXQe5R/bQkSTxKUND9jL5uXdbvz8DKlSt59dVX8ff3Z8WKFXp1j7PNqLe3N0uXLtUd++DVxpAhQ6hcuTJ2dna89NJLun0cHsXPz88gSaxbtw5PT0/q1avH9OnTcXNzw9bWljp16rBp06ZczxMVFYVGoz51aPQAACAASURBVNHbr/vBmJctW0bt2rUpW7Ysb7/9NtHR0fmKUYiiYPHfMO+4ftnLUX+w7OYmNM9gj+qnJUniYbL3iu3RAzp2zPpvAe8fm23lypX06NGDHj16sGvXLq5evQo8/jajj/Lyyy9z7NgxEhIS6N69O507d9bbBzsvnTp1Ij4+ngMHDujKVq1aRe/evQFwc3Nj//793Lp1i4kTJ9KzZ08uX7782K9DWFgY06ZNY+PGjcTFxdG8eXP+85//PPZ5hCiMfo6Bjx74XlYxM5HNXrGU3rjhmexR/bQkSeTl3vA0IOvSb/PmrP8+WFcADhw4QHR0NF26dOGll17Czc2Nb775Bni8bUbzo2fPnjg6OlKiRAlGjBjBnTt3dEt+P0zp0qXp3Lmzbm/qf/75hyNHjtC9e3cAOnfuTIUKFTAzM6Nr167UrFkzz70sHiY4OJixY8dSu3ZtSpQowbhx4zh27JhcTYgi75+b8N6urP6IbFYlYGtXO1y6d8wqeAZ7VD8tSRJ5cXcHszxeHjOzrPoCsmLFCt566y3d3g3du3fX3XJ6nG1G82POnDnUrl0be3t7ypQpw61bt4iPj8/XsX5+fmzYsIG0tDRWrVpFmzZteOGFF4CsK6H69etTpkwZypQpQ0RERL7Pm1N0dDRDhgzRncfBwQGlFDExMY99LiEKi5t3wGcH3HhgTMqqN5/9HtVPS5blyEu3bll9EJs3G9b5+BTY8LTU1FQ2bNhAZmYm5cuXB+DOnTvcvHmT8PDwx9pmFB6+pef+/fuZMWMGe/bswdPTEzMzM8qWLUt+txRp3rw5jo6ObN68mdWrVzNz5kwg64O9f//+7NmzhyZNmmBubk79+vVzPa+1ddZuKbdv38bOzs4gxuztWXNuRCREUZahhS4/wOkH7ghPfQV8C8dUiIeSK4m8rFuXNQwtNwU4PC0sLAxzc3P+/vtvjh07xrFjxzh58iTNmzdn5cqVj7XNKGRt6blx40Zu377N2bNn+d///qerS0pKokSJEjg7O5ORkcGkSZNITEx8rHh79+7NmDFjuHnzJj4+PgCkpKSg0Wh0m/YsX76ciIiIXI93dnamYsWKrF69mszMTJYtW6Y3tHbAgAF8/vnnREZGAlkbonz77bePFaMQhcmwX2H3Jf2yHjVhbEPTxPO4jJokevbsiYuLC3Z2dtSqVUtvRMuePXvw8PDAysqKN954Q+8etFKKMWPG4OjoiKOjI6NHj873t98ndvr0/T6IBxXg8LQVK1bQp08fqlSpQvny5XU/gwYNYs2aNSilHmub0WHDhmFhYUG5cuXw8/PT+0b+9ttv07ZtW2rVqoWrqyulSpWicuXKjxVv7969uXDhAl27dsXS0hKAOnXqMGLECJo0aUK5cuU4ceIETZs2zfMcS5YsYdasWTg6OhIZGclrr93f0b1Tp06MGTOGbt26YWdnR926ddmxY8djxShEYbEwAhY88H3p1XKw1Ns0e0M8EWVEERERKi0tTSml1MmTJ1W5cuXUn3/+qeLi4pSdnZ3asGGDSk1NVSNHjlSNGzfWHff111+rWrVqqYsXL6pLly6p2rVrq0WLFhmc/+bNm7qfB127du3xA544USlQysxMqQ4dsv4LWeWiWHmi94cQD7H7olLmi5Ri4f2fyiuVupJi6sj0PexzUymljNon4el5f+89jUaDRqPh3LlzHDlyBE9PTzp37gxkLQ/h5OTEqVOn8PDwYMWKFYwYMYJKlbLWzB0xYgRLlixhwIABzzbgwMCsdO/ufr+P4vTpQjf6QAhRuJy+AZ13QWaOGx7WJWBrWyhnZbq4noTR+yQ+/PBDrKys8PDwwMXFhXbt2hEZGYmXl5eujbW1NW5ubrr70g/We3l56eqeuYkT73dSF8LhaUKIwiUhLWskU87NgzTAN63AyynPwwotoyeJhQsXkpSUxP79+/H19cXS0pLk5GTs7e312tnb25OUlARgUG9vb09ycvKz75cQQojHkJ4JnX+Af27pl09/Fd6tZpqYnpZJRjeZm5vTrFkzLl26xKJFi7CxsTEYZZOYmIitbdaefQ/WJyYmYmNjk+sQUCGEMAWl4OMD8NMDU3r83GFUIdg86EmZdAhsRkYG586dw9PTk/DwcF15SkqKrhwwqA8PD9fr3xBCCJO5t77bgggI/lu/qml5CG5RhEYy5cJoSeLatWusW7eO5ORkMjMz2bVrF2vXrqVly5Z06tSJiIgIQkNDSUtLY9KkSbz44ot4eHgAWcMu586dS0xMDLGxscyZMwd/f//HenwzM7N8rVEknj9paWmY5TW7XoiHubfO0s4pKxm6P1OvqqotbGoDluamCa2gGG10k0ajYdGiRQwYMACtVourqyvz5s2jQ4cOAISGhjJo0CB69uxJ48aNWZdjslpAQADnz5+nXr16APTr14+AgIDHenwHBwcSEhJ0/RxCZDMzM8PBwcHUYYii5t4abifLe9D1g7VoNfezgU1aEluvr8K59IcmDLBgaFQx6v29det+b9GDHeFCCFGg1q3jyoDhvDryN6Idq+qKNVotW4M70H5cj0Kzu9zDPOpzU9ZuEkKIJ5Dk24120S8TbVdVr3z2xpG0r25eJBJEfsiNWCGEeEzpmdB5xWX+snPTK+93YAnDfvqiUG0/+rQkSQghxGNQCvrvg10ZLnrl7SK+Z9G6gWigUG0/+rQkSQghxGOYcBhWPPD53yj6MOuvrKIE97p4C9H2o09L+iSEECKfgiNhyhH9suqZCXz/4iVspq+DdR2L3fpuMrpJCCHyYWsUdNwJ2hyfmE6l4LdOULOMycJ6ao/63JTbTUII8QiHrkLX3foJonQJ2NauaCeI/JAkIYQQD/HPTXhnO6Rm3C8z08D61tC4nOniMhZJEkIIkYdrt6Ht9xD/wIo+C5uDT1WThGR0kiSEECIXKenwzg4498A28J82hIDnaH1RSRJCCPGADC10+QEOX9Mv710LJr9imphMRZKEEELkoBQM/AW2X9Avb10JlngX7WW/n4QkCSGEyGHKEVh6Ur+svhOEvg0WRXzZ7ychSUIIIe5ZfiprRnVOrrawvR3YWpgmJlOTJCGEeL7d21lu5wXo/7NWr6qsJexoDy7WJoqtEJBlOYQQz6/AQAgK4ohrI94f0YHMEqV1VZbmsLUt1C5ruvAKA7mSEEI8n+7tLPevY1XaD9hKSo4EodFq+SZhPU1dHnL8c0KShBDi+eTuzuUyFWj98W6u2pfXq/oydCi+VYvNsnZPxWhJ4s6dO/Tt2xdXV1dsbW1p0KABO3bsACAqKgqNRoONjY3uZ/LkybpjlVKMGTMGR0dHHB0dGT16NMVoXUIhhAlc79iN1mN/49wLNfTKR+2eycd2F4rNznJPy2h9EhkZGVSuXJl9+/ZRpUoVtm/fTpcuXThx4oSuzc2bNylRwjCkxYsXExYWRnh4OBqNhtatW1O9enUGDBhgrPCFEMVI4l1os/I6kbaueuXd/1jD9LBPsiZDrFsniQIjXklYW1sTGBhI1apVMTMz45133qFatWocOXLkkceuWLGCESNGUKlSJSpWrMiIESMICQl59kELIYqd2+ngsx3+zHTUK/c5voWQlf6YKVWsdpZ7Wibrk7h69SpnzpzB0/P+Iiiurq5UqlSJPn36EB8fryuPjIzEy8tL97uXlxeRkZFGjVcIUfTdzYT3f4BfLuuXtzy9hw1XV1GSe0Ngi9HOck/LJEkiPT2dHj164Ofnh4eHB05OThw+fJjo6GiOHDlCUlISPXr00LVPTk7W2wzD3t6e5ORk6ZcQQuRbphZ67oEdDyy30Tj9Epsb36DUxm9hzZqsYbGBgaYIsVAy+s50Wq2W7t27k5iYyObNmylZsqRBmytXruDi4sKtW7ews7PD3t6e3bt388orWStrHTlyBG9vb5KSkvSOk53phBC50SrotzdrRnVOLzrCz++CQymThFUoFKqd6ZRS9O3bl6tXrxIaGpprggDQ3FtBKzt/eXp6Eh4erqsPDw/Xu00lhBB5UQqG/WqYIGraww/vPN8JIj+MmiQGDhzIyZMn2bp1K6VL35+4cujQIU6fPo1Wq+X69esMHjwYb29vXVbr3bs3c+fOJSYmhtjYWObMmYO/v78xQxdCFFETD8P8E/pllW3gRx8oZ2WamIoSow2BjY6OJjg4GEtLS8qXvz9xJTg4GDMzM8aNG8e1a9ews7OjdevWrF27VtcmICCA8+fPU69ePQD69etHQECAsUIXQhRRs4/B5AcGUJYrDXt8oIqtaWIqaozeJ/EsSZ+EECLb4r8hYJ9+WRkL2Ncxqy9CZClUfRJCCGEMa/+BAQ8kCOsSsPMdSRCPS5KEEKJY2fIv9NoDOW+RWJrD1nbQuJzJwiqyJEkIIYqNPZegy27IzJEhSpjBd2/BGxVNF1dRJklCCFG03ds06OAV6LA1nTuZ96s0wKqW8E5VUwVX9MmmQ0KIouvepkHhlevTduQ7pJS00asObgHdapomtOJCriSEEEXTvU2DzrxQk7c+2snNBxLEnKRd9K9jotiKEUkSQoiiyd2d0+U98B66l2t2+j3SE7cHMbzKDRMFVrzIPAkhRJF06ga0DEngcikHvfKhe75gbsY+NGFhJoqsaJF5EkKIYufkDfBen2qQIPodWMLc0OFotm7N2jRIPDXpuBZCFCl/J8AbW+CaKq1X3u/AEoLXBqAB2TSoAMmVhBCiyIi4Dt6b4Vqqfvl/DywmOGU7ZvdWkJZNgwqOXEkIIYqEE9fhzS0Ql6ZfPuD2n3z1jh1m/9mUdYvp9GlJEAVIOq6FEIXe8XsJIv6BBPGhJyxoDtkXEOLxSce1EKJIOxYPLXNJEIPqSoIwBrndJIQotP6Kg1ZbIeGOfvngejCvqSQIY5ArCSFEoXQ0Dt7MJUEMfVEShDFJkhBCFDpH4rL6IG48kCCGe8Hc1yRBGJPRksSdO3fo27cvrq6u2Nra0qBBA3bs2KGr37NnDx4eHlhZWfHGG28QHR2tq1NKMWbMGBwdHXF0dGT06NEUo/52IUQOh69Bqy1w865++UgvmN1EEoSxGS1JZGRkULlyZfbt28etW7eYPHkyXbp0ISoqivj4eHx9fZk8eTIJCQk0atSIrl276o5dvHgxYWFhhIeHc/z4cbZt20ZwcLCxQhdCGMmhq9B6q2GCGF0fZkqCMAmTDoF98cUXmThxItevXyckJITffvsNgJSUFJycnPjrr7/w8PDgtddew9/fn//+978A/O9//2PJkiUcPHhQ73wyBFaIIigoCNzdOejdjbc33SURC73qsQ1gamNJEM9KoR0Ce/XqVc6cOYOnpyeRkZF4eXnp6qytrXFzcyMyMhLAoN7Ly0tXJ4QowgIDITCQ38f/H299e9sgQXzaUBKEqZkkSaSnp9OjRw/8/Pzw8PAgOTnZIIPZ29uTlJQEYFBvb29PcnKy9EsIUZTd2w/igFtT3vpoJ0klrPSqP0vZx+RXJEGYmtGThFarpVevXlhYWLBgwQIAbGxsSExM1GuXmJiIra1trvWJiYnY2NigkXePEEWXuzvb67bnrY9/ILmUrV7VxO1BTKp0WRJEIWDUJKGUom/fvly9epXQ0FBKliwJgKenJ+Hh4bp2KSkpnDt3Dk9Pz1zrw8PDdXVCiKLpm4bd6DAgjFQL/SuIwG0TCTT/C7p1M1FkIiejJomBAwdy8uRJtm7dSunS95f57dSpExEREYSGhpKWlsakSZN48cUX8fDwAKB3797MnTuXmJgYYmNjmTNnDv7+/sYMXQhRgBacgB57IMNMf9GHKVs+ZeL2SSD7QRQaRksS0dHRBAcHc+zYMcqXL4+NjQ02NjasWbMGZ2dnQkND+fTTTylbtiyHDh1iXY43SEBAAD4+PtSrV4+6devSvn17AgICjBW6EKKAKAWBh+HjA/rlGq2WhWsH8unOaVkFsh9EoSGrwAohjEKrYMgBWBChX14iM51VK/3oVuF21hWEVpu11HdgoEnifN486nNTFvgTQjxz6Zng9xOsPatfblUCQm+up83wd7P6IGQ/iEJHriSEEM/U7XR4/wfYcUG/vKwlfN8OmpQ3TVwiS4FNpnNzc2P69OlcvXq1YCITQhR7N+5A622GCcLFCn7pIAmiKMh3kujduzfBwcFUqVIFX19fdu3a9SzjEkIUcZdToEUY/HZFv7yGPfzaCeo6miYu8XjynSQmTpzI+fPn2bJlC+bm5rz77rtUrVqVKVOmEBsb+yxjFEIUMeduQbMwOJGgX+7lCAc6QjU708QlHt8T90nExcWxePFipkyZQkZGBu3atWPkyJE0b968oGPMN+mTEML0jl+Ht7fBldv65c1dYEtbKGNpmrhE7p7JAn+nTp1ixowZzJs3DxsbGz7++GNKlSpFq1atmDBhwpNHK4Qo0g5chtfDDBPEO66w6x1JEEVRvq8k0tLS+Pbbb1myZAm//vorTZs2JSAggPfffx9Ly6x/+S1bttCrVy+9zGRMciUhhAncW+p7e5NuvL89g9QHRtb3qgX/84aS5qYJTzxcgc2TcHFxwczMjJ49e/L1119Tp04dgzavv/46Dg4OTxiqEKLICQyEoCDWvNIT/+vvGyyzMfRFmPMamMlCfUVWvpPEF198Qbdu3ShVqlSebcqUKcO///5bIIEJIQq5oCBUUBBfvjGEYZ3nGVRPSf6Jca+1lJVci7h890n4+/s/NEEIIZ4vGbU8GNTtK4MEodFqWbTuQz6tfE0SRDEgy3IIIR5b4l3oatuVna/rl5fMuMvqkJ50qXxXlvouJky2fakQomi6kATNNsHOi/rltqmJbFv0Dl2OfitLfRcjciUhhMi3w9fAZztcTdUvr3I9mu8Xtqfu5Xt7z8tS38WGXEkIIfJl43losdkwQbwc9QeHZjeh7is1wOzeR8rEibKSazEhVxJCiIdSCmYfg9EHDeveqw4rr+3G6uu5stR3MSVLhQsh8pSeCR/thyUnDevGNIBpjWUORFEnmw4JIZ7IzTvQ+Qf48ZJ+eQkzWNQc+hnOpxXFkFH7JBYsWECjRo2wtLTE399fVx4VFYVGo9Hte21jY8PkyZN19UopxowZg6OjI46OjowePZpidAEkRKHzbyI03WSYIOwtYEd7SRDPE6NeSVSoUIHx48eza9cuUlNTDepv3rxJiRKGIS1evJiwsDDCw8PRaDS0bt2a6tWrM2DAAGOELcRz5fcr0GEHxKXpl1e1zdpJro6svPNcMeqVhK+vLx07dsTR8fF2G1mxYgUjRoygUqVKVKxYkREjRhASEvJsghTiObbhLLyxxTBBvFoODvlKgngeFaohsK6urlSqVIk+ffoQHx+vK4+MjMTLy0v3u5eXF5GRkaYIUYjiJygItXYd045A191wJ1O/umsN+OldeMHKNOEJ0yoUScLJyYnDhw8THR3NkSNHSEpKokePHrr65ORkvV53e3t7kpOTpV9CiKcVGMjdyVP5YGcan/5hWP1pQ/imFZSWIS7PrULxT29jY0OjRo0AKFeuHAsWLMDFxYXExETs7OywsbEhMTFR1z4xMREbGxs0snqYEE8uKIjLXwTTechP/FqjmV5VyYy7LLm9Hb/GHU0UnCgsCsWVxIOyP/yzrxQ8PT0JDw/X1YeHh+Pp6WmS2IQoLg64NaXh2KMGCaJsSgI/fNUGP9e0PI4UzxOjJomMjAzS0tLIzMwkMzOTtLQ0MjIyOHToEKdPn0ar1XL9+nUGDx6Mt7e37hZT7969mTt3LjExMcTGxjJnzhy9IbRCiPxTCv7vBLyR0oor9i56dW7XzvL7rCZ4e9jJKq4CMHKSmDJlCqVLl2b69OmsXr2a0qVLM2XKFM6fP0+bNm2wtbWlbt26WFpasnbtWt1xAQEB+Pj4UK9ePerWrUv79u0JCAgwZuhCFAu306HXHhh8ADK0+nWtT/7AoVmNcb92RlZxFTqyLIcQz4lzt8B3Fxy/blg3budUJm2dgLnKkTkCA2UNpufAoz43C2WfhBCiYG2PhkbfGSYIW+0dNgV3ZOq2CZi/6yOruAoDhWJ0kxDi2dAqmPwnBP0JD94yqFMWNr5tiXt8A/ikm6ziKnIlt5uEKKZu3Mnqf/g+2rCusxssewNsSho/LlG4yCqwQjyHjl8H351wLlG/3FwDM16F4V4g04xEfkiSEKKY+eYM9NsHqRn65c6lYP1b8EZF08QliibpuBaiqAsKgnXrSM+EIctO02OPYYJ45QU40lkShHh8ciUhRFEWGAhBQVyxd6HLuXrsdzBciSCgDnzZDCzNjR+eKPokSQhRVAUFQVAQ+92a0bXvei6XqaBXbakyWPhGCT6obaL4RLEgt5uEKKIyankwwWcS3sP2GiSIKgnRHLDbIwlCPDW5khCiCPo3EXpYduX3toZ1rU7uZu3llTh9usr4gYliR64khChivjkDXhvg96uGdWN3TmPngjY4bfxG1l4SBUKuJIQoIhLvwkf7YfUZw7ryty6zckVvWp/6MatAkTVzWoinJFcSQhQBB69A/Q25J4h3Tmzl+Of1ae1uLWsviQInVxJCFGKZWvj8Lwg8DJkPLKBTyhxmvwYfXjuKJvhLWXtJPBOydpMQhdSFpKy1l365bFhX1wHWtoK6jsaPSxQvsnaTEEXQt+fgv3vh5l3Duo/rZa2/VFr+eoURyNtMiEIkOR2GHIBlpwzrnEpBSEto72r8uMTzy6gd1wsWLKBRo0ZYWloa7FG9Z88ePDw8sLKy4o033iA6+v76xkopxowZg6OjI46OjowePZpidJdMPO/urb10JA4aLkvMNUG8VRlOdJUEIYzPqEmiQoUKjB8/ng8++ECvPD4+Hl9fXyZPnkxCQgKNGjWia9euuvrFixcTFhZGeHg4x48fZ9u2bQQHBxszdCGejcBAtEFBzFz+F002pPOP1k6vuqQZzH0NdrSH8lYmilE814yaJHx9fenYsSOOjvq9bRs3bsTT05POnTtTqlQpAgMDCQ8P59SprK9UK1asYMSIEVSqVImKFSsyYsQIQkJCjBm6EAUvKIjTi9biPXQvYzrOIN1Mfwcgj4w4DvnCMC8wk70fhIkUinkSkZGReHl56X63trbGzc2NyMjIXOu9vLx0dUIURRlamO7aEa9Pw9lf83WD+oADwRxx2ksDZxMEJ0QOhaLjOjk5GWdn/b8Ge3t7kpKSdPU5h2bZ29uTnJyMUgqNbK8liphj8dD3Zzia6gUPbB/qkHydpWv60amqgv+EmSZAIXIoFEnCxsaGxET9fRYTExOxtbXNtT4xMREbGxtJEKJIScuAyUdgxl+GE+MA2kZsZ/E3/6XSzRg4YZY1Ma5bN+MHKkQOheJ2k6enJ+Hh4brfU1JSOHfuHJ6enrnWh4eH6+qEKAp+uwINvoVpRw0ThGNyPKuX9+D7he2zEgSAVitrL4lCwahJIiMjg7S0NDIzM8nMzCQtLY2MjAw6depEREQEoaGhpKWlMWnSJF588UU8PDwA6N27N3PnziUmJobY2FjmzJljMIRWiMIoOR0GH4Bmm+DUTcP6rmkR/D2pDj2OrEPToYOsvSQKHaPebpoyZQpBQUG631evXs3EiRMJDAwkNDSUQYMG0bNnTxo3bsy6HMscBwQEcP78eerVqwdAv379CAgIMGboQjy2Hy7Cf/dBdJJhXQVrWNgcOlSrC4kfgbu7rL0kCiVZu0mIApaQBiN+g5A87hb1rw0zm0AZS+PGJURuZO0mIZ61oCDdlUDoygN8lFifq+Y2Bs2q28GSFtCykgliFOIJSZIQ4mkEBkJQEFfsXRh0sgqh5ZuBuX4TMw0MfREmvQzWJXM9ixCFliQJIZ5UUBCZkyaztNl/Gdvhc25YOxg08SwL/3sDGpczQXxCFABJEkI8oV+qN2PIJ39yrHIDg7qSGXcZZ3OGcZ3rYmGey8FCFBGSJIR4TBeSYNTvsCH5TahsWP9y1B/8L2YN9VZ/afzghChgkiSEyKfb6TDrGMw4BqkZhvVWd1KYtG0CQ3+ah7kGeKeJzJgWRZ4kCSEeQamsneJG/Q4XknNv0/2PNcwIG3N/xrRCZkyLYkGShBAPER4PQ36FfbG51zdMj2X+l51pGnUQfHxg6+WsJTVkxrQoJiRJCJGL+FT47A9YfBK0uUw3faE0TGsM/u4VML/xFrh/LDOmRbEkM66FyDEZLn3tehZdtGOiY1tu3jVsWsIMBteDCS+BvcyYFsWAzLgW4mHuTYbDzIwff/ibIZW68reLJ+SSINpUhi+agkdZo0cphMlIkhDPr6AgCArizAs1GdNxBmH1O+XarKZ9VnJoVwVkCxPxvJEkIZ5b52s0ZFLvEFa90hOtmeGMN1vS+ezVkgx5EZkQJ55bkiTEc+diMkw5AsuSfch4Nfc2/jF7+Hzcm5S3Mm5sQhQ2kiTEc+NyStbOcIv/hrva3Nu8ev535n87mJcvHoW6a2QynHjuSZIQxV5cata+0l9FQFpm7m08rpwkaNtE3v/rO8yyB/zJZDghCsce10I8CwlpMO4gVFsNc8JzTxBuGQmsCulJxNQX6VL5LmbZPdMyGU4IoJAlCW9vb0qVKoWNjQ02Nja4u7vr6vbs2YOHhwdWVla88cYbREdHmzBSUZjdugOBh6HaGvj8L0jJZZ2lKjaw1BtOfuhAz3Y1MV+9CsLCYM2arGGxgYFGjlqIwqlQTabz9vamZ8+e9OvXT688Pj4eNzc3li5dio+PD5999hn79+/n4MGDeu1kMt1z6t5kuOT3uvF/q8OZdbsWN8xK59q0gjWMbwh9a8uIJSGgmEym27hxI56ennTu3BmAwMBAnJycOHXqFB4eHiaOTphUYCApn88i+PUBTL/ShjhLr1yvj18oDWMbQIAnlC4S73ohCodCdbsJYOzYsTg5OdG0aVP27t0LQGRkJF5eXro21tbWuLm5ERkZaaIoRWEQM3kuY49ZUnnqRUb4ziHOsoxBG8dSMONVON8DhnpJ7702DgAAEMdJREFUghDicRWqP5kZM2ZQp04dLCwsWLduHT4+Phw7dozk5GScnZ312trb25OUlGSiSIUpHY2DueGw3mkoGW/n/j3H/vZNRjpcYHCPF7GzMHKAQhQjhSpJNG7cWPf/fn5+rF27lu3bt2NjY0NiYqJe28TERGxtbY0dojARrYJtUTD3eM5luw0ThE1aEkN/msdwy1OU/W6NMUMUolgqVEniQRqNBqUUnp6erFixQleekpLCuXPn8PT0NGF0whhS0iHkNMw7Dmdv5d3OIfk6Aw58zbA9X+CUch3MzGCdj0yGE+IpFZokcfPmTQ4dOkSLFi0oUaIE69ev55dffmHevHk4ODgwatQoQkNDad++PZMmTeLFF1+UTuti7FIyLIjImh19407e7dyvnGLoz/PofXAlVump9yu0WpkMJ0QBKDRJIj09nfHjx3Pq1CnMzc3x8PAgLCxMN1ciNDSUQYMG0bNnTxo3bsy6detMHLEoMDn2cziyehdfxLmw3upFMvJYOgOgZUUY7gVtl6/HbH9w1pVDhw6wdavsDCdEASpU8ySelsyTKIICA8mcNJltL77LF50ns8+hbp5NS5rBf2rAMC+o75SjIkeSkZ3hhHg8j/rclCQhTObUlAWE/JnMqsa9iC1TMc92DpYw0BM+qgsu1kYMUIjnQLGYTCeKj5t3YP3ZrM7og46D4O2827qbJTK0mR29a4FVSePFKIS4T5KEeOYytfDjpazEsOlfuJPHSqzZWp7aw/CU32i75DPMZCc4IUxKkoR4Zk7fgBWnYeUZiEl5eFvrtGS6HN3A4L3zqX8pPKsjulVNGcIqhIlJkhBPL0fH8a21oWy4YMHymj78fvXRh3qf+Zk+vy/H99hGbO7kyCQyhFWIQkGShHg6gYFkTJ7Czx5vEhLpwkandqTZlYaHJIiqtuDvDr3dodrcfXBolQxhFaKQkiQhnkjSXdg1dwObo9z4fsY1blg7PLS9VQno7JaVHF6vwP2+hsBA0GhkCKsQhZQMgRX5FpsCW6KyfvZcynuf6JxaVMhKDO9VB1tZaE+IQkeGwIonphREJsDmqKyfw9fyd5zr9Sj8bhyk94RuuEmuFqJIkyQh9DqeM9au58C/qWxu4c+WKDif+MijgawF9nxObKX3oZV4/7M3a6/ol5HRSUIUcZIknneBgcTPXsBe95ZsiSjH92VakVDWEY4/+tDqmQl02BtCh+ObaXruV0poc0yAUEpGJwlRDEiS+P/27j8mqjPdA/gXARlmBrgKAiouQivYRcvay0pcfnpto239kSZVsYCIbK25SdM07da6rQVrTa2mt2lCdtO0Xa1FawxTs7e76EWbCqi12xCxwqJUi9OrUCjQCkMHHJhn/xhhgZkzzMj8EPh+kgkw8553njy8zMM573vOmYT0XUBVM1D1t2pUmdehfl+Rw9suDgfWzAXWxAC/njYdPm2dgK6Sq5OIJigWiYnAzgXuRID6n+4UhTuP7w13tgv8TyDQftcBvsCy2ZaisDIamDXy2klcnUQ0oXF103hXVGQpElOmAKtWoe/vZbgwOxFV//0Gqh5cjjPNQFuPc11ON7Rj5a2LWJ3/X1j+K0DL6yYRTVhc3TSB9e98HY1/KkFt4hpcnJ2Is79Kwbm9JehWaS0NGh3rZ4q5H7+5UYPMhtNYfel/LfMLEGDBIeA+TjwTTWYsEveCUe6HIAL8vwGo7bA86u58/Wf4H9Gz8zWn3y7A1zK3kHa1Cul/eQNLGr9EcE+XdUNOPBNNeiwS3jbkcJEcOYKW0/9AXeQDqFUdR23yo4NFoctka2PHfn1B/kBKJJA203K2c9IMQOUHAGmAYQmws3zwcBUnnoloKM5JAGO/s5mD2/9iApp+sZy53NQNNB37P9z8qhZNIbNwY1oU6iMfQLs2zMYbOCe8swVp184g7XcxSH/8ITwYCvhOGXv8RDTxTJg5iY6ODhQUFKC8vBxhYWF488038dRTT42946ETv0eO/Ps/aRHLawpEAJMZML7xJoz/82cYAzRoPV6Nm5ca0RQciSZNFZoeSsPN7n8XhZ9vj+gkaDnwsJ277jgg1NCGhU2XkNBUh9/cqEH61UrMa/0WPgAwuwiY8dDonQwtCDz5jYiGGDd7Ehs2bIDZbMaHH36ImpoaPP744zh37hwSEhIG2zi9J7Fz52AhuBwRj32P/AE9fioYpwbC6B8I47wHYIyai55+wNh359EP9Nz5avZg5oKnAgumAwnTLF8XTAcSpgPhe4vgM2R107DDRXaKHBERMEH2JLq7u6HT6VBbWwutVovU1FSsXr0aH3/8Mfbs2XP3HcfHWz5czWb8qJ2Bv/yuwLqNg9crcpXA27/g183/xILmOixY+iASli7CglAgSmM5HcEKz1MgIjcaF0WioaEBvr6+iIuLG3wuMTERFRUVY+t44EP1r3+Fqs/Jkwnugt8UYKYamKW2nJQ2u+4fmHXyGGb93ITZP9/E3I7riGlrhK/cubxqbBEQvWj0jnm4iIjcZFwUCYPBYLUbFBISgq4uG8s2nTEwBwEg8LbR6c19YUZgTzcCTUaoTD0I7W7HrFtNlg/9zibMevIxzMr4LWZrLEVhRiCG37N5xWKgqwzYeXDI4aJGQMDVRUR0TxgXRUKr1aKzc/jlSDs7OxEUFDS2jq9csRy/BxD18w28X/J7qPp6EHjbiECTEYFPrYdqcx4C/YBAX8uy0UBfINAPUPkC/m/ssn/cPwlA3m/tx8DDRUR0DxsXRSIuLg59fX349ttvMW/ePADAxYsXh01a35XCQssypZ078R+9Xfj9jLbhE79/zHN4+zGdZ8DDRUR0jxo3q5uysrLg4+ODDz74ADU1NXjsscfGvrppgIfOkyAiuteM9rk5bopER0cHNm/ejJMnTyI0NBR79uyxOk9iUl7gj4hoDCZMkXAEiwQRkXNG+9y0d7EGIiKa5FgkiIhI0bhY3XQ3hu5CERHR3eGeBBERKWKRICIiRRNqdRMREbkW9ySIiEgRiwQRESmaVEWio6MDTzzxBDQaDaKjo3H48GHFtu+88w4iIyMREhKCzZs3o7e3162x9fb2oqCgANHR0QgKCsKiRYtw/Phxm20PHDgAX19faLXawcfp06fdGh8AZGZmQqVSDb5nfHy8YltP529oLrRaLXx9ffHss8/abOup/BUXFyMpKQkBAQHYtGnTsNc+//xzzJ8/H2q1GkuXLoVer1fsx5lx64r4zp8/j0ceeQTTp0/HjBkzsHbtWjQ3Nyv248y4cEV8169fh4+Pz7Df365duxT78XT+Dh06NCw2tVoNHx8fVFdX2+zHXflzGZlEsrKyZN26ddLV1SVVVVUSHBwstbW1Vu1OnDgh4eHhUltbKx0dHZKRkSHbtm1za2wGg0EKCwulsbFR+vv75bPPPhOtViuNjY1Wbffv3y8pKSlujceWjIwMef/990dt5438DWUwGESj0UhFRYXN1z2VP51OJ8eOHZOtW7dKXl7e4PM//vijBAcHy9GjR8VoNMqLL74oycnJiv04Om5dFV9ZWZkcPXpUbt26Jd3d3ZKfny/Lly9X7MfRceGq+BobGwWAmEwmh/rxdP5G2r9/v8TGxorZbLb5urvy5yqTpkgYDAbx9/eXK1euDD6Xk5Nj88Nrw4YNsn379sGfT506JRERER6Jc6iFCxdKaWmp1fP3epHwdv4OHDggMTExin+Uns7fK6+8MuxD5L333pMlS5YM/mwwGESlUkl9fb3Vts6MW1fFN1J1dbVotVrF1939ITcyPmeKxL2Qv8zMTCkqKlJ8/V4vEpPmcJPS3e3q6uqs2tbV1SExMXFYu5aWFrS3t3skVgBoaWlBQ0OD4uXQL1y4gLCwMMTFxWHXrl3o6+vzSFzbt29HWFgYUlJSFA/ReDt/H330ETZu3Agfm/d7tfBW/gDr/Gg0Gtx33302x6Iz49ZdKisrR70svyPjwtWio6MRFRWF/Px8tLW12Wzj7fzp9XpUVlZi48aNdtt5I3+OmjRFwpm7241sO/D9mO+E5yCTyYTs7Gzk5eVh/vz5Vq+np6ejtrYWra2t0Ol0+OSTT7Bv3z63x/XWW2/hu+++w82bN7FlyxasWrUK165ds2rnzfx9//33qKioQF6e8r1AvJW/AWMZi/bausM333yD119/3W5+HB0XrhIWFoavv/4aer0e1dXV6OrqQnZ2ts223s7fwYMHkZaWhpiYGMU2ns6fsyZNkXDm7nYj2w58P+Y74TnAbDYjNzcXU6dORXFxsc02sbGxiImJwZQpU7Bw4UK89tprKC0tdXtsycnJCAoKQkBAAPLy8pCSkoKysjKrdt7M38GDB5Gammr3j9Jb+RswlrFor62rXb16FY8++ijeffddpKWlKbZzdFy4ilarRVJSEvz8/BAREYHi4mKUl5db5WmgrbfyB1jGo71/WADP589Zk6ZIDL273QClu9slJCTg4sWLw9pFREQgNDTUrTGKCAoKCtDS0gKdTgd/f3+HtvPx8YF44ZxIpff1Vv4Ax/4oR/J0/kbmp7u7G9euXbM5Fp0Zt66k1+vx8MMPY8eOHcjNzXVqW0/nc+Cwoq339Fb+AODs2bNoamrCk08+6dR23vp7VuTF+RCPW79+vWRlZYnBYJAzZ84ornI4fvy4RERESF1dnXR0dMjSpUs9sjrnmWeekeTkZOnq6rLbrqysTH744QcREamvr5eEhAS7E2Ou8NNPP8mJEyfEaDSKyWSSkpISUavVcvnyZau23srf2bNnRa1WS2dnp912nsqfyWQSo9EoL7/8suTk5AzmrrW1VYKDg6W0tFSMRqO89NJLdlc3OTpuXRXfjRs3JDY2Vvbu3TtqH86MC1fFd/78ebl8+bL09/dLW1ubrFu3TjIzMxX78XT+Bjz99NOSm5trtw935s9VJlWRaG9vlzVr1oharZY5c+bIoUOHREREr9eLRqMRvV4/2Pbtt9+W8PBwCQoKkk2bNklPT49bY7t+/boAkICAANFoNIOPkpISq/heeOEFCQ8PF7VaLTExMbJjxw65ffu2W+NrbW2VpKQk0Wq1EhISIsnJyVJeXi4i90b+RES2bNkiOTk5Vs97K3+FhYUCYNijsLBQREROnjwp8fHxolKpJCMjY9hS5927d8uKFSsGf1Yat+6Kr6ioSAAMG4cajcZmfPbGhbviO3z4sMydO1fUarVERkZKbm6uNDc324xPxPP5ExExGo0SEhIip06dstrOU/lzFV67iYiIFE2aOQkiInIeiwQRESlikSAiIkUsEkREpIhFgoiIFLFIEBGRIhYJIiJSxCJBRESKWCSIiEgRiwSRm7S3t2POnDl47rnnBp9rbW3FzJkzsW3bNi9GRuQ4XpaDyI0qKyuxbNkyfPrpp1i5ciVWrFiBW7duoaqqyuGr/BJ5k5+3AyCayNLT0/Hqq68iPz8feXl5+Oqrr3DhwgUWCBo3uCdB5GZmsxmpqan48ssvceTIEaxfv97bIRE5jHMSRG7W3Nw8eK/lhoYGb4dD5BTuSRC5kdlsxrJly2A2m/H8889j7dq1+OKLL5Camurt0IgcwjkJIjfavXs3Ll26hJqaGkRFRWHr1q3Izs5GTU0Npk2b5u3wiEbFPQkiNzl37hwyMjKg0+mwevVqAEBvby8WL16M+++/HzqdzssREo2ORYKIiBRx4pqIiBSxSBARkSIWCSIiUsQiQUREilgkiIhIEYsEEREpYpEgIiJFLBJERKSIRYKIiBT9C7+E1xyYIDo5AAAAAElFTkSuQmCC\\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 = 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   \"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   \"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   \"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      \"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 = 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   \"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\": 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  \"toc-autonumbering\": false\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/Course-1-Week-2-Optional-Labs-5-Linear-Regression-with-Scikit-Learn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Linear Regression using Scikit-Learn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"source\": [\n    \"### Load the data set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\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   \"source\": [\n    \"### Scale/normalize the training data\"\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      \"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   \"source\": [\n    \"### Create and fit the regression 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      \"SGDRegressor(alpha=0.0001, average=False, early_stopping=False, epsilon=0.1,\\n\",\n      \"             eta0=0.01, fit_intercept=True, l1_ratio=0.15,\\n\",\n      \"             learning_rate='invscaling', loss='squared_loss', max_iter=1000,\\n\",\n      \"             n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,\\n\",\n      \"             shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,\\n\",\n      \"             warm_start=False)\\n\",\n      \"number of iterations completed: 117, number of weight updates: 11584.0\\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   \"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   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"model parameters:                   w: [110.05 -20.98 -32.38 -38.09], b:[363.19]\\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   \"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   \"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      \"[295.18 485.99 389.7  492.15]\\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   \"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   \"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    \"# 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   \"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\": 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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/Course-1-Week-2-Optional-Labs-6-Sklearn-Normalization.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Linear Regression using Scikit-Learn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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\": null,\n   \"metadata\": {},\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   \"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   \"source\": [\n    \"### Load the data set\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\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   \"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\": null,\n   \"metadata\": {},\n   \"outputs\": [],\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   \"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\": null,\n   \"metadata\": {},\n   \"outputs\": [],\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   \"source\": [\n    \"### Make Predictions\\n\",\n    \"\\n\",\n    \"Calling the `predict` function generates predictions.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\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   \"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\": null,\n   \"metadata\": {},\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\": null,\n   \"metadata\": {},\n   \"outputs\": [],\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\": null,\n   \"metadata\": {},\n   \"outputs\": [],\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\": null,\n   \"metadata\": {},\n   \"outputs\": [],\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   \"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\": 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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/data/Course-1-Week-2-Optional-Labs-data.md",
    "content": "\n"
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    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/data/houses.txt",
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    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Optional-Labs/images/Course-1-Week-2-Optional-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Practice-Labs/Course-1-Week-2-Practice-Labs-1-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\": 9,\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    \"    ### 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\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'numpy.float64'>\\n\",\n      \"Cost at initial w: 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: {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:<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\": 13,\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    \"    ### 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\": 14,\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\": 15,\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\": 16,\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\": 17,\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\": 18,\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\": 19,\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\": 19,\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\": 20,\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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Practice-Labs/Course-1-Week-2-Practice-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Practice-Labs/data/Course-1-Week-2-Practice-Labs-data.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Practice-Labs/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Practice-Labs/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Practice-Labs/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Practice-Labs/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2-Practice-Quiz/Course-1-Week-2-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-2/Course-1-Week-2.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Lecture-Slides/Course-1-Week-3-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-1-Classification.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"877b69a25bea4a39a7d0f9878b597112\",\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    \"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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c5fc2987839c4bc7ba4149330827903b\",\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    \"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   \"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   \"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\": 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\": 5\n}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-2-Sigmoid-function-and-Logistic-Regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"40cb07ccd9604cffa9c145b279c61e34\",\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 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   \"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   \"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   \"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   },\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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d30eff32883a4fff952d56a29727c7c1\",\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    \"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   \"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\": 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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-3-Decision-Boundary.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Logistic Regression, Decision Boundary\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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, draw_vthresh\\n\",\n    \"plt.style.use('./deeplearning.mplstyle')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d68e571240d24451806d614c61570a8f\",\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    \"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   \"metadata\": {},\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   \"metadata\": {},\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   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6192b6527434478fb1d36f52e6497138\",\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 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   \"metadata\": {},\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   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"71d0fb6a74da458db71148607bc84c8b\",\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    \"# 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   \"metadata\": {},\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   \"metadata\": {},\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\": 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\": 5\n}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-4-Logistic-Loss.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"846c0bed44b0446a991bfa9bef6baafd\",\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    \"soup_bowl()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b31091554f38412b997becfe337ff85b\",\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 = 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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d87bd4d219c049528d09c3cdb60f8ba3\",\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_logistic_squared_error(x_train,y_train)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4892de5c2fb74df1b7c9a798bd823a4b\",\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_two_logistic_loss_curves()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"55603070e4ce458cb48cfea712de1abe\",\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    \"cst = plt_logistic_cost(x_train,y_train)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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\": 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  \"toc-showcode\": true\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-5-Cost-Function-for-Logistic-Regression.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\": 1,\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\": 2,\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\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7084aa61782a4b91ae97262e19b19ddc\",\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    \"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\": 4,\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\": 5,\n   \"metadata\": {},\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   \"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 `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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6fc60968c9524c8999e4c738c6a870b2\",\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 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 `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   \"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   \"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   \"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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-6-Gradient-Descent-for-Logistic-Regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optional Lab: Gradient Descent for Logistic Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"760f88b544d848c7a01544ccd4e36cd5\",\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    \"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   \"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   \"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   \"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\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"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   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Iteration    0: Cost 0.684610468560574   \\n\",\n      \"Iteration 1000: Cost 0.1590977666870456   \\n\",\n      \"Iteration 2000: Cost 0.08460064176930081   \\n\",\n      \"Iteration 3000: Cost 0.05705327279402531   \\n\",\n      \"Iteration 4000: Cost 0.042907594216820076   \\n\",\n      \"Iteration 5000: Cost 0.034338477298845684   \\n\",\n      \"Iteration 6000: Cost 0.028603798022120097   \\n\",\n      \"Iteration 7000: Cost 0.024501569608793   \\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   \"source\": [\n    \"#### Let's plot the results of gradient descent:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f69ab1f5b7e04b71be28720f3957baea\",\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=(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   \"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   \"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\": 10,\n   \"metadata\": {},\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   \"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\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f0bfc3195f464e64a8a87418bdb590fc\",\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=(4,3))\\n\",\n    \"plt_tumor_data(x_train, y_train, ax)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"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\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"19269d658df54d718b8bdcf244e32226\",\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    \"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   \"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\": 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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-7-Logistic-Regression-with-Scikit-Learn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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   \"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   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\\n\",\n       \"                   intercept_scaling=1, l1_ratio=None, max_iter=100,\\n\",\n       \"                   multi_class='auto', n_jobs=None, penalty='l2',\\n\",\n       \"                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\\n\",\n       \"                   warm_start=False)\"\n      ]\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   \"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   \"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   \"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   \"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\": \"3.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-8-Overfitting.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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   \"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   \"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\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"bae2a54d54c844a59da24614d0db0787\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Output()\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ef8cb1dc72c144139d338abf816d55a9\",\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    \"display(output)\\n\",\n    \"ofit = overfit_example(False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\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   \"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\": 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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs-9-Regularization.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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/Course-1-Week-3-Optional-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Optional-Labs/images/Course-1-Week-3-Optional-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-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        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"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/Course-1-Week-3-Practice-Labs-1-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\": 7,\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\": 8,\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\": 9,\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\": 10,\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    \"    ### 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\": 11,\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\": 12,\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\": 17,\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\": 18,\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\": 19,\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\": 20,\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\": 21,\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    \"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\": 22,\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\": 23,\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\": 24,\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\": 25,\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\": 26,\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\": 27,\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\": 28,\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\": 29,\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\": 30,\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\": 31,\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\": 32,\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    \"        \\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\": 33,\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\": 34,\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    \"        \\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\": 35,\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 have time, run for 100,000 iterations to see better results.\"\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      \"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\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"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\": 38,\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"
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  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/Course-1-Week-3-Practice-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/data/Course-1-Week-3-Optional-Labs-data.md",
    "content": "\n"
  },
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    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/data/ex2data1.txt",
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  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/images/Course-1-Week-3-Practice-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Labs/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": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3-Practice-Quiz/Course-1-Week-3-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1-Week-3/Course-1-Week-3.md",
    "content": "\n"
  },
  {
    "path": "Course-1-Supervised-Machine-Learning-Regression-and-Classification/Course-1.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Lecture-Slides/Course-2-Week-1-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Optional-Labs/Course-2-Week-1-Optional-Labs-1-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([[0.67]], 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 = [[0.67]], 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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6cOcMjjzzCunXr2Lt3LwsWLOCJJ54gOTkZf39/nnzySQYPHsySJUvYunUrPXv2zHCfERERtGzZki5duvDHH3+wf/9+5s2bx/z58wEza+Pi4li8eDGnTp3i4sWLafbRtm1b+88mOjqatWvX8vjjjxMREZHmNiOR7Ka/7iRPeeqpp/j888+ZOnUqYWFhREREMG3atBv65DU5OZm+ffsSGhpKhw4dqFq1qn25nVKlSvH777+zdu1aateuTZ8+fXjyySftg7hb4evry4IFCzhz5gzh4eE88MADtGnThg8//DBVv4iICJKTk+2DVzc3N1q2bInNZsv0fleAl156iaioKOrWrcvYsWN599137VP0p6d9+/b89ttvLFy4kPDwcBo3bsykSZMoV66cvYZffvmFS5cu0bBhQ5566in7TMTpKVy4MEuWLCEuLo6IiAjq16/PZ599Zr/s6Omnn6ZatWo0aNCAokWLsmrVqjT7yM6fh4iION/o0aPTXGoaGhrKxx9/zEcffUTt2rVZu3at5ay4N2vChAlMmDCB2rVrs3LlSmbPnk2RIkVS9XnyySdJSEjgiSeecGifbdq0oUqVKrRs2ZKHHnqIjh07ZrpMW8mSJVm1ahXJycnccccdhIWFMWDAAIKCguwD1LfeeosWLVpw77330rZtW5o3b57pPBs///wz4eHhPPLII9SoUYOXX37Z/mlr06ZN6dOnDw899BBFixblzTffTPN8m83GrFmzCA4OpmXLlrRt25aKFSsyY8YMh94LkaxkM66/iUBE8qzy5cszYMAABgwYkNOliIiI5KgDBw5QoUIFNmzYkGYd2eutWLGCNm3acOjQIfvVUOnp2bMnsbGx/Prrr1lXrIgAmrBJRERERMTSlStXOHnyJCNHjuTBBx/MdOAqItlLlw2LiIiIiFj44YcfKFeuHLGxsZaX1IqIc+myYREREREREXF5+uRVREREREREXF6uuuf13LlzOV2CiIjkUYGBgTldQq6kbBYRkexyfTbrk1cRERERERFxeRq8ioiIiIiIiMvLVZcNX0uXd4mIyK3SJa9ZS9ksIiK3KqNs1ievIiIiIiIi4vI0eBURERERERGXp8GriIiIiIiIuDwNXkVEJPcYNQqmTze/nz7dfCwiIiI5x4nZ7LQJmw4cOECjRo0IDQ3F09OThQsX8tZbbzFr1izKlSvHtGnT8PDwsNx2IxITE4mNjc2eFyF5UlBQ0A3/OxORHDByJIwaheHmhm36dJgzB1JSwDDMNrlhymZxVcpmkVzCydns1NmG27Vrx7fffgvAiRMnWLp0KStXrmTixIn8+uuvREREpNn24IMPOrz/xMRETp8+TbFixXBz04fKkrmUlBROnDhB4cKFFZIirmzUKE69/SHjurzDGd9CTPvmiVRt2GwwYkTO1ZeLKZvF1SibRXKJUaM4+c5HvPHAJOK8/Pn8u6dTtWVHNjs1RZYuXUqLFi2YNGkSUVFRtGrVCoC2bduyevVqy203IjY2VuEoN8TNzY1ixYpx9uzZnC5FRNIRnwhvlL2PSqP3MqnNi3zVpCfry9T7r4ObG1SrlnMF5nLKZnE1ymYR1xeXCKPL3k+lUXt5r/UAvmzSi02lav3XIZuy2WmfvJYoUYJdu3bh5eVFp06duHDhAsWKFQPMdeFiY2OJjY0lICAg1bYbpXCUG+Xm5obNZsvpMkTkOonJ8HkMjF4Pxy7XAp//2oZ2Hs/CD9qbD+69Fx5+OGeKzOWUzeKqlM0irikhGf5vO4xZDycuh9mz2XBzY2in8fz+8d3mhmzKZqeliZeXF35+fhQoUIB77rmHSpUqcf78eQDOnz9PUFAQgYGBabaJiEj+kmLAjD1QYwY8twKOXUzbJyYklBP+Rc0Hc+b8N1GE3BBls4iIOCLFgB92Q+h06LcSTlxK22dbiZqc8itsPsimbHba4PXChQv271etWkXlypVZvnw5AIsWLaJx48aEh4en2SYiIvnHosPQ8Gd4+A/Ycy5te3D8Gd6aOYhdI6tSLO6kuTElBXbudG6heYSyWUREMmIYsOAgNPgfdFsE+86n7VMo7jTv/O9Fdo6qRpH40+bGbMpmpw1eV6xYQf369WnatCmlSpWiUaNGtGzZkubNm7Nx40Y6d+5MsWLF0myTrNOqVSsGDBjgcP9p06bpDLuIOMX6k9Bujvm1/mTadp8CMDR+BfuGV2TQkkn43NXevJ8GzMkgNFnTTVE25zxls4i4qrXHoc1s6DAXNpxK2+5bAIbF/cm+4RV5cdl7eN/dIduz2WYYhpHle80m5879dxo+MDAwTfvJkycpWrSoM0vKVVq1akWdOnWYPHmyQ/2nTZvGgAEDsn15g/LlyzNgwIAbCm9HOfqa9W9HJGfsjoXX1sKPe63b3W3wZCiMaAAl/TBnL6xWzbyPZvp086zuLYRjZrkimVM23xplc/r0b0ckZ+yKhWGR8L991u3uNuhdA16vDyWcnM1OXSpHREQEzPtYR0fBZzGQlGLd54GKMLYhVAu+ZuO1YahJmkRERLLMkXgYFQVfxEByOh9vdq1kZnOVoGs2OjGb8/z0f7Ypzv26Ua1ataJfv34MGDCA4OBgihcvzmeffUZ8fDxPPPEEBQsWpHLlysybNy/V85YvX07Dhg3x8vKiRIkSDBkyhKSkJHt7fHw8jz/+OP7+/pQoUYJ33nknzbGvXLnCoEGDKFWqFH5+fjRq1Ihly5bdUP1btmyhdevW+Pj4ULhwYXr37k1cXFyq13f9WdvOnTvTs2dPe/vff//NwIEDsdls9pkFr14W9euvv1KlShW8vb1p3749hw4dsu+nZ8+eaS5fGzBggH1Jh549e7J8+XLee+89+74PHDhwQ69PRLLWuSvwWiRU+g6mbLMeuLYuBWu7wE/trxu4Sp6hbFY2K5tFXEfsFRi6Bip/b84kbDVwbVsaoh6AGXdcN3B1sjw/eM0NvvrqK4oUKcLatWvp168fzz77LA8++CBNmzYlOjqaO+64g8cee4yLF80pN//55x/uuusuwsPD2bRpE1OmTOGLL75g7Nix9n0OHjyY5cuXM2vWLBYuXMiyZcuIjo5Oddznn3+e1atXM336dDZv3syDDz5Ihw4d2L17t0N1x8fH0759e4KDg1m3bh0//fQTixYt4vnnn3f4tc+cOZPSpUszevRojh49ytGjR+1tFy9e5I033uDrr79m1apVxMbG8vANnM157733aNKkCU8//bR932XKlHH4+SKSdS4nwbuboNL38EY0XExK26dOEVhwDyy6F8KLOb9GkWspm5XNInndpSR4awNU/A4mbDAfX69+UfjjXvOrvgtcxa/Lhl1A7dq1ee211wAYOnQoEyZMoEiRIjz99NMADB8+nClTprB582YaN27Mxx9/TJkyZfjwww+x2WxUr16dI0eO8MorrzB8+HAuXrzIF198wbfffkubNm0AM4RLly5tP+bBgweZOnUqBw8epGTJkgAMGjSI+fPnM3XqVMaNG5dp3d9//z2XL1/m66+/xs/PD4APP/yQe++9l4kTJ1K8ePFM91GoUCHc3d0pWLAgISEhqdoSExP58MMPadSokf01hIaGsnbtWho2bJjpvgMDA/H09MTX1zfNvkXEOZJT4NtdMHwdHIyz7lMxwLwE6aHK4KZlHcVFKJuVzSJ5VVIKfL0TRqyDw/HWfSoHmtn8YCXXymYNXl1ArVq17N+7u7tTuHBhwsLC7NuuBs2JEycAiImJoUmTJqkW727WrBlxcXEcPnyYs2fPkpCQYA8WMIOoWrVq9sdbtmwhOTmZqlWrpqrlypUrFC5c2KG6Y2JiqF27tj0cr9aRkpLCzp07HQrIjBQoUIDw8HD74+rVqxMUFERMTIxDASkiOccw4Le/4dVI2HrGuk8xHxjeAJ4OBU9359YnkhllszVls0juZRgw64CZzTFnrfsU9zEnSXwqFDxcMJvz/ODVeDanK8ich4dHqsc2my3VtqtBmJKSzqwmNyEuLg53d3fWr1+Pu3vqf5n+/v5Zdhw3Nzeun9A6MTHR5fctIjdv1VF4ZQ2sOmbdXtADBteBgbXB38O6j+RtymZrymYRyS5/HoEha2D1cev2AE94uQ4MqAV+LpzNuuc1FwoNDWX16tWpwmHVqlUULFiQ0qVLU6lSJTw8PIiMjLS3nz17ll27dtkf161bl+TkZE6cOEHlypVTfTl6GU9oaCibNm0iPv6/6w1WrVqFm5ub/Uxy0aJFU90rk5yczNatW1Ptx9PTk+Tk5DT7T0pKIioqyv54586dxMbGEhoaarlvgI0bNzq0bxHJeltPQ8ffofmv1gNXTzczFPd2h9cbaOAqeYuyWdks4oq2nIZ7foeIWdYDV083eLE27O0Gw+q79sAVbnLweuXKlayuQ27Ac889x6FDh+jXrx87duxg1qxZjBgxghdffBE3Nzf8/f158sknGTx4MEuWLGHr1q307NkTN7f/ftxVq1ale/fuPP7448ycOZP9+/ezdu1axo8fz9y5cx2qo3v37nh7e9OjRw+2bt3K0qVL6devH4899pj9sqTWrVszd+5c5s6dy44dO3j22WfTrE1Xvnx5/vzzT/755x9OnfpvBWQPDw/69etHZGQk69evp2fPnjRu3Nh+WVLr1q2Jiori66+/Zvfu3YwYMSJN+JYvX57IyEgOHDjAqVOnsvQMuYiYDl6AJ5ZArR9hzt9p223AY1Vh5yMwqRkU9XF6ifmCsjlnKZuVzSKu5MB5eHwx1P4R5qaTzT2qwa5u8E5TKJJLstmhweu8efPo0aMHFStWxMPDA19fXwICAoiIiOCNN97gyJEj2V2nXKNUqVL8/vvvrF27ltq1a9OnTx+efPJJ+8QSAG+99RYtWrTg3nvvpW3btjRv3pz69eun2s/UqVN5/PHHeemll6hWrRqdO3dm3bp1lC1b1qE6fH19WbBgAWfOnCE8PJwHHniANm3a8OGHH9r79OrVix49evD4448TERFBxYoVuf3221PtZ/To0Rw4cIBKlSqlWozc19eXV155hW7dutGsWTP8/f2ZMWOGvb19+/a8/vrrvPzyy4SHh3PhwgUef/zxVPseNGgQ7u7u1KhRg6JFi3Lw4EGHXpuIZO70ZXjpL6j6A0zbCVZLwt1dDjZ2ha/bQPkAp5eYpymbXYuy2aRsFslZJy/BgJVQ7Qf4Zpd1Nt9bDjZ3hWmtoVxBp5d4S2zG9TcmXOOXX37hlVde4cKFC9x11100bNiQkiVL4uPjw5kzZ9i6dSsrVqxg9erV9OzZkzFjxqT6BZfVzp07Z/8+MDAwTfvJkyez9fjiPNOmTWPAgAFpzgRnF/3bEXFcfCJM3gxvboTzCdZ9GheHiY2hZUmnlnbDMssVV6RslpyibBZxXXGJMGkTvLURLqRzm3nTEDObm5dwamk3LKNcyXDCpjfffJNJkyZx5513prqs5aquXbsC5tpmH3zwAd9++y0DBw7MippFRMTFJCbDFztgVBQcu2jdJzQYxjWCTuXB5kJT6+clymYREbkqIRk+i4ExUXD8knWfmv9m873lc382Zzh4Xb16tUM7KVWqFBMmTMiSgkRExLWkGPC/vfDaWth9zrpPaT8YFQ6PV4MCmgowWymbRUQkxYAZe8xs3nfeuk8Zfxgdbs474Z5HsjnDy4YzkpyczJYtWyhXrhzBwcFZXZclXZok2UX/dkSsLTpsTq2//qR1e7AXDK0Lz4eBTy5cfC03XjacEWWz5CX6tyOSlmHAwkMwNBI2nLLuU8gLXq0HfW8D7zyWzQ6PwQcMGMAXX3wBmOEYERFBvXr1KFOmDMuWLcuaSkVExCVEn4Q75kC7OdYDV58CMKQu7OsOg+vmzoFrXqBsFhHJP9adgLZzoMNc64GrTwFz0LqvO7xUJ3cOXDPj8OD1f//7H7Vr1wZgzpw57N+/nx07djBw4ECGDRuWbQWKiIjz7DkHD/8B9f8HfxxO2+5ug941YE83GN8YgrycX6P8R9ksIpL37YqFBxdAw59hyT9p291t0KeGuVbrG40gMA9ns8OD11OnTtkXyP7999958MEHqVq1Kr169WLLli3ZVqCIiGS/YxfhuT8hdLp5D42VByrCtofg0wgo6efc+sSasllEJO86Eg/PLIca0+F/+6z7dK0EMQ/DlAgokQ+y2eHBa/Hixdm+fTvJycnMnz+fdu3aAXDx4kXc3d0dPuCkSZNo3rw58+fPp1WrVrRq1YoSJUrw66+/AuZ1zVe3nzlz5sZejYiI3JBzV+C1SKj0HUzZBkkpafvcXhIi74ef2kM159xGKQ5SNouI5D2xV+DVNVD5e/i/7ZBsMUNRm1KwrgvMuAOqBDm9xBzj8JXQTzzxBF27dqVEiRLYbDbatm0LQGRkJNWrV3doH1euXGHjxo0AdOjQgQ4dOgDQqFEj+/7CwsJ0n46ISDa7nGQOVt+IhtOXrfvUKQITGsEdZXL/1Pp5lbJZRCTvuJwEH26FcdFw9op1n3pFYEJjaFfGubW5Coc/eR05ciRffPEFvXv3ZtWqVXh5mRdTu7u7M2TIEIf28cUXX9CjR49U2/bt20fx4sXx9/cHICYmhhYtWjBkyBBuciJkERFJR3IKfLUDqv0AL/5lPXCtGADft4X1D0D7shq4ujJls4hI7pecAlN3QJXvYfBq64FrpQCY3g7WPZB/B67g4OA1MTGRNm3aUKtWLQYOHEjp0qXtbT169KBTp04O7WPZsmW0bt061faZM2dy33332R/v3r2bP//8k7NnzzJnzhxHX4e4mJEjR1KnTp2cLkNE/mUY8NsBqPMT9FwKB+PS9inmAx80N++deaQKuGnQ6tKUzXKjlM0irsUwYNZ+qPUj9FoKh+PT9inuAx+3MLP5ocrKZocGrx4eHmzevPmWDvTNN9/QrVu3NNvnzJlDx44d7Y8LFSqEzWajc+fObN269ZaOmRv07NkTm82GzWbDw8OD4sWL065dO7788ktSUixuPsvAtGnTCAoKypK6WrVqZa/L29ubGjVq8PHHHzv8/EGDBrF48eIbOmb58uWZPHnyDVYqIpn56xi0/BXunQdbLW5X9PeAUeGwt7u5Xqun47dKSg5SNmcfZfN/lM0i2WPFEWj+K3SeD9vPpm0v6AFjGsKe7vDsbeChbAZu4LLhRx991L6W3M3YuXMnU6ZMoUOHDmzbto0PPviAY8eO4enpSeHChQGIj48nOTkZgFWrVlGpUqWbPl5u0qFDB44ePcqBAweYN28et99+O/379+eee+4hKSkpx+p6+umnOXr0KNu3b6dr16707duXH374waHn+vv723+uIpIztp2BTvOg2S+w8ljadg836B9mrgc3vIE5iJXcRdmcfZTNIpIdtpyGe36HlrPMk8vX83SDgbXMbH6tvrL5eg4PXpOSkpgyZQoNGjTgmWee4cUXX0z1lZmJEyeyYMEC5s+fT82aNenXrx+zZs1KdVnT7t27CQ8Pp2XLlhw6dIgHHnjg5l7VrRg1CqZPN7+fPt18nM28vLwICQmhVKlS1KtXj1dffZVZs2Yxb948pk2bZu/37rvvEhYWhp+fH2XKlOG5554jLs689m/ZsmU88cQTnDt3zn5WduTIkYB5Zr1BgwYULFiQkJAQunXrxokTJzKty9fXl5CQECpWrMjIkSOpUqUKs2fPBuDgwYN06tQJf39/AgIC6Nq1K8ePH7c/9/pLk3r27Ennzp15++23KVGiBIULF6Zv374kJiYC5tnkv//+m4EDB9rrB/j777+59957CQ4Oxs/Pj5o1a/L777/fytstkucdvABPLDEvQ5p9IG27DXi0Kux8BCY3h6I+zq5QsoqyOfsom5XNIlnp7wvQYzHU/hHm/p223QY8XhV2dYN3m0ERZbMlh2cb3rp1K/Xq1QNg165dqdpsNzibx8qVKwF45plnUm2vU6cO0dHRN7SvLDVypBmIbm5mOM6ZAykp5gXp/4aNs7Ru3ZratWszc+ZMnnrqKQDc3Nx4//33qVChAvv27eO5557j5Zdf5uOPP6Zp06ZMnjyZ4cOHs3PnTgD7RBuJiYmMGTOGatWqceLECV588UV69ux5w0Hj4+NDQkICKSkp9nBcvnw5SUlJ9O3bl4ceeijD2SiXLl1KiRIlWLp0KXv27OGhhx6iTp06PP3008ycOZPatWvTu3dvnn76aftz+vbtS0JCAn/++Sd+fn5s377d/rpEJLXTl2F8tDlT4ZVk6z53lYXxjaGWPnzJE5TNI51airLZpGwWcdypS+bM/h9vhYR07jq4pxyMawRhyuZMOTx4Xbp0aXbWkfNGjfrvTG5KCsyalbrNZoMRI5xaUvXq1VPdzzRgwAD79+XLl2fs2LH06dOHjz/+GE9PTwIDA7HZbPYF66/q1auX/fuKFSvy/vvvEx4eTlxcnENhk5yczA8//MDmzZvp3bs3ixcvZsuWLezfv58yZczpzr7++mtq1qzJunXrCA8Pt9xPcHAwH374Ie7u7lSvXp27776bxYsX8/TTT1OoUCHc3d3tZ6CvOnjwIF26dCEsLMxev4ikFp8I722GiRvhfIJ1n8bFYWJjaFnSqaVJNlM2K5uVzSKuKS4RJm2CtzbChUTrPk1DzGxuXsKppeVqDl82nOdVq2ae1bXi5ma2O5lhGKnOnC9atIg2bdpQqlQpChYsyGOPPcbp06e5ePFihvtZv3499957L2XLlqVgwYJEREQAZvhk5OOPP8bf3x8fHx+efvppBg4cyLPPPktMTAxlypSxhyNAjRo1CAoKIiYmJt391axZE3f3/+42L1GiRKaXSL3wwguMHTuWZs2aMWLEiFuenEQkL0lMhk+2mYuYD1trPXCtHgS/dIC/7tPAVXIhZXMaymYR15aYbH7KWvk7GL7OeuBaIxhmdYCVnTVwvVE3NHiNiori5Zdf5uGHH+b+++9P9ZXrPfww3Huvddu995rtThYTE0OFChUAOHDgAPfccw+1atXi559/Zv369Xz00UcAJCSk81EL5kQb7du3JyAggO+++45169bxyy+/ZPo8gO7du7Nx40b2799PfHw87777Lm7p/RHhAA+P1Hec22y2TGdtfOqpp9i3bx+PPfYYW7ZsoUGDBnzwwQc3XYNIXmAY8OMeqDkDnv0Tjln8jVzKDz5vBVsegs4VtFZrXqZsdi5ls7JZxEqKAdN3Q+h06LsCjl9K26eMP3x5O2zuCh2VzTfF4d9206dPp2nTpsTExPDLL7+QmJjItm3bWLJkCYGBgdlZo3NcvY/Gypw5/00U4SRLlixhy5YtdOnSBTDP0KakpPDOO+/QuHFjqlatypEjR1I9x9PT0z4j5FU7duzg9OnTTJgwgRYtWlC9enWHJoQACAwMpHLlypQqVSpVMIaGhnLo0CEOHTpk37Z9+3ZiY2OpUaPGzb5ky/oBypQpQ58+fZg5cyYvvfQSn3322U0fQyS3W3wYGv4MD/0Bu8+lbQ/2gjcbw+5u8GQoFND1NXmaslnZfJWyWSTn/HEIGvwPHlkEe8+nbS/kBW83gV2PwBPVwV3ZfNMcfuvGjRvHpEmTmDNnDp6enrz33nvs2LGDrl27UrZs2eys0Tl27jTvp7GSkmK2Z5MrV65w7Ngx/vnnH6Kjoxk3bhydOnXinnvu4fHHHwegcuXKJCYm8sEHH7Bv3z6++eYbPvnkk1T7KV++PHFxcSxevJhTp05x8eJFypYti6enp/15s2fPZsyYMbdUb9u2bQkLC6N79+5ER0ezdu1aHn/8cSIiImjQoMFN77d8+fL8+eef/PPPP5w6dQow7yVasGAB+/fvJzo6mqVLlxIaGnpL9YvkRtEn4Y450HYORJ1M2+7tDkPqmmu1Dq4LPg7PaCC5mbJZ2XyVslnE+aJOQNvZcMdvsOFU2nafAvBqPTObX6oD3srmW+bw4HXv3r3cfffdgHkWLj4+HpvNxsCBA/m///u/bCvQaUaM+G/SBzc36NTpv/tsrm3LBvPnz6dEiRKUL1+eDh06sHTpUt5//31mzZplvw+ldu3avPvuu0ycOJHbbruN7777jvHjx6faT9OmTenTpw8PPfQQRYsW5c0336Ro0aJMmzaNn376iRo1ajBhwgTefvvtW6rXZrMxa9YsgoODadmyJW3btqVixYrMmDHjlvY7evRoDhw4QKVKlShatChgTkjRt29fQkND6dChA1WrVr2hBdlFcrs95+DhP6D+/+CPw2nb3W3wdCjs6WbOIhzs5fwaJecom5XNVymbRZxnVyx0XQjhP8Pif9K2u9vgmRpmNr/RCIKUzVnGZhiG4UjH0qVLM2/ePMLCwqhVqxZDhw7lkUceYfXq1XTo0IFz5yyuX8ti1x7D6nKokydP2n+x3rRRo8wJIB5+2LwcaedOp89kKM6XJf92RLLQsYswJgr+LwaS0vngqUtFeKMhVAt2bm15SWa54uqUzZKXKZvF1RyNh1FR8HkMJKczgnqwEoxtCFWDnFpanpJRrjj84XXLli35448/CAsL48EHH6R///4sWbKEP/74gzZt2mRdtTnt2jDMgYkgRCR/O59gTqv/7ia4mGTdp1VJmNAYGhV3amnigpTNIiLZL/YKvLkBJm+BS+lkc+tSZjaHF3NubfmNw4PXDz/8kMuXLwMwbNgwPDw8+Ouvv+jSpQuvvfZathUoIpIfXEmGKVthbDScvmzdp3ZhMxjbl9EMhWJSNouIZJ/LSfDRVhgXDWeuWPepW8TM5nallc3O4PDgtVChQvbv3dzcGDJkSLYUJCKSnySnwHe7zbXg/r5g3adCQfMSpIergJuCUa6hbBYRyXrJKfD1LhixDg7FWfepFGBmc9fKymZnynDwev68xVzP6QgICLjlYkRE8gvDgLl/w9BI2HrGuk8xH3i9PvSuAZ7uzq1PXJeyWUQkexgGzD4Ar0bC9rPWfYr7wPAG8FSosjknZDh4DQoKwpbJ59+GYWCz2SzXABMRkbT+OgavrIaVx6zb/T1gcB0YWAsKejq1NMkFlM0iIllv5VF4ZY2Z0VYKesDLdWFALTOnJWdkOHhdunSps+rIMikpKakW7RbJTEpKCg5Oui1yS7afMc/mzjpg3e7hBs/WhGH1oJivU0uTXETZLPmBslmcZetp8yqo3/62bvd0g+duM9drLerj3NokLYeXynEFmU3Hn5iYyOnTpylWrJhCUhySkpLCiRMnKFy4MB4eOo0m2eNQnHnfzFc7IcXiN64N6F4VRodDBV3l6VS5fakcV6BslqymbBZn+PsCDF8L3+wCq8GQDXisKowKh/LKZqe66aVyNm/e7PBBatWqdYNlZT0PDw8KFy7MqVOnMr2kSgTMS+sUjpJdTl+G8dHw4VZzNmErd5aF8Y2gdhHn1ia5l7JZ8jpls2SnU5fM2YM/2goJ6ayjfk85GNcIwgo7tzbJXIafvLq5uWGz2TK9bMNZ99XoDLmI5AYXE+G9LTBxA5xLsO7TqBhMbAIRJZ1bm6SWG3NF2SwicuPiE2HSZnMt9fPpZHOT4jCxMbRQNueom/7kdf/+/dlTkYhIHpSYDF/ugFFRcPSidZ/qQebZ3M4VtB6c3Bxls4iI4xKT4bMYGB0Fxy9Z96kRbGZzx/LKZleX4eC1XLlyWX7ASZMm8fPPP/Ptt9/SqFEjQkND8fT0ZOHChQC89dZbzJo1i3LlyjFt2jRdMiIiLs8w4H/7YFgk7D5n3aeUH4xsAD2rQwHd9ie3QNksIpK5FAN+2mtm8950Vhgr7QejG8LjVcFd2ZwrZDh4vd7evXuZPHkyMTExANSoUYP+/ftTqVIlh55/5coVNm7caH/crl07vv32W/vjEydOsHTpUlauXMnEiRP59ddfefDBB2+kRBERp1pyGIZEwroT1u1BnjC0HvQLA58b+o0r4hhls4hIan8cgiFrIPqUdXuwlzl7cN/blM25jcPnGBYsWECNGjVYu3YttWrVolatWkRGRlKzZk3++OMPh/bxxRdf0KNHD/vjpUuX0qJFCyZNmgRAVFQUrVq1AqBt27asXr36Bl6KiIjzbDgJ7X+DNnOsB67e7vBKXdj3qLkunMJRsoOyWUTkP1EnoO1suOM364GrTwEYWhf2dYdBdZTNuZHDP7IhQ4YwcOBAJkyYkGb7K6+8Qrt27TJ8fmJiIsuWLeO5555j+PDhlChRgl27duHl5UWnTp1o06YNsbGxBASYc1EHBgYSGxt7469IRCQb7T0Hr62F6Xus291s8GR1GNEASvk7tzbJf5TNIiKwOxaGrTUvE7biboOnQmF4Ayjp59TSJIs5PHiNiYnhxx9/TLO9V69eTJ48OdPnf/PNN3Tr1s3+2MvLCy8vLwDuuecetm7dSmBgIIcPHwbg/PnzBAUFOVqeiEi2OnYRxkTB/8VAUjpT699fAd5oBNWDnVub5F/KZhHJz47GmxMxfRYDyelMwP5ARRjbEKopm/MEhy8bLlq0aKp7Yq7auHEjxYoVy/T5O3fuZMqUKXTo0IFt27bxwQcf2NtWrVpFpUqVCA8PZ/ny5QAsWrSIxo0bO1qeiEi2OJ9gLmJe+Tv4eJv1wLVVSVhzP/zcQQNXcS5ls4jkR+eumBMxVf4ePtluPXBtXQrWdoGf2mvgmpc4/Mnr008/Te/evdm3bx9NmzYFzGCbOHEiL774YqbPnzhxov375s2bU6lSJerXr4+XlxctWrSgUaNGALRs2ZLmzZtTtmxZBgwYcIMvR0Qka1xJhilb4Y1oOHXZuk/twjChMbQvo6n1JWcom0UkP7mcZJ5IfmM9nLli3aduETOb25VWNudFNiOzVc7/ZRgGkydP5p133uHIkSMAlCxZksGDB/PCCy9gc8K/Di2ELiLZLTkFvtsNw9fB3xes+1QoaF6C9HAV8x5Xyb1ye64om0UkP0hOgW92mdl8KM66T6UAM5u7VlY253YZ5YrDg9drXbhg/kVXsGDBWyztxiggRSS7GAb8fhCGroEtZ6z7FPWG1xvAMzXA09259Un2yEu5omwWkbzGMGDOAXg1Eradte5TzMeciOnpUGVzXpFRrtzUBNHODkYRkey0+hi8sgZWHLVu9/eAQbXhxdpQ0NO5tYk4StksInnJyqPmWq2rjlm3F/SAwXVgYG0zpyV/yHDwWrduXYcvOYqOjs6SgkREnGX7GfNs7qwD1u0ebtCnJrxWD4r5OrU0kXQpm0UkL9t62szmOX9bt3u6wXO3wav1oKiPc2uTnJfh4LVz58727y9fvszHH39MjRo1aNKkCQBr1qxh27ZtPPfcc9lapIhIVjoUByPXwbSdkGJx44QN6FYFRjeEigFOL08kQ8pmEcmL/r4AI9bB1zvB6p5GG/BoVRgdDuWVzfmWw/e8PvXUU5QoUYIxY8ak2j5ixAgOHTrEl19+mS0FXkv31YjIrThzGcZHwwdbzdmErdxZFsY3gtpFnFub5IzcnivKZhHJ7U5dgnHR8NFWSEhnHfW7y8G4RlCrsHNrk5yRJRM2BQYGEhUVRZUqVVJt3717Nw0aNEh1kOyigBSRm3ExEd7bAhM3wLkE6z4Ni8HExtCqlHNrk5yV23NF2SwiuVV8IkzaDG9tNNdUt9KkuJnNLUo6tTTJYVkyYZOPjw+rVq1KE5CrVq3C29v7FksUEcl6SSnw5Q7zEuGjF637VAsyz+beV0HrwUnuo2wWkdwmMRk+j4HR6+FYOtkcGmxmc6fyymZJzeHB64ABA3j22WeJjo6mYcOGAERGRvLll1/y+uuvZ1uBIiI3yjDg530wLBJ2pfPBUyk/GNkAelaHAm7OrU8kqyibRSS3SDHgp73w2lrYk042l/aDUeHweDVls1i7oXVef/zxR9577z1iYmIACA0NpX///nTt2jXbCryWLk0SkcwsOQxDImHdCev2IE8YWg/6hYHPTS0WJnlJXsgVZbOIuLpFh81lb9aftG4P9jJnD+57m7JZsuieV1eggBSR9Gw4aQ5aFx6ybvd2hxfCYEg9MyRFQLmSFfQeikh61p80B62LDlu3+xSA/mHwSl0IUjbLv7LknlcREVe09xy8vhZ+2GPd7maDXtVhRAMo7e/c2kRERPKj3bHm5cE/7rVud7fBk6FmNpf0c2ppkstp8CoiudLxizBmPXy63ZyYycr9FeCNRlA92Lm1iYiI5EdH42F0FHy+I/1sfqAijG0I1ZTNchM0eBWRXOV8AryzEd7ZBPFJ1n0iSsKERtA4xKmliYiI5EvnrphL3kzaDBfTyebbS8KExtCwuFNLkzwmw8Hr+fPnCQgIcFYtIiLpupIMn2yDsevh1GXrPrULw/hG0KGsptaXvEvZLCKu4nISfLwNxkXD6XSyuU4R84TyHWWUzXLrMpyEOjg4mBMnzCk7W7duTWxsrDNqEhGxS06Bb3ZC9R9gwCrrgWv5gvBtG4h+EO4sp3CUvE3ZLCI5LTkFpu2Aaj/AS39ZD1wrBsD3bWH9A9BeJ5Uli2T4yau/vz+nT5+mWLFiLFu2jMTERGfVJSL5nGHAvIPmLIVbzlj3KeoNr9WHZ2qCl7tz6xPJKcpmEckphgG//Q1D18C2s9Z9ivnA6/Whdw3wVDZLFstw8Nq2bVtuv/12QkNDAbjvvvvw9PS07LtkyZKsr05E8qU1x+CVNfDnUet2fw94qbb5VdD6V5JInqVsFpGcsOqomc2rjlm3+3vA4DrwYm3ze5HskOHg9dtvv+Wrr75i7969LF++nJo1a+Lr63tLB5w0aRI///wz77zzDgMHDsTNzY3w8HAmTZoEmGv51K1bF4CZM2dSqFChWzqeiOQeMWfh1Uj4db91u4cb9KkJr9WDYrf2q0gk11I2i4gzbT1tZvOcv63bPdzguZowrD4U9XFubZL/ZDh49fHxoU+fPgBERUUxceJEgoKCbvpgV65cYePGjQCUK1eOJUuW4O3tTffu3dmyZQthYWGEhYWxbNmymz6GiOQ+h+Jg5DqYthNSjLTtNqBbFRjd0LyHRiQ/UzaLiDMcvAAj1sFXO8EimrEBj1aF0eFQXtksTpLhhE3XWrp0qT0cDcPAMKz+GWfsiy++oEePHgCEhITg7e0NgIeHB+7u5kXxMTExtGjRgiFDhtzUMUQk9zhzGQb/BVW+hy93WA9cO5QxJ2L6tq0GriLXUzaLSFY7fdmchKnqD+ZJZav/8XeVhY1d4es2GriKczk8eAX4+uuvCQsLw8fHBx8fH2rVqsU333zj0HMTExNZtmwZrVu3TrV98+bNnDx5kho1agCwe/du/vzzT86ePcucOXNupDwRySUuJsKEaKj4Hby9yVwG53oNi8HSjjDvHnOafRGxpmwWkawQnwhvrDez+d10srlxcVjeCebeDbUKO79GkQwvG77Wu+++y+uvv87zzz9Ps2bNAFi5ciV9+vTh1KlTDBw4MMPnf/PNN3Tr1i3VtjNnzvD888/z448/2rddvY+mc+fObNiwgY4dOzr8YkTEtSWlmJ+wjoqCI/HWfaoFwbhGcF8FTasvkhlls4jcqsRk+OLfbD520bpPaLCZzZ3KK5slZzk8eP3ggw+YMmUKjz/+uH1bx44dqVmzJiNHjsw0IHfu3MnGjRv55JNP2LZtGx988AHz5s3j7bffJiQkBID4+Hi8vb1xd3dn1apVhIWF3eTLEhFXYhgwc5854cOuc9Z9SvrByAbwRHUocEPXhIjkX8pmEblZKQb8by+8thZ2p5PNpfzMe1ofr6ZsFtdgMxy8ecXb25utW7dSuXLlVNt3795NWFgYly9brE6cjubNm9O3b19eeOEFatasCcD48ePx8fGhV69e+Pv7U6FCBb788kv7/TYA58799z8rMDDQ4eOJSM5Z+o+5VuvaE9btQZ4wpB70uw18NbW+OFluzxVls4jcjEWHzWxef9K6PdgLhtaF58PAx+GPukSyRka54vA/x8qVK/Pjjz/y6quvpto+Y8YMqlSpckMFrVy5EoBHHnkkTVt0dPQN7UtEXNOGkzA0EhYcsm73dod+YTCkLhTydm5tInmFsllEbsT6k+agddFh63ZvdxhQC16uaw5gRVyNw4PXUaNG8dBDD/Hnn3/a76tZtWoVixcvTnVfjIjkb/vOw2uR8MMe63Y3GzxRDUaGQ2l/59Ymktcom0XEEXvOmZcHz0gnm91t0Ks6jGgApZTN4sIcvmwYYP369UyaNImYmBgAQkNDeemll+wLl2c3XZok4rqOX4Sx6+HT7ZCYYt3nvgrwRiNz4gcRV5AXckXZLCLpOXYRRkfBZzHmpIlWulSENxpCNWWzuIiMcuWGBq85TQEp4nrOJ8A7G+GdTRCfZN0noiRMaASNQ5xamkimlCu3Tu+hiOs5dwXe2giTNsPFdLL59pIwoTE0LO7U0kQylSX3vIqIXOtKMnyyzfy09VQ6c8LUKmwOWjuU1dT6IiIi2e1yEkzZBm9Ew+l0srl2YZjYGO4oo2yW3EeDVxG5ISkGfL8bXl8LBy5Y9ylfEMY0hG5VzHtcRUREJPskp8C3u2D4OjgYZ92nQkEY2xAeVjZLLqbBq4g4xDBg3kFzBuHNp637FPGG1+vDMzXBy926j4iIiGQNw4C5f5vZvPWMdZ9iPmY2964BnspmyeU0eBWRTK05Bq+sgT+PWrf7FYBBdeCl2lDQ06mliYiI5Et/HYNXVsPKY9bt/h4wuA4MrKVslrzjpgev58+fZ8mSJVSrVo3Q0NCsrElEXETMWRgWCb/st273cINnasBr9aG4r3NrE5G0lM0ied+2M/BqJMw+YN3u4QbP1oRh9aCYslnyGIcHr127dqVly5Y8//zzXLp0iQYNGnDgwAEMw2D69Ol06dIlO+sUESc6HAcj18HUneY9rla6VTHva60Y4NzaROQ/ymaR/OPgBRixDr7eZZ3NNqB7VRgdDhWUzZJHuTna8c8//6RFixYA/PLLLxiGQWxsLO+//z5jx47NtgJFxHnOXIaXV0OV7+GLHdbh2L4MRD8A37XVwFUkpymbRfK+05dh0F9Q9QeYls5J5TvLwoYH4Zs2GrhK3ubw4PXcuXMUKlQIgPnz59OlSxd8fX25++672b17d7YVKCLZ72IiTNwAlb4z14W7nJy2T8NisKQjzL8H6hZ1eokiYkHZLJJ3xSfCuPVQ8TtzLfUrFtncqBgs6wS/3w21izi/RhFnc/iy4TJlyrB69WoKFSrE/PnzmT59OgBnz57F29s72woUkeyTlAJTd8DIKDgSb92naiCMawT3V9R6cCKuRtkskvckJptXP42KgmMXrftUDzKzuXMFZbPkLw4PXgcMGED37t3x9/enXLlytGrVCjAvWQoLC8uu+kQkGxgGzNwHw9bCzljrPiV8YVQ4PFEdCjh8jYaIOJOyWSTvMAz4aS+8thZ2n7PuU8rPzOYe1ZTNkj/ZDMNIZzqWtKKiojh06BDt2rXD398fgLlz5xIUFESzZs2yrcirzp37739yYGBgth9PJC9a+g8MWQNrT1i3B3rCkLrwQhj4eji3NhFnywu5omwWyf0WHzazOeqkdXuQJwytB/3CwEcLXUoel1Gu3NDgNacpIEVu3sZTMHQNzD9k3e7lbg5Yh9SFQrraUPIJ5cqt03socvOiT5qD1j8OW7d7u0P/WvBKXQj2cm5tIjklo1xx6NzN9u3b+fDDD1m9ejXHjpkrIYeEhNCkSRP69eunteREXNi+8/D6Wvg+nblb3GzwRDUYGQ6l/Z1bm4jcPGWzSO6155x5efCMPdbt7jboVR1GNIBSymYRu0wHr/PmzaNz587Uq1ePTp06Ubx4cQCOHz/OH3/8Qd26dZk1axbt27d36ICTJk3i559/ZuXKlQwcOJCoqCjq1avHe++9B2C5TURu3ImLMGY9fLodElOs+3SuYE74EBrs3NpE5NYom0Vyp2MXYUwU/F+MOWmilS4VYWxDqK5sFkkj08HrkCFDeOWVVxg9enSatpEjRzJy5EgGDx7sUEBeuXKFjRs3AhAdHU1cXBwrVqzg2WefZd26dbi7u6fZFh4efuOvSiQfu5BgTqn/9kaIT7Lu07IETGgMTUKcWpqIZBFls0jucj7BXIru3U1wMZ1sblXSzOZGxZ1amkiukuk8Zbt27aJ79+7ptj/yyCMOryX3xRdf0KNHDwDWrFlDu3btAGjbti2rV6+23CYijrmSDO9vNtdqHRVlPXANKwRz7zLXhNPAVST3UjaL5A5XkmHSJnOt1rHrrQeutQvDvLvNtdQ1cBXJWKaD1/LlyzN37tx02+fOnUu5cuUyPVBiYiLLli2jdevWAMTGxhIQEACYN+LGxsZabhORjKUY8O0uqP4D9F8FJy+n7VOuIHzdGjY8CHeV05pwIrmdslnEtSWnwNc7oer38OJfcNoimysUhO/aQPSD0KGsslnEEZleNjx69Gi6devGsmXLaNu2bar7ahYvXsz8+fP5/vvvMz3QN998Q7du3eyPAwMDOX/+PADnz58nKCgId3f3NNtExJphwPyDMDQSNp227lPEG16rD31qmrMJi0jeoGwWcU2GAXP/NrN56xnrPkW94fUG8EwN8FQ2i9yQTAevDz74IKVKleL999/nnXfeSTOj4bJly2jSpEmmB9q5cycbN27kk08+Ydu2bZw6dYrNmzfTtWtXFi1aRM+ePSlQoACffvppqm0iktaaYzAkEpYfsW73KwAv1YaX6kCAp1NLExEnUDaLuJ6/jsErq2HlMet2fw8YVBterA0Flc0iNyVH1nlt3rw5K1eupH///kRHR1OnTh0++OADAMttV2ktOcnvdpyFVyPhl/3W7QXcoE8N89PW4r7OrU0kN1Ku/EfZLHJztp0xs3n2Aet2Dzd4tiYMqwfFlM0imcooV25o8Hru3LlUZ3edHVIKSMmv/omDkVHw5Q7zHlcrj1SGMQ2hkv5riDgsL+SKslkkZxyKgxHr4Kud1tlsA7pVgdENoWKA08sTybUyypVMJ2wC+Pzzz6lRowaFChWiRo0ahIaG2r//4osvsrZaEbE7e8W8BKny9/B5jHU4ti8D0Q/A9+00cBXJT5TNIjnj9GUY9BdU+R6mpnNS+c6y5iSJ37bVwFUkK2V6z+tbb73FyJEjeeGFF2jfvn2qSSEWLlxI//79OXv2LIMGDcr2YkXyi0tJ8P4WmBANsQnWfcKLwcTGcHsp59YmIjlP2SzifBcT4b0tMHEDnEsnmxsVg4lNIKKkc2sTyS8yvWy4XLlyvPXWW3Tt2tWyfcaMGQwePJiDBw9mS4HX0qVJktclpZhncUdGwZF46z5VA+GNRtCloqbVF7lVuTVXlM0izpOYbN62MyoKjl607lMtCMY3gs4VlM0ityqjXMn0k9cTJ04QFhaWbntYWBinTp26hfJExDDMSZhejYSdsdZ9SvjCyHB4ohp4aGp9kXxN2SyS/QwD/rcPhkXC7nPWfUr5wcgG0LO6OWmiiGSvTP+bhYeHM2HCBJKSktK0JScnM3HiRMLDw7OlOJH8YNk/0GQmdFlgPXAN9DTP5u7pBr1raOAqIspmkey25DA0/Bm6LrQeuAZ5mrfu7O4GT9XQwFXEWTL95PXDDz+kffv2hISE0LJly1T31fz55594enqycOHCbC9UJK/ZdAqGrIH5h6zbvdyh320wtB4U8nZubSLi2pTNItljw0lzHfWF6WSztzu8EAZD6kGwl3NrExEHl8q5cOEC3377LWvWrEmzEHq3bt0ICHDONGq6r0bygv3n4fW18P1usPrP52aDntXMS4TL+Du9PJF8JTfnirJZJOvsPQevrYXpe6zb3WzQqzqMaACllc0i2SrL1nnNaQpIyc1OXISx0fDJNkhMse7TuQK80RBqFHJubSL5lXLl1uk9lNzs2EUYEwX/F2NOmmjl/grmRInVg51bm0h+dUsTNl117NgxIiMj7Wd3S5QoQcOGDQkJCcmiMkXypgsJ8M4m8ysu0bpPixIwoTE01X8nEbkBymaRm3M+Ad7aCJM2QXzaW8cBc7mbCY2gsf47ibiMTAev8fHxPPPMM0yfPh2bzUahQuZHQmfOnMEwDB555BE+/fRTfH19s71YkdwkIRk+3W6e0T152bpPWCEY3xjuKqup9UXEccpmkZtzJRmmbIU3ouFUOtlcu7A5UWIHZbOIy8l0brT+/fuzdu1a5s6dy+XLlzl+/DjHjx/n8uXL/P7776xdu5b+/fs7o1aRXCHFgO92QfUf4IWV1gPXcgXh69aw4UG4u5zCUURujLJZ5MYkp8DXO6HaDzDwL+uBa4WC8G0biH4Q7lQ2i7ikTO95DQ4OZu7cuTRt2tSyfdWqVdxzzz2cPXs2Wwq8lu6rEVdmGDD/IAyNhE2nrfsU8YbX6kOfmuZswiKSs3JrriibRRxjGPD7QRi6Bracse5T1BtebwDP1ABPZbNIjrule15TUlLw9PRMt93T05OUlHTucBfJJyKPwytrYPkR63a/AvBibRhUBwLS/+8kIuIQZbNI5lYfM7N5xVHrdn8PeKm2+VVQ2SySK2R62fA999xD79692bBhQ5q2DRs28Oyzz3LvvfdmS3Eirm7HWegyHxrPtB64FnCDvrfB3u4wuqEGriKSNZTNIunbfgY6z4Omv1gPXD3coF8Y7O1mLkungatI7pHpZcNnz56lW7duLFiwgODgYIoVKwbAiRMniI2NpX379nz//fcEBQVle7G6NElcxT9xMDIKpu6A5HT+Bz1cGcY0hMr6pyrisnJrriibRdI6FAcj18G0neb8E9ezAd2qmCeTKzpnGWQRuQlZss7rjh07WL16dZqF0KtXr56FpWZMASk57ewVmLgB3tsMl5Ot+9xRxpylsF5R59YmIjcut+eKslkEzlyG8dHwwVZzNmErHcqYs/vXKeLc2kTkxmXJ4NUVKCAlp1xKgg+2mOEYm2Ddp0FRmNgYWpd2bm0icvOUK7dO76HklIuJ8N4W86TyuXSyuWExM5tblXJubSJy8zLKlUzvec3M2bNn+frrrzPtt3XrVpo2bUqLFi144oknmDdvHq1ataJVq1aUKFGCX3/91V7g1e1nzqQzLZyIkySlwOfbocr35qQPVgPXKoHw4x2wtosGriLiGpTNkpclJsOn26Dy9/BqpPXAtVoQ/Nwe1tyvgatIXnLLn7xu2rSJevXqkZycznUa/0pMTMTDwwOAJ554gueee47w8HAAGjVqxOLFi/H396d58+asXLnSch86uyvOYhjw634zFHfEWvcp4QsjGkCv6uChqfVFcqW8mivKZsmLDAN+3gfDImHXOes+Jf1gZAN4oro5aaKI5D63tFTO+fPnM2y/cOGCQ0VcDUcALy8vypQpA8C+ffsoXrw4/v7+AMTExNCiRQuaNWvG+PHjsWmFaHGy5UdgyBpYc9y6PdATXqkL/cPA18O6j4hIdlI2S36z5DAMiYR1J6zbgzxhSD3od5uyWSQvy3TwGhQUlGFIGYbhcIjNnj2bV199lSpVqlC4cGEAZs6cyX333Wfvs3v3boKDg+nTpw9z5syhY8eODu1b5FZtOgVDI2HeQet2L3czFIfUg8Lezq1NRORaymbJLzacNAetCw9Zt3u7wwthZjYHezm3NhFxvkwvGw4MDGTYsGE0atTIsn337t0888wzmV6adK1+/frRunVr7rvvPiIiIpg5c6Y9MK+aN28eGzZs4NVXX7Vv06VJkh32n4fX18L3u8HqP4ObDXpUg1HhUMbf6eWJSDbKrbmibJa8bu85M5t/2GPd7mYzb9sZ0QBKK5tF8pRbumy4Xr16AERERFi2BwUF4chts1euXMHLyzwlFhAQgI+PD8eOHcPT09MejvHx8Xh7e+Pu7s6qVasICwvLdL8iN+vERRgbDZ9sg8QU6z6dysMbjaBmIaeWJiKSIWWz5FXHL8KY9fDpdnPSRCv3VTCzOTTYubWJSM7LdPDarVs3Ll26lG57SEgII0aMyPRA8+fP59133wWgSpUq3HHHHXz22Wd06tTJ3mf37t306tULf39/KlSowKhRoxx5DSI35EICvLsJ3t4EcYnWfVqUgAmNoWmIc2sTEXGEslnymvMJ8PZGM5/jk6z7RJSECY2gsbJZJN/SOq+SbyQkm2dyx0TBycvWfW4rZA5a7yoLmo9EJO9Trtw6vYdyK64km1dAjV0Pp9LJ5lqFzUFrB2WzSL5wS5cNi+R2KQZM3w2vr4N96UzQWa4gjA6H7lXAXVPri4iIZKvkFHOuieHr4EA6k2OXLwhjGkK3KuY9riIiGrxKnmUYsOCQOYPwxlPWfQp7w2v14NnbzNmERUREJPsYBvx+EIaugS1nrPsU8YbX68MzNZXNIpKaBq+SJ609Dq+sgWVHrNt9C8CLtWFQbQjU1PoiIiLZbs0xM5v/PGrd7lcABtWBl2pDQU+nliYiuYQGr5Kn7DwLw9bCz/us2wu4Qe9QeL0BhPg6tzYREZH8KOYsvBoJv+63bvdwg2dqwGv1obiyWUQyoMGr5An/xMGoKPhyBySnMwXZw5XNe2cqaz4RERGRbHcoDkaug2k7zfknrHSrYmZzxQDn1iYiudMNDV5TUlLYs2cPJ06cICUl9eJbLVu2zNLCRBxx9gpM3ADvbYbLydZ97igD4xtBvaLOrU1ExBmUzeJqzlyG8dHwwVZzNmErHcrA+MZQp4hzaxOR3M3hweuaNWvo1q0bf//9d5qFz202G8nJ6fx2EskGl5Lggy0wYYM5gLXSoKi57E2b0s6tTUTEWZTN4kouJsL7/2bzuQTrPg2Lmdl8eynn1iYieYPDg9c+ffrQoEED5s6dS4kSJbBpoS3JAUkp8NVOGLEO/om37lMlEN5oBA9U1HpwIpK3KZvFFSSlmLftjIqCI+lkc9VAGNcI7lc2i8gtsBnXn6pNh5+fH5s2baJy5crZXVO6tBB6/mUY5kQPw9aaEz9YKeELIxpAr+rgoan1RcQBuT1XlM2SkwwDZu4zJ2Padc66T0k/GNkAnqhuTpooIpKZjHLF4U9eGzVqxJ49e3I0ICV/Wn4EhqyBNcet2wM84ZU60L8W+Hk4tTQRkRylbJacsvQfM5vXnrBuD/KEIfWg323gq2wWkSzi8OC1X79+vPTSSxw7doywsDA8PFL/JqpVq1aWFyf52+bT5iLmvx+0bvdyh+dvg6H1oLC3c2sTF/IdsAI4CHgBNYHeQNnr+v0CzADOAJWAF4DQm+iT3Tb9W8Mu4DQwBmhu0S+3vB7JVspmcbYNJ2FoJCw4ZN3u5Q4vhMGQulBI2Zx/KZtd+/XkYg5fNuzmlvZaD5vNhmEYTpsUQpcm5Q/7z8PwdfDdLrD6x+lmgx7VzMuQyhZ0ennial4GWgPVgGTgc+AAMBXw+bfPEmACMBAzIP4HLAe+BoJvoM+tGAB0+PcrI5HAVqAqMBzrgHSF15NH5PZcUTaLs+w9B6+vhR/2WLe72eCJajAyHEr7O7c2cUHKZmXzLciSy4b3709nZWmRLHLyEoxdD1O2QWKKdZ+O5c0JH2oWcmppkpUWAB8DPwGe12x/DfAFXr3B/b153eMhwH2YZ0dr/7vtJ+Bu4M5/H7+IGUTzgG430Gfxv8f7Hij877aJ/x7rPSAr/mBr9O9XRrLq9Uiup2yW7Hb8opnNn2w3J2ay0rmCmc2h+uM791I2Z0zZ7DIcHryWK1cuO+uQfCwuEd7dBG9vhAuJ1n2ah5hT6zcr4dTSJDu0Aj4A/vr3e4CzwBrgrX8ff4t5yVFGpgHFLbZfneny6oL3iZgB1v2aPm5APWDbDfQB8yzy9//W9gLmGeT1wEdkTTg6Iitfj+R6ymbJLucT4J2N8M4miE+y7tOyhJnNTUKcWppkh1Yom2+FstlpMhy8zp49mzvvvBMPDw9mz56d4Y46duyYpYVJ3peQDP+3HcashxOXrPvcVgjGN4K7y2lq/TzDC2gDzOe/gPwDM+zq/Pu4I3B7JvuxWtg+BfgQuA2o8O+2c/9uv/4TgWDMe3Ec7QNgA54CRgCFMO9beQ8omkmtWSkrX4/kSspmyU5XkuGTbeanracuW/epVdjM5jvLKpvzDGXzrVE2O02Gg9fOnTtz7NgxihUrRufOndPtp4XQ5UakGDB9N7y+Dvadt+5T1h/GNITuVcBdU+vnPfcAfYCTmOGyAGiPGUBgnpkNsH5qht4D9mOePc4uTYDymPenvMl/QXy9689QJwDbMWu8ahrWZ6hFMqBsluyQnALf7zbnnDhwwbpPuYIwtiF0q2Le4yp5jLLZNA1lswvLcPCakpJi+b3IzTAMWHgIhkTCxlPWfQp7w7B68GxN8Hb4onbJdapgzrC3EGiAOYnD+Gvab+bSpPeA1aQ92xqIeVnO9esDn8U8Q+ton6vWYp4hTbZou9b1Z6jHAi3//brK6gx1ZrL69Uiuo2yWrGQYMO+gOYPw5tPWfYp4w2v1oU9NczZhyaOUzSZls0tz2vBg69at9O7dG3d3dypXrszw4cNp3LgxoaGheHp6snDhQgDeeustZs2aRbly5Zg2bVqaaf8ld1p73FwPbukR63bfAvBibRhUGwK9nFub5JC7gZ+BU5j3exS7pu1GLk0ygPeBlcAk4Pr7oj0wZweM5r+ZAVP+fXzfDfQB816VUcBgzEurvgRGplPf9WeovTAvDSqVyevKTFa+Hsn3lM3525pj8Moa+POodbtfAXipNrxUx1xTXfIBZfPNUTY7TYYXZE6fPt3hHR06dIhVq1al216tWjX++usvVqxYAcCpU6do164dy5Yts4fjiRMnWLp0KStXrqRWrVr8+uuvDh9fXNPOs/DAAmg003rgWsANnqsJe7ublwlr4JqPtMG8NGku/826d1UAZpBk9HX17P9kzPtyhmHOiHjm368r1+zvQeA3zFD7GzNIL5N6avzM+hwDhmJOtNAGeAL4EzM0s9IlYM+/XwBH//3+eBa/Hsm1lM1yq2LOwv3zockv1gNXDzdzHfW93WFUQw1c8xVlszVls8vIcPA6ZcoUQkNDefPNN4mJiUnTfu7cOX7//Xe6detGvXr1OH06netNINVZWi8vL5KTk1m6dCktWrRg0qRJAERFRdGqVSsA2rZty+rVq2/mNYkL+CcOei+DmjPg533WfR6qDDEPw0ctIcTXqeWJK/AHWmCu92a10LejZmPOYjgQ6HLN19Jr+rQGnsW8nOlpzMCZSOrLdDLqcx5zzbpm/DeVfQ2gIebadVlp57/Hf/rfxx//+/1UB2u9kT6SKymb5WYdjoOnlsJtM+CXdFZZ6lYFdjwCH7SA4srm/EfZbE3Z7DIyvGx4+fLlzJ49mw8++IChQ4fi5+dH8eLF8fb25uzZsxw7dowiRYrQs2dPtm7dSvHiGd/dPHv2bF599VWqVKlC3bp12bVrF15eXnTq1Ik2bdoQGxtLQID5WX5gYCCxsbFZ9kLFOWKvwMQN8N4WuJTO1PrtSsP4xlDfmbPAiWs6hXmm9FbO6i/NvAtgXpKT2WU56fUJwJwE4noTHDw2mGehHVEHx17TrbweydWUzXKjzlyGCRvggy1wOZ05vNqXMWcQrqtsFmVzWnVQNruITO957dixIx07duTUqVOsXLmSv//+m0uXLlGkSBHq1q1L3bp1cXNzbDrYq/vq168fv//+O/fdZ/7k7rnnHrZu3UpgYCCHDx8G4Pz58wQFBd38KxOnupQEH26B8Rvg7BXrPvWLmuvBtS3t3NrEBV0ANgKbgAE5WolIrqRsFkdcTIQPtsKEaIhNsO4TXgwmNobbb/WeP8n9lM2SCzg8YVORIkUynJI/M1euXMHLy7yhMSAggAIF/jv0qlWr6NevHxUqVODjjz/m5ZdfZtGiRTRu3PimjyfOkZQCX++EEevgcLx1n8qB8EZDeKCSptaXfz0NxAG9gbI5XItILqZsFitJKTB1B4yMgiPpZHPVQBjXCO6vqLVa5V/KZskFnDbb8Pz583n33XcBqFKlCu7u7tSvXx8vLy9atGhBo0aNAGjZsiXNmzenbNmyDBgwwFnlyQ0yDJh1AF6NNCd+sBLiCyMawJPVwUNT68u1HJ9vRkSykbI5bzEMmLkPhq2FnbHWfUr4wshw6FXdnDRRxE7ZLLmAzTAMI6eLcNS5c+fs3wcGBuZgJfnbn0fMZW9WH7duD/CEV+pA/1rgp9UURMSFKVdund5D17D0HzOb156wbg/0hCF14YUw8FU2i4gLyyhXnPbJq+R+m0/D0DXw+0Hrdk83eD4MXq0Hhb2dW5uIiEh+tPGUOWhdcMi63csd+t0GQ+tBIWWziORyGrxKpg6ch+Hr4Ntd5prT13OzweNVYVQ4lC3o9PJERETynX3n4fW18P1u63Y3G/SsZl4iXMbfubWJiGSXGx68JiQksH//fipVqpRqYgfJe05egjfWw5RtkJBi3adjeXPCh5pan0pEJMcom/OPExdhzHr4dDskppPNnSuYEyXWUDaLSB7j8K36Fy9e5Mknn8TX15eaNWty8KB57Wi/fv2YMOFGFlQSVxeXCKOjoNJ35nqtVgPX5iGwsjPMulMDVxGRnKJszj8uJMDIdVDxO/hwq/XAtWUJ+Os++KWDBq4ikjc5PHgdOnQomzZtYtmyZXh7/3fTRNu2bZkxY0a2FCfOlZBsrtVa6Ttz6ZsLiWn71AyG2XfCn52hWQmnlygiItdQNud9V5Lh/c1mNo+KgviktH3CCsHcu2BZJ2gS4vwaRUScxeFri3799VdmzJhB48aNsV2zIFjNmjXZu3dvthQnzpFiwIw98Npa8x4aK2X9YXQ4PFoV3DW1voiIS1A2510phnk/6+tr4cAF6z7lCsKYcOhWRdksIvmDw4PXkydPUqxYsTTb4+PjUwWm5B6GAQsPwdBI2HDKuk8hL3itPjxbE7x1G5WIiEtRNuc9hgHzD5rZvOm0dZ8i3mY296lpziYsIpJfOHyerkGDBsydO9f++Goofv755zRp0iTrK5Nste4EtJ0DHeZaD1x9C8CwerCvOwysrYGriIgrUjbnLWuOwe2z4a7frQeufgXg9fqwt7u5lroGriKS3zg8JBk3bhx33nkn27dvJykpiffee4/t27fz119/sXz58uysUbLQrlgYFgn/22fdXsANng41w7GEn1NLExGRG6Rszht2nIVXI+GX/dbtBdzgmRpmNhf3dW5tIiKuxOFPXps3b87GjRtJSkoiLCyMhQsXUqxYMVavXk39+vWzs0bJAkfi4ZnlUGN6+gPXhypDzMPwcUsNXEVEcgNlc+52OA6eWgo1Z6Q/cH2kMux4GD5soYGriIjNMAwjp4tw1Llz5+zfBwYG5mAluUfsFXhzA0zeApcsZigEaFsaJjSG+kWdW5uISE5Trtw6vYc37uwVmBAN72+By8nWfe4oA+MbQT1ls4jkMxnlisOXDf/++++4u7vTvn37VNsXLFhASkoKd9555y2WKVnpcpK5Dty4aDMkrdQvag5a25Z2bm0iIpI1lM25y6Ukc8A6IRpiE6z7hBeDCY2gtbJZRCQNhy8bHjJkCMnJaU8PGobBkCFDsrQouXlJKfBlDFT5Hgavth64Vg6EGe1gbRcNXEVEcjNlc+6QlAKfbYfK38OQNdYD16qB8NMdEHm/Bq4iIulx+JPX3bt3U6NGjTTbq1evzp49e7K0KLlxhgGzDpgTPsScte5T3AdGhsOT1cFDMxSKiOR6ymbXZhjmvayvRsLOWOs+JXzNbH6imrJZRCQzDg9eAwMD2bdvH+XLl0+1fc+ePfj5aXafnPTnEfNM7urj1u0BnvByHRhQC/w8nFqaiIhkI2Wz61r2j5nNkSes2wM94ZW60D8MfJXNIiIOcfiy4U6dOjFgwAD27t1r37Znzx5eeuklOnbsmC3FSca2nIZ7foeIWdYDV083eLE27O0Gw+pr4Coiktcom13PplNw52/meq1WA1cvdxhU21yrdWg9DVxFRG6Ew7MNnzt3jg4dOhAVFUXp0ubNGIcPH6ZFixbMnDmToKCg7KzTXsNV+XlGwwPnYfg6+HYXWP3wbMDj1WBUOJQr6OzqRERyj9yeK8pm17H/PLy+Fr7fbZ3NbjboWc28RLiMv9PLExHJNTLKlRtaKscwDP744w82bdqEj48PtWrVomXLlg49d+vWrfTu3Rt3d3cqV65Mnz59GDhwIG5uboSHhzNp0iR7gXXr1gVg5syZFCpUyKEXkh+cugRvRMPHWyEhxbrPveVgXCO4rbBzaxMRyY3yQq4om3PWiYswNho+2QaJ6WRzp/JmNtcoZN0uIiL/ybLB661ITEzEw8O8NuaJJ56gV69ehIeH4+3tTffu3RkyZAhhYWE0b96clStXWu4jvwZkXCJM2gRvbYQLidZ9moWYy940L+HU0kREcrX8mitXKZtv3oUEeGeT+RWXTja3KGFmc9MQ59YmIpKb3fQ6r++//z69e/fG29ub999/P8ODvPDCCxm2Xw1HAC8vL6pUqYK3t7e9zd3dnGIvJiaGFi1a0KxZM8aPH4/NZstwv3lZQjJ8FgNjouD4Jes+NYPNs7n3lod8/FaJiOQbyuacdSUZPt0GY9fDycvWfW4rZA5a7yqrbBYRyUoZfvJaoUIFoqKiKFy4MBUqVEh/JzYb+/bty/Rgs2fP5tVXX6VKlSr8+OOPeHh4sHnzZoYOHcrcuXMBOHPmDMHBwfTp04e777471YQT+eXsbooBP+6BYWth33nrPmX8YXQ4PFYV3B2edktERK6VG3NF2ZwzUgz4Ybd5X+v+C9Z9yhU0s7l7FWWziMjNconLhq/Vr18/WrduTUREBJ07d+bHH38kJCT1NTXz5s1jw4YNvPrqq/ZteT0gDQP+OGxOrb/hlHWfQl7mzMHP1QRvhxc6EhERK3k9V26EstmaYcD8gzA0Ejadtu5T2BteqwfP3mbOJiwiIjcvo1xx6LxgYmIilSpVIiYm5qaLuHLliv37gIAAPDw8ePTRR3n77bft4RgfH09ycjIAq1atolKlSjd9vNxm3QloOwfa/2Y9cPUtAMPqwb7u5vI3GriKiORvyubsF3ncXPLmrt+tB65+BeD1+mY2D6itgauISHZzaAjk4eHB5cvp3NjhoPnz5/Puu+8CUKVKFc6dO8e6det4+eWXARg/fjw+Pj706tULf39/KlSowKhRo27pmLnBrlgYFgn/S+fKLncbPB0KwxtACa03LyIi/1I2Z58dZ81snrnfur2AGzxTwxy4Fvd1bm0iIvmZw5cNjxs3jl27dvH5559ToEDOfOyXly5NOhoPo6Lg8xhITucn0LUSjG0IVYKcWpqISL6R23NF2Zy1/omDkVHw5Q7zHlcrD1eGMQ2hcu5+qSIiLuumZxu+1rp161i8eDELFy4kLCwMP7/UHwPOnDnzFsvMH2KvwJsbYPIWuJRk3adtaRjfCBoUc25tIiKSuyibs8bZKzBxA7y3GS4nW/e5o4yZzfWKOrc2ERH5j8OD16CgILp06ZKdteRpl5Pgw60wPhrOXLHuU6+IObV+uzLOrU1ERHInZfOtuZQEH2wxszk2wbpPg6IwsTG0Lu3c2kREJK0cmW34ZuXGS5OSU+DrXTBiHRyKs+5TKQDeaAQPVgI3rQcnIuI0uTFXXE1ufA+TUmDaDvMS4X/irftUCTSz+YGKWqtVRMSZbumy4ZSUFN566y1mz55NQkICbdq0YcSIEfj4+GR9pXmIYcDsA/BqJGw/a92nuA+MaABPhYKHZigUEREHKZtvjmHAL/vNyZh2xFr3KeFrZnOv6spmERFXk+ng9Y033mDkyJG0bdsWHx8f3nvvPU6cOMGXX37pjPpypRVHYEgk/HXMur2gB7xcFwbUAn8P59YmIiK5n7L5xi0/Aq+shsgT1u0BnvBKHehfC/yUzSIiLinTy4arVKnCoEGDeOaZZwBYtGgRd999N5cuXcLNzaFlYrOMq1+atOW0+Unrb39bt3u6Qd/b4NV6UEQnx0VEcpyr50p6lM2O23QKhkbCvIPW7V7u8PxtMLQeFPZ2bm0iIpJWRrmS6eDVy8uLPXv2UKbMf7MIeXt7s2fPHkqXdu7sBa4akH9fgOFr4ZtdYPVm2oDHq8GocChX0NnViYhIelw1VzKjbM7c/vPw+lr4frd1NrvZoEc1GNkAyiqbRURcxi3d85qUlIS3d+pTkR4eHiQmJmZRebnXqUvwRjR8vBUSUqz73FMOxjWCsMLOrU1ERPIuZXP6TlyEsdHwyTZITCebO5U3J2OqWcippYmIyC3KdPBqGAY9e/bEy8vLvu3y5cv06dMn1Xpy+WktubhEmLQJ3toIF9L5O6FpiDm1fvMSTi1NRETyAWVzWhcS4N1N8PYmM6etNA8xl6RrpmwWEcmVMh289ujRI822Rx99NFuKcXWJyfBZDIyOguOXrPvUCDYXMb+3vKbWFxGR7KFs/k9CMny6HcZEwcnL1n1uK2Rm893llM0iIrmZ1nl1QIoBP+6B19bC3vPWfcr4w+hweKwquDt3rgwREblJrnq/Zm6Sk9k8fTe8vg72pZPNZf1hTEPoXkXZLCKSW9zSPa/53R+HYMgaiD5l3V7Iy5w9uO9t4K13U0REJFsZBiw4ZM4gvDGdbC7sDa/Vg2dvM2cTFhGRvEHDrXREnTAHrYv/sW73KQADa8HLdSDQy7qPiIiIZJ3I42Y2Lzti3e5bAF6sDYNqK5tFRPIiDV6vszsWhq2Fn/Zat7vb4KlQGN4ASvpZ9xEREZGss/OsuY76zP3W7QXcoHcovN4AQnydW5uIiDiPBq//OhpvTsT0WQwkp3MX8IOVYGxDqBrk1NJERETypX/iYFQUfLkj/Wx+qLKZzZV1y7KISJ6X/wavo0ZBtWrw8MMwfTrndh3gzfZDmLwZLiZZP6VNKXNq/QbFnFuqiIhIvnBdNp/d9TcT73iF9zbD5WTrp7QrDeMbQ/2izi1VRERyjtMGr1u3bqV37964u7tTuXJlvvzyS1588UWioqKoV68e7733HgADBw5Msy3LjBxpBqSbG5d//JmPLpRj3B1DORNt3b1eEXPQ2q5M1pYhIiLiClwtmy/9OJMP4iswod0rnN1g3b1BUTOb25TO2jJERMT1OW3i+GrVqvHXX3+xYsUKANauXUtcXBwrVqwgISGBdevWER0dnWZblhk1yvwC/qzYjKr132HQ/W9zxr9wmq6VAmB6O1j3gAauIiKSd7lSNi+t3JIqDd7llc4TOetXKE3XyoHw4x2wtosGriIi+ZXTPnn18PCwf+/l5cXixYtp164dAG3btmX16tUUKFAgzbbw8PCsKaBaNXBzg5QUyp45yPGCxdN0Ke5jTsT0VCh4amp9ERHJ41wpm8ucPcTxgLTZHOILIxrAk9XBQ9ksIpKvOXXJ7tmzZ3Pbbbdx/PhxEhMTCQgIAMzFZ2NjY4mNjU2zLcs8/DDcey8A5c/8zbMrptibCiZdZExD2NMdnrtNA1cREck/XCWbK5/cyzMrPrU3BSTG80ZD2NMN+tTUwFVERJw8eO3YsSNbt26ldOnSFChQgPPnzwNw/vx5goKCCAwMTLMty0yfDnPm2B8Om/8GRS6cZODid9k3rDyv7Z6Ov0cGzxcREcmDXCmbX583hsJxp3hp0dvse608r+6ejp+yWURE/uW0weuVK1fs3wcEBGCz2Vi8eDEAixYtonHjxjRp0iTNtiyzcyekpNgfFo07xd+vlePdn1+iyIWTZruIiEg+4mrZXPzCCQ4OK8vbMwdT+MIpZbOIiKTitMHr/PnziYiIICIiguPHjzNkyBC8vb1p0aIF7u7uNGzYkHr16qXZlmVGjDC/wLy/plMnfJOvpG0TERHJJ5TNIiKSm9gMw0hn2W/Xc+7cOfv3gYE3uRr5dWvJsXOnwlFEJJ/KklzJ55TNIiKSlTLKlfw3eBUREfmXcuXW6T0UEZGslFGuOHXCJhEREREREZGbocGriIiIiIiIuDwNXkVERERERMTlFcjpAm7WtddCi4iISM5TNouISHbSJ68iIiIiIiLi8jR4FREREREREZeXq5bKERERERERkfxJn7yKiIiIiIiIy8sXg9cjR45Qr149vL29SUpKStW2detWmjdvTrNmzdi8eXMOVegaMnqfnnnmGZo1a0bz5s31PmXwPgFcunSJkJAQFi1alAPVuYaM3qMzZ87QtWtXWrduzRtvvJFDFbqGjN6nn376iYYNG9KoUSNmzZqVQxW6hsjISJo2bUrz5s0ZOHBgqrYjR47QunVrmjZtmq//z+VGymbHKJsdo2zOnLLZMcpmx+RYNhv5wKVLl4wzZ84YERERRmJiYqq2zp07GwcPHjQOHz5sdOzYMYcqdA0ZvU/79u0zDMMwdu3aZdx///05UZ7LyOh9MgzDeP/99422bdsaf/zxRw5U5xoyeo9eeOEFIyYmJocqcy0ZvU9NmjQx4uPjjYsXLxotW7bMoQpdw9GjR41Lly4ZhmEY3bp1MzZv3mxv69evn7Fy5UrjwoULRkRERA5VKDdD2ewYZbNjlM2ZUzY7RtnsmJzK5nzxyau3tzfBwcGWbWfPnqVMmTKUKlWK2NhY5xbmYjJ6nypUqACAh4cH7u7uzizL5WT0PiUkJLBmzRqaNWvm5KpcS0bv0datWxk3bhy33347q1evdnJlriWj96lSpUrEx8cTFxdHQECAkytzLSEhIXh7ewNpfwdt2bKFpk2b4u/vT8GCBTl//nxOlSk3SNnsGGWzY5TNmVM2O0bZ7JicyuZcu85rVklJSbF/b2juqkwNHTqUF154IafLcFnTpk3j0UcfJTIyMqdLcVl//fUX0dHRFCpUiC5durBy5cqcLskl3XfffdStWxfDMJg6dWpOl+MSNm/ezMmTJ6lRo4Z9W3JyMjabDYDAwEBiY2Pz/R8UeYGy+cYomzOmbM6cstkxyua0nJ3N+eKT14xcfWMB3Nzy/duRocmTJ1OjRg2aN2+e06W4pKSkJBYsWMCdd96Z06W4tKpVqxIaGkrx4sX1fy4Do0ePZvv27cTExDB69OicLifHnTlzhueff54vvvgi1fZr/w2dP3+eoKAgJ1cm2UHZ7Dhlc8aUzY5RNjtG2ZxaTmRzvv/ktVChQhw+fBg3Nzedrc/AwoUL+euvv5gxY0ZOl+Kyjh8/zsGDB+nQoQN79uxh7ty51K9fP91LT/KrqlWrcvToUQICAiwn1RCTl5cXvr6+2Gw2EhIScrqcHJWUlMSjjz7K22+/TUhISKq2WrVqsXr1amrVqsX58+f1ezyPUDY7RtmcOWWzY5TNjlE2/yfHsjlL76B1UQkJCUabNm2MoKAgo3Xr1sayZcuMsWPHGoZhGJs2bTKaNm1qNG3a1NiwYUPOFprDMnqfqlatajRo0MCIiIgwevfuncOV5qyM3qerRowYka8nhcjoPdq2bZsRERFhNGzY0Jg7d24OV5qzMnqfpk6dajRs2NBo2LCh8dlnn+VwpTnr+++/N4oUKWJEREQYERERxl9//WU8//zzhmEYxqFDh4zbb7/daNy4sbFgwYIcrlRuhLLZMcpmxyibM6dsdoyy2TE5lc02w9DNJCIiIiIiIuLadFG7iIiIiIiIuDwNXkVERERERMTlafAqIiIiIiIiLk+DVxEREREREXF5GryKiIiIiIiIy9PgVcQBNpuNX3/9NVuPsXPnTkJCQrhw4UK2HsdVXLx4kS5duhAQEIDNZiM2Ntay35AhQ+jXr59zixMREZenbM56ymZxdRq8Sr538uRJnn32WcqWLYuXlxchISG0b9+eVatW2fscPXqUO++8M1vrGDp0KP369aNgwYIAXL58mZ49exIWFkaBAgXo3Lmz5fOWLVtGvXr18PLyonLlykybNi1Nn48++ojy5cvj7e1No0aNWLt2bar2y5cv07dvXwoXLoy/vz9dunTh+PHjWf0SU/nqq69YsWIFf/31F0ePHuXs2bPYbDY2btyYqt+gQYP46quv2LdvX7bWIyIirkPZrGwWsaLBq+R7Xbp0YcOGDXz11Vfs2rWL2bNn06pVK06fPm3vExISgpeXV7bVcPDgQX777Td69uxp35acnIyPjw8vvPACbdu2tXze/v37ufvuu7n99tvZuHEjAwYM4KmnnmLBggX2PjNmzODFF19kxIgRREdHU7t2bdq3b8+JEyfsfQYOHMicOXP46aefWL58OUeOHOH+++/PttcLsHfvXkJDQ7ntttsICQnBZrNZ9itSpAjt27dnypQp2VqPiIi4DmWzslnEkiGSj509e9YAjGXLlmXYDzB++eUXwzAMY8SIEQaQ5mvq1KmGYRhGcnKyMW7cOKN8+fKGt7e3UatWLeOnn37KcP9vvfWW0aBBg3Tbe/ToYXTq1CnN9pdfftmoWbNmqm0PPfSQ0b59e/vjhg0bGn379rU/Tk5ONkqWLGmMHz/eMAzDiI2NNTw8PFLVGBMTYwDG6tWr063po48+MipXrmx4eXkZxYoVM7p06WJvi4uLMx577DHDz8/PCAkJMd5++20jIiLC6N+/v2EYhhEREZHqvbv+8dVtV3311VdG6dKl061FRETyDmWzslkkPfrkVfI1f39//P39+fXXX7ly5YpDzxk0aBBHjx61f7399tv4+vrSoEEDAMaPH8/XX3/NJ598wrZt2xg4cCCPPvooy5cvT3efK1assD//RqxevTrNmd/27duzevVqABISEli/fn2qPm5ubrRt29beZ/369SQmJqbqU716dcqWLWvvc72oqCheeOEFRo8ezc6dO5k/fz4tW7a0tw8ePJjly5cza9YsFi5cyLJly4iOjra3z5w5k6effpomTZpw9OhRZs6cab9catGiRfZtVzVs2JDDhw9z4MCBG36PREQkd1E2K5tF0lMgpwsQyUkFChRg2rRpPP3003zyySfUq1ePiIgIHn74YWrVqmX5nKuhCrBmzRpee+01vvrqK2677TauXLnCuHHjWLRoEU2aNAGgYsWKrFy5kk8//ZSIiAjLff799983FZDHjh2jePHiqbYVL16c8+fPc+nSJc6ePUtycrJlnx07dtj34enpSVBQUJo+x44dszzuwYMH8fPz45577qFgwYKUK1eOunXrAhAXF8cXX3zBt99+S5s2bQDzHprSpUvbn1+oUCF8fX3x9PQkJCQEgPPnzwNQuHBh+7arSpYsCZjvU/ny5R19e0REJBdSNiubRdKjT14l3+vSpQtHjhxh9uzZdOjQwT7JgtXkCtc6ePAgnTt3ZtCgQXTt2hWAPXv2cPHiRdq1a2cPUn9/f77++mv27t2b7r4uXbqEt7d3Vr6sbNWuXTvKlStHxYoVeeyxx/juu++4ePEiYN4vk5CQQKNGjez9CxUqRLVq1W76eD4+PgD2Y4iISN6mbL5xymbJDzR4FQG8vb1p164dr7/+On/99Rc9e/ZkxIgR6faPj4+nY8eONGnShNGjR9u3x8XFATB37lw2btxo/9q+fTv/+9//0t1fkSJFOHv27A3XHRISkmbmwePHjxMQEICPjw9FihTB3d3dss/VM6ghISEkJCSkmQ7/2j7XK1iwINHR0fzwww+UKFGC4cOHU7t27XSn1L9VZ86cAaBo0aLZsn8REXE9ymZls8j1NHgVsVCjRg3i4+Mt2wzD4NFHHyUlJYVvvvkm1Ux8NWrUwMvLi4MHD1K5cuVUX2XKlEn3eHXr1mX79u03XGeTJk1YvHhxqm1//PGH/bIoT09P6tevn6pPSkoKixcvtvepX78+Hh4eqfrs3LmTgwcP2vtYKVCgAG3btuXNN99k8+bNHDhwgCVLllCpUiU8PDyIjIy09z179iy7du3K8LV4enoC5kyO19u6dSseHh7UrFkzw32IiEjepWxWNovonlfJ106fPs2DDz5Ir169qFWrFgULFiQqKoo333yTTp06WT5n5MiRLFq0iIULFxIXF2c/oxsYGEjBggUZNGgQAwcOJCUlhebNm3Pu3DlWrVpFQEAAPXr0sNxn+/bteeqpp0hOTsbd3d2+ffv27SQkJHDmzBkuXLhgX2etTp06APTp04cPP/yQl19+mV69erFkyRJ+/PFH5s6da9/Hiy++SI8ePWjQoAENGzZk8uTJxMfH88QTT9jrfvLJJ3nxxRcpVKgQAQEB9OvXjyZNmtC4cWPLen/77Tf27dtHy5YtCQ4O5vfffyclJYVq1arh7+/Pk08+yeDBgylcuDDFihVj2LBhuLllfK6sWLFi+Pj4MH/+fEqXLo23tzeBgYGAOWlGixYt7JcoiYhI3qVsVjaLpCunpzsWyUmXL182hgwZYtSrV88IDAw0fH19jWrVqhmvvfaacfHiRXs/rpmO32rqeK6Zjj8lJcWYPHmyUa1aNcPDw8MoWrSo0b59e2P58uXp1pGYmGiULFnSmD9/fqrt5cqVszzWtZYuXWrUqVPH8PT0NCpWrGiv41offPCBUbZsWcPT09No2LChsWbNmlTtly5dMp577jkjODjY8PX1Ne677z7j6NGj6da7YsUKIyIiwggODjZ8fHyMWrVqGTNmzLC3X7hwwXj00UcNX19fo3jx4sabb76Zajp+wzCM/v37p5py3zAM47PPPjPKlCljuLm5pWqrVq2a8cMPP6Rbj4iI5B3KZpOyWSQtm2EYhjMHyyJi7aOPPmL27NmpFjHPS1q1akWdOnWYPHnyDT1v3rx5vPTSS2zevJkCBXSxiIiIOI+y2ZqyWXKK/rWJuIhnnnmG2NhYLly4QMGCBXO6HJcRHx/P1KlTFY4iIuJ0ymZrymbJKfoXJ+IiChQowLBhw3K6DJfzwAMP5HQJIiKSTymbrSmbJafosmERERERERFxeVoqR0RERERERFyeBq8iIiIiIiLi8jR4FREREREREZenwauIiIiIiIi4PA1eRURERERExOVp8CoiIiIiIiIu7/8BLtVioMAwd1kAAAAASUVORK5CYII=\\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      \"[[0.25]] [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 \"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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Optional-Labs/Course-2-Week-1-Optional-Labs-2-Coffee-Roasting-in-Tensorflow.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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Optional-Labs/Course-2-Week-1-Optional-Labs-3-Coffee-Roasting-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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Th2ZOTyfRi299R8nWVWnjGR0JCQkJC4jXyTPnBnI2MkN12ideGTCZ7bhlHUnAlJCTefuLioGZNWLMm4/Y1ayzb4+Keu6mDBw/SsGFD6tevT6NGjV7aZfb+/fv07t07w7alS5dSvHhxGjRowAcffIBer3+ptsFSJ3bjxo0vfXxuIOm3EhISEhJvLbo42FMT7v9Ldri/xrJd9/yyw4EDBwgMDKR+/fq0bdsWrVb7Ql353//+x4EDB7hw4QJLlizJ9hx37959ofaeRqPR8Pnnn9OgQQNq167N559//kJ9fJrevXtz//79DNseyzfVq1dn69atL902wObNm4l7Adnt30gKroSExNtNXBw0aQKnTkH37k+U3DVrLH+fOmX5/jkGypiYGCZMmMDmzZs5cOAAmzdvxsbGJle7O3z4cIKCgqhcuTK7d+9+6XbeCAVXEHL8SEhISEhIvJHo4mB/E4g9Bce7P1Fy76+x/B17yvL9Cyi5PXv25MCBA9SsWZP169enb3+6xvuzKF++PB9//HGW372IgpsVkyZNokaNGgQFBXHkyBE6d+780m1lhbu7O0FBQQQFBTFnzpxXaktScF+AjT+cYkiFZaQm6l53VyQkJHIDkwlatIBz5yx/m80WpfaDDyz/Pp5Uzp2z7PcMd+Xt27fTo0eP9BI19vb2VKhQAYBp06ZRq1YtGjZsyIMHD7LdNn78eOrUqcPUqVNzPFdCQgIARqORrl27UrduXbp27YrRaCQsLCx9hfWzzz4D4NixY1SrVo0GDRqwZMkSFi9ezJ49e6hfvz7R0dEveudyBUHI+SMhkduEXItlSIVlXNhz/3V3RUJC4m3FbIIDLSD+kewgmi1K7aEPLP+Kj2SH+HOW/V7QXbl8+fI8fPiQ3r1788UXX9C8eXM0Gg1du3alYcOGdO7cGYPBQFxcHA0aNKBFixacOnUKsCixY8eOBWDRokVUr16dBg0acOPGDZYuXcrXX3/N119//dztPc3Ro0fp0aNH+t9169YF4MGDBzRs2JBatWoxbdq0bLfdu3ePatWq0aZNmxwV7ZSUFAwGAwB79+6levXqVK9ePT3B5NSpU6lXrx7VqlXj/PnzAPTq1Yt69erRoEEDHjx4wM6dO+nevTvTp09/oXv/mP+UglurYzEAxtZfw19zz77m3khISLwycjkMGQJPx9eYzbBlyxPlFizfDxli2T8HwsLC8Pb2BuCPP/6gdu3aDBs2jIiICPbv38/Ro0eZNGkSU6dOzXJbeHg4p06d4vDhw+mZgf/N9OnTKVOmDKdOnaJZs2Zs2rSJkiVLcujQIUqVKsWGDRtwc3Njz549HDlyhKSkJG7dusWOHTuYNm0aQUFB9O3bl08++YQmTZpw4MAB3N3dX/lWSki8DfgUc8bJ04ZlIw4ypMIy7l6Iet1dkpCQeNuQyaH4EBCekh1EMzzc8kS5Bcv3xYdY9n8BDh06RLFiFp2jVq1a7N69m19//ZU2bdqwf/9+6tevz/r16/n111/p168fO3bswPSvBfioqCjWrVvH0aNHCQoKomjRovTu3ZuZM2cyc+bMF24PLFmIH9O0aVNKly7Nw4cPmTZtGhMnTkw/V1hYWJbbpk+fzqxZs9i4cSOxsbGZ2o+OjqZevXoUK1aM0aNHAxZX6d27d7N7927Gjx8PwKBBgzh48CCrVq1ixowZGAwGHj58yMGDB9m/fz8BAQE0b96cVatWMXz48Be694/5Tym4noWcmH2+F7U6FWf/0isMqbCMyLsJr7tbEhISr0KXLrBqVUYl92lkMsv3Xbo8sylvb2/CwsIA6NatGytXriQmJob79+9TtmxZACpXrszt27ez3BYcHJy+rVKlSlmeY/jw4Vy4cAFHR0fi4uK4c+cOFStWzNBObGwsHTt2pH79+hw5coSwsDAGDhzI2rVr6dGjB6dPn36hW5RXyIScPxISuY1cIWPCzk6M394BgB/77GBIhWVEBye95p5JSEi8VRToAjVWZVRyn0aQWb4v8GzZ4TErVqygQYMGJCQk0LZtW+CJLHD9+nXmzJlD/fr1WbZsGVFRUdy9ezfdS+yxHPCYe/fuUbFiReSPFub/nSjrRdv7N7t376Zy5coYjcYMckj58uW5d+9eltset69QKNJlnadxd3fn4MGDrFu3Lj3+VxAEHBwccHBwSL+WFStWULduXfr160dYWBhKpZJevXrRo0cPxo4d+0Iu3dnxn1JwH9NxTHUm7vkQgO87bGHBJ7swm8VnHCXxJpGiNXMrTM+DGEOGFSmJ/yhdukDr1ll/17r1cym3AC1btmTFihUkJiYCFvdhgAIFCnDx4kUAzpw5Q+HChbPcFhgYyOXLlwHS3W6yQi6X88UXXzB79mwKFy7M2bNnM7Tzxx9/8MEHH3DgwAFq1aqFKIo4OzuzcOFCfvjhByZMmIBSqcxyhTY/kZJMSbwunL3tmH2+F1+vbgXAlA82MarWKlLiXiyxi8S7RWyyiZtheqISja+7KxJvAwW6gG82soNv6xdSbsESgxsUFMSCBQsyKabFixdnxIgRHDhwgBMnTvDZZ59RsGDBdDni3zJDoUKFOH/+fLqyZzabM8z7L9oeQM2aNVmxYkX6349lnKflkPPnz1OgQIEstz1u32Qypcs6WdG4cWPOnj1LfHw8ZrOZpKQkkpKS0vu+cOFCDhw4wC+//IIoiphMJrp27crKlSuJjo7m9OnTryzj/Gfr4Dq4WTP7fC+ObbjJuu+O83Wl5fSf14iSdfxed9ckcuB2hJ7ZfyWw9XQqrvZyUrVmHG1lfNLEkY8bOSCXzEb/Tdasgb/+yvq7v/6yfP8cSq67uzv/+9//aNu2LTKZDIVCwahRo/Dy8qJBgwbUrFkTlUrFsmXLstzm7e1NpUqVqFOnDuXKlcvxXE2aNGHs2LFMnDiR9evXU7duXby9vRk5ciRXrlzho48+YvPmzen7//zzz2zcuJGUlBRGjhyJl5cXcXFxdOzYkcWLF+Pi4vIidyxXECQ1VuI141fCldnne3Ht8EN++Wof4xr9iW9xF75a2gKV1X9WxPnPcfiahrnbEjh5S4ubg5zYJBNlAlV80dKJFhXypy64xFvI/TUQmo3sEPqX5fsXVHKz45NPPqF///4sXLgQURSZOnUq/fr1o0OHDixfvhy1Wp1hf3d3dzp06EDNmjWxtrbmp59+on79+owePZqTJ08yfPjwF2oPYMKECQwdOpRff/0VlUpF0aJF8fLyYsSIEfTq1Qu9Xk/r1q3x9fXNctuwYcPo1q0bnp6eeHp65ni93bt35/fff2fChAk0adIEsCS5AqhatSp169ZNjwFOTk6mTZs2mEwmHBwcKFOmDM2aNeOzzz6jU6dOfPrppy98vwXxHTN/Pba8AKhtHZ/rGKPexIx2G0mMSkOpljN2T2dU1sq86mKeMrrKMqae7vW6u5EnnLurpcecCPo1dqRnfXvcHRSIosiJm1q+3xiHs52cXz/zRCGXhO53jYTYaDw8sokzXbMGoUd3hBxcWkSZDHHl87kpv+1ERUXj5Gq5V7rUJ+Oho+PzjYfP4ukxdtrRyBz3HVnryQSYW+eXeDt5+rnJy2fh8aI1QLnGgXw0rR6yd3Thc0iFZcw+/27O9y/C8gNJTN4Qx7iOLnSoYYe1SobeKPL3mVT+92csfRo6MKS18+vupsRrIjo6Ous8FY+zJYs5uMO+hJuyRN7y9O+Z07zyn3RR/jcKlZxR2zrR/6dmGHQmJtT9g0Mrrrzubkk8hc4g0ufHCGb1cWdoG2fcHSwr8w9ijBy8osHNXsbZ21o++zkSneGdWrORyAmTCWHO7AzKrSiTIbZti/hUvIpgNiPMmf3MLMoSL4aURVniTaNmh2LMPt+Lhr1Lc3FvMF9XWs62+eded7ck8oh/QvVMXBvHtm986FHPAWuVZdzXGUTiUy1W3Gmb4vnq1yiiJbdliceYTXBjduaEUn5tMyeeujH7hbMoS7x+JAX3KQpV8mLKqY8o3TCQHfPOMrrKMuLDU3Kt/RSNmaX7ExmwKJJ+CyKYsSWO8HhpwH0e/jqdQlEfFc0fuRoZTSIjlkfTbOJD0vRm3q9sR/e6Dmw/l0alYfc59o/mNfdYIl+QyxG370B8lAhBlMkQV6wkefVGkn9Zka7kihUrIm7f8cwsyhIvhpRkSuJNpfWgSsw805OyjQLZu+QyQyos48TGm3l2PrNZZO+lND7/JYoecyMYujSa07e1Uo6IPGbxnkQ+buRAES9V+rYNJ1IoPSSYoCsaWla0pV01W4KuaKg0PIQZW+Kl30TCkhW5/g5wfpSI6bGltu7mjImnnCta9nuOLMpmUSQxzURMkon4FBNGk/ScvU6kAJV/IQgC3afVJy40mekfbOSHNhso27QAXb6ri/AKJom/TqcwbGk0NUtY07yiLWqFwPEbGuqNDaFPQwdGtXd5pfbfdXZdSKVjDbv0v0etiOF+tIHT0wOxt36yTrPjfCo969vTb0EEa772oWyBzDEIbyOiKHLkuobLwXpkMqha1IoKBdXSMwPg4oK4ew+0bEHqwEE8qNMeIdaITetOaBaasV80j6hVf+Hj6JxvA57OYCYuxYzOICIAdtYynG1lueYqKYoiCalmYpNNaPSWSdTWSsDNPr8VeOn5k3hzkcll9JlRH73GyNxe2/nz2+P8+e1xBixoTImavrl2nruRBrrNjkAhh+517fFyUnA30sDHCyMJcFOw7EsvXPP93fxvsP1sKtu+efJb7jiXyuiVMfw92ocygZb5v00VOwoOvMfFmQF0nBGBUg6DWv33XJYT00z8fSaV6CQTzrZyWlW2/W8/l2oXaLjHUue2+JAnbsiP/70x26LcqnPObyGKIjFJJiITTagUAmqlgNEkEhJjxNlOho+L4rXmhxFFkRStaFG6zaCQgZOdDHsrWZ7JkKIokqwxE51kIlUrIgLWKouM4mQnQ5YPsquk4GaDi689U0/34sDSy+xacI5Lu+8z4NcWFCjn8cJt7btkGXDXj3gy4AJ8UM2O4R+40G12OHJZPCPa5X+SmLeFFK2Ii51lIL4ToWf7uVROTQvAzjqjE4KzrYziPipGtnfhh01xrBzined90xlE7kToMYtQwEOJnVXuOkYEXUnjm5UxKBUCdUtaYzSJLNmbiJOtnBm93Sn3jijxr4SLC7E7DxORLBLgqsTO6pGP7Cc9MPXpgjIZbofrKeKtytUYbbNZJCHNTEKqCdOjiUME0nQiLnYynGxlmEVITDURHm8kwE2Bg82rCRRms8i9KANmEdwd5NhbyRCBpDQz4fEmbt3X0shZzJcJVVJvJd4GVNYKhq9tQ3Kchu/e38jPn+8FYNia1vgWf7V5NybJRJupYXz1vhP9GztkEBiHtHLimz9i6TgjnF3jfFEppDcmt0nRirjYP/LUEUXGrYnl5089Msha9tYyRBFc7eX8+bUXtcc8pE9DRxxsXo8T42OZwWSGgp65LzP8G4NRZOLaWJYfSKZuKWsC3RVcvK9n3OpY2le3Y2oP13TX7v8cahdociyzhbZAFwjo9FyW2/B4E4lpZgp7KrFWP7mPBqNIaJyROxEGingp8zwXgMEoEptiIlljRhTBSingaCsjIsGEySziai/HQSGgN4qExpoQBBOFPBSolLn724uiyIMYI6k6Mx6OCgLcZQhYqp9EJZqISTZR2EuZ5zKKpOA+g/q9y1CjUwkmNVrNz/124Oxjx9D1H6BQPp+QKooi366LY3Yf9wwD7mPcHOSsGORF7TEhfNzY8b+9mpYDvi4K/nmop2l5W1YcSKJbHftMyq3BKHI7woCvi4JKha2YuiGO0Fgjvq5585gnpZmY+3cCqw8n4WwnRyEXCI830q6aHUNaO+Pl/GLnjUww8sehZP4J1aOQQ43i1rjYyRi2LIYFn3hQv5R1uvA02Syy8UQKXWeGvVOW6pfFaBK5FmbkYrCemKQU7K1ltKpsSzEfFXKlAm8Xi/tQRIIRP9fcSSCn0Zm5F2XASingYi9HKReISDCSphNRKizvtkpheUZd7CwZv+9HGSjgIWD7CgJNSKwRpVzA302RQZh2sZfjZCvj8i2R6ZviGdUh7xfMJAcCibcJexdrph3vTtT9RKa228yMLpbsqRN2dsTJ8+Uy7f68J5GGZaz5pEnmxFkymcCU7q60mhLG5lMpfFjT/pX6L5EZP1cF10P01CxhzfEbWmQC1C9lnWGf2xF6HG1lqJUCAW5K6pe2Zt3xZD5ulL+J75LSzMz+K57lB5NxtpOhkAmExRvpUN2OYW2cX0lW0RtFtp5OYe8lDVqDSCFPBR/VcyDATUH/RZEkacyc/sEfT6cn54hLMfH10hi6zIpg/TBvlP/VBRiZnIv3dey6kIZGZybQQ0n7anbPtRit0Vk8qUr4qVD+a/FcqRAIdLd4c8Qkm/BwzDuVKy7FxMMYI462Mjwc5cgFgWStmXuRBlRKgRI+qgwKtruDSFSiidsRBor55O7Cf0SCCb1BpLiPKoMS62Qrx9FGRkiMkQfRRgp65m0y3//oks2LobZVMvnER/Sc0YD4sBTG1VzJma23nuvY8/d0aPQijcraZLuPp5OC5hVsWXs0Obe6nCOiKOZp3d/YZBPztsVTb2wIpb66T+0xD5ixJY7IhJePN+5ax57lB5MwmkRuhxuoVNgq0z47z6dS0FNJIS8VNmoZ7/mpuBOpf5VLyZa4FBOtp4QRnWTi7298OTo1gIPf+XPwO3+sVQItvg0lONrwXG2Josi0TXHUHhNCaJyRJuVsqFXCmh3nUuk7P5J+jR1oUNomgzIjkwl0rGnPhC5ujFwRnSfX+LZgNIkMWxZN1zkRXAzWYWslIyrJRKspYXScHpaeWMTDUUFCihlTDs/+gQMHKFggkIYN6lOndi2uX7+e5X4Go8jdSAM+LgoKealo36oRSjlo9CIl/VWkxoXg5+NNwwYNqF+vLrdv38bWyuKq9DJx97/99hsAeoOZZI0ZP1dFlq5FMplApcJqlgYlkqp79ULpEhLvIh4FHJl9vhdfLGkOwMTm65nUcj3alBebL0RRZFlQEgObOWW7jyAIfNrUkd/3J71Kl1+ZvJ73s+LifR1f/BJF6cHBFPviPu9PDmXdsWT0xtzrR4969vy6z3Jvb4YbqFLEKtPYuGRvEj3rPrGuVy1ixc2w55ufc4v4VBMtvgslNN7IzrE+nPkhgBPf+3Pye38crGU0nviQOxEv16fjNzSUGRLM0qAkqhZR07KiDVq9SP3xD+k0M5wbYXr+GOyVQbkFy8Lrr595YDCK/JlP8uebxv0oA00nhdJ1dgRJaSZsrGTsu5RG6cHBTNsU98x47ehkE24OlgXuAwcOEBgYSP369alVyyI/CIKAp5OC6CRTtm3Vr18/Y5/u38fT05MGDRpQt65FfsiJpDQToXFGivooCXRX4mgjx85ahkJu8V74a90yHsRklDse98tGLSM2OfcSaJnNItFJJgLcs3bLFgQBP1cFKVozOkPeyiiSgvsClKwXwOSTHxFYzoMN3x5jdJVlpMTnXFT+driBioXUz3RNqFhY/dKD2/MgiiJ7L6bSbVY4/v3v4vPxXWqNfsBPuxJI1uTeQ3burpZ6Y0O4FWbgh15u7Jvox7x+HkTEm2gwLoQj118u+VPFQmqKeqsYsSwahRy0+ox9vhmmZ8yqGIa2eRJXo9GLqPKoZNCo5THULWXN3I/dKfRUcgtvZwX/6+LGB9VsaTMllNEropm9NZ4HOSi7M7fGs+dCKken+jOjtzsda9rTra4D3es6UMJXxZK9SRy4kpblsR2q2xERb+JysC7Xr/FtQBRFPv8lihuhBg5+68fiTz0Z1taZ73u4cWVOIOULqGk1JYyEVBNKhYBaJaDV5zxh9ejRk/1BB5j2w3R+/umnLPeJTjLhZCfDyfbJCm9cihlnOxlymYCrvYJadRuxbutevps8Jb0dJ1sZOoP4wgP70t8tCm5CqhmnZ8Ty2qpllC+oZt/FrJ8ZCQkJC4UrejL7fC96Tq1LfHgqo+usZtGnuzE95/uZrBFJ0Zp5z0+V436Vi1hxKzx/FSqwjI87z6fSYXo47n3u4tLrLlVGPGDhztyd97NixpZ4Os0Ip4CHkk0jvTkwyY9Pmjjy2/4kmn8bSlxK7gjVPerac+qWlmVBSemLjE/z95lU1h9PyWBhT9OL+e4uPmJ5DDWKW/HzAA+K+mSUGSZ2cWVoa2e6zApn4toYhi2LZubWeEJinv3MXA7W0W1OBAv6e/D3GF/6NnLkw5r2TOnuxuXZgZy9o8PZVo5VNi7IcpnAoFZO/LL39S7AvA7C4oy0nBxG26q2XJ4dwHfd3Bje1pkVg7w4PtWfHefTGL8mLsc2UrVmHJ9yde/ZsycHDhxg+vTp/PRo3rdVC5jMYHyBR75JkyYEBQUxZcqU9HayIzzehL+rIpObeWyyGQ9HOX9tWE6yxpxJbgZLmFNMLiq4SRoz1ioBdQ5uzzKZgLOdjLiUvB2DJBflF0QmE/j01xZE3I5nbtetTG76JzW7vEfrr6tmub9KIZCme/ZqpUaXdwOuKIqMWhHDkesavmzpxOLPPLFWCZy5o+WX3YmsPJjE+uE+L+xS+28iE4z0nBPBzD7u6dmOAbycFVQsZMUH1ezovzCCHeP8KODxYq4JgiCweKAnvX+M4Ha4npBYI4W9VKTqzGw+mcLmkyl8282NBqUtlvIH0QbuRxnyxHU3It7IgatpnJ0RmGmlODrJyMCfo7gZqiP10W8alWik6cSHNCxjw4ze7tg8FaMRm2zip52JHJ3qn8l95dxdLW2r2vGev4pv18VS7ykX5cco5AINSltz9o42Sxf4l0VnENl2NoUNx1OISTLhZCujXTU72laze6NidY7f1HL6to6VgzwyuferFAJjO7kSEmvk592JjGznggA8bwLNpKQkHBwcAPh+6lR27dqJVqtlwcJFqNxKcevcLqZ8N5EyZctiMBjQGkScbJ/cGyulQFyyiYSEhPRt03/4gXUbt2BrrWb58mUEBATQufOHREVGolKrWbduPUajkY4d2iMIAqVLl6FR48ZcvnyZhg3q0//LkTRq1OSZfQ9wVxKdlPdlDaRMyRLvAhWbF6Ri84Ls/e0y2348x7CqK6jZsRgdx1TPMQmLSmFxDTUYxRzdO9PycH7PDlEUGbrUMu8PbuXEsi89sVIKnL6tZeHORJYdSGLLyFef97NizZFk/jiczOHv/DJYDdtWtaNNFVtGrYyl94+RbB3t88rncrKVs2mENx2mh+PnquD8fR0nbmpISDWz8lAyp25p+fNrr3T3X1EU2XwqhUldXF/53M9LVKKRXefTuDQ7IMvnKTzeyKaTydyOMBAWZ6JcATW3IwzU+uYhrSvbMrOXW7YK6pSNcYxu70LjLLwE7a1lmEWRG2EGboTqKe6b9UJM47I2dJ4ZgdEk5qqralZExBtZdiCJoCsa9EaRIt5K+jRwoHqxzJb3vOaHzfF0qG7H582dMn3n66pg4whvKo94wEf17SnqnfW9E8k6VCeD/PD992zYsh2ZWc/PP/9EhQoV+Pvvv5kwYQJlH8kP2fG0/DBt2jS2bt2KWq1m6dKlBAQE0KFjJ4IfRuDiYM369Rb5oX17i/zgHViSDz9oyuXLlxnQvSlfDhlN1w7NM7RvoxbQG0REUcyV+280gfo5xjqVQsjzkp5vjqT6luFVxJmpp3tRq1tJjq25zugqywi/lXmlp0ZxK479oyEpLWdh86/TKdT7V9xIbrFkbxIX7unYOd6PLnUcsLOyWJmqFbXm18+9aF/dnj4/Rrxy6vwVB5NoXtE2g3L7NNWLWVGrhDUjlkWz/WzqC7tQ2lvLWDfMm+97uvPPQz0Dfopk0p+xuNrLOfCdPx/WssQ3iaLI9M3xdKpln0GZzC32X06jYRmbDNmbAZI1Zjr+EE7FgmrOzSxAn4aO2FrJmNrTnbMzAzGL0Hd+RAYX2XXHkmlWwSaT65DlOiwDZ9NyNiSlmbl4P2srrSATyM1h4m6EnjrfPGDVwWQ61rBjcnc3etRzYMvpVGqOCuFayJtjLf5tXxKfNHHA2c7i8pIVX7Z0YmlQEjqDGa1eRK3MefBduXIF9evV5eO+fej04YcAfDVoEEEHDrJi5SpmzpyJKMKsGdMIOnCQiRMnERkZiSDAU+V4ORC0lw9b12XgpwP48quviIiIYH/QflZvPsCYcf/j+6lTAfj996UEHThIp04f8ueff3L+/Hnq1avPvv1BzJk7lzZt2lCmTBn2Bx2gYcMmGJ7DvS88zpK9Ma+RCUKOHwmJt4nGfcsw69xHVG9XlGPrbzK04nKCll/Jdn8rlYxKhazYcT41x3Y3n0qhQem8md+zY8HORC7e17F/oh/d61rmfYVcoEZxa1YM8uKDqnb0mPfq8/6/EUWRmVvjmdvXPct5TRAsccm3IwxM2xTHH4eTOX/v1eaUoj4qTk3zp0c9B6yUAj3mRvLj9gQalrbm3IwAKhZ6EtK09XQqGr1Iw3z8PfZd1lC/tHUGj5/HxKdawmnqlrJhSGsnfF0UfNbciVm93bk2N5DENDO950dm6V4eHm/k2A0t3evkFNst8GFNO5YGZW+hFciffAqrjyRTdVQIYXEmRrZzZmoPN8oVUPPZL9F0nR1BWj6G1SRrzGw8kcJnzbOPw3axk9OzrkOO4QXWSlkG2WPFihXUrVuXPn368OEj+WHAwC/5/c+9rFy1khkzZgAwdepUDh48yKRJFvnh3+zZs4caNWowYMAAvnosP+zfz9GjR5k0aRJTH8kP8xf9xp+b9/Hhh0/kh/r16xMUFMTICTN5v5VFfti2cz/VazfKdB6zmLu/vVzOc4UgGIzk+WKKpOC+Iq2GVGHsns4AzOv2Fwv7bsdsevKwezopaFjGhrl/J2Tbxu4LlrTtjctlH6f7spjMIot2JjC9l3smhewxQ1o7kaQxc/Jmzu7Wz2LdsRR61XfItF0URX7Zk0ClYcHcjTRw7IaG3/cnUnFoMJPWxr5QPI5MJtCioi2/fu5FYpqZT5s7MbSNM96PVqHD440M+S2af0L1jPwgb5LsaPRiBpeUx/y+P5FiPkpGd3BBIRdwsJGlD9i2ahnz+3sQm2xm94UnrqO3HsUMZUW5AmoOXk17FFeZtYub2Sxy8GparmVSTkw10Wl6OJ+3cGLDSB/aVbenchErWlexY/VQb8Z2cqHLzPBXiqfOTS7c01G/tA2u9jLiU8yZ686JIkW8lJYkVCE67Kxlz0yk0aNHTw4cPMS58xf45psxgGXSql+vLp/070d4eNij1U4ZdnZ2+Pv74+5ueb8SUp8sZDVo2JhVmw/SuVsvDh67wJlLtylWojRavUitGlW5fec2JpOJEcOHU79eXRYumE94WBh169bFbDbTo0d3Vq5cmaFvTrYyElLNOcbSafRmTt7S0qTcyyXNeREEIeePhMTbhiAIdB5fk+mnelCkshdbZ59lSIVlXNhzP8v9+zdxYPrm+GyF88gEI4v3JNK/cf4lNDKaRBbsSGBO3+zn/ZEfOBOXbObUrdxdsDx/T4fJDLXfy3peC4010uvHSGKSTPxxOJn9l9PoNS+CeuMecujay9evt1LJ6FbHniPf+WOlFKhW1OI19jhDsUZv5pc9iQxZGs2SzzzyPKPt02h05gzePU/z065EKhdWM6qdCy62clKf8vizs5Lx2+ee3I8ysv9K5ntzJ8LAe76qHJMWViioRi6DG2HZx5cfvKahpF/uJhr6N3supvG/P2PZM96XuR+706C0DdWKWvF5cydOfu+PtUrGwJ+j8uz8/+Z2hAF/N0W67JgdDUpbZ2tcAEsyyegkU/qc3LNnTw4dOsSFCxcYNXoMyRozCxcvo3+XxnzSvz9hYWEAyGQZ5Yd/06RJE44fP06fPn24cuUK9+/fp2zZsgBUrlyZ27ct8sP4sSPp8kEj5s+fT9hT8kP37t3Zt211ukxiMovIZJZ/Y5NNPIw1EBprIDzOiL117pULcrCWkaYX0ecQ5mEWReJSTDhn807kFpKCmwvYOlkx9XQvOk6oRcjlaL6pvoKrBx6kf/9tN1e2nU1l/OoYopOeKAYavZml+xMZtCSKnz71zJOU2adva3GwkeXoqisIAt3qOrDhRMornSsyIeusaJPWxllcloZ6s2+iH0YT/DHEm32T/LgdrqfvjxEvXBC7RUVbfhnoya97E6k0LJges8Np930o9caGYK0S2DTSJ1OW5dzC303B1ZCMk0WKxsTCHQnojSLDlkaz8mASl+7r8Hd7cj8UcoH+TRxZuj8xfZsqi5ihxzSvaMutcAPn7mot8cRZKGZ/n0nF2VZOhYK5o+D+cTiZKkWt6NUga2GsQw17mle0zXE1OD+RySxKvkohw9VOzt1IQ7qFMyHVxM1wA3cjDRhNEJ9qJk1nJibJ+Fx+yvb29iQnWa7zp0UL2R90gJ8X/wKiiJ2VDL3RRGpqKg8fPiQ6OhonGxkavSUmD1FEoxcxmkQ+6j+ExQtnERhYgLPnLyICx0+cpnChwly4cIHUtFQOHDzEwM8+RxRFTCYTEydNYuXKVcyeNRMgffJRK2XYqGWExWd9DaIocu6ujq517LMVbHMT4RkfCYm3FYVSzue/NGPKoa44etiwbMRBhlRYxr0LGYXwDtXtKBWgpv0P4RlyITyuXd5ychj9GjtSPpfG6OfhxE0t7g5yyuYQtiKTCfSsZ8/a47mbXCgywUQhT2WWAnNorJFm34ZSOkDF3I/dKRuo5tfPPLkwM4BhbZzoMz+SXRdytoY/C19XBXsm+BISa6T80Ae0/T6MjtPDKTkomF0X0tg6yieDRTc/CHBXcvlBRplBFC2L03O3JZCkMfPd+jiO3dQS4J5R4VIpBD5t5siSfYn8G6VCyFZ+eMzHjRzYeiY1W+XVbBaZuy2Bfnm8ADN1YxwzPnLL0k1apRD4aYAHJ25puZpPHmIywWK9fBZmkRy9keysBKyUMu5HG9K987QGM7EaKyKjE3gQbWD57z+zaNVuJk5bmK4Im83mDPJDdgwbNozp06dToEABLl68CMCZM2coXNgiP+i1aSz5cy+ffDowXX6YNGkSq1atYuniuUQlmQCBuBQzggBXQ/TpOUlkMoGYZDNpOjHXYvLlMkut2wexxiwX4kVRJCzOiI1ayNbtPrf4T8Xgnr+r5fB1DQajSDEfFc0q2OZqXEylVkUo16wgczpvYeXwIAAmBHXFw1HF39/48t26WGqOCqFMoAqVQuDifR0VClqxdphPrsZPPk18iiXj6rPwcZZz/u6rWXAdbWREJRqxt34ygJ29o2XzyRT2T/LD2U5OdJIRK6WAQg5+rkqWfO5Fhx/CWHMkmR71Mlt/c6LWe9b89Z4vN8P03I00oFYKVClslWeK7WMalLZh2NJorjzQUTpAzZojSYxfHUuyxkz9RzHAf51O4cAVDbXfy+gGVfs9a75bF5v+d52SNizYkcDALGJAVAqBqT3c6Dk3nDSdyA8fuaV/J4oiuy+kMXJFNMu+8s611bc/DiUxo0/m1cSn6dPQgS4zwxn5BtRtrlLEij0X0yjpr8bbWY6QAP+E6lErLbXeHKxlnH2gw0olUK2IFQgCYXFGNHoRf1dFlmbGlStXcPToEbRaLWO+GWs5T5Wq1K9Xlzp16gKWVduP+n9Nvbp1qVixIl5eXshkAgHuCoKjDMTGGDCYRAp6KrG3dqdAYAAPQ8OoXrMe/TvXRxSULF+2DF8fd+7cvk2LFs3x9/PH19eXU6dOMfabMRgMBho1apx+/nbtPmDo0K+pWbM2dyMN3I0y4O4gT7dQJKVZaswBfNMx/+LLJCTyk8Q0E1tOpRL6yPLQqpLtC+d0eBGs7VX8b1cn4sNTmNRyA/P67ABgzOZ2uAdaMvMu7O/O/B2JdJ4VgaudDE8nS2kQuQyGtXWma+38LQ8Ul2J6rnJoPi4KLgXnbqWBx3JAVoxZFUO3OvaMaufCol0J6XVoZTKB1lXscHeU031OJNfm2jwzlCQnvJ0V/DLQk5gkE2fvajGLMNPPjUD3vC1Lkh31S1nz1RIT5+5qqVjIijsRBj6aF4FGL2IWoXl5W87f07LtTCpyGfRtaM6Q66LOe9bM+is+U7tlA1U8iDFwL8pAwWzegfcr2TJsWQyhsUYSUk0Z3KRTtWZGrYwhTSfStbZd7l/4I64/1BMeb6Rlpey9itRKgZ71HFhxMJnve+T9YlBRbyXh8UaCow05Phe7LqRRpUjOBqIC7goexhq5H2Xg96Ur2Ln3EEaDjv5fjEKpEKhTsypf9GxChaq1SNObMRhFRo4cSd2n5IfscHJywt/fn7CwMBo0aEDNmjVRqVQsW7YMV1dX7ty5zVd92+Dl7Ufxwn6cOnWKMWMs8kPTpo1xtpVT+L1KDOjdkT6fDOLDNg1QKy25gUJijbjay3C0kXE/ypBrNZm9nOXcjxK5FW7Aw8lSGshSB1ckKtGIwQRFvPL+XRTE3A7AeM0kJj5Z5VLbWlak/gnV89WvUcSlmGhZ0RYrpcCpW1ruRBoY18k1PXYzNwm+FMVPH1smwqYDK9Cgr8W1ICHVZHHhMVmU7IBcHnBHV1nG1NO90v8+dUvL8GXRHPjWL0claN62eEJjjUz7KGflJifG/RGDlUrIIFx/8Usk7/mp+byFEwA/bo/nVpiBef080vfZfymNyRti2TfR/6XPnd8sC0pk8e5EPm7swPztiSz5wpO2U8K4/3NBUrRmus2OoJiPkv2X05jYxY22VS2TR3C0gbZTQ7kwqwBgcSWrMvwBM3q70ahs1oN/nx/D2XdZQ5kANXVLWWMyiey+mIbOIDK7jzvVi+deLFGBAXe5MqdAjosEoiji3fcuD38tlOcxFI9JiI3GwyPzs3n+rpae8yI5MtkvfeJO1Zq5G2nA0UaGXAZDl8ZQs7gVg1pZMmybzJZSU55O8ixjop6XsDgjyRoT3s6KdBcfURSJiDcSmWhCJrO4pptFS9IuZ1sZXs6W1PmxyUYSU80ZMnC/CGazxcUnNtmM9pErkI1ahpu9HH1aHM6ulnulS30yHjo65s4K/dNj7PwzOZeo+qLyk98st84v8Xby9HPzMs+CySzy3bo4luxLon5pa4p6q4hNNrH5VAq1SljzYz93XOzyvo58yPVYZnX7G7CUEBz7V3vsnC0WQaNJ5PRtLQlpZjwdLZ41eZk4Z0iFZcw+3yvT9mP/aBixIoYjk3OeU2dsiScy0cT0pxZPXxWjSaTMkGDWDfOmdMATxSA83ki1kSFcmROIvbVAnbEP+V9n10zJkdp+H0a3OvZ0zgO57HWy4mASs/5K4LfPPOg+N5IhrZxoX92WckNDuDo3gK6zIihbQE1YnBGDSeSPwV7pz86NUD2dZ0VwYWZApnbHrY4lIsHI4k89snzWLt7X0XpqGO9XtGXb2VTer2RLoIeCsDgjW06n0qiMDXNzcGXPDXZdSOWnXYlsGplzUrHNp1JYdyyFVYOzV/hehujo6CzdgEetjEmXo7IiPN5I9dEhHP7OjwC3Z8vqeqOZG6EGrFQCtmqL4vhv9/GwOOOjOsW5J/ubzSJ3IgzI5eD1qPQPWN7FmGQTkQlGzGZQKQXUCgGDybKw4uEox81ejiAIxKeYiEo0ZZuI7EURRZGEVDPRSab0fDtWShluDjJc7OSv5LH69O+Z07zyzltwb4Xp6TAtjLGdXOhcyz5D3MXlYB19foxAZxDpmUXs6KsQWNaDKac+Yu2EI+xedJ7di84zfFN7XPzs0zP9vgjXQnT8cSiZe1EWS2WjMja0q273zCRKlQqrSdWZOX9Pl+6Wk6Ix80+oHrMIxX2U2FvL+ONQcgal82Xo09CB9yeH0qaKXbpF+vg/Woa2sVj67kTo+WlnIquHemc4rn5pa/rMN5CiMee59TW36NXAkbhkM6NXxtK+mh2pWhEfFzmfL47iyD8aWlWyZXJ3N87f09FvQSTvV7JFIRfYdT6VakWfKKQKucCiTz3o82MEYzu60rGmffrKdWyyifnbE7gaoufk9/6cv6fnygMdKqXAxC6u1C2ZOavyq2KjEkhIM+X4OyRrzCjlAvI34KeqUMiKVpVt6Tg9nF8/86SAh5L4FBPujnKsVQL/+zOOmCQT/Z8qESGXCXg6yR9lh355gdjHWU68SiAiwURIrBGl3GI1lsssCdH8XBXoDSKCANZqWYYB3dlWTkS8CZ3BnGM6/eyQyQTcHBS4OZCeJObxsxCVj9WBZJIjskQ+IIoig5ZEcy/KwKlp/hky/07p7sq36+JoPSWMneN889w13/89V2af78W1ww/55at9jGv4J37vufDlby1QWSmo8QoLjqIocuaOjj8OJxMSY8TOWsb7FW1oW9XuhbzNqhWzIj7FnG4xBMsi25UHOgwmi3DtZi9n+cEklnzm+dL9zQqF3FL3d/jyGDaO8E63RJ66paVmCSscbGT8sicRgynrRE+tKtly9B/NO6fg9qznQHyKmabfhlHMW0kxHyX/hOpRKqD80Ad8UM2Oyd1cLfHL34Rw+LqWuiUt92f7uVRqFMvarXpkO2daTQnjs8XRjO3okp4t2mgS2XY2la+XxTC/nzttqtgRmWBk06lUohKNFPNWceQ75wxhVHmFrVpGQtqzXWATUs3YqPNvThne1pmmk0KZtDaWYW2dM8jT10J0fPRjJINaOj2XcgugN4BcZrFOZiebeTrJuRqiR28wo3qJuT8rZDKBwl5KIhNN3I00IBMsbtV6o4iDjQxPRwVpejOeTgpLpmyZgI1ayNBHJ1sZYXFG0nTmXEnOKggCznZynO3kmWSU/OKdV3An/hnLV+870bVOZgW2TKCaP4d503xSKB9Us8v1iVEQBDpPqkOzzyoyrfV6prfbSMl6/vSY3uC5f2it3szg36I59o+G7vUc6FnfgRSNpTTO5PWxLPrUk3qlsleY5TKBz1s4MXxZNL8M9OTn3YlsOplCAQ8lMsGSpMDP1WKByskN43ko5KXih4/c6TwjjIEtnOhWxwGTaKn5uXh3AvO2JTCuk0umeGCZTEAhFzJkF34bKOarpFwBFb6uCn7YFIcowtF/NKwZ6k2pRyvXlQpb4e0iZ9+lNGoUt2LxnkTm/2shoXoxa9Z87cPk9bFM3hBHhYJq9EaRC/d0NK9gy9/f+OLhqMDbRZmji09u0KyCLRuOp6RbO7Ni/fEUmlWwyffBKjumdndl9l8JNJzwkAoF1bjayzGaRfZd0tC8gg2bRvlkGrAdbWQ8iDZgNosvn2zk8QBuK0NnFDGZQSkXCI+3CKZqpQx1NvOi7NEEozWI2e7z/N14fb/DG/IISLzjnLql49B1Dcem+GdyobNRy5jS3ZU+C6L4aXciw9tmP3blJiXr+DH7fC+Orb/BusknGFljFeWbFqDn1LovNaYkpZnpPT+C2+EGejdwoGk5G+JSTCw/kMz4NbGsGuz13LGjcpnAl+87MeT3GP4c6sniPUksO5CMl5Mca7XAzTAD7vZynGxkVC6c++6gX7Z04upDPc0mhTK8rTMtKtpiMoPWIDL4t2j2XkpjyyifLO+TSmGpGfouMqCpI9O3xFGtmBVTN8ZjFqGYjwqjSWRGLzfkMsvC8SdNHPltXyJ1S1oTl2Ji8Z5EVg7K2qppZyXjr9E+TFoXR40xIZQJsCSduhysw89Vya8DPdJDpzydFHzaNP89aaoUseJBtJFb4fpsy+0ArD2anGExOq9xtZezY6wPg3+LoeSgYFpUsMXeWsaVBzpuhRsY1d6Zjxs9f3+SNGac7eQ5zslymYCDtYxkrchzRBE8NzKZgLezAi8nOVqDiCha3iWFXCAkxoCdlQzbHBRXQRCws7LkEbHJ5SHhdcko77SCGxJj4MwdLYtzWKEs7KWiXilr1h5N5uM8CrJ38rJl6uleHF51le1zzjCm6nL6/9SMQpWe7YYx+LdoNHqRUz8EZAjI7ljTnmP/aPh4QQSrh3pTvmDGie9mmJ4VB5O4G2FAKbdY5mqPCaFacSs2jfShgKeSs3e0LNiewOUHOuSCwL0o4yu7TbSuYkdBTyWLd1uSP+kNIo0nPKR1FTuWD8p6gr4crMPeWsiXpDi5yT8P9dQvZcOYRy7ZBqPIR/MimLElnqk93NKtDNWLWXP0Hw1z/oqnSVkbqmWxEluugJq1w3y4F2XgZqgehdxincwPl7un6dvIka6zwmlf3S7Lld3IBCPztyew4JNXs/Y/JjbZxJnbWgwmkeI+KvzdFMQkmx65sjzftQuCwNA2zgxs7siOc2mcv6uloKeSqd3dsixT8fgYQcil8kqCkCle7HkCP0Tx7U/EJCm4EvnBL3sT+aSJY7bxYYIgMPh9J7rNiWBoa6c8SdiYHTU7Fqdmx+L8Nfcs+5de4cLu+zTpV5aWn1d47jbMZpFuc8Ip6KFk7dfeGUI/utd14O8zqXSaEcHu8b4UziZ27Va4nqVBSdwMM6BSCNQraUWZQBVlhoZQ0k/JmqFelPRTcfKWlh+3J3DtoZ7YFJFTt3RZzkmvgkwm8NMnHmw4kcKPOxLpuzAKuQy0epGhrdUETfTF3THrsfnIP5pcqwrwphGRYMTeWs6s3k9cYvVGkQ4/hPPxwiim9XDF00lBzUeL4f+E6vlkUSQdqttRqXD2v5G9tYzpH7kxvpMLJ29p0T1ygX3PL3fcTV8VtVKgVwN7xq22LNRk9X5uO5vKvSgDrfJoEd9oEknVmRFFS3+sVTKMJktFjBWDPAmNM7LnYhoavUjdktY0LW/zwjl6zKKI8jnGHpnMsm9eIAgC1irhX9t4Llnn7TIxPZt3WsG9FKyjShGrZ5rbG5S24eStV0uw9DzU6V6Kau2LMaXFOn75dBf2rtYM39IBpTprQf5ysI7jNzScnBaQZbaxmiWsGdXehRmb41k55Inb76AlUey7lEb3uvb0rO9Ams7MuD9ikQmgN4i0mRqG4VGB7Y/qO/Dbl16sOZzM54sj2THO75Wvs3SAmnn9PJjZ251NJ1P4ZU8C8/t7ZBuv+dOuBD6q75ivaftzA6VcIOmpzHNKhcDSL734bn0sdceGUKWIFZ6OcvZdTiMpzczQts580cIpx9Wsgh7KbJNF5AdlAtUMae1MmylhjP/QhfcrW1zjjCaRnedTmbQ2ll4NHF7JDQ8she8n/hnL7gtpVCqsxmASOX1bh8Eo4mwrR28UKeSlpG8jBzrXsscsipjNZmSy7N9la5WM9tXtKBuowt1RjqNN9gqyzmB+5MbzSpeRJbZWMpLSTLjaZ39+k0kkVSfinwd1msV8nKberiUpibeV8/d0fN0mZ8ts+YJqtAYzMUmmbBe28pLWgyrx/hcVWDbiIHt+vcSeXy/RZUJNqn1Q9JnH7rusIT7FzJZR7lkK/60q23IpWMfcbfHM+zjj4qLRJDJ8eQxbT6fSs549fRo4oDGIbDmVwvZzqRT1VuLroqDdtHD0RpHiPkr6NHRg1WAvjvyj4aMfI7g8OzBXE26CRcntVNOeTjXtSdOZ0RtFPpwRTjEfVbbK7cNYI7vOpzGtZ+7FBL9JKOUCOoP4qMyc5X6rFAJrh3kxbnUsVUaEUKO4FTIBHsQYaT0ljEGtnPg8h1qtT2NvLcsU0/ymMPIDFzrPCqfTjHC+6eCSrrDHJpv4fX8Si3YlsvZrr2eW8XtRYpJMnLihIcBXi62VHJnMkuwIEcyAQgYmMzjYyOha2/6V3HPVSoEUzbPn3zSdiFMWpSbzClsrGdGJJjxzeIzMZpEkjRlv5/w1qrwoZrP5uWt3v9MK7pvCg2gDVx7okcmgfAE1/zvQjRvHQlk6aC/ja6/kg9HVqda+eKbjVh1Komd9hxxTaXeqac+U9XGExxvT63lFJZo49UNA+ot6P8qSvnz1MG8GLIzkj6FeGeJAAXo3dGDBjgQu3NNmsga/LEqFQIcadqw7lsxXv0Yxq497hmsxmUXm/Z3Aubs6Jnd/+ya0miWs+WxxJBM+dE1XztVKgW+7ujHiAxf2X04jPsXEzvOp/DnMh6pF87c0wcvycWNHCnoomb8jnlErYvByVhCVaKSwl4r/dXZ7ZTfpqETLxN2yoi0nfwjgfpSBHnMiGNjMETcHObO3xvPr5x4YjPDD5niO39DyQ3dHomNiEJ7D5hkXZ+DCDWN6/FJWnL+nRSkXcLfKfUuB0Syy92wq9UpaZxvjezVET7LGjKd17j4TIiJW1nlf/zb9fJIJV+I/jCiKXLyv50GMAWuVjJrFregzswF6jZE5vbazZuIx1kw8xoCFjSlRwzfbdpYdSKJ/E8ccLc/9GjtQeXgIU7ubMySuGbkyhjsRBi7MDMjgBdW8vA3FvwwmIsHE9z3c+GOId6Y2m5azpYhXAn+dSaVD9bzLomujlmGjhm+7udFlVjgejnIalsmoiIXEGOg0I4IhrZ3y3XMpv/BykuNgI8tkNbdWyZjRy51xnVzYe0nDH4eSqVfKmhVfeeX6wsPrQq0UWPu1Nwt3JtB7fiQA1iqB8HgTLSvasHOsD0V9ctfiHJtsotm3oXxQWUW5AklYK2RcDdFzL9KAt7OckBgj1YtZ4WYv5164gb2n9FQpYpUex/yi6I0iu8+k0ryCTbaKckySiRM3tbxfyRbdq1XEem7MZjh0IQVDmjUejlm/WzfC9ITHm/C2yb2kpXmBKIo4OTk9177vtIJbroCaob9Fk6oz5+h7vu9yWqZyLrnBP6F6vl0by9k7WioXtsIswld3tNR5z5oJnT2YfPIjfv9yD5unnmDz1BOM2fEh9m5P+nE30kCTcjmvxtmoZRT1UREcZUnSBPDr554ZXq4DV9JoWt6WOu/Z8L8ubvywKZ4NIzJer1wm0LaqHfsupeWagvu43aVfeTH0t2gqfh1Mx5r2FPK0BMOvO5qMt4uCTSN9XinZz+uiUmE1DtYy1h1PyZQQw95aRtuqdvxxKIkAd+Vbo9w+pmFZGxqWtSEi3khMsgkXOzk+LrkzXIxfHUurSraM+9AVjd5Mr3kRzO7jTrMKFsWshK+KzxZHcXZGILXes6bDD2GsPKLl48bP5xZdwt7M16tDuRQh45uOLpks5qsPJ/H9xlR2T/DDKRctPaIocvaOjhUHkzh5S8aYdal0qG7PmI7OeDlbrPJpOjM/70pkxcE0/v7GF6dcuqevC8mCK5EfVCqsZu+ltBxdLs/d1WL9AqENr8qOc6lM3hBHksZMaX81Cakm+i3S0622PeM/dGHE2jYkx2r49v0N/PzZXgCGrWmNb/HM5dXuRBgY8UHOi20ejgqc7GREJJgo7PXkzdtwPIVLswIzhficuq2llL+Kgc0d+W59fLa5OjrWsGf3hbxVcB9TragVy7/04pOfIvF3U9Kmsi1qpcDxm1r2XkxjWFtnvmz57mZbl8kE+jV24PtNcawf7p1pQcPRRk7N4lYMXx7NzrG+74xy+xiVQmBwK2e+bOnE3UgDeqOIn6siR2+rV2Hc6lialLNhXGeLAWXvpTSGrkhj7//88HBUcOwfDR/MieTqnAB8vWXYOuj4YFoYRyb7P1d5zaxIMsfT86cUNo7wyTQW3Ykw0OnHMCZ86IqHR94nUYtMMLLsQDIHrqQRlSRn9Lo0vungzJctHJE/yhKqM4gs2ZfI7L9S2THOB3f3N8OtPTd446WrsLAwWrVqxbVr10hJSeHhw4dUq1aN9957D5VKxe7du7M91s/VolgsD0rKss4oWGJVD1/TMPfj3IkpfMzlYB2dZ4bzdRtnfv3cMz2TYLLGzJK9ibSaHMqWMb58vKApUfcTmd1pM1NarKVqu2K0G1MDACulQKr22ab4VK0ZlVJg1aEkgEzKvNZgqQcK0LaqHf/7MzbLumn21jK0+tzP7mCrlvHzQE/uRhpYfyyZyw/0OFjL+OUzTyrkc8H13EQQBOZ87MGHM8JJ1ZrpVsc+3UKt0ZtZdTCZWX/Fs2FE5pXztwUvZ0WGjKWvSlSikb0X0zgzw1LuYMvJFEoHqNOVW7DUBi7sqWT72VQ+qGbHhM6uDF4STZ+GDs/lxm6lkrHma296z4uk7jch9KzvQEEPy6LK6sPJxCSbWD/cJ1fdGLV6M58vjuJSsI7eDR3pWseeY9c1FrerY8lUK2aFt7OCA1fSqFLEir/G+ObagsHrRDLg/nd5lbn5Rfm4kSP9FkbSu4FDlrkaRFFkzt8J9GnokC/xtysPJjF5QxxzP3ancRmb9HEpJMbA+DVxtJsWzqaR3ti7WvPDiR5E3kvk+/abmdHlLwAm7OqIk8eTMU+tFEjV5jz3iqJImk7MFO/fpbZ9ej3Zp9HqLRlUW1WyZfTKGK4/1Ge5QGBvLaDV519YQ52S1lyeHcj2c6kcuqbBZLYovrN6u72VC90vSv/Gjuw6n0af+ZFM7uaanutCFC3x0AMXR/FlCyeK5bI1801CLhNyTDaVG8SlmPj7TCrnnyqttHBnAiPbOePxyEW+ZglrygSq2HQqla617alQUE2nmvb8vj+JcZ0yL0Q9D8PaOKHVi1Qa8YDONe2pX8oao1lkx7k0tp1NZWIXVzrVzHvldtWhJEaviqVdVTuGtXXGWiXw59EUvlsfx7RN8bStYosZi9JfNlDNjnE+FHnJsoVvKm+8hOXi4sK+ffto165d+rYmTZqwcuXK5zr+f11caTs1DFsrGd3r2meY/C7c09J3fiSTurrmaoIjURT54pcoJnWxlH15GntrGYNbO2OlEhi2NJoNI3zwKODI1NO92LXwHAd+v8ypTTf5YkUr6pe2YcupFNpUzX5l9WaYnqhEE6UD1Mz5K56snKAKeCjZdtbiC6FWCpT0V1mKOv9Lwb14X0fTZ1iMX4VCnkpGtHu5QeNNpXSAmg0jvJmwJpYZm+PSa9Iev6GhbKCazaN83umJ6kU5fVtL9WJW6YLMtrOpWZaDaF3FjiPXNXxQzY5qRa0wmUVuhOl5z+/5XIrdHRT8/Y0Px29o+fNoMkFXNDjayPi8hRNNy9vkev3eIb9HYxbhyJSAdAG0ejFrhrRxZv2xFEavjKZsoIrdE/xyvfb160TSb/+7vOrc/CJULaKmYWkbOs0I57fPPTMsDqVozUxcG8f9KAMLcyn5XU5ExBsZ80cs+yb4ZnKp9HdTsuQzD3rMjWTetgRGPprvPAs6Mvt8L+6cjWB+v11MbLYeFx87hv/ZGis7FY3K2LDxZGqOuQ2OXNfiYifD1yWjElixUNZjYkFPJZeDdQCUDVRzN9KQpYJ74Z6OgrlYk/N5UMgF2lSxo02VvLcav2moFAJrv/Zi0ro4an3zkPIF1LjYy7kZpidVa2bEB850r5u7ZSv/i5y6paViYXV6Low0nZkj17WZauy2rWLHoasauta2yCFda9vx6c9RL63gCoLAuE4u9G5gz9KgZJYeSEImQPViVnzXLSDH3By5xY7zqXy7Lo69E3wzyJ81ilvzfQ9X2v8Qzq0IA30aODC8rXOeLza8Lt54BdfKygorq4xWvqCgIOrUqUP79u0ZMmRIjscX9lKxcaQPg3+LYs5f8bSoaHGJOXVLa1lt/dCVDjVydzXl5E0tRpNIhxrZD959Gzny47YEbobp0x/AZp9VpG6PUkxqtIb5Pf/Go4gzR51Lc/aONssMemazyNQNcfSs54BKIWCVTU2tRmVsGLEsmsvBOsoEqknVmlH/y/UlJMbA8RsaFvTPewHhWUQmGAm6nGZJwuOmoEFpm1xPPJCbvOdnyYB8N9KQLlCM+9D1tSaLyi3iUkzsOJtKbIoJVzs5LSvZEpdiYmlQEqduajGLUDpARe+Gjs+V+dJgJIMFIkVrxsU+83OrVlqKkYNlwnCzl1sSQ7wAgiBQs4Q1NUvkbUzJzTA9h65qODsjIJN1RRAEOtWyWFhmbY3n225v/zMhIQGvPje/CIIgMLuPG1M3xVN9dAi1S1hTzEdJdJKJv8+mUr+kNVtG+2SbZTk3WXYgifbV7LKNF5TJBEa3d6bTDIsH19OLaYUreTH7fC/O7bjLijGHGV1nNcWqefPRxPrUnRDKJ40dsmxXbxSZvCGO/o0dM4VcZOflVcJXhZ+bgm1nU0nVmbN0d03Vmll1OJm9E149uWRekqYzs/tiGpEJJhxtZDSrYIPzW2zttVLJmNLdjTEdXDh8TUOy1kz/xg7UKGb11iXbfFlCYgzsPJ9GitaMn6uC9yvZYq0SOHNHx2/7krj20JK3pnYJa/o2cnhhecpgFLF6aj5O1Vpq7Fr/K6eNlUpAb3ryDrk5vLiskRX+bsqXVpJfBVG06AWz+7pnaVyxUsnYMMKb0oMfUK2o1Tur3MJbGELl7e3NzZs3CQoKYu/evVy6dOmZxxTzUbF9rB/LvvLC10WRbsk5OyMw15VbgEPXNLSqbJtjtlyVQqBZeRuOXNdk2G7toGbq6V50mVyXqNvxtDt9mJHjLrHmSBI6w5OX7k6Enn4LI4lLMTG4tRMADcpkLcgrFQIj2rnQf2EkJ29puBdpoHzBJ8pITJKJ3j9G8HkLJ+xeY6mehFQTn/4USe0xIQRd0XAjTM+8bQlUGhbMsqDE19av56WQp5K2Ve1oW9XurVdudQaRUSuiqTbiAQevWjJ8Bl1Jo+yQYBqMC0EptyQMmfaRGwU9lPT5MYKhv0U9s5ZxUR8l5+7q0vfzclZwK8yQab/Tt7UU9bbcQ71RJDjagJfTmynQrD6cTLe69jkmg2tczoaYJBNXQ3T52LO8RxBy/kj8d3iZuflFkMkEvungwtU5gbxfyQY7KxllA9UcnezPsq+88k3hOXhVQ5sqOSdyKxOoxkol43ZE5rENoGKLQsw+34v3v6zIzZPhzGm+mgHKEFpNCWXLqRSMTwncl4N1dJoRjpuDnL6NMlv2Np5IybYf33RwYejSGM7d1VHtX3kg0nRmev0YScuKttmWHnrdmM0i0zbFUXJQML/vT+KfUD1bT6dSbsgDvl4WnSchVfmJnZWMFhVt+bCmPbVKWP8nlNvYZBM950ZQe+xDzt/TEZtsYvWRZEoOCqbO2If0nR/Be34qZvV2Y2p3SwnGhhMeMm9bwgudp7ivinN3LZUZAJxs5RiMIpEJxgz7nbqlpYTvEyXvZpjhjc8knBNXHuiJTzXTLAdvTGuVjB517Vl5KDkfe5b/vPEW3H+jVqtRqy3KWatWrbhy5Qply5Z9rmPLBKopE2g5NlljJjHNjJOtLNdjdgwmMdMqUVZYqWTpL9+/Kde0IKUbBjK/599UuXWdi0OuM6VyTQoH2pCiFQmNM9K9rj0LPvFIF6zbVrXjEhb32H+7OvWo50CyxkT778Mo5qPi6HUNMhkcuKph3dFkejVw4Kv3nV710l+aZI2Zdt+HUaO4NWdnBODwVNKBKw90fPpTJPEpZga3zrlUhMSrYzSJ9J0fgUohcGLaE5ea3/cncvWBjkAPJbfCDHzTUY1cJlCxkBV9GzvSa24Ek9bGMrFL9hmxS/mr8XKWs+1MKm2q2tG1jj3j/ojNEF8bmWBk+9lUjn9viZ35+0wK7/mp8XdTkqIxs/54MptOphCXbCnF06GGPe1r2OWYSC4vCYkx0KqyHefvajl922LVLhuopkZxq/RFLrlMoISfipAYI6X8380ajxL/bV5lbn4R7K1l6S6coigSn2omLsWEs60sx0Xl3MJgIlOdyaywVgnos5nfH9O4bxka9SnN2m+Pc2LTLVpyjzWJRRnh5ENRbyWxyWbiU0z0b+LIoPezru97I0zPvstpNCqTWaCtX8qakn5KTtzUMmRpNB9UscVaLeP0bS1Lg5JoUNqG2X3cMx33JiCKIkN+j+F6qJ6gSX4ZFo2jEo2MWB5D51kRrBvm/c4lY3pXSUwz8f7kMBqVtebagMAM2cA/XhDJrgtp9Kxnn0EWrV7Mmk+bOtJqahiu9rLndt8u5qOiiJeSjSctSUAfV/VYGpSUHjoQm2xiw4kUTn3vn37cb/uS6PHUOURR5Og/WpbsS+L6Qz1KOdR+z2JVfhOtnw9ijJT0U3H5gZ5jNywx7mUCVNR5L+MCSukAFdvOpb3GnuY9b52Cm5ycjL29xep69OhRvvzyy+c6Tm8UeRBtYMf5VLaeSuFWmAG1UkAuF+hWx55PmjqmB55nR5rOzM7zqTyMNWKrltG4nA2BWcTTFfVWselk9quqYHlpTt/W0rhc9i4McoWMQavb8PBaDAt6beP9M8co7P0eZbqXoXwBq0zukI8zJ388P4IxHV3pUMMuXdG+F2Xg4n09Jfws8T5L9iWmC+JvQlzgj9viKemvYnJ310xCiiXO1Yf640JoW+3tt46+6Ww9bVEet47xTXcNNxhFZm2NZ83X3hT1VtF6cih/n0ml7aP4cDsrGb987km1EQ/46n3nHONMxnVypd/CSLycFdR5zxp7axmT1sYyobMrMckmesyJoH8TS8mgm2F6JqyJZcEnHlwO1tFjTjgVC1nxZUsnfF0VhMQYWXUomVlb41k1xIuSr0F51OhFJq2NRSaDxmVtkAkCqw4mIQJTe7hRp6RF8ExMs7hIvUvIJTOtxCNedm5+EURRJCTWSEKqiT0XNaw8lEx0oglBAGdbGR83cqBfY8cMgvOz2jtzR8fJm1pMZpHSgWoalMrZklbMR8nJW9oc42Vjk008jDUS4PZsEUsQBDqPr0mH0dX4aeAeOHuLltyiylc1KPJhABULqnPMGfD7F570mBvBNx1c6FbnSR3P4GgDUzfGk5hm5uz0AP46m8qyA8kYTCJFvZVsHOH9Ri+2Hbmu5eA1DYe/88uUI8XDUcGSzz1p+30Yqw4l06ehFLP6NjBvWyJlC6j4rmtGOS801sieS2kcneJHk4mh9Kxnn2Eu93VVsPhTDz5eEEnX2vbPbeme8KErXeeE4+eqoFYJa75o4USzb0OpVcKa9/xUdJoRzscNHdITaf66N5ErIToWD7SE6mn1ZvotiuL6Qz2fNHFkaGsndEaRbWdSafZtKF+1dGJwqzfL6BKVaOTwdQ3dZkfQtLwNSrnAH4eT0epFvu/pStNyFu+TxDQzNs+xUPc288YruAaDgRYtWnDx4kWaNWtG3bp12bp1K2q1mjp16lCtWrUcj49PMbFgRwJ/HE5GozOnW1f7N3XkixZOxCSb+GVPIs0mhrJhhDeFssgiJooi83cksGB7AhUKWVHCV8W9SAPTN8dRtagVs/p4ZEgH3rqKLRPWxHArTJ9tnM7JW1riU03UK/Xs+EC/km5MPd2LDd8d48yW69z56zqF1n+Ae2DW6fRXDvFm+uZ4Jq+PpYSvilSdSEiMgS61HZjZx/21WbqyQ28UWXUomS2jfbJdgfd0UtC5lj3Lg5KY0Nk1n3v43+L3/Ul81sIpQ9xz0JU0Cngo0wWigc2dWLo/MV3BBXCxk9O8gi0bjifzSVOnbNuvWcKaH/u503teOGUC1bxfyZbf9iWy7lgyKVqRLrXtaVDGmm9WxbD+WDLfdnOjuI+KRhMeMrWHG62fSkzynp+apuUt5+w6K5x9E/3zrUwIWBKznbihwdNJwZEp/ukTryiK7LuUxoBFkSwc4EkBDyV3Iiw19t4lXla/HTJkCGfOnKFixYrMnTs3ffu6deuYPn06giAwZswY2rZtm0s9lchtXnVufhFMZpHf9yexeE8icclmkjRmjGaRBqVtWDPEk2I+Ks7e1THn7wQ2nAhly2ifZ7osX7in46slUSSkmWle3haFHNaviWWIxswPPd1oXiFrN+Q+DR3oMz+ST5s6ZhuWsGRfEu9Xsn2hrMAKpZwvfm2OJlnPtA6bOT3vOKfnHcdjaQsKlMs+N0atEtZsHunD5A1xfLs+jlJ+KjQGkXuRBrrWtmfbN77YW8v4vLkTn2dTTeJN5Ne9iQxs5phtAlC5TGBIa2fGr46VFNy3AINRZPmBJP4ak1nOW3M0mQ7V7Qh0V9K7gQO/7ktiVu+MngVViljhZCvj8HVNtiWv/k21Ylb8OtCTXj9GUspfRdsqtvSs60D7H8IxmUXaVbWjcy07tp9N5bf9SdwK17NpxJNY/iG/xyCKcGyKfwaDUuXCVvRv4sj7U8Jwd5C/MUnBroXo+HZdHKIIf4/xpqCnRf8QRZEDVzX0XxTFvL7QspIta4+l8FVLp9fb4TxGEEUx//LD5wOJiU9iNRMMtrT7Pozqxayws5Zx5YGOP4Z4ExJj5MftCVy4p2XTKF9c7eUsC0rklz2JHPrOP9Pq0LdrYzl0TcPizzwzWA/TdGZmbIln94VUS03LpyazpfsT+WlXImuHeWeyjv4TqqfLzDC+7ZpRWH8ekqLTmNpyHQBFq/vQZ17jDIPF6CrLmHq6FwAPYw3cizSgUgqUDVQ/l9v06+BGqJ5e8yI4MS0gx/2O39Dw3bo4to3NKld03mEyiwRdTmPPxTS0BpEC7ko617Z/J8q8/BtRFPHvf5dbCwtmeF5+25fI9Yd6pveyTDopGjOlBt0neHGhDMcv2JFAZIKRSV2zd1N+jFZvZsvpVI5e16A3mJHJBCISjETEm1ArBZqUs6FnfQf83ZR8vzGOuBQTP3yUvTvd4CVRFPBQ5qsbe/NJD+nd0IEfNsXzbTdX3q+U8X0+dC2Nwb9GUaGQFYEeSsZ/mL+LM7rUJ+Oho2Pu1Jd8eoxdcTkux317lnniofL4/OfOnWPRokX88ssvDBw4kL59+1KlShUAatasyd69exEEgebNm3Pw4MFc6bPE6+fp5+ZFnkWTWeTjBZGExZsY19GFpUGJ2FrJmNDJhWUHkpm/M4HVg72pVswKURQZuSKWsHgjKwd5ZdvmpWBLvcsp3dz4sKZdhoWpw9e1fLwgkjl93Xm/UmYlVxRF+iyIQqs389u/as4DbDqZwrBlMewa/2plN+LCUvj2/Q3pf4/Z0g73gIyC9JAKy5h9vlf63w9jjdyJ0KNSCJQroM7Ut7eJEl/eZ9d43yy95B5jNot4fnyP4J8KvDHXqtWb2XQqlRM3LF4BZQLVdK5l958ogZQTdyMNtP0+jMuzAzN9N+T3aEr6qejfxJEzd7QM/T2aQ9/5Z9pvwE+R1HnPmh71Xkyh1BlENp9K4eBVDXqjJXmpTIB9lzUkpZnxcpLTrY497avbpT9H96MM1B//kKtzArP1CDl5S8sniyI5PyPgjYiffn9yKB1r2HMnQs/DWCO/fe6ZoV8nb2npMSeCaT3dGL8mlgszA3K9okR+k9O88u5J6E8x+Ldo2lSx4+u2zlQcFsyfX3tjpZJR1EfFvH4eTPwzlqG/R7PsKy8+qu/A8gNJHLiioWHZJ6tD/4TqWXMkmcNT/HGxyzhA2ahljP/QlZgkE/O3JzC20xPhtXdDRwwmkUYTHtK4nA31SlkjirD7QhpH/9HwXbcXV24BHNxtmHq6F8fX/sPW6ScZU3U5fX5sTLHqmZU+P1clfq5vvjuvWRRRPMfYL5cJmPN5PeZysI5+CyNxtJHRrpodDtYyLgXrqD8uhI417JnU1fWtHyD+jShmLv9ioxZISDWl/y1kI0skpJqeW9CwUsnoXMs+yzJB/2bNkST+GJJzPeHeDR0Y8FNUvim4F+/riE400ammPSV8VXSbHcHtcAM96zukjxXOtnKStSK3IwwsyIcSJvnOSzz6J06coEmTJgA0btyY48ePpyu4hQsXJjXVUtLMweHNWBWXeL0s2plIVKKJv0b7EJdicU2+MicQBxsZQ9s4UyZQTY+5EVycFYCNWsb/OrtQclAwwdGGbJWjYctimNTZlS61M449giBQt6Q1Kwd70WNOBE3K2WSK7xQEgZ8HeDD4t2hKDwmmW217SgeoSUwzs/aYxWV64wjvV64p6eJjx+zzvQi5Fsus7n8zpe0mrOyUfLO1PXbOWXuC+Lkq8HN9N8Q6swiKZygNggBymWXfN4FtZ1P5ckkU5QuoaVHBFoVc4NA1DZPXxzGmowufNs2dRca3kazkisfYqAXiUy0Jw3L6yeNTzc8dfvA0aqWQpawxrlP2x6w+kkznWvY5nq9qETV2VjKO3dBS+728rdbwLG6E6rkZZqB7XXtMZpEOP4TTbU4EYzq4UPZR7qEiXkqsVAJfLYnmrzE+75zs+m/ejZEwGy7e17H0Sy/O3NHi5aTIFJ83/ANnKgwN5kG0gQB3JV1q2/PXmZQMCu6y/YkZBNas+Op9J9pMDWNEO5cMk2H/Jk50rGHPn0eTOXTVktSpXilrfuzn8crZimt8WILKbYrwQ9sN/P7lXqzsVYzensPb+gYT4K4kKtFEWJwxR6vo4Wtp6UnC8opkjZlTtzRo9CJyGXy9NIapPdwyuOJ2B77p6EKf+ZGMXB7NzD7vjuIiCAJlC6g5cFWTwUWvYRkbxv0RS2KqCUdbOUGXM/8WJrPIxuMp/PZl9paTl0EURcLjTRT3zVlgLO6rIizWmOM+ucmZ21oalLFBLhMoX9CKv8b4MPuveKoOf4CvqwKN3ozBCGULqChfQJ1jluW3FeElNNyEhAQKFbJY/h0dHbl69Wr6d+3ataNChQqIosjvv/+ea/2UeDsxmUV+3pPI0i88USsFtp1NpUVFGxxsnrxLTcrZUL6gmo0nUuhRzwEbtYwPqtrx1+lUvsjCBe/KAx0Pog2ZlNunsZTPUPL3mVTaV8+8EK1WCiwa4MGdCAOrDiWx+2IatmqBYW2caVreJlcTV/qXdGX2+V5cPfyQX7/ax7iGf+L3nitf/d4i187xJlImUM3Baxq61cn+dzp9R4enoxzb15Db4GGskYv3LVnxyxVQ80+onkG/RbP2a28qP1XWsU9DB+5HGWj/QzgyAT5p8t9Ucv3dFKTozNwK12dKztSyoi1fLYlmeFsn9l3WZKjy8ZjoRCMnbmj5+dP8kbcexhqfGVIkCJbkkQ/zUe7IjtO3tTQoY/1IBxHYOMKbH3ck0nlmOGqlDGuVwMNYIwU9lNQuYUWFLO7xu8Y7reC2rmypeZuUZsY9i7g8G7WMlpVs2X0hjX5NLEmmTtzQZtjn3F0d33XP2d2yiLcKW7VASIyBwv9atXW2k/NpM6dXvpasUFop+HJTRzZvCOb6vMNMqLMqT86T19iqZXSobsdPuxKydW1N1phZfiCJP4bmbMV7WVI0Ziavj2X98RTKBKqwt5Zz8EoajrayLGOAHGzkLPvKixqjHvBPqD5Dmvm3nd4NHVm4I4Em5Z4Iah6OCpqWt2HKhji+6+bKop2JmUpWLN6diJez4rnq4b4IgiBgoxaISzHh7pD9kBWXbMbWKn8FHdlTj0ZhLxXz+3uSmGriQYwRlUKgsJeSedsS0Lzl5Syy42UMJ46OjiQlJQGQlJSEk5NT+neTJk3i2rVrALRs2ZKmTZvmQi8l3lauhehRyoX0OvBJaeYsk0F2qW3PumPJ6a6L7o5yEjVZv3Nn7+hoUNrmmdaLJuVsOHNHm6WC+5jCXvkXdlCqjh/fHOzB6kVXuLvmAiOqrwQsbrpvgntkbtOvkQNTNsTRqYZdhnwQjxFFkR+3JdAvi9rAecntCD3jVsdy7IaWqo8UoJO3NBhNMLW7awbl9jEFPJSsHeZFo/+F0r1OzlbBdxWVQqBnXQfmbUvgx34ZldSaxa2wUQnM35HAb/sS+fPrjHKeKIpM+DOO9tXt8q0cmJ2VjNhk0zP3i002ZRsnnp+IgOyp98BKJWN4W2eGtHLidoQBvVEkwE3ByoPJhLwBCnl+8Pp/lTzE+ZHV1dNJzt1IA1mFGzvZyknTWSbCOxEGPJ0yT57PM3Tm9wSj0ZsZvSKaSsMesCvGmrQBzUnysiiHo6ssIyEi5yzObxqDWzvz95lUftqVkKmWanSSkR5zwmlWwTZPsj6maM10nB5Gqk7k0GR/No70ZXZfd5QKgfEfujJoSRSbTmSuF2ZnJaNHPQeWByXlep9eJ+2rWQSKL3+JIvkpIXFKdzdO3dJQbWQIoijS5pGL/YNoA9+siuHXPYn8lEerqy0q2LLuaM7P9NqjybSsmHN9ytykTKCaQ1c1mP/1vDrayikTqKa4rwqFXODg1bR0F6F3DYWQ8ycratSowb59+wDYu3cv1atXT/9OrVZjY2ODra0ter0+Py5B4g0mRWvGxf6JmOLpJOdOFrVlnW1lpGqfrhNvwDuHutnPow+9STpjfKqJAT9FUnHYA06oPdF92pzwgpacFV9XWs72Bedfcw9zn6blbfB2UfDxwsgM8xBYYirHrY7lbqSB3g3yL5ThRqieFt+GUbO4NdfmBLJumDfrhnmz9AsvrFUyJq2L40Zo1uNWES8VNYtbsSGHusXvOoNaOXH0Hy2TN8ShMzx5XwVB4IeP3Ji4Ng5Xe3kGj8lLwTo+mhfJjTA933XLvxwWrSvbsuZIcpZ6w2PC4oycu6ujbsnX654MUL6AmkPXNJnkZ4VcoISvirKBapxs5ey/kpbrRog3lXdawf3n0UBTroAapRyO/qPNtM+NUD0+LgpMZpFVB5Po/C+3pXIF1Ry4mnOtqLuRBpLTTPkW76oziPSYHUFcipkjU/xZNcSbuf08WfDX++n7TGu9gbX/O5Iv/ckNPJ0UbB7lw9bTKVQd/oDv1sXy4/Z4Bv4cSY2RIVQubMXUHs9OXPQyzPkrngIeSuZ+7I73o3TxdyMMFPJU0qGGPX9+7c3IFTHEp2RezatezCrbCe1tRakQWDHIC4VcoNKwYAYviWL65jjGr4klJMaEk62MBzEGaox+QPWRD2jyv4fIBNgx3hd/t7x5B/o1cWTRrgRCYjILt2Apg/Xr3kT6NMo/968qRdRYqQR2ns9+fDhzW8u9SEO2GVnfdgQh509WVKxYESsrK+rUqYNcLicgIIDJkycDMHDgQGrVqkXNmjX55JNP8vFKJN5EfF0V3I00pAvDrSvbceQfDWFxGS0Q10P16bGnMUkm9l5M44NqWVteKxSyhGD8WxD8N/sua94IN76kNDOtJodhZy3jwqwAVg32YuEAT1ZvqA9AlJsbe369xJAKyzi5+dbr7WwuIpcJLP/SE3trGaUGBzNoSTRz/45n9MoYSg0O5p9QA1tH++Sr9eyzX6IY08GFL1s6ZbDC3o8y0LKiDeM6uTJwcVS2x9csbs31h++WvPAiuNjJ2THWhzO3tZQaHMzw5TFM2RDHR/Mi6Dwzgn6NHKlWVE3NMSFUHPaA0oOD6TzTUm3hr9FPshvnB7VKWKFSCCzZl7UBw2wWGfNHLF1r278RFtwygWp8XOSsP579AsrlYB0X7utoX+3dlEf+zTvtonzs0UTo46JgcGtnvl4azZZRPuk1r4KjDZy7q2XxQA/GrIyhiLcy08pGrwYOdJ0ZTv/Gjjhm4xqxYEcCnWs7ZKpLm1csC0pEIYeFAzyQywTiU0zsu5RG0qNVzi93dueL/kdg2x3Ob7vDwN9bElD6zSzm/jQB7kq2j/Xjwj0tuy6kEZNkolIhKyZ3d8sxBvpZmMwi/4Tq0ehE/FwV6b8/WDIe/nEoib+/8c3g5qRUCOiMFgGopL+aRmVtWHMkmYH/KrOgM4jI38HkiDZqGfP6eRARb3xUF9dM6QAVYzq44OmkwGC0lJ4yixYhNK8zdFcsZMXgVs60mRLGqA4utK1ii5VKhkZvZsvJFKZujGNke5c8j9F+GkEQ+L6HG33nR6BWetCwjE2GZ+j0bS1950cwtYcbSoXA9Yc6ft2bxI6zKSRrRLxd5HSp7UDP+vY5ul6/ybxMDC6QoTQQwDfffANA79696d2796t2S+IdIcBNSWl/NZtPpdC5lj0ONjL6NXKg/6JI/vzaGzsrGSazyG/7kpjfzx2N3ky/RZH0qGef7ZxRNlCNt7NFEMwuwd25u1quhujTvVReJzO2xlMmUM2Mj9wQBEvI1e6LqcSnWOb7sb83puX/QugXd4U1E4+xZuIxPl3UhOLVfV5zz18dK5WMBf09GNPeyPrjyUQlmXC2k7P9Gx+KZVOC8WVI05m5EWbAZBYp4qXMMuPxhXs6IuJNfFQ/8zPzWF7oWc+e6ZvjOX9Pl+XiiNYgvvOJfZ6Fp5OCTSN9uBWmZ9u5VFK0Io3K2PBjP3ccbSz3/duuZh7GGpHLBALdFa/lngmCwPKvvGg9NYw7EQY+beZIoLsSURQ5fUfHD5vi0RrMLPrkzZGtv+/hRqcZ4diqZbxfKaM8cv6ejm6zw5na3S09H4jZLLLjfBq/7E3k1C0tZtEyPn7cyCHdk+9t5p0uEzR3t4kDV9NYM9QbBxs5P26P56edifRu6ED9UtYMXx5DMW8loXGWQvGrBntlqcSOXRXD2btalnzulSEJks4gMvfveDaeSGH7ON9XUsKeF1EUqTk6hNl93SkbqGbCmlg2n0yh1nvWuNnLUS7YzpYa9SgToMLXTkS9ZC8AbgEODP6zLXLF619pyi8MRpFFuxJYuj8JtVLA0UbG3UgDVYtYMbStMxULWXH+rpavl0azf1LGlPRavZmKXwfz91g/Cnkq2X42lZUHM8cAD14SRSFPJV+9YcW+31UOXk1j0c4ETt/W4WInIy7FTPViVgxs7vRashiKosivexKZuTUelUKgXilr/N0UHLqmJSTGwJQebrxfyY41h5OYtC6Ojxs58GEte1zt5dwON7B0fyJ7LqXxxxDvPFHO87pM0J/XE3Lct/N7Trl+fom3k5ctE3T4moY+CyLZMsqbUv5qTGaRr5ZEc/QfLZ80deDsbR0PYgx8WNOOn/ckUa6Amp8HeOQoFJ+9o6XjjHDm9HGnTRXbDILgyVtaPpoXwfc93GiXjRU4v9DqzZQcHMzeCX74uSqYuDaWFQeTqV7MCh9nBcKP29lUrS6B7gqalLNhUAMbJrVcj/FRzP/wP1vjU8zlGWf57xKfauKHTfGsPpKMr4tFkbobaaBVJVtGtXfOkIV73rYEQuOMTOuZ2ZPsToSBJpMecm1OIBP+jMPHWc6gf8kEoihSb9xDxnZyoWm5/4YF7V0gPN7IvG0JrD6SjL21DJ1BxFol0K+xI580ccw3w9bzkKYzM297Aot3J6