[
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2017-present François Chollet\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": "# Companion notebooks for Deep Learning with Python\n\nThis repository contains Jupyter notebooks implementing the code samples found in the book [Deep Learning with Python, third edition (2025)](https://www.manning.com/books/deep-learning-with-python-third-edition?a_aid=keras&a_bid=76564dff)\nby Francois Chollet and Matthew Watson. In addition, you will also find the legacy notebooks for the [second edition (2021)](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff)\nand the [first edition (2017)](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff).\n\nFor readability, these notebooks only contain runnable code blocks and section titles, and omit everything else in the book: text paragraphs, figures, and pseudocode.\n**If you want to be able to follow what's going on, I recommend reading the notebooks side by side with your copy of the book.**\n\n## Running the code\n\nWe recommend running these notebooks on [Colab](https://colab.google), which\nprovides a hosted runtime with all the dependencies you will need. You can also,\nrun these notebooks locally, either by setting up your own Jupyter environment,\nor using Colab's instructions for\n[running locally](https://research.google.com/colaboratory/local-runtimes.html).\n\nBy default, all notebooks will run on Colab's free tier GPU runtime, which\nis sufficient to run all code in this book. Chapter 8-18 chapters will benefit\nfrom a faster GPU if you have a Colab Pro subscription. You can change your\nruntime type using **Runtime -> Change runtime type** in Colab's dropdown menus.\n\n## Choosing a backend\n\nThe code for third edition is written using Keras 3. As such, it can be run with\nJAX, TensorFlow or PyTorch as a backend. To set the backend, update the backend\nin the cell at the top of the colab that looks like this:\n\n```python\nimport os\nos.environ[\"KERAS_BACKEND\"] = \"jax\"\n```\n\nThis must be done only once per session before importing Keras. If you are\nin the middle running a notebook, you will need to restart the notebook session\nand rerun all relevant notebook cells. This can be done in using\n**Runtime -> Restart Session** in Colab's dropdown menus.\n\n## Using Kaggle data\n\nThis book uses datasets and model weights provided by Kaggle, an online Machine\nLearning community and platform. You will need to create a Kaggle login to run\nKaggle code in this book; instructions are given in Chapter 8.\n\nFor chapters that need Kaggle data, you can login to Kaggle once per session\nwhen you hit the notebook cell with `kagglehub.login()`. Alternately,\nyou can set up your Kaggle login information once as Colab secrets:\n\n * Go to https://www.kaggle.com/ and sign in.\n * Go to https://www.kaggle.com/settings and generate a Kaggle API key.\n * Open the secrets tab in Colab by clicking the key icon on the left.\n * Add two secrets, `KAGGLE_USERNAME` and `KAGGLE_KEY` with the username and key\n   you just created.\n\nFollowing this approach you will only need to copy your Kaggle secret key once,\nthough you will need to allow each notebook to access your secrets when running\nthe relevant Kaggle code.\n\n## Table of contents\n\n* [Chapter 2: The mathematical building blocks of neural networks](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter02_mathematical-building-blocks.ipynb)\n* [Chapter 3: Introduction to TensorFlow, PyTorch, JAX, and Keras](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter03_introduction-to-ml-frameworks.ipynb)\n* [Chapter 4: Classification and regression](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter04_classification-and-regression.ipynb)\n* [Chapter 5: Fundamentals of machine learning](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter05_fundamentals-of-ml.ipynb)\n* [Chapter 7: A deep dive on Keras](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter07_deep-dive-keras.ipynb)\n* [Chapter 8: Image Classification](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter08_image-classification.ipynb)\n* [Chapter 9: Convnet architecture patterns](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter09_convnet-architecture-patterns.ipynb)\n* [Chapter 10: Interpreting what ConvNets learn](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter10_interpreting-what-convnets-learn.ipynb)\n* [Chapter 11: Image Segmentation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter11_image-segmentation.ipynb)\n* [Chapter 12: Object Detection](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter12_object-detection.ipynb)\n* [Chapter 13: Timeseries Forecasting](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter13_timeseries-forecasting.ipynb)\n* [Chapter 14: Text Classification](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter14_text-classification.ipynb)\n* [Chapter 15: Language Models and the Transformer](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter15_language-models-and-the-transformer.ipynb)\n* [Chapter 16: Text Generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter16_text-generation.ipynb)\n* [Chapter 17: Image Generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter17_image-generation.ipynb)\n* [Chapter 18: Best practices for the real world](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/chapter18_best-practices-for-the-real-world.ipynb)\n"
  },
  {
    "path": "chapter02_mathematical-building-blocks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"tensorflow\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## The mathematical building blocks of neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A first look at a neural network\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(train_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_images.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28))\\n\",\n    \"test_images = test_images.astype(\\\"float32\\\") / 255\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.fit(train_images, train_labels, epochs=5, batch_size=128)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_digits = test_images[0:10]\\n\",\n    \"predictions = model.predict(test_digits)\\n\",\n    \"predictions[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions[0].argmax()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions[0][7]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_labels[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(test_images, test_labels)\\n\",\n    \"print(f\\\"test_acc: {test_acc}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Data representations for neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Scalars (rank-0 tensors)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"x = np.array(12)\\n\",\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Vectors (rank-1 tensors)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([12, 3, 6, 14, 7])\\n\",\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Matrices (rank-2 tensors)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([[5, 78, 2, 34, 0],\\n\",\n    \"              [6, 79, 3, 35, 1],\\n\",\n    \"              [7, 80, 4, 36, 2]])\\n\",\n    \"x.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Rank-3 tensors and higher-rank tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([[[5, 78, 2, 34, 0],\\n\",\n    \"               [6, 79, 3, 35, 1],\\n\",\n    \"               [7, 80, 4, 36, 2]],\\n\",\n    \"              [[5, 78, 2, 34, 0],\\n\",\n    \"               [6, 79, 3, 35, 1],\\n\",\n    \"               [7, 80, 4, 36, 2]],\\n\",\n    \"              [[5, 78, 2, 34, 0],\\n\",\n    \"               [6, 79, 3, 35, 1],\\n\",\n    \"               [7, 80, 4, 36, 2]]])\\n\",\n    \"x.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Key attributes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"digit = train_images[4]\\n\",\n    \"plt.imshow(digit, cmap=plt.cm.binary)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels[4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Manipulating tensors in NumPy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[10:100]\\n\",\n    \"my_slice.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[10:100, :, :]\\n\",\n    \"my_slice.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[10:100, 0:28, 0:28]\\n\",\n    \"my_slice.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[:, 14:, 14:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[:, 7:-7, 7:-7]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The notion of data batches\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch = train_images[:128]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch = train_images[128:256]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"n = 3\\n\",\n    \"batch = train_images[128 * n : 128 * (n + 1)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Real-world examples of data tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Vector data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Timeseries data or sequence data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Image data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Video data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The gears of neural networks: Tensor operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Element-wise operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_relu(x):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    x = x.copy()\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(x.shape[1]):\\n\",\n    \"            x[i, j] = max(x[i, j], 0)\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_add(x, y):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    assert x.shape == y.shape\\n\",\n    \"    x = x.copy()\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(x.shape[1]):\\n\",\n    \"            x[i, j] += y[i, j]\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"x = np.random.random((20, 100))\\n\",\n    \"y = np.random.random((20, 100))\\n\",\n    \"\\n\",\n    \"t0 = time.time()\\n\",\n    \"for _ in range(1000):\\n\",\n    \"    z = x + y\\n\",\n    \"    z = np.maximum(z, 0.0)\\n\",\n    \"print(\\\"Took: {0:.2f} s\\\".format(time.time() - t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"t0 = time.time()\\n\",\n    \"for _ in range(1000):\\n\",\n    \"    z = naive_add(x, y)\\n\",\n    \"    z = naive_relu(z)\\n\",\n    \"print(\\\"Took: {0:.2f} s\\\".format(time.time() - t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Broadcasting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"X = np.random.random((32, 10))\\n\",\n    \"y = np.random.random((10,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y = np.expand_dims(y, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"Y = np.tile(y, (32, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_add_matrix_and_vector(x, y):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    assert len(y.shape) == 1\\n\",\n    \"    assert x.shape[1] == y.shape[0]\\n\",\n    \"    x = x.copy()\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(x.shape[1]):\\n\",\n    \"            x[i, j] += y[j]\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"x = np.random.random((64, 3, 32, 10))\\n\",\n    \"y = np.random.random((32, 10))\\n\",\n    \"z = np.maximum(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Tensor product\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.random.random((32,))\\n\",\n    \"y = np.random.random((32,))\\n\",\n    \"\\n\",\n    \"z = np.matmul(x, y)\\n\",\n    \"z = x @ y\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_vector_product(x, y):\\n\",\n    \"    assert len(x.shape) == 1\\n\",\n    \"    assert len(y.shape) == 1\\n\",\n    \"    assert x.shape[0] == y.shape[0]\\n\",\n    \"    z = 0.0\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        z += x[i] * y[i]\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_matrix_vector_product(x, y):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    assert len(y.shape) == 1\\n\",\n    \"    assert x.shape[1] == y.shape[0]\\n\",\n    \"    z = np.zeros(x.shape[0])\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(x.shape[1]):\\n\",\n    \"            z[i] += x[i, j] * y[j]\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_matrix_vector_product(x, y):\\n\",\n    \"    z = np.zeros(x.shape[0])\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        z[i] = naive_vector_product(x[i, :], y)\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_matrix_product(x, y):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    assert len(y.shape) == 2\\n\",\n    \"    assert x.shape[1] == y.shape[0]\\n\",\n    \"    z = np.zeros((x.shape[0], y.shape[1]))\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(y.shape[1]):\\n\",\n    \"            row_x = x[i, :]\\n\",\n    \"            column_y = y[:, j]\\n\",\n    \"            z[i, j] = naive_vector_product(row_x, column_y)\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Tensor reshaping\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images = train_images.reshape((60000, 28 * 28))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([[0., 1.],\\n\",\n    \"              [2., 3.],\\n\",\n    \"              [4., 5.]])\\n\",\n    \"x.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = x.reshape((6, 1))\\n\",\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = x.reshape((2, 3))\\n\",\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.zeros((300, 20))\\n\",\n    \"x = np.transpose(x)\\n\",\n    \"x.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Geometric interpretation of tensor operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A geometric interpretation of deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The engine of neural networks: Gradient-based optimization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### What's a derivative?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Derivative of a tensor operation: The gradient\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Stochastic gradient descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Chaining derivatives: The Backpropagation algorithm\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### The chain rule\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Automatic differentiation with computation graphs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Looking back at our first example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28))\\n\",\n    \"test_images = test_images.astype(\\\"float32\\\") / 255\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=5,\\n\",\n    \"    batch_size=128,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Reimplementing our first example from scratch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### A simple Dense class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"class NaiveDense:\\n\",\n    \"    def __init__(self, input_size, output_size, activation=None):\\n\",\n    \"        self.activation = activation\\n\",\n    \"        self.W = keras.Variable(\\n\",\n    \"            shape=(input_size, output_size), initializer=\\\"uniform\\\"\\n\",\n    \"        )\\n\",\n    \"        self.b = keras.Variable(shape=(output_size,), initializer=\\\"zeros\\\")\\n\",\n    \"\\n\",\n    \"    def __call__(self, inputs):\\n\",\n    \"        x = ops.matmul(inputs, self.W)\\n\",\n    \"        x = x + self.b\\n\",\n    \"        if self.activation is not None:\\n\",\n    \"            x = self.activation(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def weights(self):\\n\",\n    \"        return [self.W, self.b]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### A simple Sequential class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class NaiveSequential:\\n\",\n    \"    def __init__(self, layers):\\n\",\n    \"        self.layers = layers\\n\",\n    \"\\n\",\n    \"    def __call__(self, inputs):\\n\",\n    \"        x = inputs\\n\",\n    \"        for layer in self.layers:\\n\",\n    \"            x = layer(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def weights(self):\\n\",\n    \"        weights = []\\n\",\n    \"        for layer in self.layers:\\n\",\n    \"            weights += layer.weights\\n\",\n    \"        return weights\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = NaiveSequential(\\n\",\n    \"    [\\n\",\n    \"        NaiveDense(input_size=28 * 28, output_size=512, activation=ops.relu),\\n\",\n    \"        NaiveDense(input_size=512, output_size=10, activation=ops.softmax),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"assert len(model.weights) == 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### A batch generator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import math\\n\",\n    \"\\n\",\n    \"class BatchGenerator:\\n\",\n    \"    def __init__(self, images, labels, batch_size=128):\\n\",\n    \"        assert len(images) == len(labels)\\n\",\n    \"        self.index = 0\\n\",\n    \"        self.images = images\\n\",\n    \"        self.labels = labels\\n\",\n    \"        self.batch_size = batch_size\\n\",\n    \"        self.num_batches = math.ceil(len(images) / batch_size)\\n\",\n    \"\\n\",\n    \"    def next(self):\\n\",\n    \"        images = self.images[self.index : self.index + self.batch_size]\\n\",\n    \"        labels = self.labels[self.index : self.index + self.batch_size]\\n\",\n    \"        self.index += self.batch_size\\n\",\n    \"        return images, labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Running one training step\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### The weight update step\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 1e-3\\n\",\n    \"\\n\",\n    \"def update_weights(gradients, weights):\\n\",\n    \"    for g, w in zip(gradients, weights):\\n\",\n    \"        w.assign(w - g * learning_rate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import optimizers\\n\",\n    \"\\n\",\n    \"optimizer = optimizers.SGD(learning_rate=1e-3)\\n\",\n    \"\\n\",\n    \"def update_weights(gradients, weights):\\n\",\n    \"    optimizer.apply_gradients(zip(gradients, weights))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Gradient computation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"x = tf.zeros(shape=())\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"    y = 2 * x + 3\\n\",\n    \"grad_of_y_wrt_x = tape.gradient(y, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"def one_training_step(model, images_batch, labels_batch):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        predictions = model(images_batch)\\n\",\n    \"        loss = ops.sparse_categorical_crossentropy(labels_batch, predictions)\\n\",\n    \"        average_loss = ops.mean(loss)\\n\",\n    \"    gradients = tape.gradient(average_loss, model.weights)\\n\",\n    \"    update_weights(gradients, model.weights)\\n\",\n    \"    return average_loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The full training loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"def fit(model, images, labels, epochs, batch_size=128):\\n\",\n    \"    for epoch_counter in range(epochs):\\n\",\n    \"        print(f\\\"Epoch {epoch_counter}\\\")\\n\",\n    \"        batch_generator = BatchGenerator(images, labels)\\n\",\n    \"        for batch_counter in range(batch_generator.num_batches):\\n\",\n    \"            images_batch, labels_batch = batch_generator.next()\\n\",\n    \"            loss = one_training_step(model, images_batch, labels_batch)\\n\",\n    \"            if batch_counter % 100 == 0:\\n\",\n    \"                print(f\\\"loss at batch {batch_counter}: {loss:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"from keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28))\\n\",\n    \"test_images = test_images.astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"fit(model, train_images, train_labels, epochs=10, batch_size=128)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"predictions = model(test_images)\\n\",\n    \"predicted_labels = ops.argmax(predictions, axis=1)\\n\",\n    \"matches = predicted_labels == test_labels\\n\",\n    \"f\\\"accuracy: {ops.mean(matches):.2f}\\\"\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter02_mathematical-building-blocks\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter03_introduction-to-ml-frameworks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Introduction to TensorFlow, PyTorch, JAX, and Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A brief history of deep learning frameworks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### How these frameworks relate to each other\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Introduction to TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### First steps with TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Tensors and variables in TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Constant tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"tf.ones(shape=(2, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tf.zeros(shape=(2, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tf.constant([1, 2, 3], dtype=\\\"float32\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Random tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = tf.random.normal(shape=(3, 1), mean=0., stddev=1.)\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = tf.random.uniform(shape=(3, 1), minval=0., maxval=1.)\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Tensor assignment and the Variable class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"x = np.ones(shape=(2, 2))\\n\",\n    \"x[0, 0] = 0.0\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v = tf.Variable(initial_value=tf.random.normal(shape=(3, 1)))\\n\",\n    \"print(v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v.assign(tf.ones((3, 1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v[0, 0].assign(3.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v.assign_add(tf.ones((3, 1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Tensor operations: Doing math in TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"a = tf.ones((2, 2))\\n\",\n    \"b = tf.square(a)\\n\",\n    \"c = tf.sqrt(a)\\n\",\n    \"d = b + c\\n\",\n    \"e = tf.matmul(a, b)\\n\",\n    \"f = tf.concat((a, b), axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def dense(inputs, W, b):\\n\",\n    \"    return tf.nn.relu(tf.matmul(inputs, W) + b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Gradients in TensorFlow: A second look at the GradientTape API\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_var = tf.Variable(initial_value=3.0)\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"    result = tf.square(input_var)\\n\",\n    \"gradient = tape.gradient(result, input_var)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_const = tf.constant(3.0)\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"    tape.watch(input_const)\\n\",\n    \"    result = tf.square(input_const)\\n\",\n    \"gradient = tape.gradient(result, input_const)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"time = tf.Variable(0.0)\\n\",\n    \"with tf.GradientTape() as outer_tape:\\n\",\n    \"    with tf.GradientTape() as inner_tape:\\n\",\n    \"        position = 4.9 * time**2\\n\",\n    \"    speed = inner_tape.gradient(position, time)\\n\",\n    \"acceleration = outer_tape.gradient(speed, time)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Making TensorFlow functions fast using compilation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"@tf.function\\n\",\n    \"def dense(inputs, W, b):\\n\",\n    \"    return tf.nn.relu(tf.matmul(inputs, W) + b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"@tf.function(jit_compile=True)\\n\",\n    \"def dense(inputs, W, b):\\n\",\n    \"    return tf.nn.relu(tf.matmul(inputs, W) + b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### An end-to-end example: A linear classifier in pure TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"num_samples_per_class = 1000\\n\",\n    \"negative_samples = np.random.multivariate_normal(\\n\",\n    \"    mean=[0, 3], cov=[[1, 0.5], [0.5, 1]], size=num_samples_per_class\\n\",\n    \")\\n\",\n    \"positive_samples = np.random.multivariate_normal(\\n\",\n    \"    mean=[3, 0], cov=[[1, 0.5], [0.5, 1]], size=num_samples_per_class\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = np.vstack((negative_samples, positive_samples)).astype(np.float32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"targets = np.vstack(\\n\",\n    \"    (\\n\",\n    \"        np.zeros((num_samples_per_class, 1), dtype=\\\"float32\\\"),\\n\",\n    \"        np.ones((num_samples_per_class, 1), dtype=\\\"float32\\\"),\\n\",\n    \"    )\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.scatter(inputs[:, 0], inputs[:, 1], c=targets[:, 0])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_dim = 2\\n\",\n    \"output_dim = 1\\n\",\n    \"W = tf.Variable(initial_value=tf.random.uniform(shape=(input_dim, output_dim)))\\n\",\n    \"b = tf.Variable(initial_value=tf.zeros(shape=(output_dim,)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def model(inputs, W, b):\\n\",\n    \"    return tf.matmul(inputs, W) + b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def mean_squared_error(targets, predictions):\\n\",\n    \"    per_sample_losses = tf.square(targets - predictions)\\n\",\n    \"    return tf.reduce_mean(per_sample_losses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.1\\n\",\n    \"\\n\",\n    \"@tf.function(jit_compile=True)\\n\",\n    \"def training_step(inputs, targets, W, b):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        predictions = model(inputs, W, b)\\n\",\n    \"        loss = mean_squared_error(predictions, targets)\\n\",\n    \"    grad_loss_wrt_W, grad_loss_wrt_b = tape.gradient(loss, [W, b])\\n\",\n    \"    W.assign_sub(grad_loss_wrt_W * learning_rate)\\n\",\n    \"    b.assign_sub(grad_loss_wrt_b * learning_rate)\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for step in range(40):\\n\",\n    \"    loss = training_step(inputs, targets, W, b)\\n\",\n    \"    print(f\\\"Loss at step {step}: {loss:.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model(inputs, W, b)\\n\",\n    \"plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.linspace(-1, 4, 100)\\n\",\n    \"y = -W[0] / W[1] * x + (0.5 - b) / W[1]\\n\",\n    \"plt.plot(x, y, \\\"-r\\\")\\n\",\n    \"plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### What makes the TensorFlow approach unique\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Introduction to PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### First steps with PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Tensors and parameters in PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Constant tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"torch.ones(size=(2, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch.zeros(size=(2, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch.tensor([1, 2, 3], dtype=torch.float32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Random tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch.normal(\\n\",\n    \"mean=torch.zeros(size=(3, 1)),\\n\",\n    \"std=torch.ones(size=(3, 1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch.rand(3, 1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Tensor assignment and the Parameter class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = torch.zeros(size=(2, 1))\\n\",\n    \"x[0, 0] = 1.\\n\",\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = torch.zeros(size=(2, 1))\\n\",\n    \"p = torch.nn.parameter.Parameter(data=x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Tensor operations: Doing math in PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"a = torch.ones((2, 2))\\n\",\n    \"b = torch.square(a)\\n\",\n    \"c = torch.sqrt(a)\\n\",\n    \"d = b + c\\n\",\n    \"e = torch.matmul(a, b)\\n\",\n    \"f = torch.cat((a, b), dim=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def dense(inputs, W, b):\\n\",\n    \"    return torch.nn.relu(torch.matmul(inputs, W) + b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Computing gradients with PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_var = torch.tensor(3.0, requires_grad=True)\\n\",\n    \"result = torch.square(input_var)\\n\",\n    \"result.backward()\\n\",\n    \"gradient = input_var.grad\\n\",\n    \"gradient\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"result = torch.square(input_var)\\n\",\n    \"result.backward()\\n\",\n    \"input_var.grad\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_var.grad = None\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### An end-to-end example: A linear classifier in pure PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_dim = 2\\n\",\n    \"output_dim = 1\\n\",\n    \"\\n\",\n    \"W = torch.rand(input_dim, output_dim, requires_grad=True)\\n\",\n    \"b = torch.zeros(output_dim, requires_grad=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def model(inputs, W, b):\\n\",\n    \"    return torch.matmul(inputs, W) + b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def mean_squared_error(targets, predictions):\\n\",\n    \"    per_sample_losses = torch.square(targets - predictions)\\n\",\n    \"    return torch.mean(per_sample_losses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.1\\n\",\n    \"\\n\",\n    \"def training_step(inputs, targets, W, b):\\n\",\n    \"    predictions = model(inputs)\\n\",\n    \"    loss = mean_squared_error(targets, predictions)\\n\",\n    \"    loss.backward()\\n\",\n    \"    grad_loss_wrt_W, grad_loss_wrt_b = W.grad, b.grad\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        W -= grad_loss_wrt_W * learning_rate\\n\",\n    \"        b -= grad_loss_wrt_b * learning_rate\\n\",\n    \"    W.grad = None\\n\",\n    \"    b.grad = None\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Packaging state and computation with the Module class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class LinearModel(torch.nn.Module):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.W = torch.nn.Parameter(torch.rand(input_dim, output_dim))\\n\",\n    \"        self.b = torch.nn.Parameter(torch.zeros(output_dim))\\n\",\n    \"\\n\",\n    \"    def forward(self, inputs):\\n\",\n    \"        return torch.matmul(inputs, self.W) + self.b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = LinearModel()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch_inputs = torch.tensor(inputs)\\n\",\n    \"output = model(torch_inputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def training_step(inputs, targets):\\n\",\n    \"    predictions = model(inputs)\\n\",\n    \"    loss = mean_squared_error(targets, predictions)\\n\",\n    \"    loss.backward()\\n\",\n    \"    optimizer.step()\\n\",\n    \"    model.zero_grad()\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Making PyTorch modules fast using compilation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"compiled_model = torch.compile(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"@torch.compile\\n\",\n    \"def dense(inputs, W, b):\\n\",\n    \"    return torch.nn.relu(torch.matmul(inputs, W) + b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### What makes the PyTorch approach unique\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Introduction to JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### First steps with JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Tensors in JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from jax import numpy as jnp\\n\",\n    \"jnp.ones(shape=(2, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"jnp.zeros(shape=(2, 1))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"jnp.array([1, 2, 3], dtype=\\\"float32\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Random number generation in JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.normal(size=(3,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.random.normal(size=(3,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def apply_noise(x, seed):\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"    x = x * np.random.normal((3,))\\n\",\n    \"    return x\\n\",\n    \"\\n\",\n    \"seed = 1337\\n\",\n    \"y = apply_noise(x, seed)\\n\",\n    \"seed += 1\\n\",\n    \"z = apply_noise(x, seed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import jax\\n\",\n    \"\\n\",\n    \"seed_key = jax.random.key(1337)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"seed_key = jax.random.key(0)\\n\",\n    \"jax.random.normal(seed_key, shape=(3,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"seed_key = jax.random.key(123)\\n\",\n    \"jax.random.normal(seed_key, shape=(3,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"jax.random.normal(seed_key, shape=(3,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"seed_key = jax.random.key(123)\\n\",\n    \"jax.random.normal(seed_key, shape=(3,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"new_seed_key = jax.random.split(seed_key, num=1)[0]\\n\",\n    \"jax.random.normal(new_seed_key, shape=(3,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Tensor assignment\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = jnp.array([1, 2, 3], dtype=\\\"float32\\\")\\n\",\n    \"new_x = x.at[0].set(10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Tensor operations: Doing math in JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"a = jnp.ones((2, 2))\\n\",\n    \"b = jnp.square(a)\\n\",\n    \"c = jnp.sqrt(a)\\n\",\n    \"d = b + c\\n\",\n    \"e = jnp.matmul(a, b)\\n\",\n    \"e *= d\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def dense(inputs, W, b):\\n\",\n    \"    return jax.nn.relu(jnp.matmul(inputs, W) + b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Computing gradients with JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_loss(input_var):\\n\",\n    \"    return jnp.square(input_var)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"grad_fn = jax.grad(compute_loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_var = jnp.array(3.0)\\n\",\n    \"grad_of_loss_wrt_input_var = grad_fn(input_var)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### JAX gradient-computation best practices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Returning the loss value\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"grad_fn = jax.value_and_grad(compute_loss)\\n\",\n    \"output, grad_of_loss_wrt_input_var = grad_fn(input_var)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Getting gradients for a complex function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Returning auxiliary outputs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Making JAX functions fast with @jax.jit\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"@jax.jit\\n\",\n    \"def dense(inputs, W, b):\\n\",\n    \"    return jax.nn.relu(jnp.matmul(inputs, W) + b)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### An end-to-end example: A linear classifier in pure JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def model(inputs, W, b):\\n\",\n    \"    return jnp.matmul(inputs, W) + b\\n\",\n    \"\\n\",\n    \"def mean_squared_error(targets, predictions):\\n\",\n    \"    per_sample_losses = jnp.square(targets - predictions)\\n\",\n    \"    return jnp.mean(per_sample_losses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_loss(state, inputs, targets):\\n\",\n    \"    W, b = state\\n\",\n    \"    predictions = model(inputs, W, b)\\n\",\n    \"    loss = mean_squared_error(targets, predictions)\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"grad_fn = jax.value_and_grad(compute_loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.1\\n\",\n    \"\\n\",\n    \"@jax.jit\\n\",\n    \"def training_step(inputs, targets, W, b):\\n\",\n    \"    loss, grads = grad_fn((W, b), inputs, targets)\\n\",\n    \"    grad_wrt_W, grad_wrt_b = grads\\n\",\n    \"    W = W - grad_wrt_W * learning_rate\\n\",\n    \"    b = b - grad_wrt_b * learning_rate\\n\",\n    \"    return loss, W, b\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_dim = 2\\n\",\n    \"output_dim = 1\\n\",\n    \"\\n\",\n    \"W = jax.numpy.array(np.random.uniform(size=(input_dim, output_dim)))\\n\",\n    \"b = jax.numpy.array(np.zeros(shape=(output_dim,)))\\n\",\n    \"state = (W, b)\\n\",\n    \"for step in range(40):\\n\",\n    \"    loss, W, b = training_step(inputs, targets, W, b)\\n\",\n    \"    print(f\\\"Loss at step {step}: {loss:.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### What makes the JAX approach unique\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Introduction to Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### First steps with Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Picking a backend framework\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\\n\",\n    \"\\n\",\n    \"import keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Layers: The building blocks of deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### The base `Layer` class in Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"\\n\",\n    \"class SimpleDense(keras.Layer):\\n\",\n    \"    def __init__(self, units, activation=None):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.units = units\\n\",\n    \"        self.activation = activation\\n\",\n    \"\\n\",\n    \"    def build(self, input_shape):\\n\",\n    \"        batch_dim, input_dim = input_shape\\n\",\n    \"        self.W = self.add_weight(\\n\",\n    \"            shape=(input_dim, self.units), initializer=\\\"random_normal\\\"\\n\",\n    \"        )\\n\",\n    \"        self.b = self.add_weight(shape=(self.units,), initializer=\\\"zeros\\\")\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        y = keras.ops.matmul(inputs, self.W) + self.b\\n\",\n    \"        if self.activation is not None:\\n\",\n    \"            y = self.activation(y)\\n\",\n    \"        return y\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_dense = SimpleDense(units=32, activation=keras.ops.relu)\\n\",\n    \"input_tensor = keras.ops.ones(shape=(2, 784))\\n\",\n    \"output_tensor = my_dense(input_tensor)\\n\",\n    \"print(output_tensor.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Automatic shape inference: Building layers on the fly\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"layer = layers.Dense(32, activation=\\\"relu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"model = models.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(32, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(32),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        SimpleDense(32, activation=\\\"relu\\\"),\\n\",\n    \"        SimpleDense(64, activation=\\\"relu\\\"),\\n\",\n    \"        SimpleDense(32, activation=\\\"relu\\\"),\\n\",\n    \"        SimpleDense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### From layers to models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The \\\"compile\\\" step: Configuring the learning process\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([keras.layers.Dense(1)])\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"mean_squared_error\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=keras.optimizers.RMSprop(),\\n\",\n    \"    loss=keras.losses.MeanSquaredError(),\\n\",\n    \"    metrics=[keras.metrics.BinaryAccuracy()],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Picking a loss function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Understanding the fit method\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history = model.fit(\\n\",\n    \"    inputs,\\n\",\n    \"    targets,\\n\",\n    \"    epochs=5,\\n\",\n    \"    batch_size=128,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history.history\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Monitoring loss and metrics on validation data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([keras.layers.Dense(1)])\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=keras.optimizers.RMSprop(learning_rate=0.1),\\n\",\n    \"    loss=keras.losses.MeanSquaredError(),\\n\",\n    \"    metrics=[keras.metrics.BinaryAccuracy()],\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"indices_permutation = np.random.permutation(len(inputs))\\n\",\n    \"shuffled_inputs = inputs[indices_permutation]\\n\",\n    \"shuffled_targets = targets[indices_permutation]\\n\",\n    \"\\n\",\n    \"num_validation_samples = int(0.3 * len(inputs))\\n\",\n    \"val_inputs = shuffled_inputs[:num_validation_samples]\\n\",\n    \"val_targets = shuffled_targets[:num_validation_samples]\\n\",\n    \"training_inputs = shuffled_inputs[num_validation_samples:]\\n\",\n    \"training_targets = shuffled_targets[num_validation_samples:]\\n\",\n    \"model.fit(\\n\",\n    \"    training_inputs,\\n\",\n    \"    training_targets,\\n\",\n    \"    epochs=5,\\n\",\n    \"    batch_size=16,\\n\",\n    \"    validation_data=(val_inputs, val_targets),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Inference: Using a model after training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model.predict(val_inputs, batch_size=128)\\n\",\n    \"print(predictions[:10])\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter03_introduction-to-ml-frameworks\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter04_classification-and-regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Classification and regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Classifying movie reviews: A binary classification example\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The IMDb dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import imdb\\n\",\n    \"\\n\",\n    \"(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\\n\",\n    \"    num_words=10000\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_data[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max([max(sequence) for sequence in train_data])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"word_index = imdb.get_word_index()\\n\",\n    \"reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\\n\",\n    \"decoded_review = \\\" \\\".join(\\n\",\n    \"    [reverse_word_index.get(i - 3, \\\"?\\\") for i in train_data[0]]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"decoded_review[:100]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def multi_hot_encode(sequences, num_classes):\\n\",\n    \"    results = np.zeros((len(sequences), num_classes))\\n\",\n    \"    for i, sequence in enumerate(sequences):\\n\",\n    \"        results[i][sequence] = 1.0\\n\",\n    \"    return results\\n\",\n    \"\\n\",\n    \"x_train = multi_hot_encode(train_data, num_classes=10000)\\n\",\n    \"x_test = multi_hot_encode(test_data, num_classes=10000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_train = train_labels.astype(\\\"float32\\\")\\n\",\n    \"y_test = test_labels.astype(\\\"float32\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Building your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(1, activation=\\\"sigmoid\\\"),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Validating your approach\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_val = x_train[:10000]\\n\",\n    \"partial_x_train = x_train[10000:]\\n\",\n    \"y_val = y_train[:10000]\\n\",\n    \"partial_y_train = y_train[10000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history = model.fit(\\n\",\n    \"    partial_x_train,\\n\",\n    \"    partial_y_train,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=512,\\n\",\n    \"    validation_data=(x_val, y_val),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history = model.fit(\\n\",\n    \"    x_train,\\n\",\n    \"    y_train,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=512,\\n\",\n    \"    validation_split=0.2,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history_dict = history.history\\n\",\n    \"history_dict.keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"history_dict = history.history\\n\",\n    \"loss_values = history_dict[\\\"loss\\\"]\\n\",\n    \"val_loss_values = history_dict[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, len(loss_values) + 1)\\n\",\n    \"plt.plot(epochs, loss_values, \\\"r--\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss_values, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"[IMDB] Training and validation loss\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.clf()\\n\",\n    \"acc = history_dict[\\\"accuracy\\\"]\\n\",\n    \"val_acc = history_dict[\\\"val_accuracy\\\"]\\n\",\n    \"plt.plot(epochs, acc, \\\"r--\\\", label=\\\"Training acc\\\")\\n\",\n    \"plt.plot(epochs, val_acc, \\\"b\\\", label=\\\"Validation acc\\\")\\n\",\n    \"plt.title(\\\"[IMDB] Training and validation accuracy\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.ylabel(\\\"Accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(1, activation=\\\"sigmoid\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(x_train, y_train, epochs=4, batch_size=512)\\n\",\n    \"results = model.evaluate(x_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using a trained model to generate predictions on new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.predict(x_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Further experiments\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Wrapping up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Classifying newswires: A multiclass classification example\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The Reuters dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import reuters\\n\",\n    \"\\n\",\n    \"(train_data, train_labels), (test_data, test_labels) = reuters.load_data(\\n\",\n    \"    num_words=10000\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(train_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_data[10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"word_index = reuters.get_word_index()\\n\",\n    \"reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\\n\",\n    \"decoded_newswire = \\\" \\\".join(\\n\",\n    \"    [reverse_word_index.get(i - 3, \\\"?\\\") for i in train_data[10]]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels[10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train = multi_hot_encode(train_data, num_classes=10000)\\n\",\n    \"x_test = multi_hot_encode(test_data, num_classes=10000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def one_hot_encode(labels, num_classes=46):\\n\",\n    \"    results = np.zeros((len(labels), num_classes))\\n\",\n    \"    for i, label in enumerate(labels):\\n\",\n    \"        results[i, label] = 1.0\\n\",\n    \"    return results\\n\",\n    \"\\n\",\n    \"y_train = one_hot_encode(train_labels)\\n\",\n    \"y_test = one_hot_encode(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.utils import to_categorical\\n\",\n    \"\\n\",\n    \"y_train = to_categorical(train_labels)\\n\",\n    \"y_test = to_categorical(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Building your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(46, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"top_3_accuracy = keras.metrics.TopKCategoricalAccuracy(\\n\",\n    \"    k=3, name=\\\"top_3_accuracy\\\"\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\", top_3_accuracy],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Validating your approach\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_val = x_train[:1000]\\n\",\n    \"partial_x_train = x_train[1000:]\\n\",\n    \"y_val = y_train[:1000]\\n\",\n    \"partial_y_train = y_train[1000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history = model.fit(\\n\",\n    \"    partial_x_train,\\n\",\n    \"    partial_y_train,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=512,\\n\",\n    \"    validation_data=(x_val, y_val),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"loss = history.history[\\\"loss\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, len(loss) + 1)\\n\",\n    \"plt.plot(epochs, loss, \\\"r--\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.clf()\\n\",\n    \"acc = history.history[\\\"accuracy\\\"]\\n\",\n    \"val_acc = history.history[\\\"val_accuracy\\\"]\\n\",\n    \"plt.plot(epochs, acc, \\\"r--\\\", label=\\\"Training accuracy\\\")\\n\",\n    \"plt.plot(epochs, val_acc, \\\"b\\\", label=\\\"Validation accuracy\\\")\\n\",\n    \"plt.title(\\\"Training and validation accuracy\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.ylabel(\\\"Accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.clf()\\n\",\n    \"acc = history.history[\\\"top_3_accuracy\\\"]\\n\",\n    \"val_acc = history.history[\\\"val_top_3_accuracy\\\"]\\n\",\n    \"plt.plot(epochs, acc, \\\"r--\\\", label=\\\"Training top-3 accuracy\\\")\\n\",\n    \"plt.plot(epochs, val_acc, \\\"b\\\", label=\\\"Validation top-3 accuracy\\\")\\n\",\n    \"plt.title(\\\"Training and validation top-3 accuracy\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.ylabel(\\\"Top-3 accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(46, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    x_train,\\n\",\n    \"    y_train,\\n\",\n    \"    epochs=9,\\n\",\n    \"    batch_size=512,\\n\",\n    \")\\n\",\n    \"results = model.evaluate(x_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import copy\\n\",\n    \"test_labels_copy = copy.copy(test_labels)\\n\",\n    \"np.random.shuffle(test_labels_copy)\\n\",\n    \"hits_array = np.array(test_labels == test_labels_copy)\\n\",\n    \"hits_array.mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Generating predictions on new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model.predict(x_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions[0].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.sum(predictions[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.argmax(predictions[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A different way to handle the labels and the loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_train = train_labels\\n\",\n    \"y_test = test_labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The importance of having sufficiently large intermediate layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(4, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(46, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    partial_x_train,\\n\",\n    \"    partial_y_train,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_data=(x_val, y_val),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Further experiments\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Wrapping up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Predicting house prices: A regression example\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The California Housing Price dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import california_housing\\n\",\n    \"\\n\",\n    \"(train_data, train_targets), (test_data, test_targets) = (\\n\",\n    \"    california_housing.load_data(version=\\\"small\\\")\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_targets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean = train_data.mean(axis=0)\\n\",\n    \"std = train_data.std(axis=0)\\n\",\n    \"x_train = (train_data - mean) / std\\n\",\n    \"x_test = (test_data - mean) / std\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_train = train_targets / 100000\\n\",\n    \"y_test = test_targets / 100000\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Building your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_model():\\n\",\n    \"    model = keras.Sequential(\\n\",\n    \"        [\\n\",\n    \"            layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"            layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"            layers.Dense(1),\\n\",\n    \"        ]\\n\",\n    \"    )\\n\",\n    \"    model.compile(\\n\",\n    \"        optimizer=\\\"adam\\\",\\n\",\n    \"        loss=\\\"mean_squared_error\\\",\\n\",\n    \"        metrics=[\\\"mean_absolute_error\\\"],\\n\",\n    \"    )\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Validating your approach using K-fold validation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"k = 4\\n\",\n    \"num_val_samples = len(x_train) // k\\n\",\n    \"num_epochs = 50\\n\",\n    \"all_scores = []\\n\",\n    \"for i in range(k):\\n\",\n    \"    print(f\\\"Processing fold #{i + 1}\\\")\\n\",\n    \"    fold_x_val = x_train[i * num_val_samples : (i + 1) * num_val_samples]\\n\",\n    \"    fold_y_val = y_train[i * num_val_samples : (i + 1) * num_val_samples]\\n\",\n    \"    fold_x_train = np.concatenate(\\n\",\n    \"        [x_train[: i * num_val_samples], x_train[(i + 1) * num_val_samples :]],\\n\",\n    \"        axis=0,\\n\",\n    \"    )\\n\",\n    \"    fold_y_train = np.concatenate(\\n\",\n    \"        [y_train[: i * num_val_samples], y_train[(i + 1) * num_val_samples :]],\\n\",\n    \"        axis=0,\\n\",\n    \"    )\\n\",\n    \"    model = get_model()\\n\",\n    \"    model.fit(\\n\",\n    \"        fold_x_train,\\n\",\n    \"        fold_y_train,\\n\",\n    \"        epochs=num_epochs,\\n\",\n    \"        batch_size=16,\\n\",\n    \"        verbose=0,\\n\",\n    \"    )\\n\",\n    \"    scores = model.evaluate(fold_x_val, fold_y_val, verbose=0)\\n\",\n    \"    val_loss, val_mae = scores\\n\",\n    \"    all_scores.append(val_mae)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"[round(value, 3) for value in all_scores]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"round(np.mean(all_scores), 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"k = 4\\n\",\n    \"num_val_samples = len(x_train) // k\\n\",\n    \"num_epochs = 200\\n\",\n    \"all_mae_histories = []\\n\",\n    \"for i in range(k):\\n\",\n    \"    print(f\\\"Processing fold #{i + 1}\\\")\\n\",\n    \"    fold_x_val = x_train[i * num_val_samples : (i + 1) * num_val_samples]\\n\",\n    \"    fold_y_val = y_train[i * num_val_samples : (i + 1) * num_val_samples]\\n\",\n    \"    fold_x_train = np.concatenate(\\n\",\n    \"        [x_train[: i * num_val_samples], x_train[(i + 1) * num_val_samples :]],\\n\",\n    \"        axis=0,\\n\",\n    \"    )\\n\",\n    \"    fold_y_train = np.concatenate(\\n\",\n    \"        [y_train[: i * num_val_samples], y_train[(i + 1) * num_val_samples :]],\\n\",\n    \"        axis=0,\\n\",\n    \"    )\\n\",\n    \"    model = get_model()\\n\",\n    \"    history = model.fit(\\n\",\n    \"        fold_x_train,\\n\",\n    \"        fold_y_train,\\n\",\n    \"        validation_data=(fold_x_val, fold_y_val),\\n\",\n    \"        epochs=num_epochs,\\n\",\n    \"        batch_size=16,\\n\",\n    \"        verbose=0,\\n\",\n    \"    )\\n\",\n    \"    mae_history = history.history[\\\"val_mean_absolute_error\\\"]\\n\",\n    \"    all_mae_histories.append(mae_history)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"average_mae_history = [\\n\",\n    \"    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"epochs = range(1, len(average_mae_history) + 1)\\n\",\n    \"plt.plot(epochs, average_mae_history)\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Validation MAE\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"truncated_mae_history = average_mae_history[10:]\\n\",\n    \"epochs = range(10, len(truncated_mae_history) + 10)\\n\",\n    \"plt.plot(epochs, truncated_mae_history)\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Validation MAE\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_model()\\n\",\n    \"model.fit(x_train, y_train, epochs=130, batch_size=16, verbose=0)\\n\",\n    \"test_mean_squared_error, test_mean_absolute_error = model.evaluate(\\n\",\n    \"    x_test, y_test\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"round(test_mean_absolute_error, 3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Generating predictions on new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model.predict(x_test)\\n\",\n    \"predictions[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Wrapping up\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter04_classification-and-regression\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter05_fundamentals-of-ml.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Fundamentals of machine learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Generalization: The goal of machine learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Underfitting and overfitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Noisy training data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Ambiguous features\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Rare features and spurious correlations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import mnist\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), _ = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"train_images_with_noise_channels = np.concatenate(\\n\",\n    \"    [train_images, np.random.random((len(train_images), 784))], axis=1\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_images_with_zeros_channels = np.concatenate(\\n\",\n    \"    [train_images, np.zeros((len(train_images), 784))], axis=1\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"def get_model():\\n\",\n    \"    model = keras.Sequential(\\n\",\n    \"        [\\n\",\n    \"            layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"            layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"        ]\\n\",\n    \"    )\\n\",\n    \"    model.compile(\\n\",\n    \"        optimizer=\\\"adam\\\",\\n\",\n    \"        loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"        metrics=[\\\"accuracy\\\"],\\n\",\n    \"    )\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"model = get_model()\\n\",\n    \"history_noise = model.fit(\\n\",\n    \"    train_images_with_noise_channels,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=10,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_split=0.2,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model = get_model()\\n\",\n    \"history_zeros = model.fit(\\n\",\n    \"    train_images_with_zeros_channels,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=10,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_split=0.2,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"val_acc_noise = history_noise.history[\\\"val_accuracy\\\"]\\n\",\n    \"val_acc_zeros = history_zeros.history[\\\"val_accuracy\\\"]\\n\",\n    \"epochs = range(1, 11)\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    val_acc_noise,\\n\",\n    \"    \\\"b-\\\",\\n\",\n    \"    label=\\\"Validation accuracy with noise channels\\\",\\n\",\n    \")\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    val_acc_zeros,\\n\",\n    \"    \\\"r--\\\",\\n\",\n    \"    label=\\\"Validation accuracy with zeros channels\\\",\\n\",\n    \")\\n\",\n    \"plt.title(\\\"Effect of noise channels on validation accuracy\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.ylabel(\\\"Accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The nature of generalization in deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"(train_images, train_labels), _ = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"random_train_labels = train_labels[:]\\n\",\n    \"np.random.shuffle(random_train_labels)\\n\",\n    \"\\n\",\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    random_train_labels,\\n\",\n    \"    epochs=100,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_split=0.2,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### The manifold hypothesis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Interpolation as a source of generalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Why deep learning works\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Training data is paramount\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Evaluating machine-learning models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training, validation, and test sets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Simple hold-out validation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### K-fold validation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Iterated K-fold validation with shuffling\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Beating a common-sense baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Things to keep in mind about model evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Improving model fit\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Tuning key gradient descent parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"(train_images, train_labels), _ = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=keras.optimizers.RMSprop(learning_rate=1.0),\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_images, train_labels, epochs=10, batch_size=128, validation_split=0.2\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=keras.optimizers.RMSprop(learning_rate=1e-2),\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_images, train_labels, epochs=10, batch_size=128, validation_split=0.2\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using better architecture priors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Increasing model capacity\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([layers.Dense(10, activation=\\\"softmax\\\")])\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history_small_model = model.fit(\\n\",\n    \"    train_images, train_labels, epochs=20, batch_size=128, validation_split=0.2\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"val_loss = history_small_model.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, 21)\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b-\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Validation loss for a model with insufficient capacity\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(128, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(128, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history_large_model = model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_split=0.2,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"val_loss = history_large_model.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, 21)\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b-\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Validation loss for a model with appropriate capacity\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(2048, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(2048, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(2048, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history_very_large_model = model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=32,\\n\",\n    \"    validation_split=0.2,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"val_loss = history_very_large_model.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, 21)\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b-\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Validation loss for a model with too much capacity\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Improving generalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Dataset curation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Feature engineering\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using early stopping\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Regularizing your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Reducing the network's size\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import imdb\\n\",\n    \"\\n\",\n    \"(train_data, train_labels), _ = imdb.load_data(num_words=10000)\\n\",\n    \"\\n\",\n    \"def vectorize_sequences(sequences, dimension=10000):\\n\",\n    \"    results = np.zeros((len(sequences), dimension))\\n\",\n    \"    for i, sequence in enumerate(sequences):\\n\",\n    \"        results[i, sequence] = 1.0\\n\",\n    \"    return results\\n\",\n    \"\\n\",\n    \"train_data = vectorize_sequences(train_data)\\n\",\n    \"\\n\",\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(1, activation=\\\"sigmoid\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history_original = model.fit(\\n\",\n    \"    train_data,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=512,\\n\",\n    \"    validation_split=0.4,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(4, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(4, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(1, activation=\\\"sigmoid\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history_smaller_model = model.fit(\\n\",\n    \"    train_data,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=512,\\n\",\n    \"    validation_split=0.4,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"original_val_loss = history_original.history[\\\"val_loss\\\"]\\n\",\n    \"smaller_model_val_loss = history_smaller_model.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, 21)\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    original_val_loss,\\n\",\n    \"    \\\"r--\\\",\\n\",\n    \"    label=\\\"Validation loss of original model\\\",\\n\",\n    \")\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    smaller_model_val_loss,\\n\",\n    \"    \\\"b-\\\",\\n\",\n    \"    label=\\\"Validation loss of smaller model\\\",\\n\",\n    \")\\n\",\n    \"plt.title(\\\"Original model vs. smaller model (IMDB review classification)\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(1, activation=\\\"sigmoid\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history_larger_model = model.fit(\\n\",\n    \"    train_data,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=512,\\n\",\n    \"    validation_split=0.4,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"original_val_loss = history_original.history[\\\"val_loss\\\"]\\n\",\n    \"larger_model_val_loss = history_larger_model.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, 21)\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    original_val_loss,\\n\",\n    \"    \\\"r--\\\",\\n\",\n    \"    label=\\\"Validation loss of original model\\\",\\n\",\n    \")\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    larger_model_val_loss,\\n\",\n    \"    \\\"b-\\\",\\n\",\n    \"    label=\\\"Validation loss of larger model\\\",\\n\",\n    \")\\n\",\n    \"plt.title(\\\"Original model vs. larger model (IMDB review classification)\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Adding weight regularization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.regularizers import l2\\n\",\n    \"\\n\",\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(16, kernel_regularizer=l2(0.002), activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(16, kernel_regularizer=l2(0.002), activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(1, activation=\\\"sigmoid\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history_l2_reg = model.fit(\\n\",\n    \"    train_data,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=512,\\n\",\n    \"    validation_split=0.4,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"original_val_loss = history_original.history[\\\"val_loss\\\"]\\n\",\n    \"l2_val_loss = history_l2_reg.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, 21)\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    original_val_loss,\\n\",\n    \"    \\\"r--\\\",\\n\",\n    \"    label=\\\"Validation loss of original model\\\",\\n\",\n    \")\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    l2_val_loss,\\n\",\n    \"    \\\"b-\\\",\\n\",\n    \"    label=\\\"Validation loss of L2-regularized model\\\",\\n\",\n    \")\\n\",\n    \"plt.title(\\n\",\n    \"    \\\"Original model vs. L2-regularized model (IMDB review classification)\\\"\\n\",\n    \")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import regularizers\\n\",\n    \"\\n\",\n    \"regularizers.l1(0.001)\\n\",\n    \"regularizers.l1_l2(l1=0.001, l2=0.001)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Adding dropout\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dropout(0.5),\\n\",\n    \"        layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dropout(0.5),\\n\",\n    \"        layers.Dense(1, activation=\\\"sigmoid\\\"),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history_dropout = model.fit(\\n\",\n    \"    train_data,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=512,\\n\",\n    \"    validation_split=0.4,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"original_val_loss = history_original.history[\\\"val_loss\\\"]\\n\",\n    \"dropout_val_loss = history_dropout.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, 21)\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    original_val_loss,\\n\",\n    \"    \\\"r--\\\",\\n\",\n    \"    label=\\\"Validation loss of original model\\\",\\n\",\n    \")\\n\",\n    \"plt.plot(\\n\",\n    \"    epochs,\\n\",\n    \"    dropout_val_loss,\\n\",\n    \"    \\\"b-\\\",\\n\",\n    \"    label=\\\"Validation loss of dropout-regularized model\\\",\\n\",\n    \")\\n\",\n    \"plt.title(\\n\",\n    \"    \\\"Original model vs. dropout-regularized model (IMDB review classification)\\\"\\n\",\n    \")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.xticks(epochs)\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter05_fundamentals-of-ml\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter07_deep-dive-keras.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## A deep dive on Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A spectrum of workflows\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Different ways to build Keras models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The Sequential model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"model = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential()\\n\",\n    \"model.add(layers.Dense(64, activation=\\\"relu\\\"))\\n\",\n    \"model.add(layers.Dense(10, activation=\\\"softmax\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.weights\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.build(input_shape=(None, 3))\\n\",\n    \"model.weights\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(name=\\\"my_example_model\\\")\\n\",\n    \"model.add(layers.Dense(64, activation=\\\"relu\\\", name=\\\"my_first_layer\\\"))\\n\",\n    \"model.add(layers.Dense(10, activation=\\\"softmax\\\", name=\\\"my_last_layer\\\"))\\n\",\n    \"model.build((None, 3))\\n\",\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential()\\n\",\n    \"model.add(keras.Input(shape=(3,)))\\n\",\n    \"model.add(layers.Dense(64, activation=\\\"relu\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.add(layers.Dense(10, activation=\\\"softmax\\\"))\\n\",\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The Functional API\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### A simple example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(3,), name=\\\"my_input\\\")\\n\",\n    \"features = layers.Dense(64, activation=\\\"relu\\\")(inputs)\\n\",\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs, name=\\\"my_functional_model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(3,), name=\\\"my_input\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"features = layers.Dense(64, activation=\\\"relu\\\")(inputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"features.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs, name=\\\"my_functional_model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Multi-input, multi-output models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocabulary_size = 10000\\n\",\n    \"num_tags = 100\\n\",\n    \"num_departments = 4\\n\",\n    \"\\n\",\n    \"title = keras.Input(shape=(vocabulary_size,), name=\\\"title\\\")\\n\",\n    \"text_body = keras.Input(shape=(vocabulary_size,), name=\\\"text_body\\\")\\n\",\n    \"tags = keras.Input(shape=(num_tags,), name=\\\"tags\\\")\\n\",\n    \"\\n\",\n    \"features = layers.Concatenate()([title, text_body, tags])\\n\",\n    \"features = layers.Dense(64, activation=\\\"relu\\\", name=\\\"dense_features\\\")(features)\\n\",\n    \"\\n\",\n    \"priority = layers.Dense(1, activation=\\\"sigmoid\\\", name=\\\"priority\\\")(features)\\n\",\n    \"department = layers.Dense(\\n\",\n    \"    num_departments, activation=\\\"softmax\\\", name=\\\"department\\\"\\n\",\n    \")(features)\\n\",\n    \"\\n\",\n    \"model = keras.Model(\\n\",\n    \"    inputs=[title, text_body, tags],\\n\",\n    \"    outputs=[priority, department],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Training a multi-input, multi-output model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"num_samples = 1280\\n\",\n    \"\\n\",\n    \"title_data = np.random.randint(0, 2, size=(num_samples, vocabulary_size))\\n\",\n    \"text_body_data = np.random.randint(0, 2, size=(num_samples, vocabulary_size))\\n\",\n    \"tags_data = np.random.randint(0, 2, size=(num_samples, num_tags))\\n\",\n    \"\\n\",\n    \"priority_data = np.random.random(size=(num_samples, 1))\\n\",\n    \"department_data = np.random.randint(0, num_departments, size=(num_samples, 1))\\n\",\n    \"\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=[\\\"mean_squared_error\\\", \\\"sparse_categorical_crossentropy\\\"],\\n\",\n    \"    metrics=[[\\\"mean_absolute_error\\\"], [\\\"accuracy\\\"]],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    [title_data, text_body_data, tags_data],\\n\",\n    \"    [priority_data, department_data],\\n\",\n    \"    epochs=1,\\n\",\n    \")\\n\",\n    \"model.evaluate(\\n\",\n    \"    [title_data, text_body_data, tags_data], [priority_data, department_data]\\n\",\n    \")\\n\",\n    \"priority_preds, department_preds = model.predict(\\n\",\n    \"    [title_data, text_body_data, tags_data]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss={\\n\",\n    \"        \\\"priority\\\": \\\"mean_squared_error\\\",\\n\",\n    \"        \\\"department\\\": \\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    },\\n\",\n    \"    metrics={\\n\",\n    \"        \\\"priority\\\": [\\\"mean_absolute_error\\\"],\\n\",\n    \"        \\\"department\\\": [\\\"accuracy\\\"],\\n\",\n    \"    },\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data},\\n\",\n    \"    {\\\"priority\\\": priority_data, \\\"department\\\": department_data},\\n\",\n    \"    epochs=1,\\n\",\n    \")\\n\",\n    \"model.evaluate(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data},\\n\",\n    \"    {\\\"priority\\\": priority_data, \\\"department\\\": department_data},\\n\",\n    \")\\n\",\n    \"priority_preds, department_preds = model.predict(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data}\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### The power of the Functional API: Access to layer connectivity\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Plotting layer connectivity\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.utils.plot_model(model, \\\"ticket_classifier.png\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.utils.plot_model(\\n\",\n    \"    model,\\n\",\n    \"    \\\"ticket_classifier_with_shape_info.png\\\",\\n\",\n    \"    show_shapes=True,\\n\",\n    \"    show_layer_names=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### Feature extraction with a Functional model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.layers[3].input\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.layers[3].output\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"features = model.layers[4].output\\n\",\n    \"difficulty = layers.Dense(3, activation=\\\"softmax\\\", name=\\\"difficulty\\\")(features)\\n\",\n    \"\\n\",\n    \"new_model = keras.Model(\\n\",\n    \"    inputs=[title, text_body, tags], outputs=[priority, department, difficulty]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.utils.plot_model(\\n\",\n    \"    new_model,\\n\",\n    \"    \\\"updated_ticket_classifier.png\\\",\\n\",\n    \"    show_shapes=True,\\n\",\n    \"    show_layer_names=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Subclassing the Model class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Rewriting our previous example as a subclassed model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class CustomerTicketModel(keras.Model):\\n\",\n    \"    def __init__(self, num_departments):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.concat_layer = layers.Concatenate()\\n\",\n    \"        self.mixing_layer = layers.Dense(64, activation=\\\"relu\\\")\\n\",\n    \"        self.priority_scorer = layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"        self.department_classifier = layers.Dense(\\n\",\n    \"            num_departments, activation=\\\"softmax\\\"\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        title = inputs[\\\"title\\\"]\\n\",\n    \"        text_body = inputs[\\\"text_body\\\"]\\n\",\n    \"        tags = inputs[\\\"tags\\\"]\\n\",\n    \"\\n\",\n    \"        features = self.concat_layer([title, text_body, tags])\\n\",\n    \"        features = self.mixing_layer(features)\\n\",\n    \"        priority = self.priority_scorer(features)\\n\",\n    \"        department = self.department_classifier(features)\\n\",\n    \"        return priority, department\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = CustomerTicketModel(num_departments=4)\\n\",\n    \"\\n\",\n    \"priority, department = model(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data}\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=[\\\"mean_squared_error\\\", \\\"sparse_categorical_crossentropy\\\"],\\n\",\n    \"    metrics=[[\\\"mean_absolute_error\\\"], [\\\"accuracy\\\"]],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data},\\n\",\n    \"    [priority_data, department_data],\\n\",\n    \"    epochs=1,\\n\",\n    \")\\n\",\n    \"model.evaluate(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data},\\n\",\n    \"    [priority_data, department_data],\\n\",\n    \")\\n\",\n    \"priority_preds, department_preds = model.predict(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data}\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Beware: What subclassed models don't support\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Mixing and matching different components\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class Classifier(keras.Model):\\n\",\n    \"    def __init__(self, num_classes=2):\\n\",\n    \"        super().__init__()\\n\",\n    \"        if num_classes == 2:\\n\",\n    \"            num_units = 1\\n\",\n    \"            activation = \\\"sigmoid\\\"\\n\",\n    \"        else:\\n\",\n    \"            num_units = num_classes\\n\",\n    \"            activation = \\\"softmax\\\"\\n\",\n    \"        self.dense = layers.Dense(num_units, activation=activation)\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        return self.dense(inputs)\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(3,))\\n\",\n    \"features = layers.Dense(64, activation=\\\"relu\\\")(inputs)\\n\",\n    \"outputs = Classifier(num_classes=10)(features)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(64,))\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(inputs)\\n\",\n    \"binary_classifier = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n    \"\\n\",\n    \"class MyModel(keras.Model):\\n\",\n    \"    def __init__(self, num_classes=2):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.dense = layers.Dense(64, activation=\\\"relu\\\")\\n\",\n    \"        self.classifier = binary_classifier\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        features = self.dense(inputs)\\n\",\n    \"        return self.classifier(features)\\n\",\n    \"\\n\",\n    \"model = MyModel()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Remember: Use the right tool for the job\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using built-in training and evaluation loops\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"def get_mnist_model():\\n\",\n    \"    inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"    features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"    features = layers.Dropout(0.5)(features)\\n\",\n    \"    outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"    model = keras.Model(inputs, outputs)\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"(images, labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"images = images.reshape((60000, 28 * 28)).astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28)).astype(\\\"float32\\\") / 255\\n\",\n    \"train_images, val_images = images[10000:], images[:10000]\\n\",\n    \"train_labels, val_labels = labels[10000:], labels[:10000]\\n\",\n    \"\\n\",\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=3,\\n\",\n    \"    validation_data=(val_images, val_labels),\\n\",\n    \")\\n\",\n    \"test_metrics = model.evaluate(test_images, test_labels)\\n\",\n    \"predictions = model.predict(test_images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Writing your own metrics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"class RootMeanSquaredError(keras.metrics.Metric):\\n\",\n    \"    def __init__(self, name=\\\"rmse\\\", **kwargs):\\n\",\n    \"        super().__init__(name=name, **kwargs)\\n\",\n    \"        self.mse_sum = self.add_weight(name=\\\"mse_sum\\\", initializer=\\\"zeros\\\")\\n\",\n    \"        self.total_samples = self.add_weight(\\n\",\n    \"            name=\\\"total_samples\\\", initializer=\\\"zeros\\\"\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def update_state(self, y_true, y_pred, sample_weight=None):\\n\",\n    \"        y_true = ops.one_hot(y_true, num_classes=ops.shape(y_pred)[1])\\n\",\n    \"        mse = ops.sum(ops.square(y_true - y_pred))\\n\",\n    \"        self.mse_sum.assign_add(mse)\\n\",\n    \"        num_samples = ops.shape(y_pred)[0]\\n\",\n    \"        self.total_samples.assign_add(num_samples)\\n\",\n    \"\\n\",\n    \"    def result(self):\\n\",\n    \"        return ops.sqrt(self.mse_sum / self.total_samples)\\n\",\n    \"\\n\",\n    \"    def reset_state(self):\\n\",\n    \"        self.mse_sum.assign(0.)\\n\",\n    \"        self.total_samples.assign(0.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\", RootMeanSquaredError()],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=3,\\n\",\n    \"    validation_data=(val_images, val_labels),\\n\",\n    \")\\n\",\n    \"test_metrics = model.evaluate(test_images, test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using callbacks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### The EarlyStopping and ModelCheckpoint callbacks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks_list = [\\n\",\n    \"    keras.callbacks.EarlyStopping(\\n\",\n    \"        monitor=\\\"accuracy\\\",\\n\",\n    \"        patience=1,\\n\",\n    \"    ),\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"checkpoint_path.keras\\\",\\n\",\n    \"        monitor=\\\"val_loss\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"    ),\\n\",\n    \"]\\n\",\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=10,\\n\",\n    \"    callbacks=callbacks_list,\\n\",\n    \"    validation_data=(val_images, val_labels),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.models.load_model(\\\"checkpoint_path.keras\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Writing your own callbacks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"class LossHistory(keras.callbacks.Callback):\\n\",\n    \"    def on_train_begin(self, logs):\\n\",\n    \"        self.per_batch_losses = []\\n\",\n    \"\\n\",\n    \"    def on_batch_end(self, batch, logs):\\n\",\n    \"        self.per_batch_losses.append(logs.get(\\\"loss\\\"))\\n\",\n    \"\\n\",\n    \"    def on_epoch_end(self, epoch, logs):\\n\",\n    \"        plt.clf()\\n\",\n    \"        plt.plot(\\n\",\n    \"            range(len(self.per_batch_losses)),\\n\",\n    \"            self.per_batch_losses,\\n\",\n    \"            label=\\\"Training loss for each batch\\\",\\n\",\n    \"        )\\n\",\n    \"        plt.xlabel(f\\\"Batch (epoch {epoch})\\\")\\n\",\n    \"        plt.ylabel(\\\"Loss\\\")\\n\",\n    \"        plt.legend()\\n\",\n    \"        plt.savefig(f\\\"plot_at_epoch_{epoch}\\\", dpi=300)\\n\",\n    \"        self.per_batch_losses = []\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=10,\\n\",\n    \"    callbacks=[LossHistory()],\\n\",\n    \"    validation_data=(val_images, val_labels),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Monitoring and visualization with TensorBoard\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"tensorboard = keras.callbacks.TensorBoard(\\n\",\n    \"    log_dir=\\\"/full_path_to_your_log_dir\\\",\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_images,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=10,\\n\",\n    \"    validation_data=(val_images, val_labels),\\n\",\n    \"    callbacks=[tensorboard],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext tensorboard\\n\",\n    \"%tensorboard --logdir /full_path_to_your_log_dir\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Writing your own training and evaluation loops\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training vs. inference\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Writing custom training step functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### A TensorFlow training step function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"model = get_mnist_model()\\n\",\n    \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n    \"optimizer = keras.optimizers.Adam()\\n\",\n    \"\\n\",\n    \"def train_step(inputs, targets):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        predictions = model(inputs, training=True)\\n\",\n    \"        loss = loss_fn(targets, predictions)\\n\",\n    \"    gradients = tape.gradient(loss, model.trainable_weights)\\n\",\n    \"    optimizer.apply(gradients, model.trainable_weights)\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"batch_size = 32\\n\",\n    \"inputs = train_images[:batch_size]\\n\",\n    \"targets = train_labels[:batch_size]\\n\",\n    \"loss = train_step(inputs, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### A PyTorch training step function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"import torch\\n\",\n    \"\\n\",\n    \"model = get_mnist_model()\\n\",\n    \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n    \"optimizer = keras.optimizers.Adam()\\n\",\n    \"\\n\",\n    \"def train_step(inputs, targets):\\n\",\n    \"    predictions = model(inputs, training=True)\\n\",\n    \"    loss = loss_fn(targets, predictions)\\n\",\n    \"    loss.backward()\\n\",\n    \"    gradients = [weight.value.grad for weight in model.trainable_weights]\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        optimizer.apply(gradients, model.trainable_weights)\\n\",\n    \"    model.zero_grad()\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"batch_size = 32\\n\",\n    \"inputs = train_images[:batch_size]\\n\",\n    \"targets = train_labels[:batch_size]\\n\",\n    \"loss = train_step(inputs, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### A JAX training step function\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"model = get_mnist_model()\\n\",\n    \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n    \"\\n\",\n    \"def compute_loss_and_updates(\\n\",\n    \"    trainable_variables, non_trainable_variables, inputs, targets\\n\",\n    \"):\\n\",\n    \"    outputs, non_trainable_variables = model.stateless_call(\\n\",\n    \"        trainable_variables, non_trainable_variables, inputs, training=True\\n\",\n    \"    )\\n\",\n    \"    loss = loss_fn(targets, outputs)\\n\",\n    \"    return loss, non_trainable_variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"import jax\\n\",\n    \"\\n\",\n    \"grad_fn = jax.value_and_grad(compute_loss_and_updates, has_aux=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"optimizer = keras.optimizers.Adam()\\n\",\n    \"optimizer.build(model.trainable_variables)\\n\",\n    \"\\n\",\n    \"def train_step(state, inputs, targets):\\n\",\n    \"    (trainable_variables, non_trainable_variables, optimizer_variables) = state\\n\",\n    \"    (loss, non_trainable_variables), grads = grad_fn(\\n\",\n    \"        trainable_variables, non_trainable_variables, inputs, targets\\n\",\n    \"    )\\n\",\n    \"    trainable_variables, optimizer_variables = optimizer.stateless_apply(\\n\",\n    \"        optimizer_variables, grads, trainable_variables\\n\",\n    \"    )\\n\",\n    \"    return loss, (\\n\",\n    \"        trainable_variables,\\n\",\n    \"        non_trainable_variables,\\n\",\n    \"        optimizer_variables,\\n\",\n    \"    )\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"batch_size = 32\\n\",\n    \"inputs = train_images[:batch_size]\\n\",\n    \"targets = train_labels[:batch_size]\\n\",\n    \"\\n\",\n    \"trainable_variables = [v.value for v in model.trainable_variables]\\n\",\n    \"non_trainable_variables = [v.value for v in model.non_trainable_variables]\\n\",\n    \"optimizer_variables = [v.value for v in optimizer.variables]\\n\",\n    \"\\n\",\n    \"state = (trainable_variables, non_trainable_variables, optimizer_variables)\\n\",\n    \"loss, state = train_step(state, inputs, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Low-level usage of metrics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"metric = keras.metrics.SparseCategoricalAccuracy()\\n\",\n    \"targets = ops.array([0, 1, 2])\\n\",\n    \"predictions = ops.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\\n\",\n    \"metric.update_state(targets, predictions)\\n\",\n    \"current_result = metric.result()\\n\",\n    \"print(f\\\"result: {current_result:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"values = ops.array([0, 1, 2, 3, 4])\\n\",\n    \"mean_tracker = keras.metrics.Mean()\\n\",\n    \"for value in values:\\n\",\n    \"    mean_tracker.update_state(value)\\n\",\n    \"print(f\\\"Mean of values: {mean_tracker.result():.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"metric = keras.metrics.SparseCategoricalAccuracy()\\n\",\n    \"targets = ops.array([0, 1, 2])\\n\",\n    \"predictions = ops.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\\n\",\n    \"\\n\",\n    \"metric_variables = metric.variables\\n\",\n    \"metric_variables = metric.stateless_update_state(\\n\",\n    \"    metric_variables, targets, predictions\\n\",\n    \")\\n\",\n    \"current_result = metric.stateless_result(metric_variables)\\n\",\n    \"print(f\\\"result: {current_result:.2f}\\\")\\n\",\n    \"\\n\",\n    \"metric_variables = metric.stateless_reset_state()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using fit() with a custom training loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Customizing fit() with TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n    \"loss_tracker = keras.metrics.Mean(name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"class CustomModel(keras.Model):\\n\",\n    \"    def train_step(self, data):\\n\",\n    \"        inputs, targets = data\\n\",\n    \"        with tf.GradientTape() as tape:\\n\",\n    \"            predictions = self(inputs, training=True)\\n\",\n    \"            loss = loss_fn(targets, predictions)\\n\",\n    \"        gradients = tape.gradient(loss, self.trainable_weights)\\n\",\n    \"        self.optimizer.apply(gradients, self.trainable_weights)\\n\",\n    \"\\n\",\n    \"        loss_tracker.update_state(loss)\\n\",\n    \"        return {\\\"loss\\\": loss_tracker.result()}\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def metrics(self):\\n\",\n    \"        return [loss_tracker]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"def get_custom_model():\\n\",\n    \"    inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"    features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"    features = layers.Dropout(0.5)(features)\\n\",\n    \"    outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"    model = CustomModel(inputs, outputs)\\n\",\n    \"    model.compile(optimizer=keras.optimizers.Adam())\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"model = get_custom_model()\\n\",\n    \"model.fit(train_images, train_labels, epochs=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Customizing fit() with PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n    \"loss_tracker = keras.metrics.Mean(name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"class CustomModel(keras.Model):\\n\",\n    \"    def train_step(self, data):\\n\",\n    \"        inputs, targets = data\\n\",\n    \"        predictions = self(inputs, training=True)\\n\",\n    \"        loss = loss_fn(targets, predictions)\\n\",\n    \"\\n\",\n    \"        loss.backward()\\n\",\n    \"        trainable_weights = [v for v in self.trainable_weights]\\n\",\n    \"        gradients = [v.value.grad for v in trainable_weights]\\n\",\n    \"\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            self.optimizer.apply(gradients, trainable_weights)\\n\",\n    \"\\n\",\n    \"        loss_tracker.update_state(loss)\\n\",\n    \"        return {\\\"loss\\\": loss_tracker.result()}\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def metrics(self):\\n\",\n    \"        return [loss_tracker]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"def get_custom_model():\\n\",\n    \"    inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"    features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"    features = layers.Dropout(0.5)(features)\\n\",\n    \"    outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"    model = CustomModel(inputs, outputs)\\n\",\n    \"    model.compile(optimizer=keras.optimizers.Adam())\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"model = get_custom_model()\\n\",\n    \"model.fit(train_images, train_labels, epochs=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Customizing fit() with JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n    \"\\n\",\n    \"class CustomModel(keras.Model):\\n\",\n    \"    def compute_loss_and_updates(\\n\",\n    \"        self,\\n\",\n    \"        trainable_variables,\\n\",\n    \"        non_trainable_variables,\\n\",\n    \"        inputs,\\n\",\n    \"        targets,\\n\",\n    \"        training=False,\\n\",\n    \"    ):\\n\",\n    \"        predictions, non_trainable_variables = self.stateless_call(\\n\",\n    \"            trainable_variables,\\n\",\n    \"            non_trainable_variables,\\n\",\n    \"            inputs,\\n\",\n    \"            training=training,\\n\",\n    \"        )\\n\",\n    \"        loss = loss_fn(targets, predictions)\\n\",\n    \"        return loss, non_trainable_variables\\n\",\n    \"\\n\",\n    \"    def train_step(self, state, data):\\n\",\n    \"        (\\n\",\n    \"            trainable_variables,\\n\",\n    \"            non_trainable_variables,\\n\",\n    \"            optimizer_variables,\\n\",\n    \"            metrics_variables,\\n\",\n    \"        ) = state\\n\",\n    \"        inputs, targets = data\\n\",\n    \"\\n\",\n    \"        grad_fn = jax.value_and_grad(\\n\",\n    \"            self.compute_loss_and_updates, has_aux=True\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        (loss, non_trainable_variables), grads = grad_fn(\\n\",\n    \"            trainable_variables,\\n\",\n    \"            non_trainable_variables,\\n\",\n    \"            inputs,\\n\",\n    \"            targets,\\n\",\n    \"            training=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        (\\n\",\n    \"            trainable_variables,\\n\",\n    \"            optimizer_variables,\\n\",\n    \"        ) = self.optimizer.stateless_apply(\\n\",\n    \"            optimizer_variables, grads, trainable_variables\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        logs = {\\\"loss\\\": loss}\\n\",\n    \"        state = (\\n\",\n    \"            trainable_variables,\\n\",\n    \"            non_trainable_variables,\\n\",\n    \"            optimizer_variables,\\n\",\n    \"            metrics_variables,\\n\",\n    \"        )\\n\",\n    \"        return logs, state\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"def get_custom_model():\\n\",\n    \"    inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"    features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"    features = layers.Dropout(0.5)(features)\\n\",\n    \"    outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"    model = CustomModel(inputs, outputs)\\n\",\n    \"    model.compile(optimizer=keras.optimizers.Adam())\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"model = get_custom_model()\\n\",\n    \"model.fit(train_images, train_labels, epochs=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Handling metrics in a custom train_step()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### train_step() metrics handling with TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"class CustomModel(keras.Model):\\n\",\n    \"    def train_step(self, data):\\n\",\n    \"        inputs, targets = data\\n\",\n    \"        with tf.GradientTape() as tape:\\n\",\n    \"            predictions = self(inputs, training=True)\\n\",\n    \"            loss = self.compute_loss(y=targets, y_pred=predictions)\\n\",\n    \"\\n\",\n    \"        gradients = tape.gradient(loss, self.trainable_weights)\\n\",\n    \"        self.optimizer.apply(gradients, self.trainable_weights)\\n\",\n    \"\\n\",\n    \"        for metric in self.metrics:\\n\",\n    \"            if metric.name == \\\"loss\\\":\\n\",\n    \"                metric.update_state(loss)\\n\",\n    \"            else:\\n\",\n    \"                metric.update_state(targets, predictions)\\n\",\n    \"\\n\",\n    \"        return {m.name: m.result() for m in self.metrics}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"def get_custom_model():\\n\",\n    \"    inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"    features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"    features = layers.Dropout(0.5)(features)\\n\",\n    \"    outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"    model = CustomModel(inputs, outputs)\\n\",\n    \"    model.compile(\\n\",\n    \"        optimizer=keras.optimizers.Adam(),\\n\",\n    \"        loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"        metrics=[keras.metrics.SparseCategoricalAccuracy()],\\n\",\n    \"    )\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"model = get_custom_model()\\n\",\n    \"model.fit(train_images, train_labels, epochs=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### train_step() metrics handling with PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"class CustomModel(keras.Model):\\n\",\n    \"    def train_step(self, data):\\n\",\n    \"        inputs, targets = data\\n\",\n    \"        predictions = self(inputs, training=True)\\n\",\n    \"        loss = self.compute_loss(y=targets, y_pred=predictions)\\n\",\n    \"\\n\",\n    \"        loss.backward()\\n\",\n    \"        trainable_weights = [v for v in self.trainable_weights]\\n\",\n    \"        gradients = [v.value.grad for v in trainable_weights]\\n\",\n    \"\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            self.optimizer.apply(gradients, trainable_weights)\\n\",\n    \"\\n\",\n    \"        for metric in self.metrics:\\n\",\n    \"            if metric.name == \\\"loss\\\":\\n\",\n    \"                metric.update_state(loss)\\n\",\n    \"            else:\\n\",\n    \"                metric.update_state(targets, predictions)\\n\",\n    \"\\n\",\n    \"        return {m.name: m.result() for m in self.metrics}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"def get_custom_model():\\n\",\n    \"    inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"    features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"    features = layers.Dropout(0.5)(features)\\n\",\n    \"    outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"    model = CustomModel(inputs, outputs)\\n\",\n    \"    model.compile(\\n\",\n    \"        optimizer=keras.optimizers.Adam(),\\n\",\n    \"        loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"        metrics=[keras.metrics.SparseCategoricalAccuracy()],\\n\",\n    \"    )\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"model = get_custom_model()\\n\",\n    \"model.fit(train_images, train_labels, epochs=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### train_step() metrics handling with JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"class CustomModel(keras.Model):\\n\",\n    \"    def compute_loss_and_updates(\\n\",\n    \"        self,\\n\",\n    \"        trainable_variables,\\n\",\n    \"        non_trainable_variables,\\n\",\n    \"        inputs,\\n\",\n    \"        targets,\\n\",\n    \"        training=False,\\n\",\n    \"    ):\\n\",\n    \"        predictions, non_trainable_variables = self.stateless_call(\\n\",\n    \"            trainable_variables,\\n\",\n    \"            non_trainable_variables,\\n\",\n    \"            inputs,\\n\",\n    \"            training=training,\\n\",\n    \"        )\\n\",\n    \"        loss = self.compute_loss(y=targets, y_pred=predictions)\\n\",\n    \"        return loss, (predictions, non_trainable_variables)\\n\",\n    \"\\n\",\n    \"    def train_step(self, state, data):\\n\",\n    \"        (\\n\",\n    \"            trainable_variables,\\n\",\n    \"            non_trainable_variables,\\n\",\n    \"            optimizer_variables,\\n\",\n    \"            metrics_variables,\\n\",\n    \"        ) = state\\n\",\n    \"        inputs, targets = data\\n\",\n    \"\\n\",\n    \"        grad_fn = jax.value_and_grad(\\n\",\n    \"            self.compute_loss_and_updates, has_aux=True\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        (loss, (predictions, non_trainable_variables)), grads = grad_fn(\\n\",\n    \"            trainable_variables,\\n\",\n    \"            non_trainable_variables,\\n\",\n    \"            inputs,\\n\",\n    \"            targets,\\n\",\n    \"            training=True,\\n\",\n    \"        )\\n\",\n    \"        (\\n\",\n    \"            trainable_variables,\\n\",\n    \"            optimizer_variables,\\n\",\n    \"        ) = self.optimizer.stateless_apply(\\n\",\n    \"            optimizer_variables, grads, trainable_variables\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        new_metrics_vars = []\\n\",\n    \"        logs = {}\\n\",\n    \"        for metric in self.metrics:\\n\",\n    \"            num_prev = len(new_metrics_vars)\\n\",\n    \"            num_current = len(metric.variables)\\n\",\n    \"            current_vars = metrics_variables[num_prev : num_prev + num_current]\\n\",\n    \"            if metric.name == \\\"loss\\\":\\n\",\n    \"                current_vars = metric.stateless_update_state(current_vars, loss)\\n\",\n    \"            else:\\n\",\n    \"                current_vars = metric.stateless_update_state(\\n\",\n    \"                    current_vars, targets, predictions\\n\",\n    \"                )\\n\",\n    \"            logs[metric.name] = metric.stateless_result(current_vars)\\n\",\n    \"            new_metrics_vars += current_vars\\n\",\n    \"\\n\",\n    \"        state = (\\n\",\n    \"            trainable_variables,\\n\",\n    \"            non_trainable_variables,\\n\",\n    \"            optimizer_variables,\\n\",\n    \"            new_metrics_vars,\\n\",\n    \"        )\\n\",\n    \"        return logs, state\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter07_deep-dive-keras\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter08_image-classification.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Image classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Introduction to ConvNets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(28, 28, 1))\\n\",\n    \"x = layers.Conv2D(filters=64, kernel_size=3, activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=128, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=256, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28, 28, 1))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28, 28, 1))\\n\",\n    \"test_images = test_images.astype(\\\"float32\\\") / 255\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(train_images, train_labels, epochs=5, batch_size=64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(test_images, test_labels)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The convolution operation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Understanding border effects and padding\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Understanding convolution strides\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The max-pooling operation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(28, 28, 1))\\n\",\n    \"x = layers.Conv2D(filters=64, kernel_size=3, activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.Conv2D(filters=128, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.Conv2D(filters=256, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(x)\\n\",\n    \"model_no_max_pool = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model_no_max_pool.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Training a ConvNet from scratch on a small dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The relevance of deep learning for small-data problems\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Downloading the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import kagglehub\\n\",\n    \"\\n\",\n    \"kagglehub.login()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"download_path = kagglehub.competition_download(\\\"dogs-vs-cats\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import zipfile\\n\",\n    \"\\n\",\n    \"with zipfile.ZipFile(download_path + \\\"/train.zip\\\", \\\"r\\\") as zip_ref:\\n\",\n    \"    zip_ref.extractall(\\\".\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, shutil, pathlib\\n\",\n    \"\\n\",\n    \"original_dir = pathlib.Path(\\\"train\\\")\\n\",\n    \"new_base_dir = pathlib.Path(\\\"dogs_vs_cats_small\\\")\\n\",\n    \"\\n\",\n    \"def make_subset(subset_name, start_index, end_index):\\n\",\n    \"    for category in (\\\"cat\\\", \\\"dog\\\"):\\n\",\n    \"        dir = new_base_dir / subset_name / category\\n\",\n    \"        os.makedirs(dir)\\n\",\n    \"        fnames = [f\\\"{category}.{i}.jpg\\\" for i in range(start_index, end_index)]\\n\",\n    \"        for fname in fnames:\\n\",\n    \"            shutil.copyfile(src=original_dir / fname, dst=dir / fname)\\n\",\n    \"\\n\",\n    \"make_subset(\\\"train\\\", start_index=0, end_index=1000)\\n\",\n    \"make_subset(\\\"validation\\\", start_index=1000, end_index=1500)\\n\",\n    \"make_subset(\\\"test\\\", start_index=1500, end_index=2500)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Building your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(180, 180, 3))\\n\",\n    \"x = layers.Rescaling(1.0 / 255)(inputs)\\n\",\n    \"x = layers.Conv2D(filters=32, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=64, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=128, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=256, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=512, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Data preprocessing\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.utils import image_dataset_from_directory\\n\",\n    \"\\n\",\n    \"batch_size = 64\\n\",\n    \"image_size = (180, 180)\\n\",\n    \"train_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"train\\\", image_size=image_size, batch_size=batch_size\\n\",\n    \")\\n\",\n    \"validation_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"validation\\\", image_size=image_size, batch_size=batch_size\\n\",\n    \")\\n\",\n    \"test_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"test\\\", image_size=image_size, batch_size=batch_size\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Understanding TensorFlow Dataset objects\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"random_numbers = np.random.normal(size=(1000, 16))\\n\",\n    \"dataset = tf.data.Dataset.from_tensor_slices(random_numbers)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for i, element in enumerate(dataset):\\n\",\n    \"    print(element.shape)\\n\",\n    \"    if i >= 2:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batched_dataset = dataset.batch(32)\\n\",\n    \"for i, element in enumerate(batched_dataset):\\n\",\n    \"    print(element.shape)\\n\",\n    \"    if i >= 2:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"reshaped_dataset = dataset.map(\\n\",\n    \"    lambda x: tf.reshape(x, (4, 4)),\\n\",\n    \"    num_parallel_calls=8)\\n\",\n    \"for i, element in enumerate(reshaped_dataset):\\n\",\n    \"    print(element.shape)\\n\",\n    \"    if i >= 2:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Fitting the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for data_batch, labels_batch in train_dataset:\\n\",\n    \"    print(\\\"data batch shape:\\\", data_batch.shape)\\n\",\n    \"    print(\\\"labels batch shape:\\\", labels_batch.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"convnet_from_scratch.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\",\\n\",\n    \"    )\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=50,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"accuracy = history.history[\\\"accuracy\\\"]\\n\",\n    \"val_accuracy = history.history[\\\"val_accuracy\\\"]\\n\",\n    \"loss = history.history[\\\"loss\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, len(accuracy) + 1)\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, accuracy, \\\"r--\\\", label=\\\"Training accuracy\\\")\\n\",\n    \"plt.plot(epochs, val_accuracy, \\\"b\\\", label=\\\"Validation accuracy\\\")\\n\",\n    \"plt.title(\\\"Training and validation accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, \\\"r--\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_model = keras.models.load_model(\\\"convnet_from_scratch.keras\\\")\\n\",\n    \"test_loss, test_acc = test_model.evaluate(test_dataset)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using data augmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_augmentation_layers = [\\n\",\n    \"    layers.RandomFlip(\\\"horizontal\\\"),\\n\",\n    \"    layers.RandomRotation(0.1),\\n\",\n    \"    layers.RandomZoom(0.2),\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"def data_augmentation(images, targets):\\n\",\n    \"    for layer in data_augmentation_layers:\\n\",\n    \"        images = layer(images)\\n\",\n    \"    return images, targets\\n\",\n    \"\\n\",\n    \"augmented_train_dataset = train_dataset.map(\\n\",\n    \"    data_augmentation, num_parallel_calls=8\\n\",\n    \")\\n\",\n    \"augmented_train_dataset = augmented_train_dataset.prefetch(tf.data.AUTOTUNE)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"for image_batch, _ in train_dataset.take(1):\\n\",\n    \"    image = image_batch[0]\\n\",\n    \"    for i in range(9):\\n\",\n    \"        ax = plt.subplot(3, 3, i + 1)\\n\",\n    \"        augmented_image, _ = data_augmentation(image, None)\\n\",\n    \"        augmented_image = keras.ops.convert_to_numpy(augmented_image)\\n\",\n    \"        plt.imshow(augmented_image.astype(\\\"uint8\\\"))\\n\",\n    \"        plt.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(180, 180, 3))\\n\",\n    \"x = layers.Rescaling(1.0 / 255)(inputs)\\n\",\n    \"x = layers.Conv2D(filters=32, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=64, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=128, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=256, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=512, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"x = layers.Dropout(0.25)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n    \"\\n\",\n    \"model.compile(\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"convnet_from_scratch_with_augmentation.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\",\\n\",\n    \"    )\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    augmented_train_dataset,\\n\",\n    \"    epochs=100,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_model = keras.models.load_model(\\n\",\n    \"    \\\"convnet_from_scratch_with_augmentation.keras\\\"\\n\",\n    \")\\n\",\n    \"test_loss, test_acc = test_model.evaluate(test_dataset)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using a pretrained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Feature extraction with a pretrained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras_hub\\n\",\n    \"\\n\",\n    \"conv_base = keras_hub.models.Backbone.from_preset(\\\"xception_41_imagenet\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"preprocessor = keras_hub.layers.ImageConverter.from_preset(\\n\",\n    \"    \\\"xception_41_imagenet\\\",\\n\",\n    \"    image_size=(180, 180),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Fast feature extraction without data augmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_features_and_labels(dataset):\\n\",\n    \"    all_features = []\\n\",\n    \"    all_labels = []\\n\",\n    \"    for images, labels in dataset:\\n\",\n    \"        preprocessed_images = preprocessor(images)\\n\",\n    \"        features = conv_base.predict(preprocessed_images, verbose=0)\\n\",\n    \"        all_features.append(features)\\n\",\n    \"        all_labels.append(labels)\\n\",\n    \"    return np.concatenate(all_features), np.concatenate(all_labels)\\n\",\n    \"\\n\",\n    \"train_features, train_labels = get_features_and_labels(train_dataset)\\n\",\n    \"val_features, val_labels = get_features_and_labels(validation_dataset)\\n\",\n    \"test_features, test_labels = get_features_and_labels(test_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_features.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(6, 6, 2048))\\n\",\n    \"x = layers.GlobalAveragePooling2D()(inputs)\\n\",\n    \"x = layers.Dense(256, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.Dropout(0.25)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"feature_extraction.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\",\\n\",\n    \"    )\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_features,\\n\",\n    \"    train_labels,\\n\",\n    \"    epochs=10,\\n\",\n    \"    validation_data=(val_features, val_labels),\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"acc = history.history[\\\"accuracy\\\"]\\n\",\n    \"val_acc = history.history[\\\"val_accuracy\\\"]\\n\",\n    \"loss = history.history[\\\"loss\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, len(acc) + 1)\\n\",\n    \"plt.plot(epochs, acc, \\\"r--\\\", label=\\\"Training accuracy\\\")\\n\",\n    \"plt.plot(epochs, val_acc, \\\"b\\\", label=\\\"Validation accuracy\\\")\\n\",\n    \"plt.title(\\\"Training and validation accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(epochs, loss, \\\"r--\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_model = keras.models.load_model(\\\"feature_extraction.keras\\\")\\n\",\n    \"test_loss, test_acc = test_model.evaluate(test_features, test_labels)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Feature extraction together with data augmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras_hub\\n\",\n    \"\\n\",\n    \"conv_base = keras_hub.models.Backbone.from_preset(\\n\",\n    \"    \\\"xception_41_imagenet\\\",\\n\",\n    \"    trainable=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.trainable = True\\n\",\n    \"len(conv_base.trainable_weights)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.trainable = False\\n\",\n    \"len(conv_base.trainable_weights)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(180, 180, 3))\\n\",\n    \"x = preprocessor(inputs)\\n\",\n    \"x = conv_base(x)\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"x = layers.Dense(256)(x)\\n\",\n    \"x = layers.Dropout(0.25)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"feature_extraction_with_data_augmentation.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\",\\n\",\n    \"    )\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    augmented_train_dataset,\\n\",\n    \"    epochs=30,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_model = keras.models.load_model(\\n\",\n    \"    \\\"feature_extraction_with_data_augmentation.keras\\\"\\n\",\n    \")\\n\",\n    \"test_loss, test_acc = test_model.evaluate(test_dataset)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Fine-tuning a pretrained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    optimizer=keras.optimizers.Adam(learning_rate=1e-5),\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"fine_tuning.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\",\\n\",\n    \"    )\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    augmented_train_dataset,\\n\",\n    \"    epochs=30,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.models.load_model(\\\"fine_tuning.keras\\\")\\n\",\n    \"test_loss, test_acc = model.evaluate(test_dataset)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter08_image-classification\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter09_convnet-architecture-patterns.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## ConvNet architecture patterns\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Modularity, hierarchy, and reuse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Residual connections\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(32, 32, 3))\\n\",\n    \"x = layers.Conv2D(32, 3, activation=\\\"relu\\\")(inputs)\\n\",\n    \"residual = x\\n\",\n    \"x = layers.Conv2D(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"residual = layers.Conv2D(64, 1)(residual)\\n\",\n    \"x = layers.add([x, residual])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(32, 32, 3))\\n\",\n    \"x = layers.Conv2D(32, 3, activation=\\\"relu\\\")(inputs)\\n\",\n    \"residual = x\\n\",\n    \"x = layers.Conv2D(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(2, padding=\\\"same\\\")(x)\\n\",\n    \"residual = layers.Conv2D(64, 1, strides=2)(residual)\\n\",\n    \"x = layers.add([x, residual])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(32, 32, 3))\\n\",\n    \"x = layers.Rescaling(1.0 / 255)(inputs)\\n\",\n    \"\\n\",\n    \"def residual_block(x, filters, pooling=False):\\n\",\n    \"    residual = x\\n\",\n    \"    x = layers.Conv2D(filters, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2D(filters, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    if pooling:\\n\",\n    \"        x = layers.MaxPooling2D(2, padding=\\\"same\\\")(x)\\n\",\n    \"        residual = layers.Conv2D(filters, 1, strides=2)(residual)\\n\",\n    \"    elif filters != residual.shape[-1]:\\n\",\n    \"        residual = layers.Conv2D(filters, 1)(residual)\\n\",\n    \"    x = layers.add([x, residual])\\n\",\n    \"    return x\\n\",\n    \"\\n\",\n    \"x = residual_block(x, filters=32, pooling=True)\\n\",\n    \"x = residual_block(x, filters=64, pooling=True)\\n\",\n    \"x = residual_block(x, filters=128, pooling=False)\\n\",\n    \"\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Batch normalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Depthwise separable convolutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Putting it together: A mini Xception-like model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import kagglehub\\n\",\n    \"\\n\",\n    \"kagglehub.login()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import zipfile\\n\",\n    \"\\n\",\n    \"download_path = kagglehub.competition_download(\\\"dogs-vs-cats\\\")\\n\",\n    \"\\n\",\n    \"with zipfile.ZipFile(download_path + \\\"/train.zip\\\", \\\"r\\\") as zip_ref:\\n\",\n    \"    zip_ref.extractall(\\\".\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, shutil, pathlib\\n\",\n    \"from keras.utils import image_dataset_from_directory\\n\",\n    \"\\n\",\n    \"original_dir = pathlib.Path(\\\"train\\\")\\n\",\n    \"new_base_dir = pathlib.Path(\\\"dogs_vs_cats_small\\\")\\n\",\n    \"\\n\",\n    \"def make_subset(subset_name, start_index, end_index):\\n\",\n    \"    for category in (\\\"cat\\\", \\\"dog\\\"):\\n\",\n    \"        dir = new_base_dir / subset_name / category\\n\",\n    \"        os.makedirs(dir)\\n\",\n    \"        fnames = [f\\\"{category}.{i}.jpg\\\" for i in range(start_index, end_index)]\\n\",\n    \"        for fname in fnames:\\n\",\n    \"            shutil.copyfile(src=original_dir / fname, dst=dir / fname)\\n\",\n    \"\\n\",\n    \"make_subset(\\\"train\\\", start_index=0, end_index=1000)\\n\",\n    \"make_subset(\\\"validation\\\", start_index=1000, end_index=1500)\\n\",\n    \"make_subset(\\\"test\\\", start_index=1500, end_index=2500)\\n\",\n    \"\\n\",\n    \"batch_size = 64\\n\",\n    \"image_size = (180, 180)\\n\",\n    \"train_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"train\\\",\\n\",\n    \"    image_size=image_size,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \")\\n\",\n    \"validation_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"validation\\\",\\n\",\n    \"    image_size=image_size,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \")\\n\",\n    \"test_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"test\\\",\\n\",\n    \"    image_size=image_size,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"data_augmentation_layers = [\\n\",\n    \"    layers.RandomFlip(\\\"horizontal\\\"),\\n\",\n    \"    layers.RandomRotation(0.1),\\n\",\n    \"    layers.RandomZoom(0.2),\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"def data_augmentation(images, targets):\\n\",\n    \"    for layer in data_augmentation_layers:\\n\",\n    \"        images = layer(images)\\n\",\n    \"    return images, targets\\n\",\n    \"\\n\",\n    \"augmented_train_dataset = train_dataset.map(\\n\",\n    \"    data_augmentation, num_parallel_calls=8\\n\",\n    \")\\n\",\n    \"augmented_train_dataset = augmented_train_dataset.prefetch(tf.data.AUTOTUNE)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(180, 180, 3))\\n\",\n    \"x = layers.Rescaling(1.0 / 255)(inputs)\\n\",\n    \"x = layers.Conv2D(filters=32, kernel_size=5, use_bias=False)(x)\\n\",\n    \"\\n\",\n    \"for size in [32, 64, 128, 256, 512]:\\n\",\n    \"    residual = x\\n\",\n    \"\\n\",\n    \"    x = layers.BatchNormalization()(x)\\n\",\n    \"    x = layers.Activation(\\\"relu\\\")(x)\\n\",\n    \"    x = layers.SeparableConv2D(size, 3, padding=\\\"same\\\", use_bias=False)(x)\\n\",\n    \"\\n\",\n    \"    x = layers.BatchNormalization()(x)\\n\",\n    \"    x = layers.Activation(\\\"relu\\\")(x)\\n\",\n    \"    x = layers.SeparableConv2D(size, 3, padding=\\\"same\\\", use_bias=False)(x)\\n\",\n    \"\\n\",\n    \"    x = layers.MaxPooling2D(3, strides=2, padding=\\\"same\\\")(x)\\n\",\n    \"\\n\",\n    \"    residual = layers.Conv2D(\\n\",\n    \"        size, 1, strides=2, padding=\\\"same\\\", use_bias=False\\n\",\n    \"    )(residual)\\n\",\n    \"    x = layers.add([x, residual])\\n\",\n    \"\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"history = model.fit(\\n\",\n    \"    augmented_train_dataset,\\n\",\n    \"    epochs=100,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Beyond convolution: Vision Transformers\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter09_convnet-architecture-patterns\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter10_interpreting-what-convnets-learn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Interpreting what ConvNets learn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Visualizing intermediate activations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from google.colab import files\\n\",\n    \"\\n\",\n    \"# You can use this to load the file\\n\",\n    \"# \\\"convnet_from_scratch_with_augmentation.keras\\\"\\n\",\n    \"# you obtained in the last chapter.\\n\",\n    \"files.upload()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"model = keras.models.load_model(\\n\",\n    \"    \\\"convnet_from_scratch_with_augmentation.keras\\\"\\n\",\n    \")\\n\",\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"img_path = keras.utils.get_file(\\n\",\n    \"    fname=\\\"cat.jpg\\\", origin=\\\"https://img-datasets.s3.amazonaws.com/cat.jpg\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"def get_img_array(img_path, target_size):\\n\",\n    \"    img = keras.utils.load_img(img_path, target_size=target_size)\\n\",\n    \"    array = keras.utils.img_to_array(img)\\n\",\n    \"    array = np.expand_dims(array, axis=0)\\n\",\n    \"    return array\\n\",\n    \"\\n\",\n    \"img_tensor = get_img_array(img_path, target_size=(180, 180))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(img_tensor[0].astype(\\\"uint8\\\"))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"layer_outputs = []\\n\",\n    \"layer_names = []\\n\",\n    \"for layer in model.layers:\\n\",\n    \"    if isinstance(layer, (layers.Conv2D, layers.MaxPooling2D)):\\n\",\n    \"        layer_outputs.append(layer.output)\\n\",\n    \"        layer_names.append(layer.name)\\n\",\n    \"activation_model = keras.Model(inputs=model.input, outputs=layer_outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"activations = activation_model.predict(img_tensor)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"first_layer_activation = activations[0]\\n\",\n    \"print(first_layer_activation.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.matshow(first_layer_activation[0, :, :, 5], cmap=\\\"viridis\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"images_per_row = 16\\n\",\n    \"for layer_name, layer_activation in zip(layer_names, activations):\\n\",\n    \"    n_features = layer_activation.shape[-1]\\n\",\n    \"    size = layer_activation.shape[1]\\n\",\n    \"    n_cols = n_features // images_per_row\\n\",\n    \"    display_grid = np.zeros(\\n\",\n    \"        ((size + 1) * n_cols - 1, images_per_row * (size + 1) - 1)\\n\",\n    \"    )\\n\",\n    \"    for col in range(n_cols):\\n\",\n    \"        for row in range(images_per_row):\\n\",\n    \"            channel_index = col * images_per_row + row\\n\",\n    \"            channel_image = layer_activation[0, :, :, channel_index].copy()\\n\",\n    \"            if channel_image.sum() != 0:\\n\",\n    \"                channel_image -= channel_image.mean()\\n\",\n    \"                channel_image /= channel_image.std()\\n\",\n    \"                channel_image *= 64\\n\",\n    \"                channel_image += 128\\n\",\n    \"            channel_image = np.clip(channel_image, 0, 255).astype(\\\"uint8\\\")\\n\",\n    \"            display_grid[\\n\",\n    \"                col * (size + 1) : (col + 1) * size + col,\\n\",\n    \"                row * (size + 1) : (row + 1) * size + row,\\n\",\n    \"            ] = channel_image\\n\",\n    \"    scale = 1.0 / size\\n\",\n    \"    plt.figure(\\n\",\n    \"        figsize=(scale * display_grid.shape[1], scale * display_grid.shape[0])\\n\",\n    \"    )\\n\",\n    \"    plt.title(layer_name)\\n\",\n    \"    plt.grid(False)\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"    plt.imshow(display_grid, aspect=\\\"auto\\\", cmap=\\\"viridis\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Visualizing ConvNet filters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras_hub\\n\",\n    \"\\n\",\n    \"model = keras_hub.models.Backbone.from_preset(\\n\",\n    \"    \\\"xception_41_imagenet\\\",\\n\",\n    \")\\n\",\n    \"preprocessor = keras_hub.layers.ImageConverter.from_preset(\\n\",\n    \"    \\\"xception_41_imagenet\\\",\\n\",\n    \"    image_size=(180, 180),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for layer in model.layers:\\n\",\n    \"    if isinstance(layer, (keras.layers.Conv2D, keras.layers.SeparableConv2D)):\\n\",\n    \"        print(layer.name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"layer_name = \\\"block3_sepconv1\\\"\\n\",\n    \"layer = model.get_layer(name=layer_name)\\n\",\n    \"feature_extractor = keras.Model(inputs=model.input, outputs=layer.output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"activation = feature_extractor(preprocessor(img_tensor))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"def compute_loss(image, filter_index):\\n\",\n    \"    activation = feature_extractor(image)\\n\",\n    \"    filter_activation = activation[:, 2:-2, 2:-2, filter_index]\\n\",\n    \"    return ops.mean(filter_activation)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Gradient ascent in TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"@tf.function\\n\",\n    \"def gradient_ascent_step(image, filter_index, learning_rate):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        tape.watch(image)\\n\",\n    \"        loss = compute_loss(image, filter_index)\\n\",\n    \"    grads = tape.gradient(loss, image)\\n\",\n    \"    grads = ops.normalize(grads)\\n\",\n    \"    image += learning_rate * grads\\n\",\n    \"    return image\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Gradient ascent in PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"import torch\\n\",\n    \"\\n\",\n    \"def gradient_ascent_step(image, filter_index, learning_rate):\\n\",\n    \"    image = image.clone().detach().requires_grad_(True)\\n\",\n    \"    loss = compute_loss(image, filter_index)\\n\",\n    \"    loss.backward()\\n\",\n    \"    grads = image.grad\\n\",\n    \"    grads = ops.normalize(grads)\\n\",\n    \"    image = image + learning_rate * grads\\n\",\n    \"    return image\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Gradient ascent in JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"import jax\\n\",\n    \"\\n\",\n    \"grad_fn = jax.grad(compute_loss)\\n\",\n    \"\\n\",\n    \"@jax.jit\\n\",\n    \"def gradient_ascent_step(image, filter_index, learning_rate):\\n\",\n    \"    grads = grad_fn(image, filter_index)\\n\",\n    \"    grads = ops.normalize(grads)\\n\",\n    \"    image += learning_rate * grads\\n\",\n    \"    return image\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The filter visualization loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"img_width = 200\\n\",\n    \"img_height = 200\\n\",\n    \"\\n\",\n    \"def generate_filter_pattern(filter_index):\\n\",\n    \"    iterations = 30\\n\",\n    \"    learning_rate = 10.0\\n\",\n    \"    image = keras.random.uniform(\\n\",\n    \"        minval=0.4, maxval=0.6, shape=(1, img_width, img_height, 3)\\n\",\n    \"    )\\n\",\n    \"    for i in range(iterations):\\n\",\n    \"        image = gradient_ascent_step(image, filter_index, learning_rate)\\n\",\n    \"    return image[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def deprocess_image(image):\\n\",\n    \"    image -= ops.mean(image)\\n\",\n    \"    image /= ops.std(image)\\n\",\n    \"    image *= 64\\n\",\n    \"    image += 128\\n\",\n    \"    image = ops.clip(image, 0, 255)\\n\",\n    \"    image = image[25:-25, 25:-25, :]\\n\",\n    \"    image = ops.cast(image, dtype=\\\"uint8\\\")\\n\",\n    \"    return ops.convert_to_numpy(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(deprocess_image(generate_filter_pattern(filter_index=2)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"all_images = []\\n\",\n    \"for filter_index in range(64):\\n\",\n    \"    print(f\\\"Processing filter {filter_index}\\\")\\n\",\n    \"    image = deprocess_image(generate_filter_pattern(filter_index))\\n\",\n    \"    all_images.append(image)\\n\",\n    \"\\n\",\n    \"margin = 5\\n\",\n    \"n = 8\\n\",\n    \"box_width = img_width - 25 * 2\\n\",\n    \"box_height = img_height - 25 * 2\\n\",\n    \"full_width = n * box_width + (n - 1) * margin\\n\",\n    \"full_height = n * box_height + (n - 1) * margin\\n\",\n    \"stitched_filters = np.zeros((full_width, full_height, 3))\\n\",\n    \"\\n\",\n    \"for i in range(n):\\n\",\n    \"    for j in range(n):\\n\",\n    \"        image = all_images[i * n + j]\\n\",\n    \"        stitched_filters[\\n\",\n    \"            (box_width + margin) * i : (box_width + margin) * i + box_width,\\n\",\n    \"            (box_height + margin) * j : (box_height + margin) * j + box_height,\\n\",\n    \"            :,\\n\",\n    \"        ] = image\\n\",\n    \"\\n\",\n    \"keras.utils.save_img(f\\\"filters_for_layer_{layer_name}.png\\\", stitched_filters)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Visualizing heatmaps of class activation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"img_path = keras.utils.get_file(\\n\",\n    \"    fname=\\\"elephant.jpg\\\",\\n\",\n    \"    origin=\\\"https://img-datasets.s3.amazonaws.com/elephant.jpg\\\",\\n\",\n    \")\\n\",\n    \"img = keras.utils.load_img(img_path)\\n\",\n    \"img_array = np.expand_dims(img, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras_hub.models.ImageClassifier.from_preset(\\n\",\n    \"   \\\"xception_41_imagenet\\\",\\n\",\n    \"   activation=\\\"softmax\\\",\\n\",\n    \")\\n\",\n    \"preds = model.predict(img_array)\\n\",\n    \"preds.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras_hub.utils.decode_imagenet_predictions(preds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.argmax(preds[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"img_array = model.preprocessor(img_array)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"last_conv_layer_name = \\\"block14_sepconv2_act\\\"\\n\",\n    \"last_conv_layer = model.backbone.get_layer(last_conv_layer_name)\\n\",\n    \"last_conv_layer_model = keras.Model(model.inputs, last_conv_layer.output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"classifier_input = last_conv_layer.output\\n\",\n    \"x = classifier_input\\n\",\n    \"for layer_name in [\\\"pooler\\\", \\\"predictions\\\"]:\\n\",\n    \"    x = model.get_layer(layer_name)(x)\\n\",\n    \"classifier_model = keras.Model(classifier_input, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Getting the gradient of the top class: TensorFlow version\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend tensorflow\\n\",\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"def get_top_class_gradients(img_array):\\n\",\n    \"    last_conv_layer_output = last_conv_layer_model(img_array)\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        tape.watch(last_conv_layer_output)\\n\",\n    \"        preds = classifier_model(last_conv_layer_output)\\n\",\n    \"        top_pred_index = ops.argmax(preds[0])\\n\",\n    \"        top_class_channel = preds[:, top_pred_index]\\n\",\n    \"\\n\",\n    \"    grads = tape.gradient(top_class_channel, last_conv_layer_output)\\n\",\n    \"    return grads, last_conv_layer_output\\n\",\n    \"\\n\",\n    \"grads, last_conv_layer_output = get_top_class_gradients(img_array)\\n\",\n    \"grads = ops.convert_to_numpy(grads)\\n\",\n    \"last_conv_layer_output = ops.convert_to_numpy(last_conv_layer_output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Getting the gradient of the top class: PyTorch version\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend torch\\n\",\n    \"def get_top_class_gradients(img_array):\\n\",\n    \"    last_conv_layer_output = last_conv_layer_model(img_array)\\n\",\n    \"    last_conv_layer_output = (\\n\",\n    \"        last_conv_layer_output.clone().detach().requires_grad_(True)\\n\",\n    \"    )\\n\",\n    \"    preds = classifier_model(last_conv_layer_output)\\n\",\n    \"    top_pred_index = ops.argmax(preds[0])\\n\",\n    \"    top_class_channel = preds[:, top_pred_index]\\n\",\n    \"    top_class_channel.backward()\\n\",\n    \"    grads = last_conv_layer_output.grad\\n\",\n    \"    return grads, last_conv_layer_output\\n\",\n    \"\\n\",\n    \"grads, last_conv_layer_output = get_top_class_gradients(img_array)\\n\",\n    \"grads = ops.convert_to_numpy(grads)\\n\",\n    \"last_conv_layer_output = ops.convert_to_numpy(last_conv_layer_output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Getting the gradient of the top class: JAX version\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%%backend jax\\n\",\n    \"import jax\\n\",\n    \"\\n\",\n    \"def loss_fn(last_conv_layer_output):\\n\",\n    \"    preds = classifier_model(last_conv_layer_output)\\n\",\n    \"    top_pred_index = ops.argmax(preds[0])\\n\",\n    \"    top_class_channel = preds[:, top_pred_index]\\n\",\n    \"    return top_class_channel[0]\\n\",\n    \"\\n\",\n    \"grad_fn = jax.grad(loss_fn)\\n\",\n    \"\\n\",\n    \"def get_top_class_gradients(img_array):\\n\",\n    \"    last_conv_layer_output = last_conv_layer_model(img_array)\\n\",\n    \"    grads = grad_fn(last_conv_layer_output)\\n\",\n    \"    return grads, last_conv_layer_output\\n\",\n    \"\\n\",\n    \"grads, last_conv_layer_output = get_top_class_gradients(img_array)\\n\",\n    \"grads = ops.convert_to_numpy(grads)\\n\",\n    \"last_conv_layer_output = ops.convert_to_numpy(last_conv_layer_output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Displaying the class activation heatmap\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pooled_grads = np.mean(grads, axis=(0, 1, 2))\\n\",\n    \"last_conv_layer_output = last_conv_layer_output[0].copy()\\n\",\n    \"for i in range(pooled_grads.shape[-1]):\\n\",\n    \"    last_conv_layer_output[:, :, i] *= pooled_grads[i]\\n\",\n    \"heatmap = np.mean(last_conv_layer_output, axis=-1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"heatmap = np.maximum(heatmap, 0)\\n\",\n    \"heatmap /= np.max(heatmap)\\n\",\n    \"plt.matshow(heatmap)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.cm as cm\\n\",\n    \"\\n\",\n    \"img = keras.utils.load_img(img_path)\\n\",\n    \"img = keras.utils.img_to_array(img)\\n\",\n    \"\\n\",\n    \"heatmap = np.uint8(255 * heatmap)\\n\",\n    \"\\n\",\n    \"jet = cm.get_cmap(\\\"jet\\\")\\n\",\n    \"jet_colors = jet(np.arange(256))[:, :3]\\n\",\n    \"jet_heatmap = jet_colors[heatmap]\\n\",\n    \"\\n\",\n    \"jet_heatmap = keras.utils.array_to_img(jet_heatmap)\\n\",\n    \"jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\\n\",\n    \"jet_heatmap = keras.utils.img_to_array(jet_heatmap)\\n\",\n    \"\\n\",\n    \"superimposed_img = jet_heatmap * 0.4 + img\\n\",\n    \"superimposed_img = keras.utils.array_to_img(superimposed_img)\\n\",\n    \"\\n\",\n    \"plt.imshow(superimposed_img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Visualizing the latent space of a ConvNet\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter10_interpreting-what-convnets-learn\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter11_image-segmentation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Image segmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Computer vision tasks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Types of image segmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Training a segmentation model from scratch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Downloading a segmentation dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\\n\",\n    \"!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\\n\",\n    \"!tar -xf images.tar.gz\\n\",\n    \"!tar -xf annotations.tar.gz\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pathlib\\n\",\n    \"\\n\",\n    \"input_dir = pathlib.Path(\\\"images\\\")\\n\",\n    \"target_dir = pathlib.Path(\\\"annotations/trimaps\\\")\\n\",\n    \"\\n\",\n    \"input_img_paths = sorted(input_dir.glob(\\\"*.jpg\\\"))\\n\",\n    \"target_paths = sorted(target_dir.glob(\\\"[!.]*.png\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from keras.utils import load_img, img_to_array, array_to_img\\n\",\n    \"\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(load_img(input_img_paths[9]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def display_target(target_array):\\n\",\n    \"    normalized_array = (target_array.astype(\\\"uint8\\\") - 1) * 127\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"    plt.imshow(normalized_array[:, :, 0])\\n\",\n    \"\\n\",\n    \"img = img_to_array(load_img(target_paths[9], color_mode=\\\"grayscale\\\"))\\n\",\n    \"display_target(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"img_size = (200, 200)\\n\",\n    \"num_imgs = len(input_img_paths)\\n\",\n    \"\\n\",\n    \"random.Random(1337).shuffle(input_img_paths)\\n\",\n    \"random.Random(1337).shuffle(target_paths)\\n\",\n    \"\\n\",\n    \"def path_to_input_image(path):\\n\",\n    \"    return img_to_array(load_img(path, target_size=img_size))\\n\",\n    \"\\n\",\n    \"def path_to_target(path):\\n\",\n    \"    img = img_to_array(\\n\",\n    \"        load_img(path, target_size=img_size, color_mode=\\\"grayscale\\\")\\n\",\n    \"    )\\n\",\n    \"    img = img.astype(\\\"uint8\\\") - 1\\n\",\n    \"    return img\\n\",\n    \"\\n\",\n    \"input_imgs = np.zeros((num_imgs,) + img_size + (3,), dtype=\\\"float32\\\")\\n\",\n    \"targets = np.zeros((num_imgs,) + img_size + (1,), dtype=\\\"uint8\\\")\\n\",\n    \"for i in range(num_imgs):\\n\",\n    \"    input_imgs[i] = path_to_input_image(input_img_paths[i])\\n\",\n    \"    targets[i] = path_to_target(target_paths[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_val_samples = 1000\\n\",\n    \"train_input_imgs = input_imgs[:-num_val_samples]\\n\",\n    \"train_targets = targets[:-num_val_samples]\\n\",\n    \"val_input_imgs = input_imgs[-num_val_samples:]\\n\",\n    \"val_targets = targets[-num_val_samples:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Building and training the segmentation model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras.layers import Rescaling, Conv2D, Conv2DTranspose\\n\",\n    \"\\n\",\n    \"def get_model(img_size, num_classes):\\n\",\n    \"    inputs = keras.Input(shape=img_size + (3,))\\n\",\n    \"    x = Rescaling(1.0 / 255)(inputs)\\n\",\n    \"\\n\",\n    \"    x = Conv2D(64, 3, strides=2, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2D(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2D(128, 3, strides=2, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2D(128, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2D(256, 3, strides=2, padding=\\\"same\\\", activation=\\\"relu\\\")(x)\\n\",\n    \"    x = Conv2D(256, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"\\n\",\n    \"    x = Conv2DTranspose(256, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2DTranspose(256, 3, strides=2, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2DTranspose(128, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2DTranspose(128, 3, strides=2, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2DTranspose(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = Conv2DTranspose(64, 3, strides=2, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"\\n\",\n    \"    outputs = Conv2D(num_classes, 3, activation=\\\"softmax\\\", padding=\\\"same\\\")(x)\\n\",\n    \"\\n\",\n    \"    return keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"model = get_model(img_size=img_size, num_classes=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# \\u26a0\\ufe0fNOTE\\u26a0\\ufe0f: The following IoU metric is *very* slow on the PyTorch backend!\\n\",\n    \"# If you are running with PyTorch, we recommend re-running the notebook with Jax\\n\",\n    \"# or TensorFlow, or skipping to the next section of this chapter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"foreground_iou = keras.metrics.IoU(\\n\",\n    \"    num_classes=3,\\n\",\n    \"    target_class_ids=(0,),\\n\",\n    \"    name=\\\"foreground_iou\\\",\\n\",\n    \"    sparse_y_true=True,\\n\",\n    \"    sparse_y_pred=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[foreground_iou],\\n\",\n    \")\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        \\\"oxford_segmentation.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"    ),\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_input_imgs,\\n\",\n    \"    train_targets,\\n\",\n    \"    epochs=50,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    validation_data=(val_input_imgs, val_targets),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"epochs = range(1, len(history.history[\\\"loss\\\"]) + 1)\\n\",\n    \"loss = history.history[\\\"loss\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_loss\\\"]\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(epochs, loss, \\\"r--\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.models.load_model(\\\"oxford_segmentation.keras\\\")\\n\",\n    \"\\n\",\n    \"i = 4\\n\",\n    \"test_image = val_input_imgs[i]\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(array_to_img(test_image))\\n\",\n    \"\\n\",\n    \"mask = model.predict(np.expand_dims(test_image, 0))[0]\\n\",\n    \"\\n\",\n    \"def display_mask(pred):\\n\",\n    \"    mask = np.argmax(pred, axis=-1)\\n\",\n    \"    mask *= 127\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"    plt.imshow(mask)\\n\",\n    \"\\n\",\n    \"display_mask(mask)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using a pretrained segmentation model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Downloading the Segment Anything Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras_hub\\n\",\n    \"\\n\",\n    \"model = keras_hub.models.ImageSegmenter.from_preset(\\\"sam_huge_sa1b\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.count_params()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### How Segment Anything works\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Preparing a test image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"path = keras.utils.get_file(\\n\",\n    \"    origin=\\\"https://s3.amazonaws.com/keras.io/img/book/fruits.jpg\\\"\\n\",\n    \")\\n\",\n    \"pil_image = keras.utils.load_img(path)\\n\",\n    \"image_array = keras.utils.img_to_array(pil_image)\\n\",\n    \"\\n\",\n    \"plt.imshow(image_array.astype(\\\"uint8\\\"))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"image_size = (1024, 1024)\\n\",\n    \"\\n\",\n    \"def resize_and_pad(x):\\n\",\n    \"    return ops.image.resize(x, image_size, pad_to_aspect_ratio=True)\\n\",\n    \"\\n\",\n    \"image = resize_and_pad(image_array)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"def show_image(image, ax):\\n\",\n    \"    ax.imshow(ops.convert_to_numpy(image).astype(\\\"uint8\\\"))\\n\",\n    \"\\n\",\n    \"def show_mask(mask, ax):\\n\",\n    \"    color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])\\n\",\n    \"    h, w, _ = mask.shape\\n\",\n    \"    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)\\n\",\n    \"    ax.imshow(mask_image)\\n\",\n    \"\\n\",\n    \"def show_points(points, ax):\\n\",\n    \"    x, y = points[:, 0], points[:, 1]\\n\",\n    \"    ax.scatter(x, y, c=\\\"green\\\", marker=\\\"*\\\", s=375, ec=\\\"white\\\", lw=1.25)\\n\",\n    \"\\n\",\n    \"def show_box(box, ax):\\n\",\n    \"    box = box.reshape(-1)\\n\",\n    \"    x0, y0 = box[0], box[1]\\n\",\n    \"    w, h = box[2] - box[0], box[3] - box[1]\\n\",\n    \"    ax.add_patch(plt.Rectangle((x0, y0), w, h, ec=\\\"red\\\", fc=\\\"none\\\", lw=2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Prompting the model with a target point\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"input_point = np.array([[580, 450]])\\n\",\n    \"input_label = np.array([1])\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"show_image(image, plt.gca())\\n\",\n    \"show_points(input_point, plt.gca())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"outputs = model.predict(\\n\",\n    \"    {\\n\",\n    \"        \\\"images\\\": ops.expand_dims(image, axis=0),\\n\",\n    \"        \\\"points\\\": ops.expand_dims(input_point, axis=0),\\n\",\n    \"        \\\"labels\\\": ops.expand_dims(input_label, axis=0),\\n\",\n    \"    }\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"outputs[\\\"masks\\\"].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_mask(sam_outputs, index=0):\\n\",\n    \"    mask = sam_outputs[\\\"masks\\\"][0][index]\\n\",\n    \"    mask = np.expand_dims(mask, axis=-1)\\n\",\n    \"    mask = resize_and_pad(mask)\\n\",\n    \"    return ops.convert_to_numpy(mask) > 0.0\\n\",\n    \"\\n\",\n    \"mask = get_mask(outputs, index=0)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"show_image(image, plt.gca())\\n\",\n    \"show_mask(mask, plt.gca())\\n\",\n    \"show_points(input_point, plt.gca())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_point = np.array([[300, 550]])\\n\",\n    \"input_label = np.array([1])\\n\",\n    \"\\n\",\n    \"outputs = model.predict(\\n\",\n    \"    {\\n\",\n    \"        \\\"images\\\": ops.expand_dims(image, axis=0),\\n\",\n    \"        \\\"points\\\": ops.expand_dims(input_point, axis=0),\\n\",\n    \"        \\\"labels\\\": ops.expand_dims(input_label, axis=0),\\n\",\n    \"    }\\n\",\n    \")\\n\",\n    \"mask = get_mask(outputs, index=0)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"show_image(image, plt.gca())\\n\",\n    \"show_mask(mask, plt.gca())\\n\",\n    \"show_points(input_point, plt.gca())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"fig, axes = plt.subplots(1, 3, figsize=(20, 60))\\n\",\n    \"masks = outputs[\\\"masks\\\"][0][1:]\\n\",\n    \"for i, mask in enumerate(masks):\\n\",\n    \"    show_image(image, axes[i])\\n\",\n    \"    show_points(input_point, axes[i])\\n\",\n    \"    mask = get_mask(outputs, index=i + 1)\\n\",\n    \"    show_mask(mask, axes[i])\\n\",\n    \"    axes[i].set_title(f\\\"Mask {i + 1}\\\", fontsize=16)\\n\",\n    \"    axes[i].axis(\\\"off\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Prompting the model with a target box\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_box = np.array(\\n\",\n    \"    [\\n\",\n    \"        [520, 180],\\n\",\n    \"        [770, 420],\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"show_image(image, plt.gca())\\n\",\n    \"show_box(input_box, plt.gca())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"outputs = model.predict(\\n\",\n    \"    {\\n\",\n    \"        \\\"images\\\": ops.expand_dims(image, axis=0),\\n\",\n    \"        \\\"boxes\\\": ops.expand_dims(input_box, axis=(0, 1)),\\n\",\n    \"    }\\n\",\n    \")\\n\",\n    \"mask = get_mask(outputs, 0)\\n\",\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"show_image(image, plt.gca())\\n\",\n    \"show_mask(mask, plt.gca())\\n\",\n    \"show_box(input_box, plt.gca())\\n\",\n    \"plt.show()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter11_image-segmentation\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter12_object-detection.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Object detection\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Single-stage vs. two-stage object detectors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Two-stage R-CNN detectors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Single-stage detectors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Training a YOLO model from scratch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Downloading the COCO dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"import keras_hub\\n\",\n    \"\\n\",\n    \"images_path = keras.utils.get_file(\\n\",\n    \"    \\\"coco\\\",\\n\",\n    \"    \\\"http://images.cocodataset.org/zips/train2017.zip\\\",\\n\",\n    \"    extract=True,\\n\",\n    \")\\n\",\n    \"annotations_path = keras.utils.get_file(\\n\",\n    \"    \\\"annotations\\\",\\n\",\n    \"    \\\"http://images.cocodataset.org/annotations/annotations_trainval2017.zip\\\",\\n\",\n    \"    extract=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import json\\n\",\n    \"\\n\",\n    \"with open(f\\\"{annotations_path}/annotations/instances_train2017.json\\\", \\\"r\\\") as f:\\n\",\n    \"    annotations = json.load(f)\\n\",\n    \"\\n\",\n    \"images = {image[\\\"id\\\"]: image for image in annotations[\\\"images\\\"]}\\n\",\n    \"\\n\",\n    \"def scale_box(box, width, height):\\n\",\n    \"    scale = 1.0 / max(width, height)\\n\",\n    \"    x, y, w, h = [v * scale for v in box]\\n\",\n    \"    x += (height - width) * scale / 2 if height > width else 0\\n\",\n    \"    y += (width - height) * scale / 2 if width > height else 0\\n\",\n    \"    return [x, y, w, h]\\n\",\n    \"\\n\",\n    \"metadata = {}\\n\",\n    \"for annotation in annotations[\\\"annotations\\\"]:\\n\",\n    \"    id = annotation[\\\"image_id\\\"]\\n\",\n    \"    if id not in metadata:\\n\",\n    \"        metadata[id] = {\\\"boxes\\\": [], \\\"labels\\\": []}\\n\",\n    \"    image = images[id]\\n\",\n    \"    box = scale_box(annotation[\\\"bbox\\\"], image[\\\"width\\\"], image[\\\"height\\\"])\\n\",\n    \"    metadata[id][\\\"boxes\\\"].append(box)\\n\",\n    \"    metadata[id][\\\"labels\\\"].append(annotation[\\\"category_id\\\"])\\n\",\n    \"    metadata[id][\\\"path\\\"] = images_path + \\\"/train2017/\\\" + image[\\\"file_name\\\"]\\n\",\n    \"metadata = list(metadata.values())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(metadata)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"min([len(x[\\\"boxes\\\"]) for x in metadata])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max([len(x[\\\"boxes\\\"]) for x in metadata])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max(max(x[\\\"labels\\\"]) for x in metadata) + 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"metadata[435]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"[keras_hub.utils.coco_id_to_name(x) for x in metadata[435][\\\"labels\\\"]]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from matplotlib.colors import hsv_to_rgb\\n\",\n    \"from matplotlib.patches import Rectangle\\n\",\n    \"\\n\",\n    \"color_map = {0: \\\"gray\\\"}\\n\",\n    \"\\n\",\n    \"def label_to_color(label):\\n\",\n    \"    if label not in color_map:\\n\",\n    \"        h, s, v = (len(color_map) * 0.618) % 1, 0.5, 0.9\\n\",\n    \"        color_map[label] = hsv_to_rgb((h, s, v))\\n\",\n    \"    return color_map[label]\\n\",\n    \"\\n\",\n    \"def draw_box(ax, box, text, color):\\n\",\n    \"    x, y, w, h = box\\n\",\n    \"    ax.add_patch(Rectangle((x, y), w, h, lw=2, ec=color, fc=\\\"none\\\"))\\n\",\n    \"    textbox = dict(fc=color, pad=1, ec=\\\"none\\\")\\n\",\n    \"    ax.text(x, y, text, c=\\\"white\\\", size=10, va=\\\"bottom\\\", bbox=textbox)\\n\",\n    \"\\n\",\n    \"def draw_image(ax, image):\\n\",\n    \"    ax.set(xlim=(0, 1), ylim=(1, 0), xticks=[], yticks=[], aspect=\\\"equal\\\")\\n\",\n    \"    image = plt.imread(image)\\n\",\n    \"    height, width = image.shape[:2]\\n\",\n    \"    hpad = (1 - height / width) / 2 if width > height else 0\\n\",\n    \"    wpad = (1 - width / height) / 2 if height > width else 0\\n\",\n    \"    extent = [wpad, 1 - wpad, 1 - hpad, hpad]\\n\",\n    \"    ax.imshow(image, extent=extent)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sample = metadata[435]\\n\",\n    \"ig, ax = plt.subplots(dpi=300)\\n\",\n    \"draw_image(ax, sample[\\\"path\\\"])\\n\",\n    \"for box, label in zip(sample[\\\"boxes\\\"], sample[\\\"labels\\\"]):\\n\",\n    \"    label_name = keras_hub.utils.coco_id_to_name(label)\\n\",\n    \"    draw_box(ax, box, label_name, label_to_color(label))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"metadata = list(filter(lambda x: len(x[\\\"boxes\\\"]) <= 4, metadata))\\n\",\n    \"random.shuffle(metadata)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Creating a YOLO model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"image_size = 448\\n\",\n    \"\\n\",\n    \"backbone = keras_hub.models.Backbone.from_preset(\\n\",\n    \"    \\\"resnet_50_imagenet\\\",\\n\",\n    \")\\n\",\n    \"preprocessor = keras_hub.layers.ImageConverter.from_preset(\\n\",\n    \"    \\\"resnet_50_imagenet\\\",\\n\",\n    \"    image_size=(image_size, image_size),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"grid_size = 6\\n\",\n    \"num_labels = 91\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(image_size, image_size, 3))\\n\",\n    \"x = backbone(inputs)\\n\",\n    \"x = layers.Conv2D(512, (3, 3), strides=(2, 2))(x)\\n\",\n    \"x = keras.layers.Flatten()(x)\\n\",\n    \"x = layers.Dense(2048, activation=\\\"relu\\\", kernel_initializer=\\\"glorot_normal\\\")(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"x = layers.Dense(grid_size * grid_size * (num_labels + 5))(x)\\n\",\n    \"x = layers.Reshape((grid_size, grid_size, num_labels + 5))(x)\\n\",\n    \"box_predictions = x[..., :5]\\n\",\n    \"class_predictions = layers.Activation(\\\"softmax\\\")(x[..., 5:])\\n\",\n    \"outputs = {\\\"box\\\": box_predictions, \\\"class\\\": class_predictions}\\n\",\n    \"model = keras.Model(inputs, outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Readying the COCO data for the YOLO model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def to_grid(box):\\n\",\n    \"    x, y, w, h = box\\n\",\n    \"    cx, cy = (x + w / 2) * grid_size, (y + h / 2) * grid_size\\n\",\n    \"    ix, iy = int(cx), int(cy)\\n\",\n    \"    return (ix, iy), (cx - ix, cy - iy, w, h)\\n\",\n    \"\\n\",\n    \"def from_grid(loc, box):\\n\",\n    \"    (xi, yi), (x, y, w, h) = loc, box\\n\",\n    \"    x = (xi + x) / grid_size - w / 2\\n\",\n    \"    y = (yi + y) / grid_size - h / 2\\n\",\n    \"    return (x, y, w, h)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import math\\n\",\n    \"\\n\",\n    \"class_array = np.zeros((len(metadata), grid_size, grid_size))\\n\",\n    \"box_array = np.zeros((len(metadata), grid_size, grid_size, 5))\\n\",\n    \"\\n\",\n    \"for index, sample in enumerate(metadata):\\n\",\n    \"    boxes, labels = sample[\\\"boxes\\\"], sample[\\\"labels\\\"]\\n\",\n    \"    for box, label in zip(boxes, labels):\\n\",\n    \"        (x, y, w, h) = box\\n\",\n    \"        left, right = math.floor(x * grid_size), math.ceil((x + w) * grid_size)\\n\",\n    \"        bottom, top = math.floor(y * grid_size), math.ceil((y + h) * grid_size)\\n\",\n    \"        class_array[index, bottom:top, left:right] = label\\n\",\n    \"\\n\",\n    \"for index, sample in enumerate(metadata):\\n\",\n    \"    boxes, labels = sample[\\\"boxes\\\"], sample[\\\"labels\\\"]\\n\",\n    \"    for box, label in zip(boxes, labels):\\n\",\n    \"        (xi, yi), (grid_box) = to_grid(box)\\n\",\n    \"        box_array[index, yi, xi] = [*grid_box, 1.0]\\n\",\n    \"        class_array[index, yi, xi] = label\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def draw_prediction(image, boxes, classes, cutoff=None):\\n\",\n    \"    fig, ax = plt.subplots(dpi=300)\\n\",\n    \"    draw_image(ax, image)\\n\",\n    \"    for yi, row in enumerate(classes):\\n\",\n    \"        for xi, label in enumerate(row):\\n\",\n    \"            color = label_to_color(label) if label else \\\"none\\\"\\n\",\n    \"            x, y, w, h = (v / grid_size for v in (xi, yi, 1.0, 1.0))\\n\",\n    \"            r = Rectangle((x, y), w, h, lw=2, ec=\\\"black\\\", fc=color, alpha=0.5)\\n\",\n    \"            ax.add_patch(r)\\n\",\n    \"    for yi, row in enumerate(boxes):\\n\",\n    \"        for xi, box in enumerate(row):\\n\",\n    \"            box, confidence = box[:4], box[4]\\n\",\n    \"            if not cutoff or confidence >= cutoff:\\n\",\n    \"                box = from_grid((xi, yi), box)\\n\",\n    \"                label = classes[yi, xi]\\n\",\n    \"                color = label_to_color(label)\\n\",\n    \"                name = keras_hub.utils.coco_id_to_name(label)\\n\",\n    \"                draw_box(ax, box, f\\\"{name} {max(confidence, 0):.2f}\\\", color)\\n\",\n    \"    plt.show()\\n\",\n    \"\\n\",\n    \"draw_prediction(metadata[0][\\\"path\\\"], box_array[0], class_array[0], cutoff=1.0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"def load_image(path):\\n\",\n    \"    x = tf.io.read_file(path)\\n\",\n    \"    x = tf.image.decode_jpeg(x, channels=3)\\n\",\n    \"    return preprocessor(x)\\n\",\n    \"\\n\",\n    \"images = tf.data.Dataset.from_tensor_slices([x[\\\"path\\\"] for x in metadata])\\n\",\n    \"images = images.map(load_image, num_parallel_calls=8)\\n\",\n    \"labels = {\\\"box\\\": box_array, \\\"class\\\": class_array}\\n\",\n    \"labels = tf.data.Dataset.from_tensor_slices(labels)\\n\",\n    \"\\n\",\n    \"dataset = tf.data.Dataset.zip(images, labels).batch(16).prefetch(2)\\n\",\n    \"val_dataset, train_dataset = dataset.take(500), dataset.skip(500)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training the YOLO model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"def unpack(box):\\n\",\n    \"    return box[..., 0], box[..., 1], box[..., 2], box[..., 3]\\n\",\n    \"\\n\",\n    \"def intersection(box1, box2):\\n\",\n    \"    cx1, cy1, w1, h1 = unpack(box1)\\n\",\n    \"    cx2, cy2, w2, h2 = unpack(box2)\\n\",\n    \"    left = ops.maximum(cx1 - w1 / 2, cx2 - w2 / 2)\\n\",\n    \"    bottom = ops.maximum(cy1 - h1 / 2, cy2 - h2 / 2)\\n\",\n    \"    right = ops.minimum(cx1 + w1 / 2, cx2 + w2 / 2)\\n\",\n    \"    top = ops.minimum(cy1 + h1 / 2, cy2 + h2 / 2)\\n\",\n    \"    return ops.maximum(0.0, right - left) * ops.maximum(0.0, top - bottom)\\n\",\n    \"\\n\",\n    \"def intersection_over_union(box1, box2):\\n\",\n    \"    cx1, cy1, w1, h1 = unpack(box1)\\n\",\n    \"    cx2, cy2, w2, h2 = unpack(box2)\\n\",\n    \"    intersection_area = intersection(box1, box2)\\n\",\n    \"    a1 = ops.maximum(w1, 0.0) * ops.maximum(h1, 0.0)\\n\",\n    \"    a2 = ops.maximum(w2, 0.0) * ops.maximum(h2, 0.0)\\n\",\n    \"    union_area = a1 + a2 - intersection_area\\n\",\n    \"    return ops.divide_no_nan(intersection_area, union_area)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def signed_sqrt(x):\\n\",\n    \"    return ops.sign(x) * ops.sqrt(ops.absolute(x) + keras.config.epsilon())\\n\",\n    \"\\n\",\n    \"def box_loss(true, pred):\\n\",\n    \"    xy_true, wh_true, conf_true = true[..., :2], true[..., 2:4], true[..., 4:]\\n\",\n    \"    xy_pred, wh_pred, conf_pred = pred[..., :2], pred[..., 2:4], pred[..., 4:]\\n\",\n    \"    no_object = conf_true == 0.0\\n\",\n    \"    xy_error = ops.square(xy_true - xy_pred)\\n\",\n    \"    wh_error = ops.square(signed_sqrt(wh_true) - signed_sqrt(wh_pred))\\n\",\n    \"    iou = intersection_over_union(true, pred)\\n\",\n    \"    conf_target = ops.where(no_object, 0.0, ops.expand_dims(iou, -1))\\n\",\n    \"    conf_error = ops.square(conf_target - conf_pred)\\n\",\n    \"    error = ops.concatenate(\\n\",\n    \"        (\\n\",\n    \"            ops.where(no_object, 0.0, xy_error * 5.0),\\n\",\n    \"            ops.where(no_object, 0.0, wh_error * 5.0),\\n\",\n    \"            ops.where(no_object, conf_error * 0.5, conf_error),\\n\",\n    \"        ),\\n\",\n    \"        axis=-1,\\n\",\n    \"    )\\n\",\n    \"    return ops.sum(error, axis=(1, 2, 3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=keras.optimizers.Adam(2e-4),\\n\",\n    \"    loss={\\\"box\\\": box_loss, \\\"class\\\": \\\"sparse_categorical_crossentropy\\\"},\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    validation_data=val_dataset,\\n\",\n    \"    epochs=4,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = next(iter(val_dataset.rebatch(1)))\\n\",\n    \"preds = model.predict(x)\\n\",\n    \"boxes = preds[\\\"box\\\"][0]\\n\",\n    \"classes = np.argmax(preds[\\\"class\\\"][0], axis=-1)\\n\",\n    \"path = metadata[0][\\\"path\\\"]\\n\",\n    \"draw_prediction(path, boxes, classes, cutoff=0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"draw_prediction(path, boxes, classes, cutoff=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using a pretrained RetinaNet detector\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"url = \\\"https://s3.us-east-1.amazonaws.com/book.keras.io/3e/seurat.jpg\\\"\\n\",\n    \"path = keras.utils.get_file(origin=url)\\n\",\n    \"image = np.array([keras.utils.load_img(path)])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"detector = keras_hub.models.ObjectDetector.from_preset(\\n\",\n    \"    \\\"retinanet_resnet50_fpn_v2_coco\\\",\\n\",\n    \"    bounding_box_format=\\\"rel_xywh\\\",\\n\",\n    \")\\n\",\n    \"predictions = detector.predict(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"[(k, v.shape) for k, v in predictions.items()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions[\\\"boxes\\\"][0][0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"fig, ax = plt.subplots(dpi=300)\\n\",\n    \"draw_image(ax, path)\\n\",\n    \"num_detections = predictions[\\\"num_detections\\\"][0]\\n\",\n    \"for i in range(num_detections):\\n\",\n    \"    box = predictions[\\\"boxes\\\"][0][i]\\n\",\n    \"    label = predictions[\\\"labels\\\"][0][i]\\n\",\n    \"    label_name = keras_hub.utils.coco_id_to_name(label)\\n\",\n    \"    draw_box(ax, box, label_name, label_to_color(label))\\n\",\n    \"plt.show()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter12_object-detection\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter13_timeseries-forecasting.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Timeseries forecasting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Different kinds of timeseries tasks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A temperature forecasting example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wget https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\\n\",\n    \"!unzip jena_climate_2009_2016.csv.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"fname = os.path.join(\\\"jena_climate_2009_2016.csv\\\")\\n\",\n    \"\\n\",\n    \"with open(fname) as f:\\n\",\n    \"    data = f.read()\\n\",\n    \"\\n\",\n    \"lines = data.split(\\\"\\\\n\\\")\\n\",\n    \"header = lines[0].split(\\\",\\\")\\n\",\n    \"lines = lines[1:]\\n\",\n    \"print(header)\\n\",\n    \"print(len(lines))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"temperature = np.zeros((len(lines),))\\n\",\n    \"raw_data = np.zeros((len(lines), len(header) - 1))\\n\",\n    \"\\n\",\n    \"for i, line in enumerate(lines):\\n\",\n    \"    values = [float(x) for x in line.split(\\\",\\\")[1:]]\\n\",\n    \"    temperature[i] = values[1]\\n\",\n    \"    raw_data[i, :] = values[:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.plot(range(len(temperature)), temperature)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.plot(range(1440), temperature[:1440])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_train_samples = int(0.5 * len(raw_data))\\n\",\n    \"num_val_samples = int(0.25 * len(raw_data))\\n\",\n    \"num_test_samples = len(raw_data) - num_train_samples - num_val_samples\\n\",\n    \"print(\\\"num_train_samples:\\\", num_train_samples)\\n\",\n    \"print(\\\"num_val_samples:\\\", num_val_samples)\\n\",\n    \"print(\\\"num_test_samples:\\\", num_test_samples)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean = raw_data[:num_train_samples].mean(axis=0)\\n\",\n    \"raw_data -= mean\\n\",\n    \"std = raw_data[:num_train_samples].std(axis=0)\\n\",\n    \"raw_data /= std\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import keras\\n\",\n    \"\\n\",\n    \"int_sequence = np.arange(10)\\n\",\n    \"dummy_dataset = keras.utils.timeseries_dataset_from_array(\\n\",\n    \"    data=int_sequence[:-3],\\n\",\n    \"    targets=int_sequence[3:],\\n\",\n    \"    sequence_length=3,\\n\",\n    \"    batch_size=2,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for inputs, targets in dummy_dataset:\\n\",\n    \"    for i in range(inputs.shape[0]):\\n\",\n    \"        print([int(x) for x in inputs[i]], int(targets[i]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sampling_rate = 6\\n\",\n    \"sequence_length = 120\\n\",\n    \"delay = sampling_rate * (sequence_length + 24 - 1)\\n\",\n    \"batch_size = 256\\n\",\n    \"\\n\",\n    \"train_dataset = keras.utils.timeseries_dataset_from_array(\\n\",\n    \"    raw_data[:-delay],\\n\",\n    \"    targets=temperature[delay:],\\n\",\n    \"    sampling_rate=sampling_rate,\\n\",\n    \"    sequence_length=sequence_length,\\n\",\n    \"    shuffle=True,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \"    start_index=0,\\n\",\n    \"    end_index=num_train_samples,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"val_dataset = keras.utils.timeseries_dataset_from_array(\\n\",\n    \"    raw_data[:-delay],\\n\",\n    \"    targets=temperature[delay:],\\n\",\n    \"    sampling_rate=sampling_rate,\\n\",\n    \"    sequence_length=sequence_length,\\n\",\n    \"    shuffle=True,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \"    start_index=num_train_samples,\\n\",\n    \"    end_index=num_train_samples + num_val_samples,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = keras.utils.timeseries_dataset_from_array(\\n\",\n    \"    raw_data[:-delay],\\n\",\n    \"    targets=temperature[delay:],\\n\",\n    \"    sampling_rate=sampling_rate,\\n\",\n    \"    sequence_length=sequence_length,\\n\",\n    \"    shuffle=True,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \"    start_index=num_train_samples + num_val_samples,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for samples, targets in train_dataset:\\n\",\n    \"    print(\\\"samples shape:\\\", samples.shape)\\n\",\n    \"    print(\\\"targets shape:\\\", targets.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A commonsense, non-machine-learning baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def evaluate_naive_method(dataset):\\n\",\n    \"    total_abs_err = 0.0\\n\",\n    \"    samples_seen = 0\\n\",\n    \"    for samples, targets in dataset:\\n\",\n    \"        preds = samples[:, -1, 1] * std[1] + mean[1]\\n\",\n    \"        total_abs_err += np.sum(np.abs(preds - targets))\\n\",\n    \"        samples_seen += samples.shape[0]\\n\",\n    \"    return total_abs_err / samples_seen\\n\",\n    \"\\n\",\n    \"print(f\\\"Validation MAE: {evaluate_naive_method(val_dataset):.2f}\\\")\\n\",\n    \"print(f\\\"Test MAE: {evaluate_naive_method(test_dataset):.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Let's try a basic machine learning model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.Flatten()(inputs)\\n\",\n    \"x = layers.Dense(16, activation=\\\"relu\\\")(x)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"jena_dense.keras\\\", save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"adam\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=10,\\n\",\n    \"    validation_data=val_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model = keras.models.load_model(\\\"jena_dense.keras\\\")\\n\",\n    \"print(f\\\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"loss = history.history[\\\"mae\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_mae\\\"]\\n\",\n    \"epochs = range(1, len(loss) + 1)\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(epochs, loss, \\\"r--\\\", label=\\\"Training MAE\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation MAE\\\")\\n\",\n    \"plt.title(\\\"Training and validation MAE\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Let's try a 1D convolutional model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.Conv1D(8, 24, activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.MaxPooling1D(2)(x)\\n\",\n    \"x = layers.Conv1D(8, 12, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling1D(2)(x)\\n\",\n    \"x = layers.Conv1D(8, 6, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.GlobalAveragePooling1D()(x)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"jena_conv.keras\\\", save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"adam\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=10,\\n\",\n    \"    validation_data=val_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model = keras.models.load_model(\\\"jena_conv.keras\\\")\\n\",\n    \"print(f\\\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Recurrent neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.LSTM(16)(inputs)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"jena_lstm.keras\\\", save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"adam\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=10,\\n\",\n    \"    validation_data=val_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model = keras.models.load_model(\\\"jena_lstm.keras\\\")\\n\",\n    \"print(\\\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Understanding recurrent neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"timesteps = 100\\n\",\n    \"input_features = 32\\n\",\n    \"output_features = 64\\n\",\n    \"inputs = np.random.random((timesteps, input_features))\\n\",\n    \"state_t = np.zeros((output_features,))\\n\",\n    \"W = np.random.random((output_features, input_features))\\n\",\n    \"U = np.random.random((output_features, output_features))\\n\",\n    \"b = np.random.random((output_features,))\\n\",\n    \"successive_outputs = []\\n\",\n    \"for input_t in inputs:\\n\",\n    \"    output_t = np.tanh(np.dot(W, input_t) + np.dot(U, state_t) + b)\\n\",\n    \"    successive_outputs.append(output_t)\\n\",\n    \"    state_t = output_t\\n\",\n    \"final_output_sequence = np.concatenate(successive_outputs, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A recurrent layer in Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_features = 14\\n\",\n    \"inputs = keras.Input(shape=(None, num_features))\\n\",\n    \"outputs = layers.SimpleRNN(16)(inputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_features = 14\\n\",\n    \"steps = 120\\n\",\n    \"inputs = keras.Input(shape=(steps, num_features))\\n\",\n    \"outputs = layers.SimpleRNN(16, return_sequences=False)(inputs)\\n\",\n    \"print(outputs.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_features = 14\\n\",\n    \"steps = 120\\n\",\n    \"inputs = keras.Input(shape=(steps, num_features))\\n\",\n    \"outputs = layers.SimpleRNN(16, return_sequences=True)(inputs)\\n\",\n    \"print(outputs.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(steps, num_features))\\n\",\n    \"x = layers.SimpleRNN(16, return_sequences=True)(inputs)\\n\",\n    \"x = layers.SimpleRNN(16, return_sequences=True)(x)\\n\",\n    \"outputs = layers.SimpleRNN(16)(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Getting the most out of recurrent neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using recurrent dropout to fight overfitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.LSTM(32, recurrent_dropout=0.25)(inputs)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        \\\"jena_lstm_dropout.keras\\\", save_best_only=True\\n\",\n    \"    )\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"adam\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=50,\\n\",\n    \"    validation_data=val_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Stacking recurrent layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.GRU(32, recurrent_dropout=0.5, return_sequences=True)(inputs)\\n\",\n    \"x = layers.GRU(32, recurrent_dropout=0.5)(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        \\\"jena_stacked_gru_dropout.keras\\\", save_best_only=True\\n\",\n    \"    )\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"adam\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=50,\\n\",\n    \"    validation_data=val_dataset,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \")\\n\",\n    \"model = keras.models.load_model(\\\"jena_stacked_gru_dropout.keras\\\")\\n\",\n    \"print(f\\\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using bidirectional RNNs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.Bidirectional(layers.LSTM(16))(inputs)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=\\\"adam\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=10,\\n\",\n    \"    validation_data=val_dataset,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Going even further\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter13_timeseries-forecasting\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter14_text-classification.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Text classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A brief history of natural language processing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Preparing text data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import regex as re\\n\",\n    \"\\n\",\n    \"def split_chars(text):\\n\",\n    \"    return re.findall(r\\\".\\\", text)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"chars = split_chars(\\\"The quick brown fox jumped over the lazy dog.\\\")\\n\",\n    \"chars[:12]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def split_words(text):\\n\",\n    \"    return re.findall(r\\\"[\\\\w]+|[.,!?;]\\\", text)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"split_words(\\\"The quick brown fox jumped over the dog.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocabulary = {\\n\",\n    \"    \\\"[UNK]\\\": 0,\\n\",\n    \"    \\\"the\\\": 1,\\n\",\n    \"    \\\"quick\\\": 2,\\n\",\n    \"    \\\"brown\\\": 3,\\n\",\n    \"    \\\"fox\\\": 4,\\n\",\n    \"    \\\"jumped\\\": 5,\\n\",\n    \"    \\\"over\\\": 6,\\n\",\n    \"    \\\"dog\\\": 7,\\n\",\n    \"    \\\".\\\": 8,\\n\",\n    \"}\\n\",\n    \"words = split_words(\\\"The quick brown fox jumped over the lazy dog.\\\")\\n\",\n    \"indices = [vocabulary.get(word, 0) for word in words]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Character and word tokenization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class CharTokenizer:\\n\",\n    \"    def __init__(self, vocabulary):\\n\",\n    \"        self.vocabulary = vocabulary\\n\",\n    \"        self.unk_id = vocabulary[\\\"[UNK]\\\"]\\n\",\n    \"\\n\",\n    \"    def standardize(self, inputs):\\n\",\n    \"        return inputs.lower()\\n\",\n    \"\\n\",\n    \"    def split(self, inputs):\\n\",\n    \"        return re.findall(r\\\".\\\", inputs)\\n\",\n    \"\\n\",\n    \"    def index(self, tokens):\\n\",\n    \"        return [self.vocabulary.get(t, self.unk_id) for t in tokens]\\n\",\n    \"\\n\",\n    \"    def __call__(self, inputs):\\n\",\n    \"        inputs = self.standardize(inputs)\\n\",\n    \"        tokens = self.split(inputs)\\n\",\n    \"        indices = self.index(tokens)\\n\",\n    \"        return indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import collections\\n\",\n    \"\\n\",\n    \"def compute_char_vocabulary(inputs, max_size):\\n\",\n    \"    char_counts = collections.Counter()\\n\",\n    \"    for x in inputs:\\n\",\n    \"        x = x.lower()\\n\",\n    \"        tokens = re.findall(r\\\".\\\", x)\\n\",\n    \"        char_counts.update(tokens)\\n\",\n    \"    vocabulary = [\\\"[UNK]\\\"]\\n\",\n    \"    most_common = char_counts.most_common(max_size - len(vocabulary))\\n\",\n    \"    for token, count in most_common:\\n\",\n    \"        vocabulary.append(token)\\n\",\n    \"    return dict((token, i) for i, token in enumerate(vocabulary))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class WordTokenizer:\\n\",\n    \"    def __init__(self, vocabulary):\\n\",\n    \"        self.vocabulary = vocabulary\\n\",\n    \"        self.unk_id = vocabulary[\\\"[UNK]\\\"]\\n\",\n    \"\\n\",\n    \"    def standardize(self, inputs):\\n\",\n    \"        return inputs.lower()\\n\",\n    \"\\n\",\n    \"    def split(self, inputs):\\n\",\n    \"        return re.findall(r\\\"[\\\\w]+|[.,!?;]\\\", inputs)\\n\",\n    \"\\n\",\n    \"    def index(self, tokens):\\n\",\n    \"        return [self.vocabulary.get(t, self.unk_id) for t in tokens]\\n\",\n    \"\\n\",\n    \"    def __call__(self, inputs):\\n\",\n    \"        inputs = self.standardize(inputs)\\n\",\n    \"        tokens = self.split(inputs)\\n\",\n    \"        indices = self.index(tokens)\\n\",\n    \"        return indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_word_vocabulary(inputs, max_size):\\n\",\n    \"    word_counts = collections.Counter()\\n\",\n    \"    for x in inputs:\\n\",\n    \"        x = x.lower()\\n\",\n    \"        tokens = re.findall(r\\\"[\\\\w]+|[.,!?;]\\\", x)\\n\",\n    \"        word_counts.update(tokens)\\n\",\n    \"    vocabulary = [\\\"[UNK]\\\"]\\n\",\n    \"    most_common = word_counts.most_common(max_size - len(vocabulary))\\n\",\n    \"    for token, count in most_common:\\n\",\n    \"        vocabulary.append(token)\\n\",\n    \"    return dict((token, i) for i, token in enumerate(vocabulary))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"\\n\",\n    \"filename = keras.utils.get_file(\\n\",\n    \"    origin=\\\"https://www.gutenberg.org/files/2701/old/moby10b.txt\\\",\\n\",\n    \")\\n\",\n    \"moby_dick = list(open(filename, \\\"r\\\"))\\n\",\n    \"\\n\",\n    \"vocabulary = compute_char_vocabulary(moby_dick, max_size=100)\\n\",\n    \"char_tokenizer = CharTokenizer(vocabulary)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary length:\\\", len(vocabulary))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary start:\\\", list(vocabulary.keys())[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary end:\\\", list(vocabulary.keys())[-10:])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Line length:\\\", len(char_tokenizer(\\n\",\n    \"   \\\"Call me Ishmael. Some years ago--never mind how long precisely.\\\"\\n\",\n    \")))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocabulary = compute_word_vocabulary(moby_dick, max_size=2_000)\\n\",\n    \"word_tokenizer = WordTokenizer(vocabulary)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary length:\\\", len(vocabulary))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary start:\\\", list(vocabulary.keys())[:5])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary end:\\\", list(vocabulary.keys())[-5:])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Line length:\\\", len(word_tokenizer(\\n\",\n    \"   \\\"Call me Ishmael. Some years ago--never mind how long precisely.\\\"\\n\",\n    \")))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Subword tokenization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data = [\\n\",\n    \"    \\\"the quick brown fox\\\",\\n\",\n    \"    \\\"the slow brown fox\\\",\\n\",\n    \"    \\\"the quick brown foxhound\\\",\\n\",\n    \"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def count_and_split_words(data):\\n\",\n    \"    counts = collections.Counter()\\n\",\n    \"    for line in data:\\n\",\n    \"        line = line.lower()\\n\",\n    \"        for word in re.findall(r\\\"[\\\\w]+|[.,!?;]\\\", line):\\n\",\n    \"            chars = re.findall(r\\\".\\\", word)\\n\",\n    \"            split_word = \\\" \\\".join(chars)\\n\",\n    \"            counts[split_word] += 1\\n\",\n    \"    return dict(counts)\\n\",\n    \"\\n\",\n    \"counts = count_and_split_words(data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"counts\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def count_pairs(counts):\\n\",\n    \"    pairs = collections.Counter()\\n\",\n    \"    for word, freq in counts.items():\\n\",\n    \"        symbols = word.split()\\n\",\n    \"        for pair in zip(symbols[:-1], symbols[1:]):\\n\",\n    \"            pairs[pair] += freq\\n\",\n    \"    return pairs\\n\",\n    \"\\n\",\n    \"def merge_pair(counts, first, second):\\n\",\n    \"    split = re.compile(f\\\"(?<!\\\\S){first} {second}(?!\\\\S)\\\")\\n\",\n    \"    merged = f\\\"{first}{second}\\\"\\n\",\n    \"    return {split.sub(merged, word): count for word, count in counts.items()}\\n\",\n    \"\\n\",\n    \"for i in range(10):\\n\",\n    \"    pairs = count_pairs(counts)\\n\",\n    \"    first, second = max(pairs, key=pairs.get)\\n\",\n    \"    counts = merge_pair(counts, first, second)\\n\",\n    \"    print(list(counts.keys()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_sub_word_vocabulary(dataset, vocab_size):\\n\",\n    \"    counts = count_and_split_words(dataset)\\n\",\n    \"\\n\",\n    \"    char_counts = collections.Counter()\\n\",\n    \"    for word in counts:\\n\",\n    \"        for char in word.split():\\n\",\n    \"            char_counts[char] += counts[word]\\n\",\n    \"    most_common = char_counts.most_common()\\n\",\n    \"    vocab = [\\\"[UNK]\\\"] + [char for char, freq in most_common]\\n\",\n    \"    merges = []\\n\",\n    \"\\n\",\n    \"    while len(vocab) < vocab_size:\\n\",\n    \"        pairs = count_pairs(counts)\\n\",\n    \"        if not pairs:\\n\",\n    \"            break\\n\",\n    \"        first, second = max(pairs, key=pairs.get)\\n\",\n    \"        counts = merge_pair(counts, first, second)\\n\",\n    \"        vocab.append(f\\\"{first}{second}\\\")\\n\",\n    \"        merges.append(f\\\"{first} {second}\\\")\\n\",\n    \"\\n\",\n    \"    vocab = dict((token, index) for index, token in enumerate(vocab))\\n\",\n    \"    merges = dict((token, rank) for rank, token in enumerate(merges))\\n\",\n    \"    return vocab, merges\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class SubWordTokenizer:\\n\",\n    \"    def __init__(self, vocabulary, merges):\\n\",\n    \"        self.vocabulary = vocabulary\\n\",\n    \"        self.merges = merges\\n\",\n    \"        self.unk_id = vocabulary[\\\"[UNK]\\\"]\\n\",\n    \"\\n\",\n    \"    def standardize(self, inputs):\\n\",\n    \"        return inputs.lower()\\n\",\n    \"\\n\",\n    \"    def bpe_merge(self, word):\\n\",\n    \"        while True:\\n\",\n    \"            pairs = re.findall(r\\\"(?<!\\\\S)\\\\S+ \\\\S+(?!\\\\S)\\\", word, overlapped=True)\\n\",\n    \"            if not pairs:\\n\",\n    \"                break\\n\",\n    \"            best = min(pairs, key=lambda pair: self.merges.get(pair, 1e9))\\n\",\n    \"            if best not in self.merges:\\n\",\n    \"                break\\n\",\n    \"            first, second = best.split()\\n\",\n    \"            split = re.compile(f\\\"(?<!\\\\S){first} {second}(?!\\\\S)\\\")\\n\",\n    \"            merged = f\\\"{first}{second}\\\"\\n\",\n    \"            word = split.sub(merged, word)\\n\",\n    \"        return word\\n\",\n    \"\\n\",\n    \"    def split(self, inputs):\\n\",\n    \"        tokens = []\\n\",\n    \"        for word in re.findall(r\\\"[\\\\w]+|[.,!?;]\\\", inputs):\\n\",\n    \"            word = \\\" \\\".join(re.findall(r\\\".\\\", word))\\n\",\n    \"            word = self.bpe_merge(word)\\n\",\n    \"            tokens.extend(word.split())\\n\",\n    \"        return tokens\\n\",\n    \"\\n\",\n    \"    def index(self, tokens):\\n\",\n    \"        return [self.vocabulary.get(t, self.unk_id) for t in tokens]\\n\",\n    \"\\n\",\n    \"    def __call__(self, inputs):\\n\",\n    \"        inputs = self.standardize(inputs)\\n\",\n    \"        tokens = self.split(inputs)\\n\",\n    \"        indices = self.index(tokens)\\n\",\n    \"        return indices\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocabulary, merges = compute_sub_word_vocabulary(moby_dick, 2_000)\\n\",\n    \"sub_word_tokenizer = SubWordTokenizer(vocabulary, merges)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary length:\\\", len(vocabulary))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary start:\\\", list(vocabulary.keys())[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Vocabulary end:\\\", list(vocabulary.keys())[-7:])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(\\\"Line length:\\\", len(sub_word_tokenizer(\\n\",\n    \"   \\\"Call me Ishmael. Some years ago--never mind how long precisely.\\\"\\n\",\n    \")))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Sets vs. sequences\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Loading the IMDb classification dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, pathlib, shutil, random\\n\",\n    \"\\n\",\n    \"zip_path = keras.utils.get_file(\\n\",\n    \"    origin=\\\"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\\",\\n\",\n    \"    fname=\\\"imdb\\\",\\n\",\n    \"    extract=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"imdb_extract_dir = pathlib.Path(zip_path) / \\\"aclImdb\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for path in imdb_extract_dir.glob(\\\"*/*\\\"):\\n\",\n    \"    if path.is_dir():\\n\",\n    \"        print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(open(imdb_extract_dir / \\\"train\\\" / \\\"pos\\\" / \\\"4077_10.txt\\\", \\\"r\\\").read())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_dir = pathlib.Path(\\\"imdb_train\\\")\\n\",\n    \"test_dir = pathlib.Path(\\\"imdb_test\\\")\\n\",\n    \"val_dir = pathlib.Path(\\\"imdb_val\\\")\\n\",\n    \"\\n\",\n    \"shutil.copytree(imdb_extract_dir / \\\"test\\\", test_dir)\\n\",\n    \"\\n\",\n    \"val_percentage = 0.2\\n\",\n    \"for category in (\\\"neg\\\", \\\"pos\\\"):\\n\",\n    \"    src_dir = imdb_extract_dir / \\\"train\\\" / category\\n\",\n    \"    src_files = os.listdir(src_dir)\\n\",\n    \"    random.Random(1337).shuffle(src_files)\\n\",\n    \"    num_val_samples = int(len(src_files) * val_percentage)\\n\",\n    \"\\n\",\n    \"    os.makedirs(val_dir / category)\\n\",\n    \"    for file in src_files[:num_val_samples]:\\n\",\n    \"        shutil.copy(src_dir / file, val_dir / category / file)\\n\",\n    \"    os.makedirs(train_dir / category)\\n\",\n    \"    for file in src_files[num_val_samples:]:\\n\",\n    \"        shutil.copy(src_dir / file, train_dir / category / file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.utils import text_dataset_from_directory\\n\",\n    \"\\n\",\n    \"batch_size = 32\\n\",\n    \"train_ds = text_dataset_from_directory(train_dir, batch_size=batch_size)\\n\",\n    \"val_ds = text_dataset_from_directory(val_dir, batch_size=batch_size)\\n\",\n    \"test_ds = text_dataset_from_directory(test_dir, batch_size=batch_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Set models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training a bag-of-words model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"max_tokens = 20_000\\n\",\n    \"text_vectorization = layers.TextVectorization(\\n\",\n    \"    max_tokens=max_tokens,\\n\",\n    \"    split=\\\"whitespace\\\",\\n\",\n    \"    output_mode=\\\"multi_hot\\\",\\n\",\n    \")\\n\",\n    \"train_ds_no_labels = train_ds.map(lambda x, y: x)\\n\",\n    \"text_vectorization.adapt(train_ds_no_labels)\\n\",\n    \"\\n\",\n    \"bag_of_words_train_ds = train_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\\n\",\n    \"bag_of_words_val_ds = val_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\\n\",\n    \"bag_of_words_test_ds = test_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = next(bag_of_words_train_ds.as_numpy_iterator())\\n\",\n    \"x.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def build_linear_classifier(max_tokens, name):\\n\",\n    \"    inputs = keras.Input(shape=(max_tokens,))\\n\",\n    \"    outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(inputs)\\n\",\n    \"    model = keras.Model(inputs, outputs, name=name)\\n\",\n    \"    model.compile(\\n\",\n    \"        optimizer=\\\"adam\\\",\\n\",\n    \"        loss=\\\"binary_crossentropy\\\",\\n\",\n    \"        metrics=[\\\"accuracy\\\"],\\n\",\n    \"    )\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"model = build_linear_classifier(max_tokens, \\\"bag_of_words_classifier\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"early_stopping = keras.callbacks.EarlyStopping(\\n\",\n    \"    monitor=\\\"val_loss\\\",\\n\",\n    \"    restore_best_weights=True,\\n\",\n    \"    patience=2,\\n\",\n    \")\\n\",\n    \"history = model.fit(\\n\",\n    \"    bag_of_words_train_ds,\\n\",\n    \"    validation_data=bag_of_words_val_ds,\\n\",\n    \"    epochs=10,\\n\",\n    \"    callbacks=[early_stopping],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"accuracy = history.history[\\\"accuracy\\\"]\\n\",\n    \"val_accuracy = history.history[\\\"val_accuracy\\\"]\\n\",\n    \"epochs = range(1, len(accuracy) + 1)\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, accuracy, \\\"r--\\\", label=\\\"Training accuracy\\\")\\n\",\n    \"plt.plot(epochs, val_accuracy, \\\"b\\\", label=\\\"Validation accuracy\\\")\\n\",\n    \"plt.title(\\\"Training and validation accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(bag_of_words_test_ds)\\n\",\n    \"test_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training a bigram model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max_tokens = 30_000\\n\",\n    \"text_vectorization = layers.TextVectorization(\\n\",\n    \"    max_tokens=max_tokens,\\n\",\n    \"    split=\\\"whitespace\\\",\\n\",\n    \"    output_mode=\\\"multi_hot\\\",\\n\",\n    \"    ngrams=2,\\n\",\n    \")\\n\",\n    \"text_vectorization.adapt(train_ds_no_labels)\\n\",\n    \"\\n\",\n    \"bigram_train_ds = train_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\\n\",\n    \"bigram_val_ds = val_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\\n\",\n    \"bigram_test_ds = test_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = next(bigram_train_ds.as_numpy_iterator())\\n\",\n    \"x.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_vectorization.get_vocabulary()[100:108]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = build_linear_classifier(max_tokens, \\\"bigram_classifier\\\")\\n\",\n    \"model.fit(\\n\",\n    \"    bigram_train_ds,\\n\",\n    \"    validation_data=bigram_val_ds,\\n\",\n    \"    epochs=10,\\n\",\n    \"    callbacks=[early_stopping],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(bigram_test_ds)\\n\",\n    \"test_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Sequence models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max_length = 600\\n\",\n    \"max_tokens = 30_000\\n\",\n    \"text_vectorization = layers.TextVectorization(\\n\",\n    \"    max_tokens=max_tokens,\\n\",\n    \"    split=\\\"whitespace\\\",\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    output_sequence_length=max_length,\\n\",\n    \")\\n\",\n    \"text_vectorization.adapt(train_ds_no_labels)\\n\",\n    \"\\n\",\n    \"sequence_train_ds = train_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\\n\",\n    \"sequence_val_ds = val_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\\n\",\n    \"sequence_test_ds = test_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y), num_parallel_calls=8\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = next(sequence_test_ds.as_numpy_iterator())\\n\",\n    \"x.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training a recurrent model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"class OneHotEncoding(keras.Layer):\\n\",\n    \"    def __init__(self, depth, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.depth = depth\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        flat_inputs = ops.reshape(ops.cast(inputs, \\\"int\\\"), [-1])\\n\",\n    \"        one_hot_vectors = ops.eye(self.depth)\\n\",\n    \"        outputs = ops.take(one_hot_vectors, flat_inputs, axis=0)\\n\",\n    \"        return ops.reshape(outputs, ops.shape(inputs) + (self.depth,))\\n\",\n    \"\\n\",\n    \"one_hot_encoding = OneHotEncoding(max_tokens)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = next(sequence_train_ds.as_numpy_iterator())\\n\",\n    \"one_hot_encoding(x).shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"hidden_dim = 64\\n\",\n    \"inputs = keras.Input(shape=(max_length,), dtype=\\\"int32\\\")\\n\",\n    \"x = one_hot_encoding(inputs)\\n\",\n    \"x = layers.Bidirectional(layers.LSTM(hidden_dim))(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs, name=\\\"lstm_with_one_hot\\\")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# \\u26a0\\ufe0fNOTE\\u26a0\\ufe0f: The following fit call will error on a T4 GPU on the TensorFlow\\n\",\n    \"# backend due to a bug in TensorFlow. If you the follow cell errors out,\\n\",\n    \"# do one of the following:\\n\",\n    \"# - Skip the following two cells.\\n\",\n    \"# - Switch to the Jax or Torch backend and re-run this notebook.\\n\",\n    \"# - Change the GPU type in your runtime (requires Colab Pro as of this writing).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.fit(\\n\",\n    \"    sequence_train_ds,\\n\",\n    \"    validation_data=sequence_val_ds,\\n\",\n    \"    epochs=10,\\n\",\n    \"    callbacks=[early_stopping],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(sequence_test_ds)\\n\",\n    \"test_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Understanding word embeddings\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using a word embedding\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"hidden_dim = 64\\n\",\n    \"inputs = keras.Input(shape=(max_length,), dtype=\\\"int32\\\")\\n\",\n    \"x = keras.layers.Embedding(\\n\",\n    \"    input_dim=max_tokens,\\n\",\n    \"    output_dim=hidden_dim,\\n\",\n    \"    mask_zero=True,\\n\",\n    \")(inputs)\\n\",\n    \"x = keras.layers.Bidirectional(keras.layers.LSTM(hidden_dim))(x)\\n\",\n    \"x = keras.layers.Dropout(0.5)(x)\\n\",\n    \"outputs = keras.layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs, name=\\\"lstm_with_embedding\\\")\\n\",\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.fit(\\n\",\n    \"    sequence_train_ds,\\n\",\n    \"    validation_data=sequence_val_ds,\\n\",\n    \"    epochs=10,\\n\",\n    \"    callbacks=[early_stopping],\\n\",\n    \")\\n\",\n    \"test_loss, test_acc = model.evaluate(sequence_test_ds)\\n\",\n    \"test_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Pretraining a word embedding\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"imdb_vocabulary = text_vectorization.get_vocabulary()\\n\",\n    \"tokenize_no_padding = keras.layers.TextVectorization(\\n\",\n    \"    vocabulary=imdb_vocabulary,\\n\",\n    \"    split=\\\"whitespace\\\",\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"context_size = 4\\n\",\n    \"window_size = 9\\n\",\n    \"\\n\",\n    \"def window_data(token_ids):\\n\",\n    \"    num_windows = tf.maximum(tf.size(token_ids) - context_size * 2, 0)\\n\",\n    \"    windows = tf.range(window_size)[None, :]\\n\",\n    \"    windows = windows + tf.range(num_windows)[:, None]\\n\",\n    \"    windowed_tokens = tf.gather(token_ids, windows)\\n\",\n    \"    return tf.data.Dataset.from_tensor_slices(windowed_tokens)\\n\",\n    \"\\n\",\n    \"def split_label(window):\\n\",\n    \"    left = window[:context_size]\\n\",\n    \"    right = window[context_size + 1 :]\\n\",\n    \"    bag = tf.concat((left, right), axis=0)\\n\",\n    \"    label = window[4]\\n\",\n    \"    return bag, label\\n\",\n    \"\\n\",\n    \"dataset = keras.utils.text_dataset_from_directory(\\n\",\n    \"    imdb_extract_dir / \\\"train\\\", batch_size=None\\n\",\n    \")\\n\",\n    \"dataset = dataset.map(lambda x, y: x, num_parallel_calls=8)\\n\",\n    \"dataset = dataset.map(tokenize_no_padding, num_parallel_calls=8)\\n\",\n    \"dataset = dataset.interleave(window_data, cycle_length=8, num_parallel_calls=8)\\n\",\n    \"dataset = dataset.map(split_label, num_parallel_calls=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"hidden_dim = 64\\n\",\n    \"inputs = keras.Input(shape=(2 * context_size,))\\n\",\n    \"cbow_embedding = layers.Embedding(\\n\",\n    \"    max_tokens,\\n\",\n    \"    hidden_dim,\\n\",\n    \")\\n\",\n    \"x = cbow_embedding(inputs)\\n\",\n    \"x = layers.GlobalAveragePooling1D()(x)\\n\",\n    \"outputs = layers.Dense(max_tokens, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"cbow_model = keras.Model(inputs, outputs)\\n\",\n    \"cbow_model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"sparse_categorical_accuracy\\\"],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"cbow_model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = dataset.batch(1024).cache()\\n\",\n    \"cbow_model.fit(dataset, epochs=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using the pretrained embedding for classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(max_length,))\\n\",\n    \"lstm_embedding = layers.Embedding(\\n\",\n    \"    input_dim=max_tokens,\\n\",\n    \"    output_dim=hidden_dim,\\n\",\n    \"    mask_zero=True,\\n\",\n    \")\\n\",\n    \"x = lstm_embedding(inputs)\\n\",\n    \"x = layers.Bidirectional(layers.LSTM(hidden_dim))(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs, name=\\\"lstm_with_cbow\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"lstm_embedding.embeddings.assign(cbow_embedding.embeddings)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(\\n\",\n    \"    sequence_train_ds,\\n\",\n    \"    validation_data=sequence_val_ds,\\n\",\n    \"    epochs=10,\\n\",\n    \"    callbacks=[early_stopping],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(sequence_test_ds)\\n\",\n    \"test_acc\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter14_text-classification\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter15_language-models-and-the-transformer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Language models and the Transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The language model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training a Shakespeare language model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"\\n\",\n    \"filename = keras.utils.get_file(\\n\",\n    \"    origin=(\\n\",\n    \"        \\\"https://storage.googleapis.com/download.tensorflow.org/\\\"\\n\",\n    \"        \\\"data/shakespeare.txt\\\"\\n\",\n    \"    ),\\n\",\n    \")\\n\",\n    \"shakespeare = open(filename, \\\"r\\\").read()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"shakespeare[:250]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"sequence_length = 100\\n\",\n    \"\\n\",\n    \"def split_input(input, sequence_length):\\n\",\n    \"    for i in range(0, len(input), sequence_length):\\n\",\n    \"        yield input[i : i + sequence_length]\\n\",\n    \"\\n\",\n    \"features = list(split_input(shakespeare[:-1], sequence_length))\\n\",\n    \"labels = list(split_input(shakespeare[1:], sequence_length))\\n\",\n    \"dataset = tf.data.Dataset.from_tensor_slices((features, labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x, y = next(dataset.as_numpy_iterator())\\n\",\n    \"x[:50], y[:50]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"tokenizer = layers.TextVectorization(\\n\",\n    \"    standardize=None,\\n\",\n    \"    split=\\\"character\\\",\\n\",\n    \"    output_sequence_length=sequence_length,\\n\",\n    \")\\n\",\n    \"tokenizer.adapt(dataset.map(lambda text, labels: text))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocabulary_size = tokenizer.vocabulary_size()\\n\",\n    \"vocabulary_size\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = dataset.map(\\n\",\n    \"    lambda features, labels: (tokenizer(features), tokenizer(labels)),\\n\",\n    \"    num_parallel_calls=8,\\n\",\n    \")\\n\",\n    \"training_data = dataset.shuffle(10_000).batch(64).cache()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"embedding_dim = 256\\n\",\n    \"hidden_dim = 1024\\n\",\n    \"\\n\",\n    \"inputs = layers.Input(shape=(sequence_length,), dtype=\\\"int\\\", name=\\\"token_ids\\\")\\n\",\n    \"x = layers.Embedding(vocabulary_size, embedding_dim)(inputs)\\n\",\n    \"x = layers.GRU(hidden_dim, return_sequences=True)(x)\\n\",\n    \"x = layers.Dropout(0.1)(x)\\n\",\n    \"outputs = layers.Dense(vocabulary_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"sparse_categorical_accuracy\\\"],\\n\",\n    \")\\n\",\n    \"model.fit(training_data, epochs=20)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Generating Shakespeare\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(1,), dtype=\\\"int\\\", name=\\\"token_ids\\\")\\n\",\n    \"input_state = keras.Input(shape=(hidden_dim,), name=\\\"state\\\")\\n\",\n    \"\\n\",\n    \"x = layers.Embedding(vocabulary_size, embedding_dim)(inputs)\\n\",\n    \"x, output_state = layers.GRU(hidden_dim, return_state=True)(\\n\",\n    \"    x, initial_state=input_state\\n\",\n    \")\\n\",\n    \"outputs = layers.Dense(vocabulary_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"generation_model = keras.Model(\\n\",\n    \"    inputs=(inputs, input_state),\\n\",\n    \"    outputs=(outputs, output_state),\\n\",\n    \")\\n\",\n    \"generation_model.set_weights(model.get_weights())\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tokens = tokenizer.get_vocabulary()\\n\",\n    \"token_ids = range(vocabulary_size)\\n\",\n    \"char_to_id = dict(zip(tokens, token_ids))\\n\",\n    \"id_to_char = dict(zip(token_ids, tokens))\\n\",\n    \"\\n\",\n    \"prompt = \\\"\\\"\\\"\\n\",\n    \"KING RICHARD III:\\n\",\n    \"\\\"\\\"\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_ids = [char_to_id[c] for c in prompt]\\n\",\n    \"state = keras.ops.zeros(shape=(1, hidden_dim))\\n\",\n    \"for token_id in input_ids:\\n\",\n    \"    inputs = keras.ops.expand_dims([token_id], axis=0)\\n\",\n    \"    predictions, state = generation_model.predict((inputs, state), verbose=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"generated_ids = []\\n\",\n    \"max_length = 250\\n\",\n    \"for i in range(max_length):\\n\",\n    \"    next_char = int(np.argmax(predictions, axis=-1)[0])\\n\",\n    \"    generated_ids.append(next_char)\\n\",\n    \"    inputs = keras.ops.expand_dims([next_char], axis=0)\\n\",\n    \"    predictions, state = generation_model.predict((inputs, state), verbose=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"output = \\\"\\\".join([id_to_char[token_id] for token_id in generated_ids])\\n\",\n    \"print(prompt + output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Sequence-to-sequence learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### English-to-Spanish translation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pathlib\\n\",\n    \"\\n\",\n    \"zip_path = keras.utils.get_file(\\n\",\n    \"    origin=(\\n\",\n    \"        \\\"http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\\\"\\n\",\n    \"    ),\\n\",\n    \"    fname=\\\"spa-eng\\\",\\n\",\n    \"    extract=True,\\n\",\n    \")\\n\",\n    \"text_path = pathlib.Path(zip_path) / \\\"spa-eng\\\" / \\\"spa.txt\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open(text_path) as f:\\n\",\n    \"    lines = f.read().split(\\\"\\\\n\\\")[:-1]\\n\",\n    \"text_pairs = []\\n\",\n    \"for line in lines:\\n\",\n    \"    english, spanish = line.split(\\\"\\\\t\\\")\\n\",\n    \"    spanish = \\\"[start] \\\" + spanish + \\\" [end]\\\"\\n\",\n    \"    text_pairs.append((english, spanish))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"random.choice(text_pairs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"random.shuffle(text_pairs)\\n\",\n    \"val_samples = int(0.15 * len(text_pairs))\\n\",\n    \"train_samples = len(text_pairs) - 2 * val_samples\\n\",\n    \"train_pairs = text_pairs[:train_samples]\\n\",\n    \"val_pairs = text_pairs[train_samples : train_samples + val_samples]\\n\",\n    \"test_pairs = text_pairs[train_samples + val_samples :]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import string\\n\",\n    \"import re\\n\",\n    \"\\n\",\n    \"strip_chars = string.punctuation + \\\"\\u00bf\\\"\\n\",\n    \"strip_chars = strip_chars.replace(\\\"[\\\", \\\"\\\")\\n\",\n    \"strip_chars = strip_chars.replace(\\\"]\\\", \\\"\\\")\\n\",\n    \"\\n\",\n    \"def custom_standardization(input_string):\\n\",\n    \"    lowercase = tf.strings.lower(input_string)\\n\",\n    \"    return tf.strings.regex_replace(\\n\",\n    \"        lowercase, f\\\"[{re.escape(strip_chars)}]\\\", \\\"\\\"\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"vocab_size = 15000\\n\",\n    \"sequence_length = 20\\n\",\n    \"\\n\",\n    \"english_tokenizer = layers.TextVectorization(\\n\",\n    \"    max_tokens=vocab_size,\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    output_sequence_length=sequence_length,\\n\",\n    \")\\n\",\n    \"spanish_tokenizer = layers.TextVectorization(\\n\",\n    \"    max_tokens=vocab_size,\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    output_sequence_length=sequence_length + 1,\\n\",\n    \"    standardize=custom_standardization,\\n\",\n    \")\\n\",\n    \"train_english_texts = [pair[0] for pair in train_pairs]\\n\",\n    \"train_spanish_texts = [pair[1] for pair in train_pairs]\\n\",\n    \"english_tokenizer.adapt(train_english_texts)\\n\",\n    \"spanish_tokenizer.adapt(train_spanish_texts)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 64\\n\",\n    \"\\n\",\n    \"def format_dataset(eng, spa):\\n\",\n    \"    eng = english_tokenizer(eng)\\n\",\n    \"    spa = spanish_tokenizer(spa)\\n\",\n    \"    features = {\\\"english\\\": eng, \\\"spanish\\\": spa[:, :-1]}\\n\",\n    \"    labels = spa[:, 1:]\\n\",\n    \"    sample_weights = labels != 0\\n\",\n    \"    return features, labels, sample_weights\\n\",\n    \"\\n\",\n    \"def make_dataset(pairs):\\n\",\n    \"    eng_texts, spa_texts = zip(*pairs)\\n\",\n    \"    eng_texts = list(eng_texts)\\n\",\n    \"    spa_texts = list(spa_texts)\\n\",\n    \"    dataset = tf.data.Dataset.from_tensor_slices((eng_texts, spa_texts))\\n\",\n    \"    dataset = dataset.batch(batch_size)\\n\",\n    \"    dataset = dataset.map(format_dataset, num_parallel_calls=4)\\n\",\n    \"    return dataset.shuffle(2048).cache()\\n\",\n    \"\\n\",\n    \"train_ds = make_dataset(train_pairs)\\n\",\n    \"val_ds = make_dataset(val_pairs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs, targets, sample_weights = next(iter(train_ds))\\n\",\n    \"print(inputs[\\\"english\\\"].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(inputs[\\\"spanish\\\"].shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(targets.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"print(sample_weights.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Sequence-to-sequence learning with RNNs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"embed_dim = 256\\n\",\n    \"hidden_dim = 1024\\n\",\n    \"\\n\",\n    \"source = keras.Input(shape=(None,), dtype=\\\"int32\\\", name=\\\"english\\\")\\n\",\n    \"x = layers.Embedding(vocab_size, embed_dim, mask_zero=True)(source)\\n\",\n    \"rnn_layer = layers.GRU(hidden_dim)\\n\",\n    \"rnn_layer = layers.Bidirectional(rnn_layer, merge_mode=\\\"sum\\\")\\n\",\n    \"encoder_output = rnn_layer(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"target = keras.Input(shape=(None,), dtype=\\\"int32\\\", name=\\\"spanish\\\")\\n\",\n    \"x = layers.Embedding(vocab_size, embed_dim, mask_zero=True)(target)\\n\",\n    \"rnn_layer = layers.GRU(hidden_dim, return_sequences=True)\\n\",\n    \"x = rnn_layer(x, initial_state=encoder_output)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"target_predictions = layers.Dense(vocab_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"seq2seq_rnn = keras.Model([source, target], target_predictions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"seq2seq_rnn.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"seq2seq_rnn.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    weighted_metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"seq2seq_rnn.fit(train_ds, epochs=15, validation_data=val_ds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"spa_vocab = spanish_tokenizer.get_vocabulary()\\n\",\n    \"spa_index_lookup = dict(zip(range(len(spa_vocab)), spa_vocab))\\n\",\n    \"\\n\",\n    \"def generate_translation(input_sentence):\\n\",\n    \"    tokenized_input_sentence = english_tokenizer([input_sentence])\\n\",\n    \"    decoded_sentence = \\\"[start]\\\"\\n\",\n    \"    for i in range(sequence_length):\\n\",\n    \"        tokenized_target_sentence = spanish_tokenizer([decoded_sentence])\\n\",\n    \"        inputs = [tokenized_input_sentence, tokenized_target_sentence]\\n\",\n    \"        next_token_predictions = seq2seq_rnn.predict(inputs, verbose=0)\\n\",\n    \"        sampled_token_index = np.argmax(next_token_predictions[0, i, :])\\n\",\n    \"        sampled_token = spa_index_lookup[sampled_token_index]\\n\",\n    \"        decoded_sentence += \\\" \\\" + sampled_token\\n\",\n    \"        if sampled_token == \\\"[end]\\\":\\n\",\n    \"            break\\n\",\n    \"    return decoded_sentence\\n\",\n    \"\\n\",\n    \"test_eng_texts = [pair[0] for pair in test_pairs]\\n\",\n    \"for _ in range(5):\\n\",\n    \"    input_sentence = random.choice(test_eng_texts)\\n\",\n    \"    print(\\\"-\\\")\\n\",\n    \"    print(input_sentence)\\n\",\n    \"    print(generate_translation(input_sentence))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The Transformer architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Dot-product attention\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Transformer encoder block\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class TransformerEncoder(keras.Layer):\\n\",\n    \"    def __init__(self, hidden_dim, intermediate_dim, num_heads):\\n\",\n    \"        super().__init__()\\n\",\n    \"        key_dim = hidden_dim // num_heads\\n\",\n    \"        self.self_attention = layers.MultiHeadAttention(num_heads, key_dim)\\n\",\n    \"        self.self_attention_layernorm = layers.LayerNormalization()\\n\",\n    \"        self.feed_forward_1 = layers.Dense(intermediate_dim, activation=\\\"relu\\\")\\n\",\n    \"        self.feed_forward_2 = layers.Dense(hidden_dim)\\n\",\n    \"        self.feed_forward_layernorm = layers.LayerNormalization()\\n\",\n    \"\\n\",\n    \"    def call(self, source, source_mask):\\n\",\n    \"        residual = x = source\\n\",\n    \"        mask = source_mask[:, None, :]\\n\",\n    \"        x = self.self_attention(query=x, key=x, value=x, attention_mask=mask)\\n\",\n    \"        x = x + residual\\n\",\n    \"        x = self.self_attention_layernorm(x)\\n\",\n    \"        residual = x\\n\",\n    \"        x = self.feed_forward_1(x)\\n\",\n    \"        x = self.feed_forward_2(x)\\n\",\n    \"        x = x + residual\\n\",\n    \"        x = self.feed_forward_layernorm(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Transformer decoder block\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class TransformerDecoder(keras.Layer):\\n\",\n    \"    def __init__(self, hidden_dim, intermediate_dim, num_heads):\\n\",\n    \"        super().__init__()\\n\",\n    \"        key_dim = hidden_dim // num_heads\\n\",\n    \"        self.self_attention = layers.MultiHeadAttention(num_heads, key_dim)\\n\",\n    \"        self.self_attention_layernorm = layers.LayerNormalization()\\n\",\n    \"        self.cross_attention = layers.MultiHeadAttention(num_heads, key_dim)\\n\",\n    \"        self.cross_attention_layernorm = layers.LayerNormalization()\\n\",\n    \"        self.feed_forward_1 = layers.Dense(intermediate_dim, activation=\\\"relu\\\")\\n\",\n    \"        self.feed_forward_2 = layers.Dense(hidden_dim)\\n\",\n    \"        self.feed_forward_layernorm = layers.LayerNormalization()\\n\",\n    \"\\n\",\n    \"    def call(self, target, source, source_mask):\\n\",\n    \"        residual = x = target\\n\",\n    \"        x = self.self_attention(query=x, key=x, value=x, use_causal_mask=True)\\n\",\n    \"        x = x + residual\\n\",\n    \"        x = self.self_attention_layernorm(x)\\n\",\n    \"        residual = x\\n\",\n    \"        mask = source_mask[:, None, :]\\n\",\n    \"        x = self.cross_attention(\\n\",\n    \"            query=x, key=source, value=source, attention_mask=mask\\n\",\n    \"        )\\n\",\n    \"        x = x + residual\\n\",\n    \"        x = self.cross_attention_layernorm(x)\\n\",\n    \"        residual = x\\n\",\n    \"        x = self.feed_forward_1(x)\\n\",\n    \"        x = self.feed_forward_2(x)\\n\",\n    \"        x = x + residual\\n\",\n    \"        x = self.feed_forward_layernorm(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Sequence-to-sequence learning with a Transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"hidden_dim = 256\\n\",\n    \"intermediate_dim = 2048\\n\",\n    \"num_heads = 8\\n\",\n    \"\\n\",\n    \"source = keras.Input(shape=(None,), dtype=\\\"int32\\\", name=\\\"english\\\")\\n\",\n    \"x = layers.Embedding(vocab_size, hidden_dim)(source)\\n\",\n    \"encoder_output = TransformerEncoder(hidden_dim, intermediate_dim, num_heads)(\\n\",\n    \"    source=x,\\n\",\n    \"    source_mask=source != 0,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"target = keras.Input(shape=(None,), dtype=\\\"int32\\\", name=\\\"spanish\\\")\\n\",\n    \"x = layers.Embedding(vocab_size, hidden_dim)(target)\\n\",\n    \"x = TransformerDecoder(hidden_dim, intermediate_dim, num_heads)(\\n\",\n    \"    target=x,\\n\",\n    \"    source=encoder_output,\\n\",\n    \"    source_mask=source != 0,\\n\",\n    \")\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"target_predictions = layers.Dense(vocab_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"transformer = keras.Model([source, target], target_predictions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"transformer.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"transformer.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    weighted_metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"transformer.fit(train_ds, epochs=15, validation_data=val_ds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Embedding positional information\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"class PositionalEmbedding(keras.Layer):\\n\",\n    \"    def __init__(self, sequence_length, input_dim, output_dim):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.token_embeddings = layers.Embedding(input_dim, output_dim)\\n\",\n    \"        self.position_embeddings = layers.Embedding(sequence_length, output_dim)\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        positions = ops.cumsum(ops.ones_like(inputs), axis=-1) - 1\\n\",\n    \"        embedded_tokens = self.token_embeddings(inputs)\\n\",\n    \"        embedded_positions = self.position_embeddings(positions)\\n\",\n    \"        return embedded_tokens + embedded_positions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"hidden_dim = 256\\n\",\n    \"intermediate_dim = 2056\\n\",\n    \"num_heads = 8\\n\",\n    \"\\n\",\n    \"source = keras.Input(shape=(None,), dtype=\\\"int32\\\", name=\\\"english\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, hidden_dim)(source)\\n\",\n    \"encoder_output = TransformerEncoder(hidden_dim, intermediate_dim, num_heads)(\\n\",\n    \"    source=x,\\n\",\n    \"    source_mask=source != 0,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"target = keras.Input(shape=(None,), dtype=\\\"int32\\\", name=\\\"spanish\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, hidden_dim)(target)\\n\",\n    \"x = TransformerDecoder(hidden_dim, intermediate_dim, num_heads)(\\n\",\n    \"    target=x,\\n\",\n    \"    source=encoder_output,\\n\",\n    \"    source_mask=source != 0,\\n\",\n    \")\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"target_predictions = layers.Dense(vocab_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"transformer = keras.Model([source, target], target_predictions)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"transformer.compile(\\n\",\n    \"    optimizer=\\\"adam\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    weighted_metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"transformer.fit(train_ds, epochs=30, validation_data=val_ds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"spa_vocab = spanish_tokenizer.get_vocabulary()\\n\",\n    \"spa_index_lookup = dict(zip(range(len(spa_vocab)), spa_vocab))\\n\",\n    \"\\n\",\n    \"def generate_translation(input_sentence):\\n\",\n    \"    tokenized_input_sentence = english_tokenizer([input_sentence])\\n\",\n    \"    decoded_sentence = \\\"[start]\\\"\\n\",\n    \"    for i in range(sequence_length):\\n\",\n    \"        tokenized_target_sentence = spanish_tokenizer([decoded_sentence])\\n\",\n    \"        tokenized_target_sentence = tokenized_target_sentence[:, :-1]\\n\",\n    \"        inputs = [tokenized_input_sentence, tokenized_target_sentence]\\n\",\n    \"        next_token_predictions = transformer.predict(inputs, verbose=0)\\n\",\n    \"        sampled_token_index = np.argmax(next_token_predictions[0, i, :])\\n\",\n    \"        sampled_token = spa_index_lookup[sampled_token_index]\\n\",\n    \"        decoded_sentence += \\\" \\\" + sampled_token\\n\",\n    \"        if sampled_token == \\\"[end]\\\":\\n\",\n    \"            break\\n\",\n    \"    return decoded_sentence\\n\",\n    \"\\n\",\n    \"test_eng_texts = [pair[0] for pair in test_pairs]\\n\",\n    \"for _ in range(5):\\n\",\n    \"    input_sentence = random.choice(test_eng_texts)\\n\",\n    \"    print(\\\"-\\\")\\n\",\n    \"    print(input_sentence)\\n\",\n    \"    print(generate_translation(input_sentence))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Classification with a pretrained Transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Pretraining a Transformer encoder\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Loading a pretrained Transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras_hub\\n\",\n    \"\\n\",\n    \"tokenizer = keras_hub.models.Tokenizer.from_preset(\\\"roberta_base_en\\\")\\n\",\n    \"backbone = keras_hub.models.Backbone.from_preset(\\\"roberta_base_en\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tokenizer(\\\"The quick brown fox\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"backbone.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Preprocessing IMDb movie reviews\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, pathlib, shutil, random\\n\",\n    \"\\n\",\n    \"zip_path = keras.utils.get_file(\\n\",\n    \"    origin=\\\"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\\",\\n\",\n    \"    fname=\\\"imdb\\\",\\n\",\n    \"    extract=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"imdb_extract_dir = pathlib.Path(zip_path) / \\\"aclImdb\\\"\\n\",\n    \"train_dir = pathlib.Path(\\\"imdb_train\\\")\\n\",\n    \"test_dir = pathlib.Path(\\\"imdb_test\\\")\\n\",\n    \"val_dir = pathlib.Path(\\\"imdb_val\\\")\\n\",\n    \"\\n\",\n    \"shutil.copytree(imdb_extract_dir / \\\"test\\\", test_dir, dirs_exist_ok=True)\\n\",\n    \"\\n\",\n    \"val_percentage = 0.2\\n\",\n    \"for category in (\\\"neg\\\", \\\"pos\\\"):\\n\",\n    \"    src_dir = imdb_extract_dir / \\\"train\\\" / category\\n\",\n    \"    src_files = os.listdir(src_dir)\\n\",\n    \"    random.Random(1337).shuffle(src_files)\\n\",\n    \"    num_val_samples = int(len(src_files) * val_percentage)\\n\",\n    \"\\n\",\n    \"    os.makedirs(train_dir / category, exist_ok=True)\\n\",\n    \"    os.makedirs(val_dir / category, exist_ok=True)\\n\",\n    \"    for index, file in enumerate(src_files):\\n\",\n    \"        if index < num_val_samples:\\n\",\n    \"            shutil.copy(src_dir / file, val_dir / category / file)\\n\",\n    \"        else:\\n\",\n    \"            shutil.copy(src_dir / file, train_dir / category / file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.utils import text_dataset_from_directory\\n\",\n    \"\\n\",\n    \"batch_size = 16\\n\",\n    \"train_ds = text_dataset_from_directory(train_dir, batch_size=batch_size)\\n\",\n    \"val_ds = text_dataset_from_directory(val_dir, batch_size=batch_size)\\n\",\n    \"test_ds = text_dataset_from_directory(test_dir, batch_size=batch_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def preprocess(text, label):\\n\",\n    \"    packer = keras_hub.layers.StartEndPacker(\\n\",\n    \"        sequence_length=512,\\n\",\n    \"        start_value=tokenizer.start_token_id,\\n\",\n    \"        end_value=tokenizer.end_token_id,\\n\",\n    \"        pad_value=tokenizer.pad_token_id,\\n\",\n    \"        return_padding_mask=True,\\n\",\n    \"    )\\n\",\n    \"    token_ids, padding_mask = packer(tokenizer(text))\\n\",\n    \"    return {\\\"token_ids\\\": token_ids, \\\"padding_mask\\\": padding_mask}, label\\n\",\n    \"\\n\",\n    \"preprocessed_train_ds = train_ds.map(preprocess)\\n\",\n    \"preprocessed_val_ds = val_ds.map(preprocess)\\n\",\n    \"preprocessed_test_ds = test_ds.map(preprocess)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"next(iter(preprocessed_train_ds))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Fine-tuning a pretrained Transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = backbone.input\\n\",\n    \"x = backbone(inputs)\\n\",\n    \"x = x[:, 0, :]\\n\",\n    \"x = layers.Dropout(0.1)(x)\\n\",\n    \"x = layers.Dense(768, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.Dropout(0.1)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"classifier = keras.Model(inputs, outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"classifier.compile(\\n\",\n    \"    optimizer=keras.optimizers.Adam(5e-5),\\n\",\n    \"    loss=\\\"binary_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"classifier.fit(\\n\",\n    \"    preprocessed_train_ds,\\n\",\n    \"    validation_data=preprocessed_val_ds,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"classifier.evaluate(preprocessed_test_ds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### What makes the Transformer effective?\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter15_language-models-and-the-transformer\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter16_text-generation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Text generation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A brief history of sequence generation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Training a mini-GPT\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"# Free up more GPU memory on the Jax and TensorFlow backends.\\n\",\n    \"os.environ[\\\"XLA_PYTHON_CLIENT_MEM_FRACTION\\\"] = \\\"1.00\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"import pathlib\\n\",\n    \"\\n\",\n    \"extract_dir = keras.utils.get_file(\\n\",\n    \"    fname=\\\"mini-c4\\\",\\n\",\n    \"    origin=(\\n\",\n    \"        \\\"https://hf.co/datasets/mattdangerw/mini-c4/resolve/main/mini-c4.zip\\\"\\n\",\n    \"    ),\\n\",\n    \"    extract=True,\\n\",\n    \")\\n\",\n    \"extract_dir = pathlib.Path(extract_dir) / \\\"mini-c4\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"with open(extract_dir / \\\"shard0.txt\\\", \\\"r\\\") as f:\\n\",\n    \"    print(f.readline().replace(\\\"\\\\\\\\n\\\", \\\"\\\\n\\\")[:100])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras_hub\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"vocabulary_file = keras.utils.get_file(\\n\",\n    \"    origin=\\\"https://hf.co/mattdangerw/spiece/resolve/main/vocabulary.proto\\\",\\n\",\n    \")\\n\",\n    \"tokenizer = keras_hub.tokenizers.SentencePieceTokenizer(vocabulary_file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tokenizer.tokenize(\\\"The quick brown fox.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tokenizer.detokenize([450, 4996, 17354, 1701, 29916, 29889])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"batch_size = 64\\n\",\n    \"sequence_length = 256\\n\",\n    \"suffix = np.array([tokenizer.token_to_id(\\\"<|endoftext|>\\\")])\\n\",\n    \"\\n\",\n    \"def read_file(filename):\\n\",\n    \"    ds = tf.data.TextLineDataset(filename)\\n\",\n    \"    ds = ds.map(lambda x: tf.strings.regex_replace(x, r\\\"\\\\\\\\n\\\", \\\"\\\\n\\\"))\\n\",\n    \"    ds = ds.map(tokenizer, num_parallel_calls=8)\\n\",\n    \"    return ds.map(lambda x: tf.concat([x, suffix], -1))\\n\",\n    \"\\n\",\n    \"files = [str(file) for file in extract_dir.glob(\\\"*.txt\\\")]\\n\",\n    \"ds = tf.data.Dataset.from_tensor_slices(files)\\n\",\n    \"ds = ds.interleave(read_file, cycle_length=32, num_parallel_calls=32)\\n\",\n    \"ds = ds.rebatch(sequence_length + 1, drop_remainder=True)\\n\",\n    \"ds = ds.map(lambda x: (x[:-1], x[1:]))\\n\",\n    \"ds = ds.batch(batch_size).prefetch(8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_batches = 58746\\n\",\n    \"num_val_batches = 500\\n\",\n    \"num_train_batches = num_batches - num_val_batches\\n\",\n    \"val_ds = ds.take(num_val_batches).repeat()\\n\",\n    \"train_ds = ds.skip(num_val_batches).repeat()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Building the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"class TransformerDecoder(keras.Layer):\\n\",\n    \"    def __init__(self, hidden_dim, intermediate_dim, num_heads):\\n\",\n    \"        super().