KQC7StaouLnZwj1zXcCjcwpbsrHWtYFmiMJpF+i6K4Fabny5ZONKtgg0ImcPCqhh+3J6BSCKwZ6vXGx4vnNK+80wqu0tqBsX/Esu1sCh/Vd6BBGRvuRuhZvCeJy8E6rFUCFQup6dXAkeYVbLNdrTCbRWZujeeXPYnULGFNCV8VCakmtp5OpUyApeRQVrG7eUF0kpHao0O4ODuQLjPD8XZR8G1XN9weJdEaXWUZfbd0pe+CCCITTNxcUJAr+4NZNfIAAB9OrE2FloXzpa+vE4NRpM+PEeiMImM7uaZb5tN0ZjYcT2Hqhlh+7G/JZv2/NbHsGOeXqY1v18YSHm9kwSceBF3RsHBnAuuHP1kVvxWmp+V3oRz/PiD9/uc2jy2UJ25qMZnhPT8VnWtbFKT/MnEpJhJSLKv5zrm4sBSVaImpMZlF3vNTU8gze5fr4zc0jFwejVmEeiWtCYk1cuKmFoUcBjR14vMWTijkAiduaui/MJLNo3wyJaED2HIqhfGrYzgyJSDXXZ0kBVfiTeFlFVyADSdSGLo0mg+q2NGxhh121gIbjqew7EAyWoMZDwc5FQtZ0a+xA3VLWj9X0qFTt7V88UsUoggtKtqikMOhaxpCY418/6/M+a+Li/d1DPgpiqNT/OgxNwKzGWb3cU9faB9SYRmDd3ej6+wIohNN3JwfiCAIRN5N4PsOW9LbmbCrI04eklL1NLHJJlp8F0q1olZ83caZAh6WsT4mycTPexJZFpTE32OeWIpnbIknWWNmYpes40Db/xBG/VI2xKWYsLWyJPh5mh3nUhm5MoYL+VwzVRRFDl3TsOV0KskaMz7OCrrWsX+nEmPmB1q9mfAEEwoZ+Loo8uw3NJpEjt3QEpdiws1eTvViVs+0YIuiyJy/E5i7LYGKhdSU8FVyPcTA0ZtaAt0UDHrfic617DPoOBPWxHIpWMfqIV6ZKjyYzCKfLY7CaIYln3nmyXXmFv9ZBVdta7nYy8E6lgYlcelRSvcKhdT0buCQqWzQs0jRmNl6OoWHsUZs1ALNyttSNBfdZJ6HyAQjDcc/ZFArJ/Zftlh+nn7RRldZxtTTvTh7R0uryaFsH+tLhUJWmE1mFvXdzsNrsQCM3dMZW6ecU6C/zczfHs/Bqxr+GOKd5cLFmdtaeswJZ9tYX5pPCuXczMBMykWKxky7aWGUDVRhayXDZIbJ3d0QRZEDVzUM+S2KUe1c6FInb5Jc7DyfypDfoihf0IrmFWxQygWO3dCw81waX7Vy4suWTgiCQES8kZWHkth9IQ2Nzoy/m5KudexzXLSRyEhYnJGJf8ay/3IalYtYoZTDmTs6SvqrGN/JlbL/Cl04el1D/4URzOrrQbPyT1yBzGaRbWdTGbE8mmVfeVO1qBW95kXQqKwNH9XP/jn5eH4Etd6zpm8uxxDntYK77p+EHPftVMIp188v8XbyKgouWOa+pUGWkjw6g0gBDyW96jvQqIz1Swuboihy9B8tx29qMZlFygSoaVb+2RmW84tzd7UMWhLNFy2dWLwnkR1jfTOUIhxSYRmzz/di14VUes2LZPkgzwzWwdtnIljQfxcArn52DFvTBivb/MkV8qbTZ34k3s5ypmRTJWNpUBK/7EnkyGQ/BEFg6+kUFu5MZOc43yz3vxdloMW3oSgVAuM6OvNhLct4rzeKrDuWzNjVsawZ4k21Yvknd92NNNBjbgQms0i32va4OVg8h1YesngBLBrgkSGu9X6Ugd/3J7H/chp6k0gRLxW9GtjTuIzNO5mp+03CbBZZsDORBTsS8HRS4O+m4EG0gdhkM1+0dOTTptlnC//mjxiOXNey9EtPCno8eb9TtGa+XRfHkesadoz1TS+vlqwxU2pQMMen+uObTd3sVK3Fe+Twd34E5FFuldzgP6/gvksYTSLlhtzHWiVj/iceVC+W0S3zsYK7dH8iv+1LpHxBK+Y9lZI97EYcP/b4C4C6PUvR4qvK+dr//MBkFqk6/AG/femVY7a4TxZGUrWoFcdvaKha1IoBWZRzStaYGftHDGuOJFO1iBXezgouP9ChUgiMau9CizzK2nvgShqfL45ixWAvKhbKOCGGxRnpOiucD2vZE+CmYNjSaNpUtaNDdTscbWVcfaDn9/1J6I0ifwz1emvjO/OL0FgjraeE8mEtewY2exJrrzOIrD+WzOT1sSwb5J1eE89stoQJfNfNlcbZuJr9fSaF6Zvj+XuMD2WHBnN5ToEcE2Tsv5zGzC3xbBubtfD0suS1grv+GQpuR0nBlXjEqyq4/0USUk2UHfKAwl5Kvm7jTKvKGcebxwrud+vjOHFTg61alqnECsC5HXdZMeYwAMVr+NB/biPkyjfb9TAviYg3UnVkCJfnBKTHff4bs1mk8ogQFvR3p0ZxawxGkVKDg1n7tXeWdVrBsijdc14ENioZtUpYo5TDsRtaivko+bara6a5PC+JTDDScEIog1o50b+xQwblSGcQ+XJJFFGJJjYM90YuE/htXyIT18XRvY49H1Szw1YtcOa2jsV7EnG1l7NqsNcbkUzpXUQURQb9Fs21h3rm9HGndMCT5+vCPR2DfoumWlErpvV0zaTkXnmgo8P0cE58759lCSVRtLgiF/FSMrq9JVxh3bFk1h1PYW0WY8XTDPk9mkB3BYPf4BC8nOYV6Wl9y1DIBdpXtyMiwUjVImpEUczwAdAZzPy6N5FPmjhaAsfNYvrHq6gzU059RPVOxTm04iqjqywj8m7C672oXOZGqB6lQnhmKvR21e3YczGV4e1cmPt3PLvOp2baR8ASg9Ghuh0DmzvRvKIN8/t7cOBbvzxTbkVRZOKfsczu657lhOjjomDVEC9mbIlj+PJo1o/wYXovd6oXt+Y9PzUda9rz9zc+1CtlTc85EZnK2EhkZPTKaHrUc2BUe5cMMfhqpUD3eg7M6uvBwJ8j07OuHrqmwVol0KisTbZtvl/JFoNRJOhKGk428mdmfwxwUxDzHDX33jQEQcjxIyEh8fI42cppWcmGc/d0NCuf9XiTlGZmWVASY9q7cPxm5koRABVbFGL2+V60/KICN46HMazqCtZPPZFjCZR3mf1XNDQsY52tcguW0o8datix64IlS75SIfC/zq70nBfBrfDMmZBvR+gZvjyGuX09ODrFn0417WhT1Y6/x/iw7RvffFVuARbsTKRFRRs+aZLZ8qdWCizs70Fskom9l9LYfjaVGVsTCJrox5TublQtYkUpfzW9Gjhw6Ds//N0s5Zok8oadF9I4cVPL5pE+GZRbgPIF1Wwd7c2eS2kcuKrJdOyve5Po29Ah2/rAgiAwtLUTS4OSMJos73tMsokAt2cbPgLcFMQkvX1yyWMkBfcV+CdUz7pjyWw4nsy9qKzLl+QFXeo4YDTBj9sSMRhFzGbSPwAfL4ggwE1BqQA5RrNIstaQ4SOK0HZEdb7Z9SEAczpvYfEnO98ZRUhrENNdMXLCwVqG1iBSwlfFisHejFoRQ+spofy2L5F1x5IZ90cMlYcHE+iuZO7HHrSsZEv76vZUKmyVp8L7+Xs6NHqRxjkoUH6uSuysZDQoZZNlciJBEPimowsmMwRdybnM1X+ZkBgDJ29qGdAse4tSs/I2ONvK2X/Jch/P3dXRuJxNjs+AIFgU4FvhBhLSTOm1trMjNM6Ii93bNxy/TJkgCYk3mashOtYcSWbtsWTu5+O8nh3D2jgjivDHkaQsFdIus8JpVdmW9/xVPEtfbfJxWWad+4hqbYtwdO0NhlZczsFV1/Ko528uWr35uWQEx0cywmO61bHn6zbONJoQSu/5kaw6lMSqQ0n0nh9Jw/GhfN3GmW517PFzVdCumsWrqvhriHU1GEVWHkpiYBZeaY9RyAUGNHXk9/1JTNscz6zeblnmnJDLBOb2defqAz0XH4X5SeQuv+xJZHAr52wXwh1t5HzZwpFf9mQuWXTmjpam5XM2tpTyVyOXCTyMtZRXc7GTp/9/TjyMM+ZL8ty84u2TqN4ALtzT0mZKKJ2mh7H3Yho7z6fS8tuHdJkZxp2IvK9xVsRLiZuDnM2nkqkxOoQfNsfx275ExqyKASyK25yPXTl5S08J3+x95+1crJl6uhftx9bk3vlIvqm2nH+OPMzz/uc1fq4K7kcZSH2GUnH5gS49S2Klwlac+iGAAU0duXRfx75Ladhaydg9wY/pvdzzNZb1RqieykXUOca8RCUaSUozY2uVs5LVq4EDfxxKzotuPhcR8UZO3NRw4Z4WvfHNW0A5fkNL/dI22KqzHwoFQaB1ZVuO/GNZPRVF8bmK4wiCZaW8VnFrNp/MvjYdwB+Hkmlf/fUntXlRhGd8JCTeFk7f1tJ0Uigdfghn98U0tp1Jpf74h3ScHs6diNen6BbxVlHYS8nMLQlUHRnCD5vj+Wl3Ip//Yqm3WrGQmhm93Dh0VUOZgGcrU4Ig0OV/tZh+qgeFKniwecZphlRYxqV9wXl9KW8MBTyUXLz/bFntYrCOQPeMlq7eDRy4PCeAGsWsOHhNw8FrGmoUs+LynAB6N8ibfBwvSkyyCZkgUNgr59jJasWsuBSsJzbZRNNy2S+oK+QWWWLla5QlssNkFrkUrOPYPxqCo1//gtSL8jgJWNsqOSupbavacehaZguuKD7fXCsT4LEE1ryCDcf+0RIRn72S+zgha7u3UC55jBSc94Kcua2l59xwJnR2pUP1J1nJNHozKw4k0XZqGJtG+uRp8im5TKBXfQeCow30aejAtrOp3Ao34OUsJxqY0sMFvVFk+YFkxnZ6tu98lbZFqdCiEDM7bGLZkH0oVDLG7umC2ibj4JisMbPrfCoxyZbstc3L22RbG/h14uGooHoxK9YeTaZPw6wtc0aTyNL9Sczt96SGmVIh0KqyHa0qv94XWiEX0BlyVgYj4k04WMueWX+2qI+SVYeevVL3PKRozBy+rrFkYnRRUKO4FfJslPBL93XM2BLHiZtaingrSdWKxCSZ6FnfgcGtnDJl7XtdGIzic6X5VysFDI9uY/mCan7YFM+YjtnvL4oiB69q+K6bKxUKWvHZ4khqvWedoezEY3acS+XoPxp++CjrZCcSEhJ5y/EbGrrNiWBqdzc61rBLTzKVpjOzZF8SLb4LZftYH4pkkQU9P/ismSO7L6bxZQvLvxEJRkr4qrgL/2fvLMOjuNowfM9qdOOOU9zd3YsWl+K0hX4Uh1KkAkWKW1toKcVdikNxd5dixeKebDZZ3/l+LARCNkJIsHL32otmZnZmNpk957znvO/z8GNXT8xPBGq+aJz52maZXMpXS5qRpNYztd1W/hxxGIDBS5uRr4y3zfc8iDBy8FoSOqNIQR85DUu/PYJcL0OdEvZEJ5g5e09H5Y9spw5HxJvYcymJqZ+mbpddHKRPftdvZy25XCpgMFrL1tLLNNI/OaaQvyJDEaki/nIuP8iZFVyLReTMXR2PIq0CrrWL29v0In4es0Xklz3xLPo7HqVcwN1Jyr/hBkrkUjKyjRu1i79+28CsIIpgMpPhOMROLthcJCibX8mh60lp1oUD3AkxoDOKBDxRX3dxkNKttjODl0SyYrBvCuE6sP49vlkZTe3i9ilEq941PgS4L4HFIvLV4ghm9PKieYWUQZC9QkL1ovZsP59IkwlBFPJTUL6Akl71XXIkRaVvQxUtJoeQ+2oSX3/intzJjJtoDURGLY+hgI+c6kUypxQtU0j5ent7HlwK57fP9/B9ndU0/aoCdXqUxGQWmbwphpVH1FQtbEduTzln7ugYtyqKjjWc+a6TR6ovyJtmeGt3uswMoVguRSohLpNZZOiSSPL7yNPs3N4k1YvYM25VFIl6S5ori072EmI0ZmoUS3n/8YlmNp3WcDfEWodsrxBe2cdMZ7AwaWMM604kUCafEg9nKXdDDcQnWhja0o1udVLOWh9/ojA86hN3fu3vk/wZbgcbmLI5hs4zQ1kz3C/D4Dwz6I0if19O5FGkEXuFhIZlHGwGkWlRyF/Bz7vjMhwInLuno8YTn926JR34ekUUx//Rpum9e/BaEiazSM1iVsuSoS3daDEpmP5NXOlYwwkPJyn3wowsO6TmrzMaVqbhwf2286HO9gPvOmaLyBeLIvj1c2+alku5iuKglDCwmQuPoow0mxiCv7sMmdRqC9a7vorcr0ldtFttZ1YdS2DHhSQmdvFIHgwPHWsNwof+GYlSJtCq4svrQjiolEzY15GYEA0Tm29ibq/dAIzZ+gleeaxte2isicFLIjl3T0ezco442knYckbD0D8jGd/Bg661nLPvw74GpBKBce3d6fdLONtG+ydbBD0lLtFMtznhfNZQ9U5a8nk4S/B3l3H0ppY6JdJemd16LpHyBZQERaeus7RYRA5c03LoehIGk0hUghlFDjzuG08lMHlTLHIZlMyjJC7RwsDfI2lb1YmJXTxsCluZLSJ9fw4nIt7Msq98qFDQOg4ymET+Oquh78/hTPn0medrdqM1WNh1MYnAKCNOdhKalHXIclsgkQgU9pdz9q6O6kXTDsrP3NXZzMjs20BF97nh9GvokqYI2LydcfSo65xinP5DZw96zg+jycRghjR3pWk5R6QSOPzEBzdJL7JpZPoiVG87H1SUX4IjN5L4bm00hybkSjGws1hExq2OZsd5DZ/WUfHHgXgmdfPkXoiRFUfUdKnlzNj27tk2GDSZRSyiSES8ia9+j+TfcCOfVHHCSyUl+LttbKxUm4Zl7BjV1sVm4OnrokRnSjt9114mYe3Yo1zbb01Zimlfmzipkjl9vPB1ezYnEqk2M3JZFGazyNJBvm/dTO6h60kMWBhO9aL2tK3qhMpBwvVHBpYdVlPQR85vX/qkWfOgN4qcuKUlPtGMl0v6q5U5Qc95YZTOq2B4a3eb+/ddTqTX/DBWD/OjTgkHRFFkwe445u2Io25Jeyp9ZIfOKDJ/ZxwSCawc8kwF+GXQG0W6zg7FzVHChC6eyf6LABf+1TFocQTtqzkztJU1U0BrsFBhxGN+G+BjM/izWEQ++yWcAr5yxra37SeYGURRZPH+eGZvj6N4bgUlcitQJ1nYfTGRyoXsmNHLC2+XjOfvRFGk7vggvu3oToPStgeHgVFG6n8blMJK6sDVRAYtjuS3AT7Jge/T8x25oeXLReEsHOBD7eLPBheXH+hYvD+eHecT0RpEfFykdKnlTK/6Lvi55cxcY06rKG+9m7om6HlaF3o2+fFBOfe/zduqorz3ciKTN8VyZGJqL3SdwULfXyK4EWggPM7Er1944a2S8dfZRNadTGB8e3f6NXw9nyU20cyARRGcu6enY3UnAtxl/Dt2K1uq1KZ+KQfm9/V65clMgMCb0czqtgMAe2cFn69sRas5UXSt5czg5q4pJiYvPdDTc34YA5u58nmjt+dvmlkW/R3PpI0xtK3qRLPyViu+oze1rDiSQPtqTkzp5pHhyqbZInLylo7weBMuDlJqFbN7KzKU/jgQz6ZTGraO9rdZYhUaa6LWuCC2jPKj3fRQtn3jn+yNe+FfHX1/icDZTqBNFScclALT/opFqxcZ1sqNka1ds2U8+9u+eObviuPXz72pUfSZtklEvInv18VwK9jA9m/8Uz3Xi/fHs/6khu3f+Ntc+bwZqKfpjyGcnZo7xZj1VRFFkXk745izM46y+ZQUz60gJsHCrkuJ1Cluz+zeXlmaEPl1bxwnbulYMcjH5u9VFEU6zAijRUVHm2nwQ5ZEcjvEwNKBPvi4Pvu8eqPI9K2xbDmjYd93Aanqac0WkS1nNCzer+bsPR2iCKXyKOjbwIVONZzeiuc4Iz7YBGUTUzfFgACj26YMOn7aEsPRG1rWDPNF5SBl2JIISuVT0ru+C9EJZjrOCKFtVWf+18w1W+4jJtGQIuf+VrCBvy9p0egsyBbswX5CTVyd0m58DGYLToq0v4Rdy+fCSSEjNkTDjE82A1CsXh66TamT/OWzisgIGE0iraYE07eBC+2rv32zuAlaCxtOJrD/ShJag4W8XnK61VFRsaDSZkNiMovM3h7LnwfVFPSV4+Mi5WGEkRiNhf81c6VPA9VrWbUKiTHRclIwHV6wrjGYRDadSmDC+mi61FJx/B8tW0b7s3hfPJtPa1g11JdcHtZZviM3khiwKILJ3Tz4ZmUU64b7p/JzzYhFe+M4cC2JNcP8bAb44XEm6n0bxJav/SkSoGDtMTXbzyeyamjaM3/3wwy0mBzCpZl5M5UebIsZW2PYfi6RxV/6pCgHSNJbmLM9lh3nE9kxLnWDbosDVxMZ/Eckq4b6pVLeDo010Xmm1ZLpxe/vgauJjFoWhZeLlLolrZMMB64mEZ9kYUYvr3Rnzi0W8bX4Cn4IcD/wtvC2Brjfro3GyU7CqDapy3k++zUcnUFk8Zc+DF4SSbXCdvR8MsB8EGGk5eQQJnX1oHXl11fWcjfUwObTGmI0FgyzdjLk764vlbWSWW4cC2LxoAMASHxdmPpXS+TK1O3pwwgjtccFcWF6brwyMan4thEaa2LZYTWnblsH+CVyK+hTX5VhmZkoivxxQM2cHXG4OUoo4CMnLM7M3TADfRu48HUbtzc66W8yi3SdEwbA1E+fCUiJosjpOzr+93skn9Z2ZlgrN6b9FcvJ21rWD/fjbqiBllNCmN3Li1aVHBEEgY2nEpiwPoYdY/zpNieMpuUdGdvO9uR7ZgmONlFtTCBHJ+ZKtYL+9D57LwinkL8ixbVEUaTK6EBm9vSiVjppyEOWROLnJuXrT17tPp/n27XRHLmhZcn/fFLUNyfqLEzaFMOh61r2jPdPV53bFglaCw1/CKJVJSe++cQtxdjAbBGZsD6Gg9eS2PttAA42svrMFpFJG2NYvF9N/VL2FM2lIDrBwpYzGkrnVbDwC+8MJ/ytTiy8c37HHwLcbOLHDdE4KiXJq1UA6iQzFUY85tikZzNFo1dE8pGvgn5PZjTvhxloPimYizPzZkta5osB7vNMq7sG7xm1kKZzmcwGuACdZ4ZSMymE0B03AOjwQ03KNi2QHOAC7Lyg4Zfd8dnu4fm6iYgz0WtBGOokC0NbuNG2mlPyZ7zyUM/QJRHULenAtx2zvvL4MoTEmPh+bTSHridRuZAdMqnA+Xs6igYoGNfBnbL5lYxYGsn5f/U8jDByYkpucnnIiVKbWXVUzcK9cSwZ6Eu1IvasOKxm2zkNG0b6Z/r6omj1e53T14sqhdLuSKZuiiE+ycyU7l70+zmMJuUc6ZDBZEfjH4KY0MUjVfp4ZngQYaTZhCCOTsqdZqM9YmkkDkqBCV0yV9e647yGEUsjqVjQjqblHZFJ4eQtHbsvJvJVc1e++tj2jLXZInLwahKXn6hLVihgR92S9m9NJ5HTAe62DALcVh8C3A884W0NcMeujsJbJWXwC16Pd0MNNJ0YwvU5ebBXSBj8RySl81lXN55y8FoSY1dHc3JyrjeSrv/UBzeniE8y07rraco9ugeAe4ATY7d+guSFAcbA3yPI72P16n3fEUWR8//qmfZXDDcCjczp40nj5/zQ74UZGLEsCpW9hD8H+rzWzK8XMZhEpm6O4c9Daor4K/BSSbkTasBgghGtXOlW29o+m8wiPeaFE5toxmAS6VDNif5NXAmONrF4fzwrjyaw5Ws/SuZREhFvouLIQM78lPuVMo9+3BhDXKKZGT290jzmnyADraeGcGNO3uRV6MAoI/W+C+bugrzpfueO3dTy/fpoDnyfOjMjK9wM1NN6qtVv1tYqrShaSx3yeMoZ1/7lg+rwOBPd51nTrrvXccbfXUZglImVR9Tk9pSx7CtfPFXpB85xiWa2nEkkKNqEs71A8/KOOaoH9DbwwQc3mygaoODUnZQqZtvOJVKnhH1ycCuKIqdu6yia69lDVcBXQam8SvZeevfsWi7c19FtaFm+PdQFgA3fHWdsleUYdM+EixqVceTifd0766kXl2hm4O/hVBr1mDshRkrmUTB3Zyw1xwSy/ZxV/bZMPiWbRvmz9YyGc/dsew1mN/7uMn770ofjk3PzaR0V7as5sfUbfzZ97U+5AtZ0nhm9vCjkL8deIVD/2yAqDH9Ela8fcz/cyNZvAqhWxBpAdqjuxI3Heu6HZ15lMCLeTFyiOcM65Y8rOHLytvV3ojOImTKDd7aTZCiklRbLDsbTuaZzujOSAz92Zf2JBLSG9JW0n9KiohMXZ+bl4wqOnL2r49hNLUUCFJz+KQ+Dmrul2ZFKJQKNyjoyso07I9u4U7+0w1sT3L4OPtgEfeBdp3guBSdup27TVx9NoFttZ+wVEkRR5PgtLSVypxws1itpj84ocvH++2mfcuOxAVm5vMy62IOanYoSE6xheMUVzOuzO8VxTctZ2833nf1Xk6g5Noge88I4dlNHydwK+v0cQc95YYTHWcdEH/kqWDfMj6BoE+tPpq+en9MoZALfdvTg5ty8jGjtRtuqTszt48WFabmTg1uwClsuH+RDvZL2XLivZ+qWWEoMfkS1MYHEay3s/z4g2Z/V20VGu2pOLDuc/uRmRpy4paVFhfRrxovlUuBkJ+HecyrmT8cYGU0oOdtnfYxhi8UH1PSun3ZNttVv1o1lh9QYs+AY4eMqY+94f34b4E1YnNWfODrBzJ8Dfdg5NiDD4Basvtm966sY38GdIS3c3vvgNiPevXySHESjtbDxVEKySmA+bzmf1lYlp3W2qOjIt2uiuBmop3hu67agKFMKEakjN7QYTSI1iqYMCooGKAiKzj4Jc3M6nrUiIgnpWOQopKDWp62sK4rP9lsskGg0I3WQ8PXRrpxd9w+Hfr7E97VXU7VDEVqNrILwRHzcmnb57DzvggBNfKKZ1lNCqF7EjmK5FAxq7sbHFRwRRZETt3R89XsE6iQL3eqocHOS8lljF5YciM9SPWtW8XGV0ay87a+qIAgopALfd/agfikHkvQiXi7SVOJUdgoJFQracSvIYNPrzhZGs4hCJmT4d1TIheTn0Wq/oE8l1vI8eqPIP8GGLKfVXXqgZ2Sb9GdI83nL8VJJuR9upETuzKVlOygldK2tomvtt8Pq4d3g7f+Of+C/idZgYfNpDbsvJaHRWcjlLqN7HRWVC6UsT/mkihNjV0dzJ8RA4ecGhIHRJho88SL/+0oScqlAlUIp231BECgWoCAo2kSFgq/nc71OLCJIBGsf0G50FdoMr8SIyit4cCmCoeWWUatLMdqOqoxEsB77PrPtnIbhS6OY38+Lzac1lMyjZFBzV9RJFubsjKXRhGD2fxeAt4sMpVxgRCs3ZmyLpUvNN1+6Za+Q0LB02iUzYA1yy+W3o3YxOxb190FvFPFxldrMOqxZzJ6tZ18teDeaM+9gYDI/e7h83WREqc1Eqc3pBn2XH+ptpj5nlQv/6pnZK/2MsKIBCuwUAo+jTBlaNNlCEAQqf2T3Voqfvot8CHCfcPBaEl8uCqdqEXvaVXPGyU7g6kMDPeeFUaGgkvmfeWOvkPBtRw96zA1jzTA/CvkrcFAKRMRbFegu3tcx8PcI5vTxShUUxCVasu3LlqRPrXj3PBqdBWk6tR/hGiMqu7T/9FMP/ouz0rrf1QW+2xpIoTxPduZywGlSdTRjT3J6w21Ob7hN9SnNKJZLAQhYLIDZjET+bigPztgWS/kCSr7r5EHBAQ9oXNbaCQiCQM1i9mwc5UfTCcE0KeeIOsnMnRAD289piFSbqfSRHT3qqnJMICizSCUCJjMZ1lgYzSISQWT/lUSO3NRiMIp85CenQ3Vnm5L83i4yDCaRBxHGdKXiT93WJotTdKvjTKcZoQxunrYV0LZzGorlUmT5+5Bp3zeJwDuaVPDO8B9arP7AO8SZuzq6zw2jZB4FnWo44+4k5Z8gA18siiCvl4zlg3yS6+QclBK+6+hBhxmhbBrll2wF5KiUEKsxc+autV5xUX9vm5N9sYlmm3Vx7wPFcim4FWQgNtGMm6MUqVzC7Es90SYYGFN7DcfW/MOxNf8gaVCKMlXzpXyzxQySd2MckBGJOgtf/RHJ1q/9KZtfybClUclaLCoHCd928MBkhm/XxrDwC6vFUqm8Ci490NNicjACAqXzKehdX/XGrKYyg1QCZgsZjmmMpmcaEnqjVbn4/L/WLIay+RS0reqUYTle8VwKTt7WJWeZ2SIi3kRwtCnFZLizvYTmFRxZdlidZkq8xWIVofwuG8vJRFHM1LhDEKyLTB9487yfrfJLcvG+jv/9Fs6ywb4s/cqX1pWdaFDakaGt3Dj9kzWy++qJqXrnWiqGtHSj+aRgeswNRaOzsPqYmnY/BfPp7FB+6uFJwzIpV68S9Vb/2KfB07tE6cJw8aZ1JfcpgkRg9Jam9Jf+BcDJb3ZT8+wZAKTr12JXtwbExLyBu305kvQWNpxIYGhLN8yiNVB8URSioK+CZuUd6LMgjBaTglHKBOzkEr5s4kqsxkydcYEs3Bv3Zj7AE6oUtmPvpcR0j4lLNHP6jo4xq6KZ9lcsXiophfwUXLyvp9LIx8zeFpsqxVwhE+hUw5nf0vl8BpPIkv3x9HgivFIsl5Iaxez5YmEEOhvpwZfu6/hubTQjbQi6ZJYy+ZQcuZF+un9wtInQWBMSCczeFsv41VHM3hb7UinaH/jAB9497oYY6DI7lHl9vdg8yp9ONZxpVMaBQc1duTA9N/m85HSbE4bluSXH3vVVDGruSv3vguk2J4zf9sUjilbhlm5zwpjf14sGpVL3348ijdwMMqTK2Hpf8HCW0qScA7/9HZ9iu72zgtmXejJ2Y30ALAeuET1pO3fOhFoPeLgW9lUH/ds/DsgMG05pqF7ELtlr1GgSsXth9XFwc1d2XkgkOsHMgt1x1BgbhCDAZ41UDG3pikwi0HhCMONWR7215Vxl8yu5+shAlDr9RZRdFxOpXsSObec0FB/8iDXHE8jrJSOft4wtZxIpPvgR604kpHuOPvVdWHJATVI62Ya/7VPTurIjKoeUocqwlq78vCeOv6+kHveYLSLDn9RANyiVfV64Vr9ZbbrH3AszkKgTye0hR6OzsOyQmjGrovh+XTQHriWlaHM+kPN8WMEFZvwVy5h2HjaFdJRygZ8/96Ha6MdcfaindD4ln9ZR0aaKE1tOa7j2WI/KQYKLo5RLs/KlSrkQRZHpW2KoXtQ+Wd32XeKjPHDjX/j7JNSvAgo52CfE4d6mI87X/8FUsRJGaUGITmRc1eV8cX8mRdXXUDZrjGHPPnDPPgW77OafIAO5PGTk8ZIjiiJeKilXHupTKenqjSI3Aw2cm56H4/9o+SfIQP3SDtQv7cDAj11pPy0Ulb0kU6mtOoMFmTR1IP0qfFLViR83RNu896dMXB+N2Qw/dvXk4+fqXvrhQlisic6zQjh9R4veKJKgs+DvJqNzLWc+a+RCq8khFPCNo19DlxQrGEl6C189ERep9ZxVzpw+3gz+I4JqowPpXldF2fxKEnUWtp3TcPSGlnn9vLMkLvWUnvVUtJoSwoCmrriloZI8b2csro4S2v1kVTDP5SEjKNpE8x+DqFbEnrl9vTNVK/wmEUWRwze0/HkgnlO3dZgtIkVzKehRV8UnVZyzrECdnQgfUpQ/8JYxa3scXzZxtVkmIZUIzOrtSe1xQRy6oU0RtPZt4ELH6s5sOJnAtccGpLKnq7vuNCuf+lxmi8j4NdF0r+383q7gAoxt507jCcF4u8joWdf5mcaAPobEi59xtur/aCQ7Tfzxwvza/28ARvcdg497CBxsBPX3gfLtHQdkhuP/pKwXteqx6GhX9Zl6toezlPIFlMzYGsPuS1oWD/Bm8JJIWlW0ilXWL+XA4BautJseytQtsXzTNvO/E1EU0RqsQXVOajy4O0lpUcGROTti+bGr7XTcG4F6Dt/Q0qqiIyOWRbFhhC/lCzyb4PlfU1euP9bTYUYoSQaROI2ZPZesLhZ5vOR0r+NMo9IOlM2vpFZxe7rNCWPZV74pglhRFFl9LIHlh9Xs+y61eGlhfwVrhvrx6ZwwyuRT06mGE+7O1iyNPw+qCXCXsXqob7b+rvo2cKHzrFA+b+SSKuB+yvyd8Xxa24k/DsQzdUssNYvaU6WQEp3R2lZoDSK/D/CmYsF3f0LMYhHZeyXJajF0V4fFIlIqr5K+DVS0qexk05rqdfOfV1EOiTFRb3wgl2blTbeTmr0tltA4E9N6pFZ8C4s10XpqCLWL2/NlM9fkdM67IQbm7YzjykMdW0YHZJtheGB0Upr1LgubbUD5YzV0xrRnxYwZpFpIBWvdzVNMJjhxUUpgqEB+fzNNjq1FoxXYkqcZdcNOMu/ceJLqNeOn8I+T3zPjch+EShXRHz0B0rSL8t8kZ+/q+G5tFLvHW1X25u6I5VawgV+/8Ek+Jj7RTJlhjyidV8lfo/1pPTWEPvVVfFL1WV3N1Yd6eswL5fz0vDYD17hEM8sPqVl+WE1orAmLCFUL29GngQstKjpmy+9hx3kNo1dEMaePFw1KOySfU6OzsHCv1R93eGu3VEqhYO28+/0cRoLWwty+3nzkJ+dOiJFlh9RoDRam9fBk9IookvQiHao746GScjfEwIaTCTQs48CMnl4205GvPdKz8oia++FGFDJrB9+huhOql5TQt8UP66I5eUvLkoG+BHg8m6czmkTm74pl9rZYWlRyYlZvrxSpUlqDhXGro7kVZGDz17Y99N4GLBaRkcujOHVLy4CmrjQr74hCBqdu61j0dxx6I8m2ZOmR0yrKu/5Nvw7r44LPBoBvk3LuB14/r0NFOUFrofjgR1yemSfd/vbPg2oOXU9i+SDfdM93I1BPm6mhdK/rzOcNXZLFJC/8q2PK5lh0RgsbR/i9Mb/InFZRfsrdEAP9fo0gNtFM2ypOOCnh4pl9HA8rxPCCCxmUfzGCRMLZ4P+xZlWF5PdN/N9AnHIXh0Yn3+l05d4LwmlW3oGOT9wBNp/W8MveOP4eH5AiiGo/PZTz/+r462t/ft0bR0FfRSr7qdBYE1W+DuTanDwZ2sncCzOwcG88605o0Bmt47ZWlRzp38Qlx4KkiHgTjX4Ipl01J4a2cEueCBZFkUPXtQz4LYIJnd2ZtDGWXz73tul3D9ZJ5vFrY+hcw4nONZ1xcZBw/bGBxfvjsVNIWDvMF2c7CV+vjGLjKQ3tqzlRMreC+CQL609qMJlFVgz2TS5/ssXTOvsdFxJJ0osEuMvoXseZqoXtcmR8OWxpJDceG1j2lU8Kf12DSWTmtljWn9TQqYYTm05pWDfcL4XeiSiKbD+fyJA/ranupfK+nGXj24TJbFWMvvHYwFcfu9C0nCNSiVWDaMHueOwVAmuG+maLL3dGfLAJSofTt7VM3BCTocXNwatJ/Lo3Lk2blegEM/N3xrLuRAIqBykWi0iSXqRrbWcGNnNN9jHNDh5FJaUZoP7abAPKidXSrcGN15nSrcFVp7FfkwR3H4HTnX+pcWwznzzaRd7E4BTHbPPvxCHvZgA0qSmlzqxuaV7nTQe4NwP1NJkQTOOyDjjZSahQQMkve6wKvYOau/IgwsgP66I5fUdLIX85eT0VBEab2DTKH8ULs1Mf/xjE0BZuNCqbcqY/ONpEu2khlC+g5IsmrpTOq8Boht0XE5mzPZaSeZTM7euVLTONB68m8cP6aIwmkQoF7dAbRY7cSKJMPiVXHuq5PDu1TdXtYANtpgbz+5e+bD2rwc9NxrAndS2iaG20d5xPZO+3AVx6oGfnhUQStRZ83WR0rOGcrSIOL4MoiszZHseve+OoXtSeErkVqJMsbD2rwdlewM1RxrYx/jZ/rxaLSIcZobSv5kSXWtkrKGW2iOy9lMSyQ/Fcf2xAIoHKH9nRu4FLCiP7jJi3M5a9l5JYN9wPpxdWmi0WkdErowiLNbF8cNp+w/AhwP3A28PrCHDvhBjoNCuMSzPypHvc5Qd6Bi6O4Pik3Bme82GEkTk74th8WoOnSorOKCKVQJ/6Kr5s6vpGJ8leV4AL1jb3wn09+64koTeKFJBeoq32E5ykqduA7Uc6cPBs8+Sfp5/5FFk61oRvM0HRJgYsCicw2kT1IvbUL2VPs3KOtJ8RSiE/OTN7epFksLD2WAJj10Tj4iDQqpITh2/oOPC9bS/2XgvCqVXMLoXl1Iscvp5E75/D6VPfhd71VeTykBGbaGb1sQTmbI9jbHt3etXLGUHE8DgTI5dHcfiGlnol7HFQSjj/rw5BgB86eSCXCUzcEMPhCQE2+7Rz93R0nhWGn5uUUW3caFXpWT9gsYh8syqayw/07B5n7aODok2sOZ7A40gjDkoJzco5UKeE/RsfI76IxSIyaVMsv++Lp15Jq99srMbC5jMaSuZRMLWbB40mhHByci5ye9oeGy05EM/284ls+Trzlo2vQmisiaWH1Gw8pSFGY8bTWUqH6s70qpe+E0V6TFgfzYX7etYO8001pjRbRAb8FoHFAou/9EnjDNnHhwA3HS7d1zFkSSRHfky/o9t+TsP6EwmsGJL+gFJnsBAcY1Ugzu0pTxUIZQdvKsB9nsL7t9FjzigkltQrxQapgq9L/Zb885g9HXByTz3L96YaL4tF5MeNMaw4rMbdWUKpPNZUmR3nE7n2SI+bk5TQWBOI4OooISzOjEImYLKItKjoyJw+3qlW+79eHkkhv2fex2AdEDSZEEyLio4Map565TRJb6HzzFAalXXgq4+zxz/wqUff3RADcplAtSJW5eTf98WzbkTqBnXQ4ggK+soZ3MKNLacT2HkhkcX/801xvk+mhtCrvgttqjilev+bRqO1pj4/jjThoBRoVNaBIX9EMrKNW6pa+Oc5cDWRaVti2ftd9njkgXU2uc+CcKLUZj5v7EKtYvaYLCJ/X07it7/jqVHUjuk9M57MMJhEyg17xF+j/dOU+TeYRMoPf8Tmr/1TKL++SE4HuHvupx/gNi3wIcD9gJXXEeA+jjLS4Ltg7mTgkXn8Hy3frXs5j0yNzkJwtAm5TCCvl+yN+ps+5XUGuDZ5uBZOdQPRRsaYIGHRvj+5dck6xPTI5cTYbW3fuqAlLcwWkbGro1l9LIEWFRzYfDqR0W3d2H81iTshRhb082LJQTVHb1qdM7xdpERrLJjNIggC+b1lrBjkm8Jl4ymzt8cSo7EwsYttEaTQWBPVxwSycrAvNYqmHjvdDzfSdGIwKwb7plL2zk5CYkzWz2cW+chXnrwyOm9nHKGxJqZ8ajuNudPMUJqWcyQo2oRMSqp0bItFpOa4ICZ19aBeyXdPmyY+yeo3GxhlwslOoHkFRwr7K5i/K47rj/Us6p92YKczWCg+5BH7v8uVaUeLrHLsppaeC8JoXcmJ7nWcye0h41GUieWH1ey8kMTqIb5UKfxyz49GZ82SOTk5N7k8bMcJiTrrZzz+Y9qBfnaRXr/yn6/BLZFHSYzGzK1gQ7qpEJtPa5ItA9LDTiGh4FuskpcdCGYzl2p9TOVTuyl+6kCq/fcq12L03115fCKY1aOPMLnpBgpW8qXvz43fwN2mZuKGGM7c1XJmWh7iNGZaTg6hWXlH1o/w4/D1JLrOCqNaUTvyesrZclaDh7OEDSP9yeMlZ8TSSHrPD2N6Ty9WHVWz+bSG6AQzoghVi9jRtLxDcq31qds6q/rix64278NBKWFKd0+6zAylf2PXbKlZEASBSh/ZpbAxuhNixGRDMyJRb2HnhUTOTrOudhjNpEqxFgSBXvVdWHVU/VYGuE42ap9vBOqpkUba1FNqFLXn+uOwbL2Xr5dH4WwnYfkg3xR/yz5Pavu6zA5lzo645BXytDh6I4mCvvJ0PewUMoGONZzZfErD6HZvrr7tXRmsfuC/QW4PGa6OEk7c0qWZOgmw8ZSGxmVebmDtZCexGaz8J3mqkJyvMzxeC0FbUx8T0JIvlvTAYhEZX28t0UEahpVfTrnG+ejxU53Xf88vyeiV0dx4rOfqrDy4OkrJ7RnDplMa1o/w458gA31/Cad5eUf83aTk85Zz5IaWknkUuDpK2TjCl1XHNLSYHMLebwMo4CPn0gM9v++L5+8rScQlmp+cU0aXms6p9CCWHlLTprKTzeAWoICPnGGt3Ph1TxxVCqWfZv8q+LvL6GzD5kgisboy2CIi3sSp2zqW/M+HaX/F2kzfl0gE+tRXsfxwwjsZ4Lo4SG2unl97rKdO8fTHHnYKCZU/suNmYOYtG7PCwwgjPeZb65trP3dPXi4yKha0o2XFJLrOCePYj7nwd898KLj3chKVP7JLM7gFcLST0LaKE5tOaxhioyzudfH+KiNkEoVMoEddFZM3RqfpLXv2ro5Tt7W0r/bm/czeNPYJcfQf3om2v0+i6JlDNo8peuYQyo3rKFk/L5PP9kDpIOPfc2GMqbycoJtRr/mOUxISY2LVUTUrh/jh7iSlgK+C9SP9mLk1lnrfBrHkgBpvV6nVDuh8Ih+Xd8ROIVA0wGo4vuAzb4KjTdT7NpAErcifX/lyfEoeJBLwcZHR6PugZIXfv85q6FLLOd0goERuJb5uMs7e1eXYZ7YqI+qJTkgZ5UarzajsJcm1arsvJtqczSuWS0FwdNq+yW8bEkHAnL4IJCZLyjrzVyU42sSeS4nM6uNlc6LCyV7C/H7e/L4vDq0NdenniVKbye2ZcYeTx1NGpPrd+bt84AM5jSAIfN7IhR83xmAw2e7Pbwbq2XxGQ8+6H/yus4Q+xqqQ/HCt9RW83fZxwdvh4VokEoFJR7rw00lrudKlvx8ytNwy9v1x9TXe9MtxP9zIhpMJrBnmm2yhN/oTN1pVdqLy14GsPpZA5Y/sWHk0AXulhFvBRnaMCWD5IF8u3NejNUKPuio+b+zCpE0xzNsZR6eZ1pTm/d/54+NiTd09fF1L3W+DCHqhf91yRsOntdMfb3ap6cSui0kY03jOc5LqRezYcynJ5pg5JMZMHi8ZDkqBHResasu2KJFbQWD0++VqIBEEm4sJL2Iy57zF3u/74/m0tipFcPs8jco48EkVR5YcVL/UeSPV1r9vRuT1khGZgRp3TvOfD3ABBjV3I1Ev0nt+GLeCDcnbtQYLq46o6TUvlF8+90lVC/fGEEVMFtsvAIsokqAzpfkSsaYhp/VKa78pMoqe3/Qi952r1N61ymZ6MoDEYsGlb0/0q1ahX7WKiYa5fLagJgC/9NrFmMrLEUURUSTNV0iMibk7Yhm+NJLxq6M4ejMpW6T1Vx9T80kVpxR1MSVyKzk+OTffd/Lg7F0dJfMoSdSLrBnmy8+fe+PpLGPpIWsjEBprtZ/J5y1n8qeelMyjZMn+eGoXd2BOX2+WDPRlwMJwHkYYiU+04OuacUPg6yolNjHnGgJ3JynNyjuyYFdciu0OSgkJWgsms8j1x3pO3NLSwcYkjtXn8d1ZqatUyI69l9O3Tfr7ciKVXzI1Jz02n0mgdWUnnNIRVcjnLadkHiX7r6RvcZScIp8BobGmNFWkXxdCBq8PfOB106eBCm8XKW2nhXDpgT55u8EksuFkAq2nhjKzp2cKkZgPZBJ9jFUZOfosnOxqfdlKTwbr9lPdrEEwoAjfyOypv/D9jkYA7FpwiaHllnF538NMXdpiEfn7SiKjlkcx+I9I5u6IJSI+Zyb4VhxW07WWcwoRKEEQGNnajSuz8lC+gJKgGBOCAP0aqrgyKw9VCtuR21NOneL2zNgaC1j37TyfyG/74jk8IRdDW7qx74oWLxcZnzVUsXqoL93rONNhRmiKYDFWY8nQi9bFQYpSLpCgS3/CNCcoX8AOT5WUtcdTWwE5KgVrXeppDTIpaQa4sRoLju+Z+njVwnbszsCyMT7JzNm7OsoXyFmRqbXHNRnWaPeur7L5N0wPN0cJITEZf++CY6ze2W+S9+vpyiJKucDqoX6UzKOk/bQQ6owLpMWkYMoNe8TOC4msGOJH/UykJ78uRASkgu0XWBtiZztZmi8BUNnJ0nzZ2u8iF/hqUn/y3L9pvUaK+0mNYLFgHjMWVZ8eyM+fw6VXa7zGlEBW2lqzMbbKCs5vu5vqfSazyDcrI6kzLpDAKBOl8ijwUkkZvzqa2mMDuf3cBERWuBtiTJG++xSJRKB6UXsSdBZWDvGlRhF7ohIsCILAr/19mLsjlimbovllTxxtqzkTFGUiKNrIqOWR7LqQyLSe1s9VrYg9nWup+PNgPJ4qKY8i05+hFEWRR5EmvFxytiEY18GdHec1TNkUTYLW2iF6qqQU9pcza1ssXWaFMr2nl81JnI0nNTS1YZPxttK7voqfd8fZ9OEFaw3Mz7vjslWgIyLenCnBrXzeciLi05/MqF3Cnn+CDDyISPvZMZlF1h5PeCvTxj/wgTeJVCLw50AfGpV24NM5YVQa9ZjGE4IpNugRyw6rWfylNx2qf8jGemksZjjcDGIvPtkgYrv3fw7RArdnw4PV1mA3+iwuV1sy+3RLRqxtCcCyUUcYWm4Zj2+knd11+YGe8iMDmbg+Bn93KaXyKrgbaqTiyEC+XRud7f6it0OMada2ujtJ+bKpK/5uMooFKMjlIU9Rjz2rlyfbzycy9M9INFoLEom1T5JLYcKGaGZui+WPL32SM7sGN7cKle17buLT20XKw3Taf7Bm+pjMIqo3tPCyoJ8X49dGs+RAfIpsibxeMgwmkcFLIvn5M+80M9jWnUigeYV3Z1yRGTpUc+LUHR1XH+nTPObXvVaBqpycYLNYRCLVZvJ7p3+N/N5ywjMYj7xI03IOnLylIyydSXitwcLGUwm0rfpmxycfAtwnKOUCoz5x59LMvMzp48W49u4c+CE3q4f5UeE98Kx6VUSplBOf9MIiSf3I2Gq+tA7OuIUFInmy6hpw7wbdx/fFvuNHOH5XBYDNP55kTOVl6JOeNeQjlkXyb5iRc9PzMKOXF73quzCohRuHJ+bif81caTctJMOgMT1MZmvd6YilkYxfE8Wx51aGBayrxxYRtEYRxZO2oYCPnJ3jAoiIN7PkgJoT/2hRay00+DYIhVRgx7gAvFTPGpJP66jYeFJDh+pOrD6akGbqO8CFf/Uk6S1Ueu4ZM5lF9lxKZP6uWBbtjeNmYNqNZWbxdpGxY2wA90KNVBjxiL4Lwhj4eziBUSbm74zjxy4etK6cujG6+lDP9nMaPs2Ex+/bwsflHfnIV0GPeWEEvZACFRRtpOe8MAr4yLO1c3V1kBAel/GsZnicCVfH9Jtde4WEXvVdGLks0maQLooikzfFUNhfQck8b9Zq4MMK7gfeRqQSgcEt3Lg6Ow+/D/Dhu47uHPg+gB1jAqhT4u2ZrH6nkEihyFAQXnLYqPSA092frfTGXoTDzQgo5MLsSz3pO7seALM/3cnQcsuIC0+5AnYnxEC76aF828Gdoz/mYkgLN/o1dGHBZ95cmpmH03d0jFkdnR2fELBazUWqTfx5SM3QPyP5bV888UmpgwCJAHqTiOIF3QovFxl/fxuAQiZQbUwgiTqRVUfVVBgZSEScmX3fBVDQ99lkqCAI9KqnYt2JZ4J9nWo4s/Rw+qmjyw9bs9FsWRPGJppZcUTNrG2xLD2kTlWelB2UzKNk+zf+bDmTSPHBj+j7Szif/RpOqWGPcbQTyOMlp2Ru2zXrx//RcvQfLZ1rvF8TTY52Emb38qL99FAOXkuZdag1WJi1LZblhxP4sattcbHsQiIRcHGQZBi8hsWZcU3D0zctXBykdK3tzNClkTbLQERRZMyqaGoUtc9xEa2M+M+rKL+LPIxMW0V54ccbUEysZrPRe8qrqCiXPrKDjtNHpJmenBYWiYSNI2dyrW4LAL6qmY8b2++xbdoZACq0/IjCPSrSY24ox6fkwVEpwdbE309bYgiLNTG7j/dLXd9kFvlubTSrjqpxc5QyqIUr6iQLm04lIAgCv3/pQ2F/BU1+CKJfIxVjVkZzbnqe5PqbpwT0/ZfPG7tw8b6e1cP8bKbYmC0i/n3vE7akAO2nh1I8t4IJnT1SzWTGaMx8MjWEvg1d6PGkHmzjyQR+3BhNLg85FQoq0RmswW4+bzmze3tRIBsEzEJjTZz4x6qMWMBHzl9nNZy+rWNcB3fqlXRAIhHQ6CxsPJnAtL9imd7Tk+YVsncmzmQW2X0xkWWH1dwKMjxJZbKndwMXmyvsWTn/T1tiWH5ITfmCdgS4ywiOMXHhXx096qoY3dY93e/Iy3I3xECbqSFcmpU3TeX08DgTNccEcn56ngxtw8wWkS8XRXAvzMBXH7vRrLwjcimcvqNj4d44AqNMbBjpj6fqzfrg7n+Yfrp1w3zPgokPKsr/bV6HivJ/jTeiopyecnLyyCSdYaUggWqrrAJVz3Fo+Q22zT6f/PPUE11ROsjpOS+Mih/ZpSnWGJdopvzIx9miSnvkRhL9F0WglAvYySX0aaDi5G0dB68mMaqNG/9r5pLcj3+/NpoFe+J4uDB/mqUpm04l0PeXCP4a7UfZfMpU44nkz349yWrJN8ZqVxmdYKbaN4HM6OmZwmLnKZce6Gk3PYRto/1TTHIaTCLj10Sz5lgC9Urak8dLTnCMif1Xkmhb1Ympn3rkiGfznRAD5/+1TsSXzaegaICCLxZFcD/cyLcd3Kld3Gr5E5doZuXRBGZui+XP//lQ9zUITBlNVv/ZPw+quRVsHWvULm5Pv4bZM9awxe6LiYxfG41UAlUK2aE1iOy/mkSFAnbM6u1JnhxWFgYY9EcEuT3ljGydtsjThA3RaLQi03rYVsNOC4NJpPvcMCLVZoa2cKVpOUckEjh6U8u8nXGotRY2j/LL0Oc5O/hgE5RJRFHk7D0dfx5Qc/G+VfSnbD47etVXUa1IzhhHZ4WHkYmY04gvF7fYiHxCVXSmtANQM+mvrogZ7P9s6kDKnD2Y6fdZJBJWDJnGpVrPfPG+aVAQQRAQLSJT66xO3u74eR0Gdc+Fs1Jq8/cdqTZRfXQgF2bkQfUSX56hSyIIjDax4DMv6o8P4s9BvlQpZI8oiqw5lsCUzTHsHBvAyVtaJm2MoUEpe+b0Sy31XnzQQ5zsBKZ296J+KWvjnKC18PflRKITzLg5SSmZW0GbqSHc/jk/MRozXWaG4mQv4bNG1gZVZxDZfl7Db3/H07aqE2PbuyMIAquPqpm5NZY/BvpQNv+zhtdoEll+WM2cHbHsGBtAXq/sbRxFUWTjKev9BMeYcHeSEBZrpmZxe7762DXbMxg0Wgs95oWi1Yt83tiFqkXs0RutAe/v++JpVcmR7zqlnhB4GURRxGgGvdHCoWtaohLMeDhLaVDaId062Veh66xQCvvLbd67ySzyxa/h+LjKmJyGtcKLWCwiO84n8ufBeE7f0SGKUMhPTs96LnSp7Zyp+qUPAe4H3hbeRIB7L8zAH/vV7Lv6xLvVR07PuipaVnTMFtX6N80bswk62sa2cnKu1pCnc7rWQbaC2+dZ/e1xzm3/FwCpXML6MjW4PjcfqnRWmsaviUYQYELnrK+Mnb6jpeucMJZ86UPFj+woMeQRm0daM/geRRrpMCOU7nVUfPWxK6Io8tmv4fx1NpGzP+VJM7D+fGE4288lEvx7/hT2cFqDhT2XkgiNNeHiYNXCOHFLx4rBzxSRLz3Q02lmKA1LO9C7voqCvnLC48ysOqpm1bEE5vf1pkXFZ1lIFotI93nhmMwiC/p54fWcx2mMxsywpVHEJ5pZP9zvtTz7FovIssMJLPw7jliNBZWDhPA4M43KODCspetryT6KTzLTaWYYZgv0b+JCtcJ26IzWfnXh3/F0qu7Etx3ds3VsL4oiJjPIpHDilo7bIQYUMuG1r2jeCNTTckoIu8YG2HSIufpIT5ufQtg7LiBdx4a0MFus48Y/Dqg5d886PimRW0HfBiq61HJO5Y+bU3wIcDOB2SIyalkkR29q6dfQhQalHRAEOHxdy+/74qlQUMncvt7ZuuqTVd6kD256K7hpBbg3qzZg5be/ptimNZhRKZ9dw/RITdLCawAITnK+2dMRZ6Xte6g7PpD5/bwplTdzDeS1R3q6zw3lxJOV4YNXkxj4ezg/dvOkZUUn5DKBqZtieBxtxMVewtrjCVQvas+s3l74PCcSFRhlpMWkYFwdpRyemAuTGaZsjmHlETVVClkFJoKijRy6lsRHfgr2fpcLhUxAbxT566yG5YfU3A01IJcK1CpuT58GLlR+UuejTjJTYcRj9nwbkKbN1NwdsVx9qOePgTljCyCKIsExJjQ6q6efLYP67KDPgjCc7SXM6u2VyksyVmOm/fQQutRUpfAUziw3A/Us3hfPlrMadAYRR6WE9tWd6NfQhY/8csbiI1Fv4dQtLeFxZubviqNogJwvm7lR6SMlomhtQ+btjEUpF1g2yDdLM+iiKGK2iJy+o+fivzpEoEw+JbWL26frq5vTAe6BDALcBh8C3A884XUHuH8ciOfHjTH0rKuibVWrANzlB3p+2xePziiyaaRfsoL8u8pbt4L7NIBNyzooV2uo/VeGlxBFkekdtxF6Lw6AguV9GPhH0zSP33Mpkd/3q9k00i+THyI1jX4I5vNGquT67J0XEhn0RyQzenrSsqIjIbEmao4J4sD3AfyyN54zd3V0qenEb/vULPmfT4rVwESdhdk74lh/Qo1SLmFaD0/qlXRAFEVmbY9jwe44SudVUshPTpTazNZzidQsasfaYX44PjcBG6U2s/ywmtXHE4iIM6NykPBJFSf6NFCR/wXNh+3nNEzfGsv+73PZzCIymUWaTwqhZz0VXWu9vtRgURR5HGUiSS/i5yZNcxU7J+g0MxQ/Nxmzenmm6iejE8y0mBzCZw1V9Gnw6u3R4etJLNoXz9+Xk7CIVvHQHnVV9GugSjHZkNMER5u4eF+HRYTAaBOzt8cxuLkr3Wo74+EsJTLexIojCSzYE8fsXl42S9NeFotFRMT6O912zrrY46mS0rqSU4ZZZq/KOx3ghoSE0KJFC27evIlGo0Emsz4os2fPZtOmTRw/fjzF8VkNcKduiuHUHS2rhvqlWuFJ0lvoMS+MkrkVfN/55Zbyc4I3FeC+Snry+pEzuFqnRfK2FwNcgA1/Q4NbV5GFWlXdesysT7HauVOdr8Y3j/n9Sx+K585cgDtyWST+bjKGPuc/evq2lsmbYngYYaRsfiXxSRZO3dbRoZoT4zt6MH9XHOtPJFCtiB1+bjIeRRq5dF9PmypObD2rYc0wP37eHYdGZ2F2H+9kxcNrj/R0mB5CsVwKVA5Slgz0SRXE2WLxvnjO3dOxaEDaBuEJWgsVRjzi6I+531kF0KepvBdn5kUpt/17uf5Yz6dzQjk/Pe9LTShtPavhm5VRfN7IhS61nPFxlRESY2LFETXLDsYzr583DctkX92t1mBh8qYY1h9PoMQT/8Mbj/Uk6iwgCKiTLJgtIkVzKehd34VONZyzPHN++raWEcsikQjQoLTjk8m3JBL1Ij9190wz1SunA9yDj7TpHls/7zOLgg8B7vvFq/TNOf0s7L6UyPClUewc658qEBBFkXFrojl/T8+e8f5vTWZWVnjtAW666clPSSdNORMruM9z6mYS67ttSP65drdifDKicqrjtp3TsOpoAuuGZy3AvRGop/30UK7NTtnnHL2pZcKGaIKjTZTNr+TcPT0JSRY613Tmh87uuDpKWXcigYkbYvBUSSmXX4lGZ+HvK0lUL2LH7F5eHLyuZe7OWHaPC2DK5lgu/Ktj8Zc+yat5K46omfZXLNUL2/Eg0sS20X5ZmgRtPTWEbrWd6ZiOgNquC4nM2BbLwR9yvfwv6R3jRqCettNCuT47b5r97sX7OnrND+fSzDyZGqelxcQNMWw4lcCwlm60r+aEo1LgZpCB3/5W8/eVRP762j/HPbQfRBgZtzqaY/9oqVbYDonEWtJU0EeOp7OUQze0SATrt7JtFScGNHHJ9CJRRmgNFkYtj2Lr2USalncgl4eMx5Em/r6cRLtqTkzpljOp8ZB+v/LWj5Ld3d05cOAAn3zySfI2vV7P5cuXs+0aGq2FPw7Ec+TH3DbTFx2UEn753JvqowMZ0tLttc5AvS0IZjM1tixNEdxaJBJuVa5H0TMHk8WkbCGxWOg4fQRAiiD3RQJ84LJraeoWNaCZco7lw61p0BNPfopMbv2d3wkxEJ9keanVuH+CDLR7Qc2tahF7to0J4E6IgbuhBhRSgRhNNP2buuLrJmNSN09GtXFj35Uk4hItVC9qzx8DHXBUSqhb0oH200OwV0jYOS4APzcZ4XEmVh1NYPG+OKb39KJJOUdaTgpm+7nETCndXrivy1Cp29leQvkCVk/bdzXA3XAygSqF7Ri+NII4jQVPlZR21ZyoWcw+eaBZMo8SX1cZJ25pMy0IczNQzzcrItk4yj/FxIe/u4yvP3GnYWkHPp0Typ5vc2VLirfOYKHLzFC8XWQcnJCbgCem56IocuwfLYMXRzC+ozvd66heuWE/fVtLj3mh1CjqwK1gAxtPJRDgLqNHXRW+rlK+/C2CBZ95J6fMv07e3dDgA6/K6+ibs8qMrbFM6+6ZKrgFq6jPxM4eVBkdyIlbOmoWs+0T+YEXsJitisjPB7eCBAJaWj1vk7ens2by1DoIMhXkli5gR+cqtTkw2pufP9nI0VX/cHTVP3QcX41qbQsnH7fjQuIr/R1vBhqoUtg+1YRq7eL27P8uFzcD9fwbZiTAXUaC1sLcvl7Jx3Sq4Uz7ak4cuq7lfrgRO7nA+A7uyXWWXWo6cTvYQI0xgWgNIqem5MLXVcb1x9Zsgv1Xtfz1tR8f+crpODOMxQfUDGzm+tKf4cK/epZ+lfYEOVi9T7vOCcNiEdPN/Hkf+POAmo985fScHw5A2fxKetZ1TpGVV76AHS4OEk7dzno7sOFkAlvPaTj0Q64UGSElciuZ29eLVUeVdJgRyvnpedLU53hV/g0z8vGkYL5o5MKi/t7JcYzWYGHlkQSmbIlh11h/CvkpcLQTXimYfxGjSaTjzFASkqwlIIeva3FQSmhewYG/RvsxZ0ccn84NZ+0w39eeAfvWqyjb2dnh5paySPqPP/6gZ8/sm7XceTGRakXs8HdPO2jwdpFRv5QD286l73H1enj9PrjxRpH5YxfyuEBx6zUkEpYNmcavw+cQ7ZNyNtAiSLhSuX4KxWWJxULVzX+SkKi3XkMUUetNKV7585j45wGEGCV8c6wrNXuXAmB89ZXsmH+eBJ2RqVui6VjDCflLzDFIJWAwp+50I9Um9l5OZO+lJPZcTiJOY0mheufiKKV9dWf6NXKhdWWn5JrHZuUdKeyvoIi/nHrjA/Hv+y9VRz8mONrIpq/9aVnJCYVM4H/NXFl6MD7VdW0hipkLFiTv8GrDo0gjfx5Uc+m+jvIF7OhWR0WxXArGroqi2cTgFCrEmbHTeZ4/9sfzeWPXNFf1KxS0sypTvqSpeVos3BuPykHCwv7eycEtWAfPtYs7sHl0ANP/iiU64dU8CkVRZMCicMxmyOMl4/cvfdj7bS6+buvOoetaxq+J5ruO7oxaHpntVhmZ4YOK8n+X19E3Z4W7oQaCok00LZf2hI9EItCrvorVx17OA/I/jUQKdXeDW3nrz09XY2v/Zf3X1jdekFjTkp9XXn5qHWTJuH13tJPQqboz8w8kMftST8ZstU6mrJ94iqHllnH3bCi3gw3suZhEt9pZT7uVSgSMNhRhAcJiTey+lMSui0lcfqgnPM6c6lipRKBhaQc+b2QVjHxeREgQBH7o7MFHfnK8XaSUGPIYj1736TDDmj57ZEIAhf0VSCQCw1u58scBNVlJrBRFMcM2VxCwKd75vrHw73j+PKTGbIGONZzoVMOJ4GgTlUYFMndHbIpjC/i8vFXOU0RRZN7OOKZ080yz3KFbbRV5vGRsP59zscPgJREMaeHKsFZuKRbp7BVW7ZefPvXkf79H4myfvcEtwG/74jlzR4+3i5Sx7d059EMuVg3xQSYRaDc9lMZlHIjRmN9I7PTWB7gvYjQaOXz4MPXr18+2c4bHmtKse3yegn7ydL2fXheCICCX2n6BNQjKbh9clZ0MmZcny6YsJbBwaZYNmcbdhq1wdlSycO4mYp4EuRZBwvpRM/h99ALWj5yRHOQGf1SCFT/+gbOjEpWdDJMI9gppipenq5RqZUX2HJUxfMN9bhZT4T61GgAnlt9gcq3VXPsnFplbHImGzDdI1YrYs/vCsy+XKFoVdquPDuTfUKvfnaezhBiNmW6zwzh2M+26Qp3BQmisiZuP9Swd5Me/v+bnwcIC3P81PzN7e1Ms17MAq1FZB87d03E/3MB3a6OoMPwRHw14QM0xj5m3MzaFdH/Z/EoOX0+/nlGjs3D+X90bt4bJCrEaM+2mhVAyj4Kutaw1L83KO/JFE1eO/JibhmUc6DA9hES9NSCMiLeKb2QGURTZckZDlwzqirrWVvHXGU26x2QGs0Vk2SE1I1q7pzkLnt9bTruqTqzIwOohI5YdUhMWZ2bbmAB+6OxJyTxK/N1l1CvpwLJBvvRv4sqsbbG42Es4lMHzkyN8iHA/8ISc6JuzQlismfze8gxXCwr5yQl9C/rzdwqlO9TfBx6VU6Ya5+sM1Vdbg1/XMtZtLwbAT4Nct/LWQFmSuVnqb9q5cfqullHLo5C6OzL7Uk8GLm4CwC9f/M3CFmv4rrHylTQjqhSy49g/2mSPeLDWFX6/LprKXwfyKNJI9aJ2RCeYCYoxUWroI07eSr88A0BvFAmNNRGXaCYwyszKwb5E/VmAsD8K8M+8fHzT1j1FfWbVwnZEq83EPJlsP3gtia6zwyjw5QPy9n9Ay8kh/HVWg8nGhH3Z/EoOXkv/ng5cS6J0XuV7vXq74oiahXvj6VDdiUZlHGhT2YnWlZ2Y29eLU1Nys+JoAr/te7bwEBZnzvRY40X+DTcSoTbToFT6q7+f1lax6dSrjz1scTvYwJ0QI33TqSNuX80Jk0XkxC1dtl7bYBL5YUMMHao5sX6EH43KOBDgIaNEbiU/dPZgz7gAflgfQ6MyDizen7nFnuzknQtwV6xYQdeuXbP1nM4OEqLUGQdMkfFmnN+QqfbbgtbZlYUz13Gh5scptv08bzOBhUuzftSzWturdVqwfuQMAguXZsmkP9E6u2Z4/lKFoVpZkcNnpSzdBjtPSDjUpAaHChQDoOGZc2gXXnmpe+5RV8Wm05pk/9yftsRy4GoSJ6bkZk5fb7rWduZRpImvPnbjly+8+eLXcM7fS9kQ3Ao2MPiPCIoPekij74PQGqH7nFD2Xk5CKRds1nFJJQIWET6eGIRMIrBmuB/npudhTh9v7oUaqTc+kGtPDME71XBm35WkdM3dlx9SU62IfbqZBm8ryw9bhbjGdfBg3cmEFKuNgiAwvJUbeb3kbDyZwMMIIzcDDdQqnrmUIZMZkvRWYaz08HeTEat5dT/AhxFGpBIonS/9iYYWFZ049k/Gg6D0+POgmqqF7SiRxqRG34Yu5PWWE+Ah49KDV/dLflmEDP77wH+HnOibs4LKQUKk2pzhKlhkvBnVf7w/zxJKd2h0MnWKcb7O0OQsNDhoOwCutsq6vf4+6zkyiZujlN3jAohPMlNm2GM6zwplyhmBY43qcbdkUQCuT9rH0HLLSIzL2gA+wENGrWL2/LInLnnb9+tjOPaPlksz8zCvrzcFfeQkaEVOTs7Ngs+86TY3LM02916YgSFLIvnofw+pPS6IYoMeERxjZP/VJASBNFNVBUFAIgGzWWTg4khGLo+iURkHTk7KzYVpeehdX8X8XXG0mx5Kkj5ldlDfBi7M3x2X5kq02SIyf1cc/Rq+O572L4vBJDJxQwwrBvnQs66KVUcTUrQDAR4y1gzxZcrmGLQGC3dDDdwLM1Iri+nJsRoLvq6yDCcMAtxlxGTD2MMWR29qaVrOId30Z0EQaFXJkaM3X2088iLbz1vFPGf19rK5v0iAgm87unP2ro6Lb2B88s617rdv3+bXX3+ladOm3Lhxg/nz57/yOZuWc2Tv5UQ02rTTCZP0Fnac1/BxhewTqXlXEaWpA4mnge+LNbZX67Rg4cx1mQpun1I4H3RpbqZhVcjnD6U+guZ93PGYXgNBJcf0WMOkmqt4eDk8U+d7WofZbloIuy9q+GN/PKuG+uLtIiNKbWbE0kjuhxsZ2NyVWsUd+L6zJ5M3PTOOP3IjiU+mBlPAR87ZaXm5PjcfFQoqKZNfyfdro5myybbJ/PLD8QjAhpEBjO/oQWF/BW5OUip+ZMe8ft5M7OpJt9mhxCda7YVGt3Wn44wQ/glK2RBYLCKrj6r5eXcc33bM/MDgbWLFETWfNXKhfAElPi4y5uyIS7FfEAT6NXJh2aF4xqyMokddVaZl5uUyAWd7CcEx6a/GPIoyZouin9FMmgJZz6OUCzZn2jNLfKKZ++FGCmVQb96jroqbgYYsX+cDH8gOcqJvzgql8igwmsVkX860WH0sgVbZoCD6nySt1VeJNP0AuNHJlwpun+LuJGVRfx8uzchDpxrONCjtwM/9vNm+vDKzL/Wkfq+SAIyrt46h5ZZhMr58MPFTd0+WHbZ6v98OMbDssJoNI/xwdZSw7ZyG7vPC+fVzbxQyazrydx09mLghdd9/5q6OxhOC8XKRcvan3Nz9OR+PF+WndB4lc3fEMXJ5VJqTL1ce6nFQSvjjYDz/hhk5MjEXveur8HWT4amS0raqE3vHB+ClkjJ4SWSK97aq5Ii7k5R+v4QTl5jy8ydoLQz8PRKLxTqZ/r6y91IiBX3llMqrpGphO1QOEubujEtxTCF/BeXyK9l8RsOIZVH0a6jKVH9uCy+VlOAYU4b9/KNII145pCZsMIso5RmPlezkklcaj9hi7XENGSUDtK/mxPl/9VlKu39VXluAq9FoCAoKQqN5uWV6o9FIw4YNuXLlCk2aNKFt27bs3buXPXv2UKJECb766qtXvjc/N2t97Q/ro23+EURRZMrmGKoWsc92D9L3CVuBb3rb00MQILcPlPwIiuQD+ycLWO7jK+M6yloDtOizPXxTaVmmag/7NnRhTDt3hiyJRBCsJu3d54RSbfRjRBE2jfJPrl1oU8WJu6FG7oUaiE4w039hOH8O9GVwC7fkAKlPAxcuP9Czfaw/284lsutCyvoCi0Vk9rY4mldwTFOprnVlJ6oVsWfdiYTkexzU3I1200JpPz2EqZtj+H5tFJW/fszSQ2o2fe2fqVT6tw2TWSQoykSZfEoEQeCP//mw4WQCAxaFc/WhdRBqtoiEx5m4EWjk6iM9J29r+eyXMPZfScScib9v+2pOrDycfj3dysNq2ld79c49l7uMiHhzipphW5y9q6NwFvzlnhKdYMbNUcLxf7Tpdg75va01ROXyv/7U9ac1XWm9PvD28zb3zVlBIhEY0MSFb1ZFpVrleoo1o8dEy4ofJqxzhPQC4FfAUyXlkypOdKnpTJXCdsmZUy0HV2D2pZ4UrmpVUR5ZeSWTWm1+qUF1Lg8Ze8f7c/mBnlpjA3FzlPD1iijKDn/MrO1xLPvKh0ZlntV1d67hxKUH+hRZVxqdhU/nhLHwC2/GtnNPdleQy6zCU452Aidv62zWfouiyLxdcXxa25nf9qlZ+IW3TdFTmVRgXl8v9l1J4nGUMcX2VYN9cHGUUHroY/ovimDyphj+93sEJYY8wiyKbBjhl2NCR28D98KMVChg7QcFQWDlYF+WHkqg/6IIrj7JljOZRVT2Er5fF0NkvJmTt7V8/GMwg/+IfOksqHzecvJ5y9h9Mf360qWH1Tk2sVA0QMGZO+mPEcDq8/wq4xFbhMeZKJpLzoGraZdH2SskOCoFm168OU2O5jpev36dRYsWsXPnTh49emQtghcE8uXLR7Nmzfjiiy8oVapUuueQy+Xs37/f5r4XbQhehWk9POkwPZRe88MZ1NyV8gWsg/ErD/XM3xnL/XAjm0b5Z9v1XgkRTOk8zBZEknRpz2A+FZHK6v7sOEdG++3lAgn6ND6DSsGYY93Y9dNpLu/4l7FVltN0WEUqtiuSfIiDXJoqbbhtVWf2XU4il4eMgr5yHJQS5vXzxu2F2h2FTKBcfiW3QwzcDzfSuKwjVYukTGFpU9mJNcfUTFgXw9BWbvz2d1zy6n6S3sI3K6OITjDzY9f0baW611Xxw7poPm/sCsCndaw+fLsuaLgbasTVUcpvA3woX8Au3fO8zUglIJGA1iDioBTwdZOxe3wASw+q6TU/jBjNM9EOCfBDJ3d83eTcDzcy7a9YpmyOYdUQv3SVo/s1cqHFpGAalHFI4Uf4lCM3kth6VsOBCaltp14WJ3sJLSs68udBNaPb2l6N0BtFlh6K5+fP01e1TA+Vg5QkvYirI+y+mJRm9siBq0kgitRLwyooJ3l/h0rvN+9S35wVvmjkwtWHBppODGb0J+40KeeAVCLwOMrI7/vUrD2ewJav3+/B/n+RAb82xmIRGVd3LVGBCQwrv5zyTfPTfUrtTL0/t6ec1UN9aTc9hAI+csrlt+N/TV0pa2Py0E4hoWw+JXdCDeR7ota9/kQClQvZ0diGHV2dEvaUzqvkUZSJ+bvj6VrLOXmMYjCJTN0cw/XHehqWsh6Xz4YC+FMclBLaVXNi40kNw56zP7RTSJjX15ux7Uz8dTaRSLWZErkVjG3n/k6WNr0sSrlAov7Z2DiXh4yDPwSweL+aTjNDiUu0YDSL2CskJOkt1CthxydVnXFUSjh3T0f3uWFU/MiORV94Z3pVd2gLN0avjKJ8AbsUgpNP+XlPHPGJlnRF716FeiXsGZpotbmsXtR2qvXtYAOXHuhZPcQ3W6/t4iClZB4Fc3fG0bisg03dA53BQkS8mRGt3WycIWfJsSe+c+fO3Lx5k86dO7Ny5UqKFSuGs7MzCQkJ/PPPPxw5coRu3bpRvHhx1q5dm1O3kWlUDlK2jPZnyYF4+i8MJz7JgkQAJzsJPeqqmNPHG6e3pF5HkAjI0xlaSrCKTKVFVn1wX+aY7Nmf9mzv72cfomqai/z1/Hgw/Dh7Zp1nz6zz5JteA6m9jC7lcuGoSH1+uUwgt5eczrXSr0PRG0XkUoGd5xMZ38HD5nlWDPFj+J+RjFsZSaJeZOrmaGI1Frae1VClsB0uDgI+Gdj55PWSEalOGegr5QKfVH1/0ogEQaBOCQe2ndPQuab19+7qKGVISzcGt3DlToiBtj+FUL+UAyYLtKtuPaZGMXs+rePMzG2xdJoZyp5vA9JMWy7oq2DBZ970mBtKh+rOdKutwt/d6l+86oiarWc1/DHQN3lG/VUZ2NyVZhODEQToXscZf/dng5EkvYUBiyIonVdJxYJZX1X1VFk7j5rF7Bm+NAKJxJsmZR2SB0WiKHLkhpbpf8XSuZbqjQiHZFXVe+jQoZw/f57y5cszd+7c5O0xMTH079+fqKgoGjRowNixY7PrVj/whHetb84KEonAL597sf6khlk7Yun3azj2Cgkmi0iXms4c/CGA3J4fsrHeRyQSgclHu2DQmvi6+iou7nnAxT0PaP5VeRr2SX/S5ikqeynl8tvRNQPhQr1RRPZcu7v1XCKfNbQt9iMIAr8N8GH0ykj+OJBAj3nhlC+gJCLezMZTGkrlVbDjG3/+Opto097qRfJ7yQmMtr1I4OMq44vG/z3f8fqlHJi9PYSfunsmT165O0kZ1caNka1d0ehEjt5MotuccBYP8Kb9c57BtYvb87+mLvT5OYKBiyP4fUDmJqebV3DkQbiRet8F0b+xCx2qO6FykHDtkYHf9sVz9ZGBraP9cswiRxDgy2YufDo3jJ8/86JZ+ZRlFw8ijHSaFca3HbLfi7Z1ZUcOXkvCXinw2a8RzOrtidtzNqoxGjOdZobiaCehV73XX/udYwHup59+SosWqT1P3dzcqF69OtWrV+ebb75hx44dOXULL42DUsLAj934sqkrMRprapO7k+S9Vpx715EopRRcUAf1qTAiV93m4cgTOJb3gvm2jcyrF7XjrzMaetRN+8sWozFz8b6eXwraoU6ypFm36aiUsLC/D0HRRuqNDyIizkw+Hzl/f5cLbxcpRQY+RKO1pDsxEhprfqd9lc0WkUPXkpJTeyoUsKNuSftU35m+DVR8uyaaj8s7onJ49nkFQWDV0QQ+ruDIgataFvZP2ak8FaA6e1fH1rPPAmRbNCjtyN5vc7H0kJous0KJ0ZjxVElpX82ZAxNyZ0twazKL/Lo3jj8PqHGyk7BgVxxztsdSwEdOi4qO6IwiG04k0KC0IzN6edkUH3sZvmjiyvfropnd24sfN0QzaUM0DUo7IJEIHL6eRKTajJ1CYFz7d6c2++LFi2g0Go4dO8aAAQM4d+4clSpVAuCHH35gwoQJFC1a9A3f5fvLu9g3ZwVBEOhUw5lONZyJSzSjN4q4O0mRf1i1/U+gsJcx+1JP4iOS+L7JBnbOv8jO+RfpNb0uZRrmTfe9tYvbs+N8YroBbnicieuPDZQv8GwSM0FrSbfWUiETmNXLmxP/6MjjJSNGY8HdWcqOMf4UeZLC6eYkyZS6d0isCTent2PRJSe5E2Jg18VENDqR3B4y2lRxxMXB9u+4aICCogEKFv0dz1cfu6bYJwgCzvYC36yMpoCPPEVw+xQ7hYQ//udNqaGPuR1sSP6bZMTAj12pWcye3/bFU/fbYLQGC/m85fSoq2JBP29UWVRpzogNJxOYtzOOqAQzKgcJnWeHo7KPpF1VJyoUtKpqH7iaxNj27vSun/0BZqcazvy0JZaZvTzZfzWJMkMf06ScA7k9ZDyOMrHnUiKCIDCjp+dr98CFlwhwb9++zZUrV1LV6fTp08fm8bY60Fc57nUikQjZIkbzgdeHqpovzlV9uP/VURIvRvJjjVUMWdsKn4Ip0yLaVHZiwrpozt7VUbmQ7bTfeTtiaVLOAQ9nKX5uMu6Fpd/QOdtJMFtEJnTxTBHM1ivlwMZTCfSqn/ZM6ppjatq8o0InB68mMWp5JB7OUuqVdEBEZPKmaEavtDCjlxe1iz9LyWlQ2oFD17W0/SmEyZ96UekjawmAyWxh5RE1XiopnWs62/ybCIJAnwYu/Lw7Lt0AFyCPl5xvO3rwbcfUq+5gTZeJTrBgrxRe2lbCZBbp+3M4Gp2FP7/ypUw+JRaLyN5LiSz8O55fdsfTtJwDO8fnytQMfGZoVt6RG4F6xq+JZmhLN3xdZVx/rEedZKGwv4LQ2CRWDPHF5Q1NkmSlyzp9+jSNGjUCoGHDhpw6dSo5wL1+/TqTJ08mMDCQyZMnU61atWy82/eT/1LfnFXe5UnED7waLt4OzL7Uk+DbMczovJ2lIw8DMGxVC3IXt91PdKjuxA/ro7n4xLPdFtO3xvJJFacUz5a/u4y7oQaqFE67pEhrsBAaZ2bQx674uKYegjcu48iwP6MIjTWlOSmrN4qsP6lh99i3pGwuBwiLNdH/twiuPzbwSRUn3J0kHLiWxPg10Xze2IUxbd1sLj4t+MyLphODiUu08GVTl2R/2thEM5M3xvA4ysSZqWmXKtkrJHSv7cyKI+oMS8yep2x+Jb987p3uMUl6C9EJZpzsJSlWOrPCpE0xbDmjYUo3TxqUsi4qqJPMTNwQw+pjCVz4V0fX2s7M7u2ZY+2fs72EVUN96TIrjO51nNn+jR/n/tUTHmdCJgV3Zyntqzq9MWGzTAW4kydPZsKECZQpUwYHh2eDVkEQ0uxEn2fNmjWULVuWYsWKcfv2bT777DOkUim//vrrWz1Tf+2Rns2nNUQnmHF1lNC2qhNl87/5WkhRFElPDO19qMHNaL+jQo5an3q/18waGAMTiJtzlTmdtyFTShlzsPOzlTQBZvT2pNf8MKZ+6snHFRyTZ5biEs3M2xnHnktJbH/ScXSq6cyyQ2qaV0g7CF17PIFGZRxTrdT2b+zC57+GU6+Ug01xsmM3k9h7KZHDP756Xejr5uDVJAYujuC3AT7UfE5if3Rbdw7f0NL/13AWDvBJDnIFQeDHrh4sO6Tmq8URyCTg5y7jUYSRJL3I8NbudEynESyVR8njyLQtlDLifpiBn/fEs/WMBkc7AY1OpLCfnL4NXWhXzSlTK62/74tHo7Wwdrhf8iqQRCLQrIITzSo4cem+js4zQ5mWzbO1I1q7U6GgHb//Hc+xm1rsFAKiCO2rO7FzXC4K+LxbqZZxcXEUKFAAABcXF27cuJG87+TJk1y8eBF3d3fatWv3xms533b+q31zVjCaRHZdTOTwDS16o0ghPzndajvj7fL+1yZ+AAKKuDP7Uk+uHXrMkmGHmNXNmqHw3Z72uPqkrJl1spPw82fedJwZxuxe1nGC9EkwFZ1gZvrWWI7c0LJnfECK93Wt6cz0bbF0q+2cZp+y+bSGSh/Z2QxuwWpz1a22M8OWRrL8K99UGQeiaPXnLV9ASaFsFg16W4hOMNNsUojVX3V4ylr50FgTPedblaJn9ExtT5PXS86+73IxcUM05YY/pkQeBRIBrj0yUDKvgmpFlBmuzJbOp2Tz6ezzrb32SM+8XXHsupCIs70EtdZC+QJKvmzimiVnlmM3taw5lsDhCblSLMapHKRM7+nFiNZu1P02iPIF7HJ8cq/yR3Yc+D6AX/fG03JKKCKgM4rULGrH9B6eNCn75oT8BDETMnPe3t7s37+f0qVLZ+kiBQsW5OTJk/j4+NCyZUuKFCmCk5MTR48e5eDBg1k6Z1rExz8zE1Y6Zq0GIVZjVc69HWKgc00VeTxlhMSaWHssgVweMn7/nw9eqjfXKQbHaNNcOlnQeD3KH6slN8a2eHdqcLO+v4S3Pf/OuUzsdauMf4mBZfCu8qzAXmFyZtpfsYTGmClfQInOKHL2ro5GZRz4oYtH8t9XZ7BQ79sgetVT8UUT11TXufxAR9dZoawd7m/TF3XpwXhmbYtl4MeudKrhjIujlKBoI8sPqVl5RM3vX/pSI4sebG8Ks0WkytePmdU75Srt8+y/ksi3a6I5MSV3qo7eYhG5+khPjMaCykGgzeQQ7i8qkK7gyz9BenrND+fMT3le+n7P3dPRc24ovRu40KOuCh9XGWaLyL4rSfy0OYbyBe2Y0dMz3SDX8uQzL+zvQ4WCaU9yDVgUTpl8SvrbeFayA53BgtYg4mwvyVTKjz7xWXvo4pI9NVnPt7GngtK3J6qW69lA4un1f/75Z7y8vOjYsSObN28mKCiIQYMGAVCmTBmuXLH6XNeuXZujR49myz2/r7yrfXN2PYuZ5eQtLX1/CSevl5zWlR1xUEg4/6+OrWcT6ddIxbh27u9kKdLQcsuYfannm76Nd5JDy6+zbfaF5J+nnuyK0j7lZOHh60n8sMGqtlu+gJIkvXWc8HEFRyZ19UheHXyK2SJSd3wQH1dwZPQnbqn6lH+CDLSaEsLSr3yokYYgEFhFpz6dG0asxsKwlq40LuuARIBTt3XM2xVHSIyJv0b7v3QW0rvCt2ujrQsOfW2viMYnmanydSDrR/hROg2XCrAGytcfW8unSuRW8k+Qge/XR3Pge9slbE9ZdkjN8VvaTNfhpsffVxLpvyiCIc3d6FbbGQ9nKQaTyLZzGiZtiqVdVaeXLjHqPjeM2sXt+axR2u3ogl1xXH6oZ/GXr/4ZMovZIhKfZMFBIWR7vW9apNevZCrAzZs3L3fv3kWhyNpskUqlQq1Wo9Pp8PPzIywsDLlcjqenJzExMVk6Z1q8aoCrM1hoPSWEyoXs+K6TR4pBpNki8tPmGPZeTmLnuACbEu6vgw8BbuYCXGelDEOCgRNfHkreXvuPhkgVUmoX9MJOLuVGoJ67IUbkMqhcyM7mxMXjSCOdZoaSz1tOz3oqCvvJidZYWH8ige3nNMzp603TcmnPUl34V8fv++LZed4qJW+nsNaG9W3kkm2prK+TA1cTmbo5lr+/C0gzKBRFkTrjApnUzZNaaQTBT2n7UzDd66jSFdaatDEarV7kx26ZTxkCSNRbqDLqMXP6eNHQhrKlRmeh7U8hdK+jons6ddl3Qgx0nR3KuWl50g2E9122piu/LYrrOR3gng5OP8CtGpA6wL148SKLFi1i0aJFfPnll/Tq1YvKlSsD0KFDB+bNm4dKpaJRo0acPHkyW+75feVd7ZtfZ4B76YGettNC+H2ADw1Lp2yLotRmOs4MpXZxe77vZDtd9W3mQ4D76qwaf5zzO/4FQGEnY8qJrqkmO64+0nM31IhSJlCtiF2qwPZ5wmJNtJ0eiruThH4NXSiZR0F8ooX1JzWsPZHA9B6edLRR//kiJrM1DXnx/nguPdAjEaCAjzXrqHttZxzf0Pgzp9EbRYoNfsjf3wbwUTq2iNP+iiU42sTcvqlXcdNCa7BQbNAjDv6QfuZTi8nB9KqnemVbwfA4E1VGB7JuuB9VbJRfRSeYafhDMJO7etCsfOZXOn363OfW/LzppjmHxZqo/HUgj3/Ln6V7f1dIr1/J1Ddk4sSJfPXVV4SGhmKxWFK8MoOXlxf37t1j9+7dVKpUCaVSiU6neyPGvxmx+bQGlYOECV08Uq2QSCUC37RzJ5+3nLU2fMw+8PahcFZQb0UTCnQsBMDRvvu5t/pW8v4SuZW0qeJE8wpOaa7K5/GSc3BCLlpXcuTXPXF0mhnKqGWReLtIOTwxd7rBLUCFgnYs7O/Do9/yc+eXfNz5OR8/dvN8J4NbgCsPDdQtaZ9uoCcIAnVLOnD1UfoBEFj9f2dtiyVBa7s9eRBhZOURNb2yIJKw5bSG8gWUNoNbsKaifdvRg9/3xaXbHukMVu+8jFKZnR0k6AxvX7uWUwiCkO7LFuXLl8fOzo5atWohlUrJkycPkyZNAqwiU126dKF+/fqMGzfudX6Ud5L/Ut+cVX5YH833HT1SBbdgVSrfMMKPJQfVBKehSPuB95tuE2sy62IPfAu6YtCZGF5hOT9/vjfFMaXzKmlX1YkWFR3TDW4BfN1kHPohFz3qqvhjv5q200IZuDgSR6XAsR9zZSq4Bauvbddazhz8IRcRSwoQsrgA56bloX9jl/c2uAUIjDbiZCdJN7gFaFDKnssPX8631l5hdUX5YV00FovtNu7vK4ncDTXSqtKra6MsP5JA60pONoNbAA9nKWPbufHr3nib+20hiiI6o3U8kh4qBwk64/vTjmeFTOXZ9urVC4DFixcnb3vqm2c2p13r+ZTx48dToUIFpFIp69atA2D//v2UKVMmC7ecsyw/rOZ/zVxZckDN8sNq7oUakMusNid9GqioXdye/k1cGLUskn7ppAfkJCIi6Y1fRFEk4T2vwc1ov0WEhOdqdN0b58GtQS4ufHGIwN2P+GH3I4b/1RZXX9uBj1SScoBur5DQuZYqQ3uh9JBIBOwV714a3IsIQGbGv5kdI39c3pGTt3S0nhLM9509qFXMGjw/rZn7bm0U37Tz4CO/l1+l2nkhfSVMgBpF7UjQilx7pCeXpxwXB0mqDIgADxmBUSYStBac0+lYrj3Sk9frv1PTl9Wn+XlrICDZDqh48eIcPnz41W7qP8R/qW/OCg8ijFx9pGdsO3cG/h7BtvOJaHQWAtxldK3lTJ/61pKFDtWdWH5EzTdp+Fp/4P1GEAS+3tgas9HCiMoruHcujKHlllGnW3HajKj00udTygU6VnfOdDCbEf8l5W8BwTrAy4A04tMM+aatG22nhfLp3HDGtXejeG5rinN8kpkVhxOYtSOWNUOyxyN721kN03umn3XWqpITg/6IJC7RjFwqoDOKuDikXYIkCAJ5vWRceahPUwAN4MpDPfm8/ztjEVtk6tM/ePDglS7Sq1cvOnbsCJAshFG1atW30mPvnyADUzdHU9BXweRunlQoaK3P3HYukVHLo2hcxoHvOrlzN9SIxSK+kbodQRCQpTN5Iwjvpg+uYDYjSqWp9j+/PbPnPxOYYHO//eTqmO/FYVhyk5ltNuOaR0XbnxumOi6/l+MbkTV/FyhXQMn366IZ29493RTlA1eTmNU74/ShpwJUa48nMH51dLLVQmCUkcL+Cmb19qZ+qayZpCdo07Z5enqfm09riE000/zHEOwUAnIpdKml4vPGLslCIB7OUmoXt2fd8YQ0J7ZMZpGlB9XM6JX5lKkPfOBV+C/1zVnhdrABL5WULrPDGNDEhdNTcuPhLOVOiIE/DqipNS6IjSP9qFbYju3nEt/07X7AFhYzSGy04WltfwWkcgmzL/UkSa1nbJ21HFl1kyOrbtJpfDWqti2crdf6gG1ye8pIMogZ2vTsu5JExY9e3mfeXiFhyyg/Zu+Io81Pobg4SHBQSngQbqR+aQd2jgmgWK7sEe9Say0ZrvjLpSCXCrSfHsrVRwbsFAKCYBUs+7Kpi02/7p51Vfy2T83CL9IOcBftU6drh/lfIFMBbt68Vs8wi8VCeHg4Pj4+SCQvlyKh1WrZtWsXoaGhjBo1CpPJlOk0qteFKIoYTCKNyjjyXSeP5MG7nQJ61FXRupIj7aaF8Md+NRKJ1WD5A9mDfUIcvcb348Qnvbha55k9RekjO6ixZSlLJy5G6+yaLdeSfuRK+w2t+HvwQeIeq1nScjNNf6yJf5n0Jd7fZWI0Zo7f1JJksJDPS06VwnZZ9mitXdwencEawKaV+rvrYiJymZCuXcLzCIJAl1oqOtd05l6oEbXWgreL1Gbj/jL4uUm5G2qkWpHUgh4Wi8iQJZFcf6RHAE5OzU1uTzn3wwws3q+m8Q9BbBzpn6xUOaSlGx1nhFA8t4LqLwiEmMwiw5dGkstDRtVMfub3gQ9N4Jvlv9I3Z5U7IQb+DTNy9qc8KWruSuVVMqePF3VK2NNxRigj27gh+w+tkr0z6GPgcDMoMhTydX62/eFauD0b6u4GZfavujuolMy+1JPIx2omt97CuomnWDfxFF/+1phClfyy/XrvGiazyJGbWsJiTbg4SKlX0j7b0qYVMoEedZ2ZtT2WhV942xynRCeY+fOgmm3fZE3rwk4h4Zu27oxo5catYAN6k0g+L3m224MGuMu4G2KkUBrZZ6IoMnhJJPFJFvo0sNb8KmQCjyKNLN6vpv53wWwc6UeZFwRMe9VTUWd8EH8ciKdvg9QT7r/ujePSfR1zer+cZsn7RqaeSLVaTY8ePbCzsyMgIAB7e3t69uyZorg3PY4cOUKRIkVYtWoVEydOBODu3bsMGDAg63eeA5y5q8NOLlDAV27zS+XiKGVaTy/m7IilepH0axA/kHnsE+LoM7Y3ue9cpeP0EZQ+YpXvL31kBx2njyD3nav0Gdsb+4S4bL1u+0WN6bC4CQB7xh1nScvNWNLzX3oHUSeZGfJHBFVGPWbDyQSO3dTy9YooanwTyNazWZPBl0gEZvTyYtDiCPZeSkxRryeKIjvOaxixNJJpGSgT20IQBAr5K6hQ0O6Vg1uAzjWdWXYo3mZN4W/74rkfbqR3QxcqFXp2vQK+CiZ/6snotu50nxuG6ckzUSqvkt8G+ND35zB6zgtj+zkNR24k8cvuOGqOCSQ8zsQf//P90C584LXxX+mbs8rh61qkEgFnO9vfyU+qOFHpIzsW71dT6x1Ts3/v0cfAwUYQfRZOdbMGtWD991Q36/aDjazH5RBeeVTMvtST//1uHSf88vnfDC23jIhHma+ZfJ8QRZGFf8dTcsgjJm+K4chNLYsPxFN8yCPGr4nGYMqe8dPg5q5ceWhg/JpoEnUpJ9vuhxtpOy2ULrWcX3mlVS4TKJVXScWCdtke3AJ0q+3MHwfVae5fdTSBPZeSaFvFka61VMlp0Xm95Ezs4sGMnp50nhWKzpDyd+DhLOWv0X78vDuexhOCWXlEzeHrSSw/rKbB90EsOaBm62j//7z/d6ZUlHv16kVCQgJTpkwhb968PHr0iLFjx+Lg4MCyZcsyvEi5cuWYMWMGDRo0wM3NjdjYWHQ6HXnz5iU8PDxbPshTXkVFedTySIwmkdN3dPz9XS6btXaJOjNFv3rEiNZuDG7h9sr3mxWCY7Vp1h/80mQ9iolV0RrTnoE3k/7Ki5jB/swck9n9gtnM8DFdyXf3WvI+i0TCtYp1KXX+MJLnVhIeFirFzMmrEaXSV75+p5K+PJ9dfnnJNe7ttqb7le5RnPZfVkCaQaAiCLzVwYxGa6HN1GDK5rfjm3buyakyomh9xgf+FsGgFq70rJe1WvKTt7R8vTwSEahX0gGLCAevJaGQCUzv6UXlNIQVXicWi8jHPwZTq7g9Y9q58yDcaJ35PZdAWKwFLxcpiToLCz73tul1/PGPQQxq7pZCSEyjs7DpVAJ/X05Ca7CQ21NOt9oqKn2kfOueh5xWUT4fmn6tfkW/Z0lCr9sa5r/Au9o3v45nIT7JTPFBj2hZyREXByk/dbe9mvHrnjjGro4m8Lf875x4z3uromwxw77q1iD2KYIEAlpC8HYQnxvfeFSGRiezPV3ZFme23mXt98+U3X883BlHl5dPk31X+XZtNAevJbHwC29K5nn2uQOjjIxcHoXZAmuG+mZLeVd0gpkhSyI5+o+W5uUdcXOScDPQwOWHegY3d2Vwc9e3rr99Ea3BQo0xQfRrqOLLpq5Eqc0sPaRm9bEEQmON6IwgEWDFIB8+tjH+AGg7LYQO1Z3pUjN1PbfRJLL7UiIbT2mITjDj6SylYw1nmpR1+M+U2L2yTZCvry/3799PYSSv0WgoWLBgpjrBpx0ngLu7OzExMVgsFry8vIiOjs70B8kMrxLg9l0QRstKjpy8rePaQz0ze3slF6CDNd1p5LJIHkUYmfypZ5oPZE4TGqtNMz16bqPXaxNkqz4WICFRj7Nj2g3/89cofWQHHacNR5LOo2gRBNaPmpmcvpwTNcCi0YzuuzPJP3+1tS326XReLvbyt9o78ccN0YTGmljwme00nwcRRpr8EMSxSbnTNJ3PiKfB8uUHVjXDcgWUVCmU9fTnnCAi3kTXWaFoDSKhsSY+qepEbg8Zv+yJx2wRKZ1Xyd1QI6uGpk4FWn5YzanbWn794vV5yWUnOR3gXsggwK3wIcDNUd7Vvvl1PAsPI4y0mBzCkYm5aDwhmCZlHRjR2i3ZO9RsEdl9MYmBiyNQyATuLMiX4/eU3bwVAW5O1cjeXQznPkv/GEEC1ValTF9+DWyddY7DK24m/zz97KfI5O/3atn5f3X0mBfO8Um5bPrvGk0iraaG0KmGM73qZV/t5+MoI3svJZGot5DLQ0bzCo7YvyaP1ezgYYSRT6aF4uks4XaIkdrF7WlQyp6/Lyex61ISn1R24MhNHdN7eNq0Jdp0WsO6EwmsH/4hNd4Wr2wTZGdnR2RkZIptUVFRKJWZm7kqXrw4e/emlF3fv38/pUqVytT7XxfuzlKCY8z81N2TVpWd6DwzlGYTgvj8l3Ca/xhM259CqFfSARdHCR45kM7wrmGfEEf/4Z2SU4qfUvrIDoaP6ZrplOK75WsS5x2Q7jFx3gHcLV8zq7eaKQS5FPvJ1ZF3sloKzW+9mY2jj+ToNXMKvVFk9bEEhrdKbTb/lPzeclpVcmLlkbRTaDJCEASqFbFnQFNXBjR1pWrhty9139tFxvSeXoTFmSkaoODEPzpWHk3A2V7CkR9zs2V0AFO7e/LpnFBiNeYX3islIen9qEfMCYQMXh/IWf4rfXNWcHWUEKsxY68Q2Ds+gKgEM2WGPabDjFB6zguj7PDHzNgWy6DmruT1ejct2944+hjrSuvDF0TJHq61bs9q+rA+Bu79mvFxFRe99uAWoPWwSsy+1JNCla1Bx8jKK5ncest7Za/1Iov3q/mikcpmcAvWdN/hrdxYvD9707fzeMr5rJELQ1q40b6a8zsV3ALk85aze5wft4IN+LhIuf7YwJ8H1fi4yiiZW8HSr/zYOcafr1dEc+FfXar3+7hIUX8Yg2SJTD0p/fr1o1GjRixcuJDdu3ezcOFCmjRpwueff56pi8ycOZNu3brRs2dPtFotX3zxBb169WL69OmvdPPZTbuqTqw+ah3sD2jqyoUZeRndzp0m5RwY3tqNizPzUqeEPYl6kYoF33z65ZvEIYO62Xx3r2WqblYwm+k1vh/u4UHpHuceHkSv8f0QMmF98arIynjRf097JFKB+6dDmFZ3DeF3c67OJye4E2JVDy2QgZfcx+WtGQvvO7/vi2d4Kzd2jc/F6Z/ysHyQL4jg72ZdYWxR0YlaxexZezylv/W/YUZ8XD9MZqWFNU0/7dcHcpb/St+cFVwdpdQoZs+m0xo8VVJ+6+/DpRl56F7HmZaVHFk52JfDE3Jx+YGejtXfTDbWO01O1chazFZhqdiLGR97/3fr8W+ILxc1ZuaFHtg5yYl8rGZY+eWsGHP0jd1PTnLilpbmFW2LSj6lfkl77oUZ0/S0/6+y5UwiDUo7cm5aHi7PzMPRH3MzvJUroXEmDCaR4rmVjGjtyoLdqScH7oUZ8f0wBskSmQpwx44dy+jRo9m4cSPDhw9n48aNjBo1Ktm7MCOqVq3K1atXKVGiBH369CF//vycPXuWSpVe3l8sJ6lS2A6VvYSZ26wpW3KZgK+rVQVt14VEJm2I5qvFEQxo4ppuCvAbIY3ALycCQsFs5ssfvyDg3g0AJBYLHaeP4NMJA+g4fURy3WzAvRsZBqWiVMqJT3phyWA0bBEETnzSy2Y6dE4gCAIjDnSm95JmACz7bC/T6q55Z2ZozRaRzGRMKeQC5vdMWOtFDE/8dLs854dbLJcCVycJh69rk7d1r6ti46lnAa7FIrLyiJqONmpfPvCUD2u4b5L/St+cVb5q5sqkjTE8iDAC4OEswcdFxuUHev48qKb3gjBO3tbSqcY7+B1PK7B7HQHfi0GoaLEGtUfbWP99WiMbe9F63Mvck0RqVU0WMjE89ajyWmpv00MiEZhyrCtTT3YF4OLuBwwtt4wDf17L4J3vFmYLGXrDSiQCcqmAKasmte8p609q6FU3ZRuT21NOsQAF289b7cm61HRmz6VEtM8JSomiyJ8H1SnGLh/IPJkqvBMEgT59+tCnT58sXWTGjBmMGDGCUaNGpdg+a9Yshg0blqVz5gSCIPDnV750mB7C5ft6YhPNPIo00bqyI4l6C9vOJWEwiey/ksgnVZ3STNV4HZifmyCTxMbg16UN8V98BVi/FAk6M+WP76Lu9mUsHLeIpOcsdkSsNahpkdF+gIMte9JrzqjkYFZisVD89IEUx1gkEva36EG8UQRjyvM9f430BLGeR2u0JL/nVT9DRvstIiToTdgFODFgbwf2TDjJgxPBTK+3lvojK1OkYV5U9vIMA943la6b31vOo0gT0QnmdH3YTt7SZpvn29uKOsmCUibg9tz3VRAEhrRwY9SKSLaODiDAQ0YeTzmR8daBmCiKfL8uGh8XKZU/+m9na6THhxD2zfJf6ZuzSq3i9oxq40bjH4LpUsuJg9e0aHQW6pV04J9gA5cf6JFLBcasimJmLy+U8nfkiX5qn8OXKbfnsH1OMk+D0OeDWdECQVtTHidIrMe9bBCarzNEnYQ789M/7u7P4Fn9jaQpv4jSXs7sSz2Jj0ji+yYb2DHvIjvmXaT3zLqUrp/3Td/eK1Msl4ITt3TkqZl2Ov/VR3oclQIuNgRa/8tExpvJ65369zaslRv/+y2CsvmUFPSV42gnIS7Rgr1CgiiK/PRXLKIo0rC0g42zfiAj0hSZWrFiBd27dwdgyZIlaZ4gMx2rSqVCrU5d5/dU1CI7eRWRqadEq800nhBEQpIFtdaCUi5Qu7g9vRu4ULOoHRM3xnD6to6t3/jjoHz9X+SwOF1y+p8kNgbvdi1QXr2EKJEwrPQS7CZVo+zRnXR4spoa/FEJlk7+M9lHNk6bPSJTNc/sSbFi+zwWiYT1I2ek8LS1dQ3BbKb/8E7kvnM1w88dWLg0C2euQ5RKc0RkKqP9otaEbuIzVcdBuzrg4Zp28COXCW+0HvWr3yPI6y1jRGvbAx2N1kL1bx6zfqQ/RdMxVH/X0RksFBn4kOtz8yGXQqJeRGUvQS4T+HVPHPN3xdGznoqPfOXM3BrLN+3cWbw/Hp1BZM1wvzc6kfWq5LTI1JXw9Fdmyvg8+919EJnKHt6Hvvl1PwsHryXRbU4YZotVnyCXh4yutZ3pW1+Fk72Ez34NRyYRWD7I563TEEjF09Tg2IsMnb6U2VuU1gDvaWqwaAG38lB/X84GuZDymi/yKgJQtlSU0+I1qii/DIH/RDOr6zN9kmGrW5C7mMcbvKNXY+eFRKb9FcuB7wPSVOj9YmE4BXzkfP1JDj937xi1xwUy9VNPyuZXojWIuDpKkrNAlx1S8/36aLrUdGbx/niO/Zibf8OMLNoXT0S8mc2j/PBzy5oI6H+BLKkof/zxx+zatQuAevXq2TyxIAgcPHgwzQs/3deyZUt27NiRYrXr/v37TJw4kUePHmXyY2SO7Ahw1xxTs/GUho0jrQICL3Z4oijSbXYYjcs60Kt++tcQRTHbO8zkANdsxrdZXZQXzyfvG1p2Kf3sVlDs7KEUgWdgkdL89iQ4zK4AV2Un49MJA1Kt3AJcqVyfdd8vzNQ17BPi+N+gtunW4cb45OLneZuTg/Q3EeA+xXQiBOPOhwBUbFOIlqOq2jzuTQe4DyOMtJgUzPiOHnSs7pTiXuISzfRZEEZ+HwUze3lly/UsFpG/ryTx54F4zt7TYbFA8dwKGpZ2IFEvEp9oXU1uW835tQfUbaYEE5dk4X6YETuFgNEs0qqSE/2bWL+/Sw+q2XAyAUEQKJNPQfc6KppXdMowJett50OA+/7xPvTNr/tZGL8mmiSDhZk9vWz2yXqjSPUxgczt40XNTPrh5kTfniEvBH5Dpy9l9qg+b9Y+52ib1Cu3ALlaQ+2/sn7ee4vhbAYqyq8rkH8Frh16zJJhh5J//m5ve1y9069lfRsxW0TaTQ/F01nKgn5e2D0n9mS2iMzaHseaYwkc+CEAtxzyXxVFkQPXtNZA8KYWoxmKBsjpVU9FPm8Z+69aMzRyecjpUtMp2dv+TSKKIl8sjODQdS3xSRbsFAKCAF1rOvNlUxdye8q5G2Jg2LIozt/T4eIgIZ+3nB51VbSt4pji9/yB1KTXr6Q5wn/agQIcOnQorcPSpW/fvgDodLoUs8mCIODr68v8+Rmkn7whlh1SM6J12uqzgiDQv6kL366JthngXn6g44/9anZdTCRRZ8HPTUbnWlbp9KzasdhEKkXd/ys8+/dGeC6YLWEjVfhkm+yvXy19ZAdFz9p+NkqdP8w/R3akuYL7PIUuHsc1IiTdY1wjQih08XimzpfTyGr4I63mh27cKc7/dZfzf91l8Po2uOd6u+ok8nnL2TjKn34/h/HL7jjaVXXC2UHC9ccGtp/T0KmmM993yp4ZZZNZZODvEdwKNvBlU1d+G+CDWmuh1/wwZm+PxVMl5cumrkTEm+kwPYTSeZX8/Ln3azEi/31fHHdCjOhNFnaND6BkHiWRahOrjybQ7qcQ5vb1pnlFR7af03BkUm68VG//bGl4nImwODMOCoGCvm/Orkr4kKT82vkv981ZwWASWXlUzYHvcwG2y0aUcoHPG7mw+IA63QA3LNbE4gNWH8vgGBPOdhJaVnTk88YulMv/GvxQczo1+GV5uNYaWNsieLt1f1ZWcB+uhXNfZHxcoQFvdXALUKpeHmZf6snBZdfZPucCPzTZCMDUk11R2r/5ACyzSCUCqwb7MnBxJMWHPKJTdWc+8pMTHmdm7fEE/Nxk7Bjjn6PB7bClURy9qWXQx678/Jk3dnKBzWc0fL0iCqNZZGAzVwr6KrkZZKDmuCDaVHJiek/PNzZRLYoio1ZEc+G+niSDhWWDfGhWzpGHEUYW74+n3nfBbBrph4uDhLshBjaN9KN60cxNsL3NiKLIgwgTcYlmvF1k5PJ4M2OqTPnglitXjkuXLqXaXrFiRc6fP2/jHSnp0aMHy5cvz9odviTZsYKb9/P73JyfD8d00o9NZpHcn90n5I8CKTrMJQfimbUtlv5NXOhYwxl3Jyl3QgwsPaRm98VEVg/1o1TeV+sIw+J0PF/D77hlPd4DrEHu0LJLmX25V/I+i0TC8iHTuFTr42f3LqZfOyfyRCImDY9bwWym3Km9KWpwbWGRSFg2ZBoXan6cat/z1xg+piv57l5L8b5rFetS6vzhFOd/WKgUMyevP68BkwAA7XZJREFURpRKk9+f0WfIqf0AX9ctSPDVCNYNsU4oqHwc+Xxtq+T97o4Zd16vYwVAFEWO/aNl3+Ukkgwieb1kdKrhnK2TLVM2RXP+Xz0rh/hir5CQpLfQanIwNYrZM7adO6NXRKHWWlj8P1+MJpFv10Rx+YGeLaP9c3SG8tRtLQMWhbNjbACHr2uZujmGAU1d6VzTGXcnCTsvJPLlogjsFfDnV29/53L+no4522M5e09Hbk8ZcRoLCrlAv4Yu9K6vShXo5vQK7rWI9OvnS3k/+9t+WMHNft7Vvvl1PguPo4w0nRjCzbnp10HeDNTTa0E4Z3/KY3P/pQd6Os4IpWUlR/o1UFHYX0F0gpk1xxP4eXc837R1o0+D1/S5nqQGD522hNkje6Xcl53esOl53D7ekHZ68qvci630ZEHyZleps5GV445xYed9ABT2MqYc7/rGJiizyr9hRtadSCA8zozKQUKbyo5UyGFnkfm74thyRsPW0f44P6nxDYkx0eD7IIa2dOVhhJEbgUa2jvYHIEFroe8v4TgqJSz5n/cbyaZbcUTNor/j2TU2gBuBerrNDaNrTWf6NHAhv7eMlUcTGLMqGjs5jG7rTt/X1X7kIOtPJrBgVxxhcWZ8XKQExZgoGqBgaEtXGpfJ/syFLKUoP4+zszMJCSntM0RRxMPDI9vrdF6V7Ahw8/e/z6WZedNdXUrSWyj05QOCFj8LcI/dTOKrxRFs+yaAPDZ89baf0zBudRQnJufB6RWK8MPjdaksODw/7YDD7h2pAtyb1Rqw+tuUfnKZSVH2MWroNb4fJz7plWLVtPSRHdTYshTRZCTP/X+St1skEm5VrkfRF1Ojn6ubffEaz6co9xnbm4B7N5Jrd49XaZqixjf4oxIsmfTnW5Gi/BSNzoTzk2O0C64ghljV8JQ9iyEt4sagWvlQKdM/R3Y3uo8ijWw/pyEu0YKnSkqbyk745nD9RqLeQvlhj9j/Q67klKAlB+I5eDWJFUN8EQQBrcFC+eGP2DU+F/m9reJcHWaE0q6qE11qZZ8p/Iv0mh9GvZL29KxnbQsuP9CxeH88288lYjCJuDhI8HOXUbWQHVO6P0vV1hks/HVWw8ojCdwPN2InF6hX0p5e9V1eeYIqq+y5lMiwJRF8086DdtWccFBahSjO3NXx44YYcnnI+OVz7xSDpZwOcK9nEOCW/BDg5ijvat/8Op+F4GgTdb4N4u6CvOm2t5cf6Pny9whOTs6dap86yULFUY+Z0dOTVpVSWwo9iDDSbGIwvw/woVbx1zRJdrQNQwd/kjrAfdXU4Kc8FbIqMjRlgPpwLdyaBaI5pZVPdgahz9UZpwiS30SdcQ4giiJT220l4oH1O1Gosi9fLmryhu8qJeokC1vOaHgYYS3raVzW8fVkKdjAaBIpOfQRm0b6UTLPs3sYtTwKmRQmd/PEZBYpPewxq4f4UvbJfeoMFqp+E8SiL7ypUvj1ikWKoki1b4L4qbsHdUpYRaIeRRr5bV88q48lEJdoQS4VcHWU0Ku+ijFtUz/HBpPIljMalh5ScyfEiEIG9Uo60K+hivIF3j7xyx/WRbPjQiKTunnQsJQDEomA0SSy/Xwi49ZEM7SFK581yt62P0spymCd3QUwGAzJ//+Uhw8fUqJEiUzdQO7cudPsWB4/fpypc7xOqhexZ8/FRDqnM/DecymRqkXsU3yuX/bEM7qte6rg9mGEkZ0XElEnWWsQlx6KZ+DHbtl2vw6b12O/d5fNfUXPHKLU4R1cq5v51F6HhDj6TPyMgHs36Dh9BABX67RI9riVWCwE5S1CSP4i+D+4nUJQ6vljgj8qwdKJizNMjdY6u7Jk0p8pA2qdKTmwrrFlKUsnLk4Obt9G7AeWwRKvR/fTBfTLrIG/+VAeeE39gTrJzNAlkZy4paVVZSf8XGXcCjJQa2sgLSo6MuVTzxxbKT14NYmy+ZUp6l2WHVIzqZtn8vfDXiGhbVVn/jqtYWgra/p//8YuTP8rNscCXINJ5MCVJBZ85p28rWx+OxZ8Zsf8fiIms7VO+spDPV8uCk8+JiLeROeZoXg4SxnU3JUy+ZQk6kW2ntHQZVYoA5q68r9mrjlyz2kRnWBm8OII1o3wo2z+Zx2bIAhULWzPxpF+tJsWyvLD6gx1AbKTDynKb4b/at+cFfzcpDgoBc7/q6dSOoroOy4kUr2I7f1rjidQvYidzeD28gM9+68mUTqfktEro9j7bQBOdjlcN5ecGvxJ6n2vkhr8lOcDzFPdrNteDDBdy4BLaYi/mn4QWnf3y6+wKt2tweuLAfbTf1+HUnQOIggC32xug8loZmTlldw9G8bQcsuo2704rYe9WYsuURSZsc0qvli7uD1l8ilRJ1n4dE4Yvm5Sfh/gQwGf15tafeKWFn93WYrgVmuwsO5kAsd/tJYeyKQC3Wo7s+FUQnKAa6eQ0K+hij8Oxr/2APdeqBG11kLt5ya88nrJmdTVk0ldPTGaRGRS2HRaw4aTmlTvj9GYaT89FHuFwJdNXKj0kR1ao8jWsxq6zg6jV30Vo98iMa99V5LYfEbDwR9ypXDukMsE2lZ1okJBJfW/C6Z6UTtK5H49A+N0A9yCBQva/H9BEKhRowYdOnTI1EVWrlyZ4ufQ0FDmzp1L585vXtrdFr3rq/hhXTQtKzvZTFPWGiws2BXH8NbPgtRYjZkzd7QsGeiTvC0u0cyQJZGcvm0NOrxdpLg6Spi0MYbgaBMTungif8XaAIfN6/H4ImUN7vNILBY6PAlSMxPkpuVxW/rIzhSrs7ke3Sb4oxIEFirFiba9k4PRp/9W3fwnK378I9NBqdbZ1eZK79U6LbhWs9lr8799FSQuShwmV8ew5yGmoyFMr7eWhv9n76zjo7jeLv7d3dhu3CAhJLgXiru7F3eXQqG4FCkUdwqlLbSlUNxapDQF2uLuFC0OCcQ9m03W5/1jSchmJY78Xk4/+6GZmZ17Z3Z37j33eZ5zRlSm6dBKrw/S6cDctVjangWkqPX0XBFGxaL2XF9ZxEjZe15vHRN/iWLIdxFsHe+TL/7N0XKdEbnV6wUehKipnWFAKexpQ0jMa2umOmWl3A+JIL+gUBoU0M1NNkUiEbavnn4FXCUkvjKmFwSBQd+G07ySjOldPYwm/xM6utOrgTMdF4UQ4GVDBzOT3fzCjjOJtKriaERu08PBTsyMrh5M2xrFwCYubywd610XnP1fxf/XsTknEItFDG/uyrIDceye6GM2HTQiXsumE4kEzihk9hy/XUhiWhfjRemnERpG/BBJSKyWzrUc+cjfjqM3kyk3NojJHd0Y284tf36H1pSL4bUnLeRcwdicx23wLuPobPxNA4H1qAFlJ+Y9CbX3MB/5LdoLArq/V2nJlmBjK2HVjYEoElR82XgXJ7fe4+TWe/T6qi61OpUyfYO1lPE8uh/z9sRy/E4K5xYWNhrX5/T04Kd/Emi7IISjcwq/0brKaLmOIl7G7YXEanGTiY36WMTLhjP/pRgdV6+slN1njbNc3gTik/V4u0gsPgNS5/4FXW3S5h/pMfi7CGqUdGBJP0+jc4xv706fBs60XRiKv6cNfRvmXwZcdvDj3wlM+cTdoi1lEW9bhjV34eejiawanDfCppnB6jf0q6++Agxm8K1a5Tx9olGjRibbGjduTOvWrRk3blyOz5tfaFZJRuBVBb1XhrFqiDclfF4rvj6L1DB5UxRl/OxoU+V1Pnl8sh4PZwnSV1EyhUpP9+Vh1CzlwI2vi6Rt/6+aiiHfhRMcreWzHyP46bOCOaq/0OkBnQ6ndd+akNu7tZsZqSiL9Xpq7fuFCzVapNWvWvOAzYrHrQCcbNGDi027GMjnq/OJdDrO1mrN2eotDITNQjsW+6Cx4HNrxUc3W+fPo/2pkJs7pnFhqF8IyaKrHP3xX47++C9jfu+MEym4d+2AYvRYVN164mJvgwBIdu/C5ptVqAIPg4fxZCAr34ydZ+S4OUlY2t/L5GHqIpOwbkRB2i0M4cgNBe2q5T0p83CSEBL7+j6IRCARg0YnGNkJhMZqcXd6TTbVGgFJPgY6nKVidIJARLzWar3xk3ANBV0ND+Vz95XIk/Um5DYVvu42LO3vzcK9MbSv7vjGiOQ//yYzsaP1rI+6ZR1ISNYTHK2liJkSiQ/438H/17E5p/i0hSuHrysY/kMkC3t7GpVt3Him4tN1EQxv7mJR3T1GrsM/3YT+ZYyWdgtDGNvOjU9buKYtHO46l8T6zwowdUs0CpXAjK55HGHR6wzEMT25NZcaLOgNx+WECKYKWZ3vC1gRskIM5aaYtqHX5R0JtfT+/wFymx6OrvasujGQyKAEFnc6wK6559k19zyjfmpJqRoGNw+rKeN5FNF+GqFh08lEri4LMCEqErGIz1q5ERmvY+n+WL4dVsDCWfIeHk4SXsQYz7UkYhGaDCL+L2K0Jv1Wa4W3UuPs4ybhRYwGtVawKnL1OFxjMj+59kTJswgN+6b6mp1jFHC1Yc0Qb8ZsiKJPA+e3bm2m0wscv5PMlrEFrR7Xo64z7ReFvDGCm6XpZatWrVCr1dy+fZsTJ05w/PjxtFdOYW9vz7Nnz3L8/vyESCTi68HeNPlIRodFoXRaHMKY9ZF0WRpC2/kvqVXKge+GG9e6uTuKiZXrSFYZBoTNxxPx87RhQR/PNHILhnogbxcbNowuyMNQNafupZi0nxnEIhG2YhG2tjbE7DmI+uMqAAhiQzvbZq3l1ykr0L/6O6RkBbYv3ICToz3ODjaIABcHG4uv6/XbcuDzeVgqzk4VYGqzbz0F9Clp76t/6QhTZ/aloCYJJBKrbWTWh3d9f2p9rpODjfmXkx3CV7Vw6FcWgG8/2c/W9j9he/UKLkMHcmrB98jVOiS7d2E7oC/iK5exb90CclA3t/lEIqPbWI4W2NqIGNHSlV+Om/pd5gWaVZJx7YkyLTorEomoU1bK4euKtGOUaj17L8jpXOs1wT58XUGdMvlXr2YjEdG5phPbT1tfvd1yIpEe9QwK2L+el9O/sfUIaOOPpMQn6XkQqsnT/lpDitpQL2wNIpEIF5k47Rn0Af/7+P82NucU9rYifpvii7ujmJpfvKDb8jBG/BBB49kv6bc6nNFt3Kx6d3o6SwiOfj3BXrQ3lt4NnPmslVsauVUo9cQrdHwUYMf+L3z58Z8EgqPz+BkhlhiIjHvV19vqbDfU3NbZbiC7kPPU4FT4tgRH82JbaRDZgDbJuI3nuwwCUarY/zkS+iZQoIgrq24MZPR6w6LV2k//ZkKVzUQ9CjakjMdcNkTTn+8yvCE1mh9z2bBflbu6+1+OJ9K3gbPFKBzAyFauHLisICHZukVcXqJ+OSnBUVr+e6lO2xbgZZin3XimAgwka/tpOd3qGC/i/3lNQf1yb75e1d/LlrKF7PjjqsLiMYIgsOlEIr3rG/d551k5Axq7WM24q1PGAZEIrj1V5VmfcwqVRsBGLDLKHjQHV5mYZHWmsk95hizlGJw9e5bu3bujUqlITEzExcUFuVyOv78/T58+zfT9s2fPNvo7OTmZQ4cO0aZNm5z1+g1ALBYxoaM7o9q4cepuMtGJOjycHWlUQWpEWFPh5iihdmkpv19Oold9ZzafTOT74abKbdtOy+laxwkHOzHDW7ix6XgiTT6S5bifgrsHUXv/xLvHJ8g/GwPfGya3qenIdQ9sYks261dFOh01juyxGD1M3e4R8ZLPxnVj1fq/qHj2cFrt7ZCZg/lm1npw8Mrxdf2vwKaCJ84LaqOeeZKXkgAmVN7E+Ifz6LZ8Epq7J7A9FJgWgRffuI59+zaozpzPcrqyWivwMFRNvbLWH+CNKsiYsS3a4v54hY495+Tcf6nGxkZEvbJS2lR1zJK8vpODmP6NXJi0KZLNY32xtxUxuKkLKw7E0aaqIw62ImbviqFGKSnFX2VDJCn1rD0Sz9xeubcpkqfo+f1yEsFRGqR2Ypp/LEsTghrRyo1OS0KoX05KzVKm9+i383IuP05h+UDDdzUyQUe7atajn2KxiABvGyITtG/MzzfAy4Y7wWqqWBGWSFDoCI/TvVFT+A8Zym8X/x/H5pxCZi9mxUBvZnX34MSdFJJS9PSs50yjCtJMSze613Viy8lEmleSEafQEXhVwfUVxgTwtwtJNCwvxVVmeHb3qufMphOJzO6eN1ZsaUhfnwp5nxqcmqKseG79OEFt8KmVyKBYH+PU6eMt3lsBqHcBJav7sOrGQC7uf8TueedZ1OMEMJYFn4/GUaownzIed93wueVCVfrmcxVj27lZPaagmw3FC9ryKExD9RLW2wmJ0bLjrJygSA1SezFtqshoXEGa7YiqnY2IUa1dGbcxiv1TfXF0ECMRixjSzIVlB2LZNrYgC36LI8DLxkh8KSRGy9ZTiRz9qnC22ssKVBqBg1eS+C9Eja1ERMPyUuqWcTCa80/s6M7n6yOpXNSeEj7G8wpBEFiyPw5BEGheyZgDRCboMl38F4lEFC9oS2T8m1tosASpnQhnqcHqqFQhy3OiO8Eqir7B7LIsRXAnTJjA1KlTiY2NxdnZmdjYWGbNmsWoUaOy1MiLFy+MXkqlkokTJ7J58+Zcdf5NwN7WoB7Xp6ELras4miW3qRjVxo3Fe2O5G6wmMkFLtRLGhdQHLiVx46mSrnUM0aLGH0m5+VyZ6z4K7h5EHjlJSpceRttvN27PTyt3Z1ucSZBIeFHmY4sR3PTwiHhB34Wfp5FbAL/Hdxm1YAQi3dv/4b0LECQSqrZOYeqDGQCsLj2bSZU2Yhd40Ci9XBCL0Y6bkK1a3NRHqT6TD0svCIjNfHUFQeDbQ3HUmBLMjWcqPi5mTylfWzYdT6Da5CBO3knOUj+mdfHA0V5MuwUvOXApicYVpJTxs6Pt/BA6LAzhdpCKb4cZ0lIeh6np/XUYNUo60LRiLhZ3BIFVB+OoNjmIY7eScbATE5+sY8A34XRcFMKLaA1l/Oz4fnhBBq4JY+qWKK4/NUSaz9xL5tO1ESz4LYadE31xfaWY7ioTE5HJgCEIApEJurSJbGbHXnqUwuYTCWw7lciTcHWm7zGHvg1d2HQiAb2VD3rXWTnNKsneiLdwGkSZvD4gX/H/eWzOKVxlBnX5fo1caFpRliVdgl71nLn0SMn+S0k8DNFQ0tcWL5fXv7Mn4RoW7YtlTFu3tG1NK0q5kV/RldT61Iwo2suwPTfEMjVFOas/4Iv94XQn47rgVLKl/zAHyA1qdy7FqhsDadzR8F378rvvmbB8EzqtyJAynjFVPZfexyKRYa6QGfR6AWs/G41WYOKmKOrMeEFYnJbqJR0o7CFh1s4Yak57wb0X2f9djGvnRilfW5rOecmOM3KSlHqGN3fhWYSWjyYEE3hdweYxhhRZQRC48lhJ+8WhjG/nZkIuc4ttpxIpPy6Ibafl2NkYlILHbYyizvSX/Pvs9bU1ryRjehcPms99yZzdMdx9oeJFtIZD1xR0WhrGwSsKdk/yNXkGuTmKCY/PvEQuIl6Hq2P267yuP1Wy8VgCm04kcjcHn0VGiEQi+jdy5qej1rMEf/onkf6NnHPdXlaRJZsgV1dX4uLiEIvFuLu7ExcXh1qtplixYoSEhLyJfmYZeWETlBtsPpHAkn2xJCbrufyqjuFRmIZNxxP4+2YyOye+ljkPitLQaXEoN7627s+XEVGJKotDz8pmu3BYWMfqoJ2ZTVCpowcZmInHrTWk+t8+at7R4jG5temRK1Q4O5oqsaV6974Jm6DMjklW6dJshAAqngyk2/JJ7Cg8jGsedQHo/uIX6sacQhCL0WzZjq6nsShIVqYYLea8ZFoXd5pVsuwxtuN0IoeuKdg2wddo+7eH4vjtfBI7Jvjil0E04tx/KQxfG87GMT7ULp15KrFeLxB4VcEvxxO4/Mjg1ezpLCFZpadigD2FvWx4Ea3lSbiGT1u6MqatW65qY+btieHcfyls/NzHqO9ancD6fxJY/08Ch770w8fdhvA4w0ruwctJJKboKeBqQ496TvSo65xGbgEOXVPw3eE4Dn1pecX3ymMln6+P5MJif6v9P3knma92xaDRCtQp44BaC8duJ1Pe345l/b3SotlZgV4v8MmSUCoG2LOgj6dJu+fvpzD0+3B+m1rISKEwv22C7ltOCgCgbLokjg82QXmP93Vsfh+/Czefq+i+Ioxqxe15Gqnh9Hx/ohMNPrg//J3ArG4eDGzyWvDl8A0F6/9JYN9U88JVeYEJVTaz6sbAvD/x811wvg9kaZnbDPLSize7eANCTG8Fz3exdtwNHgWXB6CARyjThswwCP3l0f1e8Fss8QodKwZarpF8GaOl7owX3PumiEW18M9+jCQsXsuWMT64pCutEQSBXeeSmL0zhr+/8qNYgewRT0EQOHIjmZ/+SeDMfyno9FDKxxZ3Zwn3XqipVcoBdycx91+qSUjW80Vn9zwXYfrleCKr/ohj50Qfo7FWEAT2Xkxi6tZoDk4rZKT4/ChUzYbjifz1bzIpKj1FC9gyoLELXWo5mnW3OHEnmZk7Yji3sLDFcqk7wYbn0e1VRYy0Tqzh0kMlU7dGE5eko2F5KXoBjt9Opoi3LSsGeuXKAjEsTkujWS9Z1NeTbnVMSey3h+L55XgipxcUzlOV+Vz74AYEBHDr1i3c3NwoX748v/32G56enpQuXdro5Nbw4MEDbt68SVKSsRz2kCFDsvT+rOJtE1yAW8+VdFkWhl4voNII+HrY0Lu+M/0bu1DA9fVEfMPRBC48SOHn0T4m53gcpuaX44n8eS0JeYqAr7uEnvWd6dvQBZ1eazFit7r5LuwX1CElY/V9OmgFy8RJpNMxaUYfij66nZ1LTkMqub1av61VcpZax5uT/TJ5PKMWjOBEh4Fcq982bXu1s4do8sdm1n75Iwpnt3xrP6vHiDHUS6fH0MWfU/HyMZRiB6ZX+iFt+5xCB1Hv2W10rJOdZQW+9NhxOpHd5+TsnVrI7IMuRa2n1dyXfNXTkxolHUhSCrg7iVGqBWpODebUAn8KeZgn6gcuJbH+nwT+/NIv036kR+pjRSQSoVTrOX0vhVi5Di8XCQ3Ky7C3zV1470m4mg4LQzi7OAAPJ/OTljm7oklWCywbkHVBA61OoP6MF4xp60bfRqYDY5JST7dlofSoZzBrt4S//1UwfmMU3wzxpvnHsrTPUaUR2HwigW8PxfP7dL9s2S3EJekY9G048Qo9Axq7UN7fjniFnt/Oyzn/IIWfPitIg/LGEfF8J7gx1o8tmy47830kNe863tex+X39LkQmaFn3VwKr/ohHLAJnmZiO1R0Z3sKVShkmhxN+iaKAq4TpZvwtUxGv0LHttJztp+W8jNEisxfRtqojw5u7UD4LVhr5QnD1OkMdbczlnL3/bZLbNyDE9FZxuhP6FweZsWYtKrVh0bla+fP0GxGRJ97HITFa6sx4wYVF/iYL3qmYvDkKgBUDvdHqBKITdUjE4PVKMfjfZyp6rwrj6rIAHC0QmUV7YwmJ1fL98JwLVaWfYwAkJOs4eSeFJKUefy9b6pd1yHNxqcRkPR9NCOL4XD9KWlig3ngsgYNXFRz4IucLW3q9QP0vX9K/kaHOPyNS1Hq6LAujdWUZ49pnzXL0/P0U+n4TzsqB3nxS0zEtCKbRCuw6J+er3TEc+KKQyXMsO7gTrKLHynBKF7KlfyMXfN0kPIvUsOmEnIRkHb9N8SXAK2+j6bkmuOPHj6dmzZr06dOHFStWsHz5cmxtbWnVqhUbNmzItAOLFi1i3rx5fPzxx8hkrydgIpEoV2IY5vAuEFyAn/9J4OgtBTsm+Jr9kSlUeprNfsnKQd7UK2ccHdt3Uc7M7dH0b+RCj3rOeLkYosCbTyRw5l4K60Z6UaaQ+S/Jiqa7kC6sY/WHnVkEVxsVzbhXPriWSJyl7fdqN2Pb7HX5FkGVyuMZMnMwfo/vWvXf/WbWemy8LdcAv4kIbsb96fuYivOejfnVfxAA3oVUaMY1Tds3ql5RnO0zr6fUaAX6rg7DVSZmcT9vo9S50FgtYzdEotMJiERw/akKZ6kYhVJPSR87vF3EbJ9o+UGs1QlUnxLE9gm+b8y7LCuYvTMaOxsRX1qpbwuP09Jg5gturCyCkzTrK4aPQtV0XxFKy8qODGvuSulCdqi1An9eTWJ1YDw1SjqwfKCpYnUqVBqBqpOC+GWMj9m6X4C1h+M5818KOyf6mt1vCYIgcOa/FHakmxC3ruJI97rOOJu5xvwmuA8yIbhlPhDcfMX7Oja/79+F8RujkNmLWNTX/BiTGuW6tMTfYk38ozA1nZcaSjWGN3ehnL8dcUl6dp+T8/PRRGZ3N44Im0O+RXBVsXCkOiisiI1JZKAzU8JS+JM8IVvZRnrvXmu+vO9rbXAGeyiV2o5p3/yUtrv9AAnNJvTLdTNr/oxn04lENo8paBTRS1bpWXkwnr0Xk9g90Yfd5+RsOWUQcFRrBQq4ShjW3JW7wSoCvG2Z8oll4hWVoKXqlBfcWR2QpVKfdwXr/0ng7H8pbB5rGpRKhUojUH5cEH/P9stVavTzSA0dF4fSsLyUz1q7UsHfHo1W4NB1BSt+j6Ocvx0/fFogSyRerxeoOuUFi/t5Grm/pMf204n8fCyRE3NzV6+sVOvZf1nB/ktJxCsMVkm96jnRpqpjliPN2UGuCW5GnD17FrlcTqtWrRCbK+zLgAIFCnD06FEqVaqU6bG5xbtCcJVqPT1WhFG0gC3ze3sapUGGxmoZ/VMEAd62rB7ibTRRvvZEyYBvwtn7RSGzAjZ7L8iZuzuGwJkFza6O5QXBTVRqaXpyL53XzMp2GV0q6Txbq3WeE1yRTsfIST3xf3jLqL37NZsYefQCPC9VkfVf77Hon/umCa45cpsKAZhYeVPa386TqyDxlmWZ4IJhRe+rXTHsu5BEowpSfN1tCIrScOmRkjKF7AiP1zCtiyftqzthbysiRq6j99ehPArV8PPoglbTmz9dG0HrqjK61H5ztROZofOSECZ0dKdhees1vE1mveCboQWoVDR75DwyQcvGo4lsPZWIQqVHoxWoVdqBIc1caVfNuj3Qvotytp+Ss9fKCm6ySk/VSUEcmV2YotlM08oOPhDc/194X8bm9/27EJmgpfncEPo1dGFsW1ejNMO7L1T0/yaCwU1djGpy00Op1lNr2gvGt3dncFNTEvskXEPbhSH8NLIAjSpYfsa9kynKbyOCay7qbM4+CcCzZq6EmN4KrHgfx8vdmPvD6rS/h3zdhIpNMlHAzgSbTiSyaG8sxQrY8nFRexJT9By+oaBOaQemdnJn0HcRNKkgY3QbwwKwIAicf6Bk2YE4rj9VsWVswUzFU+vOeMEPIwrkKmL4pvH5+kiqlXAw+5tNj/7fhNO5lhNdaufOljFGrmP90QR+OZ5IQrJhHlK9hAOftnSlS62s2xQeu53M7J0xnLWS8qzTC1SaEMz28T5ULvb+fCbWxpVMZ886nY7SpUtz79497O0NF12/fv1sdUAqlVK2bNlsved9h4OdmB0TfZmxLZrqU4JpVklGAVcJzyM1XHyoZEhTV6Z0djf5sq07Es/Eju4W1Vm71nFm/yU5B68k07tB3nuaQqqK8q9G5DaNSF46jtjKmohYr6fH8smkjNdbrcHNCQSJhHOdBxkRRXMevXqxmBMdBlokt28aIp2Oevs3GZFbvVjMvRpNKH/FQMxX/TuI57ISfFN6FvIVNxDJbODvolluQ2onZtkAb6Z18eCvGwriFXoqF7OnW10nFv4Wy+FZ/kaRXU9nCR8XdaB2KSmfr4/k5Hx/i16xKq1BAv5dgkgE1krEk1L06PQCOr0hcp1dFHC1YVpXD77o4o5CKWBnK8qSojTA5UdKWla2PrjL7MU0KC/l2hNlvhLcD/jfxYex+e2hgKsNR770Y8yGKMqPj6ddVUecHMTcfK7icbiGGV09GGQl+nrgsoKiBWwtTpRL+NjyVQ9Pvvkz3irBzRekeu2mJ7eWyKI5CHoDGYM3R3JThbHSk0Bz3r15IMT0xpGJ97GbczyrpgziRXgRvt46l40TTwAwaWd7Cpe1nOFkDYOauNC3gTN/30zmWaQGqZ2I6V3cCfCyoemcEIY1czVSWxaJDM4L+6Y6UGr0c34+mrk7iMFaJkfde2vITIRLrxdISNG/8t7NfXuezhKmdfbgi07uJCkNfro5Ke+6+FBJ6yrWCbFELKJlZRkXHynfK4JrDZl+BBKJBIlEglKZc7Xf+fPnM2bMGMLCwtDr9Uav/2U4OYhZM6wA5xb7U6+slAKuEjrUcOL6yiJM6+phIgSlUOk5djs5zZPTErrVdeT3y8noBExeYFDUlSu1Fl8ChuiipZdeIuHbmT8QXNwgZpBaV7tu0mpiChqnL+hFYm7WbJrmuQsG0tnk4CbkCpXJuVO3ZdYHS/vP1mrN5vHLjNoz6k+6GmBz7Wd2/qzuz845EjSC2fu5ccZ3bJ+4PO1aJL72OE4pC0WcEZK1LKq/netHn6FQa82+zCVfeDhJ6N3Ahc9au9GtrjNbTsqZ1sXDiNymol5ZKXdfqGlb1ZEdFrxi5Sl6LjxIoYaFVNv8gCAIXHui5Ie/4ll7OJ6z/6WYXGvV4g4cu2WcHqfVCWw7lUizr15QYdxzKk8K4kGohkPXkkhQ5EzNUyQS4SQVZ5ncguH3l5VUHIlYlKn69buODyLKbw8fxua3i0IeNuyd4ss/swtTsYg9vu42fNbKlburi1gltwC7z8kzjQJ1qeXI1ccqohIyV1PNU2T02k2NyDY8ALW3mh4vEhvSkkXpxmRBbyBlep1lJeW8Vlgu2svYC9hcP9NHlt8XhWdrn0e66/Uv58mqix0YvLIxACt7BzKhymYSIrPmhJARtjYi2lVz5PM2bgxt5koRb1suP1YRK9fxeRvzGRgSsYge9Zz5+18F8hTLz5D7IWoSk/WU9H0zNnuZ4XaQirVH4lnzZzz/3ExGZ2FgrlHSgb//Nb2fMXIdi/bGUm5cEB+ND+LwjWSWHYhj++lEi+fKDkQigw1PTrVL9HqwycKajo1EZNWp4X1DllKU165dy++//86MGTMoXNg4xF28ePFMG0lNlUr/PkEQEIlE6DKxkgkNDaV9+/bcu3ePpKQk7t+/z6effopEIqFkyZJs3LjR6LzvSopyThASo6XN/JfcWl3U7H5BELjxTMXmE/H8cSWZce1daVddins6kZ0VTfImRdnFwQapPJ5Bs4ZxrvMgbjUy+OpK5fGMHtsFj4iX6EVi9kw1rYENLVYGrUjC+e7D0t4HhjTdevs3sWn+z0TYOuUqRbjnnJF8fNm0Riy1BrjU0YM0D9zCJgsewG+jBjfj/Uzdn/6+pPZVkKtRLr6a9t5CK+ohyrDcObhGAI52lttPUOioPCmI+98WM/tgVGsFqk56TqvKjhy4nERhT1ukdiJafCyjXyMXCrrZsGx/LA9C1Gz43HLNSV7i32dKJm+KIiFZT4uPHZGI4fS9FLQ6gUX9vNKiGcFRGlrMecmpBf74uNug1goM+TacxBQ94zu407iClGnboklM1iMRw42nKvZ9UQifN+ARu/lEAifvpPDLGMv3TKsTqDY5iJ0TfbMkJpNT5HeK8sNY68eWTlfq9r6npb6LeF/H5v/v34X6M1/w7bACVLESKXkcrqbF3BBafSyjdCE7utV1MhFoybcUZbAs2PToZ7g+zlB/m1mtK7x50afTnUwjt2BcG/w+ik5lU0Dr2C+3CVxzPe3vpef7YifN3fj35Y5oHB3EVoXTQmO1VBgfROsqMiITdCQp9fh52NC3oQsdqjtiK4HB30dS0seWL7u93Xt/P0TNmJ+jCInV0rqKDHsbEecfKIlL0rGwjycdahhnSSar9JQfF8Qf0wul1ScHR2tovyiUhuWkjGrtyrHbKZy+l8zQZq4s/z0OPw8bNo4umC/1p1nFgctJ/PS3wVXCEgRBoPb0F6wY4E2D8pm7ZrwryHUNrqVanqwMggBBQUEW9xUpYt0iR6lUkpKSQufOnTl69CiCIGBra3jIDx48mFGjRlGjRo20499ngitP0VNx/HPurSmKzN74nj+P1PDZDxFEyXV8XMSWK49V1CjlwKk7KfRq4MS49i6IxaI8Jbjw2nYnPaTyePrPHMrFroNNCGyD335GrNfj++x+volAWatn1YvFXGzXl9p/bk9ra+PCX0xIbnbJqbn7kKjU4moryladb/rzZHafbcUgnHyJ/EgwAE7N/XFtXzRtf2YEN7MFkxi5jg4LQ3gWqUFmJ+LA9EIkKQX2Xkji4JUkGpY3+Dj+MdPPospyXuLmcxW9V4ayoK8XnWo6pX2HBUHgxO0UPv85ku+GFaDpK1P0bwLj+PW8nJ9H+7DnnJzHYWo2jPZBoxP4JjCOg1cUBM70w9NZwtcH4zhxJ5k/ZmRPDTonSErRU3VyEIdn+VHCgtLib+flbDqRSODM/O1PfhPcR7HWB+1SHq+Hl//vpCY/8L6Ozf/fvwsdFoXyWStX2lYz1T6IV+gY9VMUlx4pSUzRMb6dGzFJevZeTKJ5JRlrhnin6W/kK8EFy9Y6KVFwur11sgVvXvTJSq0qiKDuDsP/5rfoVH5ZFeXgvNtmnuHaoacA2Dvasuh07xyrC4/bEEXFInYMa27593v0VjI9VoQhFsOcHp40quDAg1ANG44lEpWoo2KAHc+jtAROL2RRZflN4GGomrYLQ5nZ1YP+jZyNCOj5+ykM/j6CRX296Jqhjva3C3Jm7ojhl9EFqV3aniZfhdK9jhMjWrqy6UQiyw7EcXhWIUr62KHSCPT8OoyaJR2Y0fXtkXmNVqDC+CB2TvChWgnz2Xj/3Exm+vZoriz1z3Jt77sAa+NKlr5dGVOXUl9ZGUDBMFBaemUGBwcH3N1fq7GlDqAA9vb2+Pv7Z6kP7wOcpWLql5Xy+2Vju4awOC2dl4TStY4zl5cGIBKLGNjEmWUDPTg024drT1Qs+i0+X/pkjrylOLuxcvEOI3ILcLt+G/QSG3yf3Qde1+P2m/eZESH1e3yXUQtGIMri9yc9rJHb1Dbr/LHVqK1Bs4blqK1USOXxjJzUk0qnAo22Vzt7iJGTeiKVx2f5XJbIsKXtLq2LUGhlPQCSjr4gZPwZdPFZM+Z2dxKTpNQTm2R67Xq9QP9vwmhZWca0zu4IwMA1EWw6nki0XIdWJ3DkhoJ5fTzfCLkF+GJLFHN7e9GltrPRACwSiWhaScb6zwoyaVNUWsrPuPbuDG3uSpelIfxwJB4PJwmTN0dRdVIQ/71U8/v0Qng6S14d60ZYrJYbT3OezplVOEnFzOzmQe+vw3gUqjbZf/Smglk7o5nbK3v1UY9C1Ry8nETg1STC495w2qIlfMhRfqv4MDa/n+hS24ltZspCUtR6Oi8Lo5CHhMX9PKlRwoGZ3Tz5epA391YXQSKG3qvC0WjfUBqhJTIm9TYINWWssS3ay7Dd1tUQbYx7FUFMrcs93cmYgMZdNxyXF+nCVsktgADnexvEs/Kj/VSoYg2CV893mfbvn7qG/TmFpc/DCmnut7ABX18fgHcRF1QKDZOqbWHdyL9z1Lyvh8HRwxLuBKsYvi6C4j62jGvnxqaTiQxbF8mh68lI7UQER2k5fjuF3yb7vlVyCzBtWzQTO7gxuKmLSXS1blkpv072ZdKmKJJVxt+nbnWcWTHAi5E/RVJ96kueRWp4FKbmowlB7LuUxKGZhdIshOxtRSwf4MWGY4moNG8v9dfWRsSSfl70XR3Ov89M54/n76cw8sdIlva37A6RGaITdQReVbDvYhJ3grM2R81v5NvM9dNPP+WnnwwS5v3797d407Zs2ZLtcx88eJAZM2ZQqlQpPD1zVkT/rmJkazfGrI+kfjkp/q/SkVb9EUenWk4Ma+HK8VvJnPtPyRdd3NAL4O4k4fsRXnRYEE73eoaVJr0ACqXlCXBqbWhO94OBkJk75mj7AQxcPTVTEajjHQaSoBFAY74dc30Q6XTU3veLEbk1Z1eUURzraPsBJm1l9R7I5PEMnDsM/6f3DOJZGj3X6rel2tlDadc5cPogvvvqZ5IzRIlze5/dpRLkSsPA67KkLpoHcaT88h/hcy4jLuyIsCsApRW/Y6mdmDZVHdl5Rs7oNsZ9O3k3BaVa4KuenvRdFc7CvgaT7wchamwkhofh8dvJ7Dgtp121zMXMYpN03HyuQq8XKONnR2HP7Akn3XyuIipBZ1V1sF45KT7uEv65mUzrV1L3g5u64uksYdXBOEoXskNqL2JSR3cCvA3t33uhYsPRBP68piBOoafrslA+bWkY1CyJauUFBjZxRSQS0WFRCNVLOFC7jAMaLRy+oSA2Sccvn1teSc2IG0+VzNsTw6MwDTVKOqDVCUzYGEWD8lLm9/ay6Ff4JvCBw75/+DA2v310r+vEkv2xHLySRMd0aZDbTskNC3Ud3Wg1P5TF6WyIHB3ErPu0AK3nh/L7lSS61XnLqvaZka03KfpkTogJEeZVoDOIZ+Wl6FR6q6L0QlvpyffxFm/cqkgkEjHjQGe0Gh1Tam7j4aUwJlTZTJOBFeg4vnqWz9OznjNNZr/kqx4eJlmGAKsD4+lZz5mDVxRM7+LBl908uPRIyfNILY72IjaMKkC/byI4eTfFJDJqDYIgcCtITWicFhepmBolHbKli5ERzyI13HimYvt4y2VElYrYU72EA/suJtGvkXG9fIcaTrSr5kif1eGU0NtSzt+eES1djcqNUtR6dp9LYuOxBKITdRT+9CkNy8sY1tyF1pVlbzxK2qW2EwLQdXkYHwXY0eQjKTo9/P1vMkFRGtZ+6k2zitkXtItO1DFjRzSHrydTq5QDMnsRV5+o8HGTMLen51tNd87SzKhBgwYWP4zTp0+b3V6sWLG0/y9ZsmQOumYZHTt2pGPHjowZM4bAwEA6d+6cp+d/m6hfTsrnbd1otyDE8G81Rw5cSmL7eB/m7o5h9zk53w73wsv59QPZ3VFCz/pO/HpOgQMYDOitpN9mJ0U5u8c8at6RPbZiqynEe6as4HqObIRs2Lpgg5EPbvp0ZEttPWrUnoxyHllJUXa1FTFy4Uj8n94DDGR94Oqp1Dp/2MiWKODpPcYsHMkPK3cbRWJzW+cbl/FzKuWOdFFdlMuuon+pYGH97VSYXg2XMub95lqW9mFUGze6LwulVikHqpd8Tah2nZEzsIkLG48l8iRCwyc1nZDZi/ko4PUDulMtJ+bsjiEiXmuRDEbEaw0qzdcVVCxih41ExK3nKqqXcGBmdw/KFc5afem1J0qaVJSZCK9lRPNKMq49UaYRXIBkpZ7y/naMykDid51NZN6eWIY1d+XYXH8OXVdw4UEKcUk6ms5+ybbxPlQpnn/iWQMau9CtjhMHLyfxX4gaiVjE1E7uWbrOVFx8mMLgb8P5qocnXeo4pw3q8hQ9P/4dT/uFIfwxs1C2FxQ+4H8DH8bm9xNODmJ2T/Sl24owzv6nZHhzF0r62rL+aAJNPpLSbE4oQ5q5mKQwS8QiRrdx5ce/E94+wc0MqdFdS1HVvLQTShViypgSDZbtjvLazkivMx+1Dt5lrD6dGjV+C1ZFNrYSVt0YiCJBxZeNd3Fi811ObL5Lrzl1qfVJqUzfX6yALS0ry/jsx0jWjypoRDLlKXoOXVfg52HDpI5uaVHR2qWl1C79+hxDm7my5VRilgnugctJLDsQR5JSTylfO6ITdYTFaRnewpUJ7d1yVNt6/amKBuWkSO2sR5FbVpZx9YmKfo1M94nFBuGnxhWk9G1oPMOMTdLReWkYHk5ivurpycZjCbSu4ohYBHN2xxB4VcG3Q71znCqeU3St7UT7ao4cvJLEzecqRCLD86RtDj1qoxN1tJwXQpuqMm5+HYDHKz0gnV7gj6sKBn0XwffDvY3ma28SWSK4w4YNM/o7PDycDRs20K+fZVPp6dOnp/3/V199lcPumUKlUqVZIri4uCCVvj/F0FnF0OauVCpqz8//JDB3dwwancFXq2d9Z47M8sNJZvqwrlPGgZW/x1PlLfQ3I241ak+5E4FmRaDu12xiSG3OJEJsCSnObmxc+Av9vxzKxS6GGmC3qFCTKLFRWzlEdmyJznUe9MZsiRymVkcfo0S18jp3F18DoPbGZojMPCwr+Nvz7fAC9FsdRrvqTvSs54yXs5i7L1S8jNEQk6Rn1yRfs6uxMnsx/l42hMfrzBLc8DgtHRaF0L66ExeXBqSlAyer9Ow8I6fr0lB2TPSlcrHMSaQgkCU7H7FIZKKoXMDNhmeRxmlTVx4rWfBrLAenF0pTanwRraGsnx1TO3vQ+CMZA9aEc2ahP26OufvcIuK1RMTrcJKKKVbAxohwyOzF9GpgXS3VEnR6gc9/imTN0AK0qGw8QDhLxUz+xAMbsYjpW6PZOt43V9eQU3yI4L5dfBib319ULmbPqXmFWX80gdYLQolL0qHVQylfW34YUcBi5KNRBSljfo56w73NIYr2MhA8c6JPfh3y1kbI3sMQGc0oxPSm2n+PrIocXe1ZdWMgkUEJLO50gF1zzrNrznlGr29FyerWBSW/GeLNoG8jaDTrJSNaulKvjANqncDWk4mkqAU+qenEECsK4eUK2xESk7X537q/4ll3JIFVQ7xpUkGaRgj/e6lm2rZobj1XsWlMwSwvGKdCEIQsjV1ikWFuYgk+bjY8jTC9luHrIqlTxoHFfQ1ZLLN3xlCioC11y0rpVNOJrsvD+PqPeCZ/Yj44kVPo9AKPwzQoNQKFPW3S5mTpYW8rontdZ7rXzf0C2exdMbSsLGNhH2M9HYlYRKeaThTysKHnyjDufVMk08WE/ECWCO7AgaYiBl27dmXw4MHMnj07Sw0FBQVx8+ZNkpKM60v79Olj9X0ajYY2bdpw8+ZNWrVqRcOGDTl+3ECcSpUqRcuWLbPUfnokq/QEXlXwNEKDvY2hvu/jou+W71ONkg7UKOnA3WA3hq+N4PyS18bdsQrTuj6tXkD8jhSGVzoVSMWrJ83uK3v5BJVOBXK2Vuscnz/F2Y2Vi3bg7GhPpVOBlL18wmpbuSG5qe/NLCKdlTbMCUlZ224NYk8HPvqhCZG7HhJ5MoSLQ45RtG9pfFuYGrw3q+TIqQX+bD8tZ9rWKOQpeuIVehpVkPJld0+z5BYMg0Bckh4nB/Pfq+nboulax5lpGRQVZfZihjZ3pYCrhJE/RHJ+sX+mK5WVi9nz0z8J6PWC1WNP3k02EbhoUE7K+A1a7r5QUeFVitAPr/ykU8mtUq3nt/NyAr802Fy1qerI75eT2H1WzohWblb7ZgkXHqTwTaDB2N7Pw4YYuQ4PZwkjWrrSq75zrlOQjt1KxstFYkJu0+PTlq5UmRhPcJQmLS37jeLdeOT8v8X/2ticXchT9By4nERQlBapnYjWVWRpz4D3AX6eNszp6cmcnp6kqPQUGv6MTZ/7YGsl/VKny9pi4DuB56+il+YQ8odhf16T3PSR0Tfdfn5FrfNJtKpAEVdW3RjIo8thrB3xN98P/wuAGb93xjvAPEmV2onZOcGHk3dT2HAskW/+jEcihqrF7JHZiZjW2d3q2BcjNywGZ4ZHYWqW/x7HyXmFTdTDyxW249fJvrRfFMrWU/JMbbky4uOi9kzbFoNKI1i13jl+O4UWH1tO2+1V34kuy8KY1tk97Td794WKO8Eqdk3wQSQSceWxEnmKntqlDQv9jg5ivh3qTZuFoYxp65Zj65/00GgF1v2VwM9HE0BkyBB5Ea2leSUZUz5xyxenhtgkQ83t9RWmc85U1CzpQLXiDuy9YJrm/SaQ4+ItPz8/bt26laVjFy9ezPz58ylfvrzRqq5IJMp0ELW1teXo0aNG23K66iwIAj8fTWDl73FUK+HAx0XtSUjWMfS7cLxdJHz3aQGLqqdvCyV97YhX6HgarqZ4ur5ltKo6fiuFKsXt0POGanAtHFPt7CF6pKvBzYhU4ank8Xqu12+b8z5KJJQ6ejBLbaXWzWbr/On2n63VmnLVzUekb1dvbCDrZs6V/hwyeTyjFozgRIeBaX0RgFJHD9Lkj82s/fLHbNfw6rGjQK/SeHUuwb1xp3m+/SHPtz+k3Nf1kchsEQCNznBvPJzFjGnnyph2BnK45WQi5/5TWiS3ABceKHG0F1G8oClxCo3Vcu5+Ct8OL2Dx/e2rO7LqjzhO3UvJ1PS9anF7nBxE/HlNYSLNn4qrj5U8i9CYpLvY2oj4vK0b436O5NcphbC3FXHsdjKrhxr6ptUJTNoURf1yMqNr6dfIhXl7YnJEcPdekPPVrhi+7O7BL2N8kNqJ0esFzvyXwtzdMdx8rmJxv5wLNgCc/S+FtlWtp/bI7MU0/kjGhYfKt0NwP+Cdw/s4NmcXgiCw8mA8aw7F06CclI8CDKmLXZeFUbygLT+OLJCmYfG+QGovploJe/76N5n21S3/7g/fUFDrDfqS5xiZiT6lpvBC3kdS32b7eR21zqY9UE5QqqYvq24M5OK+h+yef4FFn+wHYOGpXshcTMmRWCyiaUUZTTPUbLaYG8Jf/ybTzow6eCp2nZXTwcr+VGw4lsjARi4m5DYVdjYGMj17VwwDG2dvQbmUrx3lC9ux44xlP+oHIWrOPUjhp88sz3Eq+NtTpZg9kzdHs2qwF2KxiF/PJ9G3gTO2NiKiE3V8/nMkEzu6Gy3clypkR0kfW07eTaaVlQXsrECjFei9KhytXuCXzwumaXvEK3RsOyWn3aJQto/zoW7ZvM2oufpESeVi9ni5WF9g6VjDkdP/pby7BHfjxo1GfycnJ7Nv3z5q166dpUZWrlzJ1atXKV++fPZ7mIf49s94fj0v59CXfkZkcVYPT7aeTKTTklACZ/pRxMpEUasT+OdmMqfvJqPWQUkfW3rUczabCpAXsLcV0buBC6sD4/lmqDcikQixyHgFNzxey8HLyeybVpBtq0EiEuHiYPka4lI0+VKDK9LpaB64xYhw6sVi7tdsYlSzKtbrafrHZp40aZcti530kCtUWW6reeAWHmdoKzv1sZVOBVLxivkoccUrJ6h/6YjZCG56P+Eh84fj9/guA1dPRWor5laj9pQ6ejBNqGrcvGFsXLTJyM4oMxuim6HJaX2ULqqL9nokmt8e89/Es4greFBythMu9uavsXIxWPl7CqfuJqd5y6ZHskrP/D0xDGvhanbgOPtfCo0qSHGyooQoEonoWMOJk3eSMyW4IpGIRf28GLQmHHtbgxdv+nYvP1Iy5Ltwlg7wMhvdGN7CldBYLc2+ekmPek442IpITNZz5IaC9f8k4CoTs2mscepVYU8bYhKzr575IlrDzO3RHJjuR1m/188RsVhEowoyfp/uQPuFIRy8ouCTmlkX0sgIrY4sre7a24rQ6d6OQqMo0xDu/45p/LuI/5WxObuYuyeWE3dSOLewsBGRnd/bk+8Px9NmQShHv/LLsu91bJKOXWflPAjRYGMDjcpLc1yXlhsMa+bKqsA4WlWWmX3OJav0fHc4nnnZVGB/4zAn+iQSGwhe+npUQW84LqB73qbsvs32n++ClwfN73t5MHtR4+yIVtm65jrKW7tLaWp3Kc2BFVc4tf0eMxsZlKBXXO6PxDbzqOtnrVyZ92sM9co6mC39ufxYSeA1BVeWZq6wfuJOMj+OKGj1mMYVpARHaYmR6zMlWhmxsI8nnywNxVkqomttJ6P5xq0gFX1WhbOgt6fVOQ7ATyML0uvrMFrMC+GzVq4ER2ko42fH1wfj+OmfBPo1cmFgY9N0YH8vG6JzMP/IiFWB8QjAb5N9jZ5Xbo4SPm/rRjl/OwZ8G8HtVQF5mias1WZ9fqLNQ5Hy7CBLV7t161aj15EjR6hbty47duzIUiOenp4ULVo0N/3MNcLjtHx7KJ5fpxQyIrdgyBcf1NSVQU1cWLLPsoT75UdKak0NZs2fcfh72VIxwI67L1TU/iKY5QdiTWoD8wpj27lx87mK2TtjSEw2fFPkKXo2n5DTel4Yjb80eI7tvaDIl/azCkEiYdP8nwkpWQF4nb67bfY69kxZgf6VZ2NIyQqs/fLHXNWsZqetTfN/ttqWJQshkU5nsCVaNgmxhc9WLAj0WDbJxEIo/TkGzRqG3+O7huPTWSelV5v2e3KPUeO7GfUluzZENlUL4LCwDgD6u7Ec6PkHcc8TzB4rsxfz48gCfPZDBGsC44iRG9rV6wWO3VLQaUkopf3sGNDY/KqbRitYjf6mQmonsiSUbYJapaRs/NyHr3bF0GT2Sxb9FsPS/bG0WxDCiHURLBvgbVHRWSQSMaeXF+tHFeRZpJa4JD1t5r/kt/NyJnRwZ9ckXxwz9DckRotHDhamtp5MpHtdZyNymx7OUjFTOrkb0oVygdKFbLn0yLqtkV4vcPmREicHMbN3RlN1UhAlPntG3WnBrP4jLlft5ycmTJhAgwYNGDdunMm+lJQUfHx8TKKDH2CK/4WxObt4HK5my6lE9k31NYnS2khEjGvvzic1HVl+MPPvvyESHEflicFcf6qiYhE7ihWw5fvDCVScEMSZeyn5dRlm0b2uE17OEvqvCScsnRVYUJSGCb9EUeyz5zyP1LJ0fxwbj+Xu+ZKvSBV9cq9q+Ds1NbfhAcO/olfPYveqhuPyuh41v9q3ZCeUuv35LsuiVmDYfr6PqYWQpXNm1WrpeAv4u06eWRN1mlyDVTcGUqKagWBOrrmVxV0OZDrH7VzLkaYfyWg9P5Q/rynQvlp4jVfo+P5IPD1XhvHjyAJ4u2a+8KTWgqO9dQIlFotwsBPlyIKnYhF79k4pxMqD8VSf+oI5u2NYtDeW9otC6LosjJndPOifhaiji0zMwemFGNvWja2n5Bz5N5lvDyfwOFzDzom+fNnNw2yQ4GWMNteBMY1WYMOxBOb18rC4GNesoqEEc9/FJLP7c4rSfrZcf6rK9N5ffKhMmytdfJjCkO8jKDHqOQGfPqPpVy/ZeioRpdqStVfuIBLyi5Wlw+HDh9m+fTvjx4+nQAHjcH9AgOX87ZwgvemvvePrWr2Vv8fyMkbLsBauiEVQvKCdyepDvEJHjSnB/DnTD2eZGE9nSZpK3O0gFT1WhJoVfImI19JvdTjNKsqYlk9mznFJOqZvi+bYrWQ+LmbH1Ucq7GwN8ZOhzZ2pU8aefReTcVh3hKjRdSlW2PKDIbsRXHM1otaii1J5vJEIVCoqnQqk3v5NbJr/MxG2TrmKIqePjg6aNYxznQcZtVXq6EGaB25h0/yfjaKi2Xr/H5uRaNUUevYgbbteJOJ+raaUvXTciPSGlCjP2tV7zUaJM/PuTX/uPVNXcqtRe6P3hJSswMaFv5hch7V7pA9JQvW9IU1RbCOm768dTR6yJX2ceBqu5btD8Ry6rjD45qbo8fO0YVhz63WkFx+kMHlzFGcWWjcFH7EugpqlHBhqxRjepO+vUn2vPFai1xsGohYfy7IVTRnyXTgNy0sZ1NRyu6N+jKBSUXtGZjNFudGXL1g9xNuqArNWJ1Bq9DNurCySYxEreYqeqpOCODq3sMWskr//VTBtazQpKj19GrrQq74z3q4SnkZo2HIikaW9XqeXZTRBzynSP2OfJlhf5Cju+vo7n9r+9evXWbduHevXr+ezzz5jyJAh1KhRI+24b7/9loMHD/LFF1/QvHnzPOnzB5jH2xqbc/NdnLE9mmSVwOCmLvi4ScwK4IXGaqk9/QX3vimCSiOg0gh4OktMxvzlv8dx4FISv072NfH7PnY7mWFrI9gzyZcaJd9cSrBaKzBndwzbTsupU9oBpUbg7H8piMXQs64zUzq58zBUw4ZjCfhu/Zuxf/WhWIF8TMfOTf3nG0ivfWPtZ3auRoFwovVrQgqACAp3fBXRTTfVdq8KrS5nfv8y9fYFEINjACieG9f4pn+ve9UcWxPp9QLT6+1A/apcqnr7EvSdX9/i8YIgsPdiEj/8ncDDUA1uMjExch0tKzsyvr1blvVueqwM45MajiYKxekRFKWh0ayXPPyuaI5tgwRB4MIDJWf+S0Grh/KF7WhXzTHH57sTrKLb8jDurC5icc7yKExN6/mh3PumSK5qcK8+UTLm5yguLLYeEd99Tk7gVQVbx1kXEMsuOiwKpVd9J4ufUXSijqqTg7m4pDDr/0lkz/kkRrVxpVMNJxzsDPXJ6/5KIC5Jx/4vCuWI8FsbV7JEcO/du8eZM2eIjY3Fw8ODBg0aZCul6ffff2f48OFER0cbNy4SZdmQPqswR3Aj4rW0WxBCtFxHgJcNOr2BMPZu4MLYdm64OkrQ6QV2nJYza2c0IgyF4FqdQI96znzW2o3xGyJpV93JYkQrOlFH3enBnF7gn+W0qJzgUaiatgtCaFrRgTZVZTQo72D0I1rSaCc7qtajawvwdDN/juwQ3JwSSLlChbOj6YMslSzn1kIn/f7sEvDU/QU1SUaWQ6liUenJpdreATvV6yiaRYJbsgJrV/1mMQ06yyTXTJo1wIvSlbJtQ9SqhCdXll0m5FoEAA0m16BY/cJp+0v6OGErMRCUJKWeyAQdMjsRBd0kmdazCIJA/RkvWNTPy2yKMxgmmQ1nvuDaigBcc6lUnF1cfqRk6PfhHJhWyGxd/V83FEzaZCDo7k7Z61uNKUHsnlzIbG1yenw07jn/zCmMby6eBz/8Fc+2U4nsnGgarbr2REmfr8PQ6WH/tEJULGL6e1Mp8oZUpEf6Z+zzTAhuUTMEd+3atXh5edGjRw/27t1LSEgIY8eOBUCtVjN48GBKlSpF/fr1PxDcTPC+js05+S4KgsCOM3Imb47GzkaEn6cNL2O01CjpwKQObkY1ZoIgUGF8MM5SESExOmT2IpRqge71nBjTxo2iBWzTJl+Xlvpb/I3uOCNn++lE/pzpl/0LziUSk/Ws+yueb/6M54tO7gxr7opjhnTJCVU2c7JZEy4sKoxDfqiU5gVBzCeBpCwjL9pPnypsiUS6VQH0EH8z3RtzSXDBOskVicGxGCQ9Md6WMRUbwLNmrqyJVMkaptV7nRnScUI1mgz4yOp7IhO0yFMEvF0kuMiy9/08fF3Bkv1xHJ/rZ1EleeaOaPR6WNzPy+z+t4XOS0MpV9iOhX08TeZSySo93ZaH0aSijCm5VFE+cy+FRftiOfyl9efTkRsKfvongX1TC+WqvYy4+kRJj5VhbBhV0KQMLSpBS4+vw2lSQUoRb1vW/ZXAnzNNSawgCMzeFcv1p0oCZxTKtm5JjgmuIAgMHTqUzZs3U7hwYQoVKkRISAihoaH079+fjRs3Zqkzfn5+zJ07l169eplYB0jy2FolI8ENjtLQaUkothIY3caNAU0MN+BpuJrVgfHcfK7i1ym+fLElmqhEHYnJOhb08aJhBRnPIzWs/iOO/ZfkqDTgYAdezjZ0qe3EwCau+HkaD4pTt0Th42bDxI55K/2dHj/9Hc+Fhyl8Pdh8Dc6yxju527susXKoX838BEWTiUS6gEEcVSaP5/O5wwh4eg+9WMzm8cu4Vr8t1c4eSkuvDS5enu+++tmsQFJW2nhb+9HpmDyjD0Uf3U7bpBeLuV29MRWvnjQil5mdSy8Ss3nCMrNCVunfN3zJ52aFqjI9v1jM5rFLuNbQuM43s/d1K18QkV6HOlnHoWF/pW3vsLUtEglULe6JxMrvVyTC6u/7yA0FUzdHsWuSr4lKX2SCll4rw+hYw4nxHfLv92ANO88ksuDXWIa3cKVnfWe8XSQ8Dtew+XgCB68o2Dreh6o58MHtujSUQU1dLIphgaEkov6MF9xdUzRXK7SCIBhsEv6Io/nHMuqVlaLRCRy5ruBWkIoyfnY0ryTj87bm73G+E9zETAiuiynBXbRoEVWrVqV169YcPXqU8+fPpyn+/vTTT/j7+3Pp0qUPBNcK3vexObvfRUEQmLIlmosPlegFWNjbkyYVZaSo9ey7mMSc3bEs6utJ97rO6PUCn/8cxW8XkpjT04MRLV2RiEWExGjZcCyBX04k0rqKI39eVZCk0lPI3YbudZwY0szFZBFJrRWoMC6IP2cWonShNy9A2e+bcBpXkJoox6diQpXNPOvVgl71nOlVP499cbNC6nIRGXxvoNcZ0nxjLr/eZolEOpWApGdAJhHXutlUUj7dybxoVeFPIKBX5lHevPL81euIj1Iyt/VvaZuGfN2Eio388nyxQqcX6Lg4lBI+tnw9yNskGrr9dCLzfo3l+JzCJnPxt40YuY7OS0PxdrXh8zau1CsrRaUxeMOu+TOeysXsWTs89z64zyM1NJsTwn9riliNOK/4PY6QWC2rBnvnqj1zOHMvhWHrIihR0JZPajohtRNx+ZGS368o+LSFK9M7u1H9i5f8OKIAtUqbn2/p9QJVJgfz86iC2c6WsTauWJ2d/PTTT5w8eZKLFy8SFBTEhQsXCA4O5sKFC5w5c4Yff/wxSx3QarUMHjwYJycnJBKJ0Su/8dmPkXza0pW+DV24/lSVtr24jx3fDPWmUQUp3ZaFolDpWT+qIKGxurRIyPNIDX/9q6CEjx12NiL+/booW8b5kKwSaDHnBWf/M67PqVPagf9eqshP7Dknp3cDJ2zEIrMvgAol4PlLEY52Njg7mL5EgIuDjcWXCHC1FTFm4UgCnt4DDLWjA1dPZdSyMUa1owFP7zFm4UhcbUUm58isjbe5H4mEi10Gp9Xqpl7jx5ePmwhXXejQ3+i49NCLxeyZuoJHzTta7UP9S0csWicJIhF6C5NRvVjMxXZ9aX5oGwU1Sdm6xkOXHlD8k2bE/LoT6aK62LQuAsAf/Q8R1H0BYc9CiEtWW3xlltvRuoojc3p50mlJKEO/C+fX83IOXEpi8qYo6k1/QZuqjoxr72b9JPmI3g1c2D3Zl6AoDQ1nvqDwsKf0WhmKi0zM0bmFc0RuAfo0dOaX4wlW65G2nEykc22nXFsAiEQiRrVx4/KyACoXtefqYyV3glR0qePMpaUB/PtMRR8rKVzvIlxdXUlMTAQgMTERNzc3wDBO/PXXX7Rp0+Yt9u79wP/C2Jwd7L+k4Ox/Sg7N9KNFJRn/3EoGDJYlfRu6cHB6ISZviSY4WsPPxxK5FaRCaidiUBOXtOiPn6cNDctL0WgFDlxKolZpe5b282LPJB9SNAINZ73k5J1ko3btbETULOXAfy9NrfnyG/EKHSduJ9OznnXiOriJCzvPyvO28ezUf55sY7k29X8Bqf62onRzgFR/24wiVpUWGMiryMLUWpQDcpuZ1REYyKvF6bzIEOX1zaVtlyoW/qmLW/LvrLoxkInbDQvuGyeeYEK1bYTcepLJCbIHiVjEzgm+hMXp+HhiMMsOxLH/UhLr/0mgyVcvWXEwnt+nFXrnyC2Ap7OEI7P8aFdVxqydMfgMfUqJUc/Zc17O7O4erPs09+QWoGgBW8oVtmX/Jcv1tRqtwKYTifRvlMcLYK/QoLyUO6uKMKy5K/deqLnwQEkJH1uuLfdnVncPrj9XY28LNUtZTk0Xi0UMaOzCrnN5+xyzSnC3bt3KmjVrjOqjAGrUqMHq1avZunVrlhqZPHkyS5YsyTcRJku4+VxFWJyW4S1c6d3QmcCrCoKjNGn7RSKDIMWDEA3j27uz8VgCbao64u4k4XmkhlE/RvDL5z582c0DG4nBpqS8vz0L+nrx0ygfPl0bzovo1+dTaYVsG05nF+HxOooWsP6DlkkN0Te1xuphViFIJJzrPMiEAJa/eMyEAJ7rPChXglFvC7catTcSpMqI1LTlwM9mcb9mE7PH3K/ZJFMP3MzSk8WCgMjCbyO0RHlq/7kd/4e3GDJzcJYFp6TyeMbOHoz/o9tpIli2Df2o2dIgNnHDtirreh8n8UFIls5nCV1qO3NtRRHqlZNy9GYygVeTKORhw9lF/kzpZF5c4U3iowB7Vg0pwKO1xQjbWJx/vy7K9K6euUobbl/didgkPcsPxJl9ph27pWDT8QQ+a+2Wi54bw91JwohWbqweWoCVgwvQs54zGp1hAu6RzRTrt406depw7NgxAI4ePZqm+BsREUFwcDCtW7dm27ZtTJ8+nbi4d1co623ifR+bs4sf/05gehd3XGRiBjd1YccZORHxr0WYyhW2o2ddZ345lsjaIwkU8balV31nIyG8oCgNQ9ZGsGuiL7VKO5CkFJCIoby/PUv6ebFtnA9D1kbwPNJ44FRphDeupgwQLdfh6SLBORPP0JK+toTHZ1HJL6vIDqkrM+HNpBq/TRTtZSxOlRHpI6RFexmiu+aQXZugrFodaZPA1hKBEQwpzMdbZFtsKg2p0fyYy4b2ro7Fv7wnq/bbM7jTdwCsGHiWCVU2kxCVnMnJsg4XmZhfJ/uyfbwPUYk69l5M4vpTFVM7uXN1mb9Focd3ATJ7MUOauXJ2oT8xm4oT+UtxDnxRiLbVHPN0XjStswczd8RwK8g0uKbRCoxaH8lHAXY5XtDPCmxtRHSp7cQ3Q71ZN6IAEzq4U+CVkFhUgo4i3raZXnMRbxuiE/J2oczqLO/evXs0atTI7L5GjRrRv3//LDWyZs0awsPDWbRoEZ6exqm1wcHBWexq9vHXDQWdazkhEYvwdrFhWhcPui0PZcNon7Qo7f2XalxlIn4+msC/z1T8+SqXfcOxBHo1cKF2GSlxSTq0OvjtvJzmHxsEpuqXk9K1rjObjicyq4fhmg5dU6Ttzy+4ysREJ+jxdbN8jFpjsBixyeXCVipxs0TO0tetvq8w1Nz+SfmLx0z2pZLXSqcCKXvZvE1Q2csnqHQq0OI9EOl01Nu/KVM7I0s/fb9Hd9L2+T2+y6BZw0xqcc21OWTGIPyCDOJYr5We/6TspeP0FgQeOZVjbckv+GnkGTwCnBmyuZ3F82UGZ6nhQT6kWd6kwOYX8mpQsbcVsXuSL31XhXHsVjIDm7hQ0tfgw7n7nJwrj1PoVteZKZuikCv1+Lrb0LuBMy0+luV4AUwQBG4FqXkSrsbeVkSdMgaLJpVGQJ6iz3QSnF/I3CbIFFWrVsXBwYEGDRpQuXJlAgICWLhwITNnzuTKlSsAzJkzh/r16+Pu/nbS2991vO9jc3YQr9BxK0iV5gldtIAto1q70XFJKFvG+FDm1SS3Rz0nhq6NIC5Jz72XatYMNU7HW/9PAn3qO1O/nJTQOIO4XuA1Rdpzq15ZKX3qO/Pz0QQW9DHU9MUpdFx6pGTtp3mf2pcZXKVi4pP0aLSCWcugVETE63CR5gPBTCVi1uo/8yLt9X1BVv1tM4u4ZtUmKDtWR1c/B30m2YOp0fbs1uFmjOYDPPwWos9D3A0qldKzasogjl5sx59nujOn5a8ALD3fFztp3kRXKxezp3KxrIlTvYvIz4X++uWkrBzoRYfFobSp4kjX2k44S0Vcf6piw7FEihWwZUZXdyZvjuLGM8N3pHoJB4Y2c8nTsougKA3Xn6oQBPi4qD0lfAzlHu6OYsJiM1+AC4/TZVsPJTNYrcF1dXU1ym/O7v5UnDp1yuI+S4N0TpG+P0sCtbg7iRnb7vUkaeeZRBbtjaVoAVuqFLPnQYiaU3dTKF7Qlj1TfCnsafhQyo99TuBMvzQhmU5LQoiI03JhaZG0cz0MVdN9eSg3VxXl1nMV3ZeHcv3rIiZ2JHmJJftiiU7UMKuH+ZqXJY128GRQXZ6FQrM6uavBTYWl2tGbNZuyftp3WTrHu7o/fT1xRujFYk637k3DIzutikOlr08214alWuaqZw8xaNUUixZEmbVj6RpFOh2zx7TFO/xFpudcXHEZkRKDemq7RQ0oXPW175y/u4zM+NjbjtC+Tej0AsduJbPrrJywOC3OUjHlC9ux+2wi9cvL6F7X4I/9KFTN5hOJaHQC2yf4mlV9tYbjt5JZuDeGxGQ9lYvZo1AKXHmspG01R2KTdDT5SGZxcSG/a3CDEq0PSEVcXj+D8qr9D3j/x+bsfBfC4rQ0/PIlj74vmrZNEAR+/CeR5QfiKF/YjopF7HgZo+X3KwpcZWKuLPU3+Z2VGv2cQ18WopSvHcdvJ7Py9zj+C1Wza6IvNV/VfT0KU9N2QWhaW/N+jeF5pJaNo637ceYX2i4I4dOWrnSy4Kc9ocpmEoe3plpxe0blYcaIEazVfzY8kD9tvovITOypznbD/+dlPWxW6qAdi4HiWRYuQAR1d+RsQeLZjtc+vJlgy98zuXGzFABSZzsWnOyVJ+m4H2AdMXId204l8s+tZJQageIFbRnU2IXAa0n8el7BoKYuNKsoRRDgn5vJbD4pZ0AjZ2b3yF2m3aMwNTN3xHD5kZL65aSIgPMPlHwUYMeivp6UKWRHxQlB7J7kSyUzQphgeJ7Xnv6CFQO8aVBeavYYS7A2rlidaWk0Gk6cOGExfUmrzVpaTF4PlFlFYU8brjw29pHs3cCFbnWcOXormacRGiRiEWfvpzCrh0cauRUEgRi5jiLer29P5WL2bAtWs+DXGMa2c8NFJiHAy4aoBB1//6tg4i9RTOjgzpMwDUW8bfJNNXZAYxeazn5Bp1pOVC1u/sty9a6I1vXAxYKKcHyS0qzCcSoyqv9aqh2tePUk9S8dMRu9zEuV5PzaX//SEXpYILdgSMludGi7EYk0F30V6/U0D9zC4ybtzKscO3ixefGmNDXqR43a4wLcqNOKjjvW4BX+OlJiibQKwK+TlvGoSUdcMp7fBDYcGzjBqn8vGBShi7W1IaFSVVTLr/PnjDMAOMyvg0giYnTdIrjYW76HbyNt712CRCyiZWVHWr6yDXsYquaTxSH8OMqHhuVfKwpWK+FAx5qOTN4cTfuFIWwa40OFgKytRh+8nMSM7dF8Pdib5pVkaROFGLmOlb/Hcf5+CjeeqmhSUZa/ViEW8P94feOt4n0fm7MDDycJSo1AWJw2rbRAJBIxsqUrg5u4cPiGgqBIDYnJeioWsSMsVmc2bT9arqPoK7utR2Ea/LxsGNPOjd5fh7N6sBdtqzlS1Nv2ldiknjWH4vn1fBI7xhfkxjMVjvYiSvlmnmqXlxjdxo2ZO2KoX1aKl4v5OcXJOymsGPhKRTavFYvzKhr5viMrqcLn+4BT8axFXB+sgoDumX8m9h4GEa+MStap/97/GgRd1giuU3FDmzlBsT4Qc9EQubUGkZgBcz6if5GeLO50gKjgRCZV20KZOoUYubZFztr+gCzB01nCuPbujGv/OqC37EAcFx6quLzMH/d0nKROGSmjWrvSal4IEQk6+jV0pkZJB6uZIubwMFRNu4WhjGvvxqbPC6aVhKg0AttOJ9JhcSj7pxZidGs3Jm2K5sAXviZK8ADfHkrA3kZE/XJ5m0ZtNYJbtGjRTB/mz56Z/2GtWbOGESNGYG9veSKnUqn48ccf0ywi8gLp2Xyy4ETNqcGcXWS6mpuKu8EqWsx5ybYJPjSt+Dq9uMzoZxybV5jCnrZotAI1pgbxzdAC7Dgt5/jtZOqWlaLTCxy9mYyniwSNVsDrlc/eyxgtras4MvET93yZdAZeMVglDG3mQs/6Tni5SFAo9Ry4rODpjIM8GlCXuoUT6D9rKBc6DeZ249cEtOLJQGru28jWBRvM2vtA9vxbLaUpvwsE1tp+uULF1Jl98X94K22bJYsetb0UO1WKRSuhrPrUZrQzSrMqmjEIvyeG6G5o8XIUfnzXpL/3qzdky7yfs3WNpY4eZNDXky0S5k0TDeJYadf55zN058IAsGldhPHT6mZKcP8/R3AzYtyGSIoXtDUaYJRqQ63ujjOGVKHHYRpEIijuY8vUzh4m0vrpkZiso/qUYPZ9UYiPLBDiyZuieBKu5lGYhtFt3OhV3xl3JwnBURq2nExkStvXn19+RHBfyK1P0PydP0Rw8wPv+9ic3e/C+I1RFHCVMMOCz7wgCHRYHMqAxi78cjyRT1u40rmWcdSz+KhnnJhbmAAvG+pMf8nS/p40qiDj1N1kZu+KJS5JR3l/O47eSkZmL+bjIvaIxQI3nqkp4mVDnEKPva2IES1dGdbM5Y1FpRbvi2XnWTlfdHKnS20npHZiQmK0/HIikZiFf9B9Zw+DRVJee85mxYP1/0OacnZUlN2rGv7Na+VpawsXmoTXUV5LsHWFjk9zr3Z9pDrEXjO/z8x3QavRMaXmtrS/mw76iA7jquWuDx+QJchT9FQYH8T5Rf4UziDE9dsFOSt+j0eu1BMep6VcYTuiEnWMaOHKuPZuWS6lajkvhB51nSyqvO85L2fVH/GcXeDHuI3RXHuqYkIHNz6p4YSdDdx4pmLdXwlceazij+mmVohZQY4juM+fP892Y6kIDw+nZMmStG3blkaNGlGmTBmcnZ2Ry+U8fPiQkydPcvjwYQYMGJDjNjKDu5OEIc1cGPp9BDsm+OAiM35AhMdpGfFDBD3rO/PFlmgOTLNPU2TrVMuJHaflTPrEnS+2RPGRvz2NKshoVEFGRLyWCw+U7DqbiKtMTO1S9kzt7JlWCxQj17HpeALtF4Swe7KvxclpTtGkopSdEwuy8ZicRl+GYCMRodQINKrggD9Q2y+egTMM/q5+yycBcLtxeyqeDKTb8kmI9XqGzBxslpSlIqu1o2K9nnr7N3G7fpt8FZoy53VrbXtmECQSNs3/OVMf3JCSFdgxfTW9lk4y8gK+1ag9KRp9mhewUmZe4CF9/8z1M8XZjY2LDNHdF2U+pvaf282ep/T1s1ZrffMCdu2KIbQsgvKri2iPBLHiSBBfHO6Oo1v+iRO867j1XMWh6woUSj2FPGzoWscpTTwhFYnJOq48VnLgUhJHZr32o1Oq9fT+Ogx3JwmHZhWmWAFbDlxKYvvpBAY1dWXsz5HM6elJqyqOHLqmICRGi5NURIuPHSlawJZfzyfRqILU4vPj3gsVIhFcfqykxceOnLidzOK9sej0Ao4OYnrUzR/VRCN8WN94K3jfx+bs4vO2rrScF0L1kva0zKBzIQgCS/bHESPX80kNJzycJIx+JaxSyvd1jVnX2s5sOZmISiPgIhPT8FUqXKMKMk7Nl/HvMxVL9sfSoLyUrrWcmL07htndPdk5wQmZvRhBELj4UMlXu2O5/EjJTyMLvBGSO72LB9VLOPDD3wmM/yUaqZ0IQYDudQ0EPo3cppKc1FTSjMTqeIusE6vs1H9mNRqZX8hvn12xxLA4kFXLJLAccU2/0JCdflu6DrHkdZT3r5qQ9BQjv91UaBIh7O/cLUQ83wVxNyzvd69qcn4bWwmrbgxEEa/kyya7Ob7pDsc33aH33HrU7Fgy5335f4bH4Wr2XVQQm6TDy1lC1zpOFoNnzyM1PI3QcPpuMnVKO5iQ2zV/xrPhWALfDDG4yHRdHkbv+s6U97fji63R3A5Ws2GU4dn2OFzNX/8mk6IS8PeyoUN1x7Qo7e0gFcFRGgY1seziUKWoPaGxWrqtCKdoARvaVZOx9aScET9EIhKBn4cNg5u4sGyAl1GEOa9gNYKbW0RHR7Np0yYOHz7M7du3iY+Px93dnUqVKtG2bVsGDBhgImyRW2T0wdXrBWZujybwmoL+jVxoWEGKTgd/31Sw+6ycka3cGN/BnR/+imdNYBz9Ghn8LSPitQxfF4Gfuw0F3CRsHutrJOTy7zMlHReF0qSilE1jfMyupu+7KGfp/jguLPbP04FQodSmPcI0OoGkFD0yezH2tiIWNtjOxOSvTSKTd6s3ocJV48jk81IVWblohwnxetd8cGXyeEYtGMGJDgON6k+rnz1Ekz82s/bLH3PcvrlzA1Q9e4imf2xm3Yy1KFw9TYi0SKdDL5Eg1umQJsutnsNc/zL2sfrpQAasmZatWl9r11jt7CEGZlLfqxeJ2DxhuUntMIDobgw2ew2y/8Vq+tJtWWOTY9xltlZ68BoKlZ7bQSo0OoFSvna5UjB+UwiJ0TLyxwhCYrR0qW2YND8IVXPomoLudZ2Z28uTeIWO8RujOHknGYFXwm4SqFrMnpWDvfnzWjL/PlOyaYxP2u//QYiawd+Gc35JAPdeqGg1LwR7W6hTWkoZPzviknT8eU1BzVIO6PTQva4znTJEohKTdYz6MZJbQSp6N3Bm/6Uk6peTcuGBEmcHET9+5kPRAjaIRKJ8r8F9kZRJBNfpQwT3XcTbHptz8l249EhJv9Xh1CzlQJ8Gzni5GOrbNxxPRKsV+HWyLz6vni1bTyUya2cMfRo4062OE85SMUduKPhqdywlfWw5/KUfns7G391/n6novCyU3RN96b4yjH1TfKlWwnRxT6nW035xKL3rOzP0DQvrpaj1JKsEXGVibCQiJlTZzKpr/bIeYfSsmXWRoffBBzevo9a5bcvW1XBvM5LU1L9T/82PaPv5Ppglt6nITbQ9K9F8gNJjoPoai7sjnyewuPOBtL8//7kVJar5ZL8/ZhARr+V+iBqJWETFIna4yt7SgkseIiFZx6iforjwMIXudZzx97QhKFrLbxfkNCwn5bvhBdJ4yem7yUzeHM3jCA0iQC8YZmd9GjizbIAXMnsx916o6LA4jNPzX3sHz9oZg7ujmIkd3VGq9bRZGEr3Ok4cu53CjWdKOlZ3ws1RzO1gNdeeKJnQ3o2x7dz44e8EHoZqzHrrKtV6xmyI4tjtZIp42eLrLqFOGSm7zspRawW2jClICV87q969Wb5HVsaVfCW4bwMZCW4q/nupYvOJRG4HqRGLoUZJBwY0dqFoulWQVDGYU3eTUevA2UHEs0gtI1u50r+RCz7uNoTFadl2KpENRxNQqgUuLg1IG1QzQhAEWs4NYVoXd5pVyjt15RSNziKtmFt3G3VaxdIth6nFYJz6KpXHp9WOpj+21NGDadFLc1HgvEoxlsrjzUZZSx09mEayzaUIZ6d9c1HgRKUWH2UCA+eMMLn2SqcCqbd/E9/O/AFnB5ssRYGtpTCLdDpGTuppsihxu3pjKl49afQ5vihdKU1F2dI1inQ6JgxvZVTbawnRPgGsWv+X2eiyQqlBvPgaaAztO3xeCYnfa7I1tn4xnK2kMCck61i6P469F+SU9LXF3kbEvRdqapV2YFoXD8r7v5uqiNGJOtrMf8mAJi6Mam2crhOv0DFiXQSODiJO3klGIhYzpp0bjStI6bQklJnd3Fn9RwKxSTpcZWL2TDFOL770KIWZ26I5OtefWTui+e28nD4NXdKU2MEwiV1+II5fjiWwfKAX3eq+XiHVaAW6LgulVCE7lvTzMsjzLw1hfAd3GpST8t3heLadknNklh/uTpJ8J7gvk6wvVhR2el0L+oHg/v9GbgkuQJJSz6/nkzhwOQl5iiGrom8DZ1pWNlUofxap4Zfjifz9r0F0pWgBGyoG2LH9tJxPW7oysLELvq/G9M0nE/npnwS+GezNkwgND0I0rBtRwGI/Tt9LYcqWKC4u9n+rZRoTqmxm1Y2B+ZdO/CYJZHbxNgh4ZqnCWblXed3v7KRQZ2eBw9r5raHOdkPNrhU8vBTGupF/p/094/fOeAfkzM/9UZiaBb/GcuJOCh8F2KHVG9xROtdy4stu7ni7vvsL6uagVOtptyiUSkXsWdzXEwe71wG2FLWeKZujeRKh4fdphdh3MYnPfoqkrJ8dM7u6U72EA98djufY7RQeh6kp5GHDhcX+fLkzBi9n41KPMT9HUiHAnpEtDc/kPefljF4fxfj2bkzq4GbU7tMIDQPXhNPiYxnuThJCYrUs6edl1G9BEOj3TQRiEfz0WQFW/RGPAMzs6oEgCGw/LWf+b7Ecm1PYJLqcE3wguLnAfy9V/Hw0kf0X5aSoBWT2YrrUdqKcnx2Hbyj4dUohq+9fdySeZ5Ealg3IO5uBzAiu0+K6VDr5J11fpSNnRGb2PpnVjqYe42pBxMrcOXKy39VWZJb4mauTTU/88qJ9bVQ04+YPt0pcg4uVQyIW4ffkXrb7l7EP5oj82VqtDUJYFoiyNYI7akI3/NLV8upFIu7XakrZS8eNorohJSuwdtVv5gmuSouzgw36cAUp39xM2y5bVAeRSGSV4MYl6ei0JJQaJR2Y9Ik7hTxsXp1Tz64zclb+HsuWcb5UL/nupT/P3R1DskrPUgu/2WSVnlKjnuHmKOby8teq6W3mv2R8e3cafySj2exgHodrCdtY3GgCPHWLoZawdRVH+nwdxoI+nmw+IWfvF6bPkUZfvkAsgn6NXFBqBAK8bFBrBTYeS+SPGYUQi0UoVHqqTgzi5AL/tMj4mPWRFCtoy8SO7h8I7ge8M8gLgpsXuB+i5oe/Evj1vJxktYDMTkT3us6MaOlKucJ2tJ4fwtRO7jStaLk+XhAEyowJ4u/ZfkaL5G8aaQQXsqbym5PoXX6nAOcE+U3qsousktbGR+B0+/cv2q6Mgt+Lgi6dx61IbDhn7FXjY7PR7/N7H/Lrggtpfy881QuZS9YXvu8Eq+i0NIwxbVwZ3NQVF5lhLI6I17I6MJ7DN5L5a1ahbDsXvAvYdCKRA5eT2D/V1+wiml5v0B3oWtuJSZujGdjYmdVDXi/KpQpABc7wpeGsEFpVlnE7WM32cQXTggspaj3lxwVxfG7htJTnL7ZGsf5oIs/WFTUbBY9O1FFzWjBfdffg28MJDG7qgqCHikXsaFheyvkHSsZuiOLCYn/sbER0WBTKoKYudK39Ojgye1cMCpWelQNzz4usjStvxzzxPUK5wvasHOTNk3XFCPqpOE/WFWP5QG88nCW4yjK/fa4yMSmqN7+GcLdBGx7Uamp2353qjbNVy2mJxOZnzW3q+c91HoRe/Po+i/V6yl88ZlIbfK7zoDzrj0inY9SCEWkEUazX02P5ZPrN+8xIdCvg2X84JCehT/fwyWn/Upzd2LjwF16UrmS0+HCrUXv2TFnBi9KVrNZMp4cgkbBx4S+8LFLG0L5IxJ6pK9k2ex17pq5M6+/LImXYuPAXi/0S6QzppWIfRxwX10XykSHKmDzjApprkVb7MGd3DHXLOrBikHcauQVwtBcztLkrq4YUYMS6CLS6d2t9TaUR2HU2kZFWLDdCYrVodAZ15PSWYMNbuLJ4XywarcDSAV7oBQi8mpS2/3aQioOXk+jX0IVNxxMZ2MQFLxcb1GbuQUS8Fqkt3H2h5urjFCLitWw6nsjYnyMp5WublvK864ycWqUdjNK+P23pypaTCRYVdvMSIpH11wd8wLuGsn52rB7iTcjPxYncWJyQn4uzeog35Qob6nVT1EKmY7tIJMJFJiZF/Q49v4r2gkIWxvVC7XNef2mt/vNtQSwxREpF6T4nQW+wM8pYN1xmQv72NaNPrKA3kMrTnYwXHOKuG8ht6XF53+/UOlzPmsYLGUV7Gf72rJm7SLaDN7S9BRLZ6/7V2Q6trxjSklPhXtUQqbbWb/3rspW6XUuz6sZAGvYtB8DMRruYUGUzOk0mqdAYFpmGro1gQW9PxrV3TyO3AAXdbFjcz4sutQ3uBe8jNhxNYExbN4sZImKxiM/buLFoXxzODmKTVOHSheyoEGDHgcsK5vT0IPCaArVGSKuhBVh+II6aJR3SyG2KWs/uc0k4OYjRWBDi93KR0OpjR+buieVpuIYL91N4EaNl2rYYqk99weJ9sQxv4YqdjYjbQSr+C1HTvppxBuuIFq78ej6JZFXmn3Nu8IHgZhEikcgoX9zfy4a7L9SZTiLvvlAT4P1mV4+k8njGDG9JmYvHzO7/6MoJKp0KfKN9yilSCV56kpsemUWjcwJBIuFEh4FZItb/DBhvII1Z6F8qYcyI1O0pzm78sHK3ybXcatSeH1buzhK5TUWKsxtr5r0izFNXGhPmqSt5UboSa+ZZJsyy+BjGT+9DxZOvvycOfctQo3EMAOrfHrO4wXZUyRqT98Ym6Th8XcHkTywPpq2qOFLQXcI/N5MtHvM2EBanRWYntqp+vv7veDycxARHG48AnWs5UauUA58sCSE2SUAsgvX/JKBQ6dl6MpGeK8NYMcgbH3cbbjxT0rSijGtPlZT0MW4rOlFHx8WhNK3kiJODiEfhWsZ3cGfvF4Vwloq58DCFbwLj+OuGgpW/x/JFF+P7XLGIPfEKPQrlOzT5/oAPeAdhzhYjwMuG28Fqq++LV+iIiNfh4/4O1fk9+hlCDprfF3LQsP9/CankTWRhGvumFJ6zS7aL9cl+v/Xm5w5G2+09DJHTjNdbtJdhe27TtJ1LQMfnpiS6+pqsk+iUKEPk/fkuo82duz5m1ZK1lKhieO/kmltZ2u13q/Prs/8ZLEB71jPvEQ0wsYM7p/9LISQma7Zp7woEQeDOCzUNy1n3hG1YXkpUgo5OtRzNEuF1nxZg59kkHoRoEARwlYm48ljJk3ANYzdE8vsVBd8Ne02Mn0ZoXtX3g5uj+e/nzecqAq8l4eYoYePoglx9oqJ/I2fOLyrMsgFenLuvRBAEHoWq6bM6nLk9PbC3Ne6bn6cN7o5iQuPy93N5/+L2bwHyFD2/X07iZbQWmb2IllUcqVrcHlsJnLqbQmMLVh9JKXp+Oy/n6NzC2WrvaYSGXWcSCY7WIrUT0fxjGa0qOxr5jlqbuo4c0wWPiJcW94sFgR7LJpGi0ZsVGBIwpL9aQ2bH5OX+s7VaU656IB9fPm5y3O3qjTlbqzVkOFdu27/66r4MtOCTqxeL2TR+GddrtQbItH+yqGizQlmljh40Fcp6tXRm0kdNNq/R2Y1lC7cbIrTpjjtbqzXnqrdAL5EgMvN+z7BgpkzshL1KScDyyaRo9Nxo0JYu6xfS8NB2+gAHy/TlhLQFcxvvoHL7ErT9onba+0/cSaZKcXvs7QSsSWF1qunE8dvJtKmad/XpuYVYBFq9dWIYI9fjLBWRUTdOJBKxpL8Xu8/KWfVHHABXH6uoPCGIOmUc2DzWhxrpUrLVWoHNJxL56bOCRudZ+XssTT+SMrWzB7vPJlKpiB21pgbTsaYTegGaV5KxZF8s3i4StozzpUKGWmatTkCrE5BIgPxdIP0Qpf2A9xJJSsOY/jTCMMa2rCyjUhHD76h/Y2fm74llQCNni+KQ207LaV5Jlivlz6cRGradSuR5lBbZqz60reqYM3/xRz/DleHWj0ndX2pY9s//rqJoLwjeZSCTGeHX4c3ZF6W2k9UU8ez0Ozt10PkdbZd6m08/LtYHivS03o78CRyqZEhzTq/0fXVsmr/u593noP/ub6Y1PUz4k3gmVt1CzY4l6D23vsnpjt5KpnMtJ6s18E4OYppVlHHybjJ9G+asxvdtIXUuYmtFyFOrFxCAIt7mF+R93W04NsePlQfj0OkhOFrL6PVRODpAv4au/POVn4lneJxCz+AmLhafQxM3RTG0mQvnH6joVMsJjU6g3aJQapdyoGVlGZ7OEmbtiGGxQxzzenmave+CIKDSCNjkswr9hwiuFej1AssPxFJtchDHbydjYwNRiTq6Lw+l27IwhrdwY8LGKJ6Gm672Jqv0DF8XQfsaTln2dlJpBMZtiKT9gpeotQLNK8n4KMCetYcTqDU1mJvPVQDYisXYmXu9Wu3KSG5TaUZ6iAWB5n9sxtVWhIuDjdFLBCbbsntMXu6vf+kIFa+eNHvPKl49Sf1LR/Kl/UfNO3K/ZhOz7d6v2YTr9dtmqX9NT+5j3PzhFH10m4Grp6b1t/orNeqij24zbv5wCmqSstxHV1uR2f3pP08R4Oxob/b9zo72Zt/vo0pg5sTO2KsMq6NivZ4Bq6cyZWoPGh7anvao7fhgOxOSvwbg38AnLGqwnR8P3WfD5SD+uh9FhCKFDZeDUKgtrDxjSFdWa9+tKKOfpw0SsYhbr35rGfEgRI1OLxASo6NqcdNaIZFIRK8GLhybW5gCrhIQwaK+XmwZ52tEbisG2DNpUxQfF7GnSrHX51Go9Oy9mMTnbd15FKpGqYEl/b05u8ifAC8b3BzFXH+qomlFGT3qOpmtYf7nZjIVA+yR2n14vH/AB6SHIAisDoyjwvggAq8psLUxZJz0+jqcVvNCeB6poXlFGTJ7MV9si0FvZrHr7H8prDwYx8QObjnqg1orMHp9JM3mvESlFWhVWUalovZ8dziBjycGc+2JMnsn1KrhxnjT7Q5m1GlvjDcc/7+C57sMtavmEPKHSaQwX1G0l4GcmkNG0prVfqfW1sZcNpDC1O2ptbUxlw37VbE573dWosPpkRMSrYx6TW7hdRr3kRpp5BaAuOuIz7Rl2fneLDlnEKq6fPAJE6ps5uTWu8an1Ag42Wc+xjk5iFBp3q15RmYQiUTULu3A4evmM9x0eoFLD5Us3huHnQ1WnxmezhK+7OqOWASL+nhSxs+WjtWdmNPTw4Tcnn+gJCFZT4fq5oMON5+rCIvTIQiitPlP97rO3FtdhHbVHPn3mQpvFwkOdiJWDPC0aCF09YkKO1sRAV75G2P9fxPBFQSB4Ggticl6CrhKslR0/tXuGK4+VnIqnYALwOwennx/OJ41f8YxopULreeH0LGGE+2qO+JgK+LSQyWbTyRSr5xDmsJYilrPjacqlGqBIgVsKOFjZ9LeuA2RKJR6rq4oYpQnP7S5K4FXk+j9dRgHpxeyLGrxqpZSLxYbRR3NrZHEFizMpgUb8r2ONrdIL+pkDqn1sYBJaq8179ystl328gmz+8pePkG1s4eQ2ooz7V/nNV+mfQap/a106k8jISq/x3cZNGuYiRCVOaQqWx9tP4BHzTsa9bfe/k0Wla0zg0inY+BXI7BTpZhcQ+FHd4y2CcD5TwZQsHV91E8TiPv+NjGLryF2tcNjVE3CoiGzEtDrz1QUL/j2BFrMQSIWMaCxC6v+iGPj5wXTVodP30tmyb5YQmK0BHjbotXDr+flSO3ETOvqYVSLCxAUpSEiXsc3w7xZuDeGHWcS6VLbGVeZmLsvVBy5oSBZLbAvg4DE4zANhT1t8PO0YdyGSPo2dMbORkRBNxvGtXenfjkpQ78PZ0RLN7aeSjTpv0oj8E1gHEPesH3JB3zAm0KMXEdIrBaprYgSPrbZsuBbuDeOIzcUnJ5f2CjqMa+XJz/+k0DbhaH8M9uPnRN96Ls6nHozXzKkqQsV/O2IV+jZfU7OmfspbPq8IBWLvF6YSlbpuf5URYpaoFhBG0qaGd9TMXp9JPEKPXdWFcHR4fVz49MWrgReVdB9ZRhHvvSjdCHL5zCCjR1UXQ2XM0RwleGmx1ZdbTj+fwGZKUenkigwH8nNa/GsrJDWjMJP1vot6OHhN6a1vcG7jAWp4q4bIrw5EdJ6EyrZeh2cam8sUJV6PRkFqsBQoyyWYC8zeOjGRyiY2/o3fv/6Kr9/fZWhq5vyUSN/SvrYcv6B9cUgQRC49kRlJHD0vmBYc1dWB8bTrppjWoqvIAisP5rId4fikdqJeBmjRWonJvBaMt8djmd0a1ezEe0Fe+OQ2Yvo39iFjjWdGPRdBJUnBTOwsQslfW2JTNCx/bQchUpP7/pO7DgrN7t4fu2Jknpl7Nl2Ws6RL18LYzo6iBnQ2IUBjV14Hqmh1rQXXHigokc90+vS6Q2+5cOauea7j/j//BK/IAj8dl5OizkvabcghDE/R9Jw5gt6rgjl7H8pFt93P0TN/otJ7Jzoa+LbaWsjYnwHd5pXkhESq+PMQn983SWsCYxj0d5YXsZo2TLOh++GF0SrF5i3J4aqk4KYvyeGH/6Op9OSUDouCjFq/99nSq48UrJ+VEEjcpuK9tWdGNHSNS310Rr2f2G5JlQQiYjy8ef7NftyRILeJEQ6HfX2bzKpe71Xu5lJfWy9/ZuMiKtMHs/IST1Nao0rnQpk5KSeyOTxVtuudvZQpsR14KqpNN+8OtP+iTCOoOdGKCtVbdn/4S0Grp6adn2pCwH+D28xZOZgpJlcnzmYE/UyexxwoUN/bjUxrFbbFXel4Mr62Pg7oU9QI158Fv+kWB6/ACwsJsQlqDl4OYle9Z2z3c/8xqctXQmL0zLplyhik3T8cSWJz36I5LNWbmwf70tisp4ALwl6wWBF0m1ZKIp0YgnRiVraLgilpK8tveq7cHlZEQY1deXiwxT2X0pCpRE4OMOPfg1dGPRdhFF9kCAICAgs/C2Ga0+UjGrjZtS3aiUc6FHXmQW/xiBPNv5uPg5T0291GIU9behW580M6KJM/vuAD8gr3AlWMejbcCpPCmbkj5F0XhZG1Skv+P5IfJbE6p5Havj5aAL7p/qapPTZSESMbu1Gj7pOLD0Qi4eThEMzC7G4rycXHir5ancsP/6TQP1yUv5dUYRGFQxlSckqPV/uiKb8uCC+2h3Dd4fjaTM/lLYLQszOL24+V3HuvpItYwsakdtUtK/uyNi2biw9kPk4b4SSw6DmeuvH1FxvOO5/AXqdgYBlrHEt/IlpLeyDVabRSFWs2VpQnu8ybM9uRDSrpPXZjqz3++E3+SNIlYo3ER0G8zXKllB6jInFkFtBR1bdGMiEbe0A2DD+OBOqbKaBt4Zjt5IJjTafbYVex4UHShQqgQaZ1LK+i/ikhiMBXjb0WR3OyxgtgiAwbVsM204lsrifJz7uNrSu6siDNQHY28L8X2OZtCnapG7575sK1v2VwOjWBsEqN0cJB74oxOYxBQmP1/LbhST+faZiRld3Li3xZ0FvL47dSmbJ/liTyHe0XMfR2yn0a+hMKQsLcEUL2NKovJRfLyZx5l6KUX+CozUM+S4CtUZgRMv8X4T/n7YJspO5MGd3DCfvpPBVD08afyRFLBahVOs5eEXB/F9j+LK7Jz3rmU6yp2+Nws1JwhedX69e3Q9Rc+6/FDQ6gdKF7ChWwIbW80L4d1URs+mASrWenivD8HaRMLO7Z5pwjUYr8Oc1BTO3R7N0gBftqzsx6ZdIinjbMra9u8Vri0vSUXNqMBeW+ONmofZnVu0tuC2rR7fZI6lgRmTqbu1m/DBptVUiZblqMuvH5NV+mTyez+cOI+DpPfRiMZvHL+Na/bZUPXuIQa/qY4OLl+f7L39C4Wb4rIzeIxKzeYLhPdVepQSnvue7r35+XfeaDiKdjkkz+lD00e20bZZ8aYOLlwdBIODZf0b9y9jWmVa96P3jHIv1vKnvs3aPstOv56UqsmLRjrSovrV7nBHp+24OQSUqsHz5rzjbi01WC4VENcol19L+7icLpPzIzmh7vF4dVmz/lQGBTlRuU4m5gwq/VR9JS5CnGCaugVeTUKoFWlVxJCpRx/NIDePbu9OrvhMt5rzkWYQWD2cx1Us40KOeM/suJnHkhgJ3JwlnFhbG3clypoheL7DyYBzr/0mgdmkHyvrZEZmgY+cZOQ0rSFk7ogDeLqbvFwSBHstDuf5MTRFvG4oVsCUiQcfTCA2Dm7owoYN7Wv1MftsEhaVYj8D7Sl+LkH2wCfr/jdzYBJ25l8KAb8OZ/Ik7/Ro64yqTIAgCV56omLcnBleZhM1jClqtX52zOwa1VmBR39e+jTFygyBenEKPl4uEGiXsafpVCLdXB5i1yEiPZJWeTkvD8POwYXYPj7TxXa0VOHA5ienbYlgz1Jt26RREx2+Mws/ThimfWBnnFTo+nhDMjZUBeDpb74ORTRDAqU/MC035dYRGZuo932dkxxrH1vU1AXzbPrSNAuFE66y3nx/2T2/DZimzRQCP6gZV5kzw7z/P2Tz1VNrfN2v5sGeMHK8K3YzaenR5F5+c/ZqFfQvQudb7F8EFA1eY/1ssm04kUrygLY/CNFQrbs/NIBVDm7kwo4sHNhIR154oaT0/BLUOWlWWMbCxC/EKHT/8lcCdF2raV5OxZaxPludaYXFaPvspkttBaj6p6YibTMydF2rO309BIhbxdG0RJBLLCxbtFoZQwd+e43eSEWFQro9T6LgdrKZfQ2dmdfMw8tfNDf7f+uAef2DDgl9j+PNLP7OE8FGomo6LQzg0q7CJamrbBS+Z3cOT2qWlPAlXM+mXKJ5EaGhZ2ZCGfPmRkrgkHWqtwJ4phSjrZ7qasWx/LP+9VLNhdEGzofjbQSq6Lgvl8rIA+n8TzoyuHtQpY32lqcGMYKoWt+d2sBqlWqBoAVv6NnSm5SsRqhk1N9O6UyIt5o23SKa2TFjGw2YdzZzdgMw8YrNyjDWPVkEiydRrN6NH7KBZwzjXeVBaGnKiUkv9S0eot38Tu75YSa+lkzjXeRC367cx9c4ViblfK2vetKnIkg9u8fJsXrwJwKR/YBCQah64JS1luN+8zyhvZtHhXu1mbJu9Lkv3MLOUbcDIRzenXsCjxnam8OO7Zvdl5fxC4EOU5w3y/Ml2Sqq0KYSmVm0eHLvLn88d6fnsd2aoD3Hph0Mc+E9CVIIWDycJnWo50bC8NN9TV7KKVQdj+edmMt3rOePnYUOTj2RpyqsqjcDPR+P59s94YpP0iAEXRzFDm7nweTt3k7RlS0hK0RN4NYmQWC2ODmKuPVFR2NOGr3p6mj0+OlFHg5nB/DHTj6gEHZEJOlxkYuqWkZqoFeY3wQ3PhOD6fCC4H/AKOSW4CqWeShOD2Ti6QFrkND3UWoFuy8NoVlHKOCsLxJ8sCeXzNm60+FhGilrPjO0x/HYhiaYfSfH1sCEoSsO5/5TY2YrYNrYgtTMZi+ftieFphIZf0pUypMf1p0o6Lwvj1tevyXLbBSFM6+JBw/LWz91o1ksW9fXkeaSGHWfkhMXpcHUU80kNR/o3ckkjvm/EBzc/kds04ayk2MLrYwK6mxI7xFD4DfvQZjc1+HQn84JUhT+Bhgcy7585ZEY4Ie+/N0eqQ+w18/uy2dY/P1zk0I8P0v6WDCxB4/pV0ISf5fCZOxyJaMLC6jvoP3wG2Htw45mKbacTCY3V4iwV07G6E62ryHIm6vaGkaTU025hCCV97Ghf3ZGWH8tMMkBCYrQM/i6cy49ViESgF6BMIVtmdPGgUw4J/qNQNX/9m4xCpSfA25YO1WS0XRjK+PbudLGQ9n31iZLeX4dz95si2Erg8mMVITFaHB1E1C8rNZu5khv8vyW4fb5X0LehM11qW06DnLs7BkEQmNPLy2h7uwUhzOruQUF3GzouCmFcO3cGNnExshW4+DCFzktCmdHVgzHtjAdWtVag6qQg9k4tRBkz5DcVn/0YQeWi9vz9bzKj27pZNZffc07O2J8j6VzLiSHNXXGRirkVpGLjMcM1bxvvy4qm21h5a0iWCFAqGbNELi3VsKY/xhLM7U9PVNOTI3O1o1klwLKEGAbPGmZERAF6LJuE2MpXOzN7oUSlloKaJLPENbW/3878ARtvL7P9Sz2Hq60IQSKxSkwt9cXSPc7quXK6CNF+3Xzq/LHVaoQ9NeqcvgY4FSKdjpGTeuL76C5TPt6Qtv1FUWcqBF1k0KNdOGqT6dNwHfEuBenT/yMCCtgRHqdj+2lDXenmsT4EWFAGfJPouyqMPg2daVfN8gAhCAKNZ71kUV8v6pZ1yHVEOjxOS9sFIQxv4crwFq5GA/CLaA2Dvw2nSUUZM7uZJ8Dp8YHgfsC7gpwS3E0nEvnrXwU7J/haPObGMxX9vwnn5tcBSCwsjnVaGsqo1q40qSCj24ow3J0krBzoZRQlDY3VUmNqMDVKObBviq/FhTaVRqD8uCCOzCpEKV/L4/ugb8OpU0aalo73yRJDH1pVtq4cX3lSsKENfzuGNHWhpI+hTm7HGTl/3VTwy+iCNKoge01wc0pW8roONTvIq/pPa9egSTAlngDn+2DViyInxC6715PVe5+fCxdvclEknVqyRWS1zXQR6M0HP+PfB7UMm21ERDVIpo7HVfoW/g0PuwQSnRsx+L8dPAzTMLCxC6UL2RKdqGPn2SQiE7TsnOBDeX9Twch3Db5Dn3JvTRGryu1xCh3lxgRx/1tDVmnGBe+8wKWHSnqtCmPt8AK0riIzmu9cfqyk/zfhLO3vRaeabyZqbm1c+Z+uwb3ySGl1YgrQtY4Tf/1rqlRWtbg9R28l89XOaD5t6cqwFq4mnnkFXCQ4O4j49lC8UQ0ewP2XajycxFbJLUDHGk6cuJNMg/JSAq8kWTzu9L1kZu+KxsNZwjdDC1C9hAOlC9nRrY4zf870o3oJBwatCQMwqe182qCFxZpVaSa1qjmp5TSH9LWjPZZPptrZQ2ntZKV21JJY1MCvRuD3KtKYXsDJGlLJWWbeuZn50qZPb7a0EJAZuTXud9a8iW81am9V3Tk3nsAfnziYKbkFQ5+b/LHZrGBXai2vWCSw6t9BDHn6DQD+z+V4ym0omBLFJ0030yjiAqc7vGRUWw/aV3diWAtXjs8rTM/6znRbHkpcUtbEwPITaq2QqRqxSCTC0UGEREKepFv7uNtwYFohDl9XUH1KELN2RrP8QCwDvgmj2Vcv6VjDiRldcyn88QEf8J7g0HUFPepar9WvUswee1sRd19YVgiuXsKBv/9NZtc5OWqtwM+fFTBJARYEEIsNRPdvKx7dt4JU+LpLrJJbgE61DFZoqWhUXsrvlxVW33P9qZKgKA1j2rqyd4ov7ao5UsbPjgblpawbUYDNn/sw6LsIHoa+utac1qPmdR1qdpCX9Z/WiPjJNuZFmqwhp8Quuz60WVEjzmptb07VorOj/JwbPNuRObkFyzXTGZGurndgx3V8PXkQnq6RiLUCBU9IsbvmhoddAjps6HVpFYU9bbmxIoDJn7jTsYYTQ5oZLHKmd/Gg09IwXr4HPrlqrYAsk7mIzE6MWifg5ijJF3ILUKu0A9vH+TBzRwx1Z7xk3p4YFu6NpeW8EAauCWf5gDdHbjPD/zTBldqJMv2Q3RzFJKtMHx6Dmrqw9WQCFx4oLSqSfnsonn6NXald2oG9F4zJqVormBWLygiZvQi1VqBPQ2cCryleD1oZsOpgLAFetgxu6mJCtEUiEXN6ehL/SnQmsvRHgIHI/TVrNX8s/Im/Zq1OI7nBxcvz7cwf0MTGMXD6oDTSWeroQRKVWqq+Eljyf3iLgdMHoY2KJlGpTXvJFao0D1ZLr/T75QoV/b8cakREB66eSs85I42In9/ju/T/cmiWz5+gETjafoAJeS9/8ZjV6O3t6o25Wr9tlvqfoBHM7k/QCFnqo1yhova+X0wWHW7WbGrS79r7fkGuUJm9h+lfpY4etKrunPo5pr4//TnTv9DpTD7XWgc2G5FbAUPNbUbhKZW9A2unr7N4f87Was3m8csQxGIqJt5g1b+DcNIk8sypDDMr/kCxxHC6DSiPrmcvBIG0F4gY0dKN6iUc2HIyMVMl5vxGKV87rj62rtSYlKLncZjGpMwhNwjwtuXgDD+2jffFy1mCTg9tqjpyfWURxrZ3f2fqlkUi668P+IDcIkUl4OaY+VhqaSxPxaAmLuw5n8S6vxIY397NbGrimkPxdK/rzJi2bmw4ZqpSngpNNsZ3VTortAGNXQi8puB+iPlxXhAExm+MomgBG0a1djN7TIPyUka2dOW7w/GGDWKJITroXtXwdypBa3jA8G8qyXWvajhOLMkdwcyMfGQGvc488TzdyZjMpaoD57Q9c+JGqSJN1qK3uSF2eelDm1shrawgpzZL2bEW0usMglkZ4VHNVHhKIoOGgVm7X0V7pX2/RSL48tOpLJ84FID7zysyYfkmvr+8A4XIm1WDvcz+3nvVd6ZnPWe+PRSfeXtvGaV87bj8yPpc5MpjJaV88z/zrW5ZKdeW+7Ooryd2rywrx7Z14/aqInSs8W6QW/gfJ7gC8DJGY/WYey/UZn1qS/jYUb+8DJ1eIDzOeHUnWaVn/p4YrjxWMradGy0+duR6Bh+qAG8bnkZoSEqxkjIE3HiqooSPHQVcbZjfx4tuy0L5+1+Fkf/e5UcpXH2sws5GxOdt3YhK1HLtiZI7wSp0egGRCCQSEQMbG4h4+J4/UFSuRvA36/EdMpBGRTzwHTKQ4G/Wo6hcjY0LN2Lv6c6YRSMJeHoPeE06Ry0fkybeBBDw9B5jFo5M81etf+kIU2f2xVEen2WfWWdHey52GWxC6D6+fNyE+F3sMtiiR6u58z9q3pE9U1ZkqvybHhWvnqT62UO58snNqpeus6M9WxdsIKRkhbRr3DNlBbvn/MDm8cvS+h1SsgJbF2ww8q01d/76l45YFYBK/RzrXzqCCCioSWLqzL4mXsH1Lx1h8ow+Rt67Gfuaqpb847f7je6x2l7KmrV/kOzmYfX6HzXvyN0aryPN8++OZfp/0wAomGTDT1ttSVGZHyiHt3A1a4PzptGvkTPbTiVanTjvOiunXllplqzHMoNaK3D3hYpbz1UkKHR8FGDPuPbuTOviQe8GLjjlcf1KbvFBRfkD8hsB3jbcCbbu3apU63kSrsHf0/LkrrCnDePauXInWE0hd+MJtFKtZ8n+WI7cUDC9szutKsu4asVbslhBWx6EqlEorY/v156oKOnzuk9eLhKW9ffikyWhHL6uQJdunH8RrWH4ukgeh2uY18tQfqBQ6rn+VMm1J0oS06mmD2riYryobu9hqO/0rGkcfUwlAZ41X9d/Zodg/lXTmLTkRXTXGvHMC3Xg9EhHgrKMN+2fawk5WbjIDnIaHc5u5D/jdYBBLbn1VePrkMig7S2Qemf9GjJEoG0kOlZNGcT80Z8D8OxUMlUDj3Htz6cWT/FpCxd2nZO/8165g5q6sO6vBKvHrPsrgcFN86cUSKsTuPdCxY1nKmLkOkQiEU0+kjGtswczunrQvrrjO1fP/D9dgzvvgAaZvchqrVqvlaF0qulErwamhsR/XJGz4vc4ohJ0lA+wp3xhOxKS9fx1Q0HtMlJWDPTGy0XCtpMJHL6RjI+bhOeRWhzsRDSrJOPkq9Tj4S3czLatVOupO/0Fm8b4UKmooQbg738VLNsfS3yyPq29O8EqpHZiNn5ekLVHEjh/P4ViBWxJUupRqPQMbOzK6LZuXHiQwpFBvzLgQGdEep15BV2djp33IrGViKl4MpDumQgWpZKcwM9mmRVYsmQ1lF2BpIx1qNmtH7Uk4GQJWanBzUuhrcyEssx516av4YXXta1GAlpiMfdrmhfQ+m7694xdONKqUFZIyQpsXPiLUdupfT3Wth8PW3ZK255ae7x57o8ku3pmev2WPu/C3W8w/8pP3HIz1M188kVtanUtY3SMIAj4DXtK0I/FscunVJusYsz6SKLlOn4eXdBEOOr4rWQ+Xx/Br1MLUSEXdTwKlZ5vA+PZdjoRV5mhduZFtJbWVRyZ+Il7jqPD+V2DG6m0nqJZwOE1MflQg/v/Gzmtwb30UMnInyK5stTf4gRq++lE9l5MYt/UQmb3p0KjFfAe/BQvZ0P50MdF7ZGn6PnzuoJqxR1YM9QbX3cbohN1VJsazMNvi3LgchIHryhISNbh42ZD34bONCovpc/qcJpXkjGsuflrSVHrqTwpmH1TfU2eDX/9q2DR3jhik3RU8LcjMVnPnRdqetd35vcrSeyc4MP203L2nE+iiJcNIhE8j9LSpZYTX3R2x9fdhqIjn9H50mljFeW8qOs0ggjq7sidyrAlvMn6T0siTZbwLoly5bZW2dx3Qq+Dv+tAbDrV4qyoKOdGgTr1OkqPM7YCSr2ORoHgkA1ym/peK9+heicuUfPKa7HMzze0pkTVgiaHlhnznGNzClPYM/eL1PmFJKWeZnNe0qmmE9M6G2dxCYLA0gNx7LuYxLE5hXGW5t1CuEojsOZQPBuPJSC1EyOzFxEcpaVZJRlTO7lTrvDb9dX+fysyFZ4so838EL4e7E3rKsaiDoIgsObPeH47L+fvOYXN1tk9i9TQbv5Lzi/x5/RdJcHRGmT2Ylp8LEuL+ipUeup8EYxKK/B5WzcqFbEnMVnPgUtJnH7lAbV+tA9NPjIWj0pR6xn5QyQOtiJ+/Mz4BycIAneC1QRHa5DaiXGViRj0bThaHUzp5EGPes5pkZw7wSoW741FoxPoXseJ6+P3GwiuFU6w83YYtq8kvrNCcq2RKEsqxJbIT1aVhLNDHjOrcdWLRNyv1TRP+p+dY7IqlGVJzEsbFc2YhSONSLFUHs/osV3wiHiJXiRmz1TzpHXTVz/Rd95IE0uh+zUaU/bKyUzvg0inI0EjmFxf+r5au35rn0mxrpc5d6gDbho5Mz9am7Z99vHeODgZHpYpaj0lPnvGy/XFkbzlVUG1VuCLLVEcuaGgZz1nw6RYKXDgkpzHYRrWjy5IrVI599lLUurpviwUfy9bJndyp/Qrf7kYuY7NJxLZcDSBPVNMJ8lZQb4TXFUmBNf+A8H9AANySnAFQaDr8jB83W1YM9TbRETqxjMV3ZaHsX18QWqXzvx3WHvaCxb08SRZped5pBZ7WxEtPpZRvODrRaQ95+X88FcC4fE6Svna0qeBMwVcJTwO0/DLiUQcbEXM7u7B4O8j+GV0QRpnGN+TVXoGfxeBq6OYn0aaTqhTcTtIxfNIDVJ7MbVLO+DkIKbq5CCS1QIdqjsxvp0bfq8m3eFxWtb+lcDeC0kc+MKH+l+G0OfGGWOCmx1kleTmp31MfqgDZ0Sm1ymCwh3zzx4nL5BTMTBr5Pi/5SDoIP5m1myWwIwCdTbvXV6KmmXh+1v7zJ+s6/d/7d13fFTFFsDx35b03iAhhKo0BQlFeg8dVATpEBWsIBALT0AFBQEFQUARFZWAiCCKdKT3DkGKKE1qQnpv2+77Y2UhJNmEkJDC+b5PPs/du3t3NmR35tyZOceAo6YRC17bYrl/wppeeAeYJ7VMJoUqr13m2KcB+LiV3AAXzN8Bg+bcJCHVxNA2LlT0tuF6jJ7Fu5Jxc1Tz0xhffD0K7z1k6hWemxmBva2K9/t4UreyeQySkGpkya5kZq+LZ1mIH00etS+017xXD22Aa+fkRtilDIbOvUlgVTv6tXShnJuWizd1LN6ZRGqGwrI3/ajgmfsfRL+Z4XRp4JTrtP9zM8LZ/3c6Jz+vjJdL1vMcOp/O4Nk3UaugbhU7ejd1xtlezemrmSzdnUzrOg7MfrFcnvuEU9KNPPL6ZaYM8s7xarHRpPD83Juci9DTfvuOewpwAQZ+9Bp1DuR/9hMKNgNaFDO4+Sqbo1KxYuxnAPc1A32vj7mf4w7JCQSPe95S/zfL7Ot/GaJjyvnz1bxVlvdwa4b1Vtmki4/UpfWmZfeUUbsw2p/bTPO5Ju2pcWg7LzWdQYPYU4z4ZxH6Ro05+M4Sfpt20PweOlWl/5TWrNiXzKpDySx7s0KJ2ct5KVLPT7uTuBylx95WRVA9J7o1dMJWe38NHLs4mgydwpxhPjnurf31QDIzfo9n/7SAey6fVNQBbnQeAa6PBLjiP/dTBzc53cTgOTeJiDcwrIMb9avYkZRuYuWBZDadSOPL4eXo0ch6ZuJbvt2SyMawVH59xy/Hz5veoND6/etcjzUwd5hPthqaiqLw0Yo4tp5M46P+Xry0IIrHK9nSp6kzLg5qwv7NZMmuZDo+4cicF33uOdlL/beu4uGkZsdHFXM8Pm9DAot2JFHZR0vlZVsKHuDCvc9s3lIYM5wPYgY3X0H8f7PUULgz1MUtP7OtbvVArYXa7+SvzBI8+NJCOclnHd8JZ99F61iOD/83CtQa9v96jl+mHLA8ZeruARy4bOCDn2PZM6ViicltYY2iKBw8l8GK/SnEJhvxctHwXDNnmtW8/woOd/toRSz/hOtZPKp8jtnp/ziRyqjvojk5q3KRJbXKy0Md4IJ5lnXVwRTWHEkhOc1EOXct/Vq40Kl+3jWwzl7PpPcn4Uwe6M2zTZ2z/AH9djCZkd9E8eEAr1yXIS/ZmcTGsFSebeLM1pNpZOgVKnlrGdTaNc8My7cs3ZXEN5sTcXJQ8evYCjnONn+3NYEJS2N54fRehv7ey+r5lp2KwPDf3p/6u9czeHbOezoVyHEXnUmtZtGYTznesluur3H3cxvu3WB17+it84aO+ZRjLbvl+tp3nl9tNPLW+IHZZilPNWpL3SM7siSauvxoXT6b+hMNDvxBu7WhfPne17kGtzm1vyCPKehxlbX3dTTr7Out93VrVtUpMZYRk1+xBMa7uwzINcg1qdUsHjWdo61zvkiRU/vunMG98/jds9COyQmM/HC4pR0/vTmDE627U3/3eqov/onhzWex7Oz/8Nu8GsfyPiiKwvvNllief6TZk7wVHECXBk5ZPnMGo8LuM+n/1Yw17wHxcC4BV9gLKCnNSMO3r7J3akCue3gVRaHjpOuM7+1F+3q5lxHLiQS4oqS4nwAXzJ+DvWczWLTzv4tMNio613diUGuXbNmQrUnXmegxNZy6leyYOsgrS7KoxDQjbyyM5vilTJ5+0omPB3rneA5FUegxNZzn27nSs5ETqw6nsuXPNDL1ClXLaRna1tWyEuNeXIrU0+6D64DC7+/6E1g1+6qNiDg9dcZc5ZMhXpwdu7qIZnBvfefmMDws6uC2sF7HWhB0fQ1Z3tutGcerv9xbiaKSKp8BIACejaHTgZxnW3MtszQglxe+Y0l7Qdud3xnefATwF7VdCdr+BTsnB1D5jrKDv316mD3Lzlpu15vxNC8EuefYJEVROH4pk7+u61CroGkNB6r7FmzLUGmSoTNRZ8wVNn/gzyO+uX+XPTUtnCFtXHguj0z3ReWhD3Dv16krmYz8Ngq9QaF7IyfsbFQcPp/BgX/SCarnxA9v+Ob63LRME4FvXmHXlIACLx14ft5NejZ0YueZdM6F6xjby4N2jzuiVquITDAQuiOJRTuScHFQ0X77TiYfGmo1sFp6/BoqFVTbsoZ2E0dbDTpz8lezDsx/Z16+Z/fude/ogs+W57g8Nqfz3yo/dPc+00e3riF49juoFSXbPtPclt/m1v6CPuZ+judrZjqHesY5/Z5TXNxxTcyeFORKtTqotdoc9/8CpGQYcLmjfQ7JCQT/t4/4VJsepGSaj9fduY7mvy9i8V3ncUhOYMh7wzna5wX+anc7iK6zYx0HD2bwa41nmTLEh1a1HVCpVOYB7I5IDk3eCYCdkw0fbB9gmbUM3ZHI7LXx+HloqVnBlvhUE/v/Tuepxk58OMC7xCVgyo+Nx1NZtD2R5W9b3zs4f2MCV6L1fDL03vYoFXWAG6OzPoj3tpUAV5jdb4BbmBLTjIxaGM2uv9Lp1cQZPw8tV6L1rD2SSs9Gjqw7lsbuKRWzDIrvtvpwCt9uTWTdeP9Ca9fXmxM5fTWTzvWdGPV9FO887cGAli64O2nQGRTWHElh8i9xeLtq6N7AiWsT1xQswM33Htwc3O/y4XsJvu53mbC1IOhWLdy7Z2sfRA3gB+F+LyLk9u/k1xnCN+Z8Pht3eOpiwS4OFGSvcT6e881uDXPXJzDrBW+C6jpaxhNnrmUy78VNOMQkAFDhUQ/eXt4zywX1vWfTGb80hoQ0Ey1qOqA3Kmw/ncYTle2YGexTpgPdfX+n896yWHZ8mPNKkluW7Epix+l0vh+R+1aMomStXynZC85LiLqV7dg5uSIHz2Ww92w6eoNCvxYu6AwKz7fPnpzqTo52aqr52nA1xlDgADdDZwLMV4YPn0/nxS8iMRgVXB3U6I0KzzRxYd0Ef8Z8F5Xvc6qMRuou+y5bFuOrLYOotHer1cCq1qEdNNy7gfNBT+XrtRSNhkWTF2YJREPHfMr5oKey7x2dvNA8E6jPX12ydBd3vv/4h2wJnI617IaDjTrHBE73cv7icrJND9L1plxnvS2/wzuWFt+qP3tnYKw2mXIMbsGcIVsFvDjhhWyJpu7mkJzAC//9+/Wd8TYAB5p2ybKH+/nxL7Bo6u3zpLu4M3/GMpwdswZBf7XrgVtrI40uqZm2MpFpJFHRS8PNBCMZOoXnJ/VA+/thrp2IYkKTxQz6pC2bU1xZdTCZJaP9LPtAAGKSjHy0Ipa+M8NZ+U6FfJXuKEnSdSbcHPMeTLk5qknXlbxrkZIpWZRGbo4aQkf5ciVaz+rDqcSnGKkTYMv7fTzRqGFDWJrV4BbgiSp2XI4q3H4kQ2dCo4GzN3T4umuZ9ms8E5bG4u6kJkNnotEjDkwf4k3YpUzSC5r1NbfyMzkFmDm5lWX4fkrptN2Yv2RFBckOfKdb2aXvDoJu/X9OgVNZCG7h9nss6DLwW9mu73y+Yso9uAXQJ8CmRuYMyfcS5N55IeLAoNvtv/PvYXvH7MvGb9UevvvfrEp/qPQcqDW83BH83DVMXhHHW4tieMTXhpgkI1FJRl4a2ZpRXV0Z3/Inws/H82aDxTR5+hH6T2rB9lNpDP8qks9f8KFHQydLYJyhM7FwWxJdp9xg43v+ZTbITctUcHPMezxlHpsU4ELZAyAzuPdh0OwI+rd0oWcedZ9ajb/Kly+Xt2RKvld9Z4Zz8J8M+jRzpldTZ1wdNew7m8ayPcm4Oan5cYwf9jYqAt+6wjMHd+d7BtcuMYGuowbh889pTGo1Oz6cw6WOT1Fz8nharV9q9RwF2YN7ZybhvU26ZEkSdXcgWtQJnO539rUwzpGf469/+kauSbnmj815Fr3ernU8OX8OYZ7mcj/1Y89QPeWKtbeSY6KpWzO4KqORV3KYGT7dqC2P37Vk+lrNenxzx3n0RhMudrkPGPo87sfVKBMxyUbcndTUrWRr6UhS4zP46tnfLY8dub4//uWyf4YUReGFLyIJrGouqVOaHL2Qwejvotg7NcDq/pnxP8bg5aLmrafv7cp4Uc/gxuqsf6d52WYW+uuL0qkkzeBak5RmosYbl7n2ddVsNefvdOpKJs9/EcmxGZUK7bXHL41hwR+JDG7jQu//+vpD59L5blsSbk5qVr7th7uThv6zIujZyImjo1cVbAY3PzOb1hTGMuX7zQ58LwozuVFpc5+JvAwXf2bfhnnczPDG3SaRNl4HsNdYL9uFc3Xo8U/+frcPaEZfURTOXNMRHm/AxV5No+r2WT7fmWl63m3xk+X22arVmTClIa3q5Jy47ouNCWw/lZZn5vbS6ny4jh7TwjnzeWWrWzk//jWOlHQT0wbnvJ2jqFnrV0r8dEd4eDgNGjTA3t4eg8HAoUOHaN68OS1btiQkJKRY29a+riO/H0qx+pjTVzNJzjBRu6INBbmWcPBcOif+zcTVUc30oT60quPIE1XseL2rB7umBPBYgB0vfnGTVYeSebzS7cGmYuUHQFEgw9WdDXOWElWnPjsmzeFi0FNgMFL53Mkswa1JreZ0kw7Z6tg2/e0HUtMySck0ZPtRgORMQ5afKFtnZkxdyr6mXbIc39e0CzOmLiXK1tlyX07Pv/Pn7uNJBiXH43fffy/nz+l93f0e7/u4zpDrT4O9G6h1eEeOfxe1Du+gwd4NpOqMWX5uxBj50NCDtt1+5/dK3Vgd0JWOnVfwTLsfOOv2aI7nUoAdPQaTaoI0vdHyowAZeiPpJtjZc2i2f/96OdQx3tVzKBkmyNQbydQbQTHvm731ozco3IxRuBqhEJeooFKpqBNgS+s6DtSrbPffUmXz36ejuz0fHxqKqlkNAL7o/jMb5x7N1n6VSsWbPT0I3ZGUpa5kadCwuh0aNew5m57rY5LTTfx6IJn+La2vFhHiQSrJffP9cHVUU6+yHRvDUq0+buWBFDo9Yd4TXxjzBPv+Tmf5vhRcHNS83sWdNo85EljVjlc7u3NoegB1K9kxdG4kl6N07P8ng6eftH5h3arc6uZWeg6cq2V9rEptDoburlv7z+ysNXIL0oaO+7MHyVX6m+8vzD2wuQVEZT24vfyzOUjMSR71fhVF4ZstidSb24wPLk1hU1Q75lx6mdo79jLl3GgMpkL63RVRXeSIeAOHzmdw6komRpN5rPF4JTs6PeFEs5oO2S5e2TnaMDssmA829gGg9r8X+W3QCk7vupbj+Yd3cCXs30wuRerv7f2WEo9WsKWyjw3rjuX+PZipV1i8M4mhbUvm2KTEz+BmZGSQnp5Or1692Lp1KzExMbi7u2Nvb8+gQYN49913qVu3ruXxD3IGNzndRON3rrB4tB9P5pAmOyHVSI+PbxCbbCQ+xYSNRkWbxxx4sYMb7eo65CvjWb+Z4fRq6swfYWnYalXMe6lclqytJpPCk2OvkphmYskYX1YPWsG0I9av6OoMpqyzs8asNXOPn7hI4LA+uP51EkWt5syMBUR170W59at47J1XUZlM3Hz0MVbNWkKmq3uOrxGVosfJNvcvoqQMA85WZvaSM41WZ/5SMo1Wn5+qs348TWeyejxdb/35+TlHms6Ik5XjmQYTzrn8jmpsW0vnySF57sHdNvFzLnY0LxW/GavwzWoj/ZzPMOXL/jjpzUFThtqWH6v3YVrdUfy+/XnqJvxtOYcCHJjwCRd7Dcx2fkVRcLC53ekEbFpNk/FvoMopIZlazZ+fzCeiW9YEZx72tthr1ZhMCr8fymDl/nRMioK7k5rwOCM1/W0Z1d2dlrlcJbXXquk46TrTBnnx26BfLPe/u64PbuWzZk2t/+Zl1ozzp1IeSwtLmnVHU/hgWSwr3/Gj2l3JHNIyTbww7yaVfGyYEXyPNQJ5ADO4+jxmcG1kBresup++uaT/Law8kMystQls/sA/x739t5JBdWvoyOYT6UQnmVeg9G7qzCud3KiVzwSSd3p6ejj9W7qQnG5i4dZEVr9bAb87tjUZTQoN37mKWgVD2rgS0tODkMDQ+8uinFvynm1BkBB277VORclxn3twP/g5lm0n05jfI4wnrvSynOdSaiXeOjMJZ20aiwJHoVHddX6nKtDl2L3/bRRSVu3DFzL4dFU8Ry5mUL28DYlpJjJ0CsODXBnZ1d3qqoxb3v0xBs+UJCK+3mO5753lPalQI+t7GjY/kg51HRnYqngSLBW1HafTePmrKFb9zy/LBBqYyycOnx+JWq1i0cji2X8LpXwG197eHg+P28sOfX19sbc3B5M2NjZoclh++qC4OKiZ/3J5np8bwZKdSaRl3v5g7jqdRuCbV4hPMfHN6+W5vrAaZ+dVoXsjJ977KYYJS2PzvOobHmfgz8uZPPOkM1++XI4MvULzcVeZtyGePX+lsfF4Kq99HUV0kpHHK9vmqwZgju76HRrcPTjx/UoS6zWwBLcAUd17cWbGAhLrNWD5p6G5Brfi/qiMRgJXfJ9tdvRSq47ZZ1GXf4fKaL6KvnKHkSGuJ/lsbi9LcAtgb9Ix/PxPTA6bzoim07MsPrvRvF2OwW1OrnV5mvDWHXM8FtW2U7bg9hZFUfh0VQpbTmQwro8Ly97yZMFrHvz6Py/6t3Qm5PsYft2f+0oIownsbDVMOxLMC3ODAJjeYyVzB2W9Mq3VqDCW6Mt1OevRyJmQnh50/ugGby2KZvupNPb/nc6sNfE0H3eVcm5apg4qnuU/eVHl8SPKrpLcN9+v3k2daVzdnh5TzWUAb/XVeoPC74dTCJp0Hb1RwcdVy5aJ/iQsrsb+qQGUd9fQ7eMb/H7Y+squu12PNXDySibPNnHmpSBX+jZ3oem4a4xdHMPWk2lsP5XGxJ9juZlgxM5GxZge7oXzRnOaDbPzhA5bs8/uVulvvu31pAS3JV1u+6zzORN/+EIGvx5IYe3gg1mCW4BqTlf5ueGrhGeUZ0X4XXlYbNwLFtyC+e/Lv2fOx/x75iu43RSWalm+f3ZOZbZ/WJFjMyqxdIwvu/5KZ/Ccm+gNeQ8SjCZwquLJ7LBggj9tA8CMfmsJCQwlKeb22MpGo8JUylaN3Yt2jzsyfYg33T4O59Wvo/jjRCr7/k5nzrp4Go29ikmBr16+9wvvD0qJn8G9pW3btmzduhWt1nxF8+TJk4wbN47169dnedyd0fyktr8/yCYKIUSJMmnnM5b/LooZ3Lg8ZnA9ZQa3zJO+WQghRHGwNsYplVmU4+LiGDlyJCtWrLD6uLyW6ha2iHgD12IM/HMjk682JbBvWqVclyGHXcrgpfmRHPq0Uo4FlAH+upbJC19EcnC69QQ0a4+k8PPeZJaG+DGucWi+lihbc/hyXJ6zL0fDE7HV5L4AIDpVj4ONtSXGBhytLGFO1RmtHk/P67g+r+Mmq0uoMwxGq8fzc450vfVz6IwmnKz8jtTxcfQdN4w/+w7jfNDtK5uPbl3LEyu+49dPvkft5QXA7hMmEpIVnnsimZ5jBlsSh/3Veyh1fl1smQ2eEPguXpnxvPnX15bzmdRq9k6ey+XOT2dtgKJgrzX/GwdsWk2TCW/kumT6onNlFtQKZmntAWSqbVAUaPSoln4tHPl6UxrB7R1pXit7MOTv6YBWo+bD5bE42KoZ2ytrkih7GzVHL2Qy8ttIdk0JyFIDOu5GMjOe+c1y2yakGx8NNF9NvHhTR7/PIngswI7gdq7mzInJRpbvTWb14RTmDS9Hx/pZlziXRXcuUS4KMksr7pTfvvm+ltU+YIqicPa6jqR0E+XdtSzZmURKhsKnQ3NfVTFjdTxXo/XMG14uX69x6komwfMiOT7TesKq3w6m8PO+ZFa85Qdw/0uURdlWwERetUddZuN7/lRxS86ajOzREXD+S1BMGBU15f84zbWOgThobl/ILFACsnwspz6SUJ+vUr5m7VlvVICtVkXfFs681tmNR/1smb8pgSMXMvnBynLZYxczeP6LSE58lvu4GyAlw0Sd0VfYNtGfR++qZ71o7E7+3GJO2mmwsWHOwdulDMGcc+SVr6M48W8mr3V2o93jjigobD6RxtebE2nzmAOfv+CT5Tmi4O68cHq3UhfgGgwGBg8ezMyZM/H1zb3+bHHw89Di56Flxb5kBrVxsxqUBlazx8lezbGLmTnu3wWo5W9rKTLdsHrOjwH4ZX8yXRvkf7Buq7W+Mr1F9byXQzaq7Gl1cJtpMGHt86szKlaPmxTrxxVAbeX3qyhYfT6Q5xdMPrZI57mPOq89ANbLrFRGFXSE5hoNze+8u9EIeOdVhmo0ljYmRCaQ4Gqib7sqsGMHxqe7ox/xBrW/nJclKLU16cisURvlb7VlL63aZKLF74tpGPJKtuXqKgCjEbuXQ7OcR1GrMXbviWb9WvZ6N2Zoq3m8cOFnDlx4k3Lb15CUqeKX/clM+yWepHSF4LbeuXcoKniuuStvL4pmYr/sf3uNH7GjQTV7Xvwikq9fLYfrf2V1PP1d+PjQUKa9sZuUw5fRz97AXhoR2Ls2/T6LYHR3D4bckfygko8NDarZ07eFC4M/j2Dl2Ao8FlCwzObiP9JHi/+U5L75fpgT4d3+nlh5IIWfQqy/v6FtXKj/1lXmDvPJV66NxwJsMRgVjl3MsNrXL9+XTI+GZf/CnCgk+SijkxO9EexsVFnLLNUYDefmWIJQjcqEjUqPoVxXiFuTtZTQP7Otnj+LfJSt+uFqP6adH8WYuuv5bO4EPFxsuRFr4IcdSXT66AbfvV6ezX+m8XKQ9VVCDavbo1bBPzd0WT7Td3O2VzOpryd9Zkaw8m2/LEHu85+2ZefpVH4avg6nzAzeariY2i39eXmeefvU+8tiiUs2svfjilkuyD/SxZahbV3pMyOCaavimdBblvgXtRK/B1ev1xMUFMSff/5J586d+fjjjzly5Ahjx46lbdu2HDhwoLibmE18ipGKXnlfO6jopSUuJfcshGq1iuFBbny0Iva/WrjZ7TqTxtGLmfRqeh8ZFUXJlds+trvur1nBlsPnMsw3PD1J37kXQ/+BZKzegDGwAWAOSve3HEz1d4aRuehHlP/28xoDG5CxZoPV18pcswHTHefRhf6IbuUqIr79meBWc1m0dxTvqXdSbtUS0GhwdVQzLMiN798oj86gcPBW23Lh6qAmM5e6jiqVinnDy1HJW0vDt6/y1qJovt2SwPTf4mjyv6vsKV+d4b/1BWD97KNMabmEx3xUWYLbOzWsbs+Iru4s2FS0s5sPA1Ue/xNlV2nsmwtDXIopz/69vLsWnUHJ9Tvtbmq1ipc7ufH+sthcn7P9VBrHLmXSp5n09eIeFCCDdJ2Ktuz/578++1aQXHWgecbXwzwOOJH0OJ7OWpw7/Waesb21t/de6xffqov833ktM8Ctf4dmSzkU35Bp50exqfMUXn9tNB4u5mDT30vLe308WTral2HzI0lKM+Gaz7qtGfn4XL7YwY3R3d3p8OEN+s+K4IuNCXy+Lp6OH97gtW+iGbCwJzMODQbg7N4bhASGsnLWMZbuTmbh6+WzBLe3ONurWfh6eb7ZkkhKRsmsHVuWlPgZXBsbG7Zu3ZrlvokTJxZTa/LHy1XD1RjrqcMVReF6rAEvF+tfAsOC3Dh2MZPen0YwrrcnLWrZo1KpiEsxsnR3EvM3JvDDSF+c7Ap2reJcuI79f6djMEJNfxta1MpfdmdRsrSv68D4pTG3ZwBuBauenmSs34z90904OnQ8//7tQacnHDE27E8mYDPvc3Nw65nH1URPTzI2bMbuqW4YRo3B2Ne8/GiJZxCdq/9Dq6smMtduznaeupXtsLNRMWddHC1q+ed6+pNXMqnsk/vXkY1WxSdDfQjp6cEvB5K5dFOPk72aBa+Wp0E1c3mhaUeCCdtwkRUT91Lhlx0siQ5gyIz2OZ5vYGtXGr19hU+DvXPsiIQQ1pXGvrkweLmouRpjwN0p9777ZrwBOxuVeRYsn17r7MbRi5n0nBbO+308aVnb3NfHJhtZvDOJeRsTWDraF8cC9vV3MpoUdp5J53yEHlsNtH7MgUd87z3zsyibXmjvypebEnjmSSfzyqtbweodM7rzw+fyQic/83jx1nLkgtYvvnOm+M7l1FX6Mz+5Om8/sZpqT/+Y43mb13LgmSedOXw+gxOXM2lZO/dkq8npJi5HGQjIxwQUmIPcfi1c+PVgCqev6tBqYFQ3N7o2cLLUhp0dFkxKfAbvt1/OviWn6Q1c2tUSrx7VczxnRS8tTR+1Z93RVPq3LJvZl0uKUpNkKr8eZJmg3Bz8J52QH6LZP+323lm9QSEuxYiNVoWHk5rjlzJ5dUEkhz6plOdSWZPJXGvqu62JxKeacHFQE51opFN9R0b18MhSkiA/e3DBvEfxndBozoXr6fiEI7ZaFQfPZaAzKEzq50WnPPYn5rWPN1sporvoTYrVJcCKYv045LE8WMl7ibG1Jc7m49afn2cbyLsNec1y5f07uP3fa46k8NHyOH4c40utirZZzn/uWjqD5kYzrrcHzza940vVZMx95pYcVqDeVVKq46TrfDzImybVbXI9z5s/RLNibzLn5lfN8UKMgkLPj8N5tbMbPRpnn52418stjd66TMfduyy3R/3UE79Hs3eMj4++zNZJFfH1KPHX+QqsqMsEJRpzX04J4Ka5PXMvSaYebqWpTJA1k3+JIzHNyMw7ynZl6s39u4OtCncnDZ+siiM8zsicYfeWYdRoUvhhexLfbEkkPuW/vj7JSNcGjrzZ0yNb+aGC7MFddSiFiT/H4u6kptEj9mToFDadSOWJynbMGeZDJe/SVWZNFD69QeHp6eFU97Xhs+d9spWmnL02jqV7Utn2oT8ed17oyans1L246/k6g0LAy/9yfl4Ark65/10ev5TBkDk3sbVRc+STAEvwebdvtiSy60w6S8cUzRaKSV9dIfGbnZbbb3zfhWqB2fcEv/dTDN6uGsb08Mh2TNwba/1K2R3ZFaMmNezxcFbz6e/xDG3jwoLNiSzfm4xarSJTr1DRS0tSmpGR3dzztdFcrVbxfHs3gtu5cj3WQIZOwddDi4uDOVjI1CusP5bChmOp+AAh30fRr6ULTR61zzEAuxSp55np4Yzu7sHPb7lavrwURWHP2XRGfBPFVINCzxyCjVvsbHK+imwyKfxxIo1F2xM58a858UD