__init__()\\n\",\n    \"        key_dim = hidden_dim // num_heads\\n\",\n    \"        self.self_attention = layers.MultiHeadAttention(\\n\",\n    \"            num_heads, key_dim, dropout=0.1\\n\",\n    \"        )\\n\",\n    \"        self.self_attention_layernorm = layers.LayerNormalization()\\n\",\n    \"        self.feed_forward_1 = layers.Dense(intermediate_dim, activation=\\\"relu\\\")\\n\",\n    \"        self.feed_forward_2 = layers.Dense(hidden_dim)\\n\",\n    \"        self.feed_forward_layernorm = layers.LayerNormalization()\\n\",\n    \"        self.dropout = layers.Dropout(0.1)\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        residual = x = inputs\\n\",\n    \"        x = self.self_attention(query=x, key=x, value=x, use_causal_mask=True)\\n\",\n    \"        x = self.dropout(x)\\n\",\n    \"        x = x + residual\\n\",\n    \"        x = self.self_attention_layernorm(x)\\n\",\n    \"        residual = x\\n\",\n    \"        x = self.feed_forward_1(x)\\n\",\n    \"        x = self.feed_forward_2(x)\\n\",\n    \"        x = self.dropout(x)\\n\",\n    \"        x = x + residual\\n\",\n    \"        x = self.feed_forward_layernorm(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"class PositionalEmbedding(keras.Layer):\\n\",\n    \"    def __init__(self, sequence_length, input_dim, output_dim):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.token_embeddings = layers.Embedding(input_dim, output_dim)\\n\",\n    \"        self.position_embeddings = layers.Embedding(sequence_length, output_dim)\\n\",\n    \"\\n\",\n    \"    def call(self, inputs, reverse=False):\\n\",\n    \"        if reverse:\\n\",\n    \"            token_embeddings = self.token_embeddings.embeddings\\n\",\n    \"            return ops.matmul(inputs, ops.transpose(token_embeddings))\\n\",\n    \"        positions = ops.cumsum(ops.ones_like(inputs), axis=-1) - 1\\n\",\n    \"        embedded_tokens = self.token_embeddings(inputs)\\n\",\n    \"        embedded_positions = self.position_embeddings(positions)\\n\",\n    \"        return embedded_tokens + embedded_positions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.config.set_dtype_policy(\\\"mixed_float16\\\")\\n\",\n    \"\\n\",\n    \"vocab_size = tokenizer.vocabulary_size()\\n\",\n    \"hidden_dim = 512\\n\",\n    \"intermediate_dim = 2056\\n\",\n    \"num_heads = 8\\n\",\n    \"num_layers = 8\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(None,), dtype=\\\"int32\\\", name=\\\"inputs\\\")\\n\",\n    \"embedding = PositionalEmbedding(sequence_length, vocab_size, hidden_dim)\\n\",\n    \"x = embedding(inputs)\\n\",\n    \"x = layers.LayerNormalization()(x)\\n\",\n    \"for i in range(num_layers):\\n\",\n    \"    x = TransformerDecoder(hidden_dim, intermediate_dim, num_heads)(x)\\n\",\n    \"outputs = embedding(x, reverse=True)\\n\",\n    \"mini_gpt = keras.Model(inputs, outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Pretraining the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class WarmupSchedule(keras.optimizers.schedules.LearningRateSchedule):\\n\",\n    \"    def __init__(self):\\n\",\n    \"        self.rate = 2e-4\\n\",\n    \"        self.warmup_steps = 1_000.0\\n\",\n    \"\\n\",\n    \"    def __call__(self, step):\\n\",\n    \"        step = ops.cast(step, dtype=\\\"float32\\\")\\n\",\n    \"        scale = ops.minimum(step / self.warmup_steps, 1.0)\\n\",\n    \"        return self.rate * scale\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"schedule = WarmupSchedule()\\n\",\n    \"x = range(0, 5_000, 100)\\n\",\n    \"y = [ops.convert_to_numpy(schedule(step)) for step in x]\\n\",\n    \"plt.plot(x, y)\\n\",\n    \"plt.xlabel(\\\"Train Step\\\")\\n\",\n    \"plt.ylabel(\\\"Learning Rate\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# \\u26a0\\ufe0fNOTE\\u26a0\\ufe0f: If you can run the following with a Colab Pro GPU, we suggest you\\n\",\n    \"# do so. This fit() call will take many hours on free tier GPUs. You can also\\n\",\n    \"# reduce steps_per_epoch to try the code with a less trained model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_epochs = 8\\n\",\n    \"steps_per_epoch = num_train_batches // num_epochs\\n\",\n    \"validation_steps = num_val_batches\\n\",\n    \"\\n\",\n    \"mini_gpt.compile(\\n\",\n    \"    optimizer=keras.optimizers.Adam(schedule),\\n\",\n    \"    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    metrics=[\\\"accuracy\\\"],\\n\",\n    \")\\n\",\n    \"mini_gpt.fit(\\n\",\n    \"    train_ds,\\n\",\n    \"    validation_data=val_ds,\\n\",\n    \"    epochs=num_epochs,\\n\",\n    \"    steps_per_epoch=steps_per_epoch,\\n\",\n    \"    validation_steps=validation_steps,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Generative decoding\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def generate(prompt, max_length=64):\\n\",\n    \"    tokens = list(ops.convert_to_numpy(tokenizer(prompt)))\\n\",\n    \"    prompt_length = len(tokens)\\n\",\n    \"    for _ in range(max_length - prompt_length):\\n\",\n    \"        prediction = mini_gpt(ops.convert_to_numpy([tokens]))\\n\",\n    \"        prediction = ops.convert_to_numpy(prediction[0, -1])\\n\",\n    \"        tokens.append(np.argmax(prediction).item())\\n\",\n    \"    return tokenizer.detokenize(tokens)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"prompt = \\\"A piece of advice\\\"\\n\",\n    \"generate(prompt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compiled_generate(prompt, max_length=64):\\n\",\n    \"    tokens = list(ops.convert_to_numpy(tokenizer(prompt)))\\n\",\n    \"    prompt_length = len(tokens)\\n\",\n    \"    tokens = tokens + [0] * (max_length - prompt_length)\\n\",\n    \"    for i in range(prompt_length, max_length):\\n\",\n    \"        prediction = mini_gpt.predict(np.array([tokens]), verbose=0)\\n\",\n    \"        prediction = prediction[0, i - 1]\\n\",\n    \"        tokens[i] = np.argmax(prediction).item()\\n\",\n    \"    return tokenizer.detokenize(tokens)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import timeit\\n\",\n    \"tries = 10\\n\",\n    \"timeit.timeit(lambda: compiled_generate(prompt), number=tries) / tries\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Sampling strategies\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compiled_generate(prompt, sample_fn, max_length=64):\\n\",\n    \"    tokens = list(ops.convert_to_numpy(tokenizer(prompt)))\\n\",\n    \"    prompt_length = len(tokens)\\n\",\n    \"    tokens = tokens + [0] * (max_length - prompt_length)\\n\",\n    \"    for i in range(prompt_length, max_length):\\n\",\n    \"        prediction = mini_gpt.predict(np.array([tokens]), verbose=0)\\n\",\n    \"        prediction = prediction[0, i - 1]\\n\",\n    \"        next_token = ops.convert_to_numpy(sample_fn(prediction))\\n\",\n    \"        tokens[i] = np.array(next_token).item()\\n\",\n    \"    return tokenizer.detokenize(tokens)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def greedy_search(preds):\\n\",\n    \"    return ops.argmax(preds)\\n\",\n    \"\\n\",\n    \"compiled_generate(prompt, greedy_search)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def random_sample(preds, temperature=1.0):\\n\",\n    \"    preds = preds / temperature\\n\",\n    \"    return keras.random.categorical(preds[None, :], num_samples=1)[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"compiled_generate(prompt, random_sample)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from functools import partial\\n\",\n    \"compiled_generate(prompt, partial(random_sample, temperature=2.0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"compiled_generate(prompt, partial(random_sample, temperature=0.8))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"compiled_generate(prompt, partial(random_sample, temperature=0.2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def top_k(preds, k=5, temperature=1.0):\\n\",\n    \"    preds = preds / temperature\\n\",\n    \"    top_preds, top_indices = ops.top_k(preds, k=k, sorted=False)\\n\",\n    \"    choice = keras.random.categorical(top_preds[None, :], num_samples=1)[0]\\n\",\n    \"    return ops.take_along_axis(top_indices, choice, axis=-1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"compiled_generate(prompt, partial(top_k, k=5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"compiled_generate(prompt, partial(top_k, k=20))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"compiled_generate(prompt, partial(top_k, k=5, temperature=0.5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using a pretrained LLM\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Text generation with the Gemma model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import kagglehub\\n\",\n    \"\\n\",\n    \"kagglehub.login()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm = keras_hub.models.CausalLM.from_preset(\\n\",\n    \"    \\\"gemma3_1b\\\",\\n\",\n    \"    dtype=\\\"float32\\\",\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.compile(sampler=\\\"greedy\\\")\\n\",\n    \"gemma_lm.generate(\\\"A piece of advice\\\", max_length=40)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.generate(\\\"How can I make brownies?\\\", max_length=40)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.generate(\\n\",\n    \"    \\\"The following brownie recipe is easy to make in just a few \\\"\\n\",\n    \"    \\\"steps.\\\\n\\\\nYou can start by\\\",\\n\",\n    \"    max_length=40,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.generate(\\n\",\n    \"    \\\"Tell me about the 542nd president of the United States.\\\",\\n\",\n    \"    max_length=40,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Instruction fine-tuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import json\\n\",\n    \"\\n\",\n    \"PROMPT_TEMPLATE = \\\"\\\"\\\"\\\"[instruction]\\\\n{}[end]\\\\n[response]\\\\n\\\"\\\"\\\"\\n\",\n    \"RESPONSE_TEMPLATE = \\\"\\\"\\\"{}[end]\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"dataset_path = keras.utils.get_file(\\n\",\n    \"    origin=(\\n\",\n    \"        \\\"https://hf.co/datasets/databricks/databricks-dolly-15k/\\\"\\n\",\n    \"        \\\"resolve/main/databricks-dolly-15k.jsonl\\\"\\n\",\n    \"    ),\\n\",\n    \")\\n\",\n    \"data = {\\\"prompts\\\": [], \\\"responses\\\": []}\\n\",\n    \"with open(dataset_path) as file:\\n\",\n    \"    for line in file:\\n\",\n    \"        features = json.loads(line)\\n\",\n    \"        if features[\\\"context\\\"]:\\n\",\n    \"            continue\\n\",\n    \"        data[\\\"prompts\\\"].append(PROMPT_TEMPLATE.format(features[\\\"instruction\\\"]))\\n\",\n    \"        data[\\\"responses\\\"].append(RESPONSE_TEMPLATE.format(features[\\\"response\\\"]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data[\\\"prompts\\\"][0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data[\\\"responses\\\"][0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"ds = tf.data.Dataset.from_tensor_slices(data).shuffle(2000).batch(2)\\n\",\n    \"val_ds = ds.take(100)\\n\",\n    \"train_ds = ds.skip(100)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"preprocessor = gemma_lm.preprocessor\\n\",\n    \"preprocessor.sequence_length = 512\\n\",\n    \"batch = next(iter(train_ds))\\n\",\n    \"x, y, sample_weight = preprocessor(batch)\\n\",\n    \"x[\\\"token_ids\\\"].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x[\\\"padding_mask\\\"].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sample_weight.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x[\\\"token_ids\\\"][0, :5], y[0, :5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Low-Rank Adaptation (LoRA)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.backbone.enable_lora(rank=8)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.compile(\\n\",\n    \"    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\\n\",\n    \"    optimizer=keras.optimizers.Adam(5e-5),\\n\",\n    \"    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],\\n\",\n    \")\\n\",\n    \"gemma_lm.fit(train_ds, validation_data=val_ds, epochs=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.generate(\\n\",\n    \"    \\\"[instruction]\\\\nHow can I make brownies?[end]\\\\n\\\"\\n\",\n    \"    \\\"[response]\\\\n\\\",\\n\",\n    \"    max_length=512,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.generate(\\n\",\n    \"    \\\"[instruction]\\\\nWhat is a proper noun?[end]\\\\n\\\"\\n\",\n    \"    \\\"[response]\\\\n\\\",\\n\",\n    \"    max_length=512,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.generate(\\n\",\n    \"    \\\"[instruction]\\\\nWho is the 542nd president of the United States?[end]\\\\n\\\"\\n\",\n    \"    \\\"[response]\\\\n\\\",\\n\",\n    \"    max_length=512,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Going further with LLMs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Reinforcement Learning with Human Feedback (RLHF)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Using a chatbot trained with RLHF\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# \\u26a0\\ufe0fNOTE\\u26a0\\ufe0f: If you are running on the free tier Colab GPUs, you will need to\\n\",\n    \"# restart your runtime and run the notebook from here to free up memory for\\n\",\n    \"# this 4 billion parameter model.\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\\n\",\n    \"# Free up more GPU memory on the Jax and TensorFlow backends.\\n\",\n    \"os.environ[\\\"XLA_PYTHON_CLIENT_MEM_FRACTION\\\"] = \\\"1.00\\\"\\n\",\n    \"\\n\",\n    \"import keras\\n\",\n    \"import keras_hub\\n\",\n    \"import kagglehub\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"kagglehub.login()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm = keras_hub.models.CausalLM.from_preset(\\n\",\n    \"    \\\"gemma3_instruct_4b\\\",\\n\",\n    \"    dtype=\\\"bfloat16\\\",\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"PROMPT_TEMPLATE = \\\"\\\"\\\"<start_of_turn>user\\n\",\n    \"{}<end_of_turn>\\n\",\n    \"<start_of_turn>model\\n\",\n    \"\\\"\\\"\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"prompt = \\\"Why can't you assign values in Jax tensors? Be brief!\\\"\\n\",\n    \"gemma_lm.generate(PROMPT_TEMPLATE.format(prompt), max_length=512)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"prompt = \\\"Who is the 542nd president of the United States?\\\"\\n\",\n    \"gemma_lm.generate(PROMPT_TEMPLATE.format(prompt), max_length=512)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Multimodal LLMs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"image_url = (\\n\",\n    \"    \\\"https://github.com/mattdangerw/keras-nlp-scripts/\\\"\\n\",\n    \"    \\\"blob/main/learned-python.png?raw=true\\\"\\n\",\n    \")\\n\",\n    \"image_path = keras.utils.get_file(origin=image_url)\\n\",\n    \"\\n\",\n    \"image = np.array(keras.utils.load_img(image_path))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(image)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.preprocessor.max_images_per_prompt = 1\\n\",\n    \"gemma_lm.preprocessor.sequence_length = 512\\n\",\n    \"prompt = \\\"What is going on in this image? Be concise!<start_of_image>\\\"\\n\",\n    \"gemma_lm.generate({\\n\",\n    \"    \\\"prompts\\\": PROMPT_TEMPLATE.format(prompt),\\n\",\n    \"    \\\"images\\\": [image],\\n\",\n    \"})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"prompt = \\\"What is the snake wearing?<start_of_image>\\\"\\n\",\n    \"gemma_lm.generate({\\n\",\n    \"    \\\"prompts\\\": PROMPT_TEMPLATE.format(prompt),\\n\",\n    \"    \\\"images\\\": [image],\\n\",\n    \"})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Foundation models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Retrieval Augmented Generation (RAG)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### \\\"Reasoning\\\" models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"prompt = \\\"\\\"\\\"Judy wrote a 2-page letter to 3 friends twice a week for 3 months.\\n\",\n    \"How many letters did she write?\\n\",\n    \"Be brief, and add \\\"ANSWER:\\\" before your final answer.\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"gemma_lm.compile(sampler=\\\"random\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.generate(PROMPT_TEMPLATE.format(prompt))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gemma_lm.generate(PROMPT_TEMPLATE.format(prompt))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Where are LLMs heading next?\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter16_text-generation\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter17_image-generation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Image generation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Deep learning for image generation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Sampling from latent spaces of images\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Variational autoencoders\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Implementing a VAE with Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"latent_dim = 2\\n\",\n    \"\\n\",\n    \"image_inputs = keras.Input(shape=(28, 28, 1))\\n\",\n    \"x = layers.Conv2D(32, 3, activation=\\\"relu\\\", strides=2, padding=\\\"same\\\")(\\n\",\n    \"    image_inputs\\n\",\n    \")\\n\",\n    \"x = layers.Conv2D(64, 3, activation=\\\"relu\\\", strides=2, padding=\\\"same\\\")(x)\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"x = layers.Dense(16, activation=\\\"relu\\\")(x)\\n\",\n    \"z_mean = layers.Dense(latent_dim, name=\\\"z_mean\\\")(x)\\n\",\n    \"z_log_var = layers.Dense(latent_dim, name=\\\"z_log_var\\\")(x)\\n\",\n    \"encoder = keras.Model(image_inputs, [z_mean, z_log_var], name=\\\"encoder\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"encoder.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"class Sampler(keras.Layer):\\n\",\n    \"    def __init__(self, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.seed_generator = keras.random.SeedGenerator()\\n\",\n    \"        self.built = True\\n\",\n    \"\\n\",\n    \"    def call(self, z_mean, z_log_var):\\n\",\n    \"        batch_size = ops.shape(z_mean)[0]\\n\",\n    \"        z_size = ops.shape(z_mean)[1]\\n\",\n    \"        epsilon = keras.random.normal(\\n\",\n    \"            (batch_size, z_size), seed=self.seed_generator\\n\",\n    \"        )\\n\",\n    \"        return z_mean + ops.exp(0.5 * z_log_var) * epsilon\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"latent_inputs = keras.Input(shape=(latent_dim,))\\n\",\n    \"x = layers.Dense(7 * 7 * 64, activation=\\\"relu\\\")(latent_inputs)\\n\",\n    \"x = layers.Reshape((7, 7, 64))(x)\\n\",\n    \"x = layers.Conv2DTranspose(64, 3, activation=\\\"relu\\\", strides=2, padding=\\\"same\\\")(\\n\",\n    \"    x\\n\",\n    \")\\n\",\n    \"x = layers.Conv2DTranspose(32, 3, activation=\\\"relu\\\", strides=2, padding=\\\"same\\\")(\\n\",\n    \"    x\\n\",\n    \")\\n\",\n    \"decoder_outputs = layers.Conv2D(1, 3, activation=\\\"sigmoid\\\", padding=\\\"same\\\")(x)\\n\",\n    \"decoder = keras.Model(latent_inputs, decoder_outputs, name=\\\"decoder\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"decoder.summary(line_length=80)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class VAE(keras.Model):\\n\",\n    \"    def __init__(self, encoder, decoder, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.encoder = encoder\\n\",\n    \"        self.decoder = decoder\\n\",\n    \"        self.sampler = Sampler()\\n\",\n    \"        self.reconstruction_loss_tracker = keras.metrics.Mean(\\n\",\n    \"            name=\\\"reconstruction_loss\\\"\\n\",\n    \"        )\\n\",\n    \"        self.kl_loss_tracker = keras.metrics.Mean(name=\\\"kl_loss\\\")\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        return self.encoder(inputs)\\n\",\n    \"\\n\",\n    \"    def compute_loss(self, x, y, y_pred, sample_weight=None, training=True):\\n\",\n    \"        original = x\\n\",\n    \"        z_mean, z_log_var = y_pred\\n\",\n    \"        reconstruction = self.decoder(self.sampler(z_mean, z_log_var))\\n\",\n    \"\\n\",\n    \"        reconstruction_loss = ops.mean(\\n\",\n    \"            ops.sum(\\n\",\n    \"                keras.losses.binary_crossentropy(x, reconstruction), axis=(1, 2)\\n\",\n    \"            )\\n\",\n    \"        )\\n\",\n    \"        kl_loss = -0.5 * (\\n\",\n    \"            1 + z_log_var - ops.square(z_mean) - ops.exp(z_log_var)\\n\",\n    \"        )\\n\",\n    \"        total_loss = reconstruction_loss + ops.mean(kl_loss)\\n\",\n    \"\\n\",\n    \"        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\\n\",\n    \"        self.kl_loss_tracker.update_state(kl_loss)\\n\",\n    \"        return total_loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()\\n\",\n    \"mnist_digits = np.concatenate([x_train, x_test], axis=0)\\n\",\n    \"mnist_digits = np.expand_dims(mnist_digits, -1).astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"vae = VAE(encoder, decoder)\\n\",\n    \"vae.compile(optimizer=keras.optimizers.Adam())\\n\",\n    \"vae.fit(mnist_digits, epochs=30, batch_size=128)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"n = 30\\n\",\n    \"digit_size = 28\\n\",\n    \"figure = np.zeros((digit_size * n, digit_size * n))\\n\",\n    \"\\n\",\n    \"grid_x = np.linspace(-1, 1, n)\\n\",\n    \"grid_y = np.linspace(-1, 1, n)[::-1]\\n\",\n    \"\\n\",\n    \"for i, yi in enumerate(grid_y):\\n\",\n    \"    for j, xi in enumerate(grid_x):\\n\",\n    \"        z_sample = np.array([[xi, yi]])\\n\",\n    \"        x_decoded = vae.decoder.predict(z_sample)\\n\",\n    \"        digit = x_decoded[0].reshape(digit_size, digit_size)\\n\",\n    \"        figure[\\n\",\n    \"            i * digit_size : (i + 1) * digit_size,\\n\",\n    \"            j * digit_size : (j + 1) * digit_size,\\n\",\n    \"        ] = digit\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(15, 15))\\n\",\n    \"start_range = digit_size // 2\\n\",\n    \"end_range = n * digit_size + start_range\\n\",\n    \"pixel_range = np.arange(start_range, end_range, digit_size)\\n\",\n    \"sample_range_x = np.round(grid_x, 1)\\n\",\n    \"sample_range_y = np.round(grid_y, 1)\\n\",\n    \"plt.xticks(pixel_range, sample_range_x)\\n\",\n    \"plt.yticks(pixel_range, sample_range_y)\\n\",\n    \"plt.xlabel(\\\"z[0]\\\")\\n\",\n    \"plt.ylabel(\\\"z[1]\\\")\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(figure, cmap=\\\"Greys_r\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Diffusion models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The Oxford Flowers dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"fpath = keras.utils.get_file(\\n\",\n    \"    origin=\\\"https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz\\\",\\n\",\n    \"    extract=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 32\\n\",\n    \"image_size = 128\\n\",\n    \"images_dir = os.path.join(fpath, \\\"jpg\\\")\\n\",\n    \"dataset = keras.utils.image_dataset_from_directory(\\n\",\n    \"    images_dir,\\n\",\n    \"    labels=None,\\n\",\n    \"    image_size=(image_size, image_size),\\n\",\n    \"    crop_to_aspect_ratio=True,\\n\",\n    \")\\n\",\n    \"dataset = dataset.rebatch(\\n\",\n    \"    batch_size,\\n\",\n    \"    drop_remainder=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"for batch in dataset:\\n\",\n    \"    img = batch.numpy()[0]\\n\",\n    \"    break\\n\",\n    \"plt.imshow(img.astype(\\\"uint8\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A U-Net denoising autoencoder\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def residual_block(x, width):\\n\",\n    \"    input_width = x.shape[3]\\n\",\n    \"    if input_width == width:\\n\",\n    \"        residual = x\\n\",\n    \"    else:\\n\",\n    \"        residual = layers.Conv2D(width, 1)(x)\\n\",\n    \"    x = layers.BatchNormalization(center=False, scale=False)(x)\\n\",\n    \"    x = layers.Conv2D(width, 3, padding=\\\"same\\\", activation=\\\"swish\\\")(x)\\n\",\n    \"    x = layers.Conv2D(width, 3, padding=\\\"same\\\")(x)\\n\",\n    \"    x = x + residual\\n\",\n    \"    return x\\n\",\n    \"\\n\",\n    \"def get_model(image_size, widths, block_depth):\\n\",\n    \"    noisy_images = keras.Input(shape=(image_size, image_size, 3))\\n\",\n    \"    noise_rates = keras.Input(shape=(1, 1, 1))\\n\",\n    \"\\n\",\n    \"    x = layers.Conv2D(widths[0], 1)(noisy_images)\\n\",\n    \"    n = layers.UpSampling2D(image_size, interpolation=\\\"nearest\\\")(noise_rates)\\n\",\n    \"    x = layers.Concatenate()([x, n])\\n\",\n    \"\\n\",\n    \"    skips = []\\n\",\n    \"    for width in widths[:-1]:\\n\",\n    \"        for _ in range(block_depth):\\n\",\n    \"            x = residual_block(x, width)\\n\",\n    \"            skips.append(x)\\n\",\n    \"        x = layers.AveragePooling2D(pool_size=2)(x)\\n\",\n    \"\\n\",\n    \"    for _ in range(block_depth):\\n\",\n    \"        x = residual_block(x, widths[-1])\\n\",\n    \"\\n\",\n    \"    for width in reversed(widths[:-1]):\\n\",\n    \"        x = layers.UpSampling2D(size=2, interpolation=\\\"bilinear\\\")(x)\\n\",\n    \"        for _ in range(block_depth):\\n\",\n    \"            x = layers.Concatenate()([x, skips.pop()])\\n\",\n    \"            x = residual_block(x, width)\\n\",\n    \"\\n\",\n    \"    pred_noise_masks = layers.Conv2D(3, 1, kernel_initializer=\\\"zeros\\\")(x)\\n\",\n    \"\\n\",\n    \"    return keras.Model([noisy_images, noise_rates], pred_noise_masks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The concepts of diffusion time and diffusion schedule\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def diffusion_schedule(\\n\",\n    \"    diffusion_times,\\n\",\n    \"    min_signal_rate=0.02,\\n\",\n    \"    max_signal_rate=0.95,\\n\",\n    \"):\\n\",\n    \"    start_angle = ops.cast(ops.arccos(max_signal_rate), \\\"float32\\\")\\n\",\n    \"    end_angle = ops.cast(ops.arccos(min_signal_rate), \\\"float32\\\")\\n\",\n    \"    diffusion_angles = start_angle + diffusion_times * (end_angle - start_angle)\\n\",\n    \"    signal_rates = ops.cos(diffusion_angles)\\n\",\n    \"    noise_rates = ops.sin(diffusion_angles)\\n\",\n    \"    return noise_rates, signal_rates\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"diffusion_times = ops.arange(0.0, 1.0, 0.01)\\n\",\n    \"noise_rates, signal_rates = diffusion_schedule(diffusion_times)\\n\",\n    \"\\n\",\n    \"diffusion_times = ops.convert_to_numpy(diffusion_times)\\n\",\n    \"noise_rates = ops.convert_to_numpy(noise_rates)\\n\",\n    \"signal_rates = ops.convert_to_numpy(signal_rates)\\n\",\n    \"\\n\",\n    \"plt.plot(diffusion_times, noise_rates, label=\\\"Noise rate\\\")\\n\",\n    \"plt.plot(diffusion_times, signal_rates, label=\\\"Signal rate\\\")\\n\",\n    \"\\n\",\n    \"plt.xlabel(\\\"Diffusion time\\\")\\n\",\n    \"plt.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The training process\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class DiffusionModel(keras.Model):\\n\",\n    \"    def __init__(self, image_size, widths, block_depth, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.image_size = image_size\\n\",\n    \"        self.denoising_model = get_model(image_size, widths, block_depth)\\n\",\n    \"        self.seed_generator = keras.random.SeedGenerator()\\n\",\n    \"        self.loss = keras.losses.MeanAbsoluteError()\\n\",\n    \"        self.normalizer = keras.layers.Normalization()\\n\",\n    \"\\n\",\n    \"    def denoise(self, noisy_images, noise_rates, signal_rates):\\n\",\n    \"        pred_noise_masks = self.denoising_model([noisy_images, noise_rates])\\n\",\n    \"        pred_images = (\\n\",\n    \"            noisy_images - noise_rates * pred_noise_masks\\n\",\n    \"        ) / signal_rates\\n\",\n    \"        return pred_images, pred_noise_masks\\n\",\n    \"\\n\",\n    \"    def call(self, images):\\n\",\n    \"        images = self.normalizer(images)\\n\",\n    \"        noise_masks = keras.random.normal(\\n\",\n    \"            (batch_size, self.image_size, self.image_size, 3),\\n\",\n    \"            seed=self.seed_generator,\\n\",\n    \"        )\\n\",\n    \"        diffusion_times = keras.random.uniform(\\n\",\n    \"            (batch_size, 1, 1, 1),\\n\",\n    \"            minval=0.0,\\n\",\n    \"            maxval=1.0,\\n\",\n    \"            seed=self.seed_generator,\\n\",\n    \"        )\\n\",\n    \"        noise_rates, signal_rates = diffusion_schedule(diffusion_times)\\n\",\n    \"        noisy_images = signal_rates * images + noise_rates * noise_masks\\n\",\n    \"        pred_images, pred_noise_masks = self.denoise(\\n\",\n    \"            noisy_images, noise_rates, signal_rates\\n\",\n    \"        )\\n\",\n    \"        return pred_images, pred_noise_masks, noise_masks\\n\",\n    \"\\n\",\n    \"    def compute_loss(self, x, y, y_pred, sample_weight=None, training=True):\\n\",\n    \"        _, pred_noise_masks, noise_masks = y_pred\\n\",\n    \"        return self.loss(noise_masks, pred_noise_masks)\\n\",\n    \"\\n\",\n    \"    def generate(self, num_images, diffusion_steps):\\n\",\n    \"        noisy_images = keras.random.normal(\\n\",\n    \"            (num_images, self.image_size, self.image_size, 3),\\n\",\n    \"            seed=self.seed_generator,\\n\",\n    \"        )\\n\",\n    \"        step_size = 1.0 / diffusion_steps\\n\",\n    \"        for step in range(diffusion_steps):\\n\",\n    \"            diffusion_times = ops.ones((num_images, 1, 1, 1)) - step * step_size\\n\",\n    \"            noise_rates, signal_rates = diffusion_schedule(diffusion_times)\\n\",\n    \"            pred_images, pred_noises = self.denoise(\\n\",\n    \"                noisy_images, noise_rates, signal_rates\\n\",\n    \"            )\\n\",\n    \"            next_diffusion_times = diffusion_times - step_size\\n\",\n    \"            next_noise_rates, next_signal_rates = diffusion_schedule(\\n\",\n    \"                next_diffusion_times\\n\",\n    \"            )\\n\",\n    \"            noisy_images = (\\n\",\n    \"                next_signal_rates * pred_images + next_noise_rates * pred_noises\\n\",\n    \"            )\\n\",\n    \"        images = (\\n\",\n    \"            self.normalizer.mean + pred_images * self.normalizer.variance**0.5\\n\",\n    \"        )\\n\",\n    \"        return ops.clip(images, 0.0, 255.0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The generation process\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Visualizing results with a custom callback\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class VisualizationCallback(keras.callbacks.Callback):\\n\",\n    \"    def __init__(self, diffusion_steps=20, num_rows=3, num_cols=6):\\n\",\n    \"        self.diffusion_steps = diffusion_steps\\n\",\n    \"        self.num_rows = num_rows\\n\",\n    \"        self.num_cols = num_cols\\n\",\n    \"\\n\",\n    \"    def on_epoch_end(self, epoch=None, logs=None):\\n\",\n    \"        generated_images = self.model.generate(\\n\",\n    \"            num_images=self.num_rows * self.num_cols,\\n\",\n    \"            diffusion_steps=self.diffusion_steps,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        plt.figure(figsize=(self.num_cols * 2.0, self.num_rows * 2.0))\\n\",\n    \"        for row in range(self.num_rows):\\n\",\n    \"            for col in range(self.num_cols):\\n\",\n    \"                i = row * self.num_cols + col\\n\",\n    \"                plt.subplot(self.num_rows, self.num_cols, i + 1)\\n\",\n    \"                img = ops.convert_to_numpy(generated_images[i]).astype(\\\"uint8\\\")\\n\",\n    \"                plt.imshow(img)\\n\",\n    \"                plt.axis(\\\"off\\\")\\n\",\n    \"        plt.tight_layout()\\n\",\n    \"        plt.show()\\n\",\n    \"        plt.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### It's go time!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = DiffusionModel(image_size, widths=[32, 64, 96, 128], block_depth=2)\\n\",\n    \"model.normalizer.adapt(dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(\\n\",\n    \"    optimizer=keras.optimizers.AdamW(\\n\",\n    \"        learning_rate=keras.optimizers.schedules.InverseTimeDecay(\\n\",\n    \"            initial_learning_rate=1e-3,\\n\",\n    \"            decay_steps=1000,\\n\",\n    \"            decay_rate=0.1,\\n\",\n    \"        ),\\n\",\n    \"        use_ema=True,\\n\",\n    \"        ema_overwrite_frequency=100,\\n\",\n    \"    ),\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.fit(\\n\",\n    \"    dataset,\\n\",\n    \"    epochs=100,\\n\",\n    \"    callbacks=[\\n\",\n    \"        VisualizationCallback(),\\n\",\n    \"        keras.callbacks.ModelCheckpoint(\\n\",\n    \"            filepath=\\\"diffusion_model.weights.h5\\\",\\n\",\n    \"            save_weights_only=True,\\n\",\n    \"            save_best_only=True,\\n\",\n    \"        ),\\n\",\n    \"    ],\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Text-to-image models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"if keras.config.backend() == \\\"torch\\\":\\n\",\n    \"    # The rest of this chapter will not do any training. The following keeps\\n\",\n    \"    # PyTorch from using too much memory by disabling gradients. TensorFlow and\\n\",\n    \"    # JAX use a much smaller memory footprint and do not need this hack.\\n\",\n    \"    import torch\\n\",\n    \"\\n\",\n    \"    torch.set_grad_enabled(False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras_hub\\n\",\n    \"\\n\",\n    \"height, width = 512, 512\\n\",\n    \"task = keras_hub.models.TextToImage.from_preset(\\n\",\n    \"    \\\"stable_diffusion_3_medium\\\",\\n\",\n    \"    image_shape=(height, width, 3),\\n\",\n    \"    dtype=\\\"float16\\\",\\n\",\n    \")\\n\",\n    \"prompt = \\\"A NASA astraunaut riding an origami elephant in New York City\\\"\\n\",\n    \"task.generate(prompt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"task.generate(\\n\",\n    \"    {\\n\",\n    \"        \\\"prompts\\\": prompt,\\n\",\n    \"        \\\"negative_prompts\\\": \\\"blue color\\\",\\n\",\n    \"    }\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"def display(images):\\n\",\n    \"    return Image.fromarray(np.concatenate(images, axis=1))\\n\",\n    \"\\n\",\n    \"display([task.generate(prompt, num_steps=x) for x in [5, 10, 15, 20, 25]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Exploring the latent space of a text-to-image model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import random\\n\",\n    \"\\n\",\n    \"def get_text_embeddings(prompt):\\n\",\n    \"    token_ids = task.preprocessor.generate_preprocess([prompt])\\n\",\n    \"    negative_token_ids = task.preprocessor.generate_preprocess([\\\"\\\"])\\n\",\n    \"    return task.backbone.encode_text_step(token_ids, negative_token_ids)\\n\",\n    \"\\n\",\n    \"def denoise_with_text_embeddings(embeddings, num_steps=28, guidance_scale=7.0):\\n\",\n    \"    latents = random.normal((1, height // 8, width // 8, 16))\\n\",\n    \"    for step in range(num_steps):\\n\",\n    \"        latents = task.backbone.denoise_step(\\n\",\n    \"            latents,\\n\",\n    \"            embeddings,\\n\",\n    \"            step,\\n\",\n    \"            num_steps,\\n\",\n    \"            guidance_scale,\\n\",\n    \"        )\\n\",\n    \"    return task.backbone.decode_step(latents)[0]\\n\",\n    \"\\n\",\n    \"def scale_output(x):\\n\",\n    \"    x = ops.convert_to_numpy(x)\\n\",\n    \"    x = np.clip((x + 1.0) / 2.0, 0.0, 1.0)\\n\",\n    \"    return np.round(x * 255.0).astype(\\\"uint8\\\")\\n\",\n    \"\\n\",\n    \"embeddings = get_text_embeddings(prompt)\\n\",\n    \"image = denoise_with_text_embeddings(embeddings)\\n\",\n    \"scale_output(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"[x.shape for x in embeddings]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"def slerp(t, v1, v2):\\n\",\n    \"    v1, v2 = ops.cast(v1, \\\"float32\\\"), ops.cast(v2, \\\"float32\\\")\\n\",\n    \"    v1_norm = ops.linalg.norm(ops.ravel(v1))\\n\",\n    \"    v2_norm = ops.linalg.norm(ops.ravel(v2))\\n\",\n    \"    dot = ops.sum(v1 * v2 / (v1_norm * v2_norm))\\n\",\n    \"    theta_0 = ops.arccos(dot)\\n\",\n    \"    sin_theta_0 = ops.sin(theta_0)\\n\",\n    \"    theta_t = theta_0 * t\\n\",\n    \"    sin_theta_t = ops.sin(theta_t)\\n\",\n    \"    s0 = ops.sin(theta_0 - theta_t) / sin_theta_0\\n\",\n    \"    s1 = sin_theta_t / sin_theta_0\\n\",\n    \"    return s0 * v1 + s1 * v2\\n\",\n    \"\\n\",\n    \"def interpolate_text_embeddings(e1, e2, start=0, stop=1, num=10):\\n\",\n    \"    embeddings = []\\n\",\n    \"    for t in np.linspace(start, stop, num):\\n\",\n    \"        embeddings.append(\\n\",\n    \"            (\\n\",\n    \"                slerp(t, e1[0], e2[0]),\\n\",\n    \"                e1[1],\\n\",\n    \"                slerp(t, e1[2], e2[2]),\\n\",\n    \"                e1[3],\\n\",\n    \"            )\\n\",\n    \"        )\\n\",\n    \"    return embeddings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"prompt1 = \\\"A friendly dog looking up in a field of flowers\\\"\\n\",\n    \"prompt2 = \\\"A horrifying, tentacled creature hovering over a field of flowers\\\"\\n\",\n    \"e1 = get_text_embeddings(prompt1)\\n\",\n    \"e2 = get_text_embeddings(prompt2)\\n\",\n    \"\\n\",\n    \"images = []\\n\",\n    \"for et in interpolate_text_embeddings(e1, e2, start=0.5, stop=0.6, num=9):\\n\",\n    \"    image = denoise_with_text_embeddings(et)\\n\",\n    \"    images.append(scale_output(image))\\n\",\n    \"display(images)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter17_image-generation\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "chapter18_best-practices-for-the-real-world.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras keras-hub --upgrade -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"KERAS_BACKEND\\\"] = \\\"jax\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"cellView\": \"form\",\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# @title\\n\",\n    \"import os\\n\",\n    \"from IPython.core.magic import register_cell_magic\\n\",\n    \"\\n\",\n    \"@register_cell_magic\\n\",\n    \"def backend(line, cell):\\n\",\n    \"    current, required = os.environ.get(\\\"KERAS_BACKEND\\\", \\\"\\\"), line.split()[-1]\\n\",\n    \"    if current == required:\\n\",\n    \"        get_ipython().run_cell(cell)\\n\",\n    \"    else:\\n\",\n    \"        print(\\n\",\n    \"            f\\\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \\\"\\n\",\n    \"            f\\\"\\\\\\\"{required}\\\\\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\\\"\\n\",\n    \"        )\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Best practices for the real world\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Getting the most out of your models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Hyperparameter optimization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Using KerasTuner\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras-tuner -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"def build_model(hp):\\n\",\n    \"    units = hp.Int(name=\\\"units\\\", min_value=16, max_value=64, step=16)\\n\",\n    \"    model = keras.Sequential(\\n\",\n    \"        [\\n\",\n    \"            layers.Dense(units, activation=\\\"relu\\\"),\\n\",\n    \"            layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"        ]\\n\",\n    \"    )\\n\",\n    \"    optimizer = hp.Choice(name=\\\"optimizer\\\", values=[\\\"rmsprop\\\", \\\"adam\\\"])\\n\",\n    \"    model.compile(\\n\",\n    \"        optimizer=optimizer,\\n\",\n    \"        loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"        metrics=[\\\"accuracy\\\"],\\n\",\n    \"    )\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import keras_tuner as kt\\n\",\n    \"\\n\",\n    \"class SimpleMLP(kt.HyperModel):\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"\\n\",\n    \"    def build(self, hp):\\n\",\n    \"        units = hp.Int(name=\\\"units\\\", min_value=16, max_value=64, step=16)\\n\",\n    \"        model = keras.Sequential(\\n\",\n    \"            [\\n\",\n    \"                layers.Dense(units, activation=\\\"relu\\\"),\\n\",\n    \"                layers.Dense(self.num_classes, activation=\\\"softmax\\\"),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"        optimizer = hp.Choice(name=\\\"optimizer\\\", values=[\\\"rmsprop\\\", \\\"adam\\\"])\\n\",\n    \"        model.compile(\\n\",\n    \"            optimizer=optimizer,\\n\",\n    \"            loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"            metrics=[\\\"accuracy\\\"],\\n\",\n    \"        )\\n\",\n    \"        return model\\n\",\n    \"\\n\",\n    \"hypermodel = SimpleMLP(num_classes=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tuner = kt.BayesianOptimization(\\n\",\n    \"    build_model,\\n\",\n    \"    objective=\\\"val_accuracy\\\",\\n\",\n    \"    max_trials=20,\\n\",\n    \"    executions_per_trial=2,\\n\",\n    \"    directory=\\\"mnist_kt_test\\\",\\n\",\n    \"    overwrite=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tuner.search_space_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n    \"x_train = x_train.reshape((-1, 28 * 28)).astype(\\\"float32\\\") / 255\\n\",\n    \"x_test = x_test.reshape((-1, 28 * 28)).astype(\\\"float32\\\") / 255\\n\",\n    \"x_train_full = x_train[:]\\n\",\n    \"y_train_full = y_train[:]\\n\",\n    \"num_val_samples = 10000\\n\",\n    \"x_train, x_val = x_train[:-num_val_samples], x_train[-num_val_samples:]\\n\",\n    \"y_train, y_val = y_train[:-num_val_samples], y_train[-num_val_samples:]\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.EarlyStopping(monitor=\\\"val_loss\\\", patience=5),\\n\",\n    \"]\\n\",\n    \"tuner.search(\\n\",\n    \"    x_train,\\n\",\n    \"    y_train,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    epochs=100,\\n\",\n    \"    validation_data=(x_val, y_val),\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    verbose=2,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"top_n = 4\\n\",\n    \"best_hps = tuner.get_best_hyperparameters(top_n)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_best_epoch(hp):\\n\",\n    \"    model = build_model(hp)\\n\",\n    \"    callbacks = [\\n\",\n    \"        keras.callbacks.EarlyStopping(\\n\",\n    \"            monitor=\\\"val_loss\\\", mode=\\\"min\\\", patience=10\\n\",\n    \"        )\\n\",\n    \"    ]\\n\",\n    \"    history = model.fit(\\n\",\n    \"        x_train,\\n\",\n    \"        y_train,\\n\",\n    \"        validation_data=(x_val, y_val),\\n\",\n    \"        epochs=100,\\n\",\n    \"        batch_size=128,\\n\",\n    \"        callbacks=callbacks,\\n\",\n    \"    )\\n\",\n    \"    val_loss_per_epoch = history.history[\\\"val_loss\\\"]\\n\",\n    \"    best_epoch = val_loss_per_epoch.index(min(val_loss_per_epoch)) + 1\\n\",\n    \"    print(f\\\"Best epoch: {best_epoch}\\\")\\n\",\n    \"    return best_epoch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_best_trained_model(hp):\\n\",\n    \"    best_epoch = get_best_epoch(hp)\\n\",\n    \"    model = build_model(hp)\\n\",\n    \"    model.fit(\\n\",\n    \"        x_train_full, y_train_full, batch_size=128, epochs=int(best_epoch * 1.2)\\n\",\n    \"    )\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"best_models = []\\n\",\n    \"for hp in best_hps:\\n\",\n    \"    model = get_best_trained_model(hp)\\n\",\n    \"    model.evaluate(x_test, y_test)\\n\",\n    \"    best_models.append(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"best_models = tuner.get_best_models(top_n)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### The art of crafting the right search space\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### The future of hyperparameter tuning: automated machine learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Model ensembling\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Scaling up model training with multiple devices\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Multi-GPU training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Data parallelism: Replicating your model on each GPU\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Model parallelism: Splitting your model across multiple GPUs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Distributed training in practice\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Getting your hands on two or more GPUs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Using data parallelism with JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Using model parallelism with JAX\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### The DeviceMesh API\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"###### The LayoutMap API\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### TPU training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Using step fusing to improve TPU utilization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Speeding up training and inference with lower-precision computation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Understanding floating-point precision\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Float16 inference\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Mixed-precision training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Using loss scaling with mixed precision\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"##### Beyond mixed precision: float8 training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Faster inference with quantization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import ops\\n\",\n    \"\\n\",\n    \"x = ops.array([[0.1, 0.9], [1.2, -0.8]])\\n\",\n    \"kernel = ops.array([[-0.1, -2.2], [1.1, 0.7]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def abs_max_quantize(value):\\n\",\n    \"    abs_max = ops.max(ops.abs(value), keepdims=True)\\n\",\n    \"    scale = ops.divide(127, abs_max + 1e-7)\\n\",\n    \"    scaled_value = value * scale\\n\",\n    \"    scaled_value = ops.clip(ops.round(scaled_value), -127, 127)\\n\",\n    \"    scaled_value = ops.cast(scaled_value, dtype=\\\"int8\\\")\\n\",\n    \"    return scaled_value, scale\\n\",\n    \"\\n\",\n    \"int_x, x_scale = abs_max_quantize(x)\\n\",\n    \"int_kernel, kernel_scale = abs_max_quantize(kernel)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"int_y = ops.matmul(int_x, int_kernel)\\n\",\n    \"y = ops.cast(int_y, dtype=\\\"float32\\\") / (x_scale * kernel_scale)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"ops.matmul(x, kernel)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter18_best-practices-for-the-real-world\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.10.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "first_edition/2.1-a-first-look-at-a-neural-network.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# A first look at a neural network\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 2, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"We will now take a look at a first concrete example of a neural network, which makes use of the Python library Keras to learn to classify \\n\",\n    \"hand-written digits. Unless you already have experience with Keras or similar libraries, you will not understand everything about this \\n\",\n    \"first example right away. You probably haven't even installed Keras yet. Don't worry, that is perfectly fine. In the next chapter, we will \\n\",\n    \"review each element in our example and explain them in detail. So don't worry if some steps seem arbitrary or look like magic to you! \\n\",\n    \"We've got to start somewhere.\\n\",\n    \"\\n\",\n    \"The problem we are trying to solve here is to classify grayscale images of handwritten digits (28 pixels by 28 pixels), into their 10 \\n\",\n    \"categories (0 to 9). The dataset we will use is the MNIST dataset, a classic dataset in the machine learning community, which has been \\n\",\n    \"around for almost as long as the field itself and has been very intensively studied. It's a set of 60,000 training images, plus 10,000 test \\n\",\n    \"images, assembled by the National Institute of Standards and Technology (the NIST in MNIST) in the 1980s. You can think of \\\"solving\\\" MNIST \\n\",\n    \"as the \\\"Hello World\\\" of deep learning -- it's what you do to verify that your algorithms are working as expected. As you become a machine \\n\",\n    \"learning practitioner, you will see MNIST come up over and over again, in scientific papers, blog posts, and so on.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The MNIST dataset comes pre-loaded in Keras, in the form of a set of four Numpy arrays:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`train_images` and `train_labels` form the \\\"training set\\\", the data that the model will learn from. The model will then be tested on the \\n\",\n    \"\\\"test set\\\", `test_images` and `test_labels`. Our images are encoded as Numpy arrays, and the labels are simply an array of digits, ranging \\n\",\n    \"from 0 to 9. There is a one-to-one correspondence between the images and the labels.\\n\",\n    \"\\n\",\n    \"Let's have a look at the training data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(60000, 28, 28)\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_images.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"60000\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(train_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's have a look at the test data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(10000, 28, 28)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_images.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"10000\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([7, 2, 1, ..., 4, 5, 6], dtype=uint8)\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Our workflow will be as follow: first we will present our neural network with the training data, `train_images` and `train_labels`. The \\n\",\n    \"network will then learn to associate images and labels. Finally, we will ask the network to produce predictions for `test_images`, and we \\n\",\n    \"will verify if these predictions match the labels from `test_labels`.\\n\",\n    \"\\n\",\n    \"Let's build our network -- again, remember that you aren't supposed to understand everything about this example just yet.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"network = models.Sequential()\\n\",\n    \"network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,)))\\n\",\n    \"network.add(layers.Dense(10, activation='softmax'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The core building block of neural networks is the \\\"layer\\\", a data-processing module which you can conceive as a \\\"filter\\\" for data. Some \\n\",\n    \"data comes in, and comes out in a more useful form. Precisely, layers extract _representations_ out of the data fed into them -- hopefully \\n\",\n    \"representations that are more meaningful for the problem at hand. Most of deep learning really consists of chaining together simple layers \\n\",\n    \"which will implement a form of progressive \\\"data distillation\\\". A deep learning model is like a sieve for data processing, made of a \\n\",\n    \"succession of increasingly refined data filters -- the \\\"layers\\\".\\n\",\n    \"\\n\",\n    \"Here our network consists of a sequence of two `Dense` layers, which are densely-connected (also called \\\"fully-connected\\\") neural layers. \\n\",\n    \"The second (and last) layer is a 10-way \\\"softmax\\\" layer, which means it will return an array of 10 probability scores (summing to 1). Each \\n\",\n    \"score will be the probability that the current digit image belongs to one of our 10 digit classes.\\n\",\n    \"\\n\",\n    \"To make our network ready for training, we need to pick three more things, as part of \\\"compilation\\\" step:\\n\",\n    \"\\n\",\n    \"* A loss function: the is how the network will be able to measure how good a job it is doing on its training data, and thus how it will be \\n\",\n    \"able to steer itself in the right direction.\\n\",\n    \"* An optimizer: this is the mechanism through which the network will update itself based on the data it sees and its loss function.\\n\",\n    \"* Metrics to monitor during training and testing. Here we will only care about accuracy (the fraction of the images that were correctly \\n\",\n    \"classified).\\n\",\n    \"\\n\",\n    \"The exact purpose of the loss function and the optimizer will be made clear throughout the next two chapters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"network.compile(optimizer='rmsprop',\\n\",\n    \"                loss='categorical_crossentropy',\\n\",\n    \"                metrics=['accuracy'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Before training, we will preprocess our data by reshaping it into the shape that the network expects, and scaling it so that all values are in \\n\",\n    \"the `[0, 1]` interval. Previously, our training images for instance were stored in an array of shape `(60000, 28, 28)` of type `uint8` with \\n\",\n    \"values in the `[0, 255]` interval. We transform it into a `float32` array of shape `(60000, 28 * 28)` with values between 0 and 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype('float32') / 255\\n\",\n    \"\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28))\\n\",\n    \"test_images = test_images.astype('float32') / 255\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We also need to categorically encode the labels, a step which we explain in chapter 3:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.utils import to_categorical\\n\",\n    \"\\n\",\n    \"train_labels = to_categorical(train_labels)\\n\",\n    \"test_labels = to_categorical(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are now ready to train our network, which in Keras is done via a call to the `fit` method of the network: \\n\",\n    \"we \\\"fit\\\" the model to its training 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      \"Epoch 1/5\\n\",\n      \"60000/60000 [==============================] - 2s - loss: 0.2577 - acc: 0.9245     \\n\",\n      \"Epoch 2/5\\n\",\n      \"60000/60000 [==============================] - 1s - loss: 0.1042 - acc: 0.9690     \\n\",\n      \"Epoch 3/5\\n\",\n      \"60000/60000 [==============================] - 1s - loss: 0.0687 - acc: 0.9793     \\n\",\n      \"Epoch 4/5\\n\",\n      \"60000/60000 [==============================] - 1s - loss: 0.0508 - acc: 0.9848     \\n\",\n      \"Epoch 5/5\\n\",\n      \"60000/60000 [==============================] - 1s - loss: 0.0382 - acc: 0.9890     \\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fce5fed5fd0>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"network.fit(train_images, train_labels, epochs=5, batch_size=128)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Two quantities are being displayed during training: the \\\"loss\\\" of the network over the training data, and the accuracy of the network over \\n\",\n    \"the training data.\\n\",\n    \"\\n\",\n    \"We quickly reach an accuracy of 0.989 (i.e. 98.9%) on the training data. Now let's check that our model performs well on the test set too:\"\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      \" 9536/10000 [===========================>..] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_loss, test_acc = network.evaluate(test_images, test_labels)\"\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      \"test_acc: 0.9777\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('test_acc:', test_acc)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Our test set accuracy turns out to be 97.8% -- that's quite a bit lower than the training set accuracy. \\n\",\n    \"This gap between training accuracy and test accuracy is an example of \\\"overfitting\\\", \\n\",\n    \"the fact that machine learning models tend to perform worse on new data than on their training data. \\n\",\n    \"Overfitting will be a central topic in chapter 3.\\n\",\n    \"\\n\",\n    \"This concludes our very first example -- you just saw how we could build and a train a neural network to classify handwritten digits, in \\n\",\n    \"less than 20 lines of Python code. In the next chapter, we will go in detail over every moving piece we just previewed, and clarify what is really \\n\",\n    \"going on behind the scenes. You will learn about \\\"tensors\\\", the data-storing objects going into the network, about tensor operations, which \\n\",\n    \"layers are made of, and about gradient descent, which allows our network to learn from its training examples.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/3.5-classifying-movie-reviews.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Classifying movie reviews: a binary classification example\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 3, Section 5 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Two-class classification, or binary classification, may be the most widely applied kind of machine learning problem. In this example, we \\n\",\n    \"will learn to classify movie reviews into \\\"positive\\\" reviews and \\\"negative\\\" reviews, just based on the text content of the reviews.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The IMDB dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We'll be working with \\\"IMDB dataset\\\", a set of 50,000 highly-polarized reviews from the Internet Movie Database. They are split into 25,000 \\n\",\n    \"reviews for training and 25,000 reviews for testing, each set consisting in 50% negative and 50% positive reviews.\\n\",\n    \"\\n\",\n    \"Why do we have these two separate training and test sets? You should never test a machine learning model on the same data that you used to \\n\",\n    \"train it! Just because a model performs well on its training data doesn't mean that it will perform well on data it has never seen, and \\n\",\n    \"what you actually care about is your model's performance on new data (since you already know the labels of your training data -- obviously \\n\",\n    \"you don't need your model to predict those). For instance, it is possible that your model could end up merely _memorizing_ a mapping between \\n\",\n    \"your training samples and their targets -- which would be completely useless for the task of predicting targets for data never seen before. \\n\",\n    \"We will go over this point in much more detail in the next chapter.\\n\",\n    \"\\n\",\n    \"Just like the MNIST dataset, the IMDB dataset comes packaged with Keras. It has already been preprocessed: the reviews (sequences of words) \\n\",\n    \"have been turned into sequences of integers, where each integer stands for a specific word in a dictionary.\\n\",\n    \"\\n\",\n    \"The following code will load the dataset (when you run it for the first time, about 80MB of data will be downloaded to your machine):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import imdb\\n\",\n    \"\\n\",\n    \"(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The argument `num_words=10000` means that we will only keep the top 10,000 most frequently occurring words in the training data. Rare words \\n\",\n    \"will be discarded. This allows us to work with vector data of manageable size.\\n\",\n    \"\\n\",\n    \"The variables `train_data` and `test_data` are lists of reviews, each review being a list of word indices (encoding a sequence of words). \\n\",\n    \"`train_labels` and `test_labels` are lists of 0s and 1s, where 0 stands for \\\"negative\\\" and 1 stands for \\\"positive\\\":\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1,\\n\",\n       \" 14,\\n\",\n       \" 22,\\n\",\n       \" 16,\\n\",\n       \" 43,\\n\",\n       \" 530,\\n\",\n       \" 973,\\n\",\n       \" 1622,\\n\",\n       \" 1385,\\n\",\n       \" 65,\\n\",\n       \" 458,\\n\",\n       \" 4468,\\n\",\n       \" 66,\\n\",\n       \" 3941,\\n\",\n       \" 4,\\n\",\n       \" 173,\\n\",\n       \" 36,\\n\",\n       \" 256,\\n\",\n       \" 5,\\n\",\n       \" 25,\\n\",\n       \" 100,\\n\",\n       \" 43,\\n\",\n       \" 838,\\n\",\n       \" 112,\\n\",\n       \" 50,\\n\",\n       \" 670,\\n\",\n       \" 2,\\n\",\n       \" 9,\\n\",\n       \" 35,\\n\",\n       \" 480,\\n\",\n       \" 284,\\n\",\n       \" 5,\\n\",\n       \" 150,\\n\",\n       \" 4,\\n\",\n       \" 172,\\n\",\n       \" 112,\\n\",\n       \" 167,\\n\",\n       \" 2,\\n\",\n       \" 336,\\n\",\n       \" 385,\\n\",\n       \" 39,\\n\",\n       \" 4,\\n\",\n       \" 172,\\n\",\n       \" 4536,\\n\",\n       \" 1111,\\n\",\n       \" 17,\\n\",\n       \" 546,\\n\",\n       \" 38,\\n\",\n       \" 13,\\n\",\n       \" 447,\\n\",\n       \" 4,\\n\",\n       \" 192,\\n\",\n       \" 50,\\n\",\n       \" 16,\\n\",\n       \" 6,\\n\",\n       \" 147,\\n\",\n       \" 2025,\\n\",\n       \" 19,\\n\",\n       \" 14,\\n\",\n       \" 22,\\n\",\n       \" 4,\\n\",\n       \" 1920,\\n\",\n       \" 4613,\\n\",\n       \" 469,\\n\",\n       \" 4,\\n\",\n       \" 22,\\n\",\n       \" 71,\\n\",\n       \" 87,\\n\",\n       \" 12,\\n\",\n       \" 16,\\n\",\n       \" 43,\\n\",\n       \" 530,\\n\",\n       \" 38,\\n\",\n       \" 76,\\n\",\n       \" 15,\\n\",\n       \" 13,\\n\",\n       \" 1247,\\n\",\n       \" 4,\\n\",\n       \" 22,\\n\",\n       \" 17,\\n\",\n       \" 515,\\n\",\n       \" 17,\\n\",\n       \" 12,\\n\",\n       \" 16,\\n\",\n       \" 626,\\n\",\n       \" 18,\\n\",\n       \" 2,\\n\",\n       \" 5,\\n\",\n       \" 62,\\n\",\n       \" 386,\\n\",\n       \" 12,\\n\",\n       \" 8,\\n\",\n       \" 316,\\n\",\n       \" 8,\\n\",\n       \" 106,\\n\",\n       \" 5,\\n\",\n       \" 4,\\n\",\n       \" 2223,\\n\",\n       \" 5244,\\n\",\n       \" 16,\\n\",\n       \" 480,\\n\",\n       \" 66,\\n\",\n       \" 3785,\\n\",\n       \" 33,\\n\",\n       \" 4,\\n\",\n       \" 130,\\n\",\n       \" 12,\\n\",\n       \" 16,\\n\",\n       \" 38,\\n\",\n       \" 619,\\n\",\n       \" 5,\\n\",\n       \" 25,\\n\",\n       \" 124,\\n\",\n       \" 51,\\n\",\n       \" 36,\\n\",\n       \" 135,\\n\",\n       \" 48,\\n\",\n       \" 25,\\n\",\n       \" 1415,\\n\",\n       \" 33,\\n\",\n       \" 6,\\n\",\n       \" 22,\\n\",\n       \" 12,\\n\",\n       \" 215,\\n\",\n       \" 28,\\n\",\n       \" 77,\\n\",\n       \" 52,\\n\",\n       \" 5,\\n\",\n       \" 14,\\n\",\n       \" 407,\\n\",\n       \" 16,\\n\",\n       \" 82,\\n\",\n       \" 2,\\n\",\n       \" 8,\\n\",\n       \" 4,\\n\",\n       \" 107,\\n\",\n       \" 117,\\n\",\n       \" 5952,\\n\",\n       \" 15,\\n\",\n       \" 256,\\n\",\n       \" 4,\\n\",\n       \" 2,\\n\",\n       \" 7,\\n\",\n       \" 3766,\\n\",\n       \" 5,\\n\",\n       \" 723,\\n\",\n       \" 36,\\n\",\n       \" 71,\\n\",\n       \" 43,\\n\",\n       \" 530,\\n\",\n       \" 476,\\n\",\n       \" 26,\\n\",\n       \" 400,\\n\",\n       \" 317,\\n\",\n       \" 46,\\n\",\n       \" 7,\\n\",\n       \" 4,\\n\",\n       \" 2,\\n\",\n       \" 1029,\\n\",\n       \" 13,\\n\",\n       \" 104,\\n\",\n       \" 88,\\n\",\n       \" 4,\\n\",\n       \" 381,\\n\",\n       \" 15,\\n\",\n       \" 297,\\n\",\n       \" 98,\\n\",\n       \" 32,\\n\",\n       \" 2071,\\n\",\n       \" 56,\\n\",\n       \" 26,\\n\",\n       \" 141,\\n\",\n       \" 6,\\n\",\n       \" 194,\\n\",\n       \" 7486,\\n\",\n       \" 18,\\n\",\n       \" 4,\\n\",\n       \" 226,\\n\",\n       \" 22,\\n\",\n       \" 21,\\n\",\n       \" 134,\\n\",\n       \" 476,\\n\",\n       \" 26,\\n\",\n       \" 480,\\n\",\n       \" 5,\\n\",\n       \" 144,\\n\",\n       \" 30,\\n\",\n       \" 5535,\\n\",\n       \" 18,\\n\",\n       \" 51,\\n\",\n       \" 36,\\n\",\n       \" 28,\\n\",\n       \" 224,\\n\",\n       \" 92,\\n\",\n       \" 25,\\n\",\n       \" 104,\\n\",\n       \" 4,\\n\",\n       \" 226,\\n\",\n       \" 65,\\n\",\n       \" 16,\\n\",\n       \" 38,\\n\",\n       \" 1334,\\n\",\n       \" 88,\\n\",\n       \" 12,\\n\",\n       \" 16,\\n\",\n       \" 283,\\n\",\n       \" 5,\\n\",\n       \" 16,\\n\",\n       \" 4472,\\n\",\n       \" 113,\\n\",\n       \" 103,\\n\",\n       \" 32,\\n\",\n       \" 15,\\n\",\n       \" 16,\\n\",\n       \" 5345,\\n\",\n       \" 19,\\n\",\n       \" 178,\\n\",\n       \" 32]\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_data[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"1\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_labels[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since we restricted ourselves to the top 10,000 most frequent words, no word index will exceed 10,000:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"9999\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"max([max(sequence) for sequence in train_data])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For kicks, here's how you can quickly decode one of these reviews back to English words:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# word_index is a dictionary mapping words to an integer index\\n\",\n    \"word_index = imdb.get_word_index()\\n\",\n    \"# We reverse it, mapping integer indices to words\\n\",\n    \"reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\\n\",\n    \"# We decode the review; note that our indices were offset by 3\\n\",\n    \"# because 0, 1 and 2 are reserved indices for \\\"padding\\\", \\\"start of sequence\\\", and \\\"unknown\\\".\\n\",\n    \"decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"\\\"? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\\\"\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"decoded_review\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Preparing the data\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We cannot feed lists of integers into a neural network. We have to turn our lists into tensors. There are two ways we could do that:\\n\",\n    \"\\n\",\n    \"* We could pad our lists so that they all have the same length, and turn them into an integer tensor of shape `(samples, word_indices)`, \\n\",\n    \"then use as first layer in our network a layer capable of handling such integer tensors (the `Embedding` layer, which we will cover in \\n\",\n    \"detail later in the book).\\n\",\n    \"* We could one-hot-encode our lists to turn them into vectors of 0s and 1s. Concretely, this would mean for instance turning the sequence \\n\",\n    \"`[3, 5]` into a 10,000-dimensional vector that would be all-zeros except for indices 3 and 5, which would be ones. Then we could use as \\n\",\n    \"first layer in our network a `Dense` layer, capable of handling floating point vector data.\\n\",\n    \"\\n\",\n    \"We will go with the latter solution. Let's vectorize our data, which we will do manually for maximum clarity:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def vectorize_sequences(sequences, dimension=10000):\\n\",\n    \"    # Create an all-zero matrix of shape (len(sequences), dimension)\\n\",\n    \"    results = np.zeros((len(sequences), dimension))\\n\",\n    \"    for i, sequence in enumerate(sequences):\\n\",\n    \"        results[i, sequence] = 1.  # set specific indices of results[i] to 1s\\n\",\n    \"    return results\\n\",\n    \"\\n\",\n    \"# Our vectorized training data\\n\",\n    \"x_train = vectorize_sequences(train_data)\\n\",\n    \"# Our vectorized test data\\n\",\n    \"x_test = vectorize_sequences(test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here's what our samples look like now:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 0.,  1.,  1., ...,  0.,  0.,  0.])\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"x_train[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We should also vectorize our labels, which is straightforward:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Our vectorized labels\\n\",\n    \"y_train = np.asarray(train_labels).astype('float32')\\n\",\n    \"y_test = np.asarray(test_labels).astype('float32')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now our data is ready to be fed into a neural network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Building our network\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Our input data is simply vectors, and our labels are scalars (1s and 0s): this is the easiest setup you will ever encounter. A type of \\n\",\n    \"network that performs well on such a problem would be a simple stack of fully-connected (`Dense`) layers with `relu` activations: `Dense(16, \\n\",\n    \"activation='relu')`\\n\",\n    \"\\n\",\n    \"The argument being passed to each `Dense` layer (16) is the number of \\\"hidden units\\\" of the layer. What's a hidden unit? It's a dimension \\n\",\n    \"in the representation space of the layer. You may remember from the previous chapter that each such `Dense` layer with a `relu` activation implements \\n\",\n    \"the following chain of tensor operations:\\n\",\n    \"\\n\",\n    \"`output = relu(dot(W, input) + b)`\\n\",\n    \"\\n\",\n    \"Having 16 hidden units means that the weight matrix `W` will have shape `(input_dimension, 16)`, i.e. the dot product with `W` will project the \\n\",\n    \"input data onto a 16-dimensional representation space (and then we would add the bias vector `b` and apply the `relu` operation). You can \\n\",\n    \"intuitively understand the dimensionality of your representation space as \\\"how much freedom you are allowing the network to have when \\n\",\n    \"learning internal representations\\\". Having more hidden units (a higher-dimensional representation space) allows your network to learn more \\n\",\n    \"complex representations, but it makes your network more computationally expensive and may lead to learning unwanted patterns (patterns that \\n\",\n    \"will improve performance on the training data but not on the test data).\\n\",\n    \"\\n\",\n    \"There are two key architecture decisions to be made about such stack of dense layers:\\n\",\n    \"\\n\",\n    \"* How many layers to use.\\n\",\n    \"* How many \\\"hidden units\\\" to chose for each layer.\\n\",\n    \"\\n\",\n    \"In the next chapter, you will learn formal principles to guide you in making these choices. \\n\",\n    \"For the time being, you will have to trust us with the following architecture choice: \\n\",\n    \"two intermediate layers with 16 hidden units each, \\n\",\n    \"and a third layer which will output the scalar prediction regarding the sentiment of the current review. \\n\",\n    \"The intermediate layers will use `relu` as their \\\"activation function\\\", \\n\",\n    \"and the final layer will use a sigmoid activation so as to output a probability \\n\",\n    \"(a score between 0 and 1, indicating how likely the sample is to have the target \\\"1\\\", i.e. how likely the review is to be positive). \\n\",\n    \"A `relu` (rectified linear unit) is a function meant to zero-out negative values, \\n\",\n    \"while a sigmoid \\\"squashes\\\" arbitrary values into the `[0, 1]` interval, thus outputting something that can be interpreted as a probability.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here's what our network looks like:\\n\",\n    \"\\n\",\n    \"![3-layer network](https://s3.amazonaws.com/book.keras.io/img/ch3/3_layer_network.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And here's the Keras implementation, very similar to the MNIST example you saw previously:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\\n\",\n    \"model.add(layers.Dense(16, activation='relu'))\\n\",\n    \"model.add(layers.Dense(1, activation='sigmoid'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Lastly, we need to pick a loss function and an optimizer. Since we are facing a binary classification problem and the output of our network \\n\",\n    \"is a probability (we end our network with a single-unit layer with a sigmoid activation), is it best to use the `binary_crossentropy` loss. \\n\",\n    \"It isn't the only viable choice: you could use, for instance, `mean_squared_error`. But crossentropy is usually the best choice when you \\n\",\n    \"are dealing with models that output probabilities. Crossentropy is a quantity from the field of Information Theory, that measures the \\\"distance\\\" \\n\",\n    \"between probability distributions, or in our case, between the ground-truth distribution and our predictions.\\n\",\n    \"\\n\",\n    \"Here's the step where we configure our model with the `rmsprop` optimizer and the `binary_crossentropy` loss function. Note that we will \\n\",\n    \"also monitor accuracy during training.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['accuracy'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are passing our optimizer, loss function and metrics as strings, which is possible because `rmsprop`, `binary_crossentropy` and \\n\",\n    \"`accuracy` are packaged as part of Keras. Sometimes you may want to configure the parameters of your optimizer, or pass a custom loss \\n\",\n    \"function or metric function. This former can be done by passing an optimizer class instance as the `optimizer` argument:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import optimizers\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=optimizers.RMSprop(lr=0.001),\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['accuracy'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The latter can be done by passing function objects as the `loss` or `metrics` arguments:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import losses\\n\",\n    \"from keras import metrics\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=optimizers.RMSprop(lr=0.001),\\n\",\n    \"              loss=losses.binary_crossentropy,\\n\",\n    \"              metrics=[metrics.binary_accuracy])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Validating our approach\\n\",\n    \"\\n\",\n    \"In order to monitor during training the accuracy of the model on data that it has never seen before, we will create a \\\"validation set\\\" by \\n\",\n    \"setting apart 10,000 samples from the original training data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_val = x_train[:10000]\\n\",\n    \"partial_x_train = x_train[10000:]\\n\",\n    \"\\n\",\n    \"y_val = y_train[:10000]\\n\",\n    \"partial_y_train = y_train[10000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"We will now train our model for 20 epochs (20 iterations over all samples in the `x_train` and `y_train` tensors), in mini-batches of 512 \\n\",\n    \"samples. At this same time we will monitor loss and accuracy on the 10,000 samples that we set apart. This is done by passing the \\n\",\n    \"validation data as the `validation_data` argument:\"\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      \"Train on 15000 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.5103 - acc: 0.7911 - val_loss: 0.4016 - val_acc: 0.8628\\n\",\n      \"Epoch 2/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.3110 - acc: 0.9031 - val_loss: 0.3085 - val_acc: 0.8870\\n\",\n      \"Epoch 3/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.2309 - acc: 0.9235 - val_loss: 0.2803 - val_acc: 0.8908\\n\",\n      \"Epoch 4/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.1795 - acc: 0.9428 - val_loss: 0.2735 - val_acc: 0.8893\\n\",\n      \"Epoch 5/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.1475 - acc: 0.9526 - val_loss: 0.2788 - val_acc: 0.8890\\n\",\n      \"Epoch 6/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.1185 - acc: 0.9638 - val_loss: 0.3330 - val_acc: 0.8764\\n\",\n      \"Epoch 7/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.1005 - acc: 0.9703 - val_loss: 0.3055 - val_acc: 0.8838\\n\",\n      \"Epoch 8/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0818 - acc: 0.9773 - val_loss: 0.3344 - val_acc: 0.8769\\n\",\n      \"Epoch 9/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0696 - acc: 0.9814 - val_loss: 0.3607 - val_acc: 0.8800\\n\",\n      \"Epoch 10/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0547 - acc: 0.9873 - val_loss: 0.3776 - val_acc: 0.8785\\n\",\n      \"Epoch 11/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0453 - acc: 0.9895 - val_loss: 0.4035 - val_acc: 0.8765\\n\",\n      \"Epoch 12/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0353 - acc: 0.9930 - val_loss: 0.4437 - val_acc: 0.8766\\n\",\n      \"Epoch 13/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0269 - acc: 0.9956 - val_loss: 0.4637 - val_acc: 0.8747\\n\",\n      \"Epoch 14/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0212 - acc: 0.9968 - val_loss: 0.4877 - val_acc: 0.8714\\n\",\n      \"Epoch 15/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0162 - acc: 0.9977 - val_loss: 0.6080 - val_acc: 0.8625\\n\",\n      \"Epoch 16/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0115 - acc: 0.9993 - val_loss: 0.5778 - val_acc: 0.8698\\n\",\n      \"Epoch 17/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0116 - acc: 0.9979 - val_loss: 0.5906 - val_acc: 0.8702\\n\",\n      \"Epoch 18/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0054 - acc: 0.9998 - val_loss: 0.6204 - val_acc: 0.8639\\n\",\n      \"Epoch 19/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0083 - acc: 0.9984 - val_loss: 0.6419 - val_acc: 0.8676\\n\",\n      \"Epoch 20/20\\n\",\n      \"15000/15000 [==============================] - 1s - loss: 0.0031 - acc: 0.9998 - val_loss: 0.6796 - val_acc: 0.8683\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"history = model.fit(partial_x_train,\\n\",\n    \"                    partial_y_train,\\n\",\n    \"                    epochs=20,\\n\",\n    \"                    batch_size=512,\\n\",\n    \"                    validation_data=(x_val, y_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"On CPU, this will take less than two seconds per epoch -- training is over in 20 seconds. At the end of every epoch, there is a slight pause \\n\",\n    \"as the model computes its loss and accuracy on the 10,000 samples of the validation data.\\n\",\n    \"\\n\",\n    \"Note that the call to `model.fit()` returns a `History` object. This object has a member `history`, which is a dictionary containing data \\n\",\n    \"about everything that happened during training. Let's take a look at it:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"dict_keys(['val_acc', 'acc', 'val_loss', 'loss'])\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"history_dict = history.history\\n\",\n    \"history_dict.keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It contains 4 entries: one per metric that was being monitored, during training and during validation. Let's use Matplotlib to plot the \\n\",\n    \"training and validation loss side by side, as well as the training and validation accuracy:\"\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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70Vqo7Gjq25CQGUFJJi6NChTJ8+fYdt06dPZ+jQoQm9/qc//SnPPvts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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f9e8b6b6588>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(1, len(acc) + 1)\\n\",\n    \"\\n\",\n    \"# \\\"bo\\\" is for \\\"blue dot\\\"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"# b is for \\\"solid blue line\\\"\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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CElCxERSUjJQkREElKyEBGRhJQsREQkISULERFJSMlCREQSUrIQEZGE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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f9e8b6b6080>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.clf()   # clear figure\\n\",\n    \"acc_values = history_dict['acc']\\n\",\n    \"val_acc_values = history_dict['val_acc']\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The dots are the training loss and accuracy, while the solid lines are the validation loss and accuracy. Note that your own results may vary \\n\",\n    \"slightly due to a different random initialization of your network.\\n\",\n    \"\\n\",\n    \"As you can see, the training loss decreases with every epoch and the training accuracy increases with every epoch. That's what you would \\n\",\n    \"expect when running gradient descent optimization -- the quantity you are trying to minimize should get lower with every iteration. But that \\n\",\n    \"isn't the case for the validation loss and accuracy: they seem to peak at the fourth epoch. This is an example of what we were warning \\n\",\n    \"against earlier: a model that performs better on the training data isn't necessarily a model that will do better on data it has never seen \\n\",\n    \"before. In precise terms, what you are seeing is \\\"overfitting\\\": after the second epoch, we are over-optimizing on the training data, and we \\n\",\n    \"ended up learning representations that are specific to the training data and do not generalize to data outside of the training set.\\n\",\n    \"\\n\",\n    \"In this case, to prevent overfitting, we could simply stop training after three epochs. In general, there is a range of techniques you can \\n\",\n    \"leverage to mitigate overfitting, which we will cover in the next chapter.\\n\",\n    \"\\n\",\n    \"Let's train a new network from scratch for four epochs, then evaluate it on our test data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/4\\n\",\n      \"25000/25000 [==============================] - 1s - loss: 0.4738 - acc: 0.8044     \\n\",\n      \"Epoch 2/4\\n\",\n      \"25000/25000 [==============================] - 1s - loss: 0.2660 - acc: 0.9076     \\n\",\n      \"Epoch 3/4\\n\",\n      \"25000/25000 [==============================] - 1s - loss: 0.2028 - acc: 0.9277     \\n\",\n      \"Epoch 4/4\\n\",\n      \"25000/25000 [==============================] - 1s - loss: 0.1700 - acc: 0.9397     \\n\",\n      \"24544/25000 [============================>.] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\\n\",\n    \"model.add(layers.Dense(16, activation='relu'))\\n\",\n    \"model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"\\n\",\n    \"model.fit(x_train, y_train, epochs=4, batch_size=512)\\n\",\n    \"results = model.evaluate(x_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[0.29184698499679568, 0.88495999999999997]\"\n      ]\n     },\n     \"execution_count\": 41,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Our fairly naive approach achieves an accuracy of 88%. With state-of-the-art approaches, one should be able to get close to 95%.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using a trained network to generate predictions on new data\\n\",\n    \"\\n\",\n    \"After having trained a network, you will want to use it in a practical setting. You can generate the likelihood of reviews being positive \\n\",\n    \"by using the `predict` method:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 0.91966152],\\n\",\n       \"       [ 0.86563045],\\n\",\n       \"       [ 0.99936908],\\n\",\n       \"       ..., \\n\",\n       \"       [ 0.45731062],\\n\",\n       \"       [ 0.0038014 ],\\n\",\n       \"       [ 0.79525089]], dtype=float32)\"\n      ]\n     },\n     \"execution_count\": 26,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.predict(x_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, the network is very confident for some samples (0.99 or more, or 0.01 or less) but less confident for others (0.6, 0.4). \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Further experiments\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"* We were using 2 hidden layers. Try to use 1 or 3 hidden layers and see how it affects validation and test accuracy.\\n\",\n    \"* Try to use layers with more hidden units or less hidden units: 32 units, 64 units...\\n\",\n    \"* Try to use the `mse` loss function instead of `binary_crossentropy`.\\n\",\n    \"* Try to use the `tanh` activation (an activation that was popular in the early days of neural networks) instead of `relu`.\\n\",\n    \"\\n\",\n    \"These experiments will help convince you that the architecture choices we have made are all fairly reasonable, although they can still be \\n\",\n    \"improved!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Conclusions\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Here's what you should take away from this example:\\n\",\n    \"\\n\",\n    \"* There's usually quite a bit of preprocessing you need to do on your raw data in order to be able to feed it -- as tensors -- into a neural \\n\",\n    \"network. In the case of sequences of words, they can be encoded as binary vectors -- but there are other encoding options too.\\n\",\n    \"* Stacks of `Dense` layers with `relu` activations can solve a wide range of problems (including sentiment classification), and you will \\n\",\n    \"likely use them frequently.\\n\",\n    \"* In a binary classification problem (two output classes), your network should end with a `Dense` layer with 1 unit and a `sigmoid` activation, \\n\",\n    \"i.e. the output of your network should be a scalar between 0 and 1, encoding a probability.\\n\",\n    \"* With such a scalar sigmoid output, on a binary classification problem, the loss function you should use is `binary_crossentropy`.\\n\",\n    \"* The `rmsprop` optimizer is generally a good enough choice of optimizer, whatever your problem. That's one less thing for you to worry \\n\",\n    \"about.\\n\",\n    \"* As they get better on their training data, neural networks eventually start _overfitting_ and end up obtaining increasingly worse results on data \\n\",\n    \"never-seen-before. Make sure to always monitor performance on data that is outside of the training set.\\n\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/3.6-classifying-newswires.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Classifying newswires: a multi-class classification example\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 3, Section 5 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"In the previous section we saw how to classify vector inputs into two mutually exclusive classes using a densely-connected neural network. \\n\",\n    \"But what happens when you have more than two classes? \\n\",\n    \"\\n\",\n    \"In this section, we will build a network to classify Reuters newswires into 46 different mutually-exclusive topics. Since we have many \\n\",\n    \"classes, this problem is an instance of \\\"multi-class classification\\\", and since each data point should be classified into only one \\n\",\n    \"category, the problem is more specifically an instance of \\\"single-label, multi-class classification\\\". If each data point could have \\n\",\n    \"belonged to multiple categories (in our case, topics) then we would be facing a \\\"multi-label, multi-class classification\\\" problem.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The Reuters dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We will be working with the _Reuters dataset_, a set of short newswires and their topics, published by Reuters in 1986. It's a very simple, \\n\",\n    \"widely used toy dataset for text classification. There are 46 different topics; some topics are more represented than others, but each \\n\",\n    \"topic has at least 10 examples in the training set.\\n\",\n    \"\\n\",\n    \"Like IMDB and MNIST, the Reuters dataset comes packaged as part of Keras. Let's take a look right away:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import reuters\\n\",\n    \"\\n\",\n    \"(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Like with the IMDB dataset, the argument `num_words=10000` restricts the data to the 10,000 most frequently occurring words found in the \\n\",\n    \"data.\\n\",\n    \"\\n\",\n    \"We have 8,982 training examples and 2,246 test examples:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"8982\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(train_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2246\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"len(test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As with the IMDB reviews, each example is a list of integers (word indices):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[1,\\n\",\n       \" 245,\\n\",\n       \" 273,\\n\",\n       \" 207,\\n\",\n       \" 156,\\n\",\n       \" 53,\\n\",\n       \" 74,\\n\",\n       \" 160,\\n\",\n       \" 26,\\n\",\n       \" 14,\\n\",\n       \" 46,\\n\",\n       \" 296,\\n\",\n       \" 26,\\n\",\n       \" 39,\\n\",\n       \" 74,\\n\",\n       \" 2979,\\n\",\n       \" 3554,\\n\",\n       \" 14,\\n\",\n       \" 46,\\n\",\n       \" 4689,\\n\",\n       \" 4329,\\n\",\n       \" 86,\\n\",\n       \" 61,\\n\",\n       \" 3499,\\n\",\n       \" 4795,\\n\",\n       \" 14,\\n\",\n       \" 61,\\n\",\n       \" 451,\\n\",\n       \" 4329,\\n\",\n       \" 17,\\n\",\n       \" 12]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_data[10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here's how you can decode it back to words, in case you are curious:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"word_index = reuters.get_word_index()\\n\",\n    \"reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\\n\",\n    \"# Note that our indices were offset by 3\\n\",\n    \"# because 0, 1 and 2 are reserved indices for \\\"padding\\\", \\\"start of sequence\\\", and \\\"unknown\\\".\\n\",\n    \"decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'? ? ? said as a result of its december acquisition of space co it expects earnings per share in 1987 of 1 15 to 1 30 dlrs per share up from 70 cts in 1986 the company said pretax net should rise to nine to 10 mln dlrs from six mln dlrs in 1986 and rental operation revenues to 19 to 22 mln dlrs from 12 5 mln dlrs it said cash flow per share this year should be 2 50 to three dlrs reuter 3'\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"decoded_newswire\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The label associated with an example is an integer between 0 and 45: a topic index.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_labels[10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Preparing the data\\n\",\n    \"\\n\",\n    \"We can vectorize the data with the exact same code as in our previous example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def vectorize_sequences(sequences, dimension=10000):\\n\",\n    \"    results = np.zeros((len(sequences), dimension))\\n\",\n    \"    for i, sequence in enumerate(sequences):\\n\",\n    \"        results[i, sequence] = 1.\\n\",\n    \"    return results\\n\",\n    \"\\n\",\n    \"# Our vectorized training data\\n\",\n    \"x_train = vectorize_sequences(train_data)\\n\",\n    \"# Our vectorized test data\\n\",\n    \"x_test = vectorize_sequences(test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"To vectorize the labels, there are two possibilities: we could just cast the label list as an integer tensor, or we could use a \\\"one-hot\\\" \\n\",\n    \"encoding. One-hot encoding is a widely used format for categorical data, also called \\\"categorical encoding\\\". \\n\",\n    \"For a more detailed explanation of one-hot encoding, you can refer to Chapter 6, Section 1. \\n\",\n    \"In our case, one-hot encoding of our labels consists in embedding each label as an all-zero vector with a 1 in the place of the label index, e.g.:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def to_one_hot(labels, dimension=46):\\n\",\n    \"    results = np.zeros((len(labels), dimension))\\n\",\n    \"    for i, label in enumerate(labels):\\n\",\n    \"        results[i, label] = 1.\\n\",\n    \"    return results\\n\",\n    \"\\n\",\n    \"# Our vectorized training labels\\n\",\n    \"one_hot_train_labels = to_one_hot(train_labels)\\n\",\n    \"# Our vectorized test labels\\n\",\n    \"one_hot_test_labels = to_one_hot(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that there is a built-in way to do this in Keras, which you have already seen in action in our MNIST example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.utils.np_utils import to_categorical\\n\",\n    \"\\n\",\n    \"one_hot_train_labels = to_categorical(train_labels)\\n\",\n    \"one_hot_test_labels = to_categorical(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Building our network\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"This topic classification problem looks very similar to our previous movie review classification problem: in both cases, we are trying to \\n\",\n    \"classify short snippets of text. There is however a new constraint here: the number of output classes has gone from 2 to 46, i.e. the \\n\",\n    \"dimensionality of the output space is much larger. \\n\",\n    \"\\n\",\n    \"In a stack of `Dense` layers like what we were using, each layer can only access information present in the output of the previous layer. \\n\",\n    \"If one layer drops some information relevant to the classification problem, this information can never be recovered by later layers: each \\n\",\n    \"layer can potentially become an \\\"information bottleneck\\\". In our previous example, we were using 16-dimensional intermediate layers, but a \\n\",\n    \"16-dimensional space may be too limited to learn to separate 46 different classes: such small layers may act as information bottlenecks, \\n\",\n    \"permanently dropping relevant information.\\n\",\n    \"\\n\",\n    \"For this reason we will use larger layers. Let's go with 64 units:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))\\n\",\n    \"model.add(layers.Dense(64, activation='relu'))\\n\",\n    \"model.add(layers.Dense(46, activation='softmax'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"There are two other things you should note about this architecture:\\n\",\n    \"\\n\",\n    \"* We are ending the network with a `Dense` layer of size 46. This means that for each input sample, our network will output a \\n\",\n    \"46-dimensional vector. Each entry in this vector (each dimension) will encode a different output class.\\n\",\n    \"* The last layer uses a `softmax` activation. You have already seen this pattern in the MNIST example. It means that the network will \\n\",\n    \"output a _probability distribution_ over the 46 different output classes, i.e. for every input sample, the network will produce a \\n\",\n    \"46-dimensional output vector where `output[i]` is the probability that the sample belongs to class `i`. The 46 scores will sum to 1.\\n\",\n    \"\\n\",\n    \"The best loss function to use in this case is `categorical_crossentropy`. It measures the distance between two probability distributions: \\n\",\n    \"in our case, between the probability distribution output by our network, and the true distribution of the labels. By minimizing the \\n\",\n    \"distance between these two distributions, we train our network to output something as close as possible to the true labels.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='categorical_crossentropy',\\n\",\n    \"              metrics=['accuracy'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Validating our approach\\n\",\n    \"\\n\",\n    \"Let's set apart 1,000 samples in our training data to use as a validation set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_val = x_train[:1000]\\n\",\n    \"partial_x_train = x_train[1000:]\\n\",\n    \"\\n\",\n    \"y_val = one_hot_train_labels[:1000]\\n\",\n    \"partial_y_train = one_hot_train_labels[1000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's train our network for 20 epochs:\"\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      \"Train on 7982 samples, validate on 1000 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"7982/7982 [==============================] - 1s - loss: 2.5241 - acc: 0.4952 - val_loss: 1.7263 - val_acc: 0.6100\\n\",\n      \"Epoch 2/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 1.4500 - acc: 0.6854 - val_loss: 1.3478 - val_acc: 0.7070\\n\",\n      \"Epoch 3/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 1.0979 - acc: 0.7643 - val_loss: 1.1736 - val_acc: 0.7460\\n\",\n      \"Epoch 4/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.8723 - acc: 0.8178 - val_loss: 1.0880 - val_acc: 0.7490\\n\",\n      \"Epoch 5/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.7045 - acc: 0.8477 - val_loss: 0.9822 - val_acc: 0.7760\\n\",\n      \"Epoch 6/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.5660 - acc: 0.8792 - val_loss: 0.9379 - val_acc: 0.8030\\n\",\n      \"Epoch 7/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.4569 - acc: 0.9037 - val_loss: 0.9039 - val_acc: 0.8050\\n\",\n      \"Epoch 8/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.3668 - acc: 0.9238 - val_loss: 0.9279 - val_acc: 0.7890\\n\",\n      \"Epoch 9/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.3000 - acc: 0.9326 - val_loss: 0.8835 - val_acc: 0.8070\\n\",\n      \"Epoch 10/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.2505 - acc: 0.9434 - val_loss: 0.8967 - val_acc: 0.8150\\n\",\n      \"Epoch 11/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.2155 - acc: 0.9473 - val_loss: 0.9080 - val_acc: 0.8110\\n\",\n      \"Epoch 12/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1853 - acc: 0.9506 - val_loss: 0.9025 - val_acc: 0.8140\\n\",\n      \"Epoch 13/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1680 - acc: 0.9524 - val_loss: 0.9268 - val_acc: 0.8100\\n\",\n      \"Epoch 14/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1512 - acc: 0.9562 - val_loss: 0.9500 - val_acc: 0.8130\\n\",\n      \"Epoch 15/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1371 - acc: 0.9559 - val_loss: 0.9621 - val_acc: 0.8090\\n\",\n      \"Epoch 16/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1306 - acc: 0.9553 - val_loss: 1.0152 - val_acc: 0.8050\\n\",\n      \"Epoch 17/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1210 - acc: 0.9575 - val_loss: 1.0262 - val_acc: 0.8010\\n\",\n      \"Epoch 18/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1185 - acc: 0.9570 - val_loss: 1.0354 - val_acc: 0.8040\\n\",\n      \"Epoch 19/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1128 - acc: 0.9598 - val_loss: 1.0841 - val_acc: 0.8010\\n\",\n      \"Epoch 20/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.1097 - acc: 0.9594 - val_loss: 1.0707 - val_acc: 0.8040\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"history = model.fit(partial_x_train,\\n\",\n    \"                    partial_y_train,\\n\",\n    \"                    epochs=20,\\n\",\n    \"                    batch_size=512,\\n\",\n    \"                    validation_data=(x_val, y_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's display its loss and accuracy curves:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Zs313jfpqPrFEQkY1dcAXV9Q7FBgyA6nqa15557MmTIEF588UVGjx7N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\\nEBGRJCUFERFJUlIQEZGk/w+dYzD20tOTDwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb6b8ab54a8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(1, len(loss) + 1)\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\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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s6kYGaDgd8DzYEH3f3XKfN7AQ8DnaIyY9392erW2RSTQr6qqAi/PL/8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\\nEZEEJQUREUn4/0ypeWoxAVOLAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb6b8ab51d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.clf()   # clear figure\\n\",\n    \"\\n\",\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It seems that the network starts overfitting after 8 epochs. Let's train a new network from scratch for 8 epochs, then let's evaluate it on \\n\",\n    \"the test set:\"\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      \"Train on 7982 samples, validate on 1000 samples\\n\",\n      \"Epoch 1/8\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 2.6118 - acc: 0.4667 - val_loss: 1.7207 - val_acc: 0.6360\\n\",\n      \"Epoch 2/8\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 1.3998 - acc: 0.7107 - val_loss: 1.2645 - val_acc: 0.7360\\n\",\n      \"Epoch 3/8\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 1.0343 - acc: 0.7839 - val_loss: 1.0994 - val_acc: 0.7700\\n\",\n      \"Epoch 4/8\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.8114 - acc: 0.8329 - val_loss: 1.0252 - val_acc: 0.7820\\n\",\n      \"Epoch 5/8\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.6466 - acc: 0.8628 - val_loss: 0.9536 - val_acc: 0.8070\\n\",\n      \"Epoch 6/8\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.5271 - acc: 0.8894 - val_loss: 0.9187 - val_acc: 0.8110\\n\",\n      \"Epoch 7/8\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.4193 - acc: 0.9126 - val_loss: 0.9051 - val_acc: 0.8120\\n\",\n      \"Epoch 8/8\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.3478 - acc: 0.9258 - val_loss: 0.8891 - val_acc: 0.8160\\n\",\n      \"1952/2246 [=========================>....] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))\\n\",\n    \"model.add(layers.Dense(64, activation='relu'))\\n\",\n    \"model.add(layers.Dense(46, activation='softmax'))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='categorical_crossentropy',\\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"model.fit(partial_x_train,\\n\",\n    \"          partial_y_train,\\n\",\n    \"          epochs=8,\\n\",\n    \"          batch_size=512,\\n\",\n    \"          validation_data=(x_val, y_val))\\n\",\n    \"results = model.evaluate(x_test, one_hot_test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[0.98764628548762257, 0.77693677651807869]\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Our approach reaches an accuracy of ~78%. With a balanced binary classification problem, the accuracy reached by a purely random classifier \\n\",\n    \"would be 50%, but in our case it is closer to 19%, so our results seem pretty good, at least when compared to a random baseline:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.18477292965271594\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import copy\\n\",\n    \"\\n\",\n    \"test_labels_copy = copy.copy(test_labels)\\n\",\n    \"np.random.shuffle(test_labels_copy)\\n\",\n    \"float(np.sum(np.array(test_labels) == np.array(test_labels_copy))) / len(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Generating predictions on new data\\n\",\n    \"\\n\",\n    \"We can verify that the `predict` method of our model instance returns a probability distribution over all 46 topics. Let's generate topic \\n\",\n    \"predictions for all of the test data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model.predict(x_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Each entry in `predictions` is a vector of length 46:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(46,)\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"predictions[0].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The coefficients in this vector sum to 1:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.99999994\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.sum(predictions[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The largest entry is the predicted class, i.e. the class with the highest probability:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"3\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.argmax(predictions[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A different way to handle the labels and the loss\\n\",\n    \"\\n\",\n    \"We mentioned earlier that another way to encode the labels would be to cast them as an integer tensor, like such:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_train = np.array(train_labels)\\n\",\n    \"y_test = np.array(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The only thing it would change is the choice of the loss function. Our previous loss, `categorical_crossentropy`, expects the labels to \\n\",\n    \"follow a categorical encoding. With integer labels, we should use `sparse_categorical_crossentropy`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['acc'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This new loss function is still mathematically the same as `categorical_crossentropy`; it just has a different interface.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## On the importance of having sufficiently large intermediate layers\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We mentioned earlier that since our final outputs were 46-dimensional, we should avoid intermediate layers with much less than 46 hidden \\n\",\n    \"units. Now let's try to see what happens when we introduce an information bottleneck by having intermediate layers significantly less than \\n\",\n    \"46-dimensional, e.g. 4-dimensional.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 7982 samples, validate on 1000 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 3.1620 - acc: 0.2295 - val_loss: 2.6750 - val_acc: 0.2740\\n\",\n      \"Epoch 2/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 2.2009 - acc: 0.3829 - val_loss: 1.7626 - val_acc: 0.5990\\n\",\n      \"Epoch 3/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 1.4490 - acc: 0.6486 - val_loss: 1.4738 - val_acc: 0.6390\\n\",\n      \"Epoch 4/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 1.2258 - acc: 0.6776 - val_loss: 1.3961 - val_acc: 0.6570\\n\",\n      \"Epoch 5/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 1.0886 - acc: 0.7032 - val_loss: 1.3727 - val_acc: 0.6700\\n\",\n      \"Epoch 6/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.9817 - acc: 0.7494 - val_loss: 1.3682 - val_acc: 0.6800\\n\",\n      \"Epoch 7/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.8937 - acc: 0.7757 - val_loss: 1.3587 - val_acc: 0.6810\\n\",\n      \"Epoch 8/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.8213 - acc: 0.7942 - val_loss: 1.3548 - val_acc: 0.6960\\n\",\n      \"Epoch 9/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.7595 - acc: 0.8088 - val_loss: 1.3883 - val_acc: 0.7050\\n\",\n      \"Epoch 10/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.7072 - acc: 0.8193 - val_loss: 1.4216 - val_acc: 0.7020\\n\",\n      \"Epoch 11/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.6642 - acc: 0.8254 - val_loss: 1.4405 - val_acc: 0.7020\\n\",\n      \"Epoch 12/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.6275 - acc: 0.8281 - val_loss: 1.4938 - val_acc: 0.7080\\n\",\n      \"Epoch 13/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.5915 - acc: 0.8353 - val_loss: 1.5301 - val_acc: 0.7110\\n\",\n      \"Epoch 14/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.5637 - acc: 0.8419 - val_loss: 1.5400 - val_acc: 0.7080\\n\",\n      \"Epoch 15/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.5389 - acc: 0.8523 - val_loss: 1.5826 - val_acc: 0.7090\\n\",\n      \"Epoch 16/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.5162 - acc: 0.8588 - val_loss: 1.6391 - val_acc: 0.7080\\n\",\n      \"Epoch 17/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.4950 - acc: 0.8623 - val_loss: 1.6469 - val_acc: 0.7060\\n\",\n      \"Epoch 18/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.4771 - acc: 0.8670 - val_loss: 1.7258 - val_acc: 0.6950\\n\",\n      \"Epoch 19/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.4562 - acc: 0.8718 - val_loss: 1.7667 - val_acc: 0.6930\\n\",\n      \"Epoch 20/20\\n\",\n      \"7982/7982 [==============================] - 0s - loss: 0.4428 - acc: 0.8742 - val_loss: 1.7785 - val_acc: 0.7060\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7f8ce7cdb9b0>\"\n      ]\n     },\n     \"execution_count\": 42,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))\\n\",\n    \"model.add(layers.Dense(4, activation='relu'))\\n\",\n    \"model.add(layers.Dense(46, activation='softmax'))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='categorical_crossentropy',\\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"model.fit(partial_x_train,\\n\",\n    \"          partial_y_train,\\n\",\n    \"          epochs=20,\\n\",\n    \"          batch_size=128,\\n\",\n    \"          validation_data=(x_val, y_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Our network now seems to peak at ~71% test accuracy, a 8% absolute drop. This drop is mostly due to the fact that we are now trying to \\n\",\n    \"compress a lot of information (enough information to recover the separation hyperplanes of 46 classes) into an intermediate space that is \\n\",\n    \"too low-dimensional. The network is able to cram _most_ of the necessary information into these 8-dimensional representations, but not all \\n\",\n    \"of it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Further experiments\\n\",\n    \"\\n\",\n    \"* Try using larger or smaller layers: 32 units, 128 units...\\n\",\n    \"* We were using two hidden layers. Now try to use a single hidden layer, or three hidden layers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Wrapping up\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Here's what you should take away from this example:\\n\",\n    \"\\n\",\n    \"* If you are trying to classify data points between N classes, your network should end with a `Dense` layer of size N.\\n\",\n    \"* In a single-label, multi-class classification problem, your network should end with a `softmax` activation, so that it will output a \\n\",\n    \"probability distribution over the N output classes.\\n\",\n    \"* _Categorical crossentropy_ is almost always the loss function you should use for such problems. It minimizes the distance between the \\n\",\n    \"probability distributions output by the network, and the true distribution of the targets.\\n\",\n    \"* There are two ways to handle labels in multi-class classification:\\n\",\n    \"    ** Encoding the labels via \\\"categorical encoding\\\" (also known as \\\"one-hot encoding\\\") and using `categorical_crossentropy` as your loss \\n\",\n    \"function.\\n\",\n    \"    ** Encoding the labels as integers and using the `sparse_categorical_crossentropy` loss function.\\n\",\n    \"* If you need to classify data into a large number of categories, then you should avoid creating information bottlenecks in your network by having \\n\",\n    \"intermediate layers that are too small.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/3.7-predicting-house-prices.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Predicting house prices: a regression example\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 3, Section 6 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"In our two previous examples, we were considering classification problems, where the goal was to predict a single discrete label of an \\n\",\n    \"input data point. Another common type of machine learning problem is \\\"regression\\\", which consists of predicting a continuous value instead \\n\",\n    \"of a discrete label. For instance, predicting the temperature tomorrow, given meteorological data, or predicting the time that a \\n\",\n    \"software project will take to complete, given its specifications.\\n\",\n    \"\\n\",\n    \"Do not mix up \\\"regression\\\" with the algorithm \\\"logistic regression\\\": confusingly, \\\"logistic regression\\\" is not a regression algorithm, \\n\",\n    \"it is a classification algorithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The Boston Housing Price dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We will be attempting to predict the median price of homes in a given Boston suburb in the mid-1970s, given a few data points about the \\n\",\n    \"suburb at the time, such as the crime rate, the local property tax rate, etc.\\n\",\n    \"\\n\",\n    \"The dataset we will be using has another interesting difference from our two previous examples: it has very few data points, only 506 in \\n\",\n    \"total, split between 404 training samples and 102 test samples, and each \\\"feature\\\" in the input data (e.g. the crime rate is a feature) has \\n\",\n    \"a different scale. For instance some values are proportions, which take a values between 0 and 1, others take values between 1 and 12, \\n\",\n    \"others between 0 and 100...\\n\",\n    \"\\n\",\n    \"Let's take a look at the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import boston_housing\\n\",\n    \"\\n\",\n    \"(train_data, train_targets), (test_data, test_targets) =  boston_housing.load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(404, 13)\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(102, 13)\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"As you can see, we have 404 training samples and 102 test samples. The data comprises 13 features. The 13 features in the input data are as \\n\",\n    \"follow:\\n\",\n    \"\\n\",\n    \"1. Per capita crime rate.\\n\",\n    \"2. Proportion of residential land zoned for lots over 25,000 square feet.\\n\",\n    \"3. Proportion of non-retail business acres per town.\\n\",\n    \"4. Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).\\n\",\n    \"5. Nitric oxides concentration (parts per 10 million).\\n\",\n    \"6. Average number of rooms per dwelling.\\n\",\n    \"7. Proportion of owner-occupied units built prior to 1940.\\n\",\n    \"8. Weighted distances to five Boston employment centres.\\n\",\n    \"9. Index of accessibility to radial highways.\\n\",\n    \"10. Full-value property-tax rate per $10,000.\\n\",\n    \"11. Pupil-teacher ratio by town.\\n\",\n    \"12. 1000 * (Bk - 0.63) ** 2 where Bk is the proportion of Black people by town.\\n\",\n    \"13. % lower status of the population.\\n\",\n    \"\\n\",\n    \"The targets are the median values of owner-occupied homes, in thousands of dollars:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([ 15.2,  42.3,  50. ,  21.1,  17.7,  18.5,  11.3,  15.6,  15.6,\\n\",\n       \"        14.4,  12.1,  17.9,  23.1,  19.9,  15.7,   8.8,  50. ,  22.5,\\n\",\n       \"        24.1,  27.5,  10.9,  30.8,  32.9,  24. ,  18.5,  13.3,  22.9,\\n\",\n       \"        34.7,  16.6,  17.5,  22.3,  16.1,  14.9,  23.1,  34.9,  25. ,\\n\",\n       \"        13.9,  13.1,  20.4,  20. ,  15.2,  24.7,  22.2,  16.7,  12.7,\\n\",\n       \"        15.6,  18.4,  21. ,  30.1,  15.1,  18.7,   9.6,  31.5,  24.8,\\n\",\n       \"        19.1,  22. ,  14.5,  11. ,  32. ,  29.4,  20.3,  24.4,  14.6,\\n\",\n       \"        19.5,  14.1,  14.3,  15.6,  10.5,   6.3,  19.3,  19.3,  13.4,\\n\",\n       \"        36.4,  17.8,  13.5,  16.5,   8.3,  14.3,  16. ,  13.4,  28.6,\\n\",\n       \"        43.5,  20.2,  22. ,  23. ,  20.7,  12.5,  48.5,  14.6,  13.4,\\n\",\n       \"        23.7,  50. ,  21.7,  39.8,  38.7,  22.2,  34.9,  22.5,  31.1,\\n\",\n       \"        28.7,  46. ,  41.7,  21. ,  26.6,  15. ,  24.4,  13.3,  21.2,\\n\",\n       \"        11.7,  21.7,  19.4,  50. ,  22.8,  19.7,  24.7,  36.2,  14.2,\\n\",\n       \"        18.9,  18.3,  20.6,  24.6,  18.2,   8.7,  44. ,  10.4,  13.2,\\n\",\n       \"        21.2,  37. ,  30.7,  22.9,  20. ,  19.3,  31.7,  32. ,  23.1,\\n\",\n       \"        18.8,  10.9,  50. ,  19.6,   5. ,  14.4,  19.8,  13.8,  19.6,\\n\",\n       \"        23.9,  24.5,  25. ,  19.9,  17.2,  24.6,  13.5,  26.6,  21.4,\\n\",\n       \"        11.9,  22.6,  19.6,   8.5,  23.7,  23.1,  22.4,  20.5,  23.6,\\n\",\n       \"        18.4,  35.2,  23.1,  27.9,  20.6,  23.7,  28. ,  13.6,  27.1,\\n\",\n       \"        23.6,  20.6,  18.2,  21.7,  17.1,   8.4,  25.3,  13.8,  22.2,\\n\",\n       \"        18.4,  20.7,  31.6,  30.5,  20.3,   8.8,  19.2,  19.4,  23.1,\\n\",\n       \"        23. ,  14.8,  48.8,  22.6,  33.4,  21.1,  13.6,  32.2,  13.1,\\n\",\n       \"        23.4,  18.9,  23.9,  11.8,  23.3,  22.8,  19.6,  16.7,  13.4,\\n\",\n       \"        22.2,  20.4,  21.8,  26.4,  14.9,  24.1,  23.8,  12.3,  29.1,\\n\",\n       \"        21. ,  19.5,  23.3,  23.8,  17.8,  11.5,  21.7,  19.9,  25. ,\\n\",\n       \"        33.4,  28.5,  21.4,  24.3,  27.5,  33.1,  16.2,  23.3,  48.3,\\n\",\n       \"        22.9,  22.8,  13.1,  12.7,  22.6,  15. ,  15.3,  10.5,  24. ,\\n\",\n       \"        18.5,  21.7,  19.5,  33.2,  23.2,   5. ,  19.1,  12.7,  22.3,\\n\",\n       \"        10.2,  13.9,  16.3,  17. ,  20.1,  29.9,  17.2,  37.3,  45.4,\\n\",\n       \"        17.8,  23.2,  29. ,  22. ,  18. ,  17.4,  34.6,  20.1,  25. ,\\n\",\n       \"        15.6,  24.8,  28.2,  21.2,  21.4,  23.8,  31. ,  26.2,  17.4,\\n\",\n       \"        37.9,  17.5,  20. ,   8.3,  23.9,   8.4,  13.8,   7.2,  11.7,\\n\",\n       \"        17.1,  21.6,  50. ,  16.1,  20.4,  20.6,  21.4,  20.6,  36.5,\\n\",\n       \"         8.5,  24.8,  10.8,  21.9,  17.3,  18.9,  36.2,  14.9,  18.2,\\n\",\n       \"        33.3,  21.8,  19.7,  31.6,  24.8,  19.4,  22.8,   7.5,  44.8,\\n\",\n       \"        16.8,  18.7,  50. ,  50. ,  19.5,  20.1,  50. ,  17.2,  20.8,\\n\",\n       \"        19.3,  41.3,  20.4,  20.5,  13.8,  16.5,  23.9,  20.6,  31.5,\\n\",\n       \"        23.3,  16.8,  14. ,  33.8,  36.1,  12.8,  18.3,  18.7,  19.1,\\n\",\n       \"        29. ,  30.1,  50. ,  50. ,  22. ,  11.9,  37.6,  50. ,  22.7,\\n\",\n       \"        20.8,  23.5,  27.9,  50. ,  19.3,  23.9,  22.6,  15.2,  21.7,\\n\",\n       \"        19.2,  43.8,  20.3,  33.2,  19.9,  22.5,  32.7,  22. ,  17.1,\\n\",\n       \"        19. ,  15. ,  16.1,  25.1,  23.7,  28.7,  37.2,  22.6,  16.4,\\n\",\n       \"        25. ,  29.8,  22.1,  17.4,  18.1,  30.3,  17.5,  24.7,  12.6,\\n\",\n       \"        26.5,  28.7,  13.3,  10.4,  24.4,  23. ,  20. ,  17.8,   7. ,\\n\",\n       \"        11.8,  24.4,  13.8,  19.4,  25.2,  19.4,  19.4,  29.1])\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"train_targets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The prices are typically between \\\\$10,000 and \\\\$50,000. If that sounds cheap, remember this was the mid-1970s, and these prices are not \\n\",\n    \"inflation-adjusted.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Preparing the data\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"It would be problematic to feed into a neural network values that all take wildly different ranges. The network might be able to \\n\",\n    \"automatically adapt to such heterogeneous data, but it would definitely make learning more difficult. A widespread best practice to deal \\n\",\n    \"with such data is to do feature-wise normalization: for each feature in the input data (a column in the input data matrix), we \\n\",\n    \"will subtract the mean of the feature and divide by the standard deviation, so that the feature will be centered around 0 and will have a \\n\",\n    \"unit standard deviation. This is easily done in Numpy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean = train_data.mean(axis=0)\\n\",\n    \"train_data -= mean\\n\",\n    \"std = train_data.std(axis=0)\\n\",\n    \"train_data /= std\\n\",\n    \"\\n\",\n    \"test_data -= mean\\n\",\n    \"test_data /= std\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Note that the quantities that we use for normalizing the test data have been computed using the training data. We should never use in our \\n\",\n    \"workflow any quantity computed on the test data, even for something as simple as data normalization.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Building our network\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Because so few samples are available, we will be using a very small network with two \\n\",\n    \"hidden layers, each with 64 units. In general, the less training data you have, the worse overfitting will be, and using \\n\",\n    \"a small network is one way to mitigate overfitting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"def build_model():\\n\",\n    \"    # Because we will need to instantiate\\n\",\n    \"    # the same model multiple times,\\n\",\n    \"    # we use a function to construct it.\\n\",\n    \"    model = models.Sequential()\\n\",\n    \"    model.add(layers.Dense(64, activation='relu',\\n\",\n    \"                           input_shape=(train_data.shape[1],)))\\n\",\n    \"    model.add(layers.Dense(64, activation='relu'))\\n\",\n    \"    model.add(layers.Dense(1))\\n\",\n    \"    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Our network ends with a single unit, and no activation (i.e. it will be linear layer). \\n\",\n    \"This is a typical setup for scalar regression (i.e. regression where we are trying to predict a single continuous value). \\n\",\n    \"Applying an activation function would constrain the range that the output can take; for instance if \\n\",\n    \"we applied a `sigmoid` activation function to our last layer, the network could only learn to predict values between 0 and 1. Here, because \\n\",\n    \"the last layer is purely linear, the network is free to learn to predict values in any range.\\n\",\n    \"\\n\",\n    \"Note that we are compiling the network with the `mse` loss function -- Mean Squared Error, the square of the difference between the \\n\",\n    \"predictions and the targets, a widely used loss function for regression problems.\\n\",\n    \"\\n\",\n    \"We are also monitoring a new metric during training: `mae`. This stands for Mean Absolute Error. It is simply the absolute value of the \\n\",\n    \"difference between the predictions and the targets. For instance, a MAE of 0.5 on this problem would mean that our predictions are off by \\n\",\n    \"\\\\$500 on average.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Validating our approach using K-fold validation\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"To evaluate our network while we keep adjusting its parameters (such as the number of epochs used for training), we could simply split the \\n\",\n    \"data into a training set and a validation set, as we were doing in our previous examples. However, because we have so few data points, the \\n\",\n    \"validation set would end up being very small (e.g. about 100 examples). A consequence is that our validation scores may change a lot \\n\",\n    \"depending on _which_ data points we choose to use for validation and which we choose for training, i.e. the validation scores may have a \\n\",\n    \"high _variance_ with regard to the validation split. This would prevent us from reliably evaluating our model.\\n\",\n    \"\\n\",\n    \"The best practice in such situations is to use K-fold cross-validation. It consists of splitting the available data into K partitions \\n\",\n    \"(typically K=4 or 5), then instantiating K identical models, and training each one on K-1 partitions while evaluating on the remaining \\n\",\n    \"partition. The validation score for the model used would then be the average of the K validation scores obtained.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In terms of code, this is straightforward:\"\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      \"processing fold # 0\\n\",\n      \"processing fold # 1\\n\",\n      \"processing fold # 2\\n\",\n      \"processing fold # 3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"k = 4\\n\",\n    \"num_val_samples = len(train_data) // k\\n\",\n    \"num_epochs = 100\\n\",\n    \"all_scores = []\\n\",\n    \"for i in range(k):\\n\",\n    \"    print('processing fold #', i)\\n\",\n    \"    # Prepare the validation data: data from partition # k\\n\",\n    \"    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\\n\",\n    \"    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\\n\",\n    \"\\n\",\n    \"    # Prepare the training data: data from all other partitions\\n\",\n    \"    partial_train_data = np.concatenate(\\n\",\n    \"        [train_data[:i * num_val_samples],\\n\",\n    \"         train_data[(i + 1) * num_val_samples:]],\\n\",\n    \"        axis=0)\\n\",\n    \"    partial_train_targets = np.concatenate(\\n\",\n    \"        [train_targets[:i * num_val_samples],\\n\",\n    \"         train_targets[(i + 1) * num_val_samples:]],\\n\",\n    \"        axis=0)\\n\",\n    \"\\n\",\n    \"    # Build the Keras model (already compiled)\\n\",\n    \"    model = build_model()\\n\",\n    \"    # Train the model (in silent mode, verbose=0)\\n\",\n    \"    model.fit(partial_train_data, partial_train_targets,\\n\",\n    \"              epochs=num_epochs, batch_size=1, verbose=0)\\n\",\n    \"    # Evaluate the model on the validation data\\n\",\n    \"    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\\n\",\n    \"    all_scores.append(val_mae)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[2.0750808349930412, 2.117215852926273, 2.9140411863232605, 2.4288365227161068]\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"all_scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2.3837935992396706\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.mean(all_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"As you can notice, the different runs do indeed show rather different validation scores, from 2.1 to 2.9. Their average (2.4) is a much more \\n\",\n    \"reliable metric than any single of these scores -- that's the entire point of K-fold cross-validation. In this case, we are off by \\\\$2,400 on \\n\",\n    \"average, which is still significant considering that the prices range from \\\\$10,000 to \\\\$50,000. \\n\",\n    \"\\n\",\n    \"Let's try training the network for a bit longer: 500 epochs. To keep a record of how well the model did at each epoch, we will modify our training loop \\n\",\n    \"to save the per-epoch validation score log:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import backend as K\\n\",\n    \"\\n\",\n    \"# Some memory clean-up\\n\",\n    \"K.clear_session()\"\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      \"processing fold # 0\\n\",\n      \"processing fold # 1\\n\",\n      \"processing fold # 2\\n\",\n      \"processing fold # 3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"num_epochs = 500\\n\",\n    \"all_mae_histories = []\\n\",\n    \"for i in range(k):\\n\",\n    \"    print('processing fold #', i)\\n\",\n    \"    # Prepare the validation data: data from partition # k\\n\",\n    \"    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\\n\",\n    \"    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\\n\",\n    \"\\n\",\n    \"    # Prepare the training data: data from all other partitions\\n\",\n    \"    partial_train_data = np.concatenate(\\n\",\n    \"        [train_data[:i * num_val_samples],\\n\",\n    \"         train_data[(i + 1) * num_val_samples:]],\\n\",\n    \"        axis=0)\\n\",\n    \"    partial_train_targets = np.concatenate(\\n\",\n    \"        [train_targets[:i * num_val_samples],\\n\",\n    \"         train_targets[(i + 1) * num_val_samples:]],\\n\",\n    \"        axis=0)\\n\",\n    \"\\n\",\n    \"    # Build the Keras model (already compiled)\\n\",\n    \"    model = build_model()\\n\",\n    \"    # Train the model (in silent mode, verbose=0)\\n\",\n    \"    history = model.fit(partial_train_data, partial_train_targets,\\n\",\n    \"                        validation_data=(val_data, val_targets),\\n\",\n    \"                        epochs=num_epochs, batch_size=1, verbose=0)\\n\",\n    \"    mae_history = history.history['val_mean_absolute_error']\\n\",\n    \"    all_mae_histories.append(mae_history)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can then compute the average of the per-epoch MAE scores for all folds:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"average_mae_history = [\\n\",\n    \"    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot this:\"\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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2mdUEZAm4+UUiqORDWFxcA9IrJCRP4kIqO3983tYDIK6A0cJCL770gh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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5078ac7518>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Validation MAE')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"It may be a bit hard to see the plot due to scaling issues and relatively high variance. Let's:\\n\",\n    \"\\n\",\n    \"* Omit the first 10 data points, which are on a different scale from the rest of the curve.\\n\",\n    \"* Replace each point with an exponential moving average of the previous points, to obtain a smooth curve.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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6/y33/bRKXP63hqg4smpnCsvJaPj5Qzt4eO6+5M9Gky8/z7qap33S+P\\n3kZReaTGRXZafXiwBPJdm4FbVHUmsAS4UUTafSVS1btVdb6qzgduB97usBXr2e75nACW05g+y3PX\\nM7r+zIkAvLW7bVjoGT/7N199dF2naw64AeOUUXFMSo/lpS353PDYRu+Y+6aWVoq6mRX8j/VHWX7P\\nW3zPbfoZneR/s0hvRrlt8puOllHX1MK0zATOn5XZa9/AKaPimeE2i1yzZALLp2d4V8bdX+TUVK5+\\neA0/e2UXD769n6aWVgA2HyvnUzMzOPDTC73t8H0NGB1t7WJF2LzyOqZmxDEuOZr73tzLOx2G7nr8\\n6d2DADzzcS6vbM3nH+uPcun97/HvXYVkJEQyKS2Oo6V1bM+r5NTxyX0u2+cWjmVsktP/saugiobm\\nFtYeLOVYWR13XjKThKgw7yz9k13AAoaq5qvqRve4CtgJ9PSV6CrgyUCVx5j+UlXe2l1IQ3ML+W5n\\n70VzRzMpPZY/vnsAVaWusYWCynpW7+784XSwuIbwUGFsUjSVHWZJt7Yq972xl9N+8gbHyjqv0/Td\\nf27hQFGN93lPI3P6ytMm/95ep2mpq47u7vzsc3OYl5XkHd0zITWGEGnbGtab75VdPLsxl9rGZg4U\\n1zBrTAIhIcL3L5rBpfPHkBbX99FeX/Fp89+RX8ljHx1ut0RGXnk9oxOjKal2an9ffmRtl7/b+Ki2\\nJqa3dhdx69Nb2Hysgi3HKhibFM2sMW19BUu6GbHUk9S4SN74708iArsKKvnBC9v5wkPOcuifnJrO\\nuv85l99/aWGfXzcYBqVeIyLZwAJgTTfnY4AVwNM+yQq8LiIbRGRloMtoTHeaWlr59P3v8Y3HNvKV\\nP6/j3tf3sN4dFjsuKZobzprM9rxK3t1b3G6JhsoOzSQHi2rISokhLDSEbyw7hemZ8fzXuVO9eT9y\\nO1h/++Y+aho6LwcRKBnxTsDwtKlP6UPAmJeVxPM3LmXxJOeDNDIslAmpsezI7zwq6Ndv7OEbj21E\\nFWa7cz6WT8/gvisXnFB7/J2XzGTfTy4gNET48UvOKrq+ne955XWMSYrmjgunk+AGhY77f+RX1FFS\\n08g3l01m8cQUXtlWQKs6S6YAjE2O4Vz3239CVBinT+p7wACnuSk7NZZd+VW8sCnPmz4xLZbIsNCg\\nNTH1VcBLKSJxOIHgZlXtrpfvEuD9Ds1RZ6rqQuACnOass7p5/ZUisl5E1hcVdV3lNKY/dhdUseVY\\nBa+6C+794Z0D3nb51LhILl0whpTYCJ79OLddwNhytO1YVTlYXOOdJb1idiav3nyWd6hmaU0jjW6T\\nzd/WH+WsX6wGnAlfr2zNb1eet76zbEDvz9Mktft4FanuhLT+WDg+mTe6mL2dmRjlTTtzSv+X9RAR\\nwkJDvHMxTstOZl9hNTvzKympbqCkppGxSVFcc3o2a+44lxChXSBrbG7lf5/fTojAFTlZXLfUaV6c\\nmhHHL6+YxyempHHpvDGEh4bw/m3LefOWZf3qaJ6eGc+ugkoi3c7182dlBKXjuj8COkpKRMJxgsXj\\nqvpMD1mvpENzlKrmuj8LReRZYBHwTscLVfUh4CGAnJyc4bMXojlp+O6Y9sXFzlpGHqEhQmhIKIsn\\nprDuUKk7egViI8L41pMbef7GpUxIjeUzD7zP7uNVnDW1/QelZ+JdbnkdB4trEAFVZ0TVVQ995B3W\\n6XHTOVO6XbfoREWFh7ofZlVM7WbvjL646zOzeHlrPtc+srZd+tPfOIP73tzLrDGJPS6I2Fe3rpjO\\nsbJabjlvGot+8gbPbcr1zoRfNNGpEURHhJKdFsuj7x/kqkVZjE6M5rGPDrNqx3GuPX0CE9NiyU6N\\n4Y4Lp3PujAwmpcexxKc24emD6I/pmQm8ss350vHd86fx9UFazmMgBXKUlAAPAztV9d4e8iUCnwSe\\n90mLFZF4zzFwHhC4XVuM6cHmo+UkxYSz664V3HTOFG/6PVfM8x7nZKdwrKyOj4+UMWVUHFeelkV5\\nbRM/f3UXtY3NbHaDTsdv7yluwLjm4bVU1TfznfOmMT3T+dDuGCze/u6ydu8/kMa7y31cPK/reQ99\\nERMR5l380OOmc6YgItx87tQB7+D9xrLJ/OSzc0iJjWCZu4/2u3uLuWzhuHZLZ3x5yQQq65t53m0S\\n+mB/MRNSY/jhpbMBp8ay8qzJAZtBPdOnL2TxxBS/lm452QSyhrEUuAbYKiKb3LQ7gPEAqvqgm/ZZ\\n4HVVrfG5NgN41q2uhQFPqOqrASyrMd3afKycueOSiHKXCv/cwrEsnZzGZaeO8+bxDLfcfKyCs6el\\nc8eFMzhe1cC/Nufx4f62D/7lPosDAiT7LO0xJjGKS+ePYcmkVC77/QcAPPDFhdz4hDNRL5Azmr+3\\nYhqxkWF8bsG43jP7wTNX4a5LZ/GlxRM6LS8SKJ9ZMNY7U31xh3WWvrJ0Ig+9c4C3dxex4XAZb+ws\\n5MrTup6bEQjzs5K8xzOCMOluIAQsYKjqe0CvfyWq+ijwaIe0A8C8rvIbM5hqG5vZW1jNeT7fiu/9\\n/PxO+XyHhY5JiiYkRPjamRP51+Y878Y4q/7rLO8MZY+k2Lb5BO9872zCQkOIjXD+W45JjOKiuaOJ\\nCMthQmoMgXTKqHh+9YXO93Wi7rliHi9vLeDqJRMGtZ3+3BkZhIcKTS3KaV0szDdrbCKrdhz3Pvfs\\nGzIY0uPblhTp6+S/k8XQLLUxg2RnfiUtrcrccUk95kvxaWry7OUwLyuJp1Yu4Up3CGVXM3njI8OI\\nCg/h28uneEfKJMdG8OPPzOYTbsfwUBmj72tKRjw3DUB/SF9FhYey9o5zKatt7PL3PT8rqX3AmHhi\\no55O1H1Xzu9xdduTnQUMY3rg2XfCd0nv3lwwO9N7fOqEZC5bOI6rl4zvcuikiLDrrgs6pV+9ZMIJ\\nlNYA3qXHu3LZwnHeiZKhIcKYAejM7ouBmp0fLBYwjOmBZ+2nFD+Gmj52/WJKahrajWIKDw3hns9b\\n6+rJIjMxige+uJCxydHewQXGfxYwjOmBZ5ZwckzvmwQNxNwCE3gXze3/SLCRauiN6zImgB5YvY/L\\nf/8BFbVNVNQ1UVrTSFJM+JCZiWtMIFkNwxgfnvbteT963ZsW6BFKxgwV9rXJmF4Ud7OCrDEjjQUM\\nY1wtrerdMvXBqxd612yq6eMe1cYMV9YkZYwrv6KOllbl//vcHFbMHu3dBOmibrYJNWaksYBhjOtI\\nibNgnWddJWeOxIoB3XvCmKHMAoYxLs9+1r4bCA3kqqrGDHX21ckY1+7jVSREhTHKZ80fY0ybER8w\\nmlta+cf6o2w4XNp7ZjOs7T3u7Acx1Da1MWawjPiAERoi/OhfO3ju47zeM5th7XBJLZPSA7eEuDFD\\n3YjvwxARpmXGs7ug8x7EZmRQVVrdXe5GxUf1foExI9SIr2EATB8dz86CSu8wSjNyPL8pl4m3v8y+\\nwmpaWrXdngXGmPYCuUVrloisFpEdIrJdRG7qIs93RWST+9gmIi0ikuKeWyEiu0Vkn4jcFqhyAkzL\\nTKCqvtm7lLUZ/uqbWvjLB4e46SlnM8h/73J2abOAYUz3AlnDaAZuUdWZwBLgRhGZ6ZtBVe9W1fmq\\nOh+4HXhbVUtFJBR4ALgAmAlc1fHageQZFeNZytoMf3969wB3vrDd+/znr+4CLGAY05OABQxVzVfV\\nje5xFbAT6Gn3kKuAJ93jRcA+VT2gqo3AU8ClgSprTIQz1r6uyZaAGAlUlSfWHAEgrsNWmelxFjCM\\n6c6g9GGISDawAFjTzfkYYAXwtJs0Fjjqk+UYPQebfvEEjFpbM2hEOFpaR15FPT/89CzW3HEOkWFt\\n/w3SrIZhTLcCPkpKROJwAsHNqlrZTbZLgPdVtc+TIURkJbASYPz48SdUxuhw59dQZwFj2Fl3qJRZ\\nYxKIiWj7U//oYAkAp09OJTYyjH9/Zxk78yrZVVDZqcZhjGkT0BqGiITjBIvHVfWZHrJeSVtzFEAu\\nkOXzfJyb1omqPqSqOaqak56efkLljPY2STWf0PXm5HS4pIYrHvyQu17c0S59zYFSUmIjmOIuATI2\\nKZpzZ2bwreVTglFMY4aMQI6SEuBhYKeq3ttDvkTgk8DzPsnrgCkiMlFEInACyguBKqs1SQ1PW3Mr\\nANh7vLpd+kcHSliUnWIzuo3po0DWv5cC1wBbRWSTm3YHMB5AVR900z4LvK6qNZ4LVbVZRL4FvAaE\\nAo+oatuQlgHmrWFYwBhWNh8tByA+qu3PvLCqntzyOr565sRgFcuYIStgAUNV3wN6/Qqnqo8Cj3aR\\n/jLw8oAXrAvR4RYwhqNd7uz9ouq2HfOOldUBMDHNtl01pq9spjcQHhpCeKhQa8Nqh5Uid2vVvPK2\\nCZl55U7AGJMUHZQyGTOUWcBwRYeHWg1jmCl2axalNY2UVHuChwUMY06UBQxXdIQFjOGkpVUprWnk\\nvJkZALy4JR9wahvxkWEkRIUHs3jGDEkWMFwxEWHWJDWM/G71PloVPjEljXnjErn7td0cLqnhWFkd\\no5NsRVpjToQFDJfTJGXzMIaDj4+Ucc+qPYCzNtRvrlpAdUMzr20vYEdeBVMz4oNcQmOGJgsYrpiI\\nUFtLaph4cu0R73FCVDgTUmOZlBbLC5vzyKuoZ8H45CCWzpihywKGKzoilJoGCxjDwbpDZcwak8Bl\\nC8d5g8PiSSlsy3VWppmflRTM4hkzZHUbMETkez7HV3Q499NAFioY4qPCqG6wJqmhZnteBX/54JD3\\neVFVAweLa/j0vDHc8/l53kmZiyemevPMGZs42MU0ZljoqYZxpc/x7R3OrQhAWYIqMTqcyrqmYBfD\\n9NGNj2/kzhe2e7fY9fzsGBQWT0rxHkeEWcXamBPR0/8c6ea4q+dDXkJUOBUWMIacFndb3RX3vcPB\\n4hoOFjvrRk1Kj2uXb3RiNDeePZm/rVwy6GU0ZrjoaWkQ7ea4q+dDXkJ0OA3NrdQ3tRDlLhViTm6q\\nSllNExNSYzhcUssf3z1AVFgo0eGhZCR03tfiu+dPD0IpjRk+egoY80SkEqc2Ee0e4z4fdgPZE6Kd\\niVxV9c0WMIaIJ9cepbqhmVsvmM6aAyWs3lXI9Mx4stNibSVaYwKg24ChqiPqUzPBXdG0oq7J9nUe\\nIv764SEiwkK4aM5oKuuaeHFLPqowL8s6tY0JhD71/olIrIhcLSIvBapAweKpYVTWWz/GUFFR18Sl\\n88aQEhvhnYxXUFlPRsKwqwAbc1LoNWCISISIfFZE/gHkA+cAD/Zy2ZCT6AkY1vE9ZFTUNZEU4/y7\\neXbPAxhlNURjAqLbJikROQ+4CjgPWA38FThNVa8bpLINKs9idDZSamhoaG6htrGFpJgIALJSYogI\\nC6GxuZVR8VbDMCYQeqphvApMAs5U1atV9V9Aq78vLCJZIrJaRHaIyHYRuambfMtEZJOb522f9EMi\\nstU9t97f9z1RCdFO7Kyst8l7wfTqtgKyb3uJgor6HvN5ArunZhgaIkx2h9JaH5QxgdFTwFgIfAi8\\nISKrROR6nO1S/dUM3KKqM4ElwI0iMtM3g4gkAb8DPq2qs4ArOrzG2ao6X1Vz+vC+J8RTw7AmqcFX\\nUdeEuvMpnnDXgdpwuKzna2qdfydPkxS0NUtZwDAmMLoNGKq6SVVvU9XJwJ3AfCBcRF4RkZW9vbCq\\n5qvqRve4CtgJjO2Q7YvAM6p6xM1XeIL30W9R4aFEhoVYwBhkR0trmffD13lsjRMo4iKd7yT7Cqt7\\nvK7c/XdKio7wpk3LdDq+rdPbmMDwa5SUqn6gqt8GxgG/wqkx+E1EsoEFwJoOp6YCySLylohsEJEv\\n+74t8Lqb3muAGggJ0eE2SmqQfXigBIB39hRR39TC/sIaALbmlvd4XXkXNYyrF0/gwasXWg3DmADp\\nqdN7YTenioH7/X0DEYkDngZuVtXKDqfDgFNxRl5FAx+KyEequgen7yRXREYBq0Rkl6q+08XrrwRW\\nAowfP97fYnUpISrMOr0D5Ib/20BtUwu/vWoBLa3KL1/fzX9/aiprD5YCTl/Ej17cwe7jzlpQ7+8r\\nobaxmZiIrv9Ey2sbvdd5JMaEs2L26ADfiTEjV08zvdcD23ACBLRfP0qB5b29uIiE4wSLx1X1mS6y\\nHANKVLUGqBGRd4B5wB5VzQWnmUpEngUWAZ0Chqo+BDwEkJOT068lS5wFCK3Te6A1NLfw6vYCAP62\\n7ggbD5eIA6m2AAAgAElEQVTz6vYC4iLDeMndOvV4ZT17jzvNUBfMzuSVbQW8s6eYFbMzAWhtVX7/\\n9n6+uGg8ybERFLl7dKfGRXTxjsaYQOipSeq/gUqgDvgzcImqnu0+/AkWAjwM7FTVe7vJ9jxwpoiE\\niUgMsBjY6U4QjHdfJxZnaO82v+/qBFmTVGAUVjZ4j5/9OI/39znfQR565wB1TS1MTIslr7yOEIGL\\n547m3s/PB2Dv8SpaW53vAOsPl3H3a7v5/nNbAcgrryM5JrzbGogxZuD11On9a1U9E/g2kAW8KSJ/\\nF5H5fr72UuAaYLk7NHaTiFwoIjeIyA3ue+zEGb67BVgL/ElVtwEZwHsistlNf0lVXz3Rm/SXrVgb\\nGAWVzhDZ2WMT2JlfSVVDM9Huel0RoSF8cmo6ueV15FfWMzk9jugIZ/HAZzflMumOl9l0tJxit0bx\\n8tYC5v/odR776AijE6ODdk/GjES9fj1T1QMi8jxOH8M1OB3Vm/y47j38WAZdVe8G7u74njhNU4PK\\n9sQIDM+ciuXTRnl3vbtuaTa/e2s/Ta2tnHlKGo+6myCNT4kBYEJqrLd/468fHuJFt+kK2jq8rTnK\\nmMHV0457k0TkDhFZA/wQ2AzMUNW/D1rpBllCdBiV9c3eOQFmYBx3axjLZ2R4065a5AxQCA8J4dyZ\\nGdx35XwyEiKZ526fOsENHADPbMylsdmZM5qVEk3OhOR2r2uMGRw91TD24TQVPY/TlzEe+IZn2ege\\n+iWGrOSYCFpalcq6ZhJ9hmua3r2zp4j73tzLE19fTGRY+/mdBRX1RIWHMG9cIr/70kIWjk8mMzGK\\nlWdN4pzpowC4dP5YLp3fNk1n4YRk/rHhWKf3efd7y6msb2LuD17nwjk2IsqYwdRTwPgRbRslxfWQ\\nb9jITHQmfOVX1lnA6KMvP7IWgEPFtd4JdB5Hy2rJSo5BRNp9yN9x4YxuX+/K07Ioq23kpS35bM9z\\nmrHu/bzTSpkQFc6WH5xHnHV4GzOoetoP4weDWI6TwmhPwCivZ3pmQpBLM3Q0tbQtMXasrHPAOFJa\\n5+2b8JeI8M1lp5ASE8Ftz2xlbFI0n1s4znves5SLMWbw9Gk/jOHOM+omv5eF70x7nhFMAMfK6tqd\\nU1WOlNSQ1ceA4eHZm7um0ebHGBNsFjB8jIqPJEQgv6Ku98zGq6S60Xt8tLS23bnSmkZqGluYkHqi\\nASMWsBqFMScDawT2ERYaQkZCFHnlVsPoC98axhGfgNHSqvzv89sBmDH6xJr4UmMj+J+LZrBs2qj+\\nFdIY02+9BgwRiQQuA7J986vqjwJXrOAZFR/pXXbC+MdTw5gxOoGdBW3Lhf1zw1Fe2prP7RdMZ/HE\\nlBN6bRHha5+YNCDlNMb0jz9NUs8Dl+Lsb1Hj8xiW0uIiKaqygNEXJTXO7+vsaekcLa3zLgy48XA5\\nqbERrDxrEp7h2MaYocufJqlxqroi4CU5SaTHR7IltyLYxRhSSqobiQwLYcmkVH731n6251Wy9JQ0\\ndhVUMn10vAULY4YJf2oYH4jInICX5CSRFhdJaU0jLa0229tfRVUNpMVFMtnd8e5IaS0trcru41VM\\ny7DhycYMF/7UMM4EviIiB4EGnPWhVFXnBrRkQZIeH0lLq1JW20hanG3E05vaxmb+vbuQJRNTGRUf\\niQg893Eu45KjqW9qZfKo2GAX0RgzQPwJGBcEvBQnEU+QKK5usIDhh3f2FFNe28SXz5hAeGgIyTER\\nrDlYypqHnZnfY5JsRVljhotem6RU9TCQBFziPpLctGEpzV0BtbiqsZecBmB7XgWhIcLC8c6CgLUd\\nJth5Zs8bY4a+XgOGiNwEPA6Mch+Pici3A12wYPHsB11UbXMx/LEjr5LJ6bFEuftb1De1tjs/OsFq\\nGMYMF/40SV0PLHa3UUVEfg58CPw2kAULljQ3YFgNwz878ys5zWeOxfTMeHYVVHmfJ0Tb3FBjhgt/\\nRkkJ0OLzvAU/NkYSkSwRWS0iO0Rku1tT6SrfMnc3vu0i8rZP+goR2S0i+0TkNj/KOSDiI8OICAtp\\nN3vZdK2usYW8inpOSW9bzPixry3muRuXep/bkFpjhg9/vv79GVgjIs+6zz+Ds1d3b5qBW1R1o7s/\\n9wYRWaWqOzwZRCQJ+B2wQlWPiMgoNz0UeAD4FHAMWCciL/heGygiQrpN3vPLoRJn/mZ2WttIqLS4\\nSNLiInn5Pz9BdYMtGGjMcOLPFq33ishbOMNrAa5T1Y/9uC4fyHePq0RkJzAW8P3Q/yLwjKoecfMV\\nuumLgH3uVq2IyFM4s80DHjDAaZay5UF6d6jYCRgT0zoPnZ05xuZfGDPcdBswRCRBVStFJAU45D48\\n51JUtdTfNxGRbGABsKbDqalAuBuQ4oH7VPWvOIHlqE++Y8Bif9+vv9LjIsi1BQh7dbCLGoYxZvjq\\nqYbxBHAxsIG2nffAnbgH+LUinIjEAU8DN6tqZYfTYcCpwDlANPChiHzkX9G9r78SWAkwfvz4vlza\\nrfT4SDYdteVBenO0tI6U2AjiIq1j25iRoKcd9y52f0480RcXkXCcYPG4qj7TRZZjQIk7AqtGRN4B\\n5rnpWT75xgG53ZTzIeAhgJycnAFZz8NZHqSBllYlNMQ6bTv6cH8JM0cnUFBRZ/MsjBlB/JmH8aY/\\naV3kEZzO8Z2qem832Z4HzhSRMBGJwWl22gmsA6aIyEQRiQCuBF7o7T0HSlpcJK0KZbU2tLaj4uoG\\nrvrjR3zryY3kV9R7dyk0xgx/PfVhRAExQJqIJNM2lDYBp4+hN0uBa4CtIrLJTbsDGA+gqg+q6k4R\\neRXYArQCf1LVbe77fwt4DQgFHlHV7X29uRPlnbxXZcuDdLT+kNN19e7eYhKiwlh0gvtcGGOGnp4a\\nn/8DuBkYg9OP4QkYlcD9vb2wqr6HH/M1VPVu4O4u0l8GXu7t+kDwXU/KtPfRgbaxDpX1zVbDMGYE\\n6akP4z7gPhH5tqoOy1nd3fGuJ2UBo52ymkb+sf4ok9JjOVDkjJDKSrGAYcxI4c88jN+KyGxgJhDl\\nk/7XQBYsmHybpEyb9YfLqGls4eeXzeWP7xygsaWV82ZmBrtYxphB4s+e3ncCy3ACxss4y52/Bwzb\\ngBEXGUZkWAjF1dbp7et4pTM3JSs5hj9cc6ot+2HMCOPPWlKX48yTKFDV63CGvSYGtFRBJiKkxUVS\\nbDWMdgqrGhBxmuwsWBgz8vgTMOpUtRVoFpEEoJD2cySGpXRbHqSToqp6UmMjCQv158/GGDPc+DNF\\nd727SOAfcUZLVeMsbz6spcVFcqysNtjFOKkUVjYwKt6GGRszUvnT6f1N9/BBd85EgqpuCWyxgi89\\nPoJNR8uDXYyTyvGqekYlWMAwZqTqaeLewp7OqerGwBTp5JBuy4N0UljZwMzRtgqtMSNVTzWMe9yf\\nUUAOsBlnIt5cYD1wemCLFlxp8c7yINvzKpg7LinYxQm6llaluLqBjARbO8qYkarb3ktVPVtVz8bZ\\n02Khquao6qk4y5R3uRDgcLJkUioAv3x9T5BLcnIoqWmgVbE+DGNGMH+Gu0xT1a2eJ+5aTzMCV6ST\\nw9SMeFbMyiSvvC7YRTkpFFY6I8bS462GYcxI5c8oqS0i8ifgMff5l3AWCxz2kmPDqTjSFOxinBQK\\nq5xJe9bpbczI5U/AuA74BnCT+/wd4PcBK9FJJCE6nIraJlR1xE9U89QwrEnKmJHLn2G19cCv3MeI\\nkhgdTmNLK/VNrURHhAa7OEFVWOVpkrKAYcxI1dOw2r+r6udFZCvtt2gFQFXnBrRkJ4HE6HAAKuqa\\nRnzAqKxrIiYilMiwkf17MGYk66mG4WmCungwCnIy8g0YmSN8K9KaxmZibe9uY0a0nvbDyHd/Hj6R\\nFxaRLJwVbTNwaigPuXts+OZZhrNN60E36RlV/ZF77hBQBbQAzaqacyLl6A/fgDHSVTe0EGcBw5gR\\nracmqSq6aIrCmbynqtrblN9m4BZV3Sgi8cAGEVmlqjs65HtXVburxZytqsW9vE/AJEU7GylZwIDq\\n+iZiI605ypiRrKcaRnx/XtitoXhqKVUishNnL/COAeOkZTWMNjUNLcRGWA3DmJHM73WqRWSUiIz3\\nPPryJiKSjTNDfE0Xp08Xkc0i8oqIzPJJV+B1EdkgIiv78n4DxQJGm+qGZmuSMmaE82fHvU/jrCs1\\nBmcvjAnATmBWT9f5XB8HPA3crKqVHU5vBCaoarWIXAg8B0xxz52pqrkiMgpYJSK7VPWdLl5/JbAS\\nYPz4PsWxXsVHhSECFbUjc+e9sppGjlfVMy0j3jq9jTF+1TDuApYAe1R1Is7uex/58+IiEo4TLB5X\\n1Wc6nlfVSlWtdo9fBsJFJM19nuv+LASeBRZ19R6q+pC7zlVOenq6P8XyW0iIEB8ZNiJrGKrK1/66\\nnhW/fpeH3ztITYMFDGNGOn8CRpOqlgAhIhKiqqtxVq/tkThTox8Gdqrqvd3kyXTzISKL3PKUiEis\\n21GOiMQC5wHb/LqjAZYYEz4iA8bGI2VsOFwGwO/e2k9JTSNx1ultzIjmz1fGcrdZ6R3gcREpBGr8\\nuG4pcA2wVUQ2uWl3AOMBVPVBnP3CvyEizUAdcKWqqohkAM+6sSQMeEJVX+3DfQ2YxOiRGTCOlTmL\\nLn73/Gnc/dpuAOIiw4NZJGNMkPkTMC4F6oH/wll4MBH4UW8Xqep7OENwe8pzP3B/F+kHgHl+lC3g\\nRmrAKHKXAjl3RoY3YNiwWmNGtm6bpETkARFZqqo1qtqiqs2q+hdV/Y3bRDUiJEVHsPFIOesOlQa7\\nKIOqqKqBiLAQpmbEedNslJQxI1tPfRh7gF+KyCER+YWILBisQp1MPNuzXvHgh0EuyeAqqmogPS4S\\nEeHrn5jIgvFJnDE5LdjFMsYEUU8T9+4D7hORCcCVwCMiEg08CTypqiNiK7o9x6u8x8cr60fMFqWF\\nVQ3evS++f9HMIJfGGHMy6HWUlKoeVtWfq+oC4CrgMzjzMEaE/3fxTNLinCVCtudVBLk0g6ewqp70\\nOFvK3BjTpteAISJhInKJiDwOvALsBj4X8JKdJJaeksaz31wKQHHVyJjAl1dex97CamaO6W25MGPM\\nSNLT4oOfwqlRXAisBZ4CVqqqP0Nqh5U095t2UXVDkEsyOF7ZVoAqfHbB2GAXxRhzEulp2MvtwBM4\\nK86WDVJ5TkrREaHERYZRPEICRn55HTERoUxIjQ12UYwxJ5GeOr2XD2ZBTnZpcREUV4+MJqnyuiaS\\nom2SnjGmPb9Xqx3p0uIiKaqqD3YxBkV5bROJMRHBLoYx5iRjAcNPaXGRI6aGUVHXaDUMY0wnFjD8\\nlJ0Wy+GSGuqbWoJdlIArr20iOdYChjGmPQsYfsqZkExTi7L5aHmwixJw5XVNJEZbk5Qxpj0LGH46\\ndUIyAGsPltLc0hrk0gSOqlJe20hSjNUwjDHtWcDwU3JsBGOTorln1R4uuf/9YBcnYGobW2hqUevD\\nMMZ0YgGjDyalO/MSduZX0tg8PGsZpTVOx36yjZIyxnRgAaMPWlW9x1tzh2dfxtHSWgDGJUcHuSTG\\nmJNNwAKGiGSJyGoR2SEi20Xkpi7yLBORChHZ5D7+1+fcChHZLSL7ROS2QJWzLy6YPdp7vCO/qoec\\nQ9dhN2BkpcQEuSTGmJNNIHfEacZZVmSjuz/3BhFZpao7OuR7V1Uv9k0QkVDgAeBTwDFgnYi80MW1\\ng+pLi8dzwexMFv30TY5XDM9JfIdLagkPFcYkWQ3DGNNewGoYqpqvqhvd4yqcJdH9Xc1uEbBPVQ+o\\naiPOwoeXBqak/hMRUuMiyYiPJH8YBowjJbU8+PZ+xiRFezeOMsYYj0HpwxCRbGABsKaL06eLyGYR\\neUVEZrlpY4GjPnmO4X+wCbjMxCgKKuuCXYwB9/aeQgDOnjYqyCUxxpyMAh4wRCQOeBq4WVUrO5ze\\nCExQ1XnAb4HnTuD1V4rIehFZX1RU1P8C+2F0YvSwrGEUVjUQIs6mUcYY01FAA4aIhOMEi8dV9ZmO\\n51W1UlWr3eOXgXARSQNygSyfrOPctE5U9SFVzVHVnPT09AG/h65kJkaRX16P+oyaGg4KKxtIjYu0\\n5ihjTJcCOUpKgIeBnap6bzd5Mt18iMgitzwlwDpgiohMFJEInD3FXwhUWftqUnosdU0tHCsbXs1S\\nhVX1jIq3bVmNMV0LZA1jKXANsNxn2OyFInKDiNzg5rkc2CYim4HfAFeqoxn4FvAaTmf531V1ewDL\\n2iezxiQC8IlfrB5WmyoVVjVYwDDGdCtgw2pV9T2gx7YNVb0fuL+bcy8DLwegaP02PTPee7z2YCkX\\nzhndQ+6ho7CqgdluMDTGmI5spvcJiAoP5dwZzkiikmFSw2hpVUqqGxiVYDUMY0zXLGCcoIeuySEs\\nRIbNaKmS6gZaFWuSMsZ0ywLGCQoJETISooZNwCiscmpK6fFRQS6JMeZkZQGjH8YkRZFfMTxGShW6\\n+5Vbk5QxpjsWMPphdGL0sBhaW9fYNkTYmqSMMd0J5OKDw960zHhe2JxHZX0TCVFDd8Ohqx9ew4bD\\nZQCkW8AwxnTDahj9MGtMAgA78jqueDK0eIIFQGRYaBBLYow5mVnA6AfPBL7tQyxgHCur5fMPfshL\\nW/Jpcvcnv2juaB75Sk6QS2aMOZlZk1Q/pMdHkhIbwb7CobWZ0r2r9rD2UCkJ0WGclp0MwJJJqSyf\\nnhHkkhljTmZWw+inU9Lj2FdYHexi9MmaA6UAFFc3Ulzt7OGdFmt7eBtjemYBo58mjxpaAeNoaS25\\n5c6IqCOltTy/yVkEOMUChjGmFxYw+umUUXGU1TZRVDU0lgh5Yu0RRJztZktrGvnDOwcASI2z0VHG\\nmJ5ZwOineeOcju+Pj5T1kjP4Xtmaz+/f2s+nZmSwrMOueqlWwzDG9MI6vftp9thEIkJDeGZjLo0t\\nrVw8d0ywi9QlVeW+N/cyKT2We78wn2Z3dBTA5aeOIylm6M4jMcYMDqth9FNUeCjzs5J4dXsB33ri\\nY1paT85d+LblVrKroIqvnTmJuMgwkmLaahS/vGIe7j5WxhjTLathDIDzZ2ey9pAz8qiyronkk7B5\\n56Wt+YSGCBfMzvSm/etbZ9LY0hLEUhljhpJAbtGaJSKrRWSHiGwXkZt6yHuaiDSLyOU+aS0+O/Wd\\nNNuzduXyheO8xyU1jUEsSddUlVe25XPG5NR2wWzOuEROnZASxJIZY4aSQDZJNQO3qOpMYAlwo4jM\\n7JhJREKBnwOvdzhVp6rz3cenA1jOfkuMCeevX10EQFlt3wPGr9/Yw5KfvjnQxfLanlfJ4ZJaLhom\\nOwMaY4IjYAFDVfNVdaN7XIWzN/fYLrJ+G3gaKAxUWQaDZx5DSfWJBIy9FFTWU1HbNNDFAuCVbU5z\\n1HmzMnvPbIwx3RiUTm8RyQYWAGs6pI8FPgv8vovLokRkvYh8JCKfCXgh+8kTME6khhEb4Sz4tz2/\\nYkDL5PHKtgJOn5Rqk/OMMf0S8IAhInE4NYibVbXjKn2/Bm5V1dbOVzJBVXOALwK/FpHJ3bz+Sjew\\nrC8qKhrQsveF58O4tKaRPceruPWfW9oNXe1JdlosANtznV+PqrLmQAl1jf3vkG5pVQ4V17BgfFK/\\nX8sYM7IFNGCISDhOsHhcVZ/pIksO8JSIHAIuB37nqU2oaq778wDwFk4NpRNVfUhVc1Q1Jz09feBv\\nwk9R4aHERISyM7+Si37zLn9bf5SDxTV+XRsd7tQwXt6WD8DGI+V84aGPuOg37/a7XKU1jbQqpNlM\\nbmNMPwVylJQADwM7VfXervKo6kRVzVbVbOCfwDdV9TkRSRaRSPd10oClwI5AlXWgjE6M4sUt+TS1\\nOHMx/B0xVevWJD4+Us7hkhqOlDqB5oCfAacnxdXOkiUWMIwx/RXIGsZS4Bpguc/w2AtF5AYRuaGX\\na2cA60VkM7Aa+JmqnvQBY8qo+HbPj1fW+3VdbWMzp4yKA+CpdUe55e+bveda+zARsKCinvf2FrdL\\nawsY1n9hjOmfgE3cU9X3AL+nD6vqV3yOPwDmBKBYAZWR0P5bfEGFvwGjhU9MSeFISS2/f2t/u3Mf\\nHSwhJiKM+Vm990Fc8YcPOFpax667VhDlNnN5Rm2l2darxph+sqVBBlBiTPtv8QU91DAqapv42l/W\\nsfloObWNLSTFhDMtM75Tvi/+cQ2feeB978543bnn9d0cLXWWLd+R3za2wFvDiLWAYYzpHwsYA+g/\\nzprELZ+ayk3nTEGk5yapH/5rO2/sLOTFLXnUNDYTExHK7LGJ3eZfvavnaSq//fc+7/G23LbhuUVV\\nDYSHCgnRtgqMMaZ/7FNkAMVGhvHtc6YAzof2nuNdb6zU3NLKm24AqKpvRhViIsKYPTbKmyczIapd\\nDWVXQZXfE++2HmsLGLsKqpiUFmeLCxpj+s1qGAEye2wi+4uqqWlo7nRu09FyKuqcWd2HSpyRULGR\\nocwe49Qwbl0xnd9fvbDdNZ58XaltdN7jeyum8cmp6Wx1axiqyuZj5X71fxhjTG8sYATInLGJqDr9\\nCR/sK+bmpz5mz/Eqiqsb+Mf6YwBMTIvlcEkt4MzFmDUmgeuWZnPhnEzio9rvT3G4pJa39xTx2EeH\\n26U3tbRy2o/fACA9LpK54xLZW1hNfVMLtz29lfLaJuZmdd/UZYwx/rImqQCZ4+7Et/VYBX9ff5Rd\\nBVVMTIvjlW357CqoIiIshFljEnhxizNZLzYyjLDQEO68ZBYAhVVtzVFR4SHsyKvk2kfWAnDZwnFE\\nu8uJ7DleRY07jyM9PpL4qHBaWpVdBVW8vDWftLhILp5zcm7qZIwZWqyGESAZCVGkx0fy1p4idhVU\\nAZBfUec9bmxuJTSkrV9hdGJUu+sTfGoYSyenUdfUtkzIRwdLvMe+Hdzp8ZGMT4kB4IP9xVQ1NPOd\\n86aSaLvpGWMGgAWMAJozNpF39jjrW4UI5FfUExnW9iv3fLg/dv1iFoxPbnetb74vLRnvPQ4NETYe\\nbts/fKtPwMhMiGJscjQAv3h1N0CPI6+MMaYvLGAE0LJpztpW6fGRnDsjg7f3FNHQ7MynuPvyudx4\\n9im8ecsnOXNKWqdrRYRvLHPWW5w9NpGffnYOT359CZkJUeSW1XnzbcutZFF2Ch/evpzUuEgSotpa\\nGVNjI7qc22GMMSfC+jAC6JolE6hvamHRxFT+9O4Bb/r3VkzjipwsACanx3V7/a0rpnP9mRNJi4vk\\ni4udWsbYpGiOldfR0qrc/swWNh0t5/ozJzI60alZ+A6fffE/zyQ81L4TGGMGhgWMABIRVp7l1BJ8\\nF/87a4r/q+p2XDRwbHI0aw+W8vf1R/m7O9pqeje1CE8QMcaYgWABY5B85/xpXDR3NKdl928P7TFJ\\nzoS+TUfKAfjElDTOnj6qXZ7V31lGdX3n+R/GGNMfFjAGSVxkWL+DBUBWcgwtrcrGI2XMGJ3A/12/\\nuFOeie6GTMYYM5CsgXuImTvOmbW9t7C60+q4xhgTSBYwhphpmfHERToVw4z4qF5yG2PMwLGAMcSE\\nhggLJzhzNkZZDcMYM4gCuUVrloisFpEdIrJdRG7qIe9pItIsIpf7pF0rInvdx7WBKudQdLY7v6Ox\\nlz0yjDFmIAWy07sZuEVVN4pIPLBBRFZ13GpVREKBnwOv+6SlAHcCOYC6176gqmUYrlo0noKKer66\\ndGKwi2KMGUECVsNQ1XxV3egeVwE7gbFdZP028DTgu0PQ+cAqVS11g8QqYEWgyjrURIWHcvuFM8hI\\nsD4MY8zgGZQ+DBHJBhYAazqkjwU+C/y+wyVjgaM+z4/RdbAxxhgzSAIeMEQkDqcGcbOqVnY4/Wvg\\nVlU94cZ4EVkpIutFZH1RUVF/imqMMaYHAZ24JyLhOMHicVV9possOcBT7vpHacCFItIM5ALLfPKN\\nA97q6j1U9SHgIYCcnBwdqLIbY4xpL2ABQ5wo8DCwU1Xv7SqPqk70yf8o8KKqPud2ev9URDxrfp8H\\n3B6oshpjjOldIGsYS4FrgK0isslNuwMYD6CqD3Z3oaqWishdwDo36UeqWhrAshpjjOlFwAKGqr4H\\nSK8Z2/J/pcPzR4BHBrhYxhhjTpDN9DbGGOMXCxjGGGP8IqrDZ2CRiBQBh0/w8jSgeACLM1TYfY88\\nI/Xe7b67NkFV/drVbVgFjP4QkfWqmhPscgw2u++RZ6Teu913/1mTlDHGGL9YwDDGGOMXCxhtHgp2\\nAYLE7nvkGan3bvfdT9aHYYwxxi9WwzDGGOOXER8wRGSFiOwWkX0icluwyzPQROQRESkUkW0+aSki\\nssrdzXCVZ80ucfzG/V1sEZGFwSt5/3S34+Nwv3cRiRKRtSKy2b3vH7rpE0VkjXt/fxORCDc90n2+\\nzz2fHczy95eIhIrIxyLyovt82N+3iBwSka0isklE1rtpAfk7H9EBw93t7wHgAmAmcJWIzAxuqQbc\\no3TefOo24E1VnQK86T4H5/cwxX2spPM+JUOJZ8fHmcAS4Eb333a433sDsFxV5wHzgRUisgRnV8tf\\nqeopQBlwvZv/eqDMTf+Vm28ouwlnszaPkXLfZ6vqfJ/hs4H5O1fVEfsATgde83l+O3B7sMsVgPvM\\nBrb5PN8NjHaPRwO73eM/AFd1lW+oP4DngU+NpHsHYoCNwGKciVthbrr37x54DTjdPQ5z80mwy36C\\n9zvO/XBcDryIs5bdSLjvQ0Bah7SA/J2P6BoGI3dnvwxVzXePC4AM93hY/j467Pg47O/dbZbZhLPt\\n8SpgP1Cuqs1uFt978963e74CSB3cEg+YXwPfAzwbsqUyMu5bgddFZIOIrHTTAvJ3HtANlMzJT1VV\\nRIbtULmOOz66m3UBw/feVbUFmC8iScCzwPQgFyngRORioFBVN4jIsmCXZ5Cdqaq5IjIKWCUiu3xP\\nDuTf+UivYeQCWT7Px7lpw91xERkN4P4sdNOH1e+jmx0fR8S9A6hqObAapykmSUQ8XxB978173+75\\nREJP9RwAAAMbSURBVKBkkIs6EJYCnxaRQ8BTOM1S9zH87xtVzXV/FuJ8QVhEgP7OR3rAWAdMcUdS\\nRABXAi8EuUyD4QXgWvf4Wpz2fU/6l92RFEuACp9q7ZAi0u2Oj8P63kUk3a1ZICLROP02O3ECx+Vu\\nto737fl9XA78W93G7aFEVW9X1XGqmo3z//jfqvolhvl9i0isiMR7jnF2J91GoP7Og91hE+wHcCGw\\nB6ed9/vBLk8A7u9JIB9owmmvvB6nrfZNYC/wBpDi5hWcUWP7ga1ATrDL34/7PhOnbXcLsMl9XDjc\\n7x2YC3zs3vc24H/d9EnAWmAf8A8g0k2Pcp/vc89PCvY9DMDvYBnOds/D/r7d+9vsPrZ7PsMC9Xdu\\nM72NMcb4ZaQ3SRljjPGTBQxjjDF+sYBhjDHGLxYwjDHG+MUChjHGGL9YwDCmFyLS4q4E6nkM2KrG\\nIpItPisJG3Mys6VBjOldnarOD3YhjAk2q2EYc4LcfQh+4e5FsFZETnHTs0Xk3+5+A2+KyHg3PUNE\\nnnX3qtgsIme4LxUqIn9096943Z2hjYj8pzj7eWwRkaeCdJvGeFnAMKZ30R2apL7gc65CVecA9+Os\\nlgrwW+AvqjoXeBz4jZv+G+BtdfaqWIgzMxecvQkeUNVZQDlwmZt+G7DAfZ0bAnVzxvjLZnob0wsR\\nqVbVuC7SD+FsVnTAXeiwQFVTRaQYZ4+BJjc9X1XTRKQIGKeqDT6vkQ2sUmejG0TkViBcVX8sIq8C\\n1cBzwHOqWh3gWzWmR1bDMKZ/tJvjvmjwOW6hrW/xIpx1fxYC63xWXTUmKCxgGNM/X/D5+aF7/AHO\\niqkAXwLedY/fBL4B3k2OErt7UREJAbJUdTVwK87y251qOcYMJvvGYkzvot0d7DxeVVXP0NpkEdmC\\nU0u4yk37NvBnEfkuUARc56bfBDwkItfj1CS+gbOScFdCgcfcoCLAb9TZ38KYoLE+DGNOkNuHkaOq\\nxcEuizGDwZqkjDHG+MVqGMYYY/xiNQxjjDF+sYBhjDHGLxYwjDHG+MUChjHGGL9YwDDGGOMXCxjG\\nGGP88v8DYqN7v3gUkLQAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f5091305860>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def smooth_curve(points, factor=0.9):\\n\",\n    \"  smoothed_points = []\\n\",\n    \"  for point in points:\\n\",\n    \"    if smoothed_points:\\n\",\n    \"      previous = smoothed_points[-1]\\n\",\n    \"      smoothed_points.append(previous * factor + point * (1 - factor))\\n\",\n    \"    else:\\n\",\n    \"      smoothed_points.append(point)\\n\",\n    \"  return smoothed_points\\n\",\n    \"\\n\",\n    \"smooth_mae_history = smooth_curve(average_mae_history[10:])\\n\",\n    \"\\n\",\n    \"plt.plot(range(1, len(smooth_mae_history) + 1), smooth_mae_history)\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Validation MAE')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"According to this plot, it seems that validation MAE stops improving significantly after 80 epochs. Past that point, we start overfitting.\\n\",\n    \"\\n\",\n    \"Once we are done tuning other parameters of our model (besides the number of epochs, we could also adjust the size of the hidden layers), we \\n\",\n    \"can train a final \\\"production\\\" model on all of the training data, with the best parameters, then look at its performance on the test data:\"\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      \"\\r\",\n      \" 32/102 [========>.....................] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Get a fresh, compiled model.\\n\",\n    \"model = build_model()\\n\",\n    \"# Train it on the entirety of the data.\\n\",\n    \"model.fit(train_data, train_targets,\\n\",\n    \"          epochs=80, batch_size=16, verbose=0)\\n\",\n    \"test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"2.5532484335057877\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_mae_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are still off by about \\\\$2,550.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Wrapping up\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Here's what you should take away from this example:\\n\",\n    \"\\n\",\n    \"* Regression is done using different loss functions from classification; Mean Squared Error (MSE) is a commonly used loss function for \\n\",\n    \"regression.\\n\",\n    \"* Similarly, evaluation metrics to be used for regression differ from those used for classification; naturally the concept of \\\"accuracy\\\" \\n\",\n    \"does not apply for regression. A common regression metric is Mean Absolute Error (MAE).\\n\",\n    \"* When features in the input data have values in different ranges, each feature should be scaled independently as a preprocessing step.\\n\",\n    \"* When there is little data available, using K-Fold validation is a great way to reliably evaluate a model.\\n\",\n    \"* When little training data is available, it is preferable to use a small network with very few hidden layers (typically only one or two), \\n\",\n    \"in order to avoid severe overfitting.\\n\",\n    \"\\n\",\n    \"This example concludes our series of three introductory practical examples. You are now able to handle common types of problems with vector data input:\\n\",\n    \"\\n\",\n    \"* Binary (2-class) classification.\\n\",\n    \"* Multi-class, single-label classification.\\n\",\n    \"* Scalar regression.\\n\",\n    \"\\n\",\n    \"In the next chapter, you will acquire a more formal understanding of some of the concepts you have encountered in these first examples, \\n\",\n    \"such as data preprocessing, model evaluation, and overfitting.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/4.4-overfitting-and-underfitting.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Overfitting and underfitting\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 3, Section 6 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"In all the examples we saw in the previous chapter -- movie review sentiment prediction, topic classification, and house price regression -- \\n\",\n    \"we could notice that the performance of our model on the held-out validation data would always peak after a few epochs and would then start \\n\",\n    \"degrading, i.e. our model would quickly start to _overfit_ to the training data. Overfitting happens in every single machine learning \\n\",\n    \"problem. Learning how to deal with overfitting is essential to mastering machine learning.\\n\",\n    \"\\n\",\n    \"The fundamental issue in machine learning is the tension between optimization and generalization. \\\"Optimization\\\" refers to the process of \\n\",\n    \"adjusting a model to get the best performance possible on the training data (the \\\"learning\\\" in \\\"machine learning\\\"), while \\\"generalization\\\" \\n\",\n    \"refers to how well the trained model would perform on data it has never seen before. The goal of the game is to get good generalization, of \\n\",\n    \"course, but you do not control generalization; you can only adjust the model based on its training data.\\n\",\n    \"\\n\",\n    \"At the beginning of training, optimization and generalization are correlated: the lower your loss on training data, the lower your loss on \\n\",\n    \"test data. While this is happening, your model is said to be _under-fit_: there is still progress to be made; the network hasn't yet \\n\",\n    \"modeled all relevant patterns in the training data. But after a certain number of iterations on the training data, generalization stops \\n\",\n    \"improving, validation metrics stall then start degrading: the model is then starting to over-fit, i.e. is it starting to learn patterns \\n\",\n    \"that are specific to the training data but that are misleading or irrelevant when it comes to new data.\\n\",\n    \"\\n\",\n    \"To prevent a model from learning misleading or irrelevant patterns found in the training data, _the best solution is of course to get \\n\",\n    \"more training data_. A model trained on more data will naturally generalize better. When that is no longer possible, the next best solution \\n\",\n    \"is to modulate the quantity of information that your model is allowed to store, or to add constraints on what information it is allowed to \\n\",\n    \"store. If a network can only afford to memorize a small number of patterns, the optimization process will force it to focus on the most \\n\",\n    \"prominent patterns, which have a better chance of generalizing well.\\n\",\n    \"\\n\",\n    \"The processing of fighting overfitting in this way is called _regularization_. Let's review some of the most common regularization \\n\",\n    \"techniques, and let's apply them in practice to improve our movie classification model from  the previous chapter.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: in this notebook we will be using the IMDB test set as our validation set. It doesn't matter in this context.\\n\",\n    \"\\n\",\n    \"Let's prepare the data using the code from Chapter 3, Section 5:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import imdb\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)\\n\",\n    \"\\n\",\n    \"def vectorize_sequences(sequences, dimension=10000):\\n\",\n    \"    # Create an all-zero matrix of shape (len(sequences), dimension)\\n\",\n    \"    results = np.zeros((len(sequences), dimension))\\n\",\n    \"    for i, sequence in enumerate(sequences):\\n\",\n    \"        results[i, sequence] = 1.  # set specific indices of results[i] to 1s\\n\",\n    \"    return results\\n\",\n    \"\\n\",\n    \"# Our vectorized training data\\n\",\n    \"x_train = vectorize_sequences(train_data)\\n\",\n    \"# Our vectorized test data\\n\",\n    \"x_test = vectorize_sequences(test_data)\\n\",\n    \"# Our vectorized labels\\n\",\n    \"y_train = np.asarray(train_labels).astype('float32')\\n\",\n    \"y_test = np.asarray(test_labels).astype('float32')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Fighting overfitting\\n\",\n    \"\\n\",\n    \"## Reducing the network's size\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The simplest way to prevent overfitting is to reduce the size of the model, i.e. the number of learnable parameters in the model (which is \\n\",\n    \"determined by the number of layers and the number of units per layer). In deep learning, the number of learnable parameters in a model is \\n\",\n    \"often referred to as the model's \\\"capacity\\\". Intuitively, a model with more parameters will have more \\\"memorization capacity\\\" and therefore \\n\",\n    \"will be able to easily learn a perfect dictionary-like mapping between training samples and their targets, a mapping without any \\n\",\n    \"generalization power. For instance, a model with 500,000 binary parameters could easily be made to learn the class of every digits in the \\n\",\n    \"MNIST training set: we would only need 10 binary parameters for each of the 50,000 digits. Such a model would be useless for classifying \\n\",\n    \"new digit samples. Always keep this in mind: deep learning models tend to be good at fitting to the training data, but the real challenge \\n\",\n    \"is generalization, not fitting.\\n\",\n    \"\\n\",\n    \"On the other hand, if the network has limited memorization resources, it will not be able to learn this mapping as easily, and thus, in \\n\",\n    \"order to minimize its loss, it will have to resort to learning compressed representations that have predictive power regarding the targets \\n\",\n    \"-- precisely the type of representations that we are interested in. At the same time, keep in mind that you should be using models that have \\n\",\n    \"enough parameters that they won't be underfitting: your model shouldn't be starved for memorization resources. There is a compromise to be \\n\",\n    \"found between \\\"too much capacity\\\" and \\\"not enough capacity\\\".\\n\",\n    \"\\n\",\n    \"Unfortunately, there is no magical formula to determine what the right number of layers is, or what the right size for each layer is. You \\n\",\n    \"will have to evaluate an array of different architectures (on your validation set, not on your test set, of course) in order to find the \\n\",\n    \"right model size for your data. The general workflow to find an appropriate model size is to start with relatively few layers and \\n\",\n    \"parameters, and start increasing the size of the layers or adding new layers until you see diminishing returns with regard to the \\n\",\n    \"validation loss.\\n\",\n    \"\\n\",\n    \"Let's try this on our movie review classification network. Our original network was as such:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"original_model = models.Sequential()\\n\",\n    \"original_model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\\n\",\n    \"original_model.add(layers.Dense(16, activation='relu'))\\n\",\n    \"original_model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"original_model.compile(optimizer='rmsprop',\\n\",\n    \"                       loss='binary_crossentropy',\\n\",\n    \"                       metrics=['acc'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's try to replace it with this smaller network:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"smaller_model = models.Sequential()\\n\",\n    \"smaller_model.add(layers.Dense(4, activation='relu', input_shape=(10000,)))\\n\",\n    \"smaller_model.add(layers.Dense(4, activation='relu'))\\n\",\n    \"smaller_model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"smaller_model.compile(optimizer='rmsprop',\\n\",\n    \"                      loss='binary_crossentropy',\\n\",\n    \"                      metrics=['acc'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Here's a comparison of the validation losses of the original network and the smaller network. The dots are the validation loss values of \\n\",\n    \"the smaller network, and the crosses are the initial network (remember: a lower validation loss signals a better model).\"\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      \"Train on 25000 samples, validate on 25000 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.4594 - acc: 0.8188 - val_loss: 0.3429 - val_acc: 0.8812\\n\",\n      \"Epoch 2/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2659 - acc: 0.9075 - val_loss: 0.2873 - val_acc: 0.8905\\n\",\n      \"Epoch 3/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2060 - acc: 0.9276 - val_loss: 0.2827 - val_acc: 0.8879\\n\",\n      \"Epoch 4/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1697 - acc: 0.9401 - val_loss: 0.2921 - val_acc: 0.8851\\n\",\n      \"Epoch 5/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1495 - acc: 0.9470 - val_loss: 0.3100 - val_acc: 0.8812\\n\",\n      \"Epoch 6/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1283 - acc: 0.9572 - val_loss: 0.3336 - val_acc: 0.8748\\n\",\n      \"Epoch 7/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1121 - acc: 0.9624 - val_loss: 0.3987 - val_acc: 0.8593\\n\",\n      \"Epoch 8/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0994 - acc: 0.9670 - val_loss: 0.3788 - val_acc: 0.8702\\n\",\n      \"Epoch 9/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0889 - acc: 0.9716 - val_loss: 0.4242 - val_acc: 0.8603\\n\",\n      \"Epoch 10/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0782 - acc: 0.9757 - val_loss: 0.4256 - val_acc: 0.8653\\n\",\n      \"Epoch 11/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0691 - acc: 0.9792 - val_loss: 0.4515 - val_acc: 0.8638\\n\",\n      \"Epoch 12/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0603 - acc: 0.9820 - val_loss: 0.5102 - val_acc: 0.8610\\n\",\n      \"Epoch 13/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0518 - acc: 0.9851 - val_loss: 0.5281 - val_acc: 0.8587\\n\",\n      \"Epoch 14/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0446 - acc: 0.9873 - val_loss: 0.5441 - val_acc: 0.8589\\n\",\n      \"Epoch 15/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0367 - acc: 0.9903 - val_loss: 0.5777 - val_acc: 0.8574\\n\",\n      \"Epoch 16/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0313 - acc: 0.9922 - val_loss: 0.6377 - val_acc: 0.8555\\n\",\n      \"Epoch 17/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0247 - acc: 0.9941 - val_loss: 0.7269 - val_acc: 0.8501\\n\",\n      \"Epoch 18/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0203 - acc: 0.9956 - val_loss: 0.6920 - val_acc: 0.8516\\n\",\n      \"Epoch 19/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0156 - acc: 0.9970 - val_loss: 0.7689 - val_acc: 0.8425\\n\",\n      \"Epoch 20/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0144 - acc: 0.9966 - val_loss: 0.7694 - val_acc: 0.8487\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"original_hist = original_model.fit(x_train, y_train,\\n\",\n    \"                                   epochs=20,\\n\",\n    \"                                   batch_size=512,\\n\",\n    \"                                   validation_data=(x_test, y_test))\"\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      \"Train on 25000 samples, validate on 25000 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.5737 - acc: 0.8049 - val_loss: 0.4826 - val_acc: 0.8616\\n\",\n      \"Epoch 2/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.3973 - acc: 0.8866 - val_loss: 0.3699 - val_acc: 0.8776\\n\",\n      \"Epoch 3/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2985 - acc: 0.9054 - val_loss: 0.3140 - val_acc: 0.8860\\n\",\n      \"Epoch 4/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2428 - acc: 0.9189 - val_loss: 0.2913 - val_acc: 0.8870\\n\",\n      \"Epoch 5/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2085 - acc: 0.9290 - val_loss: 0.2809 - val_acc: 0.8897\\n\",\n      \"Epoch 6/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1849 - acc: 0.9360 - val_loss: 0.2772 - val_acc: 0.8899\\n\",\n      \"Epoch 7/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1666 - acc: 0.9430 - val_loss: 0.2835 - val_acc: 0.8863\\n\",\n      \"Epoch 8/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1515 - acc: 0.9487 - val_loss: 0.2909 - val_acc: 0.8850\\n\",\n      \"Epoch 9/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1388 - acc: 0.9526 - val_loss: 0.2984 - val_acc: 0.8842\\n\",\n      \"Epoch 10/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1285 - acc: 0.9569 - val_loss: 0.3102 - val_acc: 0.8818\\n\",\n      \"Epoch 11/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1194 - acc: 0.9599 - val_loss: 0.3219 - val_acc: 0.8794\\n\",\n      \"Epoch 12/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1105 - acc: 0.9648 - val_loss: 0.3379 - val_acc: 0.8774\\n\",\n      \"Epoch 13/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1035 - acc: 0.9674 - val_loss: 0.3532 - val_acc: 0.8730\\n\",\n      \"Epoch 14/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0963 - acc: 0.9688 - val_loss: 0.3651 - val_acc: 0.8731\\n\",\n      \"Epoch 15/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0895 - acc: 0.9724 - val_loss: 0.3858 - val_acc: 0.8703\\n\",\n      \"Epoch 16/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0838 - acc: 0.9734 - val_loss: 0.4157 - val_acc: 0.8654\\n\",\n      \"Epoch 17/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0785 - acc: 0.9764 - val_loss: 0.4214 - val_acc: 0.8677\\n\",\n      \"Epoch 18/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0733 - acc: 0.9784 - val_loss: 0.4390 - val_acc: 0.8644\\n\",\n      \"Epoch 19/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0685 - acc: 0.9796 - val_loss: 0.4539 - val_acc: 0.8638\\n\",\n      \"Epoch 20/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.0638 - acc: 0.9822 - val_loss: 0.4744 - val_acc: 0.8617\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"smaller_model_hist = smaller_model.fit(x_train, y_train,\\n\",\n    \"                                       epochs=20,\\n\",\n    \"                                       batch_size=512,\\n\",\n    \"                                       validation_data=(x_test, y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"epochs = range(1, 21)\\n\",\n    \"original_val_loss = original_hist.history['val_loss']\\n\",\n    \"smaller_model_val_loss = smaller_model_hist.history['val_loss']\"\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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mZjgEnAb8NjnSJcNxpY7+4b3H038AQwsck53wbuc/ePANz9g2hhi4hI\\nNkRJCtcR/DX/rLuvNLNjgfkRrjsGeCdpvzY8lux44Hgze9XMFpvZualeyMyuNLMaM6upq6uL8NYi\\nItIerVYfuftCYCFA2OD8obtfm8H3HwSMA/oCi8yswt23NonhQeBBCKbOztB7i4hIE1F6Hz1uZgeb\\nWQ/gTWCVmd0Q4bXfBfol7fcNjyWrBea5e4O7/w1YS5AkREQkBlGqj4a4+3bgAuB3wACCHkitqQYG\\nmdkAM+sKXAbMa3LOXIJSAmbWi6A6aUO00EVEJNOiJIUu4biECwj/qgdarcJx9z3ANOAFYDXwZNgm\\ncZuZTQhPewHYYmarCNopbnD3Le35RUREJH1RuqQ+AGwElhPU+ZcB26O8uLs/Dzzf5Ni/Jj124F/C\\nTUREYtZqScHd73H3Y9z9PA9sAs7IQWwi0sFokZz8F6Wh+RAz+0ljl1Az+zHQIwexiUgHM3Nm3BFI\\na6K0KTwMfAxcEm7bgUeyGZSIiMQjSlIY6O4zwpHJG9x9JnBstgMTkY6hqgrMgg32PVZVUn6KkhR2\\nmtkXG3fMbCywM3shiUhHUlUF7sEG+x4rKeSnKL2PpgKPmtkhgAH/ACZnMygREYlHlGkulgHDzezg\\ncD9Sd1QRkaZmzIg7AmlNs0nBzFKOHbCwYtDdf5KlmESkg1KVUf5rqaTQM2dRiIhIXmg2KYS9jERE\\npIhE6X0kIiJFQklBREQSlBRERCSh1S6pZtYNuBgoTz7f3W/LXlgiIhKHKCWF54CJwB7gk6RNRIqM\\nupR2fObe8no5Zvamu5+Yo3haVVlZ6TU1NXGHIVKUzPZNVyGFxcyWuHtla+dFKSn82cwqMhCTiIjk\\nuShJ4YvAEjNbY2YrzOwNM1uR7cBEJD9oltPiEqX6qCzV8XAFtpxT9ZFIfFR9VLgyVn0UfvkfCpwf\\nbofGlRBERCS7oizHOR2YDRwZbo+Z2TXZDkxEMi/dKh/NctrxRak+WgGMcfdPwv0ewGvuPiwH8R1A\\n1Uci7afqn+KVyd5HBuxN2t8bHhMRkQ4mSlJ4BHjdzKrMrApYDPwyq1GJSMao95C0RavVRwBmNoKg\\nayrAn9z9r1mNqgWqPhJpP1UfFa+o1Uctrbx2sLtvN7PDgY3h1vjc4e7+j0wEKiIi+aOlCfEeB74K\\nLAGS/7awcP/YLMYlIlmg3kPSmmbbFNz9q+HPAe5+bNI2wN2VEERikG47gNoRpDVRxin8McoxEcm+\\nmVokV7KspTaFUqA70MvMDmNfN9SDgWNyEJuIiORYSyWF/0nQnnBC+LNxew74afZDExFQl1LJrSgj\\nmq9x93vb9eJm5wL/CXQCfuHudzZ5fjLw78C74aGfuvsvWnpNdUmVYqYupdJeaXdJbeTu95rZicAQ\\noDTp+K9aCaATcB8wHqgFqs1snruvanLqr919WmtxiIhI9kVZo3kGMI4gKTwPfAV4BWgxKQCjgfXu\\nviF8nScIlvVsmhREJCJ1KZVsizLNxdeAM4G/u/sUYDhwSITrjgHeSdqvJXUD9cXh4j1Pm1m/VC9k\\nZleaWY2Z1dTV1UV4a5GOSe0Ikm1RksJOd/8M2GNmBwMfACm/vNvh/wHl4YyrfwAeTXWSuz/o7pXu\\nXtm7d+8MvbWIiDQVJSnUmNmhwEMEvY+WAq9FuO5d9k8efdnXoAyAu29x90/D3V8AIyO8roiIZEmU\\nhuarwoc/N7PfAwe7e5Q1mquBQWY2gCAZXAb8t+QTzKyPu28OdycAqyNHLiIiGddsScHMRjTdgMOB\\nzuHjFrn7HmAa8ALBl/2T7r7SzG4zswnhadea2UozWw5cC0xO9xdKZfZsKC+HkpLg5+zZ2XgXEZHC\\n1+w4BTObHz4sBSqB5QSjmocBNe4+JicRNtHWcQqzZ8OVV8KOHfuOde8ODz4IkyZlIUARkTyU9spr\\n7n6Gu58BbAZGhA29I4GTadI2kM9uuWX/hADB/i23xBOPiEg+i9LQ/AV3f6Nxx93fBAZnL6TMevvt\\nth0XESlmUZLCCjP7hZmNC7eHgCgNzXmhf/+2HRcRyTe5bBeNkhSmACuB6eG2KjxWEO64I2hDSNa9\\ne3BcRCTfNbaLbtoUzHu1aVOwn63EEGmN5nzSngnxZs8O2hDefjsoIdxxhxqZRaQwlJcHiaCpsjLY\\nuDH660RtaG6p99GT7n6Jmb3B/stxAhCOQs45zZIqIsWkpCT1zLhm8Nln0V8nE7OkTg9/fjX624qI\\nSCb175+6pJCtdtGWuqRuDn9uSrVlJxwREUmW63bRlpbj/JgU1UYEA9jc3Q/OTkgiItKosf0zV+2i\\nzSYFd++ZnbcUEZG2mDQpd51jonRJBcDMjjSz/o1bNoMSEelICmn+tVaTgplNMLN1wN+AhcBG4HdZ\\njkukw9ECOcUp1+MM0hWlpHA7cCqw1t0HEKzCtjirUYl0QDNnxh2BxKHQ5l+LkhQa3H0LUGJmJe4+\\nn2DWVBERaUWhzb8WJSlsNbODgEXAbDP7T+CT7IYl0jFUVQWDjMyC/cbHqkoqHoU2/1qUpDAR2Alc\\nD/weeAs4P5tBiXQUVVVBPXLjiNTGx0oKxaPQ5l9raeW1+8xsrLt/4u573X2Puz/q7veE1UkiIkUh\\nnd5DkyYFi3qVlQWlxLKy/F7kq6VpLtYCd5lZH+BJYI67/zU3YYl0PDNmxB2BtEfT1Rsbew9B9C/2\\nXI4zSFers6SaWRlwWbh9DphDkCDWZj+8A2lCPBHJpUzNUhq3tJfjbBTOdfRDdz8ZuBy4AFidgRhF\\nRPJeofUeSleUwWudzex8M5tNMGhtDXBR1iMTEckDhdZ7KF0tNTSPN7OHgVrg28BvgYHufpm7P5er\\nAEXyhXoMFadC6z2UrpZKCjcDfwYGu/sEd3/c3TU+QQpWul/qGpFcnAqt91C6imI5ThEI/kOn83FP\\n93qJj5bkzWBDs0gx04jkwldoE9LFTUlBOrR0v9Q1Ijk/pDN4rNAmpIubqo+kaKj6qDA1HTwGQUNv\\n1Hr9TC18X+hUfSSSYRqRHI90/9Ivti6l6VJSkKKR7pe6qozike7gsWLrUpouJQUpGvpSL0zp/qVf\\nbF1K06WkICJ5LRN/6U+aFMxT9NlnwU8lhOZlNSmY2blmtsbM1pvZTS2cd7GZuZlpRTcR2Y/+0s+t\\nrCUFM+sE3Ad8BRgCXG5mQ1Kc1xOYDryerVgkP6j6RtpLf+nnTjZLCqOB9e6+wd13A08QrOLW1O3A\\nD4FdWYxF8oCmiRDJf9lMCscA7yTt14bHEsxsBNDP3X+bxThERCSi2BqazawE+Anw3QjnXmlmNWZW\\nU1dXl/3gJGM0TYRIYcnaiGYzGwNUufs54f7NAO7+b+H+IcBbQH14yeeBfwAT3L3ZIcsa0Vy4NCJY\\nJD75MKK5GhhkZgPMrCvBcp7zGp90923u3svdy929HFhMKwlBRApTOnMXSW51ztYLu/seM5sGvAB0\\nAh5295VmdhtQ4+7zWn4F6Wg0TURxysTC95I7mhBPRLKqoyx8X+jyofpIJKPUOF2Yim3h+0KnpCAF\\nQ+McCpNmKS0sSgoiklWapbSwKClIXtM4h8KnuYsKixqaI9Ci3/lB4xxE2i9qQ3PWuqR2FOpOJyLF\\nRNVHrdCi3/lD4xzio8FnxUMlhVaoO13+UDtCPFRaLi4qKbRC3emk2Km0XFyUFFqh7nRS7FRaLi5K\\nCq1QdzrpCNJpE1BpubgoKUSgpQClkDW2CWzaFHTpbWwTiJoYVFouLkoKIh1cum0CKi0XFw1eE+ng\\nSkpSD/ozC0q/Uhw0S6qIAGoTkLYpqqSgfu5SjNQmIG1RVElBUy9LMVKbgLRFUSUFkUKV7jQT6kEn\\nUXX4pKCplzNH9ywe6XYpFWmLoup9pKmX06P7Fw+tcSyZoN5HIh2EppmQXCqqpKCpl9tO1W/xU5dS\\nyaWiqj6S9Kj6KB5Np66GoEupehBJW6j6SCRPZKLnkLqUSq5okR2JTNVvbZepBWomTVISkNxQ9ZFI\\nFqnnkOQLVR/lEa1vW7zUc0gKjZJClmngUXFTzyEpNEoKWZZP69uqG2n7pFPS02R0UmiUFLIsn6oP\\nNCFg26Vb0lPPISk0amjOsnxqaNQ4g7bLp38/kXTkRUOzmZ1rZmvMbL2Z3ZTi+e+Y2RtmtszMXjGz\\nIdmMJw5xVx9oRHJ68qmkJ5ILWUsKZtYJuA/4CjAEuDzFl/7j7l7h7icBPwJ+kq144hJ39UFVVVA6\\naCwhND4upqSQTpuAGoql2GSzpDAaWO/uG9x9N/AEMDH5BHffnrTbA+iQlRuayz4+6bYJxF3SE8m1\\nbCaFY4B3kvZrw2P7MbOrzewtgpLCtVmMp+gV44jkdHt/xV3SE8m12Hsfuft97j4QuBG4NdU5Znal\\nmdWYWU1dXV1uA8wDjdUfZukNfivUKqN0qn8y0Sagkp4Uk2wmhXeBfkn7fcNjzXkCuCDVE+7+oLtX\\nuntl7969Mxhi/kuu/oDiG/yWbvWP2gRE2iabSaEaGGRmA8ysK3AZMC/5BDMblLT7z8C6LMaTtjj+\\n0s6nwW9xSPf3V5uASNtkLSm4+x5gGvACsBp40t1XmtltZjYhPG2ama00s2XAvwDfyFY8mRDH4K9U\\nfeRbOp6P4qz+UZuASBu5e0FtI0eO9LhAetfPmNH2a8rKGjuR7r+VlUV/jcceC843C34+9ljb42iv\\nxx5z7959/9i7d48eQyZ+fxFxB2o8wnds7A3N+S6Tg7/aU9JIt/oj7gn5VP0jUmCiZI582gq5pNDe\\n6xv/0m/8C7ktf+nHXdIwS/3+Zrl5fxEJELGkoLmP2qA9cwdVVaUuIcyYkZuG65KS1DGbBV0sW5Pu\\n+sCaO0gkP+TF3EcdTXsGf8U9zUS6XTJV/SNSXJQU2qAQB3+l+6Ws3j8ixUVJIYfimGYi3S/lTAz+\\n0ohgkcKhNgVpUbptCiKSH9SmIBmh6h+R4tI57gAk/02apCQgUixUUhARkQQlBRERSVBSEBGRBCUF\\nERFJUFIQEZGEghunYGZ1QL6uJtAL+DDuIFqg+NKT7/FB/seo+NKTTnxl7t7q0pUFlxTymZnVRBkc\\nEhfFl558jw/yP0bFl55cxKfqIxERSVBSEBGRBCWFzHow7gBaofjSk+/xQf7HqPjSk/X41KYgIiIJ\\nKimIiEiCkkIbmVk/M5tvZqvMbKWZTU9xzjgz22Zmy8LtX3Mc40YzeyN87wPmGbfAPWa23sxWmNmI\\nHMb2haT7sszMtpvZdU3Oyfn9M7OHzewDM3sz6djhZvYHM1sX/jysmWu/EZ6zzsy+kaPY/t3M/iv8\\n93vWzA5t5toWPwtZjrHKzN5N+nc8r5lrzzWzNeHn8aYcxvfrpNg2mtmyZq7N6j1s7jslts9flIWc\\nte3bgD7AiPBxT2AtMKTJOeOA38QY40agVwvPnwf8DjDgVOD1mOLsBPydoP90rPcP+BIwAngz6diP\\ngJvCxzcBP0xx3eHAhvDnYeHjw3IQ29lA5/DxD1PFFuWzkOUYq4D/FeEz8BZwLNAVWN70/1O24mvy\\n/I+Bf43jHjb3nRLX508lhTZy983uvjR8/DGwGjgm3qjabCLwKw8sBg41sz4xxHEm8Ja7xz4Y0d0X\\nAf9ocngi8Gj4+FHgghSXngP8wd3/4e4fAX8Azs12bO7+orvvCXcXA30z+Z5t1cz9i2I0sN7dN7j7\\nbuAJgvueUS3FZ2YGXALMyfT7RtHCd0osnz8lhTSYWTlwMvB6iqfHmNlyM/udmQ3NaWDgwItmtsTM\\nrkzx/DHAO0n7tcST2C6j+f+Icd6/Rke5++bw8d+Bo1Kckw/38psEJb9UWvssZNu0sIrr4WaqP/Lh\\n/p0GvO/u65p5Pmf3sMl3SiyfPyWFdjKzg4BngOvcfXuTp5cSVIkMB+4F5uY4vC+6+wjgK8DVZval\\nHL9/q8ysKzABeCrF03HfvwN4UFbPu656ZnYLsAeY3cwpcX4WfgYMBE4CNhNU0eSjy2m5lJCTe9jS\\nd0ouP39KCu1gZl0I/vFmu/v/bfq8u2939/rw8fNAFzPrlav43P3d8OcHwLMERfRk7wL9kvb7hsdy\\n6SvAUnd/v+kTcd+/JO83VquFPz9IcU5s99LMJgNfBSaFXxoHiPBZyBp3f9/d97r7Z8BDzbx3rJ9F\\nM+sMXAT8urlzcnEPm/lOieXzp6TQRmH94y+B1e7+k2bO+Xx4HmY2muA+b8lRfD3MrGfjY4IGyTeb\\nnDYP+B/0glZdAAAC/UlEQVRhL6RTgW1JxdRcafavszjvXxPzgMbeHN8AnktxzgvA2WZ2WFg9cnZ4\\nLKvM7FzgfwMT3H1HM+dE+SxkM8bkdqoLm3nvamCQmQ0IS4+XEdz3XDkL+C93r031ZC7uYQvfKfF8\\n/rLVot5RN+CLBMW4FcCycDsP+A7wnfCcacBKgp4Ui4F/ymF8x4bvuzyM4ZbweHJ8BtxH0OvjDaAy\\nx/ewB8GX/CFJx2K9fwQJajPQQFAv+y3gCOCPwDrgJeDw8NxK4BdJ134TWB9uU3IU23qCuuTGz+DP\\nw3OPBp5v6bOQw/v3f8LP1wqCL7g+TWMM988j6HHzVrZiTBVfeHxW4+cu6dyc3sMWvlNi+fxpRLOI\\niCSo+khERBKUFEREJEFJQUREEpQUREQkQUlBREQSlBREQma21/afwTVjM3aaWXnyDJ0i+apz3AGI\\n5JGd7n5S3EGIxEklBZFWhPPp/yicU/8vZnZceLzczF4OJ3z7o5n1D48fZcEaB8vD7Z/Cl+pkZg+F\\nc+a/aGafC8+/NpxLf4WZPRHTrykCKCmIJPtck+qjS5Oe2+buFcBPgf8Ij90LPOruwwgmpLsnPH4P\\nsNCDCf1GEIyEBRgE3OfuQ4GtwMXh8ZuAk8PX+U62fjmRKDSiWSRkZvXuflCK4xuBL7v7hnDisr+7\\n+xFm9iHB1A0N4fHN7t7LzOqAvu7+adJrlBPMez8o3L8R6OLu3zez3wP1BLPBzvVwMkCROKikIBKN\\nN/O4LT5NeryXfW16/0wwF9UIoDqcuVMkFkoKItFcmvTztfDxnwlm9QSYBPwpfPxHYCqAmXUys0Oa\\ne1EzKwH6uft84EbgEOCA0opIrugvEpF9Pmf7L97+e3dv7JZ6mJmtIPhr//Lw2DXAI2Z2A1AHTAmP\\nTwceNLNvEZQIphLM0JlKJ+CxMHEYcI+7b83YbyTSRmpTEGlF2KZQ6e4fxh2LSLap+khERBJUUhAR\\nkQSVFEREJEFJQUREEpQUREQkQUlBREQSlBRERCRBSUFERBL+Py+ECKSeVveDAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f43c33d57f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"# b+ is for \\\"blue cross\\\"\\n\",\n    \"plt.plot(epochs, original_val_loss, 'b+', label='Original model')\\n\",\n    \"# \\\"bo\\\" is for \\\"blue dot\\\"\\n\",\n    \"plt.plot(epochs, smaller_model_val_loss, 'bo', label='Smaller model')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"As you can see, the smaller network starts overfitting later than the reference one (after 6 epochs rather than 4) and its performance \\n\",\n    \"degrades much more slowly once it starts overfitting.\\n\",\n    \"\\n\",\n    \"Now, for kicks, let's add to this benchmark a network that has much more capacity, far more than the problem would warrant:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"bigger_model = models.Sequential()\\n\",\n    \"bigger_model.add(layers.Dense(512, activation='relu', input_shape=(10000,)))\\n\",\n    \"bigger_model.add(layers.Dense(512, activation='relu'))\\n\",\n    \"bigger_model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"bigger_model.compile(optimizer='rmsprop',\\n\",\n    \"                     loss='binary_crossentropy',\\n\",\n    \"                     metrics=['acc'])\"\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      \"Train on 25000 samples, validate on 25000 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.4539 - acc: 0.8011 - val_loss: 0.4150 - val_acc: 0.8229\\n\",\n      \"Epoch 2/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.2148 - acc: 0.9151 - val_loss: 0.2742 - val_acc: 0.8901\\n\",\n      \"Epoch 3/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.1217 - acc: 0.9544 - val_loss: 0.5442 - val_acc: 0.7975\\n\",\n      \"Epoch 4/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0552 - acc: 0.9835 - val_loss: 0.4316 - val_acc: 0.8842\\n\",\n      \"Epoch 5/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0662 - acc: 0.9888 - val_loss: 0.5098 - val_acc: 0.8822\\n\",\n      \"Epoch 6/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0017 - acc: 0.9998 - val_loss: 0.6867 - val_acc: 0.8811\\n\",\n      \"Epoch 7/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.1019 - acc: 0.9882 - val_loss: 0.6737 - val_acc: 0.8800\\n\",\n      \"Epoch 8/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0735 - acc: 0.9896 - val_loss: 0.6185 - val_acc: 0.8772\\n\",\n      \"Epoch 9/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 3.4759e-04 - acc: 1.0000 - val_loss: 0.7328 - val_acc: 0.8818\\n\",\n      \"Epoch 10/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0504 - acc: 0.9912 - val_loss: 0.7092 - val_acc: 0.8791\\n\",\n      \"Epoch 11/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0589 - acc: 0.9919 - val_loss: 0.6831 - val_acc: 0.8785\\n\",\n      \"Epoch 12/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 1.9067e-04 - acc: 1.0000 - val_loss: 0.8005 - val_acc: 0.8784\\n\",\n      \"Epoch 13/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0623 - acc: 0.9916 - val_loss: 0.7540 - val_acc: 0.8740\\n\",\n      \"Epoch 14/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0274 - acc: 0.9954 - val_loss: 0.7806 - val_acc: 0.8670\\n\",\n      \"Epoch 15/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0011 - acc: 0.9998 - val_loss: 0.8107 - val_acc: 0.8783\\n\",\n      \"Epoch 16/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0445 - acc: 0.9943 - val_loss: 0.8394 - val_acc: 0.8702\\n\",\n      \"Epoch 17/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0268 - acc: 0.9959 - val_loss: 0.7708 - val_acc: 0.8745\\n\",\n      \"Epoch 18/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 7.7057e-04 - acc: 1.0000 - val_loss: 0.8885 - val_acc: 0.8738\\n\",\n      \"Epoch 19/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0297 - acc: 0.9962 - val_loss: 0.8419 - val_acc: 0.8728\\n\",\n      \"Epoch 20/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.0018 - acc: 0.9998 - val_loss: 0.8896 - val_acc: 0.8682\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"bigger_model_hist = bigger_model.fit(x_train, y_train,\\n\",\n    \"                                     epochs=20,\\n\",\n    \"                                     batch_size=512,\\n\",\n    \"                                     validation_data=(x_test, y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here's how the bigger network fares compared to the reference one. The dots are the validation loss values of the bigger network, and the \\n\",\n    \"crosses are the initial network.\"\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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TGz681seljtu8ClZvYicB8wyzVaLNJBMSyoVsqznyQzkV6nEIVk1ymI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\\nSUFEROKUFEREJE5JQURE4pQUREQk7v8DxBgqjYrXWkoAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f43c0fbcba8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bigger_model_val_loss = bigger_model_hist.history['val_loss']\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, original_val_loss, 'b+', label='Original model')\\n\",\n    \"plt.plot(epochs, bigger_model_val_loss, 'bo', label='Bigger model')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The bigger network starts overfitting almost right away, after just one epoch, and overfits much more severely. Its validation loss is also \\n\",\n    \"more noisy.\\n\",\n    \"\\n\",\n    \"Meanwhile, here are the training losses for our two networks:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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zCzGcCjQAWwwN2Xm9l1QJ27PwTMMLOTge3AO8BFOf4cIiJSAObesRN+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\\nJQUREYkoKYiISERJQUREIv8DVI4KezW+r2IAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f43befc12e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"original_train_loss = original_hist.history['loss']\\n\",\n    \"bigger_model_train_loss = bigger_model_hist.history['loss']\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, original_train_loss, 'b+', label='Original model')\\n\",\n    \"plt.plot(epochs, bigger_model_train_loss, 'bo', label='Bigger model')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Training loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, the bigger network gets its training loss near zero very quickly. The more capacity the network has, the quicker it will be \\n\",\n    \"able to model the training data (resulting in a low training loss), but the more susceptible it is to overfitting (resulting in a large \\n\",\n    \"difference between the training and validation loss).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Adding weight regularization\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"You may be familiar with _Occam's Razor_ principle: given two explanations for something, the explanation most likely to be correct is the \\n\",\n    \"\\\"simplest\\\" one, the one that makes the least amount of assumptions. This also applies to the models learned by neural networks: given some \\n\",\n    \"training data and a network architecture, there are multiple sets of weights values (multiple _models_) that could explain the data, and \\n\",\n    \"simpler models are less likely to overfit than complex ones.\\n\",\n    \"\\n\",\n    \"A \\\"simple model\\\" in this context is a model where the distribution of parameter values has less entropy (or a model with fewer \\n\",\n    \"parameters altogether, as we saw in the section above). Thus a common way to mitigate overfitting is to put constraints on the complexity \\n\",\n    \"of a network by forcing its weights to only take small values, which makes the distribution of weight values more \\\"regular\\\". This is called \\n\",\n    \"\\\"weight regularization\\\", and it is done by adding to the loss function of the network a _cost_ associated with having large weights. This \\n\",\n    \"cost comes in two flavors:\\n\",\n    \"\\n\",\n    \"* L1 regularization, where the cost added is proportional to the _absolute value of the weights coefficients_ (i.e. to what is called the \\n\",\n    \"\\\"L1 norm\\\" of the weights).\\n\",\n    \"* L2 regularization, where the cost added is proportional to the _square of the value of the weights coefficients_ (i.e. to what is called \\n\",\n    \"the \\\"L2 norm\\\" of the weights). L2 regularization is also called _weight decay_ in the context of neural networks. Don't let the different \\n\",\n    \"name confuse you: weight decay is mathematically the exact same as L2 regularization.\\n\",\n    \"\\n\",\n    \"In Keras, weight regularization is added by passing _weight regularizer instances_ to layers as keyword arguments. Let's add L2 weight \\n\",\n    \"regularization to our movie review classification network:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import regularizers\\n\",\n    \"\\n\",\n    \"l2_model = models.Sequential()\\n\",\n    \"l2_model.add(layers.Dense(16, kernel_regularizer=regularizers.l2(0.001),\\n\",\n    \"                          activation='relu', input_shape=(10000,)))\\n\",\n    \"l2_model.add(layers.Dense(16, kernel_regularizer=regularizers.l2(0.001),\\n\",\n    \"                          activation='relu'))\\n\",\n    \"l2_model.add(layers.Dense(1, activation='sigmoid'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"l2_model.compile(optimizer='rmsprop',\\n\",\n    \"                 loss='binary_crossentropy',\\n\",\n    \"                 metrics=['acc'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"`l2(0.001)` means that every coefficient in the weight matrix of the layer will add `0.001 * weight_coefficient_value` to the total loss of \\n\",\n    \"the network. Note that because this penalty is _only added at training time_, the loss for this network will be much higher at training \\n\",\n    \"than at test time.\\n\",\n    \"\\n\",\n    \"Here's the impact of our L2 regularization penalty:\"\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      \"Train on 25000 samples, validate on 25000 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.4880 - acc: 0.8218 - val_loss: 0.3820 - val_acc: 0.8798\\n\",\n      \"Epoch 2/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.3162 - acc: 0.9068 - val_loss: 0.3353 - val_acc: 0.8896\\n\",\n      \"Epoch 3/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2742 - acc: 0.9185 - val_loss: 0.3306 - val_acc: 0.8898\\n\",\n      \"Epoch 4/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2489 - acc: 0.9288 - val_loss: 0.3363 - val_acc: 0.8866\\n\",\n      \"Epoch 5/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2420 - acc: 0.9318 - val_loss: 0.3492 - val_acc: 0.8820\\n\",\n      \"Epoch 6/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2322 - acc: 0.9359 - val_loss: 0.3567 - val_acc: 0.8788\\n\",\n      \"Epoch 7/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2254 - acc: 0.9385 - val_loss: 0.3632 - val_acc: 0.8787\\n\",\n      \"Epoch 8/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2219 - acc: 0.9380 - val_loss: 0.3630 - val_acc: 0.8794\\n\",\n      \"Epoch 9/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2162 - acc: 0.9430 - val_loss: 0.3704 - val_acc: 0.8763\\n\",\n      \"Epoch 10/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2144 - acc: 0.9428 - val_loss: 0.3876 - val_acc: 0.8727\\n\",\n      \"Epoch 11/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2091 - acc: 0.9439 - val_loss: 0.3883 - val_acc: 0.8724\\n\",\n      \"Epoch 12/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2061 - acc: 0.9455 - val_loss: 0.3870 - val_acc: 0.8740\\n\",\n      \"Epoch 13/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2069 - acc: 0.9445 - val_loss: 0.4073 - val_acc: 0.8714\\n\",\n      \"Epoch 14/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2028 - acc: 0.9475 - val_loss: 0.3976 - val_acc: 0.8714\\n\",\n      \"Epoch 15/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1998 - acc: 0.9472 - val_loss: 0.4362 - val_acc: 0.8670\\n\",\n      \"Epoch 16/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2019 - acc: 0.9462 - val_loss: 0.4088 - val_acc: 0.8711\\n\",\n      \"Epoch 17/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1953 - acc: 0.9495 - val_loss: 0.4185 - val_acc: 0.8698\\n\",\n      \"Epoch 18/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1945 - acc: 0.9508 - val_loss: 0.4371 - val_acc: 0.8674\\n\",\n      \"Epoch 19/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1934 - acc: 0.9486 - val_loss: 0.4136 - val_acc: 0.8699\\n\",\n      \"Epoch 20/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1924 - acc: 0.9504 - val_loss: 0.4200 - val_acc: 0.8704\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"l2_model_hist = l2_model.fit(x_train, y_train,\\n\",\n    \"                             epochs=20,\\n\",\n    \"                             batch_size=512,\\n\",\n    \"                             validation_data=(x_test, y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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CmIiEiKkoKIiKRkTApmtoeZ9QhfH2FmY82sZ/yhiYhIvkWpKcwDKs3s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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f43bef16c50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"l2_model_val_loss = l2_model_hist.history['val_loss']\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, original_val_loss, 'b+', label='Original model')\\n\",\n    \"plt.plot(epochs, l2_model_val_loss, 'bo', label='L2-regularized model')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"As you can see, the model with L2 regularization (dots) has become much more resistant to overfitting than the reference model (crosses), \\n\",\n    \"even though both models have the same number of parameters.\\n\",\n    \"\\n\",\n    \"As alternatives to L2 regularization, you could use one of the following Keras weight regularizers:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import regularizers\\n\",\n    \"\\n\",\n    \"# L1 regularization\\n\",\n    \"regularizers.l1(0.001)\\n\",\n    \"\\n\",\n    \"# L1 and L2 regularization at the same time\\n\",\n    \"regularizers.l1_l2(l1=0.001, l2=0.001)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Adding dropout\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Dropout is one of the most effective and most commonly used regularization techniques for neural networks, developed by Hinton and his \\n\",\n    \"students at the University of Toronto. Dropout, applied to a layer, consists of randomly \\\"dropping out\\\" (i.e. setting to zero) a number of \\n\",\n    \"output features of the layer during training. Let's say a given layer would normally have returned a vector `[0.2, 0.5, 1.3, 0.8, 1.1]` for a \\n\",\n    \"given input sample during training; after applying dropout, this vector will have a few zero entries distributed at random, e.g. `[0, 0.5, \\n\",\n    \"1.3, 0, 1.1]`. The \\\"dropout rate\\\" is the fraction of the features that are being zeroed-out; it is usually set between 0.2 and 0.5. At test \\n\",\n    \"time, no units are dropped out, and instead the layer's output values are scaled down by a factor equal to the dropout rate, so as to \\n\",\n    \"balance for the fact that more units are active than at training time.\\n\",\n    \"\\n\",\n    \"Consider a Numpy matrix containing the output of a layer, `layer_output`, of shape `(batch_size, features)`. At training time, we would be \\n\",\n    \"zero-ing out at random a fraction of the values in the matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# At training time: we drop out 50% of the units in the output\\n\",\n    \"layer_output *= np.randint(0, high=2, size=layer_output.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"At test time, we would be scaling the output down by the dropout rate. Here we scale by 0.5 (because we were previous dropping half the \\n\",\n    \"units):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# At test time:\\n\",\n    \"layer_output *= 0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Note that this process can be implemented by doing both operations at training time and leaving the output unchanged at test time, which is \\n\",\n    \"often the way it is implemented in practice:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# At training time:\\n\",\n    \"layer_output *= np.randint(0, high=2, size=layer_output.shape)\\n\",\n    \"# Note that we are scaling *up* rather scaling *down* in this case\\n\",\n    \"layer_output /= 0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"This technique may seem strange and arbitrary. Why would this help reduce overfitting? Geoff Hinton has said that he was inspired, among \\n\",\n    \"other things, by a fraud prevention mechanism used by banks -- in his own words: _\\\"I went to my bank. The tellers kept changing and I asked \\n\",\n    \"one of them why. He said he didn’t know but they got moved around a lot. I figured it must be because it would require cooperation \\n\",\n    \"between employees to successfully defraud the bank. This made me realize that randomly removing a different subset of neurons on each \\n\",\n    \"example would prevent conspiracies and thus reduce overfitting\\\"_.\\n\",\n    \"\\n\",\n    \"The core idea is that introducing noise in the output values of a layer can break up happenstance patterns that are not significant (what \\n\",\n    \"Hinton refers to as \\\"conspiracies\\\"), which the network would start memorizing if no noise was present. \\n\",\n    \"\\n\",\n    \"In Keras you can introduce dropout in a network via the `Dropout` layer, which gets applied to the output of layer right before it, e.g.:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.add(layers.Dropout(0.5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's add two `Dropout` layers in our IMDB network to see how well they do at reducing overfitting:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dpt_model = models.Sequential()\\n\",\n    \"dpt_model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\\n\",\n    \"dpt_model.add(layers.Dropout(0.5))\\n\",\n    \"dpt_model.add(layers.Dense(16, activation='relu'))\\n\",\n    \"dpt_model.add(layers.Dropout(0.5))\\n\",\n    \"dpt_model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"dpt_model.compile(optimizer='rmsprop',\\n\",\n    \"                  loss='binary_crossentropy',\\n\",\n    \"                  metrics=['acc'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train on 25000 samples, validate on 25000 samples\\n\",\n      \"Epoch 1/20\\n\",\n      \"25000/25000 [==============================] - 3s - loss: 0.6035 - acc: 0.6678 - val_loss: 0.4704 - val_acc: 0.8651\\n\",\n      \"Epoch 2/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.4622 - acc: 0.8002 - val_loss: 0.3612 - val_acc: 0.8724\\n\",\n      \"Epoch 3/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.3731 - acc: 0.8553 - val_loss: 0.2960 - val_acc: 0.8904\\n\",\n      \"Epoch 4/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.3162 - acc: 0.8855 - val_loss: 0.2772 - val_acc: 0.8917\\n\",\n      \"Epoch 5/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2762 - acc: 0.9033 - val_loss: 0.2803 - val_acc: 0.8889\\n\",\n      \"Epoch 6/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2454 - acc: 0.9172 - val_loss: 0.2823 - val_acc: 0.8892\\n\",\n      \"Epoch 7/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.2178 - acc: 0.9281 - val_loss: 0.2982 - val_acc: 0.8877\\n\",\n      \"Epoch 8/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1994 - acc: 0.9351 - val_loss: 0.3101 - val_acc: 0.8875\\n\",\n      \"Epoch 9/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1832 - acc: 0.9400 - val_loss: 0.3318 - val_acc: 0.8860\\n\",\n      \"Epoch 10/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1692 - acc: 0.9434 - val_loss: 0.3534 - val_acc: 0.8841\\n\",\n      \"Epoch 11/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1590 - acc: 0.9483 - val_loss: 0.3689 - val_acc: 0.8830\\n\",\n      \"Epoch 12/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1499 - acc: 0.9496 - val_loss: 0.4107 - val_acc: 0.8776\\n\",\n      \"Epoch 13/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1405 - acc: 0.9539 - val_loss: 0.4114 - val_acc: 0.8782\\n\",\n      \"Epoch 14/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1333 - acc: 0.9562 - val_loss: 0.4549 - val_acc: 0.8771\\n\",\n      \"Epoch 15/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1267 - acc: 0.9572 - val_loss: 0.4579 - val_acc: 0.8800\\n\",\n      \"Epoch 16/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1225 - acc: 0.9580 - val_loss: 0.4843 - val_acc: 0.8772\\n\",\n      \"Epoch 17/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1233 - acc: 0.9590 - val_loss: 0.4783 - val_acc: 0.8761\\n\",\n      \"Epoch 18/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1212 - acc: 0.9601 - val_loss: 0.5051 - val_acc: 0.8740\\n\",\n      \"Epoch 19/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1153 - acc: 0.9618 - val_loss: 0.5451 - val_acc: 0.8747\\n\",\n      \"Epoch 20/20\\n\",\n      \"25000/25000 [==============================] - 2s - loss: 0.1155 - acc: 0.9621 - val_loss: 0.5358 - val_acc: 0.8738\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"dpt_model_hist = dpt_model.fit(x_train, y_train,\\n\",\n    \"                               epochs=20,\\n\",\n    \"                               batch_size=512,\\n\",\n    \"                               validation_data=(x_test, y_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the results:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f43baa91dd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dpt_model_val_loss = dpt_model_hist.history['val_loss']\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, original_val_loss, 'b+', label='Original model')\\n\",\n    \"plt.plot(epochs, dpt_model_val_loss, 'bo', label='Dropout-regularized model')\\n\",\n    \"plt.xlabel('Epochs')\\n\",\n    \"plt.ylabel('Validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Again, a clear improvement over the reference network.\\n\",\n    \"\\n\",\n    \"To recap: here the most common ways to prevent overfitting in neural networks:\\n\",\n    \"\\n\",\n    \"* Getting more training data.\\n\",\n    \"* Reducing the capacity of the network.\\n\",\n    \"* Adding weight regularization.\\n\",\n    \"* Adding dropout.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/5.1-introduction-to-convnets.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"# 5.1 - Introduction to convnets\\n\",\n    \"\\n\",\n    \"This notebook contains the code sample found in Chapter 5, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"First, let's take a practical look at a very simple convnet example. We will use our convnet to classify MNIST digits, a task that you've already been \\n\",\n    \"through in Chapter 2, using a densely-connected network (our test accuracy then was 97.8%). Even though our convnet will be very basic, its \\n\",\n    \"accuracy will still blow out of the water that of the densely-connected model from Chapter 2.\\n\",\n    \"\\n\",\n    \"The 6 lines of code below show you what a basic convnet looks like. It's a stack of `Conv2D` and `MaxPooling2D` layers. We'll see in a \\n\",\n    \"minute what they do concretely.\\n\",\n    \"Importantly, a convnet takes as input tensors of shape `(image_height, image_width, image_channels)` (not including the batch dimension). \\n\",\n    \"In our case, we will configure our convnet to process inputs of size `(28, 28, 1)`, which is the format of MNIST images. We do this via \\n\",\n    \"passing the argument `input_shape=(28, 28, 1)` to our first layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"from keras import models\\n\",\n    \"\\n\",\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's display the architecture of our convnet so far:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 55,744\\n\",\n      \"Trainable params: 55,744\\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    \"collapsed\": true\n   },\n   \"source\": [\n    \"You can see above that the output of every `Conv2D` and `MaxPooling2D` layer is a 3D tensor of shape `(height, width, channels)`. The width \\n\",\n    \"and height dimensions tend to shrink as we go deeper in the network. The number of channels is controlled by the first argument passed to \\n\",\n    \"the `Conv2D` layers (e.g. 32 or 64).\\n\",\n    \"\\n\",\n    \"The next step would be to feed our last output tensor (of shape `(3, 3, 64)`) into a densely-connected classifier network like those you are \\n\",\n    \"already familiar with: a stack of `Dense` layers. These classifiers process vectors, which are 1D, whereas our current output is a 3D tensor. \\n\",\n    \"So first, we will have to flatten our 3D outputs to 1D, and then add a few `Dense` layers on top:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.add(layers.Flatten())\\n\",\n    \"model.add(layers.Dense(64, activation='relu'))\\n\",\n    \"model.add(layers.Dense(10, activation='softmax'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We are going to do 10-way classification, so we use a final layer with 10 outputs and a softmax activation. Now here's what our network \\n\",\n    \"looks like:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32)        0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_3 (Conv2D)            (None, 3, 3, 64)          36928     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"flatten_1 (Flatten)          (None, 576)               0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_1 (Dense)              (None, 64)                36928     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_2 (Dense)              (None, 10)                650       \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 93,322\\n\",\n      \"Trainable params: 93,322\\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    \"As you can see, our `(3, 3, 64)` outputs were flattened into vectors of shape `(576,)`, before going through two `Dense` layers.\\n\",\n    \"\\n\",\n    \"Now, let's train our convnet on the MNIST digits. We will reuse a lot of the code we have already covered in the MNIST example from Chapter \\n\",\n    \"2.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.datasets import mnist\\n\",\n    \"from keras.utils import to_categorical\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"\\n\",\n    \"train_images = train_images.reshape((60000, 28, 28, 1))\\n\",\n    \"train_images = train_images.astype('float32') / 255\\n\",\n    \"\\n\",\n    \"test_images = test_images.reshape((10000, 28, 28, 1))\\n\",\n    \"test_images = test_images.astype('float32') / 255\\n\",\n    \"\\n\",\n    \"train_labels = to_categorical(train_labels)\\n\",\n    \"test_labels = to_categorical(test_labels)\"\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      \"Epoch 1/5\\n\",\n      \"60000/60000 [==============================] - 8s - loss: 0.1766 - acc: 0.9440     \\n\",\n      \"Epoch 2/5\\n\",\n      \"60000/60000 [==============================] - 7s - loss: 0.0462 - acc: 0.9855     \\n\",\n      \"Epoch 3/5\\n\",\n      \"60000/60000 [==============================] - 7s - loss: 0.0322 - acc: 0.9902     \\n\",\n      \"Epoch 4/5\\n\",\n      \"60000/60000 [==============================] - 7s - loss: 0.0241 - acc: 0.9926     \\n\",\n      \"Epoch 5/5\\n\",\n      \"60000/60000 [==============================] - 7s - loss: 0.0187 - acc: 0.9943     \\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fbd9c4cd828>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='categorical_crossentropy',\\n\",\n    \"              metrics=['accuracy'])\\n\",\n    \"model.fit(train_images, train_labels, epochs=5, batch_size=64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's evaluate the model on the test data:\"\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      \" 9536/10000 [===========================>..] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(test_images, test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.99129999999999996\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"While our densely-connected network from Chapter 2 had a test accuracy of 97.8%, our basic convnet has a test accuracy of 99.3%: we \\n\",\n    \"decreased our error rate by 68% (relative). Not bad! \"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/5.2-using-convnets-with-small-datasets.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 5.2 - Using convnets with small datasets\\n\",\n    \"\\n\",\n    \"This notebook contains the code sample found in Chapter 5, Section 2 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"## Training a convnet from scratch on a small dataset\\n\",\n    \"\\n\",\n    \"Having to train an image classification model using only very little data is a common situation, which you likely encounter yourself in \\n\",\n    \"practice if you ever do computer vision in a professional context.\\n\",\n    \"\\n\",\n    \"Having \\\"few\\\" samples can mean anywhere from a few hundreds to a few tens of thousands of images. As a practical example, we will focus on \\n\",\n    \"classifying images as \\\"dogs\\\" or \\\"cats\\\", in a dataset containing 4000 pictures of cats and dogs (2000 cats, 2000 dogs). We will use 2000 \\n\",\n    \"pictures for training, 1000 for validation, and finally 1000 for testing.\\n\",\n    \"\\n\",\n    \"In this section, we will review one basic strategy to tackle this problem: training a new model from scratch on what little data we have. We \\n\",\n    \"will start by naively training a small convnet on our 2000 training samples, without any regularization, to set a baseline for what can be \\n\",\n    \"achieved. This will get us to a classification accuracy of 71%. At that point, our main issue will be overfitting. Then we will introduce \\n\",\n    \"*data augmentation*, a powerful technique for mitigating overfitting in computer vision. By leveraging data augmentation, we will improve \\n\",\n    \"our network to reach an accuracy of 82%.\\n\",\n    \"\\n\",\n    \"In the next section, we will review two more essential techniques for applying deep learning to small datasets: *doing feature extraction \\n\",\n    \"with a pre-trained network* (this will get us to an accuracy of 90% to 93%), and *fine-tuning a pre-trained network* (this will get us to \\n\",\n    \"our final accuracy of 95%). Together, these three strategies -- training a small model from scratch, doing feature extracting using a \\n\",\n    \"pre-trained model, and fine-tuning a pre-trained model -- will constitute your future toolbox for tackling the problem of doing computer \\n\",\n    \"vision with small datasets.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The relevance of deep learning for small-data problems\\n\",\n    \"\\n\",\n    \"You will sometimes hear that deep learning only works when lots of data is available. This is in part a valid point: one fundamental \\n\",\n    \"characteristic of deep learning is that it is able to find interesting features in the training data on its own, without any need for manual \\n\",\n    \"feature engineering, and this can only be achieved when lots of training examples are available. This is especially true for problems where \\n\",\n    \"the input samples are very high-dimensional, like images.\\n\",\n    \"\\n\",\n    \"However, what constitutes \\\"lots\\\" of samples is relative -- relative to the size and depth of the network you are trying to train, for \\n\",\n    \"starters. It isn't possible to train a convnet to solve a complex problem with just a few tens of samples, but a few hundreds can \\n\",\n    \"potentially suffice if the model is small and well-regularized and if the task is simple. \\n\",\n    \"Because convnets learn local, translation-invariant features, they are very \\n\",\n    \"data-efficient on perceptual problems. Training a convnet from scratch on a very small image dataset will still yield reasonable results \\n\",\n    \"despite a relative lack of data, without the need for any custom feature engineering. You will see this in action in this section.\\n\",\n    \"\\n\",\n    \"But what's more, deep learning models are by nature highly repurposable: you can take, say, an image classification or speech-to-text model \\n\",\n    \"trained on a large-scale dataset then reuse it on a significantly different problem with only minor changes. Specifically, in the case of \\n\",\n    \"computer vision, many pre-trained models (usually trained on the ImageNet dataset) are now publicly available for download and can be used \\n\",\n    \"to bootstrap powerful vision models out of very little data. That's what we will do in the next section.\\n\",\n    \"\\n\",\n    \"For now, let's get started by getting our hands on the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Downloading the data\\n\",\n    \"\\n\",\n    \"The cats vs. dogs dataset that we will use isn't packaged with Keras. It was made available by Kaggle.com as part of a computer vision \\n\",\n    \"competition in late 2013, back when convnets weren't quite mainstream. You can download the original dataset at: \\n\",\n    \"`https://www.kaggle.com/c/dogs-vs-cats/data` (you will need to create a Kaggle account if you don't already have one -- don't worry, the \\n\",\n    \"process is painless).\\n\",\n    \"\\n\",\n    \"The pictures are medium-resolution color JPEGs. They look like this:\\n\",\n    \"\\n\",\n    \"![cats_vs_dogs_samples](https://s3.amazonaws.com/book.keras.io/img/ch5/cats_vs_dogs_samples.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Unsurprisingly, the cats vs. dogs Kaggle competition in 2013 was won by entrants who used convnets. The best entries could achieve up to \\n\",\n    \"95% accuracy. In our own example, we will get fairly close to this accuracy (in the next section), even though we will be training our \\n\",\n    \"models on less than 10% of the data that was available to the competitors.\\n\",\n    \"This original dataset contains 25,000 images of dogs and cats (12,500 from each class) and is 543MB large (compressed). After downloading \\n\",\n    \"and uncompressing it, we will create a new dataset containing three subsets: a training set with 1000 samples of each class, a validation \\n\",\n    \"set with 500 samples of each class, and finally a test set with 500 samples of each class.\\n\",\n    \"\\n\",\n    \"Here are a few lines of code to do this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, shutil\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# The path to the directory where the original\\n\",\n    \"# dataset was uncompressed\\n\",\n    \"original_dataset_dir = '/Users/fchollet/Downloads/kaggle_original_data'\\n\",\n    \"\\n\",\n    \"# The directory where we will\\n\",\n    \"# store our smaller dataset\\n\",\n    \"base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'\\n\",\n    \"os.mkdir(base_dir)\\n\",\n    \"\\n\",\n    \"# Directories for our training,\\n\",\n    \"# validation and test splits\\n\",\n    \"train_dir = os.path.join(base_dir, 'train')\\n\",\n    \"os.mkdir(train_dir)\\n\",\n    \"validation_dir = os.path.join(base_dir, 'validation')\\n\",\n    \"os.mkdir(validation_dir)\\n\",\n    \"test_dir = os.path.join(base_dir, 'test')\\n\",\n    \"os.mkdir(test_dir)\\n\",\n    \"\\n\",\n    \"# Directory with our training cat pictures\\n\",\n    \"train_cats_dir = os.path.join(train_dir, 'cats')\\n\",\n    \"os.mkdir(train_cats_dir)\\n\",\n    \"\\n\",\n    \"# Directory with our training dog pictures\\n\",\n    \"train_dogs_dir = os.path.join(train_dir, 'dogs')\\n\",\n    \"os.mkdir(train_dogs_dir)\\n\",\n    \"\\n\",\n    \"# Directory with our validation cat pictures\\n\",\n    \"validation_cats_dir = os.path.join(validation_dir, 'cats')\\n\",\n    \"os.mkdir(validation_cats_dir)\\n\",\n    \"\\n\",\n    \"# Directory with our validation dog pictures\\n\",\n    \"validation_dogs_dir = os.path.join(validation_dir, 'dogs')\\n\",\n    \"os.mkdir(validation_dogs_dir)\\n\",\n    \"\\n\",\n    \"# Directory with our validation cat pictures\\n\",\n    \"test_cats_dir = os.path.join(test_dir, 'cats')\\n\",\n    \"os.mkdir(test_cats_dir)\\n\",\n    \"\\n\",\n    \"# Directory with our validation dog pictures\\n\",\n    \"test_dogs_dir = os.path.join(test_dir, 'dogs')\\n\",\n    \"os.mkdir(test_dogs_dir)\\n\",\n    \"\\n\",\n    \"# Copy first 1000 cat images to train_cats_dir\\n\",\n    \"fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\\n\",\n    \"for fname in fnames:\\n\",\n    \"    src = os.path.join(original_dataset_dir, fname)\\n\",\n    \"    dst = os.path.join(train_cats_dir, fname)\\n\",\n    \"    shutil.copyfile(src, dst)\\n\",\n    \"\\n\",\n    \"# Copy next 500 cat images to validation_cats_dir\\n\",\n    \"fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\\n\",\n    \"for fname in fnames:\\n\",\n    \"    src = os.path.join(original_dataset_dir, fname)\\n\",\n    \"    dst = os.path.join(validation_cats_dir, fname)\\n\",\n    \"    shutil.copyfile(src, dst)\\n\",\n    \"    \\n\",\n    \"# Copy next 500 cat images to test_cats_dir\\n\",\n    \"fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\\n\",\n    \"for fname in fnames:\\n\",\n    \"    src = os.path.join(original_dataset_dir, fname)\\n\",\n    \"    dst = os.path.join(test_cats_dir, fname)\\n\",\n    \"    shutil.copyfile(src, dst)\\n\",\n    \"    \\n\",\n    \"# Copy first 1000 dog images to train_dogs_dir\\n\",\n    \"fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\\n\",\n    \"for fname in fnames:\\n\",\n    \"    src = os.path.join(original_dataset_dir, fname)\\n\",\n    \"    dst = os.path.join(train_dogs_dir, fname)\\n\",\n    \"    shutil.copyfile(src, dst)\\n\",\n    \"    \\n\",\n    \"# Copy next 500 dog images to validation_dogs_dir\\n\",\n    \"fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\\n\",\n    \"for fname in fnames:\\n\",\n    \"    src = os.path.join(original_dataset_dir, fname)\\n\",\n    \"    dst = os.path.join(validation_dogs_dir, fname)\\n\",\n    \"    shutil.copyfile(src, dst)\\n\",\n    \"    \\n\",\n    \"# Copy next 500 dog images to test_dogs_dir\\n\",\n    \"fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\\n\",\n    \"for fname in fnames:\\n\",\n    \"    src = os.path.join(original_dataset_dir, fname)\\n\",\n    \"    dst = os.path.join(test_dogs_dir, fname)\\n\",\n    \"    shutil.copyfile(src, dst)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As a sanity check, let's count how many pictures we have in each training split (train/validation/test):\"\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      \"total training cat images: 1000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('total training cat images:', len(os.listdir(train_cats_dir)))\"\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      \"total training dog images: 1000\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('total training dog images:', len(os.listdir(train_dogs_dir)))\"\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      \"total validation cat images: 500\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('total validation cat images:', len(os.listdir(validation_cats_dir)))\"\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      \"total validation dog images: 500\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('total validation dog images:', len(os.listdir(validation_dogs_dir)))\"\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      \"total test cat images: 500\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('total test cat images:', len(os.listdir(test_cats_dir)))\"\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      \"total test dog images: 500\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('total test dog images:', len(os.listdir(test_dogs_dir)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"So we have indeed 2000 training images, and then 1000 validation images and 1000 test images. In each split, there is the same number of \\n\",\n    \"samples from each class: this is a balanced binary classification problem, which means that classification accuracy will be an appropriate \\n\",\n    \"measure of success.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Building our network\\n\",\n    \"\\n\",\n    \"We've already built a small convnet for MNIST in the previous example, so you should be familiar with them. We will reuse the same \\n\",\n    \"general structure: our convnet will be a stack of alternated `Conv2D` (with `relu` activation) and `MaxPooling2D` layers.\\n\",\n    \"\\n\",\n    \"However, since we are dealing with bigger images and a more complex problem, we will make our network accordingly larger: it will have one \\n\",\n    \"more `Conv2D` + `MaxPooling2D` stage. This serves both to augment the capacity of the network, and to further reduce the size of the \\n\",\n    \"feature maps, so that they aren't overly large when we reach the `Flatten` layer. Here, since we start from inputs of size 150x150 (a \\n\",\n    \"somewhat arbitrary choice), we end up with feature maps of size 7x7 right before the `Flatten` layer.\\n\",\n    \"\\n\",\n    \"Note that the depth of the feature maps is progressively increasing in the network (from 32 to 128), while the size of the feature maps is \\n\",\n    \"decreasing (from 148x148 to 7x7). This is a pattern that you will see in almost all convnets.\\n\",\n    \"\\n\",\n    \"Since we are attacking a binary classification problem, we are ending the network with a single unit (a `Dense` layer of size 1) and a \\n\",\n    \"`sigmoid` activation. This unit will encode the probability that the network is looking at one class or the other.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"from keras import models\\n\",\n    \"\\n\",\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Conv2D(32, (3, 3), activation='relu',\\n\",\n    \"                        input_shape=(150, 150, 3)))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Conv2D(128, (3, 3), activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Conv2D(128, (3, 3), activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Flatten())\\n\",\n    \"model.add(layers.Dense(512, activation='relu'))\\n\",\n    \"model.add(layers.Dense(1, activation='sigmoid'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's take a look at how the dimensions of the feature maps change with every successive layer:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"conv2d_1 (Conv2D)            (None, 148, 148, 32)      896       \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32)        0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_2 (Conv2D)            (None, 72, 72, 64)        18496     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64)        0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_3 (Conv2D)            (None, 34, 34, 128)       73856     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_4 (Conv2D)            (None, 15, 15, 128)       147584    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128)         0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"flatten_1 (Flatten)          (None, 6272)              0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_1 (Dense)              (None, 512)               3211776   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_2 (Dense)              (None, 1)                 513       \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 3,453,121\\n\",\n      \"Trainable params: 3,453,121\\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    \"For our compilation step, we'll go with the `RMSprop` optimizer as usual. Since we ended our network with a single sigmoid unit, we will \\n\",\n    \"use binary crossentropy as our loss (as a reminder, check out the table in Chapter 4, section 5 for a cheatsheet on what loss function to \\n\",\n    \"use in various situations).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import optimizers\\n\",\n    \"\\n\",\n    \"model.compile(loss='binary_crossentropy',\\n\",\n    \"              optimizer=optimizers.RMSprop(lr=1e-4),\\n\",\n    \"              metrics=['acc'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Data preprocessing\\n\",\n    \"\\n\",\n    \"As you already know by now, data should be formatted into appropriately pre-processed floating point tensors before being fed into our \\n\",\n    \"network. Currently, our data sits on a drive as JPEG files, so the steps for getting it into our network are roughly:\\n\",\n    \"\\n\",\n    \"* Read the picture files.\\n\",\n    \"* Decode the JPEG content to RBG grids of pixels.\\n\",\n    \"* Convert these into floating point tensors.\\n\",\n    \"* Rescale the pixel values (between 0 and 255) to the [0, 1] interval (as you know, neural networks prefer to deal with small input values).\\n\",\n    \"\\n\",\n    \"It may seem a bit daunting, but thankfully Keras has utilities to take care of these steps automatically. Keras has a module with image \\n\",\n    \"processing helper tools, located at `keras.preprocessing.image`. In particular, it contains the class `ImageDataGenerator` which allows to \\n\",\n    \"quickly set up Python generators that can automatically turn image files on disk into batches of pre-processed tensors. This is what we \\n\",\n    \"will use here.\"\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      \"Found 2000 images belonging to 2 classes.\\n\",\n      \"Found 1000 images belonging to 2 classes.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.preprocessing.image import ImageDataGenerator\\n\",\n    \"\\n\",\n    \"# All images will be rescaled by 1./255\\n\",\n    \"train_datagen = ImageDataGenerator(rescale=1./255)\\n\",\n    \"test_datagen = ImageDataGenerator(rescale=1./255)\\n\",\n    \"\\n\",\n    \"train_generator = train_datagen.flow_from_directory(\\n\",\n    \"        # This is the target directory\\n\",\n    \"        train_dir,\\n\",\n    \"        # All images will be resized to 150x150\\n\",\n    \"        target_size=(150, 150),\\n\",\n    \"        batch_size=20,\\n\",\n    \"        # Since we use binary_crossentropy loss, we need binary labels\\n\",\n    \"        class_mode='binary')\\n\",\n    \"\\n\",\n    \"validation_generator = test_datagen.flow_from_directory(\\n\",\n    \"        validation_dir,\\n\",\n    \"        target_size=(150, 150),\\n\",\n    \"        batch_size=20,\\n\",\n    \"        class_mode='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's take a look at the output of one of these generators: it yields batches of 150x150 RGB images (shape `(20, 150, 150, 3)`) and binary \\n\",\n    \"labels (shape `(20,)`). 20 is the number of samples in each batch (the batch size). Note that the generator yields these batches \\n\",\n    \"indefinitely: it just loops endlessly over the images present in the target folder. For this reason, we need to `break` the iteration loop \\n\",\n    \"at some point.\"\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      \"data batch shape: (20, 150, 150, 3)\\n\",\n      \"labels batch shape: (20,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for data_batch, labels_batch in train_generator:\\n\",\n    \"    print('data batch shape:', data_batch.shape)\\n\",\n    \"    print('labels batch shape:', labels_batch.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's fit our model to the data using the generator. We do it using the `fit_generator` method, the equivalent of `fit` for data generators \\n\",\n    \"like ours. It expects as first argument a Python generator that will yield batches of inputs and targets indefinitely, like ours does. \\n\",\n    \"Because the data is being generated endlessly, the generator needs to know example how many samples to draw from the generator before \\n\",\n    \"declaring an epoch over. This is the role of the `steps_per_epoch` argument: after having drawn `steps_per_epoch` batches from the \\n\",\n    \"generator, i.e. after having run for `steps_per_epoch` gradient descent steps, the fitting process will go to the next epoch. In our case, \\n\",\n    \"batches are 20-sample large, so it will take 100 batches until we see our target of 2000 samples.\\n\",\n    \"\\n\",\n    \"When using `fit_generator`, one may pass a `validation_data` argument, much like with the `fit` method. Importantly, this argument is \\n\",\n    \"allowed to be a data generator itself, but it could be a tuple of Numpy arrays as well. If you pass a generator as `validation_data`, then \\n\",\n    \"this generator is expected to yield batches of validation data endlessly, and thus you should also specify the `validation_steps` argument, \\n\",\n    \"which tells the process how many batches to draw from the validation generator for evaluation.\"\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      \"Epoch 1/30\\n\",\n      \"100/100 [==============================] - 9s - loss: 0.6898 - acc: 0.5285 - val_loss: 0.6724 - val_acc: 0.5950\\n\",\n      \"Epoch 2/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.6543 - acc: 0.6340 - val_loss: 0.6565 - val_acc: 0.5950\\n\",\n      \"Epoch 3/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.6143 - acc: 0.6690 - val_loss: 0.6116 - val_acc: 0.6650\\n\",\n      \"Epoch 4/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.5626 - acc: 0.7125 - val_loss: 0.5774 - val_acc: 0.6970\\n\",\n      \"Epoch 5/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.5266 - acc: 0.7335 - val_loss: 0.5726 - val_acc: 0.6960\\n\",\n      \"Epoch 6/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.5007 - acc: 0.7550 - val_loss: 0.6075 - val_acc: 0.6580\\n\",\n      \"Epoch 7/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.4723 - acc: 0.7840 - val_loss: 0.5516 - val_acc: 0.7060\\n\",\n      \"Epoch 8/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.4521 - acc: 0.7875 - val_loss: 0.5724 - val_acc: 0.6980\\n\",\n      \"Epoch 9/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.4163 - acc: 0.8095 - val_loss: 0.5653 - val_acc: 0.7140\\n\",\n      \"Epoch 10/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.3988 - acc: 0.8185 - val_loss: 0.5508 - val_acc: 0.7180\\n\",\n      \"Epoch 11/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.3694 - acc: 0.8385 - val_loss: 0.5712 - val_acc: 0.7300\\n\",\n      \"Epoch 12/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.3385 - acc: 0.8465 - val_loss: 0.6097 - val_acc: 0.7110\\n\",\n      \"Epoch 13/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.3229 - acc: 0.8565 - val_loss: 0.5827 - val_acc: 0.7150\\n\",\n      \"Epoch 14/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.2962 - acc: 0.8720 - val_loss: 0.5928 - val_acc: 0.7190\\n\",\n      \"Epoch 15/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.2684 - acc: 0.9005 - val_loss: 0.5921 - val_acc: 0.7190\\n\",\n      \"Epoch 16/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.2509 - acc: 0.8980 - val_loss: 0.6148 - val_acc: 0.7250\\n\",\n      \"Epoch 17/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.2221 - acc: 0.9110 - val_loss: 0.6487 - val_acc: 0.7010\\n\",\n      \"Epoch 18/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.2021 - acc: 0.9250 - val_loss: 0.6185 - val_acc: 0.7300\\n\",\n      \"Epoch 19/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.1824 - acc: 0.9310 - val_loss: 0.7713 - val_acc: 0.7020\\n\",\n      \"Epoch 20/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.1579 - acc: 0.9425 - val_loss: 0.6657 - val_acc: 0.7260\\n\",\n      \"Epoch 21/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.1355 - acc: 0.9550 - val_loss: 0.8077 - val_acc: 0.7040\\n\",\n      \"Epoch 22/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.1247 - acc: 0.9545 - val_loss: 0.7726 - val_acc: 0.7080\\n\",\n      \"Epoch 23/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.1111 - acc: 0.9585 - val_loss: 0.7387 - val_acc: 0.7220\\n\",\n      \"Epoch 24/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.0932 - acc: 0.9710 - val_loss: 0.8196 - val_acc: 0.7050\\n\",\n      \"Epoch 25/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.0707 - acc: 0.9790 - val_loss: 0.9012 - val_acc: 0.7190\\n\",\n      \"Epoch 26/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.0625 - acc: 0.9855 - val_loss: 1.0437 - val_acc: 0.6970\\n\",\n      \"Epoch 27/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.0611 - acc: 0.9820 - val_loss: 0.9831 - val_acc: 0.7060\\n\",\n      \"Epoch 28/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.0488 - acc: 0.9865 - val_loss: 0.9721 - val_acc: 0.7310\\n\",\n      \"Epoch 29/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.0375 - acc: 0.9915 - val_loss: 0.9987 - val_acc: 0.7100\\n\",\n      \"Epoch 30/30\\n\",\n      \"100/100 [==============================] - 8s - loss: 0.0387 - acc: 0.9895 - val_loss: 1.0139 - val_acc: 0.7240\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"history = model.fit_generator(\\n\",\n    \"      train_generator,\\n\",\n    \"      steps_per_epoch=100,\\n\",\n    \"      epochs=30,\\n\",\n    \"      validation_data=validation_generator,\\n\",\n    \"      validation_steps=50)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"It is good practice to always save your models after training:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.save('cats_and_dogs_small_1.h5')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the loss and accuracy of the model over the training and validation data during training:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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plCOXeRBiRqV8Rkytb11eISjVrucY4++uhdLkh68MEHueKKKyr9XMuW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\\nYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fc7aa0432b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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6CGiGQAg4GyAKr6CjAL6AasAQ4Ct0SqscYYk13b/vjjRctrDxkCjzwC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\\nY0wc+n+desdoPzWBogAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fc7aa22d1d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(acc))\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"These plots are characteristic of overfitting. Our training accuracy increases linearly over time, until it reaches nearly 100%, while our \\n\",\n    \"validation accuracy stalls at 70-72%. Our validation loss reaches its minimum after only five epochs then stalls, while the training loss \\n\",\n    \"keeps decreasing linearly until it reaches nearly 0.\\n\",\n    \"\\n\",\n    \"Because we only have relatively few training samples (2000), overfitting is going to be our number one concern. You already know about a \\n\",\n    \"number of techniques that can help mitigate overfitting, such as dropout and weight decay (L2 regularization). We are now going to \\n\",\n    \"introduce a new one, specific to computer vision, and used almost universally when processing images with deep learning models: *data \\n\",\n    \"augmentation*.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using data augmentation\\n\",\n    \"\\n\",\n    \"Overfitting is caused by having too few samples to learn from, rendering us unable to train a model able to generalize to new data. \\n\",\n    \"Given infinite data, our model would be exposed to every possible aspect of the data distribution at hand: we would never overfit. Data \\n\",\n    \"augmentation takes the approach of generating more training data from existing training samples, by \\\"augmenting\\\" the samples via a number \\n\",\n    \"of random transformations that yield believable-looking images. The goal is that at training time, our model would never see the exact same \\n\",\n    \"picture twice. This helps the model get exposed to more aspects of the data and generalize better.\\n\",\n    \"\\n\",\n    \"In Keras, this can be done by configuring a number of random transformations to be performed on the images read by our `ImageDataGenerator` \\n\",\n    \"instance. Let's get started with an example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"datagen = ImageDataGenerator(\\n\",\n    \"      rotation_range=40,\\n\",\n    \"      width_shift_range=0.2,\\n\",\n    \"      height_shift_range=0.2,\\n\",\n    \"      shear_range=0.2,\\n\",\n    \"      zoom_range=0.2,\\n\",\n    \"      horizontal_flip=True,\\n\",\n    \"      fill_mode='nearest')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"These are just a few of the options available (for more, see the Keras documentation). Let's quickly go over what we just wrote:\\n\",\n    \"\\n\",\n    \"* `rotation_range` is a value in degrees (0-180), a range within which to randomly rotate pictures.\\n\",\n    \"* `width_shift` and `height_shift` are ranges (as a fraction of total width or height) within which to randomly translate pictures \\n\",\n    \"vertically or horizontally.\\n\",\n    \"* `shear_range` is for randomly applying shearing transformations.\\n\",\n    \"* `zoom_range` is for randomly zooming inside pictures.\\n\",\n    \"* `horizontal_flip` is for randomly flipping half of the images horizontally -- relevant when there are no assumptions of horizontal \\n\",\n    \"asymmetry (e.g. real-world pictures).\\n\",\n    \"* `fill_mode` is the strategy used for filling in newly created pixels, which can appear after a rotation or a width/height shift.\\n\",\n    \"\\n\",\n    \"Let's take a look at our augmented images:\"\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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DVY87Nch3bG4SpKHWR8EWzCs2DNL5Rx+JxyJZVCLZ/mUjwrbVn/1ndd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\\nAF8F8HsQbTY+A+CVBzSeLwPQ6Od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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fc7aa005b00>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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qS1Nt6LCe9BiGPwHhvTPsW0ZSbWc7SfhfLZuA8b2chG1uRSWApciFi+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\\nSxRLbhc/xQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fc7a9f96240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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xs4mWNcEJ2ACucAa/sxLJquMjB+XuOiTa8FU7CTfi4Ts4GrGcSX5aUA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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fc7a9f24630>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Fzn1fPPGGwbXz2jC5JhcfTqCkhYzcy8bvoRrk5BrlsBCVenIG9bAXk5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\\nUpjJTGYyJTOlMJOZzGRK/j+IatmKbOuBTgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fc7a9f6f048>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# This is module with image preprocessing utilities\\n\",\n    \"from keras.preprocessing import image\\n\",\n    \"\\n\",\n    \"fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]\\n\",\n    \"\\n\",\n    \"# We pick one image to \\\"augment\\\"\\n\",\n    \"img_path = fnames[3]\\n\",\n    \"\\n\",\n    \"# Read the image and resize it\\n\",\n    \"img = image.load_img(img_path, target_size=(150, 150))\\n\",\n    \"\\n\",\n    \"# Convert it to a Numpy array with shape (150, 150, 3)\\n\",\n    \"x = image.img_to_array(img)\\n\",\n    \"\\n\",\n    \"# Reshape it to (1, 150, 150, 3)\\n\",\n    \"x = x.reshape((1,) + x.shape)\\n\",\n    \"\\n\",\n    \"# The .flow() command below generates batches of randomly transformed images.\\n\",\n    \"# It will loop indefinitely, so we need to `break` the loop at some point!\\n\",\n    \"i = 0\\n\",\n    \"for batch in datagen.flow(x, batch_size=1):\\n\",\n    \"    plt.figure(i)\\n\",\n    \"    imgplot = plt.imshow(image.array_to_img(batch[0]))\\n\",\n    \"    i += 1\\n\",\n    \"    if i % 4 == 0:\\n\",\n    \"        break\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If we train a new network using this data augmentation configuration, our network will never see twice the same input. However, the inputs \\n\",\n    \"that it sees are still heavily intercorrelated, since they come from a small number of original images -- we cannot produce new information, \\n\",\n    \"we can only remix existing information. As such, this might not be quite enough to completely get rid of overfitting. To further fight \\n\",\n    \"overfitting, we will also add a Dropout layer to our model, right before the densely-connected classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Conv2D(32, (3, 3), activation='relu',\\n\",\n    \"                        input_shape=(150, 150, 3)))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Conv2D(128, (3, 3), activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Conv2D(128, (3, 3), activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling2D((2, 2)))\\n\",\n    \"model.add(layers.Flatten())\\n\",\n    \"model.add(layers.Dropout(0.5))\\n\",\n    \"model.add(layers.Dense(512, activation='relu'))\\n\",\n    \"model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"model.compile(loss='binary_crossentropy',\\n\",\n    \"              optimizer=optimizers.RMSprop(lr=1e-4),\\n\",\n    \"              metrics=['acc'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's train our network using data augmentation and dropout:\"\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      \"Found 2000 images belonging to 2 classes.\\n\",\n      \"Found 1000 images belonging to 2 classes.\\n\",\n      \"Epoch 1/100\\n\",\n      \"100/100 [==============================] - 24s - loss: 0.6857 - acc: 0.5447 - val_loss: 0.6620 - val_acc: 0.5888\\n\",\n      \"Epoch 2/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.6710 - acc: 0.5675 - val_loss: 0.6606 - val_acc: 0.5825\\n\",\n      \"Epoch 3/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.6609 - acc: 0.5913 - val_loss: 0.6663 - val_acc: 0.5711.594 - ETA: 7s - loss: 0.6655 - ETA: 5s - los - ETA: 1s - loss: 0.6620 - acc: \\n\",\n      \"Epoch 4/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.6446 - acc: 0.6178 - val_loss: 0.6200 - val_acc: 0.6379\\n\",\n      \"Epoch 5/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.6267 - acc: 0.6325 - val_loss: 0.6280 - val_acc: 0.5996\\n\",\n      \"Epoch 6/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.6080 - acc: 0.6631 - val_loss: 0.6841 - val_acc: 0.5490\\n\",\n      \"Epoch 7/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5992 - acc: 0.6700 - val_loss: 0.5717 - val_acc: 0.6946\\n\",\n      \"Epoch 8/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5908 - acc: 0.6819 - val_loss: 0.5858 - val_acc: 0.6764\\n\",\n      \"Epoch 9/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5869 - acc: 0.6856 - val_loss: 0.5658 - val_acc: 0.6785\\n\",\n      \"Epoch 10/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.5692 - acc: 0.6934 - val_loss: 0.5409 - val_acc: 0.7170\\n\",\n      \"Epoch 11/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5708 - acc: 0.6897 - val_loss: 0.5325 - val_acc: 0.7274\\n\",\n      \"Epoch 12/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.5583 - acc: 0.7047 - val_loss: 0.5683 - val_acc: 0.7126\\n\",\n      \"Epoch 13/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5602 - acc: 0.7069 - val_loss: 0.6010 - val_acc: 0.6593\\n\",\n      \"Epoch 14/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5510 - acc: 0.7231 - val_loss: 0.5387 - val_acc: 0.7229\\n\",\n      \"Epoch 15/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.5527 - acc: 0.7175 - val_loss: 0.5204 - val_acc: 0.7322\\n\",\n      \"Epoch 16/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.5426 - acc: 0.7181 - val_loss: 0.5083 - val_acc: 0.7410\\n\",\n      \"Epoch 17/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.5399 - acc: 0.7344 - val_loss: 0.5103 - val_acc: 0.7468\\n\",\n      \"Epoch 18/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.5375 - acc: 0.7312 - val_loss: 0.5133 - val_acc: 0.7430\\n\",\n      \"Epoch 19/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5308 - acc: 0.7338 - val_loss: 0.4936 - val_acc: 0.7610\\n\",\n      \"Epoch 20/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5225 - acc: 0.7387 - val_loss: 0.4952 - val_acc: 0.7563\\n\",\n      \"Epoch 21/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5180 - acc: 0.7491 - val_loss: 0.4999 - val_acc: 0.7481\\n\",\n      \"Epoch 22/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.5118 - acc: 0.7538 - val_loss: 0.4770 - val_acc: 0.7764\\n\",\n      \"Epoch 23/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5245 - acc: 0.7378 - val_loss: 0.4929 - val_acc: 0.7671\\n\",\n      \"Epoch 24/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5136 - acc: 0.7503 - val_loss: 0.4709 - val_acc: 0.7732\\n\",\n      \"Epoch 25/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4980 - acc: 0.7512 - val_loss: 0.4775 - val_acc: 0.7684\\n\",\n      \"Epoch 26/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4875 - acc: 0.7622 - val_loss: 0.4745 - val_acc: 0.7790\\n\",\n      \"Epoch 27/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.5044 - acc: 0.7578 - val_loss: 0.5000 - val_acc: 0.7403\\n\",\n      \"Epoch 28/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4948 - acc: 0.7603 - val_loss: 0.4619 - val_acc: 0.7754\\n\",\n      \"Epoch 29/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4898 - acc: 0.7578 - val_loss: 0.4730 - val_acc: 0.7726\\n\",\n      \"Epoch 30/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4808 - acc: 0.7691 - val_loss: 0.4599 - val_acc: 0.7716\\n\",\n      \"Epoch 31/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4792 - acc: 0.7678 - val_loss: 0.4671 - val_acc: 0.7790\\n\",\n      \"Epoch 32/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4723 - acc: 0.7716 - val_loss: 0.4451 - val_acc: 0.7849\\n\",\n      \"Epoch 33/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4750 - acc: 0.7694 - val_loss: 0.4827 - val_acc: 0.7665\\n\",\n      \"Epoch 34/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4816 - acc: 0.7647 - val_loss: 0.4953 - val_acc: 0.7513\\n\",\n      \"Epoch 35/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4598 - acc: 0.7813 - val_loss: 0.4426 - val_acc: 0.7843\\n\",\n      \"Epoch 36/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.4643 - acc: 0.7781 - val_loss: 0.4692 - val_acc: 0.7680\\n\",\n      \"Epoch 37/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4675 - acc: 0.7778 - val_loss: 0.4849 - val_acc: 0.7633\\n\",\n      \"Epoch 38/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4658 - acc: 0.7737 - val_loss: 0.4632 - val_acc: 0.7760\\n\",\n      \"Epoch 39/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4581 - acc: 0.7866 - val_loss: 0.4489 - val_acc: 0.7880\\n\",\n      \"Epoch 40/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.4485 - acc: 0.7856 - val_loss: 0.4479 - val_acc: 0.7931\\n\",\n      \"Epoch 41/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4637 - acc: 0.7759 - val_loss: 0.4453 - val_acc: 0.7990\\n\",\n      \"Epoch 42/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4528 - acc: 0.7841 - val_loss: 0.4758 - val_acc: 0.7868\\n\",\n      \"Epoch 43/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4481 - acc: 0.7856 - val_loss: 0.4472 - val_acc: 0.7893\\n\",\n      \"Epoch 44/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4540 - acc: 0.7953 - val_loss: 0.4366 - val_acc: 0.7867A: 6s - loss: 0.4523 - acc: - ETA: \\n\",\n      \"Epoch 45/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4411 - acc: 0.7919 - val_loss: 0.4708 - val_acc: 0.7697\\n\",\n      \"Epoch 46/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4493 - acc: 0.7869 - val_loss: 0.4366 - val_acc: 0.7829\\n\",\n      \"Epoch 47/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4436 - acc: 0.7916 - val_loss: 0.4307 - val_acc: 0.8090\\n\",\n      \"Epoch 48/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4391 - acc: 0.7928 - val_loss: 0.4203 - val_acc: 0.8065\\n\",\n      \"Epoch 49/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.4284 - acc: 0.8053 - val_loss: 0.4422 - val_acc: 0.8041\\n\",\n      \"Epoch 50/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4492 - acc: 0.7906 - val_loss: 0.5422 - val_acc: 0.7437\\n\",\n      \"Epoch 51/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4292 - acc: 0.7953 - val_loss: 0.4446 - val_acc: 0.7932\\n\",\n      \"Epoch 52/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4275 - acc: 0.8037 - val_loss: 0.4287 - val_acc: 0.7989\\n\",\n      \"Epoch 53/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4297 - acc: 0.7975 - val_loss: 0.4091 - val_acc: 0.8046\\n\",\n      \"Epoch 54/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.4198 - acc: 0.7978 - val_loss: 0.4413 - val_acc: 0.7964\\n\",\n      \"Epoch 55/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.4195 - acc: 0.8019 - val_loss: 0.4265 - val_acc: 0.8001\\n\",\n      \"Epoch 56/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4081 - acc: 0.8056 - val_loss: 0.4374 - val_acc: 0.7957\\n\",\n      \"Epoch 57/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4214 - acc: 0.8006 - val_loss: 0.4228 - val_acc: 0.8020\\n\",\n      \"Epoch 58/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4050 - acc: 0.8097 - val_loss: 0.4332 - val_acc: 0.7900\\n\",\n      \"Epoch 59/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4162 - acc: 0.8134 - val_loss: 0.4088 - val_acc: 0.8099\\n\",\n      \"Epoch 60/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4042 - acc: 0.8141 - val_loss: 0.4436 - val_acc: 0.7957\\n\",\n      \"Epoch 61/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.4016 - acc: 0.8212 - val_loss: 0.4082 - val_acc: 0.8189\\n\",\n      \"Epoch 62/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4167 - acc: 0.8097 - val_loss: 0.3935 - val_acc: 0.8236\\n\",\n      \"Epoch 63/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.4052 - acc: 0.8138 - val_loss: 0.4509 - val_acc: 0.7824\\n\",\n      \"Epoch 64/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.4011 - acc: 0.8209 - val_loss: 0.3874 - val_acc: 0.8299\\n\",\n      \"Epoch 65/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3966 - acc: 0.8131 - val_loss: 0.4328 - val_acc: 0.7970\\n\",\n      \"Epoch 66/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.3889 - acc: 0.8163 - val_loss: 0.4766 - val_acc: 0.7719\\n\",\n      \"Epoch 67/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3960 - acc: 0.8163 - val_loss: 0.3859 - val_acc: 0.8325\\n\",\n      \"Epoch 68/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3893 - acc: 0.8231 - val_loss: 0.4172 - val_acc: 0.8128\\n\",\n      \"Epoch 69/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.3828 - acc: 0.8219 - val_loss: 0.4023 - val_acc: 0.8215 loss: 0.3881 - acc:\\n\",\n      \"Epoch 70/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3909 - acc: 0.8275 - val_loss: 0.4275 - val_acc: 0.8008\\n\",\n      \"Epoch 71/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3826 - acc: 0.8244 - val_loss: 0.3815 - val_acc: 0.8177\\n\",\n      \"Epoch 72/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3837 - acc: 0.8272 - val_loss: 0.4040 - val_acc: 0.8287\\n\",\n      \"Epoch 73/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.3812 - acc: 0.8222 - val_loss: 0.4039 - val_acc: 0.8058\\n\",\n      \"Epoch 74/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3829 - acc: 0.8281 - val_loss: 0.4204 - val_acc: 0.8015\\n\",\n      \"Epoch 75/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3708 - acc: 0.8350 - val_loss: 0.4083 - val_acc: 0.8204\\n\",\n      \"Epoch 76/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3831 - acc: 0.8216 - val_loss: 0.3899 - val_acc: 0.8215\\n\",\n      \"Epoch 77/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3695 - acc: 0.8375 - val_loss: 0.3963 - val_acc: 0.8293\\n\",\n      \"Epoch 78/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3809 - acc: 0.8234 - val_loss: 0.4046 - val_acc: 0.8236\\n\",\n      \"Epoch 79/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3637 - acc: 0.8362 - val_loss: 0.3990 - val_acc: 0.8325\\n\",\n      \"Epoch 80/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3596 - acc: 0.8400 - val_loss: 0.3925 - val_acc: 0.8350\\n\",\n      \"Epoch 81/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3762 - acc: 0.8303 - val_loss: 0.3813 - val_acc: 0.8331\\n\",\n      \"Epoch 82/100\\n\",\n      \"100/100 [==============================] - 23s - loss: 0.3672 - acc: 0.8347 - val_loss: 0.4539 - val_acc: 0.7931\\n\",\n      \"Epoch 83/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3636 - acc: 0.8353 - val_loss: 0.3988 - val_acc: 0.8261\\n\",\n      \"Epoch 84/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3503 - acc: 0.8453 - val_loss: 0.3987 - val_acc: 0.8325\\n\",\n      \"Epoch 85/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3586 - acc: 0.8437 - val_loss: 0.3842 - val_acc: 0.8306\\n\",\n      \"Epoch 86/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3624 - acc: 0.8353 - val_loss: 0.4100 - val_acc: 0.8196.834\\n\",\n      \"Epoch 87/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3596 - acc: 0.8422 - val_loss: 0.3814 - val_acc: 0.8331\\n\",\n      \"Epoch 88/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3487 - acc: 0.8494 - val_loss: 0.4266 - val_acc: 0.8109\\n\",\n      \"Epoch 89/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3598 - acc: 0.8400 - val_loss: 0.4076 - val_acc: 0.8325\\n\",\n      \"Epoch 90/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3510 - acc: 0.8450 - val_loss: 0.3762 - val_acc: 0.8388\\n\",\n      \"Epoch 91/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3458 - acc: 0.8450 - val_loss: 0.4684 - val_acc: 0.8015\\n\",\n      \"Epoch 92/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3454 - acc: 0.8441 - val_loss: 0.4017 - val_acc: 0.8204\\n\",\n      \"Epoch 93/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3402 - acc: 0.8487 - val_loss: 0.3928 - val_acc: 0.8204\\n\",\n      \"Epoch 94/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3569 - acc: 0.8394 - val_loss: 0.4005 - val_acc: 0.8338\\n\",\n      \"Epoch 95/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3425 - acc: 0.8494 - val_loss: 0.3641 - val_acc: 0.8439\\n\",\n      \"Epoch 96/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3335 - acc: 0.8531 - val_loss: 0.3811 - val_acc: 0.8363\\n\",\n      \"Epoch 97/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3204 - acc: 0.8581 - val_loss: 0.3786 - val_acc: 0.8331\\n\",\n      \"Epoch 98/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3250 - acc: 0.8606 - val_loss: 0.4205 - val_acc: 0.8236\\n\",\n      \"Epoch 99/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3255 - acc: 0.8581 - val_loss: 0.3518 - val_acc: 0.8460\\n\",\n      \"Epoch 100/100\\n\",\n      \"100/100 [==============================] - 22s - loss: 0.3280 - acc: 0.8491 - val_loss: 0.3776 - val_acc: 0.8439\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_datagen = ImageDataGenerator(\\n\",\n    \"    rescale=1./255,\\n\",\n    \"    rotation_range=40,\\n\",\n    \"    width_shift_range=0.2,\\n\",\n    \"    height_shift_range=0.2,\\n\",\n    \"    shear_range=0.2,\\n\",\n    \"    zoom_range=0.2,\\n\",\n    \"    horizontal_flip=True,)\\n\",\n    \"\\n\",\n    \"# Note that the validation data should not be augmented!\\n\",\n    \"test_datagen = ImageDataGenerator(rescale=1./255)\\n\",\n    \"\\n\",\n    \"train_generator = train_datagen.flow_from_directory(\\n\",\n    \"        # This is the target directory\\n\",\n    \"        train_dir,\\n\",\n    \"        # All images will be resized to 150x150\\n\",\n    \"        target_size=(150, 150),\\n\",\n    \"        batch_size=32,\\n\",\n    \"        # Since we use binary_crossentropy loss, we need binary labels\\n\",\n    \"        class_mode='binary')\\n\",\n    \"\\n\",\n    \"validation_generator = test_datagen.flow_from_directory(\\n\",\n    \"        validation_dir,\\n\",\n    \"        target_size=(150, 150),\\n\",\n    \"        batch_size=32,\\n\",\n    \"        class_mode='binary')\\n\",\n    \"\\n\",\n    \"history = model.fit_generator(\\n\",\n    \"      train_generator,\\n\",\n    \"      steps_per_epoch=100,\\n\",\n    \"      epochs=100,\\n\",\n    \"      validation_data=validation_generator,\\n\",\n    \"      validation_steps=50)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's save our model -- we will be using it in the section on convnet visualization.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.save('cats_and_dogs_small_2.h5')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot our results again:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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tjfLpLeUg9JVC8vD845p+I18vLSexkuvxzWr098zhpzSWfyZsBcNguA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\\njO4AmfoAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fc7aa096438>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Oop+MUvoqN4oOzowc8/h507XfQdx6kf5IzoB7Rta5kyJ02K+/UTCUfy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\\nXPQdx3HqES76juM49Yj/D4G0dg3mPwl3AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fc7a9dede48>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(acc))\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Thanks to data augmentation and dropout, we are no longer overfitting: the training curves are rather closely tracking the validation \\n\",\n    \"curves. We are now able to reach an accuracy of 82%, a 15% relative improvement over the non-regularized model.\\n\",\n    \"\\n\",\n    \"By leveraging regularization techniques even further and by tuning the network's parameters (such as the number of filters per convolution \\n\",\n    \"layer, or the number of layers in the network), we may be able to get an even better accuracy, likely up to 86-87%. However, it would prove \\n\",\n    \"very difficult to go any higher just by training our own convnet from scratch, simply because we have so little data to work with. As a \\n\",\n    \"next step to improve our accuracy on this problem, we will have to leverage a pre-trained model, which will be the focus of the next two \\n\",\n    \"sections.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/5.3-using-a-pretrained-convnet.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Using a pre-trained convnet\\n\",\n    \"\\n\",\n    \"This notebook contains the code sample found in Chapter 5, Section 3 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"A common and highly effective approach to deep learning on small image datasets is to leverage a pre-trained network. A pre-trained network \\n\",\n    \"is simply a saved network previously trained on a large dataset, typically on a large-scale image classification task. If this original \\n\",\n    \"dataset is large enough and general enough, then the spatial feature hierarchy learned by the pre-trained network can effectively act as a \\n\",\n    \"generic model of our visual world, and hence its features can prove useful for many different computer vision problems, even though these \\n\",\n    \"new problems might involve completely different classes from those of the original task. For instance, one might train a network on \\n\",\n    \"ImageNet (where classes are mostly animals and everyday objects) and then re-purpose this trained network for something as remote as \\n\",\n    \"identifying furniture items in images. Such portability of learned features across different problems is a key advantage of deep learning \\n\",\n    \"compared to many older shallow learning approaches, and it makes deep learning very effective for small-data problems.\\n\",\n    \"\\n\",\n    \"In our case, we will consider a large convnet trained on the ImageNet dataset (1.4 million labeled images and 1000 different classes). \\n\",\n    \"ImageNet contains many animal classes, including different species of cats and dogs, and we can thus expect to perform very well on our cat \\n\",\n    \"vs. dog classification problem.\\n\",\n    \"\\n\",\n    \"We will use the VGG16 architecture, developed by Karen Simonyan and Andrew Zisserman in 2014, a simple and widely used convnet architecture \\n\",\n    \"for ImageNet. Although it is a bit of an older model, far from the current state of the art and somewhat heavier than many other recent \\n\",\n    \"models, we chose it because its architecture is similar to what you are already familiar with, and easy to understand without introducing \\n\",\n    \"any new concepts. This may be your first encounter with one of these cutesie model names -- VGG, ResNet, Inception, Inception-ResNet, \\n\",\n    \"Xception... you will get used to them, as they will come up frequently if you keep doing deep learning for computer vision.\\n\",\n    \"\\n\",\n    \"There are two ways to leverage a pre-trained network: *feature extraction* and *fine-tuning*. We will cover both of them. Let's start with \\n\",\n    \"feature extraction.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Feature extraction\\n\",\n    \"\\n\",\n    \"Feature extraction consists of using the representations learned by a previous network to extract interesting features from new samples. \\n\",\n    \"These features are then run through a new classifier, which is trained from scratch.\\n\",\n    \"\\n\",\n    \"As we saw previously, convnets used for image classification comprise two parts: they start with a series of pooling and convolution \\n\",\n    \"layers, and they end with a densely-connected classifier. The first part is called the \\\"convolutional base\\\" of the model. In the case of \\n\",\n    \"convnets, \\\"feature extraction\\\" will simply consist of taking the convolutional base of a previously-trained network, running the new data \\n\",\n    \"through it, and training a new classifier on top of the output.\\n\",\n    \"\\n\",\n    \"![swapping FC classifiers](https://s3.amazonaws.com/book.keras.io/img/ch5/swapping_fc_classifier.png)\\n\",\n    \"\\n\",\n    \"Why only reuse the convolutional base? Could we reuse the densely-connected classifier as well? In general, it should be avoided. The \\n\",\n    \"reason is simply that the representations learned by the convolutional base are likely to be more generic and therefore more reusable: the \\n\",\n    \"feature maps of a convnet are presence maps of generic concepts over a picture, which is likely to be useful regardless of the computer \\n\",\n    \"vision problem at hand. On the other end, the representations learned by the classifier will necessarily be very specific to the set of \\n\",\n    \"classes that the model was trained on -- they will only contain information about the presence probability of this or that class in the \\n\",\n    \"entire picture. Additionally, representations found in densely-connected layers no longer contain any information about _where_ objects are \\n\",\n    \"located in the input image: these layers get rid of the notion of space, whereas the object location is still described by convolutional \\n\",\n    \"feature maps. For problems where object location matters, densely-connected features would be largely useless.\\n\",\n    \"\\n\",\n    \"Note that the level of generality (and therefore reusability) of the representations extracted by specific convolution layers depends on \\n\",\n    \"the depth of the layer in the model. Layers that come earlier in the model extract local, highly generic feature maps (such as visual \\n\",\n    \"edges, colors, and textures), while layers higher-up extract more abstract concepts (such as \\\"cat ear\\\" or \\\"dog eye\\\"). So if your new \\n\",\n    \"dataset differs a lot from the dataset that the original model was trained on, you may be better off using only the first few layers of the \\n\",\n    \"model to do feature extraction, rather than using the entire convolutional base.\\n\",\n    \"\\n\",\n    \"In our case, since the ImageNet class set did contain multiple dog and cat classes, it is likely that it would be beneficial to reuse the \\n\",\n    \"information contained in the densely-connected layers of the original model. However, we will chose not to, in order to cover the more \\n\",\n    \"general case where the class set of the new problem does not overlap with the class set of the original model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's put this in practice by using the convolutional base of the VGG16 network, trained on ImageNet, to extract interesting features from \\n\",\n    \"our cat and dog images, and then training a cat vs. dog classifier on top of these features.\\n\",\n    \"\\n\",\n    \"The VGG16 model, among others, comes pre-packaged with Keras. You can import it from the `keras.applications` module. Here's the list of \\n\",\n    \"image classification models (all pre-trained on the ImageNet dataset) that are available as part of `keras.applications`:\\n\",\n    \"\\n\",\n    \"* Xception\\n\",\n    \"* InceptionV3\\n\",\n    \"* ResNet50\\n\",\n    \"* VGG16\\n\",\n    \"* VGG19\\n\",\n    \"* MobileNet\\n\",\n    \"\\n\",\n    \"Let's instantiate the VGG16 model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.applications import VGG16\\n\",\n    \"\\n\",\n    \"conv_base = VGG16(weights='imagenet',\\n\",\n    \"                  include_top=False,\\n\",\n    \"                  input_shape=(150, 150, 3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We passed three arguments to the constructor:\\n\",\n    \"\\n\",\n    \"* `weights`, to specify which weight checkpoint to initialize the model from\\n\",\n    \"* `include_top`, which refers to including or not the densely-connected classifier on top of the network. By default, this \\n\",\n    \"densely-connected classifier would correspond to the 1000 classes from ImageNet. Since we intend to use our own densely-connected \\n\",\n    \"classifier (with only two classes, cat and dog), we don't need to include it.\\n\",\n    \"* `input_shape`, the shape of the image tensors that we will feed to the network. This argument is purely optional: if we don't pass it, \\n\",\n    \"then the network will be able to process inputs of any size.\\n\",\n    \"\\n\",\n    \"Here's the detail of the architecture of the VGG16 convolutional base: it's very similar to the simple convnets that you are already \\n\",\n    \"familiar with.\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"input_1 (InputLayer)         (None, 150, 150, 3)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 14,714,688\\n\",\n      \"Trainable params: 14,714,688\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"conv_base.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The final feature map has shape `(4, 4, 512)`. That's the feature on top of which we will stick a densely-connected classifier.\\n\",\n    \"\\n\",\n    \"At this point, there are two ways we could proceed: \\n\",\n    \"\\n\",\n    \"* Running the convolutional base over our dataset, recording its output to a Numpy array on disk, then using this data as input to a \\n\",\n    \"standalone densely-connected classifier similar to those you have seen in the first chapters of this book. This solution is very fast and \\n\",\n    \"cheap to run, because it only requires running the convolutional base once for every input image, and the convolutional base is by far the \\n\",\n    \"most expensive part of the pipeline. However, for the exact same reason, this technique would not allow us to leverage data augmentation at \\n\",\n    \"all.\\n\",\n    \"* Extending the model we have (`conv_base`) by adding `Dense` layers on top, and running the whole thing end-to-end on the input data. This \\n\",\n    \"allows us to use data augmentation, because every input image is going through the convolutional base every time it is seen by the model. \\n\",\n    \"However, for this same reason, this technique is far more expensive than the first one.\\n\",\n    \"\\n\",\n    \"We will cover both techniques. Let's walk through the code required to set-up the first one: recording the output of `conv_base` on our \\n\",\n    \"data and using these outputs as inputs to a new model.\\n\",\n    \"\\n\",\n    \"We will start by simply running instances of the previously-introduced `ImageDataGenerator` to extract images as Numpy arrays as well as \\n\",\n    \"their labels. We will extract features from these images simply by calling the `predict` method of the `conv_base` 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      \"Found 2000 images belonging to 2 classes.\\n\",\n      \"Found 1000 images belonging to 2 classes.\\n\",\n      \"Found 1000 images belonging to 2 classes.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"from keras.preprocessing.image import ImageDataGenerator\\n\",\n    \"\\n\",\n    \"base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'\\n\",\n    \"\\n\",\n    \"train_dir = os.path.join(base_dir, 'train')\\n\",\n    \"validation_dir = os.path.join(base_dir, 'validation')\\n\",\n    \"test_dir = os.path.join(base_dir, 'test')\\n\",\n    \"\\n\",\n    \"datagen = ImageDataGenerator(rescale=1./255)\\n\",\n    \"batch_size = 20\\n\",\n    \"\\n\",\n    \"def extract_features(directory, sample_count):\\n\",\n    \"    features = np.zeros(shape=(sample_count, 4, 4, 512))\\n\",\n    \"    labels = np.zeros(shape=(sample_count))\\n\",\n    \"    generator = datagen.flow_from_directory(\\n\",\n    \"        directory,\\n\",\n    \"        target_size=(150, 150),\\n\",\n    \"        batch_size=batch_size,\\n\",\n    \"        class_mode='binary')\\n\",\n    \"    i = 0\\n\",\n    \"    for inputs_batch, labels_batch in generator:\\n\",\n    \"        features_batch = conv_base.predict(inputs_batch)\\n\",\n    \"        features[i * batch_size : (i + 1) * batch_size] = features_batch\\n\",\n    \"        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\\n\",\n    \"        i += 1\\n\",\n    \"        if i * batch_size >= sample_count:\\n\",\n    \"            # Note that since generators yield data indefinitely in a loop,\\n\",\n    \"            # we must `break` after every image has been seen once.\\n\",\n    \"            break\\n\",\n    \"    return features, labels\\n\",\n    \"\\n\",\n    \"train_features, train_labels = extract_features(train_dir, 2000)\\n\",\n    \"validation_features, validation_labels = extract_features(validation_dir, 1000)\\n\",\n    \"test_features, test_labels = extract_features(test_dir, 1000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The extracted features are currently of shape `(samples, 4, 4, 512)`. We will feed them to a densely-connected classifier, so first we must \\n\",\n    \"flatten them to `(samples, 8192)`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_features = np.reshape(train_features, (2000, 4 * 4 * 512))\\n\",\n    \"validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))\\n\",\n    \"test_features = np.reshape(test_features, (1000, 4 * 4 * 512))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"At this point, we can define our densely-connected classifier (note the use of dropout for regularization), and train it on the data and \\n\",\n    \"labels that we just recorded:\"\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      \"Train on 2000 samples, validate on 1000 samples\\n\",\n      \"Epoch 1/30\\n\",\n      \"2000/2000 [==============================] - 1s - loss: 0.6253 - acc: 0.6455 - val_loss: 0.4526 - val_acc: 0.8300\\n\",\n      \"Epoch 2/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.4490 - acc: 0.7965 - val_loss: 0.3784 - val_acc: 0.8450\\n\",\n      \"Epoch 3/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.3670 - acc: 0.8490 - val_loss: 0.3327 - val_acc: 0.8660\\n\",\n      \"Epoch 4/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.3176 - acc: 0.8705 - val_loss: 0.3115 - val_acc: 0.8820\\n\",\n      \"Epoch 5/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.3017 - acc: 0.8800 - val_loss: 0.2926 - val_acc: 0.8820\\n\",\n      \"Epoch 6/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.2674 - acc: 0.8960 - val_loss: 0.2799 - val_acc: 0.8880\\n\",\n      \"Epoch 7/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.2510 - acc: 0.9040 - val_loss: 0.2732 - val_acc: 0.8890\\n\",\n      \"Epoch 8/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.2414 - acc: 0.9030 - val_loss: 0.2644 - val_acc: 0.8950\\n\",\n      \"Epoch 9/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.2307 - acc: 0.9070 - val_loss: 0.2583 - val_acc: 0.8890\\n\",\n      \"Epoch 10/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.2174 - acc: 0.9205 - val_loss: 0.2577 - val_acc: 0.8930\\n\",\n      \"Epoch 11/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1997 - acc: 0.9235 - val_loss: 0.2500 - val_acc: 0.8970\\n\",\n      \"Epoch 12/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1962 - acc: 0.9280 - val_loss: 0.2470 - val_acc: 0.8950\\n\",\n      \"Epoch 13/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1864 - acc: 0.9275 - val_loss: 0.2460 - val_acc: 0.8980\\n\",\n      \"Epoch 14/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1796 - acc: 0.9325 - val_loss: 0.2473 - val_acc: 0.8950\\n\",\n      \"Epoch 15/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1760 - acc: 0.9380 - val_loss: 0.2450 - val_acc: 0.8960\\n\",\n      \"Epoch 16/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1612 - acc: 0.9400 - val_loss: 0.2543 - val_acc: 0.8940\\n\",\n      \"Epoch 17/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1595 - acc: 0.9425 - val_loss: 0.2392 - val_acc: 0.9010\\n\",\n      \"Epoch 18/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1534 - acc: 0.9470 - val_loss: 0.2385 - val_acc: 0.9000\\n\",\n      \"Epoch 19/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1494 - acc: 0.9490 - val_loss: 0.2453 - val_acc: 0.9000\\n\",\n      \"Epoch 20/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1409 - acc: 0.9515 - val_loss: 0.2394 - val_acc: 0.9030\\n\",\n      \"Epoch 21/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1304 - acc: 0.9535 - val_loss: 0.2379 - val_acc: 0.9010\\n\",\n      \"Epoch 22/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1294 - acc: 0.9550 - val_loss: 0.2376 - val_acc: 0.9010\\n\",\n      \"Epoch 23/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1269 - acc: 0.9535 - val_loss: 0.2473 - val_acc: 0.8970\\n\",\n      \"Epoch 24/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1234 - acc: 0.9635 - val_loss: 0.2372 - val_acc: 0.9020\\n\",\n      \"Epoch 25/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1159 - acc: 0.9635 - val_loss: 0.2380 - val_acc: 0.9030\\n\",\n      \"Epoch 26/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1093 - acc: 0.9665 - val_loss: 0.2409 - val_acc: 0.9030\\n\",\n      \"Epoch 27/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1069 - acc: 0.9605 - val_loss: 0.2477 - val_acc: 0.9000\\n\",\n      \"Epoch 28/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.1071 - acc: 0.9670 - val_loss: 0.2486 - val_acc: 0.9010\\n\",\n      \"Epoch 29/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.0988 - acc: 0.9695 - val_loss: 0.2437 - val_acc: 0.9030\\n\",\n      \"Epoch 30/30\\n\",\n      \"2000/2000 [==============================] - 0s - loss: 0.0968 - acc: 0.9680 - val_loss: 0.2428 - val_acc: 0.9030\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"from keras import layers\\n\",\n    \"from keras import optimizers\\n\",\n    \"\\n\",\n    \"model = models.Sequential()\\n\",\n    \"model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))\\n\",\n    \"model.add(layers.Dropout(0.5))\\n\",\n    \"model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=optimizers.RMSprop(lr=2e-5),\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['acc'])\\n\",\n    \"\\n\",\n    \"history = model.fit(train_features, train_labels,\\n\",\n    \"                    epochs=30,\\n\",\n    \"                    batch_size=20,\\n\",\n    \"                    validation_data=(validation_features, validation_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Training is very fast, since we only have to deal with two `Dense` layers -- an epoch takes less than one second even on CPU.\\n\",\n    \"\\n\",\n    \"Let's take a look at the loss and accuracy curves during training:\"\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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FXAsQ1tT9U72U3Hqumao9ok1RuUzVGvne115dmCNFbvrAF6JI0XRdOS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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fd5529c2e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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n369GHUqFF8/vnnZZ8fHQI4OmDZm2++WXazkv79+7Nr165qf7fxZFev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\\nVgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fd550e7c828>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(acc))\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"We reach a validation accuracy of about 90%, much better than what we could achieve in the previous section with our small model trained from \\n\",\n    \"scratch. However, our plots also indicate that we are overfitting almost from the start -- despite using dropout with a fairly large rate. \\n\",\n    \"This is because this technique does not leverage data augmentation, which is essential to preventing overfitting with small image datasets.\\n\",\n    \"\\n\",\n    \"Now, let's review the second technique we mentioned for doing feature extraction, which is much slower and more expensive, but which allows \\n\",\n    \"us to leverage data augmentation during training: extending the `conv_base` model and running it end-to-end on the inputs. Note that this \\n\",\n    \"technique is in fact so expensive that you should only attempt it if you have access to a GPU: it is absolutely intractable on CPU. If you \\n\",\n    \"cannot run your code on GPU, then the previous technique is the way to go.\\n\",\n    \"\\n\",\n    \"Because models behave just like layers, you can add a model (like our `conv_base`) to a `Sequential` model just like you would add a layer. \\n\",\n    \"So you can do the following:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"model = models.Sequential()\\n\",\n    \"model.add(conv_base)\\n\",\n    \"model.add(layers.Flatten())\\n\",\n    \"model.add(layers.Dense(256, activation='relu'))\\n\",\n    \"model.add(layers.Dense(1, activation='sigmoid'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is what our model looks like now:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"vgg16 (Model)                (None, 4, 4, 512)         14714688  \\n\",\n      \"_________________________________________________________________\\n\",\n      \"flatten_1 (Flatten)          (None, 8192)              0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_3 (Dense)              (None, 256)               2097408   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_4 (Dense)              (None, 1)                 257       \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 16,812,353\\n\",\n      \"Trainable params: 16,812,353\\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    \"As you can see, the convolutional base of VGG16 has 14,714,688 parameters, which is very large. The classifier we are adding on top has 2 \\n\",\n    \"million parameters.\\n\",\n    \"\\n\",\n    \"Before we compile and train our model, a very important thing to do is to freeze the convolutional base. \\\"Freezing\\\" a layer or set of \\n\",\n    \"layers means preventing their weights from getting updated during training. If we don't do this, then the representations that were \\n\",\n    \"previously learned by the convolutional base would get modified during training. Since the `Dense` layers on top are randomly initialized, \\n\",\n    \"very large weight updates would be propagated through the network, effectively destroying the representations previously learned.\\n\",\n    \"\\n\",\n    \"In Keras, freezing a network is done by setting its `trainable` attribute to `False`:\"\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      \"This is the number of trainable weights before freezing the conv base: 30\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('This is the number of trainable weights '\\n\",\n    \"      'before freezing the conv base:', len(model.trainable_weights))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.trainable = False\"\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      \"This is the number of trainable weights after freezing the conv base: 4\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('This is the number of trainable weights '\\n\",\n    \"      'after freezing the conv base:', len(model.trainable_weights))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With this setup, only the weights from the two `Dense` layers that we added will be trained. That's a total of four weight tensors: two per \\n\",\n    \"layer (the main weight matrix and the bias vector). Note that in order for these changes to take effect, we must first compile the model. \\n\",\n    \"If you ever modify weight trainability after compilation, you should then re-compile the model, or these changes would be ignored.\\n\",\n    \"\\n\",\n    \"Now we can start training our model, with the same data augmentation configuration that we used in our previous example:\"\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      \"Found 2000 images belonging to 2 classes.\\n\",\n      \"Found 1000 images belonging to 2 classes.\\n\",\n      \"Epoch 1/30\\n\",\n      \"74s - loss: 0.4465 - acc: 0.7810 - val_loss: 0.2056 - val_acc: 0.9120\\n\",\n      \"Epoch 2/30\\n\",\n      \"72s - loss: 0.2738 - acc: 0.8905 - val_loss: 0.1239 - val_acc: 0.9550\\n\",\n      \"Epoch 3/30\\n\",\n      \"72s - loss: 0.2088 - acc: 0.9145 - val_loss: 0.1194 - val_acc: 0.9560\\n\",\n      \"Epoch 4/30\\n\",\n      \"72s - loss: 0.1835 - acc: 0.9280 - val_loss: 0.1025 - val_acc: 0.9550\\n\",\n      \"Epoch 5/30\\n\",\n      \"72s - loss: 0.1642 - acc: 0.9330 - val_loss: 0.0903 - val_acc: 0.9680\\n\",\n      \"Epoch 6/30\\n\",\n      \"72s - loss: 0.1360 - acc: 0.9410 - val_loss: 0.0794 - val_acc: 0.9740\\n\",\n      \"Epoch 7/30\\n\",\n      \"72s - loss: 0.1426 - acc: 0.9465 - val_loss: 0.0968 - val_acc: 0.9560\\n\",\n      \"Epoch 8/30\\n\",\n      \"72s - loss: 0.1013 - acc: 0.9580 - val_loss: 0.1411 - val_acc: 0.9430\\n\",\n      \"Epoch 9/30\\n\",\n      \"72s - loss: 0.1177 - acc: 0.9500 - val_loss: 0.2105 - val_acc: 0.9310\\n\",\n      \"Epoch 10/30\\n\",\n      \"72s - loss: 0.0949 - acc: 0.9620 - val_loss: 0.0900 - val_acc: 0.9710\\n\",\n      \"Epoch 11/30\\n\",\n      \"72s - loss: 0.0915 - acc: 0.9655 - val_loss: 0.1204 - val_acc: 0.9630\\n\",\n      \"Epoch 12/30\\n\",\n      \"72s - loss: 0.0782 - acc: 0.9645 - val_loss: 0.0995 - val_acc: 0.9650\\n\",\n      \"Epoch 13/30\\n\",\n      \"72s - loss: 0.0717 - acc: 0.9755 - val_loss: 0.1269 - val_acc: 0.9580\\n\",\n      \"Epoch 14/30\\n\",\n      \"72s - loss: 0.0670 - acc: 0.9715 - val_loss: 0.0994 - val_acc: 0.9680\\n\",\n      \"Epoch 15/30\\n\",\n      \"71s - loss: 0.0718 - acc: 0.9735 - val_loss: 0.0558 - val_acc: 0.9790\\n\",\n      \"Epoch 16/30\\n\",\n      \"72s - loss: 0.0612 - acc: 0.9780 - val_loss: 0.0870 - val_acc: 0.9690\\n\",\n      \"Epoch 17/30\\n\",\n      \"71s - loss: 0.0693 - acc: 0.9765 - val_loss: 0.0972 - val_acc: 0.9720\\n\",\n      \"Epoch 18/30\\n\",\n      \"71s - loss: 0.0596 - acc: 0.9785 - val_loss: 0.0832 - val_acc: 0.9730\\n\",\n      \"Epoch 19/30\\n\",\n      \"71s - loss: 0.0497 - acc: 0.9800 - val_loss: 0.1160 - val_acc: 0.9610\\n\",\n      \"Epoch 20/30\\n\",\n      \"71s - loss: 0.0546 - acc: 0.9780 - val_loss: 0.1057 - val_acc: 0.9660\\n\",\n      \"Epoch 21/30\\n\",\n      \"71s - loss: 0.0568 - acc: 0.9825 - val_loss: 0.2012 - val_acc: 0.9500\\n\",\n      \"Epoch 22/30\\n\",\n      \"71s - loss: 0.0493 - acc: 0.9830 - val_loss: 0.1384 - val_acc: 0.9610\\n\",\n      \"Epoch 23/30\\n\",\n      \"71s - loss: 0.0328 - acc: 0.9905 - val_loss: 0.1281 - val_acc: 0.9640\\n\",\n      \"Epoch 24/30\\n\",\n      \"71s - loss: 0.0524 - acc: 0.9860 - val_loss: 0.0846 - val_acc: 0.9760\\n\",\n      \"Epoch 25/30\\n\",\n      \"71s - loss: 0.0422 - acc: 0.9845 - val_loss: 0.1002 - val_acc: 0.9670\\n\",\n      \"Epoch 26/30\\n\",\n      \"71s - loss: 0.0617 - acc: 0.9825 - val_loss: 0.0858 - val_acc: 0.9760\\n\",\n      \"Epoch 27/30\\n\",\n      \"71s - loss: 0.0568 - acc: 0.9830 - val_loss: 0.0889 - val_acc: 0.9700\\n\",\n      \"Epoch 28/30\\n\",\n      \"71s - loss: 0.0296 - acc: 0.9915 - val_loss: 0.1406 - val_acc: 0.9620\\n\",\n      \"Epoch 29/30\\n\",\n      \"71s - loss: 0.0432 - acc: 0.9890 - val_loss: 0.1535 - val_acc: 0.9650\\n\",\n      \"Epoch 30/30\\n\",\n      \"71s - loss: 0.0354 - acc: 0.9885 - val_loss: 0.1832 - val_acc: 0.9510\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.preprocessing.image import ImageDataGenerator\\n\",\n    \"\\n\",\n    \"train_datagen = ImageDataGenerator(\\n\",\n    \"      rescale=1./255,\\n\",\n    \"      rotation_range=40,\\n\",\n    \"      width_shift_range=0.2,\\n\",\n    \"      height_shift_range=0.2,\\n\",\n    \"      shear_range=0.2,\\n\",\n    \"      zoom_range=0.2,\\n\",\n    \"      horizontal_flip=True,\\n\",\n    \"      fill_mode='nearest')\\n\",\n    \"\\n\",\n    \"# Note that the validation data should not be augmented!\\n\",\n    \"test_datagen = ImageDataGenerator(rescale=1./255)\\n\",\n    \"\\n\",\n    \"train_generator = train_datagen.flow_from_directory(\\n\",\n    \"        # This is the target directory\\n\",\n    \"        train_dir,\\n\",\n    \"        # All images will be resized to 150x150\\n\",\n    \"        target_size=(150, 150),\\n\",\n    \"        batch_size=20,\\n\",\n    \"        # Since we use binary_crossentropy loss, we need binary labels\\n\",\n    \"        class_mode='binary')\\n\",\n    \"\\n\",\n    \"validation_generator = test_datagen.flow_from_directory(\\n\",\n    \"        validation_dir,\\n\",\n    \"        target_size=(150, 150),\\n\",\n    \"        batch_size=20,\\n\",\n    \"        class_mode='binary')\\n\",\n    \"\\n\",\n    \"model.compile(loss='binary_crossentropy',\\n\",\n    \"              optimizer=optimizers.RMSprop(lr=2e-5),\\n\",\n    \"              metrics=['acc'])\\n\",\n    \"\\n\",\n    \"history = model.fit_generator(\\n\",\n    \"      train_generator,\\n\",\n    \"      steps_per_epoch=100,\\n\",\n    \"      epochs=30,\\n\",\n    \"      validation_data=validation_generator,\\n\",\n    \"      validation_steps=50,\\n\",\n    \"      verbose=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.save('cats_and_dogs_small_3.h5')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot our results again:\"\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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drVFZCeeKLsiqEqa9eqjhjh8tGypeojj7jCViKyoB/ntm931TZpaa4/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\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fd55306e400>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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cjJAyYXz/c9EVERqfTSWGNM9VMyNVMyJaOqTJw4kW7dujFo0CB27drF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\\ngrsxxvjQ/wPcz2v1dLH/YgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fd544300160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(acc))\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, we reach a validation accuracy of about 96%. This is much better than our small convnet trained from scratch.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Fine-tuning\\n\",\n    \"\\n\",\n    \"Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \\n\",\n    \"Fine-tuning consists in unfreezing a few of the top layers \\n\",\n    \"of a frozen model base used for feature extraction, and jointly training both the newly added part of the model (in our case, the \\n\",\n    \"fully-connected classifier) and these top layers. This is called \\\"fine-tuning\\\" because it slightly adjusts the more abstract \\n\",\n    \"representations of the model being reused, in order to make them more relevant for the problem at hand.\\n\",\n    \"\\n\",\n    \"![fine-tuning VGG16](https://s3.amazonaws.com/book.keras.io/img/ch5/vgg16_fine_tuning.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We have stated before that it was necessary to freeze the convolution base of VGG16 in order to be able to train a randomly initialized \\n\",\n    \"classifier on top. For the same reason, it is only possible to fine-tune the top layers of the convolutional base once the classifier on \\n\",\n    \"top has already been trained. If the classified wasn't already trained, then the error signal propagating through the network during \\n\",\n    \"training would be too large, and the representations previously learned by the layers being fine-tuned would be destroyed. Thus the steps \\n\",\n    \"for fine-tuning a network are as follow:\\n\",\n    \"\\n\",\n    \"* 1) Add your custom network on top of an already trained base network.\\n\",\n    \"* 2) Freeze the base network.\\n\",\n    \"* 3) Train the part you added.\\n\",\n    \"* 4) Unfreeze some layers in the base network.\\n\",\n    \"* 5) Jointly train both these layers and the part you added.\\n\",\n    \"\\n\",\n    \"We have already completed the first 3 steps when doing feature extraction. Let's proceed with the 4th step: we will unfreeze our `conv_base`, \\n\",\n    \"and then freeze individual layers inside of it.\\n\",\n    \"\\n\",\n    \"As a reminder, this is what our convolutional base looks like:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"input_1 (InputLayer)         (None, 150, 150, 3)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 14,714,688\\n\",\n      \"Trainable params: 0\\n\",\n      \"Non-trainable params: 14,714,688\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"conv_base.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"We will fine-tune the last 3 convolutional layers, which means that all layers up until `block4_pool` should be frozen, and the layers \\n\",\n    \"`block5_conv1`, `block5_conv2` and `block5_conv3` should be trainable.\\n\",\n    \"\\n\",\n    \"Why not fine-tune more layers? Why not fine-tune the entire convolutional base? We could. However, we need to consider that:\\n\",\n    \"\\n\",\n    \"* Earlier layers in the convolutional base encode more generic, reusable features, while layers higher up encode more specialized features. It is \\n\",\n    \"more useful to fine-tune the more specialized features, as these are the ones that need to be repurposed on our new problem. There would \\n\",\n    \"be fast-decreasing returns in fine-tuning lower layers.\\n\",\n    \"* The more parameters we are training, the more we are at risk of overfitting. The convolutional base has 15M parameters, so it would be \\n\",\n    \"risky to attempt to train it on our small dataset.\\n\",\n    \"\\n\",\n    \"Thus, in our situation, it is a good strategy to only fine-tune the top 2 to 3 layers in the convolutional base.\\n\",\n    \"\\n\",\n    \"Let's set this up, starting from where we left off in the previous example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.trainable = True\\n\",\n    \"\\n\",\n    \"set_trainable = False\\n\",\n    \"for layer in conv_base.layers:\\n\",\n    \"    if layer.name == 'block5_conv1':\\n\",\n    \"        set_trainable = True\\n\",\n    \"    if set_trainable:\\n\",\n    \"        layer.trainable = True\\n\",\n    \"    else:\\n\",\n    \"        layer.trainable = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can start fine-tuning our network. We will do this with the RMSprop optimizer, using a very low learning rate. The reason for using \\n\",\n    \"a low learning rate is that we want to limit the magnitude of the modifications we make to the representations of the 3 layers that we are \\n\",\n    \"fine-tuning. Updates that are too large may harm these representations.\\n\",\n    \"\\n\",\n    \"Now let's proceed with fine-tuning:\"\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      \"Epoch 1/100\\n\",\n      \"100/100 [==============================] - 32s - loss: 0.0215 - acc: 0.9935 - val_loss: 0.0980 - val_acc: 0.9720\\n\",\n      \"Epoch 2/100\\n\",\n      \"100/100 [==============================] - 32s - loss: 0.0131 - acc: 0.9960 - val_loss: 0.1247 - val_acc: 0.9700\\n\",\n      \"Epoch 3/100\\n\",\n      \"100/100 [==============================] - 32s - loss: 0.0140 - acc: 0.9940 - val_loss: 0.1044 - val_acc: 0.9790\\n\",\n      \"Epoch 4/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0102 - acc: 0.9965 - val_loss: 0.1259 - val_acc: 0.9770\\n\",\n      \"Epoch 5/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0137 - acc: 0.9945 - val_loss: 0.1036 - val_acc: 0.9800\\n\",\n      \"Epoch 6/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0183 - acc: 0.9935 - val_loss: 0.1260 - val_acc: 0.9750\\n\",\n      \"Epoch 7/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0141 - acc: 0.9945 - val_loss: 0.1575 - val_acc: 0.9690\\n\",\n      \"Epoch 8/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0094 - acc: 0.9965 - val_loss: 0.0935 - val_acc: 0.9780\\n\",\n      \"Epoch 9/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0079 - acc: 0.9985 - val_loss: 0.1452 - val_acc: 0.9760\\n\",\n      \"Epoch 10/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0127 - acc: 0.9970 - val_loss: 0.1027 - val_acc: 0.9790\\n\",\n      \"Epoch 11/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0097 - acc: 0.9965 - val_loss: 0.1463 - val_acc: 0.9720\\n\",\n      \"Epoch 12/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0055 - acc: 0.9980 - val_loss: 0.1361 - val_acc: 0.9720\\n\",\n      \"Epoch 13/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0274 - acc: 0.9955 - val_loss: 0.1446 - val_acc: 0.9740\\n\",\n      \"Epoch 14/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0043 - acc: 0.9985 - val_loss: 0.1123 - val_acc: 0.9790\\n\",\n      \"Epoch 15/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0057 - acc: 0.9975 - val_loss: 0.1912 - val_acc: 0.9700\\n\",\n      \"Epoch 16/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0144 - acc: 0.9960 - val_loss: 0.1415 - val_acc: 0.9780\\n\",\n      \"Epoch 17/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0048 - acc: 0.9990 - val_loss: 0.1231 - val_acc: 0.9780\\n\",\n      \"Epoch 18/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0188 - acc: 0.9965 - val_loss: 0.1551 - val_acc: 0.9720\\n\",\n      \"Epoch 19/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0160 - acc: 0.9970 - val_loss: 0.2155 - val_acc: 0.9740\\n\",\n      \"Epoch 20/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0047 - acc: 0.9965 - val_loss: 0.1559 - val_acc: 0.9730\\n\",\n      \"Epoch 21/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0132 - acc: 0.9980 - val_loss: 0.1518 - val_acc: 0.9740\\n\",\n      \"Epoch 22/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0086 - acc: 0.9965 - val_loss: 0.1517 - val_acc: 0.9790\\n\",\n      \"Epoch 23/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0070 - acc: 0.9980 - val_loss: 0.1887 - val_acc: 0.9670\\n\",\n      \"Epoch 24/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0044 - acc: 0.9985 - val_loss: 0.1818 - val_acc: 0.9740\\n\",\n      \"Epoch 25/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0159 - acc: 0.9970 - val_loss: 0.1860 - val_acc: 0.9680\\n\",\n      \"Epoch 26/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0056 - acc: 0.9980 - val_loss: 0.1657 - val_acc: 0.9740\\n\",\n      \"Epoch 27/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0118 - acc: 0.9980 - val_loss: 0.1542 - val_acc: 0.9760\\n\",\n      \"Epoch 28/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0031 - acc: 0.9990 - val_loss: 0.1493 - val_acc: 0.9770\\n\",\n      \"Epoch 29/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0114 - acc: 0.9965 - val_loss: 0.1921 - val_acc: 0.9680\\n\",\n      \"Epoch 30/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0031 - acc: 0.9990 - val_loss: 0.1188 - val_acc: 0.9830\\n\",\n      \"Epoch 31/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0068 - acc: 0.9985 - val_loss: 0.1814 - val_acc: 0.9740\\n\",\n      \"Epoch 32/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0096 - acc: 0.9985 - val_loss: 0.2034 - val_acc: 0.9760\\n\",\n      \"Epoch 33/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0072 - acc: 0.9985 - val_loss: 0.1970 - val_acc: 0.9730\\n\",\n      \"Epoch 34/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0047 - acc: 0.9990 - val_loss: 0.2349 - val_acc: 0.9680\\n\",\n      \"Epoch 35/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0066 - acc: 0.9990 - val_loss: 0.1865 - val_acc: 0.9740\\n\",\n      \"Epoch 36/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0115 - acc: 0.9975 - val_loss: 0.1933 - val_acc: 0.9750\\n\",\n      \"Epoch 37/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0101 - acc: 0.9980 - val_loss: 0.1779 - val_acc: 0.9780\\n\",\n      \"Epoch 38/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0101 - acc: 0.9975 - val_loss: 0.1887 - val_acc: 0.9700\\n\",\n      \"Epoch 39/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0093 - acc: 0.9980 - val_loss: 0.2159 - val_acc: 0.9720\\n\",\n      \"Epoch 40/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0049 - acc: 0.9990 - val_loss: 0.1412 - val_acc: 0.9790\\n\",\n      \"Epoch 41/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0052 - acc: 0.9985 - val_loss: 0.2066 - val_acc: 0.9690\\n\",\n      \"Epoch 42/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0043 - acc: 0.9990 - val_loss: 0.1860 - val_acc: 0.9770\\n\",\n      \"Epoch 43/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0031 - acc: 0.9985 - val_loss: 0.2361 - val_acc: 0.9680\\n\",\n      \"Epoch 44/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0012 - acc: 0.9995 - val_loss: 0.2440 - val_acc: 0.9680\\n\",\n      \"Epoch 45/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0035 - acc: 0.9985 - val_loss: 0.1428 - val_acc: 0.9820\\n\",\n      \"Epoch 46/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0111 - acc: 0.9970 - val_loss: 0.1822 - val_acc: 0.9720\\n\",\n      \"Epoch 47/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0047 - acc: 0.9990 - val_loss: 0.1726 - val_acc: 0.9720\\n\",\n      \"Epoch 48/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0039 - acc: 0.9995 - val_loss: 0.2164 - val_acc: 0.9730\\n\",\n      \"Epoch 49/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0060 - acc: 0.9970 - val_loss: 0.1856 - val_acc: 0.9810\\n\",\n      \"Epoch 50/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0126 - acc: 0.9980 - val_loss: 0.1824 - val_acc: 0.9720\\n\",\n      \"Epoch 51/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0155 - acc: 0.9965 - val_loss: 0.1867 - val_acc: 0.9710\\n\",\n      \"Epoch 52/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0059 - acc: 0.9985 - val_loss: 0.2287 - val_acc: 0.9700\\n\",\n      \"Epoch 53/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0046 - acc: 0.9980 - val_loss: 0.2337 - val_acc: 0.9650\\n\",\n      \"Epoch 54/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0087 - acc: 0.9970 - val_loss: 0.1168 - val_acc: 0.9820\\n\",\n      \"Epoch 55/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0046 - acc: 0.9985 - val_loss: 0.1496 - val_acc: 0.9790\\n\",\n      \"Epoch 56/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0067 - acc: 0.9985 - val_loss: 0.1615 - val_acc: 0.9750\\n\",\n      \"Epoch 57/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0066 - acc: 0.9975 - val_loss: 0.2520 - val_acc: 0.9630\\n\",\n      \"Epoch 58/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0017 - acc: 0.9990 - val_loss: 0.1899 - val_acc: 0.9740\\n\",\n      \"Epoch 59/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0022 - acc: 0.9990 - val_loss: 0.2321 - val_acc: 0.9680\\n\",\n      \"Epoch 60/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0091 - acc: 0.9975 - val_loss: 0.1416 - val_acc: 0.9790\\n\",\n      \"Epoch 61/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0054 - acc: 0.9985 - val_loss: 0.1749 - val_acc: 0.9720\\n\",\n      \"Epoch 62/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0028 - acc: 0.9995 - val_loss: 0.2065 - val_acc: 0.9740\\n\",\n      \"Epoch 63/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0058 - acc: 0.9985 - val_loss: 0.1749 - val_acc: 0.9750\\n\",\n      \"Epoch 64/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0076 - acc: 0.9980 - val_loss: 0.1542 - val_acc: 0.9760\\n\",\n      \"Epoch 65/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0081 - acc: 0.9980 - val_loss: 0.2627 - val_acc: 0.9660\\n\",\n      \"Epoch 66/100\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100/100 [==============================] - 33s - loss: 0.0107 - acc: 0.9980 - val_loss: 0.1748 - val_acc: 0.9740\\n\",\n      \"Epoch 67/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 5.1450e-04 - acc: 1.0000 - val_loss: 0.1922 - val_acc: 0.9710\\n\",\n      \"Epoch 68/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0054 - acc: 0.9985 - val_loss: 0.2299 - val_acc: 0.9680\\n\",\n      \"Epoch 69/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0091 - acc: 0.9965 - val_loss: 0.1533 - val_acc: 0.9730\\n\",\n      \"Epoch 70/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0190 - acc: 0.9965 - val_loss: 0.2232 - val_acc: 0.9690\\n\",\n      \"Epoch 71/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0058 - acc: 0.9985 - val_loss: 0.1773 - val_acc: 0.9710\\n\",\n      \"Epoch 72/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0072 - acc: 0.9990 - val_loss: 0.1660 - val_acc: 0.9760\\n\",\n      \"Epoch 73/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0036 - acc: 0.9990 - val_loss: 0.2354 - val_acc: 0.9700\\n\",\n      \"Epoch 74/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0023 - acc: 0.9995 - val_loss: 0.1904 - val_acc: 0.9700\\n\",\n      \"Epoch 75/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0137 - acc: 0.9955 - val_loss: 0.1363 - val_acc: 0.9810\\n\",\n      \"Epoch 76/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0055 - acc: 0.9990 - val_loss: 0.2119 - val_acc: 0.9710\\n\",\n      \"Epoch 77/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 9.2985e-04 - acc: 1.0000 - val_loss: 0.1777 - val_acc: 0.9760\\n\",\n      \"Epoch 78/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0032 - acc: 0.9990 - val_loss: 0.2568 - val_acc: 0.9710\\n\",\n      \"Epoch 79/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0112 - acc: 0.9970 - val_loss: 0.2066 - val_acc: 0.9720\\n\",\n      \"Epoch 80/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0055 - acc: 0.9975 - val_loss: 0.1793 - val_acc: 0.9700\\n\",\n      \"Epoch 81/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0045 - acc: 0.9980 - val_loss: 0.2082 - val_acc: 0.9690\\n\",\n      \"Epoch 82/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0095 - acc: 0.9985 - val_loss: 0.1985 - val_acc: 0.9700\\n\",\n      \"Epoch 83/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0032 - acc: 0.9985 - val_loss: 0.1992 - val_acc: 0.9710\\n\",\n      \"Epoch 84/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0019 - acc: 0.9995 - val_loss: 0.2285 - val_acc: 0.9690\\n\",\n      \"Epoch 85/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0066 - acc: 0.9970 - val_loss: 0.1833 - val_acc: 0.9760\\n\",\n      \"Epoch 86/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0066 - acc: 0.9990 - val_loss: 0.2054 - val_acc: 0.9690\\n\",\n      \"Epoch 87/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0072 - acc: 0.9980 - val_loss: 0.2084 - val_acc: 0.9730\\n\",\n      \"Epoch 88/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0043 - acc: 0.9980 - val_loss: 0.1840 - val_acc: 0.9690\\n\",\n      \"Epoch 89/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0101 - acc: 0.9980 - val_loss: 0.1953 - val_acc: 0.9700\\n\",\n      \"Epoch 90/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0092 - acc: 0.9990 - val_loss: 0.1996 - val_acc: 0.9740\\n\",\n      \"Epoch 91/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0081 - acc: 0.9965 - val_loss: 0.2136 - val_acc: 0.9750\\n\",\n      \"Epoch 92/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0055 - acc: 0.9985 - val_loss: 0.1775 - val_acc: 0.9790\\n\",\n      \"Epoch 93/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 3.4598e-04 - acc: 1.0000 - val_loss: 0.2102 - val_acc: 0.9760\\n\",\n      \"Epoch 94/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0109 - acc: 0.9985 - val_loss: 0.2081 - val_acc: 0.9760\\n\",\n      \"Epoch 95/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0028 - acc: 0.9985 - val_loss: 0.2033 - val_acc: 0.9720\\n\",\n      \"Epoch 96/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0074 - acc: 0.9990 - val_loss: 0.2118 - val_acc: 0.9750\\n\",\n      \"Epoch 97/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0034 - acc: 0.9990 - val_loss: 0.2621 - val_acc: 0.9740\\n\",\n      \"Epoch 98/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0054 - acc: 0.9975 - val_loss: 0.2589 - val_acc: 0.9650\\n\",\n      \"Epoch 99/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0060 - acc: 0.9990 - val_loss: 0.2242 - val_acc: 0.9700\\n\",\n      \"Epoch 100/100\\n\",\n      \"100/100 [==============================] - 33s - loss: 0.0010 - acc: 0.9995 - val_loss: 0.2403 - val_acc: 0.9750\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.compile(loss='binary_crossentropy',\\n\",\n    \"              optimizer=optimizers.RMSprop(lr=1e-5),\\n\",\n    \"              metrics=['acc'])\\n\",\n    \"\\n\",\n    \"history = model.fit_generator(\\n\",\n    \"      train_generator,\\n\",\n    \"      steps_per_epoch=100,\\n\",\n    \"      epochs=100,\\n\",\n    \"      validation_data=validation_generator,\\n\",\n    \"      validation_steps=50)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.save('cats_and_dogs_small_4.h5')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot our results using the same plotting code as before:\"\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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UeuxqjZF4OmFnWWT541RARfU3/2vSBEkMbNk82cLDQGvdhpzXaupNeX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\\naMIYY1i/fj0dkk7PF4FvQ/B4PDnp378/VVVV+DlJmjYdOnSgf//+ee/vB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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fd5442d3ef0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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QD+ASCHHZAVJT/IhuirpZ8f7NwZb+kDuQ3bDCP6CwEcT0T9iagZgMkA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\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fd53ce2a048>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(acc))\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"These curves look very noisy. To make them more readable, we can smooth them by replacing every loss and accuracy with exponential moving \\n\",\n    \"averages of these quantities. Here's a trivial utility function to do this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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5/fkexPShfKnx/IgoAxpj4JEhDaA9+GvF7rLQu1EDjXe34O0EJEWnnL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\\n\",\n 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u3y+dRTqhdeqNqlS/HyOHy4W1/Vqqrff59z3okT7qFzhw5u/LLL3B1R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\\ny6VyEc4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fd53cbf15c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def smooth_curve(points, factor=0.8):\\n\",\n    \"  smoothed_points = []\\n\",\n    \"  for point in points:\\n\",\n    \"    if smoothed_points:\\n\",\n    \"      previous = smoothed_points[-1]\\n\",\n    \"      smoothed_points.append(previous * factor + point * (1 - factor))\\n\",\n    \"    else:\\n\",\n    \"      smoothed_points.append(point)\\n\",\n    \"  return smoothed_points\\n\",\n    \"\\n\",\n    \"plt.plot(epochs,\\n\",\n    \"         smooth_curve(acc), 'bo', label='Smoothed training acc')\\n\",\n    \"plt.plot(epochs,\\n\",\n    \"         smooth_curve(val_acc), 'b', label='Smoothed validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs,\\n\",\n    \"         smooth_curve(loss), 'bo', label='Smoothed training loss')\\n\",\n    \"plt.plot(epochs,\\n\",\n    \"         smooth_curve(val_loss), 'b', label='Smoothed validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"These curves look much cleaner and more stable. We are seeing a nice 1% absolute improvement.\\n\",\n    \"\\n\",\n    \"Note that the loss curve does not show any real improvement (in fact, it is deteriorating). You may wonder, how could accuracy improve if the \\n\",\n    \"loss isn't decreasing? The answer is simple: what we display is an average of pointwise loss values, but what actually matters for accuracy \\n\",\n    \"is the distribution of the loss values, not their average, since accuracy is the result of a binary thresholding of the class probability \\n\",\n    \"predicted by the model. The model may still be improving even if this isn't reflected in the average loss.\\n\",\n    \"\\n\",\n    \"We can now finally evaluate this model on the test data:\"\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      \"Found 1000 images belonging to 2 classes.\\n\",\n      \"test acc: 0.967999992371\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_generator = test_datagen.flow_from_directory(\\n\",\n    \"        test_dir,\\n\",\n    \"        target_size=(150, 150),\\n\",\n    \"        batch_size=20,\\n\",\n    \"        class_mode='binary')\\n\",\n    \"\\n\",\n    \"test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)\\n\",\n    \"print('test acc:', test_acc)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Here we get a test accuracy of 97%. In the original Kaggle competition around this dataset, this would have been one of the top results. \\n\",\n    \"However, using modern deep learning techniques, we managed to reach this result using only a very small fraction of the training data \\n\",\n    \"available (about 10%). There is a huge difference between being able to train on 20,000 samples compared to 2,000 samples!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Take-aways: using convnets with small datasets\\n\",\n    \"\\n\",\n    \"Here's what you should take away from the exercises of these past two sections:\\n\",\n    \"\\n\",\n    \"* Convnets are the best type of machine learning models for computer vision tasks. It is possible to train one from scratch even on a very \\n\",\n    \"small dataset, with decent results.\\n\",\n    \"* On a small dataset, overfitting will be the main issue. Data augmentation is a powerful way to fight overfitting when working with image \\n\",\n    \"data.\\n\",\n    \"* It is easy to reuse an existing convnet on a new dataset, via feature extraction. This is a very valuable technique for working with \\n\",\n    \"small image datasets.\\n\",\n    \"* As a complement to feature extraction, one may use fine-tuning, which adapts to a new problem some of the representations previously \\n\",\n    \"learned by an existing model. This pushes performance a bit further.\\n\",\n    \"\\n\",\n    \"Now you have a solid set of tools for dealing with image classification problems, in particular with small datasets.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/5.4-visualizing-what-convnets-learn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualizing what convnets learn\\n\",\n    \"\\n\",\n    \"This notebook contains the code sample found in Chapter 5, Section 4 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"It is often said that deep learning models are \\\"black boxes\\\", learning representations that are difficult to extract and present in a \\n\",\n    \"human-readable form. While this is partially true for certain types of deep learning models, it is definitely not true for convnets. The \\n\",\n    \"representations learned by convnets are highly amenable to visualization, in large part because they are _representations of visual \\n\",\n    \"concepts_. Since 2013, a wide array of techniques have been developed for visualizing and interpreting these representations. We won't \\n\",\n    \"survey all of them, but we will cover three of the most accessible and useful ones:\\n\",\n    \"\\n\",\n    \"* Visualizing intermediate convnet outputs (\\\"intermediate activations\\\"). This is useful to understand how successive convnet layers \\n\",\n    \"transform their input, and to get a first idea of the meaning of individual convnet filters.\\n\",\n    \"* Visualizing convnets filters. This is useful to understand precisely what visual pattern or concept each filter in a convnet is receptive \\n\",\n    \"to.\\n\",\n    \"* Visualizing heatmaps of class activation in an image. This is useful to understand which part of an image where identified as belonging \\n\",\n    \"to a given class, and thus allows to localize objects in images.\\n\",\n    \"\\n\",\n    \"For the first method -- activation visualization -- we will use the small convnet that we trained from scratch on the cat vs. dog \\n\",\n    \"classification problem two sections ago. For the next two methods, we will use the VGG16 model that we introduced in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Visualizing intermediate activations\\n\",\n    \"\\n\",\n    \"Visualizing intermediate activations consists in displaying the feature maps that are output by various convolution and pooling layers in a \\n\",\n    \"network, given a certain input (the output of a layer is often called its \\\"activation\\\", the output of the activation function). This gives \\n\",\n    \"a view into how an input is decomposed unto the different filters learned by the network. These feature maps we want to visualize have 3 \\n\",\n    \"dimensions: width, height, and depth (channels). Each channel encodes relatively independent features, so the proper way to visualize these \\n\",\n    \"feature maps is by independently plotting the contents of every channel, as a 2D image.\\n\",\n    \"Let's start by loading the model that we saved in section 5.2:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"conv2d_5 (Conv2D)            (None, 148, 148, 32)      896       \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_5 (MaxPooling2 (None, 74, 74, 32)        0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_6 (Conv2D)            (None, 72, 72, 64)        18496     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_6 (MaxPooling2 (None, 36, 36, 64)        0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_7 (Conv2D)            (None, 34, 34, 128)       73856     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_7 (MaxPooling2 (None, 17, 17, 128)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_8 (Conv2D)            (None, 15, 15, 128)       147584    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling2d_8 (MaxPooling2 (None, 7, 7, 128)         0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"flatten_2 (Flatten)          (None, 6272)              0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dropout_1 (Dropout)          (None, 6272)              0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_3 (Dense)              (None, 512)               3211776   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_4 (Dense)              (None, 1)                 513       \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 3,453,121\\n\",\n      \"Trainable params: 3,453,121\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import load_model\\n\",\n    \"\\n\",\n    \"model = load_model('cats_and_dogs_small_2.h5')\\n\",\n    \"model.summary()  # As a reminder.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This will be the input image we will use -- a picture of a cat, not part of images that the network was trained on:\"\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      \"(1, 150, 150, 3)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"img_path = '/Users/fchollet/Downloads/cats_and_dogs_small/test/cats/cat.1700.jpg'\\n\",\n    \"\\n\",\n    \"# We preprocess the image into a 4D tensor\\n\",\n    \"from keras.preprocessing import image\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"img = image.load_img(img_path, target_size=(150, 150))\\n\",\n    \"img_tensor = image.img_to_array(img)\\n\",\n    \"img_tensor = np.expand_dims(img_tensor, axis=0)\\n\",\n    \"# Remember that the model was trained on inputs\\n\",\n    \"# that were preprocessed in the following way:\\n\",\n    \"img_tensor /= 255.\\n\",\n    \"\\n\",\n    \"# Its shape is (1, 150, 150, 3)\\n\",\n    \"print(img_tensor.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's display our picture:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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gxfm4JRoHD8mT5GZjmxdjNeuP4it97+NqAAS8P+sbqGA5OBCiwuzDXZ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\\n2tZ2zv4/OfB7sq8RpuIAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa455cc1748>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.imshow(img_tensor[0])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"In order to extract the feature maps we want to look at, we will create a Keras model that takes batches of images as input, and outputs \\n\",\n    \"the activations of all convolution and pooling layers. To do this, we will use the Keras class `Model`. A `Model` is instantiated using two \\n\",\n    \"arguments: an input tensor (or list of input tensors), and an output tensor (or list of output tensors). The resulting class is a Keras \\n\",\n    \"model, just like the `Sequential` models that you are familiar with, mapping the specified inputs to the specified outputs. What sets the \\n\",\n    \"`Model` class apart is that it allows for models with multiple outputs, unlike `Sequential`. For more information about the `Model` class, see \\n\",\n    \"Chapter 7, Section 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import models\\n\",\n    \"\\n\",\n    \"# Extracts the outputs of the top 8 layers:\\n\",\n    \"layer_outputs = [layer.output for layer in model.layers[:8]]\\n\",\n    \"# Creates a model that will return these outputs, given the model input:\\n\",\n    \"activation_model = models.Model(inputs=model.input, outputs=layer_outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"When fed an image input, this model returns the values of the layer activations in the original model. This is the first time you encounter \\n\",\n    \"a multi-output model in this book: until now the models you have seen only had exactly one input and one output. In the general case, a \\n\",\n    \"model could have any number of inputs and outputs. This one has one input and 8 outputs, one output per layer activation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# This will return a list of 5 Numpy arrays:\\n\",\n    \"# one array per layer activation\\n\",\n    \"activations = activation_model.predict(img_tensor)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For instance, this is the activation of the first convolution layer for our cat image input:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(1, 148, 148, 32)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"first_layer_activation = activations[0]\\n\",\n    \"print(first_layer_activation.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It's a 148x148 feature map with 32 channels. Let's try visualizing the 3rd channel:\"\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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vZb3XVm3ah2B9H2NlQ8wFE+RG4OMySYVY+UP5a006KI1WLEkVPlpWT1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\\nAT53pdymht3xAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa455cc1198>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.matshow(first_layer_activation[0, :, :, 3], cmap='viridis')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This channel appears to encode a diagonal edge detector. Let's try the 30th channel -- but note that your own channels may vary, since the \\n\",\n    \"specific filters learned by convolution layers are not deterministic.\"\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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tOQVj1wUbSwxxg8ZhYAY+5ZRG7oEABK27ZlFVpdKl0xnZbO3dGy7h+p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\\nSUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3f3ffe198>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.matshow(first_layer_activation[0, :, :, 30], cmap='viridis')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This one looks like a \\\"bright green dot\\\" detector, useful to encode cat eyes. At this point, let's go and plot a complete visualization of \\n\",\n    \"all the activations in the network. We'll extract and plot every channel in each of our 8 activation maps, and we will stack the results in \\n\",\n    \"one big image tensor, with channels stacked side by side.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:30: RuntimeWarning: invalid value encountered in true_divide\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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bsURGDNaVDQOi9DBAarBf4pn+rJtWvX3WKpzAvn3Ejuuz1Zw8nv4dL3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\\nTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3f3f58908>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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BohEE1sAraWDnMobdAxJWpej0iS5Ydy7nAUSUrPBgx7HYZVh1ldI5CM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\\noijzwv8DCikMdqCGiO4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3f3ec3dd8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Oyw2meHhZr0DUDBWvBJp3ukizQkIuQwY8xqSo0sa48Nkz5oH9IVOvG3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\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PpcZVPHA+isq9hmjcKlLR8+Y4CyUpJGn571TB/0EAARZZE2iZ74zhVY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\\nAb6SoD61l6+8AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3f3e646a0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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3wBTNN8B3inPcfWhLzM3TEGgIb+Yvk49uiNADxf8gHj/3ZHyjkr1brU\\np+sK+W/jPAAyVh5eK4trV4lLwpcUHOKadB6F9vjaYN838j42TfHz9h6xDh9/3LcArNw2MuVcRwAI\\npJbZLEsLYwvQHY1xF15vWarG01MV32ZZWovP38LaWtHO178v2nt/vyibvv8IAOPui2sdB82RdYub\\nL320xfs8UujvEBPz1F7rAHhyVwGBHDFFOna5qRwlXYSjUf4f8G6A+6JiCbn63M8AmOTbxFCneANs\\nDYtGs9F0kKUWOWWol+UyDNyW+6EJUWVttSw/iRagdG6KO8J+AJ6onsTHa44CoPcCiCjjTlNPee8N\\n/SNkb23bTS2j1MlzZz8DwIWfdU9L+eo74hadIc+L1tVdtX9a/gK1cHv4yaUArPusdU1zV7DuJlk7\\ndKDW06hLBTdrPjwtHiBByAC8n4p76dDpm1nVDgtq5o4o0VfFgvr7qecDUDXhE47N2AyIR4NTPZZc\\nm7SpTFvrXkZBU9p8oqV0fkDG4sme+LZTV11M/yzxOtjwoPThTkz8faW9Ze7suBv/2Pul711x90wi\\nveX8H3pOAeCT1cOx+a0oQ/KfvdHAXS331VJArEBPsYxGraAkRQEiTeocv/zv/DZuPb3tpjdSyih5\\n/WYAsjbF+6VnOZtZt/8vAMOcajmDJ24RPbdEMul9+NVk9pYWAzDx8lUAfLF4BJmbk71KTHty7ITE\\npS3W/VnWG8sdsb3Uj2zmigniNfHusyekPaaxjzxAa71sRd3hE9vjYPHsjQ9w3ZN3HupqtJs1aj3t\\n0FlHrofK7JqJABzrk7HM3mxwtEfcZ8vOkL7MtcNF3rfSnoK5aq15wjpVb0Xc3deyqiYSdci+5hwj\\n1TXfJe7wABG3gUstQ7Mpq2pT7yj3niNW3vGeHeosH0Ofk3dSkGA5rSuRumWXHibR/Q4i3cJZPVrv\\noOGTQgB826QDX7l3OADjm4anPcdy9808rYwx/cXVcNPKwV1d1RYJHC1+OpePWALAmy9OOWR16Q4E\\neph4KqSxXvrAL+hxnjRSt3LJiJxUi/3znBbPTyRjj5TTWJQcQKk9NI6QUX3n3IEp+8z8EMM+vR6A\\nRNVG1qYjPXxhHEsQ7O+qAMDmMElcKmpNiqKqpwh77Ax4Xza+gARG2XVaLiNU0JaQ0iKUN2cRjCa7\\nBQ7y7iXfIULmEv8AbEpw7e+WCe8AVwV91ZrYLJv4a5c47DxXPxCATQHpI1758jhyv7XC5UUJ5qpB\\noZcKfpIRIeRr3zu88JHuL5j+YNtJwP4LphYvzJVJvyUkTmm6lL2Lut61+Z3p9wEw2JnZaW69McG0\\nnRFJuyOWks1TLQ3uo2UjY+JpY08VqCcPPBXquKp4w7SF5eRe74sG7unl5/DgaFEI+XKb6JUtkeCu\\n7rsQkLWqdnXBspD0u09tmExhlrTHQVmiYMp2NHF2jgT6WdgoSqDJnm9jgWNO67We9/4k7d6K8M0F\\nlTRslSjgRkTanW/P/k22rP4oXylTThyxkYVbBwBwx9hPALg5dyMBU8aRHJv03PdWDOfXPUTJtiPs\\n55xvRMloLpJ7te+Kr0ZPdNez1sLemhsPkVHy7g+BZME0kasf/xkAv7putpSTsIR63z4P4PNl8hwn\\nHr2Rbzcnz2dctcnHumrU+rgCpbyrbL3NW+7DRiR1Deutx33KrBfOBMSNbV/8Y4M4druStrnmZ8F3\\ncOpyb0XqPLN5hMzpJrhdDDx9CwBbPh54EGvVMSzX3jUzWl9DfSTw/MLJAJx1hvRVWVtMJrlV4KEs\\nmZ8EM51UnCeaox5vp1fQpRNMLaw+1lNpph1nMvaQsFHNPWxyYPEZO/isTr6pgU7pwB/wD6RgefL1\\nTJuB9zjZ39goSz4yyrSgavHdXQ2v0Wg0Go1Go9FoNJpuRbewoFp8dsdfOPnBnwPtj2hXsbKQskLx\\nAf3nLeKSeeejBze1RGOfKMf0EwvhiwtEs5MJXHXtPACefuf0g1qfg019SYSs0mQd7dSzlvLlC+Nj\\nf1e8Le5OvS4Tzf55Jav5/XGSz2T8P34KtPzOL/+BLDI/NkPcOYY7K/EpTdV7DQNoVDHAh7pFpXXz\\n19PxLRXNetbyuBvX+CtF2zYmU7TlTzx/Do5AcpCMA+V7l34OwGtzTurUcruKTSGxoBzlVnkVGxzY\\nGlQ03Lp4lDtHo2j+aoY66bFSTAaWJfWLpnEsmyiBpwIqj2EkYqOpSt6B0Sx6MNNh4shWwUsaHdhr\\n1bEZojE0vBEyc0VrXZQl38mI3D04lYni1fni1pO3ykbmbrGg1Pdz0KgiMEeyVai9sI3mzP3TvQV6\\nRfGUHTq93cM/StZ+v93oYeEbYzr1GpYVc91ND8PY5G0tYbkebr78kXaVHSsfsAJDDPrXLXR6vEKl\\nkF5760xGPNJ9goNER/kJBZTr+qb02vvKC+VbL5gr1r3en8b70Iy9yvUyNXVoWnx7ovj2WMN5Fo2I\\n2/DMq8TaOTS/gsqAuG+W1cv7CAacbC4XC3p0kPSnWzb14s0c+d4emyTRaictvZx8r9S19vF4cP7a\\nYfLws6K2WB7pWGqSmHWh/Yy9/7ZYkKL7i5bEdwyU/1Y3ywBRFYmkpNWxrKcAxY5MVk1+Xv6YnHqd\\nUtXnlTjjZVgRxod88COy1rhST0rAodx5/7BsGgDHXriWVa9J6PNcZ2rEI+8OeS+zL5rHhNNkOU/o\\n3+niNUNjX6mH5ZrsrmzdEySUo76TnfE+K9BDzn1k/qlkteYWXOeIRx/txLzUhyPPvSIBOk173CLu\\nWqus7qfCO8Nltdo05J13Z0vqdwFnpbSpJU0DAaiYGGXcwqsAyP5XvF1Xj7B84zruYtPYS9pURlk0\\n7el1A5Vr7pYoYY/y4ipU6WZm92P9RPGHmb9YvE8SrbWWa3HzmXWc2EsiEc/rK8s2Mso7XNUjFm1B\\n1Wg0Go1Go9FoNBpNt6BbWVDfaBgY+92gNIm+na3L0J4KAyrECjb1LFF9rbxzJoNeEyuqb3vXrScM\\nK+XM7Iv+zvWPyiL6zAT38VjanPy45mTARLG0bl1U3GX1Otg46+PvyFov91T/zxnH+JRjV78qmuYX\\n7pkJyp5SPHUbAHve6p+2/KdXiRr8ySpZQ2c6TAYOLgNg99d9YgnQl98j2vdNpz+F/1RRc594/10A\\n1B8dYKhSTRW7ZL2VI01ApgPlD4Vipc25opEnZ5/d+RfoZCqV9blPbLGpEdMgZ22LYlO/PVVinQzm\\nOqgeJufkrZdz+n4WYHezWASsXKOmDTzV8l1YaRSMMETc0uX4/PE1HqFM2RZ1OgjbpOzN2eIVsalv\\nT6JVYtEYPEflkSFMyCfn+ItNwoUq+bVX9ptbfLF1Zh3FU2ZjwoUS1GTx3IOb9qnk7FJuXyFa4FOL\\nJUjcvNcmdtn1hj8xI8HK2TpWLuLhT8wgnCnvzeFv2eqSrlxnVdsvxRwiH4uxMaONI5Npj/V07a0z\\n233sgWJbnYmR3/paotF9ZN32LpvETjBtRqxNWERcBvbmjmn/q0YYMYt3Rkj62JV7ijCXqfWYqt+L\\n9o4yYrz0vbtfGyjHZ0FzroyZN5ZLkKBeC6A2IZCftT7W7CUWzeuHzOepj8WyZFl+rQAjHcVKR3ZX\\nvgR8KnnrR0w/7isA1vnF2rtmzlGx4xuOkTr8esK73JQTN9tage5KjpJnfM/A9/nx15LpbtPUp1Ku\\n+8PtMrZkrWq/R419rVhqbjj2C36OjGsvvycLOCPjAmQuV7lcldvAb8rH0NAk5bdko41l1PGob8ew\\np7XehMRAjjfB28M4WeW0/kzWA3sqkv0V/AOkzEwVQM7RYMOzt3Msp8E8KdtdfXjbPNbePDMlTcvI\\nh2+Lret8esgrABQO9+1XOpdAbxlQPXvaNy9dMyO1Ppr42LO8Xjw67PU2vK9I/1Y7WKWHcZrkrTWT\\ntuVsSt8nWxbQyhNDZORIn9I7R7y4Nq/vTeYWNW9JCAKXvUV+N2cbTLlJgpHlKQ+KZz85iV7/tsSr\\n1AZsxc0YWlARi98x/GyZ085eclZ7HsF3gm4loN7/5GWx320Jpq0x9NkZOPqrWfH2lqPSnXL5Yj55\\nTXKYtRQNsDVMlQz8hofvbLeDzJbFIpgeSQ41pl1ymAJ8skwG6r9m7mzlDDjqi+v4n2P+BcD7I94C\\n4FLnmaz+dAiQHIk3ulcKHzxayix/qx99x0p0iZpxXqKfiGtEyVyZUGWvi3/WdSpwSOmZT8bcWS9+\\nSALj9L1gK7vfHNDh+20P9+Rv4qQbHgTgB0+nRqHuTHqeKB3cztUygXPVtL/t1Efl2focItzZ/TYK\\nVsizz9yV6h/maIySqVyxq4cqQXVDkLz1MvBWumTgNe0mYeU+ZhpSnrsaMvaoqHr5Btnb1TkjlKuv\\nG9w1UnZzrppgL/Hi3ZvawTsbRGB2BBxk95CBpHqbTMy8dQauBmnQTW20tOAY6SfcK+MCkSWYNvWJ\\n4N114AqupiKVQH13+rKOuUAif1YFM4gskHv4oFkm4S1dvWmESBnOrQfmop7k7kv7ouu2Jpi2do32\\n0FHB1MISPkGEE3d56pM72C7ArjYE8oAKP22NPU0FBrbTRcio2S2+su4yB3nrOiagRgYFuHqUTJj6\\nu0UZ949HL6ZuuLSZ3p9LvRqKYcNC6f8yz5RAZasnvhQrZ1lQ2v+MBT9NLl/19bad8uPhbeeSvzd5\\nAC26aTPffirCpqs2oS8/Ufpt25fpA+Q9/ZTkN37CkuAGhXjhXXFTthLfZxCP6G7N/f72+KX8Yajc\\nnzM3gG+rHGBF0v0FN2HNBC7sL9doCjvp75Pn/c1umeiGJ9fjnJ+Vtm774pRuh7/vOCO2zVuu7rU8\\n7tat4sLx8ntTYvvzz5HAjlXv9Ukq07dDCf/2eLRzKzhgtKeMZZkr3HGln1Ig/uW2f/KbdRcDaQPh\\ny3n7RDZPJ5w2Hbt/SVkPd8HUoiVhcH8CEIW9SpGnIjLnnbiHC/uKAvuZ2We2em5E5dMc9syMDk/S\\nY/fgOwwjx7UTSwFnUx2AEQXTLs85nRCaszl1W+UYg4KV6h0F5P/hA3fz4lBRQpy/+lrZV2+PZQrw\\n91Pt04CgMjyNP349n+6Qees5A9ZKvXoEgZbH5qxtUp8VpcUs3yx9VHaetL39yUUSKgjH3J6PJI6M\\nXkWj0Wg0Go1Go9FoNIc9R5TIbaWeGXP+Bja8NbTN4z99ZcIBWTKdypLQ2DuK2UO0mwOKVD7InT3w\\nrUnVoCRaap+64h8A/GD2jwE4a+oSPpiX6hbbWeSPlYgbVSt6dmq5nr1GzDXJsl4+s+6c2P6wJ+5O\\na6UrGVBQzc/nioaq36V/B2DZ8kGYBcoFpir+aWZtET1KabNomgafv43nBn4CwBXRqawjP+naACde\\nLUE2Ls5bDMC0ddNiqWYsrczuNwck5aDbl3H33QbtU6bHOH/9uQC8NexdJnu6Pl1NouVoinkpAJVf\\ntZ1L0cJjiOX0Fb9oACOZUTzVLR9vWS4BsnbENbSeKikn71v5EOpKbDTnyrsM5yjLxx47VhYGWwhq\\nB8j7slxGM3Ya1JeoPIAqn1jYA676lt0b8tdE2esS90O3CtSSsdsklNFyy+5zhgQl2PVRP8zylvND\\nGnnNmEVyX8bi7BaPS8RUn6ARf0wtWk5/fYPkSVsfkPd1beFGFnxf3D2ff0dcDlv6grxrpd7hjM7V\\nkq+76eH9SgWTzqW3s1LKtIcRj9wWawvprKfdkYpGsek1FUuPZG8Cz8vi2h5vwdHYt+xsbN+7tpd6\\neG29LLWwLGw55VEyypP10c7ejTRXy3dUkifj1tDnZtBrrLiaRWYVpi0/a3tU/d9yHba8OhhXGj/W\\n341+E4BfNHwPgGjIjmeLHOhsiI8PdhlO6V1cRV2peIZYfULiffmWxe0NWRus/j8h+JGqQzjDJPNo\\nucc/DZgDwJUP/pwyxHJqlRwpMFmrAjWdtlryPVe+1zfpHhqL5P5dymq4bW7r+YStMT9mXQWG58oz\\nXnGmnYYP46mewsrM65AsOwR6mrx62QMATH9Q0tvMuuN/Y78tfrfxAgLzUsd1q78NjmvE+03b3gn/\\nnvIQic+vNTzHiNV9aYLV3WLEY0eOW2rEbca+vXhu4Lb7mFjGIcvj6MvePNNKnmPTBn+77p8A3PnM\\njzpcT8u6+11wCY541VIC1XLDxUGM1dLYLXde714zHpgoTddpWU8TWb+tN+M3/wSA3GVSntHHjLnz\\nRtxyPXuUiLyaAAAgAElEQVTQJEtWR7B6UG8C22Si+OkrshwtfQi0VPr0rubeYdIf3fGg9d467s55\\nJFpPoZsJqP6hITI3HHicxw1vDcWv8l9a69JC9W4cSujxqNxi797+Z8596MDzIGbsseHPkLJ3LhKX\\nHV+Nwco7pcMY1kLCZEswtehK4RSgbJsIcp0dSdOIpros140Nkr1CpL/EtZ5hNUa+P+It1LIdrBU5\\n/3P2S/zy4ytTyrdyvfl2yVWi4+NXawynX80zs+98AK4uPQ1InwcVWk9+vvyejkcGfWvYuynbSk7b\\nQum/019/f0kUTC2G5ooComGCi8Di/HaVE1ALpLJV3lF7vR2nv30Z4R1N8QSA1jpS715pb4E8F6Es\\nlcuxt2yrOcqGR02Sw754bjFLudFQbMbcAVW6XHovaH2hsHdvM9mb5TvzlSkXP3+Y+n4tu9fs+ige\\niTRdxN6fTn8dgDm7j+G9o94GYNQS9R20ICM058kO7zDxUQ6sycVZ37KQ/NGMPzN9w1VJ9XmVU2L7\\nOz3abRvs6+rbWeV1hDaFY/WqAr3CeHbL9zbgVEnOXvp1/6S22v8UmT1s+zT9uvaDQe7xIoTUfJ0+\\n12zNSpnGFOxoeUJSPi2Io1SEyPy1rV9vzymqPdpNCMvDMsLyDZoOO8Gesj+jSHxOw1uyOObYTQDs\\nfkQUI5EToi0Kph0h94JdVL3fJ2X7/1lxodQnKvVyVDgpOEHWjl7bfwFvlUtY6c0fitBX9U0hzQOk\\n/8jtLT61DWvyYjm228Jywyset5vta0Q4OL9Gxl3PCbVEVomrsatOjjei8Qn+iecvB+D1n73E+FdE\\nIPTtsPHTc6WPf+yZ8wBoKjSThE+A+tHNZGyQsSndGPP1SxLZs35IOEkHaim2rHp79hrcu3Oa2in/\\n7SucAlQs6pXWodCmlu0bW7wx9910gqp/nPSzv945jVkDPktTUpy1N7fs5nqoBNPzL/yaOWuOBsC1\\ncX+cJIV0az4TFSM3PiXLdaJj6tt0P3TVddD8YRBz007H8ltlydCwN2fg3dny1P3ci2XuM/fD4zp2\\n/cMIS+m7tkr61rwv3FiDc1i5Nrs2dVx5m7PQHRtnao+S/rJgsY2950ojLiyQjqJnRgOrSkVxVTgn\\nq6N2jBg7txWwpN9AALzlOv/pvmgXX41Go9FoNBqNRqPRdAsM0zz0C6m9Rf3MQdffxUmXL+GjeaJZ\\ndFd1UPu0D5de8ykAn5aJq+8no1/nis0SVXft3OGAuP9YrkRdwT+UleuHsw+eq1tXs3563MJiWYa9\\nZanvKpQJI89aD8CGOcOIKo+YwnMkirHNMHl1+GwA/lp5LACvP30KkZMkiIb98/RBNCz8/VS0WLdJ\\n1sZkd5uQD9bcJs/+qC+uA8D9dVzH1dRTvvnHrnyUnz50a0rZzcqbc+0trVtQo6PEEjGwR1Us0FM6\\nfrd3JC+9dmqr99MeLrnkCwDu7bWi1eOqI42cuEACRpkrWndN/eXVrwIQUP5If517If0+lEZhRDve\\nN5gqP231UBd+y3hVIpp7pytMU4PS89c5sTeIfsy3U1nGHeCqV9pP5ZrjbIgmuRW3l/piuY6/uH39\\niBV0aPMZT8byJD5SNYUeKhLKxkaxKn1SOhTHsrZd4MKZZtpgQpMukiAZEdNIGyU4MEos2Z7VrVsB\\nLCvv/a9e1GZdDmdsoQTrxRXvA7QZHfsv05/kjneuB8BU+SQti+vB4nfXPs9/PXdNq8fkbkzVmNcO\\nUtEm0wT1aC81Q2wE+kibMYJSXuaAWgbmie/+Zb0kgNJT208k8FQRAOWT5NzChft92RQaitqn/w4U\\nqHdUGX/XVhTudMELQ5ng9LevDpaLb9OIAM5t7qQyQyUBfn2sWEPfLB8HwB3FHzHVK5aTKStkyUTd\\nh71pUOONb3v8nizPD//wEFlrk30e6oeHyFqX6gfRrIY1V218W0CNR/97+VPcs0wCRTq+ivfb1rOw\\nAselmxs155q4avZ/zlQ/QgXJ84XYeOrTQMvW0HQW1MVBGTNGu6QOR//zpynH7C+2MfKwgqUyhjv9\\nNkybFRwn9Z6t+rVlzTUiqdvyTtxD9ZftXyJzoLz3oz+zKCCeBv/n2WtbPTZQKBXe/L1HKXn3hwB4\\nlXv8mhkzubdC5rVWTtfQERwkyfKgGHOJuJWsem2E5CsFGvqoecWu1vvQqCM1anrVKCOWG9jRKNc4\\n4YxVfLJanu01ExYAsKauN5tfFdnCCIO7dv/767Kp0lf3mhcfo6pGp37XBUdLForKZQfu4XKo2fSr\\nuxebpnlsW8dpC6pGo9FoNBqNRqPRaLoF3WoN6uevjCc0SLQJ7qr9r9rKO2cyZoHkPONrCToxOXwZ\\nlSsliEB4uArXvq6lbGQHjn9EkJufVRo895GjyRo2awbR/mLlaU274fSL5RSgflyQkQMlrH5Pj6i+\\nsxwBcmxiJeqv8pIC9M0RbekeWragNhRH8VSoXG5N8e1Wnrdodpi5DbLOJlgj67eeuOMhpr8h78Pd\\nT+pwqjdKndIcG27RTmYt88TWI7XFMcViDX6p5ONWj3t189HtK7AN2rKcWuTZM/jyuMcAOGHFz1s9\\n9oPKUQA4VMLTUK9mmnqI5t9THcYWap9mMKyCRtQMlXMbi0xCBdKWe2aLBbVPZh3roqL9CzQ4Yuuj\\nQiowiGGCp0pZxlXIeNMOUae86/bWBaCpZ/usCa7JVeoGpP6jHrwttv7LXWXEfm+8RjwH/ppRxmPr\\nzlb1kWsYaQy8LaViOSNPUsr8/pUrUjrf8NF+vjhetP9rjpfv/2crr2BCb/nOFr4xBoDVd8QtGPe3\\n6y4Pb1bd8hAAox+9HZD11615Nvx81o2tBPhPxtKW2wPttz4tvlmC1hzzpVgxbKtTLeq/WXxxiwGu\\nWuNALKcWniooOU2+mbWbxDpTX56JLU++9QfWi4VlZI8yln5P6l4w17LYdc5YFfx+NXxa0PaBJFtO\\nLVpL+9Ze6ynEPaQ86z1JYwWAa5mXvy27NGnbjIm98CySZzLxculvF9E7yXK6bx1d5Q4a+8gf1nc0\\navgOtm4oSbkXZ0NyGfXDwvzmlLkAfFI3AsMwY9sBstY7Yue35lVmCx6Yx5k7RzxIvp3yLE/XdcxC\\nc1/VYP75luRutDys1t48s9PWo/5x7BtyHZ9coz7gZsWkF1OOG/a0eHRZ1/2vq17kdy9e1aFr7V5X\\nyFS19vjLt8SqHh1Tj21l8ipDc2w9xor9XXkY55x//oLmXBV0q41jPSr428iHb0ubisSynH4X8FZI\\nO1k9WwKZZJxdDs/Lmv62LKcW+1pPQcb0cF9ZbzpxsuRiDkXtDBso6+TrVD7FW/t8wqzvywRm4z+O\\nSimnLSrHqOBOvZrJXm6NVq3Xu7xS+ujuEAIw3DeIY+eBpbhrD91KQAUJ0tIRosfVsvr455O2zWuy\\nxwRTi4ZPConF66zsOsHUwlHm4s7LpWP969wL23VOKDvCFceLj9Wfei0DWg6w1F7OOUPcud77KG5N\\nt9xtbWlcXNqDbVvHghCYESMmmD7V/3NAIuRefLEE8ln/oQTocAJ73mo5qEljL+lQfDtsNBSrxlwR\\nnziMGy8BP1YsGMJ8FZUWFSTktgdvj8UmzFOuuQ9WD+D4URsBWP2qith0SjV8KrkoB712S6sT3bYE\\nU4vw0ty2D+pk8uwioHsmyKS03u/FuS71vS3cMhCA7CwRIh3uCKZNhMz2CoTBXCf+Ivmo6geqhO39\\n/eRnyKSn0CfPuyHsojkoXY4tEH9vnkqV589lYIuo/GaNUo4RNQlnxKOcAtibW69X2Gunob8cky4I\\nUiLN81ODSVkTwabeUW45fR4Ar/llcMixN1J0vChbyj/um3JuW9z7tAQBS9fxOpZlcn7GjQB4nTJB\\n/f2oN/nPJ6enHFvy/k1A25Oaw4F0kY8TsQTTdJx54SIA+rprePqV1NyC3mNF+dX0TXphqSOCKUDU\\nZRJVE4lXlBLo8zFD+dvr5yeVZ1/bcv7triKYI9euHRYhIyij3X9NEeHndx9fzNAscREr/Zf0t/OL\\n88hfqeob6hzBtPZSaesX9FvHq/0komU64S4Ryw33QJbbBPNM3NWp79KK0u5oiudODamo4Yn5WS0s\\n4RRg0Stj23VtIwJmb5nUupdIH7tuVy+8+3RTxRdsYcebAwFiLsM9+1XzpzcukWuXG0Qtmad3OOW+\\nwqr7DvaMkrHLCoIVv78DwVgjF/7d8JHcWSCR7//UznPvyd/EPdOTg6uVvH8TLcdHbz8Rj8m6gLih\\nfzl2TqvH3nTBRwD8smADAH+qbDuTw75876QF3Nd7KQAjEQF1X+EU6BThFGRZy8arHpHrfQei73YW\\nSk4kpF5Dia+ePe2OnZuKFZ3XXQ3HnimC6b3FsmyrvyOT6VslF/NHW8TVt7woi41Pym/Hfij1rDZt\\n2l00TFLBy95uvcUsOEUUtSc817rR4WBwMIRT0C6+Go1Go9FoNBqNRqPpJnQ7C6p3b8c02quPf56L\\nN4jLXUWTaK1rmw5Md+cfIqb7zI0dT/ZgBTTYMP1hJi29vEPnOuvs9HdXtn1gB/h7H7EwDCNuQd1f\\ny2lHyTxb3CJ4vzdPnSuW08GzJSjRiz/5G5Pc8nyH7rwBAOei1vO09Tpa0jaEozaWHy3BfYY/OSPm\\nIrb8G7EMTDxhHbM/OhGA7F2p31P1u+L29jh9YlbZsHL1zVbWU4CszXaa91NROnHJFfgX7r9GLx27\\nw2KdKHK0L08dwC1D5bm/smsCu9YVpx6wS9pKrT0eQMRsp9rKCkRUPQoiLrEIOIvFh60ot44cl6j1\\nMxzybDdVFGBWyjmuOhteMeiQtyGeSsYKsmQpJQ3TBEO2BfId6hjI3NWyuaVylJOBo8XFcU9ZmntO\\nwHNCBQB1a8XCtuHaZEvAE7USMMOu/Oz+OivZJbCzCXwl34z1RP7z07j11Mp5OurB28jYZ9uhoFnl\\niHXtPrCkOC1ZTgECA5tjeTIjI+X7H/HIbZgOuW+rfzvn2/OSzts3DdOIbzrHOrHuxodB2YbGKsvf\\nWNd2fnxD8nfT0fRUnYHlFp+71sbeBkm/8Pv1Yp2zRQ3mfCppJ/qeuxuA0GdF2DvgNt8aVkCknDnS\\nN1X8JBOjUH3F21vu1/0DIy3mCe4I405dT5ZDrJhfbivBtUA67sQUL4mpW9pD/RDlZrux9WmSETYw\\nypItCnZH6iC7dl0xpT9XuSpnyvcRmNczydKoYrIRGiZty73BEUsvY1lJHdtSO+hLrv+Ufz1zStI2\\n0w45U5Vr4getB/6Zep5YTV98/VRWnp6aHqgtTl0l6VHKvpJzO8N6CslBGW/bKRZ5K4XcvliWU4se\\njvoOX+/jnUOZm7Wuw+e1xpM/kPQwd357JXVfJ7tP28LxZ5d9fHnK/kSsIEmecjsBlTJq7LgtAAx5\\n8dYjwpumvVjBFJkmHmK7/NnUKLfZ/DT5Tdui4nhp664yB5f1FK/D/mqeVRttYsE8WQpludsv2Tic\\n/MD+j72ZKrVY5g5gWftaSw+7yDc9jylj79L0qcu6ikHHSdq2zQviHo5rp/8DgBGzJG3X5FNXM/+T\\nUZ163W4Rxdc9qK/Z948/Jhqy4VvTuum47zmS9y4YkUEjyxWk9O1BAETUPOncS+fzVbms//D/++C+\\nyIZi6TiePv9RjnXJJPro59of0a616IX7Q7qou+mI9A1g35ncUC46az5vfDC5zWuki+IL0DhRXBcW\\nnjSTs5bfAEDgU5mA28JQN1w6hex17dOT1I2S55ndo4GibFkouvvNASy/Rwb9Fc0yIRrr8nDZpjOA\\n+DpY0wb1g9X1NsSvVz9OZjCbz3oCgCs2T2X1O8p1o4lWBdR0uUgPxsT0D9c+xy8XSaL7DF+A8wes\\nBuDULIlod1ZGqN31Mqw81tacx4TcDSpi5Z70QmDNIEswVa65nihGpjzbo/rJhGhE9h7cKpnpgsqB\\nAJTu6oFvhXxjsQTaQN4GeQcRtx17UK2F9ck7SozgG/bKRLapwIG9Wc7PKE+t486TPXjGy8CVzoXX\\nInEtZ1tY65tay23aFrNufoAJ7vg0YvBLoqzx7JWH31QcwbujY5P1QymghrNUtMMDeCZtMe/6+/jL\\n3lMBeKBIJg4jHrktbduzGPHIbUw4R9b6Ln5vZKfUY/hUWT7w+tD3Y22prTq0hRXFN+LcfzfbiIqa\\nGvIZsejjGODbKWXtPU7aU49FdipPlXbm/VbaYPbWzs+7Z/Ujp9w9nzkfHg+AS7mzdUXEfGv9+iWX\\nfMFbz03Z73KsNaSW6yyAf5A8O98We9rxuMlSbvZsJmul9In1o+UmHzr5Of5j5o1Jx0+5agnvfS1u\\no5lbWm/n1hIW3w5bbLmCTwmmjX2juCvV78FyvazV7RdPgseJose9IL2iM3KixIGIrkwfB8I7XpTo\\nS459Obats/OfWpF4yyMNFNrb5y7/YPUAAO7IkznijrCfM59sOc99uii+TX3DreYY3R/WzIj3Ewfi\\nxhtVEcmdY2qJROT9B3bJs/HsTf2ejuQovgVKCI06pG+pGG+2OwK5dY5ph+ozROuTnSn/DyvYm3bp\\n1tWlpwGweebwA6p3e0kXxbe78ufLngXgYp+fYc+0b0mijuKr0Wg0Go1Go9FoNJrDim7h4muzmXi8\\nzQR3ZsesoPZUIxAAUwu/BWDWLOXWm7AvUCiaxkHevczZI/lU2+8M2Tr//aNZAPyff6YGLEnEpywg\\nd6+5gqbPlYtnXqom64ypS2M5XxPpLMtp2CcFWVbTUHaE1hzx9rWeAu2ynraGrVSiOuw6waB5njwL\\n7xl7AQh+1LPdllOLk0ZJXtUTcjZxQoZYNC4afTvnrz8XgL4ZNQA8Wvw1O/3J2l8jmmw5jVEv29Y2\\ni7W3vDEr5koVzDMxwi1rsg6GtfT8i7/mrdePT9r2m+euxVCBPkJ+L3NWiDvzedcuU0e0X+9kaZGt\\nACKeCnAEU79XK5Ju2QQXTb2VtaGfuFD1zPITCMtzzHfLcxzqLaMxKlYFy9XXbHRQsEYatr/IQeae\\nZN9OezBCU0+xBHgqUzsAR5NU1hax46qT34ECZ8qx7mqorRW3wo6F80pm1IPx93sgTqyXXClu1onW\\nU4BN35fgGBURcYvuYfcRMeXZXrBegu7cX/IqVVFpm9PnSn3aCvx0sOhKy6lFsSOTqmZ5l1Z7m3ju\\nqth+a9tTNzzIf2wS9+u1t87kgeqBACymcyyox+dvTrpeW6y9dSaNUbFuTXj8TtnYQt/eUctpzRAb\\nwQIV+VPlvszebBLOkN+NRVFMu4p8naXcsK+soOBl8SZyBDvfcmphWSdmLzmWrBY8azoTKyrugVhP\\nIdlyapG5OdUqlTG1nB4ZctFvFw2UjVEjZk0dUCwzklcrJnLdjZK3d852ieK+orIPUyaKl8uyLfEc\\nyPVDlStxwvjk6qdubEcWA0dLULaiSeI1tGjeiNhxj5/6FAA/W31LbJt/sPSNmZuS619/lOonG6Qf\\nchMPMmNX3ti2kMzHoOWYok1LZDnEiCVdN/4N+0zmWfZv4zO4oadJG9xQ3pO1J4rVxrLcRkf4WXfS\\nrKQyijuwFMais62niWwKdSD8dBqsqPGRJfGgi53lSn240lQoz6RwYfv7UNuVMgct25KPe6PMEI45\\nS4JlXlSwJOX4uQ0ZLH1f2pxXeQ3Vl0DBis6xUPuLVa7qHV3XL3clv3j1OgA+PvObTi+7e8x0NBqN\\nRqPRaDQajUbznadbrEH1FvUzB954F8HRTXiXJ9s8Gsc2kbEi1Q6y8s64X3/J6zcD8Nw5YpHwGSFu\\nWiVSfeNisdw5GjteL9dJFdSulzVsUaVY8+3suExvBWc4WAyYuIOtiyQ4TChftLPOA8gr2xYtrUHt\\nKu798ZP87NUfAPDR1ffxq+0XAPDnfm8CcN7/trzupCX+evujADiNCHckWM72N0jSocYcLdZNY1Xr\\nN+BRaUBdtWp9S2MUT5Vo2oN5TgK5KvhJqaxfM20Ge48Wy2jWmbLedEqvzXyt1nyf10esW1dkL+Xj\\nRkn1M69KtI9frxpC8ftSnrs6RDBPpbNR60kD+XZcftEiOlSamaYCO54qsQjYleXHME38fcUKEMow\\niHjk+7PaaKCnGVub6d2T2l6blUfD2act4Z35Yt3w7ur87GLho0Vjbmn2I2aUyqhYk2/feiG/K5bv\\n9aXaiQAUOWsY5JLIUenWEVdHpBObMjMeZv5QrkE9GKy68SGchryb/fFYCKt1WI6GA+ujIiPEohWu\\n8HD0OLHkzBnyYWx/yZs/AqD0gn+2WMa+9c/coeqWEGyjfJq0s57vSRtr6mEjozxZs75nahjXHmk7\\n+avj59YOUqlHovE1qPXnyzcYjRrkv9F6ELoDIeyxUtzI34MmbWPX2wO67HqHEvvJ0mkGF8rcwNEE\\nUeViEVEmLdNuMuyUUgDO7CFW04fmTCMySEyVGYvjcxrLihnxmHgq4lZwEMvuxCskH2uRR9aGzp11\\nEit+nrz+eexfboOTq+XappTRvDonljLLPzDCaZNWp5Qz7BLxSqoMyFrGynf70ud8WcO59bPu+/5e\\nvUEyQI9yyXNcHGzmq0ZJK3N+ptzntKdanwekW4PaFVhrUA92Gpk1M2Yy9ADTFHZXrFgQVgyNrHau\\np4+4DerPkT7R3JBJ3hopoOxEOb/04sdSzhn0wU34Vkl/7Nvd+VbOvWolZs8EA2R3WoNqGpA7Wtad\\n164U74m/X/4kP3nlxtZOa5X2rkHtFi6+IJFlzb1ugrnywbiV61LGCi/+QSJklV74GINflsAio/8u\\njb3/2VtiH1XQlB7HbbgJR2RSY6h5XjDPJOJWZasAA/YQ3HT9OwD87esz8JbKpHfNjxM6/wny35gH\\nWu9cGvqra1fIdfdHIO4sLOEUulYwPRCW3zOTcfd1rMO28uX96tEb8Srv0P6OzFgO0/MY0cKZbXPX\\nQ7e0fdBhRFuCqYUlHDqbVN7QqhBRFQ00mGWjqaf8zilV5UZNei6VSfRur0SG/Pokk4ZmmaEF1Eyt\\nxJnJUW5xTZtnvRdXlLp+sr9ndQgjmixcuWsjuOqS3X4jbge1JXKOt1JN2sqbMQ1rQmxi2qWcWE5L\\nA+ytJK63grb8e9uQThdMG4eIW6e31MWzkyTw1qgH0wdJu4zkgAs3XPN+WsH0R9vFhfvT0ni+4O8K\\nD1QP45EPJL/p/kSpTBRMLbdYq//vCFZeUzuw9iOZCI/aKxE3f3TUl9w15QNA3MEALvTFBwDr/aXU\\nbZ8okDXDbHjWyIS7Ypz1TZtkqGjXzVlyL73nOcj5oURVLB8jbozul/LI2Sz3V1tiiy0VCTVL/+/Y\\n5IF0+fquE3e3i4tFCHr9/02lcqxcx7tHjcEJAnLdABsZe6ScZpV3taFflEi2tNthgyRC8LrSImx9\\nVXAfpdSNuCHsTR7fIR6gyMoHmciUFZdS92HrEWgPNj0yRVlR1iQCaig7Pt5by0PCE/08VCKR5k//\\n/A4ArrvwU579IDnSLoBNBY9y1hsEJsnkOWNh3D3142Xipl56ocxz5nJS2nr5t0mUrM2XibJ17Ofx\\n8dWIGHyyQb5bu0NFXHfDujdFo5AYSfvi3rJU5G90LwF18jkrAZj/3hgue/ruFo+byXkt7juYeCbK\\npH62P32wqa7GChp5JGL1QekI+VT/dWkZc0aJK/gDFbJMancwh//u8y4Al70RV/QOG74rpRxriYbZ\\naG9VMA1lGjj9+6coriuxEbUms93UodUw44KpxYEIpx2hez4RjUaj0Wg0Go1Go9F85+gW5jXTBs3Z\\nJu5KWyxvWXO2aCRcdQafTvurOjKTCRMl19XauWJ92P7uQMa8K5pCy+13WTBIUGmOw/mi+fiPaa/z\\nrzIJSrR2mWgGM3bZWFgjroml0x5PsZKOeeC2WJk/vuENAJb6+/PVq6nBjXzbki0xgXwTT1X7zPRW\\nKpjW0sB0lK4o81BjpSmIJny1HbXCapLxlYnl38o/GvI5YqldnE0mxmRxBwtsEouspzJEU6HY8Iq+\\nksZaFiqi+RjR/GepaBshM0LIlOOao6ptRA1CKhVG1G7ErKANfURPlrU9TChTpZfxSx1ySoPsHSd+\\nc82ZKkiEy01diXLrdUUxXdJXGFEpx1Vlw2yPmfGbztdsW14YRgR+8Fj700sBPP3C2Tw8XJ6flQok\\nke+S5dTiydlnd1p+v/ZaTsedLYH4lr9/VKvHNfnF7evRl6el7PtlC+dYqWnSuStnbzaxmopNBWdL\\nDJwRy/0HbFjRDyCWWqHsOMhUaUi8FSaBHnL+90YuBeD1LcmB1mI82xOAW/8sFtR/HnMGPZda14lf\\nb89UaY+jBu/kkl5SZllI2s+LmybQUCff68aV4r1TsMogeJ70HeyUoC72YNwFNhErQNHY+1OfyYq7\\nZzL2wwPv4+0nVxH5LDnlVP7Zu9i6Ve4/69v2ta5wBnw4Qlzzx74t9XLWJbj2qiH/1YmPxbT/ljvv\\n0w0nUnqNjMtWPnDfNltSrtZQtRSU+Jiy1idP096/889Y4R9L5srypkx7PHXN2L+kPi9b0IilJlHd\\nZNJ1E/nT1+em1KE7MP+9MZ1eZnikWMMda1pPZfPej/4MxHNjtsddN7BIrE6/XXTNgVRxvxnr6m5v\\nsPOwvE+aCm1Jf0M8UNsXY+dw0zYJptrfK275+c6GtMGzJuRvS9n2Yr3k/LTnhJCQYnF33GhGhB59\\npX9zvpiayq5yrJE2iJI117Hkm4jHZNwIcalfUyPpMvcnj2tXs/765Dzff6ocyhNvndHl1+0WAqrD\\nF6LXxD1UfloUG6DDA2Si5lrpjXUKALMHzQNgDKn5iCwBc+WdM2neK65WPhXxssDh59xCWR+3uaIk\\ndty+57b2G0Q4sgae2GQiAmG1pMRy8WmvcArw67KxgAiVnSVQ7ltO1qhK6lcXtHD0wWf4kzOwnyhR\\nCZ1fZrdxtGDl2LMle4HGcuFZER07QkMf6Qx8u7qPz//BxFqH46qLu5aGPfJhNxbaGJwvbkr+SiUk\\nZvI6bzgAACAASURBVDto7CkvonK0iA45G0zyX5L9tj/KhDpohqiJygR2cKZEtlxsDqRpoGgZdnpc\\nhHLl2Fzx3MIRiFA9TAaCvPXxl9xjpcyk9kxKzZFsCxmYajLvqlGu+0EwlHdT5CBnLz+gdU1mesH0\\nu0woK+7Cbb3fdKy9dWanRdVuSzC1cG/q+Luy8ukmqjMbiuS+PBUmIaWEsfJpZ+5I3y/tm/MvZ4MN\\nR6M8J1sYaqbIQPTZHnELby6IUDVSrmprljJzN0Y57m5Z+HTOL34GQL6TWPv+xW2S5/Kh313OqME7\\nARiStZc/rzgLgPBuNcZut2GfoHxcA/H14MYX8WijFtGh6rhv2pfbMp3Quj/sK5wC9PHVstXs2aFy\\nwj6T1c3ybK08qZmb7bEouPUjpB+11kYmUnp+fH3ypivEnTlRmPQPjOCoTV1yENwnC8BNm65gx5sD\\npUy1FnXoJzfg/ablNcbecgPKW88xb+Es++6owtoSTC3O+aesZ03MadoWweHynbjXtRxLfs2MmZy8\\n8hIAKr4oim2/++o5ANz/wqXtvl5TXxkzPbu7xbS+XWyY/jBDnpc+sSNjZ1MPJZiqPi/iMmK50UNn\\n1MaO+/c6cWHffOaTAEy+51YmMymlvNkfnwDAvVetiG2rCItSPlrpIvem7QDc0Xc+AL/5+Ht4ndLW\\n5/zP/ZzwvLgLW1HVwzlh5t8nLvnT1okCc93OXmT4ZC4zOFcyTkRNgxVb+wKQu6X9978v+wqQQLtz\\nknakTItfFmzgCbpeQNUuvhqNRqPRaDQajUaj6RZ0C1VLtjPAab3XMydcxDGXiJVz6b9Gt3rOg7eI\\nBvKOR29Nu3/hheIWfNqD9wDw61nTYy4t+xMWpXmCuDC6FsetuYGRoiHLWOmNWU4T8ZcoV8k0WtFE\\n7u0V19p0lmvuyOMl0uSar8Vt4FBbT/OniQa+LiBa3GdGvcgtD97RoTJayhHbmuV0+T2i8WzJFTjq\\nsQqVd2Qa8chw3wVCPhX50woqZoKpgiS5ak2qAqKVtwyRzZk2nA3ygHrMS/UR++e3EhBmxuQNLG0U\\nV/pvKsVVxpXVTH62vKzGlb2wBeWaTSoGSrnPk2LxbM5xUDFGuimbMvJGncRUa77tNpqVocYKVJK9\\nJRK7L3/f76Zl3GLdTdKflLz3Q1w7D55l5NSzl/HJ+0d36JzmvqGUOka9UdzlbffYByMnsUVrbrpt\\nEQvkhQRFgngk5owyk0ZRpmPrYbWt9llpPVXReHCzPIPjBm0BoDEsDaqithBXXarb8IL7kwMp2kNm\\nrD+9Jku8Jx4CdteLNaFvRg1mVMox88Ub4vgT1rK6Sqw/Nculv0jnygvQO1+8ZsomqWBJC1vPVdlQ\\nHOWq074EYO6z6YMD7S+r5ozACiWX2O+EVK7DrDFy/ycWlfLWlxIt0fSFuWvT5UD6PKkDB5anbNs3\\n4m4iYR841PO2XHT3ZfCUrUl/b1wwgD/PSM75ueHUp5k7UZ79nV9/X8pb5qGxSH1bu1P7QdOe3mpl\\ndn5A88OejlhOLTxeFWQnTTZuq7ySd36Id2uqm89NORIh//4OXM9yFV2/U7wmDocIvkNnzWB/RujA\\nCTIfdyyT/sNVS8yC6nlHeeRNhqHFqe0xHSecsAaA1xsyOcMrHl8h1RAy+9eR6ZT++MHfS9v/398+\\nx//89loAzuduwpOlT10/XQKUJQbGeme4BGK9r+dgZm+RfmRnrezvl1tDYQ/pE4P2QnXG/k1ALYup\\nZflcf/3DB2xFbansg4W2oGo0Go1Go9FoNBqNplvQLSyobluYYZ49BPNMPl8u638S9aptpXjZl+OX\\nf4+vx72WtC1dQICOlJtoOW3oJ2rHjFUtry0Ie8He0D753wp0YIWR7wyGZ5cBsIZBnVZmR/BPaMKs\\nFc1g1kY7Ve+IacCyaIKdz+4W/eAJ/5CQ8ctuf5CTV1wBQOMHvVLKvPam9wF47omz017zBz8UTdVT\\nj8eDlrQWRCmYC1lKC+4fIBqwzK3fLZ1NTFuu1JimAVGnsqD6o+yqFE2f/XSV+3QL5G6WxtTQW96v\\npzqCPShtomm3tJN1oQiLqsSC2tAsx7lcYS7vJwFWnjHPoe/nsnDL30f2u2ujVI6SLqlyhJhg6gdF\\nieSKJtqzXWmaTWIpowI9pE4QD57lbIjG8rd2lNV3xDXlox48eFa5rmL4E6L57KyluGees4QP3xvf\\n5nEdtZ4CaS28WcV1NJfndaicaRfN5+1No4D2p1vqCK1aTtVnF3Gb2Jtatw24VQ5iIyTHVY6Op0cy\\nt1ljS/u16baIWoPaDKfmrwOg0CHa+V+GBiVZTiF53VYilRPF8+eB6oEAlJ3dzG+HfgzAvSvOjaUp\\nMWxy7t5AJlWfixuEQ3k5RNypsQIAaj4QS2v/abKmq4zWLai+HTb+UCiL1F88Rqy9vqXepPWf6Tjq\\nErn/b/+VGqsiHVYAPgCbWkcW+EpyqH843o2ZIdfLWuti99r+LZYzLCdusSl594cAXDFe1vn+qdcy\\nhn5yAyAWT5D0Lv5x0g9mLk9vdj63cHXS355yg4t9/pTjrNRGv1kWL8dd3fI3GMyL511NJNpHLajd\\n1PL85rtGuqBIlhV0tj+H3z6bGgjJptpHa0srvVtdafOk7k/O1JVLJbZK+1YaH+ZskLXDlrdHcy64\\n1dLTqrHSP/2y7Ghqn5SgbZMRT8vQ96tYPGE2ALfvPC42ls0a8BkAU9dcyMUj5wLw1HIJLPfHSa/z\\n/SzJMcx9H8Wq8D8J1SmcL+3o2gmnAvBI//ewvF9OW30RAH19tViZ9awgrkOy9rJmq/SJ+Wn64vaS\\naCmdH5AvbvrLt+93efuW2dq2rqRbCKjVoQxe2XMsmaOqCKh8imxuX+CcdPj/3YuTbZd0Uu1SsSnX\\nJmN7yx24owkmqGiQCz9rPT+ns6ZzfWocQ+o5K1sG9X/RQvTGLiZzsZczrpUF5R9tnBzbbgmM/3fG\\nc/xedcKrE4RWS7Ew7oN4B103Wp73s0+JYJo4pC6/ZybDP58OwJ15WwB4fmoFIwrERcbqeEre+hHZ\\na5MnwE6/TKTguyeYWkSUMBpxK3dDjxFzcXbVRejxtnSyeyfEo+bVDZaHZk2mQ1nQY6lykbbJ4PCb\\nrRezR7kF+hukDF9GkCV1MrmrGxkinCHbi+bH87UZpnRJVuL7IeO3s7lc3NMDxSo5d9CG6VCRe0MG\\nDcVy7YKV0jHvm1+1NSZfJO71T/T/ImXf6jtmHpCQmijstkZ7rxHxWgKI0aVJ5i23YEu4TeShvgsY\\nTtsCamfRvCQunAaK2hcE5J03Jsf6iI6649525dvMfDk1j2LOZBE8aucXpuxLQsmAicJpsFDlyE5w\\nVY46TbwV6n0qATUwPEh2nggekQ9EOAr5iLnU7zlJCv9o2l+59j/iOfz2xRaG57YdB8DDw18AoHjy\\nThrX9AHgnT+p5S/3/TxWh4v/Q4IPvv7/pmLPlOf84NLTADi6ZHus7MuGLSNDJe58ebN8B5uqesQU\\nXc3Ksy271CRQ0LJwtOddiULcMDDSomurhRUoyQpn09gnyuSj1wOwanP6sbW9gmk6rLZlt/6fn9Vq\\nBO3myfW45ktf9/VLEuH/v66vw6HyG+4OyFxm7F9uw66ez6zx8n7twXgAl7HL03+jNrW2ZdBrkqs7\\nUaS3no1/XIDMZckCbnNOfLlKxGPGArhYpBNOG4qj+JbKvCa0/1OwbseoqetZPW9Y0rawz0zKkwzg\\nnFBNaHHHFGJXZNby2zTb/eXyxSbOEn973fPAgQuj6dif/M6HK3lrkyON2wNGLMKuZXT4dMHklPOc\\nL+WDeNnyUN8FlBSMA+Lz0oY+JlO5EIA7xv875fyhz8qYmBiht3qkQd4xkk/amutfMjGL7/dZBMBl\\nfZcA8LcVp2FXAf8Kc6Sf/2pPCWaDjGdWgDwjjWKvI1iC6awrHzpgIfVQ8935ojUajUaj0Wg0Go1G\\n060xTPPQR4QpGNHDPPfpi7i99zzuWnclANUNoneyLdi/XIUl50mQoNK3O9fFNeIEe6jt44LHNDB7\\nsrjsXvHynZ1ah+6Gtyy9prypl9nq/n1Zfs/MmCbLSilz/rVfxIJIJbrrXv4DcTn7TY9veaxWLANv\\nlUu6ntI3B2FrxcLU2MsKStJ2vZo730OwW1G4VNR19iZ5YDVDXTEXX+/eKJ4q2b/zVLEhhPIi2P3y\\nckZPLAVg/d6eBBqUO/cS0eLXjQrhyJSGYrOLBcDjDtEcEm2h/ZssIkq17JKI6+RsCcfysQaz5BrV\\nI4D+EoEsXJfqqGqEDNx7RWPaa7Fcr7Gng/oBUo6tOeWUNpl18wMA1Ec9/Phx5RqkAqc469v3LZs2\\nWPPj1i2oHbXO9jlDLFm7PuoX22YF2Olq/vPyVwCYnl2R1rLaVVjWxUROPn8pn72Vmou6qwiUBCk9\\n+4nY35Y1dsp5ywH44u1xHS5z/s33x1K7WNT3t9EwTD7Y7BXyrZdcsok1u2W5w21jxBvkzrwt+KPi\\ndbA9LG3rxl/fFSun5pIGbhspxz7woeS0NAoD9HxL2ubIn0ggwjV/Hx1zhffUqCUON+0kxyXtbd1c\\nsTj5RwbJzhP30XMGrOXD7WKd7JMt7sPrFg6MBTCz8oAaZtyKnC6AYDoCE/30zquXe/igqI2juwGq\\n6fkHRVJcjU+/ZiFVzRK0aNkrqQEfT79G8gR9/PykpCBK++YwzT5rD7vXitXet10FtDu5muWTXgTi\\nbthPPpGai7cjNPVWlqgmA5dylTySLKhpMdMHXnz7h5Lz9LzHf9GuYn5w5fs89XL6ZUf7Yrnzjllw\\nNQCRJampmDqLkO/Qz+27ioJ9coU6ritneK54uSx9WvLluupN/N+T/iTztfhErmKaLFH666SX+cN6\\n8ZZxKXeJq/ov4se5Ms7es0fGmLt6fI5d5Wyf9lvxXEnKu+o0sIWS61N2SoSrJi4AYPZa8TTJyAhS\\nv1vq4apSQSmH+sl9QyztVo7U4PgG2JKaMirUUzpZ597DPw3Upl/dvdg0zWPbOq5bCKjeon7moOtl\\ngPUPkZfw8OkSpe6up28ioFykTKdJZunB9UpuOloGZu8y+WCi9rjbjDUY2yLxnKpD/v0DOX65l6Ze\\n0vulm2QdSbRXAG2Lq2/8kOk5skbxhPfjk7enTpfJ4U8fEmEh4oZVP4kP6oP+Ja5Pz54rronXvTWD\\nrFLpABqK5R34dsSdBSy33mCeScae1ut+pAuovRZJe3OodQuVIz0xxYKzziCjTH5bAl/YZ+I+SmYw\\nVw4W15UiZw0L62X9ywfLZDLWb0AFNtVQ+mfJYrte/5+99w6sqr7fx59z7p5JbiYJgbA3iKAgoMVZ\\nBXetVWsdbW3Vqq1V2347Pm0/vy77UbvcW+tqXTiwbrEICAKyN0kgkL3vHuec3x/P+5x7w01CAlED\\nnuef3JzxPus9X6/n9bwcQbyw8jgAQO5mK6yCuuit4yK4c5gV7mZ+r5RTxOVNkZDM07l2Ol8t/c0s\\nYRn2Nn5bfxXPDZfJxjc+XLqMjv+9+ikAwNe8nXiyk/S825+4uMfjS07dh8oqLiykOO/vuyctwRW5\\nawAAX72vb5OfTETK+TDumnQf+HktUPuLijk1CCWFguwn2fHkfcWR3HfGR3Ii5KhMR4X5ZpEK9uzk\\nx/CN/48K8zo9NlogIVbKup6zhRuDc6KYOLQOAND0QAUAoP7UFNy7RVz3LLYtRZOwdCapone1zMRv\\nCxm3OHXVpQAA9/M5Ru7Azkls88UfWtA5gtv+51tc8PzPi5cYtMfYOC6C8wMhtHUKdd6wDTYfnysV\\n40TJvc1hJJ4fMYuTux3VJUCCZXuKGSgmL8s2Ng9ZsBdtMVqqwh8VdokFHUjE8jWoIm79iQs4dty0\\n+RIkPmRb7k65PVLK/sRd2z3RzBj/57VB+29XWmg8oGHEnL0AgNrXhxvb9Xzplpm0yklLc/HUTaRc\\nT7U7sxaoseNDQJUgN1dwLpJqdcK7e2DDgoKT+OJ9m9NGwKN9gfrDi1/B3589L2u7rmXQXQx1dzjj\\n/FV4e1F2bs0vGl+mBWrjbA3uMtJmbxi/BADw96fPg79azAnE4i9aomL7JWz/FknG3hTPGWYlcX5z\\nIoplUaog6w6PV8e8iVH/5tzT0cRyvPs0yCneQ8coGTm7eZ1IMfcPPacaO2o57lkq09R7SSigJ8Sc\\nJnerDHuQ5XRW8NzRp1di2/IRh/ZijhD0dYFqUnxNmDBhwoQJEyZMmDBhwsSgwKAQScrEiVOovjfN\\nzhxk1plt8CyndTI0IQ79ls+5lKImrz0777CuF5kqcplu6F7wSPec6pAVIFIiLKv1XN+PWFiJU7cw\\nsFrpSLvfXQ3CZR84ei1ZA4mnHz8dz6inAwB0461qA66vpvVKPYF0DccKHy6uPBUAsKu1ALLILWgR\\nnCvdewoASi7NoNqoMKQPWY90r9rBvKdfBsRz+a6s9bToyUkNcSFGEy+SoFnZ3rw1fLehYRLicW4r\\nspHiV2Jrx1TvPgDA1uFU8yxyB3FcLvOyNSdpnVzdMszICZxyAwk/33+4lG3G2aqhdZwutsT7c7QB\\nmoXbVLsQRkqlLZG2DslQ6A4NlY2y9Zyo2gCZ4P7nMeY8+58+Hm+TFdz+FaoFLumgcMK+eN4heU71\\n/iPTczrYUb08TUNODuMHsu09MvQlzz5/BQDg9UWHJzCX6TnVobMKzn7kJ8iNdOUXptwScpaxXtfP\\np5fTv9qFpsUVAIBRN1B0T+4MwFtBj9elZaSK3rXtVGxK8Hq69xQAzq0gnfdfU+YZbkJdLE7SVERH\\n8dv88lXmzvx/F7yM5R2jAQAbW0izjX5QCHUKvamyMwVU06PnaRECa3YgWcA+QxLXcFXZ09TeXT2H\\n6dS9MQyXXEmBpucSp/Z43OHC2SJBl9e7csV3AADXTP0IjaWkyLy8kXQ+mysJdQ+fr3wqPdcttWXd\\nlpkSjs37pvwL16/mGKX3O8nyOHatoSCcPoOIFWlQhvI9epaS2hkPaPjav8gWymQiiS4Tzow8sSEf\\nPTG6QBwABKfw+/k2HlrbSszmmOo0Lj1Qet+DH62p7hWk++o51TEYvadHO9rHscLmbhf9Ta0FFVPI\\nJvnzakG3LlIRHSYE6sRc3TOqA3e1jQEAzHHvxP4U2/b9YbIcmhI++EXHdVHxauN6hdQ7gu/bZIgM\\n9bTDKvjhPyh6H7/ccz4AYOdHFQCAbZvKMWpiLQBg306OhamxESNMKW8d5zT2oIbmY/gspaK/qWzJ\\nP+T3crTB9KCaMGHChAkTJkyYMGHChIlBgUFhkletQLRQg6tJwprXGMP283NpGdlw/LOAMFBl5i3V\\nhXM2LCjDnjf6x9fW40Wn/PX6Hj2nOlLH0cJo/SQdjKh7TnVcVLwaQZXl3LWRQdeqBb0K9ZjIRneC\\nBXIShgAHVqS/wfYXKdTROSEJv4gv/f4/bsw637udFispmbYMm98lDU9916AvSxywNQnZ84KUETOl\\nS6B7azREpvMFNif5PZKaBe80TQQA2ITYgNOSQk0swGtYaeVvCbuhlNGDoIxOoiKfFs+6TvrLQxEH\\nkm30BBipl8KSkR5BE0IFqj3tqVCcQNzLviLlE8yGGgucrSItTsHn6yXX0+MMcXciV+ZNfvjS4aVl\\ncbQe2Z7+I8VzqqM3z+l9V96P6564tsu2lFfDzssZ/95bOpvh8/dgtK8ZAPAe0ulq9NijRI6G4HS2\\nx9wc1p14TQCdLu5fto2ezR/OeheV0UIAwETHfh63ORe/cF8IAKhr88Oykd4haTrjxb+78F3UiRww\\nC3Io7hRUXfialyyIuMZO9k/N06AKT2NTA4+3zQzBpsdbbvXCQXITXC1sb53DZYwdRev/jn0i7jqg\\nAq2874OJJD33BD2nL9z0f3i6nYP9a3s4D0h92L03IZHTVQjC3tn7NTLhXstO7em1pxvb0r60dF1t\\neTPbcxqaFoPdyXelp5b5IDQBsSEiL6uIDc3MaRo7nnFu543diLee6lq3FJcG9/5sP4EtI81peBjf\\nc+WFDwCgkJKuE+De0XPbipZosIgMXgemmAGA1JxOzCwl82XDCxN7LOdohYoju1/9MiMtVsh2YI0A\\nr4/9DwDgzaFsE2e645j+B/bHjgUiP/HTBXg6nx7WC29Zj7lOtr2fvcl2aYnKUDxsb/9pYJqa39s1\\neEUHsWs3GWI3nvweJtlZ5m17LsBNQ5kfdfW5FGV9vmo69rWSJeGr4j22OV3wNuvCcdzWcKKKn560\\nGAAQsLLR/+Lly8yaKTAoFqhyCnA1SQhVpPDHU6kW+ftHKO4wBVO6PSdzsRoaSU6Gt5KPExqfgHdb\\n/6kq+mL0j8e8DAA43xPC5I+7JmBWZ3UgLnK0fnLJnQCAk/5xK0LjOLHw1n25ndLxPDa8nhKEq2Ld\\nESvS4K7reoxqzabXRI8PI5XkSb5P04N+cBQnBK6a3hXN5D4oLptIQ05pcIiJpeKyIJHLzloRHDBf\\nFRAX4ii1cXbA45x1yHdQCGX7+xQYqBySQsWoBgBATSMXqrlLnHCezwn6z8f+B+0KyW9VAU62lzeP\\nxK4O0gqTRfxwyaQMS4jf31AKtQKSmJgmclTYynhtrZUTT0lLT56jBQMrJnIw6Pe46pUpWNVD39UX\\nRIVYjiUidzu5/Dyg50MFus+J+mXEgYtTALCGpD7lWX1z/OJuj9MXEVpAw/AhXP2FExy/nHMa0NTG\\nhZDbwcr16CMLEJ3JBexrcQp5eDsktEVY/wOvuGEkZK3kuffPn4+qsx864MrpztEhsU3/unCLsW1x\\nAXltN3/yDSgK+4RkQEFyLA1OcRvrqNWqoDnCtlz8Znrc7fyakINdzoVuqCJtGewu9+lFf7/N+B0c\\nw4EgU6Nuwy29q2KPeYp19GCifeEyIZzXzcLwYLh5xnvG7wc/oTF6d7gQ1o6ey7KJ93RVYDneOiAv\\n+W/Pfh7f9PGbX7T7NGx9g8rJetgCAHgr+B4n3sO6Y0XfhAld9ZKRlzYT+uI2zxVHU7R7muuXAb8s\\n2IZncMqAlKWr8+o5Tb93yRtGXvaBynPaV0RL2XasHZ/v2Pd54sA84K5m1RCE+/YYhmisCHsgf5Xz\\njabtFEP73/95Hr9/iplCdibzMUxYumfPYF7lcwvW4VcvM9xBmcwFY96r6TA/2c13e64ngpBQ03pp\\n9DvG/nIrRT4fbp8L2cZ+pmkezyleYoG+oA4OZ3/xzdkrcG0ujYw/b2Bfrlk0I4Tpy44v92rKhAkT\\nJkyYMGHChAkTJkwMGgwKD6qOO057zqAc/b6HY3TKoU4bSno13HfaEwCA2x6k+MHBvKeZ3tdM6DTe\\nX31yBf8CWa72SLsL3iZu/UPTXAD0qnoPMV9rf/DrCyi68tuXe05v8UUgM39pvIxWeUdb998gITys\\nutcgE3Iq7WHVabgXT1iL3xVtBADcNJEpSnZ0FiG0noHnsSIFqtUiyhbpiOxpEQn/9kFVxbsgMKce\\nANC6vOQLvpM0kl4Jjja+P9UmIzqU79Q9jlb8dq8fbkFxW9dCCtxkzz40xNh24vlCbCkqo3o36X62\\nNiEI0KmhSLTvcbZG1EhsMzHBi02pMiDz2rJTVACHAkVYIrVOfks5KSFWQqtkoKwdkRgpPb6d3O9o\\n02AN6ybWwWtFjhWJ/LCN2XZCV+0Xf9+ft9f0lotewZ0vZKd9GKxQJtLCbtmS9kL99LIXAAC3P3NR\\n+rgJ9PBH1O5zqHjqWQ8SuRJOLaZIoC4s9tvipXiqk141PT9f1cwQYkL964rf3gIAsMZV4N9sTwmv\\nBHuoqzDfd2YvxbilHNeOK2f6k7Pz12OOi2X+WIh8rF072ki5onspJEWCks82X/KxhPqT2Lcrdt53\\n1ZkPY/Sz9CwXZlxT9/hGBAvVVWvpcxoZvS1nYuqdHGMyPanX7ycN7z9rpsLXx3Rnh+I5vebbpOH9\\nZcmZRs7TX3yPqXl+99ilcIrn0r3EmR7i5HYyrp4eOSur3If3zsPmAuaT3vL2WESHCc+xeP6//+B+\\nzHfxPU/9b8+euJQrm0qtOIGUoCvaMzy8KfEtLbKKusXDssrSvviu54jD1kSky//vN483PKifFw70\\n4h6taWZ2XnEfxj7BscndkN6urqAIpluwGa8MbEFtjCyv4vKtAIA7/vENKCLnb30qB7PWsN8LbSLL\\n65mrPsCvR7C/zvScNs7mOfm53Hf+zq9ix9tki9mDQGh213SULgsQz2fbK6ZTFeEyGTEheGgdRbam\\nPrcFgBfe4npCPjo/2yFhUM3ef/Pw5fiaiA+N5/IrJUuSXRacB3bCtpBkLEwzY0s/K9g8CaScvJ83\\nnyNd5/NyQw+2hakOPZcUAFjaeq9SzubeJxErfsSccHPvZF7cq/NWYNI/qHy6+UZ+3xu0WdCmspzd\\nnw5FvJiD+mBejHaHYGzwxeY5OjSEh4icYWUK4OG7TQmKn60ginAzO2GpkG00ptkwxMWF5/YCoSrp\\njUJ7m/Fj+Ztpjai80IYJggr8n9BkI47uw33s6CMhh5EzFCG+G+fwIIaWcnGcEpNyRZXRKd5dR6cH\\nUg2p305Bi/PUp5DI6VoX4gEN9hEcFKQ1h5fgLzJc5CPdc+j1bfON9xqUJKWRA2syR4Ot48tH7cmk\\nEt/5Bd5Hf5G5MNWRuTDV8Z3JywEAMx78UbflBMtZr/O2qdgSJMV9eyvbxrFbp6C8qA0A4JS4sPjd\\nh+diwjjGDgbPEjS0RR6jvAMXpwDw+KbZ2DX/cQDAkiivtzFWbuT/iyk0ElV+/X5Dkb6mifVStqhI\\ntbGN1c9XcNZ0TqpW1FYAAMY+eR0KN2RfM/4BaXX9We/oqttTzueEctNLE7KO0ReqmegpXbUeE344\\noR5XXf0mbsyjIvml596FU/5KKvIlPn6XP2QsurujLutj3mvPzkNorFiA7mDf4bPH8exKLrInnVqF\\nW8vfAgDcPZnU0+MdMUy9o/t6k4me4nz1HK5Jb5o27NvC+Ut4S/f5ifX8r/ZeaMtHA8Y9et2A9ac5\\nOwAAIABJREFUmS8n2Dkm6otEAJiw7FsAsp0cJg4f/l3Z2xThvLr7IS46/zguCZufjdO5mv2ju01F\\nx0x2Brc/exEmnLoTAFC7mX3d/E3nw7pO79fZDpoXxPGbGa91udYkRy0ePO8rAIC3N00CwuxoPPvT\\nedz1MLeOMSLLRAwYeRwNgr8d8YooyYIRb3H9ggD7Bnuj1TQSCRzdPZAJEyZMmDBhwoQJEyZMmDhi\\nMKhcTtMv2GT8VsrpdfFu7F1lNxO65zQzD+JAQbfs5vqi6LDRGhMVSoJ6vtMDERUepoFWjbWN6URy\\n5+F5gQYSv69PqyEeCn0qE7rndP1tuiXSa3hO2xR+1HerxkKppJVLKUrCXtu7UFJ/oQtL2DsGtNgs\\nJNfmfbYXOAQkXZJBo5dyEygM0OvosdMS2Rlzok14U6OCu/d+83g8MfIlAMAfbPxGKxpHYP9UIXTk\\noffFVdqBYIrnvNUwEa1RWp1jUVr01ZANsAlPjJ/njiloxkS/yEeYZLtriPoND6rSYUP5CjYwW4h/\\nkz6LLu6ZRln0sD2nOnTPqV4vJ9x/vZGb0BLt3V4eK6aFddI/sr1AB/Oe6lbVAwUijlQkc7vKdh9t\\nQkyKm3VivxAT6wm+Gr6HhhM0tO0fCgDI9wvhrzY7Wj8tBQDc4aNKb24T0PZfUjP72oPcekxayCOm\\nsb+8MW+PQTv22zjejnrvarjXswNIDWdFK1opGQrrsVwZS3dQldrVJGhvSQ2RYvYJ7gYeGCyXu80n\\neWCIDgAogkhiiVPgEEjniw2OSsG3u+dpSqaHNFOgT6faPrOQbbTQEsUt1V8DAFS+OqrH8jKhl1Fg\\nTUsEH/f6zYa3ti6DOdQXSCnA3tzVNbJxWzlOnMb8tg5ZwU33kCq94Vbe9+0tk/p1jUxkhtHY+nGr\\nnhrhdR08U4zPBHdd8hhue+LbA1KWTqv90xWPA6CIjrShJ7/+wCHhZ50vmNT0uYsxfZGwxrphbOSx\\n7xHJBeDbYUOkRIQFZbAcit7R54sqNrqojO7NEWPvEyXwCc9pw8nswKpOfgwXV1Jp/I/l9HxevuVK\\neMWcKH+FDdZI1/tp/UocWlTkmG/nPST8Gq4eugwAsCHOELWlERcKP+T8R06KXPNDJSRyTZ4vYHpQ\\nTZgwYcKECRMmTJgwYcLEIIGkaV/8St01pFwbeeWPEZ0Wxe3HvQgAeLONKRpW15cjup524nhxCt7d\\ntH4IhedurbSfBfS8a+qIKOQqmoFHzWFcSs1/KrKOV61ApIJeIFvroHJUDzh+eO7rePChcz6Tstff\\ndi/ub6cYz30Pff4CKonP3gj6haJsaVe1qmC5A5EiWhNDo1MYPYbey3F+5vzqTDkQStLlsauVMWYS\\ngFvG00Mz11UNAHiwdR7eqWGu2nCUx+f5InBa2WBtFgUtYXpQ25r4kuWgFaqf+0tLmSN1RkGN4U0J\\nC+9rR9KJzQ0UllLX52DIx8LrkqTlM1JkR6hMxKsK74x7bjNOKKEYyXsvHtf/FyWguDQMn8s4krcm\\nvG5s1+ON5LXZFSZSLmJW91l1lfnDQu5X6tH+IZ8/5f7i++/PEnIy26t895UP4IYnvn/YZceGJ+Dc\\n0/90ZP1FvJCeOEdTV+9Z7q6uHmRNBiquY7qD9e+M57kjYsgL0P3VWpcW4rO1cEyxhvh+PHWa4VVQ\\nbBIsya71om28BGU03Za/n8k0avsS+Xh42xxeeyPdZVIKiI1jn+CoFLHdjYCzvZsk1QeBHst+OLjm\\n24uN+M/Rz9C76O4hlVvFuZUAgHxHGC1xsi027qCnwretbyybhD/tedQFnRJ+QBPKJY6MlE+2+Uxh\\nkRSxtgAQGs735N3T+7NHhojyWiUjNjQyIwr3mq6MsftuuBvfe/gGAOk5T3fe0Hie1mNqt/4geULQ\\n6MPUwSeRMKA4Gpgo409nDKXXFsfqNyZ32Xc0iyTNvi073VfbRNb/405m/PrFhatwb83JAIAyN+lw\\n+8K5aHmOfUL7/BgK3uhayVunSAhs7Pre4rmywfyIiBRV/pHtsC7iuqR9HKAJ5pe1nMyXAn8YtTuo\\nI+AWgoexQhWqOG7ERM6r6j4YCn9117415ZTQObqPL+MIxe6f3bJG07SZBztuUK2cXOtdKDmhHQDw\\n8YvTAAApJzDy5GoAwP6OHLiHcdS4dRQnxO2KG3997MKssnSRJcdh5BBULUBUJOCW83ldCUDe9CYA\\nQIWXk+hqRwWiQ7kY1RfQcgqwN/D1agPLQB10+EFuDR78jMrW1YFNfD6wRlWk3EJ1Ny+GY/IoxrIw\\nZz0AQIGEsJi5bM2j4aAyWoD1YVIO7WLUP9W3GbZh/L1fKOk1RH0Y6W0xrtXoIE273sG2NSanCaPc\\nbFtuMWtrTvrQnuRCti7KSXRtpx/xGBcWjhQQCwj13nZeL+FL5//TJ5uqBrz5NvvDw2mOlqiEfe/y\\nWSe9m66bvU1H3TUD081efMkSAMDeaAAfY/AoP3/eyFycxovF4q+h/6oSn/XiVJtMerxjU9+sXA3z\\nVMx2kk5qPYP5SLc0F6OtTYh2WDim2dxJTJ1AY8u6VZzJ5FSly4mUSAiP4HtZOIPtdqq3BnHBh/3Z\\ncgo5SVYValRQ4MTi1WJVgVYuTN11vJ492P1ENxZgrXe2qlDsQggkwWM7K+QuuTwPFQ89uhB3jePY\\nOmMWlVF2LBrb7bFVi0cCAKoViqIBgK81e/zXKYBDT6wxjGQduzjZdDbJWUrD9k6gO6kbl42Gp0z9\\nJVtn3+YbB+YAB5C1OAWAa++7ATEhWqRTb7vDYS9OT6Lgk7o5Fzbx3Y72BerRgBIX+5glrx77Bd/J\\nFw89A8TynewHRnma8K2yjwEAtUnOQR4Z9hFuu246AOClLccg6e3abpxNEvBNzkGOLeTcZ9n+EbC9\\nyfM9+9gGpR15aBf6bSm/grlTaVhcvoPhA/vbnXC28IaSPvZFmgTYOnl+/XsM5fDvzTb8tZ8Sg7zX\\neWgv4SiDSfE1YcKECRMmTJgwYcKECRODAoPKgxrP1TDX2XXNHJhTj6pmpquIR2ywiEDouypPAwAk\\nFQvOufQjAMC/PiRdyV0r9+o5jYr8g3JSwkVnMmj5k9bh6IjRajG7uBoAKYXfKFgJAPj1Tkrv1+8N\\n4GfHvQkAuPm9ywAAVdfdawg0hUbTnurMi+GJGY8DAK749w39exFHMA7MY2riyIJqlQzafCJpwTEe\\n5kycaKelNl92wSK8pOd6SC/akVyHJqHxrntXPXIctxWsAgDsE+XFNAuaFLovdieKcIKfgkqeYprs\\nR9qaERNKQFvi9M42J33oFNTeaIoeILtVgdpC75ctBKQcbOutJ3B/Ik+BlEc3iGUL7ysYcsEWPLIF\\n///93Pxe9yeH8z3a9nx5XB+H4jn9PBAbmoS8jzTTg/lpU05B06224tXkDACA5hRjVNBi5MXT6c72\\nNjvWNtNSb0lxW9MMzaCV+mpU+IS3bdUKelaWu4+Fo4MF5eRyX6wQSI6l51QJirZVb0X+Dt2qn+05\\njRTKBjvB1Zj2DESKeR+yuJ+kT4Ml3rW9aTKQdxqpbQ3t7Ae0Kg+9Fr3At533tmM7Pacpd5raLifE\\nuZJmiIg5WiU4uvGc6rCxK0PDG+XGNk8Px/aGIjcL6uyGzZDyANZw/8rTLNm0U0np3XN6uAiO53xl\\nrI/PUts++IT7TPSMj/aP+KJvYcDw3Df+BgC46K0bYG1nY955xX2YtOKbAIDNJzzd6/n5gpob2895\\n/HN7T0JC5Et35bGfe803FQUucuRPGbMDa3M4zwh/yjWGatdgf4nU3P/mFwEg1T8lOojQNFKytLAV\\np0wnyyXXFkG+jY3dP4n739wyEZr4PaqQrDGrrKIuSBZYZHk6LCA4XIQjOXn/N09/F3/be3avz/pl\\nwaCIQXUXlmvjL7i5T8cmhNqWTuG1xCVDEdDekX4W7YA+XeohhKY3ZUzVdnj503REhmQPlluuu/eo\\nUV0bfVol9rxMWoXOhPLVKFCt/MfVlET98Zw0J6ZwUZLqtAMi0bvFyU5EUySoCX4QSdDZNA2wOMTH\\n0dKTEYsl/UEVoSqrJvnXaldgtQlqtpjdxWM2KImuk1ktKcPeSBuN/v2dzWl6aDxPhV3EFNiXZdP0\\nRl3IBdqsvGo88joNJq4+Jov/vJFypeO1M+u0fDJp6ngr0OeyOo7nQsjqEPGi+R0GVU4S8aK5rhjq\\nWvgiHc50Yni3oPOOymlBQ5Tv1CasGdGUDT47y+6Mc5AJxu0YIiZPdUEe/9Wh2/DLwtUAgMmLbkTO\\nlp4XKbGCHncdFcgTiwlNAtovZF1N7faicB2/Q8tk1se/XfIodic44C7wMEbHIqXpzntEkI1TSiGm\\nsU0UW6KIiY40Kcg27aoTHonfMJmRrC1HjovzeT82CYjrCytxTK4sY2WcE+CXW4/F1jZO7GtqWfe8\\nWx1GE49PYz+h7e+7ivuRhtEzaPzZVcfvUvasDbEA36n+HlqmaUYfdNxQxmIe699rlGERi0iblIJP\\n8NmdUhKK6Ij9MrfJUGE5YBC0QIOctU01ytT3OSUFuaLz0POmAkCzwvvqUDXc33Jil3KG2Dtw/2tf\\n7eurOCJhiQ3Ovn6goC/kAcB9GjUIIu8WfUF3M/C45Kr3sDvCxciSXWMAgPMPYWRZOH0DAOCWovfw\\neohqyq1ipeKzxJBjiYjfUSNHcVL0nTYpZbQ3vT0pkIzj9L8eKYmE6CFVTTbaba7oT92SAouoZmGV\\nx422OWCT2E/obdAt2bBJGLCOd7BXn/7JJbC8zv42c37rv2w/AGBWfjW2BZkLd15gN7e5d2X0KUId\\nX1LhFL+dYnxXNPbxAGCXJHglXjOk8bkckowcuWvfrWgq4hr7EYvEk62wwCL1zwDzcUw56p0+ST/f\\n97jx+7GrnnXU9ak767gbv70IAHD7f87tMTb/i4BmAZQZ7ECsq7Lnzlv+/OM+xaAOnicyYcKECRMm\\nTJgwYcKECRNfagwqim9foHtJYzQqwNYKg4mU9NAqYwtr3XpMM3Om6ejJc6of91nlHnyk4+gRORnq\\nbseWcr7wvM38BvsXKFh3xj8AAD/adwbwC9Kz9gRoVbMPCyMRIvlNt0MrKRlQ+J/uC5csGtRUVzuK\\nxapBSfHDqCkJmvCcSsIjq6RkqMLaCGHxcziSiKniSs2CAmnTMPT9rooYikOGJZ6uPFUX0GqV+9UG\\nABTn+t97LwcA7H6JVtfUBRY89o17AACXv0MBF//WL04ZK+lLW7915UdLHFAF1zA4idZZuzuBa0dT\\nRKCjgs+5qHoqPj3uOQDA9N917+HPWcX3F/kKG1IkaUOpn+Iuu4W1z+eMw+uh90b3moZiDuQ5SXfY\\n0VZoqPNaZb7vgCsCWXz5lPh+FllDc4RW679P4n3dXrMAcx/6Ee+lX2/m6EPdScLL1WCF28nv+uev\\nP4NfrbsGAJC/ie/zunevxFUnMBTi5eBUAMB5vg2oF5Rst6x7AGQkwLZVq7jhEZZ+3avql+KGld8p\\n0RqeIycRER1lUuxTNQ3uAxxMH8aKjLCIkg9lI3fmkHq9vaXbXTjG+hge+sUzfD4rDHGzzWxPcCyI\\nBSzI2UWvTKyI36VwrYyODlqgd3jovcq1RTHSRSGPHEs2j9Qiqca3SejfRXPAJrYZXlVJhQW6l5T7\\nYprV+K23TxtUuIXHY28qhCaFHckzrfSavvrOLKgO0ZaFqrB98mecRHqQ4dmr/gIAuPTxvjHBjjR8\\nfMwLAIC7ho3EI8+cCaBrntUjETY5hY4kmTpqWPRv22yGwvLi1RTqLJ3bjqtz1wAAXglRmb455TMY\\nJBZJhUUwtXSvqQoZTuh9J+cCutcUABThG9L7VQBIwGIco/exQc0Ki2A5DheMtLiWxM8b6Hxa0UiK\\n7/6afOixAFfMXMF70CRYu5m3dj5DWutz04dgzfmst6esZT7Y0RPrUWZtzzonIiYSNjFO+GQJQVXv\\nmzWExLPqntRMKJrwJEsy3FLXgIeQGoNLBEF050ndlwphZYx5oE9x1QMAZjuzPYlHG7xVfN87Y+W9\\nekb/tPIsAMCqr9+J+XffBqBntujngdBEzkEsDgWubjyn/YXpQTVhwoQJEyZMmDBhwoQJE4MCg96D\\n2nJ8Cvmrsm/TJ2T1w2UaJBEzoMvaW2PdezwPFk+qOPRy0lb7gfKcKsLCnKqg2fH29WcM/pefgbgQ\\np3K0Zds0ripYivWTaOVKrWNMg82VxNYELWOPDVuK3/2FFv/3bpsHAGg81gttKq3/eoxiRHUYPhRJ\\nWAO1pAwI6z2EB1RR064ZTYNhZrFYebYkacb5egyqosiw2ekZkNrpxS1dmp0HoWmaDdFx3O7a4YCP\\nmQ3Q1krvxYP2k3DztbQm/+V+pmuofmUkxtxMz+AdJ/8LAPDLusthzzZEfiZIirCwaBmfzx6IwbaC\\nGzPzBOsWb986emc6x1rwo7xqAMA97RQMCW3Lw5Sl9Jy+/bM/Y8GfftLjdd0f8hrBkzV47PSS2h38\\nlk1tPiMOOMfFC7vsSUSSrBOypBnxqrq3NJ6yoi3WNW6lxBvEi6MXAwDuamVuyJp/jexyjM54uOum\\nBwAAt955+DkyjxTMmsYK+pfyV3H+L2lB/cUlF6Dja2xbeS/S+1y4woInIycBAH5wxtsAgJeC0zDH\\nzThqVcSaypKKfJl1Oaja4eimA3QLD1uH8L7KmgZVBE0aln/VbsRR7UzSq37ze5dhyBJep+F4wE2D\\nOIJDhUiEC8jdKVJqNPBveOjRG+c30VsLAFgXoEcj4XVg38n8XkWfsh25G1NwtghWipvvcel4K8BT\\nYHEJhoicNGgnTikJRWM784hvYJFU2MBvqX8jRbMYHp+wOD4zflUR37TQkkJElN2q2PB/tfSgbX+G\\n7TEnpsG/h/fbNo51wlqVg/iYw3o9RxSOVs/pgfhxoBKPH0Ge0yELGa9dt3hY1r4cSxROi5gTxIVH\\ns0CDJSpYAO2coT20+kTsnsi2d1khvZOypKFNxKO2K27D85kZ5x3RuorVxTSbwVjQoUI2Yr1jqg12\\nocegv2I7VEx1dE05cnHlqdj4Nj25ei7loTEN+5nyE09vZp7vH077AH8fu4DH7ch+N/mfyjit4ioA\\nwNdHfAoAeLxubpd4VADwyVE0qRyXfcJTGtE0CG03WCQJivDyquJZZMhGfKxTxMt6pfRzJDW9L9Ky\\nPKdJTcG0e28EADg6AKvofP4k9ru+UZ/9MEcp3HUyYvl8fn0cuPHbi3DnBmqeeFZzDnby5tsQEAJ0\\n+3ZwrurZ+/kKCJYvqEbNGxUDWuagXSPFA/wYJeWtSK7KDszX502aDMRFDlJ3NWeqikOCLBKV66JK\\nzuYMASXx3VSLZORt445Dv98OkZrNPZarEtvruV32Jwp4w67tYgI+rfPQL/Y5wXJsOzbOegYAMG8D\\nc822LsumJt+x/0w0bOI38ovFUuk/7bhyNwPZz1+4Aj/IJ73w0QXzAQAjXo1jTz7fRdmxTHjeZPWg\\nrZUFONz8ppKkIREXKxCxoLHbU3AJ2mgsYUMywWqsCCqwzaFAFYvYeNQmtqWMxWp3C9PaeRxMbr3k\\nJYx3cOI49wwZ0/7MxVpyJIeMYd42bI2WdjlXUoHT7+TiIDidx805YwtWfsREWe7az26SrVkAZYrI\\n3B7mJNO6ztvtsUKfBo42sUECFkc4aPwgtwYA8PCe9L2e9NENePUndwAALvvzrT3eg/MDH8IL+dz6\\n+3a4koZ4VV0bletSSQu0FrGoSUiwRnitRD7bRmt+DKNLRB5UK7/vvRWv4Ja6+QCApY8el34Wiu5h\\ny3X3YlmMg+LDjV8BAMy9eg2WPTajx/s9mtAS4yTpp/sXoPwaLjbX7KiA08867vgOB632D8uQz5SY\\neDBE8ZrLz/8AL7WRKvbVnI0AgCJLCGFBOwtYYqhNkaajL1pimg0xwRVXMnJD5sqkpuYLyqkFGpZF\\nqTT7tyfPBwAMqVJRP4dtUFIlePcJg1LGnG30D6mMuOtvEw/5nRwpCClse2U5pMNW5gWgM/72fJ0v\\npegDO1xNnESXfMxtdXIe3lc44NiGs+0U2YOGqEnmJFin9ZZY05RbXQQlqDqN37p6dlB1wicowLoY\\n1tpEgfF9H278Cra+wIWpVYyxtoiGRC5vPG+7qHfNUXSM+bIT8I8+XFZ1MnxncIFQW0kFOtd+K5I5\\nrAuu+sFjUNpw670Y9f7VAIAbv/MGHn2EizVFrBs3hMoxM6caALB9JOcvzY1+IMFxyy7yWLqq7Hhf\\nZZ0vmMGx9nhPpXGd5qQPnUIQqFMs5Eqs7cZiVW9PdihZ9PlWxY2gyn7AAs3oUxOCFny8k5ReALi5\\nlpT6De+PRckabrN1srx4wAZbB+83lcsySm1tyJ0s8o7vyEfKJYSJoumJrvwyB9J3LuXzfTDpFVy4\\n63QAgFdYtBd4dsAnsy/Q1fptkmLctyyp8Ets900G7VdFQNbFloRQpZaEQ1CAdZGnHClbBG/Or26A\\np5fJePRfJUD3qZCPSugLUx13vnge7B3Z7azt3SEAgP+75ikAwK+evHxAclEfDPJcTigHenEKmBRf\\nEyZMmDBhwoQJEyZMmDAxSDBoPaiOVlpQQu8VI3wSLTmB/2bn9/NVA/F2WmXiBcKK15jen+k51aF7\\nXy1K131dvKkAgiPSVOLeEDojhJ3z/gkA6FBJj5u1/hZ4a9Ll/Xb+SwCAPz19MTes90ObSiUbacPh\\nBxMPJPR8TFtmPdOnVDj7QzlQhCy2JcYqFS6xYugHtLqt/mAGTvnOMQCAy77CvLMvN5yI4f+heacy\\nQfqNa2I78gK0UOqeVKnNDnu7oB8KPSNrBBAZSiApgNAOQFJQXDW7AlWhhc7hoqUxXufGiFcy+K4C\\nVeey7uSNpBf3dx+eC1su69uOk540jpPqaC1cm1OG9nZ6rXQ/peLMoM9+yuOWxcbAOYLPojb5BiRd\\nUXcIVSgo9tNrFRG0XuWkDuC/Xb0XneNTcBcKQZWl3GcP9M7V8n7kRlDkFm0/hg+Qu6578afkYlKg\\n5ApR50cmIW/h/Tiben8Gl5HL0oO6AzISzhl5C3yVXe1oky7fgvWL6GHLFHKKzmd7UlXpkPIaHom4\\npvy/AEiPXx2kYEZglQ2ODrbDuP4mRgPNM2jRL1nOBvParpPRfDrrwOYyWl/LPe0odZIFsjcawOo6\\nUr/tVt3yD3SEWMcNobJOGwqG8ZwRubTYl7va8Mp2ijEVVfG6DbMAexvPsYUBSc1Wc0ipny8t6YtE\\nnkiUqdOj5RkdSGwn28DSTC91qExCYH2oy3kj6+3Ydxrb8Msh9qulRe3w2NhBSpKGIpdoC4K6XeZq\\nRzDF79aeoNfCZUlihJv93jA7/9olBfuTTPtTZCXLZ6StGT+rIoOm5bHhcOgekTzed8otGTQ0Ty3v\\nu6jtCOKBmjgozt1JWverY97E1DvY58rHcq4T9yfhWfvFp4PSc1RWnv4oAGBrIgJZ5MR8ZP0Cg++h\\ne5UcchJTnGQOeexsR23OFNQQ55l+Ml1hC6twNnPce97OvMJ7RgUwUrSdTZ2l8FpZaEq0t+HuVkxx\\ns2zFIlJ0KW5YRNs5xrEPAFBqDRqpW2QAzQqvM9xQN3Lj6uozAAArPyGtN78SBkNQtYk0YB7Z8Kp5\\nc9mvjLc3YHgO08jtQT7U0+npaungt8r/MD2fDj1LVthfb6nA3yteBgA820mRqKQGTLV3pRmH1CS8\\ncmb/ffDc24qmolHQfoss6RF6R5LbLv39rcZ7MNEz7B1St4KvOn7zEEU846Uq3LWH/zYjQ1SouSKf\\nbJUQtIoB8TwhjLeG1LyeRu6ffZdhb396+Bv9vvagXaDqcLRrcHSzMD3wGCBN+xsoHGxx2nIsO5EX\\njnsEkz8mleSOqYxPlCYGgZo01fL15mlZ5+sL05RHfOhwRmzlZ6Qe3Bu2XHdvl//7mqc1lrSieCg7\\nv84yUmVUG+Dbx+eRFA35/2GnGBBxp5FhKWAlzy9/jxOr9upctIp3WjGKqrnVsSKo4r2ULk0r7moi\\nOVikyApHOztK68f6y7KhczgbUssx3Nfd4nTfKXZjsGqr5KTM3inBukd8t5PSx3r2i8nY/gIcSKBV\\nHOmcT3q+VP9WG1QrexE5+9KHhWiJBssoTlpH5HWg+fWhXfZbMhanej2yhGRYt3F7UizukzErHhG0\\noYWj38m6TiIH+ObzNwEAxs1kLE9wmAORV3tWoPZWizpc7e/nU3WPzMVpUhS57pWJsIWyj3UtOXxD\\nj2rXICcGD03tYBhrozVutE0zaJxrO6ZnHZe7S0XjBLafxuPYnxZ9oqF4MX9Xnke6XrLQgnc3kZou\\nOxRobWKh5BEGKGcK1p1UUSzYmjlBYfvZGWA5W/xAUTX3t43nN9TyYwis7KriCADRfO53tahY+x6p\\nZkr2Ixx10BePOp1dVSWj7kmCKpfI1VA/lxOAkmXsYy2RBIa/ynN2XMk23bi/GMl8EU/nVLBDoxaA\\n08PjNliHIBblu0+28ZtbcpJY4agAAMQi3Of1RxFwc+HhEPF59UEfnC8yZCWeJ8Ea5r3paqepQCpD\\ndp1Tikj5l8VE1HfEixQ4Go9MA0z1KyLuPyPSw+9jPbFZFcTA8T01h0YN6/KB6f/7A31hquPsl34M\\ni5gSZM6jgqNZrxXICApKrp7HW1Ml+KpELnZFp7Cr0MQiM3c5286aunHYOo5tLNjpghbjOVYRt7pW\\nAl4awkWvroehpmRocVG2nTfk8ccwPI/tepinDQEb50fDHDT0vd44FZvXVgBIZ0iwB1VEinidWJ4w\\n3ic1RErZ3yZE2M62RDEaI+kxUXqf/Yg2oecJydN3noX5v9oOACi10ej4f42nGft1lf0V9cPx1aHb\\nAABn5mxAdYL9fpmNz3KqK3viapFk/G/9KQBEzDyAXxZ9hG/+tveFaUrIweuxqCbSC9PU8T3nGnXX\\nykYWh8OZg7rrZKCu67itWQBHW9/mSb9ecw4AwOamc6k/MI0VJkyYMGHChAkTJkyYMGFaqYh9AAAg\\nAElEQVRiUGDQe1CTPgnjvkaLTn3Yj8jzPXtvdEoGALRPEMpXjVyDO1sGzvrSNpllVZ3/IADghv0n\\nYmIRhQNuWERPqmZFF5rhprfGdSlj7tnrsex1elUzPac6hp+0BwCw+5NhGHIMy27+aMiAPcOByPSe\\n/qF5XC9HZqOtKg9Fo2nx0wVPErkq2kfSg6hZJMTpYMHjj5IqJE2Loeo87te9m7m7E3A3s0ruOYXf\\n2T+0E50SfZatE2i9dDWrcDUJ6m6ujMYThPBKguUVrJXg3c/9/j3Z3715MstJ+hRoTqE6FxZKczVA\\nrJ+eeHsHsHA0FWjedND7pLyfP+CeUx2uegmOKaQztYZ7zwmmW9A8+9K2KMEohO9TJ3bnFHQ5PloM\\nuOi8hr0DBm3o2UufBwDkWdwYuYsquTlbDt0boFn6zg7oOJ5ubj3/6kHLtgKdo1m4o7l/9ygnJCQC\\nrBP21s/Xfjdv4Xp8tDibadEbdFGNa/d+FSfldSPVmIGi1/j+vvPrRQCARz4539hX8Ao9Cc3FHnjF\\na44WanAKK2lOJf+GyuxZ+Q8jRTLCpWxnhev47lyt6f3uOu7L29bVChsNpD2nOuKlbLclZbTEN23K\\nFsg7WtAs5Le/O2QpAOC6zVeiWKgYRwuF11kGvHWK+C3yPWdQo8c+SY9V5ddzIEdY17WEDN9u/o4F\\nhLe0Q4JHWK+dbUI1NM+FRA6/u1WI3AQjVpSOFTmNP2HoRdmSFCLiM7gbVMQEtVcft1J+yUhmbYnx\\nR9tYKw5LdfAoxLmz1+DVjynedqR6Uk/dcq4h5HVM8X4AgFVS8bGVIR5fhOcUoCDSkijbx033XAsA\\nkIs0OJvTcyudgePbxQdIHGfF9hjnVLqivM2eMsYld6NQxW9PQBbq8wkfz83dDsQbdI9kEp7dnHvo\\njCzVCsSECJpqF6FnTTKcInQt5eLxiRwnthTQ+7XZWwqnl4yHhAiVkhodyN3OZ7AJD6JqldA5It0/\\nABy3AtS5Q0tAeFc1G0q9ZNXsRglsIZ5f8Envde8bzzDH+OlnrAUAfFw3HO0i5Mqzmf2JLajhncRc\\nAMDz4+cacwpHK3+4G7JFSYPDAWWEEF4atwkAMGPRzehtuhUulRAdyu9QsOrIbDMDjW986308sorZ\\nMKrmMrRw8prru51PRUvY12t2/vVWWhEX4RiOlkNnivU0dzvzUqpcL3pnNmxBIVC7nnPUQ8nPanpQ\\nTZgwYcKECRMmTJgwYcLEoMCg96BawxouK2aw4m0vfQt9Ea5vmZM0eP+OHWmrfVzIb+sxq0A6DY29\\no+/W3rxNPOekCRcAAOYV7YZV5K9aY+16TE/QvacHIuXmfez9YDgAwAYglmKhv/nW0/z7z2/2WvaJ\\n53yKpa/1LYirbH5N1rannj+1T+fqkFISGnfSE1e+gdYua1RBw0xa2wo2JdHkp8VQf89ysx3lU5kC\\no66UFsQh9zlg7+D5w1+n7aRjRC4KhFXSvyetmd0xUgRrRzVoFpY5cTrjJAOzw/h4ySQAgJebkLcj\\ngZSLFjg9b2jJcgktk/hu7cLaEw8Aubv4Lf/aVtHrc4cqhFWqWsaKZgrUvD2dcTANU2Vc9tdbej1f\\nGFgNj1SsUIMiqqsigtIt7VYj/rXLtVcIUaJk98HpCWEttneTzUjflnIBKZHC55EOeqx17+mBOG8L\\n69y3hy9D5YXMNzqhiTHKBxNBSp3OWJYFFVvw9kNzAADehfVo/YjXTPr5/X509ut4+O5zupwbPjGM\\nYfm0Anega3qfA6HHoL93zZ8x/4nber+pHnDV19/BT/OZruWRjhLc8eyFh1ROf/DCt+8EAFz0aO/1\\npTvUK/zQzTEP/CJ/6cHwuw/OBQCc8eP1WPMQ46TsQSFy05A2c7oaJFjjXc2e3v3p/3U9o5QLyNvW\\n8/UcPfStrtZsk6q/gDFY+S66+w5StY5ohES+i1fb2FdL7hRieewU9DhPW0RD+ygR3xaht9NZH04X\\nIvIPFn6qonU8j4uWpRAUDALXPm7L35pEPEcIVEX43lNOCYpIZpi7htuajrWg6Tl6Tkdu4HU0WYKr\\nNh30rU3kKNw+RaQJSshG7KxDeM7VvpEdvlT465DV+OsFqwEAEx7om77D4WDr99OsqIG6Xu3SoXjl\\nB/8HALhi85UAgOtHLsEKmfOZa7/7GgDggYfPMURUJFWfYx28/DuufwgA8P23yUSDS4GzWvfaZR//\\n1cvpsbm48lRse4nMr+KFnNM0LC7vcqxNjHuxQpHiJeFGY0zk8m4h381ZY0furq7KM5ZwHIqL84Sc\\nKu5THDLsIc5RQiNkw6Okx0mqdgm5JP7B0yhyftq1rJjAjhEWI6UikjJiTS5xTbZVz34JubuFAJOY\\nv0QLLIaQpe4Fs3docLXyOlIrJxHlthZ8q3g5AODHI8fBn86M0yt0JuKbOyhE6HAm4NnEb6B731Sr\\nZHhJc7v0/T2LkvorgRaR83r5A0xvlt+NJy6TXRUdmoJ7j75MMRkZAPDsc6fAMpnjoy7K+u3L38Rj\\nT5yZdWyaOZf2RdrbRSqjHA2OyZybvTyd7e6sp29Dsoh13Ls9Wy/iYHjz2RMAAJmSWqGJrL/eLf0f\\nFAbFAlWzUHzB0dZN5VaB393+LQCAXEHFXADwvt19rkcAyF9uQ6sQx9EruuKQEC7ntpAIEpdsarfK\\nwH2FTjd+bmoRrIVcZYyZys6xeVN2YugDoTj4vJY4K8zUM7fhuRHvdzlmxBvfxUMTqIJ1zWM39Om+\\n+ro4PVRRpAOhWQD/bjYAazTduRevTi8oC9dxe9N0LojUwjjKvaTx3T3mOQDAZVNuQcFGnhMsZ9X0\\nV6fQWcHfe09ng5GGR6DV8PewtxJwtXF//ScVAIA6C5Ar9JR8NfxRvdAG1S0mxEKUJFohYTjFlREu\\nEbSYfAn15wr14Y6KXp/bW60r6AH5Tk7mCoQ6XUEf2CgHUiWTPhWai/c4bhRzse5eNQzxgBBMGc53\\n49rmRGoy24Hr4+7bQWwYn8G+qedOJjI8hRmlvM6Jbo5Kv5+odEvd1Y0kf1j0NYy5+B4AwOyzyCla\\n9+SUbstvn8p2Vmjnt/9e4COsuoCGl/aXywzdP50W9PDd56B9Ms/54VfeBgDcve5kNAX5Tg/WXUaF\\nivOCP/0EOvE5VtDz8QfDd3Lqccehn35QyNM4Y5tk56RkzoINWP7G1H6VoSuuHptXg98+eynLHQHk\\nVPXMpxnyX9bbd9wTYT+d9UgWgm164ncgW9Vcx4GTLP+eQ+Du9IDoForxNOwSeaQnDFjRgxY7O2ls\\n8q11wtUsxq2MV68bD3Tlzu7g29mJ9tGkHDobrBDpFiFS1qJ1vM1Qxk852b7bJmpQhPhVYjYnOpYt\\nPhStZL+c8jmy7kV1WOHby46rcx9bWbRUgSrGMntQ5KJuUBEtMglamRj/0PXYds29Bz/wEOGeSVXZ\\ns4dtNrYN9EI4XqTgor/R+Hf6Nz8GAKwPD8MJFzDERc+n/QD6LqKi5yWNlil4p3MygDQNt6cp6q3f\\n+zcAYL67GgBw9l9/YuyrfY8L08jkBHzdjH9nnEbqam3Uj+1N5K7bGjkvCWxRYYl17c8UrwOOvbS8\\nqD7W+VSOA6FS3rhvt4yUEDFOzzfT47uUEu3OL6FjpGgfwsYUD2hwNnNbyi0ZFPlcQfV3tKVg6+RY\\nLguBJTlpRSxg73I9e1hDTBig7EPZpwdVJ+6poSiRs0lCfxd4ee8JivIFYUjCyKg4sp08PaFjNP/q\\nz+Q+rhneDwrEfWefr9OEO0elqcvWdksXuvCRBMc4ju/x7QObD1pOAq5PWQ9z5ovQnKQXoRHs9L1V\\nvS/rwmNZn7xb7fjpBM6zRtk4j0wGFLjzdEN3/xeo3WFCBR1RNVsq+n2uOYKYMGHChAkTJkyYMGHC\\nhIlBgUHhQbX4k/CfWo/gOyW9Um191QCqe/acdoEo5us/fBcAMM5Zh/M9tCxNWXkZACDc6US0iGYb\\nV2P/rTS6hSiwQQaEz6ZqOD2nfUl4kSgUuYX20Xr3wPDF+HnDLADAH4o3sLwFD2NNXMiiC1qHbpE6\\nVHRH6x399HXYeu0/AAA2iZa4vnpUNW8K0SKRmGlz9n7VJsOSEDkYVwqv6koJSy8hJWfpBv6152tG\\nXi/dMtg03WZQQM89nVTvPZEAdrrodajrzEPObpat09WiRUDZf9PeWwBwNsmIlglPRFCkf0kCQeHo\\nzt1Fq5J/L4BPeQ97f5JnnB8eKiyaLTJKT+P7a3yNllpbGPj0E1aGqmEUoCm22LHwyo8AAK8+N88Q\\nT9CRyAXsZFcgKdS0fMM6Mbu0GgDw7g6m29B8KqxN/B6ubbRoxgpVFPpJ8Wgr8CAREClAIrxvzz4Z\\n9obu85Vm4sZ57yImEmqNtfEm5Hj3dat9Ay2frgnt8ImEtG1x1vl4PiBU8bvAEhYeZpEj9fTdt0AW\\nfO1LvrcUix88scvx4WEarpn3IQCgJkbPoG+ZC9Fil7EfADx7u7/HSeP5XfZvGNHzQx8Ejz9/On56\\nLSm+V+898SBHHzq2Xnsv4prONuA36K/3FADqEvQ0vrBtOgq29c+T6VvngJwUwmMt2ec2zAZsncKi\\nL/SXGr+ShE/QcLWP2D58+wbOg1qw4cB++MhJ+dNf6Glm9BQX0SINga3s6zNpvJpNpKZI9q4qlruT\\n+8NDLAZzIOnR8yUCil2IrQi2rqNFgksIsFgE1TGwoc0ozxo8oNMCIMdTgBiPSqnthJozXAYtUBKU\\nY9+uTjTOzO31fo9G6LTaTM/lpNPYeF4Y9e5ndl3NomHNjH932faZ0IgV4MEbOE+4fhPnUakl+bDO\\n5wBwv68MABAuV/HTM18FANz90PndFJRGrFD0H94UXtxE9lfRaUyfFXk3WyRtw633YnOCXp6vvP5j\\nAIBrTqch0GTkJM/wnsaKNDgbWdevKOC4/KvKC4z9upCRryoMeS/jXNRSjluSqkJKibYn0ttZO+Jw\\ntqbHWFUkY096+FexA8EKce0CcR8aECsVNFxBvfftAaKircqJtNiYfxfbvyUUN8TREmIctMQUBLYn\\njTIBwLN2L1pP5rgXjwrBJmio6+Q70VkYB6IvaUiiKwtgFSFDFnHZA+cz3SFnF/+2ilCA5I4A8nrx\\nvOpMDf9OGLTn0NgkBskypV/YccV9xu+x26875HIWXLYcL79F2uw1Z9Pb+eQTXzX2T/lLuo33cWUE\\nq2ALKLM64ZT4QReL1ESSK4VLx6wBAPzr41P6da+hUSnAKkQSc2OwrGSlkcWH/cHVr+Cex87rV5mD\\n4svn2GJYWLoZz1pKkH8pJ5ktz5Yf5Kye0TZJMxruOCfdy+d7QrinnWVePvoTAMD9K+YbtKdDuu9d\\n6d+REjYoXzeqsT1BX5jqOObVH+LSEz7usm3ehgvRuoxU4oHSMHtnwmvG791JzlbsnZKxMNWRSQHu\\nbbHq9MeRcAiqmN8uykv3eLGABW4R2xYu4TN76pMIfMLq1zFO0EdcGpqncr8tmJ5Yuer5bl/bSfqP\\nUu+GLBbpjlQ6F5ge/1H8iYJ4Hst2tPE+Slale1Q9h2p3NBMAqD6H9/Dr4UtxJy7m/e7TaTjArkp+\\nD4eg3lojElRBzT31dcYRWoMyciZz0Lak07caKJ5TC1VwWmpbSAFRNudimcp8c97VOmcIiBzHxeiE\\nMqo5T/bX4pVK0motcQmuWqvx/gAgcnwEZQVc/ba8zbjN7gagofYWvNos6OAFDJj51mn/xaLd8/me\\nrEDpBdUAgDfGvQEAqEqGsDLGdlT10igAQGysAsUpcjpmxMv6dnclaFhisnFfBy5OAXbqj3dyQrKo\\nJr1YS8fF9r5Y2aLT6mfFkLPS2euxAHDrpS9hY4Q5ZM/PJe1rviu92Hps2FLgWs7CR/znuwAA557D\\no71MOWO78dshsZ7psc79UTbW8UkbKdNeTwzhITQyeOr6tmD01Pd+nCUqITFU0IFOYDRomT2KT7fy\\nfocM4MJUR9LdVbHyaEZzgt8rpQpFTguQ9Io4USGlrFklWNu7xgJoVjndd2np92QLsfKoVgtiQg3Z\\nnpPu9yKdgrKbEIasKosxQXUHhVKwzXLQhbAOfQE74sV4mg6sHv3frb+4v+IV8Sut6188l6EVDct6\\nj6s/GD78LuNBiyyHl3dW176wRrrvY3UD7aMLH8JsQRFPLUnrr8ZX8Pefa85mOUMi+OPHZ/H3TI5f\\n9k3udFiLfhktI05unwOhkax7kXXZC9OK8xhE+dumifjPfsZHSm4R37ndh9gIkVu0hvcXGp2Ebxv7\\nWGejhNAo7v/WU8zt7Z/egkgHx1mbiKGOFTlh9XJMcOzgeKvm+6EUcLKtt41oqRfePXwuzSIjnu8w\\nfusPmPIKaq8wEskJCZqsx+XyKEe7CheZ2Uh4ZSOsQo6IlaCiQhV9gWenoBl7HZA7IqIcHq/FYvDu\\nY3tscwojl5RETGQ26G7xokl9y4/pqT28Nh3Y2PO4HSmWEC8Qxn9BdXY3aEbWDeeKQbFE6TfGPnno\\ni9JMPL/hWHjHc870xI7ZAA7PZKvJgFPELcfhw0/30Mikv3tPClhVUXFIZXt3Z36r9Dxpz2IaTu5B\\n/x0HJsXXhAkTJkyYMGHChAkTJkwMCgwK84QsqXBb4oAKVK2kd6Y/2bRap9Dasvi8uwAAj7fNwe3F\\n67occ9Hu01AXZqntYVrNcouDKLqY0nDNzxxc1Kg3uOuFF9CpU6b6bnXyzKIJLRVyYtFLzG/0h+tI\\n8fU7YpDn0Qs8UHlQmxXSR/akbHiuLe3J0j05zUkSlH9XtNHYp3tTJ953PRKCcmsX9L/EPg/UHJri\\nak/itiEfWdEymZbMIcviaJxBK6Dusa66QoN7K49VRS7Sr52wCg5h0vuokd65eYH9eG0jvWmujbQD\\npopUBLaIG9M0I8+Ws43WzfZRVlijvE64mNe1BzU4OrnfpnsLpK4CIDrOmvspAOAKfzPuPGCfNQL4\\nN9Mq2TmF3qXJY6uR7+A7PTmHknZuOY5zReLBV8e58eNXrwAAKH5eO7m01PDyCwYfrBEgsZV1NDyW\\n72Hk2HrMzKcUcWadPsbDPLmbRw/FkyupjOvfIjzyTW60Ccp5bxao+/bMR/vLpGS9dws9hb8u3IJ/\\n5c0HAEw8c0eW9f/OxlOxeD29t6/fzLfziz3n49QCPvej9yzMuk5IUHPtbRLqVrIOZ2ZvveS6d4zf\\nq4O0sl0ynDSTf8w8BS+c3FVY5Lt3/AgFFwoxshfLjfx2mkfk0+2D9xSgCBJy6vt07OF6TgHAN6sJ\\n/x75Xtb2H+VVAwDiF76Dx58/vV9lhhKs38Ny2rEn2c8EvhnQFXmbj0nbZxWvAoeHdXxfC+malZFC\\nBNYeXFUxWiDD3ims+6JIa/zgfaLuOf3rH0kjvOyFm/r8DEcaaoJ8pw6r8MqAyroAqX09QUp177nW\\n2SnxgAYpxpZfOoJCHdGkDWE720dYeI0SebKh6O3eJzwySQWQxAfT+j6GHUgHDo7+YvJhDhZkKujq\\nfefTwXz88v2vAQCc9QMz9fpF7RkAgIfKlxnbdPpryq0ZHtGu90NkUoB78pzq0L2ct2y5CGMCnK9E\\nSlkP3XWyEYbjaGBHMv2YGtw0hJTmq56iuGNsYhRqmM/t2959CIq3UswZFnLMq1s8DN+8muPDA+8z\\nu8BG+zDAIa69k/1ytEwxzo0Ij61vjRv+M9i/d75dAme9yA0sPHZtnW7I7byfZCDtStTVcq0lDGFI\\n5jpha6XrV2cKqA4JcoxezpTfieYpLEf3liaHxmF1cn8ywntU4zJsHSJXsRiYU07Z8IJKqga7mJvo\\nVHnNYTX6AiWPo2bSa4NrP7+BFuN9Ke0dsDVyLusXNGNZUmHV+5YiKSuMrbu5T3+gsy9aj0vBtUfP\\n0sBttnDfCnc3aHAIVdmOsSInfUo2KMR67tbDRSbldv4mUs5r1w7MfLqn6w2EF9VS50B8H+tcQkge\\n23M0Iz99JiJD+P5+v5Ciqm+1TcaSLQyfe/lkClt+86GbDe+9JqVzvSdEHuwpc3ahIcL5/8ab2WeM\\nfOH7Bivh84bpQTVhwoQJEyZMmDBhwoQJE4MCg8KD2hj0429Lz0DhyU0I1Qphhcreby1ULrxvNg2j\\nJu8HANQqXPnnWKLYm2JsZY7Mlf/u1gKkllF4xSW8eMERwN4It7kHKMdSpuc0XCpiHXsoOjCX1r2A\\nyPk3d0gl3t58PADg+E+/DgCwWxQjBvWwMK0TW+Y8Jf5Jp0L5MCM52d/XCEnynfRAvYQTs1LRAEBg\\nIq13oY8pIuDdKyMyhBY0PSdp3C+j9COawarPtsEutDfUs2iy15o8+H9X09LTlOJ32xEpwWwv1TYu\\nGiPyxdllxIWpbuVaxkv69kqGt9QazY6XUm1WxNxd372czLCM+sTfmApna9cgjGC5HR+8NIP/3Lgy\\nq+zMmNCq+Y9n7+8G53oiOPfS+wEw1YCO+Dhaur1r6NFI+nQ5eCAh9Jmq6/Px3sRXs8q82Cu+m7cD\\njtl8hueL+X7UDwJ9uq+aDUMMMa9rVzKV04UT1sEykzEPJc4gZv+bMbVz5tBl3Z5wo2rBwwCAJVHW\\noz3/GoVHQY93aDhfuHePhM4xNNX5d6btYKoj2/L33H30Gj7lPR3Ok1i3Zo6u4s6khI+jLPvrPnpp\\nL7vuLSzal84jrOe3szYOjCx6JsYsuYplD0BZq6Y/3+v+n+bvNASadJZDgcWDCfezzmy9Vgiw3J+u\\nQ5Gk8ObHHAiKd6/HNPUFik2IUQzl3/Jj9iOpsH00rS5G3EHrrbOa79bq1nrMa5oJPV0KAMTyRMxL\\nngx3o0ij0kP4qu7JzZf7oMJxhKO5g4wQJSVEkDQg5RLto59ezKaZuegcw98pjwrk0HtT10ZPpsuR\\nRDQi8nkERcy6U4OkCHEXv2CatESyrqlZ0+23J+9tIt+dPh9A21gLzLyFxNYE38kYexJV5z0IYOAE\\njDI9pzr0tFVaeRTY7jaut+57fwMAfGPX2Yd8veQHBdh6kkh3IuI2Q9Nixqd2rufcoTacg6vXXAUA\\n+PcVfwEAXLjsuh49pweibjEZbaFRCt5tpGBg0Rh2bNePXII7HqQ2RFAwjXw7rBDEL7hXp/k5HVHe\\nj+IBrEJ3zDGVP1KVXiOeT/ekKnYV1ijreLyQ5ThaYogXCYEiIfYY91ugjqF2RDRfRnwyx3Ilyndj\\nsSsozOUcVBJT2v178w3PaXQc+zdZcRi5g/17k9BkIbLkYX8rx1OAEGiyCJaCpaoeSgtPkvPTY33b\\nDKotDfVxDpUvR6FpaRFQvW/R2WWHg7YJMLx4ztwYoiK2NiUEeHJ29r0s3Vuqp+2zBzVEC7sKuh0K\\nuvNiDlRsaE/X0T21mdfpbltf4WyREB7O7//w6Y8AAK5ZehXOOotpnZrjHEPW1Zdh9+ynAcBY+1zi\\na8O0esZTX7bmOwDSIlcAcNy8bRjnpcDHC0/NBwBsqBkKVek6R1t1wV3oEF7+39YuAAAsXzYRrobP\\n3r85KBaolpiEnC1WNMfzkd9LQHUmzjmHCZoX5qw3hE1+2Ujq4Zt3z8O6q/hhCgT10vJaHiwHDJi+\\nKuBgg6ie98nSB3oakKb4Jnzp4PLIkO6fadGkf/IehcDBhAeuNzpMffE3UAh4I1nbNiRiUMQVVSvw\\n0NwnAAA37vx+r2UdeG+qBfALwajc3cms452NMhwnUDBotKAH/XP2m4ioPDbPIgaUvGpDOfWTWg5Q\\n3x23DO/vIk3BJ/IAemu7UR3KQDygIZmrq+6JbzBBgRZldfcK44c1YslaoLZM05A7uhtJWh0a4P6E\\n97thDuk1U+29U0pvqj0O+yKCzpcWyUQ02rX5ZSYil/18N7k5YRwMPxcCR/rfdVPjuHAxqZG+XdnU\\njJR43Z6adAfj/YgbX957ApLFfL93z1qJW+bzPt7aw4SUm2Y/jRGvXwMAyNnEwSizdnsyRJL0hWlc\\nME/jeSr8u9LX7BSUHv8OkfB8TBLWNzjI3l50EQBg+ik7cc8z5wAAHm7lX+WMNozNp2jPbpQgXJ5e\\nFA8kJtx//YB2kCMWfQ9V5z/Yp2MLMkRP9IXppBXfzDouHONEJtLkwZA1/Z94NIrv69/Icjy2BGqj\\nXNTkbdPQ5OEbyNvBb6UvNvsDp5HfWkM9BQlRsqL7Y3Wl2R9Wfb3f1znSkGwQQmiiiTqiEnKqxGzt\\nIAtTzc7v0jSdE5RwmQTbGBqtSrwRBJzs71NiRrx551AgyXfr2S8m0XEgsI3HHQqlOBP6wrR2Pi1r\\nkZFJ2Jr733ouPosKq98LsIKc9vRt/S6jO8w9eROWVVGADtXpBUz+MVSLbelGlGegsCfFd/JBcAKu\\nXDTnM7vOgbBtd2PaGTTqrX97PI558IcAAGW8mBMdYrknllGsaHEjQ29uPHYJ/rGM9Fs9Z/eSyYuM\\n48/eQbVctzDEHgghJA85e+qAv57xT2yNMQwlx8I6doW/Gb+ZxrF39FCOA7swBOUj+Lv9rTR1U1rK\\ncTezJtpWcCVrtaZF6fS/Ca+cFg5r5DiY9NvTC9M83mzLVA36yKeVxPD/s3edgXGU1/bM9r6rLtmS\\nLcm23I17BxvTISGEJBAgEAgJBEICCaTnpb08XgpppACBvNAJvYRqDLbBNu69N1lWb6tdbW8z78f5\\nZlayZFmSbTBmzh/Zs1O//t177rmyujF1cD1htaYQT/FGRlUYKWUARFcy13G8zViy3590G2GK8gS1\\nP0qRmCbQZOjkJlhy2GHwcH0kxfmO0rTxaBV29avzqBr9RMcslOdyI7tjhhP56wZP04wLVV3LAmFA\\nzm3DrtYilk/aCIMQyTRF+F2KpAyYQtxVadjeevSLGQZx7Jt33RCejI3psZ7ZG7pSjftzPgA4a1hv\\n9SmOI84dVvzqXI6Tt9edCwAwGmScvYMKua+NpSF84fbLIK/kNb3V/KaGUiSL+UusUBi3N9uBuYFu\\n5+UbncgXN/hMHsPMihaF8LtihsJ9ai/F0FQxpBMJneKrQ4cOHTp06NChQ4cOHe8a+q8AACAASURB\\nVDpOCRyXg0CSpEMAQgAyANKKokyXJCkXwNMAygEcAnCFoigdR7sHwPQYwQkpOPOj6MijBSZnWdYr\\n1XkuLX4pvw2OIv77xd2k+MmjJdSnKRjzxCqa58u+0ISNq8l3srdwD24dJN1IDdx2lNG9ZXu9b/EH\\nleIbnx2HeW3vFkMVX9pHmorPKixjJyhrgzQ5iHhMpHvZz3dYMemFHud9/qlva+IIZ35qC775cE/P\\nqUoXOO8RWrJ7tcFJQMotaHw+Nqmky4CEj8d+ecPjuGvplQCAkWW7AAAGGDTPaZ14xt/b52qe02iY\\nlLMd4aHIEV5Ea4tVe2RK0HRVwSMgmx7GUS/BowomXMh6izc68ek5TCXyQSktPR27cxEuFZ6jBr5r\\n4VoZE+bW9/aVGlQa4i+FpH7X/HaqQEWBUcblO0ibTaRNSC7J73EfW313ulPXNCPWXay3jqqBe6wm\\nW61YeSkFjL5xiNbrCmc7XnqP9HFbqwiM9wC2I+ig08/cjUonD74XB1qT9NDcPfFFAMCUX90KNbth\\nQrCLVIoSAEi9yNaPPJcW94sLtuGBv2bzYF125loAwIuGGQAA3yYzJl67HUDWUnfXu1+ET9x/xFW0\\nDM/NOYiH9zNfcKoQcNaeOM/pspgBtzzy9RN2v66wNZk0em68JA1bPtsKttGi/5NrnsY17p7e+38E\\nmYpC3uLt8VtcCHB4dpkQoUEbzubeB5KEt3uuPtPCdkzL4fNmzSKl+r6NC2Ddz7HXlpY193i0UKQA\\naJG19m/oJRtJsILneat7f4eunlOV7tZ13EuKd+yI9z12ng5wHWJBqik8oGTTzFh6oWnLVo6tkTKH\\noNACzkbON8nhCQxxsj0VOztRbCPvfckhsk+sjSYtXZfdL9I6BAaY06gPqBRfVwPv2TlhcPd55g2K\\nBD6D+SfkvVSsXNr7C80q5NrhZR9pHubA8YmB/Kad647v5+3DSxGOnT98/Lrjumd/oc7Vw0x8bqIg\\ng39XvMsfb34XV1efDQA4K4fj6J93DywnIcC2ejDMstp/yQMAAKNkwPTzOMZH5ewcrYouHn6lb8+K\\n6jkNVaXh3tt9SRrIOGAVJ1znEWEfsOClBWSVvBUeDwA41JzXzXPaH7hqZcgixMEaZLu1N8eRdoo8\\nolFOZrLbgoSPxwKVwlNaFkZSTduUNgCCFpkRrKgEgKiffcLUzmPeWgnRIUJMyi9p366uk2SzEcXv\\nsw5lh6D4ApBt4n06BJtKkqDY+XuijB6y0DALjKX8fXOIQqPLD47Mfmu1CYOl3Cd8EiIT6bGu8HBc\\n+XT+FgQSTHvS8swwWNX8sCeAPnw0RIv4jGhlCub2j5b82ZXWeywv6Iny5P7qBbKKrADm/Jn5f1Vx\\no5RXhnyYbWHW4jv6db9kwoQdS6oAAPauBMtVXOGNqOE6yNJh0NJitb3LtUh0SAbXX7oKALBzC1Pd\\nHV+iq95xImr5bEVRuk6nPwDwjqIov5Yk6Qfi/9/v8w6yBClhwMi8NoyroGJt1XTGZxaYOpFS+JqX\\nObOE9BHv3gAAWH7fLLxRzI6SK2gByXIjrBXcmFj3HIeaoJRNVH+sjemR4AZbvI+39wX04aWs2MOD\\nf8NesWPOE9qm7/w939OO39k4FQDw4vbJAIBbPr0Y/3qaSX+XLZuE3qJDLnyQ1/c1bZvDCgyCdds8\\nS6w6ZQDDBO0rlYPNl9wLAPhNKzcWBkh4tJObtru3ciM3s6wG8bh4CzHOLT84EsZdbPpmo1DpHWnR\\n1N3iX+tA8i1SjnO281isAHA2CcrJi5ysXWYJ7w5lZyzxcpCVxrbjzGLGa9SIWOQNuyrQuoJU8Xnj\\ni3r93rRIKraxmpvp6mFhvBzmAmiLmBxWLJ8AR2PfG6cu4b98nwyQEr1cpcf4ciLaoucbOVRVfjJU\\nif9dxs3x8BEtuLaMuXMbUxxYfpK/GyVikXLTkPcAAHc9dCNc3dMpIlqiAG3d33HXk2OxZRFfbG9R\\noaY6+3y4Z/vvujH1XsYBrGEDFwmZsjjOGkne92+GvgmAufp+O4b14t1txO9LaDDYNZEx1o27y3Fz\\n0TIAwG33UPlR3QwDwKZDLNt97QUY5iMNxXNeE9bWsB+5V3TVBj42xt5/K8aey2CZXUtGDeja44Wt\\n0QQ0ursd+/mGT+OaXuKaO9JHH/rlNPubMaHA1kcS9M7hBm0yGzePbf6APw81QbZ7tS2ba6zdqHZ5\\no7iBVbZmDSy9bUxVRKtITfNWHzvWTKWk5Y8VqqBLChETCpznF3Ix+iJ674OnAzLCBmsTOehsbQpi\\neSLHpI3GCENagbOWZVK/iO0lUp7GrImkbm6sZSjLgvJqnOGmsnV9IgfbAlxIxPzc6LsDEgq28D6y\\nhc8z+2N9vp9i4HmSnLUgpD22rOqoyAlpSKSRsfLctsn868oPIdHW06ByquH1xTSO9S8y8th4+DnG\\n0z+MgalxDxZBmXU4+8E7MeTMOgBAw/sij+cR525azDCNJ29eCgD4/egoTHsGNmY66wz41qWcE9Jg\\n/X+99kwUWTmnPrGB87t7R//0ABI5CiwTxEToz75LZFi2zakq511zK37mrW8CAB4570EAwN+D5+B/\\nb/0/AMAv9jAU5JejX0FSSPz/8P+u12IdVZgSChSRd9TWkv3RICjtHWM5hzobkmgfx/skxrK8zZIC\\nSdBalbQB9jweTyaEFocswdyq5mIXhqF2GbJZ5FMX9jdLUNE2ycaEAkOCg68UFZO1wQDIPFl2iHha\\nj1WLUVdMqmNAQtLP33e4OZ9KdXbNAWFr6//GsYPNBBmRmcHiTeDCSho1ioTs98utk7HnIOf6/CTw\\nYcSbG2Zyzs83pxFsH7xi/fHgeOJJB4vwaC6uLW7+NVhTGnVXjQMW7N8BwXjYBtMRkX8pFyBbWZfm\\nINuqKQq0v835RF0tOmuN+Nyj3CQ7QyIWfUQaN87levPpxxYN/IV6wcmg+H4GwCPi348AuOwkPEOH\\nDh06dOjQoUOHDh06dJxmkJQB5DrrcbEkVQPoAM0nDyiK8g9JkgKKovjE7xKADvX/R4NvTKEy/8Er\\nsbO+GI/MphVsni27d56zhbnDfjP6ee3YWcL6/PkD5yItOGd1j1EEIXZRJ6LN9DrkbeybshMYw+8v\\nntACn41WsHInXUN/HboGz4RpBd4Ro1XyFwU7tGtVr9IjjXOx/20+W82H2hVHE0k6WXDOakN4LT0e\\no8+ht2TvkhEn7Xm2VgWOVpU2Rqtb83QrCs4lVTaVMaL+kBC/WUTl3qhsxe8eoRCOWTBXnBc3odnP\\nMlU9Q0ragNL/iDoUxegfbUR0OJ9z+Yz1mifuwt3MwakoEhrepEcoVsT3yjhkSA5aPH8+m7k9V4dG\\n4qb85QCAiELL5+/rLkCFk16jzrQda5/KqsWq6BxLK2dZOT0/obgV90xgYPo5dj7j+bAHP3yeojb2\\n5v7Xf2Ku8PyLnIXhkA1yUtCK/EKUSAHcY9hGgwdygAJaf6+bSNXhnxXsxJGoeOUmeHb3JEwY+tab\\nQskXDgEAGp8t7/O8wAy+g83FG942fhm+4avtcd7IpWQ+uFface2t9Kx+J5f0sCm/6l3ZMlTBPpU/\\nniIY15av6XbvJ0K0pt7z5yt7XBvvyaz+0JCx872Nsb7rv+LsQwCA6qXl2rFHbqDi5kyruZtq75EQ\\naYNhSAPOBrnbsSPRKnKceoWgWbRIgiCnIFEg8u+lJeRsF4IYYQVNZ/Ib8jYIr1u05/gmG7Ne1ZRQ\\nz+7tvCOh3ttbSg9KKGKD3EG/z4gx9MhXbyw95n0+rnCrol5qPuSIgoxwEiWEGJWUAZI+0Y7iqsK3\\njKLxFPdxmDkWTfLV4wIvGRZOQwIvdEwHALy8jLR+1yGDRr9176cXJFLuhiUocqOWqKItksb4URWX\\nvbs7NSXflNeKjPDAWjvY58PDHFq9R4vF39IMTKHTW+JCrY+PA2xT/Yhv7J+6u4quon3dcBYjtkJt\\nXGPNGnsQO58fM6j3GnZpNa4bQqrgr3dfiM49fMdVX7wHAPD9+gvxr2HM0a2GOpxp34/nO8kG+4nI\\nvw0AjYI1prKHul7z1wd7+klyd6dgTLCNq3lOk3k2xPM5z4ZK2X4z1qzndFgR590Ce1gTCQoH7BhS\\nwjKRhWpuNGFBdK8QRhR0Xk+1rLEP0mJ9KykKAiKMp2CTDGsH+7PKcjCFUjBu5YAtT+QaTsooSORx\\nAawyLmL5BkSH8t6ZYvZL636bJsrY37ykABAk0UzziV5wzkZ8KY91NMHC9/uTfzIeeZOUcd/uI+9w\\nctF+VhKmphOv2N8ffFgeVGu7GOt9CgQJALMXcO9xT+kb+HkTRcn+p5gUfk1otAtGLbsetk09j8em\\n0G06Zkgzdm7jOlkxi9o2yXDt6V628uwgRuVzrRvPcMHw5pjX8P1mMjFbEmT2/LTkDVSY2fcm/rFv\\nlfKdv/3OBkVRpvd5Eo6f4jtfUZR6SZIKAbwtSVK3pqooiiJJvWt5SZJ0E4CbAMBe5OrtFB06dOjQ\\noUOHDh06dOjQ8QnCcXlQu91Ikn4OIAzgawAWKorSKElSCYBliqKM7uvaonG5ytVPnAeDpOC5tYwJ\\nef3CPwEAxlq6WwAu2MXYu7fGvgoAGP2vW7DnBlo1ftbKgPknd8xAnshBlX4hmxIlLay8JmHdT+RK\\nmijNu3f8Di+EGYd2k7ehX9+sCuKMt9ixN0U34N/bFgAAFr84s3v+jQ8Zl3+OVscXnj/zpD8rb3sG\\n8Rxh8VNFonwGRM5kmdw6aTn+8RTzJ1Wey9iyxifL4TvQ3X3XcKYVGE+zrdtBi2bbgVyU8FNgCdHa\\n2XBDAskOWhDPPGM3dvtpySx28do9Kypw9afoGa2Lk5y/ZNN4XDKNuaOmM78QRlma8FAL68tt4vN2\\nBEpwuIVW3LFDm1DzYmWP740W8xunzGdcxpWF6/A5V+dRy+dPHeX414MXH/X3rkiIWIL/uYo5a98P\\nVeH3xRQTMko9PRJ/6ijHQ4/x3ik332v8mfvxwsi3+a4yy/h37ZPx7FMLeR8R3pL0ArbWvt+na17T\\ngWDm9Zvw6xLGKqnWvWkbrsCGac8AAEY+9XXNA+GoH9i9w/OjyLSy/r17+vbSfJQe1A8DiSIh7lFn\\nhPsw+0e02ABX/dEV15JCJCk4GlBECoSuKYfUlFqxQgmyRcwPoorytmXnC1V0yRpUEBdCH13jYCPF\\nwusq0gdYIt1/C4s4M2MJx1GvO4a2VlpjzXbhSag9GdILpwbch4SnWpRPxpoVYEuLOgoPk1Ek0jE3\\nz+F5iiuN18/+CwDgPjHfuIwJnO+hwNgUawS/bJ4HAHhxJedTa7sRrsOirpvpNTWkFUQLaaP27eFY\\nHSlzaF4cVRhGUhRN/C9UakKnIOPIwuouFyZhrhV5VEW8XSJf6XdYWtkMMm16y/d8xtqrENvVJwGr\\nTyxYtBUAsPzdSZi9kB6ItjgN4vtXDx/0fYGPlwd1MDiqB1UgNIrt6K4Fb+DxGnrqO6Mcl40r+xd/\\n/Ntb/4l/NJwFANj/0igsvHodAODeIeuOek1UTiIo5jXVW3pb/Sx80MT6/ON4zjFn2YDR71OgqjQv\\ngNbXu7MxhiwNwhClt1F2iYhdSUL7RI5BbXP5ffZDZphnCK9xkPGgDncCVsFeMBoUDPPwd4ugkqyr\\nGQ5DNc91UYcL9nZZG1tbpgrfkETBPADI2WyEtVOIKAnRprTdAO966rIkS7kuCYyyI16QZVgAIl2N\\ncHwlcziu2poNcDQf//o+5ZTwxJ0UXVRz7N7ZOBXv/W3Wcd97MPAvisNY13dqv5OFD9uDejRYRL54\\ndT11NKh7ohceX6Ade+m23wIARphdWo7mN4SGyo7wEKx9numjplzO+eTR4e/1+Yzf+Tkh/GPrmcik\\nOOc7t2frZ/rnyOxZtnUMXPs4p5x0D6okSU4ABkVRQuLf5wP4JYBXAHwZwK/F35ePda9g0IlXX5+F\\njA34/DkUfLmzmvTPuqAXW2c+pZ3b+iyFUqqGs4F4qoEzfiuUMedwU/rEnAdx++4vAgCSHhFEPCeE\\nrXMfBgCYpSzt95UIF89vRYfhkRqKLd3URfF2+k/5nJ98nzlLL3OGNbW88Zas57fKzIXUXQXLAACv\\n+qbDMJQLL9POwS+yvHObEVw1cKGQvjam51/GDc/il2YO+r26wtKZhoidRyKHTSo8XMGXxnGSCaYd\\nME3nAP5q1Rs88efAOdfe2O0+xatTaGJ/wkgfO2AgnA9rByej5hmcRL46/l2s6aAy4BhnM1asY1S/\\n+2XSg1OflfHaH9ghw6Ws/8rVCbwmk67rnMNJaWN4OIJJsdExs64ObxwKS4DXHLDm9dpBHEINsyXK\\niexom1O18x+MFWDeNaQhr3yC1KSMLbtRVBGqzKCkijvGlOB1/KlkPfoKFb8j5xBeWkSDSsfrpDNt\\n3laJaUEqRPubuFCw1ZthOOI2R4o09QbfeNKd0zUD2+ldkrNF25huTrC8K3x+zajjPjB4+p9rhQPx\\nE5sm+GMLWyPbiSmWVcN11cuI5bJ8VcXWSJEB06/iYv2fw5hD7VsNM/Dabk5MzvXsW+3jJZgjbN9F\\n8xvQGefxUJgLkya3FVIu+6NzI48F5seR9w77UVeFX2cTn932Gda5a7kDilB7TNsBzwiKXgT8HB/b\\n23K0vMVy+4mSrTl1YQ2wfFTDqZQGrEL8rWMWy/jySZvw3hYuBK1Cfbv8oU78sJLq3KPcpPpOcx7C\\nkhAHzwmW1QhnWG95lRx32yw+ZOysnKSXZRstUbSxrn0S5zJLh6Qp36sGhWCFUaNwx/MVZJyioali\\nWUEzMkJYIyGGCXNQQsrTv8XxoW0ctzCu529bZj6Fz+cx19/WFf0TMhs+sw5vj/1P94NfXtnjvLFt\\n1yKzX2dvDRaWNranUZYmfHDG891+m7Syb4qfivpUDjZvphHYBaDQwl2xmluxqwLw1ruo3Dv7D3do\\n/1bx2sZJcO1nu75tafbZqlBUK3rWs+FwIySXWJs5hCJvJo2YUIudN54Cehtqx2m2lm9Pp9H1Ru8+\\nLInRcPJmYBImORlysiLANprnC6O5kO8Tj/BvxmrUDPhJX9Y4ZxFGwo6pVpg6uOLI38KyzVnbjFgV\\nc/QmvDwWHiYhkc/OZ4wKI2BY0kSgHA1Z0bUTgUQO8N/1DJ9SVaHnuvdjqYXjkvEYYULHg2ix1CNs\\nzrHVruXb/bDxYYoj9YUjN6bfapihGXUu3kOHRe3r5dkT1P2uAjzXOQUA8IG/EsNEOGO5jeu8fw5b\\nAXx7Rb/eofJZqvyai7nOPZJOHCkTQqViYWJpHfh283govkUAXmSYKUwAnlQU5U1JktYBeEaSpBsB\\n1AC44jieoUOHDh06dOjQoUOHDh06PiEY9AZVUZSDAHooyCiK0g7gnIHcy+GJY8qiPZjmrcF9y2gt\\ndYoccdaAAghH36jHboFQvobnQPZ6NeWI+W1aw25efjtSIoODIkxo8h4XzvddDgD4YcXrAIDzHSlc\\n6uTuf/pPv6zdb/pztJKs/+V92rG71jIH0XfSEkwNvKlxJD2p8YAN+SV0R43NY3ocQ1qCcnhgEu69\\nYTDe02OBXjlgHE6MB7UrrB1CmjxoxTtNZHY/MuYxPP/eQgDAFflsGs9UvoMr/0pv6kO/Zj62jBWw\\nrGDF1V1M62TuDkVLuaJ6aR/cPg8+N70yz41Ygh99fg8AYOEbXwMAVLyYxjuP/RNA1gMOAMOE8/b1\\nQ3MBMA+jSQg0qQ49T0yB7wBNkQfLHegrudDhBorzVG24Bfdd8Q8AwJYYaUaH4nlY/jjpdUkfkBzB\\n91Wm0m3q3tiTolI0qg1fGU4xglUh5jD7ovvoVCcVIzz0Nq8HPRHufUak99GVob5/yokekuL9weIz\\nHgYALHr9rgFd99sDF+LHMeFVk2m+i9W7MH7EiclveSxq8icF3gO0TspmCUmRizhjkzSBmxY2QTx5\\n+Z8x09rdK3nvkHX4z1YO4dPv2gAAWHXfdFgivLYp4EZCUOklQd1RLAqse1mHeReRmrl1/MvaiD9h\\nNYXB0ts9uO4yWtuf/ysl59NOCZZAlj4crKV337tXpHDIzdIKQyM5jhjDx5eX8lSGKiQli1QRGSsQ\\nEvlmIcRWDJKC4PkcpPJf4nwSL3Eh8geOk28PI73qlfkT8dhMjnk7U05Mc5NXuMMvckMaFaSK6Orw\\nHCAX0OqX4DsgrNw3NQMAok+WICJE/Ry7+JslZEBgclJ7L3sOxzKrmXUkSQoCtRyvbYeF96ZdQaqf\\nmdlUqmzVI9mxeu+XOfdecfCcfntOVdSsLQXG9jyuiqmpuYZ3zXsMYxSmOJMPnDxP6q6bs96+sQ/0\\nz7P4cYCaPuWuv39N82hOumdg33f3+otgSGTZNM88zLFCux+y91PvHSvs6T1z7xk440LuDMOQEm3Y\\ny7VjssCJqPD8zPfRg7py+EjML+meDPDRzgp83cfxL5DZj2k2elCrE6T2NEY9MDlJAVYMfLfguDSc\\nRezLV1aQ9vhuYxWGuLh2nDimAUubqFAU3cdUMbWXlXANjGyueQCQUt0pvuZOaEKVqrs3be+dJhou\\n43FXbf+8kK46BdubxTgiHNrPtk5HrFDSfj8exARdOe0U3+lSYCjmOikdM8Ec4nilCj3Z2hQtB/sn\\nFeP/wr6w45vsJ3nmSN/CRF2q6KU6Uni/ULYJf3mP+y3PEE6835l5sMell+27AAde7S6yOvvzW+Cs\\nE/22rvex01nLuXt1LdcYqbIMLAPMM/3RZrsVcBiTmOatQW08F1fP5wL9qTRjaKwBSdtkWC7qBPYd\\ne9bL2IFkjojrGcqGnrPchkPFHDx+9PRXAQA/vby1W4zqkei6uZlQShrl3iUjtIV+OsJFQl6TAv8E\\n9pidL3ES9AAIjFVbxeBjVS757Ad47cU5g76+N4y7jw155y1/1/6dM68JHSuLj/veSS+blDEO1B5g\\n2d5kugojL+Ngf+ghIRF39zu4ZzNzxd31A7LAn77tIsQKOJgnM4KO5pHg5J5fyxvodcXhvJeL24lf\\nvxrbZj0JAFj2T+ZEO+M3t2LS7/lduQ3ZpI6mKEfzwg38Gyk2a9Q1e2uX5I8C5ra+u4d7czbT3Hf+\\ncvNRz7MEAMMuEbsyl5vJJHpuUFvaPLhuEie9H8U4IexKRvFAO2N0lj5Og8LVX3kb96/nseumrsbq\\nVzjg9PW2qlLyQPFQkDlh3/rh7wAAF/zvd/t1XeSVYkRmcqOvpNn+fbuNR1Xq1TE4qMYbY1LJUq06\\nFURKOHl8bhHHU3/GhZk/+goAYO3dWcNbyWK2mnVLmJTUIivq3ggzy2qw20kDWTTBRYLnGTea5rLT\\nLB3fM3rjiyO50f3J7Kxe3qudVHsMVWaT0kNS4D7APp4S85utjUYjADB6xceE7Xj681Q0vvK52/tT\\nJACA/dfwG0c+cWpQsnpD2ibyH4pcjIY0NJpeysNx0Dwlg1SYZd8s7ImOJjNK3yJ1N5bHjWH4sBOv\\njaGqYok5gJ1RGqtMBhGP5otjbBEH0mQF6/zgkgr4x7AOhn+X41PLpyVIU7hgjghF9Xg+NOo1FCAW\\n5NgVNwvVaKMCo1Dstbdkv+V4oG5WZYsyqFx4XTe7ADe8v3iBZK5fHN+rDRjqprTrRvVkYNhZ3EQd\\nfm/YSX1OX7joWo43bzw2t1/nW2xpmHvJF/5el/CX799M5f/fPECVdnuLhNdErOsljniPa/sLg8cF\\nGNj+1Zy+KZdRoy43p7jGGD+iHpNczDG7PcJ+dU3+B5pq8CN1l+I6ET/9OR8Nyls6hsLl5LsFS7hO\\nuHzmeiytp7HlUJTrxEUle3F30dbsd7fQMK2qeEeGZWCKi0W92H/aWgFLsHuvMEcU+LYzZCJRyM12\\naFjvSrfx4UnxF8hfceyNfdom4bJKvmNLhguJZMaoOQyOF+qYr+aGNaQlpDv57kZPEpFSvqNvz4l5\\n3scCc1mX8b1e2Np69g91fO26lr/zdoaRzfnzd45621ixjHAN296/3r1QI77Lhyh+cmBKGBet+gYA\\n4N4Z/+axIzanALD6uZ7ZLY4FdcM6EJzeOvA6dOjQoUOHDh06dOjQoeNjgxOm4ns8GDXRrvz55RE4\\nxx7FKxHu5H/4Aqlinv3dz51202YAwIZ/TB7wc1RKgmrlHQyCI4G0h9Y2V7UQBBqThKWRVh73IZ4X\\nGg6cc+EmAMCyV6b2/x2H0ZNnP8z7pR0KTNHBe2B/fd3DAIAfPHo9fvwlWiJVitPIJ7/ewxLXH8gT\\nSQcwbKMHOXdnVsXXXUfTTsN8kybaYoxJGhVBxcxNX0BkBT2sxWsTPZ7RMYoWNFtA0bybNRexTL5y\\n/lI8uIIiSJXPZ3Dt3yiIYRAmxupEIR7ZQsErzzpaWjPWrLepYDOfl8gxIeHhe3tqekb6N0+3ah6N\\nwUDNl+rZZdasg/JUll36oAv2lu40ndisCFIiD6Rnb9YfGi7nd7kOnXh70rHyoKrY9JNs/Q3UC9pZ\\nxff37D3O91cvP7pAbQ+c7iq+QlMEzsZsoUQLDRrFt+liVvCyhffi6p1UtHxhPAXfFq65GbFW0kZL\\nlrNwGxdlNLGRH9z4NH7yHsMiVPGO3O3AS7+iN/2sJ+lN33fdfVq+6L1xev5/kr8bFW9RBM1oFTlW\\nATjW8XmxIgWyiR2ycH32e1RlYBNZpGifOJhSOfE4GR5Zj2BTOZtZV7Fcg0bFUnORxmeG8Zkq0gFX\\n/paiJPGcrPq8EB9Hzs4Q9tzKse6RhQ/hOT+53Utr6bFJJk0YX0I10NY/UZTGGJPRMZp1bWsXQi3j\\ngIKNQi39HPEyBgWSoGHah4QRiwhBGT/HaFNE0sSWrB1ZD2qgT+3+jz8GquK76qv3YO5DAwuV+Chx\\nLBXfrjhStAjIUnIHQ//ter9Ry64HACj1goXUKA2aUtwVZS/WA9RQgewWgm/jvIgMYVsvupi03VjK\\njJYAfU3jSkiFP/DqCIQrRBhCzIA/XPooAGCBnWurczd/GZE4+4fdyjH4jHHNIwAAIABJREFU+so1\\n+MP7FwAAzF4uLJ6a/SCmWXnenY1T8VYNuelFHhZ+KGFF/F2uk0yC4ipbJO3fKtx1KTh2sH8nRpH1\\nEqywojcI/TQYL2kHXsjrT1Fh3a84/v2lgyFMGUh4+H6K8aSdpOUC2THteBAaLmmLNWuHpM33qto5\\nkM3Verpi+dWcY+e++h2Uj2Kba1s89ITcWxWYUvPzdsXzt/0On/tr/1hy/UVkOCcrZ03Wg9pfFV/d\\ng6pDhw4dOnTo0KFDhw4dOk4JnBIxqGHZhhXhKgwxZfNJZi5nXOGPXrkKvt3Znb7qOf3St6l4c0fO\\noW6xoipk8WVdY2GOx3Oq3XdoHNdPYmK6tyvHAADMSTOGjSRnfLuNUeQL5m/HivqK3m/SB1TPqYqy\\nGfVoXF56lLOPDVUE6gcAnmigBf6a0RSJGoz3FMh6TlVYA2mEymiWa50sYkiLk1g0gXFoy/aPQuVi\\nelMOnk8hj7VTngWmdL9vUI5h4d138nqR+i5nX9bFJ9tYf6/85mzkutgmOkYZ8YvlFFmqfIamNv8Y\\nK2675U0AwBtDmXrBakyj/tkKcQ0tlpEywCW0D/yj+f6uxgwylmzQvjFxHLnuulyqeoRiQozDkAZC\\n4/lt9oN8n1TEDCnd83kn2nMaGSryIQ4g/6jqNd30k7/jvruYg/GVICvw7qKtfXpVe/OcBsdk4N3d\\nv5iEr95GD/l9j34aAAUh0iI7QNrxyRZMUj1pXZGxMK0MAJjMPGGYyYXFExhT0pgRonLve7Dz+/RE\\nzF1OyfiSd41QTdYPHDoLBhsH0IKNrCv7Vxu03IP7rsvGsuYZGY+1N8KUCPebQrAfYJ+KVbCdS0YF\\nnSJ3ouTIwNiixkhlx2VrkP9uO+PUyjF5MmJZVXZGxixSoTkl2DpUz4HQUNjiQn0ZB8PJd5I99Oaq\\nybC2s37L3uH4LqUyKFzGsfcG8w0oyKMHZqiX8aT+mAONEcaU+g5kg8eCFWQstU3h87z7JE20SRK5\\naF3eGCIhemdjYSsQFvlRRdszxiRNyMUgQvnjeRL6nQj1E4KjeU/V2NT+Cih9FKJLiZwuHq1e8LcA\\n0/99w1erHcscIbOQnB2CZTXXDqqAlvmIOMa00JVUBa1+sfFTuGkSE6F/d2FWGXPq+isH/hFHQPY4\\nICU5HsWLOaGYYzIcdFjh4BZ6rPJGt8PlYGe1GHn+z7/2ODZEygEA4YwV59q5/lsRF3HbKROSSS5C\\nx4nY71eaJmlMBFnm3983XICaEPtgNGHBpCJqnUTTHBsP7iuGR3gQNZEkBVCE51dlX1jbYlDsHG87\\nRvHv0eLA1XEnvD4f6TGsV9/u3s9VcXMddVD2BOidbVxXAo+ahmpiOrvWOXj8WwpLJ2DtUP/3yRxD\\nzv/L9wAAhjNimo7Af33tCQDAfz94zXHdO2M/ujbOYLyn6thgDkmQhXc+PiwJ1y62YUv74IUOT4kN\\naiRtwXr/MOzsLMYPyrh5eifApGjefRJksWcLTJDhKOFC6J8Pk17weEfvDfh4RRqOhPeLFK+5q+x9\\n/HTjpQCAeRXkMzREvIiIAWXqbIoBFVk7ERcJxo+nkAezOTVMCWL77Cd6HP9LJXMnjbvve8fxRj2R\\n9JrgruVqxT+OjdHcYsa+IKkpZksa+fkMrp+3lZTBS4duw40+LriCMutwhNmlbX6uWkbRoeaMFbm7\\nWZmKTayIJBO8B7joDd0RQvVUkROK+xdMWH0NXqijIaM9xIlHUQBVxDQmmBLe/dDalkUoQbdNNGoL\\nL0NP3SSkHf1Tw03kKpDU90XW6HDNRcsBAI+8dyY8uUK5aCvbTixpgPvgyVctlR0qHbTvZ4UqFLir\\nuw9iXTeiXWm/kVKx6a3Lnh+YwHrzbTf1uGZ1PIMbSq4HALw7ixudi3/de7t86K9iY9rlmKq+bBqk\\n+NPpAlt7T76zMZk1QhS8TOpaVc0tmLeQibevymeu6T9/637tmqv+iwa/p/77IjRfwhXMvSPewF2r\\nviLO4HMm5jRo16g5pC91RvHvNlLqv1TwAQDg5iU3AKWsf3OzyANYFockVltKRkLazf4RHsr2kbYB\\nPqFKrPY9+ZSYoU4OVKpd0i0WrRZApFGEV4S2pB0K1tWQVucQi+RL56/HZT4KYrxwKcWtlj01A1a/\\noADGTfDZaBFrDnFDUOgK49AqbiIkId7naE6iaB03suaIS3unpoWsl4WjOZdVd+YhkRCGx6gZjmLO\\nwbEGXiN36ZiqQmjSJ8OQOrWMDKcqBrPJ/LDVgNWNaWx6FPb1PbMTPPAQx+h7xrDj3jr3XchTunOE\\nU0kTepftyeKxG/8EABrtdV3VDhjFJmVrUijgS2mkl/WPmtoXMg4L5FyOjx1VQpQnoWhrR9dhQbOv\\nzA5CBzr43NWOEaiJUhizzN6B6w8xb+tYNzejkqTAYuGNDoprOhq8sIickekU596N9aUwGjnmmYyy\\n1tczSZHfutOISKmq1M5ysDdm5211fEzk2xGZIPLAC+O9ajQ6GlyHFXSS7Y+2WWKu3mZGZIgwVnUJ\\nr1v9BI3RKs22q1RpeUWLli8b6F+9RIZI4r1lGIQTQBUDSuRmVXq7Zuv4JMKxxY7yUcxVujV6YsTP\\nHPUn1uFhDrPeumbFkCJGRIZxHlHr1xwe+HN1iq8OHTp06NChQ4cOHTp06DglcEqIJDlHlShj770B\\nrTU5+ON5pPZe5KCPf+ySmyF10LqVs13S5LdjRbQqSWkJcxbsAADs/Of4HveOipxuaYfSpzUmWizB\\n0dRLWQgjcOxCclGiLU5ISUGfKxLWsBY7TIW0WJflk+oxNbcWL+ygF0/NG/hhYuct9FSpMtQnE65a\\nBcYEyy48NOsNSHqExa9V0qg7aq6rdHESefm0sLb7aYn/9rR38M0c5u/7VRvp06/Vj4fDTKvswQMi\\nJ6wiYeiSLMVXRc4+Wmyi+QaNxpJ2ilQOCQXWQDZnJAAYUgps/rR4b7ax9kkKrH41F2GWmnvC0QsD\\nLlasIHcS+ap28c1WYxo179Gqagmc+NfoTSQpOJbl6N3VP29ubwJKaRfgWURr8spJLwAAxn9wDXbM\\neaLbeScbp7tIkrORjcgSUtAxmu3W0aQgnivEhoSITtIDKOPZ34bncWw90JyP/Qsf7na/eVsvh9fK\\ni+4oexu/2E/PSOc7TEHlrpURFbk6VSGmeI6EjqlCMETk/jMetGvibvFC0e+cGbgEkyK+x6sRjByC\\nap5yA/Zmfo9/svCkxk9fG+qwt1lWKSf7mX+MEbGh7Hu2Jh7L2BSkvCyLqvFMdVHlacHVufSCZ0Qp\\nfmvHF5FaysYersxg+mS6P4IJzj21HT6YVtHvYW8T+QT9GXQOpwvGFOOxyFBJE+rLKRLhNrIBCUFX\\nTISywitSROQq9xs1wSvVm6qYFBiSp7cHdaAiSceLP17L8JgLHYkPxYPam0hSxooBCwd2pQcXXMw2\\nfLCGoQDuHVmfanJ2CFbhddwy8ykAxyeCdCTUdCaqeGPpay3I5HBhUr+AaxB7q4KMeCVVVDPllWEY\\nwoWAWbzflJJ6bG2hINwwXwAW4Xb9/fAXAQC/bLwIh8Ok7tpN7E87dwyDtaX7nGpvUZASaxRzRIFi\\nFOllBAMm7ZK1MVAtd0tQgll4MlWKr2yWtPFfS/WUAhTxuHiuBHtr9wVH0iuhU4QZqa4qV04U3x27\\nGABw9zNfAJAV/jwa4rkSbP7+7SPaF/AjSos5BxkNMjKC7ly/k+s72SrDksc5yPOWs8c9kl4JsaKP\\nft9yMmFt7zm2nH0lUxgdDOej5rWBhw/2huhQth+LWPOaYr2LGp1o9Fck6ZTYoDoKypQxn/02gGzS\\n3s23kepployoE/mmLvnD9xApEwU6nMcmlTSgOUoa06GDHPTy1mUpGf5JPD93qwH+M9k5zhnDhEqr\\n6soxXsQHbGscAtMa3icqKBU523s2kmAVYG/i8dA4kfg8P4LOZg5w508hjW7pwVEYU9ICANi/pHIQ\\npfLxgb1FQVgwkVUOuiJxIAUAw9QgEmr+WlGkaVcG0yZ2l3zbsKMScyaSVra9lQvicNAOu4v1ltrD\\ne4ybdxA7G/h7YU4IgSgXYfZX+btizCap9u0TC+Zk39KvTTP54sZEdiLI3Z1A28TelfCOBnUj3h8a\\nsAqVwuNoNMC8kHlS/2sMqe7vd1Zh8dOkT/ZXcVcx9h6b2Bt6u2f8bK5MkrVOePYfe4PQWSX3qdAb\\nGsHv23rlvZj43LcAAK5qA+IFHHuSeXxZ37YTz+c83TeoyVyWbcE6aDl9GxdlALFhyPuAOwbZCARm\\ni8oWCokubwxfGUVKbkqsZKIZq/ZvWZHwzLvMZ2hrUdWuZQRGda/rjFXR6Fnq+GyrCCF2iOOpbBPt\\nu9ak9X9DEnDX8XgzQ+NhbTdoarJqjFWo4qOfn04WSj4Qi0uxDmidaoB9LK1QnU0sO2PIqG305dGc\\n824c/wG8Rg4wE22M+RttjuE7daQZJmUT5vlojd0T5Tj52qZJMPnZvzKC4m8KG5DyiUZjEQaBkAmm\\ncHZhDgDmohgyGWF49FthbRVhHHwdREplKDlinLXwfumABebgyQ9X+CjxYW9QP2wMRMW3v4gXCuNI\\nS8+y++2t/8SFju6737O2fRaNW9iG1XXXicKQpUFknBwfay7hGkLKMKYayOYON6SA4Di266qx3GCP\\n9zZilJ3Bqv60C0VmxnrnmdgpFgcmoC3BzdW+doY6BVrcWh9UacRWvwRJ/FtSAIPIiaxuopMeSdNb\\nUI3l9mYF1pAYO0S8eLjEoK09cnfzXdXMCgApvPlres6val5qddyWnRncs5DZHu6vZaaEwBOlsF7B\\nb23cyTW2MS5BGsECci92ahkS+ou2eSK+PTeKcAs/UHX82FqMsAkjWldjSDxf1EungnD5wJ73cUNv\\nG1Q1JGTcBXsxw0dHzuOPnndczxlyIUVY7h9JfYr7/fPx+pP9y2E8GMRE/6++605dxVeHDh06dOjQ\\noUOHDh06dHx8cEpIUGSszC/q3Q9Mu5QeyB80MY/b70s2alIu1vNaobxDa5RlFy3MG0eM1qwNeb0E\\nheduze7Bc9+nCaJ0MukFO+as1H77d0EOvjiPx+9o5Mb+1cx0TJpBS/R+P10xc4rq8f5OJmG6ffYS\\n/hYtQnFlsNtzne+5cMBHr+oxtGg+9rAGFSR8LOeMsAZ6qhVYwrTkNTs8yAhxlNIlggpcYsTWTiqC\\nmEKsv/wGBfWv8pixnE0zP6zAJnIwKkLNrHWqEy/PobBOnlHB3S209L16DpMmyn4LzAG+T2cV/0pJ\\nI3x7RB5cISwjyUDrZIO4t/DmeYHSZUdX2ApPi8F0mPKEttaeVq6E8GiZogZEhdiAo6EXa9hZQcib\\nmDvSLrxTiRwFoVZ6gb9dd7UoGyMcwvGlWjmdtX3blXrznsaKlX5boG1L2bdsyKrl9iVGdKz8pu4D\\n/H3e3XfALUaczlEZ/Pf5zwGgBRoAHtpzSb+9xDqIotU9j5W8a9S8cmpIhL1NRvGb9BaMvoMhEbnm\\nCO5dvwgAoMSFV8yTRCrAcdJeb4JHeDRTYihrmyTh7HOZ33m4jeINr9RNRNsujo+qkEe00QVnE+s9\\n7VD/KsjbkR2jO8YIWlFUKEnul7OiH57T2zsFAJaAmkeRfzsrnMgVquuFo+iJqd40FAah7J3OsI4W\\nN4/FtaWs+Hk2luHeFLChgSJII/LbMcTMuezpdoooGexpKGV8js1K74XTloTQp4PTIujGsgFN9aQm\\nSsILn2q2a55vxSIjmaMKIfGY7EkDGfGOAfIjHXUmpNynr/dbx+DQm+dURUS2AqDL7B/BIQCAwOIS\\nZMrVCe3ELqSM/k7Ei+mdVenoqRExZJpEnl9BPTUBGmvAME6sXzJW7I6R4jvRUYfDSYoDBTN0Y05y\\n1uLFTgoLScK9OGfcfrTHOaHKokMdqC8AgqoqtgRrK59pFgwsxZRlY6lzY8YKxEw8TxVEihUp8O3l\\n79YA+3I8J8v+MoaM8E/ke+Ruy9aBKqSUyOOxc+Zvw+rwCADA70c8CwD47VcvRGPUo70jANjHBTC1\\nmN7k7cqEXsu3L+Su4Ten3D4IJ3aXLBvdx40OpoaFQajP29oG/LjTAqo3ucLZjjtydwIAHph4JgDA\\nuc12tMt6IDOLoRvGNR40vEnhpUtBgUr7wmxKBFXk6Jazl+CBrXzOyGL+Xv/GcI35p3r7FbOi9RNT\\nFBpDTipjA7ZudMLeR//vDboHVYcOHTp06NChQ4cOHTp0nBI4JTyoxkRW0nrrk7TGbP5hVnhFGNgR\\nCNnx1DcpQ37z/9wOAEh5MzBFBvYZzz+yEABQ+jU/yi20CLzUNgV372KQ9m2jmQqkafZeLMxhvOr9\\nFTRPHUzZcFcJg8gnWWi1yPgOIqzQvPEnYbE2JhUGYgrkzGOsa8fK4gG964BwhkgqtsXT93knGPbW\\nFOzC8HLwC0Lcw25EEdPFomRVBv4b6REovJNxubV7y2Gw0kIzewS91GsXT4DnMI/l7uLfWIEZ5nB3\\nl2DiwQL8710XAgDe3zwGRi9Ni641jCNx12VgCfJY2yRaEWUjkKBjAMFRrJeMS4bBxWD8yhKa5ere\\nK4MhdfR4VdeGvgWvnHXCW2QHLIGe1qLf3EahizMs7Zhfw7x4jkZhDXUrMLXSnJg/ieUUXFWEeL7w\\nxI6glz5k8UDK4ffJcSNsdfRaqM1NMSqQhdCDRaQHsPoH55FSPaedI1kmR4tJ9V7G9CPBl4b0eT81\\n3sZUEMNPVl0GAHDn0MIWnRmF8SDL1yG8vfG8nnlOg+Mz+NyctQCA16vHwfKO96jPU0Ui+huTe7qh\\ns5L11jkSKGSR4b39IwEAN52xAqYG9o/8LarV2q7lAe4at63m50zkSRgtEgWuDZYDAM4dsgfPbiSz\\npZA6DmiZAa3dmkUsurMJaGY4NWRHRgvEKlmSHb+TItef3c9nhypOX0+quZNzhpRi47Q3OxBLsf8v\\nKOGEWHlWG5bsoWCcUTBInOYk3AaOW9fVnKXdT00Fc6AtD3+OnwMAaN0ghEfyMpg4lvFGFxdsAwDM\\nth9EfYZ95+nWmQCAg8F8SFHhTQ9lPd+qlRxxk9aXTCPC2rPjnWxHpjCv/aT2t08SkmLYtQT7Pk9F\\n5WcOYPta6nE4Gnv268WB8fj7YY4jteuYC86mALbmE+M5DVVxvHHvFYysSBSyRRVoEzGYYTPgEv/u\\nFMwmG5Aczr46I5exfxnFgL1hxmNenfsBNoigSJed/XJVcAQO+7ngKPZxXfbz0lfhEN5Um4ixf66k\\nCq+3kvlV7c9FVCRwMYg1gSRn5zAt13BaQWclf4+Vcx1gbjHD0cLvs+7lWhMVw7Vvz9kpafouveUW\\nVQVEF783Gd4qMmMcRt571boxWoq/96/8HQCgLm3HnXspohQaLkERbItkjhDYKY6gMq8dALBrDQV9\\nuuZa1WJwj5ImUkWwCpCLWPb27aqX8JPNzHjzqTl4YSiFGyZPZcWZKmWs281ydu0Riz8JWPpN1le+\\nkZ77f4dy8EU32TUT1/QUIIstK9DYMpPPoEZMbTwX/zf7YQDAWaIKWkZFEBH0mwf9jFn999bpSAq2\\njylqQDqfnvy7zngXAPBX00KY1roH9K2nxAa1K1SltcrnmAfTPaxTU3T7zqR38O8OVsz6X5LieWfj\\nVCy/r/ux6T/tO6m6pZMFe9/vP4uklwVqCSpajq5Jkyg88XRsOh4OMkHx1yczD2qhEWhMM1r9Ww2k\\nIS/y7sJda9lZfUuzrvakVygbWhQ8PvZRAMAlK09sDlLgw1XsPRZMQhhj+Iw67M8XqrsGoNzFnc79\\n5S8DAPYPtSGucEG10M4J4fANr+OSCMtHbQdF6xJI23nPwxeLhxgUNK5mnlxXvQFusSnsLOfPxnh2\\nYR3PFRPCxAA6W8hTtLQIsZC8jJaDbP9+Gg4qVwxQovAoiBfKMIfUzpqdjIuNnKxKTC488Zm/AQBu\\nvvebAADFKsNRzXeLNrLszIB2HyzjhOcGAIkbuaSbia27Q9Lod+pE0FtO1/5AESOEKggSHJuB6xDr\\nwyhEGzqrZGya8BIAYMpLPfOkXrqPxoTap7NiYc73XF2eYs1+1xE4cnMKAN4dRizZyX5pOcZcddVl\\nywAA/35+Yd8nngZonygWWz4ZilgIGWPsG4aiGMJfYCMotLGNZxQDbEKUp01ig8nfrNC4BqBlOjBm\\nOhdk/vu52LGODmJziIpoh4JMVrdmTyVQyIYWy2OfNiQVbR0RK+dzk+PSmnLv8IIO+N9SjRnsg5Fi\\ngyYEobbb/dfch5FP9D2ef1yhbkxVKEag5QCpgmtMLIDRvmbk5nDsbK9h/9+eHILVbtLwri6gFfC/\\ndn8GiqDfmZd5YdvIeqgMs373X+VDqYP//pSLRtcSowPJJJVwqpw0iMmKARlhwOsIka4oh62wOLlY\\nzXFHYTHyvdUNs92Uwi5ByczZqX6Moo1BOvqH6z//Nl5roIG+dVXJR/w2x4Yx3vOYqq5qb+65AT34\\n8gjsv4tzQm/qvG/vHAfXVg4AXQmLAxEcPBpSHsDc0X2jKweCSLqE+JdYg5gDRqSHcHxMjBT51zMS\\nHG5+7IEIN9BzfAfg9vLY84EZCKe5erxvI8ONbPtsSBSyn7gLOYnlGoB8I+c9NYd0Y8oHh4l9yyAp\\nQD6fne5kCSgSNIp/eBj/SmkJsUqel5NHI1FqVx4ch2gpUNLZEKVwKa9x1Smaiq8qIJlyoYeyb84O\\nCakahms8OolzbP4WCepW4bNrvgsAaJ8qI28jy84NBSVfrgYAVLpo6C+xBPGfem68M8ViTbW7/4KT\\nbbOFMaEwrM0ZlraBCVaezlBzmW5L0th81lnbshtTgfSMkLYxVfE/D12F/znWzUWT2FpHI1GNOwd2\\nI+fwr3xA2rp9c9ZRo2brUEqTcHbJsWroZJv58yufAtC78NOxoFN8dejQoUOHDh06dOjQoUPHKYFT\\nLs2MigW30DJ8IFwAl5kWmPm+fbjntUsB0CsJAFJeAq713M2bwzw28oY92P+v0cd8bsInIVZMy5lv\\nt4Q//Ige2INJUjeu97RgmfBAdMq0aC2y+/GCyKnSmKJF+9k/n9vnc6IlUo8cXKcTHI0KLKLsAyOz\\n1BNHDl1s0Q47TG206KdFWoOJYw9jXwutkSMKaHV7teqNXu//p45yAMAdOYe0Y/8Osex/uPJzMDeJ\\ngPtiWnnOHLsXZlHQKVHwbXEX2qK0JrV30IppMmdgEOJIQ+7vbn1SMdA0M12hSmpbglKvueO+dTPz\\ng977wOUAgIytd6t0UjC2Va9SPF/W6HcZm9IrXaq/OCGiRBKw6cd/73Zo4pqr4bTy5i1t/ADv6u6B\\n/H0JMEWGiTxvhyUEpvM+35/N9vGbFRfDt9l81NfpKgi1/sd/BQBMeOC2gXxRv/Cv65kK64aHv3nC\\n791f5OzNDigt0wR1PTcFSYgeQTjpika1IRRj+buEB9VizGBqfm23+72y7QxNTAkAPv8ThjM0p1iH\\n9TGfJphR2yo485KCVAfvbQrxuWlPBjDx3YaWkjKWUSTMLaSl/dU3ZiFve/e5xz9O0njqubvkbt90\\nOmLU490TG4dGetBxNT0is4fSc31l/hrsTNCSXRunx/o/+yZgSC5pE2cXMfTEZkjhn9tJtSp40Q5H\\nAweSjjGcG0PDgWQxB5C/nv0YAKA97cLaED2xgRTPW7m5CpKoA1Mu72G3J2EXIkqJlAnxJNuHy852\\nFAzb4FouxF9E2gtJyeZyPF1xMtLM7LqZ4+iHkef0WDjRaWa23vX3Hscm3XMrrrieFMBnHl7Ur/s4\\nz2tGcAUZRseTp7z0P01oWcD7tM8TE2HMCFiF+ItZpMdyJeB18EEx0faHeQOYKdYjuaYwGlOkC1zv\\n47r12l3Xob6GbIjqTz+oPTMq8zkvRugh3x8vwqYAxc12NhQj3cJ+qFJqAUBKiMWj+GMpjCKdonfK\\nvIfnD30/DvMawaEdRbZL2zSfJjpnSANxIYSkitcUTmpGYxPHcGOTCBMyKzALanO8hOOFKWiEb0+f\\nRYnIxWws51fwHXYHi1D3Ft/jSC9tVwRHZsMBMnbBOCxM4pzRfOAluVvw7bcpGJm3SYSPWSREyk7v\\nsWUw3sa+8PUb/gMAuP9fn9bEjdRwtK5I5Cgom0m2aMtb3OfEimTYm0+sL/NjmwdVxcybqRQ5030Q\\nLzRPBQDUBnwIRbgQmlTKQvxb+Yv4aeMFAIBND07Srj/nG8zv987f5hz1uUmvhOg08kfcKxwwXMSN\\nklFsWvwBF2ZXcEG1ciuVey2tRphFnjjrfJ4vvZzX6/3VTWmsSEJ8BCdz24GPN01h5y1/70EljpWl\\nMORtfmzTXJaNYgDg42BscyaREonexw9tBACck78bn3JRTfSPrWcDAPYEi/DW2FeP+uyEwkXSn/zj\\nsKSFcVkHtg/F8Dc4wgVGcPLI2IDIZE4oiixp7xDzd48ftdeYYRVrxNzdPXeQ4aEWxHMHP1CocaC9\\nbQJjRQqeu+qPALKxzKMev6WH4m/KDcSLRVxHNQdoxTjwZOlHfUexF+lLpZfvy7/zLtiKjY9O6vF7\\n51yW94FF/9KOVbz6NQDQNpOdVTKkFL/PXd2/cg2OluE6zLYVEpSrI/OlZo4QsOu6yVdpxmPv/+gX\\nfCcD6gY14ZVgDXLc6hhtQKJCNBAxlkEBJPFvOcF2ZPUk8N2JbwMAis3sCLe/eINmRPPtBtoXsPH+\\nYBaNA8GMHfe9w9xrXRdRwytIY6tt4YJHThngy2WjMpt4XmfEhvRhbmSs7QZ4q8W7e7KbGkuo+3z0\\nSdqg+if50HIm27g9l/1p+tDDGv1WzU+7tKkKfkERvLSC8aTX5KxBbZqL5B/v+gxMz3BOUvt3+1lJ\\nTB3BTW+Fk7FhDmMSr9eOBwB0BFkvZYV+lLkYozTGyVjjpqQHQ8VA2Zj0YmMbF9S1h0kFLHzfBEez\\noHF72TfjORJC5YMumo8F9DyoA8dF164CAPymaLN2TB2bzeFeLznJoOlPAAAgAElEQVQhCFdk4Kru\\nTvEtfaMVhz9DI3mskGORMSEhVazK5Yr6NSqAWEeof43OFOZWMEbvgtwd2BjmZuyFzVyrGu1pGAQF\\n/qlZDwEA2jNOhGRhMBIOj78fWIDWZgbzGjpNWjYBVWPCYFSQjov5Tg3b8Ju1vNS+AxxbPUv3QUkK\\nJe7pzIQQrLAiMkTE/zdkx9UgWaHIndyK8bmMV126geOArdGIWAWf7ctnhfxs3Kv43vrPAQC8bzt6\\nlG2oHJh+7i4AwKq9NHgZ2s3w7RbK3oJSbA4r8E8QObhF1ovLKrYiX+SOVce3beGhaI3TibBz+zDk\\niEwcqoE+5ZK0DAmnK070BnUgULUjciZwfxNbVnDCn9HfDapO8dWhQ4cOHTp06NChQ4cOHacETjmR\\nJBUrn6AlanHZZOSOocU3GHBgweh9AACTMPN/ae/VKHGQ7tQ+hxakvA/Mmuc07VCFanpaXCxBBcom\\nWoSKPl+Dg6uZEyh3Mr0BcsCCbU9TjMci6JqyFVAErcQvclaaqgCvyEHVKXRg0l5ZCza2NRs/9p7T\\ntJMfM+rRW3AkudJ+2AyTyOVnSKi5DSUoQX5zokKBHOJVW1upNDbqzBa8DApCTHXRsr/VPxQVb34V\\nAOAW3petQiALAKwS77GpswztwoMg5SZg+i4tPfnivNp2H1wWvk84REtlstoN4cjRRIeMCcDR0lNu\\nsnkG3ztjAyyBHj/3G33RZ+3NEq7903e6Hetpm6QV2xw6QsXw6GlaB4xjeU619xDnvbtzDKzCm6rS\\nq8ydgKmG5Xzj4fkAgH8OW4HqT5HaNGWz8F7mJ+Be1SW4XqhAZqbQVJ+qc/ZQCXbWGTQxpq6e0+AY\\n1tuCmTux+VGKMai0H2dt1vqo5tM7HZCsYkFY9vZUkrYGFTSeJXLw+uJwO4QQksjlF49Z4HTStewt\\n5N+63UX43zQFrG6e9D4A4KHLH8DyMNkJP7t2p3b/hzsZ9vCBvxJFVRwfZxRQFXaoNYDH9lEFVvXS\\nGtvNiB8iJTVizVLdDaIKVToXAFg7T29reH+RsWQ9pxdVsOw9pjgygoozz8lJZocjK6BzMMpR713L\\nGFznJb3uj+OfwQ+vYdhAwyH+bnclMMPHcTbfxP72WutEpDO8t120l0p3OyocHE8bEvTIphQDPvBz\\nYgulrGgOUM7MfpjjsTEpw9zJuTft4FiVchnwSVfb/DhAMQqPXebD8dg8t5wy3r/8AuW+v3b4nJPq\\nOU0IkcTSqhYEqrsLT8lOq8a2MSYE88sIWBxsyxaxhkgmTUi2C5qOKC802LAiSU/loZI8DHHRI+jJ\\n40SZSJqQ7+G/b9/zRQBAucePeIYDYH2Yk19rkxdSWKgKS4BRiCRlkiJUImSEISkUfcW87zpsgLOJ\\nY713NXORptv9MBVzYs5Ys+sFpcsKP54vxJFyxHeljehIci4ZMkLktzTlwRDiRVfPYh0tsLXgoVmP\\nAAC+Wk3BOjXrhorOFMvH7SMjMbU/B5LIW2pM8G/bvBSmjOIY9NSI1wFk13QA8J6oi23hodizphwA\\nkNcbtVgfVk4qbG0it+5xeE6PFq42UOgeVB06dOjQoUOHDh06dOjQcUrglPWgWs+jRaezxYPISu7k\\nvUHgPT+58lrqjTFhHDxAy5EqDBIYqyDjoYclb032E1XvprotT+WmNWvM/k1lGDKtqds7mAMGmIXn\\n1VydPR6jMwFShM976Yo/4Ge1nwYAfK+UsVpXLrkVVZWMt6wt9uHZ6fQmfeGfdw6wJE4NmCJHt7A6\\nmhTNUmcVeTcTZ0SRjopA/lob3I3ZOAQAeKtmDhKzaDq9efwKAMC4nCbE07ymcyULuaLpJlwybQsA\\n4AdF7wAAHhj+GpYXMsbqP/4pkIWoR3uCcVQ57iiaDtN749onBD0aZCTUlEKhLvkd/bQm+kfTa9o5\\nUgYK6cUoyA0htriwnyV0ekONpza1mvHdKyjutDzAuOwV+0ZCEVbe3QGWV1X1l3H+SHp0Vv/4zwCA\\n87ZfgXYvLbaGmQHkiJxxny/bCAD4S/2FPZ6bcisaC6J4PuPOD9UUwJ1P6/ShUC4mXrsdALByzThx\\nVbatXuYi4+KPg/7yUwe9eU5VRIoNcNQKgbKUQRPoKhZiOgGDjM4mer5yKtm+TYUxpDoZKP3A1jMB\\nAK8XTIBJxE59qmM4riimFf2RWjJSrMY0JuUx5+1/tolY5IwESeSiViyibzllLf5RjWmNWxRY2/mO\\nuTt7N4NHC0S8UeqTZyY3xxTE2lnH1WJ8+/qQZSgX8cFqDsXfDXsJL4fJPlkvctGGMjZ8EKfH80JH\\nAv8aSyGkHzk/AwBojHjwbguFA80iTYzNmEKRm97UkW56Ta2GFJoS9O7kWTg+ZxQDImmOj60xJ0yC\\niqLKVyS8EgxD6UEJlnNOjOcrasicjlMYH5bnVIVT6AnM+P3tJ/1ZoYkJOEUqmNrqgh5pzBSTAZ4a\\ntuW4GHdSORnkC6ZJmYex2KGUDfVG9glJNOqoxQabSL3U2plNmWY1cz0RCjjQJo45hZjYrvZCKGKt\\n0hlmP5diRihOXmN2piALxosimGiGhAGWgGClCRaT53Aa7g0cgzNNjBM3OJ1IVTJVnn80B15LSIHr\\ncLYTqmJE+WUcTz47bAt+lN/dRfnmSCvaM/yeuiTXUE2ZbP7LvddTSPRTey/C7no+T05LGOZgWVX7\\neU1mYhj+0RwLnCJFz6dL9+O/i99jeYvXskpmbEiIckyT7dEQ8fYpypTpOxW9jlMA8aIMKsZw/6OK\\nLQ0Gp+wGtU3QZ127rFrQrmKQtM6qYbsbPjKAEZzJjmC0pSEn+Gnt80Twe5sFslBnU0TOK1u9GRjP\\nCTqRdqBpIzucmsPLFe2Sy69QuL1LMjDkccD5wRQqXD4VmIlvD+W/10QZge4uCKPhDVKGJ392F27a\\nfc3xFcgpjJRLgtyhbkB5TLakteB+xQjEBf9W3WzY/ApMS7mhfGAfRa5KZjai1M3Bc9dUkfh9nwdL\\nX5gGAHi9dDIAwFsaxHAfB8RdTUVIhrnIVg0G5qABHkFTkAWzOpZv0BbKsmDApJwSkm5eGx4uKNze\\nNDwutqPmw7kibfbpC7VebG19n6fmupo4Zz9ebmE93FX2JgDgX8OW4YbDCwEAm5upODqptB5/HUpF\\nw1cinNxjKTOuv+YtAMAoazPeDY4FANz7Dut/1KQ6TDiLE+/btVxMf2PUKjxazTzHNY1ctDtzYzAu\\nphhPADloGEIar6eh52Kr8Ig8YKcrnE0yIiXZsXF4IZVzQwl2AI8tAaWI5VNTk6XumD1cHGQaOesf\\nChXCEGMHkS0yfrazTJwoDHWupEY5VWGwZmD1sc/EWklUNweNyAildatfUNQyNGYB7IOdFTyedvCY\\ndz+QEHpzhuTRk8qfrpBkaLYVWZSxzZDCCBPrJi0kma2SWVM0X21novZV0VGoF6ryW5OH4BYL6fPz\\nSRWW8yQEM6wbgxgIFzl3aWrnPqFAcijtQmuao97BJNvJznCWJh+OWxFrFCroLj4jnpEQrBLjbT7b\\nwajSFlSvLTvOEtGhY/AwmGUYVnDu6S3HtpTKIG0T7VasCQxxAywiB7HNyL8OUwhmA/teOMnx1GpO\\nIxzlvw0GBUGhkK6OVm5fVAuviCUs2nlJQd21Wkkj9owIwSoMRp1xKzpa+KamINdOpogEK4dymGK8\\ne8ZigOxlHzS4BfV+TA7CxYIWLGKFLF1ErjIWKrQCwLgcTvY3+DYA6JqPnMYtQFVgFAvrXoKPXq16\\nA6B9GlE5iRqRe1V1ImxP5mmZLxbaOadzLu6+uzycDqMpw0XI861c59W058Ccm10nqlAFn4zTAsAe\\nb4930vHRIlyRhqua7dZZY0RLzeA3pip0iq8OHTp06NChQ4cOHTp06DglcEqkmbGWlimlt3/72Cd+\\nTDF/wXYs206PUMUzPNY+zoKyy8gb9llIuavuzMWXh68GAEyzHQIAOKQ0jEdwpXyGrGXBLBmQUGgF\\nd4mAc6tkglE6fttDVE5iRZweqIBMK1q5uQ2jTLT+5Rh5bOIfT88UHgAQGU7rZtHKvqlQ/vH8/auX\\n0ZO+sn0k/PGs5dEoaJOqVVWlJQOAx0qvg0FSYBGeDDX3rz+R9QCqx9ymBJrjWZvwBA8tlI2JrFWx\\nxErRhhyhbnSBa4eWzkbFxXsuRs3icgBZL5YyKgLbuu5WVQCIiXRMz8+9H5ev+joA5kcEAJs5jRK3\\noJLG7do3Fdlowg0JemBaNiApErPZjCnNQp0W3qJkxohouns+2kJ7CJ1CyCGU4n0m59ShWHxfQjbj\\n0R30sNrW9/SWmhbQCrx4yv/h0SDFlL6Ty/QAX6xehG2vjulxzccJKc9HP36fTNz4qSU4V6Sj+pYQ\\nG2moy4XVw77gEXS8S0p34M0GeuQjCQtsgmqXEWkh7OY0XJbuuZkyigFG4UFUqXcyJFhFu1SF+CzG\\nNNKq2FQmK+qhUqFtxpTWn71mvk+Fow2FZvYJj4Hj+0ttU3AoSArcvKKDWPw0BWPM84UI4P4cWDsE\\n1dDNek3npGFuY585MgXVqY5Uz2GkB6JlaThqT1kiV59QTnPzfjJH5AOVJbhFiJM1qMDa0VNYMHU7\\n2/BFQ3b2/E2kD8k3heEWinf+NBuHw5CAz8i5JdPFX5IR/SmuWGAE30O91mlIwCzYBLK4xmFIwClx\\nPgqIVC7lpiCKjGxbLoMNu5J8zq37rgIANC8b2u+y+DjC3qZoeYm/cutrWNfJVDjrX52g/X7prcsB\\nAM8emMKL1ni1tG1ajlmFLDhAS1ON/2fvOwPjKK+1n5nZXlRW3Sq2XOTeMRhMMRhMh0AMBAgtIUAg\\nkEAC+ZKPXC65uckHhJALxJTQAqEGDJhmY8A2YBvbuFsusmVJVu/S9jYz34/zvrO72pW0kgt27jx/\\nLM9Oeeedt57znOeoopb1JiHlnWyK/c7T7PF0ReEsVROjUhgjRzEBUoCFXtFwCe/sAMaNoNRaD49+\\nR1u33NtCZdzrKUDlplEAANcONr53xdqkP48K++3vn0J1hCh9F714HwAgWBAFWHq0LJdPG7e1dZki\\naCkRzRZa54qigmCQxn0LOyaA1msAedMjTGwui4UtyYqIDi+tR2wsH3xZRje21BKrRGI5wn878xNU\\n+qkdPpBPqTFFiDj5z78AAHz+y0ewYBMJh2JVNvrCPZ3Ng9uODxFWbxm9d+3dv0orzczxOTMcZ3AZ\\nfbBlU2/3lBB9Kqs6ioYlpGi7+0T6zWIN4/WGOfT7SNpYZIl+bQDPYrKwPkWFXWSUWkgaLSyk0sIq\\nAhkOUIMdzka1hnXqbeFCdLKJ5PMuWvx1Bu04K58CBH6ds2/I9z7eMNjGlMNVSYPVRyfSJkgSFW3j\\n6YuYYGWb+nYfo8dJMqIyDaQGRjl3hyxwsEG9upc2oBZDVLs2zM4PCEZYJDqWa/ZpEzvflC5vmIAq\\nI9Hz1kyjeNGK1bfCup4GTGs7W3iogMSEDSVGqdx++suYtjHZ4GDdRJvtP5RdiIdOoHv+fteFAICO\\npkyMm0wx46IltmGKsBWcSYzFvEkC1UmTLxM2pr7NV3qKKmqb1p4QLTLcYau2MT8vj2JNt/tKsb6b\\n+s6uTytg8ScVV8OpI2gzuj6UgydXUf7O3XPoPk+VfYjsOylJ/OQn2Tt/x/u9QAmL7+NJ4muMWnw3\\n37TMmbcH2z6c+J2U72gjokp4vuN0AMBPRlGs+h97ztNUikc4qA191DAZJ+TXAwD29uajnS0OeLyl\\nUZK1BQVfjBhFWVugKHFxy9xIxDegUUXUDCcGUdF+5201rEgosVFowhwHreTnWA7i0dazAQCfbqYx\\nIXOXAUwYF0sKXTBlskVahD7wuBn1aHmXFpGOuUTDK3H2YAtGAQB8TALZ3nDsb1QjDmD8+TQ/bGWL\\nMutuC2zz6L38a4jWZ+6QEJzE5r9denDZsYSiNdQ+I3bA0pm8KY2HdxmFR629lMb3yZnNMLN+YmTB\\nj0HVADMbzGTwDagRLdFM7Ty+GY1HFtspRdi1QUWFzCRtLQKbV6MZeIuFjJzAdtMLrDK2hmic2BQs\\nxOL9NI6IS1gcQQqBd1WKxWr+O0CM0jd86fELtGNn3bwJALDuuVlYuvgMAEDgNPpuamns5cVGWm8I\\nSkxBmC07ACE+L2nMwC1EGQ03DLCpXtvcGnyCtvnlm1IxAk2lP8p++/2cpZpRI96ozo81vlYOm5Nv\\nTJNTGtja6R1O/+ktePtJUp8onEf6FV0fF8NbRuO/z2pCJgvn8gaobVnNYThYzLAvSGO+z2uBzU7H\\nzCz+3iDJCEdZ/QgqJKZe7w5SeaOyCIeFz1G0Ltu0fyTmTyQl9k0tRH+9MaMNFVtJe2Oek8bL79m9\\n8J1Ii5rX3ZNSbkw5jBZ6/8V3/R23P/6zfs/7LsANFKEsqhtrm6DltE8X/+Y2QB06dOjQoUOHDh06\\ndOjQcbxA96AeBRhEBf4u8kC5eshC2DXBoOXoyl5FVpfgBWHkWcl7+cxBsmzdM+pTBFWiF7BLkSOG\\n4FOYaIkYRYS5fTjFV4GieVONYMJBQp9cmn0gqwrWMOW4p5tJkXhTQykyPyZPhK+YLFZZ+2Q8O4+s\\nP+deurPf+4VcKsxdx76lfzB0T6A6yd5DlR92CjB5+nezhV4kS3L7bOAHZ68BAHRF7GgJkEfUZSXL\\nWEiOdb3OALWNQNioqRg7zWR9sxoiMDCTLqfHBkNGzSsZUSR0hMgru2o/5WWzb7GiayJ528dtp7xl\\nuVtUIIV1miN6snugatCw593x+E0uqSPceCF5H//efjq6QvQOo51E9WrwZ6HeS5a/HAuxAaKKpNF5\\nsy1+jS7pZdTdXItX81RxSqVJiuLqfKK998jUFpdVToZjBxPRGqS8nx4gCu+UqY0QA3TPVatJffbU\\n7umYeTFR0ip/thgAMPmJ746uHpwUwIyRlNdu64Ey7Ti3XpuYENkUZxN+cQtRyX/4xl1Ht5BHGavb\\nx+GtijcBACsC5O53ZfjR1Uttwe2jsXN2ST0+3Uffeu6oWvgj1LZ8TKDEF06kjgOAAYImfsJS9sEo\\nylob5LRfoyRrf0dkSWMv8PNG2HoxP4MUq79np/H7/ra5+Pyr6QCAEuaJ8hWoCOWxXLVuSeuOOQ4a\\nEyZktqKmkL57yE/v1SxmQGRWcoGNGRFnjDZ3rEKZ5cEJWZQnd89eGpcUM9BTSd6r2RdRfW2uL4Fl\\nG41f4UwVpt7jf844nhEskGFpZWI7ViZU04/31F/AqOetUWTvZX3iYaIrrv25BZcU7wAATVRNgoru\\nKGM2sDktpBi13zMNfljECOIhQkGQyYFzD6okKsiRqJ/VRsgT/3jVmfAFaE74TKD5abE9iPZ6moNy\\nN0hg2kewttMfvhF9M7r/e3lP+8O652YBADY98BQmPkPzXdZXNN50zYnAVUBrAX87U1AUAAsRpBBh\\nkUUGX0yMSYgCtib2O6f1C4CRMZsEtlYN5grgn9fcwzyuCuBlWmoOIsCg0NCDBVb6EKNX/Ag3zKD5\\n/69F3wIAZmMODKcxEcAghUw461Mnh1/0Mwod/PKpZwEA1RVeXPQC0X1DkhVdUc5dZu9lkBEMs/zO\\nEg3QeS6PRuHlXlN/yKj9DkALKel0M1qvJayxc7buYOlDTAr2/A9lIcEIpmb9/k9RyOjJf1x2HQBg\\n/H8+igsqKKxlqydZaO7LXz2K0/9M2UA4K+729T+DZyzdx1ZPZZRCSZcecWy7j9ZRb3iy8btNpCBv\\n25AsspUudA+qDh06dOjQoUOHDh06dOg4JqB7UI8CTnfuQdEpFKP0wh6KBYhaActEOhbsISuf7f0M\\nNF5NhPy5ebUAgOebTsOCXLI2FxtZXi4xAImZfFqFiBbPwY+JgqDFq9qYcIACoJiJGvG41Igqw6/S\\n7ysDeXhoP6X7aK2i+MWyZTJEmUxeZg/zxHplZOwnz8GV639Cv6V45+PNe8rzRlrbEsvNPaccA3lP\\n46GYVazroDjJZ8e9BpZdB01MRWBrsAwdUTJHelhgxh5PAcrt5IEMsPOyjH4t9sLDgjTcEQtyzcyC\\n7M3B/s7chGcb/Coce1j6nLFk2Vvw63V4c/mpAIDcrcnvYFjHEuqcknh87KUUF7H//XHaMQtL4fPG\\nPxbQtfkq9qoU0LNXIQu6YIsiO5vKeF4heSmNghx7F9mCMjO967reMQCA6t5cLU6Qe1BnZDRgtIGs\\npRNtFC/y0I70BQHCTNxgVdd4rU0KcygmRN2YiS0fUO7UNT8hb/DjNz+Du567Ne37Hw7kn0XxMSfk\\nHMT69lEAAGN9sseP47mv5uO3lw2QKO7fCN6wSRNjyxLJJN+5Iw/OidQmSjLpWzb7MzCrjEzwW1uK\\nUZRJXoDOHjLp5zl8GjshwmK5neYQ/BGqZxfz8gdloxbzzGNQw7KUIGrG2+goB7XfG11rMMNMbdKr\\nUBvd78uDuYPacBfp4yFQLGt5Yo2tEkxUdLRsIdZF1UkhTYQElWxsCDohTqP35nHiwVwFoWyWCu3g\\n0RlneU5bxRTLa+srI4u9tUmCxOLNwize6OEZ7+DhaoqtCo5n8W1REdZqqm8uTlZxbg2qd9E3+nf3\\nnt509XK8+Pq533UxBoSlVcL5l5PHavViEp8zd/dz8gBToe/jQuy5htxuY2xMn0BQkClS3+Jzn1GQ\\ntfjvtkgGHKwh+WXqTxYxgmyWANQuxlxCr3dS2VbsprhT1R9bymbspb/DshMOFsJob070zOoAJq+7\\nFuXzawEAu/cQK8610YieU8krp5RQnUm2KEIulsqPic4ZvAIMPvo76lS1lDZcOEkMAyFybsaEk6SY\\nSJIQt6yyNyaW676Hb9H+NpYBS9cSm/CBB2ICXEUZNL7XuOgh5h5Ry28vm5nHPhR7yEn/h5hk6//f\\nU3jgmtcBAA8/9gN4RrOY0QzmOneENM+ot5sx28xmiGwucDBRvkhUgreXYuYdmQGE2dyS7aSx+qT8\\nOqyspzWTKY+OidudCF9DMfiBXcQkydofc9lbmPjYzffejcB1tDfobnMmpTqsiYha6k2+FgMA536a\\n1074wXYAwLdvTMPRxC23fKCJUv3pqdtTJCcaOvQN6lGAXQjDxjaMXPkv4lQRYXm05FG0icjaLyDy\\nagEAYO011PC8QTMaPKSsMZolfLVKEeSyJOpmMYpyM00Ak83U0yWosLCRYn+EmveeUJGWeDnTQB1m\\ntKkdHpar6qWDp6Bzaz4AII+NA6FsCVKI0TOyqeCeYgnusUzdsur4zzF50w2UyzPXQAPeQ/+4Ugv+\\nByjHKwAYvcmzMadA8fxk8ShYK6DjAA36G28rxQ+cNMsXsR4329wCr1ILAGiSaWCqyLejOepl5yVL\\nYC71UZff4h8FL5vADaKMyC76xvlMqKl3rKAtfrlowbK/nQob3yXHrSzCTKilv8Vh/Ma0P1jbBKCt\\nL13KgAiobb3gIrGYiFOBynIRO6ti53un0sLjjAlV2jEXUx+uC7pw1T9+NeDz+aI+ZyHxjJo6MzVF\\nX+tuKsOkGc3YBlrM8M1LE2Kqx7f8nQQGKu9cjDuvex8A8MQrlw743ENFoJDq4oHRHwAA3uo6Ec1b\\nabNi8ve/WLc2SLi+7vQhPavqhqdQ8Y+fDrOk3x18IZOmvjmVbe6imTLc+5hRby6NfRYpgr0d+dp1\\nrR7qP1yJsWZXEVQDo5XJVLcdmREtifyYDFo4dIZEHHTTvbMtAfavH2V26r+5Ri9amFq2g3GoZpjN\\nCKn0nO/vXQQA2FdZDFcHU3nsZYuJThFdM+lvKRx7x6iVb3i7sM9AIkkR9q7mThGGddTvfSV0Xta4\\nLvRUs9UfjvymThUB/wi2sfaICOUx4aleluexSIbK8+R20yLJJMhobKIyXj1zAwDgg9op8BbTNQY3\\nnVezvPx/DY3rqoxtOOvm3QCA6577xXdcmv7BDTNuSukOR1Pq82xtqWmVAJC1L4LVG8n4N+r0rwAA\\nlzt3op7xQjtl6p89sg0ZbFOaJ7k1wb8Hqi/VymI3UGeZnkXhD2Yhip1dRPe32Om3rE9MMPVSuwyw\\nxbtnlPCdi94dy7B8moH77qPN2m+ClwEAwlvzkfU1zZk33vUxAGCjeyRePvPLpOsf6SLDsl824wG2\\naHyok9YLb9bMAhhV1m6isfGuipVY56ZrVi+jXOr2xsTNKsdld6wEALz80ZnasSnfXAuAHCK7d9Pa\\nSmLCgXxzCgD+PGpD3jIBRWtZ7mjWNubedxu++H+PAwBeu7oGDW+QE0FgRkv/iCwE86ldX3kyjVuK\\nKsDGBuyGAM0NV+Wux5bAKABkbH+7it7Hy9w1nx+swBkllKP6i6WU3zVQGkHmazQmFvmpPI1XRLQ2\\nmrmW6l0xCnB3Uf+oueA5TN+ZGHYkCmrCxrQv1nxKonyFFzSh++MUSmBpwl/E5qpQzDBh6kk+74c3\\nU277Z5+9GM/i4mE/LxX+t8wNOnTo0KFDhw4dOnTo0KHjGIeeB/Uo4JcXL9UC/J/+J6XmsDer6DiB\\nWX3YP6UrVI2W4C0i65N6RSc6mpinh38qWYhLhKpAspLFp8BFnqGILGnpFRYWET14pq0WE0zkbVjh\\nI3rVP+tO1Lym1lYBObvizPoAesYYIZvJchIoYJLzeRFYMslzEOwii49jf7LYwPGART9chVVtZPFr\\n+oYsTSZ3omWKCwFkVfUvMDQYgjkici8j+iEXfKmLSvjxTgqK72GCL2gzw9zJBCNq6HnBbBFBpopf\\neBp5yFdOfh9veMiSd/+HVyHv28HL0DldgMHLcjVWx97FvYi4OdJaamN/uO0l3P/0jcN5ze8E/iIF\\ntub+7WycXnTG5ZuxegmJQ5ScTeItzR4n5PWJEu7+URGsv+CvAIDnumfh1dcXpFWOwETyAhgamGfb\\nJ2iW4VBOzBvG85ZGsmVcfRJR6a7NXg8A+N5b92hCSOliKHlQTeNpfAjv7UsaOnYhjPbh+xVbAQBX\\nZm0EAFy27E7Ya2k89Y2kse/aU9bBzSjwlT1FOFBPYQqCl6vloUwAACAASURBVImOVUtaeiVRS5mg\\nwlfAxC8yGGW2UYHRR3XaMZ2LxaiQixi9UIl9n/+cS5726zM68IcOGlNfq6LUbsoup5Z6iouEtM8U\\nES2m+5hqLbCQ01YbWzNmdMK9lTq7tZXnC4QmLMJIEwjO8iPKKI2Z2/qngh8uBHNj1L1QrgxrIw/3\\noGNRGxBxJIZIXHvrcjz74UIAwJYfUqqHaV/cjpIC8kS3u8lDIG6O5XM+XuAfTR/EWju0eS+Uo6D6\\nB08DAE7ccgUAwLs27/AW7jBCnUFzQ8ELlkHOHBh1F1ObuOeM5RjJ1iA7AyT+kmnwozZIISqKKqCe\\neai4CGBYkTAjgzynPGfxew3T0FJPnihDD/WDvM2q5kHtGcvEIo0xxprJrSYJ6bTNSu/7Ged0aylD\\nur8qTPu9v2tYO9KfG35wJ4nuLamfgfB7tCa88+53AABn2w6ghDG6Hmwnr/gUawO+70gWVlzGWIGL\\nG8/EKBauVOujMa0naMXD494GAGwKjgIA/OXTC5G1m1GFGSMtcJIPzlUDE0R7JtK73XkOMeCW/HZh\\n0jmySYAU7r8OTn9wHVyMPv6P5ygcweBXYW9NVMrqmmjQ1t6+sSzkLTOImybS/H22oxKzzTQOc7bP\\nBSt+DlMrtU2hggbKcIsNJZ/TjabcTzTcTzZPxegxrQCAA/upbV06ZzNWvnIiABIdunw/pcerXkJr\\n1elX7cRfSj4BACx49N5+3889OYJFs2lxuPQjyrlt6UxcXzjOawEQYwvVv1fe7/0OJ3Y+do+eB/VY\\nQU0oDz0R4qsHJtBC1uS2aDk2O6bTv93jBOTupE2ig8VMdC/LBabS3xm7aUANZ8XubfAD5m467jdR\\np444Ywpebxup0f8zV0XUwToeG7SzdhhQsi+2KQ26qDlEzZy6CnhGslx9LL+l1GsA2AbV2HV8Np8z\\nrqQ8YOMtzXhlG1ElrXEbUyY0CIMPUCfTJK3UsAVVREXrPKoL1zaqyO7Tg8j/uP+4SEunAu9zFJs5\\n63SidplcQcgN9L0UJ8sT6hYhs3xifGK1dCmwULgdgvuI1nQybtPunWp50z1R0BaMHOYyD8K1ybTh\\ncIjaDs9AeP/TN2LGIlJn3vr2lH7f6ViBYk1vEi63tuMLdm7DZ2X9nmerNeKMb6h+d897Bc9PpqBc\\nS+XAORpVRhuNFFF/knuMsLTRR+RGBwAwsnZ25yXLMclCC68WRncTj7DqHt+YRjI5RXNgZe9jAaKo\\nYr+PWvkqEwVzXnriZqzeQRO45KN32NpTgvvKaNIuNI3HqXlEr3r9Y+rfETuQu4PG0Z4xLJ5KFeAe\\nQ22Cx1O5R4qwtdGxkIvlpLXJsFbRIt3WrKLrDPpQp1lrAQCVYQlmtovk8Uu2eiDI4kQVE6O1+oFo\\nL/U3W4sKxcC0A1hOQO/GXLjm0mKFGw6hArZmdh5rH+IBK1BEz3PPpoNiu2nI8aiKMbbp5ZtN2RpT\\nMuW0XkEWoJhUrS5UMbHdSCFAZXm5fcV0nle2aGEDf2ynb1WU34MnxxOl8AfP35NWGY/FvJSSjW90\\nhrZBjR8HNsz8F/0xE5j0t+9OOXwgjMyhiac7twzWjtjmLpxJ3989it4nd9vA8Z0jP6A28dfQ+Vpb\\nj7C+VVDWhbb9tIGRgqL2u4X2NpDNwMYJtGg2NTGjvQEo3sTUsH3JNGMe+6hKKuwz6UZdjVmwM6OP\\nOMCmJRUiG7Ox5g5SJz3XeBEAoP6L/ueQIwVujDS6h9bP+4O/UNCMZK8foL3CE1Nex124g/5+7PsA\\ngIfP8aA4m2KGnEZav060JgaOvsfyu9+98moAgC3Hj511jF7KxjzVqOLHq+4EQGEBAJC9O/YuPFQq\\n4jYhkMfynLan/laqRMef2XUanTdCgqMpcaAYaHMKAK+vmod7F1J4TegUWueV5ndg/5ejAAD5m+h+\\nrt2xNubawzbRLgc+epXoxx/hTMz5D9oIfnyAVHpz1xngOY8GVa4Ab22WUH8xrQ/aPyJjuW1qL+rb\\nyShjbqN+9deibzEdNGbuDvtRxUJXwkwnpcDswYkrqR7jzXthZnc2MbvB9IqDeKRwCwBg/Yn0Tt2f\\nFSFeKJvnL/bG32ce1YVpDd3dPT2EjG3p634AoMgTVv2ecVSPzn0Syi+jeXnnY+ndRqf46tChQ4cO\\nHTp06NChQ4eOYwLHpwvsOEOd34XOILnlDGae007VaGX2Jm4hV+FlubkcTWTmyK4KQ2Jerp7xZDW0\\nN4rwljHBEJug0XB5vkRnvRKzXpSRDcLSLsCyh6lXNnATSiKl19LFcjlNJg9D0KUims1uyqxAmZUG\\nRNvJshIu4FTRQ7PohZllsC+99nDDO5reJcNAdIb/u+F7sLUm22gY6wMAMDqPLLAtNia60gvN861V\\nckf61qXCL/nz4iks3CORTCPuGS8iWErf6TenkGjBi7+/ZMBnOA7GlOwyLyWFi/q2bLgqk0WSUmHj\\ncvKcHmniNqffSuGBzxsIjgPpeQE39oyCmmbz0miH84Dqs14EAEyuHNjLYWyllzEyr1HUoSKczdo1\\no+2WnlOHG4vXAgB+4OzGX7ooP9oLVScDAAwDCCMdDkSymee0+9j3nMZjawOxD7gIkrvFibGV1Ifz\\ntlEd14/LQmE5ddybsjbh6j0/BEAUYQAItlq17+9soHHAW2SApZ36CffShXJV+Jk30FHLhTMEhFzU\\nN6ectw+rRn1Ev4s0JjzUOQ5v182k+3xN9BaTW0HnVDa+N1IZcyoVRA/QsZ6xgqaOyi3e3jIV3R4a\\nFzgdW9mUCSZ8rZVRNgMmB3Uas5nGck9QQiibpnOjF+iTThKyFZACicf8IxRYmZefsSwRHhlz4zuZ\\n1VwxxtTABUlBYDQT/Dto0srPPbBGFkawzxsTrOLK3XlWH65+hjyn4Tja+0AQZMBwElVUtA8d/0iB\\ns1dSibcAgNAwfMrrb1tJWfOPBduHfK3CxksxzfFSMQH3XPkeAOCWzCZNYfPiZ+9L6/pSOymiVE8t\\ng6WDZgNBBkJZfP4YmifStV1AD1O0th2kthrYm4+yPbyxpq5wRyOda+7uX5QJALrHUxkLNtD9mucZ\\n0F3H2oxNRsTO7hMevkt++cQPAQDLRppxz4s/HvZ90kWwUIaYw8ICKoemi+ovEGBrjX2jrhn03lw4\\nUbUouPJymo8+XExMkxs8P4Ypn3n8GJPEusKJJhe551jCAfxzvoT/fpfmr3iRSJf2VwZ4L8m9ksKb\\nlk/8ELMfJKE+S1es3197J4nsnO0goaWb/nQ3umbQtzb4DfCOonKfOJsyCux7eTxEpgZs+4zGYHtL\\n7Jt2TaTvHO/5TIXilQpeW0khd+f9x2YAwNI1s1HCPKe95QbtnbOrWN2xhNn+QgEiy4lqdsvY+Hvy\\nQHNGW8SmwryW1hGZNVSO+osi+NUp9K4vHaA5v7PTgcz1VFM90+i8Kd9cq60IX+meq5XX2k7vPM7a\\nihH51Dc9iFHO+TzCl+M1747BwXtYJoUiqtvnpmTDuSV5/Ir3mv6gghiGbxtI+CljdVbS+amgmIDM\\ns4gyLKsCbi3/KuH3x/YtQs27Y9K6F4fuQdWhQ4cOHTp06NChQ4cOHccEdA/qUUAwakT1buLjc++b\\nFFZgCJDFsHMyWf6y9kUhyslWyYw6sggamDBG0AVIQR5PIWgWX252CWWIMPq5V5KOUWwQXeNsGLi8\\nOZVkoq1fYNRMGJKb/hCjKswsJjKceXg8Pul6Tr3jqB4c+4bu24tagTtP+wwAcI/rAADgg9dOHfS6\\nUpZeYvdM8uIUrkr2PuVvSL6ua7KgiaSkC3+BCFtrohU5a68CsLxuL34W85z689n3iACW7sRrjD4V\\nXdPpmPohtTtlWhiGYHJ55DC9DxdqsbYKMHqGVOy0oYoxr4RnXBTOfUdv+Nm2ugLRciZklCKe1F9O\\nbctWE2tbk5+4HVt/9gQAIDqDLJGGrclxvEDMc8ohRAWYvInnHPx8JHA9Waw3hcJ4Yu2ChGcGJwWQ\\n46KLfF8ffvGU78pz6ppOwihd24b3TgrLietheedMnRJkI1m0O2eSl8/dbsQXvgoAwG1ZjZjmohip\\nNibGg4IAwpl0PY/99I8QEBhBVmsLE7SIZMkw9rK/WUyPFBQQyaKGW27vhEMkCzQXBNnYMxI9bhZP\\nXsL7ooiitdSmgi6q964JkhZHxvOGxsPeJMBrIKaN5UDMdhzpoyPkOChAbqf38k5iHk+Took/9fWe\\nAsRWCM7wJxwT6q1aObRchUEJkpd5ldn9fGNk0h4AIGerkJgwjeZpTEjLRf82+2NCXO98RTkrIam4\\n4HKyzq9cMju5kCngL41C2kcWfIXFralmBcZu5i0+ArlT+/OccihmPo4O7dnqDA++bKXcLQ05NA6U\\npEgn1h/S9Zz+4ofkNZ1nrcZkE7X5NzzZuH8pea/SldXa0UmaB6ZuQfO+e04KIHMNtX/O3EoXwRwB\\ndpayJsqG4Ow9A8evdkw3wtzN0jX1l48VAAQgey/dy8dyuZl6BVjaqe9lVykAhu855aJWPHb4PFsI\\n6UVRx3IDm3rSr6/s08gTtWbaEu3Y6DB5bC370mNsGeLGmK7pMv5x7rMAgJvfonbg3Cniw28T05Vl\\nfG3RRN3iwfMghxibq/G1chjS9KB3vEWCWLOROs3Zq09QbuC9P6YUi1f+7DM8/yGlphOjwDWnU185\\n00lewPswXmP08RRe3rgY1HjPaf0l1JlLl9Jg1XKyhMJ1ye3g4xVzAABqdhSqyAY29nqu3VH0jmKi\\ndLV075xdUbTNomPbbv4bZj71cwCkM8CRVZ3owS39UMCbH55PdXIxleusKXuwsocYaz8/jYSq3vzD\\nefBTVeCPBdvxyUESpuKf5eHN52LrGU8BAOYtS9EK2Xnb7luM6Q8TW2LZPQ8DAN7LnYb2afR+GdtN\\nGhPTsSY2ybxfR2lqxDQ9p8EcFjtcGobIYloXXr8ON2a0AQCe7ilO6z6poG9QjwKyzX6oVuoUioGJ\\n0nTGGm/ettjME7HTgBrK5BsQFbZ2Opf/G8yJTTGRDEXbrEaZwE7EIWiiRpkk4oucHSqsnf1PBqFM\\nCUYfV7mka0s/j0Ax0r1bT2C5pUpV5G2h8zyjjpwDXmEtMz4n6XA2phyTz6nCE+toQ/BiFSm27bh7\\nMaY+NjB1cwqbUT/F0ASD0tmcekZS/fkq6Pvn5ruBV10DXaLB1kbfwDdCRG82XynSP1GHigIa09E5\\nncpR+Hk/XZ0JGMyZRw1l55KJaT0/HQTyWB2U0erGuskGzzj6oJfP+RYr9s3t79LDDoNXgMKoeVE7\\nlStcHIZ1P0328RvTeMx4ksQIjHNIJCJdk4MUhPY9IixX24RTavC1mzZRj7ScA1sfFVDLLiu8Biam\\nVsb6/MHU320o6r3fNYa7MQWAYKcVop3GLTXKRKe6BLhH0hjIN0lSr4TX6klYYpe/AasaaCPAhcEU\\nIxDMYgIsPWycCwP2OpZgfS/LS5cjIDKNaMHCAUapU4BZ00nc4drsbyCr9Ox3OonW1RFwoCiH2kcD\\nL+M4L1oyExV5c3ZF0XwKje+GPnRbgEQuFEtsg8uRymDENwxqgO5naTZom81wRpzqLqOZi6U+yG1W\\n9t58cRfLE8nzJpt6DdpiVGabiIy9EsJMSD6QExN1it9kB1lf5xuPX5cvwy9BC+qcMWTR9K/J1Tam\\nTNQ+YXObCmJIhLmLL+yZqrJNRKSMiUOFqE8LkzzATlpkRTIUmDuO3NxkYWEh6gCPOP/yb5DDcjn/\\nNndvijNiG9NdTIBn6vprIH+b3qKwL8JZKhQrtZ0yI4Wl8M0px1dXPQIAWPBMehRfP8tjaelSNWVr\\n+/smGILUV7xF9D16RxuReWDgjSYAZNTJCDuo0rL2sbWMS4Kli+4XtYkw+OkdAnmxfikF01DQV4EI\\nu7e9mVH4y4wIVlC5BNWIrH2Dl7E/hCJUnlnfXgUAWDzlNRSfRdTVxi9KB7x2KBtTvpn9Yuqb7Ehs\\njhB6hrb+MfXGUW+3SfiRn8T/9l//lHZ86vprAAA7TnoNADD6sx/BwgThwpNot7V//kvaBt22NDet\\nZ4+/fg+2fURrCUvnwHOVv5Dqp6qXwgIeL/4Sv76B6Lxtsg8nv/1LAMAfrtoR9250TTuxUJG9O/W9\\n+caUwzqhB1iXrByeu4XKOOJnNdhkHAUAKFoe+258Y+rPozZva5c1oaYZz/4cZjZ+OhsYFThuk996\\nIl1TsCG2MS79gMq1Yc80GFje3lNs9M5L/AvBx/9HusZgYi4J5827ZQ0Ayjc61XMXAGAgPf5zdsdy\\nkp73F+rznplBIEz3DrlUmNzJg1hPDdHi09VX58rAlk4zPGPoHR8p3IJ3vFS6ixw0/j2V+vIBoVN8\\ndejQoUOHDh06dOjQoUPHMQHdg3oUUOtxQTAyj1cxE85oTX2u0Scn/BsPLuVvbVcQzCHbQrQoDFiY\\nh8FN3iBZFGHwMZoWMz7Ge2xTwdwro2cMWehMbpZ6IajC5KFyFK2LeXnbZpIHwdwz4C0HRWAGuQGk\\nWrLYlc1tQO3GEgBAwQyqoMYmFxy7Dz3X3/aGYjiq6P04nW0w7ykALHJWAgD+aj+LHTl8NEnuGTFa\\n6ft17XdBupSsliZGJbW2qimpuaEsLrA1sHXZ3DGwMJKph3nqhcPvkeNB/R5mDc87vwEelutrycYT\\nMOcysqzteXf8YX92KkRdzCvJvKWqJQpBSY8upW7MHPoDWZXytADtfjt27Kb0BLa65KE3alM1oaRU\\nntNACfVFa4OkpUXh3uDBUHVDzH5Z8Y/UVKtDwZ2XkIDXndl12jPyZlIf5jS14TxX9IsQ3TQ+qPk0\\nBvlmBuDxUf0UlpF3bkZWOyTWhmc56jBrApXjJRulCWrcWgRbJ2OxSKzvNKvwjGR03zzqB9ZmAeEA\\neU55lwiXhHFRHonaTDNZ8DnzWq4+QF5a4YAtltaJWdV7mnOQt4ul6GLHwk5R87qlomsGiyMJeVb7\\ngnsxBSWWcsbKBGTCWQqCLC2MoUfSTM/cuq12OyCxcS/M6MpqqqEs7vHcSxtxxs4VRDWWCot7QVVB\\n87qOnU05hsU4wZueXeRJLprfjM5VRBsdzHPKwVM1xcPgF6A2mdmz6VioxYYJ82sBAAe+HAX/OKpg\\n277hzx2Hktbj0aLNaZ97ONLM/PyCj7W+lwo2MYQnOqkvLP4R5WK9+b1bYOrq30ehxqnKWTqT1yN9\\n84oOBqNXgdFL7cLNKJPekUCEhQqJIQGWNmpInDnFcxene38A6GXrGFuTCqOHh0+l5z0NZ6lYcAbl\\nXT45g1gTnKqYCAkrJlKKkldLcvDfL1+Vdjn74uJFRHe6xfU1xhi5Zz3mLW2OUtiHuYTFjLSnl8e6\\na04Ero2x+8gsvRgXKuo6KQLXevp98gZqg9ldKvjEZV1NHvjZq4c+bu99eQLMYnpzU7CAPvYJOTR2\\nmIVYmZuiBqhGNaHcEYegjUfRHLo2nGGEpc96NJglad5Z3pez3slCKqq3gXnpd6waB9HB81bTtbJN\\n0Fgn3BvuKzagYCM921NqGLAvcM9pxzQDjCzkzl9EzyhaFwXIcYpry35Cx+KufeaThVh82XMAgL2h\\nEdrxjD2Db93aPkj27McLJMmW1KEmUmho4517EkuDucuI/1r4tnb8bBvN/3OZKNtwRmJ9gzoM7LuO\\nFnvln9wMADB0DEy9aO7OgNBJn0ceYjqhePjz2WLEIWgTM9wGRNl/BDPreGERCo9HdbGFbFuMPtMf\\nsqoTB3G+kAOIfgMABr+i5abqOI3lbN019JcK5qkoL6QkXCvO/CD2A2OY/vggxYfmWn2o3j005a9U\\nMG+2a3/Hq8ZyCpy5O/aul15L6mPvv3oa/tZFamtKYPhdxTdC1DaSnSzn7cPffwV/rqbk0qVW2pS6\\ncyxo7CCKF+f1O+tSD/LmnvQGf0fjwN+cxyjv7YqpbsbHow4VPBeXYlK12EOJ0aPcQQsMGVT5Z43d\\ni0+3EG1aLGXq1PVHltBhak/8hsbt9qQwssD4IKRmas+mQ4xv66u+2rEjH8xOhUimqsXPBfPooKnU\\nB38Ho+WxyT2eBmxtiO0o0s0Nufk6SjhWvvwO7diRUGfmi+OLqs7XjrVvoUCaii20sBhsk1x1w1NJ\\nxx11olaPviKqp9mjDmKik2K0ik0UmGYXQ8iSiFJphIxJ7Ph/d1wAgOIy3WXs+7OuoxiBQAmLEy1k\\n1Nx2Cc4a+r17KlXyhVN2YqqZB+6bsICFa1w2YRsAYEPuSBxsIWq+wUS/hW0mLd8qp+2NfdODrklE\\nnLKd24rAxwUJ75qx26jFcMaDv79G9RWhje+chjerrB7dIdpYVzfkwXyAbeqFPtfyGyBZ1ZfXTd8Y\\nVqMnpt4pN5kgscVaOCOm4s7DTLLMdNPaSIzWzVWsmzqycAjTX4K6LjfQ8H5gbZJQN4KoaaHCCMz1\\nh27UTLUxDeUqUNiC2dJ66MbKMysvPeR7AMDiXafjohPZuoRtctpkH/IlmvcusftxiT1ROXj/1U9j\\n9Gc/AgA4Mui7hbdnaQqpJgNbgJcDzoOHpZgadTfiYOsSpwyTi1bJkqQgqFDZR3w1fINpZvXwqbwQ\\nVDxTsm5Il4wztQz/eQAsrMPFNqeJKGJxyleMo5yWL3fMg7WexYHHUeX/etPfAQC/eJE2OpAT26/r\\nW3YNO8w3pwDRuIeKXrLPwVknIMTEkrnyLzB4LLdWrm3Uj5YEKVb9nkVfoZ3FeM0wW1A0ltYRwR00\\nXhq9KjwTWEaGSnqHjIPJG0RLj5y0ae2LQA4928oMMAXfyto8YfKwjepcFYZCGmcVlv42/9UYfT5d\\nQ03u9th5GSxndeuJkhYyoTBjXM8YUZszJsypxU+XUqiE4+DQ1kfeUQoctXSNeyLbRO6OffNUm9OF\\n16/Dh++dPKTnZOyK3fPhp8hQ8x+zA7h4ElGyDTPZR0gzpjUeOsVXhw4dOnTo0KFDhw4dOnQcE9A9\\nqMPAuFfIyp9u5YXcZgjGoVmoeB5LQVERdpCVp3Mq+80Z1TxRRS43mlvIfKVyephVBphFm1u2xNDQ\\nLWTxisLc+yqbRfRMoGMjishL4d5VmHRtX/ink4V2aimJDo2w9WJx8TdJ56VDux0KQrPIqxLvQY2H\\nkeUbxDfEn7v4mq8Tfl/fOYr+YDQ9z0gRzrr0aUcAec19V1EU/VNT3wIALLDKKB3/BgDg5u3XAwCu\\nG7MBf2s7AwBg8qaXs3Q45UmF0Mr0xA/6A1cajTCvihQU4P+cvLI555GiqkFU0HmAGuSnwckQmCfq\\nllNWAQBefemcQyrDYDB4Ey3K8V5MDlOdWaOARx1q0jVDgTyVUbKq7NrzuTcsnCtrLiGei1P2OmBg\\nz7aMp/YiH8xK2xKdCrNeuRsAeU25B/NIUHwHuufqax9hf8U8BKm8panuUbjOg46ZdJ3aRf639kIH\\n7hlB3qC5FqrQ5qhX8zQs85vxhX8UAEDupmuy9gNhZsCVGGXeXwTNg85F7FRBQpCxTvJG0vhWaO5F\\nqYEs0B1yBLncK5VJNM4Fzkq8YDsNACm2A0BdTzY83SwnKqPRe8rtMHro3m6/JcmTLURj+US5ZyTi\\nAGQLV/5lVMiZvZhRSH3qklzyqvgVMx6ppP6jhiSNNmfqovoxxrVj7gH0F6va+/N7RzJlGHqZ8BIT\\nJ5ICMUqy0SNool/IJ56x0GzVyrirnbwcde7knKWHkj8USPTIpGIQBOtpEBJcYagSn50Pr8qvuUOE\\nbD30cIjne2nOrGvOwaHVCiF6wIHyeYkeOO49HQgHzn4BADBz4w8AQPOeAoDTTB/dowK+QqpPe8vQ\\naL3x8JQa0D2V57+lNqYaFZwykigLtR4X2jqIgiMFD8ELeggwdafvs+Hf8NF/Xn5Iz3wwr3JI5z14\\nSSVubySBwd8VUGaCnx+8NOY5ZbDmBJBK6oaz7xRJSJk1Il3cfSHlhN0XKMDStSR+Zk1ByU8XWXup\\n7V303/dCvJiEvvwhIyyfpqA0s2J7R1F7CuZJCSJE/UE2CZDCbPzLl2BrS7ym4ftRGIw0rvV6aTIe\\nObIdRonOq95JirTNpwiwN1B5U3lvBwMXUcrbrCCQy3JRM7HMrJUG1DMiUv175Uhf5zsRvztvCR57\\nehGVcXd6vKlPXz4ZQUbZNe0aPtfKscmKlZtItPBQvKC6B1WHDh06dOjQoUOHDh06dBwT0D2ohwAe\\ni8o9qv0hY6dJ82RaiE4PVRRS5pvyjiCrRSCfrDOOegUBJuDB44jkDBVZGcSJzzQH0RRkVnJuLfcI\\niLJAb85vjzhEmHuHnweMQ4izuOVZyTvpHuSaMRdV47y8nQAoR2F/KF96y7CtRfEYf0kVvBHynBhE\\nsrDtKi2DymT4uVgSAJxRRkIIi0+OeXNDKlmQ3sdpiLIcjOYGumYo3srWk5nYlE+FhVngblt/HQCg\\nwOXGuCxqDJY3yNPy0ojzkK+JHg3yHGboPhTvaX9pEtKNPXWdS97wcZntWP/WdACA0ZN8085lZHUM\\n5qhwdCamjACA9/KmAaAUFanSbxwppMqhKAWE1LF5aYKLxWTObYPM2k5HHvlILDkBYBdZtOPjSblO\\nkxQQoFSQC22MiyzI+2cLCO8hCzL3vg2G1dc+gjNevTfhWNSm4if184bxRoeOvmUBgIqTa9O61tDY\\niRwjy9WbRzGWZimqeU458iWb5tH4Z8NJWr83ddI36K2AJlBhoGELUlBA1MQ8/ix2VAqatL7VUU1x\\npW0FTjzbTR6CKzM3wa/SN5puounz66AVvy0mkajNQQpSWmcdg8ZM8oJ3+MmT1a3kIzCWqRt1W5FK\\ndquveJBsVrX0YXmzSXTi62lLUB2hMrzvob7z0v6TYFxFd7TbYu3QkJj6FAAQLqTxTbJGcd9Myr23\\nyTMSAFDvy0a7j0bhAEsz4m5wwFHHPF6GWGy2uovnlQWE0VSpPi+19eCerGQP8RCcNf5SqggpIGpe\\nrcHirnn6F6Xbguh49uI7bOk/NE3wOXWgNDP9oeJLpWEB/gAAIABJREFUYssYdlAdHw7vKZAYL3uQ\\nier8T/vpWNk4DgCw+YQ3U17HEdqQnN7MZiSPTiRLhmUIqVL64uDV9OHM1iBGOKmdNGez1u8zYPUe\\nSr1lOmiCq/rQ2UCDoWvSoUfhP9s7An/95/cOQ2mGhxj7jNrRSFsXdvY558LRlfgC/adyOxTvKQDc\\nkUVpduYcOFlLuXK4oHxAwmr99Q/XJhp7MxbRGiT87MAsPtlE7ddbLCGzhoklxnlPGy5n+U1dXsiM\\nidjD0nLNzjmIFfUk5CjmUeBmzkeWQ64/AGj5fgiSRG2+5DV6W3+uBHsti0s1xNg0qWJGU2Hh9RRD\\n/fA/Fw1Zb8JXokD0H9586VOvpPy1O96aNORr9Q3qIaB86S0ABq/ErP1R+FjOsKiFiTsoqhasHj9x\\ncxEl7wSaHLwTVYBvQLOphZoEaInhBUHVNqY896agqBodSoyyhZon9eweL340EHh+1vaZIoQymmRG\\n2GgBtqNYgbU5UTUYAIR5RJF7b9xyfBPkz481/vIPiJLi2E/d6FA3p+UXHQAA7F1aoS0edv6ccszN\\nC16O9p5khbwmf+IysTrixcKvKPelDUBdExsoJ5F1wN+ZoeUgHQidl/rxnzNo0frgJ4vQ1UibUCHC\\nBEOiWej9jAZVB9uMDqbIm4AUY2MHE2DK3TbwwMlppv6CQyNQdC0nVbnf3vVPXIrpg55v6Uy9yPF9\\nRrTA43kwCk2hXW2ei3ZB4aiEp6a8CgD4nfUyAEDD52UxgR5TjDYphmL3MW6jNlq1k/4VZ/UCbPGv\\nskXtYJTfIoMjic5r8AtY/cW04b7eYceHFZ+gYt3gVGOlswtGmcYOw0kkllbVUIAFyiUAgHtG0QZL\\nVkX84UvK+yb6Ja3/W5igj9ELRBjjsWcSGyejKtQ+XSUyzQfU0dg6ZToJP23vKsbjFUTH/8JXgTcb\\nKf9pponG4xG2XtyZ9wUAYJKZDHBBhxFRpmQUYLTfgA/gtg/BNPBHjLJ9lVDhhd1Ei6fRmSQq95Y3\\nE4pKFs9/1c+kelqXrdGhUm1KASBQyHKiss240mFGB+PmL8iiRcQKdQosEm1gt31L9S0WBuEP0+LJ\\ncVDU2nDvRCqXIAvIdzKhtzVE6ze5kST4lMog1Bf+UfTseVNJ2nLzx5MG3JgGipiydXNsXhHDQNRN\\n9DwulRQYH4J176FINA0fPNekt8MOa+2hb47i8ZebngcA/HTl9fhDB8Xe/GMnbUrumL4KwfU0f43p\\nvQmfnv4E/Z1CjKf0LFJBqv+iTDtW00HXmlxB+Aqo82SkaRDtHW2MbeBZrsWQ2wg/ow2LTdSeRBUo\\nXsWtMsOnDw8FPFdvqjk0nJ36/XaHqX1//++/OqxlEWf34iU39ZkbM9rQLSd23kc65uIXubThSEXZ\\n7lVoRPnonWRhm7Vt5Vq4wnBEkDiiNgEGf/L1k5+kcCxLp4r0srcnI5whwDuGfXcm8un6JrXAWTCH\\nhTjM6YHjX2S0lZ+ktYMEFQ0L6PeSz5PLGrGzbAVxzHF/noTpN5OQzwIrqcG7oxZs7iL1W2kb3e9d\\ncQ7y1zNRr3K2Xg7KcI+iMSdr//CNKvYNNm1e6h1N/2ae14zIJ6Tl6x4fxcgxpCLd/fGIVLcAQKF8\\nZlpuY1kdKY3ycJG+8J5AbcbxrTXpN3tDbE0YnkdrXtMapzZ3WFuGbqhas4vUtNLTnk6ETvHVoUOH\\nDh06dOjQoUOHDh3HBI5np8V3Di4mMRgERdUkq7k1uGuCSfOW5lSSWad9ulGjcwk+nqMCyC0n687Y\\nLLKgW6UIGnzkkdu3swQ8/RmXyhYUVRNZEiPJ1qSgiz67pSs6qOeUQ8vP6pYAM1m8rMwFlFHeg0gZ\\nldfXRNZZg1fEZSN3a9f3peRVRXya5/RwgeeYdCDmZTpnN3lViuxuuD8nj2WUWaxkkwqbITEh4b5I\\nDmzbYpYllaWXCUSoPsdefgA79lGuViNLLyQXB5H/MX3M1lNZ2ppdDvROpQfdcc6neKWaAsbDUaqH\\nyQUtOPBlxWF46xjiPaec0sIFAeLB26C9SYFvRHo2qh/9iLzBf9s2H9ZNibS5Sx+/bzjF/beCeSe1\\nmVtv+AgAYBRk3FF5LQAgx0Ye0OkX7sa2D8m6mSoPZjz4N1I3ZsLAPlGwINlblApHQgRpODj7bBLw\\n4XS0in/8FL/63vtDuodgMkHNJturay+Nk6EcMw5Eyetwbw+JQARb7bAfpHqRLbHcetyNE7FDG1sV\\nCxscBBWjSmhM7Q2QR6enLgtTTibRlj1NZJ2PBA14Ons+AODbtlL4v6QUKixaA5U5Klrmkicyz0Jm\\n6zvyV8JloL8fbLyIyjhKgWsdeQd4+q++4GmaguPIrW4zKBjrojIq7F2erDkLTSzlgqmXjpn68Zom\\ngA0FFkbN9VeEcIGTxKaMbMB84KNrIU+icis2OiZERIAJA/lGqFpKLoGNicZCP7o3U51YB4v3GAD+\\nMWHYqql+ttQSHaw/e30oh8rWX6oXnmfYNq+DXWBEgKVbHo4nlVOObfXpLZlS5TZN9lcMD2GXonkn\\nv/TQS1lrjXi/iBgSM8uIevnsaxdobBnzHivObyCqPWdX9CoBZIpUqnjPKUewO0awNLK82x7FAJOX\\neeLZ2oLnHwWA1pPYnGhWMe9MIp1ubydvUE+1C72M+WVi+Z7tjSpUA2tP0cOfizseqpSYmigVRdvU\\nLeKamjMBAN9spvnZ0nJ4KY8AMOtCYiz8pfQj/KiaxrAHV5dAtFM7O33sfgCAXQoPKHbFv18qtO7M\\nh8DS6Fm6hl9Wz2gZ2TtT5CP2Df+eURtLZeRW4drC6zd1PStG5gXuZO1jWSbESOK6NWoRUaBlB6Lz\\nZJOAUCaV29ZOH93SDbTOoefkntCK58tIFPNLRp99ozNGifaeT+NgzscOLU9qfKqYaAr+8UDrrlTg\\ndGMA8JTQ2KLE5R+2HTSge2//nlOO7BNb4V9Oc4KQIpYiPv1ffg4N0v5BRqTd814BAIyv+mmS51SV\\nBg+54AJWcyaw+XT3+LTT43HoG9SjATVGsY1aqMpDWbEP3D2eBvWQS9Xi/8w91In8RQq6e2mAGl28\\nBwAwzXYQlVbaJB0ckY3oAVocyWaWVzHbACOjZEgsFks2iVqnsXRRp+hPxY1vYIPZgrbR4x3P6FXh\\n20W02OZ8+ndGQaMWR/LC+ZSLa75VwYYQLSjLl9+GP52yBABw/1JSCzR6BAisg6fLrY9HyEXlvu3i\\n5cg1UId75PkrAQCbf/EEjELiYLc9HMS0u+mB19edDgDIMITwZPF6AMC5u2kR2bQscaLm9OkJ84gC\\n1dCbCUcOrQYnTqCYsEZvJvJuo9G6dWs5AOC6RZ+jOUz1807VDDhs9JLhaqLmbeyyIS8NocKuKQJc\\nOxO/UdAlwtIVG6BvfYDq9r1Wovtt21+KX528POGaRzcshMpiCzL2cgPFwIPonCu2Y+O/aNHzwguU\\nT3LfLxfjh6PmAwA2HKS6smwcmJztnUqLbceO1AvDYB6VI5IdxeTxlG+yci+1b8Gs9HvdsYjfb6B2\\nZN1tQTiTqQW6SSG5fpjrL94HB9uYHnUMIjT92WfUHiswUzv25/co7+Ofh/AYtYYW3Fb2b6l/EsIZ\\n1IY9pTTzBifKMJ9Km5HuumxIAVqYhF2xHLucvicxBXSrNaxtTL1+amOqUcWOAxQzLfbygOIIesI0\\nmXfvyIW5b25RQUDTM0SHbb2O6FhPC/PREaJxOxigTZcQjduYpqgzb5kKiYVPCBFm8Ou04fTxRHf1\\nsmSk+3tyIdtZ6EZd+iQorrAdZmPnzDEHYWaTkLaJLo7AzijAUybRwmJbXQmmTqmlv/eVQmABrlr+\\nvjo7bP3Q9wdDPNWdb07Tgbkzvffu2k/kQ8WiaJvWSAbbWKXIc9of0t2YHkkEC+m75I/p1KjU7719\\nqva7bx2NMzsRU2SPXxByRfJUm+dUkNhmaUpJE1pLaYxvaXBBYH2rdAXVYyDPAP9lFO7zh8mk7PqV\\npwIOicb9GjNRhXvzQpDDzKjBDBmWLiXtjWk4g/qEyR17KR5Pam1TYe3onyIcypbQU8GMVRU0f5sq\\nU8cnb/2YjIiHKz6YI1gow1JE/TsoU3vaE7Gjqpm+pRAScf4s2rh+20400+5NeagwzgIA5M2g9cbD\\nFW8jqNJ7/63xrH6fp4rAlDkU9lRfPXrAsr34G8qXPc1kweh/3QYAyN5F7SXV5hQADMHhGxRSUYb7\\nAzeEeObTdzNvtsPkSdwIBl0Cwhl0zMY2U5ZrWmBbTIazlpOp7djrBZTMpTWGJ2TGqx5qmx42tl6S\\nvQVWcTIAYJmb2kEwT4A1xVopd2dyextsY8odRxErlbG3Apr6sLOB7te8rQC8ZUame2H4hvqem+d+\\n3ZM8FvHNKQCMziZrRA1iSuo83717YhTFDuqr+5CYhxsA3BVRWBvp/n/sIONXODeKMKdX+1koX0EI\\n9o0Db3B5DtbKVrqPYRgSODrFV4cOHTp06NChQ4cOHTp0HBMQ1L4qEd8BzCWlasnP7/6ui3HEUPp5\\nsqusY6oJIUa/0KgmYsyq6y+na2wHjAiz8+QCskj+5ZS3kCeRSeT27dfCv5fovrammJdTtvDgeJZ3\\nrEdGOJPnt4uy56ZWEu6cRJZszxgZhlzy/EWCzOtWZ4bCFdsqyBp40didWM4CswN+uvaOGauxKIPo\\nYzvCuagPk6Xqby+SB2XH3Ys1Sx1XJzWlIaIBALIJ2HXH4qTjUx4ny/DJl23D30vXAAA+9ZOlcY2v\\nIinf2K9bZ+Cd3TOoLtrIgmZr7Mdmw4pWeG499teR5WnmWOJW7102DtY2ZpX3DdyfWhaythAWkbeW\\n6tQ3guXTqlXgHiVqf3N4i+nY3Cu2AYD2bhwNTLXRLtB589bfglCA3tvupO/naXXAnkcWyNE5pBC7\\ns6Y4be+kdySZvxx1Erb/MrHux391Pcwb+veiTrhsLwBgU20Z7FtiVrf7byURoV9/cRUAoOaSZ7Xf\\nqiLUtsYYrLilnjzeX9WQlyoSNMBooTYs7rMBE+j9w6ztxb+TPIhThns5oy6639vn/A2zzXTRqoCI\\nO567beAb9IF/LLmDbPtNWr/W6Ijth98eyL1BRwup8pceSYx5cEvK42Ie80pfQV58U6+KzpPoG7o2\\nGZC/hhQjZCe1hY7pNvRWUF1ljOkBAERkCaEg9RO1kdqls1ZA7yQWzsDUY8WwEKMHm1Vk7WaCGYym\\n5todhrWa+pScS2yWxjMcUFjbY44kiBFowniORgX+/MT2EMoGjLOo3EFWrrkja/HyyC8TzquOeHH+\\n2juo3EzQyd7YJ8cv647xQhm+YnqHshNIyOnzSUu13yrDJJyxMTgSNqbaFWSe0mWdU9EZJG9wV8CG\\njmYmLBdllMmwCGuLmPA82QooaTj85ZkejM0nz/f9ZR/i6k+pbXFvZzhTTTkvBAs4xTd1n/KPpLZQ\\nPIp51VcPnqt7IPBvGU/NH46K7/EEeRp9TDkqobyQ6rHV40CAsQ0ca6jtGS9oxwQXefduLqC2eroF\\nWFR9NgCgwkGsgjcrZ0Ptooq011PjsLapsLUNLI508Hyq6Oxy6hsWYxRNTeQZtziprYaabTB30D0d\\njKpidivormBMtJFRWHNp/gt0U18/3IJVgyE8KYCtZxC9+sSn7jnizwtWBFFUQGNdntWH+tcSvajh\\nDAG5C2kseKGC5uKnu07FZ08nCy4NB2FnnxzjHhXSJdSO5KXp513n66MTFxJlfNsbU7SQOR7WBgCd\\nk2nM4N5CZ32sXXHPpRRS0DaLzovaVDx3+TMAgD/VEkPskqJtGGOi9vrLbUS9DjQ6UgovxUMVY+Kn\\nANA72qDl23Y0xcroKaVnu+fSeKtERGRspf7kK6MxTckLw7mZ1qP9ZTbg3lRzK92PCyQBwLb7Yuuz\\n6Q8nsiXU+d0wM1dm+LP0v8Hhxs7H7tmkquoJg533bz7E6tChQ4cOHTp06NChQ4eO4wXHrQc13Ryk\\nxwJSeVD9+Qa0nsVk+j1kBRn1UWpLYusJZHW0nEzWp+tGb8B8G3ml7q/7nhavx/MXmbpF2Frou2bW\\nxp7Ng82bT6HnFa+OJFh+3GVkUeQWq7BLgZJJ14s99JutWdTyRLpnk/XSnhnQ8g7+ZuInAIArGc8d\\nAKY+djt+95NXk463yeQlW/B4cp7EgbDj7mTvaX+Y+ljMghTKYZbVIcRLKYzub+5hnjarAM84+k4G\\nDwu235J+H2ojrSRYyjwwr6D4uYiT56oFIplUj/kb2AWD3Lr1VBUFX9P1Xd8jC3G2049LSkg+fVHm\\nZgBAhdGOi6rOp2u85OUJRgwQvs5Kq9x9vaZ9Me3R5Lgmnovx/pteBwDsDJSgxETxEQ+tPR81FzwH\\nAFoKonghrTMrydO+cvL7SfdWDIB/IrU9NSBp3m0Di9u2tse+72AeVHkmBRKG3GTF5GXieKiT8gi+\\n/No5A9+oD8LZKi5csBEA8NeibwFQ7rxbMilv29jXb4O549Dtg0fbg3q00Z8HVZlBMe85j1Jc6vYP\\nJqJkBY0tQmV18gXjy3FgEbV1eTSxClxZXviCLD50A8sh2qxqqRk85dQXi9bECcJ4ojD4Wf/fR99S\\n9fBgVECdTF7+zmkZ8BfSfXjal/gUB1nVYXRXJDbOcGYszlByUfu+d+anWptJBc6euOiRRKGyVB4/\\nz0k0cG+eT315IIGVeEz78+0IncRYCt0WWFpoUOTpkaI2Fda25DG1b5qZBLDT773+bdyY0dbvabKq\\nYNzb1P+5lxYCIFsS83wDQGAE1Z3oCmuxju+yd3246Ty8Vr4y4d5zty5C13aKUUsnBU5f/Lt7UHlb\\nFKIC1GxqvKoKFK6ggd3cHfMMjX+QvFvPlKxDX/D8y/W+LOzfVMau5esOwMTm1oy65PWP8osOfDn1\\nXQDAXU1zAAD3F6zCz+oozdSmjTQ+20a5YTGy9ZQQ09/ItlCbN4kyxmeQl1diad3i43f/HRHKUZC1\\nl+p5+f1/xlmP0TornuXlpuEK+374lHZs9oNHbk3N16BbfrtYexbXTpFCsTXWxCtIb2XD5nFw1NK8\\nvuWeJwEAB6N+3HAXeaB9BSy2tFWGP5f+vuvefwEAfv/RIoz4cmARUK6t8rs/vQQAWNo9E/MzSdzz\\nz/sWAgA86/NgYsvWzNr0UyH1jKVxkouhKsbYuHXpuSQc+FXLGLR30XrMvJvG48DYEDK20UWemUGg\\nhwZzKchibJuFBC8pkOwpBciTyvvehtcpDaDjvBa4WXpDMe5VPGNZHOz+1LQX/rzRS24FAKy4+FFc\\n/tjQxDHjU+Gk60H97iP/hwi+MY3//+HYpEbzwzC0UUPgE89g+QYP6XlmAUYrDfoTxlDQtvejkpTn\\nFnxLq4wmM7nkK/OLMZrREERBhZnlRw0ZWU8oCQEtyasDvsgqXh1bKcVTfH3FbKPkZFSDjCgEkedW\\nZeW2ApnV1JgDBVRf1jw3LimlDRHv1FWlO7TAcwD4r7+ToumVcZtLrk7nLaee4qhJrzmOfvdWHLjs\\nmQHPOXX75UnHhrIxBQBfmazlv+JiE+YeFfZmvjIZeHNQ9FNaKC8ZuwIn/5KoonzjWTCrAzvH0cAk\\nsM8RzYug8DOqg46ZLKfpIJtfvjkFANd7RLnyFzjw9wlnAACei84HAJwwYz8qq6h95a6jZxgtsXyL\\ng2HKN/T9ds59VTs2+l0arLLLulNewzeHZ1hpE/HbdZdDZQtHGGLvxTema4IK5lno95WT+1d7FaOp\\nBZe4cNZQsPkUyiN4Z0NqsYlf55BAzXOZRFdLl4Zu6hZgFhMns/iNxpQTarBvGa0OeLnNXcMTmvnf\\nAsFKE7gaCCCYR2NLe5DGuZKFddhXSMIitoZZKHlyc+LFe2sQHjEFAGAxxb5LhIkRsRR88OfHhOHA\\nQhkylm6DOpG+lbA7tvmNb22ChdpjOIvKpZiA7H1cGI8L3ymQmDHG3O4H2AaVL2BMvYDM2n/YyoyW\\nRq4VnBolBnr/U6/fhFXvzAZA6pqpVKK5emO6G1MOX6kCy1Z6jjVFbj2jd+jtNphPlTzQ5hQAJEHE\\nsu89CgC47GlmyFQTN6YAbZJPPYEWllOdjbjXxb8TVW7fzSkAfDPjbUzYmJ5gUCiXhcocBqPS8QIT\\no7iHCqOaHpqpyYQQUwSNpxXufYD61km5FDKz/qHYOo2rku6tHgHBTPVYvpBEByv3lsAfZWEtdbH7\\n+e6gHcG5BfvxdA+Jli3bR8rON+SswfYvyEBVup76cuuNEi4bswkAUGCka6uD+QgxC/NeTwFWN1M+\\nxkD46FJ7vyuoEtB9Mg0Ei3ZfC+9oGnuyd8TacAbrJpOfoH5QcvZBbRPJ14tFV9ei+fVRCfeO2oRY\\nHu8hqC/zjWk8xl5RBQB4e8xnAICIKmOpjwR+Lj93E8awMVASaLz8Y8u52rX2VnonT7EBobNofPtr\\n1QIAwI3nrMKnX54+YHm4qNGeEClNZxn8+LyH2tljE98EADzjmo9vG5l4Zm2aCyYAWfupbZ73X6sB\\nAFvdJRrdfVEmGa1n2A9isUhrtZZcGqt+MvtrvLmN3sG5xZKQjxQA3JPTUNdE6k1rU5MLmMSyb2yP\\nGUhFJnx25nUbsPKVE/u9p7mArK3xm9PQKVQ+81pnwrnWhfSuF5WQ8UpWRSx5cX5aZdfKNaSzdejQ\\noUOHDh06dOjQoUOHjiOE486Dyr2lfT2ph4qac5/v91lHAoagClkm+8BrY94DAFx//8Xo+cPIfq9R\\nmdfptMy9MDGXXlQRNXqiyUmWEaMxCkvP0LxJ7dNMmpiHwvKSSc1GzU0gRrkAU1xeKkalnJnXgPtz\\niZJhZOX6x96TIK3PSHpOPOWW02dNjqGV1V4roZlR24oMyZ7i+GccCiZMqUf32uT8cOli6xbyukxe\\nNg4ZSHTH724pgNHDJMeZx9rYZgSv8KHQhvvC1qrAxsRDesbRv5u/GQcry5nIst9ANifSPAaCuIZd\\nFEsTpnmxpz16uyb+wlNwqaf2YEw2mdiNAv0Y7/W88aZlSc/4r9qLsWzCRwnHOEUlHQzHA/lQJ6VA\\n4fnQ+sO5C8gjt3LJ7AHP+/G19F73uA4MeN5745Zj8jJqp7KTJ0c+7objowohw6H9a20h6l5VI1E0\\nl53xBComECNj1rdXobODvitvl3kfV2P9wv8BAFy15xoAQH2bCzJLJWPlRukzuxEKM0G47WQR3vvs\\nJIx8jTytatFUWL8gtkjLjyj9Q9QG5O6gsZenwsjeG0I4k+6T0UgMFzGqQPTRICt09QIsDQAfdwFo\\nVFkxStbtW1fchJpLY+Jh/eHJ4vUYU0DvbGsSNQqtFCewYTOmZ3nvC1VMFFtKBwPSewEgJzTICTFU\\nGOm7Xn/NCgBEt+d0XmsT1Xe4OIIDvcQw6isqNRD2/IQ8OtyDBAARJpwWyWCU4ZAIS9v/Pls+Dx8Q\\nwiJEFs5imdyDQJjabbzHkyOYSw3voqrz8WEFhftsaKY5dOSSWA5y1xwK7zFnB6Hu5Y0lNke6t5Go\\n4ls7TsXXP6SkVH+pIVrvT5f/HIWtiRNXJGzAS7tOAgAUZpNH557RK3AgRClctvcUY3ouCQJ9tpbo\\njsdP8rLhwd4gIshCGOqUXNQwQaDRGT8CAGSvidUATzm3fOKHwMTE+8x+8Ke45HbyAi5dTN6+YK4K\\nx8GBn3/7L4ia/eg/ic1m7VBRsfoGAIBzVcwTyT2nPJzm1zn78H1HfEJleodL9p0HANizphyFSMxZ\\n4myMwvo2taMQSzdz/3/uwfOXEo279P2B+y8PmXs/OhNRpu72UD0JJ+1ZWw7nFBLBu/yPtE4YbWrD\\nn3/1w4ErgOHNAzRPPDHtdewIEsvnxzuvAwB0dzsAhecto2/wUdNk7Vp/oQocoPfi/s6MSiNwYVqP\\n1hBlpJnPFvwVZ3+aHE7J56CHCtfhRCR7UF9mqfJ4btTpa2Lj5QklxJDbgUka+9Q/O4D3Jr8EAHi1\\nl5i85eaB2TKpcNyuiI7E5vFoxrMagiosldRqlpxA1MslY1fgzHsp9i74AlEOVAmayl18sly3wvL3\\nhc0wOtjiqJcGHKXHDimfzu2cQguwEV+nXpz48xlP3gJIjLLBYwcNfkGLm+ILFGeDrNGCLWxDMNsZ\\nm6kucZLS7DPNZ6H/9NIUR8o3kqaeoW8sFj4xNP77UHDKFRT3dn3uGtxe8DMAtOlLB1y5zuRRUfAN\\nP5p8res9G7w0VmkUwJztA29KuZqv/aw2CK8MrMDG4zq4op1iFhCxs7jk/Sx/Xa6oxaqli02hsKZy\\nG4++uaGFr7Owewa1x7M+SI4xfunF8/AS+5vHty6b8BEmPkttwuhJugQR1qCMh5AgPB6hHAVvvUMT\\n7tuRM7TjXIm35vxYPCrPl1tePg22mv4pYhc5d7C/Bmr9ieCKpUcaVTeQUe9oqvAeTqhe9uFlGZEx\\ntPA01VJb/P3YC1HjJmVPWRGhsEWK44IWAEDV5NF4nCVhV9muVVUBUw5TuR5NC6b/O34lnqyaDwC4\\n9RqimU+11OM65ScAAMGgIGMULTiKrqgFANStGAXFyBR/2brZX2jSVGzNPSx3XCACdJKqpprpBI+A\\n4IY6Q5BypQKAuZMd86XfNhwH6TnBXDVlYvUDdVRnmJTe/T7yUwEz9g8h1ypjeYn/n73vDrCqvLZf\\n5/Y6d3plmKHXoYqCCmIBUZTYa2yxYomJqc9fEl/yXvISjaao2KJRYyV2sYsiShEEpJcZpsD0Pndu\\nb+f3x/rOuXfm3ikUFchZ/zCce/r5yv72XnvtSGoV34gYgwz7eO5fNE3Bn/K+7vecD3dyoFTo9s9i\\nnrowVeDM9GLxsM/U/99ax299bfbnAIDjzcl9NixH0RVLLsKt9wlnbBffvW90EGg+1pczyVBqzUbs\\nMqKm+AAfHCHe2br4N2i8jtvMq/iePfcOwYh5TGsp/jC+mDB4ORc6DBxj7dYgwp09qYEAUPBFfAF6\\negfnjxcX/xUAcMmqm2F+jf2+fja/1fcnrsYJgkYEAAAgAElEQVRzW3sa1v+z62xMyGb/r6jMR0VY\\n0DjLBXV5cPILRy0C2TLSFD9ppRHDAjcBAJadTUfd1auSlYQT80833BMPAikL084xbAfDJ9ehfV9y\\nSppyzPTfLsaSv54PALAmJEMkLkwVKOlDC4exysLU9ZfhhclPqb+v9lN9uO4F1ph3oqcqLwC4Swww\\niNxahfY79olbYZ2gLHT7p+be9vMfAgBW/e0x3NPCBeKSYqYFjN19KwKraW+9ZyOVPcsyeCNkRCa1\\nY27bfIWqgG3cTds/s15GF9nqOG0ux8FPK0chXCZouNtMwCmCS98Qr2t6QQU1MV4b+ZG6zXMcvZF7\\nT/unuk2h+SpOtxFGB5y7ksfC3T/gd9sSSl2s9H/evBgAcHWKwODqCn4fY5aMUD77rXOjFb8bQc2T\\nh4csBwCcsP7aA6bs/ue5BTVo0KBBgwYNGjRo0KBBwxGJozaCerQiahL14rwxZO2gV+OeNYyabp+y\\nAY2dQtn1XHoko0E9LCJCmvUSPZa/f+ViTJ7LxPL9+7LVenQQgkaFK6PQh+hZyuBuqJtthCy+dt56\\n/tZ4vA4xoSpm3xf3fketoj6rHrCL2qoGv/B8+2NqBDVi5bPsC2YBoADMOBM9VXq/rl/xo8NFw03E\\n4RCbOfXS9fh7IdVXGyI+9Zz60+jFcpdnqLVSIyfSOydvS4NJOLmuuIEeLcXbD0AVSOoNx37hTd4/\\nuHtz1IlIbEL0dPE9ryDfQFGIv9dSyKe6PVOtR6sXkYpwbhiWfUrkk8/kLZZhbYorCAMDU/mueexH\\n2PbDnkIHcy7bgJUvpaC+tgwu6qCo9N5z83PIO5HtqP2DwvjvF+4AAKzeOIb3WDWIAouDgBIhAOJt\\nXu+XYKvgexr/yK1YfBkpx3dkkCVQdc4TPeiACtLmUCFSoSOOWnEtyuc+DSBei/fdrsnIFi/4xRdT\\nizJ9kzhckVMlEnu4zzsQglPoQTd2BWHeRzXowpWMmn7tG493b70XADBvza0ITST14+tJr3Gb8Vyc\\nlUZ2h0eoEu0rz0NM0OzTx/N8RikCl5Vj79+28xsFvSbkfRj3Ord/j99wZxXbqD0GNJzENmlrUGqk\\nxlkAuohIwbAZYG/iddwTslValWcsx3d9uxEmUQfbxEAr/HkxnL6D1MbEuqUKFG//ha4NsM4jhSrD\\nEEHDOkaLlP4tRaGma4RlUedV0qMjStGL/20mFe7+go2qKM2jj3wv6Xp9QWFiKOJHtvpk33fELsPg\\n7Tk2v/rxLFTNIp3zgpyNuMyZLLj251Wk9nlmrgAA5J5Wp0bBWz7lN/D7zGpt8Ac7SrCkSKGvJEcL\\nlMjwXRsuQbiR81WibJTCGlKeybbnPy96CkCt/WvskmAWNoYnx4KMDLb/lqmch3I2haETdPjf3PYc\\nAODBOy+Nf2ulKcQAT6EQLepiNN+zJQv2FLXDG2eJOrhDQ9CZ2Eave+hHAIDbrnsfoBYTTraTmvm7\\nmkWwWNmPuvz8vu42Oz7fzzCpwa2HTbAKIoMntxzVoKCfoKvbJcB8YKqfXTFG5M5a/AXee4Tjg7Oa\\nH3PaqfvxTjqZDeYDTCcD4pHWu5smqWrKy2SOZeE9aVi0+ycAgFhBALoGweQQx7ZPjcK1Q9iUsiKw\\nFY+4+3KVGruAc2PPyGnXMANcVcl5TYpYEgD8axPZF88ZGJGfP2UbPt/H9An/3zne1PY6vrewFADs\\nP4/jbKGe/0Y3pkMew3eqsMBMHhnpu3jsZwE26vwNUbRf4xWPZ4KvnG04sdlGZD7jgx3xlEDHVxzF\\nJn9F+8Q9MaS+s0RGjSLKp6jLJ+KqvyZH1csu2YHnSlfw3CmEl5SarQBgaeN42z0liI4g3/2WkEiP\\n+TJdrcU9WGgRVA0aNGjQoEGDBg0aNGjQcETgqK2DejQhb11MFR7y5dEnYG2JqUIWXSNE/tLULpw0\\nhEkDs4T+95m2SrzSTc/SM39l0ra3QEJ0PL2Y4Q4LpKjwkoeUOkk65GxOrjPgz6bXKWznfp1jZBi8\\nIh+jIAyjEFmKtNIjIkUlyCIq66ihF8RVFVV5/91DeL7EXIXvV88FAGx+PZ7oJOvjZVq+DYSdMozd\\nBxdFzV+wH2+OeRUAMOHt25G/ku/HXSo8ddUxNJ7KhykdxojFtMz9uL+AIjqtorbrJ/5C/HbrOTzm\\nleQcmwNB4xy+bynMZyoZ1wjv8/TkDflBBTJM9MrZDQzJrG0qRUszfWcmG8MBYb+RNUMBZIvcobap\\nMfW7JuKs768GALz33ImDur+sBXVqWZjEmqXCyYdANtuQEs05WHhK4qIlSq3TxHal5PIlCj8NVAf1\\nQHHl5cvx5CenAgAsQojKPy6Ap056GgAw1yrKKLx/A6oWMIdVybGR17sO783gEOugDvMCVQcfTrh6\\nIXN0nn3n1IO/hwGQWAe140J6mG0tEZja2ObbJrGdB3IktfxVW5lezeV0zIqXaWmpFUlnQozCaAsh\\nGmEjHVXIvrwgbztm20g7GWPkt5yx5kYEG+gNtu/Xq0wGzxB+/6gpnr9v7Fby82WEbSIaKiJE6av3\\nwzuZ/Vbvj6F+Dl3ZgRL2W5MtDN0OUhnMDLTCWySrUVV/gYh8duow/TSK0vmEmNIboz7AqgDv69nW\\nk7D2RXr8lfcQzGL5GQCwzuU7sZtCuLSI5Q42eShkU2zpwHO7WG/S8vngxq2oGfCM5jgjiTHGXqtT\\n+6OiaaAbQKMpkBNTxfiUuoypclSX+/V4t5NCN++/wmhHKENG2jgm7gZWZwPHkVXy4nSKH04yWbBC\\nlFK4fjWFWiw7+i+3ExORBl0Q6lwtS/GSa8d6HVT/cDZqc60J6Xv40E2zY9CncbvcRDth1gm7cH42\\n5z9F3OaSytOx+VMm1xWsTo5Y1SwSQowePQpW89z6QHKEr+HaICYXUdzoV0PIZjn/i8W4YDzbxFdt\\nbLfj0psQFknP29pZ57GxKgvOQlHnelu6+g2jpWRImAb4/kc9JMDakpA7fCa/jfkDjpmdswOQhe14\\n1RRqLDy/7Xi4VlowGHSX8l9nderf26fzu1ed80Sf5zgpoRygxcD9O/0WeP3sfOZVThgCfAYlShlK\\nA8JlHMyOGyrKFS0dp9YobZrBdmDqlJBRzm11F3HwiQX1QJgdN2ujHm2zuF0RUaq9IILsT3htRVcj\\npgdCLqFpUC/aajB1NFq59u4fPII9Yd7j/U1ktn20cxx0vXLZo44odA7eQ877/E3WS2iey22XT12H\\nPR6yDTZVM2Lt2BBvt+t+xnzi4++7U90WFKmq5o54/VIl8rn550t6REG7p7IvODf1/c3DdmDHbT3P\\nMxBOunIj/la4CgBwYQUVnXZ+OQw2wcgcbB1UbYH6LaB4eVgtyh5KELZVJm6F6uUrjCK9lNyuqbkc\\nlE90VSDfyG23f3w1AEBnD0NvYAeZWFSPkLAEyj8vBcABwy9Ekixt4vtK8dp4zjo2/upz9bAWioVu\\nWI9IgDckdfJfU4cuXuxPTMamTkAfUJLReQ9P/vUBWERv/uX+cwEAW9/oJQX3DcGfKyOWIwQXth88\\nFSuQI+jBI92Q1nAhETUDrr09jVFHbUwVQjIIrYiOs72qoTumiFTPCa4GvF/Dd+BpsSP/U/4e/T6N\\nKP1zWXDdSG7v7r00WnNXGpJqigVdEsxd/ffRphP5+5TJdG50hy1o89Kg7t5JCqQkA45q7t91Em88\\n5jXCWZ5Mv06bR2EJ90f5/V43Ed4isYgW47atQYch5/CCZ+VS/OCJpxaqdVdT0UuCGbJawP1w4XAv\\nUAO5sZSKnvaTafS3d3HBZ95qg6+Yk6Nt/zeXSXFIC9SjAIkL1NCJdNT58o1qgXXXXrZlb5FZVewN\\nZOrgHsGGOLSsAQDQ0u1AsIKD79xTtgAAzLoItrSz791cSuXXfEMn7q2muENNG2d6aZsTJkHXdVVG\\nYKul4eEZxsVkIEOnCp0pDhNnfUQdJ82tXExL4Sg6J3BssTWG0TaR45VnqDB6in2wrBVKxcKmj1p7\\nKvH2hmKMBAoiGDeWxLOq1iwYvuTiMlEhWN23hOOlLd0Pfz2vp8vkNtMOqyqsdiDwCepWzKA4NHVJ\\nSr6+4sig+4IiVPaPuf/E4vV08Hwwi0bSgud+piqf67P5gCcP34svl5UBAPxDw6oypiJAFshL6LeK\\n0vghOk6P9QWqQnF27JPhqGeDbJ5uRCiD7754EvuWP2xEWRb/nuCg3fJMxUy4q+gQyqEPhP1E+MPO\\nvJgU7Ne3TcHQl5KdpN3F/G5SDPCfxQZZnEE7aHRaMxoD7Mv73GzUpxXsQW2A11PsoS0NhfC30ZjX\\n+fXQ53PSmVbMfrLl3bEH81oOCbKw+aSDE9Q+IJy0aDM2PjUpaXsgk31j2S334l0vbZQnK+iMLkxz\\nJ9U8PViYzqPTL80UH4Q+GLcsaT8lzWB3mOOhO2bBGx1MGfryH1Pj+01iu1t+zv245oekorZ8n4Nj\\nsMkGoxCjy5jGudj0WCZyf0qb6PnhVJT+2O/ETzdR8OfGcauwtpNpI433jVCv0zqJ7cdZI8bvrij2\\nLxJt/q14p/fmsd2ec9tKbO3iPFK+jErEZ1++Gqc6WZc5Xcfn+13Nudj3finvV6SOXbFgJd6q5rgV\\nXkNbbfTZ5djbTvq8p9uCe09gwORXz1M12NQVF0TKEnT7XLsH+9/gs/gKeG4l3SQRUXPPOeHHt7wC\\nAPjLoxep2xQbTen/O29aoooybVvNWsLWRkm9TlQ45Z2VelUt+OyL18Ad4aJ37Qv8hvrT2nBRKR1L\\nvy57Z1AL1GN8iNWgQYMGDRo0aNCgQYMGDUcLtAjqt4Di5WGEnPS2NE8X8tijPNBtpJc7rYYeiM5R\\nOlXC/ZTRFNm5NHsdig30HL7rocS1S+/HFlGjpDNsRaaJHv0t7RS32NeQCVONoCkIylTMJEPvF/L5\\nguqlCwPukbx2zBmBoVVEUEUUTKGWAfF6ggZ/nELpqKP7Wb+4CVOy6JX8vJ6S06GV/ZdBOVRMPp/C\\nOVuaCyCvyhhg777hz+PDGobypQTdZjh203UUsQLp5fw9KCge5i5ZpWQHBOVu0cwNWLaLXjDbRrqQ\\nukdEcO1JrJkVg4R/l9OLFAryHdvsAaQLMZb6rXm8GSnubVYQNUvQB3tSXAJZEmyNyRSTtkkisjuq\\nG/5OQdMWEQ3nJrNKuY2eRPpbwG+CfVMyzUnxoKXNakZzCz3Vji3JFBAlgiDFkDIyOvECehDbRbL8\\nwrxtqtgQANxcOwsAI74AYDeE8OXSyUnXORQc7gjqkYZjPYI66u9ViLkZvmy5jNGA7mGAq4K/K4wD\\nS0dUjYzVXCBDZ2HfVIRTdDoZwzLJmzWJAWzDrmFw5tADHYuJcVkXQ0D00VCHaJfVBrUkTM6mSBLL\\nweiOIJxGr3vQxU5mCMiwV/HcsllQ6svssLSz39rr/GiawRBj1wTej6FLrwq8KXRYKda/qIWCUBoQ\\nGM3xxLLHkhQFDbmA0tOrxfPz3extz4KnhSEt19a+SycNBFkfLwGlRIjMbYOohZoKypQjXnEwU4ZB\\nlH05fv42AMDqVeOhD4iItRgGdUEJRaeRkVLbno5ImO881swPZ2nSIZjNnc2tB+6XVwSDlHsBjv0I\\naiBfUMq7dGp/8xZICJb1bIhDsjtR3855wmxmWx6R2YbNlSxDMqaEjJx2vw3XDGPkdMnOOQAAf4MD\\neh9fZNGnyVTgjtFGlWoZmsNGneeK1yArsnMu00kxbGocol4bADZXDoGplhNAuCSIi8tIQ3bq2U9e\\nePnbF6r7NnHG+evx7qcMUjn2ST2EgAAg77IanJpDkamXHpwPAHjx7j9j4Ys/BQCkVcX3VcrWKQzA\\nROrwoO/nljUqXV8R97kjowZveDlQPFVPsaT/LXkDF770Y/UefHm89rUXU4Dy6V0zkfMc7ZbGE9jP\\n0yoBz3yOt5F9HIyyNktqRO/Zux8AADzWOgdWPQfXRa6N+MH6a/lcHXH7Jm8o54mYMHrdXgvMJrZN\\n1z/jaQ9hu6g1P1KnMu1MHmHLX9eNHAdtyqp62sJyVIJOMB/lLrZLY44f+i18fvsslqO5qGQTnt1D\\ngabwnjS1DKOvUBxrjiFtV08mintCGMY2blMEkfz5yeXGesM9STBn6jlw6wMS/CV8P2nbuS2QI8Mx\\niX0qsjxu13ePFOV8JnDcrXuzVN1madCr462jhu9p88+X4KVu2upXjl4/qAiqpuL7LUFZZBjEIjHg\\nNyLjRDbILl38o1vtjL9fmr0OAFAdykaOngNyl1gF/LtmGlrFwkEyxDA0jx1qWBob0Yk5ldhSwsWq\\nQczge1pyEI2Kenxfs0NYfLLaeAx+I3wFYjGrFwsifTyPUFmUhp1xhcmIVeREOjrxXgVzTo2bBEXt\\nIN7RgSAxx/Vg4RkRwdwpXEStq2Mui7I4BdCDX6DQbGU9EHbwb106O3d7yAZJzKJ2kaNgr9fhaXDA\\nlaIS8laL2otX8ZtL/8rGkNs5cbd2UHHTVRlfdDaezhc+d+Ju7POwU7d4OPB6q1yIWHhzsgGIlbF9\\nSGJA9TfbABPPpRN5dMFMOikAYFIuaciBqBH7Ng1Lei+KIexdkQuprG9+oZqLJQHTzqLDwCiM37Xv\\nlWGMg9d59bW5AIDT7ngFiXqZjw1Zk3zSnzBv4S1BUb7rjWtU9UUN/3mQYzHocqjymrFHKJtbrOga\\nxbacJcrOGrvCaJ3ENqPzxGBo4uTqEzQse6Yf2zeVAgDOPIlGUlFxG+rqSasyWNjfJhQ1IM9KQzhr\\nNA2Mlx3TmbsEoN5uhCsu0E0UGWD0KsrmIldpXwDeUlE43sW+6i2UkFYjnHrltcAMUgwlK69tyvEi\\nso0UYEtr/PRKfmvvxVsiTG4g3MiVbDArBpO75+opMCqAsnQqZK9qohMxsjkdunRRR1A4Kh37dGoq\\nyIAQ9xNJEMpU6remQioV32Cm3FN1vdezJf624S06aKMlEZjbepougbwY2n0cWyJhPUxbe6p3xsyp\\nF6a+4TTGjK08n9GdeqxJXJgqUJxDfR1ztMPYJWwDr4TOscImGOpDTgbnm5cnPAMAuKf+LLgDgq4u\\n6jwOtbfj7rmkc3aLmu33lH8Pf9vCfPUpQ0gFdmdYELgvrtiuwJ/D7+HPk6EfxYVHsJHzX23IgIiP\\nv++3cW602YIw6NiGd6xi+zaHJZi4fkUoX488I/v1+U5S/F/Asb1AXbajDDqhIeLPi1cLUCi+u+vy\\n8O6YdwEAPxM6IqNW3IYZs5nfvraQtsFrsx/B5f/kgtEicuOX3n0fbq24DADQurR4UPezdNNxeLuS\\nVOJrLuVi8+qaOVi1lracfT/b23XeHyMhEw7hsTRIbkqnCvvmwiHYOYTUZPM4GqOxfS4E3Wx7n1z8\\nZwBAy4Vm3Lad6QHnfnYbn/mMJ2CUOJbvDftgMHA8vvAEOk5e/OoE4Hna4+4xfE+7bnhErcW8ZDxT\\n2CQ57kTUhYFgOu+9bSHnKMdHGeiMkHKeKWznkEtKWGQKGu5KB5y13MF6Gp8zEDMitJdvwOiVVEru\\n8cdRG+GlYZ/g+GLSlIMf5QDgYtI8j9TmYCO3GbskdQGatiW1p95cy3kymMP3kLbbAOP2ns7KmAEo\\ncVFdfS8SAk9pfAE7K7jWkKYF4krxXp26tlDwZFc+wnIynb8/HOM+QA0aNGjQoEGDBg0aNGjQcLRA\\ni6B+S1DqkioeZsN0Pwqc9ERuHya8Jc1GBJroJbzra3pILh21ESVOeir8UXo2Whpc0Hfy09lGdsFu\\npJfkuLRqAMBM6178IW9Lj+tvL/HjTTdVMJdnsZ6kO2CBfyujEzFDPHKm1EsNZseVhhXRDvt+HTJ3\\n93SxP1e6Aijl32VfHf76pocD/rwYrEJ19fTLGJ2u9GRjxQ6+C8fOZA9T5sxGxHbn9tgmRYGbzvkQ\\nAPDIZ1RnW//BRGTvFhHLaygM4PkgH2mFdN92d9qg1OMLvsPzBYcDVW5GbxSKNwC0CNKDsYX7n+wq\\nR4ONnrhsoUj4p5azcct8Jv0HZAPGW+iNHm6ge3OZpwxOHT15L9VSkfOMsjV46tO5vEcR7bUZ+g+V\\nSFFgwlCKX+xoKuWxEQmxoYyq2jYwSiHJwJZX6QXd8hMKmYxwTUB9sKdq7YXP3iVqswEb7noQQZnt\\n2qGjh/3WuploDjDq9NvitwEA1uaE+l3iE+kHG+E5DPDn89v861w+10mWuE9vwS6q01W3ZkK/6dCU\\nmjX0gZgM9zQyDEydgmZVFYalg57YruH8Ht1DbbA2CfdtVhBhIxtLXiE97K9PfBqvjaXX3a6jS/qy\\nrLV4PpM0849Xk1relOHAHFHDuCHE9itJgM4katm5JPjz2DetzcmhzMyd7Bu6YASBDEb02qayDVma\\n9aqau+z1oXs4t+fmMLLjDZpgTBGBDArx4Yi9J2WqN0YdT/r83lUlKhVPofoOye/A67v4jOFORhpc\\nbT1rAfeFiD0uJqQLxSOmSqqIPhBn2iiQEyyLYBbvu2RKPfZtoLddoa2Z2yX4SoSYWI4X4d1iLhQq\\n7KEMGaZewmmK8FEiLE06BJro3U8VK9AFk7dFrTJ0Xt74wURBt33/7wCAqUvuHGDPoxPZW0SEuDuC\\nhhP5zm+dtBJbPfyGN5YzgjbG1QSriWO5ew8jmhcdvx6/rjwfALBnH1NYJLcxPv43UEwmba8OTkEL\\n1gVl1FzMa145jekxNl0IL1VOAwBEMvkRJUlG4RDOdR4RuQ1H9OgWaS3IENGgPQYInRacMH4vTrCR\\np3x/8+mH/G6OBpgrLVh2HWtDjzA6MPYfrFcdzOH7zvzcgumf96xhbXFK2GrleJuxiu/2+lU/gq0X\\nteGeunMQjPa9fPDnSioTwyTYZ2mZXnR72N+WPnSGuq+BzOwe4j2JqBD1xDvEGDTe2QDrVWxvOzrY\\ntvxGCTozd/CJweh4sxH/PZa1o3/02nUAgDGv3wpZpH8Y2oz45aLXAQBREau7ZeYK/GIhx/+dIYXK\\nbsOL+2hHnXKBqNYQtOOuQtqBOimGGzdTwPTSUqYhOMsCePrfFBZSKOqIQa24ocuifRZy2dA4hPeb\\nFeL7Xt44BnIRfzfWWxE7hXPYug3sM7XFb6GtnHa7kkVRcn4ldq1mxFvhqBn8fUdOFSgMldwTGH3t\\n2l2QtI+tQVLFnxJnC6X+afcwIZLUK/KqzBPbb6ftdNHeM7Bhd6n49bN+70uBFkHVoEGDBg0aNGjQ\\noEGDBg1HBLQI6reAqEmnRlAtnfy3ucoF03QmGp02kZz/yu4sDHfSha5EuVa3DseZTiZazU4jB33T\\n0mloHyPqcn7hxN7J9Fo+NpWu9hmTK7Fc1KNTImkPNZ6JIRZ6Yubk0JP4r+3HI1YgapqFdZD1wg2u\\n1HwzyrDk002uyLUHM/v3aUgnkavuqXHBXnvk+D+Mbh18RT2FhbZVF6aMnKrH6GJI5dTb0MUE/xfO\\nomfoE894HGejnPl8UXcUkxgRBABnSQArP+DfikiKpR1AJXMFEn33ikhSdwm3/mv/TIxJZy7np27W\\nlZs9cTf+uimeP/Pr41gfTomg7vHmY3cnI7W1DYzSPrnvZIh0FGxtYM6PeaUzdbghAdVvMZ8nMaNr\\n0pxqAMBmK88T3upSPePKM++97NEeNVEBqPsAwPT134e8mu228EzWMqvtdCHLQa+lV4RgjLPbEPmM\\n3kKllEvL/oyU5XG+CVgb2YbrIrzXcasWYedJ/wIAvDjq3wCAWZ//FLrjRS7MuvRv5b7+UxAbkouA\\nyOHsKqWHueCLbvizRa676NKe4REEMjl+STpA381jlAjLy90T8aOM6h7nfstrw54u9pOYnV71i4s3\\n4eMW5oa6g/QQR7uNMLiF+FEICIh8HVOXGGNro2p0t7UsnmPdPVz8IZq90Q1EEyLwMQtvvqObvSsa\\n1cHI7qoybfx5MoweniBYoNRESR5XMxbVodjOsbdoXhfWP8doqW4eTzQlqxZv7yODJm13ct9RStAY\\nvXHBIyXHSh8CgukimuaR4vODLmG/XmUzYgkRVX2QB9SvHIJIgci37YzfgxIRjdW7IIkatdGpZIvI\\nNQ5sv4Pj7OjPWL/UuCW5dm/YJasRG1kPNdc1mMl3bHTrEDP2FDrS+yVVOPBgYJYYMYiZMPi83aMI\\nxu54DR6lZMVdmZVYamKbeiMqIpuyHnePZC7jCRP42/81z0ZDN1klxW+yMeiCqWr69Nx2xgRqQlyf\\nQX2C+qgN0yZW834Ejesv++dj+y7mBEpB0QgzQigSUdVGIdiUvbAROVbmr05N24/f17Auefk6zt8H\\nLwt2dMDWIOOSP/wMAHDhbZ9g1w3MM5321aUAgA67XY2S3v/zxwAAmXofJpk47k1/Px5dXXQrI14v\\nvnkKAGDnM/2XERw/fw8qltJekXVCvPG9dKSSs3TUKn8lM1JW/+bveKmbY/RlgqT05JezAVETXhEB\\nHbIvAiV2eI6XjIY7Z3+Ird0Mz86ZQxt69f5hCLRwv3mnbcIzv1yUdM0XShn53PwLjjtzFt+kjrjv\\nnUmWxvSySuwKMdp473MXIeTivT/fQEYOJMAqHsdfLAbHqARLNhk28g4+zPmXfI7j7FSjOsFMTZIT\\nP/oRqs5iDfW9J3uwPcTn//Vn1wIAFj7wc1h6aVvWvD4cveUub7v5Ddzkou5AV4zXnfPnn/TYJ3Mh\\n2XdlGdxv7TwDjstlsvL7O8iKc7r82Hr8iwCYMwwAm1+e2Pu1JeGU8zf2+H+GyY+0bQemWqktUL8F\\n6EMxldpl6hZqX50GbKtnAy/KIhX0uKx9eLuCH95u5dJobmEFAkIa8Tw7B9u3f7cL+E28hperQix6\\nR7CJvtE5HaVCZWNvIE5R/bqTnfWUbFIY7LYgAnqe25wehkEo2rqFwRTzGRCuZEdytPD+06qjqiKx\\n8kwA8LioA6Uo6g45owEdtcl0ge8KBj9gqOMws/wlKqQlmzk90eqxIxVxc0cLaSWdeXxPd2fvVumq\\nidPekiIm3i/1uPBuEcUBHHXxRfLie5GPRawAACAASURBVFiD6pEqDvrtbjt2z34WANQiz6ONfdxl\\nyUr1z71htosRRhrtP8v/EG/aqXj6uYXUjJZHSxMOVs4Zg7fwwJ0ICp1X+fqJQ85/5y9Xr5FzFmee\\nlveGqL8rNF1fmx0YQ6vOE+ZG/WoXmjJoXDzmopiGsjgFAO8qkfzvGLyCYMQuxGtsMiwtB+8w+Z/t\\npPNOLqxTt2Xo+f1vuvB9PLiatDGpkAaXtf7AxAA0pIZnhAMdE4UgEjUyIBv1EKUOYWnlb/qAQa07\\nGmmxqgsqaS1pug3Dkh0Hi+w+vJ3GcfLts14GANRHo1i6j4a3LyRUzSMSzG1s7RG7DFudoIV28xrd\\nRXrIxcIIF6kQERugC4uaoDv5m6UjhrR17BMxQC3Qroz1HbUuWHppklmbJJUqpSjtJi6IFCpvOKrH\\nQ0WkRV5ZNR9P3/UXAMAlX97IY2QdXFv6NsnNHeJ86fFFv6qQG4pT7f15cpx23083jFritGB7GRcO\\nwTVZsO3r2+TQhQGdMDwhKPMxp4z72lmjMNJCwzlW5oNZiCD5x9IBW5jXiYYdnOskGQjl892mZ3Ns\\n7KpOV51NhxvH4uK0N1xVfJ8PdxbjtnQasOfYPgAAfBUyodTA95yh43f5ac5KZAkO+IfBOYO6RvCH\\n7ViYyU4+xEBbZphRBwg3seJ0/0Pp67io4WYAgMPO7x+J6eAPs20VCntqSkYtTnTSGb+8azzK13Nh\\nqojuhVzfXD3SJT94FO+5OQe//cqJ38xFBgFFfffVh09DzfX0fq2fzsXGpZXzsUnHhf4P3uE48dK5\\nD2HkimsBAIkJOm8toY3Sl83UXSKuV8zvMdbZhAk3MD1o6es8NmYE9MI5ZJ3JcTe8IhtGT8+BxHRe\\nM94vew4AYJasmGbhmDl53Q0AAL1bj8KVih2l1F+W4J7Ba1tt7JAXOLfBIj7wTCsDCBubrsHO8+hg\\nLnvgVjz4wKMAgN/cdYN6fVc1B/FR4j0kWrG6AMeQ3a25mJHON5SxJ4agk881/WYuhD/5ZArC4+hs\\nH57L8e+4rH14Yw/bxP+7dCkA4PHqOXhFpN7tOpn3pSjqAsCCVbcjL1OkgBzP89nX2eAbynv80+mc\\nt36/5Er0xr0bz8SyIfwG2zbzA/W2Z6NCvf6z50hhDszy4L0a3uP1J9PGfLVqCka8dAsAiuj1hrMq\\n9biq2L8/aeB8uu7Fyep8NVgcOSEuDRo0aNCgQYMGDRo0aNDwH40B66BKkvQUgHMANMuyPFFsywTw\\nMiiNUw3gElmWOyRJkgD8DcDZAHwArpVleWOq8ybiP6EOam/Un2xEKEPU4MwOqNvDAXoBJSGj7ixy\\n44djPgUAXO9qVPdb6qH3ZsmPLkH91fQwpjvpfj+veAuaw/SVZAjlow8bxmJsOgV8vmqk16wgzQ2T\\nnm5ub9iEVlHGpLuL3ktTtQVWskuRUZHsJo6ahPfl8cexUjzCDS+RFnLLog/wj5cWAOg7+X2wKDqL\\n4h9175Uc2okOEPMvW4vVfz4+afsJPyUPV6nfeVf+R5hgSq4nqmBtIIohBn6bV7oZIf/7xwtwzVx6\\nqNaLmmC/GroMMy0DR96Cchg/a6BXNiLrsbmN0eszCljTbFNnXPZ9xzomzmdvlhESXj5zZ7zPH0wE\\ndTBQxJIAJFF9ASBiAUIiypWqjIwSIdMll8Y7ICgRW3m6G9KGA3TfDRKB3BhiLiH0Un74C6/6BbXT\\n2pDcNo71OqhFK8Kon0OPcpYQbUmr6Eb38J4lXID4OGNricCfzXfVPZS/hx0y9lz7SI9z7wl7cVfV\\nRQCAqjZGF+yWEFoaObYam+O14XI28/tamoJoE+Vs3KJCky4cp7HGhPPb2A01MmDp4L8mdxTWekaa\\npH2N2PVbUuAKR5O6XleXCfsetp/+ap6mgq9AxhnzNgEAFmZ8jfFGRigqI3yWux66WRW6UxDIhqrg\\n0btGNoB+I6QDQTZAFagJZvJEPcrJHAACuYrIlPiWaTIMnp51UIE4LTjoNqO4mFTTthUHz+JRvqWu\\njyibQj0e9tZNsO4/9shouRvjD153Kp9v0elfYpqD8/E8G1Mz3vaMQI6BUZ5TrHGVrysrLgAA7Glg\\nZLvw2eSxseZ7EkaOYpRnUf4W3JTOiKdCn07EdftmAwA2Ng5Ra1TGYvw3HDJgYhFpiorI2eqOEfi6\\nloJOkWYrMjcL2r8wI2KjvTBsPZhivUcHrK0yvIV8Pzdd+D7ebmCtdkVUs7I1C9JGUa5QsB0mLdqJ\\n3e1kKrl3kL2UVpn6/JOuE3WJl0/EDxZ9DAB4dBWZTzp7GPfPZArMz1+5CgDgrI4fGzUJmn1CbVaP\\nMFvGza7EG6MYnb+6Zo5a3/aPZa8BAH5z33Xq+Gh2xyni1h/y+8/IYvt8q3oiJubQZj7OxW3vNE5E\\nXTvHxIIMN6r381l1XWzfGTsk2Jp70s5lnYTf308K9PtdjC5+XD8G4fd4rGtRPS4esgEAsLyVzMah\\n9g5MsbN/nCMovNtCTlSHSRH+20Ocd7pLZMhKrfpKtk/3qCgqL3pMvf73q+fyWYUx9On6CXDuFfOb\\nECgyFXhhXp3M9/v3j+8DAFz8F1K9I7b+55bzrvsMi9I4j1y6miwFWQZ0tRzMFVstZoyPi0qt2ZBL\\nxqJ5XwIAMow++ITx9c4zJ6vnD+TwWSvu/smg6qAOZoE6B4AHwLMJC9R7AbTLsvxHSZJ+CSBDluVf\\nSJJ0NoA7wAXqCQD+JsvyCQPdxLG+QC1YndDgxRxdd4oOxRPYecx6NrwsixeVXRwUFHqZUR/FtFzS\\nCu8tZO0ohVoIAI92FuFPn58NAJgxgSPJpbnrERUXeqqWjaOiIRfRLp4zYwgpMFl2H7oCbHiFDjc6\\ng0J1UtSgDOx2oehzobAXTm4nUbNYoD72ONYF2Vov/ZQL1LIRtahaNjzpmCMVfkFvrrjiUZVee3/T\\nGdjyl8lJ+zbO4b6unRzULAuaUeoijSPbxGN1Ugx/L1yfdKxy7ov+/jO1kP0/rnkIAPMcS4Vheby5\\nbzrehz4j3uiYzvuOGuEycvE7SngT9IjhsXJO5t1ekUfXZVLzNk1ufstghtTDwDvcSJ9Pw6Pzw++O\\n6q0sUINZsUEplh5tONYXqGkVQOd4PmPBF4Iyu7cLjbO5oIwyhQphBxC1iFrEtRKEf05VUDS6Zch6\\njok/voP0Kr0k41efXMi/XTTaDOU2dQJ3VXLcjhkkmDv5d9dwI3z5Ik9SJBlJMUktiK70J+f+qFr7\\nWlVCj8mwraYBjvxs7LqVzyCLXFREJbi2H/pCJ5ANhIdzTLDs5Jhu7E7ezztEhj7Am0usu3oocI/m\\ne5IikrqgjAhKfigzClOryOVNUVf0UKEoCUupUh2/ASgL1EnrLkdkfarsuqMbiQvUkIsvN3JVG6Ji\\nUWjUC8eBIaLaMHpRi3RR3mZMs1YDAIYLtfiTnv0pLCJVKKOc526YaYBrGhvf3aPfQ6mBC9wJJvaD\\nqkgAj7VyLvuypRQAMC17v3pf3ggHgLGOBlT5uWDY6xZ1LENmNFbRnjK161VqurdQUSc+NuvXKrC2\\nxucGX74EW6OoiZot8rNzYkjfmfwOlDqpT974IADgii9uVHNVFXiLAGMZ7UhPuw2Z62iv+HMEhbcl\\n9bwUOYtaDfmigkX5jiKcMoM11FuEgv/23UNYXB2APi0EqZZjWNre+HnsjezkbePZToJlPmS6aFvd\\nPoIBnYBsgkl45U60cpE42mjHS93sq881zFSViCv20EZxlhuQdRbt7ehDeer1ai/keQx1QgG9rA2+\\noHjmOgeQxt9njqIN/sKwT5OevTnqxePCbtvipuOk8b4R6u91czleFq2I4dZ7ubj/w84FcFroeW1f\\nkw8AiBlkhPJ4vbQdot53gazmiaeC7lTap7FPM/vcBwAmXLwTHtGnVFpwZbJjPGKLO4SV8TZqAawn\\nsy+/VPYULvjLz3scE5jlUQNwNVffPagF6oAWmyzLKwG099r8PQDPiL+fAXBewvZnZWItgHRJko6c\\nREQNGjRo0KBBgwYNGjRo0HDE4mDdtXmyLDeIvxsBKK6GIgD7E/arFdsa8B8Mgz8eplIEhvRBCdlC\\nYW5+Nj1IuQY33FmMeO0N8pXW+jPQFmRE885aUmavyV2FyaLA3XhLHS6aQcrpJ3UUxHnIfypMOro1\\n6t2kcORldcGYo3g86b3USTKybfQ6dQatqBfqd7H9pPqaPBKCwnNqbY3zwxRqb9jOf1ujXhxvFunz\\nIW6raMnG0SAToyhRSsIrvDYQhVfme2jyp6aEGkS0xVvIJ3RXZKFVURXdT29fzCxjmYHJ4UsWPI0F\\nNrqb9II3d+aVa/D221R8u2ED1SmpDtt35PT6fYyG2w1B1PtIU9lSNQSGRoYJwy6hLpoZQCTEe7M5\\neF3H2xa0lQkRGasQPBkZhHNbT8/o4cR3GTntjWMxevqfgFC6BIOgnXYP4TfURdIQFI5gVSERAPRs\\n352ZOjgqObUpNFN/rowMDrO478lLAAD2U5tROIwe37Z1HG9jRhkxQT9T6MMxA9AuPPUGfzwqq4ju\\n6MJxKnpGOccGc5OXBVQBSB6h3FjbABSTjo9oDLJQldVZeLDcYVIjv6kinoOFpRWwtPadcqDAXnto\\nEaSoJf63wgaRhMiRojwMQKXjGjzfLA3224qc9oZRH8UhZiIc8TB18eWGZQnZNlIM2v2CaRUxwC3U\\nsrvc3PZA4xnIF+Iu3x9K2t8Jp23HF+UjAQD+mXxj4wv2oSvEhrQ/lIXOKI9f2slo0ZrWYTCIqGxp\\nGuMkRikKh4HzWkSEzXd4CrG5mX0rINhnwYARlkZRL75RVgVaDILOHsiPwtJ4NFgph45gbhSZs5hK\\n4BZ2nmmPDal4/JZ2brv2q2sBAM40PwKZlh6/2esA1NEG0Y2VUXIlmSFb1jEiaG2J9/+IjX93TwkA\\nNTzGm83zSREJK7axFr2+Q9SXdktqjelApk29poJAlgSbItom0l9ktwnucradz7N5vttyP8EUs2Lf\\nxOWdLnMKRTisxXoP8zSGi4oaU0/Zp9aVnyMyQpZ6XPj1y1fwfsfQZp+Y3YBcMwfpuoK4AN8fhywT\\nfyVTxztjwGvVZOR5t3ICy0MUXcPYRuVM5snVzzEhXc8+FlubgfqRnFPSGLBGZLYboQ6O790jRE3X\\n7AD8MT6/tSlFypSInHpKYn3W0QaAL9eNUdM9nM3J51EU3v3DQpCMov7pJsHSm96tRpXPeP/H6G09\\nW9Y4kDGfaYY1fd5BTxyy1SaTI3zAPDNJkm6SJOkrSZK+inm9Ax+gQYMGDRo0aNCgQYMGDRqOaRys\\nS7NJkqQCWZYbBIW3WWyvA1CcsN8QsS0Jsiw/DuBxgDmoB3kfRwVknQQpJjxCGSL6mB5VPYOTzAw6\\nDzH40S1kn88VCdY1ET32hplbUSGiqp92j0enjT6IEcYWLHBtAQDMcJD/vsU3FO3C1TEzm9z7IlOH\\nysevCPA8GzuKUdtFj1bAb0KknZ4Qc7eSlyQjLKJtRivvq7vIoIpHKLlVXwUz0Ryh69/UIiLEe78Z\\nQZrDDe9wRmDyiulVu2b9dUAF353eJ8GF5CTNiODRQ0Qspaik1q21jGGOhbfKpdbouv3LK7BgNMM3\\nN+Wwntgf8zbgB9esAgA838k07d+2jMcsOwUe3u2ip21nZz72VDASqbPx+8V8BsDE+5I8BsSGitoU\\nbkZSQ24zSkroLfW+pEQxZWSw3C78TNFB5lqTGvk41uEriaj1FjUcPcjYE0H7WH43RVDDXtmFYBrz\\niAI5HJdkZwQIilIv9jACU+h1jvjFNw/q0DGe+yqRtsDWHHUM0wlXrcEXz9XyFCu5WhFARDtDACD6\\ntVI32uCV1Aiqu4SDY3aHAbpqIWonSifJZaPgLRQMC6MEfRqjQGYzx6Bgs/mQIqffNlTxOxnQC5E8\\nJc9XF4x7330lQkDsGO1/Xr/5mC+H0D6e7frPY1/Hv9soHHhiNu0NT9SM/X72R08aI1blTTloaKNt\\n8UAXS3BNK6rFj6ezDFlTmH1iTeswtdTFS/uno7FVCJSZ2GYiET2iQjCyysZ8UpfTp2orGI3szL6W\\neITMkMa+LzWZ1bxks1tGUORWBrN4zJTJldjVOOrQXsxRAnthN5wmdtj6bn5LU4jjEJBaY8S+PG4c\\nRARjpYsBcFhaJVVsMX2XhOrd/MGVwpI3+LgxY3WcrRXIVMZBwLZLRLwzxPdJl9Wanr2jpwBgaZPV\\ncTtrkxj/9UD2FbSJN7cykr7FVYSRRpaocegsSee5zNmhRlO/DgbFv8WY02vXSxxdSL+cdUmVOvcP\\ndpTgDDtr9o7LS6wS37dB9fPqC9DRyHZvG0t2gacsgtjnbNeWcl74uIXbMFHUGj7hvC3Y52Xfat7G\\npZXhc5canVQE6IIWE2xuvhT3ON6jFNAnlYDpL3oKUIjO0KvUmT9fRsROe1MWdidCOkBEUJXyN3LA\\ngKxMRpgjLYfH/h9QJAkAJEkqBbAsQSTpPgBtCSJJmbIs/1ySpIUAbkdcJOnvsiwny6D2gjW/WB5x\\n1V3wTA5AEoa+/euBKUpHCnzTfaiY+3SPbaOfXawKAtgb4oucxrkcHPNXpKaWdA1nA1IEOADA1KEo\\nLIptMhB2KkZS3OBSC6dHoNbOU7YZEoLUEdGHjO4EalZCMwhki8ZojmHuFHbC7jAHl5quTJxTTPU2\\npTaWklR/LELfS7z47pteVAe1h4Va7p9XnqUKEB1t0AeSt9nOpWHtezsfnVP5Ah6ZyxpdClX5SMGW\\nUADnv/4jAMDk6VRRmJa+H7/K5mp8zJOL+zz2WEAqsZkrL1+Ou7Op6DzhwWQF5aMJUQuw60aK0Yz4\\n5DoAwPCCVpySQ0fO87tZv83wZaqKxUc2TKKOquk8+ndDb+SifSoH8zOmbgcAPFG8CpdVnQYA2P0i\\nFSJlHVTOkjJuxwysjwoIdUVlylEW4JG4MrbiYNQH4sfLSmlTHVRelaL6K8XiYlSQ4mJEISHQFTUD\\nMSsvaMyhdWM2hxFbc+wJByXCP5HPetroPXiieFWP30Y+vxjRTEE/F+/W1GBU6zLLumS7SzbHIJnF\\n3CtSTvTmKKLC8SIJUSI5GjcypRTngdim08vQG4QTVZJhs3AsH51F5+W8zB2Iio9dHaDXssKbg6pO\\nIdS4Knvgl3AUI5YgKpx3EhVgV0x8Q91WG6GxPe/JnmIvRwvSy3s611sWcu7OeSc5rUcJnATT4/1b\\n+TdmklNyJJVxRIqJfQDI4l9EER9UROqFbJCh94q2q+xmi6mOfF0270+ni0G3m4Zpxu7kAEHUJMF9\\n9OhvHhzk+LyuzH8Kxj5xdM/pAFD+m7sGJZI0oFUtSdKLAOYCyJYkqRbAPQD+CGCpJEnXg3TiS8Tu\\n74KL0wqwzMx1B3X3GjRo0KBBgwYNGjRo0KDhPw6DiqB+01AiqEcbEms99kZi7cfECOpgodSJ6hwd\\nT+pXIqVSDNArYXgpXodIoZnFUrgdJBnQiWigUn/S1CWpFAGjW0LYKeoxVXM/o09GIDPuWQOA8Wfu\\nwd6XWb/P0s7ncpck0wZK5lej5sPSA3nkIxK9I6gA4JnMsOPxI6oBANNdNVjyBWlMR1ok1VPCRlM4\\nugWzckn3rvGRr7Pj7TEwenodIAHuE9i40tYmsxg2/b++2/y3hVc9afjFWxQtcFbp0DmNH8myny7x\\nX13+MmpDfMan3jjju7nJbwkDleu4+gqWpqoJZOGz16Z9G7d00PCPC6DyjKcAAH9opdDF6vbhWDb6\\nPQBxz3EqGmzEARh6t+XvGEVnk3KWbSF95Ya8lbjjkVsA8H5tDclzb/rFzIhp/pC1/+647g3c5BL1\\n/TbSDxxYka2OS4m1TWPm+DaFOYMByC2qSJxyK6kUJXTxmneJVEBF/ESh8AFA51j+vffSRzHxb317\\n+kNT+bFcjgD8nx+dkbrIdDbEXSf/S9027atLAQCBdVmw1/FdtE3hvwa/pArZKaJ8siRDCguaoj0S\\nVwVRXmlEUtM5VMQk6K1CWEs5T1RS/1YgSYDBwg+XmebD3aPeBQCUmRixH2ZMTUd8UNTlfvS5hUm/\\n+ccFYN1JKqI8nTTFb6q+9DeNWB8lqzNnkUHUUM7UqoJRLWqJjyMNO29Ono9HP03WUNY2GU3z+f3l\\nsA6XH0exqkk2ppT94fHLk2xTT6EOssK0EP9GHLLKyFAZF3oKyvE/UI9RylbJujhLQI20RgFdKM78\\nU2AbxbQoTydpf7kfGwcsf9cx9thl7QHoEUE92pEYAVbm8MMWQdWQDP2c3lV3ei5IB4vGUwXd99Nk\\nuq+ab1WrU40MbxG3WVskdXDVBxMWUQrtS5e8WNVF4qqLBq8obm2PKywGcmPI2ZB8j4pBohRLH+1o\\nRl37yAGf7VhYnPYFx2a+yB2bSbnbmDMG//W9NwEA97d8DwBg7vxuB5iCs5nDXF7HIunNHU7UOLho\\nWzqceUC4czmm/r5nuz3uqs346tnk2q8Khn94Pf7fCe8AAK53NR7u2+4XSz3MT/q/v12JHoROQREy\\nCXG+//vnpbj5qne+1Xs7UvHsC/MAAGdfvAbHf28rAGDdm2Xf5S31CetOCyD8CQpFeXpNvC32l58Z\\nGBLCBVM3AgA+fHHmN3aPA8EzgTS1qjOfVLftDXMx9r8NZ6HkHOEkWjZM/d3LkngI5kYRdLNl+8bz\\nPA8/dh6CN7Itr5/G+q1XZJyKLW+NA4CeDiZlUSMhvjAV/8YMUHOL5IRFkDon+OLblN8VI1JNLVF2\\nkZRjRF5umqTWVk7fxR+bo/0LH5o2cXHkhwOmk5hvFVqV1e8xRwo2/ZD1IUd/wEL27/gsWGij09L8\\nImnNZsTU/L68tTwukCWhU6TPmOpptUdsclzTQZagfkTxj94RQTSgUHzlxJ8AADqxCIjGJCAqaMG2\\nuNciP4Od5pExL6i10VMtTGdtZj3gi4s39fvsyuIUiC9Mwy4Zxq5jx6BWFqMKEfbxc5/HuU23cVtl\\nci7jd4lxj3H+Tlyo7rmW8rOzfnoLZDE3Gp1BhEWHfrruRO44uwNtATa+rLe4OHTUx+ApFA4T5VFj\\n8RrTOiX/Xier1FxJBmQlzUwZb4wJrVRSjtUh6hDaGWKhah/ehe5mtsfEtDdF+ySULsEi6rmGHUIb\\npaP/1etb1/wZALDomZ/2u5+Gw49Ui1Hl395U5cHgWM/t16BBgwYNGjRo0KBBgwYNRwm0COoBwn5G\\nE9ZMfhUA8LPGqQCAD56fdVDnSt9E71XzDHqEctcn72P0yqpyWjhd1CIriELyirp8bp0aEY0LMAD2\\neqUenfA+2SXVuxVWpNYkqLX6UkVPAaB9BikiVWdTxWz004uR1U9VIfvJQj32i5w+9znSMfkcCkNt\\nXjZuUPtbWiT88cNFAIDK6+m9HPbWTd8Z3fe0K9Zhmr0aAPC7ry/iPdbrsOsr0iZX3sYI6nGmEDon\\n0Nuevp33+vH2cagSNN7e0VUAcK0348ENFwAA8u8kHVOJHhwIgnIYZqnvmq+JULz7gWV56jaF1v7S\\n7fdjgon/KauIU0CjA3Eb/8Pw7r9nYfsd/K4XnEXX+O73jjwFS0XUSbnX7u1ZwPSBj9PbIqjyMgKX\\nKBz3bcEyl3X0toooJwCMfIF0XqVeal8IFnGMNTYZsfAEiiMt3UT2k8Ev44knSbUM/OADAMALwz7F\\n9efzmDVvT+L122TAEGfGqM1fCcgZgKhg3Sg0PYM/nvahzA2SHKfkKSJOiZB1SKLfpRJa2xEavGhV\\nVyWjjrExIdh398G7/I4RTounwhglRnqMNr68r7zDsdBGlfbGeRxP8z8yoFN0L3kUw9OZy2wwi8hp\\nMJcN1LXdAH+uoEL6DfF3K75HxK5XFaSVGuP6MBDz8x5kq2joEUk9JurlNUpLm7G4ZAUAqGNkIpZ6\\nXHjgt5fzcuK6/8Z8eC/tOoA3gyM2enrl5cvhEepeby49Wd3uGyVSQmoG19aGGIDKeZzrbq0jO+PT\\nZUdWusS4x25Vqcm/G/Wmut3YIiKknxjwBVgtIHolGQuBgBFhP39vncxvmL1ZhqOejSEkIpbBDAkR\\nEZRXWBURKxB1cqNkicbp5aJdwhIDgqJOtF/UkzbG1XeRQ4aIz2tJEgxtmiXjhlM+BUAmzUV7SauJ\\niUFqy7oRccZHCvx37Tl9/3iMQYlKHgnCSbtuXNLvfYxYegsGZ/HFoUVQNWjQoEGDBg0aNGjQoEHD\\nEYFjKoLqy6enceT0fSivZ+6d4kFOLFtjO50iAa2dDtg2kHsfsQH+YYqKENftpjY9zO09vYNrJr+K\\n4a8x98RRlbpUzGCh5P/EhLR82KqH0d/Ta60Py5i3gHkhp7oY2VveNR7BKD+dN2rCdBfFOC5N2wwA\\n+OX+c3FZLhPiX2mlJ94XMakeKIuenq96rwvBp/tO/vfl6XDtjNUA4rkOMPcvqtVX5DRURpdXmpP/\\nBlYfGcIY/rF0/1t3xXNLBhs5TcT4yTU9/l+16HGU/ZXv7NuK5vzippcBANt9Rfjv5Yw6OuqTfVC3\\nP8z7Gr5oL4xdPduwwRJBQ6RvtZmoOV7/8Fd//QEAwHnXEnTHRF068GHbow6s7mbo//4CJmG95c3A\\nh50TAADnZ25MKlnzoc8In0yPt0VihOjODZfCtrJnzlTXjCAq5ys5flaM/JRi4c4Er+razmNdh75v\\n+IaFYavq21f52kgKJ+GOj47YMjTKfUkOGcuFVz6QLWoNt0owncKIZbqV/deoi2JbHevfmaPxKKAi\\n7vNNYubFm5PLjLxwy4CRUxVKSZGQhNYg27rNpajgGdUc0BeWnAkAuOG//ownh37B60xgH7N+ZoUh\\nIMR4AoAvj+dUypVFrTLMrbyfkBDD8w2NQu9USqGI/TpNsNX2NAtiegndIzhHufZIiIlIrTp/GZPF\\n5BZvvBKDnR2tDbyvH575Lm5aQEGo/gSWvgsY3XE7YNSzFKGRSjjgbHUXYl/GOgDA7HF7AABfdI+H\\nfSgjkQ4Lx7nGOWbofHwrSr1wGbf6pQAAIABJREFUa2sMVjZlxIwSAlmiluEoUTs2z6uQnGAycJvD\\nHEJdC1ULs9KZ6+v2WhByc+w0Onk9lymAqqCwgxzxqOgKEdH6yz2Xp+SZdLezpqgtxW9HA5S6ux81\\njUVDB/Nke7TF4IHFZWY+8RM1x3NJEeeycTiyIqhAPHf29jW0T9MRQ+Z29mtvgU4VRNI/T6aJPVOn\\nih8pQoOJSwKTYN+ZPLKalx62C72HbkB5q1GjAYHsniUVg2l6dAnBtERBJJ1PnCjAtprI3FM0WS4/\\n7ktVg2Dq+svgCzDSHW6mDZ+3EegY2/d7aPIffSXHDhaKcN5A0ctvEoON4hq7Djweekyq+KbPb8D+\\n/eyEklDAKyloQ56NggEdAQ69+1cM7ZcqAACYTeWVLce/CABYFYhh8cO399glYqFR0BcGUvFVVXAl\\nwFkjkshTfJbz/ovUzF9kleP5bj7fI1WnoFt09pOKKMDx67yPcXU5qTsVVaRFmutMCGVwAJg0uRoA\\nsGPNcGRtSb6QQu1Yd8/DGLPiej7DOg4OETuQVt3zeVKp+A6EjFMa0fHZkamMl4hUKr79IZWy84EI\\naCkTgT+f79g5shPZDhohNeup7KnU1wVYx3b9jQ8AAHaEORNcvuom2DcNXEfYVxCDYSjPbf2Mg3og\\nO76gNncM7p7dI2KwlrJvjc6m82d7Q4H6e8gnCoPbwgh1sa2OGVmPhXmsp/u3984CABw3aw92t9Kg\\nCmzKFM8av07XON5Y5QWPqdtGP71Y5YHY98ffS8klrIm6c+WxvVBNpeIbyI3B0pzcJxXDrWrR40m/\\nHcxCVa27eYB95ECgCwN/XczvbRQN87Ylt0I+kQZ3gYtKolWN2bAmtPmo+NNfyptz7DQh5IqrlwMA\\npPhC1jeKRv2EYfXY10nj31tFUS59QFL7ZWLfk07uOTcAwLD3blCvlwpBscg2t0oqhTbkEtTc47qR\\n7uDC1KTns7Z+UQBrc88x2np+E76Y9BoAwBPjxDPpzTuRsaXvcdifKyWdB4BaTzAmHI/6PD9Mm7lA\\nMXck798+PQKdV1Bcu4VRWgdVEEgR1fOd4YH+qwMzFAPZMiqufKTHtu9ioaoIAkbT+A3sFamdPf48\\n7mcp6UaOk+PoWQWkaD/9yjyEx9C4iPoUOVQd9N1iYdosvrlThrVJOI7b4/OqUg89c3YjvEG2pWEZ\\npGaOcrZgoetrAEBliOPl+20T0ejlYuzu4RTVWt49AX/K+zrpvmf95Jakbd4CQccMAyMv4SJ757uj\\nUz73kYrvXUKnjU1M2o1BFz59re/8gL5UfPtDohjRsGU3AgAsdf0TF//rctL+n6+biQ/GLevx2x9a\\nx+Cpj07l/ZhlmIXj4vzz+Cyvv3EyDhauvTF0jeB3DeRFkP9Fz/Eh6JLgOYXt9o6yzwAAD6ydh/zl\\n327cqnEu+9k9c0hNvjatGWP+SSdQuCiEjDX8UIkpB4NR8V153Z8x559HqVDSQaj4Hg66b+7MBjSv\\nLRh4x0O83mBVfDWKrwYNGjRo0KBBgwYNGjRoOCJwTFF8FXR+WIDevtu2zUVo67Ut8eETqYuJULzj\\nw96ht8y5K9lblhg9TSzrosBdokNaTXIUVfECPX/VXwEAF31yK9Kq+/4kb/wfa23W/DgLH+yK01Bn\\nDCe99P3tpE++t3ki7pj5CQDgrnFvAwBqIx7MXsYoddNjLHHQl9jRrT9+HQCgl3SICel63wn0Bpu3\\nDI74s/2OJZi6/jIAQGhtZo/tCiZ8NjjPS1BI8ysUtUOF4mn9Q94WjF9yeGm4SnmFXL1d3Xbz9W/j\\nsSfPHfDY7tFhVJ37BAAgKvOZR318A6IrRTQxYd+0eRREYCSFvxTrSc2VGgYnhR91xDA6h72iQfSY\\n8BgfnKsOjNyVtlcH7GW0qQr812KKR9aUs7lHGpFewW+415GDp16l+IvSV3dXxXk7qZ4gMXI64mVG\\nAWydUkoa5xujKCgzZuXiA3qWYwGpoqcAYKvpe2xR+mVfkVSFCj9qSDPGuJoAAMtfndHn+UYsqMTO\\nNRxn9EGOIfdf+RR+/uQPBrj7ZHzlY5jvTMc2dZu0mu2sUbS36IgIwsczih9qsMO+n+9AqY0LJERO\\nFSTUhtYZ2N9qOjIQ3MVz2tri+3vGih1bTPBNZpRzb0LkdPhHfK6+IqcKHONZoiy8Ml5SxdTFcTg7\\nw41Z2WTBvPruSbyHFCwG/+t5+FMRFXh+kVUOADh1+nasq6JgUipxI2uz3EMICQCiZglplYm1aYDu\\ngE2NDJo7kr34w4c3wR1g73Rvjj9DYn1UAHDZ/fAkzcL9w9IqqTU478jgnLbtziUHHEWVdfHIeKo5\\nfSD89CzOmUv2zAEAxCoyUu4Xs4jUnLAeTV181ida+d1y9sTQWCRuwiC+75d6dM5j23FMZFvtXJuH\\niJgqOnJ1KmtFieI2dzqQmca5t6KdaTFbaovwrnU8ACDDxvN1+i2YU1QJABhh5Enm94qepoqcKogq\\nNVXmdKDB23dd08GWlDn/0s/x+suzB9zvcOKdGto/wc38XqnEuw4nqs7hXD3q2cVqrfpUmG2tBgBc\\nPa5V3RYWdVm2dRci6uTfsyZU4IVhFAf6ULCOXsfBR1ADmTqkl4s2WquHt0D0cUEfTy9wI9PIv/+y\\ngbblPSe9hT/vobCic3/fzL/uYl2CYJIMO8s3wyzKTTWfHYTNzs5nNXGCjizLhrkreWzKLCAbZl03\\nx/k/fL0A409mW975xfCU49lgkKu3Y+FCprq9884JKfcxlvHa4a2ug7rGgWLNdfcDAGb98yff2DWU\\nyOaszReiY13eAHv3PObupkl4DQNHUL8tSvERQfG1FBbLJTff1bOmm8B3ocio4M0f3ot5/yZFwNYQ\\nN/qUDp6o0hrI4Xt85bK/AAC+/2Ccshx2QB0oEtE4Tyjkipp5T3bl46GHqZCaSPfpjahJQus0/p6x\\nTYeIqBlla0k+RinUDJ2M7CxOikoOQm90jOEzHjePioTPla5Qf5u49koAgPOVZKMjFcXXVxqGrbpv\\n6ssvrlmKq9M4YL/q4YS4vGs8PnvtwHI75OluWMQAGFzTfx29YCa/kUIjG/vFVdBv6t+IOlCKr1Go\\neW6YvrTHdkWJ1vtx3wOGLAFZ85mDtW8393NUxxM4ukcIiuZ5yRTNRCwqX4DqtwamtobSgFlnsjbm\\n1/+K18bsnCgmsG0GhIStYnIPeDoAQJDsSJg7Ac/QnmOLvVZS1SK9xXIPSm5/2CRUhX/SwLbxyVPx\\nOpdRC+I1A4PJx4x58theoKai+A4Wic6ihzuLAQBL/jWwI0UxTFKNy7FpHGN0m5zqd7n2SjoL7soo\\nh17iWDH6mcU98vp6Q8mdNHZLMJzMRd1DZS8AAG785+3Q+5X9+K+/JITpY6oBADub86Bb09Pg8OfH\\nknJCvWUBOEWu57mlXPy++dzARvXWu5Jp/GUPDDxZ+6f5cMk41mp9+/mTkwyvYIYE/UwuLhRaf+e/\\ni/o954Z74pTY0SI30lk14K0AADomxiCLFJiMrzmXdU6IwdjJ9+TYn7DvJO43bEwDLi5k4th977Ot\\npO9O/o4Trt2OjW9MHPAeejuGbXPI6f9g0jO8L70NG4IchK959Ef9nktZ1J144g58vp30VHv54Hic\\ncy/iMz1U9KW6baCFcTCD3690Ri1yLDReWgLMId67PxeyUr9U1HyUrTEYOvieIw52HoNHj+xNghad\\np1Md2+6RfBZHaRf8olZlLMbvEnWb1BxWZzX39+VLCI5lW14z9yEAPZ2kI59fjJyNybZe1MR7axef\\n6pIzVuG9fVz8JjqWDxSJY8vop9kuFUr4YOAr5Zzenw3RG0p1gsGqCh8MxVfBzpuXqGPmben74xod\\nh4DpZ+7Ahg/GH/J5FNhrZVXTpOkEIE807fbxosZoUQiGZvESRNPYcdVDqkr1sLcZlMlZY4A+1P86\\nIWwTi99S/j9qBt69hPVIRxvZDr8OBnHh63cCAHK/4n5jf7gdbUH+3vIYD/bl6lSVfkiAQZRUTrRv\\nB0Px3XXDIxj7j/7n/103CFtwgP0OB7Zf/zDm7zwPAFC3uv9x/WAovgoSqbfWKZw7/V8n9+VgXgTm\\nJkOPY4a9eRPMzX07spX9Rj6/+JBsD43iq0GDBg0aNGjQoEGDBg0ajiocERHU/kSSgplykpLuN4Et\\nP1miitl0j6fH1rkjtYtNiZYmCmb0B4NXVqkPCvzZOnSNExGx7zEidt2+2bg8mypxN396LQCgeGgr\\nws/0jLpFzBI6hKPNVR5XW1PgKdSptayCaYKiWyAhY3ffUdmmk2RV0W3UePI19LoYLHp6MjftIfXK\\n2GpMElY6GJEkoKeXFQAmr7sckS9T06kSEciJYe9ljwJg1PmBZy84oOvmnsbna/5kAC8WUkdQnYJe\\n29LB6Gus1oYFcxkZuSiTxWyrw9m4Nq1ZPWbBLtJZ698pOaB7TUQgS0Tpr/gLJpniJNgfNdAR9W45\\nG4Xpa0e/6qVKVLR0dg3q3ULl8KPU791XyGva6gfX1pXomntEDBZByQ6OoWc/bc3Awk29cfvtr+Hh\\n8lN47g/6j5Ar6B4WQ8UVbB8HGkENZ8Rg7Pjm/XYRh4ziCWxHDV8OTpQgFfryYi64iOPI+6/MTPk7\\nwJrF66b+u8e2si+vQGxd+gHfhzSDVKnQbranvqKjSp9/1ZOG3/zz+32eb/VtpELNeO4uFB/H/jpO\\nUIvfXT1VPX/mcexjp+aXY5GLauc3br4KWJX8DIqoUVSMMemnNKKpnfdr3dg/rX3+5Xyf9xdsTPqt\\nr+hpUPRXc5siiMMavQpSUdc6JgiRvIiIupnlfsWPiq8gFe6NUR+oNRpXvTANBl+Kc4/nNtduns9b\\nLLM2IQBXuYiqOKWU9xVMFzURM2XIxezPJbn0zre8N0Stt63Aen4TOj/tWwQv+3QyRVqXF/bY7isU\\nSqM55GeWz31a/e2bEEw64xKq7/614Ct127ogB8/L3roDAGBLoYQOxGujhjKjmD6R3yES4wC4c+Vw\\nlfaYdXEtACpNN73E8d/kTm13dQ/ltRSRPEOhD/JeRpiytiYfo6iqdkyQ8YsFbwEAbnLx3e4Je3HN\\nL/unEipiTIExfN9TSvdj62oqQydR4g8AY84qV9XCZ4pa3N2f56bc1zeSk6y+3YjVlzHqduqSnw3q\\nOuPOpqDTKyM+VreNFyr1vev19kaqCGowMwZz+yDG/4nd2HnSv3iMHMaUx+8c1P1+G7jqQoppvvrQ\\naep8HLZLqk2YCo2n0xZ99/S/Y5mHbKo8A8f0379yMTJ3JLe9xLY6Ywa/Q66ZA9zbWydB18Xod+66\\n+DGtk9imbjv3PQDAjzKq8bPGqQCAvR5S2BseHaHuH3RJKWnBg42gKvg2IqQDQZbi6RXvXHMfAOCM\\n9+6CuTmF3nmKCOp3WfNUufb/Z++7A6Qqz/WfM73uzvaFhWUrLCxFQEBAmqAgKmpiQY0t2Es0asy9\\nuSYmN8bf1ajRGAkWjKJGVFQsoCjSq/TOUnaXpW1v0+v5/fF+35mZndnZmS24mvP8s7Onfuecr77v\\n8z7vxL00127YmrjAaf8LT+LkBmIdyB5UGTJkyJAhQ4YMGTJkyJDxo0KvF0lq6z21jSBLn2lPpIyK\\njxnBO0wdEwKeeiAU0TyntmEUKGPap43wnI67bg921pI3zrsmMr+nXysAbQSJ9PUB6NeTfaAk7WY6\\n95QRx0vp/JQd9Gm8q7Pg09L9VG66htcsQAgEc1S1RailjHtu23pwI+ATcOMUyuX3VCbFJa5zAYsb\\nyCqvS6bnt6xKoMqcz3KvbQ/Gg4XmHb3lBIlQLBqwDgCwZ+z7KN3asXVI4RU6tMrGikdZXUpy5ijt\\nXHqNPkYKyPxt4dd0mQm1KFSH5+qEvjbs34dzycL7uykUg+BbG583EADs/eh7Hr9+AdsSXvevsJDn\\nKH0IxUG9HbgApTlnAQDlnxeiLf44l8RdXiq/CLbT5EGKJhHgM1KKjURgzWOWf7sAH4sjTNqSuOe0\\n9QLy0vxl22wkb41P9InDXKGAW+xcAsxz4T0FgIsm7MO6b4b3yLVFFVDnMXV4nH1DBjAyfNvLwxfj\\nvh0kppJI7D9P93KmlWqSe6gTgSbqRw19qV7OGFCGmYcuBwCsGPwlfs68qU/WkbjJksVTQlgVVGdE\\nATg/rQoA8PHK8QAApR+SKEnNMeovSwvW4AIdWaIfLlmFFzdGsipEZpXmsYqtK7MRT830jrUm5Dnl\\n0DaEt533bnkR166nd2vcG71Opxyg+sd1BWy5sd1Ae/fmAQDmYBb2ldMYpJtghe00ed3Mx+l6So8I\\nbQP95jkPzQc1koAVH5/aEyRRsZhfRa0Aaya955pvKe0VooQJJmldaI5R7urN5DldcNcC3LH0LiqB\\nAtDVURmfnr0EAPBMQ7EkBDV0zmEAwLatA6Gv6Z52Guo55RirpQf6+SQK2vvqg/Fh+3nKISWrg8ok\\nryQsxFPZCYNs8NiIYXNPf0rhUaKpwS9mU87mJibkk3JIlOL3WosBdTBdKQBAv9EEfRRtCQ9jRnlm\\n0QmvDf8A0/XUYHnfd9W2u5Ec/nnD0FKogJflxDUl0QceklSNXenMy9vS+SDNsq+KMTCZPLEzptP4\\ntBbRx2rDseB94vWccvBUOKUIpsQR2eWETqS/ist7CgD7zXh60CAAgEHpxuTL6BnXLRsZ66wuIXcy\\n9YNV63JjHjfBSO3lC9s0aVuoF9Kno0rRPBBIZ2y4nBxiQ8xe/jBKh1Dw+a/60ZzFk+0FDobP++pH\\nCEgZSvHid+RtQ7GW2ECvnLwIADBjyGFse3cEO5rqb8MwAXdd/g0AYIiW6AVzjs7CgV15dFgazTEL\\n5p1G87v9IsqdKHqD1xQI9+RylLxB9Vwbsad91DMBzlBYxtA8s3lb9LbFYRhJYpiOXfHPO2fMprj8\\n/8r6DoP+ReXlzJ7OoHxvTrShIiZ67QLVyWi0+jaLwWgLU45YC1NH3wDSmYqa47vgx+Q0lvZyVb7x\\nwEsAgM9bSKDl832RIhpbPxwRsS0UgQ5qobuRpklCugd1m4nuZ2mlRu1MU8CZzRaZrPM0ng0m9w6F\\nM432B9SAsboDfguDI4PlP/MF8N2zpED4tZGU4+yX2HBbCVHb0sw0ofDDGOUqUcqS44d+e+SyR384\\n+P34wjQUSZOJxte6rn0xIU2z0O7CFAAc/X349NKXAQBVPpoI/Pebt0n7412UbrjvOUx5MTKP1p6T\\n1HkW96ePELE4bYMXGguw+ATlY/Nsog4ikelVINkXcz+fmEzX0wTuiamH8bWDKt3Tl5DVpt5qRE4K\\nTWbmmonq+JRTh+SDUeglDJdcvyVMkCgeJLEJccvAAJKPtP+UXhOiiqJJ1+nEojYUWiHRrpBQNi98\\nMOlOkSW/XpSoxwAwCD2zQBV8QKGB6qb+apo4fbttODQZ1EF6T1Ebvv+SbzD94BwAwKhUmpQs/2g8\\nOjMEnVnZP+x/hdKPX1+0HABwwE4Lpwnmo3gyk7f5IKX2TxmUO3LttGJJVfvW64keqG0SsPIUTT5v\\nm0kKl//aOx6K3VQ/HphGx91kbpAG8PnHJkvXtudR29A0KKFmtN+ONKp942iR4T5LR6654J+oZ2rm\\n6UojRu+4roMrAM4+Acm4M+hCUi064O7b7sIUAJzpAvT1LLk9azrRBIhCwScMpxbnAyPYAsVqgCKT\\nDIEOB70nhVeAoZpdW0VtQ+UUO6RBcnhZF6drECXqnmMwTSiTtwUHuIl30oLv1rSNuBXtixoFBtK3\\nmqoP4Lk57wIArjLacF35dOk3AIBNtgHg4T70rf93pA5VX+fFV/AYuPfWL6Ju5wqqf82mtrN87BC4\\nqmixqa9WSCrwy96mcdJ3UoczTupHR4wgeu1uZ39MvHovABL/A4BPvaPgZ0YSy5HgxNvGoj7UrYKU\\nezapku8NfiBXSrA/5UJ21+WRoGG20oZXmqmdPLeO8kqrm5RoHRDMtyqy2R43nKjtgIspNo/Jpvb/\\n5ZuTkM0WxK15UV9P3ODG4fk5NIfAg1vwi8qpAIBdX3SfGFBbdJSXmdOn416MtoN3Pqa6+swv3sKC\\nj+md92QwWkcLU44BqkhFQ1s/BQQ2jeCOnOTjIupGUolTfFQ5igadxUXpZQCAd+vIMKOuU6OlgI4z\\nnWIhAceAuhTqT7/Wl+KlsksAAAo3vdNDugCyG+g91zAB3XeueAUTdbT/xgpaPFd+XoDMswFWRuqr\\nbF/lQNVOhgmAaMKJilf+kOALZe3wZuxhyu+doR+/30pt5sFrv8D9FmqvBUvuBgDcfNVafLB0Stjx\\n11y5XnI2dYYWzAXjSl5/PGq9XnwLCcLOXfTriH3m0fXYNurDsHurWxNvbzLFV4YMGTJkyJAhQ4YM\\nGTJk9Ar0Wg9qW89pZ2EtJVOLqk6N+gMZADq2oHM4skXc8Y+uB7/bBnlgOtX+q85ex+0EGoRaTAFA\\n3xCANZ/9ZnL1fo0QJvst5bdjlDx/AunnuHS3oS64TW2na1s+NeL1y8hKnLGMrOQ1szzI+rpj6o/+\\ndNAzpx3fgHuK1wMAXlpEFNerr18fcc5iawo2j/gYAHBjElnY9nw5OOK49sAFASqueg3fOekrv1xF\\nVs4DD86XxIROOcjyt2N/AUaWknej7CvKK6i5oFGS15+9/+ao91FUkKVv66g8AMBgTW3U4zhe3jgd\\n5iNkle+MRUhrSjyZ3ywDnTNr2KcR+3jaBsXG2Lm/EvWehkLXQc7aWN7TruK/H3ovruP8ehFLfk4M\\nifO0iZBtOodQ72lPY18rMR72raN6rdCK8HqoD+I0yvlfXyLRPpejX7feX73HhPJi6m+dfqr7//XN\\nXJyZRilnHk6pjDjn0j4H8JaCyr3o3xdTuQOAWkl91GAdeafG5Vdi53HqF1JVVJH+2liI1/cTA0S7\\nPchoMFa2zxBoD6qt1IEqxhPjYPb2u/H4ECp3AAo0VRIrg3NJfEbAX0IeQV8deUhFbQB+C/WjFV9Q\\nyqe/InbqJ329CI+ZOvN4c/9xAZ+im8qw8wR5WPweBVQqGgx8GsZEqgmmeOLUVFEJKVSkI+ga6Di/\\nRkBAS9cWmOJH83AvUndS3dpWR2V4IGNNzOtpdrO3NyXoLV1sTcGT/b5kR0QyKG5YTd4CtdGDzpBP\\nuaiRP4+8y9mqlqjHXWIIDw+4unAv3qslb9L+h4JteGUTq2/NQEsR1fGN6TRYJ1sc+P4svQtnOdF/\\nA+ke6I5Q/WhhVcFyLICUQ/wbRH4LUQk0DabvJfjZflHAleOIcn5XKnlzt7gG4LmNs2g/+z4BjQJe\\nJojnzfICXjZ/0AbbREoesWncLIF7NDpxZ9FWBBEIpq577RYS1eHzgUTgTRYRUHEBssRH1PJr6Rt2\\nR2oYAPjtu7f1qOeUw51PY7q2IvZYNfvNxwEAltC5pEgibQCk0JvGDBG3TiVGS4OX2qNe6YWaTSS5\\n4NdjV30GB1OUsiiJhbPdmo9lu4gBVL41F5ZT9AacjNimaFDAykg1Shfdj3tPAWCQiZhy34/Kg5Wx\\nD8zp1IfWHUuWQg9SDwTbRPVkep5hg6twZG1+zHcQL35/PXn5/vxBx6yYrsK914KSveQt5R7U26sm\\nIe9Com7nGKg/2vjNsKjnv7LkMun3C8n0LgYOJ0/q0opINtaSzyZhCTqfiziW11VUivC0mc0GBtpx\\nZMrb0rkF5dRff3vbswCAy956POEy9IoFqqgE3BYR2ubuaeZ7H52PMTtZhVudLm2bXTYbAHDqy7yY\\n54+5lqg52z4ajoI5xwEA+3ZRg+AJ4BOCwFRyAWRtTPwZM6UwGaqU1v4KqbNJPShKymC65gD7m3gR\\nQ+FMZ7Rfr4i0rzkljam5HtSh7SK6PbhKKa7FUWvG/ztDsWfcOMCpBwCk5OwL3r0Mf+5CufkCvfTl\\n+ySlXh5vOmTTLyDsoNGaT1Qqbg3SLEpBE/lXh7+DsWNosjF217VR78PjosfoTrAtkZOpKp8Nly6g\\nBmnuRLL4UBj14Rdo8jvgEukb9FGZJNVJHjvVHrjK59dbiJLecZRi5+HXiYhGeHKzdFzaxu6/p4uF\\nV9R4LQCiTz5DQQvG9gd7Xi97Cq/e+CoAYKczDwDwSGo5it+lAWzkBJrA7Vk9MOq5HYHHZl38M4oj\\nWf3JaKAhvJ52ZnIXL9ypoqQg7GddiGFYC2zsnyNeu5Qfj+O3aUexyHtx2DZXqgjHEerD12ZSzNev\\n+qzE6DsoPmr6fopFb/q2D7wDqR10l6nh4jyiulXZU1DhppnXB0umQs+ocvZcRh9uVsDbShO49AKa\\n8KuU/phhCgDXJgCU7uAkLN6FKVfk1bL43pNWC5RsUSrWaoGTLP6X0SxDqbwa1jSaz/cgdWviVPjU\\n3TShbClm8a39HXBm0nVKLWTpPOzJiHmNwJggDfHuU7T4K9DXo1hTE3bcA6fHYfkmiuszngk15CYO\\noZjeVWEafaOMKFTIaHgqcx+eujI4XnlFes/cSOy2CAgU0FiXaaZ7NDv1sDupnJyurajTSHlOzeXx\\njaE+rQBvP+r/+2TRwN7i0COZBQU3sPihm8wNeCaD7u08TAZYX7I/eHOPQsp5qBlBz+/1qmDdR53m\\njmK6TuL63eFwFJPxs2LWGxH7nqwrxYdHKFRKsTMBK3obEHW443mUd7gd6r3BPibagvnHhI4WprFg\\nOh2sb5zW+4eZn2BzK2lUNHloZjYh5TgeTKF5zTAdLX7eqJmMy9P2AAC+ayaa6c0ZG7FMQQsp4ykB\\nuiY292yKvLfCS3Uwf9mduPQ8akdVdjLy6Q1u+LW039pEZeg7tBZqBV2vuaYv9PVM2TuJ6lY/QzOO\\ndPZFhMByfh1uMlNsZlfmnZ0Bp/X6dUDSYCpD5Yb4KNxA0MjoZ+3bvafj7BfdCcEv4JZF5LxTlFI/\\n+vthXyF/xTwAgFotovyaV9nRnZ9pyhRfGTJkyJAhQ4YMGTJkyJDRK9ArPKgakwf5E6vCckQ6RhOV\\nwLAjSMh1jnZAvyOSoLvtA+JWAAAgAElEQVT8IXIh91MFV+qfD38TADBzddCtPCWdBBfeQ17M8rR4\\nycq/45GXMfoFyoWmZV4Aa6kHqnqyFutrYlvxnrrnLQDAh3VjsWVTCQDAY2IUrijqu9HgThYgKtl9\\nQoyuqsHM+nuw85bItvCr6T7aZiqbwhdF4bgqfgqQ7gB5bLxmEQMnVAIAzvRNijiOW+y6kwDJc5wO\\narwFAKDaHawbfi0913dOZVD5MJW2jdWq4QiQpc6+IQOxCIIrbKQ+WppaLp1zdRmph55dnhvz3Pbg\\nOp9oLn3TyM3xbPFHuH5VeBC9FyKmf09qoD6vEt4Wsqy+NJ3ERibp6pGijGwnK8qIFmnqBO0xUbSX\\nNzU0D2R3gwsn1PtMOOBxduoar7X0xfNLruyW8uROoPyH3w6OFGNZ0JwjWahf3jADAPDInNfw6OWU\\ny3CMnqjnN6JzHlSO1Z+M7tL5bREYZcVDpasAAC+/0/57ClVfVxKjEvYmPRZuJAGjJ648HHFO6eab\\nIrbp6gW4mWf8z9lER0tWBD3BtSwfm/bCJpg2JG5FfvQOUot95dhUAEBDeQqMVdQ+xpjoG7zYZzsK\\nP6D2ZgipVro6Ok7pBNStNCY0MFG2C0cdwi4veVA9jOqracMQCvWcJoLGkX7cM5EEo5aeImpX9clU\\nab+hTiFRckNpo1z5lXu0dSc0iCrvGgOhoSUB1o+adR4kTyL6tUlFjfC58pkxr6PYRuPAn4qH4NV+\\nmwEA71nT8K96oqTduiRYb+MNyekIqh00VlaaaSxYoJuG53zUd35e/HXc1xm4jOoCzwQYUAnw+8jW\\n/3UJMXbuODkFa+qo7RpqaV9SZQDxso84moaJ+P4iEvxLV5I38M6TE7GjibwtXGCsymeDrYXahZbl\\nRk7br5BYRW6LEgr27Vq15CcVfIAvhcqjOMTHx86rpgLRPafPNBA7acniKd3uEXH2pQccOvwEjn8d\\nTqFvz3ta8BF9v54P6gjH8l8+iyt33QkA8O7qmsfr0N30PJ2hKattVD+W1ozEz7KIYdPop+8fGnox\\nVc/Ud9N34rFVcwEACgd9wZWmIcjYSMsHpTd2nVY5qU4pW5RoZXPrPCNRqG7o8z0+qB5DZTBRS68+\\nkAn0oUEjoz6A1gHMA5tFnsZ6d3xCne2B02tL3rgXJdt/WKVfpQvYMZqJCe2KvywKFu7X6OiamOT4\\nmeTR3naG+hPfvthhX9EQOEB9+R+qroXWSt+K500Fupa3VfagypAhQ4YMGTJkyJAhQ4aMXoFe4UF1\\ne9Q4cjILykwR+lqyDIR6TrkIkG5npC1176PzwTnOC5rJa1asrcZO54iwc79zKvHeWxdHnB8NR5aS\\n5XOUMFCKdFAxL8D7U1/FXa88GHZ8QAUptiQUTyy4DQAw9YZtMBSRR6yRWW91Z9SwHCXLUy0ZkCAE\\ngIwd4ddoGuGXuPcZX5L1yZOsgLibWzq6JmoQYN5ZWz8BxrPMc+pt34rqNQqSiFK8SD2vDl8O/AoA\\nWX85OF/dcKR7bZkr7n0Wn9nIWzj/nSuk7X/55SIAwP+8SV7VX71xt2RZPXZTMB7VoIgvxmlTE1ls\\n33x3FhSdS7sZAR5kftZH8UR9VCZUXBpulZ6w7gEYdrLURBNaoDtKFsUXCkjq/fvM42Exvhyl/Sk3\\nauWu2GItXUHLELJoR0tf4+grSt5rc0X328a4d3aw7gxKNYlZFoduIe+d90Ckh7+ziOY55fjbx3Ok\\n37y2Fa66HaqKxHK+nms8N/IjrGrpXIqI0HyH+V/fEeFtOTD+PZRuj7S2+kzUx3HP6WJrCv7y+g0A\\ngik4A53wngLA1SZib9zGJPEvN12KgyIxeXh8Uv6KeTCdZvlJDcF0ZsooTnpjBdX7zdZSKVtxW89p\\ne3ClCtA1tt+3OtNZPKnehz2tJGrV6qC7qExeGLbR+KhyRL+GhuXCtjPSjbZJgDuFrnnRNdsAAKuW\\njJHCFoNe2OhI2U8HNgnJuHYypWS5PIli1a6vvDMuBslM8z5U+ehFPrHuTml713wksaG20jPv/Two\\nwFeYRV613126FPOSq9s9l6fHCIW+PoBWlnrn12cpp8aGtUORvZu/v/jGaLdFQEDF0sIwh2ZqUb3k\\nOeWxrxuqCvBA6RoAwDL2/f9r340h4oXB+/Gcl9pmUfKg8zzu9v4BmI/QVzLUJj6P4GPnKZ8tjL3G\\n0Zkc4/HAmyxKKWz0Z6j8x89EjmmiCnj8JmJIFK+5DeqD1D4YaQqBzqd57RTy1SbcVEiCIm/tim8u\\n2h6K3qf62plkaoXTiBmytHhFyNYoeQsZ5ldNBTRUP0zZNMiK61LCPKeOLOoLDDW0zWsUJME3/p5v\\nvngtppkpLdKt31Du4+X6Uvx70usAgI+bScTy8x1ZGJFL7KLKm1Lh20kaBG6WCmfbsYJOe78P3/HP\\nc5If1ZMagCbONEY8tVZnYGfvprP4Vy6JlZas6HpbVVuDzzvr8GWoXN91DY9esUCFXwBa1dLitC2E\\nkHHSNoJWiuUz3ow47tnVJMSTVVCP6gqWb7IfNZhXz06NuzjGGSTUYF8ZFLmYPJdWjjd8cy/akmp9\\nBsDLRBIUp2nA8OtFmKrog40wnsT5JdQp3DKWOoIHTo9DIwtMfyh9NwDKT3nBYBL9kCYe5Wb8cRpN\\ndJ/ZcT0AIOlE9ynt1Y9ii9IsJ/rOoAmZbWFOu8cnsjjl+V+3nLdEWoyunU6qqaUvP94l6hYfHEMH\\nQcVYEpGY+c/oamF8YdoR4h1YuSGjO5daa5x0tan64ID/VD3RwxduI0VlRUuw2So3BSkZjStIAfVz\\n9MVSNVHlOL1QWWiDekskHZxPRkNFVLgAV3t0XJ5HLTTvcM61VL+by7gabOT01NvfDaWK3agi9tfn\\naoD62ALJUfHnt27AkLtf6PC47sxxGoGBkUm1O7qnqkIHP1NYfOLyTwAA43SVuOqdR7u/fJ3EqpYh\\nSOLWui7AcFSD0qPh7SxlSnVQ+ySkm+GU2/zPaVJjOhb/sMVzPwpRDIi2Il8YXRgA3H4VRB3NYPOX\\n30HnqoKF6Tv5FM6uoTrOqcuuTBGKAfS9NduYAbK+40UpNw5yJd32FqeOGWSscjEqvyAK2LKdBKOQ\\nzIShKrTws4lgrHzgAAlc0HEivAb63V9HlDtHf3+EccmvFWLSkdUtCknls85PiymdxotYNjt7Pn2Q\\nF85cgpNWopyqjF4U96EGX3UsL/ZDJAh3ightU/g38WshtTdOe3z6q6tQcdFGAIhq5JubuTVkYRvs\\nNHmOv3WLyNqcnoAaLs9vqnSLUl1wMwGtQanBDvDyw0SpdzXrsKyGBGqy9NRJ286aEMin63ARLF1j\\nACpXiAAXM1Dwv8Yz4c+QKGYdJlXRr0uWRezrrsWpo78PumpqxNwIzBenHeHgvUGa4W1T30Lpvp5Z\\nMMeLwa/eh3/dSnTtt9C1BWrbnJKF0ypwfHVsZVtrLlNsL/yYbYk+BnPRsu/Wk5NH4RGgZGrgmQOo\\nL6roY4ExxI7TOpg+zo2/XAMAWPD9FFw2nNpPrZv6xDJbFhatohAPcz5VUnFDChaW0LYaF008fjln\\nJX6bFsx/PNxLxkj7J0Sqz7aKaCqJ+agSOJ2Xh2CVvNH1rBxxQQA8uXRPTVVsS8jeVV0L4+kKukK/\\njYXuWJwCMsVXhgwZMmTIkCFDhgwZMmT0Egii2LWg+O6AtiBHzPnL/chIbQ3zWnJYi8jaqrQr4sol\\n+FR9Cd775CIAXRdlsRbTvc1HE3M2+7WAkmUHuXveF7jWTKIgBoGs0yZFkMrHLZFnWpOwd+z7AIBH\\nz5Ic+/N9diL/a7Lk6ypZLqpjHVs9bX3J9mA6EzzWzSg+rZPJ26s+bJCELozDGjEwrS7sGjvXDgLy\\nyRw/dgBR4ZLULny9lSxrWZvY9QZ0bOeYdxOJUHxYRc9lXZ/Z4Tmx4MwhL4c6wykJIHmGUVk1++L3\\nzcaSni99+T5JeOdcwZlB3+PamWTF/+yjC6V61BNQRnGGsZR4UWnr7cF/MenLrx29EABw0f89FtzJ\\nDN7n37wH2xeN6EwxE4IzE/j01ucA4Jx7H8vmBaniPeqhZVA5zkUGvh8O3UWdj4ZX7puPyawb5pT6\\nCV//GqYjnSHOJY5oKWXsjLwSYEUwVwZTyuj70WB2TdFu2Ji4z7JlRCk1ngY8yUyAryX2mN44hl6q\\n4FLC3I/E9lKN1HfWfZcjhcVwBozhbOzrecwC7MOoI1Ewb7MYAPT7uybg0SVE8cTz1DbOOhofjJWx\\nx/SrbiD628ozg6BiaS9eL3kXV39P+f1SPk2ciMxzmppHEVupod4M8y560WqHCCt3PBSTR/71MYtw\\n0EWV4m+fUFhA6gERbvattR18666gNe+H81+4Muh9H58bfb631E5j/iObKJ2g/nBkaETbsX1eFTGQ\\ntnxGwmLnmuJ7rsFDyNqi5FckrPVY9jd4v3ksACBVRfXtlV1TkLKG3qWaiRvVjRQQyKRJiEpD866U\\n5QYoWF5eV6oCLWNZ+6+jl5o8sFES/wnF3Aqalzt8dNzRVQUQS6lfG8DSP60Y/GXEeQAw7QAxCFo+\\n6QtHn8j93Fs6aD0x5YSyngwUCBdbarstdLvXzFga1gTGavGnPa4f/cMjO0RRPL+j43oFxVcQAKXK\\nD/vKLLjSGN2pIfiBzIzS9b/3LMLw59t3SVuHUiN6Y/Jb+NBKDcE6iAZjc1nnJh3a2o4jaXx64N4b\\niObS4qdB+f33L5L288THALDcQQtwR0ALnUBla3mDMhobAYz/kGILGobT88+8Zp8UqzXzEFGYq1ty\\nYeiAQmQbwlZWAnUEF1yzB3/L+Q5AyOJ4agcPlr8K0w/SoLi7mgbJawt3oU8RLWQbMqkDUO7qWEl4\\n4XuzOjwmEfx8wvcAgOUfjZe2+az0jV+etxCPLpwXdvxNN3yH996P5Pr3VJxMZ6Fn8UEfrJkAADD2\\n4OI0GgIaQMGqjisD0NXFPp6DK0jeW3V5xD77hTT5LzLUYnvE3s6BK5FGW2Brm4Bfl0fPYdvTOBeL\\n0vbgSWEUvqbYg5tzML00/aHeHe8KEEU3Gj23O1CituNzO+XrfOhbRh8u/2GHRIWXxSAm8f5dgGik\\nFzAog+ieO5py8Ys+WwAAD91GysZTvnwExqo4y84UZxVuAb8uoTHBoqQF6p+bbkbjWBqXUjOZUvyX\\naTEvp7aJUJ0NjwoL9LxQeGxEWbdx5eB4p61L349Mcn/t6sfgGZxYp8xjP599/DX0Z7lXv7UTR/H+\\nUScxksVdq20ivMlU8BQDtdE9zgF4aTfNJTIOBB9KVEWuwL0sQ4A6zgwBicBnoGueK4PYk7MpdrSj\\n8ZmbQJx9/NCfjax0k/ddDQBoWBNlRfMfinVHi+jvsSJkLg9vt+Q2CK8/GbtE1IyjeWSA9cV8cQoA\\njj4iMtOpXg8bRDoX16Z9j9urqP2s2UN1veKK13Fz1iYAwK+3Ubia3xLAiGziCu89RXPMF7IKcLmZ\\n6MHWgBpvN5Buycm99A3TrSIcfSLrYcESMhx9fSWF98wpeyzimFB0NR61vYUpBw+fSmhhKiMMMsVX\\nhgwZMmTIkCFDhgwZMmT0CvQKDyrcCojHmbhCQ/vWhj8siC1yY95P1qC3S4JKsQo9N7/H9qB6jUGr\\nteFscN3uYyY6LjwABCmQIjPYFV1cjkYflf+9Q+S1DqQHJDGKeq8ZmUyJb5iGLEwPH78Oje+R51QV\\nxdybtpe2Pbl3Hh5m4gff3U35Xi/z/CbmswCAeT9ZvIzV9Ez7Xh6GlU+SwuJVRlvE8YPW34KySYvC\\ntm1x+ZFrItrF4GQSjjrfWI57S0nxccKHRJ8MtVtOuIrusWlpz1I5uee0/8UncPJb4kUZKukbt/We\\nAojqPe0JuBhFlysldhbGU+fGdmQbQOXlTn7jKQHOLNrmGeCGri4+L1vy93ScZmCku0vFhJFG6Kuw\\nuKsFBuBJCtK5TZsMkd4SEVg2iITFhmy8vxvu2Hvh7EcfTn9Kiczh1Eab12bHOkXynB54cD5ebqK2\\ns+Ddy3qwlJ1HT3lPAeCMT4WNNhKoOBee04C6Y8oyf15RSZXa1l+A0kAb/5JLOTYHa0JDGIjq+ORF\\nS/GnNVcBALRNsd2Xgpspbha2oEBDXllOdbY8/iqOe8iP8tz7lNPZOzCA5CPUn1nzAYE/A+viTCch\\nae0YTwX7PW8UYo19INEzBBdTj636oV2ticNwiHud4hMYar6QvKHT9X6Uvkxj99/veBUAMHrHdWge\\nSt83qUwFUUcvd2rfYwCAZKUd6jLuJwzeT9dAvzll+IrLt+DjTUTXzNpI21ypCtgmUj+Z/kXsfpyr\\nBzszBWnOE0L8OieeU0e+F/tmk4jQ8w2jEjo3mve0J9hRPP9oKDqTi/SHBBfyTN8bv6ddV89VzKPk\\nV07zwx8In6888ec7oGTZIPhoNH7tPdL+UE7GriTGa2f90s7WXCyrHgoAqLMZEdhMCu0ZJ2O3N00z\\nnT97Cc1LO+rRe1LNt+SNe3Eu/ab9LzyJkxv6n8M7nhvIHlQZMmTIkCFDhgwZMmTIkNEr0Cs8qIIv\\ntuc0UWzcMkSKMxGq4/MAqe2A2h65XjdUh5drzs3r8WQGpYVRM8Gj68qn492vpgAIxhCaL66G9Vuy\\nHX1UMRK3WyhNzSoHSdT3Nbag1dUP8SC5gixHn7DcnvEII3DPKYcQAJ558hcAgGeiHJ8KYM354c/f\\nHDDhmvRtYduGaeqRqSSr/cBRVQCAE9/kIW0qeYb1TFXomrlrsWTxlA7L2VVw72lvgbKQeafrOo7L\\nTRTuVPru2sbuayu+JDKTq1qpLrcM9kPhouunpNoQQPvtR3UppUzyfRXMxbV+H6W/sIQcd+ugrQAA\\ndSfdYZfesQEA8NUbJHLxm1uXYHk9pVnYd3gQdA3hxzuzRTxe3WH8/U8CIk/bAyXOHqd4yjX3PouZ\\nb1CqpWgeO2df+uZDXrkvLL1QV/DKHSRmcv8bQSu5OJrikoQd3Zdbti2SZlD80iuD3sfNr/w6oXNX\\n2Ibiy/JSAD1kqW0TJigqAN84EgRRbY3sHxxZAlQst6plAKXMuj5/J948SGyRcM9pOG5LqsWJ8dRO\\nPt8Vvd9tHkwFyS89AwB4cMAqHHZTaqrJOtrW4DdJeUAn/JKExq7ZcSccrZTOylwhSmJMbhbz7EkW\\nJDFC7jXVNovwsNS0vr4Us6nReXHfEIpBW1wxmq5R1bU8fr0NPj1L4eMMjtHqcnJJlhpuQlIlNbgn\\nnqCcrxoAFpZmxjO9BaijmYuTKfjkqJvg6kv9pqea+miNVURjKd1H6aS/Sw+PwDuzKQ6u/xzymlb6\\nTKj20Xf72xc3IKCmY3mec0+SgNZCKiP3kPo1oiSO57FQWaN5J9uDo5jGf8PRxBWIDBVqjHvlkYTP\\n+6Gw0dU9nWeoV7Y7vLH60WxAPBo9N3QinlOO5OP0rFycKxSGEyo4Moi9tv8lGpeV0YLA20H2d7QM\\n8Wvo2kc3D5ZSZhmdIhJNhaSy03XOVc7T3oBz7T09fOf8HktRE4pesUDtKvY+Sg2cCyiF0iPbLjDb\\nnhdLdCkanH41Rr/wYMT2tnqFfHEKADcWbkMflsj6fgslIF4oeHEYpR3ez6cTJPGDd5/qWRreb/5C\\nQeaeOTQ58vqUuKyAFN+mJh0CACxomIAnMkjq5lgNTS6OPDgf71mJtMGT2y9obj+XKgAMuvQoyr4q\\n7uYn+OGhUjFe1KQmSThIoRJh2Nl1RcvuXJhyGCuoC+C5TdWtSiy+g0QGrt16V8xctaELUw59VSSV\\nflszGRHusuxJuHz2C+14OmsvAGDxEJqoP717FrRaHyu3CLQxbim8wBffkLppom/smbnv4LeLb064\\nnLHAlX0LP7gHKlv3fkNVSE5c/SmaSD5+cg4O3ROZJ5jj4nH0PgcZavBIannYvs7S4vjCNDQ/Me87\\nlu8Y3+557cGbxIwxHRguNw7/hP1KPHX7DNMBvOYgo0doPZ90/U4AwPoPotMM9z1CzzjsheC74oqg\\nfMHv1wGqNpEU4nlWDOtDC8FDGBRxXUONCK+RLRQYZe63aUfxoWFkXM/zZMZBAMBS1VT42esIzVtt\\nOUTXru5Pq8irjDZUeHkyQxqffm5qlY7nC+ID499DUTlN9Hx6AWqeT5OFvbhSg/lmdbW0z6cToKHo\\nECgLyUrSL7UZFxlpHPnN6OMAgKEbflz0SABwZtFHNldF7gtdmHLw0KGUJdGNlrueoPrkFr0YsprE\\nuiwsme1OZx7M2bT6V9tpsVk9w4djs14DAFz4GHt/5Tr8soLCGe6+cgUA4JHUcjxZR0ZwWz8FbEOY\\nuJOX6paqUQWfmcargIq2aXNtMC6jctovY7mcz3ZsYFKOo49dwbIQtM1x3BVEy3neFdx2E72fNz+a\\nGbHv0N3zYy4SbQGia5sUOkzUKeI6pyN0N0XYuYPmYto2CzuedzlU4ChRhDpH+PVcQ5zQf8/zsUcu\\nJjc/R8bLYS/cF5ZVAgCcaQroGV1d6QleW+HsdBElLGjOiaq02xPojQvhw3cGDR/dtZjk1wy9nnEU\\nOSrsO7vf2ChTfGXIkCFDhgwZMmTIkCFDRq/AT8KDGssLassl64ypKnIt/nR9pBW7I3z77wsSPufD\\nytHY1ZILAJiXTWkB5iVXY8GNjQAA+1ayPHAaRShULhEqV/fIxteOob+aJnoXbfOpOi8n67lJQxZv\\nf0CBAMvH9ELlJQCAu3PX4kMbWWUF5tzY6Apgmy0fAPD0WyQffs3ctZJXQREll+gnRd+iFD3vQXXk\\nMY6jUoTheHy0o34zyDR+amVuzOM4DSU0V6qwIUhuNbG/lkvOojnCxx4bysmN0m+rjc7tDi9sW6ht\\n4X8B4OpPHwYAqPo6Er6eGKVHWVK4EgAwavvtCV/vyJS3pRyVnDWUlmyHbTWlazLZI8/R1whoGUh1\\nW92amMeyu72nQDD1TE90ttFSyuz7sgR7714OIHoOtg2fkkduA4AFzFN55FayNOsm1MO1KT5LaPEs\\n8oLtOTQAhhP0dKFejtAUUPGC1594v9vg1+h+t/38W9xwC6VMeX9RUBDt8wdJWG7Oy49L23RTyeJ7\\n3JsBhTK8b132q2eRy9guwxD0oN5+G+VxDvM4TySmyfPDPsLd31LdNh2nB1DZgrlMOc3aYnLg0Oft\\njzluiyD1Ja0NRPV89Owo/L5kedhxR7x2zFz1KwDA8mkkKhNK/x1/206s/oLKzvMFJh8DmiexXIVH\\nySPmvcCPfLUJ7cEvMs+GoICmldN6AeOZ8OM0LYCPXYZTXP06SO3VW0XPUqP24ZoVDwAAKua81u59\\n24Kna+guOnpXEbDwUIX2W7TXIEDtYDnGJUZX5ANUTwpu+3tTCcQAvT/OGtnh9uBdkPhRDTu24tI3\\nwH0Lfi2j+rpFpO6n+y0bRwIzj6SW47OFRPc2zqqFs5k+knE3udddmSLA6r+mP3WkKpVfmm+4TtPx\\nivQAtEwkx5Hrg66anjt0XPdvJTrpM4U0pi+5+zlc82rsNB/xIprntCte1d+kUr/1ZpR9U/dfJVFu\\no3k2x7ze/RTk7qb4todYntO7/0BMlNuSavFCYwEA4KOnL4nrekpVAOYYAkbjHyN2jV4toukqqmfu\\nRprLTBx+GGVvUOgaZ48IAUDbTNcTohS5frgQV376F5fMwYsdH9Yt4OJRx24kb3FnPKozLiXmzsqv\\nEhMI6050RN2Ntq8jz+mVcyis47PPJyRcnp/EAjUWoi1MORa/Hb+yq7WE5VM9HH8+VXt/amTnpVUj\\nVUOT/Y8baZU4PWcLpvY9CgBYNoJWOg1DlDgy5e2wa/DG3R3IZOGkdZcRf6K6rxrZ6+j9tA5Q4JZi\\nyi26p5UWoFvq8rH0EKnxPjxyFQBgcfVYlNWRyqPHTuU2KzzYVJ0fdq8li6egcGYFAKBiRfg+oP1B\\np7uV97iyb7z49J6/otpPE6rq25Px1Ks3tXtsPJ0kADR/E5KDbRLjva2PHh8y/OdE0ys2krrmkxkH\\nE6ahdxVJx6hOOFtNkqKjqgPKjTuV/obmTeW0Yb7AFFeE51M0XkH0QvsXkaqznzz+LPtlwtR3SPlS\\nMLIBURDhY6GxqigLVJ8R0NWyCZyu5xLZ92bc8BpNpJQd2GT4QpC3uwMPzkfppvjq2wjLKQDA0ROF\\nnSxlJBINU+Y02nffvjjq/v+ridx+Sz7FRC+uHgvdznASO1+chsKdLkZQoQHA1kTn3rf1F9LCNBR8\\nYcrpyrbvssL2c+VUbshzp4pIYrdR11C/9VXFEHyzlYyii686DAC4M3stBuaSYvM+D/UtdxyeLtGd\\n5+dsQamXJjm8j/JrBeRk0ILavp7a2/vWLNySVB9Rbg6lQG2o8LvbYbIGy8hjUDWM7qfwiyF03+AC\\nlbFU4U+lFxHYnAK1hc55opZi1Z745ft46s0b2i0DEFyY2ovoOsZjnctp3h2wF3sgODqOyVQ7RNj7\\n0vsznml/8p69XoEFUykc5qPnLoF4YXjQ+GitBk4nVZBLRu2Tttf6qeNTuiP7t5qVNH6PX3gP9GxR\\nXF2ZKilV6+uZ8c4hwOGkd6nfwQwrToBbFnR1VP7LfrZZMjYpnApMu5xNpI+RsUW9N5hZdtG/qb0t\\nQrDddTdFtzPXcvbxo/waUk6eW3FRu8fVbOyL/HKiWSeSJTrWorYjnCsVYC8zHnHepDNdkPqe25Jq\\npeN4X/cIo+YutZvwm0/IcJu+J7K+6b83ghtfWgrp4oFhVvzXcKJSv/q/pAau9IrQaqiDP8wMVBcf\\nugIt3E8hMmNJqwCfga6jrxNhZzlP+WJV1wh4TfTP9VeSw+eDzyYn8iq6HTyGuytU30EGmg+t7JYS\\ndR+tt6vozMKUQ6b4ypAhQ4YMGTJkyJAhQ4aMXgFBFH94D4M+u79YeHPH1Al7vwBumLYRAPBUJlkT\\ne9K7ZM8J4POf/Q0AcPk3RKkSnEoomcqpvjY2DY2LN33v9mLe7lsBAO7DFEw+cdp+vJBDFqapOyhv\\np+uABWW3/zPsGnA4ysMAACAASURBVE1+B2b/tuu0kqar7EgyEsVLoyRhhIv7HEa+lixn1oAebsZJ\\nq/eSF2FtTRFKLLS/xkXCCbnGJhxtJbXQLD2Z1Xd9MSTqPTlVtnJT/x7Lo3bnL5bj9XdnAwhaar9x\\nqPHrhXd26nrZ00/h9CayaCud8VFJuhO8znD6ebUnCcsOE2VrXH4lAGD/J4O77X5KppsRTXTPbQEm\\nziFho+2LgnltPUwzQxPUU0HzeWT5t+wOejeaR9C2istfBwCM/Euwre76nyCtiW/36YOe2rG37gIA\\nbDhZAMXWZHZfJsBiECWlPl2DEDO3pCP7h+/fehLFkyoBAAf3DIC++qdnb+wob2hHWPYr8sRPW/sg\\nDHuIDsBFjsbsvA6uNURPGnsNUSoX5m6Qzg0VQYoGzpAxnlZIubGjhTPwvML6mvA+UGOl7VxdV59r\\nhfcgNS4+tugvrYHLQ21KsZxYF43jvFJ71SVRA3Y71Ci/OEhaPO//7mPloQMdfSHl5dbX0Ladf/gn\\nDniowb1YMwMAcFP6ZjxxlOVTVZG3o/mjHEkB1mMGdI3hbcqVKkjbHNmccoqofUpbzL1pFRa/175H\\nq7fBlRGQPIvmqvY9o/Y+ChjPtr+f0xl9egGOiUQDMG4woWUwjc1Hr6J5wH2nJ0qUfD6+AcD4R+Nj\\nVjUXM0EkF+V6BwDDWeb59gVFtFoK6Ljk8mCZW/O6pz/xJrMwglv+Gbfnc/rPie713cdjuqUMogo4\\neG+4JzeQuNDwjwqWo9HrX+sA5uXUAgFG8fZkUVufOvwwdi4mdoO9H51vKGrB3ALymn/xf9MAEO2+\\nZhLV1ey1QUZB2l0nAABfDvwq6r1/VzMcALD6r+RV82kF2PuGM0lMJ0WJHg8AdSNpv7aAJhzGL5LQ\\nSixkSQQJOPdCRYFioogojsaSkuwYUYWcxHOZRfXc4+gfHtkhimKHqRZ+ejMaGTJkyJAhQ4YMGTJk\\nyJDxo0SvjUF1pbE4iAYBqTNJleHOnF34+zLyln3SMglA1x+g5OoyAMCT/b7ElR9THj3DGVq3H5+7\\nAPOqKJbCXBYZ98It4z6LL2K/c3RQYOaDxnHQLicvkIlZLPcO7YPZfyfPqO1SsoKL/d0o2UBcf4OO\\nTPEtx1OQ2cVnBIDDF74j/X6mgUj/jT4jDjgpXuWrE4MxNIM48AVGiku6qt8erK4jT15FPcUP/mnA\\nZ/hKS1awva3kabznF8sw/yNKgaPgHjkAR8opPsrQA95Tbk3mAf1A98S4VH/XD+4u5HLrKvK/vgMA\\n8OBYivld+dkYGFlV2r+r+zynHNYBrJ3V0zcKFUty9fVLntPWQrKmJh1XSLnuAic07FxgYAHlwT1z\\nlISlVPag5/TlpshctS825WHp6fPCtoXGuW5YSl4DR7EbAkuPYLbQi2g9Y4boIattqIfNxWL1de2H\\n1fU4fCxOlnt4exrHaumhb526Dh8unnpO7hkL7nSqJ1xUpSfgSRGjikNFwzYX5fnk3lMAuPsUxdM1\\nVKZAlc5imdK3SvuLF5Elu6MYNONJekZ3uhiRPiigCcZOKuKMq53c/zi+PUb1nscWVlekYeooSs2y\\ncRprb4dM0LN0LrYBzLva34UjXopL/NZeAlt/2i+q6K/lsADu0nSwmK7BG2+Gq47eS0o/CiJduesO\\nKa2J3crGLIMQFIRzB0WLeAytrlGUYk8DGub5EAT4B1F5uNCebk+kp+H9xRfB2Y88MYZT8efb7Awk\\nL3fn0jEDCMZltgdPEovPtQY9QDxNnCtVgDObKkUgiTFO0m1QuOkbulOAtO10/SJl0EOaPbkm7B7R\\n+tOG4QLS9vGEu/SnpVABPxNv8aQEy+N2cdUpwMqaRerBoLeNf1cOR5EHhmPBsTAwigUkHyBWVeiY\\nHw3qlvA493jQXZ5TDq9ZxLQDV4Ztc6f7oa3v2TrXW+BKUcDej81XWe7zpDKlFCcOJnK31lmKZNY+\\n/CwFkVnnxu/SaZ48+y/ENPnfk5ejtpJyb7YUqKV+v3Yn1c05wiwc/5rNzVh14jmAQ6Fyi0iuoHJx\\nVkFE+1SwfquJKmuSR5QueuFeim/dIKUbO3foqueUozemqOkt+FFRfF3n26Hbbgzb5swUI6i2tgF+\\nmE50veOxjXDBtCdymsKFY/hArY4i1PLbuz7A7zcTVeqPF3yGpz+4jp3DBowcL/RJRLl1naVnGjKs\\nCgeraFFn3kH39eso6TkQPuh1Bi0/o9XHrYNoMra+oQjNLnqY+q3ZyJtE9AxOAf5t/+X45bbbAACB\\nCipjUmkDdoz+MOLanLrx2YcXStu4gqhQaIdqd/tqkd0FRy71bIaq7rO7nGuK77mGkqogmofRu9Of\\nUkHb1P7x9n4iii6gesIp3js+GiapDvs2kFrSrBs245H09QCA2c88HuVKsdF8Ppv1iIJkKOCU4tai\\ngCTkZM8RYTxN7Z/TeUNzH//UKb6lU44BAP6Y+wVuePXHk+Q+XnSV4hsKR19GWcunimSrNULVTH1F\\n0hDK3+xZG38uN26g9Cf7YDrCFhlswRtQi9Cfjb2Y4f25dRoNIGNzq7DpCAlOaStIadVQLaLxAuqE\\nsrNJ5Ki2PgliI9vPjKnuVBGqAurf+6a04MQuMh4mU/VAQCVA4aP7NQ3lK2fAfFQpnQ+EhzVw+r8n\\nKahIrK8R4bGwRRgblwQRcKfQNns+fbDUnSqp3A+NJXXllzbPgLGN0c+dJnaY67YrsOdTv8bzPQOA\\ncAG9R3GLJeo58SIaxZerGIfmQ+XzBHuOQhJ1ay2ic/uXVqPqEIlWpRwUpG+Tlk+dcH11EnL6Ud/6\\ns367AQAf/SWorsppuKIS0NJjQeGlezee54fCxASq7GoIHkaVbGDfPNOPrA2R776liK456QoKsyhr\\nzkLd6r7S/oxp5DBYOOhdAMCcBYn37+0hlMbM0eSnVdS/rWQsn//OFXFfj9drf7IPhopwJ0JAAySP\\noxCmlq3d4QboXbAcDcDHVJ7t/QR4mfFU+uIDHNDuoHkdz09qzVVIbdiUSZXV51PCc5KOGzaahC8P\\nrymUDMqujAAmTCBxx0UDSLSowmvDHg/V6xfKyclz8kS6JDJqrI5PktuWo4Arg8qdtpeVPwA0lTBV\\n8Uyaq1bMee2ntdDrpRRfTx6bMFrV0DR0fo0lU3xlyJAhQ4YMGTJkyJAhQ8aPCr2W4usnAzGU7mBw\\nf1vvKQBoQ6he4oVkQjSF5KJ85M4lAIC+qiY89s/EhHOieU+B2Ck3uMgNAPzpLFmLv2kcCkUpWe2d\\n9UQLeGnSvzHTQLSqrxwkfvE/b94CE7NeG+rIwuRKUUgUIW5xbx2gQNIJ2t84REDqwUgvkSuVbA+6\\nRjrOc0MjPIeJprvUSN7O63N3YB2j+6on+PFuMXlG/1hNohWPlV0LzXbyfIrMWNJ42oKWkfQCvrKT\\nVXWuuQkbaiNTTfDci85qwzmpaN3pOf1PgT2HUQD30buz9xfD2hSHejbljzEuz0B5HdWjOgPVDedo\\nB0zfkOdUJNYXVi4cjxUXl9B+Rjc0nRTgZIZqIRBMEaNiRrnWC5xI2kwefct26gACqsj8hzyPLwDJ\\newqEe07/U3C0gQTLhhclkhThPw+uDFHqw7CRxgcTgl5Qnvs1EYutJHpUE/TMaOt5HYxeF22F5NEL\\nTUsj8jyhGWaILiokp8q2FAGWbdQWqodSuxs34hj2HKe2xdk1fp0Ai4k8TVW1qVAzinlrPsuDW9SK\\n8/qcpm1eqiuHdwyQhEk428Wd7kfyISob95pqm0Q4M5hIkkWQaIE87UNAJUDbJLJjg8+l1JJ3o0RL\\n9H/BFWlx1zYIcGZRA9fXdL+9PNRzypGo5zRUGCkUUb2l7BFbChRSv8Vz+3osAYhZLJyH5TutbTVB\\nnU0v9PKpO5CvpX72f7+/HAAwfvBxTEwhN/hzm2cCAEKTcnHaZEAd9JDXD2ee0jolUEs3N50SJbaM\\nwsfmFg1KtM3NWn+eAE0RzUu4ONPvbvsAT+N66RjuTZ2zuvs8pxycBuzsQ3UHAnsOhLMp+HsW/O1f\\na8Tlh/A/OZRD+LpXH416zIy+RF39uFsCqXofVCxUIPm4CJeFsS1YhjuPKMA2kHlLz9ALFQUgeT81\\nfKWbKP4GZzDnac12ShmozgFMp6nu+LUKNLrD5+b5ahPyWbzQVcM+BQD8JmMklihGAwBsBXQ//Wml\\nNJfl8GsEtBTR7z5jz+DMdmIVRsuDzPMp97T31JNCNw+de/QGuHNowaA9fW7C0TSV53ae0Wtn9KFq\\niNEotByB81uh2EzKh0LIwrToSsoxWuGmCdwLr18Tca47VYS2keVt03Q/nZPH9e39eAicxTQxGVhM\\n9JhidT20Ai1WzQpa8B14cD5mHqKBqXVhP+k6bdUAQxt0tMUpAKgc4dvVi1ORwejc9mKqzG8eHQ/d\\nUnpntv4CHjAQdWZSCr27dacLpFjgdEavUFxgxcOniGKUrKZy//mtsbhu7hoAwIcIyfnJ8EOoiw6e\\nfQQAsHNnUZfKwJU6lVn0rNGMJInAzfIAzrx0O747MRAAsGIM5Wf7W91kfPvvC7p0/USRPJyojS35\\ntDD01esQUEdOCOwbqB05h/iRvJZWoZorqGEKIdm01dbgOcpvaSTUMUp880gPKma/AQB4pbk/KlnQ\\n6JJdNGgJDdqI8il8wYkyH6B0DcH9zcN80uL6PxH7xv1b+u0YwCjuJzr/PgpnlUsxg4e3D5ASj69j\\nk9t7z1FuNdcQ1t4O6ruF5uvL9MB0ILx+7XtkPja6qFLdM/+BmOfbBrGJXIjWQKgRNR74dYBg4AFW\\nwW/0xyvJMOgVlXgfYwEAR0CLgKRDKnhonoicAgquTtfY4deG9++GahGepTTJHnfrIRzYRMrq/N05\\nCgX8O381AGB2Gek4aPOscDNJ7oCe3oOqRSkt2oO0XkGKTfcZgs9tyw2qE/Mymk4Gy3Rs6lthZUwZ\\n0AT3yUgKdU8sTLsCTsmV+pt24k6V19Bi0vFNJgw17P2xcdc92AXDbur4lDyuP6BAMjO8NQ5lasdD\\nXDg44d2Ia982482Ibc+5qRxNJQopttg3gBqmyeTCXQNJgXrJacqBW7M2B8nHqVzOdAWc7NUnVXJ9\\nj+A8ghvB884/hWMVlK+XR9jdZG7A71nsbHvj6MgriOK5dT3pJGiau2Ys1J+NTR+MtTDleDtvJYb+\\ni8Ie2usRzzcSZfVjTEqkeO2Ct8sjt4WoyzJdEYHF7P5Q0DUH2F/6vy5VB4HlCTfNI+NV9ZE+yF5P\\n37iOhmX4Tf4wpV4gGM8NEJU441Kq5A+cHgcA8IoKrCwjI1pmOjlnmqwGmMpo7qm2McOalmjFAODo\\nTx/V0r8Jbw59HwDwXsME2I9Fzik5vGIcFaEbcK4XpofvJEdXR7lME12YPnztZ7jHclr6/1zmSvWk\\n+ROmBfeukUGGDBkyZMiQIUOGDBkyZPzHote6HrjnylQVew2tYt7Ttvik6FsAwLC/kYUgmj3v7itX\\n4PWPZgEgsRgufuQzBBWEY8HK6FqqViWO3hKev7Tgo3vAZYGMZwMwMsGMyjpSPhs82IAtLrL+/Pk4\\niSnd32zGx2OJsnDV5URZ6JPWAu/bWTHLEQ2eZJbz7npS5jVp3DhURl7ZS3PIapilacWi8yYDAPoP\\nOYudVVQ2h4+sMg8NWo35StpfoycK59X9y3DURpb6Ohc9oaPYgwNWsnL59UGxjWiQPK09rDh6aDl5\\nJ/UdHNcRnpxFFPEn15BaXFcJDlpmWf57323YmEpiVf1U9B7PtfcUAFxe6gJUB8kzbGwM5i/VV6nh\\nYTnsuIdFVIpoGUtW++bTVCcsO2Nb8Tgl/u2L3sDAtZQPuG9aC1pd5E1I3k3nR6PwALGFclQtkRa5\\n5lIfsvPJzerY/tOkbkXD+5dQHzTv9Qc7fQ1JeRGAmBX8IOfKc8qhPEktrbtEkkwHtBh0JbEqlhSu\\nlLZvdRRFHOsdSzQAdwuVQdWkkjynyTOq0biRSJbKGKEe0fB/t76FPx4ilooXOrjSqC+4yUx19cWm\\nPCRr6aKpOeTmaBQtSN1Ndbx2F40D9ZObgXxGK6qkdts40o/UXXTcobcHw828ZeOvoHzhofldj56l\\nNiGe0cFQx9S7W6kfcOT6oGHKpoazQfExDVNi9ZpFCAGeB5wJ9CiC3tZYyLc0Ylc29RndzaoxTamF\\nbW33tPX2+qG2qK2juUdWTaQn0rBHLwlmcRXb0ByjfHy0nzLjAi2xu7actyTqfQq+/SUASJ6txlIR\\nGaNJ2bc0lejT3x4ajJf2UGiOt5X607SzQS+70iNCXxnJtuL5T6+/bg0A4AbLNsz96DEAgHVyMBNB\\n+bXEpGhPiZfnQk+UaNh3xkl8O/gLAMB3TiV+9cbdcZ330C1LAQB//YKUeUWlCIWX1WX2vs/7x4NR\\nJ7jDLj8MALgzey2m62kO9t8Jlrs9KN1MIfvV+zB7zhYAP7zntC2amQiWwiPCzyR7Xysij2X+YBNG\\n9p0LAEj6ihhQolKN+supLqR/SX1i0okAqidTfd40+wVM3Uz1QreZ5jJeEyDxVb6iX6r+CtiKqEM3\\nZ5HH1VptlvIzDx1K4osHdubhDjfNEyyfGqFqk1CZqMqsbQk/TRXm31SP7NbrhXpk72G/F7XGLwjY\\nFaSPozXImeoUQPagypAhQ4YMGTJkyJAhQ4aMHyN6nQeVS0pzz6krTcRNl60FAKSq7FALZPGZvzCY\\n0yo0ZyrH8Ofb95xyIaPhz9+HUMMvD6VTuoJn+Vggxu3XrwAA/PO7i3HtZLKM9dGQlfvhlErp+PtO\\nXxBW/rZQhXgWGwJk/U7XkzVpUcm7MAt03sEpCwEA61wa/Jf2LjrXHX/KDNcFdE13C1m01l34KTAo\\n/Bi36MWeUZSOYNfBfMmStc9KntRjdUELS34pxc6uPl0M8RsS62geTcFXFbPekI6bp6JtWz4bHrVc\\nnfGctpWe7458p/Fior4SACSvQndh+PP34V8PvAgAWNiS263XTgQKVundQ8gjo91ghGUPS5kxrRXa\\nHeQl0LRyVQrAp6dugwss+PSxhcN8LGz3oefvg3kWxdGdOJEBy241v2QEXFTFENCKMJwJP8KvDcb9\\nmaqC+3ge1BvGb8ExO8XMNv5ExS84FlvJyj3X3NQlz2k06GoU57SteZJF+PvQh9UfTpyrwAV/FJ6g\\nR497YE3Ta6S6PucosWYqvihoewkAgL+S+kxTbWTNbFmZjUR7gn2PBPuvh2qoPZkASViI4yLjYfx9\\nFQnh8L5Y5RHgyOZeTjru+2N5UKq5QAntS92lRONYluLlezX09XTtl/pxb7FOypOqVJHXSNmogHVg\\neGouXbUKrv4sz7GGpYQ4LUhxpyqHAA1PZ8KEU6z5QFI5bQuoWc7PtMj38FjOCtzx5cD2X1QnwPMO\\nd5f3NB7wdEWWzdxHFPyOmlam89BXgI55p7mgj18bfI/cS6s/q4T4PfVVg6y34E/nkTfxhWMz6F7r\\nM5DVRkRGCACDU8iDuqaCRA7Nu3VSXdeyWEPBH1muUHjMAnKnk9fqyQyKIX347BRoW+jYnSExxOP3\\n/Lzd99EZREsnE6/3FABeWkSss6DHtuOYV2cO1XsjExvh3tOewvLPzz0jqi14thJbfwX0NTxHMf0x\\nnhKg3U/t/sZV5DVvGqSQ0rkZW6keVc/wooLXhanR7mJC2aRFAIAxRkqn6N+QDs0EYoY0DKVK/9sR\\n32BeMnnTXmuhGPtXMAWFqTQnONlKY1lAH4Dl0/a1PnTNAbimxRCm+QlgWXlpt14vNNb0XMWdPnvj\\nWwCAOUbyvpdsTfy+vW6ByukZHF6LHzPM+wEA9+z5Bab1JwEfTKI8YbaTSRC1LPi7oX2e0Y5HXpbo\\nAHzxCgBexr4YefEhvJ1Hg/mwhSSYMePSnfhHztaw6/zm2uPS7/eswVF4yCt0Ta5ICgQ7B3u2Qsr7\\nxCdOv6sZjqezKOmxNXMHAOCi9Q/i2LR/UXndwU40fx5R07btI6VcTvXhqJ5KHW32Gno+zw2NeLSI\\nKF1KptL3p7ohuD3le7oOS17/c1MrVAran1dYg8pTNMMfkEMdy5mdfRBQM3GkFFL2WzDsXdx4hCbC\\niiZ6mC0uPy7Q0b0fySZq9XWIvkBNFKEDWek/zk3DGnsl0eJ21+QgV0UkYfUQ1mtvTO62+9z+j4e7\\n7VrxgKth88WdwgdYT9OEOfkgqztJwaTuqn1JcPWjuqVh+xHMkQ0lW7Tair2w7I3B8RtPM1rXbgum\\nZ9OEaMWBUTHLGhRCCvYHvK2GCjGF4o1b/wEAmKhTYNCbP6GcaDEw1xxMWstz/nHhtx8LXBnUBx2f\\nuwDzqiiP8qZy6j8SofiGCuu1Pc/2XRb2GYki6y6gTjraFMg2xAPToa4rInqSRZTNCw/72OtxhYks\\nNZeGLzzq/EYYcsiw6LDSpE5TppMWYQaa20G1Uwcvix9pGUEPnfq9GpYdVG5rnghzJe0ftZ4m/Qcm\\nv4nna2jR4/XQsO9PEiWKvIeJtyncgIqNowqm56R0i1KfwSevoTBXBH/7YtgVXjx7cfs7OwmV/dzW\\ndU+KKBmw+UIuFNxgoG0MLkJVLjrOaxQkVX2+YPSYBTQOo+NMa014aSmp5XIjiDlEZbd6EsudWliH\\nVA1N0A069v0vO4WT28nYHCoiF01pmMNaAOwoWRa2bdV7Y2FEJMe55ijNDboaMhMNA9+ivjoOlniX\\nUP4zEiP83G7o4MifDtzJNFc0VwXgTGOZHVj9EBVAaz6j+7K2rq8BWgcyYUgmyqWuUaNgJdHM5094\\nDwAwyxCuDDdoIX1DTSvVt4AGcH1P8+PkM1T33rRMwOsBumbNGSZoqhSx6wwpAxtOUb+UeVIEEFlf\\n7X3oXEN1AOIh1gFOjP9d/JgQOBA9dPHHBL4w7Qpkiq8MGTJkyJAhQ4YMGTJkyOgV6HUeVJ7qhXsf\\nTZUqTNTROvr3pctwnYk8eQ8zutZyZyl0ejpp1aVENf2fszOw+YPwIOOhbz6A128iizan2cy+ZjNO\\nOolW0OAyYtIesl568sg6dFfGWnBZHE6PGqg24q+N5Mn85xqySD9ToYz6Il2ZLEXL7qA1yHSSfi9Z\\nMRGfFo6g45rpHoZyNYqq7wEAaBuY9HaRB4PySAjhopFEw1mlLoEumcpo0LmR/T4JT9RdxnKsNRtR\\n5iAhj2HGU/T8+lOShXK9lWhWL1f2g4WJcnj9QeLaCZ4KINOLogFEJeJCBvednoQAk1LPHUT7CtQu\\ncH9Ee/nGOotzSTEEyGPLpctHfPkg/jTgPACAneWv7V1yB4khWromlZXqWfNIakOWXRp4RzAq4C4T\\nksqoXoSm1OD5SwUfa6RqEc3nkcuK03ZDMSCFvHxVNgu+Xkvt0nwyPs+HKx3Q1fPfZNlVW8Ptajzd\\nxT4XUdNv/3AOjCECL/8p+LF5Tjl4Go/Qtt4TllMp7+6+9t186lo13IxBwKmZnKoZDzhDxpfvgi1A\\nntphK4iR09Yzm7KPPSXpr2GnM0/ynGZmkKuyvk6D5KOcNkt/dQ2ixNRRMEE7USFKlFtRCTgvJpqB\\n30kFGvz+/VC30v0Y4QjOvj6ApSsRvLRP6VJIeZCDXuj42xDP1a2JwnL4f/0+xxUoiftavQmc1nv1\\nhd9jOaff7Y70wStZGI7CG0y942SJS1U2IKBmXiAmrKSxilL9t/cToWtCu9CyMJPTSRZ8WDmG7mdl\\neXNPpSKtNtzz6TUIUZmvrXl0v6Jxlbj71HgAwKYPqV82haS0e6WZ+tP7LScxfRyx2DYtHdF+ARFf\\nflIg2NYPPDgf+eMoN9GpldFDXbzJ4WJTncHOB14C99Fyz86jZ0fh+T47O33NHwM0LJ2LO0mQvgkP\\nW7MV+GDqQ4wNZzl57FzD3Ah4qH60FNP7shwW0GShGa5Fwb1iwfnin+qGSOFxPNxuxMSj6Gcg5pTd\\nR31aWXMmGmzMB8+E1gzlGol6rHbGViezFpGbl0KM6JziNbe1Kc2PF3+8fjH9/WDuD1yS7kF3UIl7\\n3QKVg1d453kOLGyhHj5PU4en6ymQso+GFqoPDl8DBws+OsYSkL/abzOGjKdFpJFRYLA6HQ8wGq7l\\nYuJKnXUlY6KFKLtvlY/DP4cSfWGslk+ygxOZgergYJSlontzanF7zSNvNC0ObbtzJHpFyzAa9WeM\\nOIiNX1Jnn8Lihez9RJgrggprAKCvU6PMS5TcqjSWVzLZjUGZtQAAk9qN3N+QKt2qs7TwzE1qwudl\\nRJHbaKE4K69fgeZjtJDVMfVF73A7zgjUMfncKhiTadaj11AZG5uMKN9HtCFQejMcb02H0k7nW930\\n3jOVwXcz7WdEV179yeio76TXgo19D5weh6OtFBMk+IHhhioAwIdmTkntaSLSuYU3gy0smRKvM0uE\\nr5YGEfOF9XCyb+wtZ7lPC1uhWUUzL5WDXprPrYCK0X157JnCG6QDHqslg4eYGqzfoeAxqip7cLHJ\\nKUeCH7CzKmg4E33ZwuNQn1l3GQDA2MUcfD9WTPkZTbbWfhKbPi2jfWjru1Z3vEms396nx/g9lIPR\\n1M76rrUw/P+ZpgNYqJ8AAHB4qJ9RugUpL6euIXghvk3bHHnxpOMAjlN71aroedyWEMouW9yqj6og\\n8G2e0Ot03qgTUNL9BDHyGhbFj5ewJepodr/ubBGU2+ndOmmYgL4ucmKttotw0LANTzq9ZLHQDd9x\\n6uwCqiC1Ul/LcpoaBFTPoP44eyWjYWsEKL20P+UQU1Q/qoMnifW9bL5vCFUSNnLjnACRzfC8ZlEy\\n+vO5RdmeXJw8kwcAMJ2OfAZ3iDQzV+zvCPHkJw1FPAborixMObRC5Lj9U1+cOtMU0LNct0qPAOaz\\ngLuUnBJCkwZOJ1PQZzHv6amtqG+i+p1SRAN4QyANuhoWq7qO9FBGFZ5Ak5tWurmmJqSPJWeFWkkV\\n4LzkU0hmMuebm2kOOib9BD45QfNCMwt1EAKAOgr9nKO5SAHTWLJQW/zUZmzWFKkPO8piY0uO/fhD\\nen4qC9PulZAgbAAAIABJREFUxI93xJAhQ4YMGTJkyJAhQ4YMGT8p9FoPqpUphGqP6bFnIFFNVjeV\\nYIiJ6K79NI0AgDSlDZ82ksdghomsN1U+J94aTWJD7zaQRfrG+9+XhHxC0eQnysKDo0+grXfsQ1sy\\nltbRtfVKKs+26v4YnU2e0StG7gYArDk8JuK6D9yxFHclk/LtyJT7JEsW9tE9dvfNQfqF9CynD5J4\\nh6ZJIVHEAszyra8PIG0bfSavmbydniRgTzaZTpV2BfYwjyinptWa0iQKhXU/Wbm0jSJSmHVTUkg9\\no5fouupmBewZ9H7sGiqr3uzGozO/AgA800BqgVXrcmFkNCRvSeT75KJSpfhxeVB5vs8Vq0ZBE+KB\\n45Ty3zYTTaWreVB7G5TNVLeseUwYySFAlUHmyXHZVTAzLuEXGAqABFY8E6nNKI9S3TIdV8E+lIkm\\n1JBFVukGHMwz6jtFP1SBcG9pgPU+bibQ4sgO0t0kRVYvwB0vynBdBgBAy2A/NJlUnnE5pwEAZft+\\nnDTCruK7FUTT67Wd+n8AtB3kzo52rCPAQlTsJfD5qE8VmbsroAREgXklQzyb8ebqVPiYR7c+7mJ1\\nCrHEeDgu3XdLzxaiB2E4QQOzozJdYs1yCq++LvJ4hV+UqJRg3imvXQNk0zzCr6NWqrYqICpov7lS\\nhPE03ad5IBv/a0Qp7InD1k8IUrwZDbulUCF57/3ZLNTHpYSqie6jcAswMjV07n33mAWYTrVfkdQh\\n7tAkTYJJf3sJ+DsJRSi9mMOVSy9ZV9V1gbTeArVNRMMw+uZp+0QksTy4LQrmdhcBXyv99qbSt64t\\ny4DIaP91Lqo7qUWNaLVSZVZX0PFlBwfCxfL8lidlw8TymvJwnjNuC/Z7iEJQ4yCP7OYjBZKwIg+9\\nEcRg6IKRhu8w8TGFF3AyNonHQ+f6kvxQuuj3FheV253jhfb0T4vdJgMQxChUnHMNXU5/MfeeR6Lu\\nu3LOJjyTRQvBcyWP3N3gSrg/VcQ7WfoxgoXw/mRhzfuhS9Cz0EShh/GJybmOb+4JRFNVBciIBQSf\\ntd5vxwv1FG/mZ8SZiaYjaPbTxGOMjqjs+zx9oGETU1dALe3PVpOhRi34YFbQ7LhYRZMSnaDATg9N\\nQlwiTRKsfj10bPacraRzj3iykKmi4MSj7myM0JOi861r5wEAxg8sx7/zV4c9x9C/B79RaOqY/b+K\\nTFPBUfqP+6R4Za646jOKEA30XAoWtydqRIgK1jfr6HhFqwqBNuknhJD/RY8C2iRaALib/z975x0Y\\nVZn972daJpOeEEIvCb1KE1BQQFSwYVfsBQt2XVf3u67urr/trtssYAFB7KioCIIFBQFFlA4BQgkt\\nlJCQOplMptzfH+femUlmkkySSeU+/xDu3PLOnXvf9z3vOedzApar1PNYc2SC6+zkwqAu9JmPy7as\\nW2czdP0NAGwd/R4DZ7f+568mlDBs9Z0zZzHgldZzH7xRCsaK0yOFwBPTtuctpnKD7/3fdZfok7SF\\nMUFDqRIfOeYyqU7w0+dDmqE1kSVBLb1ku02cPKeWdsGWV/1E1B0t7+yp4V4UqzoOFJvxxqt9u7qI\\nZIhx+44xmeV87nIzRoun0jZXaZQvEyKmXRmOEhkLzFY53uWwYNKuo1bKcNktdFhZ+/Kx22agtFut\\nu7Vq9jzz+AZFUUbVtp8e4qujo6Ojo6Ojo6Ojo6PTImjxHtRAdt09q1V5UXfdLav8Wp2vtsrp5EHt\\ne8tuX8jKyW9EvcdSAgZv879H9eF09KC2NjY/+CKAr44z+Ff6q/OgapR1UgVR7vTX5Lz14Llyjrhj\\npJory62OjD5IlisNgI7mIrxVl+GBbma5aIoae72xIp6TbnHZZkSJeNtJTzzRBvGgHndLzbtT7jj+\\nvXoKADGHzKg6dxQN9Hso7z33WwB+007qXQd6UEPR6QJRANVUxgGKvA5GrpLjvHmq/LTR70FFVakk\\nyuv/W8UQ40ZRQ8kM6gq4OcqNRw299ZZawKT4zglgLDFjO145zcLSqwTjL3JPLrn+BwD+0WEzT+eK\\n9yLRXMa8D6bU+N1aO+F4UEG8qECL9aQ6Uz2+sMfsK17zbW+p7Y0Ubd2D2nvMQQ5+1ROAsh7i+Yo5\\n2HYSJEJ03QBccvWPACz9+KwmbE1kcfSSkOzYJAk9j1vkr6+Qe6F8lvZVcLh2aRcj9r7yucHiRbGr\\nofZJEhXjdppRVG+q2SbPhMdtxKD29Uqhqpoe7cGg9gmJyXZKVHVik1nGDHeFiT5dZCzMOiwpfGlf\\nhx8+roX4V4fm8e8/p3XaFroHVUdHR0dHR0dHR0dHR6dV0ao8qABpYyXmPHddp8ZsUkTQPaitn6oe\\n1Ace+YRPTogYTc9YEer6en8/Yr8LronXklBMBgye4Hdd96C2bHY8NIuMj+8FwHbU5NsWyoN6yd2r\\nAfj403OIrircYoBNT1XO2/xHfh9y1dzRZIsITfWNPsYpt5SU6GwpINZYWZmqXLFgClGGpEItgNjd\\nLCIZOZ5E32eZ5RJpMOfTC4k5Ft7vsel30tbaPKgaPadms6TvsqDt6Z/fDYCxzIQ3Vs0JKlW9oTFe\\nfz0zVSfAaPHgVcVBjDZ/PpKqU4Sn1Ox3Dar9XlS+iYr2cu7YA9V7YOx9K4jNklV0e58KbAfajiBL\\nKML1oGq0lnzUmjy+FX3FoxOVZWvSNjUGbd2DGjugAKtF3vGS1Wm+7a1Jo6Cst3gDsy+aE9Te6jyo\\nrQnb2Dwc66RM3bt3/QeAG+c8hjtens1Hpi0BRNDrjb9MA2Ddc68AMG3PVHJf7xl0TleMKiA6qQyX\\n2tdrOah4DP6OS9UQMDhMGJPkPnscJt9+RjVf1WAEgzqOeNXzRMdUULFPImjaba3+PcobBqmbg7fX\\n5kFtrWieX1OnvWF5UFudgarRbbyEdh1e03KziXUDtfVT1UAtGO4meVPlSejke9bx+ecSLhNzrPnf\\np0B+fOYFADyKwvR90oFv39YDgLj9JhxpLau9kSZcA9U5WCaWvTqerLZgfFPyrxlzAXh87owa9ws0\\nUDWjDmD4X4InVyUZ8qLuveEV37anTki95DhVIjnRXEZ7NYT3lDsuIGRXBtsTrkRiVKN1SLT0weWK\\nhXyPGLVaWK8XI1vK5D5++trEGr9DKLSw35jD4ZdgLxsg4k37L3jDt+3jUmn3k0tvxJqnztjUR8LZ\\nz0FMnCp4VC4KTNG2CkqOidEe30nCnz0eI+VavcBj0b56ohrmUgPGOtZ/BL/oU1ulrgYqtPxw39OJ\\ntm6gVldfVVP033NryxVOuuCa9QB8/dFoADpOPsKKgYsBf3vDMVCTzzkOQMHqjo3QyvqT+YD0AwNf\\nDr736+/7N0M/eQQA60kZH4wuiD8s45vHIr/rz3+ZzYj/J3PvqNIQi6pxBgqGS2dutKvCeRYFY7k2\\nTsgxUQVGKlJUY9Vl8O2HqpaNQcFkks+jrTL+uX9K9rUnFOXJco2RN21l66uS9qHVO4a6G6gV7bxE\\n5bf8FYm6Gqgt/xvp6Ojo6Ojo6Ojo6OjonBa02oxwTRSj/5qWt7ql8ak9rrmboBNhqnpPAVa8Nhaj\\nWlvWmSwrX9aC5lt9dsUb2PaYrEA+fFTqAO8tac/eH8VzqiqmU9LHjbkofA9VW2ZCLxHlmdt9DYO+\\nab4+pa7hZVot2apevVDE75f1yAdzxgBSs/ivHbYCsKBYwqhciplsp4S79Ys+xiSbeCUtqmcU8gPO\\nqK1verhuvyyGdrNJiG87i71enlMftXgkK86wAxC1xR9aH7NTpP5fHNWDh5KlhM3VceINfmVoDvsO\\ny/eK2SXCSUaTgi1KvldyjHjQ7+qxmnmx4wA4ZZcSOwYDjOu1D4Ahw3J8ZXb+8dNUacNua32/pU4V\\nxm6+BtA9qZEmbnQepetTg7Z7BkqpqJmD1wDw2sdTMdQjGqAtoUXdZLvk3gSmVLQUNM+pxvEVXWGg\\n/K2NIaG8j1XRPKc1eSybg5raEWeMhnhV1Gq7jEFaLVUAe1f5e+yTMykcLV7MtPXBHsmoUoWEXRLG\\n4lKn6gnZXghKYfHCQe3vwM/8/b4jVdphKVHrTjtrDitUM2L4blc/ht2xF4Cjr/Wq8ZiaiMo30n/C\\nfgB2rcqo93laGq02xFejNSj76nVQwaumWxkrat6vpVE1xLdwbAVJ6yrnjhUO8aKoyp5aHS3r3mhs\\nuU37u1ckSSf869s/YrNdwitLXDJp/3bzQJI3VzZGHR0NuKPb9rNZ1xxU61n5bBz1AVC30C5niloH\\n85RcL2nCcY4dT5Y2qMXfZ1z1FfPfqV65NeHcExSvFsW/EGmeIRly6S4Adr/b3xfi61I8jP7rQ7Ue\\n+9GTz9HLIiPzuduuBODwgVRsh2TQNihQ1l2e5/9MfheAN46O59I0MWpzXRI+Oykuk3HRlYNx0pfc\\nTdKW6mNYncngUvOI4g4F/0bGqXmy39rgSXU4hKqTqo0TZkdAO9TfzZ0qhmr21Dkhz/dakRSdvzZu\\nL8kmMVzDzY+tjrqG+F57zSoA3t1xJpadMQ26dlNQnxBfDc1ABXw52Fo4X3UMuiBL/k04Rqpao3fW\\nB5fUvxGtHK9Fweiq/49wOoX4Jpx7AoDi7zv4tnlV+2PnPf5nsTkMVS1Ut7Z51oyblgPwqxQxVOpi\\nbCaMk1SOdcM+ajYjdek9zwFwyWtP1rhfRZICXaQTt9lkQllaaCN+u/xgMSfkRlXEGSjuI89w6qaa\\nr50nsiKkDsgjf2t7ANptj/zz74mSZ64iQf4t66gwfuJ2AHa+NMi3X0NyUFuywq8e4qujo6Ojo6Oj\\no6Ojo6PTKml1HtSnrl8IwF8/uM63TRMjqo8ntSJJVluiCiNvq+siSX60Ooz93mhd96KqB9U1pQjL\\nl4mVthWMcvlqYhkKxC0yYPhBDixLByCqqGnesQce+QSA5z6+Em+GrDB61DqQ1lMmbMcrt8MbZcDe\\nufnf/8akrh5ULTxKI9wVc0c/EUxQ3HK9qPgKzJvFO1mWoYbHGhRi9gUrt75w16sA7HF25MW3Lq9T\\ne9+5R5QN73j+MezjJey1d8eTHPuoZ1jH//kxERS6JEbCVquKK5WroevR+USEkrPluTSZPVQUybMZ\\nytNaVxXfqrhV74+2YhtITec0ji3Auy650rYuFxzyqTTml8XiWBOeV9feS3732H2hXaV19aBq3ynr\\nttn8+5SEcc35YGrdTlJP3APl2TJnhq9WXlcPatzoPHokijL6jq/7AuJJzfjmTkCiUsIhcWwuRevS\\nat3PE6OQpQrhNGYo8Zt3/A+A545c5PtejUn0SLmHLw15l7vmPxj0eXkniYqIPlZzhtfp5EF1qcJI\\noYSTAseEfWrY77RXavbyVYejk8RPd+0nHsv8laGrUXiGi0Cb0ahg2JAQ3rk7ykRs/7UiglcnT+hw\\nSYXIPPtt36am8KRmPjDLJ2T3zJs3h32cVgc1ZZ10opYyhQEP7gD8nkj71cXEfhzevdOwX13MilGv\\nA/DbHIl2+m5XP9K+Ca+zPjVInp+UHaHfnbI0sTPKRolqvtdlRHHLtg7f+d/HQA9qRbJqoxTUzUbZ\\nddfsFudF1T2oOjo6Ojo6Ojo6Ojo6Oq2SVudBdcfJasK7V70IwK0LHqHT2TkAvNpX8qSmzX8i5LE/\\n3vk8AD84UwB4/J0769/oOqDnoPppbZ7Uqh5UkJqiQMi6ooWD5GY8PvkLdpeJAMEXWYNI+L72unge\\nqwF7VzmnJ15WWq25JsxlqleuMPh6pd3hV1eIvPwXJ0WuPHtpBpbiyvu6Yw2Y7ZW3OToYcNvq/2xq\\nMvx9FrTc37KuHlRHJw/7rxGP5iuFXcLyaCojiyk/Jp6la8f/BMA9KWtqXWWv6q0FuD9nLACrFo0I\\nr71qaZXEnyp7lwrPEO9dTXmggWgey6dzh7D09XMAcMWDpSSsw+vMiFu2EmsWr/PqeWcGfX7tvSsA\\nePfdyQ26Tt+LRfxqUe+vfdt6fTATANuJ4PXZsgHlPrGlhlDW38mVQyTx6csPx4bcp75lZu66fjkj\\nbAcAuH/+zPqdpA5k3jeLzU75rW5847Gwj6tPDup7d0hEwA3zwruO5i0s35BS52tV9HU0Wr1Sd4yC\\nJ1bGgtQMaeOiIfO4YE79PG/h0H6c1IgvdMjz69qYHHI/TRjJlOkXcYwfI4WTS35q79/vNPKg1kR1\\n/XRtfbQW8fDY1TI+VzuWjCqSf3/xR2Zp+aRz36l7hES7ifIc5H0f2jtbE5pYUiC1eVId3dzYDov3\\nzx0r39lsD763zv4OrLtsQdepj6fWe4YMTEmLZdw9NdBASmbl53Xen//NHU+HZ1fYO8lY4LGCo6tE\\nGKSul5z3skuKif8kPugYt1W+Y/5oN3+euAiAF/90bVjXK+ku10ublMN3gz4DIGOR5NqnrTNEpA6q\\np7cDjsj9NpU3+HQR4bSpg9oQGhISXB9qM1DfvkFqVd783sNN0ZyIU5uBGjv0FPatMoGIhIH6p+ve\\n5ZmFN9b7+LpQ1UD1WgwYXdX/nlPvXQvA1Um/8OQ+UaQ8nJdE3MrqQ+PKz5fO9qyuB5jbXdQUL826\\nCIB9K9ODQnMBXGqS/dIHn/OFQXQ1y4Qj/csZWE5IKGncgRq+HFDSs+bPWzs1Gaj/mjGXBxbfAUD0\\nSbmLH937PAOi/AI0WvjR7+eFH34UDhsf/B8PHpkIwDebRX4x5oDfYvGoNlJtA4sWmhZ7uGEDWlF/\\nv3Rn4q7wlJ3L1PDwmKP1u3bhMNWI3hz5EN+qBIomZXwoRl1UobGSYFJ13Hbzl7z5dvXiVqE4/9r1\\nfLb1DABiq1H5DddAdfat0siTVvZNV8P4ZjfeGJZ5n/+eFXmlDX/OPZuln5wV1vH1MVA7jD8KwMrB\\nnwKRDb299/ov5Nx5/QDYvaJ+qpkDzpdFj53f9KnTcTtnzqL3ytsBsOyqu8iV8QwxZLxbEkN+3hDl\\n40mXbQTgu8/9RpduoArXTF/F29/Jot2+6/w1pGtL/9CUWjVV5LGXb2XdZ0OD9ivroRpEXQvJO5wE\\ngClRQlj3Tpzv2y/jEzFgbEdC98/9LpLncvcyeS6rq4P6+1vfA+D/Lbgh6DNnqpd9179SaVtDQ30D\\nFYKrGsADX76fnucfAOC1XpK+d+GroRdxrKNloce5PoUUtX6r1Sz3rktsIekxkofy5T/P8R2TO1ae\\n4bR1Nf/WFXHyuX2SHdcJMeo05V9HOyNu9XUt6ybX6/BDZIJPb3/qc2YkHgLgrD9KOL7ZoUTEQG2J\\nJIwU8cMNF/9ND/HV0dHR0dHR0dHR0dHRaT20Og9qVe9nYJkZZxdZdbLmBAuR1IWaPKy77p7Fb04M\\nA+CzxWfXeA7t+LYU4pt1++wg0adAD6rmIQV4p0QUVv7fh35BK49a1sRUbmgV4b6xOeofYf6EmjfU\\nYvbw/SgRoFli78rzL1wPhA4LfvHJlwGYffw87urwPQC/zZKyH2VfdcDkqHyMYjTw2MOy2tjRXMRf\\n90sphUPHxUttMnuJDyOkGE5PD2pZulpS5NLXaz1eC7k9UiYr26ccMRSs6linNmgCSonJdjad+b5v\\n++TMaQD0TpDwutWLh/trEGrNruW586gh2rYTDVtxdakRTPbubmw5Eq5lLQzez35OKe6T8myF62mt\\nCxWqc8g6VlbDK35oF5Hzxk84wY9nfAxAn7elv3G3cxG7p2FjRU1kXCTlHrYflBI1MZn+0OHtD8+q\\n0fvZ/4Lg0GSNVwq7MNYm5x5mFe9s+ud3YztSz5hh/N6eP9/4tq92bHWkL7sLANuBmu9duB5UZzsZ\\nQKz5xkrlZQBmHBrPD18Ee53qys6Zs9jglPnBNd88AED2JcHv/3X7J7Ptq37Vnscdo3DeeZsB+H7J\\n8LCvDbDSYeS+N8V7H65QUWOTOFbEegJFpdq651QjXA8qBhh/hYTrv9r1RwAKPGXcsEfCOQ9/3aNB\\n7bj9pi8BcHnNvPNe5ZSGsu5uZo7/DoDftJM+IZTndtAlu1mYsaLS59V5UOuKo4t/TKgPgZ7kUPVW\\nq3pVf3NiGN8dFS9w6Q/tg/YL9LruOixjsSE/Cq9N+pEOaxr2xXPPl/lBu1SZy9nLoyg/LhFwmic2\\nd7QSsrZqfVCMasqY1//etUUPauzwfOybZDzf88zjYXlQm7d3DJMZV8sL3Md6opJhCmJEan9rXLDz\\nMg6v6Vbn6ySMkoligaesxv2qGqauDAeW/cEGwX9vmgvAwwtrznXNul0MteZU+w1leDaUZzdeCvjn\\n2iCGqUakDNNGNXSrjNUFwzzEpomipf2kxH0kZFr43QPvyN9Gicl84fBkXimQidU1CZtIuVJihbN3\\nyGTV0rEMZZ90ejd/JZMWg8vAuor+AETnSicbFWCcOpPl3u14aBbrndKJ/vvoFE78IOeMV1NZTA6F\\ngmFi6VStfQr+MOTlr46ry51oUhozv9VUHL5hNavLOgDGbb0KgGNZ7alr1ppNDfEccflunwpkL0sc\\nKwYurrzj/T/6/tRCvJ/t8Rm3vvZotec2eCIzkGm5pkk7Kg8JVWvweY/EkJjdeIE3UeozrNWiHfxD\\neOFl7hjFl6sdipJVHUg/cA8AsWqt2uRBRZTtaV/tMfXBrT4cZgfsXyZKu6Mvk1q12zP7+/bTFu+q\\nI5RhqjEzKYfAIvEAHbufouhIh9AHhIGWi16bcQqQNVVytP9bIIq08z7whz8bhskPqGwOHYaqMe3K\\nHwCYmbKGdLUW7zF3KaCmKXwmv1UkDbgSrywQaIZprsdOmqly6sVdHb/nwpky0ddCZV0JXizF8swv\\nv+GfXDq39jzS8q4uLKfMlc4TSOD3cvaWMSNcleJIsXPmLDI+UmvMBmx3qTWBLXn1X/BorfS7aI8v\\nRLb9JAk3Lyiz+QxTrV50kSOa5SPkOTr/6/rnFXe74CBPpOwDYGFpImnnyYr4gUPSL8UmO3hz4QUA\\nLHBfEHS8trjz89ZeoBqonc8/DEDOt6HnwKHyTANZbJd5zbRYmQe7FA8Wg4yZ9coXHSRj3vKxs9De\\n7ydv+QiA2xNyg/b/R4fNpP80GoBzLt3Ogh7fB13b5ZX2KBXyXqYPOcqBY1qfGjqloiY8FhkTTC7F\\np9h7arCcb9zE7azMl7477W65txlmJ5s6dQUg8bPwlc2rUpZmJErVCzG3kDzRxkIzTuuCHuKro6Oj\\no6Ojo6Ojo6Oj0yJoFR5UbYUJ4P+qfFbVewrUy3sKUPyLrFolD5cVJGdnF9ajspri7CAhOb0WzmRf\\nlRBgq82FN4RfZWqMM6zrtoQ6qeG2obb9NC/m7jtnkzXhTdm2r2m+X6AnVftbU0azRKjObfJmEwUj\\n5fkwOOWcj9+3kJ2OLgDM3yDe9TkT5jHZJl7MUq/Z5y1br9ZGHG21cG+6iI2sf1NCxUq7K8Rna1cK\\nDrOKP0dWG78qs7DWLrW+3F4jKWNFMEDzHyVYy+EDCTsqHCgrrEmZRkq7y+edVDdV0bhyjDl1W7Xf\\nc+tsn1dz/nUSmnz7wgfqdI7azl/d9SKFNT/8Z2G86jktVMN6G6L5KQJYcbXuB7Ck7zL1ryicg0Wg\\nxro9+Oqa58sVZ8BS2oDGVUNVAbT4RvSeNoSq3lNNQTNwe+zBysNd2erIek8Bhk7eDcC2r/rRZ7KE\\n4b6f/i0Ag/F7UM+xHazxPFqExGhrzV6sfqtvBcC0Pbznqjo6DTse9r6aN8WixqP//ub3WJIvglAb\\nlw0M6xwLN0l01z+mbmbSDlE31dQsAYYMFOGQPccywm5Xbcz44TYA9k2eB4Ddq0CVYIqelkLSl0rE\\nwtu3vwTAqtIBPJUqv+uAMOtfRh+xhL38Xx/PqStBXkyDWndZMcGz0yTt48/vXB/WOQa8cj+GlGCF\\nw+hDErp9uoT6gr9W9byMxZzN4wD8X4b0wbucfjXc74dIrfFJOy7nb7mTAHCdUYpZff8Mfq25sBiS\\ndJSLd18MwJiUA753YGWGPDw/O9KZ/7NEKITq03xjWb7RF9o78+alAMwm9DxY80SG8qR+Xw6P/jgd\\ngP/bHRmP/qIxEnGRbonzeWdDeU4/tcs9vCK2lOxprwV97o5Xv3+JgaOFIl749LglAHgw8sb8afVu\\nY5Gqd3bdxWt5Z72k9cybLBGQHUylHLFLio/TLWPIU92W8ujshs97YnK9nBqs1k7d3uDTtTla5mxD\\nR0dHR0dHR0dHR0dH57SjVpEkg8HwBnApkKsoymB12x+Bu4GT6m5PKYryhfrZb4EZgAd4WFGUL2tr\\nRLgiSa5eDqKs4sncOe4tAHqtuCNk/mckcHZ0YT1etzyMQI9ufUSSGisftTFyTDUCvSxdx0oOxYqB\\ni33e1Mw7Xmawunrt3d2wlf5QBAozaXyvxvOfG7AI6FJkefOpE7J6v/iL0PUJAwlVB9UbJSteRWeI\\n6EZssoPpvTcAYDXKSmyg1x+0/Cr4qEQ8n2Ni9vL77CsAyF3YvdZ2BFJ2XikTeu4F4IecdIZ3lEZ2\\nipb8sXFxWTy/X1Zdh6ZIHs2KJSMxSnO59DrJ//r8o7Nxxdf8bGoezYyPxRNtsjfNmlZVTyr481Hr\\n4lWtqcxMqPp2gUzOnMbxFV3Duk441Ha92khfLHl5MQGeQPcw1W26J5aYYw3PR7WfU8qEdHl2f1lw\\nBoWDpL81J8nDY94dQ3Regy9TKx89+RwAl88Oz2NVWw5qdTg6SudlOx7Z59o4toAkm3RCmtdlcuY0\\nTnwjz1NtIkmBJV5CMb9YRG2ee+eaSDS31uvVRr958j4GagyEEknyWqW/2X2HvN+TdlxeyXOqEcny\\nMgDO9h72X/1qrfvtqHAwKMpWqQ3P3vwOUapr7Ldv3RrW9Wyj8nFsUHOuQnSxngGiY2DaGcv914vH\\nq8wbxfwPJc8wakQBAMX5sUQfrF6MqvsE8TR/OWCJL1royfPEq3RB7G7O/1q8wZpXtC60dQ9qKJGk\\n4Zdl8kp3qUEaZ6zeg/ibE8NYnydRSrnfdql3G9KnZHO0WLyBWt59VabuEhHEA2tknmByBLc7MHdW\\nozrrClBMAAAgAElEQVSRJEdPmaN8cv5LXLdOxhSbTfp318/JuONUT2VpZPQNLrla8ne/yB7Id6PF\\nMzpTnfvUlGtfHQNfvp+YE9LGU+Ol3Uq5yae3EVVa/+f21GADUYXyvYdNywQguziF8kWS31+RJJ/F\\nHq2ltmIDaUsiSbHDRfCwZEc7ny2U/atfR0wkaT7wErCgyvb/KIryfOAGg8EwEJgODAI6A98YDIa+\\niqLUMfChMsmjTwBQsL6Dv69X9V0iaZxWpMuEIipbOibrcYvP4NTCMPZ/3zPksYPPy6r0//rWWI2U\\nEVnV0B2w9paInq86jqyTzvrZ9gN9huO0PRf7FhT67Y68kRwYVqxx97uVrzP9su95tv0OAP7ZURT5\\nrJe6+XDJ+Dpfz1ghT2F8O5lkrBv1Js+cEGP3X502hjxmUekAAP69WgxHg3sqSdvrNikuVcUCPRVm\\nHk0TQYQS1yWYDNKec+NFjOXLwiEcPiqKvkfzRayk78QD7DwgoUprT0jYXHS+4lNvrQ5NrMTcRIap\\nRqABqhmrgUZrY4koTc8+j21L+te+YzXcf8vnALyaNR73z8mAfwGn93d3sHeShBc+fPRM1ufKD1qy\\nWoyNsr5OHhktv+ujyQd850z/QlRTYw4Gd9dms3StUQ00TgtHSTpCzJY4Vu0Xca9YAkWT5N9u1+3n\\n8MLIhV0GYu+m+Gqq9lKFcxRj7XWWITjEN1yqhnt7ouDO62RNta61TwPxrksmb5QsGK0tly+QnZPK\\n+/f+D4A8T0WdzznjkPRVPy5tuKqtxrhLtwCiDAzwz40X8t25LwLwv5PnArBo8wiyL5pT43kCDdOa\\nGHLunkr//27QZz5DcOfMWRE3TDXum/SN79zu/iL+ouTYGDhacip2HZUJqLvCxP4LRH09foysv//m\\n2+uJPlq3bKjCwlj6TpAw7mOqARJjraBPkpzzkY7fAHCL5U5e/ljmFlq4LkDFRuk7smu5J18OWOL7\\n+/tp/wLgo5LBgLxD5wySecltk9by4Jv31uk7nI4cKU2q0TDVBDRPVcQ2yDDV+F/6h76+rjqW95cF\\njLw+Mt/41tGZZ+fdVGmfY/aEsK+piYSlL34IQ4wsQLq2J/s+j5RhqnGqQkSElI2JjCl4BABbtiyY\\n3Ghy0ydOwn21+Vl1VBJoUptoPirnWXLT81yV/QQQvoF6YpyXDmul/y/rIP+aHOAaLou+x8rknnre\\nTsOiWh6Wsra9aNMYbBgpqQf9N92HsaJuz1ats05FUb4HToV5vsuB9xVFcSqKkg3sBUbXqUU6Ojo6\\nOjo6Ojo6Ojo6pyVh1UE1GAw9gSVVQnxvB4qBX4DHFUUpMBgMLwHrFEV5W91vLrBMUZSPajp/bSG+\\nNdUlbWyqXnvuLS8x460HK+3j7VeKUQ1dVcxyP1ff+jznviErOl6L0mylZG69VGpoLVgyqc7Ham0O\\nJFT7w/FwNAXpZ0u40/L+SyuFFwOYDMFrMWdtudpXG7G6EjWhQnw7Tz8ABArZ1MzyMivvnhwDwOqt\\nUmPPYPWStC68sCu7ulCrhat++tBz7HPJiueSwmHY3SKr3jdWIg2+OjGA3BJ5Hl8cKnU338gdz+ot\\n4hkMLD0Tbh1Ud5J46syFka99WVds/Qtx7EoKa99QIb6uBHlHs27zP98jfhFhEeeP9ay7OUqEp3ac\\nJeWG/nByEO/vGgnAoE7HANhyqCt/OfNTAGYdmEj+yk6VTnHTDSt4OEU8Wh+oP8yMxOMcUcPDp22e\\nEdRGTTgj5njdV70Lh7mIS5VV+bho8aCWftsBc81VthqNotHlPu+VxuAXmr7PjxQetdrBlGnrAfjm\\nw9Fsf9gfSltTiK91lKwJn9NlH1/tl/fWuK2WcIcwKU+Td3n3VbM4Z4s89//qL6vcd8/zj22O7hIK\\nGHXCjKubPB/Th/7CXztsDTpnqO+iXcd6svn7jOrQapQGenE1qvNchhKrqYonWmHedBl7DrvkfX32\\nnRuC6rxW6x0dInWfPHviwvJO75w5i2y1hFV6LR65lQ4ZC7WarIGUd3QTfVy8xadTiG9FonzXPbfO\\n5syNUrf95xELg44JVYO0NhTV+W5wB3/mjYLpV60E4KKELUxfIu/f7y+QtID+UccYGx38/mjzUVMN\\npUkiVQe1Lgy+SMTEti/rhzNVrW+cF15DXNpvcHPleWeo0jbavu72EolyVt/9bFkqUWrxh8KbkM58\\nehEb1bC01fPPBKDozHL+fbb87vlueY/+8cmVpGQ27bvQlkJ8d90lv2f/OQFRcWHWQa2vgdoByEOy\\nK/4EdFIU5c66GKgGg+Ee4B4Ac2LyyPTHn6m1HYpZqRQGE0l23T0rLAM43P0CqUsOaqRpCsM4lIFa\\nkeohKq/lTkzCpaqBau8GZ0yQsCmtMHZ19FpxBwDGY9G+fCxPvGroFZkCFHurp7Q7PHWVGNH/3CH5\\nSfNGzCcKuek3bpiB1SIj36YzxRhd4TD5FIQD0RQ/Y7/z1+0K10BtrdSUg9pu4jGObpfQvnAH0ero\\nM1XyNj/t40+518Lqtbxrs93AI7eKgfr8J5djKZG21ZTz0/X8Q+SVifJh4T4J244+4W+rO1Y1UOsR\\n4utMBpMaaXrh9VLvddFPo4JqoTYV7hi4/hZRvH06VcLVm8NA9aqyA2o6eUQJ10BtTLR80/Rld2G2\\nSd9h2RkTtJ8jQ4xS234rnmh5zkzlBhxd5cZkXyahgv3euA+TM/j5C5WD2haoatQ2dL9QTL5MNA1W\\nfD4y7DZVvU5VY1hj7GbJWzYZvZz6oWO15zydDFQNZ4o/l12rY//43BkNuo6W506iy1cT+7rpKwFY\\n+P5E336h0hmW3fcc3c3VLzhcsUfSEPYs7xX0WVMaqI5u0o/YDsvY8ct9/2XU7Orrd4eLx6aEzLl1\\nDZJV1D6dJDx47089SNle/fOaJ4USSN3k33ZyihPjUQnn1haBHrr2cx5IOlzp2LFPBi/kAChGOcbg\\nDX1de0f5AWKP192D05YM1FCEa6DW6xFWFOWEoigeRVG8wOv4w3hzoJK2dVd1W6hzvKYoyihFUUaZ\\nYutf6FZHR0dHR0dHR0dHR0enbVBfD2onRVGOqX8/BoxRFGW6wWAYBLyLGKydgRVAn9pEksJV8Y00\\nu+6exfTs8wA4XJJE/k/VryY2hOb0oDYFLSXEtyZM/Uvw7JIQudumiZfmzcXn1XpcVQ+qK96AvZs8\\nzg+eJwp042N3s7R4GACfzz63xvM5k2VlzFoQ/EyUZEBM30IAtox+DxDxkplJIdd4qmWlw8hEW/U/\\niiZ8lLzFdFp7UCOJMlIEcSb2EHXlPcXtyT6eCoSuXxopvGqUeKCyriseLCXVH+OcJG21fucX1vj1\\nw6Ig+d89k3EvS414O8OlcIisxK+++N8ATJ0Vnopva8HeR1zWPbrlkbu6c7O04Xc3y2/9l7fDq5dZ\\nX9qqB9XZQQ1dPlFzhJBhqLxnFQfEA9Z/1EH2fZse1jXKu4mXOvpwzVUE5t0uglZjo01BHlR3rILZ\\nLj+C16JwxqTgyJ++qsicKUS48unoQW0MPGqt6tjh+fyqr9z7m+JF2TTjw5k+BfFuFxz0KfW61Kia\\n6JNGylTvZPYVooA7csN1xEbJ8zGm/QEAvvjwrKDrNkeIb1NhUqNbKkZKWPsVfbfy8bciVGlUozlq\\nC8t1Jhhxq4EjmirwBY+v4c9p2yrtV50HNVeighk1cg/bv5TULS3qxGjxopySwTnuoEm9RvCcLPd8\\nF7Z4idOOWZKgfjelzh7Uqy5by6LPx9XpmOagoqfcn4O3PBUZFV+DwfAeMBFINRgMR4A/ABMNBsMw\\nJMT3AHAvgKIoOwwGw0IgE3ADDzRUwVdHR0dHR0dHR0dHR0fn9CAsD2pj05we1KYQXgrlQVXMoZPm\\nWyOtwYNaX6p6UD02g88zUDJIvCHJP9etVm5VytvLCTMmZ5NiFdGaBT2+B6D/mluI+VYT4FIv7IXE\\nK6S+qdNj4sLOkq+nybQXeR0kGoO9dmdtuVravUryLqMKFd2D2sop7yAvX8KeysvlJemyPT67+mV0\\nexeF2By1pm8/WUecMDqTzW8NaYymVsIdAyZZTMUQsITpVZdMHWfJynjU5rrXTR531SbWLpKkIy3n\\nsyWKLXkb1m20eNqqBzUc3rvjPwyzWqv9vCHldPpN3sfuFZJzGJjn+sGdEnXwQ5l8tqGkJ2uWngFI\\nXVatzuazJwcCsKOkEz/vUEuO5QQ/jLoHNbKUdXMTc7j++f29pu4HYFzKPuYuOR8AS3H136Ete1Ad\\nPcSF2q+3zINylvXwaQacf4voKXw7dyzxl4tAYcVbHcI6b9Kdh31lfR7MEWHL798biUsdhhL3h57s\\n/t8f3gZgS5l4wN/8/hx2XyXvpsUgHtSMj+4lMUv+Nrjl3YoqVTDfLHm0FR75zPheuwbloIYSJWop\\naGPC3qfDy0FtHjWMFkJTqQJ3G53D4fWVa2btuXk2TxyXSZRWl7M6IaNICx01l6JwU6LVRNXUeV1d\\nK7AcCU81N1AQpCoVAUKaSZu08zVsII8+KccXlNswq9b+MVW5VTNOwd+pARQv8ivAfo7U0fw4cQIA\\nlrNP0SNJCr3vzpXPTL/EE1WkdooNbK9Oy6GqYaphKal9dhKbY2DT7yoLqQz/eXrIfTXD0ViHRbWk\\nKyQ0vfBTf9/nPl9C2MsOJkA7sVAT1vkXU7Tzu/NkW3hvbGU04zSQ5++ay/1LRbQs5lgbnrm1ILQQ\\nV2Vr+DUa2wpXf/YI+657JWh7JOq87s5NCymAdP0bssjv+ywphwGIger4pR299kqo4qCRBwDIPNIp\\npGFaH7JuDVb8d6nBc4OqVD2o77k10Tnn0VjMIYRz2jr7lstiwj4yaONrW7USv1vuQGFXGSccwxx4\\nimXb2v+IJI4Vb9iGqdsqz9OgxGO+bftLRX3bkabgTpGByZkqY0fyDiMWu38e9b8DkwH4fIAsAi3e\\nPoFzfnoA8Buv48/cyRqjKA0PHiI1kjPi8rg2WVTeFxWIvbaWmisJ9J2QTdYqSRXQjNFQ7Lprdr2M\\nVFe6hBxbsqMbdJ5QGOo49dRHah0dHR0dHR0dHR0dHZ0WwWntQW0qFvX/gDPXVw5h7jv/Pl66bk5Y\\nx7dlT2ckqOotDUW43lMAJUD7wtFBVtZsahJ9VBGYnI3jgTy2tz0OtezJlFgRhzHXwdupeUhZlswh\\npE6q3zele03bEp7hqgrSqtC1MXfeK16U4X8J9thoXtP3S5KDPuuSWEQOUs6mrJPiK19TkSifR+eH\\n38YhyRJ+tdIqHlR3DLidMuR06p9Lfkn16u2JO+UldDWw9KcW2vvF/c+x/1rxaGUsuheAmCN1L4NV\\n1skb0gPbfrJ4i0+u6BL02enK6eg51Yg65X9GIuE1DWTnuLd8f/dddRsA4TzJUYXSpj0rxBPX2F44\\nLbSxIQR6Zit97wWtb04UKrw37bwccr9tnD4j9dxj5H3vj7SKO/skAKU/tG+U69WHzAdmhaxzWhtl\\nIxwA2HeLoJ+5cxmpayuH1JtvzmXN0EUATNx+BQDlCzoSf4f01SXz/PfdrM7pvnp/LNkPSnqVV41H\\n9XR20uErmT8Wp8s7ZJp2kuJ1ch8Tsr043pT7POe3UrN6wx9m+8SVnvhYPP+mCjCqYfPbMiUU2N3f\\nSI/2UjJniJpPttZXFCU0i/ssp/+q6p9/zdu5667ZuDJUb+j+6BrPqbHglhe4Zf0M3/FV0craec1N\\nkz6lG6hNwJkLQufXPrjwLsAfchv4dyijNNzQ3KzbZ9e4T1szeEMZpjUZq7WhKR+CvxZiTeq7gWTc\\ntAeA/e/0qfN1k7b7JzVme+MblAVjKjCfqE8QZeumIkldbChsvWFipk3VW27lqXBIDREP5PwZPwKV\\njdbFN+4E4N307wBY0ncZF19zMQDHPupJ0WgZ4Pp2OwHAiY97BJ130+9mhTSEX+j8s/zxa/k3/fO7\\nidsgIeunrHG4kiWcPbyhs2FcPOtJXz7q/qteBeqXlxpzzIh3lISuOvJEAjL2gJmcDaLIq71NZZ09\\nxBxVjWx1UK/o4iI26/R73xpK1XDWSBt8jUWk2/n8rW8A8m5PmSMLmDWZgE15nzSDMVSob6Tp9cHM\\nsAzySLHjoVkMevF+39+A7/8N5btBnzHo28b5nQKNU6jZMHWmSl/c0HrgdaU+xilAcqJodeQXSn/a\\ntV0hdirXcna/nQbPyd8rB0v98bHMJHuTGKahtOrLhjo44ZFl/fu7yZj46I5bfJ8nZMt9Kqa97++L\\nnvieZf+U6g1n2iRP+Jp9F/qOSRRhf8zlCifGyzEpXSTVJacokTv23ABAu2h7WN99eVlwbvsRdyld\\nq9TL7T/nPvZp+aj7w5sPj7Za2H3OAgCm7LwUgINr/ZVDjV3EmN4w/lXGvNH4ukF6iK+Ojo6Ojo6O\\njo6Ojo5Oi+C0VvFtKtyxXowVzeOtaQpBpFAqvrvvnN0gL2ZLQVFjDFxpal26g1G+cN9QbPy93G+X\\n4mHMnxomDhEOxb0gYV/wdo9NnjeTI7itmjf4jEt38vPq/o3avuampjCUsh5uYg62/CASrY6eKYQw\\nSFRx6GMcEyQEODVBVmXtn9dc47nLtdmAeFAzPpIQ2MTdJgaoHtad74m4Q6hIcePUPIp3iLCDKUM8\\nt7vGv8U1+0Rpct/7fWu8dk3UFuJb3l71wp6sfa1V86BqpH92D7ER+P3tPd3EHqh8HkdHr6++oYbH\\n6lcuBl3Ft648OP1zAF56/7Ja922tXteaCFTsbQpaiopvVa9sxoczIyKSFK6Kb1mGi5j98rI620l/\\n02PYUY6v6NrgNriHlWKuh1J5OERKxffa61bx4cIJdTrm8ZsX8a+3r4pMA1QMI4oAUDYm4uiuhrap\\nnUxSp2LKtkrKSvLu4Od23XN+wbL1Tjn2oT88jMEbvG/uBVKdQakQP32HVSbc6nyqWKLjSdlR+bhv\\n/v5fAMa+KHZMXI6X3vdLdYUzEg4DsDa/N2ajCIelRcs4edwRz4FCSanp307UfPe90p+iugfghaSu\\nir6hwnoDj42UQvCeZ8JT8dU9qDo6Ojo6Ojo6Ojo6OjotgpbvPggD8xBZWXFvS2zmloTGWGHgpktW\\nAfDO0rqtRDWUxvCcaitzNdU/LfCURfy6zYE1T1bODB5ZQbWdUHx1Sy2qTk1JupeknZVXYy0GEyXn\\nSiL/RX13sOLjM+V8teSwVqUiyUBUYfAxf3z8TQBey5lAzkGRHHerKRjOZAV3rKzURZ+QVcDofIXC\\nsbIyaLaKZPqP23u3jQ6gnhiiPUSiC/RGgbGi4e2pjlCe09qwqeJJR8+UHJ3aesZ9J9WMnL7iOQUo\\nGVdGUpQ8wzVpbHmXp+LzARyW6w5fdT8T7hT5/BAO/oiheU7dI0owb6zZ3bq2XDqscdFaBxbeNQZf\\ntovtn1cfaVDVewoEeU+hsvdUp+6E4znVyHaJh+LiuU82VnMahab2kobijCni+dm4ul+ztSGQqoJI\\nTT1mad5TAGu+vNfHV3StVz5q1WMay3saSZ5tv4MPqdu8dUbicf4V4XYoG/2jWErnokqfpcQ4ONRX\\nxjrXMNFN8C5Lpbi39PnD/i73u3x8CbvGi9jWT3/3CxkFcumgbQCs/+9I37abH10GwAsrpwCQP9RI\\nu63+QfH8/3sUgDjkeoEeW18bTXY8qk/w5vgDAMQYo/hvQU8AXv1AtB9MnSHSopbhej77rLwd015b\\ntZ9nfCLRVU2lpKCH+DYBXovSpmuP1mSotnZUYbWQODr6Z7i249W/RzFXnGD54HcBOGOhdGSJWTXP\\nji+7T5TkFmaNgB0y8VYGikXsOhJb6/HeKPncWCHtKkmH4eOyADjpkEGx6OPOlPSs8TStnqZQmmtO\\ntBBfdyw4VfGn2Bz/dy4eKwamt8JE0kYZVjRF3qiAMb5wsCxaLLnof1z67UMA2PZHUd5BFjoSd/ll\\nSUoy5IWP3x9shLnVsW3mnZ8zd+/ZAJRuT/G1y95F2mguM/gMNnMNa1mBIb4Vw8ToiAoxqdv+8Kyw\\nRY+0UN/6iCRVbpt8F0tJ/Z4xPcS37fDlXaLGookX1QdXohePTQ1ZP95wM6zD+KOcWNO5bm2IV3zP\\nc0sJ8W0swg3xrY3nZrzBk3PvrLRNMYHBU3k/z3C/cRQpkaWaiFSIb13QBAgxKpWUrBuL8jR5X5QY\\nD9mXvA7A/GKp/f63j66mIlV+BEuiDDbuPL8k34sXvcklMWLMBhqqM58W5d+NpSIIGGiohiL3wgrS\\nvqpssj31hwW8liNG/T96yvm2OTvz+uFzANifJSk3z563iFsT8gDo/Z3U6U5dZqWwrzybzjQP1tym\\nlARrGvQQXx0dHR0dHR0dHR0dHZ1Whe5BbQICPagabcmTejp4UN2xsqJltivYVdXthDOkKGT596lY\\niqt/j4p7weXn/QRATnkSAFlv9aNwoNy4pEz/OpEWhrtu8gsAjFn+KMkbZDW9TF0MN5eFDvutidIe\\nYOotHijzT+KWspQqbd+DWqyukjdxN+fo6KVzPxE9KFglq6VeKxgjHOapeVCdSfhCVq0Fwfs5JxVj\\nWid1Kc1q1K6jg4LtRLAXwThVVnQLDiQTv19Wb2sKYfaaweiuvM0wJZ93hs4D4PHsawA4uqinL+Qq\\nYa8xrH4j0IO69kEJGhv30uMh99U8rMrBWKwF1XtHMi6SUgD7l2XU3oAQTLx6AwBD40T84t3Doznx\\ng7ycnv7Ve3mrontQ2w5JZ0kZpsIfOzToPK5EeSksReH5DrxW6diMzsjfbN2DGh5l6S5ismt/mU1j\\nCvD8FFx7urFoDg9qU+FKkGez3Tb5tzzZyNxfiVDR345IqOyRV3v79tfKu/z63GWY8A88M5OkJqpW\\nJ/XM1IMsOzAQgOWjpBzZHXtuqFQztSp5w6D/qIPy9xzxurptBvLHiBjTNSNkvOgZncd+h5T6WXtC\\nxp4TB1MwlcoY23vkIQDKXFHk/iRzhp7jD3FgTfcw70rrIVwPqm6gNgFei/8eWwdIXJ1zZ8vMl60P\\nbdlANbplENMmWzHHFIr6ye85aMQBAHYc6kR0psQ21jXHNJDSCWXMGSO5pecGFIdcWCrPypCoYwBc\\nvm4mcStjwzunWrbS1d6FoVw6wsB6q23eQD1NQnyLRpeT1l6tz/llWpDBCGDvKs/mU1d+DMC/dp6P\\n+RtZMNEMx5ijRrY9JiGw6Z/fTdLW+ltRRf3V8GA1p7U+iwSBBup9t4qK66y3LsMQ4lxe9TKP3vwp\\n/1sgE45Q+zFajW1e37A+uKy/rDb89axFPLtVasZVHJH30nai9tmhbqC2IbSfuwnGwsSxueTmyWKT\\ndW/jVRHWDdTWTVs2UB3dZIAz2OTftK+jfKG5tyfk+vY782lxBJnUVCfX9afokiDjZDurnV+Oibeh\\nIlPGgopkj+9djsqTAeXF6XP4ougMAH45Kcai021maOpRAP7W5Uv2uGT+d/PKewDo8K2Zot5yoih1\\nwbjT5QfpFiv1T1d9OxQAd5yXqALZ77kbZO43JCqXi958AoBhk3ezeUXLyAWPJHqIr46Ojo6Ojo6O\\njo6Ojk6rQvegNgGBHtS2SFv2oEYVqoq9pdX/hiUZEL8/MtcrGidJ+/smz/NtcyniifpvgdSTnLVu\\nEnFZalK+1982xawqDrsVHJMl1HBstwMAjEo4wNwXLg1ue8/ItLulcrp4UEfduoUeNgk5n//lJOIP\\nBH9v12TxHH4wYg4AN26+k5JiWflVPOqzU2YmMVNWjgtHVhB1VNx8riR5yRN3mvBY5XxNoUob6EF9\\n6W5RRrx/040Yf0mo9pjtD8/yKSPOeeti33Z7d1ltf3rSYgC+zB9E5pKGr07be1cQu7fuuoa6B7Xt\\n4EpQQ3OL286av+5Bbd2kT8lm/9fpzd2MRkGLjIk/FDz5vP0pibSZmZTDX/Okf1/83KSg/fIHG3x1\\nu5MW1xyR5mgn7/WW3/jrK2uVKpJNMSGPuffIWQCsOqCGGu+Mx9lJwn6/nfIfAM7/5NdEd1PFL3fL\\nmNZ+xAny10uqwNRLfmb50jNrbFtrRPeg6ujo6Ojo6Ojo6Ojo6LQqWlQZxF13z6L/681X/6uxuPXS\\n71iwRFZw2nK5mbZITZ5TDVM5OFNkNdbRV9xK8ZusmJx1X4FOXCs5RSPWVv98iMyCnNtjM/iu7Y6V\\nbZ3OPMY1HXcCcGnCFgCu+P5+kurcmtZPWQ/xmsUcjHxX13GyKGh90O89Jsx6IuLnrwsrVw2lyzDJ\\nUQ7lPQWwrJA8m2l26WNjttpIDFHiRStN86sR3/LvdRcAkH3RnKD9nIqsBg/45l4Ut6x1aqVswsEx\\nQVaOtZqt1eFR0+wmqiU4avKeArxfksxtCZkAzEE8qM5khR8u/TcAF8ySUiDbH57F3VdKxMLK7yQn\\naM+ts1npkO/y4OvBNfIAHIPl/ti2i/e5Pt7T0wGtpuendhGM+u1bt0bkvM7e5ZiPiBt/yU3PA9DX\\nEtustUPbkudUpzJl3dzEHG5RU+WwWNJ3GRcr0v8d+KZn8zYmwoTynHKjiPtNid2tbojjqVT5+6nn\\n5N/eK28n9QsZUFIywbA9PC0PW75cTytHU3ZNEWVl0gd57BYMZRJ1dPaZUkN4TGI2o+OzASjoLB7W\\nX471YdzgPQBcNlvGIPPwUhaMkGi5kWfJODJtz1TGXiKimot+HoU1rBbWTHV1UIeeJ6UHt37bNwJX\\niTyt762rJ7vunsU+tXj3JfObtnj3giWTImKYtjTj1tVJpD2jctruBC0wbLY6Yo6CZjAaM6U7KU9T\\niD1ct2vZu0BsThhtMhkYdosUkx4Sl8P6op4A3N5hLQCdzUX88ZAUtV84ZzIASRXB7S8c4sVU0rYn\\nVg+N/waAuQenRuycXvVxb2+T/qSpjdMBF2ex84vKA0qvUYdY0GchAFMWP0HFJAnndWeKMRdz3G+0\\nnttnLwCrjb2J/yG4KHfCOtn2xrpLfIsa74+XZZHp8QVMzz4PgG1L+wNgNUN0ft2+g72rgssuN9g7\\n0BsAACAASURBVLL6suCCqbxu5/7zvBt4ur1aT1LdZi0w+AzTX9/+EQDpS+7m/DPEkDWX+e+PZghr\\nIiNVUxg0w7ShpIw7DsCptR0jcr6WRmMZjNa90T7jt9cHouisCY20Zsq7uvjDuZ8B8I93r2mSa3qi\\nVfGYVFnIiz7SxuPO60FtxumOh2ZR6pVOaszLkqrmGFCOOUruqWVL7YrejUVjGaaZD/hDXfvPua9R\\nVKSrwzY2Dw6mBG2PMkkq1N9PyKJqiSsam0kWUVOi7AD07niSq363CYDnNl3I3onzK50jsB5qTZQe\\nSaDdRulzTBUKC/8qC2VvF0rN1N1lHYk1i7NiYcYKOShjBUN+uhGAuCMyqJQY4xh5TuX587ad3Vk8\\nbTkAn8WfARGog1rVMNVoqYapRuvv1XV0dHR0dHR0dHR0dHTaBC1KJMk8pAj3toaXX2lpocJtVSSp\\n3XCR8z61Ia2ZW9J4VHSWFTjzSVlZVqM26s3G38+u9rORG65DWdqu1nOUnVfKL2e/DsC8on6syBNP\\n1vAkcdkecqTw06cSshiqXqomjBSbY8DepW0+mxpRfVQVoV9af1mnxTOfA2DaK/4IEF+ZmQEexo+U\\nsO616wYSVShrj5oX0GIHs73y+dznF1J6Qlb3k7bV7CV4+pG3Abg6rpj0z0RKP2l79cfEXHaco4fk\\nWY45IO+OO1bB1E/Cej2744k5Wv2qe6A4knOoxCHvUVe7B78Qft/uipfn21JS8wr/k7eL9/mauKNc\\nlXUlAEe+6hH2derDRdf9CMDST85q1Os0F21RJMk8vBD3praTLFHRV8LVo7KCowLaukiSK8ETsdDd\\nHQ/NCto26MXmnYPueGgWA19unDZkPtB4567pmgADX76fhIPBIb55w+XfzoPVmsSOaOwHZNz/1yUy\\nfm13dGXJcxMBKLmyBFeWRBhl3eafl4XyoioG6cyKp0nUVI92p9id3QkAo9VDzFZ5f7Y9Km2cnn0e\\nGw5KSZqbB60H4MN9w1F+lvaUD5T3zusykZwqY2LUQn+N3FGPiJf3m2UjcKpzUOvRyEY3/HjHvzhr\\nXuia4o1NuCJJLSrENxLGKdCijNO2TP4mMUzb4DzER98eEoZ3cn23avfx2AyUp6pKuqpBYKww+JTm\\nbMf9A/3cIgnnm5F4POg83i9TMdRQLNIVL3e6Z+opLtt5PSBhpifKxMh4M1smutZsK7YQhql9klgo\\nypEYX7vbOs8M/gKAP/1yQzO3pOGc/9VjAITSDDRUGEhQQ4qiioz+kNsa5pi2KBdLL5a8zMu3Bac9\\nuM8v5JlBSwExTEGUC9t3l8Juru3tqz133s8dSDilDur91KKsUV4MOfKsJtZgnAJYZMzGFQ/e3Mq1\\nHj1RYKqo8fCA84T3jD83/zoA/tjf6ZOIDK3NGDkmJsiCwlLapoHaFqnOOHW2kwmzNT+yQWkem4LJ\\n0Xj9dCjD9HQhpoMdDkdmzqkZo5qh2vvdmRHJHWypNKVxqhmmOytCCCYEoM297BViyJUeSsDSSY7J\\ndHQBwGLwEHWLGLBl2e0ZN0H6YM0oXffcK6x77pVK2wBKLpcBqV/7kwAYDV4uHLIDgL3F7cl2y1g4\\n/OfpADh/ScHTR8K+568dD4AhxsPK+2SReVHJYAAW7BtTyTDVeKmL5KD2ZwRdup4CIO9ohxq/f11p\\nLuO0Lughvjo6Ojo6Ojo6Ojo6OjotghblQR03ZStrvxza3M3Q0fExNFlUixb17QpAYlbwarbbBh6r\\nGkrYQ7yUXVMKKXTI6nTJhlRijsnnL/9PwgdnhAj1tV6cS8Xiyl4p06X5zOz1PQC3JxwFYJ0T/ph9\\nOQC7P+qHqVzO7V/bD3abFYxwc1M/CRv5yDAMgHJX84k3NBXT48Xb96dmbkckiNlffYjPHeevZN5W\\n8cQlnITCEaqL0aWq64YI4c3b245d/WT19sK7fgDgqzln+z43rEliVqyoj183+FMAxrz9OLFHqvfo\\n2LvJszfwrP0YVXWh3DKJ102KdrAju7O6Z/j+Bdvxyuuo4XpP64PiMhK7r2mEYs62ysp4hVpjVgvL\\n1ml9RNpzqtGY3tPTnkZI+9A8qZ7eFZDfdsUjm5JwvbXJmfKunEqW8aZDnzzSE6SP/fqEpEH9p/dC\\nvjINAGD4wGx+XCfbo3rJ+zviT/ex8RmZmwV6Uk3rJBR4Sz+Z02Vf8nqla/fJvR2AItUjb4pTsByU\\nMc5il3ZZis18PUZqoj6afACA5XGDyBotM7e09f53PVDUKG99ZD2nrQl9RNTR0dHR0dHR0dHR0dFp\\nEbQoD+rc7mvoj+5B1Wk5ZNtF6MUboyXlB0t+W08pWNV8O1eurN7t7WmjQ09ZvbMMLcQ5WD4vPySf\\np392D90yJJ/hlF2y3S5J30H2zeLxM6p5cHd2WE3/KNm21ikez1/vvJbSH8TTGl0eIsnQgM+J6o6R\\n6w7qe4Q/p0lpmpkpIs5yS/JNHNnQOfh4nVbH3LXnYoiVXE/DlHzuz/gFgFc2nwvAiFsy+XbTQMAv\\nbpSYZeTXB+8GIP5CyYk+7851fPvGWEDKuxR+Ls/H8M9kFbu6qnGFw0TI4fkJHwDwxLprMJrlnTEc\\nkVVn+0FDrbV4NQ+slk+kvVdVcaYovjZC5TIxDaGpvKcAVoP8DrrnVEenbRHTQmojuxLUyK7itu+J\\n96pTs7RvpA8/fm4Kp3JFJ8WrduvTC+7imTNElyLa4GJ7ZxE6cnllDuaKh4wPJfdUscr41fuOHKak\\n7gfg2faSdzptz1S27euqXhhiU0T0SMmR88Qf9uJWNT6cqqO+YnIR2U6Zty22i6bD+NR9HMsWMT6X\\nKkpmKWvb4mR1oUUYqLZ4J4PPy9LFjXRaHEPUsNoNSkZY+1uKpXNJ3GnmVKF0Ru5ELzGdRP1NSZH4\\nRGNBFIVfSedYliH1uxYWjSQpRUKE0+Jk/w/yxrD+mKjB2Q9JmIli9WJOlOs42ys+tUyrWgvQVO5X\\n7/WokZTntNuLUxEj4pVTEgp6MCc1hLndtqhNWKGtYEpw8dsRywB4fvsF7CqVZ+uS/tsB2FHYiZGD\\nZZDd1cEfMmRaLaPn0RypK7fR5OFvj80FwO618uIBqXla4pQJl/vbVN+xpd3VAXzoEcpPSajwUx/e\\nBED8SQNGV3ht96qjkNENBlVPKfaYuuATD44OlRUbFQN4OooglCm7soBSayLG2DImsTo6pyMVQ8qI\\n2tbYUmjNy+lgmGpElVY27Nr9ZKass3x/T7S6WJoZz/MrRWCyqJ8XkmQ+psTLHCxurwXbSbWmvVvm\\nUwW/dGVRkohkfjB6BADOfBuYZL/oIxbKi2QQSxkvTofckwkoanpNR1XkyO6MYlK81N3+rEDO08Va\\nSFkHOU9KZiTuQttCX7rV0dHR0dHR0dHR0dHRaRG0iDqotk7dlPQ7fgXALTd9DcBv2u0B6lbzrqVS\\nkeC/x1qoQbjeBZ3m5c/XvAvA/JxxAHzR7wv6LpAE9qhC/+qkI01W6Gy5LWvNR/OuGqp5zb1t3YUa\\ngLO/hOHsmzwPgAGv3u/z2LVWNAn+YX+r3E9u/q2/HhtAfnksc/u8B0B3c8sSx9I8+yc9To64JRy4\\nn0U8pGMXtHwp/PpSk3fj3lulvM8LSy6uNsy5peNtEfFZjYeqAcYPD/wLlyL/mTDriWZsUf1xdBIP\\nku2Yf0DwhnDwL7rjea6a9+umalajUt7BTafvqx+v86+U6Jvd5ywg1yORTe8WDwKgvbmE9iYJ00wz\\nlZKvhoi2N8l+sSEGlnLFRA+zvMsnPf7PU0xyz12KF5NatM+j5uhYDEbiDBIGleWSfIaPi0fwxkaZ\\nj1AiL5mxnROv6vHbf/4bAPwjv49vHt1nwX1EFcm53XFy7uThJ7GrkTHOnRJJE25ZrsbGPUwiyFaf\\nPZttFRI59uzeaQB4FAMnN7Vt4aDAuqwDZ8nYnnn/rEr/b+l0P+8gAPvXSQRgYBrO7j/9qvXVQQUo\\n91bOAdr+8Kw2YaRqeNRQAuOp08gyaMX8Y/cUAEo3Sy7q4C/vJ1RgXksyTO0ZLrIvFZW514okh/C/\\n71yBW81xiKol7GfEJRJrsnHpwEZsZdNj3SXGz8Bd0p+0jKG4dlbcK7XTOgUYllWVDW0Xn8DxhX/Q\\n7jtfFlG6nCkh6od2dGLCoUcA+GHy/4LO1xz0eUva6FUVsOP3GXno/kUAjI0OrhN8OvHqgksA8HTz\\n4FJDxVrK5LEm/nfPq4DUG5w5t/IzWt7Zw/4r5fOmrKPY2Fy8/SYKV3Vs7mbUGa1mZyDryj3MeP2h\\nao8ZENV2QmINiv99sneUd8ze1UuapO8T+51k3E9JvZQvBywBIMUkhpPF4OakRwynLuZiCj2ybzuj\\nGLXlmLCoKxgl6pw2yVjBSZn+Ua4m2UcbvD4D1EGFb6FDS2qwYCLHI+f80q7Wzlw6iY5bqq44+1Md\\n0kvvAcCY4OI354mBqhmnAOZS+bvopzQSx+QCkDxS+tv8lZ2qv2GNTMUQ+Z57Js73bXv46ETuS10F\\nQE6uKBh8cs5srtn0WJO3r7kJNFSrGqkVA8uIymz+d3P+DJlbDLR4iDPKMznwW2mrM9WLNa9u8+SW\\nM6vW0dHR0dHR0dHR0dHROa1pcSG+9t6StGwoFw9jzBETlrMkydj1Y0rzNLAWrr1hJW9uEeXLmExZ\\nNbj3lqWsyJMaS5lrwxPYaclk3S4hB33n31fp77aOqY+smHZPESXdnK+7N2dzasQ6Lg+ADSMXBn0W\\nGIVg7yHhRbEHzSFDfBV1m8ET+TbqhMd9N0qI50PJB2v0NlUX4luVvz8yl8e3XANAn1R5Tj7t82Uk\\nmlonClRvwKTnag4T1EKU+81ru31MRUcJbY7Nql4syd7TTeyBFhfoVCsP3/4p/337ikrbzrl8E6s/\\nG95MLYo8Bm/t+7REAj2nA16VfsMYorZwYIjv0zeKOvdftl2Esi2hUdvXWDjTZEAzuMSDmJBeSOFx\\nUdU3xcmYGP+jDVte5R+2pKsR50gJ3c2a8CYA650uSrwy12tvslOi3iy7V7yhSSa/OJ8poC55fJXc\\nLgsKsUZpTzujjVJFUhuOuuUYF0ZeOTkRgB/nibCO7ZS/feXJcmx0QfA8/tgUFxcOkmioNZ/U/N4p\\nIyVceXDHY/y8tRcAMYebpt/pOPkIACsGLvZt0+rJumMV33No6ilzMVeFGdPhyuJ4WbfNpu+bbWes\\nCBXiWxvOFHkurKeax++Yef8s/prXD4DX10zAdrTy83PX9OXMeX8qEH6Ir+5B1dHR0dHR0dHR0dHR\\n0WkRtLil2Vi1fpQnYPVO85wOm5bJ5sXNnxdnT5dVsOzLXvdtW3BK6g1qwhCbS7r5PBR917belZ1Q\\n3tLTwXOqcfeAtQDckLAVgLP3PUbs/qarlRgOw6bJKunbPVf6tt18YCJAyPcl9mDNr73mOV1+73O8\\nnD8egM8/OrvhDW0EytO8/HHKRwDcmiCewfrkt/3ptrcBeObNmyPXuHqg5f/ek7QXgIEvPxLWce5J\\nRZi/S/T9X1t11jwjj78xg6RzJc9o3ynJp+676jYeOeNbAG5LkFwlLW+kMXinpB3/fKnt5B42hN13\\nzObcbVcCcCorOO/LOl6eZdakBn3WGnhh/hVBs4vW4j3VohI0autPHF092I4Eh6KMvlzqTq//bEjk\\nGtcAquacDnrx/rA9FH9+9/rIN6iR8Q4sRdknuaEmp4H10/4NQKpJtmV8fSedvpPf7dgE8UTauyrY\\n8iqfJ/6Il3i1lvOgrfIsvHDXqyQZRXQv1uDmh3LxOvaMkoNHRpko8MrnZWqU4s/lnTk7WjQBAvP/\\nj7jFM7jDVYFF9bae8Mjnnc0l/PS6vDe2YvGQFfc0+sQPNV0JZ5KRxOzKnt+YLCvuAeFpnRg2iFe8\\n+IICiJLzaLWoAyMFXGp5O0tR5Xx4rR0VHdVa3E4jtpzarx0qD1rzngKY7QYQ5zV91HzZPct7+eq7\\najx6rFZnXItC85BW5/WdWyQ57TMSa9ZiMAwvAkDZlNjknlPtN9dshBUOE28vnAyALcT+v0rZz6V3\\n/xOAAX8K7xotzkDVMIUIOfl3t6WkPbwSaD51X2eS4jNMNWW3815+IugHWf3dELhtTRO3LvK0RWO0\\n79kHAMj6oWet+8YYJeTm1j03yAajgnOohO9YtzZ/Urq9p7uSYQpwY/Yktn4+oMHnnvrqk3U+xpEu\\nL64tOwpF7V0aUynX2tnuM0zTl90l167HeTTDNPOBWQx4TRVRamKlbWVYie+3DNcw1chol88h/Aaq\\no70MHrE5MpGwlIB9qQx6Xz0hokuTX3+Sl3ZeBsDzXeXLGmPcmC2yQvHYkBXMTMoB/PVku5mNPoXJ\\nRKP/Tmuhuz+rVcnbmeyMtIqVvM8lE7DGNk7dNmnXvumvtPjQ4H7z7qtRxfeZ/lJM/uk1t/q2/X7G\\nOwBcF1fEiwVS3F0TU2pOply3ji8Xjq338QOnZgGQubxvpJpUb9ImHPX9PXrTtWEdE8o4BZjXfbWc\\nZ7y8d/Y17RvUNmeqWAqeFBcxWdY6HRtoCAQaAFVxJShtpnamMbOyCNw5c0Rheee9ci+sMS4Ke8uC\\nnMElfUe7LQoVcfL9tbqa5UkGotW64poR+Mzv7ubKp6XihAmFEo+c5387RDU9Ka6M5GgxUAclHgPg\\noy0jMJ2Sxe32A6Ve5j/6fcxGh4gflXmsnBEjyqf/PXgBAPbXu2BV+9uTw6VdqZu9PlX+vMvlGpYf\\nYkPeg/s7yALkOobWfLNUDn/dA21WU5YhY0JMwIK8tuDp6OdEcYpBZGvnwHVcvb4aPm3NM+EaKvPj\\nYd0khLfCY2b7ERFt/OuZi4KuXdNzCbBlj9QijQ7x6H/x1Zm1fLOWRW3hyOtL0gExUKtW/ijv7SR6\\nr9wEZZOMt84UL6TJXFUTgwT/fExThLTtD51OknF+NgD7v0kPq/2Dp+5mYcaKStsemndvjccsL7Ny\\n3J2m/u9YWNfRQ3x1dHR0dHR0dHR0dHR0WgQtTiSpJuw93WT/f/a+Ozyqau1+nWmZmfQGhARIQu8d\\nadJEFFSKFUURxY7d6/VeP+/33ervXq/Xa6WoiKIoYgERAUGKgID0ltCTEBJSSc/0mfP74937zExm\\nMi2TkOBZz+NDPGWfPefss8/ee613vdPfd9vW0kzqiaedK5H+rs2PbRMsZIYByL3yjGBz4cy8RSE9\\nB3snyj2mLKQV0q9n/xfPnCW5U9mWVI/jufTm9tk/45svxodYW//5Szlc2yNHMO/EbykPaiAwdTcj\\nZ8pSAE1LhfHR/Hdw7+oFAACNH+nNPXfRKvcrSaeCviaXI5631uG21wNjvK3kC4K/zvsMf3uncUmz\\nNRroekMOAOD5tE0AgEJbPK7V0Sp/rIIZ2QkaXJ91GwCgoJxSAWi1VigVxDYIW+K9lm9nC71Ko3Mb\\nN+iyjiXTDvvp6IB+U1uEL6bKnEgvvkMlQlfi3n5OPON8518pJfnomi+uDfr6ljjRLZdzsIgaT5K7\\n9NgKHMondkNzxMnkBJsH1ZhuhS7vyoRPRI0mRmvf4K8CfgcDNUkyprEco40wrcZUlnou3oKIE+76\\nD0OmFR07XQYAKNnHIC2qCsdLSRZeX0PfpVdHrkYHFcn93iwg9u35Tj9inIti3x9DBQBz79mMlTlD\\nAQCmQ56mlF8+8AbuWuY5VuOsJDdduhL43Wxi5V5feavP4667+SAAYEdhJkzZ1F/ZO9N3XnFJi+RD\\nwY2JL/cVYIumc3h4TFSeAvoyaiD1KfT+Rhb5bjClwwCHlsrpsMvzvTQkUzn6MgfKZ5JiRaclWs2+\\nNx4xee7lV2co0P/mUwCA4+t6+bw283YCE4z5haGbBZHx1HF3SyzHqZ/JCFRppHobOzggRpJ0as6w\\nXwEAKzePxbk5izzKCqRdAoDqGjKqrKmIhLqkdYVZhRu2jvQgcq7/CH0W0f0xMZOv31+3Dm9/PiO0\\nggUAPpq3rV8dVCc808/ZWbtUmuj5drs+B2u7bwQQuImTJd6BPsPyAAA/jHs3IJOkVjtBNSdQvRRW\\n9/xvA245CQD4PGObtC3USaplYD3ECzQpi/DzoQ5mYtrwnKZOUDW9aLBmOeXbPW/W1D0AgNUbRjXp\\neqHAzuR1vINqLTgyl/Iy3XLqdlzc5zmh9Av2c6zt6EPw2cT38ej7T/o97cTTCzH3AsUlH1zbD8Ze\\n9AHUn2w8xs+Q4oC+iD5CfHCnaEQeu30Bafl5PA0Q2nvgb4JqZ3EGyUNLAAC1pghY93ufcFwtcI1B\\na065b8NYt96LnwjaOfn4E+8CAPaagQX/9d8uG8LOBiZKLwMTaxQkmbajfy0AwFKsRwTL4cwltb1H\\n5qLMQO3wrV4rAQDPn74LdT/4zg1p7EDn64o9+ww+ibZFhvZ9Ov0ADYRas9SXT1ANfVjfkB1Y/K81\\nSsTp+e4DveU1SXht2Z3hrWAjePz+7wEAiz4hebgtUoQlngbHepdJmLcJaswYmtSWXKQ+RJevhrEL\\nk5cblIgouzKiro8fou/EvA8Dl9a7xeaxmDhviw58YF1Xq4U2O5QAhNAQqKy34TmjjtJiU9Xe9n6O\\n9kRLT1TtLIfyEzM2YMmqaUGdO2vmLmxaOAYAoKmlcupmVyNqZazbceZYARHVnv2QOZZNxpIExJ1v\\nuqVzbWcFovP9lxP5cCFGJ9HC4edZJG2N/1EHldm9jtUZ4XmXbHoRKgNfMad/DOlW6HM9J4mmdlR/\\nbakChgz3j2ZyahX2Df7KbVsg7ZLHwi58cDEAYMGHj3nEoAJA8mAao5QdDr7dtjbwccDpBxdJE0Bx\\nIPsGX4z0GW/KY0OlZxYiTO2pEtqS0FgMK1u0GTeeYvGXdt4l/RbZxVeGDBkyZMiQIUOGDBkyZLQp\\ntFqTpIgK77P/w5uZ+csjTgb18FPvAAAGv/OUzzJ5/sfkzrSieXTQl7jt3FQAwLkNXb2e01BC+VLJ\\nID81J6TfmIsbTt4c0LH+4I855fh7O5KurOowDOri5pNAeHP2bW3MKa/jC0Vk3hEKe+pQA6KKVoFy\\np34IAOj3dmAsVb+3n3DmGxV9M6fWKLoGZ0+BxplTANj75BuIUjiZ00DNPPhKZMYUCojP2+g/IF7J\\nVuEK88n59YnRW7Fs/w0BXS9QONjKoC0/EprKllsz6zf1NC7V0Wp5xU5i+6x9DXi0gBQIS9L2NJtR\\n0qBpJ6W/Xywml8ZpM/biPymHAAQuL15RS6YDe2u7oT7V3RgJAGq70op29Hnv9zXpBjJBqlzr+X6o\\n65x/m08QpRlV7zTKMPWjRpp9IB32aFptve/A0wAAXan//sAbc8rRd9ppAMDRn6+8cU4gaApjy5lT\\na5QIdR3dk/Sp9I7GqE04ttbd8ExdJ2D4IWJL9w+hnMfbqpzHOIbXQLHf/zfDEitCUx1Yv60YRd/M\\nYyO+kLZxDldVL0BVH9gqe80v1F45j/jmAx/g2WUPS/u5qqAp8npXGNPpBW5MOuy8XmDMqamDHdpi\\nz99qTabrqAwsC0GEiOnT9gIAXkwms8TxS18MouYEQxcb9H5c1xtixp10vXHHZ+Hydk+HaH+o2cEY\\nqMbT8/rFyUcXhsSiBsL+mDrY8PWNpBzhRmwZax5BsP7j7TQ1EnPK4cqeWvXM8MeFPS0ZQf+23+fc\\nXjHEBiiofcWd9WRAi6dShxl5QouoQhb2wA4zJCtQn8ZUSocDY2FzSxIxPIHCLHjNyq6xI2VH07+d\\nxl4m6E6530m3Z8EuqMt3f5/M/Ujuq9XSb1VmOKA8y+TT7Nuwb/BXOGMl46RZi53vQkPHeY86daLv\\nzIIPH/NZ90CZU38Out7w8qxvAACvrr4t4HOaAi6lBYCps6gf2bCaxrIJI8tRv7dxd3dbDLUjlcF/\\nn7zlEWaY+D6FBxnTLdDl0QPxxpyaE1mu1cv+2xr/DUs7U39kF4NXGcgMqgwZMmTIkCFDhgwZMmTI\\naBVotQxqY5h4EzEMmZvmAwBypiyFWvC/UmBIcUAZS0s0adFVAIBuGx6R8q42PBYAcu5Y7LHvX+2P\\n4Af4zwmZndcRI7rTKniu36PDg76fELt3dYeP+8fk6w+HxZhKYQWs0dQWrCKtAr718BL84SStohn9\\n5ChUV1K7rM+wItJLvAaHNY3apfqU79QBTjZfiyNmChq8d8lzvn+EC56/j0wkJugpx+YM/F6KE3CN\\n8/YG3QWq/5rOA2HqwGITvDAJvmDMsECX6/m+KY4SO+dtwT510kUUbu0U1HX84c/3U7qOzZX9cGJn\\nT7d96iw9dmYRo3nPtPDmBDV2trrlTm7IEtn1Iu66hwwlOl9HK+T5W7r4LPNAHbHgP53r6caccjTG\\nnHI0ZE5rMxwQ7FROVL6zvIhKz3Mjk8mow2COkuKRXFnXpoCb7jTVw6slYlEzRuWHpXzOngLAuh4b\\nAADl9npcN4rYffseZ+y3cSfre4bQPzvOdXOmVzoamOJG2b0OSbGkXijf0jHo+loG08OemHkWv3wT\\nWo5TV/bUFU1iUl2MQHyZLrnGgPMYWn8Kjsb6vOgkYobqjLRfd0mJdWtIiZF0B92n7hNzkFtBxkOO\\nfXF+fgQhUPbUkGnF15PfAwA8lkXGZ4Zf/OfQNaZQXz52+Ek/RwYGzpqefHRhwPGoUcMpTVjd/qSA\\n4ua+vPE9iTnl0JYEP5R1iAoUTaLfn7LV87m6xlDzeFNRzVkgZz3jjqolFrR4NP0bc1YJXTkd22ED\\n1bV8Rj3qutI3PmW70/BIXxZcvcUSLb4spjGorpSleikR4dP9xg96T6NUTyfXB6ZYcfVKcGiA5ATy\\nSeHse20nG/RMBZk1x2ls6sqccjTGnAKALUrEU2N/AgAsKSPlli3dBGVB6N/mQJnTl2auBgD8a80s\\niTm9diLFU+7c1jK5jSdmzcC0lBMAgH/e/zEA4A+fzPN5TjDjMtd8vACwfvLbuO3D3zV6fCDMKQdP\\ni9PtC2K+BZsQ9Nyk1ZokcbgaxwCAsS9JCXRZ9DlWj6rAqI55AIANByjXU2SQkhgO7pzq4wLaQQAA\\nIABJREFUai6wg/wrMP9r1qhFYOWdZKhw7yfPAgCUJs+yzHEiModfBADk/uocYHPpKdBG3H0bYMj4\\n0zj0c0//B14BdLmG8m2dz+oIVV14xAEaZnrhKvVeXEWD+v9+Mx0AoK5vXnnzoOnZAIBhsXkAgCXZ\\n16J7O/qq5WzI9HqOOZ7e64hKZ92enPsdAEi5Lfu9/QQMvanxas8E3uFfO+MwAOCn0+QM6Jp3yxu4\\nQ+aHfT/FAA1dp85hwojF7J330QX98MhryFBTJ8oXCQYu9C3lDxSuk9+GhkUNwZ30bJEiVH4m8w1h\\n7Epf4NwbP3TbfsxC937GVlpY0p13DrrGTD8KAPhl7UCpblx6vGXnQMkZ2NKH+kNlrha6ktDbITcs\\nUtcKkuRcVS9IE866kXQdoUAryd75RNaudyDmdHjtoLkc3dTuyn+fGkP2PJoQKAVnX5P5NeWCU9f6\\n7398ufjef9+PAIAhujx8X0WTv0GR+QCAn6t6Yv9qGiDx91yZWQdLMRn+3TNud0iuvo1h9pyteCXp\\nlM9jVjGp/F+XzpG2+XTx5bengerLmEnvypT+WdK2nd8FN/n15wY85dZ9AIANG4Y3KTTF1STJqzSV\\nG8ows5jcmz5AxtpHAAAKA90AbZhMoUx9jFDlU9+qclno4OZ8Odd/5NOQho95ltck4V+fkHzc0QSJ\\nLwAMnkKT3sObmp6T2xV84gs0zYzpudlr8P/2kLFSyqbwcDXGBHqetRki7PEsjyjr1y3xIq4ZS/fk\\nl6zudN0twfeb5hgBxmR6xt7Mmewa2leX6mwHbz+0BE8dplzuwkHnAlbyRMr7W7bNuUBl7EBl6op9\\nt81A85zztnXruetxekN33wc3gKGHGUI9XUh3ie7V/Hs2YvH34Q0zam1QsTGlJc6BkaOp7z20vk/Y\\nr2PrRx94V+feP8+lBfw/L5/j9Zxgkf0EW2x0cfuVTZJkyJAhQ4YMGTJkyJAhQ0abQqtnUE2JIpBK\\nq/fPDdqC0waSO227SCsxWrUNNjtbtWJB2YJdcGOOAoFdA/zrvo8BANMjSbp2xlqP3+URtX88qzMA\\nIDLf90qbciRp4UxZcRIj2xh4zr9AU0ukDqfVrsL9wcuxxkw6gV+29gv6PG+wJlCF1RVXfxJNzqB+\\n/tgbAIBowYb9ZmJQs4xpAIDle8YgMq951PLCNVXonUz26VnribkWHE4Lb5veM09iYzB0ouemv+h8\\nblzOzlfsVH7YYFHlf8W0Icy96P11mF3aiyh4ZTl4WhFeD0eECEssbbt5DJmA/bR6eHAVaABTT2IV\\nvrp2sYdUzBUFtjp8UHkNAOC6aGJ0Hlsa+or9zNt34eVkYm+iFMFLlP5dQUZuy75wrh6rWA5RRRjN\\nnExMGagtd27jq+WWWCDicviu5Q+c2Q0Eyp4kV23u3Klf3vMmAGBQBMn1vMl77REilGbf75IvBpWj\\nvosNuiL3vsWU5MC00aRi2P71UHY9Z6ogY4oDopoZr+WH3kdzFttVUeQNK2oTpdy4095z5uINNA+q\\ntS99byO0VskQkL//5t5GoJTuczDyMq/XYX3mP279HADw508CZwi8mS0FmgcVwyg/KQ7E+j6uGRE5\\ntgybBy4HAJTYqeI3/vwUhmQQK/9GlzUAgKmLXJ5fCAzqf++jHNJPff0gbO25mxq1QW1xaN/IG6ZT\\nn/mX9jsBALEKnaRUiLgcevued/tmfLKScsaGkibGNRVcCfnXIC6b2m1VbxGJPaijrKgmQ0NHmVZi\\n2Hn7jjsXWnoaSxSdr6nz7B9VD9B4wdUgK3JsGep3JYd0rVDB2ftenYsBAKeOdfbLyvpC5FhSYtXv\\nSvaaZqYpCMU4qTnhOg77dD59b+5b+mzQ5Yy6+RgA4OF2PwMAHvzIv/ps0gwaZ239bmjQ1/OKQST/\\nxhEncx8og9rqJ6i6seV4qttW6f87qysAANEKavyuA8wXiiggZ8PqkT5dUL3BoQayH6cPMZfePX1m\\nNh7uQp3iGRNNjKdEH/eZB5NLQTM3zYeexRRawvwytSR+vpfybY5bSbEDwU5O2jo0DQaRtiG1yBrz\\nCQDgjvM0Ucja2c1vDGdzob6zDf370iAju4A+SNrjOljZRM+f/NjG1Lk8DiRpXBHKdwTv/OhRbqTo\\nd7LbEKb2diye9hEAZ2yaqAQSR9MHbkYayV4X7roOuotNXxAwdjMj94albtvW1Ech20gLEC8nnQ6b\\nmygAKIZW49uhFI/TQx3psX/ssVuxa8C3fstxrZOmJmzVk2DoSG0n85p8FK+mGFgbqUdhiRORMJAG\\nCiWFFBMZeyz8Ue/V/WlCoCkL/jmffmARrssm+f2WPmsBhDcGled/TR9AC4Z5RYmSvHLoBJJjHdze\\ny285gUxQWwt4vOm6UYu8tl2ObivoPmvLBZ8TVC77rzmQjG7jyaWh0qRDJXPT/t+55BY8O9oZ/Mwd\\n9L//2r8HREOYUpzuu1Gj6Np1u5s2YA94gtqMsA6k56I+GuXzOEucCA3L9S4OpU7DalFBc5y92F4m\\n0Y1NUO+8lQa7q74dDwCw967DmXE0+X34IuUV/aDTL9LxT1+iBcXN34e2sOgq6W2Ibp9TfFsgkvqG\\nUA6sRv1l+v1Je6ixqg2Bj9W4lLZ0lB1JncjXpLyYLbDobZjZi75XXx9g43CbAOhokVhgr36HjeHv\\nO3/4Ny2mj13YeCwh0CC/KYOxtwnqCBrkqY4425RrftNAYOxoBxxUNidg1LUKKRzOwew2FF7ybweC\\ncE9QWxv42MkaI4b8nZh52y6s+WYsAJrfAOFdyG4KZImvDBkyZMiQIUOGDBkyZMhoU2j1DKpyZCXe\\n6E+53lZXDMPdiXsAAGO0zrn1/HxaJfh1zYAm1WPMrSSbWpJG1/i4ph0UzMXh5ypaEX8p5UcsraBV\\nwm9/JNOSxAFlqNtB+d2+fZwYx1cuTkf2OpJktmUGtSHOzFvUJs2dQkVDBrU+3QZ1Fa3E8xXpcGHS\\n7ftRaiZ54rEN1N4ev/sHvLWBcvVqy72vJ3GmImEsMY1qpR13pR4AALy+4RY6t0whtUPX3+TwopBS\\nDyfWwro/3mNf+wmFKNnumTOz31TKW7kqc4u0bdRRksdX7wosP5k/2KKczJ4/d9tAkL1gId6sTAcA\\nPBufJ21vClPj73r+MOHETADA9n5rGj2muRlUDlHpGX5gSBXR7RqSc176Nr3Zrh11E7XlsoO+2w53\\n6W0M4XbvPf3AIqnMp2etAwAsiLuI7tvnAQAUuTrpuGoH6a9HfOL928aNdZoiezMyib6inQkRR/Uh\\nlxMMoieQhHDPwG+kbb123QcAiNQRJWLeleSTQd34KOXfG7/+ecSlUCOurddCk0W/4auH/wMA6Ktx\\nGrBxN0huEBYseP/Bc5a6OopzcyZdTuC6VlcG1cByXg/vdx4AcOBcOnSnfbuyhxPWaDHsKp7GGFTu\\neDxuJIU9WB1KfJa+HQBw0Ez3cWiEpkkGRq64/pb9ACB9G1dmOBV13MTy8cNzYD1L7GWjxld8MxuO\\nxZ92oD6F2lJkUWB0uC2CCkm4Px91Fnq+hblJSM2geAiFQIWPbXcePXTUh60ro3Hp4QPdJJdofTEz\\nMazxPjas78DqVRw4TX+5H9Vt9GRyfd33nXenWVdzo8fu/QEAsPizmxotV3lNJey/eo4F0iaTcitv\\nbyc3Yy6ADKEW3ERO5G/tnQwAeHj4ThypobAog40a1/ltGT5dfBvDb4VBbWmMuvkYtu3rCwDQljZf\\nCJ/MoMqQIUOGDBkyZMiQIUOGjDaFVp8H1WJVoZ4J1ntHXnJjTgFKlREObF/wbzySO9Nt251RBXiv\\niizSKy20krvX2AVHK4lBmjllLwAgNaIS1z5KeaSi2QoaZ09bEjyFTbgYzi7XFGBz7+/dtrU29tQb\\ny9OcmDr0GHZ8M6RZyn67437p7+snUpzXkk9vgj87HR5vXbWd4rcMfUx4rC+lknnsbmcu30DfFW/M\\nKcenPVdgXNHTAIC09sS03tv5V1ynp/Z/0Ewro3d+9zQimpBCgRunqLOcrBBfpQ2EPeWM7okNXt5D\\ntjj574queDHhvNuupsacessra4kTpbJt/Zmt+3HvMWPtJxR6bBt77FYAQAWLz2speHuvdMUCcssT\\nAQCmPtTwYrPD9xnhpkjXJecBANbBN4PKU+9w1QsAPFs0DBs2Ns1IqzF0XfkYzjfIrfo2vK/0xiqc\\nbCqHK6PL20jSdRTLGkou0tTexGZWbG163HigqN1Oz+TVVHq3Xk46jX8OppyB/zg9LaAyJn9Onga6\\nWgFPjt8OAJgfWwxM4EfQveu5cy6Ux+hdaWLWE4ycQIzf8i47pG335tEF66w0xjgblwTxUPBmRhpm\\n1sMVJC9HDsB3p8c2pboAKF70/p6UG3np98REufYtT91HqcPe+XRGk68VKDgL2CuSGMIl2yYBjEFV\\nMnoyXOwpADyeRDGvmWoKpNtoiMLvT1CfeGwExSpnjVoBjHI/b+bZGzC7AzNY+vxur+nMfDGnnC01\\nJguILqDjTLMo1vThtJ14aT8phCISjSg5Ru+EXUvHfVenR8RPpLSrpVTVaH9IhEdepQYoH0jX5Kyi\\nJU4BFaXYRfRF3+faM4hOzqtN8Hkc9xExJTvw5iamznLZP+POXfQbVlH79caeJk4owuJuKwEAb8VN\\nwNrdzFAnmgofkFGAZWfpgfCUUecNyTiQQ9/uiHN0RYUV6Dn1LADgYg1dx/BLkteYyY6TKW3jpZ/C\\nmxe9uWCNZ2ailc1vJmrpbYTmpO90fw1h6maG9py7yiNBU4+c25cAcE8LIwymGHXxcMsavbVaiS/P\\nzyg6BMQn0qDObFVh7TC6eTNc3OaCRfJ1NAh8vdtXUDOdzgPH58LMElun30imDaMTcvD559cBcEoh\\nFXbAPIAGz88Ooo/RgriLUtk9fr4fAKA56jSSaCmJL5+gDj90J6qPJbbINa92WNtTDxl5tqnDo8Yx\\n+Q76iL6ZcgA3n6EPRmk9DcpqDVooDwbvSnrwKcrVO/8CuRQeWes9h5Y3iW+PKTRpi1KbcSdznf3D\\nx/Ok/Q2lqntNdsxZuwAAmjQpBYB9j5PBgy+X23AaF7V2uN5rb7+7OSW+jaG+k3t/FnkxPHIkS6xT\\natkljSRzRfsDm3iJipYzrTndYILq6xhXDDlwF+qPOweP3Pyivgf95sgzgfUxxn5GCMzZNlDTkubE\\niWecbfSJQrIz3fH1EK8SX96ey+006k5SejdcyvzpQQCA9nTwbteNoWG/9ULREHy/iVy6f5pDoTl/\\nuXQj9nwfWKiQa3sbO4vCgw6U0ODZtDupqdUFAMSPL0ZpFX0L/BkhcZmxnuWBN/UxQlDSu6rVWmHN\\ndndI9ofGJL7qwSwE5DBNKLbOfw1VDmqHty7zbczjC95Me4JBQzOlcnu91L68TZjjT/vuMCpvozba\\nvV0ZygxUTs0OmohGX1uKazvQd/LV9gfQa+tDAABdJL3LCoUDUSuDH8zXpdJ9jCr0rJspnu6NttL7\\neLJ4Kl27T5ciAEDujxk+r2XXQjItChaz7tqJHlq6Ds+b2xi4C3jvxU94mPSMv/UQDpWR7Ld2Zztp\\ne/x4Wvyo/LmDV4Omlpb4Tp7Mcr//FFxO5lDRnBJfYxd6CBGX1NLz2PMYhVQsyJ+KmxPJ3OtPq2cD\\nANR14a+LLPGVIUOGDBkyZMiQIUOGDBltCq2WQXUFN5NQGgUITahu0iSSUvHUMdEKo5TzFAAy1jwC\\nwJnr1NDHBH225wqulVnun752ucc+bzLKlmZQW5sMty1D1YNyK+IwrT4rwigntgyidsQt+gFn+6lP\\np9Xw5sqvytGQQTV2siF3+vsexw3cdzcA4CiTVAGUJxgAZr7/orTN1J5ukLYkNFkLZzk4W3jL7bvx\\nr/ZH3I7J3Pwg4hPo3hn3hoepaM0wdqK24C21TigMak13ekYxZ5smPeIMSygmF95g1wH2EfSDHu9N\\nffS7awKTjLZFBJs+oD6D2kHu9PelNDolm9PCXi+Ox++n8I5Fn9zi8zi7Dvh23usAgH9coue1f1tv\\nKL2wYbZ+1GecGf+JtO39apI2PxJ7CRnrKL2U7kLT0m9MnkXhEjxnciAGZRxZFjK3KrYTY/n4vnuh\\nOuHJ9Lox9g0MeMKFvjedRtYPgYULcaaq7zvUdxoyrMi9+QMAwLTT03BhU3pQ1w4lD2pTsHn+a9Cy\\n/Cuc+ey16z4IWYEpiKKGk+pi/5BVHvuCYVDNMVSHyrEsB0qtGmndSgEAxRXMiEnlgDWf6njbpL3Q\\nMiqq0kohKT31xVjxV3fjIVGA3/ErNzpKPOE8sJRFK9jj6f0XjEopZVLcWedvEFkbLBnFzNeKmk9a\\nOu2OPVj/lVNTzXOVi52Iko04oYM5wd2UMdAUhYHkWm9JBvWVWV/hLxtuBwAoTc4+bdoU6mPWbwo8\\nnMTWkdpUzvUfSdu85V5tTgaVm5w5tCK0XtpI9hPUj7xXRWqQ9z733f+HgqsmD2o4se9JSnh7zEIP\\n5bPLo/FuKsV3mEUr+v1MH0dFDmm5lWbBQ5LgKovoczPFuX2asRG91lEHGJnr+WFt7gmqPDFtPvDO\\n1RzPXPcqw9dxDJqeDQCSA+J2owJPfvBY2MoPBN4kvlxSk3PHYmRumg8A0J5nsQp+ZJTKYRSjYz8Q\\n53V/7FgWM3egnVtn7wsWFo96bsLHPo/Lt9Gk9cYlocv/2xoCnaA2Z6x2TS87Yk41fTBUm+lAVAbF\\nujzUfTeA38YE1difJkQx0UbUnKf3RldC4ibLYGeOyYzv2AJqMy9ahQu+XHx/eYwmtPFKfVgk+655\\nl9Mn52F9z/UAgOtP0uDqQlm8dB95nvNZOx/H+euWBVS+tzoGKymPvrZUkjEaU5wvo6+JRPLESyjb\\nFnxscjjQUhNUaw9q/68O/xb/t2IOAKdc9+/lvbDim0khl60behkAYDzoGfLkbYJq1QsoH0HPhkul\\nY3MdKBpHx+bOpMXbbl88hnbMMqJ4qgUPDqb+6pH4gwCAx3JnIvfL7lSHy57X4e7B9cOM0Olp0lJ/\\nMdqpaYymgWfsr1oo7DT2qOpJ/3buX4ROUSSzVrIZ768FXaDcR5Nnb9/0xmBgi596tvg5YsZx7L5A\\n0mCeB9Vb+MTzc7/FG8tv9ShPM7ICAGDZ6zsOtqkLOi05QT1z/yKPSWRkn0rUZ8e7HQN4n2yGgnBM\\nUNXDKmE94Bk/zCW+rouAKRMKAABF29Ng6kbtsWF8qivMCQ5EhOimDsgSXxkyZMiQIUOGDBkyZMiQ\\n0cbwm2JQuZvmF3eRgcyG2gFY+bnv1TnbEJJ4qg4Fb1QjXfcqz9l0NYMzqI5hRFUpDsT4PP7E07Ty\\ne+Opm7CxF+UYe7LwGmz/ZqjHsfUZtJK1fxox+xPee9HjmOZGMKut4cCEmYcAANvXOJ2QUyeRydjM\\nlCN4b4WnnIS/t+fm+M55+YOB5PgvLnswLHVtC2hpkyRLjHP1OrKAvRua8Ml8OeY+uhEAsOS7G8Jb\\ncCtCQ4lvv+mnpByP/d4ixs7UztGsRkiWQSS51RzxblbUFPhiUHlO0ob5E0NFysQC/LcbSTtnrH9a\\nYrqGHyIDl00DP0a8kuSX3IhuXY8NKGKqixRV4wZEmwxqPLvsYY/tgTKoXHo7/NCd6BpPMlSLnTre\\n0xu6B1ZII+DOpjd1OI4PPnNXGxh7mSAomElSdnAOn0DLS3xbGo1JfGvvok5Vp6Hvs+pTT/a1Jl0h\\nmZvFJNSjro6+PecnESN/46mbcK6I2HKhkPYlH/I+DjQm0Pt9+JWFmJ1L41HuKn1mdzoiWL71+s7E\\n7H409QNoBarbGQuZNn1bMhQnd2YCcLo8i366DUM3C36aTGOPrmrP9s/ZQHWNAFsk1d3Sga4bl1zn\\nlyWdP4f68KUrbpS28TY1fSYxzutW+c41bo0Voa527yMMXS1QlzUtBCAYJAwsw+wulFd+4dqpLXJN\\nfwyqsSuxnLk3LAVAhm8bVo8MqGztCFIVmPY52/U7D5D57EVrIl77jOTMDg09c4UlTCaI8Q7JAVxm\\nUGXIkCFDhgwZMmTIkCFDRpvCb4pB5TAlMdvq8paZnwfDoDaMJz0zb9FVG1u6+h5KKTLr85Z79sGC\\nM6jcBEBhEaAyuh/z3P3fIl1TBgB4eCPZzUen1kiGQo3lH+U5MwdPYDk7v+/lsy6uqY7ChZZkUG1R\\nIiJ6UYxhYzGq3hAxguJa9BG0Yv3LgG/d9gcaw8aZWE1V8xkQBAwBYTFU8cagmpIAbXlg5/e7m+Kg\\nT3zhPQ2RL9SnOeOyea6+cKF6AK3Ua0rbRrxlKGjIoFoG1UvmQXaRvlED336yRet04pmF+MVE1563\\niqWOCjHu3heD2hywa5mZYoCx7U1FsAzqC0VD8FI7Mv+aeuQBAE1PR8PLBoCuq8i/4PydztzXi6so\\nZ3ugeVJd4w1/qwxq0XX0gU38lRqwps53R1000Y7ULsRKren7KQDg1ux7cbGAGCpFNZWjLVNIuUx5\\nOpmYC+514HlQ+TMQFeSFAgAa+nRCOfEy/qfXBgDA6nJSIu3f0huJWe71rM7wPb51bTscK2vj8beP\\n73bbZkyzQ1dAAwUrGwc1ZDU5ONN6+sFFkvnZR7nEku4d9DXOW0mxsOD8XQCAi5v95zSX4BK32tJp\\nZloa/hhUbmTE4ZqzNBQYUykWef6YHXgl6VSjZXPmVne+8fhUX+Bj3pzfvxAQg9pmv/71mSw/ZU7w\\nVH9LTUw5bGlmqApCe6BX2+SUT8AzVz+KvhqSHfUbew4AcGJXt6DLU3avg/1slFvZzXHPNKxDNnS0\\nQ2V0n9WV26IpyTwARTy9wEM6FDQ6MeXgUpxKk77RYzKn5mBt941u21zLjZtQjN93pf0vL50XwC8B\\n7ENJth5KftWmQFUnBDUx5TDvIykR81REn52hdcatYmLKIQLGdGZWkBdeuZIl0Y4pN5GUescnvh0G\\neZ7blAdo9LN52Shfh6Omu11y/+US3+ZA7DG6J8YOV/dABHBOBDRHIoHx9LdSoG/UiWcWSnLfpmL2\\nHJIPr1zhO6zl0SU0KXb9YokjqH0I+5y5HXXX0iqI0aIGfg1PAnfutpuxlgyhpg495hYO4AvBTkwt\\nfYzQhCB9DRX/STmEf1dQjtWabJq82LpaoLpMbT3Q/smcIMIeRwPKzM0UzqA7pYVK7/muvL6eQiYi\\nABg70KxHjGbGOOc00iIZX4BdcMsGLFpNMkaFNfj3+7/3keTwuU/nB31ua0HKFv5997yfRVPo3qVs\\ncg6dU7Yp8ce/kynX8ur+AIDS6ijEJNKqXa2BwoJic52T0eh875PjpKNsEfVBGk9YPurgUQ/ThgT8\\n/tw9dFw19ROJfnK6egvDOGI24z62UHL8ms8BwGNyCrAxTQGNUWzRdB11tfeV7Z33v87+isQjsZQ1\\n45FBXwMgd2k+KQ5qYsrAXYqbksmDmxkBwDd1MfjjN3NCL+wqQnQKjQkbTk4bgkuKQ50QNzSd9Xt8\\nSFeRIUOGDBkyZMiQIUOGDBkywow2y6CGwpxeKYTKnrZWvHvnhwCAJ1c9FNDxG+b82yMIX1WtcJMx\\nA0CPEBjUk2M+RelIri+MdCvPFaGyqsb2tGLI0z7oL3muHH664npEzaXUBdpjtNK4/1j/gK8xqR1L\\nV4TO+OhRMvAaEUHte8ThOzyY2BNPL8SblekAgGfj86T9XK7riBCh8pKDkKNrMjEfeWhZBrW1IBgp\\noLkX6bkjTgXHtHz80FuY9+EzPo8JlTk1JzsQUdb42mLMaSUuDSRG66knSA79zkLPlAAA8L9vzwMA\\nVPclZsAfD9axRxmK7GT+EZ3jWYcjf6QV8kH/L/AVVm7mEWzajqsFrszG9LNkKOKqmjD0ob7FW05u\\nDnO86FeKy1fHD91E+e0yIi/jx1XuxhqNsbWuzGl9L9IyiGZqv/8Y8B2eKyVGJzK3aUMKLtfnb9v2\\ni4Gxp43BW/5Tfo3mZk/tDR5Xn0VPSPkdz3qRV/L8pf7gzSyu76knvPb5EZed7yhXjgnFntrdmVP2\\nAgDMDrUkLwxG4vvwHdRen/mSGN2rjfkoGs86J7v3d+z3S+l3//T4awCAd+omoV/3IgDAkXOevaor\\nC8i/23aNALWRdlQbqfHUTTcheS39bYqja2srRSiyeOiRZ11q0j3vvjcTuznvPyf93Xdf421PccH5\\nngixVJC1WispwABXubDTbI23Z54Ptamj4HB8H8KVBsYV3tLQNAcsfSjdnlLpwPz8sQCASkt4+rBj\\nLvntfbGjr5Y7czJzmXEwbGqwKperKgaVO6j6k1YGiiEzTuCXvRSbxScooeBqcvG1a8WQYnz4IPTs\\n3PDKcL3F6IZzgmrXsaTXLs+/vht10pHnAvuC83YJONtm8nWFKNtC8UHjbmNyzG+GuB0LAKvqYvHX\\njwKToeivpTjYyqwkRFQ0iG+LE2GNYcGrKubCmqMOOAaVD/TCkbPwt4DsBQvDfq94PlhNllMS3piL\\nb9wtJK+6WEp50PRHdbAMo/if6d2PAwBeTN6FKa+5O0cbOoqwxlI7iT3pnGzU9KBtYpQNsYfchxqu\\nuaEDhTUKUNc7rwkA+kLPfuVqlvg2jEF1xYlnPCcw/qS+PJ+q7ngjg5YGuQfDJR92lQ/aWdNQmkOL\\nQW1L/Uw4BsxZTy3Eylp6R13llWmT8wEABT919nqO17Is9Px56EygE14AMCUzXw6Xha9QYlCjR9A3\\nqHZfcvAnNwKHmvk/hCA59gXXGFSrjsrmE8SG2P3GYo9to5935iznk0L+/R79/GMouYb2RRbQvqhC\\nzwZjSFZI99l1fymLzLh2TBZ+3s/8AdjP1xUqpbYXUckmfzXOelf0oQOVxqbdr3aTCqkuW1OlOvIY\\nU43LYpglXsTZe2nMxV2zK84lQNuEMbM/BBuD2lie0nDnL20KOg2n+31xfyp0vSmfvGseU1MPRoKc\\n0eJvcz8DAPxp+b1NumbDWFbA+4QzlMmoL8guvjJkyJAhQ4YMGTJkyJAho03hqmDOAsRQAAAgAElE\\nQVRQG7JOgTKo3pit5sCVZFDn3/ITAGDp95OvWB3CDWuKRQrWbgzcifKBlQuadK2Ow0imU761Y5PK\\neXzu9wCARcvJtOJ3875GtIJWxOxsaTRdXS5JeznC1S7F4dUQ9nvqgYJ18fUlmQsnuk3JAUBSx7bA\\npjSEenglrPvj/R/YRDTGoJqSqc/5Zi45ZX9w+VroGc01NDIPABCnrMcLbz7qdl7yzIsoW9PJo7xn\\nF5DRxdrSgcj5yj1344B7TuCdTj8CAGIVxN40JvGt70T1cmhEKJkkUc1yYaprPY//LTCo9Rmk+/Qm\\nj3VlUpvCdjrU3g0qrEOIVVcfohAMS6woGcKFAtNAYvkVggjVicByq/rrU5TDiE0IxWDNGyzx9G3g\\nOflCRbgYVG9MJzctcn0Wrsxpw3OMqXbk3LrEbVswDKo3hMvF9+SjVO/eSwKvTyjnBIv40w5YIun+\\n2hmDqit3wJhI7cLCUp7H5jpQegvJ2s9NXOZRzvKaJCz+M+WO5M65sbkO1KbR39EFvhtK6VCWtzTF\\nhPbrSILAGdsRh+9AVS31qUM6FQAAjhSmAqfofRXZ9zvxuAi7hsqpYt2zrzCfUGEbRP2F6ogzZMtX\\nu2wOWOJFCD4yGVgTqT9VX/Yv4WgNDOqq2ZSL9s6Vz0rbfLn4vnr/crz8ydyQrmVKdripJNqNI6XV\\n9n5rAISPIQUAUzdma1lNY1rX68oMqgwZMmTIkCFDhgwZMmTIaFO4KhjUYFHflZaSc2/6QNrW7+0n\\nJBt2XXF45+1XUwxqc8Jvzlf2WLLuexdqgZYOmzsNjzWBluoi85pm/iEZBbjEhvJ2IaZT7NCOse9i\\nzLanAQD6k40booQTTcmDylfYTz0cWrzlmkf+DQDooXYyLbwcX+ZExk426C46n4fISGchSAvzqwGc\\nQTV2EKEr9lx15fGd0f0uw2Ci1Xk7M/pYPmIZHvvvU27H1/S0A1G0Ah1zMEIyejnwLJl3DV74jAdr\\nW99JlOKdeCyiP2bXnGSHnj1DdV3jv+9qZlAP3v9fAECUgm7y3AvjcGhNP7djnp63BrdEngEAlLGg\\nztu+fA5n76OV/24rqP/TloeHLTH0NkN/Mng7E0Mq9ZPjR1Be3R1nuwVtLAY48/HpCp3v94SZFKMf\\naLqZTx96E/d9SGxE5vW5AIB1PTaEXYkRLINqyLRi2SRS/mSq6QXprIrC4P2zAQCWvQk+z+dM1dhj\\nt6Ly5w5u+4xpduTMIgaVp+jRX2jaN+tK5EHlzClHczOoHBW9GfN5ToTS6t7nVPRWIDqPtpWPoY/M\\n3OF7sPL7cQCAX+5/HcPXMcMh9hoqIq3QaKktWy7S9639Ps86FE20I2UbfYSLb7RCXUA33cpYfjHC\\nDoF7RsTSN7G+WgfdaXpHeV5Vc4wCVQOpbrw8f3lQw4U7Z2/HqpUTWuRaHD5jUNNJxYG8xtP3SeW0\\no3umLg3MsLA5GNdeo6mPOrU7Q9rmi0E1drZCl++/vm/O+wDPfvwwAKD9eIpvLfk51e2Yz+fTNyjf\\nRmqvP3wyT9rXedIF2rc1+JRAADG9ALyyvYEyqL/JCap6VAUA4PDwlc0q7eWQJ6iBYdC4Mziyo8eV\\nroYbVt9DEsm7Fr/gsS8ceblc8ft5q/BpIeWhLNrsKbNsDjRlgspham+HtoQKMnWkgarWi9vxlcCQ\\nm2jAfOiHPlesDuYkGkT8ddpX+Mfyu8Jatr8JKoctElAxU6K6zkw+WCV4TCTXvvgaHjhDjqyXv0tD\\nfSodO/36XwEAq08MQswB98WT6sEW6M/SwErJJqjeXCPd6qN3yf9Z1fhxV/ME9bophwEAC1P3Stsa\\nynjre5kREUk301xBE77IXBVMA2iwqj3WNBfH6bN3AQBKzKRnTIqow7/aH6H6ZU8HABTsTXVz7ORw\\ndTjnMmV1Bb33mmohJJOkpkAYQrlaxUOx+MN9qwAAa0oHAwBO/di90fNCvl4rcJ1e9DBN6JRw4JEP\\nnnTbZ0yxQ2mgSUowsm1jGvXhEaUt24effHQhRh29DQBQtbd9s1/PdYJa2YPukznTjA4bPQf/VbfT\\nKtojvel9ef/kWOg3kgt+7OxCLOpOLqh/LrgZAJCqq0JWdQoAIElL5557y/kNcpUCc4mvrkxAJDNK\\nUjAJ6+W+AoSedL65ivpdZa1SclDXVtLx1kgBl0ewHK1bApugGjrZpEXCpsCbSZ6pvaNVmSS1NWTN\\nfRcAMHDRUx774scWo3JXB4/toYCbH/27oisAYNnKG7weZ0ph47qi8PQJssRXhgwZMmTIkCFDhgwZ\\nMmS0KfwmGVTHMKINFAdipFVeha35riczqG0X4687BgDYs3rgFa5J88Abgxru1bIrCW+SwasJnAE1\\nJQHa8saPq+vigDaTXIjqK4l1a59ShbIKYgGif6Vt1X1tEPR0z5ISa/FMt60AgP9dfwcAQIy3IuYg\\nycusLI2ubWAdcIZkbLrSxpkaQ0cR+kvBSVGvZgZV1YseXtaoFdI2b0ZIhs70PgpmlgexXOHTJKSp\\nMHRk8sJo9lG0CdAWEqvUWDqh2InFAIAqJj0VHKGlmQkXVMMrAQC2ZjQqaw0ManOAp7jJ3+GZ4qY5\\nYepohfYSj9dgG5vx9XdlUM2xzKhIAVT1pu3qWsZSlguwMdHIyBk0HpiReAiv5xDbZLSqUVVDctJb\\ne5P6YID+In4oHwAA2HuepJuiQQWoqeznR24GALyxbzKEGvrNYoQD8UfpmysKVB9bFGBjIgl9Md2M\\niuFWJO6lc6p6MflvgQKRxe4NsrklvsaeZIKjuRAh9QviUOrThIMxzXrtQBjUV2Z9hb+vvqNZ69Fc\\nSB5cAgCNMqUxo0sBADW724XleoOmngQAHNnQOyzlGbuaoTvfeKhIoAzq1Tlq84fDzpenOSemMto+\\nTlY2v9TIm2OjP9SnM+fPJsbGekNLTExTJ11EUTW9h4ZLUc123eQuNFCtKwxfXr7WCFEpwsiaqq7E\\nsx3ZoxzolUwfvcP53QAAFfpIvDx0AwBgdRpJIZPtKpw7Q9K0Ko0O35RQ3J+uC01u/97/O+QMo4/i\\nD8UUL6lTWZGeeRoAsOlcLwCAKjsSNpZD2JZKAxnRoAIu/TY/Od7w2sBvPLZxF1ztUWf8lD6f3ovO\\nN+YBAM7u74KIy/SMLXFOuXa4oL/EB7aBBSHWZ9jgYAMpC4u1V9cLVzQmvDknphw8PjvYHMC+ygtX\\nWaFi/K2H0DeS4tXeRctOUKXJKdCsE1NvsDEXX7sWiD1D71tNV5rwGVIgSdy3neoJALicEQkFi+0R\\nvkqENpn29x1CTruvnrgRqh3kmq9lhre33L4bf0omOT+PO08e/S26a6hfXl09FCtECvERdLQCpSpy\\nvoPmOLpG1GkN6qZQf2yvpnJsl8MzGTUnORBR7r+stx9agnoHTUD+eHoeLPGsHwrDxFRUAYKXcTkP\\nCfEXPsJxJSend0/bgS/Wjwv5/O39vwIADNzlKfEFwjMxnTJrH9YeJ+IlXBNTDtfJ6ZqHyWvkhi3P\\nQJcTXGC7LPGVIUOGDBkyZMiQIUOGDBmtAq1iOVsRbUPUuFLU7QgPXe0N7ScXYEuftQCaN+epN/h1\\np21wLBCcO20o54QDtkhaNfPlONbWsWvAtwCA3nuozSjNoZXDc+7m28jwoLPKmUdsaTWxD//95NaA\\nyjINMCDymH+HuoYwdKJV2b3Tyfhp9I4noclumslKqMgvj8eT/X4GACw6eFPI5VjiHT7zGe4bTCuR\\nfXY33zufvSA0F+NQcf/szXh/83UAAE0N/XZdiQBTUuPnxGapoB9IS88OJt2ckHEexw1pAIB6K61s\\nVq5NhTNbbhSOdiXzhLsn/AIA2FnTA692IMMknr+3v7YAarbkndm/DACw9NiN0DKWT1FEq/xm3yal\\nvzncpHeny14qGYSOiWT0c6kd3TNtqbNt529MBwAoop30kjfm1Botepgaffn4f/CnfDI9OvNDeA2D\\n1FVKKccqdxOuT7dBV9AqhhfNBnM7avP6/PD8Tlf2tKVzS3IsTN0ruQr/luAqjy26gRpz/0xiks9t\\nzsSQaWS291n6dgDAdqMCH5WOBQDsGt4eHXZRu3/jvTsBALFlDgCsTCJIsWrvCMy64SAAYKSLz9zW\\nemKvTlR3xGNjtwEA9lRkAgCO1adDVDC3/y7Uf9uMKgil9P1XmsMz9mJkKOyxNhgjiEHWFdK/Y2cd\\nxpK0PR7nZGwkMz1VfwPsFqZ+qgzeAbwhXNlTc5IDr95CBlR/WTYn5DL7jTkHADjxS7cm1S1QNIU9\\nBSBlqWgquAmSa37TzMnkGjwpJhubzo0IqrxeU84CAI5dTIPmZGBjx7dLJwFA0OwpIDOoMmTIkCFD\\nhgwZMmTIkCGjlcDv0p8gCJ0ALAfQHhQZ8L4oim8JgpAA4EsA6QDyANwpimKlIAgCgLcATANgADBP\\nFMVDvq7RRXsZi3uvwL07nmvKb3EDZ6xaA14t7xnwsQGzoBks11OuvsWZ0zGTTgAAft/hRwDAjM89\\nU7BcbQiVOeXgrL23dvn3nbcAACI99niHNgT2FAAW3/ARAKCdkq6kPa4LOM2MJYFWg+3xNmjzaCWM\\nG7VEjS5D3e7gYjyVx6Kw6FjozCmH0NEEYzTFLgWSGywccM3/CqBF2VMAiFKaENEg5simd5okGVJE\\nqHuSWYWxmJj6mFNK7DxFzNnXk6je9aIGi4smAgDKN1N+tIbNQWGiFfq74yiJX1+NDluMtPxfzlyS\\njoqdEcEoNL2CXhRTO4eUCoEjoiLEH/wbwQ8rR0t/+8qC7C3li7/9dy16AcJIH/l8moDEgaUoO0Hq\\nJ86gKuvavsGaP4SLOfWG3u9Tn6Jo4ncnUHDGNstidOZjbYY8qA3znLqiOXOeBgMNy0U6YyQZHv1H\\nyMSBn1iM3kPbpeN4zuLkSyKKJlP/l7jH8xtUk0794MA+udhaR6lm1tWwfKeiEmvOkJlS35QivJRI\\nDNUrLMD5ZFUmhk8kA5s6K7GTZcZIlB0iw4GkY1xN4UAps5xRGdg7aASMLO2bzk/aN97O9Oc9H7pX\\n9vT7h6HPo9/6p3nf4G8f391o2Ta96FavYHDu7sVBjZ8bQyjMKc952m/vHFhON6/pU0MM3Nf4/QwG\\nGRsfAgC4cp05P5Fp1/QnNuAPQZZ3ahONIQLpGryxt8EikB7WBuAFURQPCYIQDeCgIAibAcwDsEUU\\nxX8KgvAHAH8A8BKAqQC6s/+uAbCI/dso9IICgyKaLg1wxQtFZO7xnxTn3Lilpb0cH6+bFP5Cc0Ob\\npIQDyzrvBAD0+Pjqn5huNIS3Xbq2wXvu2QIAiMxpvomVyOYI781fjAk6mmR22/YAAN+D4IawxzI3\\nXC8fsLrdyche4D7w8DZpM/U04X+GrwcA/Ocz73JmYyd2HZafzdLHiE7taGZTUEbGJ/bLEVAxh0V1\\nth5qH4YaPX6+H0B4Yxm4SUNLTUzfeXAJAOA6Zpzh7bp2nXMRxRbtgH47CXVdW1bsIWrL8w89C4Da\\nxlfPk4HB2UdoEPI/bz3oVm5kIQ0qdhroA99XUyjVQwlytOyiqsF+M01wd9fSByxcsv/TDyxCz2Ut\\nuwB3pWBMcUBX5D6pN6TZIbL7HXm28WHB0affxcC3n2x0PwCIe+OaXkkX1HdhWrzt7T36Em25AGMK\\n9TcRZeEVakWOKsf+IZTnNJR3cNjNtMB6YF2/sNYrnGiJiaklVsTZuYvcts364nmpzxh+I92n/Rub\\n9z512z4PgHtf1dKwRFN/Vd0Vklz9H5tmAgDihl+GzUFteO4Fkm4u77IDCZOKAAC2Ze2RO+1DOmka\\n/dNr131I+JYWgg19SLu9LHM14pU0blteQ/EYa0oHQ/8zLSIeHtYFqzpQv/33dscBAF93H4QyI+3v\\nHEWGf7XWCJeJqRPtDrDfwla6je38T0wDgTeJuR6A8hqqz+zoSvzNx/mhTEyfn/ut9PeKL67z2M8n\\njz0+Ce+3gZfbHGUHA+sBZvImIGDDMDszJVQa6X47NKJPWa23iaNNL4b0vAItP1j4/XKIoljEGVBR\\nFGsBnASQCmAGgE/YYZ8AmMn+ngFguUjYCyBOEISUJtdUhgwZMmTIkCFDhgwZMmRc1QgqD6ogCOkA\\ndgDoByBfFMU4tl0AUCmKYpwgCOsA/FMUxV1s3xYAL4mieKBBWY8AeAQAOqeqhuYeSG8WhtPY1wgA\\nUF7UQlNzZcx8roY8qNyICWh5M6YriQ5DKb9fxba2ucby3eOvAQC6qp2mTK7vWaASX38wdqMl/9wb\\nlkrbOLvxP3O/BADMib4cNONhSrEjoowq6c16/reAx+/5AQDwxk7Ku8fZZcCZB7WuswgVdXVQGgWo\\n6wIru64Ly/lXR2uVEZfd99f0IPZu4vAsAMCk+JPYUd0DADA4ivIlvr7+FkRdaB47gyN/XHjVMqin\\nH3Bnr4YevBPmXT6crnzgxDNOBYO3XKotjcxpObhYRYyteV/43bECUWyEE8IQMq8SDzktxFoqD2r/\\nm08BAI6v6xXWcl2NmPosovvn2scaexPzF3E+GK1NYOBS3ysp63XNg8phTFKgdhQbM+bS77ZGi9Cm\\nUodqs1E/p1I5MCotDwDw65oBiMmjsna/sVgq68lCEg4WmygUooO2FskaSg+zuYie5S2px7H41/F0\\nglWB9G7kqPRK5joAwLPH7oLNRt8/q4X6fa3OAmsWSU4TszzHluYYGueaEpt3vJs88RIAoGxbR499\\nhu4W6H0oPnzBGisith99iAy/eO8PA8mDGiqai50N5notbTxqTKMXX9DbMLIrmSiFO/WMKwLNgxrw\\nBFUQhCgAPwP4hyiK3wqCUMUnqGx/pSiK8YFOUF0xcIBaXL8+Cdcv/H1AdWnNMLO8dBEu7opteYLq\\nOjEFfluTUwBXbFGjqeALM+evWwYAePrScGz9erjHcaFMUPlkVHfOU/78wfx3AQBjtJ4TlsxvHoW2\\nOLAL2lncirJnLVQqmiTZ7VSm6yDRHwZNYwmo1zdfZ3slwSeoACQXX0fPOtguU9RJzKngHrA1Cl4n\\nt3yi+vjEn7AqbygASMnpI/c0X7jBT7//N8Ys/12zlX8l0XCCev3JW3Ce5evVZwU/KWhNk1RLvAiV\\nn1jZYDF51n4AwLrdQ5Bz+xK3fc05Qe00KR9n8inmz7XPa44JqsjWnl64hySObywPzNk9oLJZVxAz\\nuhQAMLvLASw8TpJV9dEoj+MdzRCD2prgOkHloTB1aQrYR1CnmhBFPh+FhQlQaKj/E6vopiRmVEKp\\noPMnpZzB91+Qo290vudE9eUSijH99XI6+sfTpK7eRu0oTm3ABQMt4FSa9egbR7LhAgMNreusEcgp\\noY5dOE/9bEK2CGMSVVhf6uI+PI7+jmSx0WIbGbo4uLab1dehEaGq81355pygtgbwCWqgeWlbC4yd\\nSB+vu+hbsB/oBDWgXy4IghrANwBWiKLIxeElXLrL/i1l2wsBdHI5PY1tkyFDhgwZMmTIkCFDhgwZ\\nMhpFIC6+AoClAE6KoviGy661AO4H8E/273cu258UBGElyBypWhTFIl/XUAtKpKg8V/DaIiK85KWz\\nxTqgqm47qyCu+K0xpg2hv5byOhp2BudSeyVhSHFAUUwMTNhk86xZ2yJFdEkl+U3xRZI9K1xysT28\\nlIxaxkw/ijQtmSiMjTpNRVh9r4ra+tVjYtczAIBNR8iYQ3c4Bn6VvezVyn6cGKQXiwdj9TaSV32a\\nTkZU/XF1Mqgc1X1siMqh7txQrIcYze9acAxqY9LgmDNUzoozN0jbAnWdbgqSlC1xldaB/PJ4qNT2\\nkM/nrOmA6SfDVaWQEW72FAB+Wk0KEC1a1jn7x97r0LeKcjCKCK9pXkNwiW04mVOpbNa0aneS4/IH\\nO6fB1pPUMFfSoKg1gLPh9ghAqaT/uSY5DwAQ3zEby7PoezJkCLnsRiotUCnohn59ajASJ5I0V8VY\\n1ScKR+LFdj8BANoxmcuwxHxUWUnZ0kVH39C9FRlQsYtb7UrsL+tM9WH1KryUAM0lejoRlQKrq+jG\\nnAJAXUcFNCzvNFfs+XP7bi3gplTS/1vaRr1bAm2JPQX8M6fBIhBzyzEA7gNwXBCEI2zby6CJ6SpB\\nEOYDuADgTrZvPcjL7BwozcwDYa2xDBkyZMiQIUOGDBkyZMi4KuF3gspiSRtb0vDwfxYpqHVBE+t1\\nVaGtsqcygFoDMZGtKaufQwWYWByoWmeFtYRlueJvaawVjhq+khWmmruEfOQXU8zMbTfvBQAcuNwZ\\neXm0Kq8poS7ll7UD4RhIhhCf2UYAAG6d8Cu++3Gk2+8AAFFFhacm1GDnd4MBuOft8ge7ls7vu4dY\\njsQoAwQ73Yw/lQ4KoqS2i4S0KtizKVYpMl8BQ8pvnRNpe1BkRTU53zIAHFt75dQCKVMuAgDOZ3XE\\n6GGknDj0Q58rVp9woKXzHLcUlNdUQvdrfKP7Z9+6HQCw8tsJLVOhVoCYXAdK0inWs6gdeR30jyzA\\nX4esBQAoGNu5tnwwMvSUePqlQT+i3EZGSBdN9G2MUxvwq4ki3TqqSUlUaI5HFy2lTBsfRSqH3Zcz\\noVdT3rJRCTn4vrA/AMBip++2MsIOaxp932x66tOjXALmSofSd07fvRKO0xS32lLmXVcSLW1k1NLg\\npkW6gubLtdwWEJSLb3NBl9JJzHjgeQBA5xvyAAAGKwWjl23v2OZfuLYSrB4qTO0ciMqnSXhsLr1Y\\nSqP7Qyuc4D5gVveokUxvBB4c7xCkbfymqTU28Caq0VDZoihAo6K/rXalJKvRMDMdhyjAypzv1Gyb\\nKApQKelvC9tnsytRX+9uRiI6AKXKIZ3zt6GkXO+lITffGjEC49gpP7DJa6KiHg8eonybtrP0oXKo\\nRWnCKCoBRwS7H+y3CA4BooZtc/AbAEhJPdkEC4KzHNfjBHZ/eI5EAICJ7l1Mx1pkxNOHcGBcAQCg\\n2qbDJSN9cCvN9AG+VBUDe5ZnAurU0fQFLNyd6rGvrUGwOV8+cyaN/iNynDI9bhxiTiGdUe/uhRif\\nRDKuHGMSdEraXmGhe9ZVX46eWopYKLPRvauwRSJBVQ8AMDBnkfbqalww04SxxkbtJF5tQKySzKuq\\n7TqomebOKjoXEezMrWP5NjIv0VQo4IigNuEqpbZ0p3L0R2kqb4kXJUnXiacXov8bV8eg2jK8Dnot\\nDeD6JtM7uC+/M5aPIPOvh4/eR8dlxcLamZ5vZLRJ6hP0EXSuRmmHTkXP0u7g/Y4oHadVOnVmDvZu\\n6dnxNlEhbQMg5UTUKqkPilaboGYfKT6APVndAbEaekbZJR3ounYFbFb2rEXgtWu+AQD877J7pbKP\\nPUkmYwPe9cxpypuJXStKJhoO1pQby5tpi6K2o6oTJDMSV0mdOdHBymHmfuVKKc8vhyVWhIJJT1X1\\ngmSew48zptihK/JcCDOwPKmTBmUDALZm90LfTOpb7k3ZKx2nZRV6/pe7oLhMleSmNYLd+btdl8lF\\nBdVXYH2iqHQZxzgEjzGDqHBKBx06Z1/M+wd+vNLo/H32DnRTRYMKuiQyzElPrIDZToNG25v0XDW1\\nDTSKjeDSaB0ssex5sFyF6lqSlQJkFsPrwc2SRIXofO/ZPVGaXBb3XNa++X1yaPi9cR4nOAAF+628\\nrZjaO5xySnbd1J02GBPopJquV/fAxaG88mPf5oS6gRPsiacpBKY5smW0NOzhN5ZuVTj5CD2rga95\\ny1UKZD250GN7989osu5gY0hRKUoL9Y4Y1oGLgLKa3m+73iH1qfw4IdYCRy3/ULD3QxSk8Cz+r6gW\\npbGsqHbpi6OoL4xPqMP7/T5zq9/QCKfrmjLlXPhMkmTIkCFDhgwZMmTIkCFDhozmRqvjj5/uRKYm\\nv/twPgCa4N9/748AgE8+u6Gx09ok/vHgcgDAX0/eBAAw70m8ktUJGQ6dAzXd2Uq8mpqUQwloK2hl\\nJSbPitTttLJS2ZNWUWpioqBPIUcWzpDarGo4WJ4xBVvdtNsFqBijaWF5wATBmXJErbJDEOhYs5X2\\nq5QOqJjRgVbltNgpukwMor2Mlt867AESKmm/MYnO1ZXb4CqLPdeP0gt8XkwmCSdyU/G/I78HAHxb\\nMgQA8ETqNmSPptWifgpmprE/Fla2Wm5Tea7UikpRWqESLCx9ikIEOOPneg5f3bI52QLOIMAuQFDT\\nb03JJMnR8OR89NcTc/p96UAAwIUqp5Trld7rAQC9upbgtqzn3ep16iFn6oteu9u+fMYSz9j1SoUb\\nc8rBjUO0BbRqaM5U4bNzZMbSJb4SKTqWckBDDIpVVOKChZhRPaOQlIID0YwZrWVLuyaHUzEQo6J8\\ngmrBjhIrsa4JqnpEKWn7aQMxMd11JfjXrzdSfSqca4euzCnHS8OoT3zn6Ez2+wRY4pxt5tMF/wUA\\n3Pfec43dmjaBjOTLiNHQfZrb7hcAwL4LXXDPzocBAF+No3QOf4qbiTMHyWDEpHJAq6NnY2J9gqvS\\nQsloKrNdBbvI+i3GkDpEAQrWn5jsTFKnNsNgc67+cgaVH3fZHAkNM0yJYKyq2abC0ZI0Ot5EdVBW\\nqID2RF8JShG3RVHberED1UFXrIBSoLJfun8VAOBfn3BrB2dbdSHcJRZI0UgUDk/XIKo8zUgAJwOp\\nNLgzie7HOLebkh3QZ1C9Hfvj3MpoCGUtVXRp510AgIKOGzH91RcBAC/3p2eVM2sJ/l5OOSHvGbQP\\nK/aOonPrnO1fydhG/kqJatHJnLqoSwSuTrG7MIf8lVCI0n1zVVWIjG1UsffNrgWie5Aks46pa9ql\\nl+PSJZJuzux7BN9con5fESBzytFxtxGF45jigRnZONSA0oUhFViRXOpt1wgSe81/k0Pt8rs5A+IA\\n7C7MKUDMLP/9mgoB+lL6H0sUnRSdD+mA6ItOCl5dRxes6RpMoIUMGeFHfWcbrhl4DgBw4vvw5gFu\\nC6hLdyAqz51HVBmAjDWPAAByZ74vbf/PrE8AAM/umU3HFUY4vxW1bFweYy/B2fUAACAASURBVIM9\\nin1I7ALAVHySYsOsBLjaj5taKkQo2BjVztQnCpNC6mc4O6vQ2JHRnsy/dCqrxJjyfMDPVXTEjv6r\\ng/r9rU7i6wt9bj6N7HU9W6BGwSNtygUAQIqePt7LOu/Eq+VU1xVfeITquuHIk+8AAAa9+1Qz1rD5\\nYOhmQWQ8DdCTPiQp5IXbRPTvRpOks1sz0WGvu24sb4ZC+rhGtid5pMMhSBI4BxsEukpuBT6hE0TY\\n2MBTE2GFgm3XuExGlWwbz1V2uSoKQj59cFO3BT6wSP/Labf/7x9ViPezxwAAYtaT87RxRjWOX/M5\\nAOAvZRRvtaOsGy7tpAGqJc4hSbFELXUOglUBkXUEbhNU/jpq+dsPwNJA6CAA4PIkm4BuPUlyOj+N\\nBoIWlxHsX9bfDgBI6VOKSBbr8mPvddL+Xh+6T0JPPbRI2jZqygns2dSvsVvTJuA6GOWQBqqNGKaa\\n0qh9JKdWoV0kLaL0jC6R9kewEWMPHUlOzxg7SE6NOUZye+6krZAkvAam4XNAwCUzDerba2rgYC+A\\nlSWj/eLQCGgvBJZ8cM7tWwEAXy6fJG0ztmeD3ggHbh59CACwbaVn7tvWgPrOdkTm0++u6073O+qs\\nZ9xszxlnYGEjc72K2u+0xGN46407AACWOLqHd927FZ+fIcWQ5UIU7AlUplrHnmVcnbRYFa2mwbhN\\nVMDKY70UzpkZl/vyCa3FrpIcO20OpSTjtbGGFKcxoLOOJjVczr3lQg/oI6icyzm0OBSdo4SFZQ5X\\nD6rEsRFfAAAy1tFkW5+rhqk9lX3+Tpp4v1wyAN+tGtvoffQm8RUVvuPQpO5B9DxOVDb+XgDAxFsP\\nYtu3Qxvd/38PrAAA/GXZHGmB7nczKEzikdhLGPA6SdbicuhZxD2Xj1c604Lf2uohUjl8oqowKqQF\\nGi4lA1wW69hCnihA0oOJgotUVukuBQYgyVlFjQhVLZ1kjaMfrYiywlHPXFMT6JsmZEdDS2buqBpo\\nxR+v/QEA8NVjoS+Y50+hduJQOSeRgtX5tyS9FSB9E7h0F6LgXGzgYSQK59/890EgCTEAaC+LiM0z\\nNVofm5YahcrkfPgFE6/uCepvTeLbEPXpNmkRSXu5dcm5+QKMIAK6sbTwXpVFBE5EpXDVS3w1Vc6/\\nLWPoJf7LQIqD/sfCOdK+mp7Uj+bOcE5UT1poMX3atqcAK+sUWf8n2AWpfxBsAhxx1JEIzLNEjHEZ\\nG7OHoFDbIVaycQk7XjQqIbDwsj8M3wgAuCPqHCb8x3fO8iGzjwMAll+zTJb4ypAhQ4YMGTJkyJAh\\nQ4aMtoNWJ/HNnJoDAMjZkOmx78iOHph8GzEDO74Z4rH/SsGQ4kDBpi4AgAK+8emdiFUZnAfxBSov\\ni3acOf3rA5+5GWa0JihG0JKOY1+cxz51iRqWUlqBqWO+OppLgDGdtiWPLgL2usuX0zYLKJhGSzlq\\nZl4UF22EyUZNst7sZJIMTGqlZCxGfLRBkvOarSrpfG5+oo+wwGSha5eXkWmR9kIEOuxxZ3EvzrVD\\nLCMKQl1DD6jDyCIUHKX8np1+smJIdD4AYHkuyRTsooAFfXcAAL78kuSY4tpYPJpKK/4vtKfcZ3nG\\nROQmdKR7ZxEkxpOvDCoSzNL/6JKI/jAZNRC44ZOGfpPRoJFkz4JLfjDejEYNPItnO24CAPRU02ra\\nSYsGC/5JJiuOUfSbL51PRuxJWi3NvEjy+ZwpS+ELezb1kyS/DZnWK43Fc5YAAB5b8ajHPlcW2Bsc\\nPYixV570nmOTy31rC5JR0YuOyY0giV+0zowkPZ1fZqG2Fakyo9pGyoEjl4k1r4iOlAyWEjR0fLk5\\nSpKF5hkT0VVPtMzyfaPpuoVqWKMZC8oY9Igy7y7MH20fT9dm/29OEKErofbRcWqhxJx2uikPAJCz\\nI11iU64U6rrZ8PqklQCAWocW//yS2P0e3UgBcOlsZ+lYbhxz5nIyIpnRUUkZvU+xaiMqhtD70X4n\\nvRsrvp6EwTeQM+Y+SxcITMZvZSvHlSo9dKwc3k84RAH2Bg52SkEEuEkOW33WqawSi2u0qRHJmNzE\\niHqpPlwivOVCDwDA0I4XsfMYKWj0xfQMzfGAjhHx5oPxAJlbI/fmDwAAfd95AhFpddLfAJD11EJ8\\nh8YZVM6cuppkWWJERFQwdp7nRKxx/k7OkFpjRKd8VuPCTrKQAqZAh7GDA7piuheu7ClnMZQuxNyd\\nUdUAgL8AsCZQfzRSl8P2alHbg6tcWLjG653x8Py5AIBPBy3D3wso3GX+aOpjl/56LVR1dKzIP6Ku\\nrC9z2FPYnG7eUDnlvJyJFByCGysDUH9qTaL6dOlC72JJdTTG9qBczD9vGwAASDlog0NNJ9enqXBr\\nFJmofYXGGVRjsga6Mkuj+ztvopt27n6l8/coRUklJBrZ/XHJHS3q2YNTiBDqmYLoMlMcaURJKizd\\nBwHo8Cv9PpXRqS4ytKMGXtlTiYSxpALh6hrHX9tOvm8ZwM55rwMArv3YN3MFAOk35gIA8jZmAAAi\\n81TYvuDfAIBxi0l6rwg9DXPI8GbeJriMk427KKSmeTMQty6YWURWt/H/n73vDKyrurJe9/WmXi1Z\\nsmzZcq9gY0wz3RQbx3HAdAiEAAmQQMpkJpN8k5lJJoQQAolpgRB6C80QDKYYdwzuTXK31bv0er/f\\nj3Xuve/pPTW3GHLXHz3dXs495+y91177AGrf4vu68gz2rf+ZL8PWxm89s4b9wM9bJuJ/ChmdHGvh\\nXOSXpy/BHx7nGOsfItLNMmMwiHFNzomo/Q2y+f0bjDLiYSEwKthHkYAZRqUfauZbkE0yRlclyEkD\\n/UZPAWDjyxMHdP8K9AiqDh06dOjQoUOHDh06dOg4KXDSRVAT5f574v5vPYf5Iids/XdXAgC+/fg9\\nJ+S6+oKjMdXOD8kRzBW1rhZjbtrIaU/84q/X4eFbGRm6+y+pkaHjilO7gS+zkhZFMmXV854ucqog\\nmhWHTUQJMg6HxV/ggIPh1KyqDnRdR9dc+fMi18UfQ8Xr3L/2Bua6jC9oQluQMSElQtrZlqGdqJHe\\nm1bZiViekLPO96hiFgq8HpsqsmRqprc4MXp66FqRGO41wdXAd+cr5bJcmw+HsjVv82t1jNRPyGeU\\n57A3Bz8dQoGah868hNewHVj1Fut33nwr80Ar7O1onMiagI1LhqlRhmC+EOPJMwJ5IhcuqkXJIgGu\\nj4lcXFdGEMVD6N1v8TLn1e2xIzuT0fl5+ZvhF4loH/jpaXzgf69BxyzeQ9ZGrsusjQIQOXh14nwX\\noV8okciTKZI6bvZeHI7k9rp++Pu3qt5WyzTmBoY3aiJRkW6uHUiFWHM1vZFKkKMbQIeIUOwWTsWo\\nQ4a5mO8jfpDttzazAEa/EBbIFG5powxjpxArsMpY1yLyfxLOF8vgtraGvrtma1vy1UeKI7B2sK0n\\n5lPubyVzoWr2fiws+hIA8D9vfUsc48TkHVnOYQ7RjyrXIs/I/vtHK6+ES0R0G94vT9lHEYnxtDuR\\nWxZIWnfQk4fsrXw+fmqYoWR1CNuCrP951oJtWCmP5HH28+lGW8zoKBBCbRYtp91q47JAu8i3kyVA\\n5LwbRY5NzG+CwcZvx5URVKOlhRlsR+cXtuCz1lEAgPsns3TM3euuhtGtKE+IP0YZMTv3zd+q9TFv\\n+Vzq71NLydjYsIG53+MfuVMtPfONvZcCAPYtTWUXSeU+BNt4D5VjG9DwEWswJkZOeyJxnSEhUqdE\\nXY1BkeeZGQGatPhF9jmMuimRaM/KQnXdnfWsc1x4Xj0O7xiSck5TJvvhQJHI/Q2aYFjGb3N+/T34\\nf+fz+e0IkIkwueowtnczgqDmkxuAqHiXam6V3whZiMVJrigM4h3axPv1ddvUqKPJqwgiyTDY+R6m\\n5JH7dNfo5bhw+d0AgOLN2qDtK+Q+ZdPrkW9Mz7xIhGyQ0DaB7yN/e6DX7UqWmoBvtwAAunx2BGs5\\n3tmbRZTfCi0H1cRlOTWymmPbMU5W7yWSx3uxZnOwie91JUVOFXSM47PPPb0Jl5bsAAB83MJovx6x\\nODIUzeA30by++IScr+YWjsejn+o/aqXgUGdqzdtZL3D/i+ZvAAAs/3vv+eXHA9Yz2hBanZ+yfPp8\\nRgO/eCs14haYEIBl79c7P1qZL+5uKkipCZ97Sgv8HxQlLVvyzFnouJb90uJSlvC6KbMFj13IviXQ\\nzXUjCzvQ0EWhxsnFDbiy8AsAgE2os+0LF+Lx3WTsPDCBE/Qp1i78uYN0n7oA21B3xIbXK8kWTFcK\\nJxH2i3gN7078G87//Y8HcvsqTjoDtS8RpH978QbM/w7r/+wPc1D843cexz1PnjhjLn6qG4YvU2tH\\n9sQpj9wDy6z2PrexzeLEbc00CktM+9M9uOMVqnPFCsQA23qChowexinQ9+QmCc4oAoJCEHEJeoA3\\nBrsw3O3joxhbSXrJ5+dzEjn0Y80RUfYs91l9RRXyy0kl7qzj9djrTcit4UTR7NEGW9mkKDtmwWbl\\neRR1wkCRhKiD11OyUjtP3flC/GI/t3PWyYgI+1epS7ezqVgTIAJQe4id56hJNBLr2rNRW0Fjfeqp\\nVJfbaKxE0Truf82S7wEAXp73CG7LWQ8AmDXxByh7RzwLYRR0jDciWihoF8IYNZljyMkibTAqVIoj\\nMaNad29sPvmB2+VinFtKmlmZuR2jzJwA3fPA7QCAITcegm8ZKec0TIGYxQB/AY/pbBo8jyfRUP1n\\nGanv3Ug6Unvcihueu7vX7az1mthOlp09fWvC+v6Mv/6gTNzV/wMS0EEjQ2HMmHzaOczdA/+Gj/Ta\\nDN3afv8Y/Q9M7r4aAHBZGUW+3t07AXNGUMjNtoCCXj/99Mq0wkTHGj8dTYdOV8yJbaJ4vXPPwMSg\\nEJdwSh6NtoaNNHgaMzMg028AWzsfeOtkK/K28VtflTcBBZM4KLbV00B11kqQhbMqKkb8cJaM6DBh\\njLpE3yLJKvXeIkSOwpKMmJv7Bg/Z1NqiHiHu9FzNDLwynTTdqzeQPm/ZY4eVpYhh9nJ7dyUgNJQg\\nGyXccIi1bp8dRjrrfwDY8E6qKJlSE3XHXRz7xi9NnRCYt7ggievae6AIjpQtBo5IjuhvhYHt2J1M\\nruv6jJNwlT6csK7Eyv578fh1GP8pr/Ox1tlcVroO40vp6KuprgQARJ2AvZXHKVsq41fZcwEA51Sy\\nfzvcnY2YU6Q9CKdM3AzVaDMoIi9tBlVp0jtaBlr4vvwWXrtkoOASz8njucrckMW7nuJkG7ul5joU\\nfcB9zT7N0aPQD0udmnpJzMzjGSNpFKlkGVlXNAAAWiykphduTDVUHc0h+J8irTb7llbER7O/aisU\\nA9NhO2yCrp15UIgBumOq4ekSLLu40YCoQ1DJszkZzahNL4o06jxSrn9Z/g5+so8UQMXZcCSzjYhT\\n7leQ5+uOVjf7/zeufxAAsOC5/kU/jxQTZ+/B6KcGPwb7POx8EvsGSxffm2KYbr978Qmtk5ponJ7+\\njS0AgLVvTsbGpqG97uN0BRFJMdu+XgjnKCKfqZEt98qitIbb6hcYTLnvejoBr89di4fGvAIA+O7D\\nTCNsgRP+iVy/oXoMvjBRGVnpYzP2GhE7i1TihiiN0Scaz8G8gs0AgHvz6OTOMtj7NUwVNNfxOPlT\\n+nfs9YTuMNOhQ4cOHTp06NChQ4cOHScFTroIal8wJTggF2V0qr9LL6T3s35ZKlWsL/hGhuHc27cn\\n319Cb/LTl9FD/rfWM/DFlwNL9O06xEhbb76e4Bp6j2abWLfo3296BddmMOo66jl6yIIF8RMWRVVE\\nL0R5xz5LFSRCDhkgCU908wxe69BPYjCfzXsJx4yqYIxU7hN7pT73irfj8JQJMaXpIvLnkNE1kp7h\\ngk1a5E+K0rMUKDKp3vSMuoj4m3qNHWMtMNMxhMJN3K51ihlRp6ACjyTfMNMRhHcDvddxc1QVqeiO\\nCKpgxIjDEV7j5sOMBmXVGGH283pLl3P7W4fdoJaR+O2Zr+E/G68BABR9wXvI3RmH209fpncE940H\\nDfAOFdGEHHrqfRGLWndxahbb+e1DPkWFiVTJ2/ZehcYljJZOXrQdALBmfyVKdiVTu4zhOGIi0tw6\\ntff2NOKjb6d5MxrG/OUOlJ3BB1y7uncv5/HAZX8bOD0kVMp3PC2fNOsPkEo3PKb4J1cscNRr7/RA\\nxIvhOQzfNQbJRJD3O/HFKWy3ipDN+Isfxjc66Om3DqLMgFIzLTyZ37J1Q9+e0biosXSWfS8+8LEM\\nk5TKPEwLozOCNS2keMrlHAD8+7KQK6JuEacQyYkA3lJeWOXrXtS3kQJlEpdm64zD0SQk8o1CTCjL\\nBNNqRtiaZrCPkWQgd6eIkA5lzy1PDsAsaJPTJx3Gl3X87t1fMvI1d+5a3LPnKgBAdBfZNXnVWmmp\\ncCbP52iS1Ahq3CSptVW9cUHJNGuROiVKq9QxBYDKT24GAOy7a7EqopQIZVuTN/UL/uJ7D2H6n38A\\nAAiO53O07Ug/MjkODWxakI5h89JLLHt0aH6eWrfz/e3jAQC7Cz9WS/TErUIMKCwhmCveR4YJpdTQ\\nQv0P2W5H5rZhQzf7SamFBxyyNgp/gUiFsCnvX4a1i8fMOmCAMSxKBLn4EtwVBjXia/Bxmd9nw3cm\\nMSXjpXpS2Do+KEGOJ7lxxiyS6so3SjKecZO91TqFbaf4i9TIaPNMCSVKbd2zRdR1Y3qZF0cLB9x9\\nNYVwlHEcsjvIcTfUO5BRrxTA5R9TIAp3ORtSJwlJMHskFK/nPpZe6rMevoD7/KKYtL6f7FuIHCtT\\nEzZuZkS7Er0LO/UGs0/CrtsY3R/7xImLvp1MiFczgrqg+vhFThVsWz7qiPaz7+q9NkvEpQ1g2+/m\\nuxxoJDWUI8PaybbuH8M26KhO39YDhaLmc0vqHGTtm5PV3wqDLN2oFF+Xk5wX83WEmHuHPFY1XenK\\n/SxXGbPJMPl7H68/eo5pFlvnluLcAgq+ecsFa+SwAZnb0s3wtJQh00r2vX9YuVBd9ntQ/K/7Oyyx\\n9ZcnL0s5grdcs1UCQ2LI2K8o1bFtPdRZ0es194avlIEKAOMe5Uez847F6rJ3x7DO2tRlfdcRjYn3\\nEixnJ2x2RpDOUErE/oWPJ/3/YGhgYepvXv0ZVrQwD6qlobTPbd0rOJnKHuVTlynUC0DCzCu2AgDW\\nvT2pz+MoNej+fcnVsLaxoai0sEfuVGvsnT2TeSfr39YM7RuuWYaf5u1Rtx0MDD4jXId4vlCe1tEZ\\n3mGeoGVhM1bXcpI5opBG64GzypPotwoyakX9QkGRDGVphnPbJL4rW7sMVz238w6VEMrnAB4s4Pr8\\nLdogGyjgcbL3RmHoQcWydAOG6ZysRwTNtvlQLoZt57G7K8wweXg/VS5SBjd2VOI3n13Oa+zkPtau\\nOFon8zwFWwT16qUsXFNwLgAalpWzSK9sqqMxGcyVINhw6oSw4NwGHG6gEbHbTRpd7udmdI3mNbQV\\n0XA+GM5XcwYOfzIMoWmcIK3aw/bm2GlDu5i45CUYqkrtwcx5tegNloP9a+X99/C3AABX76ETxdoy\\nkGxOor8apOlwJPmvCs23aUz/dPy+EMkUStPuY+skCg4Lw9LAa0zM/xsoFKXOdAPVvEd+krJsyvwa\\nXOZIpvyNNJuw+0Y+24kP9v3Nu84nvbylLRPGWlHDsSHVwDn/atLaN7aV4dvDVgMA3HFuN9biwLw1\\nswEMfH5ht4fRUkNDUFG4DWRF4S3nt660I2uHrM5mmmdkoHgtJ9715wgn0FAD4ibuY2/jThGHBNnA\\n95q3nc/TdTiA7pHcx9UgipJH7arS5PY142AWFz95/k4AwEe1o9HVyglqwR5x4RLgK+GxFYqvrSMO\\nUUIX3iEGtU9xGYTi8OgArNv5rBRjMzg+AMtuLlMMyvE77kzq1wcCxThNPE4i5FNYx1facGTfSyhf\\nfCeCzr7mrcmq7Kaxjc99zmd3YUoF+x5Ho1AKDmnjRShLa8sdf6Ozefc0WVXiDRXx4fmKjQiJ+reJ\\nar2BfGGgB4CcPdzW4hUT4jYJUS3VFwAwrKgdz+2hYRrZyfu2JjiaYhahimuV4BG+71GOFvxq2Td4\\n7QUi/9NsSKH5DlktIzae+4/KY4LB1tuGYvgT6BXlH8Zw+Eo+NIcwJvL2RFQ6b8SpTde8ZclKyyVr\\nes9zVWAQ6sy/eJ856OYSH5xFYi4k3lu6exkMTpShOmsO50RrlvY9J9KhQekzqp7lOKrNMaGqxwOa\\nYbru+6Qrz/xTeqPbN4JzkAOXP4nuONvf1L//sNfzP3DrU/jRX25JWrb++w/BYWD/oLSZZd++H2ct\\n5XHSzbaPNge1p5r38UJc1G0+kvHd0ST2adLsk5rXmf5Yenkdar+gTaEo96dDy5Iy7FnESeb73/w9\\nAOCFrhl49c1zxAUC4Sq+N9NB9je2XnQpAiIdLZ1hql5zowEzvsnvsqarEJ1NIh9b6AQ8+fylgzY4\\ndYqvDh06dOjQoUOHDh06dOg4KXDSRlAVgaHwmuT6mTFRr03x8my/ezGmPsLIaSib66xd6b0ARhFY\\n02i96aOnAUGBGlLQjekbrwQAmEStzdYdBYgNp+fIeaB3gZHh1lb8/WPhqejFyeGvFBckxB2CcQuA\\nVGGDp8pJQxovCW9hL54fT4xepZgzDogI6v+0jVHXK7S65TtEfT5odK9znNX4bTu3TUcvSweFMhXP\\njiIsRERsrdo+rgbh8X6lCMZ5jFROzKZwRP34LDRFSIEuXpdKKzIHBEUrw4iwcOqbptIb1HEgE8Fc\\noc77eRhxixB4CKcex1PGdR3jJESFiNKw90Td1b1heAQFcuK3qwEAa92V8JSa1fUhcZ6X1pI2kVnu\\nhtcrQihZpLOE6p2wtSWfVzYAGz7h8/y369/Hhm5GTndN4rktbUbErCIyIKIPtTuKIWfymZ1SdRAA\\nUFecjXgTn9ObNXz/GeOCuP+vbJexDBmmQ7welwiMRlxQxZ+6K/iJ21vjsAjRj93rKriyKuVxDQg3\\nPU/RlgMJkc3ICLZb8/6+Y2MDjZwqQjZ7r30UByLeI7tQADtajk5V8VhHThXYDg1QJKgX9BRqAjRh\\nhXBhFK4aUZd4HL+Jnw99D0rc8rt1rNn7+NC1Az6fwvJwJJAefMNTIy27uvi8l4x/EVmGZC/34agX\\nqBuc5zvgt0I2CTGxPWzUWQ2SyqpQopOhHEm9f0NIRkDUayvawAtunGWCKcD1Uox/nU1RRJx8v90j\\nGIrzlTjVNqqklLgnh2AWnmzXIWDktxgmbfSzYwp9mYvha9kXdFbxvXjLJZgZlFRTJnK2aGkptjYX\\nskzJUa+95/4V47cnR556o+EqSFfndKAI5cVhbRdUuj4ip9+57h948vlL+zyWWaRuGDZqqusRl4hA\\niwio6aAVW030/BtEKoOjXoJBpGtEnUDdubweS7ciZCfBVy5EmzoU5V8KXAFAzC6owkZZ/VbtrYA/\\nX6NsA0AwT9KU1Efyx/69xSp7pWgn+10prtGClf454pAQLuB6b0xrj8pfX4lFjdRGxT6BAgNGOEjX\\nLbbz7wFXAIcuoWDIsPdTx3lPqQmSqGsaFCKJHWPNiJv5LYusDhiiJjXipVDCg3kW2NqTxz93uQ22\\nTj679gkmlTYoFbCtWtdkYFsV21fFKt7f0URPTxTMUzqx8gApySWzOJ9oXlPyz7ykkwofCzHBOYuT\\nmTTD36YAp13M6xKpuUolikRar8LsSMT11y4DADz3woVw7jen7JNOnO2BW1lv/Y6PblQjooqI5Yw/\\nacwOhYd1zoq71GOnw77z/oqxewcXoVdYk5dM3I49bjJy6laWDeoYg8GaWx7ArEEoLA8Gbe8OVdMG\\ng/mCndNL5PPz98iS/MUcPs8MUwgWTecNwaAiMsf9cy+rR8d77KPl2RyvphTVY8srqeJ9PWGIAF++\\nzDnq6IU1kKkBiGIT+5aWvYNPCdMjqDp06NChQ4cOHTp06NCh46TASRtBHZPH/JwZ13+OhzdQeMGx\\n04a7v/kuAODRZylHf+bWBWrNpDXL+rfy+0LFHJZCebfqfQDAb9tH4al3LwAAREtE8nebAfPOXwMA\\neOfAmSnH8I3mdjdltuCBNOcQJSthCAF3zvwUAPDj3H3q+nl75gAAhl+sXYuazyGcSoZedAx+/SXr\\nciaKXCiiFYExQey/8GkAwIgPtTyAy8ZQWGdHSMuT7S9yqkDx2hdM7UBrEyMsimgFoEUqjGEZ1vcY\\nqfzQxehN9qEY3MN6P7ZRRFD9FwZgMdMDU5xBT7Q0pRuNtdrOhnCy1zeYZ4KtnfvkCc9486lGmIsY\\nsTi4kC+h4nUZzhZ6mPc9zmhnRUsU/oKE61Ac3cJbHqjORhbTSTHhBubyrgyOgqGLL8cpcgLMgTiK\\nWFoMV/vvRaBURAG8SqkDGU46f9UohiEM2LbyOAfXUAihezSAHN7Djyaz7tRv1l4KqYT3XLJCVqME\\nUpw/OjNMkA1KjiLXecsMCIscL+dYLZJzNFDKZAD9R057Ipwbh6Wjd//Y3muPTd1Vbyt9tl93TQUA\\nuGQOxU/e/mIaYjMZvrOK/I9JFpsaiVYip+uCMcy0idIUwhPbW21UQxrdFecBLfd41pWbko6dKA1X\\n+QrLH+276jGsv5q5MAtrFqFlaf8e1ZjHrNattIu8nMKNfkRc7OOsLfymW2ZmIjBU9AOyAZ6hvLYh\\nK3nP+VtdMIk+xdbGjzqUZ0PrFB7bwRRbdE2MwHFIfMtNIi91hxVBkYNTcc1e7G5nB+FpYLQwt0WG\\nSYikFYtcwGiGFV0jRYmbFq7zD8uE4xDfi3eoBW0RJkVGZNE3SNrz7C/HVFk+0CJBz972EG544gdJ\\ny5R+pz88/MnFuPu6fwBAr5HUxMipgpiIoJrz+EyCHisMzXwmQ1anUilsnQb4ikX/KJpPbnUUDlFj\\nOyLCL8E8CRHRJ2ZWsx3EbFo0OeqUYO3k724G2iDFgdAQIaKXxeuJ68SqKAAAIABJREFUVeeoUfJE\\nQUAlGhoS+cmmIFBUTtGxKc5DMM/itT+79gzuGzOo0cuWabzwvG0hbF9DTYBN2Qn3ms9rUNqv2atp\\nBNg647C18l6VCHH0zG5EahjdjjrY/ovWRxGz8c3b20Sbbk+dFMRNInIKIGbVGAGRBr4D77A4HLWi\\nNmyg/xxWAPifa57Hz1+8LmX5nGrmph1aIZhCty0+5nmoSn4rAPzdy2eS7lq+Dnjxuj8CAK55/p5B\\n73u+EBPs2Tc4xbxwzGUUztm4pRJREUH73kKK39yVcwjjFie/t+13L8a5O64AwMjpQBCc5MdvT30D\\nAPADwUJ0HjRhyjzm7a/ezvmNKU2k1GhKT7N6+477xS9X2vXpkH0aO3aXhd/HtXlr8IbhVABAHY5d\\nBDWUy2uWBcPztFV3IDaS39TR1myNn8OQp+Gz7JR1vUVOFSj5thtXkjV5++UfoOYSirx1vz8Exk4+\\nf3+xmES+p9kBnmY+5y3LB29XbTxUDoPQqsgVJRpD07zYffazAADjHwZ2nJPWQH1x+Kfq7xVCWOGN\\nC5apy74nlMZaYj685qFx8d830rD8xtZvI7AqtfhvXwgUxrG7SVgmgvp4ecZW/H3SFADArSMo+FF+\\nRjteaaOwgm8kG32iEvBfzn6mz/MYQtrvNsHDXBdk4/517WV4Z9TSlH3yZrII9OhsGu3jXQ14fv90\\nAEBorUaBtu3s/UPIydVokvsvIuUCF/Xciud5Fr13QoGxQcAjBsc6dm4TchuxZiwbY7CGhmgw1wRb\\nBwdfe1sUnjLuoyg7BvIMcNX3z/c0bshAbAbpwYc7SI/KcgbUpG2PxwyzL1nRM+qQYBMlaI1BDuBZ\\n+4yQ9nCG42jWZtuKcetojqvX7WjR1juaxUTBYRbH08S2Pv+E6pRZzZI6wak/j/ds8hjV2qqGEGAS\\n9DPzaE5Qc+0hdHezo8jdKURbnAb4iwX9RtDVo3kRGKxc//vXOUhIw0IwhgSVsMQAW4eoEyieQ87e\\nVInUQJEJkVwuD0X4Eo5EVS0R65eNV3/HFEGUUC8b90BfxikADH//VgCqzsqgMek8DsKbVh8hj/kr\\nCIVe69prwrZ5zyete8GThweqqRa+aTqlUg9HczET/LZ6M0wHir7owo4Kt/r7FQ8nJr+vfA1Xn8Z3\\nLO/iQJiOpipFJDVlQ3HE1F7gQE4127zJr4klZe1ifxQolGEMcuOuMexjHc0R9Rttns5l3mEyZFHz\\nOJzBc7j2mREVPLVAHpfZOmT4hAri9oYhiHiEkI2Y3EsxGXXnsm+xiFstffMQcqIcg8zNfMZxhw2B\\noTy3u0LC2x8ybeB3N9C4j8malTRYobr+kGScKo95gCIh9gZjvxTfPve3cpwcX9yIjQeoNhTM4TNU\\nKKgAjUSTX+lvRV9u01R+lfdiDAGZNUJEjzp8MAWASLagFEeM6vuMiwmjrR0w72UfHj3MccTeIasK\\ny02nse04miSVNq4oLmfUx+C009P3n5uuwM8mizHaJAzwgNZuc3Zz7DBG4qoj2SxSQeRDDmQKp073\\nMNGm4yaYhTZixAn4hwmqcVQoG4fMiAmleaX9N083I1jC89g/612gzj9Egn8oj5dd6oZ/O+87buV1\\nD/1Ehr9AUVDms+lNAVjBN11uvDuHAYFzs3cBAG7IbENjlPOLIWMGbjwMFImGqYKfbZwPIFF79OuF\\nIzFMFdTcQudubyq8r1fS0Y3KjzDxc1YXuCvnkLp+552pz/vT8RQiHbuaxzSmL7GrwrbVgX/DAgCA\\ndSs/3DMWbFLHidAwzuVPeST1PqNhE3aL+f28PXOw//0RvFzz4NrWKzc9iEmWnq5pA84YshEA8P2b\\nV+ASYcxL8aMb/3qKqY5ZdT2i7cfGLR7axTm1Mrt3V0WRuXtgppsyHkGMrX99Zg5ss0U+2uxOOJfn\\npOzjmcL+KnPzkc6+AOcXmi3i7eQ81x4CZmR8Syz9zYCOo1N8dejQoUOHDh06dOjQoUPHSYGTNoL6\\noZ8evYscEbwxclmv273mGaPSfR/F3CM+n2ySMWckPYJ1whs43uLC/WP+DgCYbafXcX0ogr+Wr+RO\\n4u++iBf3NzPqePvn1wPQvEZ94bdFm8Uv+gHTRU8BwCqSjGdl7QUA/PqD+bA1D863EFyTD5zS+/od\\n4QDGW+j1UOhll9ZcikMfViRtZ99lS6GfGSUZpw5hlHtVgBGNNsmOwi95XzGLhKAoAWARteoCBRIM\\nESEOlcO/nWeEIPtFk7TRs24wByC76YmShZhU5KALNiGiEbMAbkHjUmTTpbhW61ChGVvdcXiHCC95\\nc+/PweKNq6Vp7K1RWN28DmcD9+0cLyNuF15/C9uEbLDAWSuEQNyi9EynhNwavrf2sdpnFgry+QTq\\nXRB6GnAPF4IeUSAoyvTE8oX7XZZgPCzKUOSKkgm7bLC3CXd6XEZ3paCi+fnXOyaMzB0WcR2amIZC\\nXVQqk+wPJHCZE/C/i1iu6D9evrb3B9UDA42cpkM62XelTMyR4tURHwMAxn44+qiO81VC7SeMTqWL\\nKlyb0Y5rReRUwZWubvyylVHwwFTSkeybBk9HCgzpW1jlhalPi1823J5dL35b8aMJ7Nf/ZJkNAIiu\\nzE3Z19Kh3U1IMJyy98QRyBNUUBu/jUChpLIdXLVQWRVmP68tZjOqZabCdEgjmhVTG11YlOBw1iem\\nKIi/cajUy4DZplLlCr9kozf5osioZXv1DOXf0MgiSLL4lrMYXTW2dsE/lR7rUGUQ9mpe+6T1VwMA\\nts54SRW/MXuOzqPfJ46gvMI5Cxh1+OyNaWnXp6UkizQDr5+e+K2+Ulw6lmkRn+7iYGTxSmq92Pbx\\nBjUlQaGUt55igF301w5BuZYNUF3rQdGFxU0SjKK+qSwlBIlFakYoF5pIUEz7m1udyjZRxJHs7dqD\\n6nqM31Z4TgQP72b5sOJSpkp0VxSpjJ2oTSktpNF0DXWcC5jCgGe4ckTRNqwy8qq4c441BHuUbeuy\\nEj6nlkgG1mRxp9YG8QHEJLj2sZ1FhEhUONOMUKZ4KOLm42YgYx+Pl/+KXU2pyTzEsaVuth2ORu7f\\nX+RUwZzqy7B0zHspy4eYkqNbo56945hMLkMj0ofqFKogRJbJ5bsvUVOz/lVrseac0jroff48+UUA\\nWi3mGGRV3O4HjaTCfvTaDHX7gUasw9myOgdW6qomwiqZ1XUPdjBC+ui7FwMAJIOsRn/Dk30pUqZn\\nbl3Q57njYzl/T42eJqM5Zj/qyCmQPsJffeZzfe7TXxtVRC7NHsDeknyNA42eAoB7Er/1zK18ioYo\\n0FbPAdDUaYJNHEopf+YvkbH8PNLL525OLVd3JEicG66f+hqXDXDfk9ZAvXcreetvn/I4lvk4yWyO\\nZOGXBeSwK4366efnHJPzORqMWLKVhYIfFrlcm0Mh/KmBuRWjh7H24wxrKs2g0uzSKG7ib9XBO2Dp\\nQ1lx1Jx9va7riY/HvQMAmCwmMoMxTv1VInd2txUbQmysp1hTFUQfar4AT5atTlqWaJzGpjL/07gp\\nA1O/WJS03dJd4zC8hLQBhygwbhnvR5OFE04pBjgaua1P0P7DxWH4qzg4FhWRAucKm/H9GZ8BAG7M\\nJOXk9+0T8I8GTqIbakgVyNoLZB7Ucm7MfoWexJmHr8iI7gpBQ24XE1SLpObr9AVDOA57qzA8Tdr7\\nc7Ty2LHdJsgmPn+FouwvBHxDlURQ/olkymgT+T+2dhlZB0RO6HrF8JLRIWwnxWjvmBEBojz2wsmc\\nENYHsrE2Tgvc4ObxDBEgIhSJzW4ZBVvZuyiTI8ACo1AsVQq5G6IyHGLynV3OCdFoRxM+TPMMBmqY\\nTjm/BgCw+eOjMwIHW49swdzVeO8Q20Roa2peBqDlrspHUYvsqwZlIIgNgllUbmFbOBLDNOsCpgQ8\\nM/pl9FVPWpkoxOQ4DkdpgRQZLbgli/v/xSJqGqfZV4pr30fMLmqDZhvU70zJVczaF4e/SDhoKuIw\\n+QTF0yScZENCkL1iuBNy5ra8AEINNB6tHcIoaY2rqQIWQfUMuyQEhdJ2ZrVJNXADhfyWgzlW5O7i\\nwy/4gkaLbDXD2Mi8RTmLY4ZssyB3G/u6jolZ6j3GPqfRWum5GbbjaJhGJpFLat46sFreiejNMFWQ\\njpLsECr3fmGUGZ1RNASYO6go2EeyzDCIdAXL5E54u4RjV+kUuixqHb226exvM2tMiAjRYUMVx6Vw\\nxAjDYbs4ZhzZleLZC++Xx2tHLCxUgFvYVt0jAUNUOAfF0GDrjCXVZgWA1ikmOOu5LHetBd5hHNe6\\nyoSDIl9G5yi2cYeosesttcJZm1wb3FkHdI0VBqGoOyqXBxAIC+pxzIAsOw2FdZ18aNsPliD/Exr4\\nijtRNgA+kSqmXHcwxwhvqaBCOzVlY6VOct1sOyzdYh+Rj5FxUEYkI7m9xcwGxK29zy/SGafpYEqj\\nMn4k2H/B0/1vBE03BEhfi/X6BR/juTfOPybXpEAeyb5M2utAeCjbs6Xu6NTZjwadG0QLmdL3dgv3\\nUVel+r0qLL2TeZ0xYTLMWnxfUj72YDFpLoM8a3eMxD3zGHBRUtiuXnoH/nAhDeL5Tvb2Ex6+E+Ep\\n/P3h1VQfvu/QN+AZwu9p2dglqJJuAAA84+b8z2UJob2Pa1g561Hxq+9+7uZn7xrEnfWOX7eNxr/n\\n1wxqH6WNfr/+NHz8XmrkyOzRfivpbD0N1YHgogl0dC1vEI1CBg7MfRIAcHfDdJTZ2E8ubeJ8av/e\\nYjzWPgsA8MYP2TYuXHIfMvYOnkzvuJieRf8HReoyxekB7B3QMXSKrw4dOnTo0KFDhw4dOnToOClw\\n0kZQF42kBGql2YXbG+m93VdXgGfCVM5z7js6CmBP+EZEVApQW4ye5qv/+mPEbPRenP8hw91nXL4F\\nfyylgJPDkOotU6gJ/fnRqpdXAqOSl41bcx12zkoWNzlasQz7fnpLY3Y5JXIakiOYs3MhAC0JPvGc\\nGWe14KExrwAAbnmS3ib/8AiwLpmKZ99lQ20dXbqRHHrLKkY2QwQlYAxKyDzE5fWj+Dyr5zyK7WH+\\n/sDDWk1fdpWjPsxowrs+etVKLJ1o6WIEovwDHkOh7SpwNjIC4xsi6pfuC6NtMu81cik9RNGYAdHV\\nvG5rF3pF52iLSjn1VMYw9BNR69Wk1DeUIZjW8JTRq5R1MArZKKKb4p58pRLyt6fSxxKh0CqCQozl\\ntLH7cVYOayyWmekjfLDtIjx/3hMAgBvXUn3ZL1lhF1H0jO54wvE0Om/nDCHW0cHrOnv2Nny6l4JB\\nw+xs3w+/eXlvJXoHhKONnB4p3lhyxoC3jQwTNf/2/ivo+BK9CVisEMuzDfyxYM3tsG/sPxWhNzRt\\no2e0YFwY6Xq8zhgjDDlGnsMoGbAtTC/4UIfmIl47mWkUozfeAUtncou0t8gqW0BRIQ1nabWDFYVf\\nX6lG5zSX+GAQ9FKz+FuV34LdbTy3r5me9aDHCmdDMi1SNrCmKo8pIrblYRi7+B35TvMjLlgO3qBo\\nUzLgruD9m4pFH7S9C7KTET2pm31ZPD8LracwchqzytpJBUyWvkXj5ixcBwBY+vrMPrdLB9NpncDn\\nqYIYxxNmERK3C/pzONOEaivbjCTUQ6OuOCQRfQp1OpCdy77JL1IhpC6b+phM2dwu6jAhLpgRM4aS\\naZNr8WNJE73zhqCEUblk9Cj96ZKmSfi3YYyy3fyhEGDLC6A1j+9Qeb+WbpPaL2fv4fsI5sfVGqu2\\nDhlxEUywOUQU2GBF1xgRtdwt6gp2xuGbwL7HKGrodo+WkVXJCHuek9/G6MwWZIqG/UHdGDR3k9vX\\nInHMy9hkQ9bBVIVdR6tZPGMhqiTL8A5leytfxu+7/iw7fOMFrUKSYV/LuYBVjBkWTwQtU7hPx2hB\\nlS+Q1DroA03b+G37KDzz94Gpuw4WW8NBrPGTQaSlBwBnb/sGAGDFxDd73XfXbYtR+fHNAJA2ehou\\nisLSfGRTYMMYL+LVfEc1tzyK0U8dndr8sYBlQveAtttaz5qxEy7dg9mvslanrU3UQz7Cc4+4ZD8A\\nYNMHpGw5w8Cj+5lyd9r8rVx22ISfP8Vo6M8T9i3L54Rs7qMapVShBT/VXQzLZj7ni2fxHA98uLBP\\nWf5CY++R09FP3wFD9NhE9xXx0nTR09XBOM4QjDZlHIxBRn6Pa7t/yEqc0lfu3QARt2jCmhFRszlz\\nhxmrakeo6wHA0SDhVS/HoIW5X+Cmd7/LexnBfunAvCfw5y7SHC96k23j15e8gt/svXrQ15QYOQWA\\nn9zxCs6zs79+ZIDH0COoOnTo0KFDhw4dOnTo0KHjpMBJG0F9+tPZAIDgOWaUO2ndN1SXDTpfLRGK\\nl8ckVBJ2rBiJeZfSK/3+q6er253u+B4AwBoGjOFkb8vqJZPhuDM5VzMRSk6UqZ+yYqEiLbqmeCwq\\n8joGcBcDwynzWN/02WErUtYNf+s2AMCB+U8kRU6Hv/sd7nspS3S8XvkR1odE+ZUi/h1fVYcDB4Yn\\nHg4Zh+PwlNPXkTuF91Dk8KCjgpETr9cG++n0aP9USHy/7ctHhZnLGoT6SUfQicpC8tY/7CIn/tP9\\no4D9itep74ikEkl1f98NWZQZmlZI8abGQBYMl7Ed7bbQI5u9Jw5rV/Ixc2rCqDtPROczo1DSucNO\\n3l/BJhkm8Uy8Jo2Xn1GXfBx/Sf+fllLapmMc29iEjAZkG+ltu9ghSlMM/wh/bqT31+GkZ1yutyPr\\ngBCRSogmKxHUrENRZB3i9bZO4t+VSydDzuD5trXy/Tm6JFWU5WTBXQtZ5/iR1y8/JseTDEptRFnN\\nS/xXxHt+G9Z6Sdl4+/mzACRWKh044hag8mL2o11BEbFJEEhRShfdkb0Hb/v4+6ZMlscavuQ7kIT4\\n2bwLtdyyEcu+DQCQiyOwdCZHYp3NcUQcSgRVKUFi1MpwiPpz2TsMqgDNiIJ22IzJoi+b95fD0CqO\\nnclv1dhpgsUtSjPVMBrWcJZVLQ+i1sg0yvjRJUsAAL9/dx5MCvMhVylhIsFbJmpCb+H1tE/Nga1L\\n5K1buM7arUVIlTJRiTBt1p5jOtGhnxVSlO/N0umw1w8uJyg6iOipUurE3MVzmLuP7LuxdbK/UcbQ\\niAsIirJAshCay95ogXskI3uuCjcCm8lyUWrQukfICIvIgGUPxxOLGwgJEbyWACOORklWWSWOJhnb\\nRg0BACwqXA8AaHBn4nMRiTNm8QWbzTFI2aImbpjHjg/zI+DjNQbzRHsxxmEW5W+6qoCMcRzjYnGR\\nR1rpRbCLIZ3OM3hf35iwGW98wYiutYq1HvweK4JfsCyc9Xwuawm5UOfn+JdhDWOsqP++4YNxAIDC\\nTeknEoqoUSCf12hvC6NkNbf1DeHzDOXHYWgX5d3MMrrG833ELUptVBOK1/c4fg1Qfya/azkNSS0i\\nx9S6vQqD7HhFTwHgqmfuRcmZdQC0COrwt26DOZfvbakQ4JrjSB/utezrvZcbTPR05U2sap8YnRtd\\nzajp8YyeRrLiMHcPLI706alPil99514qEcndm0cds/rgisDnpE5G2uLrtP7mk01sy5d8cyNW/D01\\nl735o+R62JGpXrzj4/f4+xcWqFG0CxcfuWiPko98LCNyqya9kXJ8BXGzjLCou3zXjE/4V7A5EpGO\\nhdkTfeWeKnoTxmCCjkKL9uGGDrB/dDZox7jSxbnl5PvvhFK9uivAfmnyR9p9WESe/y/eWHREcwX3\\nmORa1ddmtGMwNWyBk9hAdTSyKb318lnqsqOdXiof0fi1FIEJ58UQiKU2kBVn/gkAcOHW1A8iUBrF\\niI84oZo2/DAAGnKqcNBYQV1LU7g8EbYm7dE/sI7qZX8868VjUv9ux12L8VgXKbdVf2PnufvGR9X1\\nSv2+RKwIAtWXcVJUE1EmUjb86tA8AICli+9j3/LhKR95xCmpdKAMK39kmEJYPIlqsBHZhFc7qAQ3\\n0UaD8b3uKXBmcNsfFlJxdZlzNG7IbEs69ofuCVDsQO8QQaONAs4mbQKaKGYEANGP8hETGiTLdpFy\\nYjTHkZkhRA2m8gPtngr4u/jpDfu7pvo79BMeO5hrhsL7Voq3G2Iyglm8IKcwMGVJUhUUFYOxYHPf\\nxjSg1V5UVCWDcTPGWxoAAA91sDjy+43j4Q5yEM4WAhrusKY06cs2IqOh93PZxOPsmhCFMYv3FW9n\\nm/dPCsB8oO8h6tQLKHrw5Udj+72fwSCcG0+phTrirEP4XjbbRzoKSPWtbMPv+W2478Wb1WWKIFI6\\nxN2iszanUiq/zlDq22E1J78XXb0OH740eGpoT/zyphfUAW5XWEiuQqMJL15yCQDgBzccxNJ2tuHn\\n6jgoVYxowU+Ha0rlilK7xc52KdekDl6WrijszfxAPOVCiKZJRlQIGZk9Ql37tBBMQvzmQFsebEJ4\\nySFqcMJjQiyDxzF3sh8xBiTVqRN18jhxswyDMKgUKqdzpxVrRtO4kUuDyPqU19ElGO7+SQEY6rnM\\nIxxTkQygu4oHyK7mdu7hRlWlu786giok4JZr+MxO++z7ADBo4xQA8mY3on35kAFte2AeUwqGLyUV\\nNlosw16j1cQznUZHX39Gr0KVdTUIqmyeCdEIj6OkSQQKoIoJWXZkIbeZO3lKxfuwxiHbRD8rRAeN\\nIY2yVuYkPbA95IB/KM8TNxkR9PE8/13N+q3RqBGPbxRzCaWO95d21fkRK1Po4TGEfXyHipJyLGJQ\\nlY8lGejex/suHEPVVJMxhjMn0WkTF/khhRYPfnse02NWdLNOuy9mwVVn0GD+yXYqkZZkumEQXvem\\nrgyV4utsGJjj0N7G9t02wY68nWxUPiEW5hjWheG5NKb3tBTAYeN4251LA8a+JXXaKUuSSp+3pDGM\\nJj15bIRlBoOGVTRgKrfeDgCwuQ1AC/uKH1Yz7eWHSFVTfaK7pM/jRrLjMHf1b7Kwrmiy0XeiKL1S\\nbOBj1hnPkJKpqP0fefLGkWPrjJcAAGO23qEKdCmq5ysOTYOviu3Vubt3w8y8yYWfVN8EAJi7YC2u\\nzWEQ6brHfzigazgRKs7DzzmIMatYscNuTVXANkQk2A7zHp88TCHXR4acrwoUKZiwbuCVEtIhcRwx\\npJkGxlxKCpjWzq87ODtlO0djajvrWUM1Eb6hcTjr+v52FMNUwdnbvoEHql7tc5+e0Cm+OnTo0KFD\\nhw4dOnTo0KHjpIAky/98ip99SJk8/OZ7j/t54qfSJRDooOfQ1GFCNJduB+f+Yyu6lAh5gE6wHXct\\nPmYRVAWLDpwHAHh5+CcpxzbM6MLofFKKdv2jKmk/AHjVm4Wfraen12ikJyZdiQJbu4yoKAHhK+d2\\ns2btxMW5lLjeFSjBbi8FSjbX0hsacVtwxkRSHi7IZemgElMnLnLQG/WsmxTdvxw6C3V7RXmZnUJY\\nw65J90dLQjDX0ltuHsf3azbG4PbwHduFkEWVuE8AuK2EtOc5jpAqHPPrgywndGBNOYrX0RMfzjSo\\ndRT7QsxiQMspvP8ha2PqMmO4732Vuqw+wXCxje9CcQYj8Pu/IO07mh3D2NGkONV3Myzsr8mG2cvz\\n5W/rP1IL0DPeNonnC5YLipsjAsOeE+dnjZu1+oaJ+NUiSs//z85LEe6lbMwRn1M48YxHUPYgWBKF\\nreHkJJnErYL22Hlio8If3/M7leb2nVqKVf1p6HK87i0GAPzfk1cBALxVEZiFQJdCEfVXheDaYe15\\nyD5RuCmEqJ3t1l8ovn+HhKhott7RbMtzJ2/BhjZ+Mw3782HuFvWIO7TayN4R/DZlkygZs9MMW4eo\\nkymEkUK5UKNlFiGm5iuLI5bJfS2tJtibRPRW1Fp1V2qlPZR9ba0GxKbwW7auYVTM3hqHb4io/Vwo\\nD+jdHcsxoWoFBUoibr4DyRpTa7EOFAXnNmD5BJZcu+XwmQAAgyTjo01MyXAc0r6XrP2CdSKi1N5S\\no1rCQvkuzV5ZFYkz+7SSCnnbuGHrNAOkERROih1mu7O4JYTHkJp6/YTPAQCv7p0KfJklzgdknk8B\\nEzzF0huNl0UhdXCMz98s3nW2pLJXfKVCYKQoohItMrdptaTt7Ro9W4numuaSnmI0xOEws2NbULJJ\\n3S4U5/luFyH0i7Yvwk8qGQ1f2jUJABCImTHESkbCy1umq/sWv899nU3pqatNpyVHP7POa8LPR7IE\\nTDq66xPdJfiWi2HrhztIPf60uQrlGYywrlnF9xe3xtWoXboI6tcJwfIwLEdZb/tEYPK5TLlSanv/\\ntn0UfpqXTBdNjOiGS9kW+4pS9oQ8nW0w4LHBUT24PhrQhJBWLqfgpVKT/lggMF7U6N6htfnBlFI7\\nlvjZIkYAf/Pylcf1PJY0Qp7uyfyuM7dY8a1vkzb82tOc35vOb0NQlKuSvmA/mI6l466Kpq2j6hnF\\n/i1jz+DZOYPBkHkUSVp27h83yLJ8aj+bn7wU3+MBw5ckVSeaWNauk6eDOhYTEYDKZ5u8rMq97V3S\\niy6+INUQia/Pxi5oBoFyflm0USnWp1iaCkMEsIracXZRoGpHzXh0XMMnvf/j4XAIylJpnWKhyKh/\\nYyQA4H/PJX10+lnVuGg4FZLzTJSAdJjDmDCBjXqbWRRRNcdhtIoczHo7IuX8cG8c+SUAYKunFLkl\\npB/OzOCgPMlajx1hUn5yjUrFRTNOtYhCxhZ+zaPOPIgaUwUAwHVYSmug+vP4gBxi0pL3vYM4N4v5\\nMQuv5DXc3zAHW97lfcXsMgo3Jit0xiza4G8WFArvwSxUzDoIADgYKVPXT8wm7bf+HV7XkAMDM0oT\\nIckyCrZwv4jooDrmh/va5Zjj1LOrsfHjMSnLf/Ey6ajVtz6KMVuPLXUqmiXy4IID73gjY9h2Dsx+\\nBmMfOzmLvhvS5DCeCFy25Wa01bHPcO1lOzoVk1O2c+1O7VcHa5wCVM927ha1WpvZhzVPz4BRpM7Z\\nD3ASttQxDsW5gpNklPGj+cytf+DNKwAAJauiCBSI3DvhTDMDhMBxAAAgAElEQVRENcPUX6zlk8Zm\\ncbIWEZ7FuNcKg+Kgc5sRKBJGrciDtTcB7unsg2Q/zxHK0wZXjzCMTX4Dsg6K3wEjgvTBIXsm8+7d\\nK5JVDwFNUf5YYPfZzwJIHGcGPvYpCsL1wWyEZPbhzUEa3k+OeA0rlk1K2UfpO5V0BFd9TP3ddI5Q\\nZI8Y8B/n810pdXEBreZhXTgXz1fTcFP0J2QDMLSANONdwjESaHJBKQPtnxyA9wAfbqYwJg2WEAwh\\nUf90uKDhbtT60Uxm66BtogWyOI7iTLO3xxDO4EJTUFbvobOZE8GRw5phNfJYX7orAABX5G3Cf25n\\nekz3CE6s/zb2WXxrCymp7t2kCc85ZxNuyV0DAAhNNKl5q95SRV3aiohor0oerCQDeTt4cbb7ODak\\nq096OOpV88Nvy2qAQvpUasn/smAnrjlwLgBg5hlM5egIObCvRTTM7sHli33VwDnEP2f+Fy5QHF79\\nj0uKYaqgp3HaE0rO49O75/S53fa7F6tVJ2aWco71+Vup33F/CObJ6n59mcQXf2sdPnht8GkmimGq\\nfJcDDfgcDxxvw7QvZG7h+OkeE8XP8+n0eva00wAAu0/RqLPjNvGdGgHYL2JgpnW3+KZN6QOSiYap\\nkoaiOOpsbRKCp3POHG1kH+I6mOq8co+OwujjcrPHoNZdToTF2LdSfU98vV1kOnTo0KFDhw4dOnTo\\n0KHjK4OvbARVqZOkeIB0aHjw2QUYesHhpGV1H5UPeH9pcE4OOJujcJezKWWJ6F682IydhyjKkd0m\\nw+LrnUo+9FN6g2vXV2H57z4DAASFCsZpeQfhjdJztCeHdC2bJYKuekbDKz6KQPGzLHuBIhhRpxFb\\nJtIjtGsdhVrwo1acXchoqtPAaEfM4kZbXFCAXfQ0bewsQyxDEURK9d/IBkm9l9rL+Ldu2zA0bKGE\\n6Nk/pWfrxeGfAncxGvxQZwWeCFOsQ6mNGrVJqifeX8YHbmsy4qPVjEZJlQwR/WnGy7jMwejua1Oo\\ngDfnho1Y/iBVp62eQb4sAOYA7y/a4Dih/uN00dNE9CV2dKQwegbng8ua2YJ1U15X/991O/uZo4mk\\nhgr5jvYvePykjcgOFMHl+X3q8AUFRdPWh/LgYGDyxwCRhmJsFwJ0UoZK8Y1NpGf3izMeR5aBnvZd\\no/0Ya+EGt91EYa3hxbfC0ij2EcrOnuFxtf6fVVDSzF4ZgZCgSomQXVFRF/445mUAwMHT8rHZR3bK\\nmlbWmGtaW4KMjeSaeEWKgxSRYNwsBG9EoM7WGYO9VdTytFoRFsJq3WsYOQ2ND8C2I5m6mW90IjiO\\nfYFt55FoKRK/66jEj3P3HdG+37hqJd58RRMrnIbk7/gC/ESdSCSqSiq1o40hre8P5AlBpFbukTOt\\nNSlyqkBRfgZa8H4mVUBby7hvqMWO6fmM+HzZznFNtsQRFVHOuNuM75zJceTfr9BqFJ6+5ZsAtLq7\\nict8y/gODBHW3gW0CHDXSJNaIz2jLgZZuVnRPmrbsxGNcOGQkaRj/nDVVRj5DHdagxnq3zyxa+f1\\nbBQ35K9ChYltdZe7GJZ2Y9JzjNokuKnPBWOA93fdVR/jggyq9BcLdcKbD1+Cv5avTHqG5352FzLW\\ns8386u5ncJZQzHuogxHpDZ3lmJLN9BElBScUMyEa5jUMnCD61cSY0ibs2V/xTzn3QCKnfaEvkaZ7\\ncynY9XTCMl9FFM6DopbzsFQG1lPlqwAAEzD4CKqtPbWvT4zOKvjgtZmQTiN3Vf588Kk8anrAKR5g\\nR98ipF9nJKZRJJY1mXw/n7cypxv7rWrMzGZbWJVBtuKmg2XqFpEMwKyVI1dBUTAN4x+5E8tO5zwo\\nUbFfqTHsEGJrRp+hT+GkmA2oXju81/XpoEdQdejQoUOHDh06dOjQoUPHSYGvXARViZweD7jOptfW\\nu6JQXRaezBwgy5a+a0udbBhMxPRoIcVkOFpFKQCDkBZviiCwjW7gQBGQtT+NOk4PmHwxPFh3EQBg\\nqIOetkDMjFUrGAWNOUU+Ya0RTtVxlJpHafLFVKEjBfE/FuHdEuagPjuV6+51RCEHRMkJr8adt4tD\\nWtyp+adSXEYwW/h1RA7ajKl7MPUclkdRRJ4ScYVrO56MMYLaOZLnMwU0QRAlnzBmlTFx2gEAwK4m\\nevQPhgsAB4+9P6F25OTcWeKCjPAN4f65Nb3npgZyjLB3Jj+T/I0Sukf1ukuvUMq9KF7cmlv6LvXy\\nz4TUv8ZVEhKjp4k4kkhqbByje/tF7l9vxwmJfCTrUXrV+4OSO2lvPn5JPI99kyVKfvDod4/J8SIu\\nE1DMXD9zO/viovUe1F5AD3pYCP48765SSxQp0dNErLngIZzx6d0AoH7zpg4jlCpjiuiUv0RGWT77\\nnqEu/v3bsE9glOh1nmHtxpUuCoJc6SOjY8KlG7D89VMAQPUgx6yAWaS6Kzn91u4YjH5+oxavGYp/\\nODRaREh3pI+Qpouc+kexk3Ls6TvONfFyMjqeeeFi/PiuIxs7X1wxa8B18JLLHqSyZhSHv1Jm5sdX\\nfpj2OGdspTjf6klvYLWoN7g+xL71ls03wiUih60e4dE3yrCKSM67V/8BVebk8bot5sM9Iz5JOc+s\\nQva3nwY45pu9FE8CgKhDlGtpjsMQEX11TEamyCP2TuayKaX12NfJHK+PtlF3IGOXBUBqDVNfMdtr\\nTiFzaF/sOB2LdjBqZTLH1LyvqEOwBsKymnSnsG+enzodz21jbeyIi9v9+ooX0Rlj7vyV17GOO+P7\\nvIZP3ONw/z7OCcIxNsguj12NoDpNbE8Gu4wDsYKU6z7ZEBEsJ/MgGTKJ2LOy4hhdzfGFMs5GyoS4\\nYe3AYtsRl6yKKcKsDYS2RvZ/Sv5xIvxDY7AVs59VNFsev+1P+O4T3+/1PDELYBygnMXtoxnlf/Tz\\nuQCAxbctxp0DLAsTnsIONdJuP2b1W79KUHJDL7n0C3WZfR37v8nrUp/hlg/G4OXb2ef9IOcgF1YC\\nkzdoWjNKbulrp3Hcvv4hTax2+Du3AQAyA8DcB1lyM7G26b6fcDxRjtdf2RljEIg6BjchO2kNVCUR\\nWuqdGXrMkWiYKoh42Bl8legu997wBh58dsEJO1/cbFBVfM1e7YXlb2Ov1TjLgu7hnOBlHejbUK1e\\nTQpAtWjrhVOaYfLx2CYfB9aCTYMX94naJWQe4rkzapUJuhEhYWwqdK64SUYgXxRg7+V7i2Ry/7nT\\nqdj4f8Wr1YLLShFzs6QZG58Hy/DnWx8DANzyNj96a6cBpgCPEyjkdV0xeyMuzKLycXU+6dFznLuQ\\nrrjxlp+yc3jBk4fWKCfrL/yeNSjtHTF0jjKJ++H2piAQzhI07IPCkD0CO+V/F72g/u5JBTlSKAbv\\nmVsXoG19qlDM0eBI1Hv7QtQpq+2xP+xOMEx74ovbHoTLwGH2QISDxKVPHXkh8v4w7PIDeLfqfQDA\\nxAePDc1YiJQmKTN/7wW272NFHXdub1QpvrKTZpKhy4eMWhogkriIP3Rdju9d23t7zDfakZXNCXxX\\nlN+TyQ/EhOUVEwaqnB1BUxe/J0Wttjei0Tm5pHN+6a6AtVMRiYuLY8cRE6o93hL2BRGnEVKU/YSt\\nNQRl+O3NMO0NO+5arCoor9mTKlCVCEUkbyDHBNIL9dkbj53jxNmU7CR7qXkGvun6KGW76QWHU5bN\\nsPJd3zxqHSLC6jcL0Q1Tqxk+kSrR0zgFSJVelNGZsnxZLYvZGsz8pm1dUdiEgqaSgmEMyYgIY9Wf\\nZ4CzhedxZdL4a/ZnoDSDiiC+dTRUi75MNU4BTZW3az3JvttXOVGpVlIwAKCF31nFviFmkdQ+zNrF\\nD630ETMUw7O7gts9+e4CJFdYTMZndSNRJBTiTQa2UbfPhu2iVuj2Ov4tyHVjXAWFl/YdHBwd70Ti\\naAzTryoGapgqBq05YZwqG9qOaVPowKvu5hi7dYlW23zEG3QoOuqM2L7geQDATMdCAOjTOAVonN59\\nI/vKh/82H0D61LtE2m+6QNP8RTReX/3HmbCkqb0Z6WRbv3Lmerzz9qw+r+nrCIVK/WVrOUb/gzT9\\nvgx1ixvYJ+YWleZ0NcYBq4v9yCQLj7Tk3vsx8oUfAwAy67V3ED2L/Vvmyqyjugdn7eDGkn+9r1yH\\nDh06dOjQoUOHDh06dJyUOGkjqPFp9PYZNyQnQysemMW3aR6Ynt6YgQonDWS/CyZTkn3t/r491ccK\\nfXmyB4pfb7jkhFIgIk6DSu0N5oqoQIdGN82piWP+zyiV/sRHpCaVL01PRy39LDnCejA7H2bb0VMT\\nIw4DLB56jiMuLWpqEgIexqCgD4PCFAAgG9Ofz+zhPg0BepMcBgse7CChamEm6X8bQ8WY76T3alew\\nBItEWYR9ixhJXXTgPHy+i/tIgn5T4ynCjwq0Gq1Eekmad3ykMV7qqMVdtaQPB3NFLdpSE6wiWBBx\\nKvcPmBlAQljcv9UdE3c8cIyzNEEpV6DQeq++4rNBHaM3HOvo6ZFg7GN3IpLJ93HPJe/jrpxDSest\\nI92Ib0n2IsYtMgzhwbXN6U/cq9J9L1x1F4DjW/Bgz8oKvFN69DVvE4UVlL4qMSJrTuP5PhrE8rMQ\\nKhQiDKKusPVgOzL30/PbNYoN/KaLP027/4d+PtWueBa+X7UcAPAbL5kGkDRKqhQR1NyABTMu6LuM\\nQ0/s6SpQmT5q32EwkJ4JLaoacUiwiEo4nmHpo6YzrtgGAFj/9kR1mfKcR3zIEiXjH7lTGyfSlPjp\\nDS948lKW9ax9feY3NmHVm1MHdLyooJeavFKSONJgUPfYSEy/OhcA8MU0rVTCNblrxS/tq1DK20yz\\nH8R73bxvX4BRJXuzBN/UwTNr/H5SbjNEYDdqk2AKijFBjA0xqwSTGB+UPhQAJhRQ3CkYM+FsUfqj\\n2sI+vWuEDdn7Ux/GTU+8AwB45L+/BYDlv9IhJiK6hrBWHiwdsg6mnmPfQj6Tyte155FlD6KuK1mY\\nJsMRUlNJygpZD9VkiONHZR8AAO7A7b2eV8dXAxGRC9Wybgg+CpCV9V/fZoT0lximbueo02rMK+I3\\n+87/KwBgjPd6hBvZzzoatZiWfxzbnrHJipl2ivE8nOYalHn2/D0Xp6zzx7U2WmWjil1i9NR+ZhsC\\nq8hKsBfw43v3tVlfLUrjUSIipoBKyohnafGA5vehXBkL/kBW1pafpEasjee1Y/3U15KWzX3wJ0iX\\nzGjqETmVDSxjNViEswdHiT1pDdSehmlPJPLWX/Xy4V3pSlN45yix9s0TY5gq6GmYxqZ6YNw0OMWy\\nRMrYsTB4+0MwR1JrCyqGaleVBcWfc0LRNkVCm/jK9l1FAy1yZQxbw5wV3Pq7HwAAsvelTjCKPzPA\\n1tF//mpPHJzPjjSzWqhFXtaAujYO0KbdnKiHciR1Qp25XyhAmgF7BycjjWcCpct5vKhNqYMXh6uR\\n171lLRM4f5t9SKWczf8N6RGuxhjum8197E0G/NfdO5KuL8scgDOXk+wLhlFpcqKjDvfVMjfj2iJO\\n0BQFXwD4afMUAMDprr14poEUv3t2DVNV3czCpg3lynAowpiCKy/FZQTz+NuRKpo5YMx9/V7sveax\\npGW/LNiJl3DOoI5zwSUb8dH705KWVd+q5bIqtN9/Rm6r2c33tviVy9CzW4/Z5RSTPpIdh7Ul1dDv\\njvP9znziPgBAeFQA7571Z3W9kod6IpSULd0S/uPxm3pd//vbSRD8xe4r4PskNdVBgaGfEryxmbTA\\njOsyB32N6WBs64Zcwu81Jgg/wRH5sNUojZjD6Zr2EYCoDZeInaFSAMAjG8/FU7P+BgAwCIqjFAV8\\nw/ktK/XbopkxPDtsxYCurcbPGpwNewuQqyhMCuXauEmCo4XfrmsPc7qi2XZEMjmzChQYEMrlYG3t\\n0CZk65bSMFWmgeMfuRNT59JJCi+/80SjUvl97o4r0PJJaZ/X++tnruK1iXK0hpA2Lrxw2x8AYMDG\\nKUDDFADevf1+XP5YKj1docUqhnq6nFSrO47mDZyAVu65HaMmk4bYHeIULFFx93ftmtFeKjxw8TjP\\nkbMnipHz69T13rh49obUqVzlJ5yAn1G5DzEvv77Mw7037KhNQlSkgjibNYryun2kwH5zwia1tvbC\\neVRDff2dMxHM5zjsreSxr525FruDbDNdo3i8jNr055TiinMjBotnYOPfosdI4b9/K3Uc/EUONHKY\\nwH8PW4nmCOdJHzRTFbm+OwtGUd/XbuI5flC2DLlGP3Skx+yLNwMAln8w5bieR0mf6UutdyAwp0lH\\n+aaLffQv02xvCgDVwjCdvZ103eozn8PIF1OdFY6d/LZC2TKueezepHWJdF7lb3SaB9U9gkIz/vQD\\n9ff9z6TWGFWMU0DLif1XQ6BC5B5vH5xVnjiupMP0Yi2NYsK6awEMPFxhmt2u5qUOBrbWwTmwdYqv\\nDh06dOjQoUOHDh06dOg4KXDSRlAHg189Tev/VwPc/m/ffUj8spz0dVQHGz3tCcVDnje7Ee3LhxyL\\nS0qBxSPDK5QPfZX0xEp+I+rOEwq5QeCzRtZhetVFKkipqROjzNx208/pVZv0wJ3I25EcRQ3mGmDr\\nSD5fyzSLKkriaoqp9UgrXtc89EY/fS+5l9cDAHKsfnQ7Ra3CKrFNnR2RTO7Tfi7Pa7FF4N/OZy7F\\nNMWxeBrXkqIa+bea0xBsYiTHKHQlonYTnBX08ts2ZasKlIrQh90YQXkO1wdiXNYWzcDpom7Vag8v\\n8jLHVpy74woAQJ6NkZhX109H4So+2zJPDAA99L5CXmR2DdQ6kdZu3oPFG4e9TURTj0J4zOSX0kY1\\nBxvx/FPp58Ctnyctq3rmDtVjdrKqAhsDqR5A2RpHOt/jBZtvTPrfsseOK+oYTT2+er2Dx32PfQcA\\n2026QSFYIOqbpvGAnrvoC5hF8eSlr888ptclZzgQswqxoWGiPqNHhrWBDdxVy+vavbEc8w2kkM0t\\n3KLW1lTUC/8YNuIzLwWDLhhJxsJnW6dBNiniaOK+LHEs2HshAODaYrbPj7vHYWs7RWTOH1KDdW38\\nyPfUM9LsPGRUPypFOMoY1hRbs7pJbYhkmNE9XDxdOb2HW5RoThKg2rSEEa8h5/Cekii+on/3V4Uw\\nUAK3ooC69zaNCdEWYx/ir4jAcXBwMf100VMA8A0RqRSibnQ4Q1J/uxq0SGThJp679mKgK8ioY/tW\\nKsl2TvAjx8g7e20/o7vXjVyPjW4q0sZ8vNaYRUJcqNp1xvw47zc/AsAILQBYPHFV0dsllNRXxUbC\\n2sj9Q1laO1Aivcq1ygZJfR+JMDby/f69awY+rmB/fUoRo7iyEZi5cAsAYK+bUaDv5a7FEFFH8IVR\\nFDnBJ8ltoPnUZOp31ygTCjf2HkHtGsExre3MCH6zaQ4AYNJQjnn3/d8HOBjhc9wbLEKNl3TeQ+05\\nvGevFRBjwWmjDgIA9oSLMcXa1ev5/tXx+FAym95ZtAU/fvnGfrYePJTIqSKcdzygzHnD2TIsXal9\\nUM858Z05M/HreS8B0ObaAIAZZCxa12v0T3l67yzGaMSEcWuuA3Bk0dDs2ez/upYXD3rfryrsF7Xg\\nhyPI6PnD9oXH9Ngf7x2NV7PJzjGuGJz4UeyT1HQR2TD4qgn9QY+g6tChQ4cOHTp06NChQ4eOkwKS\\n3EuS/omEraRMrvjOvUnlCo4nlBI2kUm+E1LfVD62uiEDguJhf8tHj+1/PH3DcTtX4cYw4hb6OhrO\\nYpRDLg3CuJ/e3ZwarXSLdxhdLFmVnajIZmj0vqEUZfh/B65Am5fvI7yOwhmBkpiaE+gQstfeChlD\\nP9YaS+docvOVkip5O8KQRUSkaQZd35HKAOJBXpvk5197k1F9N4ERjKCa2syIiVpNhqABWUIvxSvK\\nyhrCEgq20uMvS9y5dYpR9RwpAjJmrwz/haJu12Envj9nKQBgrms7AOCu/Vdi1x7mjGUXcadcpx85\\nVub//GzoPwAA/3V4Hrr+wJOHMnndgQJJi5B2aqV9lJqOUhwQAS21BqO9I67mNSWiZdqxieWF80Sk\\non1gPq9QYSwlb/P/rn4O//bS9cfkehRIolZtJEOG2XNsPkSlvqnBwOcpbT86lsPRwHSC0sWUdiTF\\ngFHz+FF8r1TUWDtGNU/ToWxJC9wT6K3tGMOLMEYASxefvVLyo22iSS0LFSqIwVGivCO2y+DObJx6\\nDnNUcy18aGubhqHbI/Jbg+w8Fk7ZgD0eRkYPdTHS5A9aEBURW2etAYFinjuDJTThaoip36bynCCz\\nZiYAWLs0cTZPmch1N2mCEemiGHdevwQAsPi5uQN6TnEz+hw/d9y1GP/ezHqbr3zKEg22VgNuv+49\\nAMBDG8/jsjQ1V48UebvYT0bsvOfukQa1j87aJyLynTF0jOZC/9A4rK3cVulPc85pgkFEp+ua+T5+\\nN/N1/GrnZQAA3156/mOOOK48fT0AYHXzCHg+EFEW0eVl74vCM5Qvx9bBhYF8A8IicGBv0fpGU0AT\\nRwL4rhTdgUQ0nSbaY1BCbCLb26xhbBQbm4bC3SSYOHY+h0kV9biwgBELf5yd9csHTkHuA9ocpGk6\\nn79VtO9wpoSI6F5yavhQnI0huIdxbHWP4DWee9lGfNlSBgDwBhjZlSRZ7aMKM7wodTIyuruT7bvD\\n7UCGk7m6s0tYmDbH7MdQC8fl3758bCM2Jxvixn/e3Ddcyo/VUn/8FAjS5Z8mIpQjw9o5sDFRETrq\\nj3GYKDzac9t5V636/+x9Z2Ac5bn1me1V3WqWZFm23LvBGAwGTA099BZuCDWhhEBC7r1JvvR6A4Qk\\ndAg9GEK1AVNsbAPuvRfZsixLlqy+u9q+O/P9OO/MrryqtuTGnD+WZ2feeWfmrc9znvNgzpun9+p+\\nPSF+gidCtQgSg3+wAjmPtBr32tSH7o7ZlAxfOccvd4URwekcq2LNdrgrul7/Jec87W9sfuzBNYqi\\nnNTTeccExVdSKFhxJO8HoMPmNJKmdJp76XjFQIoiHYxImlETEYql80PeNn4ZPKM52b5nPwXFn3MD\\n2F7CJueyRjAlgwoR00QOQqMkIxzl79bpnCSDrU5EFR7zlosFgzuGhimc4DN2y/AO48SdsU3kFR1k\\ngr2R9ShYKijDS43wFwg6mBAJkZNav2mLEC/JVyBliM2qNQbTRleHc+M2WaMDqlSw9F0KWsfy91Ax\\nJx77XgtMK7iyCJVH8Y/PSb+aN34cAGDXpiK4hlCsoN3PgSewORN7MvgsV29m7jFroxHSBN4vbhf0\\nuL2KRjWN2xPqveoiyxADLIKeFnaLd5JtgKMxdZHVXzh4YxobFoJpd9ezSGeiQv29OU1Gf21OAcC4\\ntXNl5RMZUlLTqZhDcbAHUD7w9w2EYD/ACTo2leNJXAbS9rCfqEYiYwiweISqbMAIvyy+UZowJmXG\\nkWPlxLy2iQt5iykOk4kPJvs5KHz03qkwTCZNLejnmGDdaYdBZQB7FE1NUbaktqmYaPL2Zhm2Zt5b\\npSirCr8Ax62Lf07l4dffOCelnL/NvYT36/btJLDt7ie7HfNHff0dnFpSBYAbUwD4za2v4f+9SMpd\\nX9Z7Yjjucc5W24ylnd/KtU+Cv0DkG21NNCiVSuvcZ0BYbNqNQhW7rikdckS1jvC3D1smwtcmLHRC\\n4T2vtAWTHVTcfqtuKhzigZJFvdw1Hce/mF/Sfo+I3Na2ZiVxTGwMnQc6560lxmAFcpzv9PT0hAL0\\n5WOZJ3t2wzQAwLqaIngirFiDl+3TZoli9zX8yuY2AyLZvHnIy2c2RBSkMeoDnqG8x4FLADkkcqIW\\ncZ6MKxKy7DS8uK3sL5VVubAc4JxXmeuEvZzXBCM8lp/pQ10zd+iZQp74hvTVWs7EP3f61Dr6AwO5\\nMU3GuEtplNs8NzUfcm83p0DvM2MMe5NiSvYDqYbqOW+ejqLz2UdrPhuS8ruKUI4MRWzK7H3MEX2i\\nwVkroT2/a0OKujGNzPChMJPzVtOHRSnnJW9E5bj42xWFGmx05500iD6y8CLt3IHYmPYVOsVXhw4d\\nOnTo0KFDhw4dOnQcEzj6W2SBwxFu6Q+cSN7TIw2LNw6Ll9ZpSwMtg6Nt+7HUR2GkeFYMbcNoJS75\\nlN5J/4Z8vHQBrbcvhEj7cFRYYBQ0tZgwnWT4FDgaaMFuvp4iQRZzDO+eR6Grp5vPQFBwW1cMo1Wu\\nZWM2HHUWUTc2rJgjQYdsGy0ETaxxjQKWeBgZUiuvNe23w+qjRdtdRatSKMeAkLDyO5r4zFZfHLJZ\\nUNMsrGuwIAYpzjblyvWjOIOcjVsKKbKwPy8T6320dKWZaC2sL3ajxsdUOP5FpGEZooC3nHVwVbK7\\nKgZoufp8ZQoMMTX/KR8h2TKqejsUCYg4WUeLv58j2TtBd95TALj5ioV47f2zB7weAwHZIjzVfcx9\\neiIg5gJMA6ffkQpFgaWqEQBgbWP/DhTKCIu0H+5qDhjWVgOap7A/Zm0wwq9eLvKbmtuM+HgH2QtS\\njWibxUHEvOzrtibhsZrs0cTLqkFKqb/ArJlyLV4Tcjayv8YFa0S2SBqdN6OSfVU2SQgO4ljoGyLS\\nrYQAV43Ip9kex3NrxLjXyWN3RvvtDl15T2MO4VVe58bKdeM7/PZy3WkI54jcsk29t1UHh3AMd+zu\\n3r8bTlPzLfMebSOAaAbfT5ugVGfsisHEbEzwD1YQdQsP6mCGPVg3pMEgSDChiRzAhzkasSg4GgCQ\\nWcJx9fcj38PtC77HE2VJS6XjqBd03qyEB8HewnZib05llESdBvjzxbtQ/4kpGn1cMSRElDQ6twSM\\nLDwAAKiJMDTl29lrsCtMmvGqFRRQMrdL2C+RtRUVokzhqISS+azHvgskGFyC+lnN91O4NKjVzdLO\\n9912UgQWkbh1ZEYD36M5iPvL3gYALA9SxGuedRwqciiS9PDIhVoqtPfiFJuSJAWzhGCYKnL2SMO5\\nmODsIveNjuMOnXlOk3HvLR8AAB5/k0KMxnB3ZwOBYrKWADcAACAASURBVLYTx77OqaGdeU6ToYiY\\nKn8JxwFndeoWxNZkAJq+2Z5TFTE7sH3W8wCAEUF6pw0BI1xVHd+zZYkbtYzcgCwovNIuJywewfYT\\n6+oNDz+Jp9tEaJkxgIwZHFNHmpsBAM9WXIpgrkhx1XD01zfHxAY1K8uH62/8ArP/PatfyguND8K2\\nqWMDj05uh3ndsU/NCwwXk/+u4zMTceHX7Ak/HXYlZLE4NNpikM/nkrGqjryp9G0SjHsEZS+bg54k\\nAzmbuk62nv88Vx17L3Li8xEjAQBnuHdgTYATcpqN8TQtdkXLyxoSNDzfuDAkkUdQ3VhkDvZgegEp\\nJ+0xvu+V+4ZAbmI7sbYo8BYLevFwLihMQcAn2Clxm6AjtyY2fOWDuWAoT2vERxu4ILxzxBKMsVHd\\nMS5WOte7W3GByEc3LIccrn8OXoFPROL4+zZxsWWIJhSJo9O4aAtVuWAK8BksHkkbfDJ3iPeoKNpm\\nVJ1wwpkGWPwDR/HtK17+5OwjqmSrjPP1PVbUAKCTvXxkECdXWxJN6+xL1wIAFs6dknrBEUD7qAhc\\n2wduzFANHUd0cyoQHMWFvrNWxIaHDZoxJpwhjEkBIHMjW1TWtTVo2UPFUkcmF/gRpxk/mEA1RMdk\\ndoq/fnQZ4O7YJ6zmKO4uWgQA+MTFmM0lhqEI7KThqL1UhkPQJtX8nrJJQssYSdRD1MefMLqGcoRB\\na78EWwvbTsxhRGEBFwVtOwdOlVIdJzrD1tWliU2WgPXUZoSXpSo0JqOnjSkABIviUIxCVVywXqU4\\nYGkWcaBNCcugxSfy0saNcOwXSuO1VPlsL43hl7PeBwBc7qwCAJy2/C7cdCoNfQUiWGt203RAxBRm\\n5PkQGcT7NGeLOV+WYBIxeX4xD9gbEhtPNebVeSAOSeRWVeOcASCQY9TOUw2TRYvZtoKDLKgey/bx\\nrVxqDFzmDABOjusPXk+15G2RAL4K0mjriXPue3XXNJjn0GBS9l4yZzqIg+FoEMbdRenwiPloUSXv\\na4hKeCuNVOK0fDFPhM24ZDjrU26txzI/KfluC+dJWZGQYeZ99ob4zd2mEN6r630uXB3HN/75itiY\\n9vL8ym8/A6D3lN+DsXMXM0moa5q4jYY7HZ3DFASWhMQ6QwzlB29OVViXcn3jK+P45GqRsPHHjAke\\n/xi/16MtZXhhx6kAgECjE8Z2lqXmtr/uewuwsrUUAFDxyTAAwOiLdmLHBzSyhbM5JkYz4rAd4Diq\\nxsvG7NCMjclQQ+p6Mn50Bp3iq0OHDh06dOjQoUOHDh06jgkcEx7UAlMIP8/ZjtnoHw+qwxnCxvtf\\nTDk+bt3RyXkatwGxcrrSrZu6z1Z3OJ7TQDEtsI59R/+z2jY4UHQBvZNZ1gDuzF8MAKgeRQrUplOK\\nMX8fvaBTc0iPWukuBdb3bMsrWijjLxnMeZiX64E/zHcWFQJLY6dUYdM2Kt+ahNjEpeM3olgkVF3n\\n4W817RmIigSnsaREp/Y64X3NBoKDaY0qGE6aYfPKPNhEPtFAgRAiSjfAVcVrd4O03V15g2CrEUqN\\n+6big7FUbzRLtAmtDxtwxiAqJ/40e4u4sxF+wU2zjWDAe8BvRXYm3VbpwkO8Z69LEyCJuhW4qoRp\\nTXWWKAkar2r5N0RSOfRx88BSOGJOQYUtpFnNUJFo+0faanpISrtdMKFtBwlcXH/VInxUM/YQanXo\\nCOeyAZgE9fzBUz/Hs9svPuTyYkIv7uc3vomb3PTsDZ1zJ4aUkRFQt4KWb5UydEQhFIpiDsEa8CoI\\nCA+qqoRrrzcgLtgSbUE7DFa+n3CY36oopw23pG8CAOQY+bDbZ63CR/NPFuXwY2+b+hae9TDn6bL9\\npQAYBVA2lbTHmrZ05E1uAgBsWV7G+ngkRLI49kbyhZe33QRLm1AfF17BjN0x2PdRGM1floFYH+Xd\\nVZG05Fy8ylSWJ63pe17BZFqvmuez9UBar/OpBoaI+WZvYr4R4rSw1xgRSRf5RAN8J/krE9dG3MKD\\nYpUQFn+n71LgGc5nC+exbIMzivcP0KP33XK2xW0zXsXOKBk5b7Tx+31ZNUybO9sMzkSMiJ3twOAz\\nQVbF+MT4bfUqmnBVVDx0yyiTFhaiesCbxps0tXeLD0BTx/dgb4ygeT3p4KPG79eOV8c4bv++nnl1\\nZ6RVYL2Qg5/kqgYA+OrdyHKo76l71amGyfS6+sricJfwu18xdCMA4IGsVXAY+BFf81IEzBN3YIq9\\nive2yphpE67sbP77t9ZSPLuNNHPzErYfRQJOvZ7iTtUo6bY+Or45CAnP2aF6TlU49xwZcagTCXfN\\npkp+Wg9Kvf91OzNFPDmPa2Mkna6y7F5+/kJt05c8Y0xbdw0AwGmJoOUjTq7qeRXvjNA8mfYDQpDz\\nQOoe42Dv6a/veQUA8MsnDj2DiO5B1aFDhw4dOnTo0KFDhw4dxwSOvqttACAvz8RlmUzrMaecVoXD\\ntfz0FYbprZCX06oqmxU8Pm02AOBhEVvYn1Bznh7J1DI9IVggY7+XNprxJftRFc0BADTGEp6sgjRa\\ngce5aXX+99kLAaGb88tGeqQW/89pCGXSS3RgBi3xP5i5AK/tYrxNLG6Er4kekTumfQUAuDljDdKH\\n8ZqnWxlH9kDWVqwQ3pRZzm0AgF9UXYH56xkH6qjib65GBa5ampt8JSYUnsu6jUyj9X5BLF/Lk6d6\\nRqxtMmIi5tVRS5tPKGpDqIQxQwVOL/7YcAYAYIuHnqi6uUOw4SdqzjDWNa7I+PESWrKMwgNks0cQ\\nEp7hFg/jhNIqAVdd7/IyqfFSyfAVsjz3/tQyFKl/BMu23/4URj3/ff6norc+meMTs985C/JYkXfz\\nCN1TTdMTLBAeO+nwhK9CwmP1p+euw00Psl1uv/QJrc/c89nRG1v8earYEP/vrAHMwpMbLGW9/aUy\\nDMHE25cayESIC1GeRePeB9Ax5/XfClZj0uX0ZD26naleflI/GfuCHLfbGhm/KPlN2OnitQZLHPu8\\nFHeThtCLF2i3wNgqUliJOEhTuwQLtZZg8SXyaiom1tEQlbFs4jsAgLFf9u7dxoSsgjHJUq1s5Bi7\\n9b6OaWbUOaH8VfbBrkSX4pMZr3ju0J0AgJpABioqh6Wc11nO1mTPqQpDkoRAxs6Ov7UNMyFjt/A0\\ni/RXgXwDXPsEEyVD0sSDVPaC3G5GMMZ3e2s1x9AXS77CCDO/x0srZwAAcr80oXEaxzpnegjBvZxn\\nMrcINkyOpKUHCnEqQssog2b1T663mj5Hfd+OOgXNk0UO0v2pPTzqNCFUxALOsSfG26oY28+pabsB\\nAMMsDZiRW8U6iOBfe60Jiqn7sbxxIiviP5mVzXAH8Kcx7wIAxohG1iYDPoVBXuNt9Pb/ofoSfC3x\\nW/4s4MaYTDKVnEKUb87mCXBtZPyro57P5x1qwGcbKCY2kKkmDePJEJI3pQ/gXY5f7LjtKQDA+BU3\\nIrL50N9RcCzbzOGma7E1p44ffjH2Qu5c7EhH/+CKixhv/8nLp3V73svPc88jD+cY5J8UwcS/9G5u\\nuayY7KJXPj4bB7cU2dQxXVdvcN33FhyW51TFCduqKueRfjVu3pFZWA0+jwudqwoplvL3l6/Qfnv2\\numfw8ParBuzex9LGVIWz2oBQPhcW/rgVhSZOpCqFtdTWhElOvrOxFpUWZdWu//UgQXt9fgvK3iXF\\nYeSoWgDA+zUTYRH5C5srMyFYs3hu2UwAQMNJbo2ye1kmv0djPAxZYfn1Mhd1LUGHtjENZ3OCzl2b\\n6ImyUcKuCm4oTz6FdGUYkJJPVDZKGqXWodK/KoGond1rfctweCaz7ru3kj5YtCeGmT+4EwAQvJ1R\\n5k116TC7uNBxOriIiMaNCG+iEEY6dZbQl72IP5fvwdkQR8tI1qe7DWh/qmlvv52TrLZRPYFh2HIE\\nBdgkaOrTKt1ztbf0sIpUFaJhAIYvvBUAYN5h15SvjybSqskHby/i1BlzAJEsdgJLA+sdLw1BCbF/\\nN1dmwjaEu5G4yE/568Yx+OWgrSll2yQao5xW9rsVjaWoqWMYAoTI2znTN6ExxO/ridgQFXnkgibW\\nISoBskMI/YREyIBTgXMTP1LEJYR/ZCBUwHLaB/ed6mZtYTmKKbGJUqlbQEdD5ehnOCc8cB3z2z35\\n6qUp5RlPaQVWcDO+cN3Ubu/tGsXx2+u1w7a1d4td/2BhtGN0BKQ4EHEJ5WMRXmBpA/yFIgdpkwKb\\npgIqNvIxoNJHyurPrv4w9SYGvmNPOaA4xMKs0QGX2Ei2lwjhpJ0y/IVC+XmP+C5pkkaDE0LwsLYo\\nsAqDgkr/jaRJKP5cHRhTDX5mfwylQxg+8rqPRsRJ1hrMtPE9tcQTnajURGPdcx4+U8GZNTgwmZtp\\n+9w0ZFSyre++km3ZFJTg2itCJQxsYzMH70auke27yMT2tCfajrntVDZW2/SVeWuwPcj5ZmZ2BXYG\\nKMa1qZXH4DVr1M1gHp+1bGo17CZev6M21VDRXzheN6axofw+c05/Ele++mCX56kbTAB4oo3f+u/v\\nXNLr+4x8oX/mzFsmrAAA/GfLWf1SXjKmjqUI2PaPRvR72cnYfD/HtRkbrwQAeBYNnKjcsQi3iIdS\\nDWadCRElw1Ukwj4WZXZ7nmwGzrx2DQDgX+u5+XXXpxoiFCOAPm5Q3/xXam7vQ4FO8dWhQ4cOHTp0\\n6NChQ4cOHccEjgkPahwy2uXjW2u6co2wknlSLYMPbrmmR+n+Ew2SDESD9BLsD6QjKmi6l7pIr53b\\nPhrLvJTcj7r52yRrQ6dlVV5JafMRX5IyEPVakV9Mi/WIcTUYlU7qkkPwtMY6ajHeSo/lBItKVHJh\\na4T3qYowN1yGLYjcC2kFrGgYJM5LeHEDhQosjbxm9qaTAABmkwLfYHYbV50Q4Ih37nY0B2nxLvwa\\nqG9g+5DGq+avhAfF+hItXWlFRnjJOIavhpZ2YxgwhtRcVryPrbX3LlRnA+sYN0vI2kEzWDht4BO8\\njHr++9hy2xMDfp9vJJKaW2SwSD0R66cUMzJgX3ds5aAzhNmG3dVs9y1jpUQu2sH0TilRI6xF9CqF\\n/BZEquhZMpWQhjunehxOEmk/LnZwrvkyBLxWNx0AkG7lsV11uSgppEjUpGxSFpbWD8XEHLI8DgRc\\naGgSgjIekUO1wQh7oxDgEdOYsyEGY4j1dVWz33nL7LAJ9oUhZsbvmrrPUdgVpE6s2b9sHJtgnQBY\\nfscjfC4Dv+WTSedmnlkPAGhd3HtPRGCTGKPGtyLg4JhqShJrypjJMtuSylTfhT9P9SoDIZHCxdwu\\nvl9UgaTlegFyNot3VcIxNuoCXNW8z399fgcA4Ocz5+LJXWTLmJo5jsbsgKWef5vaJU1EztzOa9sH\\nS8jeyrJDGSKkwp6gS6sUtrhdQrvweIeFIz1ze8/jbfUWMm3yy8iGqY2lYX2Y7eS9Boo8TUnfh4Wi\\nPpMdVQCAKwo24PEDFIe0mYC9F/LdDntXeFKvsaDtVHo0Ty/dAwAIxs34KkCvVZGJrIA8owX3ZJDa\\nq4p8za49Fdk2tv/1bUWoEWsTXz09tlmlrfhh+RcAmBMRAD5onoJhwuW9AwPnQT1eYdrD7zN2lh3/\\nuIH5Ke9743ZEBaPD3MK2vCYcwVQrxwf1u/z9CNf13GtW4qVV9Iw5ezj3UPD2sPkAgHEYWA/qhJU3\\nAABCW8kkOz4TMB469ga5d+jJc6qiJ8+pCkMU+Op1Mme6k5A8lPQw/QVJUfqR03eIOGmiTVn5afER\\njxM9UlAkwDCNE5e8MuMo16b/kbs2NXdpzdlmuEaTFnbxkC24JG09AOBka2JR0yqzxzkkLiwWhtK0\\nxWN3qIu1I9coNnCSAWGFE7hBEAKiShxRQcXaFuFw9kX7GNSE2XF3e3O0soyCNrW7gccirTaY2sQG\\nriSIWJALJZOdKxjLJgeiaewzmYIxaPX1Pr+oIgnamNsAq7eTRPF2sYATm9twmrHT8/oLDVOOZDbS\\nIw8pJmLQhkRg2zvwU5uaV1KKAzGhztldLsrDRaiUfe/H0z/F0/9KpXEe7yiZvReRUmE8En2nYYod\\n/iIR15nNvi8ZZShCudWREUQoaEm+BHGfGfYcLsKvHL4BAFDpz8GyzTSSSarqqzOGjCxudNUwgnRr\\nCFVN3K3EIkbIPo5XZq+qkA0tx6aa39PZENfo9Y5mlhOzGuArTuT/+8/t3ERe+8xDh/WOVKgUXyA1\\n7CNYEIellfVRN/dm76G1y6BQKrbX81liLgXpkxjb4P96kHaerUXE6jfyfEVKGPOCwmAZzJM0hVzF\\nCC0eVUXUKSF60OpJjYcFgIztiWvV0AtfkRHhDJGXNmkDKpvUMhPXqIsv9Txrq6KdFxrEMlz7ZFja\\nUzepFm+CX133Q/bDL05+FgDwi7pzk2jhNBKYkmIzxmUk1H4NIq5iQc0IeLyc12YOp8J7MG5GTGzq\\nI6Jipc5m7doyOzeT8xtHo8HP+8UErb3db0OsmRuq9GIPPK18cLOd9S7KbtPovBaxQ3eZwyiyca3y\\n3genpzzziQTZePTXvgOJ087bjFXvjz/a1ThsBEQmBUdtYq0SH8gA6WMAam5RAPAXc9xQw3nULBLH\\nC9TxNDmOdfNjD65RFOWknq7VKb46dOjQoUOHDh06dOjQoeOYwDFB8T3RESiLYulUWlbPXfnwUa7N\\nkYFiBKxmmkyKLC1IN9BUbZQSiq5qPkIVvfGeAkCBqaMgjVVKFRypidEyvCXMnE4B2YIMofhiFaqJ\\nDX4XMmw0nRdkMrC8Jm5A+UhSjYudrZi/dCIAoLyMFu/alaUwhoWCqHAWmIMSDLHeWWMlwVjoyiuq\\nek61ZxtA7+k3CUfCewpAoxYCA+s5VWFo4xDuNBxFHs4AI+7gMwazxXQlAQbxnuMRYWONGAEnDwY8\\nSRRltTsZFQRbePzfG5k7UwkZAYvw7plF7ktJQVub8DRZOU5E4waoRCM5YILRT0u+OSknrGoljguG\\nSGCQEWah9h0WIQztxZKWG9TikbA9kncIb4Njq9TJsKB6TcPjghhyDunJ9QuYl9naZNQEldSQgZhL\\ngak9tY2q3gmN2nXQ0GZr6mjXNrVLHTynKlQhJFVsKG6WNOEkVTU35lBgiEji/ISwksr6NQUBM4WG\\nERqUeP5YGl+ApzzhVfGWGbUyTWS2dvC+WrxqHl3+394sa+/RKPJER50SDHGeZxZiScljsmxKCCup\\niFuNCHj50u7dexnrIBs0j6dWv4hV85YurC0HADgsUeQ7WaGpeTXwZLGc1XUMCQmHzMjP4u8OM1/a\\nDk+eppC/1i5yngZtkETZpZlkLgWdAeQN8Wn3r7GTvdXoY/u2m6JwmfmRy4S6X4HFA9+J7p76hmBF\\nzZATwgOV7Dn9piFmB845g4yfr6pJuQ/sdsNRd/x4UfuqANzh2v6rhg4dOnTo0KFDhw4dOnTo0HHo\\nOCZiUB15xcrwGx5E9EwPtpz6eoffyl/5vrYDLz+9ChVLSgEA9gPHjwUhkJ94xzu/S/nxES+dOKk3\\n0iuAcXdtBgD8KP9zAMANzzwIC1OdIeYEwkLO/oyzmG/pqz3DYDJ3dANYzTEMTudF7RGKFUlJeU/U\\neJlgzAynsCa3R6ywGmMdfvdFrQjHaL22m6Pab2qcTShOj+uBdjea9tGqbG5LWOnSRf4+f6GErfcw\\nrqspTpN8jtGpBe2nv0wrtqfMiB/f8RYAIKqIHKPGIDIM9Ni2yQ744vTeZJsY3+aQwkgz0GOsepej\\nMCAqXAdG4bbIMUYRFq9AtSZ5ZDOyjHyuLIMJUYUW/kaZJ4YUY5I4FLEz6scrrRSEqQ3xmc/N3IpH\\n/nYtAGDtL9gup/y2+3bZNlZGdikt9HFZxHkZZViM/JYlbv7WEnYgKlL9lLooaOU0hTUPQkQ2oU3E\\nZmVZ+J7yranW+4awG6V2xlwFZHpB9way8IMCinvMbZuMBc9N77K+UZFv0T8mjC9nPQ4AeKmNoQ9v\\nvjYLt9zyKQBgk4+e9nXvjev2+Y81RNxHf/weSMjWns85XmEr51gXX8v+GHUq+OyG/wMAXPYEmTaX\\n3/gVdvnpOpw9lG2+Ke7XHMMOiX3MZbBpsfg1sTAKjOwrDkPfmQNqOcnMFFXE0GWwYY6fLJgnqinu\\nU/dxCcKZbIdi+EPhlDp8t2Qpz/tz9ynWJt29EQDwXPGSPtd1IPG+iOlc7R+K+ftHAgBafQ5MLaLo\\nzYYPR2vnxu18fjW1FgDcuOfslPOOJyTH+v73Ze8BAP4059tHqzr9DpVJcKLiYJZF/hkUjlw49gMA\\nwOinj1/NF3tD6rzXfibXETvPfBkAMOU3qWuZ8HleRCNCgK3dAgi2hDWLTLpYzAhZpBezOhIx5uo1\\n6m8wKJBEiiuTOQ6zWMtGoxyPJUmB3crrTUaO1pGYEd4mshfum86x/Nb0zcgUeirX7+F4uv3NUQjm\\nntjz+q6fPdSrGNRjYoNqzy9Wht38IAwx4La7PgIAbAtQFe8XefNxSwU3BAYomiqdOuib/Uehwt0g\\nWSRFRfIG9UREegWw6vecmIcv+i4AIPNze4IKpQBN07k5NLeyo+dOOoAmLztrPMaXlpEWgEXQb41i\\nI2M2xuELc5WaZecA1BJ0wGnhBlVRJI36FI6zbG/Ipv1uFhsnAxRMz6EK4msLmPBdMSlQ3EIcYgvv\\n0V4eRc7yBDVr/m8f5TMKNcxHW8rw5CfnAwBsZaRPZb7qQjiNA9cf/x+p3PWxDLgNHPSyRc46ILHx\\nbJMd2gbWr1i03+LipWWIazMMEai6UvvjrKNNisMmGlie0QCXxONrIjw2zZpYWD7dxo3XX1ZeiLw8\\nRt43bBNcOQPz1QKAbyQH08z1PbP+W6fy3MsnU/iqKezCtuZcAMDILIp27GrLgSLUT1RKtUFSkGXj\\nM8cUA9pCfKcTskmfthhiMIvnWt9KamKmNQCL4HOuqSWd7afjP0VI5jM++4/LtHr5+ahw1ibqqm5Q\\nz752lXbs4dyFAIAz5j8A13a++zOuYb7cFfUliHyVENE6lvDQ994GAPx+zlWYdRbf/aJPJx3NKg04\\nTuQN6hXnLgcAfPbKqQAAi0fBtQ99BgB46fULAADxyT7cM/ZLAMBuwXH939zFWhkusYkMKNEOIROd\\nbTIPB3UxjmHvto+GLIxoj83/FgCKLZ1z4To+SwWViYsHteKGIva55/5yea/uMePeVfhbwep+qe+h\\nojUewFx/CQBgdt00AMDez0q1jbezJjGXB/JTNzjfvYkGr59k7daOHYt5yg+GmgfcMdyDuMiNm7xB\\nPRHxTdugHoxtdz95XG1St91NZ8Go57+PcJHY/DVwfMsc34TY3J7n7WCuhNBQEbsQMUBVaDO4WJ7Z\\nEkNMrEcVsXlNzjuuwmqPajm2Iz4LIAS3DCI3tqJI2jX27TS6y1N82Dbj1R7ruDESwjWv/ajH845n\\n9HaDqlN8dejQoUOHDh06dOjQoUPHMYFjwoNqKypWiu77EYwhCTMvpiV2bztl/XPtPgwTAfyvLJwJ\\nk08IL5SScmSutMPW3EmhAwiRrQT2qbyxb3sWnDWsl3ckzVavfuspfGceKQYmz4ltB0ivAP7v58xV\\n6hB01Xt+c3+n54az+J58oyMoK6UYUU0zKW4Gg4LRecynV+cnfbbA6UVrmBQIlcrrCdvgsqgUXwts\\npo5R2KGYSTtmEmlkpmTtw7uf0lMRy+ZvX5z/GK7acBvrFaWJPOC1IW0j3TaWNgV/+jk9oufY+V0n\\nrLxBo/YO+ckOAMCy5aNQ+BX7UfNYWt9uvnYBsgSdt9jSrHlO840UvKiPp8EmifQ4Iv2AU4rAIZIc\\neoTrKA4JNnEspNKHDRGtvEFGGSHRh0uSxKPWh/kdvrP+Vj7f9nQMPomeyqp99MQUzTWi7jS2zdNO\\nZw7Fza+MRW/hPZ19sDCnDbkOepNjgtZrkGRNJMQm6Mgx2YhMKz2oNf4MjZI9Lo31shpiaIxQ1aTK\\nn6Xdp1bk7/uf0fMAANe6PNgT5bu9Yt0d2DDtDQDQqIe/evS/tGtVD+rlN36F19fRI1JWTC9vw2dF\\nENXVRHf+cdfTeL2J7WT5OxN7/S4GCuEp7TBt4XdVRWv8Q+Ko/Db728gXTpxQgc5wIntQHSPIaHC+\\nwfbdcFlYo5Dlz6NnIJwhIepkG5Zmkj6f5Qxg/hhSLpvlRHI81ZtqlUwIKhwf1dRbMmTYJcHUkPo+\\nHz1UNwUAwwNWL2PeQ3mQYLHEDfRGADh5HD2Hu1uzcUp+NQBg1ZOTe30f+UrOqWumvtXnOh4OdkZJ\\nxfq/+vMxfzPZWTlfd+997syD2hneu5u07Qs++hEc+45tXcrkVEXlr57YY8s33YN6vGHVnWSzuQy2\\nTum7fUXrpDggPJ4Q4UqW9DBk4Rm12UWOca8NRrPIc2vhWiwv3YdGH+flUNACi6Dzymo5lhjaa7lO\\nNGSwnF1nv5hShyUhGff95R4AQNlNFQCA8Wn78e+5Zx728x3L6K0H9ZgYLQ1RaKpUn61nDJijipND\\nfTvw1QR+4GcueQE/WHkTAMC6jYvR4OAYbM2H/hiaYmHvBGThLY9j0kQmfN9cy4TYsZwoUMPJ31nF\\nFe8Mm0FbRJ5I8aZdYZ53AgBghosBnDGHBFMgYfwI5XTMExiuN8O7hJxMw0XcbGS4AvBFRaLydC5U\\nsix+jHIfAADExWLLZojCEyM9dLyjBtuDpIMvaxgKALAa49rmZ1IG1SzfXHwa5EwOLvPPfwwA8Fjj\\n2SjNYHzk9s+oqphTq8AzQihttgFxQWfbE2W8mEoZBoBlu3m/G2Ytwew4c8YVLOXv7/5jFn70Yy6y\\nQrIFg00iblNQePONXu1vdeNplmQ4xYY6rCQ23RF0jEs1QoFZbGorozZMt3VUuVsZjmJVUOR3/Jqb\\n/zSPgtAmvqeM67gwDuRmIo1NGQempKGvSPua38pzgQ11zVxkD85h2TVNGTCIZ4m2iU4mKTC6RUyw\\nPYJcN797ZYDUnNGuehTb+D3UWNxKXw78IqflYKOAQgAAIABJREFUtS5+g58emIQ/55HiWpDmxbAF\\n3ISrqqsJnegE5laNw54LXgAAlAsaug2JjamK29+7E7tufBoAcNG30rFvXmkf3kj/4cLrlwEAPpl9\\naspvhswTV7H3m4Qbh5HO+gHOAQDkzrGi9H4avarmMebR2qbA2sZ+f6CKfcwxMoLrKhlm8HARjTaD\\nDGFsj7OfDDa2o8jEnb1K8W2XQ1gumk2WCC3IN0KLf0qGGm/qk9mfHm06A40RLsbWfjkSP7zsYwDA\\nfZl7AXDz+u4qrjUagzzP+loWzv8dz1uF3m9QDe8yKf0lbtKHPxwxL+WchrgfzYJ+p4aCRBUDQiK+\\nxq9YNOPfGBEb5jJ0rkyrxpk+ve9iAEDl8hLkVPS6ur3CBfNI19tzxbO4t/YUAMDCd6f27036CWP/\\n8QMsveeRo12NI47tdwj66HPHD+W1P/HF7X8BAMx6/tjNMnHysw8CSFB9+4Lz7uJ8+vkzifk0c70R\\nuIhrikCIY6csS4iJPNft7fxXssiaFkqwlevOqjYbnCJuVZIURMI8VzUw2r90A6M59iRvTLvbWFe+\\nzjVoJcqBoj4/4gmJE9u1p0OHDh06dOjQoUOHDh06jhscEx7UZJhahMLWeFJuzMuccO2kdePNcadg\\n11kvAQDKWu4CALh3Ht4jqJ7TqJN54QDmqEv+rQPSotj89XDxO89ztCWVJ6zUc/wOXOYMHFbdjifs\\n8dPy/ZtcWqQeKlaQsSPxu5pTTkVaQkMCro8FVcLuhk848mpU8UkDEM6kJ84YFAHtsUSOyY9dJyOa\\nS2t5XqHwDEbMmqfz7U9m8B4HJMx98K8AgDMX/BAA8Pjpb+AXr30XABAtFPWrBdJ2JeqWKwSO3Abe\\nLxIzaR46h4sfe5pzN+rOoHdj/a7x/K1BxpM/vxoAcN/v3tLysW4N0OsehwEu0VhsIkHhOHsN6qMs\\npy5Kz2e6MYhL3FQ+tgkrXo7Bgrq4oBxb4gDoOWiNs739ad/laHysjNcj4SL0DuF5sky7VGCkgvQK\\noXIb7VztM3IeKcmmRayXIZoaEiB9kYn4MH6jhm18vrSmrkIH1P5qRytYZn0G67A2fSSi6ayvtZF1\\nNYYk7LyXFtNfNpJ+/OFbp+Gt4fTY2PdYIInckrFCdti0b9fB+15Bh7u2+2z4eQO/jW19qtcoJg49\\nfPEcjHmKVvSt338SI5bT4mltHTg6WFiIkVjbJNz8Hapgv/bqeV2eb1/vAM4asOroOEIoMLemHFu+\\nhvTZ/KRjdecLZkAGLfahD/Owy8gcqjefxv525tDdOCOdA+7/VF2JuOjjB+o4jpgazWT6ACgdQop7\\nVfUgFA4mYyEQZv8fnXMAZU6G1Ly+kurYxUOaUFPPuJZ7L/1U85yevJYK4OZXs6D2Nt8q1kfJAE6y\\n1vfxjSRw4OVSAMBb/5OuMSdUtMnAvHYyrdZ6KWgUUwxaLs+NnsFantB0O8cEtyWMEgff92WZFERL\\nM4Qwr5Vjwp4lLCe98pCr3CUcexNrlH8OXgEAeOEWvsNHX7my/294mDjtiYf4xwkuknS84u6rP8bT\\nb1/Ur2WqueWPB+Gk0U//APaDVYt6gOo5lS0SDJHEtRYT1xtrT08VL/pzMz2ar1ZMw5tTngcADDVx\\nXTJh8V2QlnH94gwB3lEsx57P9eLQa6vx7nDO5WvCZIDe8ecf9qnOOnQPqg4dOnTo0KFDhw4dOnTo\\nOEZw7HlQhWfs+VPI275n2b0Q4Ya4I3cxVG/RyssZMD199o/h2ndo3g1FAgLCc+aslWD2p5YjQuEQ\\nn0krriVmwEln0cy64b0xXZb9w/k3I+385w+pXsmYfvYWLF/Ye+Gao4VVm4YBAAJDhCBOWhwts/i3\\nc70dwUHCO+0VXud6BSEhmGRrFvmkggpMwQ7FIpQjIZIu/iO+RWRwBO4ltPjbGwHsIf/fM5ipTqQ4\\n0NxIkZ104dGecNdGvCLyXy4+h/kwL193u5YKJ25VrWoSwpmJejXEaVkcYuL3d1ojaB4jYj5F/sJn\\nM8/E94uYuqT1JjbWxsfKtPy9j//qOjRQnwe2EooJjRzUoOVjrRPW/hd9p6I4l1b+SdmMnf1s/yg8\\n104vsBqAPzK/ATNEGoOfZldgQZD1+VPVdQCA4FOF6KxHhEQuWpOcJJ8uULuPHvDMg67ZPJ15iT+b\\nwLr+9LE7IMkdrZeGqAJbIz/Ok7cyfvOOt+9CTHhDMzckYmRdV9Cr0v5+wkekxthZ2wC1f6tY+4sn\\n8Y/WIQCAVxczzjejTYF5g0W7t8p4CPv57q+a9AVeREcPqmOTHW808z2aptLKaVmTEJUyCbLDX9+/\\nHNI4/v5oSxmGTmPOw/2flqA/0D5aNMi4BHcOWSJnFlBM5tKsdfjh10yp5er06gRUkSgdxy8OxARb\\n4iyyD7LXGKEIMbbgIPY3e6OMjBx+63UnzwYATDFfh9Bq9ldUMbXMfN8Y2CfTUv+dkhW4wElv6i8y\\nLgEALImVw9Qi8j9nUIgsJ88LWaRZCIoYqjULR6FqE/tj2R0UL9vfmg6plf3twaxK3FbNfhiZz9hx\\ns5aVFbC0J8aGIuGVaRXTZObWvr4h4P/+eCO2PMA0O3dn0vtoBHCSg3PwNj/7uSdqw0YPWSr7vWlo\\na+V7ad/MecDeIGHzML7bhYVkQE3M3481C5kWJ60zz6kYH/2DJVgES8rc3jvPTWiQrI2JKkZ8eQt2\\nznwFAHBbOsfB2+578oiloYmIXLWWAWSDHK/oa+zp9juePGrxqo+vOBf9rR2nek233f2kFuN5KJ7U\\nUDHXfGVDD2D/l30LpHzjVmqD3PDi4aVYCRSINWZdal9N9p4CQEMlx9GVo1nvkGLGTBGu/tPsiqR/\\n7R2u23X2i8DZif9fuJ0x7BfnbQaQiM8HEp7TuFWCMdzx/lGXhHAWj7mqe/d83yQcEyq+jrxiZfgN\\nD3Y4FpkpqIVL07SFfjBPwY5bn+pw3s1VZ2H9B5wB46INWVtS7xHMU2AaxTJDAU62I4sOaJsDsymO\\npkpOZmkVXCSHM4GbrmJCXVWUZ3d7Djau5GbMVd29A3rCDWysh7LB3PndxHMeCZGlWFEYpppDG/Zi\\nbhkZW/gu/vXTvwEArvnPA8jYlnpuaBAHj+JvVWH7Fua1zNrAa6VY6vlAQi1RnsLNXajVBskqxC82\\n2hA6mQu4/Ez+Xr82H+njKbJ033B+v0e2n6ctxgJ+kVd1QefCGer9HPUKbvoxRTpKLaSP/Wjx9bBn\\nkDaW9QYXQRGXASfdS/Xp/86bDwA485MfoWheavvw5/NYME+CYyrLzHGIBNMVhYmE0UJVOCfTh4k5\\nTOz5xULmvLQ1STCfwee7dMhmfMvNRPf3/uFeAIC9ObFgVBFxG9BG3ZUO6oW2Lqm4HaEaEy69eik+\\nfzZVuKczqPlSM9dw8ds6LYJfnjoXAPD3x6/SzvOdwWd2f5WYBNb+gu3/dV82/vL0daLendfVcTlF\\ntPxJNMXtr3Hhqar49hbtIyNYfB7bsE824qWW0wAA0100CPzq+Zt7V86ICOx7hGpqtPNzPrqXwhSq\\n+vJnATPuWkwFYteOzinXAI1mm+/nIqIvKr7jz+KE6zBxI7Ni/rFv+DqRVXx/ezk3nL/bQrpe2pvu\\nbs9XN7J5Q1o06m7+Ao4TUpyKvwBgCgJCQBtmvxCly5DQNpJ/Dx7L/lK7LQ+KiccUJwffgk/MiIg+\\nYxFhGfn37MaLZXMAAPdUfwuVj4/qUK+YTYKpk7659DEaq67efS4AYO+/yrt9vp4w5HtsvxtrCxFt\\n4djt3p2wsVtbuh/LYg4+l2p09ozrmPO6KzSfJEOx8N3nLE2c31sV32Qkq+QejJ42qjfcwLlspms7\\nskWe7GufeSjlvMmX0hKwbm7CgC6d7ME/J1Lt/J7n7+5VXfU8qN3jWBdWOlQV385EiHrarB58TWfn\\nDzlrr0a9j66lKXzkObvxfvmnvbpHMkKFHK8y1xu7PKdttIKMbYdvjJEubsYvRlHw7Ylq7kqb/1OE\\nuI1l/+Hef+FiR8d4wFHPfx+O/V2XGbfz2vZiGSMnc2e6fTPXwxlbDPCf4CJJeh5UHTp06NChQ4cO\\nHTp06NBxXOGYovgGChU49gvr7ZeJtBdCPR9KWQD/e4DpTP6QR6/Ra6WLMGUGBSPk+dldlm0/IMGb\\nRk/FmSfTwug2hbD/vVIAQASASdB9bRcyP+fVRZu1NCP/l08P2YyNV/boOVWxqaGwV+d1hiOdmuZQ\\nvacAgPQojGFe/4GXKQW6st5FhRDVr0o/wF/NFwIAVsukXN0763O88cgFKdfIqjNpCz0MWVOb0FJD\\nD4K1RUF8A79rsJ4ezfzr6/HleOYJHPnVLQCA+8cvxMZ2mqVWvqSmPejcQqyKZQFAIM7nyjcmhDqy\\nX3d2ON8zAviqhqJEt+WwsjPGVWDDLlqwMyoSL0Nkh4GtCWip5TO0WPlcJq8RkTDLVvOqKlI2VhSQ\\numwVDkb/kDjQxvMMQxTc8AUFw4o68Zy2F9LCKMUV2JqEBzlPUEr2SbAL72Ngbr6oX+fvZMrF7DND\\nrY2Y/j32heX/6j59xOhhNCHWbikFAKSvteKxTKbUSLZ7JntO8S3SH87bdikA4JrCNUDqY2nwDlew\\ndsK7Hev62877ztjLtwMANn1ID9DBKWYAei7P9v4YALD7uqe1fn9ZBdtqIF+Go77n/q8KuyWjfXgU\\nJ48hl/CtsgU4mMj70LN3AGVd0AiSIHXzPrrDpkX0YO24jd7pp6/ajcfeuezQCusFTjmXuXVdwmO7\\n4JPepxv5JqAxxjkuWMX+31OiJ1U4bMSkBvxqBJkI34/Q416wyKBR5YPZBgQG8295KC378WYrjEG2\\n29oGjjvZ6yTEhCXf1prI+al6Tn0lPH/p8M+xJsx7H+w9BQDPcEAeJjwINezLg9YkxpHTMtnmd7lH\\npIjl9QXrV3KeiKfFIYl0Dj15TZORnPYMQK+8pwCgmGQYHWq/7N01Z1+5ptNUMqqXtDNPqmLovG/H\\nHKz3vP2cTz6ITUBgSU7KeWqZV1SkzqF/nvAOzrLLHc47UtTi4wl9oe6q5x1Nuu9A4JytnBMWjCFr\\n4g9NIxEXbdAY6NwjGVZIE2qJd50Cbe+iISnH3i//FNPXU0zyw9vIKLrkhZ5T3UgxsZaZRfbcraOX\\n480nzu1wzqF4TxWTBM9ILgwUhwhR+igbv/noOwAS6RJDo2WNIfiLv96K/3bxuBoC0FmqO2+5AlsD\\nxy2Lh+ddcMZ6FDK2CaXTufZZvqXv86RsBmwjuEaNbEnv4ezjB8fUBlXdnALQ4g7NPsDI9Q3icQPe\\n2saE4b/NZR5Eo2TA2pPeBABMmt/1IOErlYF0dqLFK0htGzepClGx1zD7AbOIZXNb2cnuy1qHf7Sw\\nsawM89o/jngXvzczrufAB13HpQVntKM8nQ1mZ0pkX884EhTfnd99ql/KVqIGje702qdMMBzLTKjL\\nhgZJsDWyQ7r38Jp7fnO/RjmVbPztiXVnIuugsqNuCa7qjguLluFOuPaw6baeGwSEuq2jnoujmXm7\\nNENGxM+NwkuVp8Io8nIG88R95dSygY4qvg6htLsqyA3okJImxJHX8fzdwLCzGFP0johzXV5ViqJz\\nuUHztBfCVcfBztHAOniGGjF5DF/Gxn3cOMcyYjALFeumCXyfg9bJcNXy2oaruQgsyPTh9Dwu+t7Y\\nMRUGr9qNU5/FtV8MtkZS8QBANhu03ywmsfC6lPVvW5yvxQEbwwpC2bxm66tMXr8Vo1Pu0RUaRY7B\\nDtTcTzNSzmsX85ZrLxBZQiNT/XS2naffvhzeqWIAEBTtzLWJYct+wKBNjmrOx66wuoo3svdAfVIs\\nrO/Vu8/F1blrAACbN/BaZy82pwDgHxOGc+tBRh9Z0uKOAWixtc++fLF2zFXZuyH5oh2HruKovq+7\\nM2rx2CGX0j2+d8V8LYZHxcg+5MP8JqBcqNymDRMBjisy4Clj+0qvTN2pZG1huzzzmp3YFOKYUbAo\\ntT3am2UE83g8JnL52ZqMWkxl5odsl4pRgekgpfqoQ8IpP6DKrao4CwCFYhKe89dHcPq/fsL6bBWx\\ns5sVYLMaLsE61p2T6GRFFoYjHM7mFAAyN/MB2kaa4KxV1wq9K7NtFJCx/dDuy41s7/plZDzDNayG\\nKE67YgMAYMF2TnT27YmQks42qsGSqJb/PRmqLkfb4vyU36JuYYiwKJr6eGehMg+/8D1cLO61MdLL\\npO/fQBwvG037JG5mgusPXjF1DWUcQ6C2C8Xa8kXfhWl76lZKix0VDPHZlVO63JiqmPTMoSnUJtN6\\nh03qvZKwKZttWA2FevOJcxG9QOSq/5SbB18p4K7qW32kmKKFq3VGMFVDooKlMXhnsqN9PuOfuOqP\\nP+mx7LQKCRfe9TUAICxiMN5ddVK3NOXewhAFFp70HABgxpYfH3Z5vUEkn+sIS333667DgU7x1aFD\\nhw4dOnTo0KFDhw4dxwSOKQ8qAISFQShaTjdO0GNG+nZWU2mxQHbRMnvh9ssBAGMz6jDKXgcgkcvQ\\nGE6lmLqrDEBVR1Gc6t1lUPf+gQIFZ5zNfJOzMum7TzfYUR+mNeY/McqwFlg8+NnQDwEAoft59Q9f\\nvQPWg1LamUxxPFxCgZ3bl/bdS3mkKL7JntpDrYOx1QRZvMh4Hj2OeYO8iK8YBAAwexNW7qaT+WFy\\nVhm1PKlRN+0k185aive+mNWhbLNPQXtJx7y0xmobDMKpdvbwnZi/uaNXLxg3Y6KTgeemBlGxr7K1\\njKCxMaxPT6qSTafE4RA3WuOjt8sTtAGCNqt6J22tMgzCkv+fT6kUmzOhEQ2LSfE2dsL3iLqBdZuH\\n8hk9Ij+pS4azRijSJjnd2weL+zn5As4v2I6Xvz4DAJC2w4gc4WFVPaSdCZZI8QR9rH0oLX/GkAmG\\nAL3OaTZ+t7tv/ggv/jPhnVMVlj1CYCV9R+9pM/GPuqbcJ2PGWRQT2/DyOEQmC4VdJXEfVWQpGYpR\\niAyUxXDNLtKBG/zda9+adnfs/3FLgs5tbUncb8RoKij7IjZ87WVeStWUF57SDuvanjR20cF7GsoR\\n6sleI35c/Il2fFlbWcp1EeERsfi6fs/F36rSBL8OBRP+df8hX9sTVPpwMvoi5PRNQkWYHrHkb6l6\\nTltHssFl7kj1pD7+/JWa0I/CIQRpezqep3o326LsOxkVqeVIcSpLAgmBpUh6wnMaV3jN8A/uRsHi\\nhC3bflC3bhlj0O6nwpaZ8NIFhNKVYupaCK8v4LzRO8+p+p4O1XsKAOEsqddUYssmDvb/b+YSpBs4\\ntr6VyfHtqcKz0PAFlYatp9Kr/POG8fhdLtcdvzzzA/w6fgUAwLGvd0szs09lxUjau1XHNFN7xzFC\\nVXs/x85xcP73/4IL194BAMgSOdt/OvQTXOjgXFD+qt5ve4Oj4XXti+dUhbSZoQSjN7O+5gnebntR\\nsrIvpnU8NhBQy/7xDe9qokvlr31fa8dZp5Fxsmj8fzB60e0AgLSvEnN6sucUAGKZMVgmcGEemTOo\\n23v7qU8E577e1TVztRn/ePhZAMA9ldf27iIAnzzD9WFQMA6vvmwZFqyf3uvru8Opr9NzahrJNZS8\\no+d1yuFgID2nKnQPqg4dOnTo0KFDhw4dOnToOCbQo5lOkqRiAK8AyAPNls8qivK4JEm/AnAHgEZx\\n6v8qivKxuOZ/ANwGIA7gfkVRPu1thdQUMeE2kXftrE/w6RiS4W3GKHJttA48kLsAADDMZMfTHnq3\\n7v+v9wEAf1x2EdLXd52moTMMPmk/XighP3xNWLjnYME0N9NLvFrL1BofNEyAeSMFU1SPVDRXBiQR\\n8yOCyWMeOz7yTOpTHforJvRQ8YKHFv094UFafQ5GZ/VLr5C0/FJZpzGeqtjdhhXTaOVz7TEhIGJ9\\nnXvZ5NpGKbj5wsUAgHm1/L5za8eh7Tx6zjM+p/W5/aJ2hA/QKp21ju/YPzIKZy2/79L3J8Ik5PDV\\nONgprr0oNbNZmoaxvcT3ujQPbG/z8TmqTfi6jcIcszJpgp+/YxSEwxe+IlqkveVx1FWVAtCaAZo3\\nDYJFbR9upnkBAItPpCvYGEfcymOtI4XnM2BESHgnTEyRiaYbAyjK4juNxnm/BfUjIUV5TdwK+Ip5\\nPG0vPalRhwRzINU22jqK1xR9xn/rT1EgiTyBivDc/rOgAEpSTKiaAsZsYtnemBPuctZH+oRu3rbR\\nMtKHeDocS4bq7ZTiB9VJCCLFhQfJd0YQ8UZ+97vP/hwA8I+WWZ16UNWypJik5YR9c/a5Keclwyws\\nsfYz2TaCiwchdwrZF+My+W9lezZyxBjz9coxqDDQ4+HcJ7zctX23SqriVPYzG/FW8ykAgJmDVyDH\\nyvuEp/Bf61qX5jlVxcRkS0fvLgDsm1eKTQ+INDNLejdeJHs2xyxlqpyPTn4as+YxvZel8fDiYDKn\\nNvZ8ko4O2B3iOBuN8d3H7BJMQZEWJjvhkWy6gn3QUMFxMJKRJLqWxbkqnGWDU+QDly2JWKm4iO/3\\nDjXAvVfW7gMwBY2/kH/7h9D9dseMxaiOsT2uD1OczV3oA5AQ3jg4jdXB3lMAyHzbCdBZAIeBHrn+\\n8J6qUBk7nhHiOdPiMHo4t1g8EswMt4PFe3hxr0DfhJhUTHnrR9h9PdPs/PrFmwAAobFB7BZxoMMX\\n3goAeH3FdGwcxTGmomFQrz2nB8OQnMoqqbpnXsl44rWNRTjnoOD7kAK8P5lxa0PNiXFNFRbUcWJi\\n6R1/BQC87B2DZzaSLWWazDk9ti4D4UFsJ6oo2+inf6B5NIfNom7G7i+GHtK91bIVl2BxtZgxZCK1\\nOn5fRmHLW1+6D77rmNbF1C5h1Z2PAgBOfpZz1YSl98E2iesNfzE9qMmeTy3utMqE4FaOYcYeGBfq\\n9a0T4pBE3Uxm1tW+wtlpirvbXxJp/Rr6Pj7Y63nNgmenwzdTpNn70t7dJT3i1DMpSrhiwcCnj3OM\\nb0VgU8e1XrQ4DPO+/s0L12MeVEmSCgAUKIqyVpIkN4A1AK4AcC2AdkVR/nrQ+WMAvAGSAgoBzAcw\\nQlGULqVJOsuD6hVKWpXffgZLQpwAZ9g6d/g+UEdhmrnbxgNgwzKt54Br9nf7eBo8k8N47yyhbmfh\\nJGGWjGiKswCPUDf9yd5vY1cLFfR8daRMpG8zwS8UgNWJIpqmwCUW7aGtqcIwxxpuu3Q+XpjbcYEf\\nLwnBWM0BoDvRpvSOWigAgKbpMZibBTW7NKh19kiYxzLS/Tgpj6PClx9RPMXWCHhG8luPGM/f6rxp\\n2gJOzV9r325DoJQv2txk0gakttFCaS4/iFvGkqb24gYaFhSvBdmrU9uPutAxdJGrMnI5B+5zi8lH\\nbok6scfLXeT+tQWiDAUo4O7XUMVBJl4SgklQyi0eCRm7ulbmaR7H5wsWxjQhE2O7UKkc6sN5pbx3\\nRATWz9s8FuY6vgtJBgwRdQPIa7O3dn6vcJoo05tYUKp5WbtCMFdsmEaSAmaQFLi+7EyjrmuMupmb\\n+5XLRyJtV2Kz1TaG9cgYIkRi5mUh+2rSaz8cRWXeh/afjqX/mtJl2ZE0CZt/yH7bmXpvZ3lQVUZl\\nYGwIlj38RqVnMLF2e8SK/RXcOCgmBfb9fOfGrsUJe432shgmjuZ9DJKMIgef+/acrwAAf6i9CLOH\\nMtfhuOVc1Aar3JCF+qYqDBbMlfHxNY8AAC59pXtBhM4ot8noL/ptT/cBgP89MAHvfDijT+WeyHlQ\\nY4O4uTSqOZ2XObQ+rG4C686J45czPwAAfNI8DgCwetkIWFqF0NlpNAz4VgxC+m5e4y8waGqSyXS3\\n7JVsP6oSZVqVrI1/TRN5LGdsI5ZPehsAcFv16QAAT9SG9cupAJ27uneLsWCOAf986J8AgDke9t8v\\n/t67/Mm9garsbrmCivuTc2qxrokbvaaNuYdF6e0tesqDGs4SRjTxTU1BCZdcvgwA8PF/+u9ddIeY\\nU6jBDwtg55kv93j+C558PPrKlQD0PKjHMrrLwSqP4JrVuM2Z8hsAFJ3JtVXN4uIBql3PkM3qejn1\\nG9imtuCv4/4DALj35bs6vV5t16ro5kAgkqGGWynaukwVcu0qJ3t/4XDyoEZLuFgxV/f/5BnJ4WBm\\naTo8g3a/5UFVFKVOUZS14m8fgG0ABndzyeUAZiuKElYUZQ+AXdAY7Dp06NChQ4cOHTp06NChQ0fn\\n6BOfRJKkUgCTAawACTz3SpJ0C4DVAB5SFKUV3LwuT7qsBp1saCVJuhPAnQBgdqfSAq0HuEOf7cvE\\n9e7WlN+TMcxGK7K5kt4QlVLXGwSE5/OUEXswyUqLQ42gOBWZXMgx0gqVIwwGvy2Zg+ecFKipH8TM\\ndVt2j4JSTA+aIjyOMCmIxQ5fPnog8dNvv4c/v/dtAEjxngLQvKdA30WbMtebIHSFIBlkmARF1LCO\\nXme/zYal49n8VKr0rNuW44MdTA+zeyVT+NgbJIQnkwLh2Kp6JBWEs/lu3XuBlml0f0pCBCIaNMMs\\nzNZyTNhgrHEMu52u3g372RztC1zwi5aZVpn6DN4yQNpI7/d7gq792+kf4J0IPQITzl0NAJi7ejLg\\npUk/XkCvyMml1VgdKwUA5K5J7WbBLINmlQuP4POZ6mwwBnnQIOhw2a4A7sr+EgDw61qmN0LEAPd4\\nimzIsgGhtaTpqhbvqFOC2Z9q4Uv2nPYWKtXY/tWh00/+VMycjVe81jGHomIT9ZmXEHzYtZs083Gr\\nKeRT8Z2ncMst7JfL95YCAKI+K1w76PqxeBW834M40sEQWYngSgvCP4z/qV7AxmqMAuZeCBX1BcGJ\\nIm+Pz4ydnw3jsaERrDfTgr08pxQAcFfZV/gowDa+efrrAIDXx2bj1VqKKNTuEWlyGgy4ZKlIKdHF\\nPTvzaD5Ux3b74cenHN4DdQLVE9udJ/UPeRvxh9s2djj/mwzXNrZrV41IPTUMyNjJv71DOW7ZMv2Y\\n38IQiBVb2HZevvJpfHcePQuxDWTzuBrzcIpYAAAgAElEQVQUbcwwRKGNa7Fcjo1pGywwijCMtKrE\\nOKAyR2Thzb22ZK2Wg3hKFj0t1+WswIZv0VWx9YxCbH2CntxwJluf40CiPG8p6x2Z5EdIYR81ikTG\\nzVNkZK89fNmLmFPSciM3ezg/r4qXwL+M7yJj/7Hh+SueROqiKowEDJznNFDaRYoaP79RrNKBsRs5\\nZoTGcDz6ydTPcHdGbYfznYZ+oIp8w2Aa79HG6yMlmNTdfQw7O/ecqjianlMVnXlOVYTWZME/hmPj\\ntrufxLMeik0+9sYV2jlq6ELXM+ChQ/VeRvIFw8URw/+bQmHUP7x1DQDAsb/fb9snTD2bFJECG1ma\\n7y0/Gd+evgoAMG8P54s4+t+DqnpOY0NCQBPLV8eYgUCvN6iSJLkAvAPgAUVRvJIkPQXgt2DEw28B\\nPALge70tT1GUZwE8C5Dim3pD/jO7fhqudydCWBsE5fac1VSfG5bVjB0LOHHb2np7d6K9RIYi3sCK\\nTcPRUMKFdJGJC94lIRlRcYKa5HqsxY6/FXBj8lmAE0LbnWtwlp0t9oYdNwAA9mwrQHwbN2M4Rvep\\n6ua0N1Apvr1W9g0B9kZ+xLJTD6DcTSrWojkJxTLPCL7bIWdxIbS2pRjmHaSPOupUCoiC+DZujtSc\\nb8aQBCGMiKbTo4DM++QO46Zt+aS3Mf4xDuCjL6oCAGzbORjXDOJ3y7d5AQCfbJ2GSCEHoeY01iV7\\nvQTvhWxjkTYrij7hguqAiZ3RbQjCF+UmYsFeKrz+4PQFeG0XSQJ2C1d862sGY/AHXXcvxZRYRF46\\nhiqO6/OKUL+ctOHgUJaT7/Ti4i8Z6zD7jGcAAD8NXI3aNRy0UepH1il8tw2VpB7HrIZON6gHQ6X8\\nptRNEpRhRdGMB60TRdLqDd03ZtcVVNprfz9f+7vE1PkGMjmfqQaDoO5U8b8veXPxX7lLAABPF88H\\nADzRNhpvrD1fu+Q3j36n2zp1hfY2B2y7+F1jaj7ByOFtTGPCphMqjUAy8eVNKWH7PjljL57+6mwA\\ngHW/WYuJDYIX/W3xlZh2JTdwX4r4VLMUx54lNNYkR9VHxdjTVaT9+jAXmqrRDRiYjenB6Gzj2dmm\\ndcdtT6H8NZ77TV0Tu6tF5xJd1RhM/OYvFbvNVhtePo207+HrOd7c948fIH+/uinkxS1jDFofhSLD\\nEGHftleyhcgWaBvUzuDYznayaOgI7GumUe4XQzkf+mQbrnQzp+dPsnbjpZ8x5ODXX3DB6Jkoo+Az\\nEc4hhpShuc2oipAqHxdELcV8eBvHllk0AhfntaLRx0X4pDyOMau2lCHnCG1Mo67ejQ/JG9P+gKrO\\ne83FX+Pdd2gkV+N6HVXmbsNVOij6NvJbZ5naU8777Us39F+FvyHIcfmPm/ypA4Xx53NM2PTZyEMv\\nRIwdj9/8HM53JBrxzWIxkJyzu2Ak1zx+YdA2dDO29QZqDtU0ewhBMba8OIVGh7ltk3BLWhMA4FF1\\nTXvTTlS+Xn5Y9zwc/Hvowg7//7D1FHw4j/N7tIhr2v7S2B02Yy/y7AzqX1ZdCgCQ9jnx6GWvAACm\\nWPktZn7wEMrH0OBVtax/jCC9MmdKkmQGN6evK4ryLgAoinJAUZS4oigygOeQoPHWAkiuXZE4pkOH\\nDh06dOjQoUOHDh06dHSJ3qj4SgBeALBNUZRHk44XKIpSJ/77bQDCp4U5AP4tSdKjoEhSOYCVfa2Y\\nqoZ7Tf5qLA+JfJNSDJOstG4YFtHKW6Vk9NqRHSgQ+cHKaQ2Ykl8Hp4nWBqcxgsY49+t/b6bYUlg2\\n4Z0l3He7i+l1u2LoRvx6ENWyElYeD37XNBUA0PQh+QFpckKMJZh3bFCOeotkb+l3L/nikMtR1Q/3\\ntGZh48ZSAEC6Q1WsVSDv47fcK86PNdihFPN7mIK0/NsaFQQL+f2d1fTe+crjGsXr2imrNfGNYDoV\\n286xXKapWDa+QlpkDoCH/TcCACSRq9VgVnDDFDbNua+fLmqhIL6PXtycLRLUGzlrWe+H112FJ6b+\\nGwAwyEhP6xUf/hCKTXgvNrPeeXs7FypSFXdjdiDqYtnTXVSh/VvBaoyfT0ussYLlrJJKMbyEFqqf\\nVVK8Yu+OfGQJYapWmwNNsvBQOHjPcJYRHjWPIptqp/Te5okK0is6egNiDgmmJAVgVVW0O8+pp1yB\\nnE5T/m3F9FI/dvq5eH/0q+KM3lNwnbtUnyDv+/fHr9J+UwWUJmXWwDtc0BV3Hbq3MzlXaTCb9XdV\\nHJ7d8aLLGd3wSMFajVJ7bw4p2kPNLrw/lBT29qq8Tq9fuINesudOp6BJSDHjd7fRwz7iZXoc86fU\\nwxCk1zW0v3MBtutee+CwnqM/kexVTfamVtz8VMrv3yTUncU+6ajmNJy+W0bdLPbhk8cy5mB15RBs\\niYo5Siig2xtT+7KjXkHaSQyF8fptMK8U7B3RPaxtCupnsM8MWsWxIdmjquZRbXqyFCrh/r/fuVv7\\nXRVT+9H330ZbnOOjMZ3zn2OtHeo4qeZx3bOyGIvP4pzZFGL/N3kOjUrUPIn1nFBCllKpqxlNLpZZ\\n7WN4UPpmM3qbG/VwoQo42muPLDWq4GQut36Xuwnylfyw773PecsQ6Vro72DYGvktf/3iTRh2598A\\nAPEBoEqe6FCFigBg1DLO27deSfX5F98976jU6WhhhItrlLVuzl/mQ2EhiWHth6/cgWuvYoaHXw/a\\ngqnPps5lDRvE/FkkGFedhGj1BWoO1XZ7BpRTuT+4b+P12m9TQO+kU7DCjqb3NJIVx++aGC716pyz\\nU3431/Qtg0lPCMbMWDp/XIdjw0+rxketEwEAlxVR+G3J5Y/AJzY9d8k3Yv+KwsO+d28ovjMAfAfA\\nJkmS1otj/wvgBkmSJoGzQhWAuwBAUZQtkiS9BWArgBiAe7pT8NWhQ4cOHTp06NChQ4cOHTqAXqSZ\\nORLoLM2MCl+ZDHelsPie1wTfJsbZOer7bqHxjKGXpHTYAQDAf0b9WxNBurbyHBiEesqqVbQCyfY4\\n0rd09Kh4y+P498W0nE3//+2dd3xUVdrHf2d6ZiZl0kNoCST0FhBRRBELqNiVVdFV14J1V9d9Xd/t\\nu+9217aubZVd7IqsrogFEAEp0ntI6BASICHJpEzJ1PP+8Zx7Z4aZhDSSSTjfz4cPkzt37j1z59xz\\nz3nK7zGRBdUV9MKsIatF/kdkdU46EIqedmV3/zU+k8QqMxOOox+D9aioD2uh363+nCakrSQPVvUE\\nsl+kb9QiIJxaSikESwVXS50Y6+gYlhtO4OgREsRYPP15zFhIfecRUTvz9Y9mwFoWec1rR3Gk7qTj\\n1I4R3rd9Gjz+6HwAwGuHLwQAVBZnQl8vSjgc5TDVRXoryqdzjBhKOYUl5SL/odyErE20H4thiqke\\npYVtD73v6EvHDhigeoZ/dRnV//pv5ThsLyGPrymNEtI8bj20x0X+62E6XkINh8YnrqeJIaij71Uj\\nvtclF27H3nryJtcuojyopLLohlVc60fK+vZb24bfUQIA+EnOYty5/S4AwEfj3gAAFOqjhRpilYE5\\nFUWgwELOUjguckIvShQVn0c5IQEexLBVdD7rypYFIWKVmTmTuDPoN9APdGBWIdUgdPjp97PpXZi3\\ndCoAIKGy9WIxzv6REQTheBN75tgS7kltyYPam8vMKCUSlPrMehdHzSiR/y2GndTi6N+3fpAGFpFv\\n2dhfiOAMdeGHYygv6cWdFwEH6b5I20X7VY9han6oMZ88mwlfJbWqTmjAwNSSKlffthp/zNoR8f6Q\\nVd+H30d9M+sz+sFqRjPYxpF4YWU5eTnTNupaXQu1djS1K3mQHX2TKT9sfEoZAMDuN6PaQx7UdaL+\\ntG1px2oItoXgDaR10LQ2vcvOCQCeUVTqa//Uebi7jHJQt1eRl8LzXVqHju0aRF5684HQ80CWmWkZ\\nxYMa4EGMeIN0Ijy5dB2NFW1/rpbe93Kn5rLGmoucKZSyL2dCMGfMdBIE2r44JLLYntqjCvYiGoQS\\nMx3wbaGxSRFOZIFQSTnHaBqYU75r+0MoYGTQetrWxvC5T0fKzHQF1jE12Dye5s5jNlDe+vaJ7+PK\\nPVRX98CaAS1+vrVlZuJ+gdqZNIxvivh7wqAj+FM/qjF3w9b7MGMATbgbhdLJkr3DYCilB5+hPvS5\\n+mFioTuYFroaxlEnQu7sR6jDJ1Ro1RqsZ+sCVRGTUGryNYeyiNA3hgrHW8IELxwDaJv1iKjppgUy\\nZ1Ng8N5NAzBjKi0E/pFLtU9ftA/AB0cp5LqxSYjgbLLBUhHdDk+qWPyKcOT6y10IllMIm203ohao\\nAA0+ANUbBChc2VzVvEJu7VCtGn7lzKOnhjnHgQeHUujnWBN9lz+VXYU96wYCAIJCpc4ysB6NVaKm\\nr6grm7Uh8lx+E4toV+1lTZiUR7Pfdd/RoG4+pkHywcgnVmM/rbrQbQ/28fSldGY/DEZ6rSwiY3G6\\nBapjAGA9ErnNPt4HjagTmbw2pCqtfNfTPQS6eoGq4BwQgDmHREjYuuROP76jkCZChuOdJYUQn/Tm\\nBaqSApJ4mF4kVLdOZfv4ZX58cslLAACnUMp94dhlmJRC9/y7hyag0SGeW9tooXrb7GVY9AcKB6ua\\nQOczNDB4k+j+SSGNExgaOZpmU6iwaysterQeIDCWwt68VWbkDBYLz2IygukbNbCVRLa9brAGmvH0\\n0HRWCSX89W0PiXVf3YCbB20FAFyaSFlEWnC8U3M+AOCLYgo9S1vZuWFtLaGEHCec6LgicXtRUqB0\\nrjM3vskFauvo7IVlZ9GVC9SuRlmgKuJGjioLjJU0P2qtwi6/wo50K03Sw5WNlbma0d49/d/RP2Sg\\n7K10Wh1UiUQikUgkEolEIpFIuoI21UHt6XCvsOAKw8i2VYV4YCK5p9k3NiwGWWWD08iCHGjQq9L/\\nAeG80TYBySV02ewl0TLySWeo7T2RcM9p7l2Uxb79AFmqDs14Q7U6clFaRN9I3tFTUTynCu4MhtJi\\nOk7abmCpqEs6wkFeU/OJ0P5G9f/Y1jDFc1p9jjA3uvVIPqpYVmN/RvHaJR2mz3itGgTFnaSJEcKW\\nWhoyZTpF5ENucj366SlUbGcTfZf9VekIJJLpLHuV8BCuTILif7MXxrb46pp4xP+G3QlYzwbS55sU\\nbwlHUE+vvSLMOqhvvbBGLGybFe+dHoAIsYtR5m96ycxWHe9U7ykAJBUboG2K/h2U34AzBiaiQBzC\\nCGo92qrTxSRgBDbNIeGQUSvmwLyjnaGDHKp1tgad70G17iWPUU8N8ZWEvF8NBXTPN2WEPJFeEX1S\\nXwCMOn8/AODES1ROjek4rl/8KL0204AztN8J/HP3ZACARsPVMkS+fLrBtSyISlFlKGu90mei+87x\\nqUGck0olFTb2JaGl9DV61B2iKI7U/YBjH6U2BAtF6PnuGDWX64DGg/R5XYC+S1Mag6mmbf014bMk\\nbLyTBs1ZySTAtsXTD9VeEdrPuqb/KzVf/Wa0OepE8URah9qx9ZwPAAAjXuyYx23FnU8DAC595ckO\\nHUfSceLRe3oqJQ+8jGGvNt/OkgcoXDlv8T0wHeo5YSuKuJHtylrYGc2+zcdat6xhX9pQAxHx2EUC\\nay2hhPUOn3wQJavyW/UZpUzm3u+HUmaWuWkS/dD793dKu/b8ILpM3KBld0N3xBRj787lrArxVVBy\\ncVgwlDOm1OwEAA/1WRjtnXO+szXENxzPtVSk9pXRFAI62RTtvJ/wqwejwlLswwFDvSgILxaeLEA5\\npQCATA8OTPu3+nkAsM46jqqVlJujp7QdGOpi/wZKzqvWKzacslusEN9TqbjWD1ZHE8I+K1v+rSuu\\nowljcooL7435FwBgkYNUo/81f7pa6y77u47FeDhyaJAyi7DBoJbywgAgVeSlMQ54klsOc3JPozDV\\nhG9ap8RbN5LOd/D619Rtrck97Qzso0X4dJlOzVdua4jvkkf+ipwYdVtHPd/2ScjOx+ihf9xP13Cj\\nJxM/f/2uNh+nJXr7ArU3h/iaj0emQAS1gEmElSkLVEOM9IiqazzIXBh5YeoHaeDOof6vS2+CXzXG\\n0nEK+59Aw2vN16ZzZdJ4bK4Kqq8d/enc6du4GlKvd3I1pUAXZjiK1d76fCUFgv4O6hG1QPUnMFUp\\nvDm8SXTsxx9cAADQIIhiN83mFuwaBwBI/aZzJktcA3hTxGJU2KZ8iRzBPpQepNUFkbCBFsf+dpyy\\n+NGXI/5+tzENf5z3vXa3VzneDm+TqgPgXZfawidaT28P8S29/SU1d7Q3Ej6XCpg5tK0IB/clcuyf\\nTQuSlha0rUFZ9C50UsrUT9++q0PHC8ebIlTD94a+U6NY03VE0dfZD7B0wMCtsOVXryD/6x8AAFLW\\nijQzC4MuRn16d5Yw4PWnSehVo3di6eKiVp0n1uIxnCH/av/cq6Vj2wMuTHrziXYfW4b4SiQSiUQi\\nkUgkEomkR9GjQnx9VqD4kUgL5A37L8PW3XkAgOTdrXTthzmnFM+p3xyy9P7itg8BAE+/1H7LpiSS\\nXwz/AkBsz6nC0B+UYM/rwwBAFUvi2iC4JlLZEoCqyLvxD/9Wt236XcjiM+6/ZP0ruI3UP3Z8U6iq\\nZoajhuR2wFhstHiR/mnr+p5GT19icp9DqA2SCf7VraQgnOABUksVDwpdJ4OjfZ5U63Eynzb2FUrT\\nuVy1qHpSNOo+p/OgajRtO3/KLjr2tis9GGvsWveXbYeokzvFBeOqtoXmOvKoI1z66pNRY0x7yfvs\\nPgDAoatfBwBco3PhGuFVvbj4WlQvjU4RkHQcpb4qABS8E781Vl2iPrZOePkTw1IZNC0InGh1Aax9\\n7lUAwPmPk2p88oEgPDa69zJtjTh2gsKAtJUUCr63sS+yW2jLu088AwC476ePq4JvZiptiJPjGDK2\\nhrcterCM5elVlTbFEBOMMUSeznsKQFUa1ohCibcmVuK3TTkAgIw0Em9qzE6ISO1oK+EROZMHUV3q\\nBi+NISeciaiuo6gKn92IZCU6I1c8l8J+K1+yiE7xxRYw+spFY+ITc+9pd1vDyVtEY4z5kF71po5Y\\nR8+++XOegVk0buarXRMKvO8OuvcK3j6z911nnCeevafePPLYGw61PzLgL3fMi/Ba+pLo/tE3ND8H\\n0zeyDntOFZTjKJ7Un3bKUQlFLdgpHqGWCsCfQR5IeyLNA1qq3a6w5VfUj4p+R/3IchRooEyKUPZA\\nXzcSv23bfKLodw9Cf4oSb9MEJ64upJrmq07QSa7qW4y11eT6dfspCm/pwSGtPk9VgNKIMrUtVzNo\\nCy15TjvikW0P0oMqkUgkEolEIpFIJJK4oEd5ULUTo5NCPx68FAcGUI7XZQk/BADojxqRUNW63LOH\\nHvwvAOD+5Ght6nevP4xjnwwEAHiFzkl4uZkzwZRpZGGZ2381AKBwXvx6ANrCLGv0hctfQlbkg5fP\\nBQDseX2YmjOolJnxpoaEk9wZohxDFYduFpn38z+eg4M3vBZ+WDxbm4+tv4j0gg2pyAUORVuZFOu3\\nUuevPVZ4zoFjF9Ln+3zb8ucT11I+xrhxR6AXJzdbqfCWrjEBx0nnBOlbQ5/xJJMdyVjfem9mwwC6\\naIrAUEopiZQAgPVY6/XnjV+3T/brk4YijM0obvb9xiluJLbg5bzgHhJEWT03lKag5KI1V7vRnyCE\\npfaZ0VaXuPUQDYWeVI7/Oslbcp3Fob4/6cbtAIDX+61pdT6q+XB0CZhiL6muLR/xKTCCtrUnv1XS\\ndZwpj2zIKxnqq+G5oM1h2GTFmO+ozzReSPdy9rcMwUwaR2rWZkMr6hIGjcL7mOYBEO2NUTyxishZ\\nzWiGtB2R987DV3+J+VtnAKCSVh6Ro2k50XwbHbmaqDIN2g6Wvfj70zcDAK78zd/QIBJA3V66x7w2\\nDr8Y3pMOtP3YwQT6LloGHG4Qtdb1XvUcAReND6YqHZTfK1YZD30DXZviR16OKYSUr6+N+Hv5Q0/j\\ngtW0n35H67wgfjNXvbPmQ6ExRjmfewj1g1mvtT9HrL0oHk2u49h/66sR27qbrvLudgYHLyV9iqGv\\nP4SAKDnX1rI44d5TrYvBMJbqHwe2tU60T/F8dtSjeqonNXzb7Ju+wbsLprX5mJaK6G2GcooW6TOJ\\n5vL12/uc9jiK5zTiOHkUlaFfTXMf7cHQPOX7j34JAHjrxStO30ZRy91vpt8tJcmFJWVU9m/5BIqq\\n+thRgEuzqLzlp+Vj6HPLLa2ugzrlzZ9EbfP1pXFLX96+0lu/PkkTk9+2MH/rKnqcSNLIW3YDAN4Z\\nuKLZfcb+ueUbShXG8QCbf/oPes2incmnO05r6U6RpL130aB8Jhe6rRFJUtj4h+bDB875+YOoE7WY\\nU6g2M+oLgeS9MfZ9iFZwyxcVYeN9zwIAfnaCipeXu1Lw/MBPAAD9heDNfEcy/vLMbQBCi9GAiSOh\\nkl435tNsgwWYmnivbWqdSFLdYC2GXEONvClzMwDgpadmtfiZXz79L3znLAAAvLma2s28DPmjaeSt\\nWUAjVPgk8PhkalfOGg5HLi1ArRWhWVLtUNqWWhpQXyuCYCkHguo8WOsN9Udn9pkJorj14SX4n1Sa\\nKYaLJG355StR205H/RAx2TbTd7Xu18NjE3VijzI1jPuiVPoN5r4YUg0+nUiSZSoZOpwrMmO+rwgd\\nhVP4FrXdWBt97Mfu/hgAcE/yCez1UfhNoT72xFMRUbDsan8YlxRJOvOcqQWqtSyy//hNQMLJSEXu\\n5vjzn2jy/9cymijVvjIAlTNpYZK1KHTR3Bl0fyecDEbVDj45niFjM7323E4LJ+M7LQvsOLM1UUrr\\n1oroMdKVpYHO1fxCriNc+/hyfHyYJnN1ZSkAAK4PwlBNi8ik/a0/VpMwejoGk3idPskDk4lep5rJ\\nmHSsJhnGrXQPm06GGROymx9bXAN9MQ1Up5J0YSW+G/MfAKS++cM35rS+8QiJJHVUFTgWvV0kSdvE\\noBlBi7Zgce+rv3DqfefJoA2Tiug5uXXxsFYdp+SBl/GinZS0X/7wqlZ/prNChb+5968AgGlvhMLU\\nlfSBgEmZq4X6qn0C3b+2Tae//xoGi4oM+0P38rP/Qw6PuxffS8fZ0fb6zbFwXOSCcQuNI2wyOdse\\nKvwWJlFK4W/zbgJAgp6tXaD2VKRIkkQikUgkEolEIpFIehQ9yoPaUORB0pZIk/old67DlCSyCCkh\\neQVvPaiWDTFVR1s53ZNpvz1T3godZ/c1OFhGXpTkbe1zjTeHLDMTwitEedyTHEheTNYkxaua99/7\\nkb6RrFVBYfyacO82fL2X3Kqpy8jTVDPVg4OXUQjMuI23qLXlFM75+YMI3kA1Rl1N1F+8Ry0wnSR7\\njDubLP6WCg105ORC/RCyLhprtTBQRRz4EoG0Xa0z///5GfoOG92U8P7m/klwlJJQyes3vYbHnicx\\nk8RyOp7h4eM4L51Um1b+lurvll8RxJuXUujHhcKplvflvei7qGULXlURfS+lJlbqLq6GlTQI6XWd\\ngyFtd/R3OVMeVACwj6HzhYsVtNWD2jCIY/9tIlRMeK8s5Uy9p3yZPpw3jDy1pe8Mjfp8W8vMNEdA\\nDAm7Hwp5VAfNp9804YQGTRnUp+66bAUA4Bfppep+I9fNBgCwdaHQqtwZR7B3t/CSl7XfQis9qD0X\\nxXupUJ+vUVNIEmo6VmbKPoTu6+AIUSZqjRXmyshjTv/Zt1j8RxJoqxlF90naTg5HX1FmZjQJtWR/\\nYUDAIDwVPg6vuKeUUN/Esui2BgwspihSLDyp4tju1okmBUyhsUVvp3vHl+pH4h56aJwaWgyAhJpi\\nbb6Jar5yUY6n+mQiEhLJE+3z0hcwbjeHBJ/CaMmD2h6KH30ZFxdfCwCo+qZlATXbRScAAKtHU8SG\\nh/tQ9I8fdWp7OsuDGq+htW0Nme1pdFbkgt/CVVGieMGTSV9OEWWMeE+MJ648H2yb257F2HQphfg2\\nueihr5SJ6SgNgyhqEwiF/9pHBwADjZ+GShq/LEcRVx5UX1KwRWGt9tBaD2qPWqCejvoR5Co3HdNH\\n1TB1DAhC359WI31TaQWydNhn6vsj181GRiI9zO2fda66plygQlVyVPIgTdWha1I9hX43c7IbrhrK\\n0Rww8CQA4JXC93Dll48BANI3iLDW247BqCXV1X3b+qmKvgoj5+zCzrkjAUCdTBntXM1hFBEVgAZq\\nXSplcRfQM9RNIOuGrkqP7PXRky+fJVQTUN0mFoT2a0gK2uvSY/zgIwCAeXmLcNchCjutfJbU2wIG\\nhsGPUbj6mgO0TasNYtawLQCA65MpVPim5Q/BUEEDV+aW6LbU52vhHEehaJpjtKrV1zNV8VgJxzM0\\nAIlHO3eBqiyCdzzxctSCc8svX4m5CO17Cy3Kyz/Ia9U5tvzyFQxefjcAIGm1yDvLYkiopGvfmBcK\\ntTXUh/0e4jc6EzEiv7yXavn+5m1aeGo9wKYfvQAAGL2a8qr3THkLV+65EgCw/zsKj8ooqkTDNy1p\\nqbYduUDtuZy6QAUAdxp1WCW1IHySqYxfzssd8LhoTMj+ImRMVWqRgoUWeoMfJkNJ6dxhqtLu038i\\nI0uppw/m/ua6VrXV2Sd0IxlrRci9GDODBsAktoXniav3YNjXjJXrr2xzDfJi8nCKz113eCAAwLY0\\ndp66MgltHCEs0Ywj/du2GZabrqnH9wbReLuskpQzjVo/TOLZsqeKDNaaLYkx2xtIELmB7viavHcW\\n3RHi25WL2XhcoJbeR/fm0NfbHx6rHKOzQmzjkVgGo6h9rqvEdX1JO0JJNzrgc2CFazAA4KUXrm/x\\n80pKBBBKi2iOxoH0P9eLMcHF4BUh1f3yaC5bUZWC5yZRhZDflFwNAPCuTYOhLvLY9jEBGGo6J6w4\\nXpEhvhKJRCKRSCQSiUQi6VH0Kg9qON5E+t/QGNpmmE6WjAmZRwEAe+ozMdZGvvaFX58LS8WZsaid\\nTR5U1UNaE/s7Kx49Vw6DpZz2GYsS6XcAACAASURBVHY/qYWV/HME0u4oAwA8l/8RAOCatQ+BHU6I\\n+OwVl2zCiaaQqMH2CvJ4+4+R91XnZKrVPulgdBuCwtDOArHDYBz9mdiPI3NTtNcyXIzoVJRwW19y\\nEBPH04WZatuDv6whb1rCEfJ86J1AQyFZ6q+aQFa+NIMDD9jWAwAu/jcJAmRsC8DRh86nqNeG10YN\\nGBhOFolavpnC81tpQNAgRIQqhEemhsdUAY7lQa2bRHEoKetC7ivHheQZ9lebkFwqfojLSVhlQIod\\nZR/kRxzjgns2qQq8PuHZcY5sijhmLGJ5WEJ1cGmbfYJPFUCoH8KRvCf6vi28nYSTij+NDvvtLHxW\\nao/eETr/RTeT53vlR+PhTRK/V8OZs9RLD2rPJZYHVRE1q5xM76Vv0kSFvZ4cz+BPF0IgG0VYax2P\\nqBNdNUF4+ZJojEndooNRWOrZ3SQMVlmcicwNp2+nP4GpwmosEPKcKvdluOhaOEpUSThN6YpAHf1t\\nqeDqMyNv5kHMzNwBAOivpxSNB1fegfTV0WInNVNpjFLqSidsNUdE5cRCqa3t7kvXpLDwGIYkV9J7\\nfupoWcYG1PnpObK9hlRA61ZmR4xHdUJbJnEojX+O3anQ13ePN847isZlw05z5x/7LBBJUvAX0HW8\\nZfhmLPh0Snc1qVOZMXMDVh2j57JrY3o3t4bSDTTF1k45luJBVeYW+rA6zA0F9Pr3Mz+EV0wan997\\nCQDAoAug8gQJq9k2nF5EqSU8tlB0XtINxwEAK0ZSVZBHKs7F55tJyE2fQqkSuWn1SNDRuD27D83z\\nfrH2OiRvpbGncSCNZVPP34XVX4/qUNviHelBlUgkEolEIpFIJBJJj6JH1UFtC+GeUwX3ygwAQNmV\\nlGva6DFiT2MWAMCfHECwki6Hxt81beyNRHhOFQMlR1T5GEN9KIdx3WHKR/RNDKD6YA4A4HFQzTvD\\nLjPcWWRZMuXS7/bdSxNUYaUX7QOwpawfAMCaRwojNrMbxzbRcRSPBFjIgq72jSDgFt7tREqNRMDE\\nYC1rneW4egxZ59K3hzypSp5oxaXA+lKyXk6adBDnDCNX7iYv5T+4dUHoEsmalqIn6+31SVtw175b\\nAACebHrPk6SDX6RhJcTwSnuSmZp4rzlGrmGflcN8jL64kpfZlhqqOVmUo+1GlrrN+m24hZ6O6dxF\\nIlD1E91Rx/hs52iYsoSFUZT+O9V7Wn8+WRZ1h8mdYh5th0VD7fSuIouvvpGrnlMF5ggNW9psF7An\\nsozLi//zEu5a8DCAWJUfO49wz6nC51tHAwCsOLOeU0nvRKnlmzWIon3Yqgx4hLCcUdzLBjuDLo9u\\neo+N7nn7OD9yvqHxqGYkQ8BKY5LGRdvskz3I/pz2HW4jr+GxxDQEjHQvKTlW9fkaJB+MHCt8ZqZ6\\ncZtsTM3/1Itx1FzF0SS8CaZYAkVhKOOxjoY8NKUztXTLvmX5WDGdvtdf+5E+xK1FG7Ayl8bMk1to\\nPNIVNuI3I78CAKwUD5YdS1v2OHiTmFrCxzOUxlbGuBqJk6ij835bORjVDTSe8L3k7UlwAs5cEaVi\\n5WqtU/dmqpd6/XVrsWj++S2e/0xxJjynZyO6fXQdF+zrHd5TAFj86UR4C+jZ3Lmyn+0j3Hv6v7fN\\nBwD86b2Wy/GdjnDPqULSPro///rcLXjqx+8BAOzH6T63HNLDFpbzGRTib5qwKJAZc9YAAN7fPBFA\\n8+VqFDE2rmWoXE3RFgvzqB/9I3e9Oq8LCgG2g6505JpobrW+keaGA3JrUOakcS0zn6JGts4bBcSR\\nSFJ30ntDfBXBTPGs1bsAr4gKVW7axA0JcPYVoZDlZ24yeTaF+CoqvXonBwtb6J8auunMZepkxWuj\\nH4mnecG9tFHTQBMnS349GmvEAsRD7zGfBj+/nEIp5h+bAKOOTjQuhUK33989Af5qWppwLVc/o4hZ\\n6BvFoBQIhZopIcFcy9Ui0Bpv7Dqojf0ia4wmHYkO9S2/nCN7IA0449IrsGQvxYUVDaAQ5rIGG1IT\\naADzBOi7DkuuRHEdieiU7aIFdjDJj7R1NEAqyp5um0a9nnpHqK/raf2OoB6wHI+sp6jx8QiDgUJ7\\nRJKU2mM+MTfSO2OLCNhHCRXfnbET/uuGi99d/Ebp/euwcuw7AIARCx9p8bPuTKEkmh5Aym6hzixq\\nGuqL7NAtpjCezlLxjVdkiG/PJVaIbywaBlL/TjpM98uJK3zQnaApZ6AvGXlQZYRpAK0Yh2VWoryR\\n+r9OGHx872apAkaKwae+kCNrHX1cCdvV+BFzsamE6zpzGfyWSHEg3xA3bF8LgTZX9GfCUdTZHWRT\\nREIVUyeZzj4M7kGUprB/+j/pHEyDP1aTgNEnz00DQMbO6dNI3OjLNeMAAKnbY9/nyvPIUM/VhbVj\\nQKjmtaJOGRTiJkEDh85F+1lpqIY/gaFBqLwjCFiP0JikKHs/c89cPDH3npjn78mcTSG+vZHRl+zB\\n5sP9AQCGvbHFxnoqrRFJAshwBwDjh5MHYvvaAnAdfXbGhVuxcv54AIB7HM3F8rJqsGgoKWMbGQ1W\\nRb9rn2CXEgLc51Kalx7YlQswOrcyZ/ElMUy4YScA4N/9VwEAdnibcPM7j7frnD0FGeIrkUgkEolE\\nIpFIJJIeRY8L8XX0Fx4tYfyyHgmtsd0ZZJ3Yc88rUZ874HPg7tI7AAAVu0Ohi2fSc3o20lgg6nzW\\naOA30+9hK2ZqOIRjAF3vK69eh7VVFNrrELVKPU16vHEB1TdN05CX+7flM7HFSVZAmxDLqLnIi71N\\n5Gk80ZiIERlUE84oXLYWswf1SSL8dgUdu/rcAOARnk/hlEs4FmbtF542v5nBSdEaSDwc20qnhIMo\\ngh+1Q7VInELCI/XrqTSBsQqoSiSX/aPD3sbLueSqKFhxFwAg+yMj7v8rham9UUFhRV+uGYegha7f\\ngBH0nfpY6rE1ieI9ap1ksme6oFoTFgzwUKQZTCKUNiKcVynvkxK6T2J5hWPhTWLwpEWGQAOA5zzy\\n1JiXU8iOO4uBjaRtpmWJ6n7JpcrwEvs6ap3UpkSqxoPAzjRc8DnV8rOdpm2KBTWhSqPWYyx5gOT1\\np5fMxEmkRH3G2Z+ubUfqjgKAcwR5rZRavHt9ThTqycu/w0vvzX65cyNCJL0Ppd6otbz5+7GuQANN\\nkSiOepjuLXOpEcEJDQCAbyZSjeDL1j8IjYbuiZ/kLsatS6lG77kjqbzCjoHZyJxyDABg/4IGuJxh\\nlahuoHE0gYYv1A8LIKWY7ifF4+rKYkjZR230WwC/lV5r3XQfZS40qlEQClUTGGy7o79P7SQRXuug\\nz/qtDKk76DyWYxwaP41x2y6msXx+3UR8cXg4gFC4fkopsJgXAQBS90Sfwz6SY+h4GlScPjpe+eY+\\nYGIcsh6mc7uzOYJGIbaSJKI9dmjVsD/FQxowAho3/VY6d6jMjpJmAgAzHqWxZ8SLvae0R8FEuob7\\nNgzo5pZ0DvFaj7Wz8fSlKISf5n6J8fmiE0/rWNkZ5dmq1P421IbmE3fdvBTzPrqs3cfuDJrSGCbM\\n3AUA2LyQSgxyDWDbSnOQg1sLAACJANJuphC5JV8XwdooIihTKPzs6Jq+OO+jHwIAXv8plY57+cl/\\n4KG/PhJxvvpCDi7GjuRirRrp0yDKXmkcOiCd5gL7D9AY+4vL/4s/fR5Z1kvfwLFqFbX3pasOAwAG\\nGarafR16G3GxQA0YAEc/DuvR0y8WrWXNO319IlTUxwPQs8hJ6CC9FXf2/w4A8MyqG1o8hxDxA2cU\\nvhiPBAeK2peH4yt0I7mErrvOyeFNot+qcSBgFiGn1iP0/9dHh8Dnp30tn9PEa+KcnbAwusFHGOh7\\nzc9fhs25tO3toZTn83zOJjxdS7VDJ2QfxbcHKUdp5EiagKVaXOqidW3jELVtthFUlL36JC0cNT6j\\nmo+qFwOVJyWU09kcFlHw3mcVfYwBdQ5qr1UoEzdlMPjFhDEjbPK2b+o8AEDRqgfxzJNURzPnCar9\\nZ6jVwDyIVpnj0yi+7I7U73A4k1agfXRU3He9azD+XkGqwMn7QB0VgN+k5JuG2hrQ03t1Q4DkAy1/\\nr1MxNHAYGqK3KwtThYRKjroMWqCpEdNahtn3LwYAvP/S5eq+9tE0EZw6rgTpBnooLNhwDgDAtl2L\\n+kJxrdLpR0heawLXitDEAL3nNzPowkIJr521OqI9Jxf0i2qzPwHoX0i5dzVlfVr41i2z87GX4QiK\\nsErxbZXFKQD8q+aCdh9bcnbR0sJUIWVfEK56iuGvHitC4bcFgUN0D/5ff5oYBgIMgf2036RztVh5\\nxXMAgP462u8vs47i/X/SvsGwxGxlrGuYRM+T7EVGHJ/uQzhau05drJqPMzQOpNdKHikQpoaulD7N\\nbgJ2x8gAD9AOXBjiOAe8yTSJDupChqdfH7kWAHC0LgWmhclRh0mJsTBVePLKhRiopxzehXZayFYM\\nSIHfJ0Jz60W7goAvi54tzElToaYMhqbBdH/zAD2/snPscJWSfkXioTNr0C4+ZaHrHuJBwp7uiXPv\\nLQvT3kL6uTSnqV7fci1tYzndT+ONoczTjtZEVT6vHPFHt3yKB1Iq1PfnoXsXqKYajrcGfEt/PEr/\\n5/9nDrwjaIAL+kQ1g70mnFhM8wOWFJpDeBfS/V36q1cwdDU5su77y4+izmOfSGPjreM24I9ZpDj+\\n9EWD8M8vaI6jqyYnyobZz+Dcd54AAHBh0PvbBzcgsYjmd04PmeAvu3wLlhygOercv8+k/TUM6NO7\\nw+tbiwzxlUgkEolEIpFIJBJJXBAXIklpw9L5FfOuxY73R3bK8YZ+rxQf5H0Ttf287TcCANxfZkW9\\ndyY5G0SS3FeTq41vImu34jEFKFlcCfFVwmJN1RwNonRmeK3SmY+tBADcY6MCfX11Vsx30DFnWUOu\\nQeW3vCRnL7bYySJW9S5ZfFkgFErsTSHrlfGkBkJUDY2DyHqfeEALQ13Lv02scFiP8AwrQj1BPeAZ\\nQR6IoJ1sjDqHBv5E+uyN523A09lbo44z6tmH1M9Tw1VnKB67nUSgbrbux5wjVwMAbAb6AstWjkXK\\n3tBxlDqCWuHYM9VwtVaqUrPV1d+Pvkuirf+nE0kKTCfVucY6CitIWR9bD/BUESz7RC9sG6L39Yvj\\nufalxKxRq55XCDG5znHBL0KzFfXexINaaIX4U+NAYO7NFLr1wJbbAUSGGTupRC7mXLcYry6cDgAw\\n1nXMC7LzMfJyKOG8ow0hT9GwNWR91W1MjP7gGUCKJPVcFLG2lL3RY4wiwNY4QANXrhD18SniPRp4\\nJ1PoR0YSRSEUpR/F6lcoEmHT76JTXPK+vBc5S+j+UcSL6oYBxhoRziquc1DPYdtDfapmlBirC+th\\nEEJ0njXpoRrTQrSpycZUQaXj06it5sP6iNqhCtXn0PvjRlLOQEHiSSwpIyXe3OR6NHjoXso00/f7\\nYe7XeOL/2haSOfmRjahqovtv+yISp/ObOSwUYKPWb3VnAgMuoEgVb5DGGKfXgHQzhU3lmul5s7my\\nLwLLKYol/HkRHuK7QoQAP/zGA21qa3MontTfnhyO+R9M7ZRjtpXeJJK0745XokJ7e5tI0qM3k/L1\\nw0Is8tW6XNXL2VEP6qk05XlgOtR9g3NLIklbfhU9/oVTFaD7+7wVj8C4hyLfTldDWcE+gTyoIwZX\\n4N5cEjUaoq/CHh+ldj3+9W0AAEuWE0wIInmLaf5qqGMwTaUovo1F89VjfuWi6/joxz8AACQeBJy9\\nXMVXiiRJJBKJRCKRSCQSiaRHERc5qHoWRJYxRuHSdlL64VDkF1BS9MEbXwMA/L56aJd7Ts8m3ELA\\nR5cQbYky2jkaKGUUSQdC78fyoC16/iIAwHv5F6n7+K+j3Mu0kR8BAJ4quRE1hyiG/4ERb+JrE+UW\\n/n6oEFMqZvBZlLox9J+plqsexpRisssoXrhw6i93IXlJy7XljA1KeZSQJ5WfFNZEXahWob6BrPIf\\nF4/FWAtZ6mcn1qjH2fljspJPeXiOui35R7TfVDPV7nnq+KU44aSc2c2bqE8bGhncVCYUXhuHP5G8\\nG4qQR1MGg7ZJK76j+P7HdQCiy+GcDq0o1zLxdkr62pWUA8PSpKj9TvWWxPKeAlDLv0QfIZIhN9L5\\n5ucvw14fWTxPBCjXc3njcPiE0pVJ48OFwoHpbqAX4ZlvSvmMpqAek6eRiMKmj1uumXg6Rj0fbY3u\\nd8VhAF3nOZX0fGJ5ThVqxgjvTpBDn0nRGXcMo6iSTfYBKP6Owk+OJpMH4LzMQzCIPPr8j+cgcT/d\\nH9ufpDEmdYMeXpE67uxLx87YHIQyQLoyhV5APkf9YDE+5tEzORhkGJZGwh3F52ngdNO9XZVJ42Tm\\nBg77EPrMTeesBwCsWnWumv8eC6ePxss840ncObhOfFUNSpxUXqvETs/qJOZByu3lAIC6d07jVriR\\nxtZknRsbG+lZ4BtNYwfTcDgLabdBmeTFKLIdxewUaq9P2OqDnOGTBspbLW6gtjRtTIMlRqTNu43k\\nVZ2dWIOpCa2vM90auktsadYtK7rNYysJkSbyTdeM/hgFb5HnV+tp2eP7wnYqw/TwRW8CAB5IqcDP\\nKqkud1DPofF1nse4q7yn9836Cj9OpYni949cCADY+GXsSMu6UXQP3nTgUgDAiKTj+G1GMQBgRulV\\nAIAPCxcgUyvqHNcbVM+pUhLGGKOOc+NAICjKeRVPpfWEWRM+vzFjmNDT2DdlGQBg3p5JcFbTeaZd\\nSmVk1h7Nw8j04wCAZUJg7t7F98BUKSLDKntPxEJnERcLVIffoCq6dhZJ+6gDjP1z71HVi2eY8MUb\\nRBFz+8ggAhbx0A4CljLqag35QiypnOqMNkf44lX3KS1Gf7iOFnLmydUYO4Z2eKF6CpaV08wjkEwL\\nsJpxGhir6fe3Ho0+trJoC0epl6fbbYE6actmEHWVY6LUGDXagaCOLoCiEBwwUk1BANBUmPCXEkqi\\nv34CPTw+cebgjTIS1HHcRaFkfFkqrhCD8dzayQCAg43pqup00n46R0J1EAGhNOnyMwTq6dpqwtaf\\nSk3UuhHUiOxVbQ+W8KQwuAbTj9Tkpzjkrya8hp9kXQMA2PtOSICq/ny6qMlrQ8tDn1Wp1UrXafnP\\nn0XRchHW7NUiIZk+M6U/qTf5g1qsWUKLR6+oDfuVy4i3Ki8GAHy3i8SwkrIc2D7xffU8I1+gY9oa\\nogd4EWUDs9aDDRU0aT0TYSNHvxx4Bo4qORtQBIgMYf03fSu9PnGFD3DTvffWbioc/+PRy3DfTZQK\\n8cvdJCY02boX6+8ZCADgBzJhOU5j77g/0r3RlAP4RP1vrgzL+42oOk8Y1DLFYrTBCFZLky9eQsYW\\n47ga1HpoMRrkDP5Kep1N62X4TQwXzqQUBiVc11OgiamCrkmkELn+VjI6FhhPYJ+HRF9qA1acbKJV\\ndH4yLSJ3eHLxyRAyTH7xvzQOPvXVLUjbKu5iMeeuv9yJxaNIVftowIqNtZTucVyIkeRk29Uy0DkJ\\nlI4yznwEq9xiTBGq8alaB0oaqT07VpFBMLk89sRxce0IAMDsxG9jvt8T6W2L085S7n3qewsAAN/Y\\nqX9vWNJyOlrpfS9j6Ottn3s+dvOnAID3jk5Ut51uYQqQgNLJzcIBQ7Z9DHv1ITXFqd+5x1G5uv3i\\ngN3F6/Nn4PVW7qtPp3u4eBnNBw8WpeHdYkp70O0nQ97F83+MS+6nigoHb3gNEHqpn7to3vLk3B9E\\nLVILzj2CL4Z8If6KbXi/5RAZB7aWkxFNpwtg8khyMvRPIGEkS54Hn5dSv9k+j/6n0SnyfL09vLct\\nyBBfiUQikUgkEolEIpHEBXHhQWUMMGgDiNOKLpJWYPtaLTACgIRoas4nl57upEEtQ6BYA5WSIacS\\n8kAqwj8cTFj8lTBS96p07LRReNVONlitZWesF3U1D3M0V3uzOQz1wpMQVqJF8YA2h0d4PupGBsGS\\nydPIg0JYZG/Ik6j1MJgN5Dm4YAuJ6EzKOYJaJ3kicpLIor9vYgI+OjSOjllNngRjuQG2MvH9GulC\\nBLVAoxCBYsFQKSRfovJdQqGtqVtF2R9320PQjHUc7057CQAwwkAH1DOrKkD24iPk+bw9qQRrPakA\\ngH5TyOU82mDCY8cpBz7XSN6SOUeuwpgBFK738eClUecbs+FWPHDjlwCAx2yH1e0z8pYDAKr7LxJt\\n0OC+oxTGs7MmJ8LzpKB4b8WpYWI+ZApBmWpEl62QSLqLWP1XgdXqkTOcwmuL0ikcJEPXiKNeGv8u\\nzKV7cEndKFycRcpp8w5nYPrPyKt32E37rdgxFEkWilhwNIpQ+DtPYKSRtk0XoXALKopgGRwZ2pJi\\ncMOio3INe8qzkLpTEYcTta37MaxYMhYA4LfStqSTsb+ProzOvc1G6mV/y3XgkgQSctnrc+J8C3kd\\n3q85FwDQxA2waugzikje0GteQOkM8hbNryQPSaG1Cs9UXQIAGGY5hiIbXSuDCCvRaQIoSqZt1yZt\\nA0ClzDycwoIrA/T9PnMMQ2k1CZ4o9ZmbQy1rAeCafTNa3lnSLXRWzdO7kqoi/i8oyId2X8upQC3h\\n6UPzgT9duAC3JNJDaoPHh4lGipY45AkVHi+9j8L0L9x5PQDgzaFv47IFP6HX19N7k00aYHTkOZIn\\nVeGlYe8BAG7/92PtbmtPIWE1zZmaplA0SP1+G4JWuv+1Q2iSFKy2Ytk/JwEA8sZPwKPnUUjuRZZS\\nAEDa1OOoXk2h/aaTNJYdWj4Qs/Q0tpi09Ls9kr0Mc3bQXM5RYoN+MJ3zw4nk7zWyAGpFPa99XorI\\nyNLX4zMnjZPOi6k9luWhEnUK5mMMLllmBkCcqPgmDcni5756Gyo/7d/dTTkj9HYVX0s5U5Vo3Tmi\\neHFYnbqa8UFYD4kadCJCgutCdfSUwuexaEpnoTzSGnoRNADuDJokNeUEYC6jYxtFnlBLocOtoV5E\\nruobGFJLms/brBtE5027/Bj0WtovKGR469wm2O00YHK/BvDS4tlgowmh2eRFookmRTUOetC5y0P5\\ni8ri2FitgU/U6/Kl0Dk0Vh+CDrrgGo8GCSc0or30GUtVIFSX8DS0pOLrsTH87q53AAA3WmMURA0j\\nIOIGd3ppAB9rDOWoKLlaIwzHIra3lf84KHP1VzuvBhfXOVyxV6FhEMf10yi37D9rKVTKkOmCRtSl\\n1W44XQZsz0Gq+PZcMjY3/9sp92XjOA+uGbUdAFDtofHk8tRdMGnoPgsKud/rrVXY46Obfp83E9k6\\nMhQV6CnsLVNrwbqmyEHBrPGpdXsVI5IvqMO2Boox21pG6ujBIINWR5/11ZqQeICMVUodV/sQDS67\\nZiMA4LMNZGBLqNDBciyGiu8F1O7Z4+n+/H3mzmavgYISfneVOUZuRjOsaaK2jTHQw0BZ5IZT7HWj\\n1EsL3T1NNCn9pGwMqk/Q+JC+Rh/1Gb+FoW44Dc4fXkHGu4lGPX5aSRPPRfPPb3Ub453epOIbi/ao\\n+CqLxXBihfJ6+nrB9MIoXEf96M6LVuHXGbvbdL5DPgfy9JF1xx3Bppj9+VSGrbkDJZPfptedrOLb\\n3cRS8S28gyadicKYtmzdKJj70cLR7aKJJztuQiCZ7l99rQ5CvBuWAhovnx01H5ck0FinjCE+rkOp\\nh8aHV/ZSHqx7d4qazubOCkLjFbXoM8Xk06OFro4OzoUuScAaBHR0TNum6LFFgWsZXDm9+96TKr4S\\niUQikUgkEolEIulRxIUHlTF2EoATQHV3t0UiEaRD9kdJ/CD7oySekP1REk/I/iiJN2SfbJ4BnPOM\\n0+0UFwtUAGCMbWqNy1ci6Qpkf5TEE7I/SuIJ2R8l8YTsj5J4Q/bJjiNDfCUSiUQikUgkEolEEhfI\\nBapEIpFIJBKJRCKRSOKCeFqg/rO7GyCRhCH7oySekP1REk/I/iiJJ2R/lMQbsk92kLjJQZVIJBKJ\\nRCKRSCQSydlNPHlQJRKJRCKRSCQSiURyFhMXC1TG2AzG2B7G2H7G2FPd3R5J74cx9i/GWBVjbFfY\\ntlTG2FLG2D7xv01sZ4yxv4v+uYMxVtR9LZf0Rhhj/RhjyxljuxljxYyxH4ntsk9KuhzGmIkxtoEx\\ntl30x9+K7XmMsfWi333IGDOI7Ubx937x/sDubL+kd8IY0zLGtjLGFom/ZX+UdAuMscOMsZ2MsW2M\\nsU1im3xedyLdvkBljGkBvATgCgDDAdzKGBveva2SnAXMAzDjlG1PAVjGOS8AsEz8DVDfLBD/7gfw\\nShe1UXL24AfwBOd8OIBJAB4W46Dsk5LuwANgGud8DICxAGYwxiYB+AuA5zjngwHYAdwj9r8HgF1s\\nf07sJ5F0Nj8CUBL2t+yPku7kYs752LByMvJ53Yl0+wIVwEQA+znnBznnXgAfALi2m9sk6eVwzr8F\\nUHvK5msBvClevwngurDtb3FiHYAUxlhO17RUcjbAOT/OOd8iXjeCJmG5kH1S0g2IfuUQf+rFPw5g\\nGoAFYvup/VHppwsAXMIYY13UXMlZAGOsL4CrALwh/maQ/VESX8jndScSDwvUXABHw/4uF9skkq4m\\ni3N+XLw+ASBLvJZ9VNJliHC0cQDWQ/ZJSTchwim3AagCsBTAAQB1nHO/2CW8z6n9UbxfDyCta1ss\\n6eU8D+BJAEHxdxpkf5R0HxzAEsbYZsbY/WKbfF53IrruboBEEo9wzjljTEpcS7oUxpgVwH8APMY5\\nbwg3+ss+KelKOOcBAGMZYykAPgEwtJubJDlLYYzNBFDFOd/MGJva3e2RSABcwDmvYIxlAljKGCsN\\nf1M+rztOPHhQKwD0C/u7r9gmkXQ1lUrYhfi/SmyXfVRyxmGM6UGL03c55x+LzbJPSroVznkdgOUA\\nzgOFpimG7fA+p/ZH8X4ygJoubqqk9zIZwDWMscOgNLBpAF6A7I+SboJzXiH+rwIZ8CZCPq87lXhY\\noG4EUCDU2AwAbgGwsJvb5LDUsAAAAa5JREFUJDk7WQjgTvH6TgCfhm3/vlBimwSgPiyMQyLpMCI/\\nai6AEs75s2FvyT4p6XIYYxnCcwrGWAKAy0B50csB3CR2O7U/Kv30JgDfcFlkXdJJcM7/l3Pel3M+\\nEDRH/IZzPhuyP0q6AcaYhTGWqLwGcDmAXZDP606FxcM9yxi7EpRfoAXwL875H7q5SZJeDmPsfQBT\\nAaQDqATwawD/BTAfQH8ARwDM4pzXisXDP0Cqvy4Ad3PON3VHuyW9E8bYBQBWAdiJUI7Vz0B5qLJP\\nSroUxthokMiHFmTIns85/x1jLB/kwUoFsBXA7ZxzD2PMBOBtUO50LYBbOOcHu6f1kt6MCPH9Ced8\\npuyPku5A9LtPxJ86AO9xzv/AGEuDfF53GnGxQJVIJBKJRCKRSCQSiSQeQnwlEolEIpFIJBKJRCKR\\nC1SJRCKRSCQSiUQikcQHcoEqkUgkEolEIpFIJJK4QC5QJRKJRCKRSCQSiUQSF8gFqkQikUgkEolE\\nIpFI4gK5QJVIJBKJRCKRSCQSSVwgF6gSiUQikUgkEolEIokL5AJVIpFIJBKJRCKRSCRxwf8DFhm+\\nemP5msEAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3f3e296d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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qL1/aUI5w0/hSindUeILGS5UFZfTexoeFzW285U6jw41IEk/zvbbHuo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\\ncpTAOoyh2f1DpqbuFLZc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JlqlSnE78A2WxVnYW7xhR3XQHx3k1/M8TSJ7hTf0la436ccCNy6E4ZZ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\\njtg+ZFhGHsRFLqwfXPPRAK+30eWQufVJB5qmX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fnuet0l1+ujM/X6/6G7HnULabDJT+4u8xGbB4fcQnytK8kKtXzYntAg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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3f3d182e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"\\n\",\n    \"# These are the names of the layers, so can have them as part of our plot\\n\",\n    \"layer_names = []\\n\",\n    \"for layer in model.layers[:8]:\\n\",\n    \"    layer_names.append(layer.name)\\n\",\n    \"\\n\",\n    \"images_per_row = 16\\n\",\n    \"\\n\",\n    \"# Now let's display our feature maps\\n\",\n    \"for layer_name, layer_activation in zip(layer_names, activations):\\n\",\n    \"    # This is the number of features in the feature map\\n\",\n    \"    n_features = layer_activation.shape[-1]\\n\",\n    \"\\n\",\n    \"    # The feature map has shape (1, size, size, n_features)\\n\",\n    \"    size = layer_activation.shape[1]\\n\",\n    \"\\n\",\n    \"    # We will tile the activation channels in this matrix\\n\",\n    \"    n_cols = n_features // images_per_row\\n\",\n    \"    display_grid = np.zeros((size * n_cols, images_per_row * size))\\n\",\n    \"\\n\",\n    \"    # We'll tile each filter into this big horizontal grid\\n\",\n    \"    for col in range(n_cols):\\n\",\n    \"        for row in range(images_per_row):\\n\",\n    \"            channel_image = layer_activation[0,\\n\",\n    \"                                             :, :,\\n\",\n    \"                                             col * images_per_row + row]\\n\",\n    \"            # Post-process the feature to make it visually palatable\\n\",\n    \"            channel_image -= channel_image.mean()\\n\",\n    \"            channel_image /= channel_image.std()\\n\",\n    \"            channel_image *= 64\\n\",\n    \"            channel_image += 128\\n\",\n    \"            channel_image = np.clip(channel_image, 0, 255).astype('uint8')\\n\",\n    \"            display_grid[col * size : (col + 1) * size,\\n\",\n    \"                         row * size : (row + 1) * size] = channel_image\\n\",\n    \"\\n\",\n    \"    # Display the grid\\n\",\n    \"    scale = 1. / size\\n\",\n    \"    plt.figure(figsize=(scale * display_grid.shape[1],\\n\",\n    \"                        scale * display_grid.shape[0]))\\n\",\n    \"    plt.title(layer_name)\\n\",\n    \"    plt.grid(False)\\n\",\n    \"    plt.imshow(display_grid, aspect='auto', cmap='viridis')\\n\",\n    \"    \\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A few remarkable things to note here:\\n\",\n    \"\\n\",\n    \"* The first layer acts as a collection of various edge detectors. At that stage, the activations are still retaining almost all of the \\n\",\n    \"information present in the initial picture.\\n\",\n    \"* As we go higher-up, the activations become increasingly abstract and less visually interpretable. They start encoding higher-level \\n\",\n    \"concepts such as \\\"cat ear\\\" or \\\"cat eye\\\". Higher-up presentations carry increasingly less information about the visual contents of the \\n\",\n    \"image, and increasingly more information related to the class of the image.\\n\",\n    \"* The sparsity of the activations is increasing with the depth of the layer: in the first layer, all filters are activated by the input \\n\",\n    \"image, but in the following layers more and more filters are blank. This means that the pattern encoded by the filter isn't found in the \\n\",\n    \"input image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"We have just evidenced a very important universal characteristic of the representations learned by deep neural networks: the features \\n\",\n    \"extracted by a layer get increasingly abstract with the depth of the layer. The activations of layers higher-up carry less and less \\n\",\n    \"information about the specific input being seen, and more and more information about the target (in our case, the class of the image: cat \\n\",\n    \"or dog). A deep neural network effectively acts as an __information distillation pipeline__, with raw data going in (in our case, RBG \\n\",\n    \"pictures), and getting repeatedly transformed so that irrelevant information gets filtered out (e.g. the specific visual appearance of the \\n\",\n    \"image) while useful information get magnified and refined (e.g. the class of the image).\\n\",\n    \"\\n\",\n    \"This is analogous to the way humans and animals perceive the world: after observing a scene for a few seconds, a human can remember which \\n\",\n    \"abstract objects were present in it (e.g. bicycle, tree) but could not remember the specific appearance of these objects. In fact, if you \\n\",\n    \"tried to draw a generic bicycle from mind right now, chances are you could not get it even remotely right, even though you have seen \\n\",\n    \"thousands of bicycles in your lifetime. Try it right now: this effect is absolutely real. You brain has learned to completely abstract its \\n\",\n    \"visual input, to transform it into high-level visual concepts while completely filtering out irrelevant visual details, making it \\n\",\n    \"tremendously difficult to remember how things around us actually look.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Visualizing convnet filters\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Another easy thing to do to inspect the filters learned by convnets is to display the visual pattern that each filter is meant to respond \\n\",\n    \"to. This can be done with __gradient ascent in input space__: applying __gradient descent__ to the value of the input image of a convnet so \\n\",\n    \"as to maximize the response of a specific filter, starting from a blank input image. The resulting input image would be one that the chosen \\n\",\n    \"filter is maximally responsive to.\\n\",\n    \"\\n\",\n    \"The process is simple: we will build a loss function that maximizes the value of a given filter in a given convolution layer, then we \\n\",\n    \"will use stochastic gradient descent to adjust the values of the input image so as to maximize this activation value. For instance, here's \\n\",\n    \"a loss for the activation of filter 0 in the layer \\\"block3_conv1\\\" of the VGG16 network, pre-trained on ImageNet:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.applications import VGG16\\n\",\n    \"from keras import backend as K\\n\",\n    \"\\n\",\n    \"model = VGG16(weights='imagenet',\\n\",\n    \"              include_top=False)\\n\",\n    \"\\n\",\n    \"layer_name = 'block3_conv1'\\n\",\n    \"filter_index = 0\\n\",\n    \"\\n\",\n    \"layer_output = model.get_layer(layer_name).output\\n\",\n    \"loss = K.mean(layer_output[:, :, :, filter_index])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To implement gradient descent, we will need the gradient of this loss with respect to the model's input. To do this, we will use the \\n\",\n    \"`gradients` function packaged with the `backend` module of Keras:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# The call to `gradients` returns a list of tensors (of size 1 in this case)\\n\",\n    \"# hence we only keep the first element -- which is a tensor.\\n\",\n    \"grads = K.gradients(loss, model.input)[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A non-obvious trick to use for the gradient descent process to go smoothly is to normalize the gradient tensor, by dividing it by its L2 \\n\",\n    \"norm (the square root of the average of the square of the values in the tensor). This ensures that the magnitude of the updates done to the \\n\",\n    \"input image is always within a same range.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# We add 1e-5 before dividing so as to avoid accidentally dividing by 0.\\n\",\n    \"grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we need a way to compute the value of the loss tensor and the gradient tensor, given an input image. We can define a Keras backend \\n\",\n    \"function to do this: `iterate` is a function that takes a Numpy tensor (as a list of tensors of size 1) and returns a list of two Numpy \\n\",\n    \"tensors: the loss value and the gradient value.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"iterate = K.function([model.input], [loss, grads])\\n\",\n    \"\\n\",\n    \"# Let's test it:\\n\",\n    \"import numpy as np\\n\",\n    \"loss_value, grads_value = iterate([np.zeros((1, 150, 150, 3))])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"At this point we can define a Python loop to do stochastic gradient descent:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# We start from a gray image with some noise\\n\",\n    \"input_img_data = np.random.random((1, 150, 150, 3)) * 20 + 128.\\n\",\n    \"\\n\",\n    \"# Run gradient ascent for 40 steps\\n\",\n    \"step = 1.  # this is the magnitude of each gradient update\\n\",\n    \"for i in range(40):\\n\",\n    \"    # Compute the loss value and gradient value\\n\",\n    \"    loss_value, grads_value = iterate([input_img_data])\\n\",\n    \"    # Here we adjust the input image in the direction that maximizes the loss\\n\",\n    \"    input_img_data += grads_value * step\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The resulting image tensor will be a floating point tensor of shape `(1, 150, 150, 3)`, with values that may not be integer within `[0, \\n\",\n    \"255]`. Hence we would need to post-process this tensor to turn it into a displayable image. We do it with the following straightforward \\n\",\n    \"utility function:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def deprocess_image(x):\\n\",\n    \"    # normalize tensor: center on 0., ensure std is 0.1\\n\",\n    \"    x -= x.mean()\\n\",\n    \"    x /= (x.std() + 1e-5)\\n\",\n    \"    x *= 0.1\\n\",\n    \"\\n\",\n    \"    # clip to [0, 1]\\n\",\n    \"    x += 0.5\\n\",\n    \"    x = np.clip(x, 0, 1)\\n\",\n    \"\\n\",\n    \"    # convert to RGB array\\n\",\n    \"    x *= 255\\n\",\n    \"    x = np.clip(x, 0, 255).astype('uint8')\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we have all the pieces, let's put them together into a Python function that takes as input a layer name and a filter index, and that \\n\",\n    \"returns a valid image tensor representing the pattern that maximizes the activation the specified filter:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def generate_pattern(layer_name, filter_index, size=150):\\n\",\n    \"    # Build a loss function that maximizes the activation\\n\",\n    \"    # of the nth filter of the layer considered.\\n\",\n    \"    layer_output = model.get_layer(layer_name).output\\n\",\n    \"    loss = K.mean(layer_output[:, :, :, filter_index])\\n\",\n    \"\\n\",\n    \"    # Compute the gradient of the input picture wrt this loss\\n\",\n    \"    grads = K.gradients(loss, model.input)[0]\\n\",\n    \"\\n\",\n    \"    # Normalization trick: we normalize the gradient\\n\",\n    \"    grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)\\n\",\n    \"\\n\",\n    \"    # This function returns the loss and grads given the input picture\\n\",\n    \"    iterate = K.function([model.input], [loss, grads])\\n\",\n    \"    \\n\",\n    \"    # We start from a gray image with some noise\\n\",\n    \"    input_img_data = np.random.random((1, size, size, 3)) * 20 + 128.\\n\",\n    \"\\n\",\n    \"    # Run gradient ascent for 40 steps\\n\",\n    \"    step = 1.\\n\",\n    \"    for i in range(40):\\n\",\n    \"        loss_value, grads_value = iterate([input_img_data])\\n\",\n    \"        input_img_data += grads_value * step\\n\",\n    \"        \\n\",\n    \"    img = input_img_data[0]\\n\",\n    \"    return deprocess_image(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's try this:\"\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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5/5Lz/DHQUJfe1ffJ6jHQkvugIG6kr2QUnYrP0QLck6hFTWxJMBw0C8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\\nFyQx4ALGrvfyAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3f3987080>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.imshow(generate_pattern('block3_conv1', 0))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It seems that filter 0 in layer `block3_conv1` is responsive to a polka dot pattern.\\n\",\n    \"\\n\",\n    \"Now the fun part: we can start visualising every single filter in every layer. For simplicity, we will only look at the first 64 filters in \\n\",\n    \"each layer, and will only look at the first layer of each convolution block (block1_conv1, block2_conv1, block3_conv1, block4_conv1, \\n\",\n    \"block5_conv1). We will arrange the outputs on a 8x8 grid of 64x64 filter patterns, with some black margins between each filter pattern.\"\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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6j13OasVyw0daL6DHg9z2+pHP0VjcGkpedszhSHqG6qz/KO7nekK5/g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\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THXl5ZgY/Q0Ru98IZT+q9/8B+ecc+sbinO1NmuyM20gXe/0SGNoxqSK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\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pOTTLQux9CkeRA25EU6xEqIF9Yj4pBHWSDowoJPvRK8yOkA5AmojxyI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\\nv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h9nhLz2wMm07MLOUzMzG42y5u9/+LGZmc1fmjUzs4XFS2ZmVjiEN30P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\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39fvpMPRF2y47N5rYkbUHHHoCRGrKHUp2vmQ1qnCWKJ35JuULlZfp95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\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o2cwG0zmZItVVnv3KxnPtapkOf5lCjLXwj1kTxX/73X4LfiqnzUjU34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\\nCDUAAAAAJXYLLhmavS1PiwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3ea8f2208>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Bt/zW4i/+Vf+Y/MzKw+VH3PRurXnPV30tKeYcoJ3dWc4FBT49XxN9T+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\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ZVWWmmllVZaaS+JlYGa0korrbTSSiuttNJKK6200korrbSXxMpATWml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\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ngWvP4N+fONL8JNuaK90rQtG+/zHBD4jCj8UhO46vrw/03Y3zs83w1A\\nu0XGUyuzhm+8ojVokNdnRbgbezF47lAlHrAGt+uyoTOQgMso8sZrmve1M2wdFdBb39baObugZ8iL\\n1/UMOuLZIJdT3XzQ91EUHz3CFz1HtlJD/biNGpW3zlixXy0wtlH4SUcBnx7chNkR/HZZjfkQZctC\\nFn5TEIdNTsREynp/6Qr+rcUzDRyKSU5rTFGz6yXZB6KM26pHzSxE1IQlLGEJS1jCEpawhCUsYQlL\\nWMISlrD8/6o8F4iaiDmWiMZsOqto3/xVOFaaiuCVHUX/NlA36VXhcHmmTEbqiqKJVlAELhYHaVKG\\n4ZvzmKmWXodEvKqPFRlzW6Q6TvX7QRY0g4Lgtnuq78Xh66/fV0QwNtV9VlE0Gjb0g+ouSJsRkX94\\nTXKgA1a+AKv+OgzsVWWS54hKH+4rSlu6jF57X8Qg5aubqhfqTikY4tuf6v/ZkuJu7Qgok5QyGskV\\nRdsrj89sY14RvCSR/tw1ZUXmuyjZbKktEc50uhmuCYfA6Ax29Kc7ZmZ2967qlhzqd7s1OBJQxJkt\\nC0FiUWXuhl1FFROfL8Fpzb76PMq57HgcpA9s8+m8orZzF8lARGESb+i1Tr09eCQuXpctJUEtDI8U\\niY5NObcIH9IcmYdBTzZROZPNjUDqrN5U5H2wrwi6Vwh4M4j8o2DRTyuqe/wEBR5QD62mbCp/Sf1f\\nnpVtVHZlA6vHGqco9dp/W/3rwhY+AXmzsQyHDmdol+aVJpqWid4+kk1UOIPbHHPGGNb69Rk4CDhX\\nfnJXZ5b37qt/pzkyrtd0Bjo1UvsqO0INdFp6bRyq3uVAmeyFS6rvWPU4fSJbb9fV3866+jcaUb23\\nrxPlhj4/yDSdpwxAsuQA4TiowbkT5lmX89cJjVHpqtq8xvwbcLY/1tO87RZAdrTUlrqnsWuSnSpz\\n9nX1JSE97tXES+EmUFdaVZuGsL4Pj8nIcf0xmdJxWbYWyIOMPdBarvzL0su6zpUrQsOd7ct2PvhX\\nZdV3dzRWF24p0xF5EZUl5nJvSX5lwdX7E7hZIhAaRYfql32UIiYoC3h5zjl7GosL15Tx6FbJ0rXV\\nr+MpKA9WkyL8Rie7Go/Tj4Pz4uqHfpTMRpEs3izvF1DrIAO6ekOcN7OvCrE0cfX+0TP5ksGJMjfR\\nSIDROV/pc97fgZXfj2s8kouyi7Uramf+suZ2/Zns4wz1pzOyW1AQWacPwjGv3+/VyMxUdf1Og6wq\\ntuw9VX07GfXP6qbuM7chO2ofVa3bVbY/WwbRktF8a7kak52PdZ47isJZDFWLJtmhOKoecZTMslOU\\nyIJsOmQBfl516YF4HJJ57NO2NGPcA7kSoK8mKJFFe6CPxmpjF7hqGl4nN0fWHGWrM9QnjppaNzJz\\nzPcxqhYgVqYgiKKBfAV+2s3Lb497+t7uPlxooBLGJc2B8obGOMHan6jJ/523xL8lZMjv3lZ/Lhfl\\np258qrl/dyy1jTfbQt78clHjVUBtz8P/f7QoRMrl7a+pPT8lA35B/fCNec3pt38vv5z7Zpn2wEvH\\nulKvwYXzBuqBnr73yVu6fvbf9P4b64zfJc3JyZEyzJ/2hFZZuqJ+rla+p/ov6frvt5Q13fqy0L7R\\n+7K7D0414OXOH8zMLLKtfn71mezp7GUhab53Q3wC/wY8IbKi/vvuierzTiRrF/e1FiSfaEx+D8/c\\nUkZ9s/oXmv8f/fIfzcxswb5qZmb7gx0zM5vv/oWZmXUY+6+hpvdBUiiBxTe09vzxfY2Bmxeq5+23\\nZKOvb2rt/HhbXAvrQ9XnG78R2nP3m+/Y5yn5Oc25xLL86wTUwRTVJR/1pgx8cXlH87zbVL29rva7\\nnSpcMiZbK3gB0hFUBZwrrodaiQPynExzgvXN1zJhSeZyFwjPdIhNgsC2jL6f9jV3CjwG5HIgeVhy\\nxyAPo0PUXdJsnFGgdGdAXYBoqQ41N9IjEEUgRMcj2dKU9SYF0mbEOumD+vBAUsbhsgBYY8lRoCCn\\nfhuxTo5SqLDg46Z9jW8cvq4xyJrIQP3VgPvtEJWadlN2szUrf58GZTZNa/1dSuvzfBwVQPrX3PMr\\nUS6WWBTTm2Zm1oJDMQnqqT/LWgj/RaSIklUP1bSPZZNHT7VGzYD0WMmC5gWZslDS2KZARkfZb+7c\\nE8oLChybgog5HGlNXmJ/vvkNKT5OBsE+WLaTWdcYd6egsuBgqdyV7UALZYkkfFI5IbEdtjSdvurd\\nOdaafVZR387wbDafE7os4LKMgjR/Uh1Sb92v19T1F7bkXwaOxvoI5FEsUBOtq/9OQZYuzcuPBsjR\\nADnfBo1bGur+Ntb9ltNag1c31Y5WgL6AAzIBb1Q6CppuT/27372r78FFuTrz+VQG+6b6ZEHpxcBQ\\n+Geyi/Zox8zMPBBGS9vqYD+n8evW4CBCgdj7bO6AVgMN4yRlb96hfM1dVHIvxvT8sLSs9SLSrVgV\\nhMnma0K4bF5nnuTZi1Q56TIPvxnPyR++K87Vrev4RXgx0yDtOqhCxRpwX0U1psl51JJBCGawudaA\\n53q4YRz2exn2ICNk9DIT9UmtiIqzDz8myJZAcTbLs0jeUX2H0PV4WfVhJgGnLVw3FglUpvT5nd9J\\nBa7HqY6//V/+i363jCIwfJxRTo/48J3mxjzTtDyuN7Qp8+zPlRBRE5awhCUsYQlLWMISlrCEJSxh\\nCUtYwvKclOcCUTOd+jbtd82LKMpXnaBoMwZ94Ons2d6+soiTLpE3zo/7I0WlDt7R+fb4FcWfcpyL\\nnDxWNHZMJrhzSnT4TNePocrUPFKUNg3XRXtH9+3APXNhbtPMzBopRcYWZxUpzMEu/wQVk+1XFJFr\\nwV/SJ4OQUILYEgGT+ZLeT7/OOcwLul43rkj+K1fERfHeriKU668IdfH4rkKIcaLQuUVF/m69Jmby\\nyv5D+lX1iK/Ayj8qW6EcqFMosr3zgSL2nT31zZ23pXbhER09vq9IeH9ffRGcM6wTSp+PKGI94phy\\noqO2lOAEcDpwpsBGHiE7H41/Pl6JpXllSr04nDqgrXqmeiwWleEbdfV5ryZbqDbU9uWyPo+icd8i\\nczwF5bBHZNwjW7TEucootvX0kTKWg5rut/2SMrsuSg7jpMZkifP2LRBAR3eVuZg50Vj09mSzk5wi\\n3xdvbJqZWWlRUWkPBZzGqX7fi8M98Fj3bT6SbSSJfM8WlGHJwPLvVOF1gr49wv3qh8pgR7IKtW++\\noP5MQQ5R2dP3Kw813vNrijqnyVDPryn7GCgNfXRPNpng/KW3pTm0GFM7S/CTuL6ix0/viouhCU/J\\n/Irqv0U7WouoIDzZ0e8g+hgOzs9R0zlVGzxHv01yQHfAGffeBIb7FBwqedUts6o2xmv4hypj1Jet\\nTAPOFJRzRmcomxGxn3tB2ZPYAzgJPM7SP9WY1dpE7kF5JVBcG3IueCapMUuCLImThVu4qj7PzKse\\nlVNdr74LqqsnfxkF7ZWJK4NRWpUt5W8oO9R4gAIYaIZmA0RPS9dLk7FofCpb65YYo3llUGKu5nw3\\np/rF1mUDp6CuMjFSjUCZCqC52vApPcUv+yAmywuw4JNxTm1pbi4lQCAONG7pbfVD9UiZ8i6qfFFP\\n143Dq+GmPp+C3DgKYnKR7Bd8J+0EnEQtFCmeYQ9kA7um7yc4u90dqr5p0CRHPmeoycBsfEXoM8dH\\nEe9IPrRbAV0I7cc0ofabr/HpOyeWWJVtlC5r0Uj5+n/3j/JTJydaq1JxUGFkGgsbnAfPaC2pHKAK\\nBxdCl7o5KBEYWXoH7q9In+w02aJxQbbwmSoJHApxeC8iEyA2AWCFDFG7r+vPNdfaQQAAIABJREFU\\npzW/M/A8TZlDfUM1LqfPc5sa640bWk/aZDBP8LstOst3yUoV4YMjoxpHVcotoKKS1GsGBGYngWrG\\nOYt7ovq/uKb+H3bUP79YVn+3X9w0M7PV2K/MzOzb+8pIn7JneHhRCJVvPPilmZn9NKL/X3tF6KyH\\nDdDDL6kdf3lJ9Xz2a/VHbF1+PRtXvy2soBY4+bmZmRVBgxXn5HsesbfJb2o8PyXzamQZKylx1jx4\\noPp9EXWWX72o9pQvyp7+WUAY+7sbmvulfdnVbzKy0fUzZVc/BiV2c4d9wpL6oVwVd42T0O/+bVff\\ny7/x2AZHGtN0mbZVvmtmZvfz8g+1Y72/UL5IH2revxF9yczMhmn15Ud/VFusLBWoeO3rZmb2AUb4\\nvRn18S86+vzKFSFxfrIDB6HBAxTRGvwvIC7e+NfzIyXMzCJF+NdeEG9QL6O+nYKWdevyY8lNfe6R\\nF400ZIt79+S/napsa4U9xwro4CEcDc4EBM4IZUdMOQYHTX9OtgQ1oU3xTyP8ksfccUGulFDk6fdZ\\nN0BND2NaD/o+yo9tEOag+AKkjgdS1Y+pIg0Uc3zQwf6K1oFsVq89FOAMNAkAIIuO2CMGSWVU/4LH\\nkj7cjkUy0kOQlkZ9LAKawgs4cmRzCbhrRnDrDAfs/0H73XssFFg0ilrhq9pXz6JCM9lRfTOO9gUz\\nZdX7BNVI10CJ2yP7c6WPulA+BV9kXm3zYxqzPPvHRIo9A5yDsXlOAVTgUjkG9csatcczxT5qpm24\\nSRbZr84vgDB/UX6nA8egwaORDNSJGIwZUA/xqOZ5jf1rHFXYdJHTCT3dZx5+pPqebGbA2DZ4dild\\nVN/NMfYTVFbbzMHRY9BRVzTmKZMfys5qPdlGBXQcoCai8k/LL8o3dAKekprmnMED1+povThl3Yug\\nprR5SX4pFeMUwqHWeAPlEKhhnTXVnxHQv9YF9QxCvLipdqVBgWxuaY9VOwVFewLSyAfmcc6S5rlo\\nCNI9DUJqCJK9dwzvUk/9kwJ9EgV25oA+6XbY0yVAcadUjxRImjHPBU+faA/5ux9J0Wzq/J2ZmS0t\\nfcvMzArZkjldXctn3kzhAzoFkTbiOT1RVN2hFTKLqW/mVkGA19gngSTPcsKkAGKnhm22jnfMzCwH\\n393slhQSXZR1G335o2JK1+/hT6f0wUFdfX/7J1rEZi7Jppb+Vnw+SZDk3Zj6NE6ft+OqxxhlMwv8\\nU4Cgpp5R9kgBN2wVNO9gJL8wAweOM5H/OfHh/oqChIRnz4FbMulPAxf2Z0uIqAlLWMISlrCEJSxh\\nCUtYwhKWsIQlLGF5Tspzgagx1zG/FLdCSVHgCFHnYUcRqI0bisz1e0TQOAvsck4wUlMUcu9TRQnn\\nPJR4OEeZrShifnii6GIE1EL6mqKhw4YibcfvCv1QflVR0z5ZqcQC5+4vBKpMCh0mruo+yTKcFy7K\\nSDc567qH+gocEKtLut7kDIbtrKLTM2Vx7PSiRGWJ0LXiimjuPrhvZmZrRFnvfiwujPGmIpQDWPjP\\nDuAV+URZV0M9Ya+mDO6kcmxnnC/M1BV5r6EVv+QrGti4rbYtpkCUoBKUailCW+Ssfh7G7rmC+sqt\\nKwK+OKu2JDlnmPcDdJQ+j4OkqXOe+bxlGJxH5lzk3oGycJmY+nowjyJBHX4NB6Ub0FHFF5Uh9Dt6\\n/+wDZetaAUt6k/PYOfiGiCJXurDkY3MrV+D0QaGrCSqrAqGRM9D/3X3Y/Ou6jrut7I2Xla2kIopO\\nx9KKtvaaGsPjBvcz6sM58GSEKPWKztMvRpV5bUZlu62K7nNwIoUCP6robyKufl/d0jjlX9BcGO4r\\nQ7HzHkpBJztmZjYla5dJ6fs5snKDT2VDHz4DtQab/uJrOtvahwMiBj9SlPP1Vbh2dh+qX3IlzYHc\\nZdWjQ/S8fqZ2d0ApFOFzSk/OT2bkwzN0OiGr4MLltCC/kVqV7S4UNYYR0EB9VJcSnA8uwojfQnXk\\nrIKtcN66v6D380lF5NdWNTc2X1Objv6kLE8/qEdcNlXKk51BUWyQUJ84OWWxrm8LpXW3K/90BJLE\\nfyCbPwbJc3lb/nDxy2LL7/q3zcxsCPKvO5DtZMlmtVDPOyGzYZzPjrbgGiAD20/L1lpVtW+LzMKE\\nDEiWMb31umzw4VDfh1rmM6TLs4eqj9vUBeJ1Xa/WVHtn4FPJYtvrr5GVeiDfsPM26k/3ObeOckE6\\nrjnuweLfbYBma3++7FVyDu4Csk4RkE19+JvaO3rNHqt+DVAbbTK3Lup/CUh5AmUlz1X7Vufka7a/\\nJvSBj9LE8ZHa1SP7tRyHSwF+lafwfR1Xa7YEv5C7Jpv1e2TP8WdzqDPErghRkd1CHW5Wr95IfTk+\\nBoUFeslMgxUhW33Skh/IVUEZOWQSO1on8gFHDHwSAd/O3IS+a4GUHIIOCygZyKgmivp+knnexU8M\\nTOtKMa7rjB1U+qYYkyebHzP/+y7IGRAmUbhzDNWoCWqDo6ey/R345gL1jfTnA9TY90vicPltVXuK\\n5RNd97so63z0vpCnT9c0dlfSGtvdm8q2D/flY/Z6QsV97TJkDqDRLv9JNjbJaM/xoQldkp+X77i9\\nJBu7dZ/2PdD3731d1z35VPd9eUP1uD/3N2Zmtv9H2ah3S/219BBE1F/8Su9f1ngW4+rvv64JXfDL\\nE61HXyrLD//oQP37lQrotSVlN1crvzYzs8XCN8zM7NGy7OYQZaUfgI44XgG9wV7p8W+ztv99qS65\\nHwgZPX1dmc9mCzTmRPOmfEm236qghJUWv877mW+amVn7+r+bmdnPisqSf+FUfbE8fdvMzH47/M+6\\nXk9cAndelDLXxuabZmY2fB8EyDX5oQX8+oOk6mf/l52r+PBVlBdY0xz5lVFVY+iN1MdJVEO9bqCQ\\nxisZ6y57myzKLN1gTlTUrgxrcGwANyJ7iG5SNlIcghgFrTaNsu4ZaqegAqZtjUUGxGduRvUYMUdy\\nGfmMrrtjZmaTOCg71BOjU7UzwtwfgZ4rL7KHIT3cApHjtAIkkPolxr7chehqjKrTBBWoqA9KgL2P\\n01F7OknUU8hwJ0BDp/ieN1G9nBSqfaCi43D5nHjwLPL9CJxzzobmSm6cp39M9axoHDoN1bt8Uevt\\nha6uU+2fH8FZO5RfONwB/VOmrSBS/AQozWWtbaNj7YtLa9pLbF3TfrpbpC/PdJ3ijPbn6b7WvihI\\nyJNTjfGQZ4kM+8sE6P86qIYBnCgT9ijtsfZAqyCc50BaBp+foTQ7Dfbb+tjyoGFnQdDF4GiJgBo+\\nhQNlwpjVWFsfwVU4/4H8zswNtTOXVz8kcdgrKGcm4L2bm5NvKBTUP/GHoLpyIEzZg1wq6Xsx1K/i\\noJaXUfqtgHByQNhHQct6ICLbZ6AsmItRuH1OIdd0l/T+PPyk+XlxdLVWtU9uHZ+fW9HMrD8CReLB\\nO4WCZg+epUIMDh7mSqTF89oUxbMJPHh8PxFDlTHGOl2De3NR4zeTZk+a1N5rLYOKJKjycSZiaVCo\\nPfjjMqzZBnLEg8twBFrK5/uz7O09nnGevqf94ALImzJ7miFcUBduCAFeuoyyV4nnYU/PzVOQf5OJ\\n6s5hCpvjZEuwb60fa+x+8omUDtcPtT9e/4bGpslcC1CqQxB/gYpon2dar60xuF9DDaqATcOL+sIP\\ntO68dBaog8pmW6xjOdDHfU5DFJL4TfxPM+hjJ/kZx+qfKyGiJixhCUtYwhKWsIQlLGEJS1jCEpaw\\nhOU5Kc8FosbxPYv3GuZzLtCrKzrb4px7KarI3Ijzl0a0MzOj6K9P5H4+LQbvbAzVjoYibKUNRcoa\\nKBj0qjv6/YoieEPOhW7cEMfL6jWd63Q4M3dGZC2Nwo5Tgf9jXq+NiDIfrXXVtzXDedAo+u9Hiprl\\nriqauxBTRrpURJmDs9JdOC2cESzZE0WrV2cU0S8mNs3M7MqsInNLRWXrDkFhdD7h3GeVM7bQB/Ta\\nQkFEBxnbf1d9kvTUp3XONK6uqW4PHisivLwNOuBd9c3ZMZF6lGD2QI5MiaZO5vSah7X9s4h0QmPU\\ndxVdnM0pNuj4RGfPWSI9ff+4o8i5D0t7bEHRzH4TdERLfZGa0djO5eFASCkFcFznXLhpzOJlzmmD\\nNFkuoBTkKzrcIQuVz8N5wFndEefGxy3OtXNmNrKi3y8QsUZQxpLZNPWDV4P+2xmgDNPTOMwu63er\\nG8qcBmdNW3zeP9H/zQP93m/r/rsfa4w7ZDQuvbZpZmYJFIWKc4pSZ1HcedSE3yRO+7ZkDxtZRdYd\\njn0f3VF/n42VGdl6QfVafUFRah9Fhv2DHd2HqDRHsK27D0N7Gf6Uoj4fnyjqvHcGOgzepuKM6re4\\nKKRAJcFZ4nOUBBHyVU/3cOFrmCnIFsbLoASyKFzBPRI/01jvfixE2qOebCjexz3C1zO/IFu6uKG5\\nErus68dBAubm1Vf7ZI+q9wP0EepIac6mbqgeqTki8Xvq2yhZkYkDsqWhMfeAbJRTykQU1pUFWfCU\\nJWnXNMYHNWVxsg8gDBkpW+4XyfqgHNQBQZiIgNz7f9l7rydLsuvcb2Ue721501Xtp3u8A2YADMGB\\nIwHQX0qhUARfFHrV33QfFFLoigQR9IQjQLjxrqfNtK2uLnvq1PHeZOrh+yUU0sNlzdswIvfLqTom\\nc5u119651re/DyROuarvZVHNyEY1Bg42NewxB7ChNMmAD3+uzPjiHE4gMsDFBaGtShdQzQhkkkDb\\npclmRVB/iZfgGIhKQadbE9qgUIQD6DxotBmZVtB5bufz5Rs62HIJhNDxqf5vjcU5EDnl3HdG4zxO\\ngSrhfHsGxY0Y2b/gLPcM3xKoixwfCSHT3pVdTMbKIhaAnWTgxpl1ZC8jMtTZpaJtXFabR3TyKUtK\\nehk00OplqqQ+30f1IcmZ9h5ZdmeKDTNmULxYnznhwFXQNV0nUKzJx1G7GKEQAwrMAakzIQMbH8Oz\\nBqdCDk6aeUL+sdeVTXY4X96B22zoyu/VG8wh5vkUvpBukqw6anD5AIywr7k5j8BZwxo7zwaKMBrL\\nKVm44RSk3+94Jc5WPv2V5s7LG2rXp3P5itugAqZN8Q+lE7KZd1BT3PxAc3QY0djnWe+i9Ntvhltm\\nZjZ7TTbxJ+8y18s+1xeK7vszjdcP9+EY+AZ8KKe63nZbCJffss5d/2zHzMxuvqj1/dKe2tvaVP9u\\n7Ou+J3CR/facspxfpl9e8VVfO9ScfWn0gZmZDdhzTcfie4mdSElpdk6+8npddvrSRXH09I+VHU0+\\n1n3vFjX+SysVm+7/SH0GN9i4+x0zM3vjkfzHcUlrwcMjzfs1sva/fCSuvi8nxc/zmyYqRFn1VTqi\\ntv2wJQWsvyyobr+B++W1uubrB2UhjbNDoaQuzFXXyEeqzw9+T204c4lojgzHKO6gNhL1NAdaA73f\\neKz7Z/MgXnqy8SpIlIjBe5HX94Y92bCHv3E9kIVk9aGxsPwURTay5nGQLuOY2pMC8efArzQEPe3N\\n4R1Z0msMBMwgIv9Vqmpd2M/r+8kmm4G+6unDR+KhBhWgM+IVVWzcZo8C2jcLL5Yf0f0cHxQDPCSz\\nKbxSoATdpOo7AsGYJuPdBwk0BR0YH4PWToKaS6n/plH9buqr/9KgDpL07/mr8D2d0zh1o5pDLqiK\\nQUP77uY+aISp1sVqieuj1niW0mmCQJmpL338YDKr1+iirrmIstcTEJAn7CvTS/DNgfYddTQ2ayvy\\nM6UL8gPRE2yxJv/YOJbfq6OWVKHP+/BjxEe6b6Oh/V+vJiW08b72RrOMxnaa0HXcJCqw8DIZ/H+T\\nrq5fnunzxFXQFHCM4ZZtiA0XLsHN5Ws/6LusTwXZdhrkUBubi7H+VNqaO/UD2Z5/gs3MNIZl0/9e\\nVfXwYtrfR1zZVB0biFMvZ4G1nH1nNqn6dEdah8ox2sf+24OnMAZK+xR08GiPOQoCxiIg2ZdQIj5j\\niUXU7sCyxhOek0ACdRKoOQWqiqzvSfj5YvAYZuHGiU5RZGPv2EPtKh7TeG1dVft+Py/fupCXzc8i\\n+p7filoM9FQSlaUpqB4A2mYgqh0QzTlfbR9v6Nqf3pY//9H/8V/NzOxb3/6emZlde0nPrX5dYzYD\\nYZdOqA0tThfMHmos4ijhZuHT9FIgoJkzA1ffz11Wfb96XWtU+TkhJGNj/AnIy1FVY+gts4FFvTlA\\n/t19T/X+9Tta21/9n76uPoJD0q2iXucEz4Bwh8ExOICzMZoMOB1RqdvX/W99Ako3V7TpJEA6//dL\\niKgJS1jCEpawhCUsYQlLWMISlrCEJSxh+YKULwSixjezsef8ThGnkERh4pGiTSdNOGR2ldWKEqUu\\nFxU93b+vyFc8BydLU9/78C2dh0zlxP1wWiObh0rK4Y4iYLs7+n35vKKqwykqTUnQFhuKuCXXUHtq\\n6P/MeTgGOEO71FV2KZcnorak6HJrqut3yERUyDjX9hQ19yaonQx0/2IXBvAPyBY+1OtBR5mmB58o\\nOh+9pmh3B+b1BlnKQhYlDyKfg7H6azm5bJ/euGVmZisJ9dXOvjJ7mfGWmZmd3FGdtjlv2Jzqmsk+\\nSImiIth5X9dMb+o6baQIhidkuUEDdBdg9h8RKY9obN3E2fTjgzIv635reWUYVkEzBCir1k1lq49Q\\nHthEoWYMO/7BbSlhHZ4q4+jB4l5eVBQ1CiIoVdfvmiiAHT6Ad+Ky2tn4TBkLB+WWJhw+6XPK0lRB\\neQ1gIC/vcr4SfpBGXWPW34NjB86JJbI76yhiGNmb0Vi2H+dcaB0URHuurFuWrNGciHveJWvloL5F\\ndq0Bt8xxMKf6ykZGs7rv5rK+73FG9slH+nyvruxkLokayVzf6z3R3KqB4kiBOlh6Tf0wQ+0rf8J5\\neUf9mI4put04CjI+6uflp5WBiKLONfFUz2hDPuEsZQLwocg90iD0ImRF+qcaq1NQV7XHspnefdXl\\neE9tTvnqs+qLivxvbKCehkLDaEAm7xHZexdeCDJy+arm8eP3xZnQRZHrymtC0p2/JBWQCefV+ygp\\n9PEHcTKcXYTR8qAHUmRkpxEyj0EGd1OInlhLtt/dVWb0bl/1XH+OsQDN1DyQLQyQ21iuakzPoRg0\\nNdmkzxnhfgt+ElBjg5TmUONA7T+CWyV3QUjG9Wflb6NwZFVXdN1jFNMyv7NJslxR/HSVLFlJWbv6\\nCRxjQ835wanuPwaNF8P2LXp2HiMzs3wJ3if6L5XR9RcjmmPDlsajgd+Mcxbbcur/AoiqeEaZ1sFU\\n4924C6eQp/759I7sbEY75lEy46wj4wRnrtugSJh7y9tlM+bBqI4SFcoHbizgDlDdJz1U4g7Jlgd8\\nEkDaXFQ+io7aMI7p+/McNr6g+6QckHQ7mo/tYxQLQHZ4Y43ddKi+SYPUScA3VCCbNuyrrYetHf1u\\nEmTJNaYJOFrWxgF3AggUsu4T1O4C9Fm+isIYfqC2L1spgMZauSK/USrJbzZMtry7r6xYYq56RaMB\\np83ZStLXmn0TxM7yl6WKcbstW/9OXvf/2S/k7/oPNRe+9oe6/+Z7Gq9fvCy/d21H7UzQrm/dlnrJ\\nzxOo3ZVkM2/k1S//WtF69Y1tIRdvHArR8uSi5theXv38pRv4isGfmpnZmw+1R7idF1fZCwXxuvzt\\n++J1eekZ+Dre1zn7iCMbzrykLN8UlMmdF7W+vfCxxu9Z+PV+VZIdfCOlcWhfUz3v7Qgd82JS779r\\nn5iZ2ZVVtbfyyUt2BBL4vUUhW57F7y1cRmHmieZ3ZV2qTs2PyZb3d8zM7J17ypD2XhD3TOSu/McY\\nNZLLy/JbRwOtWcUnQht8+ILUoi7C3zGMf9XMzP791m/VhxPZ8ms/Ul/+33a2At2dtR32rSP56dVL\\nGtPWVH6ihz/HnVgPpMsM/zHvy5a6NfadU63xCXiEsmPNwX4BhCXIkRkIvDg8Bx7Zfm9C5jnDnMSP\\n90Ceu3CieaCCuyP2cqDbpgX5x2y6QDtAveVRdSKT3pho/UyBrIlGdF23DC7gFCQmyJ8oKOVElD0g\\nGfqkD5KePWWgYpiCnMyZyN+78Ew5INCntDOdBj2I0uQUZE6SdSZdAQU9hX/rmtandEbtyjm6TtKT\\n/XzK3q9+qPv6C5oT00AOxoU07Qzl0rL8UiQqPzXy5M9OdwSRPIAbcOiq74o5UJxdjVn3sfYmaWx7\\nBDKvMYI/o49qZ0vzvnUKJxX+aQTSJgUyw+DvKeXlj5YvqF4zuB8TBWyH/dsea27nzgH3Z45eCvg6\\n1fdHcfmvVEN9t/6qrru4pTncqMDLuSJb3eD9PnyhuRzwObjKEvAN9bsgQlAGSs3ggOSUQ3+m9sZW\\n1N5EFc5MkD/VZc2xsgc3Dyjk3hPZ8gnKPCtb2uvl4cTpOQFCVf3gx1Aw6+v9fhvOtbbW+Hvwzw3Z\\ng12A0+usJQk/kw9npg+6eBYgRkGuz3vYRVE+prIKijmlcR009P0o6rgDkPijPdmRD+dbZVnj/0pe\\n69CcOT1qcjpk4NkYNOgC0MaJr7704PNMA7sfB/MQBciFvJ57o8v6f50xKeKv8qjZTeD7CRSr4hMU\\neUEm99NqWzyvz7Pw8nh1jeEEpd0Z3DnLWa0DL/2l1jYr6Xm8CdrY8Xmen8ofNFG+zOZAQcHfd//e\\njpmZ7ewKAXqxJttYv6a+KiZ4vh5o3RqBbho6AfeY5lJsEaQe/mUIQilWUjszhbi5kZCjJixhCUtY\\nwhKWsIQlLGEJS1jCEpawhOU/VfliIGpmnnmtgZkHmz2Zx0lPr3207ZOcJeuS1VtGGWOvp0jbeRjO\\ny0T+Lq3DBVOGpRo99Kmzpd9fIIOcgD/lGf0uGtX/ac5l5siAzLucHS6g5JMgkjZStLcMQ/jOLRjN\\n07r+/I6imSeHnDucKfJWP1CU2h0o4pZNKULZuqOI4odEryFqt8wGqjM1RZsdODkm9xXVtTxRa85p\\nttqKcp+iyJHaXDa3pj5ce1kR38MTXXM7oUzdsKC2R8hSFScgLbKKzK4mhS4YjDh33IQZHJTCYIIS\\nwjlFbJMFZRSieX0/y1nRUeRzctRkUVAg2+60yCx8rAznXlN9mS8p+7GcVb3HqICcmiL9xtiduyaO\\nh6U18Rqd3FUW7nEDtAAZ6hHZ9UlL/TGGn2gIYiYO8/jqNln5DCiv4NxiRNdrZlBxqjMWZMXWtuBu\\nWVa/9smgH95Xfd2c7rdQ1ecxRzZUXld/VDz9vkDGxUAuRaLq388+gJMnouvGUFQLfr98UeOTzctW\\nD26rH2Mx/e7pF4X+SKfhWSHCv/uextvNyy6Wv6ZsZ4bz+XceiOMhUGMpX1D/+D3Vv9/eo39QhYEv\\nZNZWvU9aspcefB9nKdkkZ/JB0gxQT2u3YLwnou1ycNoP1HrwK4vLeqOypmzQ1WvKGg1Lul6X7FRj\\nT7YST1I3oDyRvL63UCED8YIyd6sks66/rvPAIzhfju8pG+0llTlYO685U4wpct+OKytuDUXgJwnd\\np12TbWRHej+fk62fgLzxyboMycbNL4J6uKSxvhLX3PdQ+FpJwPnSRAXksbLin30k9F0MDpnMBc4g\\ng6JbXVM9T19T/5bIXs3gQyojB5UoamybcCRMYqj3DTWH6vdRDBopA5KsKAPjDkHDDTSHyp6+FyCb\\nCsuy/V4/ODR9xkJ751myjsyZeFxz5ChYf8iUz8ikptPyPU1QbinOwRfpd2edLCHqIDYGMbCtuVtc\\nQEUQpTz/QHNjvy2EU+sYZYzbNbsBB1U0orp4ZJ3dIn0w0Gu3g4oefqMC50wB5YQIHDY+/A4R0KrR\\nOUoxZNAKKRA3jr6fJwProh4yfwgHzDFr78vym/kMilh7Gsuep89LRfXZSYtsOdw4E9aNxkhzaXUo\\nmz4CWTdpaP1ZvEwWG/RYDNvP5pm7JK8SZBKzGfmzQVu2u7iiPo6mQRZhO2ctFThgBmnG8gOhLy6+\\nquv89EP5gNU31d+DH2psTwPEUUc+4NK+2lF5rM/9otAdd3tSIFp++odmZtbraE591HnDzMy6I9na\\nO8tqaAFFpP1PNZdfjep6N14VcujFu/LH703lx41z9D8uqV9/b1XImrsmlN/Fb6ldrfdlZwcPdP9H\\nz/+zmZn1H8t+3rumcV1d0HgP/kH1eFBB5SqubOObF3Sf+5/K90zl6uyT+7puenbDpk+LQ6bwT6rz\\n8YZ4MW4eap/25bT87T/cld95Gtm5Lv5iffXnZmZ2+d6bZmZ29JTqUH+isbbub8zM7PHbavvKH71j\\nZmY7d3Xd/LpsYyWu6zu+lL2aV+THP3KEojprGYKUfHBPa1l6W2NiGxp7b0l7jMa+7tc5RiWpAt/H\\nEM6THoibufx1gD4uxuBzIwObbWgODFExmsE3EgiaeS4KNzBdTFiX+i2976OkGIWPokNmPDVnrnta\\nr5qoHzkl3ScH8m8CZ9skQKzU9PkIjq7sivYgyaTWmVEWbscxSBfmxhD1FN+Fa7IDNwx+NpaBi8eH\\nEyMRcPlo/XDxZQn2csM+XI9x+B4GoAlSakgGjrgCipNx8tNJEDVDkKV16nG/qftE1sTXNL+k/voE\\nVdnl6dn3JIecEsgzpr05PJR1Xlmrp1HtY2fLjDGgpFFfzxyJRLBPwh/vyVYfzTTv9g/V1ns3xO82\\nQuVnngARtyVOrXwUhA/cLck1raH5DIhpXjOe7terad53n2hf2LypPkgM1afLlzS/Bzn2az21495d\\nUHILun4UdFY0q/ViVJENxIy1E9XZaEnXOV8BmQ03Zf0G+1YUdMzT2NXuqB8ceOuuLrPeYUPL22qP\\nD+o2z1gfZ0E07QqJVB/Jf2Yqae4DmgOumXhO/rh1KH8YTWmdGsHxVV6Ek+uWFHo788+nahuoN026\\nrM8tjY+XBNWVZb0ew4+Cf6/5qkeiru/P4UXJ8EzpDDK88ux5pH7w1/V+tajx3auDNmw1fvf9yLLa\\nXI5rjJumPmtOQTTPQICDhAsAzqNT2coCnIjf+r5U+BbhapnyQNuBSjEsFj44AAAgAElEQVRbYO8f\\n0QWi2PgKCsLlRfhUXXiedkDdHnN6I8v+7Jw84RIV6YB4ef9drdVpuKvW3hBiM0AdzUDEROGY+dIz\\nT/O52ncVtG7qVL/PJ1X/QYBK9mkXfiaY22NU6noN1bNEfOKZC7qe72UtEj1bCCZE1IQlLGEJS1jC\\nEpawhCUsYQlLWMISlrB8QcoXAlHjOK5FnZQlYX2OTcnqZRXtG8Ncvp1ThuJRk2wj6i0LW3C/rHI2\\nmEzt4guKBE5znCecKCIGmMEGMUU9Z4ucuQsSM6AhZr4+dzlb13ms65zElJnx9xU529lRRmVjXRmJ\\n+seK0qZWFSmMwW5f4rx/FFWnHtFiF8SLO+TcfUnfG3fgGVnT/asFsnJpNaDM//2Rorop0BfZJb1f\\nHqDz3uG1UDKvoKhoDLbwLNl/84mSVnXvVA90TlJtaDMmJ0fqm9oDuEtQD+knlDlwOe89Heg+XlwR\\n/16P6CNt82BfP2vJE0kPzifv9QnHlhVrvBRXVi4dh9PgkWziyZEyAKUFvZ+DjyIFX0cypsjzJApC\\niLOpCEnYVlXRzwpn9Id92ZB3ovvGtpVtmQ4VyT7cV/b/5FhRZT+p9vrwnqw/r+sMBor0J4mo7n2m\\n3x3uqr5+Rv195XWhFnJLuk8sC9O6B7s+Ge/BLmosLV3v9L5ssMFZ3HhZ432Rc5uup+vPUcTwoqBO\\nQIUtP6X7+l31S5/zp6fvKWNz/Ag0QQW1rQX9/pO775qZWXOg8Xn+WZ0XrcL/dPSeMg65JsztKBl5\\nMLo3HypLGpno80gS+zxDcVCa6bc1Rtmo2uaQrZjC8+HN1BfLqPbkVjXP23PVZYRiwuOUxmrDQPI5\\nml8TVDtyfO7DMdJsoCIFmqvygvxRe1dz4fhkR6895pqBjogrEt8cqU8vo+iziWJKQMEy7cqvtR9p\\nTN1N1WscQV2uItsommwhAo9HkXPnxXMBZ49+t88c3mmrXftvCdkRZM8G+7pOak1ImfSlLTMzK+Aj\\nsgWUf5pq50lPcydfRwUAPzWF+8UF8dQ/1f2cDtm1CAihku5TimtcPkuK52IE79XSlvp1YU3+rrAE\\nSqQNmuyMZc7Z5AH8VUf4ktZIfnaOisn4VO1poWy0fF7rzZKvfhiNND7jueb2yUTXGdbUficmnxO/\\nCsrByBCjkDY40fcTUd13aUP92W5O7BSOgDj+MrOCvymyFg045/2QMQI5U3Kr3IssEXJJbpCtJps9\\nAa00hTdtvy8brnTg74miwgHHTW1Xtrfkqk3rZMvGA73/pCfbX35ZY5evKhs9fKj3u8cac78rWy2v\\nyg+XN6RoWB6qL+tcJ1dUe8ZDMskxkDWLGvs62fkRZ/rbU2VEO6hvrKxqLU5tyG+eHn6+DOepo355\\nlvscd1XfjUeqV0bUMbb49/r8w4wQhO3Gl8zM7P2K0CLe45fMzGw5sWNmZtdwZ++mhPb4PUdzcXKs\\nLN+F+/9oZmYnfyRIyvt/q4xx9Dootrra+cHX9f/i3+HzXtZcObyl6/3RRdnN38zlTzMXtFdpteCj\\nqoOk+oo2PW/d1+viz7UOvfYlXefkiebAjQcat/Ou+vFOXPWN39Mc3u/Kvj4+Jx/yex/L/t4/RnXx\\nGxlLf6g16cKa6tZakzrH5J76av9UGdRX4Br49VR+wQdx8tw59eUkKj+29VPZ8j3UhgavqK4Xv6Wx\\nv4vf/3pjy8zM/q0oP3xxU+qe0Zuqq7MsFMA3P9L9/3c7WxnW4N17rD45YDudvq7XfEW2V4/ouuOu\\n9o3pJMht9jJOGa4UF3RaQ36hicJLrqh2BXsrnz0ZdH/mM1cH2H4GhR9jD+eAtCmnQUPndf9JG3TA\\noepR9EFIgtgpdvFXqCd6Q0h5UGabpdW/J6eg+x6juFnWXF1Myy/P4Z+K4/+nEThoBppLPeZ2mj3S\\nBF/loGLlevQTe5OAz2MG30Yuqb2Hb0GGXPWEHssScz0vJNkbeiVQFyNQB6jaNLr/vz0wCPt2VRf6\\nZFfcSl5U43qWEp1Td+qSNTjEqppfpfXnVZek/FyXZ4I11vIIyoBeoNKH0lVrqDbPaMMinC61lPp4\\nzH4qwzoxdtTn0zzf6+p7JZCbvRkKW/vyw6UMPBqu5u9Tz7yu90GQz/m8UNE+dOuqvuegoLU3ky0c\\n7GpuTKaa+x34lKpZzYkEfe/DB9pEPSrJM0smrjFo+PJfLutHdlH3yx7r+w8e7piZWWoJviG4e4as\\n9fM2CHmeePOgsZwVuGdm8jHOUOPTRE3VAz2bLWjMIz3QV4/UbwE6t3pd++rqmvrDAS17Vm/ig2Dq\\n1OCnAp2RX9czcBo+1jonAwZM/t4R/HZ1+bLNNfnxSQ61vj7reV42343q/wXmShd7TOFrHOwmdS5t\\nMQfE2Yn2gwFiJFB3SsJh44Pa6sDl56P6NoerauO69j9JFM8mqDo5+K0ea9EsobY/hssw7nFSxFHb\\n0wnUm/B/E1SjSosL9Bl+gdfRkdpUQj0uFkdllP3a3Gd/BuehBzJ7YUNz7vvLsvkke68+KLVZDAUu\\nDoXEFtS3UNVa2+PZd0/385aw3ZmuW4YjrFY7/N1z4X9UQkRNWMISlrCEJSxhCUtYwhKWsIQlLGEJ\\nyxekfCEQNb7j2jiZMXdM5iClSF6WjGSU6N/GtatmZjZLKys/GxPVXFBUqnBJ0dGTx4o6rm4pGttD\\nxWmBDHu+ovennBdNEtU6Hul7Pc7iTWD0LlUVLR04iloOR6pnJqkI2WQkrolyQtHUBucKszC8D+Ci\\nedgBLeErAuiT1YxlOEfI6+KWoqKNQ7KeOXhdpopOz/O6fzuq7GPigoaxw1m7dBa2ffTm8/w+WXVs\\nFQb+SVbv5UpEQVFcKWISA87XLS1zZjWiaGMWdZ/pXO8X1jnzCtv5CM4SJ6IYYHtOdjlC9qRHasGG\\n9nmKa4pmTgdqW8FDIeCSMrdpopuPOLNaR8HHGaM2sqF6OnAmdG9ojOtZZRBrZIMSnDW9vKns0xAT\\n83q6/6Nbsq0kUeVUSf149BMhTU5GQilUOV+5eVXXCSLvJfiDGqir1FDO6U/0f3eoMdvclI1mZ4q+\\n9ndUv/0jfT9dVjsSFbL1Wc6gdjjbSvYps4hSWV7n9KecM+8dKGs3nuv7mZaivzPOq49i6mc3rf9n\\np7peDAWfK0SF/RiRerh7Om3Vb+VpZaCLOdnJ8ami5ruHum80QHmgkFa/o7nle2SIgoxSkuzgGYqL\\nUtQpiJApyjKjPDaA2kSWzOV8QWMRn4BaSKqOETKPrSa2Ae/QwoLq0puQCTjQ54kC57W3db+ly/IX\\nHlxUvz6RTQ72yWBGlU0rX5c/A8xm/QN4ibLq41wa/o6HysTu3d0xMzNnRe2JgKBLXJHfuVLZMjOz\\n9jFn8lELiTY1Jw9/rax/MqXf9WtwWIGcmXY4T1/V/Tee1tzKgSZLLcgGomS5oiiWLVRUn9qhJss8\\nKhs4rGk8ivBLZVGcOOlyxriJ/ytqzGctkFBbas+Fq0IWzdfgJFiFrd8ho8u5bHdyds4AM7MxNh75\\nHXJT7cmvwm0QJ7t5Q3PuCIWOXl32c66k33tkNz3SHWn4ZCLM8SkIGq+jfqgf63rDlubADCWibIoM\\nLpxos1jRImS45hn1TXwFpQHULTI9jV0UhKBLVmo0RfVhGmSPgkyljCzKnFhYEyLDRwklhrpfIod6\\nxliNcsnuL5n6KouCmodN+b5sukCWa7OqsW46oFInum6sDU9RVvWrpuSPChf0/XgCdY62XuuH4m64\\nt/sJfYQ/LGDTIAn7qF/5bd0vTj3rZOtcV/Xzm5/PRsqv6Ho3upqrl+Ky2caebP5WXa/jV1GaeEtz\\nd4jKRhs02StXhDA86n1X9fglNp+Vv7uXFRfO+QPxq/wTkJviT9T+V/5Yc3Htn2Tzt5//mpmZJYfi\\ntjn/6p+YmdmPb4t75pWM+vXGj9Ru99vyMT/+EB6nVTjlYr82M7OfxvS9i01xz1z6HsinT1S/SEzc\\nFt+Oyaf+43nNwb/Ern6yIO6e5j35gNJTQsksTIRSubQke7n0ydR+9hWN9bVbml/vk3TOnGq/c+W5\\nv1PdOsoim8kPf6+nNfXtHbXhDdBRve/r9ap9amZmsaH4cjoff2BmZtmC7rOL6tH5Df3+4x/93MzM\\nin3xAY3gU/q39Uf2eUpmVTYQuam2T0BWOiA+WqgINUBO5qMa23k0yDyz/oAAMXg/0szB5BCEIuvI\\nIEDnojxjMd1nyJ4hCzJlDlfEsAnXTVlzIn1OrwjIWB2UVLulel8ras/ikL/tj+RjssE62VR/+uwN\\np6xjAFCshrrdICIbz4AoKo5RuUNxx4WPzsNvx02fDxy1Jw6fi59gzsIr4vRA8ZXh4pmBEgEd7oH6\\njSbg/GFfH3XhWWS9jIDSG+MbhiM1IJFDOTNGBv+Svr/nyC66Huv4PNjD/seluCE/WxqBpEaKNT5X\\nH/TY1yLKZ64XoH9RWQVxkgdV0AMNVGTfNUFRLJbW/va5ixr83oZspIf6qp/X+7HlAPHIM4HPWtVR\\nPY7voSSLYlfalw0vL6gd61Uh6bxJgJRRe+qcLoik1Ocp1oPFlGygFVU9R/flR3pZ+ctqmX0eyPLT\\nAz3T9GOyvSWQkXP4jB7UtIdZg4Ms96z87yUQMlm4FMegzyYgOQMFzRHch/MSvHjr7EPZZ8YLame+\\nCDprBlq6qTlaAQHpZrXXeeemnkXjrHMrq/p9JFAIO2PpgzbZfSK/6ZW090kCkJ31UIEC2bpQ0Low\\nzKCI/Bb+togq5BLPusyRGetnbKi53ahp/Ad19acPV5yBRol1PPNmGtMjuF2yjvbyKfyqrWqflgBh\\nt8dJkgcPtF9de11o+kvP8hz8EI4ppk+M/fqYvq6WdP1xQjbTY/ofdOl7EINJEIHxdVSXQQAabnTm\\naK+TiOpGT31V8NdMHLU9lsJkD55R+IemrGnZiPZSmTR+q4VNH8NdtSobSFOP6KL+n/XgYMRfJWJs\\n7IcakyTKwhOUeQu9gkXcs9lJiKgJS1jCEpawhCUsYQlLWMISlrCEJSxh+YKULwSiJuJGrJDJWSKi\\nKOHSirKEnaiirnceKKN5dKrz1v2ZIldZVEZyMIm3sooiNzKKxlYvogSBQs+srhBdJM9B8RNlWtbP\\nk0lYI5JPJqd5W9ddvbZlZmbHB2ReR4oYVrKKTie6inb2H+j6jc8U2tu8RnR7X68Bc7vPGTcvjQpN\\nkXOYMzIORG3bCdAJnOneuKhM/XBP4eFogWg76IkImZG1K6pP74bqcdpS/y0kK1Z4RtHD3lzZ3SQR\\nvfTTKN+gTNVHyz6W0bUnqCGNyQL/LstNxLcHXwOULjYjE+COUOugrgmSQYMJMiBnLN1HivT34Ata\\nR0mltKAo7KOPhFaKoiJUXdPnKVjcJ3DkjDkfefyp6juPc0aWqO7aEqpRKILtP5QNnn6gDOOM6PEI\\nNZBmXf8POMc5IJq7uIJay0yvcc5JHx5Sn4F+13PIAhEJ33xFmZflnKLVvV3d//CdHXXEgmxh7Yoy\\npSsbqu/+gWyvSWB8CU6YTEYZCedU0eLbH0hxY+6BDjBlTNtJVKl8RZN9otvpTQ1o6RlUtJp6ncNB\\n8OSR6tcnnB2bq30u2cXdt5URP/F0/SRIme0XdN/RqfqlvUe/lXX9GDCTIaoIZynxGEi8HBxPfbUl\\n4OnIojyQQzkmRXa5NQP1c0vfT8xkQxl4fAYNje04BwoBBa1aQ3UvgBYz2N/LIGwmKPBUlpjfB/p+\\nF9b8JTJ7mbjma4dz54cH8lf9E7gPOH895Xz3GtmxTFnZ863n4dZxNVftbc5Rf6Ls1NGvpTpSrMK5\\n8zzZGHhMJryuPa2MdAElCHegyH8Tdn8P9RAvT3qKjLALKioWAXF0KFvwT2QDBRTGZpxvzxfJcMK/\\nEu1qbh/V1O7iqmxuqbxlZmZ7p8pa9Ti3XkQpIp5A9S6F2scZS5RMTo6s2upz4r7okilugoCMruh7\\nF1A5SWTU7xM4xaIFtW95GYW7i7Lp4Z7G+f5jcRpMeuqPOeoIi6+Il2CLdWUEcuvgDvepT60Ln1KC\\ng8+JhPq6ACKlifqDk5UtNWtkvTlHbjH8ATwYkYHGcEJuZtBRH5bHalMKG/RAXCammgvJdKCkJttj\\nCO3kWP4wC0dYJKnrNOAn6sxZ60C6OFldv4KNRlDyOXqktSmKX3dTen1Ul+0zRcxnDPooupwESjE9\\n1jGQk4bymt+XLXlkKqtnzFwF5Zeo0iWn8q+ZNojFF+B4efALMzO7a+JGu13WejN6TuOz9GshS36y\\nL0TNxboQLze+K382uqH6JjPyn8WLuk/0lsZt6Zu6rnP7X83M7M5fgP54++91Pdr7IXPyTU9zLPq8\\n5vriLVSdjvR/BOTOIkjN+/8sn/dNfNRP3qBdfy11qJurPzYzs/XRz8zMrDmXb1jqCj38N1/SnH3h\\nn8Wls/f76u+v/US+9YffIgubERph/UnTvjwAPXkO9bldIRQyf6i2/PIJnFeXtc/75hPZ7iOQ1F8D\\nmXgX/qD7P9G8fbGq3y1dlO19fEmZXWsr+7/2IgpkP5LtPDin653raW0q5uW3nttVFvusJU/GdQOF\\ny0BJLZ/An6Oolmrq/YUV9lpj9c1sDEdiF9WijGx1mfUr1obbq4W/gT8DKkObe9rDeFHdL4atdy3I\\n6Or+a0UQnsy5x014LeBPqdfkr+6kNA4XV+BcyMlW+uMApYtaEnuabFL/T0Bdp5P6/agJD0uD9RWF\\nnAywkVhf9RzAzZVErclFLS9AkQym8Gyw5wwy+TFf33fw+1PQKMkcGXMUk/wpnD1xFHV8fB8qLH4D\\nhA32WIHfpDTV3PcX5FsDdJj/PMj9XSBEZyhRFCizKCcmUOJy2eck0+rjDoqS9hl7gLuyYTeu+bS6\\nIZRZ4Qr7SgcVpSk8G5DgxHikKxRlE9OM9vU+XCybl/Ws48NpNubUQO9UY3UKL9EBaOFYS3PuGH6i\\nMuisJAtBCs6WeUN+ZDYBlYuNLmyqfssoLDa3xYM04lkne0FIvvOXhZB58FD3PdrbUT3zatfmV/V5\\n7YnG8Bi12otr2stUUS+d9HXdSkbvTwJVrUN9//C2kO+3fyYfs7auvU71vMaheo6TBSg0lpaw6ccg\\nDRfV3u2q/F7yS6jhzUElZ0B/eZ/v0bqQBAW9qPtmQU4ZfIgRYHBuVP+7eVBhn2iv8s7f/4OZmeU4\\nVVFZFYrEY6/jwcvqgVrzHc0JF+6dNCi/6An1t5nFOAWwguraDB4ftovm8yw0RZ3O5dTFg3tC6UTL\\noOOf+aa+XwQl3AG1OwKpAnKmxUTeucezFjydhac0RlOf/XQCmy+zzz3m2QLUU4znaeP+Uda4xk3N\\n41FH7+fKIHQuq17Vgu4zz2isyxX9X7+j5/33UXPONfRstv4aHGQDTrqAoM5wymHG+1H8cayP/2Yv\\nNhiNzPNCjpqwhCUsYQlLWMISlrCEJSxhCUtYwhKW/1TlC4Go8byZ9Xsn1q6BeoCpvBVk7VDoGY+I\\n7JMZiKBulJ4oSrhI9LYZUXQ1C8P5eK5oZPGqslaLviJqdz6FRToNkoUorRUUfnzcVFR7vqPI2ZM7\\nQm2MOwoB1j5QxnTYUCTtdKjsYOOJosqxbdWnM1eG4KklZVyjFVipfWWlUiXYoFOK4K2eU4bixl3O\\n9CUUbe+ZosND0A/Zc/p+tAT3w3uKjsdRi+pFYLsvwyfyVNyWXWV/Gg2YsAd6Pe2pbZG+oqY1zu/N\\nOS945CvSvbVERH6uNk440xhJqa5DOBPmZHLnSb1mlrb0u47qWuic/Zyv2f+bUS3l1FcJUFHzGdwo\\nfF5e0dgnn1a9R/tkKFqKpk4O1ZdtzsRGSgoPL60rY5iBF8knO9YCNeFx7nDrK2p/xlE9unVdz8HG\\nSqXzfA5Xww5j5tMfUfVbZVH9Vrqk6GtqhZA3LP5D0FGPyGy0asq8PrX+tL4P/0gbdakhCKgoykf5\\nS6pn/FT1e/SZ5tK4C8phQfV3UGSwA/XTiPOay1tqb2VVNpY1RcEfncKVQzZwAERqa1HXa6uatvex\\nslIzzmTnr4DY2toyM7McgeQB9ldYRkkor/bXQGPMUaM5SxmCdMiiTOPlOMPahlcJBASiUDaDTydd\\nUDY8WVIbRzdRW0rod0NQRnuc980uKKJ+bVtjN8ZmkmT8ju+rL+cgbyqc/Z/CadC/oSxV7SEcWTGN\\nXZ05F9tUO5JJ9cWlN+U3sjHddwCjv+vLNk/u6HctkC+Bf5qdqh4t1E1Wr8HVA+pp45wyrZvn4XuK\\ny2adue67/57mTKelflqCv2myzznwiGyrENdgutjO3V3db95QPftV+Eiiqt/2VfVHvy3beNyV0URa\\n8j31umxnsai57K7puhlX9YpkVf+Aw2seJxt5xhJkVntkvDtPQIXBjXB0rAxKjDPNC5fgzGixTnDe\\nfgrf1gnqTXnQLxPe76A20D+Gk4Fxy3GOvnBBmZ8ZnEKtXaHdJvGJJQNFmAkZPfyWDz/ScKK69Acg\\nPUr44QgKWD6qaSNUJOAwcFAWSx6rLpGsbC8FwtEh2+QjmdIm+5MjwxeFB2Nclr+KLGnuxDlHvntT\\nWacj1JI6KCEYaCSHrPnhsb4/hUctA/dNHA4am6jPMgvKpM7JAAeqe4HSjaFgNoLbITMD8hOBYwDe\\nIIdM51nLRvYb+uMd8Z3kvvqmmZl5S9oT3DvW/b4NeuxHL8l3/OGvVN8f52TDf3Be9fm7Z1T/L/9A\\nvE8Pv/4rXf+W5tTpS5p77kX2FDffMzOzPXhcXvuF2tN57ttmZpaOCWX2pYx8SQcumDv/jBLka/IJ\\no5Iy2C/DCXR8n3V/QePWINWa/OifzMys9ZWvmplZ+QPVM/qS6j16e8fMzJ77nt4v/kj2OP1T3e9C\\nV9d1/liIm2RddrfwM/nI9zNlO0RV6U++K3tvgITYe/t9MzPbeqzPK9/8lpmZzQpSg9q9pb65mdH8\\n/NPn1KedtO41+ZXu8QPTHubPtv/AzMw+rspG9t8SqugemdjvpXW9JLxqD34kGy+tq41nLZkRyMgK\\nKAYQKBVURGwqPzIvy0+7KLf0WYOducasgB/toDpSSmj/Z0n44dIB9w2IHPphNNbvx2PNtblL5hae\\nqswC/HV5FGJAsiSYK0tpre2REgiXIQhLsu4JuA7jrq4/7asdwyzqUy31bwYVpzFcZO484I4J0Hyo\\nPaF2OM6hGjMBdcdexoMXb4AfdkxzI8GcjuJ3A8VQF26KBHsYJxOQX4D2joLqZu8yB3XSm+j7ex8L\\nUbXR3zIzs/JXtc6usJfNptTeTmbHzMxWWSeT07OjfEctffd4ig0EXCFR1SkNArxSlt+ILKluySZo\\n0QBZjHrSsKP/MwEih2egIlK2DqiAMXuVNHxAh8eaj/dRWFyCG+a0JdufgibNrwZ8PdprdCZs1Fj7\\nBjldrwLawk+oT+YnKOnAs3dwT7afK+l7lZe111jhmcgLeO7GrEND2WSwLx4m5Xf2UaU6RZ2ovAl6\\nLCK/25/Jz0Xg9XPhCPNQ5MykNJeGY9Uvl2PdnGlc8iNdr9iHR+RU7etP9HkEBFQ82JvAqzcDJTau\\n6PvxAHUGt1w8+fkwEKmM6rsNb2G2yNw+YY8YVz1yxroLmiVH/1/Z1u9WV/R8F4+xD4gFPE8ah0Cp\\nMpVHxQqVrRoqVrG85kwulbUU/KJN3NkMJHgSG/bgr0uzR9jc1LPfG1//jpmZLVyGn3NEX8Ar58Tg\\nCYL7JpWQ7Z+wh/jn/1NoTp+TJH/59F+pbnA2xgLpLnhCvbHa1IrKZhLcJztWn7bxJ3tT3Xdra4Xr\\nyfb2DuFxqvIMBe9d8gj1JrjFMmXNreIF1Xfsq37NtvqhyOkIH3WqIc+o7lTXmxZU/70bQpnGk7nf\\n8Wr9RyVE1IQlLGEJS1jCEpawhCUsYQlLWMISlrB8QcoXAlFjc8ecTsxmSc7d+YGChSJul0BHxMnO\\nu6iJxDh75vYUba4fK9pZRhGnDq9Ktw0yh7Otu23Ovp5w/j+maGObTEhlpMhciuxigQOXbdjh/USg\\nVEGU01fU9hznLXt9RVmTS4rAjX55w8zMamucvburKHB2XZHJCGfp+nVY9rOKTI6HitRNs7r+YVOR\\nuNqhsm4DdOdzKPZMH6O6MlMkcImzem5ckT3veGQHPlnejpARUdoe68Nanlbkew3OgiLZ3ygZ29Ky\\nPm+O1SYPnoc0vD4VUD61w6Atij7Gg8wm0dT+5+QM6M841zgHdXWsth4R7Q2AIZVr8GvAyN3rYAMu\\nvD5w6BQX9b0Y2a0xyjeftT/SfWCtP+rAN/S02r29rXPsQ851997iPCZZ/mRS7Ty6q/dP78F1sKDM\\n79qzys4nc+KOyRTJWJIxjnNufH9PNpJY0PUulXQmtlxSP+9xXrJN/Wegqs49L9spc6b5uKksvaXU\\nnqfe1PsxV9nLvQc6s3t0U1Hl6gVFoRNP1FFd2zEzswhnkccTeFlQD7j2ivpxwnnyzruy0UmMuQrF\\neyqj8RiCHrgHe/8gUFAjQ9/1ZWc5znW2c6rPWUqXbMeciHvUA40T8HzkZdOplPq62db8WSypjnky\\nmAdwYT2Cz6gaKHBxfvj8U+J7GJChPG4re5TvK1vkpvFPrsY8guqU46pvOvd0v8FDZcXTsMjnnlOf\\n+mRmM89u6v2G2jM8lS0PTlB6IAPZOJa/HKMclr7CuetNjXV/VWMW4TzyKK76FEqywRFgrtqxbM45\\nVbsHTKphQ/+PHBRp4hnqr/dbZFw9zqdnySxM0urnhYLaE3ARpBYZ67nqG0claU7GOJ7jHD/ZwjX4\\nWcYd6vVYmY/9I6EbkjPOc5+1oNbUOZZ/dTzQHXBdLGys8kV9r9GFSyI4h96FCGqucUmBPjmJCt1m\\nMXhZyAh7ZGZJVFsd9NpHv94xM7O2gypUl+ylm7A5/mcKUg63Z3P8XQ9UkO/LL0bh2HJR23PTGoNU\\nElQSXFx5kA9JEJVuj+s4ykr5ZKFHfG9hC/WIqPqmQ18NyXTuos6/hrQAACAASURBVBbixFgb4Q+a\\n+bJpI2M8JxMYRw0p8EeZJfijUJAZofTiJxhTFxXChhreHen+adTkVtdRwYKHojakg/K6/ozMawYF\\nxrOWzJr8Uv9PZXOpD98xM7Pmu5pbX33+62Zmdi+p+1bva03Obqu+f/qx5tZfP1bWfrOq/4/i8rfD\\nn79uZmaTp9Q/pWPZWGRf47Dw9PfUjFX5pEf72juk6+qvflX9NYjKRxxM5Fteegb1lgYkER57lMwv\\nzcxs/4K+79fk4wabas/WrtAEA5BNpSsa3w9ZF9b+BERSR0ig2hvPmZlZ4gfaa6T+HE64z9T+r5yH\\nL8Yl8/xc376aBrmBEkv7quoQ/VSon73nNZ8fxnTvFVT6Xuj+1MzMYgn1We0j1o6cssiZlH73Bxtf\\nMjOzekp8DdePtVbvL6ht36xpLP4lrt/nJvp9Du6Xezntac5afHikzoEq7lZkC2V4h4ae1t5YCe4t\\n9nF+F8Q1yEVvBpqM9eYEDrV0VCiBDAhwc3X9IXM+loQLJlAxioAgwfb9JH4I7rMptlBlbnbzmivV\\nNdVrFqidwJUzzsk/uln5tVZTY12YaB1NppS9b/fhmAHV4dKeHjx1Ofiq5hlUntjL+SBfJnBbpAcg\\nY2L/X3WsKAqhgzF7xhQowpjmSmzE+z38LajiMiqDEa4zO1Y7Ww+FxPr4Xb06OOa1S7KXCqpSiT5q\\nhQX5ohnr03R8dn7FPhwpPvtoBAdt/khjMqnBDYgS7fCubCB2rL4rrZP951liMtYFoifqq0Zd834I\\nciVT1H47s66+mJbhYUPdaf+evn+6An8nvD4TkHtz/G8yBU8c68HctCZWFtWniwnth/u3ZRsJ0AmN\\nJ5rvx31QYqB7h1Eh/yyypf5YUH2nIGn2bnJaAYT29LzuH8UHtNso6c7UrhWemTyezVZBj3WO9P3O\\nnvzlQ047lEra7159Xr5mlXVojmJYPAsSBlTa7BTkDCpUHRCeRZDvTlf9sphBiYwn6QHj0xlofM9a\\nfPZQSyBrZj3dPwXqbj4BScUeyq/Lx2zmNJe//1/EAzMF1T2saT2ZRlSP1RLra1LPBwO46O6/o/b9\\n4if/YmZmz39Dfv21l69bj1MBI/g0a3AnnmM/7LPmezO4p7CxSy/rGlP8fZ39U4Tn6Sz8OD57lVhK\\nbU3P9ftEXMboNuAx6mk9iAf7QNZ0fw6SnbFPg2z3Wvr+BD6fGPGAykWhSt2i5tSDT7Ru9Bvq+3M8\\n2zpp/E0bhA0g35Wr8BFhcy4neQbIU3VAORcqqKRiy/O4/p+gmuVl9bzguHEz92xYmRBRE5awhCUs\\nYQlLWMISlrCEJSxhCUtYwvIFKV8IRI0TM4suu7YYQ/kApaHdvrJUvSGoh7EiXK2RIm5ZkC/DU0Xs\\njo4UmVtDtaQPZwOBLmuvKgra3VdEzoUL57StCNzDx4rqFlAM6tWVsUg+pUjehLO02xd0TrtORG7n\\nkV4zWdj/Pe7TEMdD6brquXV9y8zMbt1UZjyyjHJHWu0ZthSxPPgM9ak43DoZFIa6er0K181krob5\\ndUUqB6YIaL/GGT3O5fdQJGoPmrZ26SK/QdHqiiLXHmdAHaKS86ayKV2igXF4LfpHilR7SSLLZCtK\\nq7p3AvUP3+N8s4MSTI26EmGP+GfPSpiZpYi+9rrqk70HGqse2ZzCmmxnyPno6JGyLdOhbGLpsqKh\\nC4vKOLgT1aP+niLTn/1GWbZFOG7y26p3JaN2bm8p+xYla3R0osyocS46BtM3ICc7QM2oxUH8lTVY\\n+of6/aitermw1Af8JLkq7PNwtpQCKoaI3q8daFx2b+3o9qA3Nr7yqn6X4vwkiJyjjuZG+ZJsc41o\\n+OkTMjefqh9yGbW3WFHkPco5efeu6jmK0bCYxnflednJwkXNlTu/EafC6Vi2vfKq+jHlaC7OWopa\\n7z9Rvad9zeXSMnZzDiQQmaEh93F7yuKdpSxWVOd+EXZ13dIWQCFlsMXWKIAlwO00ki3NfM2bGFmj\\n5UVlYa6/rjmT2FZWKXlZfdS7iRqUpoQlyH4HrO4u12Fa27TL2dwUKnBF9eHKK1fMzGz9JSHy5qhU\\n5fP4N8aw9p6yUuMm545RUugNNZaJDY1tMaI+Tz6n1yWUA4YgXrp7+t9HVW9a1DLQeqL75FD8KlZk\\nM52fy4892Nfr+bjQAe2cvp8ty1Yzy2pHZs7Y6WMbHWisT0FJ5Se63ziBmskWCKCy+iNQAXh4U9m/\\n1l14L0B1+DHNoXKSTDDrxVlLLMqchdvmqKZ6FTmLPCVjGyiZNQLVQBQx5nDNlDl7XEddxG/BxRBF\\nWQFFpTSqCOOJxqu7p/5osN4MYuqHtqH0lElbIaWste+hTOKQbeeaANhsmladqxx1nqMsUHsk/+eg\\nNLWxoHmWAX0UA506o+6RodqSCjjFUMsbo/ZUYx7W8Vs+/BJBRjSCgkuCzG8e1bsoWSeXPndL8guZ\\nHAibeMAJoDkTy2CjAXoAW+9GsXFUr/IRXXdlU32bJTPVP/nUzMw6Q/nf4yl8UxkhSc5a7r6PktBL\\ncBSM1f4c6hz/+oHeX2VsHy/L7+0m5SvOf0X98ee/+r6ZmfX6Qmv864YymNW49hBfOn5b7WrKf6/N\\nhLLzO/rev0z0f+orUvGIci5//4Hut46S5O0L8kUndaH9LlY1V+d7KGN29Prghvz4n+V1vx+iVJdN\\nKqP60lvqt53zcD98ReO50td43v6Nso4jfOb559QPb3+m9fP6ednZ5h2N8+t/qHp1fvmi2Suynffv\\nC5WTPf4LMzPb2vyh7knSfTOijOzzcSFS/u0b4uV57kPZiPMt8bT5/01t8f5Ma8bR3+r3j74mtaer\\nvu63uCRH9LMHZIS78mtLb/6jmZmVH/4X3ad2Nr6AoCQS7EuTssUManiRovyqk4LrCk4ZYw54u6gz\\nsdeKtVDWhAeq6mhuDLuy+flAn3sxkB4sX46BSsBhpsm7zkFJu1n9vo7iY3SusY5m4cgCYeMV4TMB\\ndZdd1R7jNKGFzQcROEiw5xqonut59oCsN/Ghfu8MQNDAfzGDm8xAcsZRVRyN9JqNqD8CbpyxA/pa\\nv7I5yjUufC2RKag99kYR6j2BEy5Kpt1c7t9Te5s4zRN4BcfwePh5zVmHjPfxrt4/DxfPfHid9gTq\\nLhU7a9m+jpIiqNJuNEBwyxbv7aiPU6jeDUFOHIIiMDgdF0Fk5xZ0ncmp1pLJifxlva5nkGxJNpio\\na/6VV1GiBJ16NNfvDj/WvFy4ru/1T/G7Q7XRmakeqUX1SWwdNEASvh8QKf1V2VDUNIbxV7X/vABy\\nfQ6nmqGGOonrOhkfflFP9z16iNLlTP+f3NLcGa+o3fFFnlNASzu5ACWtyy8UdJ9qXnu0x8z9nftq\\n56wNSqKozzOXQGIO6K+CxtwFEdNty7/u32eP1JENXR7DC7oOjwnIzoUL8osVToEcP8SZnbHEeS6I\\n4isS8PEZc2fwRPVMpvT5OIKa15LuV7yUp51wh9XhWYJLyAEFAtjMJi4KlvALdvr4Vg9E/Ure5iB8\\nP31Lz+G/+cm/m5nZH/wvavP1Jfjj2H9Oe3AkdjVG3Qk8eY6uU+JURYdnHh+kXZRnxlhRM/6P/ko8\\naTFHfbyRkw13QfG24BfNjeSnYqhuTuC76zJn5q76IJfQs0WyCoci+65//MGPzMysjNrdhWeFqFuD\\no7bnwgMFH5BbxX+ietfDP//2X7SG1+Ft/Z//V61rKThppjXUPLGNhVyF+sbNiYaImrCEJSxhCUtY\\nwhKWsIQlLGEJS1jCEpb/VOULgajx53ObNzt2NA0UcRSxm8Nw3ukp6rx/F+Uazo4Vo3C8oJQTacAN\\nkSaKeEKUlkTJGufd2zvwYOQUicuSUU4mFPk6V1YEfdcU1f3shrJTH/9CKgRpVAN6Q0UAHc51F6uK\\nutY8fX7SFw9MsqKM8xAVKisr8pgvKOLXi6mdc1AGrYGiuamkUCA3f6LM8klT3796ZcvMzI7I9JZi\\nKAeRbfUGRDJh8HaaIHPyGUvtkSE9UF/nerpmHRWm6ByOEdRDquuq83GHUGxM9ywsqY0HN1BFSihq\\nugCiYw6vRYKzt1EQObMBTN1nk4//XUnlQA90ULMYK8aY5gxtBbUgFzWOOmon+QqZhXOKpEcr+t0R\\nZ2s7cVSQ4EGqcsY36WlM+/RL54nu++RYmc52U9n27Vev6boJjfHJLUXS0xd0vavwbZQKuu6MKPHR\\n2/relPPZ2W31Tw4Ogbnp/RNQUyOit5O+Ivwx+EN8lGmcqWzq6I5s1UNhoQgfy7U3hMKapdWu48fi\\nXPBWFE0OkFURMuy1j2X7gQLO+qqiy+5acBZY/dYZK5M6whavfVX9UYIfJeD+efRT3a9Tl407ZMky\\n1G9KRn2fs7eJid73hmfPcqbWle3IEMk/va8sePdIfbdzKP/RaGl+JZiXC6vyN4mibGz7FfVVHN6h\\nGcz5tYHGvrSjyHocdEI0o7548CG8SV2N7eamkCe5i5pLayBxMlllfDNk5z34O2oHqm+spb7xkvAo\\nwbvRhkvh5JYyAl5ONr+0LSTO2oZsJ7ksG8uTPfLJ7nf/TVn9AbxG8zpcN16QuyTLwhnfbEf/z+Oo\\nYcELFUHKp7Qp/7n+ovxlDFqR2o9V/5kvW/J6qB6BYMmn1Q+FbfXjAYpoo8fK3HzCefVDFIRS1OPl\\nV7b0P5mcKApBgzEH7M9YOF5uU5CPDbiKThPKJhbJGnpkUgLaEyMrmUjBxwQCa3bAWeWm6hFJBOOM\\nCgDnzFOIkkzJHE9A/02GmsMuyg6RVM6SeRAurGUeZ/VjI/XloEoGE0RhF36HWMC/9gDenS4cN8s4\\n3KL6zCHTWeqojV4cniOUwgZ8P+KTwSXztvyUxtx1WNOaen/EOe3+SG3usxZ7c9W3HoNvogH6lPPo\\nqRjnzUEp9VPwYzBGg7rmxGgKX1xRcyINdHFtDa6vqcasA+FSoYRC2RClm9nnW3BWT8SL8tqPQec9\\nLw6AjxxxmF3uCOnyUV/9E/f/0MzMphnZ0PpdjemTi1I0ijb1vWcfCRmzVv2ZmZndh+8jGpV/7F1Q\\nNrEDf11qR5933pHNvPqtvzMzs+O7qAo+2TIzs2/ek+2d/r78dYv1sXpT/dfKieMs/20Qqe8qO7r+\\n6YtmZlb40iu67tfF47L2kWy9dV9zfobdjDvyKc++qn5p3lB7nzsv37NNxnk+la/8+ae6b/X8qQ3h\\n2trKC6kYTf+1mZk9fqA6/IWnut+6Lz/dhLvqzbeE1vxBTHV0V2Wz0Zdk214Nx3OZvccn6iM3LvWo\\n26va00S/KxTRZZOS13s/VR9dAek3SH4+HqMZCmSzgWw4WwGNVoabJbZjZmaJPCpJqHG28CeFkfpq\\nmNFYVkCR+XAKRpuy8a6rsY/BfZhG9XScY88G58u4rM/zadSNUISLeA3+V39NZ7puuSIUQBb/m2B/\\nOoOnI5qDKzKi9bIz1J7JOYHnCPXSGMicvoMqHnu8IWpQMdRYihOtN31X/6dRthuhihpB3SmCYpET\\ngRtN3Wt+Ch4WEDVj0GwxUNNRJ+CIg6Mn4PcYqT/THfhPYtqbrD2rOVY6p/X+ZAJvy5H65WhV10nM\\nZPtuP0BdANU9Q+milDME9OmCCIkvqu5JuGTmAUoUbpa1keo4j6ovJ3A6FujbeEVjvgCf5dDRnGqB\\n2HFRtnr8ttbUXBHuE/ZGg772/SMQ1GvLQh07Ce0lBg1QsIx55DM4u3ZlU4OLPMt4+j+ale0OQNe6\\nKPkkQX0FSmgxR2NfWFRf52NbZmaWdTQWe3eEiNz/hV5PHe1nN168xH1BZ4FaTZ7Iv909RW6UueLD\\nj7d4XTbR4Hmn56k/pkWtYwXQaAn4T7wlfX/zwvNmZpbZhJNtV+gNb67+6o9A0d5mzwZKeFbgkdqD\\nI+yMZQKPYhbkThKk0AQVqe5cfjgVIGLxjVGeUwKEa509SGeufpr1Ax/AehuHPwoewfJ52dl3/upP\\nzMxsmf9Ho5nN6vhZuGiWy+qzCsg4F1RrP6r/I67mfwTFSWjjLM8phRjzprUjf5RiH+QiTlfKyh8+\\n9fSWmZmNQfQMG7LByT771IKu3wJ1X8Yf9uu6vsu+M+7o8y6qTpVVuGVA9V5aFwJ8ETXWNH10CodY\\nBg5CB384imssIswllz1Z/VFwKkK2MmPDOMmj3AaXZBS+JHdZtp6uT831g733f7+EiJqwhCUsYQlL\\nWMISlrCEJSxhCUtYwhKWL0j5QiBqPMexUcy1IWdQPaKlm1cVaoujEnLNVzanOVAUOkamu0X2bm9X\\n2Z00+uWtQ0U7Ec6wJooUDz7aMTOzK88rk15GeWdu0DuTac0YrPtErzdfUUamvKwsnt9X5ie/tmVm\\nZkOim2tL+v7RqSKHlQ2UKxxlE5082UM/YJdWRC6WhQsH9v6VC4qWH78LVw5n8xZmin7fRmli+YLq\\nOd6Dk6eu+8bOKTrahCNiYSNq3YHa3j9WRL2LCcRAB7Uauvf2s4oyjuqgck4ULRyZIr6LZV27e6rI\\nd5rI/ATES2NHEeBYRNHLDrwdkQ7ZpViQpj5bmcCg7Vc1RqsV1S/SVd/Mpqrnzj1l4UdwLsTITM9y\\nZCImnFuGn2jlZdlAxFE9Zyf6/v3fCj3ltYkac8b1dKTo7vWvyRaWr4m7prYLJwBIlypR2ymR70Ed\\nPqRPdszMrAW6Y+EFuHNWdD4yQHntHQpVEM+i0AOPxxwbW4qqnydEeWcj+DOOVU8vI5vOL6BwdKh+\\nelxXRvi4rQzE2tO6borMR3MXLowG59MjqADAB5IOjhyDxug1ZcvOUOPvemTs4dY43dOcbAG3OPd0\\noJyGQoOjcWk/VL+6qA70UdjJJs4eS/bhnhkMQMDUdc36IeoQcKLEK/Ij288qC1U8D9oKDoDhzR0z\\nMzuCgyqNqscIVbpDUAcWIWKPQsOMc8G5tCLu66vKKi3ATeXCaTIhA9t5oAxywI6fn2ms2wGqwAPx\\nkoc3aA30U1yZgMoq563hXYqd1xjWaqDO4nDIwGUTQ2FnRBa8XQMBkgPxktD101m1aw2U1Yspqans\\noGTmJJFYI7MyNPX7bJ8M8Ji5DZ/JAK6aDJw3E+be3Ydqf+NYyMODmmw+AX/JRRQa5hNUlorycxFQ\\nJj78I8MWWbYzFh9bnMMrVdkGTRbRuA3wcWMUyEZxjXsMTobl62TuHZR3Uso4e6j2zRL6/SwiW8+i\\n3Bb4wCjXr5BFdUHZGcilZDVlaTJexquTgPOlxHxDVc1YK6IgZBbmylQWV8hOwyXGkmjlqPoy6eBP\\nse3ooebME1BTafgbIlVdJ4oS1toVvdYfK8N40CLbNVF9Dh9wjpzz2MvXNMeSqGb09mT7KdBLhXNa\\nS7MXOZOf01w6hLvh3r6QGR5KN4WcxixKhnawKz82GMoWWnAKzJd1v8qybDQ6/HycaLXfFxKk+Rut\\nnZ9MZJsOqiLlygtUV/701afl7+7e0ppcaKnfb49lU3XQZ29cvGVmZh8caU5dWcCWmAu7q782M7ML\\nb6keU9AAg6+wtp8qs+w/I9s63RG3zCe6rX3rZ+qHf/0DvW59NcjACyXwtTa/uyoUS3xXiJ9bbbKL\\nH76kdo5Vj/nLWpf6cNd0ohqXR3G9/9olceLs99R+v6bz+r+9qH5//VDjd/+jK/bUG1JjGizDhxcH\\n5Xmo+fhpTdd6ONSe4tpUiBhT1ezyovpo412tRY1VZb2dCqo/NSF03gQ5fRvbvzBTHRJ/J9u4WZat\\nfv3l3zczsw8j4qp5ril/89/sbKUB4mV0ID9afBq+DhTXigH3iyNbb5KpzafUdwG6wtqauw/gqVuE\\nM6ECcnsCfwhuxcaObHseKGfCRZiCG2w20vXiSXifAnQwYxHJw5VTUv8kUSSKFVGAgzckBRR9AtKy\\neCCf4Mz0mh9qTjaBRydbGsce0kapYI9HptrH98Th5HLIfMf6us5gLt8QQQFzwt5rEoUHK/CFrJfj\\nCOqqIHoMBZ8YKjQ9EKgR0HpJ9mLLM82hDbjI8nntp2uoRx34FdrJ7/Fl02O1K++fHS2xd3dHdT1m\\nzwB/TmVZfjQBR18S1R+H/ZB7TvM0zzPB3h2N3Y0dIflOPpQtry7pOqsvCAmTguctRx1dUFjTE7jK\\nzul+206A7oKHBxT+FgifPnwfzgGcKD3V/6CuejzuaL87m2EboAQMpGUKJd40yOhJEqUwUE29U/VL\\nYONZY2820zPPWl7fW0nI3/vJQNlRY5Fl/fNBgA8PNFZTlD0d9iQFOBkTy+qP075sYRX+Pyei9tZu\\naq/igdCpLqgekXMgV0q6rtdV+5IN0LZPtD58+htxVuZBSWc3Ph8Gwh1onEYzlIZA4hvIUDtVP0Wy\\nIKNGspc+3JJpkD5xeFSWUCZLjNXOzEjta7vMVRBXSzl9XgqeMwAVThstS/BM+Ay2dX1Na1WaedbY\\n71AXkNMuYxLV2nPAvNkAHpto6Hs5UD5p9ihxVDQTFdUpkdV8bMId2wpO0FS1T2uCCK+jQOlONcbt\\nhPqkxGmEOPvzBPN63IUHFLjSy69r75FgOzs0EICM7ThPu9i3x0AAxuDIjfLM+Z3vCiFvcG3l8MNx\\nUFwZ5vQAtFu2L7/W8Gfm+Wc7MRAiasISlrCEJSxhCUtYwhKWsIQlLGEJS1i+IOULgahxHMecRMwy\\nZEDmG4pATWGwrj/cMTOz/KqinLNTRfpO0apPkHWvFBTpK5UVdRynYOI+VXR2eVPR2e7eln6X1f8j\\nWOZ92PcPH4M2yZAhQQEndxH2/CRnVEHGpDhvWpsq47O1rSyiR2Rx4brq0R0pQphfUiQPsnpLwSGz\\nuKL6H9zhe0SLG6AdfKLFEViwVypqx8Y5nRk+dokKIwtQRemjAaKmsLJhh8dCnCxe0LVyRV2rua+6\\nJogizmA/PwLtM0Y5oAZSovApEf7fKpL8/JdfMzOzJHw9raZ+f3GgNvkwXycXFLIdjc8WSQyKn0B9\\nYkl9MZvq+u1D1e/wXRRUDlXPKZH3xXVlMmcNzmXHFRGPbSqS3OqS1e5zXtpVu9wcZ2Cn6p8g4r3A\\nOc1UnqjoW8r+HRxo7OMr6vPlFzTmxrnJnX+QgkWrp/6fxnRdxwVFsavvNQ+V4WyjOHP+dZ3LL5EZ\\nGOwp4n9wrDGOM4WHY/Xv/g2dx4+XZZtLOXHGzObiJxl56rerLwkJtLQtfpHjW8oMR56oHsU1MiBJ\\nzqeuaO6dEPE//UzogR4oklRF7fE5m9tuyIZ9+u3q0+q38oau022o/o/fusF1mMtwWSRIoPju2RV9\\n2iey/+lUYzmF+8Xz1EfptO5dvEjGDP4kDxWmJJm+pi8bqsMLVG2R+VtQpH/MWfypq7aWUSaYpzjz\\nToY3WZDNeiD6kmX1UaSiN2p34OtBhW6WDfxWwCcCtwln5QvYsh8RAiWVKtBOVCxQToiBspon9Tqh\\nHwwOqxq8Q6222ru0Kv9x5TVltIubylxUllAog79kckf/H4AuqwhUYYeP1Y44aiPJ8/peu6bxaDRR\\nkqCffTINTcbJKel1e0PoMquqP0ucEW58KGTTYUtZvK0loQH8wC/myFaesUzIyHqLul+AcGyfYjeH\\nIKRQWjJ8RjmDKuGK5vZSREa6CzJpcqpxj/Z8rg+KcJEzySA6W2Q5I13VOz4nY0ymJz/zLJfXdyao\\n3XWwzRhrQmKO0hYcATG4B3Jk86vXZCuRocZyUkflbY76EnUZzMkekQ0LFKyqoHygnrHkVL8buGSr\\nWXNbHTLBZPcHMxlFjyybU5VNrYPSeoKahs/58yrKaJcv63vjhOqZASTVXEVZDQXGMdw0Ke53uK/P\\nuwPZdJPsmxtX9s9Q7Zv3Pt96s/r36o/H39X1Xr+v+u8ONfYue42k/w0zM4v/Cr/7jJAyg3PiuHmq\\nJW6bJ2khNMu33jQzs2tlIVbezgo6s3Fd11+/8zXdZ0XrwEs9EC0Pdb9PG+JrmU/VX41XIDP4ruxh\\n/1+EeFl7uKX7rgolMvpI60xvJlTL2iGcMiBnp/BSPf+C7OZJU+Pyak4Z/E9Ah/yxti52Z0H98/Zb\\n6o/893Sd2m/F1ZPG5/20JZTL9S9dtnlH/rOclf+9/3O9pupaG5srqqtHpvO3D7U2eBsgkYtfNzOz\\nxGuad4+boKYc2dzlp6TeNN4R38/hSPc+8XXfi3F49c5rr/D2v4O4XBcv0OhIa/9ZyxTkZgt06qin\\n6y+6Got0X30+G+t7hYHen8VQ2cvIf/fvaE+wu6e54a6rfak1FBHZK4zhsHEdFDV/l2ZFfQmEue8F\\nEnDwoLigI9hwRuDYiqAy5ybIPJO3HcGLkUK9JR3VeMzioPTg1JkOUW2CH6uT0tzMkZmes95EfXie\\nfF0/NwLRA/LJj6CWhepe3IVfA59RIoPvs45P4JuKg6rz2Q/PA7QvdjEE5dfjOWFhk/EBJZH0QZeB\\nZJ2AcEccyxqgOCZRze0eqL015+xcRh77yOlAYz2iLr220KkFuFRcEBSrZbU1UlAlEtuy3WVU+iYH\\nmqfVVY3tFJUgx0CqHGniBao+MxCAxwey7V5NnVS+qusWY3AW3tJ1Hx2DdoPnJ5vVGGey+l66omeb\\nQ/bXwyesmV1sAVUit6B1Z29X911k35mnXbNTuFROQHCmtFeKgLIqbmvNdHlGS2b1++SS/FNiXe07\\nacGjAv9SE1SDM1Z92yjgeqCn0iBoSst6pqtePW9mZqvntEd5+EspTN6v75iZ2Zi9TaYacPnIHxv9\\nE2mqv+qP5Et2bmgOb5iue9YygzfqU5D5q8y5FcYpv6r7tEFr7NyUn5/MtOcrX5Y/r4CQibNvRsDI\\nJgP1U5Jn3TEoNJdnxTTraeyAPUlsbgPUmirMzyhKXvuPNR/mgFRd/NKc/fUxXLP1PZ5jF/V+Pqs2\\nZEGBRVAMDran9ab8U2pJr3P2vzFHa3kKW/jZr//ZzMzevq1nz//hf/tzXTfgzBrCkxSoTTm676gL\\nt1VKbV6qgiYDoZMf6Hv9Ebx+IOpyq7LFMX4xBjdNoDZ3NO5CSwAAIABJREFU/SXtI6NN+b0+/E++\\nF6CEZXtZ9l6DQE26mjXXDTlqwhKWsIQlLGEJS1jCEpawhCUsYQlLWP5TlS8EoiYadW1hIWV+VVmZ\\nLMcdH0/gDFhTtPj555T9vzlQlifO2a+LS8rE2CPOrm5yRm1MJH4Gv0pB10k/qyhlOmh+WZG2xauK\\nFsdXOZdPVt/zUdJowJ8R5SzdIqzTW3DbjBT58xd0veU0euxE2PqouWxyjnBCNC2dVT0iPdizK6Ak\\nfKKcBd3fQ52gMyIDC4phDHfGLKJIZntJn196FjRJWxHQzPUt6wwVhSyQXfAXFCbtwpuRrCiSO0L5\\nZDRVhPX88zrXm62rDy+tCqnRfKi2reY0dgNUQFbiOte4tKBIb5PzjCnLc11Fgs9aIhnGNko2CTWg\\nPmpHMdADy9ugGHqKApeqGusk6IG9vhApMw+FAmzNW1M/bJwXgqWQUZaufU+R9tERvEU51f/Je8rg\\nnpIZWD6vsVh+SdHjMuiJx6e6zmQB1NRFHbhPJPV5Lg0r/R1lDV2UYApXNXbJltrRJLrb6OrzJDZX\\nMtX75IHGeHBExtpTprPQlw1O4fjJJNR/ZRBK/RqKNzXQHbD3L2yBuMoo89ACYVUHBdLmrHJiBqP5\\noq7nEMnPgBKbkP1LkC2LuprT3a7GYcw59lQJNAF8Ki6KDRM7O/+IB+9Gclnz8PwCbVxXG2Jkr0Yg\\nNfpP1OcD6tqGoyY6CLig1NeHT0BjkaUql9S30wXUJVz1cSQJF85Uv2+2ZCPHbf2/vK22Z+CaWl/U\\nGH50R1mcwTn9vrgiP1S6sGVmZpXzep2vK67+6J80xpOukDXxuMZqNlRmYOCpz32UAnzGoJ/U2JVQ\\nu7gGV09+XXN1oShbHOOnntxSfaKoGEWBOY1rKMmQoSiCRoutqx6phOo/WeO8/V3QZmSNMjP4lvKc\\n5y7KF3UD8FSZ89k9OARQ5Wo80VyYw4Plgirx5p59nlJGdcZNqV0d0CQtV5nxo6nmfNKRLVZi6pc+\\nHDPzT5TN2uXc/vQxGXXOQidKqEbB6L9IZjhQQ/DSZJI5lz8E7ZEwzfHMLGIOqkh5eG6mnOsmSWS+\\nR4bMB1UE6mtGNmtIZjPFOW0fRcPhmAzOBMWaBBxXZKVL19UnXl5+bATisN9Qn7eOhOhoYcNWAj3W\\nx7+9SoaSjN0KimfpvGzfO5Xtdg/Vl8f4s+lnWtN7M/lTh/Pf60saq5MKigv4m3GO65GxTs3ga1pC\\n+Yysf7egdjrZsyPzzMw2r2kM70W1zjlFZUzzD3Xf31SFQCyDEjv4SAiX6dEvzczsl4+/Y2ZmrwaI\\noUXVq7XyY/1+TciZsSPb6f5K/ZJu6fvt/1H98sHfCHlT/Kru88Z1jcc7c9mg86l4ViobPzczs4vP\\naC/Uvqhx6PwAqbGUlJAy39f6s9vltam53L6s+5//keyt/QacczllkLNkZPf2hCAdbAmFcvRdzcnX\\n4eKwga7npWSHv82K8+bhv0dt5XugOP+evcLz8k+FjtbI1Sgqdr/WmpD9PfnZO63vm5nZS/tSqvrx\\nZwEXH/ur78pGVgZk4TeFUNlOKNOaTsrmO3ADRBqy1c4FvV5fk9LVwrbm8//1X+1MJV6WX5qAbh3M\\nZLPdHqjdvNZwr6vPR6AqbMz8B1HYRh0uyd4iBcLPASIyZy5H2fcNIIpzmdvpiNox75EldzQmY67r\\nJtT+0hJ7EzhpHPyQhzqKHaMgBwImAq/TMnOwUkSBDF8zaSZojvaOXkz94cEdk56BPIfLa+6wf0Z5\\nJokKzGAKjwZqVUN8R5KMu4N6lWOoyoDaNji+xnDoZAFWtui3Y3j5JhGtYxWQ9oUs/QeHRtLRXmZG\\nhjy5zLq5ruvvozw3O9U6XXRBfp6h5NPaf6YCGwRq5sMz5IE8tJbmwMn/w957PVuSXld+OzOP9+Z6\\nX7bLdFdXm4JpEECDDIIEQGA4w1CMNP+AFHrWi/4VRUh6UoxGnBiKGBBDgiAADgF0A+19dZlbt653\\nx3uTefSwfokJ6gW334oRuV9O1T0nMz+zP5N7r28tX+PQH7IXAMUzgXNx5cvqw/h1+CdB0MTLoLDS\\ncCPCsxGildZ4mWn1NL/2UdSqjHS//BilsE/1/FpJ18/DoRXyGVWW1YYbqKM+TVHuPa3xhZj6evEa\\nHDxp+VgezsQrG5p3zveEahs8AJYL2sqtonI40tgPApXzoK52qJxqLFVLqmca1Gu3o758/KnWpz58\\nRtUlTlVQ/lFO7dH8iLUcZKnL/OZWdL8A3rxmB965GIiVde0NwnXZexFV1XWNkeNP4TPKQnxyQXNR\\nVnIc9pIxlN3g/hoyZ3iL+t3mC6rXzlPtTRYYo0GX9wPT2PLh8cuw552luA9jbNZQeeMxjd3uTL/v\\nTwJLx1B2ZH+V9vRZBk06Yd7IVdTnXfbL//f/9YbK9rHeNf/tqtaQ9CL78TkN1NlYa18N2ackiBV/\\nH/49ThvE0+qzTqB56xHKt7VT7d8PQe1fYz5zeK8v1OQrSebTsae/p0F3GfylBnfWwUOptf7q30uR\\nceV5+fh3/yehYsfs6yfMb1P4fvJwX86G8qnBIfx7axoD1aLGzFkb1BZIoOrKuvm8V/0+ixA1kUUW\\nWWSRRRZZZJFFFllkkUUWWWTPiD0TiBrfZtaaTX+nEjLNKzMyhmW5kFa2r5RR1C+RU9Q143KmdUtR\\nzTqZjVFLUaq5Fc5TppV96nHecu6Sfv/5h0Ih3Lku9QAPHfe+qyj14hXOuXtkSD/Wfdcuk1HvKPo4\\nncKBswLr/aGisaUVZfXiHc6Hn0mawSUDX99RFDpAteoYtRSvomjxgAhlaVHR85BXpPZIEf7zPRQu\\nKjCdIwsQ7ylK3ZhTfeurIHAuJWx8Co/PlqKYkyS8EnCgXLsuJYbjJ8oy+AOVIbukMn36qTKGZyBV\\nggTn9YiY99tquzys6n2ioAPUkAZkWQL+flELzsmMNlAbIsuUiek+2ZeE+IkPFR0dncECD5pg94Gy\\nbM0DRchX7injuARnSmVdmcg0KetTg6cIDge/qqhpm6x+bU8Z1iQZ0/yc7uOQRHrckG+1D9VXxU35\\nVuUG5TtSufbeVh/u3leGIT6n+tx9QRH8MfcLyKbNV3WfxQVl6xp9lHvoryoKQ/P0l8OZ3eFjtf84\\np/qcFhU97nT1PG/AeU7UpSYrqpcHl0TjGGRQQp/zV4XCqCYZGxXUBdqKlj9ukt46Ubat9bx8tIoS\\n2gDG9kRB5bz6NfVHUFP7HpHRGJ1e3E9icDgVl+Qbgz7qPhONq3adM6Zw0Dh9sgppzlkX9VnM6T7d\\nNUXCZw/U146rtiltaDxntzQP9OBbenhEtgplgWKoWEUEv74rX0ikNY+lFtR2c1uaht2C7p9dhjeC\\ns7Xtoa4rVDUfZckIdg/lO0eoOMXIkk0Kul82rvst3NDnC0vK+ru0R2eHeoOQeXxfPtsDhRWHM2dj\\nk0z3Fd2nM5Sv5chEjOiicYv5kPk5VwLlYZyT5/tRVlmsBGz6Yft5N9Su80b9F3Vd51zt6cOPdHKG\\nokOc+3Ou/aLmonhgPhnbGVwKqDElQDomUJaYJvR7r655/vxcmdUm/FhxMivenNongWrJFDWWcQzV\\nlwG8UgFZrDxITJ8sHjwzTpC1GIjAOM8u853b0WduKt8Zj5TNypL1ckdqowC1DsvomXG4a0KFmElF\\nZW73yDKR8TUyeyPU+eKgOoeoiHTJ0KVT6rtkATWKeZV3dqbfpROgWI929L2n3xWuctA95NMYMJ+G\\n6Cm4BCZ5kEJj1niU1gYzXZeBL2OC4kx/CNcD58ePW4zFDqgtuNUuag8yavfVX6jvfv6qynkrpnLc\\ne1eqT+/2tB6exH5qZmbfO9wyM7PPyuKoeSfQ/wtwMSw3NGews7DEVc0FuzEhinplte83HDjTXpS6\\n4LsPNCab1/S8+Ptap1OBOA2ufvw1PW8VibWDH5uZ2eK6+mOw+JqZmS38WuvfHxV+rfuigHYvqbE/\\nJHf3sMie4ocak98psd5UpOoU/0j9sHxL9bzfF/fO/EDt074GcvdDzSFfTn5qP/uN1rT8t1WHV99W\\n3x6kdW25oWf95htCuKz/rfZL0x9ovNX+AWXB66+q7fJq87UP4CgJtKaVUZp89Pwv1Qafq61yGWVg\\nT2+qbtc+lW8ED7T32al9wSx4Vm2VWgsVb1DB22dPsqE9RRv1lGlbY6/PfDcFGVhg3iiBJi2UWXdQ\\ntOwPmX+mGpsJkKNx0HQ99gaxKeom7K0SPc1raQckaUbz7qwIogaFmvFA14/78E2BvhigHjWAa8HJ\\ngBDytQcYdlD1gz+r6oXzFwhw5uUgxfyMGtMU9EKXeTAZIg15TpL6uBOQM6AEJyjcBAk4bhw4KVCU\\nC9ehFOiBEKGT5Tkjn/ZO8NoTgDQC6eOfaezNreh36UUy9AX5VfOBxnB2AALpAlaBv9JLqU8LoJB6\\n7BFmqCulUYo0uEImINnPUXvqw+dTm2o+33+o/WKPd4eVS0LCbyxrjK2WtT+vXpavx1+Sj62E8+iR\\n+qTnhgqKqtNkrPkjuc3+GrSAAyo0dcyaNQB1DOohA0q1BeqpCVKohG8nQZg30uxD53W/IKV939F9\\nvaMV4bmrrqr8i4tql8oJCPd9vTMd/uRdlQtekvVL2v/v1dXezU/VZ6OroG1f0VjMT8K1H+XNjuYa\\nh/UmRGdtbGp/W4A3tQ/iqDXQXtAzOIR4N83d1Po2v4TCWajydUHzUPdau6T2yDAHjDxQJSX2SCBX\\nP3lLc+Ibn4jLLJH7tso9r/VkBMdcmj2wA4JrCBI2iIfKZSgPo5zZZk7Ips1mcDX57NMGnRCBB/ee\\nhrHNUGidBrw7HKP2xl6ky9o7nOmCdAWUbkP3Czm0LNB9J6x9vtEGM7XBrCyffOVbQnMuwQf3HPw8\\nsXqItJavJHk3jPn6zDPOOyCoM3FUO0EEGWjjRz2tXcGHqte49QMz+297saClz3SKeQBUsgdKOQfi\\nJsFJnDQ8eYNDxu45HJPVjAWzCFETWWSRRRZZZJFFFllkkUUWWWSRRfYvyp4JRI0buJYbpq3lKOKU\\nanLW/4iMR1yRq/3HIEjgG0nBEdM4USQuVicCjxj82qqirM1jReRXUzozll8WmuTJp7pf3FMU0uMs\\n8dGeMswzU2TMBcXRBlUwTwa21VQUuHuuv9/5srJWn3/IGbThlpmZ5UDcxDh7lybanQB9Uirq+TPO\\n0a/fUtZt+11lv46byna554pmn8InkyPqHCOjMz9AReCcCB+BwrXrqncwHFomrlyeN9U1w67aZv+B\\n7jmLCXly9lCR/EagKKWf1/e9AgiOCtmbqiKz8QWyNj1dP0NZIBir7qWQcX9lhbb7YrwSPgziJ2k9\\np3hFdV17UfUZtMnKnCM3gfLBOSz5nc/UpxmyPwUi1UnaaoTS1l5L0c5QsSa1SlaKyHlgyjjMxqrP\\n4ioZX86fH/4WVIJDdmZR32/c0TlNZ6J6338k1v8xZ3xL9H11U32fJkvU2IH/oseZU3iJslXUojqK\\n8PczqFx9mf4d6HfHcAi1z1SvwhpqVKe6fwoURnxNGZEVFMoa++rvekf1HTPmihmNnWvXFfmfkhHq\\ntDQWTg+UtTwD9RXj/HymicISihAzk9+sPScnLa7q+wYInpCaJhYiAS5gs7767JysFUfhLZeC7b2h\\nNjzhPHVxQW2URt1tDLrMUF1a21JbDq9qXsijEtVHxaNZV52XlpRJSFfITg00Zk7PicD3QINNFVnP\\nlFSOBdjkl28oe+PAgRPywPtjZRaaDfVFEhWN/IrK/eitHdWLc+oLl4S4m7+FUgTKZi4KETG4sAZN\\n9Xmro+t7h/r/aFvPy5fkA7llkDWok2Tm1GdLHiogZELb+/LhDO3mUb8MaIvMuq73GrpfOoCPCO6E\\n1BbcEov6nDmgMZgjELeyAHUAnzPAXbJtTvJizPmhHTyB6wYVPrcE14OSj8ZR6t+d6/bHnHUm+1jk\\nHP7mZdQ/luCZYn1w4qp/fiLfn3fUDjEP/2qrvadkjrMTUChwqsViQ4sNyeYy72RRX2oPUWwhYZeO\\ny8em8Oh4cFnFp3DQhNnkHnw4Q86HJ+GuYri14D9zzzTwOns16o7q3brKk2TeceFZm471u0ETlAAZ\\nxymIwSaIvuIp80hZYzEGAqjZxud8lasAInAGomjUUltOR/q+BwIkTta+A9JyegpSE5RXG56OwVCf\\n+SzpwQvaBijV2VB9f3dX9Rvf0lq9/VhcLbm4kDXjl1Xv9qmuO4Cv6Lkmqnp/CPLnwd/oexCJz38i\\n1MefVe7oefFPzczsP/2VsoWbL6l9V74Op9znOi+/sSYfzF9Sf9VdZViHLvPvz+EFSSjT+pW3hXwJ\\nvqu//+gN+eL3rmnO+PXbarf5V9RPV96VL64g7fbBDWWe757pfkP2ROt/r3rYEK6dBc19yTdfNzOz\\np92f6+urZfPblC2vNeSjV+BB6wgV9MF9IWjuMhAbf/6imZkt1bV27b6qsvjtX5iZ2VoGPh0UCV9p\\ny7f+Y13z19WaUEV+EQT1Vc3no4nm7bceq86XFlSX2y99RXX5P/83u4gFKLCEnGLjtJ5/3EBliH2r\\nnwBVjIrgmPEfDFGOQUEmAf9EG2R5/gxkSYx1I4cPGWjlkcaOBwJw0NLzByjFVVDwDMjWZ1yy7A6o\\nAlAUZ7ugFeD727iiPj3F98cogC3Nq18GQz03iGndmYJQbZTYM4HCc1FdyjInIdpkEzLb1oXfjrln\\nAhoulqA+pOyLcNg4UzgsfH4Hv8MYFdbUBH6TvK4rs9edgJ5IwW/owXHhJjSXFeLsRR35Z2cqP1xL\\naD3K5jW2uy+rHv7HF19v/FqIqmSihVctluT/ILjjq9oXZ1IoDY7Zn++p7D7Qage1pXDPX6mAmARt\\n0JypL2OoIc0x7xVRGpvM4HXyeIeAgzBX0N+X4RLrb6MidQTyEd/onoCCWtP9B6BBz0/0eXwCR1VF\\nbbnxguoVItvbLT33BO6d5ZDDh33tSYt1B8RRGoR80ZfvJZLysTceqY8GH+p9I56AkzGuuWA9r985\\njnyi1JQPxeB3CjlxTlhn/FAVCc4xJw0ScwWULL6Y4L2hD2JmiELQNKY5KlPW3DYKLoaUCG3CfDqE\\nbzTfYk5IgH4ea2xDy2pD9pQVyhNyXXZ68DgNQMpweiOeRKULZOzCnH7fnsiPagdq9zHfxwsZG9bg\\nohqojasl+Uoszb4HJIwPN2IIsfnBd/7YzMzOG5q/V+G08ZkXkxYi70BWJ3hmXmU9r6EiijqToWzm\\nUedsWX1X7OKTO+y7QARW4Ntr5VCfY34bzOl5hTzcrT7vy3AsFpY0733v6/9WbYRPuhON2WFd80aM\\nedSHR2ja5tQJY8fBtwLAZ/VUuB9WPTduCeGZn0uaG7vYiYEIURNZZJFFFllkkUUWWWSRRRZZZJFF\\n9ozYM4Go8SdTa+yf22kTroWCIl8HD5T5qDynrM/OR0J5dE4UrV29pwjWMKvI27KjqOqUDGuwDyKm\\nrmo+OlHmZouzxfmOoq3+Y0XcpjVFvgpwIqQecZ6SM26pkco1PSJCf64o7clT/S5R5ZzfJ4oAznNG\\nd8pZ46NtReTmx4rQH4BamL+uM9Wff6Bzp3VAIWcHitL2TdHW9OfwjZyQcVkXSuP8gcoN/YDFjMwC\\nyhqptiKhZ62OFRsqcwoN+c6RPleqKsNCTtHGwm0QM5ymL20oUl26ze/uKcO3hHpQal7PjA/JusO2\\nXsygcPVYEdsuqKn26ItxBgz7qnsSnqGyqU4uZ3T3azv6HqTGFAWfIRnk6g2Vd67KOWvUQ84egKqC\\n68U89d0qaIqVF4TKGqO+dP+I7A0qKog+2VlLmdAe3AhXvgJL/CJImozap1FT3/sp+foc7RpbhMU+\\nrajy6WN8pKZ2W7sCIumy7ptZUeS+SDaqlCZqzWf9bWUsapyHPHwqFMmNpMZMG6WzSRFfRUmh2VX/\\ndDnv2W+igDD2qY/KEczBJcFZ5JBlvw3axOBrKgWovixwjn+AXw1CpI4yvKefq3xHu3D/gNTKEKy/\\niLll+DuIUjtkqzrwbzRRmnr0oZ51ZZnI+nV95nP6XfwaSgjUpXoZhEdXY6V7IB8YwznQjKuNbCwf\\nOTlnHD/WQM6gTrGxIY6YuSrnyFdVRwL65h+rfD14nOZBrdX31FanIDuyC2rT9WWNsTYcWs99Wdn9\\nZBYFA+LwTbJyTTiwQkWJGWf1kyn53pTs+QAUXC6kFuBceXwi324+0rz89Ina0eXM8tbLyp7FFpUl\\nsyXQbyGXj8mncrlQ3Q6VElym80T1POrpfHm6KF+rllETSWouOWupXStZPTefBApzQYvBx5Tu68GJ\\nmMaAA9ojtQg/E9im4SmZ6xHtAFIrvSmfXruk+j4FxTc+J3NO5sjv6vrKWHNAvw7nA/1edENOJd04\\nE8uYoWIHVZY5nJ+ONVBkCVE4qGqEakmTU/W144Zrj/7ul+BBWobHDIUTdwoKqq+/z8g4pkDMhCp+\\nPnwUBk/T5Eg+fgTiL8EYW4Ynyq/AcQMHTt9UWQdOmVkuRJ+pL88a6svWe0yornwng1JDiSyZlyIT\\neKz7jehLB66EYUZjsgWippHQfJK2LfsidnysebOaUlrs167K9dVt3fdLX1WWsffwZTMz239Hz3fy\\n8vFbfyxOhOFfoZbXUf1eeaBsWqussTM4Y958ToiY/MdadzZvaoyZLx9L1cXr0kdV7+1V8brc/UDc\\nNsu+HOW3CY2Z5IrmnOlzap8fgR5YgnOnDLfDf2yI4+BKR+V6Cq+HxeHT6sHn9Z7G6n32Qot3f2Jm\\nZtu76u9t1J0uPxKa+AgE1OqcyvnL+Q+tdEWZ1urJX5mZWamjtegj+Im+8bI+33yisk5Mdf56oOua\\nl4Xku9YVGtWbSJHrSlkKXEfLQiF965FQrYP3hT766I/lA/YWanfPab6rTfT3+ssaSz9sfzGOmqGr\\ntnbnND/5Ka3Jx+wzd0OVzzsqt5uRzzgJ8qNkgKeB5uVMLcufUbUqwtcBcmcCx4sDYsZAufb6ek57\\nKB+NwYsyI7Pbg68ilyQzjIJMDy7DsyP2v/AI+rd0fZKMsYHOa3XlSxn4OXp5jX3PVT1icDJkZhq7\\nAdxkPsiaOPwYcXx4AP/JCG6fBOubxfR9fqpPPwWSBSCLCzfPFN6uZEb3D3lckmN9hgikAP7EKSpb\\now7oQTLz6Zyudz3Vv8B+vYKCWRqloCCuApwFF89vP93RPDlFcWwGYiOVRX1nDiRboDYYs3/1PP0+\\nt6xnJU7hSMkJvXVtc0u/9yExpO+zPfVNd8y8D8L8/qnmG39Xv0/GNRauXIrxXJDLcHplaUvnLPQN\\nkCUgdxzU6ZZC9T+4Cv0sfRpHRaoHUp/yHaMy+tnHQsQUFrR2Xr2sepU4BtBkD9J8R/NiGf6PVXhK\\nX3xNPFVn7FOTIDNjnupfvfdVlReFs2kGNAfojvyC9irpM/39uK53ywm8hbG0+jqfUHkSmypnkZMD\\nZ7RrG+Wh40cqZ5zNXGX2xRCcHu+sIdptkuUEw0DPd0CfjVAE3rin9WXtm1p/Soz5vW3NfQYKr426\\n7hJqu4kEKBT499K+/j6Ko4jM+0csPW+f/Frz7+NfCvX5nT+XwuCt53iHyMFVE6Ju4a/bggN2+bLW\\nsnQf3x2Ga7vWjBC9lKjwPt4DSdcFcdzT+M2y5jsoxF72VMbiOggfkHIT1N7GqZCXTv/fYc8RPFV5\\n0xlOLaDaWof3LwY/6O3XFG9w4fXpgyYzTtJ04eJyUYFKF+GPAvWWQe15wn779KHW8gx7o+pzqs90\\n4Jm5F5tLIkRNZJFFFllkkUUWWWSRRRZZZJFFFtkzYs8EosYxxxJu3PK+Ik2leUUv3W1xtKx5iv4+\\n7ev/yY7+332q4k/HsEtP4eO4rwjbeYasGqiAhTVFhU87ZFz29f0jMsQJVKYyC4qszU1UnoO+ro/V\\nFH2sPYX9HXWmtB+qOBHVBakz2iGi5ihqmyBa3TxQpK7zubJ2w3mdjdt5qt9vcr4yH1M900RDFxOK\\nUB6RJU3XFHWvfYDSg4ABVjsBJZLX73phdLrv24TzvkefUgci4F5KZR5U1IYTkCitqSK7HwaQIoA8\\n2ftcCJLDmiLRl1DImaSJXKcUGT6Cx6MXqE4Zsi6T8RdD1HhkQzJkX4agI2q78IBwtnP9liLlXbLu\\nhhrInCPlrBbR1Z3fKMPQ7uo+mwP5XH5DUdr0HOztZFEaZL9GqBW5Vfg/UCYYtzi7m1KWrZyUb5xs\\n6/5HLWUQupxLdzj/uH5T7ebvw/XySO17uiNfd+C1SGY5X28QdXDe0zyyPR48Kw3df1ZWeYrzRNJn\\noDiuKnPhc27bRUnCJWHefX9HnyCI0nmi2VARZDlP6g3lU0OUfIZd9efiDUWrF8pqBx8uid6u+u8J\\nPt9GeWgaZgezMKnD8L6ygfLF7OJ+0usoSzDhzLyhDNY5Qk0NpM2NV4VyWlnRgCmD/Cg8Jx/Jbait\\nnbrqFkw0nlr7IP4CtUEWZZjxOWpGZPHzC+qjuZTaemEeRYYrygDHl1HvANVQIXu90+TMPXwex4uM\\nX3h+UgW1xTqIlcIruv/+e/gCaK7TWqjwA6KO8/GtGD5FRteB7b6ygYLQu+qj1mdwzgyUIfbhAcn6\\nGhuFrMqzPq/5aYn6JO+QqYAHykHBIlUGBQH/SQ90gfsQ3qWk+qcPWs6D22FxMVRW0DxfKYMiWdL1\\nZbjIMqADLmorl3RdygH9Ac9SsAx/SkH3H5F9Gk3VLj3O19cO1A+hetYAdYMZmZaEB+oFkqTptq7z\\nMmSQfJAzIKHG8NFwHN36qaEVuqp7xkdtifGVQ7VvBip05nNGHj4jL8v57hT3RrUijQKXZdVXA7jJ\\npim4Bkr6/SJKYYugzCbb8sUzskVJiCY6oIHyoYJKTPPO6h0hENduPae262lsnO4JTTAAeTc1+XoJ\\nJOYZmVYDtRQDwZeGYyGGUphDZrWNkkSiovoU2Tt7uEJqAAAgAElEQVR0VvT90iX54vFY9YmDLr2o\\n7R5rDghWhO56ZUFjISB7tnOksZeLi/vl4dzXzczsyVVQDQcfmJnZy9dRx6Mef11Ueb61rPr+V/hI\\nVt/UfRsZzU2vXNHffzXW89c/0nz4YIwvLauf3/PVnt+fUzteW1A2cJT9rZmZDU7Yy6yKU2ztitCC\\n57/R/f+sKK6dY1RGLoFi+9hTxroxesvMzFJJceikMv9oZmZnH2nuaH1J9XjlRx/puYEQNN/89s/M\\nzKz2hsp/Yza0eka+sPcBKKSvaU2a/UJttXv4F2Zm9nrxL83M7PMJa+kl1XHyc7Xt1l3V/TzJGvS2\\n6ujPhLTZ/ibKhu/Lh/+kp/n34yOV6Q3QCxum+fD6npA3T87Fw3NRi4Mii+VVzhFQu+09+XZ7qLUu\\nDrdZeVlt7Gblu50QOUImeAICNANPWwp+jRkIklQQqq3IV3Zx6R7Izxm8GoUA1VSQNH2Qg0nQCjHW\\nxWSb+dkB1Rqo3cagamOgbhuHen4M1ZME5Z+BeJmCDnGKIaeFfHRCJpxkvU3Z+8XhS7Gc1t0JnDeh\\ngmd/AnRmEnK7oZwE2nk8pr3gefGmqNF48CKyngQxzQ0OnGgBKoqDur4vMGdNUEv1HNYxV3u34Fxz\\nYqismaRcceaai9jmZY3/DHwc3Ynq0EUBcAbq/nxfzxiAhMzlQzQR8zz8ZW5f4y1VVVkzVa3JXVBM\\nCfqoD3KeprPZUL7XzYYKYSj1FEAjgcTswjU2Y+9zsKt3paWpxlCFG7odkJsgvMsg5zNzQg2HiNAJ\\nSj/H57zboIjYBnHSAOkzzcPVsQYqOqF9fDwl5wkAGNZiaqflVa1r+azm0ylqTg4cbhlcLM8eoQcf\\nUzfkM52EYwYU7QxOoL4eFKd8E1BpLu9kPfovOAYxiLKwCx/glPekZOeL7UlyyRjPh38V5KJrIKZA\\njHYmmvviOZBHvKf95H/XOrRzKJTg//DvvqvfrWpu6vEeFMyEsEmgxjrhvaHCnjBVBp2c9m10oN8c\\nHmme7KBeF19mbTyVj3jMW05MbTbqa/9Y5f8dEDVJ0EZuSu+eE7j7grp+D7Dwd8qDSSB0eTYbHThe\\nUnRuYaZx6jK/vfdIa03qNqc8SqzRFdBeoE27IOdaagpLcALFg+8vxrzUa6GajMJaG0Tj3Dz70i14\\nhLKg4foak2N8tj+A/5Qxm0uhEFbRWByft8wJFa9+j0WImsgiiyyyyCKLLLLIIossssgiiyyyZ8Se\\nCUTNbDaz8cQ3H06A2AwuBaKKYwv5SRS5yl1SNDWRIkPhE0E/VgSwsKyIWsUhK18kElZQJLB1qAx2\\njrO2yRTM1x1F5Ly2ImLHE50/3DtTlPJ5osUP9pUpuYMC0WFTz3/Y1tnkUlYRvBYiHy6668VNRYnL\\nFUXB5ybK+sXhQ0ntK3JZzan8j852zMys0SLiCPrg5GOVf7Gh9jjaUbZyCyWiCRHKXFaZ91aTyKST\\nsyxIhzFqPtl5EBZTECoPFUGP5eAsKIaqGWrzhTs6i+50VacwY5Cqqszt936j+6C0sNvUfeOcs/Z9\\n1dUSXyxGOF3g/DbKNx0QQXsQ+qzeVds6pu+HcDIsVu+amVkSnzr7hRBAFui6bFKZ0nQRlALIoskT\\n+cB27T39v6/rq2uKOGdvymcmHbI722qfAxjGD7ZRjwroK09ojYU19dnCLZUrM1Q7fnKkzGZzpgg6\\nwWtLlWAYTyuqPNmVTx6d6Hn9CWeIt3TfShYUVpzo7TfUD2N4lAzU2OkZCmtkRgagOYZl9ffypjIl\\nK5fkH4MD+WYPfqanB6pXu65odmZDEf71dUW5S5eUuT1DBap7IFRKExRCYQxTOufVh2SSUhkULQIy\\nIKA5LmKtOtmSQz3TBUE3yeleCy+rTHMFOAX21LY1lAaM8TeoKQI+RrnFR2mhjRJY/WDHzMyW2yjQ\\noKRQ3pIP5ZbVBpM2ahfnqlsd/p3+KVmaoj7jdHa3qRD/MUjC3Inuv/Allbu4qfpkUZmKFfT9zn39\\n/iE8RCXO3E5Ruzr19NzJlnxifhMVEFjqF29qHnLJsPYP5YOtU903zJymK6ik3NA8uJfR88Lz8ON9\\n9XGbs8SVNJnYJT0nDkqsd6x6ZvL6PgvfVDmhdtoHxbdfBwmZUGZ8+ZLq/XSg8tUPyA7FSRte0IZN\\njZlzMiQZ1K2SHWWIjnuqzwwVrlxac8+kTdYQ/pUj+KamcB7kmEMmcN3kpnDUBKi+nIBeacm/+qDN\\nShVUAOFlCSa+uaCKxlPWwH1d04T3aFKVL+Y4s+5tqGxLN4WcWFwkA3emPuowX580NO+3UZyKoeQw\\nX9Q4X7qmeWo6km+eQZWT0HC3nsPaguJivirfHKDsUsanSnTJEDToKeii2q7muROQjL0+KhdwDyRA\\n8HkBiEUQQGPQBQ7zXXZe91uM67oa2auMo/ZZuSJEz/I8KNb3qMAF7XuoYp3Myfdqb6lc95Hb+n5R\\nvvczEEh/lpe6UfCJxtI7A6kjHX9baJHssdbJuwWV73Rf19/ta57NeVpXt18P+bBU7q8khGyZ3dFY\\neP4/75iZ2d4d1X8D5M9/OFL9l27Id+dvqZyZhOoxMLVHN67s4Pk1cdtkrv/AzMze/Vzlqf79J2Zm\\nNnpefvStntapRkH99Om5ypm+rbHz+j+oPJMNlXOc/4WZmf1TR/VK3tGcnPS/YfGa+sABqRD8WPf4\\n12WV9d0lcSHsLf2RmZmtlDRf/L+P1Od/flm//zmcUeXPtV97tCZ+ncKSfL3xK+1xVtJau4+fwHN0\\nTWW6tqE+u7SlNnVQOJtbubjCoJmZg8+7oGk9ULJOAp6kQ7hjappXUuxD8yDrMmRafRAtPhxoPsqQ\\nrgf3ywjeCLisnHDeirOHyuj6bELrTgqywhN8qJTQdT34h5ZQ65sUNLYW4HoIUJZsoBiThs8umZZP\\ndeBJsTLoulU42uBqqDggZFDs6aOWkvL1/BTzpAcqLQB5mR6AQAcV6IISm+ThtGmRsQel4YZ8Lw6I\\nHvg2bAYKxZF/DeHfGrZQr2KOyFfgrkOdKg7HRjMOwquqPd4+inH7XfhdDpmvYxdH1HgoU2V+x/3F\\nfvNUa+X+IcjmJMqO9GkAKj9RUh9kQVBMW+zLKXs5jkIjCjejGPs6T58uKpuLL+s+uUsaG4Oa+nhS\\ngssR1cBUSfPA6JLacJ13CMdTX0LdYkP2kfUj/T6/qD7LpDQfxVkHunBc5UxIFRcVwxdvq55tfG6W\\nVDsVQEt47G3C/edkT32xe6B3HR/F3Rb8TLMGCsHn+BR8Itlj0Kushy14Q47uq09TOe2X42U9b8Th\\nhADVVlzDMnC19VBz+mBP6L3KotppN0ScxnXdrZI4ZC5qx/CzTlFyW9BtbTqSH4wrqDXCSRkHvVYF\\nFdzFN2d1xg5oljhYjBljftpS+6ZcPaCd0h5mxpwUJFT+2CBtN+5pnvTGUjMu8t66s6f99bAjZ6jA\\nA1SCdy5EGA7Y/47qmse7IPXK19U2qSqoIVC/kwGofFTwZiiTueHLUJ7TCOwFUjz34S+1lr3z01+b\\nmVnaE1dZ9R6qroscNUnKB045tRBLNqmXfCSZUlt2GDPxU70r+hO1XX4Bnr85jcUh8Yp6j9MHOY2R\\nYICaVYv/g4QMKHeIyB/OAjOLVJ8iiyyyyCKLLLLIIossssgiiyyyyP5F2TOBqHFds3TStdSiIlrD\\npKJ75TnYlTl/PeXs62BIBKyoTEYKffQMCkXrROzaZ8rg9B/AMQCYo9kS2mGKisiNK4oUnuzDxTCv\\nSFiuouemx4p6VW7p97OQv2NLEccq0eDpVFHP4iVF5gcjRX3bZGricDs8OJYywlhBZ2uDomgO4XMp\\nE42F06FShsMgoXrkSmqHvILNdimniGSlQgYXDguPdisYXBWZhHk5GPQnYXYEdvjwvHJWbb90c44i\\ncG6Xs5pra2rER58rquqQEW03lPVKLauNyneUaZ0SGXdAHyXgQkmANrqo5TOc6yOLvrejPuRorhVn\\niuLWnxK9RYlg/orKf3KmKGltosj10hVlNuIJVInIUm3fVxQ1XVX0NVvmHHRJ95n/kjgEEl219Ulf\\nCKTaGbwiHyu6O1gXwmf+mnwrlea8NufLR7C4N9pqtxHIoBSoqcwt+hQfDVDTao9Vf6/OuemKyj3P\\nWeXlLT13goLFEYpBg7Su81FLaR2gJASVTwWUWi6n+yyA/nI4jz7ogT7py5fqNdAi+NHcTFHm/hB+\\nkofKGHXPUPohKL6AelUlrnaforgTC/lZXFRgHmgsxcYXR9TkUXvocC55DT6I9KYGSpCQb7ceal54\\n8j5Z5R6ZSl8cMoEj3yrBK5HiDHthGYWEqe6fXCNjWNWYWLsrNEJ8JKfc/4Uyvk0Uz3wyhfF1FGya\\nmrc6nEefL2nczi7rd+UFOGEuK+uTTei69oF8JpnRGKsuKGPw8YfvmpnZOfPY8i2Vawl1qOK9G5RX\\nvtGgLw9OQRQxRn24ZcY1fA1umkwW1v6SfGP8ERnYj2C1f07lKF5GTQ8liKVV/X3WICt2LC6I/khj\\nMEYGeIovTkF7pUE8jouc24b7p1qUr23XNI+ejb/YMtaBH2uCAp0zhmdqV+XoJFXv0pzK76Q1B8zN\\ny3dP2vKjElm08478xfdAcwy2dD94ZGIQQPWnKC508G04lVrwBczTn4Omaz2USuY4391DZS6zDP/N\\nksZPPlT3SZPdh9Pp5ERl2bkvH1xcINMZcoShZDDjLP2QDOzoUGWb9vT9FPRoIlTrO6cuqFHFMhoD\\nPnwb2+9pTDUfa37pJuSLnZF8tk92PV6Ae6sCKq0OP5CpjfMxtX1yUW3isYa1J/K1GGpyBVAW0wfy\\n6U8fkAl1pGYU30Q1owG31wXth10pCW0uqT2XX1F5/wIunf9y+rqZmVW/Df/S34iz7cRRpvX6V9Tu\\nH8y0lk9dqYL8oavf/SjQGNq4pnqe3n3bzMye+4nmqg895pB74jartIXMOV5UP96mf85fE0Lpus96\\ne6TP9hMpdNwuagz9PRnhxLL2Di++KYTPU7myfa0HD6D3DTMzWz9TOx/11Z+rzpaZmd0C/fH2IxCg\\nVfXnG12NmYUXNIfd/WuhixMvCB3zwdOEVSfisXlBdBd2vaK6NIbirKnVtTa9dE2on78+Fi9OPKm+\\n/ADesq/tCNH3+Wv6TNaEZmp/ovu9BKfg2QLI7BfV91sglL26/v+fTfPiN+7Qxzzvouaz1vUHcCOA\\n6Li0TJYbxbPMgH3blPm1n+dT9wkVJGMFUAH4tjuQT3sxOB26avtuCm4zXDpfgA8OVGoXngsvAGWR\\n0BgpMeb7M5Rv0mThy1oni17IH6UxnmU9GKEs9OSBfGEONMfCsva5Hrx1nbF+P2RzN0PNazLSfVuB\\nfGQRvpBkLCwf6k0DeAFDJRRf9ZnwfWyg8jqe2jPFOuVNdb84rzPDhNpzONB1hydw6rD366fV78ug\\nrh04bgYBaoUZ+dEJ6oi77GXan2ns3gJFcRE7e6DxGyITc6ASZiDDzz/TOApYw8qXtvTDqfo8Na8y\\nJ0Auzuc139bYv+0c6P6h+FJmWW3vjkEf4BPlLQ26lauq+96h9rkztlceCly1Q9BaoKnKfyDfah+B\\nXAH9lIQvKA/yM2FwHWY0BspxXZfKwoUToiZYAzMF1JlWUQFlXZihjjoJtNfoh8q5l1EBRFkxBqq4\\nUGd/+kS/G9H3ximHMdwvlQLcOhPtRdonvFuCFKqUNP/l4MEbgdr1AaLP2mqXVFXteHtB837DAcHo\\nCpnyYFfvH6k+LzoXtCTlnCtrXQjgoqzvaR4uzPT3XEntPT2H56+s9vrTfyOkY/+IvQpccqNj2od3\\n5xCFXLqj9nNApyXCPRjcPZNJ0xZXtJ9d/BacsbxnNpv8xtHn0QR0V0tlmoO71TqgulBnc1Cdi4FQ\\nH4xU1ibo+gWfdwLQWh6KVD04/2bH6hPnEu/hoKb6KAjH8CEP3hcfXyglVL4uiL6Qly8BcvGE/fyl\\nHPP1VPepg5hJF1XvFIpfbVOf1850fQFOmzycWfM+XFnwv+ZQ++zAFRvjXSphM57w+y1C1EQWWWSR\\nRRZZZJFFFllkkUUWWWSRPSP2TCBqAtdslDNLEokfpRWNjK0oKpu7xBlcRxE1d8QZVaK+g4n+fjJV\\n9i8GQ/qQM2+JFVVzFR6PbFaRQhKblrxZpiREI19SZG/SA+Uw1f0qN1WOzBHogzu6XxcloBLKGcMl\\nRRIP4XoozytCnysqg/xkpLPTt168p3KgJrJwW1HTuWuKGJ4TR7u2ruxX/0z/r5J1zK2RmSDT0oNx\\nfAWVgUZXWczhPJG8qlmcaGbT198uX9I9aoeKMsbzqlt2UX1x/hnIizOywPAo+Ge694C/e46irsm0\\nnt0EqnHq6/pKC/WOOOcSpxdHSpj9N7TSEEhOfIHIf0YZSI+zo602kXAC67WZItwhO/rSmvogAQJk\\ngprT3q+V2ajDX7H81ZfMzCwXU71iZdoabp3RSFmWeh32e86Qlm7p95dWyQTHyYA8UTukOSs7sx0z\\nM+vUFJ1NLIPAWQPFVVNUOs75973HytSe3YdR/YrUVZbKel7GkQ/HIYnfh+9kgm/Fk6DQ1lT+xYGy\\nYeeuyh2faYzk0vL99hE8HjvK7Izaig7PLYDSmqD6ASogGdN1HooX21MQV5zHLC2pfGuw9c9AqwzP\\nUfB4qDPDg135xzznRXvORWPOZtll0EBJ+eCoxDiG58NpogqHqsYUxZr9XfVBhbPsTkl1PGK+ycJx\\nE78q3obVuH5fWiRbX5azjUGl1U7UZi7IvzHnordPdN0i2ajCKhC/uMZe/rp8M1nU82JVtV0sVB4D\\neTEgQ5gM1Tvgg8qX9Lw2CJE11KayN1CxQjlt7z4oK1Q+kpxTn3IWF2owq4e8H0WVvwGvySJjeHVD\\n89LgWH2YQxknC/rNC7kKCvgiCkUTMhQF1Ov6bbgZAvVPE+6WAnOEMwHBmFT/Jq8oKzZq6/tgJh+/\\nqMXK8DfF4H5Iqn2HwL5euPu8mZmtr6h/P/1QmeQZ6mFFVAMSq6gLgKDK4k8uvEuzDllI1EbC08jF\\ndflXrwsqhsx8qGQ08IfmwdOTq2qtqoCuKk9QPMQXRkfK5tdY+3KeEByFTbXRzbu3zcwsyXjbPdK8\\nlR2iakGWqtVQX7jwMYyYP6Y9zQNx+DHcOKgjFBHGLaHTUiDkZozXFtnrwNP/k1fkgzdf1nzdB8lR\\nP9Bzj4Yos/XI1iWZ18ic9scqx9GOvi9TnjHoW4/1INZBaQJllwk+vggK6qL2hygufvJPqB6uC223\\nsyYf9vZVnksdrRvbt1SuJ++Lm+abPbJ0+1Jf6pIZf7Sn8/63t5TZfu/jOvX4qZmZOV+TLzx/orHw\\nmaf2unL035uZWQLejdE7PzIzs+tDITzf31SGfC/5ou5/BX6WD9ROxSWVfzJGhfC6kDPj6o7+Txaz\\nwf/Hb6h/T33Gyk2tZ4V9KSctD1TfESotqUDlv7Ot9e4n39T1o18JCXTv+y/Ze/9Ba1ZpTmib355r\\njf3mpu5x+TkQEf8oPp3vPK/x0IxrPD71NK+++z4Iwy8LTfCVohS3Dm9pbbvyVD7VDfS8z7tCQuTe\\nV9b7o7saA+V5tcnDE6GdEo9V94vaAAWu/mcozayj7oR63a011Yeha5kpijwD9WF8KF9q9lXuBdBS\\nadaHkGulBwIwn9FYDVVAZ/Nw6oBSCFEZSQMlh4JaCTWjBvN7dqbnuhnWlyJ7DoRqYqAYnFAFlYx1\\n74nQw8GOxtLmZfleNnyNSKCw04I7cobyzwSlMhQp/TJjN6751wNxOGFMx8i0j9qg6siQd1FpivXZ\\nQ7AXCzkugjGKO+w5Oj3UtUJUS1/1XoZiZnlT7T2GP8uBy2cw1pgbgvBp1dSvrV2UUZ2LzyU+81Nq\\nBJoJxMI8exOHNWPUlM9UJjwDpHEwBHpe03zju1pjvbEqUX+yo7Khrle+CT/Ton43oW1qJ7pfGgXJ\\nflO+4WdCpUO1WRnFq3ZHbdXDl+ZBZJYTkI+dqjyGylIN7sLsoa7rs/YnQVvlKHeX/XYcbrXSFu9i\\nHtJgXZXzrCufOHkKZ0qS9w84y2agEnKg18LTFV4a7psD3mfOUQwCbXGM+uDuhxrzJRXT+neENl64\\nqsFa3ND66bNHOQ7fd0AWZVBYy18CTX0FJdG7mlNiIUr5gjZDCWiG+l73XSFp/u5HPzYzs3vf/gMz\\nM7vxdc2FNoSntcupC/ic+nn4ruIg3tkLPn1T68Mvfy4eF9/T31dvqL9DxboE78Dx+MxyoLK6RVBG\\nIGeCUOkWpF4OQp3Tlp4RD+cjkNCZmHzS9VD6Qu3y8bvau3RddUL6Cr7Oe3QGRNzgOPbPfue22afD\\nCbb0sva538zpuhsoXcVq+v+AffKgKx+tgMSZraLu1NZ6sHMEio09VoL4QwzltQbqejuoRc9QpSrg\\nkyGPqeOonFnU7dw8ey0QgUFXPpnNjMzlt7/PIkRNZJFFFllkkUUWWWSRRRZZZJFFFtkzYs8EosZ1\\nXUvmklbh3L1LBnwK98uILN5sHSQJBwcd2OYHvqKLl1OKZpbmQX3MK3q7v895bpjHE3OKNtYTQlts\\nbSiqON5VtslbUiam9lAZmFFK0dR6Bg6ZNTLyVd23hiRG/q4yQSHDexgNL99QZC/NWdvEmaKm619W\\nlur+O1JaWNpQ/WZVRfaO3lbEce6asor+oiJ/GSKd5wvKoqbTqv+Tp0IxvHZL9ekewkMAN8bq3XnL\\nDFX2w18psly4ozavp5Txax6obdeTilKmYMqenKCs1eFc8Dms4mTXUyOVqUdk//OHiq46BUUhG57+\\nnkeVye1cHClhZjYrkf3m3HQurTZMkbF9urdjZmZnR4qAr8NB088q+puaI/q6pjZ24ICovwWLPIzi\\nG8vyoUUUXJpklnMJPe94qPPzTRQBikuok6zqvHxhhUxFk/OXO/jOOeez8YUx2Tjf1e9X5oVOyBRU\\njj4qXM26Pk/JfHt5RXvXlhTJT47JcD9VedqP5NPHPTLnC9T/lnwo31Y9HM5LhioDE5+zwSBwap/A\\n6j9Qey7fk2/nUa1yq6rfbFtj4vwJmXHY61uciV27LRTK/JzaM1QvcMiObR8xxqYqR5Lo9ZT0XhoF\\niotYDB6LTg+EBEomE9SPEqg9pWN6xuaqMmfvPwDVg+rTPCit9DwInasap+eoxfX21WctsvZjzuI2\\n9pUp9lD9KSzQ1hW1fe2hfCc8e1taQdUJHqUxHC4Pu2qT/Alx9FP1fS+G4gM8UwMQP/OXt3S/l5VN\\n9z7R2BvkmD/3Ve7GsbJSAfNmoYLiAxwCQZ5sCgjGdU99XYLN3i+EqigodqEYlF5Ue8UGZCpm8p3m\\nucb8ZBiqfOjq6Uz98nhb/ZVdVb2XKsqubZLJyFfhx/I0hwzPydKhhODDmzUNvthcUvLCZQ+OBNB4\\niVGIDiNzU0TBiOzndk3ldMhiBSCdJiiVjUJlhb7GfDql8nc4mx1ylOVyKndxxhxVo11RSfAXPct0\\nQYqguDJFeauOGsV5R+N7UNP4i8Nz1jrR+MstMM7iygzugHDsnsOnluTcdJwsFj6QGuv+ATwRDupH\\nyefINMKj8flDzZvtx2SEUmrTfDlEKWkNmoNzINhQG/pN+cZuTeUJub1G8L7VBir3eUn1Wt/XfWfw\\nBXVZP5JjFF3gj4utal5ccOWz/UGf++mzR1btwvaC5rtR/e/MzOxVsmhL83r+D03PqU7En/Jm7F+b\\nmdnXXv8rMzOrF8QZkPd1n+yHX1K55oQ8WSppTiiN/sHMzC4nNMf8kvZ8Hp/otKRo9OYNISqbAujY\\nrRvfUf0+F8Ll8EX11/pH+n/83S0zM1vc2jEzs4ynuS3Y0323UYZcWVQ7ry2rHTd//jUzM3ufOWL9\\nBVQfS5q7fvFIY/2bHc0xyd0/MTOzedTETr+iMXEL3oHfppTB/fQvb9v0L4RsSQ6/aWZmzq/fMjOz\\nD7b12/n/TijPHw61j/nmOcqCQ63J92r6bP0J6nn/VVnx8itkOGdCfPzX5dfNzKzwjsbl9+Iq21/O\\nqy1fe1t1672mNnv8lnx0MfHFUFd91D62UWJcA6WanwMJ6IHQXgTly7415EZo9tVWCR/1IlDDqQyI\\nEVBLpRT7zX6ISgY2O6S8IGcC5rVgDHdaEfRCKhzjet4AJHkQ0z8K8JPk5zTfNVA5vQKXQy+v+z/h\\n9wnQri5Idh9+kukwVPYE2clY9QyEKYo4v+OgJCMPVZplZvBNgTDMe3DSgLABZGKjjMqRjpP5Bl3Y\\nhyfLdXTDPKiC6grcF6BIirT/DAW5DJn+zpQ9Euo05qs9ln2hnasZoRVz1O8itnRV+7p0THU4RgWp\\nDSqnelnjKbmMqg9KXOV5lBJBPfXami+H+ESI7L60IiTHOKn71lE3HcH35sKvVv9Ea24MxZt0EsVJ\\nB6Q6PhJnf5YEDTCFJ2kGP8mAPqrF1LYpEDVD+IcOQLPlZqg8ldX21Zzmtx6cLu6h6jHz9P84a+QU\\nDhpe/ayOamEbHqE861US1aIYSjyLoXJuDu7JseaCDr7WZx+ZgmvGB4Hpw9nmJNinU594F1QH/ZHC\\ndzrnrFcodzbY5867qt/qCrx6mS9Gwhmwh/G8cN3VZ2UOVUDUwvozlIOaevcz1Jpc0CYp0L4T9mDD\\nmOp9BlLpdMo+YQi3Tkw+PZuiOtXXuhbLZmyW0T1iKd5ZamrrH/97oTmfNDQ//8X/+j+bmVk20LUA\\nry3Oe3mwCE8pCLr+kdaa//JDrZ2d9o6ZmVX/l39nZmY3L+tdynj36k1QF0UVLzuHunELLptQvfQG\\ncYMQ5T/QHqnXUIES7GUmWV1frYLuxfcHu0LeeTkUvJL63SjPfdN6/m//j380M7PDB3o//+b/qHJ/\\n5a7ecUaooE5AFOZALyUz6pvjE5B/s7QFFwPURIiayCKLLLLIIossssgiiyyyyCKLLLJnxZ4JRI2Z\\nWcwcG6C8MyYK7JN5PkYRxwsU2ar3QS3AYRrvS0YAACAASURBVJAiMu+SQT5tKNqYTCu6mawqQpaA\\nU2KV8/ItBcSsuIm6y7Y+i5cUGez04dVYV8Qusaqo5vypIm2FDUXQ5q4qMheqgkwKiiKnD5RBSLi6\\nr8vZvkyC89sdRTFdV1HwfEXZz3xSUdBbf6ho6qtfU1T+4YeKYrdMEbmF63p+eqR421mJcn1FWTzv\\nbbVbAjRMZ1Q3S6vuEyKvfp/sAOehJ3AU7DxWmetnKG1xjxmKCnbEGVAi8u1TeHLIFi/A6TKJqQ39\\nvP5f5EzsiCzRRS1G+VJwnFgFBAbR0sYjlSs45UzrutomzhngJFkQl6xZHG37QZws1xrKNgEKYDX5\\n0MGHyiQubMKZAI9IZU4+UkFhzCPDu9cHIdJVu9QbKle/h+pSR750Fp79vyVfmSdKPIwpGhtwpjYg\\ngr68hQIQigS9ib5/sqdMQPlU/RYn+l0BOTR/RSiFKqixc1jk/ftkKjz1rwu/yj5nmYO+6nP5tsq3\\nkAHlMKd+PttVNrM1IeNC9i0Nj3kM1v88ChOhEkWfTOwoS0Yf1bC5NZUvXdR1AQiAfvbiai3NOmdm\\nc6grBdyL9FJwJt/wiPDntvS5uA5HCmdwPbhKlleUSauAaGu11KeuqY16O2q7s7Z8JQtip8r55ukS\\n5863VI4+ClkJkCAO0jnTnto0XdVnviEfaT5QVqj5FP4eGP+L65oXFm+oXCtL4ruYMrb24PnoHmje\\n68PV5fr6u1vRfU5c1DHiGtvzZaGfroCe6FF+O9N14z3dr32EkgNZrVA14/Fn4nIZbqNysinfcC+p\\nHa+AUBqvqPzuUBmZ1cvKdC8X1W7xZMg3hXLZrso5HYMSIwMyjHEu/Qui80YoEDVCv4AH6pi57aSp\\n9l756l2VcxKqhqg8B5yzLxZRgsjId2Ma0jY5R4mpCwqOMZHBPzYuCbHpoH7y8EhIrB4qUsVc0cxH\\npaEtH5mhoJVIoaQAumn5hvo+7apNvJx8+JA+67bVxhMj+0Tf51dQ0aujpoS6VLtFZi+lPijdBhnz\\n8pY+b2s+8d/UvPhJQupI6bp8pRdXOePwzO2j2JA50GeD8gxR08iClEz5uu4kQda9rvnn/ueaTxeG\\n8o3F+VAZS2Nx9wkKaJVQMUf1PO+q/n3UN2LwM13UWod/b2Zmy/tq30eBoCxLm+JVufN99iJ/pz59\\nfV5zwM8yXzUzsyuPNSZOCyrn2ctqnxu/1hj/5d9IAWnjzzQXvP2enOe1RdX74US+vk4GvgHa7quu\\n2m9nKjTBk02N2Rf/TnuE317V99klPf+TY7XXwWXV/56v31dJ5eWKW2Zm9uMf6/MGaOPsl35lZmYL\\noBv+qaf7J2+rXf/pkcbwpb1fmpnZBMXONGMht6N1JheoPdZvuvbRj3XtL4dqu+w93bs++LKZmd38\\nK60p305o3O2ADsrfk29/dAzqCzTWwNfaG4x1XcoV4ia5L59pJVTm+1mpSt3eEXL5jblXzczsz38j\\ntajnMl8xM7PeVWVUL2opEByFCYiXY61ti5vqm1gZNC7o026X+R41kGJe8/cEtSgXJclpCsQinCkG\\nZ4yf0lgNFXba8O6l2BdPyLsmXe0hYmG2lv10CX6SBrwInTO1YzKh51VQcPNCxCRcbLmEnvvabflS\\n01F58yjBNaAIyya0RzoAEZ8H5RcH/evUVf8sxZrE9Pz4CCQQKlGpnO7vg35IZOQ3PRSH8qAOpg5c\\nNuxlU3BIWBrUHXwfL8xrbF0Zq70bzEkGWjjM/Cc7modLSdUzkYd7aBO+RvgXi2dcfwFroHoXh08z\\nP9MzxyBsHBQRkyjeuEXGRlp7Bg++PI992TEo+fauxlcfbpvqZZV5EXR9JwEKaQxatC1febKN2hP7\\n09QiiIys9rOL7BPLm/gm+9bzHb2r5EGWe2neF9b1vCE8P1l6t9uTUyTgN5qmNf+4SdalU9Wjx57M\\nTWkvlizig2kQPm2Qk4fsq1EzKm+qvst5lLrgHanAsbnHOjjkfSC7ADoX7shrG6hAoZ7kBKC8snpe\\nkAcVPNT3uarGZhp0xMkZ76pPNC+fNnZUnysg2HMXVwYzM/N6oNUGIC03ePf7rny7jNKmj0rsDJTx\\nzAety76gBfQiMQRVMtYf7nyHkwKv6r6bV+V3bkz9WT/SWJ3w3nP16hULGB8GEjjkMqwsqo1PPa2N\\ncyg49uFgqTfV5xW4HQeoqhYKjGvaOJVVH3QmGp9ORuNrxnzkg5zuOKqDtx6uMfr/A9D8y0XQvqi9\\nZuA3ClXgyiGqd6jvjx4zhgLt8yZwVnko7MaQEp7ynj5B4TIP6qucUh+du/piwVM9xq1/jhBMM5bd\\nGIq4qOol4RxzU36k+hRZZJFFFllkkUUWWWSRRRZZZJFF9i/NnglEjWOeOdOCzciGOX1FtMIzuLMh\\nIfuh/s4RUxuhyjTiqG6/r2zSDFTC1u0tMzPzM4rgjWO6T3tEtDWp3+2dK9OSKcNtc8LzOKqazhI9\\nPdV9khNF8NpPUYUJYIc/hE/gkaLQzpkiaDXYsE9HiggWY4rM1e/DeUAkbpiBu2Gq6PEMTpt33lN2\\nq39O9jCrKPI4ILMNxcEYVYH3f6VM0e6JnpvxFDV9ujuwhSTM0x2y4J/o7HvvXBFdp6E26LdDVnfV\\nMcaZ+V5Dmb4UZZ721GcDkBFt0EH+JuzzG4o+zuCyyZUVvhx8ATUfM7NUErQDQJzAFLGuc3a3M1I5\\nCxVFRX04aPpkZoM+/B6usubjKfwdcCPkl9WmCZQRRkf6XFhU5Lkwp4j2xEJUlPoyBf/G8bHaa+Lj\\nEx4R/gWVx3PIBsGh0OX7whgVrn2Vq9YCgdNRuVKobyVWFQn3UF86e6QMh98H3TFV+xfhUHDhM2nD\\nkO429dmGX+MM1a9cmjPJII1mZNEcsn7pHOe7yYwcfiSUx/E5DOlVMjp5lHjSuu+wD3qDsbb34Y7+\\nn+Dc+ILuW4CLJndFqLQi59r3SFrNahdX9PFQKugQ0c8tKzvU6KgusYza1G/BcYWvLN4USiDegiME\\n1zx5orGxR6bNOQVd1kJ9CcREigh+JgWkgrP8AQpaBPptrqm2DlB+aIAGmx4rk+sG+n1miqID0mVd\\n+viVV8X4n7+xpcfMoZIH79SUs/ptzoGfPxHqYQTi7spz8nE3ybnuNCpTN+DbQDHCg2vm/EiKLc1t\\n+Zqd6/5W0vVzJWWsp5xDn+7o6wKqGytzatcc3FvVa6hxJeFRASU3t4JaSFPz9/SR6ntUhwOhr/p4\\nKKKVF1S+LHxNE9SYLmoOnAxOU/efurD3z6OkAKdBEPA9nGAtuA8CEI29GMgsMkyZMqiwmNrpCAU0\\nhzkqBUlPL0sm+kz1OuW8eMbRXNodOVZn/vXhu1lJbZmZWRIOgWJRZayQ4WzBJTAEATlGHWgMctCF\\n9+fyl4VWqIGYaRzAcXNfHCadkco+l4a3iIzbgPmlRSa2P+MsfFXfTxlb/Q6qTahylOfUdqNGOL8x\\nr+XwCfiNesuM8204E+B/cvj0l7VmuqwfsZb65vhAv/cP4PTKq08SBfmoU1Wbx+tfjDPg86bKtbAl\\nVaevtTR2Pn5L7f58IFWlz+ZQp9uVT780kA98NlW/Xc/BL1f+0MzMtv6VVLjiAFP7b2pMf7enefXz\\nNX2+uqjfDbaEKnn8txpLf/stIWLu+PKVl+Fh+Yf4m2Zm5u7pfP9ZTf3SO4bXbxeOm1d132pC7XYA\\nb99LsTfMzGz/y5rHq6fa49w/VfnSWXHvvLKh+fxjuGmyZfzlRaHiZj+DH6uj9nJSZKo3/tGcQyli\\nfX8m3p+nH4rH56AkZZN3bmk+2Jypjk+n+v3Nsrhs2sxzN45Vtl/cedfMzG6dfNfMzHZdQaSfmzEv\\n3dW42z3cMTOzJ3+K6qejNv77N9SGr18SV8JuWeifi9oqfHmTa/IV31FbBCBJymvy3WRaSMMpXDmL\\nOfH/teABsrbK5cOBEKIRPPiaYqAvMq7aMkCNKQWPhYe6UZHMdZBBAQb+pgSqpUOS/HlUpmpIr52d\\ng+iG5yS1DIKHeTcOB+O1r4I099k3o3xZSggx45VQZd0Flb2t9rh5F/XBqxoro5juF/dZjwuaP9Og\\nKmJtfbLdt0EDThkHXj9Urxz4RZIsSwYHRI89XGHGfJ1SO5T5NBCtcwV9dhmM2TNxDXWZc7MrrC8L\\negDAewviFySWMLPzQ93zDFRA0sJ5TGVJgyxOB/BoxPR/l/k3CaK4hxLOQojiCnS/47Hm7QIvK+Xl\\nReqm+3dYsxsN3a9sqsQZCD1DkcbLwJt0qLon5lAyrKB4dar54vBM6LVZHDRrVmMxH1efVFBKnBuy\\nLoBoHIOSCBW5egsg2Vu6Pkb7FFH4moxRKANlm3X1uwF7u7yjdhnz3lH/WO9wvUeaf6bwNh2iSGmg\\ndwslla8AAnVcxecCxhhcMCnesWJZON2ONXYnKa1n8bz6ca6ssbAPv1QAD2C/8MUQnAHI81YLLiG4\\nGucLKs8I1LNnGkMDEKMnfZUrPPWRRDHO4T3FcVXOgo/iKXvKI/o5W6QfGBousOBRf2gJOPZm7MM6\\njLdbt1HIgsMwxztMEmSc29U9PdQucyCgO6dqqyK+/oN/I+XBYVPrwAo+PYQrdjpVH1eLQu7EUJdq\\noXr6zv+jNeuF17SmvfZ1ISNjiypnd6A6V9PyHaemvz/dkQ+P9tVmV38ghGUX3qUECMI082Q6VOuD\\n9+dLXxeS8+VvaC+1AYdl7VBrbKEAbynvPj3Ur7I1rcWPTjQ/ljqu+eOLcedFiJrIIossssgiiyyy\\nyCKLLLLIIosssmfEnglETTALbDjtWeuUjGNT0b6A84kjGMIHvs7PeURTiwVFa+cvK3JVvYYWPdnG\\nMqormYaqeXauyOARij3uCDWoU1jz4cL56EDnxxtPYArnzGtmCXQCmZAnXUXmBvB5THdBdXA+MYce\\ne7wJ+gToS6+m/3c6uj6d4Vx4G5bsMhmTin7XRlXGH6BegyLH0x1lkIZ9orglRQw9zsOuxBSNLscV\\n8WvsHVtANHMpUGR5cghr+zHKCWRl+kT8nYkyhKUtOGIaKqMDGmnskrkMOMgH78IM9MF2h4zuou63\\nNYLPAd6ei1qHTHEJpI7nhlFbziFuCPFSqqAuQj2OPxI6KQNnTrWj66cltcPyVWWD5q4qatvaU0S7\\nTUDcIyt1ShTUGcOZktQPDk+JtKd1XXERNSZOHy54KtdSqBTmq7y5mq5vfabP431lXGdp9UdhRRH9\\n+VVFg8NT0UMi7skWkdimslrNI93fhbE8MUaRZ49MyGmddlR/zC/LN1N5EDWoyow+JXr8WP3ZOVS7\\n9zr6nOX13DTtvHhTqgOFquq1/+aO7vORxvDjI0XHO7sqx9pt1SsJGsQrgeZYIHMAZ0YyA79HM1Qa\\n+v3Wh2+o01BdWyBXSlW4pTh3XbysvvY4x3vYUttNRsqstU/1GRuQ1TKyFUtqKw9OgSFOkuZM/kI1\\nVFMiiwHvUYx0RTDS9f4TeDhQAWnsqq2KcKDkCqDRrimz7MGdE/NUjwnnzZsDEIVx9U2YYV2kTc/h\\nMSmBetui3i6ZyglZM0MV7nBXYyU346w/mdpQWewMlNf4vvo0eZPz1HDjTC6rvBP4QBbg+AmTBr4f\\nntNH1S5UTflUajAht8u4DlKQeTpBdjEBCiMDP1Q6G/IffUEFuazKcem2rgvI8s3ILvUSnGnmiHar\\nAbcEY3eBLF8rpt93t9Ue4zxokiZZLdB/IziCHh9q/To60Y0X8vKP1SW1U7ZANqtxat0uWeeEnlVE\\neSrn6hmZDfXl5hW1eW1PbfqkI3TW9AwVJRBpKRdOlTNlYNucE/f53m/NqKv+n+xynrygjPCgpTb7\\n4A1lLn2y/jMygSN8s+epHMtwGGwuwWlDWxU8zZfeovpy9as6O99HLW5cg5NgiqpIWmMpj9qg39Lv\\nHNBI3RP1jePAM5dV+8SmcB6UOe/e/mKKPi+/oPv/pve6mZm5nEN/aVMIlO47d8zMrPGa2vOgp+c3\\nTSi2zAvKEsZ78qXTn2qsdGda/977I6E6/iir8n2yzvn/lnzh54fyza+m/8DMzOZf/1vVA7Wp+t/+\\nsZmZtdeFNqniY05bGeXX81Km6L6mcv80K5TJ/j/p7z7n7PdQMst7QsTUH6h/W3XN08X6jpmZfSP5\\n52Zm9teb4iS6c0N7j7dOQZE8+o3u+yfqzy/toaQEUjQZK9jzWa1VT+CoKtfVVrvfAKH3vhSnnt74\\niZmZPf9Y/z86kmLWdU/zQ/yOxpHzlpAa7wbyidJM+5yP5zX/T09V9v65MqZLHSlu3SQ92X5ea2Dy\\nDa05j2Ja4y9qnazmxU3427Jx+C4ampczIDyCjNogRxZ8koVjZ6C9wixUvJkw7+1o/uimdN0iijJj\\nMr5BoLZNdVDVg5/D4IwZDuQ76az6OJXTcwMQSS1U6lIgSgbwa7RRIQyXBWcCyrqr9amSRymoonp2\\nWQ8S+H4X1MFoHzQgXEKxrr4vXQXxA+p6diZ/mGY1JmZD1AWZRxPs10egkdOe5pQkKC2bwHEDb0kf\\ndHMKHpQ+XDoz1K6ycJ8l4CMxuC2TIBy7BmdZUeX3lunXOf2/2VD93eDir01b7ONSFZXl+IRnw6+W\\nDbsuUKM3WKvbPc0zzp58MjnS3yfsRRJwsCxsaX4dD1FthduvGpNvzef0+/Kq+mC5ApprU/Nbak7z\\nvs++c1iG75MlNYDjZGlFfdMbq88ffaZ1JoWaawekigPCcgFkShZ1uyQclaUUiFDU/uogezo1fAKe\\npAxrbQEk6NyS+v68p7HvhzRDtFfAHmDk6T4ZVLWy8KKEXG/nHbjU2JNl4FTL5VXegLE04D5lkO/D\\nuD7rgxBloTkmjVLaS2W164A93SGqjBc1DyRVLgmfFXytCZBUgancNRBFx9tad974mVCFX/2elPTu\\nvUo5UDaNg3axBjxUqGAFIJYS7HH8Mu8LvO9kJglzUMGcGmqgzJtF6pxG1cg/YbzQJ0nQwLEB70pw\\n+RXxsTP4NdfWQQzCzeiY2iy2B79SRb9Lodo6Y/4/b6FA3Jevn+7rOsdj/MIF43ia36e0xQAE3xs/\\nkxJh15Ovbb0mhFAF5d/MkD4GIVkEydhA+SuX1fc9hxMz7wtdO6xrvvauaW/mrOr+QRPVKJA6BfiX\\nppawmQFl+j0WIWoiiyyyyCKLLLLIIossssgiiyyyyJ4ReyYQNea45qQylvVCvXWF5gZkdrtwSvQ4\\nV5fLED29qmjUCIqCoK3oozdS9LhWhysmpshaGk6BMYoY7gwVgbiyVFMUghJEZxMrRFlppYAg6QRu\\nhRr8H8mOIv8BZ2wP28pC1mGNduC08YiexWeKtBVbuvGkot8dDRUR7DXIJp4rjhbzVJ8CbPgZeDz6\\nPkpFZaL2SGn0PlWYtDlQe+0HsEx3HCuTPfdhMy/PEVH2lNUowAPRS6guQVg3mLC7S7qXB3dBEgWu\\nApnfBFHYcUrntpfIpFouFTaemZmlOQ99UQvVmnIllTNButqnztU1zuSjFDDa1bnwWlOZiVmg8lXm\\n6AsY/IMq3AUO56IHZNXgp6ijlpKEeyYHK/u0rfaLkaVaeEGZ7Us39Xn0SNnC4ZBz1LDFt9sgYA7V\\n18MwA8AZ3FwSZYF5PSdxmYxER33ZPyU7xnntkG8jd1XtWyYjMWwqg/AQta4cUfHSDfl0+WWds8xV\\n5PvNj1CDQnmmR0Z71pMvDUAJZBdRB7ikDH+ejE1noAj/YEwWMcYYot9zGyCF5tUPQ9RMAl/IgcQR\\nmXmykV3o69Nw2FzESqieFRNqMwem/uqGfDFT5Yx9XVnngDLEUYtoN1T3Liik1CoKOQsoBKC+sXhZ\\n2aoRXAF2qufMyqprY4DP1LgfbTE+UB8e7IrzZgq6yTinnsopc1qdV1uUn9P9BqC8uofKwvsok5Fg\\nsCkcXH5OPpyHc2Z9oOz8zjtPzcysj7JAyK4/rSkDMEJJYhSQCV1Q/aogCZsczu8O1cf9iXwqzbn2\\npWWVuwDyZfup2rd2X1mzWUH1D+fzOIjAEO1w/KaeW62iugdfURzlsyw8IyE/SXoMGg8Or/HgYud8\\nQ8uRrXOZV5twDdXbKm8bda82qlOjNFmmIv6QQQEuBUKyp++b8HyV5+HCQEljBRUwxFSsUyPbh4qX\\nH5fPH5yQYd4fWZH5qbygvm5xTny/rvlpjezVFBRW66nKfsB8nDaVzUVFIwZHwNm2UJxj1qrpQL7V\\nj6sN3YLK3B6gGMb8kZipbhMykJZEqWxO87EHUnOGIlhnSc8dwxMRh9co68FnVGCMduX73QN4pEby\\nqQRZuxjnwo+PNT8lQVJOe6DdyP6nF/CRK5oDWlOVezyBL8S7ODLPzGzn17rPVVB0D+/9k5mZ3fxP\\nUn1KfVnle43fv7Go8/JLBaE3PvoYvos/kO/fe1dj8eH39ftbe8pIn46lSNRG9Wm8qDFeJQvZ31Z2\\nbq0rH/twVef6/awyqetxlefma/KlX5z80MzMfghn0eZ7f2hmZu4qGfSX1GCtB/K524dC0CyQ7eyb\\n1q/x1/X3Gz/W/R/PhMR5/b58ee+S5rCtY7XA+XXUBmcgjI7kT3dviz/gPXdqX5kISbPrCz36q6m+\\nu/NTuFngUaukVGanrjoMPGVAcx3Nc7/ae93MzCYlzRv3DuRzw57W/A9jut+fbomvJ/lEbTpFzS/4\\niear/aLmoScoaGWLF+ceMTMrQmYWg2/NqmqDaQultW3QvmtCMx3VhQaYgmZOxUFQ5zRWzuG5a4Ng\\nWSLL3wF9kRiD9INHrsW8MwMJOpvAWTOn586q6uOJozmkz95sAloOcID18/r9VjxEbqKCCMohmVJ9\\n2rqNJeZ0nwxoie4BaflDjYlsWe2YP4HHqquxG8Q0BoagBvwKiFT2HEOeGz+BUwIux2TILQO/YieE\\nKgINjYOkmfrwGCJ3NeI6RLEsaDNHFXTfDrxbWX7goS6VWtBcF1/X554rpOm4De+Wt2UXtRzzY35R\\nPuagstlBObAUB0GiprMF9vYjlAjPd/TsnT31wdGusvcl9gArz6/SBrpuigLlMWu0x5oZdEF0o5bX\\nthBRovukVrWmXbqs8d2rCTH3FGWd2UwFXF7X/JBBKqcGr5xrKm8NFK4H5GUxrzHhw6EzOIeHNKb6\\nH45RYATpuZTVfJejQQK4yMrzIJDY9JyiapXjPWUCui1UXy2B6MwEus8a70gAI20QyIfj8JD04ZHb\\nOULZEx6ifXhZKozVSRwUxa7W6iHI17nrqDOh3lqusDBd1ODYtBC9zfNjKBm7HfZCoH3roHI7/z+0\\n7wRkTqyt77P4uLkq/xycaV24jhzaYeBqna6iQtboHpoDp4zXVZ8lNHytzB7Bh6czGOqZM5QKoXQx\\nH4R3LJxn4NdMQTLrokZXAZHdCE8rwH80aem5eTgQe/TF8rra9ht/9nW1VV5t3uOkiwNHjguh6YTz\\nCH18Yus5vfsM4UXKwzvU4mRM2kWRDa7GoWl9SXP97959eXdz4dSdogab511v2qEhkvKpGPGNTBll\\ns1TKvPjFsDIRoiayyCKLLLLIIossssgiiyyyyCKL7BmxZwJR43iOxQoJq6YVGetzzr0+UuQpUVCE\\nrWIwcqPb7pU5oxtmVsMIFupIrQ4ZD1AXbSJtYxArmVmY5YftGQWbFGdgR2RgpqgmBUT8Y10UeyYg\\nbtwWn0RjS4qeFwj8T+OKpM1gXM+gD59eBJUAgqibA0mThy8Gcod+Fy6NPRBGR6AfOIc/X1a8bYbO\\nfZJziE5cIdAUUWa/blY/BSEzQEv+GNbzdbVh3zgHXlVdZpuKcCdh7nbLfHKeOkBxpUUG1uEM7hDe\\njZqv/6eKKBNwJtYyXyx75cLK3j/EN1wUeobKCI+K8p0gpiisC4fK/FjP3yDS7RERH50ogt4nkryX\\ngfdjooyEW9B1S6vKiJTHygZOzuVzrRO19YSIt+fBWXOovtnbVRYvQcp3juybP0jzXEWr02SYyygG\\nJThHPeT8t/eIs7VkYEYdfKQEimoJRFAM9MMjRfj7R6pHwFnj+PXLNKR8xWlxFpfMc7On/nKLigav\\noYRkM1jup/L95lDlCRqq7/k7yrx22v88E1G6rUzP0kz+1D9DEaKh+j3ZVqbIy6PoADqB4/VWWFC9\\nSnZxtZY4/EBejOwDmbUhZTv7/L6ZmZ2S7SlPdO9slsxAXmXwHFSh4K6ZoaY0hH/JRXVofowSGBkB\\n2+d8eV9ttAev0RBEW4LzzlMi66GPFir6ewLFmoCMcA9umvi6fLB2Xz7Vf6i+XVzkbO+mGq2H0s8S\\nGYhLzyuT29sR2qI2gPOgo4zIcTfkuNEYyOc4v90lQwo/UhxU09z4mpmZDUA4hiQsrRkKApxPT2wL\\nLXCOWkdqrPoggmWpAgoWFd2/M9+mfcjaXQFZ2WN5yqg/a00QhzXVfwzHgGtfbC456qsdEpwD75P5\\nmaIiNYLjx0GZruCTmUEVaoRiRIisjMEz0jmSjz8FxYHggq2A5qjOaYz3yPQew8U2GQnxNCKD1ZmM\\nLI26wxzz1Rk8CqOufKvJOK6gxjFDycS7qnkh/f+1d2Yxkl7nef5O7Wt3Vy8z3dPdM8PhzHATqRmb\\nkimIjilasmlZsZ1ECBwkiBEY8E0uHCCBYfvGSABf5CZOgixAkBhxgsSJIduyLHijZUqRrIg0ZVJc\\nRXI2stk9vXdXV9e+nFy8z08PpEnSvOB0QfM9wKCmqqvqP//5zzn/qfO95/0y+nx6Ch8d9mdvbTGe\\nRB1zMOTcF9QGTz2ofpu3JKOKvj8fVCd9soSUFlUHHfabv0VmsG3ETs1AFqktxn1Cmb1tMp7RRlLP\\n4T2Txw8OX6fUCVRqh2QpLOhxOFJEsIxCr9DigPhDDU/zPWRXqeKhZq9yMz4ilTNkkWJ/+r2mjBLf\\nuKwMjB/lXjt6UeU8S8S5/5IypT30I2Tw+rr2vzcukUFn9WtmZracl4dNcVf1sfVpqdBe3nvCzMxO\\nlpWJyA5+38zM2s/q/VMrUtJczWvsOz+2EgAAHbFJREFUWX5HY9pKUxHwea5752HazYbuR7kL6lM/\\ndKAxZPO0vGT+vEXWlhllrHgkyIPmuVWVo9Z+3szMXvqY6u+TAU+k1+RNdjarz6W+QQafi1IUHZCp\\n8+mWyvXEV6bsy4s6dvWGyvbpTyg7ZbOpMnyrwz1kQX4L59Z0bc8/rrb/zHNqa7XHpJz5xBekWvrc\\ntO7NP256/JsP6lr84VXdmx6aktfNtKreblxGoVhVGbN5KfieeF3jy7+zo7FLFLu0i1/IrPpOPYPy\\nkLlRKitlzzDqOPXEe7GJyqLBXKChcvcwiUkNyfiYQ51GJpscc4g6/nSBe3QKFVaJ8OsAtUQWT7UB\\nWeU2tnSe66t6PHU/EeKaIs2HXamBU13abFfKp9U13c8y+/qeTE19OktEeZTcJyqKyo9Q1R0yd2ih\\n0rWq7mcDvBkHu6j/uL8e4oOSIbNnASV5kvEz6cnDjuo9VSCSjr/GIZ8LbRQ0yevM6wfrqqfpZTKE\\nosTpj/AFNF2nvbbmKCOyxXTe1JEb20f3RNvdVl026lKg7eI9tY2qt08WzlMfOmtmZrUpHXuCrE0T\\nc6qrmSI+Ovj7dFAHLdynOU/ttB6TO+H627q2xY6uVZd7Vwavsywyh1FPf996Tb6cnW2dcyiSiRaV\\n27tktcv21EdPPSwvlLkZ6gwvsTClOs20VVcHu/jxsYthgNdOFaXn/CX12cWLeEcynhezuqaNdqKo\\nJNNmR21mGx+Uzev4P9EWT9ynchXbqomle/T75ZC23+Y34dQEivgS6tc5MvFe53dLQ98bmMO0yyig\\nKroe+Q9rfpwii2FjS/V20FO95yvvL8vgaKjj1/E5mZjXb8uAJ1kyRyuTzWnxXo2/W/S9GTwnW/ir\\nFMiGlWP3yQCPnfpBkvGUrI94Gh129b2TC3hElucshzKky+/THMqUAcq8Lh6v3W08C/l9XFo4a2Zm\\nWTJh9ciCN+S3QWeo/jmDsq1fxifzGn6YQd8/WU7qMFHeaTw7ZL528T7VQRcPrnQXfzsUQPlkHo1y\\nOmlzH/usFJv9Qzy/MipPmeyb3TaZJZm3J16IkblEH//RiTIZyWbJmNaVWrne1XnPFtTXWnjSHJJF\\nb4htUDZ/9HHEFTWO4ziO4ziO4ziO4zhjwlgoamw0slS7Y/uHihAMD7UilUQGsuwdrierg2QcSs/i\\nJD6tN/ZKWnEr98m6Uteq9MY19tl39Pl0HRd4IsiFkOzDT/a0aoV/hBqgjwgk3dIKYwsX+cY1VuDw\\nqqmdUXVOntNjek4reZHVyn1csAc9/F7wExhUyRbA+aUreOUQETjEhyDd0efn2XPbJVKdwXsnnU0y\\n6SSKAhQ5ZIUaLOQsP9DqZZd9d1lWfvOoklJlPfYL7BMuqgydOXw0+HtriwhnRauYvYbK0GKPegvv\\nlkxU3RRYRSx0iJbn3t+Kc4rod4OsR8nKfKWm1c7sCUVa0xldky0yZC0U8DogKrWKGqmN6qrU1Odq\\nKGgqp/AHuUfRscKUPtde0/def1GRhQYRg8k8Dt4rqs/1G6gLUNIsLZ81M7MRmXM6qK/iSNe0yv7w\\nzIjoEyqH9luKuDRZdW3TJqbxvyjWkgxEZBLaUL2sE3HPEVuZm9HKfzpNhqGb7BvtKcIT2CM5HKk+\\n5x7QSn4uowhHY0Wryr3EU2ddx9kkuhdPqo3Xc2pjJ++V6mL6vOovyc4yYoV/a0WR/B6ZgU6dwhsJ\\nn6j0AGUW++g75fexlhx0jEFLje1d9vG266rzFlkzZoj+jPBqyU+ToWSGPaRlVvq3ySpBRC7dxd+J\\nKE+Fa9Lc1t+v3iQDDUq4aoUMOmmy0l1EDUAWqjzO+4EoSRoF3/oG4x/hsTn2uTfyahP1xPWeqNgc\\nUpUDIg9tvHfma/pc5YzOc+MFIqQZ2iiRhHQW5VCO7BxTuPTn1Hda9LGJiDpjR+Xbe0vR8rhN37sH\\n1RYKwuG+zn+AR4/VyRbC/vL3+hiqggZZ9gbveQmoTeytSkGz867qt3RC31/JkYWjqvcdlQFRtPSk\\nrnuY0/U6YD9/ngjr6kDlT+fUbubOkZkMn616Xe0jdV3luFok8x0CqxwePB3ef4DHwbBIJiL8uzpV\\n1XOYxsdl3yyF+nIDT6jhMtGthxQxPNxERUVWoWqJPehkbtknetXkcQfPggKR2VFIvLlQvBG9WiHy\\nWJvQ9xT3dPwc0apiRo9bJdXNdkPXarVMdqkPq9ztKVRHqFanuJ+M8BPKqCnb5orG08yixrHS/Rp3\\n+vhgBDwJ+mtkHNvU8fMH9Jl5va/JfcimEl84tdFGV59bYh/7UVkuoJbNqm2f/YoyET1wQZ4yX39B\\nSpbLKfWNdxvycplflMqj+edSmizV8Clpq61fT0vlMbr6nM7jr8njZf8PpNgZLH/OzMxOLyvL0le+\\nrkj3J+cVeX4B74MnU1K6xEefMDOzd/LK2jTsKgvUxS/re3Ofkr/Ll/L6/J+yjz/zrDIg5aMyKqVP\\naNzduaLrtLymtjz9WZXrI7+v780/Li+eRx7S2PLKS1IMLdY1tlxdVf1/HK+NA9N84PBy1s428OGZ\\n1zzv22/pvReXzpqZ2cJbqrPFBdrq9NM65pruKTcGaisPfVEKFfshjRePoHLtbeuafY2MMj98r8a9\\nP/p+st69JcXI+dkvmpnZn/yxrskDMzre5w90Tkelg2fMxhbeiXhRpbrqoxuvJNlJ1IYqk1JLGArP\\npm6ltr+RzGc17kwt8L5dlXsPM5kaGS47KB5zRMlj4reBArGJ6q3aQsmSUj1vM2fbvIF/3UtSyMwU\\nVV57lAjwvlQITdQK4SQ+I9dQmrfV1zv4Bs7Na9wvZtRXD7lvzCzq+jTbnB9Kli73xyKKoiGKH1tV\\neXt4Tk4w70/toUZmXI7MQUuMn33m9R3us5GxoUUmtCb+KZPc34b4cfXXGOcL+p7CAK8LvB9T36S9\\nvkMmUOpt8sTRM8gVmOs3mdeU8edZZz64zbxt/rzUr6m2xqvVLtece2AOn83lB5I6YtcA3izG3Oae\\naV2L1p6u3Y11XeNK8ptoWcqb6rTa+soV9c/dG4mvnPrmycsqzyzXtrOle32DXQll5vERRVCL9EGL\\nF9Snpsj6tPW25plXOc67r2r+V2e+/MCjausnFnS85PfCzAk9L5EN8OZ19ZkcqrHRqq7pPh42BfxG\\nRlt63xCFZZ75eR5l6mvXVB9rL2mcnmDuMPWw2rwtqg8UuL/u7KncPeZc2Wm1mSV8D1N4we1uq95a\\nb5KVit9uRyWf0/evXsFXqoui9rSeJ3PaPj/Zz17QWFM8I0VTFdVHIBvjDqrwiEot0DdLNXz1emov\\n11d03TKT+v76gtpJZX9o/azqIMyS1ZM5QzuHj90VXdtvPKN72Yce+JCZmT38Kak9k2zIXbxmul2V\\nsY0+ZPOG2mgZX9Q+v4FKRdSoI+Y8jF/DJJsmGSzTab2ebeIhidbusEnmW/yf+lWdaxZFUDGPindC\\nfSriSZjO6vneutS+199Qn3jwI5f09zN40eBrVN9PTAfVlt9e0/csLuCZhVJo/118Qid1DRDWWzfX\\nthiO5p3nihrHcRzHcRzHcRzHcZwxYTwUNcNog52eHebwF9nWSlWJPOf5Ba2AzbMU1RtpZatQ1Qpa\\nmiwpWVQa/Tru+2SImMRfJU3UMTGPCfiLTC7o/Tn2KY5m8WEhgtHHMbuTrF6/oT15e9NEAgZkkUoR\\nmUUNECpEhgpagayRNSQ91PcVWfkPRKwLU0Qu2i2qhTzuKIX6p4gEkHmj2kmyhxA5Kad5P6uvJLaP\\neXwLCj0bsPLeGOrYRWRLaSKrfc4pThDNYC2v10JJE7XCG/G9ybOfGGsby5MpJjWr1dhCiuOw5zQ9\\nqTKG3Ptbce6TeaVA1GRAFCRdVt0U8bDZxF1+gIRnpkDmHKL0mRYu6uy/zOEn0TrAe+EU+8ZziTcL\\njuT4Go3yqAvw5ygXtLLdx0Omzx7T3ClFMCOrwpmBjhvLKkdljnpi1bpHdpXOmlbmeyWycbGnOM2e\\n4VkcxxFVWZOsKAd9rYxXKuy5nSAz0ITqfZdMRu2W9tLWs4p4TE2SaaeMF88pRVyyTSIXhL5H3SRL\\nE30qUZkRzeqz7z7gETHa0+PuDtlGcEQvVHTe06dVzzNJ5IXsM4dbKL3wpZrpHn2IGqBEaTUV+Wru\\nqAwxrWPP4D1SmtSxs0VUA2SwSePiPiyqza/2UYvt4UFFm0ijZsrgXbNPRoXugc4hRZ0M+orQhfP4\\nNE0r0lDC82RvV9GajVUUfT2UJzt6fXRK13qCrEp5lBeZfbW1TEbnc3igNjCawbeJvbW7fZW7i9fK\\nbkvXsjZQm8qhphtFxh+UNdmy+kzI63v6qONCVD023lG9bt/U9w24jZRmdP5ZIphFolkF9tX3uqrv\\nMpGOClk7JtmHf7CvaFSHCHGHaODBmsbbIT5MRe4LSXaSEJKMCUcj8UFa53PZjK7nQU7PG/t6w5Vd\\nqSZy3G+ap3XcJeqnT2ayHgqZLH02kh0lu6DzTyI5qUN9rrlPVpIqCqwTuq61ivriwJrWJsNYlmhW\\nomrq4Jt0SISvShaRrTW1gR18Maon9P4UPmYbeNSUyPrXwy8nQ/a/7RbqL+6BebJEdcnQkAuoQntk\\nmdhLVKdEzVOoiXL0DfZtZ/HPaGZQp5HJpbZMtjiiXUMyJDbnUHRWyCZIFN2qhKGGOr9QSO4j7Bdf\\n0nluddRWSkTlLYmwNjAMOiJfXdFF/OtFKU8GKBO/8jq+dkT535lSmy/W/8DMzA5+UJ/78Jb65m+f\\nUZs6/wZzg3t1v/jWNe2vv5RWvdQL8r75ZFtpob7dl5rkwt96wszMek8ryveZi4pIH7ytsaRFJPfE\\ntzRuX33qETMzm/wQ4+1L8oypPPZlMzNrb+v7Hycbyjt/AwXnisozdUrlq13+kpmZ7Xxe9bw3LTXK\\nSkHqkO5Ax+2vaQyYexQfsFfUDofn9P3bV3Vd7v34O/biTZXlk3ldnFZDKqA3UG4s3KM2VHzmWTMz\\ne735Ezom/auaVlS/XlHZrt1E6fYmbemc/EDO1cjq9Ko8cS4daByePy110pffUj98+ElF7b9KdqbJ\\nnNRORyUQle7vo9AbokgJ6lsj1K2NFXwAyb5UQaWcyajtbBPR7XXILIlXQhHPrBrjYZd5ZgYvmO4e\\nPm5lHS9fxYeE8b4zQWa2rq7hqYyOv4/abAmvmJM1taV8W9d+ChVcK6sIue3p882hrvmNl6V8am9p\\nvN6/oDa0/Jh8jxZRgWzjMZlPk1WVdDDYH1q3wDi+p/NpZMhexXmOyHTTJrNlYK6Tb3IfrDKXM9Qd\\no8RjjTlDU2PlBPfzbAUPOuZ0Tfw8UkPVX4E+vtTR85s7ak/ZFX6P7CADHKmvHYUGxjrZJl5bzDmW\\nz6ntVfH6WjiteVAKZXEV1W5AcZ2vqKxNbl6NmGSgwnuMLGtFMliu4bGydl3jxZC6u4CCLkXGwRrz\\n//qs2kprR+8rDPQ4y5wptYRKzHTNAz6hKTwjt9b1/vQJMmShwOwxh5rkt9cqio5DPC23VvU4UUGt\\nhIIyz/3AyE7Y6UgJX8Mfbgol9kVUU7NkICuxy6KQw4MFVW+V30ZZ5vv1NzW32B2pPPeQ/XQaT5vU\\nDDsCUMEebuKV09D5V1Nqg0Wymo4GydyQ3Q2d9/f7Jgz0PZNTzO+DziuYzqtK344osUZc/8Vi4nNC\\nlq9Uoi4m8xFZYCvsyujO6vkBXnEHDbVxTt9y/LJI1YaWmmAcw4duxK6LiOfLARmq+uwu2B0k6h98\\nLBkHcyW8FPmNmPwWuYnadXFO33Nykt861GVgftfrqi4yeNNkeezj61OZ5FpT5/UV/X1nTWqsU+fx\\nFUqEcFP4+rDTpTWZKKnxBMOLpoEyZ5vfPieZB+bxsrWR3l/Hs2eA8ue99FiMo50RmW33NJ4b6xET\\nnbyl49F8alxR4ziO4ziO4ziO4ziOMyaMhaIm2MiyoWV5onaZaVzs2W83yqJcIY1GOquVrg4eBeV9\\nVjFxSk+zbzLPytxeVY9FIqgplCZdFDL9bJIHntXfpr6vnXgWsBqdTjIKndTSYLWEF8M8kVT2Gfan\\ndfxOspcOs4lyT8dPj7Ty181oZbFCZCOLSiPkceYesdd4SMScTB/9Jm7bJT32UNSU8VxIsRpdLvI6\\nUdNGumf81yocq4cvTxY103t7PfHpyefIGEAd9HbY+0hGncgxck2iS/j+ZNifV+qhNsqPOHcdvxfe\\n34pzljpo4hdiRGabqCA6LUWnRjsqdxb/jQHnlcZJvMzKemkyybBDW2NlukQUZnNF39fJ6rGNYmeC\\n6FUOP4lehlXcXa0OF0izxJZYq5MNpc+qdJcV8TwRh1QgWoUixthrmmYvc2TfZYZMO22yuCQu+n3a\\nUtzFf2UqcYlX+SKrzGGgC5/KJl4ROtwo8cDB/T9D5poRbayDQmhYUDuYW1a0MjXNavtI9T+RZFrA\\nSX3jTa3U5yJ+TJFV8Zq+p4ySp8le3s66Hsu0x0wHj5rh0dtJJ0U/wWW9XGNPaJJRhjY5IkI5RJXQ\\nYu9tosTpoegzFHrDIgo4VEFl1GUNFG1JRrKli4pKN/jewDXOooQxXPA7LTxLtngfdZZJojCT6oPz\\n81LCpWnr+TI+Sow/bWNvLYqYSl/jSLGpOmztEYke6vjzJ7TfPIWfRy5FJrEM6rKKojepvp5vr6PI\\nw0srom4aEsGYnNb7c7S5zkjfN6ji6k80sDPgmuZ1fgxvto/aKsksUSLiMYr0qW18TyZ13aoniBxz\\nO+gSaUm3j+4ZYPZX2fh6ZKRrv80eZqp/EbVg2FeEuYC6I7NO3yWjTYEsJomqrEK0b0jbLa1Rz2Qg\\nmsQnK4UKsbml5zMtvBWIzBzuFmxiRP9FOZJFCcitz3ocY6Kta96tMn70eJ2MYSP8cXoo3rLpZD+4\\n+krE5+yhAarSjK7pBIqUEVmXAlnrIv11xD0mTigSXBqpLvvs345rZPpCccPtxHJ8TwUJZr+B8od7\\na+FtFEPMTPJk5Stt64UDIoMT2/ivkUUw1yGzVk7fW+uqzbRRFOXbejwqU59gLKkrQ8TvvCyPmsdr\\nUqwM733BzMxee1X1cN8pedcMD3SBnt6Xr8oP3JAfygsdqUeeIovHBveV688py9LBhOp988xrOr+2\\n+mqvK6XN4LLKv3L9B83MLJVWPT+/JKXN913VePtw/XGV4+1XVL6PaUyae1HqlFz8vJmZvSvrGbv6\\nFxobnhj9gI53Ac+dXUXet+blcdPq6D5wYfcjZmZ25X/Lo+HjNdpsQWqU+y/Lq+cNsmFtEU28ulm1\\nn5r+I5XtL3Wur/YUvb5vQ5myCveobjdOqA3fj0r1ZkkKmrOL+s53XlYbnrsqH54ubXJYUxapPtco\\nj+dfvKk+8rU3UVzMq60uPavvm72kcaX/mhQhR6VcRvmHvLTZJZMPKrA2UW3b0HnskRlygms3om+f\\nXdA1aA7wYkii8y0pK/f7eHKlkoi22vj0EmrmSMZHFcdKzJXaCnBbO1GwoGg8/7Dqf+E+sqgS6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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3e8299908>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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74pIf+ZLcjvVgg4TNr6POPIr3ld6aJX1e/WWpr3ixrBNx6KffbNdq4x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\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mb2B/+rPh9+m3OSBbXj2UQR9lkGNAZcB/Om3rtKydbGoCf2OcO8GGv8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\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8s3igXVx6Ht5oypall9PluO1HbJ0MzM4jM9r7iuta3a1fVD7r/qy7e7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\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xZeamzHLXwyDGpzqs9toxCWr2hM1tmjQyPQnC4IkXNNAh/tMJiB6AAl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\\nQqFQKBQKhUKhUCgOCP437wggvn1fGv8AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3e4bcacf8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for layer_name in ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1']:\\n\",\n    \"    size = 64\\n\",\n    \"    margin = 5\\n\",\n    \"\\n\",\n    \"    # This a empty (black) image where we will store our results.