9qna80MGNTk84ysb6ItS7qSsmk4o+n0bQpIY97es6ArDjdDoH/knno/5e9G1x17JdzT1+BaizPj4x1YSfhxa1NvfzjO7hwbI9yaw7mkL/lllfX1EUPloei8Go0CXQqcCLWuNTjBy7mIHeCLY2anr91I+KpjTmDV7L3IFrUang40NDLZ+HDJ2JlHRTjjUuhRAiNy+2d6XV+9fo2ciJAG8b5m1IYOWBFOxtVKRmmqhSzoYr0Xq2fJD7ipXcaP7bljSsgytXYwxk6hV83bVZll4qisK+vzP4aU8ydsDz827yVGNnejRyyhKI5GTp7iSmrIzj+xHlaVrDPsv34YLNiXSZHM7mD/zztcUqL5EJ5j2Sy/elEJ1orhPcq4kzwzq4UqWcBNElmY1WxfK3/HhlQRSPjb7CoNYuVCtvw80E88pBHzcN68ZXyBrcwv0Ftzk8PyXDhK1WZTW4BXP+m0y9Qq2KNoxcGM0Xw32yBbn7/05n2m9x/Da2wv218Q4ZOhMHzmWQkm6iorcNHgGuJI7qxqu19Sx4bQvzXtwEwIS1z+Jd8fbEQlSSkWrl5TNQ1GQGt4hEJhh4elo4N+IMBNVzYGwvT8q5aVl9OJnZa+JBpaLxI/Z881r5XK825cfJy5kMnRtBdV9bnmvuzJ8hqwj46GkW70zC31PLdyPLZ1tKNXh2BM1qOTCiq3uO5zzxbwYDZkVw/LPK97R8U2dQePmrSK5G63m1szvt6pqXiuw4lc6CPxJwd9LwiK+WmwlGnOzVdA10omsDp3wV3hb5l5ppYtXBFMIumWfPnqhqz7NNnYskmOvy0XXG9fakzWOOuT4mIdVIvZDLuDlqeCzAlr4tXPB21XA+XM/inUk426tZPNo3z+X6OYlJMvLRilg2Hk+lflU77LQq9v2djouDmtBRvgRWs2fxm9s4u+c6AP0mt6J+l2os35fMqoPJ/PxW4XV2JVFRz+Am5TGD6yozuOI/ZWUGF2DPX+kM/DwCnQEGtHRhdHc3FFQs3pHIN1uT8HTW0PEJR2Y9712oW35ik40M+vwmMclGnm/nysXxq2k0pxdLdydzNcbAz2/68nilnJPnJKYZqTvmKpsn+mebCb5lyso4LkXq+X5E7plo8+PwhQwGzr5J9wZOPN/elSrltNyMN/Lj7iR+2pPM5y/4kJJhYsvJdDL1JqqVtyG4rSs1Ksgy6ZLm7xs6lu9NJjLRiJujml5NnWlc3e6BbGUzGBUqvfIvJ2dVxts19/HB/r/TeWdxDH984M/z8yI5H6FjWAc36la2JSnNxC8HUtj3dzrfvV7ecuH/fugMCtNXxbFoRxLVy9vi5aLmn3A9KuBmgoGL86vgYKtm3y//sHLqQcvzpu4egE6joe6Yqxz+JKBMrx57UKz1KxLgFhGjSaHFuGsEVrPj7+s6LkTosdFCm8ccebGDK01qODBodgTNajrw5lMFW6ZwOUpP9yk3+GSoNz0amWdbby1RNpkUJiyN4dRVHb+/W8ESRF+N1tNx0nXCZlW2upen/2fhPNPEOduMmzXjlkRzI87AwhG+Wa4kp6SbeOWrm+w5m8FjlWwZ2c2d+BQTv+xP5lqMgUVv+FKvimSzLY2+25rIgX/SWTgi9yU/X/+RQNi/mcx+0YfVh80rDVIyTPi6a+nfyoVWtQu27zs6ycBTU8Pp+IQjo3t4WALkazF6Wr93Da1aRegoX5rXciA1IYMpHZdbnrutVSumv+BbKJ1dUboUqSd0h3lZlU6v8IifDYPbuNKhnqPVMge3lNQANyQkhKNHj9KgQQPmzJljuf+VV17h9OnTqFQq5s+fT7169QqlzaL4laUANy3TRN2Qq7SoZc+RCxlEJRrxcNbQu6kzw4Nc8XXX0mNqOMM6uDI0l2R390pvUOg0+QbNa9ozub8XarUqyxLllQeSGfdjLDs+qpjjDOyCzYkc/CedRW/k/l0dn2qkXshVjs8IwMetYIPvqEQDzcdf54vhPnQJzL7V6dstCby9OJamNewJbuuKs72KsEuZLN6VTKcnHJkzzCfPmWjx8Hj9myge8bWxOk5+eUEkdSvZ8UY3dxRF4fCFTBbvTOJKtB4HWzVdAh3p29zFsurxfugNCgNm30SthmmDvKnua56JVRTzvvb+s27SqLod68ZXsIxrfp1+iL3L/7acI2V4J74d4XffbSlu58N1LNyWxO6/0tEbFWpWsOWF9q60f9zhga3WtNavyPq8IpCSYWL8jzFExBv4IyyVyEQjzzRx5texFfjhDV9a1THveZ080JvvtyWiMxTsGsP8TQkMbuNiCW7vpFar+HiQN3qDwtaTaZb7w/7NpHkthzwTVXR8womjFzKtPuZOsclGVh5I4fNh5bLt13jhi5t4uWo5/GklLkToaV7TgcFtXFk9zp8P+3sxYFYE/0ZZT8olSqa+LVw4ejGD3w4m53j89NVM5qxP4LUu7jjYqunf0pXFo/347X/+zH+lPK3rOBb4SvDEZbF0CXTiowHeWWZ/A7xt+G6ELyZFYcicCMLj9Ti52/PBviFUeOpxADrs2UPKplMFet0H5fttiXSbfB21SsXsF3z44Q1fujZw4tNVcTw3I5zk9BKQhVGVx08Ojh8/TkpKCnv27EGn03HkyBHLsXfffZd9+/bxww8/8OGHHxZ164W4J4qisOtMGp0n3yAh1ciO0+kEeNvw5Uvl+GtOZT4Z4s2jfra4OKj5qL8n8zclUlhzCGuPpmKrUTFlgFeOg8c+zVzo18KZLzcm5Pj8w+cz6JxDwHknDycNDarZEXY5/33/3RbtSKZroGOOwe3xSxlM/S2egS1dqO1vy8BWLjzV2JmJ/bw4NbsSMclGRn0XXeDXFmXPiK5ufLEpgeOXcq7EsPpwCjtPpzOotXkJsEqlosmj9nz5UjnWjffnl7f9GNbBrVCCW4DvtiehMyj8NMbXEtzeet12jzuy+YMK7P8ngz4zIixtfvZ/T9J/eT903uYAzHnhZmb2X1so7Sku8zYk0HnKDRxtVcx/uRxLRvnS8QlHPvg5lj4zI0gtAVmiJcAtZOFxBrp8dJ3NJ1IZ0MqF459VZvNEf2r42xA89yZz18dbHlvL3xZ/Ty1HL1gvoZKTDJ15Gerz7XO/Eq5Wq3gxyI0fdyZZ7jOZFDT5+FfXarinjnnd0RQ61HPE0znrMpLdf6UTlWhk1gs++Hpoafe4I+uOplqO92zszNB2rnyxISHfryVKDhcHNT+F+PHh8lhe/zqSQ+fTiU028vcNHR8uj+W5GeF8MsSbJwp5hj46ycCWP9MY3cM9x+Pt6zqy/j1zHbqmY69S541/qTXyMgecKtDxuz4AHFjxN+MahxJ3I+fgvDitP5bClxsS+GNiRSb28yKwmj01/W0Z0MqVPyZWpIqPDa8uiCzuZhaoTNDBgwfp2LEjAEFBQVnKyVStWhUwZ+jVWEl+JsSDpigKby+OYfT3MWTqFT4L9ubPWZUY3d2dxTuT6DE1nMS022X/WtdxIDndxIWIwrl4G7oziVc6uVm9IPhSRzd+2puMPoeL5iaFfPX9GjWY7mNsunxfMs+3y3nWetpv8XzwnBcT+3qyYn9yljGGo52a0DfKs/1UGmev6wreAFGmPBZgxxfDytF7RgTjl8Zw5lomsclGDp3P4LWvo3hncQwr3vLLNvYsCoqi8O2WRMY965Hr1sInqtjzUpAbaTqF4HmRVHrlXyq9cplXvo6idkhbpuwfhFqr4sY/cYQEhrL8o/1F3u7CtmJ/Mgu3JrJncoB5fFLVjtoVbXm+nSu7J1fEy0XD699EFXczJcAtTCaTwuDPI3iuuQsNqtnTtIYD7k4aKnrZ8EY3D7ZMqsjS3cmsOZxieY63q6ZAMzHRSUac7FX45bGGv34Vuyyzo49XsrNkS7Zm95l0Hq+c/6AkJsmYY5mXJTuTeLGDq+XLoHI5LdFJWWv/Pt/OlTWHU0jNLP4rPuLe1QmwY+fkAB6rZMfY0Biav3uVwbMjMCkKG96vaDVZWUEdOpdBk0ftrZbqqOVvy5geHvRp5sLOKQH8Na8KP73pR/t6Tkw7EszwrzoBMOOZ35jR67dCb2NBKYrCZ6vj+TTYm8o+2RNRaNQqPg324ex1HX/ex0xLYVCprP/kJCEhAVdX8wDYzc2NhISEbI8ZN24co0aNKsKWC3FvvtiYSNilTHZProi7k4ZHK9ji6ayhe0Mn1o2vwGMBtryy4PagTqVS4e2qIamQVlpcitQTWM16n1zZxwaN2rzU+G5PVLZl15l0q89PzTBx7FJmrvt48yM6yZhjEqnrsQaOXMigf0tnfD20GIyQlpl1HOJop2ZoW1cW7UjK9nzx8OrW0ImdH1XERqNiwKybNHjnKm8sjKJGBRsOTAugftUHs8UtPM5IQqqJpjWsb815rrkzKekm/vysEmEzK3His0qEzQxgZDd3nBy0fHZkKNP2DgTg4KrzhASGsmvpXw/iLdw3RVH4ZFU8X75UDv8ctkJoNSq+GF6Off9k8M+N4r1QJQFuIdp5Oh2VCkZ1d8fPQ8uFm1n/ccu7a5k6yIu56+NRFAVFUbhwU1+gjeZ2NirSMhVMJuuBakqGKUstvkcr2FKjgg0r9+c+a3Xppo49Z9Pp0yz/NbrcnTRExGfvVC9F6mlQ7faXQUS8AXenrH925d21eDiriYw35Pv1RMni4axhRFd3dk0J4J8vq3J0ZmU+7O9N1SLKlqk3KDjY5b202d5WhVGBcm7abCWKqjfyY9qRYHwf8SDuejLjGody7uCNImnvvfjruo6kdBPtHs99f7BWo2JwG1d+3lP6BoJubm4kJZnbnZSUhLu7e5bjn3/+OXXq1KFly5bF0DohstMbFOZtSGD+y+VwdVRTwUPD+TtmZtVqFZ8M8eb4pUzLoC5Tr3AlWp/nRej8srNRkZJHsGwwKqRnKjnW3x3c2oW1R1K5EZt7P7toZxLNa9rfVxZlDyc14XHZX+PfSD21K9riYKsmNtk8VnCwzd7ORtXtuHBTtiyJrCr72PBhfy9Ozq7MlQVVOfxJJUJ6ehQoOWVB6YwKDraqPLdVOdiq0BkV1GoVXi4avFw02Z5j72TD7LBgPtjQG4DfZx4hJDCUM/8lxCypjlzMRK2CFrVyD/LtbFQMae3C0j3FuzpOAtxC9MuBZAa3cUWlUtG3hQtLdyVjMGYNQNs97khssnnZ0u6/0rG3UVGv8r1nDvRx1VDRS8vOPK7I/n4oJVsinQ/7ezPll1jWHknJtgz5nxs6+n0Wwfjenln2LBhNCttPprHgjwS+25rIufCswXv3hk5sCkvNNhttq1VZZmaT0oz8EZZG94ZZ9+YoikJapiLZlEW+PeJny/GLmRjzuMBz9EIGj/pZD7JHL3uK/601L1v+4Y2tjGscijGPOs9FKSLOQNVyWtYdTWXgrAjavHeN7lNu8MWGeOJSbl9EetTPhoiE7BeVSrpmzZqxbds2ALZu3UrTpk0txzZv3sz+/ft57733iqt5QmSz6690KvvYWLIPD2jlwvfbsu6vtdWqGNjKheX7zIO6VYdSeKKKHRU8CyfADarnyG8HU6w+5o8TadQJsMXNMfug38dNy1tPufPMJ+FcvCuAVBSFn/cmM2tNApP6euV47pgkI4t3JjFvQwIr9ieTksseu95Nnflxd/aBra1WZZmxXbo7mV5NnHLcS5yaqWAnYwFRAvm6m1dkXLdykQjM+91r+OVvXO/h58zssGDGLO4GwMJR2wgJDCX8fHwezywe4XEGKvvYMGd9AkEfXqfZuGv0mRHB74dTsmyNqOFvm+OFrgdJAtxCFJNkpPJ/RdLrVbGjToAt/1scjcFosgzE1WoVFb21nL2u4+1F0YT09ChQkh2VSsWwDm7MWBVHhi7njubSTR2/7E/OlsWxbmU7fgzxY9qvcbR9/zrTf4vj87Xx9JsZzjPTbzC6h0eWvb1rDqfQZOxVpq+K41qMgb+u63j2k3Ce/eSGZfmzr4eWTvUdGf9jTJZZ5baPO7DmcCpGk8L4pTF0beBEefesHf6hcxm4OqgLpfZeSXc+XMe0X2MZ810UHyyL4ciFjEJLQvIwqVvZDh83DRuPp+b6mKhEA+uPpdKvZd4rEdx9zcuWWw2qA8B7zZaw84fiSUKVqVc4dC6Tb7cm0ruZM1+8VI6xvTz4+4aO5u9eZcdpc9K42GQjzvbFOxAsQI4pGjRogL29Pa1atUKj0VCpUiU+/vhjAN544w3+/fdf2rVrxyuvvPIA3oEQeYtKzLoFp2M9c033KSvjMRpNlu/wyj42RCUa+eeGjg9+juWtAlZIyMmwDq4s2pmUa0LGtEwTn6yK46Wg3PNyjOruzosdXGk/6Tr9Potg5up4PvollkZjr/H5ugRWv+tHzbtKCKVmmHhjYRQN3r7K9tPp3IgzsPJACo+NucKUlXHZLjK+0N6VFfuTOXQua26RJ6rYci1Wz9aTaczdkMCrnd1zbOOqQym0/6/EoLjtUqSej3+NY8S3UfxvSQx7z6bL2OEBc7BV07e5M99uScz1MQajwjdbknih/b1lT69c14fZYcEM/aQNADP6riEkMJTkWOuTWA/a+Qgd20+l8fcNPe/19uTrV8vxXHNn5m9KpO0H17n530rM2CRjoSX2KigpE1SIXp4fSZvHHBjUxvyHveXPVEZ9G0VcqjkALe+moWsDR345kIxWrSakpwevdXEv8OuZTAqvfxNFRLyBD/t7Ub+qPeMahzL54FA2Hk/lvZ9i+F8vTwa2zvmDpigKe86ms/9sBgaTQk1/W3o0cspS+3bFvmSm/hrLN6/58uSjt5ck6AwKi7Yn8sWGBNZO8Keyjw2pmSYGzopAq1Exoqs7bR9z4HqsnrbvXadmRVvstCqWvumXZamo3qDQd2Y43Rs6M7xj6S4dYU1KholRC6M4eC6D/i1dqFrOhqhEA8v3JePlouG7Eb6FdqX/YbH3bDovfxXJktG+NKyedblMTJKRgbMjaF/XkXef9byn8+ozjXzQ8kfL7Qmb++HsYX3PTWHRGRQ6TrrO1Rg9G97zp3bFrHuLDp5L58V5N/llbAXeCY3mjW4edG2Qe2bUoi4TlIr1gagTtzvn0l4aRtyf0lwmaGNYKl9sSGD9BH/AHEwu+COR6aviydCbEzcGVrXD0U5FUpqJqzEGpg32ZkA+Lq7di4VbE5m1NoEZQ73pEujI2w0XM+v4UI5czGTC0lge8bVh/ss+eV40T80wsepQCucj9NhoVbR/3IFmNe2zPS9Tr/Dsp+FU8NTyyRDvLIl8rscaePmrSKqWt+GL4Vlfc+vJNF76KpJhHdx4vp0rFb20xCQZGTr3JkcuZjLnRW8Gtso+Ljl60Zx99tTsysU+OC4pMnQmRn8fzeY/0+jfwoWa/rbEJhtZtjcZexsVi0f5Uq180WwFEtldi9HTYdINJvXzYmCrrJ9vnUFhxLdR5qoib/vdV6mcP77+k00LTlhuf3JgELb2xTtGPHklk6enh6M3KuyeHJDl705RFKavimfd0VR2fOhP24k3mDLAq8hLMUod3Adk/bEU5m9MZP17/ny6Ko4V+5J5vas77k5qftmXbN5bl2YiTaewZHR5OtW//+Q7RpPCN5sTWbglEQc7Fa227GBdizZU9rEhpKc7HepZLwtgTUq6iQZvX2HN+NwLw3++Np6TVzL5fqS5tl6mXmH5vmQWbU+0ZEL089CSnG5k1gvl6NbQyVK/858bOib+HIOtVsX3I31zzUpX2hmMCv1mRuDvpWVGsE+W/VEmk3lv17I9yWx83x+PB5AJsCzZfCKV0d9FEVjVnu4NnbCzUXHwXAarD6fwYgdX3n3Ws8BliE5tvcxP43YB8MiTfgz7slNhNj1Hqw+n8MO2RFrVceDguQyWhvhlqwk5f2MCG46lEJlo5MD0SlY/N0Ud4KblEeA6SoAr/lOaA9wMnYnao6+wbVJF3BzV9PoknPLuWl7t7EZskoHFO5M5e0NHdJKJyt5afn/Xj+q+9771KD82Hk9l5pp4rsca6LZ3F7uD2qE3KrzW2Y1XOroVav3JhVsTWXs0lVVjcx6sp2SYaP3edT5/0YfWdbJ+F5wP17FgSyIr9qWQlmlCq1HRs5ETf13TUTvAlvf7eFqSUekMCqsOpTB+aSxfvORD1zzKGT0sFEVh4Oc3sdGoWPBKuSzlHRVF4dutSXy+LoHtk/wLlMtFFMzfN3QMnH0TFwc1A1q54OWs5u8bepbsSqLJo/YseKUcTvaFc4Hmh7d3cnLbFQCcPez5aFvfAo9p7teLX0bSsLodMYlG/rquY+mYrON2RVHoOiWcmv62HPgnnYPTAoq8Hq4EuA+IwajQasI1mtaw59C5DNaM98fb9XbAkpBq5Jnp4dSrbMuBfzLYN61SoRU0N5oUTl/V8fNzP/PS2gFUK4TO9Yftiez5K90SvOYkOd1Ew7evsHtKQLYv2Fv7j7UaFVtOpDJrTTw3E4w84mdDfIqRyEQjz7dzZVR3jzK9/3bd0RTz1f/3/C3B/d1GfhtJtfK2VouZi5ylZZr4/VAK+/5Ox2iCGhVsGdTaJdtS+IKa2GYpujTzspsRod2pWMe7UM6bk34zwxnQypUejZx4aX4kMclG3n7ag9Z1HFCpVFyP1TN/UwLfbUliw/sVaFjdeoBZ1AFuusr61VkH5XYN7tIW1IjCVZoDXICpv8Zx6HwGKqB2RVumDvKyDDQVRWHyL3EcPJeOjVZNo+r2vP/cva0cuVfnw3XM776M4N/6U6+ybaEPJBVFodm463w61Dtb8Hqnb7cksudsOotH5TxOUBSFTL058ZVKpSIt08TUX+P4cXcy1X1tcLZXc+aajtr+tozv7UGzmrI8+ZZdZ9J4OzSGfVMDch0rjl0cg40WPh5YdP2SyM5oUtjyZxrrj6WSnKHg76lhcGtXalcs/AtbiqIwufuvxEeYt2TVaV2Rl+Z0KPTXsSYpzUSd0Vc4/XklHO3UDJx9kwy9if8940nL2ubVH5ej9Ly1KJrdf6Wzf1oAj+ZzH/L9sNavyCWfQqTVqFgyujxt3rtOi9oORMQb8HJRk65T+P1QCnPXJ9A50JFJ/bzoNT2cjcdTefrJwimholGreKKKHT9DoQS3AMcuZhBUz/oA1sXB3Jn/eTkzW4B755WdjvWd6FjfiX9u6LgRZ8DRTkXDavZlOrC9ZfHOJF7q6JZrcAvwamd3Bn8ewZge7kV+xauscbRTM7C1a65L8e/Xh7sGEXkxns/7r+HL4PUATD08tEiuot6IM1DT3watRsXCEeX5cVcSHyyL5Wa8AXtbFRk6hT7NXajgqcHTRb6+hXhQxj7jwXOfRbDrTDqDWrugN4KNRuHQ+Qy+2JjIhQg9a8f5kZqp0PaD67z1lHuWGbfC9mgFcz9fVCVSUjMV/o3S06q29e0ZnQMdmbU294Q4KpUK+zuyJTvaqZky0JsJfTw5djGTDL1C1XI2VPeVZbZ3+35bEi93dLM6EfJaFzfafXCdD57zyjF7tigaGrWKLoFOdHkAqw1UKhUfbOiDQWfknSY/8tfu64QEhtJxeF26jWhQ5K8PEJVkwNNFbSnNuCzEl++3J/Hmomiik4zY26jI0Ct0esKRyj42DyS4zYuMkAqZWq3Gw1lD40fsGDrnJjcTDKhU5uzJHw/ysiwZHtjald8OphRagFsUTCbIT6ylVpmLyOdHTX/bbEksyrq/r+toWsP6VenHK9mRlGYiOd2Em5XarqJ4lK/uwbQjwSwbv4uTWy4z/snF9H6/OY2eerRQX8fZXk18innPvkatIridG0PbuhKdZERnUPBx1aLVwC/7k3HKR5kkIUTh0GpUBFaxw06r4rttSbz8X83bauVteLGDK1+9XA4XBzU+wBNV7Nh+Kp0ejUrvUluTKfda1ndSq1T57v/v5GCrpmVtma215uwNHWN7WV/VVbWcDY52aiITDVTylosEZZnWVsPssGBS4jJ4v8Nytiw8xZaFpxg0pSWNulcv0td2tlOTmGZOmKtRq7DRqnilkxsvd3QlKtE8PinvruXguQwuRsYWaVvyS3bxF7L4FCMVPLW884wXYbMqc+Wbalz/thpLQ/yy7If199QSn1KyS3zUq2LHrr+sZ3BLyzRx9GIGjwU8XEHrvbDRqMg0WB8BGE0KBiNoH4IZ7dJswNQ2TNwxAIBfJ+9nXONQMtMKr2Zj1wZO/HJXjWqVSkU5Ny0VvWyws1Gx+UQaj/jZUM6t+K9PFiSLshClVXyquT71pvf9iV1Ujajvq3FsRiVGdHHPkhSpgoc2S0mv0sjFQYWvu5bDFzKtPm7H6TTqVymaWeSHnVajIlNvfexwawm4VlZ+PTScPe2ZHRbM/1Y+DcDS9/YSEhjKpRNRRfaavh5aqpW3YfOfaVnuV6lUlHfXEuBtg61WxbI9yfRoWDIu7EmAW8i8XDTciDVY9p/aalU5Ljm9FqPPsj+3JOrXwoUtJ9K4Ep37AP6n3ck0rG5PJR+5cpibZrXs2XDMev3CHafSqOlvmyXDtCiZ7J1tmXYkmKf/1wSASW1+4tfJ+wrl3ANbu7DxeCrHLmbkeDwpzcj036yXAhFCFA1vVw1XY8z9ofq/WYycXIvV4+1Ssvv3vKhUKoZ3cGXOuoRcy9Fk6hW++iORYUFFsz3kYdeylgPrjuZeCg/g0PkMXB3V+LqX7r83ce98q7szOyyYV7/qCMC8FzYSEhhK7I3sdagLw2ud3Zj8SxxJaTmXJj18IYONYakMaVMyvg9kNF3IqpSzoZKPli13XeW425JdyfRuVnKXJwN4OGt491lP+s6M4Fy4LssxRVH4ZX8ys9bG834uheGF2Qvt3fhua1KuV/R1BoXZaxN4sUPJ+FIQ+dO0Ty0+PjQUgKNrLjCucSjRV3Kvj5cfPq5a5g0vx5DPI/h2SwJJaea/GaNJ4Y+wVHpODadVbQeeaVJCvjtkClc8RPo2c+bnvSlWZ9Uu3NTx13Vdmajl+kJ7V27EGXj3x1jSdVkHtQmpRoLn3aRqORuCirgUyMNqeJC59nHEf7VF72Y0KXyyKp5hHVwld8dDrGbTCswOC6b3OPNF9yk9fiMkMJT0ZF0ez7w3zzVzpnlNe7pMucHGsFRLDeyEVCPzNyXQ77MIvn61XImZvJMsykVg/bEUJv4cy9rx/vjlkLr92y0JLNqexK4pAYVeGmdc41CmHQku1HOG7khk+m9xPFHFjsaP2JOhV1h3NBU7GxVfvFSOxyvJ8qS8TF4Ry/ZTacwdXo66lW//vi5H6Rn/YwxaDfzwhq/VRFSi5LryZxQLhm8EzMuHJvzR777Od/JyJl9sSGDbqTS8XTUkpBipWt6Glzq68WxT53wnuCrqLMqZausDWzuTZFEWZqU9i/ItA2ffpJybhlnPe2cLKlIzTPSeYa6/PfaZos+IHxIYyuywwu3v7xafamTEN9EcOJfOs02d8XXXcummnvXHUundzJlPhngXWjUIkd2sNfH8uDuZBa+Wo3F1O8t3//VYAxOWxhCXYuLXsdnLyYmH18ppB9m34h/L7ZlHhqDRFs58pqIorNifwld/JHI5So+7k4aYJCNBTzgyprt7kSW9y81DWyaoODvRuesT+HJTAq93duO55i64Oqo5dSWTr7ckEXYpg9XvVrDUgCtMRdXhpetMrDmSyj83dNhoVbR9zIGmNbIXhhc5u7NmnY+rhuq+NkQmGDhzTceL7V0Z96znQ5FRuqybE7yByyejARj2eXsebxNwX+dLTDMSlWjExV5doDqHRfF9eOc5dWrre21sTbeX15XmoEbcv5LSN9+vxDQjz828iY0GRnRxp2VtB/RGhQ3HU5m3IYHGj9gzb5jPA5lRexAB7i2XIvWsOZJCYqqJcu4a+jR1xqcE5AF4GCzdncQnv8fjYq+mRgVb4lKMhP2byaBWLkzs64m9rSzGFNndOR6pWNuTt37qWajnj4g3kJJhopybBjfH4pm1lQC3mBy7mMG3W5PYFJZKuk6hSjktwW1dGdTapcj+GB5khyfuncGosPdsOpGJRlwd1bSp41CkpSTEg5cYncakTr9Ybs84PBitTcn78i+Mc+o0eQS4RglwhVlJ6pvvV6Ze4bdDKXy3LZEzV3VoNSqa1bTnpSA3guo5PLALv9LfPzxMJoX9/2RwI86Ai72a1o854GwvYwdhnclo4u3GS7gV6TXt9Sj9PmhevI0qRBLgPkSkwxOiZNg4P4zN354EoNNL9ej6euADb0NRB7j6PAJcGwlwxX8e9r65KEh/L4TIj4xUPeNa/mS53Wvsk7QeULsYW1Q4rPUrcvlHCCGKQNfXA5lxeDAAm789SUhgKInR1pPPCSGEEEIUJnsnG2aHBfP++t4ArPr0MCGBofy153oxt6zoSIArhBBFRGtjLsw+fE57ACZ1+oU5wRuKuVWFR6Wy/iOEEEKIksGzgjOzw4IZHdoNgG9HbSMkMJTw8/HF3LLCJwGuEEIUscdaBzA7LBhXbwcun4wmJDCUf4uwKLsQQgghRE6q1PNhdlgwQ6a1BmBG3zWEBIaSHJtezC0rPBLgCiHEA/Lhlr6M/70XAHP/K8puMpXmNAhSCFcIIYQojRp0qcrssGA6v/oEAB8ErSAkMBR9prGYW3b/JMAVQogHyKeyK7PDgmnyzKMAvNVwMXtX/F3MrSoYCW+FEEKI0q3LK/WZHRZM3faVABjb9Efe77Cc0pyHWAJcIYQoBv0nNmf6voEA/DrtECGBoaQn64q5VfdIIlwhhBCiTHjxs3bMOj4U9/KOpMRl8GaDxXw3ZntxN6tAJMAVQohiYudozmzYf5K5Lt341ssI/d+uYm5V/kl8K4QQQpQdKpWKiZue49OD5ioQp3ddIyQwlI3zw4q5ZfdGAlwhhChmTZ5+lFnHhwJwYvNlQgJDibhYCrIaSoQrhBBClDk2duYqEJO39QNulzs8tuFSMbcsfyTAFUKIEkClUjE7LJiQH7sD8GmfNbzbYmkxt8o6iW+FEEKIssvZ057ZYcGMXfkUAD9O2ENIYCiX/yzZlSAkwBVCiBKk0mPezA4LpkZTPzLTDIQEhnJiy+XibpYQQgghHlJ+1T2YHRbMK18GATDneXMliLjwlGJuWc4kwBVCiBLota86WZYGhY7dVUJT98scrhBCCPGwqNXcn9lhwTz7vycBmNz9V0ICQ8lIKVlJMiXAFUKIEurW0qDuIxsA5tT9q2cdKeZW3SbhrRBCCPHwadW/NrPDgmnepwYA41otIyQwFKPBVMwtMyvxAW54eDgNGjTA3t4eg8GQ7bYQQpR1QcPqMvPIEAB2LvmLkMBQ4m+mFnOrxMNM+mYhhBDPTWjG7LBgKj3uDcDbjZfw2cB1xdyqUhDgenp6sm3bNpo2bZrjbSGEeBhotGrz/pf55v0vH3VdyafPrS7WNqlU1n9E2SV9sxBCiFtClnTns6PmC/HXz8YSEhjKiikHiq09JT7Atbe3x8PDI9fbQgjxMKnVzLz/xaeSKxEXEggJDOX8kYhiaYsEuA8v6ZuFEELcSa0xX4iftmcAAAd+PUdIYCi7l5198G154K8ohBDivo1f3Yv31/cGYP7LmwkJDMVkLBl7X4QQQgjxcLJ3tmV2WLBljLLq08OEBIZydt+NB9YGCXCFEKKU8qzgzOywYFr1rwXAW42WsGPx6WJulRBCCCEedrfGKKNDuwHwzcithASGEnExvshfWwJcIYQo5Z79XxM+OTAIgDWzjz2w11Xl8T8hhBBCPNyq1PNhdlgwQ6a1BmDF5KLfm1viA1y9Xk9QUBB//vknnTt35tChQ9luCyHEw87WXsvssGBmhwU/uBeVOkEPLembhRBC3IsGXaqaZ3QXdSvy19IW+SvcJxsbG7Zu3ZrlvrtvCyGEePAkhn14Sd8shBCipCrxM7hCCCGEEEIIIUR+lPgZXCGEECWTlAISQgghREkjM7hCCCGEEEIIIcoEmcEVQghRQDKFK4QQQoiSRWZwhRBCFEhBkyiHhITQqlUrRo8eneX+06dP07JlS1q0aMHJkyeLqNVCCCGEKMskwBVCCFEwBYhwjx8/TkpKCnv27EGn03HkyBHLsffff59ly5axYsUK3n///aJuvRBCCCHKoDK9RDkxMbG4m/DATdr5zEP5voUQD54u9d6/aw4ePEjHjh0BCAoK4sCBAzRu3BiA+Ph4AgICAEhISCi0doqSRfqowiH9vRBC5ExmcIUQQjwwCQkJuLq6AuDm5pYlkDWZTJb/VhTlQTdNCCGEEGWABLhCCCEeGDc3N5KSkgBISkrC3d3dckx1R90htVq6JyGEEELcuzK3RNnNza24myCEEGXW/X7HNmvWjK+//pq+ffuydetWnn/+ecsxT09Prl+/jlqttszyirJB+mYhhBAPilwiF0II8cA0aNAAe3t7WrVqhUajoVKlSnz88ccAfPjhh/Tr14/nnnuOjz76qJhbKoQQQojSSALcfAoPD7cMzAwGAwCLFy+mQ4cOtG3blhs3bgC5l78oTne3PS0tje7du9O2bVuefvppMjMzgZLX9kOHDtG8eXNatmxJSEgIADNmzKBly5YMGjQIvV6f633F7e62//vvv7Rq1YrWrVszcOBAjEYjAEuXLqV58+b06NHDsmyzuOX0ewf47bffLAmAoHT83gG2bNlC+/btadu2LceOHQNKR9tNJhODBw+mdevWBAUFERMTA5TMtt+rOXPmsGfPHubNm4evry8TJkwAoF69euzbt499+/ZRv3794m2kKPNKc7+el9La7+dHaR4b5KU0jx3yUprHFnkpzWOPvJTWsYkEuPnk6enJtm3baNq0KQA3btxg165dbNu2jZ07d+Lv72+1/EVxurvtmzZtokmTJuzcuZMnn3ySTZs2lci2V65cme3bt7N3716ioqLYtWsXO3bsYO/evdSrV4/ff/+dqKiobPeVBHe3/fr166xbt47du3dTtWpVNmzYgF6vZ8GCBezevZshQ4bw9ddfF3ezgextP3XqFAArV660dEKl5fd+6tQpvv76a7Zs2cLOnTtp2LBhqWn7nj17sLW1Zffu3bzwwgssXbq0xLZdiNKoNPfreSmt/X5+lOaxQV5K89ghL6V5bJGX0jz2yEtpHZtIgJtP9vb2eHh4WG7/8ccfGI1GOnTowBtvvIHRaMyx/EVJcHfbq1evTmpqKmDOaOrl5VUi2+7r64u9vT0ANjY2nDlzhrZt2wK323j06NFs95UEd7fdy8vLsgfNxsYGjUbD+fPnqVu3LlqttkS3XaPRsGHDBoKCgiyJf0rL733Pnj2o1Wq6du3KkCFDSE1NLTVtByxX6299Tktq24UojUpzv56X0trv50dpHhvkpTSPHfJSmscWeSnNY4+8lNaxiQS4BRQZGYlOp2Pbtm04OjqyevVqq+UvSpJHH32UAwcO8Nhjj3H06FGaN29eott+8uRJoqOjcXd3z9bGktxuuN32OnXqAOZlY1u2bKFTp06lqu2hoaEMHjzYcqy0tN3Dw4OIiAg2btxI8+bN+frrr0tN21u2bEl6ejq1a9fmq6++4tlnny3xbReiNCvN/XpeSlu/nx+leWyQl9I8dshLaR5b5KU0jz3yUtrGJhLgFpCbmxtt2rQBoH379pw9e9Zq+YuSJDQ0lJ49e3LmzBm6d+/Ojz/+WGLbHhcXx8iRI/nuu+9ybGNJbTdkbTtAZmYmwcHBfPvtt2i12lLT9u3bt9OsWTNsbW0tx0tL293c3GjZsiUajaZUfE7vbPvmzZvx8fHh7NmzTJo0iZkzZ5botgtR2pXmfj0vpanfz4/SPDbIS2keO+SlNI8t8lKaxx55KY1jEwlwC6h58+acPHkSgBMnTlC1alWaNWvGtm3bANi6datl70tJoygKnp6eAHh7e5OYmFgi224wGBg8eDAzZ87E19eXxo0bs2vXLuB2G3O6ryS4u+0AL7/8MiNGjLBcka1RowanT5/GaDSW6LafPn2aNWvW0KVLF86cOcN7771Xan7vjRs35uzZs8Dtz2lpaXtOn9OS2nYhyoLS3K/npbT0+/lRmscGeSnNY4e8lOaxRV5K89gjL6V2bKKIfNHpdEqHDh0Ud3d3pX379srBgweVt956S2nTpo3Su3dvJTMzU1EURRk1apTSsmVLZeTIkcXc4ttyanunTp2UNm3aKEFBQUpsbKyiKCWv7T/99JPi7e2ttGnTRmnTpo2yf/9+Zfr06UqLFi2UAQMGWH7nOd1X3HJqu7Ozs+X2b7/9piiKoixevFhp1qyZ0q1bNyUhIaGYW22WU9tvadGiheW/S8vvfdasWUqrVq2Uzp07W/7WS0Pbd+/erfTu3Vtp06aN0qpVK+XChQuKopTMtgtRGpXmfj0vpbXfz4/SPDbIS2keO+SlNI8t8lKaxx55Ka1jE5WiKEpxB9lCCCGEEEIIIcT9kiXKQgghhBBCCCHKBAlwhRBCCCGEEEKUCRLgCiGEEEIIIYQoEyTAFUIIIYQQQghRJkiAK4QQQgghhBCiTJAAVwghhBBCCCFEmSABrii1nJ2dLT9qtRoHBwfL7aVLlxZ38wqkSpUqbN26tbibIYQQQpRZMn4QomzTFncDhCiolJQUy39XqVKFhQsXEhQUVIwtss5gMKDVFu1H7kG8hhBCCFGayfiheF5DiAdFZnBFmWMymZg+fTrVq1fHy8uLvn37EhcXB8Dly5dRqVT88MMPBAQE4OHhwYIFCzhy5Aj16tXD3d2dkSNHWs61aNEiWrRowciRI3Fzc6NWrVps27bNcjwxMZFhw4bh5+eHv78/7733HkajMctzQ0JC8PLyYtKkSVy8eJH27dvj5eWFt7c3gwYNIiEhAYAhQ4Zw9epVevbsibOzM59++ik7d+6kYsWKWd7fnVdpJ02aRJ8+fRg8eDCurq4sWrTIapuEEEIIkTMZP8j4QZQNEuCKMmfevHn8/vvv7Nq1i/DwcDw8PBgxYkSWxxw6dIjz58+zfPlyxowZw8cff8zWrVs5c+YMK1asYNeuXVkeW716dWJiYvjwww959tlnLR3e888/j1ar5cKFC4SFhbF582YWLlyY5bnVqlUjMjKSCRMmoCgK48aNIzw8nLNnz3Lt2jUmTZoEwJIlS6hUqRJr164lJSWFsWPH5uv9rl69mj59+pCQkMCgQYPybJMQQgghspPxg4wfRBmhCFEGVK5cWdmyZYuiKIpSq1YtZevWrZZj4eHhilarVfR6vfLvv/8qgHL9+nXLcU9PT+Xnn3+23H722WeV2bNnK4qiKD/88IPi5+enmEwmy/HGjRsrixcvVm7evKnY2toqaWlplmM//fST0rZtW8tzAwICrLZ71apVSv369XN8H4qiKDt27FD8/f1zfa8TJ05UWrVqZTmWV5uEEEIIcZuMH8xk/CDKEllsL8qcK1eu0KtXL9Tq2wsUNBoNkZGRltvly5e3/LeDg0O223fuz/H390elUlluV65cmfDwcK5cuYJer8fPz89yzGQyERAQYLl9538DREZGMnr0aPbs2UNycjImkwkPD4/7er93vkZ+2iSEEEKI7GT8IOMHUTbIEmVR5gQEBLBx40YSEhIsPxkZGfj7+xfofDdu3EBRFMvtq1evUqFCBQICArCzsyMmJsbyOklJSZw5c8by2Ds7NoDx48ejUqk4deoUSUlJ/Pjjj1nOfffjnZycSEtLs9w2Go1ER0dnecydz8lPm4QQQgiRnYwfZPwgygYJcEWZ8+qrrzJhwgSuXLkCQHR0NKtXry7w+aKiopg7dy56vZ5ffvmFs2fP0q1bN/z8/OjUqRNvvfUWSUlJmEwmLl68mGX/zd2Sk5NxdnbGzc2NGzduMGPGjCzHy5cvz6VLlyy3a9SoQUZGBuvXr0ev1zNlyhQyMzNzPX9B2iSEEEIIGT/I+EGUFRLgijJn9OjRPPXUU3Tq1AkXFxeaNm3KoUOHCny+Jk2acP78eby9vZkwYQIrV67Ey8sLgMWLF6PT6ahTpw4eHh706dOHiIiIXM81ceJEjh8/jpubG927d+fZZ5/NcnzcuHFMmTIFd3d3Zs6ciZubG/Pnz2f48OH4+/vj5OSULSvi3e61TUIIIYSQ8YOMH0RZoVLuXN8ghMhi0aJFLFy4kL179xZ3U4QQQghRSsj4QYjiIzO4QgghhBBCCCHKBAlwhRBCCCGEEEKUCbJEWQghhBBCCCFEmSAzuEIIIYQQQgghygQJcMVDa9y4cXz++eeFcq5JkyYxePDgPB/XtWtXQkND7+u11q5dS79+/e7rHEIIIYQoGBk/CFGySYArHkrR0dEsXryYV155xXJfUlISY8aMoVKlSjg7O1O9enXGjBlDTExMob3uxo0bCQ4Ovq9z9OzZkzNnznDy5MlCapUQQggh8kPGD0KUfBLgiofSokWL6NatGw4ODgDodDo6dOjAmTNn2LRpE0lJSRw4cAAvLy8OHz5czK3NbsCAAXzzzTfF3QwhhBDioSLjByFKPglwxUNp48aNtGnTxnJ78eLFXL16lVWrVlGnTh3UajXlypXj/fffp1u3bgCEh4fTu3dvfHx8qFq1KnPnzs3x3BkZGQwePBgvLy/c3d1p3LgxkZGRALRt25aFCxcC2ZclXb58GZVKhcFgAMydaLVq1XBxcaFq1aosXbrU8ti2bduyfv36wv2lCCGEEMIqGT8IUfJpi7sBQhSHU6dOUbNmTcvtrVu30qVLF5ydnXN8vMlkomfPnjz99NMsW7aM69evExQURM2aNencuXOWx4aGhpKYmMi1a9ews7PjxIkTliu9+ZWamsqoUaM4cuQINWvWJCIigri4OMvx2rVrc/nyZZKSknB1db2ncwshhBCiYGT8IETJJzO44qGUkJCAi4uL5XZsbCx+fn65Pv7IkSNER0fzwQcfYGtrS7Vq1XjppZf4+eefsz3WxsaG2NhYLly4gEajoWHDhgXqRNRqNadPnyY9PR0/Pz8ee+wxy7FbbU9ISLjn8wohhBCiYGT8IETJJwGueCh5eHiQnJxsue3l5UVERESuj79y5Qrh4eG4u7tbfqZOnWpZOnSnIUOG0LlzZ/r370+FChUYO3Yser3+ntrn5OTE8uXLWbBgAX5+fnTv3p2///7bcvxW293d3e/pvEIIIYQoOBk/CFHySYArHkr16tXj3LlzlttBQUH88ccfpKam5vj4gIAAqlatSkJCguUnOTmZDRs2ZHusjY0NEydO5K+//mL//v2sW7eOxYsXZ3uck5MTaWlplts3b97Mcrxz585s2bKFiIgIatWqxUsvvWQ5dvbsWapUqSLLi4QQQogHSMYPQpR8EuCKh1K3bt3YtWuX5faQIUMICAigd+/e/P3335hMJmJjY5k6dSobNmzgySefxMXFhU8++YT09HSMRiOnT5/myJEj2c69Y8cOTp06hdFoxNXVFRsbG9Tq7B+1+vXrs3v3bq5evUpiYiLTpk2zHIuMjGT16tWkpqZiZ2eHs7NzlnPs2rWLrl27FvJvRQghhBDWyPhBiJJPAlzxUBo6dCgbNmwgPT0dADs7O7Zu3UqtWrXo2LEjrq6uPPnkk8TExNCkSRM0Gg3r1q3jxIkTVK1aFW9vb4YPH05iYmK2c9+8eZM+ffrg6upK7dq1adOmDUOGDMn2uI4dO9KvXz/q1atHw4YN6dGjh+WYyWRi1qxZVKhQAU9PT3bt2sVXX31lOb5s2bIsNfiEEEIIUfRk/CBEyadSFEUp7kYIURzGjx9PuXLlGDNmTHE35Z6sXbuWJUuWsGLFiuJuihBCCPHQkfGDECWbBLhCCCGEEEIIIcoEWaIshBBCCCGEEKJMkABXCCGEEEIIIUSZIAGuEEIIIYQQQogyQQJcIYQQQgghhBBlggS4QgghhBBCCCHKBAlwhRBCCCGEEEKUCRLgCiGEEEIIIYQoEyTAFUIIIYQQQghRJvwffup+q7yTox8AAAAASUVORK5CYII=\\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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Optional-Labs/Course-2-Week-1-Optional-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Optional-Labs/images/Course-2-Week-1-Optional-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Optional-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Optional-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-Labs/Course-2-Week-1-Practice-Labs-1-Neural-Networks-for-Binary-Classification.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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aXr9YNAowndVL/oVgshpWVFYTDYQDPb9PxeBxfffUVkskkK11XU7Im8HxfkDfTaDSi0WhAkiTmoSoWi6rLCwJerlYzmUy4dOkSzp8/j4sXL+LChQsQRZF5e2u1Grrd7lg+mTwfhiqa5B4bv9+PUCgEt9sNg8GAra0tPHz4kN2oyfOlRGjddDodtFotZsgsLS1BEASEw2Gmp5vNJgqFAlqtFmtupqYLgRwy8KxWKxYXFxGLxbC0tIRgMMh6n5Ahk8lkEI/HUalU8PTpU7Tbbayvr8Nms6HVasFms8Hj8SAWi0Gv12NhYQFarZZdVOWtAdQkS/mz0f7o9XrodrsoFAqsBFteCXgUz39iOTKCILD4mN1uZwqiWq2iUqmg0WiwzSKvKlH6mywvkRVFEW63G4FAAA6HA8DzmyGFkuh75W+uRqPBaDRCvV6HRqPB7u4uHA4HdDod/H4//H4/3G43Go2GamL4B0G3QYq3hkIhXLp0CeFwGFarFRqNBqVSCc1mE9lsFrlcDtVq9bRf9rEzWd5pMBhgsVhY1Q71slADcm+AKIqYn5+Hy+XC8vIylpaW4PV6odPp0Gg0kE6nIUkSkskkut0ubDbbWFVgv99Hu91myeJ6vZ6V2VJlpUajgd/vR7vdxvz8PHQ6HVKpFLtVKxEybummXKlUmOdpOBwyPdVqtZhXa3V1Fc1m87Rf+rEwmWdlMpng9/vh8XjYZZoOZZPJBLfbjeFwCEmS2KHcbDaZhyudTuPp06eYnZ1FMBgEAPh8PrTbbVitVhZyUpMRQzJ0Op0wm82YmZlBOByG1+sFANaYtF6vs3VG8j4KTtSQsVgsMJvNCAaDmJ2dBQBkMhmkUikUi0XWREhNbeWpjI96NcRiMZw7dw4WiwXAc7dbq9UaM0IoE54YjUbY29tDpVLBN998g2KxiG9/+9v41re+hWq1isXFRRQKBWxubqLX66lGdnLkCdMWiwV+vx+XLl3CH/3RH8HlcsHr9WI0GiGRSCCRSODJkydYX19nJaRKvT2/ClIg8oRVs9nMqnMoX0gNuR1y5S8IAhwOB773ve9hdnYWP/jBD7CwsMCq1RKJBH73u98hn8/j1q1baDQaCIVCcDgcrPNqu91GtVqFTqeDy+WC2WzG+fPn4ff7sbCwgIWFBej1epw5cwYOhwPf+c53sLu7i3/6p39ieWlKC7PQ66Ru4fV6Hbu7u+j1eqhWq+j1eqyVgc1mg16vx/b2Nr755hvm2VLaM78uFKp0Op1YXl5GIBBgXcLJW0cX8FAohMXFRdRqNUQiERSLRXz66afMs763t4cbN26wENPi4iKcTifzErfbbbTb7bHfrQYikQjOnDmDlZUVXL58mZ1zzWYTxWIRuVwOjUaDOSyOihPLkREEAWazGU6nEzabDWazGa1Wi1WWtFotFoNVy5sqx2g0wmQywWw2w2w2w2AwsH+jZm5U/ij3yJDSIDd4s9lkCofc6m63G/1+X9GVXa8L9eKx2Wwsqc5sNrOE10KhgEwmg0qlgm63O3WdNYHx8RdqNGr1ej1MJhOcTif8fj98Ph+76VJn1Ww2i93dXRQKBeTzeTSbTTbLjXowUf6HVqtFt9uFKIowm81oNptMdg6HA+FwGFqtFl6vF91uF2azeayiR8nyHQ6HaDQaqNfraLVarM8StYLQ6/VjzRWV/Kz7ITdGqZqN9Ap5ecn70uv1WNde8jA0Gg12CSevTK1Wg1arRblcRrPZZF3czWYzjEYjy8NSE3TBIPnZbDZWOdnr9SBJEprNJpvjpZhkX3lcnmLQZ86cYXFHn8+H9fV1fPPNN0gkEkin0yiVSgDUc3uWxwrdbjdmZmYQDAbh8XjY91CIqNPpwGQyvWTAkBuY4tqlUglarRa1Wg2CIMDr9eJb3/oWEokE1tfXmftXjbcmUjrURDAWi8Hn80Gj0SCXy6FcLuPTTz/FvXv3kEgkWLKrmvoRTbLfc9HzqmUfAeP6hG7MMzMz+OijjxAOh+FyuTAajbC+vo5bt27h6dOn+OUvfwlJkliid6FQGOuNQl4+kpVGo8HDhw+h1+tZv5BLly7hZz/7GfR6Pa5du4aFhQU8fPgQ2WyWKWZAefuM8osGgwESiQQajQaSySSCwSDrME5hbfkAUjXlW9EaoLCs1+tFLBbDxYsXWZNSQRDQarXw9OlT7O3tYWNjA1tbW8yAabfbKBQK6HQ6qNVq6HQ6qNfrSKfTsNvt2NraYg0rdTod7HY7LBaLquYHyrHZbIhEIgiFQvB6vRgOh+xCkUwmsbu7yy4PR/ncJ+KRoe6HDoeD3Z4odk8eGRq4dVj7cCUxueHJKjcYDDAYDMyi73Q6rIESlcxOvsHyz9vtNvNe9ft95hav1WrsZ9W0MYDxyi+qJCHXN3XVrNfrKJfLyOfzyGazLFSn1n4NhyFfA2pYD3KFT+X2Pp8Pfr+fdTOW9/dIpVLIZDLI5/OsDxUAFmZ81e8Bnnsqut0uvF4vms0ma95JXlC6VSv1YJf38ZAkCQaDAZIkod1uqyqn6iDka4reS6fTCZ/Px8KMVIjS7XaRz+eRTqeRTCYRj8fRaDTGDJh+v8/Ct4PBAJ1OB81mkyXfy0va1bAn5cjDvTSWwGQysUaBjUYD1WqVFSPIO9IfFcfqkaHhiKRsLl26hKtXr8Lr9TL378bGBnK5HFMyanuTCVrE/X4fjUYDiUQCX3/9NQaDAZsRNDMzg1arxdyXcjnQBqBk1u3tbWxsbLCbRKvVYgqWblBqkCOtI61WyxI7P/74Y9y8eRM+nw/NZhOlUgn/8A//gGQyicePHyObzbJQmxpkcBj7tSigRory6bxKhp6RwrLnzp3Dn/zJn8Dn8yEYDEIURZaAe+fOHXz11VcoFAosDn+QISuXy6QM6XbtdruxuroKv9+P69evs7lWRqORefyUityQoQRpcv1Pfp+aBmjK32udTocLFy7A7/fj6tWruHLlCpxOJyRJQqFQwN27d1EsFnH79m2kUqmxgZDkVXmVd0WeDEyGonwAsJKZrMoNhUJYWVlhl+tyuYzPPvsMyWQSiUQCzWbzWHL1jtUjQy+Y5lKEQiHMzs5Cr9dDkiTUajUUCgWUy2XV3wLkfT86nQ4KhQIeP34MnU6HlZWVseFt1M6afg54EW4jK79YLCKfz8NiscDpdMJisbDFpKZ26sCLhGka50DhSb1ej06ng2q1ivX1dezs7LBGitPWon8ylEveKzUcPvKOq+SNWVlZYfl2VGKdzWaRSqWwu7s71jfodWQwGdLtdDqQJAmlUgl7e3vMk0rt+8n7qeQEanr9VB5LH/vl/ahhHQEvG6xarRbBYBCLi4s4f/48Ll26xCpJqbQ6nU7j/v37SKfTLFFcXpDxKo/vaDRCp9Nhnbf7/f6YF0PJ0HNoNBo28ZqSpEk3b29vY3d3l+UWTRazHAXHbsgAL0ICpAS63S6azSbr5lupVFhmuNqhpNRyuYy1tTWW+0LVJV6vl+W/7LfIKZ5bKpWws7MDr9cLq9WqGgufkFeo0E18eXkZ586dw+LiIjweD2q1Gra3txGPx7G1tYVEIgFJkqbCEyM3jMvlMvPW0W3RbDaPhSvVkEhPIWp5eTnwfE8VCgUkEgns7e2xkth3beFAP0ceTnmJu1JDSvtBXs9arYZ8Po9gMMjC1m63G/V6HcFgkB3E8nCdEpF3nnU4HPjwww+xsrICn88HQRCQTqfx9ddfo1Ao4NatWyyBnDx88hD+fl49arXhdDrZQORarcbCK2oL3+l0Opb3eubMGXi9XrTbbeTzeWQyGSQSCVapdVzG27GGluReBXkGPFXelMtlFIvFsXIsJW+Qw5DHpPv9PsrlMp4+fco+J8XsdruRyWReMuroZ0mZFotF7OzsYDgcYnZ2dswLowYZ0rPqdDqYzWY4HA6cO3cOH3zwAWKxGDNktra2sLOzg+3tbaTTaWYQq0EGh0GGzGg0YkqWFK7RaITFYoEoimzPAcru8EvvK3W7pv1CN1wyZPL5PGq1GhvT8Kbs52Whkm75/lM69JwEGTKFQoFNaqb8u2aziUAggHa7jWKxqOgeOvJQNc3lunHjBj766CNIkoRWq4V0Oo1f/OIXTMe22+2xliDyDtHy/5f+pEGlZMgAQDqdZmcdGTLvamifNnJHxcLCAs6ePYvFxUV4vV5ks1mWr5hKpZBOp5k3RhGGzGQSlSiKiEajCAQCsNvt0Ov1LEekUCig3++PTaBVK3L3OCX32u12ZrmWy2V28Ew2cZPLhf5OuTbtdpspVzUoWGD8tmswGNjU4kAgAI/Hw6q28vk8Njc3mbWvxiqAw5AbxtQHpFwus0Romv3S6/XY3DK1clTPNtkcjYYHOp1OVYUqJ+VF+noybEZhJwqNKNXzu9/7GolEEIlE4HA4oNfrmQchl8uhUqmgXq+/UQiIdLB8zZhMJtaziIo7lDzigiC5UOsQSpS2Wq0sF5QG91Ihz3Hqn2PxyNDtiZoLXblyhXX6s1qtKBQKuH37NjY3N9kbq9Q39E2RW+vBYBB7e3vY2dlhIRG6ZR8kD/o6xe+9Xi+7Mcr/XanIDTKNRgOr1YrLly9jZmYG58+fx/z8PMuHWFtbw+9+9zsUCgXWOn4avDEEHTbkss1kMtjZ2WEeC4fDwW6E+XxeNfJ5VXjnXd3Xo9GIhcNtNtvYOJXJRFg1QPKidWMwGFjYstlsotFooFarseocJUOHr8PhwLVr1xCLxRAIBCCKIgqFAu7du4fV1VV2OSKv8OusKTJQnE4n5ufnEYlE4HQ60Ww2UavVUCwWIUkSG5eh1FQK0tE0XJMmfss7a0uSxHJjarUaSyg/rsvAsYWWJq01t9vNSiRrtRqLZU8mpipdyR6E/M0nuZjNZpYbQ54pqrJ41SKX975QI3Q7pOSxQCDAyhhpPgzlV1FoclqhtUWzc+hiQOFcNXgSyGijJnTdbpeFjg5K6H2bMBrJkspInU4n7HY7zGYzmzVEe1UtHi565m63yxq/AWBjHLrdLitDVvI+kyemarValnun1WoxGAzQarVQLpdRr9dZZdHrGDC0DvR6PQRBYG1G7HY7BEHAYDBgYSvyTCj9nJOHleg8E0URwPNLdr1eZzlBJ+HFO5bQ0nA4ZB3+qJX87OwsRqMR0uk0VldX8eWXXzKlSyj9zT0MMlbIinU4HAiFQgCAzc3NMY/MYcgTypxOJ3Pl0U1bLcrVarViZmYGsVgMn3zyCWZmZuByudDpdPDs2TN8+eWXWFtbw/b2NjqdDgCowtvwtsiTNSVJYrkxNJdKDWtjMBig3W6zwX3dbpfNQyKFSh7NN31W+n6qKAmHw7hw4QKuXbuGxcVFiKIISZJQqVTYYa/k5Gn5eqAQUjqdhiiKiMVizFikJNVyuYxSqTQ2fFOJCILA9gVViZLniWYkJZNJFil41eBieVNF6j+zsrKC73//+/D7/ej3+6jX62PN4Cj/SKkXDDrjgefnkMlkgs1mg8PhQKvVws7ODjY3N/HkyRMUi0W2Zo5z3RzbdV6r1cJkMrFcELvdztzgtVoNlUoFrVZL8cr1ICbfNLpJ0gLQ6XQsj0Heo+GgN1webqEYr81mgyiKqjvAyc3t9Xrh9Xrh8/ngdrsBPG8IWCqVkE6nUSgUmKtWDbecd0HukaEW4HTAK91rR3uCKodoRlK9XmcXIb1eD6PRCL1eP9YC/k31i06nY4NtA4EA3G43a+4lH3onz+1TOiQj6o0i7+JL4xxo+rOS9bXcI0MGDSXCy5vYUb7dm+SxUHWlw+GA2+2Gz+eDxWJBu92GJElsqK/SQ3NyBEEYiywYDAb0+30WhiSPzEkY/MfikRkMBrBarTh79iwWFhYQDAbhcrlQLBZRKpUgSRI71NV8+MjLN6maJJ/Po1QqQRAELC0tQavVsmmoB0EWMG0Wo9GIixcv4oc//CG7cctvokpUNvS6TSYTRFHE2bNn8bOf/QzBYBAzMzMwmUx4+PAhMpkMPvvsM/zud79Dq9ViIUw1r6PDkMfaM5kM+v0+Ll++zPI8SJ5KNmbIkKHwxvr6On7+858jEongpz/9KRwOB+bn52G1WlnfF8qjkrd1eNUFQafTYXZ2Fi6XC5988gm+853vwOPxsMvXb37zG6TTaWxvb6Nerx9rFcZxI9cR1EBxcXERly9fRjgcZiFJ+SVLyWtIDj37QUUSb5LYC4AVtVy/fh1LS0v44IMPMD8/j3K5jIcPHyKRSGB1dZVNY5fPrlIiFLa2Wq24dOkSwuEw5ufn4ff7sbq6yj5oTIw8z+i4OLaqJaPRCL/fz/qcmEwm1uGQxngr8cB9W8glTt0zAbCeMfJyvoM8C3K5ms1m+P1+nDlzBv1+H5IkKVKZypHHXMkbc/HiRXg8Htjtdmg0GlYOubOzg3g8zr7/uN2W7zsUJqjX6yyHCABrUkWKU6kyklfqDQYDFAoFPH36lHmgbDYbXC4XjEYjQqEQfD4fACCbzR54WSKZyXWQRqOB1+tlinlxcZENvavValhfX2fVlnRrV6pM5dBlwO12IxKJwG63j3ny5GXCSn7e/V77ZNuK182Hob+TgWcwGBCNRrG8vIxoNAqXy4VKpYJ0Oo1UKoW9vT1WpavkvSg3/I1GI8LhMKLRKNxuNywWCzqdDtLpNPb29lAul9Hr9WAwGJTjkaEFYTKZYDQaMTMzg5WVFYTDYeh0OvaAW1tbKJfLLyUAKfWNfRWkDKiqJpPJYHd3FzqdDuFwGMPhEIuLi9DpdGxK72S+i7x0OxaLsQ7JVquVdZ1MJpMssUqJCpaeUa/Xw2KxsOmpoigy13Y2m8XOzg5roKj0PgzHhbzXitvtRqvVUmw8Xg4dGq1WC4lEAoIgYHd3F6PRCA6HAw6HA9evX4dOp0MqlYLP50Or1UKxWByrNiKDiLwwZDxbrVZ88sknOHv2LGKxGCwWC8rlMh49eoRUKoX79+8jk8mgWq0qvnx2P9Q0huAgKGJAF8B2u80OZZfLhUgkgk6nwxKAX3XhNplMWFhYgM/nw+XLl3Hp0iWYzWbkcjlsb2/j1q1byOVyrOJLibqZkHvNXS4XotEoVlZWMDMzwwZh1ut1FItFdqk6qWc9EkNGHtKQP+TFixfZ1M9Op4NMJoPt7W2USiVm+CjZxfY60BtZr9fRbreRzWaxu7uLYDCI5eVlaDQaLCwsQKPRsOFa8jLsyb48c3NzzOq3Wq2QJAn37t3D3t4ems3mkXQ0PQ0mDRmLxQKr1QpRFNmAzUwmg3g8jkqlctov972GqgisVivcbjcajca+TbyUiCAIkCSJGTKpVAp6vR6BQADBYBAajQbhcBg7Ozswm82sgzZ5QQGwmTcGg4GFZqklwne+8x1cvXqVefp2d3dx+/ZtJJNJPHjwAMVicaxaUEl77FXI80fUChkyvV6PrQPKySNDplKpvDQlfb/wk0ajgclkwrlz5xCNRnHp0iVcvHgRuVwOuVwOm5ubuHXrFutJ0+v1FF1pSikOoiiyyzQZMnTG1+t11lTxJMORR6bd6M22Wq0IhUIsUU5egkYDESlupuYNQ8jzZHq9HgqFAra3twEAi4uLMBgMWFhYgCiKKBaLY8l1ZNFS8qHdbsfZs2dx/vx5GI1GpFIpJJNJpFIplEolxfa4kCsJm82GUCgEt9vNyvlyuRyq1Sr29vZYLwa13xzfBlo3lFyo0WgQiUQwGAzg8/nYLfQ4ps+eBPK9RBU1T58+Rb1eh8vlglarRa/Xg9VqRSAQwIULF9BsNlnLdOD5Wmu1Wmi328zY0+l0sFgsMJvNrIqF1tr6+jqePXuGQqHwUvWFEmU4zZCXu9/vo9PpoFQqwW63o9PpjJ1dhUIBXq8XkiSxNAi5545Kje12OzweDy5fvgy/3w8A2Nvbw+bmJjY2NrC5uYlarcaKWtSis+hSTZ5faibYbDaRTCaRyWTYZfOknvlIQ0vD4RA+nw+XLl3CuXPnMDc3x2LM3W4XhUIBqVRqrHkZoF6FQM9F5Y2j0QhbW1usr8CNGzdgNptx8+ZNVKtVNJtN6HQ6NmGVeqmYzWacP38eXq8XP/zhD3H58mU8efIE9+/fx71793D37t2xIXny360UyNqngYALCwuw2+1otVpYW1tDOp3G2toatra20Ol0VL923gSSgXyG2d7eHjQaDS5fvgyPx4M7d+5AFEXE43GUy2VF3rzl1UuSJCGXy+H//t//y8peu90ugsEg6wB99uxZttfkiZ2NRoNNi3c4HGPjDOjwevjwIb766ivs7Ozgm2++YZVK0zDHS43IjeB+v49ms4l4PI7RaIRLly6N6Z7BYIDV1VVUq1XkcjkMBgNWIerz+Vg15cLCAtxuN7797W/DarUimUxibW0Nn332GT777DPk83mk02mWF6PEPTcJFRGIogiz2czyX3d2drC1tYU7d+7g0aNHJx5xeSdDZjLznTKZKUGTknw6nQ7a7TY6nQ6brjqtSJLEZkxls1lWheRwOBCNRtk8k3K5zBaMxWLB3NwcPB4PMwyr1SoymQzr7UC3bKVtlMmycupJIB8IKJ/JJW/KpbRnPW7II0PuXZqMTgMklV69JIdu1hSLTyaTsNvtrLyVvC3UqoAMZXljSuqBAbxo/kYVT6lUCqlUCsVikbVYn4b1NtkGgp5ZLeuGGAwGqNfrqFQqbPYRTW/2eDyIxWKo1Wowm83o9XpwOBxs8rrX64XL5UIoFILVamXjYmi8Ac1VovYAasynAsbXCulvCt3RXlNMjgx5AaxWK4xGIxYXF3H9+nWEw2GW30ADpPL5PKrVKrtRq+2NPQgqVwOAcrnMEr9EUUQ4HMZPfvITRCIR/Kt/9a8gSRIKhQJKpRLzxlBMUq/Xo1Kp4OHDh/jyyy/x29/+Fvl8noWUlOaNkceeyUXp8/kwOzsLt9vNehI8fPgQ29vbyOVykCRJ0XHm44De71arBUmS8OzZM3z66adYWlrC7/3e78HlcrEqCr1ez+SulHUyCSnO4XCIYrGIarWKv/3bv8Xvfvc7LC0tYWFhAdFoFFevXoXRaHypISCFCobDIZtXlkwmUavV8OWXX7Jp6tvb22/U4VXp0L6iSynl6qlpr5Eu7na7ePr0KYrFIr773e8iFouxcJHNZkM0GoUkScjn8+j3+8zrR030iHq9jocPH6JYLOLTTz/F9vY29vb22EgQue5XK4IgwOfzYTAYsJxYuhiclJ45stCSvLOmRqPBYDBgSoKGR9HQLHqD1a4Y5MhLSDudDiqVClKpFKvCoM6ITqeTlVjr9XqYzWYYDAa4XC4AQCqVQj6fZ5uFBptN/h6lQYcTrSNBEFhCXrVaRaVSmbq5XG8K3YJqtRpyuRxcLhczbuQ3QzVAhgmNDNjb20O9Xh8byRCJRFhTTrotyn+e/mw2m2xQ4O7uLuLxOPucKpumASpnp9lK9Xqd5RPJm+SpATJiDQYDqtUqqtUqHA4H84CHQiF0Oh1WNUmeTSpEoDDuYDBgyb3JZJJVj8ovXGrSV7TvhsMhM1aoAtBgMJxaO4x33qH0oskrcOfOHVQqFWbdUm4M5TpUq1WWvT1txgzwwkVbq9Vw7949xONx9Ho9eDwenDlzBj6fb2z2Cbm17969i2azidu3b7Ox6LlcTrEhJWDc6KLwWD6fx87ODlqtFvR6PXPXFgoF9Ho9nqNwCLQOEokEJEnC6uoq7t27B0mS8PDhQ9TrddTrddXIUF4VQQfu2toaEokEnE4n7t69C71ef2gfC0EQ0O12WRJ5NptFrVZDu91+7WGBSmSyvUO73cZvfvMbrK2tscKCdruNcrnMckWod47SPTR00abL0S9/+Us8e/YMy8vLOHPmDCvFB56XV5OcOp0OstksKpUK8vk81tfXUalU8OTJE9aAkVpgqLW/lXy/WCwWbGxsoF6vI5fLoV6vQ5IkAC967JyU4XskhgwAlveSSqVQrVYBvFgwrVaLJVh1u132c2p7k18XjUaDdruNTCaDWq3GPC56vZ6Vt1Fcn5ITNzY2UC6XmfHT7XbHqiiULksa4dBoNFAqlaDT6VgnaLrhTKMn700gxUGDNKlcnfKM1DKwTg49C7XWp2aAoiginU6/lkeFbufUpp5umNMQEiD6/T52dnaQyWTYmAeqDqMcEDXke8gLMOiS+OzZM5RKJQCAxWKB3W5nnj1aA+T9KxQKiMfjSCQSuHXrFur1OuLxOKsGHA6HqhgLsh+kX6ixbb1eRz6fBwA24fq0xli8kyEzGXcejUYvGSu0AMgVJf/ZaWTSSu12u0in0yiVSuh0OnA4HKy0DXhRtk03RhqWJz/UlSxLkgc9QzKZxOeffw6LxYLbt2+j2WwilUoxRapGBXGUUFUPxacp8Y4+V0toYBK5d4Zc3zTC4lV7hPaYPPFcyXvqbaCydMpfpFAcVaPS+gHUo7vpOajvSbfbxc7ODoxGI2w229hz0rNXKhUW6s5ms6yAZXKOk1rRaDSs2GQ4HOJv//ZvYbVaWcI0VeWe9FoRDlNsZrP5tbXeYfMr5JnN9Plx0Wq1TmwVWa3Wtz4V5G7dNxk+J4/tv82h3mg0Tkw+b7J+gJebT8l7L5yUwXaS68disRypVSG/bdIHGbxyBfsuLt9ms/nerh9Crovknx+EfG296zpTiv6ZRF7VRZ8DR6+7T0r/vK5sJp/7bZDL5l3kc5K6+V3XzuR5/67n0utwmHyOPIttUlkqrZLmpJlc/AdtqEk5qlGekwesmp/1ODio++irvk9tTOqe1/l++Z/TyH6H8DToHAAH6t/9nvcgT4NaZfM6yJ/9tORwZIbM6zzMNL/Zk8hl8aYWrJrlyENHR4PaD59XofSQ62kgD80RapfhUewTtctoP+iZ35c8smPzyHBeHy4zDofzvjBt+mjanleNHJojw+FwOBwOh/M+w/34HA6Hw+FwFAs3ZDgcDofD4SgWbshwOBwOh8NRLNyQ4XA4HA6Ho1i4IcPhcDgcDkexcEOGw+FwOByOYuGGDIfD4XA4HMXCDRkOh8PhcDiKhRsyHA6Hw+FwFAs3ZDgcDofD4SgWbshwOBwOh8NRLCdqyAiC8B8FQbglCEJHEIT/cpK/Wwlw+RyOIAhuQRD+pyAITUEQ4oIg/JvTfk3vE3z9HA6Xz8Fw2bwarn8O5rTXz5FPv34FaQB/BeAnAEwn/LuVAJfP4fxnAF0AAQBXAfydIAj3R6PR41N9Ve8PfP0cDpfPwXDZvBqufw7mVNfPiRoyo9HorwFAEIQPAERP8ncrAS6fgxEEwQLgTwGsjEajBoBPBUH4XwD+HwB/caov7j2Br5/D4fI5GC6bw+H653BOe/3wHBmOUjgLoD8ajZ7JvnYfwMVTej0cDmd64PrnPYYbMhylYAVQm/haFYDtFF4Lh8OZLrj+eY/hhgxHKTQA2Ce+ZgdQP4XXwuFwpguuf95juCHDUQrPAOgEQTgj+9oVADzRjsPhHDdc/7zHnHT5tU4QBBGAFoBWEARREISTrpx6b+HyOZjRaNQE8NcA/pMgCBZBED4B8DMA//V0X9n7A18/h8PlczBcNofD9c/hnPr6GY1GJ/YB4P8DMJr4+P9O8jW8zx9cPq+UjxvA3wBoAtgF8G9O+zW9Tx98/XD5cNkcq4y4/jlYNqe6foR/fhEcDofD4XA4ioPnyHA4HA6Hw1Es3JDhcDgcDoejWLghw+FwOBwOR7FwQ4bD4XA4HI5i4YYMh8PhcDgcxXJonbfFYlFcSVOz2RRO6ndx+RyO3W5XnHxqtdqJycdkMilOPpIk8f11CCe5v8xms+Lk02q1TkQ+VqtVcbJpNBp87RzCYWuHNzx6T3hVGbwgnNga56iQyfXF1xOHw1ELJ2rIDIdD9ndSpFyhvjhkZM2FADyXDZfPC/Yz9rh8Xs1oNGJ7Ty4vLjsOh3MUnPbZfuIemdFoxBXoAUwaMgA/bIiDPFZ8PR2OXG58bXE4nKNGfhE/LZ1yYoaMIAgwmUzQarXo9/vo9/v7HtzTwqTnBQDMZjO0Wi3zxPT7fXQ6nbGFIv9+tTPpqZr0UO339WmRzZug1WphNpshCAKTGe3BaUUuC/JYydeSRqPhawkv9uBgMAAAJhcum4MvV9MmG0EQYDQaodVqMRgMMBgMxrzAJ8GJGzKiKKLVakGSJAyHQ7ZBph2NRgOTyQSDwcCMmU6ng16vx5TsNCE3Yg7aEPKDiBQs99CMQ4ey1WplimY4HKLVaqHX67HvmSYmjeHhcIherze2jnQ6HbRa7Sm+ytNl0tCji6dcLhrN86LXadNNwP7ecznTtKfobDcajWi322i32xgOh+oxZOQ3HJ1Oh6WlJQSDQRSLRRSLRdRqNWSzWQyHQ7Yppg2SjdFoxPLyMoLBILRaLTQaDdLpNO7fv49ut8sU7bRsEHpOnU4Hi8UCvV4Pu90Og8HAvqfdbqNer6Pf76PRaDCjeDgcTvWtkRSsVquFwWCAw+HA9evXYbVaUavVIEkS4vE44vH4mEKeFnnJn9doNEKn0zFDr9frYTAYoNPpoN1uj33/NEG6xmAwsD2o0WjQbDaZjE7yoHrfkHumaN/sM0hRldDzabVaiKIIURRx/vx5+P1+pFIppNNptFotlMvlEzvbj90jQ7ccURTx/e9/Hx9++CHW19exubmJzc1NZLNZDAaDqTx4hsMhWwx2ux0//elP8cEHH0Cn00Gj0eCLL77A9vY2arXa1Bgy8kNGEASIoohYLAa73Y7FxUV4PB72vYVCAZubm6jVatjY2ECr1WLeK9o8apfXfpCiEUURTqcT8/Pz+Hf/7t8hHA5ja2sLhUIBf//3f494PP7Sz02DvEg+RqMRVqsVNpsNKysrMJvNKJfLaLVaSCaTSKVSqj6QDoO8L2azGTabDcvLyzAYDNjc3ESxWGRedWA695hGo4HRaGQXUQDMuKOLJ6BO2ZAXU6fTwe12w+l04o//+I9x+fJlfPrpp/jss8+QzWZRLpfHwv/HyYmEluhhyPNgs9ngdruRy+VU+Ua/DnIFqdVqmdKw2+0sHEAbZdpkNBqNYDAYYLfbYbVaMTs7C4fDgdnZWbjdbvZ9ZrMZg8EAlUoFzWYTjUYDjUYD3W6XKZX9cpGmAfkFwmKxwOFwwOFwwGKxoNVqQafTjYUPpkk2hNFohMfjgdvtxvz8PCwWC9LpNKrVKsrlMpPPtCHX16SrY7EYTCYTWq0WBEFAoVBgf58GaB3o9Xro9XqYzWa43W7odDoYDAaWCtDv91GpVFCr1TAYDFRr0Mj1i81mg8PhgNvthtVqZV48+r6T4EQMmV6vh263C0mS0Gw24XQ6YbVaUa/XpzoOTQeI0WiE2WxmOUS0Ach1O03KlJLFQqEQvv3tbyMYDOIHP/gBvF4v3G43RFFk3pZms4lisYhKpYK7d++iWCzi8ePHyGazLHwJTF+pP+WeGY1GhEIhBINBuFwuOBwOlpQnTyqfJmOGnlOj0SAUCuH73/8+IpEIfvrTn8Jms+HWrVtIJpOQJAkbGxsQBGHf0nU1MxwO0e/3YbPZcOnSJczOzuLP//zP4XQ68Y//+I/Y2NjAN998g0KhoIp1I39/5SGj0WjEEldJBzscDvh8PiwsLOCTTz4Zu3xWq1W0223cvn0bd+/eRa1Ww97e3lioW+myAsY9MpFIBKFQCOFwGIFAAE6nE6IojqUAnATHasjIlSQp136/zyxaqmKSxxjV8Ea/LvS8ZNXr9XoYDAa0223Vx1n3Q37ImM1mBAIBhEIhRKNRuN1uliNDCsFqtcJkMsFqtaJQKMBisaBYLLJ11mw2mVKepsOaoNwPi8XCbkmUuEkKmpgGucjDlpRc7/f7EQgEEAwG2c2yVCpBr9ef8qs9PSY9Mi6XC6FQCG63G36/H5VKBSaTaez7lbp+5GFog8HADmAyYClcrdfrodVqYbfb4fV6EQwGEYvFYLFYYLfbodFoUKlU0Gq1kE6nEY/HIQgCarXavvtN6cgv4aIowmg0snwzyvGk7zsJTiRHhjKYu90u2u02rFYrXC4XPB4Ps2Yp0xmYHqU6GAyg1WoRDAbZh9frRbFYhCRJU2XM0HtvtVphNpuxuLiIjz76CF6vFz6fjyUb9vt9lkNkNBrhdDphsVhgMpkgSRJWVlZQr9fx9OlTrK2tIZVK4e7du2PhJrXcjCaRJ/lqtVp4vV6srKwgGo3CYrEAAHK5HMtz6Ha7EASBeUXVKBM5tJ9MJhMzlJeWluD1etHv91GtVrG5uYnHjx8jlUqh2+0CwNQWIlCiuM1mY4YwGTZyQ0apUCKqzWZjCavnzp1jl+5isYhPP/0UzWYTly9fRigUwtmzZ7G0tAS/34+zZ8+ydAk6wwaDAex2O5aXl5FOp/Hw4UOUSiU8fvyYFSSQDlI6dIbJjTSTyQS3241SqXSi59eJ5sjQQ2u1WlgsFuaV6XQ6rF+KGt7g10HuIbDZbCzcZrFYUKvVpsqIkZdaG41GOBwOeL1ezM7OwuVysbgrJTzT99Otmrwyw+EQ0WgUvV6PVTpptVo8fvx4bMOpeY1RNYFGo4HFYkEwGITf72cehlqthmKxyJSqRqMZ84qqGbmnQRRFWK1WeDweOJ1ODAYDdLtdFAoFZDIZJh/y3kwbZOBSVQoZx0ajkXnS1QKthVgshuvXr7OqtXQ6jTt37qDb7SIajeLMmTO4ePEiLl68CKvVCq/Xu2+I1mAwwOPxwOv1otPpIJvNYmtrC61WS3XtRshJIc8hojYigIo8MqQIRqMR9vb2sLOzw+L1brcboVAIOp2OhQHUelveD/mBLP+YhiRDeaMtChMZjUasrKxgZWUFZ8+ehd/vH2vkRuTz+bGMePLO6HQ6lgcyOzsLg8EAq9WKcrmMUqmEp0+fjlU2AerxQpDnczQawWKxwGazIRgMYn5+Hi6XC61WC9VqFfF4HM+ePUOpVJoaA0aOvGzUZDKxsFuv14MkSahUKsjn8yyRdZrkQ3uCelm5XC5EIhH4fD6MRiNIkoRsNot4PI5arXbKr/btkBsboVAIVqsVKysrCIVCTPc0m03k83l2MXI4HLh06RJWVlYQDofhdrshSRKePn0K4HkIV6fTwel0wmQyQa/Xw+fzAXjeIsLlcuHBgwfo9Xosj0bJBjK99sFggHK5DIPBgHq9jlarBZvNhsXFRdRqNYiiyH7muC+QJ+KRoQfY29uDyWTCuXPnYLfb4Xa7EQwGAQCJRILdEKcJWhR0i57sS6BmyEtHOTEOhwMrKyv40Y9+BJ/PB7/fz25+clkUCgWsr6+zXBhSIqIo4uzZs6zCKRwOw2azoVqtIplMIp1Oo9vtotvtqrJ3ERkyoiiyfIZYLMaaUJIhs76+jmazOTUhJULe/4Ji+xaLBaIootvtotVqoVQqjVXkTItsgPGyfarKCYfDY4ZMLpdDPB5HpVIZu4gpBdI5er0es7Oz8Pv9+L3f+z0sLi5idnYWs7OzKBaL0Gq1GA6HmJmZQbPZxMrKCq5fvw6z2Qyz2Yzd3V2sra1hMBiw5NZYLAaPxwOLxQKXy8W+Tt4bqqqkqISSoRyicrkMrVaLarWKVqsFq9WKhYUFZLNZmEwmVrhy3BfHEzVkKPESeFFyrNfrWakWR/3IE3oFQYDZbIbBYMC5c+cQCoVY/NlmswEA+v0+6vU6ut0ucrkc6vU660FEsWxKwrNYLNDpdOh2u7BaraxHyOLiIiwWC+LxOHK5HJLJJKrVquqawdEzUDlxOByG1WqFIAiskqter0OSJLYP1fDcr4u8NxGFcnU6HYbDITKZDMrlMsrlMmv6Nm2GDEGNFCnkQh1b+/0+SqUS8vk8ms0mAGWuHyqwcLlc8Hq9LA9PFEX0ej3U63Wk02mUSiWmV8i7kM1m2cXo4cOH6Pf70Gg0MBgMaDQa8Pl8iEajiEQiGA6HMBqNsFgs8Pl8aDabrFWEEuVGyENpVIncarXQbDZZDhqFl/R6PSu2OE5O1ILo9XosF0av17N4KyVLqd0D8bqoVQ5yo4FuxX6/H3a7HT/5yU9w8eJFzM7OIhKJsFteq9VCIpFAuVzGb3/7W+zs7CAej2N3d5eFU7RaLTNcJEnChQsXsLS0hKWlJQQCAXzve99DoVAAAKTTafzyl79Es9lkeTNKVirAy9U4s7OzuHnzJs6cOQOv14tms4l0Oo1kMom9vT1Uq1XmBZwmSD4OhwPRaBQ+n4/1Rnn06BHS6TR2d3fZWlGSp+EooP2k1+thtVpht9vh8XhgNptRr9fRaDQQj8exsbGBcrl82i/3rZAbs+SBWVxcxMLCArrdLprNJnK5HO7fv49+v49gMMhy8EajER4+fIgvvviCJfJ2u110Oh3odDrcuHEDMzMz+Pjjj2Gz2ViemsvlwtLSEoxGI/L5PPNmKTlfT6PRYDgcsgthoVBglaMOh4P1AOv3+6wKV/GhJeDFJpm0zqb11jOJWo2XSci1bzabYbFYWJO7UCgEn8/HBme2223UajXU63UkEgmUSiVkMhlks1mUSiVm/VOODK2tXC4Hp9MJr9fLqnKog2soFGLJ1QaDgVUyKVmhyNeNKIrQ6/VwOp3w+XwsAbrT6aBYLCKfzzOlMo1Q+JaavNHh1O12mXwohwpQprfhKNDr9SyHSBRF6HQ6thebzSbz6ClNd5OuMBgMrP+L0+lkvZW63S5rhlgqlSAIAjweD2w2GyRJQj6fRyaTYReCer3OLudarRZ7e3vQaDQoFAqoVqswmUyw2+0wmUwIBAIYDAascEE+OFlJMiTI8dDv95kMJEliHnZ5WfZJ9Ko6UY8MNcWjHAWK6U+rYp02yJg1m82IxWKIRqP4sz/7M0QiEUSjUVY90mq1EI/H8fXXX6NQKODOnTsol8vY3d1FpVJhnhSCpoQ3Gg18+eWXLOzk9/vZjcjpdOLmzZuoVCq4d+8eMpnMWOKdkiGvVCQSgdfrxdWrV3Hjxg2WhJdOp/H5559jZ2cHhUJB8c/7psirA0VRxPLyMm7evAmHw4FOp4N8Po9bt25he3ubJXlOmzcGeGEU22w2RCIRBINBuN1u9Pt9PHr0aOwQB5TlsaLzxmw2IxgMYm5uDhcuXMDs7CxsNhsGgwGSySRr23Dv3j243W58/PHH8Hq9ePbsGYrFIr744gt88cUXY7kfJLcHDx5gdXUVJpMJLpcL4XAYV65cgd/vxx/8wR+gXC5jc3MThUIBtVoNtVpNccYgMJ4q0m63odFoUC6XUSwW4XQ64XQ6Wf6rIAjI5/PHnv96oobMflOcuREzHdCGn2y0NTMzg3A4DIfDwQ7eWq2GUqmEZDKJQqGARCKBSqXCPDFUCir/v/v9Pks+oz9brRYrO6aEYEosNhgMrJqObgxKUygEvXar1Qq32w2XywW73Y7hcAhJklCv11EqlcZ6xyj1Wd8Gko/BYGCub4/Hw5pPNptNlh/T7XYV7/Z/W+h5yWNBYf/hcIhGo8EM/16vN9b0TEkYDAZ22FKPHErspU7h5XKZVd1QLme1WkUmk8He3h4bXyHP7RyNRqjX6wCASqWCRqOBTqfDvs/j8TBPtFz3KB0yEMkzA7yo4pI3GASO96w/MUOG4pI2mw1Go3Gqh/rth5rlIK9uEAQBTqcTZ8+exczMDHPd0iC61dVVrK6uIplM4t69e0y5UBhIr9e/dBCTshAEgXXXTKVS2N3dRSgUQiAQYK9DLR7AyX46BoMBly5dwqVLl3Du3Dm43W4kk0ncvXsX8XgciUQCe3t76Ha77H2YBuR5VDMzM4hEIjhz5gzm5uaQy+Vw584dJBIJtv7IGzMt8pFDxonH40EsFoPP54NGo0G73cb29jZ2dnbYYa0U5P1NdDodZmdn8YMf/ADhcBjRaBQulwvD4ZCFsB8+fIhsNoter4dWq8WGrD548ADxeBz5fH6sdwwhCAILU1MRgvziTgbRfj+rFk7zmU7UI0OxM3ozlWjRHzdqXOByaNCYz+eD1+tlfTwajQZKpRI2Nzdx584d5HI5rK+vo9frjVW6HZSgSmtJkiS0221Uq1VUq1U4HI4xr4sajJhJyJALBoNYXFxkFRj9fh+pVAqpVIrdEun7pwl6/10uF+ue7XQ6WQVbKpVCs9lknXzVetC8CnkjRY/Hwyre+v0+yyEij56SkOfGUOKt3++Hw+GA2WyGJEnodDqoVqvIZrMsfN3r9ViX9Uwmg3Q6jUaj8VKbjMnfJe/pRMgrNZUmv8PY71nkZ/tJPeuJdfYFXnRQpBJZypmZtq6++yHvbkuoRR60ucldPTMzgw8++ABer5f1GqjX68jn89jZ2cHq6iokSWJ5MK8yeOWhAEpOtFgssFqtEEWRGTGUlKb0WUNyzxJVX3i9XuZp0Gg0SCaT2NjYYEZhq9Waqj0m7xlDib3nzp3DxYsXYbfbkc/nkUgk8PjxY+zt7UGSpKlryEmQF8rr9cLhcODMmTM4f/48RFHE+vo6UqkUtra2kEgkFNdfh9YBzffz+XxYXl5mfV6AF+GRTqeDdruNVqvFPLtfffUV9Ho9EokE6vU6CxftB8lF3tiUvi7/dzUx2dSVvE5Uwk+e8uPmRD0yckOm3++z2UudTuckX8Z7h9yImexrovTFL7+h0PiBaDSK69evs/g0VQvk83nWaIrKs1/Xc0dyo+FlVI5N07KpOmXSkFGabOXGF8l0aWkJ4XAYi4uLmJubQz6fRzqdxubmJu7evcuUMj2v0p75baFQJM11W15expUrV9Dv98cMmWq1ygwZ8har0XO3H/LQm9/vZ72czp8/j0KhgLW1NSQSCWxvbyObzSrOIzNpyHg8Hpw5c4ZV9JHnhJprUs4UeWXy+fxYg9JX7R+512Uy9CT/mpJk+DrIjbjJHnGqMWQmPTI0wZj+bRrLHemNpySzRqOBVqsFSZIgCM8bxVEvB5rkrJYGbvJbC3A0SWB0q5QbMXa7nY04GAwGkCTppao5JUKucr1eD7vdjoWFBczMzLABrOVyGevr60gkEmg0Gmi32wCUbxS/CfLcBLfbDZ/Px9ZDJpNhpfzNZpOVpJ/0xN73CUEQYLFYWKNAk8kEQRDQaDRYmbG8XFjJ6+htX/9BP0eePJPJxMak0DBbMor3q9hVAySPZrOJWq2GbrfLBmnSAOCTSCE5kenXtPhpGCBNMpbffKZReVC2fLlchl6vR6lUQqVSgSAIcLvd8Hq9CAQC0Gg0KJVKzK2ptCqbyYZt8pEMk5v6TWPJtL4oDGW32+Hz+RAKhRAKhZhCpsqLSqWCdrvNFIrSlLK8aRnNU/rud7+LhYUFtlbi8Tj+6Z/+iZVb93q9l9zd08Bo9HwI6dzcHKLRKILBIJxOJx49eoSvv/4aGxsbKJVKLAF6mnP2NBoNG0ng9/vhdDqRTqext7fH+ut0Oh3Fy0n+2uWXaGD8cvi6YWfSPdRo0WazIRQKIRKJwO12s87R1WoVlUplzJhRmu6ZRH4WVSoVZLNZdDodVtTj9XohSdKJdO4/UY8Mxc3oACN3HiVzAsr2NLwJ8kVAcdlms4lms8nmedBBBQDxePzQ+Oz7jDzRlgwXuuFNulvlH2+SpEuuTLvdDq/XC5vNxsoA1YhOp2MjGKiL5nA4ZPH9fD6ParWqygGZr4K8K9Q53OPxwO12Q6PRoNfroVarIZfLsfDBNF6igBehEr1ez0rT7XY7tFotOp0OWq0WarUamw+kBujcmXwe0jk6ne4lz9zk+pi8gAuCwMak0DR1KmKgFIp8Po9CoYBGo4Fer6caeRK9Xo+NsQBedG8/KaP3RDv7GgwGdkOmw5umZirJw3AU0Bs9HA5ZsmE8HofP58Pi4iIrE/3DP/xD7OzssKGalOOhtFgrhXeorJGmV9vt9rHJ39R/YNLwOQj6GZqJcuPGDSwvL2NpaQlWqxVarZYpHZofIy+DVOIhNhqNYLPZsLS0hMXFRYTDYXi9XtZv59GjR7h9+/ZLIZNpgNYNNSWjxHIae5HJZPD06VN8/fXXTO9Mk3wI2lu0d+x2O2ZnZ7GwsACNRoNEIoGdnR2sra2hUCiwPEal6JtJ5L1OyEtAl2r6oI7Ger1+zNO7n66lf6dqKJPJhEuXLuHMmTO4dOkSotEoRqMRms0m9vb28POf/5w13KNeV2rwkNLrr9Vq2NvbYykQwIsquEm5Hcczn9jQSAonUIOcXq/HqkioCZXS39S3YTQaodPpQK/Xs1wZmuhsNpvh9/tRr9dhNBpZtZeSkL+npEw6nQ6azSa7BQIvmihRHhV560iZ7GdwyNcUzfegcBwlEstzscj7p/SO0oIgsFJSmvpNYx0qlQorPZ/G3DPgxRgMmpFDcqJEznq9jmq1OjaVd1qhBpHk3bPZbCxUUKlUmEdG6TkdZLjRlHNKRKWDljx4FouFjWY4bNghGb/U5M5qtcLn87HwJXljKC9vb28P2WwWrVZLVQNJ6TJInqf9PF3A8aeOHJshI7dY6ZAJh8MIh8PQaDTI5/PI5XJsmqhaLNQ3QZ4I1m63USwWkclkMDs7i+FwCIPBAI/Hg3K5zDwJ8pCLUmRFVUOdTgeDwQA7Ozv43e9+h3A4jJs3b8Jms8Hv90On06HX6yEYDKJSqSCRSKDb7TLXtnxTyG/dNpuNlVR++OGHiEQirAcGUa/XcffuXaTTacTjcRZWUMqak4eHtFotQqEQvve97yEUCjHDb319Haurq9jd3R3riSL/U+3IS/1jsRhisRhmZmbg8/nw5MkTNgW80+mwPIVphMIodrsd169fRyAQwPLyMmZmZrC6uoqnT59iY2MD8XgcrVaLTZlXmrxI97TbbQwGA2xvb+Ozzz5DMBjEhx9+yMZW6PV6XLx4EQaDAYlEAsFgELVaDTs7O2yMiTzMTZcnq9WKs2fPwu124+bNm4jFYiw3hubFlUol1s+p2Wyq/qybTBE4CY7VI0MHLt2MbDYbHA4H2u02Go0Gmzchd/Gq9c2dZLJqq9frscxvCglQPxSLxcJuEPQzSrtJCoLAXLulUgnb29sYDAZot9usykij0WBxcRFmsxm5XA4AmJdBnk9D4SabzYZAIACXy4UrV67A7XZjfn4ePp9vLDeEesgkk0kWfpErJyVBt0e73Y75+Xn4/X4YDAYMh0NWUlwul9Hv95nHSmnPeBTQ8EzyxthsNnQ6HTa6Qqnl90cB6VqNRgOTyYRoNIpIJAKPxwO73Y5ms4mNjQ22lqj1vBJlRa+ZIgCke/r9Pi5fvgy73c5CzcFgkOlcGutRrVZRr9fH8mZGoxHzwLhcLly8eBE+nw9nz55FNBpleoX0W6vVYl5Sij6onZNeK8fqkRkMBjAajfD5fAgEAggGg/D7/djd3cXe3h5z75LSnVbo2VOpFDQaDWKxGMrlMgDAYrHA7XZjeXkZBoMBT548YfFqJSZxUtx4d3cXgiCgUCiw2LTZbIbRaITX68Xs7CxCoRAz8CZd29QMjrpzzs7OMqNP3heiVqshm80ik8ng0aNHSKfTL7VZf9/lR8pzNBqxGTHBYBAejwcWiwWtVgvtdhv5fB7ZbJZ18J0mJhPKgfFO0MPhEJVKBblcbixU8r6/90cNyYfC1qFQCOfPn0cgEEChUEA+n8f9+/dZfx01GHzyXLhqtYrHjx+jVqvh7NmzaDQabLgshaMpSbxer2Nubo61xJCvMZPJxJJ6FxYWYLfb4XK5oNVq0ev10O12kc1m8c033yCZTCKfz6Ner6vOG0Nrg8L6oiiyPKR6vc722nF7ho/dkNFoNAgEAgiHwwgGg/D5fNjd3UU+n0elUmEZ3Gp6c98EumGPRiOk02m0Wi1cunQJ5XKZbRa3242zZ8/CaDSOTZ9ViiFDr4+8JM1mE4lEAlqtFsViERaLBeFwGDabDR6PBwDQ6XRw9uzZl7rwyp+V8mO0Wi0sFgtLnqZcnG63i2KxiMePHyOZTOLRo0fY29tDvV4/cdfnu0Kys9lsrJTY5XLBaDSy6hKKwzebzdN+uafCZDxeXipMJbB7e3svKddpggwZk8mEUCiEWCzGZnN98cUXiMfjuH//PlZXVxXp+T2M0WiEarWKtbU11Ot1XLp0iXmEKT+IyoZjsRg6nQ7Onz/Pclro/6DQktVqhdFohMvlYgnCAFjjzVwuh1u3biGXyyGfz7N1p6azjp7ZbrfD7/dDFEWWKlGr1dBsNk+kQuvYk33poJYP+6O5FpIkqWqjvC20qFutFgBge3sbd+/eRTAYxIULF6DVarGwsACr1YpEIgGNRoNKpcIqf16358FpQ2uBPCV7e3tYXV1FtVpFq9ViHgbK+TAajSxssp8hQxVLAFjOQ71eR7vdZv0vcrkcnjx5gkKhgFqtprjciMnnttvtiEaj8Pl8rNlfLpdDsVhkyZnTNhgSeCEnKsN3u92Ym5tDIBBg+qZUKqFQKLB9No3QHrRYLJidnWXhFLmhRx5QpeXivQoKbzcaDRQKBTx69Aj5fB5GoxGdTgcmkwkmk2msOy01JJX/H5SnRlVPlNDbbDbR7/dRKBRQLBbx5MkTxONxNlVdLlM1INdNer2eDYOmiyR17T+JM/7Yc2SA52Wver2exevr9TrS6TRKpZKq3ti3hZ6/WCyiWCzis88+Q6FQwNWrVxGJRGA2m/HJJ5+wPjKxWAz37t3D3bt3WUmz/P95X5En3lGI5xe/+AXcbjc++OADRKNRzM3NYW5uDkajETabbV9FOvk59WlotVrY2dlBqVTCvXv3cP/+fZTLZcTjceahUWojKjqAIpEIrl27hjNnzsDlcjHvVjKZxO7uLjKZzFi+mdKe820hXWMymVio8aOPPoLVakWz2UShUMDOzg42NzdZ2HbakPc88Xq9uHHjBrxeL4xGI3q9HlKpFJ49e4a9vT2WXK+WkD/tg263i0KhgGq1ilqtBrvdDkmScP78eYTDYYRCIZjNZpaw6/P5AIwbyuT5pRBSqVRiOq1Wq2FtbQ3Pnj1DMpnE7du3WXWcGs86+b6j3l1kxNTr9X3TAo6DYzVk6E2jfAVyuXU6HUiSxA5gznNosbdaLeTzeeTzeaRSKbhcLoTDYZhMJrjdbgSDQbZolObRojgzlUKWSiWWiEshRiqd9Xg8Bx7I8rh3u91GMplEs9lEPB5HsVhEOp1mCks+gFJJTHra6OZjMpnYHCrqWFyv18fKH9WmMF8F7R1RFOF2u+FyueBwONhk9Vqthnq9DkmSXmrEOA3IDxzKu3M4HDAajSgUCiwMS3mLgDrXEOkeGvsyHA6RTCYhiiI7m5xOJwCMJcpTyJpmuVE/LDJgWq0WKyTY3d1FNptlXaNJr6kN+R6iQhU610+6zcWxGjLkZqrVajCbzSwZkWKGtVoNgDo3zOsij72Sx6JYLLLZS71eD3Nzc/izP/szeDweLC0tweVyIZVK4cGDB2wzUe8MJUA3vW63i62tLWi1WiQSibGSWafTidnZ2QO7Q8qTx+r1Oh4+fIharcZKq+ngIgUkX2NKWm+TzaVohonBYMBgMGBeqHg8jlqtNrX5ZoPBAMPhEKFQCNeuXcPly5cRi8XQarVw9+5dpFIpNvhw2iokgRfraH5+HhcvXsTS0hLm5+dRr9fx85//HNlslrUnkA/PVBvkkQKeH76SJOHv//7v8dvf/hbBYBDBYBAzMzO4cePGWGNOyuWkZHtqdFetVvHw4UNUKhVsbW2xixnl9gFQ9VqjM2tzcxOtVovNfmu322wNncSzH5shI3/x1ISoUqkwq7/Var3U62LakZcKDgYDlMtlpFIp6HQ6lMtl1mRJnsSoVNmRR0WSJADPn5ma4pFxQr0ugBdGMSF//nq9zm5DyWSSeScoSY8Ul1JlRVBJJw2go6ZlVNo52eBN6c/7JtBzi6LIyq31ej0AML3TbDbR7XZPtHX6+wZ5OmnAaK/XQzabZftHkqSxai81Q6MKisUiy5+hitBwOAy9Xs+8CmSc0GDfer2OXC6HcrmM3d1dlMtlJBIJlEqlsc7hajUICdLjjUaD5emVy2XU6/WXGgoepxyO1ZDR6XQYDAZIJpMoFAr47//9v8Pj8WB3dxe7u7tsKq+aLdY3hRbGYDBAqVTCgwcPEI/HUa1WWaltt9vF+vo6WyxkFSsROlDoRk25U6Io4uHDh2PfcxD9fp/dhCh0QB4qJYcQ5K+b1sSTJ0/QbDZZhZckSXj06BHq9TpqtZrin/ltGI1GzGgRRREmkwndbhfxeBz5fB537tzB7u4uKpXKVBow8qRMCiu122188803yOfzePz4MfL5PGvdPy1rh/QmPS9dsMvlMjKZzNhaoYRfURRZGIoGaVJortvtQhTFqdl/8hyqvb09VCoV/OIXv8Da2hqKxSK2t7dZWP+419WxGjJ0i6amQq1WCwaDgVm1SikfPgkmDy3yVrRaLRSLRdTrdej1euaylIeUJn9eKcg3PHlbarUaqtXqGysDeV8Q+lDDoTW5LqgPCoWZ+v0+a1o2jf2YJkutyatHxu3e3h6SySRSqRS7OClxr7wrtCdo9Emr1UI2m2W5eKVSaSo9VfK1QCMsyLsgRx7i1Wq1LO+Tkn6pgek0eLLkkPyodxUljdN4BmqdcdzOihNJ9iX3nCRJbOih0md3HDfyN53yjMgwpNkWSvXC7MdBOSyvs/hpo7zJzygVeeUVVU/ISzsBdT//JJOG3s7ODgaDAcxmM5xOJxqNBtLpNOtnMY3eX/nzbm5ussO31WqxC9G0GTD7IQ9XTxYHyLuE0+FMe47L7gXU3oISqk8q2Vc47JdYLJYjeQX0O+RvvDxZ8yhpNpsnpqWOSj4HId88kz0I3laGJykfu93+VvJ524V/FGupVqudmHxMJtMbP6i8EoDWw3Hup0kkSXov95c8aV7+Qf8m/zhOxXqS+8tsNr/xg8jlIvdmnZRx12q1TuQXWa3Wd3qTD1oj++V8HJXsGo3Ge712XoW8s/bknjsKDls7JzL9mnhVPxDO/hz1gnjfeZvDZppks9/fOfszTfvmTTkp41dN8DPsYEhvn8Z5dSKGDN8wb4f8QJ829yVfK/uz317istq/586rvm8a4Ybd68Fl9Hac1jl1oh4ZzpvDNxRnEr4mOBwO5wWH5shwOBwOh8PhvM9MV7yCw+FwOByOquCGDIfD4XA4HMXCDRkOh8PhcDiKhRsyHA6Hw+FwFAs3ZDgcDofD4SgWbshwOBwOh8NRLNyQ4XA4HA6Ho1i4IcPhcDgcDkexcEOGw+FwOByOYuGGDIfD4XA4HMXCDRkOh8PhcDiK5cQNGUEQ3IIg/E9BEJqCIMQFQfg3J/0a3le4bA5HEIT/KAjCLUEQOoIg/JfTfj3vG1w+h8PlczBcNq+G6+fDOU35nMb06/8MoAsgAOAqgL8TBOH+aDR6fAqv5X2Dy+Zw0gD+CsBPAJhO+bW8j3D5HA6Xz8Fw2bwarp8P59Tkc6LTrwVBsAAoA1gZjUbP/vlr/xVAajQa/cWJvZD3EC6b10cQhL8CEB2NRv/vab+W9xEun8Ph8jkYLpv94fr5cE5bPicdWjoLoE8P+s/cB3DxhF/H+wiXDYfD4byfcP18OKcqn5M2ZKwAahNfqwKwnfDreB/hsuFwOJz3E66fD+dU5XPShkwDgH3ia3YA9RN+He8jXDYcDofzfsL18+GcqnxO2pB5BkAnCMIZ2deuAODJUlw2HA6H877C9fPhnKp8TtSQGY1GTQB/DeA/CYJgEQThEwA/A/BfT/J1vI9w2bwaQRB0giCIALQAtIIgiIIgnEbl3XsJl8/hcPkcDJfN4XD9fDinLZ/TaIj3H/C8vG8PwH8D8O95+RqDy+Zw/hKABOAvAPzbf/77X57qK3q/4PI5HC6fg+GyeTVcPx/OqcnnRMuvORwOh8PhcI4SPqKAw+FwOByOYuGGDIfD4XA4HMXCDRkOh8PhcDiKhRsyHA6Hw+FwFAs3ZDgcDofD4SiWQ/sEWK1WxZU0NRoN4aR+F5fP4VgslhOVz0EVeILw+o/cbDb5+jmEk1w/ZrNZcfJptVp8/RzCSa0fm82mONnU6/UTWzt2u/1Y5HMUOvggarXagf/JqTQ8Gg6H+z6wIAjsgzPOcDgEML5QuLyeI5cJyQl4sXmmXT4cDodznJAOHo1GL53tgiBgNBod61l14obMYX1r6GE540wuktFoBI1Gw77GZfYcuXzkRgyXEYfDOWqO0/ugVPYzZIDjl8mJGjL0gIFAAMFgEA6HA+FwGO12G9vb22g0GsjlcqjX69BoNFO9IACMHcpGoxEajQZ6vR4ajQb9fh+9Xg/D4RD9fh/A9G0gWk9yz5TRaIRWq8VwOMRwOMRgMECv15taY4bWEHlBaV9xT95zaA0NBoOxNaLRaNhlgTMuJ/J60hrSarVTs5bkl0raU/t5yWntTMs+o+c1Go3Q6XTsmUejEbrdLtPFg8HgWM72EzNk5G94MBjElStXMDs7ixs3bqBer+Mf//Efkc1m0Wg0UK1WAQBarfakXt57y3A4hFarZQvEYrHAYDCg0WhAkiT0ej1myEwzWq0WGo0GVqsVRqMR3W4X/X4f3W4XvV4PwPR6r0ajEfr9PkajEZMTvyiMH0okH/nBww2ZF5D+7vf7GAwG7Ou0noDpuUiREUNrhuQhN+zkn6sZ+Z7RaDQQRREmk4npl8FggHq9ztbNcDg8FrmciCFDD2uz2SCKIubn53Hx4kX4fD74fD6YzWYsLy/D7XYjkUigUqkw6w2Yng1CkNLQarUwmUywWq1YXl6GzWaDz+eDyWRCoVBAPp9HoVDAxsYG8zoA0yWv0WgEvV4Pr9cLs9mM8+fPw+v1jsmn1WqxtTQNxszkDdFoNMLj8UCr1aLT6aDf77OPaUUuI71eD5fLBZ1Oxw6ndruNTqcDYLr20yS0X/R6PbRaLfx+PwwGA/v3druNer3OPKBqQ75OSBZarRaiKMJsNkOr1Y55yYfDIVs7cmMHUN86kkcMLBYLO9tjsRg0Gg3TN3t7e2i1Wtjd3UW1WmWX76M0aI7dkCHrVafTIRQKwefz4eOPP8a/+Bf/AkajEVarFb1eDx6PB6VSCVtbWyiXy6jVaqjX61Nh1coheQ0GA4iiCJ/Ph0gkgj/7sz9DNBrF0tISPB4Pnj59irW1Ndy7dw+pVIq58OThA7VDsjIYDFhaWkIgEMC//tf/GpcvX8adO3dw9+5dPH36FLu7u+j1elOTV0RyIWVisVhw8eJFmEwmZLNZVKtV1Go15vmcNiYPJ1EUcf78eVitVjQaDXS7XaRSKWQymbGfU/Oa2Q95Pp7ZbIYoirhy5QpCoRAGgwH6/T4ymQzu37+PTqfDQghqkdNkEcFwOIRer4dOp4Pb7cbS0hJMJhN8Ph+0Wi3q9Tra7TbS6TTS6TQz8tSsb0jPeL1euN1u/PjHP8ZPfvITFkVoNptYXV1FsVjE3//93+PZs2eoVquoVqvMi3MUsjkRjwy9YIfDAa/XC4PBwG6GdOsxGo2w2Wxwu91wuVzo9Xqo1+sn8fLeG2jjGI1GGAwGuFwuzM7OIhwOIxAIwOfzwel0wmq1wu12s1yjUCiEWq2GUqnEjBk1bx6SE+UMWSwW+P1+hEIheL1eOJ1OmM1m6HS6qQoPkFy0Wi10Oh3MZjOcTiecTifm5+dhNpshCAJEUUS/30etVhsL+ap1vRyEwWBgMorFYrDb7ahWq5AkCc1mE4VCYczToOY9dRharRYOhwM2mw0zMzOYmZkZ85jv7Oyg2Wwyj4QamExYpdw7q9XKPOOxWAwmkwl+vx86nQ61Wg3tdpt5aCqVCrrdLpOVmveZyWSC3W6Hw+GA0+lkOkin08Hv90Ov1yMcDqPVakEQBDSbTQA4srPq2AwZ+QvUaDQwGo1YXl7GxYsXIQgCbt26hU6ng1qtBqfTie9+97twuVxYWVmBTqfDrVu3sLe3p/pDGXixaSh5bH5+HouLi4jFYvjOd74Dt9uNlZUVWK1WaLVa9Pt9BAIBOJ1O2Gw2jEYjZLNZ/OY3v0GpVEK9Xken01GtZ4Y8DhaLhXmsfvCDH2B2dhZzc3Mwm80AgFarhU6nc2Amvdqg9WOxWGC327GwsIDvfve78Pl8+OCDDyCKIm7duoVkMonPPvsMe3t7B7ZCUCOTz+n1enHlyhVEo1H86Z/+KTweD9LpNCqVCn7xi18gn8+j3W6j0WiMVQpOC6Q7rFYrrl+/jmAwiD/6oz/CuXPn2OF87949tNtt7O3t4d69e+h0OopP/p30xGi1WkQiEbhcLpw5cwbnzp1DIBDAhQsXYDKZ2MHdbDbR7XaRTCaRSCTw9OlT/OIXv0C9Xke5XGbhFDVAOnUwGLBoy9LSEpMTFVmYzWasrKyg2+1CFEVks1n80z/9E1qtFtrtNprN5pFEEY7dIyMIAgwGA0RRhN1uh8vlYtVJkiShUqkw7wwla3o8HnZ7JNRuzABgb6bD4UAoFEI0GsXs7CwcDgcsFgv0ej2TFSX+er1ezMzMQKPRwGazodVqscWhVpnRs5HXweFwsHwrURRZkhnJaloOakIURTidTvj9fszNzcHn8yEajcJgMMDr9aLRaEAUxdN+macGhavNZjPz5EUiEXi9XhaqtNlszKM3LYawHMrRIz3j8/kQDocRCoUQDAaZIePxeGC329FoNMZkpUS9s1//E71eD4PBAKfTyfbR3Nwc/H4/otEojEYj7HY7NBoNWq3WWAVpqVSCKIrodruqagMhl5NOp2P7xeVywWQyQRAEDIdDNJtNaDQalj8TCASg0+lYPiMA5pl5V47dkDGZTDh37hw8Hg9mZmbgdDrx5MkTfP7558xtG4lEcPXqVfj9ftjtdsRiMayursJsNrPKE0B9Ljl5xjcl0lmtVnzve9/Dj370I7hcLubGTSaTkCQJ6XQa9Xodi4uLWFhYgNVqxYcffohwOIynT5/CYDCg1Wqx/CK1Ia8YsFgsWF5eRiwWQywWQygUQr/fR6FQQCqVwtbWFnK53FiVBaDOdQS8KH28fv06PvnkE0SjUVy7dg2iKEKr1aLdbqNUKiGXy6HRaEydN4ae12q1wmq1Yn5+Hjdv3oTf74fT6YROp2PfS3tyGisnqbrE7Xbj/PnziEQi+IM/+AOEQiE4HA5IksR0d6/XYxWCagidyHN8rFYrLl26BI/Hg29/+9uYmZlhhhyFJTUaDdrtNgAwz4Lb7YbJZEKpVILX6wUA5PN5RRt5hNwTYzKZMDc3B4/Hg48//hgrKyuw2+3I5/PY2trCb3/7W2i1WszPz8NqtWJhYQHnz59HLpdDtVpFIpHAN998cyRVpcdiyMiVo8FgQDAYhM/ng8PhgMlkQqVSwdOnT5m3hqxXk8kEo9EIl8sFq9XKsuPJkFEjtPjlOUQLCwu4cuUKjEYjLBYLGo0G1tfXUalUsLW1hVKpBJvNhkgkAlEU4Xa7IQgCfD4fGo0G9Hq9Km+Rk0maRqMRfr8ffr8fLpcLdrsdpVIJzWYTlUoFxWJxqvJAdDodjEYjwuEwrl69ym6QdFvs9XrMyG2326pcI4dBz6vX62E2m+F2uxGLxeB2u5mxB7zoDq3W0OyroINKFEVEo1HEYjGWTK/RaFiFpLwcWw0VpvJyfKr2m5mZQSQSwaVLl7CwsACbzQa73T5WlDFZfm2xWFjulclkYl5ipSOXD3nE/X4/gsEg5ubmMD8/D0mS0Gg0kEwm8c0330Cn06HdbsPr9eLMmTMsFWB+fh7tdpulSsh/x9vI6sgNGXqD9Xo9q7r54IMPEAwGodVqWdUEJaV2Oh2Uy2Vsb2+j0+mw8mKDwQC9Xj9WNqt0a1YO3WgMBgP8fj9sNhtu3ryJhYUFnDt3jvVCKRaLKBQK+PTTT1EoFFgoThRFDAYDhMNhrKyswGw2Y2ZmBqPRCE+fPoVer2fuTEDZCgZ4+UZtNpuxsLCAq1evIhgMMhfu2toaEokEHj9+jN3dXXZbUuvNmuSi1WpZMvjs7CxCoRAsFgtLqN/e3kapVMK9e/ewurqKbDb7Upn2NCJXzoPBAOl0Gjs7O0ilUiiVSkdeJvo+M5lE73A4WLGB2WweuyB1Oh3m4UulUigWi4rNAZFXZ2k0Gvh8PiwtLcHv9+PmzZvs8LVarQCAWq2GwWDAQkYmk2ksL0jePoOqvSjspmTouQwGAywWCwKBAL71rW8hEokgEAhAq9Wy3KCnT59ia2sLGo0Gg8EAPp8P58+fh8vlgiiKWFxcRLVahdFoHDMG35YjN2TogKayT5/Ph+vXryMUCuHhw4csmY5K9fr9/pghY7fb4XQ6mSFDbie1QYpTq9UiEAjA7/fjk08+wdWrV+F0OmE0GlGv15FIJJBIJPDpp58im82yfg7A8/jiysoKVlZWYDKZEI1GWY6NXq9n74USlct+0PNYLBYEg0EsLi6OyavX62FtbQ0PHjzAkydPkEgkoNFomNdPrdB+83q9iEQiiEajCIfDAIB+v496vY6NjQ1WKvv48eN9Z1JNC5OeKDKQB4MBMpkMNjY2kEwmUS6X2aE+LTKSJ15SlVIgEIDJZIJer2eVSd1uF/V6nRkydLgr0eiTGx56vR6hUAiffPIJgsEgPv74Y7hcLpajWK/XUa/X0ev1IEkSdDodq1Ka9FhoNBrVeWTo8u31ehGNRvHRRx9hdnYWLpcLWq0WqVQKn3/+OeLxOHZ2diAIAlqtFtxuN27evIloNAqz2YzFxUVkMhl2YX/Xs+rIDRnaBE6nExcvXmSHqyRJSCQS2NzcRKlUGvOwUBMhKl2zWq2wWCwwmUws9qp0a5aQbxpRFOHxeLC8vIxwOIxgMAibzYbBYIBisYhEIoF79+4hnU4jn8+jWq2ykrZKpQKHw4FmsznWMZHkr4YbAEHPQa2vvV4vlpaWEI1GWbiy0+lAkiRUq1WUSiW0221FKtW3hVza5M7WarXodrtoNBooFovY3NxEOp1mB840YzAYmFePEhElSWIeUCpEmKb1A7zQTSQX6g3idDqZPqFwSi6Xw9bWFuLxOJOdkjzmct1IOpNaWZw9e5Y1aLVardDr9ezynUqlkE6n0e120Ww2WR6N3W6H3W6H0WhkMjCbzQgGg+j3+2xsihIr3ybbOrjdbiwvL2NmZgY+nw82mw2NRgOlUgnpdBqpVArlcpntn06ng1arhXw+j0wmA4fDwT7cbjc0Gg2KxeI7le4fmSEjf1itVotYLIaf/vSncDqdLAfmzp07uH37NstbAF4M9avX66hWq6zBkMfjgdvt3nfqs5IhRUDVWbFYDD/5yU8wNzeHxcVFuN1upFIppFIp3L17F//jf/wPVCoVpFIpdDod6HQ6tqC0Wi1mZmbQ6/XGbkPUNlwNPR3k77vRaIRer8fZs2fxve99D4uLi8zzkMvlUC6XkUqlkEgkUKvVFF8G+iZQkmEkEoHT6YRer0ez2cTe3h52dnbw6aefIpFIIJ/Ps3knSlSq7wId1KRj/H4/fD4fDAYDa9K1ubmJp0+folgsTp2MSD5utxtzc3NYWlpi/XV0Oh0LAfR6PTx9+hT/8A//gN3dXZTLZfR6PcUZfnT5I+//hQsXcPPmTSwuLuK73/0uDAYDdDod8/RmMhncvn0bd+/eZZemUCgEQRAwMzODhYUFFkYSBIElS1NfMCXrY8pJFEURsVgMP/zhDxEKhbC4uAhRFHHnzh3s7u7i/v37uH//PivLHo1GaDQaGAwG2NjYgNFoxLVr13DmzBmWK5PL5VCr1dDtdt86L+3IDRnq1utyueD3+yGKIprNJprNJmq1GprN5kvhIrlHBgDLr6FcGSVtjldBb5TVamWJZB6Ph1VNDIdDVCoVJBIJZDIZ1heGGitRWMliscDtdsNisbB4baVSYTk0aqpIoZuMxWKBxWKBx+NhfXTI80CemFqthkajoUjF+qZM3pSof4zRaAQA1lSyXq+j0WiwpmXTxmSemNVqZcUHer0eAFhDyWq1yrr7qnntTELeFCo6oHJ0KsAAwObmSJKEUqmEQqGAWq3GdI1S5UUeqEAggGg0Co/HA4PBAK1Wy2baZbNZpFIpZLNZ5PN5dDod1saAvON0rlECNDVWbLVaig3xk3FLuTFUZk0FFqPRCL1ej3kyySAhKOJC8qjVaqzHGUVfGo3GWD7n23Akhgy5HIfDIcLhMJaXl3H58mVcuHABkiThV7/6FTKZDLspAxhzVfZ6PeTzefYwdFhFo9Ext5ySodcviiJEUcSFCxfwp3/6p6yxkt1uR7fbRalUwtdff43//b//N7LZLBKJBJtyDYBZxZcuXcIf/uEfwu12o9PpIJ/P46uvvsLOzg5rj62GGyVVRuj1eiwtLWFubg4ff/wxPv74Y6ZgG40GHjx4gGQyidXVVWxtbY0ZfUpUIK9iMg5vs9kwNzfHXOLD4RC1Wg3r6+vY3d1lBvFxTZ9VAvTcS0tL+P3f/33Mzc3BarWiVqvh8ePHSCQSTF4U/gXUuX7k0CFLHs8rV67gj//4j+Hz+eD1epn+7Xa7ePLkCVKpFG7fvo3Hjx+j3W4f2yDA44L2DhWlUJj6k08+we///u+zMvxarYYHDx6gWCzi17/+Nba2ttj4ATq3AGBjYwPdbhexWAxarRaVSgXlchlra2v44osvkM1mFd0okNI7fD4fzp07hytXruDq1aswGAyoVCqo1+v44osvcP/+fcTj8bE0B/kZTzmeZ86cYVVxs7Oz0Gg0ePDgwTuF3o40R4Zmcvj9fhZf7PV6qFQqrEvmfglhZNXRwtBqtTAYDKr0yFBLfZfLhWg0Cp/PxyY2t1ottFotFItFFmfsdDpMZpS4ajKZ4HK5EAqFYDKZmLVbLpdZfohSbwBy5DdpapPu8XiYBwsAa3xXKpVQLBbZjZHmDKkZeZmo2Wxm/VHIjd3pdFjIRN4cUOnr4k2YbN6l0+lgs9nGPHqDwQCVSgWlUgmNRoOVhdKBpnZIHxuNRphMJrjdboTDYdhsNtYCg2YpFQoFZDIZFItFNBoNxTWcnHytGo0GdrudGW0ulwv9fh+SJKHVaiGXy2Fvbw/ZbBbZbBblchmSJLEDut1us7llVLVFRl+r1UK5XGZDNZW878gj43A4YLfbYbPZIAgCy90sFArY29vbt8GdXFaSJLE1Qw1NKRz3LrzTTqUXSBnuRqMR58+fx3e/+104nU60Wi3s7e3hwYMH2N7eRqVSYZtm0pVEXyMvAg2doooBpS8CIhqN4vz587h8+TLOnDnDDp5+v88s/vX1daTTaXQ6HXaI0+DNhYUFRCIRnD9/HnNzc8zNu7e3h1wuh3w+/06xxvcF8vDpdDqWUHbhwgVcvHgR4XCYtQQvFotIJpN49OgREokEG0am5Gd/HeTu3vn5efh8PtawS6vVolqtIp1O49GjR0zBUPfsaZCPHOqETXK6fPkylpeXWfPIUqmEtbU1bG9vT9WgWjJq9Xo9y12IRqNYWVlhU64FQUC320U+n0elUsHnn3+Ou3fvsvAK/R9Kkpfck2m1WlkEIRgMYjQaoVqtYnt7G8lkEn/3d3+HdDqNZDLJJjdrtVqm07vdLjY3N1nqBPD8smoymaDRaNBoNFjzSaUhL8enzvHkoBiNRmg2m6wY5dmzZ0gmk2w4735nPICxQgOTyYRgMIhOp8Oq4t62Zcg7Xzkmc2MCgQDm5+eh0WhYHDGTySCdTjPPwiTyjUB/p9uT0m/Vk8aavDeD1+tljbg6nQ7rSEuxZ/kbSkYehdwoRkmtoOW5EGoJH5CSpNwPimHb7XYIgoB+v88a39GNifKspgEKf7hcLtZDxmazsZtPrVZDNptFsVhknj01rIs3QV7xSHsnFArB5/NhMBiweS9066bLwzTJiDzgkUgEZ86cYd4YksNwOGRVKTs7O1hbW0O/31d0jx25B8rv9yMSibC5dZIkIZfLIZVKYXV1lXVVpzQHulgKgsAqTKlzNhlJdCi3223WQFCpsiJvCfWPkXvp0uk0dnd3USgUWFXtQWf2ZMNBGsJpsVjGxoG8DUfiO9VoNHA6nfB4PPD5fHC73ahWq9jd3UUymRybQQGMW1oUp5yZmUE4HIbJZGIdSKvVKkuUUpL7kpj0WFEjoKtXr7LZN8DzWGy9XsezZ8/w6NEjpFKpsWmper0ebrcbNpsN169fx+XLlxGJRNDr9ZBMJvGrX/2KNfCS35KUyGQvBlKwgUAAc3NzmJ2dhcViQafTQTabxa1bt1il0t7eniq8Ua9iMlzicrng8Xhgs9lgNpvZ3iEjr1KpjOV8TAOTYUmj0YhYLIaLFy8iFArBaDQil8vh8ePHiMfjrKKLEhHVDvXwMplMmJmZgdvtxtWrV5nHkw5pSZKQz+fx5ZdfIpVKIZPJKHpkDOkVmvnj8XgQCoUQCoWg1+vHWuenUilWPAFg3xwX6j8kz2Ok9AGLxcIuFuRpUIpuprOLGgTa7XZcunQJH330EcxmM7LZLDKZDB4+fIhEIoF6vc7kc9Dz7ZdOUqlUWIPcd+lDdCQeGfI0+P1+1nugVqshkUgwQ6bX6zGra/Ln9Xo9otEoZmZmWMdaUsbyAYhKhBaD1WqF3W7H/Pw8Ll++zKpLqLKkWCxibW2NuW3lw8d0Oh1rmnf16lV8+9vfhlarRa/XQzqdxq9//WsUi0V281bLQU5rIxwOY3Z2FrOzs4hGo6yraC6Xw507d1giOd0I1HwQTXr4dDodyx0iQ2YwGKBWq7GkQ/n0ZjWsi9eFdBONbpibmxszZCiZMx6PI5lMYm9vj7UuUDuURK/ValkDRUrilP97s9lEoVDAl19+iXg8zgwZJc+hogaSHo+H9Y4JhUKspDqZTOLWrVusk3q32z3Q00Byokol0lmUL2qxWNjwRCUhN2QCgQBCoRAuXryIDz/8EOVyGfF4HPF4nDUepZDb6+qX0WjEKm2p0olKtk/FkKFuiDRplzr8UUlWpVJ5KaQkj72ZzWbY7Xa2mACgXq8zRdxqtRRjxR4GKVOapkqJhOS2bTQaaLVakCSJJT2TdUq1++FwmJVpU4JZJpNhISU1yAl4ETIxGo1wOp3ME2O1WiEIAlOumUwGyWRyrDeKGp7/bZB7IOi23ev1WJKmWtbGm0DriJpvOZ1O2O126PV6dLtdtNttlMtl1Gq1sflB0yInCik5nU643e6xZm4UFqEqnUKhgFKpNJYDoVRoXXi9XgQCARbaoOTefD7PWjm8bZKu/MKhtPwYeRhMq9XC6XQiEAiwvTMYDFAul5m36nWLCOTedipGIEPmXWX01oaMPM4liiLOnj2L69evY2ZmBnq9Hu12Gzs7O8hmsy/19KAHMZvNcDgcbF4QdQHe29tjsTfK+FYi8tASTUulltWUSEcjGvb29lAsFlGtVlmslfJi7HY7vvOd72B+fp6Njs9ms3j06BGePHmCXC6HVqul2BbhcijJl4ZhRiIR3LhxA7FYjJWCFgoFrK2t4fHjx7h37x7rTfQq16bakPd4kCsfyv2gAW6SJMFgMCjuVvgukMtfFEVEIhGWDE2DVinJd3d3l+XGUMm+mteP/BJJHY5nZmYwOzsLs9nM/k0QBFSrVTx69AjxeJxNkidvhlIhnWwwGLCwsID5+Xl4PJ6xYcZUgk9pDa+7Jmh/TYbHlYT89ZKhOzMzg+Xl5bGZdolEAqlUCq1Wi3noDtIvch1FPWX6/T6L2uRyOWYjvC1HliNDJaA0HbXdbjNPAeW4TFpter0eTqeTJSlarVZIksRCB2TtKRUyYOg5vV4vbDYbcz3S95CblsIiNL9DFEXYbLaxTsd0ayJrluZ+KFlOcugwppui0+mEw+GAzWZja6ter7NOvp1Oh92mpwlaNzqdDiaTiVVJUM8GuVymSTb7hd4of89iscBoNKLT6bBwLu0hpbaPfxso/EH9h9xuN1wuF8vZI1n0ej2Wv0frSQ1Vb/KqJfm+kZdLd7vd19KpdNmcNO7ozFPqJZyQt3eg5pGDwYBFEOS5nIf9H2Q4U3GLPHRJ5eynasjQi6RStl6vxzK+aeIuJUvJ31hBeN7C+caNG5idncXc3BzcbjeePHnCuifSgpKXbCsBOjzIgLFarfjggw9w9uxZnD17FhaLZSzz3W63o9frsRp9kmU4HMbVq1cRiURw9epVNrwNAIrFIhsESOEoJbt8aTPQ4UxlsrFYDLFYjJXpFYtFPHnyBL/+9a+RTCZZb6JpaV4GgCUrykNvsViMHdLVahW5XA7VahUApuaAJmgtUc+YS5cuYXZ2FpFIBHa7Hffu3cPDhw/ZBynkaRgOSTrYbrfjzJkzmJubw/Xr1xGJROBwOJh+poaK1GCT8hyV7tkj3azVauF2u+H1egE8H8CbSqWYB0reSf2wNUGXLuoETKFdGudAYV4lXiboUk19dkwmE4bDIer1OpLJJLLZ7IETz+XPS/8PDZq0Wq3o9/toNBosofpdK+COxJABXhxAFPsir4q8XTFBdenUKtztdkMUReh0OkiSxHJjKBN8v3p0JUD5LWazGR6PB36/HzabjS14+W2Z+jkYDAbmdQkEAqw3CBk4w+GQNWuisAGFY5SsYIAXNyXK+idPFJX8URUbheLk7dGB6TFi5EmsFLI0m80srEQeBypFV9Il4KiQ96JyOp1wuVwwmUzQ6XRot9vI5/MsEVoup2mAqgE9Hg9cLtdY7hAAdgiTV50G06oNvV4/9sztdntMp77OmUOGjNFoHAstUQIwTQtXIpOtUOj5qLkdjcI5CPmFgsYROBwOGAwGVulFo4lO3SMDvFAA8kRDujXSm0wJwMPhEIFAAMFgEFevXsXv//7vw263o9/vI5fL4auvvsKDBw+wsbHBPDny36EE6Naj0+ng9/sRCARw7tw5XL58GQ6HYyxWWK1WsbGxgXw+DwAIBoO4ePEiLly4AJ/Ph8XFRRiNRjbxenV1FblcDvfv30cmk0GlUgGg7Fs3bfzRaAS/349QKIQrV67gRz/6ETweD+x2O4bDIR49eoStrS188803ePbsGfPWKTlm/7bIXbU2mw02m431FNrc3GSt0el7pwUy9IxGI+tQS8niJpMJo9EItVqN9WuanGGmJD3zLvh8PnznO99BKBRiOSJ0waK+TGtra3j69CkqlQrTZ2paS/KUB/qgUMmrjBj6XoPBgLNnzyIajbIByY1GgxUjpFIp1Go1RfbcoTOqXq+zjvH0+snYlctpMpmXvJxerxdOpxPXrl3DtWvXoNfrWbIwGcqnlux7EPQQtPDJmKHb4nA4hNVqRSgUwszMDJaWlqDX69FoNFCr1bC7u4tnz56hXC6zTqRK3DzkXbDZbMw1FwwGX1oI1PY7n89Dq9XCbrdjaWkJ3/nOd2C32xEOh1nTpWazid3dXdZ1km7dStsg+0HrhiYTU3Muu90OURTRbreRyWRYIl4+n2cuSzU8/5sg98hQV1YaS0DzunZ2dsbKrqcJukTZ7XbY7Xa4XC64XC5WcdFut1GpVFjzyDdJ6FQDgvB8aO3c3Bz8fv9Y/sNoNGJVgXt7eygUCuygUeOF4TCjZfLr+3kNqGI3GAzCZDJBEJ53QqbcIiVXlMqri+TVtPRvk0nN8siJ3NCjIdKRSASxWIyNTZEkiUVtTt0jQ8mF5XIZ2WyWTU09d+4cfvzjH0OSJCYEynBeXFxkrfZ1Oh0ajQa++eYb5HI5bG9vK74fCuUN9Xo9JBIJtFotbGxsMIMmEAiwhkDAi0nWdrsdo9EI58+fh9frZS64er3OWs1/9tlnWF9fZ+XpSm0TDoxvBnL7z8/P49q1a1haWoLZbMZoNEIymUStVsOzZ8+wtraGcrms2LVxVMiTDOlPeVWg/M9pYPI2SDdAyr/zeDyoVCrIZrPY2tpigzRJjmqHdASF8hcXF+H3+9m8KZLfcDhELpfDo0ePsLOzg1arxTzjStQxh0FGLBVWeL1exGIxVu1GunWylHo0ej7YOBKJIBwOs6GTVqsVAFAul7G9vc1yi5TU/kBujFB0pVwus8pYrVYLs9mMYDC4b14ZtRahTuwOhwMXLlxgFah6vR6VSgUbGxtIp9NHlj90JLOW6GGz2SzC4TB8Pt/YYU7uW8p+J1evXq9nM3Nu376NeDzODBklD22TP3symUSlUsHm5iaLQ5OXhcqlLRYLNBoNwuEw7HY7/H4/PB4P82w1Gg3WgfTzzz/H2toaa9BEoRUlbJL9oBwoKk2PxWK4du0aQqEQzGYzOp0OkskkCoUC1tfX8ezZs5fmKSn12d8F8shQxdt+jSanEdozbrebtYOYmZmBxWJBKpVCKpVinXzpgFa7ISNfCzTBeGFhAV6vF1arlbWGJ31OHY/lzUyVrGP2g9YJNWEzmUzweDyYm5tDo9GAKIrMw0lhD/IyDAYDuN1uzM3NYWZmBouLiwiHwy8ZMtlsllXhKm2Nyc/2SqWCXC7HGvuRIdPtdsdmIVKBi9lsRigUwgcffACPx4MPPvgALpeLze+idAoyZI4ih+itLQV5supgMEA2m2WLwefzod/vs3wQ+l7q90HVTdT4jnrGUD8HNd22+/0+ut0u9vb2kEgkEIlEXuotEwqF0Ol04PF4YDabYTAYmHwymQyLV5OFL++XolQ5yRN0dTodgsEgfD4f5ubmEIlE2HRraqyYy+VQq9XYuAslP/tRQHlFpFipIaBWq4XJZILT6WQJmkq5Db4t8oOaSqzJ3e90Olkbh+3tbWxsbCCVSrHZOYC6DWHSNSaTifUEuXjxIubm5sYG9VE1SrvdZnOnaLSFmvYaXTL7/T4SiQQ0Gg3rUeV0OrG4uAgAbBI6ja1oNBrsQj4YDBAOh3H27Fl2cbfZbMhms0gkElhbW2ODf5Wc6As831vU8Zgat+r1eiwsLEAURVy7dg3FYpH9HIVxg8Egzp8/z8r7aSRIr9fDs2fPsLGxgb29vZeSyE+taoliaGTBk1Xm8/lw5swZGI1GVm5M/U7y+TwKhQJ2d3dx69Yt5HI53L17l5VhqcH6J+ONenmsr6+j3W5jZmaG5f6QF4ISNam0sdvtQpIkxONx/O53v0MikcA//uM/ssnWlHOkVBlN9vowGAy4cOECzp8/j48++giXL18GAFahtbm5iUQigXQ6jXK5DODFLVqpMngX6ODpdruscy+V3xsMBrjdbszMzAAAEomEqr0zk2E0isWfOXMG586dg8lkYnkKn3/+OW7fvs0mGU+G5NQGXTJpsKjD4cCNGzfwL//lv2QVJLSP+v0+MpkMazb55MkTpruU5k04DPJgS5KEW7duIZFIYHl5mYWJIpEIzp8/j/Pnz6NcLuP+/fuoVCrY2dlBtVpll4eVlRX8wR/8ASvI0Ol0+OUvf4m1tTV8/fXX+Oabb9Dr9ca62itlndHZRX9SKD+bzSKXy8FoNOLmzZuoVqvweDxMLgCYB9RmsyEQCAAAq7K9c+cONjc3cfv2bdy+fZs1xpP/3rflyMqvJUkCAOzt7WFnZweSJMFisUAURdjtdmYFDwYDpNNp1l4+lUqhWCyqYkroQZA3qlQqIZfLIZFIsGoTGvGg1WqZm61cLqNcLiORSCCRSCCbzaLZbCp+IOR+0A3JZrOxElmNRoN2u41qtYp8Po+9vT3kcjm0221VlJkfBZNdMslLpdPpYDab4XK5mNGnVkNG/lwUXiMjzu/3s7WUy+VQKpXY7JxpmpAuh3QNdRbX6XQYjUasBDaXyyGdTqNUKjHvgxqhi0C1WoXBYGB9z6j6z2AwwOfzwWg0olwuw+l0QqPRsOqtwWCAaDQKh8MBo9GIer2OwWDAqpRKpdIblXC/73Q6HTYhfnd3F1arFV6vF4PBgEURyPPk8/ngcDjYGJBer4d8Ps8qBVOpFGtkepSyeSdDRm5wUJb2p59+igcPHsDv9+Ps2bMwmUxwu93Q6XSsW2I8Hsfu7i5r3EWdSOn/VBOU6U8Gm0ajwe7uLmZmZnDt2jXY7XZEo1Ho9Xqk02nUajXcv38fDx48QCaTwaNHjyBJ0tjcDzXJiJRrMBhELBZjQ9Z2d3fZBNpf/epXyOfz7BatNhm8CXRLoiT7drvN9p5Go4HFYsHc3ByuXbuG4XCITz/9lClUNcqM9oTVaoXJZMKHH36In/zkJwgEAnA6nSgUCvjNb36D3d1dNiCSKiqnbR3JD1by3rXbbXb4/vznP8fDhw8VP+H6MOShJWpf8fOf/xyPHz9mXmEa7jsajbC4uIh+v49SqYRWqwVgfLxDs9nEb3/7WxSLRfziF7/A+vo6Go3GS2FLJcqRzi4anyMIAnZ2dthIIZfLhYsXL8JqtbKLBM14K5fLePDgAfNqlUolPHjwANlsll1IiaOQzZFl01L2MZU1djod1gabphKTIZNIJLC7u8ua31GnRbUpFvmztNtt1iuH+lmEw2G0221YrVbo9Xrk83lUKhUkk0ns7Owgn88jn8+ragTBfsjzrSRJQrlcRqFQQDKZZO7uSqUyVv43zchL+Pv9PpurRIc67TsKQarhVngQZKDRM7tcLtZAcjQaodPpIJfLIZPJsDlm+7WUVyvyXAcyfmn2FiVz1ut1VqyRyWRUe2kiaE+QVyCTyWA0GsHpdLJCFZ/Px8ZbUF+iTqfD5NHv91lFLuUx5nI5FAqFlwxGJSLXG2TUFgoF6HQ6luws9wRTmxSqTq5UKsy7F4/HWZ4jXeaPOjXgyAwZWvS0WYrFIiRJYp1aqY8MAObKlIcJ1LxpALCwGvVAWV9fx+rqKiv7I9dlq9ViG6Pb7Y5lzatRRmS8/PrXv8bq6iqbf0KZ/5RoJg87Tju0z6jz6tbWFnq9Hmu3LndxU46D2uQ2mWfl9XpZJ2iz2YxWq4VHjx4hmUzi4cOHSCQSaDQaTImqTR77QQeMIAisb8f9+/dhMpkQiURw5coVNBoNfPXVV8hms9je3kY+n1d1+FZ+QFMof21tDTs7O8hkMlhdXYXH48GZM2dgs9lYGwgK/5NOTiaTuH37NorFIsujyefz7IxTw/qafIZyucwmhMfjcZjNZnz11VdsxqIgCKxKuV6vs6otSphuNpvHlpN2pIYMMG7512o19rn8++Qlo2rdMJOQDCqVCms2lclkWOtmQRAgSRK63S5arRZardZYabUaNgYxmUzW6/WwubmJZDLJnpeGYlJMmn5OTXJ4F+jW1263USqVIIoiyuUy7HY7KpUKM4qnAarKcTgcEEWR5ViRQUc35W63OzVGDCHPYex0OqypZL/fRzQaRbVaRTweRyaTYQeVkltfvA6TFbdkvLXbbRSLRfh8PjSbTWYU0+Bao9HIvJ+ZTAb3799HoVDA6uoq09dKrybdD5KXJEloNBrMe2cwGJBKpdiwUY1GMzbeolarsaav8qgL/Z9HyZGvVnqB1HUVGO/8R/+mtjf7dZF7qGiiLLnnKMtdPqxMrTKSPxdtEvmAULotcS/MOJOyaLfbrMdQKpWCw+FALpdjXVkBqDKMQsqVLkLZbJaVyK6trbFk8Wq1ilKpxELf07aWJvcZ9e6Ix+PY3NxEp9PB5uYmm2a8X08itUK6hpKeO50OyuUy2u02ms0mzGYzdnZ2IIoimwUor7zd2NhgZfxqNGDkkHeP/g6AjdiRj/cgnU1hb9qj8r13HDISDoufW61WxQXXG43Gia2kd5EPGXfyzpHyN/q4lMlJysdisbyWfMhrJy+llbvFgZNz1TabTUWsH2JyDU0qjKNeRye5fsxm82vLZ7JdunxPyQ+Y415HrVbrvV0/r1orJ3EQn9T6sdls77R2JnXyYRxVekS9Xj+xtWO329/57AKwb2WW3GEh//xd11atVjvwP1Cv/1AhyN9cJWe4vytcDu+GPFxHn08z0/78+zHpnZlcK9Mqs8m987o/c9jnakcuM3layUFyOW75cEPmlFCzG/JNOWyxcxkdDl9Hz9nPED7o82mGr5f92e9gfpv/YxqQ6+vDIjonKQ9uyHDeC6ZFCXCOF76OOG8LXztvzvsis0NzZDgcDofD4XDeZ6YjPZ3D4XA4HI4q4YYMh8PhcDgcxcINGQ6Hw+FwOIqFGzIcDofD4XAUCzdkOBwOh8PhKBZuyHA4HA6Hw1Es3JDhcDgcDoejWLghw+FwOBwOR7FwQ4bD4XA4HI5i4YYMh8PhcDgcxcINGQ6Hw+FwOIqFGzIcDofD4XAUy4kbMoIguAVB+J+CIDQFQYgLgvBvTvo1vK8IgvAfBUG4JQhCRxCE/3Lar+d9g6+dw+HyORwun4PhuufV8PVzOKcpH91J/SIZ/xlAF0AAwFUAfycIwv3RaPT4FF7L+0YawF8B+AkA0ym/lvcRvnYOh8vncLh8DobrnlfD18/hnJp8hNFodNy/48UvEwQLgDKAldFo9Oyfv/ZfAaRGo9FfnNgLec8RBOGvAERHo9H/e9qv5X2Br53D4fI5HC6f14Prnv3h6+dwTls+Jx1aOgugTw/6z9wHcPGEXwdHefC1czhcPofD5cN5F/j6OZxTlc9JGzJWALWJr1UB2E74dXCUB187h8PlczhcPpx3ga+fwzlV+Zy0IdMAYJ/4mh1A/YRfB0d58LVzOFw+h8Plw3kX+Po5nFOVz0kbMs8A6ARBOCP72hUAPFmK8yr42jkcLp/D4fLhvAt8/RzOqcrnRA2Z0WjUBPDXAP6TIAgWQRA+AfAzAP/1JF/H+4ogCDpBEEQAWgBaQRBEQRBOo7LsvYOvncPh8jkcLp/D4brncPj6OZzTls9pNMT7D3he3rcH4L8B+Pe8fI3xlwAkAH8B4N/+89//8lRf0fsFXzuHw+VzOFw+B8N1z6vh6+dwTk0+J1p+zeFwOBwOh3OU8BEFHA6Hw+FwFAs3ZDgcDofD4SgWbshwOBwOh8NRLNyQ4XA4HA6Ho1gOLa+zWq2KywRuNBrCSf0uLp/DsdlsipNPvV7n6+cQTnL92O12xcmnVqvx9XMIJ7V+LBaL4mTTbDb52jmEw9YO98hwFMloNAKvuONwOBwOb3jEUQxkvOxnwGg0GgjCiV1oOBwOh/OWkA6nPzWad/OpcI8MRzHIjZj9jBruoeFwOBxlINfh76q7T9Uj87oPQDftab1xj0YjDIfDsTddo9EwL4Ta5TIajSAIAoxGIzQaDfR6PXQ6HZPLYDCAJElMRmqXB4dzEoxGIwwGAwDAcDgEAGi1WqZzpn2f0dlFsgHAZSOD5ENrCHguH51OB71ez3T5cDhEu93GYDB4a7mdemhp8pY9iSAIPGyA55uFDu3RaASdTjd1Bp7BYIDBYIDZbIbJZMJwOES/30e320Wn02EbgRszHM67Q4aM3KABnhszwPToncOQXzJJHvJL5jRCOpg++v0+k48gCNDr9dDr9TAYDLBYLOh2u+h2u2ytvY3cTsyQ2c/7otVqYTQaD7Vi6bCiBTNtjEYjaLVaWK1WJi+dTgdJkiBJEgaDwdhCURv0vut0OszMzMDn80EURYiiyCz6RqOBJ0+eoF6vo9FooNPpAFCfoj3I4Oc3wNdDLjcur4MhORmNRjidTmi1WhgMBgiCgGaziW63i36/j36/D2D6ZClfR1qtFmazGRqNhhk0/X4fvV5vX508DbIaDofM86LT6eD1eqHVatHr9TAcDmGxWGAymeB0OjEzM4N2u41nz56h0WigVquh0+m8sZxOxJAhI0ZucVGowOFwQKfTMYOGvp/odDqo1+sYDAZot9tMSNMAyU2v12NmZgZWqxXBYBBWqxU7OzuIx+NotVqoVCpMZmqTDSkGvV6PGzdu4PLly2OeGY/Hg3w+j7/5m79BOp3GxsYGJElS3Y1I7qYlg56ej27IhJqe+yiYzKuSy4fLahy5rKxWKxYXF2E2m+Hz+aDRaPD06VMUCgXUajXU63UA0yXDyUuEXq9HMBiEwWBgBl6lUkG73R67YLxrMqtSoMiBXq+HKIqwWCw4d+4cTCYTKpUKut0u7HY7nE4nYrEYbt68iWq1ip///OfIZDJYW1t7K/19bIaM/A0nV5vFYoFWq2U5DlarFR6PBzqdDgaDgSlk+c9Wq1UAzw2aTqej2gP7ICiM5PF44HQ6MTc3B4fDgVarhUKhwG5FavPIyN9no9EIk8kEm80Gp9PJjLfhcAir1YrRaASHw4F2uw29Xn/Kr/xokctBEASIosiekbxVnU6H3QblP3MQalonhzFZEUF6aDLfjPOcSYPPaDTC7XbD4XBgdnYWGo0GxWIR7XZ7Ki+V8r9TWMRisWBubg6iKEKSJPR6PRgMBmg0GvT7fXZmydccoL49SM9F57vVaoXP54PD4cD8/DzMZjNKpRLa7Tb7GfKok0Oj3W4zz9+bcuSGjFxBDIdD5noTRRHnzp2Dz+eDx+OBx+OBzWaDz+eDXq+H2WweewASzMbGBn7zm9+gVCqhXq+j3W5PTcIZydFsNuPDDz/E3Nwcrl+/jkAggP/zf/4Pms0m0uk0crkchsOhqqx+8uAZjUb4/X643W5Eo1GEQiH8+te/xq9//WsEAgGcP38eRqMRS0tLiEQi2N3dRTabPe2XfyTIw6kUUlxaWsLCwgKGwyG63S7q9ToePXqERqMxtu/2C8PK883UvHcm9Q/JzmazwWAwsLBsr9dDu91W3SXgXSH5uVwufPTRRwiFQvjWt77FbtkPHz7Eo0ePkM1mpyIfRG6IEG63Gzdu3IDf78cPf/hDOBwOlEoltFotbG1tYWdnB4VCAZubm2i326hWq+j3+2xfqm3NUbTF6XTC5XJhYWEBv/d7vwev14tr167BYrEgn8+j2Wziiy++wFdffYVSqYRisYjRaITFxUV4vV5sbm4ik8kAeDMZHYtHhpQkWWc2mw0WiwWBQADhcBg+nw8+n2/MkDGZTNBoNC/dKiVJgsfjYYfa28TPlAi9iaSIPR4PfD4f/H4/QqEQHA4HU9D0/WrbHPQ8VqsVdrsdRqMRWq0WjUaDGSu0jtxuN4xGo+o8MrSXTCYTRFGE2+1GJBLBYDBAt9tFpVLB7u4uM/yGwyF6vR56vd6h/x+gPmUqhw5YvV4Pi8UCURThdDphMplQq9UgCALzLKhZDm8DyUIURbhcLrjdbgQCAej1ejidTlgsFuj1+qnwaE16ULRaLXQ6HZxOJ4LBIMLhMGZnZ+F0OmG1WtFsNtHr9dDv96HT6VCtVlleUafTQbfbVV2uJ1XR0qXb5XIhGAxibm4OXq8X0WgUJpMJWq0WtVqNneOU5KvRaGAymVgKwdtwZIYMLWrKX3A4HJiZmYHT6cT169fhdDqxsLAAl8uF/z97//Ubd5rl9+PvT+XwqZyrmKMkigqt7ukwPdMzs7PbHmMWu/BiYWDtBX6Ar2z4D/DFfm8M/wu+NGDAMPbKu+tZz3hn7J7UedStVpZIMVdg5fipnH4X8jn6VIlii5JIsYrPCyAoURRZn1PPc57znGi1WmGxWAY8MeRlUZe00Z9brRZSqRQkSUI2m0U8Hke5XH7mzXPUoQPJZrMhEAhgenoac3NzmJqaQr/fRy6XQ6FQQLlcHlDE46KM1crDYrHg4sWL8Pl8aLfb2N3dxf7+PrLZLG8cn8+H+fl5mEwm6HS6kb8h0g2QDg7yyE1MTGBxcRGLi4vodrtot9sol8uYm5tDuVxGuVzmvZJOpwE8KQ2lirdischJ4lTlNS6ePPWharfbMTExAZfLhWvXrsHlcnFYdm1tDZubm3j06BF+85vfcDhgnPbQi0A63GQysbvf6XTCZrMNVC1R6ey4o15PdImam5vD/Pw8FhYW8MEHH8DhcGBiYgJGoxE2mw2dTgcTExO4du0aKpUKUqkUcrkcPv74Y2QyGaytrSGbzQ78/FFec91uFxqNBj6fD7Is491338W7776LQCCAlZUVvlw2m03E43Hs7++zDnc6nQOVpsCL9wJ75YYMeRCcTiempqYQCATw1ltvwev1IhQKwWazsVVLt6bhnwM8SRry+/1YWFiAzWbD5uYm9Ho9isUiarUaOp3OS9Wen1bUB5nL5eJQnMvlQr/fR61WQ71eR6PReObNe1QZXsgGgwGBQADBYBDdbhf5fJ5vOXq9Hrlcjg8hyrMah/VAxqnVaoXT6cTi4iLOnTuH2dlZzM7OcjVfpVKBJEkol8vs2jaZTPwzgCdGDMXsqXpArTxGXWbD68ZsNiMYDCIYDOLq1asIBAJYXFyE2+2G3W7narfhIoSzjFqHm0wmmM1mWCwWGI1GTrpXhyfH3RtDUAWOwWBAKBTC+fPnsbi4iJWVFZjNZu5vZbVaB9YQFaokk0kkEgkYjUZEo9GxkZ3aELPZbHC5XFhcXMRbb70Fp9OJSCSCfr+ParWKRqOBYrGITCaDQqHAaSJqR8TLyOSlDRn1w2g0GoTDYczPz2NmZgbvvPMOHA4HpqenYbFYYDAY0Ov1+CCq1WrI5/NP/QyNRgO73Q673Q5JkhAIBGC1WvHBBx8gl8tBr9dja2sLmUyGb57joIRIDrRp/H4/Ll26hImJCTidTphMJpRKJdTrdVQqFVSr1bEsNVYrU8pu9/v9WF9f540APPbU5XI52Gw2WCwWOJ1OOBwO2Gw2dl0CoyUbOlB1Oh3sdjsuXbqEYDCIlZUVzM3NweFwAMBA2GR+fh7NZpPzPpaXl1EoFAYMlXa7jVarhY2NDeRyOaRSKaRSKdRqNeRyOb4QjGJvIrUCJONldnYW77zzDsxmM/r9PgqFAtLpNFqtFsrlMtrtNoxGIyKRCOr1OvexaLfb7H0YlsUoyeSoqHMbKQ2AQtkOhwP9fp/DI+qeTeMIXSQ1Gg3nd9I+vHTpElZXV+F0OqHRaNizSZcs0s2VSgUGgwGyLKPVamFlZQWhUAg7OzvI5XIcVgFGb12pq2kppPbee+9hZmYGly9fRiAQQL/fRzqd5tYY+Xwe169fRzQaRTQaRaPRQKfTgVarfarq8kV4ZYaMRqOBVqvFxMQE3njjDZw7dw5//Md/DKPRyN9Lb3ixWEQsFkMmk8GjR4/Q6XTYyie35czMDKampuB2uzE3Nwefzwev18s3TwBot9tIpVJ88I066moBSnK9ePEigsEgGzLpdBqlUgmVSgW1Wo1zhsbBwgeeKBGqCqBKLZ/Phxs3bmBra4sP6WaziWKxCEVRYLVa4XA42ACmfgTA6CgKtUFPvYNWV1cxPT2NlZUVzo2h/aLRaDiMq4b2mfrnNhoNtFot3L59G/v7+3j48CHW1tbY9U2Km+Ldo8KwV4k8MKR/2u027t69i0KhAIvFgkajwYaMwWBAOBzmCkDyVqkPaXXobVTW0ctC4dpAIAC/3w+LxcLGMBky5J0ZR5mQl468LHa7HW+++SZWVlZw7tw5LC8vo91uo1arsbez1+shlUohm80ikUhgf38fXq+XQ94XLlxAvV7HJ598gu3tbe5mO4oNPNWJ9H6/H16vF++99x4uXbqEUCgEv9/PuXupVAq/+93vsL+/j6+++gqxWIx1F+URvQov+ksbMmTAhMNhOJ1OLC8vY2FhAcFgEFqtFr1ej2+K+/v7qFQq2NnZwd7eHgqFAmKxGCtdjUbDIad8Po9kMolIJDLg6jSbzQgEAiiVSkgkEi/78k8N6hsRNQvyer0Ih8Nwu90AgEajgXg8jng8jmQyOdbN39TVSnq9HpIkoV6vP9UwiW7R1WqVyyH9fj/3cxg1uVDzP4vFAlmW4XQ64XQ6OQmODla6IauTfGu1GnsWSElQbJ/yhwKBAEwmE7RaLex2O3K5HEKhEGq1GuLxOBqNBiqVClqt1kgcVGR4eb1eWK1WnD9/HlevXoXb7Ua1WkW5XMb6+jpqtRonxhuNRkxPT0OWZZhMJnZ7dzod1Go1tNttFItF9hyToTfsqRk36P12OByIRCLweDwwGo3o9XqIx+N8OMXjcSiKMvK5aMOoCyZMJhPsdjtWVlY4vWFychIAEIvFUKlUsL+/z9GDXq+He/fucX5aJpOBz+dDp9PhhGmr1coFGqN0WSDUebCyLMPlcuHNN99EIBDgfFidTodWq4VCoYCtrS3s7+9jZ2cH6XQa1WoV/X6fIw52ux1erxcajQaVSuX1hJbULnCLxYK3334bCwsLeOONN3Dp0iVWnK1Wixso/f73v8fW1hYn2pGbV22R0uaw2+2w2WxYWVlBq9WCz+fDG2+8AbvdjuXlZdjtdkSjUVy/fn1sNhMtFLvdjunpaSwsLODixYuc81CpVHDr1i3cvXsX9+7dQyaTATBe8z1IBjabDYuLi4hEIpwQXiwWkUgkUKvVWBF0Oh00Gg3k83kYDAZ4vV4sLy+jXq8jGo2OVHnocG6Uz+dDJBJBOBzmEAntuVarxYcuGbTxeBylUomrlqxWKzweD6xWK/dyWF5ehkajwcWLF1Gr1VCtVpHNZpFKpfCLX/yCm1JVq1XOZTutkLx0Oh0WFxcxMTGBDz/8EB9++CFyuRzW19cRjUbx61//GqVSiS9WMzMzmJmZAfAkf4i8DJR3d+fOHdy+fRt7e3v48ssveRYMea5GYT09L2qvuiRJCIfDuHLlCmZnZ2G1WlEul3Hjxg3E43Fcv34dm5ubaLVavDbGRRa0FkwmExer/Nmf/Rmmp6dx/vx5+Hw+rK+v48aNG4jFYrhx4wa0Wi0ikQgA4A9/+AN2d3dRqVRQKpUQDAaxu7uLubk5vPXWW7Db7QPGzKh5Ykg+LpeL00f+6q/+ipPqzWYzWq0WFEVBNBrFxx9/jP39fXz55ZfsRVcbiZS31ul0sL+//1LFOy+lpbRaLVwuF2RZ5tJqqrKQJIkfan9/H4VCAfF4HIlEAplMBoqicNxe7VJXGzS9Xg+ZTAbRaBSdTgfVahUajQZGoxFWqxUGg+FlXv6pg55dlmX4/X64XC5ugEbt9/P5PDKZDLs1x8mIUWMwGOByuWC32/mgobwXtet/uHcKzfAgRTFKITd1siV5ZKidN/WNaTQaaDabnF9Gl4FWq8XVfFT+abFYUC6XIcsy59PQmtJoNJBlmWd29ft99mRFo1H2gp1WZUuvy2AwwGQycZmn3W6HRqNhr0q1WuWwLMXz6ZJFkPJUP3MwGOTOteSBSCaT7AEdR8i7TuuEkle73S57t2j9jVO1qPr8oUatoVAIoVAIXq8XLpcL3W4X5XIZyWQSOzs72N/fRzKZhFar5bWYy+WgKAontzYaDdTrdV4zB12qRkU/qT0plMgbiUTgcrlgs9nQ7/dRr9fZk5lIJJDNZlEoFLgohZ7faDSybqNEafodL8oLGTL0C202G95++20Eg0G8//77WFhYgMPh4KZTuVwO+/v7+Id/+AfEYjE8ePAAmUyGbz4knIOUJS2Ehw8fQlEUTE5OIhAIIBKJwGg0IhwOQ5blU6lkjwrJU6vVQqPRYGFhAT/60Y8wOTkJi8WCVquFvb09pNNp3LlzBzdv3uT6+3E0YgDA5XLh8uXLsFqtnOCcTqfZsqfwitqIodJRh8Mxkn0u6MZjNpvZaxAOhzmUWi6XsbW1he3tbaTTaaytrQ2EgkhOlCNDYxxkWcbS0hLsdjvOnz/PJf3T09MwGo1wuVxwOp0olUpIJpMoFArcNI46cZ6mNabOo6Jqvvfffx9vvPEGzGYzYrEYNjc38fXXX0Ov1+Mv//Iv+YZNiauUbEjtCyjx0uVywWKx4PLly7hy5Qqi0SgWFxeRSCTwv/7X/0I2m4WiKAP7b9RR35QNBgMikQhWVla4a3aj0UAqlcL+/j6Hdg+qOB1VhnMTFxYW8JOf/IST7C0WCx4+fIh0Oo2PP/4Yn3zyCWq1GsrlMgDwhbper/MQWwDcdZv+PqrNKKmqz+Vywev14vLly/iLv/gLTn0wGAyIxWIoFou4d+8e7t27h2QyiTt37nBaiTqH1efz4fz585iZmYHNZkOtVuPf86Ic2ZBR9yzR6/Xwer0IBALcQp+SeykRM5fLIRaLIR6PI5fLoVgsDmQqP8tCJaVOiXiyLHPCotls5oFU4wS58il26HA42B2uKAofZlRyO0qb4Xmh56IkVsphoMN6OMlw2FChtTWqSpY8MpQPZjabYTAYeNZYPp9HNBpFKpXC9vY2Go0GFEVBp9PhA5ZCIDqdjuedkDypJ4jD4UAwGOTxDxaLBV6vl/sXWSwW/p2n1StDXiWbzQaPx4NAIIBqtcq3QkVRYLPZOCGRhvupWxfUajVOHCed1u/3+cbodrs5N4LGY5AeGgfU+4cak1qtVu6CTDlo9XqdW16M02iC4V4xNpuNvXter5dnAObzeT7H4vH4gMFCqD0LpKPUYzFoX47aBYsgr6bH4+HoCxlxlUqFE513dna4Ez9579TJ82azmSM5ZAeQc+NEQkt0E6I3PBgM4sKFC9wvhjobFotFbjaVSqVw7949HqRFD/6sg1j9wOocHKvVysoFwEvF004T6lI/6lI7OzuLxcVFmEwmdDodlMtlPHz4ENFolGcMjdONSA1tfgoXdLtd3L17l70x3W730Cx3kucook421Ol0HCbT6/UcSrp9+zZ++ctfcvUNKUjq8wE88exJkoR2u41KpYL19XXo9Xrs7+/DZrNhY2MD29vbiEQiuHTpErRaLaanp+HxeHD16lUYjUY8ePCAZ52dRshzEolEuLJxa2sL169fR7vd5go2Svz++OOPkUqlOKRNhxGFTujWaTKZ2PihyhO73Y53330X+/v7+OKLLxCLxQCMfg8eqkai6fLhcBgTExOwWq1oNpvIZrNIJpOIRqNIJBJoNBpj4YlS6wma/Xfx4kVcuXIFS0tLeOedd9Dr9bCxsYFCoYB/+qd/wtraGpLJJE+2PqhSlkKTZAxPT0/D7/ejVCqh2WxyAvkoVH2pIwWSJHFLg+XlZUxNTcFgMKBWq6FWq+Gzzz7D/fv38ejRI2xsbPD4DzLc6HKq1+sxMzODt956C5OTk+ysiMfj2Nvbg6IoXDF2lPPtyIaMOoZPxgw1ujMYDFwanEwmcfv2beRyOSSTSdRqtSN3hKQyVOoWbDAYuL3xqB5WB0FvNCVn0gfw2N1Wr9eRTCZZjqNq0T8Pao+M1WrlPifDzz5ccn6aFcJRIUOGShMp56NWqyGRSGBtbW3gew9yWdNnapynKAoAcItw8lz1ej0sLS3xbctisSASiaBcLiMWi526g3r4Bh2JRDA9PQ232w2LxYJKpYKNjQ0uITaZTLyH1tbW8PDhQ779qT8URUGv14PdbofJZEIoFEI4HMbKygquXbsGg8GAmZkZGI1G3L17d2D9nTYZHRXSpS6Xi8efGAwGNJtNKIqCSqWCcrnMieTjgvqgpinWq6urmJmZweTkJCqVCif2rq2t4d69e4d2xFbn2tBljDwP1KuIQprDfZtOKxTG12q18Hg8mJ2d5b5mAHh97Ozs4P79+9jb20M0GuVzmyCdRrmPExMTcLvdHH4rFosoFotPeXCelyMbMupBfsFgEF6vF263GwaDgZvg3L9/Hw8fPuQMbqo5f5E3zWazYWFhATMzM/B6vdwj5GXiaacFdbWAXq/H/Pw85ufnEYlEoNfrUa1WkU6nEY1GsbOzg2g0ilqtNhIb4KiQLOjw1uv1fONRFAXlcnkkbjGvkmFDTe2pURvy3yYTamsAgBOno9Eo90xZWVnhhFi9Xo/JyUlIkoTNzc3je7gXgG7RVPUQiUSwtLSEyclJ1Ot17OzsYHt7G5ubm5BlGYqiwGg0ckXE3bt32Tijn0WfaUpxq9WCVqtFpVJBIpFAqVTi8NzMzAxCoRBu3rzJzQTr9frIrkm6lDocDlgsFszNzeHChQsIBoPQ6/Wo1+t49OgRdnd3kclkePDhqHtk1AYMDWL1+Xy4fPkyD6GNxWJIJpP4/PPPEY1GkU6nBzpAH/b8pL9oLI/FYsHW1hbq9Tq3IFHrstMqSzJigsEgHA4HFhYWsLCwALfbzV2Lv/zyS650jMVi3HFdvUZIV9E+stlscDgckCQJqVQKiUSCe+9QiPdYDRkK5xgMBu6g6Xa74XK5DjRkdnZ20Gq1+FA6youjh7HZbJifn+fmeA6Hg5XHKKM+pKix2dzcHC5fvswJVIVCAYlEAnt7e9je3kYsFuMmSqP+/M+CRlyoDRkqZzxrhsww5KHU6/Ucb6evH/Z/AAwYMlSZRG3Tk8kkjwMxmUyYnJyEzWbDl19+efwPdQRI/xiNRgSDQUQiEZ56ns1mkclksLOzg62tLdhsNtTrdQDgoX17e3soFot8wwSeyIf2I/WzICMnl8sBAGZmZvDXf/3XsFgsmJiYwMbGBvfuGUXUh7ndbofD4WBDhgZE1mo1PHr0CLFYDNlslkvYR3n/PUvvLiwsYHV1FefOnUOhUGCjmAwZyg/6tpAHFbBQC4W5uTl0Oh18/fXXSKfTSCaTqFQqfGE7rZCxT4YMdeyfm5uDTqdDs9lELpfD9evXsbu7i7W1NcTj8ac8MerUCZPJxAOA7Xb7QBI59Uaj0OVReeFkX8rwplJGcpdVKhUUCgUOAxDPu/iHb+Y0ONHj8fDvqVQqyOVyI6tECFooJpOJq0vsdjsvBIpRq0vYRt2N/SzoULZarfD5fNwID3hcDUCx0+d59nHJHVI/63DICHjxoXMUFqEGcDT2Q+3uPq0ypIZc1GTMZrPBZDIhl8txaweqtCLjjXrEqPOrhuWpDhWp8xyo2WK9Xuf9Osr5acM62Wg0YmZmhnsW+Xw+aLValEolHmWRTqdZ96j/76iirnhzOBxYXl7GhQsXYLfbUSqVEI/HcevWLe7LRBOrD/NE0Xqh3Bi32w232w2j0YhOp8NtM9TNPIHTK0fSATqdDj6fD1NTU/B4PLBYLDyslgp5YrEY6vX6Mwt3KC2EqjCDwSAMBgP3eorFYiiXy6jX6wMtRY7CC+XIUK29LMuwWCwwm83I5/NQFAWZTAaxWIxvMkd1Q9JNiA72cDiM5eVleL1eVrb7+/t8uxrVg50sVTLWqJMvJU0Dj2+Hm5ubiEajXFo76jeig1CXUPt8Ply8eBEzMzMwm81cMUDzSQ7rw6B21Y66jIafRe1FII6SozF8SJO8i8UiNjc30W638cYbb7DX5zQ2EqTXTWEeOoBlWcb29jZu3LiBzc1NruyjmVxqnrf3FP2uVquFfD7P8Xy1IUOyPE0yeh7U4RFKYp6amsLly5cxOzuLXC6HeDyOnZ0d3Lt3D7lcbqB/zqg9rxpKjzAYDFheXkYoFMIf//Ef44033kAqlUI8HsfNmzfxP/7H/+D5XOQl+Dbjlc4udXmxxWJBtVrl1ADqhTYKOop6viwtLeHatWs8601dnXTz5k3s7u6yXUCQnNX5tO+88w4uXbrEuj2bzeKjjz7i8BJ1iz4Rj8ww9IaQYlRXBBwlIVWdL0IbLBgMwufzwW63czlop9Ph/jTUsGqUIdcuNRayWCwAwMPHqGSdKlKI074Jjgq9/1QCSo2SJElCp9M50BulTiAzGo0wGAzcOJGSEkdRTpSrQT0oaLiayWSC2+3mkQL5fH6gnPN5nlW9zyiMR7JTx7TVvXlOgwzpdZDrmvI6qMMxtXpQe4IPGv74vDIi49FoNMLpdMJut7MhOQ6J9uobN4WWaM/VajVkMhnkcjmUy+WB5pujitqQJx3j9/sRCoVgsVg4LyoWiyGVSvGE5m8bS6EuvKDRA06nk39uoVBAPp/nIcmjkOQ7XDlpNpshyzKXolOzSapEVje7U+8Nav8QCoXgcrl4EGm/3+e5VNlsls+3l9lXL2zIqKd+Uq8BKrlSFIVLr76tukideAeAp2RfvnwZ77//PmZnZ7G0tASNRoNSqYRSqYTPPvsMX331FZLJ5KlfFMMMv1kWiwUXLlxAKBTC7OwsgsEgSqUStre38fDhQ1y/fh35fJ5zY4DRPJy/DXUiJ4UMyMKnDpnAoEeBGr653W4Eg0H4/X784Q9/wObmJvcrGqUQABkXrVYLmUwGVqsV2WwWer0eNpsNZrMZ3/3ud2G327m9AZU/HhZ2O8hTRZ7UUCiExcVF+P1+6HQ67h1Dc5tOA2pPMFVPzM/Pw+l0IhaLodPp4P79+7h//z7K5fJTrfNfxCNMiaATExP4wQ9+gHA4DJvNxsp6VPupqHMfzGYz7HY7l1zr9Xo0m01sbGzgk08+waNHj7Czs8OVJNQFehQhD4HZbIbD4cDU1NRA09FMJoM//OEP+Kd/+ifs7+9zN/lnlVnTz6SfK0kS3G43ZFnG1atX8eGHH2J/fx+//vWvOXdL3T7iNEPPLMsyHA4HPB4PvF4vzGYzut0uMpkMvv76a0SjUQ650v+jdhDUoiUQCOBf/It/gUgkgmvXriEUCuEPf/gDvvrqK6yvr+PBgwfcSPBlog1HMmTUiXFkwAwbK2TpqxXns251w+VqkiRxEzC/388xW4vFgk6nw0MDc7kcD6EaVdS3S+qYSC2bKdeoXC7zdOcXjR2OGlTBpV7U6oSx4e+l3gQ0luCgoYejJDNJkri3An00Gg3udeHxeDA1NYVKpQKbzQbg8RT4b0v4pUOI5Gqz2XgopcPhgCzLHEqp1Wo8QuQ0yY48CDRwzmQycaM2CifR+w68nMFPa8rhcCAQCMDtdg9c2MjgGUXooKJBvDQGgxq3UYoAdXhut9s8vmFUUSc3U6iD+gT1ej2Uy2UOmVAojUL/33YZon1FgxTpI51Os+eB5Dgql1HSGeRVobEmNLU7n89zAcbw/yNPr91uh8fjwcTEBHfVNhqN3DdGParoZfX0kQ0ZrVaLZrOJZDIJi8WCXC7HZZsOhwOzs7O4du0adnd3USqV+DatfrHq/BBS0HTjfOONNxCJRHD58mVcuHABVqsV3W4XpVIJ169fRzwex/b2NvL5PDfOGSXIijeZTLDZbAiFQrh06RKmpqbgcrkAAJlMhqu+KDfmtLj4TxK1gavumqmungsEAnA6nWzU7u3tYXNzE7VabeTyiegZ6/U6dnd30ev1sLW1hU6ng5mZGbhcLszOznLLg263y80naTSBupJJHa5zuVy8R9V9UsLhMFZXVwE8mSn08ccfs0eQftbrhPYMeeD8fj8WFxfR6/Vw+/ZtpNNppNNpKIryQgnQdDEDwAbx8vIyFhcXcfHiRVy9ehX9fp/7Ym1tbSGfz3Or/tctn6NAsnQ6nbhy5QqmpqYwMTHBSb6NRgPZbBa7u7vIZrMARjt5Xr0eyJtH+pYuyXfv3kUymcStW7ewu7vLCbnPk9xLZ5fVasUPf/hDLCwsIBwOc0f69fV1boZHnOb1ovbY0UXH7XbD6/VCkh4P7s1kMtjd3UUqlRrw2mo0GjgcDvZ40WTsN998Ew6HA8ViEalUCg8fPsSDBw+Qy+XY6/WyMjmyIUM3xkqlgkqlwhn9FosFRqORLbB6vQ6r1Yp+v8/ZyOoEObWFTK5zKrVeWFjA3NwcAoEAW4G1Wg17e3vY3d1FPp/nCcijpkiAJzciq9XK8VSacAw8LhelOC21Qx+VBLFXjdqLAAwmuFLSOeVK0EZTJ5qPEqQ82+02CoUCLBYLj+eYnJxk753T6USv10MqlUIul+N8KvIU0M9SGwDBYJCbxFGy7PT0NBwOB/x+P+r1Ora2ttiIXltbQyaTORXrTa1caeCc1+tFvV5HqVRCKpUamH90FNe9utoEAHt8AoEAzp07h7m5OYTDYZTLZcTjcS5koFyc0yCf50Wd5Gu1WjExMYFwOMweOfI2VatVFAoFvhyM0jM+C3VIlSpnaHRHNpvF1tYWEokE8vn8U1GFYdQ6SKPRsB6nEm4AaLVaqFQq7HUY9lycdiTpyUBW6qxP89cURXkqh4jOYqvVCq/Xi8nJSVy+fJnHPVCbB/qgPkyvyrP5Qh6ZVquFbDYLq9XKrbpnZmZgMpkQDofR7z+epEsj4Hd3d9n922q12K1N8UqK1cuyjEuXLiEYDMLj8cBoNKJQKGBvbw/xeBx37txBPB5HpVIZ+YPd4XDg/PnzmJ6e5mSoZrOJer2Ozc1NfPPNN9jf3x9o/Deqz/o8kNFRLpcRjUbhcrk4Lu/1etlAIeVKlW0zMzPweDyQJInLJEc1DKc21trtNnsh9/b20G63+RZJCfCXLl1CtVpFKBTiOPNwTholN9JcFHLv0h7sdrs8G+WLL75AKpXC1tYW93Q4TTKkSw+FfcjFncvleCbUQagPHvXX6NloZpzZbOahmisrK7h48SIA4MaNG8hkMvj888+RTCaRzWafCqefZtTP7fF44Ha7sbKygrfffptnULXbbayvryOTyWBtbQ2pVIq9CKf9+Z6F2kilMNrk5CTeeecduN1uTvDd2NjAzZs3D825JBnSwavX6yHLMjweD37wgx/A7/djenoaJpOJLwJbW1vcw2jUjN5hhl876ZVAIAAAOHfuHLxeL0KhEPeYW15ehlarRTQaRaPRwG9/+1tsbW3xIGhKEn4tHhm6MebzeZhMJsRiMWi1Wm6OFw6H4fP5MD09jYmJCRSLRdy+fRvFYhGxWAylUgmRSIST5+imODU1BYvFwuMOaEEpioLbt28jFovxzB0SwChCG8tut+PChQuYmJhAMBiEy+XivgUbGxv45ptvOGF6lDfA80Jrq1KpIBqNIhKJsCHj8/m4KZ46REnrhoZrqgcmnvaEumdBnhSaD/T1119DlmXo9XoUCgWsrKxwY0iaRXXu3LmBnDVgsPKGEjtpGKVer0er1UKr1UI6ncb29jYSiQS+/PJLJJNJTkw8TYNZ6farnkElSRIKhQIymcxzJSarDza1fGiNeTwefP/738fS0hJmZ2cxMzODR48e4aOPPkI8HseXX36JbDbL+3IUvMHDRpzX6+WQ2dtvv81DSZvNJh49eoS1tTWsra0hnU4PrJ9Rhd5r8uRNTk7i7bffhslk4nE6jx49wq1btwZ6vDzLmBlOZg2FQvjRj36EiYkJPpR3d3fxf/7P/+GIxct0tn+dPCvHkL5mNBphsVhgsVjwwQcfYGlpiQ0Zk8kEh8OBcrmM69evI5lM4ve//z3u3r3LHbHVP+tlObKWkiSJy6wVRcH6+joqlQr8fj+XchqNRlitVvj9fnb7082xWq3yLCGz2cwTs10uF8+AocTeer2OeDzON0S6db5IHPw0QMpYo9Gwm9/tdgN43LSLWjVTUy/1XJNRe9ajQAqz3+9z6SclyjUaDSwuLsJms3GXaErwnZ2dxezsLHQ6HU8Gp2Zoow7JgxQh5f10Oh00m00OSdLFQh1SovCKuqpw+KCn8slkMon19XUeDkilkKftkCa9Q7qHQq7kpXnWa1WHCShxkwoKKE/NZrNhaWkJDocDkUgENpuN2x5sbm7i4cOHyGQyXNY/irdrkoHD4cDExAT8fj+HsqPRKMrlMh49eoRHjx6hUCiM3PMdhrqcWKfTcZk5Namr1WoDewjAwJ/V7zcZfoFAAEtLS4hEIrDb7dBoNOzZ3NnZQaVSGTisRxFqd0IhR2reGgqF8O6776LVarE8FxcXEQ6HYTKZeFp6MplEoVDAzZs3WZ/TGQ682jYGL1y11Gw2kclk8NFHH8HpdHKiaiQSQSQS4Xh+r9fD4uIiK1Nyy1HNPVmqdPOjmyK1Lb59+zY+/fRTlEolFItFFuioLQ51TpBWq4Xb7cbS0hIbMpVKBTdv3sTdu3fx8OFDlEqlsbgRPS90cFPfBafTifv378PpdOJHP/oRut0uTCYT7t+/z80YV1dX8d5776FcLuPv//7vWSEDo93rg24pvV4PhUIBhUIBpVIJRqMR9+/fx8LCAubn5/HBBx/AZDLBYDBwuTAd7EajkS8DzWYThUIBzWaTjb2trS1sbW0hnU7j4cOHaDabqFarHMo8jVUqNCeqVqvxFGFSpM/ywFGsn6pKjEYjwuEwQqEQfD4fFhYW4HQ6cfHiRc4T6XQ6+Oqrr3Djxg1Eo1F8/fXXPEDxRSbzvi6Gc3+onPzq1as8hqJUKvFgxF//+td49OjRQK+T07YGjspwsq9er4fVakWr1cLOzg7nPKlHv6jbgVAoic4rl8sFn8+HCxcu4Cc/+QnnmHU6HXzxxRf45ptvEI/HkU6nB3KSRk2OFN6mQZe1Wo0r3FZXVzE5OQkAPFeJOtLn83nk83ns7+/jzp07SKVS+Oijj7hfzPM2FzwqL+Q3VtfPU9ItdUVUD/yjBjrqTqHklRhuFU7xfUrkpUGJiURioCnTqEPPTOWPBoOBF0ypVOLnH+WD+EWhRHIqAY3H42g2m/B6vTAYDAiHw+h0Ouz1s9vtqFarKBaLyGazXDY5iorjWdA6aDab6Ha7yOVysFgsMJlM2N3d5dJI9b6jf6ccEpqLQlNm6/U69vb2kEwmuSM3HeBq5XtaoNdC3phKpcLT0OlypP6+4f87XEZKg+uoQIFmxwBgZRuPx7lElKoux2FPqvUxGcs0lLVer/OBPgqG2lFRXwwPq0qithi0D8iDTjqIDGGLxQJJktirQ8n36rPqNO2j54WMOcrZzOfzSKfT8Hq9PAfPbrcPVPtRixCanZRMJrG3t8czuqrV6rEmPL+QIaO2XKla4JNPPsHa2hpmZmYwOzvLjcooIdNsNh+YIKcWWqPRwPXr1xGLxbjBFc2Cocmjo5r7QJAioRirXq/nDpqPHj3Cw4cPUSwWR9aSf1lIccTjcfzsZz9DOByG1WpFOBzGP/tn/ww2m40r5nK5HH7/+98jkUjg008/RTqdPnDmx6hCawV4ckOKRqPY39/nMAAlv6rzX2goG82NaTQaKBaLbMg0Gg10Oh2eP6Q+pE/jmqPXRMbco0eP8A//8A/o9/vsrlaPD1BDlRQmkwnBYJDz8gKBAAwGA6rVKqrVKidUb25uIpvNIhqNcrM9dbLmqOofyu2gsBzNn2o0GjzMkDxyo5jP8TyoDVG9Xg+3241Go8GdfckTYzab+dCmdgVzc3PweDyYm5vD3NzcQPjks88+QyaT4RxOdc+rUZMjvV66NNVqNXz88cdIJBJ455138J3vfId1DaVDKIrCLVE2NjawubmJUqmERCLBaSVk8BzX2nrpTD4KGWWzWXQ6nQE3br1eh81m4xEDB91o6FZA/WYSiQR2d3e5Ukkd3x9VJXIYVFquKAqq1Sobhmcdmoyq1WqRz+dhtVoxNTUFp9PJh3qn00EikWDPQrlcPpUH8cugDucC4ERTcnerP6ifE7XULxaLbMiUy2WuhFK7d9WH/2mVm9rdTx6ZeDwOSZJQq9XQbDafmbei/pr6WdWh7k6nw3Kh/imZTIYvFKNaITn8eulZm80mms0m5z7Q30e5yd/zQM+vrnCjBpzUiJSmq8uyzAmrlM/o8/ng9/u5ipLCJbFYDOl0msO2o7peCNpvdA5lMhmYTCak02kUi0XOUyRvDbUmSKfT2N3dxfb2Njsg1M6L47xgvpJZS/1+nzd+JpPBgwcPuHmV0WiE3++HwWA41JChmTLRaBTFYpEVL4WixgVyx1UqFezu7nJ5WqFQ4H4YFIcf1Y3wMpACIBlFo1H8/d//Pfe9sNvt3PGWlAjdqtU/Y9ygZ6KwAI0xUCtMiuNTDhrdGocrmtRzlQ6r0jhNqA26fD6Pb775ZiABWFGUAw+PTqeDSqXClRJ6vZ49WervJf1D3YEpkXhU5PNt0BqgoYjVahWBQACFQoGTXUcxifnboDXRbrdRq9Wws7ODX/7yl9BqtdzOwe/34/Lly2zIUREK5dPQfmq1WlhfX8fGxgbngFQqFezt7XFYbtSNGDV0YVxbW0MsFkM8Hsdnn33GOqbVanF+EfXLURTlqRlVJyGLV2LIAI+buFGypjqxipqWHVbGSYd7t9tFuVx+qgxuHBYFQTfLRqOBQqGAfr+P/f19bjBEVvA4PfNRUOdDUMb83bt3odPpsLm5yS5NcpGXy+Wxv0mqGTb0gG9PbFbnPBy1YdxpgyrbFEUBgIGD46A9Q2FrAKjVagDARp26NFmdGEvyGmU5EXTRpI9yuYxEIgGbzca3aWpbcBrzo14WtTev1WqhUChgbW0Ner2ew6uyLCMYDAIAN5C0Wq2cx0gGLXk0S6US9vb2OAmcOq8P532OA71eD5lMBtlsFoVCAVtbW3y+d7tdbvanzlcbzoM9CV5Zk4jhyZfkxqPb0vC/E8MbbXg66LgsCvVz9no9RKNR/OIXv0C/3+e8BXJlq//PWUadCE5jKijeSh/DiZ5nRWakTJ7XgBuXnCt6/c8bDlNXoDzrZ9HeVDNOXmD1e18sFrG1tcWhERoSqSgK6vX6WOXHqN9fCinFYjF88skn0Gq1bMwOD0ilSziFItV/p/wiGjtArQrG1Qgk+v0+e3fpOSnUe1AT0pOWg3SYIpRl+YWuucNNmNS3nWe+kCMqqGehKMqJSfBF5QMcrGBPIlfhJOVjs9le2k2iXjukeNSb5VXLrFKpjMT6+TaOK0xwkuvHbre/Uvk8rw56Gcrl8qlePwc1BTzJA+ik1o/Vah2QjfosetaQ42d9P30eXj9q2b0KuVWr1VO7dtSXcDWvWgaHcdjaOZa2nXTDeVHvwjhZtc/LWfMoPC/q3IhRy+t4nQjZPM1Bnhf6+lnj2xKjx5mDzic1zzJ4h7//rMgLeLbOPS1eqGPrPy5CJIdDoQHBt6PeLOOYkCg4Oc762jktB89Jc1gY8TAD5TCj56zJ8TSfWadjkIpA8JycNeUhEAheLUfVIULnnH4OzZERCAQCgUAgOM2MT2q+QCAQCASCM4cwZAQCgUAgEIwswpARCAQCgUAwsghDRiAQCAQCwcgiDBmBQCAQCAQjizBkBAKBQCAQjCzCkBEIBAKBQDCyCENGIBAIBALByCIMGYFAIBAIBCOLMGQEAoFAIBCMLMKQEQgEAoFAMLIIQ0YgEAgEAsHIcuKGjCRJbkmS/l6SpKokSbuSJP3VSb+G04qQzeEI+RyOJEn/XpKkryRJakqS9F9f9+s5bQj5PBshm8MR8jmc1y0f3Un/QgD/GUALQADAFQA/lyTpVr/fv/caXstpQ8jmcIR8DicB4D8B+BCA+TW/ltOIkM+zEbI5HCGfw3mt8jlRj4wkSVYAfwHg/+v3+0q/3/8EwM8A/PVJvo7TiJDN4Qj5fDv9fv/v+v3+PwDIve7XchoR8nk2QjaHI+RzOK9bPicdWloC0On3++uqr90CsHLCr+M0ImRzOEI+AoFAIHiKkzZkZADloa+VANhO+HWcRoRsDkfIRyAQCARPcdKGjALAPvQ1O4DKCb+O04iQzeEI+QgEAoHgKU7akFkHoJMkaVH1tcsARLKmkM23IeQjEAgEgqc4UUOm3+9XAfwdgP8oSZJVkqTvAvgzAP/tJF/HaUTI5nCEfL4dSZJ0kiSZAGgBaCVJMkmS9DoqE08lQj7PRsjmcIR8Dud1y+d1NMT7d3hcnpUG8LcA/q0on2WEbA5HyOdw/gZAHcB/APCv/9+f/+a1vqLThZDPsxGyORwhn8N5rfKR+v3+Sf0ugUAgEAgEgleKGFEgEAgEAoFgZBGGjEAgEAgEgpFFGDICgUAgEAhGFmHICAQCgUAgGFkOLY9yOp0jlwlcLBalk/pdNptt5ORTqVROTD5Wq3Xk5FOtVk9MPrIsj5x8FEUR8jmEk5SP0D/PRuiewxm3vSU8MgKBQCAQCEaW19bQh8q+6bMkSZCkEzNIBYIzgXp/nXWEznl+SEa9Xg/Ak/UjZHYw/X6fP9QyErI6GV6rR0b95tPfBQLBi/M8ivMsK1e1rhH65nDU+lnI6tkMrykhq5PntXhkyGLVarXQ6XTo9/toNptiAaiQJOlARTJ8mzxLN6RnGb3itviEYblotVr++lm/MGg0GpaJRqNBr9dDu91+3S/rVKFeG3q9HkajEQDQ6XTQ7/fR6XSe8tKcdWhdWa1W6HQ6tFottFot9Pt9dLtd/p6zRL/fR6/XO9BTpdFoXrk8TtSQUR/CWq0WBoMBZrMZ3W4X7XYbnU6H//0so35+WgTdbndAyagXw1mRl3pzDKPeJGdFHofR7/eh0Wig0z3e4rS3zqoRA4ANGL1eD4PBgFarhXa7zUr2rDNs6Op0OthsNgBAo9FAt9tFt9tFr9c7cwfzQdC60Wg00Gq1sNvtMJvNKJfL6PV6A0bfWZMXPf+wMUdn/3D47WX10msxZBwOB1wuF1wuFyYmJtBqtbCzs4NqtYpsNotarQbg7BzQw9BhTcaeVquF0WgcOKTb7Tba7Ta63S4vmHGUl9qTQLIYtuhps9ANmzYOgKc2zDhDcqKD2uVyYXp6Gq1WC7u7u6jX62g0Gmi322MvC4LWj1arRSgUgtPphM1mg91uRzKZxL1799grM6576KjY7XbIsgy/34/l5WUAQD6fR71ex/r6OrLZ7Gt+ha8fulQZDAb4fD5YLBasrq4iEAggkUggkUigXC4jmUyi3W6zh2bcof1jsVhgsVig1Wqh1+sBgA27RqOBRqNx6MX0qJy4IdPv9xEKhbC6uor5+Xl873vfQ7Vaxe9+9zskk0l8/vnnUBTlzIYJ6M3t9XrQ6XSQZRkmkwnhcHjAmMnn88jn82g0GigUCmOZ1EmLvN1uQ6PRwOVywWq1wmQyscubjBhFUdBqtZDL5dBsNgfCbnQDGHdvDcnLZrPB6XTiwoUL+NM//VOUy2X8z//5P5FKpZBOp9FsNsdeFmo6nQ50Oh0uXbqEc+fOIRKJIBwO48svv8TW1hbfHs8y6gtDIBDAwsICVldX8ed//ufo9XrY2tpCNpvF3/7t36JUKrF3BhgvnfO8kN7R6/VYXFxEKBTCv/pX/wqXLl3CrVu3cOvWLWxtbeHLL79EuVxGOp1Gq9Ua63ONvMCkq+fn52GxWOD1eqHT6VAqlVCv17G3t4e9vT10Op1XtoZOzJChh5QkCQ6HA8FgED6fDy6XC3q9Hi6XC41GA3q9/kzfjCRJgl6vh0ajgd1uRzAYhNVqxdTUFEwmE3siHA4HHA4H35TUi2IcIDlotVp4PB4YDAYEg0HYbLanDJlOpwNFUdBsNmG1WlGv11kpdzodNJvNp1y943w7MpvN8Hg88Hq98Hg8kCQJOp3uTO4p9QFts9ng8XjgdDo5DEDfI3iMJEkwm81wu91wu91wuVzo9/tQFAVarRY+nw9utxuVSgWKovD/OUuQh89oNLKODoVCcLlcsNlscLvdCAaDKJVKMBgMZ2LvDefmmUwm+P1+yLKMSCQCg8GAYrGIRqOBer2OTCaDZrPJnqqRMGToIS0WC4xGI86dO4cf/OAHcDqdcLvdMJlMmJubg9VqxRdffHESL+nUoFa0ZM16vV643W7MzMzgnXfegcvlwtLSEmRZZmMwl8uhUCjg9u3b+Lu/+zuUy2UUi0V0Oh3+nlfltjtJSB5msxkOhwNerxff/e534fV6MTk5CYfDAaPRCIPBMGCsVKtVtNvtAcOu3W4jlUrhwYMHqFQqiEaj/G+9Xm/slAvd9mZmZvDWW29hdnYW09PTbBi322025M4SpCgjkQjOnTvHbm+DwTDgWTirqPWPJEkIhUK4dOkS5ufn4XK5oNPpYLfboSgKdnZ24HA4cPPmTdy6dYtzjsZtLz0L8pY7HA7MzMwgEongJz/5CcLhMPx+P7rdLsLhMOx2OwDg888/R6PRADD+oUt6Pq1WC7/fj/feew9+vx+XLl2CLMuoVqtoNBr4x3/8RyiKglwux/lEL+upOjFDRqPRwGQywWq1wul0wuv1wmw2cyjlLKJ+4yVJgtFohE6ng8vlgs/nQygUwtTUFFwuFyYnJ2GxWPjNNpvNsNlsyGQycLlc0Gg0fHiTQmo2m2g2m6/5KY8GPZ/RaITb7YbX68X09DR8Ph8bMgaDgeOu5OKt1Wpot9twuVwDhozRaEQmk4FWq8X+/v5YKxLgyY3a6/Wy0UdezrO8z4DHa8piscBkMrG3b9zXw1Gh27TT6YQsy9DpdNDr9dDpdNBqtXC73fB4PKy7R+2i9LLQPqIogsfjgd/vh9/vh9FoRL/fh06ng8lkgsFgGMnL5IugTm2gs57kEwgEIMsyarUaWq0WXC4Xe6rU//dlOHZDhlz/BoMBFy9exOzsLFZWVuDz+ZDL5bC2toZsNotPPvkEmUzmTBw2wJMNYTabMTc3B5vNhuXlZfh8PgSDQQSDQXg8HszMzECr1UKr1aLdbnMuiNFohM/nw/nz5/HTn/4U9XodtVqNk157vR6uX7+Ob775BsDpd/+SUjSZTDCZTFhZWcFPf/pTeL1erK6uckhJp9Ox50pdZUFVXZT4TB/xeBxWqxWxWAy7u7uoVCoDCvi0y+UokGw8Hg+mpqYgyzJKpRIKhQLfhrrd7lg981EYzj2jfKtmszngrTqr8gEeP7vNZkMoFILD4UC32+U2GeSZ8Xg8MJlMY+nVPAz1getyuXDp0iVMTExgcnISHo+HddLe3h4ePXqEmzdvYm9vD4qioNvtjnVemrpAxWw2w2q1QpZlmM1mtNttNBoN1Go1/vyqq5RPzCMjSRKCwSAWFhYQCARgtVqRyWQQj8exv7+PtbU1ZDKZMxV37ff70Ov18Pl88Pl8WF1dxeTkJEKhEILBIMxmM1wuFzqdDgqFAlqtFqrVKqrVKnw+H6xWK/x+Py5cuMCZ8erKnWg0OjIHNhkXdJsJBoN44403OMRmMpmeKkEfLkdX9yggV6Ver0e5XAYA9uKMK1QGarFY4HK5oNVqUa/XUa/XucrtLNwOnwVdHqiSgm6FOp1O9JPBk9CkyWQayCEig4XyQiwWC3v5zgrDOSBms5n1tMPhgCzLaDab6Ha7yOfz2N7eRjweR6FQ4NDSadfBLwPtLY1GA4PBAKPRCLPZzF6pdruNZrPJVZPqfMVXwbEZMnQwUdIqJf1MT09zrb1Op8Pc3Bzcbjc6nQ5yuRxu3LiBRCIxEBYZpwVAb7jVaoXX60UwGMQPf/hDNvI8Hg90Oh16vR7y+Tx2d3dRq9WwtbWFarWKRCIBRVGwurqK1dVV6HQ6zMzMQJIkjvfH43GUSiVotdqRicuSmz8YDGJubg4LCwvwer0cayZPE/C0Uun3+2zEtVot9gAaDAZoNBpEIhH0+31cvnwZHo8HGxsbyOfzr+U5jwN1symtVguTyQSbzYZer/dUKfpZZpwrRl4UtZ52uVywWCzw+XwcPhr+PnXJ7FmSJT27LMuQZRnhcBihUIgrcnq9HiqVCqrVKjY3N/kcI7007n1kqBng3NwcZmdnceHCBYRCIVitVkiSxC0gMpkMtre3kU6nUa/XX1k16bF6ZGiD2Gw2uFwuhMNhTE1NQafToVKpQKvVYm5uDvV6HQ6HA4VCAfl8HtVqFcVikR90HDYMPQMZGxaLBTMzM5iZmcH3v/99RCIRVh7FYhHFYhH5fB4PHjxAPp/HN998g0KhgN3dXZTLZSiKAlmWMTExgeXlZT602+02arUaarXaQPjltEKvj8Jn4XAYq6urbNSZTCYAOPDGTBug2+1yc7NSqYRmswmLxQKr1QqNRoNwOAy9Xo/V1VW4XC5OlKbfPw7rC3iSLG40GmGz2TgUOS7P9yoYF33yKqE96HQ6OefD7Xbz4TusQ067TjkO1IYMeWKCwSDcbje0Wi16vR7K5TIKhQI2NzfxzTffcAGC+qIxbtCzUZh2bm4Ob7zxBhYWFhAMBqHX6/lM2tvbw9bWFnZ2dpDJZF6pXI7VI9PtdmEwGLgBHnlmUqkUMpkM3xj1ej0ndkYiEVQqFQBAqVQ6rpd34pBHwWq1shFz9epVhMNhOJ1OmEwmbhS0tbWFra0tpFIprK+vo1wuY2dnh2OM6vI/qkjp9x+PeajX60gkEtja2kKhUDj1m4cWMyVhhsPhgU2gVqaNRgOdTofDAvV6HYqioF6vIx6Po1arIZfLoVarwel0wul0wufzYWFhAUajEaFQCAAgyzIMBgOXrI+LMWMwGGAymWA2m9kAFOMbnsiAKiUomfUsHsgHQeG2YDCImZkZ9jIQam8MNeA8K4njpBso/2xychJXrlzBwsICnE4nzGYzWq0WGo0GdnZ2OFViuPHkuO49tSFDbVVoDUmShE6ng3K5jEqlgp2dHaytrSGXy/F6elVyOVZDhkqBQ6EQW7EejwfffPMNPvroI9TrdZRKJUQiEfybf/Nv4PV6cfXqVTidTnS7Xezt7fHPAkZ7MbRaLXS7XUxMTGB+fh5Xr17Fv/yX/xJ2u53f9Ewmg0qlgo8//hi/+c1vkE6nsbm5iVarxbOoHA4HzGYzjEYjN8ujUutyuYxSqYTbt2/j5s2biEajAE6v3EgxarVaOJ1OuFwuXLx4Ed///vcHDmNSnoVCAYqicA+dbDaLR48ecbJ4LpfjjpqRSASRSATXrl3jZOqVlRUEAgF8+eWX2Nvb49yRUT/k1crEZrPB4XDAbrdDkqSxugy8COS67vf7SKVS2NragslkQiAQGOub8vOiLsY4f/483nzzTczNzXF7A/KE0qWT5gidlZL14dYhb731Fv7yL/+SE6L7/T6q1SoqlQq++OIL3L17FxsbGyiVSmO/vtQ5Zx6PB+FwGCsrK3j77bc5QlCv17G/v49sNosvv/wS169fP5a5iscaWlInH1qtVs7ZqNfrHEIqFAo8bwl4cqscp8TMfr8Pg8EAAHC73dxzwOFwwGQycRZ3LBZDLpdDLBZDOp1GuVweyA2h5FWj0cg3b71ez7kh6XQa+XwemUyG+6mMApIkQZZlbihFPT6Ax0ZMqVRCo9FAMplEuVxGo9FAq9XC/v4+tre3kcvlkEwmOTSpKAr3ClEUhStVzGYzJyqOU+ktKUyz2cxJmtTbQ33wnJVb9LMgjyVVkKgTw4HxCjMeBXXJLOU6UO4Z8MSI6XQ6qNfrHDIZ50NaDeknh8MBt9sNp9MJi8UCnU7HMlEUBfl8Hul0GoqivHKPw2mHznqdTsejZIAnl1V15dJx6KFjMWQoN0av18NqtSIcDiMSiUCn06FeryOVSmFjY4Pf/E6ng3w+D1mW2fAhi26U3b/0Jmo0Gvh8PthsNnznO9/BBx98gEgkAq/Xi0ajgZs3byKbzeJ3v/sdtra2EI/HEY/HuTsiWf39fh/BYBB2u52TzfR6PRqNBtLpNH75y18iFovhyy+/xM7OzkiU/FGW+9zcHJaWljA9PQ1ZlgE8VqClUgnXr19HLpfDo0ePkM/nuWvt1tYWPv30U9RqNRSLRa7MoRkoAJDL5dBqtbh0lMJ7RqORw3SnWT7fhrqUPBKJYHZ2FpFIBFarFYVCgcO41Pn4VTSfGjXoUKZ10ul0uDs09SQ6S/IgaO2rq0xobwBPDJxer4darQZFURCNRrG+vo58Ps9zz8YVdSUlNQi8ePEi/H4/99dpNBqIRqNIJpO4c+cObt269VR15VlhWK+Q/DqdDndYbzQaozH9Wh0GIutMlmXYbLaBPA5FUbiUmFz81OOC4pGjDj2LVqvl9uh+vx/BYJBHM9RqNeTzeSSTSUSjUezu7nLfD71eD4vFwj+LOt5SrxWavdRsNrmiKR6Po1gsolqtnnpFQ1U26v4Uas8dNbUrFArIZDIDs4IajQYbfBSPVt+C6Aag/jrdGsYNema6UVNjRcorUo+wOIsKlqCbIa2FYY/MWUK9DuhQpo+DdIbaI6MoCjqdzlgbxOrcGIPBwGMHqCEnhfPb7TZ3Va9UKqhUKlzSf9Y4qEGi2ptHXuHjWDPHIu1er8e9CGhAZCQSQaPRwMbGBvb391GpVNBqtfhwoYejg42UzCh2RqQNbjQaEQgE4HA48O6772J6ehoXL17E8vIy+v0+CoUC4vE4Pv30U+zu7uLRo0c8g6LX68FisWB6epoNFwDseSD5KIrCRtDdu3cRjUZRLpdPvYKm7ph2ux1OpxPnzp3D1atXEQwGATyuUqpUKigWizxNdmdnB6lUCtFoFFqtltcQJT+rN5JGo2EjUqPRoNvtIplMIpvNckUcKeNxgWLWwOM12Gg02CNTr9fRbDZPvXErODmoGMPr9XIXVqfTCaPROLCOyLPcarVQqVQ41Kvu1zRO0OFrMpkwPT0Nt9uNa9eu4dKlSwiFQry3yuUyUqkUvv76a75AntX9pS7LV9PtdpHNZpFOp9FoNI5tvRyLR4YOFovFwsZMMBjE1tYWz1eg5kHqB6M/Dzc1GzVDBniSz0I9GZaXlzl04vP5oCgK0uk0CoUCtre3OdeD4qsAuJrLarXC7XZDkiRsbm4OVCM1Gg3kcjmk02ns7+8jnU4PZMufRtReOxq14Pf7EQ6HYbPZAIAVZ71eR7lc5tJGagzYarUAYEDh0i0JACsTtUGsVsIUghonRTzcX4emglerVe6vcxaV7DBqOZ3VvCG195L0NJXQkjdPjTo8UKvVxnpauDolwO12IxAIYGJiAlNTU+whp0G1hUIBsVgMsVgM9Xr9TO4vdVXbsFem2+2iWq0OePGOg2Pzf9lsNiwsLGBmZgZutxuyLKPVanGy5rOME6vVik6nw1ni1BEQOL3VNwRZ8k6nE5OTkwgEAvijP/ojBAIBLC4uwuv1wmazodPpIJlM4rPPPsPe3h6i0Sjy+fyAp0Wj0XA5G5VsU+XO9vY2PvvsMzQaDZhMJsiyzHkhRqNxJAbhqZslOhwO2Gw2yLLMSd61Wg2JRAK5XI7j+Oquxc+qZFM3HAyHwxzCU3eXpPLRUTSQD+Og/XGWwyeE+mJA3UbPamhpOPQvyzJWVlYQCoUwMTEBt9vNHhnyajabTcRiMaRSKSQSCWSzWT60x1l2lBJA09Jp9hQA5PN53LhxA7FYDOvr60ilUqjVamN1MXpeNBoNLBYLj5FROyHa7Tb29vawt7eHWq12bK/h2JJ9ZVnG7OzsgCFD8UQKBwwfJCQQ4LFBQyWAozL4kGKBVqsVi4uLmJubw09/+lNEIhF2OZK1T4bM/v4+EokESqUSK1Ya0kYj4i0WC7RaLWq1GgqFAvb29tDpdLCzs4O5uTm89957bMgYDIaRSGIlQ4aqAWw2G+fHAOAqJZILJe+SITPsTSHPHc3TsVgsCAQCPKCMGuaRZ2Kc80VILsNdM0/zejhuKNRLhgztx7MoE3WOkCzLOH/+PKamphCJRLjLOt2edTodjzuJxWLY39/nIazj7n2gZq40gsBqtbKOoSal8Xgcjx49QqFQGKjWOSuQnrFYLFyFq74ktFotboRH44eOg2PzyJC1bzabecFTx14qwVLfDkipdDoddv2PWsmo+s3LZDKwWCw8BJNctoVCAcViEQ8fPsT+/j5yudzAID9JkuD1euH3+7GwsIDp6WnodDqUy2V0Oh1MT0+j0+nA5XLB7XZDr9djd3cXxWKRB0qe9mFu6uRb8shQ4jIZIqVSCdvb2ygUCtjZ2UGpVBoIuw0/H60TqkDxeDyYmJiAy+UC8NgAyuVyfJskr8xpltNRIdkcdEk4q6hlQQn3VG1CeUNndQYVeVxMJhOXEwNPkvAbjQbnN6ytrSEej6NcLo/VnhmGLoB6vX5ggrPRaOR2Bs1mE5VKBYVCAaVS6UwOYlW3fLBYLAiFQpienobL5WI9Tg1e6RJ5nGf5sRkylOntdDo5XFAoFJBIJFCpVPjNH3bvUq15vV4fuWm95E2pVqtYX1+Hoii4desWMpkMXC4XTCYT7t69i3v37iEej+P+/fuo1+uc9EzW7fz8PN555x0sLS3hO9/5DlqtFu7cuYN+v4/vfOc7WFlZYbnt7+/jiy++QLlc5kP6NM/2oPeSGuEFAgEuFyY3drPZRCKRwOeff45cLoe9vT1eFxQyO8iQoY1lMBgwNTWFq1evDiTn7e3tcVVYs9k8ljLA14UwYp4NrQ2/34+5uTkeA0Kzcer1+qn3YL4qhhPiKeHe4XBwbgzti3Q6jdu3b2Nvbw//9//+X754jasnS53fSXKZmpoaaAlB3cOTyST29vaQy+W4Kdw4yuQgKKqg1+vhcDjgdDqxsrKCN998E36/ny8KlNtYrVYHdPdxcGyGzEFGSqfTYdc+Va1QOTFVKlGCJ3kWRlEpq2vnKV+F/l4ul5FMJpHL5fhGqD6EyAVOMVnyVNjtdpYnVTVRszgqRx6F3I/hqiIa906eFHUui6IoUBSFLftnGTFqLw0NdfN4PJwoTUmKlUoF5XJ5oCR7XFCHk86KQj0qVGZM4Wp1mBE4W2McjEYjXC4XN6Gk/A91N9per8fT0ylp/LTn3r0s1LyUPN50GafBkKS/s9ksj0cZ9w6+B0Hhfq/Xy9VupMdp3WQyGWSzWVSrVTQajWMtrjjWYnf1iyblQQ/VarVgs9kwOzuLqakp2Gw2GAwGFItFRKNRHn9Obs5RXCTqkeZUHpxIJPDVV19xNY76MFUnqlKvGa1WC4PBgIsXL7Lx0u/3eX4FKWIKxVG2/WlF3YRLlmVMTU1hdnYWdrsdWq0W3W6XZZNOpzlkRonQBxkx6psUVYZdu3YNq6urXK2UyWR4hhXN8hrFNXUQtM9o/tbwnJyzjlo+er0erVaLwwKkj57VP2XcoDUfDAbx3nvvYWJigpu8UYd1yn+hnMZSqYRSqYRyuTx24Vg1pH89Hg/ee+89TE5O4tq1ayybVquFe/fu4Te/+Q12dnawu7vLF+5RPaOOirofl9vt5uauy8vL8Pl8A97vX//610gkEtjc3OQUiuPy5p1o1x46iNX5DHa7HXa7nRUJNTIb9fHn6sRddXIhNZWi6hkAvAnIi6X+P/TvVqsVwJOEYkroJQ+G2htzWt3k6k6ZBoOBS69tNttAS+ter8fN8NS5Us9aC+qYtsfj4am0sixzN1eah3LcLs7XBclAPUSU8o1GKc/sOKB9RY3K1B7SUcvDe1HUnlBJkviyFAgEeCwITXFW6yP1RfQsyIkKTgKBAI+RoapQMoDj8Tiy2Sx7wcc11DaM+myh3Cqv14tAIMDd+Glf1Wo1pNNppNNp1Go1DuWPXPn1Qa7u4cPa5/Ph6tWrmJiY4Bt5tVpFNpsdKNUalUVCsUODwQC/349QKMTVAPTv4XAYi4uLyGaz2Nra4hCbTqeDx+PhMjbaJAQZLA8ePEAqlcLNmzc5/yYajQ4YfqdVXrQRXC4Xzp8/j8nJSczPzyMSicBsNqPT6bBnhQy/g2bhDPcsoMoLr9eLH//4xzh37hyCwSA3Y7px4wai0SgymQxKpdJYKR/yNBgMBoRCIczNzbEBV6lUkM1mUSgUnurZNO4M90mRZRlutxs+nw+VSgWKoozNGngeSB6UD7O8vIzvfe97A6GT4e91Op1YXV2F1WqFz+fjOUutVmus1hI9r9lshtlsZo+u3+/nc2l/f5+LNO7cucO5iOMig+eFLqI2mw1ut5sHQtN4oVqtxt3qo9Eod2I/bqfEiXlk1G843Y5kWUYkEkEwGITJZIJWq+XwE01dHSXUHgcySCgZijwLDocDPp+PlQHJwmg0wul0wul0wmQy8W2R5EaGDA1KvHHjBn7/+9+z5wLASCjmfr8Pi8XC06k9Hs9AWAkYvEEPL/6Dmi4ZDAb+eUtLSzh37hxnziuKgng8zt2kKVY7qp4+NXRQk6uXlAvdjBqNBhRFQa1WO7VeuuOGPFVGo5GH17ZaLe75cVagPWM0GuFwOOD1ejmkS+FI9cWp3+/DZDIhGAxCURTIsgyDwTCWidEkGxoJ43a7MTk5CZfLxdVKiqIgl8vxqJTTnot4nGi1Wq50o4iKemwDneH5fJ7TH4DjHcp6bIZMu93mZE2aG0Szlex2Ow8JPH/+POx2OyqVCprNJnZ2drCxscHtnkdpw9Athbo+lkolFAoFyLLMTZVWV1eh0+mQzWZx/vx5AIDJZILBYODOtnQoOxwOaLVatFot7qly+/ZtrK+vsxdmFJSKuiEZJVy2220Ui0V89dVXcLvduHTpEoLBIA8aDYVCuHr1KpLJJG7evIlyucw/j6Z/U9don8+HH/7wh+wO1mg0PJfpwYMH+PTTT5HL5VAqlTjR97TL7HlQG7mdTgfFYhHpdJqNZRr0R7fHswaFSWRZZoVrt9tRKBRQLpd5Ovw4rIXDUBv/tLfI4CVPjLrgYH9/nz15e3t73ARvnIeOUlXb4uIiFhYWeO4b6fN0Oo2dnR32bp5FI4aemXrEUZd6ajra7/dRLBaxt7eHWCyGdDqNSqXCeTXHuWaOzZChCh273Y5arQaDwcBl1bIsIxgMYnFxEefPn4ckSbxZdnd3sbW1hU6nM3LNheh2TM9ObfWpBT+VqU1OTqJcLiORSAAA34j8fj9kWYbFYuHGgMDjkj9aGHfv3sXdu3e5E7A6Gfo0by71jUen03G8+euvv2bjjQwZnU6HYDCIK1euIBqNYmtri6uWqNmd0+nExMQE3nzzTQQCAfzgBz+A2+1mL04qlcLt27dx584dfPbZZ6jX66yAxsEbQ6hzisiQ8fv9qNfrqNVqfHk4a5UV6l5FVqsVdrud87EAcDXcWYHWCele6o1CrTFob/R6Pezv7+P+/fv8uVAojERrhxdBrTODwSBWV1cxPz8/0Dum2WwinU5jd3cX+Xx+oHXIWcRqtWJubg6Tk5Pwer1wOp3sASdDJh6PI5VKodFocKrAcXJshkyz2USxWITFYoGiKNz5LxKJ8AToqakpAI8P6vX1dSSTSeTzeU5cBUbvtiRJElfelEol7O3todfrwWaz8YFDJdYej4cPeKpwoqxvSnhWFIW7SNI8JQq9DSfjnWbUpdWKoiCRSLD3xWazYX9/H16vlztoUkWb1WpFOp0eWBcUggsEAjh37hxcLhfMZjMAIJVKoVqt4uHDh7h79y729vYGmjGN2np6HujQpvYG6g9KKj/t6+O4ULdCoA91iwdgPNcEQe87HSYOhwOhUAgej2egoIA8LRqNZmB+WywWY2/5cBPTUYeemQw6j8fD3Y2pFJ1yzTKZDJLJ5LF2pz2t0HtOF+xIJIKFhQUEAgEeZkwDfPf29vDw4UPEYrETzSE6FkNGkiTUajVEo1H0ej1uCBcOh6HRaHDhwgWcP3+e3VHZbBYfffQRdnd3ufnZKLbAVoeWisUiAODrr7/G3t4eAKBUKsHlcnHNvdPp5P+rdv9SaWg+n8fOzg6SySR++ctfsquORjaMisdK3UeIjDxqc24ymWCz2XDhwgXodDrMz8/D4XDw1PBqtYr5+XnUajXu+0EJirIsw+fz8W2g2Wzi3r17PIvq888/55404+aJUUM3aWoaSJ5P8shQXsO4Pv9B0F7s9/totVpoNBrcbJHCvqNYUHAU1Llk5OmMRCK4ePEiJiYmuFJpuLVBuVxmz/jNmzfRbrcPrGQaZWjP0BgCCpesrq7yWJN2u41MJoNCoYC1tTXcvXsXmUzmzF0KyIB1uVyYnJzE5cuX8cEHH7CXs9/vI5PJIJfL4euvv8avfvUr7jl0UuvlWENLlCOjKArPOfH5fHwIUZ+CYrE4MNl4HOj3+9wWv9/vY2dnB61WC16vF16vl3t+0Peqy2VzuRxyuRwKhQKi0Siy2SyXEI/y1GZSALQxKLlXq9UinU4jHo+zp4U8VP1+Hx6Ph9dLt9vlXAeTyQS9Xo9Op8MNquLxOGKxGFe+0e8aNVm9CMOJ4eoD/KwpX2DQgKZSfvJkUin2OHNQuJk6rqq9urQPaX8pisJdj6m1wygUErwo1A6CElipCWm/3+eEeQrRUmHFuMriINRFLDTAWC2nXq/H+Xgkq5Oej/jKdzK5J2u1GnZ2dtBoNLCxsYFqtYqpqSk2YoxGI/L5PO7cuYPd3V1sb28jmUyi1WqNXJLvMKQom80mbt++Da1Wi1u3bsFkMiEcDiMYDHLJLKH2yCSTSSSTSQ5PUQXKqOd4qBMKAfAGaLVa+Pjjj/Hw4UMUCgXo9Xo4nU6Ew2Euh1TfLrVaLVdZNBoNZDIZ/NM//RP29/dx/fp1dodTtvyoyuuokFxarRaKxSJyuRwSiQRqtdqJxKlPG3SQU2daylvT6/UIBoOw2+0ABku1xw116Lnb7WJ7exu//e1vceXKFSwuLsJisUCj0aDb7SIej6NUKuHRo0fY2dlBPp+HTqdjnT5u8lFXclFCuNPphNVqhV6vR61WQyKRQDwe58pHag9xllDnVwUCAe6YTmuHLpK7u7tIp9MDjRNHOrQEgEMIiqKgUCjAZDIhFApx0ztFUTg5MZfLcdOccVEolBNCgw7pMKGGf9RlVI3akEmlUlwmCjwZhjjqslHfENWJqoVCAZ1OB6lUCplMBr1eDw6Hg13iw8l13W53YHhbNBpFIpHg9uGUFzLq8joK5Hmo1Wool8s8RFQdGgBOfz7Vq4bkQrqoWq0O5OGdFfr9PhRF4Yo+mjlGrQ+oUom6adOt+qxcBEg300WUijXIG04z8cbRqHse1E0laU1Qk1vKJaKBvCd94T4WjwwA7oSYz+fxu9/9DlarFZ9//jlXrdBCSSQS3HV1nBLJ1B0Q6blIWdAEWfUbrT5caA4MNcsDxnMODFVcAeBy2I8//hjb29vw+/1YWlriMmuadQI86YRcqVR4kN1nn32GfD4/UGJ9FhQwybDf72N9fR3pdJpj/vF4HAA4iRw4O0aMeq9QmPtnP/sZvvrqK558nUwmOdl+3PYWodbHkiQhmUxyFVIymYTBYOAwSj6f5wrJRCLBhQnqnzMuqKs9i8UiGo0GfvWrX2Fzc5Nz9xqNBmKxGBRFQTabHcgTOmtIksQXaxofYzQa2eO5trbGzVpfx6DnY0v2BcCW2vb2Ni8OsuZoQCS1jCfrf9wWCSkJ4PEhMmy0HQQlOp+FEj96vykRc2trC4lEAn6/H5VKBWazGV6vdyCfgdYPlfoVi0VsbW1BUZSBhMRxW0vPgtZINptFqVTiiwLdLunfz4oRQ6gP8Wazifv372Nra4vL+NUeh7OwVmjuWKFQYI+42lNHYV6qmlQf9uMI6QiqYFtfX0c8Hmf9QqFvyhs6SzpFDYVoyTlRr9cHKtmq1SpX1NL8wLEwZNRQ0itVrJBSpdDLKExsfhnUz/W8seazdhCroVlU2WwWDx48gE6ng9lsHvBsqRVQuVzmEmO1bM+i7KiqixQx5QidRVmoGT6oKYT7rGnq44raMKFJzmo9Q4UExznc7zShNnTpkqkev0Cl+ySTcZfHs6C1kM/nsba2hmw2i0QiwZ5gGp2TyWQ4hWKsDBn1BgHAN6DhA+ksHdzj7mF5WdR9PjKZzLd+P60ZOrzPIuoyY7X3T204j+tF4XmgNTLcXv8seDyHUXsb1N2ygbO7l8iQUfebUjeQPEvn00HQWigUCsjn8+j3+/j0008Hvud176kTqT981gI4y4tD8GyOsibE+hlELQ8hm0GGvXVnXT7Dz3+W5UHG/rPWyFmWzfPyOs/zEzVkxGIQfBvCuD06ByXJq/98lr0xas6Sl+HbUCfaCx4j1se3c1r1s3jnBAKBQCAQjCySuK0JBAKBQCAYVYRHRiAQCAQCwcgiDBmBQCAQCAQjizBkBAKBQCAQjCzCkBEIBAKBQDCyCENGIBAIBALByCIMGYFAIBAIBCOLMGQEAoFAIBCMLMKQEQgEAoFAMLIIQ0YgEAgEAsHIIgwZgUAgEAgEI4swZAQCgUAgEIwswpARCAQCgUAwspyoISNJ0r+XJOkrSZKakiT915P83aOAJEluSZL+XpKkqiRJu5Ik/dXrfk2nCbF+Dkesn8MR8nk2Ym8djpDP4bxu+ehO+PclAPwnAB8CMJ/w7x4F/jOAFoAAgCsAfi5J0q1+v3/vtb6q04NYP4cj1s/hCPk8G7G3DkfI53Beq3xO1CPT7/f/rt/v/wOA3En+3lFAkiQrgL8A8P/1+32l3+9/AuBnAP769b6y04NYP89GrJ/DEfI5HLG3DkfI53Bet3xEjszpYQlAp9/vr6u+dgvAymt6PYLRQqyfwxHyEQjGFGHInB5kAOWhr5UA2F7DaxGMHmL9HI6Qj0AwpghD5vSgALAPfc0OoPIaXotg9BDr53CEfASCMUUYMqeHdQA6SZIWVV+7DEAkIgqeB7F+DkfIRyAYU066/FonSZIJgBaAVpIkkyRJJ105dSrp9/tVAH8H4D9KkmSVJOm7AP4MwH97va/s9CDWz7MR6+dwhHwOR+ytwxHyOZzXLZ+T9sj8DYA6gP8A4F//vz//zQm/htPMv8Pj0rU0gL8F8G9FaegAYv0cjlg/hyPk82zE3jocIZ/Dea3ykfr9/kn9LoFAIBAIBIJXisiREQgEAoFAMLIIQ0YgEAgEAsHIIgwZgUAgEAgEI4swZAQCgUAgEIwsh5ZHybI8cpnAiqJIJ/W7rFbryMmnWq0K+RzCScpH7K/DEfI5HIfDMXLyKZVKJyIfm802crKpVContnYsFsvIyadWqz1TPq+tDr7f70OSTux9OzUcpUrsLMrnRRiWqZCbQPDiHKSjxJ4SnGZO1JDp9/u8SciQ0WjORnRL/dwvcvAKRfI0B8lULSchM4Hg+RneT8P756zvJ3FhOr2cuCGj/kx/PmsL4nm8MpIkDcjmLMrpeRheU0JGAsHLI/bTIELPnG5OxJChQ1in00Gj0fBHt9tFu90e60Oano0+9Ho99Hr9gBeh2+0OGDe9Xo8/ut3uwM8jDxb9vLOIWplotVpoNBro9XpIkoR2uz0gP/q+ceMgL9Swgdzv91kG6u9Vf6j//zjJScjnaKjlpdZRnU4H/X5/QEeNsxyehVarBfBkfZBMzjqH7Z+T5EQ9MjqdDjqdjjdKs9lkQwYY3w3S7/eh0WggSRJMJhOsVisr0X6/j1arhV6vx8/fbrdZLsNGjk6nO9NGDEEypTUlyzI0Gg2q1SqazSaAJ5ts3KBDZ/iWeNBnWj/0NVqHdJkAXp/yOS6EfF6cfr8Pg8EAq9WKTqeDZrOJbrfLcjoLMjgI9QWcLuHCkHl6r9H+Oem1cqyGjNpA0el0mJqagsvlgizLsFqtSCaTuH//PlqtFlv+9P3jgDrWbDQaYTAYsLCwgPn5+QGrvl6v86Gr0WjQaDRQq9XQbDZRLBbR6XRQr9f5e8nr0O12D7w5jivDB5Esy5iYmIDVakUkEoFOp8ODBw+QyWSgKAra7TaA8ZMLeaJ0usfbV61E1IevTqeD0WgEAP7eTqeDXq83YCyTQT0u3lEhn+dHraMcDgdMJhOmpqYwNTWFRqOBYrEIRVGwubmJWq32zHy0caXX60Gr1WJiYgJut5vXVSaTwcbGBrrdLntrzgrq999kMvG+Io+4+iw/KY7NkFE/CB3kb775Ji5cuAC/3w+/34+vvvoKqVQK5XIZ5XIZ3W537JJ/6ZlsNhtsNht+8IMf4M/+7M/YAOl0OiiXy+h0OtBqtZAkCeVyGaVSCYVCAXt7e6jVakgmk6jVatjd3UWxWES9Xken04FGoxnYSGdBuQCP15fP58P3v/99+P1+vPvuu9Bqtfjv//2/4+bNm0gkEqhUKq/ldnAcDLtujUYj7HY7ryEAMJlM7LEjL1UoFOIbtiRJKJVKqNfryOVybCQ3Gg10Oh0Ui0Ver6MmLyGfF4PCR1qtFqFQCKFQCB988AE++OADFAoF7O7uIhaLoVgssnGn9h6PMyQbvV6PN998EysrK3whvXHjBjY2NlgHnwV5EGTc6/V6OBwOGAwGGI1G6HQ6FAoFFIvFgdD+SXCioSWDwQCLxQKHwwGfzwe32w1ZltHpdFCpVJ6ZLT/K0LOo31itVguj0QiLxYJWq8WhELoN9vt96PV6yLIMv9+PZrMJo9GIRqMBSZJQKBRQKpVYsbZaradcfOMkQzUkG71eD6fTiUAgAL/fD5vNxocYyXCcIGVpsVhgtVphsVjg8/kAgD1PFouFb4xarRY2m40PalmWee00Gg3Y7XYUCgW02200Go0Bj1+32x25sJyQz9FQ6wm9Xg+DwQCfz4dIJIJAIACPxwOtVot6vY5GowGTyQS9Xj+WnvPDoGd1OBzw+/0wmUwwmUxwu90wGo1nMrxEHk+j0Qi/3w+z2QxZlmEwGKDT6dBqtdBqtVCv1/n7j5sTMWQonqjT6XjDzM7OIpfL4fz580gmk8hms2i32zAYDCfxkk4MOlwVRUGr1UIymcTe3h5CoRCmpqZQq9Wwvr6OYrGIaDSKUqkEh8PBHpyrV6+yAQgAiUQC5XIZ6+vrWFtbYxdnq9VCo9EY29uS2sXvdrsRDAaxurqK733ve7BardDr9VAUBdVqFfl8Hs1mcyzyGkiR0kFy8eJFrKyswOv1YmFhAZIksSFMciBXryzLcLvdvH40Gg3K5TKazSZKpRLK5TJarRYURUE6ncavfvUrZDKZAW/WaZefkM+Lob5Vu1wuOJ1OfPDBB7h8+TJmZmYQDAbhcrng8Xhgs9ng9XpRKpXYKzOqz30USEaSJGF6ehpXrlyBLMuw2WxQFAXBYBCFQgGVSuVMeGbooqzX62GxWOD3+/HHf/zHCAQCCIVCsFqt+P3vf4/f//737M2j0Nxxy+XYDBly6QMYuCGTW5duTrIsw2Kx8PeP202a3kBybzebTdRqNbRaLXZz1+t1FItFZDIZ5PN5/l6qxNHpdLDZbNBqteh2u7DZbKhWq6hWq9BoNMhkMhxqohvTON+aKGzgdDrh8XhgMBhQrVbZu9VqtTh/aNRRV72RFyEQCCAQCGBqagoajYYPaovFAoPBMOCdcLlc0Ol0MJvNAB4f5q1WC3a7HYqioNlsQlEUGAwGuN1u1Ot16HS6kfGMCvkcHbWO1Wq1kGUZTqcTfr8foVCIwwWkk9W37XHV089CbSjLsgy73c4XTQoznTUoGd5gMMDpdMLr9SISicDhcCAQCMDlcvFF8qQ4Vo8Mufl7vR46nQ5qtRoqlQp7Z4xGI2w2G7t8R/mG8yzoecjdVqlUUC6XUalUUCqVkE6n8dlnn2F3dxe7u7vI5/OwWCywWCwwmUyw2WywWq2Ynp6GLMsIh8Ow2+2YmZnB6uoqstkstra2kEql8Jvf/AbZbBbFYhGNRmNsbgjD+VZutxsLCwuYnp6Gx+NBu93G2toakskkUqkUh9zGKQlafRuSZRkulwvhcBg6nY7DHBQyUR/sWq0WvV4PuVwO/X6fv2az2SDLMieNu1wubGxswO12I5/Po1AovOYnPhpCPs8HyYkq/txuN773ve8hHA5jZWUFk5OTkCQJlUoFwNlu86CGWoUAj40/vV4Pk8kEo9E4cGk/C/R6Pb6Ql0ol2O12PpuuXr0KjUaD27dvY3t7G41G4yn9fRycWB8ZqgSg2zIlqRoMBrb+1d8/DptHvcDJU9JsNtFsNtFoNNBoNFAqlbCzs4ONjQ0OLZHCpQ1jtVqRyWTgdDpZbhMTEzh//jyKxSI8Hg+i0Sju3LmDRqMBRVHG9tYkSRKsViu8Xi9cLhcsFguq1SqKxSKy2SwqlQrq9fpTSdDjAB20BoMBZrMZdrv9wBshGbBUMtvpdFCtVtHtdiHLMse3yeNH3x8Oh9Hr9WA2m3n/jdJeFPI5GlqtFhaLBbOzs5iZmUEgEIDD4UCtVuM9dNbbPRyU40i6Ra/Xs2E8jrr2IMg50el0OH+McjgdDgfC4TBqtRoymQz0ej23FjnufXKiyb60IdRvvPpr47ZZ1MaEzWaDXq9HOBxm70o+n0c2m+WQEnmq1LX4lGAYjUaRSqWgKArsdjv/3el0IhwOw2Aw4J133kEikcBnn32GZrM5tiEmUrBU4dZsNpFOp5FIJDjBbJxQK0m6EFBiOCUb9vt9rq6hnDRFUZDNZlGr1bCzs4N2uw2v1wtZljn8QuEVAHypUMe0RyEBX8jn+aFLpcFggM1mg8fjwcLCAqamplCpVPDo0SO+dOn1epjNZlQqFTSbTf76OOrq50W91obbQYw76rBis9lEtVrF/v4+NBoNSqUSGo0GLBYLJiYmsLCwgCtXriCfz2N7e5v18nHtlddiyAz/fZw3BSWLybLM5Z7T09NotVrI5/PIZDLIZDLI5XJcQaCm2+2i0+lAURQAwNbWFufFFAoFXLp0CVevXoXL5cLbb7+NZDKJzc1NJJNJ9oCNU4gFGGxORbeDZDKJZDKJRqPxul/eK4U2PikQdddi+qCvkzeKEr+z2SwePXqEQqGAO3fuoF6vY3JyEk6nE+fPn+eQic1mAwCuXlHL9rQj5HM0SE46nQ52ux0ejwfz8/OYnJzE2toastksNy2l0D8lQKubl55lhtcaMD669dsgT2ar1UKtVsP+/j4AsCFjNpsRiURQLBZx5coVxONx1stqw+9Vy+u1TL/udrtoNpuo1+uoVCqoVqsDSmGcFgW9aQaDgbv6UtdMqoxQ33SehdoY6ff7rIRNJhPu3bvHNywA8Hg8sNvtqFQqaLVaJ/Kcx4VacdJt2Ol0clIi8KSD70n3LjgJqHPx1NQUvF4vlpaWMDMzw6XFzWYT+Xwe9Xodm5ubyOfznI9VLBYRi8WgKAork16vh0wmg16vh0ajwSGFbrcLr9eLfr+PUCiEbDbLuVyneT8K+RwNdYsCarh59+5dpNNpbGxsIJvNYmJiAuFwGO12m8uv2+32me/uK3iCRqNBv99HtVrlatFarcbl6bIsw+v1ol6vs/F/nG0xTsyQoQ2k0WjQbrc5ryGZTCKTyXClzjihDu2Qi9rtdsPn80FRFMRiMcTjce4DAxxsxKm9VvR9e3t7iMViSKfTkCQJkUgEH374ISYnJzE9PY1EIoF4PD6QtDeqkPKkBGjKD3K73QCelPePkxFDCZlU6vj222/j/PnzuHTpElZXV/l7qtUqH0S//vWvsb29zSWyVDpMF4der4ft7W1oNBqsra1xI8GlpSVYLBbMz88jEAjg3r176HQ62NzcRLlcBnD61o+Qz4tBOrjb7aJSqSAWi+Ef//EfYTabsbOzg1KphB/96Efwer0sl1KpxB6ZcdpjgheD1lCv10OxWIRGo0Eul0M+n0cgEIAsy/D5fJibm4MkSdyI8jibKZ6YITMcx6ZkIYpZj7PLkt5Mq9UKo9EIrVaLdruNQqHAHpnn+RkE5c60221UKhWk02mYTCa0Wi1utOdwOLgSY1SU7LOgZ6AETqooMRgMPA+mVquhVquNVYMq9brxer0IBoNwOBwwGo2o1+soFArI5/Ns0FK4kfZUu93mA5rWGCkSKinO5XJIp9NwOBxwu92wWCwwm82wWCwc5jyte1PI58UhHdJoNJDJZGAwGFAoFLjknL5H3b/pLHJWn/sw1Hmu1PKCDF0q5KGEaBrqe9yceGiJqgRIAPV6nTfOqB+4aujwpcRUn8+H6elpOJ1OSNLjDqJff/010un0kfM61DkB+Xwe33zzDcrlMr7//e8DAMLhMFZXV1GpVLC9vT3ym5GaKk1NTWFychLz8/OIRCLcNj6TyWBnZwfb29uoVqsjn3dFh4zRaEQ4HIbX68Xly5fx1ltvcc+lVCqFTz75BMlkEr/+9a+RzWaRy+VQq9We+lnAk1lC9HcarvngwQP86le/QiQSwT//5/8cNpsNbrcbbrcbZrP5VBrCQj4vB4WWqOLk9u3b/DXgcThOndA8bmNjjsqz9OdZlguFlsgjWalUoCgKXwiGc4iOmxMNLamHS9FDDk9WHTWlcBDDdfMajYZ7wlA5aLvd5hj7UbwIw/Jpt9tQFGWgssBkMsHhcHCTr1FGXRVisVi4ZwG1B6/ValAUBYqijJ1HRqPRwGq1chMuh8PBypOSVVOpFJLJJPL5PBqNxlP9c56V6K2u3MlkMrBYLFy+TB24T3v5upDP0RmutqIKFEoAVvfdEQi+DXX6xOts+XHsQyPVia4ulwt+v5/bgQ8rj1H3HBwE9bWYmJjA4uIi3G43e6UajcZAfsxRUfc4oEzydrsNu92ObrcLq9X6Kh/ltUEHjt1uh8/nY4MwmUzixo0b2NnZwf7+PndFHodGgBqNBhaLBYuLi5iYmIDT6YRWq+W1QzlWyWQSiqKwV+/bBojS1+igphCMy+UaKFU+7X2IhHxeDXTRAp7011F/nOWwEnBwexB1yO0sQh5RvV6PSCQCt9uNcDgMv98Pq9U60DfmpPTwifjG6BZDw6VMJtPYeF++DUl6PGLA4XDA6/WyO1rd7+JF3NPq71d3T+52uzwEb9TbZw97tigbnvKMGo0G4vE4z76p1+sjP2tKHZKkuWSBQICbsNHaGZ4HNNzj49tkoF6DFGJQK+bTqqiFfF4NahmoPeVnoSXGUXiWPM6ycUdrX6PRcAm/3W6H3W6HXq8/0Pg97vV0rKGl4TwR6rgJgCfK1mo1rjEfx5gjPT8l4bbbbR4jQB6UV7Up1DeGUb9J0a1Ho9GwAUzNBG02G4fUUqkUcrkcd5AcZW8MPTO12fd6vZicnMTk5CSHNqjSjVqAl0qlI3mhaE3QkMXZ2Vm8//77CAaDkKTHrelTqRQSiQT3LjotCPkcD6Snaaq8w+FAMBjkZ61Wq09dukZ1jz0vpEucTiecTifcbjc3RiwUCigUCqzHx/XsOgzaJ0ajkUc1qMusO50Ol+6r+zkdF8eeIyNJT2aaUI25JElsyCiKgnq9PpJJc9+GOi+I3vBWq4VsNssNhCg56lUpB7qVjnolGBljNNSO2l9PTU1BlmW0Wi1Uq1VuJkijL05i0upxQc9MHjy3243JyUlMTU3xQb2/v4/r169jbW0NOzs7nPfxIge1zWbDzMwM3n//fdhsNj6oM5kM4vE4FEU5VYeWkM/xQvPv7HY7AoEA+v0+J9DT/hplnfK8qNcZDUWkCeHlchnFYhG5XA65XA6KorCOP0uo+6OREUOT5cmQoX5NZAAfJyfikaFDnD4AcOkjTSsmxkUp0LOrPVGyLPOcJfIgvKh7Wr0w1J1uAaBcLiObzaJarb6SZ3ld0K3I4XDA4/HA6XRyE7xSqcQ3o1KpxLeicVg/dDiq3f0U4sjn80gkEsjlclzuSP/neen3+7DZbIhEIggEAjyTKJ/Po1QqIZ/PczfX04iQz/FAMqKk34MuBXRROiuo86HUeZ/qcBx93zjonueFqpYqlQrMZjM3xSM51Ot1ZLNZbkD5oikUz8uxJvtSvoLdbofb7eZJzvV6nTsBUrXJOLrnKCxisVjgdDrh8XiQTCZRLpefctceFXWMnmr2acBbKpXiLqajCiUwa7VahMNhhMNhTE5OIhKJ8I04Fothd3cXxWKRb1DjpEzUiZjkpt3d3cXt27eRy+U4J+h5n1sdcgwEArhy5QqWl5cRDAbRaDRwmiZM1QAAe9pJREFU69Yt7O/vc/I0hWROK0I+rx5KA6ChmepeIOO0t76N4aRvdcNNkgnJ5SwZdsCTKEu320UymUSr1eIQf6/Xg16vR7FYxMbGBvb396EoClqt1sB8vFfNiXhkzGYzrFYrzGYzTCYT58VQufA4uizpedT5QWrX24s+r/pWQK49h8PBQyk1Gg13TaZKjVFVQMMePZJhq9VCqVTiBF8qHx0nN/8w9L5TI7ejdsKm/282m6HX67mBHPU1op48VKY8anN1hHxeHvIc09RvKs1We4/H7ZkPgyrgaD3Rez6cFH2WZAI83ZyV1oX6Q11FexLr5lg9MnSjjkQifKv2+Xx8o06n06hWq2g0GgNu4nFBHUO0WCwDlUQvWsI5HFLyeDy4evUql8EZDAYkEgncv38fmUzmlT7PSaF+Rq1WC4fDAZfLBavVyt1W7969i42NDWQyGTSbzbGLUx+0D17G+KXJ6nNzcwgEAnj//ffxwx/+kBPQC4UC7t27h93dXWQyGbRaLfb0nUaEfF4d5LWiCjCXywWNRoNGo4FkMolUKoVarYZWqzXSOWjPC3n6er0eSqUSJEniixPwxANOOmdcSvGfF/WzUhhSHYokbzqljqgNnePiWA0ZdUKQ2WyG0WiE0WhEv9/nPBFy2Y2bEUOo4/n0Rqs3wvM+8/Ai0Ov1PMHW5/Px3KFOp4NarYZSqTSSAyOHS67VidIkM/LIqMNz5I0aFw7a9OrY/PMeoPRztFot9Ho93G43AoEA/H4/vF4vAHDidLFY5Gq6066YhXxeHeoKFIvFwk3x1JUnFAI/a7kg5HGgj4P6yZxF1PuGjBg639QyOykZnUjVkvpByYKlwW0UWqJDfZw2Cj0njTxXFAVGoxGBQIDnttCsoMPecHU+DMWtl5aWsLi4iHPnzuFHP/oRAGBrawuFQgHRaBTlchntdnskD/d+v88Hi91ux9TUFGZmZrjqK5/PIxqNIpfLAcDYxe/JQ9Dtdnl/kMvf4XDA7/dzfsZhSoJuRiaTCVNTU3C73fjwww9x7tw5rv7K5XLY2NhANBrF7u4uEokEe7hOq0yFfF4NpHPo0KE+TZSsWSwW2XM+jqG0Z6GuWqJBvx6PB263m1vx02y3RqPBobizgrqilAx/t9sNp9MJnU43kP6g/nycHHuODPB0wyVSRCdpsZ006mQx9WwprVbLSc8mkwn1eh0AnnnbIdkM59wEAgFcuHABS0tLOHfuHCqVCm7fvo1YLMb5McP5NKMAyYEMGeoI7XK5eNhmrVZDsVjkuTmj8mxHgZQFHdhqedhsNu6Ore42e9DaIYVDYYPl5WVcvnyZez+0Wi3s7+9zZ+Sj9l15XQj5vDpIT1CSr7oKjA5udR7juDz3YVDxCY1FsVqtsFgs7AVWX8THrcjgeVDnv8qyDIvFwhXJ6rPspORyrIYMGS2NRgO1Wo0Tx7RaLaxWK2w2GxwOBxqNBj8w3bDGYWH0ej3UajVotVo8fPgQFouFBx9OTExgZWUFqVSKFcYw6jAUheaWl5fh8/lw9epVXL58mX92Op3G9evXkUgkkM/nRzpc1+/3YTabEQwGMTMzg5mZGUxMTKBSqaBSqWBnZwd7e3solUoAxkex0vvd6XRQLpeRy+Wwv7/PngaLxYLp6Wm8+eabsFgs2Nra4n01XMYvSRJkWeY+GO+88w7nqZlMJjQaDZRKJWxsbODTTz/lydD0s07j2hHyeXmGLzfUk8dsNg9UltJUcYfDwRcGdZ+mUX3+ozAcVqL3naILzypRP2sMJ/MOJ0OfBMdmyKi9L9Q7hUZ907A3anRmMBg4u5luWaMeYqJnr9Vq6PV6WFtb4/4Uly9fRiQSwYULF2C327G9vY1ms8nPrfa+6PV6GAwGbgP97rvvckhpaWkJOzs7+OKLLxCNRvHVV19xYh71YBm10BI9v9lsxsTEBCYnJzE9PY1gMIj19XVks1ns7OwgGo2i3W4DGJ8ptGpjvlwuw2q1IplMwmg0YmlpCWazGdPT09Bqteh0Ovj8888hSRIfrsNeAlmWMTs7i0gkgnfffRfhcBihUAgmkwmlUgnJZBIbGxv47LPPUC6XUS6Xj9Q87qQR8nn1UIWWzWaD0+mE3W7n2zW1jaBLFnnRab+NiwwO4zBDhj4ET3thTnptHKtHRj3q22w2843aYDAgEAhgcXER3//+91GpVJBMJlGr1bC3t4dWqzXSm0RdkkdhtFQqBa1Wi7m5OcRiMXQ6HZw7dw4+nw+tVgvFYhHVanWgyZbRaOTZQj6fD2azGXNzc3C73SgWi7h58yZ2dnZw8+ZNZDIZVCqVp0oERxWj0cjxaZPJBJ1Oh2azySXX6mZn44R67TSbTUSjUfR6PVy4cAFer5fj0pOTk7h69SpKpRJSqRSazSZ0usfb2WAwcBXKwsICfD4ffD4frFYr78G1tTWsr6/j4cOHA+vutK8bIZ+Xh0K3BoMBLpcLV65cgdvtxuLiIrxeLzweD3q9Hl+gWq0W5wypW2a8SLPBUUF9EaW+X7VajT15NpsNdrudL+pqA+8sonZcUKI4hd9OgmP1yFDTnFQqxbHmdDoNq9WKc+fOYXZ2Fm+99RZyuRw++eQTpFIpKIqCfD4/8i47tcJtt9t4+PAhdnd34XA44HQ6EQqF8Cd/8ifodDp49913UavVuFkeYbfb+Ybo8/mg0+lQLpfRaDTw1Vdf4euvv2aPDHm71LeGUcZqtWJmZgaTk5M87ZpmK5VKpYHeMcD4KdNer4dKpYKvvvoKOzs7OHfuHLv+p6am2PVfqVSwubmJRqPBrm5ZliHLMjweD+bn52Eymbiq7cGDB0ilUvj1r3+N3/72t6hWq8jn85wTMCrKWMjnxVAnslIi/V/8xV9gZmYGXq8XFouFS2itVivm5+fhdDpRq9VQKBSwtbWFfD7Pya7EOO0/0p+9Xg/ZbJbHyhQKBR7+6/f74ff7odPpkEwm0el0xsLIfVHo2amPU7PZ5NYqJ3GxPtHQkqIo3NKYZi51Oh0YDAau3hnHWzYALoXOZrPY29uDJEmYmpri8nQAHGYjN6bVauWMeKrJT6fTqFQqPPU5m82iVqsduQHYaUOdcChJEqxWKzweDxwOx8BNmob5AeOlPA+CcqzK5TLS6TQSiQRCoRAsFgtPfrZYLGi1WnxQU4Ki1WrlMIFGo0GhUECr1UIikUAymeR5X1RKPIoJ90I+L4765kyFCJ1OZyAkbTQa4fF4oNVqB6q4yEM16uH/b0OdUE5zAS0WC1ffHmen2lGF9mOhUBgwZI5bTsceWur1eigWi2i329je3obNZuMQSa/XQ7VaRTabxd27dxGNRlEsFsdmc1ByIvB42jd5UqLRKObm5pBOp2Gz2RAMBmEymXimUKfTQafTQaPRwP7+PhqNBjcPvH//PivtRCLBvXjo96k/jwrq5oDksp2bm8O1a9fg8Xi4hwxNIL5x4waPiz+sKmVUoXXT7XY5ZPib3/wGGxsbeO+99/Dd734Xer0eCwsL6Pf7WFpaGvj/pGjV84c+//xzZLNZrK2t8fqpVCojGYYU8nlx1AnTiqIgnU7jyy+/xN7eHs6fP49AIACPxwOv1wuv1wu32412u42LFy9yNSQlUI97FRM9V6/XQyqVwtbWFvR6PXw+H8uR9Pu4GbpHQZ0ztrW1hZs3b+LBgwd49OgR6vX6iQzzPVaPDL255G0plUrI5XIolUrsnaEmU1Ta2G63x2pjqOOGlC9EvSui0Sh7YSwWC4DHU3ep90y1WuWZMbFYDJVKBbu7u0gmkygUCigWiwCebiI3iqgTnCmHgXKsaMKsuhHZqD7n80LrhnKeMpkMNBoNstksisUilzxS52P6P8BgyT81covH40ilUuzFq1QqXB04irIU8nk5+v0+X5aoA7jH4+HDmfYgNZo0m81oNpswGAxjKY9nQeusUqkgl8uhUCigXC5zQQVVw50lmRwEyaJSqSCdTiOXy7HBCxx/btmJlF9TCOnGjRvY3d3FzZs3EQqFuOlSqVTC9vY2FEUZ2SZuh0HWO4XZOp0ONjY2UCqVuE+K0WiE2WyGTqdjF2+r1eKBW+TmLpVKPOtF/fPVn0cN9SKnZ7x79y7+y3/5L9zVFwAqlQoajQbW19efms81qs9+GOp1s7u7i1QqhXa7jXg8DofDgXA4DKvVikgkwiE3jUaDaDTK3k1qjnjv3j2+PNAaHPV9JuTzYqirv0qlEq5fvw6TyYQ7d+4MJLKqc0Uo7+H27dvY399HvV4fW/kQVJ7e7/fxxRdfYGNjgxPDo9EoEokE6vU6h07GUQc9C/VFotFocCuE3d1dPHr0CPv7+5yzeRKyOZHOvuSRSCQSyGQyiEaj3NWWDuVyucwJU/T/xomDkqEoqdlsNnNDL1Ic6vh1t9tFo9EY6K+jdmuOg6zUypVCauSJUf9bt9tllz8ZyePs1qXnI++bTqdDo9GA2+1GPp+Hw+HgSgqj0QiNRoOtrS3cv38f+Xwem5ubqNVqiMViPEQUeBJeGXWEfI4G7SXaP41GA9FoFJIkIR6PQ6/X86gC+n51J+VSqcSH97jqajVarZYN5Xg8zsOPqc8QeWTGca08D1SdVK1WUSqVkM/nkclkWDYnpZuP3ZABBg8pdfIUHdbqGn31948j6rwZ4LFLrtlsQpIkLjtX54yoZ1GpK5HGVUZ0+6O1oX5O2hjD7dLHVRaE2vNQqVQQjUaRyWSQSqVgNBrx8OFDnr0FPE4oz2QyqNfrKBQKLM9xXT9CPi+G2qgBHntD6fJEHceH9dG3jVMZV6jdQ6/XQ71e5zzGsygLSnTudrtcafvzn/8cX3zxBba2tpBIJLii7aQ8VSdqyFA45CCL/qy45tTVXMATQwZ4OmFMHXIZ/vO4oc5foBHw1Wr1QINlHMpgjwq9/+VymTsaPytf6CzKTMjnaAwbMcN66KDDeVhPj6MeehZktKjLic/KmTWMOuSYTCYBABsbG/xvB51Zx82JGDKEOmeG/q7+fBY57NnPslyAp42+sy4P4PlkoP6esyYzIZ8X49vkIOT09LoRMnnC6w7xn7ghc9ADn8UFcdAzP0sOZ0k+z3PInCV5DCMU6OEI+RwNtWfmeS+WZ1G+4tJ9MKdFLidqyBCv+6FPG0IeTyNkIhCcHGK/CUYZ6awlKgkEAoFAIBgfzk52m0AgEAgEgrFDGDICgUAgEAhGFmHICAQCgUAgGFmEISMQCAQCgWBkEYaMQCAQCASCkUUYMgKBQCAQCEYWYcgIBAKBQCAYWYQhIxAIBAKBYGQRhoxAIBAIBIKRRRgyAoFAIBAIRhZhyAgEAoFAIBhZTtSQkSTp30uS9JUkSU1Jkv7rSf7uUUCSJLckSX8vSVJVkqRdSZL+6nW/ptOEkM/hiP11OEI+hyP21+EI+RzO65TPSU+/TgD4TwA+BGA+4d89CvxnAC0AAQBXAPxckqRb/X7/3mt9VacHIZ/DEfvrcIR8Dkfsr8MR8jmc1yaf1zL9WpKk/wRgot/v//9O/JefUiRJsgIoALjY7/fX/9/X/huAeL/f/w+v9cWdAoR8nh+xvw5HyOdpxP46HCGfw3nd8hE5MqeHJQAdWgT/j1sAVl7T6zltCPkIBMeH2F+HI+RzOK9VPsKQOT3IAMpDXysBsL2G13IaEfIRCI4Psb8OR8jncF6rfIQhc3pQANiHvmYHUHkNr+U0IuQjEBwfYn8djpDP4bxW+QhD5vSwDkAnSdKi6muXAYhEsscI+QgEx4fYX4cj5HM4r1U+J11+rZMkyQRAC0ArSZJJkqSTrpw6lfT7/SqAvwPwHyVJskqS9F0Afwbgv73eV3Y6EPL5dsT+Ohwhn2cj9tfhCPkczuuWz0l7ZP4GQB3AfwDwr//fn//mhF/Daebf4XFZaBrA3wL4t6K0bwAhn8MR++twhHwOR+yvwxHyOZzXJp/XUn4tEAgEAoFA8CoQOTICgUAgEAhGFmHICAQCgUAgGFmEISMQCAQCgWBkEYaMQCAQCASCkUUYMgKBQCAQCEaWQ3soWK3WkStpqlar0kn9LlmWR04+iqII+RzCScrHYrGMnHxqtdqJycdsNo+cfOr1+onJx263j5x8yuXyichH6J7DGTf5CI+MQCAQCASCkeXUdrXs9/ugHjeSJA18FggEAsGrhXTuQb3FhnWw0MVnF/UaGV4r6vVxkmvkhQ2ZYSPjVUPCEhtGIBAIjh/1odTr9fjPBxkvQjefTdRr5CCj96B1cRLr5IUMGbW19ao7A9MG0ul00Ov16PV66Ha7A/82btCC6PV6z7R0h1G/BxqNZuDzOENyorXwbXLSaDTQarUAzt4t8iA5abXaE78tnTbUe4z+THvnLMtFr9dDo9FAr9dDp3t8NEiShF6vh2aziV6vh06n85QeHleZka4BMKCb1aiNPNpb44okSdBoNKxTJUmCwWAYeOZut8sfrVYL/X6fz+/j3GNHNmRICdKL6Xa7r9Q6J+Wi0+lgNpvR7XZ5E9FiGqfFojZgSEk8j3FI74FGo2GZjPsBRXIhhfosN7ga9cE9zrI5CFIiw2FarVY7dvvoqNA+o4OK1sdZlAs9s16vh16vh8VigcViAfBYLp1OB6VSCe12my+WJCv6nnGFzh31pWAYjUbD64cuTeMInTc6nQ5GoxE6nQ6yLA9coFutFlqtFhqNxlOGDP0M9V57VRzJkFG/CDpAXxX0swwGA7RaLWZmZjA7O4tCoYCdnR20Wi2Uy+VXbji9LtShM5PJBK1WC4vFwreib4OMnk6ng1arhV6vx59HXTbPgtae0+mE2WyGJEl8c3wWzWYT1WqVbwlnabaYyWSC2WzmG1S/34eiKGi1Wq/7pb0W1N4XnU4HrVYLg8GAfr+PRqPBxs046JfngZ5Tq9VCp9NhenoaXq8Xfr8fPp+Pv6/ZbCKZTKJer6NYLKJer6NcLrM+bjabAMbLoOn3+9BqtbBarQMH90G02210u10oioJmszkgh1GUyUHhIgCQZRk2mw1OpxOTk5OwWCzweDy8h3q9Hmq1GqrVKiqVClKpFBqNBrLZLFqtFsuJjMJXKZvnNmTUt35yP5J1/rKbn6w2SZJgtVphtVrx/vvv48MPP8SDBw/w85//HIVCAdVqFe12e6RDKOqEuk6nA4PBALvdDovFgvn5eXg8Hlaw9P1qSMaKoqBSqaBarSKTyaBWqyGTyYyt1wp47F3RarUIh8OYnZ2FyWSCzWZ7yrond3i/30cymcT9+/fRaDRQqVTG2tAbXis2mw0zMzMwGAyQZRm9Xg/37t1DKpXi7x9XWQyj9krpdDqYTCZYrVZ4vV50Oh3EYjE0Go1Xos9GAfUz6nQ6WK1WvPfee1hZWcGFCxdw4cIF/l5FUXD//n2USiXs7u6iUChgbW0N6+vrUBQFjUYD/X6fPROjjDrsqNfrEYlEIMsyIpEI7HY7n3/AYz3TbrdRKBRQq9UG9taoeoHVXkqC/u5yuTA7O4tz587hxz/+MZxOJ+bm5mA2m1nf5nI55HI5pFIpPHz4EPl8Hjdv3kSxWEQ6nUatVkOz2WQvzauSzwvlyAznc7yqF6TRaGC32+FyueDz+eD3+5FIJDguNw6oDUGTyQSj0YiJiQnIsozZ2Vl4vd6Bm+JwzgzJQVEU/pBlGYqioNPpoFarodFooNPp8O8bdUhJ2mw2mEwmRCIRzM/Pw2g0QpblgXwt9Z9pA25tbZ0pdzgA9vT5/X5YLBa43W70ej0kk0lUq1U0m82xvEl/G/1+HxaLBS6XCw6HA+FwGPV6HZlMBq1Wa2w8vs+D2iNMMvH5fHA6nbDZbPx9Go0GPp8PJpMJ7XYbNpsN1WoV1WoV2WwWpVJpIHww6pButtlsmJ6e5nVClybgiR5vtVpwOp2o1+uoVCowGAyo1+uo1+vodrush0cFnU434GHp9/tseBiNRtjtdlitVg4/ms1mmEwm/v8Ufuz3+6hUKrBarSgWiyiVStDpdCgWiygWi69cLs9tyNAhQAuWPr+sJ4Y+U17M6uoqlpaW8NZbb2F5eZkt3FFP9KVnNBqNMBgMCIfDuHr1KtxuNy5dugSn04np6Wk4nU72PKhzHEjeOp0OkiSh0Wig0WigXq+jUCggnU7jF7/4BZLJJNbX15HNZkf+hqROgtbpdLhw4QImJyfx3e9+F++++y4MBsPAJgLAMepOp4Nut4vPP/8cm5ubyOVyKJVKY31IqY1drVaLYDCIDz74AB6PB+fPnwfwWEnfuHEDe3t7iMViZyJJcfgyMDs7i6tXryIQCODcuXNIJpPY399Hs9nk3CtgvA089b6amJiAz+fD+fPnceHCBTgcDnQ6HX5+g8GA+fl59Ho9LC8vo9Pp4Ny5c9jc3MS9e/dQLpfZsFHnHI0apG9cLhfOnz+PcDiMn/70p/D7/XC73TCbzWi322i323zgUyJ0s9nE/fv3kU6ncevWLdy5cwflchnxeBy9Xu/UX8Tp/XI6nZidnUW/30e5XEar1UIqlYKiKHA4HJicnITH40G/30er1UKxWITBYIDRaIRWq4XRaITP54PX68XCwgKazSbefvttVCoV3LhxA7FYDLdv38bt27c5LQJ4+UKVI3tkhpXCyy5YddhAr9fD4/EgHA5zHoROpxsIYY0ylOUtyzI8Hg8mJibg9XrZgAmFQgNWf6/XYwuXFCzlEFksFrTbbTSbTciyDIPBgGAwiF6vh2g0OvJGDEH5DGazGV6vF5FIBOFwGJFIBFqtFiaTiUNJhNoAcrlcMBgM35pLM07QbdFsNsPj8cDv9yMUCqHX68Fut/O+Gmej7llQ+Nrv98Pv9yMQCKDdbkOv1585WQx7ZCisr9frBy5RAGA0GiFJEicBk1cvm81ClmX0+332QoyqHEln0voIhUKYmJhAIBCA2WyGXq9HrVZDvV6HXq/nPD3g8cWpWq1ClmXkcjkkk0n2JI/KJZzWgtvtBvBYHvV6HblcbiBU1ul0oCgKut0uJ4iTTtFqtQMRB71ej1AoBIfDgVQqhX6/j3g8DlmW0Wg00G63X8m5/kJVS8Cria/TZjEajQgEAnC5XLh27RrefPNN2Gw2jrdlMhkUi8WRzG+gRWw0GqHX63HlyhVcuXIFk5OTeOutt2CxWOBwOKDRaFAqlZDNZpHP51EoFNjb0mw2kc/n0e124fP5YLVaIcsyKx6PxwO73Y4f//jHKBaLnLhYqVSgKAqA0SnNHq6usdlsuHTpErxeL3784x9jfn4ewWCQn6fdbnNVRafT4ef3er3weDycAD0qyuRFUV8wjEYjrFYrJ+ZZLBZO8qW11Ww2x8bYPQy1kqTndbvdmJ2dHbgMUDLiqF+WjgpdrignD3jsbW+32yiXyxzadzqdnM9Ht25ZlqHX69nz8Ktf/QrVahV6vX6kqncoFOLxeOD1enHp0iX8+Z//OVwuFwKBAHQ6Hb755hukUik8evQIjx494nCTy+XCG2+8AbvdjkAggHA4DLvdjsXFRXzzzTfIZDJs9J3G80vtSNDpdAgGg3j//feh0WiQSqVQqVRQqVRQLpexu7uLZrMJo9GI3/72t7weyJghA0av10OWZQQCAdjtdqysrMBqteLKlStYXV1FKBTC7Owstra28Omnn3IqxMvsvRe+pr7KcmvaKD6fD+FwGDMzM5z9rCgKqtUq6vX6yB1Gwwl1FFJaXV3F5OQkzp07B71ez7HUfD6PUqmEWCyGRCLBmd+1Wg37+/vodDqYnJyE0+mE1+vlD1IyCwsLqFarCAQC2NnZQbPZHMlbN71m8irMzMwgEolgeXkZCwsLHF4DnihdSjpMJpNQFAVarRYOh2OsYvffhjpES7dsuhU1m02O3ddqtZGL3b8K1GvK5XJBo9Gg0+lwGFKdHzNqe+YoDOc20iFGlah0waxWq0gmkxx6pCo4vV4Pq9UKu92OWq2GpaUl7j0zajoaeBJmM5vNcLvdiEQiuHDhAmw2G4xGI1qtFuLxONbX1/H111/jxo0bcLvdfKmamJiARqOB1+tlD7Db7UapVILZbEar1TrVulhdvWa32zEzM8Pe7mKxyF64UqnEl+RerwetVgtZlgc8MWQUu1wuTE5OIhgMYmZmBlarFeFwGBaLBZ1Oh9fa119/zeffy8jntfjbabNoNBqYTCZ4vV5897vfRTgchsFgQCwWw97eHqLRKO7cuXMsWc4nAS0OvV6PixcvIhQK4Y033sDy8jKsViuazSZXAJRKJc5toY9Wq4VqtYpWqwVFUdDr9ZDP5/nGTeVvs7OzsNlsmJ+f5ySrUQ3F0SFitVpZqVy7do3DcGTEkPLpdrsolUq4c+cOJ5JRuG16enqk1suLMnwwUe7HuXPnMDk5iV6vh83NTWSzWUSjUWQyGTQajYH+F2cBWjPtdps9dWov3lkoS1cf2nR5fPfddxEOhzE1NQWHw4FyuYxisYgHDx7gF7/4Besvt9uNa9euwWKx8M/R6/VwuVwoFAqw2+0ol8sD5bWnfW2pCyqcTidmZmY4xK/RaLC3t4disYibN2/i3r17iMViaDabKBaL2NzcRD6fh81mg8/nw5/8yZ9wiNJms8FqtfIB/yojGSfF8PtHlx911a26Wo3OO61Wi0KhgEwmA6/Xy96Zt956CxMTE3C73VhZWUG73caDBw+QzWaxvr6OWq32wvl6p8KQcbvdePfddzE1NYV6vY5EIoHbt2/jxo0bSCaT7JYjRmEhqK1ck8mE8+fPY3V1Faurq1hcXES73Ua9Xkc2m8Xnn3+O/f193Lx5E/v7+1yNNPzzgKcbELrdbkxPT8Pn86HVasFsNrPRM8qGjMViQSgUwszMDN544w1MTk4OuKzVh1K5XMadO3e4b0G328XMzMzrfZDXAK2JUCiES5cu4dy5c4hEIigWi9jZ2cHe3h729vaQyWT4Fj7uqCvV6DP1XKILAhky7XabD55xhYwMqv6bmprC22+/jUgkgsnJSdjtdhSLRWQyGTx8+BA///nPYTQa0Wg0EIlEsLCwgEgkAuBJibLD4eBKJ1mWuU0GMDq6ut/vw+FwYGpqCsFgELIso16vY29vD4lEArdu3cKtW7c42bfZbCKXy8FsNqNer8Pn82F5eRnBYBB6vR42m417OI3aelLnw6jfP/JaqqHKRzWkmzudDkcLwuEwJiYm4PF44HK5EA6H0W632Tjc3t5Gt9tleR317DpRTabOf6DGZjMzM5ienobH44HZbMbu7i5SqRS2trYQi8VQKpVG9lA2Go2cuLywsIDZ2VkOd+TzeWxubiKVSmF9fR2ZTAb5fJ4PYvUC0uv1cLvdXKWj1+vZ+6DT6VAulwEADx48gNFoRDqdZmUyCrci4IkyoZK+YDCIixcvYmJigptSkULodDrcv2Fvbw+pVArb29soFosIh8NwOByw2+1nIgdEDR3CdrsdwWAQTqcTwGMFVCwWUSgUxqos/3kYzpHRaDTs0dRoNAMXhnGWibqijTwG09PTmJiYgMvlgt1u56ZuiUQCDx8+RDQaZc9VPB5Hu93G3t4ebDYbl69TngSVblO+Q6vVOvXyJJlQAYXT6UQgEIAsy2g2myiXy9je3kYsFkOxWHzqEkkHbrlchkajQTQahdfrRTAYhN/vh8lkgs/n43VG+ngUoNwpqrKlKtrhc3j4edShWY1Gg263i1QqhXa7jVu3bqFWq2FycpKN4cnJSQCA3W7ns+9FcolO/EpGnhitVouJiQn85Cc/QSgUwvT0NHQ6HR48eICvvvoKW1tb2N7eZq8GMQoLgd5wq9WKd955B5FIBO+//z6Wl5fZmt/Z2cHPfvYzJJNJfPnllyiXywOGHt2W+/0+ZFnGxYsX4XQ6MTU1Bbvdjjt37uDOnTuoVqtIpVJIp9NcTttqtQbcfqMAGauyLMPn82F1dRV/+qd/CrfbDY/HA71eD+CxPBqNBhRFwdraGv73//7fyGQy+Oabb9DtdnHhwgVOKFMbP+OKWrno9Xq+/Zw/f56rDxqNBhKJBKLRKOr1+tjL5FmQF4pCAcViEfl8/nW/rBNBndSp1WrZ1U8hJbvdjlKphGKxiFu3buGjjz7i9dLv93Hz5k3Y7XZMTk6i2WxiZWUFbrebPae9Xg+Li4swmUzcrJN+72nU2eQ1oDC22WzGxMQElpaWYLPZUKlUkEgk8Nlnn2Frawv7+/tsnNGhTp/T6TSXF1erVXznO99BMBiEw+HA0tIS9vf3uTPyaUZ96aXK2E6nw8YM5ZMd9n6qjTytVotOp4MHDx7AYDCgUqkgEAjggw8+wPe+9z0AwNWrV+HxePD555+j3W5zHs5RL6EnZsioG5TRjcjj8SASicDpdKJcLqPT6SCbzSKXy6FarXJS0CjdrNUWqV6vh9Pp5A1Pb2Y+n0c6nUYqlUI2m+X4vDo+SM9MyYmBQAA+nw8TExNwOp1IpVLs3RmeQzWqHixCXXJtNBoHPDHdbpc7R8ZiMSSTyYEqHJoVQ4bPWUGj0XAlm8Ph4ETFTqczkOh7lpKfCdoXZOhZLBbIsoxarcYXi4Num+ME7SGbzQabzYZAIAC/3w+XywWtVsv7qlgsIpVKIZPJQFEUlkur1eKmb+VymZM+1aW2brcb1WqV9+yo5IXQeSTLMlfhNBoN1Go1lEolVCqVp0Jl6pAlFRxQ1Sk1J9Xr9QgEAuh0OjCZTKjVagMFIKcNynuh107NRikKoM6LeZ7Xry5N7/f7KBQKAIBsNotCoQCj0QibzQa73c5hSfWaO3WGjPoN7/V6CAQCWF5exptvvok/+qM/QvX/396b9dZ5bumBz57neeAeOA+iqMGiZNmyPByXU3WOg4qRAgIEqTT6oi9y043+AbnIVSO/IEDfN9BIGkiA7iRIlcvnuI7rWJZlTdZkkuIk7ol7nmfusS9Ua+ndWxRFUbTEb+t7AEE2tUnub+33Xe9613rWs2o1fP3114jFYixg1u12uSXwJH7o+2E4o2I2mzE3N8dk3G63i+3tbdy5c4czT+RMiSQmEllJuTYQCODP/uzPMDU1xXVslUqFWq2GUCiEWCw2MCVcKvYSIXYhkcIq3R7JCVcqFdTrdfzwww/405/+xGU5qq0aDAZYLBbYbDbWvRhliOtNo9FgaWkJExMTOHfuHCYnJ9FqtZDNZpFKpfgPzYIZdduIIKdotVo5qzA/Pz/Qjk4D7kYNlHUgLaWzZ89ieXkZp06dwocffgi9Xs+HzLVr1/D48WM8fPgQjx494n0FgNdNMpmE2WzG5OQkf41GPZw/fx5utxubm5tIp9MsjXBS1xp13rjdboyNjWF6ehqzs7MDnaOxWAy7u7sDpFaCGMy0223s7OygXC5jamoKpVIJVqsVX3zxBcLhMB4/fgzgaefPSZM+oMsvlQTL5TL0ej1Onz6NXq+Hb7/9lkv3zWbzld83/fxwOIxIJAKn08njDRYWFqBSqbCwsAC1Wo1SqYRCoXDomYOEN1paooPJZrNxhsFisbB6YCwW47kV9NqT8mEfFiLJl9oUqR5PHTaJRAKZTAalUgmtVuuF3SP0c7RaLex2O1wuF1wuFx/WZrOZBeHod4vfK2VQNkpcAzQvKZ1OIxQKsfQ18UKopksqk8Mkz1HCfi20NpsNXq8XNpuN5eRJwmB4xsm7Bgr2aH3o9XoW/BIHRo4SxPVP/sjhcLDIG6mINxoNtFot5HI5xONxZLNZVCoVzoyKLdmNRoOlDujmTj/bbDazOKcUMjL0voh7SJIFpPtC6unNZvPAg5XsQ8M06XKqVqvhdDpRqVRgMBjYLic1K0OXYVK4NhqNUCqVLOMg0h1e9b0TJaDX66FcLrNOmk6nYzFGauWm178KftVARuwSoInWDocDX3zxBT7//HNoNBqsrq4iFovh7t27iEQiKJfLz7WsSQViSyKJKxGbn1KUq6ur+Omnn5DL5ZjU+6IbMnXlNJtNlMtllEoleDweGI1GqFQq3hzDP0NqdgOecWR0Oh3sdjsTdo1GI/N+1tbWEAqF8PDhQ+zs7DAXiFr4SYcoGAxCo9Fwh4FUW9FfBrFkYjAYMDc3h+XlZfh8PigUCuTzedy9e5cJ9DTjRIp76zhAB26n00GlUkEul+PypJg5GCXbUFaFbsHnzp3Dp59+yllLktiv1+usSFutVp/zJ5R9SCaTPKogn8+zTIKUbUaXRVEBvNFocMnxZZkBsg/poVBmg/YllXttNhuPSTkp/kgs//R6PRSLRcRiMb4I6nQ6BINBnDp1Cr1eD5lM5kifNVUp+v0+8vk81tfXYbPZ+CLv9XrRbDafa+w4LH71jIwYfdL4gfn5eZw9exb5fB5bW1uIRqOIRqMs+iZVR0vPSkQp6sDR6XQc5adSKYTDYZ4YC+zvOMlx0OaguUoiiZdEqER7SdFuwODsF7odEcmMarOUiUkkEkzSJCci1ripA4M2J4CRzESIxHmtVssjHKxWKwCgXq8PiCs2Go13rqwkgmzV6/WYA1EsFll3adQgElpJBJAEytRqNdRqNSsa7+3toVarDfBBgOfb16vVKndK1uv1gZK4VEEkcHHGHZXFgOf1VIYhZp4oq0GlceJlkU87Smvxm8DwpZm6+SjT63K5WD/oqKBnr9frXJGgyzz5bsqm07o77Lr61QIZMTvhdDphsVjwySefsEhXtVrFzs4O/vjHPyKZTLLE/ElMub0K6HARW9fUajXa7faAWrGYjRmGyJNpNpssTlWtVuH3++F2u/l3SF0LZLibghQhPR4PtFotWq0WNjY2UCwWmVuUTCY5Yid7k3IylQ6IE0ICcOKmGQVQ0KxWqxEMBuF2u+H3++HxeKBSqVCpVJDJZLCzs4NkMjlAzhwVGxwWlBEmgUWDwcBZiFKpxATDUbKNqNNlMBhw7tw5nDp1CtPT00xmTafTKJfLePjwITKZDLa2tpDL5dBsNvct69OFgkoPw+3IlI0YLtOdVJuK8/3Gx8dhsVjQ6/VQq9UQjUYRj8cHgrrDQvTrtE9PeumS3hvNjCIh1m63C71ez632r3M+0/fVajUkEgkmlQPg7Aw1xRCf9rC/71c5BcUPT61W83C2jz/+GJcvX2bBt3A4jO+++465DqMiSCVKNQ8HMvTsVBJ40YdEqcpWq4VSqYTHjx+jXC7jgw8+GKh5k9LtSXUWL4MYyNCoiomJCbjdbmi1WlSrVayuriIej+Pu3bt4/PgxP784aZeCGeJBVCoVhEIhDmTq9bpk1EZfBrFkq9FoEAwG4ff74fP54Ha7+VZFgQwdTlRmkPrzvwrEbkkS4hIDGbp9Sv0CJULM9Or1elgsFpw9exaXL1/G1NQUtFotyuUyYrEYUqkUrl+/zlpMuVyOM1f7gVSRaZitKFRKmQg6gE7ywQ08C2SoG5QCmWq1yiXHVw1k9ltD4n496aBxL2IgQ11d1Hxz1M9VDGSazSYHMhQoAU+76ugC+yoXz2MPZMSbotlshtPp5Lq9w+EAACQSCWxtbWFrawuVSoXLLKPiZMUUI2WZxDQjRbb02pc9M7U/Us0WACsDi62jUrWdSqWC1+vlMQvz8/Pwer3MoI9Go4jFYqxYTBhuNad/G745SsGpHhbic1AZbmxsDH6/n1OzlUoFhUKBLwhixuFdwXAHoVar5QNLp9OhXC5zEENaKaMQ5BLoEmm1WplzRiRw4GnZcXd3F8lkErFYDJlMhruQRNDeoT96vR5ms5lvzgBYUiIajWJ3d/fEj0gRfSVdKEn0jXDUYOykPvPLIPJ8aFBkrVaDwWDgsuvrnjNkU6PRyOMxqP2fEhlHXTfHHsjQAW4ymeDz+TAzM4N/8S/+BSYmJmAymdDv9/HLL7/gv/23/8Y6KmKr7SiAAg/6YGjcucFgYIY21aWBF6dexcU1XL+mydjkNCh9LiXQglWr1VhcXMTs7Cx+85vf4De/+Q133KRSKSasZjKZF8pYi1lAkqCnyP8kO9WjQCynWa1WnD59GtPT0xgbG4PJZEI0GkUkEuEbd61W4/UxKgf1YSB2pZhMJiwsLODixYtot9uIx+NIpVLI5XLcJTkqgZ54mQwEAvB6vQgGg/D5fOyDC4UCHjx4gN3dXTx48ACFQuG5TIx42aKypNVqZf0Zm82GTqeDVCqFeDyO27dvIxqNcus18HJ+ydsEZXJJW4i0UsQ/rwKxhLRfae0kry/67InHaTQakcvloFar+bL8upk28sNutxsLCws4deoUpqam0G63EQ6HudvrKOq+xxbI0AOSOJLL5cLc3BxPazYajSgWi1wfI+EgKaTbDgvatESc2tvb44BGrVbDaDRy6zS1wx5mUYi3yuFbg9RBNrNYLLDb7dxS3mq1UCgUkM/nUalUDuQVkYYMtblTqx/pzohZm5PqVF8V1HJN64oG1BGZLpfLoVwuP0dafJcgkuKp5dpoNCKdTg/wpqTA53gViAe00+mEx+OB2WyGTqfjvVGr1VAsFlEul7n7jw5yEWRDaloYGxvjsQbEZajX65yVyefzA8M3pWBPcR7SMGH3MBCzeaKQqfjz6Gee5AsVnV3UWEIZf7VaDZPJxC3YR2m9Bp4FciSGa7FY+HdWq1XW2DkKn+hYAhnqrun1evB6vfD7/VheXsa//Jf/EjabDW63G51OB3/4wx/w6NEjrK2tYWtrayDdLYUF/zLQ4UJRrcFgQCaT4eyUyWTC4uIilpeXWTPnMARMUoj0+/08iOwk33QOAzFFqdFo4PP5MDs7C5fLBZVKhVwuhx9//JGnoFPb3/CNsdfrQavVYm5uDpOTkzCbzeh0Ojx4NBqNcolJ6sRo4PmBqzTSgWa6NBoNhEIh3LhxA6FQiDvdRmmfHQYiR480mChQvnfvHq5du4ZIJDLAm5K6bcRMDJXSPvroI0xOTvIYgnw+j0QigZ2dHWxsbCCfz6Pb7fLeoEOE/BKtMxr495d/+Zc8A81ut6NeryMajWJnZwdra2vY3d3l9yMVew53HbVaLS6tvEo3G2VJ6Y9SqUSr1eKRKi9r8nibIN/aarWQyWRgNBqZv+JyuWCz2bC6ugqbzXaoagLwbD2SDUkheHJyEu+//z4mJyfRbreRz+fZV6fTaR5R8NYE8RSKp1OLaXDW5OTkgDBXPB5HKBRi8iXxRk7ah/q6oM1AN596vc4OwWq1wu12o1KpDBBWD4LYBTVq5QFKaZIqr1qtZuY8lR4bjcaA+jHwLBAigi9N4FWr1SzqVCqVmP8wShCfXWxXp9sN3bbJEY9aNuqwGO7c0Wq10Gg0qNfr3CkpZW7ZQSDfSiNS6DZNonaiUKJImqe/aV/SyA+PxwO32806TTSUlTgV5XKZfd1w5vikQyybiNmAV/Ub1GZNDR7ilHXiOB5lIOKbgNgpS++VKgqUySRulMjNfNmzUIBEsYFer4fD4YDL5YLJZGLSfbFYRC6XO7KNXiuQEetlxGp+77338Pnnn2N8fBwGgwHlchm///3vEY/H8dNPPyEUCqHZbA4IuY0KxEOWiKrUNu3xeOByuTAzM4PPP/8cNpsN6+vrvPn36ygRa7XDM6ekbDt6JpVKxbM2vF4vfD4fWq0Wtra2sLKygh9//JEVIMWNRn9bLBYOmJeXlzExMQGDwcDqo1RaGrVABgAHb06nE06nEw6HAwqFAnt7eyz2Jk52fpcg7hu6ARJ/iPghOzs7PMTvJHMXXhV0uFB2gMqONHus2+1y6YBKS8Qjo/lcpOlhs9ng8/ngdDpx6dIlOJ1OnDlzBi6XC+12G9lsFuFwGD///DPi8TjPi5NS00a/3+dOLOKRGY1G+Hw+zuK+iOQqcvz0ej3Onz+Pubk5tlEmk8Hm5ibC4TBrX7Xb7RNrH5EWQVkkmlbtcDhw4cIFVuC/d+/egFzKsI+lki5lBo1GI86dOwefz4fLly/j8uXLqFQqbJ8HDx4gGo2iUqkcqXz12hkZehCSGg4EAjh9+jTfjhuNBlZXV7Gzs4NIJIJsNjtSxN5h0IdKDiOZTLIMOAA4nU7Mzc0hnU7DbDbzDelFh61YXzypG+CoICImRftWqxWVSgWlUgnJZBKRSASVSoVVV8XupH7/qeqk2+2G1+tFIBBgRVu6AZGM+qiBgkBRVt1gMLBTplvOYdK/o4bhfaRSqbi0RByQer2OQqEw0twhuglTlyTdimmmDh3e1OYq8hupo8TtdmN+fh5utxvLy8twOBxwOp0wGAwsJJjP57G7u4t0Oj0wm0kKEEsf4qGs1Wp5iOGLzikxuKHMld/vx9zcHLxeL0wmE2eUM5kMl6petWTypkEdnxTMkLaLx+OB3+/H1NTUc/OW9iMAU8ZYp9OxqjQ1JSwuLmJiYgKRSASrq6tIJpOIx+NIJpNHUvUFXiOQoTdOm+W9997DxMQEzp49C6/Xi3w+j7/7u79DIpHAysoKUqkUGo2G5CL2o4Jux5ubmygWi3j//fe51czj8WB+fh6fffYZkskkbt68iVKpNDD4EQCXXGw2G2ZmZjA5OQmj0cjTVml2jhQzDpRloiDGbrfD4XAweVCn03H6Wq/XD3CJnE4nq0SfP3+e2/gUCgXW19eRSqWwvr6OfD7P2ZyT7DxeFXQg7e3tDRxIw2XHUd9jB4EOGIPBAL/fj2AwyINW6fYtxX1zFIjrgFL7s7Oz+PM//3MmdQJPL1kmk4mDGKvVypksr9fLJPxms4m1tTU8fvwYGxsb2NjY4InPUih906WIDm2aMUWDC81mM8bHx7lJAxjUI6L/73a7MBqNmJmZgdvtxrlz53Du3DlYrVaW+r979y6SyaRkVLXJV3Y6HUSjUQDAmTNn0Ov1EAgE8MEHH8Bms3GLdiqVQrvdhtFo5GnWNpuNW6wtFgump6dhsVgwPz8Pp9MJs9mMQqGAWCyG+/fvY3d3d9/W/1fBkQIZseZON8OzZ8/i4sWLmJ+fh8fjQSQSwTfffINkMonV1VVUKhUWcBt10AfSbDbx5MkTZLNZhEIhjI2NsQrr3NwcPvvsM4RCIS4/kVom8OzWTYHM9PQ0pqennwtk2u22ZB2yQqEYmEXicDj41kKDMklGXSQfLiws4PTp05icnMSHH34Io9HIJLT19XWsrq5ifX0duVxupMicIsgJUz271Woxh+pdh8gh0uv1zOtQqVSo1+vvVCAzvOZJfGx2dhb/5J/8Ew6EFQoFy9ATeZza1mk/9vt9ZDIZ1Go1rK2t4bvvvkM8Hsf29jYHMVK6MJAfJQJ0rVaDSqXiQKbRaHAWT+RtiJkctVqNubk5BINBnD17FufOneO5eLFYDD///PORp0a/DdCZ3m63EY1GmQPV7Xbh8/lYj6hSqSCbzeLevXtoNBrwer2wWq0YHx9HMBiE3W5HMBiE2WzGzMwMlzh1Oh1PuKZAJpPJsH2OaqPXysjQdE9608FgEPV6HQ8fPsTjx48RjUZRKBQkl3I8TtCcpCdPnkCn03H02u/3MT4+DpVKhYsXL8Lv9yORSLBWTLvdhs1mQyAQwMTEBMbGxuBwONBqtZDP55HL5XhKrVRBraCksFosFqFUKplL9Mknn3B6k6BSqXiteb1e2O12KBQKtkUoFMKTJ0+Qz+dH+rCiWrZIJlQqlfx1se36XYSY2rZYLLBYLCiXy8jlcvu2XY8K6CAgwnsul4PZbIbb7WYxPApmJiYmBi5P1KJNN2oKTDqdDvL5PJrNJnZ2dnjo3+7u7sDMMymi3386P6pQKKBQKPCoHJokv7CwAKPRyNld8imkC+bxeLC0tIRAIACLxYJutzsg+FoqlZinJ5XLFGmXZTIZ9Pt97OzswGq1wmw2c8bu/PnzKJVKsNvtaDabcDgcMBgMPLyXZnsRF7bdbqNcLgMAYrEYdnd3sbm5iUwmg2KxyBfVo+LIGRmSR5+ZmcHY2BjOnz+P9957D3/84x/x3Xff4cmTJ7h//z6nvN+FTIwISl+SRsO1a9ewsrKCcrkMs9kMh8OBixcvYmFhAVqtFplMBjdu3GDCU6VSwfj4OC5duoRAIIDFxUU4HA7EYjEUi0U8efIEW1tbrMUjhQ0igjZLuVyGQqFAIpFALBaDx+PBzMwMgsEglpeX9xWXog4u6qgoFoucorxx4wYePHgAYDBzOEog29XrdZ7fVavVAIB5aVRCode/KxDJ8SSt7vP54PV6cevWLR6iSVkI8kujZCMKZqvVKkKhEFqtFhwOB9RqNXcHWiwW+P3+575X7AQkPk2j0cDKygqy2Sxu3ryJSCSCnZ0d7Ozs8PdIkS5AJdpcLodut4tYLIZoNAq9Xo9gMAidToff/va3SCQSuHPnDg817vV6PBNufHwcX375Jfx+P3Q6Hfb29vDw4UP8zd/8DRKJBBKJhKRGg4gSIpubm4jFYpicnEShUMD777+P5eVlmM1mzM3NcbaGkhoqlYr1mihGaLfbKBQKzEtrNBq4d+8e7t+/j2g0is3NTbTb7dcu/79WRkY8VMTWz2w2OyA5TOluKXyQvwb6/T4fNERibbVacDqd6HQ6cLlcUKvVmJmZ4dRbpVLhlLjX6+WyQaPR4H+nNDlBKvYV3ydlD0gQiUSSiKC4H4mMWkO73S637SUSCcTjcVSr1YHuiVHFcIuoqNdA2Zh3PSNDFygqae/t7XGXzqi2XdMz0SFC+kuxWAz9fp/LRvtJX1AQSONPWq0WdzdFIhFkMhkkk0lkMhkuhR80l+mkQ6QA1Go1ZDIZxONxOBwOLoP4/X5oNBpks1nodDomBrtcLkxMTMDn88FsNkOtVqNYLLKGGAl5khieFH0RzT7MZDLY3d3l80ij0cBsNgN42q0MDHbRNptNni1IDS/NZpNVtKPRKFKpFBPuj0OV/rXSJETWNBqNqNVqSKfTCIfDPOCQonuppNSOG2KUSbNvfvjhB4TDYSwsLKBYLMJut2Nubg56vR6nT5/mtrdarcZiZ8QX6XQ6iMVi2NraQjgcRjablVTKchgU+FL6krQuPB4Pq7FSuUQEHdrJZBIPHz5EMpnEN998wxOviXAoRZscBuJnTgc21bVpNAOp+gLSCXCPC8QvI99E0hC5XA6hUAjFYnHg9aNiH5HD0el0UCgU8P3338NgMGBjYwNutxsff/wxPvroI/YtdAGlIKbX6yESiSAUCqFSqSCZTCKfz+P69esoFArIZrMsF0Et3VK0n+ibc7kcCoUCbt68iVqthqWlJfzTf/pPodfr8emnn6Lb7eL8+fMoFotsJyr7k6+pVCr4/vvv8eTJEzx69Aibm5uc9ZMCAfpFaLVaePDgAba3t5FMJhEOh+F2uzEzMwOTycSZK+oQzWazyOVyyOfzvIaocpDP55lGQOVdAMcSCL92vYfSj9SKR215UiE3/dogGxApl2rKRqMR8XgcnU4HMzMzzDei9tBGo8H1faoxUjYmn8+jWq1ySu4offdvG8M3x3K5jGw2y+tHp9MBeCaYN5zFIcG7eDzOKVwSVBrlIAYY7LoYbh8le9JN512D2BY7nC2gDAPZalRBz0Zp/Wq1CqPRiHq9jpmZGWSzWbTb7YFMuqjRlM1mkUgkeH8RGZYGkFLGU6qZGMJ+vnl3dxdOpxOFQgEWiwVutxs6nQ4ulwsGg4FtRPSAbreLXC6HSqWCRCLBEiP7CQ1KEf1+n7OYiUSCOTE0bodGypBoKWXtqMGFeIvVahXFYpHF9Pb29ljr6DhwpJ9CUWar1cL6+joikQiSySQsFgs2NzdRLpcHpJil/EEeF6hMQtwFSrU5HA48fvwYdrsds7OzcDgcrA9SLpe5bT0UCqFUKuGnn35CJBLB7u7uSIwqAJ4Son/55Re+Cf7888+wWq3w+/2wWCw4deoUE6T7/T4ikQi/9saNG+xwiYw3CjZ5EcQAsFarIZ/PY21tDaVSicuPNPpimFv0LkD87ElTiFpEc7kcisUiE6NH1SbicxGZl9L5tVoNKysr0Gq1sNlsHMgAz4LAdDqNdDrNUv2tVgu5XI4zfFK8OB0EysyQYF02m0UsFoPZbMbExASr1ZvNZjSbzYHSSb1eRzgcRrlcxubmJgcxo6BRRO+duiI3NjaQTCZhMplYLZpoEe12G71ej6fKEyeG1hCVuskvHfcaOnIgAzzdJJlMhmfjaDQaVKvVd1KI62UgB0tBTKPRQD6fh9VqRb/f5xlDnU4HDoeDpdTJ+RLZbnNzE4lEAo1GY2Scca/X4yi+Wq0ik8nA6XQin8/D5XLxrC7aBLFYDOvr6wiFQlhfX0ej0eDg+aiCSlICHT6UpUun0wDAIx5IYHGUsw6HAWWrKpUKlEolc8pIYPFdAKXvqZxWr9cRi8VYTp8gZu+o2WBYKA4YLMmMCuh5KBNeKpV43tDc3BysVivOnz8Pr9fLox2KxSLi8TgHMFQ6Ib88SrxQWgOZTAbZbJbL/qQcTWNlAKDRaHCZibhoBHGg5n78x9fBkfM6lN6mNDYxsylql7E/REdAM4WIHZ7JZGCxWGAwGHhGFRGmEokEms0m0uk0p8dHYZMAz2r6vV4PhUKBnzMej8NkMmFzc5NF8QAgk8nw9OJarca2kHIt+qioVqu4c+cO80BUKhV2dnbe2SBGFPSqVCqIxWL4H//jf0Cv12N1dRX5fJ7T2u/CWqFnJJ9Dc++os00ErRlS+xVLI2IgM6oQyc5EjyAFcRr+22q1WPWWJofTxG8irY5aRnj4eai1n85+sdRNwe9+BOdhYvmxvseDfqDJZHrpbxtOS75tCeZarfbGVpDZbD7ypyHemMXb0H4bYLhE8Dolu2q1eiLtI3I+9utUGn4tff24pzq/SfsYjcbX2s3DIxvexK25Xq+/MfsYDIbX2l/0t+ibfu0DudFovDH7WK3WV7aPSOzdz/cP+5bjtle5XH4j9jmqbz7IDx2E42hBP6m+WcRwt+R+A0fp7+PeYwfZ57WZNqMUeb5JiB0GL7Oh+O+jzDs67DO9C7fDV4Vsi/1Bt0XZPk8x7Ete9JrDvG4UcZRnfdfsQ/uJLgfD++tt2OPYApl36cM8LoxqQHIUyOvo1TFcNhj++rsM0Qaifd5128g+5+WQbXQwTqKvfrfkdmXIGEGcJIdyEiHbR4aM0caBHBkZMmTIkCFDhoyTjNHqo5MhQ4YMGTJkvFOQAxkZMmTIkCFDhmQhBzIyZMiQIUOGDMlCDmRkyJAhQ4YMGZKFHMjIkCFDhgwZMiQLOZCRIUOGDBkyZEgWciAjQ4YMGTJkyJAs5EBGhgwZMmTIkCFZyIGMDBkyZMiQIUOykAMZGTJkyJAhQ4ZkIQcyMmTIkCFDhgzJ4o0HMgqFwqlQKP4/hUJRUygUYYVC8T+96fdwUqFQKP53hUJxR6FQ7CkUiv/rbb+fkwZ57RwM2T4HQ7bPwZDt82LIvvnleJvr521Mv/4/AbQAjAFYBvA3CoXiQb/fX3kL7+WkIQ7g3wP4EoDhLb+Xkwh57RwM2T4HQ7bPwZDt82LIvvnleGvr541Ov1YoFCYABQDn+v3+xj9+7f8GsNvv9//tG3sjJxwKheLfAxjv9/v/y9t+LycF8to5GLJ9DoZsn4Mh2+dwkH3z/njb6+dNl5ZOAejQg/4jHgA4+4bfhwzpQV47B0O2z8GQ7XMwZPvIeB281fXzpgMZM4Dy0NdKACxv+H3IkB7ktXMwZPscDNk+B0O2j4zXwVtdP286kKkCsA59zQqg8obfhwzpQV47B0O2z8GQ7XMwZPvIeB281fXzpgOZDQBqhUKxIHztAgCZTCbjZZDXzsGQ7XMwZPscDNk+Ml4Hb3X9vNFApt/v1wD8vwD+D4VCYVIoFJ8A+CsA//ebfB8nFQqFQq1QKPQAVABUCoVCr1Ao3kZn2YmDvHYOhmyfgyHb52DI9jkYsm8+GG97/bwNQbz/DU/b19IA/h8A/6vc3sf4dwAaAP4tgP/5H//7373Vd3SyIK+dgyHb52DI9jkYsn1eDNk3vxxvbf280fZrGTJkyJAhQ4aM44Q8okCGDBkyZMiQIVnIgYwMGTJkyJAhQ7KQAxkZMmTIkCFDhmQhBzIyZMiQIUOGDMlCDmRkyJAhQ4YMGZLFgX3wJpNJci1NtVpN8aZ+l9FolJx96vX6G7OPw+GQnH0KhcIbs4/NZpOcfUql0huzj9VqlZx9yuWybJ8D8KbsI/vmg2E2myVnn2q1+kL7nBhBn+E2cIXijX2mMmTIkCHjFbCfbIfss2W8LbyVQGa/TdDr9fi/aUPIG+NwIHv2+330+/0B+8k2HH2nS587/bf4uY/Sc8p4exDXF/0t+hrgmb+R19y7DYVCMeCTDnrdceFEZGTEhyYj0H/LOBzIhrLtBiFuplG0zXAQIz7vKD2njJMD0deIe0r23TL2w4suksOB8OvgjQYyvV5v34dSKpUwmUxQqVT8mk6ng06nA0DeFPth+MBSqVTQ6/VQKpXodrvP2XDUMHxDfNHXFQoFlEol2wgYXIdSX1sKhQJqtRoKhQIajQZKpRKdTgfdbhfdbhedTmfAYZAt3kXQ597r9dDr9QayVmQXOaPwDGQn4FmgQmtMo9FAo9FwMNPr9dBsNvl7yL7vgi3Jl5DfVSqVUCqf9tG8C88vgtYC+VvR7xJofRwma3NYvLFAZj8HQl9XqVQwmUxQq9Xodrvo9XpoNBojewgfF3q9HjtgjUYDi8UCtVqNVquFbreLRqOBdrs9cpvpoCwEOV76ukqlGthQCoUCrVaLbSd10GevUqlgMBig0WjQbDaxt7eHVquFvb09udz4j3jRJYmCQaVSyWvkXQftn06nw4EeBTBarRYGgwEmkwn9fh/dbhftdpv/Jju/K3YkW9HZRWvoXXl+YLCcRDag/aTVaqFUKtlP0/6jixZ9/+vgVw9kxAjNZDJBo9HAbrfDarUCeOpcDAYDZmdnodPpkMvl0Gg0EAqFEIlE+GcA7150+yJQ1K9Wq2GxWDA2Ngaz2Yy5uTlotVokk0lUKhXEYjHUajUAo2G7g7IswLPMnkaj4ddrtVqYzWao1WrodDr0ej3s7u6iVCpJMutHNlCr1dDr9TAajZienobRaITNZoNer0elUkG5XEaj0UCxWESr1UK5XEa73Uaz2US3232nHC0dqnRZMplMMBgMAJ461V6vh1qthk6ng729PXS73bf8jt8Ohi8IWq0WDoeDfbZWq4XT6YTBYIDT6YTT6USv10On00G9XsfW1hYqlQri8ThKpRL/nFFdZ/RsFAQ7HA5otVq+SPR6Pc7SjKoNCHSBVKlU0Gg0sFqtCAQCvIbU6qehRr/fZ/uUSiXs7u7yvnud7MwbCWQ6nQ5UKhVcLhdcLhfOnDmDpaUl9Ho9tNtt2O12fPbZZzCZTFhdXUUymcTf/d3fYXd3lxcCYdQXxMtAC0aj0UCv12NqagqfffYZfD4f/uzP/gwmkwk3b95EOBzGn/70J4TD4X3Te1KDWJNXq9VQq9XQaDQwGAwc2KjVaszMzMDhcHAAbbPZ4Pf7odVqYbVa0W638e2332JzcxOVSgXtdhuAdNYV2UCr1cLlcsHv9+Orr76Cz+dDIBCAxWJBKpVCOp1GsVhENBpFuVzG2toaO45yuTyQqRpl0DpQKpVwuVywWCxYWFjAwsIC2u026vU6arXawCFcLpffqUAPAGcUxGDGbDZjcXERVqsVp06dgtlsxtTUFOx2O6ampjA1NYVut4tWq4VsNovvvvsO8Xgcv//971GtVtn2gHT212Ehkp11Oh20Wi3m5+fhdrsRi8Wwu7uLVquFWq32TlzEyR46nQ4GgwHT09P4/PPPYbfbMTMzA51Ox68pl8uoVCrY2NjAH//4R1SrVeRyObRarSNnyX+1QIY+PKVSCYvFAq1Wi4mJCfh8Pvh8PlitVv5gbTYbzGYzjEYjrFYrms0m356AwY6mdx1kM4PBAJvNBo/Hg/HxcXg8HphMJmi1Wg4QR8FuYhBLNx+z2Qyr1Qq9Xg+LxTKQnZmcnOSbIgBYLBZ4vV5otVqYTCa02234fD52tJVKRZIORq/X814aGxuD1+uF3W6H2WzmLJPBYIBSqUS5XEa9XkepVEK73YZKpUK73eYgblRBB43BYIBWq8X4+Di8Xi+mp6cxPj7ON8FqtYput4tyuYxSqYRqtfq23/obwXCnm16v5xKlTqeDz+fDzMwMLBYLxsfHYbFY4Ha7YTabodVqOZsFAFqtFmNjYwAAh8MBk8mEvb2950qbowaNRgO32w2j0Yjx8XG4XC5UKhUkk8kB8vOog7hTLpcLbrcbU1NTGB8fh9VqhdfrhV6v55Kj0WiE2WxGLpeDwWBAq9V67d9/7IGMuDl6vR7MZjPOnDkDj8eDr776CqdOnUKlUkGlUoHRaITH44FWq4VCoUCz2YTNZoNarYbH44HNZkO9XucHHdXNcBiIqTulUolgMIhTp07hwoUL+N3vfgetVsuH1cbGBtbW1pBOpyV9sxSzMEqlEna7HUajEefPn8e5c+c4fUmZBaVSyQEdAM7S6HQ6/vdOpwONRoNYLIa///u/Rzwef8tP+ero9/sIBoOcibl8+TKXaoGnAUwgEOC92Gg0EIlEUC6Xcf36dYTDYWxvb+PJkydcGiD7jArouYxGI2ZmZuB2u/Gv//W/xoULF6DT6ZgYT2sil8shn8/jP/yH/4B8Ps9kaWA0/Q5lYAAwp2FiYoIz5qdPn4bL5cLs7Cz0ej0cDgeUSiXz7tLpNNbW1qBWqznwuXjxItrtNsLhMDqdDnZ3d7G7uzty9hMzLHa7Hb/73e/g9/sxPT0Nu92OXq+H7e1tzoKPciDX7XbZ71osFly9ehVXr16Fx+PBwsICdDodTCbTvhwZhUKB27dvQ6FQIJVKMQVFfO1h8auWlhQKBbRaLTweD98efT4f2u02MpkMBzD9fp+5HMDT6F6j0UCtVg8sBhngbgG73Q6fzwePxwOn0wkAyOfzKJVKKBaLKBaLaDabb/ndHh1i6pZKSVarFTabDT6fDxMTE1w2UqlU/FqbzQadTsdEs2ECZ7vdhtvtRq/Xg8ViectPeTRQCtflcsHpdMJisXC2qdvt8u2IyJlEbq5UKpyJKBQKiMfj/D2juL+ICO1yuTA2NoaJiQlMT09zpoCIiL1ejwNep9MJq9WKer2Oer3+th/hV8FwqUOtVjOXYWxsDOPj45ibm4PdbkcwGOT9R2uoXq8jn89jd3cXOp0OFosFdrsdFosFCoWC+TO5XG7gd47CYS7ajrJXfr+fs320FylLfJydOScRIlfT7XYjEAhgenoaDocDTqdzwPfS33TuWywWGAwG6PX65xqAXhW/SkaGHK3VasX4+Dh+97vfcTr/1q1buH37Nu7cuQOz2YyxsTHmLxgMBly8eBF+v//pm/vHUsK7DJERr9FosLS0hPHxcXz00Ue4cuUKzGYzWq0W8vk8vv32W+zu7uL27duIxWJoNpuSs98w4dBkMuHs2bNwOBz44IMPuDw5NjbGWb92u82k1s3NTS5NGo1GaLXagbZ0AFxmELMYUkOr1UKhUIBer+cuNUrdit0AjUYD/X6fy7dffPEFqtUqxsfHYbfbkUgksLa2xm3aUofIITKZTBgfH8e/+lf/ClNTU5iZmQEA/PGPf8Q333zDh5HL5cIXX3wBi8WCTz/9FPPz8/jxxx9x48aNkWrVB561vmo0GphMJuj1egSDQdhsNnzxxReYm5vDxMQEgsEgvzaZTOL7779HNptFLBZDsVjkrLrT6cT09DQmJyd5n547dw56vR7NZhPb29tv+5GPFbS+zGYzPB4PZmdncfHiRYyPj6NaraJer2Nvbw/tdpuzDqMEhULB2U6NRsOZmC+//BKnT5/G/Pw8FhYWAAB7e3uo1+vY2NhAo9GA1WqFTqeD3+/H2NgYjEYjpqamoFar+TVHxa+SkSFCpslkgsPhwMLCAvx+P37++WfEYjGsrq7i5s2bMJlMcLvd0Ov1cLvdsFqtmJ6ehtfrBYCBfvx3EWJpBXhqD7/fj7m5OSwuLuLs2bPcMVAul7GxsYFQKIRYLIZ0Og2VSsU8IymCiHTBYBB+vx8XL17E/Pw8DAYDDAYDGo0GCoUCkzbr9ToikQhKpRJsNhtnZyjz0u12OYVusVieuwlICZ1OB41Gg7uQxAOXDiAKaIinplKp2BaFQgHpdBr9fh9bW1sj02Ehtt2bTCZ4PB5cuHABs7Oz7IS3trbwzTffAHi6pyYmJrC0tAS1Ws2HciQS4RT3KBFWRfIzccyCwSDcbjeWlpZw+vRpzqhUq1Vks1lUKhXcv38fkUgEOzs7yOfzzLGiCwVJZxBXptPpwG63v+3HPXaQ/dRqNWw2G5xOJ4LBIILBIJ48ecLdgbQnRxFiJ7LZbIbL5cLi4iIuXboEr9cLt9vNHZOVSgVPnjxBpVJhfpXRaMTY2Bg0Gg0cDgdqtRpnkY+KXy0j43A4cPnyZQQCAajVatTrdayurmJtbQ2xWAzA01tlsViETqdjUS9yQETQe1dLS3SgkMOhG/RHH32E06dPY2JiAv1+H7lcjm26vb2NeDyOZrPJZRWpYLi12ul0YmZmBh6PB59//jk8Hg8CgQD0ej0ymQyy2Syy2Sy2trZQq9WQTCbRbDaRy+XQbDZhNpthNpuh1+tht9thMpkwNTUFs9n8XHeGlEDvu9lsIpvNQq/XP9dC3u12sbe3h0wmg62tLajVavh8PhgMBiYmBoNBfP755/B6vSiVSigUCtjZ2UGj0ZB8NxN1q505cwZzc3OwWq1QKpW4efMmIpEIHj16hGazyXssk8ngT3/6E3w+H65cuYLx8XFMTU3hzJkzKBQKiEajkj+UKNC1WCwwm83w+/04f/487HY722h6ehoWiwXNZhPRaBTxeBwPHjzgrF02m0W1Wh0QNOv1elyqo7Zag8EAh8MBo9H4th/72EHPTIew1WqFWq1Gv99HIpFAOBxGMplkAjkwOtpNYhBnMpngdDpx9epV+Hw+LCwsYGxsDCqVCqVSCfF4HPfv30cmk8G1a9dQrVY5kGm328yNPXfuHGw2G+7evQsAKJVKXEl4FZv9KoFMr9eDw+HApUuX4HK5oFKpOMV08+ZNrj2TYJdOp+MWNqVSCaPRCL1eD71ez4tEiofOUSE+KwV2S0tL8Pv9uHLlCs6cOcP112w2i3v37nEgQ3VpKR5GROpVqVTweDz44IMPEAgE8Nlnn8HlcvEzZzIZ3L9/H+FwGDdv3kS1WkU6nUar1eLbEGVtjEYj3G433G43bDYbcyKkuJ5EwalGo4FMJgOTyfScuFun00Gz2UQikcBPP/0EtVqNhYUFLqV5PB4Eg0EsLCzAZrMhGo1id3cX0WiUyXtSWzsEso/FYsHZs2cxNTUFm80GpVKJO3fu4Mcff8STJ0+45AY8DfyuXbsGt9uN8+fPw+VycSATCoUGZCCkaBcqN1Lzxfj4OM6cOYOvvvoKdrsd4+PjzFVQqVRIJpNIpVJYXV3F73//e2SzWWxubqJarbJfEbN/7XYbrVbruUBGr9e/5Sc/fgxnZKg5hUpwW1tbSCaTqNVqA6rbUga9f7EsabfbEQgEcOXKFUxMTGB+fh5jY2MoFAooFosIhUK4du0aEokEfvjhB5TLZXg8HqaTXLhwARqNhps2gsEgWq0WyyEAeCXJkGMLZEShLiL/eL1emEwmjuSJgErOcr/6s0jSHC4tSdWRvCpEZ+zz+eD1enH27Fn4fD44nU5oNBom9IZCIWxtbSGTyWBvb+9tv/VXhhhQKJVKeL1eZrxTmptaPZPJJEqlElZWVrCysoJMJjOwpsQsVr/fZwG4YrHITlVKWSoRlBGw2WywWq2YnZ3F6dOn4fP5oNPpADxf+mi326hWq9xmbDQaufPE7/dDr9fDZDJhcnKSyXck5CU1Xgi9X51Ox/yFyclJWCwW3L9/H+12G5ubmwOHDD1bv9/n8mwmk0EikYBKpcL09DRnqKSqMk52oUui3+/HmTNn+OAhnoxSqUQmk0Gz2cTm5ia2t7cRiUSQSCRQLpcHAlzxT6/X4yCGyrujKigoXrQMBgNcLhdsNhu63S7q9Tqy2Szi8Tiq1epIZGEoYBW1y9RqNbxeL5aWlhAIBDA+Ps7lxXK5jJ2dHWxvb2NrawvhcBi5XI7Xzt7eHhQKBbLZLKLRKJxOJyYnJ2EymeDz+TijTjpOr4JjzcgQyU6n02FsbAxLS0totVr44YcfkEgkXijGJW4Q4nUQ0VeqB89RIZI17XY7E1z/2T/7Z/B6vZxVWF9fx8OHD/Hw4UNcu3YNtVptIF0ulU1EjpaC11OnTuHq1auYm5vDZ599Bq1Wy6WU27dvY3NzE/fv38eDBw848wA8C1BobfX7fU55000SeLoZyQFLJStDzoTars+fP4/Tp0/jyy+/hMlkYql4MZNCNkun0ygUClzOLZVKA/VsKgG73W7cvXsXnU4H5XL5SOndtwmRhOlyuTA/P4/Lly+jVCrhP/2n/4RYLIb19XVks1nm0BB6vR4KhQKazSYeP37MHWFXr15Fr9fD3//93zMZWkqXKZHLQOX65eVlfPnll/D5fDh16hRzWyjQi0ajTHQmOQdae2Qz8i9EoK9WqyiVSsjn8zCZTAOBzqhAlIFQKpWw2WyYm5uDx+NhP7O9vY2VlRXkcrmRsgF9/mazGXa7HUtLS/jn//yfw+v14tKlSzCbzcjn80gkEvjxxx/x7bffIpVKYWNjgzPkRC+p1+t48uQJfv75ZywuLmJxcREulwunT5+GxWLB7u4ustksgFdLXBxLICOSUqlbicTZ2u02CoUCK/e9DGIm5l0MYoCnt0qj0cjtkJSSMxgMfPOhm2Mul2OmvFSDGHK0RPr2+/1wuVycacjlcqy6Go/H+dChdDndHMRnF+e9iDM/KLvTbDYlccsW14RKpYLb7UYwGITX64XZbGaNHOok6PV6KBaLSKfTnAkV07XpdJq7BWq1GvNJHA4HHA4HjzZ4nQ6CtwHyP1RKJGHARqOBbDaLdDqNer2+r2YOHfh0KBcKBTgcDm4PpUuVVDMNdPA6nU64XC7mjKlUKnS7XRSLRdTrdezu7iISiSCdTqNUKqHVanEA97IhiKM+JJHWF82aslqtrBRdLpd5DEitVuP5dlK3hdhkAgBWqxV+v5+7Rp1OJ+swZTIZ5HI5xONxpFIp7iIVx6HQz6P2fRIlpTJdrVbj8qY4VPIwdjy2jAwdKl6vF6dPn8b09DS0Wi0KhQJWV1exs7PD2Rixx37YaKTnQBoy4oyGUYYYDAaDQYyPj2NpaQmffPIJO1UA2NzcRCaTwfXr13Hjxg1ks1lJz8+hhbywsIBgMIhPPvkEn3zyCUtaU1s51VpDoRAajQYfKrQ+9ntuyhBaLBY4HA54PB64XC7UajXk83mUy+UTfcMWO0z8fj8cDgeuXr2K3/72t/xMxImhFvx6vY7bt2/j9u3bSKfTiEajA7Nfbt++jZWVlYGRITMzMzCZTLh8+TK8Xi+uXbuGUqkkmT0nckAmJibwm9/8BhaLBVtbW0gkEtjY2GCey35ZJvJFnU6HxdxILdnlcjEPSZSbP+kQLwk6nQ4ffvghTp8+jfPnz2N8fBw6nY5lC65fv45UKoV/+Id/wJMnT1AoFFAul/n7X+RXKMAhiQPSUKGBpVKx1ctA+7DX67F+15kzZ/Dhhx9yxSGZTGJzcxPpdBqAdEvYIuhM0ev10Gq1uHTpEj777DNMT0/j8uXLUCqVqFarSCaT+Prrr7G6uor19XXughyegE42yWQy+OWXX2Cz2dBqtaDRaJhwToKmzWYTzWbzzWZkgGcHMalAknS8KP192EnMw5HgqEN0OsRVIEfqdrvZlp1Oh1N4qVQKmUyG2fEn+UDeD8NdSna7HWNjY3xj7Pf7PKskmUwiFoshk8mgUCgMROovemaxDVev1zP5V6fTsaOWAqeIntNsNsPpdMLtdsPj8TARnrpG2u02SqUSSqUSEokEotEoisUiK7GSYyEJ/mKxiFqtBpvNBoPBwD+/UqlAq9U+9/tPOmgdmUwmJoYXi0UUCgVUq1U0Go0DdalEYU66TVKJmwQ6xczfSYa4t+hi6HK5EAwGYbfbeWBmo9FApVLhsj/dpmlwJpX6X/Q7yBZkH7p4jlIQMww636xWK8xmM8rlMksZVKtVtFotLm+PCkgJ2+VyMSfGYrGg0+nwTDfq2Mpms6jVavtOkqf9s7e3x8KKYhdUs9nkJIao7vtGMjLiL6PU98LCArxeL6fwaRovpXX3ezgiJObzeZ7eLM4LOunO4ygg29HNhlLZly9fxl/8xV9gbGwMMzMznJkol8v4h3/4Bzx69Ai7u7tIpVIDmRgp2oiIc2fOnMH777/PA8ZyuRw2NzcRi8Vw48YNLqMBL29nFO1qtVqxtLSEqakp+Hw+2Gw23Lp1C5ubmyd6PAEdEjQY8/z581haWsLi4iJnYvr9Pmt85PN5bpWNRqOIRqO8/0Tpb5IHp1szCXcR0brdbrM4pdhme1LXFj2b1WplDZNAIIBYLIY7d+4gnU6j0Wgc+P7JdykUCiaUh8Nh3l9TU1MwGo2o1+tot9sn2h6EXq8HvV6P6elpuFwuXLp0CZcuXYLVaoXRaEQsFsPdu3eRTCbxzTffIJ1Oc8MAXQAO41NI2dbn83HJin6G1FvWgWe+hHROZmZmcPXqVQSDQSSTSaTTaaysrCAcDqNSqUiKVzYM8imi76EW6WAwiCtXruDixYvQaDRcHrp+/Tq3Wm9ubmJvb487tfbLfAJPhfLoItXpdLg5iLpNj6Ip81qBjBh105skkTvS66DWvJcNDyOnWa/XUalUOBU+CpvhMFAoFDxMa2JiAufPn+fyQbPZRDweRz6fx/b2Nn755RfUajVUq1WWiJYSxM1CY999Ph+mp6fhdDqhVquxt7eHRCKBWCyGSCSCZDIJ4Nk6O6yzoOGKIqekWCwiFotx+vwkg7g9JITo9Xq57EaBSiwWQyKRwMOHD9mh0jBMkbugUCj4ctDpdHiekChuRUGMRqNhot5Jdcy0jgDwTBeLxQKr1Yput4twOMyCiS87lMlOlUqFszKVSoXLCe12+0gzYN4GyC5qtRoulws+nw/BYBATExNc2q/X69ja2kIsFsPjx4+ZoAq8mhAp8RtoiCvJz4+a36ZsE7Xmm81mVCoVFAoFDmioK0fqEH2zVquFz+fD7OwsJicnEQgEOKNSLpfx5MkT1hzKZrOcxTwINDGd/AsALk9SNuaNdy2REyQ5eK/Xi4mJCTSbTdy7dw/RaJTLHwcRwmjz0MEmRoejCLqx0Mhzi8WCS5cuIRgM4sKFC3A4HGi1WtjZ2UEul8P333+PZDKJUCjEpEUpasWI0Ov1PNCPOCC9Xg/ZbBZPnjzBTz/9hFQqxdyEwwQxIsPeZDJhYWEBH330Eex2O/b29lCr1QY66E5iJmt4bbjdbszPz2NqaopTurlcDqFQCIlEAteuXWM+DCmLigeR+HxiB9Qo7C8aeGgymWC326HX67n1noIS8lEHgfwNBT3kYGmqsThv6KTeusVyKokfkny+x+OBSqVCq9VCq9VikbtUKsWdf4fZC2JTh0ajwcTEBC5dusQyAK1WC1tbW4hGo0gkEm/isX81EPdKqVRyOenUqVO4ePEiisUiwuHwQMlb6iRfkdJhNBoxNzcHl8uFK1euYHFxET6fD3t7e4jH43j48CESiQTu37+PVCqFarX6wtIt7S3RNwcCAbhcLi5JUpb4qGKlx1ZaomyC1+tFMBjE9vY2Hjx4wH31dLN70YPSv5HDIGeyX9ZH6hju8jp16hQCgQC++uornD59GkajEQaDAalUCuFwGJFIBH/4wx8Qj8eRTqcHapBShl6vx/z8PPx+PwKBABwOB/L5PAqFAkKhEH766SfuBBhuL94P4maxWCwIBAI4deoUPvzwQ6jVaiSTSa7nxuPxE8uRoefQarWYm5tDIBDA3NwcpqamWLcjk8nwyI8bN24wyZAOshfdqMW19yo16JMKeg4ah0KBTKvVYj7QYQIZAEycJpG3fr8Pg8GAiYkJAGBxzpMKMZCh8QwXL17E1NQU3G43lEold9eQ4B0pqR52LdD+otEfwWAQy8vLcDgc0Gq1aLVanDWmLKqUQWuHOkgXFhawvLyMX375BdevX0ckEuFAhrR6pLyfgKdrwGAwsFYMEcVJMZzarCmQKRQKh6oMUIXFbDYjGAxyZyqtS+L6HWWI7bEEMkT4omDGYrFAq9WybPVh0oy0AChdTn9arZako9xh0CFFJFQarEkjCAwGA/r9PhqNBnK5HDY2NrisVKlUJNudBBw8OVav16PX6yGfz3OmodFocLBxEKlX/NtsNkOj0WB2dhZLS0uYmZmBWq1Gs9lEKBRiXZXh1sCTBgrc7HY7nE4np12ppJhOp7G9vY1kMjkQkO0XwIhBi8Fg4JlL1PJOpQBKF4sE4ZNom5dBvFm+6vun9dDtdtFoNAYGcp50kG8h+XyHwwGbzcYdj3SbDoVCnNmltXOQrYaDXspQ0HBJGkdAbf7JZJIzngQprSPxECWF+ZmZGczMzMBisaBQKCCbzTJPURwOKaXnFEGfMQ1bdbvdmJycxPj4ODebFItF5PN5RCIRhEKhQ0uq0N6hQM/n82FxcRGBQAAqlQp7e3uIRqPctk3SGK/im1+bIyMKLjkcDi4ThEKhQ6V2RadDqU/iyZAwFyDdBSJCbBPV6XRwOBwIBAJ8awoEAsyEp2Fb3333HTKZDCKRyEhkYujGqNPpYLfbce7cOUxPT8Nms3H76/Xr17Gzs8MBx4uyMWJmgUptbrcbTqcTn3zyCX73u98x3yObzeLHH39kImy9Xh8QYjxJoH2lVqsRDAZZZl+j0aBWqyEej+Px48f405/+xHuMDpj9nkUsmVDHgc/n48m1RAJOpVKIx+MczEix+2K/tPSrfL5kRyIkUjAjhengxH0yGAyYmppinzI2NoZut4tKpYJHjx7hu+++QyQSQS6X4zLkQWtHJO5SKWB6eppv6uPj4+j1egiHw8hkMlhdXcWDBw9Qr9dP5P46DLrdLkvx2+12fPrpp7h06RL0ej3C4TALc5KPknqZn3SUKJM9NTWFDz/8EBMTE/B6vdBqtUilUnj48CEePHiAW7duoV6vHyrRMCzKePbsWfzFX/wFHA4H1Go1CoUCbt++jXA4jHA4zAHwqxB+j40lSrdsUSPmsHoCVJMzGo18GyKi8CiNQqd2SIVCAZvNhvHxcb7RWCwWKBQKHqSZSqWQSqWQz+dRKpUk4UhfBjH1bTQaYTKZmMtCMt8k5lYsFgeIqMM/h0AHj16v5xHxJNhktVrRbreRTCa564kcj/j9JxVKpRIajYbF8BQKBZrNJreP1+v1gbLAMERSNbU1+nw+nj1FYm8kMU+XB9qzUl9vr4PhEpwUMjIABj5v2hMajYbJ3TSVuFar8f7aT9eLfhaBfLvD4eBMaiAQYFG0VquFXC7HbchSEZzcD+LlWiSQ22w2Fkyk56zX62zzk+xLXgYKGoxGIzdHUIs5NeHkcjnmBFG15KAGHtGORJS22+2sKK7X61Gr1VAul1nAkyo4b7RraeAH/aOAHela1Go15HI5PpAOglarxcTEBOtjtNttNBoNnhMDnOwD51VADP+lpSX85V/+JTweD86cOQOz2Yxms4lUKoWbN2/i9u3bCIVCWFtbYxKU1AeQUdRvMBgwMzOD6elpjI+Pw+v1Ip1Oo1Kp4OHDh7h+/Tr29vZe+NmLJDziA5w6dQoulwt//ud/jvn5eR7nsLa2hr/9279FKpXC/fv3WbH0pGcayEHQ4EtqSUwmk7h37x42Nzc56BAvEPS99Kfb7XKAZ7PZ8NVXX3HJLRAIoN1uI5PJIBaLYW1tDZFIhDuepL7WjgIxaKHnl8rsKbFbiQYams1mLvs0m02Uy2VWyh7mIgwTwYFnly/6OVevXsXk5CQuX76MS5cuQa1Wo91uI5vN4tq1awMZT0B6WmCiPXQ6HWZmZuDz+Tgz8fjxY3z//fcIhUKIRqN8mEs1IyO2l2s0GszMzODLL7/kLkmLxcLztm7cuIH//t//O3cVDwcxYjBMPDMqY5vNZnz++edYXFzE+++/j9nZWeTzeWxsbCAcDuPu3buIRCIolUpHeo5j7dsVHSmlOQ+TSVCpVMytEbUu6BYhZdCz02FD48/HxsYwOTkJh8MBs9kMrVbLLZ+ZTIZrhtRrPwokMuAZ94NmBJFIXafT4cF9JHonfs/wjZHaIUlRlEY5BAIBBAIBHo9RLpcRi8WQTqc5iyEVOXXKNoklsGaziVKpxOvioGnVdBMyGAwsT09tuDSMs91u863oVYUrTzKOeoCKNqd2a5qlI4UMFe0vKgsOz7UDBvVChjNvYou52EVKXBu/34/JyUm+Vbfbbd636XQaqVSKB0ee1NLtizDMA9JoNJyJoawodT3mcjlWVJf6BZPWDAWsfr8fXq+XM7Y0eDeTySCVSqHVau2rLzW8P2jt0IwmCgjtdjtnCfP5PLLZLHNAj+p7jn1opJgOJ6LUyzpNdDodFhYW4Pf7oVQqkc/neWYFQYoLRcwcuFwuWK1WfPzxx7h69SrLXFNLZ7lcxq1btxAKhXD37l3cu3cPjUbjUN06UoHYjWOz2WC322GxWGA0GtHr9XjGT6PR4HUEPLtd00ax2+2w2WwYGxvDmTNn4HK5cPnyZdjtdk5ZbmxsYGtrC+vr63jw4AFzHaRGyqODgC4GjUaDAxlRzI5eA4D3n8fj4UzVp59+CpfLhdnZWS4PKJVKlMtlrK+vIxQKYWdnB/F4nG9SUrGRCHKeLxLlehHE7ks6uG02G9LpNO7evYt8Po9msyk5u1DWhS5Ls7OzuHDhAjY3N7GzszNQXqL2ddL0oC5Um82G9957D3a7HbOzs9w2m06nkUwmsbKygng8jlu3brGy62EF9U4axI4sn8+Hs2fPwu/3o9vtYnd3l6c7D+s0SRUiyddqtfK5ZLPZoFKpUK/Xsba2ho2NDWxvbz83ukTMfIpfp7XjcDjw0UcfwePx4OOPP8bU1BTa7Ta2t7extraGr7/+Gul0mvcXcLRLyLGPKCCuDBE6yaEcVH/VaDRwu93wer1cvxW7nU56GeAgiDo7drsdCwsLuHLlCiwWC9xuN8vGNxoNRKNRPH78GDs7O4jFYpIn9g6D1ghxWqiGTxOuKQvXbrehVqs5kBm+OZJMeDAYxLlz5+DxeLC8vAyLxcIBSzabxcrKCkKhEFKpFJMapehcgWdlOVGVd9guYmssERWpffuDDz6A2+3m7icKfKicKfKxtFqtZNcdlUKGg/+Davn078AzYS7KFLbbbcRiMS6RS2n9iOtDq9VCrVbD6XQiGAwil8vxgUFZb51OxyUkyhyTjtGHH34Ip9MJp9MJg8GAQqHAYnBra2vcqURtyFJcP2JpjsQhiW9Hw1iLxeJAt46U1sN+EHmLWq0WZrMZY2NjMJlMLGZLGlWUhRIvmcOZc4JOp4Pb7YbP58O5c+e4VDU2NsYaQ7u7u1hfX+fEBTVs0Lp8lQzoawcy9EGSGmahUEA+n4dKpcLi4iJMJhN2dna4e0kkz5nNZrjdbpaPdzgcWF1d5TbZ/X6XFCAuDpvNBqPRyHXBxcVF2Gw2vg2XSiXcuXMHmUwGt2/fxs7ODrLZ7IAjkMpzvwy0VqiMVK/XOfCg8tDc3BwuXLjAugIqlQp2u52HP+r1ekxMTDC3ZmFhAWq1Gru7u2i32wiFQsjn81hZWcHa2hqKxaIkyXhiS3SpVOIUtt/vx3vvvQez2cyHq8lkGpgJRMMxA4EAzp07x9kvUXWV9urjx49x69YtpNNpni8kNTspFArs7e1xl4zZbOY/nU5nQEtm+NnIJ4n2Ja4eTfSly9VJb0enSyQRb61WKyqVCiwWC2t8uN1unDt3jiUyiCyuUCh4YB8FMlarFWNjY6w2rtFokMlkUKlUsLGxgfX1dWQyGayvr6NarfIF5CTb6CDQuWS1WvHee+8hEAhgdnYWVqsVjx49QjKZxM7ODpfOgNHxzQTxAkANN9SA0e12YbVaB0qXJpNpoFRJJexAIIDl5WXY7XYsLi7CaDQiHo8jEolgZWUFKysrPN+r0Wg8JzT5qmXc1wpkxNQacQ8okFEoFDh16hR0Oh2uXbsG4FkaioRx6FCanJzkLhOSWk+lUnx7p98lJZBzsFqtsNvtWF5exvLyMiYmJmC1Wrn7JB6P4/vvv0csFsOjR4+QSqUAQLLPfRDEMkmj0WACYqvVgtlshkqlwuzsLM6fP49KpYJkMgmdTofZ2VmYzWaMj4/DZrNhenoa09PTMBqNcDgcKBaLuHnzJtLpNG7evIlIJMKDNcUskNRsSS2z5XIZ3W4XKpWKbzh6vR6pVAq9Xg82mw1arRZOpxNGo5Fbb51OJ6ampgYyFLT3crkcnjx5gtXVVdy5cweVSkWSbaT0XpvNJrehUxBjsVg4c0X2EyFequjf/X4/pqenBwIZkoF4WVbnbYP8MXEPKJCpVqtcMvN4PCxINjExwVk+hULB2RYS5NTr9XA6nQPDIB8/foxQKIRr167h+vXrqNVqKBQKAJ4RRqUMGklB84VmZmag0Wjw9ddf486dOxzIUNB4ktfDUSEGMp1OhwOZTqcDq9XKfpzK1xqNhkuUCwsLmJ+fx+zsLD755BPo9Xoed3Ljxg2Ew2HcuHEDt27d4g468s9HycQQji0jQ3MmEokE1tfXoVAo4Pf7oVAosLS0BKvVyvVTWvDT09N4//334XK5+HZOLbJ7e3uSPHzI2dEgrNOnT8Pj8WBychIejwdarRaNRgOpVApra2s8NTSVSqHRaAz8LKk9+2Ew7GgzmQwPJ7NYLJiamuIgp1AoQK1WIxAIMGHVaDTCZrOh2+0in89jd3cX+Xwe9+/fRy6XY30M4hdJEeKBtLu7CwBIp9Osu+Dz+dDr9TjTQBkZumWTRgyJA/b7fb4xZ7NZVKtVbG1tYWtrCzs7O6hWqwNdCFJZd+J7pb2TzWaRSqWgVquxvLyMTCaDUqmEQqHAjplABEcSkDOZTAgEAvB4PKjX61hfX8fu7q5kpA/IHnSLzufzSKfTPKtLq9VyGbff7yMQCDw3a4tEy1QqFfr9Pmf9SK/owYMH2N7e5s6k4XEYUgaVTEwmE7xeLywWC7LZLDqdDl+MKFiWyh45LIiDR80EJNpqNBpx7tw5aLVaDorpfKNSpTjx3OPxwO12w2QyoVAocPa3Xq/zyKLd3V3WoAGO55w7toxMtVpFtVrFo0ePoFarcfbsWXz11VeYmppCqVRCPB7Hjz/+iEgkAofDAafTiYsXL+Kv//qv0ev18PjxY6TT6QF+iNQCGbGkpNfr4fV68dvf/haTk5NYXl6Gz+djHYLV1VX8l//yX5DJZPDLL7+gWq0CkH7N9SDQWmk2m3xAb25uotlsYmlpCR6PBx999BGWl5c5ralUKlmNlmxTKBR41gnNY7px4wbLrQ+rQkrNnnTbazabePDgAaLRKBYWFpi3sLi4iJmZGVy4cGEg20Qt2nRg0f6kwLlcLuP27ds8KPDx48c8AA44mhLu2wYduLlcDv1+H9vb23j8+DEcDgf++q//GrFYDNFoFABYp4KekQZNEiGROCF2ux3Xrl3DDz/8wA5XzA6fVND7IwVfAFhfX0e9XofVaoXD4WAyNPE/xH0iZtdJ/iKbzaJcLmNtbQ25XA5/+MMfsL29zTObaK0C0ttnBLGzlA7nhYUFqFQqbG5uIp/P45dffsH6+vpLZwZKDfQMYhCTSCSwt7fHOkF/9Vd/xUK1tVqNy9h0zpGAJI0w2NvbQ7VaZVHTW7duIZfLcaWFuJDA8bXnHyvZFwDK5TLi8ThcLheSySTX1brdLiYnJ5mEaLPZ4PF4ADzdeMSAr9frfEM46Y7jRaDNTdE9cTu0Wi06nQ63u4rZp1EYAvkyiKUlcYCjQqGAy+UaaHsFBgMfADwHJ51OMwGNMjK1Wg2NRuM5kS+pguxEHSCJRAIulwvA0wnzAFhAcvg5iTBNwWCtVkMkEuGp37u7u8hmszx8VArZhpeBSkRUkqRDyWw2c3nNYDCgXC5zfV+v1w/4IovFgmaziUwmw7yYwwh6niTQuqHPPR6PQ6lUYmpqCkajkfeFuM+AZ+rP5J+q1SrK5TISiQQqlQpCoRCTXYnTIJbapLzXCER4Ja5Zv9/nbJ6YQQBG43mBQa0kGrRK2TZq2AGene80G4nWEYnX0rgLytqUSiXs7u7ybLtCoYBKpTJQdThOGx5LaQl41lkUiUSQTCa5XOL1evHhhx/ysC1qFyVSWjgcRjKZxB/+8Acehy7VQ0jkY2i1WhgMBiZaklR+uVzmWSc7OzssTDUsajaKEEsmlUoFnU4Hf/u3fwu73Y5wOIzx8XG4XC44nU5+LXUgkZZBo9FgKetiscjtwsRhEB20VG1JwUm32+VD9euvv8a9e/dw5coVXLlyBTabDcFgkNdNv99nnks2m2XRM5otdefOHeTzeSSTSbY9BTFSvTAQ6OKgVCqxs7ODZrOJCxcuYGJiAkajEf/m3/wbtNtt3L9/H4lEgpVaiWDe7/dRq9VQr9fx7bffYnt7mzMRUrKPeFGggOz3v/89bDYbKpUKFhYWmD8kHmA0rC+ZTKJarfIFq1AoIBwOo9lscqeOOJtJ6vuMQH7bYDDAbrfDarVCq9WiVqtha2uLu7HosinFjqwXgdYMZdhWVlbwH//jf2SSrtlshsFggFar5fldlHmhv6nJolAoIJPJIJPJcBBMwRFdrn6tM+5YRxT0+30m3en1eoRCIbRaLVy6dImJr6T+2+/3WQyHhHay2eyA1ofUQZOKm80mjzmn2jXVDfdTSBxl0DrpdrtoNpucGYjFYiw+Jga7NG21Xq8jn8+jXq/zRHAiGorllVGyI7Wkd7tdZLNZtNttTExMMPGOulFoTxWLRc5ukgBgIpHgQW/ijVp0yKNgM3qGer2ObDaLbDaLQqHAbfqkT0UaRtRN6HK50Gq1EI1G0e12USgUeGDpqw6uO0kg35PNZtFoNBCPx7kTyWq1AsDAPmy324hGo6hUKhzIFItFRCIRtFotvnDRrCVgNNaNCOJ+0MWAgjwSvpNSZu5VQWdytVpFLBZDuVxmLSGz2cxSBOSTqHOLMlXRaJQ5WTS+gTqdyHZia/Vx49iVfelAKZfL+OWXXxAKhZDJZNggxHBut9v8sJQCpQUjlRvQMMjpUcYhGo3iP//n/8ydS3q9np0szasQWzpHzTG8CBTMkIZOtVrFzZs3YTQaWc4aeEYMpllTNJWZWrdprUiNpHpYiPuJlC+vXbuGra0tmEwmLseJhFe6AVUqFVZcbbVaKBQKPNFao9GMnL1oHVA7/71791Aul+F2u/Hxxx/D6XSylgU51FwuxyTx27dvI5/P48mTJ6hUKkea93JSIJbliTP2448/YmVlhXVyCFRS6na7PHmYbufEm6ABpqOaNabnoUGhdCB3Oh14PB4oFAo8efIEuVwOwKAC8KhA9CGxWAxqtRrxeBxqtZr14CgQpvObgmW6ADQaDda4IhoAgDcS+B5rIAMMdjERMahYLPJNSK/X80ZpNBrcTUBRm5Q3Cr1vyiwUCgU8ePCApfRJ7pmchchPkOozHxViCpxS+wqFAlqtluuy9BqqyZO9aI2NcgskQSRg0kEdj8dZvEp8DUmH0xqjmWXivvo1b0VvG1SKpJR3oVCA1+vF2NgYms0m5ubmMDk5yd1L1PUWj8fx8OFDFvyijKCU7USXBSohRiIRJoSTpgzwLCtD+0xUU6euLinzFQ8LuoA2m03U63UmwBuNRlitVvbfLxpkOwqgjjeqEqTTaRYIJL6MTqcbWDP0t5hJFzlY4iX918xo/SqBjKjwq1Ao2KlSjVFUKRVTdqO2OIisqVQqWWeHnlnqM6SOA2JAQqBDiP5dVK4VnemoZRRehmGiNNWc6d+AZ3L0tMaohVJ8zSjbbJjPQm3It27dgsViwcbGBux2O2cD8/k8wuEwiw6SPaV8mRIhPgPtI8q+iBCJuxTkiHtzFGxxGBDhNRQK4b/+1/8KhUKBSqXCfKPhYaKjiP0+dzEoFtWixT9i0Lvfuvm1y3LHHsgAzx9QdOumKJdeAwwKv43SAhG7TuhDHM4mjPot5zAYFkESg7zhktGokAuPAjFYER3Ki14nZqzeJYjPTIHM7du3nwtyhvek2Ck3SvtSzBJTACeuG3EvSVHy4jggEp+73S43YgD7+5xRt89+658CGTFjBwye429z3/wqgQxBzM6I/0//Lb5uFCEGLcN2GNVnfl28aI2I/y7jxXZ4FzIvh8Xwvhv+t+H/HnWbHcYeo26Dw+DXLoNIEQetnZOAXzWQAV6+QU6qYY4Lo3S7+zXxLgS2xwE5CD48ZFs9gxyoHA5kn3ctk3kYnOS186sHMsDJNoAMGTJkyJAhQ7pQyCk0GTJkyJAhQ4ZUIdc9ZMiQIUOGDBmShRzIyJAhQ4YMGTIkCzmQkSFDhgwZMmRIFnIgI0OGDBkyZMiQLORARoYMGTJkyJAhWciBjAwZMmTIkCFDsvj/AZy+P0obpTCaAAAAAElFTkSuQmCC\\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    \"        Dense(units=25, activation='sigmoid', name='Layer_1'),\\n\",\n    \"        Dense(units=15, activation='sigmoid', name='Layer_2'),\\n\",\n    \"        Dense(units=1, activation='sigmoid', name='Layer_3'),\\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\": 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      \" Layer_1 (Dense)             (None, 25)                10025     \\n\",\n      \"                                                                 \\n\",\n      \" Layer_2 (Dense)             (None, 15)                390       \\n\",\n      \"                                                                 \\n\",\n      \" Layer_3 (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 'Layer_3/kernel:0' shape=(15, 1) dtype=float32, numpy=\\n\",\n      \"array([[ 0.39041346],\\n\",\n      \"       [-0.27156866],\\n\",\n      \"       [ 0.159729  ],\\n\",\n      \"       [-0.05295599],\\n\",\n      \"       [ 0.09760153],\\n\",\n      \"       [ 0.5046477 ],\\n\",\n      \"       [ 0.0829522 ],\\n\",\n      \"       [ 0.22344196],\\n\",\n      \"       [-0.28368717],\\n\",\n      \"       [ 0.4707337 ],\\n\",\n      \"       [ 0.06744999],\\n\",\n      \"       [ 0.05795258],\\n\",\n      \"       [ 0.36314076],\\n\",\n      \"       [-0.37443805],\\n\",\n      \"       [ 0.22470719]], dtype=float32)>, <tf.Variable 'Layer_3/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: 0.6703\\n\",\n      \"Epoch 2/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.5421\\n\",\n      \"Epoch 3/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.4166\\n\",\n      \"Epoch 4/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.3080\\n\",\n      \"Epoch 5/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.2327\\n\",\n      \"Epoch 6/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.1829\\n\",\n      \"Epoch 7/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.1491\\n\",\n      \"Epoch 8/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.1250\\n\",\n      \"Epoch 9/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.1071\\n\",\n      \"Epoch 10/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0933\\n\",\n      \"Epoch 11/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0822\\n\",\n      \"Epoch 12/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0734\\n\",\n      \"Epoch 13/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0662\\n\",\n      \"Epoch 14/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0600\\n\",\n      \"Epoch 15/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0550\\n\",\n      \"Epoch 16/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0506\\n\",\n      \"Epoch 17/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0468\\n\",\n      \"Epoch 18/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0435\\n\",\n      \"Epoch 19/20\\n\",\n      \"32/32 [==============================] - 0s 1ms/step - loss: 0.0407\\n\",\n      \"Epoch 20/20\\n\",\n      \"32/32 [==============================] - 0s 2ms/step - loss: 0.0381\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fceec930a90>\"\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: [[0.05003172]]\\n\",\n      \" predicting a one:  [[0.9880077]]\\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\": 20,\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 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    \"        \\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(a_out)\\n\"\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      \"[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\": 22,\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\": 23,\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\": 24,\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\": 25,\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\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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N5MTAwMDAwMGgoDOXFwMDAwMDAoKEwlBcDAwMDAwODhsJQXgwMDAwMDAwaCkN5MTAwMDAwMGgoHkl5EUKEhBB/KoTICiFmhRC/fJfjhBDit4UQsc3HbwshxA7n/eXN82WFEN8QQoQe5TqfFIZ8dsaQz84Y8rk7hmx2xpDPzggh/qYQ4gMhRFHUNpu823FHhBDfFkJEhRD3LEcvhPgnQoibQoiqEOLLu3nNHxeNIptHtbz8LlCitk/IXwH+sRBidJvjfh34OWobpx2jthX8b2x3ws3P/z7wVzfPmwN+7xGv80lhyGdnDPnsjCGfu2PIZmcM+ezMEvB3gX92j+PKwL8GfvU+z3sZ+OvAxYe/tCdOY8hG07SHelDbBK0EDOte+wPgv93m2HeAX9c9/1Xg3F3O+/eAP9I9H9j8Hu/DXuuTeBjyMeRjyMeQjSGfvf2gNkl/9T6OG6xNl/d93p8AX37S97efZfMolpdhoKJp2oTutcvAdtr96OZ79zrujmM1TbvNZkd8hGt9Ehjy2RlDPjtjyOfuGLLZGUM+BvueR1FePECq7rUk4L3Lscm64zx38a3WH7vTefcyhnx2xpDPzhjyuTuGbHbGkI/BvudRlJcM4Kt7zQek7+NYH5DRNm1Ij3DevYwhn50x5LMzhnzujiGbnTHkY7DveRTlZQKwCCGGdK8dB65vc+z1zffuddwdxwoh+gH75vc1EoZ8dsaQz84Y8rk7hmx2xpCPwf7nEQN6vgb8S2oBYi9QMyGOAr2ABvRuHvebwDjQCXRQ6wS/qTvPDJsBPJufTwEvbZ73D4GvPengJUM+hnwM+eydhyEbQz6PKB8L4AD+PrVgZgdg2XxPA17Z/F9svnd483UHYNed56vogloB2+YxbwO/tvm/6Unf736UzaPeZAj4BpAF5oBf3nz9pc1Gb9Xd5O8A8c3H7wBCd0Np4JDuvL+8eb4s8GdA6En/oIZ8DPkY8tk7D0M2hnweUT5foTbh6h9fAbqpKWhNm8f1bnPcjO483wd+Tff8zW2Of+VJ3+9+lI1spLuKEOJvA2uapv3+fRz7IvA3NE370q5fyB7FkM/OGPLZGUM+d8eQzc4Y8tkZIcSvAKOapv3WfRxro5Z9dUzTtPJjv7gnzF6TzWNRXgwMDAwMDAwMHhfG3kYGBgYGBgYGDYWhvBgYGBgYGBg0FIbyYmBgYGBgYNBQWHZ60+12N0RATDabvesuqI8Tl8vVEPLJ5XJPRD4ej6ch5JPJZJ6IfIz+tTOGfHbG6/U2hHzS6fTHLh+j7ezMfpDPjsrLx0F9wPAOu7EbPIUY7cPgfnmQ5AOjHRkYPF4e99j9xJQXeWPVarWWs715Y0II9TB4utE0jWq1qv4XQmAymdTz7drN046Ul0T2M/lX37/2i7z097iTArPd/e4XGew2+n5njM/bU9/XDLlspb4/6sfv3eCJW17qf3DjxzeQmEwmLBYLQgjMZjOaplGpVO4YNAw+Qt9/6ied/c7D3KNh2duKXsmV7We78Vnftp5W6u//aZeHHn07kjS85UW/ShJC4Pf7sdlsygKTy+UoFovb3rzB/ke2C7PZTCgUYnR0FJ/PR29vL5lMhh/+8Ickk0nVXqQyk8/nKZfLmM3mXdXuGwmTyYTVasVkMmEymRBCYLPZMJvNatVTKpWoVCoUi0WKxeKTvuRHot6aZLfbcTqdasVXrVapVCpbjpftpVQqbVGCpYzk+Z429DITQuB0OrFYLDidTsxmM5VKhY2NDfL5vJKdPPZp7G9CCFwuFxaLRY1FxWKRcrms3n9ake3C4/FgtVqBWvsqFApqzNkN+Twxy4vJZMJsNuP1enE4HKozyAFHdiRDw3960E9GJpMJj8dDT08P4XCYkZEREokEFy5coFAoqAFTDqTlclkNHE8jsj85HA4sFotS4uQAKyfnfD6vFgeNrrxIZHtxOBx4vV6g1pY2NjYolUrqODmAbmxssLGxoV4z+AgpS6fTidVqxe/3Y7VaKRQKShGUY7SU4dOGnJMcDgd2u52NjQ0lk1KpZMxXfKTcORwOoNZm9P1xN+b1j0150Q8SNpuNvr4+wuEwb7zxBl1dXWryeeeddxgbGyMSibCysgLsTy1Wv9LZznQt71muoJ8WqtUqNpsNn8/HgQMHeOONN/B6vXg8HiKRCF6vl2q1ypkzZwiFQmrl89ZbbzE1NcX6+jqZTEZN5vsZ2X5sNhtNTU00NTXx0ksv4ff7cTgcWK1WmpubcTqd2O12LBYLKysrxONxfvSjH/GjH/1IDSrQeP1M9h23243P5+P06dO89tprWxTbTCYDoKwHiUSCQqHA0tIS2WyW9fV1CoUC0WiUTCZDqVSiXC4r6xU0nlweBDmJWCwWHA4H3d3dBAIBTp06RSgUoru7G7fbTSqVIp/PMzU1RSQSYWZmhtnZWXK5HNlsFtjfcpJIpdhisfDss8/S3d1NNpulUChw4cIFJicnMZvNWCxPPCLjY0e2JZvNhsvl4gtf+AKDg4Nqbv/BD37ApUuXKJVKFIvFR7bafewSlqbt9vZ2urq6+MQnPsHBgwcpFouUSiUSiYSagCKRyL7tEHUbYW15XbJf7/1uSHmYzWacTidNTU0cPHgQp9NJuVwml8ths9lwOp2MjIzQ2dmJ2+2mWq0yPz/P+vo6uVyOVCr11MhOysvv99PS0sKpU6doaWnB7XZjs9no6OjA6/UqN8Dc3BwrKyvMzMxgsVioVCpUKpWGlJdU3qxWKy6Xi76+Pj75yU8qq1OxWCSRSCgZVSoVotEo2WyW27dvk0qlWF5e3qK0SKuvPlB1v1t/pZvW4XDQ1dVFS0sLzzzzDO3t7QwNDeH3+5XcwuEwMzMzVKtV1tfXqVarSkHczzLSI9tDb28vIyMjrK+vk81muXnzJhsbG0+lG00/b8m2dPToUU6fPq1c1BMTE4yPj6s+tuctL/qBwOl0cvDgQZqbm/n0pz9Nd3c3HR0d2Gw2LBYLdrudUChEc3MzCwsL+27wkIOt3W7H4/GoWA59YzeZTFSrVeLxOIVCgcXFRTKZzJbBdD93Dk3T8Hg89Pf309PTQzAYpFgscu3aNWZnZ5mfnyefz/Phhx+ysrLC2bNnaW5u5qWXXmJ4eJh3332X8fFx1tfXWV9f35fZEbIdmc1m3G43ra2tvPzyy3R2dnL06FECgYByE7ndbkwmExsbG2iahtfrxWKxcPToUV5++WXm5ua4efPmPTN19iKyHxSLRZLJJGtraywtLdHU1MSBAwdYX19nenqabDbL2toamqZht9sxm80MDAxsCT6dn58nkUgwNTXF8vIyq6urxGIxKpXKvoxjkL/3xsYGbrebnp4e2tvb+Zmf+Rna2toYHh7G4/HgdDqpVqu4XC6sViujo6P09vYSCAQIBAJcu3aNWCzWcG3nYahWq5hMJkKhEH6/n7a2NlpbW8nlcqTT6Sd9eU+carWKxWKhqamJcDhMKBQiEAhgMpnQNI3e3l76+vpYWFggm80+cpv52CwvcoXU19dHd3c3zz//PF1dXTidTkwmEzabDU3T8Pl8BAIB5SvbL4oLbJ10vF4vHR0dnDp1SgU1ScWkUqkwMzNDMpkkHo+TyWRUPJDMutkvMtGjV3I7OztpaWnB4/FQLBaZnp5mZmaGWCxGLpfj1q1bZLNZnnnmGTweD8ePH6dUKpFOp0mn01QqFWKxmHK77SeZyXYkAyrD4TDHjh2jq6uLvr4+PB6PUlakP17GebhcLtxuN/39/Rw/fpxqtcrt27e3ZHE1kpyEEJRKJTRNI5VKEY1G8Xq9+Hw+stksmUyGWCzGxMQEQgi6urrwer309vbi8/kIhULY7XaWl5dJpVJcuHCBmzdvYjKZSKfTaJq2JW6mkWRzN/Qua9mO2tra6O3t5cyZM3R2duLz+bBYLCrWxWazYbPZCAaDWCwWdY5EIsH777+vzrsf5LMd+sWj1+slGAwSCoUIBoMsLCyo/vY0I8cPn89HU1MTPp9PKcAmk4n29nY6OzuVNbQ+Df9BeazKi34y6ujooKWlhZdeeomOjg6ampqw2+3qwguFAuVymfX1ddbW1sjlcvumM0g5yGyIvr4+XnjhBdra2jhx4oTyj0oLwcbGBmtra6TTaZqamohEImpwTSaTZLPZLRNNo8tIysfhcOB0Ount7eXUqVN0d3dTKpVIpVIsLi6yvLxMoVCgUCgwMzPD+vo63/3udxkfH+fo0aO0tbVx/PhxWlpaOH/+PDabjWQySSwWe9K3uGtomobFYsHlctHe3s5zzz1HT08PR44cIRgMKveIvn1IhVd/jq6uLp5//nnlcovH4ywvLwM0TKyQbPfS9SXjLzKZDJlMhuXlZc6fP8/a2hq3bt2iWq0yMTGB3W7nwoULuFwuuru78Xg8yrXW399PX18fhw4dYmZmhunpaa5evUqhUFCrxUbvczKex+v10tXVRVtbG6+88gptbW34/X40TSMSiVAqldQqORAI4HK5tozdfr8fj8eDw+FQv8F+GbO3Qy4eh4aG6OrqoqOjA7/fTzwe5/bt26TT6S1Za08b+rk8m82q7CtptbNYLCp5YDd4bMqLfrB0Op0MDQ1x4MABNWm7XC7MZvOWNLN8Ps/6+rryr+pN2Y3cKeQK2G6309TUxMjICD/90z9NS0sLBw8eVLVM9Mdns1my2Swul4u5uTkuX77M8vIy09PTW9xI+yGgV64CHQ4HTU1N9PT0cPLkSTwej1JelpaWiEQiyn86NzeHxWKhVCoRDocJh8N0dXVx/PhxnnnmGSwWC8lkktnZ2X2jvMjf3Gq14vV66enp4Y033qCjo4PR0VHsdvsdWVd3G0y7urpob28nnU4zNjaGyWRiYWFBfaZREEIo104ul1OKi4yZO3/+PKurq0xPT1OpVFQqubQkDA8P09TUxOnTp+np6eHYsWP09fWxtLTEysoKb731FktLSyQSCeUaaCT51CPHDKvVSlNTEydPnqSvr49Pf/rT+Hw+XC4XGxsbRCIRkskkly9fJh6P09vbS1NTE263m1AohMPh2KK87Pc0Yb3VfGhoiMHBQaXs1Ssv+9mtvxPSwl0oFFTJk3K5jBBCKS9yPNoNK9VjUV6kmdrpdBIKhdQqT2qqNpvtjgYuXSfDw8NsbGzg9XqxWq0kEgk1+TSaAqO3uNhsNoaGhjh+/DiHDx+mra0Nq9XK3NwclUqFVCqlAgtlGh5AOBzG4XDg8/lIpVKMjY0xNzdHJBJhdXWVcrms0vMaSTZ6pJw8Hg+9vb10dnaqTKJsNks6nSaVSm1Z+coBIpFIUKlUuHjxIrlcjpGREXp6emhra+O5557DZrOxvLxMqVQil8sBNOzgIgfQUCjEqVOnGB4epr+/n2AwCHxUX0FOrpqmkU6nKZVKatKWbVEij2/ECVnfvzweD83NzSqIe319nVgsRjweVwHcsm9J62a5XGZlZYVUKqXcZ+l0mtXVVQKBgApYffbZZ1Umm3QPNNpYBCg3kc/no7u7m4GBAT7xiU/Q0tKC3+9HCMH09DTJZJL33nuPtbU1ZmdnyWazalIuFApALZNLZtnI88L+U1z0v7V0h/T29nLgwAEymQzZbJbl5WXW1tZUCYf9JoP7RcrJ5XKpEihWq5VcLkc+n2dtbY3V1VVyudzerfMi6274/X5lgv3sZz9LU1MTXq9XWVz0RaLsdjt2u50TJ07Q39+vfNE3btwgGo0CW6s+NgJSifP7/YRCIY4fP87P//zP09zcTE9PD/F4nGvXrikLgd633Nvbi9frpb29HY/Ho1xLH3zwATdv3uTChQtcvHiRVCpFLpdT1WgbDb3/PRAIcOjQIfr7+2ltbVUByzIDLZ1Oq6A5OUisra0RjUb5wQ9+wNjYGD//8z9Pa2urUoIArl+/TiqVIpPJNEzb2Q5pwWtra+ONN97gwIEDjI6OYrVaVWyLPr13Y2NDZe65XC5sNhuBQEApL1IJbNQBV7Ybl8tFIBCgq6uLgYEBSqUSq6urrKysEIlEyOVydyho1WqVYrHI/Pw8mqYxOTmJyWQiEokwOjrKT/3UT6mYIKvVynvvvcf169fvKO7XSHKT7cfn8zE6OsrRo0f57Gc/q6zgiUSC69evMzc3x5/8yZ9sSRYIBAI4nU6lvBQKBZLJJPl8Xil0jSSLB2FjYwOr1UooFKKtrU2NURMTE6yurjIzM8PCwgJWq7UhFwG7hWwDcr6TVrlkMkkul2NxcZGFhQW1mHjUBfeuznb6bJpgMKjM+H19fUoTA1SFS2nClDU5TCYTbrcbs9lMf3+/qpgaj8fJZrPKArPXO4k0idlsNkwmE/39/YyMjHD48GFaWlqoVCpcvXqVSCTChQsXSKfTrKysqOA5q9XK8vIyLpeLUCikgjLdbrdK0SuXy3g8HhYWFpiYmKBQKJBKpZ7wnT8YeouL2+2mt7eXoaEhWltb0bRateX5+XmWlpbIZDLk83lga5yP/Cvv/datWwQCAdra2mhra1NBmfoU2EYbYOR1y2Dbjo4Ourq6CIfDW6xIpVKJWCxGoVBgdXWVfD7P4uIi+Xye4eFhFQBtNpvJ5/NkMhlloWhExU7KRcYAud1uvF4v6+vryuKyU6HL+sFT0zSWl5exWCy0t7cTDoepVqt0dnbS2tpKIBAgk8moIn+NIi+5UPR4PPj9fgYHBzl69Cj9/f3YbDY2NjZIpVKsrq5y48YN5ubmSCQSqhaHxWLB7XZvqZgqi7JJN+V+L1gnaybJ0gMOh4Pl5WWmpqYasu/sFnrLlCwSOTg4yIEDBwgEAlgsFqLRqPIUpFKpXfMU7KryIht0KBRiYGCAw4cP84UvfEHFJFgsFtXYZdCpLJ4lV0Yytcrv93PkyBFaWlrI5XLMzc0RjUYbImBOH6jsdrt5/vnn+cIXvkBbWxsHDhzg8uXL/Pmf/zkzMzO89dZbFAoF8vm8UubMZrMKcrLZbFitVo4dO0ZHRwcvv/wyzzzzDKOjo+Tzed577z2++93vsrS0xNjYmHIdNAJyYmlubqavr4/Tp0/z8ssvq/TMeDzOpUuXmJmZYXV1VdWT0FsKZDDqysoKKysruN1ulpeX+cxnPsPIyAgtLS10dnYqZRkaz/2ot+ANDw9z5MgRjh49qlbMctGQy+W4cuUKq6urvP3226ytrbG2tkalUuEXf/EXOXnyJE1NTVitVrLZLJFIhPn5eW7fvk02m20oC4w+W8HhcKjsj3A4TDKZVK5VfRbIdvcmFVlpBb516xa3b9+mUCiQSCQ4ffo0r776Kmtra3R2drK2tqbqmzQK0kXW1NTEiRMnOHXqFJ/73Ofwer3Y7Xa1SJienub73/++ShuvVCo4nU61iJKxivKcxWJRjV16d+V+QrYdu93O8PAwvb29Sg5jY2O89957RKPRhuo7u42MBfL5fFvKVnR2dmK327l9+zZXrlxhcnKSlZWVXWsru6K86FdAdrud5uZmDh48SF9fH6FQCLfbTalUUn5SWakxn8/j9XqVOVtWApXmN2nB6e3tpVAoYLfbt6yg92pjkQ25tbWVjo4Ouru7CYfDCCFYWlpiYWGB6elplpeX1V4h+nLl1WpVZV+VSiXMZjOLi4sUCgVaW1sRQiiFsLOzk9HRUWw2G7Ozs6p6Iex9C5V0A3o8HlpbW1VAoCwwJrOMVlZW7iuTQQZ+Z7NZFShms9nw+/3qvI2Yzijbk9/vp7Ozk+bmZuVPlu6PlZUVYrEYY2NjrKysMDc3RyqVwmKxqJpCMt5MykC/et7Y2Njz7aUeeb0yJkymZObzeSKRyAMpGbItSoU6mUyytLREX18fpVIJi8VCc3Nzw5V/lwHeMkC3p6eH1tZW5YpOpVKsr69z48YNpbTIOBZgi2Vcxk3BR22nkZS4B0UfX2e32+ns7KSjo0PJJ5lMkslk1FjztKEfS/XykWO5jOWMRCJqPNpNdkV50Zslm5qaOHbsGF/84hdpaWmhv7+farWq6nOsra0Ri8X4+te/TiQSUTEdvb29qqiN2+2mr6+P3t5eent7ee2111R6o8wogL03OevdRVarlWeeeYazZ89y4sQJBgYGmJiY4OLFi1y4cIEf/vCHKu0X7gwilQqIPGc8HkcIwe3bt2lvb+eLX/wiP/dzP8epU6c4fPgwb775Jjdu3FDFuqQ2vFeR92Uymejo6ODkyZMMDAwQCoVUUbG5uTneffdd1tfXd1TI9JY46YuXJm+Px0NnZyfxeFyZyOX377X2U49+UWCxWDhw4AAvvvgiAwMDag+fcrlMLBbjRz/6kYpVkKXuTSYTn/jEJ+ju7mZwcJCBgQFVkViuxsvlslKgYe/1qe3QW1LMZrNa4DQ1NWEymYjH4ypF+n7dGfXuo6WlJUqlEq2trcTjcaxWK0eOHMFqtXL58uXHcl+7jVTEfD4fXq+XkZERPvnJT9LV1UVTUxOZTIaZmRlu377NH//xHyvrpd6SIhUXu92Ow+HYsrt7sVikUqk07KLgfpCZMn6/n7Nnz9Ld3U08HieRSDA/P68yIPe6N2C30f/esm7ZSy+9RG9vLydOnKC5uZm5uTlisRgffPABb7311pZtJPZEwK4+bsXn86n893A4jN/vV4WOZPri4uIia2trygcmA+42NjaIRqNqlSyrh0o3lKwzsLGxcUfF2b2C/EGtVitOp5NAIEBLSwsOh4NyuUwikVDl2aVlBT4aOPUVP+vrcuhXhCaTieXlZZaWltS+LoFAgHA4jKZprK2tfcx3/mDo5WSxWPD5fFtieorFIvF4XAWbFgqF+xocZdpsPp9XiqEMtPP5fMpf3yj+edkO5F4hfr+fcDiM1+tVxdni8TgrKytMT0+zsLBAOp0ml8tRLpex2Wx4vV6amppUwK4QQrmYpHwbdQNUeb1Op5NgMKh2lK5UKhQKhS3F5R7kfIDaRE7uwi03CnW5XA0jJ3mdwWCQ7u5uZbVzuVyUSiWSySQzMzPMzc0pd1h935D79MgaTLJas2w/0t3dKDK5X/TxcTLeJxAI4PF4WFlZIZFIqJ3sG8lVv5vIhZU0WnR1ddHZ2YnNZqNarbKyssLi4iLxeJxcLrfr25A8kvKi9zk7HA5GR0d5/fXXGRoaYnh4WK388/m8mrTffPNNVldXGR8fJ5VKMTc3pzJl5C64drudF198Ue17dOrUKRYXF+nq6iISiRCPx/ekuVI2YhltPTAwwOjoqFrJXbp0ib/8y79UloS7BY9up5lKy8z6+jrJZJKf/OQnFItFnnvuOd544w06Ozs5efIkU1NTau+RvYpURPx+P36/n6GhIY4dO4bP51P7z1y6dImbN2+qjfPuVT9Brv6SySTlcpnV1VXi8ThOp5Njx46RTCbx+XxbSnnv9QFHWjQDgQDd3d0MDw9z+PBhfD6fyg55//33mZyc5Bvf+IbKyNrY2FCr5d7eXg4fPqw2aJSxCrOzs7z//vtMT0+rtgh7XyZ65KKpra2Nw4cPq0BvqcDq7+t+kX1PZuaUy2UKhYIK4l1ZWWmIVHu9y+PEiRN89rOfZXBwkNHRUQqFApFIhLGxMf7Vv/pXrKysMDExQbFYVBZbOX7IwpoyDd1ut1MoFJifn+fixYssLi6q79tvyE1Pe3t7GRwcVJtWvvPOO0xNTRGPx8nn88qt20h951GQ836lUsHr9araSK+++iptbW1YLBZisRg//OEPuXr1qqqGrt/sdDd4ZOUFwO1209TURFtbG52dnarSp6ZpZDIZEokEkUhEWVui0agyOcpt1uUPXywWsVqtyjRXKpVUbQppPt/L6GN/pFJXKBQoFotq4pTVgx8EKZ+NjQ0qlQrr6+tKqy0Wi5jNZlpaWpR7aa8PJrI+hyx05Xa7VUB3Op1WSuqDlN2W6cFyk0+5O7BcMUsFWX7/Xkdeq6xhIl2qVqtVBb3L3ddlETU56cgYBVk9VlpdpBVPyjiZTO5JK+b9ICcMaek0m83qt3+UeAy9BVT+1btR9jp6y6bValU1a2SafCaTIRqNKgt4LBbb4gKS55CLSdnu7Ha72nstn8+TTCaV27vR2s79Il0iHo9HLZDS6TTJZFK1sadNcYHa2CSzizo7O9WWEg6Hg2g0SiKRYGlpieXl5Yea7+6Hh9IE6i9kZGSEF154gWPHjvHiiy+iaZoaTKU5+y//8i9ZW1tjenqafD6vql3WDxTSD7++vk4kEiGbzW6pzLeXBw95D/JHdbvdSnnJ5/Pk83lyudwjB/1pmqZqMAQCAUZHR6lWq5w9exaTycSf/dmfPbDJ/ONETiqyZkJXVxd+v59CoUAsFuPWrVt873vfIxaLqV1a71deMvhUFkby+/00NTURDAZVJVDJXp2w9RkOFouFkZERXn31VQ4fPkwoFKJYLBKNRpmamuIHP/iB2hlZBg7KkgN+v5/29na6u7txuVxomqYsCTdv3uR73/se+Xy+YQuMyXuVAbvlcpnFxUWi0ahyy+7WPUlLzF62aEqkMtrU1KR2Zj9y5Ag2m02V/P/hD3/I5OQk4+PjKgBVvw+YVF5GRkYYGBjgwIEDhEIhtRBLJpNEIhEVH9Nobede6Mt+9PX10dnZqRTjiYkJbty4oQqLQuP1nYdF9gOv10t3dzdDQ0P84i/+oiolkM/n+fa3v8309DQ//vGPmZubU5bg3ZbRI2kC+p2g5UZ6MrZAauarq6tqbx5Z9l/GMOjNbfXmWumHr380wuAh3WD6wljSBfCoG3hJOciKsVIZknuVOJ3OhuhIsm6JjGWSmTO5XE5lQMgArwc5J7DFPGk2m5XVrtHSGW02G263m2AwSHt7O36/X5n0s9msCs6Waa16C4HMMJJWJ9kO5QaNUs4yZqERkcqLxWJRhfeku+hRFA39hCQfMgNQn3K/V9EvouQOyDKOUAa0y6qw9cHa8rNybA+Hw7S2tqrPb2xsqHgyaeFqpD71IEi3vgx4lu7IZDKpLC/wdCgu+jhMIYTKKm5tbaWtrU0ptjJLT2auZbPZx5bJ+FCWF6lYSIXl5MmTnD17lkAgANRKto+NjbG4uMj58+dZWVlhdnZWVWPcSQuTg4UMwpOKUDabJZFIPDYT1G4gJw/Z8aXZVp9eqFfCHuQHlQOxHJTNZjMOh0NNUtIHqS9fvlfR13c5ePAgra2t2O12MpkMt27dUlUY78ckLe9T3rff71cBwKFQaEuAZaOsEKV8Ojo66Onp4dSpUzzzzDOq/k0sFuPKlStcv36dmZkZFXQrlRaPx6N2be/r66O5uRm73b5F6W2EkgN3Q2+ZkuNES0sL6+vrqh6QvnDag9yfXiZy8WGxWEin0yqduBEWUAD9/f2cPn2a3t5enE4nS0tLTE1Ncf78eX7wgx+oXbP1rqKNjQ0sFgsdHR2EQiHOnj3L6OgoLS0tCCFYXl5mbm6OpaUlcrlcw8jiQZHtQO7L19raqjbGvXXrFrdu3VIhDo3Wfx4GOWfJnez7+vr42Z/9WRUIXiqV+OEPf8ji4iI//OEPWVpaIp1OK/nsGeVFImNd5MPhcKBptY2ZpE91eXmZWCymam/sFLMiNV0pIFkNVLoAZPQ/7H1tV9+p5QpRKjN6c/bdzI71yod8X1oPpKnc7XYrc/DDZFg8SfRKnswUkht63WvlfDf5bOejlyvERtu23uPxEA6HldtL3qPcJ0SWH5BKnoz8DwQCdHZ2Knecfi8xmRpdLpcbShbboU/jdTqdpFIpyuWyikV40PurT/+UJQ/MZrNyZcsJf68iFS8hanvxyKrKcl8iub/M6uqqsiLpxx45ToVCIVpbW2lpaaGlpUUpv6lUSqXi36sAYKMiZWK1WrHb7SrLqFgsbtn482kL1AVUHF0oFFI1g+T+RYuLi0xPT6tkCWmVe1zyeSDlRd9QTSaTqora39+Pz+dTptWFhQXefvttIpEIN27cUGXdpelafzN6awTUVgvt7e289NJLvPbaa0SjUb71rW9x4cIFlpaWKBQKd6QW7xXkCkaW2pb77gghaGlpoa+vjxMnTrC0tMTNmzdVgbB7nVMIoVJdvV4vLpeLM2fOcObMGVXfQsYwTE5O7nlTrgx8W1pa4sqVKzQ1NTE4OIjFYtmyU61+Gwk9evehlI8MFnv++ec5cuQIJ0+eJBQKsbi4yMzMDBMTE2ofKNi7Vhi5EjaZTHR3d3Ps2DFaWlq2xHqtra3x7rvvsrCwQKVSUamsgUBA7TL9yiuv0NraSjAYVJOwyWRicXGRiYkJFhcXtygwe1EW90KfwuvxeEgkEnfUa7lf9BYXk8lEV1cXR48eVbVx8vk8169fV3FYexHZJ+SiRu4rJ2MR5ubmeOedd5iYmCCXy6l4MhkLZbPZ6OnpIRwO84UvfIEDBw6oTDVZcOzatWv85Cc/YXJyUgXE7+U4xAdFFt/z+Xz09PSomB+73c7ly5dZXFxUrpBGc0M/DPqUcbvdzuDgIJ/85CcZGhri5MmTQK0q9fz8PN/73vdYWFggFottKQOyJ5QXiWywgUBgy07RMpgrlUqxsLDA2traloyR+hvRDy7yPbnBWkdHB+3t7aytrTE1NcXS0hLZbHZP+xn1GVPZbJZMJkM6nVZxBzLqv1QqMTc3R6lUukN5k+fRW1pMJpMq093U1ITP52NgYICjR4+qtNlMJsPs7KzaI2mvI4RQxeik20Pep6y0fC/FDj5afbtcLlVnaGhoiObmZqxWK8ViUVn/ZEbFXs3G0sfsWCwWvF4v4XAYj8ezxVInA3bT6TQWiwUhBG63W6Xnd3d309/fT1NT05YaLnLlvLKysiUzaS/2pXuhd+3I/cD0u0bDg7uLAJVRFAwG6ezsVLsty32jZJXQvSgzvTtNBmyHQiFsNptSPpaWlojH46pv6TfytFgsyuIyNDREX18fwWBQpUfncjlWV1eZnZ0lkUjsy/omeveIrGIuwyHS6TTr6+sNEfe0m+iVl6amJoaHh5U7WoZzrK6uMjc3x/LysprXHrdy90DKixzsgsEgHo+Hnp4eVRUVYHFxkStXrnDhwgUmJye3+NblTcjBVF9HwGq1MjAwQGtrK2fOnOHgwYPY7XYuXrzI+fPn+f73v080Gt1yrr3YaeRAIGNz3n33XbLZLCdPnuTEiRMMDg7ypS99ifn5eY4cOUImk2FtbU0VZZOdRlogZBE3s9nM4OCgciF4vV61O/fNmzeZnJxkYmKC8+fPk81mt/ix9yLyt5Olo2WxK5fLxeDgIPl8ntdee03FTOlTOOXKWCrM3d3d+P1+FTszMjJCT08P2WyWa9eu8f777/Od73xHVQ7dyxO2HBDlyrmzs5Pe3l78fr96X9M0wuEwL7zwggq6tdlsqpDjmTNnVBC0fpdpGZw7MTHBhx9+SCQSaeg0T31avNxk0mw209raqrYk0TRNBSTL0g31yPFI9rvDhw/T29vL888/z5kzZ0gkErz77rtcv35dlc6X37/XqM92lBmPspR9PB5ndXWVZDKpLAdWq1VtitrS0sIbb7xBW1sbR48exe/3Uy6XWVtb49y5c0xPT3P+/HlmZ2fVTt17UQ6PiqbVCkPK8dbtdpPNZpmYmGBqakqVptjP6MsEAMoKfPz4cV588UWVPCCLxkrDwsdpXLhv5UVvHZDxFqFQSFXvhNqkPTk5yezsLKurq1vMq3ernWC1WnE4HHR3d9Pb28vIyAiHDh1ifn6eubk5pqenmZycVAWU9vJgq49JyOfzTE9PA7WU4NHRUcLhsPITyq3CFxYWyGazLCwsqE5jtVpVZV6pwBw7doz29nZVLTYWixGLxVheXua9995jYWFhSzDhXpURfJTVIKPTZcaU9C93dXVx8OBBzGYz169fV/s7yc/KVZHH42F4eJhwOMzp06fp6uqipaUFv9/P5OQkS0tL3L59m6tXr6o4D7hzK4a9gn7lLGNX5K7i8v1qtYrX66W/v19lDjkcDrVfzeDgoIo90y8aZJbEysqKygRodKSJX8Z7yYw76XbcKS1ePw5J5cZqtdLd3c3Ro0cZHR3l8OHDfPjhh2ofssdRJXQ3qR9T7XY7drtdyUdag/VVcWWc1IEDB+ju7uaZZ55RsS4Wi4W1tTVSqRSTk5NcvXqVmZkZ4vE4wL52m0i5yJjCbDbLysoKkUjkscdy7EWCwSAjIyMMDw8rFz98tEGntGp/nPPPQ7mNZGqifOj9pjLgUp9yBx9NOj6fD5vNptLv+vv7CQaDHD58mLa2NvL5PDdu3ODSpUtcvnyZhYUFlaW0VyedeuR1yvobAEtLS3R0dNDf34/dbmdgYIBqtcrIyIiqb1KtVpWyIi0v0gRXqVRYXFzk+vXrpNNpVldXVRbXzZs3lcUF9rbiAlstL5VKhdnZWSYmJmhtbaW3t5dwOMwzzzyj/O9Sm5eDpcVioaWlBafTSVtbG263G6/Xi8ViYWZmhlwux9WrV1V1x0wms6cnHYm+zzgcji2xKvostUAgwLFjx9Tn9LuQyy0QpKUhm81SLBY5f/48U1NTXLp0ibm5ObLZbMMOwPKaC4UClUqFiYkJfvzjH3PgwAEOHjxIb28vp06dYmFhQWUe1btlpTtW1mI6cuQIHR0dPPfccxw8eJBKpcL777/PBx98wI9//GO1OajeVbLXZCevR78Brr6St37j3GAwSDAY5NChQ4TDYU6dOqXCAOx2O6urq+RyOc6fP8/i4iJvv/02t2/fviOuaD+hV/7cbjdtbW14vV4V4Ly4uMjy8rKK9dnPyPFGjkHNzc309fXR2tqqXKtyESoDmOu3GXnc7eSBA3b1fmb5kDeh31NEf6y8CZvNplxOIyMjhEIhpelL8/i7777L5OQk169f59y5c2qXZDngNALyfmOxmHILLS0tcfDgQYrFIr29vQwNDakqvNIfLd1Gcj8Nk8lEOp2mUCgwPj5ONBrl+vXrzM7OsrS0xNLSEqlUilgspuIkGmVgEUKoTJlIJML8/Dx2ux1AuYE6OjpoaWnZos3L+wyHw8q3bzabVWq13FH5ww8/5OLFi6yvr5PL5Rqq/cjFgWwL9dft9XpVLEa9NUEfyCytWzLN99KlS9y6dYvV1VVg7wYt3y8y+3BhYYGrV68qa1VbWxtDQ0MIIRgbG1MpwLC1nIHFYiEQCOD1ejlx4gQjIyMcOXKE3t5eLl26xPj4ONevX+fKlSsqAw72ruVO/pbSGiUfsh1J5Vbuh9bZ2cknPvEJWlpaOHHiBHa7HbPZTKlUUsX+ZAjAtWvXWF5eVrFF+w39wk8uHmSphfX1dZWlJXcqb/S+cz9IpVfu09fR0aGq5+vLf8hx/OOOtbxv5UU/+Mu9YeTmgF1dXQSDQTo6Ojh79qxyi0hfvFzl2Gw22tracLlcyi0iS1aPj4+TzWY5d+4c4+PjzMzMNISraCekzNLpNAsLCyrQsqWlhevXr2O1WlVxLb3CJ7V/QK2cFxYWSCQSrKysEI/HlZ9R+l8bST5S2ZWTyuzsLG+//TaJREJZEWS7OHDggJJLtVpVG6HJoOgbN26QzWaZmZkhFouxtLSk/sptKPbqZHM35I69Mu1XxjDJAVavzOkf8FERurW1NbVyXlpaUjEL+pVzI7WZu6FpGsvLy1y6dEm5PzY2NvjkJz/JoUOH6O7uVpsIypWh3Ina4XDQ3NyM2+2mvb0dn8/HzMwMN27c4PLlyyq7pFAoPLZCW7uJvL50Ok25XGZ+fp7bt2/T1dVFV1cXx44dU/E9DocDv99Pb28vdrudbDZLLBZjcnKSRCLB1atXicVijI2NEY1GyefzDbU4ehjqS3VIxVYiayhJSy7sjz5Ujz5EJBwOq13pu7u7VUmCXC7H8vIyCwsLXLlyheXlZVV89uOSyQNZXvR7O8gV89LSEoFAQBU2cjgcdHV1qaC57u5uVd1UKi9yValpGrFYjEwmw40bN7h16xbnzp1TacTSVdRok49ETipyL4xIJML169fxer00NzdvqQyqv0c5OUmzf6lUUruYyslJnltm2zQaegVmbm6OVCpFNpvF6XTS3t6O0+nE6/XS2tqqZCODw2Q6fjKZ5MMPP2R5eZkLFy5s2VVZnruR2o9+5azfo0feh35lo7ew6O9R+qBXVlaIRqN8//vf5+bNm0xPTxOPxxtmf56dqA/+l3s0+f1+BgYG6Ovr46WXXiKXy3Hy5EkymQwLCwvKfWS1Wunp6VF7RtntdmKxGOl0muvXr/Phhx9y6dIlrl27dkd5iL2MvD5pxl9YWGBqakplKB49epTh4WE1Zsjxp1AosLi4yMrKiiow9t5776lNUeU4vNf3ldsN9DFDsr6L7Gtutxu3263KLexH6oPaw+Ewhw4dor+/n66uLorFIul0mpWVFT788EPm5+e5evWqaiuSPRnzIlfAlUqFqakpHA6HKozmcrlwu93K1C8DMPUTVTQapVwuK21+cXGRVCqltLdoNHpHDY9GRw58ep90Mpm8L8VDmqv1mr4+eLXR5SOEUIG709PTKsp/bm4Ov99PZ2enit/Y2NhQm8HNzs6qQMJ4PE4kErkjoLLR5COvVQ6OMkZlYGAAIQQ2m0251gBliZLtSSp1mUyGiYkJ4vE4t27dYm1tTW0h0UjyuBfy95Xu6pmZGVVfSpr/ZVZSIBBQ23MASsldXl6mWq0yOztLLBbj0qVL3L59WwWlNqqVSohaNdzr16/j8/kIBoO4XC68Xq9qB5lMRrlCrl69ytraGlevXiUej6taJrB/xuF7oXcp2u12vF4vPp8Pl8ulsiEdDoeaz/azTOS9ud1umpubVcHYeDyudhOXCu7q6uqWIPCPiwdSXupN09euXWN+fp50Ok2pVGJ4eJgTJ04oc6Q+yFAGkUnz/oULF4hGo9y8eZNEIqHqfegnnL2+0rlf6u+jWCwqLbU+kPBulWP1lpb9gj4TJpvNkkqlmJqawufz0dbWRiAQ4MCBA0pZk1VOc7kct2/f3lJ3od590ohy0q+cs9ksV69eVTFfso6L2+1Wx8v3UqkUN27cIB6Pc+HCBWKxGJcvX1Z7sEjXWSNa6HZCyktaqm7evEkkEuHQoUMUi0Wampro7+/H6XSq8vYyJm9lZYVsNqtqlsjkgLW1NdbX17dYXBptkpLXOzMzQyKRUNuI9PT04Pf7lTKSSCS4ePEic3NzaiNUWX9Kr7A0Yl96UPSWWrlLeTAYVEUzi8UiHo8Hp9P5VMhDtnufz0dHRweBQACz2UwkEuE73/kOMzMzvPXWW6pI5pOYnx7KDignWTlwTk9PY7VaVSqZNDHKBiEzkfL5PFNTU2rFnEwmiUaj5HK5Xd0BthG4m/Z+r+f7EdnwNU1TSq5UZvP5/JZJSm4TIQN0t0vN2w8yk1ZKGWSZyWTwer0Eg0F1jJSHnIQzmQyTk5Ok02lV12U/FhLbDpPJpLaXkDEwfr+fxcVF7HY7Pp8PIYRyxcXjcVV0TbpYZB2XRsnauxv6bKxkMsnt27ex2+3MzMwwMzOj+lssFuP69etEo1FV8r8+6LJRZfCgyIlXLrQjkQg//vGPVTaajKNLJBINEf+0W6yurnL16lVSqRTxeJwbN24wPT3N2toa8GTbh9ipUqDb7d6xjKDcKVmasz0ejxpc62M4ZDZSIpFQE5R0hcjjH3aVk81mn4gEXS5XQ5RZzOVyT0Q+Ho/ngeSjV3alxa5+MNXvFl1vbXlYMpnME5HPTv1LrgKFECpQUGanSaQ7UWarlctlstnsHftqPap8nlT/utf4U49sM3LxZLVa1Y7aMgBeuh9lXYp8Pr9lk0q9xeF+5fak5OP1eu85PmuapqpWy20UJIVCgfX1deV2e1wFQNPp9McunwdtO/CR20hW+m5qalLtoVwus7Kycl9Vvx+Evdq3pF4g43zkNhyysKr0vkgelxKzk3x2JQJLDgb6Kqj1yEFDDhb1WwY0onnWYPfZznWmZ7/7miUytgxQxdZkoTE9chCRwdz6DQmfBjlth1woSTnog02lgiOz1vSFtfYrUkmT96t/XWZ0Pk3l7u+G7C/SU5BKpdS8JPvZ0zT+yH4kC3zKcAd935LHPgkeSXmRWQ7yJqW5ejvqb1CfIfE0NAaDeyPbgYzN2GlAfRrajBxACoWC2tx0p2Pl36fBJ78d9feuL1e+02fqY4H2S9vSZ6BtbGxsyQaRPE1xLfeLDImol9fTtsAWQtwRn6mXwZOWxa7mvunzw7d7r/7Hf9I3b7C3MdrHRxaY+r1G9O/r/3/aZbad5W67MWk7Oe1n2dW3n8flItpP7NTXnjb0c/tekcOuKC/6gMv7OdbAwOD+MVbFD8aDBL0/DeORoaQ8OPVWvKdVfttZWfaKLHbV8rJXbsrAwMDAGI8MHgWj/dTYq3LYMdvIwMDAwMDAwGCvYdijDQwMDAwMDBoKQ3kxMDAwMDAwaCgM5cXAwMDAwMCgoTCUFwMDAwMDA4OGwlBeDAwMDAwMDBoKQ3kxMDAwMDAwaCgM5cXAwMDAwMCgoXho5UUI8TeFEB8IIYpCiK/ucNwRIcS3hRBRIcQ9i8oIIf6JEOKmEKIqhPjyw17fk8aQz70RQoSEEH8qhMgKIWaFEL98l+OEEOK3hRCxzcdvix0qJwkhfnnzfFkhxDeEEKHHdxePD0M+d8eQzc4Y8tkZQz470wjyeRTLyxLwd4F/do/jysC/Bn71Ps97GfjrwMWHv7Q9gSGfe/O7QAloBf4K8I+FEKPbHPfrwM8Bx4FjwOeB39juhJuf/33gr26eNwf83m5f+MeEIZ+7Y8hmZwz57Iwhn53Z+/KRGy497IPaBP3V+zhusPZ1933enwBfftTre9IPQz53vX73ZucY1r32B8B/u82x7wC/rnv+q8C5u5z37wF/pHs+sPk93id9z4Z8DNkY8nnyD0M++0M+RsyLwZNiGKhomjahe+0ysJ12P7r53r2Ou+NYTdNus9kRH+lqP34M+dwdQzY7Y8hnZwz57ExDyMdQXgyeFB4gVfdaEvDe5dhk3XGeu/hW64/d6bx7GUM+d8eQzc4Y8tkZQz470xDyMZQXgydFBvDVveYD0vdxrA/IaJu2x0c4717GkM/dMWSzM4Z8dsaQz840hHwM5cXgSTEBWIQQQ7rXjgPXtzn2+uZ79zrujmOFEP2AffP7GglDPnfHkM3OGPLZGUM+O9MY8nmEoB4L4AD+PrVgHgdg2XxPA17Z/F9svnd483UHYNed56voAloB2+YxbwO/tvm/6UkHMRnyeSwy+hrwL6kFiL1AzYQ4CvRuyqJ387jfBMaBTqCDWif4Td15ZtgMXt78fAp4afO8fwh87UnfqyEfQzaGfPbOw5BP48vnUW7uK5s3oX98BejevMCmzeN6tzluRnee7wO/pnv+5jbHv/Kkf0xDPo9FRiHgG0AWmAN+efP1lzYbvXXzuQB+B4hvPn4HEJvv2aiZHQ/pzvvLm+fLAn8GhJ70vRryMWRjyGfvPAz5NL585JfsGkKIXwFGNU37rfs41kYt+viYpmnlXb2QPYohn3sjhPjbwJqmab9/H8e+CPwNTdO+9PivbG9gyOfuGLLZGUM+O2PIZ2f2knx2XXkxMDAwMDAwMHicGAG7BgYGBgYGBg2FobwYGBgYGBgYNBSG8mJgYGBgYGDQUFh2etPj8TREQEwmk7nrLpaPE7/f3xDySSaTT0Q+wWCwIeSzvr5uyGcHnpR8jPFnZ7xeb0PIJ51Of+zycbvdDSGbbDZrzF07sNPctaPyYmBgYGDwdKJP5ti+2vvTTX2yiyGjrdxPMtCjyOyJKS/VanXLzZlMJuPHN7gnunoB6jnUOoH+8bRl0VWrVWCrPORfo18ZPCjG+Hx3pFykjOr7miGnrWw3Nu2GnJ6I8iKEwG63b+kQ5XKZjY2NJ3E5Bg3Gdo1e/9rTprjoB1BJvTyMAXV76pU9gxqGPO5N/QT8tMtsu3G3XrmTz3dDVh+b8iIv2mKx4PF4eOWVV2hpacHv92MymfizP/szrl+/bmj4BsBHDV0+NjY2MJlMOBwOzGYzFosFk8mE1WpFCEE+n6dcLislWN+O9qMyI+VSrVYxm804nU4sFgsOhwOAUqnExsYG+XyejY0NKpUKmqZhMpkwm81P+OofL/XWOfma/n+5aq5fDerbzdO4kpZtxOPxYDab1b1nMhlKpdJTJw89sq3Iuczv92Oz2VRbyuVyFIvFp1ohlmO1bEcmkwm3243NZsNsNmMymSgUChSLRTY2NpTB4mFk9bEqL1AzP9rtdvr7++np6SEcDmMymXjrrbc+Kvv7FP7oBjsjJxapvNT/TSQS5HI5qtWq6jxPg/vIZDJhsVhwuVxYrVa8Xi8mk0lNNtVqlVKppAYVMCwxEmPVvBX9pOtwOLDb7UouxWKRYrG4ZfX8tCIXAF6vF4fDQbVaVQ+5SKh3Ke13thtnzWazWli53W4sFgsWi4VkMqlcSY/ibflYlBeplJjNZoLBIC0tLfT29tLX14fT6aRSqWC1Wj+OS2lI9BYI2SmkVrsfV0J6i4LdbsfpdNLW1obf7+fIkSN4vV4CgQB2u52mpiasViuXL19mcXGRDz/8kKmpKXWO/SgbOSj4/X4OHDhAKBTi8OHDeL1eenp6MJlMLC8vk8vlmJmZIZ1Os7y8TDKZJBqNkkwm77A6NDr6icJut+Nyuba0AZvNhhBCyS4UCmGz2XA4HJhMJnK5HKVSiVQqRS6XU9aqYrFIPp/fl/1MUm/htNvtfPKTn6Szs5OmpibMZjN/8id/wpUrV+6IhXka0N+vzWajr6+PcDjMG2+8QVdXl7L4vvPOO4yNjRGJRFhZWQH2R9/aifpxxGq10tXVhdvtprOzE5/Px8jICK2trbhcLux2Ox988AEffvghCwsL3Lp1Sy3AHpSP1fJiMplwuVz4fD6ampoIh8MIISiXy1gslqeuUzwI+kbyNMipWq1isViw2+14PB66urpobm7m1KlTBINBmpubcblctLW1YbPZsNlsBAIBFhYWmJ2d3ddWBjlIeDweent7aWtr45lnnqGpqYnh4WEsFgszMzOkUimCwSCxWAy73c7a2hqFQoFMJsPGxsa+U+6kdc5ms+HxeNA0jUqlosYdOUjabDa6u7txuVx4PB4sFgvr6+vkcjlWV1dJJBKUSiVlscrlcur8+xn9omFwcJDh4WE6Ozux2Wy8/fbbjI+PU6lUntrYROmmbm9vp6uri0984hMcPHiQYrFIqVQikUiwvr5OJpMhEons+/ZSj5RPOBwmGAxy8OBBwuEwzz77LD09PXi9XlwuF9VqlbW1NdLp9JZg3geV12NXXuRFycnlzJkzdHd309PTQzAYJBKJEI/HKRaLj/tSGga9oiJXiq2trfh8Ppqbm9Ukvbi4SC6XI51OA/tjcNU0TblBWltbOX36NC0tLZw6dQq/309PTw8Oh2NLjIfJZGJwcJBgMMjs7Cxra2tEo1Gi0ei+iqGSpulAIEBfXx8DAwP8zM/8DIFAgO7ubmw2m1oFut1u7HY7Xq+XcrnM6OgoyWSSsbEx5ufnuXXrFrdv335kv/OTRPYRq9WKxWKhs7OTgYEBQqEQPT09aJpGsVjEYrHgdruV8mKxWGhvb8fhcKjYjmQySaFQIB6Pk0qlKBQK5HI5bt26xZUrV8hkMqyvrwONJ6ft0GfMSAun3W6no6OD5uZmuru7aWlpoaWlBYfDwec+9zkOHjzIjRs3mJ+fZ3V1lZWVFaUw7kf0Fimn08nBgwdpbm7m05/+NN3d3XR0dGCz2dQiKxQK0dzczMLCwha39X5oL3r0bUdaJD0eD8eOHSMcDnP27Fmam5tpa2vD7XbT0tKCx+NRsurp6eHMmTOYzWYikQi5XI5UKgU8WFbbx2Z5kRPSoUOH6O3tpbm5GZ/Px8zMDMlkknL5qdk0+b6QncZisSjlpa2tTa2GPvzwQwqFAoD64fdDJ5FuEWlVOX36tFrlyAlZf5+VSoVqtaoG3QMHDjA5OUk+n2d1dXVfyERPtVrF6XTS1dXF0NAQZ86cUXKpVCokEgk2NjZULNCBAwewWq3KHRIIBJienqZUKjE/P0+pVKJSqTSsnKRFV068p06dor29nYMHDwJQKBRUkoAMGDSbzWpSdrlcWCwWpbAkk0my2SyZTIZ8Po/L5WJ5eRkhBOvr6/tiMtJbcOX/UtEdGBigtbWVcDhMIBAgEAjgcrl4/vnnGRkZUUpgoVAgEok8ydv42KhWq1itVvr6+uju7ub555+nq6sLp9OpLH2apuHz+QgEAipofj+0lbtRv8B2Op0cPnyYnp4eXnvtNVpbW3G73VitVmVdkbS0tHDo0CGi0SjNzc3E43GSyeQDX8NjVV6kD9Xr9XLw4EG6uro4dOgQHR0dOBwOyuUyc3Nz3L59W03ATyN6DR9QWVlWq5WOjg58Ph8vvfQSAwMDtLS0EAgEWF9fZ25ujnw+3/BuJGmulu4gqaR1dHQwMjKC1+tV2TPykU6nlam2Uqlw4MAB/H4/TU1NDA0NkUqlmJub2zeDh36Sbm1t5cSJE/T19amAwUgkQjKZ5IMPPiCTySjF9+jRozQ1NamBpKenh0AgQKFQoFwuMz8/z+3bt/dFG5IWFr/fT2trK0II5TaSSq+MFZMxdnrTtQzCdDqdBAIByuUysViM2dlZbt++zcLCQsO7TPRjjcvlorm5mVAoxLFjx3C73SqWbH5+nmg0SrVapampCbvdTktLC06nc8u59kv/qkdvceno6KClpYWXXnqJjo4OJQ9577Ivra+vs7a2Ri6X23eyqc/Wk8kSHo+HgwcP0t7ezosvvkhbW9sW+cjkCfgoe8/tdqvj/H6/kteD8rEoLw6HQ1lcpOnNZDKRz+eZm5vj5s2bD6V57Sf0gXAy2t/pdNLT00N7ezsvvPACx44dw263Y7VamZ2dJRwO74vVoMwQ8vl8DA4OcvDgQV555RX8fj/t7e1ALduhUqmQzWYpFAosLi6SyWRYWlqiWCxit9txu92Ew2EGBweZmZnZNynB+kw9q9VKS0sLJ06coLm5GafTST6fJxKJsLS0xHe+8x3W1tYol8s4HA5KpRK9vb0MDg4SDofp7u7GarUqeZrNZmZmZtRv0IjtSG+llDF1LS0td/z+epO0jPmRSrDH41HxVTINX1pbFhYWyOfzmEymO/ppI6GfIKrVKg6Hg97eXnp6evj85z+Pw+Egk8mQzWa5evUqhUKBYDAIQFdXl1Ls9jt6OTmdToaGhjhw4AAvvPACbW1tuFwuzGazagsyqHt9fZ1oNEo2m70jRrHR2spO6ONXW1tbVSjICy+8QDAYVG5E2cf0n5EuJofDQVNTEz6fj0Qi8VDxd49FedEPJl6vV62ku7q6lEltY2NDRfdLt9F++oHvRn3QraZpWK1WgsGgigtyOBzK7DYyMkI4HKalpQWTycTCwgLxeJzx8XFmZ2dJJBL7JhNCpvUmk0lmZmawWq1MT0+zsbGhskHi8TiFQoHl5WXy+TyZTAaA3t5eHA4Hfr+fo0ePMjU1xdjYGNlsdl/EBFWrVcLhMF1dXfT19dHc3IzH41FBpufOnWNpaYnZ2VmVTWS1Wjl37hyTk5PMz8/T2tqq3I5+v59Dhw4Rj8cJh8PkcjkSiYQaZBoFfT+qVquUy2UVG6RXVIrFokplrVQqxGIxcrkc8/PzZLNZwuGwWhEGg0G8Xi8ej0e5BWQ9oUZOv5fyCAQCKuj0zJkzeDweEokEhUKBa9euKaslwNDQEG63m6amJpxO55a04P2IVOKdTiehUIiuri6ef/55Ojo6VF2X+nFEWvGGh4eVp8FqtZJIJIjFYsD+UGD0C4SmpiZGRkbo7Ozk5MmThMNhHA7HHeUH9P1FvpdIJFhbW2N+fl5lQT7MmPPYlBfZAFpaWjhw4AAnTpxQZkchBKVSiUKhQCKRIBqNUiqVHsel7Cm2S3mWK6COjg4CgQDDw8P4/X6Gh4cJBAL09vbi8/lUReLJyUmuXr3KBx98wPj4uCrI1sjIRi4V2mg0yvj4uFJkisUimUyGQqHA0tIS2WxWtRlpoRoYGMDhcHDy5En6+/u5ffs2ly9fVu6URlXw9G1GZlsdPnyYrq4uqtUqmUyGubk5/s2/+TcsLS0xNzenglSFEMzOzmIymTh69CidnZ187nOfw+Px0NTURGtrK+vr61y6dInV1VVisVhDBWDWKxFy4tFnxGiappTecrlMLpcjn88zNjbG+vo6V65cIRaL0dPTQygU4vjx4wwMDNDR0YHb7VbBrFarVa0cG1F5kZOnEILW1lZefPFFBgYG+NSnPkU6neYnP/kJ8/Pz/Omf/inRaJR8Po/T6eTEiRN4vV7lvtavpvcjUgH2+/309fVx6NAhPvvZz9LU1ITX61UWF70MZLDziRMn6O/vJxQKYbfbuXHjBtFoFNhaXbYRkXOVxWJRJRreeOMNOjs7eeGFF1Q2nx75XMpKtr+1tTXGxsa4efMmMzMzFIvFh7KS77ryIlduspbCiRMn6O3tJRQK4fF4ANQAU6lUlBLT6L7kndBnRcjaEjKA0Gq1KmuBz+ejp6cHt9tNe3s7brdbmbNlFkkkEmF6eppYLEa5XG5YE7YeOWFK90c6nSaTyahBUzZsGUQpzfl3q4Aq3W5+v59EIgE0Znq5HDA8Hg9ut5u+vj4OHz5MZ2cnZrNZ1XGZm5tTRfoAFZiqP088HkfTNObm5lTwd2trK16vl+bmZgqFAmazuaHkJJWJtrY2uru7GRkZUXFhANlsllgsRiKR4ObNmxQKBfWYm5sjnU4zPz+vUsdXV1cBiMfjnDx5Uk1CnZ2dRCIR2tvbSafTxOPxhkozl8qv1WpVVt3R0VECgQDxeJzV1VVVnySfz2+5t3Q6zfr6OuVyWcU5yIJj+wnZ1+x2O8FgkK6uLo4fP05fX58qRgcfWYf1tbZkf5PKbn9/v5JXPB5X7RAab5zWjwdCCMLhMKdPn6a3t1dl9kmrpDw+n88r62axWMTpdGK1WgkEAng8HgqFgkoplxXA5fkfhF1tgXp3kfSnfv7zn6e1tVXVC5CabalUUivqdDq9r7ONpFwcDgehUIjW1lYGBwdVYGBzczPPP/88Ho8Hn8+nMozkpK5pmnKvTUxMcP78eWXmfdgCP3sJOQgkEgni8bgKWB4dHeW1115ThQ3j8Tjr6+ssLi6ytrZGpVJRqa9yAJEdwOPx0N7eribtRhs04COfcVNTEwMDAzz77LO8/vrrOJ1OVVX4/fffZ3p6mkgkQiaTUTVg6rdGmJ+fZ35+nvb2dkwmE2fOnKGvr4+WlhaGhoaoVCqMjY2pCqF7XV5yMrZYLBw5coSXX36ZQ4cOcfr0aWX5XV9f5+LFi8zOzvLNb35TVR2WsVNyHKpWq9y+fRshBBMTEzQ1NVGtVunv71cpoKVSiWvXrrG0tKTizBoFffG5cDjM8PAwn/rUp0gmk1y7do3JyUm+853vqPo2Mn3abDYr836xWMRms+H1emlqalJFAOX593p7uRfSpRgKhRgYGODw4cN84QtfIBwOEw6HsVgsagGZzWaVoiPHHrPZTCAQwGQyqWKaLS0t5HI55ubmiEajW6xfjUS1WlVj9ODgIL/0S79EW1sbBw8eVBZJ+KidJRIJMpkMFy9eJBqNKjf1yMgIPp+PdDqtxvB0Or1lm5cH4bHNetJMViqVSKfTTE9Pq1x4k8mkyk1LE+9+sCDcDanVu1wuuru76erq4siRIypV0+12A7Wg1Gw2i9lsVlo81BpPLBZjeXmZ9fV1CoWCSm/db/KSWRChUIjOzk5VpVEGDgYCAdLp9BYzo5SBbHOaplEul1UWQKMiXT/Nzc309fWpKpXVapX19XVWV1eZm5sjEomoWI/trFD64EEZgDoyMgKgVtPyuxqhPcl7kVYAWZckFAqp8uMrKyssLCxw48YNlpeXSSQSakUo3ZP64Fspo1wup+pPTE1NEQqFaGtrUyXO7Xb7k7z1B0If6G0ymQgGgxw4cIBgMKhWv7dv32Z+fl7tDQYf9adqtUo8HsflcpFOp/elhVy/4Lbb7TQ3N3Pw4EH6+voIhUK43W5VsDCbzZLP55mamiKfz+P1elWcotPpVO5F6WoMBoP09vZSKBRUKQP5fXu9n+nnY1nNu6mpie7ublpbWwkEAlsC2zc2Nkgmk+RyOcbHx4nH49y4cUMVymxqalJjkaxcXalUHukaH4vyIgfBYrHI6uoqq6urRCIRtSGjz+ejUCio2grS/bHXf9CHRZoa29raePHFFzl8+DA/9VM/pVI5M5mMqr0h/YoHDhxQUe2VSoVr165x/fp1bt26pVZ+jW5xkdR3lI6ODp5//nmOHDnCmTNnVCCY2+2mt7cXTdO4fPnyFr+zHKChZrHIZDIqbbFRJmWJlIfb7cbhcDA6Osobb7xBd3c3Pp+PlZUVJiYmuHr1Km+++SbpdFqZ+7eLV9HHsUiX48GDB1X9Cq/Xq+p36JWdvSoz+bsHg0Ha29tVO5HBlJFIhO9///tMTU3xve99TwVt64tqSWQfkjJPpVJkMhkuXLiAEIKzZ8/S39+P3+8nHA6r+KlGQP6Odrsdh8PB0NAQr7/+OqFQiKWlJcbHx/nmN7/J+vo62WyWjY0NZfGV8UMTExOsrKzw6quvqom43urUKPLYDhm/IuPAjh07xhe/+EVaWlro7+9XC8dcLsfa2hqxWIyvf/3ryo0oq1yHQiECgYBy7/b29tLb28trr72G3W7nwoUL5HI5lWCwl2VWX8PFYrEwPDzMCy+8wJEjRxgeHlYbLepd/hMTEywvL/O1r32N+fl50uk0QggGBgYYHR3FYrGo4Hlp+dSP2w/KYw3YzWQyzMzMIIRQJknpKyyXy0r7ehr2y5ADQrlcJp/Pq8G0UCiovWc2Njbw+/1bVsG5XI5CocDa2hrLy8tkMpktk/Ze7gT3i/ztbTYbTqeT1tZW+vv7aW9vV51EulCkxU7KQPqc5R5IMm5DHxjdaEh5eDweVbUzHA7jcrmoVCqk02kWFhaUq0gWK9ypLegtofl8XinKUnmRFYul+Xwvo3cNhsNhvF6vCmjXNE1ZFaT5ulAobGkH2ylmUj5yMsvn82pxpd9DrBH7m5yYOzo66OnpUXWBVlZWlIz0Mqm/x/q+ZrPZ7ti1vRHloo9b8fl8dHV10dHRQTgcVuOw3E4jk8koV0ckEmF1dVVZ0zc2NohGo/j9fmUxd7vdyg0lC/3JOXGvLw4ksp6L3W7H5/PR2dmp9gSTlu+NjQ2y2SyJRILp6WkWFxdZWVlRgcp2u121GfkZqfA8an/aVeVFnzWSy+WYmppicXFRbaDX29ur6nHoTbn73fIiLQqpVIpbt26pWiXlclkNHnNzc3g8Hj71qU8pE6TZbGZpaYnV1VXOnz/PBx98sKUA0n6Rl1zlyZT65557ji9+8YvKGlAul8lkMsTjcWZmZpidnaVYLKo0VlkzoKOjQ7lVpMwbsdaLVOb7+/s5cuQIJ0+eVGmYmUyGqakpvvWtb7G0tEQymVT1Wu7VHqQ1VE7O5XIZj8dDf38/y8vLuN1uZQ3cy/1RDn4HDhzg5MmTdHZ2blG80uk0s7OzKjNNZuTdj3xkEcRMJqNqdkj0K9K9jv46e3t7OX36NJ/4xCd4/fXXuXDhAn/0R3/E3NwcS0tLagUs+4reEup0OvH7/Xg8HlwuF36/H7/fr7JL9mobuRf6OERp3Xz99dcZGhpieHhYyULWIltZWeHNN99kdXWV8fFxlU4uYw5l3RO73c6LL76o9j06deoUi4uLdHV1qa1wGmFBJZV4mTI+NDTE888/TyAQUKnh0s06MTHB/Pw8X/va11haWmJmZoZSqcSBAwdoamoiFArh9/vVAsNqtaog3kfpT4/N7yDjDmS0scfjUWZJs9m8JViukQaFh0EOtoVCgVgshslkUgXE5OSjz6GXVVMLhYLy3ctskr2+Kn4YpKLh8Xhoa2tT+zfJIK5yuUwymVSrab0LQA4eMl1RX0is0S16MmNKxlpIK5xU5GSWzP3eo6ZpyrolXSxyMNH7r/eqzOR1ySBtGTwqC6cVi0WVHaO3KDyIIiZl4Ha7CYVCatf7UqlELpdriD3Y6uMHPR4PLS0tKhlAVg6W1anv5m4EVKVv+agPBG9U9K7ZpqYm2tra6OzsJBgMKuttJpMhkUgQiUSUtSUajSqPgRyLpUyKxSJWq5V4PK48DXa7Xe3p0whu/vrfVc5Jbrd7S9aVDHqPx+PMzc0xPz/P2tqaKkkghCAQCNDW1qZigwAVi5jJZCgWi4+0CH+sAbv6/6WGK39MGQAlo/8bvTPshDRNxuNxLl26pMxolUqFXC5HV1cXv/Irv6K2T3C73UQiEWKxGH/+53/O2NgYCwsL6sdulDoc90Lvk7fb7QwPD/PGG2/Q09Oj4n1kptW5c+eYmZlhbGyM1dVVZXmR6ecyrVyuCorFIqlUqiEmm3qkXDwejypGZzKZyOVyLC0tsbi4yMLCgrIK3E97kKu97u5utUeLDC5slMWDVFh9Ph8+n4+BgQGOHDlCc3MzmqYxMzPD+++/z7Vr17h27Zpyp92P9U3KQO58e/bsWT7/+c8TDAZJJpMqgHdlZaUhVs7w0bjT2dnJiRMn8Pl8zM3NMTU1taWq+XbtR7YHl8uF1+vF5/Ph9XrV/ln6GKtGsr7Ut/ORkRFVvfzFF19E0zS1QJqenmZhYYG//Mu/ZG1tjenpaRX0vZ0iJ7OR1tfXiUQiZLPZOzJHGwWpVMhaY21tbfh8PpUxHI1GuXjxIjMzM/z5n/850WiUubk5VXvL6/Xy6U9/miNHjjA6OkpTUxOZTIZUKsXk5CTnz59XC/k9p7zokStr2ZnkaloqLvVFbPYb8p4qlcqW7BcZvCT99tIca7Valc89Go2ysrLSsIPFTsgJQ2a8+P1+mpub8fv9alKVMUJy1SNdbvJz0qonlWL4SK6NvumgrAOk7zPZbFZZYOQK534UD9m3/H6/2uVVyljfD6WVYq8qM9JCKU3PcmNFud9VJBJR7eRBrJT6DCafz6eK+JlMJhXzIAOj96pstkMIoZR7IYS6F7ny1R+33WelrKXFXGaLNIoCdzekUiGzGqVlSu5wnEwmVaKJDHGQi+36OkoSfcyUPu6uUePvhBC4XC6CwaBaGOrjymT8z+LiosqCBZR7sa2tjY6ODrXlhiwSmU6nVfHRPRPzsh167VQGSG1sbBCLxVhZWVEBddJsvd/RBwZKk1woFFINBFCR27OzsywvL5NKpdQGc406EW+HlIN0F3V2dtLb24vH41EDrVwpnjt3Tilx8NFOr0ePHmVgYIADBw7g9XpJpVIkEglWVlZUfYpGlZne5WE2m1UAvCwmJqtS73R/MrVVxlENDw/z/PPP09fXh9PpVIXuZDD4/ZxzLyCVMVl2IZFIMDMzwwcffEAkEtmi4N4L/cTS09PDoUOHOHToEJ2dnczOzjI+Ps7169eZnZ1VA3SjjVVCCFKplEoO0LvAdvqtbTYbLpdLxUI1uitWXr9UWE6ePMnZs2cJBAJArXT92NgYi4uLnD9/npWVFWZnZ1UxtZ1iy2SbdDqdBINBtQiVAa0PuwHhx4X+2uRcc+DAAZ5//nlVl2xjY4NyuUw8HufKlSuqVIO8N7fbzalTp+ju7ubIkSMMDQ3h9XoBWFtbY2ZmhqWlJeWyhIcfaz42y4s+IAxQm1nV11t4GtArcrK8vXSpSatBPB4nGo1uqVWyHy1TciXt8XhUJVm73a4sAlIOsny9tKTIWJdwOKy0e6vVuqUE/KNUb9wr6IOzy+WyWv3fz+pXH+vhcrlUdk5LS4uqLSR35s5kMg23opZykVl8uVxOVe68300m9SmhshZKW1ubCjCU21XIyadR6wbpLXf69nMvGektLnqrQqOP2TLWRT4cDoeyKESjUZXdGYvFVFrvTjErUlG2WCzKGiyrYMt5rlEWBvBRf5AVuL1er4onrFQqFAoF4vG42pdQBj+73W5aW1vp6OggFArh9XqxWCxomqa8CXKfOng0WTz2XaXlCrm9vZ2WlhbViaQZtpEGy91E/tg9PT309PTQ0tKC1+tleXmZeDzOuXPnmJqaUtHp+83qIpExDO3t7TQ1NW2xPi0uLvLmm28q64t0BQghlLXq+PHjnDhxgnA4rFLyFxYWSCaTW9wgjYy+lsLq6qranFIG4NYjTftCCBXD8alPfYqRkRGOHTtGf38/AEtLS2oPqJmZmXsGb+5V9JPogyr4cgIfGBigubmZl19+meeee05tVrmwsMCFCxeYnp5+IGvOXkNaTqTF5X73KNIrzjJDrf5zjdC/9EHMJpOJvr4+Tp8+TX9/Pz6fTyVILCws8PbbbxOJRLhx44ay9OrTfCX1ySayvMNLL73Ea6+9RjQa5Vvf+hYXLlxgaWmJQqFw13T0vYK8n2AwiM/no7u7W9U5kgvKdDpNLBZjZmaGRCJBc3MzbrebY8eO0dzczKuvvqoSL/Tz1srKCjdv3lTV0fVxQA8jj8dueZEWBrfbjcvlAj4qxbxdR3iakPsayYwSGakuA76Wl5eVmXo/Y7VaVZaVzWajXC6rlNe5uTkWFhZU8K30wXs8HgKBAC0tLbS1tW1ZOSWTyW2LaTU6+slHmlzrlbP6idzpdOL1elXataxYnEwmSSaTxGIxVldXSaVSDS8vveJyv1YXOYDK2Ieuri56enpUdmAqlVL1UKQVrxGVF/io/pbMUNtJsddnosmUVtkvG81Cp0fGXQYCgS07RRcKBRXkv7CwoDJnpKzqleLtFOZAIKBqxbS3t7O2tsbU1JRK2X9UN8nHgbwvm82mtqvx+Xxb9naSWcQy+zUQCBAMBhkaGqK1tZW+vr4tWYCyrWUyGWKx2B11hR6WXVNe9IFJEqmZ1isvmlbbl0S/N8Re/kF3C32AqtPppLOzk1dffZXu7m6ampool8vcunWLubk5ZYHZz1YXidzjSq4Ii8WiKmP//vvvs76+rrKLAoEAgUCAz3zmMxw4cEDtvi0Hnhs3bnDx4kUWFhbu23XQSMg2JO9N/tXXtrFarSoG6OjRo7S0tHD69GkGBwcRQpBOp7l48SIXLlzg5s2b3LhxY8uqcK8jLUtyASTTv30+Hy0tLZTLZRYWFu46QUv5mUwmenp6CAaDfOYzn2F0dFTtZzQ/P6+2F5CbODaaRUqPHHPlQkma8vVuVWkpN5vNqlDbpz71KQ4dOoTf72d1dZXp6Wlu3LihMkUaBX1VZo/HQ09Pj9pYEGBxcZErV65w4cIFJicn1cSsbz96lxmgtgMYGBigtbWVM2fOcPDgQex2OxcvXuT8+fN8//vfJxqNbjnXXu9n0ivg8/lwu91barLIzOGuri4+97nPYTKZ6Orqwuv1MjAwgNvtJhwOq7ld0zQWFxdJJpOMjY1x/fp1VcDuUXksu0rD1oA6mRpst9tVJ7FYLCr/XSov+8HEfy/02TVNTU0MDQ3R1taG2+0mmUyqCG65T4SsZrlfkYNBsVhUvlMZv5BMJllYWCCTyag6CW63m0AgwJEjR9QOwi6XS2VQRCIRZc6sr3exH9hu9adfNMjVcktLC01NTYyMjNDZ2akUZJk1MTc3p8zZKysrSvFpBFnJSURaEYQQKvvI5/MRj8eVfx62rwArFwWyuOHIyAgnT57E5XJhs9nIZDLMz88rq8uj1qR40kjlRV8AU5+NpW9LAF6vl3A4zKFDhzhx4gTxeHxL9qNM028Eeej7h5yUQ6GQ2mASaoG6k5OTzM7Osrq6umUPp/qUaNme5C7d3d3d9Pb2MjIywqFDh5ifn2dubo7p6WkmJydVYchGaj9yc2WpoMksI5mBFgqFGB0dxel0cvDgQVwuF83NzVit1i3uNLlRo/QkyMQAPU80YFcqKIFAQKX9trS0qBQ9v9/P4cOH1UZXFouFM2fO0N/fTzab5fbt28zNzalBp1F+4AdF/qDhcJhjx45x6NAh+vr6sNvtzM3Nsbq6yvXr15mfn1cpeftVFhKZBbGwsMDi4iJLS0tYLBaampo4dOgQv/ALv0A2m0XTavVg+vv7CQaDjI6OEg6HlWkylUqxurrKwsIC09PTrK+vP+E72x2kVWVjY0Pdt91u31KoTyp1ciD1eDwMDAzg9/vp6+tTxclkbYZbt27xwQcfMDk5ST6fb5g+J5X4XC7HxsYGy8vLzM3NMTAwQFtbG729vbzwwgt4PB5Vk0PGLEikvKTL4JOf/CS9vb0cOHAAp9NJJpMhn89z6dIlvv/977OwsKC+r5EXEZqmqaJ+8/PzNDU1kUqllHVXTqxyu4jnnnuOnp4eWltbsVgsTExMMDU1xa1bt4hGo6oqbyO0Gz1y0SwfclKWC6ZisaiUFBmgK9uMrHPS2tqK2+1WY9Hhw4dpa2sjn89z48YNLl26xOXLl1lYWFBJA43Yduo9KdLo4HQ6aW5u5uTJk2rTRikrvbU7nU6Ty+V4//33GRsb48aNGySTSUql0q60nUdSXvSR+larlVAoxODgIC0tLQwODioTksPhIBgM4nQ61R4qIyMjdHd3MzExgRCCaDRKLBZ7KqwvPp+PkZERBgcHaW9vp1QqMTk5ycLCgkpbLZfLDetbv1/kgJnL5YhGo+oRDAYJBoN0d3fz8ssvUygUKBQKSsv3+Xx0dHSoGBmoTWiJRILV1VVWVlZUNHsjo1/xbWxs4PV66e3tpVqtqqC3jY0NbDYbzc3N+Hw+jhw5gt/vp6enB7fbrQZcac27ceMG77//vmpvjVL5Ez5ybRQKBUqlkiq30NnZicPhoLW1ldHRUZLJJB6PR2U41I8pFotFBRQeO3aMoaEhmpubsdlsavO9yclJPvjgA/L5vHKpNeIEBB9NQi6Xi3A4rAofSkunXCnLcdzlcnHo0CGGhoYIBoOYTCYWFha4evUqi4uLpFKphtt6Q19yQLZ5uYecdEPKdqU/VrYbm82mXE4jIyOEQiGeeeYZWltb6e3txe/38+677zI5Ocn169c5d+4cpVKpoQuLbme5NplMah+5cDisxibZxqTbEVDW8/Hxcc6fP8/i4uIdhTWfaLaRpmm0tbXR19fH4cOHOXPmjNpQTtYIkOXbTSaTaiSxWEwVAlpbW2uoNLIHRf6w8kfv6upiZGSEjo4OLBYL6+vrXLx4kfn5eZaWllhfX1ca7H6Uh0R26lKpRCqVYnFxkWvXrtHX16fSVY8cOaLiG6xWq/KnynYlMyAmJye5ceMGkUikIc2021EfQOv1elUsSzAYVKmrcrVst9tpbW1V1aw3NjYYHx8nnU4zNjbGysoKV69eZWZmhmQy2ZC1leRks7GxwezsLDabTVUBFULQ0dHB6OgoP/3TP63alIy7k6tGp9PJoUOHCIVCKu5hfX2d5eVlzp8/z8TEBJcvX96yL1IjIuW0vr7OzMyMqoc0PDzMz//8z7O8vMyVK1dU8KXL5eLgwYPKnR0IBJQc3n//fcbHx0kkEg1ncdErD+l0GoDl5WWWlpbo6uoiGAzS0dHB2bNnVf+x2WyEQiEV4Guz2Whra8PlctHS0oLD4SAQCGCz2RgfHyebzXLu3DnGx8eZmZlp2DFIXmsqlWJpaYmpqSnGxsZoamqivb1dHaeP/dHfo8xIyufzfPDBB6pOkl5x2S0e2fKiaRrt7e2cOnWKs2fP8ulPf3qLViX/l1HKa2tr5HI5VbdD7kDZyMXE7oVeeQmFQrS3tzMyMoLP58NqtZLNZvnwww+Zn59neXmZfD6vlL39jGz0pVJJpSlevXoVq9XKM888g9/vV8Wj6j8jzb0y5f7WrVtcvnyZ5eVlisXiXatgNjJerxe3201nZyeHDh1Sr8vBVbpvq9Uq6XSabDbL+Pg4c3NzvPPOO0xPT5NMJslkMsoU3ojI/iQDafv7+zl58iRer5fW1lYVUxaPx7l9+7ayTknrsJykZdl7s9nM/Pw88/Pz/OhHP+Kdd95RlYzl5NWISDmtr68zOzur6vsMDg7i9/uZm5vD5XKpImo+n4/XX39dbaS3sbHBj370I1XOfWFhQbWxRhur5XiRTqcpFApEIhGWlpYIBALKjSgDUWV4Q3d3t1LwpfIi4zQ1TVOZMzdu3ODWrVucO3eOmzdvKotoI7YdvfJSKBSYnp5mbGxMxWbqlRT9/nISGb+YTqc5f/48V69e5caNGywvL2+x1u1G+3mk0Ut/o3JXZFn8qv4Y6VeUwV5TU1Mkk0lmZmaIxWKq1Pl+Rb9C9nq9OJ1OTCYTiUSCRCJBKpV6oL1q9hNSIZFBcw6HgwMHDhAMBunq6lJ+akBZYeLxOPl8nlu3brG+vs7Y2Bizs7NqwmnktiTlsba2xsTEhNpOXh9w6Xa7t2TdrK6uqs3SisUikUiEVCqlKs7KdE3ZzxpdPoCKZ5mcnOTdd99V1l+z2UxHRweBQACfz6dcInLnX7kfVrlc5vr166RSKcbGxlheXmZhYYFCoXDfO1HvRfRjrqZpRCIRLl++jNlsxufz4fF41GaEp06dIpFIcOvWLYQQzM/PqwrF+Xyeq1evqoq8st00okzgo/ixSqXC1NSU2hwXUJsPtrS0UK1WsdvtBAIBZenTNE3F+kSjUVWHKpVKceXKFZaXl4lGo8oa0ahtBz6qCaRpGtPT07zzzjvK4ibHZFm2QW60KEtbFItFotEoyWSSyclJVe7jcShyu7L0isVi3Lx5k3Q6zcrKyhaTkkSusBcXF8lkMty6dYtMJqNqvTSqX/B+kPKwWq0Eg0GVrlitVonFYqytrbG+vq5Mmo24snkU5G8fjUbJZDJks1mcTie9vb34fD414QDKxH3z5k2i0Sjvvvsui4uLjI2NEYlElK+6kZH9YH5+nkqlou7Z7/fT1NREOBzG4/FQrVYplUpkMhmmpqbIZDIsLCyQTqe5efMm6+vr3Lhxg0QisaVsQaP3MzmBSqvbpUuXyGazvPDCC3R2duLxeFQhvnrXm3SlxGIx0uk07733HtPT00xMTCgrcDab3Vfj0ezsrCrJXiqVGB0d5dOf/jQ+n4+2tjZWVlbUzu1yXJbBlaurqypuqJHlIcdTmaF27do15ufnSafTlEolhoeHOXHiBH6/n97e3i0xHHITYbnQvnDhAtFolJs3b5JIJFhbW1PWTPk9jSgrvdIrd1IfGxtjenqapaUlKpUKQ0NDtLe3K+uV9KJks1nm5+fJZDLMzs6SSqW4fv06sVhsy7YKuzmvPbLyIoQgn8+rgj6yoesjleUFVyoVVUBMamuNXhhrJ/RR61arldbWVg4ePEhHRwc2m41kMqniXGTZd9ifcT/3gxwo4vE4Y2Njqk3JLRSgprwUi0Vlubt9+zaxWIxcLveEr373kL9/JpNhdXVVFdGSRaP8fj+Tk5NomqYUm6WlJfL5PNFolFwup3zMMntiP7YpveVX1mSR9UlaW1ux2+34/X7l6tC7rZeWlkilUly7do3l5WVWVlZIpVKUSqV9JStpbZD3Pj4+rvahEaJWJ0juU5PJZNQGe9K6IIN690vZAWlJkXWhpqensVqtrKysEIlEVGyUfg6T1YWnpqbUrsjJZFL1tf3mNZAy0seoRiIRrly5otqHjFstFAokEgkKhQIrKytqw8Z8Pk8ul9sSF7Pr17mT8uDxeO5Ls9AX79Hnx9/xZTqz425qYplM5om0HL/fv6N8pFykSfKVV17hi1/8Im1tbQwPDzM9Pc1f/MVfMDMzw7e+9S0ymcxj+bGTyeQTkU8wGLxvzVQOsvXBlU1NTSpDQLaxSqWiVpGymNSjWKvW19f3pHxkQK6M15DuDln9Ej5yo8n9fKTLQ2ZN7Ebsz5OSz/2MP/raG52dnSoA98CBA4TDYVU0zG63k8lkePfdd4nFYkxMTJBMJpmbm1P9TsrrQdvRkxp/vF7vffUvOQnLfiX39YGPFgxra2uUy2WlrEi3x264itLp9McuH7fbfd99y2634/F4CAaDAHfEcEhLhBxz6ncs18vqQclms3ty7pLokwJk6ZNQKKQMFdIyLJ9vbGwoo4Q+IeBh289Oc9fHGrG3X1eA90Les9xYTzb2mZkZpqamiEQiatB4mqk3W8rVkd7VITuT7CiNWqb8QZALArk4kGXa9a/JAeRpa0d6pTeXy6nVX6VSYW1tjWw2q2Kmcrkck5OTpFIpZYGRG8PKc+1n9CnByWRyy2vlcnlLyqtkv8tE9p2dJlnpapSTdf2WAY0cB3Q/6K13+XxeuaFl35GhH/rx+OOQx64VqQPUCnkn9vOPfC/i8TgTExPqB5+fn+ett94in8/va/P+/VBvlpam3bvt7aT3LTeif/l+kPemryYrLSr11LebRs0kehikjOR+TTJeRR//pK/noa9tIj+/n/udnFylYnK34n31fWk/y0Tft2SwaSqV2vbYejno5bSfZQRb06D1Vl75Xj3b1f95XDLa9RFuv/+YD4KUhXR1JJNJZmdnldYqg+GettXy/aAfbO/2/tOClAXcWca9/jj936cJKaMHscQ9Davmu1G/Qn6a2w7cWU22/r36dvK0yEm/mJR/6+W0XdtpGMuLwfbIBl8qlVTk9vj4OPBRY3haTNYPw37IHNotnvbJ5X4w2svO6NtQfTt6WtuV3ip1P8c+zdyvov9xyclQXj4G9FHr5XIZ+MhMa7PZnvpOYWBg8PFijDlbMeSxM3tRPjtmGxkYGBgYGBgY7DX2Z6SjgYGBgYGBwb7FUF4MDAwMDAwMGgpDeTEwMDAwMDBoKAzlxcDAwMDAwKChMJQXAwMDAwMDg4bCUF4MDAwMDAwMGgpDeTEwMDAwMDBoKB5JeRFChIQQfyqEyAohZoUQv3yX44QQ4reFELHNx2+LHareCCF+efN8WSHEN4QQoUe5zieFIZ+dMeSzM4Z87o4Q4m8KIT4QQhSFEF/d4bgjQohvCyGiQoh7FrUSQvwTIcRNIURVCPHl3bzmjxOj7eyM0X7uTqPI5lEtL78LlIBW4K8A/1gIMbrNcb8O/BxwHDgGfB74je1OuPn53wf+6uZ5c8DvPeJ1PikM+eyMIZ+dMeRzd5aAvwv8s3scVwb+NfCr93ney8BfBy4+/KXtCYy2szNG+7k7jSEbWbr+QR+Am1rnGNa99gfAf7vNse8Av657/qvAubuc9+8Bf6R7PrD5Pd6HvdYn8TDkY8jHkM/HIqe/C3z1Po4brA13933enwBfftL3Z7Qdo/0Ystn+8SiWl2GgomnahO61y8B22v3o5nv3Ou6OYzVNu81mR3yEa30SGPLZGUM+O2PIx+BhMdqOwb7nUZQXD5Cqey0JeO9ybLLuOM9dfKv1x+503r2MIZ+dMeSzM4Z8DB4Wo+0Y7HseRXnJAL6613xA+j6O9QEZbdOG9Ajn3csY8tkZQz47Y8jH4GEx2o7BvudRlJcJwCKEGNK9dhy4vs2x1zffu9dxdxwrhOgH7Jvf10gY8tkZQz47Y8jH4GEx2o7B/ucRA3q+BvxLagFiL1AzIY4CvYAG9G4e95vAONAJdFDrBL+pO88MmwE8m59PAS9tnvcPga896eAlQz6GfAz57J0HYAEcwN+nFozqACyb72nAK5v/i833Dm++7gDsuvN8FV1QImDbPOZt4Nc2/zc96fs12o7RfgzZ1F3nI95kCPgGkAXmgF/efP2lzUZv1d3k7wDxzcfvAEJ3Q2ngkO68v7x5vizwZ0DoSf+ghnwM+Rjy2TsP4CvUBkz94ytAN7UJtmnzuN5tjpvRnef7wK/pnr+5zfGvPOn7NdqO0X4M2Wx9yEa6qwgh/jawpmna79/HsS8Cf0PTtC/t+oXsUQz57Iwhn50x5HN3hBC/AoxqmvZb93GsjVr2zDFN08qP/eL2AEbb2Rmj/dydvSabx6K8GBgYGBgYGBg8Loy9jQwMDAwMDAwaCkN5MTAwMDAwMGgoDOXFwMDAwMDAoKGw7PRmIBBoiICYRCJx111QHydut7sh5JPNZp+IfDweT0PIJ5PJGO1nB55U+zHkszNOp7Mh5JPP5z92+Riy2Zn9MDbvqLw8Kg8aDLzDTuwGBsCDtykw2pXBR8j2Y7SJrWzXrwwZbU+9rAw5PRkeq/Kipy6PXCGEUA8Dg/ulWq3eU5Gpb1dPQxuTMqn/+7T3MymHarUKgMlU85g/rfKQbFPjQ7UTTdOUfJ52OcHWNqRpmmpDYMjnSfBYlJe7TSr6wbO+oxgY7MTdlN6d2trThl4Wd5t89K89jTzN93437kdBqW9bTzP6Oetpl8X98jisVY/N8qJfGZtMJux2OxaLBYfDgdlsVhdfLBapVCrqLxgNwmArmqaxsbGhBg2z2YzP58NqtSKE2LICkhSLRUqlEuVymWKxuOW9/da+NE1TFgXZt6xWq+p3ZrOZbDZLqVSiWq1SrVafukWDbCculwshBLlcjo2NjSd9WU8MqcSazWYsFgsej0f1o2q1SqFQUOPyxsbGlrH8abPiSVlZLBZMJhMejwez2Uwul6NSqVAul1WfMrgTOebIh9lsVuPUo8jssVpe5MVZLBacTidWqxWfz6cuHiCVSqmOIpUXAwPY3oJnNpuxWq14vV4cDgcWi2XbDpDL5cjlcuTzecrl8kPFyjQaeqVFLhLcbjcWi4WNjQ2q1SqVSuWpnLRNJpOapIUQSpFTpcafoolHPz6bzWbsdjs+nw+LpTYdVKtVUqmUmpTla09DH6pHf88mk0nNYVarFagtkuSk/LS1owfhcchlV5UXORBYrVacTider5fOzk4CgQBDQ0N4vV66u7txOBw4HA42NjZ49913WVxc5IMPPmB+fn6LVcbgzlghvblyP8pJH6uhaRqVSgWLxYLP58PlcnHgwAGCwSCvvPIK4XAYr9erBhI50GqaxtraGtFolOnpaa5du0Y8Hmdubm7LINzI8tPLx26309rais/n4/Dhw3g8HpqamnA4HLS3t2O327l27RpLS0uMj4+zuLhIqVSiVCoBjS2HnZAykvIJhUK8/vrrmM1mvvnNbxKJREgmk5RKJWVReBqQljqv18vAwABdXV185jOfwe12A1AqlZifnyeTybC8vEw2m2VtbY1MJsPq6irJZJKNjQ2lBO93uW1sbGC1WmlubiYcDvMLv/ALtLS0MDY2xurqKm+//TZTU1NPVRu6H+R47PP5cDgcNDc3EwwGWV1dZXV1lVKpRD6fBx6uDT0W5cVsNuN0OgkEAvT19dHc3MwzzzxDMBhkaGgIt9uNy+WiXC6TTqex2WyMj49v3XTpKW8E9QGX+v/3q2zqV3ZykBVC4Ha78fl89PT00N7ezgsvvEB3dzeBQAC73X5H4OHi4iJLS0v4fD6SySQA8/PzW+JkGr2dSfmYzWYCgQDNzc2Mjo4SCoVoa2vD4/Fw4MABXC4XdrudQCDA+vo6sVhMuQIa+f7vBzke+Xw+2tvbOX36NBaLhffff59UKkU6nX6qxhx927fb7bS1tTE4OMirr75KIBBA0zQKhQKTk5MkEglmZ2dJJpPMzs4Sj8cplUrkcjmlOO93menHIK/XS3NzM2fPnqW3txe3283c3BxXr159Kq1SO6FfcDudTnw+H93d3XR1dWEymchkMmiaRi6Xe+g2tCvKi1zNSndQf38/L7/8Ms3NzRw8eBCPx0NHRwcOhwOPx6PMk5qm4Xa78fv9OJ1ObDbbvlkZ3y/1lhVpeTKbzcpHb7PZ8Pv9OBwO5WbLZrPk83mq1eqWeJBGlFm9gmaxWLDb7YTDYUZHR5XS4vF46O3tVRORw+EgnU6TTCaVa8jj8eB0OvH7/djtduUquXz5Mjdv3mxoi4NsJ9IF4vV66ejoUIuDcDjMqVOn8Pl8uN1urFar6m8nT56kr6+PcrmMEIKpqSm1WpQu3P2GnHhsNhtdXV10d3fT0tKCyWSipaWFVCpFPB4nm81uGze139C3H2lJOH36NL29vXg8Hmw2G5VKBYfDQW9vL+VyWf2NRqNkMhnGx8eZn5/n8uXLXL9+fVur8H7EbDbj9/sJhUJ4vV68Xi/t7e0AymJlUENaXKxWK1arlZMnTzI8PMzhw4fp6enhO9/5jlpAJRIJ4AlbXvRBTXJl3NLSwtDQEDabDZvNpla9eu3f6XSqjmOxWFTsy9NggpODq1671ysvZrMZr9eLy+Wis7MTn8/H6uoq6+vraoUEqM/q0z93ysTZq+gtdw6Hg7a2Nl566SWampro7+/H7XbT0tKCzWbD6XQCkEwmyefzlEolpcTJSTsYDLKxsUGlUmF9fR273a5M3Y240pbtRCovPp+PoaEhurq6OHv2LOFwmMOHD6ugVPhoYTE4OMjGxgbT09Osra0Ri8W2LV2w35DKcDgcprm5mUAggBCCYDCo4u+epngO2e6tViuBQICDBw/S3t6uYhJl/2tra1PBvHKlXCwWaWlpYWZmhmQyyfj4+FNjwZPxY3Jx5HQ6CYVCStkzqKFPHpCxrgcPHuTZZ5/l2LFj9Pb2Mj09zblz55T15WF5JOWlfiXY19fH6Ogox44dY3h4GI/Hg8vlAmp+1Eqloi5YTj7S9L+2tkZnZyfj4+PMzc0pJWa/pqTp3WsWiwWXy4XL5aK/vx+Px0NbWxtOp1OZ/EOhEC6Xi2QySTabJRqNEo1GWVlZYW5ujnQ6TTQa3RI81ijIaw6FQrS0tNDc3MzAwADd3d2cPHkSt9tNKBSiWq2ysLBApVLBarVSqVS4cuUKsVhMBWCePHmSwcFBNVmZzWYlW5fLxcbGhvKzNhKyvVitVlpbWzl06BDd3d28+OKLBINB+vv7cTgclEolisUiy8vLFItF7HY7VquVYDCo/M6HDx8mEokwOTnJxsYG5XJtx/r91sck1WqVbDZLoVBQMvT5fAQCAbWogsZUaB8EOSbYbDYCgQDBYJBQKITT6WRtbY18Ps+1a9eoVqucOHECn8+nFkTlcpmNjQ3C4TA+n4/l5WWi0SiRSISZmZl96XrTL8g9Hg9Hjhyhr68Pj8dDtVrl1q1bTE9Ps76+/qQvdU8g25fFYsFms/Hss8/S2dmpxuS1tTXm5+dVrFAmk3kki+dDKy96d4fZbMZms9Hf388bb7zBwMAAQ0NDyhxdqVRU5sfa2pqaqGw2Gz09PZhMJsrlMn19fZRKJdWR9nMgnZSb2+1WAYVNTU188pOfpKWlhSNHjuD3+7e4j6xWK8VikXK5rIKerl+/zvvvv8/S0tIdGQKNgnTt+Hw+hoeH6e/v5+zZszQ3N3Ps2DGVUZRKpbhy5QrpdBohBMVike9+97vMzc0pV9DGxoayWoVCIUwmk1ICXS6XOq6R0Kep2mw2Ojo6eP755xkcHORTn/qUyiza2NggmUwq834qlSIYDOJ0Ounv7ycUChEOhzl06BA3b97E5/ORy+UolUr7so9BTSGTqb+FQkHJ0Ov14vf7Vbr904JMqPD7/QQCATUOR6NRVldX+da3vqX6YldX15axRAhBR0cHTU1NygJ8+fLlLYHw+0WB0bvCpPIyMjLCwMCAUl6mpqa4cuUK8Xj8CV/tk0f/28vF+OnTpzly5AgnT56ks7OTH/zgB3z44YeMj4+zurqqXNgPywMrL/XFikwmE93d3fT29nL8+HGGhobweDysrq6Sy+XUCjCRSJDP59XKua2tDbfbzeHDh7esAObn59E0jcnJSW7durXlexu9U+izsdxuN83NzUpJkbEcIyMjeL1elS0ilTeZAisH20AgoEy6Xq+X27dvAxCPx5menmZjY2PP+/GlPKTJuqenh2PHjtHT06MsUEIIstksc3NzrK6u8tZbb5FIJBBCUKlUmJmZIR6PK3fQjRs3VHZAd3e3sjo0NTXR2toKwOrqakMpeLLOTSgUorOzk4MHD3Ls2DFaW1uxWCx3xCTE43EuXLhAIpHA6/XidDo5c+aMCt7t7OxUMSCRSIRUKqWU6f2GtAxLK7CmaZTLZTKZDOl0+qlJo5domkY4HObMmTMcOnSIYDBIPp9ncnKSpaUlNXbMzMxQKpWIxWLk83kqlQqapvH8889js9nweDwMDw+zurqKw+FQ9ZQafYzeDqnwNjc309zcDEChUGBtbY2lpSXlvn9a0fcfq9XK4OAgzc3NjIyMMDg4iKZprK6uMjU1xfXr15VO8Khj8CO5jaRVYHh4mFdeeYXR0VGOHz9OMplkYWGBxcVF3n33XTKZjAqMm5qaolqt0tXVRTAYBGBgYIC+vj6CwSDFYpHm5maq1aqakPeD4gIfTUJOp5NgMMjAwAA/8zM/Q2trK6Ojo7hcLlUsSu/60f+VLjqHw0FLSwu9vb2cOXOGy5cvU6lUuH37tjLj7mWk4lKtVpVb6NChQ7z44ou0tbUxPDxMtVqlWCySTCZ57733mJ2d5Rvf+AaxWEydR19AC+D8+fNMTk7S2trKc889pwba9vZ2uru7qVarTE5OPolbfmikaycYDHLs2DFOnz7NCy+8gNVqxWKxkMlkmJ+fZ2VlhW9+85ssLS0p5cVms+FwOMjn8xw/fpxPfOITDAwMMDg4yODgoFIA97qi+7BIpczv9+P1etE0jVKpRCKRIB6PbxlE98MYczf08U3t7e186lOfUsHeCwsLXL58mdnZWcbGxtA0jbGxMWKxmFKGs9kslUpFJV+EQiFOnjzJwsICLpeLfD6vJvH9Ikc57+gtnu3t7WiaRjabZWFhgenpaTKZzJO+1CdGvTHD4XBw7Ngx+vv7eeaZZ+jr62NmZoaFhQWuXbvGe++9p5JOHjVZ4KGUF3nBdrsdh8NBa2srfX19BAIByuUya2trXLlyhcXFRcbGxsjn88o8nU6nqVarRCIRMpkMly5dIhqNomkanZ2duFwu+vr6GBgYYGBggEQiQTQa3fL9jdY55MBhs9mw2+10dnZy7Ngx+vr6lMXFbDZTLpdZWlqiXC6TSCSUi6harWK321UGTiAQUIojfGQBk6/tdfnUK2Uypb67u5vW1la8Xi8bGxtkMhlmZ2dZWFjg6tWrLC8vk8/nVWDu3c6pV/xkNpashNlohRDl5GsymfD5fLS1tREMBrFYLMryVi6XWV5eZnFxUSkxsqqwNM2ur68rdyyA3++np6eHlZUVLBbLvjL5S/QZfB6PB6/Xq9LqXS6XKuC3XxU3iZSDtHAPDAzQ2dmJx+NhfX2d1dVVFhcXVe0NTdOYnp4mHo8zPz+vJptqtcrs7Cw3b97E6/Xi8/lUwoW+xtJ+Qh+oK5Mpcrkc6XSaQqGg5LWf+s2DUq1WsVgshEIhQqEQw8PD9PX1qRi8+fl5pqeniUQiqirxbmTGPrTyItNSw+GwiiauVqtkMhlu3brFX/zFX7C0tMSVK1dUoTF9Rkw0GsVkMhGLxZTSc+LECXp7exkeHiaTyVAoFLh+/Tpra2tAY+7PIn3uGxsbuFwuWltbeeaZZ/jSl75EOBxmcHBQyS2dTjM2NkYikeDGjRukUimVBtzW1obf7+eFF17g8OHDKtAXPkpNs1gsDWX6l4qGzJY5fvy4MjOWSiUikQhvvfUWU1NTfPOb3ySdTlMsFtXvrw/mrh805fvSelMoFFQcVaOgdzPKQN2DBw/S0dGB1WpVsT2FQoGxsTFmZma4dOkS6+vrSuGRgfLLy8s4nU5SqRQAbW1tnDp1irW1NaxWq6rAC423ONgJObCGw2HC4TButxtN02hqaiIej+/rgN36PtHV1cWZM2d49tlnOXLkiBqrJyYmGB8fZ21tTW0HcO7cOUwmk3ou3UYXLlygWq3yzDPPqDEpHA5TrVZZWVkBaIgF1P0gM9VCoRDBYBCbzYamacRiMdbW1pQC8zRWrNYjx6j+/n66urp48cUX6e3txW63k81muXjxIu+99x43b94kkUiohfbHprzUm4eEEIRCIbq6ulShsHg8TiQSUVp8MpncdkDQDxbZbBaA6elpZeL2er14PB76+/uJx+P4fL6Grc8hi4jJAfTgwYMcOHBAxbQkEglyuZxKP7x+/TqJRIKFhQVyuRyZTIZKpUKpVMLn83Ho0CHy+Tw2mw2TyUQ6nSYWizE/P8/s7Cyrq6sNMwjL/a5aWlro6elRAbbSSpJMJpmZmWFxcZFCobAlK+ZulhcZSyRX1cVikXw+TyqVIpFIkM1mG2Z1qK9T4vF4VCE66f6QSrEsHCb369EHG8rFQjqdZm1tTVUeNplMNDU10dzcTGtrK9lsVqXgN0LbeVD0qeNSoZWK8H5GWu6k60y6600mE4VCgfn5eZaWltRiEWqykhY7qdTqSzro06NlG9tvbUafSevxeFS9JCkbuRff05Iqvh36ccZms9HW1kZHR4eyyK2srKgQkpWVFVWQbrfqkT2U5UVqTgMDAzz77LP09/eri33zzTe5evUqY2NjVCoVbDabusH6cwAkEgmSySTf/e53+eCDD1TefEtLC5/61KcAmJycJBaLsbS0VLtoy2PbT3JXkZ1emlaPHz/Oz/3cz9HZ2UlfXx/JZJKxsTEWFhb41re+xerqKuPj4+RyuS2DhaZpqpBff38/fX19Kn16eXmZd999lw8//JBvf/vbW+qd7EX0WWqBQIBAIMCxY8d45ZVXVEBlsVgklUoxPz/Pm2++yfr6OtlsVmUSSfRKsJRTe3s7R48epbOzE7vdrjrP7Owst27dUsrLXpWPHtl+vF4vXV1dDA0NMTo6isPhUNYpKatEIqGqxeoHCNlXFhcXWV9f5/r167S0tNDR0cHhw4dZWVlhcnJSuQnqrVqNjr69SZmVy2Xi8TjRaFTFvOzHbUlkv3A4HLhcLnp6evi/2Puz2MiyJL8T/l3f992dTtK5r8FYMyIjl8jMyq2nCtU9pWpMDwRMqQcQRpBGmNHT9z0JmIfGQJgZ6X1mID0IDXV/Go2EVrek7mpVtaoysyq3yNhXMoL76vR9393p30PEOXnJDDL2DLrz/gFHBMnLS7/mduzYMfub2RtvvEEoFGJnZ4dEIsFvfvMbVldXicVikocgDhACarmouTPd7LzAt2MB+vv7ZS8cMeRU9JdSE767UQb7Qe30i7Ts2bNnGRkZkbK6evUq8/PzfPXVV8zOzsprXxSeygtQ13GL4Xg+n082/yqXy6RSqV0lu2LuDOzebNT3VEdg4vE4W1tbhMNhWconSFKxWOw7fInDrjCKouByuejv76e/v59wOIzT6ZQVWIuLi2xubhKNRiUxrlqt7jopAtLLF5u0OHHncjk2NzdJJpOSDyL+7mGF+OxsNpvsWCkqjgBJqMzlcrLEXu1w7DWmgKzGEvqinpIr5rCo57F0EvbKSTRWK5VKbG9vs7GxIdfdftNtG40GlUpFdiTu6enBZDJht9vxeDy7iOLdAHXZptFoxGQySf3aG0Hodtjtdvx+Pz6fT5aHi+7CsViMTCbznYjdXg6UcFBsNpu8h4gIi8G64nc7HWrdMZvN+P1+vF6vXHeC7C2eu1MOQy8aIjJlNptl0YXX62VnZ4dKpUIymZRRvUaj8cI5mc/kvAhPPhKJyPb/+XyeaDTK/fv3iUaj3zGC6jes5igITyyXy5HL5bhy5QrlcpmPPvqIqakpJicn+fjjj7lx4wYrKyu7SJeHXWHEqeTEiRN89NFHnDx5kjNnzlAoFFhbW+PmzZv86Z/+KZlMhs3NTenFqx0+sdkKIywaAFWrVZLJJPfv3+e3v/2tJNuJaw8zhGEUUZK+vj7MZrPUhUwmw61bt1hYWJBkwf1SRUI+Pp8Pu93OyZMneeedd2T/IPF6keHK7wvifYdCIdla2+Vyyb5Ja2tr/OVf/iXr6+t88803lEolaSTUm0673ZZ9TkRTsd7eXmw2G36/n+HhYbLZLHq9ftfhoJNkpYbQL9EE0u124/f7JdH9KEB8jjqdjqGhIc6dO8fZs2cZHh4mk8lw+/Ztbty4wY0bN6TTq95Y1F26RbGB0WhkeHiYU6dO4fF4KBQKZLPZ7zQc61S9gd26IziKr732GpFIRNrdubk55ufnicfjlEqlrozaPQ7C9ppMJkKhkGzh0N/fT6FQoFAocOXKFa5du0Yqldplg18UnmmXExuoxWLBarXSbrcpFAoyfF0ulwEeeVIW2BuFERGFbDbL9va2rLYRw8PW1tbk7KPDXjEinkkseL/fL6drGwwG2e9GREzy+fyu8PWjYDKZZKM1s9ksm7RVKhWZrz7sG47aoCrKg0FnXq8Xm822q6FhqVQiFouRTqdpNptPFA0wm80yN+1yuTCbzS/1WV4m1I69kFMwGJR9b4ThEL0mUqkU5XJZ6sDjoObCqP9eN0GsBbEGxfiRw7w+XhT2RqWdTic9PT0yYlKtVtnY2GB7e5tSqbSLBL/fvYxGIxaLRc6iE7ZYkHm7ieytjryYTCZcLpesqBKtG7LZrDxsdluq9XEQzyuiLuFwWPZtMxqN0iZlMhmZhXkZsnkq50WEpEUY2+PxyHk7iUSC2dlZ5ubmaDQaT3z6F8ZYGFORf+3r62N0dBSXy8Vbb71FsVjE4/HIk7jwjg8jxELu6ekhFApx9uxZLly4ADyosrp27Rp/8id/wvb2NltbW9IZe9TziMhCT08PAwMDDA0N0d/fTyaTIZvNks/nKZVKh74bsTpXbrVaMZvNDA8Pc+LECcLhsHTqisUiS0tLfPrpp2xvb8tU2N5n2xshCAQCRCIRenp6ZMND+JY3ouY9dALa7bacvj49Pc2FCxckV0GkgNLpNEtLS8RisUdGXcR94NshaX6/X46eqNfr5PN5eVjYS8TsVKhPz8JB9nq9smP1UYEI00ciEV5//XX6+vrQ6/WsrKzwr//1v5aEyketL/j2dK0oiiyzHhgYYHBwcJez3Enr6nFQE3XFNORwOIzf76dYLJLNZlleXmZxcVFSHTp9vTwNRMpVRDX7+vr4/d//fSKRCMFgkHa7zSeffMLi4iL37t0jmUx+57D0ovDUdxTlY2JmisFgkHwX8RLtxp/mAxXXVqtVOao+m81KwqJoo3/YUyJqOBwOOQzO4XDQbrdJp9Oyr0IymZSlrPudetQThAOBAA6HQw5QK5fLsmriMJN090LMl7FarbL3hqhsqFarsqmh6Am01zCqHRehi16vl2AwiMvlkpVYYqMXhrZTJparuWViErvH45FRTtG0TpB1S6XSvvOshDE2Go2YzWZsNhtOp1P2FdobueumTUicDMVg2KM2CkDYDsFTMZvNsrtwLBYjm80+NrIp7LjD4fgO70qk8IXudYts1VE7kWEQ7QREZZ9Yc0cVYk8SWQXRvVxEg9U9XV5WVO6pOS/iQ7VYLNIgiNkhonzsWT0tdUg8lUqxsrKC3W5ncnJSdqVtNpskEolDbWRFaeLU1BRvv/02g4ODtFotFhYW+OKLL7h79y5ra2uynfbe06D6xCMcn/Pnz3P69GnZ8j6dTnP//n02NjbI5XL7OkCHCepNWZT/itk7er2eSqXC1tYW0WhUhrTh0QRdMXl8YGAAr9fLj370I06fPs3Y2Bgul0s2uVteXpYyF8Sxwy4nePCcFotFDhD0+/2ycq9Wq8mhnBsbGxQKBYDvpEXUeiR4H8PDw0xMTGAwGEgmk2xsbMgeH53g2D0JxOlQ3d/F7XbjcDiAbyOj3dqcTs1NFGTdYDBIrVZjeXmZaDQqHVbBidoLISORcnvttdc4ceIEExMTOJ1OotEoKysrxGIx2c6h0/UGduuOaLrmcrmw2Wzk83l5QBeHoW7Vob3YG+kOBoO8++67jI2Ncfr0aSwWi2yRMj8/z9LSEpVK5aVyDZ9J8nvf0KOIkc8KoTz1el2mQxTlwWwfNd/jMEIdrhYj53t7e7Hb7TSbTbLZLCsrK9IrfVTDNDUvxGAw4HK58Pl89PT0SJKliLqI6ISY+Nop4Uvh3IousSLVIfhMjUZDlrSqoTaywulzuVwEg0H6+voYHBzcVQkhIjhbW1tkMpkn5s8cFohZVuIlnNxWq7Wr8Z5wgg/67EX0RrTJV5QHM6NE9dHeiq5Oh4i8WCwWedASUVuxTjtJF54Fwg6JTugApVJJzipSVybuVw4tuC4iLetyuWT/pEwmIw8EnVjFdxBEWsRqtUrdabfbUm57q7OOAsSaMhqNOBwOBgYG6O/vlxwo0a05m81SLBZfejbgqSIv4o3UajVKpZL0QJ1OJ2NjYywtLTE4OEixWCSTyTxTBEYYlVKpJIeCGQwGPB4Po6OjKIrC/fv3D+ViEcZQ8IHGx8fljJ6trS1mZ2f5/PPPyefzuxruiWcWG7LoLeDxePj4449ltcDAwACtVotMJsPc3By/+c1vWFtbk5yZTjkFiOdtNpu7SsCFkfR4PFgsFkneVW+qIt8aCoVwuVy89957jIyMcOzYMcLhsIwE3r9/nxs3bnDlyhU+/fRT2cBNjU4wOsJQ7k0LCRmK114CrtBFsXmfP3+ekydPykGoc3NzzM3Ncf/+fcm7OsycqWeBSJnYbDa5AQnHWD16oxshUoWiMtThcMg0s3jug9KMQqf6+voIBAKcOXOGc+fO4XK5qFar3L9/n1//+tcsLi52nP15FNSOiDgUTUxMyLE3JpOJRCJBo9HoqIrXFwWhEx6Ph8HBQU6ePMkPf/hD3G63rHz9+c9/ztrammy4urcv14vGMzkvor5ffJAmk0me6txut6x9fxaIxSP+hiCTCW/ParUeWoVRE1KdTicejwev1yvZ6dlsllgs9p3GRuqqLL1ej8lkwu/3EwqFmJqaYnx8XDozItqSSCRYX18nm8123IlZbSDVOXNRnmi1WrFYLHI+j/gdISvRLVQQCEdGRvD5fNhsNrnZi+naq6urbG5u7pJRpxhZ9Rp63Gaznw6I1EF/fz+jo6Oys3O1WiUWi8lKJXGq6iQ9Ogji4CRS24K8qu730+0bkDq6Kfg+agK7Gnv1R6wzj8cjCw9CoZCM+om5R4KX2E0yFHrj9Xpl93i9Xi+5ZnsjL0cF7XYbs9lMMBgkHA4zNDSEyWSSVcbLy8usrKzIMS4v2548k/MipmiKRmKimVw4HCYSidBut+WGoe4dcdCDqNMlonW5mDTdaDTIZDLMz8+zubl5KKMu8G3kxePxMDAwQCgUwuv1yujK8PAwb7zxhmznryaFCUJmX18fbreb119/nWAwyMzMDD6fj1qtxtbWFteuXWNhYYErV64QjUZllVEnQE3K3tnZYXNzk/n5eSwWi4yauFwuhoeHee+991hfX+frr7+WTqzJZJLVRD/+8Y+JRCKcOnVKchparRbRaJRUKsW1a9f4+uuviUajHRneVRSFSqVCNpuVs1TcbrcM/wuoHUGxOen1ella/dprr9Hf388777zD5OQkdrtdTsQVwy47UT6Pgvo5RLVIb2+vlIXJZCIej0vSfDwep9FodJXT9jjsjd6pU0TicGAymeS4jp/+9KeMj48zNjaGXq9nbm6O1dVVrl+/zsrKyveySX1fEFwXk8lEIBDg+PHjkmMook0rKyskEgny+fyuKtluh9CTQCAgJ9M7nU6y2azs0rywsCCjU9+HPjyT8yL4KKL7qdfrxW63S35GOp0G+M6J8HGeqnB2xDwJv9+P1Wql0WhQKpWIx+NSaQ4jxLOqu5babDbJTxCESZEfFDl5m81GX18fTqeTiYkJvF4vb7zxBoFAAL/fj9lsZn19nUwmw9LSEjdv3mRtbY1cLtdRPBcBEbYWOdJisSjD1BaLBZ/Px8TEBAA3b95EURTq9bo0Kr29vbIVdV9fH3a7XaZWstks0WhUznoSjnanyAm+TSWKaiDBTVFHHdW8s73rS6fT4Xa78Xq9zMzMMDw8zOjoKL29vbJZXTqdZnNzsyMjd08CwZMT04/F6blarUp5qvPy3fb8+2G/51TrjhgC2t/fz5kzZ5iensZut6MoCvF4nHv37kl79DIJmd8n1M8vigl6e3sJBALo9XqazSbxeJxoNCoHMoqqxqMEp9Mp7a7JZKJWqzE/P8/q6qp06kThwMvWiWd2XlqtFuvr68zOzmI2m4lEIgwMDPDRRx/JmTulUknOSxEfsvhXRCnEvyInPTk5KaefHj9+nEKhwK9//WuuXLnC5uambIB3GJVG5PfEdNV79+7R09MjHbGTJ0/idDpJJpOsrq6iKIok0/X09GC1WgkGg1gsFvx+P3q9nsXFRSqVClevXmV9fZ0bN26wtLS0y3B0GkRacHl5mWazSTgc5vTp07JKKxQK8dZbbzEyMkIwGJQpSpPJJEmDY2NjcjSFoigUi0VKpRKXL1/m6tWr3Lp1S5J0O1FOwmFrt9vk83kymYwstxd6IiJ56XSaaDSKTqejp6cHr9fLO++8Q09PD8ePH5dOsKIorK2tsb6+LqdQqzujdiv2q6bplojToyD0Rwx3FdPExYgAs9ksU0hGo1FW/Ylo91tvvUVPTw+jo6M4nU62t7fJ5XJcvHiRr7/+mtXV1a7oCaRGu93GZrPR398ve0Y5HA5SqRTRaJQrV67IKpqj0C9IXWEkKtamp6cZGxvDYDAwNzfH0tISN27cYHt7W0Zcvi+deKamKYKwlEgkWFlZYWRkBEVR6OnpkSG2b775RnbaU9d5qwUiSjnhW49/aGiIM2fOcOzYMQYHB7lx4wbXrl2TDW8Oc55abALpdJpGo8H6+jpra2uMjY3R19eHxWKhr6+PfD5PLBZDURTZyyMYDMqKKkCSobe3t9ne3ubixYvMz8/L8kQxHLOTID4z8ZmLks0LFy5Qr9dl7yDRT2JgYICenh6pbwaDYZdzp+77Uy6XyWaz3L9/n4sXL7K9vU2hUJATvTsR4rlLpRLFYlFygETXz56eHo4dOyYnthoMBsbHxwmHw7z//vuy/4LoD7Ozs0MsFuPevXuyGWS38RX24qAo7WGN4L4oNJtNarWa7EsC4HK5ZM8swfuxWq2Ew2G8Xi+vv/46oVCIH/zgB3KCuU6nI5vNsra2xtzcHDdu3NjFR+sWCE5HKBQiEAjg9XoxGo3kcjni8Tj3799ndXWVWq3W9SMB9u7TIuIyPDxMb28vxWJROi8LCwuyZ9D3eRB6JqsuPrTNzU0URaG3t5eBgQHMZjMej4fp6Wl++tOfsrGxwVdffUWxWCSZTNJoNOTYdbvdjl6vl02kRkdHCQQCnD9/nqmpKVqtFpcuXZLchUQiceiHDor3JZrz3L59m1arJT9YkSIyGo309vYC3zo8Ij0QjUapVqssLy+Ty+W4evUqsViMxcVFkskk5XJZ8oI6FWruS7vdJh6Ps7GxgdfrJRQKyYiUoiiywkqE9202myQhttttOZDy5s2bkkOTSCSoVqsda2DUFWhCPrOzszgcDk6ePCmd3P7+fj788EMKhQKvvfYaOp2OcDiMy+WSZePwQLdmZ2fZ3t7myy+/5M6dOywvLx/69fSi0Ww2icViRKNRWebbSc0dnxTqtKOIvORyOdlt+NixY/z+7/++JMtbLBYGBgaw2+0MDg7KlhSlUomlpSUKhYKMtiwvL8tRJt3CdREQ6+e9995jfHwcu91OPp/n6tWrLC0tkUwmZYq7m557Pwgah16v35WqFyTdK1eusLq6KtNo8P3akid2XtR5dfF/YQSGhoYYHx9nYGCAvr4+pqen6enpYX5+nkqlQjwelydIEQq32WxYLJZdTdhEyevg4CBXr17l+vXrXL16lW+++UZyGg5zjlU0fBKOyO3bt+Xp1mazEQwGiUQiclicgKj8KJfLsunc119/TSwW45tvvpHyEwaj0yIue6F2XkTDNeEI+/3+Xb0pXC7Xrgid+kTQarVIJBJkMhlu3LjB/Py8JI2JaolOhaIo0mkTPIPh4WHpkFksFnp7e3dVgAAyGmWz2dDpdBSLRSqVCtevX+fGjRtcunSJubk5mTI4rGvpZaDRaEjeQjabpVAoyGnk3QaRNhLNGrPZrOTiTU1NYTKZJDnXZrPR29srm4+2Wi2ZJrp16xYbGxv85je/YWlpiVwuJ3tvdYvc1Okv4bwEg0FsNhuJRIJr166xsrJCKpWiVCphMBi65tn3g7pnmZhfdPr0aVlYIQYvCg5Qo9H43tfSM0dehGffbreZn5/nk08+YWpqikajIYd39fX18e6775LJZIhEIpRKJdnJMxQKYbVaCQQC2Gw2hoaG8Hq9coz2jRs3uHnzJuvr67vSTp1gaMXGKUK1t27dolqtymZqdrsdr9cLPFCSarXK1taWdF6KxSKLi4sUCoVdfU667aQjHJGlpSV+/etfMzw8TDwex2azSZKlaPcvnD3RxVmkhS5fvkw0GuXWrVtsbW2RzWY7Rk8eB/Ec5XKZeDzO+vo69+7d26U/6jHzIg0JUCgUqNfrzM/Pk0wmuX79OnNzcySTya4aovc4CGMqiLqbm5tygnu3P79w8OPxOHNzc7Jvi8FgoLe3V6aVhLOys7MjT9HLy8tks1muX79OPB5na2uLUqnUsRyy/bC3Qk2MGxEy2dzcZGVlhY2Nja579oMgnBcR5RUFADqdTkYvRUM6eDUc1Kd2XoTjoiiKbPh048YNFhcXef3112m1WoyNjRGJRHC73QwPD1Mul1ldXZVpkXa7LTdxkZMXE0p//vOfc/HiRW7fvs2dO3d2/d3D7u2qGesAuVyOTCZDMpnkypUrBAIB+vr6JIFXyLJcLrO+vk6xWGRra0tGJETZ68sYJ/4qoeY+7ezsyDTG2NgYZ86cwe12Ew6HcbvdDA0N4Xa7cblc6HQ66vU6xWKR27dvE41G+cu//EuWlpZIJBIUi8VdHXs7HULn8/k8Gxsb3L9/n6tXrzIwMIDL5ZINDcUJWlwvKiOy2SxffPEFy8vLfP755ywtLX3n/t0MIY92u02pVCKbzbK4uChTH936/Oq0IzxI71+9ehWr1cqxY8ewWq0MDQ1RLpeJRqOUy2W2trYoFArMzs6Sy+W4ffs2mUyGhYUFWdUIdHzKej8Ix0VEn5rNJmtraywuLsrxGSLidBQg9EccHEVKv1wuS9L/xsaGPKC/Cpv7XJEX8f9ms0m5XGZzc1N66rlcThqOer1OKpWiXq9LTy0ej0vioclkkp1Wb9y4wcrKyq4Szk5WGBH+FxN8xbC4dDot5Viv18lkMtRqNRnqBbou0qLGXie4XC6TSCSYm5vDbrezubmJ3W5ndXUVu93O/Pw8iqLI9uazs7OkUik5UXm/ybidDiGfYrHI6uoq33zzDSsrK2xtbT2SjCwasSUSCUqlErdu3SIWi1EoFHalfLtNTnshIlaCTNhqtSiVSqytrUnifzfqi4A6zS/GklgsFvR6veTe1Wo1UqmULJ0XG5M4RIk0/957diPEZr21tcVvfvMb2u02hUJBOnVCDt3ouB0Esf/u7OzIFP/c3Bxra2uvvEO1chDj3uPxPBEdX+TPhddqs9lkWFsdNVB77eJ3AJmCajabZDKZ70wTfpzCZLPZV7Kq7Hb7E5crqNu5A7s+dHVKSDzrizQUpVLplcjH4XA8sf6oU2OC12M0GrHb7ZhMJjmPR0zh3uvsqeX1tLIrFosdoT8mkwmz2YzVasXj8TwyGimc5Wq1Kps71mq1A7vwPg6vSn+eRj5qCLsh0o6iok8MfFVPQ34R6+xVycdqtR4on72NP0XXb0F4bzabsmGkmHckSMzq4ZXPe4CsVCrfu3weJ5v94HA4CAQCtNttaV+E8wsv3oF7FbKBx9tmsT5Ed+UPP/yQP/iDP+D27dv85V/+Jdvb27Ig5WVGXQ6yzS+0hlQMVBR4VKhRPVwOdo8DENOp1R10u9nb3w9H8Znh2xy9emaPoijSiIiUiNCTvYMWu1VuYhMSYzeehLMinDr10M6jAhHVa7VaVCoVmW7c2dmR8ujGxnz7QRwOqtWqjP7q9Xppr9Vy2TvapdMj30+LRqMhG6EKmRwljpiAOBiJBrGrq6tcvHhRturI5XLyulf2Hl9E5GVvy+m934PHd3ZUX7f32scJqBMiL68Shz3yst/MnoO+flT641kXUidEXmD/tXUQXkSaqNMiLwKPktfLSJsd1siLwKPksJ/z9rS290nQSZEX9bgEgZfpwB3WyAt8d56cwWB4pIP7MvHSIy/q/Kr493Gbz+Putff/GroXe/VHjSdxho+SnqiJzgfhqHBbHoe9tugoykPNUVTL4yAO1FGUk8BR1xcBoTciilmtVnfpy6vm/7zw1qNHLcyo4cVhLxFc/e9Bv3NUoF5bmlwejxcZnet0CN3Z7xB5VOWyF/vxyI4q1M9+2PpmvVDn5Sh/yBpeDDQdejJocnoyaHLaDU0ej4cmo+/iMMrkQM6LBg0aNGjQoEHDYcPRKlrXoEGDBg0aNHQ8NOdFgwYNGjRo0NBR0JwXDRo0aNCgQUNHQXNeNGjQoEGDBg0dBc150aBBgwYNGjR0FDTnRYMGDRo0aNDQUdCcFw0aNGjQoEFDR+G5nBdFUXyKovy5oiglRVFWFUX52T7XKYqi/FNFUVIPX/9UOaDrjaIoP3t4v5KiKH+hKIrved7nq4CiKP9IUZTLiqLUFEX54wOuO6Eoyi8URUkqivLYpjuKovwLRVHuKYqyoyjK332R7/n7hCafx0NbX/tD05+DocnnYGjyORidYHueN/LyfwJ1oAf4O8D/rSjK8Udc9w+A3wdOA6eAnwD/46Nu+PD3/znw3z+8bxn4v57zfb4KbAH/BPiXj7muAfxb4O894X1vAP8TcPXZ39qhgCafx0NbX/tD05+DocnnYGjyORiH3/aIwUtP+wLsDx9uUvW9PwH+j0dc+yXwD1Rf/z3g633u+78B/1r19djDv+N81vf6Kl88WCB//ATXjT/4OJ74vp8Df/dVP58mn5cmF219afqjyUeTz6uQSUfYnueJvEwCzXa7fV/1vRvAo7yz4w9/9rjrvnNtu91efPiAk8/xXjVo6DRo60uDBg2vAh1he57HeXEA+T3fywHOfa7N7bnOsU9ubO+1B91Xg4Zuhba+NGjQ8CrQEbbneZyXIuDa8z0XUHiCa11Asf0wdvQc99WgoVuhrS8NGjS8CnSE7Xke5+U+YFAUZUL1vdPAnUdce+fhzx533XeuVRRlFDA//HsaNBwVaOtLgwYNrwIdYXue2Xlpt9sl4N8D/6uiKHZFUd4Bfgr8iaIow4qitBVFGX54+b8C/j+KovQritIH/H+BP1Y9xIqqrOz/B/xEUZT3FEWxA/8r8O/b7XZHnQwVRTEoimIB9IBeURSLoiiGhz9rK4rywcP/Kw+vMz382qIoill1nz9Wl/IpimJ6eL0CGB9e33H9ejT5HAxtfR0MTX8Ohiafg6HJZ390jO15TlayD/gLoASsAT97+P33gBXA+PBrBfhnQPrh658BysOfmXgQNppW3fdnD+9XAv4D4HvVDOxnkM0fAe09rz8CBniQT/Q/vG74EdetqO7zK+Dvq77+9BHXf/Cqn1eTz0uRkba+NP3R5KPJ51XI59DbHvFHXigURflfgES73f7nT3Dtu8D/3G63/7sX/kYOIRRF+UPgeLvd/sdPcK2JB+zsU+12u/HS39whgCafx0NbX/tD05+DocnnYGjyORiHyfa8FOdFgwYNGjRo0KDhZaGjcnEaNGjQoEGDBg2a86JBgwYNGjRo6ChozosGDRo0aNCgoaNgOOiHTqezIwgxhUJh3ymWLxNWq7Uj5FOpVF6JfDT9ORgOh6Mj5FMsFl+JfGw2W0fIp1wua/bnALwK+6PZnoPh8Xg6Qj7ZbHZf+RzovLws7CUJK/tP0D7y0GSlQYOGV4l2u63ZHQ2HDt+78yJqtHd2dgDQ6XRycWgLZDfUchLQ6XSanHjgxAnHTtVDYJceqa/RoEHD00O9tkA7PGl4egidUdtr9b979/4ntduvJPKiKAo63XfpNuqH0fAAmiwej72Kf1TwJAv8KMlDw8uBpkOPhhYVPxh7nZb9fi6ueVr5fS/Oi+yIpygYjUb0ej12ux2dTkexWKTRaFCv12m1Wuj1eunYHHVl0Ol02O12jEYjrVaLdrtNtVqlXq8DR1s+YkEIfTGZTOj1+l3y6daoi7rL5M7OzncidIqioNfrdx0SjrKuHAQhFyHHVqvFzs4Oer1eyvAoRvCEvTYYDCiKIuVy1FNIYq2JlzprIGzRUZaPwF7HBL5dYyaTSfoBer2eZrNJrVbb2+H3sfheIy/CeTEajbhcLgwGAzs7O1SrVVqtFq1W6/t8O4cewnmxWCw0Gg1arRbNZlNuzhrAYDBgMBiwWq0YDAZarRaNRuPIbTaawXx+iE1Ik+W3MBgMu6LkR21d7YejGu19UuzVE7WTZzQapb02Go1Uq1V2dnbk/vakeKnOy15Ogs1m46233qKnp4cLFy7gcrm4c+cOyWSSW7duEY1GyefzFIvFXafJo6IcQk46nQ6Xy4XT6eQP/uAPGBkZIRqNks1m+eKLL7h9+7a8Ho6OfNRhyGazicFgYGpqilAoxMjICD6fj88++4ybN29Sr9ep1+tdtRkJ/dDr9ZhMJiwWC16vF7PZjNvtRq/XS+dte3ubSqVCoVCQzpyaC6ThAYSNsVgsGI1Genp68Hg8JJNJksmkjAofNYhN5uTJk/h8PtbW1shms+RyOYrF4pGMMIi1Fw6HsVgsBINBLBYL5XKZWq1GLBYjm83KjRiOxlrbLzIpsgVibQ0NDREKhejr66Ovrw+r1YrVaiWVSrG4uEg0GuXq1as0Go1H0kr24nuNvJjNZkZHRxkeHubDDz/E7/fjdrvZ3NykVCrJTalSqRzZKIxQAovFgtPp5LXXXuPMmTPMz88Ti8WYm5vbde1RWByPgth0enp6GB4e5syZM4TDYebn55mdnaXZbHalfIRzazabcTgchEIhHA4HfX196HQ6Wq0WtVqNRqNBLpejUqnQaDTk73abPJ4XYr2J02A4HKa/vx9AHqJqtdqRkZtaR/R6PX19ffT398vTcblc3kUDOCpQHyw9Hg8ul4vR0VGcTieZTIZSqUS1WqVQKBy56NSjnlcdfDAYDFgsFnp7exkbG2NsbIzR0VHsdjsul4uNjQ2Z9r927dqrSxs9YjolDoeD8fFx+vv7uXDhAr29vTgcDvR6PePj4/T29uLz+UgkEly9epX5+XlWV1dZW1uT9z0qC2VnZweDwYDX65WnQIfDIT/co5qDh28XhF6vJxKJ4PF4uHDhApOTkxiNRnni6bbcvPisRZh1aGiI119/Hb/fz/j4ODabDb/fj06nk2nF1dVV8vk89+7dI5VKMTc3RyKRkPLppojU80BsSCMjI/T39/POO+8wMzPD3/zN35DP58lkMhSLRaD7bZB6fQUCAdxuNydOnGB4eJh4PE4qlep6GeyF2mlxOBw4nU7effdd+vv7mZ6exu12s7q6SiqVolKpkEgkaLfbXRf13Qt1FPxR1UPCRjudThkdHxsbo7e3l1wux/z8PAaDAZPJhMlkYmpqimq1Kve4J8FLi7yoIycWi4Xx8XGGh4c5fvw4oVAIq9WKXq+nv78fRVHo7e2lVCpJEk+pVGJ1dXWXkLpVEdQQCuB0OvF6vTgcDmw2G0ajUYZqH8fi7kYIh004d4FAgHA4zPHjx5mZmWFzc5N0Or2LSNdNECFri8UiDwE9PT0cO3YMq9WKy+WSxEqRNioUCni9XjY3N0kmk6TTaem8HMWw/16oDW84HGZycpLz589z/vx51tbWuHTpkow0QPc7L4B0XlwuF4FAgOHhYcbHx/nqq68kIbXb1tbjIGRitVrxeDycOHGCiYkJTp48icfjwefzEY1GuXnzJkaj8UimGfe2q9Dr9fT29hIKhXj99dcZGRkhHA7j8/m4cuUKd+/elb87NDTE5OQkW1tb6PX6J/6bL9R5EQ9gsVhwOBy4XC4GBgYIh8P84Ac/IBQK4fP5sFgsKIqyq0LCaDTidDqZmZnB5XJRr9eJx+MUCgUymYysnOhWA6I+XVutVoaHhxkeHsbpdKLT6UilUjLvLHKC3SqLR0HoVqvVQlEUBgcHGRoawu/3Y7VaWV1d5d69e2xtbVGr1eR13SAjEVESoerp6WmOHz+O2WymWq2STqf54osvqNfr1Gq1Xc6bw+FgcnISgJMnT3L79m2Wl5ep1WoyHdINMnpaiOiT3W7HarUyOjrKzMwMFouFXC5HNpslm81SrVa7XkZqZ8RgMGCz2ZiYmCASieD3+zGbzZRKJWKxGJVK5Yn4CN0Gg8FAJBKht7eXcDiM3++XaTTBPQsGg/T29rK9vU2xWOxKndkbcRGOil6vlwftqakpPB4Pp06dwu/3Y7PZ2NnZ4dKlSySTSRYWFlhcXMTv9xMOh3E4HMTjcfL5/Hf6mh2ElxJ5sVgshEIhBgYGuHDhAn19fbz77rs4HA5ZIi3IPCJCYzabURSF6elpxsbG2N7e5v79+2xsbJBIJNDpdF27aNSKIJyXoaEhxsfHcTgcKIpCOp1mY2ODQqEgyaoGwytp0/PKoHZeBgYGGB8fJxAIYLPZWFtb4/Lly2xublKtVrtKX4Tz4na7mZiYYGpqiuPHj1OpVFhfX2dzc5Nf/OIX5PN5yuUyiqIQCoVwOp288847hMNhIpGILEfMZDJyY4ajEVEQEBE8sWHb7XbcbjcjIyMcO3YMi8Uiiam5XE46L92OvfZnYmJCkuBNJhOFQoF4PC5TKEcFQleMRiORSISBgQF6enrw+XyydYXZbMbj8RAMBgmHwzLN2O0RKvF8goMXCAQIBAJ8+OGHDAwMcPz4cdxuNwsLC8TjcS5dusTFixeJx+Mkk0nGx8cxm83Y7XYSiQTZbPapZPZCdj9x0rNYLNjtdsbGxrhw4QKhUIgTJ07gdrux2WwYDIZHVhGpORwibBSJRHj99ddxOByUSiXq9TrlcvlFvN1DCZEOcblceL1e+vv76evro9lskk6n2dzcZHl5mUKhcOSiLvAtN8Hn8+H3+4lEIvT19VGtVonH40SjUaLRqDwZdoN81MbBaDTicDgIBoM4nU7gAaFU8MPm5uYoFArSuYvH41itVgBCoRCTk5OSI1Or1bh79y65XG7X3+kGmT0OajtjMpmYmZmhv7+f0dFRQqEQ6+vrpFIptre3KZfLkuzc7bIR9sftdhMMBmVhRTKZZGNjQ1bRHCW0220MBoNcd2NjY0QiEex2+670htjXzGYzFosFvV7fdTQHdcRFpJ1F0cDU1BQul4uRkRE8Hg9TU1P4/X70ej2VSoW5uTnu37/PysoKuVyOVqslAxzj4+MALC4usrGx8VSFOi/MeRFvKBwOc/r0af723/7beDweent70el00sHZy1QXG42aLGYwGJicnMRkMmG320kmk6RSKcl47zZjK55dURQ8Hg89PT2Mjo4yMjJCtVolm82ysLDA3bt3KRaLuxr5HQUI+ZhMJsLhML29vYyPjzM6OkqpVCKRSLC6usrKygrNZrOrwvyC66LT6XC73fT39+Pz+QDI5XLcvHmThYUFrly5QqlUwmg0AsiGj+vr6wQCAf6H/+F/YHR0lFOnTtHb20u1WmV2dvZVPtorgbq6yGaz8frrr3Py5EmOHz9Of38/t27d4urVq6ysrMjQf7fo0n4QEU2TyUQgEKC3t5cTJ04wMDDAL3/5S5aWliTZu5simgdB2BwRUejr6+PkyZP09/fjdDqlwyKiMoITY7Va5RrsRoi9XjxvX18fH330EaFQiOPHj+N0OgkGgxgMBvL5PIVCgcuXL/Pll18Si8XIZDIy2tLX18epU6dYWVnhyy+/JJFIyCrRJ8FzOS/iwxO5rrGxMWkIPB4PNptt13WAVHzRlC6Xy9FoNLDZbJhMJsxms/R2w+Ew09PTVCoVFhYWyOVy1Ot1qtVqV3q2ZrOZ4eFhBgYG8Hg8WCwWEokEqVRKpgTE5nyUIPhOIp8aiUQIBoNYrVaWlpbY3t4mk8l0XXm0WDdmsxmr1SpzxG63W2441Wr1O9EBdWfdQqEAwMrKCm63Wx4wgsEggUCASqVCqVTa9fudDrHBihLNVqtFuVyWqWqj0cjAwACBQICxsTGGhoZoNBpsbW2xtrbG8vIymUymq3RpP6jtssViYWxsjMHBQUk83dzcZGlpSaZCjgrUDt34+PiudNHeyEq5XKZarZJMJkkkEl3Fd9kbcdHr9ZjNZnw+H1NTU/T390t6Q6VSoVqtsrm5yc7ODrVaTUbGRb8pcUAPBoN4vV7JGxJpI/XffBye2XkRD9NutwkEAoyMjPDuu+/yX//X/zVut5uenp5d+WU1uUf0TqhWq8zPz5PJZBgaGsLr9crSYL/fj8/nIxKJ8Oabb/KrX/2K2dlZ8vk8lUqla3oNiGcQpXjnzp1jdHSU/v5+3G43V69eZWlpiVgsRi6Xk+Soowa9Xo/b7eaDDz6QUSmDwcDt27e5desWm5ub1Ov1pyq1O+wQBtRms9HT08PQ0BDHjh3D4XCws7NDvV6nWCxSqVSABxvQ3oqQVCpFJpPh0qVLZDIZfud3foeTJ09y9+5dRkdHiUajFAqFrpKZCGnbbDaCwSDVapWNjQ2Z9jCbzbz22msMDw/z5ptvMj4+zuzsLAsLC1y+fJmvvvqKfD7/ip/k5UMdwRYp6/fee4+hoSGsVivFYpFbt25x6dIlubEcFbTbbRqNBna7nffee4/BwUHGx8dxuVyyIMBoNEo+YiqVYmVlhYWFhafehDsBOzs7NJtNye+ZmJjgpz/9Kb29vZw7d45Go8GVK1dIJpNcu3aNXC6HxWIB4N69eySTSZluikQinD59mnA4LPtRraysUK/Xn2pPfybnZW9Yx+Vy0dfXRzAYlBGXvW9AVBeJ6EksFqNYLHLnzh0ymQx6vZ6dnR2sVqvM6QOYTCYcDodsJywMczeQocRzCMMhTtahUAiAarVKNBplbW2tq/k+B0GkTaxWKw6Hg97eXoLBIPl8nnq9zvb2Ntvb211JrBQ6bjabcTqdOBwOHA4HZrMZQM4Is1gs+4byxSGjVCrJKKdIQfX19VGpVGQ/pW44DMC3z9xsNqlWq7uqz8QBaWRkhJGREYxGI+VymbW1NRYWFiTXRfTpOAoQzouomnE6nTKCkM1mKZfLMq19FCDWgYh4Op1ObDabPHjvvbZarVIsFmUERuhaJ2NvVZHgiIn2DAMDA4RCIcxmM6urq9LRTafTrK6uUiqVpJ0S7QbE4crn8zE4OCi5eZlMRs42VP/tx+GFpI36+vo4f/48x44do7+/n3a7vSt3JSIujUaD5eVl0uk033zzDfF4nG+++YZMJsNPf/pTTp48KclR4t4mkwmr1Yrdbv/OnI1OhzqvOjk5yfDwMOfOnaO3t5ednR0SiQSXLl3i2rVrxOPxI5F/FxDOrjrvHIlEOHXqFKFQiK+++oqNjQ2uXLkiO+p2C1EXdh8QnE6nLNMMBAJyfQkOkOBB7e2JpHZG0uk0RqORUqkkS80vXLhAu93m1q1bu/5uJ8tQPLe6U3ej0aBarWK1WmXa8Uc/+hHDw8OUy2VWV1f5L//lv/D111/LnjhHidshylzFpuL3+/nlL3/J2toaq6ur5HK5IyWPnZ0dSZAPBAKEQiH8fr/so6Ruyrazs0Mmk2Fra0t22u2mBnWC4yL6jp0/f54//MM/xOFw4PV6icVi/Mf/+B/Z2tris88+I5/PSxmJ5xeOr9lsxmQyMTExwQ9+8AOuX7/Ob3/7W9bW1mTl49PwhZ7LeRGnwGAwKEsz95Yiik2oUqmQz+dZWloiHo+ztLREKpUilUpRKBR29VVQo1uUYC/Um5PZbKa3t5fe3l6cTidms1k2FUun0/LE3I1y2A9q/TGZTPT19dHb2yt7BOVyOZLJJKVSqevbtwsSu1hLoudGOp2W5DeRSto7lFLIpVqtks/nJYlO5J6tVmvXNR5TVzSKULQoiRacMqfTiV6vJxaLkUgk2N7eJp1Of8f+dDtE5Nfj8eB2uzGZTMADMngqlZJ9g46C4yIgeFFerxev1yt7AYmfiTVVqVRoNpvEYjHW19d3bdydDnVVnl6vx+/3MzIywuDgIKFQCL1eT7VaJZfLsbm5STQalSnsR9li0VPJ7XbLdim1Wo1kMvkdPpW6+vggPJXzovY4FUVhfHyckZER3n77bc6dO4fNZttlPAVprlAocO/ePdbX1/lX/+pfsbm5ycbGhiSg6nQ61tfXsVqtnDhx4pHebTcohMBeArPP5+P9999naGhIzqi5ffs2KysrLC0tEY1Gj0RX1Ee1nNbpdAQCAX70ox8xNDSE3W6nXq8zPz/PvXv3yGQyNBoN2YG4GyHC+q1Wi0KhwNzcHL/4xS9wOBwMDw/TaDQYGxsjmUyytrZGrVaTkRgR6k4mk2SzWebn54lEIlitVvr7+/F4PJhMJprNZleRwUXaqNlsyvEkAwMD/O2//bfp6+vD4/FQKpX45S9/yZ07d7h27RrRaFTK+ihAcKqsVisnT55kaGgIs9lMvV5naWmJ+/fvy0hdtx4i90LYHqfTyalTp6TeiMGnwrFptVqymOKTTz7h8uXLpNNpeZDqVFukrvzd2dmR6eo33niDn/zkJwwMDDA5OUk8Hufy5cvcu3ePzz//XNrhR0Xoms0mOzs7DA0NMTU1RSAQIJ/Ps7m5yd27d6nX67vW3EutNlJ7Y4K1b7fbMRqNu/6wEEK1WmV7e5utrS1isZj0tsQHbTAYds2lOQoQi8Bms+H1egmFQpJ9XavVZO+SUqkkx4R3u/HYq7Qmk0mW3vX39xMIBKhWq1QqlV1Tf48ChFFpNBoUCgU2Nzdxu934/X5qtRpWqxWbzfZIo6koijQgomO1TqeTqdhu25jUeiRGKvT19RGJRAiFQng8HsrlMvl8nu3tbaLRqDxIqVPT3XRg2gv14cBsNsvu57VajWazSbFY3NU36ChApNDElHZRlWc2m3fxXYRTnM1mZYQ8m81Sq9XkfTpVZnujtmLoq9ifTCaTjHaL/lMWi0VW9T1q/xaHblHIAxCPx2UTyGfVsSd2XoSXDg8Iuna7nfPnz/Pxxx8TiURwuVzSWxMnvmazSblcZn19nb/+679mfX19V/8Ei8UiIy8i/C0Y3HsF2S1QV195PB7Onj3L1NSU5PtEo1Hi8TifffYZ8/Pzu1janbogngU6nY6enh4++OADxsfHeffddzEajdy9e5eNjQ2++OILlpaWaDQamEymrpaNur1Ao9EgGo1y8eJFHA4HyWQSvV6Px+NBr9fL1v+wm/siWhNsbGxw69Ytpqen5ZrtNtmp+wI5nU7Gxsb4gz/4A9mMbmdnh88++4yVlRUuX77M0tISzWZT2p5utDtqqCs1LRYLwWCQd955B7fbzfr6OslkclfTsG6q4NsPQmc8Hg/Dw8McO3aMH//4x3i9XnkoaDQasuy+VCrxzTffyOh4Op3uKl6Q+MzPnDnDRx99JJ24eDzO3NwcdrudwcFBbDYbP/nJT9ja2uKTTz6RjS8B6ciIooLp6Wneeustbty4wVdffcXy8rLkBz2L3J468iJyV6LTaSgUkhOi1W+43X4wWTOdTpNMJuWmXK1WpaHQ6XSYTCbZ18XpdMqcazdCzeMQfUv6+vro6emRjY+EN59IJEin00duyJdgpYvIgJCPx+Oh0WiQTCbZ3t4mm81SLBa7jsT9OAjei+h1FI/HMZlM2Gy2fbtTqg8Dgvsifr+bI50mkwmfzycjd+LUV61WicVibG5uks1mZVfmo6RHIvJrt9vlEEaLxSI7DJfL5V3px6PgvAiZuFwuPB4Pfr8fl8v1ncNDJpMhl8sRi8VkdVqnR6jUTrsolDEajfh8Pnp7e9Hr9ZKXur6+js/nw+fzsbOzI6sg1Q1nxX0URcFms+F0OqUTU6lU2N7eJp/PP5Kf90LTRuJmYp7OmTNnmJqa4sSJE4TDYQwGw65GWYLtv7a2xl/91V+xsrLC9evXdzWqgQcnyr6+PrxeL2+//TavvfYakUhk19/tplOQuoLKbrczOjrK7/3e79HT04PNZiObzfL555+zsrLC+vo66XRahjKPAsRmajAYsNvt9Pb28uabbxIKhWi326TTaT755JMjNyZBOBgmk0luNJFIhGw2y40bNwBk6nW/QYLi62KxKMtgn6YVd6dAGEyDwUBfXx8/+clPGB0d5cSJE+h0Om7evEk0GuXTTz9leXmZbDZ7JDZn2M1ZbLfbsnpkamqKkZERyuWyHNypJlEeBdnAA7mIJo49PT1y6KvIIojCkp///OdsbGxw/fp1EokE5XJ5V8agkyFswsDAAJFIhOnpaQYGBvjmm2/45S9/STabZXt7G5/Px/Hjx6VshCOinlsovj5x4gRjY2PY7XbW1tYkn6rRaHwnqvc0+/0TOy8iWmA0GgkEAgwMDOD1erFYLN+pLmo2m9RqNXK5HMvLy3KgYLValSdlk8mEyWSSnT57enoIh8OyK69AtxkWdd8S0e7d6/UCUKvViEajbG1tUSqV5Id7lE6E8O0sH6vVis/nw+l0yoZsoq+L2gk+KqF+0WtBpFgLhYKscFATdA9aL+L0KIxUN8ltb08K0c8lEongcDgkl2xjY4NYLEYqlTpyVXxqqLstm81mKpWKrADtJvL200D0uxFlvYJIKg7k1WpVdmJOJBKSP9bp+9ReO+B0OgmFQtjtduBB9dnCwgKFQoFEIkGhUMBqtcqDeKlU2pV1EVVsRqMRv99Pf38/iqLIqJWoeFTvbU9rix7rvKgdE5fLhcvl4ty5c7z//vuSYCquE6HXTCbD3Nwct27d4ptvvqFUKuF2uwkEAoTDYZxOJ9PT03i9XsbGxvB6vUxMTBAKheQcJGCXUnTLBtVut3G5XExMTDA6OipnP62trbGyssK1a9dYX1/vqgGDTwvR10XkmyuVCrdu3WJ9fZ35+XlisZhktneLXhwEYTjhwYbj8Xjo7++n1WqxsLAAcGB4Xy0fQRAXefxu0S+RTmu1WgQCASYnJzlz5gzvvPMOVqtVEnP/4i/+gvX1dTY2NiiVStJuHRUI585msxEOh3nzzTfxer1ysOna2hqbm5vSqesW/XgSqDMCarsi5CAO5ZlMhkQiIae0d5MOieDC+Pg4b7/9Nul0mj/7sz/jzp07bG9v02q1MBgMlMtlZmdn5Z4v2vw3m0156J6eniYUCnH27FkmJib47LPPuHnzJisrKy+E1PxUkRfRMC4UChGJRPbNUdVqNRKJhCwla7Va+P1+7Ha7nNtz4sQJOVbA7XbLWT6iLFp9b0E27IYcvWjWEwgE8Pl8srw8m82SSqVIJBIkk8mOz6E+DwTR0m63S3b7xsYG6+vr5HI5isViV228j4Na70XU0uVy7XJAnmSjEWtYyLWb5Ke2QVarVQ7w7O3tpd1us7KyQjweZ3l5mfX1dYrF4ncim0fBCRYbrVhjvb29WK1W8vm8PBUXi8WuJHIfBLF+xGa81xkR+1Cj0ZAzfLqlpwvspoYYjUY8Hg/hcJjt7W0WFhbY2tqiXC7LlGyz2ZTzv8R+LRrMiQiwSG8HAgHcbjelUkna8L3ZmmeR41MRdsUHLB5wb2mUeEOtVot6vY7H4+HDDz/E4XBw9uxZXC4XoVAIi8WCz+fbVWEkpnQKBarX6zSbTba2tlhaWpLzEjqxDbz6Q9Lr9fT29vLhhx8yNDSEwWAgk8lw9epVlpeX5fiEbghFPg0EWc5kMjE5OclPfvITOe+qWCzKKiPRqrxbTjr7Qf25i3SZOAzo9Xqmpqaknhy08EV6SJDlzpw5w7vvvovdbt81B6mTjbBw7gT59MyZM/y3/+1/SygUkjbkz//8z1lfX2d9fZ1sNisjEN1c2fgotNttWSkyPDzM5OQk+Xyev/qrv2JtbU02fjwqtmcvnzMYDDI5OUkkEtm1Nmq1moxKZTIZ6eB1yyFK7ONi+KQYfJvJZORMQfWzqntICUdXHMxHRkbw+/386Ec/IhKJsLa2xuzsLLdu3SIajcqWBALPuvaeutpIeKViFtGjoDYmk5OThEIhPvjgA8niFuRCca36JQQk+p0kEgnm5uZkKmXvBN1OgnDMRJosGAyi0+moVqusr6+zubm5q+692zdoAaG8osdCMBjk9OnT+P1+zGazrKoR6SKhO0chZQQPIpmiM65oMyD6cuzNGe8lvwn5iIhLJBJhamqKarUqu8l2ugzFc5rNZrxerxz8ZjabZXXIjRs3ZHVRtVrt+pYM+0EUDPh8PgKBAIFAgHq9LtPWor29yWQ6UvZHRKMcDoesblTbl0ajIQcwitlX3ZQuEg6Iy+Wip6dHBhVEZVCr1dr1rGJ/2nswN5lM9Pf309fXx9TUFH19fdy+fZu7d++ytbVFPp+XwQ94vrX31B12RedKscEK70u8iZ2dHXw+H+fPn5c5QqvVKhvcqPP36nC3aKIlZiJtbGywsbHB5cuX+fWvf00ymZSzStTeXydA3UNAhNKEZyva3G9ubhKLxaRsjhoEwctms+F2uwkGgxiNRjY2NiQ5Tn1iFr/TzRDGolwuS6Lp0tKSPN0UCgWmp6dJJpNsbW3JnkkCagLr2NgYQ0NDDA4O4nQ6WV1d5c6dOywuLsq11ynrSUB8/iJyOzk5yYULF5iZmcFqtRKPx/n6669ZWVlhfn5ehquPQt+SR0Hog8vlknyEVCpFNBplY2PjkZvUUYFwXsQkcrfbvWuf2dnZIZ/Py0GV6gxAN+iSWP+iKajT6cRoNO4bnRTyEhQIh8PB9PQ0Ho+HY8eOYbfbWVhY4Pr163zzzTcsLS2Ry+VeaCf0p468qJ0U4X2p2/eLxeHxeIDvtnxX58iAXaEodUvvRCLBwsICt2/f5ptvvtn1NzptcQnnxWazyeF6YnEUi0XpwKTT6a4sX30SiFOMSCV6PB5ZfRWPx2VfF6E73WAwngQiMtdsNmW/pN7eXkZHR0mlUgwODmIwGNja2to1lVUdnRItCaampgiFQthsNjKZDLdv35a/9yIIdN831FUNYtzBm2++STgcxmw2k8/n+eKLL+TsFbHhHJXWA49Cu93GZrMxODiIz+cjn8+TTqdl5Yw6BXCUIOgQ4vBkt9t3yaHdblMqlSgUCtRqta4bSSL2VpvNJmeeCfKuumBGvW+LPkGiy/4PfvAD/H4/w8PDAPzpn/4pc3NzzM7OEo1G5Yw29d97HjyR8yLesCiHunTpEgBDQ0P09PTIsjKRUnoUgVDt9IgBcmLIXL1eZ3l5mVQqJae6xuNx4vE4a2tru0JTnQT1+xYzjKanpxkaGsLpdFIoFGQeXowGP8onQwGhI6I0sdFoyFCjuhqt2yF0QHDLtra2uHLlCjMzM4yNjeF2u7lw4QKrq6tkMhny+bzkBIkRHuIU+fbbb3Pq1CkAZmdn5SuTyci/1Wk6J96vSIW99tprjI6OUiwW+c1vfsO9e/eYnZ0lm83K0t9Oe8YXATWvw2Kx0NPTw9TUFAB3795lcXGRWCwmI1PdsiE/LcQhU33AbrVaVCoVcrkc6XSaTCbTldEpsTaq1SqFQgGTyUQgEODcuXPUajVZUCLGAdhsNtmfTBwWTCYThUKBzz77jEKhwPXr11lfX6dUKr0UbtBjnRc190LMNLhy5QqZTIYf/OAHmEwm3G63zGEJz+qgks12+8GMlkajQSKRIJ/P880337C4uMj169dZWFiQG5g64tLJhken0+H3+5menpah+0KhIKtoEonEkTceaoiyV3HKEUM+j9IGpHZeWq0W0WiUa9euYbVaaTQa0ikJh8Pcv39fdh9uNpvSmExNTcmS2LNnz/LVV19x9+5deSLqBm7VwMAAb731FidPnmRkZIQbN27w29/+lsXFRebm5qjX67sOBEeFK6WGIMSLtMjU1BTb29tcunSJ5eVlSdQVB4SjssYeBfV+I+aBiSnb2WxWUhe6SUZq56VYLGIymfD7/Zw7d46enh62trZYWVnBYrHgcrnw+XxMTEzIVFutVuPevXskk0l++9vfEo1GuX//PtlsVhYGfO/OixrCM93Y2KBaraLT6djY2CAQCMg8mSA67TWIQhFE2+nt7W1KpRKbm5vkcjnm5+dlNYXgvXQD1GkO0XhNEJYrlYocVCkiUd2UR30aCN5TuVymWCySz+fR6/VEIhGazSYDAwNyfMJRaywm1lOlUiEajbKwsMDFixfx+XwMDAzQ39/Pxx9/TD6fZ2tri0ajgc1mw2QyMTo6Ktfk6uoq9+7d4/bt28RisVf9WM8MYRtEeH98fJwTJ05gsVi4f/8+s7Oz3Llzh3g8/sg0SLfYlqeBOBQZjUaazSaxWIytrS3ZRFRdOXOU1tajINabwWCgWCwyNzfHxsaGLLcXh6lugvjsU6kUiqJw5cqVXfxSh8PB6OiovL7VarG8vEyr1aJUKlEqlZibmyObzUp+i7oX18vQqadKG4ky6IWFBRYXF1leXsbj8RCJROjv76e3t5fh4eFdNd/qzbvZbJJKpSgWi9y5c4d0Os3q6qoMdwsG995IS6cvJrEYRMWHMCDFYpG1tTWi0Sj1el22xj9qEJ+vmJgsQrQ+n4/x8XHMZjNjY2OYTCZWVlaoVCqv+B1/vxDpWMGPggeN/I4fP87MzAyBQIDh4WHq9Tqbm5s0m00cDgcmk4lQKITZbGZ+fp579+5x9epVLl68SDKZlPfvtPUlbITf76evr4+TJ0/y1ltvMT8/z9WrV7l8+TIXL16k0WhokUy+W80n9GRpaYlbt26RzWaPRPuBJ4G6mlZU21y6dInV1VVZMtxNXBcBse9sb29LB2ZhYYGpqSkmJyfxeDyMjY1RLBZJp9Pkcjnu3LlDNptlfn6ebDbL7OysbK4KSDnC088teqL3/Ky/2G63ZX5dURTZWjqRSOw75EwwtqvVKmtra/KELQiJ3ZAe2gv1s8Tjca5duyYri9bX11lcXCSRSOwiQ3XT8z8pRGSu0Wiwvb3Nl19+KcsWk8mkDG0f1bblAoqiUCqVWFtbQ6/X89VXX+F0OuXQQXEAqFar1Go1CoUCOzs73Lx5k83NTdlLqFardfQpW1EU6vW6DHOLAbCxWEyG9o8KN+pxEJ+xiGxubm5y8eJFSYQXh4FO1YUXAdGaI51Os7KyIgdVigqjfD5/JPRJ2OBEIiGpHdlsFofDgcfjoVqtks1mKZVKMnsi/t1bCfyy9empm9TBt95TLpcjk8mwvb0toy1ms/k7LG31/0V6pFarSW9/bz6sWxaROre+s7PD/Pw88XhcyqlarcoNWZAsu+XZnwbqarNqtSp7AhgMBtmrQ5AuxQj1owhRKZPNZrl69SorKyuy+ujdd9/F4XDIrruiVff9+/dJpVJ8+eWXLC8vU6lUpOPSqdwGsa7q9Tr5fJ5YLMbq6iqLi4vcu3dPps7UZfVHGcIe12o16vU6pVKJpaUlGekU0aluiyY8CdRRX0GK/+abbxgYGMDhcBCPx4lGoyQSia5tY6G2Aa1Wi2azyfLyMjs7O9y9exer1Sr3LDUPsVwuy2yMsE37ZQ5eRqr2mSIv6k1Z/F/0b9kbPdjrvKgnTqqvU9+7m6B+nkajIZnXYhK3uumaht3cF8Hz2NnZoV6vd1U77ueBWD/ValXm4G/fvo3FYsFiscjmUc1mk42NDZmGExFOgU5ea2IzLpVKLC8vc/HiRdkBVUyK1vBdCL0QPbO09fQtdnZ2yOVyLC0tSacumUzKKexAxzr8zwrRq020MBHOjYhsqvVnbwDiZeuWctAfcDqdj/3re3//cW/4UR/88ypDoVB4JdpktVqf6tNRO217+5W8zAVRqVReiXyeRH/2w35yghe/MF6V/jgcjmd6iL0GQ5x6HhWqFRvU3l4uT6NvxWLxlcjHZrMdKB/xDKIsXBQUiNf3hXK53BH2B3avK/h+eIWvwv48q+0REUnB1xDOntCtF41XZXs8Hs8u+RzESdnP1n4fTlw2m933jzw3O/RZiThHyXtVY6/xgKMri71Q65Imp8dD3ZdCjM3YD9+Xo/x9QuiJ2jETz3ZUhi2+CHSLPrwIiP5S8O3BSehSN8vpSdbJQZzUV9F+4IWVtnSbYXwZ2K+EXMMD7G0//STXHUU8Dz+s2/RtP2LgUdeR/dDtafrnhYhidmPxyLPiSfb2V7Hejl5d7iuGthg0vChouqThWaHpzsHQ5HP4cSDnRYMGDRo0aNCg4bDh6NXGadCgQYMGDRo6GprzokGDBg0aNGjoKGjOiwYNGjRo0KCho6A5Lxo0aNCgQYOGjoLmvGjQoEGDBg0aOgqa86JBgwYNGjRo6ChozosGDRo0aNCgoaPwzM6Loij/SFGUy4qi1BRF+eMDrjuhKMovFEVJKory2KYyiqL8C0VR7imKsqMoyt991vd3GKAoik9RlD9XFKWkKMqqoig/2+c6RVGUf6ooSurh658qB3RJUhTlZw/vV1IU5S8URfG9vKd4OdD05/HQ9Gd/aLI5GJp8DoYmn/3RKbb5eSIvW8A/Af7lY65rAP8W+HtPeN8bwP8EXH32t3Zo8H8CdaAH+DvA/60oyvFHXPcPgN8HTgOngJ8A/+Ojbvjw9/858N8/vG8Z+L9e9Bv/HqDpz+Oh6c/+0GRzMDT5HAxNPvujM2yzGG72rC8ePOQfP8F14w/+3BPf93Pg7z7v+3tVL8DOg8UxqfrenwD/xyOu/RL4B6qv/x7w9T73/d+Af636euzh33G+6mfW9EfTH002r/6lyUeTzwuS06G2zRrn5eVhEmi22+37qu/dAB7l3R9/+LPHXfeda9vt9iIPF+JzvVsNhw2a/uwPTTYHQ5PPwdDk0wXQnJeXBweQ3/O9HODc59rcnusc++RW91570H01dC40/dkfmmwOhiafg6HJpwugOS8vD0XAted7LqDwBNe6gGL7YYztOe6roXOh6c/+0GRzMDT5HAxNPl0AzXl5ebgPGBRFmVB97zRw5xHX3nn4s8dd951rFUUZBcwP/56G7oGmP/tDk83B0ORzMDT5dAOeg8xjACzA/84DspMFMDz8WRv44OH/lYc/m3n4fQtgVt3nj1GRggDTw2u+AP7+w//rXjV56Rll9G+A/4cHBLF3eBBCPA4MP5TF8MPr/iEwC/QDfTxYBP9QdZ8VHhKcHv5+Hnjv4X3/FPg3r/pZNf3R9EeTzeF5afLR5PMcsukI2/w8D/hHD9+w+vVHwMDDD9D/8LrhR1y3orrPr4C/r/r600dc/8Gr/kCfUUY+4C+AErAG/Ozh9997qPRGlRL8MyD98PXPAEX1gReAadV9f/bwfiXgPwC+V/2smv5o+qPJ5vC8NPlo8nkO2fzRI2zoH3HIbLP4EF4YFEX5Q+B4u93+x09wrYkH7OxT7Xa78ULfyCGGoij/C5Bot9v//AmufRf4n9vt9n/38t/Zq4emP4+Hpj/7Q5PNwdDkczA0+eyPw2abX7jzokGDBg0aNGjQ8DKhEXY1aNCgQYMGDR0FzXnRoEGDBg0aNHQUNOdFgwYNGjRo0NBRMBz0Q5vN1hGEmHK5vO+Uz5cJh8PREfIpFouvRD5ms7kj5FOr1V6JfHp6ejpCPrFYTFtfB+BVrS9NPvtD27sOht1u7wj5lEqlfeVzoPPyfeJRxOEDJo8fOQj5aDLR8DzYu840fdKgQcOz4HHFPi/bthwK52VX7fbDB9aM6gPs7OwA7JKP+qXhAcRCUstJp9OyogJ79QjQdEmDhqeAWEMCR3ndqB0XtW1R2131fv4ycCicF9Cclf2gKMoup0V8T8OjocloN9SOyl49Et/XoEHD4/GotfKyN+hOwKuyua/UeVEbVovFgtFopNVqsbOzQ7PZpNFoyJ8fNQhv1mq1YjAYMJvNGI1GKpUK9XqdRqNBs9kEjqZ8BIQOGY1G9Hq91KNKpUK5XN51zVFDu92m1WrRbrexWq0YjUYpn52dHVqtFrVajUqlIq/vRrTbbbmehDz2PqtOp0NRFPR6/a5oVLfKRMOTQeiBTqfDaDTu0otKpUKz2WRnZ4d2u41er0ev17/qt/xSoV4PYr3Y7XZ0Oh06nY52u02xWKTZbH4nyvui8cqcl71GwWKxYDababVatFotKpWKdF6OGtQfuNlsxmw243A4MJvNGAwGuSk3Go0j7bioYTQaMZlM2O12bDabNC5HdfPZa2RMJhMWiwWXy4XVaqXRaFCv11EUhXq9zs7Ojtzgu1Gn1JGnJ8VR1R0Nu6HT6dDr9VitVuncioMBQLPZlP8/KpEY8YwGgwGHw4HBYECv19NqtahWq/KQ8DLxSpwXNS/BYDBgMpl4//33mZqaIp/PU6lUuHbtGtevX991+ul2pRALQqfT4fV6cTgcfPTRRwwODjIwMIDH42FhYYGtrS0uX77MlStX5An6KOZfhR7p9XpOnDjB4OAg09PTRCIR/vqv/5r//J//M61Wi2azeWRkI9bKzs4OOp2Ovr4+HA4Hr732GgMDAwwMDBAKhSgUChSLRW7cuMFvf/tbCoUCyWQSoOtOj0ajEafTidlsJhgMYjabsVqt0ra0221yuRzVapVkMkk+n5fOXLfqzV7nVsN3IWyL0+nE5/Px8ccf4/V6sVgstNttlpeXyeVyRKNR8vk8iUSCbDYrIzHQnbJtt9vodDqsViuRSIT/5r/5b3A6nRgMBvL5PP/23/5bNjY2qFarL3UNvfK0kV6vx2QyMTExwRtvvEEqlSKXy7GxsSGv6UYF2As14VRRFBwOBz6fj1OnTjEzM8PU1BSBQIDr16+ztLTE9vY2N2/ePLLpo73Gt6+vj+npad544w2mpqaYm5tDr9d/h0x2FKA2nB6Ph2AwyIkTJ5icnGRqaopIJEImkyGbzVKv17l165YM84rf73RZqfk9BoMBl8uFzWZjaGgIm82G2+3GYDDISG88HpcOTLlcptFodO0GtPdE3K3P+bwQm7TJZMLj8XD69GnC4TBOp5N2u43H4yGZTGKz2UgkElSrVfL5vExTdqM81YEHo9GI1+vl/Pnz+Hw+jEYjyWSSv/mbvyEWi1Gr1V6qLXllzsvOzg56vZ7e3l78fj/j4+OMjY3RaDTIZrPA0VhM6pOyxWJhcnJSKkRPTw/nzp2jr68Pq9VKs9kkHA5jsVjIZrPodDpWV1eZm5ujXq9TrVaBoyM3RVHw+/04nU5OnTrFm2++STAY3LUJHwWojaXJZMJkMjEyMoLP5+PNN9+kt7eXiYkJgsEgNpuNWq0GINNsHo+HcrncFfJSP4Pb7WZ8fBy/38/Zs2dxu9309/djMplwOBwywtRut0mn0zIStbi4yOLiIgsLC/Lnnbym9lYq7uVmqKs994b7j3LFnqIo7OzsUC6XSSaT3Lhxg2g0yujoKA6Hg6mpKWZmZjh16hSlUonr168zPz//Hd3pNginzmw2Y7FYdlE+RNTp+3juV8558fl89PX1ydfa2lpXfuAHQWw+BoOB4eFhent7effdd+nv72d0dBSXyyV5CT6fD5/PJ0PbRqOR9fV1AMrl8pFJHwlj7HQ6CQQCjI+Pc+zYMXmSPoo6JE5DdrudiYkJ+vr6eOuttxgYGMDtdmOxWGi1WpLrItIngk8l7tMtcDgcTE5OMjAwwO/8zu/gdrsJhUKYTCbMZrPkMgDkcjnK5TJWqxWXy0WpVGJpaek7KYBOJPEK+yJIlTqdDoPhgekXG7RYM4KAKnBU7MmjIGRTrVYpFAosLi6SzWZxOp0ATE1NEQwGpc0REb1KpcLy8vIu3ekmCFsj1pHJZMJoNO4iL38f+N6dF/FgBoMBi8XC2NgYk5OTBAIBTCYT2WyWlZUV0uk0zWZTLrZuhdhEfD4fPT09fPTRR/T39zMxMYHD4SCdThOPx4nH45TLZQYHBwmFQgSDQc6cOUOpVGJ+fp5YLEYmk5FecbdDLCCfz0c4HMbr9eJyuUin05RKJer1+vd2AnhVECdltdNy4sQJ/H4/Fy5cIBwO09PTIyN1zWZTGhuDwSC5IOFwmEKhIKv9xL07bdMSm7TX62VgYICxsTHef/99bDYbmUyGZDLJ5cuXaTQaMkrp9XqxWq3y31AohNfrpVQqkU6n5frrRF0S71mQ/iORCGNjY3g8Hvr6+mSFVb1eJ5PJUKlUWF9fp1wuE41GqVQqFAoFGo2GvPYoQUQzG40G5XKZ9fV10uk05XIZl8tFJpMhHA5LJ2ZoaAi73U46nWZra4tMJkM8Hge6J4Il1pjJZCIUChEKhfD5fDgcDknU/b7wvTove4m6wnk5ceIEgUAAo9G4y3kRgujGhaMO41osFhll+eCDDxgYGMDhcNBut7l16xbxeJz5+XlSqRQ6nQ63200gECASiZBIJIhEIjQaDRYXF3fdu5shntHr9Urnxel0kkqldjkv3QzhvAjemMvl4tSpU0QiEd5++22CwaAsX9ze3iaTyUg5ORwOLBYLTqeTnp4eEokERqNR3rcTIeyL2+3m+PHjzMzM8IMf/IBqtcqVK1dIJBJ8/fXXZLNZ4vE4rVaLkZERPB4PZ86cob+/n+PHjzM0NEQikWBjY4PFxUWSyWRHVmOJjcZsNuPxeJienuZ3fud3iEQinD59WlbOlEol1tfXyWQyXL16lWQyyfXr10mlUpTLZarV6pGLwAhdEp+5cF70ej3Ly8uYzWZyuRz9/f2SVzY4OMjw8DDr6+vMz88DsL293VVyEzbHaDTS09NDT0+PJDEnEgngu838Xha+N+dFbRANBgOjo6MEg0HGxsYYGhoCHoRuc7kc+Xxehra76YMXEApgtVrx+/2Ew2Hee+89IpEIbrcbnU5HMpmkXC5z5cqVXR7/3NycNLrDw8O43W4GBgYoFouYTKaOPjk/CUT42+/343A4JBHV4XBQqVRYXV3l3r17RKPRXSTUbpGFms8jTj9Op5Px8XECgQBnz57F5/NRr9eJx+NsbGxQKBSIx+OUSiVOnDiB3W6X4d1KpUI6naZQKMiwb6fKym6343A4GB8f5/XXX8fpdHL79m3S6TSff/45mUyGpaUlqtUqxWJRytJisVCv1wkEAhSLRZLJJIqi8Prrr+P1ejGbzaRSKdbW1joiFaB+LpPJxMzMDDMzM0xOTjI6OorBYGB1dVX20xJryul0cvbsWcrlMj6fj3Q6LZ2+ZDJJqVR6ZEOyTtWX/SAKSRwOB1arlZ6eHux2O+FweFevF1F1VCwWSafTuN1ubDYbFotFpmm7Geo0pNCB73NtfC/Oi9rgCqN7/PhxhoeHOX36NOPj42SzWVKplHxVKpWu7ZYqcsxms5nBwUEmJyf56U9/SjAYJBAIsLOzw9bWFrFYjE8++YT79+9jNBplnjoWi2EymRgfH8fn8zE5OUkul8NsNktuTDdCXUouUiJvvfUWJ0+exG63UywWuXfvHp9//jkrKyuyD043hWzVJ0Kr1cro6Cj9/f18/PHHBINBTp8+jdFo5P79+2xvb/PrX/+a9fV1KpUKrVYLj8dDJBKRefpisSijMqLCxmAwdNyaa7fbuFwuhoaGOHnyJB9//DHRaJRf//rXrK6u8otf/GJX8yxhcKPRKACLi4vY7Xbi8TgzMzNMT0/z0UcfMTAwgNfr5d69e8RiMdkgEg6vXRI64nA4cLvdvPXWW/zu7/6uPChtbGxw/fp1arUaxWIRh8PB9PQ0Ho+H119/HZ1Ox4kTJ0gmk5jNZubn57l27Rr5fH6XTe7G0RLCxohoVU9PD2+//TZ+v5/p6WksFgs7OzvU63WuXbsmq/aETXY4HNjtdnw+n+w31Y1QE79fFbXje4u8CO9eVDcMDw8zNjaGw+FAURSSySSxWIxEIkGhUOjKyIswKgaDAavVSjgc5vTp0wwPD+Pz+bBareRyOUqlEjdu3GBzc5OtrS2y2ax0XjKZDBaLhVKpJJ0UtffbTfJ6FETpYjgcJhKJ4Pf7cblc1Go1SqUSsViMzc1NCoVCV0Wf1GlG0XBOlIf39vYyMDCAy+Wi0WhQKpWYm5tja2uLubk54vE4fr8fu92O3W7H6XRKfkuz2ZSvTjO0ezkdQ0NDvPHGG/T29hKNRmUUTjgdIqqkXifiX8FrWFtbk6k4m82GwWDg+PHjNBoNrl+/TqlUktyyw6hb4nBoMBgYGxuTEVqHw0EqlWJxcZGtrS1u3bpFvV6nUqlIvkIoFKKnpwebzYbD4UCn03Hy5En8fj8AHo+HQqFAqVSSuiP+hcPrzD0phOzsdjuhUIjTp08TDAY5efIkTqeTUCgkO3kLx79QKBAKhaSuCOenmw+RhwXfW+RFGARRXXT+/HkmJibw+/0oisLi4iK3b99mfn6e7e1tWUrd6QtCDRFxEcTA6elp/tbf+lsEg0EGBgZoNpty0/kP/+E/sLy8TDQapVwuSy9XeP6CgClOkerX90ma+r4gNlbR6XJqakqmHEOhELOzs2xvb7OwsMDs7OyuEHenQ5360uv1uN1uIpEIo6OjfPTRR4RCIY4fPw48yLHH43H+y3/5L8zPzzM3N0c+n+fDDz9kaGiIYDBIKBSiUqlQrVYleVXd16RTINaTx+PB7/dz7tw5fvaznxGPx7l9+zZzc3N89tlnlMtlSWzee0IUX9dqNarVKteuXePGjRvS+T116hQ/+tGPsFgsXLp0aRcx/rBBbJaiy/S7777L+++/j9/vx+v1cvPmTf78z/+c7e1t5ufnpdPqcrmYm5tjdHSUqakp+vv7CQQC6PV6gsEglUqFvr4+7t+/z+zsLOvr6xSLRUqlEuVymUKhsOt9dOK6E3uU3W5nZGSEmZkZfvazn8n9Sq/XU6/XAbDZbDL6W6vV5POaTCYZlalWq/L6TpTHk+BVk9hfqvOiNrqiGkKEuQVnQYSrl5aWZCmayCt304cu8qgGg4FAIMCxY8dkDwqn00mz2aRQKDA/P8/GxgaJRIJcLic9eBF5CQaDu8qns9ksGxsbJJPJXW2quw0iYuX3+wkEArLrsMlkkr2B4vG4jNp1kxyEkbDZbLJS5OTJk0QiEQKBAE6nU1ZF5PN5MpkM+Xxepkng2xbnIszbLWtLURTJR3A4HDSbTbLZLAsLC2xsbFCr1Q7Uhb1jAwQxN5VKsbS0RDAYpFAoUKvVMBgMMnV7GCHsrdATsVZarRaxWIzt7W0ZyRVrRJQCp9NpLBYL9+7dI5/P4/F4JIFb9KAKhUIoikJ/f7/Ur2QyydbWFuVy+dA6dY+DsM2iA/OxY8cYGxvD7/djMpmIxWK0Wi3S6TSKojA0NITZbCaZTFKpVLDb7dIOmUwm6vW6bHTYLevsMOKlr0ShGCJN8ru/+7sMDQ0xOTmJ3W7n888/Z21tjZ///OfcuHGDWq22K7zbDVBzFOx2O8ePH+cP//AP6evrY3R0FJ1OR7FYZH19XUZc5ufnKRaL8h4mkwmbzcbrr7/Oxx9/jN/vl70oPv30U7a3t+XMo26LWKmrJs6cOcPAwAAffPABQ0NDGI1GisUiS0tL3LlzR5Z4ijRlJ0PNFWu1WgQCAU6cOMGZM2f46U9/isViwWq1otPpZDOt1dVVNjY2iEajskpG9GEwGo3fIdgBryxn/bwQkZRIJMK5c+fw+/2kUilmZ2f5T//pP1EqlWRDvv2eb28PF3FwEmM4dnZ2GBkZIR6Py/TUYV1bIroUCAQYHh5mdHSUsbExGU26fPkyN2/elDYZHsilXq+ztLREMpnk3/27f4fX6yUUCmGxWOjp6cFqteLxeJiZmaGvrw+/3082myWbzXL37l0uXrzIysoKFy9epNFoSGfxsMpJDRG9s9lseL1eTp8+zd/5O38Hv9/P4OAgqVSK3/zmN6RSKe7du4dOp+MnP/kJgUCAubk5crkck5OTMqVkNpslQb5YLHbVPnbY8L04LwaDAa/XSzAYJBwOEwqFaDabFItFNjc3WV5eliTdbs4TWq1WAoEAPT09hEIhebqp1+uk02lJVs5ms7vC+KISwOv14vf78fv9GI1GGbIVDbYEummxqNNF4vTX19eHy+XCYrFQqVSoVCrEYjHW19cpFApdNVtE5OGNRqPsJSFalIv8+87ODsViUUbhNjc3KZfLMq34KGKl+v+duuaEQ2K32yUvY3NzU24cwnF5FjQaDZlaq1arHTEfS+i8aD6oHuQajUbJ5XLyOfZ22BUTxre3tykWixSLRSwWC/l8HpvNJjlTIlIlSK1+v5+JiQkAuf6SyWTHRGDU4yMGBgbo7e3F5/NhNpvJ5/Mkk0nW1tZkhMlgMLC0tEQmk2F5eZlyuUx/f7+M2InBhCJap+Hl4aU5L+K03Gw2sdlsnD9/nrGxMc6fP4/X65WpkV/+8peyl0m9Xu/YU+B+UPe26evr48KFC5w5c4Zjx47JLp+FQoFr166xsrLCysoKsViMZrMpNyadTsfExATj4+OcOHGC0dFRGQIW/1Yqla6SGyA7fhoMBux2O8FgkHfffZeRkRF5MhTy+uKLL/jiiy92OcCHfbM5CGKD2NnZIRwOMzAwwNtvv82Pf/xj3G73rghApVJhaWmJjY0Nfv7zn8uoS6PR2FX6vJfzITYidT+LTkG73Zap1L6+Po4fP86dO3f47LPP5CaqjjA8KdQRmFqtRqVS2RXBOcw6tTdtZLVaMRgMbG1t8fXXXxONRmUl2aOeo1KpcOPGDfm1GL4nUv4Wi4Xp6WkGBgaYmppicnKSwcFBzp49y9zcHG63m8XFRX7xi19Qr9cP9YBPccARre2PHz/OD3/4Q0ZHR4lEIqTTaS5fvszi4iJ/9Vd/RTablTy6aDSKxWKRXB+RwhZcl2Qyyfr6OrVaretsshqvOqr0UpwXYRBF8yxBMOzt7ZVjxVOpFNvb2ySTSTKZjCQ3dRPUctDr9Xg8HjnLSZyKRJVDLBYjHo9TqVR2RV1ER9RQKEQkEsHlcmEwGCgWi2xtbZFOp6nVapLb0E0QXCkRvu7t7SUQCOD1etHpdDQaDTKZDLFYjGw2KzesTod4BpPJJEmTQ0ND9Pb24vV6sdlsuzZYMchUcJ9yuZzUIVF54nQ6cblcGI1GmWIS3BjR1O8wb8xqqLt0i1lOoj15JpPZVYn3tNjb/l8QMEU6pJOcPHVp/ZOQK9vtthymJ56z0Wig1+up1WoYjUYcDocsOrDb7RiNRuks9ff3k81mO0KPxOdss9lkl27RcG5nZ4dSqcTa2hqbm5vStghybjqdxmAwyJQsILlQIgIjDg7djq4i7IpF0mq1cDqdjI6OMjExwe/93u8RDAYxGo1kMhk+/fRT7t+/z+LiIul0eteckW6BUGS3243H4+H48eN8+OGHBAIBLBaL9NKXl5f57LPP2NraIp/P7wrtRiIROY79rbfewu12U61WuXTpEn/2Z3/G5uYmpVJJVh11A9QlsMLh+73f+z3ZGVTIoFgs8vnnnzM3N8f6+rpsY97pchAGob+/n1AoxMcff8wHH3wgO3mKZ8xms8zPz7OyssK///f/Xoa4xSh6nU5Hf38/Pp+P8+fPc+rUKQKBANVqlfv373P9+nWuXr3KnTt3dp2UO2HzAeQsGUGWLBQKstXCsz6Dup2BOFwIfkc2m93V3O6wQZyEK5UK+XyeSqVCvV7H6XQyMjJCtVplZWXlwN9/FCG51WrJqMPNmze5ffs2t27dore3lx/+8Icyjfnuu+8CDzbyw54yEfb12LFjvPnmm5w9e5b333+fSqXC9vY2t27d4v/9f/9fmc4Xh8N2u00sFkOv1zM4OEggEMDn8+F2u2UJtZgTdVj15EVhb/82oX/fV0HAC3Ve1B+W6Efh9/vlDAS32029XpeEJkEybTabuzoXdgvUkReLxYLD4cDj8WC322Xb9kqlIjs0ipy0musiGiWJBnbNZpNSqUQqlZKVA4Ko1y2yEwvAYrEQCATo7e2VkQeHw4HBYJD9NtR61OnGYi951OVyyWZ8vb29u6J1pVKJbDZLNBqVL1FFIiIuJpOJYDAodUeMrW+1WuTzedmcrlwud1RnXXVUUqwlUer8oqo8RD8h+LaMWjgDcDgdPPGems0m9Xpdds+1WCzS7jzpPQT2phVF9MnpdFIqlaSjrCiKjBIfdoiUo4iG9/f3EwwGsdvtsvIqkUgQj8elTRbP2G63ZVRFOM+iq269XpdVXN3uvIiUfqPRkPu3uqJRpCfVjs2LxkuJvOh0OoxGI729vXz88ceSZLizs8Ply5fZ2NiQHI9KpSIf/DAahOeBWPQi3Gqz2bDZbJKvUKvViMVixGIx6byo5zmZzWbefvtt2Y3YZDKxsLDAysoKs7OzxGKxrsqrCkUXM3fGxsb4yU9+IrlCdrsds9lMpVLh4sWLrK6u8tVXX7G0tESlUun4Kivx/KL08ty5c7z99tuMjY3hcrloNptyxsrVq1fZ2tri0qVLZDIZtra2dvGDRA+l999/Xw4pFN2bRTO2q1evSn4VdM7wOCGnnp4eJiYm0Ov1LC4usr6+zvb29nNFIcWadTgccgJ1IpEgFouxtbV1qLkvwukVh0MRfRGdqAuFAhcvXpSN5Q7qkKs+VcO3pG7hQE9PT/P6668zMzOD1WolkUhw//59VlZWDnW6RLw3Ufjw2muv8e6772K328nlcszNzfHzn/+clZUVMpnMI6vVRDr72LFjTE1NyVE39+/fZ319Xbat6Abu3V6IiFW9XicWi+Hz+Ugmk3g8HtxuN41Gg0gkQqlUksUU4qAufv9F4aU5LxaLBZfLRV9fn0wXVSoV4vG4PCUWCgUZNehGCIU3m81yTobw+AGZGxXeq7qRlsViwW6309PTQyQSweFwoNfrKRaLkuPRjdVZ4lTkdrtlT5twOCwHd4oeCtFoVBqKXC7XFa3KxQlFROmCwSD9/f0yJC0aymUyGVZWVtjc3GRtbU0SShVFwel0YrPZCIfDkiszODiIx+PBbDbL03KhUJBRl07sqaQoiixvFaR3tRxEj5KnhTplKThCIupSqVQOddWReF+CaCyqpcxm867pvyKSpP6d/e4l/hUnaYfDgdlslnbJ7XbTarUol8tsb2+TzWYPfcRBHJBcLhder5dAICD5PrlcjvX1ddmVWYwjUUOn0+2qoHU4HLK/Sy6Xo1qtHnoZPA8E365cLlMul6lUKjL6JKJRopL2Za6VF+q8iFKx3t5eTp8+zfHjxzl79ixms1mGtv/6r/+atbU1EonEY73/ToVQXJfLhdVq5dSpU5w/f56ZmRmZ9tjZ2cFgMODz+fB6vfJkLUKvp0+fJhwOc/78ecbHx3G5XADE43EWFhZIJBLSUHeL/MSpd2hoiN///d9naGiI1157TRIDK5UKt27dYmtri1/96lcsLy+TyWQ6XgZqAqrBYOC1115jdHSU06dPMzAwgNlsptVqkUgkWFxc5Nq1a/z1X/81hUKBVCoFIFMD7733Hj09PZw/f55gMEhfXx92u10a542NDba3t1lbWyOTycgZYp0kO9FoT0yBFmTlfD7/3BFcIYtIJMIbb7xBpVKRjpHYlA5rozqxyWYyGclr6unpob+/n+npaflZr62tce3aNckba7Vacl6TwWCQm7NOp5POSiQSwePxyA68AwMDDAwMkM1muXr1Knfv3uWXv/yl5IccVn0Shz2fz8fw8LAcbCqI/6urq8zOzpLP57/T3FA4MoFAALfbzcjICGNjY9hsNjkIdWlpiXQ63VG9bp4GQsfq9Tqbm5vYbDY5R25mZgaLxcKHH37I2NgY2WyWWq0mncAXLYuXEnmxWq309/cTiUQIBoPs7OxIh2V5eZmNjQ0ZNej0UP+joK4WEeHnoaEh2Z9F3dVTNDayWCxyCqzNZpOdiHt6evB4PACS7yIqKsTIhU4J9z8pXC4Xk5OTMhctZNZoNNje3mZjY0NWAnRTlZpIt4ZCIUZGRggGgzidTunUlUolEomElEGtVpNdPa1WKy6Xi5GREfr7+5mampL9KvR6vYxKqKuzOqV/iRoisium/vr9fqLR6K5y5ueBCIsLvlE8HiedTu9qvnZYIT7HarUqO8ImEgnC4TBut5v+/n5mZmYwGo2yxb9oZyF+X7RvEGXoYuba4OAgoVCIEydOMDIygsvlwuVykc1m2draYm1tjYWFhV0cmMMKsUd5PB5sNpts618sFsnn86TTaSqVyq7oiZqHKObzeb1ePB4POp2OZrMpu1uLardOOxQ8KRRFkSTuYrFILpeT0WGr1UokEkGn08nIpbp69tCljcQbE2Wtw8PDfPDBB/T29qIoCplMhi+//JLV1VW2t7fJ5XJd0QF1Pwh5iAm+ExMTnD59etdGJBrT3b17l62tLdnO+8KFC/j9fo4dO4bL5cLpdFIoFJibm2N7e5ubN29Kgmq3OH7qUL1ofBUOh/H5fBgMBlqtliSnfvbZZ9IRflz31E6A0AeRBnE4HExOTvLaa6/JduwiDRCNRpmbm6PRaHD27FlJKnU4HAwPD+P3+3nrrbdkM0OTyUS5XKZer8uI1ZUrV5ifnycej8uyWOiME6J6AxEbrMVikZFMcc2zPIvo+yH0TqTrVlZWmJubI5PJHPpqSDVht9VqMTs7Kx1ci8WCoiicPn2a0dFRXnvtNQqFgmxomEgkUBRFVm+JJpBi6KDoG9NsNqlWq8zNzbGyssLa2hr37t0jnU53BA1A2JpAIMD4+LjkNZVKpV2z5ATxXVTOirEuHo+HH/3oRwwODvLaa6/R29vL1tYW8/PzXLp0iS+++IJ4PN61jgt8W2YuppJvb29jsVjkQcrj8VCv1yVNQn1Yf5F4YZEXdVWN1+vdleool8ssLS2xvr4uSWRi4+3WD1jkVQV3o7e3V26yopxO9HfJZrPSm3/zzTfp7e0lEolgMplIJpMUi0VWVlaYn59nfX2dXC63K2XUDRDheIfDgcvlkix+caoplUqk02kWFxdZW1uTc3u6xYGDb53/UCgk00Xi+avVKvl8nlQqhaIoksAt5HXs2DE8Hg8TExPYbDYAObemWCyyurrKwsICt27dYm5uTv7NTtQhwQszGAyS7K/Gs/AN1KleEe0UPIbt7e2OWm8iQrS9vU2r1WJwcFAStsPhsJxGns/n5Vra2NhAUR6MFhAHCDHSxW63y3W2srLC9vY2KysrfPnll2xtbbG4uLirq/VhPkwIh0RwygSXsFarkUqlyOfz35mNJg4XTqeTQCDAyZMnGRsbo7+/H6fTyb1791hbW2NlZYXl5eWuigbvhdB/QRGp1+vk83kKQ4F26wAAk+FJREFUhYKM4opDmCB37+2d9KLwQpwXdT+ToaEhhoeHZWWDCCfeuXOH7e1t2QyrUwzBs0A8WyKRoNFoMDExwdjYmGw0JyIv4qRjs9kYHBzE6XQyODiIy+VCURSq1Sq3b99mY2ODX/3qV9y7d08SdUX+uhsgog/BYJATJ04wPT1NIBCQ7ciLxSI3b95keXmZtbU1OSitWyrUxKlEbMiivbsIz4pDgZiDZTKZsNvt8lrRCFLdqG1zc5N8Ps/du3dJJBLMzs4SjUaJx+NAZ0Ra9oO6BYHRaCQSiUhZiWnJas6B+ln3VtEI2QeDQVwuFxcuXODkyZNSdiK98iji5mGFeJ/5fJ5Go8GXX37J9vY2Ho9HRg96e3tlutHtdhMIBHaVh4teMUtLS5TLZTklWXRGX15eZmVl5TuE707RK8HzEWXNRqNRNnIUJGRROBEMBvF4PJw9e5aenh5Onz6Nz+cjFouxvLzM559/zr1791haWpK8qG7e3+DbA0S1WmV2dpZKpSL7/IgAxvT0NI1Ggxs3brC1tbUryvwiZPPC0kZinLiYD+F2u8nn88RiMTY3NyWRSRDDuhnigxEdTNfW1lhdXcVoNDI4OCjZ2nq9XpbFioqiYDCI2Wym2WxSq9VYXl7m3r17XLlyhbm5OQwGg+S5dIoxfRzEZuR2u5mYmJDkQJPJJBfI0tISS0tLJBIJstmsJBR2E4QzZjQaMZlMchMWm4qYuyKqbERPBUD2XCgUChSLRZaXl+XYBEHSzefzHbnR7AfhvIRCIex2O2tra5hMJtrttjwciDJNoSuP6jorDhG9vb0cP36ct956iy+++IKbN28Si8Uol8vSsXxZp8gXCfG5imqQarXK8vKyrK7p6+vjxIkTBAIBOSB3YGAAvV4ve8SIKrbbt2/v6ge0ublJKpWiUChQKBR2NRftJH0SXB8RMdLr9dhsNux2u5xOLmYeTUxM0NPTw7vvvisrIM1mM+vr62xubnLr1i1u374tW1d0o23aC3XJ9MrKCoryoPOwmDMmDuKVSoXFxcWX8h5emPOiVgDRCEvUzS8sLOwalHYUPljRxKfVahGNRrlz5w42m40TJ05Ij97v9zM9PS2bJYnW7fl8nnv37pFIJPjqq69YXFwkn8/vclw6yVDsB6E3ooV9OByWRF1hAISjJ8o+D/vG8SwQn6WYjRKPx9nY2MDlcslRAOqojCgDFmHbSqXCxsYGhUKB2dlZue7y+Tyrq6sUCoXvyK6T9Uec4IrFIqlUCr/fz8DAAKVSiVKpxPb2NrOzs7K0XB3pFONJhM4FAgGcTienT5+mv7+fVqvFl19+ybVr15idnZVcFyGvTtS/Wq0mZSAGeKZSKex2O1euXJHpbeHoia7C1WpVzokSDfpE/w4xNqDT9Eg8YzweZ25ujnA4zPT0tOyALiZqC06mxWIhHA5LXpnZbCYWi1GpVPjqq69YXl5mYWGBZDIpHZdOk8mzQlEUms0mqVQKnU7HJ598wsDAAO+9956UZzgcplwuEwgEWFtbIx6Pv7DBuS+02shgMEiSjujiOTc3J/OqtVqt67kuAuKDFc7L3bt3GRgYkGkAq9Uqy1sVRZGkw2KxSKFQ4Pr163LMvGj81G1diPc28QuHw0xMTBAKhXadXtrtNtVqdRfBtJugdl7K5TLxeJz19XVGRkZwOp0yyrY32ia4MJlMRjYt/OSTT0gkErKiTziI6t/tVP0RhwJ19VUmk2FwcJCpqSmZXrtz5w65XE52IRbdZkWliOAKicNEKBTijTfeYGBggF/96ldcvnyZu3fvcu/ePXnC7GTU63XpwAhduH///q6S6EdNmRZ9qERqBb5NF3Rq5FfovnBeZmZmaDQaeDweyRl788035fXCVgvU63UZZfn666+5d+8e0WiUQqHQlZWfB0HscYlEgnK5zKeffkokEmFqagqHw8HMzAwAuVwOj8dDs9kknU7TbDYlP+Z5iLwvxHkRG6po8lMul4EHzdnC4TDVahWr1bprEXRiY6ynhVjoghB369YtfvWrX+HxeCQh0+1202w2ZS+Je/fukUwm+eabb3ZVZon7dZPMxPOoq6+Wl5dpt9tEIhEZaRIRBlEB0E1QL2DhjNy/fx94sOhFOtHhcMjfESz/SqVCMpkknU5z9epV0uk00WhUcjTU6aFuSRUJebVaLZaXlzGbzZKz0Gg0GBwclBtxuVwmnU7L5mGA5Ao5nU5pn+x2u+xwPT8/z/LyMtls9hU+5YuF+Mz1ev13+BjCodlbFqz+nvoQ0embs3j2XC4nD5XhcBiXy4XP55OtKgSazaaszBOp16tXrxKPx6WeCMe409fWs0Dsca1WS3IRL126RCwW49y5c3i9Xnp7e6nVaszOzmKxWGTkTvz+s+KFRV4URaFer5NMJuV0X5vNxvDwMI1GA7vdLsO4R8FxgW85DKL2X4TYJiYmeOedd/D7/dhsNiqVCqurq8RiMf7qr/5KDgYTzPduJoCpq2lisRh3795Fp9MxMzMjS6fFz+v1etd1FIbdkZdmsymjbtvb24yOjjI4OMjAwIAkxmezWTY3N8lkMiwvL5NMJrl27ZrkOIiQtzAs3QYRebl79y6rq6vSpoyNjXH69GmGh4c5deoUlUqFVCpFtVollUrJijZR1SaazbVaLa5cucLq6irXrl2TpN9uwUG243G9a7pNf4Qc0uk0xWIRl8tFo9FgaGhIFgqIaKeI+G5sbJBOp7l48SKxWIyvvvqKZDJJqVSi0WjIQ9ZRgjqNKpyXjY0NUqkUf/M3fyMbGfr9flmEcvnyZWw2m5wN9bwO3wtxXoSCi/b/c3Nz/PKXv6RYLLK0tMTW1lbXbjyPg4gsiCqQ5eVlyf1xuVzMz89Tq9XY2toik8mwuLgoO592kwF9FNRpgFarRSqVYm5uTrYuNxqNGI1GWZqZSCS6sj/Q3moYETlZWlqiVCrJzp/ilFwoFOQhIR6PUywWZcM5cb9udHTVEJFegJWVFaxWq2wuJgbDCb1SlAdDLsUhQaSvARnCFr1vRHfUbj4wqLFX947K84q1sr29jdFoJJVKkUqlcLvdhMNhaZsqlQqbm5uy11Yul6NQKMhDZbfZomeBOorXaDSIRqPU63V+85vfyBlRpVKJ1dVVKpXKC6sUVQ4Kw9tstieK0Yt7iOoHh8OBz+eTD1Sr1XYNq3rRjbHK5fIrWXEOh+Op5CNq40U3VLPZjNPppNlsUiwWJVFOHYZ8ETIqFouvRD5ms/mJ5SMI32KwoM/nk/yoRqNBKpXaRcB8kUa2Vqu9Evn09PR8Rz4iqiBSIYJHZrFY5DWNRoNyuSzLPdVr+GUY01gsdijXl5CTKBcXp2efzydbD4hp3G63m1QqxZ/+6Z/K8L9o/ieIqHub9j1pZdGrWl9Pan9eNV6FfB63d+1NlVmtVtmbRLRogAdrTdhm9eBceDFr7VXtXXa7/YXqjlqegicWDofl6ISdnR1yuRzFYvE71x6EUqm07wUvfLYRPGhPLXLMwHecFuj83PvzoNlsyryfkE2tVts1ifQoQR2CFCkiMWxRpJUE4VB9fbdDnGRE9E58T3xf/B+OjkzUUPNf6vU62WxWzjgql8tYrVZ5snY4HORyORnhFGlcse6EPI+iHI8yxEbaaDSkTVa382i1WlJHBIn5qPJbHoe9kfRisSgb/ok97kVyFl8YYRe+ZamLmQfqD7ibSINPCzVhTnjrgtScz+e/c20nliA+L9Shx0qlIuWz9+fdLhfx+auJ7dVqVabS1NfBt6e/bpfLo6CWlQhXb29vS6dXzfkRhlXd9n0vXkSnXg2dAfV+pN63HvWZq23PYR3K+arxqMoh9YTxvVmEF2GvXtonoW4ABY9+uKMIdShaLaMX/cF2MtSby1HhHuzF3pTFQVHLoyab/bA3uvs4aGtOA+yOGMD+TqvGbzkYamdQ/LtXli9ynb1Q50X9xvayrzXj8C32ev3iexqOBtn0SbHX8d/7fQ3fQtMbDc8DTX9ePF62s/dSIi+aEjw5NFlpeBJoeqJBg4ZOwPdlqw6sNtKgQYMGDRo0aDhs0JJ4GjRo0KBBg4aOgua8aNCgQYMGDRo6CprzokGDBg0aNGjoKGjOiwYNGjRo0KCho6A5Lxo0aNCgQYOGjoLmvGjQoEGDBg0aOgqa86JBgwYNGjRo6Cg8l/OiKIpPUZQ/VxSlpCjKqqIoP9vnOkVRlH+qKErq4eufKgd0slEU5WcP71dSFOUvFEXxPc/7fFXQ5HMwNPnsD0VR/pGiKJcVRakpivLHB1x3QlGUXyiKklQU5bFNmxRF+ReKotxTFGVHUZS/+yLf8/cJTXcOhqY/B0OTz8HohPX1vJGX/xOoAz3A3wH+b0VRjj/iun8A/D5wGjgF/AT4Hx91w4e//8+B//7hfcvA//Wc7/NVQZPPwdDksz+2gH8C/MvHXNcA/i3w957wvjeA/wm4+uxv7VBA052DoenPwdDkczAO//oSw5Oe9gXYHz7cpOp7fwL8H4+49kvgH6i+/nvA1/vc938D/rXq67GHf8f5rO/1Vbw0+WjyeUFy+ifAHz/BdeMPlvMT3/dz4O++6ufTdEfTH00+h+vVKevreSIvk0Cz3W7fV33vBvAo7+z4w5897rrvXNtutxcfPuDkc7zXVwFNPgdDk4+GZ4WmOxo0vDx0xPp6HufFAeT3fC8HOPe5NrfnOsc+ubG91x5038MMTT4HQ5OPhmeFpjsaNLw8dMT6eh7npQi49nzPBRSe4FoXUGw/jB09x30PMzT5HAxNPhqeFZruaNDw8tAR6+t5nJf7gEFRlAnV904Ddx5x7Z2HP3vcdd+5VlGUUcD88O91EjT5HAxNPhqeFZruaNDw8tAZ6+s5iT3/Bvh/eEDweYcHIaDjwDDQBoYfXvcPgVmgH+h7+BD/UHWfFR6Smx7+fh547+F9/xT4N6+axKTJR5PP9ywbA2AB/ncekOUsgOHhz9rABw//rzz82czD71sAs+o+f4yKkAiYHl7zBfD3H/5f96qfV9MdTX80+RyeVyesr+d9QB/wF0AJWAN+9vD77z1800aVAvwzIP3w9c8ARfVhF4Bp1X1/9vB+JeA/AL5X/WFq8tHk8z3L5o8eGgn164+AgYcGwP/wuuFHXLeius+vgL+v+vrTR1z/wat+Xk13NP3R5HN4Xp2wvsQfeaFQFOV/ARLtdvufP8G17wL/c7vd/u9e+Bs5pNDkczA0+ewPRVH+EDjebrf/8RNca+IBu/9Uu91uvPQ3dwig6c7B0PTnYGjyORiHaX29FOdFgwYNGjRo0KDhZUGbbaRBgwYNGjRo6ChozosGDRo0aNCgoaOgOS8aNGjQoEGDho6C4aAfOhyOjiDEFIvFfadYvkzYbLaOkE+5XH4l8nG5XB0hn3w+r+nPAXhV+mO32ztCPqVS6ZXIx2KxdIR8qtXq9y4fTTYHoxv29gOdFw0aNHQvBFn/gAn2XY+9BQtHWRYang6a7rxaaM7LIcGeGnsJne5BZk9bGBpeBIR+7ezsPOiVoCi7XkcFj5IDcCRlcRD2k89RxiN6ohzZdfQqoTkvhwia0j859ivx12T4ZFAb2aMss72bzVGWxaOwV0c0+TzAo+ShOS7fL16Z8yI8+v1wFIxru91mZ2cHnU6H2WzGYDBgt9vR6XQ0Gg1arRaVSoVms0mr1dp1Uj6KaLfbUg6tVmvXz4Rc9Ho9Op3uyMpoP4hTopCP3W7HarXSbDap1Wo0Gg2q1SrQvesNvnV6xVpyu92YTCZpj8rlMrVa7cin1MR6stlsGAwGdnZ22NnZkbpyFCF0wmQyodPpcDgcu2RTqVSo1Wry66OqO98XXonzojXG2y0DvV6PxWLBZDLh9XpRFIV6vU6z2aTdbktjunfDPopQO7SPCtlqOBh6vR6j0YjD4cDr9VIulykUHgx1Fc7LUYBOp0Ov1+N0OrFYLHLD2dnZketub8rkKEHIx263YzKZaDab7Ozs0Gq1qNfrR1ImQheMRiMmkwmXy4XFYpGyabfbUnfU12t4OXjpzos48e3s7AAPNhqdTofT6cRgMGA0Gh/5AddqNWq1Gq1Wi2az+bLf5vcORVEwm8243W6CwSBvv/02fr+fqakpTCYT2WyWcrnMwsICqVSK69evs7W1JZ2ao7QohDEwGAwEAgEsFgs9PT2YTCb582KxSLVaJR6Pk8vldkX2jpKs9kIdcdHr9Zw+fZqxsTFOnDjBzMwMt2/f5ssvv2RjY4ObN2/SbrcxGAxdJzP1YcFkMjEyMkIgEOC/+q/+KyKRCI1Gg0ajwZdffsndu3fZ3t4mFosBR0d/xAHJaDQyODiIx+Phhz/8If39/USjUXK5HL/97W+5e/fukYouqO2I3W7nvffeo6+vjzfeeINgMEixWKRcLvPLX/6SK1eukMlkyGazMhLcbVCvpVf5+X9vkRe1Auh0Omw2G2azGbPZLEmpahSLRdrtNvV6XYYpu2mh6HQ6GWnp6+vj3LlzhMNhzp07h8ViIZlMUiwWcbvdRKNRNjc3SafT8mR4FGEwGPB4PLjdbiYmJrDb7dJJEfKqVqtSd46KcT0IaufFZDIxMDDAyZMnuXDhAm+99RZWq5X19XVKpdJjU7ndAJ1Oh9FopLe3l0gkwoULF5iamqJWq1Gv18lms2QyGYrFItvb20dOf8Qh0+PxEIlEePPNN5mcnGRhYYFYLMbc3Bw6ne47ZNVuhRwC+HDfMpvNjI2NMTk5yUcffcTg4CCpVIpCocDCwgL37t2jWCxKOkC3Ya99eJU68MKdF3WkxWAwYLVasdvtDA4OYrVa8fv9WCwWwuEwFosFq9WKXq/fJZR2u83s7Cyzs7MkEgk2NjYApBfbqYtFvO+BgQHOnDmDz+eTJ8BTp07JaBSAw+HAYrFw5swZxsfH2dnZYXR0lOvXr7O0tCRPit2cLhG6ZDabCYVCBAIBfvSjH9HT08Pw8DBWq1VeJyJVly5d4t69eywvL7OysvJqH+AVQm10DQYDx48fZ2BggPfff58zZ84QCoUolUrkcjkSiQSFQqEr9UhdFWK1WpmamiIYDPKjH/2IgYEB+vr6MJlMGAwGzGYzPp+PYDDIxsaGlN9RCP8L+2uxWHC5XJw6dYqRkREikQh+v59kMkm1WsXpdGKz2ahUKtL+dDsE9+fEiRP09fXx1ltvMTQ0hMvlYmdnh2w2SzKZlE5vN9lltR0RkSR1NEm9vgQfUeBlO28v1HlR54lbrRYGgwGLxYLX6+XYsWN4PB4GBwdxOBz09fVhtVolIUydJ2y32xiNRrLZLNVqlVarJT3fTlcInU4n00ShUIjp6WkcDgc9PT27TjQWiwWdTofb7abValEsFvH5fCQSCTY3N9nZ2aFerwOd68w9CYQu9PT0MDg4yHvvvUd/fz/9/f3SeYEHkbpKpQI8iNCUSiXpvByFzWcvhB7pdDoMBgMjIyOcPHmSU6dOcfLkSRqNBpVKhWKxSC6Xk7LrVjnt7OxgNBoZGRlhYGCAt99+m0gkgtVqlVHQdruNy+XC4/FgsViAo6M7QldMJhN2u52RkREmJycJhUK4XC4cDockeZvNZur1+q6NrRuh3oitVivHjh1jcHCQY8eO0dfXh81mY2dnh2KxSDqdljZIpPW7QS7qohLxEgdsRVEkD0rwfUTkTvz8ZcrghTgv4kMWxFOHw0EwGCQQCHDs2DHpvFitVgKBACaTCbfbjdFoxGAwSA9NHbWZnp6m1Wrh8/mkgqRSKZlr7SSoHRKbzUYwGGRkZASfz0cgEMBoNFKv16nX66ytrVGr1YAH8uzv78dut9PT04PFYiEej2Mymbh//z4LCwtdGeYXOiA4Qf39/Xz88cf09fXR29uLy+Wi1WpRrValcyv0YnR0FLvdTrlcJhqNUigUyGaz0jh3O9Rr0WAwMDo6it/v5+zZs0xPTxMMBtHr9TIVuba2JqNW3Vbdp4649PX1EQqFJF/B7/djNpvls1arVRqNBplMhkQiQblc7uqNWQ11pDwQCBAOhxkcHGRgYEDapq2tLRYXF9ne3iaXy9FsNrviMPkkEBEHr9dLIBDA4XBgNptpNBo0m03u37/P7OwsKysr5PP5rpCNOuptNpuJRCKMjY3h8Xjo6+uTz1ev18lkMlQqFdbX16XdrVQqFAoFGo3GS5PFC4u87OzsYDKZsFqthEIhTpw4wfDwMB999BEej4f+/n5MJhNGo1Ey2dUPJIyu2IimpqakkhSLRTY3N6VidKLzIoyD3W6Xzovb7SYQCNBqtSgUCuRyOe7evUs+n5fEOYPBQE9PD6FQiL6+PorFIg6Hg1qtxvLysvR8O3mh7IWaOBgOhxkbG+PDDz+kp6eHnp4ejEajjBzk83nq9ToWiwWj0cjo6CgTExNsbW0xOzvL1tYW6XS6q+RzEATPRxwkpqamGBoa4vTp00xOTuJ2u9HpdKRSKebm5lhfXyebzVKpVLrKudt7ap6YmGBoaIh33nmHcDiMzWZDr9fLSHGtVqNSqZDJZEgmk5RKpV28jm53ZIRz7/f7H+m8RKNR5ufnicVi5PN5DAZDV5JR94PBYMDr9eLz+XA4HNhsNgqFAuVymfv373P58mU2NjYoFAry4NDJUB8gPR4P09PT/M7v/A6RSITTp0/L/btUKrG+vk4mk+Hq1askk0muX79OKpWiXC5TrVZfWgTmuSS8N78VCAQYGxtjZGSEt956C7/fLyMGOp2Oer1OKpWSxLharSYNpghJuVwubDYbAD09PUxPTwOwsLBApVIhl8sRjUY7ihCl9tynp6eJRCI4HA5MJhP1ep18Ps/du3eJx+N8/vnn0nkRzt7AwADT09MEAgF6enpotVrMzc3hdrupVCrS0HaDcVWXI3o8Ho4dOyajVHa7fRcHYWdnh7m5ORKJBFarVTovfX19BAIBjh8/jqIokr/QLTJ6FMRaNBqNmM1mRkdHCQQCvPHGGwwPD8sIXjabZWtri6tXr/L555+zvr5OLpejVqt1jWyEQ2+1WvH5fEQiEd5++236+vpkX5e9z2o0GgGYnJyk1WrhdDpl6jqVSgHd7cAI58Vut8vNWfQBEnZ7e3ubSqXStTJ4FNRcD4fDgdPpRKfTybR9tVqVnJdqtdoVERdAtu6YmZlhZmaGyclJRkdHMRgMrK6uysIRsQ87nU7Onj1LuVzG5/ORTqe5cuUKiURCHgb2a+z3rHhu91CdD+vv7+ftt9/m+PHj/PCHP5Tep7o/wMrKCtlsltnZWfL5vPywRZRhbGyMvr4++vv7GRwcJBAIMDk5Kb26zc1Ntre3OyZdomaqh8NhXnvtNXkCVhRFlvd+/vnnrK2t8Z//838mk8nQarWwWCwoisLk5CThcJi+vj4GBwcJBoPcuXOHQCAgc63Q+eF+dWWMwWAgGAxy/vx5IpEIwWBQykNEF5rNJpcuXWJubg6bzYbFYuHHP/4x4XCY/v5+3nzzTarVKtevX6fZbNJoNLpyA1LnpU0mE06nk/PnzzM8PMwPf/hDhoeHZcRzaWmJhYUFPv30U/7Tf/pPu0peO+Uw8Djs7OzQaDRwu92MjIwwPT3Nj3/8Y/x+P06nU0Zc1BFcER4/c+YMo6Oj+Hw+zGYzc3NzJJNJgF2OczdBzY9yOp14PB5cLhd2u13al+3tbdbX1yWxu9tkcBDUhymv1yv1p1arUSqVSCaTbG1tyehlJ68joQsOhwO3281bb73F7/7u78qI3MbGBtevX6dWq8kswPT0NB6Ph9dffx2dTseJEydIJpOYzWbm5+e5du0a+Xx+l/PyInpzPZfzIioZRDh/ZmaGqakpwuEwOp2OVqtFuVyWoaVcLsedO3fI5XKsrq7KPLu4j16vJ51OEwgEmJmZodlsYjabsVqteL1ehoeHZQ6tEyAcLKHQNptNRhB0Oh3FYpHl5WVWV1eZm5tje3tb8l3EJh2NRtHpdGxubhIMBuXJ2m634/V6qVQqXWNQxSbsdDrp7+9nZGSE4eFhAoGAPO2IjVboS7PZpFwuk06nabVanDp1itHRUXQ6HaFQCJ/Ph9vtplQqdTXBWfRn6enpIRAIMDU1xfDwMC6XC71eL59/cXGRmzdvEo1GZdPDbtmM1KFur9crQ9wjIyOyGR0gT81iwxZpbBF50Ov1jI6O0mg0pE0qlUoyAtMNslJDrCez2UwwGCQUCmE0GtnZ2ZH9bqLRKIlEQkbLu00Ge6F26KxWKxaLRRKWRWFFPp8nnU6Tz+cplUod33+r3W7LyjuRQRkeHsbhcJBKpVhcXGRra4tbt25Rr9epVCo4HA6q1SqhUIienh5sNhsOhwOdTsfJkyfx+/3Ag9L7QqFAqVSSvdvUPdyeRW7P5LyoK4L0ej0zMzNcuHCB48ePc/78eUwmE3q9nmKxSCwWY3Nzk1/84hfEYjHphQn+injTYkG43W5sNhsfffQR+Xye8fFxTpw4QX9/P+fOnUNRFP7mb/6m4yIvRqNRkk8DgQAGg4FMJsOnn37K0tISn3zyiYygCFm0223m5ubY2NhgZmYGu93O8PAwPT09BINBBgcHKZfLXXNyFlwXv9/PG2+8wczMjOx7I6J3Ir3h8XgwGAzU63WKxaIsqz927BhDQ0MEAgEmJiZYWFigp6eHZDJJNpsFXn4J3/cNsWmbTCamp6cZHBzkgw8+YHh4WJJSk8kkqVSKL7/8kk8++YRkMkmr1ZIbdzdAhLJ9Ph9jY2PMzMzwt/7W3yIQCMg1J1oMiL42YiyHKAH1eDyyyu/EiROEQiHK5TJra2skk8ldZaPdALV9cjgcjI+PMzo6KlNGs7Oz3L9/n7t377K0tNS1jdceBREFdrlceL1eGZESB/NYLMba2hqxWIx0Or2r+KTTIKKQdrsdm83Gu+++y/vvv4/f78fr9XLz5k3+/M//nO3tbebn52k2mzSbTVwuF3Nzc/z/2/uz2MjSLE0M/K7t+77RSDMadyfp9C18icUjIzMrMztRXYXKQlWhW5USpoFCtwRJT9LLCNAADaExM92AHlua1oNQGM1MF5RAq9RVnVGRmdWxeXiEh+90574Zbd/3fZ0H+vn9mjlJpy90d7t2P8DgTvLSaPfcfzn/Od/5zuTkJObm5tj+JpVKYbfbUa1W4Xa7Gak5GAyiVCqhXC73qHsTXmRevXTkheM49kC9Xi+8Xi9j8He7XRZOI9Kk3+9nYj5UIXIYIa5araLT6SAcDmNjYwMqlQo+nw+tVotpDAwSaAKQpDTp2jSbTZRKJUSjUaRSKTSbTRb654fX6KRDXv7o6GgP8TccDgviJMQv1TQajfB6vUxFt9vtMkY7CYfNzMywU5HJZGJRGNIuMZlMUCqV7CRQLBYHxuF9EZDdNBoNDAYDvF4vxsfHodPpIJfLWV46FAohEAggFosxkrNQNmFaR/jRg7m5uR6uFFXzlctlVKtV7O7uolqtQq/XQ6FQwGQysTJgOpRRBMfn86FWq0GpVDL5dyFFOxUKBWw2G0ZGRuB2u+F0OtHtHvR5SiQSiEaj7JA0LI4LcLD+qlQqjI2NYXR0lEXvpFIp2u02MpkM4vE4KpUKgMFue0OfXaPRwGQywWq1smKSeDyOWCyGSCSCXC6HRqPBCmtqtRoymQxUKhU2NjZQKBRgMpkYh6zT6UClUsHhcIDjOIyOjqJQKKBUKrF0W6VSQTabfWH7vZLzcubMGZw9exZXr17F1atXmWouRVzW1tbwV3/1V4jFYlhbW2NhewBH5gYpzXTnzh3s7OygUChgbGwMCoUCLpcLZrN5YLxbfoRKq9VCr9fDaDRCJpMhn88jEong/v37zHkBnkYFKPJCYlDBYBAmkwkTExNQKBQYGRnBwsICK50eZLluflmeWq3G+Pg4rl+/DrvdDrVajXw+j42NDcTjcXz55ZcAgH/0j/4RK8efnp7G6uoqut0u0uk09vf34XA4WGptZGSEnbTp7w2infrBT5M4HA4WcRkfH4fVaoVMJmOh2q+//hq3bt1idqQTtBDsQGNfp9PBarXi3Llz+JM/+RM4HA5MTk6i0+mw6odkMol0Oo1f/epXiMViGBkZgU6ng8/ng8VigclkYjonPp+PVUwqlUrcvXsXlUpFUByzTqcDrVaLixcvYnJyEu+//z7sdjtLk62srLDqEfqdQb/v54Gf7jebzfj444/h8/ngdrthNptZxHdzcxMPHz5krQAG2S5UsWqz2eDz+TA5OYmpqSncv38fDx8+xJ07d1j7EHJgqQhnd3cXqVQKv/rVr2A2m+FwOFgLFzpcLiwsMImCXC6HXC6H1dVV3Lp1C36/H7du3WLNiIGTza0Xcl7oocpkMlY65nK5YLFYoNFoIJFIUK/XkclksLe3x0562WyW9eThL5j9H5Den1IEfDbzIGpQ8DU3FAoF25xlMhnr5kvCRkctCvx0CXUs5TiO8V7oJEDXDvLiotFoWFk4CYXV63UUi0WEw2FEo1FEIhF0u11sbW0hm82y0k1KQbZaLebwEYSQUuODxhWdDA0GA3w+HyO4E8+l1WohlUohk8kgFosx/RI6QQ/qOOGDz1sxGAwYGxtj1WZ0UKCWESS5kEwmEYvFkEgk0Ol0oNFo0G63kUqlYDQaGe9Fq9WyNJTJZGLXUfuJQZ5rZDeVSsWi56Ojo9BoNJDL5SgWi0in08hms8jn84Js0XIY+sUdVSoVrFYrLBYL6yZNVUb5fB6ZTKbnUD6ooDWF9ihKp5JuC2n79KcNKdVfr9cRi8XYPFOpVCgUCtBoNLBarT2Vou12G0qlElarFTMzMwDAyOCUmj0JXsh5oY1Tp9NBp9Nhbm6OVYPo9XoUCgUkEgk8ePAAf/3Xf41oNIrV1VV200c1YSTwGchKpZKVpun1egAYSI0X4iJQ3tRmszGSaaFQ6JFmPywkS/dbq9VQLpcZYVmr1bKNijb5QZPr5qcMATAF3XPnzmFsbAytVgvxeBw7Ozv47W9/i2g0irW1NbRaLSQSCWi1WiYURTnnarWKdDrNiGF8ATuhgNQstVotXC4XfD4f/vE//sdwu92Ym5tjqdVyuYxvv/0Wm5ubrG0C6Q0JAXzxR5VKhcXFRfzkJz/BzMwMZmdn2XyqVquMm/DFF18gkUiwasdAIMA2Kn7PtevXr7O+R5cuXUI4HMbY2BhisRjrMTaooAORXq+Hx+PBzMwMq9KzWCxot9tYW1vD/v4+tre3EQwGnzl4Chn8snFqlkuOHcdxyGQySCaT2N3dxdbWFkql0sDbpT9tRIfsSCSC7777DtFolDVsPexeq9UqHj58yL6mlD5lHVQqFc6cOQOPx4O5uTnMzs7C6/Xi0qVLTPZjZ2cHn332GRqNxonSky+8ilGOVKfTwWAwwGg0suaKFHVJJpOMy9FoNJg090lBJ0qj0ch6/BBBaBAhl8sZY53ay1PunVjXJ4kM9EsvE8lwkEOWFIYkYpzL5WJk3Gq1ing8jng8jkQigUwmg1qtxpwVCt8D6GGtU6UWjRkhduImB99mszEBQwrXUrqoWCyyXDVpuQx6KScftODSJkMl8lTO2u0edBvP5XKIxWIs2pJKpVCv13vWFBob9XodcrkcmUyG5feVSiWrwhCS40faJVQCTL16Go0GW8epSk1Ic+d5oFS/2WyG2Wxm+xBFdknIsFgsolqtspSLkGxEBwP+63nX1+t1looEwKr1aE7pdDqmv6TVaiGXy5mzNDo6ytJvJ8ULzUR6SE6nEx6PBx6PByMjI6wEMRwO48aNG1heXsbjx49ZlOBFF0si9ly+fBmzs7Ow2+2oVCoDKaTFcRwMBgM8Hg8cDgf0ej1yuRw70VCO72VwkkH1LoMGutFohNlsxuzsLC5fvgyLxYJWq4VQKIS/+Zu/QSAQwPr6OhPjk0gkyGazPWOBQrckTMYnOVM4cxBTj3yQvYgA7na78aMf/QgTExOssScJH+7s7CAajeKrr77C6uqqIPLyhP4xPz8/j48++gjnzp3D9evX0e0eNOosFovY29tDKBTC3/7t3yKZTGJvb48dGviRYHpPqkbKZrOIxWIol8usGklo6UeNRsPSjS6Xi0XP0+k0Hj58iK2tLWQyGbRarYGupDkp+KlAm82GTz75BDMzM5iammItSYrFIm7cuIHd3V3s7e0JpvUIrQ2kWl6tVtFoNKDX6zExMYFarXZso1sqt+9Hu91mAnXkFzx69AgjIyP42c9+xsbd9evXARxQUkgu5Hk4sfPC3yjJY6JoArGvK5UKK0ml3iDPSxX1/w0aBJQWIaVLACxFMGgg0TVaADudDiqVCotKHeeA8G3HnyC0kZ3UM34XwVdzNBqNMJlMsFgsUKvVrJw1Go0yRn+j0WChy/4OpvycLfGvSJiu0WgMXErtKNAiodFoYDQaWXWIVquFUqlkAm10Osxms8hmsz0cIKGA5pTFYsHo6ChrIkgp2Xw+j0QigVgshmg0ilwuxyIJpOvSD5pX/XOLXoOcLuKDKvsosqBUKiGVSlmzzmw2i3Q6Lchxcxj4PDKO46BWq5lmEqU/6vU6Go0GUqkUEokEc4KJkznI6wt9dn6kmiqFiMR+0vcg9M8ZSuHr9XqUy2XUarUeDqdCoXihz/xShF1aOPV6PXQ6HQvNxuNx7O/vI51Ov/CDpMVCp9NBrVbD5/Ph3LlzGBkZYW3HNzc3EQqFXila8TbAJ+Y2m03GX6GHd5zjQTocGo2Ghdq63S6azSaLRtGgGLTJQ5/X4/Hg8uXLWFpagtfrRblcRiwWg9/vx6NHj5DL5XrKNPtPwPyFx+l0YnZ2FiaTidmIuiYPmn0IfOI3KQ/PzMzg3Llz+OEPfwiDwcA0TGjjuX37NmukR/1FhFBSDzxdK8hhuXjxIt5//32YTCYAYJUM4XAYt2/fZusShfiP427wNy+z2Qy5XM5acPAPZYOI/pJyt9uNq1evwuPxMHLy/v4+AoEANjY2sLOzg3q9zg4MQgfRG9RqNVwuFy5dutSTWaC5tbe3h52dHZRKpWcKSgYVNO6LxSISiQSLvjidTrz//vsoFou4desWK6Th81P70c9lJOeFSMBnzpzB5cuXsbCwALVajWQyic3NTfj9/hc6HLxUApcIbsRVoNMeee18ldgXeai0aPSfwtvtNqrVKpLJJPL5/MCx/KmyiO/RntRbp02HlHX5fTUotPc8B+hdBN/h0Gg0rDpErVajVquxRnlU6kvX9qd+aCzQeNRqtazKpFqtss7Tr6Lk+C6AopKUO6aIi81mY0TCVqvFKmsSiQTi8TjbsIW4ARHXhV4qlQrdbhe1Wg2pVIpx74jA3Ww2j+WsEA9EJpNBrVZDp9NBKpWiUqmwuTboKs10j2q1Gnq9Hg6Hg3GEGo0G8vk8stks0+I4bpMSEsix49vGarUy0UIaV6VSiTXRHcQswFHgc76q1SpbO0mGgRpS1ut11Gq1nt856r3oX1p7qNGy0+nE2NgYjEYjy9jEYjGWgjspXsp54TdlIuZ6s9lkEQYSoTtuwPO9MqoYUCgUuHr1Ks6dO4crV65gcnISjUYD8Xgcq6ur+PWvf41EIjEwg4YmPbVDCAQCCIVC6HQ6WFhYQKVSYc5Z/0MjD1ShUDChpJmZGWi1WlSrVWxtbeHzzz/H+vo6arXawDl0AHpShFarldmCoizPc/Co7A4AU9UlBeK1tTWsr69jeXmZtQ4Y9Lw0lW3Oz8/jD/7gD+ByuZjoIXCw8GxsbCAUCuHx48fw+/2oVCqCqRLhO7wSiQQTExN47733MDk5ycimtVoNoVAI33zzDWKxGNbX11GtVgGgJ3JH4KeHAGBychIjIyP4+OOP8eMf/xipVAp/93d/h7t37yISibAoVv/7vMvgO/rdbhdGoxHz8/NYXFzE7Ows25TS6TQePHiAvb09FAoFQZXUPw+U3jCZTFhaWsLi4iK8Xi8MBgM4jkOlUsHGxgYCgQD8fj/i8TgjpArBPrQ2ZrNZ1Go1bG5uwul0YnR0FGfOnGGR60AggPv37zNHjvZ+AIwXRf+SszI2NgaTycSqtogvm8vlcO/ePayuruI3v/kN0un0CxVWvHR7AHJa+Plh+pef1jnJpko5L41Gg9HRUczOzrIOsJlMBsVikZHtKFQ3SAOGOkcXi0WUSiXI5XKmHUENKY9qGiiXy5mWh8VigVwuR7vdRi6XQygUYhszMDiy93xHjaIJlFd+EYY7vRfJudvtdha9KZfL2N/fRyKRYCeFQVYH5TiOVfk5HA5MTEywCBPxqJrNJpLJJOLxOOO7CK3KCngqcGkymXo6RVPErlAoIBQKIZlMsvlBc4tvC/74op+ZTCamFTMyMsJKYiORCOtfQ9cPEvifV6VSwW63w263MyVqSo1RZR/1fhom0CHa5XLBbrdDr9dDrVYDQA/XhaLBFEEXAmh8UOCBqs1cLhdra7OwsAC5XM4k/imIQb9PmQHa0yhz4vV64XA4cPbsWUxMTMBgMMBgMLAO94FAANvb2z0cmJPgxM4LP2eezWYRiUSYABZ58+Pj43jvvfewu7uLQqHAQlDA042VPhxJcBPH5eLFixgbG8P777+PM2fOwGw2o9FoYH9/H9999x2Wl5eRTqcHrmyP4zi2EVNnbZIdt1qtGBkZgVwuZ43yyE6Um56ammJqutPT06zJXi6XY+HwQQXxeWjDMBqN6HQ6rEokFosxOXY++CQwclY++OADLC0twWKxIJFIYGdnh6kXD9J44YMOAwqFAgqFAvPz8/jkk09w5swZjIyMsMWi0WggnU4jFArhd7/7HYLBIFNtPmzTHlTQMzebzdDpdPB6vZiamoLFYgFwUO24vLyMu3fvYmtrC5VKhY0fun/+QQsAawcwNTUFp9OJa9euYW5uDkqlEvfu3cPt27fx93//90ilUj3vNUj2JFkFKveemJhgqrGkEbW9vQ2/34+HDx8iEomgXq8LJqpwHPg8IJVKhcnJSfz85z+H2+1machisYh4PI4bN27A7/ezCixg8JzYo8An7JLOD2mHqVQqcByH8+fPY3JyEhcvXmTCoaRazXEcO0SQ9pjD4ejRjaG09vr6Ovx+P+NWUYDiedmafrxQ5IUmba1WY5GEcrkMtVrNCG7ULJCEskg9FuitWKJyT71eD4PBgOnpaczNzWFychIul6tHIZSIuqRsOagEKRJNIwEfrVYLk8nEIgPk2NG9SSQS2O12jI2NsWaMhUKB5eCpcmIQU0bAU4eYGO10yiHOAvGbDqsqonsm+enJyUksLi6yKFcymWRhfvpbgwj+wupyuXD+/HmMjo6yBnHAwbjK5/NsrgQCARbSHdS50g/+OOBHIq1WK1trcrkctra2WMSNHwHuL4mm8UORTY/HA5/Ph/n5eZw5cwbBYBCBQAB7e3vY2trqUbceJHvynS26V6vVypw1uVyOcrnMehhFo1HEYjEAgxPJfVUQ14UiUnR4lslkjG+Zz+dZeXSlUmHEb6GB5kwsFkO73WYHBJvNBpfLBbfbjTNnzjCBx1KphFAoxMrLSTmXSM+kVs1xHPx+PyvEuHnzJiKRCHZ2dno4my8y5l7IeaE3pk2FJvfo6Ch0Oh1GR0dx7do1jI2NwW63I5fLsQZomUwGUqkUNpuNTSC1Wg232w29Xo/FxUWMjIzAbDZDrVZjf38foVAId+7cwb1793p0PQZt8bBYLPB4PJicnITP54NCoWBEVY/HA6VSyUilFBK3Wq0wGAz46U9/iomJCbhcLrTbbWxtbWF7exurq6ssJTKoiwwtqsTrocUgHo/j66+/RjgcZtLkdFKmU6TZbIZGo8G1a9cwOjqKxcVFjI6O4ubNm1hZWcHGxsZAkrv7IZFI4Ha7MTU1xcKuRqMRwIGTRy0SvvzyS4RCIcRiMRSLxYHcaE8KmUzGolEk2d5fgUeLIRF0idRtMBigUChYefnk5CTMZjMWFhbgcrlQrVaxvr6OBw8e4OHDhwiFQoz0PIjzjL9mGgwGuN1ueDweuFwuVikaj8dx79497O/vs/YRg3ivLwI+f0omk8Hn82FpaQmXLl1i7W6oJxZFXEKhELLZ7MCOhZOA7qtQKKDZbOLmzZuIxWIwmUyw2WwwmUwYGRlhTYaNRiNsNhsrvQfAtGJ2d3dRqVRQq9XQaDRYKndvb4/x8fjr84uuVS/lvBBzP5FIIBKJMEY2VYxQzjAej8NoNCKfz8Pv90Mmk2F6eppNIo1Gg/HxcRgMBoyMjECv17MFN5vNYnV1Fevr66wF98vc4NsEnRbJsaMKEUofKRQK2O12AAcOIaXE5HI5JicnYbVacenSJUxOTkKr1aLb7SIYDOLBgwfw+/3I5XIABssmh4HypKTnk8vlWMMzvjAdP3Wp1+thNpuxuLiI6elp+Hw+WK1WpFIpPHjwAIFAgJFVX0Td+V0Bf1LbbDbWJdntdrMNmUiW5OSTdD2R64S2wJJNqCKIXpSapbA0PxrJT32QrolOp8P8/DwsFgsuX74Mp9MJn88Ho9GIb7/9FltbW1hZWcF3332HRqPBxDEH1Z60pmo0GoyMjLDqEarIy2az2NraQigUYuT/YQCtK1KpFA6HA5cuXcLMzAzrUk8p7MePH2N/f79H1VuoBwO6p0qlwhyPvb091t7G7Xbj7NmzsNlsmJ2dhVarhcfjYZmSRqOBQCCAcrmMx48fs96GlUoF4XAY6XSaqX+T7fl/90XwUoRdyv2urq6yEkKtVsvKVIn/QqVm1Dab0iBKpbKnFb1CoWCleqSM+fjxY9y5cwf7+/vPnLwHDdRvJ5PJIJPJQKlUsjTA9evXUalUcOXKFRayI+EtjUYDl8vFSFL5fB4PHz7E8vIyYrHYwJ+uiXtQq9WQy+UglUoZh8XlckGhULCye6VSCaVSyZzc9957j23qJpMJu7u7rAPu7u4uisXikUJk7zpo81AoFExJd25ujp14qGwzGo3i66+/xv7+PtbX11mUapDHxFHgOw/FYhEAWKPOsbExtrC+//77cDqdrHqR1JbpZOhyuaDRaFgrBVp/1tbWUC6X8d1332FtbQ1+v39gU0WEfkIy9enxer3Q6/WoVCoIhULw+/3w+/2sknMQ7/VFQWuP2WyGxWLB3NwcLly4wNbbZrPJevXt7Owwx04oui4nBc2BRqPB9NzS6TS0Wi3u3r3LBEb50c9cLodarcaaLZKPQC1xXleV1gtzXoCnEto0yQ0GA8bHxzEyMsLSHWazGZ1OB1NTU4yVTCE6/gmaFqRarYZms4l4PI5oNIqVlRXcv3+/JyIxqDlGOt1QvxSDwdCjsQA865TRw6UTZTAYxN7eHpaXl7G8vNxTDTCoE4kiU9VqFblcjnEXqJ06SfxT2Z1er8eFCxfgdDrx+7//+3A6nQAOnOlf//rXWFlZwcOHDxEIBBhXZFBBRF06Lc/NzcHlckGpVDKthVgshhs3biAUCmFjY4NF8wZ1PDwPtEAWi0V2/xT5lclkjGQ5NjYGi8XCToVUuknOCxFXu90u0uk0SqUS1tfXsb29je+++w4bGxs9PccG0QEm8NcIasHh8XiYymkwGMT+/j729/eRy+UE2afnMJDzYjAY4PV6MTMzg/Pnz/eo6eZyOSSTSWxvb7MU/TCk1PigyCPxUiQSCTY3N3tKog/rMk3Vj3wZENrvX9eceqnVnQY2CTb5/X58++23GBsbQyqVglKpZHLlBoMBwNNKAbpRPtmw3W7D7/cjnU5jbW0NgUAAq6urPc3kBnEy0cOqVqtIpVJYX1/H559/zkJvKpUKer2e9cwg4hiVnXW7XZYe+v7777G/v49IJMIiUYO+yNCYoLJvjUaDbrcLh8OBH/zgB8jlcpidnYVEIoHBYIBGo8HU1BT0ej0kEglyuRz29vaQTqdx//597O7usuq3QXfsgKclwSQGSZEkftlmMplkfYsGvUnnSUBl4a1WC7u7u1CpVGwdIhVqh8OBTqcDpVIJk8nUU/FHVVipVArVahXhcBiFQgHLy8usmSyNy0Fdd/pBY4hfUVKpVJDJZLCzs4NgMIhGo8Eiv0K455OA1md+hJY23XQ6jXv37mFnZ4elr4HhITETaCxQo1P++tKvkQT0NnQEntqLHJ/XiVd2Xur1OlZWVpDJZFh7db1eD5vNBqvVijNnzrAbP+w9qGfNrVu3sLW1hfv372Nra4upWQ7yyYfPEapUKmg2m4hGo1hcXGTk5ZmZGdTrdYTDYbTbbSahTHLd33zzDfb29nD37l1GHhz07sC0mZBGUCqVwvb2NotCjY+Pw+l0sjYBMpmM6VFYrVZ0Oh0EAgGkUil88cUX2NnZwcrKCuLxeE+rhEFdhPkTn3gdpJ1AXVqpXw+Rmvv5HUIE3RsdeB4/fsxC041GA7Ozs7hw4QKMRiN8Ph9bRCnsXS6X2SHp7t27SKVS2NjYYCdsvqIsMPgbFd0/RbxpHaY2EvF4HMvLywiHwydqnSBE8NXiyTGm+fXVV18hGAwimUz2SH4Mk32OW0ef16bntOfPK8fVOY5jDRkpF0/cF4PBgEgk0uN90e8QSDb/0aNHiEQiyGQyLLIglIFC90122t3dxa1bt2AymeD3+1m6rN1us+oJUpt99OgRI2Lyc/CDDnJgALATIIkhUcSu1WpBqVSC4zjmzKZSKdRqNayuriKdTmNzcxPRaJSJRg2iDkc/+IeDSqWCra0tfPnll7BYLHA4HMhkMtjf3+/pkMz/PaGDxg4J0u3t7UEulyMejyMWi7ENiX8KpPYlpEG1tbXFysvpYCE0+/FPyI1GA5FIBHfv3mWVM4FAAMFgkEUrB33evAgo6lIqlRCNRvHo0SNG1JVKpfD7/djd3WWqr/zfE3EAvi3eRlUndxyzXKfTnYh2TqcbfpibevFQiP8oUJuBXC7XowkDnHyglEqltzKiNBrNC9HyKdKgVCqh0+mgUChY2oiqaugEQItvJpNhFQAvu8BUKpW3Yh+DwXCsfeieNBoNNBoNlpaW8MEHH2BsbAznzp1jJyLqf1EsFnHv3j2kUincvHmzp7sr39F9UfsUCoV3cvzQXNDr9dBqtVCpVNBoNKyvVb1eRz6fZ+PqtPC2xo9Wqz3WPnTfCoWCzSmK3vHXHHJqW60Wcrkci8LwN6VX6QxcLpffin1UKtULrT80fqjMlfSUKFp5WqjVam/cPs+zDV9vTCKRQK/Xs6pZjuOY9g1/bp3G5vw2bAOcfG9/2zhub38tjEYaCP1t5E/y0InQQw0G6Xohe7jtdpvputDk6G9mSYsv3y70cyGB4zhmD+oums1mUSqVehp/1mo1VCoVbG9vI5fLsbLFfgVeodmHIgwUfSL5bmryedI2CkJGu91mqVTg8DFAREKKVPW3DBD6mgMcHBT5DSaJUHmajsu7Cn7qmjIGhUKB/Yw4QMM+t95lvJbIC+Gw9zrpw3/ZUzMwOJEXwmGKsf0hOMKr2IXwrkZe+CBCFz96B/Sml+j0TBsRH69in3c18kLgOyj9ok5vYsN9VyMvhH77HIZ+O/G/flUbDkrkBcAzdnoTvJ53MfLCx1Fj53Wsvc+DGHk5HqceeSHwNxoAJz4VvolB8i6BiGHAyZw7oduFbMDvUPo8DNOJGehdYPvzy8Nw/yfBcevNYSnXYbPbUU6wiGerZIZpbRlUvHYhjGFeHF4Eg17J8DrxopGEYRtXVEkk5PTYq4DGjXgQOB7iZnw4Dhs/op3efQyuipcIQUJcNI6GaJvjIdpHxKtAHD+DhWM5LyJEiBAhQoQIEe8axNyFCBEiRIgQIWKgIDovIkSIECFChIiBgui8iBAhQoQIESIGCqLzIkKECBEiRIgYKIjOiwgRIkSIECFioCA6LyJEiBAhQoSIgcIrOS8cx1k4jvs/OI4rcxy3z3Hcnx9xHcdx3L/kOC795PUvuWOK6jmO+/Mn71fmOO6vOY6zvMrnfFsQ7XM0OI77rzmOu8NxXJ3juL885rqzHMd9xnFciuO459b1cxz3v3Act8FxXIfjuH/yOj/zm4Y4fo6GOH6Ohzh2jodon6MxKHPrVSMv/xpAA4ATwC8B/M8cxy0ect0/A/ALAOcBnAPwhwD+88Pe8Mnv/xsA/9mT960A+J9e8XO+LYj2ORoRAP8CwP/6nOuaAP53AH9xwvd9COC/BHDv5T/aOwNx/BwNcfwcD3HsHA/RPkdjMOYWv6fDi7wAaHHw8Gd53/vfAPw/D7n2JoB/xvv6LwB8d8T7/t8B/P94X089+Tv6l/2sb+Ml2ufEdvoXAP7yBNdNHwzXE7/vDQD/5G3fnzh+xPEjjp136yXa58R2eqfn1qtEXmYBtLrd7ibvew8BHOa9Lj752fOue+babre7gycD7RU+69uAaB8RrwJx/Ih4WYhj53iI9hEAXsV50QEo9H0vD0B/xLX5vut0R+QO+6897n3fZYj2EfEqEMePiJeFOHaOh2gfAeBVnJcSAEPf9wwAiie41gCg1H0SQ3qF932XIdpHxKtAHD8iXhbi2Dkeon0EgFdxXjYByDiOm+F97zyAlUOuXXnys+dd98y1HMdNAlA++XuDBNE+Il4F4vgR8bIQx87xEO0jBLwioeevAPxbHBCgPsJBiGwRgA9AF4DvyXX/BYA1AKMA3Dh4yP8F7338eELgefL7BQAfP3nf/w+Av3rb5CXRPq/dNjIAKgD/DxyQ5VQAZE9+1gXwwyf/5578bOHJ91UAlLz3+UvwSGUAFE+u+QbAP33yf8nbvl9x/IjjRxw7785LtM+xthmIufWqN2kB8NcAygACAP78yfc/fvJQ5byb/FcAMk9e/woAx7uhIoAzvPf98yfvVwbwfwKwvO0HKtrntdvmnz8Z8PzXPwfgebIAWJ9c5zvkOj/vff4ewD/lff3FIdf/8G3frzh+xPEjjp135yXa51jbDMTcoofwWsFx3H8PINntdv/NCa69DuC/6na7/8lr/yDvKET7HA2O4/5TAIvdbve/O8G1Chyw+891u93mqX+4dwTi+Dka4vg5HuLYOR6ifY7Guza3TsV5ESFChAgRIkSIOC2IvY1EiBAhQoQIEQMF0XkRIUKECBEiRAwUROdFhAgRIkSIEDFQEJ0XESJEiBAhQsRAQXbcD3U63UCweUul0pEtyk8TBoNhIOxTKBTein20Wu1A2KdcLr8V+4jz63iI4+d4iOPnaBiNxoGwTT6ffytjR61WD4R9qtXqkfY51nl5HTismunwthAiRIgQIULEu4+jqnTFve3N4VSdl0PEbyCRHJ+pGvaHT3bi2ww4sAu9RIgQIeJFwV9Pnrf5iuvMsyCbdTqdnq8JZDOJRCLa7w3g1JyXwzZe+v/zfk988AeQSCRQKBSQSCRot9vodDrPODUingXHcUNho5PcoziXTg4hj5ujNtrjrhfHzuEQHbx3A6fivHS7XXQ6HchkMqhUKvZvp9NBoVBAq9ViGzENgGFPL/U7exqNBhqNBj/60Y/gdrtx584d7O/vI5vNolQqDWUUpn+sdLtdNpakUik4jmOnnuPG1aCDH82keUSnQQLZgW8PcbwcjBf+mJDJZD3RYCE6MJ1OB+12G1KpFFKpFBKJBFKp9Jnr+POp3W6j2+2y64cZNLdoPmk0GshkMigUip451Wg00Gw2Ua/X0Wg0BDvn+Afoww7T/fd9Ws7eqaaNpFIpNBoNFAoFdDodOp0O6vU6OI5jiy5h2KMK5MjRAqPRaGA0GjEzMwOfz4dAIIBUKoVischsJMSJcVL0O3vDDv6mK9RF83WA79AMk41obVEoFJBKpZDL5QDQM2ZofeZHeYcdfPtIpVLIZDJotVoolUqo1eoeJ7BcLqNWq6Hb7aLRaLytj/xGQOvNYfPpTc2r1+q8kPOhVCqh0+ng9Xrx4x//GCaTCV6vF+VyGV9++SVKpVLPg+92u4jH4ygWiwgEAkgmkz0cmWHw/CUSCbRaLc6ePQuLxYKlpSWYTCZYrVZIpVI0Gg2Uy2W0Wi0Aw7Vh8ycITRKFQgGZTIbx8XFoNBpks1mUy2UUi0VUKhVB2ofunzYhlUoFs9kMpVIJs9nM5kmn00E2m0WtVmP/NhoNtFqtoZlPwLMOrlqthsfjgUqlglQqRbvdRjAYRLFYZJEGIYHGi8VigdlsxsjICHw+H5RKJbRaLbsGOLBPo9FALpdDpVLB1tYWCoUCUqkUarXaC6X+hQCynUQigUqlglarxblz52A2m3H27FmYTCZYLBaWUQCAeDyObDaLO3fu4O7duyiXyygUCixiIwTI5XI2f+RyOaRSKbRaLTiOY1FNcoDJwSmXy2g0Gmi32+z7r2MMnYrzIpPJoNPp4Ha7cfXqVTgcDszOziKfzyOZTCKbzcJisTDvv9PpYHd3F6lUCtlsFslksuf9hAzajGmS+Hw+uN1ufPDBBzCbzUgmk8jn82i1Wmg0Guh0OkOxeDwPlIocHR2F1WplKbV6vY5yuQxAmIssLagKhQIajQYOhwM6nQ6jo6NsPjWbTYTDYZRKJTZmms2m4OfS8yCXyzE6OgqdTgeZTIZWq4VMJoNKpYJOpyO4uUXPW6vVwul0YnJyEufOnYNGo4HBYOi5BjgYN7FYDIVCAeVyGXK5HIVCgUUTAGHOqaNAa7NSqYTBYMDU1BRGR0fx4Ycfwm63w+l0QqPRsH0qEAggkUggn89jZ2eHpeCE4rgAB9kUlUoFuVwOtVoNuVwOk8kEjuOYc1IsFtFqtZjzQnboT22/Kl6L88LPf7XbbZhMJly+fBlzc3OYmpqCVqtl+UCr1QqdToeFhQXo9XpIJBJ0u13MzMwgl8vB6/Vib28P2WwW2WwW6XQa0WiUhe2EBLKXwWDAwsIC3G43fvKTn8DpdMLj8YDjOHzxxRfY2trCzs4Ostnsa/VcBwX9HAW1Wo0LFy7A4XDgRz/6EUZGRnD//n2EQiHcunULuVzu7X3YUwDdP516PB4PLl26BIvFgomJCebEyGQyFlGIRCKoVCoIBoPI5/NYX19HIpFAMpnssc+wjCOO46DVavHee+/BbrdDo9GgVqshHA4jl8uxU6MQ7EGbhMFggFqtxtWrV/Hhhx/C5XJhfHycpabpWlpbO50OpqamUKvVMD4+jlwuh5WVFSSTSayvryMej6PZbAp6DeJHXJRKJSwWC65cuQKXy4Wf/vSnsNlsGBkZgUqlQrvdRrlcZnYwGAxQKpW4cuUKZDIZVlZWcOvWLdRqNVQqFQDPr7Z910F7FgC2d1erVRZ5abVaPalHALBarVAoFMwRLpfLzG6vMoZeW+SFTyA0Go2Ym5vD9PQ0RkdHIZFIWPSAPP7Z2VnYbDY2iUZGRlAsFqHT6TAyMoJAIIBwOIxut4twODzwD/0w0EBQKpWYnZ3F+Pg43nvvPdhsNigUCpRKJfj9fty/fx/RaBSlUomF6oYNfE6QSqXC7OwsfD4frl+/Do/HA4lEAoPBgK2tLUHyGrrdLjvteDwedvqbmZmBWq1m6UW5XI5Op4N4PI5KpYJAIIBsNsvsVq1We5wXIdnoOHAcB5VKhZmZGYyPj0Or1aJSqeDTTz+FTCZjp0Qh2IPug8bF3NwcPv74Y5hMJthsNnQ6HeaskSNCUTtyfj0eD0qlEkwmEyKRCDtMttttNJtNQZcD0xigqMLS0hI7MJhMJnZNpVLpIeaq1WoYjUacOXMGKpUKrVYLa2tr4DiORYMHHXznhSgMlFakMUUOLl3ndrthMpmgUqlYwU6xWHzlMfRKzgtNEoVCAblcDqvVipGRESwuLuLChQswmUwoFovIZrO4efMm0uk0Njc30e12sb+/D6vVirNnz8JqtUIul0Or1WJ6ehoOhwMulwvBYBDdbhd7e3vMqwOE4b2Sd6/X62G327GwsIDR0VEolUo0Gg2sra0hnU5DoVCw05LVakU6nX4tD37QQNVrer2eRRympqag0+lYyk2n07GQJk2eQd6M+KF6iUSC0dFRLC0tYX5+HgsLC9BqtT0pkFqthmQyiXa7DblcDolEgrGxMTgcDmSzWajVamSzWcRiMVZRMqi2eVHQokvpV4VCwRZboaVEaG1wOp2YmJjA6OgoTCYTpFIpisUi4vE4NjY2UKlUkMlkoFAo4HA4oFarMTIyAqVSCb1eD5PJhIWFBYyNjaFer8Pj8eDx48fY399n67FQIjD86K5EIoHdbsfFixfh9Xrx/vvvw2w2AwCy2Sy2t7eRy+Xg9/tRKpVYJGthYQHj4+Not9twu92w2+0wmUwsbQJg4A+e7XabOStUfFMul6FQKGC326FSqTA9PQ2JRIK9vT2USiVYLBY4HA4AB3OsWCy+lhTta3FeiIE9MTGBy5cvY3Z2FufOnUO320WpVML+/j5+/etfIx6PY3d3F51OBysrK7BarfjTP/1TzM7Owuv1wmw2w2w2Qy6Xw+l0wul0Ip1OQ6vVolarodlsvtLNviughZQ2Y7vdjvn5eTidTqhUKtTrdSwvLyMWi0Eul8Pr9bKwG5HqgMGfCCdFv7NntVoxOTnJUpJ0qtZqtcx5oVzroC+sRNCVSqWMCzU9PY35+XkAT6v0KIS9vb2NZrOJiYkJ6PV6uN1uKJVKFtVcX1+HTCYTzFw6KWgMNZtNNJtNlsZ+3Xn4t4n+Igen04np6Wm43W4YjUZUq1UUi0Xs7u7id7/7HTKZDPx+PzQaDebm5mA2m3Hu3DlYLBYsLi7CaDTCZDIx3obP50O1WkUymWRRByEdovjRXZvNhuvXr2N8fBxXrlyBQqFAJpNBLpfD7du3EQwGcfv2bSQSCVY88Ad/8AdotVrweDzs0GA2m1GpVHqoFYNsLzoA9Ms16HQ6jI+Ps7FDa06n04HZbIbdbmcRGVqfX5WD90rOC5FwlEolTCYTfD4frly5AqfTCa1Wi2KxiFgshmg0ing8jlQqhUajgW63y1jst2/fRiQSwZkzZ+B0OjE+Pg673Q6lUgm73Y6xsTFMT08jkUigXC4P/EmJBq9MJoPJZML8/DxLoWm1WmSzWeRyOTx+/BjxeBzXrl2D3W6HTqdDKpVCIBBAu90e+OjTi4BOd0qlkp0kbTYb9Ho9arUaOwFotVqo1Wqo1eqesvxBB0VelEolkx7go16vMz7L559/jlqtxojypLNEIW2dTgeNRiPIzYcPfhpIKpWy0la1Ws2qJYQGWhdVKhVUKhXcbjemp6dhNpvZmru1tYWVlRWsrq6iXC4jnU5DqVSi1WpBq9Uil8vBZDKhVCrBZrNhYmICOp0OdrsdcrkcFy5cgEQiwdbWFjuICoEDQwcAnU4Hq9UKr9eLmZkZ2Gw2VCoV5HI5bG5uIpFI4O7du4hEImxPqlQqkEgkiMfjCIfDjMBK+yKlbYVEmOdXnlHKbHZ2Fm63G+fPn4dCoUA0GmVrFvD6Nbde2XlptVpQq9Us9fHTn/6UCYYVCgXs7+9jb2+PEQfppBiNRiGRSFAul2EwGHDlyhWMj4+j2+1Cp9NBrVZDq9ViamoK586dw+bmJvx+/0APgH7NAJvNhmvXrsHn82F0dBQcx2FjYwPBYBDfffcdkskk/viP/xhXr16FXq9HKBTC8vIyE5waJpA41MLCAnw+H0ZGRmA2m5FOp1GtVhnnhTbner0+8BwG/uenDZhSY/xrqtUqdnd3sbu7i1/96lesymhychIejwd6vR5arRZWq5U5MFR9NMjz6SjwnzfZT6PRQKfTMVsIrbII6K0uMhqNmJycxNLSEiwWC7rdLiKRCG7evInV1VXcvn27p5x1Z2cHMpkMDx8+hMFgQCqVwujoKKuAHB0dhdfrRbfbxdjYGH7zm98gEomwCNYgO8EUPWi329BoNJiYmMDMzAzOnz8PuVyOfD6PTCaD77//HoFAAJ9//jlisRiUSiWkUimbS/v7+zCZTHC73WxDt1qtyGQykMlkjAMyyGsS0Du/yHnR6/W4dOkSpqamcOXKFcjlcoTDYahUqmcOW68LL+W88KsfZDIZ7HY7pqamYLPZABwQeZrNJtLpNLa3txEMBlnJJvD0JEn5sna7jXA4jE6ng/n5eVQqFajVaigUCuj1eoyOjiKbzUKj0TC9ikFFp9NhZa4WiwUzMzMYGRmBVCpFvV5HPB5HLBZDqVRCvV5Hs9nsyZkOK2jM6HQ66HQ6dpKp1WoolUqIxWLI5XJIpVKoVCqCyscDvb2t6J5arRay2Syi0SiWl5cRDAZRrVbRbDYRiUQglUqRyWQYp8xoNLJKNvpdIaLfISNOh9PphNlshlarhd/vZ6KP/WvToIIfedHr9cyRl8lk6HQ6qNVqKBaLqFarjA/W73TU63WUSiXs7OygWCxicnISjUYDXq8XJpMJZrMZPp8PCwsLbOzt7+/3EDQHyYZkM41GA7VaDZ/PhwsXLmB6ehoqlQrNZhPJZBLxeBzb29uIRCJoNpvsgA48G4WgyLpOp4PD4UAymYRMdrDVCmkdJ8qIwWCA1WqFzWaD2WxmfKh8Po9cLsdUrPP5PNMqex1r8ys5L+RgzM7O4qOPPoLP52MbSqFQQCAQwNdff81ypMTz4FeD5HI55olGIhEsLCxgenoaUqkUJpMJLpcLS0tLqFQqMBqNTPhnEFNHdNIlFrvX68VHH30EvV4PmUyGfD6PjY0N+P1+5HI5VKtVlhbhl54JaVM+KUjbxGKxwGKxQCaTodvtolAoIJ1OY3l5GaFQiJWUU7pACOh3XCQSCWQyGatG29rawt/8zd/0KDCvrq4ikUjg+vXrsFgssFqtsFgsTG+pWq0iFAqxkzf9HSGBv05RddrY2BjUajW+/PJLpi1FZZuDnIql6AGdgolvYTQaWW+0SqXCxgitJeS80GGgWq2iWq3i9u3bbEMPBAL46U9/CoPBAK/Xi/HxcahUKoyMjODWrVvIZrOoVqsDW1HT7XZhMpkwPj6OS5cu4Y/+6I9gsVig1WqRTqexs7MDv9+Pb775BslkEs1mk0U/++cmVSkplUpYrVZMT08jk8lApVKxtZx+b5DBL9YZHR2Fz+fDxMQE3G43c4Cj0SiCwSBz9DKZDAqFAkuzvVXCLnlcbrebkcKAg9KpRCKBVCrFvC3g8AdG32s0GqhUKigWi8jlctDr9eh2u1CpVLDZbLBYLKxMjZyXQRwA/FMhLRoSiQTNZpPlVqkkETg4CVUqFUbupQWF/16DaIeTgk6HpCpLkRdakHO5HJLJJBPSIkKZUBw8csJUKhU0Gg30ej2USiU7SSeTSaRSKSYsRqhWqygUCshkMkilUjAYDEzWnP4Vgn2OA90f8T/cbjfju5DeBJEI+SfpQQWtB8QzUKlUjM/SaDTQaDRQr9ePVOmmOUPzqNFoIBwOAwAmJychk8lgs9lY6tHj8SAajcLr9bKI56BEsPgHSblcDpfLhcXFRUxOTsJsNjNZgUKhgGQyiXQ6jVqt1hP1P+oe6fu0bg2yU3wcqALU6XQy7SSZTIZEIoFMJoN0Oo1sNsvsUSqVUK1WmdL3G3Ve+Jslx3GYnp7GwsICrl27hkuXLrGTbiaTwfLyMtbW1hCNRplAT/8CQZs3cODwdDodRCIR7O7uQqPRwOl0wmQywWQyIZ1OY2JiAqFQCNFodKBz9XRKAsD0FWij2dvbg9/vZ3nkbDaLRCIBjUbDlDKtVisqlQqTwX/XF4pXBb+ayOVyweVysTTbzs4Odnd3Gfm03W6jXq+zRQkY3OaMRIinNgAOhwNjY2PQarVoNpvIZDJ4/Pgx9vb2GHmQKk1yuRzq9To2NjYglUpZ9EWn08FisUCj0TCRKSGCzxUymUy4evUqPB4PDAYDGo0Gk3DgL6aDDj7nhQ57BoOBNXMtlUooFouo1WrsfvvXDn6LiUajgTt37uDRo0eo1+vY2trC+++/j/n5eTgcDni9XuZIP378mKX+6bO8y+sSRbINBgPMZjOuXLmCX/7ylzCbzRgdHUW1WkUikUAgEMCjR4/YPnbUxsuvJgKe7pE0H4W2TpODq1arcfbsWUxMTMBms0GpVGJ7exuBQABra2vY3Nxk983vX/g65ttLRV7ogVCZr16v75H6bzQayOfzKJVK7GRzFOjGSPwom80iEomwvDw1wyL1UCGckIiFrtfrwXEHDdGq1SoqlQrK5TKq1SrrlEy2ViqVTBJfoVAwQuqg2+J5oMgDlW1qNBqmhVOv11kotlQqsR4+g0wePAzkvOj1eqjVakgkEsb1SaVSPQqxBCIgkrrnYXyzYQDdL9+ZJdv09zPip7MHGf0bJzn0tVoN1WqVCas97z263S7jA8ViMaY55XA4WLUfP0WlVqsZT+9dB9mIeCl2ux1ms5lFdal1RCqVQjKZRCaTOXQf6x8rh9mVX1YsJNAYo+o2Wlco0tdqtU61Z9hLRV4UCgWUSiXGx8extLQEp9MJiUTCFkjSdonFYqwh01FOB32P9BdWVlaQyWRgNBoxOzsLtVrNUkhCePgcx7EqozNnzkAul6PRaCCRSCAajSIajSKRSKDRaLAKCXJ0iCtjMBh6qmmEBr6nTt794uIipqam4HQ6odfrmaw7OS3b29ssXCmXy3u4HIMI/unE6XRicXERPp+P6UbQqXBlZQWpVOrQclXSWcrlcqzLrVAX0uNAJz4aT1RQcFj6RGh24TiONeikNYaq8573ezSHWq0W7t+/j8ePH0MikaBer+P8+fMwm82wWq04f/48qtUqRkZGkM/nkUgk2OHrXQM9XzoUT01N4YMPPsDS0hIcDgf7zLlcDg8fPsTW1hbu3buHYrEIAD2OPz/aQgfJ/p8LseEngB5uDxHD+S0n6L75r9d9YHqp2A09JNJOoM2C8vClUomFKl/kwZGIFJ0OhPTgaTMiJWGHwwGj0dizQPBfRMqUyWSsJI9OUrTwCMU2/eDfFzHayWlTKBTgOI5xo+hVLBZRKpUEwXfpjwZQWwCFQsHk2ymiQgQ4uvaw9+ofJ0IdN0eBP1/oVFir1ViqGsDAO7uHoX8TJWmL4yLhh4Ff1ZdOpxGLxZDP59mhlNR47XY7W9PedVBFls1mYwrEVBHEJ5wSn470yQ6DWq2GwWDo6TTdbrdZKw7qWi6U8nzay1QqFZM0UavVPXsZZVz46887ofPCD8Wq1WoolUrI5XJks1mEw2Fsbm7iwYMHLywDTJsORWn4m9AgP3R6gNTNdWpqCteuXYPNZmO8A3pRWTQtCBR1AZ5yIIS2yB4G/qCnKJ/X62Ut6Le2trC/v4+bN29if3+flQgDgz1W+kFll5Q2BA7I7fF4nL1IvJFOfXTK4TiORUn5J20h2eekoHmWy+WQz+dZlLO/7FUo6F8/X+V9CJ1OB5ubm8jn8zAajZiZmWHVJpVKBT/72c+wtrbG2rm8i2ltWj89Hg+8Xi8+/vhj/OQnP2FCauVyGclkEo8fP8Znn32GTCaDVqvFqmT7nVypVIq5uTmMjIzg937v93D16lXodDpWbv75559jf3+fHeT5h89BBEVViLA9NTXFBGaJCJ/L5ZDJZFgA4rQOk69kxf4JQidC4m/UarUXfk+ZTMZ6JQllUeGTlBQKBfPUNRpNj7dK+WXKxRqNRiZ3z0/LDQvIkZXL5UxcjOM41nad1IgLhQIrJ+dv3EIAv/KD3xCtXq+jXq8zkTDg8OoRhUJxqKKsUOxzUtA8azabjP9BkReh2YJSZPTiVzUSd/BlD0CkylssFlnZr1wuZ9wRk8n0Tm/O/AORTqdjERMqfSa+Jm3Ax1W2kl2tVitrB0ANUvP5PLLZLJLJJAqFgqAOnXx9F4PBwKpgSdW8UqkwIjx/PX7d8+ylS6X781n0PSLCUfrjpA+MHq7T6cSZM2fgdruZ/olQoi9SqZRJlFPEiuM4NBoNRCIRxONx1jTuypUr8Hg8GB8fh9lsRj6fZ7yFQbbD80DpR2o7YTAYMDIyAq/XC5fLxUrK/X4/dnd3Wb8nWpSFAP5hoN1uIxaLYWVlBWNjY+z+k8kkU6zmg5/TVyqV8Hq9mJubY93cheTYvQjonulgRWlGIbXaoGdLYnSlUgnlchkymYw1x3O5XEilUkygkFL+zwNFrqhaid6DUrp6vR4zMzNIpVIDYc/DiOscd6BF8uDBA2xubqJQKLADeD8PDwATAvz444/xwQcfMCG/5eVlLC8v486dO9je3u7hGAlh7nHcgZbQ3NwcZmZmWDsgctY2NjawtbXFnDYSs6W1idb3V8Ur6bwAzxIA+byMk24mfAdHpVLBYDBArVYzx4VfYjXI4FcBkOogRVSolJGIblarFS6XC1qttqfRIN8pFMJEOAoUqdJqtdDr9SxSRd2Ti8UiCoUCk+YGhFMtQqAxT/dLWjZkg+NaINA4I/v1V9oI6ST4PPRzPygnf9jYEQIoMsePyhE3wWAwPCMmd9K1hCpJKBLIryQlLh9xHwYB/Z+TeCqJRAK5XO7ISlmKYlFq3+VyMQmDTqeDXC6HYDCIZDLZw8MTCiiiazQaodfrWWSXr9NG9gPAouevm/bwys4L8DRMSXmw6elpnD9/HrFYDI8fP+6JwPQ/RH5KheMO+kHodLqeqES9XkehUOhZwAcdfGevXq9jd3cXwWAQrVarR7XQZDJBqVSy6hK/349QKCTYXD2NpVarBY1Gg/n5eZw5cwZTU1NQKpUIBAKIRqPw+/0IBAIs6iI0x4VAnBc6vQBgncXJ2T3snun7xLMiom8qlYLf72dCiELUoOCj37mjtBGFtoWUNqJnmcvloFAokEwmWVsVm82G2dlZ/OAHP8Dq6iprjHtYav8wriFtPNRB+TDZikG0Izn5VJW3traGb775BolEgnUdp/uiIhUSsvvwww8xOjqK9957D3a7HTs7O4hEIvjd736Hzz///NDIzSCD9mmpVAqNRsNK5uVyOVqtFjY3NxEIBLC9vY39/X2WRXA4HDAYDKzlDUUEX3XdeWXnhb9pUD8Hk8kEp9PJxMKOK8/kb+IU6tZoNOyk2Gq1mPYJnZb4f3fQQRwOukeygV6vh9FohEKhYA0sM5kM08/hix8JEd3ugfS03W5nqp4SiYSJ+fE3bz5RVUjgL5oUqaNIJOlpHHXPtDAQh4x+r1KpIJ/Ps0OA0CN4/aBUHDnIQgF/I+BvENQnjrSCPB4PMpkM0zOhDbq/wq//MMBPRxIfsX/9oYPHIIHv1FKVbCQSYcUm/P1JLpdDpVKxPlnUJNbhcECpVKJQKCAYDCIYDCIQCLCxRg6SUEBl5qTiLJFI0Gg0kM1mme5UsVhkTi4doMiefM7VW3FeiKzEX1TpAbvdbnzyyScIBoNQKpVIpVJYWVlh8sr9bG2pVIqJiQnY7XZcunQJFy9ehNPpRLd70KmTVB63trZQrVbZ3x/kRZcGc7PZhE6nw/vvv49MJgOv1wu5XI75+XmMjY1BIpGgUqmwaEMulxNEC/qj0O12GQFwbGwMFy5cgMfjAcdxyOfzuHXrFvx+P+uXBQjHkeWDNhBSiPV4PLBYLGzOUYVf/+/Q4kA9oKidAPWsiUQibBwJMWXCB5+oyuc48KNNQnJ4aU0hLs/u7i4cDgfjPNlsNrz33ntwOp1wuVyIRqO4f/8+isUi4vF4T5dpat5IJHgqHpiamoLb7cbZs2eZUjMAVCoVRCIRZDKZgXNggKcRGHp1u10meGk0GmE0GlmE5dKlS7BarThz5gxMJhOq1SqCwSCWl5dx9+5dBAIBdsgWitNCawutPXq9Hk6nk3Usp15pwWCQHYzIprTH8ytrXwdeS+SFXuRRmUwmTE9PQ61WI5VKQa1WY3Nzk6nu9YdqJRIJ7HY7vF4vPB4PxsbGoNPp0O12kU6nsba2ht3dXSSTyZ6T9qDhMA+cIgwTExOwWq1M04VaIwBgaTPqryEE/k8/+NwmmUwGrVbLNm2HwwHggGy5v7+Pvb09FItFNJtN5jwDwkkZ9TsVKpUKRqORqeuSA3MUp6y/Wk0mk7HeNrlcDul0mjl+Qgd/8aSNmb9mCWXMAE/HS71eR6fTQSqVQjgcZkR3ajGi0+lYZ+1sNsvGQ7PZ7Kl+BJ52QSbBTK/Xi+npaYyMjLAeWfQ3X0bb610C37klLiIVWZhMJiwsLGB0dBTnz5+HxWJhcvgUDaaDAanx0p4oFNDcIQeGeIikqp/NZpHNZnvEH/m+AdDbVftV9/CXcl6IELa6ugqZTIalpSU0m01GDtRqtXC73Yy7kkwmWT8ePmeF4zgm23327Fm43W7MzMzAaDSiUCggEolgbW0Ny8vLSCaTPZNikJwXeoCNRgOFQgG5XA6JRIJJvpOOSbPZZNEWyiUmEgnk83msr69jdXUV+XxecJU1/LRip9OByWTC4uIizp49i6mpKcjlcsb1CYVCiMfjkEgkUKvVghIyJPTbpFQqsXHQaDSg1WoxNzeHer3OcsmUWyfi5HvvvccOAyaTCYFAAOl0mpV/CrGNAoE2X4PBAI/HA5/PB6fTCbVajZ2dHcTj8WfWISGBDogrKytIp9MsiuJ0OuH1eqHVajE+Ps7IlsViEZFIhIljtlotJJNJRvrtdDqs6nFiYoI14lOr1SgWi6yPzW9/+1sEg8GBiAz3fza1Wg2r1YqFhQX84he/QD6fZy0RHA4HLBYLPvzwQ6YqrFAomHL1V199hc3NTdy9exd+v/+1dU1+F0FyH1Qar1KpsLGxgXA4jJWVFYRCIdTrdRbI6HQ6yGazaDQaPfSPN97biE/eajab2N/fR6vVglqtht1uh91uZ0qFGo2G9aPJ5XLQaDSoVqvPqFqSpsvc3BwcDgdzeOLxOMLhMAKBAHZ2dgZ+sSFuS7VaZblVYmDL5XI4nc4eD5U0KbLZLGKxGCPr1mo1wRB1D7uHTqcDrVaLyclJ+Hw+1iRtY2ODMfjz+TybRELV6uBzDkj4ibogq1QqeDwexONx1qSxWq0yQp1KpcLs7CxmZmbgcDig1WrZybhYLLL3EZrNCLRoyuVyjIyMsN47UqmU6QO9iIzDoICeJ1VU7e/vIxKJsOaDAODxeJi6rMlkgtVqRa1WQzqd7nFeIpEIqtUqq2ibmpqCzWaDzWaDwWBgm3MikUAwGMTGxga+//57lrIahLHF/4xERB4fH2ccxEQi0UM4PXv2LDswtdttxr17+PAh7t27h0AggEQiIUgSPJ8fR9wfs9mMVquFQCCA/f197O7uIpFI9DQ67XQ6rNCGWnK8rvHxUpEXCu9Ho1EUCgXIZDJUKhUsLi4yGXMKJ1LPg7m5uR7JYNqAKaRLjb2IkPr999/j9u3b2NraYt08B3VA8Elh5XIZu7u7+PTTTzEzMwOz2cy6wJK3SieffD6PL7/8Ent7e9jf30etVhuIU81JwScDUmWMXq/H7Owsrl69ipGREVSrVcTjcdy7dw/BYBDZbJZ10yYIwRZHgeM4RgT0+/3Y2dmBTqdjvIPf//3fRzQaxd27dwGANc5bWFiA1+vtCevSqUeIYn5Ar8NH6WVqGieXy9FsNtkhgF9pJCQbAE8jvURM3t7eRrfbZVL31DmZ0o9qtRoul4v9frvdhsFgYLIMAHr4LaVSiak7b29v49GjR2xuvusFFfS5SqUSkskke1FKzWAwwOv1sii4VCqFTqeDTCZjVWqkL/Xw4UPE43Hcv38f4XC4J+Lyrt7/q0Kj0WBsbAx2ux1KpRL1ep2RlKmtD9D7/Pl0kddJ6n7pyAsARKNRpjlBfJSZmRmmIEshbIrC8Eui+8WRyCMjL+7777/Hp59+imq1ysqqBjlVwnFPu23u7e0xZvaVK1fgcDjgcDggk8nYQ6Ymal999RVWV1eZiqwQw5E0oDUaDUZGRjA9PY1r165BLpcz3YV79+4hGo0im82iVqsxrovQTs980HOmkksS5pucnMTCwgLrrk0nHgC4fPkyXC4X5ufn4XQ6oVKpGPmQerQMg8YLPwpFmlG1Wg37+/s9zssgrylHgcYNRZd2d3cZmbbZbMLr9bJN2eFwMNFM/nzqrz6ik3Qmk0GpVML29jYePHiAtbU13Llzh/U+etfXaUrJUkd2cl6oopFoD1R8QhyYZrPJejo9ePAAkUgEv/3tbxEIBFhUgd/WRqjQaDQYHR2F0+mEUqlEu91m6fxyuYxGo9Gzt1MGgV/B9rrs89K9jWhAk0eaSqWwtraG3/zmNzAYDLDb7dBoNCw3arfbGQmq2WwimUz2tGanMuhHjx5hb28Pu7u7rzU/9i6AJkKn00G1WkUsFsOtW7dgsVgQCoUgk8nQbDZRq9WwtraGVCqFeDwuOD0KPigKJ5fL4XK5cOHCBUxPT8NoNKJarSIQCDBtF+pW21/GKVTw77PdbiOZTOLBgweoVquw2WwAgJGREcjlcvz0pz8FAMzMzDDFU47jkEqlUKlUsLm5ic3NTaTT6Xf+dPyy4KeVpVIpFAoFDAYDtFot21iEspacBPzqjkajgWg0inv37iEcDiOZTEKv18PlckGj0cDlcrGIOUVtut0ustksSx0RCbhYLGJjYwObm5uIRqOsinQQbEtjnlSq19fXmVqsRqOBUqmEVqtlBFRKt5bLZVald+/ePaTTaRZp4h/KhTan+kGRcZfLhXK5jFwuh2QyyarMjrr/07DNK5VKk+QvpXVKpRK2trZgtVp7CF42mw2XL1+GXC5HvV5HuVzGrVu3mLw0x3HsVHj//n1sb28zD/9d9+RfBPycdKlUQiAQwGeffQa9Xg+PxwOpVNrjvBBpjMKRg7A4vAj6y+98Ph8++ugjTE1NwWq1IhQKsU3X7/czrovQqouOA7/6IxgMsoohUmBeWlqC1+vF+Pg4OI5jnDOK9EUiESQSCdy/fx8rKyuIRCLsQCCUedUPfl6eupHzq9KGBfz0Ubvdht/vx97eHnQ6Hex2O5MjMJvNWFxchEajYelrUtHd2tpiHCFKZ+dyOYTDYcRiMRbFGxTHkGxCvYfu3r2LWCyGYrEIj8cDs9kMo9GIcrnMUmFra2vIZDJ4/Pgxcrkc9vb2WPEJCT0KdS7x0e12YbVaceHCBRiNRhSLRaRSKVZEcZSS8Gk5dK+lVJqiKdVqlXWTbLVa0Ol0yGQyMJlMrEqGwtcrKysoFovsRERknnA4jGKxyKIyQvNk+fdTr9cRi8UYGVMikaDdbqPZbLLyxUEhv70KyEGlRaBWqyEajSIYDGJzcxP7+/ssWkDXDwP4VUfAQal4Op2G3+/H7du3MTo6yk7LVIFGHabz+TzK5TLu3LmDWCwGv9+PdDot2NQjgU7BlDKi9HUikUAmk2Eky2GYV/3gp5NIaoCiK61WCyqVCjqdjjkv7XYbwWCQSdwT+bJSqaBcLvcUXgwaaG/hR2C++OIL6PV62Gw2lMtlhEIhFAoF1hWaeC0UaaL3GRbQXs8XpKN1hRpYvsm15ZWcF34kgTpK53I5cByH3d1dljdUKBSwWq2Myd5qtZiTw7/RfiVeoXuzpVIJ+Xz+GRITbeZ8zQGhgp82oudNMt1ra2v4/PPPkcvlWAdbvkM7LJEX4IBnVigU2KKxs7ODiYkJlMtl2O12LC4uQqFQMAnulZUVJJNJfPbZZ9jf3++RKhfqvKK1g8roDQYD4/1sbW0hkUhgf38fsViMRZ+GYfPp5xrQ4YhEQKVSKR4+fMgq+Mhp7nQ6rDKNIiz9WiiDaj/67Pl8Hvl8HqlUCvfv34dCoYBarUaz2WS90wqFAiM/E2gdEvLazAc/ilcqlVAsFrG+vo5oNIpAIMCaT75Je7yW3kb9oIHPJ38VCgXmuZH3dlzDqkGdFCJeDDQhGo0GYrEYlpeXWTQhGAyiUCgIWkn3RUH24p8YKexNpffNZpOl2SiCNwzNPPs5QrlcDisrK6zdSC6XQz6fZxwOEQcgUiXx8fgRPyHqKBH499lsNpmkfaVSQafTYREWijrxf28YwXEHfbMeP36MSqWCQCDABPneyuc5bmDqdLoXHrX973fc+x83CF5kgJRKpbcymgwGw2uZ1YfZ6HVOkEKh8Fbso9VqT2QfulcqmydQ/5nTXjzL5fJbsc+rzq/+dBv/Gtp0+jeflxlXb2t+nXT89IPukSK/wNOoDH88va5xNUjjpx+H2eB5Tu6LjqG3MX6MRuOJbdM/Hvrvv/9+X+fanM/n38rYUavVLzV2qEiHH6A4zXYQ1Wr1SPu89shLf0if792etFxq2DzbYbtfQv9YaTabaDQaz2y2/Bf/+mEEfz6dpLlgv+2EjP7xQfwx/vf4qQ5xPD1rg/5/h2Hs9HPL+HvVcU7MMII/p4DDW968KZxK2gh4vrcqDgQR/ZvGSRbKYd5oCC+jlzAM8+2wsfG8hVUcTwd4mTElJBy2CQ+rLY7Du1T9e2rOCx/iIBDxOiBuNE8hzqlXhzieRPAhzqnBwrGcFxEiRIgQIUKEiHcNw1HnJUKECBEiRIgQDETnRYQIESJEiBAxUBCdFxEiRIgQIULEQEF0XkSIECFChAgRAwXReREhQoQIESJEDBRE50WECBEiRIgQMVB4aeeF47j/muO4OxzH1TmO+8tjrjvLcdxnHMelOI57bl02x3H/C8dxGxzHdTiO+ycv+/neNkT7HA/RPsdDtM/x4DjOwnHc/8FxXJnjuH2O4/78iOs4juP+Jcdx6Sevf8kdI+jBcdyfP3m/Msdxf81xnOX07uL0II6f4yHa52gMim1eJfISAfAvAPyvz7muCeB/B/AXJ3zfhwD+SwD3Xv6jvRMQ7XM8RPscD9E+x+NfA2gAcAL4JYD/meO4xUOu+2cAfgHgPIBzAP4QwH9+2Bs++f1/A+A/e/K+FQD/0+v+4G8I4vg5HqJ9jsZA2OalFXa73e6/AwCO4y4DGDvmug0AGxzHTZ/wff/1k/etvexnexcg2ud4iPY5HqJ9jgbHcVoAfwLgbLfbLQG4wXHcv8eB0/F/7bv8/wLgf+x2u6Env/s/AvinAP5fh7z1LwH8Tbfb/erJtf83AGscx+m73W7xdO7mdCCOn+Mh2udoDIptRM6LCBEiBg2zAFrdbneT972HAA6LvCw++dnzrnvm2m63u4OD6M7sK31aESJEvHaIzosIESIGDToAhb7v5QHoj7g233ed7gjeS/+1x72vCBEi3iJE50WECBGDhhIAQ9/3DAAOS+30X2sAUOoe3tTtRd5XhAgRbxGi8yJChIhBwyYAGcdxM7zvnQewcsi1K09+9rzrnrmW47hJAMonf0+ECBHvEF6lVFrGcZwKgBSAlOM4Fcdxsic/63Ic98Mn/+eeXKd48rWK4zgl733+kl+OxXGc4sn1HAD5k+sHzskS7XM8RPscD9E+R6Pb7ZYB/DsA/wPHcVqO4z4C8EcA/jeO43xP7ON7cvn/G8B/w3HcKMdxbgD/LYC/pPfiOM7PK9v8/wL4Q47jPn5CCv4fAPy7QSPrAuL4eR5E+xyNgbFNt9t9qReAfw6g2/f65wA8OMhHW59c5zvkOj/vff4ewD/lff3FIdf/8GU/59t6ifYR7SPa51TtYwHw1wDKAAIA/vzJ9z8G4Acgf/I1B+BfAcg8ef0rANyTnylwkBI6w3vfP3/yfmUA/ycAy9u+V3H8iPYRbfPsiybxawPHcf8pgMVut/vfneBaBQ7Y/ee63W7ztX6QdxSifY6HaJ/jIdrneHAc998DSHa73X9zgmuvA/ivut3uf3L6n+zdgDh+jodon6PxrtnmtTsvIkSIECFChAgRp4mBysWJECFChAgRIkSIzosIESJEiBAhYqAgOi8iRIgQIUKEiIGC6LyIECFChAgRIgYKxzZm1Ol0A8HmLZVKR7a4P02o1eqBsE+1Wn0r9tFoNANhn0qlItrnGLwt+4jrz/HQarUDYZ9yufzG7SPOreMhhLHz0l2lT4qjqpm4Q1uLiDgKZEfRbiJEiBAhYthxqs4LbbjdbhedTgcAIJE8zVSJG/HzQTbsdDrodruQSCTMbqL9DgeJGHU6nR5b8cfesIE/F/vHkQgRBI7jeg6ctO5wHNfzAo4+mAoNtHcR+DYQ8fZw6pEX4NmNQ3zwL47+hUO04fHgjznRVk83JdEWJ9t0RTsdQFxv0OOsiY7LAfrn0Nuwyak4L3RjEokECoUCMpkMGo0G3W4XxWIRrVYL7XZbTIUcA5ooMpkMEokEWq0WSqUSlUoF9Xod7XYbrVYLgGg/oHcycRwHrVYLnU6HTqeDTqeDRqOBcrnMl8AeKsjlckilUvZvrVZDrVZ72x/rjYGee6vVOtEYkEqlkEqlQ7dZ0bojkUggkUig0+kgl8vRaDTQarVQr9fRaDSGxiYSiQQqlYpFKrvdLmq1GltXhg38TADtPzRP3nQ099QiLzQJ5HI5lEolTCYTut0ums0mGo0GarXaUG4iLwqpVAqZTAaDwQCNRgMAbOKIJ+ledLtdtulotVpYLBa0Wi20Wi2Uy2VUq1Vmt2EB3atMJoNcLodarYZSedA7rdFoDJ0zx58vx82fYXNa+iGRSCCTyaDX66FSqdihiTasYQClVzUaDaRSKSQSCdu0m83mMym2YcTbnCOnFnlpt9tQq9Ww2WyYnJzEL3/5S3S7Xdy4cQPxeBw3btxAJpNhXpuIpyC+hkwmw5kzZ+BwOPDJJ59gYmICf/d3f4fbt28jkUggHo+zzXpYwd+cJRIJJicnMTIygitXruDy5cvI5XJIJpN4/PgxPv30056IwzCMu1arBY7j4HK54HA48N5772FmZgZffvklvvnmG1QqFZRKJQDCtAc5KFKpFCqVCl6vF2q1Gmq1GlKp9JnNh76OxWKIx+NoNpuo1WqCtA0ftBG3223IZDLY7XaYTCb8yZ/8CXw+H5aXlxGJRHD79m1sbGxAJpMJet2hKJ3T6cQf//Efw2g0wmg0olwu42//9m8RjUaRzWZRr9eHgj9G84KitwaDAR6PB81mE8lkEvV6HblcDu12+405/qfmvJDXqtVqMTIygt/7vd8DABSLRezu7uL27dtot9tDTaI8DrToOp1OTExM4MMPP8TS0hJ2d3extbWFXC6HTqcj2u8JJBIJ5HI5nE4npqenceXKFfz85z9HLBaD3+9HrVaDQqFAs9kcqnQlkZYNBgOcTifOnz+Pq1evIhaL4f79+yyNInRbkPMyNjYGg8EAo9EImUz2TLqRH5nL5/M9RGeh2wh4uknpdDrY7XZcvXoVS0tLLAKxvr7O1m2hOy+dTgdarRbnz5+H0+mEw+FAoVDA999/j1wu1zM+hgG0pysUCuj1ekxOTqJWq6HVaqFUKqFQKLDD0pvAqTgvtKHW63WEQiFYrVYEg0FoNBo4HA7U63XI5fKhevAnBdlEoVBAq9Xi3LlzOHv2LKRSKSKRCBKJBFKpFKrVqhi1wtNKAJfLBZvNhg8//BCXLl3C1NQUOxWsr68jGAyiUqkMVb6eFhuJRAKDwQC73Q6dTgeFQsFO2kKdg3Rv3W4XBoMBi4uLcLlc+Af/4B/AYrFAq9VCJnu6/NGYaLfbaLfb+Oyzz5BIJAAcHLj41wgR5OQqFArodDosLi7C6/XCZrNBqVSC47genqLQQWNHJpPBarXC6XRiZGQEGo0GWq0Wcrm8Zw4JeWwAT505uVwOk8mEubk5/Nmf/RnK5TK++eYbxGIxZLNZNJtNdv1p2+TUOC8SiQStVgvJZBLxeByJRAJWqxVmsxn1eh0ymWwoCU8nARF11Wo1JiYmsLCwgEajgXQ6jWw2i3w+zzZhoU+a54EWXYvFAo/Hg8XFRVy7dg0ymQztdhv5fB77+/uIx+NDl7MHnlZdabVamEwmli4Zhs2IFly1Wo3Z2Vn4fD58/PHHsNvtUKlUPZEDmkvNZhOtVgvb29v49ttvUSqVhmJzIsjlcmg0Gvh8PkxNTcFoNEIulwPAUJJUJRIJjEYjTCYTrFYrI/DKZLKh47wQp1Cn08Hj8eCHP/whCoUC8vk8NBoNvvvuO2EQdoGni0ej0UCpVGKsbeJp0OIxTIvDcaCcs1wuh8/ng9PphNPphMFgwOPHjxGJRBCLxVAul9FsNoci13oUaBGVy+VQKBQ4c+YMzp8/zzgN6XQayWQSDx8+xK1btxCPx9lmPQw2I6fOZrNBr9fj8uXLuHz5MjiOw8bGBsLhMAqFguAiUfxqGbPZjImJCXg8Hly/fh1Op5OR3qnikeZSqVRCo9GAyWSCVqvtSS8OA4hjp1arodPp4HK5MDIyAo7jUK1WWfq1VCox8uqwgJw2Sin2pxqHBRzHodVqIZ/PI5lMYnd3FwqFAmfPnoXRaMSNGzcgkUiQTqffyP50Ks4Lvy6eSnpLpRI0Gg30ej3zWvk3NyybyvNAPJbx8XGMj48z5yWRSGB1dRWxWAyVSgXAcE2cfvDTaxqNBnNzc7h27Ro8Hg90Oh12d3exubmJR48e4datWywdMCw263Q6kEqlLOR96dIlfPzxx/j222+xurqKcDiMfD4PAILZiPghfIlEAqvVimvXrsHn8+H69evQ6/UsZdZut9HpdJDL5VAulxGLxVAsFjE9PQ25XD5UVWn8DVmlUkGn07E0CcdxqFQqiMfjCAQCqFQqQ3do4oteDtO44IMfmSwWi0ilUtjb24PL5cLi4iIsFgu8Xi+azSby+TwjuQ+c80KgRaTVaiGTyUCpVGJkZATtdhsajQYKhWJocobPA5/NrVQq4XA44HQ6IZFIUKlUEAwGsb6+jmw22/N7w2Y3voaQTCbD1NQU7HY7JiYm4HK50Ol02KngwYMHCAaDQ0NKJVB4V6VSYW5uDhMTEzAajWi1WkgkEtjZ2UEmkxGcTWix1Gg0MBqNGB8fx/nz5+FyuViaiMZPu91Go9HA9vY2EokEstksarUa7HY73G73W76TNw8aCxR5MZvNMBgMCIVCLPUai8VQq9WG1nkZdhChvVqtIh6P49atWxgfH4fNZkOj0YDH40G320UikWAl5adZHHHqCrvkvMRiMcjlchiNRkZGVSqVqNfrQz8w+A4caXGMjo5ifHwcUqkU1WoVW1tbuHv3LtPHGVa+Cz9dpFKpsLS0hOnpaSwsLGB8fByRSAThcBiPHj3CV199xcpdqVxW6KC5RNyFS5cu4dy5c2yBCYVCePToEeLxuOCq1WhOaLVajI+PY25uDh9//PEzJOVOp4Nms4lKpYLl5WVsb28z4cfZ2dme9xM6+DYBDqqMTCYTHA4HLBYLvvnmG2xtbWF9fR2hUIg5xsMA0Wl5Cppb7XYb9XodgUAAn332Gebm5rC4uAiFQoHZ2VkYjUZsb2+j0WigWCyi0Wic2hpz6pEX4GAQ8MmS/bLtQjsBvgy63S7kcjlGRkbgcDgwMTEBr9cLmUyGZrPJwtzDOpn4pfdKpRI+nw9msxlnz56F1+tFq9VCKBTC48ePsbu7i42NDVaVJaQN+nkgsvfo6ChsNhs8Hg/cbjeKxSJyuRxCoRDi8TgqlYpgHGDaZNRqNbRaLcbGxnDhwgXMzMxAqVT2kCvr9TpqtRp2dnaQTqextbWFYDAIr9cLi8UCnU7Xo6YqdPB1krRaLSYnJzE+Pg61Wo1ut4tsNotEIoFqtTpUaVc67JB+1DCtIc8DP5sSi8Wwu7sLo9HIOGUulwu1Wg2VSoVx7wYy8kKhpkqlgkql0pOTFsri+TrQ7XahVCpZieLly5cxPj6OVCqFXC6HVqvFTkfDaDNyXiwWC6xWK/7wD/8QXq8Xly5dgtVqZWmiTz/9FLdv30Y8Hkc8Hh+ahYefdlSr1Th37hzGx8dx8eJFTE1N4fPPP8fm5iYePnyIra0tdq0QQJEDjUYDt9uN8+fP4xe/+AUj39Lzb7VaKBaLyGQy+PTTT+H3+/HgwQMUCgUsLi7i/PnzcDgcrBprGCpr+NodZrMZ77//PiYmJmAwGNDpdBCJRLC9vY18Po9Wq8UKLYTs2NEepVAoIJfLewT5hmFMHAdaTymK2263cfPmTfh8Pvzpn/4pJBIJ7t69CwBIJpPI5/ODGXkBng4ElUrV0yNCxAH4OUGFQgGHwwGXywWNRgOO45BIJBCNRlEsFln0ZdjAJ+eOjY3B5XLB5/NhdHQUUqkUlUoFOzs72Nvbw/7+PrLZLKrV6lv+1G8W/DJGqrKZmJiAXC5HrVZDJBLBzs7OM+KGQpqLVCBA6Ve+Fke9XkepVML6+jqSySQCgQBisRirtlIoFFCr1T3aL8MC6mFkMpngdDphs9mYsnCpVEKpVHqj4mNvExTFIyVZhULB+vMNw/2/CGi+VSoVpjtG6WqtVnvqh6NTL5XmC/1YLBbIZDI2EYTsvb8oqB/P0tISI1gCwN27d/H48WPs7++zFMgwRBII/DGi0+nwgx/8ABMTE/jBD34Aq9UKv9+PUCiE//Af/gNu3LiBUqmEarXK0ifDAIo8KBQKjI6OYmxsDD/72c8wNTWFdruNaDSK7777Dl988QWy2SyLeAppMaZICaVY+Y0Vm80mMpkMwuEw/u2//bcIhULY2tpCuVxmrQL0ej3j4wnJLsehf9xMTExgcXERIyMjSCaTLC1A8u80ZoS+bpPzYjAYWE85tVrNfibiAJQ+ymazsFqtLOprs9mQzWZZsAI4HWrIG9kFSVpao9E8s6EM82DgE3XJazWbzbBYLKhWq8jlcojH44jFYkMXSSDwFxKTyQS73Q6bzcbE1qrVKvL5PPL5PIrFIur1+tByg+RyOYvcmUwmaDQa5PN5RCIRZDIZFItFRl4WGviEd3rRWtNsNlkvsEQigWQyiUqlgmaz2XNSJNVdvnif0McRpY1oo6boU7lcRiaTQblcZnNKiOPmMPDXZJlMxngv/J+LOECn00G5XGb8FuAgQq5UKgdT54UPEj+i3hAKhaJHnG7YwddXMJlMmJ+fh8fjwd7eHpLJJG7fvo1Hjx4hn88L7rT8PNDJUKVSYX5+Hl6vF0tLS3C73ZDL5ajX6wiHw/D7/SxVNEwRF6CX66LX6/Hhhx9iYmICbrcbarUa9+7dw/r6Ora2tpDNZgUZuaPwNfE2zGYzUxMGgFwuh5s3b8Lv92N1dRXpdJrp4FgsFrhcLni9Xni9XgAH3bapzFOoaxRFqjqdDpRKJSYnJzExMQGtVguO4+D3++H3+xGPx1mqcRjWH34aX6VSQalU9qQgRRyAeKvNZhPRaBR6vR71eh06nQ5arRZGo5EpERNed/TljUVeiPTEJ+oOw2Q4CvQgZTIZNBoNPB4PxsbGoNPpIJPJUCgUkEqlUCgUUC6Xh4rpD/RypfR6PUZHRzE6Ogqj0QiNRoNut4tGo4FcLodUKsVKyIcNfK4LcRbsdjuazSYKhQISiQTC4XDPyUiI4Pfloahcu91m1TKkTl2tVnv0J4jTQC9S261Wq6x8Wojzjl84IZPJYDAYmOPSbrdRLBaRz+dZlegwVYTybUMvfmWsiKfodruo1WrHRrxPa9y8sSMq3Rg/FCeXy9FoNN7UR3hnQCdFpVIJo9GIyclJ/MVf/AUrb202m3j8+DG2trZYWSsgLHLlcSCBI71ej7GxMUxPT+PP/uzP2AlZqVSiUqkgn8/j/v37ePDgAZLJ5Nv+2G8cFJnSarW4ePEiJiYm8MEHH8BsNmNzcxPxeBxffvkl1tbWkM/nBUc6pMgItRyx2+2Ym5vD6OgoVCoVEokEvvvuO+zu7uKzzz5DLpdDvV5n1TJ0eCCyrlqtZqTvra0thEIhwRJVyW5KpRIWiwUTExMYGxtDp9NBsVjE3t4etra2WKdgoY2d54GcYYq8DFM096QgR7dUKrGUNIk/8p1euvZ14408kcN6QggxfP0ioMlhsVjgdDoxMTEBu93OysqTySSLKPBDtsO0gCgUCthsNtjtdoyMjMBut0OpVAIAyuUy8vk8MpkMstks6vU6gOFx8PpFDUdGRuByuaDX66FUKpFOpxGJRJBOp5HP5wXLdQEApVIJpVIJs9kMh8MBg8GAbreLarWKSCSCSCSCbDbb02SRv6jyT9n1eh3FYhHVahWNRkOw8gTkvKhUKmg0GhZ5abfbaDabKJfLKJfLPRvQsKBfzgNAz5gR0QtqvdJ/mCDC/GnhjbmT1AitPxwH9BJXhQx+GZ5SqcTExAT+6I/+CD6fD/Pz85BIJFhfX0ckEsHNmzexu7uLXC43NI4L38FVKBRwuVz48Y9/DJ/Ph8nJSWg0GkYQu3fvHqsaCYfDTLZ8GNDpdFiLDZfLhZmZGfziF7+Aw+EAx3GIx+P4+uuvsbm5iVAoxPrRCNU+ExMTmJ+fx9WrV/Gzn/0MEokE+Xwefr8fX331FRKJBCqVCtrt9jMkQpIfoH+ppFrIxG+KbOp0OoyPj2NqagpnzpyB2WzuabyXTCZZg71hhJDTrK8T/L1JJpPB5XKxyLlKpWIRmYGsNuKDvxgIcWF4Hvjhar1ez7reEteFyhNTqRQymQyazebb/shvFFRar1arYTAY4Ha74XQ6oVarGUm3VCohGo2yzsh89U+hO3gEUmQ2mUywWq3weDxwOp2o1+soFAqIxWKIRqPMNkKGXq9nkSdS+aTIXDweRyaTYTboHyOUwqaTdaPRQLVaRbPZFKzzAjzlSmk0Guh0Ouj1emg0GqbvUq1WmcMHCC/y9KIQ6jh4naC5Rfo4p51qfCORF6r/VigUqNfrrLSqPyUidNCJR6PRwGazwev14sqVKzAYDKjX60gkEvjmm2+wt7fHHJdhirpQeeLo6CguXLiA+fl5XLt2jTHZs9ks7t69i2g0in//7/89gsEgksnk0Cyw/MiUXC6H0+nEJ598gsnJSbjdbjSbTfzmN79BIBDA6uoqotEoKwcWmm34oX2LxcIadEqlUqRSKdy4cYPZgKrQ+iO91L19ZmYGOp0OzWYTwWAQy8vLiEajaLVaLAQuJPAjwAaDgTkuEokE4XAYoVAI+/v7CIfDaDabpx7+FyEctNttxGIxBINB5HI51Go1AAPIeeF7q3TCabVaaDabaLVarNfRsE0MmUzW0wBNpVKhUCigUCggGAyyNAiVcwrdPnz+AVU+TExMYHx8HCMjI5DJZKhUKsw+wWAQu7u7iEajguUkHAU+6V2v18Pr9WJ0dBQajQa5XA6BQAC7u7uM4yHkdBE59RqNhpVGd7tdlEolBINBxGIxlMtlNJvNZ8TnyFE2GAywWq1QKBSMrJpMJlEulwWvbUKcF+r/BIDxyKjKke/0iRDxPHS7XVQqFcaXOixV+7rwRhozAgc31Wq1WI8jyoMNQziOTjpKpRIGgwEzMzP44Q9/iDNnzkAulyOdTuOzzz5DIBDA8vIyU7QclkWDCF9jY2OYmprC+fPn8fOf/xwWiwVyuRylUgmPHz9GOBzG7373O0SjUWSz2aFzXKis1WazYXZ2FktLS/jkk0+gUCiws7ODcDiMBw8eIBgMssaLQgStGWq1GiqVCna7HW63G91uF9vb23j06BFu3LjB9Fz4fYpId8rj8cBiseDixYtYXFyEVqtFpVJhlROkKCs08HsZmUwmTE9PY2xsDO12G/l8Hjdv3sTe3h7y+fxQ8BBFvD6Qo0uCouQUn9Ye/8YIu7SBN5tNxuIfBscFeFrSShEXl8uF+fl5jI2NgeM4tjlTqLZQKDyj6ihU8JVM9Xo9IxDOz89DoVCwCpBwOIz9/X1sbGwgkUiwyNQw2AjojbiYTCZMTExgamoK09PTqFaruHPnDsLhMCKRCBKJBJMgEOrmQ5wftVoNnU4Ho9GIcrmMZDKJSCSCvb09VKvVZ6qL+A0+XS4X0w8CgHq9zrpOCzkqzNeXcjgcMJvN7MS8u7uLvb29Ht0kUeNExElB5ffEUZRIJEwM8XXPpTfS2wh4GqY1Go1M40Qul6PZbAp2UvTfO3W8XVpawsWLF9HpdPDgwQPs7e1heXkZ8XicsfuFuGgeBo7jMD4+jrGxMZw9exbXrl2D1+uFWq1Gq9VCIpFgPARy7GhjHhY70eQ3Go2w2+04f/48/uE//Iew2+2oVCqIRqO4efMmAoEA0ul0T/d2IYJfkabX62E2m2G1WiGRSFAul5l2SbVaZTl3WlS9Xi9MJhMuX74Mh8PBmns+evQI4XAYa2trSCQSrNGckGxIhyiLxQKPx8M6aev1ejQaDUb0TiQSjO8jOi4ingfa5+nFp4acZpDijSrvUI+jRqPB2gSQ8yKkReIwcBwHm82GhYUFzM/PY3p6GrFYDA8ePMDW1hbr+CtUUayjwHEcXC4Xc+ouXboEg8EApVKJZrPJFFJ3dnYY+bJ/YRU6+G0SnE4npqam8P7770MikaBarSKVSrG0WqFQYEJsQrZPt9tl4nLEH6NyeaVSCb1ez6oegKftE5aWlmC32/HRRx/BbrfDYDBAIpHA7/djeXkZ+/v7yOfzLFcvNHS7XWg0Gni9XkxMTGBychIymQz5fB6lUgm5XA65XK6ntFV0XEQchf6xQQKs/e01Borz0i/0Q4SwdDqNdDrNGn4J2XGhB+d2u+F2u3H16lVcv34der0eiUQCGxsbjMNBbecB4UcUiH9gt9thNBpx8eJF/OAHP4Db7YbNZoNMJmPVV3fu3IHf72d5eHLuhGwfPvhVWHa7HefOnYPP54NKpUIymcT333/PQv1UEizETbcfHMehVquxNhrRaBTdbhejo6OsrUR/UQBFXnQ6HTweD1QqFaLRKHK5HB48eIAHDx4gkUgwLp7Qxhi/Us1sNsNoNEKr1aLRaCAYDCIUCglamO9FwZ9HfLG6YbcLHxKJBHK5nDXJLRaLrAkqkXbpIDUQaaN+JV2qNAIOnBdi89frdcHKTvNz7C6XCxcvXsSVK1fwwQcfIJfLIRgMYmtrC3//93+PYrHYkwoROsg2FosFXq8X586dw/Xr16FWq6HVapmWCzkvwWAQ+/v7qNVqgo8o8EF2ImfW5XIx50WpVKJQKODGjRsIBAKM4wEIf4ElDgvfeYnFYrDb7RgdHYXL5cLZs2cP/T2K+FJk7969eyzqcv/+/Z7+a0KFUqmEyWRifcLq9TpzXuhAKeIpAbVfaZePYbcV6bpIJBLUajWUSiUkk0mWeiW5j9Nor3Dq1Ub8PkYcx6FcLrN+GcOgrMtxHPR6PZxOJwtRp9NpfP/991hbW2PkQCHb4DCQGB1xn4ivQI0Wd3d3sbu7i52dHWQymR7C1zDZik/UVavVMBqN6Ha7CIVC8Pv92NzcZBwFYHhsw3Ecms0mqtUqMpkMQqEQpFIpUxmmdNFhYetGo4FAIMB6Y+3t7SGbzQre6esHv4gil8uxdXmYiPCHgarTqEUCEbk7nQ5rGzFszSoPA63hNpsNNpsNcrmcSRXwU6+nZaNTTxtR4zPaoPL5PGuQJtTwJP/EDABGoxEejwdmsxkSiQSxWAy/+c1vWLffdrs9VBEFAlWL0NgoFAoIh8PY3t7G119/jVgshtXVVTZWhH4i7kd/OwmdTger1YpOp4Pt7W2sra3h/v37KJfLQ3UCpDHQaDTQaDQQi8WwtbXFWm5QtQOAHhHMTqeDZrOJSqWChw8fIhQK4fPPP2cpydMKb7/LoEZ6qVQKqVSKcRCHdWOmaEur1UKxWGR9rohrVy6XUavV0Gg0mI2G0U7Ew5PL5XC73XC5XMx5oQMoX2T1NHBqzgv/A+fzeXz77bfQaDTIZrPIZrOMXCfEB88vzQSAWCyGhw8fIp1OIxaLscqGXC7HrheiHY4C3Ws+n0coFMK9e/fY16lUCuFwGLu7u8jn86jX64KXtz8KZCdSZg6FQrh58yaTsd/Z2WERF/71w4ZUKoX19XXGVdFqtbBYLNBoNKwKCQBzdLLZLB49eoRoNIpkMskOEEKfh3R/5XIZe3t7rL9RJpPB9vY2ksnk0HHKDgOt3/V6HfF4HP/xP/5HViWbz+cRi8VQqVQEL2L4PPAJ3fV6HZFIBBzHIZlMMtL3aUbwuONObDqd7qWOc/2hWipdlEqljESXzWZfm2dWKpXeyghSq9Unso9er4dWq4VSqYRKpUKxWEQ8HmclsMDpbjzVavWt2Eej0RxrH2o1r9frWbkmnXLy+TwAMGn207RPpVJ5J+3DB8dxrKqGnJlGo4F8Pn/qDeTeln1Ouv5QV2nivFitVvh8Prjdbly7dg1yuRydTgeFQgHffvstYrEYvvzySyQSCRYFfhVdpbe1/mi12pdan5VKJVuP9Ho9ms0ma0dC+kmvE+Vy+Y3b50Xm1lHodrtQqVRwuVyQSqWsDDiVSr02rbK3NbdeduwQqKrIZDLh/PnzsFgsOHv2LNrtNv7qr/4K4XC4R4T2Zdfv48bOqURe+iMP7XabSU3Thj1MYclGowGO41Cv15n2xLCoCx8HsgGdYprNJhMxHKbxcRJQtKVYLLKQLeXdhx3tdps1pJTL5ajVaqzMvt1uQyaTodPpoFKpYH19HdlsFvl8fuh6qxHIXiTfTl+La9KzaLVaKBQKkEgkPfNumERWjwLHcT2BCKVSiXa7zdZz/nWn8vdPI/LSD369N/vDr3HBeNcjL/z778+TvolF812NvAC9VVmEN72ZDELkBXh2Hr0pO73rkRegN9p7mEQDXUMb9OtMRQ5S5IUOlv3zrn9Nep0b86BGXoDT37sGNfLCB801ssnrJDO/8cjLYehfdIcR/aI9w2qHfpy2mJGQwLeTGJ06AH8jphPfUZtvv17HMNuvf96J4+lwHOa8iHgKOhD0F6qcNt6Y89J/Q8M0AGiRFDfoZzHsG8iLgOM4xgES8RTDWEL/sjjJ4WnY0yF8iHPu+Xhb+9sbcV7EReUAoh1EiBAhQoQQ8ab3t2M5LyJEiBAhQoQIEe8ahldGUYQIESJEiBAxkBCdFxEiRIgQIULEQEF0XkSIECFChAgRAwXReREhQoQIESJEDBRE50WECBEiRIgQMVAQnRcRIkSIECFCxEDh/w/gVllRVpZ9wwAAAABJRU5ErkJggg==\\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\": 27,\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\": 28,\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    \"    Z = A_in@W + b\\n\",\n    \"    A_out = g(Z)\\n\",\n    \"    \\n\",\n    \"### END CODE HERE ### \\n\",\n    \"    return(A_out)\"\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      \"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\": 30,\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\": 31,\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\": 32,\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\": 33,\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"TensorShape([1000, 1])\"\n      ]\n     },\n     \"execution_count\": 33,\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\": 34,\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\": 35,\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\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\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\": 37,\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\": 38,\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\": 39,\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"
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  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-Labs/Course-2-Week-1-Practice-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-Labs/data/Course-2-Week-1-Practice-Labs-data.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-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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    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-Labs/images/Course-2-Week-1-Practice-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1-Practice-Quiz/Course-2-Week-1-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-1/Course-2-Week-1.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Lecture-Slides/Course-2-Week-2-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Optional-Labs/Course-2-Week-2-Optional-Labs-1-Relu-Activation.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\": \"dfdb52ff3b004b4fa1baaece08327cf0\",\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\": \"4b598e2399b144aaa4fccd64917941e0\",\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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Optional-Labs/Course-2-Week-2-Optional-Labs-2-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\": \"7a491470472141e2873e115055f300c9\",\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 1ms/step - loss: 0.7907\\n\",\n      \"Epoch 2/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.2971\\n\",\n      \"Epoch 3/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.1247\\n\",\n      \"Epoch 4/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0811\\n\",\n      \"Epoch 5/10\\n\",\n      \"63/63 [==============================] - 0s 930us/step - loss: 0.0659\\n\",\n      \"Epoch 6/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0573\\n\",\n      \"Epoch 7/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0523\\n\",\n      \"Epoch 8/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0474\\n\",\n      \"Epoch 9/10\\n\",\n      \"63/63 [==============================] - 0s 938us/step - loss: 0.0441\\n\",\n      \"Epoch 10/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0407\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fad6c2d5a50>\"\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\": 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\": 6,\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 960us/step - loss: 0.9268\\n\",\n      \"Epoch 2/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.4168\\n\",\n      \"Epoch 3/10\\n\",\n      \"63/63 [==============================] - 0s 917us/step - loss: 0.2041\\n\",\n      \"Epoch 4/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.1172\\n\",\n      \"Epoch 5/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0834\\n\",\n      \"Epoch 6/10\\n\",\n      \"63/63 [==============================] - 0s 937us/step - loss: 0.0668\\n\",\n      \"Epoch 7/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0567\\n\",\n      \"Epoch 8/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0496\\n\",\n      \"Epoch 9/10\\n\",\n      \"63/63 [==============================] - 0s 1ms/step - loss: 0.0448\\n\",\n      \"Epoch 10/10\\n\",\n      \"63/63 [==============================] - 0s 980us/step - loss: 0.0400\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fad6c102450>\"\n      ]\n     },\n     \"execution_count\": 6,\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\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"two example output vectors:\\n\",\n      \" [[-3.63 -2.12  3.23 -0.61]\\n\",\n      \" [ 3.92 -1.39 -4.48 -7.29]]\\n\",\n      \"largest value 13.5404215 smallest value -16.33019\\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\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"two example output vectors:\\n\",\n      \" [[1.02e-03 4.65e-03 9.73e-01 2.09e-02]\\n\",\n      \" [9.95e-01 4.94e-03 2.23e-04 1.35e-05]]\\n\",\n      \"largest value 0.99999964 smallest value 1.0650191e-13\\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\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[-3.63 -2.12  3.23 -0.61], category: 2\\n\",\n      \"[ 3.92 -1.39 -4.48 -7.29], category: 0\\n\",\n      \"[ 2.48 -0.84 -3.51 -6.06], category: 0\\n\",\n      \"[-5.    2.64 -2.87 -3.94], category: 1\\n\",\n      \"[-1.85 -2.97  3.2  -3.99], 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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Optional-Labs/Course-2-Week-2-Optional-Labs-3-Multiclass.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\": \"9b7c12252e664cc2bedf037bea81bd05\",\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 988us/step - loss: 1.5989\\n\",\n      \"Epoch 4/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 1.5179\\n\",\n      \"Epoch 5/200\\n\",\n      \"4/4 [==============================] - 0s 998us/step - loss: 1.4369\\n\",\n      \"Epoch 6/200\\n\",\n      \"4/4 [==============================] - 0s 992us/step - loss: 1.3756\\n\",\n      \"Epoch 7/200\\n\",\n      \"4/4 [==============================] - 0s 985us/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 978us/step - loss: 1.2188\\n\",\n      \"Epoch 10/200\\n\",\n      \"4/4 [==============================] - 0s 979us/step - loss: 1.1791\\n\",\n      \"Epoch 11/200\\n\",\n      \"4/4 [==============================] - 0s 976us/step - loss: 1.1446\\n\",\n      \"Epoch 12/200\\n\",\n      \"4/4 [==============================] - 0s 981us/step - loss: 1.1129\\n\",\n      \"Epoch 13/200\\n\",\n      \"4/4 [==============================] - 0s 959us/step - loss: 1.0827\\n\",\n      \"Epoch 14/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 1.0516\\n\",\n      \"Epoch 15/200\\n\",\n      \"4/4 [==============================] - 0s 985us/step - loss: 1.0225\\n\",\n      \"Epoch 16/200\\n\",\n      \"4/4 [==============================] - 0s 985us/step - loss: 0.9967\\n\",\n      \"Epoch 17/200\\n\",\n      \"4/4 [==============================] - 0s 960us/step - loss: 0.9681\\n\",\n      \"Epoch 18/200\\n\",\n      \"4/4 [==============================] - 0s 955us/step - loss: 0.9392\\n\",\n      \"Epoch 19/200\\n\",\n      \"4/4 [==============================] - 0s 3ms/step - loss: 0.9092\\n\",\n      \"Epoch 20/200\\n\",\n      \"4/4 [==============================] - 0s 971us/step - loss: 0.8771\\n\",\n      \"Epoch 21/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 0.8461\\n\",\n      \"Epoch 22/200\\n\",\n      \"4/4 [==============================] - 0s 976us/step - loss: 0.8099\\n\",\n      \"Epoch 23/200\\n\",\n      \"4/4 [==============================] - 0s 973us/step - loss: 0.7771\\n\",\n      \"Epoch 24/200\\n\",\n      \"4/4 [==============================] - 0s 943us/step - loss: 0.7485\\n\",\n      \"Epoch 25/200\\n\",\n      \"4/4 [==============================] - 0s 963us/step - loss: 0.7215\\n\",\n      \"Epoch 26/200\\n\",\n      \"4/4 [==============================] - 0s 952us/step - loss: 0.6967\\n\",\n      \"Epoch 27/200\\n\",\n      \"4/4 [==============================] - 0s 957us/step - loss: 0.6742\\n\",\n      \"Epoch 28/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 0.6540\\n\",\n      \"Epoch 29/200\\n\",\n      \"4/4 [==============================] - 0s 961us/step - loss: 0.6352\\n\",\n      \"Epoch 30/200\\n\",\n      \"4/4 [==============================] - 0s 961us/step - loss: 0.6187\\n\",\n      \"Epoch 31/200\\n\",\n      \"4/4 [==============================] - 0s 971us/step - loss: 0.6030\\n\",\n      \"Epoch 32/200\\n\",\n      \"4/4 [==============================] - 0s 933us/step - loss: 0.5884\\n\",\n      \"Epoch 33/200\\n\",\n      \"4/4 [==============================] - 0s 948us/step - loss: 0.5746\\n\",\n      \"Epoch 34/200\\n\",\n      \"4/4 [==============================] - 0s 963us/step - loss: 0.5621\\n\",\n      \"Epoch 35/200\\n\",\n      \"4/4 [==============================] - 0s 954us/step - loss: 0.5512\\n\",\n      \"Epoch 36/200\\n\",\n      \"4/4 [==============================] - 0s 948us/step - loss: 0.5414\\n\",\n      \"Epoch 37/200\\n\",\n      \"4/4 [==============================] - 0s 995us/step - loss: 0.5323\\n\",\n      \"Epoch 38/200\\n\",\n      \"4/4 [==============================] - 0s 983us/step - loss: 0.5236\\n\",\n      \"Epoch 39/200\\n\",\n      \"4/4 [==============================] - 0s 992us/step - loss: 0.5150\\n\",\n      \"Epoch 40/200\\n\",\n      \"4/4 [==============================] - 0s 978us/step - loss: 0.5072\\n\",\n      \"Epoch 41/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.5006\\n\",\n      \"Epoch 42/200\\n\",\n      \"4/4 [==============================] - 0s 994us/step - loss: 0.4944\\n\",\n      \"Epoch 43/200\\n\",\n      \"4/4 [==============================] - 0s 985us/step - loss: 0.4888\\n\",\n      \"Epoch 44/200\\n\",\n      \"4/4 [==============================] - 0s 999us/step - loss: 0.4830\\n\",\n      \"Epoch 45/200\\n\",\n      \"4/4 [==============================] - 0s 989us/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 967us/step - loss: 0.4673\\n\",\n      \"Epoch 48/200\\n\",\n      \"4/4 [==============================] - 0s 980us/step - loss: 0.4624\\n\",\n      \"Epoch 49/200\\n\",\n      \"4/4 [==============================] - 0s 989us/step - loss: 0.4574\\n\",\n      \"Epoch 50/200\\n\",\n      \"4/4 [==============================] - 0s 985us/step - loss: 0.4530\\n\",\n      \"Epoch 51/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.4491\\n\",\n      \"Epoch 52/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4451\\n\",\n      \"Epoch 53/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.4414\\n\",\n      \"Epoch 54/200\\n\",\n      \"4/4 [==============================] - 0s 973us/step - loss: 0.4374\\n\",\n      \"Epoch 55/200\\n\",\n      \"4/4 [==============================] - 0s 993us/step - loss: 0.4336\\n\",\n      \"Epoch 56/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.4295\\n\",\n      \"Epoch 57/200\\n\",\n      \"4/4 [==============================] - 0s 996us/step - loss: 0.4261\\n\",\n      \"Epoch 58/200\\n\",\n      \"4/4 [==============================] - 0s 972us/step - loss: 0.4225\\n\",\n      \"Epoch 59/200\\n\",\n      \"4/4 [==============================] - 0s 989us/step - loss: 0.4193\\n\",\n      \"Epoch 60/200\\n\",\n      \"4/4 [==============================] - 0s 978us/step - loss: 0.4161\\n\",\n      \"Epoch 61/200\\n\",\n      \"4/4 [==============================] - 0s 991us/step - loss: 0.4131\\n\",\n      \"Epoch 62/200\\n\",\n      \"4/4 [==============================] - 0s 983us/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 967us/step - loss: 0.4029\\n\",\n      \"Epoch 65/200\\n\",\n      \"4/4 [==============================] - 0s 957us/step - loss: 0.3994\\n\",\n      \"Epoch 66/200\\n\",\n      \"4/4 [==============================] - 0s 941us/step - loss: 0.3957\\n\",\n      \"Epoch 67/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 0.3920\\n\",\n      \"Epoch 68/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.3878\\n\",\n      \"Epoch 69/200\\n\",\n      \"4/4 [==============================] - 0s 976us/step - loss: 0.3837\\n\",\n      \"Epoch 70/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.3792\\n\",\n      \"Epoch 71/200\\n\",\n      \"4/4 [==============================] - 0s 990us/step - loss: 0.3755\\n\",\n      \"Epoch 72/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.3718\\n\",\n      \"Epoch 73/200\\n\",\n      \"4/4 [==============================] - 0s 998us/step - loss: 0.3683\\n\",\n      \"Epoch 74/200\\n\",\n      \"4/4 [==============================] - 0s 971us/step - loss: 0.3643\\n\",\n      \"Epoch 75/200\\n\",\n      \"4/4 [==============================] - 0s 991us/step - loss: 0.3600\\n\",\n      \"Epoch 76/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.3550\\n\",\n      \"Epoch 77/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.3491\\n\",\n      \"Epoch 78/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.3425\\n\",\n      \"Epoch 79/200\\n\",\n      \"4/4 [==============================] - 0s 978us/step - loss: 0.3367\\n\",\n      \"Epoch 80/200\\n\",\n      \"4/4 [==============================] - 0s 983us/step - loss: 0.3293\\n\",\n      \"Epoch 81/200\\n\",\n      \"4/4 [==============================] - 0s 927us/step - loss: 0.3228\\n\",\n      \"Epoch 82/200\\n\",\n      \"4/4 [==============================] - 0s 987us/step - loss: 0.3156\\n\",\n      \"Epoch 83/200\\n\",\n      \"4/4 [==============================] - 0s 992us/step - loss: 0.3080\\n\",\n      \"Epoch 84/200\\n\",\n      \"4/4 [==============================] - 0s 956us/step - loss: 0.3006\\n\",\n      \"Epoch 85/200\\n\",\n      \"4/4 [==============================] - 0s 976us/step - loss: 0.2933\\n\",\n      \"Epoch 86/200\\n\",\n      \"4/4 [==============================] - 0s 948us/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 960us/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 1ms/step - loss: 0.2570\\n\",\n      \"Epoch 91/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.2498\\n\",\n      \"Epoch 92/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/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 1ms/step - loss: 0.2274\\n\",\n      \"Epoch 95/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/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 978us/step - loss: 0.2060\\n\",\n      \"Epoch 98/200\\n\",\n      \"4/4 [==============================] - 0s 976us/step - loss: 0.1995\\n\",\n      \"Epoch 99/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.1950\\n\",\n      \"Epoch 100/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/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 955us/step - loss: 0.1804\\n\",\n      \"Epoch 103/200\\n\",\n      \"4/4 [==============================] - 0s 980us/step - loss: 0.1758\\n\",\n      \"Epoch 104/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.1709\\n\",\n      \"Epoch 105/200\\n\",\n      \"4/4 [==============================] - 0s 961us/step - loss: 0.1662\\n\",\n      \"Epoch 106/200\\n\",\n      \"4/4 [==============================] - 0s 956us/step - loss: 0.1616\\n\",\n      \"Epoch 107/200\\n\",\n      \"4/4 [==============================] - 0s 973us/step - loss: 0.1575\\n\",\n      \"Epoch 108/200\\n\",\n      \"4/4 [==============================] - 0s 959us/step - loss: 0.1527\\n\",\n      \"Epoch 109/200\\n\",\n      \"4/4 [==============================] - 0s 969us/step - loss: 0.1480\\n\",\n      \"Epoch 110/200\\n\",\n      \"4/4 [==============================] - 0s 960us/step - loss: 0.1439\\n\",\n      \"Epoch 111/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 0.1396\\n\",\n      \"Epoch 112/200\\n\",\n      \"4/4 [==============================] - 0s 960us/step - loss: 0.1357\\n\",\n      \"Epoch 113/200\\n\",\n      \"4/4 [==============================] - 0s 963us/step - loss: 0.1315\\n\",\n      \"Epoch 114/200\\n\",\n      \"4/4 [==============================] - 0s 969us/step - loss: 0.1277\\n\",\n      \"Epoch 115/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 0.1240\\n\",\n      \"Epoch 116/200\\n\",\n      \"4/4 [==============================] - 0s 977us/step - loss: 0.1207\\n\",\n      \"Epoch 117/200\\n\",\n      \"4/4 [==============================] - 0s 969us/step - loss: 0.1171\\n\",\n      \"Epoch 118/200\\n\",\n      \"4/4 [==============================] - 0s 967us/step - loss: 0.1139\\n\",\n      \"Epoch 119/200\\n\",\n      \"4/4 [==============================] - 0s 955us/step - loss: 0.1110\\n\",\n      \"Epoch 120/200\\n\",\n      \"4/4 [==============================] - 0s 990us/step - loss: 0.1084\\n\",\n      \"Epoch 121/200\\n\",\n      \"4/4 [==============================] - 0s 987us/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 979us/step - loss: 0.1001\\n\",\n      \"Epoch 124/200\\n\",\n      \"4/4 [==============================] - 0s 985us/step - loss: 0.0975\\n\",\n      \"Epoch 125/200\\n\",\n      \"4/4 [==============================] - 0s 976us/step - loss: 0.0951\\n\",\n      \"Epoch 126/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.0925\\n\",\n      \"Epoch 127/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.0902\\n\",\n      \"Epoch 128/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.0882\\n\",\n      \"Epoch 129/200\\n\",\n      \"4/4 [==============================] - 0s 996us/step - loss: 0.0862\\n\",\n      \"Epoch 130/200\\n\",\n      \"4/4 [==============================] - 0s 997us/step - loss: 0.0840\\n\",\n      \"Epoch 131/200\\n\",\n      \"4/4 [==============================] - 0s 990us/step - loss: 0.0826\\n\",\n      \"Epoch 132/200\\n\",\n      \"4/4 [==============================] - 0s 963us/step - loss: 0.0807\\n\",\n      \"Epoch 133/200\\n\",\n      \"4/4 [==============================] - 0s 972us/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 971us/step - loss: 0.0741\\n\",\n      \"Epoch 137/200\\n\",\n      \"4/4 [==============================] - 0s 990us/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 975us/step - loss: 0.0700\\n\",\n      \"Epoch 140/200\\n\",\n      \"4/4 [==============================] - 0s 971us/step - loss: 0.0685\\n\",\n      \"Epoch 141/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.0670\\n\",\n      \"Epoch 142/200\\n\",\n      \"4/4 [==============================] - 0s 993us/step - loss: 0.0657\\n\",\n      \"Epoch 143/200\\n\",\n      \"4/4 [==============================] - 0s 986us/step - loss: 0.0645\\n\",\n      \"Epoch 144/200\\n\",\n      \"4/4 [==============================] - 0s 978us/step - loss: 0.0634\\n\",\n      \"Epoch 145/200\\n\",\n      \"4/4 [==============================] - 0s 969us/step - loss: 0.0622\\n\",\n      \"Epoch 146/200\\n\",\n      \"4/4 [==============================] - 0s 965us/step - loss: 0.0611\\n\",\n      \"Epoch 147/200\\n\",\n      \"4/4 [==============================] - 0s 969us/step - loss: 0.0601\\n\",\n      \"Epoch 148/200\\n\",\n      \"4/4 [==============================] - 0s 968us/step - loss: 0.0591\\n\",\n      \"Epoch 149/200\\n\",\n      \"4/4 [==============================] - 0s 973us/step - loss: 0.0582\\n\",\n      \"Epoch 150/200\\n\",\n      \"4/4 [==============================] - 0s 974us/step - loss: 0.0575\\n\",\n      \"Epoch 151/200\\n\",\n      \"4/4 [==============================] - 0s 973us/step - loss: 0.0570\\n\",\n      \"Epoch 152/200\\n\",\n      \"4/4 [==============================] - 0s 935us/step - loss: 0.0563\\n\",\n      \"Epoch 153/200\\n\",\n      \"4/4 [==============================] - 0s 975us/step - loss: 0.0553\\n\",\n      \"Epoch 154/200\\n\",\n      \"4/4 [==============================] - 0s 976us/step - loss: 0.0541\\n\",\n      \"Epoch 155/200\\n\",\n      \"4/4 [==============================] - 0s 981us/step - loss: 0.0530\\n\",\n      \"Epoch 156/200\\n\",\n      \"4/4 [==============================] - 0s 943us/step - loss: 0.0519\\n\",\n      \"Epoch 157/200\\n\",\n      \"4/4 [==============================] - 0s 981us/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 964us/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 972us/step - loss: 0.0466\\n\",\n      \"Epoch 164/200\\n\",\n      \"4/4 [==============================] - 0s 999us/step - loss: 0.0458\\n\",\n      \"Epoch 165/200\\n\",\n      \"4/4 [==============================] - 0s 993us/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 979us/step - loss: 0.0429\\n\",\n      \"Epoch 170/200\\n\",\n      \"4/4 [==============================] - 0s 946us/step - loss: 0.0423\\n\",\n      \"Epoch 171/200\\n\",\n      \"4/4 [==============================] - 0s 977us/step - loss: 0.0418\\n\",\n      \"Epoch 172/200\\n\",\n      \"4/4 [==============================] - 0s 996us/step - loss: 0.0414\\n\",\n      \"Epoch 173/200\\n\",\n      \"4/4 [==============================] - 0s 982us/step - loss: 0.0413\\n\",\n      \"Epoch 174/200\\n\",\n      \"4/4 [==============================] - 0s 952us/step - loss: 0.0408\\n\",\n      \"Epoch 175/200\\n\",\n      \"4/4 [==============================] - 0s 997us/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 962us/step - loss: 0.0388\\n\",\n      \"Epoch 178/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 0.0384\\n\",\n      \"Epoch 179/200\\n\",\n      \"4/4 [==============================] - 0s 971us/step - loss: 0.0384\\n\",\n      \"Epoch 180/200\\n\",\n      \"4/4 [==============================] - 0s 991us/step - loss: 0.0377\\n\",\n      \"Epoch 181/200\\n\",\n      \"4/4 [==============================] - 0s 955us/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 996us/step - loss: 0.0363\\n\",\n      \"Epoch 184/200\\n\",\n      \"4/4 [==============================] - 0s 985us/step - loss: 0.0359\\n\",\n      \"Epoch 185/200\\n\",\n      \"4/4 [==============================] - 0s 961us/step - loss: 0.0353\\n\",\n      \"Epoch 186/200\\n\",\n      \"4/4 [==============================] - 0s 999us/step - loss: 0.0348\\n\",\n      \"Epoch 187/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0345\\n\",\n      \"Epoch 188/200\\n\",\n      \"4/4 [==============================] - 0s 995us/step - loss: 0.0343\\n\",\n      \"Epoch 189/200\\n\",\n      \"4/4 [==============================] - 0s 977us/step - loss: 0.0339\\n\",\n      \"Epoch 190/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.0337\\n\",\n      \"Epoch 191/200\\n\",\n      \"4/4 [==============================] - 0s 975us/step - loss: 0.0333\\n\",\n      \"Epoch 192/200\\n\",\n      \"4/4 [==============================] - 0s 1ms/step - loss: 0.0330\\n\",\n      \"Epoch 193/200\\n\",\n      \"4/4 [==============================] - 0s 970us/step - loss: 0.0325\\n\",\n      \"Epoch 194/200\\n\",\n      \"4/4 [==============================] - 0s 978us/step - loss: 0.0321\\n\",\n      \"Epoch 195/200\\n\",\n      \"4/4 [==============================] - 0s 979us/step - loss: 0.0317\\n\",\n      \"Epoch 196/200\\n\",\n      \"4/4 [==============================] - 0s 984us/step - loss: 0.0314\\n\",\n      \"Epoch 197/200\\n\",\n      \"4/4 [==============================] - 0s 952us/step - loss: 0.0310\\n\",\n      \"Epoch 198/200\\n\",\n      \"4/4 [==============================] - 0s 988us/step - loss: 0.0306\\n\",\n      \"Epoch 199/200\\n\",\n      \"4/4 [==============================] - 0s 960us/step - loss: 0.0303\\n\",\n      \"Epoch 200/200\\n\",\n      \"4/4 [==============================] - 0s 962us/step - loss: 0.0300\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f47086392d0>\"\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\": \"2b7bbf93988f45bd8c4d49c39cb6efb5\",\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\": \"e92fbfdec7e6464185edea658737fa3d\",\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\": \"e31d3a5a796e4126abf2e87b81092d96\",\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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Optional-Labs/Course-2-Week-2-Optional-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-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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    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Optional-Labs/images/Course-2-Week-2-Optional-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-Labs/Course-2-Week-2-Practice-Labs-1-Neural-Networks-for-Multiclass-Classification.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\": \"2224c2e3603c4408a604d7b072a4d335\",\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\": 3,\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\": 4,\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\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3991345bd541429f8c41c4593f9a2a08\",\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\": 6,\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\": 7,\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:  [ 0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  8.56e-06  1.94e-06 -7.37e-04\\n\",\n      \" -8.13e-03 -1.86e-02 -1.87e-02 -1.88e-02 -1.91e-02 -1.64e-02 -3.78e-03\\n\",\n      \"  3.30e-04  1.28e-05  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  1.16e-04  1.20e-04 -1.40e-02 -2.85e-02  8.04e-02\\n\",\n      \"  2.67e-01  2.74e-01  2.79e-01  2.74e-01  2.25e-01  2.78e-02 -7.06e-03\\n\",\n      \"  2.35e-04  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  1.28e-17 -3.26e-04 -1.39e-02  8.16e-02  3.83e-01  8.58e-01  1.00e+00\\n\",\n      \"  9.70e-01  9.31e-01  1.00e+00  9.64e-01  4.49e-01 -5.60e-03 -3.78e-03\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  5.11e-06  4.36e-04 -3.96e-03\\n\",\n      \" -2.69e-02  1.01e-01  6.42e-01  1.03e+00  8.51e-01  5.43e-01  3.43e-01\\n\",\n      \"  2.69e-01  6.68e-01  1.01e+00  9.04e-01  1.04e-01 -1.66e-02  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  2.60e-05 -3.11e-03  7.52e-03  1.78e-01\\n\",\n      \"  7.93e-01  9.66e-01  4.63e-01  6.92e-02 -3.64e-03 -4.12e-02 -5.02e-02\\n\",\n      \"  1.56e-01  9.02e-01  1.05e+00  1.51e-01 -2.16e-02  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  5.87e-05 -6.41e-04 -3.23e-02  2.78e-01  9.37e-01  1.04e+00\\n\",\n      \"  5.98e-01 -3.59e-03 -2.17e-02 -4.81e-03  6.17e-05 -1.24e-02  1.55e-01\\n\",\n      \"  9.15e-01  9.20e-01  1.09e-01 -1.71e-02  0.00e+00  0.00e+00  1.56e-04\\n\",\n      \" -4.28e-04 -2.51e-02  1.31e-01  7.82e-01  1.03e+00  7.57e-01  2.85e-01\\n\",\n      \"  4.87e-03 -3.19e-03  0.00e+00  8.36e-04 -3.71e-02  4.53e-01  1.03e+00\\n\",\n      \"  5.39e-01 -2.44e-03 -4.80e-03  0.00e+00  0.00e+00 -7.04e-04 -1.27e-02\\n\",\n      \"  1.62e-01  7.80e-01  1.04e+00  8.04e-01  1.61e-01 -1.38e-02  2.15e-03\\n\",\n      \" -2.13e-04  2.04e-04 -6.86e-03  4.32e-04  7.21e-01  8.48e-01  1.51e-01\\n\",\n      \" -2.28e-02  1.99e-04  0.00e+00  0.00e+00 -9.40e-03  3.75e-02  6.94e-01\\n\",\n      \"  1.03e+00  1.02e+00  8.80e-01  3.92e-01 -1.74e-02 -1.20e-04  5.55e-05\\n\",\n      \" -2.24e-03 -2.76e-02  3.69e-01  9.36e-01  4.59e-01 -4.25e-02  1.17e-03\\n\",\n      \"  1.89e-05  0.00e+00  0.00e+00 -1.94e-02  1.30e-01  9.80e-01  9.42e-01\\n\",\n      \"  7.75e-01  8.74e-01  2.13e-01 -1.72e-02  0.00e+00  1.10e-03 -2.62e-02\\n\",\n      \"  1.23e-01  8.31e-01  7.27e-01  5.24e-02 -6.19e-03  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00 -9.37e-03  3.68e-02  6.99e-01  1.00e+00  6.06e-01\\n\",\n      \"  3.27e-01 -3.22e-02 -4.83e-02 -4.34e-02 -5.75e-02  9.56e-02  7.27e-01\\n\",\n      \"  6.95e-01  1.47e-01 -1.20e-02 -3.03e-04  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00 -6.77e-04 -6.51e-03  1.17e-01  4.22e-01  9.93e-01  8.82e-01\\n\",\n      \"  7.46e-01  7.24e-01  7.23e-01  7.20e-01  8.45e-01  8.32e-01  6.89e-02\\n\",\n      \" -2.78e-02  3.59e-04  7.15e-05  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  1.53e-04  3.17e-04 -2.29e-02 -4.14e-03  3.87e-01  5.05e-01  7.75e-01\\n\",\n      \"  9.90e-01  1.01e+00  1.01e+00  7.38e-01  2.15e-01 -2.70e-02  1.33e-03\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  2.36e-04 -2.26e-03 -2.52e-02 -3.74e-02  6.62e-02  2.91e-01\\n\",\n      \"  3.23e-01  3.06e-01  8.76e-02 -2.51e-02  2.37e-04  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  6.21e-18  6.73e-04 -1.13e-02 -3.55e-02 -3.88e-02\\n\",\n      \" -3.71e-02 -1.34e-02  9.91e-04  4.89e-05  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00  0.00e+00\\n\",\n      \"  0.00e+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\": 8,\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\": 9,\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\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"290ce20fea4b46ff885be4c989450d3d\",\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\": 12,\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\": 13,\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\": 14,\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\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"[layer1, layer2, layer3] = model.layers\"\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      \"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\": 17,\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\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e849a9feb3d143bab7ea64f6962172c2\",\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\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"102753ef246f48ccaffd7a939d210a72\",\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\": 20,\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\": 21,\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\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"df916c8b55ed44db80540679bac5c960\",\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\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e964498ab08f4927a6eab02f3db9fda7\",\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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-Labs/Course-2-Week-2-Practice-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-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    \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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-Labs/data/Course-2-Week-2-Practice-Labs-data.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-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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    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-Labs/images/Course-2-Week-2-Practice-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2-Practice-Quiz/Course-2-Week-2-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-2/Course-2-Week-2.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Lecture-Slides/Course-2-Week-3-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Practice-Labs/Course-2-Week-3-Practice-Labs-1-Advice-for-Applying-Machine-Learning.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\": 1,\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\": 2,\n   \"metadata\": {},\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   \"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\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b8652adf8a8843949a14a686e40ca0ef\",\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   \"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\": 5,\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    \"    err = np.sum((yhat-y)**2)/(2*m)\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    return(err)\"\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      \"\\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   \"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\": 7,\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\": 8,\n   \"metadata\": {},\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   \"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\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b3762d59151d4759b9143ee4798d4278\",\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   \"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\": 10,\n   \"metadata\": {},\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   \"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\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a86a5d327ef04a30b89830caae5bf4c9\",\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   \"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\": 12,\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\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2675504ec7d34c46942ae836d86af3bf\",\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   \"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\": 14,\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\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"634b0dc0c792482aaef51ad5bba0aaff\",\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   \"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\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"5f01dad9e4724a5e9c12d1b1f2e114b9\",\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   \"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\": 17,\n   \"metadata\": {},\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\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"de58cbc7343649f49ef147cc11f575d9\",\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   \"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\": 20,\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    \"    incorrect_ttl = [1 if y_i!=yhat_i else 0 for y_i, yhat_i in zip(y, yhat)]  \\n\",\n    \"    cerr = np.sum(incorrect_ttl)/m\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"    \\n\",\n    \"    return(cerr)\"\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      \"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   \"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\": 26,\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    \"        Dense(units=120, activation='relu', name='Layer_1'),\\n\",\n    \"        Dense(units=40, activation='relu', name='Layer_2'),\\n\",\n    \"        Dense(units=6, activation='linear', name='Layer_3'),\\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=SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=Adam(0.01),\\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\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 1ms/step - loss: 0.2867\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.2307\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.2261\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.2168\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/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 3ms/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 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 3ms/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 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 3ms/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 3ms/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 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 3ms/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 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 3ms/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 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 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 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 1ms/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 3ms/step - loss: 0.1750\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1545\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1614\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.1764\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1645\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1538\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1485\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.1422\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1299\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 [==============================] - 0s 3ms/step - loss: 0.1049\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1111\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1079\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1021\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1048\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0971\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0985\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1026\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1111\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0991\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0890\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1423\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1381\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1105\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1005\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0846\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1125\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1129\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1219\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1161\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1137\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 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 3ms/step - loss: 0.1096\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1006\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.0978\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0879\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 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 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 3ms/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 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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 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 3ms/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 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      \"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 3ms/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 1ms/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 3ms/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 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 1ms/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 3ms/step - loss: 0.0713\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0688\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 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 3ms/step - loss: 0.0696\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1189\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.0579\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0721\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0463\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 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 3ms/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 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 3ms/step - loss: 0.0510\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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      \"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 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 3ms/step - loss: 0.1648\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 2ms/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 3ms/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 1ms/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 2ms/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 2ms/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 3ms/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 3ms/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 3ms/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 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 3ms/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 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 3ms/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 3ms/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 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 3ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0484\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 2ms/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 3ms/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 1ms/step - loss: 0.0290\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 2ms/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 3ms/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 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 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 2ms/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 1ms/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 3ms/step - loss: 0.1534\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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      \"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 3ms/step - loss: 0.0258\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0269\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 1ms/step - loss: 0.0237\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 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 1ms/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 3ms/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 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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.0218\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.0254\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.0210\\n\",\n      \"Epoch 744/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0241\\n\",\n      \"Epoch 749/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/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 1ms/step - loss: 0.0768\\n\",\n      \"Epoch 782/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0394\\n\",\n      \"Epoch 787/1000\\n\",\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 1ms/step - loss: 0.0298\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 1ms/step - loss: 0.0250\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 1ms/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 1ms/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 1ms/step - loss: 0.0229\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0271\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 4ms/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 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 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 3ms/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 1ms/step - loss: 0.0217\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0729\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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      \"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 3ms/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 1ms/step - loss: 0.0189\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.0300\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.0432\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0203\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/step - loss: 0.0161\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0328\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fc68f230590>\"\n      ]\n     },\n     \"execution_count\": 27,\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\": 28,\n   \"metadata\": {},\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      \" Layer_1 (Dense)             (None, 120)               360       \\n\",\n      \"                                                                 \\n\",\n      \" Layer_2 (Dense)             (None, 40)                4840      \\n\",\n      \"                                                                 \\n\",\n      \" Layer_3 (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   \"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\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3f290495fb154d958862949f68777752\",\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   \"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\": 30,\n   \"metadata\": {},\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   \"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\": 32,\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    \"        Dense(units=6, activation='relu', name='Layer_1'),\\n\",\n    \"        Dense(units=6, activation='linear', name='Layer_2'),\\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=Adam(0.01),\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/1000\\n\",\n      \"13/13 [==============================] - 0s 967us/step - loss: 1.7306\\n\",\n      \"Epoch 2/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 1.4468\\n\",\n      \"Epoch 3/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 1.2902\\n\",\n      \"Epoch 4/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 1.1367\\n\",\n      \"Epoch 5/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.9710\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.7947\\n\",\n      \"Epoch 7/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.6499\\n\",\n      \"Epoch 8/1000\\n\",\n      \"13/13 [==============================] - 0s 875us/step - loss: 0.5378\\n\",\n      \"Epoch 9/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.4652\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 915us/step - loss: 0.4184\\n\",\n      \"Epoch 11/1000\\n\",\n      \"13/13 [==============================] - 0s 883us/step - loss: 0.3860\\n\",\n      \"Epoch 12/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.3641\\n\",\n      \"Epoch 13/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.3487\\n\",\n      \"Epoch 14/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.3316\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.3201\\n\",\n      \"Epoch 16/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.3110\\n\",\n      \"Epoch 17/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.3026\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.2953\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.2880\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.2824\\n\",\n      \"Epoch 21/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.2768\\n\",\n      \"Epoch 22/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.2716\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.2690\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.2618\\n\",\n      \"Epoch 25/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.2606\\n\",\n      \"Epoch 26/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.2560\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.2516\\n\",\n      \"Epoch 28/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.2500\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.2497\\n\",\n      \"Epoch 30/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.2424\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.2406\\n\",\n      \"Epoch 32/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.2386\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.2371\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.2355\\n\",\n      \"Epoch 35/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.2328\\n\",\n      \"Epoch 36/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.2311\\n\",\n      \"Epoch 37/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.2289\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.2271\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.2278\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.2269\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.2244\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 883us/step - loss: 0.2250\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.2228\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.2227\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.2230\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.2198\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.2188\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.2156\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.2156\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.2165\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.2155\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.2130\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.2121\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.2122\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.2105\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.2116\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.2121\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.2084\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.2122\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.2101\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.2095\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.2092\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 775us/step - loss: 0.2116\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.2085\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.2120\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.2087\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.2107\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.2090\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.2084\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.2053\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.2060\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 0.2061\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.2075\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.2067\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.2039\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.2036\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 884us/step - loss: 0.2062\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.2017\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.2044\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.2055\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1999\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.2028\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.2019\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.2042\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.2016\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.2068\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.2005\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.2011\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.2000\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1998\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1992\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.2001\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1997\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.2008\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.2015\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 895us/step - loss: 0.2011\\n\",\n      \"Epoch 97/1000\\n\",\n      \"13/13 [==============================] - 0s 945us/step - loss: 0.2006\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.2031\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1991\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.2006\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.2010\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 973us/step - loss: 0.2018\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.2026\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1988\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1974\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1966\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1963\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1969\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1987\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1978\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1962\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1979\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 884us/step - loss: 0.1944\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 932us/step - loss: 0.1987\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 903us/step - loss: 0.1934\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 931us/step - loss: 0.2009\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 942us/step - loss: 0.1943\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 903us/step - loss: 0.1969\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1951\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1964\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1957\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1970\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1960\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1973\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1961\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1957\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1949\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1946\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1944\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1969\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1926\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 0.1925\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 889us/step - loss: 0.1933\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 877us/step - loss: 0.1942\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1976\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1939\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1931\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 942us/step - loss: 0.1947\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1941\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1917\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1922\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1917\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1944\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1948\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1921\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1920\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1925\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 890us/step - loss: 0.1899\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 868us/step - loss: 0.1913\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1914\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1944\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1920\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1949\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1904\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 874us/step - loss: 0.1917\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 895us/step - loss: 0.1898\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 922us/step - loss: 0.1913\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1905\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1898\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1910\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1913\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 927us/step - loss: 0.1930\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 939us/step - loss: 0.1913\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 910us/step - loss: 0.1907\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1910\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1891\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 901us/step - loss: 0.1940\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 892us/step - loss: 0.1914\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 934us/step - loss: 0.1914\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 944us/step - loss: 0.1893\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 932us/step - loss: 0.1894\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1879\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1924\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1887\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1876\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 781us/step - loss: 0.1861\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1922\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1977\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1881\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1894\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1906\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1894\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1872\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1893\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1885\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1867\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1866\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1884\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 782us/step - loss: 0.1907\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 780us/step - loss: 0.1890\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 770us/step - loss: 0.1880\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1863\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1904\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1857\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1859\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1856\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1879\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1884\\n\",\n      \"Epoch 199/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1894\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1860\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1869\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1837\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1861\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1869\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1846\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1881\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1841\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1902\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1850\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1883\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 868us/step - loss: 0.1863\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1856\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1860\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.1890\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1855\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 902us/step - loss: 0.1891\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 908us/step - loss: 0.1834\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 904us/step - loss: 0.1887\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 909us/step - loss: 0.1857\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 874us/step - loss: 0.1844\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 910us/step - loss: 0.1846\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1843\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1878\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1884\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1851\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 885us/step - loss: 0.1844\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1824\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1849\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1879\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1860\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1834\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1882\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1851\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.1874\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 913us/step - loss: 0.1822\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1841\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1876\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1923\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1867\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1832\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1863\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 889us/step - loss: 0.1978\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1946\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1871\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/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 820us/step - loss: 0.1836\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1820\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 876us/step - loss: 0.1857\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 949us/step - loss: 0.1829\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 879us/step - loss: 0.1838\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 883us/step - loss: 0.1828\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1842\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1832\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1830\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1830\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 897us/step - loss: 0.1833\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1826\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1796\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1876\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1819\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1826\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1827\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1820\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 858us/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 860us/step - loss: 0.1835\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1812\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1817\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1836\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1801\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1868\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1869\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1815\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1847\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1787\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1841\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1804\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1861\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1816\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1797\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1807\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1815\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1822\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1813\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1815\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1829\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1849\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1805\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1807\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1801\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1793\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1815\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1784\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1867\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1805\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 0s 778us/step - loss: 0.1855\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1816\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 781us/step - loss: 0.1798\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1817\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1823\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1878\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1788\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1850\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1827\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1818\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1811\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1827\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1814\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1854\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1785\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1831\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 778us/step - loss: 0.1775\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 781us/step - loss: 0.1820\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1801\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1792\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1847\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1841\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1811\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1841\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1785\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1815\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1792\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1829\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1800\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1783\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1797\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1846\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1790\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1815\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1801\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1803\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1824\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1849\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 774us/step - loss: 0.1835\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 778us/step - loss: 0.1797\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1805\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 780us/step - loss: 0.1796\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1807\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1794\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1808\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1790\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1797\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1804\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1838\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1832\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1819\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1800\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1789\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1787\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1784\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1846\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1826\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1802\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1792\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1786\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 835us/step - loss: 0.1802\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1781\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1800\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1821\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 896us/step - loss: 0.1789\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.1798\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 880us/step - loss: 0.1815\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 935us/step - loss: 0.1799\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 938us/step - loss: 0.1811\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 945us/step - loss: 0.1785\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 935us/step - loss: 0.1776\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 882us/step - loss: 0.1784\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1819\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1771\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1799\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1780\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1773\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1769\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1770\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1766\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1768\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1794\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1799\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 0.1768\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 904us/step - loss: 0.1805\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 895us/step - loss: 0.1782\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 865us/step - loss: 0.1843\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1763\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1790\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 901us/step - loss: 0.1781\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1771\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1809\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 909us/step - loss: 0.1807\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 874us/step - loss: 0.1792\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 881us/step - loss: 0.1767\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1767\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1763\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.1768\\n\",\n      \"Epoch 395/1000\\n\",\n      \"13/13 [==============================] - 0s 895us/step - loss: 0.1789\\n\",\n      \"Epoch 396/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1801\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1805\\n\",\n      \"Epoch 398/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1783\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1775\\n\",\n      \"Epoch 400/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1796\\n\",\n      \"Epoch 401/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1776\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1771\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1765\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1775\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1753\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1759\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1776\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1779\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1759\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1798\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1807\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1778\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1771\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1760\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1760\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 773us/step - loss: 0.1782\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1756\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1762\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 779us/step - loss: 0.1756\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 779us/step - loss: 0.1773\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 773us/step - loss: 0.1761\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1753\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1777\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1754\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1779\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1781\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1739\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1757\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1755\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1775\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1775\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1773\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1777\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 888us/step - loss: 0.1781\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1761\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1775\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1788\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1762\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1752\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1742\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1765\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1776\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1755\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1773\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1763\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1764\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1792\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1746\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1752\\n\",\n      \"Epoch 450/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1773\\n\",\n      \"Epoch 451/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1772\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1764\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1754\\n\",\n      \"Epoch 454/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1748\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1752\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1753\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1785\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1744\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1758\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1759\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1750\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1745\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 782us/step - loss: 0.1792\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1752\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1756\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 900us/step - loss: 0.1752\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 913us/step - loss: 0.1774\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1748\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1767\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1813\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 817us/step - loss: 0.1793\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1748\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1762\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1822\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 868us/step - loss: 0.1788\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 0.1760\\n\",\n      \"Epoch 477/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1758\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.1763\\n\",\n      \"Epoch 479/1000\\n\",\n      \"13/13 [==============================] - 0s 885us/step - loss: 0.1751\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1749\\n\",\n      \"Epoch 481/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1742\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1745\\n\",\n      \"Epoch 483/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1763\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.1767\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 892us/step - loss: 0.1780\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1739\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 876us/step - loss: 0.1781\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1755\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1766\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1783\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1769\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.1752\\n\",\n      \"Epoch 493/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1772\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1739\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1750\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1798\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1744\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.1750\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1750\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1735\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1783\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 875us/step - loss: 0.1749\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 891us/step - loss: 0.1749\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1741\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1767\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 0.1752\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1764\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 844us/step - loss: 0.1719\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1791\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1746\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1786\\n\",\n      \"Epoch 512/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1737\\n\",\n      \"Epoch 513/1000\\n\",\n      \"13/13 [==============================] - 0s 765us/step - loss: 0.1781\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/step - loss: 0.1766\\n\",\n      \"Epoch 515/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1730\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1738\\n\",\n      \"Epoch 517/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1729\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 894us/step - loss: 0.1747\\n\",\n      \"Epoch 519/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1759\\n\",\n      \"Epoch 520/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1748\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1762\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 865us/step - loss: 0.1750\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 911us/step - loss: 0.1751\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 901us/step - loss: 0.1747\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1739\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1731\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 894us/step - loss: 0.1783\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 874us/step - loss: 0.1810\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1770\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1740\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 868us/step - loss: 0.1743\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1759\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1786\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 874us/step - loss: 0.1766\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 888us/step - loss: 0.1755\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.1749\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 876us/step - loss: 0.1713\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 877us/step - loss: 0.1774\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 880us/step - loss: 0.1741\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 894us/step - loss: 0.1774\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 906us/step - loss: 0.1734\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 891us/step - loss: 0.1754\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 889us/step - loss: 0.1735\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 911us/step - loss: 0.1758\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 916us/step - loss: 0.1723\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 885us/step - loss: 0.1786\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1743\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1750\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1747\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1768\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1732\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1736\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 898us/step - loss: 0.1725\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 889us/step - loss: 0.1748\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 880us/step - loss: 0.1733\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1727\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.1754\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1781\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1805\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 923us/step - loss: 0.1764\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1784\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1715\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1730\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1733\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1718\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1750\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1751\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1728\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1730\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1761\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 879us/step - loss: 0.1798\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 855us/step - loss: 0.1762\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 902us/step - loss: 0.1727\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 911us/step - loss: 0.1722\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 931us/step - loss: 0.1717\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 957us/step - loss: 0.1730\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 931us/step - loss: 0.1751\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 916us/step - loss: 0.1741\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 890us/step - loss: 0.1732\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 939us/step - loss: 0.1725\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 948us/step - loss: 0.1731\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1709\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1727\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 852us/step - loss: 0.1742\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1721\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1730\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1728\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1718\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1710\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 872us/step - loss: 0.1787\\n\",\n      \"Epoch 591/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1789\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 905us/step - loss: 0.1745\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 900us/step - loss: 0.1775\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 901us/step - loss: 0.1727\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 920us/step - loss: 0.1738\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1746\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 895us/step - loss: 0.1734\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1738\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 881us/step - loss: 0.1707\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1735\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 831us/step - loss: 0.1731\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1727\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1722\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1720\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1747\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1770\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1741\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1748\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1731\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1743\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1725\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1706\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1732\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1746\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1729\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1711\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1722\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1802\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1725\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 773us/step - loss: 0.1773\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1710\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1746\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1728\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1709\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1776\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1717\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1728\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1711\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 794us/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 790us/step - loss: 0.1711\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1752\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1731\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1758\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1713\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1744\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1728\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1725\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 964us/step - loss: 0.1718\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1732\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1736\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1700\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1705\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1725\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1711\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1723\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1719\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1718\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 858us/step - loss: 0.1740\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1737\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1705\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1699\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1712\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1704\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 945us/step - loss: 0.1705\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 979us/step - loss: 0.1701\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 960us/step - loss: 0.1701\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1739\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/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 805us/step - loss: 0.1718\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1720\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.1725\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 0s 859us/step - loss: 0.1694\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1700\\n\",\n      \"Epoch 666/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1740\\n\",\n      \"Epoch 667/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1693\\n\",\n      \"Epoch 668/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1722\\n\",\n      \"Epoch 669/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1732\\n\",\n      \"Epoch 670/1000\\n\",\n      \"13/13 [==============================] - 0s 813us/step - loss: 0.1704\\n\",\n      \"Epoch 671/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1696\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 949us/step - loss: 0.1733\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1726\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1740\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1699\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 884us/step - loss: 0.1712\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1711\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1718\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1795\\n\",\n      \"Epoch 680/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1709\\n\",\n      \"Epoch 681/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1703\\n\",\n      \"Epoch 682/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1717\\n\",\n      \"Epoch 683/1000\\n\",\n      \"13/13 [==============================] - 0s 822us/step - loss: 0.1758\\n\",\n      \"Epoch 684/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1699\\n\",\n      \"Epoch 685/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1753\\n\",\n      \"Epoch 686/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1728\\n\",\n      \"Epoch 687/1000\\n\",\n      \"13/13 [==============================] - 0s 850us/step - loss: 0.1733\\n\",\n      \"Epoch 688/1000\\n\",\n      \"13/13 [==============================] - 0s 875us/step - loss: 0.1706\\n\",\n      \"Epoch 689/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1705\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1698\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1721\\n\",\n      \"Epoch 692/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1712\\n\",\n      \"Epoch 693/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1716\\n\",\n      \"Epoch 694/1000\\n\",\n      \"13/13 [==============================] - 0s 908us/step - loss: 0.1692\\n\",\n      \"Epoch 695/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1718\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 907us/step - loss: 0.1704\\n\",\n      \"Epoch 697/1000\\n\",\n      \"13/13 [==============================] - 0s 912us/step - loss: 0.1711\\n\",\n      \"Epoch 698/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1708\\n\",\n      \"Epoch 699/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1702\\n\",\n      \"Epoch 700/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1737\\n\",\n      \"Epoch 701/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1720\\n\",\n      \"Epoch 702/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1701\\n\",\n      \"Epoch 703/1000\\n\",\n      \"13/13 [==============================] - 0s 846us/step - loss: 0.1710\\n\",\n      \"Epoch 704/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1690\\n\",\n      \"Epoch 705/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1719\\n\",\n      \"Epoch 706/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1718\\n\",\n      \"Epoch 707/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1680\\n\",\n      \"Epoch 708/1000\\n\",\n      \"13/13 [==============================] - 0s 782us/step - loss: 0.1756\\n\",\n      \"Epoch 709/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1754\\n\",\n      \"Epoch 710/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1721\\n\",\n      \"Epoch 711/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1751\\n\",\n      \"Epoch 712/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1714\\n\",\n      \"Epoch 713/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1716\\n\",\n      \"Epoch 714/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1703\\n\",\n      \"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1704\\n\",\n      \"Epoch 716/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1749\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 0s 782us/step - loss: 0.1676\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 776us/step - loss: 0.1713\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1690\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1700\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1713\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1712\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1697\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 854us/step - loss: 0.1718\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1741\\n\",\n      \"Epoch 726/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1719\\n\",\n      \"Epoch 727/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1716\\n\",\n      \"Epoch 728/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1713\\n\",\n      \"Epoch 729/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1694\\n\",\n      \"Epoch 730/1000\\n\",\n      \"13/13 [==============================] - 0s 909us/step - loss: 0.1764\\n\",\n      \"Epoch 731/1000\\n\",\n      \"13/13 [==============================] - 0s 903us/step - loss: 0.1758\\n\",\n      \"Epoch 732/1000\\n\",\n      \"13/13 [==============================] - 0s 888us/step - loss: 0.1735\\n\",\n      \"Epoch 733/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1700\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 936us/step - loss: 0.1698\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 947us/step - loss: 0.1699\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 949us/step - loss: 0.1716\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 915us/step - loss: 0.1701\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 924us/step - loss: 0.1720\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 930us/step - loss: 0.1737\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 876us/step - loss: 0.1730\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 888us/step - loss: 0.1700\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1684\\n\",\n      \"Epoch 743/1000\\n\",\n      \"13/13 [==============================] - 0s 889us/step - loss: 0.1713\\n\",\n      \"Epoch 744/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1695\\n\",\n      \"Epoch 745/1000\\n\",\n      \"13/13 [==============================] - 0s 865us/step - loss: 0.1715\\n\",\n      \"Epoch 746/1000\\n\",\n      \"13/13 [==============================] - 0s 884us/step - loss: 0.1690\\n\",\n      \"Epoch 747/1000\\n\",\n      \"13/13 [==============================] - 0s 876us/step - loss: 0.1706\\n\",\n      \"Epoch 748/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 0.1687\\n\",\n      \"Epoch 749/1000\\n\",\n      \"13/13 [==============================] - 0s 885us/step - loss: 0.1694\\n\",\n      \"Epoch 750/1000\\n\",\n      \"13/13 [==============================] - 0s 858us/step - loss: 0.1700\\n\",\n      \"Epoch 751/1000\\n\",\n      \"13/13 [==============================] - 0s 875us/step - loss: 0.1697\\n\",\n      \"Epoch 752/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1696\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1707\\n\",\n      \"Epoch 754/1000\\n\",\n      \"13/13 [==============================] - 0s 900us/step - loss: 0.1719\\n\",\n      \"Epoch 755/1000\\n\",\n      \"13/13 [==============================] - 0s 885us/step - loss: 0.1716\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1766\\n\",\n      \"Epoch 757/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1752\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1689\\n\",\n      \"Epoch 759/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1709\\n\",\n      \"Epoch 760/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1696\\n\",\n      \"Epoch 761/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1684\\n\",\n      \"Epoch 762/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1731\\n\",\n      \"Epoch 763/1000\\n\",\n      \"13/13 [==============================] - 0s 905us/step - loss: 0.1725\\n\",\n      \"Epoch 764/1000\\n\",\n      \"13/13 [==============================] - 0s 864us/step - loss: 0.1754\\n\",\n      \"Epoch 765/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1697\\n\",\n      \"Epoch 766/1000\\n\",\n      \"13/13 [==============================] - 0s 901us/step - loss: 0.1735\\n\",\n      \"Epoch 767/1000\\n\",\n      \"13/13 [==============================] - 0s 802us/step - loss: 0.1705\\n\",\n      \"Epoch 768/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1699\\n\",\n      \"Epoch 769/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1701\\n\",\n      \"Epoch 770/1000\\n\",\n      \"13/13 [==============================] - 0s 888us/step - loss: 0.1693\\n\",\n      \"Epoch 771/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1708\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 905us/step - loss: 0.1693\\n\",\n      \"Epoch 773/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1697\\n\",\n      \"Epoch 774/1000\\n\",\n      \"13/13 [==============================] - 0s 891us/step - loss: 0.1712\\n\",\n      \"Epoch 775/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1704\\n\",\n      \"Epoch 776/1000\\n\",\n      \"13/13 [==============================] - 0s 815us/step - loss: 0.1681\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1704\\n\",\n      \"Epoch 778/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1721\\n\",\n      \"Epoch 779/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1706\\n\",\n      \"Epoch 780/1000\\n\",\n      \"13/13 [==============================] - 0s 808us/step - loss: 0.1747\\n\",\n      \"Epoch 781/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1722\\n\",\n      \"Epoch 782/1000\\n\",\n      \"13/13 [==============================] - 0s 825us/step - loss: 0.1714\\n\",\n      \"Epoch 783/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1697\\n\",\n      \"Epoch 784/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1691\\n\",\n      \"Epoch 785/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1710\\n\",\n      \"Epoch 786/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1770\\n\",\n      \"Epoch 787/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1710\\n\",\n      \"Epoch 788/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1672\\n\",\n      \"Epoch 789/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1706\\n\",\n      \"Epoch 790/1000\\n\",\n      \"13/13 [==============================] - 0s 884us/step - loss: 0.1718\\n\",\n      \"Epoch 791/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1678\\n\",\n      \"Epoch 792/1000\\n\",\n      \"13/13 [==============================] - 0s 845us/step - loss: 0.1691\\n\",\n      \"Epoch 793/1000\\n\",\n      \"13/13 [==============================] - 0s 871us/step - loss: 0.1715\\n\",\n      \"Epoch 794/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1784\\n\",\n      \"Epoch 795/1000\\n\",\n      \"13/13 [==============================] - 0s 877us/step - loss: 0.1659\\n\",\n      \"Epoch 796/1000\\n\",\n      \"13/13 [==============================] - 0s 865us/step - loss: 0.1756\\n\",\n      \"Epoch 797/1000\\n\",\n      \"13/13 [==============================] - 0s 858us/step - loss: 0.1708\\n\",\n      \"Epoch 798/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1706\\n\",\n      \"Epoch 799/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1695\\n\",\n      \"Epoch 800/1000\\n\",\n      \"13/13 [==============================] - 0s 890us/step - loss: 0.1668\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1703\\n\",\n      \"Epoch 802/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1683\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1704\\n\",\n      \"Epoch 804/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1701\\n\",\n      \"Epoch 805/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1691\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1712\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1679\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1688\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1704\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1699\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1693\\n\",\n      \"Epoch 812/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1678\\n\",\n      \"Epoch 813/1000\\n\",\n      \"13/13 [==============================] - 0s 807us/step - loss: 0.1694\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1676\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1698\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1717\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1712\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1681\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1723\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 811us/step - loss: 0.1733\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 778us/step - loss: 0.1692\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 783us/step - loss: 0.1745\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1762\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 874us/step - loss: 0.1713\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1697\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1698\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 959us/step - loss: 0.1720\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1696\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1707\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 787us/step - loss: 0.1693\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1691\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1689\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1716\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1669\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1683\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1673\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1684\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 860us/step - loss: 0.1688\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1695\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1689\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 819us/step - loss: 0.1702\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1711\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1689\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1682\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1694\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1678\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1693\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1707\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1699\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1683\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1688\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1751\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 816us/step - loss: 0.1707\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 810us/step - loss: 0.1680\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1688\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 836us/step - loss: 0.1690\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 908us/step - loss: 0.1676\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1720\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1691\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1692\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1705\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 804us/step - loss: 0.1675\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 800us/step - loss: 0.1715\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 805us/step - loss: 0.1684\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1703\\n\",\n      \"Epoch 866/1000\\n\",\n      \"13/13 [==============================] - 0s 901us/step - loss: 0.1702\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1695\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 842us/step - loss: 0.1728\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 803us/step - loss: 0.1682\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1681\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 785us/step - loss: 0.1684\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 789us/step - loss: 0.1680\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1720\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1705\\n\",\n      \"Epoch 875/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1686\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1676\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 828us/step - loss: 0.1750\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 795us/step - loss: 0.1728\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1733\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1690\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 814us/step - loss: 0.1721\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 862us/step - loss: 0.1754\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 857us/step - loss: 0.1727\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1697\\n\",\n      \"Epoch 885/1000\\n\",\n      \"13/13 [==============================] - 0s 895us/step - loss: 0.1670\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1675\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1723\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1701\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 894us/step - loss: 0.1677\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 917us/step - loss: 0.1712\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 849us/step - loss: 0.1684\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1695\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1680\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1694\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1683\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 782us/step - loss: 0.1694\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 790us/step - loss: 0.1714\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1682\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 843us/step - loss: 0.1704\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1664\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1683\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 798us/step - loss: 0.1682\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1669\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1688\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 784us/step - loss: 0.1686\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1739\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 853us/step - loss: 0.1693\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 851us/step - loss: 0.1689\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1673\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 900us/step - loss: 0.1700\\n\",\n      \"Epoch 911/1000\\n\",\n      \"13/13 [==============================] - 0s 876us/step - loss: 0.1672\\n\",\n      \"Epoch 912/1000\\n\",\n      \"13/13 [==============================] - 0s 879us/step - loss: 0.1672\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 892us/step - loss: 0.1702\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 920us/step - loss: 0.1662\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 918us/step - loss: 0.1716\\n\",\n      \"Epoch 916/1000\\n\",\n      \"13/13 [==============================] - 0s 914us/step - loss: 0.1669\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 936us/step - loss: 0.1704\\n\",\n      \"Epoch 918/1000\\n\",\n      \"13/13 [==============================] - 0s 881us/step - loss: 0.1659\\n\",\n      \"Epoch 919/1000\\n\",\n      \"13/13 [==============================] - 0s 866us/step - loss: 0.1725\\n\",\n      \"Epoch 920/1000\\n\",\n      \"13/13 [==============================] - 0s 911us/step - loss: 0.1718\\n\",\n      \"Epoch 921/1000\\n\",\n      \"13/13 [==============================] - 0s 897us/step - loss: 0.1670\\n\",\n      \"Epoch 922/1000\\n\",\n      \"13/13 [==============================] - 0s 888us/step - loss: 0.1695\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 869us/step - loss: 0.1670\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1672\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 839us/step - loss: 0.1685\\n\",\n      \"Epoch 926/1000\\n\",\n      \"13/13 [==============================] - 0s 821us/step - loss: 0.1681\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1698\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 832us/step - loss: 0.1660\\n\",\n      \"Epoch 929/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1704\\n\",\n      \"Epoch 930/1000\\n\",\n      \"13/13 [==============================] - 0s 838us/step - loss: 0.1678\\n\",\n      \"Epoch 931/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1703\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 806us/step - loss: 0.1700\\n\",\n      \"Epoch 933/1000\\n\",\n      \"13/13 [==============================] - 0s 792us/step - loss: 0.1699\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 771us/step - loss: 0.1691\\n\",\n      \"Epoch 935/1000\\n\",\n      \"13/13 [==============================] - 0s 777us/step - loss: 0.1689\\n\",\n      \"Epoch 936/1000\\n\",\n      \"13/13 [==============================] - 0s 791us/step - loss: 0.1680\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 796us/step - loss: 0.1701\\n\",\n      \"Epoch 938/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1681\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 818us/step - loss: 0.1693\\n\",\n      \"Epoch 940/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1703\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 826us/step - loss: 0.1674\\n\",\n      \"Epoch 942/1000\\n\",\n      \"13/13 [==============================] - 0s 824us/step - loss: 0.1667\\n\",\n      \"Epoch 943/1000\\n\",\n      \"13/13 [==============================] - 0s 820us/step - loss: 0.1682\\n\",\n      \"Epoch 944/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1706\\n\",\n      \"Epoch 945/1000\\n\",\n      \"13/13 [==============================] - 0s 823us/step - loss: 0.1679\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 833us/step - loss: 0.1647\\n\",\n      \"Epoch 947/1000\\n\",\n      \"13/13 [==============================] - 0s 830us/step - loss: 0.1759\\n\",\n      \"Epoch 948/1000\\n\",\n      \"13/13 [==============================] - 0s 873us/step - loss: 0.1712\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 887us/step - loss: 0.1679\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 897us/step - loss: 0.1669\\n\",\n      \"Epoch 951/1000\\n\",\n      \"13/13 [==============================] - 0s 875us/step - loss: 0.1733\\n\",\n      \"Epoch 952/1000\\n\",\n      \"13/13 [==============================] - 0s 848us/step - loss: 0.1662\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 829us/step - loss: 0.1751\\n\",\n      \"Epoch 954/1000\\n\",\n      \"13/13 [==============================] - 0s 847us/step - loss: 0.1705\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 863us/step - loss: 0.1661\\n\",\n      \"Epoch 956/1000\\n\",\n      \"13/13 [==============================] - 0s 893us/step - loss: 0.1658\\n\",\n      \"Epoch 957/1000\\n\",\n      \"13/13 [==============================] - 0s 890us/step - loss: 0.1676\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 879us/step - loss: 0.1718\\n\",\n      \"Epoch 959/1000\\n\",\n      \"13/13 [==============================] - 0s 876us/step - loss: 0.1644\\n\",\n      \"Epoch 960/1000\\n\",\n      \"13/13 [==============================] - 0s 879us/step - loss: 0.1697\\n\",\n      \"Epoch 961/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1654\\n\",\n      \"Epoch 962/1000\\n\",\n      \"13/13 [==============================] - 0s 884us/step - loss: 0.1667\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 861us/step - loss: 0.1757\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 870us/step - loss: 0.1661\\n\",\n      \"Epoch 965/1000\\n\",\n      \"13/13 [==============================] - 0s 841us/step - loss: 0.1713\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 840us/step - loss: 0.1671\\n\",\n      \"Epoch 967/1000\\n\",\n      \"13/13 [==============================] - 0s 917us/step - loss: 0.1697\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 856us/step - loss: 0.1716\\n\",\n      \"Epoch 969/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1688\\n\",\n      \"Epoch 970/1000\\n\",\n      \"13/13 [==============================] - 0s 878us/step - loss: 0.1672\\n\",\n      \"Epoch 971/1000\\n\",\n      \"13/13 [==============================] - 0s 886us/step - loss: 0.1664\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 827us/step - loss: 0.1684\\n\",\n      \"Epoch 973/1000\\n\",\n      \"13/13 [==============================] - 0s 834us/step - loss: 0.1660\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 786us/step - loss: 0.1678\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 797us/step - loss: 0.1675\\n\",\n      \"Epoch 976/1000\\n\",\n      \"13/13 [==============================] - 0s 788us/step - loss: 0.1710\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 868us/step - loss: 0.1722\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 894us/step - loss: 0.1648\\n\",\n      \"Epoch 979/1000\\n\",\n      \"13/13 [==============================] - 0s 904us/step - loss: 0.1716\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 837us/step - loss: 0.1666\\n\",\n      \"Epoch 981/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1666\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 809us/step - loss: 0.1696\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 793us/step - loss: 0.1703\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 801us/step - loss: 0.1655\\n\",\n      \"Epoch 985/1000\\n\",\n      \"13/13 [==============================] - 0s 799us/step - loss: 0.1658\\n\",\n      \"Epoch 986/1000\\n\",\n      \"13/13 [==============================] - 0s 812us/step - loss: 0.1691\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 909us/step - loss: 0.1665\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 932us/step - loss: 0.1680\\n\",\n      \"Epoch 989/1000\\n\",\n      \"13/13 [==============================] - 0s 932us/step - loss: 0.1682\\n\",\n      \"Epoch 990/1000\\n\",\n      \"13/13 [==============================] - 0s 919us/step - loss: 0.1664\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 867us/step - loss: 0.1682\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 908us/step - loss: 0.1685\\n\",\n      \"Epoch 993/1000\\n\",\n      \"13/13 [==============================] - 0s 914us/step - loss: 0.1672\\n\",\n      \"Epoch 994/1000\\n\",\n      \"13/13 [==============================] - 0s 914us/step - loss: 0.1660\\n\",\n      \"Epoch 995/1000\\n\",\n      \"13/13 [==============================] - 0s 947us/step - loss: 0.1705\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 905us/step - loss: 0.1678\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 897us/step - loss: 0.1689\\n\",\n      \"Epoch 998/1000\\n\",\n      \"13/13 [==============================] - 0s 907us/step - loss: 0.1701\\n\",\n      \"Epoch 999/1000\\n\",\n      \"13/13 [==============================] - 0s 899us/step - loss: 0.1711\\n\",\n      \"Epoch 1000/1000\\n\",\n      \"13/13 [==============================] - 0s 896us/step - loss: 0.1628\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fc68ef28a50>\"\n      ]\n     },\n     \"execution_count\": 33,\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\": 34,\n   \"metadata\": {},\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      \" Layer_1 (Dense)             (None, 6)                 18        \\n\",\n      \"                                                                 \\n\",\n      \" Layer_2 (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   \"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\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7a4fa5b5577b44abb5bf9e0b9e6cc945\",\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   \"source\": [\n    \"This simple models does pretty well. Let's calculate the classification error.\"\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      \"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   \"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\": 37,\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    \"        Dense(units=120, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.1), name='Layer_1'),\\n\",\n    \"        Dense(units=40, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.1), name='Layer_2'),\\n\",\n    \"        Dense(units=6, activation='linear', name='Layer_3'),\\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=Adam(0.01),\\n\",\n    \"    ### START CODE HERE ### \\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\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 3ms/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 3ms/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 - loss: 0.5482\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5393\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5135\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5322\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5148\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5021\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5041\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5086\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5108\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 3ms/step - loss: 0.4977\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5227\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5380\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5101\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5247\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4910\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4799\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4673\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4877\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4816\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4969\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4812\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4776\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4696\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4759\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4731\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4599\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4623\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.4669\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4545\\n\",\n      \"Epoch 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 3ms/step - loss: 0.4954\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 4ms/step - loss: 0.4411\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4591\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/step - loss: 0.4630\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4543\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4465\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4328\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4386\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.4468\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.4348\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4419\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4371\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4542\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4331\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4236\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4470\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4431\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4460\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4281\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4470\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4480\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4627\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/step - loss: 0.4260\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.4316\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4302\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.4595\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4779\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4529\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4328\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4336\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4206\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4214\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4343\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4415\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.3963\\n\",\n      \"Epoch 199/1000\\n\",\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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.4036\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.4007\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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      \"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 1ms/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 3ms/step - loss: 0.3858\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3804\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3810\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3883\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3922\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3879\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3801\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3715\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3690\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3733\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3863\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3808\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3927\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/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 3ms/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 1ms/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 1ms/step - loss: 0.3791\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.3764\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3819\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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      \"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 1ms/step - loss: 0.3584\\n\",\n      \"Epoch 402/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3600\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.3730\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3534\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/step - loss: 0.3673\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3961\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3836\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3501\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3725\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.3558\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 2ms/step - loss: 0.4088\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3998\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3510\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 2ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3489\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3452\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3429\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3425\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4209\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3978\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3565\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3443\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3419\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3654\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3516\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3480\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3599\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3539\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3668\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3593\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3483\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3536\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3456\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3486\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/step - loss: 0.3522\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3642\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3473\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3546\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3543\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3561\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3643\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3590\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3484\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3427\\n\",\n      \"Epoch 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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3568\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.4029\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 2ms/step - loss: 0.3356\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3463\\n\",\n      \"Epoch 680/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3406\\n\",\n      \"Epoch 681/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3549\\n\",\n      \"Epoch 682/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3399\\n\",\n      \"Epoch 683/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3363\\n\",\n      \"Epoch 684/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3415\\n\",\n      \"Epoch 685/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3470\\n\",\n      \"Epoch 686/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3487\\n\",\n      \"Epoch 687/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3424\\n\",\n      \"Epoch 688/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3321\\n\",\n      \"Epoch 689/1000\\n\",\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 1ms/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 4ms/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 1ms/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 4ms/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 1ms/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 3ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 2ms/step - loss: 0.3650\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/step - loss: 0.3407\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3493\\n\",\n      \"Epoch 757/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3608\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3780\\n\",\n      \"Epoch 759/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3424\\n\",\n      \"Epoch 760/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3436\\n\",\n      \"Epoch 761/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3541\\n\",\n      \"Epoch 762/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3457\\n\",\n      \"Epoch 763/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3317\\n\",\n      \"Epoch 764/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3496\\n\",\n      \"Epoch 765/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3551\\n\",\n      \"Epoch 766/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 2ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3339\\n\",\n      \"Epoch 780/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3368\\n\",\n      \"Epoch 788/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.3355\\n\",\n      \"Epoch 791/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3734\\n\",\n      \"Epoch 792/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3761\\n\",\n      \"Epoch 793/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3444\\n\",\n      \"Epoch 794/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3632\\n\",\n      \"Epoch 795/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3406\\n\",\n      \"Epoch 796/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3788\\n\",\n      \"Epoch 797/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3315\\n\",\n      \"Epoch 798/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3506\\n\",\n      \"Epoch 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 3ms/step - loss: 0.3287\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3403\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3452\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3836\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 1ms/step - loss: 0.3420\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3308\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3247\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3456\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3698\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4228\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3441\\n\",\n      \"Epoch 832/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3515\\n\",\n      \"Epoch 833/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3434\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3518\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/step - loss: 0.3347\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 1ms/step - loss: 0.3459\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3653\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3272\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3222\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3736\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3834\\n\",\n      \"Epoch 866/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3725\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3334\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3360\\n\",\n      \"Epoch 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 1ms/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 1ms/step - loss: 0.3594\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3704\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3419\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3713\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3464\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.3283\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4247\\n\",\n      \"Epoch 910/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3561\\n\",\n      \"Epoch 915/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.3225\\n\",\n      \"Epoch 924/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 0x7fc6846d3550>\"\n      ]\n     },\n     \"execution_count\": 38,\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\": 39,\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      \" Layer_1 (Dense)             (None, 120)               360       \\n\",\n      \"                                                                 \\n\",\n      \" Layer_2 (Dense)             (None, 40)                4840      \\n\",\n      \"                                                                 \\n\",\n      \" Layer_3 (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   \"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\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"880f0e7120f0430da4d5fccfaaacbc07\",\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   \"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\": 41,\n   \"metadata\": {},\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   \"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\": 42,\n   \"metadata\": {\n    \"scrolled\": true,\n    \"tags\": []\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 1ms/step - loss: 0.2867\\n\",\n      \"Epoch 6/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.2261\\n\",\n      \"Epoch 15/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.2168\\n\",\n      \"Epoch 20/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1963\\n\",\n      \"Epoch 29/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1904\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1733\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 3ms/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 1ms/step - loss: 0.1889\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1953\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.1877\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1716\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.1762\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.1490\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1855\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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      \"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 3ms/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 1ms/step - loss: 0.1678\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.1485\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.1422\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1560\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1319\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 2ms/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 3ms/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 1ms/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 3ms/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 3ms/step - loss: 0.1205\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.1049\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1111\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1079\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1021\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1048\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0971\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0985\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1026\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1111\\n\",\n      \"Epoch 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      \"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      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1105\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1005\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0846\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1125\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1129\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1219\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1161\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1137\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 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 3ms/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 1ms/step - loss: 0.1041\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/step - loss: 0.1150\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0987\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0947\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/step - loss: 0.0780\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0767\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/step - loss: 0.0799\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 3ms/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 1ms/step - loss: 0.0741\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0890\\n\",\n      \"Epoch 297/1000\\n\",\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 1ms/step - loss: 0.0733\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 3ms/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 1ms/step - loss: 0.0772\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0674\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 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 3ms/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 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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.0579\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 1ms/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 3ms/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 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 3ms/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 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 3ms/step - loss: 0.0510\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/step - loss: 0.0663\\n\",\n      \"Epoch 395/1000\\n\",\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 3ms/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 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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 2ms/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 3ms/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 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 1ms/step - loss: 0.0310\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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      \"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 3ms/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 1ms/step - loss: 0.0419\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0356\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 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 3ms/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 1ms/step - loss: 0.0414\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0288\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 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 1ms/step - loss: 0.0265\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1205\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1474\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 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      \"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 1ms/step - loss: 0.0343\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 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 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.0231\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 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 3ms/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 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 3ms/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 1ms/step - loss: 0.0225\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 2ms/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 2ms/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 1ms/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 3ms/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 1ms/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 3ms/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 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 3ms/step - loss: 0.0247\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.0323\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1098\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.1236\\n\",\n      \"Epoch 686/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1437\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0897\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0441\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.0274\\n\",\n      \"Epoch 710/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0265\\n\",\n      \"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 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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 3ms/step - loss: 0.0270\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.0194\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0213\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.0690\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.0932\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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      \"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 1ms/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 1ms/step - loss: 0.0250\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 1ms/step - loss: 0.0229\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0271\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 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 1ms/step - loss: 0.0227\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0228\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0271\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0291\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.0309\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.0272\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/step - loss: 0.0712\\n\",\n      \"Epoch 925/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.0311\\n\",\n      \"Epoch 934/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0210\\n\",\n      \"Epoch 939/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.0374\\n\",\n      \"Epoch 953/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0234\\n\",\n      \"Epoch 958/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0190\\n\",\n      \"Epoch 963/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0161\\n\",\n      \"Epoch 972/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0384\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0541\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.0380\\n\",\n      \"Epoch 983/1000\\n\",\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 3ms/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 1ms/step - loss: 0.0175\\n\",\n      \"Epoch 991/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.0199\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 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 4ms/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 1ms/step - loss: 0.2910\\n\",\n      \"Epoch 10/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.2574\\n\",\n      \"Epoch 19/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.2742\\n\",\n      \"Epoch 24/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.2579\\n\",\n      \"Epoch 33/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 2ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 2ms/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 3ms/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 1ms/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 3ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2478\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2232\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2125\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2099\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/step - loss: 0.2035\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.2184\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2133\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 2ms/step - loss: 0.2202\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.2109\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1978\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2058\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2035\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2134\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2120\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2032\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2208\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2110\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.1934\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 3ms/step - loss: 0.1963\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 [==============================] - 0s 3ms/step - loss: 0.2124\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2030\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2046\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1955\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1952\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2066\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2206\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2010\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1978\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1886\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.1953\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1950\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2228\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2118\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2069\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2012\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2153\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2126\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2101\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1979\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1987\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1946\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1889\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1984\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1975\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1919\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1904\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1942\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2016\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1996\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1913\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1909\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.1962\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2024\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1985\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2075\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1942\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1964\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1922\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2000\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.1950\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.1934\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1802\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.1756\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1861\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1793\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1913\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.1721\\n\",\n      \"Epoch 453/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1777\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1811\\n\",\n      \"Epoch 476/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.1657\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 3ms/step - loss: 0.1626\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.1742\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1743\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1815\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1871\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1805\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1800\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1729\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1697\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1675\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1669\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 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 3ms/step - loss: 0.1760\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1692\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 4ms/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      \"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 3ms/step - loss: 0.1633\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.1715\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 3ms/step - loss: 0.1754\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1664\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1660\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 1ms/step - loss: 0.1728\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1634\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1657\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1728\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1716\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1674\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1601\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1609\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1690\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 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 3ms/step - loss: 0.1785\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1685\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.1616\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.1608\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 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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 [==============================] - 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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 1ms/step - loss: 0.1671\\n\",\n      \"Epoch 714/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.1651\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1606\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1602\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1585\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1671\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1553\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1588\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1534\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1654\\n\",\n      \"Epoch 726/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1566\\n\",\n      \"Epoch 727/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1590\\n\",\n      \"Epoch 728/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1607\\n\",\n      \"Epoch 729/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1564\\n\",\n      \"Epoch 730/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1612\\n\",\n      \"Epoch 731/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1658\\n\",\n      \"Epoch 732/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1772\\n\",\n      \"Epoch 733/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1629\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1657\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1574\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1603\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1598\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1714\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1832\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1698\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1638\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1561\\n\",\n      \"Epoch 743/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1575\\n\",\n      \"Epoch 744/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1536\\n\",\n      \"Epoch 745/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1615\\n\",\n      \"Epoch 746/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1653\\n\",\n      \"Epoch 747/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1647\\n\",\n      \"Epoch 748/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1587\\n\",\n      \"Epoch 749/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1592\\n\",\n      \"Epoch 750/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1620\\n\",\n      \"Epoch 751/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1577\\n\",\n      \"Epoch 752/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1612\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 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 1ms/step - loss: 0.1578\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.1551\\n\",\n      \"Epoch 763/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1670\\n\",\n      \"Epoch 764/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1811\\n\",\n      \"Epoch 765/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1565\\n\",\n      \"Epoch 766/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1609\\n\",\n      \"Epoch 767/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1624\\n\",\n      \"Epoch 768/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1588\\n\",\n      \"Epoch 769/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1516\\n\",\n      \"Epoch 770/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1537\\n\",\n      \"Epoch 771/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1596\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1531\\n\",\n      \"Epoch 773/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1655\\n\",\n      \"Epoch 774/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1720\\n\",\n      \"Epoch 775/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1744\\n\",\n      \"Epoch 776/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1563\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1590\\n\",\n      \"Epoch 