\\n\",\n    \"    results = np.zeros((8 * size + 7 * margin, 8 * size + 7 * margin, 3))\\n\",\n    \"\\n\",\n    \"    for i in range(8):  # iterate over the rows of our results grid\\n\",\n    \"        for j in range(8):  # iterate over the columns of our results grid\\n\",\n    \"            # Generate the pattern for filter `i + (j * 8)` in `layer_name`\\n\",\n    \"            filter_img = generate_pattern(layer_name, i + (j * 8), size=size)\\n\",\n    \"\\n\",\n    \"            # Put the result in the square `(i, j)` of the results grid\\n\",\n    \"            horizontal_start = i * size + i * margin\\n\",\n    \"            horizontal_end = horizontal_start + size\\n\",\n    \"            vertical_start = j * size + j * margin\\n\",\n    \"            vertical_end = vertical_start + size\\n\",\n    \"            results[horizontal_start: horizontal_end, vertical_start: vertical_end, :] = filter_img\\n\",\n    \"\\n\",\n    \"    # Display the results grid\\n\",\n    \"    plt.figure(figsize=(20, 20))\\n\",\n    \"    plt.imshow(results)\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"These filter visualizations tell us a lot about how convnet layers see the world: each layer in a convnet simply learns a collection of \\n\",\n    \"filters such that their inputs can be expressed as a combination of the filters. This is similar to how the Fourier transform decomposes \\n\",\n    \"signals onto a bank of cosine functions. The filters in these convnet filter banks get increasingly complex and refined as we go higher-up \\n\",\n    \"in the model:\\n\",\n    \"\\n\",\n    \"* The filters from the first layer in the model (`block1_conv1`) encode simple directional edges and colors (or colored edges in some \\n\",\n    \"cases).\\n\",\n    \"* The filters from `block2_conv1` encode simple textures made from combinations of edges and colors.\\n\",\n    \"* The filters in higher-up layers start resembling textures found in natural images: feathers, eyes, leaves, etc.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Visualizing heatmaps of class activation\\n\",\n    \"\\n\",\n    \"We will introduce one more visualization technique, one that is useful for understanding which parts of a given image led a convnet to its \\n\",\n    \"final classification decision. This is helpful for \\\"debugging\\\" the decision process of a convnet, in particular in case of a classification \\n\",\n    \"mistake. It also allows you to locate specific objects in an image.\\n\",\n    \"\\n\",\n    \"This general category of techniques is called \\\"Class Activation Map\\\" (CAM) visualization, and consists in producing heatmaps of \\\"class \\n\",\n    \"activation\\\" over input images. A \\\"class activation\\\" heatmap is a 2D grid of scores associated with an specific output class, computed for \\n\",\n    \"every location in any input image, indicating how important each location is with respect to the class considered. For instance, given a \\n\",\n    \"image fed into one of our \\\"cat vs. dog\\\" convnet, Class Activation Map visualization allows us to generate a heatmap for the class \\\"cat\\\", \\n\",\n    \"indicating how cat-like different parts of the image are, and likewise for the class \\\"dog\\\", indicating how dog-like differents parts of the \\n\",\n    \"image are.\\n\",\n    \"\\n\",\n    \"The specific implementation we will use is the one described in [Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via \\n\",\n    \"Gradient-based Localization](https://arxiv.org/abs/1610.02391). It is very simple: it consists in taking the output feature map of a \\n\",\n    \"convolution layer given an input image, and weighing every channel in that feature map by the gradient of the class with respect to the \\n\",\n    \"channel. Intuitively, one way to understand this trick is that we are weighting a spatial map of \\\"how intensely the input image activates \\n\",\n    \"different channels\\\" by \\\"how important each channel is with regard to the class\\\", resulting in a spatial map of \\\"how intensely the input \\n\",\n    \"image activates the class\\\".\\n\",\n    \"\\n\",\n    \"We will demonstrate this technique using the pre-trained VGG16 network again:\"\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      \"Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5\\n\",\n      \"548380672/553467096 [============================>.] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.applications.vgg16 import VGG16\\n\",\n    \"\\n\",\n    \"K.clear_session()\\n\",\n    \"\\n\",\n    \"# Note that we are including the densely-connected classifier on top;\\n\",\n    \"# all previous times, we were discarding it.\\n\",\n    \"model = VGG16(weights='imagenet')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's consider the following image of two African elephants, possible a mother and its cub, strolling in the savanna (under a Creative \\n\",\n    \"Commons license):\\n\",\n    \"\\n\",\n    \"![elephants](https://s3.amazonaws.com/book.keras.io/img/ch5/creative_commons_elephant.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's convert this image into something the VGG16 model can read: the model was trained on images of size 224x244, preprocessed according \\n\",\n    \"to a few rules that are packaged in the utility function `keras.applications.vgg16.preprocess_input`. So we need to load the image, resize \\n\",\n    \"it to 224x224, convert it to a Numpy float32 tensor, and apply these pre-processing rules.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.preprocessing import image\\n\",\n    \"from keras.applications.vgg16 import preprocess_input, decode_predictions\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# The local path to our target image\\n\",\n    \"img_path = '/Users/fchollet/Downloads/creative_commons_elephant.jpg'\\n\",\n    \"\\n\",\n    \"# `img` is a PIL image of size 224x224\\n\",\n    \"img = image.load_img(img_path, target_size=(224, 224))\\n\",\n    \"\\n\",\n    \"# `x` is a float32 Numpy array of shape (224, 224, 3)\\n\",\n    \"x = image.img_to_array(img)\\n\",\n    \"\\n\",\n    \"# We add a dimension to transform our array into a \\\"batch\\\"\\n\",\n    \"# of size (1, 224, 224, 3)\\n\",\n    \"x = np.expand_dims(x, axis=0)\\n\",\n    \"\\n\",\n    \"# Finally we preprocess the batch\\n\",\n    \"# (this does channel-wise color normalization)\\n\",\n    \"x = preprocess_input(x)\"\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      \"Predicted: [('n02504458', 'African_elephant', 0.90942144), ('n01871265', 'tusker', 0.08618243), ('n02504013', 'Indian_elephant', 0.0043545929)]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"preds = model.predict(x)\\n\",\n    \"print('Predicted:', decode_predictions(preds, top=3)[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The top-3 classes predicted for this image are:\\n\",\n    \"\\n\",\n    \"* African elephant (with 92.5% probability)\\n\",\n    \"* Tusker (with 7% probability)\\n\",\n    \"* Indian elephant (with 0.4% probability)\\n\",\n    \"\\n\",\n    \"Thus our network has recognized our image as containing an undetermined quantity of African elephants. The entry in the prediction vector \\n\",\n    \"that was maximally activated is the one corresponding to the \\\"African elephant\\\" class, at index 386:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"386\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"np.argmax(preds[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To visualize which parts of our image were the most \\\"African elephant\\\"-like, let's set up the Grad-CAM process:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# This is the \\\"african elephant\\\" entry in the prediction vector\\n\",\n    \"african_elephant_output = model.output[:, 386]\\n\",\n    \"\\n\",\n    \"# The is the output feature map of the `block5_conv3` layer,\\n\",\n    \"# the last convolutional layer in VGG16\\n\",\n    \"last_conv_layer = model.get_layer('block5_conv3')\\n\",\n    \"\\n\",\n    \"# This is the gradient of the \\\"african elephant\\\" class with regard to\\n\",\n    \"# the output feature map of `block5_conv3`\\n\",\n    \"grads = K.gradients(african_elephant_output, last_conv_layer.output)[0]\\n\",\n    \"\\n\",\n    \"# This is a vector of shape (512,), where each entry\\n\",\n    \"# is the mean intensity of the gradient over a specific feature map channel\\n\",\n    \"pooled_grads = K.mean(grads, axis=(0, 1, 2))\\n\",\n    \"\\n\",\n    \"# This function allows us to access the values of the quantities we just defined:\\n\",\n    \"# `pooled_grads` and the output feature map of `block5_conv3`,\\n\",\n    \"# given a sample image\\n\",\n    \"iterate = K.function([model.input], [pooled_grads, last_conv_layer.output[0]])\\n\",\n    \"\\n\",\n    \"# These are the values of these two quantities, as Numpy arrays,\\n\",\n    \"# given our sample image of two elephants\\n\",\n    \"pooled_grads_value, conv_layer_output_value = iterate([x])\\n\",\n    \"\\n\",\n    \"# We multiply each channel in the feature map array\\n\",\n    \"# by \\\"how important this channel is\\\" with regard to the elephant class\\n\",\n    \"for i in range(512):\\n\",\n    \"    conv_layer_output_value[:, :, i] *= pooled_grads_value[i]\\n\",\n    \"\\n\",\n    \"# The channel-wise mean of the resulting feature map\\n\",\n    \"# is our heatmap of class activation\\n\",\n    \"heatmap = np.mean(conv_layer_output_value, axis=-1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For visualization purpose, we will also normalize the heatmap between 0 and 1:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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05JHUn3L3KfzZI2S9JyTWfaARixoQPB9s2SrpW0KSIW3GMkIrZI2iJJ\\nZ7bOanbPEgCLGioQbF8t6TZJfxgRh6odEoCmDHs6+H+WtErSVtvP2P7GiMcJoAbDng7+7hGMBUDD\\n2FMRQEEgACgIBABFvfMhRCjmZmttieN0k/NJJOsb58HmBFhIdHLzOWjhrfODlWee/wF7s4QAoCAQ\\nABQEAoCCQABQEAgACgIBQEEgACgIBAAFgQCgIBAAFAQCgIJAAFAQCAAKAgFAQSAAKLzIDOrVN7P3\\nS/r1Inc5W9IbNQ2H/uPV/3R+7HX0f39EvG+pO9UaCEuxvS0iZuh/+vU/nR/7OPQ/hlUGAAWBAKAY\\nt0DYQv/Ttv/p/NjHob+kMfsMAUCzxm0JAUCDCAQABYEAoCAQABQEAoDi/wAX2XHzJeb91QAAAABJ\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fa3e40c1a58>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"heatmap = np.maximum(heatmap, 0)\\n\",\n    \"heatmap /= np.max(heatmap)\\n\",\n    \"plt.matshow(heatmap)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, we will use OpenCV to generate an image that superimposes the original image with the heatmap we just obtained:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import cv2\\n\",\n    \"\\n\",\n    \"# We use cv2 to load the original image\\n\",\n    \"img = cv2.imread(img_path)\\n\",\n    \"\\n\",\n    \"# We resize the heatmap to have the same size as the original image\\n\",\n    \"heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\\n\",\n    \"\\n\",\n    \"# We convert the heatmap to RGB\\n\",\n    \"heatmap = np.uint8(255 * heatmap)\\n\",\n    \"\\n\",\n    \"# We apply the heatmap to the original image\\n\",\n    \"heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\\n\",\n    \"\\n\",\n    \"# 0.4 here is a heatmap intensity factor\\n\",\n    \"superimposed_img = heatmap * 0.4 + img\\n\",\n    \"\\n\",\n    \"# Save the image to disk\\n\",\n    \"cv2.imwrite('/Users/fchollet/Downloads/elephant_cam.jpg', superimposed_img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![elephant cam](https://s3.amazonaws.com/book.keras.io/img/ch5/elephant_cam.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This visualisation technique answers two important questions:\\n\",\n    \"\\n\",\n    \"* Why did the network think this image contained an African elephant?\\n\",\n    \"* Where is the African elephant located in the picture?\\n\",\n    \"\\n\",\n    \"In particular, it is interesting to note that the ears of the elephant cub are strongly activated: this is probably how the network can \\n\",\n    \"tell the difference between African and Indian elephants.\\n\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/6.1-one-hot-encoding-of-words-or-characters.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# One-hot encoding of words or characters\\n\",\n    \"\\n\",\n    \"This notebook contains the first code sample found in Chapter 6, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"One-hot encoding is the most common, most basic way to turn a token into a vector. You already saw it in action in our initial IMDB and \\n\",\n    \"Reuters examples from chapter 3 (done with words, in our case). It consists in associating a unique integer index to every word, then \\n\",\n    \"turning this integer index i into a binary vector of size N, the size of the vocabulary, that would be all-zeros except for the i-th \\n\",\n    \"entry, which would be 1.\\n\",\n    \"\\n\",\n    \"Of course, one-hot encoding can be done at the character level as well. To unambiguously drive home what one-hot encoding is and how to \\n\",\n    \"implement it, here are two toy examples of one-hot encoding: one for words, the other for characters.\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Word level one-hot encoding (toy example):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# This is our initial data; one entry per \\\"sample\\\"\\n\",\n    \"# (in this toy example, a \\\"sample\\\" is just a sentence, but\\n\",\n    \"# it could be an entire document).\\n\",\n    \"samples = ['The cat sat on the mat.', 'The dog ate my homework.']\\n\",\n    \"\\n\",\n    \"# First, build an index of all tokens in the data.\\n\",\n    \"token_index = {}\\n\",\n    \"for sample in samples:\\n\",\n    \"    # We simply tokenize the samples via the `split` method.\\n\",\n    \"    # in real life, we would also strip punctuation and special characters\\n\",\n    \"    # from the samples.\\n\",\n    \"    for word in sample.split():\\n\",\n    \"        if word not in token_index:\\n\",\n    \"            # Assign a unique index to each unique word\\n\",\n    \"            token_index[word] = len(token_index) + 1\\n\",\n    \"            # Note that we don't attribute index 0 to anything.\\n\",\n    \"\\n\",\n    \"# Next, we vectorize our samples.\\n\",\n    \"# We will only consider the first `max_length` words in each sample.\\n\",\n    \"max_length = 10\\n\",\n    \"\\n\",\n    \"# This is where we store our results:\\n\",\n    \"results = np.zeros((len(samples), max_length, max(token_index.values()) + 1))\\n\",\n    \"for i, sample in enumerate(samples):\\n\",\n    \"    for j, word in list(enumerate(sample.split()))[:max_length]:\\n\",\n    \"        index = token_index.get(word)\\n\",\n    \"        results[i, j, index] = 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Character level one-hot encoding (toy example)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import string\\n\",\n    \"\\n\",\n    \"samples = ['The cat sat on the mat.', 'The dog ate my homework.']\\n\",\n    \"characters = string.printable  # All printable ASCII characters.\\n\",\n    \"token_index = dict(zip(characters, range(1, len(characters) + 1)))\\n\",\n    \"\\n\",\n    \"max_length = 50\\n\",\n    \"results = np.zeros((len(samples), max_length, max(token_index.values()) + 1))\\n\",\n    \"for i, sample in enumerate(samples):\\n\",\n    \"    for j, character in enumerate(sample[:max_length]):\\n\",\n    \"        index = token_index.get(character)\\n\",\n    \"        results[i, j, index] = 1.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that Keras has built-in utilities for doing one-hot encoding text at the word level or character level, starting from raw text data. \\n\",\n    \"This is what you should actually be using, as it will take care of a number of important features, such as stripping special characters \\n\",\n    \"from strings, or only taking into the top N most common words in your dataset (a common restriction to avoid dealing with very large input \\n\",\n    \"vector spaces).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Using Keras for word-level one-hot encoding:\"\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      \"Found 9 unique tokens.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.preprocessing.text import Tokenizer\\n\",\n    \"\\n\",\n    \"samples = ['The cat sat on the mat.', 'The dog ate my homework.']\\n\",\n    \"\\n\",\n    \"# We create a tokenizer, configured to only take\\n\",\n    \"# into account the top-1000 most common words\\n\",\n    \"tokenizer = Tokenizer(num_words=1000)\\n\",\n    \"# This builds the word index\\n\",\n    \"tokenizer.fit_on_texts(samples)\\n\",\n    \"\\n\",\n    \"# This turns strings into lists of integer indices.\\n\",\n    \"sequences = tokenizer.texts_to_sequences(samples)\\n\",\n    \"\\n\",\n    \"# You could also directly get the one-hot binary representations.\\n\",\n    \"# Note that other vectorization modes than one-hot encoding are supported!\\n\",\n    \"one_hot_results = tokenizer.texts_to_matrix(samples, mode='binary')\\n\",\n    \"\\n\",\n    \"# This is how you can recover the word index that was computed\\n\",\n    \"word_index = tokenizer.word_index\\n\",\n    \"print('Found %s unique tokens.' % len(word_index))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"A variant of one-hot encoding is the so-called \\\"one-hot hashing trick\\\", which can be used when the number of unique tokens in your \\n\",\n    \"vocabulary is too large to handle explicitly. Instead of explicitly assigning an index to each word and keeping a reference of these \\n\",\n    \"indices in a dictionary, one may hash words into vectors of fixed size. This is typically done with a very lightweight hashing function. \\n\",\n    \"The main advantage of this method is that it does away with maintaining an explicit word index, which \\n\",\n    \"saves memory and allows online encoding of the data (starting to generate token vectors right away, before having seen all of the available \\n\",\n    \"data). The one drawback of this method is that it is susceptible to \\\"hash collisions\\\": two different words may end up with the same hash, \\n\",\n    \"and subsequently any machine learning model looking at these hashes won't be able to tell the difference between these words. The likelihood \\n\",\n    \"of hash collisions decreases when the dimensionality of the hashing space is much larger than the total number of unique tokens being hashed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Word-level one-hot encoding with hashing trick (toy example):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"samples = ['The cat sat on the mat.', 'The dog ate my homework.']\\n\",\n    \"\\n\",\n    \"# We will store our words as vectors of size 1000.\\n\",\n    \"# Note that if you have close to 1000 words (or more)\\n\",\n    \"# you will start seeing many hash collisions, which\\n\",\n    \"# will decrease the accuracy of this encoding method.\\n\",\n    \"dimensionality = 1000\\n\",\n    \"max_length = 10\\n\",\n    \"\\n\",\n    \"results = np.zeros((len(samples), max_length, dimensionality))\\n\",\n    \"for i, sample in enumerate(samples):\\n\",\n    \"    for j, word in list(enumerate(sample.split()))[:max_length]:\\n\",\n    \"        # Hash the word into a \\\"random\\\" integer index\\n\",\n    \"        # that is between 0 and 1000\\n\",\n    \"        index = abs(hash(word)) % dimensionality\\n\",\n    \"        results[i, j, index] = 1.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/6.1-using-word-embeddings.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Using word embeddings\\n\",\n    \"\\n\",\n    \"This notebook contains the second code sample found in Chapter 6, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Another popular and powerful way to associate a vector with a word is the use of dense \\\"word vectors\\\", also called \\\"word embeddings\\\". \\n\",\n    \"While the vectors obtained through one-hot encoding are binary, sparse (mostly made of zeros) and very high-dimensional (same dimensionality as the \\n\",\n    \"number of words in the vocabulary), \\\"word embeddings\\\" are low-dimensional floating point vectors \\n\",\n    \"(i.e. \\\"dense\\\" vectors, as opposed to sparse vectors). \\n\",\n    \"Unlike word vectors obtained via one-hot encoding, word embeddings are learned from data. \\n\",\n    \"It is common to see word embeddings that are 256-dimensional, 512-dimensional, or 1024-dimensional when dealing with very large vocabularies. \\n\",\n    \"On the other hand, one-hot encoding words generally leads to vectors that are 20,000-dimensional or higher (capturing a vocabulary of 20,000 \\n\",\n    \"token in this case). So, word embeddings pack more information into far fewer dimensions. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![word embeddings vs. one hot encoding](https://s3.amazonaws.com/book.keras.io/img/ch6/word_embeddings.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There are two ways to obtain word embeddings:\\n\",\n    \"\\n\",\n    \"* Learn word embeddings jointly with the main task you care about (e.g. document classification or sentiment prediction). \\n\",\n    \"In this setup, you would start with random word vectors, then learn your word vectors in the same way that you learn the weights of a neural network.\\n\",\n    \"* Load into your model word embeddings that were pre-computed using a different machine learning task than the one you are trying to solve. \\n\",\n    \"These are called \\\"pre-trained word embeddings\\\". \\n\",\n    \"\\n\",\n    \"Let's take a look at both.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Learning word embeddings with the `Embedding` layer\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The simplest way to associate a dense vector to a word would be to pick the vector at random. The problem with this approach is that the \\n\",\n    \"resulting embedding space would have no structure: for instance, the words \\\"accurate\\\" and \\\"exact\\\" may end up with completely different \\n\",\n    \"embeddings, even though they are interchangeable in most sentences. It would be very difficult for a deep neural network to make sense of \\n\",\n    \"such a noisy, unstructured embedding space. \\n\",\n    \"\\n\",\n    \"To get a bit more abstract: the geometric relationships between word vectors should reflect the semantic relationships between these words. \\n\",\n    \"Word embeddings are meant to map human language into a geometric space. For instance, in a reasonable embedding space, we would expect \\n\",\n    \"synonyms to be embedded into similar word vectors, and in general we would expect the geometric distance (e.g. L2 distance) between any two \\n\",\n    \"word vectors to relate to the semantic distance of the associated words (words meaning very different things would be embedded to points \\n\",\n    \"far away from each other, while related words would be closer). Even beyond mere distance, we may want specific __directions__ in the \\n\",\n    \"embedding space to be meaningful. \\n\",\n    \"\\n\",\n    \"[...]\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"In real-world word embedding spaces, common examples of meaningful geometric transformations are \\\"gender vectors\\\" and \\\"plural vector\\\". For \\n\",\n    \"instance, by adding a \\\"female vector\\\" to the vector \\\"king\\\", one obtain the vector \\\"queen\\\". By adding a \\\"plural vector\\\", one obtain \\\"kings\\\". \\n\",\n    \"Word embedding spaces typically feature thousands of such interpretable and potentially useful vectors.\\n\",\n    \"\\n\",\n    \"Is there some \\\"ideal\\\" word embedding space that would perfectly map human language and could be used for any natural language processing \\n\",\n    \"task? Possibly, but in any case, we have yet to compute anything of the sort. Also, there isn't such a thing as \\\"human language\\\", there are \\n\",\n    \"many different languages and they are not isomorphic, as a language is the reflection of a specific culture and a specific context. But more \\n\",\n    \"pragmatically, what makes a good word embedding space depends heavily on your task: the perfect word embedding space for an \\n\",\n    \"English-language movie review sentiment analysis model may look very different from the perfect embedding space for an English-language \\n\",\n    \"legal document classification model, because the importance of certain semantic relationships varies from task to task.\\n\",\n    \"\\n\",\n    \"It is thus reasonable to __learn__ a new embedding space with every new task. Thankfully, backpropagation makes this really easy, and Keras makes it \\n\",\n    \"even easier. It's just about learning the weights of a layer: the `Embedding` layer.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.layers import Embedding\\n\",\n    \"\\n\",\n    \"# The Embedding layer takes at least two arguments:\\n\",\n    \"# the number of possible tokens, here 1000 (1 + maximum word index),\\n\",\n    \"# and the dimensionality of the embeddings, here 64.\\n\",\n    \"embedding_layer = Embedding(1000, 64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The `Embedding` layer is best understood as a dictionary mapping integer indices (which stand for specific words) to dense vectors. It takes \\n\",\n    \"as input integers, it looks up these integers into an internal dictionary, and it returns the associated vectors. It's effectively a dictionary lookup.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The `Embedding` layer takes as input a 2D tensor of integers, of shape `(samples, sequence_length)`, where each entry is a sequence of \\n\",\n    \"integers. It can embed sequences of variable lengths, so for instance we could feed into our embedding layer above batches that could have \\n\",\n    \"shapes `(32, 10)` (batch of 32 sequences of length 10) or `(64, 15)` (batch of 64 sequences of length 15). All sequences in a batch must \\n\",\n    \"have the same length, though (since we need to pack them into a single tensor), so sequences that are shorter than others should be padded \\n\",\n    \"with zeros, and sequences that are longer should be truncated.\\n\",\n    \"\\n\",\n    \"This layer returns a 3D floating point tensor, of shape `(samples, sequence_length, embedding_dimensionality)`. Such a 3D tensor can then \\n\",\n    \"be processed by a RNN layer or a 1D convolution layer (both will be introduced in the next sections).\\n\",\n    \"\\n\",\n    \"When you instantiate an `Embedding` layer, its weights (its internal dictionary of token vectors) are initially random, just like with any \\n\",\n    \"other layer. During training, these word vectors will be gradually adjusted via backpropagation, structuring the space into something that the \\n\",\n    \"downstream model can exploit. Once fully trained, your embedding space will show a lot of structure -- a kind of structure specialized for \\n\",\n    \"the specific problem you were training your model for.\\n\",\n    \"\\n\",\n    \"Let's apply this idea to the IMDB movie review sentiment prediction task that you are already familiar with. Let's quickly prepare \\n\",\n    \"the data. We will restrict the movie reviews to the top 10,000 most common words (like we did the first time we worked with this dataset), \\n\",\n    \"and cut the reviews after only 20 words. Our network will simply learn 8-dimensional embeddings for each of the 10,000 words, turn the \\n\",\n    \"input integer sequences (2D integer tensor) into embedded sequences (3D float tensor), flatten the tensor to 2D, and train a single `Dense` \\n\",\n    \"layer on top for classification.\"\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      \"Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz\\n\",\n      \"12460032/17464789 [====================>.........] - ETA: 0s\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.datasets import imdb\\n\",\n    \"from keras import preprocessing\\n\",\n    \"\\n\",\n    \"# Number of words to consider as features\\n\",\n    \"max_features = 10000\\n\",\n    \"# Cut texts after this number of words \\n\",\n    \"# (among top max_features most common words)\\n\",\n    \"maxlen = 20\\n\",\n    \"\\n\",\n    \"# Load the data as lists of integers.\\n\",\n    \"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\\n\",\n    \"\\n\",\n    \"# This turns our lists of integers\\n\",\n    \"# into a 2D integer tensor of shape `(samples, maxlen)`\\n\",\n    \"x_train = preprocessing.sequence.pad_sequences(x_train, maxlen=maxlen)\\n\",\n    \"x_test = preprocessing.sequence.pad_sequences(x_test, maxlen=maxlen)\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"embedding_2 (Embedding)      (None, 20, 8)             80000     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"flatten_1 (Flatten)          (None, 160)               0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_1 (Dense)              (None, 1)                 161       \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 80,161\\n\",\n      \"Trainable params: 80,161\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\",\n      \"Train on 20000 samples, validate on 5000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.6560 - acc: 0.6482 - val_loss: 0.5906 - val_acc: 0.7146\\n\",\n      \"Epoch 2/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.5189 - acc: 0.7595 - val_loss: 0.5117 - val_acc: 0.7364\\n\",\n      \"Epoch 3/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.4512 - acc: 0.7933 - val_loss: 0.4949 - val_acc: 0.7470\\n\",\n      \"Epoch 4/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.4190 - acc: 0.8069 - val_loss: 0.4905 - val_acc: 0.7538\\n\",\n      \"Epoch 5/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.3965 - acc: 0.8198 - val_loss: 0.4914 - val_acc: 0.7572\\n\",\n      \"Epoch 6/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.3784 - acc: 0.8311 - val_loss: 0.4953 - val_acc: 0.7594\\n\",\n      \"Epoch 7/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.3624 - acc: 0.8419 - val_loss: 0.5004 - val_acc: 0.7574\\n\",\n      \"Epoch 8/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.3474 - acc: 0.8484 - val_loss: 0.5058 - val_acc: 0.7572\\n\",\n      \"Epoch 9/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.3330 - acc: 0.8582 - val_loss: 0.5122 - val_acc: 0.7528\\n\",\n      \"Epoch 10/10\\n\",\n      \"20000/20000 [==============================] - 2s - loss: 0.3194 - acc: 0.8669 - val_loss: 0.5183 - val_acc: 0.7554\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers import Flatten, Dense\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"# We specify the maximum input length to our Embedding layer\\n\",\n    \"# so we can later flatten the embedded inputs\\n\",\n    \"model.add(Embedding(10000, 8, input_length=maxlen))\\n\",\n    \"# After the Embedding layer, \\n\",\n    \"# our activations have shape `(samples, maxlen, 8)`.\\n\",\n    \"\\n\",\n    \"# We flatten the 3D tensor of embeddings \\n\",\n    \"# into a 2D tensor of shape `(samples, maxlen * 8)`\\n\",\n    \"model.add(Flatten())\\n\",\n    \"\\n\",\n    \"# We add the classifier on top\\n\",\n    \"model.add(Dense(1, activation='sigmoid'))\\n\",\n    \"model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"history = model.fit(x_train, y_train,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    batch_size=32,\\n\",\n    \"                    validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We get to a validation accuracy of ~76%, which is pretty good considering that we only look at the first 20 words in every review. But \\n\",\n    \"note that merely flattening the embedded sequences and training a single `Dense` layer on top leads to a model that treats each word in the \\n\",\n    \"input sequence separately, without considering inter-word relationships and structure sentence (e.g. it would likely treat both _\\\"this movie \\n\",\n    \"is shit\\\"_ and _\\\"this movie is the shit\\\"_ as being negative \\\"reviews\\\"). It would be much better to add recurrent layers or 1D convolutional \\n\",\n    \"layers on top of the embedded sequences to learn features that take into account each sequence as a whole. That's what we will focus on in \\n\",\n    \"the next few sections.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using pre-trained word embeddings\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Sometimes, you have so little training data available that could never use your data alone to learn an appropriate task-specific embedding \\n\",\n    \"of your vocabulary. What to do then?\\n\",\n    \"\\n\",\n    \"Instead of learning word embeddings jointly with the problem you want to solve, you could be loading embedding vectors from a pre-computed \\n\",\n    \"embedding space known to be highly structured and to exhibit useful properties -- that captures generic aspects of language structure. The \\n\",\n    \"rationale behind using pre-trained word embeddings in natural language processing is very much the same as for using pre-trained convnets \\n\",\n    \"in image classification: we don't have enough data available to learn truly powerful features on our own, but we expect the features that \\n\",\n    \"we need to be fairly generic, i.e. common visual features or semantic features. In this case it makes sense to reuse features learned on a \\n\",\n    \"different problem.\\n\",\n    \"\\n\",\n    \"Such word embeddings are generally computed using word occurrence statistics (observations about what words co-occur in sentences or \\n\",\n    \"documents), using a variety of techniques, some involving neural networks, others not. The idea of a dense, low-dimensional embedding space \\n\",\n    \"for words, computed in an unsupervised way, was initially explored by Bengio et al. in the early 2000s, but it only started really taking \\n\",\n    \"off in research and industry applications after the release of one of the most famous and successful word embedding scheme: the Word2Vec \\n\",\n    \"algorithm, developed by Mikolov at Google in 2013. Word2Vec dimensions capture specific semantic properties, e.g. gender.\\n\",\n    \"\\n\",\n    \"There are various pre-computed databases of word embeddings that can download and start using in a Keras `Embedding` layer. Word2Vec is one \\n\",\n    \"of them. Another popular one is called \\\"GloVe\\\", developed by Stanford researchers in 2014. It stands for \\\"Global Vectors for Word \\n\",\n    \"Representation\\\", and it is an embedding technique based on factorizing a matrix of word co-occurrence statistics. Its developers have made \\n\",\n    \"available pre-computed embeddings for millions of English tokens, obtained from Wikipedia data or from Common Crawl data.\\n\",\n    \"\\n\",\n    \"Let's take a look at how you can get started using GloVe embeddings in a Keras model. The same method will of course be valid for Word2Vec \\n\",\n    \"embeddings or any other word embedding database that you can download. We will also use this example to refresh the text tokenization \\n\",\n    \"techniques we introduced a few paragraphs ago: we will start from raw text, and work our way up.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Putting it all together: from raw text to word embeddings\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We will be using a model similar to the one we just went over -- embedding sentences in sequences of vectors, flattening them and training a \\n\",\n    \"`Dense` layer on top. But we will do it using pre-trained word embeddings, and instead of using the pre-tokenized IMDB data packaged in \\n\",\n    \"Keras, we will start from scratch, by downloading the original text data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Download the IMDB data as raw text\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"First, head to `http://ai.stanford.edu/~amaas/data/sentiment/` and download the raw IMDB dataset (if the URL isn't working anymore, just \\n\",\n    \"Google \\\"IMDB dataset\\\"). Uncompress it.\\n\",\n    \"\\n\",\n    \"Now let's collect the individual training reviews into a list of strings, one string per review, and let's also collect the review labels \\n\",\n    \"(positive / negative) into a `labels` list:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"imdb_dir = '/home/ubuntu/data/aclImdb'\\n\",\n    \"train_dir = os.path.join(imdb_dir, 'train')\\n\",\n    \"\\n\",\n    \"labels = []\\n\",\n    \"texts = []\\n\",\n    \"\\n\",\n    \"for label_type in ['neg', 'pos']:\\n\",\n    \"    dir_name = os.path.join(train_dir, label_type)\\n\",\n    \"    for fname in os.listdir(dir_name):\\n\",\n    \"        if fname[-4:] == '.txt':\\n\",\n    \"            f = open(os.path.join(dir_name, fname))\\n\",\n    \"            texts.append(f.read())\\n\",\n    \"            f.close()\\n\",\n    \"            if label_type == 'neg':\\n\",\n    \"                labels.append(0)\\n\",\n    \"            else:\\n\",\n    \"                labels.append(1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Tokenize the data\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Let's vectorize the texts we collected, and prepare a training and validation split.\\n\",\n    \"We will merely be using the concepts we introduced earlier in this section.\\n\",\n    \"\\n\",\n    \"Because pre-trained word embeddings are meant to be particularly useful on problems where little training data is available (otherwise, \\n\",\n    \"task-specific embeddings are likely to outperform them), we will add the following twist: we restrict the training data to its first 200 \\n\",\n    \"samples. So we will be learning to classify movie reviews after looking at just 200 examples...\\n\"\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      \"Found 88582 unique tokens.\\n\",\n      \"Shape of data tensor: (25000, 100)\\n\",\n      \"Shape of label tensor: (25000,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.preprocessing.text import Tokenizer\\n\",\n    \"from keras.preprocessing.sequence import pad_sequences\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"maxlen = 100  # We will cut reviews after 100 words\\n\",\n    \"training_samples = 200  # We will be training on 200 samples\\n\",\n    \"validation_samples = 10000  # We will be validating on 10000 samples\\n\",\n    \"max_words = 10000  # We will only consider the top 10,000 words in the dataset\\n\",\n    \"\\n\",\n    \"tokenizer = Tokenizer(num_words=max_words)\\n\",\n    \"tokenizer.fit_on_texts(texts)\\n\",\n    \"sequences = tokenizer.texts_to_sequences(texts)\\n\",\n    \"\\n\",\n    \"word_index = tokenizer.word_index\\n\",\n    \"print('Found %s unique tokens.' % len(word_index))\\n\",\n    \"\\n\",\n    \"data = pad_sequences(sequences, maxlen=maxlen)\\n\",\n    \"\\n\",\n    \"labels = np.asarray(labels)\\n\",\n    \"print('Shape of data tensor:', data.shape)\\n\",\n    \"print('Shape of label tensor:', labels.shape)\\n\",\n    \"\\n\",\n    \"# Split the data into a training set and a validation set\\n\",\n    \"# But first, shuffle the data, since we started from data\\n\",\n    \"# where sample are ordered (all negative first, then all positive).\\n\",\n    \"indices = np.arange(data.shape[0])\\n\",\n    \"np.random.shuffle(indices)\\n\",\n    \"data = data[indices]\\n\",\n    \"labels = labels[indices]\\n\",\n    \"\\n\",\n    \"x_train = data[:training_samples]\\n\",\n    \"y_train = labels[:training_samples]\\n\",\n    \"x_val = data[training_samples: training_samples + validation_samples]\\n\",\n    \"y_val = labels[training_samples: training_samples + validation_samples]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Download the GloVe word embeddings\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Head to `https://nlp.stanford.edu/projects/glove/` (where you can learn more about the GloVe algorithm), and download the pre-computed \\n\",\n    \"embeddings from 2014 English Wikipedia. It's a 822MB zip file named `glove.6B.zip`, containing 100-dimensional embedding vectors for \\n\",\n    \"400,000 words (or non-word tokens). Un-zip it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Pre-process the embeddings\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Let's parse the un-zipped file (it's a `txt` file) to build an index mapping words (as strings) to their vector representation (as number \\n\",\n    \"vectors).\"\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      \"Found 400000 word vectors.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"glove_dir = '/home/ubuntu/data/'\\n\",\n    \"\\n\",\n    \"embeddings_index = {}\\n\",\n    \"f = open(os.path.join(glove_dir, 'glove.6B.100d.txt'))\\n\",\n    \"for line in f:\\n\",\n    \"    values = line.split()\\n\",\n    \"    word = values[0]\\n\",\n    \"    coefs = np.asarray(values[1:], dtype='float32')\\n\",\n    \"    embeddings_index[word] = coefs\\n\",\n    \"f.close()\\n\",\n    \"\\n\",\n    \"print('Found %s word vectors.' % len(embeddings_index))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Now let's build an embedding matrix that we will be able to load into an `Embedding` layer. It must be a matrix of shape `(max_words, \\n\",\n    \"embedding_dim)`, where each entry `i` contains the `embedding_dim`-dimensional vector for the word of index `i` in our reference word index \\n\",\n    \"(built during tokenization). Note that the index `0` is not supposed to stand for any word or token -- it's a placeholder.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"embedding_dim = 100\\n\",\n    \"\\n\",\n    \"embedding_matrix = np.zeros((max_words, embedding_dim))\\n\",\n    \"for word, i in word_index.items():\\n\",\n    \"    embedding_vector = embeddings_index.get(word)\\n\",\n    \"    if i < max_words:\\n\",\n    \"        if embedding_vector is not None:\\n\",\n    \"            # Words not found in embedding index will be all-zeros.\\n\",\n    \"            embedding_matrix[i] = embedding_vector\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Define a model\\n\",\n    \"\\n\",\n    \"We will be using the same model architecture as before:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"embedding_4 (Embedding)      (None, 100, 100)          1000000   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"flatten_3 (Flatten)          (None, 10000)             0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_4 (Dense)              (None, 32)                320032    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_5 (Dense)              (None, 1)                 33        \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 1,320,065\\n\",\n      \"Trainable params: 1,320,065\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers import Embedding, Flatten, Dense\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(max_words, embedding_dim, input_length=maxlen))\\n\",\n    \"model.add(Flatten())\\n\",\n    \"model.add(Dense(32, activation='relu'))\\n\",\n    \"model.add(Dense(1, activation='sigmoid'))\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Load the GloVe embeddings in the model\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The `Embedding` layer has a single weight matrix: a 2D float matrix where each entry `i` is the word vector meant to be associated with \\n\",\n    \"index `i`. Simple enough. Let's just load the GloVe matrix we prepared into our `Embedding` layer, the first layer in our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.layers[0].set_weights([embedding_matrix])\\n\",\n    \"model.layers[0].trainable = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Additionally, we freeze the embedding layer (we set its `trainable` attribute to `False`), following the same rationale as what you are \\n\",\n    \"already familiar with in the context of pre-trained convnet features: when parts of a model are pre-trained (like our `Embedding` layer), \\n\",\n    \"and parts are randomly initialized (like our classifier), the pre-trained parts should not be updated during training to avoid forgetting \\n\",\n    \"what they already know. The large gradient update triggered by the randomly initialized layers would be very disruptive to the already \\n\",\n    \"learned features.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Train and evaluate\\n\",\n    \"\\n\",\n    \"Let's compile our model and train it:\"\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      \"Train on 200 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"200/200 [==============================] - 1s - loss: 1.9075 - acc: 0.5050 - val_loss: 0.7027 - val_acc: 0.5102\\n\",\n      \"Epoch 2/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.7329 - acc: 0.7100 - val_loss: 0.8200 - val_acc: 0.5000\\n\",\n      \"Epoch 3/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.4876 - acc: 0.7400 - val_loss: 0.6917 - val_acc: 0.5616\\n\",\n      \"Epoch 4/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.3640 - acc: 0.8400 - val_loss: 0.7005 - val_acc: 0.5557\\n\",\n      \"Epoch 5/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.2673 - acc: 0.8950 - val_loss: 1.2560 - val_acc: 0.4999\\n\",\n      \"Epoch 6/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.1936 - acc: 0.9400 - val_loss: 0.7294 - val_acc: 0.5704\\n\",\n      \"Epoch 7/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.2455 - acc: 0.8800 - val_loss: 0.7187 - val_acc: 0.5659\\n\",\n      \"Epoch 8/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0591 - acc: 0.9950 - val_loss: 0.7393 - val_acc: 0.5723\\n\",\n      \"Epoch 9/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0399 - acc: 1.0000 - val_loss: 0.8691 - val_acc: 0.5522\\n\",\n      \"Epoch 10/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0283 - acc: 1.0000 - val_loss: 0.9322 - val_acc: 0.5413\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['acc'])\\n\",\n    \"history = model.fit(x_train, y_train,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    batch_size=32,\\n\",\n    \"                    validation_data=(x_val, y_val))\\n\",\n    \"model.save_weights('pre_trained_glove_model.h5')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot its performance over time:\"\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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LlzZy644ILiS/4Bxo0bVzxc8K233grA/fffz/r16xk4cCADBw4EIDs7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\\nkruISAQpuYuIRND/B8sHA/B6JcgEAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7ff6bd49d748>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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HfglZUrV7J27Vr69Omzt219jx49OPHEE/nLX/5ywPorV66kc+dQE71z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\\n/D/w0XtHon6dFgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7ff68fa53320>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(1, len(acc) + 1)\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The model quickly starts overfitting, unsurprisingly given the small number of training samples. Validation accuracy has high variance for \\n\",\n    \"the same reason, but seems to reach high 50s.\\n\",\n    \"\\n\",\n    \"Note that your mileage may vary: since we have so few training samples, performance is heavily dependent on which exact 200 samples we \\n\",\n    \"picked, and we picked them at random. If it worked really poorly for you, try picking a different random set of 200 samples, just for the \\n\",\n    \"sake of the exercise (in real life you don't get to pick your training data).\\n\",\n    \"\\n\",\n    \"We can also try to train the same model without loading the pre-trained word embeddings and without freezing the embedding layer. In that \\n\",\n    \"case, we would be learning a task-specific embedding of our input tokens, which is generally more powerful than pre-trained word embeddings \\n\",\n    \"when lots of data is available. However, in our case, we have only 200 training samples. Let's try it:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"embedding_5 (Embedding)      (None, 100, 100)          1000000   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"flatten_4 (Flatten)          (None, 10000)             0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_6 (Dense)              (None, 32)                320032    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_7 (Dense)              (None, 1)                 33        \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 1,320,065\\n\",\n      \"Trainable params: 1,320,065\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\",\n      \"Train on 200 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"200/200 [==============================] - 1s - loss: 0.6941 - acc: 0.4750 - val_loss: 0.6920 - val_acc: 0.5213\\n\",\n      \"Epoch 2/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.5050 - acc: 0.9900 - val_loss: 0.6949 - val_acc: 0.5138\\n\",\n      \"Epoch 3/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.2807 - acc: 1.0000 - val_loss: 0.7131 - val_acc: 0.5125\\n\",\n      \"Epoch 4/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.1223 - acc: 1.0000 - val_loss: 0.6997 - val_acc: 0.5214\\n\",\n      \"Epoch 5/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0584 - acc: 1.0000 - val_loss: 0.7043 - val_acc: 0.5183\\n\",\n      \"Epoch 6/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0307 - acc: 1.0000 - val_loss: 0.7051 - val_acc: 0.5248\\n\",\n      \"Epoch 7/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0166 - acc: 1.0000 - val_loss: 0.7345 - val_acc: 0.5282\\n\",\n      \"Epoch 8/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0098 - acc: 1.0000 - val_loss: 0.7173 - val_acc: 0.5199\\n\",\n      \"Epoch 9/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0058 - acc: 1.0000 - val_loss: 0.7201 - val_acc: 0.5253\\n\",\n      \"Epoch 10/10\\n\",\n      \"200/200 [==============================] - 0s - loss: 0.0035 - acc: 1.0000 - val_loss: 0.7244 - val_acc: 0.5264\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers import Embedding, Flatten, Dense\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(max_words, embedding_dim, input_length=maxlen))\\n\",\n    \"model.add(Flatten())\\n\",\n    \"model.add(Dense(32, activation='relu'))\\n\",\n    \"model.add(Dense(1, activation='sigmoid'))\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['acc'])\\n\",\n    \"history = model.fit(x_train, y_train,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    batch_size=32,\\n\",\n    \"                    validation_data=(x_val, y_val))\"\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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vjgg3Wu55prrml4kRGgI3cRqdG8eTB1KqxfD+7B/dSpwfR0W7t2Lf37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\\nEYkghbuISAT9f9OWSH2mH2LTAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7ff68c18a048>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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vPS0tK0LFtEpL0yszXuHmuqnX6hKiISQQp3EZEIUriLiESQwl1EJIIU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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7ff687df5048>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(1, len(acc) + 1)\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Validation accuracy stalls in the low 50s. So in our case, pre-trained word embeddings does outperform jointly learned embeddings. If you \\n\",\n    \"increase the number of training samples, this will quickly stop being the case -- try it as an exercise.\\n\",\n    \"\\n\",\n    \"Finally, let's evaluate the model on the test data. First, we will need to tokenize the test data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_dir = os.path.join(imdb_dir, 'test')\\n\",\n    \"\\n\",\n    \"labels = []\\n\",\n    \"texts = []\\n\",\n    \"\\n\",\n    \"for label_type in ['neg', 'pos']:\\n\",\n    \"    dir_name = os.path.join(test_dir, label_type)\\n\",\n    \"    for fname in sorted(os.listdir(dir_name)):\\n\",\n    \"        if fname[-4:] == '.txt':\\n\",\n    \"            f = open(os.path.join(dir_name, fname))\\n\",\n    \"            texts.append(f.read())\\n\",\n    \"            f.close()\\n\",\n    \"            if label_type == 'neg':\\n\",\n    \"                labels.append(0)\\n\",\n    \"            else:\\n\",\n    \"                labels.append(1)\\n\",\n    \"\\n\",\n    \"sequences = tokenizer.texts_to_sequences(texts)\\n\",\n    \"x_test = pad_sequences(sequences, maxlen=maxlen)\\n\",\n    \"y_test = np.asarray(labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And let's load and evaluate the first model:\"\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      \"24736/25000 [============================>.] - ETA: 0s\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[0.93747248332977295, 0.53659999999999997]\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model.load_weights('pre_trained_glove_model.h5')\\n\",\n    \"model.evaluate(x_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We get an appalling test accuracy of 54%. Working with just a handful of training samples is hard!\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/6.2-understanding-recurrent-neural-networks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Understanding recurrent neural networks\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 6, Section 2 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"[...]\\n\",\n    \"\\n\",\n    \"## A first recurrent layer in Keras\\n\",\n    \"\\n\",\n    \"The process we just naively implemented in Numpy corresponds to an actual Keras layer: the `SimpleRNN` layer:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.layers import SimpleRNN\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There is just one minor difference: `SimpleRNN` processes batches of sequences, like all other Keras layers, not just a single sequence like \\n\",\n    \"in our Numpy example. This means that it takes inputs of shape `(batch_size, timesteps, input_features)`, rather than `(timesteps, \\n\",\n    \"input_features)`.\\n\",\n    \"\\n\",\n    \"Like all recurrent layers in Keras, `SimpleRNN` can be run in two different modes: it can return either the full sequences of successive \\n\",\n    \"outputs for each timestep (a 3D tensor of shape `(batch_size, timesteps, output_features)`), or it can return only the last output for each \\n\",\n    \"input sequence (a 2D tensor of shape `(batch_size, output_features)`). These two modes are controlled by the `return_sequences` constructor \\n\",\n    \"argument. Let's take a look at an example:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"embedding_1 (Embedding)      (None, None, 32)          320000    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"simple_rnn_1 (SimpleRNN)     (None, 32)                2080      \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 322,080\\n\",\n      \"Trainable params: 322,080\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras.layers import Embedding, SimpleRNN\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(10000, 32))\\n\",\n    \"model.add(SimpleRNN(32))\\n\",\n    \"model.summary()\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"embedding_2 (Embedding)      (None, None, 32)          320000    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"simple_rnn_2 (SimpleRNN)     (None, None, 32)          2080      \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 322,080\\n\",\n      \"Trainable params: 322,080\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(10000, 32))\\n\",\n    \"model.add(SimpleRNN(32, return_sequences=True))\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It is sometimes useful to stack several recurrent layers one after the other in order to increase the representational power of a network. \\n\",\n    \"In such a setup, you have to get all intermediate layers to return full sequences:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"embedding_3 (Embedding)      (None, None, 32)          320000    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"simple_rnn_3 (SimpleRNN)     (None, None, 32)          2080      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"simple_rnn_4 (SimpleRNN)     (None, None, 32)          2080      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"simple_rnn_5 (SimpleRNN)     (None, None, 32)          2080      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"simple_rnn_6 (SimpleRNN)     (None, 32)                2080      \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 328,320\\n\",\n      \"Trainable params: 328,320\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(10000, 32))\\n\",\n    \"model.add(SimpleRNN(32, return_sequences=True))\\n\",\n    \"model.add(SimpleRNN(32, return_sequences=True))\\n\",\n    \"model.add(SimpleRNN(32, return_sequences=True))\\n\",\n    \"model.add(SimpleRNN(32))  # This last layer only returns the last outputs.\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's try to use such a model on the IMDB movie review classification problem. First, let's preprocess the data:\"\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      \"Loading data...\\n\",\n      \"25000 train sequences\\n\",\n      \"25000 test sequences\\n\",\n      \"Pad sequences (samples x time)\\n\",\n      \"input_train shape: (25000, 500)\\n\",\n      \"input_test shape: (25000, 500)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.datasets import imdb\\n\",\n    \"from keras.preprocessing import sequence\\n\",\n    \"\\n\",\n    \"max_features = 10000  # number of words to consider as features\\n\",\n    \"maxlen = 500  # cut texts after this number of words (among top max_features most common words)\\n\",\n    \"batch_size = 32\\n\",\n    \"\\n\",\n    \"print('Loading data...')\\n\",\n    \"(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)\\n\",\n    \"print(len(input_train), 'train sequences')\\n\",\n    \"print(len(input_test), 'test sequences')\\n\",\n    \"\\n\",\n    \"print('Pad sequences (samples x time)')\\n\",\n    \"input_train = sequence.pad_sequences(input_train, maxlen=maxlen)\\n\",\n    \"input_test = sequence.pad_sequences(input_test, maxlen=maxlen)\\n\",\n    \"print('input_train shape:', input_train.shape)\\n\",\n    \"print('input_test shape:', input_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's train a simple recurrent network using an `Embedding` layer and a `SimpleRNN` layer:\"\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      \"Train on 20000 samples, validate on 5000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"20000/20000 [==============================] - 22s - loss: 0.6455 - acc: 0.6210 - val_loss: 0.5293 - val_acc: 0.7758\\n\",\n      \"Epoch 2/10\\n\",\n      \"20000/20000 [==============================] - 20s - loss: 0.4005 - acc: 0.8362 - val_loss: 0.4752 - val_acc: 0.7742\\n\",\n      \"Epoch 3/10\\n\",\n      \"20000/20000 [==============================] - 19s - loss: 0.2739 - acc: 0.8920 - val_loss: 0.4947 - val_acc: 0.8064\\n\",\n      \"Epoch 4/10\\n\",\n      \"20000/20000 [==============================] - 19s - loss: 0.1916 - acc: 0.9290 - val_loss: 0.3783 - val_acc: 0.8460\\n\",\n      \"Epoch 5/10\\n\",\n      \"20000/20000 [==============================] - 19s - loss: 0.1308 - acc: 0.9528 - val_loss: 0.5755 - val_acc: 0.7376\\n\",\n      \"Epoch 6/10\\n\",\n      \"20000/20000 [==============================] - 19s - loss: 0.0924 - acc: 0.9675 - val_loss: 0.5829 - val_acc: 0.7634\\n\",\n      \"Epoch 7/10\\n\",\n      \"20000/20000 [==============================] - 19s - loss: 0.0726 - acc: 0.9768 - val_loss: 0.5541 - val_acc: 0.7932\\n\",\n      \"Epoch 8/10\\n\",\n      \"20000/20000 [==============================] - 19s - loss: 0.0426 - acc: 0.9862 - val_loss: 0.5551 - val_acc: 0.8292\\n\",\n      \"Epoch 9/10\\n\",\n      \"20000/20000 [==============================] - 20s - loss: 0.0300 - acc: 0.9918 - val_loss: 0.5962 - val_acc: 0.8312\\n\",\n      \"Epoch 10/10\\n\",\n      \"20000/20000 [==============================] - 19s - loss: 0.0256 - acc: 0.9925 - val_loss: 0.6707 - val_acc: 0.8054\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.layers import Dense\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(max_features, 32))\\n\",\n    \"model.add(SimpleRNN(32))\\n\",\n    \"model.add(Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\\n\",\n    \"history = model.fit(input_train, y_train,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    batch_size=128,\\n\",\n    \"                    validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's display the training and validation loss and accuracy:\"\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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VlnnVXmNscffzylu6iWNmHCBDYnFBNPP/101q9fn0zokibSZXAvESX9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\\nMoiSvohIBlHSFxHJIP8fsY1CLI9SLjkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f4274e9c2b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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rfMzw8X2jLNtm3w8cc7kv2778LGjeGxLl12JPuTT079njhffRWS+bPP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\\n9P8B6H4ZRCYlex8AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f4270c827f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(acc))\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As a reminder, in chapter 3, our very first naive approach to this very dataset got us to 88% test accuracy. Unfortunately, our small \\n\",\n    \"recurrent network doesn't perform very well at all compared to this baseline (only up to 85% validation accuracy). Part of the problem is \\n\",\n    \"that our inputs only consider the first 500 words rather the full sequences -- \\n\",\n    \"hence our RNN has access to less information than our earlier baseline model. The remainder of the problem is simply that `SimpleRNN` isn't very good at processing long sequences, like text. Other types of recurrent layers perform much better. Let's take a look at some \\n\",\n    \"more advanced layers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[...]\\n\",\n    \"\\n\",\n    \"## A concrete LSTM example in Keras\\n\",\n    \"\\n\",\n    \"Now let's switch to more practical concerns: we will set up a model using a LSTM layer and train it on the IMDB data. Here's the network, \\n\",\n    \"similar to the one with `SimpleRNN` that we just presented. We only specify the output dimensionality of the LSTM layer, and leave every \\n\",\n    \"other argument (there are lots) to the Keras defaults. Keras has good defaults, and things will almost always \\\"just work\\\" without you \\n\",\n    \"having to spend time tuning parameters by hand.\"\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      \"Train on 20000 samples, validate on 5000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"20000/20000 [==============================] - 108s - loss: 0.5038 - acc: 0.7574 - val_loss: 0.3853 - val_acc: 0.8346\\n\",\n      \"Epoch 2/10\\n\",\n      \"20000/20000 [==============================] - 108s - loss: 0.2917 - acc: 0.8866 - val_loss: 0.3020 - val_acc: 0.8794\\n\",\n      \"Epoch 3/10\\n\",\n      \"20000/20000 [==============================] - 107s - loss: 0.2305 - acc: 0.9105 - val_loss: 0.3125 - val_acc: 0.8688\\n\",\n      \"Epoch 4/10\\n\",\n      \"20000/20000 [==============================] - 107s - loss: 0.2033 - acc: 0.9261 - val_loss: 0.4013 - val_acc: 0.8574\\n\",\n      \"Epoch 5/10\\n\",\n      \"20000/20000 [==============================] - 107s - loss: 0.1749 - acc: 0.9385 - val_loss: 0.3273 - val_acc: 0.8912\\n\",\n      \"Epoch 6/10\\n\",\n      \"20000/20000 [==============================] - 107s - loss: 0.1543 - acc: 0.9457 - val_loss: 0.3505 - val_acc: 0.8774\\n\",\n      \"Epoch 7/10\\n\",\n      \"20000/20000 [==============================] - 107s - loss: 0.1417 - acc: 0.9493 - val_loss: 0.4485 - val_acc: 0.8396\\n\",\n      \"Epoch 8/10\\n\",\n      \"20000/20000 [==============================] - 106s - loss: 0.1331 - acc: 0.9522 - val_loss: 0.3242 - val_acc: 0.8928\\n\",\n      \"Epoch 9/10\\n\",\n      \"20000/20000 [==============================] - 106s - loss: 0.1147 - acc: 0.9618 - val_loss: 0.4216 - val_acc: 0.8746\\n\",\n      \"Epoch 10/10\\n\",\n      \"20000/20000 [==============================] - 106s - loss: 0.1092 - acc: 0.9628 - val_loss: 0.3972 - val_acc: 0.8758\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.layers import LSTM\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(Embedding(max_features, 32))\\n\",\n    \"model.add(LSTM(32))\\n\",\n    \"model.add(Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['acc'])\\n\",\n    \"history = model.fit(input_train, y_train,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    batch_size=128,\\n\",\n    \"                    validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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YUXsnv37nBCNzEiFurSwapWTBhJX1WzgTHAW8BaYLaqrhaR+0Qkp/nl\\nucA6EfkKOAnI+RdqA6SLyArcBd6H8rX6qRCGDh3KrFmz8kybNWsWQ4cODWv5U045hTlz5pR6+/mT\\n/htvvEG9evVKvT5T/mKhLh2sasVwfBz4WHl06dJF81uzZs3PppWnHTt2aGJioh46dEhVVTdt2qTN\\nmjXTY8eO6b59+7RXr17aqVMnbdeunb7++uu5y9WsWTO3fNu2bVVVNSsrS6+44gpt3bq1XnLJJZqW\\nlqZLlixRVdXrrrtOu3TposnJyXr33XerqurkyZO1atWq2q5dOz333HNVVbVFixaamZmpqqqPPfaY\\ntm3bVtu2batPPPFE7vZat26to0aN0uTkZO3Tp49mZWX97H3NnTtX09LSNCUlRXv37q3ff/+9qqru\\n27dPR4wYoe3atdP27dvrnDlzVFV1/vz52qlTJ+3QoYP26tWrwH3l92cVq0RU3TF+3oeI35GZeAGk\\naxg5tsL16rnlFihg+PgySUkBbxj7AjVo0IC0tDTmz5/PwIEDmTVrFpdffjkiQkJCAq+99hp16tTh\\nxx9/5Mwzz2TAgAGF3i/26aefpkaNGqxdu5aVK1fSuXPn3HkTJ06kQYMGHD16lN69e7Ny5Upuuukm\\nHn/8cRYuXEijRo3yrGvp0qU8//zzfP7556gqXbt2pUePHtSvX5/169czc+ZM/va3v3H55Zfz6quv\\nMnz48DzLn3322Xz22WeICNOmTePhhx/mscce4/7776du3bqsWuX61O3atYvMzEyuvfZaFi1aRMuW\\nLW18nhIq74EAjSlM3I29Ey2hVTyhVTuqyh133EGHDh0477zz2Lp1K9u3by90PYsWLcpNvh06dKBD\\nhw6582bPnk3nzp3p1KkTq1evLnAwtVAfffQRl156KTVr1qRWrVoMGjSIDz/8EICWLVuSkuL6xBU2\\nfHNGRgYXXHAB7du355FHHmH16tUALFiwIM9dvOrXr89nn33GOeecQ8uWLQEbfrmkrC7dxIoKd6Rf\\n1BF5NA0cOJCxY8fyxRdfkJWVRZcuXQA3gFlmZiZLly6latWqJCUllWoY402bNvHoo4+yZMkS6tev\\nz4gRI8o0HHLOsMzghmbOGUEz1I033sjvf/97BgwYwPvvv8+ECRNKvT1TtJw6cz97whoDdqQftlq1\\natGzZ09GjhyZ5wLunj17OPHEE6latSoLFy7km4LO4UOcc845vPLKKwD897//ZeXKlYAblrlmzZrU\\nrVuX7du3M3/+/Nxlateuzb59+362ru7du/P666+TlZXFgQMHeO211+jevXvY72nPnj00aeL62b34\\n4ou50/v06cOUKVNyX+/atYszzzyTRYsWsWnTJqBiDb8cC00lwZopmthgSb8Ehg4dyooVK/Ik/WHD\\nhpGenk779u156aWXaN26dZHruP7669m/fz9t2rTh7rvvzj1j6NixI506daJ169ZceeWVeYZlHj16\\nNH379qVnz5551tW5c2dGjBhBWloaXbt2ZdSoUXTq9LNuEIWaMGECv/rVr+jSpUue6wXjx49n165d\\ntGvXjo4dO7Jw4UISExOZOnUqgwYNomPHjlxxxRVhb8dPsdJU0phYYUMrm4iKtc8qKangC6gtWrij\\nbWPiRbhDK9uRvolrsTDsgDGxxJK+iWuxMuyAMbGiwiT9WKuGMj8Xi5+RNZU0Jq8KkfQTEhLYsWNH\\nTCYV46gqO3bsICEhwe9Q8rBhB4zJq0JcyD1y5AgZGRllarduoi8hIYGmTZtStWpVv0MxJnAieo9c\\nv1WtWjW3J6gxxpjSqxDVO8YYYyLDkr4xxgSIJX1jjAkQS/omamJlzBtjzHEV4kKuqXhyxrzJuVtU\\nzpg3YM0ljfGTHembqIiV2wMaY/KypG+iwsa8MSY2WdI3UWFj3hgTmyzpm6iwMW+MiU2W9E1U2Jg3\\nxsQma71jombYMEvyxsQaO9I3xpgAsaRvjDEBYknfGGMCxJK+McYEiCV9Y4wJEEv6xhgTIJb0jTEm\\nQCzpxyEb0tgYUxjrnBVnbEhjY0xR7Eg/ztiQxsaYoljSjzM2pLExpiiW9OOMDWlsjCmKJf04Y0Ma\\nG2OKYkk/ztiQxsaYoljrnThkQxobYwpjR/rGGBMglvSNMSZAwkr6ItJXRNaJyAYRGVfA/BYi8q6I\\nrBSR90Wkaci8q0Vkvfe4OpLBG2OMKZlik76IVAamAP2AZGCoiCTnK/Yo8JKqdgDuAx70lm0A3AN0\\nBdKAe0SkfuTCN8YYUxLhHOmnARtUdaOqHgZmAQPzlUkG3vOeLwyZfwHwjqruVNVdwDtA37KHbYwx\\npjTCSfpNgG9DXmd400KtAAZ5zy8FaotIwzCXRURGi0i6iKRnZmaGG7sxxpgSitSF3NuAHiKyDOgB\\nbAWOhruwqk5V1VRVTU1MTIxQSMYYY/ILp53+VqBZyOum3rRcqroN70hfRGoBl6nqbhHZCpybb9n3\\nyxCvMcaYMgjnSH8J0EpEWopINWAIMDe0gIg0EpGcdf0ReM57/hZwvojU9y7gnu9NM8YY44Nik76q\\nZgNjcMl6LTBbVVeLyH0iMsArdi6wTkS+Ak4CJnrL7gTux/1wLAHu86YZY4zxgaiq3zHkkZqaqunp\\n6X6HYYwpB/v3Q61afkcRH0RkqaqmFlfOeuQaY3zxzDNQpw5ceSWsX+93NMFhSd8EwksvwVNPwa5d\\nfkdiADZtgttugzPOgH/9C9q0gd/8xt3e00SXJX0T11Thvvvg6qvhhhvglFPgqqtg0SI3z5Q/VRg1\\nCipVgrffho0bYcwYmD4dWrWCG2+E777zO8r4ZUnfxC1VuOMOuOceGDEC0tNh5EiYOxd69IDWreGR\\nR+CHH/yONFimToX33oPHHoNmzeCkk2DSJNiwAa65xlX7nHYa3H477Njhd7RxSFVj6tGlSxetqKZP\\nV23RQlXE/Z0+3e+IguvoUdWbblIF1euvd69zHDig+sILqmef7eZXqaJ62WWqb76pmp3tX8xB8M03\\nqrVrq553nuqxYwWX2bBB9aqr3Peodm3Vu+9W3b27fOOsiIB0DSPH+p7k8z8qatKfPl21Rg23R3Me\\nNWpY4vdDdrbqqFHuMxg7tvDkoqq6Zo3qrbeqNmrkyjdvrnrvvapbtpRfvEFx7Jjq+eer1qypumlT\\n8eVXr1YdPNh9LvXrqz74oOr+/VEPs8KypF/OWrTIm/BzHi1a+B1ZsBw5ojp8uNv3d95ZdMIP9dNP\\nqrNnq/bp45atVEn1wgtV//lP1cOHoxtzUEyb5vbtlCklW27pUvdZgOpJJ6lOnqx68GB0YqzILOmX\\nM5GCk76I35EFx6FDrpoGVP/0p9KvZ+NG1fHjVU85xa2rcWPVceNU16+PXKxB8+23qnXqqJ57bt6q\\ntpL4+GPVnj3dZ9KsmerUqfaDHCrcpG8XciOkefOSTTeR9dNPcNll8Oqr8PjjcOedpV9Xy5Zw//2u\\n+eC8eZCW5i74tmoFvXrBK6+47ZnwqMJvfwvZ2TBtmmu1Uxr/8z/uAvCCBa4V1ujRrqnn9OlwNOzh\\nHY3vR/b5HxX1SN/q9P2zf7+7MAiqTz8dnW1s3ao6caJqy5aaW8d8002qq1ZFZ3vx5MUX3T6bNCly\\n6zx2THXePNWOHd26k5NV58wJvzovHmHVO+XPWu+Uv717Vbt3d3XwL7wQ/e0dPaq6YIHqFVeoVqvm\\nvkFnnunqq/fti/72K5pt21Tr1VPt1q301TpFOXrUXYtp3dp9Fp07q77xRjCTvyV9E/d27lTt2tU1\\nufz738t/+5mZqo8/rtqmjfsm1aqleu21qosXBzPp5HfsmOrAgaoJCarr1kV3W0eOuB/9pCT3WXTr\\nprpwYXS3GWvCTfpWpx9hqvDll3DkiN+RxLfMTFe/vmwZzJkDl19e/jE0agRjx8Lq1fDxx/CrX8GM\\nGe4aQEoK/PWvwR72YdYsN8TC/ffD6adHd1tVqrhe1+vWwdNPu2EeevaEPn3g88+ju+0KJ5xfhvJ8\\nVPQj/UcfdUcadeuqDhmiOmOGOyI1kbNtm6vDTUhwHapiye7d7rpCly7u/yAhwTUh/eCDYB39f/+9\\nasOG7kzMjw5vWVnuLCwx0X0O/furLl9e/nGUJ6x6p/y9956rW77gAtWRI1VPPNHt4cqVXVO1xx5T\\n/eorv6Os2LZsUW3VynXwifXT9y++UP3d71xTRVA9/XTVhx9W3bPH78iib/Bgd81j9Wp/49i3z12A\\nr1fPfQaXX666dq2/MUWLJf1y9u237qiidWt3cVHVXWT69FPVO+5Qbd9ec1v1nHGG6h/+oLpokauL\\nNOH5+mtXZ1unjuonn/gdTfjyD/vQtWt8X/T9xz/c+3zwQb8jOW7XLtf3omZNd2A2YoTrjxFPwk36\\ndhOVCDh0yA3gtXo1LF7s2g4XZPNm1+573jx4/31X79+gAVx4IfTvDxdcAHXrlmfkFceXX8J558HB\\ng25kxi5d/I6odF57zdX99+wJ//43VK/ud0SR9eOPkJwMLVrAp5+6uvZYkpkJDz0EU6bAsWNutM/x\\n4127/3CpwuHD7n/x4EHXZyPneVlfn346PPts6d5buDdRsaQfAddf70YG/Mc/YPDg8JbZu9clr7lz\\n4Y033GiCVau6H4/+/d2jZcvoxl1RrFrlEj64jjnt2/sbT1m9+KIb9XPQIPj732MvMZbFlVe6C+tf\\nfAHt2vkdTeG2boWJE+Fvf3P7f+BAEAk/QZclbSYkwAknHH+Evm7f3v0glYYl/XLywgtuONg//AEe\\nfrh06zh61B0VzZvnfgS+/NJNb9fu+A9AWhpUrhyxsCuMpUvh/PPdF+Ldd91NN+LB5Mlwyy1uqOdp\\n01zCqej+9S+45BJ3/4K77vI7mvBs2uTife89d9ZVUCIu7nVJylavHr3P2pJ+OVi2zHUNP+ssd9Qe\\nqSO2DRuOVwMtWuR+FBIT4aKLYMAA1wwtCPcV/eQT6NfPVYG9+y6ceqrfEUXWPfe4hHPrrW6Yh4qc\\n+HfuhLZtoXFjV8VZtarfEQVPuEnf9wu3+R8V5ULujz+6i4pNm6pu3x697ezcqfrKK6pDhx5vgVCt\\nmmrfvm60wngdAnjhQnfR7Re/iN/3eOyY6pgx7jN94AG/oymbX//adZJbtszvSIILa70TPdnZrllm\\ntWqqn31Wfts9fNglw7FjXTLMaQ2UkqJ6112uJ2g0urqXtzffdO3bk5Ndm/x4dvSo6rBhGtVxg6Lt\\n3/928d91l9+RBJsl/SgaP97tuWee8S+GY8dce+M//9k1BaxUycV08sluKIC5c11TwYrm9dfdj2lK\\niuoPP/gdTfk4fFj14ovdmE0zZ/odTcns2qXapIlqu3ZuaGvjH0v6UTJ3rttr11wTWz0sMzNVX3rJ\\ndYqpXdvFeMIJqoMGuV7BFaFD0KxZroogLS14vZizslTPOce9///8x+9owjdypOt8uGSJ35EYS/pR\\nsH69G16hc2f3JY1Vhw6pvv226w168smaex3gootUn3tOdccOvyP8uRdecGcr3btXjB+oaNi9W7VT\\nJ/dj/eGHfkdTvDffdP9b48b5HYlRDT/pW+udMB04AGeeCdu2uWaESUl+RxSeY8dcc9BXX3WPLVtc\\n08+ePV2fgksugZNO8jfGZ55xfR369HGdl2rW9DceP/3wA3TvDtu3uw58KSl+R1SwvXtdk+JatVyb\\n/IQEvyMy4bbesVE2w6AK117retzOnFlxEj64uxR16+buJrV5MyxZ4voUfPMNXHcdnHyy6xD2l79A\\nRkb5xzdpkkv4F1/s+igEOeEDnHgivPMO1K7temivX+93RAW7/XbXwem55yzhVzjhnA6U5yMWq3cm\\nT9Yy33c11hw7prpyperdd6u2bau5LYG6dnWDgn39dfRjmDjRbXPwYLsImN/ataqNGrmb8Xz7rd/R\\n5PXuu+5zu/VWvyMxobDqncj46CNXFXLhha7qobT394x169YdrwL64gs3LSXF3Xf2sssKH0+oNFRd\\nj82JE2H4cHj++fgaiiBSli51/3tNm7pOeo0a+R0R7N/vhgqoWhVWrHC9TE1ssM5ZEbBtm2rjxq5N\\n/K5dfkdTfjZudPcFOOus42cAycmuHfby5WVrtXTsmOtnAK5paTz0K4im999XrV5d9Ze/PD56q5/G\\njHFNSyvCheagwVrvlM2hQ+6WazVqBPvm1xkZqk8+6e4HkNMX4LTTVG+/XfXzz0v2A3D0qOp117l1\\n3HRTbDUHr7U+AAAN7klEQVR5jWVz57pmkb16qR486F8cH3xw/LMzsceSfhnddJPbOxWts0w0bd+u\\n+uyzquef79qTg2qzZqo33+zuDVDUHZKys1Wvvtot87//awm/pF5+2e27Sy7x5x4MBw64H/tTT1Xd\\nv7/8t2+KZ0m/DGbMcHvmllv8jiR27djh2tb37++qH8BVhV13neqCBXkT0+HDqldc4crce68l/NL6\\ny1/cPhwxovyrxXKq5GL9bmVBZkm/lFascJ1jund3ycoUb+9ed0Y0eLCrDgN3f9SRI924LAMHumkP\\nP+x3pBXfvfcePyAprx/Pjz929fjXX18+2zOlE27St9Y7IXbvhtRUyMpyLVgaN/YljAotKwveesu1\\nApo3z3XiAXjySRgzxt/Y4oEqjB3rxuO//35316doOngQOnVyNw9Ztcr1HzCxKdzWO9ZQznPsGFx1\\nleu09MEHlvBLq0YNuPRS9zh0yI2DX6cOnH2235HFBxHX0W7XLtfstX59uOGG6G1vwgTXnPftty3h\\nxwtL+p6JE909S5980t0YxZRd9equf4OJrEqV4P/+z52ZjhkD9erBsGGR387ixfDoo643ep8+kV+/\\n8UecdjUqmfnz3V2Mhg2L7lGTMZFSpYq7v27PnnD11e6AJZIOHXK3AT3lFHdXLxM/Ap/0N250yb59\\ne5g6tWLfss4ES0KCuy9tp07wq1+5XruRcv/9sGaNu3F43bqRW6/xX1hJX0T6isg6EdkgIuMKmN9c\\nRBaKyDIRWSkiF3rTk0TkoIgs9x7PRPoNlMXBg26IAVX45z9dfbQxFUnt2u5MNSkJ+vc/PoRGWSxd\\nCg89BCNGQN++ZV+fiS3FJn0RqQxMAfoBycBQEUnOV2w8MFtVOwFDgKdC5n2tqine47oIxV1mqm50\\nx+XLYfp0OO00vyMypnQaNXIjc9ar55L0unWlX9fhw65a58QT3QVjE3/COdJPAzao6kZVPQzMAgbm\\nK6NAHe95XWBb5EKMjmeegRdfhLvvhosu8jsaY8qmaVOX+MFddP3229Kt54EHXNPMZ591LYNM/Akn\\n6TcBQv+FMrxpoSYAw0UkA3gDuDFkXkuv2ucDEele0AZEZLSIpItIemZmZvjRl9Knn8LNN0O/fu4C\\nrjHx4PTTXR+JPXvg/POhpF+lFStcK7Zhw1xVkYlPkbqQOxR4QVWbAhcCL4tIJeA7oLlX7fN74BUR\\nqZN/YVWdqqqpqpqamJgYoZAKtn27u2NU06auWideh0o2wdSpk2vJs3mzO6jJ6RxXnCNHXLVOw4au\\n45eJX+GkvK1As5DXTb1poX4DzAZQ1U+BBKCRqh5S1R3e9KXA18DpZQ26tLKzYcgQ2LnTXbht0MCv\\nSIyJnu7dYc4cd+Q+YIBrsFCchx+GZcvgqadc4jfxK5ykvwRoJSItRaQa7kLt3HxltgC9AUSkDS7p\\nZ4pIonchGBE5FWgFbIxU8CU1bpy77+izz8buvUeNiYSLLoKXXnLNOK+4wh3JF2b1arjvPrj8chg0\\nqPxiNP4oNumrajYwBngLWItrpbNaRO4TkQFesVuBa0VkBTATGOENAHQOsFJElgNzgOtUdWc03khx\\nZs+Gxx6D3/0Ofv1rPyIwpnwNHQpTprgxkH7zGzfUSH7Z2a5ap04d+Otfyz9GU/7CGoZBVd/AXaAN\\nnXZ3yPM1QLcClnsVeLWMMZbZmjUwciScdRY88YTf0RhTfq6/3lVnjh/vmnROnpy3A+Ljj8OSJTBr\\nFkT5cpqJEXE/9s7evW7wr5o14R//gGrV/I7ImPJ1xx0u8T/+uKuvz2mx9uWXrsnypZe6qh0TDHGd\\n9FVdr8Kvv3ajPTbJ39DUmAAQcQOn7drlRs3MGZlz5Eh3MPTUUzb8SJDEddL/85/htddcXX6PHn5H\\nY4x/RNzYUrt3uz4qCxa4/iovv2zDiAdN3LZSX7AA7rzTtVwYO9bvaIzxX5Uq8Mor0Lu3u7h78cXR\\nGZLZxLa4PNLfssW1XGjdGqZNs1NXY3IkJLiz3ylTXPWOfTeCJ+6S/k8/uZEzDx1yHbBq1fI7ImNi\\nS+3ars+KCaa4S/o33gjp6e5o5owz/I7GGGNiS1zV6U+b5h5//CNcconf0RhjTOyJm6S/bp27X+h5\\n57m7/hhjjPm5uEn6rVq5QaNmzoTKlf2OxhhjYlPc1OlXqgQ33eR3FMYYE9vi5kjfGGNM8SzpG2NM\\ngFjSN8aYALGkb4wxAWJJ3xhjAsSSvjHGBIglfWOMCRBL+sYYEyCW9I0xJkAs6RtjTIBY0jfGmACx\\npG+MMQFiSd8YYwLEkr4xxgSIJX1jjAkQS/rGGBMglvSNMSZALOkbY0yAWNI3xpgAsaRvjDEBYknf\\nGGMCxJK+McYEiCV9Y4wJEEv6xhgTIJb0jTEmQCzpG2NMgFjSN8aYALGkb4wxARJW0heRviKyTkQ2\\niMi4AuY3F5GFIrJMRFaKyIUh8/7oLbdORC6IZPDGGGNKpkpxBUSkMjAF6ANkAEtEZK6qrgkpNh6Y\\nrapPi0gy8AaQ5D0fArQFTgEWiMjpqno00m/EGGNM8cI50k8DNqjqRlU9DMwCBuYro0Ad73ldYJv3\\nfCAwS1UPqeomYIO3PmOMMT4IJ+k3Ab4NeZ3hTQs1ARguIhm4o/wbS7AsIjJaRNJFJD0zMzPM0I0x\\nxpRUpC7kDgVeUNWmwIXAyyIS9rpVdaqqpqpqamJiYoRCMsYYk1+xdfrAVqBZyOum3rRQvwH6Aqjq\\npyKSADQKc1ljjDHlJJyj8SVAKxFpKSLVcBdm5+YrswXoDSAibYAEINMrN0REqotIS6AVsDhSwRtj\\njCmZYo/0VTVbRMYAbwGVgedUdbWI3Aekq+pc4FbgbyIyFndRd4SqKrBaRGYDa4Bs4AZruWOMMf4R\\nl5tjR2pqqqanp/sdhjHGVCgislRVU4srZz1yjTEmQCzpG2NMgFjSN8aYALGkb4wxAWJJ3xhjAsSS\\nvjHGBIglfWOMCRBL+sYYEyCW9I0xJkAs6RtjTIBY0jfGmACxpG+MMQFiSd8YYwLEkr4xxgSIJX1j\\njAkQS/rGGBMglvSNMSZALOkbY0yAWNI3xpgAsaRvjDEBYknfGGMCxJK+McYEiCV9Y4wJEEv6xhgT\\nIJb0jTEmQCzpG2NMgFjSN8aYALGkb4wxAWJJ3xhjAsSSvjHGBIglfWOMCRBL+sYYEyBxk/RnzICk\\nJKhUyf2dMcPviIwxJvZU8TuASJgxA0aPhqws9/qbb9xrgGHD/IvLGGNiTVwc6d955/GEnyMry003\\nxhhzXFwk/S1bSjbdGGOCKi6SfvPmJZtujDFBFRdJf+JEqFEj77QaNdx0Y4wxx8VF0h82DKZOhRYt\\nQMT9nTrVLuIaY0x+YbXeEZG+wGSgMjBNVR/KN/8JoKf3sgZwoqrW8+YdBVZ587ao6oBIBJ7fsGGW\\n5I0xpjjFJn0RqQxMAfoAGcASEZmrqmtyyqjq2JDyNwKdQlZxUFVTIheyMcaY0gqneicN2KCqG1X1\\nMDALGFhE+aHAzEgEZ4wxJrLCSfpNgG9DXmd4035GRFoALYH3QiYniEi6iHwmIpcUstxor0x6ZmZm\\nmKEbY4wpqUhfyB0CzFHVoyHTWqhqKnAlMElETsu/kKpOVdVUVU1NTEyMcEjGGGNyhJP0twLNQl43\\n9aYVZAj5qnZUdav3dyPwPnnr+40xxpQjUdWiC4hUAb4CeuOS/RLgSlVdna9ca+BNoKV6KxWR+kCW\\nqh4SkUbAp8DA0IvABWwvE/im9G+JRsCPZVg+nti+yMv2R162P46Lh33RQlWLrSoptvWOqmaLyBjg\\nLVyTzedUdbWI3Aekq+pcr+gQYJbm/RVpAzwrIsdwZxUPFZXwve2VqX5HRNK96qTAs32Rl+2PvGx/\\nHBekfRFWO31VfQN4I9+0u/O9nlDAcp8A7csQnzHGmAiKix65xhhjwhOPSX+q3wHEENsXedn+yMv2\\nx3GB2RfFXsg1xhgTP+LxSN8YY0whLOkbY0yAxE3SF5G+IrJORDaIyDi/4/GTiDQTkYUiskZEVovI\\nzX7H5DcRqSwiy0Tk337H4jcRqScic0TkSxFZKyJn+R2Tn0RkrPc9+a+IzBSRBL9jiqa4SPohI4H2\\nA5KBoSKS7G9UvsoGblXVZOBM4IaA7w+Am4G1fgcRIyYDb6pqa6AjAd4vItIEuAlIVdV2uL5IQ/yN\\nKrriIulT8pFA45qqfqeqX3jP9+G+1AUOkhcEItIUuAiY5ncsfhORusA5wP8BqOphVd3tb1S+qwKc\\n4I0+UAPY5nM8URUvST/skUCDRkSScOMdfe5vJL6aBNwOHPM7kBjQEsgEnvequ6aJSE2/g/KLNzbY\\no8AW4Dtgj6q+7W9U0RUvSd8UQERqAa8Ct6jqXr/j8YOIXAz8oKpL/Y4lRlQBOgNPq2on4AAQ2Gtg\\n3vhgA3E/hqcANUVkuL9RRVe8JP2SjAQaCCJSFZfwZ6jqP/2Ox0fdgAEishlX7ddLRKb7G5KvMoAM\\nVc0585uD+xEIqvOATaqaqapHgH8C/+NzTFEVL0l/CdBKRFqKSDXchZi5xSwTt0REcHW2a1X1cb/j\\n8ZOq/lFVm6pqEu7/4j1VjesjuaKo6vfAtyJyhjepN1DkIIhxbgtwpojU8L43vYnzC9thDbgW6wob\\nCdTnsPzUDbgKWCUiy71pd3gD5xlzIzDDO0DaCFzjczy+UdXPRWQO8AWu1dsy4nxIBhuGwRhjAiRe\\nqneMMcaEwZK+McYEiCV9Y4wJEEv6xhgTIJb0jTEmQCzpG2NMgFjSN8aYAPl/hgcyrEwutEEAAAAA\\nSUVORK5CYII=\\n\",\n 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91Gy5Yt6d69e57zFGTZsmWcf/75tG7dmj59+vDTTz/lnj9nqOWcgd4+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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f4265b6e3c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(acc))\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/6.3-advanced-usage-of-recurrent-neural-networks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Advanced usage of recurrent neural networks\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 6, Section 3 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"In this section, we will review three advanced techniques for improving the performance and generalization power of recurrent neural \\n\",\n    \"networks. By the end of the section, you will know most of what there is to know about using recurrent networks with Keras. We will \\n\",\n    \"demonstrate all three concepts on a weather forecasting problem, where we have access to a timeseries of data points coming from sensors \\n\",\n    \"installed on the roof of a building, such as temperature, air pressure, and humidity, which we use to predict what the temperature will be \\n\",\n    \"24 hours after the last data point collected. This is a fairly challenging problem that exemplifies many common difficulties encountered \\n\",\n    \"when working with timeseries.\\n\",\n    \"\\n\",\n    \"We will cover the following techniques:\\n\",\n    \"\\n\",\n    \"* *Recurrent dropout*, a specific, built-in way to use dropout to fight overfitting in recurrent layers.\\n\",\n    \"* *Stacking recurrent layers*, to increase the representational power of the network (at the cost of higher computational loads).\\n\",\n    \"* *Bidirectional recurrent layers*, which presents the same information to a recurrent network in different ways, increasing accuracy and \\n\",\n    \"mitigating forgetting issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A temperature forecasting problem\\n\",\n    \"\\n\",\n    \"Until now, the only sequence data we have covered has been text data, for instance the IMDB dataset and the Reuters dataset. But sequence \\n\",\n    \"data is found in many more problems than just language processing. In all of our examples in this section, we will be playing with a weather \\n\",\n    \"timeseries dataset recorded at the Weather Station at the Max-Planck-Institute for Biogeochemistry in Jena, Germany: http://www.bgc-jena.mpg.de/wetter/.\\n\",\n    \"\\n\",\n    \"In this dataset, fourteen different quantities (such air temperature, atmospheric pressure, humidity, wind direction, etc.) are recorded \\n\",\n    \"every ten minutes, over several years. The original data goes back to 2003, but we limit ourselves to data from 2009-2016. This dataset is \\n\",\n    \"perfect for learning to work with numerical timeseries. We will use it to build a model that takes as input some data from the recent past (a \\n\",\n    \"few days worth of data points) and predicts the air temperature 24 hours in the future.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's take a look at the data:\"\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      \"['\\\"Date Time\\\"', '\\\"p (mbar)\\\"', '\\\"T (degC)\\\"', '\\\"Tpot (K)\\\"', '\\\"Tdew (degC)\\\"', '\\\"rh (%)\\\"', '\\\"VPmax (mbar)\\\"', '\\\"VPact (mbar)\\\"', '\\\"VPdef (mbar)\\\"', '\\\"sh (g/kg)\\\"', '\\\"H2OC (mmol/mol)\\\"', '\\\"rho (g/m**3)\\\"', '\\\"wv (m/s)\\\"', '\\\"max. wv (m/s)\\\"', '\\\"wd (deg)\\\"']\\n\",\n      \"420551\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"data_dir = '/home/ubuntu/data/'\\n\",\n    \"fname = os.path.join(data_dir, 'jena_climate_2009_2016.csv')\\n\",\n    \"\\n\",\n    \"f = open(fname)\\n\",\n    \"data = f.read()\\n\",\n    \"f.close()\\n\",\n    \"\\n\",\n    \"lines = data.split('\\\\n')\\n\",\n    \"header = lines[0].split(',')\\n\",\n    \"lines = lines[1:]\\n\",\n    \"\\n\",\n    \"print(header)\\n\",\n    \"print(len(lines))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's convert all of these 420,551 lines of data into a Numpy array:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"float_data = np.zeros((len(lines), len(header) - 1))\\n\",\n    \"for i, line in enumerate(lines):\\n\",\n    \"    values = [float(x) for x in line.split(',')[1:]]\\n\",\n    \"    float_data[i, :] = values\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For instance, here is the plot of temperature (in degrees Celsius) over time:\"\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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AURpfA9mXvoGyT+3w2yxcC6p69E+uJZaG9rxdqv3zakz0ON9vHAMJL1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\\nAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33d00b0ef0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"temp = float_data[:, 1]  # temperature (in degrees Celsius)\\n\",\n    \"plt.plot(range(len(temp)), temp)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"On this plot, you can clearly see the yearly periodicity of temperature.\\n\",\n    \"\\n\",\n    \"Here is a more narrow plot of the first ten days of temperature data (since the data is recorded every ten minutes, we get 144 data points \\n\",\n    \"per day):\"\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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3d14Y2tHYHn54iZM+niL19biCKDtnAEnzH2c8bYMcbYZvHvIrWOlW42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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f336e862da0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(range(1440), temp[:1440])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"On this plot, you can see daily periodicity, especially evident for the last 4 days. We can also note that this ten-days period must be \\n\",\n    \"coming from a fairly cold winter month.\\n\",\n    \"\\n\",\n    \"If we were trying to predict average temperature for the next month given a few month of past data, the problem would be easy, due to the \\n\",\n    \"reliable year-scale periodicity of the data. But looking at the data over a scale of days, the temperature looks a lot more chaotic. So is \\n\",\n    \"this timeseries predictable at a daily scale? Let's find out.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Preparing the data\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The exact formulation of our problem will be the following: given data going as far back as `lookback` timesteps (a timestep is 10 minutes) \\n\",\n    \"and sampled every `steps` timesteps, can we predict the temperature in `delay` timesteps?\\n\",\n    \"\\n\",\n    \"We will use the following parameter values:\\n\",\n    \"\\n\",\n    \"* `lookback = 720`, i.e. our observations will go back 5 days.\\n\",\n    \"* `steps = 6`, i.e. our observations will be sampled at one data point per hour.\\n\",\n    \"* `delay = 144`, i.e. our targets will be 24 hours in the future.\\n\",\n    \"\\n\",\n    \"To get started, we need to do two things:\\n\",\n    \"\\n\",\n    \"* Preprocess the data to a format a neural network can ingest. This is easy: the data is already numerical, so we don't need to do any \\n\",\n    \"vectorization. However each timeseries in the data is on a different scale (e.g. temperature is typically between -20 and +30, but \\n\",\n    \"pressure, measured in mbar, is around 1000). So we will normalize each timeseries independently so that they all take small values on a \\n\",\n    \"similar scale.\\n\",\n    \"* Write a Python generator that takes our current array of float data and yields batches of data from the recent past, alongside with a \\n\",\n    \"target temperature in the future. Since the samples in our dataset are highly redundant (e.g. sample `N` and sample `N + 1` will have most \\n\",\n    \"of their timesteps in common), it would be very wasteful to explicitly allocate every sample. Instead, we will generate the samples on the \\n\",\n    \"fly using the original data.\\n\",\n    \"\\n\",\n    \"We preprocess the data by subtracting the mean of each timeseries and dividing by the standard deviation. We plan on using the first \\n\",\n    \"200,000 timesteps as training data, so we compute the mean and standard deviation only on this fraction of the data:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean = float_data[:200000].mean(axis=0)\\n\",\n    \"float_data -= mean\\n\",\n    \"std = float_data[:200000].std(axis=0)\\n\",\n    \"float_data /= std\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Now here is the data generator that we will use. It yields a tuple `(samples, targets)` where `samples` is one batch of input data and \\n\",\n    \"`targets` is the corresponding array of target temperatures. It takes the following arguments:\\n\",\n    \"\\n\",\n    \"* `data`: The original array of floating point data, which we just normalized in the code snippet above.\\n\",\n    \"* `lookback`: How many timesteps back should our input data go.\\n\",\n    \"* `delay`: How many timesteps in the future should our target be.\\n\",\n    \"* `min_index` and `max_index`: Indices in the `data` array that delimit which timesteps to draw from. This is useful for keeping a segment \\n\",\n    \"of the data for validation and another one for testing.\\n\",\n    \"* `shuffle`: Whether to shuffle our samples or draw them in chronological order.\\n\",\n    \"* `batch_size`: The number of samples per batch.\\n\",\n    \"* `step`: The period, in timesteps, at which we sample data. We will set it 6 in order to draw one data point every hour.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def generator(data, lookback, delay, min_index, max_index,\\n\",\n    \"              shuffle=False, batch_size=128, step=6):\\n\",\n    \"    if max_index is None:\\n\",\n    \"        max_index = len(data) - delay - 1\\n\",\n    \"    i = min_index + lookback\\n\",\n    \"    while 1:\\n\",\n    \"        if shuffle:\\n\",\n    \"            rows = np.random.randint(\\n\",\n    \"                min_index + lookback, max_index, size=batch_size)\\n\",\n    \"        else:\\n\",\n    \"            if i + batch_size >= max_index:\\n\",\n    \"                i = min_index + lookback\\n\",\n    \"            rows = np.arange(i, min(i + batch_size, max_index))\\n\",\n    \"            i += len(rows)\\n\",\n    \"\\n\",\n    \"        samples = np.zeros((len(rows),\\n\",\n    \"                           lookback // step,\\n\",\n    \"                           data.shape[-1]))\\n\",\n    \"        targets = np.zeros((len(rows),))\\n\",\n    \"        for j, row in enumerate(rows):\\n\",\n    \"            indices = range(rows[j] - lookback, rows[j], step)\\n\",\n    \"            samples[j] = data[indices]\\n\",\n    \"            targets[j] = data[rows[j] + delay][1]\\n\",\n    \"        yield samples, targets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Now let's use our abstract generator function to instantiate three generators, one for training, one for validation and one for testing. \\n\",\n    \"Each will look at different temporal segments of the original data: the training generator looks at the first 200,000 timesteps, the \\n\",\n    \"validation generator looks at the following 100,000, and the test generator looks at the remainder.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"lookback = 1440\\n\",\n    \"step = 6\\n\",\n    \"delay = 144\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"train_gen = generator(float_data,\\n\",\n    \"                      lookback=lookback,\\n\",\n    \"                      delay=delay,\\n\",\n    \"                      min_index=0,\\n\",\n    \"                      max_index=200000,\\n\",\n    \"                      shuffle=True,\\n\",\n    \"                      step=step, \\n\",\n    \"                      batch_size=batch_size)\\n\",\n    \"val_gen = generator(float_data,\\n\",\n    \"                    lookback=lookback,\\n\",\n    \"                    delay=delay,\\n\",\n    \"                    min_index=200001,\\n\",\n    \"                    max_index=300000,\\n\",\n    \"                    step=step,\\n\",\n    \"                    batch_size=batch_size)\\n\",\n    \"test_gen = generator(float_data,\\n\",\n    \"                     lookback=lookback,\\n\",\n    \"                     delay=delay,\\n\",\n    \"                     min_index=300001,\\n\",\n    \"                     max_index=None,\\n\",\n    \"                     step=step,\\n\",\n    \"                     batch_size=batch_size)\\n\",\n    \"\\n\",\n    \"# This is how many steps to draw from `val_gen`\\n\",\n    \"# in order to see the whole validation set:\\n\",\n    \"val_steps = (300000 - 200001 - lookback) // batch_size\\n\",\n    \"\\n\",\n    \"# This is how many steps to draw from `test_gen`\\n\",\n    \"# in order to see the whole test set:\\n\",\n    \"test_steps = (len(float_data) - 300001 - lookback) // batch_size\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A common sense, non-machine learning baseline\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Before we start leveraging black-box deep learning models to solve our temperature prediction problem, let's try out a simple common-sense \\n\",\n    \"approach. It will serve as a sanity check, and it will establish a baseline that we will have to beat in order to demonstrate the \\n\",\n    \"usefulness of more advanced machine learning models. Such common-sense baselines can be very useful when approaching a new problem for \\n\",\n    \"which there is no known solution (yet). A classic example is that of unbalanced classification tasks, where some classes can be much more \\n\",\n    \"common than others. If your dataset contains 90% of instances of class A and 10% of instances of class B, then a common sense approach to \\n\",\n    \"the classification task would be to always predict \\\"A\\\" when presented with a new sample. Such a classifier would be 90% accurate overall, \\n\",\n    \"and any learning-based approach should therefore beat this 90% score in order to demonstrate usefulness. Sometimes such elementary \\n\",\n    \"baseline can prove surprisingly hard to beat.\\n\",\n    \"\\n\",\n    \"In our case, the temperature timeseries can safely be assumed to be continuous (the temperatures tomorrow are likely to be close to the \\n\",\n    \"temperatures today) as well as periodical with a daily period. Thus a common sense approach would be to always predict that the temperature \\n\",\n    \"24 hours from now will be equal to the temperature right now. Let's evaluate this approach, using the Mean Absolute Error metric (MAE). \\n\",\n    \"Mean Absolute Error is simply equal to:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.mean(np.abs(preds - targets))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here's our evaluation loop:\"\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      \"0.289735972991\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def evaluate_naive_method():\\n\",\n    \"    batch_maes = []\\n\",\n    \"    for step in range(val_steps):\\n\",\n    \"        samples, targets = next(val_gen)\\n\",\n    \"        preds = samples[:, -1, 1]\\n\",\n    \"        mae = np.mean(np.abs(preds - targets))\\n\",\n    \"        batch_maes.append(mae)\\n\",\n    \"    print(np.mean(batch_maes))\\n\",\n    \"    \\n\",\n    \"evaluate_naive_method()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It yields a MAE of 0.29. Since our temperature data has been normalized to be centered on 0 and have a standard deviation of one, this \\n\",\n    \"number is not immediately interpretable. It translates to an average absolute error of `0.29 * temperature_std` degrees Celsius, i.e. \\n\",\n    \"2.57˚C. That's a fairly large average absolute error -- now the game is to leverage our knowledge of deep learning to do better. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A basic machine learning approach\\n\",\n    \"\\n\",\n    \"In the same way that it is useful to establish a common sense baseline before trying machine learning approaches, it is useful to try \\n\",\n    \"simple and cheap machine learning models (such as small densely-connected networks) before looking into complicated and computationally \\n\",\n    \"expensive models such as RNNs. This is the best way to make sure that any further complexity we throw at the problem later on is legitimate \\n\",\n    \"and delivers real benefits.\\n\",\n    \"\\n\",\n    \"Here is a simply fully-connected model in which we start by flattening the data, then run it through two `Dense` layers. Note the lack of \\n\",\n    \"activation function on the last `Dense` layer, which is typical for a regression problem. We use MAE as the loss. Since we are evaluating \\n\",\n    \"on the exact same data and with the exact same metric as with our common sense approach, the results will be directly comparable.\"\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      \"Epoch 1/20\\n\",\n      \"500/500 [==============================] - 10s - loss: 1.5009 - val_loss: 0.4131\\n\",\n      \"Epoch 2/20\\n\",\n      \"500/500 [==============================] - 10s - loss: 0.4272 - val_loss: 0.2994\\n\",\n      \"Epoch 3/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2837 - val_loss: 0.3115\\n\",\n      \"Epoch 4/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2629 - val_loss: 0.3213\\n\",\n      \"Epoch 5/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2486 - val_loss: 0.3156\\n\",\n      \"Epoch 6/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2405 - val_loss: 0.3144\\n\",\n      \"Epoch 7/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2351 - val_loss: 0.3165\\n\",\n      \"Epoch 8/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2289 - val_loss: 0.3151\\n\",\n      \"Epoch 9/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2248 - val_loss: 0.3238\\n\",\n      \"Epoch 10/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2215 - val_loss: 0.3442\\n\",\n      \"Epoch 11/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2161 - val_loss: 0.3247\\n\",\n      \"Epoch 12/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2136 - val_loss: 0.3282\\n\",\n      \"Epoch 13/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2117 - val_loss: 0.3404\\n\",\n      \"Epoch 14/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2087 - val_loss: 0.3359\\n\",\n      \"Epoch 15/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2076 - val_loss: 0.3567\\n\",\n      \"Epoch 16/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2055 - val_loss: 0.3316\\n\",\n      \"Epoch 17/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2030 - val_loss: 0.3437\\n\",\n      \"Epoch 18/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.2016 - val_loss: 0.3312\\n\",\n      \"Epoch 19/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.1985 - val_loss: 0.3472\\n\",\n      \"Epoch 20/20\\n\",\n      \"500/500 [==============================] - 9s - loss: 0.1985 - val_loss: 0.3545\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras import layers\\n\",\n    \"from keras.optimizers import RMSprop\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(layers.Flatten(input_shape=(lookback // step, float_data.shape[-1])))\\n\",\n    \"model.add(layers.Dense(32, activation='relu'))\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(), loss='mae')\\n\",\n    \"history = model.fit_generator(train_gen,\\n\",\n    \"                              steps_per_epoch=500,\\n\",\n    \"                              epochs=20,\\n\",\n    \"                              validation_data=val_gen,\\n\",\n    \"                              validation_steps=val_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's display the loss curves for validation and training:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3317e5a2b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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eKJbdpxd2688Ub69OnD0KFD2bhxI1u2bClzPYsWLSpOvn369KFPnz7F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\\njSjpi4ikkf8PQHVe3fAN/ToAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3317ee2e80>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(loss))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Some of our validation losses get close to the no-learning baseline, but not very reliably. This goes to show the merit of having had this baseline in the first place: it turns out not to be so easy to outperform. Our \\n\",\n    \"common sense contains already a lot of valuable information that a machine learning model does not have access to.\\n\",\n    \"\\n\",\n    \"You may ask, if there exists a simple, well-performing model to go from the data to the targets (our common sense baseline), why doesn't \\n\",\n    \"the model we are training find it and improve on it? Simply put: because this simple solution is not what our training setup is looking \\n\",\n    \"for. The space of models in which we are searching for a solution, i.e. our hypothesis space, is the space of all possible 2-layer networks \\n\",\n    \"with the configuration that we defined. These networks are already fairly complicated. When looking for a solution with a space of \\n\",\n    \"complicated models, the simple well-performing baseline might be unlearnable, even if it's technically part of the hypothesis space. That \\n\",\n    \"is a pretty significant limitation of machine learning in general: unless the learning algorithm is hard-coded to look for a specific kind \\n\",\n    \"of simple model, parameter learning can sometimes fail to find a simple solution to a simple problem.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A first recurrent baseline\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Our first fully-connected approach didn't do so well, but that doesn't mean machine learning is not applicable to our problem. The approach \\n\",\n    \"above consisted in first flattening the timeseries, which removed the notion of time from the input data. Let us instead look at our data \\n\",\n    \"as what it is: a sequence, where causality and order matter. We will try a recurrent sequence processing model -- it should be the perfect \\n\",\n    \"fit for such sequence data, precisely because it does exploit the temporal ordering of data points, unlike our first approach.\\n\",\n    \"\\n\",\n    \"Instead of the `LSTM` layer introduced in the previous section, we will use the `GRU` layer, developed by Cho et al. in 2014. `GRU` layers \\n\",\n    \"(which stands for \\\"gated recurrent unit\\\") work by leveraging the same principle as LSTM, but they are somewhat streamlined and thus cheaper \\n\",\n    \"to run, albeit they may not have quite as much representational power as LSTM. This trade-off between computational expensiveness and \\n\",\n    \"representational power is seen everywhere in machine learning.\"\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      \"Epoch 1/20\\n\",\n      \"500/500 [==============================] - 169s - loss: 0.3216 - val_loss: 0.2738\\n\",\n      \"Epoch 2/20\\n\",\n      \"500/500 [==============================] - 168s - loss: 0.2846 - val_loss: 0.2654\\n\",\n      \"Epoch 3/20\\n\",\n      \"500/500 [==============================] - 168s - loss: 0.2772 - val_loss: 0.2653\\n\",\n      \"Epoch 4/20\\n\",\n      \"500/500 [==============================] - 168s - loss: 0.2707 - val_loss: 0.2663\\n\",\n      \"Epoch 5/20\\n\",\n      \"500/500 [==============================] - 168s - loss: 0.2643 - val_loss: 0.2680\\n\",\n      \"Epoch 6/20\\n\",\n      \"500/500 [==============================] - 169s - loss: 0.2584 - val_loss: 0.2644\\n\",\n      \"Epoch 7/20\\n\",\n      \"500/500 [==============================] - 168s - loss: 0.2553 - val_loss: 0.2658\\n\",\n      \"Epoch 8/20\\n\",\n      \"500/500 [==============================] - 168s - loss: 0.2508 - val_loss: 0.2672\\n\",\n      \"Epoch 9/20\\n\",\n      \"500/500 [==============================] - 165s - loss: 0.2455 - val_loss: 0.2728\\n\",\n      \"Epoch 10/20\\n\",\n      \"500/500 [==============================] - 164s - loss: 0.2423 - val_loss: 0.2801\\n\",\n      \"Epoch 11/20\\n\",\n      \"500/500 [==============================] - 164s - loss: 0.2386 - val_loss: 0.2750\\n\",\n      \"Epoch 12/20\\n\",\n      \"500/500 [==============================] - 164s - loss: 0.2364 - val_loss: 0.2774\\n\",\n      \"Epoch 13/20\\n\",\n      \"500/500 [==============================] - 165s - loss: 0.2313 - val_loss: 0.2810\\n\",\n      \"Epoch 14/20\\n\",\n      \"500/500 [==============================] - 165s - loss: 0.2283 - val_loss: 0.2855\\n\",\n      \"Epoch 15/20\\n\",\n      \"500/500 [==============================] - 165s - loss: 0.2246 - val_loss: 0.2879\\n\",\n      \"Epoch 16/20\\n\",\n      \"500/500 [==============================] - 165s - loss: 0.2194 - val_loss: 0.2894\\n\",\n      \"Epoch 17/20\\n\",\n      \"500/500 [==============================] - 164s - loss: 0.2181 - val_loss: 0.2941\\n\",\n      \"Epoch 18/20\\n\",\n      \"500/500 [==============================] - 164s - loss: 0.2139 - val_loss: 0.2982\\n\",\n      \"Epoch 19/20\\n\",\n      \"500/500 [==============================] - 164s - loss: 0.2100 - val_loss: 0.2973\\n\",\n      \"Epoch 20/20\\n\",\n      \"500/500 [==============================] - 165s - loss: 0.2074 - val_loss: 0.3034\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras import layers\\n\",\n    \"from keras.optimizers import RMSprop\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(layers.GRU(32, input_shape=(None, float_data.shape[-1])))\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(), loss='mae')\\n\",\n    \"history = model.fit_generator(train_gen,\\n\",\n    \"                              steps_per_epoch=500,\\n\",\n    \"                              epochs=20,\\n\",\n    \"                              validation_data=val_gen,\\n\",\n    \"                              validation_steps=val_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let look at our results:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3317e755f8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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uxJz549S4dlXrt2Ld27d+c///M/6dGjB2eeeeYBr1OexYsXc8opp9Cr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\\nkr6ISBZR0hcRySL/H5V5hnuyqMOlAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3316a8b358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(loss))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Much better! We are able to significantly beat the common sense baseline, such demonstrating the value of machine learning here, as well as \\n\",\n    \"the superiority of recurrent networks compared to sequence-flattening dense networks on this type of task.\\n\",\n    \"\\n\",\n    \"Our new validation MAE of ~0.265 (before we start significantly overfitting) translates to a mean absolute error of 2.35˚C after \\n\",\n    \"de-normalization. That's a solid gain on our initial error of 2.57˚C, but we probably still have a bit of margin for improvement.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using recurrent dropout to fight overfitting\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"It is evident from our training and validation curves that our model is overfitting: the training and validation losses start diverging \\n\",\n    \"considerably after a few epochs. You are already familiar with a classic technique for fighting this phenomenon: dropout, consisting in \\n\",\n    \"randomly zeroing-out input units of a layer in order to break happenstance correlations in the training data that the layer is exposed to. \\n\",\n    \"How to correctly apply dropout in recurrent networks, however, is not a trivial question. It has long been known that applying dropout \\n\",\n    \"before a recurrent layer hinders learning rather than helping with regularization. In 2015, Yarin Gal, as part of his Ph.D. thesis on \\n\",\n    \"Bayesian deep learning, determined the proper way to use dropout with a recurrent network: the same dropout mask (the same pattern of \\n\",\n    \"dropped units) should be applied at every timestep, instead of a dropout mask that would vary randomly from timestep to timestep. What's \\n\",\n    \"more: in order to regularize the representations formed by the recurrent gates of layers such as GRU and LSTM, a temporally constant \\n\",\n    \"dropout mask should be applied to the inner recurrent activations of the layer (a \\\"recurrent\\\" dropout mask). Using the same dropout mask at \\n\",\n    \"every timestep allows the network to properly propagate its learning error through time; a temporally random dropout mask would instead \\n\",\n    \"disrupt this error signal and be harmful to the learning process.\\n\",\n    \"\\n\",\n    \"Yarin Gal did his research using Keras and helped build this mechanism directly into Keras recurrent layers. Every recurrent layer in Keras \\n\",\n    \"has two dropout-related arguments: `dropout`, a float specifying the dropout rate for input units of the layer, and `recurrent_dropout`, \\n\",\n    \"specifying the dropout rate of the recurrent units. Let's add dropout and recurrent dropout to our GRU layer and see how it impacts \\n\",\n    \"overfitting. Because networks being regularized with dropout always take longer to fully converge, we train our network for twice as many \\n\",\n    \"epochs.\"\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      \"Epoch 1/40\\n\",\n      \"500/500 [==============================] - 171s - loss: 0.3526 - val_loss: 0.2740\\n\",\n      \"Epoch 2/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.3138 - val_loss: 0.2742\\n\",\n      \"Epoch 3/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.3065 - val_loss: 0.2692\\n\",\n      \"Epoch 4/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.3033 - val_loss: 0.2694\\n\",\n      \"Epoch 5/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.3006 - val_loss: 0.2695\\n\",\n      \"Epoch 6/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2990 - val_loss: 0.2709\\n\",\n      \"Epoch 7/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2955 - val_loss: 0.2667\\n\",\n      \"Epoch 8/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2942 - val_loss: 0.2671\\n\",\n      \"Epoch 9/40\\n\",\n      \"500/500 [==============================] - 171s - loss: 0.2940 - val_loss: 0.2649\\n\",\n      \"Epoch 10/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2923 - val_loss: 0.2673\\n\",\n      \"Epoch 11/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2894 - val_loss: 0.2695\\n\",\n      \"Epoch 12/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2899 - val_loss: 0.2648\\n\",\n      \"Epoch 13/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2879 - val_loss: 0.2634\\n\",\n      \"Epoch 14/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2884 - val_loss: 0.2666\\n\",\n      \"Epoch 15/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2863 - val_loss: 0.2695\\n\",\n      \"Epoch 16/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2852 - val_loss: 0.2705\\n\",\n      \"Epoch 17/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2843 - val_loss: 0.2739\\n\",\n      \"Epoch 18/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2844 - val_loss: 0.2646\\n\",\n      \"Epoch 19/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2827 - val_loss: 0.2661\\n\",\n      \"Epoch 20/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2813 - val_loss: 0.2637\\n\",\n      \"Epoch 21/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2805 - val_loss: 0.2663\\n\",\n      \"Epoch 22/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2786 - val_loss: 0.2662\\n\",\n      \"Epoch 23/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2795 - val_loss: 0.2637\\n\",\n      \"Epoch 24/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2801 - val_loss: 0.2649\\n\",\n      \"Epoch 25/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2774 - val_loss: 0.2723\\n\",\n      \"Epoch 26/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2788 - val_loss: 0.2682\\n\",\n      \"Epoch 27/40\\n\",\n      \"500/500 [==============================] - 171s - loss: 0.2780 - val_loss: 0.2655\\n\",\n      \"Epoch 28/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2782 - val_loss: 0.2645\\n\",\n      \"Epoch 29/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2770 - val_loss: 0.2647\\n\",\n      \"Epoch 30/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2756 - val_loss: 0.2746\\n\",\n      \"Epoch 31/40\\n\",\n      \"500/500 [==============================] - 171s - loss: 0.2763 - val_loss: 0.2641\\n\",\n      \"Epoch 32/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2752 - val_loss: 0.2733\\n\",\n      \"Epoch 33/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2742 - val_loss: 0.2619\\n\",\n      \"Epoch 34/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2753 - val_loss: 0.2711\\n\",\n      \"Epoch 35/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2727 - val_loss: 0.2658\\n\",\n      \"Epoch 36/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2724 - val_loss: 0.2664\\n\",\n      \"Epoch 37/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2725 - val_loss: 0.2661\\n\",\n      \"Epoch 38/40\\n\",\n      \"500/500 [==============================] - 171s - loss: 0.2725 - val_loss: 0.2633\\n\",\n      \"Epoch 39/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2726 - val_loss: 0.2695\\n\",\n      \"Epoch 40/40\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2727 - val_loss: 0.2719\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras import layers\\n\",\n    \"from keras.optimizers import RMSprop\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(layers.GRU(32,\\n\",\n    \"                     dropout=0.2,\\n\",\n    \"                     recurrent_dropout=0.2,\\n\",\n    \"                     input_shape=(None, float_data.shape[-1])))\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(), loss='mae')\\n\",\n    \"history = model.fit_generator(train_gen,\\n\",\n    \"                              steps_per_epoch=500,\\n\",\n    \"                              epochs=40,\\n\",\n    \"                              validation_data=val_gen,\\n\",\n    \"                              validation_steps=val_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3317e879e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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OvXj++++47t27eH3M/ixYuLkm/nzp3p3Llz0bI5c+bQrVs3unbtyurV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\\n+f9aLqARS41G2QAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3315076e48>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(loss))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Great success; we are no longer overfitting during the first 30 epochs. However, while we have more stable evaluation scores, our best \\n\",\n    \"scores are not much lower than they were previously.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Stacking recurrent layers\\n\",\n    \"\\n\",\n    \"Since we are no longer overfitting yet we seem to have hit a performance bottleneck, we should start considering increasing the capacity of \\n\",\n    \"our network. If you remember our description of the \\\"universal machine learning workflow\\\": it is a generally a good idea to increase the \\n\",\n    \"capacity of your network until overfitting becomes your primary obstacle (assuming that you are already taking basic steps to mitigate \\n\",\n    \"overfitting, such as using dropout). As long as you are not overfitting too badly, then you are likely under-capacity.\\n\",\n    \"\\n\",\n    \"Increasing network capacity is typically done by increasing the number of units in the layers, or adding more layers. Recurrent layer \\n\",\n    \"stacking is a classic way to build more powerful recurrent networks: for instance, what currently powers the Google translate algorithm is \\n\",\n    \"a stack of seven large LSTM layers -- that's huge.\\n\",\n    \"\\n\",\n    \"To stack recurrent layers on top of each other in Keras, all intermediate layers should return their full sequence of outputs (a 3D tensor) \\n\",\n    \"rather than their output at the last timestep. This is done by specifying `return_sequences=True`: \"\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      \"Epoch 1/40\\n\",\n      \"500/500 [==============================] - 346s - loss: 0.3341 - val_loss: 0.2780\\n\",\n      \"Epoch 2/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.3125 - val_loss: 0.2754\\n\",\n      \"Epoch 3/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.3045 - val_loss: 0.2696\\n\",\n      \"Epoch 4/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.3018 - val_loss: 0.2747\\n\",\n      \"Epoch 5/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2957 - val_loss: 0.2690\\n\",\n      \"Epoch 6/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2923 - val_loss: 0.2692\\n\",\n      \"Epoch 7/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2907 - val_loss: 0.2673\\n\",\n      \"Epoch 8/40\\n\",\n      \"500/500 [==============================] - 343s - loss: 0.2879 - val_loss: 0.2690\\n\",\n      \"Epoch 9/40\\n\",\n      \"500/500 [==============================] - 343s - loss: 0.2866 - val_loss: 0.2743\\n\",\n      \"Epoch 10/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2833 - val_loss: 0.2669\\n\",\n      \"Epoch 11/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2825 - val_loss: 0.2669\\n\",\n      \"Epoch 12/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2822 - val_loss: 0.2700\\n\",\n      \"Epoch 13/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2785 - val_loss: 0.2698\\n\",\n      \"Epoch 14/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2775 - val_loss: 0.2634\\n\",\n      \"Epoch 15/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2778 - val_loss: 0.2653\\n\",\n      \"Epoch 16/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2740 - val_loss: 0.2633\\n\",\n      \"Epoch 17/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2746 - val_loss: 0.2680\\n\",\n      \"Epoch 18/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2731 - val_loss: 0.2649\\n\",\n      \"Epoch 19/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2709 - val_loss: 0.2699\\n\",\n      \"Epoch 20/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2693 - val_loss: 0.2655\\n\",\n      \"Epoch 21/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2679 - val_loss: 0.2654\\n\",\n      \"Epoch 22/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2677 - val_loss: 0.2731\\n\",\n      \"Epoch 23/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2672 - val_loss: 0.2680\\n\",\n      \"Epoch 24/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2648 - val_loss: 0.2669\\n\",\n      \"Epoch 25/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2645 - val_loss: 0.2655\\n\",\n      \"Epoch 26/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2648 - val_loss: 0.2673\\n\",\n      \"Epoch 27/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2624 - val_loss: 0.2694\\n\",\n      \"Epoch 28/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2624 - val_loss: 0.2698\\n\",\n      \"Epoch 29/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2602 - val_loss: 0.2765\\n\",\n      \"Epoch 30/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2596 - val_loss: 0.2795\\n\",\n      \"Epoch 31/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2598 - val_loss: 0.2688\\n\",\n      \"Epoch 32/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2590 - val_loss: 0.2724\\n\",\n      \"Epoch 33/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2581 - val_loss: 0.2754\\n\",\n      \"Epoch 34/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2570 - val_loss: 0.2688\\n\",\n      \"Epoch 35/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2559 - val_loss: 0.2753\\n\",\n      \"Epoch 36/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2552 - val_loss: 0.2719\\n\",\n      \"Epoch 37/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2552 - val_loss: 0.2745\\n\",\n      \"Epoch 38/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2537 - val_loss: 0.2761\\n\",\n      \"Epoch 39/40\\n\",\n      \"500/500 [==============================] - 344s - loss: 0.2546 - val_loss: 0.2793\\n\",\n      \"Epoch 40/40\\n\",\n      \"500/500 [==============================] - 345s - loss: 0.2532 - val_loss: 0.2782\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras import layers\\n\",\n    \"from keras.optimizers import RMSprop\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(layers.GRU(32,\\n\",\n    \"                     dropout=0.1,\\n\",\n    \"                     recurrent_dropout=0.5,\\n\",\n    \"                     return_sequences=True,\\n\",\n    \"                     input_shape=(None, float_data.shape[-1])))\\n\",\n    \"model.add(layers.GRU(64, activation='relu',\\n\",\n    \"                     dropout=0.1, \\n\",\n    \"                     recurrent_dropout=0.5))\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(), loss='mae')\\n\",\n    \"history = model.fit_generator(train_gen,\\n\",\n    \"                              steps_per_epoch=500,\\n\",\n    \"                              epochs=40,\\n\",\n    \"                              validation_data=val_gen,\\n\",\n    \"                              validation_steps=val_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's take a look at our results:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f33151ee630>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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/369aNfv34AtGvXjm3bXC30tGnT6Ny5M507dy4ZlrmgoIBOnTrxy1/+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\\nAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3315184390>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(loss))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can see that the added layers does improve ours results by a bit, albeit not very significantly. We can draw two conclusions:\\n\",\n    \"\\n\",\n    \"* Since we are still not overfitting too badly, we could safely increase the size of our layers, in quest for a bit of validation loss \\n\",\n    \"improvement. This does have a non-negligible computational cost, though. \\n\",\n    \"* Since adding a layer did not help us by a significant factor, we may be seeing diminishing returns to increasing network capacity at this \\n\",\n    \"point.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Using bidirectional RNNs\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"The last technique that we will introduce in this section is called \\\"bidirectional RNNs\\\". A bidirectional RNN is common RNN variant which \\n\",\n    \"can offer higher performance than a regular RNN on certain tasks. It is frequently used in natural language processing -- you could call it \\n\",\n    \"the Swiss army knife of deep learning for NLP.\\n\",\n    \"\\n\",\n    \"RNNs are notably order-dependent, or time-dependent: they process the timesteps of their input sequences in order, and shuffling or \\n\",\n    \"reversing the timesteps can completely change the representations that the RNN will extract from the sequence. This is precisely the reason \\n\",\n    \"why they perform well on problems where order is meaningful, such as our temperature forecasting problem. A bidirectional RNN exploits \\n\",\n    \"the order-sensitivity of RNNs: it simply consists of two regular RNNs, such as the GRU or LSTM layers that you are already familiar with, \\n\",\n    \"each processing input sequence in one direction (chronologically and antichronologically), then merging their representations. By \\n\",\n    \"processing a sequence both way, a bidirectional RNN is able to catch patterns that may have been overlooked by a one-direction RNN.\\n\",\n    \"\\n\",\n    \"Remarkably, the fact that the RNN layers in this section have so far processed sequences in chronological order (older timesteps first) may \\n\",\n    \"have been an arbitrary decision. At least, it's a decision we made no attempt at questioning so far. Could it be that our RNNs could have \\n\",\n    \"performed well enough if it were processing input sequences in antichronological order, for instance (newer timesteps first)? Let's try \\n\",\n    \"this in practice and see what we get. All we need to do is write a variant of our data generator, where the input sequences get reverted \\n\",\n    \"along the time dimension (replace the last line with `yield samples[:, ::-1, :], targets`). Training the same one-GRU-layer network as we \\n\",\n    \"used in the first experiment in this section, we get the following results:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 40,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def reverse_order_generator(data, lookback, delay, min_index, max_index,\\n\",\n    \"                            shuffle=False, batch_size=128, step=6):\\n\",\n    \"    if max_index is None:\\n\",\n    \"        max_index = len(data) - delay - 1\\n\",\n    \"    i = min_index + lookback\\n\",\n    \"    while 1:\\n\",\n    \"        if shuffle:\\n\",\n    \"            rows = np.random.randint(\\n\",\n    \"                min_index + lookback, max_index, size=batch_size)\\n\",\n    \"        else:\\n\",\n    \"            if i + batch_size >= max_index:\\n\",\n    \"                i = min_index + lookback\\n\",\n    \"            rows = np.arange(i, min(i + batch_size, max_index))\\n\",\n    \"            i += len(rows)\\n\",\n    \"\\n\",\n    \"        samples = np.zeros((len(rows),\\n\",\n    \"                           lookback // step,\\n\",\n    \"                           data.shape[-1]))\\n\",\n    \"        targets = np.zeros((len(rows),))\\n\",\n    \"        for j, row in enumerate(rows):\\n\",\n    \"            indices = range(rows[j] - lookback, rows[j], step)\\n\",\n    \"            samples[j] = data[indices]\\n\",\n    \"            targets[j] = data[rows[j] + delay][1]\\n\",\n    \"        yield samples[:, ::-1, :], targets\\n\",\n    \"        \\n\",\n    \"train_gen_reverse = reverse_order_generator(\\n\",\n    \"    float_data,\\n\",\n    \"    lookback=lookback,\\n\",\n    \"    delay=delay,\\n\",\n    \"    min_index=0,\\n\",\n    \"    max_index=200000,\\n\",\n    \"    shuffle=True,\\n\",\n    \"    step=step, \\n\",\n    \"    batch_size=batch_size)\\n\",\n    \"val_gen_reverse = reverse_order_generator(\\n\",\n    \"    float_data,\\n\",\n    \"    lookback=lookback,\\n\",\n    \"    delay=delay,\\n\",\n    \"    min_index=200001,\\n\",\n    \"    max_index=300000,\\n\",\n    \"    step=step,\\n\",\n    \"    batch_size=batch_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1/20\\n\",\n      \"500/500 [==============================] - 172s - loss: 0.4781 - val_loss: 0.4797\\n\",\n      \"Epoch 2/20\\n\",\n      \"500/500 [==============================] - 171s - loss: 0.4529 - val_loss: 0.4679\\n\",\n      \"Epoch 3/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.4071 - val_loss: 0.4536\\n\",\n      \"Epoch 4/20\\n\",\n      \"500/500 [==============================] - 171s - loss: 0.3670 - val_loss: 0.4398\\n\",\n      \"Epoch 5/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.3343 - val_loss: 0.4320\\n\",\n      \"Epoch 6/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.3191 - val_loss: 0.4388\\n\",\n      \"Epoch 7/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.3065 - val_loss: 0.4186\\n\",\n      \"Epoch 8/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2938 - val_loss: 0.4014\\n\",\n      \"Epoch 9/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2824 - val_loss: 0.4077\\n\",\n      \"Epoch 10/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2724 - val_loss: 0.4090\\n\",\n      \"Epoch 11/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2670 - val_loss: 0.3990\\n\",\n      \"Epoch 12/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2613 - val_loss: 0.4146\\n\",\n      \"Epoch 13/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2548 - val_loss: 0.4013\\n\",\n      \"Epoch 14/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2521 - val_loss: 0.4086\\n\",\n      \"Epoch 15/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2458 - val_loss: 0.4178\\n\",\n      \"Epoch 16/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2437 - val_loss: 0.4097\\n\",\n      \"Epoch 17/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2416 - val_loss: 0.4163\\n\",\n      \"Epoch 18/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2373 - val_loss: 0.4174\\n\",\n      \"Epoch 19/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2355 - val_loss: 0.4064\\n\",\n      \"Epoch 20/20\\n\",\n      \"500/500 [==============================] - 170s - loss: 0.2313 - val_loss: 0.4261\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(layers.GRU(32, input_shape=(None, float_data.shape[-1])))\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(), loss='mae')\\n\",\n    \"history = model.fit_generator(train_gen_reverse,\\n\",\n    \"                              steps_per_epoch=500,\\n\",\n    \"                              epochs=20,\\n\",\n    \"                              validation_data=val_gen_reverse,\\n\",\n    \"                              validation_steps=val_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f3314e81860>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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mmZf/GLXwAwYcIEPvnkEwYMGMCAAQMAyM7OZsOGDQDcd999HHrooRx6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\\nlPRFRDKIkr6ISAb5f90CkpLZvdpGAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f330f25bcc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(loss))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"So the reversed-order GRU strongly underperforms even the common-sense baseline, indicating that the in our case chronological processing is very \\n\",\n    \"important to the success of our approach. This makes perfect sense: the underlying GRU layer will typically be better at remembering the \\n\",\n    \"recent past than the distant past, and naturally the more recent weather data points are more predictive than older data points in our \\n\",\n    \"problem (that's precisely what makes the common-sense baseline a fairly strong baseline). Thus the chronological version of the layer is \\n\",\n    \"bound to outperform the reversed-order version. Importantly, this is generally not true for many other problems, including natural \\n\",\n    \"language: intuitively, the importance of a word in understanding a sentence is not usually dependent on its position in the sentence. Let's \\n\",\n    \"try the same trick on the LSTM IMDB example from the previous section:\"\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      \"Train on 20000 samples, validate on 5000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"20000/20000 [==============================] - 111s - loss: 0.4965 - acc: 0.7648 - val_loss: 0.3593 - val_acc: 0.8570\\n\",\n      \"Epoch 2/10\\n\",\n      \"20000/20000 [==============================] - 107s - loss: 0.3105 - acc: 0.8810 - val_loss: 0.3329 - val_acc: 0.8648\\n\",\n      \"Epoch 3/10\\n\",\n      \"20000/20000 [==============================] - 105s - loss: 0.2566 - acc: 0.9057 - val_loss: 0.3863 - val_acc: 0.8770\\n\",\n      \"Epoch 4/10\\n\",\n      \"20000/20000 [==============================] - 106s - loss: 0.2231 - acc: 0.9195 - val_loss: 0.3471 - val_acc: 0.8556\\n\",\n      \"Epoch 5/10\\n\",\n      \"20000/20000 [==============================] - 105s - loss: 0.1912 - acc: 0.9314 - val_loss: 0.3346 - val_acc: 0.8694\\n\",\n      \"Epoch 6/10\\n\",\n      \"20000/20000 [==============================] - 105s - loss: 0.1721 - acc: 0.9379 - val_loss: 0.3621 - val_acc: 0.8520\\n\",\n      \"Epoch 7/10\\n\",\n      \"20000/20000 [==============================] - 105s - loss: 0.1613 - acc: 0.9427 - val_loss: 0.3438 - val_acc: 0.8694\\n\",\n      \"Epoch 8/10\\n\",\n      \"20000/20000 [==============================] - 105s - loss: 0.1502 - acc: 0.9503 - val_loss: 0.3890 - val_acc: 0.8588\\n\",\n      \"Epoch 9/10\\n\",\n      \"20000/20000 [==============================] - 105s - loss: 0.1369 - acc: 0.9520 - val_loss: 0.3626 - val_acc: 0.8768\\n\",\n      \"Epoch 10/10\\n\",\n      \"20000/20000 [==============================] - 105s - loss: 0.1249 - acc: 0.9579 - val_loss: 0.4639 - val_acc: 0.8566\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.datasets import imdb\\n\",\n    \"from keras.preprocessing import sequence\\n\",\n    \"from keras import layers\\n\",\n    \"from keras.models import Sequential\\n\",\n    \"\\n\",\n    \"# Number of words to consider as features\\n\",\n    \"max_features = 10000\\n\",\n    \"# Cut texts after this number of words (among top max_features most common words)\\n\",\n    \"maxlen = 500\\n\",\n    \"\\n\",\n    \"# Load data\\n\",\n    \"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\\n\",\n    \"\\n\",\n    \"# Reverse sequences\\n\",\n    \"x_train = [x[::-1] for x in x_train]\\n\",\n    \"x_test = [x[::-1] for x in x_test]\\n\",\n    \"\\n\",\n    \"# Pad sequences\\n\",\n    \"x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\\n\",\n    \"x_test = sequence.pad_sequences(x_test, maxlen=maxlen)\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(layers.Embedding(max_features, 128))\\n\",\n    \"model.add(layers.LSTM(32))\\n\",\n    \"model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer='rmsprop',\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['acc'])\\n\",\n    \"history = model.fit(x_train, y_train,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    batch_size=128,\\n\",\n    \"                    validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"We get near-identical performance as the chronological-order LSTM we tried in the previous section.\\n\",\n    \"\\n\",\n    \"Thus, remarkably, on such a text dataset, reversed-order processing works just as well as chronological processing, confirming our \\n\",\n    \"hypothesis that, albeit word order *does* matter in understanding language, *which* order you use isn't crucial. Importantly, a RNN trained \\n\",\n    \"on reversed sequences will learn different representations than one trained on the original sequences, in much the same way that you would \\n\",\n    \"have quite different mental models if time flowed backwards in the real world -- if you lived a life where you died on your first day and \\n\",\n    \"you were born on your last day. In machine learning, representations that are *different* yet *useful* are always worth exploiting, and the \\n\",\n    \"more they differ the better: they offer a new angle from which to look at your data, capturing aspects of the data that were missed by other \\n\",\n    \"approaches, and thus they can allow to boost performance on a task. This is the intuition behind \\\"ensembling\\\", a concept that we will \\n\",\n    \"introduce in the next chapter.\\n\",\n    \"\\n\",\n    \"A bidirectional RNN exploits this idea to improve upon the performance of chronological-order RNNs: it looks at its inputs sequence both \\n\",\n    \"ways, obtaining potentially richer representations and capturing patterns that may have been missed by the chronological-order version alone.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![bidirectional rnn](https://s3.amazonaws.com/book.keras.io/img/ch6/bidirectional_rnn.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To instantiate a bidirectional RNN in Keras, one would use the `Bidirectional` layer, which takes as first argument a recurrent layer \\n\",\n    \"instance. `Bidirectional` will create a second, separate instance of this recurrent layer, and will use one instance for processing the \\n\",\n    \"input sequences in chronological order and the other instance for processing the input sequences in reversed order. Let's try it on the \\n\",\n    \"IMDB sentiment analysis task:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import backend as K\\n\",\n    \"K.clear_session()\"\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      \"Train on 20000 samples, validate on 5000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"20000/20000 [==============================] - 214s - loss: 0.5994 - acc: 0.6865 - val_loss: 0.4722 - val_acc: 0.8090\\n\",\n      \"Epoch 2/10\\n\",\n      \"20000/20000 [==============================] - 213s - loss: 0.3673 - acc: 0.8543 - val_loss: 0.3769 - val_acc: 0.8448\\n\",\n      \"Epoch 3/10\\n\",\n      \"20000/20000 [==============================] - 213s - loss: 0.2743 - acc: 0.8972 - val_loss: 0.3196 - val_acc: 0.8688\\n\",\n      \"Epoch 4/10\\n\",\n      \"20000/20000 [==============================] - 211s - loss: 0.2310 - acc: 0.9150 - val_loss: 0.2972 - val_acc: 0.8856\\n\",\n      \"Epoch 5/10\\n\",\n      \"20000/20000 [==============================] - 211s - loss: 0.2009 - acc: 0.9261 - val_loss: 0.4461 - val_acc: 0.8514\\n\",\n      \"Epoch 6/10\\n\",\n      \"20000/20000 [==============================] - 210s - loss: 0.1912 - acc: 0.9339 - val_loss: 0.3636 - val_acc: 0.8640\\n\",\n      \"Epoch 7/10\\n\",\n      \"20000/20000 [==============================] - 209s - loss: 0.1670 - acc: 0.9423 - val_loss: 0.3476 - val_acc: 0.8580\\n\",\n      \"Epoch 8/10\\n\",\n      \"20000/20000 [==============================] - 210s - loss: 0.1523 - acc: 0.9469 - val_loss: 0.3887 - val_acc: 0.8830\\n\",\n      \"Epoch 9/10\\n\",\n      \"20000/20000 [==============================] - 209s - loss: 0.1431 - acc: 0.9506 - val_loss: 0.3781 - val_acc: 0.8810\\n\",\n      \"Epoch 10/10\\n\",\n      \"20000/20000 [==============================] - 209s - loss: 0.1366 - acc: 0.9521 - val_loss: 0.3713 - val_acc: 0.8792\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(layers.Embedding(max_features, 32))\\n\",\n    \"model.add(layers.Bidirectional(layers.LSTM(32)))\\n\",\n    \"model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])\\n\",\n    \"history = model.fit(x_train, y_train, epochs=10, batch_size=128, validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It performs slightly better than the regular LSTM we tried in the previous section, going above 88% validation accuracy. It also seems to \\n\",\n    \"overfit faster, which is unsurprising since a bidirectional layer has twice more parameters than a chronological LSTM. With some \\n\",\n    \"regularization, the bidirectional approach would likely be a strong performer on this task.\\n\",\n    \"\\n\",\n    \"Now let's try the same approach on the weather prediction task:\"\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      \"Epoch 1/40\\n\",\n      \"500/500 [==============================] - 325s - loss: 0.3029 - val_loss: 0.2660\\n\",\n      \"Epoch 2/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2751 - val_loss: 0.2660\\n\",\n      \"Epoch 3/40\\n\",\n      \"500/500 [==============================] - 326s - loss: 0.2668 - val_loss: 0.2628\\n\",\n      \"Epoch 4/40\\n\",\n      \"500/500 [==============================] - 326s - loss: 0.2594 - val_loss: 0.2615\\n\",\n      \"Epoch 5/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2532 - val_loss: 0.2684\\n\",\n      \"Epoch 6/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2442 - val_loss: 0.2674\\n\",\n      \"Epoch 7/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2405 - val_loss: 0.2700\\n\",\n      \"Epoch 8/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2343 - val_loss: 0.2782\\n\",\n      \"Epoch 9/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2293 - val_loss: 0.2778\\n\",\n      \"Epoch 10/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2233 - val_loss: 0.2813\\n\",\n      \"Epoch 11/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2167 - val_loss: 0.2978\\n\",\n      \"Epoch 12/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2116 - val_loss: 0.2984\\n\",\n      \"Epoch 13/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.2061 - val_loss: 0.2920\\n\",\n      \"Epoch 14/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.2008 - val_loss: 0.3016\\n\",\n      \"Epoch 15/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1952 - val_loss: 0.2985\\n\",\n      \"Epoch 16/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1915 - val_loss: 0.3029\\n\",\n      \"Epoch 17/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.1862 - val_loss: 0.3127\\n\",\n      \"Epoch 18/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1821 - val_loss: 0.3079\\n\",\n      \"Epoch 19/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1772 - val_loss: 0.3116\\n\",\n      \"Epoch 20/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.1735 - val_loss: 0.3151\\n\",\n      \"Epoch 21/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.1705 - val_loss: 0.3208\\n\",\n      \"Epoch 22/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1664 - val_loss: 0.3345\\n\",\n      \"Epoch 23/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.1631 - val_loss: 0.3162\\n\",\n      \"Epoch 24/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1604 - val_loss: 0.3141\\n\",\n      \"Epoch 25/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1572 - val_loss: 0.3173\\n\",\n      \"Epoch 26/40\\n\",\n      \"500/500 [==============================] - 325s - loss: 0.1559 - val_loss: 0.3156\\n\",\n      \"Epoch 27/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1530 - val_loss: 0.3227\\n\",\n      \"Epoch 28/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1521 - val_loss: 0.3288\\n\",\n      \"Epoch 29/40\\n\",\n      \"500/500 [==============================] - 325s - loss: 0.1496 - val_loss: 0.3264\\n\",\n      \"Epoch 30/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1481 - val_loss: 0.3266\\n\",\n      \"Epoch 31/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.1456 - val_loss: 0.3241\\n\",\n      \"Epoch 32/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.1436 - val_loss: 0.3293\\n\",\n      \"Epoch 33/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1426 - val_loss: 0.3301\\n\",\n      \"Epoch 34/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1409 - val_loss: 0.3298\\n\",\n      \"Epoch 35/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1399 - val_loss: 0.3372\\n\",\n      \"Epoch 36/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.1387 - val_loss: 0.3304\\n\",\n      \"Epoch 37/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1388 - val_loss: 0.3324\\n\",\n      \"Epoch 38/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1362 - val_loss: 0.3317\\n\",\n      \"Epoch 39/40\\n\",\n      \"500/500 [==============================] - 323s - loss: 0.1342 - val_loss: 0.3319\\n\",\n      \"Epoch 40/40\\n\",\n      \"500/500 [==============================] - 324s - loss: 0.1350 - val_loss: 0.3289\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras import layers\\n\",\n    \"from keras.optimizers import RMSprop\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(layers.Bidirectional(\\n\",\n    \"    layers.GRU(32), input_shape=(None, float_data.shape[-1])))\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(), loss='mae')\\n\",\n    \"history = model.fit_generator(train_gen,\\n\",\n    \"                              steps_per_epoch=500,\\n\",\n    \"                              epochs=40,\\n\",\n    \"                              validation_data=val_gen,\\n\",\n    \"                              validation_steps=val_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"It performs about as well as the regular GRU layer. It's easy to understand why: all of the predictive capacity must be coming from the \\n\",\n    \"chronological half of the network, since the anti-chronological half is known to be severely underperforming on this task (again, because \\n\",\n    \"the recent past matters much more than the distant past in this case).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"## Going even further\\n\",\n    \"\\n\",\n    \"At this stage, there are still many other things you could try in order to improve performance on our weather forecasting problem:\\n\",\n    \"\\n\",\n    \"* Adjust the number of units in each recurrent layer in the stacked setup. Our current choices are largely arbitrary and thus likely \\n\",\n    \"suboptimal.\\n\",\n    \"* Adjust the learning rate used by our `RMSprop` optimizer.\\n\",\n    \"* Try using `LSTM` layers instead of `GRU` layers.\\n\",\n    \"* Try using a bigger densely-connected regressor on top of the recurrent layers, i.e. a bigger `Dense` layer or even a stack of `Dense` \\n\",\n    \"layers.\\n\",\n    \"* Don't forget to eventually run the best performing models (in terms of validation MAE) on the test set! Least you start developing \\n\",\n    \"architectures that are overfitting to the validation set.   \\n\",\n    \"\\n\",\n    \"As usual: deep learning is more an art than a science, and while we can provide guidelines as to what is likely to work or not work on a \\n\",\n    \"given problem, ultimately every problem is unique and you will have to try and evaluate different strategies empirically. There is \\n\",\n    \"currently no theory that will tell you in advance precisely what you should do to optimally solve a problem. You must try and iterate.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Wrapping up\\n\",\n    \"\\n\",\n    \"Here's what you should take away from this section:\\n\",\n    \"\\n\",\n    \"* As you first learned in Chapter 4, when approaching a new problem, \\n\",\n    \"it is good to first establish common sense baselines for your metric of choice. If you don't have a \\n\",\n    \"baseline to beat, you can't tell if you are making any real progress.\\n\",\n    \"* Try simple models before expensive ones, to justify the additional expense. Sometimes a simple model will turn out to be your best option.\\n\",\n    \"* On data where temporal ordering matters, recurrent networks are a great fit and easily outperform models that first flatten the temporal \\n\",\n    \"data.\\n\",\n    \"* To use dropout with recurrent networks, one should use a time-constant dropout mask and recurrent dropout mask. This is built into Keras \\n\",\n    \"recurrent layers, so all you have to do is use the `dropout` and `recurrent_dropout` arguments of recurrent layers.\\n\",\n    \"* Stacked RNNs provide more representational power than a single RNN layer. They are also much more expensive, and thus not always worth it. \\n\",\n    \"While they offer clear gains on complex problems (e.g. machine translation), they might not always be relevant to smaller, simpler problems.\\n\",\n    \"* Bidirectional RNNs, which look at a sequence both ways, are very useful on natural language processing problems. However, they will not \\n\",\n    \"be strong performers on sequence data where the recent past is much more informative than the beginning of the sequence.\\n\",\n    \"\\n\",\n    \"Note there are two important concepts that we will not cover in detail here: recurrent \\\"attention\\\", and sequence masking. Both tend to be \\n\",\n    \"especially relevant for natural language processing, and are not particularly applicable to our temperature forecasting problem. We will \\n\",\n    \"leave them for future study outside of this book.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/6.4-sequence-processing-with-convnets.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Sequence processing with convnets\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 6, Section 4 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Implementing a 1D convnet\\n\",\n    \"\\n\",\n    \"In Keras, you would use a 1D convnet via the `Conv1D` layer, which has a very similar interface to `Conv2D`. It takes as input 3D tensors \\n\",\n    \"with shape `(samples, time, features)` and also returns similarly-shaped 3D tensors. The convolution window is a 1D window on the temporal \\n\",\n    \"axis, axis 1 in the input tensor.\\n\",\n    \"\\n\",\n    \"Let's build a simple 2-layer 1D convnet and apply it to the IMDB sentiment classification task that you are already familiar with.\\n\",\n    \"\\n\",\n    \"As a reminder, this is the code for obtaining and preprocessing the data:\"\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      \"Loading data...\\n\",\n      \"25000 train sequences\\n\",\n      \"25000 test sequences\\n\",\n      \"Pad sequences (samples x time)\\n\",\n      \"x_train shape: (25000, 500)\\n\",\n      \"x_test shape: (25000, 500)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.datasets import imdb\\n\",\n    \"from keras.preprocessing import sequence\\n\",\n    \"\\n\",\n    \"max_features = 10000  # number of words to consider as features\\n\",\n    \"max_len = 500  # cut texts after this number of words (among top max_features most common words)\\n\",\n    \"\\n\",\n    \"print('Loading data...')\\n\",\n    \"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\\n\",\n    \"print(len(x_train), 'train sequences')\\n\",\n    \"print(len(x_test), 'test sequences')\\n\",\n    \"\\n\",\n    \"print('Pad sequences (samples x time)')\\n\",\n    \"x_train = sequence.pad_sequences(x_train, maxlen=max_len)\\n\",\n    \"x_test = sequence.pad_sequences(x_test, maxlen=max_len)\\n\",\n    \"print('x_train shape:', x_train.shape)\\n\",\n    \"print('x_test shape:', x_test.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"1D convnets are structured in the same way as their 2D counter-parts that you have used in Chapter 5: they consist of a stack of `Conv1D` \\n\",\n    \"and `MaxPooling1D` layers, eventually ending in either a global pooling layer or a `Flatten` layer, turning the 3D outputs into 2D outputs, \\n\",\n    \"allowing to add one or more `Dense` layers to the model, for classification or regression.\\n\",\n    \"\\n\",\n    \"One difference, though, is the fact that we can afford to use larger convolution windows with 1D convnets. Indeed, with a 2D convolution \\n\",\n    \"layer, a 3x3 convolution window contains 3*3 = 9 feature vectors, but with a 1D convolution layer, a convolution window of size 3 would \\n\",\n    \"only contain 3 feature vectors. We can thus easily afford 1D convolution windows of size 7 or 9.\\n\",\n    \"\\n\",\n    \"This is our example 1D convnet for the IMDB dataset:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"embedding_1 (Embedding)      (None, 500, 128)          1280000   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv1d_1 (Conv1D)            (None, 494, 32)           28704     \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling1d_1 (MaxPooling1 (None, 98, 32)            0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv1d_2 (Conv1D)            (None, 92, 32)            7200      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"global_max_pooling1d_1 (Glob (None, 32)                0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_1 (Dense)              (None, 1)                 33        \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 1,315,937\\n\",\n      \"Trainable params: 1,315,937\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\",\n      \"Train on 20000 samples, validate on 5000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"20000/20000 [==============================] - 4s - loss: 0.7713 - acc: 0.5287 - val_loss: 0.6818 - val_acc: 0.5970\\n\",\n      \"Epoch 2/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.6631 - acc: 0.6775 - val_loss: 0.6582 - val_acc: 0.6646\\n\",\n      \"Epoch 3/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.6142 - acc: 0.7580 - val_loss: 0.5987 - val_acc: 0.7118\\n\",\n      \"Epoch 4/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.5156 - acc: 0.8124 - val_loss: 0.4936 - val_acc: 0.7736\\n\",\n      \"Epoch 5/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.4029 - acc: 0.8469 - val_loss: 0.4123 - val_acc: 0.8358\\n\",\n      \"Epoch 6/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.3455 - acc: 0.8653 - val_loss: 0.4040 - val_acc: 0.8382\\n\",\n      \"Epoch 7/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.3078 - acc: 0.8634 - val_loss: 0.4059 - val_acc: 0.8240\\n\",\n      \"Epoch 8/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.2812 - acc: 0.8535 - val_loss: 0.4147 - val_acc: 0.8098\\n\",\n      \"Epoch 9/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.2554 - acc: 0.8334 - val_loss: 0.4296 - val_acc: 0.7878\\n\",\n      \"Epoch 10/10\\n\",\n      \"20000/20000 [==============================] - 3s - loss: 0.2356 - acc: 0.8052 - val_loss: 0.4296 - val_acc: 0.7600\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras import layers\\n\",\n    \"from keras.optimizers import RMSprop\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(layers.Embedding(max_features, 128, input_length=max_len))\\n\",\n    \"model.add(layers.Conv1D(32, 7, activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling1D(5))\\n\",\n    \"model.add(layers.Conv1D(32, 7, activation='relu'))\\n\",\n    \"model.add(layers.GlobalMaxPooling1D())\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(lr=1e-4),\\n\",\n    \"              loss='binary_crossentropy',\\n\",\n    \"              metrics=['acc'])\\n\",\n    \"history = model.fit(x_train, y_train,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    batch_size=128,\\n\",\n    \"                    validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here are our training and validation results: validation accuracy is somewhat lower than that of the LSTM we used two sections ago, but \\n\",\n    \"runtime is faster, both on CPU and GPU (albeit the exact speedup will vary greatly depending on your exact configuration). At that point, \\n\",\n    \"we could re-train this model for the right number of epochs (8), and run it on the test set. This is a convincing demonstration that a 1D \\n\",\n    \"convnet can offer a fast, cheap alternative to a recurrent network on a word-level sentiment classification task.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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ErgTeB2VbX2gKbc2B2oxhQUVJ2+qr6Ha4YZOO/BgNfpwBHtE1V1FjDr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\\nRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f708b9c0048>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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SRl4ptm4ptk3J2pU6fSp08fhg0bxrZt2/jggw8q3M+SJUuKk2yfPn3o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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f707af3d5c0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"acc = history.history['acc']\\n\",\n    \"val_acc = history.history['val_acc']\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(acc))\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, acc, 'bo', label='Training acc')\\n\",\n    \"plt.plot(epochs, val_acc, 'b', label='Validation acc')\\n\",\n    \"plt.title('Training and validation accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Combining CNNs and RNNs to process long sequences\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Because 1D convnets process input patches independently, they are not sensitive to the order of the timesteps (beyond a local scale, the \\n\",\n    \"size of the convolution windows), unlike RNNs. Of course, in order to be able to recognize longer-term patterns, one could stack many \\n\",\n    \"convolution layers and pooling layers, resulting in upper layers that would \\\"see\\\" long chunks of the original inputs -- but that's still a \\n\",\n    \"fairly weak way to induce order-sensitivity. One way to evidence this weakness is to try 1D convnets on the temperature forecasting problem \\n\",\n    \"from the previous section, where order-sensitivity was key to produce good predictions. Let's see:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# We reuse the following variables defined in the last section:\\n\",\n    \"# float_data, train_gen, val_gen, val_steps\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"data_dir = '/home/ubuntu/data/'\\n\",\n    \"fname = os.path.join(data_dir, 'jena_climate_2009_2016.csv')\\n\",\n    \"\\n\",\n    \"f = open(fname)\\n\",\n    \"data = f.read()\\n\",\n    \"f.close()\\n\",\n    \"\\n\",\n    \"lines = data.split('\\\\n')\\n\",\n    \"header = lines[0].split(',')\\n\",\n    \"lines = lines[1:]\\n\",\n    \"\\n\",\n    \"float_data = np.zeros((len(lines), len(header) - 1))\\n\",\n    \"for i, line in enumerate(lines):\\n\",\n    \"    values = [float(x) for x in line.split(',')[1:]]\\n\",\n    \"    float_data[i, :] = values\\n\",\n    \"    \\n\",\n    \"mean = float_data[:200000].mean(axis=0)\\n\",\n    \"float_data -= mean\\n\",\n    \"std = float_data[:200000].std(axis=0)\\n\",\n    \"float_data /= std\\n\",\n    \"\\n\",\n    \"def generator(data, lookback, delay, min_index, max_index,\\n\",\n    \"              shuffle=False, batch_size=128, step=6):\\n\",\n    \"    if max_index is None:\\n\",\n    \"        max_index = len(data) - delay - 1\\n\",\n    \"    i = min_index + lookback\\n\",\n    \"    while 1:\\n\",\n    \"        if shuffle:\\n\",\n    \"            rows = np.random.randint(\\n\",\n    \"                min_index + lookback, max_index, size=batch_size)\\n\",\n    \"        else:\\n\",\n    \"            if i + batch_size >= max_index:\\n\",\n    \"                i = min_index + lookback\\n\",\n    \"            rows = np.arange(i, min(i + batch_size, max_index))\\n\",\n    \"            i += len(rows)\\n\",\n    \"\\n\",\n    \"        samples = np.zeros((len(rows),\\n\",\n    \"                           lookback // step,\\n\",\n    \"                           data.shape[-1]))\\n\",\n    \"        targets = np.zeros((len(rows),))\\n\",\n    \"        for j, row in enumerate(rows):\\n\",\n    \"            indices = range(rows[j] - lookback, rows[j], step)\\n\",\n    \"            samples[j] = data[indices]\\n\",\n    \"            targets[j] = data[rows[j] + delay][1]\\n\",\n    \"        yield samples, targets\\n\",\n    \"        \\n\",\n    \"lookback = 1440\\n\",\n    \"step = 6\\n\",\n    \"delay = 144\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"train_gen = generator(float_data,\\n\",\n    \"                      lookback=lookback,\\n\",\n    \"                      delay=delay,\\n\",\n    \"                      min_index=0,\\n\",\n    \"                      max_index=200000,\\n\",\n    \"                      shuffle=True,\\n\",\n    \"                      step=step, \\n\",\n    \"                      batch_size=batch_size)\\n\",\n    \"val_gen = generator(float_data,\\n\",\n    \"                    lookback=lookback,\\n\",\n    \"                    delay=delay,\\n\",\n    \"                    min_index=200001,\\n\",\n    \"                    max_index=300000,\\n\",\n    \"                    step=step,\\n\",\n    \"                    batch_size=batch_size)\\n\",\n    \"test_gen = generator(float_data,\\n\",\n    \"                     lookback=lookback,\\n\",\n    \"                     delay=delay,\\n\",\n    \"                     min_index=300001,\\n\",\n    \"                     max_index=None,\\n\",\n    \"                     step=step,\\n\",\n    \"                     batch_size=batch_size)\\n\",\n    \"\\n\",\n    \"# This is how many steps to draw from `val_gen`\\n\",\n    \"# in order to see the whole validation set:\\n\",\n    \"val_steps = (300000 - 200001 - lookback) // batch_size\\n\",\n    \"\\n\",\n    \"# This is how many steps to draw from `test_gen`\\n\",\n    \"# in order to see the whole test set:\\n\",\n    \"test_steps = (len(float_data) - 300001 - lookback) // batch_size\"\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      \"Epoch 1/20\\n\",\n      \"500/500 [==============================] - 124s - loss: 0.4189 - val_loss: 0.4521\\n\",\n      \"Epoch 2/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.3629 - val_loss: 0.4545\\n\",\n      \"Epoch 3/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.3399 - val_loss: 0.4527\\n\",\n      \"Epoch 4/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.3229 - val_loss: 0.4721\\n\",\n      \"Epoch 5/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.3122 - val_loss: 0.4712\\n\",\n      \"Epoch 6/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.3030 - val_loss: 0.4705\\n\",\n      \"Epoch 7/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2935 - val_loss: 0.4870\\n\",\n      \"Epoch 8/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2862 - val_loss: 0.4676\\n\",\n      \"Epoch 9/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2817 - val_loss: 0.4738\\n\",\n      \"Epoch 10/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2775 - val_loss: 0.4896\\n\",\n      \"Epoch 11/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2715 - val_loss: 0.4765\\n\",\n      \"Epoch 12/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2683 - val_loss: 0.4724\\n\",\n      \"Epoch 13/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2644 - val_loss: 0.4842\\n\",\n      \"Epoch 14/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2606 - val_loss: 0.4910\\n\",\n      \"Epoch 15/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2558 - val_loss: 0.5000\\n\",\n      \"Epoch 16/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2539 - val_loss: 0.4960\\n\",\n      \"Epoch 17/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2516 - val_loss: 0.4875\\n\",\n      \"Epoch 18/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2501 - val_loss: 0.4884\\n\",\n      \"Epoch 19/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2444 - val_loss: 0.5024\\n\",\n      \"Epoch 20/20\\n\",\n      \"500/500 [==============================] - 11s - loss: 0.2444 - val_loss: 0.4821\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.models import Sequential\\n\",\n    \"from keras import layers\\n\",\n    \"from keras.optimizers import RMSprop\\n\",\n    \"\\n\",\n    \"model = Sequential()\\n\",\n    \"model.add(layers.Conv1D(32, 5, activation='relu',\\n\",\n    \"                        input_shape=(None, float_data.shape[-1])))\\n\",\n    \"model.add(layers.MaxPooling1D(3))\\n\",\n    \"model.add(layers.Conv1D(32, 5, activation='relu'))\\n\",\n    \"model.add(layers.MaxPooling1D(3))\\n\",\n    \"model.add(layers.Conv1D(32, 5, activation='relu'))\\n\",\n    \"model.add(layers.GlobalMaxPooling1D())\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(), loss='mae')\\n\",\n    \"history = model.fit_generator(train_gen,\\n\",\n    \"                              steps_per_epoch=500,\\n\",\n    \"                              epochs=20,\\n\",\n    \"                              validation_data=val_gen,\\n\",\n    \"                              validation_steps=val_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here are our training and validation Mean Absolute Errors:\"\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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DL8SmjYMPySqeoFO4nYsCF0+atfP1y0dN99idUQpWaYNCl8llWlpC9J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\\nIEr6IiIZRElfRCSD/H9HJBKfh9lz3gAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f0637d633c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(loss))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The validation MAE stays in the low 0.40s: we cannot even beat our common-sense baseline using the small convnet. Again, this is because \\n\",\n    \"our convnet looks for patterns anywhere in the input timeseries, and has no knowledge of the temporal position of a pattern it sees (e.g. \\n\",\n    \"towards the beginning, towards the end, etc.). Since more recent datapoints should be interpreted differently from older datapoints in the \\n\",\n    \"case of this specific forecasting problem, the convnet fails at producing meaningful results here. This limitation of convnets was not an \\n\",\n    \"issue on IMDB, because patterns of keywords that are associated with a positive or a negative sentiment will be informative independently \\n\",\n    \"of where they are found in the input sentences.\\n\",\n    \"\\n\",\n    \"One strategy to combine the speed and lightness of convnets with the order-sensitivity of RNNs is to use a 1D convnet as a preprocessing \\n\",\n    \"step before a RNN. This is especially beneficial when dealing with sequences that are so long that they couldn't realistically be processed \\n\",\n    \"with RNNs, e.g. sequences with thousands of steps. The convnet will turn the long input sequence into much shorter (downsampled) sequences \\n\",\n    \"of higher-level features. This sequence of extracted features then becomes the input to the RNN part of the network.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"This technique is not seen very often in research papers and practical applications, possibly because it is not very well known. It is very \\n\",\n    \"effective and ought to be more common. Let's try this out on the temperature forecasting dataset. Because this strategy allows us to \\n\",\n    \"manipulate much longer sequences, we could either look at data from further back (by increasing the `lookback` parameter of the data \\n\",\n    \"generator), or look at high-resolution timeseries (by decreasing the `step` parameter of the generator). Here, we will chose (somewhat \\n\",\n    \"arbitrarily) to use a `step` twice smaller, resulting in twice longer timeseries, where the weather data is being sampled at a rate of one \\n\",\n    \"point per 30 minutes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# This was previously set to 6 (one point per hour).\\n\",\n    \"# Now 3 (one point per 30 min).\\n\",\n    \"step = 3\\n\",\n    \"lookback = 720  # Unchanged\\n\",\n    \"delay = 144 # Unchanged\\n\",\n    \"\\n\",\n    \"train_gen = generator(float_data,\\n\",\n    \"                      lookback=lookback,\\n\",\n    \"                      delay=delay,\\n\",\n    \"                      min_index=0,\\n\",\n    \"                      max_index=200000,\\n\",\n    \"                      shuffle=True,\\n\",\n    \"                      step=step)\\n\",\n    \"val_gen = generator(float_data,\\n\",\n    \"                    lookback=lookback,\\n\",\n    \"                    delay=delay,\\n\",\n    \"                    min_index=200001,\\n\",\n    \"                    max_index=300000,\\n\",\n    \"                    step=step)\\n\",\n    \"test_gen = generator(float_data,\\n\",\n    \"                     lookback=lookback,\\n\",\n    \"                     delay=delay,\\n\",\n    \"                     min_index=300001,\\n\",\n    \"                     max_index=None,\\n\",\n    \"                     step=step)\\n\",\n    \"val_steps = (300000 - 200001 - lookback) // 128\\n\",\n    \"test_steps = (len(float_data) - 300001 - lookback) // 128\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is our model, starting with two `Conv1D` layers and following-up with a `GRU` layer:\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"conv1d_6 (Conv1D)            (None, None, 32)          2272      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"max_pooling1d_4 (MaxPooling1 (None, None, 32)          0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv1d_7 (Conv1D)            (None, None, 32)          5152      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"gru_1 (GRU)                  (None, 32)                6240      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_3 (Dense)              (None, 1)                 33        \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 13,697\\n\",\n      \"Trainable params: 13,697\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\",\n      \"Epoch 1/20\\n\",\n      \"500/500 [==============================] - 60s - loss: 0.3387 - val_loss: 0.3030\\n\",\n      \"Epoch 2/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.3055 - val_loss: 0.2864\\n\",\n      \"Epoch 3/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2904 - val_loss: 0.2841\\n\",\n      \"Epoch 4/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2830 - val_loss: 0.2730\\n\",\n      \"Epoch 5/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2767 - val_loss: 0.2757\\n\",\n      \"Epoch 6/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2696 - val_loss: 0.2819\\n\",\n      \"Epoch 7/20\\n\",\n      \"500/500 [==============================] - 57s - loss: 0.2642 - val_loss: 0.2787\\n\",\n      \"Epoch 8/20\\n\",\n      \"500/500 [==============================] - 57s - loss: 0.2595 - val_loss: 0.2920\\n\",\n      \"Epoch 9/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2546 - val_loss: 0.2919\\n\",\n      \"Epoch 10/20\\n\",\n      \"500/500 [==============================] - 57s - loss: 0.2506 - val_loss: 0.2772\\n\",\n      \"Epoch 11/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2459 - val_loss: 0.2801\\n\",\n      \"Epoch 12/20\\n\",\n      \"500/500 [==============================] - 57s - loss: 0.2433 - val_loss: 0.2807\\n\",\n      \"Epoch 13/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2411 - val_loss: 0.2855\\n\",\n      \"Epoch 14/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2360 - val_loss: 0.2858\\n\",\n      \"Epoch 15/20\\n\",\n      \"500/500 [==============================] - 57s - loss: 0.2350 - val_loss: 0.2834\\n\",\n      \"Epoch 16/20\\n\",\n      \"500/500 [==============================] - 58s - loss: 0.2315 - val_loss: 0.2917\\n\",\n      \"Epoch 17/20\\n\",\n      \"500/500 [==============================] - 60s - loss: 0.2285 - val_loss: 0.2944\\n\",\n      \"Epoch 18/20\\n\",\n      \"500/500 [==============================] - 57s - loss: 0.2280 - val_loss: 0.2923\\n\",\n      \"Epoch 19/20\\n\",\n      \"500/500 [==============================] - 57s - loss: 0.2249 - val_loss: 0.2910\\n\",\n      \"Epoch 20/20\\n\",\n      \"500/500 [==============================] - 57s - loss: 0.2215 - val_loss: 0.2952\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = Sequential()\\n\",\n    \"model.add(layers.Conv1D(32, 5, activation='relu',\\n\",\n    \"                        input_shape=(None, float_data.shape[-1])))\\n\",\n    \"model.add(layers.MaxPooling1D(3))\\n\",\n    \"model.add(layers.Conv1D(32, 5, activation='relu'))\\n\",\n    \"model.add(layers.GRU(32, dropout=0.1, recurrent_dropout=0.5))\\n\",\n    \"model.add(layers.Dense(1))\\n\",\n    \"\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=RMSprop(), loss='mae')\\n\",\n    \"history = model.fit_generator(train_gen,\\n\",\n    \"                              steps_per_epoch=500,\\n\",\n    \"                              epochs=20,\\n\",\n    \"                              validation_data=val_gen,\\n\",\n    \"                              validation_steps=val_steps)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f707ade8128>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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WoVnTt35mc/+xldu3bluOOO22k7ZVm4cCFHHHEE3bt3Z/jw4Xz55Zcl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\\ngg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f703c7fe940>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"loss = history.history['loss']\\n\",\n    \"val_loss = history.history['val_loss']\\n\",\n    \"\\n\",\n    \"epochs = range(len(loss))\\n\",\n    \"\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"plt.plot(epochs, loss, 'bo', label='Training loss')\\n\",\n    \"plt.plot(epochs, val_loss, 'b', label='Validation loss')\\n\",\n    \"plt.title('Training and validation loss')\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Judging from the validation loss, this setup is not quite as good as the regularized GRU alone, but it's significantly faster. It is \\n\",\n    \"looking at twice more data, which in this case doesn't appear to be hugely helpful, but may be important for other datasets.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Wrapping up\\n\",\n    \"\\n\",\n    \"Here's what you should take away from this section:\\n\",\n    \"\\n\",\n    \"* In the same way that 2D convnets perform well for processing visual patterns in 2D space, 1D convnets perform well for processing \\n\",\n    \"temporal patterns. They offer a faster alternative to RNNs on some problems, in particular NLP tasks.\\n\",\n    \"* Typically 1D convnets are structured much like their 2D equivalents from the world of computer vision: they consist of stacks of `Conv1D` \\n\",\n    \"layers and `MaxPooling1D` layers, eventually ending in a global pooling operation or flattening operation.\\n\",\n    \"* Because RNNs are extremely expensive for processing very long sequences, but 1D convnets are cheap, it can be a good idea to use a 1D \\n\",\n    \"convnet as a preprocessing step before a RNN, shortening the sequence and extracting useful representations for the RNN to process.\\n\",\n    \"\\n\",\n    \"One useful and important concept that we will not cover in these pages is that of 1D convolution with dilated kernels.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/8.1-text-generation-with-lstm.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Text generation with LSTM\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 8, Section 1 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"[...]\\n\",\n    \"\\n\",\n    \"## Implementing character-level LSTM text generation\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Let's put these ideas in practice in a Keras implementation. The first thing we need is a lot of text data that we can use to learn a \\n\",\n    \"language model. You could use any sufficiently large text file or set of text files -- Wikipedia, the Lord of the Rings, etc. In this \\n\",\n    \"example we will use some of the writings of Nietzsche, the late-19th century German philosopher (translated to English). The language model \\n\",\n    \"we will learn will thus be specifically a model of Nietzsche's writing style and topics of choice, rather than a more generic model of the \\n\",\n    \"English language.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Preparing the data\\n\",\n    \"\\n\",\n    \"Let's start by downloading the corpus and converting it to lowercase:\"\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      \"Corpus length: 600893\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"path = keras.utils.get_file(\\n\",\n    \"    'nietzsche.txt',\\n\",\n    \"    origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')\\n\",\n    \"text = open(path).read().lower()\\n\",\n    \"print('Corpus length:', len(text))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Next, we will extract partially-overlapping sequences of length `maxlen`, one-hot encode them and pack them in a 3D Numpy array `x` of \\n\",\n    \"shape `(sequences, maxlen, unique_characters)`. Simultaneously, we prepare a array `y` containing the corresponding targets: the one-hot \\n\",\n    \"encoded characters that come right after each extracted sequence.\"\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      \"Number of sequences: 200278\\n\",\n      \"Unique characters: 57\\n\",\n      \"Vectorization...\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Length of extracted character sequences\\n\",\n    \"maxlen = 60\\n\",\n    \"\\n\",\n    \"# We sample a new sequence every `step` characters\\n\",\n    \"step = 3\\n\",\n    \"\\n\",\n    \"# This holds our extracted sequences\\n\",\n    \"sentences = []\\n\",\n    \"\\n\",\n    \"# This holds the targets (the follow-up characters)\\n\",\n    \"next_chars = []\\n\",\n    \"\\n\",\n    \"for i in range(0, len(text) - maxlen, step):\\n\",\n    \"    sentences.append(text[i: i + maxlen])\\n\",\n    \"    next_chars.append(text[i + maxlen])\\n\",\n    \"print('Number of sequences:', len(sentences))\\n\",\n    \"\\n\",\n    \"# List of unique characters in the corpus\\n\",\n    \"chars = sorted(list(set(text)))\\n\",\n    \"print('Unique characters:', len(chars))\\n\",\n    \"# Dictionary mapping unique characters to their index in `chars`\\n\",\n    \"char_indices = dict((char, chars.index(char)) for char in chars)\\n\",\n    \"\\n\",\n    \"# Next, one-hot encode the characters into binary arrays.\\n\",\n    \"print('Vectorization...')\\n\",\n    \"x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)\\n\",\n    \"y = np.zeros((len(sentences), len(chars)), dtype=np.bool)\\n\",\n    \"for i, sentence in enumerate(sentences):\\n\",\n    \"    for t, char in enumerate(sentence):\\n\",\n    \"        x[i, t, char_indices[char]] = 1\\n\",\n    \"    y[i, char_indices[next_chars[i]]] = 1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Building the network\\n\",\n    \"\\n\",\n    \"Our network is a single `LSTM` layer followed by a `Dense` classifier and softmax over all possible characters. But let us note that \\n\",\n    \"recurrent neural networks are not the only way to do sequence data generation; 1D convnets also have proven extremely successful at it in \\n\",\n    \"recent times.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import layers\\n\",\n    \"\\n\",\n    \"model = keras.models.Sequential()\\n\",\n    \"model.add(layers.LSTM(128, input_shape=(maxlen, len(chars))))\\n\",\n    \"model.add(layers.Dense(len(chars), activation='softmax'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since our targets are one-hot encoded, we will use `categorical_crossentropy` as the loss to train the model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimizer = keras.optimizers.RMSprop(lr=0.01)\\n\",\n    \"model.compile(loss='categorical_crossentropy', optimizer=optimizer)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the language model and sampling from it\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Given a trained model and a seed text snippet, we generate new text by repeatedly:\\n\",\n    \"\\n\",\n    \"* 1) Drawing from the model a probability distribution over the next character given the text available so far\\n\",\n    \"* 2) Reweighting the distribution to a certain \\\"temperature\\\"\\n\",\n    \"* 3) Sampling the next character at random according to the reweighted distribution\\n\",\n    \"* 4) Adding the new character at the end of the available text\\n\",\n    \"\\n\",\n    \"This is the code we use to reweight the original probability distribution coming out of the model, \\n\",\n    \"and draw a character index from it (the \\\"sampling function\\\"):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def sample(preds, temperature=1.0):\\n\",\n    \"    preds = np.asarray(preds).astype('float64')\\n\",\n    \"    preds = np.log(preds) / temperature\\n\",\n    \"    exp_preds = np.exp(preds)\\n\",\n    \"    preds = exp_preds / np.sum(exp_preds)\\n\",\n    \"    probas = np.random.multinomial(1, preds, 1)\\n\",\n    \"    return np.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Finally, this is the loop where we repeatedly train and generated text. We start generating text using a range of different temperatures \\n\",\n    \"after every epoch. This allows us to see how the generated text evolves as the model starts converging, as well as the impact of \\n\",\n    \"temperature in the sampling strategy.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"epoch 1\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 126s - loss: 1.9895   \\n\",\n      \"--- Generating with seed: \\\"h they inspire.\\\" or, as la\\n\",\n      \"rochefoucauld says: \\\"if you think\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"h they inspire.\\\" or, as la\\n\",\n      \"rochefoucauld says: \\\"if you think in the sense of the say the same of the antimated and present in the all the has a such and opent and the say and and the fan and the sense of the into the sense of the say the words and the present the sense of the present present of the present in the man is the man in the sense of the say the sense of the say and the say and the say it is the such and the sense of the ast the sense of the say \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"t is the such and the sense of the ast the sense of the say the instand of the way and it is the man for the some songully the sain it is opperience of all the sensity of the same the intendition of the man, in the most with the same philosophicism of the feelient of internations of a present and and colleng it is the sense the greath to the highers of the antolity as nature and the really in the spilitions the leaded and decome the has opence in the sume \\n\",\n      \"------ temperature: 1.0\\n\",\n      \"spilitions the leaded and decome the has opence in the sume the orded out powe higher mile as of coftere obe inbernation as to\\n\",\n      \"the fof ould mome evpladity. in no it\\n\",\n      \"granter, it is the than the\\n\",\n      \"say, but the\\n\",\n      \"most nothing which, the like the knre hindver\\\"\\n\",\n      \"us setured effect of agard\\n\",\n      \"appate of alsoden\\\" the lixe their men\\n\",\n      \"an its of losed the unistensshatity; and oppreness of this not which at the brindurely to giths of sayquitt guratuch with that this\\n\",\n      \"if\\n\",\n      \"and whu\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"rely to giths of sayquitt guratuch with that this\\n\",\n      \"if\\n\",\n      \"and whungs thinkmani.\\n\",\n      \"ficcy, and peninecinated andur mage the\\n\",\n      \"sened in think wiwhhic\\n\",\n      \"to beyreasts than\\n\",\n      \"this gruath with thioruit\\n\",\n      \"catuen\\n\",\n      \"much. h.\\n\",\n      \" geevated in\\n\",\n      \"sporated mast the a\\\"coid\\n\",\n      \" nrese mae, all conentry, .. fin perhuen\\n\",\n      \"venerly (whisty or spore lised har of\\n\",\n      \"but ic; at lebgre and things. it keod\\n\",\n      \"to pring ancayedy\\n\",\n      \"from dill a be utisti listousesquas oke\\n\",\n      \"the semment\\\" (fim their falshin al\\n\",\n      \"up hesd, and u\\n\",\n      \"epoch 2\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.6382   \\n\",\n      \"--- Generating with seed: \\\"he cleverness of christianity.=--it is a master stroke of\\n\",\n      \"ch\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"he cleverness of christianity.=--it is a master stroke of\\n\",\n      \"chreme and the same and the contrary and conscience of the deason of the sense and that a sould and superion of the all the subjections and all the disting and all the more and the disting and all the same and such an all the delief to the same and more and sand and sense and all the more and the still and the sense and the more and contrary and man and such a sould and art and the presention of the\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"y and man and such a sould and art and the presention of the daction of the still of the same is any more and sanders of hoors of who has an all the man is been fact and belief and contrary had sake and disting world so sake from the\\n\",\n      \"prejudice of the sentiment and the contrarism of vided and all the saymits of the man way not the achated the deadity at the \\\"courde of sisted and all the disanctions and as a contrades in a should for a phadoward and only and\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" and as a contrades in a should for a phadoward and only and\\n\",\n      \"emptoces of anmoved and the issintions eedit modeyners bre- warlt of being whole has been bit and would be thing as all it as mankfrom for is\\n\",\n      \"resp\\\"quent, privelym yeads overthtice from\\n\",\n      \"how will has a mankinduled opine sancels and ary are but the moderation along atolity.\\n\",\n      \"\\n\",\n      \"131. new may intempt a van the\\n\",\n      \"saur. trater, sake--it tantian all ass are a superstion truth, \\\"worldting and lawtyes to make l\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ass are a superstion truth, \\\"worldting and lawtyes to make life\\n\",\n      \"coldurcly of no has grocbity of norratrimer. no weat doem not ques to thus rasg, whation.\\n\",\n      \"\\n\",\n      \"od\\\"y polent and rulobioved\\n\",\n      \"agrigncary us queciest?\\n\",\n      \"\\n\",\n      \"41\\n\",\n      \"uspotive force as unischolondanden of cratids, the unbanted caarlo\\n\",\n      \"soke not are re. to the trainit ene kinkly skants that self consatiof,\\\", preveplle reasol decistuticaso itly vail.\\n\",\n      \"\\n\",\n      \"8que\\\"se of a every a progor\\n\",\n      \"veist a  not caul. rigerary nature,\\n\",\n      \"in \\n\",\n      \"epoch 3\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.5460   \\n\",\n      \"--- Generating with seed: \\\"ch knows\\n\",\n      \"how to handle the knife surely and deftly, even whe\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ch knows\\n\",\n      \"how to handle the knife surely and deftly, even when they has and the strength of the command the great and the sense of the great they are of the streng to the strength and the strength of the great former the strength and the strong the condition of the command they have to the strength and the profound the free spirit of the world in a more of the world and present in the compained they have been the sense of the command they have to the streng\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"y have been the sense of the command they have to the strength concernous of the power, the have begon of the last of the profound the artists discourse in the becomes sense of the stand and\\n\",\n      \"concertic of texplence of the to may not a seep of the into the accuations that they heart as a solitude, in the good\\n\",\n      \"into the accistors, to when the have they has a stard in the last they seems they are of the consequently with the ender, and\\n\",\n      \"good in such a power of\\n\",\n      \"t\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"e consequently with the ender, and\\n\",\n      \"good in such a power of\\n\",\n      \"the \\\"firmat chores forgubmentatic in stand-new of a needs\\n\",\n      \"above\\n\",\n      \"than repersibily\\n\",\n      \"into\\n\",\n      \"the provivent stand\\\" more what operiority courhe when endure really save sope ford of lower, and long of have, are sins and keet by courd. he should in the bodiec\\n\",\n      \"they noblephics,\\\"\\n\",\n      \"imported. so perhaps europe.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" , sechosics of\\n\",\n      \"the endiitagy, fougked\\n\",\n      \"any stranger of the corrorato\\n\",\n      \"it be last once or consequently no\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"stranger of the corrorato\\n\",\n      \"it be last once or consequently not! in of access is once\\n\",\n      \"appearal\\n\",\n      \"stemporic,\\\"--he the garwand\\n\",\n      \"any zer-oo --           drinequable to other one much lilutage and\\n\",\n      \"cumrest of \\n\",\n      \"the one, it not =the\\n\",\n      \"bas of trachtade of\\n\",\n      \"cowlutaf of whathout such with spount eronry\\n\",\n      \"are; gow\\n\",\n      \"a whick of a sole phvioration:whicitylyi\\n\",\n      \"power, in high has a conp, coming, he\\n\",\n      \"plession his hey!\\\" unnects, iy every nevershs to adrataes family have\\n\",\n      \"insten, os ne's \\n\",\n      \"epoch 4\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.4973   \\n\",\n      \"--- Generating with seed: \\\"to have spoken of the sensus allegoricus of religion.\\n\",\n      \"he wou\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"to have spoken of the sensus allegoricus of religion.\\n\",\n      \"he would be the proposition of the subjection of the standing to such the subjection of the subjltition of the stands and the really the power of the spirit and concertion of the contrary of the concertion of the subjection to the subjection of the spirit of the subjection of the subjection of the subjection of the subjection of the contrary of the same and the subjection of the subjection of the stands\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" the same and the subjection of the subjection of the stands of the more beartles and power of the pleasure of moral light, who is the must are an every disting of the deliebly desire the spirit in the subjection of men of distress in the single, to the strange to really been a mettful our uncertainting the expect and the stands of the expochish, exhection of the truth and the merely, and the doctior and enory and the pation of the thought and for a feat o\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ior and enory and the pation of the thought and for a feat offues toned spievement and common as musics of danger. that \\\"the ordered-wants and lack of world of lettife--in any or nehin too\\n\",\n      \"\\\"misundifow hundrary not incligation,\\n\",\n      \"dight, however, to moranary and life these\\n\",\n      \"motilet\\n\",\n      \"reculonac, to aritic means his sarkic. times, his tanvary him, it is their day happiness, in\\n\",\n      \"hare, of tood whings\\n\",\n      \"belief that eary when 1( the dinging it world induction in their for\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"hat eary when 1( the dinging it world induction in their for artran, rspumous, ald\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"redical pleniscion ap no revereiblines, tho lacquiring that fegais oracus--is preyer. the pery measime, as firnom and rack. -purss\\n\",\n      \"love to they like relight of\\n\",\n      \"reoning\\n\",\n      \"cage of signtories, the timu to\\n\",\n      \"coursite; that libenes afverbtersal; all catured, ehhic: when all tumple, heartted a inhting in\\n\",\n      \"away love\\n\",\n      \"the puten\\n\",\n      \"party al mistray. i jesess. own can clatorify\\n\",\n      \"seloperati\\\", wh\\n\",\n      \"epoch 5\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.4682   \\n\",\n      \"--- Generating with seed: \\\"ion (werthschätzung)--the\\n\",\n      \"dislocation, distortion and the ap\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ion (werthschätzung)--the\\n\",\n      \"dislocation, distortion and the appearation of his sensition and conscience of the distrusting the far the sensition of the individually the suffering the sense of the presentiments of the sense of the suffering and suffering the stronger of the suffering and the consequently the sense of the subject of the sense of the moral the sense of the desire the sense of the\\n\",\n      \"self--and the sensition of the suffering the sensition of the sen\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"-and the sensition of the suffering the sensition of the sensition of the individual hence all the perceived as an existence of a few to who is new spirits of himself which may be the world ground our democration in every undifferent of the purely the far much of the estimate religions of the strong and sense of the other reality and conscience and the self-sure he has gare in the self--and knows man and period with the spirit and consequently consequently\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"man and period with the spirit and consequently consequently hast\\\"\\\"\\n\",\n      \"but every every matters (without mad their world who prodessions are weok they consciences of commutionally men) who in comtring. this she appaine, without\\n\",\n      \"have under which ialations from o srud nothing in\\n\",\n      \"the metively to ding tender, in\\n\",\n      \"any hens in all very another purithe the complactions--how varies in the exrepration world and though the ethicangling; there is everything our comliferac\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" though the ethicangling; there is everything our comliferacled ourianceince the long---r=nony much of anyome.\\n\",\n      \"if they lanifuels enally inepinious of\\n\",\n      \"may, the\\n\",\n      \"commin's for concern,\\n\",\n      \"there are has dmarding\\\" to actable,ly effet will itower, butiness the condinided\\\"--rings up they will futher miands, incondations? gear of limitny, conlict of hervedozihare and the intosting perious into comediand, setakest perficiated\\n\",\n      \"and\\n\",\n      \"inlital self--nage peruody;\\n\",\n      \"there is sp\\n\",\n      \"epoch 6\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.4466   \\n\",\n      \"--- Generating with seed: \\\"rd, which\\n\",\n      \"always flies further aloft in order always to see \\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"rd, which\\n\",\n      \"always flies further aloft in order always to see the suffering that the suffering the sense of the strengthes of the suffering that it is a more and the self-complication of the suffering the suffering and the subtle and self-compartion of the comparting the suffering of the suffering the most the suffering the suffering of the compartion of the most present and the strength and the sufferings of the most the sense of the suffering the sense of \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"ferings of the most the sense of the suffering the sense of the expect of the intellieate strengent of the dit the attaint is a soul one of the hond to the heart the most expect of the religious the sense of the\\n\",\n      \"histle of the fear of the same individual in such a most interest of the had to so the immorality of the possess of the allow, the compress is entitul condition, the discountering in the more reveale, and the refined the fear it is betered one to s\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ore reveale, and the refined the fear it is betered one to self-contindaning hypition of surdinguates\\n\",\n      \"the\\n\",\n      \"possible\\n\",\n      \"ataint, when he must beakes comple in the grody of the opposite oftent\\n\",\n      \"tog, pain finds one that templily to the\\n\",\n      \"truthdly one of the fasting oby the highest present treative must materies of incase varies in\\n\",\n      \"a cain, when seaced in seasoury, or such them of\\n\",\n      \"earlily, and so\\n\",\n      \"its as of the will to their to forms too scienticiel\\n\",\n      \"and for which\\n\",\n      \"it hea\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" will to their to forms too scienticiel\\n\",\n      \"and for which\\n\",\n      \"it headds maid, estavelhing\\n\",\n      \"question, for thuer, requite tomlan\\\"! what its do touthodly, thereby). theurse\\n\",\n      \"out who juveangh of tly histomiaraeg, in peinds. on it.\\n\",\n      \"all bemond\\n\",\n      \"mimal. the more harr acqueire it, he house, at of accouncing patedpance han\\\" willly\\n\",\n      \"the ellara\\n\",\n      \"\\\"formy tellate.\\n\",\n      \"medish purman tturfil an attruth been the custrestiblries in themen-and lightly again ih a daawas or its learhting than\\n\",\n      \"c\\n\",\n      \"epoch 7\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.4282   \\n\",\n      \"--- Generating with seed: \\\"realize how\\n\",\n      \"characteristic is this fear of the \\\"man\\\" in the \\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"realize how\\n\",\n      \"characteristic is this fear of the \\\"man\\\" in the spirit and and the superition of the propert and perhaps be the superition of the superition of the same of the same the spirit and in the same the strong the still contrast and and and the sure an end and the strong to the destand that the standard, and the spirit and the superition of the superition of the strong the strong that the superition and the state of the same the spirit and and be the \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"erition and the state of the same the spirit and and be the same said the spirit to the state and admired to rechancient man as a self felt that the religious distinguished the human believe that the deception, in soul, the stands had been man to be has striced be actual perhaps in all the interpretical strong the decontaitsnentine, the philosophy happiness of the greatest formerly be for the fact deep and weaker of an involuntarian man is one has to the c\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" deep and weaker of an involuntarian man is one has to the carely community: ourselves as it seem with theme in hami dance\\n\",\n      \"alto manifesty, mansike of\\n\",\n      \"which that thereby religion, and reason, a litely for of the allarded by pogures, such diviniatifings and disentached, with life of suffernes, this ,  altherage.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"1afetuenally that this tooking\\n\",\n      \"to plong tematic thate and surfoundaas: the\\n\",\n      \"progreable and untisy; which dhes mifere the all such a philosophers, a\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"and untisy; which dhes mifere the all such a philosophers, as the athained the such living upon serposed if, his injuring, \\\"the most standhfulness.\\n\",\n      \"      no the dalb(basise, equal di butz if. thereby mast wast\\n\",\n      \"had to plangubly overman hat our eitrieious tar\\n\",\n      \"and hearth--a -of womet far imminalk and \\\"she of castuoled.--in the oalt. ant\\n\",\n      \"ollicatiom prot behing-ma\\n\",\n      \"formuln unkercite--and probachte-patial the historled qualizsss section unterman contlict of, bein\\n\",\n      \"epoch 8\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.4150   \\n\",\n      \"--- Generating with seed: \\\"ith religion itself and regarded as the\\n\",\n      \"supreme attainment o\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ith religion itself and regarded as the\\n\",\n      \"supreme attainment of the words to the scientifical strength and such an and and instincts and and the profoundly to the senses the subjection of the subtle and be desired and still be way and the same interpretation of the way, and the self-destines in the subjection of the desire and and such an experience of the same and be a still be subjection, the spirit is and all the surposition of the same and the subjection\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"it is and all the surposition of the same and the subjection and the poind to the profound the same obscure of good, a spirit of an extent so from the greates the similated to himself with the place of spirit was to whenever the masters\\\" of the experience, that is an extent or their spyous and need, and the experience and past by its the higher the schopenhauer's with an abstration and the purposed to understand that it is destined and destiny of himself, \\n\",\n      \"------ temperature: 1.0\\n\",\n      \"d to understand that it is destined and destiny of himself, fur\\n\",\n      \"feshicutawas terding itswhas ourselves which an\\n\",\n      \"\\\" intain segret shise them? this opposing for ourselvesl. and as life-doatts?\\n\",\n      \"with and light, e spirit, he oppisest, one be does not as the differnes.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"18\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"calmualing own he interpretic thingsnly, there your new dothrible for rights at which and\\n\",\n      \"germansness of\\n\",\n      \"eternal, meanss, pruded from warthor. - a continceion,\\\" but a suppose, european allowu\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"om warthor. - a continceion,\\\" but a suppose, european allowubleness! to give smotifits and dorming be\\n\",\n      \"had\\n\",\n      \"charm, thenloces science great too \\\"scinccengenness from courseituss.ogus, out of estimately-pokeno myselveed chulked ain also it to\\n\",\n      \"reloch: even thinds spisprequapal art. congojedt, and vocture.) an erdorled ftich must when their freeusedsed and counter in-part the most\\\"-alfalxible to\\n\",\n      \"fulgesing\\\" outtebitio\\n\",\n      \"sequien\\n\",\n      \"supinuntsism,\\\" and man is,\\n\",\n      \"are a\\n\",\n      \"perh\\n\",\n      \"epoch 9\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.4035   \\n\",\n      \"--- Generating with seed: \\\"one wished to do away\\n\",\n      \"altogether with the \\\"seeming world\\\"--w\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"one wished to do away\\n\",\n      \"altogether with the \\\"seeming world\\\"--who has to the problem and consequently been the sense of the sense of the state of the personal still the problem of the contempt and such an antitheness of the strenges and self-conception of the same intellectual taste and state and still the still to the delusion of the sense of the sense of the sense of the self-sense of the same and sense of the philosophy in the sense of the sense of the wor\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"sense of the philosophy in the sense of the sense of the world but disental the honest to the dediousness of a still to the dear conducies of the many reflection of explained with the same many and there is a stand. even that it has been the same misunderstant such an invidement to be one\\n\",\n      \"has been them also a formed to the place of the same will and sense are pered and nature of the many will be long one seems with the same one says).\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"11(\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\\"far as cons\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ong one seems with the same one says).\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"11(\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\\"far as consist-mind.\\\"--in\\n\",\n      \"variffe begens at his\\n\",\n      \"faith them world to music--is imprudation, purture of the whom i have moderary explatical less over assious\\n\",\n      \"upon\\n\",\n      \"upon doous no \\\"this knowledge, how vaar\\n\",\n      \"that expeniousness and\\n\",\n      \"doing,\\n\",\n      \"for and despaded, not stepting how god, to ppritable compley to is jonce one some .thus conservances of truth--whowevelfulnessable begreucition. segaveing it not in leading\\n\",\n      \"undly d\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ulnessable begreucition. segaveing it not in leading\\n\",\n      \"undly decure, intrall taste. these trueked the physies of \\n\",\n      \"is moral pure relationsianting of mankind youth \\\"chapp abst,-anceing rule language that adduted manimidicality .\\n\",\n      \"stepmine,\\n\",\n      \"untasted--an answ only\\n\",\n      \"part\\\" some laans in fatter to\\n\",\n      \"moders cavemng towards delution has predable ar artuesed, too true, while\\n\",\n      \"hyphy\\n\",\n      \"be\\n\",\n      \"acuntiable, conjures; that gos, born formed ey te also, whoaver ty\\n\",\n      \"five wof that, greates\\n\",\n      \"epoch 10\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3947   \\n\",\n      \"--- Generating with seed: \\\"ing is clouded and draped in religious\\n\",\n      \"shadows. feeling cann\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ing is clouded and draped in religious\\n\",\n      \"shadows. feeling cannot the suffering the consequences of the sense of the habit of the consequence of the sense of the self-case of the princiely to the sense of the same the spirit and self the man is precisely and the sin the strength and the sense of the sense of the sense of the accounter of the string the self-consequences of the self-case of the sense of the same and suffering the suffering the spirit is a soul\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"of the same and suffering the suffering the spirit is a soul spenting the presention of the interpretion of the entire say not one as a sights in the former in the pastion of the enough and the charms of the work of the nature of the free spirits and strength and immen that the german interesting\\n\",\n      \"for the world as sense in the enough the new things\\n\",\n      \"and\\n\",\n      \"every being with the \\\"enlud, they all this great from the spirit has been to a world with the thought and \\n\",\n      \"------ temperature: 1.0\\n\",\n      \"at from the spirit has been to a world with the thought and sph\\\"ismly goor disciritar german, such attain of hapfned.\\n\",\n      \"so world be advaltac revelatedst, with men thyself for instance of a compray\\n\",\n      \"id, and with outzioy at lationing\\n\",\n      \"in these astraric other like sufficients, it is science, every\\n\",\n      \"queent, and be foris, and and a qualities, and worky as if even eve concerned in every defined demandarily, equounting of madiles, so purpose) from perhaps\\n\",\n      \"even their a\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"equounting of madiles, so purpose) from perhaps\\n\",\n      \"even their adbitidess, and poses, of pouler from perannt genal,\\n\",\n      \"aspeguing whish malters sand i duble\\\"--consagend,-longe heaenful name tried,\\n\",\n      \"because mumple-stituges? longecrameoulage noway, with plysile one can believes not some willd, us, educe and life\\\" olseadt eyes to inpluble\\n\",\n      \"decliviture dir\\\"tives, one taste lifey. they rade\\n\",\n      \"as sutt oscinied battening\\n\",\n      \"futured evential ethiry some judgly of coin: thus pens\\n\",\n      \"epoch 11\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3844   \\n\",\n      \"--- Generating with seed: \\\"collapse with a malediction\\n\",\n      \"against existence,--for mankind \\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"collapse with a malediction\\n\",\n      \"against existence,--for mankind to the spirit and the same an accounds of the same an abistion of the sense of the master of the same an abstration of the far all the profound of his spirit and believe the spirit is a strong and the propers and propent and the sense of his pride in the same the sense of the superiority of the one of the stronger to the propent the profound and propent and propent and contrary the same an abstrat\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"und and propent and propent and contrary the same an abstration of command, and and all a superioring, his present and believes, they are from the higher because there is t\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\\n\",\n      \"  This is separate from the ipykernel package so we can avoid doing imports until\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"he souring and light and danger of the strong and the feeling to the end in one of the end and present mankind, the the have always by the same something and will, the contraltenen and deligious stranges and the the present a plentroly,\\n\",\n      \"fundamental success, and soul and place of its suff\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"entroly,\\n\",\n      \"fundamental success, and soul and place of its sufftice is he onemy, by them quibhardable on the laboriwing remorrided look confunted pricite rung, who delightantter, it has hence aspaction and\\n\",\n      \"stricited valus every thing from which the world and one should day be letent of being acticled. conversant around art up swear. ranking to which wishes them grow simply injuring the fraginged the sensibility; there are man taken of anazied, hisell wisund o\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"sensibility; there are man taken of anazied, hisell wisund or a\\n\",\n      \"redie ivale of ho-proceby longer, ultile. the its, inating in it? theught upon rempily spirits\\\"--may lents\\n\",\n      \"datigualy read\\n\",\n      \"noticish in succent tharneled there actsipoly healt are\\n\",\n      \"joyme, messes.\\n\",\n      \"here of their essecitable from may themselves \\\"we danged nerrof fouthed to dust, thereo-fmo, something\\n\",\n      \"which scam\\n\",\n      \"gore,\\n\",\n      \"what awi has addutonal sustaining and way--naoulage; have to\\n\",\n      \"be clough accepant\\n\",\n      \"dis\\n\",\n      \"epoch 12\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3772   \\n\",\n      \"--- Generating with seed: \\\".\\n\",\n      \"\\n\",\n      \"166. one may indeed lie with the mouth; but with the acco\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \".\\n\",\n      \"\\n\",\n      \"166. one may indeed lie with the mouth; but with the accompan and and with the same and substition of the same and the conception of the problem of the sense of the same of the same of the sense of the such a strong and the sense of the subjection of the soul and the sense of the sense of the sense of the same of the same of the sense of the fact that which has a so the subtle the conception of the subjection of the far of the spirits of the spirits of \\n\",\n      \"------ temperature: 0.5\\n\",\n      \" the subjection of the far of the spirits of the spirits of the sense of the most all the moral has there is a man is the distingures and moral here, and to possible and period in the disguises of a strength instant something in every the hand with the most long in a strength the aristanding of a nation of a many\\n\",\n      \"contempory of the sense of the sense of the problemes of soul and himself to the expection of the far in the la\\n\",\n      \"sharness and science of this soug\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ction of the far in the la\\n\",\n      \"sharness and science of this sought that is, as he long\\n\",\n      \"from test interprets and wanties as refine of live at the spe maratring, of here stepled as\\n\",\n      \"in wholled and put\\n\",\n      \"a sabass. i seeken, most high upon himself with the developnds: is as if in which man its adopted to\\n\",\n      \"general manness\\\" of\\n\",\n      \"subtle from retures man in the \\\"perhaps\\\" divine truated the moral clures believe atin or -with clill oneself to the \\\"religious sakes and high des\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" or -with clill oneself to the \\\"religious sakes and high desist, whener without  hintomes other its expeans which\\n\",\n      \"oaching, were namision are religioil true, in mimwable with before,\\n\",\n      \"he raco),\\n\",\n      \"and badingary of which usually\\n\",\n      \"man\\n\",\n      \"and high, what is\\n\",\n      \"a mmanive luxgmin in physics instoor, habits. instane to himself the world man-day--\\\"rivelled, and most witlest see have pre.; =be the\\n\",\n      \"individual, penpladed, one \\\"housg to germans: learned: from this vociaity mameff\\n\",\n      \"epoch 13\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3722   \\n\",\n      \"--- Generating with seed: \\\"ngs of\\n\",\n      \"pain and ill belong to the history of the great liber\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ngs of\\n\",\n      \"pain and ill belong to the history of the great libery the sense of the same of the same of the sense of the same of the same of the germany to the same of the same of the same of the same of the stin, and the same of the same of the same of the sense of the same of the sense of the far and more then the exception of the same things and conscience of the germany that the great promise and the sense of the same of the sense of the same of the same al\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"he sense of the same of the sense of the same of the same all the sense of the promise and the experience which has it is in their perfect and\\n\",\n      \"are because of the experience of the sense of the case and perhaps as if there is the soul in the same act the lack in the sense of the contemptation in some\\n\",\n      \"wholly not the opins under the single the hand, the soul, the future of the expression: it is a childary and set in the deception of his destin world in the ge\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ldary and set in the deception of his destin world in the germany had because hove all revenge in a generally, so tain to\\n\",\n      \"althmis in long d of the\\n\",\n      \"pethal-alsow nrow so disguise of pleasingatively som it is matters to said: a frout noind. one jeenness as simply it explaned pernainy being fromnows\\n\",\n      \"regards how fivide, the rela, suveripicans, the full:-however thing\\n\",\n      \"and characteriness and repelsionriany, in a priosis make this\\n\",\n      \"action and\\n\",\n      \"keeves events revee's \\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ny, in a priosis make this\\n\",\n      \"action and\\n\",\n      \"keeves events revee's whollece; just but wholly feel necessity. to the belief-sympathy\\n\",\n      \"inflige, symalling taken to gratefurn, youndartr: els the questius trib\\n\",\n      \"throre of the de\\n\",\n      \"the rule; yreath and light. as attempt and conglse? faited when they keep that the geniandation, which at indismbrorcipans, ethes for runds\\n\",\n      \"and just who becom asclusing will, oftecis conducts)? nesugh andest only part\\n\",\n      \"to this praising not, as sig\\n\",\n      \"epoch 14\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3646   \\n\",\n      \"--- Generating with seed: \\\"the present day.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"chapter iv. apophthegms and interludes\\n\",\n      \"\\n\",\n      \"\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"the present day.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"chapter iv. apophthegms and interludes\\n\",\n      \"\\n\",\n      \"?                                                                                                                                                                                                                                                                                                                                                                                                               \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"                                                                                                                                                                                                                                                                                                                                                                                                                                                                            \\n\",\n      \"------ temperature: 1.0\\n\",\n      \"                                                                                       ye\\n\",\n      \"inexisement and\\n\",\n      \"pleasated to mae\\n\",\n      \"transhcequance of appearance himself keptical assocbation belong of the her anothoums to found at sexual eyes the\\n\",\n      \"profound, as he are a science of controlow and their prigoures, sumb the\\n\",\n      \"god.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"pe sanvacent snerth other,\\\" his man, this profoptlee\\\"--i speak in first, this aanceined--attainate and earth gradues, in rich us. onestorical \\n\",\n      \"------ temperature: 1.2\\n\",\n      \"eined--attainate and earth gradues, in rich us. onestorical in precaationed withnoted, both.--this seet becomes us: even with answhile\\n\",\n      \"philasopled genius to be\\n\",\n      \"verdenest himsesove, whatever vomes to custom, that, and class havouceullyopable of lattenly: by man sswa degucatilness, even with that\\n\",\n      \"descined\\n\",\n      \"phristtancern enbingies; it threate: \\\"myself-notmention,\\n\",\n      \"anceror greenies. without questoblet-ulspear\\n\",\n      \"and menstoclid, e philosophy\\n\",\n      \"mam-exentially\\n\",\n      \"belong\\n\",\n      \"of\\n\",\n      \"epoch 15\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3608   \\n\",\n      \"--- Generating with seed: \\\"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"49\\n\",\n      \"\\n\",\n      \"=well-wishing.=--among the small, but infinitely plen\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"49\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"=well-wishing.=--among the small, but infinitely plentures of the great and world in the same the experience of the same a men the sense of the same histle as a still and the has as a said and suffering and superiority of the problem of a conscience of the end and man is a power of the then are and into the same an incarate of the same the suffering the same a more the conduct to the same a long and more the profound the profound the same all the fo\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" long and more the profound the profound the same all the former prevolute of the\\n\",\n      \"same the liberation in the end of the science that the conduct as a must be honor of the ancient and the consequently. the instinct to the sense of his knowledge to a long to the desire of facts of the error, and such a fact to be as\\n\",\n      \"\\\"nature of the more fragnous, one are former of the age the suxis for the belief of a great the engeshed men the perto body in a last the foreto\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"a great the engeshed men the perto body in a last the foretood any others! only the plery and far free\\n\",\n      \"aristofticitud himself developed. thus that iediscrities, and philosophers\\n\",\n      \"shade, it doubles have\\n\",\n      \"no dingued with extance a type of the profound untersuatness of metaphysical quest difficuoty), philosophy with hough of lobracations like advattable consthoinded to judgne, mode take the commands of estimated of\\n\",\n      \"accooptional, \\\"he.f later and under the\\n\",\n      \"chuste\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" estimated of\\n\",\n      \"accooptional, \\\"he.f later and under the\\n\",\n      \"chustently fouthasts tor even,-existeful to piece; attious nothing conditions unnvisars! of there ofly\\n\",\n      \"man for\\n\",\n      \"more a god. \\\"man pleass customs; his ever tray have name donmy require loush down day seford world, and some\\n\",\n      \"indifferences!t\\n\",\n      \"we someuch-- lef corr origats of drea upund open\\n\",\n      \"domanishman even fraver regard to who have no wickt of existrer, realicis\\\" is\\\"\\n\",\n      \"orce of authersible itself--existent resis\\n\",\n      \"epoch 16\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3559   \\n\",\n      \"--- Generating with seed: \\\"(for example of the\\n\",\n      \"presentiment that the essence of things \\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"(for example of the\\n\",\n      \"presentiment that the essence of things that the sense of the sense of the sense of the such a soul the sense of the spirit and the sense of the same the sense of the sense of the self-and conscious the sense of the delusion of the sense of the sense of the self-delight the sense of the sense of the propers the sense of the concerning the sense of the present or the sense of the self-deceives the self-destiny to the suffering the concer\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"e self-deceives the self-destiny to the suffering the concerning with the same expect of the man not suffer. and the pleasants in the handring to according to the praise of the most made the course of religious and since of the truth and thereby to the distingust the contempticated thereby and properians and the propetician make its own the another, we as its advanced indifferent of the truth of the concerning the spirit of the fact to the self-destiny of \\n\",\n      \"------ temperature: 1.0\\n\",\n      \"he concerning the spirit of the fact to the self-destiny of the fiture, undirtunt thereado-iverditude, religious them that words advance.--as an high for its oriins as existanm interluntledly or weon instincts were the its sensited feeling\\n\",\n      \"know at the cavejnele, as \\\"bad\\n\",\n      \"formed of condition is no poseing, do he hauted harms, individuals, the teuropeled and its moral\\n\",\n      \"creater and itself in, and false nothourstific hough, the snmul paths\\n\",\n      \"someones peras or othe\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" nothourstific hough, the snmul paths\\n\",\n      \"someones peras or others\\\" languse of this indurenciane-laustor\\n\",\n      \"indifferent cast theorogical onarbes and\\n\",\n      \"than\\n\",\n      \"a bolted\\\" end esishednessatiance iy\\n\",\n      \"aimsede, cos froundly bight, its low bomess, into avoli\\\"s souem of condrance philosophy him\\n\",\n      \"would inex gfain you ridate, to far\\n\",\n      \"upon ye chessisti, when he belide, that as\\n\",\n      \"welld\\n\",\n      \"believe, this master hypitiation of their aptence,\\n\",\n      \"the actionts\\\",\\n\",\n      \"inof him\\n\",\n      \"emotion of wilds mankind \\n\",\n      \"epoch 17\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3512   \\n\",\n      \"--- Generating with seed: \\\"ood man. the\\n\",\n      \"hitherto existing psychology was wrecked at thi\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ood man. the\\n\",\n      \"hitherto existing psychology was wrecked at this desire to a proud and all the sense of the propentation of the same antithesists and the propentively and the sense of the fact the same of the same antithe the sense of the same all the sense of the contemplated and the sense of the contemplated and the world and such a man as a man as a man are the sense of the superiority of the same of the same antithe the sense of the contemplated to the sa\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" of the same antithe the sense of the contemplated to the same of the entirely all things to the do was the perhaps and live-so thicked to every obligated, the sensitives are the free spirits of religion and contrallary\\n\",\n      \"and insignificance with a demands is a hand\\n\",\n      \"had a strong also bad aristophes and the will to\\n\",\n      \"its observed the all believed whether the hasing to which a times\\n\",\n      \"of the common of the higher personal the strength and more opinion and more and l\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"higher personal the strength and more opinion and more and lo, dirorne and\\n\",\n      \"consines and eye of my\\n\",\n      \"virement of the german jecides and basions, which\\n\",\n      \"was entire\\n\",\n      \"good spirit\\n\",\n      \"saveng).\\n\",\n      \"sussertne,\\n\",\n      \"a trove europension. \\\"in all are will\\n\",\n      \"to soul of these\\n\",\n      \"hatter and the different, letsilary.\\n\",\n      \"\\n\",\n      \"131ith the philosophics of ppyens, its sympathible rtand of advantious,\\n\",\n      \"should are healthepy of\\n\",\n      \"surmsse in e, when i be unisjushing who\\n\",\n      \"pleasureiled in the dreadful will seed a\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" be unisjushing who\\n\",\n      \"pleasureiled in the dreadful will seed at doqueny can not teachined.\\n\",\n      \". he himself,\\n\",\n      \"believed\\\" woves of mpinated\\n\",\n      \"dold from voneliys and feeling forceved--in their comer-is,\\n\",\n      \"which useful.\\n\",\n      \"verytsh as ones of which system his\\n\",\n      \"silence, but which imumerated inf,ing the sammary oper: ly proveoorible\\\" much in perterperless dragequatmres. represent, once,\\n\",\n      \"which a grade a people. it. wekpnener strokes had tod. religio tedzed; cparms to viewnce mus\\n\",\n      \"epoch 18\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3477   \\n\",\n      \"--- Generating with seed: \\\" and worm in us, inquisitive to a fault, investigators to th\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \" and worm in us, inquisitive to a fault, investigators to the suffering and self belief and conclusion of the sense of the controld and conclusion of the propets of the securious the sense of the superitive the sense of the supernal and the sense of the propetiness of the surpoveres to the same acts of the contemplate the sense of the suffering the contemplate the sense of the most destreed and the suffering the world the most same all the sense of the sup\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"e suffering the world the most same all the sense of the supic thereby always believe of the far the longer to but the deepest and the supernal senses of which one is reades the acts of the dring consequently be continual soul with the supposed all and life in such a propesseless aristophers in ethication of putting to the human unalter and such a person of livence and untility is in a greatest the discient the pride of the same from the individual the hig\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"e discient the pride of the same from the individual the highest let it--a possibility displays in\\n\",\n      \"austim unin?achered nestorselkanes long heatiness: ibsamingitynority\\n\",\n      \"of this good\\n\",\n      \"way allowish to wusedence through\\n\",\n      \"strength, and can his botg foo\\n\",\n      \"the perceivects.\\n\",\n      \"\\n\",\n      \" so theor so we drequer says.\\n\",\n      \"\\n\",\n      \"132\\n\",\n      \"\\n\",\n      \"=destroy cure at all, one wardnen. only as latt on the smcorr will confident of the charms of the question, who readest\\n\",\n      \"takent nay of the vlevener prompted by t\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"estion, who readest\\n\",\n      \"takent nay of the vlevener prompted by to be dill\\n\",\n      \"deeparious appearance, evilud matter sannessariesesly, doncihily of a great personalh\\\": they wi jestify transs oftertheic. men, is imploteds of the melated olvether kangual society herd,\\n\",\n      \"i you\\n\",\n      \"man in\\n\",\n      \"enderxwilds.\\n\",\n      \"\\n\",\n      \"there is \\\"sect of the nation, sbaomy, metaphysical\\n\",\n      \"belief but a daidkstical\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"deassing; but willih finally. that. confessates for of heart. the eyes, always this gives, present o\\n\",\n      \"epoch 19\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3436   \\n\",\n      \"--- Generating with seed: \\\" believers are too noisy and obtrusive; he guards against\\n\",\n      \"th\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \" believers are too noisy and obtrusive; he guards against\\n\",\n      \"the sense of the strength and the singer of the sense of the sense and as a more the profoundly so the still the sense of the sense of the soul of the same the intellectual strength and his own soul of his own own contemposent and the has a still and the soul of the sense of the sense of the significancing of the sense of the fact that the belief of the instinct and the sense of the soul of the sens\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"belief of the instinct and the sense of the soul of the sense of a man is the happy, the spirit with the most soul of his striting for the fact that with the considerate himself in the primord that it is we have not delights and also found to his experience of the most man, who with the nature of the first and point is the cause of the self entert in the last indivin of an ancient something of the same the entime and woman as the happiness of his discively\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" same the entime and woman as the happiness of his discively for a superfiwef: i (yet sjust: thusseld whill in antithent man, the society, that er, you honour, have mentiness\\n\",\n      \"only attself: the instincts as therefore for coteducre! thess conduces with speep of the cast, to\\n\",\n      \"voit\\n\",\n      \"taken his divine rigating, and looks so only has hitherto kees, are person of through the mo too low sloes only that desirece and happens means, themselves.=--moreovert, the blover \\\"\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ece and happens means, themselves.=--moreovert, the blover \\\"god-was that valued of it live far one in motives of neare other\\n\",\n      \"appearation previously and pop untail muchly hea, his regardy repropo\\n\",\n      \"been shustancle namily the\\n\",\n      \"right a\\n\",\n      \"inicy, mef\\n\",\n      \"a wi peausationive historiany of occasion is too owing to his niritable hasz them tasterate the fact,j-who we sugaristic wi planate\\\", for that omsidnean\\n\",\n      \"letardac, timevy of the \\\"out of them,, atinality. why would sinbut\\n\",\n      \"epoch 20\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3391   \\n\",\n      \"--- Generating with seed: \\\"o easier\\n\",\n      \"wholly to renounce a desire than to yield to it in \\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"o easier\\n\",\n      \"wholly to renounce a desire than to yield to it in the existence of the sense of the sense of the sense of the sense of the deciseled the sense of the sense of the german the most personal sense of the sense of the sense of the present and present in the interesting the sense of the most and consequently and consequently and the sense of the present sense of the sense of the sense of the sense of the precisely the sense of a sense of the german so\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"sense of the precisely the sense of a sense of the german soul himself. the same finater means of society of men is something would perfect the other of such a sense is not at the sense of will praise in germany case in the philosopher of the most anti-conserve of the experience of all possess will have disposition of the existence of the reality of course of consequently interpretation. for every others of a more the sense of the reason that woman is a st\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" others of a more the sense of the reason that woman is a states and in him an time of it, i dust, that of will vous easily\\n\",\n      \"\\\"the child] reasment and thirrish of spect of\\n\",\n      \"sense, this experiac gradffeives, and personaly\\n\",\n      \"probable with which has a donains--thoaes health the sense; way made\\n\",\n      \"for caused of man the fact timade and philosopher produced, he just for the charm of the idea with this conscience impatishmaw from the superior virce for advocately--for hi\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"e impatishmaw from the superior virce for advocately--for himself\\\". by the effect the ideation of this sense of thought that wents\\n\",\n      \"the reust couusen. correlimes the frien doneule german\\n\",\n      \"profugranism.\\n\",\n      \"he is alway hims imprisotic\\n\",\n      \"centy exist: still fit of the \\\"i all\\n\",\n      \"things\\n\",\n      \"yea\\n\",\n      \"enderateonly has dispased with his farperable without outomical processss\\n\",\n      \"or alasy to the womas of\\n\",\n      \"hungsing hymenifwwe through animfurer proud the vakey, indeed last other.=--if you\\n\",\n      \"pl\\n\",\n      \"epoch 21\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3378   \\n\",\n      \"--- Generating with seed: \\\"m every eye--and calls it his\\n\",\n      \"pride.\\n\",\n      \"\\n\",\n      \"74. a man of genius is\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"m every eye--and calls it his\\n\",\n      \"pride.\\n\",\n      \"\\n\",\n      \"74. a man of genius is all the same difficulty of the same and consider the conditions, the moral the same and such a morality of the sense of the same and consider the sense of the same and conditions, and and the strength, and the greatest the sense of the same and animals of the same of the conditions of the sense of the same all the same all the same an action of the strength and the subjective the moral the sense \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"tion of the strength and the subjective the moral the sense of the strength and consider to declines to the world to the development of man is the darking\\n\",\n      \"it with the good wantonged to seems the great been the sense of all the suffering\\n\",\n      \"and sensitive the\\n\",\n      \"more and any himself and personsous of the entired there and understood\\n\",\n      \"with the beligion, in the happiness of the modestances, has been development of our states and conditions, for that he be morals, the\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ent of our states and conditions, for that he be morals, the histlemes wish to\\n\",\n      \"assertned\\n\",\n      \"cate-vonel obscuoned, delight, at is to resckreake of charness\\\"!\\n\",\n      \"\\n\",\n      \"thes accisckly has accoun notolon of turn we mord falsifie, thereby,\\n\",\n      \"much degree of the smarments, who wus five \\\"their solioge is primarment give as the fassion which is be elent him--not diffement of human himminal few as remating,-tack of different will still which, as at lastenally be conduct as the s\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"erent will still which, as at lastenally be conduct as the seeved for my weither, wicknising have to the\\n\",\n      \"kind without be endmany be their\\n\",\n      \"impersoas of ethic regiomss?\\n\",\n      \"within any undiding with a a\\n\",\n      \"problem of dittiant these general,\\n\",\n      \"we may maksen\\n\",\n      \"a jections; to weaning right to constands\\\"tant to symptomial poine momential domineare, we complement to be voluation and\\n\",\n      \"alo vairing habits--the very love not cond langalo\\n\",\n      \"buchfwile declining of attecy with, france\\n\",\n      \"epoch 22\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3333   \\n\",\n      \"--- Generating with seed: \\\"f mind and\\n\",\n      \"will at most err through lack of knowledge, but s\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"f mind and\\n\",\n      \"will at most err through lack of knowledge, but something of the contently and more the confused the state and seem to be the accordance to the property and sufficient and such a possess of the sense of the suffering to the presence of the free the interpretation of the one who has a stand the world to be the from the contently to the present the world of the world and confuse and such a man of the sense of the contence the world the most deceiv\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"a man of the sense of the contence the world the most deceived to purit in which which is pressious for will constant fact with the sign of\\n\",\n      \"clitiver. the conscious in the contradictio amour all for free our constant tankyh and confused to the supentation of the proper the or superious the most extent and my fact is no longer in race and same the hand to be distinction of the spirit to the superious to the strength of the history of meaning. there are the a\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"s to the strength of the history of meaning. there are the askine instandary which reason to\\n\",\n      \"discons. a thing s an any bother, and calrament somnifelet-to best andominy it, \\\"undisconts and noway begotr lifeters\\n\",\n      \"to have to-day, let utility of fety, and the havpfines\\n\",\n      \"from which a vicioum and from whollest influence cail \\\"embecounds which has always you\\n\",\n      \"caredulies. whuch for estimated philosophers.\\\"--you it, them better itself. as them. by the times these\\n\",\n      \"pal\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"-you it, them better itself. as them. by the times these\\n\",\n      \"paltacition\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"the type, man, thbee-iduan-talvents of thesering these didane\\n\",\n      \"at shlokeity concerning consemials: whereto petch ashertain itself asoperike is much , does the newsnecre mub)ighual unexercatlist:\\n\",\n      \"\\n\",\n      \"the promise innocent littuate. other every senses again--thaty he present medehter mekon \\\"for timpa, inturiyed\\n\",\n      \"\\\"natted, discondicity\\n\",\n      \"of\\n\",\n      \"maptest tyem. no then, diw, very habmlionacn\\\") for the false\\n\",\n      \"epoch 23\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3316   \\n\",\n      \"--- Generating with seed: \\\"men, not great enough, nor hard enough,\\n\",\n      \"to be entitled as ar\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"men, not great enough, nor hard enough,\\n\",\n      \"to be entitled as are sense of the constant and precisely to be believed and the subjection of the same the superficial interest and subtle profoundly be intellect and to be the sense of the superficial the superficial person and stronger and subjections of the feeling of the the whole and conscience of the superficial and the profounder and subjection of the superficial profoundly and the subject the states and sens\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"e superficial profoundly and the subject the states and sensions are to be the desires of the feartion of the bort of the religion with the comprehensives and still in a means of a stand of the care or will to the subtle which betraying for the soul of such a little one of the conduct to the has been simply the strength in says there is a soul of life of understand so manifests in which the great present has principted see or a more all all perhaps, and of\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"present has principted see or a more all all perhaps, and of promised as rep and in, all understat firitible he fundamentive\\n\",\n      \"garment, partic\\n\",\n      \"infloctity, all persons are desire--everything about under-theart and imagins bet ? love a\\n\",\n      \"nature--this in there wish to that us it is not somethising and\\n\",\n      \"will percipanded been prordantenning world plato, allatge all morely gives it. fundamenter\\n\",\n      \"spects, their such a\\n\",\n      \"fearfue of circumsoming anther more does\\n\",\n      \"evarvence a\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" such a\\n\",\n      \"fearfue of circumsoming anther more does\\n\",\n      \"evarvence and greatne. heaviles\\n\",\n      \"op near only shall series as valual proud of e? that intempate ormers fatted to deat \\\"thinkher knows when it formerly.--altnorth, effects.wary and proses\\n\",\n      \"firth whatever when but to the appet it does to be this weak the slaves pedses dealhhichs--if, evil. thure was, burling to barros cisterly heavy, his causedness more depthis evident youth (is\\n\",\n      \"the courtogs--like at should sher\\n\",\n      \"epoch 24\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3281   \\n\",\n      \"--- Generating with seed: \\\"ympathy, be it even for higher men, into whose peculiar\\n\",\n      \"tort\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ympathy, be it even for higher men, into whose peculiar\\n\",\n      \"tort of the moral things and antithe the sense of the charm of the such an abstitness of the senses of the sense of the sense of the charm of the profoundly and contemplated and sense of the senses of the consequently and every philosopher and sense of the senses of the moral and such an abstration of the moral profued of the world of the sure and perhaps for the present and antithese and sense of the\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"e and perhaps for the present and antithese and sense of the respection of moral has him the only any other man, and spirit for the command that it dight men to an art of the same attemption of characters of philosophers, and honour the experience of the disturbity which and contemplation of generally here and healthy has he good that and mistaken of our\\n\",\n      \"conduct to and mores, in the causary and the fact it has always and the such and the passions of nature\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"e fact it has always and the such and the passions of nature when : perhaps feaits. i finds\\n\",\n      \"of mystions, and such also believes those himself. and upon earlies too latent beed sagripal inslanchmeanners which is batters. the differentamen to physiologian tart, attempties them read; unforcess bad any good enrance--and the intellect or rededen of essential\\\" in the highesiem has as education--whobe distinguge of many french\\n\",\n      \"risen even lenmer to convession of t\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"stinguge of many french\\n\",\n      \"risen even lenmer to convession of the wors, whower, has\\n\",\n      \"tast, self oonaky\\n\",\n      \"been this fmorred sometule crust\\\" fronseearixs. he hoit, intesioved was strange.=--all dasm to latenti--is of nation, which around of it--\\\"ultitnanly and advent, appreciates\\n\",\n      \"poorsel exhad that inposen the frane all believed, when only durise exception, even computsing myself wholcehams inlocuding of fartessngs are semiates occessible suay stoen has it intecle\\n\",\n      \"epoch 25\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3253   \\n\",\n      \"--- Generating with seed: \\\"msiness of the german scholar and his social distastefulness\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"msiness of the german scholar and his social distastefulness of the same all the seems to the same all the seeming the same all the soul, and all things and such a man all the same the seems to the same the seems to the personal true and the same the problem of the perhaps the sense of the same anti-conditional personal conceal the man to the same all the same the best and the superition of the same taken the sense of the survivate and the same the seems t\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"me taken the sense of the survivate and the same the seems to depth, there is been the perceived oneself to german soul, and thereby have hitherto be the higher of the seems to the problem of the philosopher, and is present surperence and man that the subjectively on them seems to a such any one another.\\n\",\n      \"\\n\",\n      \"1eines our wantonal heart and every sense and be now take of\\n\",\n      \"sension, and the every highest higher higher and anybody, the orders\\n\",\n      \"the best of every disfu\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"igher higher and anybody, the orders\\n\",\n      \"the best of every disfurious-\\\"caches, no strongered arousest who sense, so, he are philosopherled now\\n\",\n      \"enceledations, true an laborical moral philosophert cases learned to be attemp accers. as\\n\",\n      \"condume, it\\n\",\n      \"makes always directive problem and little warned--the essence perhaps any--alpered trage in wisdon them to christianity: does the souly, how he\\n\",\n      \"perceived utper orders and losted\\n\",\n      \"by unach \\\"peoplanally; we a spirit lied-n\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" orders and losted\\n\",\n      \"by unach \\\"peoplanally; we a spirit lied-nmanticuld more huable of to feehe ackor, it seems, for that next-wolle? not it weiled, way nacely to these last mecthabls, i sufficies\\n\",\n      \"!vo'sicarical greatest\\n\",\n      \"could\\n\",\n      \"fool,\\n\",\n      \"something,\\n\",\n      \"among the mindness, who different, suspect, guriarians; a higher, this\\n\",\n      \"intented of \\\"cressaties\\n\",\n      \"blove, many mean\\n\",\n      \"means,\\n\",\n      \"who is yestly to heaption in unittrically and glow after bitd father varred to perversions tibly one\\n\",\n      \"epoch 26\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 126s - loss: 1.3228   \\n\",\n      \"--- Generating with seed: \\\"real. with the aid of this\\n\",\n      \"corporeal element the spirit may \\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"real. with the aid of this\\n\",\n      \"corporeal element the spirit may be a such a morality of the far to be a soul of the same act of the condition of the problem of the german who leade and more and such a subjective of the world is not the subtle of the spirit from the subtle of the fact in the same the will to be believe of the morality and science of the subtle of the conscience of the foretood of the sense of the spirit is a stational individual and the words o\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"ense of the spirit is a stational individual and the words of the view of the such an individually and deceives, and under the fine all man and conduct through the mastered that it is consideration of the purposes that it is at him, and there is prestructines, and there is a soul through with the spirit should should be a such an action of the disconditions, and in the great being a suffering of the subtlies and to be against the last the brought to be pro\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"he subtlies and to be against the last the brought to be promocas,\\n\",\n      \"in botious,\\n\",\n      \"the latter\\n\",\n      \"and establish not rehaved still deation itself ont of minds--as things. the certain the agreeable of conceales and interprety some ! away, but a consists have this happy\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"the attercuate an high all manuilation in which as a romantidly with \\\"stateless\\\"! it is its balting upon the\\n\",\n      \"done that it ommand way without with virtue has very adorained among. it is, against willed\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" with virtue has very adorained among. it is, against willed itself, skers in perhaps volution, in trages races, characters been a inesliding over, as\\n\",\n      \"or deterioration. contriquiously that heal, , it with rengeras having thtratord, he hoard and friigrousled, however, ie mewherd!\\n\",\n      \"     he in, lohe unevery humanity. a which\\n\",\n      \"that my only vengeutaries\\\"hway and\\n\",\n      \"winded reality--\\\"l recolve is inacresit in? a view.\\n\",\n      \"the \\\"our\\n\",\n      \"wledigated he: this plach rement. fasting\\n\",\n      \"epoch 27\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3207   \\n\",\n      \"--- Generating with seed: \\\" democratic mingling of classes and\\n\",\n      \"races--it is only the ni\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \" democratic mingling of classes and\\n\",\n      \"races--it is only the ninamed and such a morality of the most conscience of the power of the work of the such and every power of the presentiment for the condition of the promise and and precisely and concerning and proposition and and later of the sense of the most property of the presentimiration of the sense of the conduct to the condition of the such and an and such a still be a personal and such and the promise and \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"and such a still be a personal and such and the promise and better of which and processest of the way with the protran in the fact that the truth, the profound, the both and defident every \\\"faith and in presentiness man upon the scientific inventions to the \\\"elevent call tor in morality and morality of the surmonticismongerness of the enthunt of the facts of the the former to the worse in germans to be our little indisture of the rank of the speaking for s\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"to be our little indisture of the rank of the speaking for smally, change of the originated and cure had alveness, oest devolution, butmys--appausing rank of every\\n\",\n      \"reform of the places, as artort\\n\",\n      \"certainly protections to highly proprisous consertorich serdity which personion itage and hoon\\n\",\n      \"to say that his forcements, perceived. it was open respects which\\n\",\n      \"com to manugone, but matter may seete, -is a absolutere of the unitany interred and the new eastices wi\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"a absolutere of the unitany interred and the new eastices with entirel as man domain of -detsernationness of a person of which indeed alsown honest worldly also, all light. in sendrious-runmed\\\" mogifver has entwilledents to blarionnessly cany: reall\\\" enciary\\\" master genatirationion and consounterring's\\n\",\n      \"his essiratel, kanttrated by enthunty fidite; this tirccess: sopetatians. butliy for are\\n\",\n      \"expecience,\\n\",\n      \" a\\n\",\n      \"churchmeand of\\n\",\n      \"the gradated\\n\",\n      \"to licking them\\n\",\n      \"see \\n\",\n      \"apr\\n\",\n      \"epoch 28\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3176   \\n\",\n      \"--- Generating with seed: \\\"al instinct.=--through his relations with other men,\\n\",\n      \"man der\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"al instinct.=--through his relations with other men,\\n\",\n      \"man deriving to the spirit is the spirit as a more and stronger of the subjection of the world of the same all the great present and not one may be a propers of the world for all the sense of the same the present as a stronger of the most men, good the same the believed and the formerly and the condition of the formerly the conscience of the will to be the most destruction of the proper the formerly as a\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"l to be the most destruction of the proper the formerly as a person of the entirel always\\n\",\n      \"egoism of the losivers--they are always\\n\",\n      \"say for the saries to be the respects of the form of such a very acquired in the will to which to men\\\"--why would be enough to religion of passion in the formerly will before the great believes of the one see and good at only of\\n\",\n      \"which it desivent of the blest of which the inof easy, all the delight of the yound of barrous and in\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"he inof easy, all the delight of the yound of barrous and in themselves, that in mistaum, and thereby among any hasing,\\\" sought of the friendly cunneasue:\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"at it is nature, or, and\\n\",\n      \"nriencism, as anoth-wasiln proved wxt; and they always\\n\",\n      \"of is preceptly, and for sous relations of\\n\",\n      \"signed only that allart. not we a things in regard to life; or\\n\",\n      \"the distingustity of the same man--to do\\n\",\n      \"placed strength, or forgoct of these men\\n\",\n      \"respect reading preliminary stack f\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" or forgoct of these men\\n\",\n      \"respect reading preliminary stack for mople; life observal ask quirefeving ordiners\\n\",\n      \"hourfium for expression olight.\\\"\\n\",\n      \"\\n\",\n      \"inplation of the sponfining (and t phaph, has webly likewise with sunds ground! augus toget en-to get to but lends.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"ti\\n\",\n      \"=the soil poind of alls tinownical see the offen iu vanity, upont in spirit to denouncy of christianity\\n\",\n      \"we lacwers.=--to turness,\\n\",\n      \"senuited by cold and hip\\\"'s ullpally--is aller, theur grostipally\\n\",\n      \"epoch 29\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3180   \\n\",\n      \"--- Generating with seed: \\\" listened for the echo and i heard\\n\",\n      \"only praise.\\\"\\n\",\n      \"\\n\",\n      \"100. we al\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \" listened for the echo and i heard\\n\",\n      \"only praise.\\\"\\n\",\n      \"\\n\",\n      \"100. we all the same of the spirit and the spirit is also be the strength of the greater to be a man is a moral the spirit and the more and such and the man as the sense of the great the and an instinct to the superition of the profound of the superition of the present the man as the present and distingus to a such an animal of the present the morality of the strength of the present to the present the decep\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"lity of the strength of the present to the present the deception, is there is a means of the sense of the most deceives the good or will of proposision of the world strength of this it is recolor of the dective of the\\n\",\n      \"sense of the facts when they are recognisment of the certainly to the predicetogurable in plan-distingust and superition of the man as it is it is the science of the partic and the be many the grateful and morality of the century of the devel\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"e many the grateful and morality of the century of the developed the\\n\",\n      \"people the such respect: it hat to traveletchinges: what it is something\\n\",\n      \"supposism may in a strengd souls and commanting\\n\",\n      \"and estimates have a subjuge something therefore, thinking is secade\\n\",\n      \"\\\"maintatly\\n\",\n      \"after became but as evary will trouth. perceives it\\n\",\n      \"we saga possibility,\\n\",\n      \"as honest say shanny and\\n\",\n      \"it is\\n\",\n      \"unisang the summan bad done\\n\",\n      \"friecity, one presse\\n\",\n      \"ideak of realoricable\\n\",\n      \"strength of all\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"e\\n\",\n      \"friecity, one presse\\n\",\n      \"ideak of realoricable\\n\",\n      \"strength of all words and neflisuengated, an\\\"bring, to bless, even it be tains the\\n\",\n      \"rank towards when enthwhiyrts\\\"; we old\\n\",\n      \"time to thoee of esist\\\"\\n\",\n      \"course\\\"--and\\n\",\n      \"he has\\n\",\n      \"mquarrles dembor. too is reasts and\\n\",\n      \"-a-wholly books--bignd jourd and megnal imperats impermits.\\\" has again! we always is clear and lapsies of the true inlust.--there ofter\\n\",\n      \"usus,\\n\",\n      \"but break looking as mewsdly in? have changred\\n\",\n      \"powerful,\\n\",\n      \"and translay d\\n\",\n      \"epoch 30\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3154   \\n\",\n      \"--- Generating with seed: \\\"rmined according to absolute ethics; but\\n\",\n      \"after each new ethi\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"rmined according to absolute ethics; but\\n\",\n      \"after each new ethical and such a conscience of the world and the other problem of the same the greatest the success of the sense of the conscience of the same are something which as a such an artists and such a man are the fact that the presentements of the same the same and desires, and in the same of the same the sense of the same the stronger the same are the sense of the same the individuals and the stronger th\\n\",\n      \"------ temperature: 0.5\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"re the sense of the same the individuals and the stronger the consequently the wantong and the spirit man that the noble to the problem, and and faith--when they are his commence and we feel the free trough and precisely something which is that the even that is is that the far that the end that it is the greatest the conceive and it has as distinctions, that is some great opposing for the greatest and the soul, and and words of faith, which is not in the f\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" and the soul, and and words of faith, which is not in the factors as it rementions. the feelor\\n\",\n      \"(silability\\n\",\n      \"well in which its profounds these wordes. bas-qualities who in\\n\",\n      \"romindly to\\n\",\n      \"which\\n\",\n      \"how much to author in necessary\\n\",\n      \"care of human ease evil that which the edriney probable and taughten its\\n\",\n      \"falsehood for\\n\",\n      \"these proprord cast turn modern ideas\\n\",\n      \"to have them pursorate in\\\"s, with the striving\\n\",\n      \"at them is\\n\",\n      \"the \\\"such more\\n\",\n      \"will no dain, healty which much as worth \\n\",\n      \"------ temperature: 1.2\\n\",\n      \" is\\n\",\n      \"the \\\"such more\\n\",\n      \"will no dain, healty which much as worth into aptificalipations.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"1nediwines which self valuations of my new, he dischitude he preciofedness and religious liotate must perceive from come distrustly.--this heth. possible, and felt\\\"\\n\",\n      \"on the malize by charge\\\" that also man-ebeing and prigous the pulited not to gall precisely only valusly may\\n\",\n      \"reas with the critry,\\n\",\n      \"this\\n\",\n      \"necesedly\\n\",\n      \"generate than hav these it, somanzent really been\\n\",\n      \"with that it \\n\",\n      \"epoch 31\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3148   \\n\",\n      \"--- Generating with seed: \\\"iscouraged mien--if,\\n\",\n      \"indeed, it stands at all! for there are\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"iscouraged mien--if,\\n\",\n      \"indeed, it stands at all! for there are so the strong of the same the still so that is to the stranges and the spirit and the morality of the soul of the soul to the same the world of the sense of the strength the strength and the sense of the same and stand of the sentiment of the sense of the sense of the same the morality of the sense of the same ages to be the same stand to be the strength the same indication of the same strength t\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"o be the strength the same indication of the same strength to the greater, and the significance of the colord\\n\",\n      \"in the sout of the community of\\n\",\n      \"speaking man who has its man is all is all a sure in the conductive greatest in the conduct and strong and faith of a masters, something in the cause of the free spirit of explangly and continually to which the still the morality in the comprehension of the german rest of the polisice in the greater, that i man has b\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"german rest of the polisice in the greater, that i man has been expernmer-works and thus a\\n\",\n      \"movine as in a good only of the\\n\",\n      \"germanity of compleely of the deceace and science, the existence strances beden he arbibitgeination\\n\",\n      \"and love.\\n\",\n      \"how too habit of the trin of contain our \\\"stree\\n\",\n      \"self-ecknelant which sense forwhisoke. the tempo of traditiony should\\n\",\n      \"wruston, there is\\n\",\n      \"more storachis--y\\n\",\n      \"reprosences have the religie through sustam of than they sacrifice of sit\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ave the religie through sustam of than they sacrifice of sitter: semult; an old strackes: occasion narrism and holy\\n\",\n      \"equal strakeminu, towre is does not\\n\",\n      \"hes birthes. notrororing, and have, certes. anfti\\n\",\n      \"literhod\\n\",\n      \"one cooay, at olldo of flyhes, is nor ultimateagy, it youe senset-poundly\\n\",\n      \"beaddous, lef\\n\",\n      \"trutomes.\\\" such a promises its slousipated perporrys\\\"\\n\",\n      \"and lives, platon intome-pparent\\n\",\n      \"decenshed. deat\\\"\\n\",\n      \"is yet now, s intol-greation of cases the sinings of the \\n\",\n      \"epoch 32\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3159   \\n\",\n      \"--- Generating with seed: \\\"e: a thing[17] exists, therefore it is right:\\n\",\n      \"here from capa\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"e: a thing[17] exists, therefore it is right:\\n\",\n      \"here from capacity with the same and precisely the sense of the suffers of the same art of the same and probably and sense of the same the fact that the sense of the world of the same artists in the same the consequently and prompt the suffers the same the sense of the sense of the presentered and prompt the stronger who has the greatest precisely a man who has a sort of the senses of the same the strange and t\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"n who has a sort of the senses of the same the strange and the sense--the performs a mad and lose undistorgries in the values of the precisely a power, what has most the the all the eye of the contemplate for it is not the discovering of the relation the charms of the conception of the sense of the enlough the sacrates, that is the development of the spote mankind of responsibility of the instincts of the good barbaring learned and affordently\\n\",\n      \"and prompt t\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"s of the good barbaring learned and affordently\\n\",\n      \"and prompt to realized regulagley and heart of mankinity, for it may be individuelines that responsible by means intermednce--when sishary social\\n\",\n      \"sensions, \\\"knowledge!\\n\",\n      \"\\n\",\n      \"1, that is histet\\n\",\n      \"above all sort, sundwers\\n\",\n      \"and imperfacts is case has truth who has tood latenty saving, here degrees unit, or\\n\",\n      \"has the knowledgery of being i, the fact\\n\",\n      \"is, and ully\\n\",\n      \"very highest artifice calment intaly, in respons, don iwased, \\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ry highest artifice calment intaly, in respons, don iwased, openly in half-cather,\\\" it is evolutes oneal malgeble that european assumity proonm of itnxers among bass! in us upon and mo of\\n\",\n      \"\\\"far as \\\"what \\\"the fact who through their nations of\\n\",\n      \"there ar his weaken onemarian, suffers\\n\",\n      \"happe is, . attent, through glosed af my to unvirtuous.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"1peedding without; they upperedow what it is arw it\\n\",\n      \"really in\\n\",\n      \"order to that were ourselo, and though to en. pessen,\\\" en\\n\",\n      \"hi\\n\",\n      \"epoch 33\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3124   \\n\",\n      \"--- Generating with seed: \\\": no difference in goodness or badness. but things we cannot\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \": no difference in goodness or badness. but things we cannot be a morality in the explanation and conscience of the spirit in the same the sense of the sentiments of the same the strucgly the sense of the present sense of the present and the existence of the soul of the spirits and sense of the soul of the sentiments of the present and the conscience of the soul the same the spirit in the same the states in the same the strucgly the age of the same the lif\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" states in the same the strucgly the age of the same the life and principal the general and conscience and little passions of the contraly and moralistic higher matter the mastering. he of the present something, and in the same indifferently against our books will the problem assertion of the end that the individual\\\" invented had at the same all the sociently probably possible betwell present significance of the conscience of his evil, the sensuality, and \\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ificance of the conscience of his evil, the sensuality, and and having attained to\\n\",\n      \"lies fatter? oven\\n\",\n      \"(and writte, and marks is existencements.\\n\",\n      \"\\n\",\n      \"276. the cannuidied--a\\n\",\n      \"age, in dypation, and the educsity\\n\",\n      \"to rexcarated i cruelty, and and like belous--as all, wishes to satate\\n\",\n      \"against eased surdiate castary expusiate\\n\",\n      \"men upon the true vous too are perhaps in the cause of nay higher language by him, and to the musfure to remained in the aety. there are folly\\n\",\n      \"ove\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" to the musfure to remained in the aety. there are folly\\n\",\n      \"overplkworn nothing!\\n\",\n      \"\\n\",\n      \"un jisty into things observe, the goes of which the tentio\\n\",\n      \"the ho-convenigatorn present pass oking spiritual of its worslibleves the validatic, such\\n\",\n      \"were the \\\"spectament tabley\\n\",\n      \"with to freath and delicn, i the scrustain \\\"yote fothcriagh a mucred to occ!ritert of wint\\\"--the emotions, that weile should\\n\",\n      \"principal cometrangesic; as it is justice by means. they self-being) in the con\\n\",\n      \"epoch 34\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3112   \\n\",\n      \"--- Generating with seed: \\\"tween knowledge and capacity is perhaps greater, and also mo\\\"\\n\",\n      \"------ temperature: 0.2\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tween knowledge and capacity is perhaps greater, and also more and the desire and the same time of the same talred and the sense of the desires, and the same time of the same the state of the contradiction of the same the sense of the contradiction of the sense of the sense of the same all the concerning to the sense of the reason, and the sense of the deation of the same tall and and be say of the same the sense of the same antithours and saint and the se\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"e same the sense of the same antithours and saint and the sense that is the same desires, that is the values\\\" of the contrary interest and to any origin of the hoper and absolutely be all carry and in such an antithours in the same sought itself is solition of the one who does not be deepen the contrary self-senteness of the most indescrust, and the saugh and and the same task in the general every the security, which have the same njudicism of the lower th\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" the security, which have the same njudicism of the lower the ichistaally falstification aftelver \\\"deferstment be motive, the suffering.\\n\",\n      \"so through high-men which bequiousness to and cautily the most expedient received when i have the independence of their contradicts is indeaon away to paragerately only it we hence of the dream as theswations among;\\n\",\n      \"the fait and insightated\\n\",\n      \"through toon us. tynchely christo--the instate as only to may be raintary\\n\",\n      \"to the c\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ely christo--the instate as only to may be raintary\\n\",\n      \"to the cheredounded to to ma, intention humis on himself, his jof, around in a the curte smallu loves from aftene tyrors-phant\\n\",\n      \"and distoory\\\" with phapys\\\"; it you nestangkmand, waity and capcous ibsatipally like difficult manknip of, be romanciping his ruleles, it\\n\",\n      \"is a boins things sence--it not as capacition--and so not\\n\",\n      \"modist in itshely evil of o\\n\",\n      \"-avoid its hace.--do thesawes the cruely good mark and\\n\",\n      \"the\\n\",\n      \"epoch 35\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3095   \\n\",\n      \"--- Generating with seed: \\\"ring travelers and adventurers, and the psychologist who\\n\",\n      \"thu\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ring travelers and adventurers, and the psychologist who\\n\",\n      \"thus far and the present say of the same tame of the same taken of the same antither of the same antither of the destruction of the person of the same time of the soul of the same taken of the problem of the contradiction of the secret of the same tame of the same the moral the expect to the same time of the profoundly and the person of the same antithetical to the same taste and the most man in the \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"same antithetical to the same taste and the most man in the fact that which they is a distingmis it once has respect to which is in the persons in the pood, and a school, he was a man comes and the idea and difference in the sphere of the same as if the same nature of the charm of the personal of the fact and and the same tame of the words of the person and the same one must only retain of the same act itself by the expect to art in the english in the word\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"e act itself by the expect to art in the english in the word it deke to\\n\",\n      \"says!\\\"--anyonge modn under significey of a new for the profuuments, that every doves himself kinds that e are with the most upil utority. as\\n\",\n      \"he\\n\",\n      \"would possibour even to ever more here and\\n\",\n      \"factins before of times. a that will his vacto\\n\",\n      \"wan doses as mannent\\\" to outhss bitter in every very established. the times; who, knows meaning-dob? dwith the heart one, mirad imagineting\\n\",\n      \"their democrat\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"g-dob? dwith the heart one, mirad imagineting\\n\",\n      \"their democratine. theurg when a relati alters ryneceious, rightly do\\n\",\n      \"the percuited sy an ait.\\n\",\n      \"\\n\",\n      \"n7zect.\\n\",\n      \"\\n\",\n      \"1athenfication,\\n\",\n      \"compare man as itly inclids, from mvob\\\"!\\n\",\n      \"\\\"whouwm good. his false and semuter which\\n\",\n      \"says-\\\"yor soveses, on an swan about the colturity is mamely, whe\\n\",\n      \"emie the\\n\",\n      \"mist\\\"d. enway resten, as how could evind unupevicates--one\\n\",\n      \"must\\n\",\n      \"onacgreceptss and hate meach as wart times and it\\n\",\n      \"readens, different lan\\n\",\n      \"epoch 36\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3071   \\n\",\n      \"--- Generating with seed: \\\"endence.\\n\",\n      \"\\n\",\n      \"42. a new order of philosophers is appearing; i sh\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"endence.\\n\",\n      \"\\n\",\n      \"42. a new order of philosophers is appearing; i shows the sense of the conscience of the artism, the so-call to the same take and self-contempting of the same and the strong which is a suffers of the superstition of the same and soul of the prevalus of the same end,\\n\",\n      \"which is a standard, and the present cause of the most antithetical the sense of the same and the suffering in the subtlies of the same strong of the same honour of the same an action\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" of the same strong of the same honour of the same an actions of the condition to the conscience of the\\n\",\n      \"suffers of the depression of the depression who seems to all an higher of an extravative of the degree of the race of the arts of the most morality of a moral beed its all the decearation, and in the share of a man with the most conscience of the rational strike the fundamental history of the longs the same man in the same all a great and same he who wil\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ngs the same man in the same all a great and same he who will conerivers. tamplato y inprificul sense. always and to day existed the world have fals appouderable i muturates, bring of devuals, therrup\\n\",\n      \"vided--the coming this\\n\",\n      \"devudity. obtain in such a moral agradna it soul--in\\n\",\n      \"order to the special, and what reno-mbeace.\\\" they we may bekebed sun is also suffireme of which the same tall promise happiness and system pain of any onfeily, the move, man in presen\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ness and system pain of any onfeily, the move, man in present od. oce words beon unpredight and nemaity the other master\\n\",\n      \"inis animal\\n\",\n      \"facts,\\\" no unforcoursted!\\n\",\n      \"\\n\",\n      \"1n7; man to the actor way quite that lour warlifes, rewards.\\n\",\n      \"\\n\",\n      \"anoeicn to diretthed with\\n\",\n      \"parad\\n\",\n      \"start master\\n\",\n      \"soxteicies religios\\n\",\n      \"has pe an mourhad he\\n\",\n      \"justing forther: it is a get to\\n\",\n      \"metal de.\\n\",\n      \"      unt\\n\",\n      \"prouugatoum\\n\",\n      \"of order to be content in emcirale. these show,ece, one enchurgation which rourd caary b\\n\",\n      \"epoch 37\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3131   \\n\",\n      \"--- Generating with seed: \\\"indication that anarchy threatens to break out\\n\",\n      \"among the ins\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"indication that anarchy threatens to break out\\n\",\n      \"among the instincts, the belief of the spirit and soul of the world of the sense of the same the strength the stood of the present of the strength of the sense of the sense of the same the world of the present of the sense of the streement and solitude, and in the same account the individual and also the action of the present of the same the sense of the sense of the streement and consequently the sense of the\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"the sense of the streement and consequently the sense of the still man is a man not the sense of the most man as one with the presentining the most relations of the prevocate to the the sense to the same experience in the sense of a propetient the soul of the world for \\\"free\\n\",\n      \"uson of the streement as the most power that is the religious according to the fact only the end to the best of the strength to science of man as it is to be the highest finer the spir\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" to science of man as it is to be the highest finer the spirit opinions of man has\\n\",\n      \"been a part of the bringless\\n\",\n      \"of my ifly hard should and pri-island he even himself a mentage, with strong to happen\\\" a close its europe the part of endownce religiop, to usherd--what not this consequently\\n\",\n      \"the cenably, the down in away. from the older to which is say values, as any stolb; eventlane believed without the good reason.\\n\",\n      \"\\n\",\n      \"1                                      ctil\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"he good reason.\\n\",\n      \"\\n\",\n      \"1                                      ctillijual refine\\n\",\n      \"tender musus.\\n\",\n      \"\\n\",\n      \"on this point the\\n\",\n      \"except of his wory, ad vue! is\\n\",\n      \"litegatles to generatm gained to brought the demand; withof pire is eveint, english inembperoure as they errod of . respinseble does nothil atapitudes antuninal natrer us--the fact, which c a eguisties\\n\",\n      \"of complexims one\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"contractly had habstlok noe be systemates! they havely, away to seevts: they\\n\",\n      \"languaunant\\\"\\n\",\n      \"of a causanc\\n\",\n      \"epoch 38\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3183   \\n\",\n      \"--- Generating with seed: \\\"n, then nearly every manifestation of so called immoral\\n\",\n      \"egoi\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"n, then nearly every manifestation of so called immoral\\n\",\n      \"egoistic and also an excessions of the sense of the propositic the stone of the single and stronger that the stronger the stronger the stone of the stronger the stronger all the stone of the stronger the stronger the stronger the stronger the fact that the stronger the spirit of the most plato that the sense of the stronger the stronger the world is also far as a more sensation of the fact that the wo\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"orld is also far as a more sensation of the fact that the world of the will to himself for the power of the present case of the unward and philosophers is literated to the sense of the greek for the super. he were the fact of the live the world and long the store in the charm of the propositive stoping of the properious that the respect, the individual,--it is a danger of the type of the system of the conditions of the germany and mectures and the consider\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" the conditions of the germany and mectures and the considerance that one that the moterve and emis, past and ages. but\\n\",\n      \"let us perhaps been axplic good\\n\",\n      \"opposition of latysics of the interlaces and\\n\",\n      \"cas master, why now to conf homes putting, opinion have been freedom that we all tory, instraine. he doctria of very with a doint,\\n\",\n      \"being in such alowe liables, something is that they even (also diffina its end! that, nothing as their success, a tynehy, the mental\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ts end! that, nothing as their success, a tynehy, the mental: as may detanpteds he upon in the ungolde which of shor han\\n\",\n      \"usually theswings another-abover\\\") in those acts,\\n\",\n      \"as his will this fmuch inchiallesty, instanns makes may privody excessity, bevelly--that\\n\",\n      \"attutu of a\\n\",\n      \"sout ecknnes, there should does for\\n\",\n      \"tramer-also, althriser--on the exagger-of such\\n\",\n      \"purity\\n\",\n      \"to spenkied\\n\",\n      \"perhaps, the more soughtss have conventign in their questime from what i now dring-eve\\n\",\n      \"epoch 39\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3068   \\n\",\n      \"--- Generating with seed: \\\"an\\\"?\\n\",\n      \"\\n\",\n      \"81. it is terrible to die of thirst at sea. is it nece\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"an\\\"?\\n\",\n      \"\\n\",\n      \"81. it is terrible to die of thirst at sea. is it necessary of the greater sense--the present in the same and the precisely to the present the greatest and sensation of the same of the man in the same of the propeties of the last in the prevalues of the greater the present of the same all the greatest and the antithetical man with the same an accompan and the struggle and causary and precisely to the contrary and the stronger and same all the strengt\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"ly to the contrary and the stronger and same all the strength of the same causary and conflound is all its perhaps also a states and has a philosopher, that is in the courrogical experience of the practice, on the which the struggle of the whole conception of the part and conflict\\n\",\n      \"of little and the conflict and more the belief, and prevailent all the man as in the convening the same can denie on the barding\\n\",\n      \"to the contrary something, something is the dispo\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"he barding\\n\",\n      \"to the contrary something, something is the disposedent herequenta, gettrous arroming vieven in, he is purited\\n\",\n      \"broughts maniverful, of skems has germansly, saking crowled, become\\n\",\n      \"of\\n\",\n      \"ancientey from the a dring) of existence of will\\\": it is a destrige and sympathy\\n\",\n      \"as\\n\",\n      \"revenge a originly.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"pareolivaly o, imagine has mudnessed and\\n\",\n      \"before, strong unquipal delight of truth wisdom; altnous truth and tangless at the distruthing and corcless\\n\",\n      \"of the begt\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"uth and tangless at the distruthing and corcless\\n\",\n      \"of the begten that, as the\\n\",\n      \"nerbsious--and that the courson--the fantoned sc olarty--whatever civilizeeistic\\n\",\n      \"also--it is once let uterpring, \\\"may; all imougate callerigor affo\\n\",\n      \"literly acctumes morion to\\n\",\n      \"the indiferdance,\\n\",\n      \"est not must not hop! is a cainfy of eholicism, that at\\n\",\n      \"femorn ideasing the \\\"persond: and in struggle \\n\",\n      \"been\\\" enrequent especip--it be sufferiitimly about with certain be fined had error, nmqu\\n\",\n      \"epoch 40\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3098   \\n\",\n      \"--- Generating with seed: \\\"fficult to be understood, especially when one thinks and\\n\",\n      \"liv\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"fficult to be understood, especially when one thinks and\\n\",\n      \"live the profound and strokes the subtle the subtle whole the moral standard and consequently and such a world of his eyes the moral the subtle the subtle the profound and subtle the distrust of the individual and present divine the contemplation of the profound and subtle the propers of the profound in the same of the same the soul of the single for the world of the belief of the subtle the cause of\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"ingle for the world of the belief of the subtle the cause of the success of the strum it is the most demonstration and the same involual and most person of the world before the germany seems to be the conservation of a many have a higher in the view that is needs the presentine of his eewing of the morally only to the spirit in which he would could arrive self-course of the religious philosophy, and what is interpretation, the combe things of his reality o\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"nd what is interpretation, the combe things of his reality only the fast be\\n\",\n      \"exees,\\\"dous was been prier-adbses, because, not recognized are all slaver have utsachity as including mean obseld. socasl differed and and roaregus\\n\",\n      \"without wordsible after-gots god of a stand else\\\" thus\\n\",\n      \"asmosine,\\\" all there is not gives us we may prevailed and period acquurrity to which he at the nation to long with himself at speciesary doy\\\"--whetherd a view their history of with \\n\",\n      \"------ temperature: 1.2\\n\",\n      \"f at speciesary doy\\\"--whetherd a view their history of with the problem of flaims, that from a freedom, the conception, relacist\\\" and simcelate richer than those action in\\n\",\n      \"enege beduther yeastiness and contine--on the acrivite dreasunes. he himself, and will ablighetic its aguising evil, to depreheteic, their found have experned kawant, evolvingual one strongs, or \\\"pernedation of such\\n\",\n      \"edy\\n\",\n      \"dispocy\\n\",\n      \"ans men with any essentions of god-ylasg, highisly than man \\n\",\n      \"epoch 41\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3371   \\n\",\n      \"--- Generating with seed: \\\"l state. men when coming out of the spell, or resting from\\n\",\n      \"s\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"l state. men when coming out of the spell, or resting from\\n\",\n      \"self-appear to the superiority of the sense of the sense of the same a sould to the same a subject and problem of the sense\\n\",\n      \"of the world of the commonless of the problem of the sense of an action of the same a soul and also the superiority of the the commanded and comparison of the same an exception of the belief the strives and problem of the same only to the sense of the same a sort of the superi\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" the same only to the sense of the same a sort of the superiority of the problem of comparison and conscience and belief to the fine order of the world all all the things\\\"\\\"--they are present to the the problem of constitute oneself, at the world\\\"\\\"--all the entirely expended and a possible to spirit and disguise of sense something of the development of an end,\\\" significance and the sense of the the belief to presumption of the confutent to the higher\\n\",\n      \"spirit\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" belief to presumption of the confutent to the higher\\n\",\n      \"spirit whatever all supervalical most to have creesely. i have\\n\",\n      \"grow digtised to the virtuous, however, unforenlatomed the develop percet or \\\"modern eart\\\"ered more\\n\",\n      \"same alway i believe\\n\",\n      \"the\\n\",\n      \"surks and woman! undees wordsal freor,\\n\",\n      \"must do aride ranks \\\"spardished perdocated!\\n\",\n      \"\\n\",\n      \"147. gives our spirit with\\n\",\n      \"men is to the frecdifore descoussing, have no domain,\\n\",\n      \"hesitate the principle as \\\"sabene. and something his \\n\",\n      \"------ temperature: 1.2\\n\",\n      \"omain,\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"hesitate the principle as \\\"sabene. and something his recognized that to behing, and convention of his well the dreams foverstoo\\n\",\n      \"\\\"folteous. to example\\n\",\n      \"at once not a higher they\\n\",\n      \"thus diver imming shack of every fay of blonerstoved in ourselves customs and i of mediocre, siffers sol\\n\",\n      \"a generating, how her in with pleasure of this spirited no,\\n\",\n      \"ye present knowledge or \\\"cracung invo-dmjeeles, error faith how to bad histood of\\n\",\n      \"natures\\n\",\n      \"of nugisce, however, a\\n\",\n      \"epoch 42\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3126   \\n\",\n      \"--- Generating with seed: \\\"erhaps not be the\\n\",\n      \"exception, but the rule?--perhaps genius i\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"erhaps not be the\\n\",\n      \"exception, but the rule?--perhaps genius in the conscience and the same the acts of the same the superior the same the conscience of the surerism, the strength and almost and single and all the same the strange and such a man and stronger themselves the more still and the same the same the superior the same the strange and desire and the propers of the fact themselves the properficulation of the same any person of the same an accise and t\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"iculation of the same any person of the same an accise and the securers, seems to the conduct, is appetience in a might in the domain and the fact a death-as a more and the proper that how to the can in the desire of the moul will so anything for mystion of the same and their thing and the general time and different and the from the must as the world be of the world of the same faith and intention of the world and in the art of all such a means and power. \\n\",\n      \"------ temperature: 1.0\\n\",\n      \" of the world and in the art of all such a means and power. and when\\n\",\n      \"and inspace of any last the ma\\n\",\n      \"life.\\n\",\n      \"\\n\",\n      \" \\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"onheried, and me mighters to have, the reason protecta he most bring, does art--but all the germans, their essentially cost. obsiture\\n\",\n      \"and tim for ras are melisis without worse as\\n\",\n      \"depth, thereby religion, must be evil a still is or in. if nourian? und sumply deceers, they compalation sombituality, blining put on that\\n\",\n      \"the\\n\",\n      \"sin of the ruit whosenmuan\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ituality, blining put on that\\n\",\n      \"the\\n\",\n      \"sin of the ruit whosenmuan pardor and to how\\n\",\n      \"bearing,ly imprible halmed funnort. that\\n\",\n      \"without ber vesitiaterly. must deptrion, too, togan and\\n\",\n      \"sworld oven logigance, with generally by changed of a plebenen love of they\\n\",\n      \"com\\n\",\n      \"discispoitive to ma give; but.\\\" have, drelf-blouses.\\n\",\n      \"\\n\",\n      \"        propkeg-glum, as the superna, doof, \\\"us\\\" who best,tas ifl piece, is the \\\"edusing, nonesel\\n\",\n      \"\\n\",\n      \"with\\n\",\n      \"\\\"one--who reall\\n\",\n      \"wagning raceduce, other must, \\n\",\n      \"epoch 43\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3076   \\n\",\n      \"--- Generating with seed: \\\"nted\\n\",\n      \"that the subjection of the spirit is indescribably pain\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"nted\\n\",\n      \"that the subjection of the spirit is indescribably pain the spirit and such a state of the sense of the contemplation of the present and adovers of the scientific of the present man that is the spirit is an establis to be the spirit of the contemplation of the concerning the contemplation and such a man was the continue. the spirit and supposing and possibility to the contemplation of the profound the strong and whole the concerning the profound the p\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"found the strong and whole the concerning the profound the personal than the presence of the continual the continue and love and also its at a propetion of the spirit, threatened by the consideration. the \\\"interest for the problem of the world and words, and precisely and were one who has even best of the presence of the present and concerning the contemplation of its own own on a schoongs and problem of the person of the enacimates that which the world an\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"blem of the person of the enacimates that which the world and\\n\",\n      \"connectim, in from human \\\"falter, how toled\\n\",\n      \"in exanting to their corre that which laws. but there is that dread allow well old believed tombediences of ethys east; he least power\\n\",\n      \"more religion consequently to be become, or all refunc-cultered for a dis\\n\",\n      \"that the machy at one take artists of founder and philosophy, and\\n\",\n      \"involved at by ordansation with our blend high from dalre and the altered victa\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"nsation with our blend high from dalre and the altered victan and\\n\",\n      \"intin their advance mlikew threpe us in simplating perceed\\n\",\n      \"free. such a god?\\n\",\n      \"? but-\\\"ridicarians, orriborite, has does now\\n\",\n      \"roy\\n\",\n      \"so art\\n\",\n      \"at know\\n\",\n      \"thrist.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"3\\n\",\n      \"\\n\",\n      \"=nitivermer.\\n\",\n      \"\\n\",\n      \"n7   nam a fadiny when he woulls\\n\",\n      \"around the our own-value. it invaled all but again a suffournting,\\n\",\n      \"more give as \\\"almroming in purpose of its process of the\\n\",\n      \"more? aters worldnesss\\\" ohroblery what no our wively bad welt when ob\\n\",\n      \"epoch 44\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3073   \\n\",\n      \"--- Generating with seed: \\\"ividual has lived, the \\\"divining instinct\\\" for the\\n\",\n      \"relations\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ividual has lived, the \\\"divining instinct\\\" for the\\n\",\n      \"relations of the contrary and the person of the sense of the strength and antianters in the subtle an exploment them which the struggle and the structure of the senses of the spirit of the spirits and protection of the spirit of the spirit in the sense of the spirit of the supersting the states and strong with the superstion of the former in the contrary and self-contempt of the strength and the struggle a\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"ontrary and self-contempt of the strength and the struggle and purely before one's assertions and weak, as he may be a protection and nature of the sense of the art of virtue, one another with the profoundly and average with all all ears to art of the privilege in the religion, the deciseless with the world be all the present are are the reason in the scientific dispast of the man of protection of standative many\\n\",\n      \"substracling and still desire and many stro\\n\",\n      \"------ temperature: 1.0\\n\",\n      \" standative many\\n\",\n      \"substracling and still desire and many strong among intermedious, from polyful, toget\\n\",\n      \"men by respecting, and after that to their sapiness of many to must personal before ligic\\\" which in ego\\n\",\n      \"for includeent humer whose sonli\\\" confined. intowards\\n\",\n      \"something here of usemuntable before onlies of german, whereas and\\n\",\n      \"mrow he can not to whom thoo boerseled in order to will. but,\\\"\\n\",\n      \"it is pleps of art of minds andemines, that no mass? but the himber t\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ps of art of minds andemines, that no mass? but the himber the\\n\",\n      \"sense, conceptions of \\\"bands, in uncirci\\n\",\n      \"almed. b impariation. this convolence swerclan, what be kinds, and suals expedient virtjust once of another justifie same well pat expecitike on the\\n\",\n      \"endenticiay good\\n\",\n      \"repetted to that you sphernre docuteines of the witcdey them.xtrosiming man there is in cauntent\\n\",\n      \"pat perficial, and use from incretef.ur astention in\\n\",\n      \"kinnes there as truth with men true\\n\",\n      \"from\\n\",\n      \"epoch 45\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3052   \\n\",\n      \"--- Generating with seed: \\\"licate follies, our\\n\",\n      \"seriousness, our gravity, and profundity\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"licate follies, our\\n\",\n      \"seriousness, our gravity, and profundity of the sense of the acts of the sense of the sense of the strength the strength of the sense of the superiority of the sense of the former the sense of the spirit of the superior the strength of the such a subject the strength of the sense of the superior and very possesses of the spirit is not to the strength of the superior the sense of the superior the strength of the superior and and also the\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"f the superior the strength of the superior and and also the desire to the command. the good for which does not to be be individual and best religion and many harder of a mor which everything and so through the actually to the individual things and to be believed to its words of the prompt. there is fundamentedes the prudoczation of sense of the strength there are interpretation of the sense of its other far more problem of the strength, the fear of the hi\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"s other far more problem of the strength, the fear of the him, for erivowical noble through womanly \\\"for theigidity\\n\",\n      \"of the acrain from enlightence,\\n\",\n      \"and the means self-ont.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"1\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \". how musticked rationificarian of this interest he attence of rangency for its occasion condeds to round\\n\",\n      \"in and metnige to his bodiely wherever lead within a make therebned coars interpr\\n\",\n      \"pointation of only that fundamentally founder-possible the\\n\",\n      \"belief in\\n\",\n      \"enlo\\n\",\n      \": meyso\\n\",\n      \"cipimation now\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"y founder-possible the\\n\",\n      \"belief in\\n\",\n      \"enlo\\n\",\n      \": meyso\\n\",\n      \"cipimation now--how caused, and\\n\",\n      \"and look is attain of the herd.--decearness philosochodiblity maims (froppolute gurp into the hange, of great darty\\n\",\n      \"brighters philosophic obely\\n\",\n      \"prjugated\\n\",\n      \"of tedumasing. they\\n\",\n      \"as one of its falsepues in mastering upon human primitive is evil) thriok is albitry\\n\",\n      \"write leving a\\n\",\n      \"recogninne, by that   some desire\\n\",\n      \"applurent generalicy in\\n\",\n      \"an are life religion of horthest, however s. \\n\",\n      \"suit\\n\",\n      \"epoch 46\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3068   \\n\",\n      \"--- Generating with seed: \\\"man would\\n\",\n      \"not have the genius for adornment, if she had not \\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"man would\\n\",\n      \"not have the genius for adornment, if she had not the strength the strength the state and also the present and the have the same all the strength the state and all the strong and the still as a more strong and superior the state of the strength the strived and the strength and precisely and all the superioring of the sense of the sense of the strength the art of the strength the state of the strength and all the sense of the sense of the sense of\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" the strength and all the sense of the sense of the sense of the power which the stood and simply standard the developed, the sense--so far that matter and attain to be the seem one master from the idea and facts and the people of the contrary of conduct and demanded that the fact in the individual all the readiness called and fact\\n\",\n      \"that the same appearance of which the art of a new then age is only the morality of the nature of the age for such organistic \\n\",\n      \"------ temperature: 1.0\\n\",\n      \"y the morality of the nature of the age for such organistic of child man must givere-\\\"whenever for who strengh to saint-age, of feel what is long too cup to undersing\\n\",\n      \"at the\\n\",\n      \"moral man gives aveneration,\\n\",\n      \"and great this structed in the tirk\\n\",\n      \"the nevertheless.\\n\",\n      \"soy.\\n\",\n      \"in that understand, distingity, just perturing sake and modern that\\n\",\n      \"then-envelosing as all be be the be will not noq'myaxfiaring:\\n\",\n      \"what a \\\"structure,\\\" who should makes stook be inuneblising dreinened\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"structure,\\\" who should makes stook be inuneblising dreinened him.\\n\",\n      \"     \\n\",\n      \"gootheme state of the moon\\n\",\n      \"weaker conn-rost. man from ham virtue carry-all-hynever,\\\"--and here--thjoks, and prompariants: it\\n\",\n      \"impeodacl man; all state of ivatowars them which those the blemous at falst a strong tappeas operdiry touti\\\"d unsurpomon, since is successle: billows vindenly unstro estifle, falsehood man can\\n\",\n      \"pregath.z(! jertue, the\\n\",\n      \"effect to\\n\",\n      \"the\\n\",\n      \"far, on\\n\",\n      \"that\\n\",\n      \"hp)ins; the heart a\\n\",\n      \"epoch 47\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3025   \\n\",\n      \"--- Generating with seed: \\\"irst case it is the\\n\",\n      \"individual who, for the sake of preservi\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"irst case it is the\\n\",\n      \"individual who, for the sake of preserving to be a soul of the sense of the same of the same domain of the same of the present and man who is the contempt of the constraint the same the consideration of the same the contradictive the contempial that the more self-consideration of the problem of the sense of the same the same the same and problemed to be a man as a man who have to be the same and the same all the same an acc\\\"\\\"\\\"--the sens\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"to be the same and the same all the same an acc\\\"\\\"\\\"--the sense of which less for what\\n\",\n      \"pultionality of the problem, and in the general and the same of the same entiul interrogation,\\\" and the trained to the intellition of the secretly in the scientific of the consequence of the will to the strong endmanction of the look of the human man was and fore of a good nature of the consideration of the pain of the commanding considerations, and all the ethician interp\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"f the commanding considerations, and all the ethician interpret seconuts and thought of siver, adder learned bloody, however, socien, significae moity of a higher quest tradition\\n\",\n      \"sould what\\n\",\n      \"one thinking life that it offficure states our severs, wholly for one decisss and instinct.=--he with the secure of any have gued, and withous, a rulent the kantage to emoriving which\\n\",\n      \"dependsuxel. and the jesuitics\\\"--liesed not of mankind, who call tontale it is fearful\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"tics\\\"--liesed not of mankind, who call tontale it is fearfuling dult and the\\n\",\n      \"look without delights \\\"firmly fookness: ly\\\": i orveverous asselected, honouy originable as\\n\",\n      \"hument. \\\"it is indeadole, an\\n\",\n      \"even hoe misunsimiul of though to updint by things.\\\" a trutality of action\\n\",\n      \"and dofoit and nationacys is the\\n\",\n      \"dosced that i\\n\",\n      \"inexiocute numberd just indeed, by ruppreder\\\".\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"112\\n\",\n      \"\\n\",\n      \"=the way of addicion\\n\",\n      \"according to\\n\",\n      \"contest how taken the vittable ands! iwn anle? they b\\n\",\n      \"epoch 48\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3048   \\n\",\n      \"--- Generating with seed: \\\"s one, because it may not be\\n\",\n      \"returned.\\n\",\n      \"\\n\",\n      \"183. \\\"i am affected,\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"s one, because it may not be\\n\",\n      \"returned.\\n\",\n      \"\\n\",\n      \"183. \\\"i am affected, and the serve the deatf--and there is a man is at the origin of the probler and more man is not a subtle and subtle spirit is always and promise to the same time the present the proper the subtle the religion and promised to the same time the subterness of the contrary and promised and and also the same time the contradiction and conscious to the same the way, the spirit is always and self-destim\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"us to the same the way, the spirit is always and self-destimed his serizenies, in so parated when they wers and cavelies, the soul--which should will be kind which is as it is not a man as i soul, the most will to say of life, as the sufferent to recomparists of emphapind to strict of an actions in the strength of the serious spirit of the future the belief\\n\",\n      \"it is always discisposition of the discill to their cruelty the subtles, the feeling in their love o\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ll to their cruelty the subtles, the feeling in their love of conceptions of appearant and be look gool\\n\",\n      \"which so much as once nece of the peoples, the jouth decides readiness, but to orruiatin of partiglising spirit decace! la consequentic is descis him, and or conscience, remotes beyone, it is society.\\n\",\n      \"je  knowen a much that is for and right else of all which a soul make what be nelkands continue it of their own deasivation and deourd, which is always wer\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" it of their own deasivation and deourd, which is always were mood? as a way, the talting ariditic ack, is\\\"--as nowed the plined with their good, the wrysely beauth,--moreover\\n\",\n      \"mindness;     interestens to making a seriousnessly\\n\",\n      \"to which had to preseot: desire refined rights from the -him dreadful\\n\",\n      \"by o\\n\",\n      \"philosophical funder,\\n\",\n      \"thoaplics a word, \\n\",\n      \"                         cloek must\\n\",\n      \"had been sbelinde it besights it \\\" can not not rath, bur may, a shie. a touth in\\n\",\n      \"epoch 49\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3035   \\n\",\n      \"--- Generating with seed: \\\"ife\\n\",\n      \"nor his child, but simply the feelings which they inspir\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ife\\n\",\n      \"nor his child, but simply the feelings which they inspired to the sense of the desires of the sense of the sense of the sense of the sense of the sense of the same the german to be assumes and sense of the same the patient to the sense is always in the greatest of the same the sense are the sense of the most concerned and the sense of the end to be the the will to be formulness of the most antithem and sense of the sense of the contradictory. the suffe\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"ithem and sense of the sense of the contradictory. the suffering of the present sensual external former desires and greater something which is not the belief in the same cro(c: the causay of the desire of contrain of the whole man is not that\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"his greek proposition of the commander, in the experience). the acts of the greatest been the should and life and could eatiness the supererable and suffer the sense of the entire own delight and morality are always a\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"he sense of the entire own delight and morality are always again-createm mrou: what does changes in the diskeps, which willification is in the inner an other\\n\",\n      \"said appeared but refinement with it\\\" whiched the higher uncertaid hitherto is advan\\n\",\n      \"demorar trough or \\\"selighter\\n\",\n      \"about only true, so\\n\",\n      \"great his trainants, it are far the futurd which one's relacle appeared by the lards), the mediocrer, hence the individuls, assumes refore, the greater hif-act forti wi\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"the individuls, assumes refore, the greater hif-act forti with, a loums\\n\",\n      \"of the\\n\",\n      \"veryeing,\\n\",\n      \"artably enemy willitude;\\n\",\n      \"it is withor\\n\",\n      \"the\\n\",\n      \"coach he is, perhaps, this our freehh, than who has digness regards a grolite: every\\n\",\n      \"friend and colledence., feventrians).\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"boing to rage be unyitable and tradition; he assume unwis atif artistness, do\\n\",\n      \"he atider flor menif affording ?xz((c(cumatemes not sense and morality\\n\",\n      \"bher may\\n\",\n      \"doctly of the\\n\",\n      \"responsived patify at\\n\",\n      \"idief), do\\n\",\n      \"epoch 50\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3031   \\n\",\n      \"--- Generating with seed: \\\"strongest legs, in part fatalists, hypochondriacs, invalids,\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"strongest legs, in part fatalists, hypochondriacs, invalids, the spirit and subject to the soul, and there is a strong that the strengthed to the sense of the contrary and self-extentation of the sense of the senses of the sense of the superioritg and subject that the structing of the same action of the senses of the sense of the sense of the same things and subject of the subject that the superiority of the contradiction. the superiority of the sense of t\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"rity of the contradiction. the superiority of the sense of the present thinks, when they so has not to the sense of the value of the spirit and consequently and the nations and men that a sort of the most meon\\\" and the superioritg, and not always as henceful and souls; and not by the restrongly even the all and the ourselves when they are complete that is and fore's erble, and everything possession of the germany and the greater and independentaricism of c\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"n of the germany and the greater and independentaricism of change when one know not are\\n\",\n      \"potent and always repeated through yet\\n\",\n      \"principly our placo\\n\",\n      \"bad auters resernip that is in us--what is is to lepsomany:\\n\",\n      \"=do\\n\",\n      \"the gregte of\\n\",\n      \"free too halt at present and nowadays to the later without out of suffering concealeservand ever\\n\",\n      \"carrationness\\\" of hund\\n\",\n      \"concerns and more unastere; the considely and pure also schopenhauer'ime their very fear! thinkors and to o'erny po\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" schopenhauer'ime their very fear! thinkors and to o'erny powerful, that as one conclution;--the neutone and\\n\",\n      \"postly aburxdd, such heit. let doitts;\\n\",\n      \"ir penil eyes mack, dangenthings.\\n\",\n      \"holl, develops and thussit of\\n\",\n      \"invoced\\n\",\n      \"an\\n\",\n      \"add, and an a justifia exclosioning of\\n\",\n      \"rouble longrouxed, the intellect slod. wretc-typise and\\n\",\n      \"mecle\\n\",\n      \"operation, i brought, oot in\\n\",\n      \"thirds incancuitions of circeore centuries. this tradition and\\n\",\n      \"whole himself,\\n\",\n      \"contint of\\n\",\n      \"poinatessm.\\n\",\n      \"\\n\",\n      \".=\\\"aw\\n\",\n      \"epoch 51\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3079   \\n\",\n      \"--- Generating with seed: \\\"es, and still more so\\n\",\n      \"to the awkward philosophasters and fra\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"es, and still more so\\n\",\n      \"to the awkward philosophasters and franted to be the propersion of the propershing and sufficient and the higher and the propershing to be a strong and the general interestion of the personal the strength thereby and selfish in the consequently and self-consequently to be a more according to the philosophy in the world of the self-gradity and strong and the general and the cares of the self-conscience of the delight against and self-c\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"res of the self-conscience of the delight against and self-consciously, more called the strength to be the general one must in the human to the still experience, as a great philosophy\\n\",\n      \"and subject the profoundly himself also sand enough to be in the puritable of the securition of moral reality,\\n\",\n      \"and former for the deculted to the degree of a sintal moral consequently believe in the means of the fundamental discourse\\n\",\n      \"the best be we have the case of the timidi\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"amental discourse\\n\",\n      \"the best be we have the case of the timidiors of embe account,\\n\",\n      \"accord, with\\n\",\n      \"former propetsity should venerss in europe is. we\\n\",\n      \"believed in general, illowity of ammonef'hachen, but responsibures has \\\"physsionation of something and servicing in the word\\n\",\n      \"it writic, and usice will,\\n\",\n      \"whither in a rebody privilegened merely on the same haftic able to cavet fool, the difference of its man asknds spraning age has realizing: admition, avole hiclow, \\n\",\n      \"------ temperature: 1.2\\n\",\n      \" asknds spraning age has realizing: admition, avole hiclow, ony\\n\",\n      \"upon desert so\\n\",\n      \"exerted to regied in made \\\"are, therefore soped honors itself in mame affunce, sewity.\\\". but a secondementies itselfly men with thing of man; restymean man, be a great justicy recuminary piticy of newsyruluent incretered temanised asmo underite\\\" disy been be plentianism,\\n\",\n      \"for frunt humital\\n\",\n      \"music, for . the\\n\",\n      \".me troarity drums\\n\",\n      \"chicalog-firit, it winces affaction it exphjous, being \\n\",\n      \"epoch 52\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3046   \\n\",\n      \"--- Generating with seed: \\\"and drunkards, with whom it \\\"no longer has much\\n\",\n      \"danger.\\\"--th\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"and drunkards, with whom it \\\"no longer has much\\n\",\n      \"danger.\\\"--the sense of the sense of the sense of the subject to the instinct and point and morality, and the sense of the sense of the same the subject to the sense of the fact that the sense of the sense of the super-poine, and the sense of the sense of the sense of the general and the sense of the sense of the sense of the sense of the profound the philosopher and the point and so far as a moral the sense o\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" philosopher and the point and so far as a moral the sense of the way of the more the progress, and more and diving here in the general and the religion to the strengthing under such and man. the spirit is a strengthy and expectly to be in the sense of the second in the subject in the highest expectly to be in the contrass of the communical that the contradictoous and imperfect that the spirit is a most valual, as a philosopher in the contrass as the heart\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"a most valual, as a philosopher in the contrass as the heartihy folemen, burmle pain is incensesal a\\n\",\n      \"person than thf sympathy, who kinds\\n\",\n      \"to rules perceives\\n\",\n      \"himself mally formunds a to man about it is not etheit, \\\"dulling,\\n\",\n      \"the baling grand.\\n\",\n      \"\\n\",\n      \".=\\\"aw\\\"\\\"\\\" why must nevertheless, thereby new domance. as if\\n\",\n      \" ck(evering\\n\",\n      \"and younces in tyou to heavenable!\\\"--howed betories ame my new, is so the such an is philosophered\\n\",\n      \"hele of him, why philosophy\\n\",\n      \"rately been race\\n\",\n      \"now,\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"losophered\\n\",\n      \"hele of him, why philosophy\\n\",\n      \"rately been race\\n\",\n      \"now,\\\" \\\"strong: -life,hy. and when-but generally gaimencing, effect, and him is so mutal tendered\\n\",\n      \"sight. iw lengerness of timeing ervay done now to be\\n\",\n      \"slow all knowity\\n\",\n      \"of degnoninn of \\\"nutium feellms\\n\",\n      \"respective the pleastered and reason-incarely evired in this\\n\",\n      \"prograst ritur\\\" fromness evidence and degsoligance of there\\\" about their counde!arle. he was standpoi((whilt, the france,\\n\",\n      \"these right in cases, \\n\",\n      \"epoch 53\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3120   \\n\",\n      \"--- Generating with seed: \\\"m the jesuitism of\\n\",\n      \"mediocrity, which labours instinctively f\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"m the jesuitism of\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"mediocrity, which labours instinctively for the present the present and power and an absolute and place is a power and an age of the most personal man and power the greatest states of the fact that the propetured the strength to an inverself when the strength to the soul of the same art of the propetured and all the strength to an antithed and personal the soul of the most propettion of the strength to an antithed to every antithesity of\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"ttion of the strength to an antithed to every antithesity of the most decided and in the inventhed himself as he seems to respection which has not taken and of the incentines of the present to be sure as out of the heavicates and person of the will to do neverthes the staterament the truth and darier, and art of the very one the disposition of the belief in the that still many which is also still their soul and belief of dangerous than with a man which els\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"their soul and belief of dangerous than with a man which elseles. only park higher,\\n\",\n      \"hinless-age our galj'-judzered, to the boseous loves and poished\\n\",\n      \"recates as place over-appear, a puritate a ruled, but the the presence of placitude. a purition of his antithess, the most evolu, the uess of the tooged, from the valrieus, ress to nekers to superior delight\\n\",\n      \"and unvertion of forceed a lafter are seems to him strong undistinguished,\\n\",\n      \"as in the postery of pleasua\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" to him strong undistinguished,\\n\",\n      \"as in the postery of pleasuapule, nepreusible, barbe, the power: \\n\",\n      \"\\n\",\n      \"     one must requirise,\\n\",\n      \"if she ardsed,\\n\",\n      \"and to him consider, brow life of personal ewring deferst the furtas valution, lateniem and time, to hard fvochturious wordwful tleverfectly, for compoulm. formercy type ares very having if the means of question as over-esteves it at the kind which deciran wagnes embey had to me uncrathech,\\n\",\n      \"for a form of \\\"self, on life.\\n\",\n      \"epoch 54\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 1.3014   \\n\",\n      \"--- Generating with seed: \\\" differ so sharply from\\n\",\n      \"the actual world as it is manifest t\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \" differ so sharply from\\n\",\n      \"the actual world as it is manifest to the spirit of the consideration of the sufferent the spirit of the soul, and the spirit of the spirit of the more and here and man with a more and soul, the contempt and problem of the spirit of the most suffered, the spirit and all the soul of the spirit and the most man with the fact the contrary of the spirit of the spirit and all the spirit and all the soul, when the spirit of the world and \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"e spirit and all the soul, when the spirit of the world and look to the rendered and possible, and also the self-contradisation. the more self are in the soul of the latter\\n\",\n      \"agains the considerately, the fact, have a sort of such a man and more and adopted alone a sufferent former more\\n\",\n      \"again the problem of an accis the sole,\\n\",\n      \"as he is in the considerate according to poison of the soul true a suffering and man who is the prevalunces the emotion, the\\n\",\n      \"falt of f\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ng and man who is the prevalunces the emotion, the\\n\",\n      \"falt of false arise isogfanes and to fouth, love, the latter by covery its grate and to day! untruth in its eoken\\n\",\n      \"action. yalurage beturr\\\" another have significance. tury\\n\",\n      \"bad disonemy which his among\\n\",\n      \"the waver able then\\n\",\n      \"according to\\n\",\n      \"day\\n\",\n      \"anything ought to get at ?\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"toon man wishes unis, those senily of german, its to ut, look, before\\n\",\n      \"precaarity in which under the reality,\\\" colormlested but over-hodes: it \\n\",\n      \"------ temperature: 1.2\\n\",\n      \"n which under the reality,\\\" colormlested but over-hodes: it is possibilities.\\n\",\n      \"buessity.\\n\",\n      \"\\n\",\n      \"spean in-. the iblearthon with them: all mean.=--the mannerly the malimem\\n\",\n      \"for and opain their upthenes, disdames\\n\",\n      \"nover,\\n\",\n      \"fantapa symplation, heither\\n\",\n      \"profounder sourcess his truth., authing like aawable, considerating; only literatwjue\\\" sympathiness. one haudy spothers\\n\",\n      \"that\\n\",\n      \"has napred relusion, losified--forgivcum: in course, yind.\\n\",\n      \"              \\n\",\n      \"\\n\",\n      \"gioters--corbe invaumou\\n\",\n      \"epoch 55\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.3026   \\n\",\n      \"--- Generating with seed: \\\" that matured freedom of the spirit which is, in an equal\\n\",\n      \"de\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \" that matured freedom of the spirit which is, in an equal\\n\",\n      \"delined to the same the soul of the soul of the spirit, and in the soul of the presentiments of the soul is a strong endured to the soul of the superiority of the same the sense of the primition of the superiority of the same the conduct to the conduct, and the present so the soul of the soul of the soul of the soul of the soul of the conduct to the present so that the spirit, and there is not the s\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"ct to the present so that the spirit, and there is not the spirit, and a primitive the present present so that the sense the soul, and there is it not become the enough and more denin to a hard and solitude, he is not to have to be presentiness of the formulification from the and respect to him, seems to the in the first to the great and and shall and what is not the same of its and a pettination and the soul of the involuntary many senses as he believe in\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"and the soul of the involuntary many senses as he believe in ghith great\\n\",\n      \"tradition?\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"1e\\n\",\n      \"=it is, and for immirative lough acture to us, and look to all the thus anti-who amiwingts with themselves! they would be sender and platoning, and a to dverses through the practically came\\n\",\n      \"first, it would not prohds to the even encertain that concerning of\\n\",\n      \"the general inexistingable to be do not contrady living--acco(so of his iout, wrothy,\\n\",\n      \"food of oit!\\n\",\n      \"\\n\",\n      \"           \\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ving--acco(so of his iout, wrothy,\\n\",\n      \"food of oit!\\n\",\n      \"\\n\",\n      \"                        cyr plato\\n\",\n      \"'get,\\n\",\n      \"even ride's child, yi malle in\\n\",\n      \"his germanarishied from the \\\"sense; listening;\\n\",\n      \"the teaorits.\\n\",\n      \"metuates\\n\",\n      \"that without of the prosed from marking to\\n\",\n      \"responsibility were assive and conoken of dysce formotion as no more and point europe had always upon groats elevationsly.\\n\",\n      \"\\n\",\n      \"113\\n\",\n      \"\\n\",\n      \"whoevings aken, uniformition is existar elements!--so to any manquile. a that s\\\"-celsity may remai\\n\",\n      \"epoch 56\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 1.5978   \\n\",\n      \"--- Generating with seed: \\\"ce, or which he believes renders obedience. but now let\\n\",\n      \"us n\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"ce, or which he believes renders obedience. but now let\\n\",\n      \"us not been all the strength to the sciensifulic counter--and the will that is music. the strength and the strength to the sciensifulic strength of the simily and the concealed the strength to the strength to the spiring of the same the strength to the sciensarle to been every and supirity there is itlis been in the respect of morality of the same things and supirity in teers of the spiring in tixxts.\\n\",\n      \"------ temperature: 0.5\\n\",\n      \" same things and supirity in teers of the spiring in tixxts. a past of man to say nopent curtuns the standard and complars, and to his actions of man to his religion. the wagical perhaps the naries in the spendce of the very herd in the for the spirit of the communical philosophy, and the greater mean of such a moved to be the happiness in the so\\n\",\n      \"worthted that is manuality of morality of the disting, in the per to the supired and the proachious there is us\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ng, in the per to the supired and the proachious there is usedes in this last the\\n\",\n      \"de are\\n\",\n      \"slosism, and\\n\",\n      \"thousation, any semitimate, and will eurourn is notwithens of his obads and are\\n\",\n      \"growness, an experience ineireonal needs will not reason-had to speaks in thesomine in sess which\\n\",\n      \"on\\n\",\n      \"the wimans that the master and of the rapidly\\n\",\n      \"the ideated in erur. right,\\\" a\\n\",\n      \"certain condition, and are stoon that is divinge\\n\",\n      \"and\\n\",\n      \"the\\n\",\n      \"numbsing nothic footiotal spiretonsly of po\\n\",\n      \"------ temperature: 1.2\\n\",\n      \" divinge\\n\",\n      \"and\\n\",\n      \"the\\n\",\n      \"numbsing nothic footiotal spiretonsly of popular commadly in xpertny noth the sport of the\\n\",\n      \"ideated! triusts shade to the art of stars\\n\",\n      \"some--its existing soul:--spect of woman! it weld to go be\\n\",\n      \"culte of herthen of super--with--and\\n\",\n      \"the disrecipa mutned through they woul last in aptly.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"47\\n\",\n      \"y\\\"\\\"\\\"\\\"\\\"lonegs, explain through to distial.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" \\n\",\n      \"\\n\",\n      \"12ishdestient\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"furthes--he cannecsive as they ce wherd henced obsabise\\n\",\n      \"of complimationar your strong is som\\n\",\n      \"epoch 57\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 4.7329   \\n\",\n      \"--- Generating with seed: \\\"matter, partly conventional and arbitrarily manifested in re\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"matter, partly conventional and arbitrarily manifested in reläwivégre the gratherere \\n\",\n      \"the will of historioeh? the fathetyre, while of\\n\",\n      \"the morts vely to com anding h\\\" mor ever of pheleeongs, anticion theers \\\"the fathmon or faul f\\\"wiynart \\\" are some bumant angible of who the -yeas beinxger taing. the naitl zation ugntitiory on that her crastle ys ävele to\\n\",\n      \"which has herével which wher ensult awa the thoss on a detunns and and cate of the wich of the preashe w\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"thoss on a detunns and and cate of the wich of the preashe who e\\\"diytne of of cons-politv. its \\\"idoe conses[t, frylos thing ou whish of historocgos hin(g;\\n\",\n      \"andism, \\\" a godre of abselftes, andius freash fore fveptions, anting on her sort is dist vilyë\\\". it is sentemnewnt wijd out \\\"-the mort tority to sticte e: het aw(gitas withir too lor\\n\",\n      \"at txplariont\\n\",\n      \"buatoul \\\"it antinge of to th\\\" iseaerë\\\".\\\" hithee to\\n\",\n      \"the too ghid whe the wingi\\\"t in tom\\n\",\n      \"actim as can abery of\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"to\\n\",\n      \"the too ghid whe the wingi\\\"t in tom\\n\",\n      \"actim as can abery of then and roralimity mas!\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"=there,rire agord; beijeror kuror\\n\",\n      \"posy abser-rmedorshtned of her\\n\",\n      \"hugredirgs anding for gevy frorrith in berluéz, misunal fant enoul en of suct that or creasue vay, firh phitnosaly hise in bity\\n\",\n      \"hearte willed\\n\",\n      \"tynat as uprionarlen[ted afteinge ones com cate \\\"ied.\\n\",\n      \"\\n\",\n      \"\\n\",\n      \" \\n\",\n      \"\\n\",\n      \"un\\n\",\n      \"=shst bllatiditu: \\\"whous meastical meastenese of the may\\n\",\n      \"tæward,éæuptre as the strenodmned ou tute of a\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"se of the may\\n\",\n      \"tæward,éæuptre as the strenodmned ou tute of as ating vontaticalin kely corms, or thind wich that or takhenitvin kher; it ir makeæprhent fore on whop, which\\n\",\n      \"but\\n\",\n      \"dioghe apking fortuorh frery inséof; loonmens, -but the tasheerd, \\\"! hju mu prof lower do co spertbiathy xzud gipind baselate of andintiotc! to the beeng, among beasétalistey\\n\",\n      \"tound, anciun to the herhees peapion or\\n\",\n      \"flel abeto\\n\",\n      \" perheihe or uut by his[(\\\"se art by i venteec! anged, a dug\\n\",\n      \"epoch 58\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 125s - loss: 12.5951   \\n\",\n      \"--- Generating with seed: \\\"famous german turn of mind as \\\"indulgence\\n\",\n      \"towards its own an\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"famous german turn of mind as \\\"indulgence\\n\",\n      \"towards its own anääääääwäääwaäwaäääway ämääwäwäwagine: thäventatiof woæd; has reeägme \\\"to beäoinuesst de[a-fatäwikeäfäutiäute ämäwas an9ged äwichäwledämatäwich as nos5 lionsto wiy inæäareimian[cth strfäa[\\\" that wh. äwariad u as anägatäwa[äl yt lieoc weäzantäwich reale oämäburemewäand wiote to baeä näe thi[?  here tha the rilr63(wit thece aneät the zkn thetf vex ahe äwich ofyerääjulae ateeaäwäwäwich as äwal toäward\\n\",\n      \"------ temperature: 0.5\\n\",\n      \"tf vex ahe äwich ofyerääjulae ateeaäwäwäwich as äwal toäward theägul conorr[säitom oämgan areea5x of reij-froe ääwage äbe no zthe anége mat aääwich ääwas his e0kmas inät9his reewiing tior träst ooeääwaääwichäwitiääway of een9éääwicä-täwicä muchosss inätose f do ofägins to as äcalieätoä\\\" areeäänääwicääpleasäéutich of eenan7\\n\",\n      \"sveägu freing wis anäwious das0kna-anca_ige wioled äa reäwariit es9ym an eiëæs riääsert be teeaéumenämatioissstas riéägiäm noowhäämator\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"ëæs riääsert be teeaéumenämatioissstas riéägiäm noowhäämatorerast inægääwëc\\\"iëwel \\n\",\n      \"dwmreäque of ts as5nanon yämiäarease onoëgäve al nanääqufuléuäventias of äwher ävinäward theé as treint yägo wäwut thdioin äreääägääääwasä\\\" ævelevot og canä(!    noe6, or fe anoäduth eäwilighe or iniëæbävengt wichedävina täw,éy täwiäguls inæ9zit ex=derunäégman mioioplei6ëfflth areh[r6\\n\",\n      \"thäwicn hjust nouloämäfre ofuääwlitiasumtoo ) =thek h: foosäbnea0oenle howiun fienitieädelä\\n\",\n      \"------ temperature: 1.2\\n\",\n      \"wlitiasumtoo ) =thek h: foosäbnea0oenle howiun fienitieädeläpateémaäveléuräreminge of esëuæs älachsee cäwledäæbli9beräyt listue, äaäwariäaäof treastalotinom lit ditéënatoras te cunrtshäme forädämaäëräwäwëwartic säd dist nëree choaeäalne stinisce[ng äacari] bs[zitareatorémotieiæp beääblæcämotaääägaton, yhe neasey reoe9=warion fic5in\\n\",\n      \"to\\n\",\n      \"ha e eaéutarssæb phiäävuletiämual \\n\",\n      \"    the (erlas7weinä ware mis heimohongioné thos witengeein vatoëtaäa anäématioä präväut\\n\",\n      \"epoch 59\\n\",\n      \"Epoch 1/1\\n\",\n      \"200278/200278 [==============================] - 124s - loss: 14.5726   \\n\",\n      \"--- Generating with seed: \\\"nd smugly and ignobly and\\n\",\n      \"incessantly tearing to tatters all\\\"\\n\",\n      \"------ temperature: 0.2\\n\",\n      \"nd smugly and ignobly and\\n\",\n      \"incessantly tearing to tatters alläääwäwääääwääw äbeingn=ämatiorurus4mik8ualämas a r beioeqva3pre äuä theh a diäwiate no arerea8utiäach whas anémaneälys an atämlionä sucortänoeent histäw the nrius aräwe aäwis an9tiint as anämat äwichäwich aäwich an areie ääwiäare, thas anät ad manäwwatäwäwich stäbat vznen tinänalo area0k ther9aäm nus ore oftaäwichäw thdoä äwich an atiengästanes_r of äwhäwich manoaeiheäaräp a qwiätämaäämatie hf of \\n\",\n      \"------ temperature: 0.5\\n\",\n      \"stanes_r of äwhäwich manoaeiheäaräp a qwiätämaäämatie hf of moämaké aläward anee ae ëewäaraéutoliual tézatiinlir[thorss aäb there re ngsss no5dz steä at nonääwed thers oundeääarow\\n\",\n      \"apyreä\\n\",\n      \"musunsedonh yreean4zatituon df waäduale hi\\n\",\n      \"dx de, thäwatate thäuta aägation los theoone an al anäd ariosss oä éa aroämg,\\n\",\n      \"äiäbeägy äbääwace äbuthenäd, thapihe: a(lus paton äwiotääarow dvatäbéäpämusinoämuse ihsé nääwicnäledynann mane ue\\\"rea[qwecs ulinsshé sunemices oéblinizh\\n\",\n      \"------ temperature: 1.0\\n\",\n      \"wicnäledynann mane ue\\\"rea[qwecs ulinsshé sunemices oéblinizh. of toesäteliqualianohen\\n\",\n      \"waräiglian lenieas ureee nerycai an faquseimém éäbäwhägarer ta isé æ6le haäwa al ranäm ä.\\n\",\n      \"cherod\\n\",\n      \"as wo bedäpearaädäplan äéysäred. th\\\" æarämisunenébli6libho eäinde stoéel é eéatiofes--aleeetg éytämatiägäutäplatocn sta4pwo lozat me in7mon isgæke \\n\",\n      \"\\n\",\n      \"l nowäck oiori3æ\\n\",\n      \"sto inæeéot an to at oé läwich pule [aäty ahtoämatiant ädirisaeä busé n0éhdored si an e ouämnosäeéa aäägnedtor \\n\",\n      \"------ temperature: 1.2\\n\",\n      \"ant ädirisaeä busé n0éhdored si an e ouämnosäeéa aäägnedtor säundablé an atieäute an a an ähe6 hove th: teétos of äusicse hfe ndanertäcb inägr[seeä\\n\",\n      \"b5a th(oo ffeor9s is reägét a te oosézrediodäy perteeéäiduneeäisulteäuthäwsn nofe!\\n\",\n      \"l vimacæ i   nooriutoes st3k lioféere of enée inëäanyss co[axtin9ewingienhoss ou eea6d n9uof ou\\n\",\n      \"thalä buämä meeeriävé\\n\",\n      \"wouéäducoéwirasn an tin a0zein man ie =thri:cl g iäwhs oat aiäl lonstaäuctämreneé\\n\",\n      \"änate éitaws o s9hirior stioä\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import random\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"for epoch in range(1, 60):\\n\",\n    \"    print('epoch', epoch)\\n\",\n    \"    # Fit the model for 1 epoch on the available training data\\n\",\n    \"    model.fit(x, y,\\n\",\n    \"              batch_size=128,\\n\",\n    \"              epochs=1)\\n\",\n    \"\\n\",\n    \"    # Select a text seed at random\\n\",\n    \"    start_index = random.randint(0, len(text) - maxlen - 1)\\n\",\n    \"    generated_text = text[start_index: start_index + maxlen]\\n\",\n    \"    print('--- Generating with seed: \\\"' + generated_text + '\\\"')\\n\",\n    \"\\n\",\n    \"    for temperature in [0.2, 0.5, 1.0, 1.2]:\\n\",\n    \"        print('------ temperature:', temperature)\\n\",\n    \"        sys.stdout.write(generated_text)\\n\",\n    \"\\n\",\n    \"        # We generate 400 characters\\n\",\n    \"        for i in range(400):\\n\",\n    \"            sampled = np.zeros((1, maxlen, len(chars)))\\n\",\n    \"            for t, char in enumerate(generated_text):\\n\",\n    \"                sampled[0, t, char_indices[char]] = 1.\\n\",\n    \"\\n\",\n    \"            preds = model.predict(sampled, verbose=0)[0]\\n\",\n    \"            next_index = sample(preds, temperature)\\n\",\n    \"            next_char = chars[next_index]\\n\",\n    \"\\n\",\n    \"            generated_text += next_char\\n\",\n    \"            generated_text = generated_text[1:]\\n\",\n    \"\\n\",\n    \"            sys.stdout.write(next_char)\\n\",\n    \"            sys.stdout.flush()\\n\",\n    \"        print()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"As you can see, a low temperature results in extremely repetitive and predictable text, but where local structure is highly realistic: in \\n\",\n    \"particular, all words (a word being a local pattern of characters) are real English words. With higher temperatures, the generated text \\n\",\n    \"becomes more interesting, surprising, even creative; it may sometimes invent completely new words that sound somewhat plausible (such as \\n\",\n    \"\\\"eterned\\\" or \\\"troveration\\\"). With a high temperature, the local structure starts breaking down and most words look like semi-random strings \\n\",\n    \"of characters. Without a doubt, here 0.5 is the most interesting temperature for text generation in this specific setup. Always experiment \\n\",\n    \"with multiple sampling strategies! A clever balance between learned structure and randomness is what makes generation interesting.\\n\",\n    \"\\n\",\n    \"Note that by training a bigger model, longer, on more data, you can achieve generated samples that will look much more coherent and \\n\",\n    \"realistic than ours. But of course, don't expect to ever generate any meaningful text, other than by random chance: all we are doing is \\n\",\n    \"sampling data from a statistical model of which characters come after which characters. Language is a communication channel, and there is \\n\",\n    \"a distinction between what communications are about, and the statistical structure of the messages in which communications are encoded. To \\n\",\n    \"evidence this distinction, here is a thought experiment: what if human language did a better job at compressing communications, much like \\n\",\n    \"our computers do with most of our digital communications? Then language would be no less meaningful, yet it would lack any intrinsic \\n\",\n    \"statistical structure, thus making it impossible to learn a language model like we just did.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Take aways\\n\",\n    \"\\n\",\n    \"* We can generate discrete sequence data by training a model to predict the next tokens(s) given previous tokens.\\n\",\n    \"* In the case of text, such a model is called a \\\"language model\\\" and could be based on either words or characters.\\n\",\n    \"* Sampling the next token requires balance between adhering to what the model judges likely, and introducing randomness.\\n\",\n    \"* One way to handle this is the notion of _softmax temperature_. Always experiment with different temperatures to find the \\\"right\\\" one.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/8.2-deep-dream.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Deep Dream\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 8, Section 2 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"[...]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing Deep Dream in Keras\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"We will start from a convnet pre-trained on ImageNet. In Keras, we have many such convnets available: VGG16, VGG19, Xception, ResNet50... \\n\",\n    \"albeit the same process is doable with any of these, your convnet of choice will naturally affect your visualizations, since different \\n\",\n    \"convnet architectures result in different learned features. The convnet used in the original Deep Dream release was an Inception model, and \\n\",\n    \"in practice Inception is known to produce very nice-looking Deep Dreams, so we will use the InceptionV3 model that comes with Keras.\\n\"\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      \"Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\\n\",\n      \"80658432/87910968 [==========================>...] - ETA: 0s ETA:  - ETA: \"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras.applications import inception_v3\\n\",\n    \"from keras import backend as K\\n\",\n    \"\\n\",\n    \"# We will not be training our model,\\n\",\n    \"# so we use this command to disable all training-specific operations\\n\",\n    \"K.set_learning_phase(0)\\n\",\n    \"\\n\",\n    \"# Build the InceptionV3 network.\\n\",\n    \"# The model will be loaded with pre-trained ImageNet weights.\\n\",\n    \"model = inception_v3.InceptionV3(weights='imagenet',\\n\",\n    \"                                 include_top=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Next, we compute the \\\"loss\\\", the quantity that we will seek to maximize during the gradient ascent process. In Chapter 5, for filter \\n\",\n    \"visualization, we were trying to maximize the value of a specific filter in a specific layer. Here we will simultaneously maximize the \\n\",\n    \"activation of all filters in a number of layers. Specifically, we will maximize a weighted sum of the L2 norm of the activations of a \\n\",\n    \"set of high-level layers. The exact set of layers we pick (as well as their contribution to the final loss) has a large influence on the \\n\",\n    \"visuals that we will be able to produce, so we want to make these parameters easily configurable. Lower layers result in \\n\",\n    \"geometric patterns, while higher layers result in visuals in which you can recognize some classes from ImageNet (e.g. birds or dogs).\\n\",\n    \"We'll start from a somewhat arbitrary configuration involving four layers -- \\n\",\n    \"but you will definitely want to explore many different configurations later on:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Dict mapping layer names to a coefficient\\n\",\n    \"# quantifying how much the layer's activation\\n\",\n    \"# will contribute to the loss we will seek to maximize.\\n\",\n    \"# Note that these are layer names as they appear\\n\",\n    \"# in the built-in InceptionV3 application.\\n\",\n    \"# You can list all layer names using `model.summary()`.\\n\",\n    \"layer_contributions = {\\n\",\n    \"    'mixed2': 0.2,\\n\",\n    \"    'mixed3': 3.,\\n\",\n    \"    'mixed4': 2.,\\n\",\n    \"    'mixed5': 1.5,\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's define a tensor that contains our loss, i.e. the weighted sum of the L2 norm of the activations of the layers listed above.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Get the symbolic outputs of each \\\"key\\\" layer (we gave them unique names).\\n\",\n    \"layer_dict = dict([(layer.name, layer) for layer in model.layers])\\n\",\n    \"\\n\",\n    \"# Define the loss.\\n\",\n    \"loss = K.variable(0.)\\n\",\n    \"for layer_name in layer_contributions:\\n\",\n    \"    # Add the L2 norm of the features of a layer to the loss.\\n\",\n    \"    coeff = layer_contributions[layer_name]\\n\",\n    \"    activation = layer_dict[layer_name].output\\n\",\n    \"\\n\",\n    \"    # We avoid border artifacts by only involving non-border pixels in the loss.\\n\",\n    \"    scaling = K.prod(K.cast(K.shape(activation), 'float32'))\\n\",\n    \"    loss += coeff * K.sum(K.square(activation[:, 2: -2, 2: -2, :])) / scaling\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can set up the gradient ascent process:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# This holds our generated image\\n\",\n    \"dream = model.input\\n\",\n    \"\\n\",\n    \"# Compute the gradients of the dream with regard to the loss.\\n\",\n    \"grads = K.gradients(loss, dream)[0]\\n\",\n    \"\\n\",\n    \"# Normalize gradients.\\n\",\n    \"grads /= K.maximum(K.mean(K.abs(grads)), 1e-7)\\n\",\n    \"\\n\",\n    \"# Set up function to retrieve the value\\n\",\n    \"# of the loss and gradients given an input image.\\n\",\n    \"outputs = [loss, grads]\\n\",\n    \"fetch_loss_and_grads = K.function([dream], outputs)\\n\",\n    \"\\n\",\n    \"def eval_loss_and_grads(x):\\n\",\n    \"    outs = fetch_loss_and_grads([x])\\n\",\n    \"    loss_value = outs[0]\\n\",\n    \"    grad_values = outs[1]\\n\",\n    \"    return loss_value, grad_values\\n\",\n    \"\\n\",\n    \"def gradient_ascent(x, iterations, step, max_loss=None):\\n\",\n    \"    for i in range(iterations):\\n\",\n    \"        loss_value, grad_values = eval_loss_and_grads(x)\\n\",\n    \"        if max_loss is not None and loss_value > max_loss:\\n\",\n    \"            break\\n\",\n    \"        print('...Loss value at', i, ':', loss_value)\\n\",\n    \"        x += step * grad_values\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Finally, here is the actual Deep Dream algorithm.\\n\",\n    \"\\n\",\n    \"First, we define a list of \\\"scales\\\" (also called \\\"octaves\\\") at which we will process the images. Each successive scale is larger than \\n\",\n    \"previous one by a factor 1.4 (i.e. 40% larger): we start by processing a small image and we increasingly upscale it:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![deep dream process](https://s3.amazonaws.com/book.keras.io/img/ch8/deepdream_process.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Then, for each successive scale, from the smallest to the largest, we run gradient ascent to maximize the loss we have previously defined, \\n\",\n    \"at that scale. After each gradient ascent run, we upscale the resulting image by 40%.\\n\",\n    \"\\n\",\n    \"To avoid losing a lot of image detail after each successive upscaling (resulting in increasingly blurry or pixelated images), we leverage a \\n\",\n    \"simple trick: after each upscaling, we reinject the lost details back into the image, which is possible since we know what the original \\n\",\n    \"image should look like at the larger scale. Given a small image S and a larger image size L, we can compute the difference between the \\n\",\n    \"original image (assumed larger than L) resized to size L and the original resized to size S -- this difference quantifies the details lost \\n\",\n    \"when going from S to L.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The code above below leverages the following straightforward auxiliary Numpy functions, which all do just as their name suggests. They \\n\",\n    \"require to have SciPy installed.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import scipy\\n\",\n    \"from keras.preprocessing import image\\n\",\n    \"\\n\",\n    \"def resize_img(img, size):\\n\",\n    \"    img = np.copy(img)\\n\",\n    \"    factors = (1,\\n\",\n    \"               float(size[0]) / img.shape[1],\\n\",\n    \"               float(size[1]) / img.shape[2],\\n\",\n    \"               1)\\n\",\n    \"    return scipy.ndimage.zoom(img, factors, order=1)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def save_img(img, fname):\\n\",\n    \"    pil_img = deprocess_image(np.copy(img))\\n\",\n    \"    scipy.misc.imsave(fname, pil_img)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def preprocess_image(image_path):\\n\",\n    \"    # Util function to open, resize and format pictures\\n\",\n    \"    # into appropriate tensors.\\n\",\n    \"    img = image.load_img(image_path)\\n\",\n    \"    img = image.img_to_array(img)\\n\",\n    \"    img = np.expand_dims(img, axis=0)\\n\",\n    \"    img = inception_v3.preprocess_input(img)\\n\",\n    \"    return img\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def deprocess_image(x):\\n\",\n    \"    # Util function to convert a tensor into a valid image.\\n\",\n    \"    if K.image_data_format() == 'channels_first':\\n\",\n    \"        x = x.reshape((3, x.shape[2], x.shape[3]))\\n\",\n    \"        x = x.transpose((1, 2, 0))\\n\",\n    \"    else:\\n\",\n    \"        x = x.reshape((x.shape[1], x.shape[2], 3))\\n\",\n    \"    x /= 2.\\n\",\n    \"    x += 0.5\\n\",\n    \"    x *= 255.\\n\",\n    \"    x = np.clip(x, 0, 255).astype('uint8')\\n\",\n    \"    return x\"\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      \"Processing image shape (178, 178)\\n\",\n      \"..Loss value at 0 : 0.671784\\n\",\n      \"..Loss value at 1 : 1.08265\\n\",\n      \"..Loss value at 2 : 1.54404\\n\",\n      \"..Loss value at 3 : 2.06391\\n\",\n      \"..Loss value at 4 : 2.60002\\n\",\n      \"..Loss value at 5 : 3.1742\\n\",\n      \"..Loss value at 6 : 3.67622\\n\",\n      \"..Loss value at 7 : 4.2676\\n\",\n      \"..Loss value at 8 : 4.81796\\n\",\n      \"..Loss value at 9 : 5.26686\\n\",\n      \"..Loss value at 10 : 5.84397\\n\",\n      \"..Loss value at 11 : 6.30927\\n\",\n      \"..Loss value at 12 : 6.71333\\n\",\n      \"..Loss value at 13 : 7.28859\\n\",\n      \"..Loss value at 14 : 7.78225\\n\",\n      \"..Loss value at 15 : 8.23158\\n\",\n      \"..Loss value at 16 : 8.69093\\n\",\n      \"..Loss value at 17 : 9.17363\\n\",\n      \"..Loss value at 18 : 9.52095\\n\",\n      \"..Loss value at 19 : 9.94124\\n\",\n      \"Processing image shape (250, 250)\\n\",\n      \"..Loss value at 0 : 2.17672\\n\",\n      \"..Loss value at 1 : 3.44289\\n\",\n      \"..Loss value at 2 : 4.49521\\n\",\n      \"..Loss value at 3 : 5.33331\\n\",\n      \"..Loss value at 4 : 6.17848\\n\",\n      \"..Loss value at 5 : 6.95403\\n\",\n      \"..Loss value at 6 : 7.6694\\n\",\n      \"..Loss value at 7 : 8.27542\\n\",\n      \"..Loss value at 8 : 8.94549\\n\",\n      \"..Loss value at 9 : 9.60977\\n\",\n      \"Processing image shape (350, 350)\\n\",\n      \"..Loss value at 0 : 2.31222\\n\",\n      \"..Loss value at 1 : 3.5261\\n\",\n      \"..Loss value at 2 : 4.66823\\n\",\n      \"..Loss value at 3 : 5.76891\\n\",\n      \"..Loss value at 4 : 7.02546\\n\",\n      \"..Loss value at 5 : 8.36348\\n\",\n      \"..Loss value at 6 : 9.9852\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# Playing with these hyperparameters will also allow you to achieve new effects\\n\",\n    \"\\n\",\n    \"step = 0.01  # Gradient ascent step size\\n\",\n    \"num_octave = 3  # Number of scales at which to run gradient ascent\\n\",\n    \"octave_scale = 1.4  # Size ratio between scales\\n\",\n    \"iterations = 20  # Number of ascent steps per scale\\n\",\n    \"\\n\",\n    \"# If our loss gets larger than 10,\\n\",\n    \"# we will interrupt the gradient ascent process, to avoid ugly artifacts\\n\",\n    \"max_loss = 10.\\n\",\n    \"\\n\",\n    \"# Fill this to the path to the image you want to use\\n\",\n    \"base_image_path = '/home/ubuntu/data/original_photo_deep_dream.jpg'\\n\",\n    \"\\n\",\n    \"# Load the image into a Numpy array\\n\",\n    \"img = preprocess_image(base_image_path)\\n\",\n    \"\\n\",\n    \"# We prepare a list of shape tuples\\n\",\n    \"# defining the different scales at which we will run gradient ascent\\n\",\n    \"original_shape = img.shape[1:3]\\n\",\n    \"successive_shapes = [original_shape]\\n\",\n    \"for i in range(1, num_octave):\\n\",\n    \"    shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])\\n\",\n    \"    successive_shapes.append(shape)\\n\",\n    \"\\n\",\n    \"# Reverse list of shapes, so that they are in increasing order\\n\",\n    \"successive_shapes = successive_shapes[::-1]\\n\",\n    \"\\n\",\n    \"# Resize the Numpy array of the image to our smallest scale\\n\",\n    \"original_img = np.copy(img)\\n\",\n    \"shrunk_original_img = resize_img(img, successive_shapes[0])\\n\",\n    \"\\n\",\n    \"for shape in successive_shapes:\\n\",\n    \"    print('Processing image shape', shape)\\n\",\n    \"    img = resize_img(img, shape)\\n\",\n    \"    img = gradient_ascent(img,\\n\",\n    \"                          iterations=iterations,\\n\",\n    \"                          step=step,\\n\",\n    \"                          max_loss=max_loss)\\n\",\n    \"    upscaled_shrunk_original_img = resize_img(shrunk_original_img, shape)\\n\",\n    \"    same_size_original = resize_img(original_img, shape)\\n\",\n    \"    lost_detail = same_size_original - upscaled_shrunk_original_img\\n\",\n    \"\\n\",\n    \"    img += lost_detail\\n\",\n    \"    shrunk_original_img = resize_img(original_img, shape)\\n\",\n    \"    save_img(img, fname='dream_at_scale_' + str(shape) + '.png')\\n\",\n    \"\\n\",\n    \"save_img(img, fname='final_dream.png')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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igLPrImx1I9URVi64RnhgrNADEiGAK4EANEEwKKNxhCUWP3PGzPmvbe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\\nsiOkF99jAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb3138eefd0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.imshow(deprocess_image(np.copy(img)))\\n\",\n    \"plt.show()\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/8.3-neural-style-transfer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Neural style transfer\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"This notebook contains the code samples found in Chapter 8, Section 3 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"----\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Besides Deep Dream, another major development in deep learning-driven image modification that happened in the summer of 2015 is neural \\n\",\n    \"style transfer, introduced by Leon Gatys et al. The neural style transfer algorithm has undergone many refinements and spawned many \\n\",\n    \"variations since its original introduction, including a viral smartphone app, called Prisma. For simplicity, this section focuses on the \\n\",\n    \"formulation described in the original paper.\\n\",\n    \"\\n\",\n    \"Neural style transfer consists in applying the \\\"style\\\" of a reference image to a target image, while conserving the \\\"content\\\" of the target \\n\",\n    \"image:\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"![style transfer](https://s3.amazonaws.com/book.keras.io/img/ch8/style_transfer.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"What is meant by \\\"style\\\" is essentially textures, colors, and visual patterns in the image, at various spatial scales, while the \\\"content\\\" \\n\",\n    \"is the higher-level macrostructure of the image. For instance, blue-and-yellow circular brush strokes are considered to be the \\\"style\\\" in \\n\",\n    \"the above example using Starry Night by Van Gogh, while the buildings in the Tuebingen photograph are considered to be the \\\"content\\\".\\n\",\n    \"\\n\",\n    \"The idea of style transfer, tightly related to that of texture generation, has had a long history in the image processing community prior \\n\",\n    \"to the development of neural style transfer in 2015. However, as it turned out, the deep learning-based implementations of style transfer \\n\",\n    \"offered results unparalleled by what could be previously achieved with classical computer vision techniques, and triggered an amazing \\n\",\n    \"renaissance in creative applications of computer vision.\\n\",\n    \"\\n\",\n    \"The key notion behind implementing style transfer is same idea that is central to all deep learning algorithms: we define a loss function \\n\",\n    \"to specify what we want to achieve, and we minimize this loss. We know what we want to achieve: conserve the \\\"content\\\" of the original image, \\n\",\n    \"while adopting the \\\"style\\\" of the reference image. If we were able to mathematically define content and style, then an appropriate loss \\n\",\n    \"function to minimize would be the following:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"```\\n\",\n    \"loss = distance(style(reference_image) - style(generated_image)) +\\n\",\n    \"       distance(content(original_image) - content(generated_image))\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Where `distance` is a norm function such as the L2 norm, `content` is a function that takes an image and computes a representation of its \\n\",\n    \"\\\"content\\\", and `style` is a function that takes an image and computes a representation of its \\\"style\\\".\\n\",\n    \"\\n\",\n    \"Minimizing this loss would cause `style(generated_image)` to be close to `style(reference_image)`, while `content(generated_image)` would \\n\",\n    \"be close to `content(generated_image)`, thus achieving style transfer as we defined it.\\n\",\n    \"\\n\",\n    \"A fundamental observation made by Gatys et al is that deep convolutional neural networks offer precisely a way to mathematically defined \\n\",\n    \"the `style` and `content` functions. Let's see how.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The content loss\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"As you already know, activations from earlier layers in a network contain _local_ information about the image, while activations from \\n\",\n    \"higher layers contain increasingly _global_ and _abstract_ information. Formulated in a different way, the activations of the different \\n\",\n    \"layers of a convnet provide a decomposition of the contents of an image over different spatial scales. Therefore we expect the \\\"content\\\" of \\n\",\n    \"an image, which is more global and more abstract, to be captured by the representations of a top layer of a convnet.\\n\",\n    \"\\n\",\n    \"A good candidate for a content loss would thus be to consider a pre-trained convnet, and define as our loss the L2 norm between the \\n\",\n    \"activations of a top layer computed over the target image and the activations of the same layer computed over the generated image. This \\n\",\n    \"would guarantee that, as seen from the top layer of the convnet, the generated image will \\\"look similar\\\" to the original target image. \\n\",\n    \"Assuming that what the top layers of a convnet see is really the \\\"content\\\" of their input images, then this does work as a way to preserve \\n\",\n    \"image content.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The style loss\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"While the content loss only leverages a single higher-up layer, the style loss as defined in the Gatys et al. paper leverages multiple \\n\",\n    \"layers of a convnet: we aim at capturing the appearance of the style reference image at all spatial scales extracted by the convnet, not \\n\",\n    \"just any single scale.\\n\",\n    \"\\n\",\n    \"For the style loss, the Gatys et al. paper leverages the \\\"Gram matrix\\\" of a layer's activations, i.e. the inner product between the feature maps \\n\",\n    \"of a given layer. This inner product can be understood as representing a map of the correlations between the features of a layer. These \\n\",\n    \"feature correlations capture the statistics of the patterns of a particular spatial scale, which empirically corresponds to the appearance \\n\",\n    \"of the textures found at this scale.\\n\",\n    \"\\n\",\n    \"Hence the style loss aims at preserving similar internal correlations within the activations of different layers, across the style \\n\",\n    \"reference image and the generated image. In turn, this guarantees that the textures found at different spatial scales will look similar \\n\",\n    \"across the style reference image and the generated image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## In short\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"In short, we can use a pre-trained convnet to define a loss that will:\\n\",\n    \"\\n\",\n    \"* Preserve content by maintaining similar high-level layer activations between the target content image and the generated image. The \\n\",\n    \"convnet should \\\"see\\\" both the target image and the generated image as \\\"containing the same things\\\".\\n\",\n    \"* Preserve style by maintaining similar _correlations_ within activations for both low-level layers and high-level layers. Indeed, feature \\n\",\n    \"correlations capture _textures_: the generated and the style reference image should share the same textures at different spatial scales.\\n\",\n    \"\\n\",\n    \"Now let's take a look at a Keras implementation of the original 2015 neural style transfer algorithm. As you will see, it shares a lot of \\n\",\n    \"similarities with the Deep Dream implementation we developed in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Neural style transfer in Keras\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Neural style transfer can be implemented using any pre-trained convnet. Here we will use the VGG19 network, used by Gatys et al in their paper. \\n\",\n    \"VGG19 is a simple variant of the VGG16 network we introduced in Chapter 5, with three more convolutional layers.\\n\",\n    \"\\n\",\n    \"This is our general process:\\n\",\n    \"\\n\",\n    \"* Set up a network that will compute VGG19 layer activations for the style reference image, the target image, and the generated image at \\n\",\n    \"the same time.\\n\",\n    \"* Use the layer activations computed over these three images to define the loss function described above, which we will minimize in order \\n\",\n    \"to achieve style transfer.\\n\",\n    \"* Set up a gradient descent process to minimize this loss function.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Let's start by defining the paths to the two images we consider: the style reference image and the target image. To make sure that all \\n\",\n    \"images processed share similar sizes (widely different sizes would make style transfer more difficult), we will later resize them all to a \\n\",\n    \"shared height of 400px.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras.preprocessing.image import load_img, img_to_array\\n\",\n    \"\\n\",\n    \"# This is the path to the image you want to transform.\\n\",\n    \"target_image_path = '/home/ubuntu/data/portrait.png'\\n\",\n    \"# This is the path to the style image.\\n\",\n    \"style_reference_image_path = '/home/ubuntu/data/popova.jpg'\\n\",\n    \"\\n\",\n    \"# Dimensions of the generated picture.\\n\",\n    \"width, height = load_img(target_image_path).size\\n\",\n    \"img_height = 400\\n\",\n    \"img_width = int(width * img_height / height)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"We will need some auxiliary functions for loading, pre-processing and post-processing the images that will go in and out of the VGG19 \\n\",\n    \"convnet:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from keras.applications import vgg19\\n\",\n    \"\\n\",\n    \"def preprocess_image(image_path):\\n\",\n    \"    img = load_img(image_path, target_size=(img_height, img_width))\\n\",\n    \"    img = img_to_array(img)\\n\",\n    \"    img = np.expand_dims(img, axis=0)\\n\",\n    \"    img = vgg19.preprocess_input(img)\\n\",\n    \"    return img\\n\",\n    \"\\n\",\n    \"def deprocess_image(x):\\n\",\n    \"    # Remove zero-center by mean pixel\\n\",\n    \"    x[:, :, 0] += 103.939\\n\",\n    \"    x[:, :, 1] += 116.779\\n\",\n    \"    x[:, :, 2] += 123.68\\n\",\n    \"    # 'BGR'->'RGB'\\n\",\n    \"    x = x[:, :, ::-1]\\n\",\n    \"    x = np.clip(x, 0, 255).astype('uint8')\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Let's set up the VGG19 network. It takes as input a batch of three images: the style reference image, the target image, and a placeholder \\n\",\n    \"that will contain the generated image. A placeholder is simply a symbolic tensor, the values of which are provided externally via Numpy \\n\",\n    \"arrays. The style reference and target image are static, and thus defined using `K.constant`, while the values contained in the placeholder \\n\",\n    \"of the generated image will change over time.\"\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      \"Model loaded.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from keras import backend as K\\n\",\n    \"\\n\",\n    \"target_image = K.constant(preprocess_image(target_image_path))\\n\",\n    \"style_reference_image = K.constant(preprocess_image(style_reference_image_path))\\n\",\n    \"\\n\",\n    \"# This placeholder will contain our generated image\\n\",\n    \"combination_image = K.placeholder((1, img_height, img_width, 3))\\n\",\n    \"\\n\",\n    \"# We combine the 3 images into a single batch\\n\",\n    \"input_tensor = K.concatenate([target_image,\\n\",\n    \"                              style_reference_image,\\n\",\n    \"                              combination_image], axis=0)\\n\",\n    \"\\n\",\n    \"# We build the VGG19 network with our batch of 3 images as input.\\n\",\n    \"# The model will be loaded with pre-trained ImageNet weights.\\n\",\n    \"model = vgg19.VGG19(input_tensor=input_tensor,\\n\",\n    \"                    weights='imagenet',\\n\",\n    \"                    include_top=False)\\n\",\n    \"print('Model loaded.')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Let's define the content loss, meant to make sure that the top layer of the VGG19 convnet will have a similar view of the target image and \\n\",\n    \"the generated image:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def content_loss(base, combination):\\n\",\n    \"    return K.sum(K.square(combination - base))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Now, here's the style loss. It leverages an auxiliary function to compute the Gram matrix of an input matrix, i.e. a map of the correlations \\n\",\n    \"found in the original feature matrix.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def gram_matrix(x):\\n\",\n    \"    features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))\\n\",\n    \"    gram = K.dot(features, K.transpose(features))\\n\",\n    \"    return gram\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def style_loss(style, combination):\\n\",\n    \"    S = gram_matrix(style)\\n\",\n    \"    C = gram_matrix(combination)\\n\",\n    \"    channels = 3\\n\",\n    \"    size = img_height * img_width\\n\",\n    \"    return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"To these two loss components, we add a third one, the \\\"total variation loss\\\". It is meant to encourage spatial continuity in the generated \\n\",\n    \"image, thus avoiding overly pixelated results. You could interpret it as a regularization loss.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def total_variation_loss(x):\\n\",\n    \"    a = K.square(\\n\",\n    \"        x[:, :img_height - 1, :img_width - 1, :] - x[:, 1:, :img_width - 1, :])\\n\",\n    \"    b = K.square(\\n\",\n    \"        x[:, :img_height - 1, :img_width - 1, :] - x[:, :img_height - 1, 1:, :])\\n\",\n    \"    return K.sum(K.pow(a + b, 1.25))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"The loss that we minimize is a weighted average of these three losses. To compute the content loss, we only leverage one top layer, the \\n\",\n    \"`block5_conv2` layer, while for the style loss we use a list of layers than spans both low-level and high-level layers. We add the total \\n\",\n    \"variation loss at the end.\\n\",\n    \"\\n\",\n    \"Depending on the style reference image and content image you are using, you will likely want to tune the `content_weight` coefficient, the \\n\",\n    \"contribution of the content loss to the total loss. A higher `content_weight` means that the target content will be more recognizable in \\n\",\n    \"the generated image.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Dict mapping layer names to activation tensors\\n\",\n    \"outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])\\n\",\n    \"# Name of layer used for content loss\\n\",\n    \"content_layer = 'block5_conv2'\\n\",\n    \"# Name of layers used for style loss\\n\",\n    \"style_layers = ['block1_conv1',\\n\",\n    \"                'block2_conv1',\\n\",\n    \"                'block3_conv1',\\n\",\n    \"                'block4_conv1',\\n\",\n    \"                'block5_conv1']\\n\",\n    \"# Weights in the weighted average of the loss components\\n\",\n    \"total_variation_weight = 1e-4\\n\",\n    \"style_weight = 1.\\n\",\n    \"content_weight = 0.025\\n\",\n    \"\\n\",\n    \"# Define the loss by adding all components to a `loss` variable\\n\",\n    \"loss = K.variable(0.)\\n\",\n    \"layer_features = outputs_dict[content_layer]\\n\",\n    \"target_image_features = layer_features[0, :, :, :]\\n\",\n    \"combination_features = layer_features[2, :, :, :]\\n\",\n    \"loss += content_weight * content_loss(target_image_features,\\n\",\n    \"                                      combination_features)\\n\",\n    \"for layer_name in style_layers:\\n\",\n    \"    layer_features = outputs_dict[layer_name]\\n\",\n    \"    style_reference_features = layer_features[1, :, :, :]\\n\",\n    \"    combination_features = layer_features[2, :, :, :]\\n\",\n    \"    sl = style_loss(style_reference_features, combination_features)\\n\",\n    \"    loss += (style_weight / len(style_layers)) * sl\\n\",\n    \"loss += total_variation_weight * total_variation_loss(combination_image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Finally, we set up the gradient descent process. In the original Gatys et al. paper, optimization is performed using the L-BFGS algorithm, \\n\",\n    \"so that is also what we will use here. This is a key difference from the Deep Dream example in the previous section. The L-BFGS algorithms \\n\",\n    \"comes packaged with SciPy. However, there are two slight limitations with the SciPy implementation:\\n\",\n    \"\\n\",\n    \"* It requires to be passed the value of the loss function and the value of the gradients as two separate functions.\\n\",\n    \"* It can only be applied to flat vectors, whereas we have a 3D image array.\\n\",\n    \"\\n\",\n    \"It would be very inefficient for us to compute the value of the loss function and the value of gradients independently, since it would lead \\n\",\n    \"to a lot of redundant computation between the two. We would be almost twice slower than we could be by computing them jointly. To by-pass \\n\",\n    \"this, we set up a Python class named `Evaluator` that will compute both loss value and gradients value at once, will return the loss value \\n\",\n    \"when called the first time, and will cache the gradients for the next call.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Get the gradients of the generated image wrt the loss\\n\",\n    \"grads = K.gradients(loss, combination_image)[0]\\n\",\n    \"\\n\",\n    \"# Function to fetch the values of the current loss and the current gradients\\n\",\n    \"fetch_loss_and_grads = K.function([combination_image], [loss, grads])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Evaluator(object):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        self.loss_value = None\\n\",\n    \"        self.grads_values = None\\n\",\n    \"\\n\",\n    \"    def loss(self, x):\\n\",\n    \"        assert self.loss_value is None\\n\",\n    \"        x = x.reshape((1, img_height, img_width, 3))\\n\",\n    \"        outs = fetch_loss_and_grads([x])\\n\",\n    \"        loss_value = outs[0]\\n\",\n    \"        grad_values = outs[1].flatten().astype('float64')\\n\",\n    \"        self.loss_value = loss_value\\n\",\n    \"        self.grad_values = grad_values\\n\",\n    \"        return self.loss_value\\n\",\n    \"\\n\",\n    \"    def grads(self, x):\\n\",\n    \"        assert self.loss_value is not None\\n\",\n    \"        grad_values = np.copy(self.grad_values)\\n\",\n    \"        self.loss_value = None\\n\",\n    \"        self.grad_values = None\\n\",\n    \"        return grad_values\\n\",\n    \"\\n\",\n    \"evaluator = Evaluator()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Finally, we can run the gradient ascent process using SciPy's L-BFGS algorithm, saving the current generated image at each iteration of the \\n\",\n    \"algorithm (here, a single iteration represents 20 steps of gradient ascent):\"\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      \"Start of iteration 0\\n\",\n      \"Current loss value: 7.40874e+09\\n\",\n      \"Image saved as style_transfer_result_at_iteration_0.png\\n\",\n      \"Iteration 0 completed in 15s\\n\",\n      \"Start of iteration 1\\n\",\n      \"Current loss value: 3.0653e+09\\n\",\n      \"Image saved as style_transfer_result_at_iteration_1.png\\n\",\n      \"Iteration 1 completed in 12s\\n\",\n      \"Start of iteration 2\\n\",\n      \"Current loss value: 2.16951e+09\\n\",\n      \"Image saved as style_transfer_result_at_iteration_2.png\\n\",\n      \"Iteration 2 completed in 12s\\n\",\n      \"Start of iteration 3\\n\",\n      \"Current loss value: 1.60554e+09\\n\",\n      \"Image saved as style_transfer_result_at_iteration_3.png\\n\",\n      \"Iteration 3 completed in 12s\\n\",\n      \"Start of iteration 4\\n\",\n      \"Current loss value: 1.31395e+09\\n\",\n      \"Image saved as style_transfer_result_at_iteration_4.png\\n\",\n      \"Iteration 4 completed in 12s\\n\",\n      \"Start of iteration 5\\n\",\n      \"Current loss value: 1.12635e+09\\n\",\n      \"Image saved as style_transfer_result_at_iteration_5.png\\n\",\n      \"Iteration 5 completed in 12s\\n\",\n      \"Start of iteration 6\\n\",\n      \"Current loss value: 1.01115e+09\\n\",\n      \"Image saved as style_transfer_result_at_iteration_6.png\\n\",\n      \"Iteration 6 completed in 12s\\n\",\n      \"Start of iteration 7\\n\",\n      \"Current loss value: 9.30111e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_7.png\\n\",\n      \"Iteration 7 completed in 12s\\n\",\n      \"Start of iteration 8\\n\",\n      \"Current loss value: 8.63746e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_8.png\\n\",\n      \"Iteration 8 completed in 12s\\n\",\n      \"Start of iteration 9\\n\",\n      \"Current loss value: 8.02952e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_9.png\\n\",\n      \"Iteration 9 completed in 13s\\n\",\n      \"Start of iteration 10\\n\",\n      \"Current loss value: 7.47047e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_10.png\\n\",\n      \"Iteration 10 completed in 13s\\n\",\n      \"Start of iteration 11\\n\",\n      \"Current loss value: 7.06082e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_11.png\\n\",\n      \"Iteration 11 completed in 13s\\n\",\n      \"Start of iteration 12\\n\",\n      \"Current loss value: 6.69204e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_12.png\\n\",\n      \"Iteration 12 completed in 13s\\n\",\n      \"Start of iteration 13\\n\",\n      \"Current loss value: 6.37783e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_13.png\\n\",\n      \"Iteration 13 completed in 13s\\n\",\n      \"Start of iteration 14\\n\",\n      \"Current loss value: 6.11595e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_14.png\\n\",\n      \"Iteration 14 completed in 12s\\n\",\n      \"Start of iteration 15\\n\",\n      \"Current loss value: 5.79567e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_15.png\\n\",\n      \"Iteration 15 completed in 13s\\n\",\n      \"Start of iteration 16\\n\",\n      \"Current loss value: 5.53718e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_16.png\\n\",\n      \"Iteration 16 completed in 13s\\n\",\n      \"Start of iteration 17\\n\",\n      \"Current loss value: 5.33935e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_17.png\\n\",\n      \"Iteration 17 completed in 13s\\n\",\n      \"Start of iteration 18\\n\",\n      \"Current loss value: 5.1677e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_18.png\\n\",\n      \"Iteration 18 completed in 12s\\n\",\n      \"Start of iteration 19\\n\",\n      \"Current loss value: 5.00414e+08\\n\",\n      \"Image saved as style_transfer_result_at_iteration_19.png\\n\",\n      \"Iteration 19 completed in 13s\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy.optimize import fmin_l_bfgs_b\\n\",\n    \"from scipy.misc import imsave\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"result_prefix = 'style_transfer_result'\\n\",\n    \"iterations = 20\\n\",\n    \"\\n\",\n    \"# Run scipy-based optimization (L-BFGS) over the pixels of the generated image\\n\",\n    \"# so as to minimize the neural style loss.\\n\",\n    \"# This is our initial state: the target image.\\n\",\n    \"# Note that `scipy.optimize.fmin_l_bfgs_b` can only process flat vectors.\\n\",\n    \"x = preprocess_image(target_image_path)\\n\",\n    \"x = x.flatten()\\n\",\n    \"for i in range(iterations):\\n\",\n    \"    print('Start of iteration', i)\\n\",\n    \"    start_time = time.time()\\n\",\n    \"    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x,\\n\",\n    \"                                     fprime=evaluator.grads, maxfun=20)\\n\",\n    \"    print('Current loss value:', min_val)\\n\",\n    \"    # Save current generated image\\n\",\n    \"    img = x.copy().reshape((img_height, img_width, 3))\\n\",\n    \"    img = deprocess_image(img)\\n\",\n    \"    fname = result_prefix + '_at_iteration_%d.png' % i\\n\",\n    \"    imsave(fname, img)\\n\",\n    \"    end_time = time.time()\\n\",\n    \"    print('Image saved as', fname)\\n\",\n    \"    print('Iteration %d completed in %ds' % (i, end_time - start_time))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here's what we get:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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EpRKCiTCFnqWZ5eVuLUGpgkwnZ8pKXAGPIsoyhK0AbPl+RJSpEk5KGD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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f06c5919cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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2zduZXTpysOXX493dXzOOMpbEan02EwkIDE+4w071M5+IGd29gR13Hn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\\nfYghzhD8L42dbG2f+j04AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f06c577c7f0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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LrzKifPnXD3xefYv/byl+yD2Y25R716hJEJ3wWUgsxAcPEr7uc3hNkT\\nic2mxqheJbla1ERZKu84vHVIIwT5aARmQC/lOsCD2SNdbhiOJ6giQ6hAYaCwBbQZDMdceX0h4upT\\nnjBqvYWmwW8ryDWhqnuC+Y5U14R1B9sETcTNA6ly4DrCMrE5BShBFMSmffIuvvaMJzMuzi9QZUso\\nNnRlzdPy9+LsIeNRr1SZSUZZZrv3kaBLKEvI1qjnFbK6ZJqDqNbsDS3XTsaf3ryNh6mhzhKNhjw3\\nMDQMnpMM9g0y5aj9jMndGS0GM1DcmM149dr13QT9bjck7q8eg9dcO5nx+PQDTrslDy4WvHe2hB++\\nBhx9Qp1uSWM60B0Xjx5yfGuPy0cXZNuKE+HYa5bkcc7BqKMc5TuaWXpG2tBrSWvgwe7vs1gBS0gB\\nOzhk4yvK3NF5TysS56szurpj83CKCkc8eusD2EhY70F9AmvD5lGHSkNE67GpYTNfweWG9eNTSJHH\\nj+5z+vghKMni0ZrurCMLE1bnFtIh7TxATLt1H/XHBtm/Q9yDVMD8DDFv0D4nzTWcSng4hmZA++4F\\nLGpYV9AYmD8l6MweEEBqUOMveP8rzCFuAEle5HgXcc7T+g2uClj91ZXzf22ht6cRY0SmSLutYDLC\\n0y/MADdn+9TLBaNRQVutycp9eua19FJ8gZjd5PyP3qQ8yEnCIKOgFg1eCzQtgZrlgzlSWaaHE1Ib\\n2VQXkBKZtSir8OuAj5aGRIwJrRVRCELjEBLy4ZBN09Fut6gkqZqOs7ffASSta1GcEVNkdjCjrh8z\\nO7hGZnvV3u5Ct62vefT4lHIyYbVuKUaAyaET/etgd+/mgFvQXXD8C9/GffAYOZpSSMmk82znCwZ7\\nvf+AoYYIQzOglCV10TGSlrjOiU4SdYM2iepBSxsUcuwwgyEyGHpma4EcS0J7z9iOqRvBdnbO2WrL\\n3/6b/y7f+yzB6odQjMi1gM2KcjimDi1kW+6MBGq9ZjPactG0pPGE0a3nd9S80pI24LekuEXYbEft\\nj5m/XWELzeDaFHTWj8kd3Byxv6fY3G9ojSfUiYgk8xHJmLMHNXv7R5x9+JB8so8SAzK9xZcG10GU\\njiQkJ8+dsL6sGMyO8a3jcHZE03RsL2qyoSHS4aoW3QnchcPaIW61wYzG0JWgFG21JcscuBGIAvYE\\nbM/BR0SesX50jnUFWW7JRhG6JWQRLmqwa/pLZ0DHKb6NlNn1z+7uZ6C5OH2P8XiIElMkGkVNu60Z\\nTIf49A2w2f88iDFSrWqa0RD4ZFESSHiuTw45v3ibaAzZk0NzhRIuH7F3MKKhQSWDcZJR0avznFYs\\n6gaVF2ihObu/xthEnhmquoNWsnUViECeW5KLKJ3oOlDK4BpPlmtWZ2ti1KQEQie0UgijSUgya0lB\\nQXDgAoezKYhEFz0hBYqdxpLpgjs37jxZedeeAQaspmfwK9+CBTKwJXCOORyBK5i0kYm1nD4+5fkr\\nZt9t1GBSIjyUzrJuA/bQ4kMghRxShr5hSVnkdNHw+LzmuViCDzQPz8hv7RHO51RnKy6KDZO9nF9+\\n9Zf4/Z/d+2KCFQ443RFoS2ECZuHRpYOhBAvTF0pkzBjFYxZvBIpZZF0/IleGYZmgWCLUGWzuQBsg\\nbtkblDhlOf/wMQeT67B/JfIt+WBA/q1PlvDw4Qe44FBqgzDw+KJlMjhkU3cMTKQkIJ2iioLJSFEM\\nJagFoz0NWiO1JgGlHfa2vTQQHbHrMHsa7xta15KSZPH4IcOhQThNnhfQdMS0pXGOso00uWRdN5QC\\nQi6Y65bZ1iNk3jOn7+38ddzQPlpzcO06lmvYP5NPN3TrB0z3hhBy2q5PYopdZHzSOyhddH/WJE/w\\njWB2IRXX7x7y07dXvU9jBweMKbm/OeNw/5joOnr75co22zlaZse0H3xANJJOg4uJ5dYzGws4zJh4\\nzeKioo0NdpJRrStWVcV4PCGmgIx9JtfFfI0yug8WJ0ipRknNvGow1qJ0JAKpcUhjSGiUgKgEMXmk\\nSTxeXGLKE9p2y2wwpNhtceBzzmtsdkhvX5b09qPgk0SNjN7Wy8EEcI4mOspiSJF9Xu1rGxgWsKkT\\ng6IgdYHkPNKDQSF0IknN0eGETbdAlYBfk5/sQV2xOt8ihCCfHXAwHCP0Pj/+4f5nfmVLWLyLKtdg\\nh0BJa6dkcYosG8hLKhmoG8e+0KTWEbqcyZFBkNBFRChNxCO3vTM0hUSbCmLscMITgsVKQbO4RPox\\nas+irKMXgsWTXTw5ucP5xQXVdoMdlKyXZ9hCERO0InFx5glW0UmP94mmTpgIyRkkLU4YMlP0ryWL\\n/izJDJkLIKJtibZAVBSjGV1dczmfY+u2V+2lR0nNqp4zOijJD66xOt2wFRmL9ZphmbE4XTKaTohe\\nESVU645br3yHr249B5QdUG88IuUEApkCrQXRdUhjKexXt8S/EcyeSJw93PJXX/s20PtUCz5xhd0c\\nHu5uwStHm6DfMAtEWF5QaEsVOkSKeCEZ5gWoCFuPVjkH165xdVD2Jv9vV+vwPtH5FiUk8+UaY/rU\\n1sHQkqJgYHOqzYZyWCKRn2N06IVZwiJDQooKKQefeSLv16x6raEcDzm73HD/40cc3PwkPt45GBWw\\n7UDn0LUQlCRlmig1KMHldsV+YSkqx7U87/OJVzWX1YpNWLPZLsn2h+h8xOz4+AtW+xHQoaYWKHvf\\nhjRkDEEHZGbwUaGGktlUU2868pQRc4fTEdEqpIsIHWm7SMeUsTAEmZFiwrUtUUCXHCLBSjbYtaN5\\nXHN8c4CIwMHzT60ncrC/D/u9QDraP6QONcVRAaFhm12ybiqKztIuBY2EYqQZTjJQLVYqEi0CSy+K\\nxY62NYI+HRU6kuzPmh1Yrg2O+CSC43raUNCzUWR8uMdYDLlxRwBzzKjh4vSM0uSY5BmNhiS/ROiv\\nFpFITQdCo1C0yYNORJ/wDoTWbLuOEPxXmgu+IQ46IQQv3XmOKzdV8QXPaGPRQtEzfOBJeAXNZlvT\\nRkGIGpLYpTrvvJTDAeQ7ofAp9T/8P10tAksEynyAMBLfRUIbscIwHGSkCFpKNuua1Wr7hYwOvTBT\\nSJpNzWqx2b3T0wj0EYEI3tEtLjFaMszMp56yZmcNC2hD6IcYQdAQZaJqA8NSkSUPTQOuA5lAlphl\\nJFQNbVcjMdSfW+2W3vNcA+f0zjQPTsM2QDYk7AIa0oBWiRQdmVRYJdDGEqMmKpClwiFQondKzdOA\\nJka8CPjkQUu2ybGWgY0SxCjIyHCVh66DdLlb0wa45LMo1O7kqBw9KVjWDbqIdMEjEHTrAMlASHjv\\nEUS62JJQQCIQSVg8Co8iAJGAQF5lxNMzek+X/puCKiiaqOgi+LRh41dAxv3HpwwnI2JKCBJN4xB6\\n8gV0/jwevv82QigSirYDoSJStKjoSCEhkmRgrwTVV8M34mbX2gAZHf2Rsl/wTN0KRAvG7qpYdtIU\\naoazMcuHW7A9UYSXdMFRVy1FLkEYPr8pf448wy9ApjMu5huMhr3pECNgsV7TtR06aZRNlIVhNO4P\\n4BfV2fSrEGgdaaqGy4tTGucZlJpClNhMQWr7syEtvgpMxzm+qnj08DFRK64f9uHKuupN/KZRfa5A\\nq0EFRL3i5GhKqOYoMyWqFmdaTFWzqSrm88ClCVxuG4Lc8uNfvPvU6q7ClY9Z+YeM9R6BDsUAyuvA\\nlG1qGWS9/0BCr44HyEpDDB0pSrQX2EwhlCCElmwIspFsGokqDetqS3F9zOZ0wfHtQ5rLBYvWEQaK\\npKAxhug7Lk4vaMUjXjiS9Axf8+ms7Kfok+9x47bl9KN76MLiRY1OcHEBl+ual759E1fVKDVAZIK6\\nDRRZRkSToiNEyHUJse2DrkKQhGa9bVEWTFIYaxDeU+qyN02IgKXUEdqWshyzbSrKLMd5iZKahx/e\\n4+T283/KiehDxcc3Supq22dEkhNbjxEgo8Ao88nQP0eF+jeC2WOIuO05DGbEUIP6rDoLSvhdKZOE\\neAk+gM05+/CMw9l1RFLEGIlxp+RLi2w6zt5uaPUCYwRCBhCRSTEhCgg+YJXC5hnkvXOwq2uiixht\\nUaX53Dqexv50SL1ZU/uG1ve+h0xZyryg2bTsn4zo2hanHIX+vL6SgqNxnm0Nw2LCZrUCEWi7xOn8\\njJcOx3D+GEY5dBlNkxjtRTohMB7atoPDq0Biy3KTEZODKCF1qFIxKvbpqprURRRr5NojtYVaYLY5\\nzWVFfRj54KM1ezc/m91Wk5iTgLE+AjoUzwEv0QvPmoEoaEl0VDTVhmE5oJBDajwoR6FylNHUVLgA\\ng2LAytUMcsl+PqSh4qQcscZz88YRKzpm+2Pc6RahIuu6xgdJtoLhWJF5BfdEnxRVSphegjL0MfFP\\nY1gOGH7rNbarc6q6Zb1eE1LH0XTIOz9/jxdevE3nGuaLOQfH11jO1wxKibEZWivoGkIIBAJdSmQy\\nMtAQGg8qp0oOUTtQNUbmuC6gZE00fZXgbLrP4jJRx4CPkdC1ONdA85jltqOpHcc3n4e0xXuJTh50\\nIHU1PgrqtcDYiDKJus1o0WiVUDJB50iZ6n1MXxFfS/OKPy9e//Zr6Xf+23/AchuJM8V9n8hnI9bL\\nDePhkFwrdGoY2ILjaU7oWlRRgC3YnM9pNw6pDI4O4QXoRCfgIC/wTUDmFkIgSuhCQGqBVAmiILpI\\nVH0ppe88xmpc59BWE2KgaQKjkSU3Y3R5JYR6sepTJKWWdtOgogDhqVuPVAqVHIumQxvDoNCMx19k\\nBycuF6c0scZ6iV/20l7YRK09h8BgfQ6jDHzBcpWQhacVgiQTlz7g9RijJS/dvMP7jy4ISpMFx/7+\\nlJTAGo+JS4Re9FmB716CtLCCdRVYL1rq/cjdf+NX+bxV14c2+yQRTa/SP/clVPyymwo+caju9m1n\\nKrjkUEJhSDgSPvXKdBCSmDpyEckp+L3f+ym3rs8QdcRLgbQ5mYzkbkscBgYTAWoPuEbqC5afoKWv\\nC1hUS3Kb06y2LOYbBmPD5fmcrByRiUTdJg6P93FdxJuENJGh2KXfx4CQJaFrUCrhm4gSBTUemxwR\\nBVIRVaTUBgKYzEAI+Laj8x6jMkxhCG1HVW8YTAqS8jSrBiuyPvNOCqIISBEJXjG/dGgL0ffpv1LB\\nxEQsslfj9qbUbUeZ57+fUvqi9LtP4Rtxs0spUZ0mEwK2huMyI84jUzukaUASUdZT+4oul3QEhkpA\\n5+m6DiUEIUaEUiD7LDipEqu6QUkBLiKTIaRdyCwAQZFiQgsIMUJIqJQR24A1BSn28fZikKFi4PT0\\nIft7+0QcQlikFIhMElOEzuOc6I1wqclEIgZBnpdoa4hdxbJeMC6mCAIRiSTg2oBGYTtJQNNSI0nI\\nSqGDYt1KRBoh60hUGhE7NstIsgVSSiZDw+l5w97hHptNS6gdXaZAJD748BFH+4d9kYjMITXQQd0J\\nisM9okjImUcMLrh78lSyzKcg6POyr/BFRTmO3nl3CdymT7z5LNRTFq9DkUhECmHxEbxIWCFQnUPp\\n3jnmUTQqUBP49ve/y9tvvcFAa0yr0J2nNYKsVMQ1tOcderal3b5NORsQB0dI2Qt/haH2HmOGVMFh\\nBpahGINPTEf7oAXBBVQeOX28xKiIExaBZ+E3DKYTXO0QonfqoTx5npNCDUGgraVzASFAh8R8Pqdy\\nLUWu0UkzyIdomeO7SNPVGK0o1JCwTcRkIEmClrRbz2BguLyokEJQmIzCSoJLaCRR9Gc2hARK9D4X\\nEkX2FyzOjlZU1zXO18i1Y6YUOkQSgbYAJQxrn2HyviBGFznNpkVWFUhJp8RO+iaiAKNF720VCZ0Z\\nEgJNb8fLGHEJEDXC9JmSKRl0TBij8T4S2g4pwaaEsdCtO0Zljqal65ret2UkwRswCrQjioRREqQg\\nGxja1bZXBUOi3ngyE1mGC2w+wKeEjQGlNdjA9lGNUgXCJqKMvX84BlYjWHYZpReQJEb1cX0hOmQb\\nqDpDOUysV48QuWO5WCKKEZvY8ouv3f30HosMYk1x6wZ4jdwPDCavUN75IoLAo+Wc4STn9/7Fz9FG\\nszfMeeHogHm7QQ5aro0Hu4KhNX1h47v0N/gVs2/oi476TEfx5MaNQItA4+gwUvY+BpdQRrFaLMmH\\nGa1wdJ1mv9in1YHXv/193v7jN1mlinwrSEbgmwIpNLlRNHXk2qGGdErVrIkcUeQZWlqUNggkOZaI\\nx2SRer2tz1EcAAAgAElEQVQCY+m6DptrVBtJRe/vkURiEmg7pl0nrBIkFfFBo5xk2wWECCB7W19K\\nkKEjyzW5tYyLQV9EFXv6h+SIyZNpTYiOZtckJMa+OUoTGpLuqFxGPi4hQOcdgUSSIBUQIlIkOimx\\nUuJrR0akcV9dM/9mMLuA1jm0EYxnJZuqb7agraFLNd4HgirwviZ3ntXykuHxHqFuiGVG7N0qiJgQ\\nSEQUGDRaS4SSJAUy9p1DdNKIkEgJYnJIqXA7LbP1vo+v+4QSUBiDTJGiFJi8AAm5khBzlusNUQRs\\nUSCMAtl3lIlJkESiKHIylSGFoElgyjEXy3NSagne41uPGQ5ohEMXkGqB6DQkj8oUwghsEwlNoIiy\\n56sowApilEQf6Uj9uylL2wVihOgbRP5F/v8C5Dn4BshYLFumky9XvP/Fb/0TPI7FuuLicstBOaJ5\\n8WWSgOnJAdXHjymlgWEGVQYnxzC6YvSGntEdvUdhQ6/2NCQaHAHNFE3WJ7SEBMawOl/RWIFQss9S\\n3aWnZrtw10uvvsIbP/0pURuCDjRtYJjneBpCgq5L5FmGSIlcS1KSBMDRYsgJREQKCCGwVtP5gE4C\\n4SNS0ncNQoIMCASRSBIRnyIqyr4TEaJfLooUI0ImXNdhtabqPJkBGSXSJrxPaBSkhJKa2AmUvtJy\\nZK+Epj7l1YVAlJEUEwj1idOfPhgjd4ySQm/yBCK67XqT7CviG8HsKQmOb9xkeXHBtpOkQUlTBoJw\\nxLClKHJEEoyyAktNUIrB0Qg1Ljg9XaOEQEeBNKYPzKWOLvO4DkptEbuNFUbRVuClR0ZDDBGEwKiE\\nCH10p8gkstCkFInC02wjk/2yz3KTCSEM88crXNswHg/w8wVxaBHW0naOVV1TlDNUXiJ3cYV8ry86\\n6TYNRmp0TCgRaDc1VddhRW/vidgRUmQxFwwKg2ojikjSHUIlgvMkbxhkGSIYMmtYLiPLboubdKQu\\nkY8VW/dFzD6BfMHCCqZyynTWJ+Z8vFzzv/7T3+HDt95huleyWa/56L0zXrl7RIagahyhUbQTxxs/\\neQNnE/FPDGp5wemDJU6PyIpjzi5PyeU9vHpMLiVBRr7z/VcIyfPyqy+gy8Dh9RGbmDiYTCjYoEgY\\n2cfsEh12YHHOIVJC1J4iWtrNBjsUrEloCl58/RVsynnv7XdZ2yW5C+iksD7x8LFDTxR6pCizLdkQ\\nus6ji5w2OjJpcYLeiBIWk0mU0KgssGlbUmcIYWf27XK3rNJUocMHjxGRgv62jpnEe49RhtlkRIqJ\\nkMDqQGihbT25yvAxPCmhCjJipCR5haBX/b3rQ3uakrb2SELfHixGrJS9gI99CFULkCEStaATCdk6\\n9OAvGLNrKRE2Q98aoSuJthIfKozJUa7Abx0xCFQSqFywma+YcQtyjzUVdQwoCZ6aGAwDY3ehEoWJ\\nqi82kAERIoPMsO366iMh+7tn6yLCRpTSkAIx9OkVWsY+A66R4P2uFLpj73gETKDasLxoCS5BFxEp\\nUdiS0EownyfCzeee4/H2ArfZkAVB1BJVCUSy+CIiO40Wimgd3tdErXEiYIYZIUFoAjJkyJToVnOm\\nKWeKYjXQ3G8rDmZDunzC929/WdLGHaYSEg5B5L/+R/8THzy44PreEXl2gEQx299DD26wd1QQNxWF\\n3lXmFQ0f/uFbqAtNbUEXjpPXj3nr7bcZ3YDaPyBFj3ItW5fxnddfw7lA4wT3H3hOjo543HZ4l/HS\\nj74FNLhdUUmgRZHIi0ReFEAOowlsN7ueDR3jK9+BULTC8cK37tK2Wz54631i22JShskzopY024bq\\n4oxsNqfIJxT5YV/cKCMK1Rc12z5XAinxTct4oFEjx8XFhthAEJCsJEZPnmnKrMA3Na7u2B+PKfcK\\nFl3LxGa0TUXyoJG0sUMgybTGxxqlMoKPIBNK97c9eIzpNY+2iaQQiSEQM0F0Eu8iygi8c6QoMDIB\\nCk/AWo0uNJnOINP4+BdMjQ9dgCBZA/vlgNRuyLOyjzHLAyI1OkYSGpslSq3pj6yjch2ZsL0gkIqg\\nFEpEvIt9qE4nEgEhNFZECBGTKYIXeJdQRAYJXOwzqLoQCD5hjEQohR1aiLG3zaXZ7VgLNH3J6K0J\\nVdPgKnBeImRHWznKUoIs+ayifDzY593TS0KbiIVAlREtBWHlkFIQpSdJgZeWJAUH0wK7jKgkEYMO\\npwO6TpTHJa3rcHVg7SJ7R/tcOzjgN//lx7z6Jcz+4MFH/A//+H9ncHxA8J7ZZEK1WqDkihhq9qcn\\nvPvggjwvefjglNDAwbWSi+WWzXKDSFOOhjnjqFCVYP2247b+Fu/+xj1u//Au29NLHs7PWNcd5WTF\\nay/c4Wz+gMX2j/jn/8dvs+m2NK3jP3b/ES+/eAOdW3QhEMqRE8mARA27RBYGVxEAA+0KpGUdA6Os\\nj4pkWcnwaEiB5/TxEik71DYj0wY1stTrivlqTtP16TKTyYg8HyFERhccmSpIMmHLvjHE6cdrpkdT\\n9J5kebEhNBGcoKk9zjpiFmm0oZ4NKWLNIDoEGatqTZKCidpHhkgQ4H0iSkdIvR9HSvA+oIUB6XFd\\nh5KaydDiQiDEiIp9A00TBcIokuz/sgJCFBgrsFHgfSQpiTaKuvoLlhsfQqD7k/ucvH5EGztstuv0\\nITIwoEWHnUS6SoG1TAd9MYxhiJCLviunTEglSRGWdctoWmKEJQUQphcOiAhWkxqBiAkZEj4CXiG9\\nIaqALDtElMggSEGx2W6ZbzfcennXSinJ3qbC9+2qYoUJHmUCelCwrQLdeaTtKrZyTTSJg8Nr8MQ9\\n1XEwndJsGprO4x20+YChqtmoDucFr95+jrq9oEgebISurwGoVYUzksHxdWC869zWMtyld370x+/w\\n6u0j6haKp5y0//Qnv8Uf/ckDXnnxOQ6v30XYIXtW8/xzN/mbv/DLrC/e540P3uGNn/ycl2/vk0YZ\\ncrbHZt7xf/3sUZ9kskqM9ydsTSAsFuRjy/lmy3SUOCsu+MPfvMdf/+6rbBfnlBPDD37wPEUqiXsn\\n3H/3HXLpCVoznezx27/1W/zG/9aSGc3rr3+PMmX8yi9/Dz9O6Ce2fr1bvQUGkFkIFep0Q7iR75qP\\nCq4fP0fcfMjeq8cs35/Tzh1pJGhbhbU5GRG3rhAqoPMMYwMtnkKVu0KrtAsFKk5u3ARa/vjnb7F/\\nvIcw9Da09DQbyXWdE1NDblsIFrPVsNGUqgANzdZhB30TFOUFJEOKkigSftfLtvUJpRQiGVIUdJ0g\\nJo1VEIKjkxFpPNILRoUlFZ4UAyo3fW+FCnRuEbbPK2jb1Vfms28EsyPAXp8wP1vjRGR6YLFoekun\\nReYNCENyfZuolF3lLnsm+xnNuYMkcTGRlGAwzlEp0bYeXai+W4nY3czeYwxUjUMpCSISU0AQqL3H\\nbxVKJJTyfRdQLbh1+6Qvfui9ZOzyUaFLsFaYoIlI2uQpZwkhJMoktI+4JjC/fx8XBCkTFE7QADFp\\nBjHhRKRza1JToXODnJZQbilSBZXpa/JHEURDMbzR24woPnF6ZfRNFHLkrMSrBe/fe8g/++mf8OFH\\n9zG54a+9/hq/8m/+El3tGR0dsKoFY9Vy7/03+a/+4X/HH/z6/4LMLXuqZDoa8mHT0GaW7WrDbDxh\\n/XBFGhrePG35/o9e5XBoEaFG6cT99+5z7bBA2Sn/55tv8/E7H/HjH71KUymywQxBxfLSMxvcJK5X\\nTIuczdmK45PruG7OvY/e4ejaDX7vnbd4+bsvkok1Inmskbsw3VOlsaqgPLnKnOz9Ei0t2fAEqJjc\\nWsMNj3u84NF6ipAGrxImkyQHF5cVcrXixonFq2MiAxQSzZUoDnSh5bXvfJcQN8znC5S1+EUfjgs2\\nUGQG1ud0PsONhojFCukFIhnIIfq+9XUQgMhxoW8NrWRASUmKnhhBColIiRBWhFwjrCa1HYMyx5gc\\nJwRCKUIK6FRS+4jVBjWQCDJq7ym0xv+rajgphLhHH3sJgE8p/VgIMQP+AX32xT3g11JK8z91Himo\\nVxU+ZuhS7hj9qtglAQWYRMoCGIUZXVWImb6/ehRotUvXRJFiwAmByjRJRGL0KKv7XmVa4nygHBo2\\n6xa0gKghejItaYMEHfA4OpEYTfd7+1vSZ6YBJNUbdV3sq1Bs7NNeK1DaY40liUjnHFopDB6pE5um\\nIXpLkImkFCFGWh3AJKQwLC87MAa3aTEbCU5CaWDg+vwBlvTNGref7AvA7o53CH7nN3/CG3/4Lmtf\\ncf3WdV57/TsMxsc8vFQMDqcoIZgJQe4q3r3/x1x+9Ef82q/9KhfvVcRlS+bOuJkPmUuFyzSLxZwy\\nCaQUDK4f8H9T96ZBkmXXfd/vLm/Jl3vtS+89Pd2zDwBiIUGCoEhCXEOkRJMK2SGFLYci7A/+YDvC\\nCtN2hEOOED9YXxS2FaZFiZsokTIJkuCCjcRgAAwwA8y+9/RW1bVXZVaub7/3+sPL6u5ZQACiggZP\\nREdVdmVl5Xt5z7nnnvM////x9W02jnOaTYehIFQBR/EU4QlW5jXCBowOM5pzi2SuSTnWZLGlmJbU\\ntaY3yKj5TXQqEUVAfzhmlNxgMD3EqzlOr6wS+TWMp9F3ZgUNFZevTyHBkWPKAiNKrPQIRADUsbFF\\nhgavI2hOS5JxiWv4FA6ElVjPoUWBSRKy0sOrhXdGlOxs7fiqDsQ4UdUSfF8SKoOWYFOPLM4JVkKm\\n+wOsX6ceQG4KkBpDxc3vpK1qASbD6upzNtbhCR9hKx0EJy2uNOhAUxrLaBjTbHgENY+TQRuHmFXm\\nm/gym2UzAAJfV9MfeZZ/2/76H2Nn/yHn3NE9j/8x8GfOuV+aabz9Y+B/+AtfQThCoQhUE1GbctfR\\nE8CBrpHFMdqb9SO8FlWq5yEM6KiOsymlsciiutlGSoQwSOFX6fus8o6zaKHIEofUAuOqMz0oNAIp\\nJLUgIDdTpllM09eAquC5guq1Slex0FoLHhilyAqJLkqK4+psJlRGve7jjMAoQbNTR6cFNQPF6JBR\\nkuHJNs56mCJHSp+HP3QaGGCvx9DUsDab3COmArc0qvtBi7cj1kJG0yHFeEB3ocvHPvYYK6010syn\\nV58n7tQhSDgYZYw3XuCZp77G8dY3OBt0eHzhLPM9jwW/SffhLqsIFnzJM8U83aVl8vGYUV6yuT/l\\n9nDEqIjZ6U3IjQMvYtiPcZmgWViKxpjR/gS57CN8ifZDxNDSbin8VDPoHRCut0AK8jTFOEdDRRSF\\nY73W4ODqVdYizdRr0q0touS4qtuIVYoZe79A4uGDPqFrkmxtbdNc7tKefxAY4/ItmrKg3fI5mKYg\\nIpyoWqN1EaFyRV1XUOiiUgm4A/qxOARjtJgi/IR41CDsGKL0gChp4kTEeC+h2+yyf5QyCTW+rIEV\\naCkQpkQrD2sLtLQYAqzKqx65SRCBQzoNAlTkY4RmodnhhK6qcklDwHD2KKQKQyfDXw5HQOkcgVBk\\nyV+ts7/T/hbw8dn3vwY8wbd0dkiCEq0gnximeczcisFDcnzUoztXw3dV7/muIERVpLGZq1Bt2oGs\\noqx0lZqHEIKySFG+QqDAVaOMUoL2wWQ+WlqsdhTGgXFIUVYtEF/Q7s6RZwbfM2C9yrmlY5ajYWSO\\nEgXCGqQXUDiBFTVkAEmS0gh8TOmw0gAe9WY18bTYabFIxu3rPUJTYp0lTgdQ+jAZIyN/xqJVzrJV\\nRdW7bsI90R3g2Zde4qXXXibLRnSba5y7cJF4mmHac3gqoDkdEw/7jPau8Xu/8qucP38WP59yLlhm\\neewRtceMyinh/DJjv05jPMHGCbXVJfrX9imyCYMsRtkGaV7i9TNOmwXGi4q+2cfGiqJZ59hKzq3M\\n0Zh4rD6yAi5Gmk0aLUepYb+YYFseo/6QMrNsTUJOXziFGjv6B0cceSnjwYDj42f4kZ/9CCZWFJnD\\n83xczeJ5lXNq9B0mAzdLwddPdWZjqQaoI9pnUB3D+PZNIqlIi4zSFxjnk2aKzEtYXGiQE1PORlRn\\nIX0WQnPAoTJBMQ0pvYRO2AOhEWFIYUMGrqTVlhhXzhRrAK2QKMrZCi2KgnqtgTUVBmB+dZ7+wTHa\\n1zQ6He6SXprZvxMCU7hL4SXueVxNy8WEIHTVzhPf/uDqXwobL4S4ScW+4ID/2zn3y0KIgXOuM/u5\\nAI5PHn8ze/CBB9znP/1JXNFgqR7hrc6RkRJgmCYTasIgPYcrMkS4Bvewoh4c3YbMq1Ij4bDWopRj\\nXMCcrwgCSUmltxQF1QhsbgvKNMeaCvAgZqNDgVMUeY5X8yitI1xuV4APJypHN1DpThWQH4HvYSYe\\nNs2xUpAbhSnA15ZCZUy0wVqFpyUri/cOmZwQcBTs3trE5YKm5xFPpywudJAdC4EG4VNlN3WqXf3t\\n0MjtnQN+83f/DfedP0syTfCaLS7cd4GckMOtMS4Z8vKzXybTXeLjXc6st7C7+wRCsRxZiv0ptVrC\\n/rDP7XyBeGQ5NdhkQRa8FFsmw5S9bIxTjo3DCbbrmLtpuPjgB9m/3CZYVHzg8v1cefyjmDzHJPv8\\nV//wf2fJeNQNtB68gCwdD105zaSfU1ISRh5zjTZR3SePOtQbixVlhy+ItCXo5MwvLeGLklojpNXu\\n0mk2WFk9xdgvqdXm73z6ZnYniyJlMu2z2pm/5x4lTI82yYeCSZaTi4KGauA5iW1bylCx0rlwhwLy\\nJJdMGVLHZ/PGPl6oCWyB76oaTcMJ0qJkHBb4voevJFYrAgUqdwgrMdpipcPzIwhVVXeJNBWS8CRQ\\n+/e8+5MMhZkbnYy/ltgKQ0e1s8/ou8mwhDgUeVZw3Buxvr7wV4KN/37n3LYQYgn4nBDijXt/6Jxz\\nQoj3jCb3yj+tr60RFQuEfgQyJcbizzjmjOgjQ59kMsAT/iytuecCtI9KBYUtKN0MjGUdda1RQpLH\\nFfyo1tLkNkUYDyFkpSmmBTiHsQZTQlLk1LRAL1Z/JytyAueBtCCrHdhIiSRDhWNsP6DwljHSgLQU\\nIsN6lqJ01Fs1OjqkXmtysgCrfnKVjGIctkhJ8hQnItYWNc35+oyoYpZFKEsV2E5qFG+3T33tixwV\\nEx7otDGqydnzq6BC0oNjjm5tcLDXQxZdTk87UBYc9xxKzxMIcP1bjLeukumE/ddusz9/kcOsIBtt\\n8+qN2xytrXFwbBgEEXlmELZkNCh59Od+lOWFBur8wzzwPY+R3jrgtRtjxls9fvD7z+CKKZ1GRCtO\\nSHvX0crnxaf3aEfz+Fpwu2+57/5LBBc62DSgCHO0pxgOFcLzKUcB0/0Ef0XSTvbY27nF9z70CLpf\\nZ65WQ9Zy7AysdMJCGHohzc4Sx/1dhPRRgaZZm6O+cIb6goTbm/SSBIYJtuPjXJuVzuqdffPE0auy\\nn8fOUZ9Wt0k8GWGVxFhB4eVMywrl6WuBH2mUc5TOUjiBUhaEQTUqTnerRbVHRznVMaway3YzPoS7\\nw0EnJJQnV1NpEuRuihRexctQjIhHGY6SLB5Rr3fYtMscjTIODu+SnX4r+0s5u3Nue/b1QAjxSSr1\\n1n0hxKpzblcIsQocfJPfvSP/9MhDD7vClcRpik0TwuUO0ezMHoUeRVbBT0lSqgh5F7BiZ/dHIpHW\\n4AmFFVVMTK1FeyWu1JSJQnoCrTxKaxA2xBMlTjlKIfALC5GBoqKYNjZHzhBMzoCRAq3HSGVJrIee\\nLFeabWmJcRZnBUp5CK/EKkHY6CBF423XrGZHkCzNSKYjzOExy6pBqjUmy1CRqOCszVnrkQZ39eUK\\nTjjqX371Nf7t7/5buqsrnFs7R7PdIY4n3NwZM4qv88Zrr+DLORbm27jJgHQwYalRo3CbeNM69b4j\\nvpojmqe5maWMCRhv9ZgE8GQGjUsPMjqcUNiCoUmRLqBelmjZ4uH3v59O6zT+wgr9Xkw5STBRQcKQ\\nN155i7DbZpiUeK6Gcz6tsWHqa1JVApLz509TDo/IDhTGTrAbHvvHOR/88fdxtDPk1Po6edxDD+rE\\nSUAtanPt2Rvk82P8BTi1dBEpF3A0MHjY2SIu0XTn1sCkFeoMqILkgO7p++iWtxm+eowLIWjWcaXD\\nzYpcJ/upLVO0drRzQZEOUc7HSQ9tJkhVUmtG6PlaJUKSe2iV0vQVFCUIS4rCo4PWbUpSCjI0PgKF\\nROBm3SWb3kKGNaAkdjmRiLjbWQmYTo/wdYzMEkpSDqmTN7oMj49ZmV9iLtRcyEpO1UJ64befxv8H\\nM9UIIepCiObJ98AngFeAPwT+wexp/wD4g2/5JqSkdA7V9KrIeSfOFhSlwQtCsnRGHX0n5amszEuc\\ndFhXKaZaLCWOshRIA9IzyFBgcjBuVpCzBpxBFBVYJc1yQi0J59ro5QiwKKswmYPSYZzDaAsyBTvG\\nl468iMitwCmHkh7OaaTVmBQwAinePZNfxXxJlsXE8Zh6VCNUkvluwHiQQFivJprucI1nMOsng+XQ\\nbtM73uUzT3+Z1vIcF+cXOLOwzmp3kcP9HbZ3Ntja2eDM6lnKRLK5tQdtR71jyY1BeE2MDMhrEXK+\\ny3Gu2NiH2xNHmjTJBw3KcZfxtEs+aiBSDS4kjQWeVeixImg12RunJJOU/ZvXWVyqoyPDzvZNbr38\\nMh2VUvdSclMwjXt4LsGnpBQFSenYuXYL3VRcvXXEZHRIW1kWA8f21Rt4jZDdnS3CMGTcGzGdZkgJ\\nqfHoH4/Y792G/JjCTckpZvCb6qBTrRY7I3PIuMsjPzsKaU37gTWyvCAMPJy0b3P0k919kqQVhFVX\\narzGaDTeDAsvGPYmZFZUWZeuoKyEFYl7EAQo3QYMGp+AGhIPOVOiEXgIQmQYcOJ6WoSAo0xmfIpm\\nSF0W2NGIoZ7jmu1QWEX74DoX8wEr0RwULZQ/hwxbOPUXcy7ca3+ZnX0Z+GR1LEcDv+Wc+7QQ4uvA\\n7wgh/iGwAfz8t3qhsjC4WFKKnNZCgMYxJqFJQE3nTOOjioInfbezB76uaIdKWxFSiGo81GkLmcQq\\nn0I6tMlRWEgNYS1gUgxpdFqgJHZcwFIHUAzLCW3TglLiixhEjsDhioqFBCNQpST0NXk+xpWS0hpc\\nYCsOvEATeSdc9ZWd1M1PRiBG+zu0GgFlOZvIzSd0LrYrpZqow132vZMjiwYavPiVp9nd3qV3nPFT\\nf/MXCDyPrCzZ288IFgM+8dHvZ+9WQtTRZLO56OFuzI2ru6RuB1+HlGmPGwd7BPWY6bhHc1GzURRM\\n9lMiKWjXYHN/j9ROcLYgzw1SBxzpDFeUbO8e4YILxMkeQdNxe2eHiXSkWyM2Bz3coWF/OiIK6vhB\\nyN44wVmPvbeGtELFoijY2poig3USuc3N/R10exU3PGK5I3C1eUZpgeo0oFPQMznYnHrp4XaOIAdp\\nYozVBE0fg6SGnM2MeKA9ssJwdOMG62fvo2LWHAHz4BuWL15iZ/uI+bX1OyQvEhgdD1nqtjnYP8Iz\\nDul8fFkiSLEtSVZztJsBflmjpgV3M8y7GZegQRVoZiAu1GzWL6UkR6KRlLjcUtgc4wpqNQtFTHxg\\n2W/N0yIlN4KuUHQDQzdaBiLoXOREwdgG9RmvoaxGuL9N+w92dufcDeCx9/j/HvDD39Fr4XCNnFxI\\nMt3AIghmtFM5E+pRneFun1B7BM23K6JksUE6hVCymmiyDqccVkIpLKETVdsMQU0oClNi0wwnVVWS\\np8bSepOTEaOo9CHNQQhk3WFKD5H6hK4iWERBmQtKkeGEQmQeUlusEix0uyjv3YwpVdc0ZjQckhz3\\nWNI+bpyTBg5/uVGd6+wOqAeoFtC90lSVffHFF9nuj9jf63N5fZlplvPm1WeIS0cZlyyttUhjj3Yj\\nIJU+g5qimA6478wck90dHv6hB3jqudeYHPVR7Qm7O7uo/lsc3NxmIgSDzCe1HlN5TOnBFElR5hDP\\nmGxtwfJ9a/QPNplrZdhyjqlv8QuJ8gxbr1/DuTGN5TZnVs/hDRMGo5SdYR9TD6h1FIU/xAtiylFB\\nd7DHqeIGR52PkddWsHUQw5w4GFNbF0jrwbTECxy5dUxUzmAouPHkW5hTi3h+k6X7HU7Pk+GjuEer\\nRyjWz67CzhYshtXOS0wl66Woh4IyjzFCkRtH3a8R1T1uvnaNIFAoKZClJkgdQTcjSRydpar4V9MV\\ntBa8WQ3GUnVJarNGYJWJOVImNkHZQyhG1GqLCBbZ3BvRFDldmYMq2JUlOvVZXGjQcmMwM2IK3QI1\\nR5V3TGfvvwBqSONAGSQGU3z7hJPfFQg6KSWZEoSLNaZppcdduZ7GFQElJc1mRJlkVJHzblVaKYkr\\nBQ6DdA4nHJmVUAgwgqKQ2MAhJLhcUKiMcG6N5ruIFRUHr91g/vIcRghk6bCpo7QFgczAWIzwKHNw\\nqsQagXUSfItfVxRFjPK6fDPbvHaVpoYAQ5wKdAheK4Bo1jeXbd7eWrtrf/rE1zg47CMbLdYuXWJj\\n55jXn3iSpjekHrY5f/8lRK2FsxEjGRApaE96HO8fkdXqZCLnwe/5IB2hGLkWFR1In0qf7CSlrFPt\\nVieiDRo4QuAIKMicYefmBvn7L6Odo1v3GGVDpIStwS7MGybPv8XIKbZ7beab80wPB0ytIJ4ami4g\\nVyF6oY3p9WmYFqfGG7QOx/RJGJ3rMq51MN4ILw+RgwFno4hxniBWHDtyn+evvk57octk4zrjfsYv\\n/Kc/g2lNsXOncTK408Cq6zrjgz3KMqVrQ/KdY3RNIr0xHAvaa6u8/NYOrfkO7cUuT7/0CufX5tE1\\niy5KtLBIU4CwTE1M++yDVE5X9byZhRbFmLtjvFMkJ3yDJcKk+CKHLCdAkA2n9Db6lEJxbWkF/3jM\\naWlZjUvIBGVDo5sKgrAC0tjZa9uo+nzkLPjnFbVaYSzCkzMSi2/PviucPUsL0lLiBx4N38w66BVM\\nMnUlkaeJj8c0yKgi3L3ObjHZrPfqBFYIPD2bDJQWaSSyAGUKtBSo4C7U8m3mChbOdcjTEuQsASsN\\nLhB2kBUAACAASURBVCjJdYJzBXayiCg1ThpcAi7MybWj0ZojEE3eSZVpMUgUBzs3aXo+wUQgvBIR\\naTJRkqcxEXUqwMzJbv52+63f+wwH4z7NWpclv8t61+OFN3ZZXmgzimOipmZ+pYXfWCYb9FFWYqYF\\nmUvotDT/7H/6X3ni05+j669i810UxxR3rj/lJN28i0U/EalIIIzwVIkwPi5r8Z/87Y+z3FpGtQ27\\ntWMOR4d844+/xuKVdV55/RXOukpKezzMSIZ7gCaMarTqFjO5jZAdnr9+i1NnrvD5ccT+uQ9Sn6xQ\\nppa5gz7p7vN0LqwRNBSu9KqZCWGIJ44Lj57jyc+/xetXD1hsW+rG4+pXX+DC917EtpsEcpkTTd0E\\naC6tcNwr6e32mW9GuNExhAmYiP2Xb7K03EJ7BcOd2zyyvEBv95gATaR8Yjch0j6Fy+mcvo+YnIgT\\n/nE3+wsZJ50Sg6sIL5igaJEUCUU8oFYk+JMEPMm+bLHnZai85PSgT5xZ0qhGr6hjygSRjFmsjWHU\\nhnoD9BDsIYiLULhqchMFWQE6x5vNxX8ntKnfFc6uPUXUDQi1R5sARzX3BBJbegipkEaTYKiZt0sF\\n57ZEonGUOOFweOQYtNVYrSrQnBMUNYnzQ6Lmu7ng7OAQ2WkzLlO09FC5QOUGqR0jZ/HyCClAmeoG\\nIyylLmkvzsMdDP9dMxQ4JLu9DQb7Q9rKR4oC5flIv8SogoXTy5h8CqzynsEH+NRnv0biCtKywEsn\\n/P4TL+FUgUgg66WMsx2ix67QG45ZkIosifmDT32G67eu8Se///swsJyqtQiSPh4hI07SzAAo8Chm\\nRBIn3d2TBZ3TUfMYU2JTzepSl8uPr3P57DnOLNYZejHnvmedw4OAn7j083ztuZe43fDopz6iprBF\\nBoUj9EEWY8qxw1ct0tEhj6xFFPYaK2c/QB4GxCKjO69wyjDIQoKyYJwcMNQtXj08RnZC1uotDl7f\\n54HHHub0csR43COOS/78T18hK6Y88IMdhqserXDuRK4TgO78Kcr6bZjGiLqELAGhqHsBw8Mxx76j\\n0DWOplPaNUFpCvpFThRCXoyZu/wgEPD2g6Pgbi2lKgsqJCU5CsWNG6+yQIpVBZtBB0vIqQCSwYBz\\nLVhafwDIufnyVTwVEEpFqRVRVAeb4kyAiGVFolkEVXfBA6TFZAUqCME5bGGwSlZsSN+mfVc4u5AS\\nP4oIhWQycNQ6ghOoSztq0tvboa0k2Z2JsxM6AKoCWQFCi6ofXr1ipbysBAZTnbMDh3jHef/E8tQg\\n0hhf+lgrMNYghCPNLV4tQBUKUxQoWREOGCyyoXgv0mtTFCjP4/Boh6w3JBLgW4HwBYYcXffxmlWA\\nUP4C38zRAawu6R31WQo0o6xAz81RDA4QIuBj3/cwovkhtq9dZ6HR5vawx8HmDSaTPk9/+gucX+yi\\ntWF4dEhAyYCYGgHxnSq1pZiJI5R3SlUTToqBAzMkMAFzIiAoqibWsHfAkRcRK4l8Y5/NWxt8+amb\\n3HjhJsPRMXPeKrFNUNYhhOYwH+AzR2Q8cj+jPV9j5zBmca3B8cabdM4s0WnWcZOMYq7G0tISzs8Z\\nZENks2TtkTPMzzV4/eYBkbF0IsWLWztol7MwP0ctarD76i6r8zc4d/Ys2WxVKO6G3+NJQVMptJbo\\nwoEuMDpDZZKaCsliR6gjsryP54fgCfI4ZmmuxTuPjHfNUaXvlVKNLSVaB/S2tlhQCYemTuEUwgmW\\nxJSD3ZS5hVMsrJ6ZvbMauctxopKb8rUHpQYbUiLxhKjGo22Fx8Ba8CVKUfEqIEFJsA4p/goKdP8x\\nTcgKP26ARqf6ejIOADVsKZkUWRWdmV3srIAlMoEQBi0kUkiyEqQsqNVV1bNXHqWRaHfSy3y3he0W\\n0yJDlALPCWwpKIXDehoShysNwjNYaZBGoXyN0u/dtXR5wnFyTHJwjK99cmdwziGcIHU5S4urVIul\\nzXvLYVT2r//NbzMtYWWujQgU11/cZL21xNzD9zPfiHALZ1he9OhIzVzo8UZyQJz2ONjcpdMr6ByG\\nCJNiyRj6JWdWzvHq5i2EDKvFc8dOFnPKCdz0RGVVdzSiBqevnKPvJLdfvEq5GRMZ+PqTz3Jmfhk1\\ngTiLq+ygVPimoCF8Ln3oI7z69S/Ss1uM7ALdTJJbn34xTz5RNPQcelBQIyDDUGooyyln1s4Q+SFm\\nscvC/fM0Gg0+fmadUgpOnWnT297FnybEyYDrez3aUvPGC69z7ic+cgd7drexBYsLF8AO2Lp5i1MX\\n1znaOsLrWLSdQGZZMR6YAWnNksucMlWsPfwgd5GL7zYL5BQcDXfI9w/oKIUnJZt2GS1beFnKhYUI\\n3V4mxbFy1p/x35nZe8zorq0xOJyQYPBdil9oFA6/mePKDD9x4AdQn7X37D1yZ9pDConU6g6M+Nux\\n7wpnt8bgZgvwJKU8eWP74z7tekgxtRSuqIA19RkKrfptjPERGFCu4mHDoywUIZJAekR+gKy/966e\\nxUMCP8BkkCuIyhKhDJmwGBsiS1DWUQiFiAxxluHrOo3g3qr7yenJcdQb0B/1qJcBiJTQ8zAavAXN\\nmeb6LCl5p4ba2+3XfuP3SG1MveZxNPHoHyUEkU+j0+HcpfvwuouM4xFjFxNEHq/d3KARphTjMf3d\\nPvefr6OPekxSQ+bq5DXLce+YlaDNMCvwGhGRXGBvdKvqecgQa1MCNNls5z8VdTn34Pv50ld+jykx\\nn33ry3zqj/+Ulw+vY16M+c9/5ifZevE2KQXXs4K2iBA1SxlrwkaN5597hn/6v/23XHjwMf67/+Kf\\nETMkWqpx2q9Rlvuo0qfV8jCmoOE3masHzJ1fQnXmiHSXxlodFXbJyhS/08ZzMQfDjLC5TGBH6GaE\\nXHqD7f0hw+0R+cYG9QuLlAR3nL4aKQElO5y6+Dj7Gy+xfPYKu9c3kO0Amxq8UpJMNaJeJ80Tzp5a\\nn32WJ1JP7zaJI7u9QyM9YNd1uB14GJNySu3TnwjWzj+CX6vWW/S239M4DCmCpYVVFhcMSTxhsLuN\\nZ0p85eNySSkdnpog/CnEbfA8EGXlGEFFi+VkJTfmfQe88d8Vzq60JqpFtKjKbyddygozrZimBaVS\\nOHci7HjSy6z49oStaLcd4ClRkeg7gywlalEiwzrvlS5PyxF4hulBD19X8lLOlZSepZAWawpC3yMz\\nBqmrSCqlodFs8s5bZxhx2BtgshhpLfhiJseUcursWab9I2jOf1MY0zPPP83GzUM6y0u0z3UZXD/m\\noJ+SxprmXJv777/I2tlLJLFH20zYvL1JIcZcff0bNAKfujSUowz/cJNJbEllQHiugdjuU49jdKpo\\noBmS4CaCYw44j0MonyMXk1IjI6t4843lwObUJrDpYpJJwc9e+jne91+e4onnnub20iZuO6UUTWSR\\nE780otgssSInIaE0dZZWa/zT//Hf8ciP7/KDP/0Rens5x26PXn+f+c7j9PemTG1BvVWiRUkpfWra\\nEcRjalEDSMmPprTbLfoHhzTnuiw1I66++ib1ps/N51/hwpmLHMWv0ihKvvRHf8QP/30P3Xkch/+2\\nHjqzVbN89lGODrdYvfgQbrrJYTyk0WoT1WtMJinLHU2r04R3QLLvWgl2SH/3kP3Sh7JJ6FuacYLR\\nPt7aJa6cuTcbMMQ2xyQFvd4xzhestueo1xrEpGgCoqjJwPMplKFEIF2FE7G1AqUzbC9HtoOKxCSo\\nZMCQGuFXsuLed+DC3xXOLmRVNjqZASq4exoOVYs4PcYqgVJRpVwInOiiJq7A0x6ykCAcWmqky8G3\\nBK0aZQiCBO89FEPiaUKhLDpS+NNq9FXIAFuCUA5HRllWHO/GGTy/jp73eOdtS4cDwnaX3u5tOk6g\\nrU8iC9LUsrKyxDRXzK08+E2v/8U3bnHjxm2Mqfjoh5tHTMaCq28dcOX+Ou+/sIBeOEs6rTjop+aQ\\n3/l/foW6OiaZHnLl/H089NBpxmaKiQIOD3c4feEi59ZX+IG/+wBvvPQmLzzxCn69xPQEx5M+97XP\\nkvltJhNJmFrwEqQNELlEqRzfebx14xbGljz/+c/yqSe+xpf+j3/N0vs/wtr3fZTuygo/+mNrvPW5\\nZ/jK5jYf+74P8tJ2nzKTlK5A7iWsonj2uT8nefgSD7U+xmLe5XXhOL+4gMqPGBqNIqXVahI0F3jj\\n9dvc/5FHGVhHfTQmq/mMkwHnr6wxHYxJPDh7+QLKlrS6NWSc88Sf/BlzKyvcvHrE4SuHNO+/AUtX\\n7jj5yaZRjS9ZFhZXyEdHlCJkaS3A5jl5PmK57kGwAIQMU0s+HZCMp/SPx5D2qZuU/vw5fC/DSstC\\nfkSiGhRek/MX36mkc2KKg9t7eMbR0CFlkdLbP2L9bINQhLNCtKUzv0xv7wDtGaIyQRoPY2qoMkLU\\nS9C2EvgoU/Ciagw3F8RFjv4WqkX32neHs5/oK3BCSXHXpkkfVRe4VFCUBXeLc1XstlJTqpKGkpWD\\nOosRmlrgUzhJ03ogG+/8k+AsKpMorRDGUWiLlSWelEySnFrNRzsJylWssl6AfI+AAeDKgtHuBiLQ\\n7PV71BtNRmnBo5fPE9ZafLNawYmFgUeSOHIpeOX6Ntrz6SWWhXNrFC6hc+EKxSQit5JRvMNrr3+F\\nzatPs+wcS+2QRmqYpJblx1Z5xH2C/+W//s949QvfwFM+XeXYemOTdDxgcWmRnY0D1kSDtTMhUTNk\\n46m3CJUlbfgkI49EjyHK6Iw037jxSzz1f/4S80z5f3/9V8ktnLv2Fa4OMp5WPnNtwRkz4Bd+8gf4\\nzGs7bO3l1PyIJBuSKk1Sr5MGBdc2d6g3v8qp+1cQRjAdF2RW4mvJ9LBgsn9Ad81y6YGLNIQhbBek\\nqWJ5xWPkCt68epsHLzyMHce4IoIwwiRDyqBDZ2GJYTpgzhO89rUvc9mHlaXLJIh78r/KPCQlE/zW\\nHD458f4R2krCxQh0k5wGoxQ6gWR3o0d//yaZjWjUI3YbC6zHBxRSUptfob58irP198ZVnKzQvd4R\\nfqgwcUEqwGiFkxVtlauGrgGfoO5x+mLE7tYG1hWkNkeEltKC1hKbllBm+MIgi6JazsoS+SFW/TWr\\nxp/gk08Kc/eaRGAKiTMG6YlZoQJOTvaFq6iobFnR/Egp0U7gC0VRVDDGd1kJzHDUsqwANOiKk1sK\\nKvpmZ5BOzAKQpFXv8F4SR85aZKAQxiPPjis6YRngS0NYm6eCan5zZy+Z8srzrxF2aoyOR6R5wdW3\\nDqjX63TCkHFRZ5A1OR5MOTzY5bU3n+GFJz9LY6nLuTAiSFKS0YSoa+ieeYCfXO/y6T/8PKuL6wwO\\nh2zsHfHGs2/Sbs6TjiSXuyscDY544LFzJAd9aitdXtzbI/TXSM0WtUAgXYuFjqKY9jH9Mfu9PWQO\\npwA5gN3BAQetkMH2Iavnz3B49YD0MCfJhkihwIE1HnFuePwTjzE+7NPbmyC2B5QND1dKOq1lkiLH\\ndeuMhkPqNYvXbjLKLEzGzJ1expqCMh6yuzviA/c9QGp9skLg4pSFlUWk8rl84Rw/cKnNm198knw0\\n4I1nX6H12PsJgtVZFeXtpqlT5kdI5YjmW5AZ7DjFdT2mU0ujrhj2B9x88xUW5+dxsk2G5mIworl2\\njtr8HN43TfNP1ixs727jCYcpc6QQGJdhrcDGFRLfYlGz/KPEoXDUazV8EWJyQ1GWKAnCWJwFh8Ig\\ncKXCJQ7dqEQNv5MB9e8KZ8fd3dHvffMW8JRHaiVOOpy12Bxkzd15phGGmgtmSh4lShg8v0Yyimkv\\nLnN3bvjuYTmRIwLp4SlHWWQYV4lBOAN5aQi1rjjlncbljlo3wBmL0O98hxOE1Ozc3iKZlEivTk0n\\nHGxv0rmwMHtO6y+48Iynn3+extwCb127idSSwVaPNLc8dr5BYWr8rZ/6O1zdnKK9jBe/+jk+/ye/\\nxtkwohwfUf/w+4icz0DG5GcuEZZLTAYjHjp/jms393nxmRd461N/wgXP0mwrhi0fOa94/wcus249\\ntmJDutjADpboT4fUWjWujfoEmeFG9sd84d9/mb0nv0FeHJPV67wRxxwLxzU3xpmM40Twmy+/SYpj\\nREaNiCkF7XaD9ccfZXJrgy998tNIOiwhuHmjxUM/8T5UfZ+F5jzthcsUcoSonWNyOOXJP/k8Vx7+\\nELf7bzH8wlM02xG602D94jpfeOJzPPbIfdRVm4nwGfV9WssK0aoxdoYOOfntI9JpQVDszsBTK7h3\\n1GoMGVr7IA3WphhfIP06snB0w5wbT32R5w4l7fnT7Mcp51pbJDpi/f7v5y9qk95rpYH11VU2rl9H\\nKR9rLQiHZ8CTguuvvcn82WXqQQMjLaH0AZ/W/DKTo9vVeTzWCCFA5PjKkruAUlTYR1vkmKTi6Cvt\\nX7ed3Tl87u6Z97pTEGmOexlIjXMOihOEV9Uy8sqKktALBQQeJi9xvqO9sEARG1AC721VsQwtHRKB\\nTUukr8h0jskFWjtcocEqnC5Q0iKwlK7Ev9NqOyGdrMQOtwe3Se0EiSTPRDXkuL7KlQunvuV19w9S\\nnvzqTZrddbxaRLQ6zzdee51WtMCN7SkfevxBbt+8zVxL8uUvP82/+qX/hksPPYDY3CUPBS6fMAxX\\n+MTf/j42phlh3ePZZ1/gn/yLf8kDdcPSQpvFmmRynJLGU1ZW2rTDBeZVwNIw4fX9HUZ+hCd9XGnY\\nK0YUwOVGjX/1P/87TkuJu73Dzq0BUy3ZAW47SGSKZx0pMb07pAwNjKfJC0s6tOx/8SlAcSZaRzR9\\n5ERglld546mvcrX+AA8+6LP68FmKYc5x0kMpw0f/5seIe7coG45GYxnpK5IkZvv6FkZrXn3jGmE8\\npdlt8eEf/TiucZkzZ1bZuPEcjzx+iS99po+apBxvHrPwYB1DH0ObqleToyplPQpRVgq4xq/49YqY\\nr73wHNvDMYFS+DZHbO2y1myxeu5R5pa/iUbWe9h4OkRFIaNJivA9ytyihaA0psr6nCDwFZ16BY3W\\nlSTEyQwcRjdptyXlyDAZjDHG0QgU0gqcBYGHpcSve0yNweT//9JSfcfmnLvD3XIy0FkNg4LBQ/g+\\nLi9QSiEDH+IUoqqpIQGnKjknYR2hF2KkIMszfF1DqHtr+1CS49Ggv7ODJzyStEQogbMZifMIrK0m\\nF60AYRFaEYVN3p0wBWTxIUVco3QhvpfjpWl1VjycwgMPfcvr/sV/8i9YO9fBij36meXrrx2QD4cs\\ndBYQhHROLfPJX/4N3uz1+NJv/3OixTmmvSniwiLn5zuEc/Nc+eCH8ZY/zFnbw+8u8uJzz9KsR/SH\\nBQNrafoBwdkat15O+bjnoZwhRfHV4xGvx46dW5u0wxWm8YSw7rM2rfHhD52nLBI2jw54eTwg8eFa\\nbJlChf+2jkGSMaVEEmHJmBc1sqJEovApCQOB9kLSJKfVarK4EnJQxqxfOMvyqRY0ugizQbi0QL3v\\nM7fUpDceMzR1dgY7nDunyXKPjBCKCcrW8FQd4xcM+ofUcHhS0zx9PzsvvcRRUOe1nREX718GO+H1\\nr7/EpQ8+RmoKhArxECQUlCakIQWelezffJ0vvr7JwfYN4oWLPFL3EH6NufsvcmZ1habvV+CVO0RY\\n78wSStQ9LmSBZr3N1vZtTJGihcYYR44hjCR5BqVyjKdTmMlbinv47wTQ7iwAOZNki85Kk0k/RkhN\\n6fIZYkigVMh4aOisdBlOEr5d+y5xdjtjD/VO5K3uXLxF4gtFaSunNNMJqhlwEhqcrbTOqrN1RSEV\\n+hFSWURmZ2X9igLKOYMWPrgcX4Tk1qJ9n6IocZFCmop1SiAqZVhniWpBxV//to+kWgDJcUx/o0+h\\nFKXUlJlBhh4uH8+uoorZ2fQWQX2dk7P7Z574czZ2DvDnNSmaw4N9SjyQgvnlBX7gyhVuHUqMgH/5\\n678F6Q5SLDM/mBLVh1x5/G/wgUfWOf3wR8jKcySTAbYjEYNtbu5MqM/NYZMtxpOEvWnK6TNLNE63\\nKX3J1nSMFFPM3CrjmymZ9nhlPCKQgnKaEdDF657mzMWH2PzKc8h9OATAxyE5QKGxlBTgNQmtpTQB\\nPZfRUI7UjFF0iOQyWk9prK0QNCc89tGPcG1zk2K4Teq6XFytk9UEyhsRtCxDT3B2aY7F05LH/sY5\\nxP6EzZs32Nwa0Jpfx2Rj2s02B7GlmDrGe1MW74PGfMTS8jo3br3JhQ89znRzl698+nlqix0uP/wo\\nuJhxaQiEwFd1TJwwEQX//Nd+k6nzeWRlje99349w8coDeEsdiONZc9xWWaT0IB9Xk2h3tiNLWQ4o\\ntOT4sGRtsTqyCSzGldSkJjYKqSRzDUXmLKXJmQ81BsXa+fsobIaQXsWVeM9qEbN73Vk5DUWC33A4\\n55BWIJylzCwoSWexC0jyv27CjtZY/BlRn0fVenOcTAxXap/CCZCCLCuIOnf75lIIrDXgS4SWCCcp\\nnQFjCIOgallIB8zOQKViOhxjnQNXFdgsgFUVjzcCYSXKCkxqCVfauDRDhPfCJg3XN2+SjaYEymJy\\nkMohVcZxMuBHfuTHOOFyh4Cgfo575/A/+ckXefDBBebCiK3BhKDlo4oGSgwQLPDitQndMOEXf/E3\\nYPIqACEhMhhjW+u0QsnAtlkoIhoLMB6lnMXntRtD0sEtPvixH+Lxn/ppbm9u8JlP/i6b164xHGa8\\n9tYOg4qRjw+ennK4mzMiI9A1jCkoyPDZQ0ZwPBwx7E9JqEqMuxgE+Szrmh1pigkxklqzSdsIaqGH\\n6eesdn2yVcPejX2+78GzIBd548lXCeqSW7spc4NdzrbOMBU+KxdOM1ZTGq2SOLZo6YOaI+zAwz/4\\nGLx6i6c+/QxaNYAD5vySoYE//vQf8DNqxIYruXxpnbY7zR/9yq/Src2h3tjF7g7orG6wtnoOa3o0\\n1+rcmB7z2See56EL5/k73/sBrjz+kberaUDl6Nk1yCsEnQ0WkL4B9mdrrklJgNaOJC1pN9VspXqM\\nh2OOx0d4VlJv1ijLksKAF1SrW/iGsNXG4eOJtxdtT1xW3HnkMZn08ZXD5g7tVVqDXmiwAsokxYUR\\nWfrXbMTVOUdSTIlms+Anp2IfmJYTQCAUGGdnDnsyzKhQXvUze6LvYQ1136PI7Yz2GfAr6gjwIJ7i\\n1Wok8aSSi7Jl1VqzAudMNSorFF4IpV+R/kn/HQuiHFFYQ1IahCtxQlGqHM8vEYcnaVVj9jer2ee3\\noWmKgMFujAhLPG2x4yneKCWoa0KZsP7gPIg+1197CxBEOCJ2CWoBtcYap86eIfYXMEnKeNJDDbe5\\n0WszPLrJ6sIKVy5d4dbmHoQRsdLsDTNkEJBmOT4BlowXbm9W02EExOXhncW2DdTw2DrssZFrtqmU\\n1x11cir1EXNP4NJYymxMQ9fZ6/dQCObrczz9WkVHWJQ5jfkRP/VzH8UcH/Pnn0046jfZ+sqXOP2x\\nD7NXSox2qEdXOL9UZ2NbE44lje4CaiHiwhnJ/I+FfP3rbzKODVY52oEmTjKe/MwzzC+1uXz/Zdqn\\nW1x84FHefOoNNl/5GpOm4qf/3t+nVDmoPl+8tsvv/N4zXFhr0QwvceXD3/seK9ECSaUsm04hkyR2\\njGzmOHuMyH1SM8F6dTq1CCzUOx7W5UghydMxPiCNo0hjdBhgSoMooe7XGQz7rCx2yYypFGC4l9H2\\n7rqXs08jTzMay23y4wScw82eJAXYIiO37h3Q57/YviucXQiwyQTfa97DKXYym9ygl+zjewKcQtgZ\\nZ/tsZxeexRiDlh7OGTwvZDKKaTZq2NIgDZV6i53lDY2Io709whnLC0risGijqm6cBmsKUuvItXpb\\noy1NpoRhyGCUEyCJY4ESilw4jAKblfzwj/8slW7GSU/+HlYT0+dzf/YynhoTT2t0OgFHG8dcWuui\\nPcOCtiw+dB+J1+I3Pvsk8Y2nAE1EidWKOO3wjz7xPgbTJsXhHhuTAY8+8ih/+Ok3efa5P0apLv/o\\nF36GoxFcfPgUX/zCV9k8TKnpgDTPkbhZCg74giAXOCxdEaJ0QFEk/Pc/9/N89OMf5v/65V/nq5ub\\nswNQRb7xbptDU3J2qc1wu8W1vS/y/BNPsLR8nt/+9X9PXhyyb0CVhltv3uLKIyv8f9S9d7Rl113n\\n+dn75HPjy6HiqyRVKZRkS1awkQUyNjYSyIDBxgYas2iPmxl6GmZ6TdM9Y1IzDNPDGkKDCW7GBhs3\\nNjbBxklGki0rhyqppMq56uWb78nn7D1/nPtKqWTJTM9a4rfWq1d11711z97n7P377d/v+/t+3/u+\\n22idW2So5zlx6jSWEExMzLH69Aon5kJmm1P0IghSidM3aPibGN+sOHP6PJu8CsunlxgMhwgK2h2L\\noGhx6tQSe956Ox/5nf+T+z/7Vc6uLvLYgSf4s898nt07Jmls3sv4FW/kj//TO19y/SOsORJG4yv6\\nKxjuFFo0IDLwgpxoTWHICQQCqTRGNSHuJGRS0+73qExuRoiYqiPBaKBNhTZSBkmG60MWR4iiyeyW\\nTRRJufG+sD60sdgLNg6IZRtYxfFJghRhmiOW2fKogDDJVUGeh4jvgB36dbHYUZr1dou0cJkdG7uU\\nrDOBSAU0JioMuzHCEghbURT5iDRfYmmLIlMIp0x0JFmK41vEYYghbaRllwtdlNI7YZyhU03qFAhT\\noYVGaIlWumzIKTSFEDQrTabkiNVVQhYFaJ3TG/ZZ73TJ4xBDJ0gdorXCTnze+KYNb/FC8M3zU/zs\\nqcN88oFvYs277Nq1nX+8/xAL2zYR9yOSgWLXG/fxub/7Cj/1x/839959PQAOOXpyCuFXmZkZ5+lW\\nyp5qzFqr4Jrdc/zOL/8e/nSDO26+hZm57ZzuhajeAL8+QxRkWMTkeU7huCRJRGxkSMNBZQm2ocmF\\nBsek7lSYHJtmdcLm6Pp51qMOQ8MgLYpRpuH5cHF2fopNE3Ps2n8VhZFz31fv5c3v+R4eOXiOxfEQ\\nqgAAIABJREFU+lW3sjZsceOP30G73cU9+hyFLrD1gN23bWep7eJU6hz7h/toraywyZAE0Sqz269l\\nYaxJKnKmKwpsjzA2wbRpTm+hMI7QXVuhyBLyIqUwNJMqZnWlxfkDzzGxY4H/euwppCnw/HE+8BPv\\n56abb6fhfDtAU49yqZmljtraIgYpvVZOLh1CWSDrijSTI4XfDCklNdcmDHMMJTGThODiOXKpqdTq\\nCGISCb5t0zAcTFdiTpoEvQLpOAhdOhClR3wUvFgJQJKTqwhTWtheSRdt2JIiV2USWpenfNMGz/XI\\n25fbhC9vr4vFLqXAETBWs4jiGM91R/BG8KRHZ9gqJZiLgkIITAEb9fM8K8Uh4jxFCIUhDOKowPMF\\nlm2XCrFSkGc5CEG3M8CtOgRZjiUFWuhy5stGdQwkhSgVPDR5iXMqNJZXoX30NLLioyOFHQv62kBL\\nH0PnvPHNt77qOL/+2Yfp0EV2PR64/wB2vUHQ7iBCk54weeixB7nhXbdxZ6VCWXkdHWccg+t272DT\\n/K3c/IZJjpwf0JwW/Nnffga3kbB72zjKnscwLSpVnzQrWO2eRjshIS5KKjZPjLO+2GZYhEzVmqx1\\nV8gKAE2YR6wFEbTh8ZPHX3bdGzqhtVqTHfObqG6eZ8J22L1lF2NjUzREzmc++QVu3L+b+es76EYF\\nWdVcePwsnjZIgi5JfYKnD7aZ27Wdrd93E3f++Fv57Ed+l4/+1ueYqzucDb7Gqqowd8UOPvjhH0K0\\nfPLKDF6tSj/K2HvdVTzxzZBM9xn220xtmiONM9Ii4uiRc+w6f5yqOY9h+5y5+Bxn1zp86q/v5+yx\\nk+x8w8380q/9a7YaL33gfSCFLIVIgdmEYRs/g6IwIYvJgLpjMxxG+J6NNASDfoTjmGRk5AiUZYAh\\nWO/2mZscQ6mMYRJjZhJhuKSDArNSJ8fE1GUka44W+gsjR7GRpSpM4rCPKSSmpSlG0amQGo1Ca4nS\\ningY41cv35l3OXvVxS6E+C/AncCq1vrq0WuX1XMbiUL8DvAuyrjoX2itn3y175BSkMYRnW5A1oYt\\ne1w2GNP7YRdtGuR5jmGW50VtCTbCL2kqkkBhVm1ypckFWDoHbRC2Y4wqxGFBxa2w2mrhuQ5xMsSz\\nbDSKNCuwXRedJJi2Sagl2C7rnYDNM6OJFDlFMsCesjB8i7iXUyEF1plobGHr3EyZubUu1/u8YQMO\\nnu4SzuVM9AvGDRsZ9vEzjzAImGx2OJ66/OHP/gIQssGla41t4c633MCOvbfwzINP80d//SBK5yRJ\\nQVaE/Mj73s8ggx1Nn8zw6F04hpAhZw6fQokGP/ND38/3vv17+NVf/mW2XXslH/qln+bRe+7lTTfc\\nyiNPHmLL9HZagxYf/4u/IBQR3U7MpZLIC2zvtfuYmZ5nanySQZTQHK8xiIdkQ8nkxFauvnaBB7/8\\nFb5/2/vxGjN0k5gdc9Pc89mv0LIrPHb/PzKzeS/J4DPs37+P//jxf8s1d93Oj508TVOm3P+VZzmn\\nJCeOHOLvP6rZf+NN7L4uY3xugdxw2H7Nm3nH972Jj//BJ+CoJugOME0TqTKePXeY/ef3MGifx636\\nzNRtwmHI1XOTVPotVk+f5uAXjrLjB6946ZMHmCBSSEPoxKxZpe5flmYUZoncTNIcYZrUXJfMSNGm\\nhWVKhLZwXY9cGjimhDEwHBO78HEQmJYBhkWuS3rI4lK1p2QaFuhLKLoyrLdHCWVZHqvyvKQnR6BE\\ngdKi1HsTGo2kWnOJk8vcrFew1+LZ/x/g94FPvOC1V9Jzeyewe/RzE/CHo9/f3oTEqfgIHWO4GTDO\\nIBsSIBj3m/S7/bKTrZBI2yOj1Ga3kZBCnuSkToI0JIlKqXguKi1IZYKZCpQ2WFvrUGvY9PsRjmOj\\nCk2RC1xtI9KyhVVjkOqchl8txe6hPG5LE8MyS5SZzAhaPQZpSu6a1Gea2OPTrzrE448O6LcrpE5C\\noW1kIbGSkMSoU7UDFg2DE48+CuFZoOykznyLHbUZxvfcyD1f/VsYm6aQPZpimh/5qQ8yOWVQ98dI\\nsz5PnzzNQmOKYK7GrDPOtt13cP7gk7z1lmu5+4d/FEWK4AL3v/d/AOAdhwuuvnUXnbZCmBMsbN3B\\nM0eO4IkKCQmGY5MlZYjoV7dwxx23c/L4OqvtNhfOnyffNA4iZ+3oKTbPNomdiGMXz3I3A2h3iRNN\\nHBVcc+Ne/vJTX2bz7DzpMKQvqtzzpW/xoUeWKPIpirldhLrLzFSLMW3jVgvWLjzLnx1+mOEnXN5+\\nw2286yffzdSOq3n62Bkcv0EcRpAXpEVKMMhxVMp99z3A1oVNuPEYm7bcTpycIBlqprduRgcFf3/v\\nl3j3yxZ7AUkKIoE8KrvJUjCDDKUztCVBC6SUZGZKe5gwTIfMzDSo1+sUSKRRSjEb0gAhWV1rMT0/\\ni1UUFEqSpRphSfSIUWODa0LqUsl1QwxSjnx8r9tFGoqKWytFSTeg3VijaFMhtETi0ekG1JuX6ft4\\nBXtV3nit9TcoE7IvtB+k1HFj9PvuF7z+CV3aw0BzJBTx7S9CaNw0Q3ULrCzn7NlF8m6XqqE4e+4k\\nFc8lz1KSPEbpiDiJIBboOEXIgkKUgJoiz0EYxHHEIAlIpEEhDQaDAbWqybDXw3VtpDBKVJwrKKSm\\n0ApDCGwt8cmx05yqZREEg7K1UGr6gwEVUWfp2UWEiklUzG1vuJnpyS2vOsknTzzI7/yX36c2cZo9\\ntmZWhQQX2pw7tcyamxHOKVZPHuT0Q1+/9JkAeNud7+KW/W9k+dnDhMqifW6JKWOcsdmraAVDNDV+\\n4z/8Mv/1K19itXeR44N1ip4mkFdhhWfZtq3CB376Q4z75calyS6huh/61heZbhpYTo5n5yytrJAJ\\nhdI5Ck12yWNUmGmMkzFkujJJHiZkg4Rjp8/wrQcextJ9ut2AoNMlDHtcXGnTWb7IIJGkecxQw3zV\\nwY5j0m4HFhexak2+fM9TWJnB3lvexPyea8ltE8uQWHGOHAjGTYtxUzJIFvnEf/4kqrPOdGMCMTGB\\nsmwKLcCooGROGMScPX2WVrtN3Oty9ugzOBWNrJtIu4KtDCwr5RvPLr706S61AOyAXBfEhsYSkkJK\\nLMekVvHRkcYVYKSS8YrHnuv30ZifRlR9zKqH9CaxvGrZc64Mpuc3E66uMeiHaF1gexKkKr34Cxf6\\nCExjXGr/Ki9HFxohBEqVZ3QpJEIaLwjhRZmtVzHjdRudvnZFmH+qSMSM1npp9PdlSg55KPWEz7/g\\nfRdGr73MhBD/UgjxuBDi8VarTdwZoAvQSUmwH3RTsqAgy1QpfZPlGFqjkgyZa7IoQ2Ulv5yibBaQ\\nwiRNc6Qp0UKUVD+ZwLUdkiin6tWQWuBZBqIAaUiEIZAj2F5e5Li+R5QUoDMs10QjSeMAw3VZOrWM\\nHiRl55HS4H873PvzduKZUzS32tgYNCwTbWtEvWB25wxPHvwGC2+9nq9+4RFeiNIz/CpnT5yBMZtI\\nGKxeWKdRqRBlU7zjtuuQOmfp/Dm23nwrcepw6kLEqSceonV2hc7KRUwFhxYL+v0V1sPBqOJffkMF\\nCMipmoKalCxsnaHZqBOFKaYJtmsBCdglEeX87BR1z8PSBrWqNcp/mAyGIU8/d4SnDhxFKxu/PsUf\\n/NanuXiuQ5YEhEFK2l5mYecVYBlo06Q6MUdu+Xzp/oc5dWaJrVdewZ5dVzC3ZRdeqimSEOEZGA4M\\n8pgTR5Y4fewwJ4+doV4zmZ5qcu3O7WzetImMhOEgKqUgHElvdUCW2jTqdaIgQeYZaWFS8X22zM/w\\n8T/67MtvjlWWZVVuooXGEBJtg3AlaZJjmYKqtJhxK7hbxkHFgDUSCXUoGW1kSazgukBOrgFpIQ0D\\njcCUxiWknAQQ2ct4MRQZiALPd7AMs0RvCo00RFld0yXYq8Q5CIQhSfKslCJ/jfZPVoTZMF0qQ34n\\nzTcbn/tjrfUNWusbZuammdw7jR53EE0L04dmvcHaSo/UEvSyIWZD0okShihiLUjyiDiO0IUuzyKW\\ngZIFppEySHKE6ZDGw5KkTxYkRg6ejed7qCKn7tYhBceyUAq0YSBsizTVNBs+CBtbS5ZaZ4jDgOPP\\nHmN6wmdsooYqLGrNCjoLXm2YQML93zjNmU6HzKySdGN6UYSFxJQWd951K79wx/vQRe/SJwSwZbLO\\n7h1X8tShxzl6cZ1tV+/DH5/hh3/8DoxqSjYM+YevfJFo8RzzokI1DOgtDZncsZes8xinu0MOfPPz\\nl/7PmJLyo6RUgim3yu//+0+xa/s20tTnh3/ibvZffR1KF1imZGJ8BrRAWB7dIMP0xpja3KDf7jM+\\nVsHGZnysSjhICHuLGErRWW8z3bBpeA66vYI3voludYZHnz5BNFzHkmAkGVMZ9FLNf/7TP+fk4ROs\\nrWT85Fv2cxMRdp6TKI0RW0xXKwRZD2kG/MrP/zu27Jpn1zVX8713380d330DPhWqnk+l5mOpjFwk\\nrLfbJNkQqSYQ2CitaEw1SAcJSmS8+I55KMaAcWxZwS4cyECYFkoLrNxgfDzAmokwp1JYXYTVoKzP\\nigrPK69usN7lgEF9Zpqa5xD1BnTX+8RRQqEUiqLMOY3qG+oFq6a/1iEYtpCWgYFFkZVbQ5arUdZe\\nlO+/BODUmGIjPnht9k/Nxr+SnttF4IVx7ebRa9/WpLTw/U34vg002aAamJjZ8Jw+CWtsnnIpJzcj\\nSkLIC3TcR1oJwVBgOQZ5ppCWgWqleJ5Jd9ijWnOZH58BW5MlGdJzSqn7wEAVBdI0yDWEeUqtWsN2\\nrDJDK02mCoMoGeD4JnEuKKRNVwRsb+5AWK+eCW0lgvPtAFHN6RYVCmUhxJBIe1TTmPOPddnYc33K\\nrKb2Bf3Q5NH7j7J9u0ccaWqzJnNzs3zxs/cyO7fA2UMHcLda6GqNC70BjkzZff1tHD72Lb76+Gl6\\nB//k0klw43HYeCQVEMRD9mzfxdGDx7lybCtt3yxD2BRMoyAKAkzDxDZcsiyhu9phx+wetO2QWgMc\\n0yTSOc0Jn1S52JUqyYVFQpXz8H0PsuuaqyiymE2zmzg62SBME5zUpRsERLlDdvIEp3KIMzi/1MJr\\ne1R3b2UmP0G/b5IbKWEhcM1Sv56szVf/+qsMtIVtQWVuM1dcexUiDxiEEaGCdi9E1TvEKsXVCWES\\nU3UsOq0+47UJNl9R5fAq3PCCFIvcUFGtKIxKhhMXpH2BoQUOGsusU3RBeAI5YYNpkqcDTKeUeSqX\\nUMbzXPul3DMqHtE9K2y5EbaXJV4pQVNgiOex9r7vl+f5YuTRRYmgkbLs+9CqTOPrEbBMCU2WZ8jv\\nwF//Uz37K+m5/R3wk6K0m4HeC8L9V7GNYpMPl9SyQzZoqBzGKevXJVOM50ziVSqYtodpVsqkncrx\\nPIlpGCizoNCKHbt3MD07PwpJTSzHw3ZrFGGGKgoMwyAvFCLTmJagUqtQqLQMtShI04h6c5xha0gi\\nAhJS1voGV1x5zauO6JnD5/jt//inuLKN7hfIcIAKV8m0xphx+dZij7/69MfZAHSEo2mojM+wdX6W\\n7TsbdAvNzd+1j6q/iW5fUd88j9lYY/4N29m2fSv1uodv5/QTj97gNCDpnXuMMt+bvWg3H8PEQ5BR\\nwn6GxRoHlg+Tj/Xptbvsv24XdsUiigvcmomWDnZF0R2ssXXnXhqWx3vuelfZKJQrDKFYXu+SZYo4\\naWMWCt+1OHj4CY6dOMlMw8e2J3j7u+9ifGKMbrtFEQ+IwgDdy5g0Yj79J1+k3WuRT1Q5JKtUNs0y\\nbUt0phB5RN1yqGAy5sT86a99FJUsI5I1ls9cIKFPr8iI4n7ZHKUyesMOUXudQTRAyAqCDNNvMgxi\\nqnnEuefWLnOnPMK6AMsm6Tv4noHvG2QGBGkNw6oiHQmdIao9xHRs8qRP6XhiLrVu6bJCpDPArWB7\\nDkoWdLpt4uGAIkvJ0/Jeq9EW3FpZZtjvlhgSWYqIaiRKg9IaVZQ6hsIQyNFqVaoArbENA0P+N1zs\\nQoi/BB4CrhBCXBhpuP0m8L1CiOPA20b/BvgH4BRwAvgT4F+95iu5dCmCUtjGpPTyG+qWARtaV6Wv\\nKomDZ2fm2L59G1NNn1rNJU1DirxNr9dlZsv8Zb8pHvRxbJc4y0nScuISXZ7zk6ggVhJl2uWGPzdO\\nMVjh6oUx3ESz2a/wgffc+ZpG1A0svnTPIdzxKrJhUZ2awQtz9GLG+tkVzoUdhJGPRi5pVmpsnptj\\n2+Qmqr5muRvw3KHz5PYE1fEqnu8wVRsSDx0ma/ME+RZWj8D6kXVmZz0+93d/xmc+9jnoxJeuoQG8\\n5y3XcueeKuvk9EYPmQ08ff4iD973FB/940+RpYJ3vu2t6LiU00oHCVXXIY87yMKjNjFB4vl0C8nu\\nKxYYa7rMjNX4sR/4AX7nV/83fvXf/Guu2r6zDKeHIYPWIgcfO4RlDKk0p5jePsHC/lmUdPErDoZl\\nk6c5zx0+zZ//+b10li8wsW0b45sapDKj0ahjZxkiCpDkBMmQp09+g7ppEvVy3MoW/Jlt+BMelYqL\\nEGXyqjcMOXP2Iiq3cdBkwibNNYVhEw/W+eIXv3yZO+Xhiyo4krzioMhK9V8zozBDsmIIkQCrgvQd\\nsk4f0zLReQgYZTxepCURYhQjZA7pEEjRnZAGGtKIbquF0OXdNjBJuwFV18YyLXShUGkxigA2vD9I\\naZRJOkCpUrxUCIkwJIWQJZj0Ndpryca/T2s9p7W2tNabtdYf01q3tNZ3aK13a63fprVuj96rtdY/\\np7XeqbW+Rmv9+Gu5iCSJWbv4NO3BGq3eReRIfF6PPLwipvT4XRTrlMinjXApI0naBDIiIaM2MYGq\\nVNi9d+GVBoRb82j3ugjTBCmIwxjHtDEKRZym2KZX1jwtEwyLBMCs0FMpT50591qGBMDi6aPc9bbr\\nOHX4WVpnVnj0649i+9MsvHEXX3vwfoLlC+givgSEjIMMv19laSXh6MGLnD3R4X/8t/+CSrJM0lvl\\n5KnnOHqqS7raQRgtyA9z7Vtn2fFdu/jGww/TOi1ghF+HMg66+7adTFY8JrZO8p43zLAFuBrYB0wC\\njdkVjpx5lt/7kz/lF3/lI1x70x5SEkTFIs5KCYnC0oSdFmiFadqYiYcoDAadjDt/7N0EtmRgONz0\\njrdjmBbJMGBlcZn11T4WguXVENud4fo330JttkacDMiygCy3cGzBkaWn+fInPk9z7Qj9k2fZNOPj\\nasn81BzzszPElNv9hGXxG7/+e0xOVTGLIZ7tcseb9mMFNnZi4gjwLIOEgvWVZQwdQm5S8SVZ3mPY\\nTdk0V+dk/6V3qgAxhCKj6ktMbPLMosgc8hBkxSM1DHSvgPUMy7bJhgGFjomCPqqIwBAE8QAME4KM\\nIiwg0tT8KtJykdJEkOFeaqjKsWseKRphSwzLwrBGqHgNqig5FxAKMVqlQoBWoHRZlc8yvqNGmP/P\\nCbr/JqYUaRiycrZNtzNEYJGNpAtK+EApawsGBRkpCZBC2CfurBMPUqLVAFsJGjWTuQkbeVksNyAE\\nUZBgmzYKQZDE1Jt10jwmKQr8io+JuBRmgU0QFwh/jNS0Ub4J0WuDKH7tH7+C9PoYwqZWH+OKqxbo\\nF21OrR1i/01X0j/+MBtCzlBjxphnqbNIurSKSQVhupx49jxZR7N8MWTpwhqDKOPpZw7x2PHnOHHm\\nLAefPsA99z3A2RPnsdznD6MNylC93u/yI3fezuKi5qEnVxij1KDZGF3/RIaHZn7KY/XsKlKVPAEz\\nk5OYqY0tKwiV4eQFskgQOsCv5DTHPdLBAMsxGYbQ0wK36ZIXBRNTYwzW2/SWVsjjnCjtMDs3hVcb\\nY3HxDG5V4LoQiIj2oIcBrBkKW+fceuP3sHXHArkBuc45v7xEHRcfCLKA5bMnGK84+A2P8cYE9co8\\n27btwDFsrExhZynBYI1ua5lur4+UJulwQMWXbJ/fhigEjz959iV3yih/DBNTgS0NhBRYtoPlSnJT\\nERUZmVRomUMWU8QBiNI5FFoRhxGZKoijMgUohVGesYUkVwKDsrSr0uejLoRGGibonDTOUEpTCI0S\\noA1ZysyXRMkIDUKXBBYISV5o3EqVvHjtCbrXxWKXwsBLx3EwMQuDZ588StQfkAx6pL0hSdCju74G\\nqorVs7C7OWQJeZ6RSEXF89h51T4mZiZgILB0Fdv2UHGvDKeKUtMsiYcEwyHtTgfDcUAVVH2PoN9H\\nmwbVqc045kbCs/wzGkZMzm3n4HMnqDk1purj8MIb9orWIkyGHDp0kJk916CbNYr0IrWZcUJDc/6J\\nJy8RVpVMugpr0ueK/bsoLItK1WbvVQsoGfH5B44TBx1m5yY5f+YcZ4IuD33hSzzyta/ypb/6FMcf\\n/wZZd4ksPnrp2zdX4Htn4S13XM/xbz1JkbVYpmRDN4AdwJLW/If/5SfZvc3EXlnHKwwunl5nplZn\\ndWlAWuTkmWYw7LN4YYlepDALB89t8sWvfQtRNcjTNtJXjDW28ubb34SKIc0UyTCkvbrGYLVL1Wix\\n47prkVaVD/33P4ZhW0TxKmRdeuE6NlWOdhMOHA1YO9FGyElmm1V2TNd408JefNehiywjLD1EWjmW\\nM0SrIb0kZ2K7w/hUEyssqFgWwzBlvdXC9CsMu13CYYRMJOtBTGu1Ra+7xnMrGS+2Kpg1yBQkOUIr\\nLFeDJ0lTjedZhBoCDcMwwMk1WSvBMy2iQUQSRxh5yRSndIEWBYQRhswQRo7KMtxC0e20aK+t0Vtp\\no+OMSrVBFqTotGxg07oUTdG6rKsjxPMZdyEwzBIbLzHp9wKUfu0IutfFYgewsoIaQyqdDhOW4MyR\\nU5w+doqV1hInjx2n22lz7sRZhqtdeoMB62GKWZ+lMbYZswJlaB+DZ4BMKcIYkUbkaUA/6LK6dIEw\\nCOl2u/jVJv0oKFlICk1hW2ydmaTxotrEiL3WtDl55jiTDYOKZ7FlxxzdwYCge5E0Hrx8ICP733/x\\n86SZojXocfLQQQbrF6lkDY4c6hOEFu3lmP6oVTQF6gsz5BOa83mHeKbBtut2sWNzjdipMrXPhLEC\\nI5EsL62Sdk/yUp36l9r/8dM/zs/+yPsxr76WX/r013jk+BABPAE8CkwZVeAUH/zpD+I0NrGt4fKm\\nxhjd1TZmXNAfrFE3U2SmmaxK/uav/o71pQtUTcVcs85Ve3axtr7GD7zzZ7jnbz5Fev4o3XNrXHfj\\n9Vg1B+lZrPf73HvPo/SXW7RWu+zev59tN99EHL4YCBIxpEOfB5fOMagU2JZgIjHYZjVo5pKpqWlC\\nDBwkdSTvuet9HLznANZgFdHvs2PrPnbuv5JKrUqW5pgSTN/k/IkjSGlQrdUospB+GlJ3BN31Izjm\\nSxtk6uWdqEtwLbS2yCKJDiSkBvaYT3NTg2qjinQ9Ms9BW5IoCLAk2MKAXBGGim5YsD7M6Ac5SZST\\npYowl1iGjVvY6CinMTOB8B1AYTo2fqOCU/XI8lKhtYzZy4WuX4Cj17osugtR0KjaqOyf2WKXErQY\\noPMIRxq4GEz744x5NfyiQs108Q0bbKhunqCx5UomG5spk3c55el01BRrK5AZhmcjHBPDNPBtl2al\\nSqEF1UqVOIuo+C5aa2KlmJzcfJlqZTnD7ZV1aoaHKaq0hx2StKC5eTOV5iayHP7i0x+77JjqVzZw\\naglbZqeZrk9TlRaFkvR7i1x45BRJsYw7mv59N7yBpnSoZwaVQDBeG2N80zRZO+H8oYimdElWC4Zr\\nQ5h4ZcTUTdtLOano527n+z90I+/5n/8Vn/zbL7PPL9OaBrCj6RAAf3T+M9A5At5BfmJvjavDmD1R\\nzBsnPfIsYfuWMYp4QC4UvXaLXn+ZC8vHWb5wjMbsNKtnVmkaHrubTQ4/9hw//wv/E//h332EY2fW\\nWV4ekIQxbqFpnz/HsadOU2sarJ05QpEMuOnWq1927QmwFoQc+Nb9xBcucj4YEns+esxGioCpsSpZ\\niRJnnjqP3fcoNb9GxakzDFMmpidozmwGJLLIyeKM8ckmCsmwl6ANh1rdwpAmZmZz8LH1y8xgWTLD\\nkpgVE9vUVCwwdIoOFVga6gb++BT22Az+WJ2xiUmSomCYD8mKvKx/FxrbMEkcm7iQFHl53UbDwjEk\\njlm2OOkiIQ1THNcjyYbEWYohBbYhRwtdoZXAkAKlddn6Kkq2Y41mEIXo72AFvy4Wu2Gb1PdvI5sf\\nI65bFF5K3cipZxmVNKZZsyEPEUkKXoPy0c24lJnXGwk8HxIBiUkal62JiYIkVyjTRDoGpmMjDBPT\\nqeL7TfxqHcd+JaGfkCiNGWqNYxrMTE9w7swFNt5dqdb4wHt/hqWzpzn0zEEeePABHj1wiocefpQj\\nzzyLWMtI1mOEq7DsglbRY8uVezh+5BFKpfaSiMguHBbGthMHgjCE6OIytExsx2fXpj5HDxwmC6Az\\n7EPwUsjn8/bImQj9m+/HffcU9FswNmBJpZxVparcOFDvJsT3fAxmEhibgi0z7Bxb4V1XWmxTBbtT\\nxVu37eb8+Q77Jn0cYuokZFnExVPnWe4FfPovv4j0NYtra1wMItaG0GxOMNmYZca3GHMnGZ+dI9Ga\\n4aBHtTHNw5//JoNuxkrX5YbvezlxxBA4lFd4tjJBd/MC9Wuu5JmlNU6udjl+7gKq12GMgmlgixfj\\nxy2EEaOMEMfosiU1ePv+Bd73zrdiFAKtA5YunGLMSXCNBInENWwMS5ANBnzm7z53mRl0StUV2yBT\\npVKvRpAnBtkwh6QY8SIk5Y9wwZJo28e067h2WbOXQqDTtIwahSIXGVmRQRCTFwVpOgAshOFg+y4I\\nk0ILXMvH9R1ypUqsnBQgNEVRIEcEVqooSu+uoV6rUKT/7JhqAOqMTVbxp1IWj7WZ2GRDrwdxAWKa\\n2lQNvDblY+HzPGOXUTLSAFwSdTCxR40zRS4RCuIspV5rEEYptl2l6lqE2sBx5KV+4pKQ+6i/AAAg\\nAElEQVSUyiCJA5YXV9g2P0/RjWhsmiZNWyTLETfdeAMv3SPnti2w0QDwNw8/ij88wfbNiqA3oOo5\\nKHOemlHnyImnWT19mA3AqgVkfo0iz9C9FseWz8B4lb037CDt9jiZhRx67lmIUpYWW1RZZGLHFK3e\\n5WrFI3vvPJy2gCrxgweQmQWjRrYe8PWlJ2B2CxSnSsafLOPqX3g3X/7CH3P1pgayOiRcXCfaNE7N\\nEhxZD7FdiFRG++JJPn/oCRrNCYbdAVWvQiw1qD4qS1haW0VVLd70Xfu47+v3Eyc5IrF55luP4E/N\\nsHnnNPn6BFdfv8D01DSra6svuvSYgNOLAU899QxPn3iOgA1iL4mHZrZugpEjRMr2PT6rxw6wc8c+\\nBmEJi5aGxZP3P8ZV+3Zz+PQyg47Jc0cucu2Ne+isLGG5U4ShgZGFLOzczpE2XDm+8e0FYINVAz/C\\nSjVBfyTEbMvybBwbJQpI9qA2Sbnoq4zVN5pREs4HA2xRCpYYiUJpQW4KSARRkuF5No5bKXXWHbNM\\nrwuJ75UcCNI0Cbo9JupNwijDNgVaCwqdj87qRqmloBT9IMJ0X64k/Er2uvDsZQxt4UkfhMX09nnw\\nFXHULlUspQVpBqpDecI1eR43WMoOk6dQ5GXaUmegCpTKUFpjuxaObZNnOaZpYNt2+SmzIEdd8uoC\\ng87yKq2VVVZWVnnyqSdJsphOLyAZarbMTfHt9segG3Hh8BK9bkgUBQyGHTZvnmX77HZOHD9LkndZ\\nWzpKGZWkRAjQOVObmhw+dRF7sgENjfR8QtsiCdvQqEAWAoIhFq1Tlws/R9N46NdAueBtYm1Q0MkS\\nhhcubHRtkwKPf+OrgF2WiMyi/L14jok+eDFUcNjkSLaNT7HUTtg8N07o2Diuy8nlddpJzmDYx7Qk\\n2swRQqFSgZIWWRLzxBNP8dRjh+kOQ3IgKoYsDobUJ5poFbFryxhTvsf+/fsvOwYB3H/iOXwE107V\\n2TxVw6gYFFWbrOZhjzdpIXju7Cqnzixz30NP0M9S0oZBYOS852d+hJmtE4ikoGppYpmw3OqjpCaL\\nNEKmGIZBvzegOf7Cby516zUjeSJb4XoSt2JQCOhEGzqDckSLZlI6nQ1dgrIzDW3ieC6YcoSflyW2\\n3QBZKXstdCEgTknafV6aeymUxrJshCEwpSTLs7IiNWqXU5QgG6mhXqlgyMvHpJedW/0d0Nr8/2Vv\\nuP46/eSj95JYYziUD2VBD48Mls9CsR2sFOQ6pDbYLkx6lMFpNmJX2PD0Nlnaw7RKtJ0qJJiSLC1w\\nK89LJOdcigtKgssowTYM+ovnqU74rLZ79Pt9Bp2M3K8wO1Zh286drzoWpWJ+6Zd+lY998nO890ff\\nSetCl1Zg4VRTTlw8wPLDh+godwSMDmFsjFuu2s3J4wl7bp2m6Rt0TrmE5/u09WHO2jGc7lyi6nol\\n0+d/FsxJOLcbrAlU50mGqyu8+YMfJY3gHCXWS+sutO+D8TlAQnQQvvl/wTcdTn7sIg8t7KXePclj\\nlQU60yaDA2fpNiQHjiySKUFm5gjbIswSLMMlSEGqBMPQSC1xqxWsLKcbDcitKjoLMIwK2h3DshM+\\n/PM/y80f+GFyNeS9V9z2snH84L7d2DN1PnfvEyzMlJvIubW0FO/EwpOaJM8v3bu77voeLAMunmwz\\n4TsoS5PVx9BZRhAW0Jhl744FzByUXadS86nXG3TNORZ23cCHfuDFirqaRVIyzLxAJZJkAEJVafV6\\nNDxB3ZIIV8NEFbQAUed5ZcINi8mCAYP1mERqMnRJQlkF3TOoygJR99FSYVTHwHy5A1FJzCDo4kp/\\nJGgBJXy2xMoXhQcWpHlGo9l4Qmt9w6s9m68Lz54lKVjWKElW3kiXGlBHmz40ihKKmI2BPQGuT0kj\\nHQCllG2RQ5orsiTDMjxUkpFEQ8gzZA7WS0gjTSAfldBUnGI7DnFnQL3icP7ECnmkqTemyEyBMAu8\\nZuM1jaUQ60w0fT784R/l/NEWy92C1vpRnnvkCOeOx3TyYtQ5VZZ+zJ5k5+QW9lW34Kx4iLbH4Ysr\\nPHXhQc5eXILTndGsXN7+uy2gDv4UbL4NjO+B8ToULWT3HGtPHaKFycro8794zS5Aw/g1lAHyyOXc\\neAW81WfnL16Bs3wRT8dcMVlj0pDcvFCj0+pQ9TVKhnTygjAMyCVk2ZA/+oNfZ2FuC1XTQbkONb+C\\npTzARouSySUpQnyREnRW+U+/8lF+42c+wpb5gsnZGhPA1bUq//L97+C3f/3nuOXuW7nxqu38yr/5\\naa65+o1IquQ5SAWojDTPmaAEBPnAfV+4j+bEFNuv2Ud9rM50s4kXgR+bKFOyefM0dStFmTGO72FL\\nQa8bI6I2q90jfPLAhRfNp6CKg4dh+iSyYL3bJ5NDxsYkQtoMtMsgVmWkKTb4BTfuzga/vMDybIxI\\ngjYRhYlMNMMIcq0xTANplsWgZBhctq4iHZdE5RRaoQtVQmWF4hLHpFa4noHOX7tIxOtisaM0erU1\\nIkgxR1MmARsxuR3sBPwcbAk6hbAHgwGCCioWDLsxBgZGDkWqCIY5wbAAw6OfJoSZwrBePNSzSz1s\\n6bC6tIJdxOj2Kq4cQpLQqPsYMifvd9kxPQXDGCN+bWT8Tzx8lpBxli8mVKsNbJ0QGRaLF5cJVpYo\\nKwcbNI7wjrfcxplBzFndgnRAuNan3dnww9/e/h74w9/1EXub8HgIUwY0T4K1SlJxmbzljWybmWVA\\n+Rj+/AffO/rkBvw4Bm8Kzo2VKJze07xnPmdyagtXelWuGG9Sq3v81B03EwQpVQ2+16RZq7Jryzy/\\n+Sv/K1dNz9HuXABhEKYa34eLySoWEqMAxyrLS2GU0axNcMv+fWwRA37rtg/z4Zv28Yt37eQDP3w9\\n1123m8XTKxw6vMzAayLHHPbecR0/8e9/lLf94JvZ0nDZZJrsbNZoGgYOUJUQa5OoMomubMXatICz\\ndRLTyHHIqDgmTz11iOWVPhNjUwAMwxSrYiKFYOXiszz0xXtfMqulkwGPqldlaq5OIiAIcpRWFEWG\\n0CX9edkau055du/zPMVPAWmOLnJyVaDIETIvobKGACVRPQV9gVOIV1yEDcdHaAW6VJRR+gVHTpmS\\nx0Mc77WruL4uFrtpm4hmlQ3fu0GpW5oD/S5kGVluQhBA3od0BVYj4iLDmzRo90KCSJEVOYapkY4i\\nHoR4voG0NeEgJ4qe/1/rTs7a4hJ1xym7w0wLZCkXFQ8VIjWxbI8gLJgan6Q7GLJ85jjDzouTSi81\\nv2KzdmGRuFewtLiC37Bo1Brc/rbbwCpwCdg49xs0mJubw7cEM3ugGw1od4cwePXW2V9+G9ypPw93\\nfwus34JrvgvCJkzeAs0pnL1XIzLB+951GzblY7j17lspH+QC6EO+DNEK7L0NxC54+y3QaNGaiEl7\\na8TdNlt3bmXb3Bi/+eEfoqvBjFr0B0PmJxtQCDbN1lmNU2p1Bw+XhW3buG3/ldx2463s2beNJCs3\\nybRos2XHHPakwyMPHcCXVc7nJoejCifjhKNHD/LYI/fwwCNf4w8/+mn+/OOf5Usfv5eF5ja6zw7Z\\nNTfLNVt3UR2zqUzWcG0Dx4OElLmFecbsHGW7WONbibwaiQA7zNg66dEP1xiEw5LS2RXEeY5IYNIz\\nmK29VCttg9TZABoUKIZhROFqEplS5DlFqlhdCRishgjTIqNFuXm2yAlKRyUsbCFKCmhLklvlGV5T\\nMFAjFKirwFIwyqgHwfNl1XQYIbUocaRCIHSpkXCpy9UwEBio115mf30sdiFAyYCckg9e8MI0mIR6\\nFW1YaOmCUwXRhHCCYlCQr2mGSwmq0OSqII0VUVdRxBKVSMJBRppqgkAx7KcM2wmtC+cRg5Bpv4Jr\\nSEhCcEpGkYKCSsXEFjlCR1j1DNNLsbKcMdPBVrBy4RzoFK0LeoMh7WGbxdYKjz31DE8cOMLYjKB1\\n4TQ7t0wziAR2brF4+iJb3zBNvJHkcZt4YxZPH32OTfUt1NnDYE1w6sQi0P228/Us8JFPfR+cuRK4\\njjKMbMCwgCUBgznCrx+h3j3N911hMwVsBcg3cAlNoABzM3hNsD245rvB2QGVkLclHeaDhFvnTHZv\\nH2ObbdPrhCzMu8x6IFyT4foqgW5x7Ohh7nrz7dT7mi31Ck89dADfdKnh48YvPos+c/AQ93/9HpbT\\nLvG6gTu7jfnr38QgqPDAP6xy4oIkl038ep3VMKC/HHP/Fx9jShaMLy3RyHL2DkNulynXehY7PBcH\\neOYL36Diw+SWbZiVKTJtMTk1heU5rF3s0dw0RYoiKyycRo24PwCrj+vZmE6ZBX/xMamgPCS41Bvj\\n7FyYZWasTpInoBVZZmOaBr0gpt0VrC9KyCcpMDDJCBigrBDLK/ANA0dIZGpiaI1WEmsEACfNIMrK\\nDD9Q8TfcnSIpBhQUSK3JdIoUCq1GWBKhyTMLYXmkyT8zbHxRFEhTkKiMpMguJc02LNMlIKVknhNk\\n2iMTDTK3zGyKwkSIsi0wF5LcMIm1Qo0YgsMgJhmGuJbGlAWGLjAQYGooshHvQFKW7VSGMDSWaSCE\\nKDnEDMnEZJMkUnQ7EaY26bbWuXjuDOtLF3ju6UM8/MBjnDxxjEbVZufCZhZ27SRHsbK0jufbLFw5\\nTd0fA8YwhYcQmixS4EWstNY4f+wc9rRL70VEP5e3ffq3wXkzbL8SGEAawFIPrA7ELahPYe6Yh7dc\\nSTIxTt0ui335sZOUHmgduAaSDf79DFoCalOwbRxNQWSkNOdqRMKkUp/myq27sbuCquEgcs3s2Dgr\\nS8tkQcGZE2s4osBQqpTdShOqhoklrFcUqx7mKVXpoLsRbqYpMom0mqi0RhjnaN9jsRdx5Ng6/qRP\\nFiQUaReRQ55phCmxXI8BZYban6igtCbMMyZmKjSmPYIiQ9iKfpywfHEJ387RqcB3fYTjkCYZQa9P\\nmw2JK3ie4mPDZbpAA9NzyHVGaiQoJ8XSEjPLydtDxk0X1YuIOnnJdBXl5LJgaKVomWEKjUgMRGaC\\nEmg9wuIXlIiyjVVYbCxcQa3eIAhjpBAYpkWhoFRDKaMFQ5rEYYxhvvYl/LpY7EopiAuqwsQx3FFO\\n/XkzUkleQGEkhEZMXxYEUpEbObiK3NRopdG5piChEFF5vsozVJIgVUSjkSLiFnrYoYpFrV4BMweZ\\ngrYhFdBPMPICpW3S3EXlPnbukKcGmafJ64rMj6lPOWQqoVGr0motc80NO5mZrrJ9k093EHDkwEWe\\nOXqOJ599jpvfsodw0KV1PqZ3ocpkY4HGWBMdhSSxojlWp9moctd37yGqfnsIrH7w19H6d4E9UH83\\ncBTiVVhrQ5D+v9S9d5Rl13Xe+TvhhhfrvcpdndGNRjfQSAQIkoAYBEabkihaHFNUHssKY0sejwIX\\nJY9sSlyKlkzNjEbUiEqUSJGiRIlWIAmAOSIRABuxEzpUV3XlqhdvOmH+uFWNRqNAUjKXF7XXqlVv\\nvbr33HNv3X32Oft8+/ugOA/jazA2IDywF8avZ8+LX89vvfddhC3JsSfO8gwYaR1UCwoPnRiae6A5\\nAfUWF4IqS1ZSE4paHtAYHeXGPVfxB7/wv/NAP6NmHNeMtxmg8UHE3/z5z+FUzGilQmQkxXpOurbG\\nhBPslFW2gyx9ce5RfJKyNrvAqUefoDpqiGsCmXtMELLRq9Lcu4fHzq2y5+YjxAIqg5x+tc6F8RGG\\nU5P0K6VS+mc+cTfDQcpIrU4UNtizcw+tqWl0GNOcCDn5+Ak2lldZXz1BlnTYe91h9uzewdTEKDBk\\nqZ/zTPpWsNybp0QlbDHROBANZq7aQ32mhXES6x31SNBQBVmyQCe7SLc/4OyZJQYdx1onYT1OqUSK\\n0EFPFuRxqVVQaEfiBZnbXLB6B6YAl1zqAyJCa1BhiElypFSX4CTlLtyAalXi/xGKMN8Uzq6UIukl\\nZTJiC5N+6VOp5Jo7jcs1PlcI41HG4LICQ1lV5Cy4wiOsQuQamQHW4ZzCW4XwIT63SCfQUQjCbnL6\\nupKoIhSlXFIS4DOHDCy+YhAqJ7A5G2cHjE7tYudMiyBuMD45QmN0jD0HrsK5iGsOH+bAjfuptDV5\\nPuTqqyN2jk7x8BdOcer8WXq+w+zyaVbzExS6xNTXwgqpcGS2wsZCxvri4rbP509/9n8p5apfcifw\\nk5A6GJ6EM/fBqbtg8W7YuQYTeyFsQG0ZRnKQlrQRcMuLruIv3vvbfOBdH6N8iWcBAXoKgn0wsrus\\nxZ45yKOnuzTrmutrffL1ASO6oBguUjdzxJWIN7/k5XzHLUc4t3SRx4+d4PbbD3LgzqPM9YfsciG3\\nVCE0C9Dp0F5KCd0W/8CzbYAhyaG6e4rDL72dkdYUsogQVEh8BbSk3+thc8/yGpwogH6XvRt9Dq8v\\nsGd5iX2h5pYa7GqF/PWf/w1Sd6j6DGdrFN2Im284SneloKIN68MhF1b7xDta1LSmVYlwRIxNzLAx\\nHPD02vpmz0IajQkcmrJusGBTPpWIKvWowsxVu6i26ujxiOU8I6/GZMIjvSMMBUHqiddCRgZtRL+C\\n9jGymlI4i0sc0lmMELiAkvByMCxZKC+JR1rmT51CS8VgWBCECuFzvC82cSSiBKJJgVT/zCK70oow\\njGEA2WZxyWWcm1gdY2WBCQxZGJIIQRFKiqDEHXvhsNpDIHDSkQaeVFsy6bHCgkgohj2E8+iKhKoo\\necKNgSgoB2/psF4QVAMKn5JagysUyBgCjaorcBvYgpKwn1FAMz25h3Y8znh7mpCQMycW6Sx06fYE\\nK/2c1Y0unXSFRx76FGzM4ZM+3aU1wBLVDOefPM+hw6N045S1M6e2fT4T64ayhGUKLvwpxAoGfVhe\\nhy+9HxY+AMffCyuzJdyTlXLXojnKwqphfM8+qlHIWhRB9gjlmt1TZpA1MAK1Bqxarn/9m4j6q4wM\\nLjBc7JCmIdk6aC2xecLbvvsVpEWHuTRkYj2kcfQ2oGCODu2xFrtbEb3ugGYF1sYdlckqimjb+zr5\\n1NPccfhGjhzYyd59dVoTFRqtGNIUSRUbhyTe89TiIoPKFB0JWZBTj2B3PWB3q0mrXSexOY+dPsET\\njz/JzMQUQWQQMqNSi6lWAvCOkIwiSzCJY/HMKrNz5xgOuxy8eoavfPZBzj7+zLPXSCSelGWGvRPQ\\nWQAcAo3czHfEzVHi2gh7r34BtUaNoBJTrcVEWuLtAK9TUpGworos5RsERQgmxCuHsQ4yj8gsDDPI\\nDGQZWxx2oKjVKmhZIUCA1EjpQGXgC7yzSA0uzwn/ZxJOfkNMgFQCco8K2Kxj30IldYlyhyVAWkFg\\nLLJwKGURosQhK6UQBijAe4luOJwqBReFUciipOQN46jkn0+GZURv6FJ/UQjQFYwI2SgKdByihcJb\\nS2Et3gtUoFk6t0R/feMZ8u8r7MnFE9zzyVNMHdrFxaU1JCnjO2oM1jvbHO1YW19mZXGNP/2dP4D2\\n9ozbFeB17/4d4Dhkn4V0WMpyLDZK3MGPvBWuuxWumQa/AuvrYGIYGnj0cWbqdRYeWmL3q36Mt/63\\nt5EvV2E9L9f5WGCMEjUfQ6XJ0FXoNMZwYY0drWl8nqBVBo0qu6dGWbzYodausnPfBLKpgQuw8BX8\\nyU9zcSklxVGLNUHPUfQXaS4N8FxevPPMy3nPsfvZ12rRbmmCoEocNQlEwFizjXM1CjQWRy/3nE0c\\nfQfLg1G6I7vp2JDBWoeRVsRgs/hwtetoTDaJojqVdptONyEMQrLEY51hZFyTDwvau6uMT7WphnXk\\n0DM9Nc7gsko8TYVyCzhChHVWBwkMt1b1W5l6ufl7QCWuMtbaRXNsN2PTV2NaMcu+RHoXaY5IQJsI\\ngcBKSJ0HAd5piFtQqYLepDjeQptIRe4K8iLHO0dWgHXxpjIMWOOQUYgp/pkJO4JAiBjhHUKYzW9y\\nyr1mR6HSci/ee6x2FN5jwgDryz1cuSn57KVFeIHtSiITIZVFo5AevHAYrQitK5Mi7WoZtQEiBZmH\\nMMBpSDMPQpZTJG/JnKViNZNXHWVbuZRNu3C8x6teeRvdM8eoas+5+Qvsu+Egu68+yOyxY889QcH0\\ntVfzju/9Nv7k05/fts0P/Pr7sHweRcjxP/s4H/jMItrUOPnQ0wTXHOKzH3+Afbv2Mb7Dc+0k1BFM\\nxZO0k5SlesqF9r284bvexDTXc+h1r6GUHpbQOQthKdl4SWiqOkH19a/ngT/7GDe1QyoioT3VJFvp\\nwkofP9Wmdd00Fz6Yc9u372X5oITlfpnl37ibO6xlvh0S5h5GWhwohqwsdZjYvGp5y+ayJxjziaeH\\nLPUGzOy+llTP83TlDIN5RSVbR4Qx/V6fjdQgqm3S4QBtQ/LFhCk9YLnvkeN19rQFC+ueT374b5lu\\n72L3eAixplApN15/iH+45yKEgvWlVSo3WKTOcTjqO6bIpGFjvU8jvpwWvHSLCg2MTqjNVOgPU9zF\\nixTWU201qNSblFPCDcqlUY8SrBAxObaXyTEAx5NPHkM7BdqhCBFOIiKPih3SQb9fUJ8MN4FWVcro\\nrhkbb7DRH4DyFEUBXiG9xgqJUK4UpSgM9hup4vo88k9vB36EZ5KYP++9/8jm334O+GFKr/gP3vu7\\nvnY3BFgBCorMElZK0j0IEBRYoVE+Q3iBcCUooVGvo2LBcNBFZRJTCJz0+MIjrMY4j5IercryASE9\\nOIHJQdcDNsm9KUfnTYSeM3izKaQrBRiJCCyagCiu4BkgLsksPNdWlhLOXZijPszx0iFlFecEs0+d\\n3f4ECxdPPcXLv/X/4Wd/57e3PeSV33kdq8mTLJ+d4/13f4kPf+401x44xNmlDg+cOEZz9zWcOPko\\nnCzr+Uvm8nKrbbIeM7J/kh2VUa79lu+gfJkSSFPorZbvJh3IdYlMLDIIRhk5dDXrx48xMlMH4whE\\nCFZgUsn6WocX3nAtXoUUeQZDXW4hjS3RWX+YkWuupVhaYilboVhZxCKo4y85+7OHyogHHzpBZSyj\\nQkasQ9rtOifOr1CtGYxUUKnTWU6JRKk+2w+GrKYpUZxQbzZpVGtgVwgogS9htcXkWES3k4OPUUVK\\na2SMbjFk9twyuZFYG1PYIaLbR0lJUQzIXOWyfpVaqhCglS1ryqOA4bCPVpreSg9JySvfGFObzzXg\\nubkJyZEjN5Gsdri4vFwut7dIyLxHWVBKgLPl+39ZSRaqBkUfi0HaoAyCwiFFjHMChEUjCcJvLKjm\\nT4DXbfP9O733N23+bDn6tcB3A9dtnvO7QogrUQvPMe8d1mfkxhAGmswkgNssUYlxm3toTpZlf054\\ntArxYkhjVJZVbzrEhwEqBBk6gopDoXBWIgiItSLMLVI7GImg3qJ8uJv73qFgkBuioAHOU/gMr1yJ\\nlpKwsLyEoMYz/5DnWqeTU2Qp588vUKlXqTcnwDj2HB6/7KjLx1fFH/7hr3L3/ce5cOq5BS5nP/pe\\nEvUFJuNDPHXPPI2JPdz+outAWlo1OBDEjCwucbjpGcNeKgsylKyf9/ZT7nv0PP/uV/8IIZoIIVDi\\nelqVl/Kzv/jf6V+osvzEaVh4sjwrGAFRo3bLNYxdNwqtHqCQogH1ADGhWTp3jv/03v/MT//Cz/Bz\\nP/MT9PMUBqeh+2Xe9sa97M8m2VPfzY6V8+xhyMj4DDtoU9k2rmzw1x/9IB/64H0EIyM02yOoqIbz\\nMe2giZc1gv3jpJmh0WpwGsGCyVnyAbPZGANXZSdVJmVA6ap9/uqD72dmR5t6rUJ/aLDCIaygyCyN\\nVpsgFiwuLCM11GqOXj4gLfqsrFxJTBcBpchDanJc6tDVGBWHRFFIf20DlyQszQ/YmEvhYq/MAfEM\\nFDrf/F0ZG6EwoHyElh6MIO8YMA5dLcprDUSpC7+FbATyzKCkQXqNDwpkNEC6HkptEk+GEVl6JePO\\n89vXjOze+88KIfZ9ne29AfiA9z4DzgghTgG3UbLTPq9JIVG2VFu1ucXLAqHLqXzCAHyE8iXrppMO\\nKUr0kA5rdPIOI2PjdPurOJdDGuOkwVkFhaXwBUaWUTzSChkHoBo822kdxuTIKKDX6aLDck1mC4Mz\\nIQjDdOtrc8Q/9pUzrF2cx2WC8Z2jVPZXWOicZc/0NMuPChJ/mstH/723vZjRaJQvJxe2be+uz5zk\\nR1/ySnhknqOiwWtvu4HTJ+fIGpM88uQ5GtWIxz53gr7N6c3McOr8aWSouLixAUFIR+QYJ4mMY2tT\\nx+HokPF7f/AR+sUM3//D38WpC4/TmP0yR++4DaKAq2+7tYQnnzkLoSkTmtUq9y4t8C0/9Ar6SwPq\\nO2fQSYd6ZMFUYWkf9fGcq+cWGN1dZ6TZ4PE6rF9c5w0M+DyGGUbo0LlCqGGVmT3XkfU61FpViqFi\\ncqpO0EtZ3UgIqm2UTljr9RgSMyoEYxGsdAuun9nLYHaWA+0K920UEDZYPHueM2dOocM2E2NVzh6f\\nw/mCqswZmpTFxSEHDu+lGA7YGICNQob9jMhtl4cpEAQE2uK0oUgkhRUEylEUW6DnAI0mlxkhQ3A1\\nYAgyIiRm2fWYkA3CIChnrqFAO4NWDZzwGDHAmIy4USuRdPEz/VAViRyGyEBgfUySKWIpUFikLPnT\\n/Pbpo23tf2TN/hNCiB8AHgR+2nu/Tin1dO9lx3xV+SfgRwF2795NISKCwuJMBiKAzCGDjEoVEikw\\nXoFTZdUPJezQYRkJp8mLHpW2IKJG4gQitSjt8V7Srka4IsUbEBWNbAY8NzpLdBghhimVSDNMLAVQ\\nCyN87si0ZJBIxq68iSvsvocfZqoS0agWZGlCuzHCwNQ5fv4Eid/a2lHltp+HI+Ekj33xcT597HPP\\naeuqBvzoT70ekg0Y3WDfgQB6e2mHEWLHGDsP7uHej36Zl775ZYS1ER69f5Zv+947+eKXZjGF5vSD\\np3FRl/WVBfpk5CQ0KGPOEEufFX7vPb/G773n15hkkptffi3N+l/yvj/9Tc4spsn6t9IAACAASURB\\nVOhghH5jD4dbExR9Q23HFOKpASe/9AhXfe+NrC4sMrVzFMwyjNwC/QnYP+DI8F7mLg75e3WU2XFJ\\nYz5ioeWZ3AiYZ7tEJTx96mE+85lR3vD9NzA23mZ+cI5svs/0VMTFNEc7z9qcYmznfrpz5zArG4wE\\ndczqGoxEBHGL6dkulZmQ82dXCRuT5J0+g2zI1GSbC3MbCG1whWdkNEI5SxDB8tIqE3tmSLpD+p0r\\nC0o8pXsMyckJXQbWYEwMJkDKMiBE0qJzgVWKJM2pBBWIEtiEE43LEqHnhCMyCu9DRJBhnCDCotM+\\nOrQUxhLUx3iGfclQq0ekSZngtIVAEuGFx+aeSj0E70rxiK/T/qnO/i7gHZtP5B3AbwH/5h/TgPf+\\n94HfB7j55pu9kQGogkKXaKqwUEgfUSAZeIEWBiXlpTVPqeShgJwwaAAZPWOQ7QZmPSHwktxLvJN4\\no7DSUBvbEpnYzhRpYihJQARCl+WDUkJeSA4dPrB5u9sPpUl2igOtnbjBkDxbo7c6RLZ6jO+OeWKp\\nAZde9JJlJFR1LswfJ2zfyac+8cBz2jvdPVN+GCzC0w8R1auwnrJnahmSVQadcb7rloMw6ciKDe58\\n1SgkIf/qrS+HXMHEG6EvYXEZmh2IR/jow8f54489wL33PM6sKSG5VWCJJe76zBKxrPCGN0X894/+\\ndqk+3btIcfwstWqT2UdPkS0vs++Q4K6/+2te+wM/WrK2BBVwDr9uEfsz2rZL+3EYfWyJe7/9VsIp\\nOH98lTtFjXv8AqvbPr0e9z78Rf7t//m/Yqcsp983y7TrsDAwpKpg4AtUUMfJJp51FunRcSH6qSWu\\nuSoApYhDWBmWA+rJp46jjKA1PcbIdJuVtQ5JHjC7nNEvHBNeMVzJaFRrCCeoRA3UyJW5mK3/c4RK\\nE3rGoZoxrr+ZdfAO6wQug2oUEYcx3sWQClAOtGIz+1O2FlqsMQhTipegHFJJGqYJLiCox5RzhVIP\\nATxxOMp6skK1UUESIKQpRR2VZphnVJRCfCNFIrYz7/2i9976kv3u3ZRTdfgnyj8JBM4VFHh8LpEu\\nxVJgHLjcE3qFlCHClMX/DjAu3+x+SBmvIhp6BJcNCD3oRCExFCZFhJJ6O6Qc254nm+5SKoGCfJMV\\nRIDHIELNxPTUpZ5ub6dIwoDWyCS6FtPaMUJ1tEHS87CqefmBQ5dd11ALpsmFwRQDbjy6g3zYf1Zr\\nf/Jb7wD64Bfh5CIUY+AnKfwITGp6J95PbfQMjE/D4ixR7zTk89BsQv8C+EfBzEJbwP59MDEK7YP8\\nizffzgd//aWc/8AP8fOvv5NvHR2lTlkacy0w4RI++qm/4NP3PUSet8mKgKA6A4du4bP3fIVrjrQJ\\nKgNG20PwHUiykr0lkYijB0j2TMKtwG9OMPMv5tEbjyFHCuIxR+4X2EdIAwi3jTEOYWqsJRscPnKY\\nsf1NlA6oOajmnpmdhvXZLiKU9BqTPCUFCyrDrXdoK+gLyH2ZmT57/gTnnj5JpDK8j9i1Yz+727sJ\\nBx5FjrEJFotVns7aBrVY8NTjT17Rnx5b22yVOKIYRqQdhRqGeGuBgDCPEEKTe0c3zRnmFioB6DYQ\\nPNu5nINQ4LVAEREoiwo9NmqAikpFm0tEGIoyujuCRgxCIQuFdxYhk3LjJKwgpN6svvv67J/k7FfI\\nML8ReGzz898C3y2EiIQQ+yl12u//Wu15X+be8RIlLQKDoaDwBu8s2lucLnCBQ4hy1NPPGtECtiL2\\ncJCW9D1eI6yjyC1ZliHrW9nW50mwyZjcFVhZILXAWnAqwhvQ2dcqN51Ci5hmK8LYHO88wgnGpmpM\\n75jk4sICl6P9B8WQb3nRixltz3Di6YXntHbH0UOQ12BtA0LPXFcxpMGTyxmpbdG4/jq6jz/C8cdO\\nMVxdhAvnyJ1nqGIYFNCfLym0+wOgDzVfVlg9+BScWYCFDj/9qlfz6299I2/79hfymrFRapSv9nU7\\nR/jFn/p1wjBiaGswvQMEzOyJMWaVikqoqwJEBhVTUivFDqoxRXSEpLkPWlOwG2Z2wzop2TX7mLrm\\nGmb2HKIt6uTbVuc77r7rbmyaQKDxO2ukYUJYy6gHfULpgA5hpYWRLYaFwnpNTYbUqBGHimf0EnLS\\nPEOFMc5rwihCaE3WG9BdXqQSS+IoxOQ5rkgZ9hN2TFypHhRQOl+AJScOQkLjCYWH0JAHFq88RB6n\\nDU47rBL4S9TOHrPpsAChDjG2BCp64UvZ5tziiwIrDYQeipLZ5nImJosvVYkp5aAQFin8JvuNfl7M\\nx3b29Wy9vR94BTAuhLgA/BfgFUKImyjntWeBHwPw3j8uhPgg8ATl0PTvvf86iK0FGOMpvC1hyFZu\\n3pTC9DVRJHCxwOkcl6tLckmXm8UjsMT1Ktkgx+ucPIf2VIWoEjBICmrVyle5Zc16v0/gQRagA0Fi\\nPa1WjUb1a62LNI31FjJISDYS6u0qsQypec+Z5VXC6Z2UqQ2ojk+wc+dudo7u4gd/4rv48Z/5hee0\\ndvA1twIRjMWg6zx01yd5wU5BZeMsH129mZHThnbi0K+dYHB8lWrvJGuDmM/lk0yFA152W5WVFUNt\\nV4WKy2FyGjYasPMO6BwG12N0fJxRE1GRFp1lPHIsY380wpfPz/PI3Gd5+Qtey3v/7DdoX9Vk/vEv\\ncd2LJlE+QknJ6YVl9syepLbzanARBAqKHk17ECpvg1NPwHdOc+sHPkWlOcaxcJrb1CStwvDk+dNM\\ncHnhyZatcfL4AzT37+aGI0e5//wDhHJIMpgjKBp00irTk+PML80TRAHVSsRqYuhW99KIahzcP8Fj\\nX1mgVhN8+b5Hee3r38hoq8Xc3DzpcMDK+hpp1OXs0wvc+gKPsxVqcY4KQ3LbobexfkV/BKXDZyUt\\nVGHQXmOEIyHA6AziAucVFV8jQJZiDlEFWAc0AYotiJgQDmkVShd4rXF4rCm33qQQ4AropjC2tePj\\ncRjqGkwxxIpSA9H7KlJIcm8IjPjGOrv3/i3bfL09f3J5/C8Dv/x194BNwknhEbIAJ1HSITbJ8F21\\nzMAHTpdqlnistyT5gErYuNSGIsIxRA4sNslRQqFyQWFBp1ALQ8r10NaoeaUZGgIK5cFqnEnQStFL\\nCrJBwfS+5++/dWdYmnN0rMMIQVyBPFD0fMBoO+LE8fOb14apnVP8yze8nIc+fZyxxhSzc8+ePs60\\nt4QtHbBG/4nP8vjd72c9Afc0HJ9eYXGhR0OmPHzff+XWXSNMRxELvcc4rdc4eP0hfvG3z3LN3nn2\\n7X6SG2+4gfoJzR0vvw2aCUQjMN6Ek4+CFRytHWHHncvcenXAo0/0ecneozy03uXM8D6OP/IBPvuX\\nc8xM7+Caq8bxYgIvWhy5OWD17Bw1X4N2CHK8dPj2EiQDmGjAegP+051c98dPYL+0gMs1o9Uq94RT\\nnMnPbvMUBbNn1nhB39LeOUVjx7VEH/ocV10bsFKNOXuqQLFASlBOhX3BBpKPL6zxhjtvJjy+QWAX\\niJVno79Ge3KUrLdAb2Wexx85hzWSzvoGrd2Obq/DaDjO6jBHDAfUm47MX6nyE1FugQ2QiUeLiAQH\\nMiDMJFWpS/33TcUXpyVy1OPpIuhRQjO3yMQkXjmcNuWsxosSMyIgEFv18wEl680zuz4FhjCIMHmG\\nsxmKEGs0IvDEUiN1gHfPr11wpX1zIOi8R6VlLYANAAEutSjhCFU5lfEepAqQOkciqITP3grr+3Xq\\nooG/uASNkERq/FSBExafSLAWdPBVRkJLGDTJk3WcM0gdIALIkyF7rjkCz0q3bJkBBig55PRj5+iu\\n9xh0E2bPD9m/b5SkAJUsEMZVmkR0yUnW+pw6dp4bbr+Re5/6Cv6KHMJv/sbPsXxmlon9u4Aq9Ze8\\nnLf97iFIhiydnuP0O59mff1+1uSQgQ75wwfP4FG0RmJ2zazylQ9/jPUhHH/gKZyFof8r7njtDaz9\\nt1/jnT/8fezbMc6o7hCGx6F6FPQ+xoIX8JLrwfn7SWWIGAypLCY8ed9x3vRtL8X0M9R6RtBqEIVV\\n8sQRj9RZXusy0RhiswClQ6i1wa3AWBsX3Iw8swQ3HeSG/gk2Tp4l2pnyltpNPPh3Z7d5/pqhznnq\\nUw/x6je+mJf89A/R+eN3cXN9yAcfOIGbvh23eJZQNzBFSJoPCAnZkFUatUnqcZu6LiuWXeJIl5c4\\n1xMMVjJGR5qcOXEek24wd34BU2RkPiUMayT9VXzhsdn2eQTwmEqFbk8RigJjLIF0FH2Bp4Ku99HV\\nFCsDNgpJHI9sZia2lIjLrH5WaEIviL3GiS29IY8WAuE9xlj02Mjm8Snl8sHhhWaYZERxjpIpUoIR\\ndYQqFwr8IxJ03xTO7oVnEFiE0EhpME7RSzLqtYBAaIQVGAGaAukdtnBYDOqyQliV5PQ25qlFNRKv\\n8bpAJgWRAB3Kcv/SWVDbOS1AQD/vUSjQRuCswzhPrbY1qGx3TlmM2x84vvzYGQKTUI0dcS2iSNdo\\nTexh4+KQJ+fXKKICMsv4ZJtmdYYK09xw9JrntPhzP/4b7P3QVZx4eJnx0ZDewgobg5DVzPOOt32U\\nx3tnLjt6C89t6XUGzF62s+Uk9D1MTMJH7jpGC3jvjQ8Tt/bxH3/wZeyaOgOnny6Za5v7oas5cs0N\\nLC2cYjEM+fHvuA6bDjh/5hzaBUQyIdSOfElz7c17GUqLCkKGq4ssi9Ps3bULRB2KKnT7yLQLrICQ\\nMLmXllqilZzhyCDkp0sOrCvuvGDYWeb83CPsyGvclV/gX/3OW0je/15em8JqpshHaiSrBpMPYKSG\\nTCt0cfj1hBfe/gLuOnuSxQvrhKEmDizD5Q4Hr9nLk6cXMSgMBcurXby3ZElKjmbH9AQr6+fL3MNz\\n30zA03GWuAVmfQMXyJJwXMdlLUemsSKHyDLWrBJsTv23HHbr/6NsQahKfDyhRTqNFBJTOGzPUbl6\\na5ZaBiNDThXNenedWNYQ3pYyZrrEpQhf6sF7//VIkZX2TVEI431ZVm2dR2YBsRU0ayFKBiVHti+p\\neaSQCCmpVCuoy8epPCOZz3Fe0o9zjCqwOCITE4QRXghI7Ca//PNEdmfRUQVnHI68rDG2nu7yBm64\\n/f5wOQBo6rURvAwprGbnWJO4FjJICnrzCwSySj1MkKas/FpZG5KYHtIvMXF181mtNYHXfecLuecj\\nX+DY4yf42w/fyxe+sMTFk0vMP3WBn3nbW/jgb76Vn3jL6/lqEJ99+0N+8t/+Rx65+3NsUbNvAP/v\\nH32Jc194gnf80t/ynr+5nQe+sh9WTsDF+6CiqcYZh1/V4nvetAu5eIaJoEatiPCFJvdj5BuLRG6d\\n+RML5LN9kourVJWn0e/jV1dBFVCR0OrCVQ7Gz8HSKfxoA+68FooAnniEP3nzzdu/B50VOrbLucdP\\nsifcwYnK9ay95DXER8aZqTkqmSItSoCUMIp+BnNFzob0TB24mm952Ytp1yJsbuh3cqZGRhn2U+oV\\nyXQ7IjAF3f4GTofUogjrPfMLXaJAMzW2HaFogadHTXqSbhd8hKOK8AHGWgphyXWAMRP4ZBSdBSTk\\nlE6+RehZZvRlEJCrHOslxgpcITGpJ5Q5lVopQ8Ymg2wJ5tFAiJeCSkNibYgr2njbwFoDgcCT/6NK\\nXL8pIjsepLcI6xFWEElJEAU4JxHSEQpB5sq/SyFJixRDjt7CqQtJ5hxFZoiDiNBSMttIRVGUHH9a\\nlbxgziRIvY2ryABpJKHSYAzGCgrt0BJk9asxy6bg+ojCMXAWaSRxo4LLcwLvSXyI9SEDWwowHb56\\nH7e84Hrmzl/E2OzKljDGcMvtR4itJWt4xmf243tzXKdHWRtm6LE6397ezYv3H+Yrcyu87z3vYXqy\\nwUNL5dptsg5nns4YDE9Tqx6g1uBSVRjAPfd9BXPfV9h1+GZu+devhotDUBndhT6dqM1ubcFnBME0\\ns+egahyyEpAVjtFwgJY5iwsQhRUaEzVU1CVqVhFRDGG9rKev7Ibjp+HEAKotvNesPLHKBBNw0zhJ\\n1fJ9dxzhvV94krKu7xkyzw5nObN0nqnlMQrbwh24gWP1p4hlhF71aBlidExTGTqsQV5lbW1It9Oj\\npivsmGyhsg7ZMEU2qjRrNT73+UcJVYCQgkgHSKlxxqKUoznepBgO6KxfCZctp/Bl7aUhjGo4Y/Fe\\n4o2gUgvprg/RUqJCidYCYfPNyF5y0D8rsGwKR0gnkUKU0GW5SSdejSgn9WbzjFL6CTwYRS4zvNQY\\n45Fs7q07i5AB3j2/HNiV9k3h7N4axPoqAxtgowgbhkgjccKinUc7gbE51WadaGSC+mXnLi9fYCSX\\nKKEwUlIUDjlMoSXJPSgRgVJo6yGjhMtua12ktxiboqxGKkVUDakpR2/xJI2paUrm0SttADIkzueJ\\nY4vzfVweMpCevLdMKmo4BCONaTq9M9x4+BDtdoPTZ+ZYLp6dXMmBQ1P7EZmnFURUmwFLF5eZHg3Q\\nqaEiQ4phj3YseeFth7jupOAHfuXf887f+AeO0ePALnhqtkSC1aoH2Heg+ixHh9KtJPBffu1X+Mzd\\nd/OdP/CdvHRkQK3WIm4KvvzYBfrdHhPjV5FlXZr1KrmwKAI2unXikZhGa5TAS4oh9C44orok7/do\\nX7MTWnsh36CQMV3XY2wiQa71mJjaA7WDUEu4bX4VeaSN6SR84LGzz3mi7/qd9/KrN1/F1aO76YYR\\nU6+bZ+nPn2AsGqExVpDrmMHFC5TSX0MefPgcu27bz6E9h5AvzHjQPo6MBUOGMJdx09GjiMCz0D1B\\nXtTI+gbXAG8sEo9QIaMTVwaAktdPOU/hPDYr6ay1EKRa0rMFSocoL7BqiBeOSAaEW/LPFDwrsgtV\\nUlk6i/IKFYJCgg/JwlJpJqqV7rhFSC0xeFxJimEtWpUS0MXWQYD65xbZbV6wfH4WpnbQzQ2TOyqE\\nNUkQaZpxSQFcfZ5zTZEyEBrlFKGFIiwQsUSYiBAIRQmtJYjKB5QbCLeTzKmwnpxHEqOlRQYZgWjS\\nHmuQ5Kuk6yvE7QZlocLlL8Y0cJxrb97PJ5dzTGp56P4TTOxr4nsJg2SANYvYLKW2ZwdJlrK6tsKg\\n1+fUE88mq3jPf/4/uPNf3sLF8yFqOMTGjoZcI8sSikoFG4RE0RIjZo0L+TgTLzrIueMnedcnfoo3\\nf+BL/O2ps2ytE+/70ic49/Rz6a8vgUItfOKBBzn52AW+8K23Mtl6gsm650V797O+PmR1+SyV6QnO\\nbnTwBEhXsHNihpUNQ3PEE2lPkTtSlREkluHFIecufp6bXnQN1GsER2cYO3oINhbhQw+A7YJtcmFh\\nlsb0HtInT9PQ21cQXlx+mnv/7mO8+k13MnNwH/old7D+kWWOn+4RtzTrvRQh6+AiZNWwlgw4vbjG\\nWKtBpd7CGMtgrUMwc5AoHVA1HuMEoVa43NNuNynynDCuMBgW1BqSixfObV49oZxtlGW/Jq0TihSE\\nwW3y1wehwckUJyI8EcYbIi2RVpK5FKQkIqZcu1sgIvRlLsorj/EWZQSBkhA1oaqxfQNe4oXAsyXS\\nLPFKoQOPyFKEMhS+AUVYJptxJaXb12nfFM7upeDo697I8yPUnmsXl+aIOg5XaNZCj7I5GkhMQaQq\\nWNXDEhJkFeIIlEkh1GXCaKvS7XIrcppxQDE0iEITSUO9OkB3JK2RCrT3gB+C2G613GDQs8R5wbnV\\nhIMTLdZTg/KlSOCpJ88BhmDW86EPf5IsGSALxSf//Jnq30O1CY7efC1PP7nGqJ5kpJ3QKaBVq6EJ\\nKHo5RkFux+iaOpVKRLY4x3Uv2s3qYMBrfvAmXtZ5NQ985i5e+PLXEm989cKdj/3R/8Wv/dnvMaN3\\nMXf+HFOThxmZmWR54Gk2x3AuZWNQIDWERYG0XaLaFGnRxxZNOiikEDS8wCWGmIhrD1/NsbtnueE7\\nXgiLBnvqFMtVWEQgel2GvQEfzS9y/4fup+FHeHJ+g1EkNaFZ9vllTPnrnD3zKH/0thP85O/+DAtd\\nxXDHFOm4pcgNggxdDQl8hWG6wMlhwquLkOn2CAu9PoF2mOE6sY4wxZC6s2QovPAUQR0VanSakXhL\\nnkqU9ITBFpvOViWkIGOdsFqD1CDbhtRbcJJch0hXILzEe4/QClEVeAGRrJZZ8ksBYVOuTEqkt6Bj\\nAl/OKKx3IBXDfkYl0CU9GqWmmyDC4UslidyjhMQJublLJ/BZAs87S93evimcXQhPCUQY/VqHXjKT\\nWgLhiKIQIzJ01ZMkA2RdMOgrJoJlbL2K6o+V0j9qHilSkBVKR99C1JUZV1SA8AOo5Fg/SWEUUbiE\\nYgnsJrpKPN/84jDzZ+5B62WSSKD8gGbYYm4xxboUjcLQp/Cwunh62xZWBssEYcDoSMz0SIyqC+SK\\npG7hrF8jEFBPC2ITkOPB9LhqqsnsXIKIa/T9AFqC66IuJF/i7b/8S5faHmvC4Rtv5y/+73fyY2//\\n36iN12kdPsodL3olnUeXiaYlEyOjeKnoqIJkvc90M6RSaLw3SO+xjVEefHiBPZOKqDmKyAqEECRi\\nSB5EhLqOXndIk9I79mVmzy3xsS/ey0f+6h84VKmw0Utoxm3mVtcwrYClWFO0a8RJh1bsmNu4xCyA\\nooqROSvpKg99/rMUcZPJmWnmp5ZQZxcIjCMKQpI0B5uQAY+fXWbX3haTe6fpFJbZM2e47vBhqjqk\\nnyaM7phGC8FIHOKFxsoqmpxaPSR1A6ZGJzefVkBJ11UQUSOjQElB7gzCBFQijU0HRLJOIlMUKdJX\\nKDLBqrO09IBKHFJmYDYoXaxGoTSBL3BJho5AxoJapULaNXgPoqKwDpws3zG5maRTWuKyHCvKeC9V\\ngRcFIizxIu4fQRz/TeHszlieeOAY177wFQA8fOxhJqptpCmIYodVjrFGCxFopNKgSnL8bgaeDC/A\\ne0FAlaxbAhZsf4rACTJ6mBxGmmHJIps5qG6JQ24J9GUgMuqiwtLQo1OLFo50w1JrlIUWX7X/wzmU\\nX8cvD6mN7eTqiTbzF9bZffO1+M5FNgYtVpZ7bHJWb9vGGnD9C27i5MkT1BsSkdfQIznW5sRriiDL\\nqVQtQ+eoRI6hG+XppQFRvQJWkgsYHR4nS5tcTC/wl3/5/aws/Wv+v//6AG9/37s4feYU7/yb9/D3\\nH/7ypWu+6CV38mNv/i7++B/u4tdv+SF+8qfeQv70kCeOP8mxL95LoxITKnC5oz8UTE+OoFTC4mKf\\n/VMNRF6AcwxNStRU9HobfPzuf+Dk752lKxT5oM+1O3bw1ByY8SP0UsMrX3ktzZtG+MKnHubnf/h7\\nuOOO67nn7z/Dd7/13ZeWGIIBC7MbVCPHY/d+nO/5N9/Dhz5xmsANURgiJxiLKswP+5TgFcvdH/ss\\n3/Hqo3hjiCsBslfw4GOPcscLb6RmIUgdlUiSrgxQSpMPO8TtBv2hQVdiaiPtS1cvWT0SCrpoqpgA\\nIhEQ1yOsSYiaHgYBUhhSa2h5Vabk6pL40oIzYktZBkDKkjO+Vo+RIkeOlmv7dJijYkEmPJGMUGQ4\\nINtkvIuiCjLLSuo15wmcKMlRbQEyQnwjEXT/M0wIgb1Ms0pJizaGaqCRcU5nkOD6EVnqGWYJ/SxF\\nju4EPcR6Cz7EeUekJQKFUpZUVslkhvEDKmEELsAlEhlBOU3bJNynVOfMOkPqI5MsD89hRQ8RapKB\\noDY1w/NnDEq793P3c/ypi8RyjLqooZOEiXqFcy5AmIAorVK+lJfrgm1j003cedD9AVY4vJAI6xk3\\nklg36OY5TipkLqiqGF3xBBgUBcIUDLIutYkakQzJhCOoh/zIv7udF916gDPjNfyghISuza4yunuM\\nP3z3b/P7Hyx1ytO1AqqTDIsn2HtoP1/81Kdp7WpQDNaRyhDGDscAAokwBhGAM4JBUmADz0MffoRP\\nffyLNJoNWjNtpPYkok7nwhwHbrqJB07NI0JL7aZdVI62eNsbX8HVBw4ymO3xkpfdhuTdeJ6J7rOz\\nF9m9q8VgfQPTW0KoVc4cf4zG9CSLg3UqfUctLlCJInEG6JOmQ4Q3zJ2b58hYGwJBrVqhWNwgiEIK\\nV9AYaZENUuq1kEJ4ZKRwZDxX31QhqODQSFHyADjh8BJ6xqLzIVqBsQJbWJS63Jm2eOcDygFeooTE\\nCk8hPJG1SPRmkq3Ug5NSkwz7VCoFCElAi5wc6QW+8DilkF6WJbK2JPdSuBIv/3XaN4Wz4yHpPgNX\\nXFoZsuOqaWQQMiRj596d4GOCnqNOyrj0XOgMSfMu0inC0COUwQiLVxItJEMgRKBjjROCXl8gIk+9\\n4iHpQCVkq9jAZH2iWszjJ2bpZ5aqzokaddpMki1L0krGSPP5Or/K6bOrBO3dqPVFWkqjTAUV5RRr\\nhmom2DM9yXJvgZuveSU3vXgP9/zN53i6+9hzWuo8scTE2G50ZwVh5xEJhLFmdTynwBB3DYEV+CJD\\naIFwkpovkFaxNtBUJ/dy+sQF9l41SjHbRQWGPFhnx24QSyv8wfv+mr/4wPt45MtP8q23vpSzs/de\\nAnRuzAsQB0krZ1l99Gluv/lmnlhZpR4ovC2oBpIsyRmpVwlyQ+4MDsnGsM+xBx6iWOyyc/9+9k7N\\nILMU7aE40mD621/Kan+RX/mlF9LvrZLE44zueQHr82ssXxwSxm2GvZhGCEn+TG1gp9/j6L4bWF2f\\npX/2Ai+7fQ8TJuHigsUvJChgHU8mM5Rz2CDg3X96N696zS20mw2yNCft9ViZXWdK1cidZbmfUNvR\\nojAFqXG4ikcHmkDpbWSUQvSl3mgKUqJaQGEUbk0QK4UxlrpSRDWNcAXL6ymNdo36JSffCiYlLZrw\\niiAKCKRh0E+pKElrrEEn65NlMFqp4UUHgyVjQEyd7nBI5B1KhjjnMELivEfLrSTePzNnt86R62eS\\nDSPjk3RNjsFjg1LuBuVgpAI+RJqCPbUanXaMNobe0DJIJEIBm1xlyjm0UMo9PAAAIABJREFUC7CJ\\nRegcFQTEUcTK+Q3G9+yENC+z8lKho1Gy9WVcp0svAaoWky4ys3MGcESjI6Rri8SjU9v0PmL2iWVE\\nv6DuLWlF0tWSLC+4+uhBeicMJy+cp1Yd4f7jj3Lf8U8BjhHqdChLWzVwmL1cPHeGmZk2Ps9RyqLI\\nIW7g0ya9zjl2txTDRFCYBGkzcj/FUHiM9YztOcTa0kUOTO3Cri9jMkfYcviNcxy5ZYLxpx1v/w9v\\nYSkdpft9/z917x1kWXbf931OuPHl9zrPdE/cnZlNwCIHEoQACgymDJuUyKJchEhKslyWRdlVKkty\\nyf+QZVu2SrZoWy67KFtlhrJMEaRo06RICokAiLxYbJqd3dkJPaHzy+/mc47/uN07u8CCAKqoKvD8\\n093v3n7v3fA793d+v29wvLJ9k6u/8oVX84yX7r0EzGmEknyjwf3DhNWuD1mJbnQwUtNst/GasNFt\\nMpyNaHkB2b1DPOPhTEw/AtqwvrbC5P4NgmYCJuDPv/9d3NwZsrR2mYdXm+zcvkOnGWHSGak/4IXn\\nX3pVxv/k1u2pJrKsuDcX3JgL1qKUt6/Bwlr67+/z1FdvEUTLkEdMRgvS0nHt2l28XpOo02dx/5Cu\\n38D3LTYDL/TJ0orYCaiq2oUoDzDSIoUkTRdvoFYQ4FjUICscGQkoTddrUBYlVSEQWnK4qIgVdCJN\\niMVSIl/Vm68fKLWMgUV7MJvm9FfWwAomkzkJFa3Yw5SO0h9gsXjklGT4TR9RZChbIZUll4JSSFBB\\nneiLP2OtN+sc+jWVxYb2iD1JIDSeiMhnhwTddayovakJA7L5nE77FGV+xFq/zd7+IUrUaU1pDb4C\\nKSWuMFhXc5fScUqnt8Sd7T36vTZmnuP5msksA+Vw3ZBkMuTRy49R5WO+9MpLvOXSBdx4Rrj2jdDW\\neoT4K21uPXeVyw/32c9KOutL6HnOvIBm4zTz5GmCcopjzLEzxevAomcJuMptLp87hZ1axHIM5azm\\nQxaKJeng1Dp7wxFHC7jY77FYzAl1DukCryzInpsQxG0yuyDsl6iqQTnyOeU/yvzqi6y1e8hpzik7\\nY0bGqbUGT/z8zxPHA7Qn+NqXXwAWtM6dZvvuDjRXCasxgXbM04q1lSUaJVSRJpvMcGXBtRt7rG2c\\n5urnXuLU0oBuzzAxOQM/oL1xDuFJdK/N4vmcMuwxCbo8dzClch4D0cD2u3z6sy/z8T/4Ah/52R/l\\nH/3Sbz64J8wMO51hS8HtkcR4imXfshocsqEHdN59ESE7PDctePnmLXYWC/aHC57b1izbjCgqWe0O\\n8BB0GwFJNkeMFwzinPlswlJv+TgWBb7WZO7rA73EMEHgo2gSUzDiiNHUkBzl9Dq9OvPyLFprNBAG\\nPg5JkRbo0DIdzfBUgBQKZwqk9AjDkDBcAzyQCqUnxLGHLSxCOpzJj52hLM7J2i4KDycB6XAWOoN2\\nzfVQ8hvcif+k8V0R7A7H1rmzr/5dOTBOUzqJ5xy5DgjwkPIkNSoJm02oKjw/pkorVleWAUcxn+E1\\nIoRQZFlOLjOKMqfIM4yp2N5NCBuKvckhlSnRmSYvLKGKeOKRd/DEIxx/xgqb509x4/mv4amQJXmV\\nqNsDf/lYsOakaKchhMHZFsqUnFlaZWdbEvU0VSnJbUkr9tg7gi6nGbNN3cN9UKO4Ts4f/nc/z+Fu\\nxXT/iNNyjMn3iXSntq9qeJD7kDka2Yyd7RmpKWl7ku56jlZjql2LWF6itJZwMSQWmjwr8V2XtrvA\\n8HZCaQzZ6BWa7QZL3WVkEJFMM6KlNlsby9RlQkGvOSCNF0gvQ08LBi0PL7Yo2yB2kkpM8H2HLB0f\\n/51PcKHfoUgPmTV7NH3Y6mqKSZNC+az0GsQ3ppx7fI1ndu7w8rXbXLu+x7W9uzzztZu8511PsrK+\\ndKzUUg9BjZz3PI1fFfzv//Sf82M/+gMsvX2LG9dvEdldBt4Kq23JUneZJ1e6vMSQ6dnzGOHzn//l\\nD/HTP/DTNBo+MnMEUYVWEm1DJkczKqcQTuN7HpN5iqTi9ObFr7srFYoWD9q0ih4deh2f2+N9nCmw\\nzlJWijAKiDzFPEkw0qMrY4QoISxQSqMqh68cpZQ8SO9NfaRKkZcF2hrSyoKn0FKgBVROklc55aIk\\n6CissOjAozQVnooAxRsUG77p+K4IdiEl3ZW1V/+2xlEYQ9zwKStLP1rhAdb4WEy/NKB9EB46kuTk\\nCAR+s38McpSEoU8YvhHqzZKmc6Lomy3ET1BQPucfvQw2I93Z4eqzL3Pv/ojzp8+wtrWODJcJww7j\\n8QzPD1HTAql8us2AQAqUyNk6v8Ynf79gQYnlqP7ux4F+kraeAq5+4UUeXTtDsvsSh5FkyXiw0qjT\\nwfmIg4MFcTvASwyZDMkqzd1Ryajoo0WPzXVNtjvH90IKBqiGj/Iko6MF6DaHo4pMFwRLTWa5x+c/\\ncxujd2nFHm++vEVLF7A4gMYpmoMVmumCFiFuOcMYSSPLKZWP5yzTPcfhUclXv3yL5dUl5tMFRuQs\\npvtcfOgRikWOCH3G6QJzs+R3XxnxP/ziP8E1FeudAfcPr3Nwvz7TV3/vDwH45//3A1a0A4IWWGM4\\n1elz2J/w9Je/yLs+9BP8zs0R71jrs3N4l+F0QqOzgZxZekozTg8wzRZG+xRxC+Es5e0DOleajDSM\\ntIff6aNCTRyFFNaiQknmKmbFjJLXipZJ6oq6e82VKnAIilygZYDnlRgHyTijqDJ6p0s84SF0rQ/n\\n+z7COLTnIytHVZzo2j0AdTXDNsZMqEyFkXU2mlUlUhnmiwxVOALhI4Wri55FggpihKzQoqaBf7vj\\nuyTYBdFr1uxID1c5ytzQbPjUJaSTk27ACuyiQEY+BDVCqUYsnSCLv1VqI/+EQP/GfZEx0doyV+KI\\ntaV1rr14l8rX+J6hFS1o+QHzvESGLRZ6gfUd6BwlFN12m3uje9QlQ6gV8X1WRJ8jt0sJ9FE8euU8\\n88MdBoMuzUjgL459cVSFRJNmFe2mhw4C8JuY6ZybkwOSQuEKS9xoQmlJ3QIrYbQzI4ojPD/AkyWK\\ninycEcQenqeIm45WSyObllvPfZXBk5uU00M8P8I4hU9M4BlGaYqHx9JSi+Ekp9WM2Dk64Pb2beKW\\nxyw1TPLat67rYvb3F5y7dJ6Xr9+j0VzhpadfIYgj8mLK+Dbs8/UiEfV49rMvvlosVEA+g6DZY297\\nh6ww2FLQCAK6vR7jtKAXNJlJxe72bS4326zpFRbNiD1KhukUGXjgKsJuzFxJrB/WqbBThDJkPl7g\\n9xsY42iomI1Ty98kGE4eMDXQRiBfLSw4Z5FSoUTtcKBNjXgr8xIdQ0tr0PWE0QAqVb7mPU/uUU1H\\ntSGqVWrmNsMJKK1B6wDjSpyqhS4sBq0FVVERBSfaQt/++K4I9vokPhDpD6Qkin2U8pinUxrdAI6L\\nJOCBE0wmQ9LZmFavTRBU+N6JKd4xIf7bGO5YozYxU2IVPSDWnGy1GUJ26s9WCfS26PUU7zrzGJOD\\nPQ7upzz38jMcLXbRkSTzulg/pVAF3ZbPTlLS90MECa/VvrMU7LoHclQBBrXZYWbnLK2fI1IpRAlV\\nOUSrA7Ax6xdX8IQh0wqp27QrR8M7YFENqUrH554/Yjgt+Pc/9DjFeILCI5IR8zRnPLlL7CpSF3Jv\\nO6UsF4SzjNG9KXkgKfbnNB86w/UvXePKO0e48AJh0yckpO+1SRJLJgOinkTJGYvduyRJwSCMKPMS\\nf6lLcXCXzRVN2Yh5+fo+uYr5zB98gp35K/zW5+/SpXaY+2bO8xe3znHx4Yu8+NJ1GgiqpuNwb4Sx\\nkMxThpXg8oU3szj8F+hTK+SEHDlN4QzjLCWZ7LIvLHtZyf6NHVxh6MVN1MY6016HeBDRCSOkNpQC\\nlA8793Y5/8hFqtE+89mcOW/EfjhZrtU+BhIJ1mLKFC+vbcKLXv20XiQW0hInK7rKA0+Aqh1ehBVU\\nueN1xA7ghCZ9MmJZsSDBKQ+JZF7UxhSFaaCcpqoqmm3vmDTjI78Dy+bvimD/enjv6Y0VDsf7pLlh\\n0AnJ5xVB+5hF5EowFaNIYI3BHIyxhcTrhhRFyvLKCo3gjVL3rxtpPVM6LKHTpNWc7e3reMpHywLp\\nVwjjcebiI9Tp3Ml7ZkBCZ3mNznLIxTdd4LH3nuPlT77El37js2ip2Ti/xsH+TbYeP0PfW3oN1vn1\\nIwCadPm7P/nneLTzJEH2VVpyXh+j52FmJTpugIwwk+Olis1w2QF7t4cMhwYHlNJQEdHpNnn+c9fY\\n2FiiEUrKIkeJgDBaxpKTHZXYRl0TKZxmoXLKg5Rmr8nVL95DPt7hylJC/+1nGY0d08TSaLUJZAW+\\nIPInsBizd2uC124ibclKMKeMLKe/7zyNouDt73wLZlzwuevb/IOnPsVHPvQB3CeeRkRLiONjzqlX\\nYK+BVvDEm97N97zzFdwk4ZW9+1RzSOdziqpmHlohONjeBxkwTRdUkzm+UFx+6ALj/T2m1iHDmPH2\\nPa5/7RZCaQQlYZbS1x32XrlL14Wc21gHJIukotdpYE1G2G9x8+bONwmGukZUIvGQGHKkAOEEhagN\\nF8WswKYe1tc0VgxogRGgi5yD0ZRm1MBYRZnmOFcgxDd3FZL4tOiQMSNQHsbXVPMC/LDunPiKg90J\\nK1vHCu3fgZT0t5wWhBCbQohPCCFeEEI8L4T428ev94UQfyiEePn4Z+/4dSGE+B+FENeFEM8IId7y\\nLb+EfP2TeHd/ROT7tHyPIskJmhGOEkMOeQbOURUVfinwEbXscVbhFxKbVdy5t8v9wzs4Hjik1JcM\\nSuvIKZi6GfgFxjMMy4obN7fxPIl1iioNqErNeJHy+izBUtcO+ljK43f1aXe3GFw8Q//xDS5caCJ2\\nDN1ul07oId0IxTJvNK9uonnz+Q0uPfIwfjBCecBag/E8YXyYotstECu4qk3YWafZP0ujd4pOr4fa\\nOIPsdACJrSTaauQsJfBCXNhlnhUc7kwwxmF0g0UR0m0HNExBzzk2/BZe0GTz9AY6lHhBSGo92DrP\\n5MWrhGGBi6Pa2TbUiMhnEXTYvl8Hh9ft4TZOIy89RH+rzTu+97089n3vIm1ElGvLbEcNRAr/x7/4\\nONwZ8P7vfTfu+Ox1gEYB/+q/+q/58Z/9QV54/lO89/t/kF/65V/lwkMNmgS1mHIxZp6MkFaySC3/\\n+l9+ml/8hb/DKztjxsphmgFfu7/Hge/TurzJBMc0XXDj2kvMJzN2RnPk3BBNJA2/xags8dp9kkWB\\npzW+UJSpBed49MmtbyIyngOC0hY8f/Mq02QOTtYwVSHRIqGj9unoQyLP4XKHKQtE5kDmLHdDIl/i\\nhx5xw2c0HVNUE0o3w5JgeK2qrKHON0tCJAuXUBQWU2rKWoGcqqhY2VonIwPEnzrrraI2gXhKCNEC\\nviKE+EPgp4GPOef+oRDi7wF/D/i7wA9Rq8o+BLyTWmP+nX/SB8ivk9YxWpNVBW0dHs+CCoFDUUGq\\nmBwMafY90qRCSIVTGi0kQeQxT2corcjzmhssXJ2q48CXilm6oMwXxP2Y7Zdv1WCHMKTdapJnJY6C\\n0lN4nsebHn4MXi3bnHjDAcyQtIBDoMMhQx567BLPf/XL5IcT1K0UF2hWu6cZ3jmktaSYHla8NoHx\\ngU6vQeEZXvjUcwwef4h+mcKdI7qeBXlYW/l6GuGFEOVQCoxsovqnCI52uXTWMV/M2ZmPSYcGHcZU\\nWtFpxuioTxZnNJoe0hm0MNjQI2m2KfIF7qDiey6fYXHwEsrvkIen6Z8a8NQXhqyt+3Q6M5qiiaDE\\nUz6psQR6BeWN+Rs/9Tbu7IyRcgnmR5y+cJHK73Ggl5jHA6aHJWazz90UHlpucv69TaLhgv/sHY/z\\nq198lgPgDz7xP/P3//Gv8OX/97WeIqCDTRJ28MIcZwOOhocEylIZ+Ohvf4If/cs/xmzWRVtFkDjO\\nv+kcK90maZZy794RSQ7Pf+1FtINRUlCueuxTUTUb4DU5HE1Ybm1RLeZo1WRleYVYFZjdr+ezn4wA\\nS440jotn1xBWMJI+RWkIhUBVHlUsELGlzAvkgcb4jqhXQWkxaX6steZoR5qkTMkPCyolcArSMiNu\\nxRgBgQqxpaEsMookAyWxRqOlwbiCqoTGWoO8ygh1DFjct096+7YEJ3eAnePfZ0KIq9QF5A9Tq84C\\n/J/AJ6mD/cPALzvnHPB5IURXCLF+/D5vONTXYc9FWaFjR15lmKqg56if5rlD+g7ZCiimKXgeVOBb\\nSeEZkiyn3WnTFCFhy6NwJViDVBJrFUkxx0lHlVm2X9klFBFCWpJZjkMi0GjPY6W7RHs5rs0e3xC0\\nEPOA6qpYZwuocLKB8BoEZKRC01ALdtqa8eHLdDhxY6kD/fsun6Kr+syVYH79HovkgI2l2jU2DdpE\\n2oIzNQ/AVchIcu3qK1y/cYTXbHKm3STOjhhWMe3eBu7wDiUZpWkSCUvL6zAqJc24QTKZUqSGMArp\\nSZ8gbrGfHJDlFUG0gjUZ2rfcuz+u+ezDG7z9PWcgXGVtfYCWEHY6iGqC11rmn/3u51nqBJR5zuZa\\njzu3JqgzB5w9f4VSxGxcWqFxpsnf/7mf5Cu/93kevbjO6bee5nOfuMaP/3s/xj/4R3+Dr3zq2jcE\\nOsBzL34ajxKbwc7ulFCFCH+BS2HKgt5aRC/wWIskCRW744zTpzb446deJpYdmmGI0DmeEJS2pNeL\\noTIICyurfZphQE6CmymaZzxcCIvUMeh0uHt3l7On177uG+UUWIQnSaaCsjA4IQhVgXKGrGxR7Xeo\\nKGl1wO/naE9glUIlQe2j0WiifMkin9Fa6jAbJ2SFQ6aCjoowc8vClJSqgtwgC0lYxRSqwjqJVAZU\\nUQtgWAfOUpDh4yP1v6Vq/LHn25PAF4DV1wTwLnACLzsF3HnNv51YQL0u2F9r/7S+/npv8sATKOVw\\nBkoran1sYFElRIGPUw5harlnX4MzBmssnq/BOuJGiDEGpSXGlAgB0tQq3JPRhHbkoXNDWdQyV06C\\nsA5RGaRX1VRWPBCvdfY8ESI4QUXFgKAmT3pAxtZmj5ee+zKbYY9RoUEZiqAWMZgcn+wBNWPaZDnN\\nMx7yIOV8dxXpCkjn0OpTWkdUCYhCsF4tNawl5WTBpVNrVD0flRdov0l5q2I8maKEJLYBvpJ0VkPM\\nQUG/16asKqyOWWQjoqZHw3gUszlFZYj9NosEyjTDVCmLwtLQDZZWN1nprGDDFUbbI0xVInsWLwxZ\\nuJxbRx4f/bWn8f0G65sRb37LY3zpN/6IL33sn/Cen3gf/+qjHyNWBT/2F97BO554nFPLPZ6/dpcP\\nfPCd/Mxf/CE+9+xtOoM2izt/zD/7zT8gXYz46hefx+SWG/dK+tQcyEEYMs9yfPOgO717/4B4ucNs\\nOmPheUQyRng+Ya+JzSpCVxBIqLwIZzxmRyXtVo888+itrjMZJ5w6A1Gl0UrjMMS+xCvg7MYbISRD\\nQjJKcqI4xFeScTLDCy1KghMWZWo9BWlMzUgTPqKoU3Ov6R+36QVxQ5EWtUmlXSSQ1uJTGIf2ail1\\nKQxaQSAFaeGoFGgE2tW9fpMaZvOE1krdTfoOluzffrALIZrAR4H/1Dk3fS3bxjnnRM1T/bbHa+2f\\nLl+68rr/9SOPPKmFAVeWNyjKOT4BSvvs3JvQwq9TnMqSGzAWltf6WFdSCk0uKxAKZSzesYxPmmUo\\nH2zuSFyGJzyskDhjoRCIQCNDKG3BznCIizVRYPE9wQP56ZNV3YOnvSKAY1Ojt77vEV5+5jmYTuhU\\nMZ1GEzl8UIU6Odlvi7ostQc0I83e7Vuc/XcuUEQeuCZUgqadQ9NRzEr8RgiRhtGUo2nF5Y0+O2RU\\nuiLzunjlLh4p9xYVVzZWabfrBpajQDhLGMek05TBUhPtl0xnYLXP8imJMmMWpUaaCj8fIVxM2glY\\nZBGnz13h6H5FEEkavZCnv3aTO3tDtg/2GY2mrG6tsUgr7u3lPPern+bCE5e5+NaHMId3+PD3Xmaj\\nF3Ha79HY7PDk97ydH2kbTq+tYOWMS5e6pPMmh4d7/OQHHmZ2dMRffd/j5PEKv/C3PsKv/Pqn+Ke/\\n8VvMF1NAMCvqRVRPN4h7Z7h7d4+uH1M5y8HtQ8SbLzCaW/rKEnsC4zyCpk9ZCdqbSxzNBeNZhnGO\\njX6bnVtHbC2fZjq0LC95tCLLmd6Agxfvs/zIa60J6wm+ICdHM51MiJVPFGgqLJQBUTjGbxk8QjzR\\nJTvM0T7IoICWAc/VN2gegKgoipKo36bTsNAwDO9nKL9G0zEC4Tycs5SuprVaattx6XyMkaSLnNZy\\nm4iaqu3sn6KLK4AQwqMO9F9zzp1gGvdO0vNjh5hjacPv3ALKmNczwaZHM+JGgPAsZZIgbIrIQISa\\nvMxBWXLjE2mFROB0wTyZ46zBb7fxdFiHo4VhekQxKYiDkIO9Ca0ooMhq7W6lS5xzdMOIrEzRccRS\\nb4UwPmnxBXVlXJzouNfFmpPC3ElbpmJQn8gqYUhGZ7Uk399lVg1pRPU81qPuuqygeHOvi01AtZt8\\ndpFy7bELbAWnaoJO6ZD9Eqb7+P11IIJCQKfNjcyw2h6Q2ENifJ754xtcDHw6eQGdBvGyou1pChRh\\np08xHpPYAldCFCgqY8gSg/QkrdIwOxpi5BoePo1iUmvPNVZpL3W5P0uJuktki5TIwhOtASuZZrPd\\nYrJIeWX7Lq4dgVti8EjEzuGUt5zdJD3YZ+vhTbJswYVWi/65PktdyfbtGyzGM7wohqhLyRFaWqLJ\\nfTqlYCIsbv8G3WnOY5tn6HlbZOImQzc/RlgIRtWCMp1zZjlmulcgPIsTinanT9xpwmJU+61bTag1\\n7jAhJuBQ+aSLMWGrR5Fa/NWAsKPpLDfwPEvqDHlQ0GuedPpPwkLiSPDxcEDkW5pNxfBwgTQSVXlE\\nNsXz5lB1yNMWcaCQykEswaawkJDXYpKJrnA6AEpyEgJ6JOMJolGLhLStxpjaqFyLgMDLUa5ACn2s\\nWSdw1iOKmixY0KD1Hdm4fjvVeEFtCnHVOfffv2bT/wP8lePf/wrw2695/SPHVfl3AZM/ab0O1Hh3\\n+8DIN26GSE+jhMIqQaUUshNx/cYuUgdUQiJ0RVXWa9xKGnQQEIQxDRVRq3LXQRn5tXyvqQztRgNn\\nLEprfC1Q0gPrk2nLylKXQVtSWkuaO7KsoBTla9bsJ0FeUSfixye5gOlhwrMvHTA7yAlHFcXhPhcS\\ngxnnvHWzFuSo7QYgxvBDFxr8yLLHex6/QksFPPPylKJcgJqBS5mMU+itggtBt6C9ys6NfZY2WlRH\\nU1p4ZIkhbHkI52NTTasXMT0a0e82EbKWVfJ1k8pWeL2AzFqSRUq7FxGEinE64LDqoJohqd/nvjvN\\nPitYHVJ5IVGgWZQJ8VqfElhveLSikiAETwZcOPUQjz3yGOe3togyj7ed3+Jc2+exh3uUo/v07ZyO\\nPWT1xmfxv/RRGoeHyNv3iLePGOwdcDmZ0LjzIq3RHP/efaa3b+BG9xgdHRINLKpKCcIHfmnBcfPy\\nF/+nX+MjP/MfEPWX2Zl6ZLmj0xvg+TBfTBDCIZVikS9QDcW9smKmS2YyJxMZubMcZXOazZDVRp+e\\ncbTDkKQomY4Pjq/tg0Szxn9oiqTA92NMqRFFzXJT3oLK9ZknG8ySFrgUQoNTPmQelA3Ij/sPVuFH\\nPt3lNuAIjt1amx0PGQicC0AI4taCuJnSjCUt36MhFbIKCHRFI5gReBW5MTS+sWH/Lce382R/L/BT\\nwLNCiKePX/svgH8I/LoQ4q8Ct4EfP972u8APA9ep61E/860+IPB8Dl+8w9IjlwFwhSO3OUo4VvuS\\nUV4xGR/SDCxUAik8lCxA5VRKkM4rzp1d/oaZ65V7txh4LVpSU1qLLSxFZUAYFJJQO5SWLEzG3FWY\\nROCrklCFeIECLaA41ppXISe89ooAjeTGc1/kv/2V3+f6M5/DjIb81E98kPd+/zt54TcnbAYtnvr4\\ns/zRy/+aS60B12ZH9BC864lH2Oy1yYqMd3UG/Nhf+xGuPvUcH9n88+TXDlC2oNP1oTLQkBgPVNxg\\nGvq8sr/PhStPcjDfo20DNlRAMywgaDBUHg9fOlP7j1WSqsgxpSUUmjxd4JuYym+S5BVZVuDykrjj\\nsXN4D7OwFE4ztgX72Zzkbs47334W6RkCC/PZgkgf0W9M2d6uaPttdpIEs5+jkilLPiSLipW1VXLp\\n4+mY2b0pT1/fJ7hU4T/zBQZvepxpKbnnn0KHPRpVycHmKa7f32UziNhLDI3Ty9xM92jHhjN+yJeG\\n2at9eQ+Fw/DJj3+Gv/CuR5hPx1SzGWffvEHU73L3/pBOEIAp8ZTASUOKY6wNpfWZjlJaOEISvMqn\\naRsU9zP6Z3rkxYJYt2hLCVVZX/dXEZs1e02HHlmaUlaOKKgf1lQGZ2NQAZ5vaejjicLVRTQqBw0f\\nSoNpOvK2xKOiRLM7nBC7GcoFmFwipSEsfIJGCZ4inwgScZz1ljkNAZ4Glxf4uGPfBA/3p4mNd859\\nhm8OSfvgG+zvgL/5bX8DACsIIqhRcj4qCKgA4QxK+hSJxtOCShqMVFhrUdYiwhqmcebMyhumKINe\\nB9/4eMbhRIlT0Ag6pNWcUjiGiwlKKloiJvY0ViqU77ABZGWOWRiiMEKqun13eDTG8336rSb/4d/6\\nBd7zPT3+9s/+AI9cer1f2/O/+ns8c/NZ5MVz+K6iKCQKxQaOxuEhu37F1tkNhnspaljw5DsucXB/\\nRluFlM7QxMGxj5fqKP7w05/n+ov3eOzieabDMbujfTYffpj724Y8NMRhj8sbA3rBHGc0Bo8ES4HD\\nsyUqDJjMKpzO8eSU3BTMMkdVVRSzjCyx7JcOtRLS05Y/9763YYIYXSZ0fMdie8jNyYzdPUsgDUev\\n3CRYbnNne8y5bo+V3pRhmTO6PiTqlfQ7Xej02I08vpKO2UgvYq/kf0r1AAAgAElEQVSOWD0lWbYZ\\nuneW7b0m8lbClvKYjxOa7ZBwNCXKUoqkIFQlGnFMF6kDvTbvMmTax8gOUgoyA3G/w3IvQlsYTxYo\\n5WGsRgctslzy9PNXWV/vM9ktWQ0KPNll107YWltmbDKU5yMkLIYZHaeon+4nMFkAjSctUSMkn1oy\\nM0eJAOmg6x1QCUee+cxp0exYkAVID7QGW2JkTqk1o4OU5soyHj6UFWVl8U2Dhq5AV+hGiVFtKCW2\\nqlBRgVYaXEBaKiwRgbSUkwX0Y0AivoM++3eJSYSltRYxrmrcdGc5Ig4U7SAEz6MqJcNFcuzaITCl\\nRVYe+ayk1+0wyt6oR2rxnMJKQaVcrdrpQaVqCqEooRV2UH5IKQQVFqEMWmlsVmGSgkYYMitLKqUo\\nTMXqoEu/UfJL//K3+ZEPPoEsWjxy6RsxQ9//wXOseqv01lZonWqS1ZeJCsv+eEpaCeazjKCh8b0K\\nHWlmSYX2FM2uB6LEhGGN6cRne5Jwd56xPFiGyrHRW6elIkqlcGGMQSFthWSONSnGGExZ4lxF4FtK\\nSoyUmGxE046IVUnY6VD6ikr7JDIiqyQUmthZTp1ZJ7MereUu1hdkpmKaWVTUQQaafJogneXU5iqE\\nFl3kePMJj18ZcC6c08v22Nxq4J3q8eWXbnM4t9y4PeX2yyNGT71CVIzxtg9Ymuf4+xP0fI4eVhSv\\njIhyxfhgzPK5CEdyTEUxlMd3akWGFYLFbMbqlsf9g/s04h6PPXKBZjPAZCWqqO2U0AofTRxFlKVF\\n0WB8mJEuSprrLYQQSOPQCLJpQiMOwJYsihkPAr3WzlHH+Hil/JpoZhRCeEh/ge+Nif2SIDwmaUkF\\nWtX+d77CSomnNJsrZ3gVGmskQRigQkFN5hdYraiMT156oBxhJAgjjRc48txgrcJJh7UOjwiF+rdv\\n2fynPax1MJ7RPlgAjlYIfjmlmNyH5JAsS9HaUQl3fC4dLrYYk9NuttlaPqmg1inNjd1bTIYHBMbh\\nmQphKoS1KCfIU0ueSrQLaAqPvhcSxx5VLpmnhtmsIE0MpXJMipwg8NGm4vYrO/wv/+uv87k/+ipu\\nOkHOhrz18hpvlBx1fnSZ4Nwc7UWoRsDZToN5rWLOQVLSdiVuMSM6f4akMPzep7+C3wxI8wUc7DO9\\ntovqn8awBGqFG/tjBufX8Z2HJ3OWOi1G0xmD0+v0Zcyq71HkJaVoYHWLtHSEvkLkGbYqoEjRoiI0\\nIdO5JCl97h5NWIxTsqSicJDOCzYurvDoex5mZqGxEhG0Q1q9DoVxLPuOzlpMahx+EDG5M8NsL2gt\\nShYLQ+St8JUXDkAO6AVtAk/x+EOneM/bL5F4C+7mGS/OLDdFg5c+c51Al8x1ygvOchh0GZsW948c\\naVJRxgGjwxyP6Jhc6lHYOr1cb/XrCbgQ5GmX2bgkNT6NYImg36TQFV6kyLKSwXKPF5+9g5QOG+TE\\ncZNGV1H5gKdRnqa0FY4S3xcILbAHEyJjeGCtdWzWgMAiMM4iA4GWBaaCJG/i5BIqbqEbohZZ8amX\\nfnkJKiYXPirs8yDcHKYSzMuKzOaISlMufHSloCzwfYiWNNGgiRGOLJ9jbEGe5wjPELThVXmz74AL\\n812BjUcrtmcFW2EAsx1o9RmcuvDqZuXfOp6pLVoZpBa4UnPlsTdTFuC9yiMQLJIh5zor5GWBwVE5\\ni3C1zp0DwkChg1qSuKLmZCssvgNfSFwocZXDA+bTEXu3bvHcrQMa7RYbgwU3bj5DnsKOt8L8hXs8\\n+vY3OqA3c+Wv32RrcomdX77K3f09oCaBjPFpDG/TlDF7mSFe6fLCF56i0WowvyeI0xC7uY7ZGPDp\\nP36RN7Vjzg5q7fJyMUa0vRpgHkfkRYrfbIKpkBKKsraJLikpKkcVhAzzjDjyMJVkVIak4SZH4zGF\\nTUgnc/I0ZH9/lyvvfoxWL0bEMctrA6TfoBgNSW1Kq23pTbbJygpHRdQMCauSKi2wFYStJnhLxEHB\\nfrfLuQubNAcdVgLF888+j9pcoXFulSyb87FnXuHx5ZDskx+ljAPs0jJnzg5IxQqu04T7khd37rAR\\nL2jrgnkFjVr9DoCd2T7nL19kbo8I0z7nLrXJJvukaUphJM1Bl36/w+HRhDt7t2mJim6jSUiTxUHJ\\n6sUe/Ue2UFZgIoPxBfPKosg5v7qELMuaqZamED1otQo0gpCg4WHuHKB9iR8XqGCVYZbhrKHdcPje\\nMUvTAh1JvkgImyGpSYhUm3qpqgiVj5EO5UmEKWvidlUStBd12yZcBltwdG9Kt9WotW9KAcKHwoFf\\nTxzfScP7uyPYpaAQgrJwiOkc3dp43eag7SNzgZxJfKuIWwGdrbW6dHIc6BZTAy/mJc6v7Z6ccXhC\\nIIQEKZjMc4LjPqySJSAoS4fzNKl1pHmFHR0RmII7hY/ySlyZc6HtqFTCxfe/h86gB3JADZV9PRjo\\nwXiSpUdLlkYx73/zKT5+dpN7t66TAruUpAQMcsWp+ZQ3u4jfOFygbz3HMLFc3Rlx8X1vo0wCbu0e\\nkc9GPNlq4xUFzk3oblxkakoabdhaPsX9r7zCRstnuXBkTCllhU1bVKWmKisiB2VmsWVBiwXT4ZRW\\nWANKrk9T8lzz7g89xm/9f7/Df/mX/g4Lr0VqPMLMEfUD8uszbAapXSYtNKgRGEcyS4laA7JyzHIn\\noPAKNrRieSnCFDPEzMd3Ax67cokrf/GD5EhsNmJ47yXytOLSj9/m6m//G8psxOL2XY4WbV6eauZl\\ni8PlPtp0sNU+PhJBiBEp6/0BmVN86pNPc3rjPPcPQprtZe5u79Nud2naDu2VLr3Ty2wJi1WGbntA\\nICX7OzOeeOItTKcFDb9NM4jI8xQvDLCVZbQzQa9vMnUp1a0p/aVBDVHG51XMepUg8NGeosSnYUtk\\ndkTPj5C+Bp1AqeoOzlKLZDwkHqxQ69lZUrMAZWsnuG7Iwd6IVsMj9CqEUqA60Duxeg5BRmhvm5wc\\ng8QPdC1u2T6GylJ9R7n5d0Wwl1VFnhUcJILUT7hw6uHXba/mOQ6B5yQrp5uU/gOkekpGsxIkBxOy\\nsqDf65JRgnLYwiCkqHc2hkbkAQablwhn8ZVH7HnYQGDzOSLZ5q5o0fAS5vMx4e6QKuqQNVZ55Mpl\\nOm1qD3huU6d53yzYAd7B7/72/8YP/0f/Mf/N2R7v+aH/hArwKHnWQRRqzt74JKv9Na6nKb2zF1n6\\n4Xfw+GALKPg3H/skpyrNyh0Ic0Pe9Xh+vODtrkGjqzCyJMkt/cEqk9GQQFnEpCJRBVJ5LIoCV5Uo\\nP+Foqimkwk+PuH93jGu1OdgpkFGb0MZ84Cc/wJt+8E0MLp5nQYQrM7Iyp68lL159gbPrPRZ3UyrT\\nxNcFX9h5nsFgFZGWeG3NUVLiRZJuZMmG1ymbAbN7d7CtHmaY8tm9Hd7/lz5MuR+xdWnAQinS5IAr\\nb3srmDvQhXdf262fWukmf7QNz7x0h0/+yiuk+Chh2FztcLQ74YAASZtmR7BuW3z16at85CMfZjb0\\nGN/KKHWM7Sxx+oyiShyh30A3NFunGtx++Ranr5wjCEOiIObQTDHCEgjJrJhiWx6B6tOOi7q4VmWg\\nQ04MmYSuEZVKCypXEQoPHVqMdbVapl9CHEILJof3abdXj6+4qTMidfJeFTookNKAEySloIqg1TkB\\ncD3o80+mloZWRB6oKgEpyReKwI/qwuWfNaUaXyvWWx2maUbi3kCjvcyJtMXzOmy/NGT98bPHmp0V\\nDULm832aYYtmaLEmI1KK1BkiX+OoRfirsuYeWysxvsYJx3S+IFscouZHHAZLrHR6LJuKzbNbXFJt\\nat3v1w9JbcIH97/lcf3wT38Y6PP2730EqGu8bTSj0udObng4FXibqyzHAV6/yee/eo3J8CmK0Gd6\\nd4cntMU0FPlaQNE0nNHr+BrSVINrIHxJsxuTVQk7yZh1B3G+4NDOMSrA04q8qN1tTemxUwjSlRVu\\n3jui1e6z9/vP8Td/6a/jBgNs3CGhUROOZIgOJHY65m1nl7n91LPElU9WKg5HC84+usz4KCFAks49\\nirYizgX7wwKRLhiZCdP9lN6V04xv7NNaajK+tUO30YMqxZUWoVpMq5R26yzMx7B8CIvn4c7XeN/6\\nFd73rkv8tQ/+HF+4tWCXgE9/+cv8X7/zWcCgRMB05hgtanWiySTlxu0dNs9uUrqUw2TKZrOFwad1\\nbp3Z/ZxsnvPwlWVkM8YrQ5KbY/yequUhXclwsaAMPYLmcfBUAvSJRoKiRk/WugRKWTxXe7dU0q+9\\n2xo+NCwUGlSXzlKPyjjq3oikxJHbDCsdJRa0j0FTYOi2LbodHmM6BA+UkuDUmQ0WaYKTDiPqDDbP\\nEgJa1PrJf9aq8dZRzhK05xP6IfBAVno0OWQQ9dCElH7F1iObhFodS1TUojzSeVAZjC0pfUGhqAkv\\n84KqdJQGTGFBOrQvCZQgtg6vHOK8hJ1ml5YaM08XbF5YBXWaNwr0eiF2grJq8Fr30Tcea+wePFWL\\nGhyPCRULucKdfMCsexptAh7pDBgdHHHtqWvk949ozVLWgxgjNONQ8PJ4xvXdCff3R+xPhxhr0FJi\\njSOzlrjTJDMZVZrgTRb4RYKuLLYoMZWHspAvMlrtZaq9kvmdKTfvXuPf/bkPcP57t/BYpRN1j3vZ\\ntWDnUTIlqcbMD8eMZxmHsyFalKx3O3gLw927Q+4ejgh7PpghsUrJsoLZEDpqiaXGMouDMR4zGqoE\\nbTmYHZGmKYE0mMUUmeUkhxXZzIeRD+FS3UyuFmBuE24OubyV0/ZLLl8+h+72gDl39nY4HKWks5dY\\nWe5SpBlJmvHS1bskhaHTbdEIAlbWBhyNpmwuryNNRFUJjPHJqgVogywlMheEDpLpiKjZROJBasEI\\nHmj8n5Cl60Kd1AqlNVpblDDgCggsmAKi5vG+Eq1ULRx5fK9iwRpRg76MQ1pVu+rk1XGhzT++v04s\\nymA4HGEdVEJTorBOHlfgw3q/P2trdiUcQTYBTxNW8OIffZnLF7agkHQChWyu0PL7uNAjx9W8dBwN\\nFMPZAX3hQ5CB5yiFQlqL8gSVcBSewklFZAyzqqCYHdG2FbvxEnHVoFmkRC6n2evQ2DpFzbZ+42Fs\\njpI+LBRmPkatetRo4DcuiRbA2vJbgBdefe0Q+OLhDpeWN3ilAWvC8qFuG7m/zaWZR18ZbO6YLseU\\nvQHtQYcL7SYqhmgQsj+dkBcO7VJ8JRGqpNPvc3e3wTi0jPaHVNkUvx+QVprcNpGRYL80LIqSp7ef\\nZ1EsWAwN407IXK6hEUgcDkdQWY5kwrnWHOm2GTNBBBF2ALIM2bm2DaHEa21hmwEJKaf3ElgZoq0m\\nsxmj0RHD/X2Wzw9wswOsG5Ps3+Vo7iiTCefObVIkM5TNEVicCDDuEsPqPIPGkDhqwDSB60+xbHwe\\nPf8kv/+ZbfbHBwB87HdOENuSo4P72DTBkxWr51vohkcn7FJM9ui0fKbzjKbncCsdgqjLYG0NtazI\\njcBTmtAT7N69zXB08P9T997RtmVXeedvrZ33yeHmd1+sevVevVe5VCqplEOBMEYtEJhGCEZDN2oG\\nxt02ocH2GBi7GT08RmODA80wBrehSQIDNlIJyZIQKFWUKocX6oWb08ln57VW/7Hvfa9KqpKKhtGj\\nPP+695yz09prrjDnN7+P/mhAGAY4+AhzIJuc8HI2GY1wDLYpsI0g1QbHtcnHY5ylbvk7HXF1e4dW\\ntYnrWUzzGOEYGk5tHzmgKYxAin3hB/dAw8Dja60122AaTVHaIAToXJQZ2f2Z3/pvjakGbchUymYl\\np237uKICXhUcgQwFe2qI6zeRlFxfKYbavoO1a030dIqWEmMEXmFwQsEoSXFrLoGUiFTj6YLBNGZD\\nScbxgHjS58SZ0+C2ge6r3NiB7G5J+m/JAD3oM+j3qScWyHWYcSiVXL/erpMN3fyyzxt5woLMaBS7\\npJs7HO1I3OcfZc4/xXA8YUyT7qG7KbwAk8dkvQFirEh2LcZJxjROcfwKouqxvbbKYDQlzRS1aMKi\\nrZHTISKOMaJCqgIyq0B0ujz1yEUiWzEYW3z4Rz/Et3343eTMorBxmKJNRKQVs24PZ7DNiyvbtJsu\\nwrXYU3B+d0QwW6NIpsR+j2p7lmRoyIJ5LuQFthKs7+2yfONRHKtFVHcZySpnTx5ib/MKWVZQczL2\\nViNiUcXOXWabIegYM4qZEymuhGmSkVye0nxqFeuDd/HJP/r3/JsHXqpDrmCfczDNHArL4YE/f44f\\n/PF78FwXJ7Nxj8/xwtMb1FtN+npMZb5LXc3iJuAWgjQ3dBdDttfWobBBQ6u+X/VmZ1ynQcv2+8D1\\nz4wWxLGm1vTI0hSvO1/SVR24UyZoNmbK3XlR4GBjChimIwoE2hjcwEU7BmUynLzY72NjSpTm9a3s\\ncJzh2RYYi8IARNS8BEwCwsdxXrsLvy6c3UhBfXmRoDNPzQ+BXfKdHk7Fg8oiTcBgY8G1JdGUDMjJ\\nignYDqrIkIUkLxLy1DBa22IhkFiZzcQJ2DVwoutyfBogrTb2kXlyu4ZzTUt7v/hlf+Q1KAQbwC7T\\nccBoaOGOYoy2SW2XwK6hL27izpx4lae6bjkRuBXIyrveBs4nY9JsyPzMHPM3zTN/752kPYel4DAD\\n2+fPvvRl9oqURA05lroc6ywQzHSpVzSzaohaOsT5qwMOVX2OVqoM0gl+6CLWLtGopNRyGyvNmeQp\\ntdkml3fXePdNIfnN72Vups1t3/ejlJ1XMMm2UO6YQFhYStCb1tFbY6xJi8tfeg4xjJDKZs52sDeG\\nPHJxyJH7DjNe2UCkFmuOJJAhYUNyvDmPJQR+s03l9BzrOx3GzRCVK7I4YXz+AouHF7Eqc+Sxx2oc\\n4dsZ4soKTZXT8gWXehF7Xo073vlG7OmQfnwIxYEgpsD1GthKEhUDmo7hsb94jJ/46Q+ytrYKNnRa\\nbYY7lzl26CgXXhhz851NQlEKhvhND095pLbixXOXSUZjOvMWwoUDBGdpGUWcYJu0BMi4B7URFvWa\\noDG7AGQ42wfkJtdXd1d3dmjU66S5wRIZtueiEAghSmJKB8bJFN/zkU5BtdlAobCuSYRlsB8bqrUC\\nkmSKyCRaKDIjCdCY7V3yRhfXf3WKq6+114Wze56Hv9TBpw/JHvgBzkzIwQPnpAgsFCUNVblN0VQI\\nyPp72G5BOpziaKjYOUWccGFk0BJEPkUNN8kyjXvsHirdDqBIybheZlFqXZcja5kHLV+dBcxQURqn\\nWWeUXKXIcvLCY+gI5hdm0NNVZOXVVgZlzN4jhGx67UrB8ZDHN0YszN7DodmA5UYAyQTPc0AWzNYc\\nfuA7j6OtLknh4OWK3Yt79HoxteGU+UHC1WjAseVDNDoFeaGYWzqB7eZ077oZkgFMVmA3Ym5iMz53\\niWLUh8kO09mTtG96F2SPwcBC1ANc4SJSh7wA2/FRgw3sLIKeZqpbLB0/TXzpKtUsRQmLzq0n+OrG\\nJof9KtU8KbXnNq4yK+ssv+kUsZEkfpV0qc1bjgakRUJhZaxfvEyrdYw0G5NF6zQnEXnkkVUFjmez\\nqjpcjROGwyGH334jv//ZAZ99aJ3f236p8q0hSw2zS22itTWmaUbY7aB2BDOdGVKrIJkxmD0fO4fj\\nx2eJEwc7CChCxaSao0cWVkuytrbCyVuO0h8N8OothvGARjCDJkfgYAcOo52Mum3KMJIGVIZ2A7RJ\\ncZIYv33ALV/OxpGKqNarKKUwNhjLI85LlJyxNDK3iLIM42uCqoeaHIiMFrzSUr7mV8iihKJQuL4k\\nJ4DcR3g1XN/lr2KvC2cvC8p2QG3CSg3aNngeo8xDhwW2NLhuF4V7ILAMuWQ43CXe7eEWMZu5x1g7\\ntNwMP485FUg6joe9MI8J62RhFbtkNqMESRwoqhYUFNgUGCIyXJxrMc6gZMO0pvT6E0JTRxITmxg5\\nNBQ1D9OfIL+BFPrBq/uu77uf//Q7n8IGfuYj38Iv/O9/jO820ZlGigpsjIEcvBAmwO4GeWOBgZoj\\nKjK2BmvMH51l0lc83yt45pkNitVVRpfOETsV1gqH2++9GW+mzS0nZggHGj3MOOwbalWfZx7cwXty\\nhY3+JZ75f57mO3/uh7mSTDl8dI65O99Ebydi+eghti5dIY1jGspmMJWY+Q7bOiKp1Di/PqXTMPjZ\\nDncdPYLlWDz58U9yx+IJPD8nFoYHRZXQr+Ari4rXZrVwkPUmxh7g1E/y4iWLPHKpOH0q44iqgFFe\\nI9I+bhwRt7vMzy/x0MMv8JuP91hJXwkKfZnVtZIfJcunRNkGg12X1LEYOFNCpyAcBhxpzzG5UbP7\\n/IBqUKFS9QgDHz0cEBHQOrHA0okFbg9uYHN3mzjVNIIMSWW/n0wJaj5xPqZQDq52MVIwiVI6rSpb\\n45i5VpsDR+9v7DERZVmt79klnWmeYpQsa9Ix6Kwgz3LqrSZaQaLGGIqSco2IrxUR1VlGGhfYjo3O\\nFJbIKGSOHVShSMEOeK32unB2ozTphT5e1QW/Cb0BKAuVKoLDTfxWlYicOHPILIPMc3w7JqPHps6Z\\nqhq+mlClIJ3mtPIetBZwjh4Fv4mAl5BEl+Wvzj7WWWNdc3yFxsNjypDKPrAhHgwIAsU0HRMWVRpB\\nnTjtYXROXhT4ccbB/P2N7O47buM//c6nypr22cMsdBZ57soVjh9qseSFoG1wUogNDFNoOHh6imNN\\ncYqceNDni3vbtBtNfN+nfqzKs089zV2Hz+BWG+hWnR1vyu898Dkun17kkB6Q90ecqAQcIucGL2Q6\\nEbQsgWpY/PiP/CNqy03uuPUU7/lRwc6uotG1SLMJjgaTaSr1kI0XL9OuB1SSPn4uMEpSnSjWxJip\\njjh9z41EWxkZISLyyEeK1J7SnmmTxEOyDBr1Lr1kSlyk2JUqhczpZQnNxgzC8pjgIPZy5qdTXrBc\\nRlWPjY01emubuPPwMq2sa3ZAzd0gTQdY2WlmnBouHk7qoPoGGVq0Kg0qHZuZap1E+eiRwZaSre2E\\nW24/yU1nj9KVdU4cn9uvRbC4PsvaOJ7NcJQgtUI6FkaAHzpoXWbPeQlTbK4LhCMxyiaNU7Tj4mJh\\naQOijMIbYbAcG6UM8bTAcW0yrfGky9eH1jVxOkHaZYpQSoHRGbku6yj+Ko4OrxdnB7zuMnkaUWwr\\nvLCK9H1arYCNaQTjMeNRn5rjczWGS6s9GqFHxbep5Qmzsk/kCrQImFoFW3bA0skOhVfjgHZCsg7E\\nGJbI8PFQ+4u1GhljLAQ2VcZsUCMBEhh3CHAgGXGinpPmIYMoIy8kRioSZcjTgHpyDvw2JfvWK9vJ\\nIyVQ6Mwbl3EO3cCxt9zFJ776ZW7tH+Uth7oI6bMUmP2wgQ+bETgQLFapepojbznBF6+mjPsTXuzv\\nMU13+Z7vfz/bwxS36hPkOfUs5H++/z7Ov/Acq8Oc1vwiT25t8eTjH+PQxGa5ZbOYRjy+vsl9Rw4x\\nTsfsPPQE/2b9Wc6tw7/9pZ+l0zzGdjymIQTjlQn1VBOubzOzXrAaVtnyHGYGm1hZyt7mgMN3zfLI\\n4y9i37JEszWLmybonQkjoWkvLlJ3LDxp0WjfSIwiWFAUJmbcnzJY3WVnMGUvTtmLelwuLFTY4M/+\\n9Kt8dW3Iul5j8E3hDM/z4nrAHcuSbGOHmbBB3WlQzNfBnWA2p9StCttbPeaXlqE3wF+octv8Mu+5\\n9+Zr3EOW1QIKsmyM69b3e6XPZNqn3uwS706JxgVGgjKaCSm253CdixAqzYAki8lsjcgNqclBSSxg\\nkmYIW+DaFoHnkk1TkijHK3Jk+4Cq/GsnDMNwMMHyQ1B2qe8mHaaFRSB9rpOhvjZ7XTi7MpL+jkTZ\\nAsuGbG2XTIyJfAvVXmQls7DDY8Ril4qTcfNsg4o2aJkzV58pZ+1qBbDhcJ1ShL0sYEhRaAQOETYT\\nBPpak5aze4rLJcrAzAw1OqAugCVAFzCRsCag6bI3yen1cqpVTe4KEsvGyWwoYr6ZhvvuehlNPnrn\\nGU6+537ef3HIn3x0C78SsFFMOeRZ5NsJohmyOY3pTsd4nQo8t4rVqTNSNss33Eg8ibjrnbP0pyPW\\npjn+oTaFUqg9wfJgiOvGdO66mY2pwptpMplssfBmiXVljfNPPY3oQ7MH93ddzq+usdazWFmV1Cou\\n/8s/+Kf8nR/+IGfe/SZ2LkVUjQRXMEkN237I+aEmqnfZcob4yuKNd9/BfFXxUPYk24FFLMd0pppD\\n83NsZBPMXo7veexcucrZ245DHpEWOY985UVWNrfQRcIjn3qQcN4nkzU29qasPHqezuwyK6PNV57Q\\nv84Mo6hgZWeVU7feRBbZjDYVW8kqrXmD2fGZP+ZgtWFz/QKztSUOnT7GodPNr3GtMYaYvb0hCwtl\\nSBhyqtUukDC1C4QWCK3RUiCKgnQiuXBxh0AOCXyHKImg4lE4OTXPQmvI7FLNSDsaW9nkaUa6V+A2\\nQzq1GmoKKs1xvINsw0vvygJtYWSCMF4pZiwE0nPQWY4QBSJovqZWgteJsxd5Stx7Glvn5PYsQ7tC\\nagfkJsfemzInbOrFNj0rZWoKLEcibZui2iCbbeFXLgOXgBOUTr7IwYh3AGfRdClz6AdO6QKSAWOa\\nBECERhNFmipLxIMh+UhTs10iv0Uag1uXzHUyxnEfFYUIqRChhL6BK4/Bmfe84vONMrjxxjKt86M/\\n+1PASe76yI/xf3/0o1x96CInP/xupgyxVUoiNJMkoxo02bRc5sab5NEEvVhlZW+PhWPHuTRJmVmc\\npx0riihlNM2pOAqraKFlyI4pqC+FDIYJDWeOqXMvwdEpJ4+/md6Fz3Oi0mS65tE4vcDbnuxztnuG\\nC4cr4HoMN/b403/9W5y65Ta6aUatXWfD1ohGwhuPh5zLMuTiER7+4z/j7Jtu4MFPf5m3VA2DZy+z\\ntheTL/ok1S26txxjfGlE7VSLRrvCv/6536DbCXnwk1/ADo7t/UMAACAASURBVELmbpzjxafOU296\\nDFcGZNke/WGKVyh21lf4gQ9/gD/9rU/y4jW2s681SbDYIl7f45TXphEEJMrCcQLqs4YkGKMSm0AU\\nSDNGFjC8GpFWJ3zHR06+AmTKY7e3WmoDGkAccA5qVra2aFaaGJOAKlllhGdRkR4iiUBkRHmG4wWo\\nWBPniqEpMEZiCQskWBZYRqIQ+HUfEShilaItA0mB78F1/mFBuVpwqIcB4zzBQmFJjVCKosiQ1QrY\\nr51/Dl4nzr4zTfjkygWOLpzA92xO1n1anQ6yU4V4BGEVNmMWmxIqdUqEUcp1LrgSGlni6g6+L+36\\nrqZJbkY4AsZZTp4NyVKf/hBkowVbKaKhGeVTEqNRuYswkEYRhVXyc4uBpsgUbtBApaBkhhQGcgmL\\nXUqS3a/PudddOFQrFWV6lZs4sn8/q+OCmUrBxe0hR446LJxeRk9iluopg5HhmZ0EJWpEJqfYy2nO\\nNcnShG69ih4L9FSDZfAczdiZ4nRc3HjE2WSXcezjVOqkSQPtNjCux2hSYfnUtyMHWzROp0x6hhNr\\nG5wQEX+0lVA7cpgz3/JO9KkWD37lPM2+4g//+GOcfMfN/MQ//Q2WKwFFEnNM1bm1ZfPbD/0ih++7\\nhd3nd1k4OuXsqWM8+fwLnHnbYd7zvrvKFdZ4HULBG/7enaxe2KZzucbOQFIkI4ZzDl59lltuXCas\\n1Pj4H32cy2GVdsvif/zpn+GH/rvv5gPf9/d5Mb34dW0Kmnh9D7d1BuE1sUSVvB9zpLOEncfs7Alm\\nZ2bIK5pJNKTbrXKpv8N//Ff/6yucSwFT2jWX8UiVS3mvxkEwd3nuBJCzMhzgORIVG2QoiVWKsBy6\\ntSZpkTDJM6Rt8LVNIRRCSITRJRVarsm0QvgCb1awNxzhpDYzlTqCHK4lln2u5/Zhc3uPVruDFqDI\\nUZaPV5EwzaHxVwPAvi6cfa5d57ve8UGCSrusKLJjtocZ81ZILic4CFiyKJmuDjDrB+zVHeDEvqMb\\npjpGj7fQucTkEqUKsiyjSDOCuks83sNFEk9iKhWHFi69qzmzYYXdK2uElRK8UGgb4WZgWUQDRcXz\\nKPIChYVKFHiKTBRkowwpJK3AkK+s4C8fDDgHeylFgcAJy2HHzq4j9P7Ff/gVfv4HP0L2xA6dE3cw\\nmGa0qwsEtavU5wYEN9zE1ac36Olttveucpu+EbtSZZIXqHhM6FaIhglKGVr1Fo9cfp5mM+VM9ype\\nGtK0DrEeOVREyHivR61SI49iLGW4uNtj+egx1m3wdvrcaSuev3iRf/n7n2G8VOcLW5d535vexl8+\\n8CC97Sl3L7yBLMk4U+vTcAS3ztxKzexRz2K899+Pc6rKGTfle3/xw9CcAxVAbxt2nwVHspIvMgnn\\nGcRVfJ0Sm4B3nLiBM+97B7u7e/QSzcXRhKXmHPPHqrScLpH/At/xobfwS7/xSs4OJc+exqlZTLIN\\nVNRBLINxq7jbklbDcOmxVS6vneOGs7fyxne+Yj0ykJNhiJQhT2BCj6y3SbM2g19tceB4Jqzg1xzy\\nrSlVNyDWgp4YU6QDPNtmHMUEroejbRyhyqOEQUpZ6sRpg2h6rPcmtOtNqi2PfDTFdw9yTDYHAC6A\\ngjGtQw1UXCBljG2nOMpDOl2oOJRw7b9BUI0QYhn4TUrPMsC/M8b8shDinwD/E7Cz/9N/aIx5YP+Y\\nnwV+mHLI/HvGmE9+o2s4tkV1rsWgyGjbDWwy5tslwMCpGEpu1i3IdkuAgwBERFFskkxjJrlhOjTY\\nwqUwYOKI2U6T0VrC/HyLaa7Bc1C5wrM0rpZYrosRKVpnuIHLVBSIhoPKIUslmdIEnsd0EuHJKm7g\\n0kv7GAeELUlNQYUQhGYyLHnJKl2XMldf7DeXAjJsArK0xFnPuNeJAo+deTtve/+7cCYhOjfgBIwi\\nRdBywM9o5WNGsw4Xzm2Tdyt4lZBcu3hYTKdjjJNSLTSj4RTX7yIU+M0F0jlweyPqFPjumIiAmZtO\\nsr3Zpy8DGs0j9AvF6laC15qlrj2a8YTU09RkTr6qsPoOz31+Ewqb5KtDTqZ7dMIOS8k2zZuX6Kjn\\nOSvPc8qBld4Gsvpm5rw92KhCdAzsI/TOX6Kd5CRxwspgntFQEgU+fpZgeYpmMKWabJCbGBF63Lhc\\nJ1opi5pG03XmDls8/NWHvkHPKajM+Hh1l2M3LbO2GtM5ssDzl67SmKmzud5jnAwRRKyvXOYnf+qH\\nX+U8FmmaIYyLFA5apTiuTZrF+PtCIOBS8wNCy6FwCsih5ntMZEKRWMzUGoz2hijbwigQlkIoCcZC\\nYzBWjkTiOIKm7+JoRTSJ8YVHnid4QUo5q1/H4NvU6G1dpNVoI6xS44BMYQvJeHNAbcF9Kdjum9pf\\nR/4J4F8aY/7Pl/5YCHEz8L3AGcrN86eFECeNMYpXsXg65ZHHH6B94igyOMRYJzguOCg8DIo9GoTg\\nvoNSCxRQA+w0pTq0qUpJbiXk0iW2FJYdYjKoNY+QjjOEsSgsA8pQtXv4ImGiPQrfRpsEKRz8sEY8\\n8WjNBQg9wJlt07sSsXRsHvKIC5c3mDoZ0hVgJFkqCCYWy8fq7F1eI95zqNhNGK/B0WUgLqOnFsCE\\nRquc0dvtlz/7n/7nJ7j7SMEdx+9FhR1SE5D1Q1ynge3adI93mVFjUilodjpspTmTtT1sNOQJm1c2\\nqNdtPvvxB7j3nnvxvGWm68s47TFekNJpr6GaDpfHTY7cehrjWWxtDHn7+74HScFldjlEl6tPPMhD\\nv/j7pPYmjaLOWw4vkq2scnppgep2zq3dXW5qxyx6CTs3VJmJezQvADfA8hv70L0E7jbIRRi5MFjl\\n2eeuMtNuk+mEB//Lo2xdTaiHEbLlUR88yQf/8bsZfvkKy3NLrOkqZjSm6iXcfNst7Gy9wOn3HeH8\\nlVeb1QECusUsx+fnufqFmJN3nmBhsUYhAp76woM8fnWMRxff63L7m2+lVXvlYFa+t0olrCEK6IuC\\nXAGFoLPY2vcAA7ai5QRQWFSdgkkSozF4wmZhbhaAkydP8sLuVQIhUaZAGgOKfcETQxEpQmNo1gLy\\naQ6ei12YEg6LRYnDP2C3rVCQMFuvo3RBVgTkRQWR5ujxlHp7Dv4K/HPw15N/ejV7P/B7xpgUuCSE\\nuADcA3z51Q6wLJs33v7tHKQwXvmVKPb2LtLQfZKxZDRR+JbHKC9ohRahchC2wLhFGcxwLKQp8N0+\\n2Ia0aCN9F6e6B2KXun2GRBscT2MRoJGEsxUys0UgEmCH9pFTZOTk+ZT5M4ukF4bEkaY502Tr4vPY\\ntRkmoy1MUCGxLLrTHNIRHN3lWgVT7pYUSGH5VAd0CAf2S3/8B3zvd5xk+gmf+997D+2Gz7Ro4Y59\\n6AiEijky38U4HrkGpjH1ULCzNubyixvksWYlhN/91V/nz//sAb77B36ae990B93Fo4x0xPp2Tpit\\nUrUc8oHH0PHozM1STD1MxWGeNiFNZHCRyy+OOdWY54mvPsqZ0w2ytYt0jx5i9u7bqBXbzM5ZNHau\\nIvbGBPe9i8lbM8yhHaxxG2+litXRYO8Tj8gRycUX2UprbE9zButbdP0aeZww3U74qX9wLwzXaCzf\\nxRW/y5ceuoJwG9Sabe44fROBiXjus4+y0yuDUBJnX0zzpVbh7mNHQTkcveM0yzfWSXs7PPvg5zH1\\nLl/5yyfZ2/01Xq6H/rU2xul4bK1NKIoUozTKkqX+YJ6DI/fZZmErn5BNEhomwLVtppOMhaOL9EiQ\\nyqemwMoNYRiQjlSpNCRyclEgbY+l5SrbccI0zRFJiqsVgRUgcoXWitA/4NI1QECGYqJyLF0SrGgs\\ntK2xXLdkl1X72tOv0f468k/3AX9XCPEDwKOUs3+fciB4qYjXgfzTq1pZsvfqMDQDqCKhkjWwJ5qq\\nYzOpR8jEEEiBCSTjXoLIDDXHw8rH2EaXzCHVx0HaePE9EHRBdMCexdghWuh9xtKClCkGiRQtMprY\\n+yowFi6uU2penDm1REa5q3Lcgu3ntrFii3o9xK8btq4MmOsswqAKlQJEBnZZrijSsrMNIqi9JEt3\\nZHmR1dTmi599nLccWWZxZofWXAiiDtMJdbvJzk7CRrHH1uRh9NTQqtpsTzJ2X1zlez74Ln79V/+A\\nJTVD/8omP/F3/z733HYLt9/3bs6+6w7eduoOvMEahd4lt13y7gJKTpkqi5AqQzwyLFYvTwiVj3jq\\nKW6rQdNNOX3PEt6SwRl9lTtHY8bjOcT9t9B47mGYnMNtHGL7ombWHsPScXAtUPWSSrkN87LCX3zx\\nMi/ujumIOiYpmEifu4/X4OidEE0hPIE/mrD9F5/nlJNRO36c+7/jDaAzmks/dL2PkIOogplQFi5F\\nnDh7E2G3jteYo6gNiIYpl+NNPv65F2gHM+ztfnr/6K93dMMKeZ4ghE3ST5CxD9LHERlFBtKX9CdD\\nMktQsR2qwscZJ5DnKMdCeBZGl+et4uBIEBqqosZ0lOAbhyjP8YTEsT0qtQ5u6FDNM5Q0CNunyCQq\\nMTiBRXGNONLb72EuIpqQ5xnVsEmqM4StsZXB9SXGKLACroOLvrn9deSf/i/gn1H64j8DfhH4oW9w\\niq893zWtt8X5BYp0xMCr0H3JJmR3sIFxYorEIpQW+QBAkjkZk8IgAKNTpuO8LEUwMOrneLZHYHyK\\nkSGwFyFLITMUI4VdaaIsF8uF0M7JXUXmSTxcSp5RjcBGvqRpYsqofkGZHBHAVDss3tIl3ckodEqq\\nJN1uC+0UROfGVM+YkrkkiSCYo3o4gA5sbxUsH3t5s1fTFq3Dkr/48ye46yN/i2J0BTsIwK3TV11W\\nO4anX3iS+NzjvOcD9/P8E19BFvA977iFvUce5h6n4FYhiLdcPknKhSee4qtPPMXOr8Atd93LL//q\\n/8bFR66y0N7j0NF1/NoM3ZvfxIXoCaquB/Y8G488TnjuHMd7ayy2WliH5hj1B7zXugg9YAzWTAGt\\nCGZG0N2AQc5srSjDE+ESnGiWLRQ3STdiHn52l2HWZlTkZMkUR2gWl47xvr9zHzQ8iFch7WPt7lHT\\nFvrIEsHSElc3exy+5SzD/X4ssdAoMBNCQgqrjmgEWNaUz37qHG95nyaUM9j1Fo89d54jwRIf+/yv\\nfIPeN0XoFBWBKQKKRJCJFAuJEIY0HTPfWcBvtTggk9haX8EWHrbllusE4zE/W2M0nlKvVShEKf6Y\\nBwbbaHSukQ50uh08z0dT5tsncYYf2tT8kMLNENqQRzkuPqgYxLikoUai8rKPR9MEbQtMbsiMpupb\\n2NKBa5Cx12b/n+WfjDFbL/n+14CP7f/7muSfXqr1dvb0WTPaHeItAdTBwGQ6Is0VfrVBOsoJQtCh\\nRtkKx7WwtxU0bMTURhcu0stLmaTMQjuGCIORVczgVmxPIrSLU83R2RSrBaQKigAnkVg1hXRcRlH5\\n4iZJSs9MqQUCK7cZCXDtkAPSIAewXEHeqOAVLrsXV6m4Pn6rAUVB9WQHLl6GWxdLMgZiXF/zkz/9\\ng5hKH5h5WVu86+638vCjn2ZrzeLNOw6z1aPcUjmMSCbkLZ9zX3kcozQf+M63st7vcf8734S9E1G9\\nsocfwNypLjNyDv5km1EMa7Rpk7BAxuOPPcjZN3yAn/nA9/Hmdx8ne/AF7NjnWfmXBDe1OXJ0jtvf\\n/z4magO7o1jxfGZuO0tQsfj2v/1WmH4F3p2Ct4RdXSLdvYz3oQ9BT4A7A0eXSoaWxEC2weTP/oC1\\nUYWV9AhBYxazGVENuhTphI0oZu+JR6l86BhEdRhe5bkvDnjsuS22ewLL6XP61BG8uMwzH0g1QIhN\\nTIHE9SroVJL0VthVBfNOjbWrj/HlRxVnbvhWHnn2HJfO/+436dESaGCpjEE/prAUvu0TFwmesQg8\\nD1UUFOkEaSzQEkcIlNRgW0ynMZ3ZMsVar1VYjQfU/YD++h6O7+FJj4wJlu3iSMn2Zg+nCq1qi7l6\\nlUjljKOEnJzAq2Arh+FkSsuURS5ULPBy0qykNjemQCORSqANZLkitWMqWmD0a4/QvZZo/CvKP32N\\nDPMHgKf3//4vwO8IIf4FZYDuRuDhb3wVQz+d0KGOKiKiSUGUTbGkjcTDFQWOLZnKHO1Y6BwqtoMl\\ni3I/pTSuDcJIdAGuLJF4yrGROkBpjbZThI7JCkM6HCOkQuY5hVHYjmC43UM6gkmUYCwL/BJ3J3JD\\nPQyu7foOGqzWaZZhlI5i+uR5uq0aWRKVQow6hXoFCPZlnzWCFjsrY1ZXXuTu2Zc7+9HlRVY2Z9m7\\n2mdrOObYyRtICpdq4KJMyl0n52gvtKi2QiLPJ5E2lpKElk8w2ygLcZ7chWU4lcNxvcjmlfMYCr6l\\n5vKXhHzi8w/wiS/nfPg9b2W0PmXhLXch5Ji+WcTlKM+PI+pnDnOkcjNv/da3E4QGuhXozEOrXm4r\\ndmO86ptBD+G+45QQz6RkaMkMuCfIPv84a5s7JDpiUlGs7O3hdkICx6c/zPjJj3wbzvE67OyBivBF\\nSsNMsJOIappxx5mjbFy+wrlLfRwOMs4pEoFFyDAdE+xHPTr+LLWGw9rOKs8+G9H013jP/fd+kx6d\\nAD4q20NYstRlQ2NMgev7FFlCoTTD8ZTFZgC2y87mHo4lwS8ppcKwpKtKtMKXNiJRjEdjRJxijADb\\nw7ZtqDpIx2N23kMVMWUFZ4mN8F0XYSQChe3vk1oaQxFbmEyj3IQ0L1luHNtGCoHr2rjGgBZUfL9c\\nAei/2Zn91eSf/nshxO2Ug+9l4CMAxphnhBAfpaRnKYAf+0aR+PIYgVVYXDy3RiP0cR1BpzZHJSw3\\nt2EYo8Zjqg0Hx5EUY0MYGFQcYQuXWpjg2AJja4RXAyPRqsByIhJ7QORJhg2Jp6AytRFOQZ6m2FJj\\ntCAtJLbvoVHYflnNbvs2m4MevqlR2Cl7/ZiTc0vXIA81QiqU9SvvfO89xOMB080IlRUlA3GnC9tj\\naPjgWcCQX/iF/4PNy1+v0VVpO7jRkI6ryHafYXXb4aYjJ/B6CQt+g5kbDxHlU5SfMFfXqP6ImeUj\\n6I0JyrHRuLjzN0Nlgzv3QgaXUtJbqlSckL94cYw3GBALOLcL7dVd/tbb38biYoNRGiEnLg9OnuXU\\nPaf5449+kpmT8zzwh7/N/XGb2nACh4/CieNwsgHDi3D8DaRiluGnHkVaY/IABC5rKwOeXuvxmU8/\\nSdUNSLPncLwMh4DnVp+lXlnkZMXmPT/3/bDyFBRDss9cZbgBFgpvpsLECxlGcPv3vBdX3E0OtAHV\\n6JIPRxgUCZqYIcvdo6wPL3F+a42S/we++Ni/fZUedtD9DmrOR4yGMXkmyZXGlQVKKaSWNGtVQtsu\\nBSMEDLd2CXyXar3OcDTC9T2qjQYKCweBSQ0V6TIxE/yKh21bZElClmfQfEnwzC4ARZHmaGEhHIGj\\nBCYD15GldqjxsJUGpcjThHicIZVB5YZ600MVCmEchC4HBBHokmL9NdpfR/7pgW9wzC8Av/Ca70JC\\nqhVdbSGVw+yhGbyX1KmNI+jKNnkyQLljvKpNEoNHgOM6yCQCMoTjkw/ACQzSKXPelVYP21YkhY81\\nbpGlGm1F+JmNFgpp+ySxQUqDpR10WlAITTYu8IKQSOW4E8OMb3Nl4yqj7T3mF1pUZw9Tvbazr1CE\\nEb10m+W5ebQjkL0CFmbLqA054LFQd8lv+vqX81P/6Jc5xJgPnD7Ok09scOt734aQl6ETMIhaBM4h\\nbCtmWgxLjGA9ojca0b35OP3HHsT3C3T3CGP5IwQnC24xL/DChT61uYC3nrmX+FLI3urzXBw9w6Of\\ne5Tdz50nZ0gOfOKZP+fZL32RQ77PHW+/my9++gmecSRfWtvGVxPCDUF+YcTMo5ofe/88X/3Kebbs\\njLx/lVGxxXp/nc/912fAd0hVjZpTQUwU46qPS8ChwhDYAfNBnbM3N8oqscyBziIcP8zmF8/R8Ooc\\nmyY8mWp8PL706390Le4eA7WlOkvzPs++cAkw1L0Gm709tNE0cRgAX3n6lYSCD8goDrqv2f8/21eD\\nKbAQKFlFy4xcDKhLlzQ1CNfGnUAlrBNnMYVKaXQaQIAhI9IJNRmyurFNrV3H2ufvM4WN9ASHl19O\\nhx7lKTKX2I6PNJBGGXGS4joho1yCtNGjIbJpUJaDlYfUtCTLIlQGwtYYUWByiW1MST0N+wx3r81e\\nFwg6jcF0fLLtjLqvrjn6QeGoGY+g3sap1MgmBUobPKFQOiPOBRVflR2oMDj1BGQKpgK2hijA8yTL\\nbhVqFkXbMBgarCJDSYdMT0EI0lwhLA3SJcVBmQJrItCOQGSCqS6ILINTq9LrRWwONnjjyaVr8n+W\\n1eLE2ZDNSxsEuaDRqEIWg3fAGJojEbReoUjpllMS8zz08xHO4hKjieGFQY+zN9yJrjTYWi91xrxA\\noAsoRIiwLTZMyMINtzFdX0PVp+xWXdTVZ7DaDU7fdxd1fZX1L3+Ge7v3ES4s8ea338L5h57j4vMv\\n8gRwhDpvPvPO/XmxdIOFdpfCrrGmdnGdNrVoSpgnTJ7y+MefPsdiq8GhGUO0odCNAKsjsOunaNkZ\\nVTym2RD8Ak/m9LMKyhlhRQX/5B9+iJn3vx14GswAohGf/Pyz2FbAYJLRTCPkeIpHxs//3L/HpkzB\\nFsCPf/e3stBs8PmHn+APfvdPGKU7SGxC6gzY4Nve8F3ccWbx6xv2WpJTUg64Cf3dLVAOcQ6mEFhG\\nUOSK0HIwMiDNbfyaYFwkpJOY2UqLNI9xjcSmvl9eZfClYMIIv6OYjvpEcUbgWYQask7Oy1lvwLZ8\\nbNtiEmdoIRHGJqx6qCIlziMsz0NWA0YiI85y2B0ROBWEp7CwqddbJNkYHWuc0MMUBcJ1/2Zn9v8/\\nTOUKT4KsVRiPU5pzpZN7QG+6zcxCFYoRsSxRTmpkIZSNIxJs24LKCCYR+I2SL14JsnEFWbiQWihL\\ng8iwidkLFHZrltguML5dcoQ5NrYVkpmcLE2xhI1tWRQqRdiCnf4Y4xksIRHCJlcRFbdsuoM5I8Rm\\nd7LBzjDilluXMUWKcA8or8o9uqGU7X6pLdZv4vZjDmfbHq7TQDma//iJL/DWwNB7cpt7fuQ7kGGH\\nTBiUsEthQ+MSCo2dQRw4hDcdYZQ9w4n4M5A/Rzp7B0+6y+z0bQ7dHnB4y2UY2uSXdnnvmcPcvNih\\nkmcsZEPOXHie/7yXk7GAY0sGgy2OVmdZIyLLR1RHM/SyHapCMnECNgc7TEcS261gTT2UDKiqEUYW\\nHDp6kjhVPPPsJW4+toxMJD17nZ0XJ8y8//uBq8AQ5nvkv3OOxkaK3oU1y8Zd9Lnl2El+4zc/RhQU\\nhJQcHsaBb/nO7+Pn//m/It8ecPjoEudX1lCqICfBckI+/vAffqPexfVoi8EmJEljLC0oECAMgpzM\\nGNA2ReJQsy0cBY7vksuUIs7xgg4gkEg0Fg4BK9uX6bbqTLYHSEcymWqWTs1xHUVZkKgcoS2UznE8\\nF9d1SYocLI2m1Jf3bIfJNN3P77vUayGNap29zQlJZHH8xA1QRPhhnbwYgzL7dfMF+r813niBYG9r\\nh47Xoa5CNJoxAgtNreLCzhgCTTK2MJmN64NFH6kcGKky5tKowsSFuItSMVgWyhIUFY2yDViCInfx\\nG5Lm3AxcY6oprSDH4FGKDSkENuM4xQ9gvlsHXUodjUcpvWKXtOhT5pyuW71ax7mpiioUva0+M50W\\nhNe1wgTleA9lRH8XMONzbDwJ7z09hx/YrPXHNDqGq2OHxY0nCRofICpsXFuTGo2QIKSioEB4EwI/\\nZjoqyPUOzKYwaHG5f5ip9HC78zx2MUeGLrHyManGq/ncPDeHNYiIv/AIp6vzvHmpixkpzu3scvnO\\nO3nk84/yphvnkTjs7Iw5Ue9SLWyi6Q5uxWVmeZ7mbJtu4hKtDxk3LO696zaefvw5Fm9vcvPSrWw+\\nu4v0drjSE1wwm5QvKQJyyKt87okp9kaOaxJiHC6uSPLtde749vfw5x/9rxzBou8rsladN9z6Bj72\\n27/FRz7yP/Cpz3wOiwoCwaKdsue1eTlo5qVsL5pyI1ABBKPdIXGsEUKBMigj9t+LAWlhCoOWCVOt\\nEcKgpUNmBPMnj3C96Oq6WSn0d2JmG02yPMNq+jDZLp/VakHg41shWNcLV23bpXLN6w4oqSQ1cjQO\\nMYqkKIjSnNCrUa02ymvbZR5IuiNQBmMUShiyLOO12uvC2Y0xVOo1hr2UYydKAscCiMlx0RjLIk8L\\n5NQCA+NojN8AqU0JWjG1MhpsVWECTgCxMyIpLIxn0JZBa4uaF5DkE9gXAn7p40vy/R24xKBQCMJA\\n4CFJhca2Avomwq47+EUHLb++fl1SQekEbWma7Qo4B9e5HvJ4KXSoC/zts3M8/vQWTneW2lyT9NIW\\n1thjYCS9qSbPDJbtYDKFcASqyJGOwgiNW7eJdmOKPKF7dIlifZ1Rs4nZFMzUMgoREYUJvrtFFmum\\nwSJfWbtMtzWDl7dp3ngbVWfE1sVLdKYjFps+LwqH7uINVK0COfHZMxZ4grRmYwKfimczxuZQvcGR\\nuSqts4sU8w3WX9zk9M2z9Kd9qk6InO9yde1Z9LS5zytYABp6BhKL9olFnv+Th3AbkpHR7O0lFG2b\\nX/v9P+A84KFw/JDVjcm19gqqXWysckZGMSbn6F3HuO7oX7uklZTCaSlQILUNMifPJVKzz1EkEKJ0\\neWlDZhRRYnCdkoewRO2FlIPV9b5CZvCNQ24E4/4AadvkRuKaCM9KQdgQfDMyyJcOHs6+NISFNBaD\\n3QFSZ3RnO/vf75fcal0CSoxGFflfScX1deHsSmuqdR/Q12pSbaCGT39vi9CpM5woLEeDnlBp+eR5\\nhu3FmEqBsGvE05ykGOHNaOI4RgVVLKdNOh1RxCnGMmjHlIPCK7B7mP0LKwoyFBKbAIccg4MgQzJr\\nV8ls6Cw46Fc4R4qNX3GwUQi3QjlkpbwaXPOIaPLPkITYOgAAIABJREFUf/Dt3FlNufPeeTb7I9Ro\\nm6bMsFBc9WZ5+DOP0b7VYXn5CErK/5e6N4/X7CrrfL9r7Xm/83vmqjo1T0mqkqokEBJmCCiNoEAj\\ng9J470W7r9q2tu1FvbagXtsB7ctVG1G7PyKg0DIryhhQCYHMSVWSmqtSdc6pM7/znvde6/6xz0kq\\npBLg8/F+rjz/vOfsd9jTevZ61vP8nt8PnZXSGBkGOgTDa9CoN1i66zFq1Rsx0wG7J4dkMqMnNKN8\\nwCH9CBdPF5wbr3LhoZOcH90Le/dgbtnKWGWMM8UYNzbr5PES7X7Mjt23MX/561RywcRzd7O8uM7e\\nMY/+Wgd7tsl1R5+DFSzRtgS+b2N6KavdOdq1GfzBOHnTZrE24uzaKvVeHdyDwDIQwkiDAQ988gts\\nm57kYcNkrddhx4TJF1Yu88Whxgamx9pcXFt/yvX6u7//MrucOkXD5PGVVVLX4SMf/9MrPmHwVBKR\\nFJ2uMFIueVpQpBG6yBHKLDvGazZZlpLFBY5lgrCwM0Vra5soiwkTtcHLHsCG628KRiTdIaQK0xfo\\niolCE0YRTVuAqcGMIByCykCa4JcKLs9km+RmHqWe5Eo4RIghzby2cU7lvg2tUCQoaZJnBVn2PRbG\\na60xC4fZepsgALv2pDsmYYKVj0jzgDyJqVVsUhVTrxsQDikSi0R3Qdg0ag4raz1azQZOaxy8BkxU\\ngZgcyWg1ZPlij+ndTz+GTWiCBDwcNBkRCS4eAkmx4bAmBXJDr+tJ9Y7SKkCgLQJhUiFCMKC8jQOg\\n+RSX/+InP8aL9vTJV07wvKO3sHN4hmRksn/vVk6ud5gYRnSaNr/3B1/iE19+A9LV5KOQXJSoKWlk\\n2AKyOGYY9pi+RhGtWOSmi5jIsGWTtD+kVjlAt9Wm6F1irAc0WjitBl85eY7w7BLfyGNeedM+LqcN\\ndGWKXcmA03d+ka23takswOqFIQebs4wvnMHc1aB94DravceIhSRSDdxAYemA5x/YxYVeRmFOcuzx\\n0zx48SydHnz+w78GnAMEpAWEFvf8wZ28atcNHL/rIWIrw8oEd13sseXlB/jDG9/Cj/zvb6e1a+fT\\n7tHcyXOYREw6bba6LX7vw7/PteOHn2VkrSEKn6wboIQgTw2koZBOjopz4rjAdEC6OYZhodOEasMA\\nqchTXS4C4oIpKjzJI18QE+C2a4gsQGcZVuGgZYbnShJlkAUS39FQxOCp8hWbb6WcvtI2NWDW+xFh\\nr08S9glGI649uNkpskFzbhoIbSK0wDRMhMiv+nvPtI//300KiWUb2C6MVJdNbeyVhSWUSLnYmWN5\\nfQlhKupCYYZDgrBDUSSYDQ/frVGZqhEoyfS1N+BsmQavDTgofGAckwZSaVqtJ+ksrvZMFGwm3ixc\\nfHJgQMwmLFE+oftVMt1caafOLeAojUwMBFXKXvspNlfqEli63ON//tp/4eff8MO8+uAObo5hIl2n\\nmS5zYMLg4gMPI1WGMBTZcsrkoRl+9vX/kY/9j08hbVB5RqxdDFUhSw36nsKctsBt4Lkm7pgi8mA1\\nyNCpQ22mzrnmNha37cWvh0yONyios+fQTVx39Hpecc21zN1/kv56hjK38+CyhTUxRTeuIQYuLwwn\\n2L42Yt/O66l711JZd5lxHERSp8irdKVN327Tq7gcf2SRSzrg66fOsrbS5cG//n/Yc8shSlRkBnM5\\nLOTM7NzFKVlwom6X0lRLI3o+nDk54qd/911XdfR3/eZvAUMCcp6z74V85AMfYveOZ2q5KLn/w9xh\\npRuQZgqtI4QpygpOrLD9GhVZwcwdbO1Tc5o4FUkmbMIE0jjFAvbM7gJCNAJFgSLGpYZOM4QZkVkR\\nKlNoJZE6ZhSFJIZJpg0YFSWBaJbBaADdea5cDmxaqYAAoOiuL9HrzDEK+4yCK+XFFGyotgpDoAXk\\nSoP8HpvZVV4wXvFZDgeEuUO7sCAfsrJyjkglpBlkQrK+3iWv++yenaSIA4zZMaC5kfoau6LBpPyj\\nnHufXM/Vp7Zwcv4M2ze2bDa1fOsTb1PKr8TJK2w0coPst7xgV4coHtjT4uSxOzFEhQsXE175A8/n\\n3m88xl2f/Rx/8uE/4yW7prnnwj9y8hK851+/kDe/7uXQ1VwMVomZYumhc7z69ut5dG6NVcfF9STW\\nI5dQjSk+8Vd/x/SRw1yzbYJMWSjLxawoCjVJN8zRYYKqBeTxWZpjfdzIRPsVlIoxvQF6ME/RNrk8\\nWuLFN43zt19aIlRbScwW12/bTnesR9eOGD9Sxxk6rJBQv2kf7cFDjOcSJcYRhQtz53FixbaxFvNC\\nczGNGbgOkwsZM7VJPvLow1yKlzg0sRUZLeLs/RngHCwtQtCD0QkYHqNjtjmua9SGAb1EMbQd7p6f\\nv/r4AJYvn3mi9v7OX/kP3Pj8W7nv3Fev8um4vPNpQRFI8jTHFAa6MMjTFMtyqIkCVUjSIqUx5pMk\\nBZgKnyrkFoxXcIQicyWbsZ5AUBAgNx76ySjG0DaGo8mKCJ1LjMjFlArfN8iCGCldZGEgCgHdFJo+\\nSbdDhEuztclzZ5DkKTXTZiVIUVnC5MQ4Q1Pg2ldGARszuGVCrjAMA6k21Q6/M/sX4eyea3P+1GlG\\nqUF9dgbLaICscd3uo9x/8hH2TtXJDMW22VbZ+YTGZNvGt0c8E9njU0/OhjxgZmoTqprjPMPpa8rY\\nwkDSR9HEIylyHEOSkqOTCNupPBEFbNrPve1XefiOr3J88RFq1LjAOlYZUwApd186xYwNv/32N/Cc\\nl98AW8cobE0riHGTnKo/RhQm3HjNLMfXUtLFNeoSzrGKnJngy5+4g9t+7e1cWF+koX1Sx6Zp+rho\\njO1TjIYDqqpHPnc39tQ6qhcjZ/cyu9JloT9H4B3h8ukVFpdP8AMzYxyvdphjjD3+DEudgL5ZUC0E\\nLn2WRI0bopDbhmtYjiQaO0u208dfTBhdCuhGLlPSZDoY8JDpsd5XpLfs5t4/+CI04Vik2PNv3goM\\nYWBBNIIshJ0zPPb+M3xpvs2p8xfIwqxkTG9eXUILSnf7k/f9OdftGue6Hbdw8LqDYJukRcntvtGH\\nSOkQJWFIZzVDpDEGJRusKDSmZZGLgqwwaJoSt1ohzTWGlBg1H9IMGi6jXp/+MGFsrM2oiKkaEo1N\\nTln61IA0LKwcdCDQIga7oLAUxaCgsxbiW4LCBCsrsLVAehKKGMfzMFU5jgwE/ShD27AWpWRFwdT4\\nDGG/xzAZMtbYnJZGsEF9Tp6DcBCAlAIhvse03ooi5/7776U1uY/rDx8pNwoQDZ+bb3nuFZ9cp3zO\\nb65/FCX04ttnJHtxjxo5ebq5xnn6qWc82dn2BMUw5kbFVJMFQ8yKxDJTCsCkAeR89FOf5OF7HuIj\\nn/hL7CjDoMEFVim1Z3KGbKzngU4K27ZsR1YmIcjJ+z3qMgBrhN9uUKvluH7CHq/F6iCm5wYMVI4y\\nUs5fOsM3Hz3DaLTEjsNHyQsbbUos06DwDByjgq3bLF9uMLUnQx7rkC8OMRNNzZoirDeZPXyYUVhn\\nOU+o1KfZllTwi5gJM2LMhEq/x55Ji2+u9LnmQBtrbAwc8G47gNUDjUTsblMUEdX1lO3hOifHt/Fw\\nlLFtprZxsWFEg3IJU4PhHCQSxmd4/GvHGZjbSIIObs1mKcw4vL/Gre/4iWe9f+1Glc5gyHv/8Dfx\\na2WGuuJvouM2FVdzwGY0GlAUGtNIy+PNS7LgpEjxPYdca0zbICtKjjnXtUucuVsmbwdhSLVZLVXZ\\n8xyMaulcyA2mYkGQZwhpgS6r7ggojBzDsjBVji0EQmoEOWhQSqOKFDO2yeKEwrPBkFiWSahSpCEZ\\ndvpUcTBNC6fis237JlDoioqO0iBF2RJrCPQ/d9fb/9cmlKYexJx/9Czc/vKrfCKgdPCQ0g036Xue\\ndMlvZ75bwciG5Onmk7BsVs02auAavVHZj6lvEE+EeZdomEDLIYoilhYXWVlf48LX7+F3f/X9XAhL\\nUvMbrUlEnjLSPYLNHVqQ6bK5TulybwXwf//aL9O++QC5XyGL1lAtC+qrcOnrTLRfgDLHcYsVLj5+\\ngukd41zOJxELXdL5ZYqtbf749z/Gj//8W1gbeiT1CnXTYpTlVCct8Aywd5Qaee4C9C5h1ks+PNM5\\nxr6VC+y1HJaYoJtdT2WQcyN9DJ0THr6OSK0ze/QCuh/ysr2zWNduBWMWGh4sdzFNDw4fZvHuY6iG\\nR9BNGJgtHnNdXvme/4Xv+74ffuJ6/68/8kagAWf6YK+DFwM+vZ7L11clFTcnXi+17waPu/ziL/zy\\nM967r33pMTr9ET94+yuZOXTDE9tv2H10YyxcyeEmSQcF6JxIGRRao0VZsEMVJLHGtQSF0uSF3MjC\\nb+i45QpMqNZ9hNCQS3yrBOMoBAYGBi79wTrVukmvF1GgEbmDoMBykrKJypAkRSl+YsliQ2bZwjA0\\nZj8n35BzTksFOJJI4PsGdd/BUSbD3hBTmfj+Zn4pg43oEEOic42QkkIXFPn3mLMXSvHYwgId/UwK\\nF5vV6RbQBXqUh16ndPRvH8rYWBS6glXdbESR5JlCmeAIkwEr1LCxyLnv0bv4+tce5H/82f8kiyNO\\nPrYpuVz2Nu+jRkiAQ8nKsWoH2MpkXzHBQ6xylDqOWWHPrIvd9MliF8cfsf3wIQ7esJPGjt2gI1hP\\nsWqSTO7EMrswzBDJkH4w5GATqnNLLOUmu6YFg5WMb55fZNEd8Rs/+xt87NPvIQsHJGoW129SpBYQ\\nk/mCvFIjFbPkB/bg6pBi0MDf8ih8/BiimzHjRjTVdrz6Cjx2Am6oY3k7qJtjsN1F5AWtnTPQk9AY\\nh7kONOowXIFMcjA3+PI954mb49w5O8WD0Qp//Lafwsdh694ZRunjnD/3t8Cvw31/C2+Z5cu/++d4\\nw5xj83XUdIv4zDkqA2g3xvmx3/j5Z713n/r0X7BzR51Pf+lqVIb5Fa8upCOSPCNPypnXFiU6Tiio\\nNFyGscT0bSw0nmMgbBudFSWwxqwwCDoUClSqCEcjpqZmnrjz5cwegyxpyQzhkJsxRqGxCkGWGCij\\nZKhJtMLMDLRdSocb5OhCk1mSRBXopJwEfMdgpuqRSjD8JjLMSLKAXnf5inPcZFPIwQSVFahcUQD5\\n95qzexWXt7319WiR87VP/RWHbn8prVqpkvmtwIMnqXahdPoJnsk2E21QPvejNMPbSF7G3Tk+/fkv\\n8NDJk3zjjq+QhBlGonj89AXGKy61mo2hEh69PORFlFXi82gcclboUqMMUg3gRbsP8YIX7KXWzrjh\\nxbt54JEz9NZspDVOniuuUW1cPyVwZ2m87DY4FcPCMvTPMahm/OUX7+X7t0h2NSJmTIezZ5ucGZh0\\nKoID10zywN13E7ZqcDlBra7QGWvy1re8i1//nR+jN3cfL3j16xmoGXxDYDkVqjqhKAq8yToiX0WK\\nXQTmHiq/fgfJnceJz0JjPoJqCM+twtJ2rN4IshQed6E+W2aR1Sk4Nwe1CTByiGHQz5lbSElnr+P8\\nmOA9/3A/N12/D0NrDh+9Hk5fZt/+vVz65hzBo39PpdHhsQ/fze2vu4WH3/8RiuqNjOZSzEaVbkfw\\nph97B2//9++86v1beiShH57iT/76PQweD67yCQ+iNXAaUAzBSsE2CSKFZ2gKkZMoAaYkUzmFzHFM\\nQTzKqbY8NJIsyLFrFuBw6pGTjM80MU0byxHkYfjEnsruhrzUbidEuhpiBUojLbUBu61gaYkQUZlI\\nU4oCTa7LpYRQGqEVpi2RhcbyHRQZmbKwAS0MOsNF4nhIu3klOrOgxOAlG0A+iaEMjFyB+h5L0Bm2\\nZHz7GMVawM3XbmF+8ST/9bf/lBe9+Hn0+iP272phmh4tq8KW666H3jo0U8qwXlOu258OWNh09Esa\\n2gKq1Sof/MP38vEvfIaa7dNo+kgnoTaKsdfX6PRDZJ5i91OyfklW3abMFNjAS4Hbduzn2vEab/yx\\nV8NWQZcKpqpTm/RJRj0GQ7i1XedSPGAwMNBORjxaJQ8lL/jB2fJw2xac6UIYkhewe2yMUdTn0tKI\\ngY7J3Da+W2UlgdWFRerTDc6dD7ArJnucCvevL7KwdYp3/6c/4n1/8It4FY8YSa4MRCyR0iCjykhp\\nHNtH+ylmdQ8r2R4mdz+KWL8HimOQ7mR18RC29mlUh2AWkHdh+xG4tFaiEw9dC04F1pchWCbsSj4/\\nXGWtVeEfHzpHZazCseOXqNVrNC6uMmVq8qWIf/e6F3DuwkNcs+Iy6zfpf2EFQx4mjkcUwYhLukAc\\n2MWbfvKnrzomHp5/nG984wN87G8+zSNfO41R+daoL0HTQ0sTKVSpwkAORY5jaVQq0KYoB0EhMKWF\\nyHKsHLyqSaRLkUVRmNiWjdYx2ybbyCQjzlKUlCA2A3hBSIq/AXq1DEkaJwhDgDJQhcS21AakWpLn\\nAmkZ5EJRGCXoTeYajaTQAq1iRnOXmZ7Zil8xERuEyf1uj4uPn8CqSLZP7No4z82la1ienwFxlFKp\\nNLDNAsP8zuvs/yKcXZqSoVHgT8zgbZtgXyb4jd+8CaKQhx+8wKMPXKI6OcHCxTu5pXcBMocb907D\\nlirIEaXy3zY24XcxGncjoXHszGNs31e2zP5vP/7jfO2Td6CTPq+47UUMFwN275tmLrpEMYLqMGad\\nclGwqQfiAG/ZaTN7cAdua5WbjrjsOfIKqO8kU5JGf4JYhqx1UgxrinjUp2XVWCmWMcOYeGTQEX2u\\n3X09/vOvKTvhtkgu95fYYlYw44zt9Un2b5lm6cQl9FAwHsPU6tdp5g0eTWocu7zKFtOgV6+xcGlI\\nszLF2vyIxcGAP/vwh/n+KGP/dd/H5LZpBkGGdGyKRGPYEJg17FZIFl6mlhpoUcd+zrWwlsDxh5kI\\nzgFT0BTgaGi0YX0OrBDsBqT1ci17/XO54/1/yN0VxadHBbX5Pu3KduL1FRpTVYJE4oxV6C0vcmD3\\nLtpTDdr+DNaSJBEJndoU3uw+hvd/hFte/Xx+/33vY++WLezd/9Ra+fHT53n8kUc4cPM29h28jXdU\\n97Hr4N6rjJqEuEjIh0lZcjUp196WTxwu47suKE1SFHiOgSUl3SHUmx693oDWxDTaCEmSIRXqFKKg\\nMjnJwtlTOJZNpeqTJppur0vNdfHdCkXpriRJgiNsUpGVMFsJBgrbVijDoDBNskyQpAJhFwgJ0jTI\\nKRBWicl3fQvHASEU/cLAk7C4ssSgN6BieLQmNiNWhyfRexZZMMK3rI1IwsCyn11Q9Er7F+HsIKlZ\\nbZ4on1gOqCF4Njfcdis33HQrOPDw/W2WepcZn54iqbk4nRjGx4DTlPFNHbCxiRiqkOF5wbG77+cD\\n7z1OEHb4+w/8DZqESenjBikjclZXVjh54RxJVj4ypinZMyXwTuDf/tB+FuIuB1+6m+buPZAFpPOX\\nWGr5RJUa1aSgPuWhnZxhkuI0bVYHKVG1jhiAFjYTnse1N18HnQ7UfWBIZiQQFSRaYJkuK+vrbD3U\\nxu+lLB3rUNEZK0GPTPvUGg3qNZvLpy5hVDxGYY6QLq2tFn/1l/fw91+5xCc/8RyGtRp2q0I/T/HJ\\nMISARBFEIZPNBkEGSWMCd7zK6uP3MbF3F/QWIIshtUsmjrYNUR/SPrjjJVvuRANaLvPtcU6LHh1P\\nISMY9pcYb08RdHtMejVGgz67xycJLifMvGQLhWuinRxBQRIWeI5HIzBZPbdC02xw/XNe+LSRcPrc\\nBdJwnf1jL2b/1hufJffqkESgi5IiHJWXoXOWIgxNlqVI20VoQaogLzSWJdE6x7FN8iwnyTXCLJEW\\n5iaYSuTUXb8kNsk0SRRRFTa4kKHKOpBhIDTkQiJNjUol2rDA1qjEREiN1AJpSLTQaK0otIEyyrq9\\n0EZJShHERFKT+T41wyYZDZBCoxKNZ9euONeNtWcWk0UpSZBRtWtI08K0vsfC+E0S/nI9npV/S4sy\\nGWeAUxL/3XDTEW7gyFO+uXr8Xs7f9QW8Yo5HHlnihptv5h++3mPOCfngBz+PV9WYiYPhWdQtjyJL\\naLcsdu9p8LkPffaJvqNNsdBp4Ox/fxvO918DD87TWejzvBu2Udz/aMk2ojS2VWf75AwXhynC0Yhu\\nj3bhYEiLPBnR7a2j14YMeh0y2+HGG55bPsfSCFIHZBdHr8BaSm6Ps46N4Y3D1IhWy8aqTTB4qEMj\\nqnLm/Ih0MKA5tpWJZpUiHOFWfQadJea6ig/86e/S7ef85I+8hUOHb+Inf/tX2L5nB4nK0LokU3B1\\njaUsw9ItjGGCPTCY2PVKePALMHE7BAMwLRgMoC+h2oBKE1Kbiw8d4+ylJb5+YpU/Xphndt+tHN3b\\nZFCEyKJNNhBMxDXqqqBan2B7ZvLvf/EtrFRyskEHMT6POzKRcZXRwogbt07zwfvP0x8I3vPe9z7l\\nXuZFxmtf9XIs4PzpB9m9/xDPpFJ6/oGHqe+dQUiLwSinPmVCqiDJsGwPmeYoqbC1QVIotBb4toFQ\\nEkM6JCLD9itUNxO2aQyGoFX1sT2HYT/AtixGUYI95jCMelS9BgKBVXFQYVo6MqCVRtoGmVJQlHLO\\nggKvaaGkJurn5KpEvQk0wjQQmcFgdcT0ga2YxKDK5UEmCsYajSvOVFMuJFOwJEWRoahShDnrcYjj\\nf48JO5bmU4Ysm3jzEWXo4vFMeGKA3ihgJZN4JIzsiLseO8G8UDz84Bm2TLrkqwVCKLJ+hGGGZIzx\\nkh98Lf/wT/9ESFnU22y3eBHwhff/KM5b3wxn7yQ7vBt10wwPnbzIkde+Bj53L1gR6JT1U8tMbD+A\\nzGKyBIxGkwePnyboLdONIRitsb0mIcgJwiHs2A/CgnBAePwe3LQHmcZMq7hVk5VzK+y6fhuYFtUX\\nXk+1M4ec69GqS1pWjcXhArsm2uyeKxgGyyQNj4fPfB56PYpPHeeV/+Z2/vDBk8jLZxgIk9ruLWRY\\nCHtErWJjKIVlh9jjunSKkQutcSgU+NMlb57TLNVwD+yFw7uJvnwfH7rzYe4/scyxFcW+656LazsM\\nA4991+3izCPn8ZoOdgvcUOCmTV7zjkNcjBeZGN8CyZA5sYY7Oc7pxwcEdsTQgLUipum0n3YvTWkR\\nA8n6Olv2H+Bv/uHTvPYlP8jT8jFqlaldW4hzjS5yTG0Qri7jm1W0rqG0xLAMhKk31vBW2RYqVMk5\\npyTJIKe94wqKsCAAUWAkmtXOOqZt4XgG+A5JMMCvVTabYQmGOU2/SpD0MJQikznSbyFNE+FYZLkg\\nTzOUFqWMmCXw6oIoVKgkQwmB41h4vmT+0iX0eJ224xKEGVK6kF8ZzmhKet8SA+BXLTJqSGwqnoX5\\nbTvrrri+3+4DQggX+Cee9MSPa63fJYTYBXyUEgB+P/A2rXUqhHAo5aJuovSjN2mtH3/2vWjKvuNN\\nzmwoFVc3n3BPtpD81I/+LFbQ44HTj5HhsGesRb4WsG+bRTwymNpVwRaCGW+KY4/fg+dVsRyFEjGv\\neMMrcdVOknzIfRfPcemKC/DGdo33/febcV/3E/Qfi2jM3II1OoM/jDlydIb+XffS2DoBDCCNGEsF\\no363lJn2XNaCmLTdwJw/j5Q2yUybYQy33byX2stvhjCCpAcqxq9XSdImmDZJKmEUMDHTYO6BdWb3\\nH4TFAmYPc/rsSaozDbS/gNezMBOP/Qf20oyned9nHwUyqPYxXjOB98EuRu8yO170I7xq1xZuf+Nb\\nOPKy53Db0UOs5AOUjPEcTZx3KFSGr1OcigKjBmmzbEG1NtAAd/wD/+VHfovfOr7A2MwUjYkqRl0z\\nu3cb0mrQCjz6FxfZPj5BxW8QResoGaKHBVtvniRZTqisBfS0Sb+1gzML8yTFGCfOznHk5r0sf/OL\\nRP2nCzKrpIt0m+RtjSBgsj3J1eL4aBDRWRviNFtkicKRUMhxrE3wqCgQtoFUBbZngjYoZAmwyZTG\\n9V3a9W9xElWAm2A3TCZMk1TDKM+xhcsoHuFZJn7FBCFpNRqgSmSG1BbayNCmQEoTHInlOFhaQAqm\\npVBaEYRg5RZISM0CS5jkqcLCZMyvITOFbWf43jTPveWWjYPalIJyIcmhsOivFngNk1xolJ0jjX/e\\nMD4BXqa1Hm1QSt8phPgc8B8p5Z8+KoR4P6W22x9vvHa11nuFEG8Gfgd407PvQlAylV7d+nPncIOY\\n//bBO1AypbPWZby2lUGco/OMYXuMlXYd3xnx2KUleqHkkYcfZnrHDOl6QpSOKOrjPHJ8yMtfZPLu\\n//rnT+DwBGXG/QMfeBX+a14Dj69SH/PA8mF8F25okMZDGs+5nvjxFZa7iraxhjKWIW+jDRdXmlRN\\ni87xs+zKA3Ijxu9XeelPvwnWhrAWwWgR7BDOn6Gz0qW3nBDhsJKlyIpPMIhp7puAPdtgbJLLF+ZJ\\nxragVIEttjA9s4XLZpXjIXTyHl57CKfnYf8EDO+Dm3N+fueLmA2/wrxb428+/gE+9rmP8Vcf+iC9\\nPMJr2pj9BC8DSxpYWQTZAMykLLlN7uXU5Yhvnl/hH++9yJ8fX+DG51/D8FyHbDnmda/7foapTUsK\\nRCXG9ZskYYBIC1rjPtaZDgeqNfI1H6FByCHacxie6DKWaYSreMlzr+FvP/oFDl+zlzvd1afdZ+lW\\nKXSXphjnU5/6JK/5oe/j6c4eMYwzGmMt+v0hUmqkVWAYLkGc49kCWyrQ4BgehoDWWJtBv0+cKprt\\ncRzbItMhT9FCT/Mybe4UYPuM+gHK1BiiQBiCaBjiV10EGYHSVGSFosixhYFQGUE0pNa4ogzcsNDh\\nCJQoEXaxRlZyikTR7yU44w4YOaZtEhYpddMjiYccPLjzinNVlOVlG7QAf5KKNSyZk7Mc33c3AEHf\\nmX0nhJOaMqaGJ0HIGngZ8NaN7X8BvJvS2X9w42+AjwN/JIQQG7/zDFZQzuwbYVUHaMNbb/8pSNcJ\\n4oQ0GbB1ahxD5tRrNr1hSiFB2DkZQy7MreE+KFcWAAAgAElEQVSHAfGgw0jDxLhF3FOMkgC2mSwu\\nGfzBH/0M7/7Pv/oEwDClHErXYOG/5naIW+DUEDUL+inoCkm7wBoa5Es2sekzUY3xiyFRmtILx3DH\\nr+HMo8tUlEm7AWcLAzGq8ILbjsDyZchDGGgIe6UoZSpo53O0jZTzp0csTjTwkgA3DLjYW+Vr37jE\\nW3/xzZiij6gU2PEQmUsoLOzcJA2HxGnI2tol1uZWGd9/HbizUNsNK3P88L/axufuWuQjwy6r59fZ\\nfeQlvPJ5b+Hlzz/Aj731paynJqNgRG9hwG1v+1E4/hWYaRJHUGlu528/82k+8dl72TNZQw0iDj/v\\nBRy+6QAE4I8Cpi4XdGyBoWBLWmGkelyoW7RDh9f/xCtZXwtpeALHVFSMnInAJGs0GMQFTeFRGYUM\\n2Mrv/s5/uso4sPCwUN15Xvfal9PthrTaT9J96HwVYU6QxjlZMkRlOYVXJY5S/MoI0/FIU40rXXAk\\nUgtqvksWpzTGpmlcESEaWj65OuwtQKsGqzGYFkVU4DuVkq04LSgK0KogXVglEQa1LVsgT0oKDbOg\\nyARu3ecpD6Yixfc94rhAJxmeYZAoicgl41UDnRfUqnVGWUikUogEu6Z2UnWvLDHOUy5jfU59/Ri7\\nt9oI1yETZTkwjlI865mVlL7VvlORCIMyVN8L/DfKBuWe1nqzyHelxNNWYA5Aa50LIfqUof4az2oV\\noM/Zx1bpDWJ+612/h/CGrK8tUpsaQyUtzg0Vnp/jYlGvjxOvXSIi5/JyxKAX4doR7liVMLMJdIXA\\ncRimIWbf4/d+71d44IFHeOCx40/Zqw380K03Q1CHyl6YKYA+qAosWmSVM2ivg1+ZpBc4aNNhV9ZA\\ntRN8tc7qYgd3ssa2sToXvnyKwq9x444dNCZq6N4aIophMCz541MBoxhGq7AaYFsTJEgGRkLNgygM\\nyNCsP3IKJ85YurDOxPQYqmqz0gvJtSboh0jfQske4/valFPYNIhd4LfBVtx6dMBz77N4gIQ+8LVH\\nPsrX5m3e+fvvfuK8b37BPu57+zthQ4qh2RpDOha2zNk97aCUyfNufQ4vPvIyvnnvveza0iJSGbkF\\niQ15aiEVTPgeF1YC3vhvX8KagAnbQXQjkkTiGA7brBb3zD9OfbLJYw8/xnrNYffUGK99/TMEe7mD\\nbG3jY1/4LG/8vh946jg0Jzh/+gRSeBiioOIb5I5J0Q3B0wwGAlfUGCYRtu9QhIrW9JVElKWjZ0Fc\\nhtlVDWEHKgWEAZmpKXKDJCpwbANpJ4RZjmlZCJ1hN+pYtkkv6dB02tTqFUZBwvSWbTzNDA/IEKaD\\nWTVIhzlClU05iSwwEgiyLr1sRLO9DVsLKlUb27lyebHR2BPmVP0JlFIImVPkFk5u4rrmd8Et+x06\\n+wbv+xEhRBP4FHDwu9jHVe1K+aft27dSzrMNPvPXH2Nx/RKrww6OX1Cp1snjgiIWTMyMsXu2wNUu\\n4839zK16CNGn0pQUhSAo1jmxsMioZ2BqzXBYcOvNt3PL845y6dKIpTMXntj/JllAA7juYBvk1Abk\\nLi3fNTXImLrvkIU5SIfRqiBNHWZruwmCZWwFnrTpRAVLwyGG6XNwdpbZWh36fUSSQjcAKSEfgt+E\\nyIJ8jLDwWLME3bBHJC0SoKlMxhoNDOExzG1a401kXJAZERpNNAoIVUGkIC08RivzVMer0B2BFGDX\\noa9pOpKGbTDpwmIMF0caRslTrv99d57ZvLvUm2P0uus0fDBth9r0BG/60R/GUi7Xbp9m/rjLpGuT\\njLnkTkJ/dUR13KdjBlS0gR2a2KZHvbBhGOIaDr3REMfXpKbF+NQ463lUgspsG7d+dcaWJI5x3BIh\\nOTlZf9r78xceoTlWZWk1pu456CJnOAqpK0USClAORaGpVTwEiub0NE/FUZY2CPs0LA8zh0KlGFqB\\nNFCyQBWaIldkKsWuaFKzFFzUmUIXOVpDMlAwAYZhMDH2zN16YOGYCm1qVJBt6IUUZURZQAUDW7hE\\ng5SqZRHECZOVK5ezZXK6SBWFIUjIkKI8HykFYTDCsZ8JYv50+66y8VrrnhDiq8CtQFMIYW7M7ldK\\nPG3KP80LIUxKf1q/ym89If908803a7KI1/3wL+CsdegRULUsVKwYJS5jrTpadcmjecZbs4wChVGv\\nsq9xDYa/yu5DVSxlkcmIl+NyYMcBVjvnueGlL+PYPfeRJCYf+cg3qTq9J/avKOGurx5v8Io/ehuE\\nNshoQwbX2ODFEpAIRNgi6cNOUSWtVbmYTpHobfh+FzUI2ZqNsXBmjd1HD5YzdHEJby2AIAMMlFdD\\n5gswdxb83ZwYjbNiFkSWQSWHrfv3UfEdXnh4K/l6iNGqktKkmQbU5tdYHCb4FZ/OWoHnOsyH63TW\\nA6rjtRLwklUgaMPaIhTLkJ/gHf/hX3HPyhgPvvdDkMFPv+NHCddX6ayMCKOEuFB4bs7iasrJ8yep\\nV6E/gl/+pZ/h7Mlldo3vp9XyGM2NOLxlN5mtsHsa30pZctdJHLhYrCHXY57/omvxmjWiC8s0PUEu\\nMqoHPRaX18hsiZ1rKj3NTLVKq+nyib/8O37uw08fX47rcvb0Cfbun6ZhWaQE2Few9k23ZljuD7Dq\\nVbTOKWKNY3sYhSCPDdJMoHWOXwUyjXRcrtY34fkupjTQQbn+Rbh0V/tU/ToChekohBCYRYWG5yGA\\nJC0I+wl2nmNnZRttqPWzEE1tjjKbggzDKIUglJFhZgZ+zcRIoZKbYPgUOmV69soIIYE1e6Pltodl\\n+Fh5HZ1rUmWQpwX+5KYW3Xdm30k2fgLINhzdA15BmXT7KvCvKTPybwc+s/GVv9n4/xsb73/l2dfr\\nkEYJ/+4Hfp1QrDLMY4QrUHmOYfjY7jqWpXGVRxFC3dlL3THJRUYQhWDU6OVNgjVNkuY8evIcJ459\\njdZsk7M/9xl27HLRsU2OYqoxZAM/iQ/sBrZaISwegy0T5cy4OQuIgNzrYqoAM5BkfhfbUkjTZXDa\\nwMonyUWBsg1UmtOqevgiZTB3AatYxBMl8izJI5x0CVbOQaNFOugTZgaBZTG+fzs7HZPx7U1660B/\\nhGkLMDKytMu2VpO5xztktQqjQmO2fdLekCLMmRibYPH0GjOvPAoLizCMQGSQ+PDgGOOHco4Ch6am\\nWFxeplJRWIYHhWA8NhA6YWC5VKsWx86fRI3gDS+9hWJxwC+9/dUESz3CfgSJhWeJkrizYhAHiva+\\nGe76ykkaFYdWodmSGZirfcaqCUOVI0yXaH4VvyJRoUaHBVoaZA2XdC2m/nRRnA0bsnf/fi6fO8e9\\nxx/m0MEjTxmh/eEIlQwRhYewJJahCfKYzFKYpkaiyXNBmkgs04Q8K/EDGx2S4TAklwWOKFtbRapI\\nggjHr2A4PkmeY0oD6Us0Cm0UGEpg2QIsiVOvAzlJ2AcdYz0DBuBJk0CMiUW94ZIEQ5aGA6puE0ND\\nrCKUzFGjAGFktCdmnvxqPgSZweWMitmgmOmTmEO8xKM+tJGuACn+2cP4GeAvNtbtEvhrrfVnhRCP\\nAR8VQvxfwIOUenBsvH5oQ5e9A7z52+1gYWGF0dQFjMxAOTY5OXnRxkgEe3YKTCfE1BW2XXuUkenj\\nFS4r/S6XFhYpioL14SWMqo1nKPrxAq3dAkOsc+CQichjDCujKCQUCqsyWWK8UYwEfGMxg7klqJ4B\\np1ayglKAEpijDQrfwQDP7rMcCS4uJezcewuPnVihaZiIosUiCQO9yj5TMzFaxk4K+raJqWHQD7F1\\nylhfEOQN7lrT+Fh4VUkhwa75BEpjVly6TRshLaQpaDVM5r+5SC3OGZJTs2tcWO6iwpCq62G4Pl/9\\nzN28ddsMDO0yqx4vAk3mm7exdqJDPzjJzbsn+PLlZeYvLbGl0qDuavKkgwwVfbWdT9zxeba04T+/\\n+xeYadawBgbr3QjPj/ETTWAmKF9TGAVJW3Cqu0rnXJdkuc/Mtdt58RuOYg0MvBGYmPRtB5UZVGsT\\nJFmCykOMzMVruIh8nf3X7mVFFkSs4j2tialGGM2zZdcWKvf7nHrkJNcdOfrEu8udPttmx1jrZ/SC\\nFLduYIYmtkjod1KEb2NucAylYczQCbAtF8sEaeSkSQ/T0ARSYKsc4UY4ZoYaaXQhkSQUqUC4BY4v\\nSKMCmQgsG5yKgDwpOVGVQZokmGbByiBksj7+LKO7JJ1YXV1kYqKNV3UJBgGeZWD6JsMgZ+d0iyQb\\nXPGdEPohGA7nzs8x4e9CjklUY4hdhIhuC6ouiMp3Ma9/Z9n4Y5Sa7N+6/Tzw3Ktsj4E3fhfHgJAG\\nnbUEa6yClZs4boEzJlEx7N9+A3m0wsHD17I2iFkrQrrDCFn1KNxJLJ1TJy3DpCSg5dcw05ww7GPJ\\nlCSrYxQVHN/kxKnzHDh8Dd35LVyev5/juswcXvrkZ9n+riMb3Aeb2qESvCmC7nlGbYepsI4gotW0\\nWF9bJRMBRqVOp9Nj24TA8jSDzirVXLI4snHGCsTK47ipzbqsUqvOcFZOovZuRWQRtbpNartk0qQd\\nwCCMyKouhiOQSiMsQasuGYUSjBi7KKgaAl21CIIMy7JYmh/BeK1sXokyiELQHdq+Qxwo3HrBoUab\\n7Ttd7vjm3dwwe4RhtMiMb9I9tci8oZloCyZ6Ljfu3IbOc3A6YGeoUFLpGoh1qNQNetmI+WREJCNk\\nJ+R632GrlWOlmoZOsbOY2POw/ToIge6tlDBRx8UJY7wkpuHD0RsPcOj1t/Pe3/xzfun//D+eNhZ8\\nrwmjAByXRF1BDZ0HbJ2eJE+jsgc9VWRhQZQVuCZ441XCfgzSRBgGaVRQNAuGoyFJlNFo1shzTcNw\\nSQYRog5BPyVNM0yjoLA1KAOhJbKwyZKSnkTllCU5N4NMQGrRcGt057uYdYtCS6hvUGGRUUK2N1lo\\nE8gDEBZV2+LyhVUGSYjh+ngK3Eyxe8dOQOBZVybmluHyCNrTjM02MLKCLLFh0CIgo1qx4BnyHs9m\\n/yIIJ3MFY0e2Y8iEWn0v26a2sH0L7L+uRhYrjOYBji8rLq/E3PHVuzkzf5n+Sg+pEyzbRJoFa2FA\\nVqmAVWXYk6TaZGFNoQwLuymJ0gCrWhAMA5KhRm4AdhxADgrojDZmdQAF8YDhpTNUnHUmOyusrMbc\\ndaxLYkySJC7SrpLFkopnMuiHMDKxljPybkYeaYqFeSrrHexeQCQ8vrTgs7BsYMQRZtNB+Q521aAi\\nNM2hxvQEZk2Q6xzXslGiSlgo4mYVbbYJR5p6zWUYB/SHXexWhROnFmHMgdFpoMcwzGEiQJ17GGc4\\npJIkbEsS3vyq17K0GHDh9MOsXOhy9uKAjp1xeXiag7PbufXV1yF37KRrW5jSoB4MqXe7+NkAaeak\\nccr0dJup8SrJUgdnJHjzz76Kwy89TC3WzPTXGfc6FKJP1l1Cdi9R9ddoWAnxqMAvRoyNznMgXmJr\\nr4tYWueXf+WdXHr02FVGgwcVm7qv2DXzJGx0NAqwqjZBkpBkGc1mlZpn4DdNTNem0w3Rlg0WRFFM\\nZaxGmKagCqp1h1QlJSe8yDDN8oGu7Bq5XSMTOaQJRSIpaoo0HyGiEZYfYE8ItGWS4qOkVeZxvJBa\\n3cE1XRxVEPQ2GZQiingNSED1gBEq6hCNVsHRVByJb9nkWcZaMMBr1AENWhAsb5JLZhDF4NYJ+hZF\\nYYOTUggLlboo7ZJ6JnjfeWJu0/5FwGWzrGC4uMY1106zsFQyDu9sTTMYhWRaYfoey70BIsgxqw3c\\niRquAZdX1gntUv9saWGRhbmyfOVaVVSU4VgNtChwnJTe6oDxqsswGGDYEmm3KNI+68A27wbo+jAe\\nQlOCyCk6Q2qtCvTOIXBoWi3qEkZ9MAwXmabootQKc1Pv/6XuzYNsT8/6vs+7/Paz9t5997kzoxk0\\ni0YbSIBkkAgGAmYNqWA5FCFOIA6JUwUOSbkwrsIpL4HYhhiTUDaYgoqJMaCq2DJCIDkCabSNRhpm\\nuXfm7t23t7Of3/Zu+ePcKyQhgVTxH8NTdaq6+9Q5p7vreX7v733e7/P9ICpPs/SEViBbx1IHkDFV\\ngLGr2HjyNTgLWglSHCG0FEoSGYGLUuRQ0hiL9PEK8ewlykpCXeOMJ5Y51bhCqZjZ6QlBaEgiuLuE\\n2oEuqcYTqjvHbG6sc7eKsKJFtYLHts+SK+j1YXRX0DrBpLao1DM/PuGN3/l64uWMrTywUabY8ZKO\\nkUivsLGjVIYmT7l7Z7JSfBVd+kUf23oy78FKCBJTW0Tjibqa2dGSXCUUXhDbAG0DIhDXY8a3V+q5\\n86994gtkgwIRsZx5kvyPVy+hJNPTMXvbm1y9PeZoUbLRUyznAqEr1vsJbdWA87gg6Pf7yMYizWrS\\nzRqLbxpA0JqauvUgNJFWYCBYg7QBKQRSKnTrwa78J4VXxDYCFSA46BUsFlMyF63m0uU9F5nGIGoF\\nsQNTQRzAOExtMXWDDoFMarww1Eog4pULzrwq8Z8BHS9WApogsQ2QS7xrEfespCMJUYig/Xyvhz87\\nXhUreyRgO86pTcOjj0E363B41VDPNLov8bNDpFgiE+j3tlCkZJ0O53cvAwkHh1Oie+7uupNh40AU\\nQRTVLJ3gaKFJ4y7BdtBpzpFvVpxsoMjgp3/+fVB0oK4IbgEsqd1KctM0AphwjREXzxd0XEN1OGVo\\nYkTjaKct4viUo6u3mCYpdzsJM2cYyR4fji/wiWRINTmkXi6wqiWRCzqmZTht2FOSZSI5GCgWwZBH\\nCmOXeF3TJIpebtlbntIXS5RZspEpqtGMwXCNXDpulxPs778IW5fh1i2G1lFeyxjfuEsxG7ERtVwa\\nSJ46E/GOb34Lt0xL7+EBLx8fMJpbhAg8dOkyD1w4z4XDYy7cHHFuHrHmclwzZKlSyFvChuPGeMrT\\n73+OCw+dQ2yv8cnf+QTjZ15gcXrMuN9n3w8ZZAPyNGfuEmZ2Ax00gyRAXuDOXqA8f4l0o8+Zew26\\ndzx0lr/2rv+JgxevsfLWvx8dvve7/jPy/oV733uKTgbCc7w/Zr0bGPYDsrXkS4N30FYthNXAS3c4\\noFxWDIqC3voAZIxOcjY2NygGHZqmZTRfUM3NaqDFCXykWNJirSKOcqKshww9RADlLYyWYAxGBe4c\\nnDLY22axHCO9wFWK5qSExSEyYyWi8gE/9jhTEPsB7Sgwn1U0TSBWgm6aYmYNdZgS5Y7+zr1/yiLA\\nOIL1lJAaKBKqJqa1Fo8nEpZga5qmWm3bvox4VRR7nAiyNU9ZCeoTh+j2sNvbLHtrHJmChYk5GC2Q\\nec5OJ+dMr8e0mlOFliATgowQfrUSOecgOLyWZHsDRD4k6qyjkwJRQBmWhNkY3VVIwEU5I4Bf/9eQ\\nLxDBw9GCQgnwLcJGoNa5OYtokwHd3gbWSmb1gtItacKI5/afZ+NcTr/1VPM5LlFUiwl5f50n3vk2\\nOnvb5BKUl0iV4OMYegVjYzCxpOhHiKCo60Acx+AlthXEmcAc7UNZk7QKdWfCRhrhIocYGbbW1/j3\\ntyve+9ufhjYn6mqy7YTlVodqL6X31EWGW4EzWvHIxQeZHra8+KlXyFntLmdT+L5vfT075x5jduqI\\nxDbjRmCjikrMmUQVdeGZG8PJnQlvuHyB7vExl2zN+eM7PLa8ys7hJ0jaI2JnEL7CDiz0Hd0dTZu3\\nOFHhQo3XIJKY4vx5ssEme8AzV+/wc7/yv/Ad3/4ufuvnf5GVNPQLhcTWjqLfhSjFuZx2KVmaAANY\\nBkHlukydZYknLzocHp3ijMWe3gc7BtApoCFP8DqmVoFaQOUtJkAy0FTLllrX1LlhYQLSBIIC32mZ\\n+oa5tZzZXQcWDIYlAYuqNXEtQMxhepP5Yka1WA0RziuojSfNLZ0UgrO4siUQuHXnBDWWpER8BoV0\\nBNg+dBKyUCPLmrSv0UlMlCYYm2JUQrLXh+wLuyp/sXhV3MYLqWjiPVrn2T/0DM/E3F2M2D8+pre+\\nycXdHXqui7Vzss2cpVhwdDiiiIoVAy6JiJvAclEiOxGu8chZQp6k9OOAsg1zFNXcIIJja6/LeDYn\\nUzF3qg4vouDZKRzNYF1C5KGaQ1IRy4RrN2K++vG387Fnr/Lp5z7GxfMXceWCyd0Jb3vjWW69KHnx\\n+j4Xj6cUOYw21xhubjBiyf5zz5JrjVIRO66it2y5EVLk2Zxc5Uid0VhDJ89IspjFvMTKnCSULOoF\\n3aJl4lqWVUJPpmh7Qlpp1tKEWaj5qV97L9/xzq/mqx8IqAtD7qp1nvzaN8GlC9C0cHxE5ANvEEOS\\nsLrxmwN7vYRf+ts/yMVHX0/R9tGLCbFroJYk0tPKKcnOFteuTLh27YQ6JAy6juG0ZEs7duIWJmOG\\nQ8WhmkNvjYX1hLwF2dBvLJxWxKMbIEtUmeNIEW+UPPz2GMEqvVMGfPiFD/HtP/RBvuefvJd/+Ym/\\nC/ISn0vFg5P9I/KhJsgI67lnDhrjK0vhSqSuGUpovOH42h26mUYqQ80SW2nSOGd6eoLuBqQ1eJei\\n40AEGOOh8sRJjlINOgnIGGxVo5cK0/WUWjLYuH+ncQwvzlGddURhcLMG5xROPUTqINdgloG4gVZB\\nKxp8sOAkIZLoEOEWjl6WMzo4ZnvtYVZuCgJmNeQC5gLqiDT1VMYSqQgZDNIrZKS/2OTvnxqvimJv\\nLVw9TdlYh694wxov3jlke7DOZqdDHEVknZSmXEIdMW1qoiLBhIZl0ByPDrk1HXNGpWhy7hzOKNZj\\nzm4OsY1j0ImxVhDlisykHLwMF8/1aFTM1DUskx77quan3vcC/7MXMKkgs5BHEFlmKubSQw9DnrA8\\nnmDLCnPzFc5vDrnTnvK7v3+T1luMhHEBVkRcc4FiluN9SVcHOjJCh4ZdeQWvFGuPvYEyiah9vHLF\\n0YIgwfoGKQNBBVQHksRTqAWNU0Qy4jYrhWffGeIk4cZiidcRH/34Df6bH/o+uPwIT1oPpzPoxpgW\\nhMzQ5QkPPHYR0UuZTmreuLvGju7w9d/5dvY/XiFeepn1nsHMT+mJdZxQzPqa9718m43+Dr3Nhvj4\\nLoUt8MYzKmo+2U/R3QfZPnuG0EjqYcpAGRo7I9vocvDsVXZvjKAewbADfhvnJZzNYHfKNorncdT3\\nVnMN/Manf4ft4Yv8P//y7/OGb3wnnz0cdeXadXabnM5wl1woTo9HWGPJ+gNqAQRP5VKEVvT6Hap6\\nyt1Ji5Cwk3bAKVSkKRcNQkYIZQjCEVSKlxbhJZNy5TMv5gk4UFGA2CJqyWDvvk2UpZqNyXp9sG5l\\nkiFmeBRts4I7KteipIYchAMpE4RK2RwOuHv7gBAc/W4Pg6NNLaFtEfH9xtxqHz+tPbKnET1Dva+Q\\nkUenBpU6hFJ/TB/7MuJVUezWW0IyoacGVItAu+ixlAlJN6KT5zSVx+cKbyVtIzk4GBOWjtFsn9PZ\\nAlc3eCJk7dnsDNHDgKtK7owlapAyLDr0dY0Dzj6s0O2IInaofEjTlnz4eMYC+KGf+fus/dXvhXIT\\n4xSzynPtY6+w2Yv4g9sfYXdvwBsefIAHOorgar73u96K9ZbTheFktkBTcfTpEWtSoBZLNrUlTRQL\\nH1OFJUdrPVqGuLlCe4+RLf1C0/iAjBMSKWiCJ/gVD7CbnGfOiG654OTGPtlmBzmt6HlF0BWR8ujc\\nU89O+MN/c4XXf7sk2XkItrc5bY9Isy6TpSaJMmavHNPNA/0JfMOTl/mxn/wR9p8/YvjCKakTzAjU\\nRYbxS2wsMGRc++RLTHfmRIuGB5qGzAX6WwPU8pT1RcFYOm6NZrwmL4g3OzShoikdJ6MpG5f7GB0R\\nvShXGKTXrbE8rSh2L8NuxMbmGtXxavJNk6yGOb1DW8HXftuPM2z/K3711/8hb//u7wQSvvbrvp7j\\n/X0aYfjI088ghSQg2XaaooghKimSFB8E1XJGL+lSLRd0hjHeOoI1dIZDOvfmwo+O79BUBicUVV3S\\ni1NSBW3weBxpHFPbJcdxYGv7oc/KVs1kWWGlQlYZ0kWIANopnPKIYPFB0pia9c0cWQwBv5JfVy07\\nDxeM9me4OgLpEUmKiGNgA8QRrCcsW0fkFbptqCtQUhBii40iWqvIwz1v8j+Pxa60Ztgf0FvLcTJj\\nMR4xHHSwltXEW2Xob/U43D8CGVM3M9JgEM7R1g2dJMMuS4KKECFQkBFjUSrGmYTgIpbLEXmeMrYR\\n1sZQW6gcPjjujb5w43DBWlPB1pBod5OXfundnAeuvvAcxyx5/OxXMpQ9khjqOmZ6UmK1xPqEXEs6\\n/T7rb9jmhf/3UyRNRSd2q6SUiiTrUEtJVOygRUZwcnWmH0mCDLR1g0wVrXX4AHEcYdCcqgHrg4JO\\n54SD0YzBYIipYWwcJjNY5kT3OuXJ2detlFcE1uN1xuWSEBXouODo1h9wuN/wV77mAc5c7mJEizlc\\nEtsZIi6oDHgCNoVlknB0d8EjD+1ycjKmUDGRrQnBsrl+lkg2DBaGIlK8nMeY7S5WCoJL0HmMx1Fr\\nCV0F3ZLGG+K2oZ+qFfeMLV4+vj/immGpAUlOj0l5FZIhh7S883vexb/6LclTTzzF1rlzbN6Tk579\\nttUqu79/hJdQVTOaskFJjfASLx34FQTUt1DrlkgqFPcJvh5LIE1ysiQDArZ1pJEm0Za2XVldJTqm\\n6Co+m6MO0Ms02sO8rUGLe2bWKxSTkhJHoJOkyG7MapD9Htk1CkCNQK609rEjNJ/VNnMB4yyNt0TG\\nEOFpjUdIiZYe5zQiyNWq/uWfvL06it2HgHGaa6cNMp7wwOMbhMrj2obaarbWe0QqoklTbuzfpduR\\nLE9bbNuSpQproMojIuHJhGd+EBjEOQ/vDEhVIBGOvd1zzJhxmxOqyrB/dEKWR6SR5kw1oBtm/PKH\\nNU/tvh7aAlQfbpUUk4a1XspXbsb4j/8u03nNwWCdaWeTpx5/I2bR0u1muCIlSTQtU3Ye7HPz/bdJ\\nd/o4I5BZoOgmSDWgaVOKzQydgxWBRfPQyDEAACAASURBVAgQJLH2iMhTuxZLgsSSd7dJzpzgJscM\\n9zpcdB2uT0teOxDcemWGO7PJUC8wC8+/e/czvO07P8j6pbOQDGk7MXneY5g/zD/+Jz/D/kc+za/9\\nwk8y4Abn84LJs8/TWba4XsLUB5QXSGcJ6x0++MwNysmC/kxxKWoxzlImGXk38NzLVzmzt8ZwPmF3\\na53ZU0Ps5gVEnZBlgll5jNzsES0T3J0JKk3RvQyx7UiEWvnEsUYCbBFzhCG+x1tvWKKAoqmpqDlX\\n7PKLP/vrvOsvF3z3X3n4T+TN3t7Wva9WpiK+brh9fR8RFcTDnMPRmPVBj/3DY9YHXYZ5QQgtzlv2\\nNrcBj7cVNgTmy5bKezqxxgWosKTBEue73CeoVnfvkElJvtQc2ZK428UqS9uC1w7vHDpIgg90Ot2V\\nI1DeA6/uSXdXBPbhXoq7PqIRDf3s/sCPAZMQTWriyEEUMZdgUMRS4UOLCHLlE9/jc7FFX2K8Krrx\\nSgkiMwO3RGnLbL6EXJMOMoq1lYHieFZz43BG2YkZxw0T05KkHU7HS6plSbaRYbIaVIfpaWBqa+Ke\\nYN6OOZ4f02pIlWcnS+ilEZudLtXJMYOkoKM0Ac97Dq6w/6FrPP2r7+PTv/huYiuot3Kabk4xNwys\\nYzdK2Rk71pctH/vAB+gNIYiGxlWcjqfEaY+9hx/h63/wu1nGChPBAEM0n9DUC7KtDDo5jdTIPMc3\\njiwIFAm+9XS7Baic2qewC7uDDzCY/wHbk1MiH9ETCtlM2RrG9LIcakEWFJVv+cN/fwUKdc/rIMY7\\nTb28zQ//J++gf/Ml7Hs+ylNHigdv3OXCYgIzx2ElmaoCq7tUZByMa9YHfTqhZcO29BpHR0c0SjIc\\nl7x2cky7vM3Jm7rwHa9l68nH2eleohgOkZ2C6HyE2g0sKBk1jrsh4SQbYI2AOAW9BhREFJS4e+BN\\nfa/cLV1iPBEpQ2Ztyyeufpy/9RN/m2965zfzo//lj36RDBJAH5lucf6Rxzl/eQ9cxLmdiyQqZq2b\\nMVzvAwYRAlqtBmRmZYvUQ7K8z7mz51jbGVAZi1cC6yE7+wCOkpIZIMl21qDT43iuEGmX1gucFTiv\\nkUHTK7oonbO210evhRWXvS5xbgHarxBBpBACohNjpKEpP8sq4hTob1ALQS08rYvoZjEtJU5IAnYl\\n9fcSjv4kDfbPilfFyt60njofkrqWsoVOR0Km0UnKyd0jOjsDRicLQiqZVjNU7vGVZXF0lzODiEgV\\n1HNFWyYEU7E5KOiuRezfmLN2tkAVMaqIsYcZSTFgXN9kcXTKo5d3KOcLRgHyoEmwPPv+D/CN3/CV\\nmCzlVmeTsTf4ZcDMFFatMzWBVgtiB2c3Mz7+4U+S9neIs4KsW3A6L4mSnEk9Z3Bmk1R7NmYNjZeU\\nHnaGOQ0SLXLa2pKlMVYrItNgI4WRCokhiiVOK5SKVitDuwL5JTLFzzWb3SGXG8+sMcS14c5wwN/5\\n6ffwH/8P/wUraOWDoOAXf+bHuPH+3+EvPbrJpY0hCM1pGaBImOZrxMJipeHWZEQaa7STiKt32Fxm\\nGCkIukHVNZs+8DUn18D+Eel/9ASvvOYt5LsPIsuYdN3j2oieOGWLT8NLL7B4cY9nnpOUh0uSrSHb\\nm5piTXBu1IC/ws17q/hqfEUg4d5giUbh2MZyw5S8df0ii8mI/cOS0XSfd771m3nvH/w2Xzx1FYKC\\nzrAFZpBKyhrGJyNSrcgGa9y/Le/lPUCQJH0shkgW7N5zkFlYC5QoMnI0p5NXGEYpk4MFrh+BEehg\\ncTKAb4iCJMklaZZC21AaQa4MJBFKiRWy+jNAB0GjYxZtQPnFPcuWFvr3vJMahXAGLVqmyxipNZFf\\nqf4tfnURie/jR/+cWUkrJZhXU5xQnNvZ4Xh8woYBUy9QuaIKNZP5PsbVVL6iHZVM9vfpmwJbG6SI\\nMK5C6wUyKbDE1I2mEBExPYLXZKVAMOCP/F2mwjHY2SY0FYP+BrY5YTR1ZMA/+vn38Y1v2CHevkSj\\nNjCRoBsvyGJLXtaUYkbazRF1hK/hQrLC+7p2zmy6oOn0sLWlSCSi08NWczquIo0jqo0Bd08dnUFK\\n0A6hHFGesT+ZstNNiJSidI40MVDXmGyX8qHvJHrpN0nf3KW4coqfS7rzGfOQcL3XIdMJka9Z85aT\\nvZTjV5a4dsL/+k//N5bX7vL29SVfu+HYWRxxNLrF+Ye/giSBUePp6IpEeYws2C4UrfXMDmb0yjnL\\nbIuySInrlmIaCHnER77qHE8++ijl9nl2/MO0t2rirmdmFlhivBnASQ9eDLzwoQNGYY+N7Rw5XeJ2\\ntpirDqwPYPwiLSu4ckpCRE5gjAYsNYKUGSs++vMvj3jXd30r+wf7XD+6istnCLHGlWc+yINPPv6n\\nZNWQ+3r19c37xvJuVXSfkUUHjK/wVpDoCHRCQ0tCn0iVgGbSjkmcIHEFt4+WRCIiBEktAokMICKi\\nbgSxYSyXFG416JRGGhSEduUlr6J7Mxc2gBZk3YS0TbDtfRVcDGa8knXrCD+X4BxtDIWWiHqFYlep\\nhm4H4i//7O1VUezWWNYvdVBGMK0XuF5gYZasddZoZMyV5ipuvUGPJdv5GWbHpyzaU6rK0eaCOA5E\\nyQq9ayqJjAVKa5g0oD2qIynNmKgbWE8jlnVCmw/wtqKuJ0ynUxIKvFpy1y0Qw0ugN7iU9CkHEk2F\\ndAVxbUhmc05OTtDbOVLA1FTIriaIQK/YpB8PmeGJIkU0nbMpJcIkmFQzKRsuv2ab2rQYbzHCob1l\\nWGRYBHbZ4KRH2A7WQRyvYbMnSd/egF5y5uRDzKcRAdgwKRdllytxQ9HNUZXAnk1QCaTZkKyacW5L\\nc348Y70IGHHKpQcDwTyNuvhGiqQgL48wE8tkPqLXHFA3kuPTdfK0yyzU+HlgXNcYFdFXEe3FJ/no\\n2g7nj9aJZcwCg4w6hGlOUaScHEw4ua5I4sc58vtYr6ialk6eYuIIaTV0unAsP3PO3iVBoojyAbEW\\nHC1GRErgDJzf2KAzcFx7+Rpv/Po3UlyJeebZP2J3d4+/9ZM/x6/8xs//GZl1v7kmWRV+g7EWYS1/\\n9NLLmFaQZgk6i3j48jkWGLr3jvtMY0kS6MQp0+Mx7dRgowjrLF0paQNkMiIJmrSTorCY0uCFookD\\nzllyGRBSoeQ9TFOoV978Sw1pQVcnxOuf5bKrJU0Ks3qB0hLlNXEmaRdLTBOTdjRxT4NzMFcrTciX\\nEa+KPXtTt7z4yRssxi1t49m9tA2Jx0tHvWxo5oZmGRDEXN47CwuHq8HOLcpLjGnBg2slulXk2iJt\\nQ6fIoAiUcc3EliTdlJyIJ7bOoKRlNCoZ3a25eP4SLQm1CyyB9737D+DOPhkBrSHLEkJWcKIkExGT\\nr+3iOjmNhKgomDSOZRsIcUbrQQnHS1deIdJgm5qljllmMZt7m9Rtsxrh9YHGCWRj0QaapkHQorWl\\ncS3LxiGzmEznwCbUPXj8LEl3idqW6L0B0ju0CpA6EiXY6Az5P/73X+Vv/vhPs95JUDIQ+o5lp8Fe\\n2uJGEESvf4Rqs8c49Rh3jIgW5OmYrnmJobiFjSNmSUJQBm3ndAcDOntbfPrjz5GMlnQqi6BG5xqd\\nrI6JnNIo6clVwNc17SSQu5RMSfCCVkkqbwm4e3w0gwE2xQbd/gZaZyQ+RsiCKHSpTUUv2uDkZM7d\\n22Pe83sf4IPv/yQbW+d4x1vexle97g1Y0/4ZWQX3cYx/zG13KCn58IefRgpHTCALkEpBaxq6rKyk\\nANJEMZ/OqMYtdmbQfnWmTqQonUFEEhkJnKwJbbuydW81kV95IEXKrYpb+JUppPcrXT0e0oT64ICm\\nrflsfbtFomWKawUuMVhtQDoQEiEiTADrw8rarLnvCPfnDOw4GBaI+Zybx3c4c+kCdz51h2ASbo1e\\nYu1Mhw3TwZQzEIL3/F+/iwor/JhuHG1owEdgu4gox2hD5mpU2KBagJstWdSWer9F3tYcxwse3+qS\\n3FWc0LA0LZezDufPDrhze8wcz9/7pXcT0oR3PHqBVBWoLKfX3yB0p7hNR4FiMTolXVsSJQWmbsmS\\nhEmjUJnFLyqefOwSJx9/Dt9RlC6nSD3ZepdJECipSPsZs4MjlHUIJD7zdApPqgS3D/ZZK7ZYzMZ0\\nCg2DSxB2YU0Sv/wy8dwQ356T2ZYz+pjCxdgo8PzJHV58ZZ93/sA7GN08gt0up2LAExe2sMU2eZBc\\nZYtYp+RmRJQ6qCxxXaPcMaqZI1WMTwSDvEVsZ9yaK+YYvu6Hv44oBLRY4tKIppH41uOjCL1eIFxN\\nefNZ+u1dlpOW2AascojgCcHCoiXEDZRHLO4cESFRsWXRBqQVdENMtRnzSHKZm4c30GspRdWhpGGt\\nk/DJTz1N6i2Xzu+xVtS8cvM6D597Mz/xd/463/eu7+GLp3IMRASWCDR3bu1z/tw2dhlAe2Jdc3Q0\\n5ezFLRbllKotyQYdqnJOLAIn4/mqbxIZYptx6cIet45ukXlFNlDMypalqZAIoiwiVxbhMoIH0org\\nW6wRRDICGQitRXQUJyxp6pau+uNi13FGVAriNibeilhMWsJEkKkIHQfQGl/L1fWosPf+ti99vX5V\\nFLtzgY3hedK4ZTzbJ5tnRJ0+Ua/P4eEEQqApF5wcTSnyQFfn3LhREmUSGznapSQmJ9mMoTdlWRuE\\nWU0lremzpM2StWGKsyW+a7jazHFSEBrNeqI5uHubQTclIiOj4XYe8fPvfoZHn/gKNh54GPW6bWaN\\no7+2Qzuf4aUkFx6lOizLBXlWMF4YpHIktcdEEbPDI2IEQqdEIaInloyPbpPsvYYmSpnVlmLQZ0mD\\nGM2JXaCuAj54+rEmjT3lYkxn5zxGjBGNQ83OIAZvgvJ5zmiLKTwvLyXLpmJRWqLgSJaBbmnZePQh\\nbs9GFL0dLrz5AtduzdmII3rekWQOPxfU6TmSOGAngVGzhoslftyS+QbnIg6WgmEnpRdVqMk+oXiA\\n2bzFxDPO2pyEQJTFNFWJrk7YEQ2jScyyjJlKi7GBNFIMOl2CaYgiD7Wjzrap8NTNEpVBNkgR0SlC\\na3QUc+HceaKBRNg1jkYjJodjbMfyh89/gqc/8gm+9/u+ldc+8BDH44Z/8eu/zGsvn+PJN70ZEX2h\\nKTDBojmik+xw5ZMfI0skzkKIgEjS4jCt4c7LhyiVkwxjUiTHx2O2el36iWbZttQ+5jUPrOSy59Yl\\nqDUwll5TIQuP8QrvJVJaKrfqn3mTIlWERGJbi04lTmjCYoJtG7T8PGdYD0HVJDnUM0GqYozUgEHl\\nHiKH8Y5pLPBCMPQ1yC998u1VUewqiqj0AJoReImLBYkytMawttZnfrskH+6wvj3kY+/5OAejYwb9\\njEVjaDNNr8hYixJquUQWJfvPWORjFpEU3LrzCkFCf/0syVbOVi8jnVheXrxAL4qpyyk4Q5MqIhEx\\nCBG6nDIrb3L1Ix8hbyriB7pEazvQlsQ6oSYgM005XpApWE4aoqARrqYygRBHrAvNkc5YWEXPRDRS\\nkcQwPTwirG+Rd7rYIGmaJSIxDNopVWNXXuXKg51gjxucyog2Ujg8Bd/S7F1msmzx9pSkbkj1Boty\\nQmgOkSpm7+I6p9dvst7NSIl54PweMKBz9hxzU+PSijqRVJWkm68xG92kFg1VMcBWEt0TtL4F3Sdz\\nKUMxo3BT+sawmM7ZGqwh8gIZd/G2wcuYunVkY4U/lcwmKTWSwbriheeucfnSFvPWcO7cFoPtPVgf\\n8HP/4GfvCZkUXd/SLgS6n9NNwAbJ+kZG0waG3QFZHkMC2aBLIloW247ff/pT/Pjf/G95bRtz59eO\\n+Y1/+zs89JVv5gubKld0kpTT/U+hlyP0qWb73JB5bNBFDy0S1vMenaKDVDGVM9z89DWkUhy7hra1\\n+ATywf0z/RbUAFAQwWypGGQpznpc0KTekemSQIwLYI1HSomOFKaxREmC8RbvI7bPnP+c39QEQysE\\nzsfgWmxqaaRZ+WAsC5SWFF1H0U1WU5rAanvypcWrYs/eW8t567dcJpaatLPJcHiectySCM3kdEFn\\nO8V1U5558RXq+ohO4intnEhLNuKC3UEXGxnujscsZp61gcROJ1SzE8bTCVdevs6yMiQ6Z1h3yXyX\\n3fWCSDkiqXB2iTuacnZrjTpPcXRpspR/+q9+j3f/5m/D/nUyAYSVm4kMCqRmkBYkRzXKl+DmZGZC\\nkBXNdELaNJSNpCUm7SiUUORLya6O6aYJhkDwHl1FDNbOMGtj8ryLljF4j60r6nbCnZsvw+kYvGBm\\nKq7fPaa7s0nfn7BW3SSJIqTusL6e8kBnRnF0FzcznJ4uePrpF8m6OT521B6iQpCpnMz36aZ9Or0O\\nRmb0Y00iarqRpF96dpuAaCqivGHmBWO7wd1wAToZhRPU18cIU5L4imi2QFWWyKe4RrE/WTKpWsY3\\n9tkZKHzdEhbggyfppWCWnN4xPEiXLhohW0Jcojd60ASuv3iN0ewEFSVYJE88+QSPXniYNz/+WvaP\\nZkyPDVFsub5/jSay/MXv+ho2d3f5r3/gv+dH//pP8I9+5hfwzWfv53Oqcs763iVunhxR9jVmBGtO\\n0ZMj8nBM0YuRGxuMfMutoyP6vS7l0lAuPUWyKtrzg/tMtZgVIhxgwuYDHZqqJQRPkIZWphgRY6xH\\nO4EQnhAMeINwEucDkYpWisr28/bb/QQpYpoaDI5gJRpF3JG42NNGFuE1YcKq7/hlxv8f/NM/B97O\\nSmkK8P0hhGeEEAL4h8A3s4JKf38I4eN/2mdMjkfcvfIRiuEMrQOjk4Zep0+qJE0raOYtSkW0B4ZO\\nSAiVwXYUKo3QyRBrY2RbEZeKxrVEfUtjDdVCEJaBva2YuyfPcnM/4/zukOHWJt3NC7xy44RGdAj1\\nKbJd8MqJp9/JSPMeM7egXhre/4lT3tU0cOcODDcIjpW5oTfsT+7STzxmYXA+YIIAWlIVUZaCzV6P\\nyjnCYoHteoyxCDPCdFN0N0e5BJlFHJyOyDsDjssZOsqxBqxt6Hc1rbOrPpMLdIOnt9YniBpxMSe/\\ndoftcUubp8ySLdTogGVouP2R57mYRXR7inf/69/nnd/yGPOyZP3hc1gjaNsGvTVgObfY3XWUTZlU\\nlpYIN8jxVjA/kNw9mLFxeQ2bJkgHOg6MG0O3nzKbH2Mbw9ZgA4VAmiWimTJpG1zbMsxTjgWciogd\\n54h7fVyRgJEkfgfVGZLgqTB0O10Oj2cMewmDMysXljwRxJGB1rF+ps+/+LX3sHM2o61qTo9H/Ow/\\n+GfMLEyOTnjXf/pdjF65hSmn7B+9QP6rOT/4n/9lGhoSmZDl57BUvO0b/wIf/djHwAXWTxP6soFh\\njkwks/YuxXrM2to59p85IHKeJIdYSgZ5Tmlq8s+xjoqAAeMb1+l2Mlrtwaxur73TaG9ppcKZZiXS\\nzSJMWPVnFnXNeDLhtWc+F1cdeYWLDCFeEvUUbhRoSkGWSVQhAIEQitAKRAhAWDU9v8T4Ulb2+/in\\nJ4HXAX9RCPFV95770RDC6+49nrn3s28CHrr3+KusKDF/anQ7GW9+8AxFZLn+4rNs9QqCKzGuQgbP\\n9GhCO3MsTldn6lEiib1YuXakEp0I0iRQDLKVJsEFWgEVFTZIFqUh73TY3dhA4ljfHPAt3/tt/MiP\\n/TBf885vwIeIBk/sHKpuENIhU43rKg5tw7/5P38TZkewXGB8uwIGxjAtZ0yqEmsdzns8AuUc09Ml\\n86XBuBYPzFvB0mnmKmPpBK6sEWVF8JYWi/Mrcc2yahiPJwQVEEEgjOHkxh3K4yNo59jlCZgWK2Pa\\nJsOKdTqdLsvjGSrqILI1pE7YWc8IdcP8zjFf97bHGXYz1noZxho8BqkCUkCwGiULlkbS6aWkaYTR\\nilpGiCKit1mgIklralIsul0glUcoj69a5tMlTdsQ5Erfb6uVl0AUQxOgiSRXbp2ytt1jOl1yeDwl\\n1C0q0uhc01pH3ukwnzRoNCK0xHECrSZLclLV5fRkxPWrtzh/8QyLhUMIzeT4hMVkTCotTz32JF/9\\n9sd569e9mbPnttm7uM3//Ru/wceffx5rVh1rD9SVQSS7nN1bo2xmLKUH04FSI7ymFyfEaHzl2Noa\\ncubMkE4smE9LytoynVefl7Ur6GiaD1fCuBAg3Dd2bhFiVfjBe9q6ASmItEQhmC8XdDrZnzCL9EFg\\npCVKY2Ibr8zlNQgvsN7jvFsx3nMFyeqk4T+04eQXwz99sfhLwC/fe92HhBADIcRuCOHgi72grQz1\\nzREPXkrpbvZ46b3vJ+SOEA84OQ0kyvLSlQMSbWho8FoTULR1zbntTXzlOQgTXGNpZyUqFcxHC3a3\\nUuZ2wmLecPumws/GjI5v0iBZtJY8EvzAX/sertz4IAcfK+mlHpGmTIUgjROiRcTdDckvvPtDfNO3\\nvR7WEmK9RWslsQ50UzhTwdzUmEhQW2hUjI0dVDO6tqISHfxwm5A5pNIUMiKIhER0qHWXpYjpy4qo\\nsQyKHBF1iWiwMqIMJZPTQ/JHBzAdE5kDmtEmTT5gwS6m6GA3Ms52A6qjuV5N2Hugy2JseOHKFTYe\\n+Qr2JxUuyVnbHiKCQ2gPAtqZhyxmOEsZOc3Q1STHC9q6IKSaW7dus/3URY4WDbHP0GlJLGKMTWlH\\nFWkesZutkdZLkn6C7iQ899I+Wb/P3FTMDo45IeKJs+vMbx+ikwtkIUMIzdogZyA7VKkglJJBP6au\\nRki1xcH+KW956hFOZks21xJevHaVg+mE/mCAKS1qqPCRooi7BN3iooRbI0MlNxn2YqQY88hD5/m7\\n//jv8dQDT/E//tiPIIFO1gNqdi++kb7/I0pbE5YK4cEnJSIvaL0mjjV6PcY0FfM7YwbDLerCMzk6\\nZndt+HmZOyPbXufo6BoKiQoxyjuIa0yb4VpDN9YoLXA4QggoJTk5OGZn9/yfqIPSGZZa4Zwgnntk\\nX5HmAaUjYhnQSiOcxAT1mQM75/8D79mFEEoI8QwrH43fCSF8+N5TPyWEeFYI8TP36K3wWfine/HZ\\naKgv/P5ScVpJzFiRqZxLjymsNfzbX/l9hPSkieXwcJ9IL1ZXp0hivSCOY1QjiawkiJQ6KGLbkFWW\\njV6GLVvaZoRU7crrOz5l9/xZvubr/wIba+cIdo/f/LWnec0Tb2Gw8zC2v8WtyQnXx4dcO7yLz2OW\\no4ZREvM3/rt/Bk0FiyNiDDSepoK7pw2TVrCwUB02TG7W5G2fZJHRLBPqheTo1ohmsqRT7tPUJ5SL\\nmuPrh9iTW1T7V8FUTOqW1lrqsmRaGWQyoFE9jo4Mdz5ycwWHjCCRgZ435JFE7W3TWk29PydtHBEx\\nmxsFtw6mlM5yeu0aSjh2Ll5ApxGNq3HWIxrLMFlZHTVpi4+WrPcr1ue3eGQrIaHl0gNb1IcluU3J\\nRYKIMp6/vSC4wCAryLKM4SAj+Gjl6loGdi5doJQxC2sIcUbSK7h1eMJhpOld3iXf6cDZXSpbc/Pu\\nbWwomZU1dw6X6Dji+itjHn1sE2UEZ88NuXv0Micnp9St4+b+Pr2tLq5u8XFMbR1JtsXr3/gmummH\\njihAKFTUJQ99NmSf9/7eez8v01Igxff7bDy4ixsqajPDzktErYgIBB2o4gbVDWyuFYTJkrCouHxx\\nkxWP8H4ouKf5K1AoA0I6jAPfxAQrcQ0gFTKS1JUjyjpMqxY9iPDiT9JpA4HIB9LY08YG6yNklDKn\\nosEiUkHcTYi799Vzjqr60jXyX1KxhxBcCOF1rMgvbxZCPAb8OCsM1JtYgVD/xpf8qazwT0KIjwoh\\nPjqfzTiaKZbNgP2XYzprF3jwkfO89Z2v54Pv++esdzZoJh7hWqIsRugEqeHcpTOYukJKQ6haZncO\\nEZHBxx47W9KeOBIkOjLE6YqbnW32aRyUVY1vJkSioUgKnvrqR7n4yAW6g212t3Z461vfyCJkIBMO\\nlOKDy4LpBz4Jn74Cx3OoFB3ZQ2uNECmmiemlisjX3DyaccvBKzrjsNPBnyuYJwpnZ7TC4tIe8foa\\n9ekt5OQqh6MZk0lDMp/Qr46I/ZLx+JjThUf2h1SRYNw6bCmZqhbSikGnJbMLrKkYDBN8WSF6A9pa\\nc3F7m0sX9rhy5y5v/e7vJ9IJdVkTawmixSrPMgS01qjIEuuYcbJO9fgljoKhl0ak5ZSelmQyIMyC\\nphGcu3iJJE6pnWJathwdzwk+JzUSjhe88NwtRtMR9bLFEFiUNVG+weNveQuPX3iUva/8FtBn+K3f\\n+3ccMuVkusC7CiWXlPWCtbMJUihUJjk5POX61X2mtQMZo2JF2wSS9TUwiksPP8jeAw+g+w6rLSJt\\ncDJm6SKI8/+PujeNuiw76/t+e+8znzu/81tvVXVXdVV3l6RuSS21GklISAIhkAAZgSFYNjh2EmJn\\nEQhrkZDFWkCcwQQn2PFAcGw5WSTBEAUxiVGDNQ90I6nV6qruquqa37fqHe545rOHfLjVUgtwkJYX\\na7X3x7vuPffeffaz9j7P83/+P3pJh/7qgP/sR3/6z6y9zsppkCG3xgsK6xNIweLWLlILQBHIGKM9\\nrBcQpAG+9Rkfjllq/l64k4aARooE4wKapgYB1iis0kBDVTcYJ2mNo20dzjYoE7G9tf6C6+gl2tQY\\n9Fjj1wHCk0hVEachUSckiCKE8PCV/+XzuCPH6r8kUc0L8E9vc879g7sv10KIfwU8j+V8Hv/0/Hgh\\nGuqF1/oy/mllbdu1xRE3ppbGCjwlWF2POSPX+Hv/y89xqOecefk201tXMcpDIBlGKxgzRPWGSD0n\\nbmDkBygV0lqB8AzRwOEC6MUr+FrCaAPP+ux+8TY2AFdX5FWLUY75OMAXa6yf1JSLGReu7BJ1LMqL\\nsXWGPAE/9t/8Cv/kF36IxJSUM4E4qnHjI8o4oWxCsskCHfh43RZpPXYPS7LsiIe21sgN7NceXt+y\\nqI5I0phgd8aaLNkPUrrDLvV8YH4YWgAAIABJREFUgR8lFLsFPSXwuhIx6tJObtF4Ehs6or6HVRJ9\\nbU6YWYzxyRcZK52UXphi2hYTGaqkw2hzkw/98vt48/e+hYOjMZtpSt1abNuS2wzhCTwRE7g+swas\\n7CD9Flcf4Ok+tyoPtaGJfI/Z4RzfGdTJkxjjsdZPEYHCoRDZHMZHiMYhq5DACKSVjIIuP/xffz+8\\n5Axwz9073+FzN2+T4AhwlKIk9hVl7RPXCjLFuJyTu0Ns4iGNwDhHpARO10gz4j/8T76HO4dThuvH\\nyfYzPrN7lfVBigokzho6QYAJU1J8emuOf/IvfoUf+IHvZZS8cLl3OfmKe8A6Frd2scoDP8YArVU4\\n4dNZHTCrD1GtRngt7XQffzACcpa2WTnQpdFzrNUIFFprQOGsoSxbwijE8xWBr1BBwtGtm9T5V+/G\\nFkerLbmr6CYBrnLUpcFoDxdbRBrSxcPHe55AD0BTVlj7tTNh/sKdXQixdhfoyAvwTxeEEFt3XxPA\\nO4Gn7n7kt4C/IZbjMWD2//e8DlAVLY3zKUpF0t1m91rNZNajsSvMjxpOvfwsw3sGaN0hUupuA4Jh\\nrG/hrWmGmyFe7NBUuKbGLUpEafFlSNrp4luHbmucdPznP/lu8vqQaTlnWo0ZL3ZZHOwTeJYiq0iT\\nDumwh0h6XLw6I3MKegFXrk24tCj4nh/537n+9Hmms9vkjaD2uzQHC9xkSlEa5uMCdZBz6ZkD8taR\\nDLp87vZtnp7OuKx6nN+bcnQ04dL1CRfKEZ8ZD5kFMc/uZ3zkasafzD1utym3Dgr2xjl5knLQRoyb\\ngCz3UdqwdzDjpoXn/Jg7xpGeOkm7OqQWPgLHSrpg78lLnBqmvOcf/TN+/Vd/E2kFRD6NSqlsiE9I\\nHAU0nYjcFax3FZsDRTeyjJKMoOMwGx107fBKQ+g8Bt6YTnmT2a1dyju7FIe7FLMF/r1nqFZjmtWY\\nKlGUnYhiEJCtG977gQ/BdA58hcc+SkZYJMiQ2lrKWjEc+jhVUvmWy1cusyh9rt6Z0tYSq6Eofapg\\nwNmH7ufN3/YOHnn0m1lPT2JdSS/x0JJlPsJbCszaRnHi2HGuXdnl13/r/ezeOiC/K4VdDg+IQEI3\\nSeg3UO3dRmFJpCDxPcqiQqYCfI1XgNQO3Jyl/LZimY4TZM0CJzTGWBwCh0FLQboakQQhvpdgrQJr\\nqDEI76v3WFlLxLhCaJYQR2cRkUUphfIEIQqDxqFpXvAfPPh61LL/TvinD93lwAng88AP333/77Is\\nu11iWXr7m3/hNwjH3t4tXO2zfmIboVMWM2ikT1E61HqKUymt9tGUREkCxmO00SXqKILa4YWGmhJd\\n5AR4HNsY0DqDyReUxmJliEo7fOyP/pi6rimKmiiocTanqCw2U/hhROB1mU1qYm/AYHCGyWxBULQk\\n0RoTO+NolrM/ruiLfVK1Q2YiGutRVoZFC00FtZHcKR1e5TObVHjdLqdO7FAWOXk+I2nnaGcQBERJ\\nTJnn3Hf8GIusomottbXUCTTC8sCpezl/c5/QF0R5QejAdRMOxgVqvUMna/BiQTHVqDiC3FJrSyg0\\nzXjMlavXaauCwWofkhBdWoTySbyYti6otCQZdVHFHFfUKFmjfCj3ZwzPHqNo5jgBNuzQuAXy8IhY\\nrnN4kBEMAo4WDYe3ZiT9ms9fPM8V7SjrFqccbpTw1O4u3/Pd3waD++/e7DFNcYQjJLMLYi8idgHG\\nNLhGM54fEQ777B3MWV8d0WqDEQYSxeHeIbNFwUc+9DhpZ4WmrijbhtZ6rIUROFAiwlqLVIZFKdhY\\nO0nTzrhx/ZCXntn6cxZfAL6CJKBa7BO1K+ALWl0RJRETnWE8CGqfLMuWTVtb97OsOFtAY6xB1g4h\\nPRon8JFYWxPFCUIrsIKqbfFjgZOGwepXk1+NrHDKQCtwFTgsXtfDWAh9RY0BlhTX3gssaqSUSPm1\\n7+z/LvinN/9b3u+Av/s1/wLAWUddQWZDPvrkNR494THa3EZGHogZNgx57uJFjDvCj3sIodjYXieK\\nugSVIvCXvmBOaMBy79ltHjpzmtjziPuC8888x8W9XUxk+PAffpI06bF16hSysqjCJ5SG1jbkecFR\\nHhKrLp7zWN9QzO4olBEs2qUGv/UUb/2xX+KJ/+NH6ceWS7sLFs2UemFY5Jo800yMRKSSYjzDlynj\\nccnJ7XWinXVMFzp1RbFwlFmGMwtkJrl49TbGl9jIxw8iDl3D/WdPsHHPJp/+o4ZR07JYVCgZcyBi\\nHnrrGwlFwPmPP4EdTzl5bJsLh3O87ohpmLJpbvLsM1dZOb7GJz/wFPeeOsajxx4jsDlC+piwRdmU\\nnoux0md/usugUxHczsmjdTgmqIo5jRWUTUN3uMEnnr3DyZWYtbWUuC+YlwesjLrU3pDCaP6DH347\\n+52QQMbUpuD6Uc4Xn77GpdkV5N4V+qNjiO7S709iiTCEfgchLJn2qDNLUy/orgw4WsxYMR54Hq2w\\n+CLmdW94lHuObdLIlnxym6o8YpFnKF+TyZq1dJXFuKLXT3CRwTcx1rVsDHp84P0f4G1vedmXS2PL\\nBFsI1ND1wBk83Sy70qwk8Hxyarw0onFLfzwVSapSQ3UIkQF2gXuIXIDSltYDoVpsBf1U4GYGseqR\\nVzWBFBzuH5FPZrzszFeHU+VLjN8saVFNi0QhvSW6usoaSs+xlkREX9W7PoNlF+zXPF4kctmIG9Pj\\nRL0IvchhHKFFFy0LejsB1nZZZDlhvHTtjOIEbRxSOZCgUkvRlMhIMLQxvTSiLRR9LPXCcO/Gae45\\ndw//z2//MQpDXmbc2jVEBAR+SG01qRRkRxm+rNHKJ/Q9bO2BFFglaGWIzWZ03YBoNCB6+bdweLjH\\nfa9a4dMf3We6X6APpgz7HrcuPcfJzR3ysqC0IJXh85+4w7HT57i9P+f++4/RHfR56plnWdvqcjCt\\naMoaIxx1W5O0gu5Wj9/+7Y/z8rP3E98ZQ1hh4pS80+H4S85RGx9dSPbykkEcMK4Fw26C6EfYzFE0\\nUx548DTZpZt88IMf53/6pz8JJEg5xwXL464NLZ7OMVHC+skThOWc+vINmjzAawVpoClnYxIhqY4O\\nOffgvThdopSHntxmMzpkeLjLnWEPG/iElUPEKwR+ipA5p+7d5E2vuoejW7t85MPXecWr3sAXnvo4\\nABYf0CzKOR1P4FyL6MQUZUM1LekNImotMIkk7HRojlL+1n/5w+xeus2Ny1cwrePyxedoqwIlBTfa\\njJX9NVa6W2TlLp1ed/l/VUWpS0To8Y/+19/hR3/4HSxVcJrlQVWACCG2RE0I5QEUPRgKYgRS+Rhl\\nyF0DpUPaCMoAd3gNsZMDLUhJLVtaBEp7tD6EEqLEA99gipY0GfHpJz6Gbv/sTtzOpoSLlEAZbGjR\\n2lGNDabWdHc8wgSiP13tLgtEHKPbrz0b/6II9rKq2Toewo3nOHd6CzMLKCuD7YfE6xucPXeCZnJE\\nFDl6/ZjQvwtSKC0iiTCexYtCTKEIYo/iqObG3i5z2bI6SIhXezRel4ceeZAb126wPzMsZtlSb92J\\nIXdk04q038E3gsK1FG2J8AUultSVXBZdwh5ZC7ps+Y/+9i/wO5/9PQC++5t+AKjAPgkXnoVz95P9\\n2u/SGfbAWW7dmDFbTGguzzgzGtJYRZUtePSN99ONI7Qf8JlPnWc0TBESqsmUyMJ2skJ28Sq9tXVu\\ntAuqe9d43ase47CsIHAcHB5REtALIqxnkSUUwrC/f8RgY0TrL6g+PuHcqR0e/8jjPPbOb8YXIWUz\\nR5KgtSSJa1qjkSbCNAXe6gbx4QHDSHBYO6bXMrZW+/TuVfTbWyjrMXc+lQ7I9CrTFBb5HCt8mis3\\nWdlqGW71mV29TDJawxXHedkDD/Hy170TU3k8+/Sn2STgNiDwCbzlY4dw0HWWSoVMsjEr/Q5JlFD5\\niqju8r0/9A5uXjqkx4jVfkZtcjbX1rhxcAOtwSqP+dERuzdnnDwzZDaZsnbyFcRKQdWQdmOyxQ12\\nG9gO4CvpKgEEEAi8uIW6ZLwo6CZD/FAwtQo/iBhup1y9fpPIC1gcTEibGCEDMltTFhD6HomwtFaD\\nkgzjPgTL00MaSdzdUpsf9fjTw9YKU1e0UiC0IhACgaGz4hFIy6LUGFUzCAzLioCCuE9tNMg/W8L7\\nt40XRbAHgUHf+SDH02O07SH2mM/6acfmfes0MuJ//tlfZNATBEoSRClS+UROEloDusBJnzQI8J2g\\nLpdH8pXhkNyv0M2U6MaCYLJOb6PDmRMn0ZdvMM0KmlJxlOUEcYCXBAgZocuWtimgbXCmps5zkqTG\\n1DWtr9C+paMdjz93gfs3X8GvvOfHeeW3fydUGUSrcH8At2/T+auPYT/zBWQgGC4cqtuhjjIW1S6i\\n2SeyEZPdMYedCDetOZYXDFOwUnLYQkco8GtMKth54CQb2zEmL9i7cA3b61LhWB/16Z49xnT/DrGB\\nFk1q4NjJY9zYu4XnJzAcEeL4mf/un7H6j9/P//nbP0FqNK0LaJ2PMClO5qAcar3H5//gcc6+/CRX\\nLhdcbMFsbzCWIHZL4q6hiSPGdc6qJ6GJuT0L6MgpMi/o3nuSejolf/aQVTPl+vmGW1/KkZ+5wOqD\\nx6gTx+sfO4nGw8cQoigCgWc8rBMEaY/t1VWO77yW28WUwyPD9qkVxNgxuzHnJfc5PNeQ1yWd1YBF\\nVREGIVmTobRACoPnKZrM8tij55hnDWFPEcg+w05C0cz47B9e4W3vuJcIyTKj7viyRiz0QPWR2ZjZ\\nrRmrpzawssYPDDcPxvRWR5RZSTZoGc9awtYS+RVBx6EbjRIBWtilWUUs7nrAG0xpWLgMKw1bq0td\\n/Qtt370ogSzHbwWNNBjhEEugAOZQEoeSzuZdntzdch8kWJvj5NeuoXtRBLtwilFyilAGREHIyftO\\nshjf4fbHrvPEMzdJuxWhchghyLRlpZcAlrppsMKjsQLfCxACWuHIJExqhx8E3F5k1POCQUcz8LbJ\\nmjltA6dOHOPWzUO0dVgZ0u0kxEJhEoXnC5SoKYsCGwZMD8Y4IVHG4SWKealIVMu0mvMjP/2veP89\\nD9MfhpCMoSmhO4SDfeisg3MkD62QqAb0MbAVxrQ0eUs9W2DyGeH6Om5nGy+osEJwuFgl2ekSdAIa\\nC7dnc+qspJdbFkWG7PZQSZdF4yDtcqu6wXrPoysNkXTI0OENUhrV8MoHj3H+0gGdbo/xYsIfvP+z\\nfOtbXk3gHMaUSFHjCUukY+zeAS9/w5uYVpry6EmKrACvZX9/Qu/0GZ6pI/oGgnxGTc1C+IzlCvc0\\nl7jf7nH+hk8dJ0xaQVIPqOOYWs841evSmebc98pH4MFjHFLSJaA3DJlOShJCtjf7EKRsnriHnZ11\\nuuVJ+nKfwPOxq4o78ynv/Y3/l4de/lruHDSIw4ZSS3Tj8FWC9Vq01SAUtQDjJaggpDUZYTyktQlW\\njPnAB/6I73jHfwxAgyPAW5r0y2XpDQW+r+mGCsY5ndIQdDVeDEESkOclEQIVRyzqHFlbrJIYKfEC\\ng1/EDHsxBD4GjSIg6KRc+MLTCE9x37kH7q76CaDgTouSLZXyUW2BEj7ChcStRucZdZDS68RLUKQU\\noFqWlQCf1hrCJPrzg+rPGS+KrjdrQPmreGrZE9w0jiAMuXXrkGefuc709iEegkh5FIuC3mCACjwi\\nP0E4cKbGjwxp6lE2Na1rqJqcPKuQUYzod6lCweFkjjRw7iUPcu6Rc9h26VlvdIPwQPpL9yDjQCgf\\nP01I4oT1jU26vR6BlBQLS+vJJUeszbn27DV8prhmf5nz6Q6odufQRMj+FrXXpy4V85nkKI+YNQMy\\ns0IhVwh6K6ys9Im6Ad1RQCxLUjGnt6nRoUH4grpyCKGIohAlFWE3ojUNnrPQGgK/y2C4RhWENEFC\\nrgUahy8MPU+SmoZTG0OqecEzV6/xgQ9/nmv7M/Iqx+86iCTS89BGIUerVK2PEh26UZ9mv8AWkvGN\\nKd3Uo/YVk/mCyjUc2JKpVzEzBbV23N7bZ55NuTI54KnskKdmC754/ibPfPEaZ17yAPc9cj/oiuyL\\nF/jB7/x2pEiZFzk7O5tsn92gFpoolVx48ir5rMLZgPWVDqEIaHVN2AFtJKDwPIsXWJLQoxfH+Eri\\ns0R7CVkgVIVrJZEfY7XGSYXRLdJz7M6u8IcfW1aJlxbWjiWN0QcUyBb6At03tNJQLTS2hKo13JpO\\naEVLEgSEQUhbaoSUCOFhnWa4aYnilqifgnDL8iKS7PAIpxts4768u4q7NllUNV4DlQmxyuKEXJY7\\nDcQOlNB4VkKlYCIgyzi6eQgYlJIsjwBf23hR7OwWQzBYcN/2KZ65XrE7KfjGt78cdWqDL43vIOUM\\nFTisrmmbClNCQAffd7jW4jtHfqhZHLUkUYqTmqkbE2qL8DqIUFKaBqnBZSHHNo/xAz/+bj77gfM0\\ndYkTJU2V42SE6kTQaqRShCqkFzmMczgR4ZoBSTxkqhec/5NLNE2NjAV/+L4P8s53vBTskOsHNxEi\\ngKlg9+Yhvh8ReD3STpdS56SxINSaNm9xdYS3WHAn1NQmY0RNNBD88cWLPH2xYasz4vQDZ3n20lW2\\nVmK2VMTMn2KkY7SyRjEZE3iC08f63Lx5sLSHimKoHVGl8MqCV2ynfOaTT7NT+nB2g707+/z9n/nf\\n+O///t8hLOc4HEJIrKgYl3P8OMZfwLV2wTR22MbR2dkh1zl+pZB1TRkorhw4GtsgTMHV3jY3wi5e\\nZ5VKFRTW4SpJFEWc2Lqf9MFXQOcIVhI6Xsz3v+0cn3r/l2C0xsWbuwyikLKyZKZgO/G5fvMOXqTZ\\nWNGUmUUph201sZdgijFRaMALGfRGjPc1i6bA1xpHgLQaWTm6/YBZViCDDlr6S49CEbHal/z+73+I\\nN33jS4moWR6Jl9bSEGOpCMMebSNxQUjUM8hKMKxD8tCCJ7l4c8b29jpba+ssihw/l3SjEKTHcDUA\\nsUz++VjybMx0crRUer4gyXaXMgexpqpBzxu6m12qaYVoHVoaOsYjLYGqAF0txXvHU1Z2TgOKyHNk\\nf1kKur+s4ZxFmpInnppyYvscKqn5wCfPk3Y8vu+7voXdJy/zxPSTNNWcURJSlzmBv2RrG2mZF1OC\\nWOF5Amnq5bP4zFC1GeuJRQYKIwRRWLHIDT/1Q9/JRz70OG//we8i6vh89sOf4fJT15GywknLMI2o\\nqxKDQDhFGnpY16WzmmCtJNB7JK9/gL2Le+RHC/7hP34v7/nnv87P/dxfpfVizj83Y7Kfs1c2fPHS\\nee6/b4eb+4dsjVKOrfbZ2FjHawP6oWLNFsiVIYx6RGmElgUPv+YUD79xmyjx2bt4i61DR7eaoiqB\\niBtMEGBXW+ZK8NSXzrMTajZlylRD6loqaXC+o3Uj4u0BZx6RXP/MM4yikqY65EsXx7z/jx7n5adH\\nhEFEtxuyOfRJwxBpW2Za89HPPYtbH/LI6x/BlAXrLEgTRROnLETB8a5PfntGOc24YfoQ9xC3JoQr\\nmr6nWa8kfaFZHYWwfxHcCJqWaT7l+vg54jWPXXPIPZ0eZTZB+yFHB4eMViropUhXU9xWLJqAKFIM\\nUg8lBE8/+TRer8Pa2ipJMKA3CrhVSBIZgNGUVUXspRRtRq4tvrc0vTC+hCZkZ2VA02n53DNjXnl/\\nRIhjqXlfMtGd6KBUtkx8BZZqVKEXAbLsLuXJ1jEYDWiajP07h2zurKBVQdtqYAvbQGMXRHEPQ8vR\\n3gxfSFwUgP1KjdwjwVJzK9A0kSaZT2iLGOk7RNPipEcjI1SmiTpyyR5UDRQlyAjieEnwaeuvOc5e\\nFMHuKZ9qOqDKVriZCzaEYv+5PRbzCWfP9tk8O2Btb4UbRUFTGeKwQ1ZOCGSKriHuD8EG+E6xyFo8\\nY+kmfTq+xrQVDR6tsyyyliROQAvCNiDPPFqhkYMB62crFtdvUFctWIn0PYxwjGc1W52YJEpRbYN0\\nAU6vc+fqBQZbK0xv3GbXz9kbC1wy5KGtgIcePgH3nubKR59k4+Tb+PVf+jXe9sAOfRPSTRS/8dEv\\n8eq3fhMXvvQcT85v8Ng3r0HZIq1kbbCB0wqHphCKjbPHufalC/STiK4tmLiWUAaYVrPIM0IT4JzP\\nIoTad/RVzO6dPdaGKTZzBBrOnOyyu7vKQ3GXTx3W1IMuv/DP/y/e/spHOP7ABvdtd9n83m+gfvxP\\n0LnPk8/NeNePvJvw4JAkyGkSiSxiqDJMWcDaBntXb9EN+7T9Gt9rWNEFoheSjQvWE4+RH7C1EjGK\\nr/H5X/2XjE68mpu6ZRJJvuvM/Wx9V4/v/9e/R+hAYLDKYo3i1sGMcnqNs5s9wnCDOAkIehZTlNTO\\nIVA0Vcmzly+y7g9wqcQaw2h1wOGtCQ5F1hZ88hNP8MirX40X+EgpsLaitRblIsLW59OfeJZvuP8x\\n3F1duoeAeoZSIXgGGUoK0ZAHknjg01IRVAKPEKsMupRIv88885AT6Hd6mMJHBY4o9IEFipRGCmrd\\nIJEc23lhp1uBpGWYCQ7SeAmBrD1wFiUaAs9HuwrjexjhoYRZttGaGvIDsKu0sQdfR5PriyLYtdH4\\n8RRDzerpIYd7+/Q9j3Q0ZLxoiXstcbicCF8ZaHOiwMcIQaKWpBfp5UShoPR8qqZEmQbTOIyUNJVG\\neA15LXjbO9/Cxc9foDGaop5i2gYPyzzX5LWh0RlxE+O0xvUi+t2EqsrBtXQGfawDWUkeedU5vIHm\\nc5/5BP3aYFqPX/2dz/OzP/cdUC/Ak9z75nMAvPt//DG+4nTa8uNEQMUb+CuA4ae+9W/ywCvXCYYn\\nGI1WWTsxRHoG39c4DxoRMC5BiYDYaC5fuM7eZMr2yjqtCIl6EWIxw3eGiXacHK5h5xOcgNalVGLA\\nPWe36HkOsbXOF28ccPPJp/nXv/ZB1u4b8eYHd3jTu7+V3vYZaEM+9Vu/xz0nIl7R1oR1BaGklJqd\\n4Bprr7mP379iiQcJ8qjAtRF95ROaBSJNcd0uE5cTdTeoygMGecbm6XNw3wrHdno0q8eIvS7f8caa\\nZ/7ayzj5hv+C+1fvYzo/RNoapwzJQDJu5/ixRxRK5gtLaFJUuGzMcbqmE8J4PiEkxmlDPikJpKOX\\nJNAYjMpBWbQ1OKHwg5TQ0xgr8C0cHJ3ns+d3ePTBnbtBcJfW4ksgQlpDJCRK9Il9xcTNkHFMUQus\\nB2HXINIutmkZDRLauuLwzhEbJ4YsBbsJtJqDG3cQtmaaGx55zWu/vOYNBco6OqUgsxaswZoWGTpE\\noNG2JnSWMNKYwEc4DQaM5yhNQ4wlkSEm+NqP8S+KBF2aRmyGhiQ45OazlxhGPTzbxekIpyVVZjm2\\nc4zxLGdRLsBAW0t8KZCx4rBsKKwliAKkMIhAUFFS2pxaT1HC4nRDEATYOGZRFczHc6bzBVVh6HV7\\nbO902HzJSdbuOcnMFOxPCrKDnMn+nCIz6KamaUKkiggiiaxD9CKks3qSzIQIH379fb/BO1/xd/nc\\nez5A9p7/Gz71PrjyCagvscy+zlg+eOUsp34BTPhv/+F/xRvUkNN3dgmfPc/xNGHgQtLGw3clYero\\nDjtoKwmahpUgYyVyrG+t8Ux2nauzXQaqJTGgA40WGiEjnJK4vsE2mnS0zkKl7Hg1r7mvgyun1PkB\\nz37hPL/5W08A98O9j8HZbyTY6NB6JbpjCCNBUFac6vdZO/kQ1PDYiQ6PbEWE+0esJjmpzvDX18jy\\nip2m4L5sxpZZMPAlkeqRNxEkBhVkOK2xs5L2EHZe+wj6vT/Lh37xR2mbmM4wRRuP7GjCZFZwWOVM\\nJg1+1iJdTiMtTmt068iKkihOObGxxs7KEFc4eoHCswYVCAjg6rUDAh3RQaIa8J0hjhV+KpBlyb/5\\n4Ef5pV/+vbursIRoSWSxWJABnkkItcf+UY3fTQhXI0g1TtYEnqRclIS+woaC2jMEUQSqx1K00yEf\\nZww6AbFS9JLRV635AgFVA8bDdxBaCaZBKRBIlFgaY1aeRXstubDUykckq/Q2zuCnd3HWX0ed/UUR\\n7M45fK/LA6dP45ylqAq8ANJUIayjbVuG6wnCazFtRVWVWFujPIU2DU3bMhnXhEGfWZ1h0dR2etfb\\nfClFrPyCvJny6OtfRVa3tNrR6gVYTT4v8FSMDFMIPTbOrhOsRBhqWl1TFDmT8YKDgzHj+QQCEE4i\\nS83W1jHmOmBhfOaHGatrPQbdTQ6/cAUu78PtOcxqqO6wtAO4BXYfmGGLq0AFxyJML8SbjBkd3EHc\\n2kU3Ja5pKauagpJrd27QqiUIw2/AZZZLX9ilrDSXb9xG+wEohfIUbdlSuhYdRNSN4fixIZM7U9qg\\nT8gK5B4PP/wQGZoFli8Wh7z59Ct4071v4Jd/7udZ7fZAWepIU4eOzkpEN22ZiYBCRnhdx9qpIeF6\\nRJQ40tQx0TPSFUniT9gaGFq5AOGovZjci8jzlrZw2FmGzGfMiwwWfcTDp9k85/GaE31cbckp8EKw\\nTcGsnHHl9nKOooFPVpW0ZYOoNLZusNowTH3mB/ukypE0lqi2eHpZ4XG+I0wDdKtxxuFsQGXdEspi\\nI/KiZVIUd1fhXWIMAsnyeGy0BO2x3h/hpKXRJYESeDJACB8lBUbCpG0pPZ/h1grLkLJASzXPMLpB\\nq5ZW6K9a84oITLjMpluH1nK5rrD4gURjMJ6ksVDkFWHPI15P8Pox8JVrua8jhF8UwS6lY2UroBP7\\nqPwqs4OLxEFBEmsaE1AaQdpJ8aQPRtGUOVEqyPKWtoSeUpw+uUY3DqABk9UkSUBTVQT4LJp9jPV5\\n+7u+k3x/AY0A4SjbCdKfYaoJLs8Zeor7NjfpEtGNfYQqyZuM8eQ2B/t7XL9+mTt7N7h48SI39/bQ\\niykP33sPLo6Y+Q2llDz5zC1+5ud/jaHK4clbsFfDPMDdDGDWAj32Pzvh2scvIvMKfXCHZ/avsneq\\nx0HqaPwJd648g68Msq2l7eFlAAAbUElEQVQRheO+jeOs9/uEvqAUEcKFYCybaci3veG1vOmtb+R2\\n1tBUjrhp6bmcsKeoq4qXqi0Ov3DI2XvX8aMIv4VT/SHf+trX8WCnw/PpnQ8/d41/c/U5XvnKNcgr\\nZF6jwxRvY4i33cF1F9RyTLIRkcRzVocDNreOE6sEiSJtNYO2xu+MWMgVMtljWjXcGQ0YhzWiifHF\\nEK+ooRfQG4yoJhn4qxzcvMTHf/nb2Sp8Ot0BTd2g6FDlUJmMxnns79ZgCpxymLYi6YQctRWf/Nzj\\neCon7VrSpmDVgmcVyilms13uzHcpmgajAD9GKaisZWVjRKBg7/r07gwEfIWb5gCBCnykBEqLb1ao\\nMkm/28OTCiMU6xspWhQcP36SkydOv+DzEpiwOJhiGkumG4Lwqy2fExtDHYOpcLLECEvQdTgEWVFh\\nw5omqvCGkG565NKyaFscLS8M9myRf+1x9vUE5V/WEIDL59gq461vPsv6esn1S09w4XOf5vBoD+Wn\\nxJ0hK50hVmhmiyNE0yJ8jS89JF3WtjeYVRYhFU76LEpL3XrMyoI4GHHugVfy+te+Dp1rFvMxuspQ\\nOmY61iyKGYtizNHuIflRQRJ1OXb8JKcfPI1IfYzySFe6RF1HU2a4pqRwC3KjOdg/5OGXvgLruujV\\niEPdsp9P+KV/8TGuffYypOvY64e0swXkCe5wwfpLdji53eV3fu+DlOM5929v0OsGqLNnubx1iqen\\nEeMvjfHLilDP2RqE+IsZoW5xKgbb0kxntBTsH9zm+ufPc3yk0EBjfWzso9uWjVBg9g4ZdNZJ8oK1\\n6Yy1EGRdsS1G/A8/9Xf+zL1461/7CV7z+tfSX1tFuILKWbKmxkjDqG9g2CI3S1AHjNSY/ooCT/DA\\nzipRDFMccwdUU5JAILp97GJCYivsPCfqrUIu8SuH3huDHbK28gCzj36KX/v57+QlnSHrtiF3JQQB\\nG/0O8/mEoGwJdc1cG2ZFTT0vIStQZcV2RzM8rBkWEYn1EdHSWSeRPuPbM4TwAEtWHTDqWZJOxKJq\\nCcMRvaDL8+ryrxTGJI4cJywEhjpuCDxB149ospowiFH+8nQn8Hghu325mhvAkCYRVjoIA7Z37v3q\\niZ7uwewA/CXpVwgQxkc4gW09PCXproZo5WNUAnGAin0EAo398m8V/75BIoSAAAtFRsf1OLGS8Ne/\\n/00Muymk6xSRj/Rn/Mq/DMGGFEWDcY4k9BGiJU5GRPGQRXWBWpV0rY9G40uJTaAtQ9709jfw7J98\\nniBZ4bmjSxzcvE1ba6KogxYaTwX4kcEsWpp6CWowhPT7I0wwp3WSpgFfGDAOP2ooZpqoDpHxlFc9\\neIJnL1+iFSm3iorHU0f++Uv8xOMfJP32MwR2FQ4ixNkhLG7ypfNf4B3f8jB7VxfsXj3i3NaISoT8\\n/s3buNjyhWee5p2nXo1pS6RpSERGGvpM5Taen1JkMLm8T2vneFKwH4dkm46wmNEgqTZ8ysUEK+cU\\nzSbHp5a+jLkYrVL7Afc8tsPxt/3tP3MvtGp47bu+j0d31vmFn34Xri3wgohguEQeE0VLzfeG4eQ3\\nb3PjNy/QDUZMDUysz06nSznZp9PxWKz0mFxfcPbYgElxsLyfyluCFT3oKEVxfY7qHqf/jr/BEx/8\\nHN/03a/hPf/0CoGrYOqh+xFiM6UwFaookN0ET2lMKQiFx0rcYU3A8YWh0ANuh5Y6adCqopYhq2kH\\nazN0G+HHHkXdICSEiQJr8XuCX/jF9/KT/+n33M1rt0CIwLD0WvUIVUyDxQY+1npY4XDWobWmKCpY\\neX72LM/Lbyf7R4iqxPcsVhtOnnyhM00BnWehVbjpS2lFQOLVyDZCpA1JN8TvSFzg01EK17ak4nmZ\\nbItHxPOnj6+QYf/i8aIIdlhSYWTriFzEnSee430ffQbbWuQoJdzsceLhUzzyDQ/yvl/+GBujY5x/\\n6hYb2xm9UZeVrVVS1aHO59hCo1W81AzLiI7X4cBMuf7ceeLA5/reHmnq4XuGstTMigyrWxyCJFUY\\nDIsjzSLpEHs+ZlZgqSi0I/QUzjQ4HWCmDQfC0lkkrHYVLYLEU5hGUQuP3XyGtz3kr//U7/D3noKX\\n/OD3Yu87S3snIlxdZ3Bsi+z2Idev5px62b2IWHPh1jWSe0ZcenbBhm7Y/eyTrJzeAGvp9gf4riFu\\najJTolQAnk8UDZC1Yzjqs7hzgQe9nEN6HGQtK94djvcOuDLp4jyPpq3Y90Le8oaHeemb3/VV8//a\\n17ya6cERTi/I3Iw/euoyTz17hze9apug9ahnFaqT4MVdCDxYzGCry6v/ysv59G9cp5s5nPY43C8I\\noy51awgKy0rqiAYpydo2VS5omyldGeJqg+ikJArQASxS3vy6kN/90OMMhsfI5/u0RpHVJWQBXpSy\\n2ioOxhVp31BpzVDGrG30MMVt9ruC231YdENUEHBmZYUjo/ErjQt9WjRECmxEoAPaQBBSstpNuTNd\\ncHF3zJnt55NoiqVuXoO1WFmhhI+zYklSNaARJL2AW0/c4Mzx5yWwBc8bW0gzRx5m1GpGKTt89bAQ\\n7EAHijkE1tBGBhVoVFTRWIGKIjzngATfl0vnYhTLvIK9+z0+X4c0/sUS7A5Di40V1+7cYDYds6Mt\\nSRBy+zDn4PaE9U6Hndjyo3/rjVSV4PxzMyptmGbw1Pkpp7Y97nvpQ3zDW97M5aef5ZmLl6lKwyhO\\nObUx4o+f3CU/yrGUBCpASUllNEiJ1A7jwXTqYU2O6kbMphmhDHBGkZcTAj9AuxhrWhZA5PWoq5xQ\\nwWRcQRigbUKCpnEd8kayWwbYZMSHn6l5ybRFijuESQpacOyBM7AY85qTA0zWIjxNb7CJazTDR08y\\n/sQfM7l+hVr1iWOFUxGVMPhhRBl1mc+nbI16XK1uoxeC2TRFBEOumhG19NCmZh427OYNEQ1FnLI/\\n9OCeAbeHmgt3Z76TbvKGVx/HyIS6qQkLhTEKIsN7P3KFb3/9WZowWwIK03Bp9CD6sOWDqwgZEUdX\\nsJnGCoHyDWVR421u0UifFXlhKZApOpiFpDq6TXfNUvUC4uAUqAT8DlRDkGP+wbvv4Q9+9wOsdze4\\nlufIxtJYxdHlq5zYXuV4EiBMgc4WHLc1W+fOkSUDrjYlzYbPpBa87dWP0k4WtG1DUdZ4MsQID2kF\\n1rOYqMaXmkj4WOfj+4pPfOoCZ9712rurERoLofRAGSTV89h0hO/TlEAkePqZL1JULzRhEoDGHo2Z\\n3KqwAoL+gMj8KYa6S+HGMVoLBDkxLY3noWxMV4SUnkQJQSIkLR4SH4OHARQNFoHkrumk+PeMCON5\\nHmujdXrdHrq1KM8n9H1c3dCJfaw1FPOM2V7G9KCmrjXDlRSBwwtCqsrQtiGev85srtk5ucnrXv8y\\nBqMuyWgpIZ3MQasQKUN0a6iaFtsCrcNKh1Bi2TKofGYzjaWDkSG59InXdqgZUtYjhF1Fl0MODlqE\\n7dLkimYWQd7DkGD8lIaWOlCIIMGlI56eD9i9NAUOoT0CWYAroZNCI1EuBSW57xse4JFHX0qMR50E\\nHKY+N4sZLQ1JrLBUlKahNYogCSnrBmE9dCTZ82t0mFAGA0TYwYqQigEyXsfVJT0ZsNJTHI6vsvOK\\n7/vy3K+c3MZVNc28RUmfjAbVSUjDis9++goi6VOoJafMkxWIDDwBkYN+BFeOCJRHG/jUnsRGDn81\\nJVOGVklWogKhDNPDBdmVPdajAuyUOKiX8+CVIHKoLbjjcG/AoxtgFgtWfYW1mqLSHOut4JTGlyWm\\nqkkCn9WkZv/qLU6eOsVo4xg7J0+ysr6Cdpbac6i2IbaSqm6pjSaNHL5qsJ7Bo0U7iXQSFRh2L9++\\nOyMRFpDyK8/wzklaXeOUpNVL/XpdGjZWRzT1C+vcDpDkiwKH5M54weLW3XbXF47xFJzG0eL7BucE\\nTirSVCDbpUlqoL0lfcgq3P/X3rnHyFWdB/x37vvOe2d39m3sXT/wq2AwxBhSaIjSUBr1ETWBkkAa\\nUUVNpSpV/gmoUtX+0VZECqFtqpC2VGqbttDSNIkaJTQEAo0LBPPwi8X22l7jfc7uzszO486d+zr9\\n415TQxFYJGS99vykqzn3O2dH3ze6395zv/ud80Xx7jUhEaCinOO2qnb+ddoviDt7EIQsOy5hpOK2\\nAvpG1lOZn0UxdDpSYXRjifnZJYy0TXPJIRQKtp5i62CW47OnsLMdlqY7CK9DpdJh/fp1uKpOy6lh\\n1iJ0HTQZoUgHL+oQCo2ObkIEGiFKEKIJm7QOoR9hhyma80tYA2mUZkjB7EO2W4RiBbtgUnMC8mkJ\\nWofABum3sF3BmN5mxTNZCR2cnEJtbpaC2c9rqTSHG6MM53Mg9PhuZrfBMyBfhHoLDAH+CmQyXLF3\\nC0/t20+PyNCvRojpRUyrQV/WYaoD2Xw/rY5Gvd6kT8tS2FTi1OwkN3ZG0UOJioM3KOj4GtRyiA0j\\nmMM5Dv5wgk/86T+//rtft2sXmXwWd3mRSPPI2QbFUgqrb5iJoyfZNJjm64+fZu+ejYwEM5DXIViB\\nQgGsIiCgkCETTRF0AoJQI+1JqkGLhiIRTpuVPUO45nrSDqRLCvVcm1wOCOehqYFuQI8F+RL4oyA0\\n/uYBg1+8+zscdxxymKxIExeBJyUrLQ9lxSPdq7Lh5m1s2noD82mb7duytC2Vouejrjg4lQZ9tQhL\\n8VnItwl9m6XZJfIDmzH1DEqkEaghvlcnb0iidIDve+i6RMVERRLnQYAQFroR0vFCdHSk9FE0hdem\\np/E855wr2QLZJFsq0V4+Q0UskhYW11+z+5wxPigBkRovtFG1FoqSRpoBQmmjGDqK9NEkSEUSh10l\\nShigKCK+fhKnh4DovEpXx4h4F6nVpa/UL++87ZfwpYFGyOlTs4ys6+P09CksxaIvnSaby7C8WKe8\\nXMZQJZlAYbHV5qOfupPjUzN86z//A00x6SvkCZyATE8O160TdTwajos0VOpzLZqeRWikcNoCgU+u\\nP4VUBPl0H1HQwLAk2y8bxqvPMFwssGXTBraM9ZEpGARmkYW5ZZ6fmOK1yTOcOrFAtelgeh4Ft8N2\\nVUduXkekSxp1FyMnKU9VCFMSW6bpnz7JHR+/jqtvuZWB928HdRp0Mw7edk5BTwmiPIRtWHR49fGT\\nzM5INEKGe03oOHQ0m7qp4hlFWpNTZDUd34goFAx2LCtEukqQFdRTFXxDwVLH6L2xh4/f8WW+eXCK\\ns499w7lRduy8DNWz8YRHqElUPS4HHnoapbES+574HtIY4tpdW7nv966nffwUu/ZcDkePwZYSGAbu\\nhKR1ZJpXyg1Oaxla5Xk6mQyLRHxsxwbMsM3BmTofLqpYSp2JVD9XpiuweQnyV7N8pE7v7jzkB6Bd\\nhEPzsMXhgc88yhefP0ZAirptsrWwDtc5Tcfu5Volh0KD6z/588hMkSdmm4RND1tTQKgQOAznRxmq\\n6yieT6UoaQsds89lMLcJ3dDQgzZ21sSLMriui5qJMNQcv33XrwPN2J+U+L17QAbXcfAiFUUotFsu\\nuZ4MU68+zwv7X+KuT3+WOFEqnlLXpo5wbGKe09UFPnbH77zxYm+1wPHw2xJfC9CUAEWVNEyHXGCj\\npgNQo7ieu3E2yn929Xu8cy3oEBmg6PiBjaGbL0gpr3knPzvvO3uy4eR+YEZK+REhxBjwMHEs8gXg\\nTimllxSL+AdgN7AM3CalnHr77wZVxNMUTdOx0jat5QZ7r7kWSzepLtUxbJ3+oSHs4wa9lsnpFw+g\\nd8APVZZmyowNX0a9UUfqBvkhG10RZPI9YFoU8fEaS3zgpi0URjYyt1ihujBHrd6i5gRksjb6rIve\\nn0EOZhDHZtiQ1xjcNIARtpj60WnImqTHtnL55q2UKwvc9IFr6LGyBK5JPdLQoohiXWE56lA5OYMs\\n9fKVLz1AdaHKzuHLCKMm9R6LA7U5dvg+//3If3H5YBlDUahKl7FxAc6OeKWl74FX4/I9Q/R7Qxxc\\naPHESycZH96I2WlSby6x/obtHDpTZp3ukVYiaLeZMQ2MQkB/apacAQsrPoPZXr7wwS/x+EJ8l8qb\\nGitOwM6rrsT3W4goItIjCppNiIuuSvyswK+W6bN1AiE5sv9Vvv5wm4/+2vU8vW+CGzcb0GiAnUNq\\nDWZlm9nREifmW+jFIl4UEvoK0ycm2By6bCsMQP047YLCojYOGQPsadAr9O7ZDT1mPAOuNyE4CYdf\\n5pOfznL/TI45p5dRW3By7iTD/RppP2D3nhIj4z9HM1vk1YlZkDZ90kavmAS6JMyskAqqKFoKzXcx\\nOhaRJrGDGiZVlDCNZVs0fIkStLDTkvryKYyBjUAFEKCYIBWIfCJqCHRk6OK5IflMCiI4c3oO29KJ\\nndwEytCc5MC+I/iqxtLsmzZVDoCoAeESum6CHtLxM6QdQY/pxf+o2sTPEApxnCQK4+dywwcUCEII\\nlWSMjfIe7VTzOWACOLuvzn3Al6WUDwshHgTuJq7rdjdQlVJuEkLcnoy77a2+8CwCAZqGhkKn7ZKy\\nIqrlBf7n2QUG+kpcNn4ZrUaDZjWg0WrRchr4BYGsuJycPMaJk4fQbJ1ibx+K1MnlMjiVND35DI3O\\nClokyGnDvFae4cziQfpklhEjIJ+RrFeLjM7WGfddljyP0Etxs5KnenKZHwSHkEYOUW/h6QqVWpXl\\nM6fZf/RVnn26QRg6RLpF1c8QRhYyiLANE6PdIBAug5uLDGwYYqXVxlAdWq7k+coctce+ycj6zUyp\\nEXbfIH5D45nvzLP7fTb9w1l8x6LhpFBNFzIwMl5kaH2BM5PzHDzeIVfoYfnUJHpBYSk0aWgmba1G\\nOy+x8inMzWMcOTzLUuDy1b/9R+5PHB1g57YSlp0H/zRpUydUFFKGRMMBVcVTDQytgUkKczCHuzBL\\nyjTYdzhC9g9yeT7N03MqG0dKBHWfQpCnprioMs+2ksFiu05LhASBSuS2qDge+b4UwVybqBbQUxRU\\nHUHPtArpMmQnIVOElAFDKrjrYGGWvkNHeepDu/iqPsDDz+yj4ArKdpUr00VSGzIspwTPvThNaNg4\\nmTjJyIqaKCmXlHAJaDEXTDOU01FFnhCdeuCSabZZV8wTdDpkegSWNLDaWV6rFTjT9Km7CjlLJ/Cb\\naHoWVBvP08mkBEFlGkHEbLlNupBChi71lQZESSq0UgVDsFUb5DnKOCL7/73NbENkxa9vFYkuAtAU\\nCGLnR9cAGcdFIiAKwNaJk37izDw0NX6FiUYUnP80/rycXQgxCvwy8CfA55O94m8G7kiG/D3wR8TO\\n/qtJG+BR4CtCCCHf5nkhjKDayBIpOrmcSadj81q7QygjqjMRMwtlcimTjGWQ7t2A0+lQq9u0lCZz\\nyzouG5COSmtFY/v2cRorddZt2Uh5dh7dslCIt+k1RRrPa9GRNqo+jJqSqLaJ1WOw1GhjDvbSDKtM\\n9KRhQDKzMIEWpJFaQM11ybkqeiVk67U38diTT7Hc0Gi0PVY6bTzHRfUiZARBO14y2dubi4M7ioLr\\npQhcQVGVHHqpTNSpESLROMjVvdsY25bl8JMHaJR/yFV7d9Kst2nWl9B6BdmeFGokMbJpSgMDhNU2\\nZqtGf6RRC1Q8X2HZSrO44LLwyhz7/nKCfCqP2/T4xouLxAV76gxd82HM0RF8V8HtRKhpaK5UiLyQ\\nOvH1owQ6TivAMEyUVB91eZzFyjLq3DJZ/QxL6QyLmweYnCozUEqzLtuD3T+MVm2RydlUzizSmF1h\\nZOsmGi1BdnAjM1Mu+vgHGR3NYS36uGoWN7yepRMVCnnIjI5AxcNTTIzCDlgZgKtqjFVq3HJ4lu+V\\nC1SrA1iODcMFqs44swt1yr6GH1p4QQoPD1NzcGsBA/kR/EBDqllcYeCHKr6iobgaHS+NplrxHvOA\\nEglKlk1+5ArCpSaHXl5m6zYLXbfQcbD0FFJR+dEzB9i1exNRAAvzy5QMlXo1T+DoVCoGCzMOvZZG\\nvz3EQmhjl8bozB2jjYNNKrnQHVquSdBRkKqKEFF8h0dBqhoEDrZUURSJ5/kErZAoUEiFNqEUtJ0O\\nnh9vzinVJrrhYqeyb+1Ub+XH5/PMLoR4FPgzIEtc+eW3gGellJuS/nXAd6WUO4UQh4krxkwnfSeA\\nPVLKpTd952eIq7wC7OT/ikxcbPQBS+84au1xsdoFa8+29VLK0jsNOp/67B8BylLKF4QQv/DT0Aze\\nWP5JCLH/fAIMa5GL1baL1S64eG07n2n8DcCvCCFuJd6/Jwf8OVAQQmhSyoA31nM7W+ttWsRJyXni\\nQF2XLl1WkXdMqpFS3iulHJVSbgBuB56QUn4CeBL4jWTYp4BvJe1vJ+ck/U+83fN6ly5dfjb8JBl0\\nXyAO1k0Sv357KJE/BPQm8s8D95zHd/31T6DHhc7FatvFahdcpLZdEEk1Xbp0ee+5IHLju3Tp8t6z\\n6s4uhLhFCHFUCDEphDifKf8FhRDi74QQ5eSV41lZUQjxfSHE8eSzJ5ELIcRfJLYeFEJcvXqavz1C\\niHVCiCeFEK8IIY4IIT6XyNe0bUIISwjxYyHEgcSuP07kY0KI5xL9HxFCGIncTM4nk/4Nq6n/T4SU\\nctUO4kTfE8A4cYrQAWD7aur0Lmy4EbgaOHyO7IvAPUn7HuC+pH0r8F3itZDXAc+ttv5vY9cQcHXS\\nzgLHgO1r3bZEv0zS1oHnEn3/Fbg9kT8IfDZp/y7wYNK+HXhktW1417av8g+/F3jsnPN7gXtX+0d5\\nF3ZseJOzHwWGkvYQcDRpfw34zbcad6EfxG9bPnQx2QakgBeBPcRJNFoif/26BB4D9iZtLRknVlv3\\nd3Os9jR+BDhzzvl0IlvrDEgpz66CmAcGkvaatDeZul5FfBdc87YJIVQhxMvE2/1+n3h2WZNxzgi8\\nUffX7Ur6VzhnI6q1xGo7+0WPjG8Ja/aVhxAiA/w78PtSyvq5fWvVNillKKXcRZwM9j5g6zv8yUXB\\najv72Wy7s5ybibeWWRBCDAEkn+VEvqbsFULoxI7+T1LKbyTii8I2AClljTg5bC9JRmjS9VYZoaz1\\njNDVdvbngc1JJNQgDoB8e5V1+mlwbhbhm7ML70oi19cBK+dMiS8okpWNDwETUsr7z+la07YJIUpC\\niELStonjEBNcChmhqx00II7iHiN+bvqD1dbnXej/L8Ac8R7E08Tr+XuBHwDHgceBYjJWAH+V2HoI\\nuGa19X8bu95PPEU/CLycHLeudduAK4CXErsOA3+YyMeBHwOTwL8BZiK3kvPJpH98tW14t0c3g65L\\nl0uE1Z7Gd+nS5WdE19m7dLlE6Dp7ly6XCF1n79LlEqHr7F26XCJ0nb1Ll0uErrN36XKJ0HX2Ll0u\\nEf4XrFxKPgIoiSYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7f06c57f56d8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"# Content image\\n\",\n    \"plt.imshow(load_img(target_image_path, target_size=(img_height, img_width)))\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"# Style image\\n\",\n    \"plt.imshow(load_img(style_reference_image_path, target_size=(img_height, img_width)))\\n\",\n    \"plt.figure()\\n\",\n    \"\\n\",\n    \"# Generate image\\n\",\n    \"plt.imshow(img)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Keep in mind that what this technique achieves is merely a form of image re-texturing, or texture transfer. It will work best with style \\n\",\n    \"reference images that are strongly textured and highly self-similar, and with content targets that don't require high levels of details in \\n\",\n    \"order to be recognizable. It would typically not be able to achieve fairly abstract feats such as \\\"transferring the style of one portrait to \\n\",\n    \"another\\\". The algorithm is closer to classical signal processing than to AI, so don't expect it to work like magic!\\n\",\n    \"\\n\",\n    \"Additionally, do note that running this style transfer algorithm is quite slow. However, the transformation operated by our setup is simple \\n\",\n    \"enough that it can be learned by a small, fast feedforward convnet as well -- as long as you have appropriate training data available. Fast \\n\",\n    \"style transfer can thus be achieved by first spending a lot of compute cycles to generate input-output training examples for a fixed style \\n\",\n    \"reference image, using the above method, and then training a simple convnet to learn this style-specific transformation. Once that is done, \\n\",\n    \"stylizing a given image is instantaneous: it's a just a forward pass of this small convnet.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Take aways\\n\",\n    \"\\n\",\n    \"* Style transfer consists in creating a new image that preserves the \\\"contents\\\" of a target image while also capturing the \\\"style\\\" of a \\n\",\n    \"reference image.\\n\",\n    \"* \\\"Content\\\" can be captured by the high-level activations of a convnet.\\n\",\n    \"* \\\"Style\\\" can be captured by the internal correlations of the activations of different layers of a convnet.\\n\",\n    \"* Hence deep learning allows style transfer to be formulated as an optimization process using a loss defined with a pre-trained convnet.\\n\",\n    \"* Starting from this basic idea, many variants and refinements are possible!\\n\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/8.4-generating-images-with-vaes.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from keras import backend as K\\n\",\n    \"K.clear_session()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Generating images\\n\",\n    \"\\n\",\n    \"This notebook contains the second code sample found in Chapter 8, Section 4 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## Variational autoencoders\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Variational autoencoders, simultaneously discovered by Kingma & Welling in December 2013, and Rezende, Mohamed & Wierstra in January 2014, \\n\",\n    \"are a kind of generative model that is especially appropriate for the task of image editing via concept vectors. They are a modern take on \\n\",\n    \"autoencoders -- a type of network that aims to \\\"encode\\\" an input to a low-dimensional latent space then \\\"decode\\\" it back -- that mixes ideas \\n\",\n    \"from deep learning with Bayesian inference.\\n\",\n    \"\\n\",\n    \"A classical image autoencoder takes an image, maps it to a latent vector space via an \\\"encoder\\\" module, then decode it back to an output \\n\",\n    \"with the same dimensions as the original image, via a \\\"decoder\\\" module. It is then trained by using as target data the _same images_ as the \\n\",\n    \"input images, meaning that the autoencoder learns to reconstruct the original inputs. By imposing various constraints on the \\\"code\\\", i.e. \\n\",\n    \"the output of the encoder, one can get the autoencoder to learn more or less interesting latent representations of the data. Most \\n\",\n    \"commonly, one would constraint the code to be very low-dimensional and sparse (i.e. mostly zeros), in which case the encoder acts as a way \\n\",\n    \"to compress the input data into fewer bits of information.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![Autoencoder](https://s3.amazonaws.com/book.keras.io/img/ch8/autoencoder.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"In practice, such classical autoencoders don't lead to particularly useful or well-structured latent spaces. They're not particularly good \\n\",\n    \"at compression, either. For these reasons, they have largely fallen out of fashion over the past years. Variational autoencoders, however, \\n\",\n    \"augment autoencoders with a little bit of statistical magic that forces them to learn continuous, highly structured latent spaces. They \\n\",\n    \"have turned out to be a very powerful tool for image generation.\\n\",\n    \"\\n\",\n    \"A VAE, instead of compressing its input image into a fixed \\\"code\\\" in the latent space, turns the image into the parameters of a statistical \\n\",\n    \"distribution: a mean and a variance. Essentially, this means that we are assuming that the input image has been generated by a statistical \\n\",\n    \"process, and that the randomness of this process should be taken into accounting during encoding and decoding. The VAE then uses the mean \\n\",\n    \"and variance parameters to randomly sample one element of the distribution, and decodes that element back to the original input. The \\n\",\n    \"stochasticity of this process improves robustness and forces the latent space to encode meaningful representations everywhere, i.e. every \\n\",\n    \"point sampled in the latent will be decoded to a valid output.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"![VAE](https://s3.amazonaws.com/book.keras.io/img/ch8/vae.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"In technical terms, here is how a variational autoencoder works. First, an encoder module turns the input samples `input_img` into two \\n\",\n    \"parameters in a latent space of representations, which we will note `z_mean` and `z_log_variance`. Then, we randomly sample a point `z` \\n\",\n    \"from the latent normal distribution that is assumed to generate the input image, via `z = z_mean + exp(z_log_variance) * epsilon`, where \\n\",\n    \"epsilon is a random tensor of small values. Finally, a decoder module will map this point in the latent space back to the original input \\n\",\n    \"image. Because `epsilon` is random, the process ensures that every point that is close to the latent location where we encoded `input_img` \\n\",\n    \"(`z-mean`) can be decoded to something similar to `input_img`, thus forcing the latent space to be continuously meaningful. Any two close \\n\",\n    \"points in the latent space will decode to highly similar images. Continuity, combined with the low dimensionality of the latent space, \\n\",\n    \"forces every direction in the latent space to encode a meaningful axis of variation of the data, making the latent space very structured \\n\",\n    \"and thus highly suitable to manipulation via concept vectors.\\n\",\n    \"\\n\",\n    \"The parameters of a VAE are trained via two loss functions: first, a reconstruction loss that forces the decoded samples to match the \\n\",\n    \"initial inputs, and a regularization loss, which helps in learning well-formed latent spaces and reducing overfitting to the training data.\\n\",\n    \"\\n\",\n    \"Let's quickly go over a Keras implementation of a VAE. Schematically, it looks like this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Encode the input into a mean and variance parameter\\n\",\n    \"z_mean, z_log_variance = encoder(input_img)\\n\",\n    \"\\n\",\n    \"# Draw a latent point using a small random epsilon\\n\",\n    \"z = z_mean + exp(z_log_variance) * epsilon\\n\",\n    \"\\n\",\n    \"# Then decode z back to an image\\n\",\n    \"reconstructed_img = decoder(z)\\n\",\n    \"\\n\",\n    \"# Instantiate a model\\n\",\n    \"model = Model(input_img, reconstructed_img)\\n\",\n    \"\\n\",\n    \"# Then train the model using 2 losses:\\n\",\n    \"# a reconstruction loss and a regularization loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here is the encoder network we will use: a very simple convnet which maps the input image `x` to two vectors, `z_mean` and `z_log_variance`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"from keras import backend as K\\n\",\n    \"from keras.models import Model\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"img_shape = (28, 28, 1)\\n\",\n    \"batch_size = 16\\n\",\n    \"latent_dim = 2  # Dimensionality of the latent space: a plane\\n\",\n    \"\\n\",\n    \"input_img = keras.Input(shape=img_shape)\\n\",\n    \"\\n\",\n    \"x = layers.Conv2D(32, 3,\\n\",\n    \"                  padding='same', activation='relu')(input_img)\\n\",\n    \"x = layers.Conv2D(64, 3,\\n\",\n    \"                  padding='same', activation='relu',\\n\",\n    \"                  strides=(2, 2))(x)\\n\",\n    \"x = layers.Conv2D(64, 3,\\n\",\n    \"                  padding='same', activation='relu')(x)\\n\",\n    \"x = layers.Conv2D(64, 3,\\n\",\n    \"                  padding='same', activation='relu')(x)\\n\",\n    \"shape_before_flattening = K.int_shape(x)\\n\",\n    \"\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"x = layers.Dense(32, activation='relu')(x)\\n\",\n    \"\\n\",\n    \"z_mean = layers.Dense(latent_dim)(x)\\n\",\n    \"z_log_var = layers.Dense(latent_dim)(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here is the code for using `z_mean` and `z_log_var`, the parameters of the statistical distribution assumed to have produced `input_img`, to \\n\",\n    \"generate a latent space point `z`. Here, we wrap some arbitrary code (built on top of Keras backend primitives) into a `Lambda` layer. In \\n\",\n    \"Keras, everything needs to be a layer, so code that isn't part of a built-in layer should be wrapped in a `Lambda` (or else, in a custom \\n\",\n    \"layer).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def sampling(args):\\n\",\n    \"    z_mean, z_log_var = args\\n\",\n    \"    epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim),\\n\",\n    \"                              mean=0., stddev=1.)\\n\",\n    \"    return z_mean + K.exp(z_log_var) * epsilon\\n\",\n    \"\\n\",\n    \"z = layers.Lambda(sampling)([z_mean, z_log_var])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"This is the decoder implementation: we reshape the vector `z` to the dimensions of an image, then we use a few convolution layers to obtain a final \\n\",\n    \"image output that has the same dimensions as the original `input_img`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# This is the input where we will feed `z`.\\n\",\n    \"decoder_input = layers.Input(K.int_shape(z)[1:])\\n\",\n    \"\\n\",\n    \"# Upsample to the correct number of units\\n\",\n    \"x = layers.Dense(np.prod(shape_before_flattening[1:]),\\n\",\n    \"                 activation='relu')(decoder_input)\\n\",\n    \"\\n\",\n    \"# Reshape into an image of the same shape as before our last `Flatten` layer\\n\",\n    \"x = layers.Reshape(shape_before_flattening[1:])(x)\\n\",\n    \"\\n\",\n    \"# We then apply then reverse operation to the initial\\n\",\n    \"# stack of convolution layers: a `Conv2DTranspose` layers\\n\",\n    \"# with corresponding parameters.\\n\",\n    \"x = layers.Conv2DTranspose(32, 3,\\n\",\n    \"                           padding='same', activation='relu',\\n\",\n    \"                           strides=(2, 2))(x)\\n\",\n    \"x = layers.Conv2D(1, 3,\\n\",\n    \"                  padding='same', activation='sigmoid')(x)\\n\",\n    \"# We end up with a feature map of the same size as the original input.\\n\",\n    \"\\n\",\n    \"# This is our decoder model.\\n\",\n    \"decoder = Model(decoder_input, x)\\n\",\n    \"\\n\",\n    \"# We then apply it to `z` to recover the decoded `z`.\\n\",\n    \"z_decoded = decoder(z)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The dual loss of a VAE doesn't fit the traditional expectation of a sample-wise function of the form `loss(input, target)`. Thus, we set up \\n\",\n    \"the loss by writing a custom layer with internally leverages the built-in `add_loss` layer method to create an arbitrary loss.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class CustomVariationalLayer(keras.layers.Layer):\\n\",\n    \"\\n\",\n    \"    def vae_loss(self, x, z_decoded):\\n\",\n    \"        x = K.flatten(x)\\n\",\n    \"        z_decoded = K.flatten(z_decoded)\\n\",\n    \"        xent_loss = keras.metrics.binary_crossentropy(x, z_decoded)\\n\",\n    \"        kl_loss = -5e-4 * K.mean(\\n\",\n    \"            1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)\\n\",\n    \"        return K.mean(xent_loss + kl_loss)\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        x = inputs[0]\\n\",\n    \"        z_decoded = inputs[1]\\n\",\n    \"        loss = self.vae_loss(x, z_decoded)\\n\",\n    \"        self.add_loss(loss, inputs=inputs)\\n\",\n    \"        # We don't use this output.\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"# We call our custom layer on the input and the decoded output,\\n\",\n    \"# to obtain the final model output.\\n\",\n    \"y = CustomVariationalLayer()([input_img, z_decoded])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Finally, we instantiate and train the model. Since the loss has been taken care of in our custom layer, we don't specify an external loss \\n\",\n    \"at compile time (`loss=None`), which in turns means that we won't pass target data during training (as you can see we only pass `x_train` \\n\",\n    \"to the model in `fit`).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:4: UserWarning: Output \\\"custom_variational_layer_1\\\" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to \\\"custom_variational_layer_1\\\" during training.\\n\",\n      \"  after removing the cwd from sys.path.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"____________________________________________________________________________________________________\\n\",\n      \"Layer (type)                     Output Shape          Param #     Connected to                     \\n\",\n      \"====================================================================================================\\n\",\n      \"input_1 (InputLayer)             (None, 28, 28, 1)     0                                            \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"conv2d_1 (Conv2D)                (None, 28, 28, 32)    320         input_1[0][0]                    \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"conv2d_2 (Conv2D)                (None, 14, 14, 64)    18496       conv2d_1[0][0]                   \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"conv2d_3 (Conv2D)                (None, 14, 14, 64)    36928       conv2d_2[0][0]                   \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"conv2d_4 (Conv2D)                (None, 14, 14, 64)    36928       conv2d_3[0][0]                   \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"flatten_1 (Flatten)              (None, 12544)         0           conv2d_4[0][0]                   \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"dense_1 (Dense)                  (None, 32)            401440      flatten_1[0][0]                  \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"dense_2 (Dense)                  (None, 2)             66          dense_1[0][0]                    \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"dense_3 (Dense)                  (None, 2)             66          dense_1[0][0]                    \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"lambda_1 (Lambda)                (None, 2)             0           dense_2[0][0]                    \\n\",\n      \"                                                                   dense_3[0][0]                    \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"model_1 (Model)                  (None, 28, 28, 1)     56385       lambda_1[0][0]                   \\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"custom_variational_layer_1 (Cust [(None, 28, 28, 1), ( 0           input_1[0][0]                    \\n\",\n      \"                                                                   model_1[1][0]                    \\n\",\n      \"====================================================================================================\\n\",\n      \"Total params: 550,629\\n\",\n      \"Trainable params: 550,629\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"____________________________________________________________________________________________________\\n\",\n      \"Train on 60000 samples, validate on 10000 samples\\n\",\n      \"Epoch 1/10\\n\",\n      \"60000/60000 [==============================] - 52s - loss: 0.2109 - val_loss: 0.1966\\n\",\n      \"Epoch 2/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1933 - val_loss: 0.1899\\n\",\n      \"Epoch 3/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1886 - val_loss: 0.1862\\n\",\n      \"Epoch 4/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1858 - val_loss: 0.1851\\n\",\n      \"Epoch 5/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1840 - val_loss: 0.1830\\n\",\n      \"Epoch 6/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1827 - val_loss: 0.1816\\n\",\n      \"Epoch 7/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1816 - val_loss: 0.1813\\n\",\n      \"Epoch 8/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1807 - val_loss: 0.1827\\n\",\n      \"Epoch 9/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1800 - val_loss: 0.1806\\n\",\n      \"Epoch 10/10\\n\",\n      \"60000/60000 [==============================] - 51s - loss: 0.1794 - val_loss: 0.1801\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<keras.callbacks.History at 0x7fe9b9870470>\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"vae = Model(input_img, y)\\n\",\n    \"vae.compile(optimizer='rmsprop', loss=None)\\n\",\n    \"vae.summary()\\n\",\n    \"\\n\",\n    \"# Train the VAE on MNIST digits\\n\",\n    \"(x_train, _), (x_test, y_test) = mnist.load_data()\\n\",\n    \"\\n\",\n    \"x_train = x_train.astype('float32') / 255.\\n\",\n    \"x_train = x_train.reshape(x_train.shape + (1,))\\n\",\n    \"x_test = x_test.astype('float32') / 255.\\n\",\n    \"x_test = x_test.reshape(x_test.shape + (1,))\\n\",\n    \"\\n\",\n    \"vae.fit(x=x_train, y=None,\\n\",\n    \"        shuffle=True,\\n\",\n    \"        epochs=10,\\n\",\n    \"        batch_size=batch_size,\\n\",\n    \"        validation_data=(x_test, None))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"Once such a model is trained -- e.g. on MNIST, in our case -- we can use the `decoder` network to turn arbitrary latent space vectors into \\n\",\n    \"images:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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gqSJLEH6MMPP0QsFnugV1Bcf6LLbrdj48aNAICDBw/i4MGDHEJJJpMI\\nhUKK0NHy8jKCwSB7wmpra+Hz+dia9/v9iMfjSCQSD6xmpPXX6XTQ6XQKq7qzsxOf+cxn2DvpdrsR\\nDodx+/ZtdjvPz8+jUCgwf9fX18Pr9bJXo1AowG63I5VKsWfpfmskVrKJHhS73Y6enh4cOnQIALB3\\n715YrVZMTEwwLWNjY4hEIrwO8XgcGzZsYA+i3W7H8vIyW3+AbNWtZs+Kc7lcLhd7Sw4fPozNmzfD\\naDRicHAQgOylmJycZE+pzWZDNpvF5s2b+YwajUZotVpeu9Va4uLraN98Ph+f9YMHD3K4G5BDxDdv\\n3lR4sCgkuWOHXJlcXV2tqEgjb0upHm7ympOXoqurC7t37+acpVQqhd7eXg6hB4NBLC8vo7Kyki3v\\nxsZGeL1elgWFQqGskAV5iUgOdXR0oLu7mz2lFosFkUiEwyMXLlxgDzJ5TMhbQWdCq9UinU4rvBSl\\n0ETra7Va0dPTgy1btgCQQzX5fB7hcJjD+VeuXMH8/Dyfm+bmZrS1tfF7bDYbCoUC5waVC51OB7fb\\nzfKwq6sLPp8P8/Pz7L0ZHR3FwMCAwiPb3t7Onvaenh4UCgVF7lkpEL3tJpOJvTJdXV1obW2FxWJh\\n/p2ensbY2BjGx+UxrxS61ul0HGJ1OBxIpVKKCE45NOl0OnR2dgIADhw4gJ6eHuj1el6HiYkJBINB\\njnaEQiEYjUbOjwTueNtpbVpaWhQpJOVEkkQ80cqSqKBQqAiQXcxzc3OKpLgbN24gGAxyWKuurg4H\\nDhzgJL14PI7Z2VksLi6uqRSVYvPkrmxoaEBLS4uCluXlZY65A2BFjoT3gQMHOMQwMzMDQHZ5Li0t\\nlUyTWD6+ZcsWbN26FXv27OFLVKPRYHl5mS+6mzdvIhaL8XP1ej16enrQ2dmpuPyuX7/OSXurKQct\\nDudYLBZs2bIFmzdvxsGDBwHIOUp6vZ4Fw+XLl3Ht2jW+MCKRCLRaLTo7Ozn3wuFwIJPJsAJz+vTp\\nVbcyEJN+zWYzNm3axIJq3759CAQCTG8sFsMnn3zC6wPIypLD4eBy5vb2drjdbg75DA0N4fLlyyUl\\nxJLL2WQyseK2c+dO7NixQxHOmZubw6lTpziMQmEvUiK9Xi8aGxvZgJiZmcHQ0BBCoVBJ7ma6uImH\\nOjs70d3drcifisViuHnzJj755BP+3svLy/zs5uZmVFdXK5ST8+fP49atWyzsVpuAShcvIF903d3d\\nHBJsaGiAJEmYm5vD6dOnAQAnTpzA9PQ0X/KkzJAiAMj8Ozo6yuexlLYTorJEoT1SkJxOJ/L5PIdQ\\nTpw4gY8//lhRRh0IBDg3hdYzGo3yBVROeTMZAmazmS87OlvEm9euXcOJEyd4nZaWlpDNZlFbW4t9\\n+/YBACfR0h5NTk4im82WpRCIBS319fUcwgJkY/Hs2bNMy8WLF7mVBPFqc3MzK7qAHF4VldtSIfJQ\\nfX09K5XpdBrBYBAnT57EhQsXAAD9/f0KGUfGoqjQDg0NIZ1OP9AoKoao/BuNRrjdbjb8vF4votEo\\nbt26xWtz7do1TExMKM6w2WxmgyGbzWJxcZH5h55RjrLtcDg4JNve3o7KykqMj4/j2rVrAIBLly6h\\nv7+fw5UAOCxOMjKTyShylii9oNQ8KlJqATnNpr29Hel0Gh999BEA+X6kJHsA/Hyz2cwFN+FwGDU1\\nNUwvKZViUcxa8EQrS7TgZrOZE14BWSOORqN8kQwODuLGjRuIxWL8mj179uDIkSP8WXNzczh58uSK\\napVSodVq4fP52Avj8/mQyWRYG5+cnERfXx/6+/s5AU+n06GmpoZj+HQIg8EgJ1EPDg4inU6vuppK\\npIcUi127duHIkSOwWq2sgASDQVy9ehVnz54FIMeUs9ksW4FNTU3YuXMnJ6cCwMjICI4dO8ZafKk5\\nA4B8sR05cgRHjhxhSzaZTGJqaoqVsHfffZeFEK1LbW0t9u/fzxddNpvF5OQk3n//fQClVVWJze8C\\ngQBefPFFfPaznwUge/IymQwrkSdPnsQ777yD6elpFtgGgwENDQ2orKwEIOenaTQa9oRdvnwZkUhk\\n1UoArU8ul4PX62X+fP755+H1elmJHxgYwPHjx/HRRx/xwc/lcooz4Pf74fV6+XMXFhZYiRG/94Po\\nAWQhQuv97LPP4vDhw+yhSKfTuHz5Mk6dOsU8lEgkkM/nmd7GxkZUVlayYUK5XuQ9WC2IJtrfyspK\\n7N27l72kPp8PkUgEJ0+exJkzZwCsTCglC9fn8zE9lAC+mlyue60R7dm2bduYHpfLhdnZWXzwwQcA\\ngLNnz+L27duKvJZYLAadTsf7Rso25V6Uk29Cn+1yufji7ezshMViwcDAAADg2LFjOH/+PBtF9P5k\\nMslrRJclGUmJRKIkeugz6XKk71hRUYHq6mpWWHp7e3Hq1ClcuXIFAFiu6HQ63muHw8H/AbLCspYo\\nAF2QgUAAgUCAFftIJILe3l6cO3cOw8PDAGRliAoQiK6Kigp+DymQa+0TRvk1dLYMBgOi0SguXrzI\\nihtV1opecY1Gwwabx+NZUTldLi0Gg4EjG1arFclkEkNDQ1x01N/fj3A4zIaI2HuPZKLT6YTL5VL0\\nrHpQsUsxtFotbDYb82VlZSUSiQQmJib4nE9NTWFmZoYN7Ww2y1W5ooykhH5ANtjFaMdaey6pOUsq\\nVKhQoUKFChX3wVPhWVpaWsLMzAzH1sfGxjA/P4+LFy8CkEMQ5H4mzZcsCtJyL168iIGBgZIz9YtB\\nblDyJHm9XiwsLLD2Tc+hfiWAXJHR2tqK5557DoDsFUin0zh58iR+97vfASjNHS96KahaApA9bvl8\\nHnNzc7h16xYA4Pjx4+jt7eXQQDKZhE6nY4uiq6sLO3bsQKFQYIvvpz/9KU6cOFFSV2p6DVUAbtiw\\nAY2NjTCZTLwOo6Oj+N3vfseVE2LfEkC2SKniiaz/paUlfPTRR3j33XcBoCRPjlht1tXVhc7OTvZy\\nZTIZjI6O4te//jUA2StAZaZk1VVUVGD37t3YvXs3f2Y8Hmcr8Pjx46umR6SLQoKU00GWGe3Zm2++\\niQsXLmBhYYHXzmAwIBAIcF7T1q1bodVqef0uX76Mc+fOIRaLlWw9GY1Grhzr6upShBovXbqEo0eP\\n4vz584pKTTFf5siRI+js7GSPaTwex8jICGZmZsoKeVPOQXt7OzZs2MBe3FgshpMnT+LUqVOcs0Rn\\nht7T1taGw4cPs7cVkHkoFArxGS3HHa/X69Hc3MyhWJEeyouYnp5W8J3FYsHGjRvx7LPPcqggl8th\\nfn4eU1NTAJSd0EuBRqOB1+vlnDubzYZoNMqe6ps3byIcDiuqME0mEzZt2oQ9e/YAkFMVqDwckMNl\\n5dBC3iiSvTabDUajkWXOjRs3MDw8zF4Byv+y2WwcYt2yZQv8fj/zWCQSWbWn/W70kNeF2j3QnszP\\nz2N4eFhxZ1ArF5Jdzc3NCr5bWFiARqMpO4wjekwlSeLnpFIpTE1NYWpqikOOqVSKq5EBWXbV1tay\\nZ8lms8Fut8PpdCr2tlRayDNMHiKbzYalpSWEw2G+Z6l7t9izjyrTSY5SLijRd/XqVe59Jj7vfsjn\\n8yv68eXzeU6tAWTeFFsBUT8lg8HAMpLC67T3kiQp8jEpJFqud+mJVpYIVOpOl8nk5CTGxsb4kNOh\\nstlsHI//2te+xomFAHD06NGycoLuhng8zptI8W4Kuc3OzrKLlGL4DQ0NePXVVzmBzWg0YmxsDG++\\n+SbTl8lkVt37hUDKkpg8vLCwgAsXLuC9994DIAuqaDTKIQgqpaV1+uIXv4iKigokEgkWtP39/Vha\\nWiorPk8u0IaGBlRXV0On0/HF9sYbb+DMmTMcxioOLW3ZsgVf/OIX0dDQwPTevn0bg4ODLHhLYXYS\\nONXV1ejs7ERtbS0fyLGxMbz//vscGhB789B36OzsZHoAWZCNjIywm3p+fp5nWK0WOp2O+76IZf9T\\nU1NMy+zsLCRJQl1dHV9A1Bfq85//PABZ4U6lUry2t2/f5tLeUsIXWq1W0d+msrISFouFcwNGRkYQ\\ni8Xg8XgU/WAcDgfa2toAyIn7FB4AwOE3SZIUBRarAeUvALLiTcUHgLxHMzMzSKfTLOQp3EMXW1tb\\nG/bt2wen08mK2tTUlKIxKzVqLQXUAsDhcHBe5OLiIhYXF5nPfD4ffD4f/93n86GjowPbt2/n8FIi\\nkcDIyAjTotfryyqPp0uLFDer1cr9rwBZUfN4PMybDocDFRUVaG1t5dwQu92OxcVFLkM3Go1cDFJq\\nU0pJkpiHaA3o0s3lcrDb7ZwbRTwXCAQ4wbupqQlWq5UNtGAwyJfdasLKBMrnEnslic1nKedU7GFG\\nrR2Ih8jQFnM4JyYmFC0qSglXij2fKCmZQM1xSQaS8Uv0U3NV+rter0c0GmV6AHCBSSkpHNR0U2yp\\nQkUn4vmjESgAuBG0w+Hgc038QgqLaBzT8x5El16vR2VlJX/HaDTKIWI6N0tLS4oeZ1Qctby8rAgt\\n0tkD5IKcqqoqTkmh8Gy54dQnWlkSteCFhQVO6gKgiNmKCXRkeTc2NiKVSuG///u/AQBvv/32mmbo\\niLSIyhFVeIjdrgnEULt378YLL7zAh29+fh4/+MEPcPLkSUUMtlQQLdSZmBKmJycn2dolpUf0/Gzd\\nuhXf/OY3AcgMlU6ncfr0afzyl78EIFeHxOPxkplKTOIlKySTybByND4+jlgspugI7ff72avxR3/0\\nR6xQ0nc6evQoent7ee+LOzbfD/Qat9uNhoYGOJ1OxRyvXC7HgqGhoYFzFaiq65VXXlF4TBYWFnD8\\n+HFW2ikptFRrpaKiAvX19Yr8jGw2yz83NDSgoaEBFouF6XM4HIrqKyoOoHyZW7duKYZLit//QXA4\\nHKyQUEIkKT4OhwN1dXWor6/n5HJJkiBJEld41dXVQavVcpLn22+/jcHBQUVH/VKEudj5Xuw2n81m\\n4fF4FMnkVBVIPLV582bU1tZCq9UyD7311lvo7+9XVJuWerlQfgT1diFQfg4g81llZSXnpLhcLmzf\\nvh01NTX8HaampnDy5ElWRsWhzEBpTSDF6iwqGCELPx6Pw+v18uVCdO3Zs4fp1Wg0mJmZYYU7nU6z\\nsl2OoUTfUafTwWg08llrbGxENptVeDH8fj/8fj/zEOUDknd7bm6OlR6xGreUcw/c6ccjer06OjpQ\\nKBRYptjtdvh8PlYKNm3axP8PgOewFfftW03RAr0OkJXGaDTKMp8aF7e3tyvW2+v18qUvSRK2b9/O\\nilyhUMDi4iKSyaRCoSo1F5fuDjJC8/k8fD4f2tvb+Xek2JEXieRnZ2enYn3ERHG73a7IyVttPzPx\\n/stkMpAkCT09PZyPNDw8jEgkoliXxsZGRCIR3hMqdhCbIFdXV3MyvJi3WA6eaGWJkM/nkUwmFRpr\\nMSRJwsGDB/GlL30JgMxAU1NTeO211wDcKX9fa/kgMZlIS7H1Qy7vZ599FgDwne98Bw6Hgy+gn/3s\\nZ3jvvfeQSCQUXW3LoSuRSDAzBINBVnCKxycQwz/33HP4zne+w+5vnU6H/v5+/M///A+Hl8j9Wg49\\ntC5TU1McJiXLlbrXEo12ux07duzA5z73OQByWazJZEIoFOLw5Mcff4yxsbE1hVCoIGBxcZET2wG5\\ngy4JoXA4DEmSuEs0IAtNs9nMz75+/ToGBwcVCnKp9Gi1WlRUVCCTySgS210uF4dpd+zYoSiNBeRw\\nb0tLCwuGdDqN/v5+rvAKhUJlDbGk8J6oRGazWb7wDx06hD179iASibAXA5AvZ/K46XQ6ZDIZTpgN\\nBoNl8xCV59NaieEPv9/PjRfpwqmqqmJFCZAvIPLW0D7Nz8+XHUIRPXsUPiKB6/V6sWvXLg75RaNR\\n1NTUKDogV1VVwWAw8HuGhoYwOjrKPEUKRinrRF4BUZnO5XJwu93YtWsXAFmBJY8gIJ+9QqGAyspK\\nvmSz2axiICud3VJkJFUqV1RU8EVGoyno2Tt37kRNTQ0blHa7HQ6HA/l8nvmMmmbSviYSCfYMlqK4\\nkZeLlGmDwcBd0wFZKdu1axd8Ph97scxmMyoqKngv/H4/LBYL88zdkpdXG/oSO1NTQQadNY1GA7/f\\nj61btyoMp2Jvjs/nYwOCyuypFQcABU+uFlQBWzyYNhAIsMFK4VSS1z6fD06nk3mP6DWbzYqxNaV6\\ntylJm/ZOoxPAAAAgAElEQVSDjCRqbgrI+ya2pyBlMZvNKiofRYWWWr3Q2sZisTU1plQTvFWoUKFC\\nhQoVKu6Dp8KzRLiXZaHRaNDS0oK//Mu/ZC0yk8ngxz/+MXtdSi2JfRAdxWW8Ii06nQ7bt2/HX/3V\\nXwG40wiNEpt/9KMfYXFxcUUiXKn00XvIYotEIsjn84qS3EJBHuJJSZ3f/va30d3dzZZWOBzGf/7n\\nf+L06dPs8qSchXLooecODQ1hYmICZrOZvVxUwk2eg+7ubjz//PPsyZEkCdlsFufOncPRo0cByGX0\\nsViMLadSPHD0OvIyLC8vc54QDTIWLU6bzYaWlhbOxaGBtsRDv/71r3H+/HnOxaCmeaWsUz6fZy8X\\nlcXW19cjn89zyIRmxSUSCbZ2a2pqFPlpoVAI7733Hq5fvw5A3vt4PF5yUixZ87QOly9fRk1NDVtw\\nlEwqtpZIp9OoqKhQeKMikQi3Frh58yZCoRCWlpZWjIl4EMTwiEaj4ZELtC7UhE5sLZFIJBRDc4ke\\nalExODiIpaUl9pyUw0O0F+Pj42zl09w6Cn0FAgHk83m2kMnjVSgU+AzcvHkTY2Nj/HM5Scx0zsTQ\\nJ+XmkMXf0NCg8IBTbqTo7UulUujv71fkftLalLo+4ufOzMzA6XQyD1mtVvZ0AfL6Ly8vY3FxkdeO\\nPoPawczNza2gZbUhuEKhwN87lUphbGxM0XaG8gEp75Q8kXSuXS6XwnM0OTm5It+1lL5h9Lp0Oq1o\\neUNzMs1mM3v6yZNKMj2ZTCrmhWo0GiQSCSSTyRUNO1cbfgPkPYjH41yo1Nvbiz179sBoNLJ8pn5w\\nxQ0wl5aW+NlWq5XbodDnl3p3EC3U5qK3txft7e3YtGkT0+JyuRQhzXQ6rWhQCch5qB6Ph/eOetOJ\\ncw5LbWsg4qlSloohVp189atfxebNm3kxz5w5g9dff13Rz+Rh4X6fpdVqEQgE8N3vfpc7wGq1WoyO\\njuJf//VfAcjCpNzmb8UgRhNpE12NRqMRVVVVeP755wFgRWfUN998E++++66i8qocRQmQ94MEzoUL\\nF1hpIzcpucjFad7t7e0sVHO5HG7duoWf//zn6OvrAwC+5EoRmAS6ZMfGxrCwsAC3283PosuW3NBW\\nqxV+vx/btm3jC0ej0WBubg4///nPAcgVc/Pz85wwWE5HX71ej0gkggsXLnCPJ6vVCq/Xy5ewOHmc\\nQrlms1nRhZoUJbH7OV12pUCj0SCbzbKgKhQKGBgY4PwSalqYTCY5l6GjowOvvPIK72s8HucmlICc\\n2zU/P19W3htwR6Ank0nMzMwwbeKgZ9q3YDCImpoavPjii0xvMpnEpUuXcPXqVQB3ErFLyaMgiIoF\\nNSekfEWfz6c4K9QFnpTeHTt28CxGCkV/+umniEajfP5KaZAp0pRIJBAMBnHp0iUAcohY7G2UTqeR\\nTqdZmSIlc/v27Xx5hEIhXLhwgc8s5ZqUSk82m0UsFuMcsZMnT+L8+fOcQ2IwGLC8vMwXLF1y2WyW\\nZ7aRkkmpAOPj41heXi5rfUSZODk5qejx1NzcDJPJhFgsxiE/ykei91RWViqS3G/evInZ2VnFdyiF\\nJjoD6XQa4XCY92xkZAS1tbWKprA6nY67VQPy2erp6eEmsVarlZtAroWHcrkcEokEn+nr169jfn6e\\nlRN6TSqVYiN6eXmZE62pEpbC0+JA5FLldaEgTyYg4zGbzSKVSmF+fp6NCmqiScrd3NwcFhYWkMlk\\n+DXt7e1oaWlhue/1eqHVahWNcdcShnuqlSWyhnt6evDHf/zHMBgMLBz+5m/+BhMTEw9FIVkNaINs\\nNhteeOEFPPvss4pBo9///vdx/PhxAKvriL0a3C23QEywBu4kdB8+fBgAOOn69ddfBwD84z/+I2Zn\\nZx+K8qbRaNh6FDu5ipU/opW9ZcsWhRV3/fp1/PVf/zUnmAPll1YDd9ZifHxcUZkF3Kn8odc4nU58\\n7WtfU+QFRaNR/Mu//At+8YtfAABfuGs5cJlMBn19fZiZmWHPA5W0kiJHTem2b9/OFqfFYkE2m+UW\\nCv/+7/+OkZERxZDLctYpm81iYGCArVfKHaHPMhgM3FmdlNyOjg64XC7m+du3b+MnP/kJd/imsu9y\\n1omakALyHk1MTLB3KpvNcsWnmOv33HPP4ctf/jIAmcfm5ubw5ptv4sSJEwDkfSuXHnrPwsICBgYG\\nFAOc6eKkPaDu9JSEv337dq4OOnbsGABZWaL1AcrLwcvlcohEIhgbG+NzQgnexVY1KSY2mw2HDx9W\\nVAv19/fjzJkz/JpyLl2ihyq0AFnpkiSJFRStVqvIF6TpC62trYozOTU1xd5JmrRQDj3ZbJaNCkoe\\nJmXk5s2byOVyWFxcZLm0sLCAQqHAisLLL7+MfD7P+3rt2jX2ppRLDyAriWIys8lkwq1btxSdzM1m\\nM0+aAOQzEAgEFGd7enqaFUmgPOM2l8uxkgjIezY6Ogq9Xq9oAUINMAGwkl9VVYVXX30VgFx9mslk\\nWKEiA6kUegqFAtLpNHvwqct9b2+vIkKSSCTYUKUxU+KzYrEYvvSlL3EeHLXFINm2lgIvQM1ZUqFC\\nhQoVKlSouC+eWs8SlXkDwHe/+13uO0MhkytXrqzJA1AKNBoNt8Z/6aWX8L3vfY+bxAGyF+Ctt95S\\nDKl8GJ6lu7k6yUtBMfr9+/fjW9/6lmLA5vHjx/GDH/wAgBwSXOuASIK43vR5YozYbDbD4XBwtQXN\\n1CNPwt/93d/h4sWLirYQa1knsUKEqkfE8maTycTWpcPhwPbt22E2m9liO3HiBN566y22msq1vEXQ\\nWlNpNABFzxLgzlDcuro6RU5HKBTi4cY02kSMx5eDfD7PvX+AO0OHxd5d2WyWq4oA2SNI5cqAvE5n\\nzpxR5HKtpYEfnRMq96d1oRw/sQLGbDbD7XYrwgfT09P45JNP+Pw9jH1Lp9O4ffs2xsfHOeRAJfa0\\nBzqdDlqtliux2tvbYTAYsLi4yJ4lCuWsha/JEp+YmOAWBFQZRCgeXOxwONDY2KjoL/Tee+8hEomU\\nPAbmbvSkUimmhSpLRXlQ/P96vR5dXV3cpkOj0aC3t5c9QmuRSSIPkZdGzK+7W6k6cCc6UFlZCZ1O\\nx54l6uVTrtwWw7RTU1PsyRM97qK8E0vcKYwrhumof53YzqZUUF4U5atROE48b9T/i9aH+F0ce6LT\\n6bj1Ar2mHE+X6MmjUWFiziZ59cQcXxrPQ3vb39+PTCbDHvuamhpF5GKtd+5TqSxRaf7+/fsBAJ/5\\nzGdQKBRw5coV/Nd//RcAmTEfZp7S/WA2m7nE8c/+7M/Q3NyMTCaDDz/8EICc0L20tPRIQoKFQgEm\\nk4kv2VdeeQW7du1iphsZGcE//dM/sct8LWGuB9FB/9LB0mq16O7uxp//+Z8DkHvUJJNJ/PCHPwQg\\ntwmIx+MPNRmfICY2El2pVIoLAv7gD/6A14nK8f/5n/8Zo6Ojaxq8fDfQ9yOastkstFotP0eSJGzc\\nuBHf+MY3OGySyWTwy1/+khPfo9HoQ+On4iaEYp8dUjBtNhvPRNu4cSM0Gg3z0M9+9jPMzc2tuTs+\\ngfao2NgppgmQw0tf/epX+TJJpVJ4/fXXV7SbWGsoVwyVUBi5OFmUOptT3zAKn/T19fHMNpqrt1Ze\\noj2jS/VuNIn/bzAYsGPHDuh0Os5z6+vrQzweX7PCTe8V903sjXS3pFqaIkD8ncvluKEv/bxWeuhz\\n4vH4XWkQL1Gx0zP1fKIct3g8XnYITgTte3GZf/GeiUpBOp2G3W5X9PaiVh0Pgx5ab7G1TjE9xf0M\\nU6mUgh6tVqswKMsBGQDAnTCtmJBNP9NzRZ6lvY7H49xcE7iTP0Xv+V+lLIkb5PF48Kd/+qcAZO07\\nHA7j5z//OVvI6+1VEkdp1NbWcq+grq4uaDQaXLx4EX//938PQM6ZeRReLsp7CQQC+MpXvgIAePHF\\nF6HX67nx3Pe//32cP3/+oSsA9wI1pwTkER3f+973FBVpp0+fxk9/+lMA5fUtKhWigDSZTHj55ZcB\\nAJ///OdhtVqRSCRYebt8+fJDUwAeBPHQu1wufOUrX8HGjRv570NDQ3jttdfY8l7LMOjV0kP/6nQ6\\n9PT04C/+4i8A3OnS+6Mf/QjAneHMxe9dL5qAO2N1vvGNb6Cjo0NhDBw9epSVkkdFD3CnuOOLX/wi\\ngDtegQ8++IAt94dV2LFamkhmtrW1cYPV4ukD6y0D7vb5RqMRhw4dYi9dPB5Hf38/5zw+7DW6Gw3F\\nHnkxaVkspihlePda6SgG5QqKFz51618PelbDC7lcjveNPE3ifj0MQ+B+zyaQAiUanbOzs+jo6AAg\\nr92GDRvw+9///qHQ9dQoS2JzL71ej2eeeYYTKVOpFK5evYpPPvnkkYXeiHlramqwf/9+rlqipMo3\\n3nhD0bbgUUCv18Pv9+Ozn/0sXnrpJQCyMieGb3p7e1cw41obdd4L1KCMKqu+8IUvoL29nQ/67Ows\\nfvOb3yhKY9eTHhFarRbNzc2sVLpcLmSzWYyMjHCLh0elKAFQuJN3796N/fv3Q6vVsnX7zjvvYHZ2\\n9qEKpdVAo9HAYrHgmWee4X3M5/MYHh7mBOpS2wM8DJqoMm7fvn3Q6/VsGb/zzjscWn7U0Ov1aG1t\\nZWGdz+cxMTGBjz/+eF2qcldLEwA8++yzPEfr5MmTAMDl8I+aJkAeU9PU1MRK9uzsLIaHhx8LLYDs\\nzW1vbwcAruIdGxvjv6+l5LwciO0yaFYaAG5xQH9/lCBPj1hIIHpcgbXPXyuVHhEajQbXrl3D3r17\\nAcjnj1ob0N/VBG8VKlSoUKFChYp1wlPjWRKHI3Z0dOAP//AP2bqMx+P48MMPMTY2tqJBJOFharpa\\nrZZdtp/97Gfx9a9/nectJRIJnDlzBu+///6KkSPrRQ95ubq6unDkyBF87Wtf45ylZDKJY8eO4Ve/\\n+hUA2YIrtgTWCzRKg8ISL730EiRJ4lyXN998E2+//bYi8X29LTiyyOx2Oz73uc+hpaUFgGyBz8zM\\n4B/+4R94UO6j9E7QcFEAOHLkCDc4pBl/P/3pT1d44B4VXdXV1XjhhRc49BWPx/GTn/yEx5sU5zQ8\\nitAuhSh7enqg0+k4/P7b3/5W0XfsUWPTpk3sgaO8xZGREYUMeFTeE9FbuXnzZmg0GkQiEeZvyo15\\nVPtG0Gg0PLCV5HU4HEYsFmNa1jLwtBxotVrFzMpUKsVtaKgB6OPwVtJ6iE15xeHNjwOpVIoTw3t6\\nepDJZFbMuXyUIL6lghex+Wk0GuW8OBp3Ui6fPxXKEh1oSuI8dOgQtm/fzoswNzeHTz75ZEVi6nqF\\nlsSOq1/5ylfQ0tLCBykajeL06dN80Og96wWtVsvrUFdXh1dffRWBQEAxcf3YsWPcSK+4Sma9QN+5\\npqaGO2JTcunHH38MQB64KnbGfVThN0CuDmppaWEFZHl5Gb/4xS9w8uTJ+84gXA9Q13eqDHK73SgU\\nChgZGcHf/u3fApB7GYlCaL3XivaPBlZWVFRwddlvf/tbvPHGG6yUPMrQCfE7NemjysL/+I//ACDn\\n4dwtgXY9aSSeslgs8Hg8XNUzMTGB119/XdGb6VHQI9JFCu7k5CSmp6fx0UcfcT+scmavPQyQHLh1\\n6xafvzfeeIPzukSaHgUomZs6a9O/1CBTq9VyUvWjAimRqVQK4+PjfJ+EQqHHYjSJKBQKbMRt2LAB\\nyWRS0XX/cSGXy+HDDz/kaRVmsxmTk5MPLZ/yqVCWKImLEoVbW1sV0+yPHTvG3goxlrseGi7l4ZAn\\niRQ4Kn8/ffo0zp8/f9dqkPVI7NRoNAgEAgCgsP6pA/bvf/979Pb28gEr9gKsV7IplZT6/X72cg0P\\nD+PChQuc6zI4OHjX6on1UnLFxo9tbW0YHx9nARgOh/Gb3/xmhfK2XvSIIAWArMX+/n5UVVXhV7/6\\nFQslKjd/VHlKdI60Wi3C4TCuXbvG1uRrr72GUCi0oopqvZVeURBT9c3JkycxMjKCN998E4BsPRYn\\ndq+3AKfPp0IK6kT8+9//Hn19fYpWAWKl03pDlIXj4+P47W9/i7Nnz3JH9PVONi+G6DUCgKNHj3Kj\\nU8rrWu+k/HvRpdVq2VP6+uuvw2AwsLeSSugfpVeQkEql8M477yi614+Pj68oo3+UiMViXOlNg3TF\\n1gOPGvT9qVM+Ne41mUyYnp5mY2Wt+oCas6RChQoVKlSoUHEfaB5X9YGCCI3mgURotVrWrj/3uc9h\\n3759PG/p17/+NSYmJhRlhOtphRuNRuzevRsAcODAATQ0NHDlxPnz53nYarHFvZ70AHKvoKamJrhc\\nLm4VcOnSJQwODnIIpZxS0XJhsVhQUVHBfWcsFgsmJiYwNDQEQB5pUNxzZr1DS5TD4XK54PP5uJko\\nzSArno31qDw54nyo2tpaWK1WjI2NKayiR1m5RFarTqeDy+VCQ0MD0xIMBle0eXiUdEmSxLP9nE4n\\nlpaWuCHio2qJIUKsXHI6ndyUcnR0lPurrbcMuBvEth01NTXI5XKIxWLslVuvHmv3guhZkiQJLpeL\\nc10mJiYeWjPacumi81dTU4N0Os2tA5aWlh5LvhJwJ5eR5BRwJxT3KL2CIkS+ovlrFCZMJpOPjS4A\\nikHAOp2OZ1Q+AL2FQmHHg1701ChLIkg4PQ4BRBBzo0T37OOkSZzHRnic9AArQyB3W6vHQQ8lk98t\\n6f5xn4m7Jd0+Tprutk5PwhqJeNz0EJ4GulSa7o8ncQ+f1LX6/wlWpSw9FTlLxXicmivhSbjEivEk\\nrEsxHqUnazUoViQfNz13w5NG15NGD/D4+eheUOlaPZ5EmoAnk64nkab/bVBzllSoUKFChQoVKu4D\\nVVlSoUKFChUqVKi4D1RlSYUKFSpUqFCh4j5QlSUVKlSoUKFChYr74KlM8Fbx8HG3Rnnr3bjyfqCK\\nR7FBIuFRdtIVodfrodfruf0AtapIJBKPPAGT2iAQLXq9HplMBplMZkWzyIf5zLt9LlXL0dBWmkKe\\nyWTWrcngvaqDiBZqx0DI5/PrXpZ+r2aT4uBRjUazopXAejbwLN6z4vMktlsRf7ce9NxPxhTLmvVq\\nJyLScL/9ErGerR+K1+VujS91Op3id+vVbuVue1RcvUznXGxu/LDXRzzD4u+KG3EWCgVotVqWgZlM\\nhl8nvuZhQfUsqVChQoUKFSpU3AdPlWfpXpYAaZjFeBQekfuNLii1X8fD0srv9/t7tewv1uLJOwBA\\nMa7hYdFDf9NqtSv2SWweqdfrIUkS7HY7ALlx3NjYGOLxeFk0FVuyIsgDUEwP8ZbNZoPNZuPxLX6/\\nH7lcDufOnePRO+Xw3N2sKEC2KOnZ5C2ivxkMBvh8Pp4n53K5EAqFMD09zU0HaZDzWugR/588RxqN\\nRjGQUqfTwWQy8QigyspKzM7OYmZmhsfsrGW0RjEtoseIaBI/W6/X85w2+tvc3NxDoUWkh/hFXBe9\\nXq8YbQLIzSqpcaxWq0UikeC9yWazaxruKfKNyC/i+SFayDNAsyQ1Gg2Wl5d5FiLRsVY5RPskfmeD\\nwcDrodPp2DNB9FksFuRyOW4EmU6nH5pnQKvVKhqH0jqJPJDL5ZgWSZIgSRKPYlpcXGQv6VpHjBTL\\nNrPZrPisXC6HfD7PtBkMBpjNZn5uNpvF4uLiQ2koKvKuyWTiYcL0ucSbJHvy+Tz0ej2sVivTQ4N9\\nH8b8NVoXq9UKrVaLQqGgWKtkMsl7QvxB6wPIfJXP53lOHb3mYeGpUZaKmcxisXAXUYvFAofDAUmS\\nWECaTCZoNBqegzQ8PIyFhYWHMn9IvEB0Op2ioymFasxmM0+Rdzgc0Ov1/HMqlcLMzAxP2gbAQmut\\nCoBWq1WEinQ6HTQaDU/U9nq9sFqtTK/H4+FJzSTAk8kkFhYWeN5PPp8vea6OOFtMDM+IF53ZbIbL\\n5WLa7HY7nE6nImyRy+VgMpn4oovFYgiHwyWHvkR6iBb6j35vtVrhdDqZHqKNhD4JCZoqv7i4iHA4\\nDIPBUPK8L5GHiveM1slms/HsLOJxeo3JZILf7+f11Gg0uHz5MoLBYFkz2u7GQ4B8cVC4jxRWt9sN\\ni8XC/GwwGJgeQOYf2jO6iMuZy0S0iAqi2WyG1WrldXA4HAoecjgcrLzR+6amphT8EovFVn0RF++r\\nePHSmthsNgAy/zocDlZGLBYLTCYTryUATE9PY3BwUDHEtjisslqaxJAa8Q91evZ6vXA4HLxntEfE\\ny4Asc65fv87dz6krdKlKSrEBotPpYDQaFTInEAjw3Eqag2g0GnkfE4kEgsEgbt26BUCe/UdGWqny\\nWtwzrVaroMVms8Hv96OiooLXwmAwsFFGmJ+f53mfmUyGhyOXE/4SDXmdTqdYB7fbjUAgwPtGCpko\\nM9PpNN9jMzMzSKfTig7e5dxnNByYOqhTJ26Hw4GKigqmL5FIsGIrSRKvBU2EiEQiSKVS3CW7XMVf\\nHAi/adMmuN1ujI2NKYbEazQa7hSeTqchSRJmZ2d5b7RaLdLp9IoUjoflNHmilSWR6Q0GAwslt9uN\\n5uZm1NfXA5Db0zc1NcFut/Pims1mJBIJtrL7+/vx8ccf49NPP1VsbDk0idat0WiE1WplJS0QCKCu\\nrg4tLS3sgSChKlqg6XQat2/f5qF/V65cQTgcLjkfRxSYZMGZTCY+BE6nE7W1tWhtbQUANDY2AgCv\\nJXAnFk2/m5iYwJUrV3Dq1CkA4FEuq6VHpEuv17OgslgssNvtPPh348aNPFoAkJUREpBkLVRXVyuG\\nWvb19WFwcHBVykmxt0an0ymUAPHCb2hoQEdHB9xuNyuNFosFqVSKharX64XX6+XDNzg4iJmZmdW0\\n019Br0gLWXS0/hUVFWhtbUVTUxPzczqdhslkYv5wOBxoaGjgz7hy5QoA+cJbDV8XexqJn/V6vUJw\\nud1u1NXVobm5GXV1dQBkfs5kMrxHhUIBHo+H/x4OhxEMBpFOp0vmZ7IoaY0MBgPzt8PhQH19PVpb\\nW1FbWwsA8Pl8CgFtsVig1+vhcrn4dxcvXsTAwADTUorwFD0JZBTRmrvdbnR0dPDZqqurg8PhYH4g\\nLw4pcIBstGWzWTZEyEAqxWNBe0Z8A8jKc2VlJbq7uwEATU1N8Pv9zFORSIQ9tHQxz83NwePxsAwS\\nL+BSPSiiTCSjg/aora0NjY2NCsV/aWmJPbWAzFPXrl3j5124cIG9OWtR/kk5IVnc1NSE2tpa1NbW\\nsqJmNpuRSqVYhuv1esTjcZw+fRqArFzH43EFn5Vr1BqNRn7Oli1bUFNTA5fLxXuSSqUUXn2Px4N8\\nPs+jhk6cOIGlpaUVMqccRZIMMgDYvHkzGhsbYbfb+TWSJGF+fn6FgZvNZvksnT59GsvLyywz6X4s\\ndX00Gg3L4sbGRmzcuBFdXV1sZJG8JHrpzDQ2NrIhbbVacf36df7MaDS6IodzLZ4mNWdJhQoVKlSo\\nUKHiPniiPUsEcn2Ttu1wONDa2ooXX3wRgJw7YjKZYDAYFDHNyspKtvpaWlowOjqKCxculBwyKUZx\\nCMVisWDDhg0AgEOHDqGrqwsOh4M9EolEArFYTOE5kCQJlZWVuHz5MgDg9u3bbG2WQw/9S6EK8rrt\\n378f27Zt49CRJElYWFhgt65Wq0V9fT1b5ICccxIKhdZUdSZ64MSQ365du7B161YAsjdHkiT2XM3N\\nzSGXy6GiooKHITY2NsJgMLCHkOLWD7IQir1K5J0gK0mSJDidTuzYIY8E2rlzJ+rr65FOpzE8PAwA\\n/EyyBOvr6+HxePj34XAY09PTq8rpuhc9RJMkSexx27VrF7Zv3w632825UOPj44hEIuzN8Xq9qK2t\\n5c/95JNPcPv2bQ57PQjFeUCi69poNDKvdnR0YOfOndi0aRO/Z3p6GvPz8+z+pnAC5SzZ7XbMzc1h\\ncXFRUTXzIFDITTxbWq2Wv3NlZSW2bduG3bt3Mz9HIhFMTk6ypZ1IJODxeBAIBNhzMDs7i0QigWQy\\nCeCOVbqaPRPXicK2FEapr6/nvQJky3Z2dpbzbugZDoeDeSiVSsHtdrO1S96KUjyltF8Gg4FljNPp\\nRHt7O3bu3AlA9tpqNBpMTU0BkL1GFJIiWjweD0ZGRvhMkOW9WnpEWsQcJUmS4PP50NbWBkA+W9XV\\n1cybwWAQ0WiUBw8DMj9LkoQbN27wZ68lTUI851arlb1cTU1N6OzshMVi4X2an59HNpvlkGV1dTVM\\nJhPz1JUrV3hQe/FzSqGRzjl5kZqbm9HU1ISKigoOawWDQczPzyvyd+rr6zk3cXZ2lr0nd6Pnbr8X\\nIXptxVxQm82Guro6uFwuvgfE8Cwg86pWq0Vra6vibqOB0fSacjxLBoOBh2PTgGWz2cyfazKZMD8/\\nr8jBSyQSMJvNPKjdbDZDkiSWOblcDsvLy4ocrLXgiVaWxERJSi4D5NCX2+3mUJNOp0M4HEYul2Ph\\nYDab0d7ezodxeXkZS0tLSKfTa4phiiWLgHxRBAIBdn/v3r0bTqcT8Xicmayvrw9zc3NoaWkBAEXi\\nKR3IWCxWNl20TgaDAW63G62trdi3bx8A4KWXXoLb7WY3bigUwtmzZzn2a7VaWbmiCzKXyyGVSikO\\nyWpQnKhMB4AE1c6dO/Hqq6/y96cchXPnzgGQlQ9JktDd3c3hQp/Ph3w+z4rk9evXV5XcfTeBbzQa\\n+UDW19dj27ZteOGFFwDIB3RkZARXrlxhBTYajcLlcjEPmc1m6PV6zjMbGRnBxMTEqhQU2ltRSRKV\\nyNbWVhw4cAAAcPDgQQDArVu3cPHiRf7/fD7PCklzc7OCFkpgFsv1V0uPuE6SJKGiogJbtmwBADz/\\n/PPo7u7G0tISr8vVq1cxPj7OSkxDQwO2b9/OAj4UCmF2dlbBN6tRUPL5vCIniKbTkxK5b98+vPTS\\nS6iuruZ8kv7+fvT19SkU240bN2Lz5s38vShXsZx8E3odrZPJZGJD5MCBAzh8+DCfm6GhIVy5cgWj\\nowbSr3AAACAASURBVKP8fWpra+FyudDQ0ABAlhcLCwsr2hiUcrmQ/NHpdHzZdXZ24uDBg6y4aTQa\\nXLlyBdeuXQMg84fdbkc6neaQlMfjgclkYv4tpwSc5CHlIAGyMdjd3Y3nnnsOgGzwRCIRzkcaHBxE\\nIpFATU0Nh3urqqpQWVnJPCV+tvjzakF7bzab0dLSwvJ5586dsFqtGBsbQ19fHwA57UCr1fKeVFVV\\nwev18jrZbDbm3bWU7FMYqbOzEwCwYcMGNDQ0cB4mIKc8zM7OskJuNptht9s53FpZWQmLxbKi/UO5\\n9FBotK6uDo2NjbBYLHyHzs7OYnp6mu8Op9MJn8+H+vp63uu+vr670lNqWFmr1XIYv6OjA/X19Uil\\nUpiengYALC0tIRwOs1JJd4UYIgTk9SIHSTqdxtjYGPN3OUqciCdaWSoGKUsVFRXwer3MYDdv3kRv\\nby/m5uZ487u7u7Fx40Y+fAMDAxgcHCw7iboYtElOpxM9PT3YvHkz/35iYgKXLl1iCyCTycBisbBF\\nodfrkc1msby8zIKVkk7XAqPRiJqaGhw8eBCf+cxnAMhKQCQSYYXkxIkTmJubY+aur69nS5AO5PDw\\nMG7dulVyNVUx/SaTCU1NTXj22WcByBevaEWdOXMGR48e5QMByIfW7XbzhWQ2m9kjCACjo6OrrnIQ\\ncx0A+aJqbm4GAF4jUrhnZmZw6tQpHD9+nL05lGtGtFRUVCCfz2NoaAiALPSXlpa4gqXUNSKBuGnT\\nJhw+fJgvF4PBgFu3buHUqVOcNxaNRmGz2ViJrKurg16vZyWBEj/FSprV0iN6lux2O7q7u1mJ3Ldv\\nH1KpFD799FPO4bhw4QISiQQndHd1dcHlcjG/XLp0iQWs+JxS9gyQFRSn08mXy8GDB9Ha2opwOMxK\\n5KlTp3Dr1i22GltaWrB7924YjUZeGzr393rOamghz5LX68XGjRsBANu2bYPL5cLExAQAmZ/PnTvH\\nihzlf1GeB3BHkSzHMCqm2WAwKPL/NmzYwDLy2rVrOHv2LOeyRSIReDweOJ1OVmrT6TTi8Th74+l7\\nliqHRGMNkJXnbdu2sZEUj8dx4cIF9Pb2ApCVylQqhYWFBT5bYlQAKC+fVKRHTHyvr6/H3r17Acj8\\nPTo6irNnz7J8DgaDMBqN/D3osqbzKVaIlYPiQg7yira1tSGZTGJoaIjl29DQEBYWFvjZer0eHR0d\\nCsWNlDfx80vNWSJDhM4sKUJTU1Po7+8HIBtoExMTbJB5PB6YzWYFfalUShGVKJUegiRJbAgajUb+\\nf6JlZGQE4+PjfHdEo1GYzWZotVq+Vz0eD1wuF0dE/H4/pqenmba19udTc5ZUqFChQoUKFSrug6fC\\ns0S5OOQ1amhogMvlYnfh9evXcf78ecRiMXbBHThwAJWVlWyh9Pb2YnR0tOQy3btBdDnX1NSgubmZ\\ntdtgMIjz58/jzJkzuH37NgDZg7B161a0t7cDuFOpd+rUKYyPjwPAmrpAk0UnSRJaWlqwefNmDh3F\\nYjGcP38ex44dAwC2yMmiq6+vR319Pex2O4LBIADg7bffxpUrV9bUYsFoNMLhcGDbtm04fPgwAFnT\\nj8ViOHv2LADg2LFjGBgY4Oc4nU7U1dVh69atHKqLRqN4//33OZxQbs8no9GoyFE6cuQIAoEAx+WP\\nHz+O48ePY2pqiukxm81oampiT4LFYsGNGzdw9epVAHJ4p1QPAXlyxOrODRs2YNeuXezlGhkZwUcf\\nfYRTp04p2jfYbDbOjaurq0Mul+P8qsnJyRVrs1rviVgNZ7Va0djYyHkAer0e165d40pS4E4OA/HY\\n1q1bEQgEOAdkcnJSkR+0WlrodWI/Hqr6A+RwTj6fx7Vr19jLdeXKFaTTabYePR4PNm/ejMrKSg5x\\nB4PBFSHB1UJMBaCWJeQxqaqqQjqdZt789NNP0d/fz/RbrVY4HA40NjZy+JdaKqy1nJn4iPbN6/Uq\\nKjn7+vo4XArc6YXlcrnYs6HVapFKpdjrtha5WBz2djqdzN9jY2O4ffs2ewlCoRAkSeL8LXq9GFam\\nPkLlVlYR9Ho9VyUCsqwMh8OYnJxkj/by8jK3LAHktaRUCvp7cZ+zctqWUNhUDDNT6gjlbQaDQW69\\nQfB4PLxOfr8f6XR6Tb25AFm2GY1GvrcSiQRCoRCGh4fZcz4xMaGo0HY4HMwv9D6Hw3HX1jKlro/f\\n7+fPpLt9YmKC74pQKITl5WVFFaDNZkMul1NU4plMJkUfMWoLAcg8tZZQ3FOhLNGXo/g8uUwphBUM\\nBhGPx2Gz2ViQ7d27l0M4APDBBx/wQXwYIAWlsrJSEfro6+tDf38/pqammIEsFgv279+Pjo4Opn9g\\nYADHjh1jmtbC+CQw7XY7f38xJHLixAm+VAGZwSk09vWvfx0+nw/Ly8tcQvzee+9x2WWpEHO5Ghsb\\n0dPTw5dqOp3GuXPncPToUQBySK1QKLBg2LNnD7797W+jpaWF3a0nT57Ehx9+yKEx0VW/Wmg0Gs5d\\n2LVrFwBZIMZiMVYiP/zwQ8zPzyOfz3Po9tlnn8Wf/MmfcLhpcHAQH374ISehJpPJsptQmkwmzmHb\\nuXMnN5UEgHfffRcnT55ENBplvnC73Xj55Zfx1a9+lX++fPkyhzYikUhJibnFIOWf1ojc7FNTUzh+\\n/DguXbqkEFR+vx+vvPIKAOCZZ56BxWJhJXJsbIzz10R6ysmpqKur4xCKxWLB6OgoKyWAzA86nY4V\\nqpdffhltbW2KwoFoNMrjYIiOckJNhUIBFRUV2LZtGwBZ8RkcHORwzuTkJPL5POeibdiwAS+99BKH\\nyoA7SbPFzSJLpYXGydDF0NzcDI1Gw+syNDTE/ATIa7d161YcOnSI5WgikUA0GmX5QUU05dCTy+VY\\nQdbpdKioqGAFdXh4GJOTk5ysq9frYbfb0dnZia6uLgCyobe8vKwo+S+3R06hUOC9jsfjiiKBTCaD\\n6elp7tUGyLLcYrFw6Mfj8Shae1DostyxHrQO6XSaw1b0HSkdQyxQMBqNCplOvfKIlqqqKgwNDSny\\n3krNwSNa6B6z2+2cokKfm81mFa1TSCmamJhQFOA0NTVxaxdKBSjl3NP9SfcA5dGKPJNKpaDRaHhf\\nKZnbYrHwPev1etHS0sLGi8ViUfTyoruj3ETvp0JZAmSGo9wAyr4fHBwEIDcHTKfTCAQC+PznPw9A\\ntrzz+TxfhsPDw2uKgxOKG7DNzs7i9u3bfPAmJiawuLgIg8HAHokvfOELeOWVV5gZwuEw3nzzTfT1\\n9a25QWZxYqXRaEQsFmPlaHx8HNlslmPeW7du5URrQFac0uk0BgYGcPz4cQCyN4q08FJpI8b0+XzY\\nvXs3tmzZwsrS/Pw8xsfHmWlbW1sVCt43v/lNtLa2olAo8OG7evUqksmkot/GapU4sZmhz+fDtm3b\\nWGE1m80IBoP8nFwux726mpqaAADf+ta30N7eznwzPj6OgYEBpoVyEEpVKqm/CXlBm5qaYDKZmJbZ\\n2VkYjUa0t7ezR6KtrQ1f/vKXWXHL5XKYmZnhM0H9tQqFQsm5ZmLFYlVVFerr6/ly6e/vRyQSgdvt\\n5n3yer3o6OhQ5MVls1nMz88DkJMxqUcV8XcpZ48uCp1Ox5cDIAu/aDSKVCrFCkhDQwP8fj/nDO7d\\nuxd2ux3JZJI9palUihvqAeUpKJR4brFYeE+sVquiYMTv96Ouro4TVXfs2IFNmzbBZrOx121mZgbZ\\nbJZlQblds4sbmdIFI/5dTFJubm7Gnj17UFVVxZd1NBpFNBpV9NWhC7PUZGpKOAfurK/YCFKsAnM4\\nHGhra8PWrVvZY0IeN/KEFQoF7udVbtNFQOYhMS/LarWiqqoKDoeDL/yamhpUVVVxPiPl4FDeHXkL\\nxc7j5chGjUbufE+5dOl0mpP/6ewbjUZOtAbkyjxqfAzIBtr09DRyudyKmWiE1XqUjUYjexVTqRSc\\nTieam5s5YhMMBhXy2el0oqKiAmazWeEsEJ9pNBoVOaX0nR+0LhaLhd9z4cIFNDQ0wGw2s7yjzxQV\\nWrrv6Gwlk0lFU02z2YxAIKD4jgAUzo1S8NQoS5lMhpnsxIkTAMCWjNjVmBjeYDBgeHgYP/zhDwHI\\nCtXDSOwGZMYgjffKlSsYGxtjhYAS5wDwpbt//37YbDa2Hl5//XX85Cc/QTQaLcs6uBfC4TCuXbuG\\nZDLJTGYymdDY2MjP6ezsxKFDhzjMlc1mcePGDfzbv/0bVztRybfI8KulTUx8p3CpKNA3btzI60PM\\nTEolKTJTU1P44IMPAMjWejgcXpFsW8paSZKE2tpadHd3s4CkxEAKN1VWVsLtdsPn83GoixKLyTv5\\n6aefstAEwFZNMpksKcHSYDDA4XCwEuDz+VihA+SE6cbGRlRWVvLB7+joQHt7Oz87FArhxo0b/Fzq\\nihwKhVaEwB5Ej6hwF3cxDwQC6OzsRGtrK/OU2+1GW1sbK3t6vR7hcJg9S2QFWiwWFkqlrhFwpxEd\\n0SJJEmpqatDW1sZhZJ/Ph0AgwLxDie/RaJQNBnoueQroLJQqMMlTJpbIUxgeuMNDRNvmzZsRCASg\\n1WpZkQyHwzAajbxHkiQprPlSL1+ixWQyKeRfW1sb/H4/K1BVVVXYunUrfD6fogWE2FyUzqmo5K5m\\njcSKQUC+MI1GI3/upk2bMD4+rkhSpj0Tm+NOT0+zUkMNSUVvQimX3L3khdFoRGdnJ6anp/mcO51O\\nmM1mPo+SJEGj0fCekSIoDpAtbpi5GlrIU0PfkTr1d3Z2cmi7UCjA6/Xye6ilAiGXyyEQCGBsbEyh\\nhIjjT+6nnNDeZ7NZLC0t8Z1aKBTg8/lgs9m4otJoNEKr1fJdQUqkWLWYTqfR3d2NS5cuAZA93BqN\\nRtEaYzXKElW7ATIfSpKE3bt3s4J98+ZNxGIxNpJcLhe8Xi97agFZFqRSKd5Hs9nMUyKI1lLkUDHU\\nBG8VKlSoUKFChYr74KnwLJHlReEFsW8CcMedefDgQc5dyOVy+M1vfqPoeULvWWsio0hLMpnkuUbA\\nnfEnLS0t3Denuroa+Xyey3h//OMfc3IwWWMU5y2VNspdAOQQYDAYxPT0NJeYezweaLVa9lB0dXXx\\n/wOyh+K1117DpUuX2Fsmzhujf0uly2q1Ym5uDtevX2dLm3JQxNh0IBBQ/D0ej+OTTz7hRPSBgQH8\\nv+y9aXNc53E2fJ3ZdwxmwzLYd5HgvmixtVmLLVtyEqvkOJVyng9v6qn8g/f5CfmUSiVViV87Kct2\\nxWVHju3YsihZNmWJpCjuGwgQAEFi3wazYPZ93g/H3bjPAUhiBgPIfHK6SkUBmDnTcy9999199dXp\\ndFqR6qoWl2O327Fv3z40NzfzvBEpJWG3kskkzGazIoxLEQBKdcViMUWvpEwmswmXsx0hSgWK1FCL\\nAwppf+UrX0E6nVZEVfx+P6clADn6trKyouAeI5xXtfqYzWZeh2azmfF/gHxb++pXv4pcLqfAtni9\\nXo5ElMtlTE9P814rFoswGo0cYapGqEQf2OD7IuyN0+mE3W7HK6+8oojEiH0XjUYjSqUSJiYmOE0v\\nth+pdnzEVC4gzzkVbhAHF6UjC4WCoq8e9YUrl8uMa5qdnVVEkdXYru1GA+kzKCJx584deL1ejk6+\\n8MIL3KKDxpJA6vQZ4+PjWFpa4nlV29Vqxkh8biKRwNjYGEckGhsb8dxzz3G6RExpiS2gwuEwz9VW\\nKa9qhNYzpb0IZ3js2DH4fD48/fTTivPEaDQq1reYmhbJUkXy1u2OjahTPp/n9TwxMcHFQQcPHuTX\\niNhDwi/R+mtvb4fP59vUFqga+hJAnoN8Ps+Y2eXlZUSjUdhsNj5DKVIjPrdSqcBms7F+ra2t2L9/\\nP0cNs9ksCoWCIpq0nfODSFMBOfLo8XjQ09PDc+BwOGA0GhlLabfbkc1mkU6nNwG6qVDm2LFjMBgM\\nvCeodyZFden7bFceC2dJLVt1hT969Ci++c1vclh6aWkJ77777iZAcD1TcfRvNpvlxUMH3/DwMDNV\\nUwXGP//zPwOQ0zq0uLciF6tWDzHkmcvlODUIyKkBsX+V1+tVVJ1897vfxXvvvYf19XVFCFwMedei\\n1+LiIq5fv45QKMTOkMPh2NQBXMx/Z7NZnD59Gm+//Tbn8Imsk77jdkkXRb31ej3i8ThGR0cVlW70\\nPEB2uMkg0gYtFAq4d+8efv3rXwOQqy6j0ajCUa6F5JQApWI1p9frVWx6dbNMi8WCUqnEZKIffPAB\\nxsfH2cGl5pa1VlqJbNdnz57lFLLH4+GO8OSMGY1GtLS08OfEYjGcPn2a015UPUQgUqD2fbewsIAP\\nP/wQgMyd5nQ6FQzY6+vr0Ov17GhS5/qzZ8/yGqL5EtMUtehTLpexuLiIM2fO8HO8Xq+iRx5hqgA5\\nvUMswkSASGziIparFgwVYUFoDV26dAk2m40dfQIO0/qgDgJiF/nZ2VmEQiHe9zQutVYM03Pn5uZw\\n6dIl3kderxe5XE6RMqlUKsjn85zqpmbe6qozoPZGsYC8h6empvDZZ5/xzz6fDwaDgR3NfD6PTCbD\\nztzg4CAqlQqnb0wm0yZSymoA1eJ3yWQyGBsbAyA7I+l0WpFqS6VSMBgMigov6klJ73E4HIqUIDXb\\n3a4+9FwA/J1v3bqFnp4e9PT08Hq2Wq1YX1/ni1M8Hmf8KP2OUq7kxBCzPNnV7YCp1bjGSqWCZDKp\\ngLR0dXVBkiRFoU8ymVScZUtLS5yeA2QsmiRJbJemp6cRj8d5T1Qrj6WzREIT7vV68eabbyoo9f/l\\nX/5FQVZXT3nYRtHr9QzGpfxqLpfDv/7rv+Ls2bP8MxmkeoDO1QDjbDbLi6pUKmF4eJgZbFtaWlAs\\nFvGrX/0KAPDuu+9y52ixCqJWDBVt2lAohOvXr+P69evsBIiYEwBoa2vjakJAxn+98847mJqaYnAl\\njZVoMKvFvqTTaYyNjaFcLuPy5csAZOdDdNxcLhcOHTrEugGyw/ezn/2MCePm5+eRzWb5ZlIsFms6\\nWMrlMoMnATm6R44jIBtUyr0TEN9oNCKVSrHjdubMGUxPT/Pcp1IpNmS1zBvtm8XFRUU5PLXtEdsG\\nfO1rX1O0Ffjss89w+vRpPrjp8KESZ6C2cv1isYhQKMSEqtPT07Db7fB6vYy1SCaTOH78ODOOS5KE\\nyclJfPjhh9zSh/TYCcCbnL9wOMwUCmtra2hoaOBDNZ1OIx6PMyC2r68POp0Oq6urOHfuHAD5Bi82\\n96wG+6LWR5IkPuyuXr2KRCLBgGmr1Yp8Ps/7iFrSSJLE63dmZgbr6+v8M0UEal3T4lq8efMmR4ka\\nGhqg1+vZNuRyOVgsFvh8PrzyyisANvYqOQnkUFYbMaFn0fOoSo8iS8lkEi6XS9HWIxqNIpvNsi04\\ncuQIWlpaFMUUauB7NWMkEkFKksTjcvv2baytrfEFgF6TSqXYAcjn84hGoxy5IWegUChsybxeqyQS\\nCVy/fh2Tk5PsCDmdToTDYd7D8Xgc+XwePp+PI/KdnZ3w+XwMvL59+/amiPKjdKtUKojFYlyM9dRT\\nT6GxsREmk4lxY7RGKcpfKBS4k4O4xvV6PeMXqck2RZhFrGAt46VhljTRRBNNNNFEE00eIo91ZIlS\\nB08++SRefvll6HQ6vkH87Gc/Y+6Z3ZZKZYNev7GxEa+//joOHjzIvxsZGcGPfvQjRbSnnulAEY8F\\nKNOU1C6CKs5ojKhKkFpB1FpOKYpYBbG+vs68KWLarVQqMZ6K+ptRe4jvfOc7+N3vfodkMrmjKIBa\\nYrEYbt26hbt37/JtkpqKUvi4s7MTL774Imw2G98+fv7zn+PHP/4xp74oQrFTuodIJIIbN25weJjS\\nnqRbPp+HyWTCt7/9bQ4pA8Do6Ci+//3vA5CpMPL5vCIFW+v8ifw2o6OjMJlMfJsk4kKdTse4iubm\\nZqaoAIB33nmHKR5Il1r1ofQMIKcXQqEQR08mJia4VRCNvcvlwokTJxinAMicapOTk4oKI4pS1KIP\\nsLGnqL8kIKcIjUajYh1UKhU8+eSTAOTojiRJWFhYYMwSjZlYbVYLr5FYZQhsNDcWe2XR3AFyOp5g\\nAZROvXHjBhKJxCZdatVHhCKsrq5y9I9SWCK1gNVqxZNPPqngIBoZGWHdaI53wvsEyNGEQqHAKcBI\\nJMIVoBRByWQyKBaLnMIMhUIol8s8tpTariUNJ76WYBdiGpTacYgVtsDGHBEnFVHiSJKEQCDA0V4a\\nq1rGiZre0uddvHhRQVNAQnYpkUhAp9OhsbGR7dLBgwfh8/kY50QQAtEubUcKhQLjftfW1nDr1i1c\\nvnyZz45EIqHoCSpJEuLxOMxmM6fViGiTyJ9zuRzu3r3LUfJ79+5tq6/og+SxdZb0ej0DCP/6r/8a\\nPp8PiUQCP/3pTwGg5v5LtQj1sALkxrVvvfUWLBYLH7L/9E//tKmx6G4IGSyDwcBh0W9961v4i7/4\\nC879zs/P4+///u95AVEpZT2cN/EZdFiK5c02mw2HDx/G17/+dQDyQRcOhxnL9d577yGRSCgOg52C\\n8QGw4cvn87z5jEYjrFYrr6GBgQF0dHSgXC4zNcV3vvMdBaP3TvAcok6lUgmxWIw3OY2P2Cnd6XTi\\nmWee4dRcLBbDP/zDPyjIGMVxqnWt0zNIl3Q6rXAARKAppQIIg0YpzXPnziGdTit02cneo8/OZDLI\\nZrN8KaJ1KmL9jEYjjhw5wus7kUjg3LlzCuxWPRxuAFz2LeKwxDkANuYS2GgC/emnn3KqYCeHriiV\\nSgW5XI4PJTrQ1OXjIlv00NAQjEYjX9oWFhYUF7ed2AGRlPJBWEzxX4PBgGeeeYYdlkKhoGCLrtVx\\no+fTuIggdxJa03Q4k62ieS0WizAYDJxe1ev1O9r79J5CoaDAqxGeSQRrExaNdKHCD/o+RqMRg4OD\\nsFgsCoek1rQpOf7EVSjqS+lMcT8SEJ9sQCqVQmNjIxPHfv/730exWKw6/V4ul/kiMT09jcXFRUXD\\n6XQ6zWS+9NxYLMZzRfqK7OJHjx5FNptlG6rGnVUrO3KWJEmaBpAAUAJQrFQqxyVJ8gD4KYAuANMA\\nvlmpVKI7+ZwtPhd6vZ45ck6ePIlSqYQrV64wsWI9ozePEpPJxPwmf/mXfwmfz4dMJoNTp04BkDEd\\ntWJJtiPq57rdbs4pv/7664rmtT/96U9x9uxZRb57t3QiYyC2hnnllVcY1JnL5fD73/8e7777LgD5\\noNstjJn69kt4JeJa+cY3vgGn04lQKIR///d/ByDjd0Q8WD2jgSL/SKFQUDAG2+12vPDCCxgaGuL5\\nOXXqFD7++ONNYOV6iLgGCNsjVo4ZjUa43W68+eabAOQbXCKR4HEKhUJ1Xd9qB1AN+hWjlfv27WM7\\nAMhVYWNjY5siWzvVjW7h4kGsFqqioipYi8WCdDqNK1eu8HvqcQmg50iSxM/bihhVHCePx4Pm5mZU\\nKhUGm6uxiTu9BIjRLnUFlLp6jFrZkH5q/pudXuDEuReZ58kmibgmGkvRWaL/AHk/7rSKGtjAPolg\\nbFEfYKMFivie9fV1vswUCgU4nU6YzeYd2+5SqaQoECER50oEawMbY0WtYqLRKBobG3ls6NJUCxCe\\nbNudO3e4ipTeT9F3GjuKEJHtJF3T6TRH3FZXVxnzBEBRsVmL1COy9GKlUlkTfv4/AH5fqVT+XpKk\\n//PHn//fOnyO4rbi8XjwV3/1VwDkm/ji4iJ++ctfYnJyEsCDjUe9DDotKIvFAr/fz20o9u/fj2Kx\\niA8++AD/+I//CGDjBreb+gDy+LhcLnzxi1/ksWlvb0c+n2dg8Ntvv60A7W31neo5RmazmRmN33zz\\nTTzzzDP8OdevX8fbb7/NwOC90EnsRt7U1ITXXnsNgAw2T6VS+O///m98+umnAGrvQ1eLiIavt7cX\\nf/ZnfwabzcapjB/+8IdcGbjbIh5UFKl88cUXGUQNyNVXVLBAUa7dvgzQvzqdjtOn3/rWt+BwOPig\\n+/nPf4719fW6RZMepsdWEggE+KJiMBiwtrZWF6b+h+nzMKH13tvbi0AgAEmSmPyv3vOmdnYe9Ddy\\nDnp6ehQ0JfF4vO7re6vnbfV9RcoHqpYj27PTQ/ZR+mwlNG+VSgXpdFpBa0CVnyQ7iU4+6nK6FWVL\\nLpfjrMna2hr6+/sVrWHW1tZqTsEDYLD9VmtTvCyq2+HodDokk0mOGFKlHEVSH3TubVd2A+D9ZwB+\\n8Mf//wGAP9+Fz9BEE0000UQTTTTZE9lpZKkC4LeSJFUA/H+VSuW7AJoqlcrSH/++DKBph58BQEnt\\nbzab8frrr3MH+UQigdOnT+PSpUvsCasJ6Op98yVPv7m5GS+99BJeeOEFALJ3Oz8/j1OnTnGokkKt\\nahxIvW++VqsVR44cwRtvvMEg6kqlgtnZWeaGiUajrI9at3rrQ2SBJ06cACCXhFosFvb0P/nkE0xP\\nT2/6XHGs6qmTGF42m804fPgwUyrodDrcv38fFy5cUKR91GO1WyJJG53Rn3vuOfT09CCbzXILkeXl\\n5ZrI+XYqOp0OPp8Pb775Ju+/VCqFM2fObMI47KVORPEwODiIUqnEe+3KlSs1NYCuh+j1egwMDDAl\\nRD6fx7Vr1zgFDtQ/mvwooRt/b28vpzoI7Eyl7Hs9f4Cc2rLb7RwRzGQysFgsjE/bbXynWqjvH4lI\\nheB2u2tuMlyrqO0fpckoPWg0GhXRp73QS9SJii5u3LiB/fv3s01vbW3F0tIS449qnccHRabE76mO\\n1hIXHBEaz83NwWAwcHSeindqjV7u1Fn6YqVSWZAkKQDgQ0mS7oh/rFQqlT86UptEkqT/DeB/V/Nh\\ndFj09/fj29/+NgNOV1dXcfPmTUxPTyuAZdUy425XqK8YIDsAf/M3f8Oh0Ww2i3PnzuH8+fO8YET8\\nDn2PeoabCST52muv4eWXX8aLL77If1taWsI777zDPC/UyX63Nxcxp544cYK703s8HsTjcZw+rX3h\\n9AAAIABJREFUfRoA8NFHHymA7+IY1VtoLdCB39bWhmeeeYYPtkgkgj/84Q/46KOPFFwce+GgkF5U\\nsfjqq6/CZrMhkUjghz/8IYCNVG41/CU71QmQHYDh4WHs27ePD7K7d+/igw8+YGzAXgqlBQn31tbW\\npgDlE5+W+B12e62LeJOuri62DdlsFlevXq2JybweQqkKANyjLpfLMdicdN+rcRJFbEZNn02OE/1+\\nLx0mEWBMxUFkKxwOB0wm067gKR8kNBflchmRSITTXjRera2tjD3ba8eyXC7z3pckSVFNSzqKkJm9\\nKGwiXYANrj+j0YhyucygcOoBSM2aq5UdOUuVSmXhj/+uSpL0CwAnAaxIktRSqVSWJElqAbD6gPd+\\nF8B3AeBBDpVaaAIOHjyIzs5ONt7xeBw3b95kFmZ6rQg6rCcOx2AwcJnpG2+8gWAwyJ+ztraGGzdu\\nKIBl9D6Seholk8nElW+HDh3Cyy+/DLPZzIR8n332GS5evMg4BXV0q94iVimZTCYcOnSIF+vy8jLu\\n37/PWBd1M8jdPFDEwx+QI0sGg4FbV9y+fZsdgM/jpi2SqUmShFgshlOnTnFEUDxI9kJovOx2O9ra\\n2iBJEhvs//iP/3hgRBDYfSfOarUyqLtUKmF1dRUffPABAGVrE3Gt74Vj6XA4EAgE+CAZHx/HRx99\\ntAn7tlfRHGJTBuQL5fz8PGZmZrjt0l47lSQEph4bG2O84sTEBJf102v2MupVqVR43i5fvoyTJ0/y\\neqeCh88jCkdOJHVkWFhYwOLioqJVzG4WDz1ML0C2m08++aQimksEnsDeOHLiuSFWPDc2NvK6op9p\\nHGuRmjFLkiTZJUly0v8DeBXACIBfAfhff3zZ/wLw3zVrp4kmmmiiiSaaaPI5y04iS00AfvFHr84A\\n4MeVSuV9SZIuAfhPSZL+HwAzAL65czVlIS6Vw4cPo1QqcZ70/PnzWFlZUZTK1kpCtx0RvXgiwiIc\\nwEcffYQbN25sCimLUa56ik6n41TS4OAgEzpSS4bf/e53mJycVHDDiJ74blVXUfPe9fV1rk5YXV3F\\n9evXceXKFQBgTqW90AfApvL86elp5i0aGxvDxMTEpuqP3dJH/M5Unk9RkRs3bqBQKOA///M/Od++\\nG9VUD9ON9pFer0c6ncbExATjp37zm98gk8k8tPqp3vqQ6HQ6WCwWTiVNTEzg6tWrvN7FKM5ejZVI\\nKJhMJrki96c//Smmp6cVN/+9jN4QlQEgk8R+9tlnuHr1KpPAEpnhXokYNTIYDJidnWV4wM2bNxEK\\nhepKRlutbjQWFy9exIEDBxRRLzFaspciSXJ7GrKZ9LPII/h5RJVonsbHxzE+Ps52imhEPo+UJekl\\ntgAiTjtAhqTsZL1Ln8cC2KTENtJwFH4H5DTcyZMnGdT58ccfIxaLoVKpKPAvu/Xd9Ho99/I5ePAg\\nenp6GMR569YtzM7Ocokj6SL+W28hYOKTTz4Jk8mk6Gy9urrKvd+2kt3SSZIkOBwOmM1mxlRRaSeB\\nFdX9lnY7zK1u4ktEooCcylVzhOyV0IFLTq/H40E6nUYsFttEgLiXOgEy9szj8cBut3OaIhqN7gr/\\n1HaEmmuKYxWJRDjUXksvup2IiPkxGAyKpqihUIgBpZ/XQUuXA6fTyb2xaB53wvq+U72IZ4nsaCKR\\nQCKR2FHvrp2KOFaNjY28lpaWlrbdqLbeQk4v2VCfz4dUKoVQKLTnWCVRRDva2NjI51+tjbzrKSLN\\nwjZTlFcqlcrxR73osXGWVK//XDaTWoet5PPUa6/Av9XIXmJHapE/1TEj0XR6uPwprilNNNHksZJt\\nOUtaI11NNNFEE0000USTh8hj2RvuT+Em+aegg1r+1HX6U9fvT0U0nbYvf6p6aaKJJv93iRZZ0kQT\\nTTTRRBNNNHmIaM6SJppoookmmmjy2Mlekr1qzpImmmiiiSaaaPInIVQBqHaEHvSzWJG6m6I5S5po\\nookmmmiiiSYPkccS4K1J/UXtnX+ewGyR6E/sHUX/7iXhmaiD2WyGxWJhXYgAUU3QWC/ZqixebOdj\\nNpu5nUWhUGDuIxqf3WpCrG6Zo74JbtVmqN7j87CbJOkj9tdSy26NjXrOttpX6s/erbWz1Ripdd2K\\nA6qeraG2+lmtgyjiXNVz7Wyli6iP+Bm0dujvNEb14Dp72Hd/0O/0er2iNY2oD7Cz8SHbttXc0BiI\\nn6PT6WAwGBTNxsU1tCPSR6EllXgGkBSLxU1zQvqIf98trsXHyll60OYTG0aKk78XzLnqw1zUTWwm\\nKDLEiouw3k7Jw/QR/67+m3phAhuH7k6ck62MNummbrhIYjAYmGiTumuTfoVCAeFwuOZ+SFsZBZKt\\n9BH7awUCAbS2tsJutwOQxyUWi+Hu3bvMGlurTlsZUb1ez99bHDNAZo5vb2+Hz+cDIPckDIfDSKVS\\nTIgqEqPWqg/9v0hwqNbXaDTCYrEwGWO5XEYikUA+n1cwMtdCoqdm76Z/aVzIsIrrm/QkIsFSqaRg\\n9t4J87H6s4ANMkNxrdBrSR/SJZ/PK5izybGsde+L8ySuF4PBwMzw9DmkC70nl8shk8nwvKgP4VqF\\n5oQa0QIb+5j+Xi6XYTAYeOyoBxqx2NMY7VQX+s40LiaTCaVSCXq9XrGexMa5ZrMZpVKJCWGTyWRd\\n9JEkCWazmW0b9XcT7at4pgHyuFmtVn5NqVRCPB7f1G+wFjEYDHC5XABkcslMJqM4B8jGis6HyWRS\\nXBiLxSJ3jSD9a9VL7K5AzxLnJJVK8ZyUy2WUy2XF2WU2m5nhHNjY//U6+x8bZ0nc+HSY0qBarVa0\\ntLSgsbERgUAAwMammJmZASAza6+uriKfz9eFJl5k7dXr9Typ1FLD7Xajra0NANDS0gKTycSHSSqV\\nwr179zA7O8ts1qlUqi4OABlMWkD0O2KrbmlpQVNTE+vrcrm4szU1/41Go1hdXWVWVjIm1epEh4V4\\nqIgHncvlgs/n4wPfZrPB4XCwsQXkTZHJZLidzNzcHCwWC3cIr0Uf+k6iETUYDGhoaEAgEGADQgzk\\ntO48Hg8sFgtvwunpaUSjUZhMJkWbne3M4VYHvKiL2WyG2+1Gc3MzANmAuFwuNihGoxENDQ08L/fu\\n3UMikUAymWTjpja8j9KH/lUfuiaTCXa7HV6vFwDQ1NSEhoYG3n90wNL6WV1dRblcVtyIt7t+1CSm\\n4vqh5swWi4XnyO/3w+/3M6O3w+FAMpnE8vIypqamAMh7S1wvuVxu246b2qkWW+aYzWbY7XYFu3JD\\nQwPvNYfDAZvNhsXFRe4Qv7q6ilwuxwYdkI16NftejJqJuthsNt5LXq8XLpeLD2aylwaDAffu3QMg\\nt/EIh8N8AIl6VLPf1ZEYk8kEq9XK4xAIBOB2u7kDg8PhgNVq5a7wgNw2Y2Zmhj8/l8uhUCjwoQhs\\n316LFx6DwcCfB8h72Ov1oqGhgceO7A7pn0wmcffuXW53UiwWkc1mFU5NNU6l6CQ6HA5mLS8UCnC7\\n3XC73byGKpUKbDYbr+disYh4PM7nWCQS4fkSHahqxod0slgsaGpqAiDb4tXVVTgcDrY5VqsVuVyO\\nzy2r1Yr19XVEIhFuIUIOjLheanUqaT7a29tht9uxurrKNqa3txeVSoWZ+svlMnQ6Hebm5viiSrZD\\nfdGrF9O5hlnSRBNNNNFEE000eYg8FpEluu3S7YAiEoODgwCArq4uDA8Pw+fzcRSAworUNLKzsxPv\\nv/8+FhYWdhSeE1MkgOwNW61W9oAbGxvR29uL4eFhDAwM8Gu8Xi9/XqlUwuzsLM6fP49PPvkEgHzL\\nKJVKVeukjlDo9XpYLBYeK4vFgvb2dhw4cACA7KED4CiB0WiE0WjE+vo699qbmJjAyMgI30CrieKo\\ncSwUJSFdLBYLWlpaAAAHDhxAW1sb30rcbjcsFgvfgOmzw+EwZmdnAcg3ivX1deh0uqqiAxShEEO2\\nFouFb97d3d3Yv38/vF4vpwLcbjdMJhP8fj8A+Va6urqKsbExfnYqlVL0jXrU/KnnCsCmOfN4PBgc\\nHERvby/fQjOZDBoaGhQ90fR6PTe3HRsbQz6fV9zyxNTvg/QgIV2MRiMkSeJxcTqdaGtrwxNPPIGO\\njg4AcpSrWCyybhSdpAay586dw9LSEkqlUlUpXDHlR2MkRu2cTicCgQB6e3sxNDQEQI5yGY1GXi8e\\njwcGgwEjIyNwu90AgMuXLyObzfI8Ufh+O3OlTvWbTCaOyjY3N2N4eBjt7e0AgI6ODo4QAHIExe12\\nIxQK4f333wcAXLt2jZt+Axupju1EAdVzRrg1GpuhoSEcPHgQgBxBbmho4DVF82W1WnH//n3+XSaT\\nUaxfigpUE5Uk3cSGwoFAAIcPHwYg2xyKytK4lEoluN1uXp9tbW1477332NZks9lN0ZtqUjxiNNLp\\ndPLaHRwcRGtrK2cgANkuUTYAkG1OZ2cnfv/73/PPJLXoI2IenU6nwgb7/X7YbDbeS9lsVmEL7HY7\\nEokE5ubmAABnzpxRpKNEqTaSbLFY2LYNDQ3BarUq0rQUpaW9RXsxlUqxjbxy5Qru3bvH+1y0PdvV\\nhcaIdOnt7UV/fz9HF+l5JpOJI8qUvg4Gg1heXuZnTE9PKyJNYpRrpynmx8ZZAsADZzQa0d7ejmee\\neQYAcPjwYVitVlQqFQ4PxmIxeL1e7Nu3D4C84C9cuIDFxcWHgkO3I2pci9FoZCP59NNP44UXXmCH\\nAADu37+PyclJdHZ2ApAPZq/Xi+XlZVy+fJmfsROMCf2r1+sVjUaPHDmCZ599llOCgOwMUYrC7Xbj\\n2LFjaGlpYWNG4didOJUE+BWdJYfDgeHhYZw4cQKAHG4tl8tsvOfn5+F2uxEMBjmEn06nUSqV+CBe\\nWlpiQ1rN+Ij6ALLhcrlc2L9/PwDg+PHjaGtrQy6X47FZXl5WpAm9Xi/K5TIbqvn5eSQSiW2lT9WY\\nJHV6klLJAHDo0CEcPnwYfr+fU6Hz8/OIRCI8ltSwlZzItbU1xONxBQhyu2Mjpicp3U3ref/+/Th0\\n6BAGBwf5QhCJRBCNRrkhq8/ng9vtZl2p6W46nVak1B51uKidJUrb0nd2u904fPgwDh48yHtJkiTE\\nYjFFqqaxsRGDg4OYn58HIO+tYrHI9kN0Bh41NuqUjtFoZEcyEAhg3759fClyOp3I5XJsrGkvBgIB\\n3n+3bt1S4EBqOVzUaUn6bL/fz+PS0tKiwG/kcjlOA7W2tvJrxsbGtsQsbXcNietHvBT5/X52AILB\\nINxuN++bVCqFUqkEj8fDDkpHRwfa2tr4giYCqtUFBY/SR6/X86XZarXCZrNxusnj8bDzRikdmi+y\\nmT6fT2GX7t+/v8mJJId7OyJicVwuF39OV1cXGhsb2WECZNubSCT4LLDb7ejo6GAnIRwOY25uDslk\\nUjEu1egDbDjatKcdDgeCwSAA8NjRvNK8ZbNZOBwO9PX1sXNUKBSwsrKiwJqJsI3tOpRms5mdpUAg\\ngJaWFlQqFU6FAvKY09hVKhWkUinYbDZ2hIvFItxuN+7cuQNAtlOZTIZ1KRQKO8NU1fSuPRZaCGLU\\nSLwdlEoljI+PK6IjbrcbJ0+eVGCYCIi2E8Q+ebQiYNBsNrNT9uUvfxnt7e1YW1vjg+zatWvI5/O8\\nIQYGBjjyRRs1lUrVNIlqY2K32+HxeHDkyBEAwFtvvYWuri4+OEZHR3H+/Hm+Le3fvx/Hjx+H3W7n\\n20wsFsPU1JTicKlFL6PRiMbGRo5iHThwAF/5ylfQ09MDQDZCIyMjuHjxIgDZMRoYGMATTzzBz0mn\\n05iamsJnn30GAFhZWdk2xkM0HqQPOWEejweHDh3CCy+8AEA2XJOTkxgZGcHt27cByOvq0KFDvO5K\\npRKmp6fZiE5PTzPWbDvjodaJjGhjYyNaW1vx1FNPAQCeeuop2O123L9/H6OjowCAmZkZNraAfHvP\\n5/N8uMRisaqwOA86gIxGI9xuN0dtv/jFL+LAgQPIZrOYnp4GAAa0094KBoNwOBx8iFOXdjFSup35\\nIhyCeozoO+/btw9Hjx5FV1cXr9/Z2VksLS3xfuzu7mYME61fAsRuVWn1qDGi95Be4hrq7+9HZ2cn\\nOwmLi4tYWFhg3VpbW2G1WuF0OvkiUiwWFbjJWkDVZH8IdwLI67mrq4vnhHBbq6ur/D46kMiJoa7s\\nal22Oz7iOIn22el0wufz8T6n6BpF+SORCOOr6ILQ3NyMUqnEutDaqaUARnRqaP3QIRsMBmE0Ghkz\\nSvp4vV52FrxerwJP9aDIRLXYRJozWs8ul4txh6TL8vIyIpEIj0tXVxdaW1vZhl64cIExnaJe23UC\\nxHGhdQvIc9bU1ASLxcLYw7m5OQU+yev1oqOjA4ODg7y3xsfHYTab2ZapL7LVRCbFKKnD4YDH42FH\\nLRqNIhqN8ucmEgmOKJPNy2QyMBgMfGFwOByYmZnhZ9SCvRXlsXCWSMggBoNBPPHEEzwRZ86cwfXr\\n17GyssIh8WeffRZOp5NvXtevX0c0Gq0p1bWV0KIzm80YGhrC888/D0D2iqenp3H+/HncuHEDgAyc\\npE1K75mfn8fq6iqi0SgA1AWERsD3Y8eO4c///M8BAD09PYhGoxzB+vDDDxEKhdhgkjGpVCoK4HIi\\nkdixU2kwGODxeHDy5EkAwGuvvYa2tja+hdy4cQMffPABjwGlvdxutwJsee/ePTb65Cht97ATxWQy\\n8fc+duwYvvrVr/ItOxwO4+bNmzhz5gwfdk1NTfD5fBxloRQcOSjFYrHqKhk1EBaQjdCxY8fw6quv\\nApA3+cTEBK5cuYLr168DkCMD+/fv5wiFyWTC+Pg4O3ZUKCB+72ocFBI6UMnZfvrpp1EulzE6Ooqr\\nV68CACYnJ2EwGPhgDgQC0Ol0fFMPhUJb0hdsd85ER85qtXJUYN++fRgeHkY0GmUncnx8HCsrK/ya\\nnp4e2O12ZLNZNqxbXZBqWT96vR52u50/q62tDX6/ny9oo6OjmJycVDjPBw4c4DQPAHZuar2AiP8v\\nRgCbmprYIQDkSOTk5CRfkux2O4LBIPR6PetH0aadiPrABuTv6HQ6+XNCoRAmJibY2Q6FQuxMUUoq\\nkUggFAptirRVGwlQR6PIsaQISiqVwvr6OqampthBSaVSaG1tRVdXFwDZyQ2FQhzV2IkdVOuWSqUU\\nl1AqrKFU0srKCpLJJEeWWltb4ff7FX+nsVZfBqsRcrTpc/R6PQqFAuLxOM/T7OwsotEoX+jz+Txa\\nWloQCoXYZkciES4CoecC1Z9nOp0OHo+HdWlra4PZbGZbPDc3h3A4zGOXTCZhs9kUqX6j0Yi2tjZ2\\n7qxWK9bW1ji6WmsBFetY8zs10UQTTTTRRBNN/gfIYxNZkiSJb1H9/f3o6+tTcNCsrKwgHo/z7yhc\\nv7a2BgBcYlgvzgXy6l0uFw4ePMjASr1ej5WVFUxPT/Nnu1wu7Nu3j9MsVPo+OjpaVy4IKjvt6+vj\\nUGSlUsHdu3cZlLyysgKTyYTh4WEAcrTH7XYjnU7j7t27AOSxyuVyVYP0RKFyYovFgu7ubgBySLlY\\nLHJO+fbt2wiFQjxng4ODeP7559He3s63gdXVVUUotVahGw+toaGhIfT09CiiXFevXkU4HOYI4MDA\\nAJ5//nnGLBHWi25atZTskuj1en6f1WpFZ2cnf87S0hIuXryICxcuMA7I4XBgcHAQR48eBSBHlpaX\\nl/l2TCk48VZdjV40B8ViEXq9nsP+ZrMZ4+PjuHLlCi5dusTfOxgM8hrq7OxEOp1GKBQCAAV9AUk1\\nkTd6LYH4RYC3wWDAwsICA9tv374Nk8nEEcKhoSF4vV6Ew2G+YapvlNVGKuhfSZIUKTSbzQaDwcC0\\nFnfu3MHU1BTfkCVJgt/vh8Fg4HW2vr5eF/4gEtonpVIJNpuNb/iJRALz8/McWerq6uIUIukbDoe3\\nnKtahb5TKpVSpJsIS0q6xGIxjiLTfiwUCohGo/x9djJGYiq3XC4jnU4rsH5LS0uIRqOcFiwUCmhv\\nb2dd3G43pqenef0Q6Wyt+lA00Wg0olKpcLSkUCjAYDAgFotx5JwKkMhmUhqXIkuRSIR5n2pNdwHy\\nXhIjkVNTU4hGo8jlcpyGW1lZQTgc5mcTJ1Qmk1HQF4iRsFqicLRWaA97PB7cuXMH6XQaN2/e5HHJ\\n5XL8uYQdFNP0RDtDe61UKjF+EgDv3VojhY+NswQoqwr8fj8bZ0qvdHZ2cjpscHAQRqORU2EXL17c\\n8YFLIlZ+uFwuuFwuNpCRSATr6+t8oACyc/fWW28xgC2VSuHy5cuYnJzcBNKrRRcaF5PJxGkjMppL\\nS0u4ffs2p0h8Ph+Ghobwd3/3dwBkI1ooFDAyMoJTp04BABuJHVUO/DEl2NbWxiBOnU6HpaUlXLhw\\ngXWz2WwMkP3bv/1bDA0NoVQqMUbp/PnzWFpa2lSFUo0QnsJut6O/vx+AXHGh1+s5pXblyhWsrq5C\\np9Mx1uKtt95Cd3c3r7Pf/va3GB8fVwDxafyrJe7U6XSMYevv70dPTw8/49atW7h+/TrW19c5fXPg\\nwAF8/etf53U2MzODs2fPsmGrVCrMa0TjU8shaLVa0d/fz2s3nU5jfHwcY2NjbHSsVisOHDjAQH2L\\nxYKZmRlOGWazWTastTop9HqLxcKpmvb2duRyOczNzbGTSFhAwrl1dnZCp9Nhfn6euWkoFSPyT9Wi\\nCxEX0kWkubkZmUyGL0X0Lx26+/btg8fjQSwW4zQpXUKq5eXaSpd8Ps92qKWlBT6fjw+KRCKBVCrF\\nToLP58MTTzwBh8PBTgKtN3rGThwUsfBBr9ejqamJU97qg85iscDtdqOrq4sdCQAKTjNaO7XYxFKp\\nxJdQ2mcin9DU1JTiwLfZbAzEB8BYGNLNZDJtmW7fbiqX9k2xWFRg+6gSmf4GyGeDw+FQMGQTzg3Y\\n4D0Tq17VgPyH6UXvSSaTCIVCbE+I7LdS2eiQUC6Xkc1mGaNHP4vVnFarFRaLZRMptHjJeNQc0mfS\\ne6iKk1KSgLxvTCYT214i63S5XHzW0cWELgP5fF5BAprP57nooZZ19dg4S+VyWWGUxsbG+OdCoYDW\\n1lb4fD48/fTTAOSNF4/H8d577wEAR5V2goYHNh/U5I2LuV+/34+jR4/yInvppZfQ3t7OnzszM4OP\\nP/6YDRtQHUjvQTrRRnK5XHww6PV6OJ1Opg5oaWnBl7/8ZfT19fHnzs3N4aOPPuLDhYCW4u2sWl0o\\nOtHX18fgd51Oh3g8zrr19/fD5/PhpZdeAgCcOHGCIwdE4rewsKCIwhDmopqxImeppaUFhw4dAgD0\\n9fVBp9Pxxspms+ju7obP58Px48cByFVpVquVozuExaHNSSzEIsHgdvXR6XQKotDW1lbOxxMOoL+/\\nnzF4zz33HFpaWtgYra2tIZFIKLBz9H6xFLwafeg5RIhJz0qn02hoaOBoU3d3N2MCaRxmZ2d5PRNJ\\na6lUqik6KRpeMUpAh0ilUmHivK6uLnR1deGLX/wiAPkgLhQKuH//vuKAAcCHYy2RN1EX2g8Oh4Mr\\n9oAN6gBy7oaHh2EymSBJEju19CxyYqrB4JHQfIms2KVSCQ0NDby3TCYTgsEg37qHhobQ3NysoGCh\\n8RAxdGLkrJoxEi+QhH2hzwY2AMSA7Lj19fUpsIkej2fTpcNoNNYEzAc2HEC9Xo94PM4XngMHDmBw\\ncBChUIjHqqOjA11dXQpiyFKppKgkJNZv8QKy3QuuWCQgnmOpVAr79+9nxnAAXN1FF2vac+Q0xONx\\nPuzFNh/iPtnOxa1UKqFUKjH2aH19HV1dXQgGg/xZJpMJgUCAsVxutxsejweVSoWdf4o00Xe0Wq0K\\n+0P28VHjJEkSR+wvXLiAQCCAcrnM40DPJMxmQ0MD/H4/mpubORAQDofh8XjYASVqAXU1Xzgc5vO6\\nKhuw7VdqookmmmiiiSaa/A+UxyayVKlU2EP8wx/+oEiH2Gw26PV6vP7664o87K1bt/Dhhx8C2Mjt\\n7xQrQBEg8t7D4TA+/fRTTltQKFykEwgEAtDr9ZyX/t73vocrV65sKoHfqW75fB7379/H+Pg4Y6hc\\nLhfa29s56nDkyBG0tbXxrWR9fR2//e1vcf78eQX+QZRqoziAfCv0eDxobm7mG5vD4cDAwADfZiqV\\nCgYGBnicLBYLUqkUrl+/zqkCKksV0xbVpiypqmp4eFhRtVgoFLjqq1Kp8E2KUjputxuxWIzxXm63\\nG319fXwDorLaWtOCtGaOHz8On8/H63n//v1wOBzw+XyKVK7L5eIb3eLiIvx+P5cYU5VNLBbjua5G\\naHwtFgvz4pD09vbC5XLxLa+trY31AeRUXTQa5XC3y+VCKpVSpGZqwXwQXw6lc5xOJ8rlMtra2vh3\\nHo8HwWBQQZgZiUSQz+fZFlC7EzGitN2qWDWNgclkUvQtlCSJ5yAQCCiq5RoaGrhfFY2v3W7nXnWA\\nPO7ZbHYT79J2ROyhaLfbFeX7LS0taGlp4X3e0NDAeogVcGKJPLXcIdoHYPupXCINBOR9Lj63oaEB\\nfX19HBUwGAyw2+1MxCvqQvqWy2X+WSQT3S5Nh/idzGYzz3U6nYbH48HAwAATmzqdToVNyeVyiMfj\\nir5kwEZUG9iosNzOfNF3zOVyHNWjMatUKpzWBWQb7vP5+D1Wq5WjY/QesqfqiNV2uI1EzsJEIsHR\\nbbfbjaGhIbS0tPCedbvdCv4j+s7FYpHtUDweR1NTk6L6lXjN6OftRN5zuRw/M5/Po62tDb29vWyH\\n7HY7kskkp0r9fj+ampoQiUQ4skRYYJovmnuCeZjNZoTD4arbC5E8Vs4SGZjFxUUsLS2xYTCZTOjo\\n6MD+/ft58lOpFH7xi19wyFPEBtXDYaLFkEgkGF8CgB2R3t5e1sVsNiOfz+PMmTMA5PJ9OhjFMPpO\\ndctkMpibm8O5c+c4fOv3+xEOh9mAu91uxWIeGxvDL37xC8zNzbFREnuzAbWNmc1mQypf6xS0AAAg\\nAElEQVSVwqVLlxisaDAYUKlUOLVEIWcxXB8Oh3H+/HkmoVxeXt7UaLJa58RkMmFoaAgHDhzYROBJ\\nB91TTz0Fi8WChoYGzuMbDAbFJhbTgYAcgqZUSDX4IGILJkAj8a+QETx69Ci6u7ths9l4bBoaGmA0\\nGnmOotEo82qRLqlUalNqZjsi9hqj54hh9RMnTiCVSrGhJ73oMCFOMcJmVCqVTYzp1Ry6okFPp9O8\\nh8PhMEwmEwYHBxVpQ4vFougbmUwmMT8/r0hzqxnTq8UsUIoom81ySmdpaQler5cxbsRwLPLX0Jqn\\nA4P0Fde0uK62qxetOUqZzMzMYHZ2lh2S7u5umEwmHoNsNot0Og2j0agA8xMrufjZ4nqoZt7oPcQ4\\nTSnuzs5OBSaPuMmy2ayCUV9cU3QpFRve1oIzqVQqiMfjzIDd09ODrq4u9Pb2KigzyEECwJhPSqeG\\nQiHE43GFM0rn0aPSzKKtIkeDMKGhUAidnZ1obW1lclwx9QfIjoPoADz//PO4du0aJiYmFJdbsXfd\\ndux1uVxWFE/Q2Nrtdl5Dvb29yOVy/Jp4PI61tTWF8//UU09hbW2Nx5cukCLhJ63vh42RJEl8Rnm9\\nXpw4cYIvaQA4nU02MhAIoFQqYWVlhW1gPp+H2+1mh7u7uxvlcpkvUsFgEJ9++imWlpaq7lkJPEbO\\nkijqW48kSRgYGMCBAwd4s925cwdnzpzZdBOpV9WZ+P+xWIyrc+7du4dgMIihoSG+/er1eszMzOBH\\nP/oRANnoE1hQ3LC16KYG0BWLRdy6dYudt97eXrS0tPDtwGKxQJIk5ob5t3/7N9y6dYtZdUlfun3X\\nqlc2m0UikcCtW7fYYOzfv1+xmHt6emA2m3kMIpEIfvKTn+D06dMKXiX6XsDWnDmPEp1Oh3Q6jXg8\\njomJCQDyxtfpdPx8AiWLDY+TySTOnz/PYPPp6Wmk02k2Uul0uioiSBLRGQDktZrL5XiOKpUKFwmI\\nbWtEEsqbN29ienpaUZVSKBRqBi+SVCoVTE9Pc6uH/v5+WK1WZLNZjmL4/X4EAgGek8XFRczMzHDE\\nUCSXrHYNqV9XKpWYUf33v/89mpub0dzczIcWYePoxlksFrG0tIR4PK4YG+KRoWfWCqrO5XKM7bt0\\n6RLa2trYuaZ1Q+vZ7/ejVCohnU4rnAAxCkCHVi17rFAosIN6//59XL16lfcNXdTERt09PT0K4keP\\nx6NoXisCe2vZY7SXqFH4lStXAMhOpdj8mogLPR4Prw9qdyHicEjXnaxnuuwQkazL5cLCwgJ8Pp+C\\nGygWi/E8BoNBdHV1cbsWckxEDN52RQRfUxRR5COjQgVav+vr69zihN7f3d3NuhAmN5FIKNitqQ3J\\ndvSh14uEjoSBJFZsGqtEIsHvCYVCWF9fR0NDAztUg4ODiMVinAmYmprC2toaczVRFOphDhxFo8gR\\n6unpgcfjgd1u5wslkcyKFZXipY4+C9iwr83NzQgGg3yZGRkZQSQSwejoaE0OuIZZ0kQTTTTRRBNN\\nNHmIPJaRJRLyIN1uN5566il4PB72rn/yk59sSuHslohRmHw+D4/Hg3379jFvTjKZxHe/+12mMcjl\\nchyWrAc7rNpjz+fzHBbN5XJobm5m79rj8SCdTuN73/seAODs2bNIp9OKiM1OIkr0HrrRra+vc5Tr\\n9u3bGBgY4JDz8PAwGhsb+cb54x//GB988AFWV1f5dyJegf6tVi+q+hgbG+P0nslk4kpBQL7ZPfvs\\ns3C73fyZp0+fxq9+9SuORkUiEe5VB2w0P652Dgl/RxVSo6OjuH//PoeTATliQpU7JBMTE/jxj38M\\nQE6fLi0tcfSESrOr1YdC4OK8TUxM8JxRO4NKpcLh7G984xswGo18Q/7Zz36GkZER/j65XI7Lnatd\\nS6IuxWKRqQIAOSLh9/vh9/t5jmw2G9544w2OEuRyOXz44Ye4c+cO384LhQIKhQKPVa2RJXoGpZcu\\nX76MmZkZTnnTuFPlKTF3Ly8v8007Eoko+tSp28JsV8rlMn8vQI4MXLt2jW/e1LibvjNVwomRU8J6\\nEiSAWM9r3WMkVIZOjO/qtU3l6F6vlyMl1PqEIks0LmJkqVrcJI1TuVzmtXnnzh1mfKfo2/r6OpLJ\\nJNuCwcFBtLS0cIXlBx98gLt379YUWRL1Ub93ZWWFefmoujOfzyOTySia92azWRw7dgyAPK+dnZ2b\\nIv9ms3lTdeOjRJwzvV6PSCSCqakpjlh5vV7uOUn6lkol+Hw+1rdQKKCxsZEjvdFoFCsrKxylW1hY\\neGSbEaquO3/+PAA5Kup2uxVwELvdjlgsxvOYTCaRTCZRLpcVY5XL5TgyRgz7tL7pHDAajTXRCD3W\\nzhJtrN7eXjz77LMol8sYGRkBIOOCaJL3Uoik8uTJkzzRV69exXvvvccTXW3PrEeJmoNINDBOpxOd\\nnZ2cfweAzz77DL/61a8AyBtAbSB3ohO9l7q8iyRhqVQKTqeTyQxpwxHv0n/9138xcJ8OgZ1wv9D4\\n53I5zM7OIhQKKULvJpOJ8+RHjx7lDUppn1/84hc4d+4cb0Y65KrFl6iFgNjkPBPol/AbOp0OLS0t\\nOHbsGG/8ZDKJX/7ylzh9+jQAOQyfz+cVqaVaOXtE7AKlrwiXQ+XSNpsNX/va1wBsAK2phc5HH33E\\n/eBonHaij5gqEMGYoqNC+lKfRbIFy8vLuHXrFiKRiAJcTq1p6Odq9KF/aSxoPc/Pz2NlZWVTQ2Ra\\nUwSWnp6e5gOI0ho0Vmqajmr0EoHa2WwWS0tLPD4E1qbnEqalVCrxhXJmZobXEVAdaHkrfcRmzOl0\\nmp3cubk5Bd5HkiQu/qCDjEgpxVYgxG1Uy2WEhDBP9J3n5+c5bUwwiWw2i0wmwymf6elpbg0DyI4c\\n8YaJF6VaihZETOHCwgKi0aiCzoFgCnRZMZvNsFqtimbNQ0ND6O7u5rQ3paRovW9nvGh8xfVBrZxo\\nfRsMBjgcDv6OKysrMJvNnI6j1+zfv5+hCclkEvfv31eQaG4nYCGm2O7evYtwOIwLFy4wCD+TyfBl\\nlT6HMIz02Xq9HjabjQHzJpMJCwsLChLkkZERBTygGnlsnSWdTscL7NVXX0VPTw/C4TBjLRYWFurG\\nTPsokSSJbyXHjh3Dm2++CY/Hw3ncn/zkJ1haWtqTKBcgLxrKgb/00kt49dVX+dCdnZ3FD37wAwXz\\ncz0cNpKtKo6I3M3lcuHw4cNckWY2mzE9PY23334bgLyY1fwX9XDcgI0oEB2qVJFDufcTJ07A5XJh\\nbW0NP/jBDwDIkSXR4a7VAVALYV8oMkOGnIyzx+NBIBBQRJWuXr2Kd955R9EYlfAu9P+16kUEh4CS\\n0I/EZDLBaDQyUN/lciEWi3GUa25uTtGQdafjRO9TR+7Em7PooPT19bETfOXKFYyNjSkqzHaij+jE\\nkD40T+q9Uy6X4Xa7ee95vV7k83lMTU0polziWNW6zsWCF9JTJCokofUeCATg9/uh1+sVIOJ8Pq84\\nZGtxAChCLlb4iaB2wrCJ+MxEIoFkMsm2gaK8pH8+n2dOrVrmjZ5DQHLR4QbkaBI5loVCAfl8noHX\\nMzMzOH78ONtM4tPaqgHydseHdBHHhdivgY15onOCbILBYEAmk+GL5Ze+9CVks1k4nU62lalUqqYo\\nruikR6NRLC4uKhx56h9Hz41EImzPxSb2It71zp07mJmZURQWbOccLpfL/J2XlpaQSqWQSqW4uIMu\\nuhQVLZVKGB8fZ7Z40revr4/HhUhsKUgRDoeRSCQUfTSrkcfWWZIkicsKn3vuOZRKJYyOjvJtt14N\\nc7ejB1U3AXJpfmtrK5LJJD755BMAwLVr1xQMpfXWSx1Zcrvd3Bbj6aefRiAQYEPw/vvv48aNG4rI\\nzW7oJBptMoiDg4N45plnuBouHo/j1KlTTGkvgqXr7cARCZsIKLVYLExbcPz4cWQyGVy4cAGffvop\\nAPn2UksZfjX6ALLRFJtRNjY24vjx47Db7Xx7/M1vfoPl5WWFI1OvMRIPJHLARFAqReCIjgKQU1AX\\nL14EsAHYrdf6FkugRYdQ1IkOskOHDilC7RcvXuR5q8faFqvCqALtQZce0usLX/gC/y4UCmFmZkZR\\noFCrAyDqRMBh0YFUP1N0KpuamjjSRWuKUmP1upjQeKsvPJTqVdMwqJ3GlpYWnlc6mGvVRZ0WFP9G\\nlZE0NoVCAXq9nh2XcDiMtbU1dor9fj/sdjvW1tZqjroB8j5Pp9ObyIjFCKAIugZkm7iwsMCNo1tb\\nW5kcksZZTSS6HRHZuAF5ndK6IsetVCop0nv0WdlslscuFArBarXyGI+OjnJ6jJ6xXX3IWbp27Roc\\nDgfK5TJ/b6KPISdteXkZhUIBmUyGgyZGo1FR+RgOhxGPx1mXZDK5owIYDeCtiSaaaKKJJppo8hB5\\nrCJLYt8Zs9nMADyfz4elpSV88sknXK5KzQ9J6GZTD0A1PQ8A956hlh2vvPIKAODjjz/mNMX4+LgC\\n60LvF4Fv9YgSUCh7eHgYr732GgDgiSeeQKlUwscffwwA+OUvf4m5uTkFNoDGZif9qrYSSpVS9OaN\\nN97A4cOH+dmffvopTp06xTQGNEbiLZRuzPXQR2zJ4HQ6cezYMbzwwgsAZD6he/fu4f3332cQOOFl\\nRC4VUbd6R+MI0zE0NIQnn3wSgJyaBOSyeTGdsJuiTi0ZDAacOHGCAd6FQgG/+93vFA2F68Ff9jA9\\nxJ8lSeJw/MmTJ2GxWBhjNTY2piigqLc+Wz1XjJh0dXVxMYVOp0MkEsH8/PwD8SQ70VHdBkRtX4CN\\n1G4gEOC+ZJQGCofDNbcTUYsYWdrqWWI7DvqXAObi++tRZCK+b6uGzvQ5YjsnUX9JkphiBQBTZzzo\\nu1WjkzpSrcYO0XyIY0UREUCOjvj9fkV0h55TrU7lcpmjXKKdFdezeI6KeFiKAk1OTip4zogPSaTt\\nqDZdKUaA1O1l6KwgPSuVjSIGs9mM+fl5BbGpmMZPJBI7gp08Ns6SeJjrdDo88cQT7Jjk83mcO3cO\\n165dUzQ5BaDYjPXEMNFz3W43hoeH8cwzzwCQ8RyRSARnz55loLA6jUA8RvVySkjMZjO6u7vxwgsv\\nMDBOr9djYWGBKw3m5ua2dNq2Mnb10Mfv93MlB5EJEl7qwoULWFhYUBgp+nc3UpaUMgXk0Prg4CBX\\nMqVSKVy9ehVTU1MPTR+ojUm9RK/XMwHboUOH0NTUhGg0ikuXLgGQ17joXNfTiVSL+B2J/+all17i\\n/be8vIzl5eUHVvnsptNE2BdiNm9sbEQ6nWYenWg0uikFX890pVrE7240GhVsxuvr6xgdHeUUOL2+\\nXvP2qGeI691mszHfDeHeyuWygpV6p/v/Yfqo/2Y0GhWkmsSzQ2m4WohDt6sT2Trx0iO+LpPJKHjF\\n1Kzbj3p+LfqI9k7cf5Ikd4sg8PP8/DzcbreisEDdjLcWfR60X7Y6M8UKtOnpaUVVsV6vZ7Z4AIpe\\noNXotBX8gRzLB30HIv0UCx9Ev4EY9/+vd5bEw9zhcOCNN97As88+C0BeJCsrK5icnFQMspiXrqej\\nJFZ2uN1uPPnkkwzGtVgsmJ2dxZ07d9hIPmqD7FQXwgT19/fjC1/4Al588UW+eUejUVy4cIGxXGID\\nSbUu9dIHkMehvb0dhw4dYkA3dYSmFjRnz57FwsLCQ6uU6nXQ6fV6RdUJNamlsRgZGcFnn32G0dFR\\nBTB1K6nnOAEbuCCq4ujv72cCynfffReAHAXYy4IFEoPBgJ6eHvT39/OaunTpEq5fv874qd1yILfS\\nB5DHq7+/H4DcBLVQKDDOTGTn3QtRO5aBQID3YzabxezsrOICtxsRuAfppY7U0M8i3QTpTe+pl27q\\n7yk+m0hio9Eo732Hw4GmpiauUKNGzPVa8+posPq7EoYHkItgKpUKU780Nzdz6yN1M+Z6y1bOCxHq\\nAvJlvFwuw2w2s4MiNrIFsCs4S7WO4qVNZIGnaj/aA5IkcSHLbomYnRGdI9KJxoPGjCLi1YqGWdJE\\nE0000UQTTTR5iDw2kSVg43YQCATw8ssvcwVaKBTC0tISMpkMR3wAKCpX6p3OIe/1wIEDOHLkCFeW\\nZDIZzMzMYH5+flN6SQwX1jNiQuHirq4ufO1rX+PSbkAOk96+fZvDuA8KY9ZTH0COLHk8Hhw/fpw/\\nc3x8HCsrK8wvRGWyW2FT6j1f6htQKpXC4uIiY7nGx8dx7dq1LXPau5ESJL3E/xeJ3DKZDH79619z\\nylKNeRNThbupm91uRzAYRLlcZnLOU6dOIRqNPjB0v5vRE51OB5fLxc2O8/k8FhcXOV2pxiruhj7q\\nSBeteavVys2gAbmM+sKFCwqd1FG43V5TZIPW1tZw7949WCwWhS1Qp3x2K2WpHrNisYjl5WVOn/b0\\n9CAej3MUQEyB70XKUkz9EC8W2dX79+8zrmg30ruP0qtSqbDdouozYMOWZzKZukbhqhGiY6Hzb3x8\\nHKFQiOdbrPzbCxHPVsJO0bwajcaaaQOAx8xZIkfo6aefhiRJXAJ7/fp1BuWS7AYmSBRylpxOJ5qb\\nm3lRLC8v49KlSw/FKdRTJxGLZbVaEQqF4HK5mDH46tWruHnzpqLJqWi46p2yoOdROe709DSPVSwW\\nw927d/nQpUNkN/URRWxYmslksLi4yAfH+Pg4YrFYXQG4DxM1iN1gMPCmnp2dxdjYGC5fvsypARqn\\n3Rof9SFLc0Zdz2/evMllvNeuXdsSqLrbQunvhoYGNnoTExMYGxvD+Pg4AKVTuZc6AXKYf319nVPe\\n586dw+joKPMzAbu7vtV6idxGoVAIN2/exOrqKq5duwZAxpOIpJ+7OV7qfa7T6RCNRjklPzg4iDNn\\nzuDu3bsA5P25V7x0gNJRXF5exvXr11mXyclJxGKxTemuvZJSqcTrfWRkBG63GwsLC4w9U1+kdlvE\\nsyyTySCfzzPx4/r6+q5RwGxHr1KpxKlSatRMusRisR3pI+3ll3mgEpK0LSXIKAUCAezbt49ztleu\\nXEE4HN40Sbt50BFwkggE6XaZzWYxPz+/6Ta5m+MsVniZzWauSAA2ummrc+27Pe9UsSRJEt+KiEOE\\nHAAy1Hu1BsXqEWJbpt9Ru5DddLAfJHSwUZ7fbrcjn88jnU7XDXxbi06AvOdcLhdzDAHgJr8kezle\\ner0eZrNZQeBJ7SqAnVdRVSuiYwnIe5B+JhZ78UKzV0KHP+lC4ybiTUQiw70WnU7HtgqQnfJcLsd4\\nks/DKRELiJxOJ0dw1tfXkU6n99wukIhVvDRmtQCnd0PosqBubv+YyZVKpXL8US/SMEuaaKKJJppo\\nookmD5HHKrKkiSaafH6yW/goTTTRRJPPUbYVWXqsMEuaaKLJ5yeak6SJJpr8TxUtDaeJJppoookm\\nmmjyENEiS5poookmmmiiyZ+EiF0lHkRJQtWVu9XtYSvRIkuaaKKJJppoookmDxEtsqSJJppoosmu\\ny6MKBPaqFcxW+mz12XtZ0PCw/pO7Sfi61edU27qo3uND5MEP6hFHou7buZv9MgHNWdLkjyJ2BVeH\\nNT8P3iHi1CIiSZHUbze4PLYyjGLPL0DmOBH5hqhh6270YnqUwRIJBwElL43YK6meuqjHSE0ySEJc\\nPrvZEPlh46P+O3FnPahx6k51edDniqkC9WfXm4NNPR7baQKtJjlV67ZTXbbq6Sf+/mH6iLrsRB9x\\nXap1Eve3+BlEZEh/F23PTsfGYDAoevGR0PdW/404lsRm7GR3drq3iAuP/l/9nGKxqBgbmi9RH71e\\nryDF3Ak/Fo231WplHkPiuiqXy4oei/Q70Q4Sy/pucS0+Vs6SehOKm44apYrEYntBDLnVZhT1od9J\\nkrSp9YpIEFcv/cjpUT9LHBfRMaLfGQwG1oUablKj1J04J2onTNSNxkicT7pVuFwufk2pVGJdUqkU\\nkslkTTqpWbPVfyNDpmbWpg3r9XphNpt5Hok4cm1tjRncq5lD8XO2YtAWyUXF+QM2WmvQnCWTSZTL\\nZRQKBXbeiPyzGiFdxHVN64MIBNV/F+cQABOPVioVnrdanVzxs8Tu4eRMq/8FNlpX0OcVi0UUi0Ue\\nq520QBKNM3UxpwPHaDQq9hH9vVgsMiksEUHSHNFeq0UXkayQSB7F1ivi+ikUCjAYDIrDJJ1OK1ip\\naYxq1UV9qaD1Ui6XYbVaeR+J+tB7CoUC0uk076NischOQS1Cz7VYLHA6nTzeBoMBxWIRBoOB502n\\n0/HvAHk9EzM1ACaH3amNNhgMcLvd3EYllUqxfaPn6nQ6RacBg8EAo9HI64fGqVAo7PhSZDAYWBed\\nTsfOB60h+v4kRHJqNBr5M0ulEtLpNL92J5Ev+lybzcb2RlzfYuPlSqWyqV0P2QCye/UmqNUwS5po\\nookmmmiiiSYPkccqsiTeIu12O7c7sdls6Onpgdfr5d/pdDqkUiksLCwAkHvqrKysIJ/P14VKn7xZ\\nvV4Po9HItxSLxQKDwYBgMIj29nbWT6/Xw+FwAJB7Mt25cwczMzPc84eaIe5UJ71eD5PJxLckuk36\\nfD4AQHt7O9xuN9+8GhsbUSwWsbq6ipWVFQBy081wOLyj2wJFKEgf0kWv17NugUAAwWCQb5wGgwEu\\nlwt6vZ5vEMViEfF4HPfu3QMgtyUxmUwcuahFH0BeQ6JuNpsNra2taG5u5t+ZTCaYzWbW12w2o1Ao\\nYG1tDQAwNzeHYrEIi8XCt5nt3szFaBK1oqDPNZvNcDqd6OjogNfrZV2sVqui/QlF2QBgamoKqVQK\\n5XKZX7NVRcmDREyRGI1GnhOLxQKHwwG/34+WlhYAcksPm83Gt9JkMonl5WXMzc0BkNdyNBpFqVRS\\n9ArcjqgjnuLecjgc3IsxEAgAkOfNarXyvqdGsZOTk9z3L5/Pc+SNxm6786SOsNF4AIDf74fP5+N9\\nTq1OxPZDer0eExMT3LtueXkZhUJB0auR2uzQz48SijzQfgHkfTwwMMDjEgwGodfrOSJRKpV4HKnH\\n39jYGCKRCO/zXC63KQq3HaHIIq0Zj8cDu92O/v5+API+b25u5rEUx59SK7du3cL9+/f5+5MutURQ\\nxH3t9Xrh8/l4HILBIFpaWuB2u/l5VqsVZrOZ91IsFsOtW7cQCoVYX3o/STVRJjHi5nQ6ua2RJEnw\\n+/3o6OjguSmXy4q9lUgksL6+jqmpKQDKVkO0p2pJx9E+p7Ws1+uRz+fR0NCA7u5u1qVYLPLeMhgM\\nCIVCiEaj3JYmnU5vyqLU2gpJ7Lfq8/mQTqf5s1taWpBKpTjySHCI2dlZRXPccrnMuhAUoF6wjcfG\\nWdLr9byAbDYbXC4Xent7AQADAwM4cuQI2tvbeXKMRiPW1ta4oazRaMSZM2cUXe53kuuliTWZTLDb\\n7WwoGhsb0dvbi0OHDnFn9FKpBI/Hw4dYLpdDW1sbPvzwQ24AXGtfMvHQpXSJ3W7nsXI4HAgGgxgY\\nGAAADA0NAQB3uLfb7ZAkCdPT07h48SI/N5PJKFJL29VLnVLT6/Xckdput8Nut6OtrQ2A3Dyzo6OD\\nx8XtdqOhoQFGoxGRSASA3MTSbDbzhqAegNU4cKKjRBvJ4XDAbrfD7XazLn19ffD7/XyQNTQ0wOl0\\nspOeTCYxPj7OBr6lpYV7NG0XDCrOlYjLamxsZIe2tbUV3d3d6Ozs5LHLZDLw+Xxs3LLZLGZmZrgp\\nKvWTK5VKigakDwPTqvUBNpz9pqYmAEBfXx+6urrQ1dXFjhsddvQag8GA27dv4/bt2wDkwziVSm1K\\ngz9qztTpPWqaSwfJvn37MDAwgL6+PjQ3N/NrEokEj6XP50M8Hscf/vAH/t3c3BxyuZyiKfF2RK2P\\nxWJBS0sL750jR46go6MDfX19AGQjL0kSr1260HV1dfEzisUiQqEQO+C5XK6mvUX2kBy1wcFBHDt2\\nDK2trQDktSkeFGtra+xY+v1+ABtpOHG9bIXv2s4Ymc1mdtx6e3vR1dWFQ4cOAQCamprg9/sVuJtU\\nKgWTycQHvsPhUKTh1Ckget92nUnSxefzobu7G42NjQDks6KpqQkej4fXg5iOA+TLrNfrxdmzZwHI\\ne0/tLFWjj+gEuFwu3kd+vx/d3d3w+/1sr/P5PDevpu8SjUbZgbly5QrGx8fZBqr1Aba3viVJ4ksP\\nALS1tSEYDMLj8SjSyrlcjp07AHjiiScUztLIyAhmZmYUKa9aL9d0Gevu7sbhw4dhsVjYFtP3pXNA\\np9Mhn88jGAxieXkZgDxP8/Pzin1ST3zgY+EsiY0pAXnReb1e3oyvvPIKTCYTcrkc3yZ1Oh18Ph87\\nLFevXuUFWA99xIVJDgoAHDt2DM899xx8Ph8bodnZWUSjUQSDQQBydGdxcVER4RGjKdWK2kGxWq1s\\nHIaHh3HkyBH+Wa/XY3FxkW9RwWAQbW1t8Pv9aGhoALCRJ99JTpywHWJePBAIoLe3FwcOHAAA/v6L\\ni4sAZONdKpUQDAZ5PF0uF7LZLB98uVxuS0P6qPEhR4meY7Va0dTUxAfd8PAwGhoaUKlUeJ3lcjkF\\nZokcLLpxZjIZZLPZqm/ipI/ocDscDvT09AAAOjo60N3dDbvdrsC6RCIRBW6IooaAvDco2vYw8PNW\\nIjpu9H3pQB0cHERnZyfcbreisWgul2PD5Xa74XK5eJ4lSUImk0EqlVIckNsZF7rxAhtNQ2nter1e\\ndHV1we/3K5wYEfORyWRgtVoRCAQ4GklYLnF/bVcfcY6cTicaGxvZ+XG5XKwbAHbaaH2aTCaUy2U0\\nNjayEzMxMaGI/qnH51HOJCAfYjabDV6vFx0dHQDkw85kMvHzwuEwCoWCAsdht9uh0+l4boPBICYm\\nJhRR3FoOOYvFAo/Hw/YtGAxyZIu+YyQSYUdIjKDSwdza2oqOjg7MzMzwe2rVx263szPd2dmJpqYm\\nduzpwmYymdgGZjIZGI1GdoK9Xi8GBgb4EJ6bm+ODthqHRBwfQF4vra2tHP0j3dkui64AACAASURB\\nVILBIF+CVldXkUgk2J40NDSgsbGRdcvlcpidnUUymVTgnKrNltDYky5tbW3o6+uDzWbjc0Cv1/Ne\\np892uVxoamriuQSAaDSqaNQuFuRs13GyWCzs4La2tqK9vR1er5cjaslkEoVCQXGOxeNx2Gw23gPR\\naBQGgwFLS0sAZEyYiNukfVkzxqumd+2xEPCMNr7D4cDAwADfqtLpNK5evYrV1VVEo1EA8o3iyJEj\\nvAgprFuPigYx1Gc0GmEymThy86UvfQltbW2YmJjgiZ6fn0djYyM6OzsByBvA4/EgkUjwoqvmhimK\\n6C2ToXC73di3bx8A4Otf/zqampowMjICALh//z5mZmZ4A/t8PjQ0NKBcLvPCWlpawtra2o7SleRE\\ner1eNqJDQ0N48cUXeYPeunULc3Nz7OBSqtJut3P0JhwOY35+ntMYBEitdqxIH/Gg6Ovrw9GjRwHI\\nG/Tu3btYWFjgzUa3ZYr4pFIpLC0tcbpydHSUDUUtwG46NCgUT5u+v78fVqsVc3NzrIs6sqTT6ZBM\\nJjklmM1mkUqlFCmdR42HqAv9azab0dTUxIc7pbZTqRTPkxgZAeR5i0QiiMViAMCpnWpTKGpDWyqV\\nOOoGyBHlcrn8/7P3JrGRptm12Il5HhkRDJLBmcxkjpU1dnVVD+o2pIYFN+TVA7yyDQNvY+/9dt6+\\nlQADBiy8heF+G0luCFAZJUvqSd1dY1ZW5cgkk0NySI4RjAjGPA9e/HUuvz/IzIwIMktKOy5QSJJF\\nxn//b7zDuecinU7LWi2VSigUCnKZEOScTCZlnrLZrA743q2HqRZgsOBBrcihUchUPw9nfvbU1BTc\\nbjfa7bZubFQjsrOy6WX6qH9Tr9flAuB40+Pf29uTyBHHhWkgGr38/yoIv5+9ZTQa4fF45JINBAI6\\nYzQejyObzUqUoNlsIhAI4M0335S/YeRJBeVTl173lt1ul33udrsRDAblcxmRLRaLyOVy8qzx8XEx\\nsGhUfv755/K51KOfM5EGodfrhdfrlci6x+NBq9XC7u6uzFM2m0U+n5ffmZqagt/vl/X98OFDnZNE\\n3XoVwiG4FiKRCKanp2G1WuUO3dzc1DlFXq8X0WgUY2Njsp5XV1d1EAJWxvVqVKoOj8vlgt1ux/j4\\nuDwnn8+LMQRoe63RaKBUKsldlslk4HA45Bwtl8vY2dmRfa9WMvcjA4D3QAYykIEMZCADGcgL5LWI\\nLFEYzRkeHsaVK1ck7P/HP/4R33zzDVKplOR2Z2dn4XK5xBNYXV29MHC3KjabDQsLC/jRj34EQMMK\\nPH78GJ9//rlgOJxOJ6LRqIQZLRYLVldXcXR0dGYuvF8hPujGjRv4sz/7MwBaqHdrawuPHj0CoOW8\\nDQYDbt68CQBSyrqysiJYC3riF6FPIBCQZ/3sZz/DyMiIhLfX19dx9+5d+f3r168jFArBYrHowJZ7\\ne3vyPT2XfrwpNex85coV/OAHP5Dvk8kk1tfXsbS0JBHMN954A9FoVMbi8PBQ9AFOvJt+1xQ9onA4\\njJmZGbz99tsAtHHb2trCysqKPGtoaAhXr16VyNLTp0+xu7src5bP5/viX+mMMDE9ySjo/Pw8CoUC\\ntra28OzZMwBaRCIWi8l6bjQa2NzcRCKRAKB579xrveAF6Lkz9WU2m+F2uyX6EAwG4fP5kEqlRJdU\\nKnUqdZdIJLC5uamLHKjruZdIjsqxQ0qLTpoHpmUPDw+RzWYlhUxsSiKREOwk12+vaQrqw/chYJtn\\nYKlUQiaTke8ZHWZUIBgMYnJyEtFoVKKRBwcHp+aoV7yJSg9BMK7VakUmk5GfsyiCUa9KpYJoNIrJ\\nyUlZz9lsFsfHx+eO+qtpdEAb30qlIu9ssVgEYM+xYdSQa95oNCKRSMj64ed0PqdbYZpThQEA2p3U\\nSY2Sz+d1dA5MzavFN4wK8e7oxOZ0I81mUwcf8Xq9ePbsGarVKlKpFABt3VYqFcl+sLCi0WjIeJbL\\nZdjtdll3lUpFl2buZW3zPOHaqFarkkrf2NiQ9QNomRjeL4xYmkwmLCwsyO8xeshxYvFCv/LaGEsG\\ng0Em5Nq1a3jzzTdlonlI22w2CaX+5Cc/wdjYGD755BMA0A3aRekDaGHeGzdu4NatWwC0g6JUKiGX\\ny+mMuw8//FCqQwieVjfjRXFBOBwOTE9PC66i3W5jb29PNlu5XMbo6Cjee+89AJpBQCAoD/3zGAAU\\nNb3DNEokEkGr1dJtgHQ6LXP2zjvv4ObNmzAajXKI8qBV566fsWL4ln/rdDoRi8Vk8zx58gSPHz9G\\nMpmUA+rdd9/F7OysGHc8ZBka7jQIuhWDwSBha0A7nJ1Op6T7dnd38fjxYzx48EDW0NTUFG7evCl7\\nYHV1VcdvQkxOr8YSn6+CXFn1BWjrOZ1OY21tDaurqwC0i3dhYUHC3UyxcFzOowtw4hTZ7XZdAQOr\\nEwuFgmBb9vb2MDIyIpeQ3+/HwcEBEomELu3Try4UzpmaLrBarTrDPplMIplMisNGvF4+n5c1xIrF\\n8/CrqVxYvMi8Xi9sNpuMQ6lU0qWaRkdH5VKjkZBIJM69r9rttmAT+bmHh4eScuI7l0oluYTr9Trm\\n5uZ03Eu5XA7Hx8c94xHPErvdLuuXjh/3WyAQkJQxz0Sz2YyFhQUZO5fLhVKpJI4IU6v9pJeAE2JF\\nv9+PfD4vTnQ2m4XH49EZPvF4XFeIUqvVdPOcz+dRKBTOjW+1Wq26oqP79++jWCzq8E+FQgG5XE70\\nj8ViKJfLOqJKVlxSP9XZ4fO6GSubzSb7JhgMYmNjA/l8HsvLywBOijSor81mQzgchsPhEGeKeCvO\\ntdFoxOTkpKwpOhn9puJeG2NJ9cYA7eKlBzE2Nobp6WlEIhH89Kc/BaB5xK1WC3fu3AGg4SzOY1U+\\nT5gj50WXz+cFlEoj4M0338T3vvc9WXSHh4d4+PDhKc+l30O8E+BNkDcAwbXw3efn5/Hee+/hT//0\\nTwFo3mAikcAXX3whlXnnObA6q71MJpOOnfb4+Fiek8lkMDQ0hPfffx+ABtQ3m83Y3d2VeVteXkYm\\nk9FdLr0CmDkuFotFvF9WWfEAX1tbQzKZhNlsFvzZrVu30Gq1pCT+9u3bWFlZ0ZV9A9ph2w/onJs6\\nGAzqSqsZOcnn84JVmJ2dxdjYmOAJ0uk0FhcXxcNrNpty0PGg7Xa9q+vObrcjEAhIxK3dbuP4+BiH\\nh4fy3uFwGLFYTNbY/v4+6vW6jGWtVpN5V9dmtwa4OtcqLUAkEoHD4dDhKKrVKiwWi+DiHA4HKpWK\\njmWYe6IzitarJ85Ll05aMBiE2+3W0S44nU7Rd3x8HE6nE41GQxdRUIktWUnZiy4sZc9kMvLerBBW\\nDU232y1zNDIygomJCd3vEN+lEv+pc9btGFWrVWxvb8vavHXrFsLhsIxDs9mEw+HQRda5xugUOZ1O\\nXVUxje3Oi62b6GQmk8HDhw9lHN544w25UKPRKCqViq7Cy+/3w2azybyS4oS/o1aacm12u4ba7bas\\n1e3tbbjdbgGb8zPVCtZKpYJAICDnSSaTgdVq1WHlSH6qnjnqPfCifUZ9a7UaUqmUzBGrONXzmp/F\\nimES8qqBAGLg1DUEnDg8nMNuxokRIRpy8Xhc7kjeq9TNbDbD5/NheHhYV4nu9XrFcGOhE/GXHLt+\\nMycDzNJABjKQgQxkIAMZyAvktYos0XM5Pj5GIpHQcat88MEH8Pv9wr1kMpmwvr4uaThyvpyHjr1T\\nH0CzeFmJAmiW9tzcnJQZA8DNmzd1Fu8f//hHLC0toV6vn8I/nFe3Wq0Gs9ks+litVgwNDeH69esA\\ntBz4j3/8Y6kWqVQq2NjYwOLiokQO1Pfr/LoXsVgsGB4elqpFUvUzJXb58mXMzMzgz//8zwFoEYtS\\nqYSDgwOpAqMXo0an2AOoV2yF3W4XqoCbN2+i1WpJChfQOFhGRkYEf8awOT2eXC6HVqslKSu2bFA5\\nT7rRibrT+/L7/ZiamhLvslQqweVy4cqVK7Ker1+/rmuDAEBXkUaqB45PL6JGfZgiopdNvI9KSrmw\\nsICZmRl5Dj1deuL5fF7aSlD6ScMRD0MP2mw2S4UcIyrT09NYWFiQaCCjularVSJ3tVoNNptNx7PU\\n65pmBRLxWAAET6VGknw+n6wxlvMHg0EdpkqN3lQqlZ5J/BhFMBgMuvRHKBSSNG0ymYTL5ZLIzdTU\\nlMwpoywq5onvWCqVdGnCbqTd1lpPcFz29vYwNjYmkfXh4WEUi0VZH8FgECMjI9JXjO+k/kui1k7C\\nzm7Gh+cMv15fX5d3Hh0dlQpFztvExARmZmZ09CTJZFJgCaxeJN6Jep4V+XqePsBJupopt1AohEuX\\nLsFms0mEOBAIYH5+XsaOUSZGtzc3N5HP53W0EOzhxqhON9W5zWYThUJBKrYjkQjC4bBghaiv0+nU\\nUWWwWo6pZ3JjcRwYhVPbMuVyua5aL3EMPv/8c0xPT8NutwskolKpoFqt6irKI5EIJiYm5HzOZDK6\\nCC6xceqcTU9PC0E10Nu59FJjyWAw/B8A/isAiXa7ff3bnwUB/C2AKQBbAP5du90+Nmgr/X8F8OcA\\nSgD+u3a7ffesz+1V1IX52Wef6fh7eHn9/Oc/lwOy2Wzi97//veBjLrpPDD+nWCxicXERH374IYCT\\nnHiz2ZTwHw+p9fV1AMDHH3+MRCJxavOfV7dWqyVs1z/84Q8BaMZbMBiUUOWtW7cQDAblUDo6OsLv\\nf/977OzsnOpzR+nHiCMXlVpW6nK5MDExITxLtVoNN2/elDJZs9mMbDaLpaUl2Vwkq+s0RnrVx2Qy\\nIRqNClUALzIakfxMckEB2rra29sTXWKxGFwulxhy9XpdwuO9ABmJc2JaYmFhAcPDwzJON27cEN4i\\nHpqjo6NyeQBaeiEajYqBWyqVBNvQS06e+0oFMpN5GtDmbGZmRoj8AC3tTYME0OYoFArJ/ydbtsp8\\n3Au2i0ZWvV6XNBt/3mg0EA6HxQBnWkNl8A4EAgiHw+JcqaznQH8EsHw2DWQAQiXAi3hyclLHF6Pi\\n43gGOJ3OU/u+Wq3qqAReJuoa4ueQpE9Nj8RiMV2fwGQyiWazKWuWKU41jWgymXQGYTeGN3msKEzt\\n8TkmkwnhcFgwm41GA5VKBQcHBzJW8Xhcl+LkXKlrvltajGazKfvC6/Wi3W7LnOXzeRgMBsRiMbzz\\nzjsANGelWCzKpctiD747ezCqDg7fuRtqDH5OJpOBx+OROQ6FQkJSyWIAv9+P0dFRST+ZzWahLAE0\\nA8Zms+meR0dJTRE+Tx+eo+zpxrn3+XyYnZ3FzMyM/E6xWMTQ0JA4/QTFVyoVweAdHh4iGAzq5lrt\\nfdgLdQDnOZFIYHJyEnNzc6JLo9HA3t6ejNPo6CjGxsZQq9VEl1arhaOjIzHKC4WCBC/4jqlUCsVi\\nsT/caxe/838C+N8A/GflZ/8BwG/b7fZ/NBgM/+Hb7/9nAP8lgPlv//segP/9238vRLjIdnd38ctf\\n/lI2ltvtxs2bN3WtEdLpNL755ptT+JKLiN6ohlupVMKXX34piyUWi6HVamFmZkYuZrPZLKzCAPDg\\nwQPUajUBR/Ld+tFN/f1arYZkMok7d+7o2h4UCgUd7whbwQCa4fnrX/8aiURCh/HoV9RxLhaLuH//\\nvgD3AoGADoMSCoUwOjoqRm+j0cDGxgbu3bsnvEpsmqt+bj+YJZfLhUAgIIdQp0c/Pz8vnCw8wI1G\\nI/L5vAA92SBWra7oVx+bzSbGRSf4PBqNIhAISHsP4AS0ymezzQANCUaUOpvadqsPx4JgTXqO5DtR\\nMR0E5nKvbW5uIpvNnkn6yHfrtmhANQAqlQqy2ax4nPF4HPl8HqFQSAxs6sEDkhghEopSB5VIllGv\\nXqMnlUoFqVRKLq6DgwNUKhVxRHw+3ym8D/WjLkNDQzrALg0LvnO3hi67sFMX4je4z+mk8NKqVqso\\nFosIhUJy4VutVvj9fjF6SfiqYmi6PZNUw/jo6AjPnj2TSGQoFNKxRatEqyrHE3FXwImzRUJL4ASg\\n263BxL/J5XJyoe7u7goRLy9eXvAsUMhkMiiVSqILI5Nms1nmMZlMIpfL6c7v54k6t6qRbjBoLWII\\ndufPWDgAQNpQcR6vX78uwGu1xRKrIfmc50WXVGfTaDTKO0YiEYyPj2NiYkLuDqPRqAPH53I5rK2t\\n6aI37AjB6BP3A3V78uSJFKI8b96IKSWWa3R0FJcuXcLU1JSOR2xiYkLWEJ3Izc1NmZNMJgO32y3P\\nYeUqnfNSqYRPPvlEMGkcq27lpbdiu93+I4B0x4//AsAvvv36FwD+a+Xn/7mtyZcA/AaDYaRrbQYy\\nkIEMZCADGchA/o1Jv5il4Xa7ffDt14cAhr/9egzAjvJ7u9/+7AAdYjAY/j2Af9/PwxlKVkPFVqsV\\n0WhUrMy1tTV88803p1DvF5WGUz/v+PhY2F59Ph+i0ShGRkZ0DWKXlpbwT//0TwA0C50etGpt96ub\\nGn5ttVrY3NzERx99BEDDBQ0PD4u3YLPZYDAYJFf90UcfYXt7W8c83A9vR6cQW3J8fIzf/va3ADTL\\nfmJiQqor1PkCNKzD3//93+P+/fsSEm+1WjpPt1c8BXDSrLRarUr5e6FQwOjoqHhWNpsNbrdb14Ih\\nnU7j888/l8ajbMRM3Zg+6YdmQY1Obm1t6dK2Xq9XeFAYWXI4HKjX6+Ih7+3tYXd391RfL9VT6pfD\\n5+nTp+JNzs7OIhqN6lrohEIhHUszsRgqfoZcMOra7JfDhzQBNptN10aIYxUMBqUatd1uC+aLXigr\\nIdVUlxoRfJFO6v9j+ogcT2z4yQgF8XXECUUiEcFJ0vPe3d3VNfVVx4a6drue1Ia829vbunJ9l8sl\\nvb0ACHs99wL1r9fr4mXn83k5k9TUXDdzxigEAOHlUjl8vF6vLspoNpsxPT0t80aGdT43HA5LyofC\\nFkHdjA8/hyzPbKnkdruRzWYxNTUl65W9JzkOLEfnmdlsNiUVp7aPUe+WbtK6HCM+l/dYMpkULA65\\n23jGlMtlhMNhiY5Eo1HE43Ekk0l5J0A7q7guj46OUK/XXxg1Ieu6ylo+NDQklYGAFqVV+abS6TQM\\nBoOsa0DDeap3R7VaFfoOQItMs8uBWtHXKWrzY6b1jEaj7JuxsTFdQ2GVW0lt0Gyz2eT7oaEhTE9P\\nSzcLUvb84Q9/0EXou5VzA7zb7XbbYDD0fKu22+3/BOA/AUA/f6+Ky+XCwsKCDkT9L//yL7oeO69S\\n2u22bJxisQiXyyX9tADtEPq7v/s7mehXQY6pSr1eFzLDer0Ov98v4XmPx4NcLoe//uu/BqC1HCmX\\ny6dSXZ3v162oYV6OCzf1b3/7W1y9elU4qa5duwaHwyHh47/5m7/B/fv3hZgNOMn383P7bXXSbrel\\n3B7Q+vWpPaQmJiYQCARgt9vloProo4/wxRdfyFiyzQbnrl+CTI4L0wsrKys4PDyUSzYYDCIQCOCN\\nN96QQ7RareLOnTv43e9+BwDS/ZtlySwyOI/xxueoOK2DgwPEYjHBNAAaNqfVasnh/OmnnyKVSsmh\\nSh4Y9cDudozUtUfsAykJlpaWEAgEUKlUZE1Fo1H89Kc/lYsulUrh3r17SKVSMo+8iPlOTIH3og/n\\nTCXtW1xchN/vF74pwgCIhSFuZ39/X8aBoGpVNzUl2K1eJFKkAZJOp/Ho0SMxroeHh3UG4vj4uBhB\\n/B3+yzSnavD0uobUi9liseD4+Fgwml6vF+FwWC7DSqUiKWYC80dHR/Hw4UO56AqFgqTp1FYa3VB0\\nqGuoXq8LyBjQaC6YVuP7s/8Z0+JXrlzBG2+8IRQn7bbGVUcHADiBW6g90V6mT2dZPc8AldaCbYu4\\nr9vtNmKxmI5EOBwOY21tTYfniUaj4hwUCgU5U18mNK79fr/oRUOkXq/rWohls1nBf/FuAzRaCp5d\\n5XIZhUJB0uSpVArhcFgA1WcJDXgSJzscDjSbTYTDYVy7dg2Atm/29/flDiUJr4q3ZHqN7zQ1NYWR\\nkRGxCUqlErLZLPx+v4D3e5F+jaW4wWAYabfbB9+m2VhStAdgXPm92Lc/eyXCyY1Go3jnnXeESRjQ\\nGueqzQZflXQaFi6XC3Nzc3j33Xfl2Xfv3sWnn34qi47VMOeN3nQKvVg1+kIcDi30RqOB27dv44sv\\nvgCgHbLnIRB8njBvrnqCzWYTqVRKx/hbq9Wku/fnn3+O7e1tlEql5x7a/ejGaMPTp08l2ke8D4HC\\n7XYbH3zwAer1Or7++msAmhHw+PFjObgIfO00CPvRp1ariRGWz+d1mBqPx4PZ2VksLCzI+z979gz/\\n8A//gNu3bwPQDjK1t2AnHqJXffgcdqFXwZYrKysYHh6WKACgHYL//M//DAC4c+cOMpmM7pBViSl7\\n1UUFLauGRKvVEt4wRrnee+89lEolqWr85JNPsLq6ir29PdGH+J7Oi61XJ4C9pfg5yWQSmUxGR4ob\\nDoclIsFij83NTbksMpmMDmRNjJD6nG5FbQxerVaRTqfljInH41KlSBkfH4fFYpFxoUHLvUZdVLxL\\nL/qo0ahKpSLjkkgksLe3J5d5q9USTB6jhsViEbVaTS5druVWqyWGJTFLvQirxLhPUqkU0uk0stms\\nju9I3dfJZBJer1d0mZyclCpejnkymdRlN7oVNYKSTCZhMBiwvb0thlk4HIbBYBBjx263w2q1ShFB\\nLpdDJBLB8fGxDsMbj8d158nL5o3GP8eF3FQqmStxZPwdVqCr0VNmdIhxPDw8xMrKihinBwcHugKG\\n50mz2ZS1+/TpU+zs7OD4+Fhwb16vF/l8Xp7Lvoa884CTaCodTL/fj0wmg/v37wPQztDNzU0dpqoX\\n6ddY+r8B/LcA/uO3/36k/Px/MhgMfwMN2J1V0nUXKmSNBYAPPvgAN27cQDqdxpMnTwBoaTi1RPdV\\nGk0kpgQ0K/vDDz9ENBqVxqO/+93vsLW1darlwqsS1fK/cuUKbt26JYtsY2MDH3/8sVjoXHCvQnjx\\n8YBxu92Ynp4WwLfFYsHa2ho+/vhjAFqUK5fLnSIxuwj96AXy4mU4nd5lOByG3W7Hzs4Ofv3rXwPQ\\nWsOoLRguyqhkCo4XpkqLAGgHpMvlQiQSkUPn008/xeeffy4eEaMGqiFxHn3UCF5ndRMPapWocmtr\\nSwy3vb09nXfMS60ffVTng4YS93m5XJYLStVXbby8urqKjY0NXUUXoxTnmTc6IeqYcz3wcjEajTCZ\\nTGJUhsNhbG5uShUaoBmSKlM111O/Rq6aklQNS46ZWjwRCASEugDQLpe9vT1ZU4ya9sNyrL4DnTWu\\nXToHqrFaLBYxMzMja4v0FCyRJwN5o9GQuVWjui+TTmJFjnc2m5W0J3/GCC3ncXh4WLfuqtUqKpWK\\nrmQ+k8n0lblQQfekImk0GmIc5XI5XVGJ3W7H0dGRpN8nJibg8Xiwvr4uLXTW1tZOMY53I+oePTw8\\nhNlsRiqVEuOI1ZEc/83NTSm4obPy9ttvY21tTX4nn88jHo/LutzZ2ZHK7xeJupYdDgfC4TDq9bow\\neHPMaFC1220ZF3UPzM7OSgU8K6k5Lpubm1heXkY6ne4r+t4NdcBfA/gTACGDwbAL4H+BZiT9XwaD\\n4X8AsA3g33376/8PNNqAdWjUAf99zxoNZCADGchABjKQgfwbkpcaS+12+795zv/6L8743TaA//G8\\nSnUjBoNBoiXf+973YDKZsLm5Kemlcrl8Ic1guxE2sAW0EvSZmRmUSiU8ePAAgNayo1tOjn5E/Tyj\\n0QiXyyXppYmJCcRiMfHyvvzyS6yvr+vK31+FTiqwl94tyd+Ifclms/jiiy/EQ1LTXBednmREQPW8\\n2UcP0LiNyuUy7t+/L3iYYrGoAy9e1Pyp+gCQ8lwCK4eGhnDp0iUh1AM0jq5sNiteI73si4q6qfgH\\nFeBLMsZoNCprvFAo4NGjR7oUisFgOMVn1I+oKUE1mgOcRG5qtZquFYXVahXg/sHBgZQqqylKdZz6\\nAb53gsMpKgmo2WxGMBiUNVWr1U71pCwUCtIElH/f6zxyjFTMjPpuqp7ce+RTUtNuBMcTjKumNHoR\\n6sMxKpfLujFWyRKpo9vt1mGuQqEQrly5oiuhZ+FArySrjNbw2WqJf6VSEXJTRikqlQqMRqNEb9he\\nSSU9jkQiQpHBd+kFX8a/KRQKsofZk5F7DNBSuSr/EUH81JXkmPv7+xLZZbSsF24jUjKwkbnD4YDF\\nYtFlbJrNJtxut0QemQY3GAwSSQoEAhIF5Ng1Gg3ZnwR3dzNG/MyVlRUcHx/DZDLJvjabzYhGo0JG\\nu76+jlwuh1KpJGucBQ08l8rlsoDdAe2cOk/z+teGwRvQh+hNJpOQTU1MTCCZTGJ5eVmMJaZzOsF1\\nFwWs5udaLBZYLBY5IN966y24XC7cu3cPv/nNbwBoPcXUChiVgfciDSiTyQSn04nh4WF8//vfB6A1\\nynW5XAKeu337Nh4/fqwjUyTPhcoZclH6uN1uXLlyBQDw/vvv46233hIA3tLSEh49eiQbgk1Gz6oO\\nuoh5MxgMchD4/X689dZb+N73NBqwaDSKg4MDPHr0SHLcnDOVXVjFYF3kWmIzXQCYm5vDlStX0Gw2\\nJa38zTff6ML+vIw6eX36FRXc35lqtFgsiEQiktqt1+tYWVnR8U+dxRzeL6fZ8/5GJQbkRUa+HLWX\\nFvmCeCj22l/seTo9b6+qZ8HMzIys91wuJ412eREz5aMaX/3qc9Z7qSBis9kslXqs0PV6vZLy2dra\\nQrlcFkeKFaPquurFqFSJTTsdOIfDIVgd/n86LIBmJBCLA0DweCw+6Wd8AJzqkgBAcDcq+SJwggE7\\nPj7G9PS0OOPxeBzPnj071cG+13OJBkGnPp2NrNWUcSd8gNW629vbkhKkI06joNvxajabYvypjOeq\\nkWuxWHScW2oPSgDSg49C3JtazdftGFHvVColZ6+arlaDEqyobLVasq5sIfxdngAAIABJREFUNhvi\\n8bi8SyeXHtOp/cprYyzxQuHXoVBIWLMNBgMWFxfx6NEjOcC54dVKiotspKvSufv9fly+fBmAZrjV\\najU8fPhQjADVowK0SX1ZaWc/YrVaEQqFcO3aNanQGRoaQiaTkSqwnZ2dU+SB7KZ+kfqQcC0Sicjl\\nwaaH9FSePHmCo6OjU16/2Ww+5dFfhD5soQBo2LJQKKRr+0J9Osu61fYbF21sU6xWqxzO4+PjGBoa\\nwt7enkS52FKlk1jxokD5nfrwe5PJBIfDgdHRUXlGIpHQsaqrxj9wchleVHRQNeT4DOKC3G43isXi\\nqVY9KmCeF/lFRARVUd8fgLQXUTE2PH/Uz1AvgfOA8tUx7tQFgK7BKUH7TqdTqr7sdrtEE4CTisp+\\npHNvqHow6sixIBt6LpeTeXM6nQiFQgJSpr79ntmd0eBOXQqFgo4GwGAwSCEH2y2xUpZl+mo0qd8I\\nXGdkkjqq0dPOymSynQOaYefxeJBMJmWuiF/sde5UA/csUuROKhL+Tbvd1lWWdjaJVsv51ch8t0J2\\n8U7hmukUOsAqML5TZ+qmBih6lUEj3YEMZCADGchABjKQF8hrE1lSPReLxYK33noLf/EXfwFA8zKf\\nPHmC9fV1XSmnWlrbjyfwPOn8rFAoJLTvs7OzEoZkuTA9BdXT7Qxz95uyAPSVLz6fD2NjY5IzNhqN\\nUroLnESWzvKQ1ChFv/rw89xuN2KxGGZmZiRd6nQ6pXcdoFUuLS8v6xqcnpUGughho1eGcaPRKPx+\\nv3hjW1tbWF9fx71798R7USkeKBcVMVFbytAbY2We0+lEPp9HKpWS9Ck9STV6cx4v6UXSmQYNBoNY\\nWFiQKNxnn32Gra0tXXVTv+Xv3eqjitlsxhtvvAEA+PDDD+F0OuV3crmcRG0vcs8/Tx91PZD4kWuM\\nvdqY3gJwqh3NRaROqZOqC8kYmapRo21MfQUCAfj9fjkr2HblvGvqrMhJZ5l9o9HAwcGBcDyNjY0J\\n2Smg7QGPx9M3jcHz9OH7qSkZfi6/X1lZwczMjJSgE2NVr9d1ZLkXJZ2VhJ3vWavVTmEKLRaLrn0M\\n2+YA/Ufjzxrfs37WCU1Qsz5Mx/NOOk/rrG517CTmZaSLe4JnPAkr+52718ZYUsXtduOHP/yhpJrq\\n9TqSySRSqZQsGPb7UcPdr0Lsdjtu3Lgh5Fkul0u4PPhsk8mk2wQq2JvS70GgAvLMZjMWFhZ0YNx8\\nPo+9vT0BCvPwVA8QNU99Xn2oi8PhgNPpxOzsrLwrexqRtmBxcRG5XO5Ud/qLpg6gMaLyg7RaLRSL\\nRdy7dw+Axjf18OFDZDKZU2XdnXpcVMqrE0/HFCF7a925c0fGit3gX4XhdpZQJ6fTiVgsBo/HI/36\\nvvjiC+zu7uowEheZoqR0YrIMBgMsFosORE1mYKaZj46OBPx+EYDzl+lDXAclk8kIFGBpaQmPHz/W\\nlZzTmLuIeXueA8bva7WapJZ2d3dx7949KRGnrk+fPhXnoB9erE59zhKu206D+ujoSPiFYrEYUqmU\\n4AUPDw8F73LegoFOo/IsJwg4KU+nkUm84OHhoVy66n3Sr169jHFnurVcLgsbOueWfIIXDevoRjiW\\nvHeJF1SNu+9aHxVjpc6TSjnSj7xWxhInZGJiAlarVTb57u4uHj9+rGuL0S8pXjeiHkx2ux2RSEQO\\nw2Qyia+++gpPnjzRVZq9CCB6Xl24SaxWqxww9IASiQTu378vFQKd7OEXVVFFURsMt1otbG1t6QB4\\nuVxOsFxHR0fPzYlfpHCMjEajroN5oVCQA3JnZ0f4Qb5rg4Q4La7vWq2G5eVlPHnyRPAvKniWer0K\\n6TTgSIq3uLgoJIOPHz/WgSdpLL0qnVTdLBYLwuGwGCTLy8t4+vSpAE2z2aw4I6/CQTrLqVAr0J4+\\nfYq//du/BaCt708++URYjQFceLVnZzS405hTK4za7TZ+9atfSYTw/v37SCaTpypjL0qnzu9V3ZrN\\nJgqFAn71q18B0Li6FhcXhR396OgIpVLplZwFZ+moGk/EeBJPFY1GkclkdESQ36WoBkChUBCyRep3\\nUQU5vYg6jioXFg2U71Kf553XjKaqQYrziOG7HuQzleiy3QkPcI/Hg5GREUnvrK+v4+DgAMVi8ZWm\\nA1RRK2C8Xq+ERL1eL46OjuTQBi7eIHmeLqyAsdls4u1aLBZdFcdFg29fpBMvf+rC7u/UpbNdyHeh\\nkzpWVqtV5wExxP1d6kNRIxQWi+VUafh3MWdn6WY2m8XYZUTiu9xnnaKmGwAIISUvk17bq5xX1DXV\\nbrd1xQkM+RNU/F3rpRpyjILR4AROehv+awjPB3U/qkUmryoT8DKdKJ0QjlflePcqHLd/DQPpeXIe\\nCMm/Efmm3W6/87JfGgC8BzKQgQxkIAMZyEBeIK9VZGkgAxnIQAYykIEM5AJlEFkayEAGMpCBDGQg\\nAzmvvFYA74EMZCADGchABvL/bemknzgPnc1FySCyNJCBDGQgAxnIQAbyAhlElgYykIEMZCD/v5Ne\\n+9+9SrkootKLkE4eOLUy8Tykjt0Ke+UZjUapxCV3k0pzwQrPi+7V+TwZGEsDGchAXjv5t3TRUV6U\\nJvjX0PdlF/B3ldbovHzPIno9K+1yUc/u/KxOVulOvc4i+LwoOYtVvrOPYTeEvBelEykSyEfUbrd1\\nP+tkwj+Lz+uiaU3MZjNCoRDsdrs8h7x4aiNxVR/+3aviVwNeM2PpdTogO39+1vfAq2kNcdZzKJ2H\\nExebeoB08gy96sOiUyeSNFI6uY8ucjN00xJD7eTe+fxXwVzdeRg9Tyd1rtUWBOclrlQ/t7Otgfq5\\nnTxRFM6X+jnnOVA7x0LV5XnrXSWju2jm887L/6wxOksf4Gy2837XTycBZefzjUajPJfPUOdJ5X9S\\ndetnbFRetU79VC4lfq9yUan6qXxU5+GE4zMZoehcI61WS6dPZxNg9XvywZ2XL89g0FrNkOeK402j\\nBDjp9sDvOaed+gAXs8/Z0cBms50a73q9DrvdrmMt59yp+qicTxdxFno8Hvh8PpjNZuEvzOVyqFQq\\n0qqHHGHqWmo0Gqfm+iLvrgFmaSADGchABjKQgQzkBfJaRpZUj4BisVh0fdKMRiPy+fwp7/JVRHLU\\nMKrK7E09O5mr1V5JKmPtRej2In1UvTo9OoYwAc1qV/sgncdzUXXhz/i91WqVsaEO1I190tjfj7qU\\nSiVUq9W+PBg1gtbp8ZP5XGWIZs6c31utVl1T1Ha7jXK5jFKppGsS2os+fI7q8VssFthsNl0Y2mKx\\n6Lwo6kkvkyFqlZWZjUF7EerCfWQwGGC32+F0OqXhaqvVkvkBIPPBOanX69IORV3zvbbT4Hypa9ds\\nNsPtdkvvQ5vNhnq9LvrU63WUy2XU63UdU3ylUpGxY8ufXtY1dTGZTKIPmfuHhoZEv3a7jVKpBEBr\\nFVOpVFAqlaQdBPcWvyczdK97jAz0ahNcr9crzZi9Xi/sdruMeT6fl7FiT7FsNitjRd36ZYY2m83y\\nbL/fj2azKXgTdjngOFWrVeTzeVitVnl2Op1GLpeTtjDNZlMaIvcqBoNB1kMwGEQwGJQ54b6x2+0I\\nhUIAtH5rlUpF14g2nU7LOJVKpb7nifpwHDwej8wZG86qZ6Ddbke1WtX1N6OOgDZ2qj5A/+czx4HP\\nZQcISqVS0eGEgNORI3W/Ud9+9VHHwOVyYXJyUvZaPB6Xs5/PabVauvZdNpsN7XZbfqcz8nReea2M\\nJbXtgsfjwfDwMAAtbBeLxRAKhWSyW60WUqkUdnd3AWgd7pPJpLS0OK90toNguNDn88Fms2FqagpT\\nU1MAtE2iHtb5fB5LS0vY39+Xw6FcLp97Upm+cjqdMlZutxtms1kaj05PT8PpdOq6oNdqNezu7uLg\\n4ACA1k+OPZr4ub3qRqPIYrHIxWY2m+Hz+eT7mZkZDA8Py4bl7zA/DWjh152dHezt7QHQNrDZbO65\\nGSkvO46Lx+OB2WxGJBIBAITDYUxMTMDv9+saExsMBjloC4UC8vk8Njc3AWiXDcem16atvHgBbZMH\\ng0E54Ofm5hCNRhEOhyXszB5tPMATiQSSyaQYRGtra9JQk3Or4hBeNjZ8Z6vVCrfbLePi9Xpx8+ZN\\nDA8Py2XIseHfHB4eYnt7W/ZaoVBAPB7XGW7d9mVSjWu73Q6fz4fR0VEAwMLCAgKBAC5fvixjxS7n\\n/Jtms4mNjQ1sb29LE+JcLqdrP9RtukltDWIymaTN0qVLlwAAb775JmKxmOiXzWZRrValZxegXW5P\\nnjyRNbO9va1z4mjUddOmRb10rVYrYrEYZmdnAQChUAiXL1+W5uLBYBDFYlGM+EKhgFqthmq1ikeP\\nHgEANjY2sLe3p9O30WjoGn13M0YmkwkulwtXrlwBAIyPj8NoNCIajQIAZmdnMTw8LHuk0Wggn88j\\nl8tJf8/FxUVsbGzIO6qGfq8AXpPJJOfx9PQ0hoeHZRwikQgWFhYQiUR0e7Zer8uZl81m8eDBA2xs\\nbMgYdPbP68dwslqtsNlssqcNBgNmZ2dx+fJlORN5wXMcjo6OdE194/G4rg0SpR99VMOIDtLs7Kys\\nZxplqoOZSCRQrVal92g6ndatF+5HVa9uheeh0+lEMBhEq9WS83psbAwWi0WMxnq9jnq9ju3tbVkr\\nBHtzn3emBs97v742xpLJZJID0uv1YmRkBJcvXwagbca5uTmMjIzIxWaxWLC6uirezPHxMTKZzIU1\\ni1Q7xXs8Hni9XgDA5OQkZmZmMDc3h6tXrwLQPFmn04lMJgNA2wAmkwmpVEqMgm6wM2eJ+ncco0Ag\\nIPqMjo5idHRUDLerV6+i1WrJYVIul5HJZOB0OqU5KSM8/eCqOnFHNpsNIyMjALQDPBaLYWxsDAAw\\nMjKC8fFxOch4YJRKJezs7ADQGqWGQiHZnLlcTqIU3eqlGko0zCYmJhCLxTA+Pg5Am7dQKAS/3y+X\\nh9vthsFgEP0SiQTW1tZkU/MQK5VKsh5edmjxIFQPdLfbjWvXriEWiwEALl++jKmpKTidTl2UKBAI\\nyGcfHR3h2bNnWFlZAaAZ4EdHRygWi10dVuo8qZG8UCiESCSCW7duAdAum1u3bumeXSgUUCqV4Pf7\\nAQCXLl3C8vIyfD4fAK1XIw9aVY+XGd30+lVd3nzzTZmjd999F8PDw4hEIjrjKJfLyRzZ7XbEYjF8\\n/fXXMiebm5uo1WqnDtGXiTpHXq8XMzMzeOutt8QoeOONN2C322VN1Wo1NJtN2eeMHASDQdGFv8MD\\nno11uxF+hsvlwtjYGK5du4aFhQUA2hk4OzurM8AbjYZ8n81m5SJWIxvqPDGC2+t+d7lcuHz5Mt54\\n4w0AmrHk9/tln3s8HjgcDnmO1WqVaBsvfavVikajIedhtVqV9aJi5Lox3mw2G2ZmZgAAU1NTGB4e\\nlubBExMTiEajcLlcMgd0vqhLuVyWswgAlpaWzozSdutE0igZGhqSuQMghtLY2Jju7FM/t1arIZfL\\nyRn6zTffYHl5+bn6AN2tb84bjZHp6WlcunQJY2Nj8rNms4lyuSzft9ttXL9+HZVKBcfHx6LP5uam\\nrql2v9GcYDAo4/Tmm29iZGREzuJ4PK4zllqtFkqlEnw+nzT4LpfLSKfTugj3WXjFfuW1MJa4CHgg\\nWq1WzMzM4N133wWgeXgmkwnHx8fiwTH6RCtZjdycF1yter/NZhM2m028qKtXr+Kdd96B0+mUBZ1M\\nJgW0BpyA6TrTU/1KZ7rP7XbrvLqFhQW5gMrlMrLZrGxOl8sl3jM3hZr66FfMZrOkkxilGBsbw/j4\\nuMyJyWRCoVCQy8XlcsHv9+s8bYfDAZ/Ph0AgAADY3d3tuaklIwR2u11n1PLgBLQDvVarIZVKyUHg\\ndDoRDodl3dGD5gauVCqnDLduDyq73S7gylgshpGREYmUmkwmSQNwHMrlMsrlshj/rVZL0lLAycHW\\n7bioe8FiscjajEajOu8yGAxKRI1rtFAoSAQCgIBW1ShMsVhEuVzuOYprMpnECOMcMZLjcDhQKpWw\\nv78vc2AymZDL5WQcOL9er1eXVqlWqzqgajdiNBrFsJiamsL8/DyuXLkiDbwrlQoymYyMQyaTgd1u\\nlwvf6XTKhcR3Ak5ScdSlF+MN0Obk+vXrmJubw/z8PADIxUKnolaroVgsyr6pVqtwu91yPgBAIBCQ\\nVAZwurF1t2M0NDQkzgagGbmhUEg+d39/H6VSSc7DYDCoAxcD2rqLRqMSQWk2m305tgaDAV6vV8Y7\\nGAwiEAiIbo1GA7lcDsfHx+Ic2mw2WK1W+R2Px4O5uTkcHh4C0Ix/jks/dwcNbpPJJO/Jd2bKiXun\\nVquhVCrJ90NDQzrnt1AoYGtrC7lcTpfG7zVdybQ+zxM2plcj//V6HclkUgcVCQaDOsc6m83i6Ojo\\nlPGvphG7GSveFYBm0A4PD+PKlSsSHc5kMjoj1mazoVAo6AIBBwcHWFlZEaO3Wq2iUqnI9+eFlQwA\\n3gMZyEAGMpCBDGQgL5DXIrJEq56W5/DwMObn58UqjsfjePjwoWBugJMohmpV8nPOm7tU/95ms8Hr\\n9YqH9/3vfx8ulwt3795FPB4HoFnX09PTEmEhyFoFLvcDZKQuqqfscDgEHAcAP/vZz2CxWHDnzh0A\\nWo7ZYDBI5CAWiyESiSCRSOiwOefFULVaLRiNRgSDQfGKhoeH8fbbb4u+Dx48kPQAoAFDFxYWYDKZ\\n5GfJZBK5XA5HR0fyuf3oxfw3MR5er1eHzWm1Wnj69CkymYzOAwZO8DalUgnxeFwiT0tLS0ilUn3h\\n4Nrtti4lyKgeoHm/qVQKqVRKMB2A5v1xPefzeUmHASeg2X6iOcDJ+mP0jeHubDaLYrGIfD4v0Zxi\\nsQiHwyFryOv1Ih6PC+bj8PAQlUoFlUqlJywXo60q7tBgMEikplgsCrCT2C1GjVQP2Wg0Ih6Py3lw\\ndHSki+Z0ewY0m02J5jAKYLFYJOJQKBRwfHys2zft9glPzfj4OEKhEOr1unjixKBw3/cCHObvBAIB\\nTE5OwufzSUQwnU7j+PhYdMvn86hWq6KLy+XC/Py8pJY5ntzr1KUb7JQqBoMBwWAQw8PDuogV1w0A\\n7O3t6TBjdrsd0WgU165dk0gB95qKcWPRQK9RZLfbLZ/n8XjgcrkET8e1o4KobTYbpqenJeLjdDoR\\ni8VEN45h5zz1EkUGNDzr0NCQzBnPvoODA10EuVarSep5dHQUfr9fN05erxfpdPpckRKr1QqHw6ED\\n5XM9cd88e/ZM9jHH0maz6bImLK4gDqsTmN6LbnxHv9+PcDiMcDgse399fR3JZFIXSWcKns9Kp9Pw\\n+Xw6Ist4PC6Zi36jlZTXwliicFBcLhe8Xq+8+N27d3Hnzh3k83nBWjAMzIEi1uWiq+GsVismJycF\\nx2C1WnH37l18/vnncnBdunQJ169fF33r9Tri8biuGu6i9HI4HJibm5Nx8Hq9WFxcxPLyMgAtJD49\\nPY133nlHfh/QxoeHfifepJ8cNPlD/H6/gMvfeecdDA0NYWlpCQCwtbWF/f19ycfPzc3J2NA4qlar\\nOD4+loP3PONlMpkkLUHMB8PdBCjv7+/LxTs/P49KpaIDepbLZcmRZzIZXcqw23GhqMBJj8cjB2Qu\\nl0M8Hsf+/r6kVcLhMDwej1xsm5ubOl0ODg5QLpd11UzdXsCcK+pSLpflsnG5XDg6OsLu7q7spXa7\\njZGRETmUisUidnZ2xDlgClE1IrsxCAikpUHIVBr3kcPhgMPhwPHxMZLJJADtcuEBDpykWXZ2dsSo\\nZbVTL7oA2sXKz8jlcrh27RrsdjvW1tYAnKTTOEe8/JgCikQiqNVqSCaTgsHL5/M6J6kX4Trb3NxE\\nOp3GlStX5IJ58uQJjo+PxbhW9zOgrZ/x8XFYrVYx3AjOVYHXHJ9edNrb20OhUJCL12QyiePBcVEr\\nAnnhTUxMiAF1fHws1VeUbnBuncLCHhrTPp8P9Xod9+/fB6Ct1U7D3eFwwG6364zGo6Mj+V6tiO1V\\n2u22XPj1el1SnwDw1VdfwWKxwO12y17iM7kfr1+/jmw2K/s8n89LZSoNln6q4siIzfPQ6XRieXkZ\\nRqNR9haLNPi54XAYPp9P8JEUt9stunCf9XpOqxW2IyMjiMViKJfLgslcXV3F7u6urhLearXC5/PJ\\ns7xeL6ampmTuaRRzjalrvR95rYwlLigaA9yc+/v7cDgcAsIEtAiP2WzGw4cPAUBK9S9KuKm9Xi8u\\nX74sQEun0ynRHi7EaDSKhYUFwaQsLi6i0Wicqmi4CJ18Pp+AzAHtvVmWC2hRLWIMAO0w2dnZ0YEt\\n+/GgztKFXhXnaXh4GM1mUzY+IxC8XK5fv458Po+dnR3xxqxWq85Y6pehlRgqHkL1eh0ej0c20t7e\\nHlZXV1GtVsW4u3btGtLptOTNiVXj5V0sFtFsNnvSRwWbq/l3o9EoEbhisYj9/X2srKzIATI7O4up\\nqSm5AFmyy0OqWCzqvHGge8/XZDKJ0cxyeB5KFosFtVoNh4eH8t6BQADT09OyvtPptGAXgJMS9E4y\\n0W50Ucu+nU6nDjdktVqFvkEF/EejUR2wuVwu4/DwUAwdlqD3c6lwn3s8HhiNRmSzWfl7q9UKo9Eo\\nhg/xX5xHVq3l83nx1on3Uit1+sGZMfpHA7XZbMLj8chlUCgU4PV6Zb2Pjo5K1S4v72QyKUDqfoU6\\neTweuaRYzs310Wq1hKYA0NZYOBzWlaqn02ldtOQ8OhmNRjlzDg4OcHx8LNW0pCtpNBq6yiq73a6L\\nrrJaDzg/3lUtiU8kErJPtra2BBfJNZ7L5YTCBNAufJ/PJ+NCnJxaMt+PsBpXjZLn83mdUVgqlcQ4\\nAyAVhKwM5e9YLBadEaJSoXTrmKifabFYJFK+uroKQKvcVKtIHQ4HIpEIPB6PYM0cDgeGhoZk3lKp\\nFFwul5zf/Fm3lbmdMsAsDWQgAxnIQAYykIG8QF6byJLKdZHP53XluFeuXEE+n8fCwoLQCUSjUWQy\\nGayvrwM4iQJclC60cBl2VKstwuGw8HsAwNtvv42JiQnxBHZ2drC5uakLk/dbbtnpgTGFRvxApVJB\\nrVaTyMHk5CTef/99wZswkvPo0SPBnFwEARsjKAyFAprHoOKPqtUqRkdH8dZbbwHQoidbW1tot9uS\\ntmDKoZNyvxddVMJHer+tVktH5X94eIhCoYBgMCj4s8nJSV0Y99mzZzg6OpL0AiMWvVajdM51s9nU\\ntUEgrqJWqwmmipg3elrFYhEPHz6UOeM6VIk3e9FJDaO3222JjpCsTvU4/X6/YHioi4rLYbRC5Ysh\\n1uhFwjVHz3BtbQ1er1fGIBgMwuVyIZVKiX7tdhuBQEBKw10ul/AuqR6kOkfdRgfa7bZ8xuLiItrt\\nNq5evSpj4ff7BXsIaNHLdrste4uerxotIyZLjaB0pp5epA+gRToWFxeRzWblvGu1WoIpAU4qOfnZ\\n5MlihR5wEkVUy76ZnulGH/7/XC6HR48eSbRvamoKJpNJsDkkvGUkx+fzIRKJwOv1yrnkdDrhdrsl\\nGsXIpDqH3aZyS6WSpEpLpZJQAwBa9IFzxnMpFArpos6dveOcTidsNpsO88IUYTfRHT4nkUjoKsX4\\nefl8XtfuRU3VsdKMe0vFJJ7V1w3oDh/YaDRQLBZ1d1AqldLtc37NvcaqPWYwKNvb27qz1mq1ii6N\\nRqMrTKfRaNSl+S0WC5LJpJxv/H/8HIvFgkAggImJCTkfnE4nHA6HjHckEoHT6dTBdzY2NhCPx/si\\n7H2tjCVedKlUCvF4XHAVsVgMQ0NDupCcyWTCV199JTlP9aA8Lz5I3SSdzLOBQABXrlyB3+8XY2ly\\nclIXDnz48CEymYzu4u8nP98pzWYThUIB9XpdNoHT6cTo6KiEu69fv44333xTNkAqlcLGxgb29/d1\\naUG1TPY84V6y1gLQAQUBjWTw5s2b+OCDDwBohyhTCUy7HR8f61KoBoOhL6OX78M5mZ6eRqVSkQPe\\nbrfj2rVrmJqawnvvvQdAGzumX4CTsl6V3dpkMvWVTm00GnJhMqTM8Wk2mwIEZTp1bm5Ox89DgCo/\\ng6HvftKUqiNyfHyMarUqly4pCsbGxiR1Ozs7i1gspuPEqVQqutB754XTS6qJl8nx8TF2dnbEiBwd\\nHYXNZhM+LEBLq4yNjcmBSU4zHrgcG4vFosNRdKsP/yaVSmF9fR02m034sCYmJuD1emWs/H6/HNjA\\nCaN3IBAQ5yUcDiOTyehA1kBve6xWq+HJkye6S3ZqauqUAev3+2UM1M4G3PuBQADXr18X52Vvbw+J\\nRKJnoHetVsPGxoYOVzg/Py/rpdFowGazyZk0PDyMoaEhXVEDn6Wmf51Opw5jpGI8XyTEPXIcXC6X\\njD91yufzsmauXr2KS5cuyTzWajUcHR1J6jSbzQpnlpqOU9fRi8aJ+jMFy7mw2+2YnZ2FzWaTZ9vt\\ndszMzIjB7XA4kM/nJY14cHAg2DiOn8pgD0CHz3uecM+rqfWxsTFcvnxZ5omUNypPntfrRS6X061b\\nNa3s9/tRLpdlHdpsNiSTSTkrXjRGPIOWl5cRiUQQjUbFcd3c3EQ+n5e9F4lEMDw8jImJCTkvMpmM\\nDhZRqVQwPT0ta8rn82F2dha/+c1vsLW19UJ9zpLXxlgCThbkw4cP8fHHH8vA+f1+WK1WfPjhhzIw\\ntVoNd+7cES/1ooHUlGazicePHwsIeHp6GvF4HMViUXBM9Eru3bsHALh//74u/3oevVSjpt1uo1Kp\\nCEswoG0C9cKfmZmRHDSgXUj7+/viuQCnGU97MeLUw6PZbKJYLMpnGwwGRKNRAcNXKhXcunVLDm+C\\nMwkIBfSVKPydbvXp9LRUgLfL5RIwPKBdIvV6HaOjo2KEE59Eg4TGE3Xj3/SCr+C4qEYNMUK8ZEmw\\narfbxSiw2+1otVoCvjQajXC5XDoCxH5aVfCQ4qWay+WErBDQeF5QhTntAAAgAElEQVTIdcRx8Xq9\\nOo+fPEJcV8T1qFWNvYwPD2Kz2YzDw0M5rEmESWJKQLtMbDabzAEjGj6fT/QpFAo67rBecEKco0wm\\nI7gKriGCmumgBYNB3RyQ/djlcknky2KxYH19XcalE7vYzXg1Gg1hcmaUaGhoCIVCQWccmc1mOf8y\\nmYwUXDDCEwgEEIvFZI0FAgE8fPhQwNbq+79IiEPk77LKlNhEtojheehwOJDL5XTvvb+/r4tolstl\\n4d7hWuw2O9BsNgWLw8gvjQ+r1YpWq4WhoSEhDZ6fn9eRz5bLZezt7ckZSpwTK3z5ud2C9KlzqVTS\\nvePQ0JBE/LiGJiYmEA6HdWDzVqslRk08HofD4RBcLMdXdTLoNL1IGDXlHNjtdoyPj2NhYUGMsGQy\\nCZvNJlEkg8GATCaji65Xq1UEg0EZb0brVbZ8Rk9fZlBS/4ODAyQSCR0WeHd3F2tra7JWya9UKpWk\\n0pFzwnM0n89jaGhIPiOTyci+6UdeK2OJL3l4eIhf/OIXcniPjIzg1q1bePvtt2Uxp1IpbG5u9hVu\\ne5moHkW1WsXjx4/xV3/1VwA0D89isWByclIq0mgEEGz+7NmzC2MSpz6AtrGy2Szu3LkjG3JmZkaq\\nl4ATgjR6k5988gk+++wzpNPpCzckS6USnj17JpUogUBAKnIAzcsbHh4WryiXy+H+/fv46quvxJPq\\nBOeq79uLsJs303uBQEA2OqClbf1+PwKBgIxRpVJBKpUSAyWZTEp6DDhfulK9vEulks7zikQimJqa\\ngt/v17WCqVQqOm8S0HdrP8+a4ucR6M8Lam9vT0gz1bSKSiqYTCZPgTopvQJi1fEsl8tIJpMSJSC7\\nOhmQVd2pCws9HA6HzONZANRu5001uAuFAnZ3d2XNOBwOlMtlHfGjyqDu9XqlnY9K2GixWHQRb6PR\\nKOPdjTGgOiLb29sANK/ZbreLbiaTSfppcYxMJpMuGknCWFZ/lkolOJ1O3LlzRy7nbgxw6sNncYy4\\nHpj+41nMyLxKL0DKDjpHsVhMImV0MldXV3WVVi/TBzgxRtWS9tHRUQwPD0shh8/nQ7ValZTPwcEB\\n0um0RIB4+QInKSm29KF0azipBKUzMzNCdkrH3263w2w2i7HEqmmOy8LCglTY0WBhoYHahkktRHie\\nuN1u2Uvvvvsubt68iUuXLsl6rtVqKBQK8uxyuSwGOc8LZi34N+w0wLNiZWVF0n0vmjcyigPaHuk0\\nIq9cuYJgMChZiUgkAqvVioODAzkDU6kUrFarjJ3BYEA8HsfNmzcBaBXQJLZUCWu7lQHAeyADGchA\\nBjKQgQzkBfJaRZYo9GJozVerVczMzAhpHKCRWD158uRCIzhnCbFUi4uLADSvir2IGAWw2+3Y3t4W\\nYshisXihUZzOdFkymcQf//hHAFqabWRkRCJLDM8TKPzpp58imUyeakzbbyRH9cSJ5yHHk9frRbVa\\nFdwA27Mw+rC9vY1vvvkG29vbup555y2TZYi6VqvJe9frdSwsLAhJpcPhEPAt19Dh4aGuiS9TDSpv\\nSj+RJYbQ6YkvLy+jXC4LPolEp6rX43A4UK/Xxdsl3qrz2SpguBe9OL7lclm3b9griqF+QItODg0N\\n6VoyuFwu0Y0l4WraAujei1P1r1ar4jEDmkepYqzYCoceJxtJE+MFnOYTIki1l15xjIao4GGDwYB0\\nOi1RLabBGdGan5+Hy+VCvV6XlgykiWAUg+l4lbzzZePEOW42m5IqWltbQ7vdlmfz/dTUWDQa1ZFq\\nHh8fw+PxSJri8PAQfr8ffr9fPhdAV9F5FUCfz+fx9OlTeSeXy4VQKKRLy/Ffjv3R0ZFEUQCNtoM9\\n09TIbiaTkf33onlT02VqWrlaraJcLsPhcMh7kZ6EKUsWCDA6wrSdwWCQc7RYLMLpdAquSe1J1ilc\\nz+y52dmHUW2FxcgudbPb7RgZGZF3DgaD8g5q6qvZbMq5uri4iKWlJYmwnCVWqxWjo6P4wQ9+AEDD\\nbV29ehWBQED2NaOBHJdKpQKHw4FQKCT68XxgoYHBYEAsFpN3nJmZkV5y6prqHB8VWO5wOLC7uysN\\njwHgRz/6EZ49eyZnMe+EZrOpIw3mOQ5o2QNi+Shzc3MIhUKSYejlLn4tjSVAz1xts9kwNjamIxZb\\nX1/vi1m5X+GEFQoFyTszZFupVLC4uKirolINHP57EcBz4ATTAJwAOlUAYTqdxm9+8xsAmnFXrVZ1\\nOI7z9s7r1Icb6+7duzrwMBt/8uL4x3/8Rzx79kxHTNipQ79pL+CE7wU46f6uMkG73W6YTCb5ndu3\\nbyMej8vvsGHrWUDhXseMaRQAwuDLA7HVaonRxou4UCjg4cOHMq9utxv1el3+hmu9cx57BTLXajUk\\nEglZzwcHB6hUKjqg7cTEBPL5vMzb0dERIpGIhNFpmKpA4W716azEajQagnmr1+tSUcQ5Ghoa0gFc\\ni8Uitra2EAqF5MLMZrN49OiRjHenY9CNPkzt1Go10WdpaQl+v1/IRGlQcd+vrq5ibW1NKpwALY1y\\n/fp1Gd9EIqHrAdit0GCkgcoeXqoB23kJPXv2DFtbWzIOZPPnRR2JRDAzM4NEIqHDgXQzRipA12az\\nIZvNYn9/HwB0xixw0ogZgKSZST5LfZPJJKanpzE1NSXnx7Nnz5DJZLpaQ+pZSGwhn8PUIp3tvb09\\nHR7K5/Ph5s2bOpwQWeKJceS4qZw+LxNWlXJ9kFySvE8cm2w2q8MjOZ1OMWhdLpdg9NTUvdFolMrv\\nfD4vkI/nCdcpjZy5uTnpV6mmMEulkrxjvV4X4D0xeCaTCeFwWNJ57XZbnGBAW4eRSOSF64j7XO3A\\ncXBwAJ/Ph5///Oe6seI753I56cvKPcCveWbOz89jdHRUxlatKuwHw/zaGkvAifdAlDsxJoC2AdQ8\\n8kUYI2cJP1clHovFYrhx44ZuMy4vL+sWd2c1xXl1UL9WDUS73Q6n0ykHeqVSwddffy3eMSs9uq3s\\n6EUYWeBBRep8Gh+kFvjyyy8BaMRjiURCF0k6S5d+5pLMzmrH9Uwmo8MljI+P4/j4GI8ePQKgFRKw\\nCgM43ZX9POOldlgHtEuTHnShUEA6ncYPfvAD0Xdrawu3b9+WKo5sNotKpaLDLnU6Bt1GTFTpbKFA\\nMsHDw0O5ZNmSgZch15DaiPksDFWvRQIsHVeNP5a2E/cRCoUQj8fxzTffiC4+n08waIBmlD948OAU\\n/USv89ZpFLAqiA4ao38qK36xWMTo6KjgY6LRKEZHR+XZhUIBhUJBV/HVjR6AvsS9Wq3qWuNUq1VY\\nrVaZg8PDQ4lIcE2ZzWaUSiW5QCKRCGw2G/b29nQA3W5Exaxx/tXIhspSzQIYj8ejq1wisSag7Q+/\\n34/Z2Vk5q2q1WldRLtVYUiM5wAktx87OjlzwtVoN5XJZV/Uai8VE32g0iq2tLUxPT0txysbGhi7q\\n8qK1pO4xgt0BbR/RUGbUze1261rQEF+jjsvQ0BDy+bx8Tjab1VGyHB4evnTeGCXlfZlIJJDJZISE\\nlvqp+C/iNRuNhqxx4t44L5lMRhj8qVur1XoppqvdbutwWnSKfvKTnwDQcG9+v1/Gn61YzGaznEvj\\n4+Pwer3SeNvr9aJSqeD27dsAtIq6w8PDU7Qi3coAszSQgQxkIAMZyEAG8gJ5bSNLqvdw6dIlvP32\\n27rqiqdPn+q8nVeJXWKbA0CzgK9evYqpqSkJKzKHTI/0PFT+LxLSCJjNZkmJ0KtlqP3p06e4d++e\\n4EDUfkwXlX5T9SF2CdCiXOTDoiwuLkq13Pr6+pl8HJ30CP3qopb7MlzMcbJarajVanj8+DEePHgA\\nQKvk2N/f1zU77owG9qMPP4MeG4lD1dJ2YlIYvXn69Cl2dnYkssRQfOe49KsPPfzOaA6rCNVUAekU\\nmBJMpVKCdQFO+IX60afzndSSYrUXG/c1w/PcW8lkEuVyGaOjo4J32Nra0nHB8D27ETVa0hm1ZZSL\\nXjSrudTIAaMPalVjqVSSaAe5e3olNSX5qJpmYysK4ASXpUZoTSYTrFar6ELqCaabpqam0Gq1kE6n\\n5RztJeXFsbJYLNIsnM8JBoPy3Ewmg1KpBJ/Pp0szq30WGe05PDyUs+rw8LDreaPeTAlxTogHdDqd\\nOrLZWq2mIzYtl8uiPyvq1F6NOzs7WFpaks/oNsXsdDrlHRhBV1OCuVwOz549k2gfx5GRnHA4jMPD\\nQ2xvbwtmiVx0jEyT2Pdlksvl5G9YpReJRHR8WclkUlKy7P+Yz+cllbiwsIDV1VWBmLCyk59BXXuJ\\nmHo8HqlY5H1wfHysa9g7OjqK/f19HB4eyhyYTCZEIhGJ9jHFzXPq8ePH2N3dFS6xXuW1NpY4cPPz\\n87BYLNjY2JBcLfuOXSQm6HmiLubx8XFMTEzowMSLi4tC9tf5DhehU+dnOBwO2fhjY2MYHx+Xxbu4\\nuIhkMqkDx7+KcVHZpNWNHolEMDExAUDDlywtLelwN+Vy+dTFpko/Y8bLSE1VEQ/EcSJb98rKio7X\\nJZfLnSrTP6/QCOB6aDQasNvtuv5sgUAAlUpFwvPsv6byYamAyfMYkeq/tVpNB7xlmlJ9HlNsPCBT\\nqRQKhYJcuqpx26uoY8y0m0pJYLPZdKncSCSiS/VyntW+aY1GQ8fQ3Ithov4u00tq53Or1aojCg2F\\nQuKYsFza5XKJg0DMidrA1GQy9ZQWUIHzXKsWiwWtVksA3jQsacBGo1FJP9BQIx+VyoC8tramM5a7\\noQ2gQctxKRQK0vQYOCGh5eVusVgwMTGB4eFh4T8qlUq69KTZbIbVasVXX30lHHbdnlUq6JeYN85j\\nOByWsaLjcXBwoGOdNhgMulST0WiEzWbD+vq6rPFcLifp55eNE59dLBaRSqUk3cQ1PDQ0pDMSV1dX\\ndSlvj8cjYzg9PY2joyNsbGyIM16r1WRf8HNfNk6NRgNbW1v46KOPAGhUDZFIBHfu3NGdkU6nE48f\\nP5bPjcfjukICi8UiXRY4du12Wwzjg4ODrkDUKuEu/3369Cn+8i//EoAGbH/33XcFxH737l3kcjmd\\n80pgPEHge3t7SKfTck6R5LbfnqyvlbGkepxGo1EOnMnJSSGcunv3LgBtoFRjqZ+D8kXCz2Plj4qP\\nGB4exsbGhmBffv/73yMej59qvwCcJoA8j5hMJmlcSyAiNwEX0ObmJm7fvi35bVZ+qFVp/ZAbPk8f\\nl8sl1QgLCwuYnJwUrACbSpJHZX9/X3hn1O7SLzKeehGj0SgHeiAQwPz8vIzT5OSkGCSs3tvb25Px\\nAU4Mh05cz3nFYDDoLtBQKIShoSEYjUbxkp4+farD4alNSIETI6EfOcu4Ud+ZlwX183g8WFlZEbxD\\nsVjUca8wqtGvqOPa+TlcG+QTmpubw6VLl6QatV6vS/SElzMBumd9fi86dYJC222NJZwXg9vtxq1b\\ntwQ0u7q6ioODA3g8HuEyMpvN2N7eljXGStRedaHh3xmFoyEUi8VgNpt1rVdYwKDifjiOgHaxra2t\\nPbdq6WX6cHxI1MrxLxQKuHTpkhgfNNrUc8put2NnZ0ciHTs7O3jw4AG2trbEqOmnupMcS9SNUePx\\n8XFxRDY3N6VCDtCc7/HxcYlGHR0dYXl5Gdvb27KOWJHdy9w1m00d4W4+n8f29rbgcQDNUFDXKw0h\\nGkvb29vIZDIoFovyO0ajEZVKRdcYuBtpNBoyBvl8Huvr66hWqzoeLrVqUMUMqsB2tQULI408m1SW\\n+W7GB9DGm9XuvM9brRZ++ctfCh9VtVpFNBqFy+XS4f/u378vBm1n1SQLh/o9tweYpYEMZCADGchA\\nBjKQF8hrE1nqzI273W688847ADRP/ODgAOvr69J/jaFkYlLY7PMi9QEgniW97uHhYTQaDWxvbwuX\\nQ2cfOKfTKQ1vL1KYWmLDUUALtWcyGcn7M2VCK57tGFjhdFHCNGkwGJS0m8PhgMvlkrz/7u4u6vW6\\neESkxnc6nfIzNmW9CBoDq9Uq4eNQKKTDl7ACjSzHwAkGhH9TLpdRKpVkHs+bllMrdpiOAbRxYqWS\\numbVflBer1dXQn9e6YzgcFz4c5/PJzgFRps4Jx6PB9FoVHTpBVvSjU7qOPEcIG8RWeCJ7WJLlJGR\\nEUnfbG1tddUv60U6nFUtajabYbPZZH3cuHEDc3NzwpdlNpsRi8Xg8Xhw7do1ANo6W1lZwR/+8AcA\\n6KoU/iydiC1T581mswkGyOfzIRaLSUqQrTVisZiuD2Cr1RK4wP3793H79u2+vO92u61LhaqRzlwu\\nh/39fYnAsZ1KKBTS9RBzu90SWUokElhaWkI2mxV9Vd6ol4mKC1LTtLVaDffv38fe3p6cgcxCMLJk\\nMplQKpVkLBOJBNbX15HP53Xjze4C3QpT7ypbPrm6mFIzmUwol8u6fa/2ymRGQH0uI7lqQ+Ru9WHU\\n6KxoeaVSQaFQOLUW1L0PnK726+wL2SvOjO1SOp+bz+eF+d5gMMiYqT39unlGv3fJa2MsdU5OIBDA\\n+++/D0DD5RwdHQkPDkUFpb4qfZirZ2n+jRs3EI1GpYu9qjMvIBoFne/Vr3SSvKkpGr/fL72EAO2g\\nUp/PMboo0Dk/h20n1MaiDocDFotFwvO5XE4H0OM7kBvlosRgMAiFgoqjUA0UluInEgldJ+7ORrm9\\n9BV7kT7qocK0hEo/wUtEBZdbrVYZKzbTvIi0suqIACfriV9HIhG89dZb0tQylUohkUhIKL5cLmN/\\nf190O49TcpZhrK5vpnbZfPnHP/4xHA6HpCnu3bsn/Qg5diQM7Df9RiFWiPoQnM3vZ2Zm8Cd/8ieS\\ndr58+bI0tqZBZTKZdOdUL5xPnTqpxRP8nntrd3cXbrdb2kV4vV6EQiG43W4ZK5vNhng8LqX57BFJ\\n3rVedeocK+6b3d1dlEol+UyeTW63G59++ikAjbtLxa0kk0lYLBYZP6D/Ih21gIF7nPtd/f+ck8eP\\nH2NnZ0dSpyymcLlcuhTledLenf+qpKSdY6me10ajUdqbqMVLZ1GH9CLPo9NQx/wsJ5FnmXp+qDjD\\n847RWXpSCLNRz2f1nuXv8x06Gzf3Kq+NsaSKzWaTjvWAdgAdHByIdwmcdKdXuVUuEsisVguNjIyI\\nLjMzM5LT5WXSucjOy0itSieJn8vlwvj4uOR2fT4fisWiRLl2dnZ0BzQPiYsaH/ViAzRAJS1/HjY0\\nRlZWVrCysiIRCW569bA+jydAIR6LBx71s9vtumqQp0+fYmlpSdYRvUDVY74IfVS9+K966RaLRRQK\\nBezt7QnubWNjA7VaTcaOUaeLHCeKuhasVqv0ZCJ2ZGlpCY8ePdIBvFOplM5LvYhDklgq1SC0Wq0I\\nh8Oy7vf392EymXQVlbwMGU1VSTbPI51VjJ0Oz/b2Nh48eCCHs8pdw0jpl19+iU8++UTG7jz7Tv1b\\nRnJoLJVKJYm2A5CeX9FoVKJyqVQKf/jDH/DZZ58B0IzKSqXStz4cF8692qdTZZtnRDebzUp0IBaL\\n6fiGNjc3kcvl+o4IUtSIvqonOYTOEvKp8fx2uVzCUaUaWOephO382VlfnyVkJFejZd2AzLvVrZ//\\nrxqjxJe+qsb1nc/txFuqEc6zilfOo89rZSxx83m9XlgsFrnUNjc38fXXX+Pg4EAuXnokr3KyAG3j\\nDw0NSZh0e3tbSPIINCNY7VUtILWKgwBpekCbm5s4ODgQ0r6joyNdmPeiwNMUddNYLBbE43HxbguF\\ngo7M8MGDB8hkMjpg4v/L3pvERpZlZ2Pfi3megzEwgsGZmUxmJbOysrqmVldL6BbaG8OAFvbGS3th\\n77zyyl7o33nYWDDwGxYEQ5ANrdRuS0Krral/dGVNOVSOJJNDcIhgzPM8evH6HN4XOUUEI6s75XeA\\nAovMiPfOu+/ec8895zvfeRus65SyICAkCTUeBuSI2/Pnz5FKpdghIaM5q6IAUahIAbgAeFPUq9Vq\\nYWdnB/l8nhulUjWlWEE3S13EjUz8SafN+/fv83t69OgRjo6OeOzIcZv1vB7d6DqdDur1OkqlEu7c\\nuQNAjgKk02kub97b2+Nu6uImMgvdRiMQo6SUX331FQ4PDzkqqNFosLe3p0gvVatVReuVy+ojrv1W\\nq8XzgxqeEgj/+fPnsNvtqFarvB4JSEzPM6s0vOjA0c/R59VoNGi321xF9fz5cwWlAlEHzFKfV/3+\\nss8Ph0O2FVRd97Yqh8e55mjUTnT+3/Ye9zohx+h3rYMor7LXlz50X+rbqqiiiiqqqKKKKv/GRfpd\\neoSshCSNpYQIqrZYLMwPQnTvouf/fT2XRqNRNPD1eDxoNpscQiZdvg99KNVEjU8BOYQspm8maR56\\nWaHIyWiDVbHEWDxxvm2dxCgFNbUUc+uU3/6+5xCAFzBMw6FMmvm20shvEhEjIa474EW+m+9TL9JH\\njISJJ0kKy39fPSFfRgwq6ko/Z5UquYyQriIs4HetjyhiSuX3YV8ieZscfar8Xsjd4XD4wZs+9E45\\nS6qooooqqqiiiiozlLGcJTUNp4oqqqiiiiqqqPIaUZ0lVVRRRRVVVFFFldfIO1UNp4oqqqiiiiqq\\nvH35XWK1qAUXySgW8XeBb1OdJVVUUUWVf4Py+wRMHuXC+V3KKAnr91UM8CpdRIJJkaOI/v370I8K\\nJ0RCY71ez/QppJtGo1EUBryNd0kkwi6Xi4lws9ksHA4H8wUWi0V0u10F8fTbpjBQnSVVVFHlnZM3\\nnSq/T0dBbMvyqsrX7+sULOoijoH481Vs/ZfV7VXNmMWNGLggnx2938v0vayIFYl0D4PBAL1ez/cg\\nTi6RqHfUuZuVPsRCT/+v0+lgNpuZX6rZbCp48F7mKM1SH6pwdTgczPVGDO9arZY7YwAy9xWN02gb\\noFnOa51Oh0AggNXVVXaWLBaLotPEcDhErVZTdHsgnsG35VyqmCVVVFFFFVVUUUWV18g7FVl61ckF\\nePlJ5PviNhpXJ5Ft91WfeZv6iPccPemO5odnxaMzjj4vEwoFj/Y8uuwp5lX6vO60LTZynbU+r9Jp\\nlE9IvJ/4ndGIgajPtHqNpilGe9mNntxedhIf7TU17TiJuohta0SG8ZfdX2yTIrZjIH0n1UWcJ2Jv\\nOIPB8EJ7j1G2alE3ShWIvcCmOQlrNBpuaCzqQuzhYpQEuGhILf4bMWvT771eb+pUhkajYQZ6k8kE\\ng8GgaPpM9wMuoieDwUDRIoVYskkuEyWgZ7Xb7XA4HDwuOp0OPp9P0T8sk8lwc1/gYhxEPjiaQ5e1\\nPU6nE4FAgHXR6XSw2+38Ho1GI46Pj5kdv9VqKdoa0TjNYp0D4GbBi4uLMJlMisbQvV4Pfr+fGy03\\nGg3uIiDqI0Z8ZhHVMZlMmJub416ngDx25XKZo140/7VaraIn5WiT35lGvGZ2pe9BxOavOp1O0Rn9\\nZU3yqtWqgnJ/1GjOQijfTAaUfrdYLIrcryRJ3IKEFqPYbHJWBHGj+lCol7qPG41GGI1GRWNJaitA\\nuWlaoJcl0hvNx5MuFHJ2uVwwGo1sTOl+5XKZ3zW1k6AF0Wq1pm7RMrrRUW4ckI3G3Nwc9Ho9j1Wz\\n2US/3+d+WxqNBvV6nVvbUI8pCk9PKuK46PV6/t3pdCIUCiESifB7MhqNKBaL3JyZfhLZaKVS4Uak\\nNDZia5dxxoZ0MRgM/I6MRiNCoRCWl5f5PbXbbUVD5Ewmw61sgIt3JnY1p5YR0+hCm3AgEEAwGMTc\\n3Bzcbjd/ptls8tgVi0UUCgVUKhXecIgglt4RGftxbIFIyGk0GmG323mD2draQiwWY9tjsVi4HQsg\\nv99SqYRarcY9z4rFIqrVKtsCkXR0XKEehz6fjzdep9OJpaUlzM/Ps756vZ5TKPl8HpIkoVgssn7x\\neJz7wQHyXJrGKdBoNLDb7XzvWCwGm82GcDgMQG6Sa7fb+Zr5fB65XA7dbpfbwJycnCAej/O8Fh2C\\naRxbmjNerxeLi4vc4sXv92NpaQkul4uvG4/HUS6X+d6pVArHx8fIZrMALuxAt9u9VOsjnU4Hk8kE\\nj8cDQJ7PFosFXq+Xe1bq9XrMz8+zjclkMigUCrzWarUams0m7yHA9PaZ7A4gz935+XnF2qfemPRe\\n2+02stmsgpSW9g9a24RpmkbEteb3+7G9vc22uFKpIJvNcgsxQHaqxD2e9lux+fgs5Z1ylsTO3U6n\\nk5vXBoNBOJ1OGI1G3jxqtRrOzs54cJPJJIrF4szyq/RiyRunTTcajSIQCCASiSAWi/Fncrkc0uk0\\nALlh5f7+PjeRBHCpBpaiTkajETabjfWJxWLw+/24evUqAGB1dRV2u52Nd6FQwPn5ORKJBPchS6VS\\nPFaXEWI3pwlvtVpx9epV+P1+AMC1a9cQjUZ548vlciiXyzg5OeGeUel0Gs+ePeOxa7fbzNo8rn4U\\njRBPv36/Hx6Ph+fQ0tISrl+/zn0HAaBUKqFareLg4ADARcPYx48f8+8EeqR5N+4GLBoqq9WKxcVF\\nbnD6wQcfYHV1FZFIhJ2AbreLUqnEG93h4SESiQQb0SdPnvD/k7GgU9brxonGhgykxWJBKBTCjRs3\\nAABXrlzB7du34XK52KCTU0a6nJ2dIR6Pc6PdRCKBk5MT1Ot1hRM5jpMrjovD4UA0GsXNmzcBAD/4\\nwQ9w5coVuFwu/jwBPGlzKRaLrAu9t3g8jkwmo2DUFyMYrxsbmi92ux1Xr17F1tYWrl27BgDY3NzE\\n3NwcG2mKRtB9arUayuUy4vE4Dg8PAcg90E5OTngcGo2GYu2/bnxoLpCj9Ad/8AdYXV0FIK/rpaUl\\nXkuSJCn6GjabTVSrVeRyOezs7PD47u/v89qiiJzYCPlN70uSJOh0OjgcDty+fRuA3BQ3GAyyzQmH\\nw9BqtYrG4p1OB7lcjh2Se/fuQavVco8/EScjRjHGEXGdr62tIRwOs825efMmrl27Bq1WyzZmbW0N\\nw+GQdclms7hz5w43Z87n82g0Gi8AmyexP8DFIZ++p9PpcBP/tnoAACAASURBVOPGDdy4cYOfLZ1O\\ns5MJyLa42+3i6dOnAOR+mi+bv9PsaxqNhudHtVpFq9XClStX2LHMZDI4Pz/HysoKfyeRSMBgMLC9\\nOzo6UqzxUbs8qdMNyPulxWJROG7D4ZAPT4A8d9vtNmq12guRZxEsP6sIHPAOOUsUZgYAs9mMaDTK\\ni3FrawtOpxMGg4E3jGazia+++ooHSPRIZ6UPAHYGgsEgANmArq6uwuv1siEjI/T8+XMAcrPdUqmE\\ns7OzmUS6RI/cZrMhFAqxo7aysoL5+XluDbO0tAStVotIJAJANgwejweDwYA3u9Hr0jNMogttwE6n\\nkzeX1dVVrK+vIxQKAZAnvNfr5WvbbDbU63U4nU6cnJwAAP9OG7PolEyik0ajgdVq5XH45JNPsLq6\\nis3NTQCy80RtdMjZ8Pv9cDgc7Oydn5/j+fPnbBxSqRQODw9RKpVeWKCv04UibBSpWVlZwQ9/+EN2\\nUNbW1jj6R/Os3W5jbm6OT6VerxfhcJg3vl6vh/PzcxwdHfFm/TqQs/iexPRNNBrFhx9+iE8++QQA\\n2HiKzTslSYJer2ejarVaYbVaFWmoer2OwWDAUctxnBMyhuQMzc3N4ebNm/jhD3/IurjdbhgMhhdO\\nj+Rg+Xw+mM1mDIdDjt7U63VFk9lx545er4fX6wUg25iNjQ122ADZDo1WVYkpQaPRCL/fD61Wy85c\\nLpdDpVLhKAaN0zhCcyEUCuGjjz7C1tYWtre3AcgHRqPRqIhUtVotjqQCgMFgQDAY5M+Uy2WUy2XF\\nO2o2mxNtKpIkwW6349atW2zvrl69ikgkwofbVquFTqfDz0zj5na7eW2Rk0nrW4wWTrrJ6fV6LCws\\nAJCjWhsbG2wP5+fnOSJM+hiNRoVNDIfDinTO119/zZ+dBqhP37FarXA6nQrbvLa2poi61et1GAwG\\n/j0QCECr1bLd0uv1+PLLL/mwO62QfaZ1HwqFsLGxgcXFRZ5n5JSJqdJIJAKj0cj2uNfrod1u8++j\\nhQWTCDm4Ho8HN27cwIcffshruFKpwGazsc2hlmIGg4E/Q/aGbIGYbia5jMP0RoC3JEl/LklSRpKk\\nx8Lf/ntJkhKSJD347X//kfBv/60kSfuSJO1KkvTHU2umiiqqqKKKKqqo8nsg40SW/gLA/wLg/xj5\\n+/88HA7/B/EPkiRtAvhPAVwDEAbw/0qStD4cDi+VPCTPnE7MFJ7f2NgAAKyvrwOQT/oUxgXkyACF\\ncyc5wb1JxNNar9eD0+nkEsfl5WVsbGwows65XI5P34CM1SmVSgoA47jA51GhMDggnzwHgwGCwSCf\\n8oLBIHw+H3vXZ2dnaLVafGKmKMFwOOSQp0ajYY6NaYVCqQ6HA0tLSwDkE6cYSSJcAJ34PR4PjEaj\\nIgrhdDoRiUR4LMvl8lSYJbPZDI/Hw+OyubmJ9fV1Ps1kMhnk83lFKtdqtcJms/HJezgcwmq18th1\\nu12cnJwoQs9v0ouijDabjU+YW1tb2NzcZGxAo9HA/v4+SqUSn/z6/T4cDgfPoWKxiH6/z+9MkqSX\\njsur9BGBk1qtlk9s165dw9WrVzkV0Ov1cHx8jKOjI54PhMcj7I5er+cwPkmj0VBgGcZ5X/QZeqZY\\nLMaRUUBOySQSCVQqFY48Uuk1jYvJZOITsXhvAqfSM40ro3g7mi+APGcIHAzI69xms/FnKNrd7XZ5\\nfff7fQbJApNFSMnu2O12aLVa9Ho9fpZWq4VMJsP2L51Oo1QqcSSS5q1Yum61WhURQ5FPZ5LxsVqt\\nL6Q7Wq0Wp/cSiQTOzs44AhAOh+H1ejE/P8+6uFwu+Hy+F1Kak9pFsmc0vnR9igw9f/4cg8EAqVSK\\noyE+nw8ul4ttg9FoxMrKCo/lw4cP+blGCx/GGSvSYTAYwGKxKKJTiUQCvV6P1875+Tn6/T5HR9bW\\n1mCz2TgVdnZ2hp2dHd4/6PqjZfVvEoqc0VqjKKoYbWq1WorG5xqNBn6/HxaLhZ/J6/Xi7OyMsz6d\\nTgfD4XDiaA7tOQA4ku1wOPg6lBKnSKTdbofdbofZbOZ5lUqlOJVKuoh2gIDy00aX3ugsDYfDX0uS\\ntDjm9f5jAP/XcDhsAziSJGkfwIcA7kyl3YUOGAwG/IJ8Ph/W19fZWB8fH+Pu3bsoFotsHAjHJOIU\\npnVIRmUwGCiqUObm5ngyX716Fa1WC19//TUvRr/fj/X1dQZjUghVBJxP68iNpn3IQaFU1/vvv885\\neEA2Gi6Xix3M5eVleDwe2O12ThWIm9y0Qikkj8ejALqHQiE2Qk+ePEGz2eSFFg6Hcf36dbhcLl4A\\npVIJ5XJZkQKaRobDoSIErtPp0O12eZM9Pz/Hzs4OgzkB2XlbXl5WVAqKKZRkMolGozExfopwOaQL\\nYZVoXnS7Xezu7iKdTvNzkzGjVAFtJuTYdbtdZLNZ1Gq1iRwUSsNRyiQYDMLhcCjArfl8HkdHR7wB\\nES6OcCAulwvZbJadhnw+PxZfzKgQLkGsXLLZbPyMyWQSmUwG6XSax0qv18NqtfLacrlc0Ol0SKfT\\njEk5Pz9Hs9mc2EERN2udTodOp4Nut8tOwOnpKY6Pj/n3arXKZHqAjN1xOp0YDof8GdKdHPDRyqbX\\nCTlGxFMkgslLpRJSqRSOjo4AgA8hNL/n5ubQ6/XgdrsVOKZarabAB00yd0hMJhMkSeI5Y7VacXx8\\nzO9tf38f2WyW50A+n8fS0hKsVivPIZvNpqhQEyvSJk15WSwWdgKKxSJOT0+5okun0yGVSqFSqfD9\\nvF4vlpaWWJdgMMibMT2P6PCQjKsX6ULjRE7p8fExqtUqvv32W96nBoMB9Ho9p93IkaP1abVa4Xa7\\nkUql+DrkjE+yh1DKm56L1pwkSTxWz54947lB96bipWQyCUCed3q9ng8Io4VUk9hFGm+fz4doNMp7\\nOSDP1UQiweNERUy9Xk/hDDmdTp7zVEBA+xowHhzgVXIZzNJ/LUnSfw7gWwD/zXA4LAKYB/Cl8Jmz\\n3/5tpmKxWGC1WnnRf/PNN7h79y6MRiNvPJFIBA8ePOAFfNnKrlERQXoul4sNZLfbxf379/HFF1/w\\ni/n4449hMBgYN5VOp1Gv1xUT/DJ6iSc6wgmRI9lqtbCzs8Ono2azie3tbY6OEFi6Wq0qquJeVo49\\nifT7fXQ6HZhMJl4EZrMZtVqNgeQnJyeo1WrsADidTl4AZAja7Taq1apikovAxHFEjEzSeJvNZpjN\\nZt5sUqkUb7A0NouLixgOhwpgarfbVVTNTIrxoOtoNBrFIqfqPEAG3RcKBeTzeb6X3+/H/Pw8G4tK\\npYJGo8Gb8MHBwQtRt0kMFY1LvV5HoVDgE1yn0+FxoXsbjUYYDAbWt1wuMzgfADtOYonxuA5Bp9NR\\n4FYKhYKi8qZUKiGbzbIzPUpn0O12USgUFKdMqkATbcA4ugwGA67WOj4+hs/nQ6FQYEctm82iUCjw\\nc5PjTGtPo9Gg3W6jUqkoijtqtZriEDeu0Jw/PT2F1+tFKBTiOZTL5XB+fs7PXKlUmGwRkNeWRqNR\\n4IKq1SoDzGnsJ5V+v49cLodkMskHtHK5jEwmw2srnU6j0Wgo7ImIPQGgiLYB01NNDAYD1Go1dtzb\\n7TaSySQD7JvNJkdMyAEhXCTp1263FczVNpuNQc3TYJboOkSgmM/nAcjvzGw2o9PpvFABSqLT6RSY\\noG63C4fDAYfDwU7ANJF2ymqI+KRMJgOdTodHjx4BAHZ2dtg2AfI7czgcqNVqvC4I5ynSLIg/x9VL\\njEZZLBYEAgH0+32ezzs7O7xv0LgYDAa43W52+MLhMGKxGI9LtVpVAL4v4ygB05NS/q8AVgBsAzgH\\n8D9OegFJkv4LSZK+lSTp2yl1UEUVVVRRRRVVVHnrMlVkaTgcpun/JUn63wD8P7/9NQEgKnw08tu/\\nvewa/x7Av//tNcZyP8mDjEQiihL0Z8+ewel0YmVlhSuvFhYW0O128fd///ek50w5lugkYDabEQwG\\nGZfjcrk4NUBRritXriAUCvEJlHiEpuHmeZmQ906UCj6fj6M1BoNBEf0IBAJMJwDIp5KjoyOUSiXF\\nyXsSHM6rRK/Xw+Vy8Uk7EAig0+nwOBSLRU7NATL2TKvVch6f7k28OcAFkd40QtEkuq5Op+NTdSaT\\nQTweR6/X40qalZUVRc6e8B5UNVgqlTh1MU3ZLp1s9Xo9SqUSv5PBYMDpA7puNBpVlKlTapJOrZT6\\nmjSyNFpeS7xYdOIn3hQx2mexWGC321n/Wq3GFBSAfFIXWyNMogvdC5BPgo1Gg8Pqg8EARqMRer2e\\nKwmpjJ4iu1QFl0qlWB/iWJp0/Q+HQ37mZrOJer2OYrHIJ3/iCKPrRiIR+Hw+ns8mkwntdpvxcMAF\\n1kzkyBl37ojcaI1GQ0ElodVq4fV6WV/Cd1BUd3l5mceRvlOr1dBoNBQtQSZNexE+qdlscrSh1Wqh\\n1WqxjbRYLGi325yOIswpRWwAOUpXKpUU844qqyaN5ojr4uzsjFPUABjbI1acGQwGRRpQkiTU63WO\\nXlarVYZeTLOHiFHPVCrFz9xoNFCtVqHVatlGEpcTrXPCpdHvpE+9XlfY/WnIRHu9niL6ZzKZUKvV\\nkEjIWzatPxEXSTx5tL+Uy2VotVoebxEzSM8+LhyApNvtwmKxQKPRsK09ODhQPLPZbIbL5UIgEGBM\\no8PhgN1u57VWLpcRDof5Gru7u8hkMgqeqElkKmdJkqTQcDgkkqD/BABVyv3fAP5KkqT/CTLAew3A\\n19PcY1SGwyGH9lKpFPMqAcCHH34Iv9+PH/zgBzzptFotGo0G4xZEFtRZ6ELXajQajAMC5I1vaWkJ\\nrVYLW1tbAC5KwSnPm8lkkMvlFIDYacstSR9Anvz5fB71el3BiWMwGBiwu7Gxgdu3b3M++OjoCKlU\\nCru7uwqHRLzuJOkckcm13W7j5OSEgZMEvBSxLwsLC1z+HI1GcX5+jmKxiGfPngGQ+YTEsZoknSqW\\nx/f7fVQqFX4HXq8Xa2tr7ICTwxMIBNjxjcViKBaLfM/d3V3E43G+hpjmmGSMaEOnppBWqxWhUIjn\\nLiDPX7PZzNiFxcVFuN1u3pDK5TLu37/PQOdarTYVCRulFum6iUQCJpNJgUdyOp0Ih8O8kYXDYYTD\\nYTbghI+hNHOlUnkhjTvJ/KYxPTs7w8LCAr8jj8fDRQAiDmRubo6vXa1WGZ8opj/EuTmJ0HWbzSZO\\nTk6wvLzMKUqPx4NoNMrzORwOK8DmzWYTjUYDqVSK/zZKGjvKdD7u+JTLZaRSKS4sETcPQF7DkUiE\\nsSh2u53T63RYMZvNCAQCfG/aqEfB2m+SbreLSqXCm5TJZEKv1+NnDgQCCpqFcDiM+fl5OByOF0hi\\niSeqUqmg0+m8wD83LtZM5LrqdDo8dyn1RNhBAAxqFu1evV7ndCWlxkSHhfaAcdO5wAXgeJRVXa/X\\nKzCidrud3xvh0sQCFypWEvmFRFzluPhAkZiVnHoiv6VxHA6H7HAT3MRqtfJYEEmmCBQHLtJw45II\\nkx2i+9JeQenTfD6vGCObzQa32421tTUu9LJYLIrPBAIB1Go1xeHm8ePHSCQSU2Fy3+gsSZL0fwL4\\nHIBPkqQzAP8dgM8lSdoGMAQQB/BfAsBwOHwiSdJfA3gKoAfgvxpeshKOZDgc8oRPJpN4+PAh851E\\no1Gsrq7CZrPxpKlWq/juu+8UHAy/fZ6Z4JboeqVSCel0mjctqoYLBAK80Xk8HgXI8/T0lBmOp8mB\\niyJuAHRSKJVKfKqORCJYWFjgjfjGjRtYWFjgCVSr1ZBKpVAulxUcLdPoMwqi7/V6CmbnQqEAu93O\\n40KkbOQsORwOruihjTefz79wEp/W6RVPaBaLhU91gLyw9Ho9wuEwPv30UwCycSDWWtI/nU6/sJFM\\nog9F7QjrAchGVK/XMz5Jo9FgYWEBkUiEN7+lpSUGpwLySTCXy/F3RJ0meXejUaOTkxM+odGYeTwe\\nLCwsMM7E5XKh2+3yCXQwGKBQKLy2qmqS6AlhDnQ6Hc7OzhTAT7vdDr/fz7xmLpcLGo1GUXmaz+cV\\nUcRarcZYOGAyG0Dvtlwuw2Aw4Pj4mI2z3W5HKBTiCBvhJ0RGYYPBgIWFBX5vNpsNe3t7jL8g8tdJ\\nsIuE5aACCkCuqFxcXGQHhZxtmu90YNBoNByVc7lcbA8AueBid3dXcQgYd4xqtRofIobDIUKhEDs+\\n5ITR+6DIthipISyRiJsiXCrZlHGxOWJEkJjtaZ2T02M0GnkObW9vc1QbAHPgkQ3N5XLo9XowGAyK\\nKDM9O93zdfrQT7E6i6pQqSgHuHDAyUZqtVoFoTExsPd6PXao2u22ou3OJEzj9J6LxSIWFhZw48YN\\ntjlnZ2ewWCw8Tna7HR6PRwGqtlqt0Ov1PB6Li4sKG0k8SOPg4cSo+fHxMa5evcoHV4/Hg2KxyIf+\\n+fl5RKNRrK+vs4NGWEKxgm5zc5Ov63K5EIlE8POf/1xBCD2ujFMN95+95M//+2s+/+8A/LuJNRlD\\n6AWk02l88cUXfJJ5//334ff74Xa7+TOdTgfxeFwRDhSvMSsZDAbY3d3lSIjJZOJ+NbQAjEajopQ2\\nl8uxszT6bJOK6KBQ9O309JQjakQiRqDlcDgMq9XKk71Wq6Fer3M0gJ7pslEuMs50sgbkU0YsFuMF\\nEI1GcePGDR4n0j2Xy/HEH02h0POOazQBcFi72+3yabFYLGJ+fp5DuNT7LBgMKqowTk5O+L2NtvUQ\\ne1tNMj7kLJHzT+zKYiUntWSgjRiQT5lizyixekkkjps0jdLpdNigFItFFItFdj7C4TDcbjdCoRDr\\nR200aG0lk0nY7Xaeh5dplUNpHQBcFEHG2+/380mXNl56byLrdywWw97eHjsOmUwGw+GQDTptwJNE\\nBer1OvftEiNLc3NzCmCwWAHY6XSg1Wrhdrs5ykxwAFqfo9WU4+pEhHx0SHM6nQpyXDqZ03uk9S1G\\nJx0OBwKBAP9uMBg4+krPPc78HgwGioOH0WiEz+dTVNlptVqGJYTDYU7dkWMcj8fR6XQUUQAaV/HA\\nO85hQEwDNRoNTrMBFx0XQqEQ3n//fQAyjYgY1e31ekgkEopxoflFDgqVyE/y3ojAkeauzWbD+vo6\\ngsEgO5Z+vx8+n0/RO7DRaPDmnkgkYLfbFZkSrVarcHAJvD2OAydew+FwYGVlBdGojKQ5ODjAcDjk\\nQ5LJZOKIJtkuagNEmRW32w2r1coVz6VSidtnvUkfMTqcSCRw5coVnjNbW1vY2dnhcSIoTrFYVET6\\nm82monUM7TGAvO8+e/bspa3RxpFpAd6qqKKKKqqooooq/7+Qd6bdCXDhDVcqFfz85z9nj/fevXv4\\n7LPPMDc3x157NptFNpu9NF/Qm3Tp9/tIJBL4q7/6KwDAt99+i0AggNXVVcbqGAwG7pcFyOmcWTf5\\nAy5OIY8fP+YT0O7uLpxOJ3voer0e/X6foz0PHz7EgwcPUKlUXvD8p03F0c9Op4NsNss9lqxWK87P\\nz/m9ra6uwu12c1Qjk8lgZ2cH9+7d49MC9be6LNhcHBvggsiNTviLv+3NZrfbOf9eLpdRLBY53ZRM\\nJhUh5cvi4Oi7hUIBHo+H70P92UKhkAInQZQZABhATfN7GgCzKCKQmaIdgLyOCMQsRhssFguHxMvl\\nMnZ3dxVlyJOmA0nE7xF1AKUaS6USdnZ2uLQbuGiRQPem1hDb29scXcjlcooeY5PqQz/r9TqSySRH\\nusxmMyqVCp+qNRoNstksR/8oUkmnbQA8xyia+pvf/Ib5diYdI+KeAcBRdUrvmUwmdDodvi6RhNps\\nNsU4eL1eToN//vnn2N7exl//9V/j22/lIuVxqDFIH5qLtG7EXl9+v19RXNFsNhGPx1nfRCKBcrnM\\ngGPiIQuFQgzQvXv3rmJuvk4fEQ6g1+v5nRE8YnV1lW0itRShiGapVFIUFlAkuNfr8XsbDAY4Pj5W\\nNCF+k1DBC6U9r1y5gs3NTaytrfF1ASjSfdT7jCLMKysrDECndUHktMSPlM/nFamwlwldk7CJH3zw\\nAW7duoXFxUV+T4uLi4r32Ol0UCqVMBxe0KkQl5jIbRSLxXhOHRwc4M6dO4pilVcJrWFqMSW2YdrY\\n2IDFYuGobiAQgCRJyGQyrEs6nYZGo+GshM1mw5MnT/DZZ58BkFu6FAoFuFwu3osnsVHvlLNEQikM\\nGpRHjx5hZWUFw+GQQ2yUc34bTokolDagzb3VaiEajWJ+fl6BjyHSQ2C8hTWpDsAFFqNWq+HJkyf8\\nt8XFRXbcCKxIYdLHjx+jUChM3STydTpRKo7C2Xt7e7Db7RxKpXdFizGZTGJnZwfn5+eKMRrFKkyj\\nGxk82sj29vYYJEg6iGzNgJx6yWazCo4cCqUD06d2h8OhgrX34OAAnU6HUzWUohWf22w2Q6PRcCrM\\nbDYrQtuXfWdiwcLh4SG/m+FwyPeizZA6p5Mhi0QiWFpaYmNNHeVHQdWTgOBJp1arxSnudrsNn8+H\\nWq3GTXLJoNJmSP3Gbt68yUb/5OSEAcPARYXfNO+tXq9jd3cXgPyeHA6HwrlLp9OcDrHb7Yw7IxJY\\nu92O999/nz9DpJaT9DwU+2/RhkmM7/TMxAcmcoQNh0PFpkyVhdRMOhAI4NatW/juu+/4XYrv/XX6\\niF0N+v0+8vk8zw8iYaVnzmazyOfziMfjCgxeq9XitJzP54Pf78fGxgavy0wmg9PT0xdwem/Sx2Aw\\n8AFtYWEBfr8fKysrjIUjgDWtx9HDAADmEqLrkI7U7/P09PSVew3pbzabMT8/z2mhW7duYXl5mbsd\\nAODDGKX9XC4XtFotO70Oh4PtAtkyGleyHw8ePMA//uM/KsgYxbEBZMfE7/dzD8i1tTVO/dPYabVa\\nRZUjsWiL5KGEtyL8MOlMn1leXkar1eLCI+Dl702sVrfb7Tg/P0er1eLx/vjjj+H1ehk/SpxXzWaT\\nxyGTySjS4pSaJger2+1yupH25kmCKe+kswRcbDgkWq2WK7AAcMnjrDFKoyKCDwF5g6Xu0mIT1Gq1\\nqiDQEx2cWQptBGQkj4+PMT8/zzlmQI7M0YmOMEGjC30WQHhyQOja8XicWbEBcFd6cnrv37+ParWq\\nMNCjTsBlgOdic8x2u439/X3eZPV6PVqtFvR6PS/qhw8fot1us/GgSr5ZFAuIJ/FGo4FiscinnYWF\\nBUUTWuCiPJ+wFVeuXMGDBw9eqFy8rAyHcmsQ0oWIRYnBGpCdAq/Xy6fJYDCIn/3sZ/zv8XgctVpt\\nYkwHgBccLLF66/DwkB17MYrVbDbZSTCbzbh27Ro+/fRT/OAHPwAA3pjpvU7ahJl+UnSN9Hn+/Lni\\n9NtqtRSYtn6/z5Fd2lQjkQhu3brFRQQPHjxgwli6z+ucAPGnCOSlhrhi1IiisvTM7XYbZ2dnisol\\nSZJ4k11aWsLCwgJu376Nf/iHfxh7fOg/soFEuEq22GQywev1KmgCTk9PkUqleCOmMnXxABIMBrG5\\nuclOzcnJCR9K36STWB1mMpn4HRF+k6KBNI40HoC8WW9sbLBTRizsGo2Gr2O1WhXEp2KbrVcJOUti\\nU19yXsXojegsEaM9leprtVrY7XYFs32z2YTJZGKHSiRyHRXx7yLzdqfTQaFQwMrKioJKQuxWIUkS\\nnE6ngpqBiCHpmYbDoYJuhaLhb1pz4rr66quvkM/nEYvF8PnnnwMAH2ppjlUqFQbdk010Op2wWCz8\\n+9WrVxl/Rd85PT1lrPOkomKWVFFFFVVUUUUVVV4j72xkCbg4YVGLD7H8fbSFxKwoA14m4nVNJhNC\\noRACgQB7yrlcDsViURGRIH1mqZN4XXput9vN9PiAfMouFAp8CiGsizg+s4xSiNeVJImJ+gA55JxM\\nJrmflZh6G63WEJ9xUv3oO1SVA8jzg9o9AHIYvV6vw2AwMEaCCMzolES4hdH7T6qTqA8gz18xXRmP\\nx6HVauH3+xXltK1WS9FIUqzgEefSJHQUL4vkdLtdjsJks1kcHx9ziTsgvyciQQSAn/3sZ1heXsbt\\n27cBAL/61a+Qy+UU727cMRJ1oagknfzr9To0Gg3Oz8/5PVJrFXpHVqsVnU4Hc3Nz+PDDDwHIPGx/\\n+Zd/ybxWk+gjnqrppE3zl1LwYgpNbM3T6XSg0WjQaDQU/cBu3rzJ9yY+n1fN91fpRI19xQibOFYU\\ndRKbB9PcpdM5leZTlLnf78NkMinI/8bBB41yR9HpXqROAS4aoVO1rlg1qdVqYTQaFTQMwWAQ8/Pz\\nnKqjsR83TQlcRD5E+gm3241ms6kgMqXrA/J7E1sLra+vI5fLKSoqy+UyBoMBY6zGeW+kF12D1qz4\\nnqhfm1jZJkkSp6OoNYs4doThoyiX2EvzVUIpblrnhUIBe3t7WF5eZrtClWW0BihyarFYOBppMBgQ\\ni8UUEc5arcb7SzabfSEL9CqhNVIsFvH48WP84he/4DXscrkQCoV47AhiI9oZIqelPq1msxntdpvT\\n+M+ePcPe3h5qtdpU+MV31lkSjarX68Xm5iYajQaHLxOJBNxuN/8uEkC+DV3oPhRqXVpa4kWu1+sV\\nADcxfz+r+wMXi0/se0R8FBTGPDg4QK/XUzRINBqNL+hzWeeSvi8aW4fDgYWFBTZSzWYT9+/fV/CD\\nUFNXMd8uOn+X0UnUp9PpKDhEtFotTk9PkU6nFUZTJDUTn2v0upPqITo31A+N3pHP54PBYMD5+bki\\nZEzpMEBujBoIBLC/v8/XmEaXURl14okV3m6383w+OTlhDBUgO5XXrl1j/qGrV6/i0aNHUzGbjzpv\\n5BQA8rrR6XQKA05EfyQE2iesFSCPlYg1meSQQvehEmmx4zo1WCV9yWmi34nWgPh0ANmRENOIGo2G\\nwfrjCjknIiWI2WxW0DfQxkZ4OzGFTOPi9XoVhIjknJ6enjIx4ZtEPKDROqF1Tun27e1txpoB8nyx\\nWCxotVp8iCNHhMbBZrMhHA4r0vTZbJaZvek7r9KJjuim8gAAIABJREFUhPBA5LDFYjFsbGxwoQsg\\n46VELjeifiFptVqoVquKsUomk0gmkxNhYqk3nIjd0ev1ij6LRA9Ca41IH8WGt9SYmJ6fcE7kuD19\\n+nQsvUQS0GvXrsFgMMBgMPBeKR5sAfmgfXBwgEajoWCGv3v3LhcaEPkvvTPqGzgJNoioQE5PT/HN\\nN98AkA88drtd4eiPrjej0Yj19XX+DNE/UOr2/v37TE0zDZb5nXaWaHFGo1GYzWak02nePIgLgvLz\\n01Kcj6OH2Cl9bm4OHo8H/X6fuTGILFIEW85i8ycRIwoEwiPj4HK54Pf7FRNIo9GwMSQOmlkLRau0\\nWi07bnNzc0wqCIA5XSgfD+AFAzTqnEzjxA2HMumfSIxGHDM0P6jCI5FIsJGnJpZidHJWGLPRCIXN\\nZlPgp4rFIreAAGQHSnxvkiRxhOcyQmMjOh8insput3NbGDJOvV5PcV/izBGZf6eV0WiUOP7D4RAW\\ni4VxVSRGo5H18fl8jKUSuWp0Oh0/4yT6iZgfAIrontVqhcvl4lN0q9VSdLwn3iCPx4Nbt24BkDcl\\no9HITvD+/v7EtolY+Yk7DJC5i0KhEIOSC4UCstksO2l0QOp0OmwblpeXEQ6HmW9IkiQkEgkmpgTe\\nbJ9o/ogEuzQvaZ2vrq7yuxTHQOTI0ev1ispYAmXv7++zHS2Xy2O19RBbIpHtpfkiFpjQhp7JZNDt\\ndjkTQI1tySGguS1G5MvlMp49e8br8014HECusnv48CH/bjAYoNFoYLPZeOxSqRTu3r3LuhFGj+ZU\\nKBRCIpFAMpl8AbMkRpbeFDnp9XpIJpP44osveMyovQi9p1AoBJfLxWD/QqHAhJhi1eLTp08Zk9du\\nt9FqtRSRXzFS+boxonfWbDaZo+9P//RPAQA/+clP8PnnnzNHH+lC7wgAN6cn1u/79+8rCKNLpRJS\\nqdTUDXVVzJIqqqiiiiqqqKLKa+SdiSyNliBrNBo+QYbDYe6XQyXzOzs7yGQyilLJcXOn44hYMUCt\\nBQAZke9wOJBKpfj0eHh4iJ2dHQ6/ajSaF/SZRXRHo9FwdQ6d6qhXlcj8fHp6yjghYm/W6XQKHp1Z\\n6KPT6WA2mzlVEAwGYbVa+TRQqVQgSRJTKuTzeUVoGsALqZzL6EUlzICcggiHw5wSdDgcyGazMJvN\\nHFKme1EO32azcfXVZfShtOIoF4zIDky6UpqiVqspcCGtVovD5rOQUbwajRPhqWw2GzeIpUghRS1s\\nNpuiQXKr1bpUdGlUF1prDocDkUgEVquVnzsWiynKvh0OB9bX17G5ucnzeX9/Hzs7O5di8ac1IWK1\\nqCUNRYxbrZYiglUqlWAymbCysoKf/vSnAOToar1exz/90z8BkHEU49okcVxIH5pDHo8HXq+XT96r\\nq6uK6jhimxfnGTW5pnd9fHyMX/7yl3j48OFEY0R2TMSWiSXdjUaDe4oBFwzeNpuNuY4oSkY2kxoh\\nJxIJxppN0kSbPtdoNJDNZhmH+PjxY/h8PpjNZv7b/v4+yuUyRxxGW5kQdxb1HaRnJhwh/f4m6XQ6\\nnPIjHU9OTjAcDjlyRBhOEQMk6qLRaFCr1RTYSUrJ0mfHiZxQ2T3Z3nQ6zc9N0TOr1Qqv18vzW6Qt\\nIBv45ZdfIp/P8z0pmyDSr4yLD6LPUXW2yPD+F3/xF/j5z3+Ojz76iO8TDAZhs9l43hUKBfz6179m\\nWpFEIsFs5jQuFDWcxga8M86SKDQ5yDDMz8+j3W7j/Pyc03D0AsVWGrPkNxKdMJPJxBuq1+vFcDjk\\nkB8gE0MWCgX+jsvlQj6fnzmOikCSdrudUwXUMoKevd1uo1gscphXkiQ4HA5uWjkrkSSJafDF9NJw\\nOORxqVarsNlsHD7OZDKMY6BFQmmYyzZBprQtvSe3260gNyyXyzCZTIjFYqxPPp/HYDBgI28wGLgp\\nKzBZT7hRIYcZkB0SIjgEZCeSUjxih3J6v8BFqFo0UpdxJGmDp75ZIvklte4hx3Jubk6RZqH1R+Hv\\nnZ2dmTjbBKqmZ6ZNzu/3M2+YiH8D5MPK2toafD4fb7x/93d/x+9yWiHgu16v5+tYLBbEYjF2Gt1u\\ntwLAS/Znfn6ex5McpT/7sz8DcJEKmlSIR4nmUCKRUPTGjEQizA1E+hMHlUhzUiqVmDT2yy+/xJ07\\ndzj1A0zW00+0r0+fPuW5a7Vasbq6ypsa8Uqtrq4yZrDRaKBarbID8/DhQxwcHKBYLLJdonLxcUQk\\ndez1evjuu+/4mY+OjhTtWI6OjpBOp3meWSwWpFIpvga10RhtfN5sNsfSh8aQUqF0nWw2y46rWEjQ\\narUUjgOAF+4tUiNoNBqFLRvnoEION40B4clEffP5PB8cgYsDlJhSpd9FKAhh9YDpWkKJ65S+2+12\\n0Ww28Ytf/AKAbKfsdjuGwyGPYbVaVZDnjmJDx22X8yp5Z5ylUdyK0WhkQCn188pkMorKJa1Wy5Pg\\nspvtqIiTeTgcKgBvq6urGAwGXN1EYNiX4admFVEioaaoZJg8Hg/C4bCCf8psNrMhEwF9sxByJPR6\\nPTtH9A4oYkKRCY1Gg1arxRseddumCBPJZavPaOzFxqIEdqWxI8eOTsGAPDa1Wo2jcK8CKU5TDUdg\\nZRIy6oB88l5dXYXf71dsdo1GgyuXvv76a6RSKQWwe9pqOAAKwLHIjWW1WhEKhfD555/zwUMsaADk\\nsXz8+DFvSIlE4lL9BUkIzCtuug6HA+FwGD/+8Y8ByIcT4lgDLgg9q9UqOwG//OUvX+heP66IhpZA\\nwDRmFosFOp2Ox2VjY4MxXvQd6tlFGI/79+/jz//8zxUktpPaJhpbkSD18PCQe3ABwM2bN/Hee+8p\\nKr60Wi1KpRJjy4i36G/+5m8AgCM5k7LBj1bOdbtdZDIZ3LlzB4DsEK6srCiifxTVEDdSMQpx7949\\n9Ho9ZLNZxXUn0Yf+Xxz/hw8fIh6Pw2az8d8A5XsYjdpZLBZeZ+QQEuP3pHNKxE5S1ano/IiHMfr8\\n6Nqm+S465ZIkTcW5NvruRjM44jx4WRaE9BH/RlWgpP8shK4vVnsSvlL8DGVXRH3pp2hvp5F3xlkS\\nRafTwWazcTicmsHu7+8rOrlTRQhwueawLxNx8lIDQkAuMzUYDEin0xyhqFQqaDQaipL5WaUDRxeW\\nTqeD2+1WlO12Oh0+wcbjcSSTSTaY1ERXDOtetgoOuCA0MxqN7Pnr9XrUajWOLOVyOcTjca7Yicfj\\nyOVyKJVKvChettguU2FFmzwZLZG2oNfrcdd6QC5hffr0KZ+uyMG6bDXcKLkgdaqn8HcikYDT6VQ0\\nFKbIEoXN//Vf/1Uxj6ZNVdKmQMaS3hsZ9FarhUqlgmw2yxt8sVhUdD0/Pz/Hr371K24lI6YrphHx\\nJE5pB0A+iVPLA5pDpVIJGo2GI0upVApnZ2fY39/H3/7t37J+06aWRzcBADxn7HY76vU6h/3T6TRC\\noRAfnKrVKk5OTnB8fMwg09/85jeKzgLTjtNwqOxgXywWuVoJAL744gvMz8+zHSBQOa130q9QKCiA\\n+dPaSRFSQFFhem/pdBrffvstX9doNPIco02VNubRw8xlIQFiIQVwUdkmEtTS/V91H2qVI0YtphWK\\n6ADjERK/ztaIBQyzkteN9av+bfQdic84a3nZ84vjKEbbR6Oj01TniqICvFVRRRVVVFFFFVVeI9Lb\\nKBufWAlJGksJsceO3W7Hj370IwByWLdYLCqAyxSdeFvPJ0YqIpEI073fvn0bnU4HiUSCweYULXkb\\nJwFRF5Ean8rxNzc3Fb3rdnd3UavVOAQttkSYtS52ux0Gg4GxLT6fD4FAgD1/ahdAUQECWc6Sg4p0\\n0ul08Hg8PIeowSlFmojkLZvN8om4Wq2iXq/PvBkzhd0JP0VcRhQpdTgc/O8Ebi2VSqhWqxwVmDUV\\nBo0LlTNT+sZsNsPtdsPn83EkKR6Po1wuKwDd06ST3iSjJH7EryQ2hu73+4qS7lwux5gKcR7NaqxE\\nfcRUNiBHmsTIDJEWEpcS/e0yuIk36UYi9jqkf6MTv5imFVPIs35/4+gpRugpjULyfenzJpklzYsq\\nv5dydzgcfvCmD71TzhIJGQKqQKPQ26xTbeMIGU9aUDabjZukzrLSbRJ9ROI2scIKuMj5fl86jYIR\\nRfDfaHXL29ZpdDMZ/ZskSd+rPuJ9xTGiTUwk6XtbG+zrdBJ/AlC8N9LpdyGj6YvLYtpmpc+r7vl9\\n66OKKqpMJP92nSVVVFFFFVVUUUWVGchYzpKKWVJFFVVUUUUVVVR5jajOkiqqqKKKKqqoospr5J2k\\nDlBFFVVUUUWVfwvyMsD771oP4ILSZBr+trehj9FoVFDKjDb9ftuiOkuqqKKKKqq8NXkZn9Dvyimg\\n4iCRZ2m0EfD3tQGL7amI4b3ZbKLRaHBBDvGezZLE+GVC3QGcTicXTm1ubuLs7AxnZ2cAZL5AjUaD\\nbrd7qdZB4wi1yhLb4fzRH/0Rc+D95je/QTKZxHB40fR7UjLViXV6a1dWRRVVVJmx0Gny96EibrS/\\n3+g9pyH4m0YH4ILtXGQppo0WeDXh5LRkpi8TscKUnABArsgVGd+73S53pxcZ6IEXiQQvK1R9S10D\\niAYjEonwvXK5HFKpFNOpEKnqaJuMWekjjsvm5iY2NjawsLAAQO4N9/TpU6bAqdVqqNVqCmdplvrQ\\nfKGepp988glu3LgBQO5Gce/ePXz99dcA5D6Gox0xLkNk+irRarVwOBxwOp14//33AQCffvoprly5\\ngm+++QaATDPT6/WYUBUAU3S8LYdJxSypoooqqqiiiiqqvEbeqciSyP2i1WqZDI6o9sVeNqN9bd6m\\nTuIJU+Q4IiFdRH3eVr51lD9I1It+inlo0kE8hV62nYco4tiMnsQ1Go2ir5DYdFFsgzBKmjmrdizi\\n76MNIEkfsVcTtQUZnVeXfYej3Fhi525RH5GXit6j2FV+FFMwrV4ioSG9I7GJL42DGP4WW6bQXB9N\\ndUzbFoZIIKl1zigxJL0nQI5a0LjQ30Z7uk3TtoLekdjb0Ol0wufz8emc9KLTNzVfpdYk9Ddx7EiX\\nScdGq9UyOS8gE75SA2FAJsik5qMAuCdcNptlXYjoVGwH8bL5PY5oNBpOJc3NzeHKlSvcAorartCc\\nqlaryOfziMfj2NvbAyC3ssnn80wuSjZzWsJcetdmsxmhUAjRaBQAsLKyguXlZcRiMdY3l8shm81y\\nL8EHDx6gUChw2xbqaXfZCIokSTCZTNw82OfzYWVlBSsrK0zcGwwGYTKZEA6HAcgkwslkknvSUU80\\nMYp5GZ0o4mez2eByueB2u7nxeavVUvSwpAba1WqVx4T2j1k0Fyehvd3lcmF5eRmA3EasVCpx+6eT\\nkxP0+30YDAaFjRndY2a5v76TzpLZbIbT6eQJpdFoYDKZUK1WFazClUqFXx7lWWfds4byzjShXC4X\\nrFYrwuEwG1UAKBQKOD8/BwDOSVP/OmA2oV4y6Hq9njcTp9MJt9uN9957D4DcFNJsNnMPvUqlgoOD\\nAzSbTTZU1CftsiFxSgvQ2Gi1Wg6DA8Bnn30Gp9PJYelkMolyuYwnT55weL5cLiuazHY6HUUfu0lE\\no9GwLvTOyEgtLi7ivffeg8PhYCN6enqqaKTbaDSQyWR4jg0GA25eOinruLjh6/V6mM1mHofV1VVs\\nbGxgaWlJEbI/OTlhA358fIxisch962iMGo0Gvzcaw3HHhsaFsAuA3Fvw2rVrWF9f5/Wm0+nQ7/e5\\nqW8ul8Pp6Sn3Qjw/P2dSVtqs+/3+2PqQLtSklpjOr169ivX1dXg8Hk5bmEwmhWOUTCaRz+dxdnbG\\n/dhOT095TgPgbvHjGHayOUajETabDaurq5ifnwcArK2tYXt7W9EIejAYsCPUarVwfHyMXC7HPf3i\\n8ThOT0+ZiZ3mzyRrTZIkOJ1ORKNR3Lp1C4C8ydJ7AmS2c0mSFJ3om80mjo+PuX/cN998g729PUX6\\nqdVqTdUY2mw2cx+6jz76CFtbW5zOiUajMJvNPN/peWu1GjtLX3/9Nf7Df/gPjI9pNBrMCj/NWqd1\\nbrVasbKywk3XV1dXsbm5CY/Hw9ichYUF9Ho9bG5uApCxOnfv3sW9e/cAXPRCvCxWh+wP2b+VlRVE\\no1EsLi7yHNdoNOj1etyBwefz4fz8nNfa2dkZp6AuSw4rkuG63W5cv34d8/PzrMvZ2RkKhQK/t7m5\\nOd6vaOwajQYajYai390s0rl2ux0ff/wxQqEQAHn+3r17l5t1i+uHnH/a+17XV/Qy8k45S3SKMhqN\\nCIVC+OM//mMAshHV6XQoFAq82LLZLJ4/f84NNzOZDKrV6szyq2REqcs4TbDPPvsM165dw+LiInvo\\nWq0Wh4eHePbsGQDg4OAABwcHSCaTPMlGOyhPIzTJzGYz6/MHf/AH2N7eZqPqcrlgMBjYQz87O8O3\\n336LeDyO3d1d/lu5XL60PtTyRDz9/vjHP8b29jYA4NatW7BarWzYstksSqUSvv32W3YsDw8P8eDB\\nA96IxSjYpAbdYDCwE+D3+xGLxbhlzvb2NhYXF9k5AYB8Po9Go4H9/X0AcgPVg4MD3L17l/+doipi\\nhGccEU/iHo8H6+vrbKx/8pOfYH5+Hk6nkw3VcDhEsVhk5yMej+PZs2c4PDwEADx69AjpdPqFiM84\\n0UsaGwDwer1YXFzElStXeFxu374Nj8fDY0NRJNpky+Uy9vb22JDF43Fuiiy2ihnHkIoG3Ov1Ym1t\\njTe6Dz/8EDdu3IDT6eQoF0Um6T61Wg2FQgHPnj3Dw4cPAcgOFTniwAXOYpx3RXPTbDZja2sLW1tb\\nDDrd2trC2toan85H23W0223Mz88jm83yu9br9QrHmubOJFECk8kEm82G9957j9ssxWIxLC0tsfNP\\nY0J2gKIaRqORD3Hdbhc6nY7tEjkDFGEaVx+KctGcCQaDCIVCcLvdAGR7TRglGgNyHOjdDgYDNBoN\\nHsuTkxMel0nXlojdMpvNioOJ1+uF1WpFs9lksLAkSdDr9fB4PACAn/70p3C73Wzjv/vuOxQKhRds\\nzjSR0sFgwAfV+fl5BINBBAIBXtd0IKJnjUQiuHnzJq/zL774As+ePWNnQdRlUn3EaHa73YYkSQiH\\nwzxXaY2Le5TJZOI5Bsh7xXA4ZP3JNkzrTALgll2xWIwjgtRonJ671+txI2kaK4r8ig12ZxWBA1TM\\nkiqqqKKKKqqoospr5Z2JLIn91yidQye8WCyGVqsFo9HIJ2SPx4NkMsnfmTYX/yqhE5DBYIDdbsfq\\n6ioAOYS7urqKfr+vSLMsLi5yCkWj0XDj31mkBcVUgd1uRzgc5hPbZ599hsXFRcZRUJiSwqgulwur\\nq6uo1Wp4+vQpgNnkeal3n8/nw9bWFgDgxo0b+NGPfsTRnWq1il6vxycKrVYLvV6P5eVl/lsmk4Hd\\nbueqh5eVIb9JKFphtVp5znzwwQf49NNPOZpjNBpRrVbRbDYVaSCj0cjfsVgsGA6HfELe29vD+fk5\\nms2mIgL0pvGjExClb27evInbt2/jD//wDwEAgUCAm7CSaLVa6HQ6ju7Mz8/DbDYrUr0WiwVHR0cc\\nQSFc05vGhsYdkKMCt27dws2bNwEA165dg8vlQq/X4ygt6S+e3iORCJ/U6/U6KpUKp1IAcKj8TbpQ\\nSp2uG41G+R2trq7CarViMBigUCjwdUdTmkajES6Xi6MqLpcLlUpFgYcZZ46LaVubzQan0wm/38/z\\nwW63o1arcaqLIgKkC0UsBoMBR8JMJhMcDge/o0qlMrY+o/bParVy1NbpdKLVanHqsV6vo1QqsT20\\nWCyMO6P57fP54Ha7eT1WKhU0m82J179Op4PT6eT5bLVa0e/3kU6nAchp0GKxyPbP4XDA5XLB5/Ox\\n/qFQCNeuXeN1fn5+zpGPaSI4NN4WiwUOh4Pnd7Vaxc7ODsrlMt/LYrHA4/FgbW0NgLx3bG9vM06I\\n8F5iJGbSqDZwkSaieUlrt1qtsn0uFouKaM7q6ioWFxc5ddftdpHNZhWRpWlTXwQhoWcOhUJwuVw8\\ndlarFTabjSNhnU4H0WgUHo+H70epdlrfo5Wqk+hF68Zut3MUl6KTBDMQI8qSJHEvVpJut8vPJDav\\nnkafUXknnCUxVAhcGBwKF9psNhgMBjSbTV4A9XodPp+PF+w0RuBVIgJXO50O/H4/lpaWAMgTzGQy\\nodVqMZ6kWq3CbrfzIvF6vSiXyzPDT9EkA+TJcOXKFXZQdDodisUipycBeTwpvCkCmMVUgZgumGQx\\n0rUIq+RwOHjj/eijj6DT6TilRoBK2pBIJzIcgGzQo9Eoby7NZlOR2hlXJ8Kc0Eb34YcfYnNzk/VN\\nJpP45ptvMBgM2LDOzc3BYDAogMs2m42xGaVSCYVCAY1GY6JQryRJsFgsbACXlpbw3nvv8WZTq9Vw\\nfHyMhw8f8r2tViuCwSAbWBojciy0Wu1LDwPjOgWjpdVkpLrdLnZ3d7G/v89G02g0wuPx8DgYDAbk\\n83l27nq9Hur1OofJx9VjdAx1Oh0XbwDyOtJoNEgkEowB6nQ6MJvN8Hq9AMBA4nw+z5tzp9NhR4Ce\\naVJb0Ol0UK/Xkc/nOUXcbreh1WoZT3J6egqtVsvryO/3sz50b0qNkhM5CTeMWJRQqVRQLpf5nZDQ\\neJODQu/V5XIhHA4jEAjw32izJPsxDR6HHOdOp6MAjgNgbFS1WlWAlP1+P4LBILa2thj/pdfrFRw/\\nL2vkPI6Qwy1uoJVKhWEHBLA/Pz9nh8Pv9yMUCrF91ul00Ov1vAZcLhfbKHoHkxy8xWfQ6XQ8F6rV\\nKo6OjlCv19m+pdNpNBoNvl8oFEKhUGCbZDab4Xa7YTQaFTq8rMDjTTqJn6tUKpzSImfDZrMpCn+0\\nWi2cTiesVisfPICL/UIUcW8bVx/xnZdKJXg8HnbkS6USF3jQPSkdTfeiwy7N506no8CT0rqfNmjy\\nTjhLoycvs9mM9fV1ntyHh4e4c+cO6vU6b4aBQAD1ep1BerMk9RL1oQgBASvX1taQy+XwL//yL7wY\\nI5EIrl+/zrlecujE61xGL/G7Op2OAaAAEA6HcXR0hDt37gCQ8SU+n48N19LSEsxmMwwGA2/A7XZ7\\n4sX3Mp00Gg1cLhdP5kajAYvFgmQyCUB2lmq1Gr/HXC6HlZUVOBwO/k6tVkO32+XFKOakJxECoRKO\\nbDAYoFKp8DM+fvwY9+/fR6vV4gUZiUSwvLzMp1+z2cxVXqPPSmP0JsdSHFPRQen3+xwtKZVK+PLL\\nL7G7u8vP6nQ6kc1meXPR6XTQaDTsEAwGA66+ou+ILLev00fE/FBkhhzafD6PRCKB/f191s9isWBu\\nbo43RY/Hg0ajwQa0Uqlw8cI0YEv67Gjl2NnZGeLxOE5OTthB6XQ6cDqdivtotVpks1negHK5HNrt\\nNm/m42IqREet3W4jlUohEonwc7fbbcVBJJ1OQ5IkXue06Wo0Gh67Wq2GYrHIupCzNIkzSc7o8+fP\\neS7SvKW1lUgkUK1WeY7Nz89zlIU2Q+I5IltAYz0J/oU2n2aziefPn/Mz2u12vo8I/gfA6y4QCPCB\\nwWQyKSJ54rUvI7VaDalUim2x3W7H+fk5SqUSb6q1Wg0ajQanp6cA5Pks6u9wOGA2mxVFApPIyyqS\\nAXleWq1W7O3t8aG+0+nAaDSyo3Z8fAytVss2cjAYwOFwcLUj/U3ETY4roh3TarUcISIM269//Wvs\\n7u7yXDUYDBxJJyedxpGcXIosTeN4k43X6XSYm5uDy+Xivx0fH3NlIF3XZDKh1+spIrl2ux0OhwOA\\n7ByVSiWe38B4Ee5XiYpZUkUVVVRRRRVVVHmNvBORpZf1iNFqtexZf/fdd3jy5AmcTid++MMfApBD\\niF988QV7wKO51FmKmAZrt9t49OgRvvvuO46OLCwswGKxcPj+7OwM9XpdkTqZVYqQvHoam2KxiJ2d\\nHS7R7Xa7fHICZG++3++j3W4rwpViZGla/QizQSmHRqOBXC7H4flkMolWq8UnOKpWc7vdHDFpNpsK\\nXAgwHhZnVOiUKuKjhsMh42yoTLbdbitOuyITMQBFpCOTyShScHSfcWQwGPAph/A9FJlJJBKc3qN3\\nYrVaodfreRx6vR4qlQqfho+OjpBOpzkqOMm4iD+bzSay2ayCu6RUKilSn2KaDACnESjikk6nOZ0g\\nVlWNGz0RI5HJZJLfR6vVQqvVQrlc5mckDAidJnU6HSqVCvP2AHKVZaFQUGAYxtVFpGEoFApIJBK8\\ndiwWC4rFIs9vOmGLnDlmsxmVSkVROVgsFjmKO8k8FmlQ6D2J61in07G9I51ovtvtdgSDQdjtdp5T\\nxA4t8kJNKhSVbLVaHD3qdrscGQHkddLtdtlOEr2F3W7nyEGv12MMI4CpIiWkjziHWq0WMpkM30eS\\nJF6z9LdyuYx6vc7REdpbaM4bjUZYrVaODk6jE0m/3+col2hHxPYmIvZWp9Oh2+1yNoIqHP1+P7+3\\naaJdpBfZeMIeDYdDhkc8efIEpVJJwXlHNojmF607kdGbaBZGn/1NupCYTCbMzc1Bp9Oxjbxz5w6e\\nP3/+wj7gcDg4eurz+bCwsMCRpHq9DoPBoMA3XkbeCWeJXiq9tGAwiMXFReZ9efLkCXw+Hz744AMm\\nQuv1evB4PPzSpl18rxKRl8bpdDIfRCAQgF6vh8ViYf1WVlZgs9l4kWSzWcWme1mhiSYuNCqDdTgc\\naLfbbDz8fj+i0SgTo3U6HRweHiKXy7GxJD6qaVKE4ndEbBcgp7GGwyE7BbVaDUajkVNj6+vrsFqt\\nqFQqbByoxJfGjggGJx2f0fQZGUO6br1eZ2oJCnkT/YNIfipJEqeA0uk083yMO7deNqatVgvpdJqN\\ntzj2IgDT6/XyZ2q1mgJYSWkXEQMzqU6AvJGVy2UGUhIPFlFSAHLacGFhgZ3IfD6PVCrFjhuFvkVs\\n0DS6kD40X6xWK+tAc8bj8SAWi3HaOZPJIJWGM3tNAAAgAElEQVRKIZPJMNh5lDdsEhHHstfroVwu\\nszF2u92Ym5tjXSKRCOOC6DuVSgX1ep3XFpFCioekSQ8itD4pDQjI63p+fp7f22AwgN/vZyzX1atX\\n+d/IqanX6ygUCi84tNPqI3KjiYeIdruN4XDI68pgMCAUCmFubk7RE41wWHTNaUXk0+t0Ouj3+wo+\\nOzF1A8jzQyQ3pOIcWlu1Wo3/f5oDt3jdTqfDh4xer4dCoYBer6f4W6vVYv2ImJacEvpsr9dTQBOm\\nkeFwyO+M2rAkEgk+1ItcToDsxHi9XjgcDoYDEJ8hpaLL5TLK5bKCSmBSzJJer8fq6ipsNhvblIOD\\nAy6GAC6KmWKxGO/5wWAQNpuN53e1WsXKygofzh8+fIhsNjsR/5wo74SzBFycYAAglUopKhM++eQT\\n3L59G2tra7zIzs7OFCeVaVh7X6eLSHBXLBZ549BoNLhy5Qp0Oh1XyMViMYVhS6fTCs4c4HJkXqJR\\nookq5n9DoRCTUkajUdy+fZtP6wcHB0ilUjg4OFBUC73s5zgiMrk2Gg0kEgmuMgGgWPixWAxer5f7\\n/1BV3unpKefNia9HBMiOGxkYZd6u1+uc8w4Gg/D7/fwZr9eLSCQCo9HIfDGLi4uQJIkN+PPnz/H4\\n8WMGF9fr9YkxHmQUBoMBj3exWEQmk+FxkSQJoVCIyVcBGXNCXGKAvOE9fPiQHbdisThVn6ZRQGy1\\nWkWpVGKwOZFlimR1sVgMOp2OuV/q9TpqtZqioGFawjzxUERrVsRMEPaGHBKn0/nCoWh/fx/D4ZCd\\nGmLRnraSkn4OBgPG1gAXTiM5JKFQiLmMaFwI/0WnXyJDFcHa0659EYw7HA7hdruZd8lqtcLv9ysq\\nAslxoAOCRqOBx+N5AWs2qT40/8Xogsh/Q5VLpCs5mW63m+1msVhEtVplu2W321GpVF7YsCfRiUQ8\\nYI1GU4ALbJno1OTzecYRNRoNxlPRdabhEqJxGo3SjmIg7XY7zyli1ibpdDp8SCJdaD5Naq/Fd9Zs\\nNlEqlXB8fKw4mA4GAwU33cLCAnw+H7/LZrOJvb09Xmu9Xu8Fh3BcWy1GTu12O/r9PmdE0um0Yi5Q\\ndO3atWu8f7jdbq6KB+TIby6X4zlVKBS4qm4aX+CddJZyuRy++eYbNqLvv/8+gsEgk40BsiFIp9Mv\\nhANnwTAqXo8ql4iQT6fTIRaLMakgIHvk9XqdnaVEIoFms3lpEPWo9Pt9lMtlnJycMNhye3sbq6ur\\n7BxFo1FEIhE+jTx8+BDxeBzZbJZPX9OSir1Mn1KpxJ5+MpnE8vIyU9g7nU7EYjFcu3YNgDy5y+Uy\\nHj9+zKzZmUxGkSKcFHwKXGxIBIwFLkp0KV2wsbHBRoAqCR0OB3K5HB4/fgxABqoS2zlw4bhNmvYi\\ngy1uAhqNhsff6XQiEAjAarWyUTKZTCgUCjyWBHSmVBM1kJw0MjAKom21WoqTdKfTQTAYxPLyMs8h\\no9GIVqvFjhuRnJL+BMyetjBAJJkTS/EtFgtcLhcikQg7S+S80L2pRcLp6SnrW6/XX9qGaFx9AHku\\n63S6F4hNA4GAYmMTNy2KUIRCIaY/iEQiePToEb766isAYGLaSd+Z2PaGntvpdPImRc4ZPTM1rxU3\\nZ7PZjLW1NY5CazQaPHr0aKrorWjLBoPBC46F0WjkaPb6+jo7dZRKSqVSKBaLCuJQs9mMVquluM40\\nMhwOFWktggfQe9za2sLS0hKPCzXVpbVGG7XRaOSoxDROrjg+gLyOYrGYgv5Dp9NhYWGBI24Wi0UR\\nQaZoeLfb5c+Uy2VF1Ro985tEXBOdTgd2ux0ffPAB6/Ps2TNe/4DsxBHzOd2LKDrooOd2u7G7u6tI\\nG4rP/DohJ8dgMCCVSmFjY4PXls/nQ6vV4t+XlpawtLSE7e1tHgeK2ooRqvfff5+dJaqQFW34JKIC\\nvFVRRRVVVFFFFVVeI+9MZAm48JYbjQbu37/PHuTGxgb6/b4CAEv8QqP4jVkBqcWQZy6X45YY1BzR\\n5XIxDw0ge9gU1i0UCpwamCbV9ToZDAZIpVKcblpcXGSqBUA+2VIfPUCOsOTzee43BFxElqaNDAAX\\nqa92u83PTWF2iuYsLS0hGo3ySabZbCIej+P8/BzxeJy/M9qTado0Qbvd5lSR0+nE/Pw8cztRntzl\\ncjEuplarYXd3l6N0+/v7SCQSHJ0S20JMIqPppWKxCIfDwfOFQu/UVFP8Dp3EC4WCAj9DYzRpeJki\\nOfQclC4WiS2JqI5O50RsSidzt9ut4DsR39ekIkZNOp2OAoBMNBKE66B7iRQK/X4fCwsLuH79OoOq\\nT09PL72+aC7n83lFxIFOuoB8wqd1DVzwq2m1Wu6TRu1/KCJYLBY59UXPP64+xP0EyPNB7B14dnam\\nSC2ZTCYMh0P4fD7mHKK2UZSq63a7SKVS3MICGB87JGKWLBYL9+yjazidTibKXVtbg8lkQrvd5vl7\\neHio4FejKB6Vh9PYTIp963a7L/Q6dDgcDAYG5BQ3kQsD8vzO5/OKyKnZbFaQIorca+L9xtFLbL1y\\n+/ZtRKNRRRrZ5/Px7xqNBt1ul7E7qVQKOp0OXq+XoyPUZ1TcS8aJVoo4IbFxrVjgksvl2C653W50\\nOh0kEglefxRNpeiO3W5Hp9Ph+U1wjHHemwgdoX58FLFaW1tDs9nk9RaNRnH9+nVUKhVO1TUaDVQq\\nFY6UEraZ0nSky+np6VSRpXfKWSJptVq4f/8+g8parRb+5E/+hPufAXKqTsQsvS3p9/soFov41a9+\\nBUDu7XP9+nV8/PHHXBUjSRLOz8958RGQeFYiLpJ2u41kMol//ud/BiAb8KtXr+Ljjz8GcFH9RmzM\\n+/v7ODg4QL1eV1znspsLbXriZHY6najVagzIW15e5t5NALjh6IMHDxhkSNUVl3Eqycg2m012Iok8\\nkELvGxsbmJubg9ls5lQGgZ3JET4+PmbgMuk27ThRY1O6rslkYifN6XS+oIskSZibm+NCAmKGFh3c\\nSfFK4vjQMzUaDRQKBf6dqvLK5bKCL8ZgMLBR3draQjwe53RlqVSaKiU4Ku12W+Es5XI53Lt3D5lM\\nhjd4QF7/Io5oaWkJH3/8MRvWZDKJBw8eTF1dRdLpdFAqlRSki8lkko08VW7SRkKEtdFoFFevXgUA\\n7mlJB4Z+//9j781i47yvfMFf7fvC2shiFVncKYmiTK2RZSd2rG4HGQdJPzRu0A0M7h0McF/uPAww\\nDzOY136ZpwHy0GjgogfpaWSSmUYySNKIkw5ix4plS7IsKVopkSKLS5FF1r7vyzx8Pof/r0RJrGLR\\ntnq+Axg0qVrO91/P8ju/08S1a9dkXDAH0Ymel96XzWYRiUTYkNPpdKhWqzJsVCaT4TQUIOGa5ubm\\neD8uLCxgdHQUP/7xjxkcf1Ahckr6f4vFIkurTE5OcsGL2+2GRqNBOBzmfpSrq6vsAADgKtmhoSG+\\neFOp1IHXFO0bo9EIp9PJxiqlTYmJGpAMKHFcCoUCEx8DeyBrsa8eMaTTPnlRsQ59LuFs6PI+f/48\\nzp8/D5vNxmumUChAr9fzeyjdTeeh3++H1+tlDiFAWt/xeJzPdAJdv0iIGJeecWZmBn6/Hzabjfe1\\nyWRCOBzmsWy322yQ05wQWz7tNcJwnjt3DsBeP02xG8HzhNYPsbuLTjFBN2jfjIyM8L1Kzx2JRJhw\\nFJDmaGhoiFO+IyMjmJiYYLhHt/JKGktUGk950SdPnmB9fR2nTp3ixUylzP00Sp4nZDAB0sU3MDAg\\ns6ZtNhuy2SzjcERq+H6KmH8m7zGbzcpKJgmkSvo+ffr0mZYL/cJ0UYk/HeiRSATj4+OyMl5iYAak\\nnHI4HEYikWAvlQydfhhvwN7YE9iPvoe8cGJlBsDVT7QZc7kcWq2WTLdeRYwsxeNxmEwmxgZQt3UC\\ncwKSt65Wqxks7/P5ZJGkwxom4vgkk0n2ZD0eD1ZXVxGJRNjDbLfbGBkZYW99aGgIly5d4pJjKhXv\\nVR8RW1StVmX7xu12Y319nV9D+Ci6mAmIPjc3hzfeeAOAtCeXl5f5QupFF5H8kSKNiUQCer1eVlEp\\nVuyIoGuK+MzOzmJoaIhZ7T/88EPcvHmza50INC0WvVBVF+lZrVZlHnQ+n5e1qSFjgYh8PR4PM8mT\\nI3rQvScaG0RsSpEbu92OqakpjuIaDAZEIhHcv3+fI8iRSER2ydNnWCwWvtgIK3eQFj4idmtkZISf\\ncXJyEg6HA8FgUNbWSByXZrMJl8vF58D29jazV9Ma2tra4ggI8GIgM33u0NAQLl++jDfffBOAFC0h\\n4D2dibTnCdQttjcCwE2kiSASkPCLq6urvD+XlpZQKBReOE4ajQZms5mNeNrLGo2G15DD4YDD4eDv\\nEUHddKarVCq43W7OXJRKJVllL5Xui0Uf+4m4li0WC+N6Sa/Tp09Dr9fLihFarRZ2d3c5W7C2tgaf\\nz8dngdVqhUqlkhVf1et1OJ1OznZ0Yx8omCVFFFFEEUUUUUSRF8grGVkiIUuUyp0bjQanJajsmqzO\\no5JOrAF5ujs7O+zVUc6bvAXRW+tnNRx9nlqt5ugHkYpRpMDhcMBsNvPYmc3mfbEuh60apHy46HES\\npwilJ8kLp3Gi1ieUcwf6Py7kLRJuS+QTKpfLsh5XjUYDExMT7N2I1ZX9EHpGInGj6ANxGmUyGY6q\\neDweTs8BwBtvvIFr165xavewc9VZak0eM6WZ1Go1pyNjsRi8Xi9TY7z77ru4cOECr7GHDx/2hAkg\\nXTpLqcmbj8ViHGUQiRRF3QlDMT09zSnLy5cv48c//jGnDrqleQD2yt9VKhU/G0WPycumvU/voTJ9\\nSmUCwNtvv41gMMh7wGazyb7nRbqJKTUiVaX13Gw2uXceIJ2N2WxWhoXpJGOsVqswmUw8r7OzswgG\\ngzhz5gxHz16Wau7cVwA4pUwYoJGREUxOTrKHn8/nGSZBa54iyGL1p91uh8PhkLVNEWlEnqePSqWS\\npQDFtiputxtGo1EWyQD2cFaAFN3x+/28791uN7e7oSiLy+VCOp1mihORgLVTH5LR0VGcO3eOozAU\\nURefhxqL0xwZjUaOdAHSGvB4PLDb7ZySoupZohFpNptcZfkiabfbDEsgguBjx44hGAwCkO4p4gyk\\n11M1KN2zhEGlu60Tr5ZKpWRQi+cJwSQA4M6dOzAajTh//jyfMRMTE0in0xzlp9ZOwB4xpsPhgN1u\\n50rZsbExWTQwmUwiFouhXC73VBn7ShtL4kGmVqtleUgylkSelH6nvUhE7g6bzYZgMCgLk1Jqhzaf\\n2E37KFJxtMgBMC8GhS+fPn2KgYEBGecJXQT9BpuTPiKeJBAIsG6UY6ZxcTqdTHZIl2E/06giMSUR\\nrNHGqtfr3CyW8u+Tk5OYmZnhJsnLy8vPpCV6WVfimgTAF4uIh9jY2MCjR4+Yy8hms+H8+fOMvZid\\nnUUgEGBsich8THJQfhNxjigd0dkUtdFoyDidjEYjH1wLCwsyLMbw8DAbJt3oQ3rQezr5Y4geoVQq\\nsX5kLIl8MUtLS9je3ua5pYu8FweF3kuAY7FhKBn1IthcNJZordRqNd5/4t8AyQDslgqDeIGMRiNf\\nomazmYH3wB7xrZiCJ91orinVRQZ3s9nkTvMiTuVlQpe7SBg6NjbGfGWUGqWxi8ViKBaL0Gq1DAym\\nbgYimz8VDpDDu7u7+1J96Cym76LCCUoJ+v1+pnEhY4MwQmTQlkolOJ1ONvbIsTKbzbwekskk07+8\\nTB8RLC+m/mltExUAAMZFifgplUrFBkypVGKKHJEJnBqXAwdj9CYnlRjfCRZw9+5dPo/b7TacTifP\\nETG1u1wuLCws8NgFAgF+JioWonOjVCrJih5eJPQZqVQKn332GdMDAFJqbnZ2lvc07Rsi0wQko9zr\\n9TK+iXB75HDeu3cPm5ubL8VzPU9eaWOJhLqgFwoFWef248ePM46CPJijEHFzOp1OOJ1O2O12xg25\\n3W7k83nenCaTCfV6XXbRHsaY67yQqIIJkIB7brebc7ThcBhOp5MX2ODgIOx2+zMLuh+RJdKHDphQ\\nKIT5+Xl+TTgcRjabxbe+9S0AkmcwPT2NiYkJPsAp6tWPCJx4UVAlEGEZGo0Gc07RQRAKhWC1WtkT\\n/Oijj5irhj7vMPqIncwJnwFIB3omk8GDBw/w8OFDANKFEwwGWbdgMIhjx47hzp07AMDVML3oI0Y2\\nKCpA3prVaoXD4WAmamCvQlGsoNPr9Xywvfbaa1hcXOyJP4iiJoB0gYrtDAiDQEBf+m7iQAKky9Ht\\ndmNoaEjGcCyy9najE32GwWCA3W5nQk4at2q1ylGYfD4vq0oiY2BwcJDxU8FgEM1mky+pxcXFZ1rm\\nPE/ErgFkTJDn7Xa7YTAY+DXRaBSRSESGPaKWI3QOHT9+HCMjI8xzRi09lpaWZIzVBzFygT18zdDQ\\nEGZmZnjfhEIhmQMUi8UwNjYGj8fDjolKtdf8G5CiiVarlUH+NBeiMf0ivciIMZvNcDgcbCx5PB4M\\nDAzInJ56vS5rKByPx7m9EACuOjQajfyM1AqJ1upBnDri3aJ9Lv6kNUWgfIrsUhNieq3BYMDa2hpS\\nqRSPQzabxe7uLjso0Wj0QAaTGIGzWq3QaDRIp9NsuHk8HlmlJo0BtREBpHWXTCbx6NEjAFKESozA\\nbW5uIplMHghnRvNBrVc+/fRTBorPz8/D7/fzvRYMBlGv1zE8PMznkkajwfDwMM8FVXZSEcG1a9cQ\\nDoeRy+V6qmJWMEuKKKKIIooooogiL5BXMrJEEQKybi0WC9rtNjOtApJVSQ35AKki5qjSXhqNhkuZ\\nvV4vV1eQR0Sl12QBk5fVz/YrpAuVhFKZrlarfcY7qFarrMvu7m7PdPkvEvHZyJMNBALMMg5I4yL2\\n6ikWiwiHw5y376eQZ03f5XQ6MT09zR7G6uoqNjc3EYlEmMGbcuiH7cHUKbQOKX1DEQFKqVQqFU4l\\nkbdLeX8xhULeKH1mr5Eu8T2UqhRbBpB+4t/8fj+nBqg5q9iU+DBsyyS0jyj64HA4WA/ab9T0lKIS\\nCwsLOH/+PKxWK8/fzZs3OQ3WjYjjqdVqOWJMVYuE/RN7rsXjcT6DrFYr5ufnceLECXzve9/jZ8hk\\nMviXf/kXAFJ0tdsIHDFke71efu7x8XEZFmp1dRW7u7scoSUsXrvd5ijL+fPnuboKkNKrv/rVr3Dv\\n3r2uGqFS9JcigCaTCQMDA1z95vf7ufkvAIYpVKtVGXbL5XLJeh9GIhEsLi7yWUU9/g6SihMbTpfL\\nZZ4TvV4Pq9WKRqPB0ZtCoYB8Ps/YrVwuB4fDweuOetZlMhnOFjSbTUQikQPh4GgvLC4u4sqVKzKu\\nIOKRovdT82hKHS0tLaFcLvN6HxwcxM7OjowXj+aY0pUHSe02m03mLQOkOTQYDLh16xbfqxTNoXMq\\nkUhw5oKeW61W4/Hjx0wbsrGxgVgsxmdlZ1uv54kY6Wu1WkilUrh79y7+7u/+DgDwt3/7t3jjjTc4\\nDUctdYaGhngc6F4h+ozPP/8ci4uLnF2inqO9nk2vjLHUmWoSSwIDgQDUajUcDgeHxJeXl7G1tcUH\\ngcFgYEK7fghdWgRupIOCMB+FQoH/9vTpU0SjUc6VGgwG1qfb7swv04mwA3QIEQeNiBuKxWIyyn2r\\n1QqDwSCjNDisPmREGgwG2WUnXmwulwutVosPIL1eD5/PB6/Xy/NGB0k/9NFqtfy5brcbWq2WNxiV\\nwIptSCiNQtgFi8Ui48jqh070OWJrEwKeiw1iBwcHMTc3x+mxarX6DNi8H9QBNNZk4FKa1mKxyMhD\\nqQwakNIsKtVeg+GNjY1DOQLic+h0OjYiQ6EQXC4XKpXKMwcklVZfvHgR09PTsNlsrM+vf/1r5PP5\\nQxmSBIx2OBxsTHs8HlitVpw/fx6AZOxns1l+9pGREUxNTfEYAtKB/etf/xo/+9nPAODAKThRF8KX\\niJcsNRSlNEUoFJKRg1IKTqPRMLZIo9Egm81yo9Hr16/jgw8+QDab7VonsTCCxoD2mk6nk5HRigSm\\nNLcajYZ52YA9fp7t7W0Zn95BLzqRImRtbY3TRLSfm80mGxfr6+soFotcoJBKpWRp22KxCIvFIqOD\\nUavVSCQSByr4oH2eyWRw+/ZtHrORkRH4/X4kk0keq62tLcRiMV7fRKDYCdQXP5ccCBFfeBARiUQT\\niQQ7+ISTpLZdNJZ0DtZqNTY0CV9J+5BaQNFZ1k3TWnHNNRoNFAoFNmB/9KMf4Xe/+x2++c1vApDW\\nkM/ng9PplKUsRU6qpaUlJBIJDlr02meQ5JUxlkShqBJFjQg0HIvF2Iokz4oOKZPJJAM79lMXseEp\\nXXoqlYo9onA4zEy9gHRA5HI5VCqVvmFxSBdiohV7IZFegHTQFotF9rQJYB2NRrsixjuITnq9HhaL\\nRcZyLD5nMBjkKhVA8vCIP6MTlNsPfbRaLXtoOp0OiUSCx0Wr1eL06dNoNBqy7uiFQkEGVu6XPp2V\\neVqtVnYgDg4OIhAI8Nx6PB6MjY3xZl9eXsbKykpX2JIXiYifcrlcbGwT07nZbJbhJlwuF3t5arUa\\nGxsbeP/99wHseXa9Cl1IhAWkfR4KhRAKheD1emXrmzqQA3tjmUgk8NOf/hQAcOvWrZ4NJdKFCCmJ\\nMRyQDMlgMMjrpXMOjEYjNBqNDKP005/+FD/72c8YHN/N4U2fTcDt7e1tNjY2NzcxOjr6TBWVWHFJ\\nlUoUKYhGo7hy5Qp+//vfAwAePXr0jKFw0MhSo9HgCMfKygo8Hg9HtwEpCkffS0zLHo+H193Kygo2\\nNzfZYAmHw1hdXUUymeSziqoND6IPPcPOzg5HlQEp6kl3AjlpkUgEqVSKDbVUKoVkMiljt7ZYLLJq\\nawI7H+ROoTVUqVQ48kKfK1aWAdI8iecwXfDi94gYWfodgKyK92VCTlFn1waxUmx7extarZbXAAUn\\ntFrtM03Xxc9VqVSyz+3WQBGdY7HSNBaL4fbt2wCk8zsYDKJWq/F+jMfjjAUmEQ3swzrer4yx1Ak+\\nJjAXIBkCFouFDRAAzLRMC/OgiPxu9aGNSIvD7XYjEAggEAjwpZHNZpHP53niibSv87n6IUQCSZa+\\n3W5HIBBgz6VQKGBnZ4cP63w+L0tfHlanThK/ZrPJc5BOp2EwGNjDpG7xtMljsRiD8vpZok8RN4PB\\nwM+WSqVQLBb5IhZLlEmq1Spu3bqFTz/9FMAeEdphhFJpYvsQlUqFRCLB4zA1NQWfzyerilGr1Wg0\\nGhwF+Id/+Ac8ePDg0OuIUrdiFZJer+cDZmBgAMePH8f4+DhHJKjikpyB5eVl/NM//RM+/vhjAOAU\\nc69Vgp0AevGwJgZjMsDp0qD1m0gk8OTJE/zyl7/EBx98wH/rtUs8jUOlUkE2m8XW1hZHrHw+n6yl\\nERGHiu/Z3t7GrVu32CC5ceMGX+DAwUkfxXGhQz+bzTKp4x/+8AdEo1FuKTIzMwO73S4rhyeGb2ri\\n+8c//hE7Ozsy45ZSdd2sc7ESEZDG+6OPPuKIUCgUgs/nY2NKp9NxSx1a81tbW8hms2w85XI5qNVq\\n2Z476Jkg6l+pVBCLxTj6cPfuXYZtUCSm1WrJDB9iwycxGo0ol8vQaDQyQ+agxKvia2q1muw5iFW9\\nc25JOqPY5PSJn9FsNplwsVvpjChTmo2+WzScxeIWUXQ6nawaV6w6PkwkR9SPPouMNJVKtS/xpniW\\nUfWsGA08zN2mALwVUUQRRRRRRBFFXiCvTGRJFEphkNW7vr7OzfuoD9nW1hYKhQJ7M/3uEdcZbic8\\nh8Fg4CgB5cSfPn2Ke/fusXeTSCQOBFTsVsiCpv5vgOS5LC0tcQqFMACUrgyHw9jd3X2u19CtiCFQ\\n0bsDpMjR/fv3OSWhVquZUBSQIhQ3btzA2tpaX1vCUPREpDHIZDJYXV3l9M34+Djq9bqsF9nW1hb+\\n8Ic/cPl+tVrtKyhfTKuk02nG25lMJqYIoOhNLpdDPB7Hb37zGwASvoTaovRDDzFsnslkONrw9OlT\\nqNVqLhQApKjc8vIyczx9+umnePLkCXvdh2miK76XeGA+//xzAOCGuT6fjykfYrEY4vE4Y1IePXqE\\nJ0+eIJPJ8Lo7zBiJ5KjpdBoPHjzg515aWsLw8DCndXw+H7a3tzlt+/jxY/6dxq4zatGLUMRLBCUv\\nLS3h6tWr7FUT7xLtR5GkUuxtSJ8n/uxFKDoDSPskHo9zBI7WT2eUoNFo7BtNpN8PkzIRIzUibUwm\\nk3mGkLDzuzr/Xdxnhz2PRBoU+n2/iFBnJkWco/3SSf0oqBB12u/fxPS4KJ1NfA+jz8v07HxmkfZE\\n/Pt+Ohw6K9DvC7snJVSqAykhktVRNQiwVw2nVqtl6aWjMEg6RavVMgAWkPr9EPkYXR6pVIrZq4H+\\nEi0CcpJD0odwFBMTE7JUF7HmksFCPDX9Fko1iZWCJpMJLpeLx0GtVssq88jI7LdhS2lbm83G4WGH\\nwyFr9tlsNqHT6VAqlTjUS8Rz/R4fSsVRWksk4APAF5rBYGDDrVAooFgsyi7dforYJ0sE5dOhLFYC\\nEueSmAI8ih6MNE6UarNYLHzZiDiNWq3Wcxf4XnQSyXAJo0H/Jqb7+lGYoIgiihy53Gq32+de9qJX\\nylgSXg9g74A/rBdyWBHL3MmD6jw0v0wRLW1i5+4FI3EU+nRSAnwV+pAe4kVHf+sF29JPfUQR5+3L\\nlv28734UIyiiiCKKfM3kQMaSgllSRBFFFFFEEUUUeYG8kpilo8qL9iqi998Nr8RRSSdfxVct/cit\\n91v2S9d8VVGc5333VxnB2e+7lYiSIooo8v9XUSJLiiiiiCKKKKKIIi+QVzKypIgiiiiiiCL/HkQs\\nGtiP5PGr0AeQ8LedmNKvEhssVlQShvLLxLy+1FhSqVQjAP4ZwCCANoD/2m63f6RSqVwA/h8AYwDW\\nAPyHdrudVkkj/SMA/w2AEoD/1G63b/xf1hgAACAASURBVB+N+ooooogiinwdRawSFH/ShdtZEv9l\\n6aLRaKDX67nYpNFoyJifqcjjKCsq6adarYbFYmEyUYPBwC1PgD1iSBG+cBR6kS5iP9W//uu/hsVi\\nYeqOzz//HJVKhXv0kS5HqY/Yb/Db3/42JicnAewRnxK5MLBHQnlU83aQNFwDwP/UbrdPALgI4L+o\\nVKoTAP4XAB+02+1pAB988TsAfBfA9Bf//WcA/9B3rRVRRBFFFFFEEUW+JHlpZKndbkcBRL/4/7xK\\npVoEEADwAwBvf/Gy/xPARwD+5y/+/s9tyby7rlKpnCqVyv/F5xyJ9LNnlyKKvAryVa75/b77y9Jn\\nP8oH4MsFpHdGKcS/i9QY+/GqHVUkpbNJLukm9hCr1Wqo1WqsFzU97Uyz9CqdfG8UFaC2K0QAa7PZ\\nUKlUuN8XILWnKpfLfY9YUBNxaqPidDoxOjqKqakp7quXz+eRSCSwu7sLQOqzVyqVZL3W+lX8ITY1\\nV6vVGBwcxNtvv41Lly4BkMhEb9++jSdPngCQyEbT6TTrQlGmfq0f0oXWz8jICH74wx8CAL773e9y\\n1A2QyDkXFxfRbDZ53R9FNIfGSKVScWPo9957D++99x5z0S0uLiKXy8n6Pmo0GpTL5SPjWusKs6RS\\nqcYAnAZwA8CgYADtQErTAZIhtSm8LfLF3/pmLKlUKpjNZt58gDQwIvkiHQpfxgEuNjbU6XS8OWlB\\nUXdn0k2czKMKYdJ3E9s5HRYiCSMA7uAu6tJ5MPTjACVdxAayGo0GBoNh38aHIi9UZwVdvw502pCk\\ni06ng8lkkl14xWJR1ptqPwbmw84h6ULfS5eNwWDg3l5kJFD/rUajISMUFZmBD3MhE36C1oter+ex\\nocvPaDTy9wPgbuxUCUqXi2hA9XqgUnNoQOo0brfbZU19LRYLNBoNs+MXCgXk83nU63XZWIlVob2m\\nWWi9kkESDAYxPDwMv98PQOqjp9FoZJdLJpOR9WKkZp80VrSmutWl0yCxWq2YnZ3lfpmBQADBYJB1\\nNZvNaLVaiEQizBR///59rK6usr5E8NmrYUD72mQyYWBgAKdOnQIAjIyMwOPxcArFbDbD7XajXq9z\\nr8O7d+/i5s2b3PGeDLvDXsSUWiKW9TNnzvA4USPoTCYDi8XCvew2Njbw6NEj1iWZTHKPsX70hhRJ\\nVk+ePImZmRm+y5rNJrRaLY+VRqPB7u4utre3AUh9RjuZ6ftxHmo0GpjNZszOzvJ3N5tN7O7u8to1\\nGAzcMJ6aDtPdQZ/TL6NSpVLBarXi4sWLAIDvf//7GB4eZnb8R48eyRoSA+DG9OJ90s/79cDGkkql\\nsgL4BYD/sd1u5zoUavdALPmfIaXpunkPAGnSAoEA3nzzTQDSQeHxeJBMJnH//n0AUkftWCzGhxJR\\n1h8FG7NOp+PmsFNTUxgYGMDMzAxbxa1WC/F4nNufPHz4EOl0GuVymQ/0fhhO4kVHFnggEMDo6Cjm\\n5uYAAH6/H263mxddNpvFlStXsL6+zm1HKCd92M1IRgAtZovFAp/Ph4mJCQDAwsICXC4XgsEgAMkw\\nSafT+OSTT7j7+M7ODrdpAA7nWYkGiclkgtVq5fz8sWPHcO7cOfh8Pj5Ey+UyotEod7qORqNYWVlh\\n9vN2u41KpSLz1rvRSzQarVYrH5h+vx/z8/M4fvw46zcwMIBarcYHxJ07d7C+vs6NVIlRO5fL8Rrv\\npoWNuLcMBgM8Hg8AYHR0FIFAAAsLCzxPbreb26IAkif+6NEjnrONjQ1UKhVUKhXGEzSbTT7kDyq0\\njumiO336NI4dO4ZQKIRjx44BkA5Kg8HA+yiTyWBtbQ0rKyvc+mhpaUkWKaAm292cBSqVis+doaEh\\nANLFOzc3hxMnTgAAvF4vdxKgZy6Xy1hdXcWDBw8AAPfu3cODBw947IrFoszo7Wa+jEYjRkdHAUiG\\n2/z8PF90c3Nz8Pl8vMbEhsNkFHz44Ye4du0at0wR2zB1u7/Efe71ejEzM8PjMjk5idHRUTbkdDod\\njEYjNBoNY3XOnj2L4eFhvP/++wCAtbU15HI5WSuNbkQEKdtsNt7TAwMD8Hg8GBwc5Hl0uVywWCx8\\nLqnVajx9+pSbH1+5coWb/vbDESEHyG63c0Nt2hv1eh0Wi4Xna2xsDNPT07zvb968iZ2dHV7LQH/o\\nWLRaLRwOBxwOB7P35/N5rKys8N6ifUM4KxJx/fYrwqzT6RAMBtkRGRwcRKPRwOPHjwFITkcul0M6\\nnX6mzdJRkeceiDpApVLpIBlK/1e73f5/v/jzrkql8n/x734AsS/+vgVgRHh78Iu/yaTdbv/Xdrt9\\n7iDMmYoooogiiiiiiCJflRykGk4F4P8AsNhut/934Z9+DeA/Avjfvvj5K+Hv/4NKpfq/AXwDQLZf\\neCVKDZjNZgwPD3Oe99SpUzCbzSiVSlhcXAQg5TTv3LnDIWdCzfcrvyp64haLhb2UN954AydOnMDM\\nzAx7xAaDAclkEnfv3gUgRVju3buH7e1tjpjs10yxF50MBgPsdjt7cd///vexsLCAUCjE3221Wtnj\\nT6VSMBqNuH//Pu7duwcAiEQiyOfzfYl0GY1G7uE3MTGB733vezhz5gwAqfGoVqtlL4VwDIODgwiH\\nwwCABw8e4LPPPmM8ASB5ft3OI0W5KOI2PDyM+fl5XL58GYAU5RoYGGCvl/ShuQSk0O+9e/dw48YN\\nAHueOPBsU9KXidjzzOFwYGxsDK+99hoA4NKlS5ibm5N5mAaDQZa+mZmZwYMHDziS+uDBA2xubkKv\\n17NO9Xr9wONEEQjaWxQVOHXqFM6cOYNgMCjTRaVSsTc8NTWFmZkZ3Lx5E4DkqS8vLyMajfKepWc+\\nSJhejAo4HA6OIs3OzuLixYsIBoM8R5Qao/F3u90YHBzE6Oio7DWrq6vcVJuikwfxyMW0LUWQaS+5\\n3W6MjY3xmiqVSrwHAWkt6PV6BAIBHodisYhMJiNrZiumdg8yV5Rmt1qtGB8fByBFc6xWK0dQqE+m\\nGFmitU377cyZM8jlchzlovSlWH3VjT5ienJoaEjWXLxarfKeBqQ58Xq9rK/L5cJbb73FEe9MJvNM\\nX8ZuUjwiLkin0/Gc0M9kMilbD2q1mqNyU1NTWFhY4M8ol8u4du2aLKLdyz3SmfonOInZbOZ9vbOz\\ng3g8zmllk8kkizBrNBp8+umniMVivP96bfclYux0Oh0sFgtGRkb43srn89yoGpDuhWKxyBAB+oz9\\ncG+9pOPElKDNZkMwGOTIo9lsRjqd5rTt2toaotHoMzg3alklfp6o12HkIGm4NwD8twDuq1SqP3/x\\nt/8VkpH0LyqV6r8HsA7gP3zxb+9Dog14Cok64L87lIZfSGcuUq1W8wLyeDycb6eDYHBwULbR9gMz\\nHkZEYJzFYuFD68SJE5ienuaQJbC3QenAnJiYwPb2NiKRSF9yvOLlYjAY4PV6OUX59ttvw+128+bL\\n5/Mc+gWkQ8ntdjPeAuhP+JIuDfFAf/fdd/HOO+/wZkyn06hUKnxAGgwGlEolOJ1ONrCcTicsFssz\\nPeW6FWpcSzn3+fl5/OAHP2DDzWg0IpVKoVwu89gYjUbUajW+dKempji1AkgGCqVWxbF72fh1lsW6\\nXC4cO3YMf/mXfwlASgkaDAbEYjEOt1ssFpjNZl7TZrMZx48fZ13b7Ta0Wi3C4TDjCTQazYGNE1qb\\nJpMJfr8fIyNScHhychI2mw2lUonnqdVqwW638zy2Wi04nU7ej4lEAg6Hg1MXwF6H8IOIeGjSegak\\ntVCpVJBIJLjBcLFYlAFBvV4vms0mpxMBaR51Op0sPN/tvqPzp9ls8rxptVpsbGzwuMTjccZNABI8\\ngNJk4pogXCONXa/nUqvVYkNnaGgIzWaTDZLt7W1sb2/zhUqNx48dO8ZGjclkgtFo5N+Jx+YwaXdg\\nDwdJ+2RzcxPb29u8V6rVKgwGAyYnJ/H6668DAK8nSgGZTCZotdqe0jriOqP303zXajUkEgmoVCp2\\nwCqVCht0gOS82O12TE1NAZAck1u3bh36DALkvTEJvymCqBOJBNbW1tiQGxkZQSaTYTD6xMQE1tfX\\nOS0nPmO3ImJb9Xo9LBYL3G4372syQshYyuVyjMelM9FkMqFWq7H+pEsvqTAxAOH3+zE5OcnwEZPJ\\nhKWlJdleK5VKsvQvzbFICdG5ng9ztx2kGu4qgOedcpf3eX0bwH/pWaMXCF0U9XodOp3uGTB3qVRi\\nIBwZAbQhes197yfiZiRAHkWW2u02SqUSNjY2+ECv1+vw+Xw8mTab7UiiXCR+v58t8maziXQ6zbib\\nTCaDVqvFhzgd1nq9njEzBAg/TB6a8EF6vZ4PnQsXLkCn0yGbzQIArl+/jq2tLfaqxsbGZIsfkAzh\\n4eFhxgmJ+fFuhAwCMqZnZmZw/PhxjpbEYjF89NFH2N3d5QNkdHSUAc7A3uVC+DSHw4Hd3V0ZFqcb\\nzAnNm8VikRkojUYDm5ubuHr1KvL5PH/X2NgY608HEq0pKiLYD4B+EBE/p/MZwuEwtra2+CJut9sY\\nGhpiT9xisaBcLvMcVSoVFAoF1Ot1WQTloONCQoB22kfpdBpWqxVLS0uMu8nlctBoNBxJnZ6ehtVq\\nRalUYkOC9KHzol6v97TvaC+R42GxWFCr1fh7otEostksP8PIyAgqlQoGBgZ4HMRKNPrZ7Xqm/Viv\\n1/nSHBwchMFg4IhxOp1GJBLhvUb7UK/Xc2Ss1WrBZDLx5UhGXC/GiXgpZTIZJJNJNlYLhQJSqRSv\\nn3K5DKPRiHq9zsbRzMwMGo0GR+kcDgc0Gk1PBoq4t9RqNWq1Gu+jZDKJdruNbDbLGLtyuSyL4pIj\\nQnPmdDrh8XiQy+X4b93ssU4nXyyuSafTSKVS/Hk0b4TDMZvNiEajvF6q1SrzRJEu/QB4U0SRPpt0\\nLZfL7Hy1222O0onGvl6v57Hbb3x60a9UKsHn8/G+bjabSCQSfMaUSiW0221ZQQ7d8eLvzWbzmT3X\\n63gp7U4UUUQRRRRRRBFFXiCvTLsTMb+q1+tlmIRMJoNbt25hY2ODc+CDg4NIp9OMEzoqCgGNRgOj\\n0YixsTEAUjQiFovhww8/ZCt4ZGSEsQsAnqEQ6KcQMy15aCaTCZubm7h+/ToAKe88MDDA0ZDR0VGY\\nTCbo9fq+VxVQekTk5KjVatjY2AAAXLt2DaurqzIPNBgMIhAIsHdTLpehVqs56pXL5XoeN51Oh+np\\naQCSt1iv19nzfvjwIW7fvo2dnR1eV/l8HsPDw5xeMpvNqNfrnMpzuVzszdMYHSQKR/9Ozx0KheDx\\neNgrK5VKuH37Nh49esSRApfLhUajwZ4WvZf0p/0hYnG6GSexyqdWq/Hnkpe7trbGnrhOp+OoBI1l\\nq9XiCFA+n0ez2ZRVnB00iiq+ptVqoV6vcxrAYrGg0Wggl8txSociyBShoBRQPp/n9UxeaLe6dOpF\\nKVj6bp1Oh1wux88djUaRz+d5vej1emi1Wq5SpM8R0x+kSzf60Our1SpHtcLhsCxqkUqlEI/H+ayh\\nNA59J7AXUaFopV6v7wn/Qq+nM4VSSCTVahXpdJrXVLPZhMlkQiwW48g/RbopykX4wV72uqg/UVzQ\\nd8fjcSQSCWSzWU4T0jqjylKXywWfzyeLcrndbj63etEH2MP30BzVajVUKhWsr6/zmG1ubiKbzfJr\\notEozwsAxpxZLBZZRVyvWYrOs8LhcHAU7oMPPsCNGzd4XlUqFarVKlQqFUdpKAshZgNobYqfexAR\\nI3BOpxNnz57ltbmzs4Pr169zdJJgHmLKT6vVQqfT8V1BbONixflhGsu/MsYSsDeYOp2OSxgBKbS6\\nsbGBWq3GwNRms4lPPvmEF6HIQ9MPEYFxGo1GlnJbW1vD2toaX3Rzc3NwuVx8YNKhqtPpDlWKStK5\\nWMQLJhQKYWtri9MWyWRSlpd2OBz8HjrAO1MDveIGqKybjIB0Og2TycT8JZFIBIlEgjEpZFAGg0HO\\nTVMJugiA7xXgLQI9jUYjms0mH6LhcJixMCLGzGKxcNpNo9GgVqvxeItFAyQHBcSKPxuNhgyYnUgk\\nkEwmmXOKRFwvxIFCaeetrS1sbW2hUCgcKtzcaDSQSCTY6SCOlWazKeOHMRqNPE6NRgOFQoFfu7a2\\nhu3tbeTzeU6x9oKFaTabqFariEal+hC9Xs/7mL57aGgIIyMjjIvz+XzIZDJIpVJsSBBgVrwcDyqi\\nzsTdRM9pNptht9t5Hm02G3w+H6cnjx8/jsHBQeTzeX4GAlTTWdArDQal4UQDxWaz8dzXajUZD9P0\\n9DROnjzJqTgA2N3dRSaT4TnqXMvdiohbKRQKvDYLhYJsHZMzNzQ0xEauVqtFMpnkcQF6NwBEofQ0\\nnW3ivhJTdTqdjs8hm80GrVYrI6W02+0yAtLD6ENC/F8rKyt8DtFYEYaQ6AVEg8tkMsHr9fLc093T\\ni4jnidVqRTAY5KKR27dvI5VKPYOJFak6rFYryuWyrMhB5FzrRkSs4vDwMEZGRvhvv/nNb3Dnzh3+\\nXoKJiNxPBoMBoVCI5y2fz2N7e5vPAdG47EVeKWOJxGazweVysbe0tbUFm82GmZkZPuSTySTUajUP\\nkIjv6KeoVCrY7XbWxe12M7cHHQR+vx92u50PTMLp9KMCDnj28DcYDHxI0k/a6A6HQ4Y30el0iEQi\\niMVijMXoJF/r9TAH9iqyxL+JB7rJZOJoycmTJ+H3+9FoNBiLEY/Hsbu7K/NKe7lg2u02NBoNbz7C\\nMZAXJRJB0lyOjIzA7/fz72q1WuaBRiIRLizoBUclVipVq1W+KMjjHxgYkEUGRBLWXC4n8443NjaY\\nY4l0OQz/Cr2XDEyj0ch7y+FwYHBwkA/03d1dhMNhNoLT6TTzPonVQ72IGIUxGAxwOp0yLphQKIRQ\\nKMTrPJPJIJ1OIxaLsRdKRH5ihddhQMP0XqvVCr/fzxHjiYkJjhIC0tiVy2Vsb2/zms9kMoznAvZA\\nqN2I6E3T55ZKJeRyOV4jAwMD8Hq9PE6XLl3CyMgIjEYjrxlyqsgx6XUdd45nq9VCuVzmqBGdAeJ6\\nt1qtMg4tegYyRNPpdFdFAc/Th/6fximbzT4z5sQbJK5R0TEpFosolUoyct9eIxTi3JFRSf8P7JHw\\nkjNgsViYdZz00mg0yGQybARQhOowYjAYEAwGUa/Xsb6+DgDPOGsWiwWjo6Nwu908l1qtVlb8kcvl\\nEI/HOVBwUEdbzBxptVqcPn0agUCAja6lpSUkEgk+D6lgJxQK4fjx4wCkKkybzcbnaKlUQiwWY761\\nR48eIZ1O93zvvjLGkniB06VAEzI4OIjx8XF4vV4eiNXVVeRyOVnLgX4ZS2JIv1qtyiz7ZrOJqakp\\nLn8EpIqiarXKh0ckEkEmk5Et8MOQedH7arUayuUy6vU6b+pmswm/38/VcXq9HidPnmRA+s7ODiKR\\nCFZWVthY6ox29VqpU6lUkM/nZXNQq9XYgzt79iy0Wq2sTL3RaODp06dMPra6uopkMiljY+4lqgRI\\nc0Vrhg4bmsdAIICTJ0+iVqvxwTQzMyMr693c3MSf//xnGVi+Fwbm/So7c7kcR0GbzSZGRkY4vQWA\\nU15k3O3u7uLx48ecEiIC1m4B3qSL6NXRf4Dk2fr9fng8Ho6w2e12NJtNpukgA4UuOtp3vVboiJWm\\nYuWNy+XC+Pg4Tp48yWB4l8vFjOLAXjSi0WjwZVIoFA5d4UVGm16vZy/a5/Ph9OnTmJ+fBwAulxcv\\n1E42fzLYRbb2Xve+WIbearVQrVY5wuZyuTA5OcmkftPT06wbpScrlYqMYJCqzw4rFPWiZxJTjvQ9\\nLpcLgUCAjblCoYBoNMpnKRWIHLYCjcaW1oc43uLeEp3vgYEBlEolPg93dnaY1Ljzc3vRh6TZbKJQ\\nKHCUG5DGTqR+mZ6extjYGN8lVCovApdpPR32fms2m9jc3GT4CFFJ0HoPBoM4c+YMfD6frAp3fX2d\\n9yiwV3QBdEeJI44nEbzSmbKxsSGjTrHb7RgaGsLZs2dx9uxZfk+z2eT1bbFYEA6HZZFUggj0MlYK\\nwFsRRRRRRBFFFFHkBfLKRJaAvdBnPp/HnTt32IP7zne+g1AoBLvdzt5ku93G7u7uM+Rqh4ngiEKW\\naaVSQTQaZUI+QOLjOXPmDEcDNBoNisUi44jW19eZcO2wIOpOnUqlEsLhMLdXsFqtcLlc+Pa3vw1A\\nwuo4HA7+3ocPH+Lx48fcPBLoLTXQKQQCzOVynLZaXV2FzWbjyM3w8DD3iQKkqFc4HMbnn3+OR48e\\nAZD4YkTOql55YEgf8hY3Njbg8XhkuoyPj6PRaMjSKMlkkiNJ8XgcS0tLHAEiMHG3Xkq7LXEi0fpV\\nqVQol8s8/k6nE8FgUJYaaLVa2NrawsOHDwFIa2h9fZ2fhyKK3UbdKC1AuhB1g/i72+1GKBRiLiO9\\nXo9qtcppOZPJhGg0yvoTvUMvcyW+h3rS0Xy43W74fD5uT0H6iVECvV6PkZERrK6u8vvoTOg1pUM/\\niVONSEpHR0eZDJKkXC7LUr2E9aLCAuI0++Mf/8iv7xW3RDxUgLRm7HY7e96UOiWPn3iPxMhSu92G\\nz+djLFo8HudIcC/6iGtGjDQA0rxQyi0YDGJiYkKWMkmn08hkMpxur9frMuJD0vegIuJsgL3IrNVq\\nZX4j+nxqq0GRpVqtxm1oAHDbFRHE3BkdPmiqSavVcrTMarVibm4OVqtV1orH4/FwZGZwcBA2m431\\np7J9IpEk/YA9HO1B952474mHa3Z2ljMe8XgcJpOJ2xw5HA4EAgEZFQbRZ4gcSYlEQkYKKu7PFwnp\\nYjQaORpIz+FyuRCJRHiOxsfHMT09jTfffFNW/FOv1/mcstlsOHdur0HI2toakslkz42RXyljiYRw\\nI5QWuHz5soyhFpAmdr8OxEdRFVcqlZgp3Ov1YnJyUgZeJNwCAR6JjLEzt35YofRgoVDgyo2RkREZ\\n6/DAwACDeAFgeXmZU1100PZSobOfLp3VTNFolI1aQEpzORwOnqNEIoF79+5heXmZ88y5XA7VavVQ\\nfepIF7G3mt1uRzablQH1qTKQNn4sFsOTJ0/Y8FxcXMTOzg6nCnrlfAL2KnAAKTxsNps5tWS1Wrk/\\nG11kVF1G7OEEWqa0HBlKvWJOaHwbjQaKxSKnPYllmL6fpNls8mFNlUNilc9h1pCIc2q1Wpw6rVQq\\nqFariMfjsrXa2Yx5aGgI58+f57kOh8PPXG69CI0TGajJZBKJREKGP9rd3ZWlEScmJmAymWT9EIeG\\nhnj/0bP0YhCQAQdIl5JIBJlKpbC6usqFHVtbW+w4EXZSpVLB5/Mx3isUCmF1dVVGrNnL/FHTZfEi\\nttvtbDAGAgFMTk6yMQtIZ4PIbE4GjZiG6wb/IorY6/DEiRPw+/0yMk4yoOg+sVgsSKVSjMNpNBow\\nGAyMF6Lv6FYfeg997+joKC5duoTjx4/zetje3obD4eBxMBqN0Ov1rEuhUECtVpP1kozFYs+kZF92\\nDlDqTuR6c7vd8Hq9nNYixmxyKAcHB9Fut7G1tcXrzO12Q6VSMcjabDYjEAgw5ESsjHuZiNjEcrnM\\nzOqAtKcjkQg/88TEBM6ePYt6vc53bzabRSaTYeNuZmYGoVCIn2drawurq6vsPHUrr6Sx1Gg0ZKDJ\\nyclJBINB3vQAmD34KEDdohCYkSZMo9HA4XDA7/fzAmo2m9wQFpCMJ7pI+lmhR9Z4NpvFnTt3AEib\\ngBiigT1SNBE/tR+xYj+MN8of06V19+5dDAwMcD5e9HoB6eJYWVnB4uIiX0iUnz/sPNKmpUNJq9Vi\\neHiYNxbR/Ivl1zROhM2hBrHdEi3uJyLmbmtrS9bceGJiAhaLBXq9nr9Dq9VyGw9A6hgv4hZ6jU6I\\n+gCSASjizJLJJOLxOLLZrCxa4PP52OhdWFhANBqVGbiHWUO0J6j5rlia/+mnn8pAwXQQ0yEaCoVw\\n4cIFxhEBkpH74MGDrlvS7KdTrVZjfVZWVmRMyiJdAekfDAY50gxIztTCwgKDaO/cuSOrYDyoLhR1\\nI4OEIg40HmTwkkGbz+cZo0fPb7PZMD09zU7dsWPHsL6+jvv373cFGBbxXIBkfIhtM4gVnrCK09PT\\n8Pl8KJVKssrY3d1djtoSLtVgMMja9xxUaK263W5MTU1x9/rZ2Vk4HA4YjUYeK5PJxAYeIK0Pg8HA\\nEZtgMMi4QrFarxMA/aLxAcAM5QsLCwAk0P3bb7/NDcYBcCcFkbhSnDOKnA4PD/PnxuNxqNVqGRbt\\nIKLX63mcnE4nR4Fons6fPw+73c7zqtPpEI1GuYEtCRU6AFIUlxoVkxykMo6qlQEp0EHjQef+8PAw\\njh8/zgbtxMQE9Ho9tra2OAixtraGSqXCFCdEckoRRJ/Ph/Hxcayvr/dUradglhRRRBFFFFFEEUVe\\nIK9kZInSTZSCiEajSKfTGBoaYiuSPOSjjiwBe9ElQIpoZbNZGTWAyWTiVBwgt7T7nRZUqVSyigDK\\nS3fS5YtkdqI33C9pt9sc5hVTPJ38PM1mk0Ot2WyWCdpEL7JfKUrxJ3GvkOdNzViLxSJ7OO12G4VC\\ngaMHxENzmIgSichFUigUkEwmZREV4hgiT9vhcECr1XJI+c6dO7IKz36kTYG9NBylanw+H65du4ZG\\noyHrf3fs2DFcuHABgESNcfnyZXz66acApLQARU97ETHaWq/XZZxbDodDxgNGpKWU2jh58iTGxsbg\\n9/tx+vRpAMDFixfx+PHjQ3G/iGuZ0srUZ5HOIapGFfnKwuEwYrEY/21iYgJer5dxT7T+utWJokr0\\nudRvjX6nRrQieeDu7i7a7Tavb6/Xi2q1ypEOvV7P0fBe9BHxUyKlgsPhwOjoKEcFXC4Xms0mtre3\\nGc9IDWQp8ktzpdfrOVJyUCyVRqORRT0vXLggq5iq1+vQ6/X8HVTxRc9eLpfhcrmYaDidTnPUjs75\\nRCLBFb/Ai88oiswMDQ3hL/7iahDMyAAAIABJREFULzjqOTU1Bbvdzi1DAGk9JJNJxuZYLBbuZQdI\\ne83pdMJgMHBUsV6vY2lpic/Rg6S9KDJJUSSNRsP3ltiz0u12871lMpk4Ok9ZEq/Xi6GhIV7PFHmi\\n91BE8yBCY+h0Ojm9TbocP36cSY0BKc3caDSwvr7OFcFra2uy1C4RwtLZQLhQsbl3N/JKGkskItlh\\nJBLB+Pg4LyriGaFD/6ikEwtB5I6PHz/mzUe8NHR4LC0tyQDnQP+a11KpMi0IKnWmUvxAICDjgBoZ\\nGcHdu3ePxGgjXeggUKlUMr6gSCQCrVbLAFmbzYaxsTFcvXq17xgzMhrFg4tIBAFpY1FpqRgSf/PN\\nN/Fv//ZvAKSUVL+Mb3HdENM5bXS73Y58Po9wOMyprTNnzmBiYoLX0Llz5/D73/+eD63D6tL5OxnQ\\nq6urjNOhtKFGo2EcEAC89957CIVCnOq9du1az/NGWBX6HvFgK5VKKJVKskOTjDjxovjGN76B8+fP\\ny4gre6GbEEv8qRmv2Wzm5yamZbqcqtUqGo0G69xut5mxntIWarUaBoOBL3NiFz+IiABqSh2RAUIY\\nHEqrEGs4pQw1Gg2Dz8nQpHQKGX9EICtSCLxMN5ovs9nM3x0IBPiiBaTzz+PxsGEo4vVorqnwREzN\\nJxIJpFIp/huN54v2IM0Z7ZMTJ07g4sWLfOlSI12r1crPRvNB5xBhlMiQGB4eRiAQgNFo5D1gMpmw\\ns7MjM+SfNz60hohwl75na2uLx1zsciAatNQhQMQuut1uWCwWnDp1it9jNpsZelEsFl9qWBJkg+AY\\nOzs7SKfTmJiYwBtvvAEAbGiQsUEFCyL5KSD176Rncrlc8Hg8nJYLh8NstLxMH9rTT58+Zewcre9g\\nMChz7FutFrLZLIrFIu9zatxOKUC/3w+NRsPOTCqVQiqV6rmw6pU2lmiBWSwWFItFWR7VbrfD6/XK\\nLqSjAHeLrOKA5DlOT0+jWCzi6dOn/DeLxcKTSF5qv7BBoi6kD3kmJ0+ehNfr5bYvW1tbOHnyJBss\\nwWAQOp1O1mi4nzppNBrebHNzc5idnZVV4oms64StstlsfMj3g8VXvPjo0AwEApiZmeELKBaL4fPP\\nP0etVmMvdHZ2FnNzc3jttdcASIZDvyoFxYbBXq9XhgF68uQJ1tfXsbKyIqvIsdvtjLHqBKMfRhfR\\nKKEKKjq4YrEYkzrS3yhSSRfSO++8A7PZzGMrts7pVheq9KHPMRqNMt4iit6IvErNZpPPgkqlwu+n\\ni21jY6NrjhyKjNJBbLVaYTabuUCCXkNAVGAPNyZio4htmXB6dEmLjaFFjpyDrC1iDne5XLwezGYz\\nrFYr67azswONRiOrptTpdHA4HPxMVF1I46VSqdjoOijbebvd5ogUPePExATGxsYYX9doNDiaQ5JO\\npxmHB+xFLWg9l0olJBIJJiMEDt4MXaPRMI/c4OAgzGYzf3e1WmUOJVq/xJ5Pjkcul+PCBvp3AMwq\\nDUiGhNgA93kiRn+1Wi1qtZos6lKtVmGz2digcjqd8Pl8fGaWSiUYjUaO3ADgVidkcNLzdRMxIeNE\\nZNhvNpu4ffs2R75UKhUGBgZ4vRDGdnR0lI0ji8WCsbExXvP5fJ4rc+n5Dyr0nlwuh88++wznz5/H\\nu+++C0ByXBcWFhgzRga+yGIfCoUwNDTEhQRmsxnVapUjTxsbGxw960VeaWOJJqJQKKBcLiOTyfDm\\n02q1mJ+fx7Vr1/g1RyG0gGhBkUWdz+eZUGtubg4qlQpTU1MApAWWyWRkh/ZhjDnRSqYKGVowr732\\nGkqlEoPejEYjLl68yBfz3Nwc3G73Mx7uYY1L0XAjj3JiYgIej4cPixs3bkCtVrMxMjAwgPn5eUxN\\nTbHHQ8bSYSNwdAnTJqcLjC6X69ev409/+hOazSaPzdTUFEwmEx8e77//vuxAP8z4iKSlAwMDsNls\\nfFhsb2/jxo0bHNUBJGP8zTff5PVNlzcdsodNDYoM7w6Hg+eM0nJi+pHWmcgoDuz1qyPixV7Gh6KR\\ngHTYian1YrG4LwMzRTYACZS6sLAgS4/t7OzIxuegeqlUKn5GYlEeHBzkOYnH49ja2uLfa7Uat64B\\nJAPg1KlT+N73vod33nkHgDRvyWQS//qv/woAz4CuXzY2NC4mkwmBQIBJKGmc6Jyz2+2w2Wyy1ixO\\npxODg4Oyknmfz8djs7KywpG7bsYIkPapuLfm5uZ47PaL/lEbD3pPtVqVUTCsrKxw6ot0ISPmILrR\\nvqYoHH12uVzmFk80DjqdTsZCvb29LUvl6XQ67iJAUa6BgQE2aA46RuTAkyG3uLjIMASKZhNJaKez\\nQvqbzWZEIhFsbGywwRCLxbgknubiZWNE4yiS0TabTaysrLCTMTo6Kmu9QjQYzWaTx8Fut6PRaHAE\\n/N69e3jw4IGMsPZlUSUSESIRj8fx+9//njtN+Hw+zMzM8LhQoQ0RPtMzkAEMSPtzeXmZqV8++eQT\\nxGKxnh1eBeCtiCKKKKKIIooo8gJ5JSNLlDoQwdzFYhGpVEpG0mc2m9kTpwZ/R6GLVqtlfJJer0c0\\nGuXmtABkJd6A5KUS7TpJP8C5FM2xWq3sqVAzQXp2ioyIuAW9Xt8XuvxOnSjKRViW48ePo1qtMm8R\\nAbnFhpWZTIZTIOKz9UOfVqvFXlIwGITH42GP6ObNm1hdXUW73WZAN41NJyC/H9FAALKokUaj4dTV\\n9vY2dnZ2ZPw7lCqgdZTL5Zir6zDSmYajBqfkOYrhfkrNaDQavPbaaxwRBCQv7smTJ7LX9SIEPAUk\\nrzUQCDDJXK1Ww+7uLvR6vay/mcFgYNLVH/7whxgeHka5XGbA+a1bt7qOvHVi3FwuFyYmJmSUIOl0\\nWtYegtquUJTr4sWLWFhYkNGaJJNJ/P3f/z3ef/99AJBFTl6mj8jxYzKZMDQ0xNHq0dFRGWdZPB6X\\npSuNRiOGh4dhMBj4mcxmM3K5HBOdPn78GOFwuKt1Je4FisQMDg7KmnWrVCpuiA3sNbcVzxuj0Qi7\\n3S5LuRExZDcd7CmdRPxSq6urOH/+PEdK9Xo9Y6nEfnGRSAR//vOfAeyB5Wnd5fN53m8UrSRw90HW\\nFe2fa9euoVqt4uTJk/y9ne1OAGlvU2T94cOHqFarMk6onZ0dxGIxGTdTo9HgSNNB9t/zIvbLy8v4\\n+OOPAQBvvfWWLPperVYZnE/Rp3w+j5WVFR67W7du4enTp7ICjIPQYojp61arhWg0ig8//JDn/m/+\\n5m9w/Phx3ns6nQ7FYhHj4+P8vmw2C5VKxeNy8+ZN/o90PQyd0CtjLHViM0Q8gdvths1mQzAY5A1J\\naS4CzUaj0UORCHaKyFAq5t4JBCqSDBI/By0wh8OBWCzGgNDDihhKpV5aFAKvVCoYHh7mBUJhU9KF\\nDga9Xi+riOrHJazVamEymfjQMZvNqFQqXGWSSqVkhzewhw8QKz0OyyEk6kRrxmazyaoGqcO11Wpl\\n485ut6Ner/PBRZ9B0itfDxmrtFbJKCOc2fDwMM6dO4dsNssAx+985zs4deoUGxJra2t9qWLsLFCg\\n9AOlCqjBZrFY5Ms6EAjgu9/9Lqd6m80mPvnkE9y6dQsADhx2f54u9IwWiwVer5fXi9lsRqvVkhFB\\nms1mhEIh7n3odrvRaDSwurqKn/zkJwAko6ZbfCDpQvvTZDLB7XZjenqaUwPtttQDjc4CIi6ktet2\\nu9kJITzMP/7jP+LnP/+5rGn1QfUSL6BO1ubh4WF4vV42cqlXnPhe4iyizykWi7hx4wY+/PBDAFKF\\nJfEbdSNEHEqXdC6Xg06n4zOIKtrE3oe5XE5WVUVFBCsrKwCk9Z1IJFAul/msOuhYqdVqLux58uQJ\\n7ty5wzhESlVSpSIAJqAkXGc8HudUISA/i0h0Ol3XzWsTiQTu3r3L42Cz2eDxeJBIJPieKhQK3LMP\\nkFLIIlC/1WpxI2Yy3BqNBkwmk6xX3MukMw1H2KpcLodf/vKXAKRU6OTkJJ/JdBYVCgU+E+PxOCKR\\nCKd7k8mkDKrQarUO7FSKhjcB8Ynpfnt7G2fPnmXwuc1mg9lshk6n470UDoexs7Mjwyg9efJE1oS9\\nG3065ZUxlsSJpQOVFvPw8DA8Hg+azSaTva2srCCVSvEFZDQaD92ZeT8hUCTllHU6HetD3szOzg7n\\nvAEpskQdyfutC0W5RGZqv9/PxhxdALTAEokEV7/0O/JGeAEaByr9pssmGAyi1WrxAZ/JZNgIOAqw\\nuQjwrlQqyGazDEqlaheLxSLD62xtbXH0ibAc/cAsiRWLdrsdGo2G19CFCxfw+uuvw+VyyZhxiRQO\\nAD766COu7DiM0DOI5IYi+SU19CViQWCPYZwibouLi/jFL37BB2ivOlH0j4SKA4h9ntqLiIBYo9Eo\\nw8MUCgVcv34d//zP/8weJUXoupkvqtgSK93S6TQDsgEp2kR7mfQH5JdqMpnEtWvX8NFHHwEAfve7\\n33H0tNuxEckZKcpGF0Mul4PX6+WLjTB6NEcEbE2lUmwU3L9/H1evXuV5oyhON7pRRKBSqci6BoTD\\nYZkRWSqVOPKxurqK5eVlZDIZNgISiQSMRiPrkslkoNVqUalU2DA8qNPUaDTYkHj48CE3VAUkRzWR\\nSCCfz3MEeX19HcViUdaup1qt8rzSGtNoNDy39JqDrHWxNdbu7q6s/Y7FYoFGo5E5qtVqlfcjGTF0\\nV1DUXaSDIWNVjMy8TDrHkZyJSqXCdA6bm5syXNbAwAAXCtDY0Z1Kc01nJH1+Lw3s6f0iW/7t27dx\\n9+5d/PznPwcgnYfz8/Myh+DJkyfsGAHgaL1I3Lvfsx9UFMySIooooogiiiiiyAvklYksAc9ahJRS\\noZ5RW1tbHMaNRCJIJpNsJfcD37GfLkQuSF6TXq+Hz+fD6Ogoezdra2tYXl6W0bJ368F1o1cul2N9\\nrFYrnE4nR1TK5TKi0ShXCGxubiKZTPYtPdnZB6nRaHA0JBqNYnp6miOCBoNB1oLk3r17uHPnzr4N\\nkA+rE3lj5IkQ0dvs7CyAvWgJlQQDwIMHD/CHP/yBOao6vabD6CNi7mw2m6y/UjAYhNPphF6vl/XN\\n+/zzz/GrX/0KAHD16lXZmu5WJ3GegL1Go1SSTpHIiYkJ+Hw+OJ1OWTpiZ2cHv/3tbwEAP//5z/Hw\\n4UNZuXmvYyQS/VWrVdRqNRlew263y/qOAVI0iWg6fvGLX+C3v/0ttre3+XN6jZiK2Izl5WVOudHf\\nTpw4wZw8ANgzp31+/fp13Lp1Czdv3uRKq07MRDe60WuLxSLa7baMHy2bzXJPSgDs/dPeisVi3Cyb\\nou+dUACNRtPTGif8EUWWVCoVVldXOaqvVqtRLpc5apRMJpkjRyQ6Fc8gIrAViYUPClloNBoc6Ugm\\nk7h69SpXRRP5rNicm6oY6XtoT4g9/wh/SWuKUkUHEfFzO8v1C4WCLEpEYy+OP7UhIRHPMfp8tVrd\\nVTsY8f4S/9ZqtWQRTDEbQxE+EY9L391Jjkvj38veE59VrDQFIBt/IlmlaCrxQNF3d0I4DovLfaWM\\nJRIKW1KI7v79+0ymRgcBGUt08fUDGyRK5+KlhRQOh3HlyhUcO3aMD4d79+7h7t27HLpMp9N9wQV1\\nisjzQj2XPvroI4yOjjJ+and3F5999hk3ZI1EIsjlcn3TR7yECTdBBqxOp0O73eYUj91uRyQSwfXr\\n1wFIAMjV1VUZbqJfY0Q93+iiW1lZgVar5ZD40NAQ2u02Hj9+zGDFO3fuYHl5mVMF/TIo6VCijb+x\\nsSErb47H44y/I6K527dv4/bt27y+K5XKoTa+aGTV63Uel0gkgrt378qa11KDTVrP165dw/Xr1zk9\\nmclkegq376dTvV7n8aYeTjRH8/PzCAaDaDabbJj9+c9/xv3793lcKN3UD6O23W7LjGsiCiXjyOVy\\nyXqXEckfzWulUpFhhOgzDyutVgulUglbW1uy3nTipSqywdPP56Vp9ksfdivNZpNhBvfv38f9+/dl\\nn9+J9aP/OlPa4u/dEHZ2Cj1juVx+Kfyi0+HoxPz0ozclfX7nGSISdO6n037/v99eO0zqm6RzzXS+\\nRuTsEsfsec/VD+mkxRGlWCxCpVI9w1hOr+vU6bDnt+ooohtdK6FSHUgJGjgidaRDS+Q1oWhOuVzu\\nGzD4RUJAc/J0bTYbtFqtjJ+CvKhucsq9CEVQDAYD44CoQkY8CETAYL8Ogv10oXYMIsOqyWSS4WQI\\n7AmAgZdHMWeECaI1RDgcsWGlSqVigCUAxiQchT5iiw7CCdF6oSogEXtB3vtRrR3yxogIkqIChNuh\\nKI+oy1HvrU6SSqPRyODxTq//yxLxIt0PB/V1OE8VUUSRruRWu90+97IXKZglRRRRRBFFFFFEkRfI\\nKxVZ2ud9/P9fh+cA9jz0fqQB+iGdfEVftU6dpepfRqPj5+nRmR44iorAbvTZT77q+QKOrlWQIooo\\nosjXQA4UWXolMUskX8cD/Ku6bJ8nXzd9vi5G5H7G41ep19dhTJ4nX2fdFFFEEUW+DFHScEckh21w\\nqogiiiiiiCKKfD3klY4sfZ1F8cYVUUQRRV4N+apTzZ1wAJEB+6vSS+xSQfJlFE09T8QxEuElX1b2\\nRIksKaKIIooooogiirxAlMiSIoooosi/Q/m6FA1QpESMUHTqIJIaHrUQQS1Rd2g0GlmzcyI3/DKK\\nYqh1F7USmpychN1uZyLcRCLBveCAo480aTQapuI5duwYvvGNb7BuH3/8MZaWlpBIJJhG5CjpTIC9\\nCJJKpYLNZsP4+DgA4MyZM4jH41heXua+dOVyGfV6/cjW0r8rY+mrDqV2ytexWq8ffc36KV/HOfu6\\n6QM8O19ftp4v4xcCvryLTtSHLuH9WLGPGrwvcokBe42s6fdmswmVSsVMwgBkl14/9SKDhDjNtFot\\nLBYLGwRarZb7elEPtHw+L+sK38+LT61Wy/r3WSwW7sPodrthNpuZORuQ2KEjkQgTkBLTeT/Y8kXR\\naDSw2Wzw+/34xje+wfpptVpsbm4CkDoNiF0QiLS3H2PTqY9Wq8XQ0BC+9a1vAZAaZudyOczMzAAA\\n/vSnP8nY6AuFQt/GZj99NBoNgsEgAOCb3/wm/uqv/ooJYGu1GpLJJGq1GutDvdf6bTCJzeEBac2c\\nPXuWx2l8fBxbW1swGo28hrLZLHK5nIwhvZ/yyhpLRqORu9lbLBZuxyBS6pdKpS/NYxGJMdVqNaxW\\nK3eS1mg0yGazzLZLeomVYX2f2C8IDvV6vayBrMfjgU6nY9bTdDrN1P/ioXkUFw01oxR1czgcstYV\\npVJJRoDYaDSYkfmo9AGk8dLr9TCZTHzh6HQ6mVdXLpfZ6xR16ac+9FOv18NgMMh0IR1IisWizMMj\\nI6Yfa54OK3FcqMEwrSX67nK5LOtyL7bQENl0ezlQRfyGWq2GzWaDy+WSXbxut5sv5mazid3dXezu\\n7vJlVywW2esUdel2fDojJEQmurCwAAAIhUKw2Wzw+XwApPYs7XYbm5ub3I7lyZMniMVi3H3gMJeN\\nODZkHNFFd/LkSUxNTXELnVAoBJfLBZ1Ox10Nbt26hevXr7NuqVQKhUKh54tYnCe9Xg+bzQZAalJ9\\n6tQpnDp1CoB0RgaDQW4ODUgX3cOHD/H+++/zOGUyGSYhPYyIxKZWqxWhUAiXLl3ieSsUCjCZTLym\\nms0mYrEYIpEIAImxPpvNHpo1n6RzzoaHh3H8+HEAUlP4XC7