778/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1543\\n\",\n      \"Epoch 779/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1617\\n\",\n      \"Epoch 788/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1532\\n\",\n      \"Epoch 789/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1497\\n\",\n      \"Epoch 790/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1545\\n\",\n      \"Epoch 791/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1577\\n\",\n      \"Epoch 792/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1562\\n\",\n      \"Epoch 793/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1554\\n\",\n      \"Epoch 794/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1573\\n\",\n      \"Epoch 795/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1554\\n\",\n      \"Epoch 796/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1542\\n\",\n      \"Epoch 797/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1676\\n\",\n      \"Epoch 798/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1574\\n\",\n      \"Epoch 799/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1515\\n\",\n      \"Epoch 800/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1504\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1536\\n\",\n      \"Epoch 802/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1482\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1533\\n\",\n      \"Epoch 804/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1660\\n\",\n      \"Epoch 805/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1520\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1538\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1547\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1503\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1535\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1703\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1708\\n\",\n      \"Epoch 812/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1572\\n\",\n      \"Epoch 813/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1517\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1531\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1550\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.1579\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1608\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1555\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1793\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1690\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1628\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1627\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1586\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1757\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.1585\\n\",\n      \"Epoch 834/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1480\\n\",\n      \"Epoch 835/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1504\\n\",\n      \"Epoch 836/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1528\\n\",\n      \"Epoch 837/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1586\\n\",\n      \"Epoch 838/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1555\\n\",\n      \"Epoch 839/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1596\\n\",\n      \"Epoch 840/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1552\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1612\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1690\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1602\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1541\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1550\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1606\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1592\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1574\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1516\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1509\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1519\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1667\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1508\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1507\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1633\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1582\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1661\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1703\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1607\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1603\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1549\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.1584\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1518\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1543\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1512\\n\",\n      \"Epoch 866/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1637\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1567\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1579\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 [==============================] - 0s 1ms/step - loss: 0.1532\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1611\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1620\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1547\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1618\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1671\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1656\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1590\\n\",\n      \"Epoch 885/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.1662\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.1552\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 2ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.1530\\n\",\n      \"Epoch 917/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/step - loss: 0.1545\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1488\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1513\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.1436\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 3ms/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      \"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 1ms/step - loss: 0.1529\\n\",\n      \"Epoch 974/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1445\\n\",\n      \"Epoch 983/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.1414\\n\",\n      \"Epoch 988/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.1615\\n\",\n      \"Epoch 997/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3363\\n\",\n      \"Epoch 34/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3586\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.3105\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3219\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3489\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3533\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3558\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.2994\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 [==============================] - 0s 1ms/step - loss: 0.2729\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2967\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3043\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2981\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2903\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2808\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2722\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2721\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2803\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3002\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2898\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2991\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2872\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2926\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2694\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2795\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3005\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2835\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2709\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2765\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2873\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3138\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2971\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2815\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2687\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2741\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2962\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2732\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2687\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2779\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2684\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2700\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2771\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2724\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2769\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2847\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2696\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2718\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2793\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2713\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2727\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2656\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2646\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2694\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2741\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2780\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2863\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2836\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2751\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2735\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2831\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2756\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2754\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2655\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2590\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2792\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2796\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2747\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2658\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2601\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2806\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2803\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2601\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2837\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2661\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2565\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2792\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2663\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2869\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2754\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2606\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2567\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2627\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2783\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2639\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2750\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2751\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2665\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2544\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2694\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2743\\n\",\n      \"Epoch 199/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2783\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2641\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2676\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2561\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2634\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2984\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2652\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2517\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2660\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2698\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2635\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2600\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2638\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2641\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2654\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2647\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2613\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2652\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2633\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2727\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2648\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2533\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2562\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2620\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2624\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2690\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2606\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2601\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2579\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2482\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2564\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2612\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2889\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2680\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2667\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2484\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2593\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2539\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2570\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2918\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2661\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2573\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2561\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2707\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2673\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2800\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2518\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2584\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2528\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2575\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2596\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2584\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2605\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2468\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2540\\n\",\n      \"Epoch 261/1000\\n\",\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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.2592\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.2490\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.2588\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2675\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2444\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2785\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2503\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2636\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2643\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2736\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2964\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.2616\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.2740\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.2625\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2695\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2709\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2469\\n\",\n      \"Epoch 327/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2454\\n\",\n      \"Epoch 328/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2682\\n\",\n      \"Epoch 329/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2556\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2563\\n\",\n      \"Epoch 331/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2618\\n\",\n      \"Epoch 332/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2738\\n\",\n      \"Epoch 333/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2578\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 [==============================] - 0s 3ms/step - loss: 0.2609\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2503\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2435\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2478\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2632\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2413\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2544\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2412\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2631\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2413\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2606\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2671\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2560\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2726\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2537\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2434\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2699\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2456\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/step - loss: 0.2514\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.2489\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.2383\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.2437\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.2661\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.2450\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2569\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2570\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2564\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2358\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2354\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2492\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2510\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2410\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2705\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2500\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 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 3ms/step - loss: 0.2525\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/step - loss: 0.2672\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2353\\n\",\n      \"Epoch 423/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2391\\n\",\n      \"Epoch 424/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2473\\n\",\n      \"Epoch 425/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2449\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2405\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2400\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2449\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2364\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2378\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2360\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2445\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2462\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2504\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.2455\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2678\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2632\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2504\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2432\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.2452\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2328\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.2616\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.2420\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2382\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.2448\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2504\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 1ms/step - loss: 0.2464\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/step - loss: 0.2627\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 4ms/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 3ms/step - loss: 0.2402\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.2434\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 2ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2328\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2532\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2409\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2248\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2315\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 3ms/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 [==============================] - 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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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2334\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.2460\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2305\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2367\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2255\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2348\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2537\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2377\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2329\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2212\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2405\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2316\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2244\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2280\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2265\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2217\\n\",\n      \"Epoch 660/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2351\\n\",\n      \"Epoch 661/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2234\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2297\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2287\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2537\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2334\\n\",\n      \"Epoch 666/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2385\\n\",\n      \"Epoch 667/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2348\\n\",\n      \"Epoch 668/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2443\\n\",\n      \"Epoch 669/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2447\\n\",\n      \"Epoch 670/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2217\\n\",\n      \"Epoch 671/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2320\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2542\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2308\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2357\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2376\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2288\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2243\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2216\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2363\\n\",\n      \"Epoch 680/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2322\\n\",\n      \"Epoch 681/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2361\\n\",\n      \"Epoch 682/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2318\\n\",\n      \"Epoch 683/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2287\\n\",\n      \"Epoch 684/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2226\\n\",\n      \"Epoch 685/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2378\\n\",\n      \"Epoch 686/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2428\\n\",\n      \"Epoch 687/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2273\\n\",\n      \"Epoch 688/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2278\\n\",\n      \"Epoch 689/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2287\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2282\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2288\\n\",\n      \"Epoch 692/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2276\\n\",\n      \"Epoch 693/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2293\\n\",\n      \"Epoch 694/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2335\\n\",\n      \"Epoch 695/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2300\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2249\\n\",\n      \"Epoch 697/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2279\\n\",\n      \"Epoch 698/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2341\\n\",\n      \"Epoch 699/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2225\\n\",\n      \"Epoch 700/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2300\\n\",\n      \"Epoch 701/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2200\\n\",\n      \"Epoch 702/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2195\\n\",\n      \"Epoch 703/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2355\\n\",\n      \"Epoch 704/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2407\\n\",\n      \"Epoch 705/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2296\\n\",\n      \"Epoch 706/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2239\\n\",\n      \"Epoch 707/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2318\\n\",\n      \"Epoch 708/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2419\\n\",\n      \"Epoch 709/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2438\\n\",\n      \"Epoch 710/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2251\\n\",\n      \"Epoch 711/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2309\\n\",\n      \"Epoch 712/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2541\\n\",\n      \"Epoch 713/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2406\\n\",\n      \"Epoch 714/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2223\\n\",\n      \"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2201\\n\",\n      \"Epoch 716/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2311\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2446\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2268\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2434\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2284\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2355\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2216\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2356\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2228\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2337\\n\",\n      \"Epoch 726/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2289\\n\",\n      \"Epoch 727/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2364\\n\",\n      \"Epoch 728/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2410\\n\",\n      \"Epoch 729/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2280\\n\",\n      \"Epoch 730/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2242\\n\",\n      \"Epoch 731/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2416\\n\",\n      \"Epoch 732/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2303\\n\",\n      \"Epoch 733/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2363\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2381\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2343\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2324\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2306\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2212\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2472\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2357\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2545\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2449\\n\",\n      \"Epoch 743/1000\\n\",\n      \"13/13 [==============================] - 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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 [==============================] - 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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 1ms/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 3ms/step - loss: 0.2200\\n\",\n      \"Epoch 914/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.2331\\n\",\n      \"Epoch 923/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.2304\\n\",\n      \"Epoch 932/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2208\\n\",\n      \"Epoch 947/1000\\n\",\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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2220\\n\",\n      \"Epoch 966/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.2120\\n\",\n      \"Epoch 975/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.2188\\n\",\n      \"Epoch 980/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.2284\\n\",\n      \"Epoch 984/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 2ms/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 4ms/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 3ms/step - loss: 0.5749\\n\",\n      \"Epoch 18/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.5401\\n\",\n      \"Epoch 23/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.6280\\n\",\n      \"Epoch 27/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.4785\\n\",\n      \"Epoch 38/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4753\\n\",\n      \"Epoch 39/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4653\\n\",\n      \"Epoch 40/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4723\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4891\\n\",\n      \"Epoch 42/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4702\\n\",\n      \"Epoch 43/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4732\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.4776\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4861\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.4627\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4228\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4406\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4350\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4590\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4822\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4671\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4562\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4280\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4492\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4325\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4285\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4278\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4370\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4308\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4205\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4119\\n\",\n      \"Epoch 72/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4073\\n\",\n      \"Epoch 73/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4198\\n\",\n      \"Epoch 74/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4399\\n\",\n      \"Epoch 75/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4173\\n\",\n      \"Epoch 76/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4132\\n\",\n      \"Epoch 77/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4055\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4231\\n\",\n      \"Epoch 79/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4440\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4192\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4298\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4079\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3980\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3879\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4066\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4000\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4279\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4109\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3996\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3867\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4078\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3978\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3892\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3779\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3963\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3768\\n\",\n      \"Epoch 97/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3881\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3877\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4175\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4077\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4039\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3933\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4009\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3857\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4077\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3677\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.4007\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4203\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3973\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3845\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3824\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3772\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3690\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3707\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3662\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3780\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3907\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3794\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3938\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.4135\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4511\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4021\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3951\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3796\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3678\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3692\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 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 3ms/step - loss: 0.3615\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 1ms/step - loss: 0.3855\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3661\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3872\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3924\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3909\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3643\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3576\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3714\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3759\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3635\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3615\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3911\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3709\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3614\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3586\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3585\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3561\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3813\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3619\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 4ms/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 1ms/step - loss: 0.3694\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3572\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3820\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3957\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3766\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3955\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3758\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3532\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3594\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3514\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3787\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3697\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3533\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3442\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3461\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3525\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3613\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3605\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3438\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 3ms/step - loss: 0.3600\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3791\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3554\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3624\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3596\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3528\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3519\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3416\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3580\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3424\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3612\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3465\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3457\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3360\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3572\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3500\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3893\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3804\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3567\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3602\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3329\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3503\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3516\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3517\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3583\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3363\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3360\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3358\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3414\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3392\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3709\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3593\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3367\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3393\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3365\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 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 1ms/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 3ms/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 [==============================] - 0s 3ms/step - loss: 0.3543\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3425\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3580\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3592\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3357\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3556\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3444\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3462\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3388\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3560\\n\",\n      \"Epoch 262/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4042\\n\",\n      \"Epoch 263/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3634\\n\",\n      \"Epoch 264/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3352\\n\",\n      \"Epoch 265/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3447\\n\",\n      \"Epoch 266/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3742\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3910\\n\",\n      \"Epoch 268/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3617\\n\",\n      \"Epoch 269/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3334\\n\",\n      \"Epoch 270/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3425\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3341\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3346\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3531\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3263\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3432\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3320\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3291\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3319\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3497\\n\",\n      \"Epoch 280/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3418\\n\",\n      \"Epoch 281/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3284\\n\",\n      \"Epoch 282/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3330\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3388\\n\",\n      \"Epoch 284/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3451\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3696\\n\",\n      \"Epoch 286/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3386\\n\",\n      \"Epoch 287/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3480\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3440\\n\",\n      \"Epoch 289/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3327\\n\",\n      \"Epoch 290/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3536\\n\",\n      \"Epoch 291/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3434\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3260\\n\",\n      \"Epoch 293/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3390\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3573\\n\",\n      \"Epoch 295/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3478\\n\",\n      \"Epoch 296/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3311\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3295\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3466\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3404\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3817\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3411\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3372\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3265\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3286\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3379\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3374\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3393\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3401\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3263\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3299\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3181\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3202\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3444\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3382\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3359\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3503\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3354\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3276\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3359\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3239\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3312\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3254\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3388\\n\",\n      \"Epoch 324/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3462\\n\",\n      \"Epoch 325/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3258\\n\",\n      \"Epoch 330/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 3ms/step - loss: 0.3521\\n\",\n      \"Epoch 334/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3389\\n\",\n      \"Epoch 335/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3308\\n\",\n      \"Epoch 336/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3369\\n\",\n      \"Epoch 337/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3485\\n\",\n      \"Epoch 338/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3181\\n\",\n      \"Epoch 339/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3173\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3333\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3273\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3430\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3294\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3224\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3210\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3267\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3359\\n\",\n      \"Epoch 348/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3467\\n\",\n      \"Epoch 349/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3143\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3377\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.3193\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.3349\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3275\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/step - loss: 0.3110\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.3148\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.3062\\n\",\n      \"Epoch 432/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3096\\n\",\n      \"Epoch 433/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3419\\n\",\n      \"Epoch 434/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3591\\n\",\n      \"Epoch 435/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3488\\n\",\n      \"Epoch 436/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3521\\n\",\n      \"Epoch 437/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3330\\n\",\n      \"Epoch 438/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3322\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 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 1ms/step - loss: 0.3159\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.3020\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3299\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3170\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3237\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3257\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3189\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3158\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3154\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3216\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3479\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3514\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3584\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3095\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3101\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3171\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3234\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3147\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3233\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 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 3ms/step - loss: 0.3405\\n\",\n      \"Epoch 478/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.3169\\n\",\n      \"Epoch 485/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3098\\n\",\n      \"Epoch 486/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3041\\n\",\n      \"Epoch 487/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3023\\n\",\n      \"Epoch 488/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3132\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3005\\n\",\n      \"Epoch 490/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3167\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3306\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3144\\n\",\n      \"Epoch 493/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3028\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3128\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3036\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3265\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3311\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2980\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3035\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3243\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3230\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3174\\n\",\n      \"Epoch 503/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3331\\n\",\n      \"Epoch 504/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3173\\n\",\n      \"Epoch 505/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3048\\n\",\n      \"Epoch 506/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3183\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3079\\n\",\n      \"Epoch 508/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2933\\n\",\n      \"Epoch 509/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3031\\n\",\n      \"Epoch 510/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3006\\n\",\n      \"Epoch 511/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.2988\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3011\\n\",\n      \"Epoch 522/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3191\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3209\\n\",\n      \"Epoch 524/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3043\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3172\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3132\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3062\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3107\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3495\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3183\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3230\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3114\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2985\\n\",\n      \"Epoch 534/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3265\\n\",\n      \"Epoch 535/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3200\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3079\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3586\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3209\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2946\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3151\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3265\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3297\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3176\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3109\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3033\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3208\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3347\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3134\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2954\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3015\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2957\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3070\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2918\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3121\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3453\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3336\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3204\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3091\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3226\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3070\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2953\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3058\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3203\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3274\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3077\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3020\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3044\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2970\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3055\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2993\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3173\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2986\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2993\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3036\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.2922\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2914\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3151\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3550\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2946\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3024\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3277\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3074\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3123\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3039\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3194\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3047\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3001\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2973\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2899\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2931\\n\",\n      \"Epoch 591/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3086\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3075\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2965\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2962\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4034\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3768\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3273\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3137\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3107\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3082\\n\",\n      \"Epoch 601/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2931\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2938\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3229\\n\",\n      \"Epoch 604/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3136\\n\",\n      \"Epoch 605/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3287\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3176\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2975\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3015\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3131\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3224\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3182\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3333\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3201\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3101\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3082\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2993\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2890\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3229\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3134\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3101\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3149\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3099\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2953\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2911\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2859\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3012\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3119\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3220\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3136\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3089\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3103\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3292\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3372\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3033\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3340\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3206\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3102\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3004\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2979\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2924\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2917\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3073\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3169\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2949\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3102\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3222\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3319\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3124\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3107\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3047\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3042\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2905\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3161\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3146\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2960\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2891\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3069\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3077\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3205\\n\",\n      \"Epoch 660/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3287\\n\",\n      \"Epoch 661/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3002\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3192\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3300\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3133\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3039\\n\",\n      \"Epoch 666/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2982\\n\",\n      \"Epoch 667/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2915\\n\",\n      \"Epoch 668/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2929\\n\",\n      \"Epoch 669/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3197\\n\",\n      \"Epoch 670/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3089\\n\",\n      \"Epoch 671/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3145\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3342\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3395\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3112\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3089\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2862\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2934\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2899\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3091\\n\",\n      \"Epoch 752/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3558\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3366\\n\",\n      \"Epoch 754/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2889\\n\",\n      \"Epoch 755/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3006\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3145\\n\",\n      \"Epoch 757/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3105\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3002\\n\",\n      \"Epoch 759/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2846\\n\",\n      \"Epoch 760/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3078\\n\",\n      \"Epoch 761/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3121\\n\",\n      \"Epoch 762/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2981\\n\",\n      \"Epoch 763/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2834\\n\",\n      \"Epoch 764/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3100\\n\",\n      \"Epoch 765/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2970\\n\",\n      \"Epoch 766/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3009\\n\",\n      \"Epoch 767/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2938\\n\",\n      \"Epoch 768/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3139\\n\",\n      \"Epoch 769/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2899\\n\",\n      \"Epoch 770/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2825\\n\",\n      \"Epoch 771/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2937\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.2948\\n\",\n      \"Epoch 773/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3288\\n\",\n      \"Epoch 774/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3042\\n\",\n      \"Epoch 775/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3027\\n\",\n      \"Epoch 776/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3461\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3248\\n\",\n      \"Epoch 778/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2962\\n\",\n      \"Epoch 779/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3004\\n\",\n      \"Epoch 780/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3098\\n\",\n      \"Epoch 781/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2900\\n\",\n      \"Epoch 782/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2843\\n\",\n      \"Epoch 783/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.2883\\n\",\n      \"Epoch 784/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2918\\n\",\n      \"Epoch 785/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2853\\n\",\n      \"Epoch 786/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2971\\n\",\n      \"Epoch 787/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2940\\n\",\n      \"Epoch 788/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3046\\n\",\n      \"Epoch 789/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3025\\n\",\n      \"Epoch 790/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2957\\n\",\n      \"Epoch 791/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3428\\n\",\n      \"Epoch 792/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3225\\n\",\n      \"Epoch 793/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3232\\n\",\n      \"Epoch 794/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3264\\n\",\n      \"Epoch 795/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2912\\n\",\n      \"Epoch 796/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3264\\n\",\n      \"Epoch 797/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2908\\n\",\n      \"Epoch 798/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3391\\n\",\n      \"Epoch 799/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3217\\n\",\n      \"Epoch 800/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.2843\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2797\\n\",\n      \"Epoch 802/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2839\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2877\\n\",\n      \"Epoch 804/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.2821\\n\",\n      \"Epoch 805/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2999\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3031\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2934\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3543\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3538\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3074\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3039\\n\",\n      \"Epoch 812/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2831\\n\",\n      \"Epoch 813/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2890\\n\",\n      \"Epoch 814/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2991\\n\",\n      \"Epoch 815/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2894\\n\",\n      \"Epoch 816/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2962\\n\",\n      \"Epoch 817/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3112\\n\",\n      \"Epoch 818/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3029\\n\",\n      \"Epoch 819/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3103\\n\",\n      \"Epoch 820/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3399\\n\",\n      \"Epoch 821/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3342\\n\",\n      \"Epoch 822/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3019\\n\",\n      \"Epoch 823/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2950\\n\",\n      \"Epoch 824/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2926\\n\",\n      \"Epoch 825/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3002\\n\",\n      \"Epoch 826/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2912\\n\",\n      \"Epoch 827/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2837\\n\",\n      \"Epoch 828/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3008\\n\",\n      \"Epoch 829/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3228\\n\",\n      \"Epoch 830/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3516\\n\",\n      \"Epoch 831/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 2ms/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 1ms/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 [==============================] - 0s 3ms/step - loss: 0.3144\\n\",\n      \"Epoch 841/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2857\\n\",\n      \"Epoch 842/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3091\\n\",\n      \"Epoch 843/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2989\\n\",\n      \"Epoch 844/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2895\\n\",\n      \"Epoch 845/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2857\\n\",\n      \"Epoch 846/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2840\\n\",\n      \"Epoch 847/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3037\\n\",\n      \"Epoch 848/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3005\\n\",\n      \"Epoch 849/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2726\\n\",\n      \"Epoch 850/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2887\\n\",\n      \"Epoch 851/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2878\\n\",\n      \"Epoch 852/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3102\\n\",\n      \"Epoch 853/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3152\\n\",\n      \"Epoch 854/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2965\\n\",\n      \"Epoch 855/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2880\\n\",\n      \"Epoch 856/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2866\\n\",\n      \"Epoch 857/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3041\\n\",\n      \"Epoch 858/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3138\\n\",\n      \"Epoch 859/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3153\\n\",\n      \"Epoch 860/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2968\\n\",\n      \"Epoch 861/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3007\\n\",\n      \"Epoch 862/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.2812\\n\",\n      \"Epoch 863/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2802\\n\",\n      \"Epoch 864/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3240\\n\",\n      \"Epoch 865/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3275\\n\",\n      \"Epoch 866/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3517\\n\",\n      \"Epoch 867/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2986\\n\",\n      \"Epoch 868/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3207\\n\",\n      \"Epoch 869/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3152\\n\",\n      \"Epoch 870/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3251\\n\",\n      \"Epoch 871/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2918\\n\",\n      \"Epoch 872/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2823\\n\",\n      \"Epoch 873/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2778\\n\",\n      \"Epoch 874/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2865\\n\",\n      \"Epoch 875/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2906\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2993\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3067\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3285\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3180\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3107\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3246\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3173\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3372\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2983\\n\",\n      \"Epoch 885/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3149\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2987\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2804\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2996\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3048\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3328\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2987\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.2931\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3163\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3025\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 4ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 4ms/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 1ms/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 3ms/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 1ms/step - loss: 0.3058\\n\",\n      \"Epoch 928/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.2935\\n\",\n      \"Epoch 929/1000\\n\",\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 1ms/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 3ms/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 1ms/step - loss: 0.2862\\n\",\n      \"Epoch 937/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.3066\\n\",\n      \"Epoch 946/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.3142\\n\",\n      \"Epoch 955/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/step - loss: 0.3016\\n\",\n      \"Epoch 964/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.2940\\n\",\n      \"Epoch 978/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 4ms/step - loss: 0.2934\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3374\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 4ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/step - loss: 0.6332\\n\",\n      \"Epoch 28/1000\\n\",\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 1ms/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 1ms/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 3ms/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 [==============================] - 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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 3ms/step - loss: 0.5427\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4969\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4850\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4701\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4800\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4764\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5024\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4932\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4790\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4675\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4867\\n\",\n      \"Epoch 92/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4786\\n\",\n      \"Epoch 93/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4648\\n\",\n      \"Epoch 94/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4594\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4748\\n\",\n      \"Epoch 96/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4565\\n\",\n      \"Epoch 97/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4674\\n\",\n      \"Epoch 98/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4644\\n\",\n      \"Epoch 99/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4918\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4890\\n\",\n      \"Epoch 101/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4837\\n\",\n      \"Epoch 102/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4679\\n\",\n      \"Epoch 103/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4682\\n\",\n      \"Epoch 104/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4611\\n\",\n      \"Epoch 105/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5015\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4530\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4619\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4723\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4631\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4550\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4570\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4484\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4437\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4412\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4397\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4498\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4729\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4633\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4572\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4637\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4878\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4727\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4443\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4451\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4334\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4442\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 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 3ms/step - loss: 0.4411\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/step - loss: 0.4458\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4567\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4315\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4510\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4523\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4632\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4445\\n\",\n      \"Epoch 142/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4293\\n\",\n      \"Epoch 143/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4443\\n\",\n      \"Epoch 144/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4528\\n\",\n      \"Epoch 145/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4282\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4272\\n\",\n      \"Epoch 147/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4712\\n\",\n      \"Epoch 148/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4470\\n\",\n      \"Epoch 149/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4293\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4223\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4212\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4222\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4386\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4262\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4304\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4284\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4283\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4284\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4102\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4304\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4475\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4396\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4155\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4343\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4685\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4743\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4786\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4517\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4378\\n\",\n      \"Epoch 170/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4403\\n\",\n      \"Epoch 171/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4281\\n\",\n      \"Epoch 172/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4228\\n\",\n      \"Epoch 173/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4366\\n\",\n      \"Epoch 174/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4419\\n\",\n      \"Epoch 175/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4219\\n\",\n      \"Epoch 176/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4462\\n\",\n      \"Epoch 177/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4348\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4284\\n\",\n      \"Epoch 179/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4334\\n\",\n      \"Epoch 180/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4211\\n\",\n      \"Epoch 181/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4400\\n\",\n      \"Epoch 182/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4298\\n\",\n      \"Epoch 183/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4150\\n\",\n      \"Epoch 184/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4077\\n\",\n      \"Epoch 185/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4090\\n\",\n      \"Epoch 186/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4143\\n\",\n      \"Epoch 187/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4180\\n\",\n      \"Epoch 188/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4205\\n\",\n      \"Epoch 189/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4023\\n\",\n      \"Epoch 190/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4018\\n\",\n      \"Epoch 191/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4158\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4258\\n\",\n      \"Epoch 193/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4130\\n\",\n      \"Epoch 194/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4186\\n\",\n      \"Epoch 195/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4047\\n\",\n      \"Epoch 196/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3990\\n\",\n      \"Epoch 197/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4228\\n\",\n      \"Epoch 198/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3965\\n\",\n      \"Epoch 199/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4067\\n\",\n      \"Epoch 200/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4085\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4336\\n\",\n      \"Epoch 202/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3972\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4041\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4079\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4638\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4349\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4181\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4282\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4402\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4173\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4352\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4110\\n\",\n      \"Epoch 213/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4028\\n\",\n      \"Epoch 214/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4062\\n\",\n      \"Epoch 215/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3995\\n\",\n      \"Epoch 216/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4097\\n\",\n      \"Epoch 217/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3970\\n\",\n      \"Epoch 218/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4177\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4025\\n\",\n      \"Epoch 220/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4018\\n\",\n      \"Epoch 221/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3892\\n\",\n      \"Epoch 222/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4103\\n\",\n      \"Epoch 223/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4064\\n\",\n      \"Epoch 224/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4411\\n\",\n      \"Epoch 225/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4357\\n\",\n      \"Epoch 226/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4371\\n\",\n      \"Epoch 227/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4695\\n\",\n      \"Epoch 228/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4047\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4031\\n\",\n      \"Epoch 230/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4150\\n\",\n      \"Epoch 231/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4023\\n\",\n      \"Epoch 232/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4166\\n\",\n      \"Epoch 233/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3921\\n\",\n      \"Epoch 234/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3898\\n\",\n      \"Epoch 235/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3890\\n\",\n      \"Epoch 236/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3963\\n\",\n      \"Epoch 237/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3975\\n\",\n      \"Epoch 238/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4240\\n\",\n      \"Epoch 239/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4148\\n\",\n      \"Epoch 240/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3973\\n\",\n      \"Epoch 241/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3937\\n\",\n      \"Epoch 242/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3993\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3967\\n\",\n      \"Epoch 244/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4038\\n\",\n      \"Epoch 245/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4092\\n\",\n      \"Epoch 246/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4170\\n\",\n      \"Epoch 247/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3849\\n\",\n      \"Epoch 248/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3901\\n\",\n      \"Epoch 249/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4000\\n\",\n      \"Epoch 250/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3942\\n\",\n      \"Epoch 251/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4171\\n\",\n      \"Epoch 252/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4092\\n\",\n      \"Epoch 253/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3991\\n\",\n      \"Epoch 254/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4139\\n\",\n      \"Epoch 255/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4082\\n\",\n      \"Epoch 256/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3862\\n\",\n      \"Epoch 257/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4039\\n\",\n      \"Epoch 258/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3929\\n\",\n      \"Epoch 259/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4006\\n\",\n      \"Epoch 260/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3875\\n\",\n      \"Epoch 261/1000\\n\",\n      \"13/13 [==============================] - 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 1ms/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 [==============================] - 0s 1ms/step - loss: 0.3776\\n\",\n      \"Epoch 271/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3922\\n\",\n      \"Epoch 272/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3930\\n\",\n      \"Epoch 273/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4171\\n\",\n      \"Epoch 274/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3856\\n\",\n      \"Epoch 275/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3944\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3852\\n\",\n      \"Epoch 277/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3848\\n\",\n      \"Epoch 278/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3844\\n\",\n      \"Epoch 279/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3799\\n\",\n      \"Epoch 283/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3908\\n\",\n      \"Epoch 288/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3891\\n\",\n      \"Epoch 292/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3820\\n\",\n      \"Epoch 297/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3886\\n\",\n      \"Epoch 298/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3947\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3908\\n\",\n      \"Epoch 300/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4260\\n\",\n      \"Epoch 301/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3924\\n\",\n      \"Epoch 302/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3945\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3913\\n\",\n      \"Epoch 304/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3748\\n\",\n      \"Epoch 305/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3760\\n\",\n      \"Epoch 306/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3864\\n\",\n      \"Epoch 307/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3908\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3862\\n\",\n      \"Epoch 309/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3814\\n\",\n      \"Epoch 310/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3687\\n\",\n      \"Epoch 311/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3687\\n\",\n      \"Epoch 312/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3703\\n\",\n      \"Epoch 313/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3827\\n\",\n      \"Epoch 314/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3809\\n\",\n      \"Epoch 315/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3814\\n\",\n      \"Epoch 316/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3744\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3760\\n\",\n      \"Epoch 318/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3701\\n\",\n      \"Epoch 319/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3733\\n\",\n      \"Epoch 320/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3833\\n\",\n      \"Epoch 321/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3963\\n\",\n      \"Epoch 322/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3963\\n\",\n      \"Epoch 323/1000\\n\",\n      \"13/13 [==============================] - 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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.3673\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3872\\n\",\n      \"Epoch 341/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3766\\n\",\n      \"Epoch 342/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3723\\n\",\n      \"Epoch 343/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3673\\n\",\n      \"Epoch 344/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3606\\n\",\n      \"Epoch 345/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3598\\n\",\n      \"Epoch 346/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3622\\n\",\n      \"Epoch 347/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3856\\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 3ms/step - loss: 0.3769\\n\",\n      \"Epoch 350/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4017\\n\",\n      \"Epoch 351/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3680\\n\",\n      \"Epoch 352/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3890\\n\",\n      \"Epoch 353/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3754\\n\",\n      \"Epoch 354/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3651\\n\",\n      \"Epoch 355/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3694\\n\",\n      \"Epoch 356/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3624\\n\",\n      \"Epoch 357/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3780\\n\",\n      \"Epoch 358/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3945\\n\",\n      \"Epoch 359/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3878\\n\",\n      \"Epoch 360/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3899\\n\",\n      \"Epoch 361/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4105\\n\",\n      \"Epoch 362/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4582\\n\",\n      \"Epoch 363/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4070\\n\",\n      \"Epoch 364/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3699\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3682\\n\",\n      \"Epoch 366/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3625\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3679\\n\",\n      \"Epoch 368/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3897\\n\",\n      \"Epoch 369/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3988\\n\",\n      \"Epoch 370/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3720\\n\",\n      \"Epoch 371/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3604\\n\",\n      \"Epoch 372/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3585\\n\",\n      \"Epoch 373/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3769\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3546\\n\",\n      \"Epoch 375/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3783\\n\",\n      \"Epoch 376/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3752\\n\",\n      \"Epoch 377/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3589\\n\",\n      \"Epoch 378/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3739\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3793\\n\",\n      \"Epoch 380/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3803\\n\",\n      \"Epoch 381/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4060\\n\",\n      \"Epoch 382/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3681\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3737\\n\",\n      \"Epoch 384/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3598\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3663\\n\",\n      \"Epoch 386/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3548\\n\",\n      \"Epoch 387/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3733\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3716\\n\",\n      \"Epoch 389/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3627\\n\",\n      \"Epoch 390/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3487\\n\",\n      \"Epoch 391/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3634\\n\",\n      \"Epoch 392/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3779\\n\",\n      \"Epoch 393/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3564\\n\",\n      \"Epoch 394/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3581\\n\",\n      \"Epoch 395/1000\\n\",\n      \"13/13 [==============================] - 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 3ms/step - loss: 0.3748\\n\",\n      \"Epoch 399/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 1ms/step - loss: 0.3593\\n\",\n      \"Epoch 405/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3516\\n\",\n      \"Epoch 406/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3570\\n\",\n      \"Epoch 407/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3647\\n\",\n      \"Epoch 408/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3587\\n\",\n      \"Epoch 409/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3787\\n\",\n      \"Epoch 410/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3846\\n\",\n      \"Epoch 411/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3894\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3757\\n\",\n      \"Epoch 413/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3575\\n\",\n      \"Epoch 414/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3544\\n\",\n      \"Epoch 415/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3712\\n\",\n      \"Epoch 416/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3780\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3834\\n\",\n      \"Epoch 418/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3596\\n\",\n      \"Epoch 419/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3664\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3501\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3819\\n\",\n      \"Epoch 422/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3949\\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: 0.3629\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3592\\n\",\n      \"Epoch 427/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3591\\n\",\n      \"Epoch 428/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3646\\n\",\n      \"Epoch 429/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3617\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3552\\n\",\n      \"Epoch 431/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3792\\n\",\n      \"Epoch 439/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3463\\n\",\n      \"Epoch 440/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3438\\n\",\n      \"Epoch 441/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3577\\n\",\n      \"Epoch 442/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3750\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3666\\n\",\n      \"Epoch 444/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3588\\n\",\n      \"Epoch 445/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3658\\n\",\n      \"Epoch 446/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3522\\n\",\n      \"Epoch 447/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3682\\n\",\n      \"Epoch 448/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3577\\n\",\n      \"Epoch 449/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/step - loss: 0.3667\\n\",\n      \"Epoch 455/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.3489\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3788\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3629\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3605\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3618\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3469\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3526\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3660\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3435\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3747\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/step - loss: 0.4114\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3492\\n\",\n      \"Epoch 482/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.3526\\n\",\n      \"Epoch 491/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.3464\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3493\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3473\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3728\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3925\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3429\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3410\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3588\\n\",\n      \"Epoch 501/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3531\\n\",\n      \"Epoch 502/1000\\n\",\n      \"13/13 [==============================] - 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 2ms/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 1ms/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 3ms/step - loss: 0.3438\\n\",\n      \"Epoch 514/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3602\\n\",\n      \"Epoch 518/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3600\\n\",\n      \"Epoch 519/1000\\n\",\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 3ms/step - loss: 0.3611\\n\",\n      \"Epoch 523/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3710\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3556\\n\",\n      \"Epoch 536/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.3378\\n\",\n      \"Epoch 537/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3968\\n\",\n      \"Epoch 538/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3557\\n\",\n      \"Epoch 539/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3544\\n\",\n      \"Epoch 540/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3885\\n\",\n      \"Epoch 541/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3800\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3745\\n\",\n      \"Epoch 543/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3643\\n\",\n      \"Epoch 544/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3504\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3475\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3593\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3749\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3630\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3370\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3386\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3394\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3395\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3357\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3471\\n\",\n      \"Epoch 555/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3829\\n\",\n      \"Epoch 556/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3821\\n\",\n      \"Epoch 557/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3660\\n\",\n      \"Epoch 558/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3455\\n\",\n      \"Epoch 559/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3674\\n\",\n      \"Epoch 560/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3462\\n\",\n      \"Epoch 561/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3361\\n\",\n      \"Epoch 562/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3482\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3494\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3496\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3387\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3521\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3385\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3406\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3433\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3335\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3617\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3443\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3416\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3526\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3370\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3322\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3480\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4143\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3493\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3312\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3583\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3470\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/step - loss: 0.3323\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.3425\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3412\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3367\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3413\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4272\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4031\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3599\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3473\\n\",\n      \"Epoch 599/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3397\\n\",\n      \"Epoch 600/1000\\n\",\n      \"13/13 [==============================] - 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 1ms/step - loss: 0.3372\\n\",\n      \"Epoch 603/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 [==============================] - 0s 1ms/step - loss: 0.3401\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3579\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3480\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3584\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3431\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3386\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3462\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3406\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 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 1ms/step - loss: 0.3792\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 0s 1ms/step - loss: 0.3405\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3470\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3458\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3504\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3577\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3509\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3407\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3346\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3255\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3427\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3487\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3370\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3290\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3396\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3397\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3437\\n\",\n      \"Epoch 660/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3602\\n\",\n      \"Epoch 661/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3335\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3310\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3546\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3446\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3383\\n\",\n      \"Epoch 666/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3420\\n\",\n      \"Epoch 667/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3244\\n\",\n      \"Epoch 668/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3214\\n\",\n      \"Epoch 669/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3419\\n\",\n      \"Epoch 670/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3272\\n\",\n      \"Epoch 671/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3389\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3752\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3986\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3959\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3344\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3342\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3275\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3239\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3372\\n\",\n      \"Epoch 680/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3329\\n\",\n      \"Epoch 681/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3464\\n\",\n      \"Epoch 682/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3324\\n\",\n      \"Epoch 683/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3308\\n\",\n      \"Epoch 684/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3345\\n\",\n      \"Epoch 685/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3396\\n\",\n      \"Epoch 686/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3393\\n\",\n      \"Epoch 687/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3373\\n\",\n      \"Epoch 688/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3254\\n\",\n      \"Epoch 689/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3936\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3678\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3424\\n\",\n      \"Epoch 692/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3462\\n\",\n      \"Epoch 693/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3388\\n\",\n      \"Epoch 694/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3413\\n\",\n      \"Epoch 695/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3331\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3298\\n\",\n      \"Epoch 697/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3293\\n\",\n      \"Epoch 698/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3415\\n\",\n      \"Epoch 699/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3392\\n\",\n      \"Epoch 700/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3387\\n\",\n      \"Epoch 701/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3316\\n\",\n      \"Epoch 702/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3209\\n\",\n      \"Epoch 703/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3198\\n\",\n      \"Epoch 704/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3448\\n\",\n      \"Epoch 705/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3384\\n\",\n      \"Epoch 706/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3431\\n\",\n      \"Epoch 707/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3416\\n\",\n      \"Epoch 708/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3378\\n\",\n      \"Epoch 709/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3513\\n\",\n      \"Epoch 710/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3481\\n\",\n      \"Epoch 711/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3312\\n\",\n      \"Epoch 712/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3759\\n\",\n      \"Epoch 713/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3535\\n\",\n      \"Epoch 714/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3329\\n\",\n      \"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3257\\n\",\n      \"Epoch 716/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3310\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3459\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3696\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4027\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3473\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3392\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3264\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3577\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3511\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3742\\n\",\n      \"Epoch 726/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3558\\n\",\n      \"Epoch 727/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3311\\n\",\n      \"Epoch 728/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3416\\n\",\n      \"Epoch 729/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3327\\n\",\n      \"Epoch 730/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3392\\n\",\n      \"Epoch 731/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3409\\n\",\n      \"Epoch 732/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3441\\n\",\n      \"Epoch 733/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3286\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3408\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3281\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3344\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3351\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3552\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3518\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3398\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3642\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3936\\n\",\n      \"Epoch 743/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3476\\n\",\n      \"Epoch 744/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3306\\n\",\n      \"Epoch 745/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3215\\n\",\n      \"Epoch 746/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3225\\n\",\n      \"Epoch 747/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3181\\n\",\n      \"Epoch 748/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3368\\n\",\n      \"Epoch 749/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3707\\n\",\n      \"Epoch 750/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3405\\n\",\n      \"Epoch 751/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3510\\n\",\n      \"Epoch 752/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4010\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3922\\n\",\n      \"Epoch 754/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3427\\n\",\n      \"Epoch 755/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3329\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3407\\n\",\n      \"Epoch 757/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3505\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3532\\n\",\n      \"Epoch 759/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/step - loss: 0.3238\\n\",\n      \"Epoch 760/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3408\\n\",\n      \"Epoch 761/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3569\\n\",\n      \"Epoch 762/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3362\\n\",\n      \"Epoch 763/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3258\\n\",\n      \"Epoch 764/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3463\\n\",\n      \"Epoch 765/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3366\\n\",\n      \"Epoch 766/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3368\\n\",\n      \"Epoch 767/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3302\\n\",\n      \"Epoch 768/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3504\\n\",\n      \"Epoch 769/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3349\\n\",\n      \"Epoch 770/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3195\\n\",\n      \"Epoch 771/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3392\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3385\\n\",\n      \"Epoch 773/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3724\\n\",\n      \"Epoch 774/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3553\\n\",\n      \"Epoch 775/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3283\\n\",\n      \"Epoch 776/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3809\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 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0s 1ms/step - loss: 0.3206\\n\",\n      \"Epoch 949/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3159\\n\",\n      \"Epoch 950/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3215\\n\",\n      \"Epoch 968/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.3273\\n\",\n      \"Epoch 977/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3239\\n\",\n      \"Epoch 982/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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      \"13/13 [==============================] - 0s 4ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 4ms/step - loss: 0.7193\\n\",\n      \"Epoch 41/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 1ms/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 2ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/step - loss: 0.6040\\n\",\n      \"Epoch 78/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.5611\\n\",\n      \"Epoch 95/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 2ms/step - loss: 0.5539\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/step - loss: 0.5777\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.5392\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.5492\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/step - loss: 0.5477\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.5251\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.5151\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 2ms/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 1ms/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 1ms/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 3ms/step - loss: 0.4993\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.4851\\n\",\n      \"Epoch 201/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 2ms/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      \"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 3ms/step - loss: 0.5147\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.4994\\n\",\n      \"Epoch 219/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.4906\\n\",\n      \"Epoch 267/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.4582\\n\",\n      \"Epoch 276/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/step - loss: 0.4457\\n\",\n      \"Epoch 285/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.4550\\n\",\n      \"Epoch 294/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4710\\n\",\n      \"Epoch 299/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4486\\n\",\n      \"Epoch 303/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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      \"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 1ms/step - loss: 0.4580\\n\",\n      \"Epoch 308/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4400\\n\",\n      \"Epoch 317/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4804\\n\",\n      \"Epoch 326/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 3ms/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 1ms/step - loss: 0.4381\\n\",\n      \"Epoch 340/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/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 3ms/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 3ms/step - loss: 0.4236\\n\",\n      \"Epoch 367/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4298\\n\",\n      \"Epoch 385/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.4205\\n\",\n      \"Epoch 403/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/step - loss: 0.4301\\n\",\n      \"Epoch 404/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 2ms/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 3ms/step - loss: 0.4556\\n\",\n      \"Epoch 412/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4202\\n\",\n      \"Epoch 417/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4094\\n\",\n      \"Epoch 421/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4146\\n\",\n      \"Epoch 426/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4254\\n\",\n      \"Epoch 430/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.4339\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.4080\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 1ms/step - loss: 0.4589\\n\",\n      \"Epoch 456/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4453\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4091\\n\",\n      \"Epoch 458/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4247\\n\",\n      \"Epoch 459/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4114\\n\",\n      \"Epoch 460/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4077\\n\",\n      \"Epoch 461/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4066\\n\",\n      \"Epoch 462/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3998\\n\",\n      \"Epoch 463/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4065\\n\",\n      \"Epoch 464/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4224\\n\",\n      \"Epoch 465/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4093\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4367\\n\",\n      \"Epoch 467/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4290\\n\",\n      \"Epoch 468/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4419\\n\",\n      \"Epoch 469/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4647\\n\",\n      \"Epoch 470/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4229\\n\",\n      \"Epoch 471/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4145\\n\",\n      \"Epoch 472/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4521\\n\",\n      \"Epoch 473/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4735\\n\",\n      \"Epoch 474/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4743\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4702\\n\",\n      \"Epoch 484/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4070\\n\",\n      \"Epoch 489/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 [==============================] - 0s 1ms/step - loss: 0.4177\\n\",\n      \"Epoch 492/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4315\\n\",\n      \"Epoch 493/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4128\\n\",\n      \"Epoch 494/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3983\\n\",\n      \"Epoch 495/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4084\\n\",\n      \"Epoch 496/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4207\\n\",\n      \"Epoch 497/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4822\\n\",\n      \"Epoch 498/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4362\\n\",\n      \"Epoch 499/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3974\\n\",\n      \"Epoch 500/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4184\\n\",\n      \"Epoch 501/1000\\n\",\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 3ms/step - loss: 0.4484\\n\",\n      \"Epoch 507/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/step - loss: 0.4106\\n\",\n      \"Epoch 516/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4168\\n\",\n      \"Epoch 521/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.4390\\n\",\n      \"Epoch 545/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4002\\n\",\n      \"Epoch 546/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3999\\n\",\n      \"Epoch 547/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4273\\n\",\n      \"Epoch 548/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4036\\n\",\n      \"Epoch 549/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3978\\n\",\n      \"Epoch 550/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3958\\n\",\n      \"Epoch 551/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4007\\n\",\n      \"Epoch 552/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4029\\n\",\n      \"Epoch 553/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3997\\n\",\n      \"Epoch 554/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.4204\\n\",\n      \"Epoch 563/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3995\\n\",\n      \"Epoch 564/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4233\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4073\\n\",\n      \"Epoch 566/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4120\\n\",\n      \"Epoch 567/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4113\\n\",\n      \"Epoch 568/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4200\\n\",\n      \"Epoch 569/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4056\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4390\\n\",\n      \"Epoch 571/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4195\\n\",\n      \"Epoch 572/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4159\\n\",\n      \"Epoch 573/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4151\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4285\\n\",\n      \"Epoch 575/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4161\\n\",\n      \"Epoch 576/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4002\\n\",\n      \"Epoch 577/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4012\\n\",\n      \"Epoch 578/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4826\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4323\\n\",\n      \"Epoch 580/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3979\\n\",\n      \"Epoch 581/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4081\\n\",\n      \"Epoch 582/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4111\\n\",\n      \"Epoch 583/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4020\\n\",\n      \"Epoch 584/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4224\\n\",\n      \"Epoch 585/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4452\\n\",\n      \"Epoch 586/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4275\\n\",\n      \"Epoch 587/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4059\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3806\\n\",\n      \"Epoch 589/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3815\\n\",\n      \"Epoch 590/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3795\\n\",\n      \"Epoch 591/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3950\\n\",\n      \"Epoch 592/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3908\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3986\\n\",\n      \"Epoch 594/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3971\\n\",\n      \"Epoch 595/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4547\\n\",\n      \"Epoch 596/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4217\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3903\\n\",\n      \"Epoch 598/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3899\\n\",\n      \"Epoch 599/1000\\n\",\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 3ms/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 1ms/step - loss: 0.4062\\n\",\n      \"Epoch 606/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4323\\n\",\n      \"Epoch 607/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4111\\n\",\n      \"Epoch 608/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4204\\n\",\n      \"Epoch 609/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4018\\n\",\n      \"Epoch 610/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4288\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4345\\n\",\n      \"Epoch 612/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4396\\n\",\n      \"Epoch 613/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4220\\n\",\n      \"Epoch 614/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4076\\n\",\n      \"Epoch 615/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4151\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4375\\n\",\n      \"Epoch 617/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4107\\n\",\n      \"Epoch 618/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4320\\n\",\n      \"Epoch 619/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5052\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4725\\n\",\n      \"Epoch 621/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3986\\n\",\n      \"Epoch 622/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3926\\n\",\n      \"Epoch 623/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3904\\n\",\n      \"Epoch 624/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3843\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3796\\n\",\n      \"Epoch 626/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3971\\n\",\n      \"Epoch 627/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3997\\n\",\n      \"Epoch 628/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4224\\n\",\n      \"Epoch 629/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4081\\n\",\n      \"Epoch 630/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4002\\n\",\n      \"Epoch 631/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3999\\n\",\n      \"Epoch 632/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4189\\n\",\n      \"Epoch 633/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4124\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4116\\n\",\n      \"Epoch 635/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4069\\n\",\n      \"Epoch 636/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4228\\n\",\n      \"Epoch 637/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4017\\n\",\n      \"Epoch 638/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3951\\n\",\n      \"Epoch 639/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3887\\n\",\n      \"Epoch 640/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4111\\n\",\n      \"Epoch 641/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3916\\n\",\n      \"Epoch 642/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4167\\n\",\n      \"Epoch 643/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4325\\n\",\n      \"Epoch 644/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3954\\n\",\n      \"Epoch 645/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4429\\n\",\n      \"Epoch 646/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4494\\n\",\n      \"Epoch 647/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3927\\n\",\n      \"Epoch 648/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3984\\n\",\n      \"Epoch 649/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3853\\n\",\n      \"Epoch 650/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3866\\n\",\n      \"Epoch 651/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3837\\n\",\n      \"Epoch 652/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3766\\n\",\n      \"Epoch 653/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3934\\n\",\n      \"Epoch 654/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4094\\n\",\n      \"Epoch 655/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4130\\n\",\n      \"Epoch 656/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4033\\n\",\n      \"Epoch 657/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3898\\n\",\n      \"Epoch 658/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3824\\n\",\n      \"Epoch 659/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3789\\n\",\n      \"Epoch 660/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4124\\n\",\n      \"Epoch 661/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3841\\n\",\n      \"Epoch 662/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3863\\n\",\n      \"Epoch 663/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4046\\n\",\n      \"Epoch 664/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4014\\n\",\n      \"Epoch 665/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3959\\n\",\n      \"Epoch 666/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4139\\n\",\n      \"Epoch 667/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3785\\n\",\n      \"Epoch 668/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3723\\n\",\n      \"Epoch 669/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3906\\n\",\n      \"Epoch 670/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3916\\n\",\n      \"Epoch 671/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3856\\n\",\n      \"Epoch 672/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3960\\n\",\n      \"Epoch 673/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3896\\n\",\n      \"Epoch 674/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4161\\n\",\n      \"Epoch 675/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3998\\n\",\n      \"Epoch 676/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3994\\n\",\n      \"Epoch 677/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3928\\n\",\n      \"Epoch 678/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3879\\n\",\n      \"Epoch 679/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3903\\n\",\n      \"Epoch 680/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3870\\n\",\n      \"Epoch 681/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4057\\n\",\n      \"Epoch 682/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3845\\n\",\n      \"Epoch 683/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3840\\n\",\n      \"Epoch 684/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3963\\n\",\n      \"Epoch 685/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3952\\n\",\n      \"Epoch 686/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3890\\n\",\n      \"Epoch 687/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3848\\n\",\n      \"Epoch 688/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3721\\n\",\n      \"Epoch 689/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4237\\n\",\n      \"Epoch 690/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4118\\n\",\n      \"Epoch 691/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4134\\n\",\n      \"Epoch 692/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3938\\n\",\n      \"Epoch 693/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3770\\n\",\n      \"Epoch 694/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3862\\n\",\n      \"Epoch 695/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3836\\n\",\n      \"Epoch 696/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3806\\n\",\n      \"Epoch 697/1000\\n\",\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 [==============================] - 0s 1ms/step - loss: 0.4032\\n\",\n      \"Epoch 705/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3955\\n\",\n      \"Epoch 706/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3935\\n\",\n      \"Epoch 707/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4077\\n\",\n      \"Epoch 708/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4006\\n\",\n      \"Epoch 709/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4320\\n\",\n      \"Epoch 710/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4190\\n\",\n      \"Epoch 711/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4116\\n\",\n      \"Epoch 712/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4650\\n\",\n      \"Epoch 713/1000\\n\",\n      \"13/13 [==============================] - 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0s 2ms/step - loss: 0.3735\\n\",\n      \"Epoch 876/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3825\\n\",\n      \"Epoch 877/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3949\\n\",\n      \"Epoch 878/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3843\\n\",\n      \"Epoch 879/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3764\\n\",\n      \"Epoch 880/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3726\\n\",\n      \"Epoch 881/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4077\\n\",\n      \"Epoch 882/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4082\\n\",\n      \"Epoch 883/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4647\\n\",\n      \"Epoch 884/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4117\\n\",\n      \"Epoch 885/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4137\\n\",\n      \"Epoch 886/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3766\\n\",\n      \"Epoch 887/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3847\\n\",\n      \"Epoch 888/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3827\\n\",\n      \"Epoch 889/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3771\\n\",\n      \"Epoch 890/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3975\\n\",\n      \"Epoch 891/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3751\\n\",\n      \"Epoch 892/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3799\\n\",\n      \"Epoch 893/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3984\\n\",\n      \"Epoch 894/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3896\\n\",\n      \"Epoch 895/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4349\\n\",\n      \"Epoch 896/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4009\\n\",\n      \"Epoch 897/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3860\\n\",\n      \"Epoch 898/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4113\\n\",\n      \"Epoch 899/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4130\\n\",\n      \"Epoch 900/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3933\\n\",\n      \"Epoch 901/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4191\\n\",\n      \"Epoch 902/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4013\\n\",\n      \"Epoch 903/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3950\\n\",\n      \"Epoch 904/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3678\\n\",\n      \"Epoch 905/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4050\\n\",\n      \"Epoch 906/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4526\\n\",\n      \"Epoch 907/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.3866\\n\",\n      \"Epoch 908/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3975\\n\",\n      \"Epoch 909/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.3820\\n\",\n      \"Epoch 913/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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 3ms/step - loss: 0.3732\\n\",\n      \"Epoch 927/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 3ms/step - loss: 0.3825\\n\",\n      \"Epoch 941/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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      \"13/13 [==============================] - 0s 1ms/step - loss: 0.3784\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 4ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.9171\\n\",\n      \"Epoch 31/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 [==============================] - 0s 1ms/step - loss: 0.8262\\n\",\n      \"Epoch 44/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8449\\n\",\n      \"Epoch 45/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8335\\n\",\n      \"Epoch 46/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8717\\n\",\n      \"Epoch 47/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8457\\n\",\n      \"Epoch 48/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.8543\\n\",\n      \"Epoch 49/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8180\\n\",\n      \"Epoch 50/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7972\\n\",\n      \"Epoch 51/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7949\\n\",\n      \"Epoch 52/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7942\\n\",\n      \"Epoch 53/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7766\\n\",\n      \"Epoch 54/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7629\\n\",\n      \"Epoch 55/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8018\\n\",\n      \"Epoch 56/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7752\\n\",\n      \"Epoch 57/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7784\\n\",\n      \"Epoch 58/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7531\\n\",\n      \"Epoch 59/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7732\\n\",\n      \"Epoch 60/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7617\\n\",\n      \"Epoch 61/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.8013\\n\",\n      \"Epoch 62/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7902\\n\",\n      \"Epoch 63/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7824\\n\",\n      \"Epoch 64/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7560\\n\",\n      \"Epoch 65/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7429\\n\",\n      \"Epoch 66/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7496\\n\",\n      \"Epoch 67/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7481\\n\",\n      \"Epoch 68/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7445\\n\",\n      \"Epoch 69/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7267\\n\",\n      \"Epoch 70/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7464\\n\",\n      \"Epoch 71/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.7165\\n\",\n      \"Epoch 80/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.7136\\n\",\n      \"Epoch 81/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7057\\n\",\n      \"Epoch 82/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7064\\n\",\n      \"Epoch 83/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6878\\n\",\n      \"Epoch 84/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6923\\n\",\n      \"Epoch 85/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6891\\n\",\n      \"Epoch 86/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6944\\n\",\n      \"Epoch 87/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6866\\n\",\n      \"Epoch 88/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7030\\n\",\n      \"Epoch 89/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6948\\n\",\n      \"Epoch 90/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6955\\n\",\n      \"Epoch 91/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.6932\\n\",\n      \"Epoch 100/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 [==============================] - 0s 1ms/step - loss: 0.7019\\n\",\n      \"Epoch 106/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.7179\\n\",\n      \"Epoch 107/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6857\\n\",\n      \"Epoch 108/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6916\\n\",\n      \"Epoch 109/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6818\\n\",\n      \"Epoch 110/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6770\\n\",\n      \"Epoch 111/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6666\\n\",\n      \"Epoch 112/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6476\\n\",\n      \"Epoch 113/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6423\\n\",\n      \"Epoch 114/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6374\\n\",\n      \"Epoch 115/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6376\\n\",\n      \"Epoch 116/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6437\\n\",\n      \"Epoch 117/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6676\\n\",\n      \"Epoch 118/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6745\\n\",\n      \"Epoch 119/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6791\\n\",\n      \"Epoch 120/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6592\\n\",\n      \"Epoch 121/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6358\\n\",\n      \"Epoch 122/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6411\\n\",\n      \"Epoch 123/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6409\\n\",\n      \"Epoch 124/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6411\\n\",\n      \"Epoch 125/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6261\\n\",\n      \"Epoch 126/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6329\\n\",\n      \"Epoch 127/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6430\\n\",\n      \"Epoch 128/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6203\\n\",\n      \"Epoch 129/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6177\\n\",\n      \"Epoch 130/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6144\\n\",\n      \"Epoch 131/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6298\\n\",\n      \"Epoch 132/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6359\\n\",\n      \"Epoch 133/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6049\\n\",\n      \"Epoch 134/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6333\\n\",\n      \"Epoch 135/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6214\\n\",\n      \"Epoch 136/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6172\\n\",\n      \"Epoch 137/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6090\\n\",\n      \"Epoch 138/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6361\\n\",\n      \"Epoch 139/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6228\\n\",\n      \"Epoch 140/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6284\\n\",\n      \"Epoch 141/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.6155\\n\",\n      \"Epoch 146/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.6206\\n\",\n      \"Epoch 150/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6107\\n\",\n      \"Epoch 151/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5987\\n\",\n      \"Epoch 152/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6050\\n\",\n      \"Epoch 153/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5992\\n\",\n      \"Epoch 154/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5925\\n\",\n      \"Epoch 155/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5953\\n\",\n      \"Epoch 156/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5899\\n\",\n      \"Epoch 157/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5960\\n\",\n      \"Epoch 158/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5963\\n\",\n      \"Epoch 159/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6052\\n\",\n      \"Epoch 160/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6213\\n\",\n      \"Epoch 161/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6517\\n\",\n      \"Epoch 162/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6613\\n\",\n      \"Epoch 163/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5941\\n\",\n      \"Epoch 164/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5938\\n\",\n      \"Epoch 165/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6148\\n\",\n      \"Epoch 166/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6169\\n\",\n      \"Epoch 167/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6042\\n\",\n      \"Epoch 168/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.6015\\n\",\n      \"Epoch 169/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 4ms/step - loss: 0.5925\\n\",\n      \"Epoch 178/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 3ms/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      \"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 3ms/step - loss: 0.5839\\n\",\n      \"Epoch 192/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.5524\\n\",\n      \"Epoch 203/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5573\\n\",\n      \"Epoch 204/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5715\\n\",\n      \"Epoch 205/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.6330\\n\",\n      \"Epoch 206/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5756\\n\",\n      \"Epoch 207/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5767\\n\",\n      \"Epoch 208/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5743\\n\",\n      \"Epoch 209/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5912\\n\",\n      \"Epoch 210/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5746\\n\",\n      \"Epoch 211/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5682\\n\",\n      \"Epoch 212/1000\\n\",\n      \"13/13 [==============================] - 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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/step - loss: 0.5426\\n\",\n      \"Epoch 229/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 3ms/step - loss: 0.5363\\n\",\n      \"Epoch 243/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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      \"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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 1ms/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 3ms/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 1ms/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 1ms/step - loss: 0.4916\\n\",\n      \"Epoch 365/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.4981\\n\",\n      \"Epoch 374/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4977\\n\",\n      \"Epoch 379/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.5069\\n\",\n      \"Epoch 383/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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      \"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 1ms/step - loss: 0.4963\\n\",\n      \"Epoch 388/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 2ms/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 1ms/step - loss: 0.5247\\n\",\n      \"Epoch 397/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/step - loss: 0.4802\\n\",\n      \"Epoch 420/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 1ms/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 1ms/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 3ms/step - loss: 0.4955\\n\",\n      \"Epoch 443/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.4700\\n\",\n      \"Epoch 452/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.5010\\n\",\n      \"Epoch 457/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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 3ms/step - loss: 0.4620\\n\",\n      \"Epoch 466/1000\\n\",\n      \"13/13 [==============================] - 0s 2ms/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 1ms/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 3ms/step - loss: 0.5125\\n\",\n      \"Epoch 475/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.5035\\n\",\n      \"Epoch 480/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 [==============================] - 0s 3ms/step - loss: 0.4695\\n\",\n      \"Epoch 525/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4746\\n\",\n      \"Epoch 526/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4856\\n\",\n      \"Epoch 527/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4507\\n\",\n      \"Epoch 528/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4539\\n\",\n      \"Epoch 529/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4873\\n\",\n      \"Epoch 530/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4694\\n\",\n      \"Epoch 531/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4829\\n\",\n      \"Epoch 532/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4654\\n\",\n      \"Epoch 533/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/step - loss: 0.5128\\n\",\n      \"Epoch 542/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 3ms/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 1ms/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 3ms/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 1ms/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 2ms/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 1ms/step - loss: 0.4689\\n\",\n      \"Epoch 565/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4586\\n\",\n      \"Epoch 570/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4621\\n\",\n      \"Epoch 574/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.5203\\n\",\n      \"Epoch 579/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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      \"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 3ms/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 1ms/step - loss: 0.4629\\n\",\n      \"Epoch 588/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4427\\n\",\n      \"Epoch 593/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4749\\n\",\n      \"Epoch 597/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4340\\n\",\n      \"Epoch 602/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/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 1ms/step - loss: 0.4673\\n\",\n      \"Epoch 611/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4697\\n\",\n      \"Epoch 616/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.5116\\n\",\n      \"Epoch 620/1000\\n\",\n      \"13/13 [==============================] - 0s 4ms/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 1ms/step - loss: 0.4462\\n\",\n      \"Epoch 625/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 1ms/step - loss: 0.4590\\n\",\n      \"Epoch 634/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/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 [==============================] - 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0s 3ms/step - loss: 0.4833\\n\",\n      \"Epoch 713/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4445\\n\",\n      \"Epoch 714/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4452\\n\",\n      \"Epoch 715/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4250\\n\",\n      \"Epoch 716/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4150\\n\",\n      \"Epoch 717/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4374\\n\",\n      \"Epoch 718/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4288\\n\",\n      \"Epoch 719/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4501\\n\",\n      \"Epoch 720/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4521\\n\",\n      \"Epoch 721/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4426\\n\",\n      \"Epoch 722/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4292\\n\",\n      \"Epoch 723/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4361\\n\",\n      \"Epoch 724/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4311\\n\",\n      \"Epoch 725/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4439\\n\",\n      \"Epoch 726/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4526\\n\",\n      \"Epoch 727/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4452\\n\",\n      \"Epoch 728/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4704\\n\",\n      \"Epoch 729/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4262\\n\",\n      \"Epoch 730/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4394\\n\",\n      \"Epoch 731/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4449\\n\",\n      \"Epoch 732/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4180\\n\",\n      \"Epoch 733/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4271\\n\",\n      \"Epoch 734/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4488\\n\",\n      \"Epoch 735/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4186\\n\",\n      \"Epoch 736/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4248\\n\",\n      \"Epoch 737/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4305\\n\",\n      \"Epoch 738/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4641\\n\",\n      \"Epoch 739/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4604\\n\",\n      \"Epoch 740/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4374\\n\",\n      \"Epoch 741/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4710\\n\",\n      \"Epoch 742/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5218\\n\",\n      \"Epoch 743/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4587\\n\",\n      \"Epoch 744/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4657\\n\",\n      \"Epoch 745/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4437\\n\",\n      \"Epoch 746/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4377\\n\",\n      \"Epoch 747/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4214\\n\",\n      \"Epoch 748/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4213\\n\",\n      \"Epoch 749/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4994\\n\",\n      \"Epoch 750/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4640\\n\",\n      \"Epoch 751/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4234\\n\",\n      \"Epoch 752/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4783\\n\",\n      \"Epoch 753/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4549\\n\",\n      \"Epoch 754/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4373\\n\",\n      \"Epoch 755/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4336\\n\",\n      \"Epoch 756/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4585\\n\",\n      \"Epoch 757/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4700\\n\",\n      \"Epoch 758/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4325\\n\",\n      \"Epoch 759/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4507\\n\",\n      \"Epoch 760/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4573\\n\",\n      \"Epoch 761/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4541\\n\",\n      \"Epoch 762/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4458\\n\",\n      \"Epoch 763/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4274\\n\",\n      \"Epoch 764/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4645\\n\",\n      \"Epoch 765/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4691\\n\",\n      \"Epoch 766/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4310\\n\",\n      \"Epoch 767/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4348\\n\",\n      \"Epoch 768/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4404\\n\",\n      \"Epoch 769/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4509\\n\",\n      \"Epoch 770/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4635\\n\",\n      \"Epoch 771/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4313\\n\",\n      \"Epoch 772/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4288\\n\",\n      \"Epoch 773/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4356\\n\",\n      \"Epoch 774/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4749\\n\",\n      \"Epoch 775/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4567\\n\",\n      \"Epoch 776/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4502\\n\",\n      \"Epoch 777/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4717\\n\",\n      \"Epoch 778/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4722\\n\",\n      \"Epoch 779/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4331\\n\",\n      \"Epoch 780/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4403\\n\",\n      \"Epoch 781/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4867\\n\",\n      \"Epoch 782/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4367\\n\",\n      \"Epoch 783/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4404\\n\",\n      \"Epoch 784/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4350\\n\",\n      \"Epoch 785/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4190\\n\",\n      \"Epoch 786/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4218\\n\",\n      \"Epoch 787/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4293\\n\",\n      \"Epoch 788/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4209\\n\",\n      \"Epoch 789/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4268\\n\",\n      \"Epoch 790/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4713\\n\",\n      \"Epoch 791/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4677\\n\",\n      \"Epoch 792/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5326\\n\",\n      \"Epoch 793/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4860\\n\",\n      \"Epoch 794/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.5062\\n\",\n      \"Epoch 795/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5388\\n\",\n      \"Epoch 796/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4840\\n\",\n      \"Epoch 797/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5376\\n\",\n      \"Epoch 798/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.5497\\n\",\n      \"Epoch 799/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4734\\n\",\n      \"Epoch 800/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4402\\n\",\n      \"Epoch 801/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4257\\n\",\n      \"Epoch 802/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4150\\n\",\n      \"Epoch 803/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4158\\n\",\n      \"Epoch 804/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4205\\n\",\n      \"Epoch 805/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4295\\n\",\n      \"Epoch 806/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4292\\n\",\n      \"Epoch 807/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4171\\n\",\n      \"Epoch 808/1000\\n\",\n      \"13/13 [==============================] - 0s 3ms/step - loss: 0.4304\\n\",\n      \"Epoch 809/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4788\\n\",\n      \"Epoch 810/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/step - loss: 0.4305\\n\",\n      \"Epoch 811/1000\\n\",\n      \"13/13 [==============================] - 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0s 3ms/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 3ms/step - loss: 0.4018\\n\",\n      \"Epoch 987/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4166\\n\",\n      \"Epoch 992/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/step - loss: 0.4242\\n\",\n      \"Epoch 996/1000\\n\",\n      \"13/13 [==============================] - 0s 1ms/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 3ms/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\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"0bdeaa8d14be45f596151dbac5307f65\",\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   \"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\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ae8d4a5b6105426aa5012b09318b58b5\",\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   \"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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Practice-Labs/Course-2-Week-3-Practice-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Practice-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Practice-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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Practice-Labs/images/Course-2-Week-3-Practice-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Practice-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Practice-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3-Practice-Quiz/Course-2-Week-3-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-3/Course-2-Week-3.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-4/Course-2-Week-4-Lecture-Slides/Course-2-Week-4-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-4/Course-2-Week-4-Practice-Labs/Course-2-Week-4-Practice-Labs-1-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\": 1,\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\": 2,\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\": 3,\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\": 4,\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\": 5,\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\": 12,\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 = len(y[y == 1]) / len(y)\\n\",\n    \"        \\n\",\n    \"        if (p1 != 0) & (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\": 13,\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\": 14,\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 ind in node_indices:\\n\",\n    \"        if X[ind][feature] == 1:\\n\",\n    \"            left_indices.append(ind)\\n\",\n    \"        else:\\n\",\n    \"            right_indices.append(ind)\\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\": 15,\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\": 16,\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(y_left)/len(y_node)\\n\",\n    \"    w_right = len(y_right)/len(y_node)\\n\",\n    \"    \\n\",\n    \"    #Weighted entropy\\n\",\n    \"    w_entropy = w_left * left_entropy +  w_right * right_entropy\\n\",\n    \"    \\n\",\n    \"    #Information gain                                                   \\n\",\n    \"    information_gain = node_entropy - w_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\": 17,\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\": 18,\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    \"    max_information_gain = 0\\n\",\n    \"    \\n\",\n    \"    ### START CODE HERE ###\\n\",\n    \"    for feature in range(num_features):\\n\",\n    \"        information_gain = compute_information_gain(X, y, node_indices, feature)\\n\",\n    \"        \\n\",\n    \"        if information_gain > max_information_gain: \\n\",\n    \"            max_information_gain = information_gain\\n\",\n    \"            best_feature = feature\\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\": 19,\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\": 20,\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\": 21,\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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-4/Course-2-Week-4-Practice-Labs/Course-2-Week-4-Practice-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-4/Course-2-Week-4-Practice-Labs/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": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-4/Course-2-Week-4-Practice-Quiz/Course-2-Week-4-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2-Week-4/Course-2-Week-4.md",
    "content": "\n"
  },
  {
    "path": "Course-2-Advanced-Learning-Algorithms/Course-2.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-1/Course-3-Week-1-Lecture-Slides/Course-3-Week-1-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-1/Course-3-Week-1-Practice-Labs/Course-3-Week-1-Practice-Labs-1-KMeans.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    \"        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    \"    ### 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    \"    ### 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 0x7f0dd5984510>\"\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: [5 5 5 5 5]\\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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-1/Course-3-Week-1-Practice-Labs/Course-3-Week-1-Practice-Labs-2-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\": 2,\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\": 3,\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\": 4,\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\": 5,\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\": 6,\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\": 7,\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\": 8,\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\": 9,\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    \"    ### 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\": 10,\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\": 11,\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\": 14,\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    \"        \\n\",\n    \"        tp = np.sum((predictions == 1) & (y_val == 1))\\n\",\n    \"        fp = np.sum((predictions == 1) & (y_val == 0))\\n\",\n    \"        fn = np.sum((predictions == 0) & (y_val == 1))\\n\",\n    \"        \\n\",\n    \"        prec = tp / (tp + fp)\\n\",\n    \"        rec = tp / (tp + fn)\\n\",\n    \"        \\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\": 15,\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\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[<matplotlib.lines.Line2D at 0x7f6cde913f10>]\"\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\",\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\": 17,\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\": 18,\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\": 19,\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"
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  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-1/Course-3-Week-1-Practice-Labs/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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-1/Course-3-Week-1-Practice-Labs/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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-1/Course-3-Week-1-Practice-Quiz/Course-3-Week-1-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-1/Course-3-Week-1.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Lecture-Slides/Course-3-Week-2-Lecture-Slides.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/Course-3-Week-1-Practice-Labs-1-Collaborative-Filtering-Recommender-Systems.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\": 4,\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    \"        \\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    \"        \\n\",\n    \"    J += lambda_* (np.sum(np.square(W)) + np.sum(np.square(X)))\\n\",\n    \"    \\n\",\n    \"    J = J/2\\n\",\n    \"            \\n\",\n    \"            \\n\",\n    \"            \\n\",\n    \"            \\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    ### END CODE HERE ### \\n\",\n    \"\\n\",\n    \"    return J\"\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      \"\\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\": 6,\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\": 7,\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\": 8,\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\": 9,\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\": 10,\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\": 11,\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\": 12,\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\": 13,\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\": 14,\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\": 15,\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\": 15,\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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/Course-3-Week-2-Practice-Labs-2-Deep-Learning-For-Content-Based-Filtering.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),\\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    \"# 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 126us/sample - loss: 0.1254\\n\",\n      \"Epoch 2/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1187\\n\",\n      \"Epoch 3/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.1169\\n\",\n      \"Epoch 4/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.1154\\n\",\n      \"Epoch 5/30\\n\",\n      \"46549/46549 [==============================] - 5s 117us/sample - loss: 0.1142\\n\",\n      \"Epoch 6/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.1130\\n\",\n      \"Epoch 7/30\\n\",\n      \"46549/46549 [==============================] - 5s 117us/sample - loss: 0.1119\\n\",\n      \"Epoch 8/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.1110\\n\",\n      \"Epoch 9/30\\n\",\n      \"46549/46549 [==============================] - 5s 117us/sample - loss: 0.1095\\n\",\n      \"Epoch 10/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.1083\\n\",\n      \"Epoch 11/30\\n\",\n      \"46549/46549 [==============================] - 5s 117us/sample - loss: 0.1073\\n\",\n      \"Epoch 12/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.1066\\n\",\n      \"Epoch 13/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1059\\n\",\n      \"Epoch 14/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1054\\n\",\n      \"Epoch 15/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1047\\n\",\n      \"Epoch 16/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1041\\n\",\n      \"Epoch 17/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1036\\n\",\n      \"Epoch 18/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.1030\\n\",\n      \"Epoch 19/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1027\\n\",\n      \"Epoch 20/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1021\\n\",\n      \"Epoch 21/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1018\\n\",\n      \"Epoch 22/30\\n\",\n      \"46549/46549 [==============================] - 5s 117us/sample - loss: 0.1014\\n\",\n      \"Epoch 23/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1010\\n\",\n      \"Epoch 24/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.1006\\n\",\n      \"Epoch 25/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.1003\\n\",\n      \"Epoch 26/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.0999\\n\",\n      \"Epoch 27/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.0997\\n\",\n      \"Epoch 28/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.0991\\n\",\n      \"Epoch 29/30\\n\",\n      \"46549/46549 [==============================] - 5s 116us/sample - loss: 0.0989\\n\",\n      \"Epoch 30/30\\n\",\n      \"46549/46549 [==============================] - 5s 118us/sample - loss: 0.0985\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<tensorflow.python.keras.callbacks.History at 0x7fb191a888d0>\"\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 32us/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\": 18,\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 = np.sum(np.square(a-b))\\n\",\n    \"    ### END CODE HERE ###     \\n\",\n    \"    return (d)\"\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      \"\\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\": 20,\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\": 21,\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\": 22,\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\": 23,\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\": 23,\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"
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  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/data/content_movie_list.csv",
    "content": "movieId,title,genres\r\n4054,Save the Last Dance (2001),Drama|Romance\r\n4069,\"Wedding Planner, The (2001)\",Comedy|Romance\r\n4148,Hannibal (2001),Horror|Thriller\r\n4149,Saving Silverman (Evil Woman) (2001),Comedy|Romance\r\n4153,Down to Earth (2001),Comedy|Fantasy|Romance\r\n4161,\"Mexican, The (2001)\",Action|Comedy\r\n4167,15 Minutes (2001),Thriller\r\n4228,Heartbreakers (2001),Comedy|Crime|Romance\r\n4232,Spy Kids (2001),Action|Adventure|Children|Comedy\r\n4238,Along Came a Spider (2001),Action|Crime|Mystery|Thriller\r\n4239,Blow (2001),Crime|Drama\r\n4246,Bridget Jones's Diary (2001),Comedy|Drama|Romance\r\n4247,Joe Dirt (2001),Adventure|Comedy|Mystery|Romance\r\n4254,Crocodile Dundee in Los Angeles (2001),Comedy|Drama\r\n4270,\"Mummy Returns, The (2001)\",Action|Adventure|Comedy|Thriller\r\n4299,\"Knight's Tale, A (2001)\",Action|Comedy|Romance\r\n4306,Shrek (2001),Adventure|Animation|Children|Comedy|Fantasy|Romance\r\n4340,\"Animal, The (2001)\",Comedy\r\n4343,Evolution (2001),Comedy|Sci-Fi\r\n4344,Swordfish (2001),Action|Crime|Drama\r\n4366,Atlantis: The Lost Empire (2001),Adventure|Animation|Children|Fantasy\r\n4367,Lara Croft: Tomb Raider (2001),Action|Adventure\r\n4368,Dr. Dolittle 2 (2001),Comedy\r\n4369,\"Fast and the Furious, The (2001)\",Action|Crime|Thriller\r\n4370,A.I. Artificial Intelligence (2001),Adventure|Drama|Sci-Fi\r\n4386,Cats & Dogs (2001),Children|Comedy\r\n4388,Scary Movie 2 (2001),Comedy\r\n4446,Final Fantasy: The Spirits Within (2001),Adventure|Animation|Fantasy|Sci-Fi\r\n4447,Legally Blonde (2001),Comedy|Romance\r\n4448,\"Score, The (2001)\",Action|Drama\r\n4638,Jurassic Park III (2001),Action|Adventure|Sci-Fi|Thriller\r\n4639,America's Sweethearts (2001),Comedy|Romance\r\n4641,Ghost World (2001),Comedy|Drama\r\n4643,Planet of the Apes (2001),Action|Adventure|Drama|Sci-Fi\r\n4700,\"Princess Diaries, The (2001)\",Children|Comedy|Romance\r\n4701,Rush Hour 2 (2001),Action|Comedy\r\n4718,American Pie 2 (2001),Comedy\r\n4720,\"Others, The (2001)\",Drama|Horror|Mystery|Thriller\r\n4728,Rat Race (2001),Comedy\r\n4734,Jay and Silent Bob Strike Back (2001),Adventure|Comedy\r\n4776,Training Day (2001),Crime|Drama|Thriller\r\n4816,Zoolander (2001),Comedy\r\n4823,Serendipity (2001),Comedy|Romance\r\n4865,From Hell (2001),Crime|Horror|Mystery|Thriller\r\n4873,Waking Life (2001),Animation|Drama|Fantasy\r\n4874,K-PAX (2001),Drama|Fantasy|Mystery|Sci-Fi\r\n4876,Thirteen Ghosts (a.k.a. Thir13en Ghosts) (2001),Horror|Thriller\r\n4878,Donnie Darko (2001),Drama|Mystery|Sci-Fi|Thriller\r\n4881,\"Man Who Wasn't There, The (2001)\",Crime|Drama\r\n4886,\"Monsters, Inc. (2001)\",Adventure|Animation|Children|Comedy|Fantasy\r\n4887,\"One, The (2001)\",Action|Sci-Fi|Thriller\r\n4890,Shallow Hal (2001),Comedy|Fantasy|Romance\r\n4896,Harry Potter and the Sorcerer's Stone (a.k.a. Harry Potter and the Philosopher's Stone) (2001),Adventure|Children|Fantasy\r\n4901,Spy Game (2001),Action|Crime|Drama|Thriller\r\n4903,In the Bedroom (2001),Drama\r\n4963,Ocean's Eleven (2001),Crime|Thriller\r\n4973,\"Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001)\",Comedy|Romance\r\n4974,Not Another Teen Movie (2001),Comedy\r\n4975,Vanilla Sky (2001),Mystery|Romance|Sci-Fi|Thriller\r\n4979,\"Royal Tenenbaums, The (2001)\",Comedy|Drama\r\n4992,Kate & Leopold (2001),Comedy|Romance\r\n4993,\"Lord of the Rings: The Fellowship of the Ring, The (2001)\",Adventure|Fantasy\r\n4994,\"Majestic, The (2001)\",Comedy|Drama|Romance\r\n4995,\"Beautiful Mind, A (2001)\",Drama|Romance\r\n5013,Gosford Park (2001),Comedy|Drama|Mystery\r\n5014,I Am Sam (2001),Drama\r\n5015,Monster's Ball (2001),Drama|Romance\r\n5025,Orange County (2002),Comedy\r\n5026,\"Brotherhood of the Wolf (Pacte des loups, Le) (2001)\",Action|Mystery|Thriller\r\n5064,The Count of Monte Cristo (2002),Action|Adventure|Drama|Thriller\r\n5065,\"Mothman Prophecies, The (2002)\",Drama|Fantasy|Horror|Mystery|Thriller\r\n5066,\"Walk to Remember, A (2002)\",Drama|Romance\r\n5072,Metropolis (2001),Animation|Sci-Fi\r\n5108,John Q (2002),Crime|Drama|Thriller\r\n5110,Super Troopers (2001),Comedy|Crime|Mystery\r\n5128,Queen of the Damned (2002),Fantasy|Horror\r\n5135,Monsoon Wedding (2001),Comedy|Romance\r\n5171,\"Time Machine, The (2002)\",Action|Adventure|Sci-Fi\r\n5218,Ice Age (2002),Adventure|Animation|Children|Comedy\r\n5219,Resident Evil (2002),Action|Horror|Sci-Fi|Thriller\r\n5220,Showtime (2002),Action|Comedy\r\n5254,Blade II (2002),Action|Horror|Thriller\r\n5265,Death to Smoochy (2002),Comedy|Crime|Drama\r\n5266,Panic Room (2002),Thriller\r\n5267,\"Rookie, The (2002)\",Drama\r\n5283,National Lampoon's Van Wilder (2002),Comedy\r\n5294,Frailty (2001),Crime|Drama|Thriller\r\n5296,\"Sweetest Thing, The (2002)\",Comedy|Romance\r\n5299,My Big Fat Greek Wedding (2002),Comedy|Romance\r\n5313,The Scorpion King (2002),Action|Adventure|Fantasy|Thriller\r\n5329,\"Salton Sea, The (2002)\",Crime|Drama|Thriller\r\n5349,Spider-Man (2002),Action|Adventure|Sci-Fi|Thriller\r\n5377,About a Boy (2002),Comedy|Drama|Romance\r\n5380,\"Importance of Being Earnest, The (2002)\",Comedy|Drama|Romance\r\n5388,Insomnia (2002),Action|Crime|Drama|Mystery|Thriller\r\n5400,\"Sum of All Fears, The (2002)\",Drama|Thriller\r\n5418,\"Bourne Identity, The (2002)\",Action|Mystery|Thriller\r\n5419,Scooby-Doo (2002),Adventure|Children|Comedy|Fantasy|Mystery\r\n5444,Lilo & Stitch (2002),Adventure|Animation|Children|Sci-Fi\r\n5445,Minority Report (2002),Action|Crime|Mystery|Sci-Fi|Thriller\r\n5449,Mr. Deeds (2002),Comedy|Romance\r\n5459,Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002),Action|Comedy|Sci-Fi\r\n5463,Reign of Fire (2002),Action|Adventure|Fantasy\r\n5464,Road to Perdition (2002),Crime|Drama\r\n5481,Austin Powers in Goldmember (2002),Comedy\r\n5502,Signs (2002),Horror|Sci-Fi|Thriller\r\n5505,\"Good Girl, The (2002)\",Comedy|Drama\r\n5507,xXx (2002),Action|Crime|Thriller\r\n5528,One Hour Photo (2002),Drama|Thriller\r\n5574,\"Transporter, The (2002)\",Action|Crime\r\n5577,Igby Goes Down (2002),Comedy|Drama\r\n5608,\"Das Experiment (Experiment, The) (2001)\",Drama|Thriller\r\n5618,Spirited Away (Sen to Chihiro no kamikakushi) (2001),Adventure|Animation|Fantasy\r\n5620,Sweet Home Alabama (2002),Comedy|Romance\r\n5621,\"Tuxedo, The (2002)\",Action|Comedy\r\n5630,Red Dragon (2002),Crime|Mystery|Thriller\r\n5669,Bowling for Columbine (2002),Documentary\r\n5673,Punch-Drunk Love (2002),Comedy|Drama|Romance\r\n5679,\"Ring, The (2002)\",Horror|Mystery|Thriller\r\n5785,Jackass: The Movie (2002),Action|Comedy|Documentary\r\n5791,Frida (2002),Drama|Romance\r\n5810,8 Mile (2002),Drama\r\n5812,Far from Heaven (2002),Drama|Romance\r\n5816,Harry Potter and the Chamber of Secrets (2002),Adventure|Fantasy\r\n5872,Die Another Day (2002),Action|Adventure|Thriller\r\n5878,Talk to Her (Hable con Ella) (2002),Drama|Romance\r\n5881,Solaris (2002),Drama|Romance|Sci-Fi\r\n5902,Adaptation (2002),Comedy|Drama|Romance\r\n5903,Equilibrium (2002),Action|Sci-Fi|Thriller\r\n5943,Maid in Manhattan (2002),Comedy|Romance\r\n5944,Star Trek: Nemesis (2002),Action|Drama|Sci-Fi|Thriller\r\n5945,About Schmidt (2002),Comedy|Drama\r\n5952,\"Lord of the Rings: The Two Towers, The (2002)\",Adventure|Fantasy\r\n5954,25th Hour (2002),Crime|Drama\r\n5956,Gangs of New York (2002),Crime|Drama\r\n5957,Two Weeks Notice (2002),Comedy|Romance\r\n5989,Catch Me If You Can (2002),Crime|Drama\r\n5992,\"Hours, The (2002)\",Drama|Romance\r\n6003,Confessions of a Dangerous Mind (2002),Comedy|Crime|Drama|Thriller\r\n6016,City of God (Cidade de Deus) (2002),Action|Adventure|Crime|Drama|Thriller\r\n6058,Final Destination 2 (2003),Horror|Thriller\r\n6059,\"Recruit, The (2003)\",Action|Thriller\r\n6155,How to Lose a Guy in 10 Days (2003),Comedy|Romance\r\n6156,Shanghai Knights (2003),Action|Adventure|Comedy\r\n6157,Daredevil (2003),Action|Crime\r\n6188,Old School (2003),Comedy\r\n6218,Bend It Like Beckham (2002),Comedy|Drama|Romance\r\n6281,Phone Booth (2002),Drama|Thriller\r\n6283,Cowboy Bebop: The Movie (Cowboy Bebop: Tengoku no Tobira) (2001),Action|Animation|Sci-Fi|Thriller\r\n6287,Anger Management (2003),Comedy\r\n6294,Bulletproof Monk (2003),Action|Adventure|Sci-Fi\r\n6297,Holes (2003),Adventure|Children|Comedy|Mystery\r\n6323,Identity (2003),Crime|Horror|Mystery|Thriller\r\n6331,Spellbound (2002),Documentary\r\n6333,X2: X-Men United (2003),Action|Adventure|Sci-Fi|Thriller\r\n6367,Down with Love (2003),Comedy|Romance\r\n6373,Bruce Almighty (2003),Comedy|Drama|Fantasy|Romance\r\n6377,Finding Nemo (2003),Adventure|Animation|Children|Comedy\r\n6378,\"Italian Job, The (2003)\",Action|Crime\r\n6383,\"2 Fast 2 Furious (Fast and the Furious 2, The) (2003)\",Action|Crime|Thriller\r\n6385,Whale Rider (2002),Drama\r\n6482,Dumb and Dumberer: When Harry Met Lloyd (2003),Comedy\r\n6502,28 Days Later (2002),Action|Horror|Sci-Fi\r\n6503,Charlie's Angels: Full Throttle (2003),Action|Adventure|Comedy|Crime|Thriller\r\n6534,Hulk (2003),Action|Adventure|Sci-Fi\r\n6535,\"Legally Blonde 2: Red, White & Blonde (2003)\",Comedy\r\n6537,Terminator 3: Rise of the Machines (2003),Action|Adventure|Sci-Fi\r\n6539,Pirates of the Caribbean: The Curse of the Black Pearl (2003),Action|Adventure|Comedy|Fantasy\r\n6541,\"League of Extraordinary Gentlemen, The (a.k.a. LXG) (2003)\",Action|Fantasy|Sci-Fi\r\n6548,Bad Boys II (2003),Action|Comedy|Crime|Thriller\r\n6550,Johnny English (2003),Action|Comedy|Thriller\r\n6552,Dirty Pretty Things (2002),Crime|Drama|Thriller\r\n6564,Lara Croft Tomb Raider: The Cradle of Life (2003),Action|Adventure|Comedy|Romance|Thriller\r\n6565,Seabiscuit (2003),Drama\r\n6586,American Wedding (American Pie 3) (2003),Comedy\r\n6593,Freaky Friday (2003),Children|Comedy|Fantasy\r\n6595,S.W.A.T. (2003),Action|Thriller\r\n6618,Shaolin Soccer (Siu lam juk kau) (2001),Action|Comedy\r\n6620,American Splendor (2003),Comedy|Drama\r\n6708,Matchstick Men (2003),Comedy|Crime|Drama\r\n6709,Once Upon a Time in Mexico (2003),Action|Adventure|Crime|Thriller\r\n6711,Lost in Translation (2003),Comedy|Drama|Romance\r\n6753,Secondhand Lions (2003),Children|Comedy|Drama\r\n6754,Underworld (2003),Action|Fantasy|Horror\r\n6755,Bubba Ho-tep (2002),Comedy|Horror\r\n6764,\"Rundown, The (2003)\",Action|Adventure|Comedy\r\n6773,\"Triplets of Belleville, The (Les triplettes de Belleville) (2003)\",Animation|Comedy|Fantasy\r\n6867,\"Station Agent, The (2003)\",Comedy|Drama\r\n6870,Mystic River (2003),Crime|Drama|Mystery\r\n6873,Intolerable Cruelty (2003),Comedy|Romance\r\n6874,Kill Bill: Vol. 1 (2003),Action|Crime|Thriller\r\n6879,Runaway Jury (2003),Drama|Thriller\r\n6888,Scary Movie 3 (2003),Comedy|Horror\r\n6936,Elf (2003),Children|Comedy|Fantasy\r\n6942,Love Actually (2003),Comedy|Drama|Romance\r\n6953,21 Grams (2003),Crime|Drama|Mystery|Romance|Thriller\r\n6957,Bad Santa (2003),Comedy|Crime\r\n6958,\"Haunted Mansion, The (2003)\",Children|Comedy|Fantasy|Horror\r\n7090,Hero (Ying xiong) (2002),Action|Adventure|Drama\r\n7137,\"Cooler, The (2003)\",Comedy|Drama|Romance\r\n7147,Big Fish (2003),Drama|Fantasy|Romance\r\n7149,Something's Gotta Give (2003),Comedy|Drama|Romance\r\n7150,Stuck on You (2003),Comedy\r\n7151,Girl with a Pearl Earring (2003),Drama|Romance\r\n7153,\"Lord of the Rings: The Return of the King, The (2003)\",Action|Adventure|Drama|Fantasy\r\n7154,Mona Lisa Smile (2003),Drama|Romance\r\n7160,Monster (2003),Crime|Drama\r\n7163,Paycheck (2003),Action|Sci-Fi|Thriller\r\n7173,Along Came Polly (2004),Comedy|Romance\r\n7254,The Butterfly Effect (2004),Drama|Sci-Fi|Thriller\r\n7263,Miracle (2004),Drama\r\n7265,\"Dreamers, The (2003)\",Drama\r\n7293,50 First Dates (2004),Comedy|Romance\r\n7317,EuroTrip (2004),Adventure|Comedy\r\n7323,\"Good bye, Lenin! (2003)\",Comedy|Drama\r\n7324,Hidalgo (2004),Adventure|Drama\r\n7325,Starsky & Hutch (2004),Action|Comedy|Crime|Thriller\r\n7347,Secret Window (2004),Mystery|Thriller\r\n7360,Dawn of the Dead (2004),Action|Drama|Horror|Thriller\r\n7361,Eternal Sunshine of the Spotless Mind (2004),Drama|Romance|Sci-Fi\r\n7367,\"Ladykillers, The (2004)\",Comedy|Crime\r\n7371,Dogville (2003),Drama|Mystery|Thriller\r\n7373,Hellboy (2004),Action|Adventure|Fantasy|Horror\r\n7438,Kill Bill: Vol. 2 (2004),Action|Drama|Thriller\r\n7439,\"Punisher, The (2004)\",Action|Crime|Thriller\r\n7444,13 Going on 30 (2004),Comedy|Fantasy|Romance\r\n7445,Man on Fire (2004),Action|Crime|Drama|Mystery|Thriller\r\n7451,Mean Girls (2004),Comedy\r\n7454,Van Helsing (2004),Action|Adventure|Fantasy|Horror\r\n8014,\"Spring, Summer, Fall, Winter... and Spring (Bom yeoreum gaeul gyeoul geurigo bom) (2003)\",Drama\r\n8361,\"Day After Tomorrow, The (2004)\",Action|Adventure|Drama|Sci-Fi|Thriller\r\n8366,Saved! (2004),Comedy|Drama\r\n8370,\"Blind Swordsman: Zatoichi, The (Zatôichi) (2003)\",Action|Comedy|Crime|Drama\r\n8371,\"Chronicles of Riddick, The (2004)\",Action|Sci-Fi|Thriller\r\n8373,\"Stepford Wives, The (2004)\",Comedy|Fantasy|Thriller\r\n8376,Napoleon Dynamite (2004),Comedy\r\n8464,Super Size Me (2004),Comedy|Documentary|Drama\r\n8528,Dodgeball: A True Underdog Story (2004),Comedy\r\n8529,\"Terminal, The (2004)\",Comedy|Drama|Romance\r\n8533,\"Notebook, The (2004)\",Drama|Romance\r\n8622,Fahrenheit 9/11 (2004),Documentary\r\n8638,Before Sunset (2004),Drama|Romance\r\n8641,Anchorman: The Legend of Ron Burgundy (2004),Comedy\r\n8644,\"I, Robot (2004)\",Action|Adventure|Sci-Fi|Thriller\r\n8665,\"Bourne Supremacy, The (2004)\",Action|Crime|Thriller\r\n8781,\"Manchurian Candidate, The (2004)\",Thriller\r\n8783,\"Village, The (2004)\",Drama|Mystery|Thriller\r\n8784,Garden State (2004),Comedy|Drama|Romance\r\n8798,Collateral (2004),Action|Crime|Drama|Thriller\r\n8807,Harold and Kumar Go to White Castle (2004),Adventure|Comedy\r\n8810,AVP: Alien vs. Predator (2004),Action|Horror|Sci-Fi|Thriller\r\n8861,Resident Evil: Apocalypse (2004),Action|Horror|Sci-Fi|Thriller\r\n8865,Sky Captain and the World of Tomorrow (2004),Action|Adventure|Sci-Fi\r\n8873,\"Motorcycle Diaries, The (Diarios de motocicleta) (2004)\",Adventure|Drama\r\n8874,Shaun of the Dead (2004),Comedy|Horror\r\n8907,Shark Tale (2004),Animation|Children|Comedy\r\n8910,I Heart Huckabees (2004),Comedy\r\n8914,Primer (2004),Drama|Sci-Fi\r\n8917,Team America: World Police (2004),Action|Adventure|Animation|Comedy\r\n8947,\"Grudge, The (2004)\",Horror|Mystery|Thriller\r\n8949,Sideways (2004),Comedy|Drama|Romance\r\n8950,The Machinist (2004),Drama|Mystery|Thriller\r\n8957,Saw (2004),Horror|Mystery|Thriller\r\n8958,Ray (2004),Drama\r\n8961,\"Incredibles, The (2004)\",Action|Adventure|Animation|Children|Comedy\r\n8969,Bridget Jones: The Edge of Reason (2004),Comedy|Drama|Romance\r\n8970,Finding Neverland (2004),Drama\r\n8972,National Treasure (2004),Action|Adventure|Drama|Mystery|Thriller\r\n8981,Closer (2004),Drama|Romance\r\n8983,House of Flying Daggers (Shi mian mai fu) (2004),Action|Drama|Romance\r\n8984,Ocean's Twelve (2004),Action|Comedy|Crime|Thriller\r\n8985,Blade: Trinity (2004),Action|Fantasy|Horror|Thriller\r\n27660,\"Animatrix, The (2003)\",Action|Animation|Drama|Sci-Fi\r\n27706,Lemony Snicket's A Series of Unfortunate Events (2004),Adventure|Children|Comedy|Fantasy\r\n27773,Old Boy (2003),Mystery|Thriller\r\n27801,Ong-Bak: The Thai Warrior (Ong Bak) (2003),Action|Thriller\r\n27808,Spanglish (2004),Comedy|Drama|Romance\r\n27815,\"Chorus, The (Choristes, Les) (2004)\",Drama\r\n27831,Layer Cake (2004),Crime|Drama|Thriller\r\n27846,\"Corporation, The (2003)\",Documentary\r\n27904,\"Scanner Darkly, A (2006)\",Animation|Drama|Mystery|Sci-Fi|Thriller\r\n30707,Million Dollar Baby (2004),Drama\r\n30810,\"Life Aquatic with Steve Zissou, The (2004)\",Adventure|Comedy|Fantasy\r\n30812,\"Aviator, The (2004)\",Drama\r\n30822,In Good Company (2004),Comedy|Drama\r\n30825,Meet the Fockers (2004),Comedy\r\n31221,Elektra (2005),Action|Adventure|Crime|Drama\r\n31433,\"Wedding Date, The (2005)\",Comedy|Romance\r\n31658,Howl's Moving Castle (Hauru no ugoku shiro) (2004),Adventure|Animation|Fantasy|Romance\r\n31685,Hitch (2005),Comedy|Romance\r\n31696,Constantine (2005),Action|Fantasy|Horror|Thriller\r\n31878,Kung Fu Hustle (Gong fu) (2004),Action|Comedy\r\n32029,Hostage (2005),Action|Crime|Drama|Thriller\r\n33004,\"Hitchhiker's Guide to the Galaxy, The (2005)\",Adventure|Comedy|Sci-Fi\r\n33166,Crash (2004),Crime|Drama\r\n33493,Star Wars: Episode III - Revenge of the Sith (2005),Action|Adventure|Sci-Fi\r\n33615,Madagascar (2005),Adventure|Animation|Children|Comedy\r\n33660,Cinderella Man (2005),Drama|Romance\r\n33679,Mr. & Mrs. Smith (2005),Action|Adventure|Comedy|Romance\r\n33836,Bewitched (2005),Comedy|Fantasy|Romance\r\n34048,War of the Worlds (2005),Action|Adventure|Sci-Fi|Thriller\r\n34072,\"March of the Penguins (Marche de l'empereur, La) (2005)\",Documentary\r\n34150,Fantastic Four (2005),Action|Adventure|Sci-Fi\r\n34162,Wedding Crashers (2005),Comedy|Romance\r\n34319,\"Island, The (2005)\",Action|Sci-Fi|Thriller\r\n34405,Serenity (2005),Action|Adventure|Sci-Fi\r\n34437,Broken Flowers (2005),Comedy|Drama\r\n34520,\"Dukes of Hazzard, The (2005)\",Action|Adventure|Comedy\r\n35836,\"40-Year-Old Virgin, The (2005)\",Comedy|Romance\r\n36401,\"Brothers Grimm, The (2005)\",Comedy|Fantasy|Horror|Thriller\r\n36517,\"Constant Gardener, The (2005)\",Drama|Thriller\r\n36519,Transporter 2 (2005),Action|Crime|Thriller\r\n36708,Family Guy Presents Stewie Griffin: The Untold Story (2005),Adventure|Animation|Comedy\r\n37380,Doom (2005),Action|Horror|Sci-Fi\r\n37384,Waiting... (2005),Comedy\r\n37386,Aeon Flux (2005),Action|Sci-Fi\r\n37727,Flightplan (2005),Action|Drama|Thriller\r\n37733,\"History of Violence, A (2005)\",Action|Crime|Drama|Thriller\r\n37741,Capote (2005),Crime|Drama\r\n37830,Final Fantasy VII: Advent Children (2004),Action|Adventure|Animation|Fantasy|Sci-Fi\r\n38038,Wallace & Gromit in The Curse of the Were-Rabbit (2005),Adventure|Animation|Children|Comedy\r\n38061,Kiss Kiss Bang Bang (2005),Comedy|Crime|Mystery|Thriller\r\n39183,Brokeback Mountain (2005),Drama|Romance\r\n39292,\"Good Night, and Good Luck. (2005)\",Crime|Drama\r\n39446,Saw II (2005),Horror|Thriller\r\n40583,Syriana (2005),Drama|Thriller\r\n40629,Pride & Prejudice (2005),Drama|Romance\r\n40732,\"Descent, The (2005)\",Adventure|Drama|Horror|Thriller\r\n41285,Match Point (2005),Crime|Drama|Romance\r\n41566,\"Chronicles of Narnia: The Lion, the Witch and the Wardrobe, The (2005)\",Adventure|Children|Fantasy\r\n41569,King Kong (2005),Action|Adventure|Drama|Fantasy|Thriller\r\n41997,Munich (2005),Action|Crime|Drama|Thriller\r\n42011,Fun with Dick and Jane (2005),Comedy|Crime\r\n42723,Hostel (2005),Horror\r\n42738,Underworld: Evolution (2006),Action|Fantasy|Horror\r\n43928,Ultraviolet (2006),Action|Fantasy|Sci-Fi|Thriller\r\n44022,Ice Age 2: The Meltdown (2006),Adventure|Animation|Children|Comedy\r\n44195,Thank You for Smoking (2006),Comedy|Drama\r\n44199,Inside Man (2006),Crime|Drama|Thriller\r\n44555,\"Lives of Others, The (Das leben der Anderen) (2006)\",Drama|Romance|Thriller\r\n44665,Lucky Number Slevin (2006),Crime|Drama|Mystery\r\n44840,\"Benchwarmers, The (2006)\",Comedy\r\n44972,Scary Movie 4 (2006),Comedy|Horror\r\n44974,Hard Candy (2005),Drama|Thriller\r\n45081,Silent Hill (2006),Fantasy|Horror|Thriller\r\n45186,Mission: Impossible III (2006),Action|Adventure|Thriller\r\n45431,Over the Hedge (2006),Adventure|Animation|Children|Comedy\r\n45447,\"Da Vinci Code, The (2006)\",Drama|Mystery|Thriller\r\n45499,X-Men: The Last Stand (2006),Action|Sci-Fi|Thriller\r\n45501,\"Break-Up, The (2006)\",Comedy|Drama|Romance\r\n45517,Cars (2006),Animation|Children|Comedy\r\n45666,Nacho Libre (2006),Comedy\r\n45668,\"Lake House, The (2006)\",Drama|Fantasy|Romance\r\n45672,Click (2006),Adventure|Comedy|Drama|Fantasy|Romance\r\n45720,\"Devil Wears Prada, The (2006)\",Comedy|Drama\r\n45722,Pirates of the Caribbean: Dead Man's Chest (2006),Action|Adventure|Fantasy\r\n45728,Clerks II (2006),Comedy\r\n45880,Marie Antoinette (2006),Drama|Romance\r\n45950,\"Inconvenient Truth, An (2006)\",Documentary\r\n46335,\"Fast and the Furious: Tokyo Drift, The (Fast and the Furious 3, The) (2006)\",Action|Crime|Drama|Thriller\r\n46578,Little Miss Sunshine (2006),Adventure|Comedy|Drama\r\n46723,Babel (2006),Drama|Thriller\r\n46965,Snakes on a Plane (2006),Action|Comedy|Horror|Thriller\r\n46970,Talladega Nights: The Ballad of Ricky Bobby (2006),Action|Comedy\r\n46976,Stranger than Fiction (2006),Comedy|Drama|Fantasy|Romance\r\n47044,Miami Vice (2006),Action|Crime|Drama|Thriller\r\n47099,\"Pursuit of Happyness, The (2006)\",Drama\r\n47200,Crank (2006),Action|Thriller\r\n47518,Accepted (2006),Comedy\r\n47610,\"Illusionist, The (2006)\",Drama|Fantasy|Mystery|Romance\r\n47629,The Queen (2006),Drama\r\n47997,Idiocracy (2006),Adventure|Comedy|Sci-Fi|Thriller\r\n48043,\"Fountain, The (2006)\",Drama|Fantasy|Romance\r\n48082,\"Science of Sleep, The (La science des rêves) (2006)\",Comedy|Drama|Fantasy|Romance\r\n48304,Apocalypto (2006),Adventure|Drama|Thriller\r\n48385,Borat: Cultural Learnings of America for Make Benefit Glorious Nation of Kazakhstan (2006),Comedy\r\n48394,\"Pan's Labyrinth (Laberinto del fauno, El) (2006)\",Drama|Fantasy|Thriller\r\n48516,\"Departed, The (2006)\",Crime|Drama|Thriller\r\n48738,\"Last King of Scotland, The (2006)\",Drama|Thriller\r\n48774,Children of Men (2006),Action|Adventure|Drama|Sci-Fi|Thriller\r\n48780,\"Prestige, The (2006)\",Drama|Mystery|Sci-Fi|Thriller\r\n48877,Saw III (2006),Crime|Horror|Thriller\r\n49272,Casino Royale (2006),Action|Adventure|Thriller\r\n49278,Déjà Vu (Deja Vu) (2006),Action|Sci-Fi|Thriller\r\n49286,\"Holiday, The (2006)\",Comedy|Romance\r\n49649,Eragon (2006),Action|Adventure|Fantasy\r\n49651,Rocky Balboa (2006),Action|Drama\r\n50794,Smokin' Aces (2006),Action|Crime|Drama|Thriller\r\n50872,Ratatouille (2007),Animation|Children|Drama\r\n51077,Ghost Rider (2007),Action|Fantasy|Thriller\r\n51084,Music and Lyrics (2007),Comedy|Romance\r\n51086,\"Number 23, The (2007)\",Drama|Mystery|Thriller\r\n51255,Hot Fuzz (2007),Action|Comedy|Crime|Mystery\r\n51412,Next (2007),Action|Sci-Fi|Thriller\r\n51540,Zodiac (2007),Crime|Drama|Thriller\r\n51935,Shooter (2007),Action|Drama|Thriller\r\n52245,Blades of Glory (2007),Comedy|Romance\r\n52281,Grindhouse (2007),Action|Crime|Horror|Sci-Fi|Thriller\r\n52287,Meet the Robinsons (2007),Action|Adventure|Animation|Children|Comedy|Sci-Fi\r\n52328,Sunshine (2007),Adventure|Drama|Sci-Fi|Thriller\r\n52458,Disturbia (2007),Drama|Thriller\r\n52604,Fracture (2007),Crime|Drama|Mystery|Thriller\r\n52973,Knocked Up (2007),Comedy|Drama|Romance\r\n53000,28 Weeks Later (2007),Horror|Sci-Fi|Thriller\r\n53121,Shrek the Third (2007),Adventure|Animation|Children|Comedy|Fantasy\r\n53125,Pirates of the Caribbean: At World's End (2007),Action|Adventure|Comedy|Fantasy\r\n53322,Ocean's Thirteen (2007),Crime|Thriller\r\n53464,Fantastic Four: Rise of the Silver Surfer (2007),Action|Adventure|Sci-Fi\r\n53519,Death Proof (2007),Action|Adventure|Crime|Horror|Thriller\r\n53894,Sicko (2007),Documentary|Drama\r\n53953,1408 (2007),Drama|Horror|Thriller\r\n53972,Live Free or Die Hard (2007),Action|Adventure|Crime|Thriller\r\n53993,Evan Almighty (2007),Comedy|Fantasy\r\n54004,I Now Pronounce You Chuck and Larry (2007),Comedy|Romance\r\n54259,Stardust (2007),Adventure|Comedy|Fantasy|Romance\r\n54272,\"Simpsons Movie, The (2007)\",Animation|Comedy\r\n54286,\"Bourne Ultimatum, The (2007)\",Action|Crime|Thriller\r\n54503,Superbad (2007),Comedy\r\n54881,\"King of Kong, The (2007)\",Documentary\r\n54995,Planet Terror (2007),Action|Horror|Sci-Fi\r\n54999,Shoot 'Em Up (2007),Action|Comedy|Crime\r\n55118,Eastern Promises (2007),Crime|Drama|Thriller\r\n55232,Resident Evil: Extinction (2007),Action|Horror|Sci-Fi|Thriller\r\n55247,Into the Wild (2007),Action|Adventure|Drama\r\n55269,\"Darjeeling Limited, The (2007)\",Adventure|Comedy|Drama\r\n55276,Michael Clayton (2007),Drama|Thriller\r\n55282,30 Days of Night (2007),Horror|Thriller\r\n55290,Gone Baby Gone (2007),Crime|Drama|Mystery\r\n55442,Persepolis (2007),Animation|Drama\r\n55721,Elite Squad (Tropa de Elite) (2007),Action|Crime|Drama|Thriller\r\n55765,American Gangster (2007),Crime|Drama|Thriller\r\n55768,Bee Movie (2007),Animation|Comedy\r\n55820,No Country for Old Men (2007),Crime|Drama\r\n55830,Be Kind Rewind (2008),Comedy\r\n56145,\"Mist, The (2007)\",Horror|Sci-Fi\r\n56171,\"Golden Compass, The (2007)\",Adventure|Children|Fantasy\r\n56251,Futurama: Bender's Big Score (2007),Animation|Comedy|Sci-Fi\r\n56367,Juno (2007),Comedy|Drama|Romance\r\n56587,\"Bucket List, The (2007)\",Comedy|Drama\r\n56775,National Treasure: Book of Secrets (2007),Action|Adventure\r\n56941,P.S. I Love You (2007),Comedy|Drama|Romance\r\n56949,27 Dresses (2008),Comedy|Romance\r\n57368,Cloverfield (2008),Action|Mystery|Sci-Fi|Thriller\r\n57504,\"Girl Who Leapt Through Time, The (Toki o kakeru shôjo) (2006)\",Animation|Comedy|Drama|Romance|Sci-Fi\r\n57640,Hellboy II: The Golden Army (2008),Action|Adventure|Fantasy|Sci-Fi\r\n57669,In Bruges (2008),Comedy|Crime|Drama|Thriller\r\n58025,Jumper (2008),Action|Adventure|Drama|Sci-Fi|Thriller\r\n58047,\"Definitely, Maybe (2008)\",Comedy|Drama|Romance\r\n58156,Semi-Pro (2008),Comedy\r\n58293,\"10,000 BC (2008)\",Adventure|Romance|Thriller\r\n58295,\"Bank Job, The (2008)\",Action|Crime|Thriller\r\n58803,21 (2008),Crime|Drama|Romance|Thriller\r\n58998,Forgetting Sarah Marshall (2008),Comedy|Romance\r\n59022,Harold & Kumar Escape from Guantanamo Bay (2008),Adventure|Comedy\r\n59258,Baby Mama (2008),Comedy\r\n59315,Iron Man (2008),Action|Adventure|Sci-Fi\r\n59369,Taken (2008),Action|Crime|Drama|Thriller\r\n59387,\"Fall, The (2006)\",Adventure|Drama|Fantasy\r\n59501,\"Chronicles of Narnia: Prince Caspian, The (2008)\",Adventure|Children|Fantasy\r\n59615,Indiana Jones and the Kingdom of the Crystal Skull (2008),Action|Adventure|Comedy|Sci-Fi\r\n59725,Sex and the City (2008),Comedy|Romance\r\n59900,You Don't Mess with the Zohan (2008),Comedy\r\n60040,\"Incredible Hulk, The (2008)\",Action|Sci-Fi\r\n60069,WALL·E (2008),Adventure|Animation|Children|Romance|Sci-Fi\r\n60072,Wanted (2008),Action|Thriller\r\n60074,Hancock (2008),Action|Adventure|Comedy|Crime|Fantasy\r\n60126,Get Smart (2008),Action|Comedy\r\n60756,Step Brothers (2008),Comedy\r\n60950,Vicky Cristina Barcelona (2008),Comedy|Drama|Romance\r\n61024,Pineapple Express (2008),Action|Comedy|Crime\r\n61240,Let the Right One In (Låt den rätte komma in) (2008),Drama|Fantasy|Horror|Romance\r\n61323,Burn After Reading (2008),Comedy|Crime|Drama\r\n62155,Nick and Norah's Infinite Playlist (2008),Comedy|Drama|Romance\r\n62374,Body of Lies (2008),Action|Drama|Thriller\r\n62849,RocknRolla (2008),Action|Crime\r\n63082,Slumdog Millionaire (2008),Crime|Drama|Romance\r\n63113,Quantum of Solace (2008),Action|Adventure|Thriller\r\n63131,Role Models (2008),Comedy\r\n63859,Bolt (2008),Action|Adventure|Animation|Children|Comedy\r\n63876,Milk (2008),Drama\r\n63992,Twilight (2008),Drama|Fantasy|Romance|Thriller\r\n64614,Gran Torino (2008),Crime|Drama\r\n64716,Seven Pounds (2008),Drama\r\n64839,\"Wrestler, The (2008)\",Drama\r\n64957,\"Curious Case of Benjamin Button, The (2008)\",Drama|Fantasy|Mystery|Romance\r\n64969,Yes Man (2008),Comedy\r\n65088,Bedtime Stories (2008),Adventure|Children|Comedy\r\n65230,Marley & Me (2008),Comedy|Drama\r\n65261,Ponyo (Gake no ue no Ponyo) (2008),Adventure|Animation|Children|Fantasy\r\n66097,Coraline (2009),Animation|Fantasy|Thriller\r\n66203,He's Just Not That Into You (2009),Comedy|Drama|Romance\r\n67087,\"I Love You, Man (2009)\",Comedy\r\n67255,\"Girl with the Dragon Tattoo, The (Män som hatar kvinnor) (2009)\",Crime|Drama|Mystery|Thriller\r\n67734,Adventureland (2009),Comedy|Drama\r\n67923,\"Fast & Furious (Fast and the Furious 4, The) (2009)\",Action|Crime|Drama|Thriller\r\n68135,17 Again (2009),Comedy|Drama\r\n68237,Moon (2009),Drama|Mystery|Sci-Fi|Thriller\r\n68319,X-Men Origins: Wolverine (2009),Action|Sci-Fi|Thriller\r\n68554,Angels & Demons (2009),Crime|Drama|Mystery|Thriller\r\n68791,Terminator Salvation (2009),Action|Adventure|Sci-Fi|Thriller\r\n68954,Up (2009),Adventure|Animation|Children|Drama\r\n69122,\"Hangover, The (2009)\",Comedy|Crime\r\n69406,\"Proposal, The (2009)\",Comedy|Romance\r\n69644,Ice Age: Dawn of the Dinosaurs (2009),Action|Adventure|Animation|Children|Comedy|Romance\r\n69757,(500) Days of Summer (2009),Comedy|Drama|Romance\r\n69784,Brüno (Bruno) (2009),Comedy\r\n69951,\"Imaginarium of Doctor Parnassus, The (2009)\",Drama|Fantasy\r\n70183,\"Ugly Truth, The (2009)\",Comedy|Drama|Romance\r\n70286,District 9 (2009),Mystery|Sci-Fi|Thriller\r\n70293,Julie & Julia (2009),Comedy|Drama|Romance\r\n71057,9 (2009),Adventure|Animation|Sci-Fi\r\n71464,\"Serious Man, A (2009)\",Comedy|Drama\r\n71530,Surrogates (2009),Action|Sci-Fi|Thriller\r\n71535,Zombieland (2009),Action|Comedy|Horror\r\n71899,Mary and Max (2009),Animation|Comedy|Drama\r\n72011,Up in the Air (2009),Drama|Romance\r\n72226,Fantastic Mr. Fox (2009),Adventure|Animation|Children|Comedy|Crime\r\n72378,2012 (2009),Action|Drama|Sci-Fi|Thriller\r\n72641,\"Blind Side, The  (2009)\",Drama\r\n73017,Sherlock Holmes (2009),Action|Crime|Mystery|Thriller\r\n73321,\"Book of Eli, The (2010)\",Action|Adventure|Drama\r\n74458,Shutter Island (2010),Drama|Mystery|Thriller\r\n76077,Hot Tub Time Machine (2010),Comedy|Sci-Fi\r\n76175,Clash of the Titans (2010),Action|Adventure|Drama|Fantasy\r\n76251,Kick-Ass (2010),Action|Comedy\r\n76293,Date Night (2010),Action|Comedy|Romance\r\n77455,Exit Through the Gift Shop (2010),Comedy|Documentary\r\n78209,Get Him to the Greek (2010),Comedy\r\n78469,\"A-Team, The (2010)\",Action|Comedy|Thriller\r\n79057,Predators (2010),Action|Sci-Fi|Thriller\r\n79091,Despicable Me (2010),Animation|Children|Comedy|Crime\r\n79134,Grown Ups (2010),Comedy\r\n79139,\"Sorcerer's Apprentice, The (2010)\",Action|Adventure|Children|Comedy|Fantasy\r\n79293,Salt (2010),Action|Thriller\r\n79592,\"Other Guys, The (2010)\",Action|Comedy\r\n79695,\"Expendables, The (2010)\",Action|Adventure|Thriller\r\n80219,Machete (2010),Action|Adventure|Comedy|Crime|Thriller\r\n80463,\"Social Network, The (2010)\",Drama\r\n80489,\"Town, The (2010)\",Crime|Drama|Thriller\r\n80549,Easy A (2010),Comedy|Romance\r\n80906,Inside Job (2010),Documentary\r\n81229,Red (2010),Action|Comedy\r\n81537,Due Date (2010),Comedy\r\n81562,127 Hours (2010),Adventure|Drama|Thriller\r\n81591,Black Swan (2010),Drama|Thriller\r\n81845,\"King's Speech, The (2010)\",Drama\r\n81932,\"Fighter, The (2010)\",Drama\r\n83134,Tucker & Dale vs Evil (2010),Comedy|Horror\r\n84152,Limitless (2011),Sci-Fi|Thriller\r\n84374,No Strings Attached (2011),Comedy|Romance\r\n84392,\"Lincoln Lawyer, The (2011)\",Crime|Drama|Thriller\r\n84772,Paul (2011),Adventure|Comedy|Sci-Fi\r\n84954,\"Adjustment Bureau, The (2011)\",Romance|Sci-Fi|Thriller\r\n85414,Source Code (2011),Action|Drama|Mystery|Sci-Fi|Thriller\r\n86190,Hanna (2011),Action|Adventure|Mystery|Thriller\r\n86833,Bridesmaids (2011),Comedy\r\n86882,Midnight in Paris (2011),Comedy|Fantasy|Romance\r\n86911,\"Hangover Part II, The (2011)\",Comedy\r\n87430,Green Lantern (2011),Action|Adventure|Sci-Fi\r\n87869,Horrible Bosses (2011),Comedy|Crime\r\n88163,\"Crazy, Stupid, Love. (2011)\",Comedy|Drama|Romance\r\n88405,Friends with Benefits (2011),Comedy|Romance\r\n88744,Rise of the Planet of the Apes (2011),Action|Drama|Sci-Fi|Thriller\r\n88785,\"Change-Up, The (2011)\",Comedy\r\n88810,\"Help, The (2011)\",Drama\r\n89492,Moneyball (2011),Drama\r\n89774,Warrior (2011),Drama\r\n89864,50/50 (2011),Comedy|Drama\r\n89904,The Artist (2011),Comedy|Drama|Romance\r\n90405,In Time (2011),Crime|Sci-Fi|Thriller\r\n90866,Hugo (2011),Children|Drama|Mystery\r\n91077,\"Descendants, The (2011)\",Comedy|Drama\r\n91485,\"Expendables 2, The (2012)\",Action|Adventure\r\n91500,The Hunger Games (2012),Action|Adventure|Drama|Sci-Fi|Thriller\r\n91542,Sherlock Holmes: A Game of Shadows (2011),Action|Adventure|Comedy|Crime|Mystery|Thriller\r\n91658,\"Girl with the Dragon Tattoo, The (2011)\",Drama|Thriller\r\n92259,Intouchables (2011),Comedy|Drama\r\n92535,Louis C.K.: Live at the Beacon Theater (2011),Comedy\r\n93326,This Means War (2012),Action|Comedy|Romance\r\n93510,21 Jump Street (2012),Action|Comedy|Crime\r\n93840,\"Cabin in the Woods, The (2012)\",Comedy|Horror|Sci-Fi|Thriller\r\n94677,\"Dictator, The (2012)\",Comedy\r\n94959,Moonrise Kingdom (2012),Comedy|Drama|Romance\r\n95167,Brave (2012),Action|Adventure|Animation|Children\r\n95441,Ted (2012),Comedy|Fantasy\r\n96610,Looper (2012),Action|Crime|Sci-Fi\r\n96737,Dredd (2012),Action|Sci-Fi\r\n96821,\"Perks of Being a Wallflower, The (2012)\",Drama|Romance\r\n96829,\"Hunt, The (Jagten) (2012)\",Drama\r\n97225,Hotel Transylvania (2012),Animation|Children|Comedy\r\n97304,Argo (2012),Drama|Thriller\r\n97306,Seven Psychopaths (2012),Comedy|Crime\r\n97913,Wreck-It Ralph (2012),Animation|Comedy\r\n97921,Silver Linings Playbook (2012),Comedy|Drama\r\n98961,Zero Dark Thirty (2012),Action|Drama|Thriller\r\n99007,Warm Bodies (2013),Comedy|Horror|Romance\r\n99112,Jack Reacher (2012),Action|Crime|Thriller\r\n102123,This Is the End (2013),Action|Comedy\r\n102407,\"Great Gatsby, The (2013)\",Drama\r\n102481,\"Internship, The (2013)\",Comedy\r\n102903,Now You See Me (2013),Crime|Mystery|Thriller\r\n103141,Monsters University (2013),Adventure|Animation|Comedy\r\n103341,\"World's End, The (2013)\",Action|Comedy|Sci-Fi\r\n103772,\"Wolverine, The (2013)\",Action|Adventure|Fantasy|Sci-Fi\r\n104211,We're the Millers (2013),Comedy|Crime\r\n104241,Kick-Ass 2 (2013),Action|Comedy|Crime\r\n104374,About Time (2013),Drama|Fantasy|Romance\r\n104879,Prisoners (2013),Drama|Mystery|Thriller\r\n104913,Rush (2013),Action|Drama\r\n105844,12 Years a Slave (2013),Drama\r\n106100,Dallas Buyers Club (2013),Drama\r\n106782,\"Wolf of Wall Street, The (2013)\",Comedy|Crime|Drama\r\n106916,American Hustle (2013),Crime|Drama\r\n106918,\"Secret Life of Walter Mitty, The (2013)\",Adventure|Comedy|Drama\r\n106920,Her (2013),Drama|Romance|Sci-Fi\r\n107348,Anchorman 2: The Legend Continues (2013),Comedy\r\n107406,Snowpiercer (2013),Action|Drama|Sci-Fi\r\n108932,The Lego Movie (2014),Action|Adventure|Animation|Children|Comedy|Fantasy\r\n109374,\"Grand Budapest Hotel, The (2014)\",Comedy|Drama\r\n111113,Neighbors (2014),Comedy\r\n111360,Lucy (2014),Action|Sci-Fi\r\n111362,X-Men: Days of Future Past (2014),Action|Adventure|Sci-Fi\r\n111781,Mission: Impossible - Rogue Nation (2015),Action|Adventure|Thriller\r\n112138,22 Jump Street (2014),Action|Comedy|Crime\r\n112175,How to Train Your Dragon 2 (2014),Action|Adventure|Animation\r\n112183,Birdman: Or (The Unexpected Virtue of Ignorance) (2014),Comedy|Drama\r\n112290,Boyhood (2014),Drama\r\n112552,Whiplash (2014),Drama\r\n112556,Gone Girl (2014),Drama|Thriller\r\n112623,Dawn of the Planet of the Apes (2014),Sci-Fi\r\n112852,Guardians of the Galaxy (2014),Action|Adventure|Sci-Fi\r\n114180,\"Maze Runner, The (2014)\",Action|Mystery|Sci-Fi\r\n114935,Predestination (2014),Action|Mystery|Sci-Fi|Thriller\r\n115149,John Wick (2014),Action|Thriller\r\n115569,Nightcrawler (2014),Crime|Drama|Thriller\r\n115617,Big Hero 6 (2014),Action|Animation|Comedy\r\n115713,Ex Machina (2015),Drama|Sci-Fi|Thriller\r\n116823,The Hunger Games: Mockingjay - Part 1 (2014),Adventure|Sci-Fi|Thriller\r\n116897,Wild Tales (2014),Comedy|Drama|Thriller\r\n117176,The Theory of Everything (2014),Drama|Romance\r\n117529,Jurassic World (2015),Action|Adventure|Drama|Sci-Fi|Thriller\r\n117590,Horrible Bosses 2 (2014),Comedy|Crime\r\n118696,The Hobbit: The Battle of the Five Armies (2014),Adventure|Fantasy\r\n119141,The Interview (2014),Action|Comedy\r\n119145,Kingsman: The Secret Service (2015),Action|Adventure|Comedy|Crime\r\n120466,Chappie (2015),Action|Thriller\r\n122882,Mad Max: Fury Road (2015),Action|Adventure|Sci-Fi|Thriller\r\n122892,Avengers: Age of Ultron (2015),Action|Adventure|Sci-Fi\r\n122900,Ant-Man (2015),Action|Adventure|Sci-Fi\r\n122904,Deadpool (2016),Action|Adventure|Comedy|Sci-Fi\r\n122906,Black Panther (2017),Action|Adventure|Sci-Fi\r\n122912,Avengers: Infinity War - Part I (2018),Action|Adventure|Sci-Fi\r\n122916,Thor: Ragnarok (2017),Action|Adventure|Sci-Fi\r\n122918,Guardians of the Galaxy 2 (2017),Action|Adventure|Sci-Fi\r\n122920,Captain America: Civil War (2016),Action|Sci-Fi|Thriller\r\n122922,Doctor Strange (2016),Action|Adventure|Sci-Fi\r\n122924,X-Men: Apocalypse (2016),Action|Adventure|Fantasy|Sci-Fi\r\n122926,Untitled Spider-Man Reboot (2017),Action|Adventure|Fantasy\r\n129354,Focus (2015),Comedy|Crime|Drama|Romance\r\n134130,The Martian (2015),Adventure|Drama|Sci-Fi\r\n134368,Spy (2015),Action|Comedy|Crime\r\n134393,Trainwreck (2015),Comedy|Romance\r\n134853,Inside Out (2015),Adventure|Animation|Children|Comedy|Drama|Fantasy\r\n135133,The Hunger Games: Mockingjay - Part 2 (2015),Adventure|Sci-Fi\r\n135143,Fantastic Beasts and Where to Find Them (2016),Fantasy\r\n135536,Suicide Squad (2016),Action|Crime|Sci-Fi\r\n135569,Star Trek Beyond (2016),Action|Adventure|Sci-Fi\r\n135887,Minions (2015),Adventure|Animation|Children|Comedy\r\n136020,Spectre (2015),Action|Adventure|Crime\r\n136864,Batman v Superman: Dawn of Justice (2016),Action|Adventure|Fantasy|Sci-Fi\r\n137857,The Jungle Book (2016),Adventure|Drama|Fantasy\r\n138036,The Man from U.N.C.L.E. (2015),Action|Adventure|Comedy\r\n139385,The Revenant (2015),Adventure|Drama\r\n139644,Sicario (2015),Crime|Drama|Mystery\r\n140110,The Intern (2015),Comedy\r\n142488,Spotlight (2015),Thriller\r\n143355,Wonder Woman (2017),Action|Adventure|Fantasy\r\n148626,\"Big Short, The (2015)\",Drama\r\n150548,Sherlock: The Abominable Bride (2016),Action|Crime|Drama|Mystery|Thriller\r\n152077,10 Cloverfield Lane (2016),Thriller\r\n152081,Zootopia (2016),Action|Adventure|Animation|Children|Comedy\r\n157296,Finding Dory (2016),Adventure|Animation|Comedy\r\n158238,The Nice Guys (2016),Crime|Mystery|Thriller\r\n159093,Now You See Me 2 (2016),Action|Comedy|Thriller\r\n164179,Arrival (2016),Sci-Fi\r\n166461,Moana (2016),Adventure|Animation|Children|Comedy|Fantasy\r\n166528,Rogue One: A Star Wars Story (2016),Action|Adventure|Fantasy|Sci-Fi\r\n166643,Hidden Figures (2016),Drama\r\n168250,Get Out (2017),Horror\r\n168252,Logan (2017),Action|Sci-Fi\r\n176371,Blade Runner 2049 (2017),Sci-Fi\r\n177765,Coco (2017),Adventure|Animation|Children\r\n179819,Star Wars: The Last Jedi (2017),Action|Adventure|Fantasy|Sci-Fi\r\n187593,Deadpool 2 (2018),Action|Comedy|Sci-Fi\r\n"
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  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/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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    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/data/content_y_train.csv",
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    "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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7,0.4126075,-0.36223966,-0.3201397\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/images/Course-3-Week-2-Practice-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Labs/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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2-Practice-Quiz/Course-3-Week-2-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-2/Course-3-Week-2.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-Optional-Labs/Course-3-Week-3-Optional-Labs-1-State-Action-Value-Function.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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-Optional-Labs/Course-3-Week-3-Optional-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-Practice-Labs/Course-3-Week-3-Practice-Labs-1-Reinforcement-Learning.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 0x7F1C6C0F35D0>\"\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\": null,\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 441 | Total point average of the last 100 episodes: 35.557\"\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\": null,\n   \"metadata\": {\n    \"id\": \"E_EUXxurfe8m\",\n    \"scrolled\": false\n   },\n   \"outputs\": [],\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\": null,\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\": null,\n   \"metadata\": {\n    \"id\": \"3Ttb_zLeJKiG\"\n   },\n   \"outputs\": [],\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"
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  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-Practice-Labs/Course-3-Week-3-Practice-Labs.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-Practice-Labs/images/Course-3-Week-3-Practice-Labs-images.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-Practice-Labs/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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-Practice-Labs/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": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3-Practice-Quiz/Course-3-Week-3-Practice-Quiz.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3-Week-3/Course-3-Week-3.md",
    "content": "\n"
  },
  {
    "path": "Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning/Course-3.md",
    "content": "\n"
  },
  {
    "path": "Course-Certificates/Course-Certificates.md",
    "content": "\n"
  },
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2022 Shantanu Umrani\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": "-------------------------------------------------------------------------------------------\n\n<p align=\"center\"><img width=\"auto\" src=\"https://cdn8.openculture.com/2022/06/21191516/Stanford-Deeplearning.ai-email-social-2.png\" /></p>\n\n-------------------------------------------------------------------------------------------\n\n# [DeepLearning.AI, Stanford University - Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction?#instructors)\n*This Repository Contains Solution to the Assignments of the Machine Learning Specialization from deeplearning.ai on Coursera Taught by \n[Andrew Ng](https://www.coursera.org/instructor/andrewng),\n[Eddy Shyu](https://www.coursera.org/instructor/eddy-shyu),\n[Aarti Bagul](https://www.coursera.org/instructor/~77736236),\n[Geoff Ladwig](https://www.coursera.org/instructor/geoff-ladwig)*\n\n-------------------------------------------------------------------------------------------\n\n## WHAT YOU WILL LEARN\n\n- Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)\n- Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods\n- Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection\n- Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model\n\n---\n\n## About this Specialization\n\nThe Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. \n\nThis Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.\n\nThis 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. \n\nIt provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)\n\nBy the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.\n\n---\n\n## Applied Learning Project\nBy the end of this Specialization, you will be ready to:\n\n-Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.\n- Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.\n- Build and train a neural network with TensorFlow to perform multi-class classification.\n- Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.\n- Build and use decision trees and tree ensemble methods, including random forests and boosted trees.\n- Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.\n- Build recommender systems with a collaborative filtering approach and a content-based deep learning method.\n- Build a deep reinforcement learning model.\n\n-------------------------------------------------------------------------------------------------------------\n\n## There are 3 Courses in this Specialization\n\n```mermaid\nflowchart TD\n    B[\"fa:fa-twitter Machine Learning Specialization\"]\n    B-->C[fa:fa-ban Supervised Machine Learning: Regression and Classification]\n    B-->D(fa:fa-spinner Advanced Learning Algorithms);\n    B-->E(fa:fa-camera-retro Unsupervised Learning, Recommenders, Reinforcement Learning)\n```\n\n### COURSE 1\n### Supervised Machine Learning: Regression and Classification\n\nIn the first course of the Machine Learning Specialization, you will:\n- Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.\n- Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression\n\n* [Supervised Machine Learning: Regression and Classification](https://github.com/shantanu1109/Coursera-DeepLearning.AI-Stanford-University-Machine-Learning-Specialization/tree/main/Course-1-Supervised-Machine-Learning-Regression-and-Classification)\n\n### COURSE 2\n### Advanced Learning Algorithms\n\nIn the second course of the Machine Learning Specialization, you will:\n- Build and train a neural network with TensorFlow to perform multi-class classification\n- Apply best practices for machine learning development so that your models generalize to data and tasks in the real world\n- Build and use decision trees and tree ensemble methods, including random forests and boosted trees\n\n* [Advanced Learning Algorithms](https://github.com/shantanu1109/Coursera-DeepLearning.AI-Stanford-University-Machine-Learning-Specialization/tree/main/Course-2-Advanced-Learning-Algorithms)\n\n### COURSE 3\n### Unsupervised Learning, Recommenders, Reinforcement Learning\nIn the third course of the Machine Learning Specialization, you will:\n- Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.\n- Build recommender systems with a collaborative filtering approach and a content-based deep learning method.\n- Build a deep reinforcement learning model.\n\n* [Unsupervised Learning, Recommenders, Reinforcement Learning](https://github.com/shantanu1109/Coursera-DeepLearning.AI-Stanford-University-Machine-Learning-Specialization/tree/main/Course-3-Unsupervised-Learning-Recommenders-Reinforcement-Learning)\n\n-------------------------------------------------------------------------------------------------------------\n\n## Certificate\n\n1. [Supervised Machine Learning: Regression and Classification](https://www.coursera.org/account/accomplishments/verify/77SF7ZRAHG2S)\n2. [Advanced Learning Algorithms](https://www.coursera.org/account/accomplishments/verify/EYY44BAYYAGU)\n3. [Unsupervised Learning, Recommenders, Reinforcement Learning](https://www.coursera.org/account/accomplishments/verify/DZGJW22HB7VZ)\n4. [Machine Learning Specialization (Final Certificate)](https://www.coursera.org/account/accomplishments/specialization/8F3W88UHF7CL)\n\n--------------------------------------------------------------------------------------------------------------\n\n## References\n1. [Supervised Machine Learning: Regression and Classification](https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction)\n2. [Advanced Learning Algorithms](https://www.coursera.org/learn/advanced-learning-algorithms?specialization=machine-learning-introduction)\n3. [Unsupervised Learning, Recommenders, Reinforcement Learning](https://www.coursera.org/learn/unsupervised-learning-recommenders-reinforcement-learning?specialization=machine-learning-introduction)\n\n----------------------------------------------------------------------------------------------------------------\n\n## 📝 Disclaimer \nI made this repository as *a reference*. Please do not copy paste the solution as is. You can find the solution if you read the instruction carefully. \n\n \n## 📝 License\nThe gem is available as open source under the terms of the [MIT License](https://opensource.org/licenses/MIT).\n \n-----------------------------------------------------------------------------------------------------------------\n\n\n"
  }
]