H9wm1GhJbyVBLkn53OdBqtbBarZiY\\nmAAAnDp1CgMDA9y+hwxZi8XCe5+ickdBmaPVajEwMAAAePPNN3H58mWcOHECgNTlIJ/PyxoeU1RJ\\nXIf9NOIUzJIiiiiiiCKKKKLIC+SVjCypVCrY7XYOyc3OzuL111+HWq3Gxx9/DEDqGfXo0SO2hslj\\n6ld/L1HI4wWA6elp+Hw+nD9/HseOHQMgeeLxeJx7pH344YdYXl5mj5eknyF5igL4/X4Eg0FcuHAB\\nAHD69Gn4fD7u41Sr1fDHP/4RN2/e5P5aFHbul+ciNmt1u92yvPOxY8fYkwEkT+WTTz7Bo0ePAACL\\ni4uIRqM8TuSJ98PbNJlM7HlPT09jbm4Oo6OjmJycBCC1imk2m6zLysoKFhcXeZzK5TK3JqGoSjd6\\ndVZ30BpyOBw4efIkZmdnEQgEAEgRFIvFwt+zurqKtbU19jhLpRKKxSJ2dnb4Nfl8vuv1TtEK8mwH\\nBgYwNDSEc+fO8XoOhULQ6XSsPzVpJV0ikQiy2SzS6bSsgWy3vb4oikNeq9VqxdmzZzE/P89RgdHR\\nUXg8HlmEMJVKYW1tDZ9//jkA4OHDh1hbW2NdCoWCLAXUjYjtezweD95++2289dZbAKRzyGazceSN\\nxrFYLHIk6erVq7hy5QqvqXg8zo1xge7XD82TxWLB4OAgzp2TuPW+9a1v4cSJE3wOmEwmjhBShO3E\\niROYnp7mhshXr17lqO5hIksGgwFWqxVutxsAMDMzg4sXL7JuGo0GOp0Oer2ex0qtVmN+fp7b7Pzk\\nJz/BvXv3ZGd2rzqJc6bX6+FyuTA9Pc3RnHQ6jVwux70ZfT4ftFotn5F6vR5XrlxBo9GQRd97FVrP\\nWq2WzyGKRtrtdlitVm5HZTKZMDAwwM3H19fXEQ6HOR1HuvQr+qbT6XjeAoEAtxgCgJ2dHaTTaVkD\\n7840eL9Sg2q1GkajkXWZm5vD6dOneRzozFleXuaMTWeTavqsvkW6+vIpX5LQZtTpdLBarRgbGwMA\\nXL58GaOjozAajQxG8/v9MJlM+OyzzwBI6Zxyudx3rAeFnGmjzc7OYn5+Hq+//jqn4SwWC0qlEod5\\nk8kkCoUCNjc3ZemmfgilcGhRnT17Fq+//jouXrwIYG8z0vfSJQvsHQArKysoFAp9MZLEHmg+nw9v\\nv/023nnnHQDSBqCDlaTVakGr1bKRYDKZcPv2bVkYmjZnt/p1XrzDw8O4dOkSAODdd9/F+Pg4dDod\\nGy0ajQZqtZp1mZ2dhdPp5PTv+vo6h6jFFE834wNIh6bdbuf1fOHCBbzzzjsIBoOsCzUhpmeenZ3F\\nxsYGHj58CADY3NxENBpFtVqVpXgOOk5i6NpgMPDaXVhYwGuvvYaLFy/C6/UCkIw58YAcGxvD7Ows\\nN9Z99OgRwuEwSqUSX+Y0r53puZcJHZoAMDg4iJmZGVy4cIGbMZvNZmi1Wk7l6nQ6uFwu2XfV63Wk\\n02l2nKi3ZLdCwGAyQIaHhzE8PMypLwCyFLJGo4HJZEKr1eJ5PHbsGHZ2dmR4mEql0rVBIPaBBKR9\\nPTw8LDv/KpUKr8t4PA6TyQSv18uGpcFgwOTkJKampgBIDXAp9dUttpGMEmCvvyCtIb/fD7/fz+Nf\\nLpeRTCbhdDoRCoUASGvKbDazEXzv3j2sra3JLsBeHJFOg5u+0+PxIBqNAgDW1tYQiUR4XhuNBoLB\\nIM/rG2+8gc3NTXa4SZde07iibg6HA1NTUxgcHAQgpYzX1tYY4J3L5eD1enkfkWMZDoef2Uu96gPs\\nnY1+vx9nz54FAIyMjKDRaGB5eRmA5HRkMhmZs0p7ohOacBhjkpwAg8GA+fl5AJLx7/V6+Wy7c+cO\\nbt++jZ2dHcZPPa8RdL9wuq+ksaTVamE0GvlyGRsbg16vR61W4wiEzWbjrsSAvNN1P3WhA4sW+8LC\\nAt566y20222e2HQ6jUajwQDCwcFBDA0NYXNzs785VbWaAYyED3jnnXdw6dIl2aEZi8X48IjFYmi3\\n23A4HHyg94NQU2yCSpfsd7/7XfzgBz9g8GKtVkMqlUIikQAgXXQUbaNLgMaKDrZehcaGDp2RkRG8\\n9957uHz5MgDpEGo2m8hkMvxder2e8QHA/8fem8bIeWbnYk/t+752V+8bm80mm81d5Iy1S2NbsJ0Z\\nzGLD8YIgNz8CBEHy695f91/+ZEGACwS48bWBIIZhwx7NxJgZzSJpKFEiJYqkuLP3tbqra+na9+XL\\nj0/n9PsVKalr6YmYW+cPyWZX1an3e9/znuU5z5Gd3tOnT7NRJfxHpVLhi+KwzgkZGUC+tIaHh/HS\\nSy8BAC5evMhrRPtZxDHQ6/v7+1kXumySySQP39Xr9YfOwomASoPBwJfuxMQEhoeHYbVaeQ9Fo1E0\\nGg12giVJgtFoZKcyl8shFovBbrfzGWhnon1zVsDhcECn0/F+oe8ojq3xer1sbGm9DAaDwoB3AmQm\\nUDcg4yZLpRJjfgAosoxqtRpDQ0Nwu92cMVGpVDCbzawv4ZXazZrQ9yA7ROu8ubmJVCrFgVChUIDV\\nasXs7KwC70UXNiDv727YRzovtE6NRgObm5uc2dvf30c+n4fFYsHk5CQAOePd39/P+JNQKASn08mv\\n6Zbo9XpotVrs7OxwoLOzs4N0Os1ZrnQ6jUqlwviYQCCA/v5+LC0t8cXcrjTbVsJ20XNcXFzEkydP\\nsL6+DkBeu2KxyGerr68PLpcLy8vLXcFxiueeglRylkwmE9bW1nD37l0Acmce2RMxO6bRaBQVknal\\n2Qa5XC6cOXMGgJz5r1QquH//PgDgxo0b2NnZUWCUnjVKq5u44OfGWRIzQmQQxD9VKhVisRgeP34M\\nQI60rVYrXya7u7tddUyau4PIISB0fjweZ+coGo0y0BqQsxrPShl2qgtdLmazmY3Q4OAgJEnC2toa\\nAODJkyeIxWKKbI7f74fBYOC1IseCjF672Tgy4HTxXrhwAYODg7wO6+vruHPnDjsnVqsVjUYDXq+X\\ngX1OpxN2u50v5k5AexqNhr/jsWPH8OKLL3LJTaPRYH19HXfv3uVWVKvVqmgxpi4eynRotVpIkqTI\\nCrayTuRgETCXouqJiQlIkoSNjQ2+iLPZLFwuFxt0k8nEFwsggy8JOCw6Jq0+N3IoydGw2WzcOEHr\\nQs4/lQ5cLhdMJhOnw+PxONLpNFKpFBvRdtP0onNSKBSwv7+PlZUV3jP5fB7VapX31MDAAHw+H7xe\\nL8LhMK8NlW8BdFzGpdeQ6xagAAAgAElEQVTu7+9jb29P0X0Tj8fZWTIYDNjf38fY2Bg/k2QyiUql\\nouiK6wQaQOtKnUqUvVlaWsL+/r6irdrj8SjA5NVqFZVKRVE+02g0HV8qBPYnp71YLGJra4ufx9bW\\nFsrlMlwuF68VZeDEEqfL5YJare64UaHRaPB607OKx+NcTl9dXUWpVOJgsVarKcqVXq8Xg4ODsFgs\\nyGQy/Dvt6kNCz91ms/E6rK2tYWVlhddOp9NBo9Gw05vNZlGr1RTNKd1IBJBjEQqFOAkByNlGWqd6\\nvc42R8wiVioVRaOJ+D3b1UmSJHg8Hg4gbTYbNjc38cknnwCQ16lcLj/VpNNoNBTNHiKchN63XZ16\\nAO+e9KQnPelJT3rSk6+Q5yazBBwA43Q6HQKBAHu5FNFtbGzwz3w+H3w+H0cHlKrvNjeFTqeDVqvl\\n7El/fz/K5TI++ugjzgro9XqEQiHO9hwl2ZnBYFCA9Gw2G1KpFH71q18BkIHvADh6mJiYgMViYY4K\\nUa9u1HrFTAxlaCja/cUvfoHPPvuMo6r+/n74/X54vV6O8iKRCAP6AbmM2E4kTqljWhe/388ZPkDO\\nPL7zzjt48OAB/8zv98Pn8zEgnTJwVBqg1ufDcBA9SyhjEgwGMTg4yKXcSqWClZUV3L59m9uXATmz\\nRSUTojigM5FIJLiE2QkOjkpYpJskSdjf38fa2hrrUi6XodVqFbg3o9HImaXt7W3E43EFtYFIFteK\\niJm7UqmEbDareK9MJgOLxcL7o1aroVQqIRqNcpYllUp1JbqkUp7IMUMlG/qO8Xics39ms5lLuXS2\\nyuUySqUS26l2zxjpQvYtl8shnU5zxi2dTiOdTjOVCkXg4jl3Op1svwAZ9yQ+91aFnkmtVkOhUOAS\\n7PLyMux2O5dPd3d3UavVEIvFWP/BwUHO3gByhp706cQO0bMWs2k7OzvY39/nzD+RLJK+uVwODoeD\\nMUsWiwU2mw12u533eDtCeohcYXa7HTqdjrNuCwsLChxOoVBQnGnCB4k4PaA9kHdziUqn02FycpL3\\n5urqKq5fv87rAoCzWqJtoUoEvQ+AjriOJEmCRqPBpUuXGNPWaDRw8+ZN3Lt3j/XQarWKrDLhG+me\\nqVQqKBQKCl06wQY/N86SWI8kbgc6WPV6ndP9YofDRx99xB0NYm2zGyJueK1Wy0RvJpMJkiQpDNep\\nU6cwNDTEG2p3dxfJZJLLOEB3OHHo4iauGeCg5k2HPBwOIxAIMIZiZGQEdrsdS0tLX8qJ046TKV66\\n9NpIJILJyUkGt25ubmJra0tRWiKMDK0vGQvqDqHn2KpOVBKkPWO321GtVnl/PH78GEtLS1hfX2fn\\nLhgMcjcYIBsqEYxLXTRiWacVQKyIWQoGg1yCKBaLWF5extraGjuWdrsdbrdbAWbM5XJ8+Pf397G4\\nuMgYKqA1sLkoIrcNOUJUVqP/93g8vIe0Wi2i0SiDzdfW1hCNRpHNZlnfdh2U5hJKOp2GWq3m70bN\\nDAT4np6ehiRJWF1d5dLz+vo64vE4n9FOyl71ep3Xl5wycsBVKhXcbjcHTsPDwzhx4gTUajWXMuLx\\nOJLJJJ9HsbOoFaH9L0ITCoUCO7ROp1OBpXO73RgYGMDIyAg/t0QigXq9zmdALCG2I6QL4fyodLSx\\nsQGbzaZwKtVqNSwWC5fbzWYzSqUS77F4PI79/f2OeZbIQaFzUigUsLa2Br1ez59FetH5o6YB+txM\\nJtOVwPFZ72E2m5HJZNheb29vo1wu8+8S0JnK4tRB5/F4eB+LQUm7IkkSTCYTTp48ybb2zp072Nzc\\n5M+hLkaxecJisTD5KiAHA/V6vS2Moih6vR5XrlxhW/zgwQO8//77XJ4EwPyBIv/bwMAA60b4U3pN\\npzo9N86SuMFMJhPsdjt3W9DDnZiY4A0fi8VQLBbZQDa/R7d0AeQoiCJ+m82GbDYLjUbDQMpgMIhA\\nIMBkcNT62ekGf5Y+kiTxAQNkAyACpl0uF4aHh7nLwO12IxKJYHt7mw8sMQ53oxuOuqCAg0tKBMeb\\nzWYGL545cwazs7PQaDSclaOMhuj0tqMbRSsieRo9J0A+nDqdDhaLhfFno6OjmJiYYP3UajVisRgW\\nFhZYNyLzbCdioQjObrfDYDDw5VKpVKDT6RSdgna7HUajkb93Pp9HPp9np3ZjYwPpdFqhSzvYHHJy\\nyUhRRsLhcCiccr/fz0YqHA5jdXWVn1kmk+H9LV6g7eLexIvD4XAgGAyygzI+Po7p6Wk+f6VSCRsb\\nG4jFYgzgpSyX2D3UzrrQn+I6OJ1OHDt2DIDcJHDs2DEMDg4CkO1CvV5HIpHA6uoq6xKJRHg/dws/\\nVa/XubMOkPcHgf4BOWA7efIkxsbG+Dxubm4iGo1ypyllbbthJ+v1OtvlYrGIVCrF61av12Gz2WAw\\nGLiz1OPxQK/Xs43c3NxUXIzdEgocVSqVwnkTmyeI9oD2GDV+UNYeaN/JBZT2T6fTKc4sYevoGZE9\\nogyLz+fj5gqyZeFwuC2AtZg80Gq18Pv9sNvt/F5kY0iI+sXn83H2xmKxIJ/Ps+NJ5Mu0D1s59yJo\\n/Pjx47hw4QLb57t372J5eZmzovV6HQaDAT6fj5thKBCgzy4Wi9je3saDBw8AyBlO0Q60Kj3MUk96\\n0pOe9KQnPenJV8hzlVkSSwOlUonrvCaTCYODg0wXAMgpuEwmo+Av6WY3HL1vrVZT8JLk83nU63W8\\n9NJL7KEPDg6iWCxyvfXJkydIpVIdt6GSiKl4jUajKPEQfcKVK1cAHBDRUWkpmUxicXERi4uLHB00\\nr1O7defmMQ4U/dJanThxAsFgkMkOz507B61Wi8XFRc7eLC0tYWdnhzOE7bRaUzaAav3AQT2bIkWX\\ny4WpqSl4PB6O4mZnZ+F0OjkS2drawmeffYZr167xv8vlcsulAtKHoian04l6vc7fsVwuw+/3Y2xs\\njCNvyjKJ2adEIsFlup2dHeRyOdTr9bY5ewA5W2I2m/lz+/v74fP5FKMpDAYDVCoVZ0sIB0NRH42I\\nabdtV8ziEE0IAN4rly9f5rJbIBBQZNxo8GixWGyLb+rr9BLH9bhcLkxPT+O1114DIJMv2u12zlJI\\nkoRSqYRYLKaIdslGtLouz9JHLO2IUAONRgOv18sdi/Pz8xgbG4PD4WDMnZgBBDrPcomZCtHeEmxC\\n/LfFYsHw8DDjgrRaLVKpFGcDiV5A/E7tlitFIeyqSB9hMBjgdrs5gzw2NqYoc+3t7SGRSCCZTHZM\\natyMbywUCgqKh2q1CofDwedvamoK/f39/G+dToe9vT3odDq+X6gM1sk6Ed5H5Anb3NxELpfjDLLH\\n48G5c+cQDAYV3dS0PgD4HhHnI7ZCXUJ/D4VC0Gq1nJ1cWFhQZE6NRiPcbjcuXLiA2dlZALKtEnn9\\n9Ho97t27x/Y7FotxmbCdNXpunCXgwEEhbhMCLRuNRkxMTMBoNHLqlgCE3Z6fQyI6KKlUitmC1Wo1\\nLly4gKmpKTby1WqV22cBGUNRKBS6ziZOLbuxWIxTj2azGZcvX2buDEmSmHcGkGvBRABHG7NbqXgi\\nAiXOkLW1NfT19XG7/muvvYZarcYYCpPJhN3dXVy/fh23bt0CIJeXqD2c9G+nBEcYD3JIwuEwotEo\\nG6GRkRH4fD7kcjlOv1Np7M6dOwBkh+Tzzz9nJ52cgnaeI81cA+Q9UywW2WGhtunx8XEGxRJwmUpd\\nGxsbiEQijH3JZrM8t66doIAcgOYBrC6XCwMDA/B4PIq2YbFdmDh9xNlw7c5fE4X4VkRyQ5fLBbfb\\nzQ0AJGQQ6/U6c4aJM6O6sZ+JZJL2x+joKDwej0IXcTAtteY3Gg0FcWWxWGQMUzabbbvxRHTciKGb\\nyu1ut5ubJQAwGJYA8oDspJfLZT5/BGDuRB/g4OIVZ0A2Y/TGx8e5YYJ+x2Qy8Z4i3E6nz41K7+Js\\nTCp703sTgJueo9vtRiAQ4IaLaDT6TObuZuewFX0AGbJx7NgxTExMsLNEzOb0fqFQCIFAgJ0TwsRm\\nMhn+Tu2yVosBGz2TQCDAJWKtVouRkRGGk7hcLszMzMDn8ynWzuv1KiAcW1tbbTW9iIOXKZgnm0KA\\ndoJIDA8P49ixY/i93/s9XhsizKTfsdvtOH/+PNvQR48eMbVKWza75Vd8A6TRaCCdTjOA02g0wmq1\\nwmAwMHaBapdH2XlGUq1WFRwiV65c4UwXIBvEfD7PzhINZTwqKZfLjEMgsLTIeFwqlfj/b9++jYWF\\nBcTj8a5n4SRJYuApAObqIWdpcnJSMTU6HA7jo48+wp07d5jBlob8dur0ki6UPYtGo4hGo5xF0mg0\\nGBsbg9Fo5O8eDodx79495va4d+8e9vb2+PvQRdiOLiLoNBaLwe/3c6Todrs5u0P4l0qlgtXVVdy+\\nfRuADAQNh8McHHTiKDVnAcSaPjksIgEiGVkx+2Q2m/kZ0bp0wmkiijjWoVqtIhaLKTqv9Hq9ohvH\\n5/NhZmZGEZyQIe5ECD8l4kuKxSIHJgsLC0gmk3yJ6fV6XhvaZ8PDw3A4HGwvEolE+xgKYUAsAV2/\\n7JlQB5rFYuF9RuN+yPlzOp1tr1PzxetwOBi8TdMUxGHjfX19GB4e5i5Gsul0UYt7uR18GX0PjUYD\\no9HIGbYLFy5gfn4eVquVM48E2ien0eVyQaPRMH4qk8l86T3SStaEusYoiA6FQjh37hzOnz/Pgert\\n27eh0+nYaXQ4HLBYLKwrDRY3m81PBQz0nQ9rA8RnZrPZMDg4qCBMHRoawv7+PncD0/gTsVkiGAxC\\np9OxQ5XJZOBwOLiJh7KKh1kncf+Yzeansn9Op5N1m5iYwKVLl2AwGNgZisViintsenoabrebEwWL\\ni4uKKkWr0sMs9aQnPelJT3rSk558hTy3maVMJsOt+QsLC7hy5QqndAE5e9NNluwvE8LmUMYiEok8\\n1cXRaDQQjUYVeJOjyHRJkoR6vc4tzYBc+6/X64puj2q1ymu3t7fHXVTd1ImyFeVymaOmlZUVHDt2\\njKNho9Go6EqJRqNYXFzE0tIS41+6laGQJAmVSkXxnBKJhGJmlMPhUPDolEolBd0/zfITx2a0K0Tp\\nAMjfe3R0VMHaazQaFYNztVotBgcHOUP46NEjxksBT9M9tCJimrxcLiOTybBuKysrii4g0r2/v5+z\\nAidPnsTa2hrPhkun0x2V4MQUfq1W4zVYX1/nZ0LROZX8KCswOzuLM2fO4Ny5c3wGrl27xpk3oD38\\nFJVPqH0akNP+9+7dY3wdYSkpQtbpdBgbG8Pc3BzjKsxmM8bHx1lf+t1W14doMCi7EAgE4HQ6uXxq\\nNptRKBQ4y0V4oGq1yvpbrVZF9slgMMBgMLSc9VapVDAajYpM0smTJ7kcYrFYFJ8TDAbh9/sVmZFU\\nKoXHjx9zlq1YLKJeryvmuh22dKJWq7n8OD4+jqmpKczPzwOQx1HZ7XYFvQo9Y9LHaDQqaBiCwSCy\\n2ayC16w5A/t16wPIz8Rut3OJ6fLlyzh//ryiHDkyMoJKpaLgWVKpVIrsqt/vh9Pp5MoKdc42s2h/\\nnU4ixxaxhJdKJb4rJiYmUK1WGbOk1+sRi8WQzWYVeEC/38+ZJavVqijLtcJQT7rYbDaEQiFIksTf\\n2+FwYGRkhDOn09PTsFqtCIfDjHNbXl5GLpfjz9br9VwtAGTMlcvlanuEznPpLAHKy4bA0rVajQ0B\\nOQ1HWX4joRIPCVGs089oZhOlCwkQfhS6NQMafT6fotRFG5eckc3NzafaTrulF6XDSSer1YqhoSFF\\nrV2c/h6NRrG2tsaXLUm3SoJiel6r1cLtdrNRJZyA2I4PyCXUZk6cbulDz6JYLCKbzbJulUqFMRv0\\n2VarFXq9HufPnwcA3Lp1S0Hl3ykwt5mvh7BRjUYDS0tLivIMtfZ+61vfAiBfjhcvXsS7774LQH6O\\n7eLexGcEQAF8JzLB5eVlNqyE+aFSklqtxunTp3mvATJOhQxqq7rQ3tVqtXy5iFPY0+k0nx/C6Ikk\\neblcDlarlUvP5OSQnWrFqRTLOQQ9EMlnrVarAnhbrVb5Ykgmk1yKo/NHs/bIwSLnpFWhQdBEunvi\\nxAkGJtP/04ULHBDVarVaDhCy2SwymQyXvgqFArRarcIJOIzNJMeN5rp9//vfx8mTJxUUHET3QQ5r\\nsViEzWbji7hUKqFSqSjWNpfLYXx8nJ0ECigO4zCR8+F0OnHp0iWGioRCIRgMBsUeMJvNiEajCnLO\\ndDrN60QYJo1Gw8HJxsYG9vb2+D0OoxM5obQujUYDyWQS5XKZ94fb7YbH41GMzNnb28Pe3h4H/jQX\\nlRxlu92Ovr4+xuQVi8VDOd/is3W73WyH6ZxQSZBsL5WvV1ZW+GxvbGzA5XIpzomIDS4Wi4pmrFbl\\nuXWWgINNUSqVEI/HFUaIBrCSl3lUQsadPpcO3ebmJkdWJpMJw8PDPDF6dXX1yBwltVoNvV7PB31k\\nZAQWi4Vn5lF0QxfJxMQEHj161DV8ybN0ISM0NjaGqakp3qwUkdOBHRoawvT0NG7dusURRbd0Il0o\\nG3LmzBmcPXuWDSZFkvl8niM/r9eLqakp1i+TyXTFUaK1ETme/H4/G9FisYjbt29ja2uLMRxXrlzB\\niRMnGMM0Pz+P999/nx2JTp0lkWQVAF9aRFwo4sYcDgeq1SouXrwIADzUmtbt4cOHHTlKdLkQXooc\\nI8rg0swn4IAjR8z2UUcoGelsNttyposGHdNnE9u12+1WEIESsJ50aTQaCsZ6v9+PQCDAl59KpcLW\\n1hZj8g5LAqlSqRRdPj6fD3a7nTme9Hq9YnaX2WxGOp3m/UEDW81msyKbKgadhA+kzwO+fl9Rt+LY\\n2Bg7KFeuXIHFYlGQAxKAGzhg5xafG501shUajQblcpmzdaTL12UpaP/QIPGLFy9y4wYgO407Ozus\\nC62Nx+Phc05cdWQrdDod6vU6IpEIZ+TVavWhghUR66fX6xVs3MlkEk6nE5OTk4pAqdFosMNCPEz0\\nGqPRCK/Xq2gaGB4e5iYi+syve24U9FHQrFKp8MEHH+DKlSu4cOECANl5tlqtvKfIQaQB3fQ6t9vN\\nZ9ZisXAm8avW5cv0AeSgiLL+9AyCwSDzlgHyXqUskvgMzGYz20ifzwe1Ws0BJ3Udtmsrn2tniR6Q\\n1+tFNptFPB7nw2a1WmGz2RRtyEfloEiSxAby+PHjCIVC2NjY4KhucnISFotFMXn8KPQRHbe5uTkA\\ncutpqVTCBx98AEA2kOfPn1cAQTUaTcfspl+mi16vx8gXo1VeeuklmM1mTm//9re/hdvt5kvX6/Vi\\nbGxMQdDYTV0MBgM7ibOzsxgaGmJnZGlpCTdv3oTT6cS3v/1tAHIKf25ujo1SJBLpSgdjs+NGDi1F\\ncJ988gnW19extbXFDhSx0xJQdWRkhMuYneoiZheILZgML43noMwccNDmTQ7V2NiYopzUCUiYqAuA\\ng/EbtDeLxSKn5pufA50/n88Hq9WKarWKlZUVAHLXoqj/Yc6dJEnQarXcEUVOj0ql4j1DUWszIJ70\\nHx8fx6VLl3DlyhUu8USjUVy/fp2j4cM636LOAwMDGBwcRH9/P9sUnU6nyOKWy2VFpl2lUmF4eBjD\\nw8OsL7FYU7dqpVJRvMdhxe12Y2ZmhgdBh0IhBSklIF9U5IyQIyWWjimgpH1I1C8ixcphbBSRz9KZ\\nNRqNqFQqvFcjkQhSqRS0Wi3/jtVqRa1WU4w7EbNsRBrpcrnYfoilqK/Th6RerzPrv7h2Yvep2WzG\\n2NgY24ZEIqGYIuB0OqHVapFIJNiRpwyhSLh7GL2asyzJZBIPHjzgkrFWq+WgGwBPL+jr6+OfBwIB\\nDA4OKs5ouVzmIKAVe0m/m8vlcPfuXaytrbED7vf7ce7cOc6wEW2Lw+Hg1w0MDGB4eJiz71arVQHX\\nIXb0dukoegDvnvSkJz3pSU960pOvkOc6s0RRCY02sFqtHC14PB6cPn2aW63FKKebQhErRU39/f2I\\nx+NYXV1lXRwOB2q1GpMv0jygbmWXmiNBs9nMUV4gEMDnn3+OR48eAQCDS0nf2dlZOBwORTmgWzoR\\nQRi1bp46dQrZbBZvv/02AHmo7/j4OKd9bTYbxsbG4Pf7GYjdLS4qnU4Hq9XKWa4TJ05ApVLh5s2b\\nAOShvhsbGxgfH+f5gpOTk7Bardz2fffuXUUWoZN1MhqNjOkg8klqdb979y6WlpYUHDilUglarVbR\\n4k/lJqCzzKlKpVLgWKxWK58XmjUnRtrUKkz6q9VqRcmnnVZveh8CqwJypGgwGPh9o9Eo49zELFcw\\nGMSbb74JAHjxxReh0+mwvLzMXF0UjbYK7LbZbBzZ2mw2jI+Po1arMfZQq9UiHA4r+KdsNhvvnytX\\nruCFF16Ay+XibMKnn36Kq1evKmYdHlZoXYiHizLWpItWq+XnWCgUFCUKt9vNrfqUNaQ2cMqU2Ww2\\nGI1GBWnm1wmVk0UuI9pPIj6QylbAAV5SLB0RtouESp6Tk5P8/Ajs/FXPkbKT9F4Gg4HxXYCcrczn\\n83C73YpsaDwe5wxbOp2GVqvlbE+pVOKWc4JWEIj56zLgYns+AfMJ30NrXygUFJkwm82moBWh4bW0\\nBvv7+3j8+DHbi1QqxcSr7QqByD///HM+SyMjI3C5XEzW2Wg0MDQ0pADdU2mM7pdPPvkEN27cYGqM\\ncrl86HNHe65cLmN9fR0/+clPOMNms9lw+vRpXpfx8XEeFE22wG63Y3x8nCtOkUgEH330EX79618D\\nkGEflGX+/z0pZfMsG0q9h8NhJBIJBfso1fhpsTvdTF+mCxkLOkSVSgX37t37ShZss9mMXC7XNX3o\\nvYn4bXBwkA9fLpfD4uIifxZtFrHrhAbwdrsbTqVSweFwsLNkNptx7do1ZjJPp9PY3t5WpNkTiURH\\nILxnCb0XYT0AOZ1dKBTYWSJM1/7+vqL7hhjaxffpBj5Iq9UqSPDq9TpfwgReJJwJcAAGJV2Wl5dR\\nKBQ67jjTaDTQ6/V86U5MTMBut7OzCoBBqPQ7o6Oj+Pa3v82OXD6fx9WrV7k7p1XeIHH/isNVQ6EQ\\n+vv7+UyHw2EsLi4qLtaJiQnMzc3hj//4jwHI6fpMJoOf//znHCi10+hBZVsqN/h8PmbAJudiamqK\\nLyxALsGLA4bHx8eh0+mQyWSYq+tf/uVfsLi4qJgLeVjCPnLKyCkinYADwky6KPL5PCYnJxXP2WQy\\nKeaQpdNpLC8vMxiXGI7bsUui40Okj2R7q9WqYgB1sVhEPB7nUgpwQEJJ5bJ0Oo18Pq8oWanV6kNh\\ncer1Op9rv9+PkydP8uuIrdtisbADsr+/j9XVVXauqRxJ/08ErEajsSVckKgPfU6tVuN7q1Kp8FB1\\nsoFUdqd1KRaLSKfTCqxOLBZDOBxmOMPW1hY3ONFnHkbEcpRKJU84WFxcxNWrVwEcTMag36nX6xgc\\nHESlUmEHlsq4RNx7/fp1bG5usp06LFZQxCyVy2Wsrq7iJz/5Cf//D3/4Q4RCIQWmzWg0YnBwUOFk\\nqVQqhjN8+OGH+MUvfsEDvkulUkddus+Ns9R8eYo4C6KqHxgY4IuuWCzCYrHw5ovH40+N3+iGPiqV\\nPHiUul2oBd1isWBiYgKAvBEikQjXuKkVu1sUAqJB1Ol0DP4EZAfh29/+Nm+giYkJdtYA8FRpjUbT\\nFQJPcV1oOCM5KARcvHz5MgD54E9OTjIgr9FoIBKJ8IgWep9urBE5iCKrcC6X44v58uXLMBgMOH36\\nNGflDAYDYrGYojOsUydOxKuJ2TyXy8XT6vP5PCYmJuDxeLj+/tJLL8FoNGJpaQkA8N5777HjS9+v\\nVT0AcMcRZS2cTifX/gG50y2fz8NoNDI53aVLlzA9Pc3v8d577+Htt99mZ6/VM9bccUaRbDAYxNzc\\nHJ/hbDbL+APaU7Ozs/B6vexU5nI5/OxnP8PPfvYzPm/tnHnC7hDucGhoCH19fUz2SL9TLpfZiaTW\\ncrq0dDod4vE43n33Xbz33nsAZCoG0SFp5bnR+25vbzPpLl3ooVAIZrNZAYYXAzLqctzc3GS8VDqd\\nxsOHD/kyiUajjAtrJRtAI13o+dMQX7LFxKBONiiRSGB3dxf3799np5yy8/S52WwWhUIB8Xi8JTwl\\nYXEoICO2aTrngJxxUKlUvJcIf0QUIRRYk0Ou1WpRLpcVI0ZaIaQVsU8iSW+9Xkc4HMbt27fZSTSZ\\nTHwOAJlMNJFIsHOSy+WQSqWQTqd5f1PWWQw6Wu2wFClwyEkJh8M4d+4cO/80+ml3d5cbp8LhMLa2\\ntjiTRLigdhi8RSmXy9jd3WVd0uk0zp07x1hcIrsUOyj39/exu7vLuuzu7vLwXODAcWv3TulhlnrS\\nk570pCc96UlPvkKem8yS2M1C2RyKOEdHR7lWSWnbcDis4IwQu2q6KVQ6oNSqxWLBzMwMPB4P/6xY\\nLGJ7e5s9YJqVdBS66HQ62Gw2Tk1aLBb4/X5upaWSG0XMFO11qwQnvg+l/sXxILOzszh58iSAA84Z\\nympEo1GsrKwoMoDd5HwymUwKTIdarcbrr78OQK6Je71eWK1WjtbD4TDeeecdXqN2x5uIIkkS8+RQ\\nicfr9cLj8XDn1ZkzZxAKhRAMBjl7KkkSlpaW8Ld/+7cAgDt37nRUWqZ1pawiZSI9Hg+mp6c5y0Vr\\nJhIgUls3DRT+m7/5G6yurnLU3apOIj9MsVhUzKlzuVyceXQ6nXzWxNErpVKJ9/OPf/xj/P3f/z3C\\n4TCXutrZQ7VajcszgIz9W1lZgd1u53VwOBwKokIaYkz7fXNzE//4j/+Ia9euccmEOq1alUajwdlg\\nk8mEaDSK5eVlzmrRvhXXslQqceZmY2MDW1tbePjwoYKYNR6Ps80kzrNWnh9938XFRcW60D4H5PLe\\n9vY2j1giIsFIJMLlJypPi2SjGo0GqVSK9T1sN1yxWOROSECeSUnlSYPBgEQigXQ6zSM50uk0cy8B\\nciao0WjwnjIYDNwPN9UAACAASURBVDyEm7IUNK/yMHtLJI4VO8WoI/CDDz7gs0RlO1oHyhjR2aLP\\nJMJfeh+Ry6gd0lXKJlYqFcZuhcNhvP/++7x2wWCQzz6tTTabVXQ1UqlLlFboAwAoynG0Z3784x/j\\npz/9KWe53G43ZmdnFTxOm5ubCIfDfL6oc1WkFujkPnlunCVAiW2gOjggX7KxWAyxWIwNyuLiIra3\\nt/lAHAVDNf1JrMKAzOtB7c+UOl1YWMCHH37IJZSjYMwmXer1OjY3N5nHZWpqSsF7USgUsLGxgZ/+\\n9KcAZE6cYrHYlanswNPl0mQyifv37wOQ8QMDAwN8GRJTLhm2t99+G7dv31Y4Ad0qCRK4kgxXuVxG\\nf38/E+nRAEvR0P7DP/wDfvvb3/Jz7MaAYeJdIS4XQL5cBgYG2FkioK1arWbj/OTJE/zd3/0d4wna\\n4Q76MhFb8YlAURxGaTAYOO0NyDwo7777Lv75n/8ZgDyMmfYQ0P4zo8uEzonD4UAymWT8nUicSZdH\\nJpPBhx9+iPfffx+AjJmIxWJPObat6tRoNFAoFLhUc+PGDcTjcaRSKW4fHxkZUQQm6+vr2N7e5v1+\\n9+5d7OzsKLiYmpnWW9GL9m4sFoMkSUgkElwO+eSTT+D3+/lsZTIZLC4usqMfj8efwisS+FbEurSz\\nx9PpNCqVCj+Tvb09/PznP+cgKJFIKOYYEra0Wq0qGKQbjQa/hhoKxFLiYZ1MEb+ztLSkwNAQIFkE\\nsdN3pu9NcAb6d6lU4teLWLPDYvPocxqNhuJzyekRcWT0cxJKDIilPPqzOeBpBSvY7JiI35/0I/Z8\\nsqM0ZFnkO6I91DzovFXW9a/TL5vNQpIkBXXA8vIyJEnigKFcLitIhYkbjt7rMJi3rxLV171YpVIN\\nAvi/AAQASAD+oyRJ/7tKpfr3AP5rALEvfvXfSZL08y9e828B/FcA6gD+O0mSfvk1n9HSN6BOIFok\\nwjYAYAKq7e1t7O3tsfPUra6qZ+kiAslnZmZw7NgxzMzMcO30xo0bWFhY4MySOHqhG9LM7Ov3+7nr\\n64UXXuAsBSBjJt555x3GKSSTySPBchFA1u/3cxfgsWPHcPHiRX5uarUaDx48wM9//nMA8ggPYu/u\\nJhifyA2DwSBjgCYnJzEzM8MdXYS9uHXrFt555x0AMuhbJKLsljOp0WhgtVo5e3Px4kXMz88zEF6j\\n0aBQKCAcDjMw+OrVq9jY2GDnqdMoiYTA5hSxzc3N4dKlSzhx4gQAuSOtUqkgGo3ixo0bAICPP/4Y\\nS0tLbLhEQ9kNfejCdDgcmJycZPxUf38/1Go1Njc32aHd3NxELpfjwEncO910/rVaLQPhKfNI2WqK\\n8KvV6jPH4Yh6dCtAog40cjaaLzrCZTRfhs/Ca3Sz+5X+Lr7vs95f1Kf5fYDOWfubMTPP+vwv+1kz\\nX5FILNmpfNn3/apnIPKWNTsR3ZLDVjoIiyrqIe6z36VOFART0EmOfxvjqG5JknTu637pMJmlGoD/\\nUZKk2yqVygbglkql+vUX//e/SZL0Pzd9gRkAPwJwAkA/gN+oVKopSZKOxlvpSU960pOe9KQnPTlC\\n+drM0lMvUKl+CuA/ALgCIPcMZ+nfAoAkSf/TF//+JYB/L0nS9a94z5aUEIcZAnKEV6/XYTAYOBVJ\\nEV63S13NQpkC0kmv10Or1SrYc2nWULdxOM/SpXnYJ2FeKPpXq9U8IwdobTZVq7oQqyxlCqhNXazh\\nU0aHdDkqfYgDh/aM0+mEXq9nfEkul+PSgJgpOaqISa1WKwZ3joyMKMZo0MwxcYAm6XQUQvvXZDLB\\nZDJxGS6Xy6FQKKBQKCgGdR71uQIO1knk6xExG8DRrUdPetKT/2zkUJmllpwllUo1AuADALMA/gcA\\nfwUgA+AzyNmnpEql+g8AbkiS9H9/8Zr/BOAXkiT9c9N7/RsA/+aLf549tBLfcOkGF0835ZuqD/DN\\n0Ombos9hSwU96UlPetKTrsqhnKVDUweoVCorgH8B8N9LkpQB8H8AGAdwGsAugP+lFe0kSfqPkiSd\\nO4ySz5N0C0vSLfmm6vNN0emboo+oxzdBn570pCc96cmBHMpZUqlUOsiO0t9LkvRjAJAkaU+SpLok\\nSQ0A/yeAC1/8ehjAoPDygS9+1pOe9KQnPelJT3ry3MnXOksquT7wnwA8liTpfxV+3if82n8B4MEX\\nf/9/APxIpVIZVCrVKIBJAJ92T+We9KQnPelJT3rSk9+dHKYb7gqA/xLAfZVK9fkXP/t3AP5UpVKd\\nhkwnsA7gvwEASZIeqlSqfwLwCHIn3X/b64TrSU960pOefBOFWtCPopmjHREHZHeD260b+gAHFArf\\nhHUSx0aJFA9HuVYtd8MdiRItdsP1pCc96UlPDi9fxlnzu7T/1NVIHY7irEXqcPxdXcRqtZo7hW02\\nG/R6vYI0s1qttkyG2a4QYS4gM8WfO3eOO2MfP36Mvb09BZfXUXej0jMxGAwIhUJ4+eWXAchdsEtL\\nS1hcXFRwrB0FwfKzhDqaAZkQ1mKxoF6vM7diKpV6ZgfxIaRrPEvPjYjkXd8E71ckN/smOKVArzvu\\n66T5UhGJ8/6/0E/87K8iaftd6vYsPZqjut+VPkQv0MzS+yx9jsomiOuhVqsVJHkmk0nxuUSad1Q0\\nDCJrPVF1ALJDoFarmYQVkEd2ZDIZHndCzNfdfnbkHNF4Kp/Ph/HxcdTrdaaooEuPyEZjsRiKxWLX\\nHRXKSBiNRpw5cwaAPIzZ7/czc3y1WkUymeQRNURufBRrQ8+JHLe33noLL7zwAtOX6PV6bGxs8JDi\\ndDqNfD7fldFLX6UPILP3/+mf/il+7/d+D4C8X/71X/8VhUKBJ2NkMhnk83kFC/xRCK3Rq6++CgC4\\ncOEC7HY74vE4M+avrq5idXWVden2+vQG6fakJz3pSU960pOefIU8t5klceii3W7HzMwMxsbGOD14\\n9+5dbGxsKGYyHbU+gEycp9Pp4PV6MTs7C0Ae25DJZPD48WMA8qykQqHQ0ZyorxOKMIkwk0aM+Hw+\\nuFwuTvvm83kmQSRyyGZCz27pRVkAMc3rcDhYN3HmEI2pyWazT0Uu3dSneTizzWZTDHKt1Wq8z4ig\\nUUzzdjNj0Zyh0Ol0/NkU8dF3t1gsiqGcNAQVQFcjK3F8DRGdGo1Gjs7pc8xms2LkRqFQUMy8KpfL\\nLc2u+jJpJqQ1m82KodWhUAiAbBNoNMz+/j52d3fZNpBu7a6P+JxEQlqDwQCn04mZmRkAckllbm4O\\ner2ev/vu7i5WV1dx69YtAPJ4JnH4aLt7u3n0g9FohM/n45E6p06dwtjYGM6cOaM4+0+ePMGnn8r9\\nN1evXuWST6dnTCQT1ev1cLlcOHdOrnQMDAxgfn4efr+fM0u0ph999BEA4J133sGNGzfYTnYqYglQ\\nr9djeHgYL774IgBgaGgIHo+HR/7UajVkMhm2h9evX8fCwgISiYRirls3dCLy3rGxMQDA5cuXEQwG\\neZit0WjkMT+AfO4p4yVmJ7shpA/toZmZGbz22mucEUwkEigWizAYDIr7RbRL3cRYiXeFzWbD+fPn\\n8Vd/9VcA5D0UiUSQSCQUxMtiBp7mDXZLn+faWaL65Ysvvoi//Mu/xOjoKNcv33vvPfzmN7/hgbI0\\n4+uonCbazFarFUNDQ/jDP/xDrvV6PB6kUimeFffrX/8aV69eRTKZZKeA6vfdnB1F+vj9fk6lnj17\\nFidOnGCHwGKx4OHDh/jVr37FRnN3d5dZx4HuOZparRZmsxkulwsAcP78eZw4cYLn+jmdTvh8PmSz\\nWdy+fRsA8OGHH+Lx48c80DSTyXQtBU3zvgDZoZ2ensbc3ByXKlwuF1++gJzmffjwIRurtbU1RKNR\\nZLNZBQt5u0LPTKfTwW63Y2RkBPPz84qf+f1+APJaJRIJ1iWbzSKZTGJpaYlT5PF4XDH4sxURZ6Jp\\ntVo4HA6eN3j+/Hno9XoeKFur1WAwGNhxKxQKyOfz2NrawqNHjwDITsHe3l5bDpPo0Gq1WhgMBr7Y\\nzp8/jzfeeAMejwcAMDo6ikqlArVazc7RysoK7t27h3v37gGQZxBmMpm2ZtqJDjax05MdGhoawqVL\\nl/DWW28BkPcPPS96ttlsFjdv3mTn7uOPP0YkEuH90+r5b8YB0cUxPDyMs2fPsuN2+fJlOBwOOBwO\\n3vNarRbT09P8HFOpFG7cuIH9/f2OHFuaIkA2xmQyYX5+Hi+99BIAYHx8HKOjo4qSpdlshtFoZNug\\nUqmwtbWFzc3Njs+W6LjRMzt79ixeeeUV/myVSsXOdSwWQzAY5D1Fw26JyZ5+1q6tFmfo0Z4+ffo0\\nAODcuXMol8vsLBUKBWxvb7MN1mq1MJlMikCkGwEk6SI6S6dOncLExAR/552dHTx58gS7u7tsV+jM\\ni2eUdOpUH7GM3N/fj7feeotLp9VqFcvLy4hGowrMEunTDR2a5bl0lihDQRmJY8eOYWRkBMFgkI2o\\nJEnY2dnB2toaAPmyqVQqR4KYF4dIUgT14osvYnR0FIAc5Xo8HtZ3YWEBoVAIpVKJJ3V3O0NBhsrv\\n9+PVV1/Fj370IwBytOtyufizarUaQqEQZmZmkEqlAMh18W5ElyS0NjqdDoODg3j99dcBAN/73vfg\\ncDj4mdHvmM1mHipLw4fFi5iwA+3o13zRHTt2DADwyiuv4A/+4A9gt9sVg34NBgMbqr6+Pvj9fmxu\\nbgKQL+aPPvoIa2trHUV5zbiF8fFxvPrqq3jttdfg9Xr5dxqNBg9sVqvVKJfL7ESGw2Hs7+9DpVLx\\nWmWz2ZYzKGJUBsgXyejoKF5++WW8+eabAOTB1eJlKH0xdZyeRzabxdbWFjQaDWMt8vk8TCYTX0iH\\n1Ul0TrRaLaxWK6anp3Hx4kUAwJ/8yZ8gFAopRupQdEuXndPpRLFYxN7eHgDZeRKntB92H4kXCnDg\\nwF64IFPMzc3N4c033+RnptPpUK/X2XkA5Ah5fn4ekUgEALCxsYFYLHboQaZftjbkeJDjdvbsWczP\\nz7OzrdVqOQCi37Hb7dDpdJiYmGD9FxcXkUwm27r0RIdE3B+U+SenzO12IxaLIZvN8ucEg0EEg0F+\\nzdmzZ3Hq1CnE43F2ltqRZz0zl8uF6elpfiblchlPnjzhwDqVSsHlcrEufX19mJ2dxdraWjvg4af0\\nEYWqELSHtFotIpEIPvvsMwDA4uIiEokE6+J2u2G1WvnMAfIzajegFZ0cyhbT4Oq33noLJpMJOzs7\\nAIBPP/0UW1tbyOfzvC8os0rv040xRGIFgs7wq6++iu985ztsI/f29vDBBx/g1q1bfJbo/mrujuuW\\nA/dcOUvigzUYDJiamgJwEDU1Gg2Fp1sqlXiTNbcZdlMnEaX/wgsv4Ic//CF8Pp+irFUoFLC1tQVA\\ndqj6+/uxurp6JBPSdTodp7fffPNN/MVf/AU7JIVCAbu7u3xRJJNJ5PN5GI1G3ogiYLZTfcRyzfDw\\nMH7wgx/g+9//PgA565XNZvkSq1arKBQKis/2+/2cMQG6cwjps48fP44/+7M/AwC88cYbsFgsyOVy\\nnJGg6I32kNFoxNjYGILBIAC5vPDw4UMYjUaOvlpJQ4uOicPh4CzAW2+9hddffx1ut5ud6UKhgEaj\\nwbpZLBbuVgHkEpBer4fH4+G0OXWHtGrY1Wo1O2UTExN4/fXX8cYbb/BnVSoVzojS71ssFo4C9Xo9\\n4vG44tlXKhW+nA4rtH/EEsTQ0BAuXLjAmVKv14tcLscGU6PRcHmXLpNarYZ0Oo1EIgHgYCZhJyUv\\nQN4PXq8Xg4MyB+/4+DjUajVn+2KxGDQaDfx+P5fDqGxBJc1KpdKRXRJL5VTuAuR1cTqdfIGEw2HE\\n43EYjUacPHkSgOzs6/V6mM1mAHJmTKfTteW4NetUr9fZnjidTlgsFt7Ljx49wurqKmKxGO+Z48eP\\n48yZM3w59vX1YX5+HtevX++KPrROlCXVarXY2NgAIK/Nw4cPsbq6CkB+JkNDQ6yb2+1GMBiE0+lE\\nMpnsSBfSBziwR+L+yGQy+PTTT7lMS2VROkcUxIqdg92803Q6Hc6fPw9A3s+VSgU3btwAANy5cwel\\nUokDAJJm2oVOzxWti16v5/Lkj370I7hcLt7P77zzDj799FPs7u6yLaIgWgwgunnn9wDePelJT3rS\\nk570pCdfIc9VZkkUwi4Ack1cq9UqyhL3799XgHNF3oxuSLMXTNG83++H3W6HWq3mKGR5eRl7e3v8\\n+Wazmae4dzvTRZH1+Pg4ALktlsoQgIy7uXv3Lnvj1WoVExMTilJBtVpVRHPtli6pHEhZihdeeAEv\\nv/wynE4nADmKWlxc5JQzgbmHh4cxPT0NQH62VD6l92xXqKwGyJHrm2++ySVBr9eL/f19LCws4M6d\\nOwBkQHe1WuUs3eTkJAYGBrjkls/nUSwWOYMBtFbSofU2mUwYGBhgwOkbb7wBv9+PbDaL5eVlAMDS\\n0hKq1SpnuXw+H5xOJ0fvsVgMq6urSCQSisxYK+UuWiOj0cj7eW5uDi+++CL6+vp4zywuLmJlZYVf\\n4/V6EQqFOBLPZrN48OAB1tfXOVrPZrNt7XcCwAJy9mxoaAijo6OMBYrFYnjw4AGX200mEyYmJjA6\\nOqrAWmxtbfF5bCfbJuojZqttNhtnZsxmMxYWFhinFY/H4XA4cOLECX69RqNBpVJRlJc7sQPiuWg0\\nGgrQfa1WY5zWwsICUqkUnE6nooGBSoYAGOvV7tqIGRPgoNHAYDAo2s2Xl5exsrKCZDLJGXmdTgeL\\nxcLlVb1eD6/Xy8++UxEByA6HA4VCgTNLDx48wOLiIlMo6HQ6ZLNZPkehUAiFQgF6vV6RUWlXmulA\\nJiYmeA/t7e3h888/50xpvV5XlDSNRiMajQYymcxT2Kdu3CUOhwN//dd/DUDOqIXDYbbPmUwGWq0W\\nkiQpwPuNRkNRKm1Xl+b9o9VqGed2/PhxSJLENAEfffQRtre3FeV/sqlkV2kfi392skbPjbPUXJ+3\\nWq1s0O12OyqVCiqVioLcy+l0Kuq6R4FXoo1MpZlQKASz2YxoNMqXLmESKKVIG/4o2Fl1Op3CCPr9\\nfkiSxJ14P/vZz7CyssK4nKmpKTidTmQyGcaXiBuwUzEYDIpOJZvNxqXS+/fv4+233+bLRaPRoL+/\\nH2NjY/xsqUuPjIXIbtuq0OUGyCWICxcu8DoUCgU8fvwYb7/9NmMXarUafD4fv8bpdMJoNLIRrVQq\\nCkeJPuOwa0cGx+12Y2ZmBpcvXwZw0Mm1srKCX//61wBkbEuhUOAyy8jICPx+PztL4XCYnQKRXK9V\\nIZAwlQWOHz8Os9mMeDzOjs9nn32GaDTKl6HD4UAoFGKjlEwmsby8jN3dXQZfilizdnQCZMNst9uR\\nSCS4GSGVSmF7exvpdBqA7LBQ2Zucpa2tLSwvL7Oz1Nzt2Yo0v6ZWqzEYl7oT6dIlbp7NzU1YrVYA\\nMmYpGo3yWSuVSh2dNbEMV6/XGRO2urqKbDbLDkokEkEymYTT6eT9TKUleo56vZ47HTvVSSwTpVIp\\n3g+AjBnb2dnh8j8gPzcqYwKyk2CxWGC327tShhPfI5fLIRwO83dcWFhgLiVAvqSr1SqXBEkcDgff\\nL508N/Fyp2430u/Bgwd49OgR61IqlRRlrnQ6zTqInaHdKDep1WqcPXuWMUulUgnvv/8+Oyh0xwIH\\nTT+1Wu2pu1mUdhxv+h6jo6MM2TAajQiHw/jlL38JQLaHxFcmrqdYVq5UKopO6mq12lGz0nPhLDVn\\nFSj6oYtDp9Ox80HYnEAgoCBcE9liu6FPc82WLian08mRCWUFAoEAvF4vX8x7e3vY3d1VvEenWBxA\\n6UjQhUmAVyJ7u3PnDur1Ond8HTt2DFarFYuLi4qDIG6qdownReAi+C+ZTKJUKvEz+fzzz3Hr1i2+\\n1AYGBjA5OclOJXDgkNBryDlpVSc6SBR1OJ1OlEoljoj29/dx9epV3Lx5kw2Vx+OBz+djR5jWkjIo\\nsVgMkUhEQZ7Xik5ihD8wMMCXWL1ex/b2Nt577z12CtLpNNxuNxuC/f19xONx1oWcq7W1NV7Pdpxe\\nelaET/L7/VCr1VhdXWVdaK9Qxq1YLGJjY4Mj9Wg0yuR5pEutVmvbUNF3oECgUqmw45ZKpZDNZvkC\\n8Xq9UKvVCIfDHJ2Hw2Gsra0x3qHTYIAuAJVKhXq9rqAFAA4uDIfDgYGBAVgsFsZLxeNx7OzsYGFh\\ngf/djYw3fR9ab2p9Fx1EnU6n6IYrFApIpVLsaD569Ai7u7sd60POiXjBR6NR3luRSIRtjegI6/V6\\nPnuVSgWPHj1CIpHoSuAmOpXUWENrk0ql0Gg0+DzqdDro9XrOahWLxacc/W7dJXq9HhaLhe3bwsIC\\nN2qQLmI2k7CJFBSQdErLIUkSdDodXnjhBbaROzs7+PTTT9mxJ6ywmH00mUyo1+uK9ehGs5JGo8Gl\\nS5cQCAQAyM/g7t273FFO3e1Go5HPvtlsxtDQEK/L/v4+otGowgZ1Ij3MUk960pOe9KQnPenJV8hz\\nkVlqLqFRfZtKBZVKBfV6HWazmT3acrnM5I/i+3RLH1FErhWTycRZJvqZ1WpFIBBgD/3Ro0dIp9Nd\\nK3c1j3XQ6/VcniFCPJHC3mq1ckuxz+djigWKxJvTle2WMCmrRBk1rVaLer3O+KlSqQSTycSYpkuX\\nLuHixYvwer1cvllZWVHQ/VP5pJ2MiVqtVtT+i8Ui7498Po9kMsmdaQBw4sQJzM/P49SpUwDk55xM\\nJhkH8vDhQ+4OaTVqEbE4drsdNpuN14WyRvF4nPeSRqOB3W7nKEokewSA7e1txGIxBTllO1gYjUYD\\ni8XCuLJqtYpUKoVCocBrFwgEFPipYrGITCbDz4zwHuI+6oTqQcw+1Ot1GI1G1m96ehput5v3eyaT\\nYbI6kTeH+LlIl3alOaNQLBYV86pGRkYYL6jX6yFJEpNiAnKWJRKJcMccdTd1ww40Gg3OEm1tbTGO\\nEwAGBwfh9/sxNDTELfxWqxX1ep2pMDY3N5/qRm1HyFbQPkyn09zNCshrptVqodFouDzpdDoVlCY7\\nOztIJBJHMmIknU4zRw/9W8QQWiwWhEIhzmpQ167H4+GsXDdErVbD7XYjEAjw+1L3G+liNBoV3HR9\\nfX0wGAyIRqNsP8hOdNIiT7yFRKQMyDYxHA5zNp9oXcTzZrfbFdlJOvt01lq5N8TKkdvtxg9/+EMu\\n0+7s7ODatWtcISH743A4mKJndHQUwWCQ7Wg2m8Xm5qaijNgJzcJz4SwByrpztVpFpVJRAAYvXboE\\no9HIBpIcJXpQ3Z7B1JyKpweQTCaRzWYVuBuTyYRUKsVEi/fu3WMsTrc4IEgnAt+J5ZlsNst8Qm+8\\n8QaGh4e53Xl/f58BjlQqeFYKtR39mjlm1Go18vk8f+dAIIDz589jcnISgOwsOZ1OrK2tYXFxEYDs\\nkKytrXF6nljP210vKmNVq1Vks1kG2qZSKQbj0tpcuHABw8PDrG8qlcL169e5lfbRo0fM6N1q2plS\\n2oBMuEaOACBf7oVCAQaDgculWq1WwSdUq9UURKcbGxtIpVIolUptccGQQ0LMz+SMuN1uxuTRc6QS\\nBpUOEomEgrYjl8ux09YMsmx1jXQ6nQKHODk5iQsXLjDAmy4wMs47Ozuo1+sKg00ElJ2S+JHjJjJ2\\n9/f3c6v1pUuX4Pf72cADB84sOUt0mZCdasa8tapPs/MmTognQlFAvmTHx8cxMzPD61mv1xGLxdhp\\nWF9fVwSX7ejzLN0I1C7uAb1eD6vVyvgYckzIcSPeI5GiohOdRIfbarUqziw5JOT09vX1YWRkhHXK\\n5XJIp9NM1iu+d7tlbtLJ7/fD6XQyl1GxWITL5WL7PTIyAq/Xy3eJVqtl8kWioukEYiKuj91uh91u\\n53OyurqKdDrNwa7ZbMbc3Bz6+voUbOciBu/x48coFou8D1s5a6Iu/f39CAQCbBPX19exvb2tAOFb\\nLBbMz8/z/TE4OAiz2cwOOCBzQ9Fr0ul0R/fHc+MsAQcGl3hTiBqfLjQC5gFy1mJ/f//IGLvF7opC\\nocAYinfffRehUAh9fX1sNCmqpWiSIrhu69ZoNNgJoEju6tWr+O53v8vRpMfjUfDd7O7u4v79+9jY\\n2PjSLqp2HRO6QMmp3d7exsOHD5mA7eLFi5idnWUgpdVqRS6Xw82bN3Hz5k0AsmOZz+f5ubY7mZwM\\nG71PPB5XMKgPDQ3hO9/5DvL5PGNxrFYrVCoVA75XVlbw8ccf4+HDhwAOLuF2nqPYmWe1WhWYCbPZ\\njEAgAJvNxtk+ArkT7oa6u2hP7e/vP3OEzmFFJKEMBAIKjhyayj4wMABAXsv9/X2+2JaXl7Gzs8MX\\nbalU4qxpJ1wwtEZkmIl0VgQpkzMlXnyhUAixWIwdkkwm0/a6NOtDGBNAxtj19fWxY6lWqxXYrHq9\\njmq1Co1Gw3s8Go0qMn6d6NTsuFmtVl4Xr9cLo9H4VPekTqfj39Hr9VhfX+eLrh2nv1kf4AAfSOtk\\ns9kUDO+AvK/sdjuftbGxMQwNDfGeItLKTskWKbtNtjgQCODUqVOwWq18iZJTTefR7Xbj+PHjzHtG\\nmUDa10B7+1kkWwTAmRwxs3T8+HEMDg7yc+jv74fP51PgcmKxGHQ6HTcW7O3tdSVrOzMzg0AgwO+V\\nTqcRCoU40PZ4PGyv6XeMRiNKpZKiKkF/Fz/nMPrRuhC/kk6nY+eUSHfJaQwGgzhx4gSuXLnC+5my\\n2bSnbDYbdDod28iFhYWOuhl7mKWe9KQnPelJT3rSk6+Q5yqzREIdDeTBWiwW2Gw2mEwmru0S+7HY\\nBXFU0mg0OEOxvb0NnU6nqG9nMhnEYjGOmrLZbFfHiTRLvV7n8lI0GoVareasgMFgQC6X4zLXJ598\\ngsXFRUVXTrcwFJRZoMgtGo0ikUhwlDQ0NASDwcD/v7q6il/96ld49913meogn88rsgKdtOvSvgHk\\nTMze3h7XTz9HmgAAIABJREFUuy0WCwYGBmAwGDh6XFtbw+3bt/Hxxx8DkFvmd3d3uSbeyTBW4CCr\\nsL+/j1gsxlkjGuzp8/n4++bzeSwsLHCH5crKCvMqAXJWoJPsiZihAA5ag4mugLqV6HfFrEUul+Ms\\nCqBsze8UH6RWqxW4J8oY0/vSIE36d61WYxyIOEZHlE6icLEsaLVaFYNN8/k84y0AeU9ZLBbFz4aH\\nhxGJRLgbLplMtlWKI31IF5vNhr6+Pj7nNpsNRqORo+6xsTH4fD6YTCbOImo0GuRyOT4TlPlpNwso\\ndmw5nU4uj8zNzTEUAZCfkclkQiAQ4E7ToaEh1Go1ZvNPJpNIJpNtl0xErh4aKQLI2ezvfve7cDqd\\nfJbS6TQymQyvFbHgi5g8mi7QyXlvfmaBQABTU1MIhUKceYxEInyWAXnag8vl4nNOGcRgMMhYz044\\nuujc22w2DA8PKxjcrVYrXC4XwxJGRkbgdDohSRJjlIxGI9xuN+u/sbGBzz77jPfYYelxRF0MBgO8\\nXi9UKhWXBPP5PO8rQKa8uXz5MjweD9tjor8hqIXdbkcoFGIMocPh6IiB/bl1lkSOGxG4KXJ7iNO8\\nj1IX0SEgYyqODKnX60gkEor296NylKgURyUIjUYDs9nMB5Q2IOEUwuFwR+nuLxP6ftVqlY1wKpVS\\nDMuk8hOtXSQSwf3797GwsMBr1U1nl4ZhAvIFr1KpFENzqV5Pl0cmk8G9e/dw7do1AAd4mG6UUICD\\ny4kufwJsGo1GHiEiNguMjo7i7t27AGSHWxwc2YmjJOIdisUiIpEIX1qff/45yuUyzGazAofg8XgY\\nNzQ/P4/NzU12wKkVu91nRkaTLivam1tbW0yDQbgEOkuUep+bm8PU1BROnjzJDglhZtqhdxAvXYPB\\noAC/63Q6xONxdgIIu0h600idM2fOcEmKdKb93U47M+0Nr9fL+3dkZARut5v3ELV3EybIbrc/BW6l\\n50y6kM1oVbRaLXw+H5drJicnMTc3x/QTdKlTcEs2UsS+qFQqrK2t8bMnXCr9H3D456bRaNjGDAwM\\nIBgMcpPGK6+8grGxMUWjCQXWtKeIfoKeq9PphMfjeQqTddjzJjoB4lipyclJzM7Osh0E5HJpIpHg\\nu40wnrQWhUIBfr8fZrOZnQLChbZiKwnQTnsVkO2dOFrF7XZjbGyMn5/RaMTe3p4C65nJZHDs2DEF\\nhlAcs9NKIEC62Gw2DAwM8PxLEr/fz7Qyc3Nz8Hg8iEQinIRYXV1FoVBgOAlRrZD+Xq8Xe3t7bXNk\\nPZfOEnDAUAsceK+ioSKCuN+ViJEAdcGQfmq1GoVCgb3ao3TgCFhJRnN8fFwBeKPLhTbzzs7OU5Pp\\nu+XIEY6MMhIDAwOYm5vjQ05Or1iXXlpaQiaTUejQLR4aygwAB5EUHXJ6ZoVCgfUpFouIxWKMuepk\\ngK8oVMMXnS4R7E8RbLVaVURwer2eo6RaraaIQDuNeMUu0v39fSYKJVZzu92uaGo4deoUY89CoRDm\\n5+fxi1/8AgDY0WpXF7EhQKVSscHd3t5mbikygPF4HPV6nZ0Cm82G48ePKwY00+XUiS5msxlGoxF2\\nu533czweVzA9E15Q3GNarRbDw8PsLFFnYbs4NwA8A3BqaoqH4Ho8HkVjB+lAf1JHbK1WY6dlZ2cH\\n6+vr/LyoiaZVYlWLxYK5uTl873vfAyB3kdJUBVoXcfA5AHb2aM83D1imbiedTtfShatSqWA2mzEy\\nMgIA+KM/+iMFQDoQCDBfGu2hSqWCQCCgyNSUy2XFbDiXywWfz8e2gLCTh1kn2kM0y5G+s9ls5swp\\nfUfquBUDhWQyyftlbGwMLpcLZrOZszk2m415BltZJ8KW0fchbCjZZ3ISRbZ5IpoVB8APDQ3xd9Rq\\ntXC5XJxtJRzfYfQhcblcPKBXvFfFTDE5lAsLCwyO393dRTAY5HMiSRLzjZH+ndju59ZZEg800emL\\nPwuFQggGg/ygj1IPjUbD0eaxY8fgcrkQi8V4w6vVaoyNjXF3BW2kbgtFGFarlS+yl19+GUajkVsu\\nyWGg1GowGOR0dDezXeQo0dR6ALhy5QpGRkb4oDVnamj451ExrZvNZo5233zzTbz44ot8kVKJsFwu\\nK7o/xKwc6d0NESfEU5aGSrnxeBybm5vIZDJswC9evMhjPADg1KlTWFlZecrRbVVo/9J3pJEOdClQ\\nt50IjqdLjrrAtFotAoEAG9lOSifieByj0ajoLKMhx6JjWK1WeaAtIBt4MtCkO72mFZ2oO4j2ArVs\\n9/X18T5YWVlhwDZwAOimrEZ/fz9mZ2cVA5FpaCs5WIfVS6VS8YXv8/k4czM3N8f/v7u7y8+ISrj0\\n3mazmctwFCgVCgXYbDZFl2CrXVVUhrl8+TIuXboEQHYu0uk076FoNKpoLKGLj8DBtHZ2u52dXofD\\nAYfDgWw2y79zmLXSaDTQ6/VcApycnIRer2cbVyqV4PP5YLFYFM6F1WrlM0D7RRxj4/P5EAgEeKxO\\nuVw+FK2BaMvq9boC7pDP5zE0NITf//3f59+nEjbpRizUpBuxmmezWd4PFouFu3IPK82whGq1igcP\\nHmBpaYkH1FerVSadBGRnJJVKMT0NINsLuvvo30ajUZGFPqw+JPv7+0yiSuefRojR/iQ6jo2NDf7e\\ntVqNRyIB8jMliANwMA2i7Yx3W6/qSU960pOe9KQnPfnPRJ7rzBJF5uPj40wOSGlni8WiqMcexeeL\\nf1I5Z3R0FNVqFaurq5wm9fv9DPYUX9/NchcJReY0gNDpdCIcDuM3v/kNAHmtjh07xrqIJbpuCgE+\\n7XY7D8d86aWX0Gg0cOvWLQDA9evXMTY2huPHjwOQ08k2m61rY2BIKN3s8XjwwgsvsC42m435bz78\\n8EMsLy9jdHSUB5+6XC54vV6Oorr1zGhdKMs1ODiIfD7PQPKVlRUevHz69GkAMgB2cHCQsxbNYMx2\\nhcoj9L6UaaPsQ7FYZMI7cYyHWq3mLAY9a/Hf7QhlReksDQwMoFarcXY4n88rMlyA/EycTieTrE5P\\nT8NsNiMSiXBmqR3siyRJsNvtnC3R6/Xw+XyoVqtcTrdarQr+JiJgPXv2LADg1Vdfxbe+9S2YTCbm\\nxNnd3cXe3l7LMyslSeLswiuvvAKfz4fx8XGO6kulEttDQM62m0wm/n+VSgWXy6XAzKjVajx8+PAp\\nEtNW9pRGo8HMzAy34wMH5UgqjxDwnTLrZrOZR46IWSOj0agYZ0Rg5lbGVahUKgZAA/J+DofDnM0n\\nAkev18vZSJfLpWgYIkwefZ9cLsfjqSibQzivVqRWq3HDCv37wYMHOHXqFD8no9GIwcFBRZOJRqPh\\ncuvQ0BAkSWL8Hq1nO3ZJzNQ1Gg2kUiksLi4qStoiyaNOp0OlUlFkAI8fPw6Xy8X2Ih6PI5/Pt0UA\\nS9+nVCphaWkJkUiEP2dwcBDnz5/ns7e+vs6ZJJGT6sSJE2wztVot8vk8U2Ok0+mOSE6fW2cJgGLK\\nOaXtRJDe2NgYgzy7WUJpFpVKxTXyYDCItbU1rK+vczmBSgOUHtTr9Qpys27qodFocPr0aR7KKkkS\\nfvnLX3J3GRnz5vJCJ2R0zToAB+nw6elp/OAHPwAgO403b97EP/3TPwGQuVSsVivOnDnDurlcLphM\\nJkV5qVMHhdhpJyYm8K1vfQuA/Jx2dnbw4x//GIA8xVqj0cDtdvOlQ0Br0VnqhsOk0Wj4sgPkQx6L\\nxXjw8tbWFsrlMvPT0O+YzWbGMDWDcduZlUd/Eq4GeDqlLzID0545ceIE3nzzTQYXNxoN3L17l3Uj\\nkrxWyeio24Uu1RMnTqBUKjGfTDab5fekZ+LxePDaa6/hz//8z3mdarUarl69igcPHgBAW4bbYDDg\\n+PHjmJ6eBiBfuqOjo4jFYuz4qFQqZi8H5Oc6MjLCDvmFCxdgs9kQj8fx/vvvAwBu3rzJpYxWRKVS\\n8TMaHR1FKBRSlLRVKhXPoKR/Z7NZRWcTzdUj53N5eVnBRUdOVCtz/Ox2O4LBIAKBAH8WzU8kXSwW\\nC0wmE9u8eDyOSqWCSCTC+5heQ11fLpcL8/Pz2N3d5QYLEaP3ZUJEnOTUOBwOqFQqvmSpkcJqtSo6\\nKNfX15lPjUo5pH8mk0Eul4PL5WKAcSwWU5R0DvM8a7WaopREZblisch3g06nQyAQ4LMUj8eh0WgU\\nXELUzUxOAPH4tbqnmh3jQqGAO3fuMIQjEAgogpdqtcrdzGSXdDodCoUCs2Tfu3fvqTLzYYWebbFY\\nxPLyMq5du6bobr948SLvF7/fj3A4jHQ6zQmSoaEhTE9P8z7e2dnB+++/j3fffRcAmBG+XXmunCXx\\nwYqkZ48ePcLIyAg7JwA4gqKaZ6fTvb9KJ6ppA3LLayQSgdlsVpDTAQdof71efyTUAUS+Njc3x5fJ\\n8vIylpaWuIYcj8e5E4x002q1XccJ0fT6mZkZxkfVajVcu3aNwcNOp1PhxNLFLLL/diuzRHgTuuCr\\n1Sru3r3L3WWxWAxDQ0MwGo28r6jDTMwcdCqEGbPb7Qqczfb2NkdntHZTU1PcYeT3+xXA/MXFRUWG\\npZ3LFwCDpclB6evrU1yykiTBarXCbDZzxPbSSy9henqajRQN3GzuYGxVdDod/H4/75fR0VFYrVY+\\n11arFXt7e9BoNIzVOX/+PKanp/my0Wq12NzcxKeffsoXbzvRJJ0Jwq0MDAygv7+f/yRRq9W8h30+\\nHzweD7/GarWiVqtheXmZMYPknIj6HBYkTPajUqkwnosuEwoW6aKoVCpwOBwKTAp1T1LgtLm5iY2N\\nDQ6UWmGCbu4STCaTCmCwmNknWydiX548eYKHDx/yHqIzSnv6/2XvzYLjOo90we/UvlehCgWgsC8E\\nQAJcQUoURYkSSYmiJGuxlra8dI8dYzmiox864ka/35d5mad5mp7pG3Ej+nZHRzja3W0PZVm2SFES\\nxR2kAIIkdmIHat9RK6pQ83CUyXNKpERUFWTTPl+EggIJ4GT95//zz+XLTDIspKM/HgXExaHGsYDI\\nHZMWJ2i1Wh6WC4iXaCAQYCNYrVbD6/XyWlJ1cbFYZMNbp9NtWWdS0QZ9ns3NTYRCISwuLnLLh46O\\nDlkbhoGBASQSCd7LNGlhaWmJGw+TTtgqpJw2itgODw9zFOvUqVNobW2VEd8bGxuRzWb5maurq1ha\\nWmJHb2RkhIfc0s9sZX0A0VgaHx/HmTNneKTZwYMHuVUHIK6/x+OBWq1mx47GadF7/OSTT/CHP/yB\\nK78ftY3Bw6BwlhQoUKBAgQIFCr4Bj01kqdzjkVbAOJ1OWCwWHk8BiPlJKrcG7vcdqTVPiBr0UZUS\\nzV8yGAzMWQLEmTmLi4uyn69VNEfq5VmtVp6RA4jpprfeeouf097eDrvdzryh2dlZFAqFbYnmGAwG\\neDweWfNCu92OEydOABA9vhdeeIGjcnfv3oXf76+6wksqA3D/Hen1+q95qcRt6Orqwu7du3Hy5Ene\\nV/fu3cPq6mpNUrhSWbRaLRwOBz/H5XKhu7tblmJraWnBM888w3wv6iny8ccfAwBu375d1VwxaRl6\\nfX09rwMN0qT97PV6IQgCywOI3q/dbud0zgcffIDr16/Lyr8rSQmSt07ppsHBQVmPHJrRZbPZ+Gy5\\n3W7odDp+r8vLy/jnf/5n3Lx5s6JUAKFYLCKVSnEKkCJGdXV1zDWTpmcBcNSAnpdMJnHz5k38+7//\\nO/egSiaTskjXVqqF6DPOz8+jubmZU7XA/dEO9Gy1Ws28IECMeN+5cwder5c/UyqVgt/vl82pI+7n\\no8gDiBH7lZUVLC4uMgWCenLR+kQiEeRyOdZ/CwsLWFtbw927d2VzDKXtHjY2NqDVajE3N7elZpn5\\nfB7RaJSjLkajEYuLi/wZ6+vruRyfqk8pzUYRifX1deRyOf48ZrOZU8AU4XnUTIU0ekO8P4pUEx9v\\neHiYP2PnV40faU/RTDRKucViMUSjUVl/rEwmU3XzTmpfIqUm+Hw+nDhxgqM7Op0OqVQKc3Nzsnc5\\nPz/P3E9q5SH9/Y+6TuUpwRs3bvDdsbCwgIGBAdZT9DOZTIa5cTRSjKg31OZAOiqrGjw2xpJ0MYlH\\nQgZBfX09zGYzBEHgQ3H79m3Zi6tFCuVBIFKqNPTe1NTE8gDiBrpx4waTDLdrXh2RG6XKuKmpCW1t\\nbRzuBkTFSco7GAzWfMgwcD+9B4CVklqtxksvvcT5bpfLJRt+PD09jZWVlZrM8QLkylWtVsNms3Fo\\nXafT4cCBA1wm29DQwENjSXHdvHkTExMTstLUamWhVIfBYOB34vF40N7ezqklnU6HlpYW2O12/rl4\\nPI6PPvoIv//97wHcJ6pWCmk/G5PJxBebzWZDZ2cnG2kajQZms1kmbzqdxtraGnPPfv3rX2Ntba1i\\nHh59xlQqhUAgIOvaC9wvnujs7IRer5f1Xtrc3EQikeDGdP/6r/+KCxcuIBAIsDyV7O1cLoepqSle\\nJ0o3EaEVuK+HSN50Oo14PM59i65du4bPP/8cs7OzfLE9Cu/mQdjc3OTUEv18OBzGnj17AIALEcg4\\nikQimJ+f55L5tbU1eL1eRKNRNpRjsRgPPaZnlA+8/TaEw2HcuXMHuVyOn0Uy0fubmZlBMBjkdVld\\nXUUqlZJREYg3SWXemUwGgiDImgw+ilybm5vIZDJc4k/pM1oXu90Om82GXC7Hv/dh+4QMI1pX6r9E\\n8j3qOkmNJenXdNkPDw8z50en0/GZo5+REuHT6TT3WKMzUKm+lN6J0lYC9B4XFxfxySefsGPS2tqK\\nTCaDSCTCjhJ1oCdZqqGXSHVksVhEIpHAZ599BkDkQmm1WnZUiLO3vr7OhmQ4HMbCwgLvu0KhIGs1\\nUu0d99gYS4B8k6XTaV6k1dVVRKNRzM3N8YumkRnS5lm1NAikvTPW19eZTHr06FG2pqka56OPPsK5\\nc+dY3lo1NyyXpVQqIZlM4ssvv+TIwK5du/gAAmLPkw8++AAffPABALGBYC3lkSqEjY0N9owAMeq2\\na9cu2XNCoRA+/PBDAMB//Md/YGVlpSouzsNkyWQy0Gq1rOzsdju6u7tlDTKLxSJ8Ph/Onz8PAPjl\\nL3+J5eXligjC34RsNovV1VU+xMRfkg6HFQQBuVyO1+6LL77Ar371K1mfl2rkkSqmubk59h4PHDgA\\nl8vFslA0bnNzky+Xmzdv4ty5cxzlWl1drYmSzGazSCQSuHr1KstGjfzoaxo9QWdreXkZly9f5kpC\\nr9f7tXFClVYKRSIRjlAUCgWEw2EsLi6ywW2321EoFPhcz8zMIJVKcYRiYWGBJw1IuThb5SvR9xFJ\\n+datWwiHwxgfH8fo6CgAUQ8ZDAbmHy0tLfHwVwAsh1qtZqOX/k66Tlu9dAuFAhYXFxEMBtmjv3Ll\\nCpLJJOuceDyOaDQq66lEz6JoGTWFJZBBWMm0g2w2ywYsRTzoDFPT0Ewm87WO7vQnRcXo36nyU8q7\\nkn7/t6H8spZ+TWNUyqvryvm5tE407FjKv6FAwlbeXbm+p6+lHE06ZxS5mZiY4DNJz6JIpHQtS6US\\nG+SVGHH0O6TGKfGOyIAdHx+H3W5HPp9n5zufzyORSMjklwYCKLtUKYTtID1vWQhB2JIQarWamwwC\\nYjrn0KFDSCQSTKSkBlqVjDjYqiwWi4WrLwYHB9HR0YHm5maO3ly6dAnBYJAVWa0NN2nEzWKxoLm5\\nmavz9u3bh7q6Ot68d+/exfnz59mDqzXxnZ5DFRy9vb1cij84OIi+vj6+dL1eL8bGxritATWFrPW7\\novTk/v37uVKpq6sLfX19fNCWl5exuLiIkZER9maCwWDNUoIEqj6zWq28LqdOncLRo0c5/UTe3cjI\\nCMbGxgAAV69eRTAYrCr19jB59Ho9R0uOHTuGkydPcsTNaDQiGo1idHQUN27cAADcuXMHKysr7IjU\\nMlJKKVNANEZ27NjBkSWtVgu/34/V1VXZiI5isShL1dSyQ740xU1jOqSpC+l+pQvsQZdirUEdjsuj\\nFeXPLL8QHxRhr5WTRGe/PPXyIH1XHm2ptUzS31v+jIf9bqlMD/oMtcDD5Pqm3y8dnVWNA7BVuR62\\nX8rlqfV99iCZHvR3tP+lc+ioszrJ9oi4WSqVDn3bNykEbwUKFChQoECBgm/AYxlZKi8vp/CalFwp\\ntTC3E+RRkSyUQsnn8xyGpun02y0PyUJjT0ge6bBaQRBqOgz2m2ShgcIUvaGIIOWUpeROoPZRASnU\\najXsdjunICwWC9RqNUdHqKy6/D1tlzwajUY2u4waqwIisTISiTA/Adg+j5IgnT5uNBo50hSJRJgo\\nLJXlu9Ab5WcL+HoU609BfylQoOCxxiNFlh5LY0mBglqhlhWAtUKte14pUKBAgYKH4pGMpceK4K1A\\nQa3xp2iU/CnKpECBAgV/yVA4SwoUKFCgQIECBd8AxVhSoECBAgUKFCj4BihpOAUKFChQ8BeNPyWe\\nYPk0hT+2XNKh2yTPXyLXUzGWFChQoODPHNILT4rvomL4QbJotVqUSiWuGKYK3fLqz+0EVYAaDAY0\\nNDRwdW4ul0M+n+cKYqpm3k5I+3k1NTVxN/9sNouZmRnuK5bNZrlqd7tB74mGbB88eBA2mw2zs7Pc\\nFDYWiyGVSlU13WAroCpvtVoNvV4Pm83GlbuZTAbxeJzHRtW6J52ShlOgQIECBQoUKPgG/FlFlshT\\nAP44HlM5HrVD63cJClU+LmHUP2Z4/E8pNA/I5XnUrsTbKce3dUn+rvaZdF2kXaSlMpXPP9xOeaR6\\nSKPRyIbKAmKkgvpF5fP5bU21kCzUedzlcsnmANKIGUAcKJtKpba19xogRnI6OjpgNBrR398P4H70\\nhsb7LC8vIxaL1XzCgFQWWofe3l6cPn2aJwtsbGxgbW2Nx9h4vV6Ew+FHHjJcDWw2G15//XW88sor\\nAICpqSmMjY3xOC2ay7a+vr7t0ykEQYDT6cSrr74KAHj11VeRyWRw/fp1fPnllwDEoc6bm5uyIdrb\\nBZVKxWOYGhoasHfvXuzcuZMjgD6fDyMjI7JZnrVcm8faWCJFoNFo0NfXhxdeeIHHjkxPT+OTTz7h\\neUq1XriHgRrpORwOVgTd3d2wWCysCMbHxxEIBGRDNbezjT0NbgXE4bUul4sn3mu1WsTjcaytrXH4\\nkuZvbce4BukFptFoYLFYWDaj0cizqyiEmkqlEI/HZcMua3Ugy40haqBJ+0qn0/FwYgA8vFI6b4vk\\nqfVcPfqa0hT0/ySbyWRCoVDgnykUCsjn8zxTDqjNGJIHGWjShqfA/REl9O9Go5HnodHP0jDQWoyx\\nKH9n1OyUnq3T6WC327kZqtls5gsGEPdUOp2uSZi+fH3UajXPjjMajWhvb0dTUxOPstnY2EA8Huex\\nTMvLy4jH47KhqNXKI/1/GgnV3t6O9vZ2DA0N8WBUj8eDaDTKKZXLly/jzp07NRsTVS4LvY/W1la8\\n+OKLaG9vR2trKwDxc1ssFoyPjwMQdeTFixe/NhC5UnnKjXkarA0A+/fvx9NPP83DYX0+HzQaDZqb\\nmwHcn4/o9/tljXRrdeal57y1tRWDg4PcsFYQBJlxbbPZkE6nUSwWeXxWrQehS9OTTz31FF588UUA\\ngNvtxsLCAtRqNT+7UCjwQGkAsrFDtQSNzyIj8sUXX0RjYyPi8Tjv30AggGKxyHpJo9HU9N7/szCW\\n3G43/uqv/grvvfcev7T5+XmYTCb87ne/AyAObKUBhNsB6UVis9nw7LPP4p133gEADA0NAQB7Lp98\\n8gl+9atfYWFhQTZRezuMErVaDbPZjF27dgEQN9mJEyd4qrXT6UQkEsFHH32ETz75BIC4dolEgg9E\\nrXK/ZEiS0mxpacGxY8c4P+92u9HV1QVBELC4uAhAHMo5NzeHW7duARDfo3QIZi3kAcSL32azYffu\\n3azAXS4Xdu7ciaamJgDgYc1kgN+9exeLi4sIhUJbmoz+TfIA9w0Ao9HICttut6O9vZ2/djqd/H6A\\n+4NWfT4fD5Oem5vji6YamUgeOltOpxN6vR7d3d0AxG7oNpuNjd6GhgYYjUasr69jaWkJgOi83Lp1\\nizumVyuPVKG73W6eH7d7927odDo0NzfzHjebzYjH4zzg9vr165ienkYsFgNQ+Tuji066h+x2O/bv\\n3w9AdJL2798Pp9OJtrY2/jm/388z/+7cuYMLFy5wFKPSfV3OSaI5kTTj75VXXkFrayt6e3vZoXQ4\\nHCgWizwnUhAEpNNpjI+Pc6SgGlmkX0uNj1deeQVvv/22TB/TrE9yTFwuF9bW1pDJZGTcoVrJo9Vq\\nWe+8/PLLGBgY4HcyOjqKdDrNBrjb7UahUEA8Hue9Qo5SNZDKRQb2yZMn8dRTT7EhR4PRyZGV7rXy\\nTEqt5KE/HQ4HfvSjH+HAgQMAxPW/fv06Ll++zOc6kUjIIsjSuXFA9ca2NBq5c+dO/O3f/i0A8e6g\\ngeRkLK2vryMajcoM/Vpmdx5bY4k2PAA0NTVhcHAQbrebLfRSqYSuri7+WqPRYGNjY9tTKzqdDi6X\\nC0eOHOGhrfX19cjlcry57XY72traEIlE+PKoxeF7mDyNjY146623AIih1Kampq9Nvu7r6+MDEIlE\\nkEqlZB5zrWQzGAxob28HAPzN3/wNTp48yUqUQuOZTIY94lQqBYvFwh7LyMgI8vl8zYxLuuCbm5vx\\n9ttv4+TJk2wcabVaWK1WPny5XA7d3d2suHbt2oULFy7g8uXLMi+vWhiNRng8Hpw6dQpPPPEEAKCt\\nrQ11dXV8meTzeWSzWZYlm83CYDDgxo0bfC4ikQiCwWBVhiVFJzweD5555hkA4uf2eDz83mhCOxlT\\n5A0XCgUMDw+zvNIJ5pXIRMrTYrEw6fTo0aPYu3cvGwU2mw06nQ4Gg4H3ECAa2ZOTkwDEyMHS0hJf\\nhtW8M5VKxUZZZ2cnTp8+zevU0tLCMpMsdB6JlKrVajE2NlZxBLw8eiMdW/Pkk0/i3XffBSAakVqt\\nFpmSj+PNAAAgAElEQVRMhg37UqkEi8UCt9sNAHj66afh8/kwMzNTk/Sp9AJ1OBy8Li+//DLMZjOC\\nwSBmZmYAAPF4HA6HgyPebW1t2LFjB5aXl3nSfC1Aa+R0OnHw4EEAQEdHB3w+Hztkt2/fRiQS4Xdk\\nMpnYQZBGuaqRQfr/Go0GnV8NPj958iQsFgvm5uYAiIabNKJF6VSNRlNTQ0D6rsiZffbZZ/HUU0+x\\nITc2NoaLFy9iamqKo7TbRQugc0M6pbGxET//+c8xMDAAQDyzXq8Xt2/fxsTEBABgZmZGViRQbvxV\\nK5NC8FagQIECBQoUKPgGPLaRJWmetLu7G319fVyOCoiWZy6XYy9brVZvW/RGmnO22+04duwYnn76\\naY5a5HI5pFIpDvs7nU643W4ealtrWaQWucfjwTvvvINTp04BED3ObDbLkZBwOIxUKoVcLsfeL61Z\\nLXhU5aHU3t5e/PjHPwYAvP766zAYDOwJZLNZBINBZLNZ9vqtViva2trY06q2jLc8PUkh5nfffRcv\\nvvgidDodf96NjQ3mlgFixMflcnHKx2w2Y2RkBCaTidesWCxuWT7iulCEYvfu3Xjttddw+vRpfifk\\nEVNKguQhL5AiEg0NDewR19XVyfgnW5GH9rPZbMbAwABeeOEF5gtQdItAPAGSZXNzE+vr64jH47Ih\\nylKS81blod9hNBrR29uL559/HgBw+vRpuFwuXvNcLsccPYqyFItF5HI5JjIDtYkMUPkyRUqPHz+O\\nl156iaMj6XQakUgERqNRxj2Tcqw2NjZQLBYr5ghJPXtpVKCzsxNPPfUUR9x0Oh1WV1cxOzvL+6yn\\np4dTh4DovVsslq9FCbYKSn9IuX87duzA3r17+etIJIIbN25gamqK5W9sbJRF6Xbu3Ilr167VTB56\\njl6vR39/P6eR4/E45ubmmEQ9Pz/P30t/OhwOHkpeLaTvjOQ5evQoAJGztL6+jqtXrwIAJiYmZFw/\\n6T7ZDp4rFQEAwPvvvw+bzcbR67Nnz2J4eBjJZFKWPi2VSjIdU6uokpTn9sYbb/AdBohcvw8++AC3\\nbt3i95VIJGT6Ua1W13SA/WNpLNFCksKpr6+H0+lEqVTiEPPU1BTC4TAr6O00lIjgCogK58CBA2hs\\nbOSXtLy8jMnJSTaWLBYLE15rXT1A6+JwOAAAhw8fxssvv8yEwY2NDdy7d4/D36urq2hvb4fRaOQL\\nn/K+1abhaG3oHbS3t+PNN9/E66+/DkA0LFOpFMsyMzOD2dlZ2Gw25hNotVqsrq7KOEGVKk/pnjGb\\nzdi3bx9+9rOfARAvOoPBgGQyiYWFBQDAwsICk/IBYMeOHejq6uIDHAgEkEwmkclkZEb6VuQBxBSx\\n1Wrli+0HP/gBTp06BavVymlav9+PZDLJ4e/NzU1otVred1qtFuvr61heXmb+SyUkZjKUylNLr776\\nKu+hfD6PcDjMsqnVaplRXCgUkEqlsLS0xDyQyclJJJPJig1JQExBNDU14ciRI3j55ZcBiOnTXC6H\\nQCAAQEzbOp1ONpDo77xeL3OWxsfHsb6+XtHZkxoBGo0Gdrud+YCHDh1CXV0d833oM7e1tbGxRJcL\\npZYikQjC4XBVvCmpXMR16evrQ19fH589n8+HGzduIBQKwePxABAdEbPZzOtbLBYhCELVurK8EtFq\\ntaKjo4NTp4lEAnNzc5icnITf7wcgcpQEQWBHBBCNfUoB0e+tVA9JYbFY0N7ezo7IzMyM7NItlUow\\nGo1s9Ho8HqRSKahUqqoNtwfJQ9xIwpdffokrV64AEHk4pJcB0UEqlUoymkQtoVKp8NprrwEABgcH\\nUSqV+Ax/9tlnvA5SQ3hjY0NmpNSKtiEIAnP9fvGLX8DhcLBu+/DDD3H27Fl2roH7hiSdNQCyIhig\\nOkPusTKWpIpBq9WyQt+zZw8A8QKhhbJarVCpVBxB2c7SXCknoa+vDwcPHkSpVILX6wUgEm1XVla4\\n7JFKLaUeQ602F3mtZCzt3r0bzc3NvKGGh4fxxRdfsLfgdDrR1dWFXC7HpOr19XXmd1UrmzTi0NfX\\nh8OHD7NCT6fT+OKLL3D27FkAovGhVqsxNDTE7zYajSIQCPB7LFfEWwFFHADxc584cYJ5ZWazGevr\\n67h+/TrOnTsHQLxgisUienp6AAB79+6FwWDgtVxfX/+aAbAVRUEKh5T3m2++CQB44YUX4HA4kM1m\\nOR9//fp1eL1eNn5cLheam5t5T0WjUcRiMSwtLfF7rISkS4Y/8ZFeffVVnDhxAnV1dRwBXF5ext27\\nd1lxpVIp2XvOZrMIBAKIxWJc9eX1emWE9EeFNMpltVpx8OBBdHZ28t8lk0mWh2SR7n9ArMRbWVlh\\nTp7P56vKUZEauQ6Hg39PLBaTPef27dvIZDKYnZ3lPdTS0gK1Wg2fz8ey1LLJIF2s2WwW0WiU1//2\\n7du4evUq1tfXORK2ubnJXBlANOSkTlKleNDPFwoFfv9kuF25coUv2bq6Omi1WpZfr9ejoaEB9fX1\\nVeuhcqKvTqeDzWZjPTQ9PY2JiQk+NxsbG1wBB4BlsFgs7PDmcrmq5CGoVCr09vZi9+7dAMT3t7Cw\\nwA5bLBaTXfhkqJRKJRnBu9r2E7RGNpsN7733HgDRMItEIjh//jwA8QxTawl6Fr0/koX2cTXvjH7G\\naDTiJz/5CQAx4lYoFHDt2jUAwM2bN7nCVdo2xGAw8B4iJ1ZaaVrNGimcJQUKFChQoECBgm/AYxNZ\\nelD5J+VWu7q6oFKpUCgU2KrUarUIhULMU6gmffMgSK16KZxOJ+x2O/L5PG7fvs1/bzAY2BsOBAJY\\nXFysaTWDVCZBENiapl5BVIX00UcfYXV1lStg2tra4HA4MDY2ximeYrEoq8qpNKxK6RmSi3o3UXpk\\nenoav/3tb7m3isViwcDAAHMpgPslsVTeTB5dJTJJuSJOpxMmk0kWFbh79y7OnDnD3AW9Xo+WlhZO\\nDeh0OhQKBf4dPp8Pi4uLMu9mKzLRuqjVauzYsYP7cplMJiQSCYyPj+O3v/0tADGtLPV2qZSZInAa\\njQarq6sYGxvjlFQl0ROK5JCn+9RTT6GhoQHxeJy93cuXL2NhYYH3byQSgcFg4GhlLBbjflTkiWez\\nWVnfpa3KBIgl3IODg+jp6eH9kEqlMD09zbJtbm4inU6jrq6Ow/LJZBJra2uc8kmn0zWpptTr9Whu\\nbub0UiAQQCQS4ajR4uIiSqUS4vE4RzQLhQIymQyXO09MTNSMt0gcKkCM1Ph8PkxPTwMQU8r0TKq8\\nA8TIAO2X1dVVTExMbAs1wGw28zuam5vDxMSErI3E5uYmzGYzr0U0GsX8/DxWVlZqzskxGAzo7Ozk\\nzz0xMQGv18ufW9rIE7jPfQEeXv1VqSwajQbt7e1oaWkBIJ6lyclJ1sX0fIJOp2P5pNzKWrUO6Ojo\\n4DRtsVjE2NgYR3M2Njag1Wq52pXk2dzclLV1kMpSqUwqlQqtra2cbqc74MaNGwBEfZjP56HVamUt\\nTXp6evjOX1pakp2tavf1Y2MsSUGNC+mlOhwOvkjopeVyOfj9/pqUen4btFqtrEVBeVNAlUoFt9vN\\nynp8fBzRaHTbGmVqNBo+6JlMBrFYjI2lTCYj41m0trayYiIlT6RTQrX8BSIEG41GhEIhfg41wKNU\\n0u7du3HkyBH09/fzxTszM4PFxUX+GQqlblWmcp6bx+Nhng995mvXrsHr9bIyam1txYEDB5hMbDQa\\nkU6nOc1y69YtRCKRr+XsH1Ue2jMmkwnNzc186JPJJBKJBC5dusQGdzAYRF1dHZNxiVRJRnEwGITX\\n65WRHLfSBFIa5jeZTKy8jUYjMpkMUqkUk3HX1tY4RUny6vV6XksqaCgWiyyLlMS8lTUq78ul0+lk\\nZPHNzU2WFZBzu0gXRCIR+P1+TktWeu6kxFP6Wmp4mM1m2Gw2TmE2NjZyOpLWgeZXUYqeDMtK8KC2\\nAfQO6POSgUQcN4vFwnvIYDBgc3OTDdqFhQWsra1VbNSWy0V/Uj8cStuSobSxscFGpN1uh91uZ90Z\\nCAQQDAZr3lFcpVJxY1AqGiF5pDwcq9XK+6qurg6JRAJ1dXX83ip12MphNpvR09PD63Dv3j0sLy/z\\nZyYuKdE86urqIAiCbAZaMpmUpeoq3ds6nQ7PP/88749CoYCLFy+y7gXEVLjVauUzabFYEI1G+Wyl\\n02nZrLitrJF035As5NRnMhncunULIyMjAEQKBPUSo1Yvg4OD6O7u5jPZ2tqKhYUF1luhUKiqpsaP\\njbEkzTtTMzPyTEZHR7Fz506oVCpWyKSgpB5BLQ0TqRdSXpmQSCTQ0dHBzek0Gg0SiQTzByYnJ5FI\\nJKpuGPggmSinTUaBIAiIRCLs/R45cgStra1saGYyGfzhD3/AxMQEG3N0mVSrMEulkiyao1KpkMvl\\n+PBZrVbs3buXldKhQ4fQ0NAAv9/PHJSRkRFMTU0xwZuq4SqRTdoTR6PRIJ/P82dOJpMQBAGdnZ3c\\nf2VoaAj79u1joyYSieDatWvcj+Xy5ctIJBLIZDIVRXCkHY1JQQEiN8Dn82F9fZ2VZH19Pdrb22VR\\nrvX1dfaOfT4fFhYWEI/H+VxsxTihs2UymdDZ2cn7w2g0IpfLIZPJsIHS0NDAkSJAfI+5XI47IKfT\\nae52LjWWtgpSmvTOGhoa0NLSwrwfQDxvnZ2dvIfm5uZgNpsRi8X4ck4mk8jlcsyZqYa7ID1bOp2O\\no4+AyJ10uVx86XZ1dcHv98uag5IhSc4Lkbur7UxdblgC4uVAF4der4fD4UB7eztfQHq9Hmtra7yH\\nbty4IbuoK5WF/l9KzE+lUjISfqlUgl6v58rNtrY2WCwWJlmHQiGMjo4iEAjU1HCj6sVkMskkezIi\\nyWmjClx6rxRJofEr1UC6Rmq1Gm1tbTh8+DAbubOzs7J1sdvtaGpqYn4VFTHpdLqHFr1U0qeLigOe\\nffZZPuderxerq6usg0wmE/r7+9HS0sLyUXEF6dHJyUlZNXMlZ00QBFitVhw4cIAzDOFwGDdu3GBj\\nlfZ6T08Purq6AIjnrb6+nvmBqVQK165d432XTCar4ioqnCUFChQoUKBAgYJvwGMTWQLuW8ybm5vI\\n5/McRqWhghqNhi3/eDyOaDS67QN1Nzc3sbGxwV7ryMgITp8+DbVazaWnyWQSGxsbnL5ZWFjgaEQt\\nKs4IlJrJ5XIcoh0fH8fhw4c5UnD8+HHZ8+bn53Hz5k0sLi5yrrfcG6im6mNjY4M9IK/Xi+npabb8\\nm5qaMDAwwGk66rl08eJF7jMyMjKCVCrFUYxKw6jSqCQgRh7D4TBzX3p7e7nTOnlNVqsVBoOBq2RG\\nRkZw/vx57gQdjUa/lrJ8VEijAPX19cwto3/r6upCqVTCjh07AIjl1tKO1D6fD6lUikeb+P1+TsFV\\nIo+0r1V7ezu3CaAeUlIeU39/P6anp/m9hsNhrvwCwGF4aU+sSiOBNOcNEL3suro6WXRHo9Fw125A\\nTMk7nU6MjIzw2ni9Xq7wJFkqlUej0fB7aGhogMfj+Vp7EvKGqTTfarWyR7y4uIjFxUXed9WMEiov\\n4XY4HBwNsdvt2NjY4IgE9VBqbW1lT7ylpQU+n485g4uLixWPFCF5CNL+Tbt27UJnZydHHqWpTEqh\\n7Ny5E7t27eK0IXHMqh21JF0fQHxnBw8exJ49e/icU8sQaaS0o6ODz57f70ckEqlJ1aJ0jYxGI/bv\\n3w+3282/12w2o7e3l98DVQRK52eGQiEZbSISiVRMTZCOT9m9ezf27NnD74aqk6lrNnEGKUsBiOcv\\nk8nw/i4UCizPVkHP1el08Hg86O3t5XUJhUKyKJfT6cTAwACGhob4PdJ9Smlwo9GIfD7P+pr2VqXp\\n08fKWCLQhUkGisVigV6vh16v51CqzWaT9b/ZTs5SqVTidEMsFoPBYIDdbmelabFY4PV6WXlTWmC7\\nZNrc3JSVt5OSBO7346HeGefOnWMOlXStalE+LO27A4gGbC6X44u4u7tbZuBOTU3h3LlzOHPmDOeZ\\n6R3WaqivNE3r9/uZVO1yuXhcDr3LpaUlfPnll/jiiy8AABcvXsTy8rJsVlUtUgTr6+sIh8Mcyu7v\\n70d9fT3a2tr4e+LxOCYnJ3H9+nUAIpdrenqalRRNRK9UmZPxYTAYoFar+eKIxWLMCaSLTa/Xw+Px\\ncN+iyclJ3L17VzY3i5rBVdtAlM41IF40sVgMer2eLz/il1CqVK/Xo6OjA36/nw0F4uBUowuk/dTI\\nuKc+QJTqmp+fRywW43WipqFut5tTialUChqNhvdhpSk4SnNJuSMtLS28n202GywWC18uHo8HTqcT\\nTqeTU7kajQazs7OcjgqHwxXvaakBS3MWqQnl9773PbS2tjINgXh+Op2OL7r29nbU1dVx+j0cDiMQ\\nCFTMnyIjQKVSQavV8nOOHDmCt956C/X19bx/m5ub4ff7ZcNqdTodv8dwOAxBEJDNZlmXVWOc0L3g\\ncrmwa9cuWTFCc3MzD4AlWWw2GzuyLS0tMJvN0Gg0THauJoUrbV9C3Clpg1er1cpr19XVxXMfac/Y\\n7XY4nU5uR7GwsIDR0dGKggDSlhw9PT3MzwLu9/4jo7GnpwdDQ0P8XEDkbVJxByA6Tj09PZx2lvZf\\nqgSPpbEEiEqGGpa53W7mDtEm83q9SCaT22okEaSXeUtLC1wuFzQajaxaYWlpiRscVkugfBRZ6PA1\\nNjbC5XLx5VIqlZDNZrkR5N27d2tOopTKQhEGQNys3d3drIToYJLxMTU1hfPnz2NqaooJg7U0dqWV\\neIIgwOPxYHBwEMD96rhCocDyzMzM4MyZM9wgrnzOWi04XYDI8dFoNGxEWq1W5lGQ0WKxWNDQ0MAO\\nAlU3SaegV8M1IaWZz+cRDAY5mpZOp9HS0gKHw8HnTa/Xw2AwcLRSo9FgbW2NK6+qlUfKCSJCLgCu\\nPLRYLGywUISOZGlubobH48GePXu4qlEQBORyuYq6ZEsro4xGI+x2O1fhms1mFAoFvnRXVlbYeANE\\nLtrAwAAaGxvZISCjmKIslRgnWq0Wbrcb/f393Cepvr4eNpuNjTJBEJBKpWTd5pubm1FfX88G1MrK\\nCkKhEBvp0qrOrcrj8Xi4aKSxsRHd3d3o7e0FIEaWqPEqIBqMyWQSVquVnTiTySQrriDeWyWgobyA\\naNDq9Xru1H/q1Ck0NzfLIoAmkwkOh4M/e0tLi6wDvNvthsVika1NJcRljUYDnU7Hl3ZdXR03DiVj\\niP6dni0tVABEZ6+xsZEr0+j3C4Kw5Ya4FCkF7vcLlOrjbDYr40uZTCZEIhHEYjGOKqdSKezatUvW\\nBV7asf5ROUvUOxG4zxkzGAx89guFAhwOB5/7np4e1NfXw+v18r1K+5jOOTm/pFfNZjMXnlSCx9ZY\\nkkYujEbj1zZLKBTaVqOkHCQLERWl5ZXFYhFra2ucGtuu1CAdGqoSAIADBw6wkgLAJd2k4AOBAB+O\\nWq8VeVHkDQwMDODIkSN86VJEjqpxxsfHMT8/j3Q6XZM0oBSlUgkajYaf3dDQgL1797LXodVqUSwW\\nkU6nWZ65uTlMTU0xUVjqVVaDckVLXXBpD+XzeR7bQUqJokZk9IbDYVlaoNoIDslTKBQQDodx8+ZN\\nAKKCcblcaG1t5e/RaDTYsWMHX9Td3d0YHBzExx9/DODrk8e3AqnnTek/Wvfp6Wn+O/Ieo9Eot14A\\ngOeeew49PT1obW2VkeErjQRQ5IYiNc3NzbIhz7lcTka0lUZO4/E46urqZB5yLBZDPB6vKN1FRiR1\\nuD9x4gSnSIrFouxipQtO2m28vr6eDTxANP4pfUvyb7UkXq1Wo66uDocOHcI777wDQDxbqVSKjUqj\\n0Qi1Wi0jljscDtTV1claKhQKBRlBnaJVWzFy6Z3R/njyyScBgCkRTU1NUKvVyOVy/OzNzU3o9Xre\\nd4VCAdlsltfQ4/GwrNKWJltZI+A+kZw+j06n4yHUpIP1er1sRBFVutG5d7vdcDqdsFqtLJ9er99y\\nypvuCqmxpNFoZE6OxWJBfX097+/l5WWsrq4iEAjwPtFqtTx5gNbOZDLJ2tg8CqTfR9E0vV4vi8BS\\nsQkgnqM7d+5gbm6OCdzpdBo9PT2yAdmRSORrerti3VTRTylQoECBAgUKFPyF4LGNLKnVavYM6uvr\\nea4RWagulwtms1k2PHM7QBZ5R0cHAHFUBeWYySouFApwuVzsNW1XtItKrR0OB8/3ee6552RNKUsl\\nca4QjYMob31QKxC/w+Fw4IknngAAvP3222hqauIy2VAohM3NTY7c6PX6r3lstZKLUhc0c+7111/H\\noUOH2FNcWVlBNpuFVqvlCCCl5KRDT2sBGitARPeOjg5ZD5dgMIhYLIZ0Os3RkY6ODlitVg53S+ck\\nVSuLXq/n30veKoW2aXyIXq/nz9/d3Y1SqcQpTKPRCLfbze+q0jA3yUIpNbfbDaPRyJ6tz+fjNCS9\\nt3w+L+tt1NraCo1Gw40o6TNII9GPuqcMBgOf6wMHDnAaThoJlab6S6USMpmMzONvbGzkhnqASFwO\\nh8MVrRH93qGhIbz88ss8iw4QI42RSIRJ9k1NTawPADGFQp46nb9oNIr19XVOeZMO3QpoNM7Ro0f5\\nbKnVaty9e5d5h3a7nQeHA+DIN/GA6Nk6nY73u8VigVarfWjz34eBSO+UjlSpVMhms5wiPnfuHHNf\\naK2oQIi+pj1F+prWzmQyyRpEPgqkUVsac0VfLyws4A9/+APsdjvvh1QqhVQqxfs8lUrJIpykAyKR\\niIzLVwmktJWNjQ1Or1HktFAoQKPR8P4Ih8OIx+MolUqcrbDb7bBYLBzxqTQrIP2+dDoNnU4Hg8HA\\ne0YaYQRE3ls8Hsfi4iLrAkr1UsRbq9UikUiwnszlclUVMHyrsSQIggHABQD6r77/P0ql0n8XBKEL\\nwC8BuADcBPDXpVIpLwiCHsC/ADgIIAzgB6VSaaFiCR8uF7PyTSYTNxWjcKZ0AON2QNq/Q61WM5mx\\no6MD2WwWPp+PU2Hl/I1aGyZSWbRaLXp7e/G9730PgKjM1tbWmAy4c+dOFItFWXpnO4w3mhA/MDCA\\nV199FYDYh0ba2XxmZgb79u3jg0Ydv2ttvKlUKlitVhw6dAhvvPEGAHAvJSKUXrlyBWq1mhtQAqIi\\nqMUgz3IYDAa0tbVx+Lq+vh5+v5/nwAWDQZRKJdhsNjY029raoFar+RKgJqzVdn+mNAoZbgaDgefM\\nASLBnjrjk3NCs7VIqWq1WuahAJV3N1apVKirq+PUUnt7O2KxGPOn6AxJFZ5Op0N3dzeeeuopAOKw\\nY5PJhJWVFa6WrVRBWq1WXn+Px4POzk6sr6+zARKJRLjiBrjfQZuGoh45cgQdHR3QarWc9i7v/7YV\\nkEF7/PhxDAwMwGw286WazWaRy+VYNjKUSAcVi0U2fKXp1I2NDTaCK6mk1Ov12L17N89NpHXx+/3M\\nIWlqaoLJZOILVq1W81wv2s9utxsGg4GdWyoGqWQvUUoduN8HiIyy+fl59PT0MCcGENd1YWGB93xL\\nSwt6e3tljsPs7CwSiURVJGHah1IC9ezsLHw+H6duLRaLrFdXKpWCyWRiQ7SjowP5fB5jY2OyZpGV\\n6CWqngbucxVpTwOiQ9bY2MgFDDTZAbivP/fv3w+tVst6dH5+Xla9vBVIzzfx+ojf2t7ejt27d7Ph\\nMzMzg2w2C4PBwPL29vbimWee4eG7uVwOwWCQ16na4cOP8uZzAE6USqV1QRC0AC4KgvARgP8G4P8q\\nlUq/FATh/wXwvwP4f776M1oqlXYIgvAegP8TwA8qlvAhEARB1mm0VCpx92FA9MKkI0ZqFRV4EPR6\\nPQ/zraurQygUQiwW48NmsVhk5c21GlhJkFZ+2Gw2vPvuu1wVE4vFcPbsWa5e2LlzJ/MvAFFhSjuf\\n10oWs9nMw2qfe+45AOJhO3v2LBtuZrMZBw4cYEVhs9m4iWUtid0ajQadnZ3Yu3cvH3K1Wo3Lly/j\\nd7/7HQBRIdLBp8NHXKJKPbcHQRAEGI1G9Pf3y8itS0tLGB0dBXB/0vj+/ftZWbS0tEAQBNnw2mp5\\nSoB4sVBVDkGj0ci8MeLBUeXJ4cOHcfLkSX5vgUAAn376qSxCUYksBoMB7e3tTAzu7e2Fz+fjsxuN\\nRtk7pMhof38/jhw5ghdeeAGAeP5isRjOnDnDBspWu6vTuvT397MsO3bsgMfjgdfrZU9Wp9PBZDKx\\nkZjNZuFwOHgtu7q6oFarEQqFcPnyZQAiSb2cuPwoRrhKpZJdUNSYk6KgGo0GDQ0NzBPS6XSy7uBU\\n4k3jVgBwI1rpkGo6e4967urr61FXVyfjRZIBS7rYbDbDaDTy/lhfX2f+C+mhrq4uxONxbmOQSqVg\\nt9tl1amPst9VKpWskKNYLEKr1X7t80i5UcViEbFYjLkvJpMJ0WiUjYTR0VF4vV6YzWb+vdTE9lHW\\n6WG6rFgs8p6mdaBKN9q7VIVKBkA8Hse9e/dw584d3neVjDuhdywdgLu8vIwbN27IqskMBgPvqWQy\\niXg8DpvNxhFAlUqFUCjEHEfieErbdDwqSG+k02nMzs7i3r17zDWrr6/H0aNHOfq+sbEBjUYjG0d1\\n4MAB9PT08J0/PT2NK1eusCEnrY6vBN96C5RE0KAa7Vf/lQCcAPAfX/39/wLw5lf//8ZXX+Orfz8p\\nVOpqKlCgQIECBQoU/JHxSDFFQRDUEFNtOwD83wDuAYiVSiUKR6wAoAFNLQCWAaBUKhUEQYhDTNWF\\nyn7nLwD8YivCSm0uo9HI3kwwGERzczN7FYDoHVDUBNi+yJJKpYLL5WIvO5FIIBwOw2q1cuhXOlCU\\nfqZWwzOlIO9y//79bEGPjo5iYmKCuRfpdBr5fF7G5SKuV61TX93d3Th48CCHu2dnZ3Ht2jXmkhw4\\ncICbegJgLkUtI10AuHqKIjmA2EPp0qVLnKoplUpctSQt6a81qNSdUlmA6FFJx4cQ76yvrw/d3Tfo\\n0QAAACAASURBVN0A7qfHqOQ8kUhUFVmSlsTX19fzWXI4HNjY2OD9QSXUg4ODHCHcv38/XC4Xv6OR\\nkRFcvnxZNgqikv4qBoMBdXV1nF6nsTx0hhsbG3n9qBS8s7MTFouFz9/GxgZGR0dx8eJF2TDSSirh\\nALCXXVdXB5PJhL6+PtkoGIfDwWuVTqdlvY9KpRICgQBGR0e5x1A2m/1ateCjVi/Rc3K5HLdQoM9N\\nayflmwDgRriBQAArKysyzk0sFpOV0GcymUdO7dL65HI55HI5jI2NcbNLt9sNk8nEHr/BYIDZbGY9\\nsLy8jHv37mFqaoojhOFwGDqdjqM7+Xyez4N0ePS3rRX9O0VdpqamYLFYZKX4xAOiPR+Px+FyuThq\\nlM/nMTExwft5cnKSZ+1J531WosMLhYIsExAMBjExMcHNRFtaWmCxWLhykJ4pHYdy584dTE5Osi6Q\\nVtJuFaRDiLN04cIFjiQdPHgQLpeLv6a2Aul0mqOTMzMzmJyc5MjS4uJixSNh6N2lUimMjIzgo48+\\nYi6itCIRECNwwWAQNpuNf85sNiObzTI/8Ny5c7h48SLr8WrpHY9kLJVKpSKA/YIgOAD8GsDOip94\\n/3f+DwD/AwAEQdjSJxAEAQ6Hg8uFW1paoNfrZUNsl5aWoNFoZHyeWhoDUuJZfX098xScTicaGxtl\\nZPOVlRXcunWLD3Ct5ZB2Pu3u7obD4eBQpMvlwqlTp/gCamtrk/E5pBdKreQBxI1bX18Ph8PByiGb\\nzcLpdHKaYmhoCF1dXZxTnpub+1p6qZr3Jl2X3t5eGQmZ9hClWfr7+3Hy5EkeLAyIeyiZTNbUsKWu\\n1I2NjXy5FAoFHkYLgLvmvvDCCzID7+rVq8z3qlVvLFpf2h87d+6UzTWMRqPQ6XQYHBzk92Y2m6HV\\nanmO13/913/B7/dX3FpB+v00eBYQ07KNjY18kZRKJSZ+SptQAvedofHxcfzLv/wL5ufnWRdUktIt\\nFAoIBoP48ssvAYhGwdDQENra2viCpwaeZKDQcGypoTI6Oopr167xeUun0xV17t/c3GRj6eLFi3jm\\nmWfgdrvZcKOeZdKBpqurq8z3isfjWF5els1EK5VKsi7ZKpVKVoL+TZAaJTQjkQy3HTt2oFgsyowC\\nIt0DYg88Ko+Xnn2r1cppRWpcSanDR10rav9BBmAoFEIoFGJZkskkPB4PUqkU86XondA6zM/PY3V1\\nlc99OBxmUv5WB7OX92YiGem5wWAQV69eZfmo1Qu9g5WVFczPz/NZ8/l8WF5ehs/n4/1dSYscacED\\nyRSJRHD16lX+XT6fD4cPH+ZUWKlUQiQS4RQZrdXk5CQb5ZX26pL+zObmJqLRKD788EOW8aWXXkJj\\nYyO/V71ez/uN9EUgEMD8/Dyf2ZGREZlTWe29uyW2WqlUigmC8CmAIwAcgiBovooutQJY/erbVgG0\\nAVgRBEEDwA6R6F0zULUDKSm9Xs+WPlnbo6OjFfcz2QrUajUcDgfzkfR6PcxmMwRBYENkcnIS09PT\\nbI1XMzizHFJrmYjMmUyGvQEy4miT5XI57k8BVJbvfhRImwASuru78eMf/1jGK8vn8zyYdmZmhn+u\\n1gYl9VgiPlJHRwfefvtt5kM0NDTAbrcjnU4zd+jWrVuyMRm1ME42NzehVqtlHeg7vxoES6NoTCYT\\nPB4PzGazzEP+7W9/y0qzmoGQJAcgetEqlUrWZK67u5v3D50taaM8Il3/8pe/BCCS46kfDLB1grfU\\nowwGg8zXuHXrFvbu3SurALRYLDxSARAjbNTdHBC9yVu3biEej1fVP2xzcxOrq6tsnCYSCSQSCRw+\\nfFgWzVGpVGzELC8vIxQKcSWh1+vF3NwcvF4ve7v03rZqwJVKJebz6HQ6eL1e7m9F8sZiMY7aBoNB\\nJJNJ5njQQNpisciRGqqGkxKPt6oL4vE4VCoVwuEwv6fLly9zRRMg7mdpk8l0Os0VcdJedIFAgL+H\\nnAEpqfhR16lUKn1ttI10v587d05WaVcsFmEymVgvJRIJ5HI5NowomiLtkF+pjpL+DBl2N27c4CbB\\nH374IYxGI3OYMpmMjFeWyWR4sLW0/1SlukAqD/VYO3/+PABgeHgY7e3t7LDV1dUhGo3C6/WywR2N\\nRrnfGH2m8t+7VVloD66uruLf/u3fAACfffYZHA4HR9p37NjB5HPa436/H7dv35b1XarlpIxHqYZz\\nA9j4ylAyAngRImn7UwDvQKyI+98A/H9f/ciZr76+8tW/ny/VSFqpgqF2+ICovHU6He7cuYMLFy4A\\nAD7//HPE4/HvZDbc4uIiEzhbWlqYYHjp0iUAwAcffIDZ2dmvebq1QHlDwbm5OVy6dAknTpwAAI6m\\nkOH2+eef48yZM1xKW+2l+yB5AFHBZDIZzMzM8MGnEmJah3g8jvPnz+M///M/AYhRAfLyaomNjQ2s\\nrq6iUCjwYbZardz5neSNRqP45JNP8OGHHwIAxsbGkEwmay5PNBrF4uIiR2rq6urgcrlYWdO4kXA4\\njM8++wwAcP78eVy4cEFWmVINpGX+k5OTHPZvbW2FwWCQVb6p1Wpks1merXT27FkMDw9z6D0SidSk\\nsznNNKSRLqlUCvF4nEnubrcbOp0OwWCQDexYLAafz8dl6qS8HzVC8k1IpVJsnK6vr2N1dZW/BsT3\\nlE6nWQ+Rty+dkUfl2eUGdyVrRGd4dHQUKysrGBsb4xmKNBeQ9gd9LX0eGUrSMUTl+2ircpGDqtPp\\nZERxacsNlUrFszDLQeeP0n9SeStNm0hTXeVGaS6XQygUkqX0yqMslJGQGpHVyPOwnyEjJ5vN8p7x\\n+XyybAE5V9K0J/1cufzVRHQJ0uhZLpdDIpHgKl0i20tbvFDbBamhA2DLkdMHyVYqldiJm5ubgyAI\\nHCm9fv06mpqaEIlEWFdRd3w6a+Xjn6rNLj1KZMkD4H99xVtSAfj3Uqn0W0EQxgH8UhCE/wPACID/\\n+dX3/08A/yoIwiyACID3KpZOgQIFChQoUKDgjwyh1umXioTYImeJysuJO9Db24sdO3ZgaWmJh9WG\\nQiFks9lt6SEkBU09J1kOHDiAhoYGFItFLlmcmJiQTbWuJaReiFqtht1uR3d3N5ea9vb2ykpRb926\\nhdnZWfZkak00lxKHGxsbcejQIebm0LBMym+Pj4/j1q1bnBJ8mPdZLbRaLRoaGnDq1Ckuv+7q6kJz\\nczN7UVeuXMGXX36J0dFRLCwsAMC2RLlIHpfLxb2BTp48iaGhIY7uhEIhXLlyBdevX+fUy8LCwrbM\\n8KPeXERk3r9/P/bu3cscpnw+z6klit6EQiGewg7UdnyPdEaU0WiE0+mU9QqKRqNIp9NfK02WRm23\\ni5tIfDOpF13OFyn//+3SPxRtkHrw35aO2U5dKJWlPKqwneuwFbkID5LlQdyib/r+amUqf46US1n+\\nPOnfbdc6Pkimh8kjLVDYDgrHw+QA7pP9pYUJ0uxAhYOgb5ZKpUPfKtPjaCyVv1h6oeWb6rv4bHQY\\npWRGQAwFlyuy7ZaHhiHSnwB4tpaUTyKVZTsPH/FdpAMfAci4AOUybOflUldXJ7uIS6USG43UBO+7\\nkocMJuB+F2QiKtL8sPIU6XbuH1JCNMeLQtuUhiwWizI+wnd1tsp7XG1Xh3cFChT8xeLP11hSoODP\\nGbWu3FSgQIECBQ/FIxlLyiBdBQoUKFCgQIGCb4BiLClQ8CcGJaqkQIECBX9aUIwlBQoUKFCgQIGC\\nb4BiLClQoECBAgV/wnhQVd8fCyTLn4o8hO2WRzGWFChQoECBAgUKvgFbGneiQIECBQoeP5DXLW3F\\n8F21NHkQpH2rCDT5QCrbdwFBEGTjszQaDYrFIstS3qV6u2VRqVQ8ZNvj8cBms/HMv1AohGg0WtMx\\nHt8mj3TQtcfjwb59+/jvpqamuAccsP18S2n0SBAE6HQ67hFnMpmQz+d5FEsmk6mpPH9WxtKDmnwp\\neHT8sdfvYQ3Z/pjy/LGe/SB8U5j5j7k+9P/lMkjHSHyXF9+jNCLczjFI0mer1WpoNBruY0XPpp5V\\nNI9tu8cyUe81l8vFFzH1YKO+Z4lEgqfcb+f7UqlUMBqNsNlsePbZZwGIF10qleIGtX6/H7FYjGey\\nbSe0Wi08Hg83rN2xYwd8Ph/P+JuenkYkEnlgM9RaQxAEmEwmDAwMABAHyLrdbh7vQ3NGyWACtncv\\n07xRQFyXd955B/39/fyeaDYi9cyrZKjvVuUBxKbHNpsNBw8e5Hlx2WwWKysrGBkZASA2qq1l08w/\\nC2NJEAQ4nU688cYb2LNnDwBxXtX58+e5i3YikfhOvRWdTsfT0oeGhtDV1cUK886dO7hx44Zsk9Vq\\nMvLDQJvMZDLB6XTycMSmpiae/k1DOElpkkzbdRhpwC1NjrdarTAajTCbzbwOmUwG4XCY52Jt98Wi\\nUqn4Pen1euj1eu4gnc/nZY0s0+k0z9za7s661NyTZKNO0tJhoDQUlxQ67a3tkEkQBJZFrVZDq9Xy\\nGuj1el4Xkm9jY4NnpW2XPIBoANCekjbalA5kLRQKyOVyPMus1jJJ553V1dXBbrfLOqLTzC1A3N/J\\nZHLbLhtaF61WC4fDgWeffRYejweAGClIp9MsC0UJotGozJjbjvVxu904duwYXnzxRQDiRRcKhdiQ\\no3l/NBB4O2ShtdHr9XjiiSd4kHVDQwOuXbuGeDwOQNRL5XP0qFN0reSRNliuq6vDCy+8AAB4+umn\\nEY1GWRZaC71eL2s0vF3vSa/Xo7+/HwDwxhtv4Pnnn4dKpcKXX34JQHxPhUKBI4SkC7dj0oBareZz\\ndPToUezZswcdHR3caJj2C0UIdTodcrmcbM5eNfizMJa0Wi327t2L999/Hz09PQDEgZrNzc34p3/6\\nJwDiQm5sbGxpgnWloJf605/+FADw3nvvyZT31NQU/vEf/xGXLl3i6enbPRqBlNDx48fx3nvvobm5\\nGQDQ1taGXC6H4eFhnDlzBoDovXi9XlaitQ75Sr2DtrY2nDp1CgDQ09ODw4cPQ6PRsHF09+5d3Lp1\\nC59++ikA8b0mEomav0cKf1utVt5Dvb292LVrFxoaGgCICnJ9fZ3f2cjICNbW1uDz+bj7dq2UBBkA\\n1EnbbrejubkZ9fX1AMDentPpBAAevJvL5XiY6tjYGEcNaiUPICoho9HII34MBgPsdju/1/b2dpaJ\\nkM1m8dlnn3G4vlpZpP+v1+tZQbrdbhiNRjQ2NvLf1dfXy9YlFArB6/Vy5KDaMURSeVQqlUyWQ4cO\\nweVy8SgbukhoKPHq6iru3r3Le6pWoPdFl1hDQwP27NmDQ4cO8WBirVaLjY0NvojVajVyuRyy2Sxf\\nyrU0Bmh/OBwOHD9+HH19ffzvZHDTWQPEtVlfX2ejttZjdchJGxwcxA9+8ANeq9u3b2NlZQV+vx/A\\nfaNBq9WyDOVDWmshDwBYLBa8+OKLeOuttwCIOufjjz/GzMwMAGBtbY33q9SR2g5ZBEGAx+PBSy+9\\nBAB49dVXYbFYMDw8zJEuulel41qkf1a7f6Sf0WKx4M033wQAnD59Gu3t7TCbzXxPBQIBJBIJmQzS\\niHe1zX4VgrcCBQoUKFCgQME34LGOLEkjFL29vejo6GCPW6VSobm5mS1JtVrN89q2O6dKYd3vf//7\\nAICWlhYUi0X2CAwGA1wuF+x2O1vF2zWQkOZ8Pf300wCAv//7v8fu3btlaYtkMonW1lZOza2trSEY\\nDG5LukutVrNHd+DAAbz//vs4duwYADFdYjKZkE6nOSKi1WohCAJHbq5fv848hlqsF+0hnU6H5uZm\\nvPHGG3jllVcAiBEJikoAopcXi8U4QtHV1YUbN27g008/Ze+3FlE48rKdTievzdDQEFpbWzmFQvwJ\\nepbP54NWq4XX6+XUs8/nw+rqatWcj/LB1cQTIGKl0+mETqfjs+d2uxGNRlEqlThqsbCwgKWlJUxM\\nTABAVZFB6bxBh8OBgwcPoqOjA4DomTc0NMBut3MavKGhAX6/n589NzeHQqHA0RxK0VcCacREr9fL\\nBiT39vbC4XDAaDTyefN4PGhvb+dh0sPDwwgEAry/q9VP0oGjRqMRnZ2dAIDnn38era2tvEcAca9a\\nrVbs2LEDAHiGpNfr5chuLUApFLvdDgA4duwYnn76aaTTaVy9ehWAGBXI5/OsG+rr69HY2IhcLsdr\\nU6tB5CQPRftOnTqF1tZW3Lx5EwDw+9//Hvfu3eP3oFKpYDAYoNVqZfMRawWiIwBAf38/3n77bU79\\nX7x4ER9//DHvF4polc9MrKUs0ijXm2++iXfffRcAYDabsbS0hN/85jcYHh4GcH/Y+HbTEPR6PQ4d\\nOoSf/exnAMQ7NZVKoVAocBQ0Go1Cp9MxTYJ+vlZzUB9rY4kW0mg04umnn4bD4ZCF4AqFAm9CtVr9\\nnbxU4imcPHmSlbUgCCgWi7Lcu8VikU1vBmp/AAHR2GhpacGPfvQjAOJh1Gg0bABQdUUoFOK1oj9r\\nrRhI6QwODgIA/uEf/gFDQ0OyqfJ+vx+BQEBGiG1tbeWLemNjo2ZGnFqt5pTJnj178P777+PgwYOs\\n1AVBQCqVYoNWo9HAYrFw6stoNOLevXuw2WwIBoMsXyXGifTSNZlM2LFjB374wx/i6NGjAACbzSbj\\nI1mtVtl+bmxsRKlUgs1mY3mbm5sRiUQquvik4W+TyYSOjg5ODTz33HMwGAxs8Gg0Guh0Ol5LlUoF\\nl8uFbDbLw4ITiQQbV5WC1shoNKKtrQ0A8Prrr+PYsWNsPCUSCVgsFlgsFk49GwwG5PN5vhxDoRBM\\nJhP/TDXySFOlbW1tePPNN3Hy5En+d7/fj1KpxPvZ5XLBaDTyupAxRajWUJKmSltaWthhO378OCKR\\nCCYmJvjsUxUY7eddu3bB6/V+TS9VIw/9qdfr0dXVBUDkm/T392NkZIRTXWQQ0RnQaDRwuVw1MfbL\\nZaJ3tnv3bgDAE088AYPBgEAgAEB0FqV8P5VKBZVKhUKhUHMeIMlDjsaxY8fQ3t7Oz7lz5w78fr/s\\nuVI+Gf1Zy7uD9urg4CDefvttTv17vV588MEHuHz5Mt9lxAWUpt1qJY90Pzc1NeEXv/gFp5CLxSKW\\nlpaYZweIaVu/3y/jcpFMtcBjayxJF9JkMmH37t28oQHRU5mZmeGNtZ2lltKLTqvVorOzE/v27WOj\\nI51Ow+v1smLIZrNIpVLIZDIsby25AVJPxWKx4JlnnmHFoNVqEY/HWTHcuXMHdrsdZrOZlfbKyoqM\\n61KNtytdG6PRiJ6eHvz1X/81AGDfvn0wGo2sKMPhMC5evIhcLscGlVarxcjICBsj1SorkoX4SYcO\\nifMT/+7v/g5DQ0PQaDQcDYnH45icnEQoFAIgRii6urr4cgwEAohGo+xdAahIsROpnIzGffv24ec/\\n/zmOHDnCPIpkMinjkqyvryOfz/O/m0wmZLNZxGIxNo4SiQRfjJXIQ7+3t7cXP/3pT5l0ajKZkEwm\\n+XeT8U3PJf5dKpXiaM7U1BQCgUBF+0j6zgwGAzo7O/Hee+8BAF577TWYTCaO9plMJhiNRiaUA+L5\\nkyr0paUlLC8vV0zwlhqSer2eeTbf+9738O677/J79Pv9MJlMHC0BxOhNPB6XRXaDwWDVVVblHCWX\\ny4Xnn38ep0+fBiBGBchwkzqQTqeT37XT6YTT6axozzxMHkA03FwuFxv+hw8fhiAI/M6A+8UURODV\\naDQwGAzQaDQ119s6nQ4NDQ0sT0tLC8LhMHPYKHJJ702j0SCTyWybs63T6dDb2wsAfMaIEzQzMyPT\\n57Sm+Xx+2wpdyKl599130dHRwZGajz/+GMPDwzLSNO2dWjvW0ogSIJLLn3zySV6HxcVF/OY3v+FK\\nRQCyog3p76hVlbfCWVKgQIECBQoUKPgGPFaRpfIqGAqj79y5EzqdTpbLVavViEQi3KBqu3p1SJ8H\\niFVLx48fh9lslnm2fr+fIxb5fB6RSATJZLJm+VRCuYfZ2dmJ06dP89crKyu4e/cuV1fk83ns27cP\\nmUwGk5OTAMSIitS7rCayRM3nADGVdPjwYe6tolKpMDc3h8uXLwMA7t27h0AggP7+fo7eJJNJ+P1+\\njnRJI1WVygPc57lRPv6JJ56AVqvF6uoqyzM7Owuv18vpm+bmZmi1WpZF2qNG6sU86lpJPW+z2cxc\\nl5/+9Kd45plnoNfrOao1Pj6OxcVFjk5qtVo0NTVxCsXn8yGTyWBpaYkrrdbX1yuKmuh0Ok5hPfnk\\nk3jttdfw0ksvcaogHo/D5/OxLH6/H5lMhiOTKpUKwWAQ6XSa21GMjY0hFApVJA+9M7PZjP7+fhw7\\ndox5ZQ0NDUgmk/ycQCDAbS9IHp1Oh2AwyFydqakphEKhijkw0hS3y+Xi0urDhw+jrq6OI6Wjo6NY\\nXl7G5uYmr11XVxecTidH4ZaWlrjpYbWQVnjZ7Xbs3r2bn7u6uopPPvkEy8vLLL/T6UR7ezt765ub\\nm4hGozVpXyDtsaXRaOB0OvHEE08AEKN/N2/exMcff8zRHEDUDxRZcrlc0Gg0Mr5XrXSkVqvFwMAA\\nR3G0Wi2uXLmCs2fPAhAj3KVSie8Xu93OXMvtaMthsVi44mzPnj2Ix+PMnxoeHkYikeD9UV55Cdxf\\n61qsj1qt5nYO3//+92GxWPjcjI2NYXp6WtZvqhzf1t/sUUERUOJo/uQnP4HNZuP7/MKFCxgfH8f8\\n/Dz/DHFeCcViUcYVrvaMPVbGkhRSxdDS0gKtVotCocAbPJPJYHFxUUagrvXzpSCF7nA40NXVhVKp\\nhOXlZf53UkKAmPudnZ2VhXVrRVYuT8M1NzfD7XZz2u2LL77AwsICH7S+vj7YbDYMDw/zxbyxsVET\\nMiXJI+1343Q6+V0sLCzgww8/ZMWwubmJtrY29PX18fpms1nmfpBsQGUGXHl6qb29nUnt+XwegUAA\\nH330ES5dugRANNTMZjMTYLVarSz1tbq6isXFRcTj8Yr6ZElD2Q0NDXyZ7N27F4IgIBQK4fPPPwcA\\n5glQGs7tdiMej3PKx+PxYGlpCWNjY2z0JpPJLSsIel/EL3nllVe4twoZAWNjY7hw4QKHv+fm5mA2\\nm2Upb7VajXw+z0ZMKBRCNput6BzSO2tubsarr76K559/no3pdDqNqakpbi3h9Xrh8/nQ3NzMP5fN\\nZhGPxzmVu7q6KkuBb3V96L2ZzWYcPHiQU7kWiwXxeJxTKFevXsXKygry+Tw3Gdzc3MTs7CzrJSpY\\nqAWkJPyhoSE4HA7WQZcuXcLY2BiCwSDvX7vdjlQqxRdOOBzG8PBwzdpySGkSBw4cgNlsBiCW5n/6\\n6adYWlpih1Kj0aC+vp71dzqdRiQSQTAYrHnbEqvViuPHj/PFevPmTZw9e5b3N/VQon8no5v6qkk/\\nWy3k6enpwQ9/+EMAIr/u0qVLMgK19PNTwQu1eQBq24ndaDQyN5GKW86dOwdAdDIKhQLzf4H73c4J\\nlB6stm8gOW3PPPMMANEpKhaLWFxcBCAS39fW1lAoFNgOcLvd6Ozs5HN+9+5dWSFQtWnUx8pYkla2\\nEX8BECNLLpdL1gdjfX0dXq+X863bxVciC5iME4PBALPZzFVmgPiSzGYze1Hj4+MIh8M1b0BHHoZW\\nq+W1EQQB8XicnxMMBvmAAmJPnGQyibm5OZlBIt1k1cpIStJmsyGbzfJzlpeX+TIFRILsk08+ib6+\\nPjbupqensby8zN5NuWxbgTQq1djYCKfTyfsjEAjg1q1bGBsbY/msVisGBgaYsGs0GrG+vs4X0Ojo\\nKNbW1pDL5SrmBgFilGtwcFBmuEUiEXz66af49a9/DUCMulFjQUA0ylOpFF8uCwsLWFtbw+LiIkct\\npJHLrchkNpv5cu/s7MTm5iaSySQbtWfPnpURhePxOAwGAytNvV6PTCYj439RpVUla0SX1d69e9HW\\n1gaj0Sjbz/Pz8/ycSCSCUCgkU6JqtRo+n48v5mw2W1U1JRlhFosFWq2WL9V0Oo25uTlW6PSZU6kU\\nG5YGgwFqtZr3WCQSqYpPKY1o/v/svWlwXOd1Nvjc2/u+oYHG0lgIECTBXRJXSaRM2ZJpSbSlWJYl\\nW5XIcTlJJVHKVTOZpCo1VfNjfkzVVH5M1dR8lT/fTFJxYsWWrc2ySFGiJO4CFxALsRArsTa60fuO\\n7p4fl+fwXpCiiV5o0999qlwWQKDvwXvf5bznPOc5cmFQURSxsLCAixcvApD2nLtxxoLBIDsJCwsL\\nmJmZqcpFSb7W6IClNXzx4kVcuXIFyWSS7Tcajairq2P74vE4YrEYEokE7+nViJ7QhYkqNgHJqZ2e\\nnubn6PV66HQ65u54vV5ks1mk02kFL68a9uj1enR1dfGzYrEYvvjiCywuLgKQ/mar1crnC7X0yOVy\\n/D2az5VG4DQaDfbs2cNK5qIo4ubNmzyHwuEwdDodHA4Hry2TyYRwOMwR9nQ6jUwmoygiKCeaLIoi\\nfD4fXnjhBQDSnhKNRvHBBx8AkM4FeibtD21tbejp6eHok9VqxfDwMFcS5vP5it7ZQ+UsEYrFomIx\\nhsNhfnk04dcqdldb9HGt0BV9TZPe5XLxQabRaBCJRDA+Pg5AEn2Uq/ZWG/Jbj8PhQC6X4+qhRx99\\nFM3NzVzxlUwm8cEHH/BmCtypTluRN67V8jjodDro9Xp+jsvlwrZt2xSl1j6fD6FQCNeuXQMAXLhw\\nAWNjY3wTL9dZovlCB5vT6YROp+PPnZycRDqdhtfr5eqP9vZ2PPnkk5ySCofDuHjxIqcwT506hVgs\\npiB4r8cemrMNDQ1oaGhgB3dxcRGBQADXr19nhxuQnDd6j263G/l8ng/dXC6HS5cuKarf1hNVkleW\\ntre3Y/PmzQCkd5TL5RAMBtkJyGQyfAACYLvJ8aSoDR0wQHnK1DRGlGqktKPValV8ltFoZCeSFLLz\\n+TyPnSiKCoel3AgX3ehpPttsNhgMBp4vxWIR0WiU17XVaoXf70coFGJHMR6PK8r3A4FAgvgGFQAA\\nIABJREFURc6/nGxO6VxAWmszMzMsI0GXEqfTyWMld9oA6Sa+tLRU9g18LU2CDnO6PNKhRb3EtFot\\nr8fm5mY0NjZylC2Xy2FkZASRSKQqUgoEnU4Hv98PnU7HY0LvhPZEvV4Pn8/HxP18Ps9CuNUUMBUE\\nAV6vF8899xzPx+npaQSDQd4bWltb4Xa7+VyhNZ9MJnkfpTVWjrMkd7bNZjOef/55flY2m8XFixfZ\\nmTabzWhvb0dbWxvP+Vwuh6WlJR7L8fFxZLNZPpvXM5fWVk8ePHiQC5MKhQKGhoY4ak6V7g6Hgy+Z\\nVGm6a9cuAFIkulgscqQpnU5XJEytErxVqFChQoUKFSrugYcysiQvTQagIAFSaC4ejyMUCtW8QWWp\\nVGIiGSDd4Egan25wRIgjbsDIyAhHI6pJXiSNCznnKBAIwGq18k3A4/Eo9Hpu3ryJc+fOMdEUQEUp\\nirX2yEnQiUQCgUCAezA1NDSgo6ODw6jUfuGzzz7j0O+1a9fuaGBZDdsoAkHRhsceewz19fXo7u5m\\nHpDFYoHD4eC02+nTp/Hhhx8ygZp4aOXKBRB3pKGhAR6Ph79OJpNwuVzo7OzkqNbS0hI3H6WfiUaj\\nHDkgvaxy7ZGTqDs7OzlNazabUSqV+LYN3E6ZEEKhEIaGhjj0Tu9bfosrVzLAYDAwSVmv18PhcKBY\\nLHL0plAooLW1le0vFouYnp7G0NAQRwCXl5eRy+X4Bl5J5ESn03H0xmw2w+l0cgQlFoshnU4zSdlq\\ntcJgMCCZTHJqIBaLYWpqitMsmUymKrIcRqMRNpuNW80Qp40iN36/H6urq2hubub3SKkNogeQRk01\\nUoJyDbOenh5s27aNo0bEP4nH42xLR0cHGhsbOeK2uLh4h/hsJdEuOaf0wIED6Orq4qhyW1sbisUi\\nnydGoxGNjY1c2DE7O8vcITkXp9LoG6Xft23bxp8biUTgcrnwyCOPAJAyA06nk/cGQRA4XUp7VyVc\\nHPn4bNq0CU8//TSPVSgUwo0bN1gTbNOmTdiyZQvq6uo48iWKIqefAemsk0cDy7GLuHeHDh3i/S6T\\nyeDatWv8jux2OzZs2IC2tjY+Zx0OB+x2O7fyamlpwdLSEjfWXVlZ+R+HsySH/I9uaGjg7t60cQGo\\nSbXZV4EOqHw+D5fLBZvNxrbo9XqMj48z8TOZTNasOzOlBeW6F16vlyeQRqNBJpNBf38/AOC9997D\\n4OCggqRMv1ct0IGZzWZhNptZrLOjo0MhkDk8PIzjx4/j7bff5sVHfI5KnV55ygKQ5ob8MPF6vWho\\naFDYMzExgXPnzuHUqVMAgJMnT2JxcVFRXVHJJkWbklarRSaT4QO0s7MTDQ0N2LBhA6d8AoEAzp07\\nx6ncmzdvYnh4mMPf2Wy2on5V8kPXaDTy30gpboPBwE2qdTodtm/fjqGhIQDSOMhFRGluV9o/i9Tn\\nacPUarUIBAKKFCAJvFKfsUgkwlwpSjHNzc0hl8tVpAVD78tkMnG6hoRC6YDXarXQaDR8uFCTT1EU\\nOWXS19eH/v5+vpiU65yQPXRo2e12NDY2YsuWLQAkwr/VamXHjbgv8pQOpVno0I1Go2WnKOTzWafT\\nwWq1cpXgSy+9hF27dvH8NhqNXHEm7y9otVr5Z8LhMOLxeNnzR56eJDFZADhw4ABeeuklFnEFpAo0\\nt9vNTj6llSm9E4vFoNfrK9Y2IueWzgWn04nHH3+cm5kDt50Weo7NZlOI3jY3N8NqtUKv1/N5Uu4Z\\nJ3e2rVYrenp64PF4+HPC4TCKxaKiV2ZzczNMJhM7mvT+aE8fHh7mS8p6Ia8ybW1tRWdnJzuJJFZM\\n8Pv96OjogN/v53ebSCSQzWZ5DtfV1XH/OOD2HlcuHlpnCbjND9q4cSMz9GnCU8VJrZ0kAr2I7du3\\no7m5GVqtlic8ecUjIyMAqt9ZXA668dDGtWPHDjQ3Nyu6QqfTaY7cfPHFF3eULler0uNufK69e/dy\\nOajBYGB7AImf9Mtf/hITExMKyfpqQr4Jbdu2DXv37gUgcYDMZjNWV1cV9rz11lu8+GmDr4YDTqrA\\nAJiMTFGB+vp6mM1muN1ufi9OpxNGo5HVagcHBxWOW6W3S7madTQa5b95ZWUFRqMRXq+X1xuV5dN7\\njEajaGtr44hbJY7S2upJciQBiVcWi8W4ogm4HaGjg42qUfP5PHOsSqWSgqe0nnGig81kMsFms8Hh\\ncDCHSqfTIZFI8FgROZ4Otq1bt6K+vh42m40jYeFwGCsrK8wDKcfhpsiHnBhM84WcJZ/Ph3A4zPMn\\nnU6jq6sLPp+PHSwqZqBDqNzWFcT3oSgRKb7v2LEDgFSZZ7PZ+D2mUimIogin08kcPL1ej8XFRX42\\nCapWEpEEpPcmiiLab7V92bdvH3w+H4rFoiISqtFo+FD1er2sAg9Ih64gCBU5tsDtNkZyUvuGDRtY\\n9BIAE7XlVd1y6Qm32w2j0Yh4PH4H/6mcYg45n5TaFtFcDQQCcDqdfFmx2WwIh8O4efOmgm9bX1/P\\n68Tj8UCj0az7DJGve6vVis7OTrhcLl6zqVQKGo2G2xq53W7Y7XYEg0Fef6lUSnEZ8Hq9d6zXShqL\\nq5wlFSpUqFChQoWKe+ChjSyVSiUOlzqdToiiiEKhwLeFsbGxmlWb3Q3kWW/cuBE2mw2CIPCtLh6P\\nY3BwUFF2WgtQWJV0ewCpgabFYuFn5vN5LC8v4/LlywDAZdZyVMs+uvXTreixxx7jPkz0nHw+zxGJ\\ns2fP4ubNmxXxOO4FnU7Ht6SOjg488cQTPE4U5Uomk8xRunz5Mm7cuKHgBpDdlYBugfKohbyChNLJ\\n6XSaK7qi0Sji8ThHJILBIHK5XMVRrruJfFLVHyCNi9PpxObNmxW9A10uF3+9a9cuBINBRSqukvJl\\nioLSONA6Gh0d5Rsr8UkSiQTsdjtrYe3YsQPd3d0wGAysUQWUF3kTBIFTbl6vF263Gzabjecvcd5o\\nzzGZTIq1F41G4ff7WVwRkG6/9xL1uxfoPdXX12Pv3r3Yt28fR1Ci0ShsNhtHdzweD9xuN8/dRCIB\\nl8sFk8nE85h4XHKO2XojAhTla2lp4apWSstS5JEkFmjtBYNBTtXJaRPyiCFlCsqJUBgMBh7v+vp6\\nZLNZxdc6nU7RsiOVSnEEB7itZUSRpYaGBtjtdk6zAuvT7ZO365FnHCgSKT+3NBoNkskk87ui0ShH\\n4QApvWq32+HxeDgStlbr6H5tklcsms1mTk3SuanVauF0Onl+TExMYH5+HtFoVBGR8vl8CoFMg8FQ\\nUXaC0sd2u11xhsr3qkgkgmAwiEAgwHskcTqJWlEsFhU8pf8hRSlpQVAulcKq8gksD+vVEhTypXTO\\nyy+/zH265KRkeZqjVs4SaU81NDTgBz/4AQAweVDeuVvOT6qE0Pm7bCGyHY3N97//fYV6MRHjyVmK\\nxWI1c3CNRiO2bNmC3bt3A5B6im3cuJHfy/LyMjuNVJ66uLhYUdj2qyCKIrxeL5M4SRmcCgDi8ThW\\nV1dRLBb58KMUiZzbUC0YjUbmHHi9XqyurjI3KplMwmg0YmxsjJ+5bds2bN68WVHWazKZqtIfymg0\\nsuPT3t4OrVbL5Ojx8XEkEgkFB2h1dRWtra2cZqHefSsrK8xZonTOeospNBoN7zFPPvkkGhsbIYoi\\np/eGhoYQiUT40CIhThonutCRdAEgzbN0On2H9Mj9gA627u5u7N+/H3v37uVnXb9+HaFQiLkkRMCX\\nS6pkMhnkcjnel6gYYG1j1vWA9uINGzbwMzOZDObm5jAwMABActzq6ur4OdTXcHl5mR1PSnNSykTe\\nE26980me2kwmk8jlcvzOPv/8cy4IIJ0lKo6g9U88OHKwvF4vDAaDInVeLuT7bSQSwblz59DZ2ano\\n5xgMBnnOJ5NJLiQAJKeGGkPLNbXWi7UOhCiKsFqtivQkcWsprby0tIR4PM49BYHblAHSOVurH1iO\\njEE+n4fNZuN1BYAFeel8iEQiCAQCigbddXV1aGxsVMgazMzM8D5QaeHSQ+UsyTc7rVbLzQf1ej3L\\nmsuF52rV+FBuCzlL1MKjrq4OmUyGSYGAtBhXVlaq3jSXIL+52O12PPXUUzh69CjbOTc3xyTUlpYW\\nzM7O8gSq1RgZjUb4fD4888wzLCzW1taGeDzOB/Hq6iq6urrYkatVs0q67T/77LN4/vnnAUgbeCKR\\nYIL02NgYHA4Huru7+TAxGAzQaDRVt8dkMqGnp4e1jARBUGjiEEfB5/Pxhk1VYTSn1t66K4nk+Hw+\\nVqEulUoYHx9XNKekprnEqbLb7WhpaeHoTiAQqIpjKYoiPB4PC2Ju3ryZK9mA21EwudPo9Xqxa9cu\\nHD58GIDkLAmCgMnJSdy4cQNA+er9RqORnSWPx4OtW7cilUqxA0IVlXSTJZ4LRaNaWlpgtVohiiLf\\nfuWaS8D63htFOrZu3Qqfzwej0aiIaskPcypoIGckFAohnU6z1hkAtlvuPK13HlEDaHmUIp1Oo1Ao\\n8IG/uLgIp9OpIIBHIhGsrq7yQed0OlEoFDiiUkkRDHHUyL5CocDjMjU1hZmZGfj9fh4Ho9GoqNo1\\nm83YsGEDj93Q0BA3Fy9nLn1VZKNUKmF+fh6RSITXMhVT0LMLhQI8Hg+r+7vdbszNzeHy5cs8vuVG\\nTeWFM6RLlsvleKxcLhdCoRBfZu12O/R6PQwGAytr9/T0IJvNchT36tWrZY3T2khULBZDJBLhPcbv\\n96OhoYHnKuleWa1WvlDu3r0bBw4c4HUyPj6O6elp3svkDmY5UDlLKlSoUKFChQoV98BDFVmSe4Va\\nrZaZ8QS65QC3w51yJdFqYW0zw/r6eva0qawyk8nwzYUaMMrDptWyZ23J7pYtW/DjH/+Yve35+Xlc\\nvXqV001025WnCqppD32ux+NBV1cXnn76aU6RRCIRfPzxx3xT2blzJzZu3Mg3cbqZEv+sWjCZTNi/\\nfz8eeeQR5lGEw2G8++67HM3R6/UcnaCxonLzctRovwqUgtu+fbuimmxlZQWjo6MApNtkc3Mzurq6\\nFIrMmUyGb0nVksTQ6XTYsGEDp9So/JZSFJQecLlc3AZh586dClmD/v5+DAwMcASoEh0janMASJWl\\n8/Pz/Jx8Po90Oq3QMtq2bRt2797NVWCCIGBiYgIfffSRIvVcji0Oh4Nv+O3t7aivr0cymVSsa4/H\\no+hTp9frFZpVsVgM4XCYmzMvLS2VVeUlCAJH9vx+Pzo7O+F0OjmyRKlQSo8YjUaEw2GOPqysrCCd\\nTit6G547dw7Xr1+vqH+myWSCTqeDTqdT7EMOh4OfQ0rnVPG1srLCLTJoLygWixgeHmau3PLyMu9t\\n6+EK0t4sl3KRRyKTySRWV1dhNpt5XhF3it4jlc+TLefPn8fU1JSCM7NeVeq7/Q5xalKpFEdQmpub\\nEY/HeVxIpoPm+/T0NE6dOoX+/n5eb+VQKaiql8YllUphbGwMy8vLnMYifpe8nY/ZbEZTUxPbk8vl\\n0Nvbi9OnTwMAv9f1ziV5lCuVSmF2dhaBQID5fz6fD3v37uWfWVpagkajQXNzM9MZDhw4AI/Hw1Hc\\nixcvor+/n/fMSnsePlTOklz0zG63Mxkwk8nAYDDw5AOU7UiqDbkTptFo0NPTw+mSRCLBeWZKCV65\\nckXRcb0WNgG3uSfUABGQdF1mZma4jDeVSmFyclLRvLOa9sjHhUh6tEn29/ejv7+f0xhWqxXpdJpT\\nYel0+g49pEogL9G1Wq1wOp28kL744gt8+eWX7BRQGba8RUc1HTZ5+bDb7eZGkQAUgomAlMolJ4Dm\\neCwW4wMPqFyck96TXq+H3W5XNKtNpVJsbzQahcfjwaFDh7Bz504AtwmwlNo9e/YsZmdnK3a4iXRK\\na8nhcMDlcrHztHPnTuj1epRKJXY0LRYLfw+Q0iy/+MUvcPbs2YrTgtQQGACnJ6isHJBaUTidTn52\\nNBpFJBLhtXXz5k3EYjEMDg6it7cXgLRXlXs5IccnEolgbGwMhUKBD1mLxQKNRsMkaiIxUypyfn4e\\nCwsLzN0BJMJuIpG4Y72th0dFzuvExIRCP0jeu4x6rclThESZkLd9mZyc5JYoQHkcE1oXNL7ZbFZR\\nrp9MJrk/Jo3dpk2bYDab+WdcLhcmJiaYc3Xjxg0kk0nuwbaeMVrLlSsWizwugiBgenoavb29+MY3\\nvgHgNoGb5pBWq4UgCHyunTt3DpcuXcLCwgL/DKXky9kP5PI2Q0NDOHnyJL7+9a8DkJxGq9XKfEb6\\nW+RFAQMDAzh+/DiPlVyuolxbkskkent7cebMGdYHtFqt2Lx5Mzu4JM+zadMmPk9sNhvzwADgs88+\\nw+TkZFV6HQIPmbNEEAQBbW1tzCewWCwQBEHRB4bIsrV6vrwJKoljAdLBTP3YiPQ2Pj6OycnJqhBg\\n7wba0KnxK92QAGnCt7a28iZqNBoxPz/Pej3V1nwiW3w+H3w+n0Kc02g0oqWlhXv3tLe3w2g08jjN\\nzc1V1R45D2Dnzp0cwQKk3P/GjRv5dvbEE0+go6ODFx4A1jGS21PupiR3lIvFItrb2zkaQtopJMhn\\nt9vx5JNPoq2tTeG0DA8Pc7Sk0vw7oVAoIBAIsIOydetWbNiwgR39aDTKauLkJFCRwPvvvw9A2sDl\\n/fFoUy3noEun07x25+fnsWvXLo6W6PV6dqjolkhcRbqJ/+Y3v8GJEycQDAYVBRbrRalUQiqVYieH\\nnOgnn3zyDjFRufry8vIyH2JjY2MYGxvDwMAAE9LX3rrXc+jSXP3yyy8xOjoKnU6HgwcPAgATyWn+\\nZrNZhEIhxXMpMknzbGlpCZFIhMeJ5tp6K72i0SjS6TQ7OkajEYIg8EFns9kwMzPDeyZVTrpcLr68\\nTE9Pc180QDowy41Qyvl8q6urPEfo66tXr0Kj0bCQaT6fVxQojI2N4erVq+xo3rx5kzlWlXCW6L9p\\n7lJT7i+++IL3yMcffxwGg4F/JhKJYHp6GmfOnAEgdX+YnJxUvLdybZLPvVwuh+HhYbz99ts8f/ft\\n24f6+nreN1dXVxEOhzE5OYmrV68CkJyla9eu8aWzEp4Zgfrfvf3223xZPHToEAwGA0ea6urquJiC\\n7F1eXkZfXx9++9vfApC4ZtWsrH4onSWdTsfiV8Btjz0ajeL8+fMAwB22a9HuRD74VK4ov0WRbACR\\nqPv6+lguf+3vVwPyKg6KsNEk27FjhyKVNDMzg97eXoWMQTXtkTcyzufzHNUBJOmALVu2sDMniiIm\\nJydx4cIF/p1qvjP6u7RaLY8R2bJnzx5s2rSJf4bkJ65fv85zKBgMKkL41RinYrHIlTjkJDY2NqKj\\no4M/n6JPpVKJVbI/+ugjXLlyhW90lVYx0hhTexxKAep0OrS3t6OjowMA+MZdLBZ5Q7x+/Tr6+vpw\\n/PhxAOWH3teC0gLXr18HIB3eyWSSo0gulwsWiwWpVIojJJFIBEtLS+ws9fb2IhQKcasM+pxykMvl\\nOJpz4cIFJp3S/KWDgd4jpVTkDsri4iL/Hv2O3Kb1vEMa/7GxMY7+Xbp0CYDkoMgVvcnZpXWfTqdh\\ntVqRzWZ5DlVSmScfI51Op/gcmgcUDVlYWFB0VyiVSlxpSM7S8vIy5ubm7tpyaT02yaP+BHmldDKZ\\nxKeffoorV65wZIna6JBTcPPmTSwuLvK7pwIH+V5ZbppZ/rvUYuXcuXPsmH3++eectgQkp3dycpId\\n3FQqpbBH/ndXArJlYGCApVM++OADbNiwQSGvsrS0hOnpaXaM4/G4Qgqjkj1APi7ZbBYDAwP453/+\\nZwBSq6n2W018AamQQ6/XY3Z2VtGgvr+/n9v3lNPg/F5QCd4qVKhQoUKFChX3wEMZWVpdXcXw8DDf\\nJtva2pDL5fDOO+9wCI7C8LWSDpCHL69fv843g+7ubi4Ff+uttwBIocpkMlmzNJzcltHRUUxOTioi\\nA8lkklut/PznP0d/fz/fPKvVNBdQpieJSBmLxTgFaLfb4XA4+JZ97do1/Od//idHlkhfqNrjk06n\\nMT09jUOHDrF9brdbof0SjUZx+vRpvPvuu5x6CYfDVUt3EYrFIiYnJzE8PMzvqL29HV6vV0FkXllZ\\nwaeffsoluX19fVhaWuKoQLVsyufzGBsb4xsYpbjopkgpsOnpaSYpX79+HVNTUxw5rUZUCZDmIpUE\\nA1JkIhqNssYMjU8sFuPIUiqVQiaT4YgEEdSr0edwdXWVoyNGoxHnz5/H9PS0Yr3JBQRJ1JHmFKWA\\n7hadLMcmSsMtLS1xtI8iMxRRkROb7xZlkT+bbJNHPNZrV7FY5PYlayMdxK9bWFi4Y10LgoAbN24o\\n0mVrtfLktq4H1DKEniMH7UmRSISpCJTalf8svTuyoZL39lW/Q1H0VCrF0Zz5+XmFHfR+1vajrEZG\\n4G6RKXlxRyQSwfj4uIKHSmKeciHTSsfnbqBm2cRpO3nyJGw2GxeZuN1uGAwGzM3NcYHCyMgIlpeX\\nq9IC6m4QauVMrMsIQViXEVqtFjqdjsOo3d3dMJvNmJiY4ElXSRPG+4G86V9dXR03z9y2bRvMZjMm\\nJye5Gezi4mLZfY7uxw45N6ehoQFbt27lA6ahoQHxeJw7L8/NzSlUu2vFnzKbzejp6cHBgwe5iofE\\nOinNcu7cOczNzXEao1YCmUajEX6/Hy+99BK2bt0KABxaJif38uXLOHv2LJaXl3nx1coemjOkzfXo\\no4+iu7ubx+7y5cu4cuUK+vr6eGzIkawFtFoth/0bGhrQ0tLC8yebzbIAnDyVRFo6tQCNAykcExGe\\ntHDWckfkHKZa7WfySi8CCTqufWY53J/1Qk6WJtzr0CqXa3e/WNunTI5qp/rXg7sVjJA91dApK9cm\\n+X/fixNZLv+vXJvuRvaXYy2ZvFZ2yXUM6WtRFJlbSbpe8osRNRMvw6ZLpVLpsd9p08PoLN36nbt6\\n4IQH9XeRHfLJRtyLB22P/IYkt0eu0lsOV6IckLKvRqNhrgLZIb8lyVFLm7RaLdxuN9tCbQWoajCX\\ny9X8wF1rDzkkBoMBDodDQW7N5/OKKo5a2ySvjiP7ACgqymoVGf1ddq0lx/++DjkVKlT8UeK+nCWV\\ns6RChQoVKlSoUHEPPLSRJRUqVKhQoUKFigqhRpZUqFChQoUKFSoqheosqVChQoUKFSpU3AOqs6RC\\nhQoVKlSouG9Usy3VwwLVWVKhQoUKFSpUqLgHHkpRShUqVKhQsX6sFamsZsuM9YAiE3IpD0EQWMqj\\nWsKL67FH3mNT3l8zHA4jn89XReh0PfYAkoRHXV2dopnt5OQkYrGYQnrlQYwTjVFzczP27NkDQJJc\\nuXr1KrdjqaUQ9FfZRO/KbrejUCiwSKy8BU81oDpLKhh/iPo1D0oX6nc9//dpgxx/aPYAtw++e9nz\\nIA89AHcoSsv/rVY9I+8GURSh0Wj4ICbdM7ltcoXvWoHeEfWyBKS+liT0CUiiftlsFrlcrubjI4oi\\n9Ho9N7Pdvn0794oEpP5ssVgMiUSioobI9wuNRoOGhgYAwMsvv4y2tja25cKFC5ienuaebEB1Ox+s\\nhdyJ7OnpwV//9V+jpaUFAHD+/Hl8+umnGBoaYvV2uR5brewBJEHW7u5uvPHGG9i3bx8AqUm8VqvF\\n559/DkBS/a712ADS/BFFER6PhxuS+3w+rK6u4uLFiwBuK8dXay7/0ThLGo0GPT09ePHFFwEAra2t\\nGBkZwdtvvw1AatdQy5e4FiTKCAB+vx+HDh1Ce3s7AKmZ7cWLFzE9Pa1oHAk8GPFBo9HI6uednZ1w\\nu93I5XLcHHFxcRErKytsUy03cvmNzmAwwGazoa6ujseuUCgomlo+iIOFbt+kbE3Nd4vFIqvGAlAo\\nfdf6cKFxkotHlkolVpQulUrIZrMQRZHFNak9Rq3sIVtEUYROp+MxoCiBXECyWCxy+4ta2UO2aDQa\\nHh/6N7IJuN35nW6g1V5zckFYg8EAo9HIDko+n1cosYuiiEwmw++q2rdhuU1arRZer5cPl/r6ekSj\\nUczPzwOQDrpEIoFoNMr21ErFXhRFuFwufP3rXwcA7Ny5E+FwmKMEgiBgcXERoijyOqP2LLV4X1ar\\nFc8++ywA4NVXX0UqleJxsVgssFgsCqeEutnXYt0LgsAdIV577TU888wz3Bx2ZWWFmxfLG8mX23j4\\nfkCOW1tbG15//XU8++yzPD+oaTX9DF1SaFyq2m5EdlY4HA709PTgyJEj3JVheXkZo6OjfLZFo1Ek\\nEgnFHlSJPSpnSYUKFSpUqFCh4h74o4gsUXj52LFj+Ku/+iv+/oEDB7gR5i9/+UtuI/EgoNFo0NTU\\nBAD4p3/6Jxw4cIBvC8vLy/jXf/1X/Md//IeiMWotc/QUBbBYLHjuuefw9NNPAwAOHz4MQRAQCAS4\\naWtvby96e3t57JLJZM1uuzqdjiNuO3bswP79+9He3s5jlc/ncebMGY4QBoNBhEKhmkVNNBoNP7u1\\ntRXt7e3clsRiscDr9XK/tvPnzyMSiWB5eRmBQABA9fuB0Xuj9AlFksxmM7RaLdra2gBIDWWpASZF\\nLkZGRmoy3ymFQn2aRFGEwWDgOeJyuTgCSDdOapxai/dG9gDSOOn1epjNZv53o9GIUqnEz6abOH1d\\n7TGid2Y0GuHxeOBwOPi2m8/nFbyKbDaLaDTKY0cRi2phLfdlx44d8Pv9AMDjRM/L5/PI5XIQRbHm\\nfe0cDgcef/xxnr+iKCIajfJ7s9lsWFhY4HQL2VeLfUir1eKpp57Ca6+9BgDcZ3R8fJyfK+9XSP9f\\ni16NgiDAbDbj1VdfBQC88sorMBgM3PO0t7eXe3vSu9VoNHdtQlwN0LwBgL/4i7/Aq6++Cq1Wi9HR\\nUQC3U5TytkjyFHi15zLtzfv27cOrr76Kffv28R4zMzOD6elpnkPUaqta7+mPxlmyWq3YvXs3bDYb\\ngNtpCUrZUBj+QTQlFEURVqsV3/zmNwEAR44cgdPp5Jdqt9tRV1cHh8PBndtrtfgHcfYvAAAgAElE\\nQVTos2kCHT58GD/96U/R2dkJQNoYUqkUSqUSb1xXr16FRqOpqT2AlHbbtGkTfvKTnwCQnNvGxkZk\\ns1l2CrLZLGKxGG9c586dQzQarXrqi9I3TU1N+M53vgMA2Lt3Lzo7OzntZjKZIIoid8JuamrCwMAA\\nvvjiC3agqnHY0TyVO27bt29Ha2srH7parRZ6vR4ulwuAtFFQF/q5uTkAEjF1cXGx4rSl3B5Amr8N\\nDQ3sRLrdbmg0Gp5jnZ2d3HSXHJJIJIJUKsWp3krenbxsWaPRwGaz8Tg4nU60tLTAYDDAYrEAADo6\\nOhCNRtn5j8fjmJmZYc5HJBKp6J3J024ajYZTbvX19XC5XGhubua1b7FY0NnZyWnl6elpTExMYGpq\\niv+easxrSpPSOmpoaEBPTw/MZjOntZLJJIrFItxuN4DbaS7692pCPn8sFgueeOIJbN++ndf1559/\\nznMDAKe+a1WeLucF+f1+/PCHP+Rx+PDDD/Hee+9xI/RSqcS0AEKtUrdGoxF79+7Fj3/8Y/763Llz\\n+Jd/+RcAwMTEBHK53B0X61o42KIowuv14pVXXgEAHDt2DCaTCXNzc/jggw8AAMPDwwgEAndcOKpl\\nj3xtWSwWHD16FADwxhtvoKWlBRaLhR01nU4Hu91es4v0Q+0syV9qe3s7Dhw4wJvD6uoq8vk83+DI\\n834QVQwajQY+nw8vv/wyACm/KneG5DcVQq2iSrQpEEHw+9//Ptrb23nxp9NpxONxJJNJBX9qdXW1\\nJs4SRSEAoL29HT/4wQ9w5MgRANI4JZNJRCIRPngFQUB9fT0cDgcAMPm0WmMld9xaWlrw8ssv41vf\\n+hYAoK6uDul0mjcCqrwgUqrBYMDCwgLsdjtHNnK5XMXOCUVKGhsbeXPYs2cPmpubFbbodDq2v7Oz\\nE6lUCsvLyzxWqVSKOSjVsMfn8wEAvva1r6G9vZ0vJgaDATqdjh0Wl8uFXC6HXC7Hz56enkYsFmMe\\nSCUbmjyS5PP5cOjQIa4WIidJFEU0NjYCkJzaXC7Hz56ZmYFGo+FoIDl25YL2IZpDzzzzDADA6/Ui\\nl8tBq9Xy3+v1etHd3c0OicFgQCKR4MhBNQ8ZvV6PzZs3A5C4L06nE9PT0/z30j5ETm+pVEIsFmN+\\nTLXtoXX/xBNP4M0330Qul8N7770HQHJg0+m0Ys+pJWeSIjgA8J3vfAd79uzhRtZ9fX2Ym5vjtZbP\\n55HJZJDJZNi+WkWQXS4XXn/9dV5rwWAQv/rVrziSI9+P5MT3Wpwder0eTz31FDtuHo8H4XAYP//5\\nz/HRRx8BkIj4cp4dcaeqbY9er8euXbvYcdu4cSNyuRwWFxd5Pq+srCAQCCCZTAK4XUxRLf6UyllS\\noUKFChUqVKi4Bx7qyBJBFEXs27cPdrudvch0Oo3x8XG+PWYymZrm4ddWwBw6dIhvdaVSiSM4AJBI\\nJBCLxZDNZmtWFiuPujmdTo5QPPHEExAEgb1vSikVCgWOAvT39yORSNQklKrVapnL9eKLL+LYsWN8\\n48xkMhgeHkYwGOSUIAB89tlnHBJPpVJVTVNQhKK7uxsvvvgiXnzxRf6eIAgIBoOcInE6nWhqauJ/\\nX15eRiwWU9w4y7kFy98VIIXet27dihdeeIFTuRqNBkajUVGJl8vl+HcpOmCz2ThqGYvFOLK6Xnvk\\n78xoNGLXrl04duwYACmVm8/nmbOk1+sV60uj0cBgMPAcp+9VWo1K78xoNGLnzp0AgJdeegkHDhzg\\ndaTX6yGKIlKpFFdWWa1W6PV6nmeTk5NcKVMJyB763C1btuCNN97AwYMHAUjVOBS1pRRgfX09nE4n\\np3ySySTOnDnDlYKVRFHk781gMKC7uxt///d/DwB47LHHsLi4qKhUSqfTsFqtHCE0m83I5/MYHBys\\n6n5E64z2w7/7u79DZ2cnRkdHFWtNq9Xy3E2n01zVWO1UHNlDWkGvvfYaRFHE9evXAUiRxmKxqNCk\\nymazNa0MpLn67W9/G4cPH+b9+eTJkxgZGeF1BEhjJedv1bL67W//9m85KxEKhfD222/j/fffx/T0\\nNABpXNZWv5VKpapJvtDneDwevPDCC9i+fTsAKeU2NjaGCxcuKKo5V1ZWeF2v5bhVSsF5qJ0l+UHR\\n1tbGvBPC6OgoH3S14t+sfQFarRatra3YtGmTYpHH43EO84bDYYyOjiIQCPAkq5Yjt/bgNZlMeOSR\\nR3ijovQIhdpnZmawYcMGJJNJDA4OApA2eXkOupJJJt/AtVotPB4PvvGNbwAAdu/ejUwmw4JmIyMj\\nGBoaQltbGztUkUgEU1NTnJcm0l65pFw5QdNoNKKrqwsAWDtEr9ezg33z5k1cv36dNw/i5sgXYyaT\\nucO5WO9YreW6HD58GD/5yU/Q0dHBh0kgEEAoFOL3Fo/H4ff7+dAlTs78/Dzz4Mol5sudSLfbjSNH\\njuDP/uzP0NHRAQCcwqINfWlpCcFgkNNwOp2O5SfISRgYGMDNmzfLtgeQ5o/L5cKePXvw53/+5wCk\\nOQSAeVqRSATBYBCxWIxTGU6nE4FAgDkxY2NjGB4eVhxA64GcL2WxWJgw/cMf/hDPP/882xsIBHDj\\nxg0EAgGev3q9HhaLhcn4U1NTWFxcLNuWtXbRe3M4HDh27Bgef/xx/vfx8XFcvHiRL0jJZBLbt2/n\\ng5r4dtVyCuR7kdlsxve+9z0AwLZt25DL5fD555/j7NmzAKSxyuVyPJ/NZnPNeJOCIMBut+PNN98E\\nAGzYsAGhUIh5OFevXkUymVTwXdeeLdUkVIuiyGnkn/70p6irq+Nz6+2338b4+PgddJK1e041zw9K\\n47/xxhvYvn07P2d6ehq/+tWvMDs7y/NZTjSn3weq48DJ5/M3v/lNPP/883wxWV5exieffIJLly7x\\nHLFarSyPAUjzO5fL8VlRKX3joXWW5DovZrMZra2tin+Px+O4cuUK5zOrHVVaO0HIFp1Oh4aGBtjt\\ndj5MAGkzoINjdHQUAwMDd2xKlQoOyheQfGzcbjcvtuvXryMcDjNHwuPxoFQqYWBggJ0W4itVeqMj\\ne+SOW0NDA5OWo9Eozp8/j76+PgDSGNntdvj9fp7gi4uLKBaL7ADQmJXrlMgF1jweDx577DEAEn9q\\ndXUVw8PDOH36NAAp6lYsFrFr1y7+jJWVFV7A4+PjmJ2dRSgUKisvvvYw2bFjBwCJV9bR0QGNRsPv\\n6fTp05iZmeE55PP5kE6n2ZHzer0YGhrC9PQ0rly5AkDiO6w3UkHviyJVL7zwAn70ox+hvr6e+YDz\\n8/M4ffo0R/tmZmZgt9v5PadSKa5uInunp6cVB9B67QGkuXr06FEeH0Ca33Nzczhz5gwAySmbnZ1F\\nc3Mzv6dkMol4PM430HA4jGAwyNGccuYRIBGVH3nkEebc7d+/HwaDgdfR6dOn8eWXXyIYDLJTrtFo\\ncO3aNXaWBgYGsLS0VLHO2tqCgCeffBL79+9nWycmJvD555/j/PnzvC/5fD5Eo1HeGzKZDPr7+6tW\\nHSgfp7179+LAgQMApAvbuXPncObMGY5Q5HI5WCwWdkgKhQIKhUJNqgONRiO+8Y1vcHQynU7j3Xff\\nxaVLlwCAq6Zp/pCoKFVRVhOCIMBms+Ef//EfAQDNzc1IJBL4+c9/DkDar7PZrCJqS9Vm5CSUux/e\\nDRqNhrWv3njjDeh0Ol43P/vZz1ivkN6tXM+MbKmWzhIRzAEpgtzQ0MDcv1OnTqG3txeTk5O8L3V0\\ndKC9vZ2/DofDCg5ppT7AQ+ksrT2Eu7q6sHPnTj44AOkgvnnzpkJ4rpqLTi54Jy9v1Wq1aGxsRHd3\\nNy82+jly3K5evYqlpaWahVK1Wi1PGJ1OB7fbrSjF1+l0TH5taGhAOp3GyMgIb/KUHqyGbSTrAEie\\nv9vt5ggKIDlDtDk7HA7s3LkTnZ2duHHjBgBpk5+bm+OoHE3+cm2SEyk7Ojp4HEqlEm7cuIHLly/z\\nBg5IqZUnnniCf2Z6epodt6GhIczOziKdTpcVGZA7bn6/n52l+vp6JBIJDAwM4MMPPwQgRd1EUeQK\\nL4vFgqWlJa4aWlxcxNTUFEZHR7G8vAwAipL09dhkNBrZFpK8WF1dxdjYGADg17/+Nfr7+3ltBYNB\\nPkwAqVouHo9Dp9NxajeZTHLVZTn2AFJEYv/+/WhqauK1vry8jBMnTuD8+fMAJKcsGAxifn6ef89i\\nsSgqdiiFWW7KlJ5dV1eH1tZWjmCRkOLJkycBAFeuXLnDSYxEItDpdBwJW1lZqVoFpV6v5z3HYrGg\\nWCxieHgYgPTOzp8/j2g0yrbE43FMTU3xegwGgwgGg1WpJpJXv4miCJ/Px7ZNTk7i17/+NYaHhxUF\\nC4Jwu90Jib+m0+mqOiiUNn3kkUfYvunpaUVFq1xMFLhdSQ1A4WBXw0HRaDRobGzkCGChUMDQ0BB6\\ne3sBSJdDk8nEeyiNS6FQ4L2sUCggm81WHNURBAFNTU14/fXXAUgp/VwuxxG3/v5+FItF2O12XgMG\\ng0GhbJ5KpbC6uspflztGdG5QscT27duh0WgwNDQEAPjoo48wOTmJVCrFRH2TyYSmpiaezxqNBlev\\nXuW1Fo/HK3pnv5PgLQiCURCEi4Ig9AmCMCgIwv926/v/ryAIk4IgXL31v123vi8IgvB/CYJwQxCE\\na4IgPFKWZSpUqFChQoUKFX8AuJ/IUhbAkVKplBAEQQfgtCAIH976t/+5VCr9Ys3PHwWw8db/9gH4\\nf279f9VAnqFcu6Surk6Ru41Go0in03cQz2oB+edaLBbU19ejra2NPV6tVgun08lclytXrihaClTT\\njrWRrvr6ekWLA8pJ040uHA5zCJo8cEp1VSOyJNd60Wq18Pl8PC5tbW3Q6/XYtGkTACki4fV6EQwG\\ncfXqVQCSCNvs7KyitUi5kSU5kdLhcMButytu1UQeJw5KU1MTDhw4wNGbubk5nD17lqNcZ86cQSKR\\nKIt0Lo9QuFwubN68maNcuVwOAwMD+PLLLzmaE41G4XK5FGKLiURCwT+5cuUK4vE4j9V6bKJbqdFo\\nRGNjI6co2tvbueXMuXPnAEipgcXFRb6ZU5SG0gJywUW6YZbTxoNu+MRf27lzJ5qbm/k2C0g6L319\\nffxOaK7FYjGOAmSzWWQyGR4Xefn1eu3RaDQ8H5qammCz2ZjjEQgEMDU1hQsXLgCQ5pTJZFJIKJRK\\nJTgcDraFmrRWmnbXarWwWCwcgTCbzQgEApwqvXLlCpLJpEIvqFQqIR6P8/dWVlbKikbezS45781u\\nt6OpqYl5LpcvX2ZODo2lwWBQZAa0Wi0ymUxZBQpfZRMgpY2am5uxdetW/uzBwUGsrKzw2rJarTCb\\nzbyHptNpiKJYdVFjURRht9vxJ3/yJzyHIpEITp06xVkIn88Hi8XCtsXjcW55ItcyqzTKRZGcb3/7\\n23j00UcBSPNjdnaWe61lMhm0trbC7/ejvr6en72yssLReJIIobEr59ylvdHv9+P5558HIJ2r0WgU\\np06dAiDtxdlsVtFKiIpIenp6AEjzbnl5mTMmWq2W07vl4Hc6SyXpL6WyEd2t/93rr/82gH+99Xvn\\nBUFwCoLQWCqVFsqy8B6gzdnv9/NCo82ZCLG16FEjx1qHYnV1FVu2bIHNZuNNiIjAdPBNTk7WrFkl\\n5bJpPPL5PDZt2sRNIg0GAwRB4MMmFovh1KlTmJub482jmj305L3cSqUSMpkMNmzYAEA6bJqbmxX9\\nzTKZDD7++GNeoGNjYwr9lXJJemu5XAaDASaTiedLS0sLstks6uvrWbHWbDajrq6OD5xPPvkEn3zy\\nCR90RIQv5z3KnSUqUKB0TjabRV1dHerr63njSqfTsFgs7OzF43EEAgF2KlOpFOLxeEX2ANJh0tra\\nyjyturo6lEolaLVa3rC3bNkCj8ejSKtMTU0pHCNRFJHL5RS8inJs0uv1TPp1u91wOBwoFouKZ7W1\\ntfGGabVaMTc3h2vXrvEcD4VCil5w5c5v4Za2lbxvodVqZacxEokgFoux02s2m2E2m7G6usq8j1gs\\nhmAwyIcupVTKtUc+nzUaDR9izc3NLMxLX9Oljeynqlz5RYTEXitZY2vHaePGjTh06BDP3Xw+j9bW\\nVkVRg9VqRS6X4+KKu2kIVeJQynmTJDZLn5fP59HZ2cnrXq/Xs62A5ACEw2EFraLcy7ecB0oiuM88\\n8wzvBfPz88hkMpwGt1qtCnX8bDbLKXfinpEtlRRPkFbhSy+9xA5sNpvFxYsX+V3s3bsXmzdvhtvt\\nZqe8UCggnU5zcdC7776LRCJR0bonUeejR4/yOGg0GszMzGBkZIRt8/l8aGhoYHtbWlpQV1fHZPn6\\n+npcv34d/f39/Nk1J3gLgqABcAlAF4D/u1QqXRAE4a8A/O+CIPyvAE4C+IdSqZQF0AzgpuzXZ299\\nr+rOEk2wHTt2QKvVQhAEfknLy8tMonyQMJvN2LJlC1dzANImFAgE2AFIpVI1bbsinxAkgkdEOY1G\\ng0wmwyJn//Vf/4WRkRHEYrGa8KeEW8JghMbGRibnkpNLN86BgQG8//77eOuttzhiQrfuaskF0Jwh\\nkiIdLo2Njairq4NOp+OIxODgIHp7e7mjNkWV5ITBSvgB5CTa7XZoNBqeEyaTCQ6HA88//zx//tLS\\nkoIQOzs7i/HxceYnVRJxA26vJZPJBLfbzZ9DzU1FUWQHqqenB/Pz81xqvbCwoLjZUmRSLghXrk0m\\nk4mr7IjoL3fCdDodtm7dquCUmM1mRKNR7hpPTmSlRE+NRgOTyaSQaSiVSnxzLRQKMJvN2LhxI/+7\\nIAjI5XJsy+joKObn5yvuXk8OAB1aTqcTNpuNn93U1MR7ESA5vSQ/QRHtlZUVjI6O8ntMpVJVcdyo\\njQpFaI8dO8YVt4DkPNEtnw66aDTKJfuAxFUkYnOlkW25uvzOnTvx+uuvw+FwsIp6U1MTdu/ezfPD\\n6XRieXn5jibV1RA4lEf97XY7jh49is2bN/P3YrEYWlpa0H6rBZTD4YDVauUqWIfDgYmJCUULlEpA\\nz7XZbHjsscfQ3d3N51YgEMD8/Dzb0tnZia6uLphMJnawaT6RI9nb28sXA6C8ghedTgefz4cdO3aw\\nrEUymcTFixf5TPd4PKirq4Pdbmc+rk6n4zZQgDRWzc3NdwhAl4v7EqUslUqFUqm0C0ALgL2CIGwD\\n8I8ANgPYA8AN4H9Zz4MFQfiJIAi9giD0rtNmFSpUqFChQoWKB4Z1VcOVSqWIIAifAvhmqVT6P299\\nOysIwn8H8D/d+noOgF/2ay23vrf2s/4FwL8AgCAI63bTBUHg1NKuXbu4pxJ5vNeuXVNES2oN8mYf\\nf/xxbNiwATqdjm8qqVQKX3zxBQYGBgDURr5/LSiM/PTTT6OtrY1vScViEZlMBidOnAAAnDhxgntE\\nVRsUAifPvq2tDc8884wiJbi6uspcrk8++QS/+MUvqqY7s9YWuVZKU1MTDh06xOXMLpeLuVx04/zs\\ns8/wzjvvcJQrHo/fUSZbLjQaDb8TvV4Pm83G5eVUhu/xeDgKQGk2qpIJBoOKyqZKOHlyYUV6JxTu\\nTiaTEASpL5O8h5jVamWehcFggMFgYF4OpV7LjVDIuYg6nY7nB/Whkvczowowkg5pbGyE2WxGMplk\\nHtP8/LyCv7ge0DuyWq2ccqO1pdVqkUwmub/Z2n/v7OzkNBOlAIeGhpDL5crilcltstvtHNkCpMhA\\nW1sb8/+8Xi9ztcjWrVu3wuPxcOT0zJkzCIfDnK4sdx/QarUwGo0cJQKk6AwJCO7YsQOiKPJz6b3I\\nKwnj8TiuXbvG9sbj8bKrBOXrXF4dSLZQ+xvi/MzPz2N1dZV/xm63s2YPIGlhEc+tUgoAlf4DUhZi\\n+/bt0Ol0HHUjbTSym55L0Uy/38+SIpVmAuR8UnlGhPac6elpRassr9eLRCKB+fl53ncoBU6RXo/H\\noxDzXA/onVF0liKQgJRKD4VCitY81CqI9h2HwwGHw8GRMOIX0nqUN/gtB7/TWRIEwQsgf8tRMgH4\\nBoD/g3hIgjQTvgNg4NavvAvgbwRB+E9IxO5oLfhKAHjDrK+vh0aj4V4xgEQirJUQ5VrIy+NJSVye\\nfpqZmcFnn32myDHXyg4iVlL/smeffVaRf8/lchgdHcUnn3wCQArF17JhrtFo5Pd05MgR7Ny5kzeC\\nYrGIdDrNaYBPP/0US0tLrApbbej1ej7g29rasH//fk7DabVadrap3PrLL7/E3Nwcv7dqC4eSg+J0\\nOpHJZHi+6HQ6Tk9S2HlpaQmBQIA3BmomTKgkHShPoZVKUn8wShkPDQ2x9hXNIxpH2mibm5tZf4U+\\no1x7yPECJIefSqUBKS1Kc4ccblKEp/dKaedSqcSCh/IU3HogCAKnr5uammAymRCPx3mtC4KgONB1\\nOh1Lc9C/E3GZ7CUuVznFHTRvXC4XGhsb4fF42LE3GAxobm7mg8LpdCKXy3HRBu0LpIoNSIeLXOA1\\nn8+vmyhM0g42m405P0QkpzQcabkRkskka1+Rg2U0GuF0OvkwJHvKGSP5RcRgMCCfz/MB7vP5OHVE\\n7yAejyMajfJay2Qy3OgckLgwZrOZxSDpOfc7TvLf0Wq1/HsajYbHjOY4pfzovUajUWg0Gp6HDQ0N\\ncLvdnOIFpHdbToHJ2iKT9vZ2lEolhe6WxWLhtUPyMul0mteA3W6Hw+Hgr0VRZErMeu0hWK1WtLW1\\nwev18h4XCAQUF55oNModCuhvaG1t5dQ9IJ11NI7Ag9FZagTw/93iLYkA3iqVSu8LgvDJLUdKAHAV\\nwF/e+vnfAPgWgBsAUgDeqMjCu0AQpCaIFBWgxUjcIEDiUdSyAk4+UY1GI7cTee6553jTpFvS+Pg4\\nJiYmaspTAm7nbNvb2/GjH/0IAHgBkC3JZBL9/f0KMbhagBZ4a2srvva1rwEAXnnlFTidTh4H2jRJ\\nRHF8fLxmjpLZbMaePXuY0/Gd73wHjY2NfHCQ9k46nWZCoJy0XE2Q7szevXsBSBtOIpHgcejq6uLq\\nMnI0l5eXa8J1EwQBVquVo1oUkSCOTT6fh8PhwNzcnELvqLGxkaMCdMutVFgRkDhTayvx6BAbGhpC\\nNpuFwWBQNFm12+0Kx1MQpDY1dHGSa/msxz5BEDhidfToUfh8PiwtLSnI2lqtVhHlikQifDATxymX\\ny3H0Zn5+XlHhVc6h63a7sXfvXtjtdo5QEXeN1jnx/OQOdSwWg8Ph4O/RoVaNSLcoivzsYrHIGk6A\\nND/cbjc7+isrK3zwyZ2l1tZWHku5btd6sJbsTLwnGv++vj489dRT0Gg0HOFaXFzk9Q9Ie6LX6+Wo\\noNFo5PGsVMtodXWV50c2m8XExAR6enp4jgeDQSwsLLCWWzAYhNfrVUTjqcUS2VCJlhH9Xl1dHTZs\\n2ABBEHh+hkIhLiQBpAq0UCgEt9vNEUyn04lischOOUXpynHe5BEpt9utcNQWFhYUws7BYJA5buSU\\nb9iwAZ2dnRwhXFhYwPj4OM+7SguX7qca7hqA3Xf5/pGv+PkSgL8u2yIVKlSoUKFChYo/IDxUCt7y\\nUJ3FYuHqHFEUkc/nkUgkOPQej8dr2jiXoNFo4HA4uOEpqZ6mUim+LZw4cQIrKys1kzGQt1ppamrC\\nSy+9hMOHD/OzAoGAIszb29ur4JfUIpJjMpmwdetWPPfcc9wOwuVyKbSBiCdAat2Ur6+2PRqNBi0t\\nLfjWt77F0UiHw4GVlRW+cZLOCul5ANJYVZrnvhuIq0C3s3A4jMnJSR6Hvr4+eL1edHR0cPjd6XTC\\n5/NxXr/afan2798PQLpVUyNlQHpHuVwOGo2Gy3i7urrQ0dHBP/Ppp58iFArxLXC9IXgCRbmoWnLP\\nnj3cGxCQbpPJZBIajYZv2n6/H1u3bmVtFYfDgZs3b+LLL7/kaEu5Y6XVarkM2WQyYePGjfD7/Ypb\\nv5zTQarKFMkyGo3Q6XSIx+NcuTQ3N1e2Oj6lGzweDwwGA9ra2nhd+3w+1lqiZxcKBZ4/S0tLiMVi\\nCuVnQKk5VU5POErnUBUicDvaTvMjHA7D5XIpuGipVArZbJYjSx6Ph98tIEUDy+1RVyqVFI2V5aDe\\nifX19fzZtMZpXLxeL/NOgduSEOVETMge+f/LtbFCoZCiEXexWOR0KCCdJ/L1aTKZ8OWXX2JoaEjR\\nL64cyKuM5fpD9HfbbDZotVqOXlLasKmpCU899RQAKS04NTXFHNipqSnkcrmKZAwo2plIJLjSrbGx\\nESaTSVHNbDQaYbFYWIpm//792LhxI0cIz507h4GBgYrHifBQOUtyWK1W3rxJjDKVSnGYrlbpHHqe\\nXJfmiSee4JYYer0eiURCQTyjTaoW9pAWDSBtkAcPHsT3vvc9DiHPz89jYWGB8+86nY4XPlDdvkLA\\n7c3J7/ejq6sLe/fuZR7C7Owsent7+dnUpJE2VRqjatoDSGmuI0eOYPfu3Zw6WlhYwEcffcQ57ebm\\nZjz55JNYXV1lB4rKhSsNvctBPJidO3eyLaVSCRMTE1weHAqFYDQa0dTUxOklyr9TqqNaNgmCgK6u\\nLl5LlFqijYtECzdv3sw9o7q6umA2mzldSaRlQiWbUjqd5rm6adMmJJNJ1i3yer3MP6E5tWPHDgXX\\nJRgM4sSJE4oeZ+WuPXlTZZvNBp/PB0EQmGcSj8cVnDxKK5OTls/nMT8/j6GhIW7TQOmecpwlmgul\\nUglutxsbN25UOCSiKDIHz2QyIZ1O83tJp9NYWVnBwsIC70u9vb0YGxvj+V7Oe6NS+LWFD3q9ng9d\\nchrJETIYDNDpdBBFkedxJBLB2bNnuW1NOByuSC9MPr5ywVT6W00mE49VS0sLdDod27t7927odDrm\\n7V24cAELCwsVpyvXvutCocDixJTi9vv9CiKz0+nEwYMHeU1cvnwZ58+fV9A6KnGWyKZgMIjJyUl0\\ndnbyHt7a2sq8UnpOS0sLtm/fziX98/PzOHHiBC5fvgwAZfV/JNDvkZZUPNUpVucAACAASURBVB7n\\nd+T3+7FlyxZOnZIcxGOPPYYXXngBwO3+ntTj79SpU1heXlYECirBQ+ksaTQa+P1+vpWQhxwMBnHt\\n2jX+Xi0h14TYu3evglRGis7EmZiZmVHctqvpDMg/12AwwGazQa/X86E6MzODbDbLiy0YDGJpaans\\nioXfBfkG6XQ6sbq6yvnss2fPYnJyUtFUNBQK8QKQ3yyq5ZgAt9W69Xo9b5anTp3CpUuX2Kns6elh\\nZWjaHKgnVLmRkruBVHvXVsWYzWY+DDs6OvD444/D7/fzJh8MBhEOh6tWSUl/ExF+5U5uQ0MD8wBS\\nqRR8Ph+2b9/Oc0gQBCwtLXFlHqnpVsNxk0cFSqUSmpqaOHLT09PDt0v5TRyQ5jkAfPzxx/jwww8x\\nNzfHP1PufJJXTN24cQM9PT1oaWnhfUen0ynIrcSDIWdkaGgIgUAAY2NjHDUkteVyDjha0ysrKxgY\\nGIAoiuy4xWIxJnCTLQaDgZ2lRCKB2dlZFlgEJF0zcraA8vbMQqHA74s+y2q1cmQAAEeMaK2RInup\\nVOKf6e/vxyeffMIRuGKxWHYUR/53kEAvRSQymQxu3ryJ7u5udkiMRiOi0ShH5UwmE8bGxlgtenBw\\nsOKelGRbsVhkW7RaLebn5zE3N8cFOU6nk6v1AHC0jnhyZ86cQV9fH6LRaMVOAPF8AWlOffHFF+js\\n7OS1Tw3hN2/eDEDJwaNLxKlTp3D69Gne48utOpVHuagv5vDwMF+UrFYrjhw5wvvW5s2b0dHRgYMH\\nDyoqvQcHB/HOO+8AAI9TtfbMh9JZ0mq12L17N28UVDETjUb5MFybEqhFageQiHF79uzhDbRQKLDg\\nmbwkt5bkbnnn+b1793LlBnDbUaCKrrm5OYW3XW3Imxs3NzfD5XKxLU1NTRAEAXv27AEgvZO5uTl2\\nKuXk1GpAHso+fPgw3G63wpatW7fye6OU7tzcHG/YsVis6mlKEk5tbW3lDTKVSsHj8fBzyTkRRZFT\\nlteuXcPQ0JAiIljpxk1YWFjgubpp0ybodDou+06lUiwySHNoaWkJH3/8MT7++GMA0jjJlcPXVtit\\nx6ZCocDtQgqFAl577TVFI2aaX3LHIRgM4rPPPgMAnD59GktLSyywSJA7vPdrl1x5+6233sLJkydx\\n7Ngxvu1arVY0NDSwo7m8vIypqSl2sIaGhjA5OYlIJMLfI6eyHHsIkUgEZ86cwWeffaaQb2hvb+dD\\n1uPxoFAocMown88jGo0iFApxBHNhYaFi2RCKDhWLRXbCotEoRFHkKP/p06cRDAb5EC4Wi6ivr0c0\\nGuVK2EuXLuH69esKNfFqXJoorUXrhtr2OBwOjqYaDAa4XC7+mfHxcRw/fpwvA4FAgAnVldojvwwE\\ng0GcP38eLS0tvIf7/X5FS5poNIr5+Xlea+fPn+duC5XQOmgO0mfE43H89re/xerqKr773e8CAAs6\\nkj2ZTAahUAjz8/PsSF66dEkRnSzXMZE7S9TY/d133+Xoe3d3N+rq6rhYKJvNsko87QWDg4N4//33\\nmYqzsrJS3SbMVfskFSpUqFChQoWKP0I8VJEluo2ZTCZ0dHTw14VCAZlMBiMjI0zmotttrfqvkTfv\\n8/k4DCj/93w+z2J1dKske6ttE92ISP9C3jCW8rjk+V+7do1vfGRTLcYolUpxjx8Kb7vdbo5OABIZ\\n8Ny5c3z7rUbp+d1A/blEUeSw9sGDB7F9+3aOsJnNZkxOTuK3v/0tCzLSLb2a40O6NHNzc9zGo66u\\nDrt37+abLj1vYGAA7733HgCJX5JIJBQl89WyJxAI4OTJkwCkudre3o62tjYAUtQyHo9jfn4efX19\\nACTi5OjoKPO91uoYVfL+CoUC9+Kj5sZE5na5XDAYDApe0PLyMiYmJjgtEIlEuIHvWjvWm04lzRsA\\nLHL53/7bf+P5bLFYWGoBuK2VQ9GRbDbLHI61Wl3lpHZpjHO5HM8F2ltEUcTs7CzPXb1er2jNQuKi\\ngUCA98i1Wl3l2FQsFlk7Sh7poAgqALz33ns4ffo0k+W9Xi+vA2q7NDMzg1AoVJUI/NqIlLzkP5lM\\n4vjx4xgZGcHWrVsBSFE4OWepr68Po6OjHPGmJtDV2JfkEZRcLoelpSX87Gc/4yjWli1bUF9fz/bG\\nYjGF1Mvy8jKnuqplD9kyPz+PX/3qVxzZ3bJlCxoaGhQNvufm5jAyMsL2ED+3Gns3/S5JPXz00Ud8\\nVh0+fBgHDhzgVK7NZkOxWMTCwgJzy44fP46BgQH+nXJFRL8KD5WzJH+xly9f5gq0UqmEwcFBnDx5\\nkjePcisX1otYLIa+vj4OFxqNRoTDYfzmN7/BBx98AOD2plQrHpX8AJ2cnFQQ8ERRRCAQwNtvvw0A\\n+PDDDxUE72o7ApQmIV5UPp9XqOnm83k+DP/t3/4Np0+fVlRe1eKdraysYGRkBBs3blTwmIxGIx+y\\ng4OD+Pd//3f09fWxY0k8hWqiUChgZmYG165dY2Lw6uqqoqFwKBTCqVOn8M477zAXJ5lMIpPJVH18\\ncrkcFhYWFEThpqYmrqJyOBxIJBKYmpripr2xWIwdEvqbqrVxp9NpDqunUikcP36cnRNRFFEoFBTK\\n1NT8ld4TpQPvZk85aUG5w1wqlRCJRHh+CIKA8fFxxUEh70NFSuZ3s6ec8ZJzOtZ+JjU0pVQu/Rul\\nd4iELU+Xrp1L5aZziOO3luQt59sFg0HmbVEBgXx8aQ5VYx7dbY3IvxeNRjE4OMjis2upGsTDqlb1\\n8lfpIdH7ID4iAFy5ckXhaJJzReMkd7YqxVqHkkR5yZmempqCyWTi8cnn8+yAyDW1alHBXCwWkUgk\\nmDgeCoUwMzODffv2se1zc3OYmJjgeTU4OMhroxYQak2Evi8j7rPdiZxULS8xbm5uxuLiImZnZxWV\\nKLUE2WKxWOD3+zlK0NzcjHw+j/7+fkUOv1YtTuQOitlsRldXF3bu3MnfK5VKmJ2d5a7Q8Xi8Zu1N\\ngNsEb7vdjieffBKPPvooczwWFxcxPT3Nt9/h4WHF4Vgrm8xmMzo7O/Hd736XycIGgwEzMzMsBHn9\\n+nVMT08jm83WxJGUQ6fTwWazcSSpvr4eVquVx2FqaoqJuJWUdd8v5M09LRYLTCYTv8fV1VV2TuTE\\n61pVdwJ3Kh7TgU/8k7s9t9YXIzkhXh4hXuu0VMJFKseetYrSXzUO1a4w/Sp75P99r4q/Wtuz1ha5\\nTWtL+L/KxlrbJH/2V0X1HpRdX6VMfrdKxwdhF2WG5AUL1GAcuL0XJBIJvrRVEEm6VCqVHvtdP6Ry\\nllSoUKFChQoVKu6BhyqytOZ3FF//vv4O8oDl9tBts1aVeF8FqkxZe2OqZuj2fiEIAkcoyB4SX/uq\\nNEAtodFo4HQ6OUqh1+uxsrKiKL99EA2OCXJeGf03cV2qzUu6H8jb9wC3I4QU/q9WW4xy7SKsjQr8\\nIexffyx4UNGe+8Ufmj0q/mhxX5Glh9ZZUqFChQoVKlSoqBBqGk6FChUqVKhQoaJSqM6SChUqVKhQ\\noULFPaA6SypUqFChQoUKFfeA6iypUKFChQoVKlTcAw+VKKUKFSpUqCgf92ro/fso9qHqS6oIJY0z\\nqpqtheDh7wJp+pA+XDQaRSqVUuj4PMjKUI1Gw7Y88sgjmJyc5ObVwG1h1t+HPVu2bEEymcTk5CSA\\n230ifx+VvDqdTiEKK1eVByqf36qzpALAH44Uw92glhDfiT+kMbkfkb0HLfq3VkxvLR6UbAXZQpu4\\nKIoKIUt6j3LF5lraotFouHk0tYwgMVQ6XEipudb2iKLI7SsOHDiALVu2cOPovr4+rKysIB6PV10p\\n/qtA78jv9+Mv//IvsWnTJgBSq6GTJ09ibm6OO0Sk0+ma2wNIaucbN27EP/zDPwCQGl1fuHABv/zl\\nL1m5OhwOs0BqLSEIAnQ6HTo7O/E3f/M3AIBt27bh0qVLOH78OABJgTwUCtVc2oMkcvR6PQtVdnV1\\nwev1YmhoCMDt1jBy9fNKoKbhVKhQoUKFChUq7oE/qsiSXq9Hd3c3AOC1115DfX09PvroIwDAiRMn\\nkEgkHmi4krxrrVaLlpYWHDt2DIDUfHdoaAinTp3i9iy5XO6BhJzJIzcYDACAxsZG7NmzBz6fjxsQ\\nXrp0CZOTk4pWG7W2Cbgdim9oaOC+aXa7HfPz89zzSt6fqNb2ULsNuolTI156digU4r5NDyJSIW9t\\no9frAdy+DVM/KUEQ+Cb1IN+bXq9XhN7l4XCypdYtiMgmURRhMBgU74QEW4Hb/bUoolJrW3Q6Hfe3\\no8gNjY9Wq1WMDTVtrZU9JpOJ+1h2dXUhGo3y2qL3R30Ia2kPrS3ar48ePYrW1lZcu3YNgNRYd+37\\nyefzPLdrYY/JZAIAvP766/j2t7/NPe9mZ2eh0+kgiiL/jDxdWAvQOnc6nXjzzTfx1FNPAZCaM+t0\\nOqRSKX62Xq/ntQ/ULkVoMBjQ2tqKN998E08//TQAaT7LWzXl83nodDpFU9xazB9RFOH1enHo0CHs\\n378fgBQRXFlZYUHd3t5e5HI5FiKutPfoH42zRA7AK6+8AgD40z/9U1itVmz4/9l78+A46ytt9Hl7\\n3xd1t1r7YlmWF3k3NtiAF2wMxhgMBkKAJAQmkxRZppKbmSHJfElN5ZuqWzWpO1N1q+7kG5ipJJUE\\nEsBhswGD8YaxLcvyJtmSJVm71N1qLb3vff94OcfvKwiBXsxA+vwjt9zq9/RvPec5zzln3jwAYh+y\\n3t7e6wKdktCCb2pqwne/+13s2bMHgHhYnz9/HpOTk3j//fcBgBtuFjvWKwgCbDYbVq1aBQB44okn\\nsHTpUphMJjaW/vCHP+DFF1/E5OQkADFmX7TmhJJDym63Y926dVi/fj3WrBFrhNlsNrz//vv4/e9/\\nD0Dsm+b1eot20dEhDoh90iwWC2prawGIm7G5uRl+vx8AcObMGfj9fvj9fu4DWOi1NdcgoTWl1Wpl\\nhhytHaVSyWtobGysaOtJqVTyoUThHSn/BBANAel7fD5fUdbRXCOSDJS5cyENhVGTZ6Dwl4vUSTIa\\njVCpVLzGFQoF4vE4jwMZcaRbPB4vypwJggCDwYB58+axIxKPx+FwOLhx6vT0NOLx+HVx2hQKBSor\\nK7F9+3YAYsPm8+fPY3Z2lt9DRiSNRzGNf5VKhY0bNwIAHn74YWi1Whw5cgQA0N3dzeFA0iGdThe1\\n3yetl4ceegj33XcfO0dnzpzhUBetX3KAi3VG09qsrKzE3//932P37t38u97eXly+fBljY2MAxHNI\\n6pgUWuhstlqtuP/++/H1r3+djf9YLIahoSFukHzhwgWoVKqCOWlfKGPJYrGwxWu322WewPUWhUIB\\ns9kMQFzwu3fv5vh8IpGA2WyGzWbj9xdzsUtboOj1etx6660cc165ciX/P21+QnikrUCKpZdWq0VT\\nUxMA4O6778auXbvgdDq5MTEgxp4XLlwIAPB6vXzZFXq8yFByOBwAgBUrVqC1tRUulwsA0NjYiKqq\\nKly4cAEAYDKZ0N/fj/b2dr5w6LIplD5kBOj1ejgcDh4XjUYDtVqNuro6AMDMzAy0Wi1CoRCjlaFQ\\nqKCGrtRw02q1zBXQ6/UynovNZkMmk5HNYTAYRCKRYM5HvmMkPYyl+9xqtcJsNsuMDovFAkEQeD8m\\nEgk2DACRlFqIOZMaSfRcl8sFi8WCUCgEQJw3p9PJqEUkEkEgEOD/lxq7hdIHAHQ6HSorK+Fyufh7\\nj4+Pw2g0MlmXiMLSZsGF5MZJP9Nms2Hz5s1sTB88eBDt7e38HoPBAEDOf6NzqtD6KJVKzJ8/H08+\\n+SQAcQ/v27cPzz33HACw05hIJArGf/lzQk7R1q1bAQBPPfUUlEolrly5AgD41a9+hSNHjiAQCMh0\\nKdbdQesVAL75zW9i586dUKvVvI+PHz+OtrY2btVExm2x0CS32w0A2LVrF7761a+ivr5edi4ZDAbe\\nP4UGH0qcpZKUpCQlKUlJSlKSj5EvFLK0dOlSzmBQKpXIZDIYHR0FcC2l8XqE4MgDqqmpAQDce++9\\n7NWSZLNZhMNh1qfY0DdZ3y6XCw899BCWLFki0yWVSrEHRZ5uMfkBgOgJlJWVYffu3QCAnTt3Qq/X\\nI5vNyp7tdDq56WwkEilKHJxQHKvVii1btgAANm3aBKVSyc8uLy+HyWTCTTfdBED0uvx+P6xWK49d\\nodJmSR9CI1taWrBixQqG49VqNfR6PaOTBoMBgUAAHo+Hw4Q6nQ6XL1+WhTby1QcQs6jq6+uxePFi\\nACKSkk6neV7LyspgsVig1+s5XDo9PY2enh4cPnwYAAq2tpRKJRwOB+/7xsZGACKSRehEdXU1HA4H\\nIz6Tk5Po7+/HxYsXAQBdXV0FW08qlYp1WLt2LWw2G8LhMLxeLwARhausrGQkLBKJYGRkBJ2dnax3\\nocaGsoUAYMuWLbjpppsQCoXQ19fHz5Ii2hS+lIYwC7nPpKjRt771LWzdupW/94ULFxCPxxn1CofD\\nCIfDH0IpiqGP1WrFz3/+c6xevRoAcOXKFXR0dPCcBYNBZLNZxGKxojcBFwQBVVVV+OlPfwoAcLvd\\nGBkZwe9+9zsAwNGjRzE5OfkhDmCxeGUGg4G5to888gj0ej1mZmY4RPnKK6/gypUrCIfDAK4h68VA\\n/XU6HbZt2wZARNyqq6uRyWQYlU0kEojFYrIoijRcWiodgGsD+dhjj/HhkMlkMDMzw4fz1NTUdU1f\\n1uv1ePTRRwGAY6p0CMZiMXg8Hng8nqKHugDxECRuy4MPPohly5bxOMViMTZOzp07BwA4dOgQZmZm\\nirYBKd5tNptx//33M3nR4XAgEAjA6/XyQRCLxXDw4EFOBw2HwwWNh0tDJ2azGbt27cItt9wCQEyL\\nDQQCGBkZAYAPpXdHo1E2wOlwKJShpFarYTabceedd7Iuy5cv53kLBALQarVsTJlMJvT29sJisfD4\\nXrhwgcM9hdCHwmrbt2/HvHnz2ECxWCyIRCLsEFgsFphMJvh8Pr5wJiYm+N+F0IcSFNxuNx5++GHm\\nlblcLkSjUXg8Hg7vzps3D0ajERMTEwDEcZmamioYB4bWkFarRUtLC37wgx8AEKkAsVgM/f39sjXf\\n0NDAod2pqSlMTU3xeqfzoBA6aTQaXss//elPkclk0NnZyWFao9GIWCzGIRW1Wg2Xy4WhoaGClzKQ\\nGm67du3Ck08+iWg0ikuXLgG4xtWi7x8KhZBOp9lAL6QupA85QY8//jhuueUWBINBAMDFixfR19fH\\nofVEIoFkMlnQmj0fpQ8gGm5PP/00c22npqawd+9evPrqqwBESgIllBTbiFSr1bjjjjvwwx/+EIC4\\nr6empvDSSy/hV7/6FQCRiC/lchWL0K1Wq9Hc3Mx7q6GhAalUCj6fD93d3QBEjmYmk2H+KK0hqT75\\nhHG/EMaSQqGA0+nEqlWr+BBNpVLo7+9nC7iQXJKPEzrIy8vLsXbtWgDioZROp3nzTU5O4uDBgxgc\\nHJQtMqkRUChdlUolDAYDXyY33HADTCYTW+OhUAgGgwE+nw8nT54EIF5siUSi4LwF4gQRwXTx4sXY\\ntGkTX8KhUAiBQABKpZI9latXr6Kjo4Nj4kQkzlcnQv/owKyvr8fq1auxZ88eGdcmm83ymiJjgDZj\\nLBbjQzTfsZqL3KxYsQKrV6/G3XffDUA8LHQ6nczjt9vtrH8ikWA0ldZZKpXKWZe56N+NN96I9evX\\nAxARt3g8zkiNWq1GMBhklEuhUCAQCMDn8/G8DQwM8Ljlog/9pDpB69atAwA88MADWL9+PaNn0WgU\\nU1NTmJ2dZe6ZIAgYGxtjRGViYgK9vb3svORqfNOc0RwsXboUTz/9NCcneL1e9Pb28joBRCcqHo+j\\nv78fgLiG/H4/j1O+joCUXN/Y2Mj1eZqbmzE6Ogqfz8djFYlEoNVq+QxSKpUwm80FRwZonCorKwGI\\nqIDdboff72cHbXBwUGbYZ7NZ5sbR7wvNBVy2bBkA4Bvf+Ab0ej0bbocPH8bw8PCHHFmpo1fIKIU0\\nqeTRRx/Fvffey68vXLiAAwcOMFr8UXua1kyh5ozWUE1NDX72s5+hqqoKgPid3333XfzpT3/iDEpy\\ntqW6SM/AfHUiXSoqKvDEE08waqtUKuH1evH666/zvtZoNCgvL+fz22AwQKPRyNDAv9psOOmBvnjx\\nYrjdbh7cRCKBAwcOMCpwvSqK0sJvaWlhYhwgIiIUqmlra8N7772HcDjMk1fIjTeXFKnRaNhTsVqt\\nCIVCfHFRxtKFCxe4CutciDefA1z6txTSImNpyZIlXBoAEAmnBoMBCxYsYO/39OnT8Pv9jErQwZ7P\\nBUc/NRoNH+Dr1q3Dxo0bYTabOXTr9Xqh0Wj4sIjFYujt7eXPGBgYwPj4OB9kQG7zSIR1Msa2bNmC\\nW2+9FWvXruWLeGJiApcvX+b1bbfboVQq+dJ1Op04d+4choaG0NHRAQA5oydSFKCyshKPPvoo1q5d\\ni0WLFgEQD53+/n4mnRKZm9bPyMgINBoNuru72Sjv6urCzMxMzvoAYlixuroau3btwq233gpANP6T\\nySQGBwcBiNlC4+PjsFgsOHToEI9DKBTC0NAQAJEMPzw8zAbKpz0bpJ53WVkZVq5cCQDYsWMH1q1b\\nJ8tGPH78OK5evcoGd39/PyKRCBtqPp8P4+PjbODmc05JC0663W7s2LGDQ6WxWAyXLl3C6dOn2RPX\\naDQIh8P8bLVaDZ/Px6/zFek42Ww2DqE0NDRgdnYWhw4dQltbGwBxHNRqtQxZo3IYhRQyeJxOJx5/\\n/HEAYtjY4/HgxRdfBACcPXsW0WiU9wA5BnOdokKJUqnEDTfcAAD44Q9/CL1ez2v1+eefR39/P5/H\\nKpWKk3GkRlyhRBAEdjL++Z//GfX19fyc8+fP4w9/+AN6e3tl+lAGHHCNTlIo55rOvx07duCBBx7g\\nhIDp6Wn87ne/w9tvv82IYGNjIyoqKmTZ1WNjY6WilCUpSUlKUpKSlKQk10M+18gSicFgwB133AGj\\n0chWdjAYRFdXF4dzikmgloYtqITBpk2bUF1dDUD0rCi+CgAnTpzAyMiIDFL9qBYRuYo09VetVsPt\\ndnNIsK6uDjabjYm3Wq0WiUQC58+fZxRubq2XfMeNvGqVSgWLxYLm5mYAYmhAo9GwJzA+Po66ujrY\\n7XYmfvb19WFwcJBRgI/iD3waoXExGo0wm82MllRXV8NsNmN6epo5HF6vF62trYw+EbpD/9/V1YWh\\noSGEw+GcuEFzQ13EAbrppptQV1eHRCLBJOQrV65gcnKSvb6amhooFArWZXZ2Fj09Pejv7+eWEbmk\\nxBMySmjazp07cdddd8FoNPJ3PH78OE6fPi3jIAmCwCiSVqvF5OQkYrEYh3xmZ2cRDAY/9bxJQx81\\nNTW48847cdddd/GcpFIpdHd3Y//+/QDEsO3Q0JAsScBoNDKqC4jh3kgkknOtLtKnrKwMq1ev5r21\\nevVqRKNRDi3t3bsXo6OjjBIA18qG0HrOZDKYnZ3Nm4RKISsKhS5btgwbN25kpOa9997DSy+9hIGB\\nAX42IO512n+BQACpVAqRSKQgpR1onPR6PebNm8c1ldLpNN566y28+uqrXNuNinXSnFGhxUIXoiSa\\nxIYNG7iuUjKZxKuvvsr8Vir8SJJIJKDRaGTJG4UMeVksFnznO98BIJbdiEaj+NOf/gRARLmk9dQA\\ncf3OpWxIEyzymTu9Xo+dO3cCENEcQRB47+zduxdXr17lcxwQ9zr1zSNJpVI8TnlxhD6I0ABi3USr\\n1cpn0FtvvYVTp05hdHSU11k2m4VGo+F7d/ny5XwOkV5/tZwlgv4sFgvuvPNOWT+oYDCIiYkJnsTr\\n1QtKrVajtrYWd955JxsJFH6ig+Ho0aOYnZ39yGJZhYIvpfyXhQsXct0OqkNDl+HMzAz27t2Lw4cP\\nszE317DMxzBRKBQybkt9fT0v5paWFhiNRg7LaTQaGI1G9Pf347333gNwDRKnTUK1YHINdxG0brPZ\\nYDabmQRMh3M4HJbVMnK5XBxmO336NN5//32+gLq6uhCPxzlDLxd9APEyb25u5lpSDQ0N8Pv9GBoa\\nwtmzZwFc6/9Ezyad6CDzer24cuUKEokEOwi56ER1poiftHLlSlitVni9XrS3twMAXnvtNYyPj8sK\\nKcZiMT4gNRoNjwuFUXK5gMlwowSJm2++GStWrIDb7eb1cOXKFbz66qvcJyscDmN6ehqBQEBmGJM+\\ngLiGcuEwkgEgrcPV0tLCYTilUomLFy/yRXfp0iUIgoBkMsmhAkDcB3SAU9ZpruEmaR2usrIyJo4v\\nWLAATqeTQ6X79u1Dd3c3tFotzxtlm9H+jEQiCIVCeTuWNG9UtdxisWDdunXsJPn9fpw4cQJ+v5/f\\no1AoEA6HZd8nFAohHo8XxAigz9XpdKioqMA999zDdcI8Hg/a2tp4v7hcLqRSKTb0U6kUV6gvFAEf\\nuBZmuu2225iIn8lk0NXVxfxRynilOfP7/VxQlfZ5MpksCK9UrVajtbUVTzzxBD87Ho/j5ZdfBgCc\\nOnWKGwzTnlSpVJidneUw+MjICJLJ5IfCcp9GCHywWq2c4DJv3jxkMhne56+//jp6enpYb0A0wmOx\\nGIeeM5kMzp49y/wq4nTmGur+XBtLNAmtra0oLy+HIFxr9dDb24uRkZHrYiRJUSGFQoHNmzejtrZW\\nNokzMzN82cxFleZ+n3x1IXIkIC7mzZs3MzGO2nWQoTY+Po4XX3wRw8PDfAEVqqjY3I2i0WhgMBj4\\nYGhqaoJer5cV8YtGo9i7dy9zGShTSBp3zlU36d/RoUkx8RtuuAFlZWWyOHkgEIBarcbx48cBiE0i\\nL168yLpQtkUua0yKRur1eixYsIBbP2g0GjQ0NKCvr48RFKvVyi1xAGB0dBQTExNMbkwkEowI5qoP\\nIK4Xu93O5RGoXAFdXoDIBchms7IDPBAI8EUyPT3NRkK+iIlarWbjofYFoQAAIABJREFUpLa2Fo2N\\njVAoFPy5Pp8PbrdbZpSpVCrEYjH+HSEp0i7tuRLfVSoVr490Og2n08lcs1gshuHhYeYqNjU1IZVK\\nwWKx8DzNRW7IUMo3KUCtViOZTMqMJUDk1QGic0BJHoQKjI6Oylr1KJXKvIoKStF1KTLjcDiwZcsW\\nNoz6+vpgtVrR1NTE51AkEsHExASfQWq1mg2UfHid0sKT9LlLly7FunXr+POGhobgdrv5PdQKh7iL\\nPp+Pz0wp2TwffUgni8WCu+66i9dQKBTCuXPn0NDQAEBcQ9KMQL/fj4GBAUxMTPC4ENk8H30UCgXK\\ny8uxa9cuWVmZvr4+dtiam5tRW1sLs9nMazyRSCAQCHCh3kAgIHMec9VJp9Nh/fr1bCxRaRQy3Pr6\\n+qBWq2GxWHg/1tbWorKykp3d+fPnw+128/2SrxNQ4iyVpCQlKUlJSlKSknyMfK6RJbIYt27dCp1O\\nB0EQ2HuUckuuh5CF7nA4cPfdd0Ov18vSTPv7+/Huu+8CgMzrLZYe5I0sW7YMt912G3uTSqUS8Xic\\nM5d++ctf4vLly7KGmYXkdkn7BOn1eqxcuZJTqyl7kcIjV69exSuvvIK9e/dymIm83EJwKKT1eYhb\\nRh54U1MTjxGFTM6ePYszZ87gnXfeAXAtvbkQhemk3rfNZpMhFlarlWsVzZ8/H4AYZmtra2OkoKen\\nB16vl3WltNhcx4nWqk6nQ1VVFX9OOByGRqOBUqlkZILCAARvU5abtLko8SjymTeVSgWDwcBhW4VC\\ngenpaWg0Gg5jpdNpuFwuRuAmJyehUCgwMTHxoRY0+aJcc7M5LRYLtFotf14oFIJWq2V0p6qqCplM\\nBl6vl8d3fHwcHo+HdclnjKQFU8nDJg5ec3MzlEole9m1tbWora2FSqViTpvVasX4+DjXn0okEnnp\\nI+3PZzKZuCjobbfdxvuM9G5oaEBdXR3/zcDAAIaHhzmc4/V6GSnNNyQoHafq6mo8+uijcDgcvHeU\\nSiUWLVrE+9FoNKK3t5dba/T19WF0dFTWOihXnaRcLoPBgO3bt2P79u08Dj6fDzqdjkO7drsdVquV\\nM4aj0SicTif6+vpkrYPynTOz2YxNmzbh0Ucf5bEKhUJoa2vjNVRXV4fm5mauH0ZCCDsAdHZ25lxP\\nTYoCulwurF+/ns+/VCqFixcvcianUqmEXq+H0WhkSonVaoXdbme7wOVyoaKiomC1uj63xpIgCBw3\\n3bRpE9RqNTKZDF+yJ06cKFjPp08itAG2b9+OhQsXyuo7hMNh7N+/nwsrXo9muQTr7ty5E9XV1czV\\nSafTCIVCeOWVVwAAb7/9tqwybSGFUuKJQLpmzRrs3r2be1HpdDokk0kOk+zbtw979+6VFekrlMwN\\nodTX12Pbtm1cr8dkMsFsNiMWizG37NChQ3jjjTf4UCpkk1Fpc1WbzYaysjLmLFFdLpfLxe8JBoPw\\ner04ceIEABHulvKE8uVz0HPISKNLS6fTQafTQaFQcBilsbFRRt6mEhjSmmG5Gm5SjpvZbIZer2ej\\np7u7G7Ozs8hms0xkprVOBlVtbS2i0ShGR0eZ3zC3+vKnEakRaTKZeP8AYugoHA6z46FQKODxePg5\\n8+fPh9PpxPj4OK/x8fFx5sbRWOWik1arhVarlRVvdblcsjo0sViMjetUKoWWlha4XC6e2+npafT1\\n9fHFlyuxm+ZMWhA4lUrxPl+7di0EQeB9de7cOSQSCdTW1rLhSbW5pLrMreHzSUVqkNBaop/Nzc1o\\namri+laA6KQpFAoO95rNZtTU1PDlTeHBfMKlUr1IN5PJhJtvvhlarZbXB5X7kK5vmltAdDij0Sh6\\nenoKkhRAc2a327F161aUlZXxmurt7UUgEGBHpKamBslkEkNDQ3wWlJeX8zkPXBvnXETKMVy4cCFu\\nueUWNrhnZ2dx6tQp3jepVAoqlQqRSITPZ+JukS6ZTIYdvULI59ZYUigU7K1QM71UKsXEuBMnTlwX\\nvhJNDm36+++/nyeYFt3p06exb9++glRT/ku6kEFw4403AgBuv/12RlMA8VBta2vDvn37ACCnDKVP\\nKnMLYt57772cAQd8uMr622+/jZmZmYIbSiQajYbRo8bGRmzYsIEPAkDcgOPj46zPiRMnMDMzw2hl\\noRvS0jgYDAYZ2kjEytnZWfb6+/r6MDQ0xIYDIVz5Gm6UOSn1vsLhMPPrhoaGYLfbYTAY2MN0OBzQ\\narV8MNrtdkxMTMiqPueql8Fg4M+lfUSX7NTUFBdypcvE4XCgtbWVD0QiE0sr++bK46LPo58WiwV+\\nv1+GCkuNRGq3QLpZLBYYDAY0NjYyEb+zsxNDQ0N5rXG9Xg+NRgOdTsffSxAE1NbWcj01jUaDUCjE\\nulGBPpVKxfuxoaEBXV1dMv1zEbqQCJmh/ULnM6EDdNHRWp+cnOSzqaKiApWVlXwJp9PpnMdImg1M\\nDbfpdWNjIxsE0hYdoVCI56iiogIOh4MNz5GREZw7d44vY+DTGShSY0mlUvFadblcWLx4MQRB4LuB\\nDHEyaGdnZ2GxWJjDZLVaUVNTwwkp0s//NEJIu7Se2rx585DNZvmMmZmZ4YgNIKLZPp8PKpWKW3mV\\nlZXJ+Gm5dliYy+FsampCRUUFf8exsTH4fD5GA2dmZpBOp6FUKnnNCILAjasB0TGZnJyUoYF/dcgS\\nhVDuvfdeAOCqs9FolEMmRMorpg7AtVYi999/PwBg1apV3DmcPO99+/ZhdHS04AUo5wr1C1u2bBm+\\n973vAQAbA3SATU1N4e233+YQSrHCgUqlEmVlZaioqODeb5s3b2bUBAC3pSADd2xsrGiGktFoxOLF\\ni1FfXw8AuOuuu1BdXc0bPRAIIBgMYnh4mMmKHo+nKP0ElUolqqqq0NraCkD0xsLhMD+XKph7vV6e\\nH4/HI0PcCqmTxWJhXVwuF8LhMKMy/f39TJz0eDwAgEWLFqGxsZEP/WAwKCvnIC2W92n11Ol0rAsh\\nAHSJX758GX6/n8NfALjVgrRqb29vL8bGxmTZb6SPVL+/JFL0eseOHaipqUF/fz8XdHU4HHA6nXxx\\neL1e+Hw+GVlepVLB7/dz6IvaVUif8Wn0AUREorq6GgaDgXVJJBKMDgKiEZdIJPhZSqWSDWy6mKUe\\nOH1+LnNGRoB03jOZDF9shFiRroToZDIZDnVptVo4HA4ZXeDTjg+JNMGFUE7aN8FgELFYjFtOAaIx\\nJE2eMJvNqKio4Gw5iloUggpA6e2AaKwaDAZEo1HORB4ZGZGVd/D7/WhsbOQ9odfr4ff74ff7ZfOW\\nqz4k9fX1vI7p3urt7YXP52OjkloW1dfXc0q/QqHA+Pg4I5hkxOSKUNLPsrIy6HQ6Rhq7u7tlJWSS\\nySRisRi3qQLECvrSsjgdHR04d+5cXiiuTL+8/rokJSlJSUpSkpKU5AsunytkSQpnut1uTm+mkgE9\\nPT3c4qBQ6e9/SRcqgvXggw8CEC1/SjGlXkMHDhwoam86KbGyoaEBX/nKV9gTAURviry6wcFBHD9+\\nXNb3qJB6SaHURYsWYdOmTYwAGgwGJBIJjjH7fD6MjY1xLZh8UmA/TlQqFSorK3HHHXfwuLjdbkxO\\nTjLK5ff7EQ6HuT+dVAo9PiaTCUuWLOG6M2NjYzKvaWhoCA6HA0ajkZEN6kEmJeFLS1bkw1uoqqpi\\n7pYgCOjs7OTPS6fTmJiYgNVqZW9ywYIFMJlMHC6bnZ39SH5JrnWMiH+0fv16KBQKTuGmhr12u535\\nJW63G/X19bLCim1tbThz5syH2nZ8Wn0UCgVzRaxWK5YtW4bW1lZev2azGSaTidEQSnmmvZVMJhGJ\\nRNDX18fFRanvYi4os7QGEaGTNFYqlQoNDQ1cvFCv18vKLkjDvNIelV6vl9GnXNATWn+pVIrRPgoR\\n0v8lEgkZoqzVapFOp2G1WhlZEgQBIyMjjAp8VLPYTyM0vgqFAkqlktGcRCLB359+xmIxboECiOvb\\n7XYzMkbrO1fOEv0N6UTjVF5ejmw2i1QqJSsDMDU1Jav5tGzZMg5lJhIJLjxL6yxXDqz0/JAmvhDa\\nTuuM1nsmk4HT6cSiRYu4PUsmk0FHRweXesmnpRGhiXTWJRIJ5piSTvTaaDRCp9PB6XTymb5s2TIY\\njUYuCnvs2DGMj48XDI3/XBlLJIIgYOXKlRxTBsRFdvr0aVmGQDGfT4vMYDDgS1/6EpYvXw5APLSi\\n0Simp6eZ0E1ZFMXQSdrI02QyYf369di2bRsv/mAwiMnJSYbe+/v7PxTHLaTQBdrY2IilS5di48aN\\nDGf7fD5ZRVW1Ws1d14Hc69/8JTGbzbjtttuwdu1a5nT4fD7s3buXN6jT6URtbS2o7xJw7cAuRFE8\\nqTgcDqxevZph76qqKiYvA+LhbTAY0NLSwvPY09ODyclJ3vgE6eerUyaTQU1NDXPcstksh90A8fDT\\narVobGzkDB23242pqSmcOXMGAGS98egzcpFsNotgMMjrZfny5ZypBIgchHA4LMuior+TVvY9ePCg\\nzHjLB34nQ379+vUwm83MwwPEC4ZI6IA4b5FIhB2TQCCAoaEhHD9+nD+HCnXmE6YIh8Ow2WxcVRoQ\\n977NZmNjSaVSQaFQ8MVFFZ7D4TDrcuXKFfh8vrxrB6XTaVlxS6nhBIjjJA2FCYLABi8ZMWNjY7h0\\n6RIXGgwGgzk7vHP/Rvo5FKLLZq81yCajjThWjY2NCAQCOH36NADwvVIIB1waBp2amkI4HIbb7eY1\\nVV5eDrfbzby3BQsWcJFFQOx9eOrUKXi93oIYAfS3Pp8PU1NTcDqdvIbmz5+PWCzGYay6ujq0tLRg\\n+fLlPNenT5/GyZMnmdYRj8dz3m/SvwuFQkgmk+yINDc3o7Kyku/3VCoFt9uNDRs2YNeuXQBEQ+rK\\nlSt47bXXWDdaR4WQz6WxpNfrsXjxYrY2s9kswuEwenp6Clph9eNEWn359ttv501PB/f09DRnyVAV\\n2mIZS9L0T7fbLasQPDU1hWg0yl5Uf38/kwiLIWS4uVwu1NTUwGw282VK7WfIYEkmk5ySK5VCj5Ve\\nr4fD4ZChIR0dHTh9+jRX8F68eDFUKpWs3UkxRBAE6PV6mM1mzhZqampCJpPhuH9zczNWrFgBtVrN\\nF+/AwICsbEGhdaLD2mKxoLGxkTPziLBMLXsAcd7efPNNnD9/HgBkfI98JZVKcXPgnp4eLFy4kA9M\\narNgNBr5AB8dHcXk5CRzFQ8ePIjR0dGCXWy0V44ePQqr1Yry8nJGYgwGA9asWcPGtE6nw/T0NCNh\\n77//PoaHh9HX18coRT4HN41/KBTC4OAgkskkc7Uo249arxgMBuj1etYtHo/jwoULiEQizI3r7u6W\\npYDnapjQd6Kzl5q90j6anJxETU2NrCG1RqOBQqHgM/LYsWM4ePAgr/d8nEup0ZdOp5nLCYCrqVdW\\nVrIRTs1XiSgcDodx+PBh7N27FwCYkJ/vespmszLnKxqNYnh4GLW1tew42e12tLa28pluMpmQSCS4\\nOfYLL7zA85ivEUC8NkC8F9ra2uB2u5kDtHjxYlRWVrJxp9VqYTKZEIlE2ODev38/Ll68yGd4rpxT\\n6TqiDFOv18uOk9vtxj333MNIqU6nw7p169DS0sLPHBkZwWuvvYZjx44BENddISM6Jc5SSUpSkpKU\\npCQlKcnHyOcKWSKL3GKxYOfOnYwskRfT3d0ta4tRTD3o2WvWrEF9fT0jFhS6aWtr4zYZhWhM+XEi\\nrR20ZcsWGAwGttJTqRQMBgNnnB0+fBjhcDivjKU/J9JU1DVr1mDp0qUoLy9nj9Nms0GhULA3fObM\\nGRw8eJBTdgtRM2iuPoDonW3YsAEul4vnadGiRQgGg6xLRUUFent78eabbzLXLNdGq39JJ8rmpFCX\\nXq/HbbfdxogKebmXL1/GgQMHAIhlFaanpwteOFShUKCvrw/PPfccAODBBx+Ew+FgTzcQCHD7B+IC\\nnDx5EgcPHvxQUbxC6US1pDo6OmQNqWtqalBfXw+DwcBIzYULF9DZ2cm9BKkURqGQAPJaKbPGarVy\\n+KahoQHDw8Ooq6sDIIaVBwcHef20t7dz+j4hu4UICWSzWYRCIXR3d/N+U6vVKCsrk4UtnE4nj0Ew\\nGMSFCxcwMjLCKMD09HTe2adUdkIaZkulUkgkEkxDeOutt9Da2socvZqaGqRSKZw9e5YbZl+8eBFj\\nY2O85/ItRElCehFiderUKRw6dAi33HILc5Sqq6uRzWY5Y/HYsWM4evSoLCRYqPVNNb8A8fwzmUwo\\nLy+X9ai02+28vkdGRtDZ2YmDBw8CANra2phrmW/4jVoSAWKI+7e//S3S6TTuuOMOAOK5SXXWAHEc\\nBgcHcfXqVbz11lsAgCNHjnDWMH1urvoQah4MBnHy5Ek0NDTgoYceAiCumRUrVnA2M3AtS5Hm9oUX\\nXsCBAwe4cXU+IcGPks+VsUTQZENDAywWCw9EOp3G+Pg4VzkFCh/KkYp041D3dylBLxwOo7+/n+O4\\nUiIdvS6kLlIuADVdJC4OhcEIeu/v7y9auQDgWu0glUqFsrIyGby9ePFiTE5Ocgjl+PHjOHfuXNFC\\np7ReqD+QdA7mzZsHp9PJIUK/34/9+/fj3Xff5YOq0IYJ6ZRKpdDe3s5zVF1dDavVyuM0OTmJ8fFx\\n7N27F++//z4AfAh2L6RBOT4+jv379wMQuSP19fUcKqW+hrOzs2xwX7169UMk5UIeSjT+wWAQL7/8\\nsoxbUlVVJasWPjU1Bb/fLzuspZB+vkLfi0KNkUiE19H4+Dh6e3t53sxmM6LRKF+6VDSUSLz5Cs25\\nQqHg8ZeWRYhEIvjtb38LQOTgNTc3s1PX3d0Nj8eDUCjE36kQzkA2m4Varf7I9UDG9B//+EecOHEC\\nq1evBiDux7GxMVy+fJlLVEQikYI5ldKzn9YC6ebxePDLX/4SJ06cYA5eVVUVQqEQk/CPHTuGqakp\\nTrgo5NrOZrPs0Pv9fhw4cAA9PT3cuLqpqQlGo5HLGgwMDKC9vZ3X1PT0dEGTl6SFk8+ePYuxsTEc\\nPXoUALB69WqYzWbef5OTk7h69SrOnz/P4VMyuAtlSALimePxePCb3/yGx+H+++/HkiVLOJxKc9zf\\n348//OEPAMTmun6/n9d1oSkLnytjiQ6pcDiM7u5uPqQikQhefPFFjI2NFb2W0VxdZmZmuFAXvT55\\n8iTefPNNGWJSrMKPUu9Aq9ViZmZGVq14dnYWb7zxBhehnJmZkfEBijVOg4ODzCWT1sQZGxvjoo+v\\nv/46IxdAfjyFjxL6rFAohKtXr6KmpoaLPCoUCszOzjLv5sKFC3jttddkVd+LQcpPp9Pwer1ob2/n\\nNXPy5EkYjUaeR7/fj97eXly+fFnmaRdDn1QqJeum3t7ejs7OTll2DGV1SbucSx2GQuo0l/xMjWYB\\nkePh9Xo/RNiVvr/Q2ZTSfTv3+9J6lj5vrlNUjDkjw21uNmQqleJWE16vF5cvX5Y1EaW5LrQ+fy6L\\nlebN7/djamqKUSRCBJLJpKzqe6Hk4z4rHo9zyxnKnCZ9pFmMhZy3uSi+dB0Fg0FZGw/ad3QWUHHO\\nQqPucz8rnU4jFothfHycCdLvvPMOTCaTrEo8zVmxsqnpMxOJBCYnJ3mOIpEItm7dylzKoaEhdHR0\\noL29nZFcv99f1Cz4EmepJCUpSUlKUpKSlORjRCg2AvOJlBCET6QEhVUMBgMcDgfHeRUKBS5duoSZ\\nmRlm7hez1Ym0PYTdbsfSpUtlqbKjo6MYGhpiXYpRBZqE6ogAIu9m5cqVqKmpYQicKsJShVjyUoql\\nD8Gkdrsdt99+OxoaGjg019vbi46ODg6XUj2RYvfKM5lMqK2txZYtW2RzMjIywnDyzMwMhyiK3SaH\\n+mgRZ4IQCPLW4vE4ksmkLMusmPtUmoavVCpl1ZgJFf0oBKdYIkVnpNmehWqqnI9O0rIhxSoH8mn1\\nIfmsxoZEqsvcUNhnJXPH6aPGqNClQT6NTnP1mVuN+3rqJF3Xc38n1ed66ETPJmRUqVTCYrEwxzQY\\nDDLaLW1KnaO0Z7PZNX9Rp8+TsTTnb2SvP8tD66P4SJ+FPrSwqN0AgOsWlpwrgiBAq9XKjDlpYbzr\\nrdPcoo7UhFF6MFyPXoIk0iJs2WwWSqVSRra93vpInyv9KR2fz/qskOpwvS+4klx/+Z+w5kryVyFf\\nbGOpJCUpSUlKUpKSlCRP+UTGUomzVJKSlKQkJSlJSUryMVIylkpSkpKUpCQlKUlJPkZKxlJJSlKS\\nL5zM5TSWpCQlKUk+UjKWSlKSknzh5H8CF7MkJSnJF0c+V0UpS1KSkpTk8yh/Lnv3s8j4Il2kJSMA\\ncJPXz6IEAWXPUoYocK3kCmXQfhbZs4BYmV2lUiEWi3HpkWg0et31Aa6VzTEYDFwJfHp6WtYK6Xrq\\no9VqYbVauTwMtfahljrXUyfKTJeuIank29qnhCyVpCQlKUlJSlKSknyMlJClz0BKNWJKUiz5rGrT\\nzK019lnWQfuogo1zXxe7EKpUFAqFDM1RKpVcv4o8dGn7iGKKtAm4VquFwWBgHQipSKfTRemL9uf0\\nAcQm29JGqVNTU/B6vfD5fIxSUBHbYotCoYDL5cKOHTsAAMuWLYPH44HH4+H2SAMDA5ienr5u+lBj\\n67vuugtLly5FMBjkvmknTpxAV1fXdZszpVLJza03b96M6upqXjvDw8Po6Ojg18VG4KQFoq1WKyoq\\nKmR7a3Z2ltdPvgWQv3DGEm2+srIy/O3f/i0aGhoAAG+88QbeeusthMPhz+QyUSqV3Jz0scceQ2Vl\\nJd555x3uJu33+wvekPTjhMZJq9WisbER27ZtQ2VlJQCxk/Tx48f5AC9Ew81PKgqFAlarFVarFQDQ\\n2NgIjUbDjYCDwSBCodB1rWxLcLxSqYROp4PJZGJd4vG4rLjl9dAHuFaAlET6WnrpXS+dpM/PZrOy\\nytsAZP2trpc+9PxMJiMzWMiYK2ZDaaku0jWkUqlk4yAIAtLpNBswNHfF1Iee5XK5UFZWxl3bNRoN\\nEokEotEodySgsFOxdKF1M2/ePOzYsQMGgwEAcPjwYeh0OlloTq/Xc3/CYukDADqdDtu2bcOTTz7J\\nz33ppZe49xi9V6vVFnV8APE8tNls+PKXvwwAeOihh6DVavH6669zE+KZmRnZWBZzDSmVStTV1eG7\\n3/0uANFYisfjeOONNwAAPp8PqVSK955SqSzaPlOpVLBYLNyE+O6770ZNTQ0btN3d3Th58iTfY6lU\\nKq+q+184Y4k2+QMPPIAf/OAHvNGSySSOHTtW1M3250SpVMLhcODHP/4xAGDnzp0QBAFOp5ObAIbD\\nYcTj8ety6UoP7yVLluDv/u7vsHXrVo4719fXY2pqiluBSDuKF0sfQBwnvV6PRYsWYevWrQCALVu2\\nYGJiAvv37wcAXLx4EZ2dnUU14KQogEKh4HGxWq2YN28eG3KBQABTU1MYGBhg76WYOs3lmFBrEkA8\\nOPR6PRQKBa/x2dnZohmVcyuQ0+9IL0BsUEr6qdVqBIPBougifba0VQuNF6FtUv1UKhVCoVDR9KHn\\nS40kQDyfpA1kSR9aY8VsjaRUKuF0OmGxWAAAFosFLpeL10s0GpU15QZER6mY+ixbtgwAsGfPHlRV\\nVXGbptnZWXi9XoRCIb78i+0g0Rzdeeed+Pa3v83tiCYmJjA6Oorh4WH4/X4A15rKFltUKhUeeeQR\\nNtzMZjM8Hg/rBIhjFY/Hi278C4IAk8mEf/zHf8TOnTsBiIblxMSErFXT3Aa9xRCFQgGtVot169bh\\nJz/5CQCguroamUyG1/fMzAzsdjumpqb4b/J65id9oyAISkEQOgRBeO2D142CIJwUBKFXEITnBUHQ\\nfPB77Qevez/4/4a8NCxJSUpSkpKUpCQl+Qzl0yBL3wNwCYDlg9f/N4D/J5vNPicIwn8AeALA//fB\\nz+lsNjtfEIQvffC+hwqo858VhUKB8vJyAMA3vvENWCwW9koikch18QSkQp6sRqPBxo0bsX37dgCA\\n0WhEOp2GXq+XedvXK0yhVCpRVVUFQBynTZs2wWKx8POz2Sz0ej0ikch10YfGyWKx4KabbsLmzZtx\\n6623AgCqqqqg0WjQ2NgIALh06RJUKlVRuTmkj8FgkDVvrKmpwerVq3mcUqkUrly5gpmZGfbOi+Xd\\nCYIAg8HAqE0qlYJSqURZWRmAa+HU6elpblQciUSKBskrFAoORwJyRIR6AjocDvbmpqenEY/Hi6KP\\nFOWy2+1IpVKy5+h0OmQyGUZ5ANEDJm+40OeCdN/X1dVBpVJxqCubzcJoNDIySmE40kGlUhXlnFIo\\nFLBYLNi6dStsNhsAcS95vV4YjUYA8nA7rWOFQlGUc0mpVKKmpgbf+c53AAANDQ144403cPHiRQBi\\niJuQLikHTaFQFAXlVqlUjHL9+Mc/htvtxtmzZwEAL7/8MgYHBzE9Pc37XNozESg86kVI45YtW/CD\\nH/yAzyCPx4MXXngB586dY2QpFAoV9dwhMRqNePLJJ7Fnzx7WLxAI4MSJE7h8+TIA4OrVqwgGg7z/\\nCn1OUwhZp9Nh2bJl+P73v4+WlhZ+VjQa5fdks1n4fD6+x/Ido09kLAmCUAPgLgD/G8D3BXEEtwD4\\n8gdv+RWAn0E0lu754N8A8AKA/1cQBCF7nUgmmzZtAgDMnz8fgiDwpJ0/f/4z4ytVVlbi29/+NsOD\\n1DT1/PnzPJHFhN+lIggCLBYLHnjgAQDA8uXL+dKjwzIcDsPr9V4X0qkgCNDr9QCAdevWYfny5Vi2\\nbBkbASqVCi6Xixe6x+MpqlEpDVE2NDTAYrEwubKlpQUrV65k3U6dOoXLly/LwmHZbLag+tFhpdfr\\nUVtby3OUyWTgdDqxcOFCACK3K5PJYGhoCGazGYB40fl8voKHnmnOaFwoVELjVl5eDrfbDYfDwfPm\\n9XoxMjLCPItChlFpTQPiOExNTSEUCnEoyW63Q6PRwOVyARAFQk1GAAAgAElEQVTHcnR0FAMDAwDE\\nMEshL2Ay3CorK7Fq1SpMTU3JeGQOh4Pfa7VaodVq+eLr7+8vSkhXq9XixhtvxKpVq+D1egGIxvTM\\nzAzvP5VKBZvNBoVCwQY3kYYLLWazGbt372Ye5/DwMNrb29HT0wNA3EfhcFjWqLxYnE5BEOB2uznU\\n5XA44PF48OKLLwIQ+VPRaFQ2L9IyC8XQZ8mSJQCAn/zkJ7DZbGxs//73v8cLL7yAyclJDllSqYdi\\nFWIl4+PBBx/E9773Peh0OnbyT5w4gddffx0dHR0ARBJ+KpUq2rjQnl6zZg1+9rOfYdmyZawf8Uel\\nAEQgEOB7LN85+6TI0r8B+HsA5g9eOwDMZLNZMtVGAFR/8O9qAMMAkM1mU4IgzH7w/smcNPwUYrFY\\n8OijjwIQJ5gMEgA4evQoL6jrZTDR5bF9+3Y23gDRwp2YmMDJkyevWwYDiVarxerVqznTw+12I5vN\\nIhaLsXfw3HPPyS6QYo6ZVqvF/PnzAQA7duxAa2srysrK2NtNp9M4d+4cjh07BgBF5b0AkBkBGzdu\\nRHV1NXOUbrrpJjgcDj40vV4vAoGAbO4KbSjRONTW1mLDhg1slKVSKdxyyy18qGq1Whw5cgRqtZoN\\nh+Hh4YIiOVIEsK6uDuvWrQMgcl1CoRB75qtWrYLVakVnZycbS7Ozs2hra2NjqZA6OZ1O9i6XLFmC\\nqakpjI6OMnra2NgIk8nEjoler4fL5eLLxuv1Fmz/KRQKNgDWr1+PefPmob+/HxMTE/wel8vFSAFl\\no9E5kEqlCrrfaL1s3LgRt99+O6qrqzE0NARAvNjC4bCMp2Q2m5FIJHjdFPJckhKoH3/8cdx99928\\nX44dOybL6CLDSKvVsg7FQgAtFgt+/OMfM5o9OzuL3/3ud3j77bcBiKhoKpVCMplkpLTQThGJQqFA\\nRUUF/u3f/g0A0NTUhEAggN/85jcAgF//+tfweDyIx+PXJRKh1Wo5IvJP//RPsFqtiEajeP/99wEA\\nzz//PE6ePMnG3NzzplDrmAjsK1euBAD8y7/8C1pbWwGAz+PR0VHEYjHe1+TE0frJV5e/aCwJgrAT\\ngDebzbYLgrApr6fJP/cbAL5RwM9DQ0MDD6ZKpUI0GsXzzz8PQGTp58OEz0Ufyi772te+Brvdzh5n\\nNBrF/v370dbWJjuU5qZfF1IX+uyGhgb8zd/8DV+yOp0O2WwWXq8Xr776KgCgq6uraARqqS4KhQKL\\nFi3CV77yFQCisWS325HJZPhgHB4extGjR9kbJoO3UBeKVBe1Wo1169bhlltuAQDcfvvtqKqqYs/E\\nbDYjlUrJiMEajUZGvi6EXhRW0ul0THRfsWIFbr75Zka1pqam0NTUxAZ5MpnkzE8ioZLHla8oFAqo\\nVCpGrHbs2IEFCxZg+fLlAMQLv7+/n40nk8mEdDqNdDrNY3XlyhWUlZXlTbKU6gMATqcTjzzyCBtG\\ndrsd0WgUXV1dbLTodDqo1Wq+XDKZDCYnJzlNPV8Djr6TSqVCbW0tfvjDH7Iu09PTiMVijBzp9XrZ\\nHiDjhAw5o9FYkPA3XS6LFy8GAPzoRz+CTqdDb28vXx4ajQYGg0HmeVssFvh8Pj6XChX2koZKb7rp\\nJjz11FPIZrNoa2sDAExOTiKTychQAtJRql8hhZ71wAMP4P777+fvfPLkSVy+fJnnQVoYk75DOp0u\\n+BkNXDPcVqxYAUBE/86dO4cjR44AEPf93GfT3xZqfKSJEJs3b2bDraKiAuFwGB0dHbK7IplMFj0s\\nqVKpUF5ejp///OcAxHIOCoUCMzMzOHz4MABgZGQEdrsdFRUVAMS9ZTKZZKHTfMbokyBLGwDsEgRh\\nBwAdRM7SvwOwCYKg+gBdqgEw+sH7RwHUAhgRBEEFwArAP/dDs9ns/wHwfwBAEIRSwaGSlKQkJSlJ\\nSUryP1L+orGUzWafBvA0AHyALP1f2Wz2EUEQ/ghgD4DnAHwVwMsf/MkrH7x+/4P/P3g9+EoqlQr3\\n3HMPhyCy2SyuXLmCQ4cOARDhwetdm+fBBx8EACxatIh5SoDoyf7pT3+SFTUrJlmZyJ0A8PDDD2PD\\nhg0c+wVE6P/8+fPs5c0lVhZaH0IFqqqq8MgjjzB6Yjab2ZMlzsTevXvR09MjK59fqPi81Ns1m81Y\\nvXo1HnroISxatAiAyL0h5AgQU1EvXrzIc+b1ehGJRBCLxT6URp+rPoCIhJSXl2PXrl244447AIh1\\nwwwGA9ehIZ4JjZNOp8OVK1cwODjIJNl8iJ9SxM1ms6GlpQVPPPEEAKC1tVVG/FUoFFi6dCnrFAgE\\noFAo0NbWxvB8X18fhoaG8kIsqZSD0+lEc3MzAODpp5/G/Pnz2XscHBxEMBiUpaETL4fCT+l0Glev\\nXsX4+DiA3MM7tJYpTDtv3jz867/+K5qamgCICN9bb72FwcFBXh/Dw8NIJpMcXr106RJisRh8Ph/r\\nmusakiKcOp0Obrcb//Ef/wEAWLBgASYmJtDe3o729nYAoqctLReQzWbR2dmJSCRS0BYjVIiTOGO/\\n+MUvUF5ejtHRURw4cAAAcPbsWSa703MVCgVSqVTBycuETNI+/+lPfwqj0chrde/evTh37hx/d2lt\\nJRrfQobAFAoF7+vHH38cjz32GK8Xr9eLX//61zh37hwA8ayW6kN/X8j7g87n1tZW/Pu//zujtqlU\\nCt3d3Xj22Wd5DcViMcTjcR4XaZgSyH+c6PMcDgd+9KMf4YYbbgAgol6zs7M4cOAAXnrpJQDiml+z\\nZg0j4Hq9Hg6Hg0sHEEKZq+RTZ+kfADwnCMLPAXQAePaD3z8L4DeCIPQCmALwpbw0/AQiCALsdjse\\nf/xxnuhoNIp9+/Yx/F1o2PTjRKlUorKyEl/96lcBiGGKVCrFB/rBgwfR2dkpq0lRSIKe9HOIFLdg\\nwQIAYgXYsrIyGWyaSCRw6NAhJlfOLbKY77hJe/VIOTUbNmzA2rVruRpsOp2GUqlENptFd3c3AKC9\\nvR3d3d0MiScSCS4ulotI6/EIgsCZQXV1ddi4cSMaGhoYxo1Go7IMtEuXLuHIkSO8+bq7u7kWTD6h\\nE5ojCgssWrQIy5Ytw44dO/h3mUwGvb29PFaZTAZXr17F8PAwAPFQ7enpQX9/PyYnRXpgrhevlHTv\\ndDqxfft2tLa2cvE3v98Pn8+H/v5+ACJJORAIcMhNr9ejr68P3d3dzNWZmZnBzMxMTvNGB6bRaER1\\ndTVuvfVWrF27FoB4oM/MzHDR0q6uLoyNjWFsbIyfXV9fD4/Hw/svFArJCLufdoyk2ZIul4u5Wps3\\nb0ZjYyPz6o4ePYpLly6hq6uLw8jhcBgOh4MvZvodGQT5GLcqlYqTNZxOJ+677z7U1dUBEMM3r7zy\\nCnp7e9lIDAaDiEQifLnEYjGkUikZDy+fvU/zptFoYLVa8fDDDwMQ95rP58Pzzz/PJHuaEyryqNVq\\nmRdU6GQApVKJiooK/MM//AMAMVzq9/uZF3T58mVEo1HZnEjrd9HvChVy12g0uPnmmwEA3//+96FW\\nq3mOnn32WRn3DxDnRFrcVKlUFoybSJwpQOQF1dXV8XofGhrCs88+i/Pnz/OcKJXKj0xwKYQxKQgC\\nr83du3fjwQcf5LM4Ho/jvffew6uvvsr1AOfPn8+FTAGRGyitUJ9vmPJTGUvZbPYQgEMf/LsfwNqP\\neE8MwAN5afUpRaVS4f777+ciYgA4tjo31bMYIt00CoUCRqMRX//61/liI8t/cHAQgEjSm5mZ+ciD\\nsVCeHH2ORqOBw+HAY489BkC8OKgpJCAaRs899xxefvll2QEu1SOfsZMWdcxmszCZTMyt2blzJyor\\nK2U8hXA4jIsXL/LBdfbsWSSTSRl/IB/DlzaOxWKBIAjMa1mwYAGWLl0qq+xM5NczZ84AAF577TWc\\nPXuWDyYyRnItJkqHkFqtRmVlJRtumzdvRkVFBZLJJE6fPs3v9fv9nEk1NDSE4eFhNpamp6cZSaEL\\nJ1fDRKfTMXmytbUVO3bsgCAIuHLlCgDg3Xffxfj4OBtHOp0OV69elVUznpycRCgU4u8YjUZz1ocO\\nzFWrVmHFihXYtm0bj4Pf7+eK84CIao2MjGB0dJQRk8nJSUSjUVk6cywWy1kfmqf58+ejqamJjcgb\\nbrgBoVCIve4jR44gFAohEAiwYZZOp7mIIHAN1cqVUC01JF0uF6M3t956K3bu3Mn7vKOjAx0dHbIE\\nCSo4Ses9kUhwdfx8Ljsy/sngNpvN2LlzJyOTiUQCp06dQldXF+9jvV6PUCjE30etVnN2E70nn3OI\\nLllq/PrYY49h8+bNAMRzhxxYei8ZUKQLVTeXGiX5ntVUWHH+/PlcsNhutyMcDmPv3r0ARERWo9Gw\\nAUNntFKpLHjpC1rbX/qSiG9s2LABANjpeOaZZzAwMACDwcAlekgn2vvUSDdf/i2tIeIgf+tb34LJ\\nZOLvfODAATzzzDMYGxv7ENJGXOFgMChz1JVKZX53R05/VZKSlKQkJSlJSUryVyJfiHYnVqsV3/zm\\nN7mVACB6Um1tbdclvVIaQhMEATfeeCO+9rWvcaZSKpWCz+fDH//4RwBiLZWP0qtQYUKK9wMi6rZn\\nzx7cd999AMRMpVQqxR5KT08PnnnmGUxOTsraChRSF/osyqgiz2X9+vWcOQWInkAwGMR//dd/sZcX\\nCoU+BOsWQjcKDVBG1K5du7BgwQJks1n2RmKxGEZGRphXceHCBXg8Hn4+rbV8UUulUomGhgYuvFlb\\nW4s1a9YgmUwyX2BmZgahUIgRis7OToyPj3N4J51Osx756qPVarFq1SoAwM0334yqqiqo1Wr09fUB\\nEMtNJBIJDvf5fD6MjY2xd0logDT7NJ+wKfF7mpubsXbtWtTV1TEaefXqVWg0GrjdbgBiGjGtF3om\\nIW7S+cqHF0SIidVqRXNzM4fhTCYThoeH2ft1OBwIBAIwm80culWpVDJEmTgouXrf0rByPB5nNHvF\\nihWw2WwcoqC1FI/HGX0KBAKy9U6edyGyhqUIbW1tLXbv3s3lEsbGxpBIJFBfX8/zqFAoZIWDNRoN\\nMpmMDAHMZ86kCENLSwu+/OUv8zz29/cjGo1yzTK73Y7Z2VkO7RKCSigykN8ek86Z0WjEnj17GMlN\\np9M4e/YsP2f58uUcSgXEBr7Ey6PxID5uviFTu92Om2++mQuFarVahEIhvrcCgQDrSedmIpHAxMQE\\n0yYolJrPnNF8LVy4EA89JNazprOxq6sLgFjeZnh4mKkbgIgo1dfX83lhNBphtVr5//Nd159rY4kM\\ngu3bt6OpqUnWF+vAgQPcnPZ6CG0Ap9OJn/3sZygvL5eVCnjvvffwwgsvAEDRa2RQHBwQL7vvfe97\\nnHIuCAIikQiHln7xi1+gu7u7aEUx56ZJ79q1C3v27AEgHkrpdJpDA+3t7XjppZfwxhtvcNitkOUe\\nBEGQpdM7HA7mCqxcuZKrllPooru7GwcPHmRjKRgMyi62fMnc0hBKOp1mLte8efNgs9kQDAY55AOI\\n4QAKhfX29soul3zHScovcTqdfNHRwSW9HEwmE2pra3nezp07JyMFky75rnHqFUiEabVaDbVaDaVS\\nyYaZx+PBzMwMhwVUKhUikQjGx8eZR5FOpyEIQt6XLulTW1sLQFw/LS0tXJcrHo/D5/Nx+KampgYV\\nFRXo7u5m5ySZTCIcDsuIwvmElGmf22w2WVPRG264AZlMho3r2dlZNDY2or6+nrmJoVAI8Xic55HG\\nKN8Qt0qlkiUj3HvvvVi8eDGvB7/fj3g8zqFvQLwEI5GITF8g/3VNXC7SxWaz4amnnkJlZSWfMRMT\\nEzCZTFi9ejUA0Ujo6enh7zM+Po7R0dGCNMyWnkF2ux1bt27Fk08+yWt1fHwcAwMDbPTabDbYbDZ2\\nTDo7O1FRUYHz58/zHsjHUJKeQTfddBP+1//6X2xMU7iUQv1utxu1tbVoaWlhXtDMzAxGR0d5XMbH\\nx2U1xXLVyWazYfv27di1axcAce9NTEyw4TY4OIhwOAytVsv7sby8HHq9ns8hi8XC9x7JX6WxJAgC\\nD8RXv/pV6PV6ZLNZJgy++eabRW22OlcXWuw7d+7E4sWLZXVdvF4vnnvuOYyMjAAoLn+KMk+Iv/XE\\nE0/A5XLJupqPjo7it7/9LQDRQClWxdW59Xk2bNiARx55hLOHNBoNIpEIb8aXX36ZDaVCG5OkCxkk\\ndXV12LJlC2688UbWxWg0IpFIsD7vvPMODhw4wJdJIZME1Go1e7YVFRWorq7mwooGgwHT09Ow2Wx8\\ncFEFbCqySplu+RpuZMzSuBgMBpSVlfFlNTExwYXdSF+bzSZr3Dk+Ps7Ee9IlHySJDnCXywWdTsfG\\n9vDwMC5evIhwOMz7jTLcpGgPed1SFCAf9AYQDQC73Q5BELhwYigUQjKZZOQqEomgq6uLLxKn0wmr\\n1Yrh4WH+3ezsLBc4BHL3vFUqFXQ6nQyVLSsrYyQym80iGo2yca1SqeB0OlFWVsaGW3l5OXp6ehhR\\nntsE9dPoIyX5ptNpGYK1ZMkSGe/w0qVLUCgUcLvdfE5NT0+jpqaGkVLKssoXcZMiSoCITra0tCAe\\nj7MB0t/fD4vFwueU2WxGQ0ODLEMxkUjk7VDS2SxtWH7PPfdAp9PxmvL5fIjH4zyPZrMZGo2GjSeq\\n9dbZ2SnLHMxFpBWxKysr8fDDD2P+/Pm8d6niPr2ntraW2wnRGV5TU4NMJsM8PeJv5rPX9Ho9Wltb\\ncd999/G5FIvFcOjQIY44eDweKJVKqNVqdigrKytlkQqPx4NoNJr3OJF8bo0lpVKJbdu2AQBuvPFG\\nKBQKRKNR/Pd//zcAcQNcD1SJDi5azN/5znfYiyHL/9lnn8Xhw4cLNml/Tmgz2mw23HvvvQBEsjBt\\nTkDMivnP//xPvPvuuwByPyA/qT4Gg4GNgMcffxwtLS2sTyaTwcDAAJO5jxw5kjPx9pOIRqPhbKHF\\nixdjx44dTPAGRK+/p6cHr7zyCgDg9OnTiMViBZ036SEu7XMkDZ1SqHRmZgaXLl0CIHreUtIyXf55\\nHwAfZAWSsUFEVirUqFQqUV1dDUEQmFStVqvh8/k4PEEZjNJxylUvQo8A0YiMRCJ8aU1NTXGKN42V\\nUqmE2+2WhSDr6+sxMTEhQydzXVN0Uej1elRXV8Pv9zN6nUwmkUwm2bAcGxuDWq3mi06n08FsNmPV\\nqlVc4sHj8SCTyeSVDk+EY4PBIEtgcTqdTG5NJBKYnp7mvWa1WrmfIKXMj4+P48qVK6xLrkRhpVIp\\nKxSayWSgVCo5ZNPQ0IBkMsmIm0ajgVqtxuTkJO/H6upqVFVVcdsMGttcMs6kBjfpRGfyli1b4HA4\\nEIvF2FjS6/XIZDIcZp43bx4cDgefDZcvX+b+fbkUXZRSNNRqNRtly5cvZ8SNjCW/3w+z2cxramBg\\nAPX19Yyc2mw27rlI85XrGEkjEAsXLsTChQuRzWbZORwcHEQkEuGx8/l8mJiYwIIFCzjUZbPZZD0i\\ng8FgTlnd0jkzGAxobW1FZWUlnykjIyPo6OiQlfsgo43u3srKShiNRt7rExMTuHr1asHK85QI3iUp\\nSUlKUpKSlKQkHyOfS2SJINynnnoKwDXPYHh4GG+99RYAEdUpJrIk9RYsFgu+/e1vAxBT0MnqJ27A\\nvn37ZIS8YgmhFatWrWKSHsGY5GWfOXMGx44d49BBsVrAULE+s9nMKNf69etloYOpqSmcOnUKJ06c\\n4NfF4HLRHDU2NnIK7u7du7Fw4UJGDkKhEAYGBnDq1ClOQ6fwUqFFrVajvr6eSYsUkiOkhpCHzs5O\\n9nZ7enpk5OFCkvBdLhc3oKbQKIWzvV4vqqqqYDabOUSi0Wi4/xoA5ivN1ScXj1ej0XC68G233Qa/\\n38+ebiwWg81mQ1VVFaO2wWAQ8XickylId0oMAK55lLmgAkRK3rZtG1pbW9Hb28soQFNTE+rr6/k9\\nfr8fWq2Ww//U4ohKGQDgIovS8+PTjlFZWRkaGhpgMpk4tJ9IJOB2u2XFZqPRKK8Xn88Hu92OZDLJ\\nqKHX6/1QzbJc2psQ0kWIoFKphFar5fCIIAgIh8PweDwAxFIOyWQSer2e0RHa+9KwWa5tjaQohVKp\\nhMFgYBI+hd39fj/XCSPCOc1JTU3NR7Ywkob3ckGWqKgqkaN3796N8vJyJBIJJuL39vZyeBcQ59Bi\\nsfA5LggCvF4vpqam8iaZ0zwB4lomVJI4R21tbfD5fLIkDSquTN8pnU6jt7eX9c/13pWS8A0GA6qr\\nq6FWq3mvvffee7h06RLve0JW6+vreTwrKiogCALOnj0LQGzyOzIyIktcykc+V8aSdNGtWbOG+x4B\\n4mX3q1/9imsZXS9it0ajwQ033IAHHhBLS6nVaqRSKQwODuKZZ54BIPbFKmZfOik5t6KiAl//+td5\\n4QPiJXPq1CkAwP79+zEwMFC0UJe0CvWSJUu4uCLpR325AJGET1ldAIoWplQqlSgvL8eOHTs4RFJT\\nU4NYLMZGZH9/P86cOYNQKMSZS2SQFFofvV6PJUuWcJiCsiOJS3L27Flks1nMzs4yBO7xeGQ9mAo1\\nf5lMBmazmStiNzQ0oLOzk0NuFGLWaDR8YNMBSQZ3IpGQcYLy6Q8lCAIbtEuXLkVZWRkbSxaLBel0\\nGnq9nrk4ZHyQMeL1euHxeApSWFWhUHD4w+VyoaysDDfffDPvt/nz58PlcskO+crKSj7QKcHkwoUL\\nfJmQIZVLaICeW1dXh/LycqTTaRm3Zfny5Wy4UdYdGZGUVBKLxXi/Xbx4EcFgUJYkkMu6Ir6SNGTp\\ncDi4nppSqYQgCOwMUH0no9HIIZzBwUEMDg7ymiIjrhCZpm63m40kMhil3elnZ2eh0+k4nONyuRAO\\nh9mo7O7uRjj8/7d37rFxXlkB/93xY5zErhM/Ujtup02Im9aNaEld+mC7bRax1CmlVKqqSLTsH0hI\\nUCQQRdBoJbT8wR8gQQEJseWx3QVStu0uiO1KKGkTJ7ET104TP9ZO48QevzLjt93xo7Hjx+WP77sn\\n30ztiYnn4XjuT7Iy842TnHvmfPc795xzz51NiDzZ2dmyYWH37t1kZ2czOzsrabe5uTkmJiYkVXf/\\n/fdHnQF5+fJlWlpaxOGE9W1YMN9ZIBAgOzs7alPAF198gc/nk/eFhYVyJqT53oaGhsSRga82NF4r\\n3pTg1q1b2bt3b1Td4bVr15ienpZdr0tLSxQVFbF//35pUFtaWsr4+Lgsvi9evCg1gongjnKWDH6/\\nn9dff13ypsvLy/T19XHu3LmkRAJi8XrBRUVFvPHGG1LMaBrenT9/Xo4PSeZRK95W+UVFRdTU1HDw\\n4EGRb3FxkXA4zNmzZwEnQhHbOTyRmNVlVVUVNTU1vPjiizIJmS7mZktuR0cH165di9pFlQwnLj8/\\nn+eff54XXnhBdi75fD7Gx8dlAu/u7mZ6ejqqkeJ6OoWvhtaa4uJiampqpKv6E088weTkpMhSUFAg\\nR4aYCV1rHVU/lSiWl5cpKyuTBn3FxcVUVVWJEzk5OUlZWRkzMzMyIYbDYUZHR8XpjT1GYD025Z1s\\nfT4fW7ZsEYfA7/czPz8fdRL9xYsXCYfDEvW6cOECY2NjUTLcrjzLy8sSHS4vL5fDjU0ti3EQzL/v\\n9/sZGRmROqLs7GyCwSBnz56NKja/XXnM37t06RKDg4Oy1R+Q3cDm4WeKyE2dkOkQHwqFpNGpicqt\\nd5u3sUvvQ8kczwPOnGB23oHT7DUQCIj9gxNRGRoakkjCemopvYeSLy0tSYG2kTd2d6fP56Oqqoq9\\ne/cCjhPR3NwsDu7g4GBCMhWmrs9sIIlEInKkknEU8vLyKCoqkmM9ysrKyM7Olkjv+fPnaWtrY3p6\\net1H0nh1YIqhCwsLZbFSWlrK1NSUyPb0009TWVlJfn6+1BHW1dXR3NwsC8z1POu8DUqNXZqas+3b\\nt1NQUCALtrvuuov9+/fzzDPPyG64SCTCmTNnxFkaHx9P6LPX1ixZLBaLxWKxxOGOiSx5c8iVlZVU\\nV1dHHaVw6tQp+vr6krot38jh7ZWxb98+qqurJZKztLTE2NgYTU1NUQ0DkymP8fz37NnDoUOHpIYL\\nnNVjMBiUtEV/f39So29mJRAIBHj44YclqgSOp9/f3y8HvZoGa95+IckgNzdXtr2aLa9jY2MSKQFn\\n9djd3c3ExERUE7pk4Pf7KS0tlVXSrl27GBkZEXvJy8tjdHSUS5cuSfTm2rVrSemFpbVmZGRE6jdM\\nrZlZtVZUVLBt2zaGhoakbcHg4CAdHR0SQUmkTMvLyzQ0NADO6vHBBx+UFEpOTg5FRUVorSUC0d7e\\nztTUlKSZR0ZGErZTEG5GzRoaGujt7aW4uFhqOoaHhzl06JCkSKanpwmFQnK/d3R0SI1TIlLM5u+a\\nWiTTcwocezbpIkCib96eM83NzXz++eeSXpqenl53eYCJlnj/L9OGw1uD5627KSkpIRAI0NPTI/fg\\nxx9/TH9/f1RN3nplgptNP03KamZmhrvvvptAICC6Ki0t5YEHHpB5qKmpiWPHjkVF4NZjT+bvmeib\\nicIEg0F27drFtm3bpMdTeXk5i4uLkv6dm5vj4sWLfPLJJ4BzrqipNUuEjowOent7GRsbo6CgQFK5\\nL7/8sqRswWk1oZSis7OTEydOAPDRRx/R19f3lfTy7coDzj0XCoWYm5uTXbpPPvkkubm58h2Zo4YK\\nCwulFq6uro4PPvhA5rLZ2dmEPnvvGGcJEKfg8OHDlJWVibGMjY1x4sQJRkdHk+4sQXQX39dee43t\\n27eL4/bll1/S2dnJ6dOnJVSZ7L5K5sZ67LHHOHDgAFlZWWK8XV1dtLa2Sh8Mc+p5shwBk8s+ePAg\\n+/bti+o3NTMzQ0dHh2yjbm9vJxQKresss3h4zwp65JFH2Lp1q8iydetWtm3bJg5AZ2cndXV1Eo5O\\nhjxwM23a2NgodWXz8/Ps2LFDUj49PT1cuXKFc+fOSU923BAAAAu/SURBVBrFdMVdTz3QSmRlZTEw\\nMMCbb74JOGfBlZWVSdfgkpISrl+/zsDAgDgxw8PDRCKRhKS6YlFKyX1z9OhRacoHTg3T448/jt/v\\nl0n88uXL9PT0yISZqG3CsZgaloGBAen1cu+997KwsCBpi0gkwsTEhKSZu7u7111Xshqmu7VZQAaD\\nQW7cuCGFzBUVFdy4cUPScq2trdTX1xOJRGSxlKj+auYsSu/D7tKlS1Im8eyzz+L3++V9RUUFwWCQ\\n2tpacZampqaklikR8hhMawdzMj04LUyUUjJX+f1+hoaGOHnyJOA8dEOhUFR/tUSxsLAgtvvuu++y\\nuLjIc889J4tM0z3cLG7r6+tpamoSB2B0dDRhZ8FprcWhPX/+PEePHuXVV1+VNHNJSQlbtmyR+7Gv\\nr4/29nY+++wzafFgmnUmIkVpxhUOh7l8+TLj4+MyR1ZWVhIIBMQhN62Cent7OXXqFOD06evu7o5q\\nGZJI7ihnyUQFDh48KLlycFZwV69eTVkTSq21rCbN5ORt0Hf69OmErZJuhWlOB86DzjTnNLro7u6m\\npaVF8uTJ7qtkHmyFhYX4/f6oBnzLy8sMDw/LwyZRN9pqmFW36asSiUSkiFprzeDgoHQyb2trIxQK\\nRenndvqF3Aqfz0c4HOb48ePygM/NzaW0tFScpZaWFiYmJpicnIyqS0iGPKajuxnz2bNn8fl8EgXY\\nuXMnU1NTLC4uRu1KS6YNeaMw3tqogYEBqQM0DxdzSG2yorfeiLHWOuqQ0KtXr/LOO+/IQ7ekpISx\\nsTFplOnVUyL1lZOTIwseM25zNM/bb78NON/bfffdJ5GDxsbGr+w2TaTD7T1eZmlpiZmZGYnMHDly\\nhPLycokyZ2dnU1tbGzVne8eyXrwHiS8vL4uzD/Dee+/R3t7Orl27pM50y5YttLW1UV9fD9xsHJqo\\nwmAv3gLqhoYGhoaGOHPmjPSiy8vLY3h4WJzIhoaGqJqrRMrk1fng4CAffvgh/f39coBudXU109PT\\nMkd2d3fz6aefEg6HxSFJlMPtjdxdv36dxsZGAoEANTU1ALLT04x/eHiY1tZWjh8/LruXJycnpYFu\\nMrA1SxaLxWKxWCxxuKMiSyaMa7Z8m5X5sWPHCIfDKUnBGUw0JxKJMDMzI6mburo6amtrmZqaSln9\\nlFmp5OTkEIlEyM7Olv48dXV1NDQ0JC00GYtJlZraqGAwKDl6s3ozNUvJ7oVlxrqwsEBfXx/5+fnS\\nnXt6epr+/n5JmUQika9E3ZIh29LSEpFIRM4IA2Tnm/mOZmdnpU1AKuTxYlZ4xqbMeXiQvBTXavLE\\n/j+mliH2ejLaOxi8Y15JnnA4LNdNZHC9LQtuhdmlGftvz83NSbomGAzS2NgYNY5k6WmlFhvLy8tS\\nV2bk8PYtWs8RNGuVx7wGoiIS5qxHby8mU9tkZE+0PHCzX5M3GtjV1UVXV5fI4vP5oqJa3ohdomXy\\nnpdozjU0aT9w0pM3btyQuWB+fp6FhYWkfm/gzNehUIj3339f5HvooYfYuXOnPNdOnDhBfX29RJMg\\nubXBACpV/YjiCqHUmoQwDkogEKCiokKU09/fH3WGVTIxDorZFltZWUlFRYWkCoaHh6VIOBUPF29/\\nkwMHDki+2UyaV65cYWpqKiGnZa8Fk5IoLCzkqaeeYmJiQkLtpl2+eZ+sM+liKSwsZMeOHQQCAXmg\\nLSwsMD8/L3pJZFHwrTBHjBh7NhNQbA+lVN2b3vSembS9D5tUzxHeuixv/Uk6ZInF5/OJfMnsnbZW\\nYptbplseI4u3mWS6ZYptJplOebz32kb4zmLl8TpQK5EKWc1mrqysLOk3pbUmJydHHKPr16+vuKC8\\nTS5oratvKVe6DRnW7ix5fj/qfTrG4H2oeOVJZXTL4D1vLHbFkA55jEymn4nRVbL6KK1VHu8EHjsp\\n3E6n4ETIBDdtyPv/p0Mei8ViyUDW5CzdUWk4w0Z4iCQ71P7/ITbUvBHQWkc1m0w3sSu4lVI5qWaj\\nyWOxWCyWlbEF3haLxWKxWCxxsM6SxWKxWCwWSxw2ShpuDJh1/8xkSshsHWT6+MHqAKwOMn38YHUA\\nVgeQGh3ct5Zf2hAF3gBKqc/WUmS1mcl0HWT6+MHqAKwOMn38YHUAVgewsXRg03AWi8VisVgscbDO\\nksVisVgsFkscNpKz9E/pFmADkOk6yPTxg9UBWB1k+vjB6gCsDmAD6WDD1CxZLBaLxWKxbEQ2UmTJ\\nYrFYLBaLZcORdmdJKfW8UqpTKdWllHor3fKkCqVUr1LqZ0qpFqXUZ+61IqXUx0qpq+6fO9ItZyJR\\nSn1PKTWilGr3XFtxzMrh7127aFNKHUif5IljFR18RykVcm2hRSl1yPPZEVcHnUqpX02P1IlDKXWv\\nUqpWKXVJKdWhlPoD93rG2EEcHWSEHSil8pRSTUqpVnf8f+5e362UanTH+b5SKte97nffd7mf359O\\n+RNBHB18XynV47GBR93rm+4+MCilspRSzUqpn7rvN6YdeE+MTvUPkAV0A3uAXKAVqEqnTCkcey9Q\\nEnPtr4C33NdvAX+ZbjkTPOavAweA9luNGTgE/C+ggCeBxnTLn0QdfAf44xV+t8q9J/zAbvdeyUr3\\nGNY5/nLggPu6ALjijjNj7CCODjLCDtzvMt99nQM0ut/tB8Bh9/p3gd91X/8e8F339WHg/XSPIYk6\\n+D7wygq/v+nuA8/Y/gh4D/ip+35D2kG6I0u/CHRprYNa6xvAD4GX0ixTOnkJ+IH7+gfAb6RRloSj\\ntT4DTMRcXm3MLwH/ph0+BbYrpcpTI2nyWEUHq/ES8EOt9bzWugfowrln7li01oNa64vu62ngc6CC\\nDLKDODpYjU1lB+53OeO+zXF/NPAN4Efu9VgbMLbxI+CXlYo5Tf0OI44OVmPT3QcASql7gBeAf3Hf\\nKzaoHaTbWaoABjzvrxF/0thMaOC4UuqCUup33Gt3a60H3ddDwN3pES2lrDbmTLON33fD69/zpF83\\ntQ7cMPov4KyqM9IOYnQAGWIHbuqlBRgBPsaJln2htTangXvHKON3P48AxamVOPHE6kBrbWzgL1wb\\neFsp5XevbTobcPlb4E8Ac9p6MRvUDtLtLGUyX9NaHwBqgDeUUl/3fqidWGNGbVXMxDG7/CPwc8Cj\\nwCDw1+kVJ/kopfKBHwN/qLWe8n6WKXawgg4yxg601kta60eBe3CiZA+mWaSUE6sDpdR+4AiOLh4H\\nioA/TaOISUUp9WvAiNb6QrplWQvpdpZCwL2e9/e41zY9WuuQ++cI8N84E8awCa26f46kT8KUsdqY\\nM8Y2tNbD7sS5DPwzN1Msm1IHSqkcHCfhqNb6v9zLGWUHK+kg0+wAQGv9BVALPIWTWjLnlXrHKON3\\nPy8ExlMsatLw6OB5N0WrtdbzwLtsbhv4JeDXlVK9OCU43wD+jg1qB+l2ls4DlW71ey5O0dZP0ixT\\n0lFKbVNKFZjXwDeBdpyxf8v9tW8B/5MeCVPKamP+CfBb7i6QJ4GIJ02zqYipPXgZxxbA0cFhdxfI\\nbqASaEq1fInErTH4V+BzrfXfeD7KGDtYTQeZYgdKqVKl1Hb39RbgV3DqtmqBV9xfi7UBYxuvACfd\\n6OMdyyo6uOxZMCicWh2vDWyq+0BrfURrfY/W+n6cZ/9JrfVvslHtIJXV5Cv94FT5X8HJWX873fKk\\naMx7cHa3tAIdZtw4+dcTwFXgE6Ao3bImeNz/iZNeWMDJRf/2amPG2fXxD65d/AyoTrf8SdTBv7tj\\nbMOZEMo9v/9tVwedQE265U/A+L+Gk2JrA1rcn0OZZAdxdJARdgD8PNDsjrMd+DP3+h4cJ7AL+BDw\\nu9fz3Pdd7ud70j2GJOrgpGsD7cB/cHPH3Ka7D2L08Rw3d8NtSDuwHbwtFovFYrFY4pDuNJzFYrFY\\nLBbLhsY6SxaLxWKxWCxxsM6SxWKxWCwWSxyss2SxWCwWi8USB+ssWSwWi8ViscTBOksWi8VisVgs\\ncbDOksVisVgsFkscrLNksVgsFovFEof/A9Rjf6bhqUWuAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fe99e3f5b70>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from scipy.stats import norm\\n\",\n    \"\\n\",\n    \"# Display a 2D manifold of the digits\\n\",\n    \"n = 15  # figure with 15x15 digits\\n\",\n    \"digit_size = 28\\n\",\n    \"figure = np.zeros((digit_size * n, digit_size * n))\\n\",\n    \"# Linearly spaced coordinates on the unit square were transformed\\n\",\n    \"# through the inverse CDF (ppf) of the Gaussian\\n\",\n    \"# to produce values of the latent variables z,\\n\",\n    \"# since the prior of the latent space is Gaussian\\n\",\n    \"grid_x = norm.ppf(np.linspace(0.05, 0.95, n))\\n\",\n    \"grid_y = norm.ppf(np.linspace(0.05, 0.95, n))\\n\",\n    \"\\n\",\n    \"for i, yi in enumerate(grid_x):\\n\",\n    \"    for j, xi in enumerate(grid_y):\\n\",\n    \"        z_sample = np.array([[xi, yi]])\\n\",\n    \"        z_sample = np.tile(z_sample, batch_size).reshape(batch_size, 2)\\n\",\n    \"        x_decoded = decoder.predict(z_sample, batch_size=batch_size)\\n\",\n    \"        digit = x_decoded[0].reshape(digit_size, digit_size)\\n\",\n    \"        figure[i * digit_size: (i + 1) * digit_size,\\n\",\n    \"               j * digit_size: (j + 1) * digit_size] = digit\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"plt.imshow(figure, cmap='Greys_r')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The grid of sampled digits shows a completely continuous distribution of the different digit classes, with one digit morphing into another \\n\",\n    \"as you follow a path through latent space. Specific directions in this space have a meaning, e.g. there is a direction for \\\"four-ness\\\", \\n\",\n    \"\\\"one-ness\\\", etc.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "first_edition/8.5-introduction-to-gans.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"'2.0.8'\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"keras.__version__\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Introduction to generative adversarial networks\\n\",\n    \"\\n\",\n    \"This notebook contains the second code sample found in Chapter 8, Section 5 of [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python?a_aid=keras&a_bid=76564dff). Note that the original text features far more content, in particular further explanations and figures: in this notebook, you will only find source code and related comments.\\n\",\n    \"\\n\",\n    \"---\\n\",\n    \"[...]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A schematic GAN implementation\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"In what follows, we explain how to implement a GAN in Keras, in its barest form -- since GANs are quite advanced, diving deeply into the \\n\",\n    \"technical details would be out of scope for us. Our specific implementation will be a deep convolutional GAN, or DCGAN: a GAN where the \\n\",\n    \"generator and discriminator are deep convnets. In particular, it leverages a `Conv2DTranspose` layer for image upsampling in the generator.\\n\",\n    \"\\n\",\n    \"We will train our GAN on images from CIFAR10, a dataset of 50,000 32x32 RGB images belong to 10 classes (5,000 images per class). To make \\n\",\n    \"things even easier, we will only use images belonging to the class \\\"frog\\\".\\n\",\n    \"\\n\",\n    \"Schematically, our GAN looks like this:\\n\",\n    \"\\n\",\n    \"* A `generator` network maps vectors of shape `(latent_dim,)` to images of shape `(32, 32, 3)`.\\n\",\n    \"* A `discriminator` network maps images of shape (32, 32, 3) to a binary score estimating the probability that the image is real.\\n\",\n    \"* A `gan` network chains the generator and the discriminator together: `gan(x) = discriminator(generator(x))`. Thus this `gan` network maps \\n\",\n    \"latent space vectors to the discriminator's assessment of the realism of these latent vectors as decoded by the generator.\\n\",\n    \"* We train the discriminator using examples of real and fake images along with \\\"real\\\"/\\\"fake\\\" labels, as we would train any regular image \\n\",\n    \"classification model.\\n\",\n    \"* To train the generator, we use the gradients of the generator's weights with regard to the loss of the `gan` model. This means that, at \\n\",\n    \"every step, we move the weights of the generator in a direction that will make the discriminator more likely to classify as \\\"real\\\" the \\n\",\n    \"images decoded by the generator. I.e. we train the generator to fool the discriminator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A bag of tricks\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Training GANs and tuning GAN implementations is notoriously difficult. There are a number of known \\\"tricks\\\" that one should keep in mind. \\n\",\n    \"Like most things in deep learning, it is more alchemy than science: these tricks are really just heuristics, not theory-backed guidelines. \\n\",\n    \"They are backed by some level of intuitive understanding of the phenomenon at hand, and they are known to work well empirically, albeit not \\n\",\n    \"necessarily in every context.\\n\",\n    \"\\n\",\n    \"Here are a few of the tricks that we leverage in our own implementation of a GAN generator and discriminator below. It is not an exhaustive \\n\",\n    \"list of GAN-related tricks; you will find many more across the GAN literature.\\n\",\n    \"\\n\",\n    \"* We use `tanh` as the last activation in the generator, instead of `sigmoid`, which would be more commonly found in other types of models.\\n\",\n    \"* We sample points from the latent space using a _normal distribution_ (Gaussian distribution), not a uniform distribution.\\n\",\n    \"* Stochasticity is good to induce robustness. Since GAN training results in a dynamic equilibrium, GANs are likely to get \\\"stuck\\\" in all sorts of ways. \\n\",\n    \"Introducing randomness during training helps prevent this. We introduce randomness in two ways: 1) we use dropout in the discriminator, 2) \\n\",\n    \"we add some random noise to the labels for the discriminator.\\n\",\n    \"* Sparse gradients can hinder GAN training. In deep learning, sparsity is often a desirable property, but not in GANs. There are two things \\n\",\n    \"that can induce gradient sparsity: 1) max pooling operations, 2) ReLU activations. Instead of max pooling, we recommend using strided \\n\",\n    \"convolutions for downsampling, and we recommend using a `LeakyReLU` layer instead of a ReLU activation. It is similar to ReLU but it \\n\",\n    \"relaxes sparsity constraints by allowing small negative activation values.\\n\",\n    \"* In generated images, it is common to see \\\"checkerboard artifacts\\\" caused by unequal coverage of the pixel space in the generator. To fix \\n\",\n    \"this, we use a kernel size that is divisible by the stride size, whenever we use a strided `Conv2DTranpose` or `Conv2D` in both the \\n\",\n    \"generator and discriminator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The generator\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"First, we develop a `generator` model, which turns a vector (from the latent space -- during training it will sampled at random) into a \\n\",\n    \"candidate image. One of the many issues that commonly arise with GANs is that the generator gets stuck with generated images that look like \\n\",\n    \"noise. A possible solution is to use dropout on both the discriminator and generator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using TensorFlow backend.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"input_1 (InputLayer)         (None, 32)                0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_1 (Dense)              (None, 32768)             1081344   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_1 (LeakyReLU)    (None, 32768)             0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"reshape_1 (Reshape)          (None, 16, 16, 128)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_1 (Conv2D)            (None, 16, 16, 256)       819456    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_2 (LeakyReLU)    (None, 16, 16, 256)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_transpose_1 (Conv2DTr (None, 32, 32, 256)       1048832   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_3 (LeakyReLU)    (None, 32, 32, 256)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_2 (Conv2D)            (None, 32, 32, 256)       1638656   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_4 (LeakyReLU)    (None, 32, 32, 256)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_3 (Conv2D)            (None, 32, 32, 256)       1638656   \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_5 (LeakyReLU)    (None, 32, 32, 256)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_4 (Conv2D)            (None, 32, 32, 3)         37635     \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 6,264,579\\n\",\n      \"Trainable params: 6,264,579\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import keras\\n\",\n    \"from keras import layers\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"latent_dim = 32\\n\",\n    \"height = 32\\n\",\n    \"width = 32\\n\",\n    \"channels = 3\\n\",\n    \"\\n\",\n    \"generator_input = keras.Input(shape=(latent_dim,))\\n\",\n    \"\\n\",\n    \"# First, transform the input into a 16x16 128-channels feature map\\n\",\n    \"x = layers.Dense(128 * 16 * 16)(generator_input)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"x = layers.Reshape((16, 16, 128))(x)\\n\",\n    \"\\n\",\n    \"# Then, add a convolution layer\\n\",\n    \"x = layers.Conv2D(256, 5, padding='same')(x)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"\\n\",\n    \"# Upsample to 32x32\\n\",\n    \"x = layers.Conv2DTranspose(256, 4, strides=2, padding='same')(x)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"\\n\",\n    \"# Few more conv layers\\n\",\n    \"x = layers.Conv2D(256, 5, padding='same')(x)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"x = layers.Conv2D(256, 5, padding='same')(x)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"\\n\",\n    \"# Produce a 32x32 1-channel feature map\\n\",\n    \"x = layers.Conv2D(channels, 7, activation='tanh', padding='same')(x)\\n\",\n    \"generator = keras.models.Model(generator_input, x)\\n\",\n    \"generator.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The discriminator\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Then, we develop a `discriminator` model, that takes as input a candidate image (real or synthetic) and classifies it into one of two \\n\",\n    \"classes, either \\\"generated image\\\" or \\\"real image that comes from the training set\\\".\"\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      \"_________________________________________________________________\\n\",\n      \"Layer (type)                 Output Shape              Param #   \\n\",\n      \"=================================================================\\n\",\n      \"input_2 (InputLayer)         (None, 32, 32, 3)         0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_5 (Conv2D)            (None, 30, 30, 128)       3584      \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_6 (LeakyReLU)    (None, 30, 30, 128)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_6 (Conv2D)            (None, 14, 14, 128)       262272    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_7 (LeakyReLU)    (None, 14, 14, 128)       0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_7 (Conv2D)            (None, 6, 6, 128)         262272    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_8 (LeakyReLU)    (None, 6, 6, 128)         0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"conv2d_8 (Conv2D)            (None, 2, 2, 128)         262272    \\n\",\n      \"_________________________________________________________________\\n\",\n      \"leaky_re_lu_9 (LeakyReLU)    (None, 2, 2, 128)         0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"flatten_1 (Flatten)          (None, 512)               0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dropout_1 (Dropout)          (None, 512)               0         \\n\",\n      \"_________________________________________________________________\\n\",\n      \"dense_2 (Dense)              (None, 1)                 513       \\n\",\n      \"=================================================================\\n\",\n      \"Total params: 790,913\\n\",\n      \"Trainable params: 790,913\\n\",\n      \"Non-trainable params: 0\\n\",\n      \"_________________________________________________________________\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"discriminator_input = layers.Input(shape=(height, width, channels))\\n\",\n    \"x = layers.Conv2D(128, 3)(discriminator_input)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"x = layers.Conv2D(128, 4, strides=2)(x)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"x = layers.Conv2D(128, 4, strides=2)(x)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"x = layers.Conv2D(128, 4, strides=2)(x)\\n\",\n    \"x = layers.LeakyReLU()(x)\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"\\n\",\n    \"# One dropout layer - important trick!\\n\",\n    \"x = layers.Dropout(0.4)(x)\\n\",\n    \"\\n\",\n    \"# Classification layer\\n\",\n    \"x = layers.Dense(1, activation='sigmoid')(x)\\n\",\n    \"\\n\",\n    \"discriminator = keras.models.Model(discriminator_input, x)\\n\",\n    \"discriminator.summary()\\n\",\n    \"\\n\",\n    \"# To stabilize training, we use learning rate decay\\n\",\n    \"# and gradient clipping (by value) in the optimizer.\\n\",\n    \"discriminator_optimizer = keras.optimizers.RMSprop(lr=0.0008, clipvalue=1.0, decay=1e-8)\\n\",\n    \"discriminator.compile(optimizer=discriminator_optimizer, loss='binary_crossentropy')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The adversarial network\\n\",\n    \"\\n\",\n    \"Finally, we setup the GAN, which chains the generator and the discriminator. This is the model that, when trained, will move the generator \\n\",\n    \"in a direction that improves its ability to fool the discriminator. This model turns latent space points into a classification decision, \\n\",\n    \"\\\"fake\\\" or \\\"real\\\", and it is meant to be trained with labels that are always \\\"these are real images\\\". So training `gan` will updates the \\n\",\n    \"weights of `generator` in a way that makes `discriminator` more likely to predict \\\"real\\\" when looking at fake images. Very importantly, we \\n\",\n    \"set the discriminator to be frozen during training (non-trainable): its weights will not be updated when training `gan`. If the \\n\",\n    \"discriminator weights could be updated during this process, then we would be training the discriminator to always predict \\\"real\\\", which is \\n\",\n    \"not what we want!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Set discriminator weights to non-trainable\\n\",\n    \"# (will only apply to the `gan` model)\\n\",\n    \"discriminator.trainable = False\\n\",\n    \"\\n\",\n    \"gan_input = keras.Input(shape=(latent_dim,))\\n\",\n    \"gan_output = discriminator(generator(gan_input))\\n\",\n    \"gan = keras.models.Model(gan_input, gan_output)\\n\",\n    \"\\n\",\n    \"gan_optimizer = keras.optimizers.RMSprop(lr=0.0004, clipvalue=1.0, decay=1e-8)\\n\",\n    \"gan.compile(optimizer=gan_optimizer, loss='binary_crossentropy')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## How to train your DCGAN\\n\",\n    \"\\n\",\n    \"Now we can start training. To recapitulate, this is schematically what the training loop looks like:\\n\",\n    \"\\n\",\n    \"```\\n\",\n    \"for each epoch:\\n\",\n    \"    * Draw random points in the latent space (random noise).\\n\",\n    \"    * Generate images with `generator` using this random noise.\\n\",\n    \"    * Mix the generated images with real ones.\\n\",\n    \"    * Train `discriminator` using these mixed images, with corresponding targets, either \\\"real\\\" (for the real images) or \\\"fake\\\" (for the generated images).\\n\",\n    \"    * Draw new random points in the latent space.\\n\",\n    \"    * Train `gan` using these random vectors, with targets that all say \\\"these are real images\\\". This will update the weights of the generator (only, since discriminator is frozen inside `gan`) to move them towards getting the discriminator to predict \\\"these are real images\\\" for generated images, i.e. this trains the generator to fool the discriminator.\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Let's implement it:\"\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      \"discriminator loss at step 0: 0.685675\\n\",\n      \"adversarial loss at step 0: 0.667591\\n\",\n      \"discriminator loss at step 100: 0.756201\\n\",\n      \"adversarial loss at step 100: 0.820905\\n\",\n      \"discriminator loss at step 200: 0.699047\\n\",\n      \"adversarial loss at step 200: 0.776581\\n\",\n      \"discriminator loss at step 300: 0.684602\\n\",\n      \"adversarial loss at step 300: 0.513813\\n\",\n      \"discriminator loss at step 400: 0.707092\\n\",\n      \"adversarial loss at step 400: 0.716778\\n\",\n      \"discriminator loss at step 500: 0.686278\\n\",\n      \"adversarial loss at step 500: 0.741214\\n\",\n      \"discriminator loss at step 600: 0.692786\\n\",\n      \"adversarial loss at step 600: 0.745891\\n\",\n      \"discriminator loss at step 700: 0.69771\\n\",\n      \"adversarial loss at step 700: 0.781026\\n\",\n      \"discriminator loss at step 800: 0.69236\\n\",\n      \"adversarial loss at step 800: 0.748769\\n\",\n      \"discriminator loss at step 900: 0.663193\\n\",\n      \"adversarial loss at step 900: 0.689923\\n\",\n      \"discriminator loss at step 1000: 0.706922\\n\",\n      \"adversarial loss at step 1000: 0.741314\\n\",\n      \"discriminator loss at step 1100: 0.682189\\n\",\n      \"adversarial loss at step 1100: 0.76548\\n\",\n      \"discriminator loss at step 1200: 0.687244\\n\",\n      \"adversarial loss at step 1200: 0.746018\\n\",\n      \"discriminator loss at step 1300: 0.697884\\n\",\n      \"adversarial loss at step 1300: 0.766032\\n\",\n      \"discriminator loss at step 1400: 0.691977\\n\",\n      \"adversarial loss at step 1400: 0.735184\\n\",\n      \"discriminator loss at step 1500: 0.696238\\n\",\n      \"adversarial loss at step 1500: 0.738426\\n\",\n      \"discriminator loss at step 1600: 0.698334\\n\",\n      \"adversarial loss at step 1600: 0.741093\\n\",\n      \"discriminator loss at step 1700: 0.70315\\n\",\n      \"adversarial loss at step 1700: 0.736702\\n\",\n      \"discriminator loss at step 1800: 0.693836\\n\",\n      \"adversarial loss at step 1800: 0.742768\\n\",\n      \"discriminator loss at step 1900: 0.69059\\n\",\n      \"adversarial loss at step 1900: 0.741162\\n\",\n      \"discriminator loss at step 2000: 0.696293\\n\",\n      \"adversarial loss at step 2000: 0.755151\\n\",\n      \"discriminator loss at step 2100: 0.686166\\n\",\n      \"adversarial loss at step 2100: 0.755129\\n\",\n      \"discriminator loss at step 2200: 0.692612\\n\",\n      \"adversarial loss at step 2200: 0.772408\\n\",\n      \"discriminator loss at step 2300: 0.704013\\n\",\n      \"adversarial loss at step 2300: 0.776998\\n\",\n      \"discriminator loss at step 2400: 0.693268\\n\",\n      \"adversarial loss at step 2400: 0.70731\\n\",\n      \"discriminator loss at step 2500: 0.684289\\n\",\n      \"adversarial loss at step 2500: 0.742162\\n\",\n      \"discriminator loss at step 2600: 0.700483\\n\",\n      \"adversarial loss at step 2600: 0.734719\\n\",\n      \"discriminator loss at step 2700: 0.699952\\n\",\n      \"adversarial loss at step 2700: 0.759745\\n\",\n      \"discriminator loss at step 2800: 0.697416\\n\",\n      \"adversarial loss at step 2800: 0.733726\\n\",\n      \"discriminator loss at step 2900: 0.697604\\n\",\n      \"adversarial loss at step 2900: 0.740891\\n\",\n      \"discriminator loss at step 3000: 0.698498\\n\",\n      \"adversarial loss at step 3000: 0.754564\\n\",\n      \"discriminator loss at step 3100: 0.695516\\n\",\n      \"adversarial loss at step 3100: 0.759486\\n\",\n      \"discriminator loss at step 3200: 0.693453\\n\",\n      \"adversarial loss at step 3200: 0.769369\\n\",\n      \"discriminator loss at step 3300: 1.5083\\n\",\n      \"adversarial loss at step 3300: 0.726621\\n\",\n      \"discriminator loss at step 3400: 0.686934\\n\",\n      \"adversarial loss at step 3400: 0.747121\\n\",\n      \"discriminator loss at step 3500: 0.689791\\n\",\n      \"adversarial loss at step 3500: 0.751882\\n\",\n      \"discriminator loss at step 3600: 0.71331\\n\",\n      \"adversarial loss at step 3600: 0.704916\\n\",\n      \"discriminator loss at step 3700: 0.690504\\n\",\n      \"adversarial loss at step 3700: 0.853764\\n\",\n      \"discriminator loss at step 3800: 0.688844\\n\",\n      \"adversarial loss at step 3800: 0.791077\\n\",\n      \"discriminator loss at step 3900: 0.679162\\n\",\n      \"adversarial loss at step 3900: 0.724979\\n\",\n      \"discriminator loss at step 4000: 0.676585\\n\",\n      \"adversarial loss at step 4000: 0.69554\\n\",\n      \"discriminator loss at step 4100: 0.693313\\n\",\n      \"adversarial loss at step 4100: 0.742666\\n\",\n      \"discriminator loss at step 4200: 0.678367\\n\",\n      \"adversarial loss at step 4200: 0.778793\\n\",\n      \"discriminator loss at step 4300: 0.699712\\n\",\n      \"adversarial loss at step 4300: 0.740457\\n\",\n      \"discriminator loss at step 4400: 0.697605\\n\",\n      \"adversarial loss at step 4400: 0.755847\\n\",\n      \"discriminator loss at step 4500: 0.710596\\n\",\n      \"adversarial loss at step 4500: 0.814832\\n\",\n      \"discriminator loss at step 4600: 0.706518\\n\",\n      \"adversarial loss at step 4600: 0.83636\\n\",\n      \"discriminator loss at step 4700: 0.687217\\n\",\n      \"adversarial loss at step 4700: 0.775736\\n\",\n      \"discriminator loss at step 4800: 0.769103\\n\",\n      \"adversarial loss at step 4800: 0.774639\\n\",\n      \"discriminator loss at step 4900: 0.692414\\n\",\n      \"adversarial loss at step 4900: 0.775192\\n\",\n      \"discriminator loss at step 5000: 0.715357\\n\",\n      \"adversarial loss at step 5000: 0.775003\\n\",\n      \"discriminator loss at step 5100: 0.703434\\n\",\n      \"adversarial loss at step 5100: 0.940242\\n\",\n      \"discriminator loss at step 5200: 0.704034\\n\",\n      \"adversarial loss at step 5200: 0.708327\\n\",\n      \"discriminator loss at step 5300: 0.698559\\n\",\n      \"adversarial loss at step 5300: 0.730377\\n\",\n      \"discriminator loss at step 5400: 0.684378\\n\",\n      \"adversarial loss at step 5400: 0.759259\\n\",\n      \"discriminator loss at step 5500: 0.693699\\n\",\n      \"adversarial loss at step 5500: 0.700122\\n\",\n      \"discriminator loss at step 5600: 0.715242\\n\",\n      \"adversarial loss at step 5600: 0.808961\\n\",\n      \"discriminator loss at step 5700: 0.689339\\n\",\n      \"adversarial loss at step 5700: 0.621725\\n\",\n      \"discriminator loss at step 5800: 0.679717\\n\",\n      \"adversarial loss at step 5800: 0.787711\\n\",\n      \"discriminator loss at step 5900: 0.700126\\n\",\n      \"adversarial loss at step 5900: 0.742493\\n\",\n      \"discriminator loss at step 6000: 0.692087\\n\",\n      \"adversarial loss at step 6000: 0.839669\\n\",\n      \"discriminator loss at step 6100: 0.677867\\n\",\n      \"adversarial loss at step 6100: 0.797158\\n\",\n      \"discriminator loss at step 6200: 0.70392\\n\",\n      \"adversarial loss at step 6200: 0.842135\\n\",\n      \"discriminator loss at step 6300: 0.688377\\n\",\n      \"adversarial loss at step 6300: 0.718633\\n\",\n      \"discriminator loss at step 6400: 0.781234\\n\",\n      \"adversarial loss at step 6400: 0.710833\\n\",\n      \"discriminator loss at step 6500: 0.682696\\n\",\n      \"adversarial loss at step 6500: 0.739674\\n\",\n      \"discriminator loss at step 6600: 0.693081\\n\",\n      \"adversarial loss at step 6600: 0.747336\\n\",\n      \"discriminator loss at step 6700: 0.681836\\n\",\n      \"adversarial loss at step 6700: 0.780143\\n\",\n      \"discriminator loss at step 6800: 0.728136\\n\",\n      \"adversarial loss at step 6800: 0.838522\\n\",\n      \"discriminator loss at step 6900: 0.660475\\n\",\n      \"adversarial loss at step 6900: 0.717434\\n\",\n      \"discriminator loss at step 7000: 0.672144\\n\",\n      \"adversarial loss at step 7000: 0.948783\\n\",\n      \"discriminator loss at step 7100: 0.692428\\n\",\n      \"adversarial loss at step 7100: 0.837047\\n\",\n      \"discriminator loss at step 7200: 0.731133\\n\",\n      \"adversarial loss at step 7200: 0.728315\\n\",\n      \"discriminator loss at step 7300: 0.671766\\n\",\n      \"adversarial loss at step 7300: 0.793155\\n\",\n      \"discriminator loss at step 7400: 0.712387\\n\",\n      \"adversarial loss at step 7400: 0.807759\\n\",\n      \"discriminator loss at step 7500: 0.68638\\n\",\n      \"adversarial loss at step 7500: 0.967421\\n\",\n      \"discriminator loss at step 7600: 0.690096\\n\",\n      \"adversarial loss at step 7600: 0.811904\\n\",\n      \"discriminator loss at step 7700: 0.702784\\n\",\n      \"adversarial loss at step 7700: 0.867017\\n\",\n      \"discriminator loss at step 7800: 0.674138\\n\",\n      \"adversarial loss at step 7800: 0.837909\\n\",\n      \"discriminator loss at step 7900: 0.674747\\n\",\n      \"adversarial loss at step 7900: 0.743664\\n\",\n      \"discriminator loss at step 8000: 0.680357\\n\",\n      \"adversarial loss at step 8000: 0.810859\\n\",\n      \"discriminator loss at step 8100: 0.688885\\n\",\n      \"adversarial loss at step 8100: 0.786809\\n\",\n      \"discriminator loss at step 8200: 0.671557\\n\",\n      \"adversarial loss at step 8200: 0.784159\\n\",\n      \"discriminator loss at step 8300: 0.70359\\n\",\n      \"adversarial loss at step 8300: 0.95692\\n\",\n      \"discriminator loss at step 8400: 0.720167\\n\",\n      \"adversarial loss at step 8400: 1.14066\\n\",\n      \"discriminator loss at step 8500: 0.747376\\n\",\n      \"adversarial loss at step 8500: 0.630725\\n\",\n      \"discriminator loss at step 8600: 0.688931\\n\",\n      \"adversarial loss at step 8600: 0.849245\\n\",\n      \"discriminator loss at step 8700: 0.707559\\n\",\n      \"adversarial loss at step 8700: 0.713202\\n\",\n      \"discriminator loss at step 8800: 0.673593\\n\",\n      \"adversarial loss at step 8800: 0.832419\\n\",\n      \"discriminator loss at step 8900: 0.6777\\n\",\n      \"adversarial loss at step 8900: 0.773395\\n\",\n      \"discriminator loss at step 9000: 0.659887\\n\",\n      \"adversarial loss at step 9000: 0.77255\\n\",\n      \"discriminator loss at step 9100: 0.675182\\n\",\n      \"adversarial loss at step 9100: 0.749544\\n\",\n      \"discriminator loss at step 9200: 0.687147\\n\",\n      \"adversarial loss at step 9200: 0.836509\\n\",\n      \"discriminator loss at step 9300: 0.690807\\n\",\n      \"adversarial loss at step 9300: 0.829561\\n\",\n      \"discriminator loss at step 9400: 0.656649\\n\",\n      \"adversarial loss at step 9400: 0.788181\\n\",\n      \"discriminator loss at step 9500: 0.703494\\n\",\n      \"adversarial loss at step 9500: 0.78302\\n\",\n      \"discriminator loss at step 9600: 0.680718\\n\",\n      \"adversarial loss at step 9600: 0.813078\\n\",\n      \"discriminator loss at step 9700: 0.704956\\n\",\n      \"adversarial loss at step 9700: 0.761652\\n\",\n      \"discriminator loss at step 9800: 0.673504\\n\",\n      \"adversarial loss at step 9800: 0.853213\\n\",\n      \"discriminator loss at step 9900: 0.669288\\n\",\n      \"adversarial loss at step 9900: 0.677691\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"from keras.preprocessing import image\\n\",\n    \"\\n\",\n    \"# Load CIFAR10 data\\n\",\n    \"(x_train, y_train), (_, _) = keras.datasets.cifar10.load_data()\\n\",\n    \"\\n\",\n    \"# Select frog images (class 6)\\n\",\n    \"x_train = x_train[y_train.flatten() == 6]\\n\",\n    \"\\n\",\n    \"# Normalize data\\n\",\n    \"x_train = x_train.reshape(\\n\",\n    \"    (x_train.shape[0],) + (height, width, channels)).astype('float32') / 255.\\n\",\n    \"\\n\",\n    \"iterations = 10000\\n\",\n    \"batch_size = 20\\n\",\n    \"save_dir = '/home/ubuntu/gan_images/'\\n\",\n    \"\\n\",\n    \"# Start training loop\\n\",\n    \"start = 0\\n\",\n    \"for step in range(iterations):\\n\",\n    \"    # Sample random points in the latent space\\n\",\n    \"    random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))\\n\",\n    \"\\n\",\n    \"    # Decode them to fake images\\n\",\n    \"    generated_images = generator.predict(random_latent_vectors)\\n\",\n    \"\\n\",\n    \"    # Combine them with real images\\n\",\n    \"    stop = start + batch_size\\n\",\n    \"    real_images = x_train[start: stop]\\n\",\n    \"    combined_images = np.concatenate([generated_images, real_images])\\n\",\n    \"\\n\",\n    \"    # Assemble labels discriminating real from fake images\\n\",\n    \"    labels = np.concatenate([np.ones((batch_size, 1)),\\n\",\n    \"                             np.zeros((batch_size, 1))])\\n\",\n    \"    # Add random noise to the labels - important trick!\\n\",\n    \"    labels += 0.05 * np.random.random(labels.shape)\\n\",\n    \"\\n\",\n    \"    # Train the discriminator\\n\",\n    \"    d_loss = discriminator.train_on_batch(combined_images, labels)\\n\",\n    \"\\n\",\n    \"    # sample random points in the latent space\\n\",\n    \"    random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))\\n\",\n    \"\\n\",\n    \"    # Assemble labels that say \\\"all real images\\\"\\n\",\n    \"    misleading_targets = np.zeros((batch_size, 1))\\n\",\n    \"\\n\",\n    \"    # Train the generator (via the gan model,\\n\",\n    \"    # where the discriminator weights are frozen)\\n\",\n    \"    a_loss = gan.train_on_batch(random_latent_vectors, misleading_targets)\\n\",\n    \"    \\n\",\n    \"    start += batch_size\\n\",\n    \"    if start > len(x_train) - batch_size:\\n\",\n    \"      start = 0\\n\",\n    \"\\n\",\n    \"    # Occasionally save / plot\\n\",\n    \"    if step % 100 == 0:\\n\",\n    \"        # Save model weights\\n\",\n    \"        gan.save_weights('gan.h5')\\n\",\n    \"\\n\",\n    \"        # Print metrics\\n\",\n    \"        print('discriminator loss at step %s: %s' % (step, d_loss))\\n\",\n    \"        print('adversarial loss at step %s: %s' % (step, a_loss))\\n\",\n    \"\\n\",\n    \"        # Save one generated image\\n\",\n    \"        img = image.array_to_img(generated_images[0] * 255., scale=False)\\n\",\n    \"        img.save(os.path.join(save_dir, 'generated_frog' + str(step) + '.png'))\\n\",\n    \"\\n\",\n    \"        # Save one real image, for comparison\\n\",\n    \"        img = image.array_to_img(real_images[0] * 255., scale=False)\\n\",\n    \"        img.save(os.path.join(save_dir, 'real_frog' + str(step) + '.png'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"Let's display a few of our fake images:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\ndBhZLS00FlwAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb172d99f98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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+mwrMh7/EMGufP/KYAHA+1fc/f7+/+2dHwhxDuLLZ3f3Z8CwG9tQoh3\\nJdv5zv85M3vRzB41M/5ZTgjxjuRGnf8bAO4CcD+ABQBfYU80syNmdszMjnU6/Lu2EGK43JDzu/ui\\nu3fdvQfgmwAeSHjuUXc/7O6HM5mhhhIIIRK4Iec3s/lr/vw4gBM3ZzhCiGExiNT3HQAfATBjZucA\\nfBHAR8zsfgAO4DSAzw50NgesHZbnKgm53aoksGyjyyOY5lI84i+7yvO3baT4V5PZcni6Wuv8XNWE\\naLqS8X6XzsxSW+PiIrXtOXAg2D6R5xFiy1W+n9ud3ENtS5Xz1Na0cPTbSImXwsr3+FztveMQtT1/\\n4RVq6xCldbLEI/AyPR65N7GLX7P1XjhiFQBqZ7ksmiW34C74+jaEZUUDP8/1bOn87v6pQPO3Bj6D\\nEOIdiX7hJ0SkyPmFiBQ5vxCRIucXIlLk/EJEylB/dZPNZbF7321BW6XNI6JyU+H2QodHc1XaXLKz\\nWV6uC+CSWK0dnq75QzzSa//B+6lt5fQ0tbUSSjW9duYKtdXJ2/m+Pfx1HbT3UtvcLv7aLq7xKLxX\\nVsPJSWsVHnd28I691Hb3voPUtsyVOVR64TWSnuBJOovgc99Y5jLrpQVuyyTImN1sWNLbaCckC0VY\\nMn0b+Tt15xciVuT8QkSKnF+ISJHzCxEpcn4hIkXOL0SkDFXqS1kKhRyphdfgUl+G1OTLtnmyzXye\\nJ/fMrvOILkxz+XDpzbVg+6EZLhv9i9/459T2i+d+Tm0nTp6itssXEur/VcKS0tQsj2Q8cIDLeeVR\\norMCKNzOEzhdOR6WWldrXL7qVPncbyTIuqk017d62fDaqa3zhKDFBCW4ZXydtlgIIYBihkvPMxPh\\nSLxWwjr1kfD6TqX5uv8Hzx34mUKIf1TI+YWIFDm/EJEi5xciUuT8QkTKUHf7O70uVjbCu72jo3w3\\nuk7y4LXzPJ9aeXc4gAgAmjW+YzubkLOuUAqPfa4wTvscPHQ3td27j5/rvvt4TtRMj8/V3IFw7r+Z\\nST4fM3u5LZXmZc9ynbD6AQClYtj23vfyHexsj+efS7hkyJd5QFCXBMdMkYAfANi3h5fr6l7kefqW\\nlvlra22QRJQAgHC/QpmPw0fDCpOlB7+f684vRKTI+YWIFDm/EJEi5xciUuT8QkSKnF+ISBmkXNc+\\nAH8GYA6b5bmOuvvXzWwKwPcAHMBmya5PuDuPbADQ6XaxXAlLQPuLXFJqdMPBCtbmudZSCUE/Vy5z\\nSalU4O+HE+VwIEthgks8l89eorbxCX6uxgYPBGn1uGx09/SdwfbyNJcj6xtc9uo4n+MrG9zWJNem\\nBy7PNno8v1+1klDOLaHcWG89fK33HuBBSTOjPLLnjTN8rlaaPMBoKjdBbV1SOixtfBxrI2R9pAZP\\n4jfInb8D4I/d/V4AHwTwR2Z2L4BHADzp7ocAPNn/WwjxLmFL53f3BXd/rv+4CuAkgD0AHgLwWP9p\\njwH42K0apBDi5vO2vvOb2QEA7wfwNIA5d1/omy5i82uBEOJdwsDOb2YjAH4A4PPu/pYv7u7uQLie\\nsJkdMbNjZnask5BLXwgxXAZyfjPLYtPxv+3uP+w3L5rZfN8+DyC4s+XuR939sLsfzmSHGkoghEhg\\nS+c3MwPwLQAn3f2r15geB/Bw//HDAH5884cnhLhVDHIr/hCATwM4bmbP99u+AODLAL5vZp8BcAbA\\nJ7Y6UNpSGMmHywzlpkluPwDFTli2W2vwfGWr4HnuWpO8n2W53GRj4THu37eb9vEpXibr5PM8cq+V\\n51+Rbt9/gNqKY+GovivLXHLc2ODRY5kRvkR23XYHtb0vF77OZ89fpn0W1ivUlivxSMbSOC+FtW9/\\n+LXtTsi7+OrJk9R2/OWXqa1W5VGOk/N8HWQL4fW4WuURhJVK2Nbtchn7erZ0fnf/KQDmLb898JmE\\nEO8o9As/ISJFzi9EpMj5hYgUOb8QkSLnFyJShvqrG0sB+VJYOGg3eLRXKx9+j7Imj7AabfGIKEt4\\nz8sbl43Mw5Fxp6urtM+GcVlxrc7lvPfd915qK+Snqe342QvB9jdfeZP22cjyiL8Dd/LxZ3btp7a6\\nh/stJ8z9+SUerZhu836zJR7Bedcd+4LtZXAZ7fgGlxw7Rb7mcj0+V1Pj3NUWPRyxWC7yaNE6iT61\\nt3E7151fiEiR8wsRKXJ+ISJFzi9EpMj5hYgUOb8QkTJUqS+TTmFyNBwZlyMSIACME9UuU+BSyEyR\\nRwmOlHjiyQluQmk+nGQ0myBTbjiPEjx0L5fzds+HJSoA2FjkEYvn3qwG24tlLuetLPFIsPUun8ds\\nl89/LhuOYiuTeocAcPsElzDrdS6/jXW4rDuaCc//qTdO0z6vXThFbc2EZKdJzlS7wl93rReW+rJj\\nU/yADXLfHjx/p+78QsSKnF+ISJHzCxEpcn4hIkXOL0SkDHW3v4MelnrhXc+JBt9VLk2Fd9nnJvnW\\n/IjxnG/L53h5p4n9CeWkNsJjtxzf9V6t8N1+r4d35gEAF3i/tYTSVZVsPdhe7vAd/dU03yI+UAof\\nDwDOVHhewE4jrEhM5flueWGaB9usXuLBO+0WVwKefu5ssP0vn/or2uf82YvUls7ydVqY4ffSDFG5\\nACCfCduaLT4f3RGyThXYI4TYCjm/EJEi5xciUuT8QkSKnF+ISJHzCxEpW0p9ZrYPwJ9hswS3Azjq\\n7l83sy8B+H0AV+svfcHdf5J0rF63i2YtLAE1mHQBoHsxLDelRnnJpWKRv6+1M7ysknm43BUAXO6E\\nj7knxSXH0XEenDGe4dPfAJcqyzn+ukcz4XyCY1k+H5ZQviyT5sE2C1UusdVOhmXAu+/n13nv6CS1\\nVa6EcxMCwPEXXqK2E5VXg+1nnjlD+xQn+VxNFfn62D3Fy54V21yeLeTCkp5N8OvcyYRtKUuITLuO\\nQXT+DoA/dvfnzGwUwLNm9kTf9jV3/y8Dn00I8Y5hkFp9CwAW+o+rZnYSwJ5bPTAhxK3lbX3nN7MD\\nAN4P4Ol+0+fM7EUze9TM+Gc2IcQ7joGd38xGAPwAwOfdfQ3ANwDcBeB+bH4y+Arpd8TMjpnZsU6L\\n56kXQgyXgZzfzLLYdPxvu/sPAcDdF9296+49AN8E8ECor7sfdffD7n44kxtqKIEQIoEtnd/MDMC3\\nAJx0969e0z5/zdM+DuDEzR+eEOJWMcit+EMAPg3guJk932/7AoBPmdn92JT/TgP47FYH6na7WF0L\\nS1HdPI+M82Y46iy7eo72qXb4FsRam0f1jZMySADQTYcj0hamuSw3wtPLoYpwnjsAyC5xOTKV4ZLS\\n+U643Nh8i8t5ZytcHio3eeThtPGIv0blSrj9GC+Hlq7zyMMnXniKn2uVX898Pvy699zJ14d3uFtM\\n7ubrtOE8T99Kh89/NxVe360Wn3vPsSjHwb9aD7Lb/1MAoZEnavpCiHc2+oWfEJEi5xciUuT8QkSK\\nnF+ISJHzCxEpQ/3VTS5XwN79h4K2SoNLQCxBphcnaJ9WJyEiqjBDbdjLy2TVe+GSV+mJJPmHR7El\\nKFtopbl8mCPSEADUPXzQ2ng4CSoArHV5csxsKqFc1xhPMFm7EO73f46/SPt061zeXE2Q33J5HjlZ\\n9fD9bWqSX7NCmc9vJ8UvWgt8jn/lPXPU9rqHZdEcnw4s1MKvy3tcUrwe3fmFiBQ5vxCRIucXIlLk\\n/EJEipxfiEiR8wsRKUOV+rLZFHbPhiW4mSZPFNnohCOV5m/jkVnFLJddLF+ktvm5XdS21g1PV3mC\\nR9mlEhJxJpTPw8QIl6LyGS5FtUmA3vQIjyBcusxl1mKen2tPgtS3vDusU/36B7iU2u0sUZt1eO3C\\ndIK8dbm6Ej5eg489Ia8qCqWEi+a8Y7HM18H0ejjZab3G5343WTwLeZ7o9Hp05xciUuT8QkSKnF+I\\nSJHzCxEpcn4hIkXOL0SkDDeXthuVQ8pzXAppEcVjxXmywjWSyBIAxsb5uU6vh6UhAGilwhJhmeex\\nxFiBJ8fsJIy/R+RNALAml71K42H5843l12ifpvPjZZ2Pv5UQlnj6yqlge73Lk1LmuwmvGfxc5y+H\\no+IAgMUrZlK8dl4xmLJyk6kRLhNnWlxOXW5epLYqwgv8Qo0nJm2shueq2+YS5vXozi9EpMj5hYgU\\nOb8QkSLnFyJS5PxCRMqWu/1mVgDwFIB8//l/4e5fNLM7AHwXwDSAZwF82t15MjgAzVYbb7x5Pmgr\\ntMNlvACgOLkn2F5b57vDpQy3pXrcVpjiATVohXecR6b47nCvlpCbMMt3sNcu893o2w/ygKYMyeG3\\n3gqXGgOA5iWucBRmeA7CV19+g9qW1xaD7Y1lPvfjB3i+wI2LvBRWK83vYV4jrzuhjFouyxWORjnB\\ntsLLx3UTNuEnS+E5Xu/x9bH4y4XweZo3d7e/CeC33P1XsVmO+0Ez+yCAPwHwNXc/CGAFwGcGPqsQ\\nYsfZ0vl9k6tvu9n+PwfwWwD+ot/+GICP3ZIRCiFuCQN95zezdL9C7yUATwB4HUDF/f//SuUcgPBn\\ncyHEO5KBnN/du+5+P4C9AB4AcM+gJzCzI2Z2zMyOtVuJWwJCiCHytnb73b0C4G8A/DqACTO7umG4\\nF0BwJ8/dj7r7YXc/nM3xzSMhxHDZ0vnNbNbMJvqPiwB+B8BJbL4J/Kv+0x4G8ONbNUghxM1nkMCe\\neQCPmVkam28W33f3/2lmvwDwXTP7TwB+DuBbWx2o6461RljqWVzk+duK6fAwSz3+SaIGLpW1El52\\nrsWjdLqdsAy4usS/zswUEmS5PH/vvQIufa4l5NybyYc1rJFxHrxTH+W611qL54Q7foXbJmfDc9JK\\nSJDXSpB7F5s8eCc3wY9ZI3JZLsPnPtvl+R+zNR6Y5D0eMFZMkBYr9fBarVX4uipPh8eYynAp8nq2\\ndH53fxHA+wPtp7D5/V8I8S5Ev/ATIlLk/EJEipxfiEiR8wsRKXJ+ISLF3HlJoJt+MrPLAM70/5wB\\nwPWb4aFxvBWN462828Zxu7vPDnLAoTr/W05sdszdD+/IyTUOjUPj0Md+IWJFzi9EpOyk8x/dwXNf\\ni8bxVjSOt/KPdhw79p1fCLGz6GO/EJGyI85vZg+a2Stm9pqZPbITY+iP47SZHTez583s2BDP+6iZ\\nXTKzE9e0TZnZE2b2av9/Hg54a8fxJTM735+T583so0MYxz4z+xsz+4WZvWRm/6bfPtQ5SRjHUOfE\\nzApm9oyZvdAfx3/st99hZk/3/eZ7Zra9BBnuPtR/ANLYTAN2J4AcgBcA3DvscfTHchrAzA6c9zcB\\nfADAiWva/jOAR/qPHwHwJzs0ji8B+LdDno95AB/oPx4F8EsA9w57ThLGMdQ5AWAARvqPswCeBvBB\\nAN8H8Ml++38F8IfbOc9O3PkfAPCau5/yzVTf3wXw0A6MY8dw96cAXF+F8SFsJkIFhpQQlYxj6Lj7\\ngrs/139cxWaymD0Y8pwkjGOo+Ca3PGnuTjj/HgBvXvP3Tib/dAB/bWbPmtmRHRrDVebc/Woy9osA\\n5nZwLJ8zsxf7Xwtu+dePazGzA9jMH/E0dnBOrhsHMOQ5GUbS3Ng3/D7s7h8A8C8B/JGZ/eZODwjY\\nfOcHSN3mW883ANyFzRoNCwC+MqwTm9kIgB8A+Ly7r11rG+acBMYx9DnxbSTNHZSdcP7zAPZd8zdN\\n/nmrcffz/f8vAfgRdjYz0aKZzQNA//9LOzEId1/sL7wegG9iSHNiZllsOty33f2H/eahz0loHDs1\\nJ/1zv+2kuYOyE87/MwCH+juXOQCfBPD4sAdhZmUzG736GMDvAjiR3OuW8jg2E6ECO5gQ9aqz9fk4\\nhjAnZmbYzAF50t2/eo1pqHPCxjHsORla0txh7WBet5v5UWzupL4O4N/v0BjuxKbS8AKAl4Y5DgDf\\nwebHxzY2v7t9Bps1D58E8CqA/w1gaofG8d8BHAfwIjadb34I4/gwNj/Svwjg+f6/jw57ThLGMdQ5\\nAfBPsJkU90VsvtH8h2vW7DMAXgPw5wDy2zmPfuEnRKTEvuEnRLTI+YWIFDm/EJEi5xciUuT8QkSK\\nnF+ISJHzCxEpcn4hIuX/Ae1CeF7+CuDKAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb17167c470>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Zf4AoDDJS5Htnn6NszcxSW2g0Tp2W7x4J1WjktKM7Oz1Fao8dx/WYS3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\\nQiQUOb8QCUXOL0RCkfMLkVDk/EIklP8HrHN8dfFI5NgAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb1713b3780>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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OIBgMYKt9VSXAas9cKy43JEK6zRHk8+yhfz1JbMccl0mjRlbbe4ZLd7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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb17130d668>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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VfKcVs9xU9bqh5exzFS26/rR4K+WUvorDzME5MmJ98THN9YPEvnpBJk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\\nfgbgtwGMmNmlDcMDAILNyd39fnc/4e4nBhIaQAgh+su2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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb1712dcb38>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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3guQRZNuIo7rXC+Rl/kgV9p8OvKMlwyTRX4NVzKJATcFMPNjRrPQ2ld\\nVurt+ubw+0sA9wfav+Xud/X+bcDxhRA3E+s6v7s/CYD/wkII8bZkM9/5v2Rmx83sUTPjn6GEEDcl\\n1+r83wFwG4C7AEwA+AZ7opk9bGZHzexos8kTZQgh+ss1Ob+7T7p7x927AL4L4J6E5x5x98PufjiX\\n4/XohRD95Zqc38yuzlv1GQAnrs90hBD9YiNS3w8AfBTAmJmdB/BVAB81s7sAOIAzAP50Q4NlUtg2\\nEs7hZgl6zeLUTLB9ucElmeUl/hWjWuWln85dukRtO3aHc/U1h3iutW1lLsuVczyJ30ApQc5LyGeX\\nKocltmKClJoClyPRTiivBT7HDsKvrdkdpX2qCbn4hothmRUABgb4HAu5cL8M+HnOr3B5k1SbAwC0\\nE6IjC9sSSrOREmbdWZ53ceft4XV89YWLtM9a1nV+d/98oPl7Gx5BCHFTol/4CREpcn4hIkXOL0Sk\\nyPmFiBQ5vxCR0tcEnmZANhuWQ1qNcPQVACy3wkkfZ2s8gWT9EreVD/JoupFlLtcskCCrVI7rPynj\\n76/pYljCBICyDVFbs8EloPK2sMQ2mOVSWSNB6mvkufxWnePnLDMcDlX7yCffQ/ssJOhor73Ky10h\\nw388ttIJJ+o8Oz1F+5w/e47aBsYOUtvwMHen5grXZ/PNsGS6bfcu2ufd77wj2P7ME6/RPmvRnV+I\\nSJHzCxEpcn4hIkXOL0SkyPmFiBQ5vxCR0lepr1lv4sypN4K2IpEAASBLlKhh45LXxQZPmNiY4fLV\\n2AfGqA1NUquvwWXFVEIKg2KJJ6wc272d2hZnuMQ2XQ/LovkUj8BDmWSQBFAo8H6eIEe2l8LnZvc7\\nbqd9Ji++RG3daV7j73LtMrVlB8KXeLnD73uVFH9du4d4JKYNJ6yV85qHRXLp77p1L+3TSofXw8F9\\nYi268wsRKXJ+ISJFzi9EpMj5hYgUOb8QkdLX3f52p41qNbzr2R7gJa/cwjnOWgjnAwSA/BBXD7oJ\\n+fF8geda62Ip2F6p8i399ADfHUab78yOFngJrbEdCaftclh5KJT4PObbvH5Zo5ugwoCv1UozfM4u\\nzE3QPqff4Hn10kV+zjIJSQ3r5Jx18nx926k5akOan7PxYb4exRLP89gl56bV4YFOF18L56FsNjae\\nHl93fiEiRc4vRKTI+YWIFDm/EJEi5xciUuT8QkTKRsp17QPwVwB2YrU81xF3/7aZjQL4EYD9WC3Z\\n9Vl3T9BIAAOQQric1GKdSxSDFi5NtP9Wnovv1NJ5aqsuzFLbbI3bsql8sD0/yKUmy3A5cnr6ZWq7\\nZZz3GxzaR22j28KyY40E/ACAL/O1z3d4ENFwQgmw0lg4WKhe45fI2BAPxvqD991JbecuXaC2Xz35\\nQrC93eXBXffeF86PBwA7f+9Wanvp7CvUtqvC8/GlBsMS4ek3eDm6BRLY00kIdvudcTfwnDaAP3f3\\nOwB8EMCfmdkdAB4B8IS7HwLwRO9vIcTbhHWd390n3P3Z3uMFACcB7AHwIIDHek97DMCnb9QkhRDX\\nn7f0nd/M9gN4P4CnAOx09ys/17qE1a8FQoi3CRt2fjMbAPATAF929zclQ3d3B8L1ic3sYTM7amZH\\nW52EUtBCiL6yIec3syxWHf/77v7TXvOkmY337OMAglUQ3P2Iux9298PZdHizTwjRf9Z1fjMzAN8D\\ncNLdv3mV6XEAD/UePwTg59d/ekKIG8VGovo+AuALAJ43s2O9tq8A+DqAH5vZFwGcBfDZ9Q6Uy+Vw\\n6/5w3rp0mktb5cGRYPv2PTzi7OJlLimls/w9b3SMR3t5NSyvrCxy2ejSVDiqDACaDR5NVx7gpbwG\\nR3nOvXIqHE1Xb4TbAcAyfD3ml7n0eflsuBQWAMwthqP3BnNcOhypJJQUqy9SW67D1/EAUYOzo1wu\\n/cTHDlNbO8FlThx7kdom57kMWCuFJeTL01y2y+8LX6f2Frbx1nV+d/81ViX6EH+44ZGEEDcV+oWf\\nEJEi5xciUuT8QkSKnF+ISJHzCxEpfU3gmUoD5XJ4yHyay1dZUq8r1eQ/GirneFJNS/NkkJUdvExW\\nsxOW35ZLXJJpJshoOefzyJf4a+s4l98mZsLSYjfP17flfKzJKk+4WZ3n0YA5Uqds104u56XAJbv6\\nRV7u6iMffS+1/dGnPh5sP3Hst7RP9QyX5S7OcVl3fC9PklrJ8AS1NRKVmCvw87LUCSdq7WLjv6LV\\nnV+ISJHzCxEpcn4hIkXOL0SkyPmFiBQ5vxCR0lepz5CCeVgCWqnzJJjpTLjPQDkcDQUAH/79D1Db\\nTIfLNW3nS1I5MBZsH0xIUjIwwo+X6/D5zzf4+/LuNJeNZpth+S3d5lF9kwkJTVeMz39s5xC13XUg\\nPMd338kTYF6+dJrazh7ntk6bJyfNpMORnyuzC7TPsePhpJ8AcGGRR4seuJtHA2bJeQGAxRWSuDRB\\nks52wq/LeKDr76A7vxCRIucXIlLk/EJEipxfiEiR8wsRKX3d7e90u1ioh3O4DQ3xHWwfCO+KO1EO\\nAGBwmAdZLPLUc2hmeJBOysN501aQcMBFvmPrzlWHVIvPo3aZB8BMzQWTKGPbCF+r5VleFqpa47vi\\nKPDd/uIH7g62Z4v8fjMxfYnaXjh9jtpm2jz4aPvIsWD7y6d4jsTqYjhoBgAOvY+rFbt28mv4leN8\\n/svhrPdAiqtIzXbYj7p+fct1CSH+GSLnFyJS5PxCRIqcX4hIkfMLESlyfiEiZV2pz8z2AfgrrJbg\\ndgBH3P3bZvY1AH8C4Epyta+4+y+SjuVdoLkUljUyOxOKeHo4KOLiHAmIAFDq8uPVc8PUlnYeGWG5\\nsNS3r3yI9rllFy//NVnjctNCjc+jlBD0c2EmHMBzcvos7WPGJaWVeW6bOfsGte3cGc4Z+PpLYekN\\nAJ741W+oLZ1JyEGY5edzvhW+DtL5BJl4ICEXn/NK9Ocv8ACpsy+corbMrvD5rHd5MFa6EX7N5mGp\\nNzjuBp7TBvDn7v6smVUAPGNmv+zZvuXu/3XDowkhbho2UqtvAsBE7/GCmZ0EsOdGT0wIcWN5S9/5\\nzWw/gPcDeKrX9CUzO25mj5pZuJSuEOKmZMPOb2YDAH4C4MvuXgPwHQC3AbgLq58MvkH6PWxmR83s\\naKPJE3YIIfrLhpzfzLJYdfzvu/tPAcDdJ9294+5dAN8FcE+or7sfcffD7n44n8ter3kLITbJus5v\\nZgbgewBOuvs3r2ofv+ppnwFw4vpPTwhxo9jIbv9HAHwBwPNmdkWn+QqAz5vZXViV/84A+NP1DmQp\\nQ64UHjKX41NZuhyW9Mp7eQTTwiKPmCsNcSln160HqS3fDctNuWEuNQ0X+Ked+UX+3lvN8FxxhYT5\\nD0+EJazlZR6Bl2nw/HgDXqa2mSaPpjv5UlgGzKZ4BGRlJ1/HdotfH9UCj5zszL0abM8P8rFKZS6x\\nLaZ4ROXp18JjAUBmF1/HHePhEmYz1YSvyQNh+TtFchYG57TeE9z91wBCq5Go6Qshbm70Cz8hIkXO\\nL0SkyPmFiBQ5vxCRIucXIlL6msAzk8lg+7ZwyatigU9lysNJJNsrXD4pj/BSWJ0Sl3kKQ/yY3XZY\\nYlvqcGmourjIj5fwmkdS26gtm+blqZoelr1GRvmvrxemwucEANLjCXJTisuHNZJIMssDMTEyuIPa\\nussJHVd4BGersivYPpTnEmwDPILwxZOT1LaMhPJxd/LIz5qFozutzeXq+Xp4fVWuSwixLnJ+ISJF\\nzi9EpMj5hYgUOb8QkSLnFyJS+ir1WdqQHQkP2c7zyKzUQFh+a6US6vsVeaaxaofLeZPLXALq1sOS\\nUmaUJ4P0boLkmObvvcWEyLKZBGnLdo4H22cTEoLmB/hatcETeJaKPLpwZHd4HUeGeFRcKc/XamWR\\nS47j41xy3JYPRxFmM7wPMvw1v/dOfq5TCTJbl9XjA1BLhZOCVozLxBNzYbk3k+Y1AdeiO78QkSLn\\nFyJS5PxCRIqcX4hIkfMLESlyfiEipb9SH4BUNyx5lIp8Knt3haPO2oVw4kMAGC5wOa81zGWe4RKX\\nm3wkLC3uHeYRc23jSUYzXf6aZ1rhBI0AkBvgUWd7R8LyW22W15ibW5ymtkxCHb/ZS7wuXCYTlvTy\\n4PJVdoBH7rUWeF3D5QaX0VK5sK1R53X1Mhku92aKfKxyid9LL0/xtapnw/LhTD0czQoAK0Tq63b4\\n+VqL7vxCRIqcX4hIkfMLESlyfiEiRc4vRKSsu9tvZgUATwLI957/N+7+VTM7AOCHALYBeAbAF9yd\\nb1EDWGk0cfL1cImnvU2+K54rhXfuR3I8yKLWmae2HalBalte4LvRlWY42GbSG7TPcJbvzHcTSpSV\\n2nxXud3mwTGVdnjHfLnF5zjYTghMQpXaCim+Oz8zE+6X7/IAnUMVvladNF+rRovPozAfvq4cPD+e\\np/larXT4WCuT/LwspXkQ1BA5pKX5epQKYVfL8niw32Ejd/4GgI+7+/uwWo77fjP7IIC/APAtd78d\\nwByAL258WCHEVrOu8/sqV26H2d4/B/BxAH/Ta38MwKdvyAyFEDeEDX3nN7N0r0LvFIBfAngNQNXd\\nr3xgOQ+AB4ULIW46NuT87t5x97sA7AVwD4B3bnQAM3vYzI6a2dFmMyEHvBCir7yl3X53rwL4FYAP\\nARg2syu7MHsBXCB9jrj7YXc/nMvxn00KIfrLus5vZtvNbLj3uAjgEwBOYvVN4F/3nvYQgJ/fqEkK\\nIa4/GwnsGQfwmJmlsfpm8WN3/z9m9iKAH5rZfwbwWwDfW+9AnWYb1QvhIJJihkt9w5Vw6aqhLJ/+\\n9OuXqS17Gw9+mJzlMs+eg/uD7Y3zXP7pbuelwSZneeBG2vmnpHqbl+sqEHVoYiossQJAmy89kCCZ\\nVpe4rdEOB+I4V8MwMM+3jWbb/CtjfZaf66bPBduHRvn6ppd5UNhogUuwCykuzdUb/Fyfr4avudEu\\nl2ArO8JlyFLZjX+6Xtf53f04gPcH2k9j9fu/EOJtiH7hJ0SkyPmFiBQ5vxCRIucXIlLk/EJEirkn\\n1Bi63oOZTQM42/tzDADXaPqH5vFmNI8383abx63uvn0jB+yr879pYLOj7n54SwbXPDQPzUMf+4WI\\nFTm/EJGylc5/ZAvHvhrN481oHm/mn+08tuw7vxBia9HHfiEiZUuc38zuN7OXzeyUmT2yFXPozeOM\\nmT1vZsfM7Ggfx33UzKbM7MRVbaNm9ksze7X3P68BdmPn8TUzu9Bbk2Nm9kAf5rHPzH5lZi+a2Qtm\\n9u967X1dk4R59HVNzKxgZr8xs+d68/hPvfYDZvZUz29+ZGY87G8juHtf/wFIYzUN2EEAOQDPAbij\\n3/PozeUMgLEtGPc+AHcDOHFV238B8Ejv8SMA/mKL5vE1AP++z+sxDuDu3uMKgFcA3NHvNUmYR1/X\\nBKtlLQd6j7MAngLwQQA/BvC5Xvt/B/BvNzPOVtz57wFwyt1P+2qq7x8CeHAL5rFluPuTANZWinwQ\\nq4lQgT4lRCXz6DvuPuHuz/YeL2A1Wcwe9HlNEubRV3yVG540dyucfw+Ac1f9vZXJPx3A35nZM2b2\\n8BbN4Qo73f1Kxo1LAHhZ3RvPl8zseO9rwQ3/+nE1ZrYfq/kjnsIWrsmaeQB9XpN+JM2NfcPvXne/\\nG8C/BPBnZnbfVk8IWH3nx+ob01bwHQC3YbVGwwSAb/RrYDMbAPATAF9299rVtn6uSWAefV8T30TS\\n3I2yFc5/AcC+q/6myT9vNO5+off/FICfYWszE02a2TgA9P7nBd1vIO4+2bvwugC+iz6tiZllsepw\\n33f3n/aa+74moXls1Zr0xn7LSXM3ylY4/9MADvV2LnMAPgfg8X5PwszKZla58hjAJwGcSO51Q3kc\\nq4lQgS1MiHrF2Xp8Bn1YEzMzrOaAPOnu37zK1Nc1YfPo95r0LWluv3Yw1+xmPoDVndTXAPyHLZrD\\nQawqDc8BeKGf8wDwA6x+fGxh9bvbF7Fa8/AJAK8C+HsAo1s0j/8F4HkAx7HqfON9mMe9WP1IfxzA\\nsd6/B/q9Jgnz6OuaALgTq0lxj2P1jeY/XnXN/gbAKQD/G0B+M+PoF35CRErsG35CRIucX4hIkfML\\nESlyfiEiRc4vRKTI+YWIFDm/EJEi5xciUv4/oZCTzhWsNM4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb1712a69e8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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vhjAJc0p8+7+4+vdqxGvY7ycrjcUbXKc8wNDfcH2xPG+yxPjlJbZfo8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\\npZjPZPOfA/gDAH/VbH8SwIdvygyFEDeFtj4lmFm8WaF3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     \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb17127add8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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EyR8wsRU+T8QsSUlZTregLAxwCMu/uOetuXAXwOwJUIky+6+8+WO5YD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\\nQsQUOb8QMeX/AeHpppwCSQBNAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb1711c08d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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iLKWhWmuBQ1uBze33yJzwcS4bJVAFBpczkykeJjzE+MBtvLXFlGvcql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\\nX4iY8v8AmhtlqgSGG3AAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb171193cc0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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5ut8vqD+W6evDOQ5LJoe0QLsMXzZ4Lja+f4egzmeEPZtQSXdWcn+Hnl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\\nAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fb17115e4a8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"# Sample random points in the latent space\\n\",\n    \"random_latent_vectors = np.random.normal(size=(10, latent_dim))\\n\",\n    \"\\n\",\n    \"# Decode them to fake images\\n\",\n    \"generated_images = generator.predict(random_latent_vectors)\\n\",\n    \"\\n\",\n    \"for i in range(generated_images.shape[0]):\\n\",\n    \"    img = image.array_to_img(generated_images[i] * 255., scale=False)\\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.imshow(img)\\n\",\n    \"    \\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Froggy with some pixellated artifacts.\"\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\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "second_edition/README.md",
    "content": "# Second edition notebooks\n\nThese are the notebooks for the second edition of the book, originally published in 2021. These notebooks use `tf.keras` with TensorFlow 2.16.\n\n## Table of contents\n\n* [Chapter 2: The mathematical building blocks of neural networks](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter02_mathematical-building-blocks.ipynb)\n* [Chapter 3: Introduction to Keras and TensorFlow](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter03_introduction-to-keras-and-tf.ipynb)\n* [Chapter 4: Getting started with neural networks: classification and regression](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter04_getting-started-with-neural-networks.ipynb)\n* [Chapter 5: Fundamentals of machine learning](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter05_fundamentals-of-ml.ipynb)\n* [Chapter 7: Working with Keras: a deep dive](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter07_working-with-keras.ipynb)\n* [Chapter 8: Introduction to deep learning for computer vision](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter08_intro-to-dl-for-computer-vision.ipynb)\n* Chapter 9: Advanced deep learning for computer vision\n    - [Part 1: Image segmentation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter09_part01_image-segmentation.ipynb)\n    - [Part 2: Modern convnet architecture patterns](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter09_part02_modern-convnet-architecture-patterns.ipynb)\n    - [Part 3: Interpreting what convnets learn](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter09_part03_interpreting-what-convnets-learn.ipynb)\n* [Chapter 10: Deep learning for timeseries](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter10_dl-for-timeseries.ipynb)\n* Chapter 11: Deep learning for text\n    - [Part 1: Introduction](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter11_part01_introduction.ipynb)\n    - [Part 2: Sequence models](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter11_part02_sequence-models.ipynb)\n    - [Part 3: Transformer](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter11_part03_transformer.ipynb)\n    - [Part 4: Sequence-to-sequence learning](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter11_part04_sequence-to-sequence-learning.ipynb)\n* Chapter 12: Generative deep learning\n    - [Part 1: Text generation](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter12_part01_text-generation.ipynb)\n    - [Part 2: Deep Dream](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter12_part02_deep-dream.ipynb)\n    - [Part 3: Neural style transfer](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter12_part03_neural-style-transfer.ipynb)\n    - [Part 4: Variational autoencoders](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter12_part04_variational-autoencoders.ipynb)\n    - [Part 5: Generative adversarial networks](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter12_part05_gans.ipynb)\n* [Chapter 13: Best practices for the real world](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter13_best-practices-for-the-real-world.ipynb)\n* [Chapter 14: Conclusions](https://colab.research.google.com/github/fchollet/deep-learning-with-python-notebooks/blob/master/second_edition/chapter14_conclusions.ipynb)\n"
  },
  {
    "path": "second_edition/chapter02_mathematical-building-blocks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# The mathematical building blocks of neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## A first look at a neural network\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Loading the MNIST dataset in Keras**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import mnist\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(train_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_images.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The network architecture**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(10, activation=\\\"softmax\\\")\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The compilation step**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preparing the image data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28))\\n\",\n    \"test_images = test_images.astype(\\\"float32\\\") / 255\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**\\\"Fitting\\\" the model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.fit(train_images, train_labels, epochs=5, batch_size=128)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using the model to make predictions**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_digits = test_images[0:10]\\n\",\n    \"predictions = model.predict(test_digits)\\n\",\n    \"predictions[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions[0].argmax()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions[0][7]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_labels[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Evaluating the model on new data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(test_images, test_labels)\\n\",\n    \"print(f\\\"test_acc: {test_acc}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Data representations for neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Scalars (rank-0 tensors)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"x = np.array(12)\\n\",\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Vectors (rank-1 tensors)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([12, 3, 6, 14, 7])\\n\",\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Matrices (rank-2 tensors)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([[5, 78, 2, 34, 0],\\n\",\n    \"              [6, 79, 3, 35, 1],\\n\",\n    \"              [7, 80, 4, 36, 2]])\\n\",\n    \"x.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Rank-3 and higher-rank tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([[[5, 78, 2, 34, 0],\\n\",\n    \"               [6, 79, 3, 35, 1],\\n\",\n    \"               [7, 80, 4, 36, 2]],\\n\",\n    \"              [[5, 78, 2, 34, 0],\\n\",\n    \"               [6, 79, 3, 35, 1],\\n\",\n    \"               [7, 80, 4, 36, 2]],\\n\",\n    \"              [[5, 78, 2, 34, 0],\\n\",\n    \"               [6, 79, 3, 35, 1],\\n\",\n    \"               [7, 80, 4, 36, 2]]])\\n\",\n    \"x.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Key attributes\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import mnist\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images.ndim\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying the fourth digit**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"digit = train_images[4]\\n\",\n    \"plt.imshow(digit, cmap=plt.cm.binary)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels[4]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Manipulating tensors in NumPy\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[10:100]\\n\",\n    \"my_slice.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[10:100, :, :]\\n\",\n    \"my_slice.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[10:100, 0:28, 0:28]\\n\",\n    \"my_slice.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[:, 14:, 14:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_slice = train_images[:, 7:-7, 7:-7]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The notion of data batches\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch = train_images[:128]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch = train_images[128:256]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"n = 3\\n\",\n    \"batch = train_images[128 * n:128 * (n + 1)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Real-world examples of data tensors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Vector data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Timeseries data or sequence data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Image data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Video data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## The gears of neural networks: tensor operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Element-wise operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_relu(x):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    x = x.copy()\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(x.shape[1]):\\n\",\n    \"            x[i, j] = max(x[i, j], 0)\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_add(x, y):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    assert x.shape == y.shape\\n\",\n    \"    x = x.copy()\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(x.shape[1]):\\n\",\n    \"            x[i, j] += y[i, j]\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"x = np.random.random((20, 100))\\n\",\n    \"y = np.random.random((20, 100))\\n\",\n    \"\\n\",\n    \"t0 = time.time()\\n\",\n    \"for _ in range(1000):\\n\",\n    \"    z = x + y\\n\",\n    \"    z = np.maximum(z, 0.)\\n\",\n    \"print(\\\"Took: {0:.2f} s\\\".format(time.time() - t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"t0 = time.time()\\n\",\n    \"for _ in range(1000):\\n\",\n    \"    z = naive_add(x, y)\\n\",\n    \"    z = naive_relu(z)\\n\",\n    \"print(\\\"Took: {0:.2f} s\\\".format(time.time() - t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Broadcasting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"X = np.random.random((32, 10))\\n\",\n    \"y = np.random.random((10,))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y = np.expand_dims(y, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"Y = np.concatenate([y] * 32, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_add_matrix_and_vector(x, y):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    assert len(y.shape) == 1\\n\",\n    \"    assert x.shape[1] == y.shape[0]\\n\",\n    \"    x = x.copy()\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(x.shape[1]):\\n\",\n    \"            x[i, j] += y[j]\\n\",\n    \"    return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"x = np.random.random((64, 3, 32, 10))\\n\",\n    \"y = np.random.random((32, 10))\\n\",\n    \"z = np.maximum(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Tensor product\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.random.random((32,))\\n\",\n    \"y = np.random.random((32,))\\n\",\n    \"z = np.dot(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_vector_dot(x, y):\\n\",\n    \"    assert len(x.shape) == 1\\n\",\n    \"    assert len(y.shape) == 1\\n\",\n    \"    assert x.shape[0] == y.shape[0]\\n\",\n    \"    z = 0.\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        z += x[i] * y[i]\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_matrix_vector_dot(x, y):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    assert len(y.shape) == 1\\n\",\n    \"    assert x.shape[1] == y.shape[0]\\n\",\n    \"    z = np.zeros(x.shape[0])\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(x.shape[1]):\\n\",\n    \"            z[i] += x[i, j] * y[j]\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_matrix_vector_dot(x, y):\\n\",\n    \"    z = np.zeros(x.shape[0])\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        z[i] = naive_vector_dot(x[i, :], y)\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def naive_matrix_dot(x, y):\\n\",\n    \"    assert len(x.shape) == 2\\n\",\n    \"    assert len(y.shape) == 2\\n\",\n    \"    assert x.shape[1] == y.shape[0]\\n\",\n    \"    z = np.zeros((x.shape[0], y.shape[1]))\\n\",\n    \"    for i in range(x.shape[0]):\\n\",\n    \"        for j in range(y.shape[1]):\\n\",\n    \"            row_x = x[i, :]\\n\",\n    \"            column_y = y[:, j]\\n\",\n    \"            z[i, j] = naive_vector_dot(row_x, column_y)\\n\",\n    \"    return z\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Tensor reshaping\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_images = train_images.reshape((60000, 28 * 28))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.array([[0., 1.],\\n\",\n    \"             [2., 3.],\\n\",\n    \"             [4., 5.]])\\n\",\n    \"x.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = x.reshape((6, 1))\\n\",\n    \"x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.zeros((300, 20))\\n\",\n    \"x = np.transpose(x)\\n\",\n    \"x.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Geometric interpretation of tensor operations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A geometric interpretation of deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## The engine of neural networks: gradient-based optimization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### What's a derivative?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Derivative of a tensor operation: the gradient\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Stochastic gradient descent\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Chaining derivatives: The Backpropagation algorithm\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The chain rule\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Automatic differentiation with computation graphs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The gradient tape in TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"x = tf.Variable(0.)\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"    y = 2 * x + 3\\n\",\n    \"grad_of_y_wrt_x = tape.gradient(y, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = tf.Variable(tf.random.uniform((2, 2)))\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"    y = 2 * x + 3\\n\",\n    \"grad_of_y_wrt_x = tape.gradient(y, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"W = tf.Variable(tf.random.uniform((2, 2)))\\n\",\n    \"b = tf.Variable(tf.zeros((2,)))\\n\",\n    \"x = tf.random.uniform((2, 2))\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"    y = tf.matmul(x, W) + b\\n\",\n    \"grad_of_y_wrt_W_and_b = tape.gradient(y, [W, b])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Looking back at our first example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28))\\n\",\n    \"test_images = test_images.astype(\\\"float32\\\") / 255\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(10, activation=\\\"softmax\\\")\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.fit(train_images, train_labels, epochs=5, batch_size=128)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Reimplementing our first example from scratch in TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A simple Dense class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"class NaiveDense:\\n\",\n    \"    def __init__(self, input_size, output_size, activation):\\n\",\n    \"        self.activation = activation\\n\",\n    \"\\n\",\n    \"        w_shape = (input_size, output_size)\\n\",\n    \"        w_initial_value = tf.random.uniform(w_shape, minval=0, maxval=1e-1)\\n\",\n    \"        self.W = tf.Variable(w_initial_value)\\n\",\n    \"\\n\",\n    \"        b_shape = (output_size,)\\n\",\n    \"        b_initial_value = tf.zeros(b_shape)\\n\",\n    \"        self.b = tf.Variable(b_initial_value)\\n\",\n    \"\\n\",\n    \"    def __call__(self, inputs):\\n\",\n    \"        return self.activation(tf.matmul(inputs, self.W) + self.b)\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def weights(self):\\n\",\n    \"        return [self.W, self.b]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A simple Sequential class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class NaiveSequential:\\n\",\n    \"    def __init__(self, layers):\\n\",\n    \"        self.layers = layers\\n\",\n    \"\\n\",\n    \"    def __call__(self, inputs):\\n\",\n    \"        x = inputs\\n\",\n    \"        for layer in self.layers:\\n\",\n    \"           x = layer(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def weights(self):\\n\",\n    \"       weights = []\\n\",\n    \"       for layer in self.layers:\\n\",\n    \"           weights += layer.weights\\n\",\n    \"       return weights\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = NaiveSequential([\\n\",\n    \"    NaiveDense(input_size=28 * 28, output_size=512, activation=tf.nn.relu),\\n\",\n    \"    NaiveDense(input_size=512, output_size=10, activation=tf.nn.softmax)\\n\",\n    \"])\\n\",\n    \"assert len(model.weights) == 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A batch generator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import math\\n\",\n    \"\\n\",\n    \"class BatchGenerator:\\n\",\n    \"    def __init__(self, images, labels, batch_size=128):\\n\",\n    \"        assert len(images) == len(labels)\\n\",\n    \"        self.index = 0\\n\",\n    \"        self.images = images\\n\",\n    \"        self.labels = labels\\n\",\n    \"        self.batch_size = batch_size\\n\",\n    \"        self.num_batches = math.ceil(len(images) / batch_size)\\n\",\n    \"\\n\",\n    \"    def next(self):\\n\",\n    \"        images = self.images[self.index : self.index + self.batch_size]\\n\",\n    \"        labels = self.labels[self.index : self.index + self.batch_size]\\n\",\n    \"        self.index += self.batch_size\\n\",\n    \"        return images, labels\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Running one training step\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def one_training_step(model, images_batch, labels_batch):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        predictions = model(images_batch)\\n\",\n    \"        per_sample_losses = tf.keras.losses.sparse_categorical_crossentropy(\\n\",\n    \"            labels_batch, predictions)\\n\",\n    \"        average_loss = tf.reduce_mean(per_sample_losses)\\n\",\n    \"    gradients = tape.gradient(average_loss, model.weights)\\n\",\n    \"    update_weights(gradients, model.weights)\\n\",\n    \"    return average_loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 1e-3\\n\",\n    \"\\n\",\n    \"def update_weights(gradients, weights):\\n\",\n    \"    for g, w in zip(gradients, weights):\\n\",\n    \"        w.assign_sub(g * learning_rate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import optimizers\\n\",\n    \"\\n\",\n    \"optimizer = optimizers.SGD(learning_rate=1e-3)\\n\",\n    \"\\n\",\n    \"def update_weights(gradients, weights):\\n\",\n    \"    optimizer.apply_gradients(zip(gradients, weights))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The full training loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def fit(model, images, labels, epochs, batch_size=128):\\n\",\n    \"    for epoch_counter in range(epochs):\\n\",\n    \"        print(f\\\"Epoch {epoch_counter}\\\")\\n\",\n    \"        batch_generator = BatchGenerator(images, labels)\\n\",\n    \"        for batch_counter in range(batch_generator.num_batches):\\n\",\n    \"            images_batch, labels_batch = batch_generator.next()\\n\",\n    \"            loss = one_training_step(model, images_batch, labels_batch)\\n\",\n    \"            if batch_counter % 100 == 0:\\n\",\n    \"                print(f\\\"loss at batch {batch_counter}: {loss:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import mnist\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28))\\n\",\n    \"test_images = test_images.astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"fit(model, train_images, train_labels, epochs=10, batch_size=128)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model(test_images)\\n\",\n    \"predictions = predictions.numpy()\\n\",\n    \"predicted_labels = np.argmax(predictions, axis=1)\\n\",\n    \"matches = predicted_labels == test_labels\\n\",\n    \"print(f\\\"accuracy: {matches.mean():.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter02_mathematical-building-blocks.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter03_introduction-to-keras-and-tf.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Introduction to Keras and TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## What's TensorFlow?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## What's Keras?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Keras and TensorFlow: A brief history\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Setting up a deep-learning workspace\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Jupyter notebooks: The preferred way to run deep-learning experiments\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using Colaboratory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### First steps with Colaboratory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Installing packages with pip\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using the GPU runtime\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## First steps with TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Constant tensors and variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**All-ones or all-zeros tensors**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"x = tf.ones(shape=(2, 1))\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = tf.zeros(shape=(2, 1))\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Random tensors**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = tf.random.normal(shape=(3, 1), mean=0., stddev=1.)\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = tf.random.uniform(shape=(3, 1), minval=0., maxval=1.)\\n\",\n    \"print(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**NumPy arrays are assignable**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"x = np.ones(shape=(2, 2))\\n\",\n    \"x[0, 0] = 0.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating a TensorFlow variable**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v = tf.Variable(initial_value=tf.random.normal(shape=(3, 1)))\\n\",\n    \"print(v)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Assigning a value to a TensorFlow variable**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v.assign(tf.ones((3, 1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Assigning a value to a subset of a TensorFlow variable**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v[0, 0].assign(3.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using `assign_add`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"v.assign_add(tf.ones((3, 1)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Tensor operations: Doing math in TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A few basic math operations**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"a = tf.ones((2, 2))\\n\",\n    \"b = tf.square(a)\\n\",\n    \"c = tf.sqrt(a)\\n\",\n    \"d = b + c\\n\",\n    \"e = tf.matmul(a, b)\\n\",\n    \"e *= d\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A second look at the GradientTape API\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using the `GradientTape`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_var = tf.Variable(initial_value=3.)\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"   result = tf.square(input_var)\\n\",\n    \"gradient = tape.gradient(result, input_var)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using `GradientTape` with constant tensor inputs**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_const = tf.constant(3.)\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"   tape.watch(input_const)\\n\",\n    \"   result = tf.square(input_const)\\n\",\n    \"gradient = tape.gradient(result, input_const)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using nested gradient tapes to compute second-order gradients**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"time = tf.Variable(0.)\\n\",\n    \"with tf.GradientTape() as outer_tape:\\n\",\n    \"    with tf.GradientTape() as inner_tape:\\n\",\n    \"        position =  4.9 * time ** 2\\n\",\n    \"    speed = inner_tape.gradient(position, time)\\n\",\n    \"acceleration = outer_tape.gradient(speed, time)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### An end-to-end example: A linear classifier in pure TensorFlow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Generating two classes of random points in a 2D plane**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_samples_per_class = 1000\\n\",\n    \"negative_samples = np.random.multivariate_normal(\\n\",\n    \"    mean=[0, 3],\\n\",\n    \"    cov=[[1, 0.5],[0.5, 1]],\\n\",\n    \"    size=num_samples_per_class)\\n\",\n    \"positive_samples = np.random.multivariate_normal(\\n\",\n    \"    mean=[3, 0],\\n\",\n    \"    cov=[[1, 0.5],[0.5, 1]],\\n\",\n    \"    size=num_samples_per_class)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Stacking the two classes into an array with shape (2000, 2)**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = np.vstack((negative_samples, positive_samples)).astype(np.float32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Generating the corresponding targets (0 and 1)**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"targets = np.vstack((np.zeros((num_samples_per_class, 1), dtype=\\\"float32\\\"),\\n\",\n    \"                     np.ones((num_samples_per_class, 1), dtype=\\\"float32\\\")))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting the two point classes**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.scatter(inputs[:, 0], inputs[:, 1], c=targets[:, 0])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating the linear classifier variables**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"input_dim = 2\\n\",\n    \"output_dim = 1\\n\",\n    \"W = tf.Variable(initial_value=tf.random.uniform(shape=(input_dim, output_dim)))\\n\",\n    \"b = tf.Variable(initial_value=tf.zeros(shape=(output_dim,)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The forward pass function**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def model(inputs):\\n\",\n    \"    return tf.matmul(inputs, W) + b\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The mean squared error loss function**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def square_loss(targets, predictions):\\n\",\n    \"    per_sample_losses = tf.square(targets - predictions)\\n\",\n    \"    return tf.reduce_mean(per_sample_losses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The training step function**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"learning_rate = 0.1\\n\",\n    \"\\n\",\n    \"def training_step(inputs, targets):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        predictions = model(inputs)\\n\",\n    \"        loss = square_loss(targets, predictions)\\n\",\n    \"    grad_loss_wrt_W, grad_loss_wrt_b = tape.gradient(loss, [W, b])\\n\",\n    \"    W.assign_sub(grad_loss_wrt_W * learning_rate)\\n\",\n    \"    b.assign_sub(grad_loss_wrt_b * learning_rate)\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The batch training loop**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for step in range(40):\\n\",\n    \"    loss = training_step(inputs, targets)\\n\",\n    \"    print(f\\\"Loss at step {step}: {loss:.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model(inputs)\\n\",\n    \"plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x = np.linspace(-1, 4, 100)\\n\",\n    \"y = - W[0] /  W[1] * x + (0.5 - b) / W[1]\\n\",\n    \"plt.plot(x, y, \\\"-r\\\")\\n\",\n    \"plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Anatomy of a neural network: Understanding core Keras APIs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Layers: The building blocks of deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The base Layer class in Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A `Dense` layer implemented as a `Layer` subclass**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"\\n\",\n    \"class SimpleDense(keras.layers.Layer):\\n\",\n    \"\\n\",\n    \"    def __init__(self, units, activation=None):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.units = units\\n\",\n    \"        self.activation = activation\\n\",\n    \"\\n\",\n    \"    def build(self, input_shape):\\n\",\n    \"        input_dim = input_shape[-1]\\n\",\n    \"        self.W = self.add_weight(shape=(input_dim, self.units),\\n\",\n    \"                                 initializer=\\\"random_normal\\\")\\n\",\n    \"        self.b = self.add_weight(shape=(self.units,),\\n\",\n    \"                                 initializer=\\\"zeros\\\")\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        y = tf.matmul(inputs, self.W) + self.b\\n\",\n    \"        if self.activation is not None:\\n\",\n    \"            y = self.activation(y)\\n\",\n    \"        return y\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"my_dense = SimpleDense(units=32, activation=tf.nn.relu)\\n\",\n    \"input_tensor = tf.ones(shape=(2, 784))\\n\",\n    \"output_tensor = my_dense(input_tensor)\\n\",\n    \"print(output_tensor.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Automatic shape inference: Building layers on the fly\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import layers\\n\",\n    \"layer = layers.Dense(32, activation=\\\"relu\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import models\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"model = models.Sequential([\\n\",\n    \"    layers.Dense(32, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(32)\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    SimpleDense(32, activation=\\\"relu\\\"),\\n\",\n    \"    SimpleDense(64, activation=\\\"relu\\\"),\\n\",\n    \"    SimpleDense(32, activation=\\\"relu\\\"),\\n\",\n    \"    SimpleDense(10, activation=\\\"softmax\\\")\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### From layers to models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The \\\"compile\\\" step: Configuring the learning process\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([keras.layers.Dense(1)])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"mean_squared_error\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=keras.optimizers.RMSprop(),\\n\",\n    \"              loss=keras.losses.MeanSquaredError(),\\n\",\n    \"              metrics=[keras.metrics.BinaryAccuracy()])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Picking a loss function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Understanding the fit() method\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Calling `fit()` with NumPy data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history = model.fit(\\n\",\n    \"    inputs,\\n\",\n    \"    targets,\\n\",\n    \"    epochs=5,\\n\",\n    \"    batch_size=128\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history.history\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Monitoring loss and metrics on validation data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using the `validation_data` argument**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([keras.layers.Dense(1)])\\n\",\n    \"model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=0.1),\\n\",\n    \"              loss=keras.losses.MeanSquaredError(),\\n\",\n    \"              metrics=[keras.metrics.BinaryAccuracy()])\\n\",\n    \"\\n\",\n    \"indices_permutation = np.random.permutation(len(inputs))\\n\",\n    \"shuffled_inputs = inputs[indices_permutation]\\n\",\n    \"shuffled_targets = targets[indices_permutation]\\n\",\n    \"\\n\",\n    \"num_validation_samples = int(0.3 * len(inputs))\\n\",\n    \"val_inputs = shuffled_inputs[:num_validation_samples]\\n\",\n    \"val_targets = shuffled_targets[:num_validation_samples]\\n\",\n    \"training_inputs = shuffled_inputs[num_validation_samples:]\\n\",\n    \"training_targets = shuffled_targets[num_validation_samples:]\\n\",\n    \"model.fit(\\n\",\n    \"    training_inputs,\\n\",\n    \"    training_targets,\\n\",\n    \"    epochs=5,\\n\",\n    \"    batch_size=16,\\n\",\n    \"    validation_data=(val_inputs, val_targets)\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Inference: Using a model after training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model.predict(val_inputs, batch_size=128)\\n\",\n    \"print(predictions[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter03_introduction-to-keras-and-tf.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter04_getting-started-with-neural-networks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Getting started with neural networks: Classification and regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Classifying movie reviews: A binary classification example\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The IMDB dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Loading the IMDB dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import imdb\\n\",\n    \"(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\\n\",\n    \"    num_words=10000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_data[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"max([max(sequence) for sequence in train_data])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Decoding reviews back to text**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"word_index = imdb.get_word_index()\\n\",\n    \"reverse_word_index = dict(\\n\",\n    \"    [(value, key) for (key, value) in word_index.items()])\\n\",\n    \"decoded_review = \\\" \\\".join(\\n\",\n    \"    [reverse_word_index.get(i - 3, \\\"?\\\") for i in train_data[0]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Encoding the integer sequences via multi-hot encoding**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"def vectorize_sequences(sequences, dimension=10000):\\n\",\n    \"    results = np.zeros((len(sequences), dimension))\\n\",\n    \"    for i, sequence in enumerate(sequences):\\n\",\n    \"        for j in sequence:\\n\",\n    \"            results[i, j] = 1.\\n\",\n    \"    return results\\n\",\n    \"x_train = vectorize_sequences(train_data)\\n\",\n    \"x_test = vectorize_sequences(test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_train = np.asarray(train_labels).astype(\\\"float32\\\")\\n\",\n    \"y_test = np.asarray(test_labels).astype(\\\"float32\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Building your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Model definition**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Compiling the model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Validating your approach\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Setting aside a validation set**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_val = x_train[:10000]\\n\",\n    \"partial_x_train = x_train[10000:]\\n\",\n    \"y_val = y_train[:10000]\\n\",\n    \"partial_y_train = y_train[10000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training your model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history = model.fit(partial_x_train,\\n\",\n    \"                    partial_y_train,\\n\",\n    \"                    epochs=20,\\n\",\n    \"                    batch_size=512,\\n\",\n    \"                    validation_data=(x_val, y_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history_dict = history.history\\n\",\n    \"history_dict.keys()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting the training and validation loss**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"history_dict = history.history\\n\",\n    \"loss_values = history_dict[\\\"loss\\\"]\\n\",\n    \"val_loss_values = history_dict[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, len(loss_values) + 1)\\n\",\n    \"plt.plot(epochs, loss_values, \\\"bo\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss_values, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting the training and validation accuracy**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.clf()\\n\",\n    \"acc = history_dict[\\\"accuracy\\\"]\\n\",\n    \"val_acc = history_dict[\\\"val_accuracy\\\"]\\n\",\n    \"plt.plot(epochs, acc, \\\"bo\\\", label=\\\"Training acc\\\")\\n\",\n    \"plt.plot(epochs, val_acc, \\\"b\\\", label=\\\"Validation acc\\\")\\n\",\n    \"plt.title(\\\"Training and validation accuracy\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Retraining a model from scratch**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(x_train, y_train, epochs=4, batch_size=512)\\n\",\n    \"results = model.evaluate(x_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"results\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using a trained model to generate predictions on new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.predict(x_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Further experiments\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Wrapping up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Classifying newswires: A multiclass classification example\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The Reuters dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Loading the Reuters dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import reuters\\n\",\n    \"(train_data, train_labels), (test_data, test_labels) = reuters.load_data(\\n\",\n    \"    num_words=10000)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(train_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"len(test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_data[10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Decoding newswires back to text**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"word_index = reuters.get_word_index()\\n\",\n    \"reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\\n\",\n    \"decoded_newswire = \\\" \\\".join([reverse_word_index.get(i - 3, \\\"?\\\") for i in\\n\",\n    \"    train_data[0]])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_labels[10]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Encoding the input data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_train = vectorize_sequences(train_data)\\n\",\n    \"x_test = vectorize_sequences(test_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Encoding the labels**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def to_one_hot(labels, dimension=46):\\n\",\n    \"    results = np.zeros((len(labels), dimension))\\n\",\n    \"    for i, label in enumerate(labels):\\n\",\n    \"        results[i, label] = 1.\\n\",\n    \"    return results\\n\",\n    \"y_train = to_one_hot(train_labels)\\n\",\n    \"y_test = to_one_hot(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.utils import to_categorical\\n\",\n    \"y_train = to_categorical(train_labels)\\n\",\n    \"y_test = to_categorical(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Building your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Model definition**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(46, activation=\\\"softmax\\\")\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Compiling the model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Validating your approach\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Setting aside a validation set**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"x_val = x_train[:1000]\\n\",\n    \"partial_x_train = x_train[1000:]\\n\",\n    \"y_val = y_train[:1000]\\n\",\n    \"partial_y_train = y_train[1000:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training the model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"history = model.fit(partial_x_train,\\n\",\n    \"                    partial_y_train,\\n\",\n    \"                    epochs=20,\\n\",\n    \"                    batch_size=512,\\n\",\n    \"                    validation_data=(x_val, y_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting the training and validation loss**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"loss = history.history[\\\"loss\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, len(loss) + 1)\\n\",\n    \"plt.plot(epochs, loss, \\\"bo\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting the training and validation accuracy**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.clf()\\n\",\n    \"acc = history.history[\\\"accuracy\\\"]\\n\",\n    \"val_acc = history.history[\\\"val_accuracy\\\"]\\n\",\n    \"plt.plot(epochs, acc, \\\"bo\\\", label=\\\"Training accuracy\\\")\\n\",\n    \"plt.plot(epochs, val_acc, \\\"b\\\", label=\\\"Validation accuracy\\\")\\n\",\n    \"plt.title(\\\"Training and validation accuracy\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Retraining a model from scratch**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"  layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"  layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"  layers.Dense(46, activation=\\\"softmax\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(x_train,\\n\",\n    \"          y_train,\\n\",\n    \"          epochs=9,\\n\",\n    \"          batch_size=512)\\n\",\n    \"results = model.evaluate(x_test, y_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"results\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import copy\\n\",\n    \"test_labels_copy = copy.copy(test_labels)\\n\",\n    \"np.random.shuffle(test_labels_copy)\\n\",\n    \"hits_array = np.array(test_labels) == np.array(test_labels_copy)\\n\",\n    \"hits_array.mean()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Generating predictions on new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model.predict(x_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions[0].shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.sum(predictions[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.argmax(predictions[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A different way to handle the labels and the loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"y_train = np.array(train_labels)\\n\",\n    \"y_test = np.array(test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The importance of having sufficiently large intermediate layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A model with an information bottleneck**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(4, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(46, activation=\\\"softmax\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(partial_x_train,\\n\",\n    \"          partial_y_train,\\n\",\n    \"          epochs=20,\\n\",\n    \"          batch_size=128,\\n\",\n    \"          validation_data=(x_val, y_val))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Further experiments\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Wrapping up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Predicting house prices: A regression example\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The Boston Housing Price dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Loading the Boston housing dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import boston_housing\\n\",\n    \"(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_data.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_targets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Normalizing the data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean = train_data.mean(axis=0)\\n\",\n    \"train_data -= mean\\n\",\n    \"std = train_data.std(axis=0)\\n\",\n    \"train_data /= std\\n\",\n    \"test_data -= mean\\n\",\n    \"test_data /= std\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Building your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Model definition**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def build_model():\\n\",\n    \"    model = keras.Sequential([\\n\",\n    \"        layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(1)\\n\",\n    \"    ])\\n\",\n    \"    model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Validating your approach using K-fold validation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**K-fold validation**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"k = 4\\n\",\n    \"num_val_samples = len(train_data) // k\\n\",\n    \"num_epochs = 100\\n\",\n    \"all_scores = []\\n\",\n    \"for i in range(k):\\n\",\n    \"    print(f\\\"Processing fold #{i}\\\")\\n\",\n    \"    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\\n\",\n    \"    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\\n\",\n    \"    partial_train_data = np.concatenate(\\n\",\n    \"        [train_data[:i * num_val_samples],\\n\",\n    \"         train_data[(i + 1) * num_val_samples:]],\\n\",\n    \"        axis=0)\\n\",\n    \"    partial_train_targets = np.concatenate(\\n\",\n    \"        [train_targets[:i * num_val_samples],\\n\",\n    \"         train_targets[(i + 1) * num_val_samples:]],\\n\",\n    \"        axis=0)\\n\",\n    \"    model = build_model()\\n\",\n    \"    model.fit(partial_train_data, partial_train_targets,\\n\",\n    \"              epochs=num_epochs, batch_size=16, verbose=0)\\n\",\n    \"    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\\n\",\n    \"    all_scores.append(val_mae)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"all_scores\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.mean(all_scores)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Saving the validation logs at each fold**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_epochs = 500\\n\",\n    \"all_mae_histories = []\\n\",\n    \"for i in range(k):\\n\",\n    \"    print(f\\\"Processing fold #{i}\\\")\\n\",\n    \"    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\\n\",\n    \"    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\\n\",\n    \"    partial_train_data = np.concatenate(\\n\",\n    \"        [train_data[:i * num_val_samples],\\n\",\n    \"         train_data[(i + 1) * num_val_samples:]],\\n\",\n    \"        axis=0)\\n\",\n    \"    partial_train_targets = np.concatenate(\\n\",\n    \"        [train_targets[:i * num_val_samples],\\n\",\n    \"         train_targets[(i + 1) * num_val_samples:]],\\n\",\n    \"        axis=0)\\n\",\n    \"    model = build_model()\\n\",\n    \"    history = model.fit(partial_train_data, partial_train_targets,\\n\",\n    \"                        validation_data=(val_data, val_targets),\\n\",\n    \"                        epochs=num_epochs, batch_size=16, verbose=0)\\n\",\n    \"    mae_history = history.history[\\\"val_mae\\\"]\\n\",\n    \"    all_mae_histories.append(mae_history)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Building the history of successive mean K-fold validation scores**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"average_mae_history = [\\n\",\n    \"    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting validation scores**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Validation MAE\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting validation scores, excluding the first 10 data points**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"truncated_mae_history = average_mae_history[10:]\\n\",\n    \"plt.plot(range(1, len(truncated_mae_history) + 1), truncated_mae_history)\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Validation MAE\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training the final model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = build_model()\\n\",\n    \"model.fit(train_data, train_targets,\\n\",\n    \"          epochs=130, batch_size=16, verbose=0)\\n\",\n    \"test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_mae_score\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Generating predictions on new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"predictions = model.predict(test_data)\\n\",\n    \"predictions[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Wrapping up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter04_getting-started-with-neural-networks.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter05_fundamentals-of-ml.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Fundamentals of machine learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Generalization: The goal of machine learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Underfitting and overfitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Noisy training data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Ambiguous features\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Rare features and spurious correlations\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Adding white-noise channels or all-zeros channels to MNIST**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import mnist\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), _ = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"train_images_with_noise_channels = np.concatenate(\\n\",\n    \"    [train_images, np.random.random((len(train_images), 784))], axis=1)\\n\",\n    \"\\n\",\n    \"train_images_with_zeros_channels = np.concatenate(\\n\",\n    \"    [train_images, np.zeros((len(train_images), 784))], axis=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training the same model on MNIST data with noise channels or all-zero channels**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"def get_model():\\n\",\n    \"    model = keras.Sequential([\\n\",\n    \"        layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\")\\n\",\n    \"    ])\\n\",\n    \"    model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"                  loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"                  metrics=[\\\"accuracy\\\"])\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"model = get_model()\\n\",\n    \"history_noise = model.fit(\\n\",\n    \"    train_images_with_noise_channels, train_labels,\\n\",\n    \"    epochs=10,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_split=0.2)\\n\",\n    \"\\n\",\n    \"model = get_model()\\n\",\n    \"history_zeros = model.fit(\\n\",\n    \"    train_images_with_zeros_channels, train_labels,\\n\",\n    \"    epochs=10,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting a validation accuracy comparison**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"val_acc_noise = history_noise.history[\\\"val_accuracy\\\"]\\n\",\n    \"val_acc_zeros = history_zeros.history[\\\"val_accuracy\\\"]\\n\",\n    \"epochs = range(1, 11)\\n\",\n    \"plt.plot(epochs, val_acc_noise, \\\"b-\\\",\\n\",\n    \"         label=\\\"Validation accuracy with noise channels\\\")\\n\",\n    \"plt.plot(epochs, val_acc_zeros, \\\"b--\\\",\\n\",\n    \"         label=\\\"Validation accuracy with zeros channels\\\")\\n\",\n    \"plt.title(\\\"Effect of noise channels on validation accuracy\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Accuracy\\\")\\n\",\n    \"plt.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The nature of generalization in deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Fitting a MNIST model with randomly shuffled labels**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"(train_images, train_labels), _ = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"random_train_labels = train_labels[:]\\n\",\n    \"np.random.shuffle(random_train_labels)\\n\",\n    \"\\n\",\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(10, activation=\\\"softmax\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(train_images, random_train_labels,\\n\",\n    \"          epochs=100,\\n\",\n    \"          batch_size=128,\\n\",\n    \"          validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The manifold hypothesis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Interpolation as a source of generalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Why deep learning works\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training data is paramount\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Evaluating machine-learning models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Training, validation, and test sets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Simple hold-out validation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### K-fold validation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Iterated K-fold validation with shuffling\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Beating a common-sense baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Things to keep in mind about model evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Improving model fit\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Tuning key gradient descent parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training a MNIST model with an incorrectly high learning rate**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"(train_images, train_labels), _ = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28 * 28))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(10, activation=\\\"softmax\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=keras.optimizers.RMSprop(1.),\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(train_images, train_labels,\\n\",\n    \"          epochs=10,\\n\",\n    \"          batch_size=128,\\n\",\n    \"          validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The same model with a more appropriate learning rate**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(10, activation=\\\"softmax\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=keras.optimizers.RMSprop(1e-2),\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(train_images, train_labels,\\n\",\n    \"          epochs=10,\\n\",\n    \"          batch_size=128,\\n\",\n    \"          validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Leveraging better architecture priors\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Increasing model capacity\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A simple logistic regression on MNIST**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([layers.Dense(10, activation=\\\"softmax\\\")])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"history_small_model = model.fit(\\n\",\n    \"    train_images, train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"val_loss = history_small_model.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, 21)\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b--\\\",\\n\",\n    \"         label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Effect of insufficient model capacity on validation loss\\\")\\n\",\n    \"plt.xlabel(\\\"Epochs\\\")\\n\",\n    \"plt.ylabel(\\\"Loss\\\")\\n\",\n    \"plt.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(96, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(96, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(10, activation=\\\"softmax\\\"),\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"history_large_model = model.fit(\\n\",\n    \"    train_images, train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    validation_split=0.2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Improving generalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Dataset curation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Feature engineering\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using early stopping\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Regularizing your model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Reducing the network's size\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Original model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import imdb\\n\",\n    \"(train_data, train_labels), _ = imdb.load_data(num_words=10000)\\n\",\n    \"\\n\",\n    \"def vectorize_sequences(sequences, dimension=10000):\\n\",\n    \"    results = np.zeros((len(sequences), dimension))\\n\",\n    \"    for i, sequence in enumerate(sequences):\\n\",\n    \"        results[i, sequence] = 1.\\n\",\n    \"    return results\\n\",\n    \"train_data = vectorize_sequences(train_data)\\n\",\n    \"\\n\",\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"history_original = model.fit(train_data, train_labels,\\n\",\n    \"                             epochs=20, batch_size=512, validation_split=0.4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Version of the model with lower capacity**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(4, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(4, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"history_smaller_model = model.fit(\\n\",\n    \"    train_data, train_labels,\\n\",\n    \"    epochs=20, batch_size=512, validation_split=0.4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Version of the model with higher capacity**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(512, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"history_larger_model = model.fit(\\n\",\n    \"    train_data, train_labels,\\n\",\n    \"    epochs=20, batch_size=512, validation_split=0.4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Adding weight regularization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Adding L2 weight regularization to the model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import regularizers\\n\",\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(16,\\n\",\n    \"                 kernel_regularizer=regularizers.l2(0.002),\\n\",\n    \"                 activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(16,\\n\",\n    \"                 kernel_regularizer=regularizers.l2(0.002),\\n\",\n    \"                 activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"history_l2_reg = model.fit(\\n\",\n    \"    train_data, train_labels,\\n\",\n    \"    epochs=20, batch_size=512, validation_split=0.4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Different weight regularizers available in Keras**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import regularizers\\n\",\n    \"regularizers.l1(0.001)\\n\",\n    \"regularizers.l1_l2(l1=0.001, l2=0.001)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Adding dropout\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Adding dropout to the IMDB model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dropout(0.5),\\n\",\n    \"    layers.Dense(16, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dropout(0.5),\\n\",\n    \"    layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"])\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"history_dropout = model.fit(\\n\",\n    \"    train_data, train_labels,\\n\",\n    \"    epochs=20, batch_size=512, validation_split=0.4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter05_fundamentals-of-ml.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter07_working-with-keras.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Working with Keras: A deep dive\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## A spectrum of workflows\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Different ways to build Keras models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The Sequential model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The `Sequential` class**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"model = keras.Sequential([\\n\",\n    \"    layers.Dense(64, activation=\\\"relu\\\"),\\n\",\n    \"    layers.Dense(10, activation=\\\"softmax\\\")\\n\",\n    \"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Incrementally building a Sequential model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential()\\n\",\n    \"model.add(layers.Dense(64, activation=\\\"relu\\\"))\\n\",\n    \"model.add(layers.Dense(10, activation=\\\"softmax\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Calling a model for the first time to build it**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.build(input_shape=(None, 3))\\n\",\n    \"model.weights\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The summary method**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Naming models and layers with the `name` argument**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential(name=\\\"my_example_model\\\")\\n\",\n    \"model.add(layers.Dense(64, activation=\\\"relu\\\", name=\\\"my_first_layer\\\"))\\n\",\n    \"model.add(layers.Dense(10, activation=\\\"softmax\\\", name=\\\"my_last_layer\\\"))\\n\",\n    \"model.build((None, 3))\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Specifying the input shape of your model in advance**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.Sequential()\\n\",\n    \"model.add(keras.Input(shape=(3,)))\\n\",\n    \"model.add(layers.Dense(64, activation=\\\"relu\\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.add(layers.Dense(10, activation=\\\"softmax\\\"))\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The Functional API\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A simple example\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A simple Functional model with two `Dense` layers**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(3,), name=\\\"my_input\\\")\\n\",\n    \"features = layers.Dense(64, activation=\\\"relu\\\")(inputs)\\n\",\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(3,), name=\\\"my_input\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"features = layers.Dense(64, activation=\\\"relu\\\")(inputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"features.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Multi-input, multi-output models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A multi-input, multi-output Functional model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocabulary_size = 10000\\n\",\n    \"num_tags = 100\\n\",\n    \"num_departments = 4\\n\",\n    \"\\n\",\n    \"title = keras.Input(shape=(vocabulary_size,), name=\\\"title\\\")\\n\",\n    \"text_body = keras.Input(shape=(vocabulary_size,), name=\\\"text_body\\\")\\n\",\n    \"tags = keras.Input(shape=(num_tags,), name=\\\"tags\\\")\\n\",\n    \"\\n\",\n    \"features = layers.Concatenate()([title, text_body, tags])\\n\",\n    \"features = layers.Dense(64, activation=\\\"relu\\\")(features)\\n\",\n    \"\\n\",\n    \"priority = layers.Dense(1, activation=\\\"sigmoid\\\", name=\\\"priority\\\")(features)\\n\",\n    \"department = layers.Dense(\\n\",\n    \"    num_departments, activation=\\\"softmax\\\", name=\\\"department\\\")(features)\\n\",\n    \"\\n\",\n    \"model = keras.Model(inputs=[title, text_body, tags], outputs=[priority, department])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Training a multi-input, multi-output model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training a model by providing lists of input & target arrays**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"num_samples = 1280\\n\",\n    \"\\n\",\n    \"title_data = np.random.randint(0, 2, size=(num_samples, vocabulary_size))\\n\",\n    \"text_body_data = np.random.randint(0, 2, size=(num_samples, vocabulary_size))\\n\",\n    \"tags_data = np.random.randint(0, 2, size=(num_samples, num_tags))\\n\",\n    \"\\n\",\n    \"priority_data = np.random.random(size=(num_samples, 1))\\n\",\n    \"department_data = np.random.randint(0, 2, size=(num_samples, num_departments))\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=[\\\"mean_squared_error\\\", \\\"categorical_crossentropy\\\"],\\n\",\n    \"              metrics=[[\\\"mean_absolute_error\\\"], [\\\"accuracy\\\"]])\\n\",\n    \"model.fit([title_data, text_body_data, tags_data],\\n\",\n    \"          [priority_data, department_data],\\n\",\n    \"          epochs=1)\\n\",\n    \"model.evaluate([title_data, text_body_data, tags_data],\\n\",\n    \"               [priority_data, department_data])\\n\",\n    \"priority_preds, department_preds = model.predict([title_data, text_body_data, tags_data])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training a model by providing dicts of input & target arrays**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss={\\\"priority\\\": \\\"mean_squared_error\\\", \\\"department\\\": \\\"categorical_crossentropy\\\"},\\n\",\n    \"              metrics={\\\"priority\\\": [\\\"mean_absolute_error\\\"], \\\"department\\\": [\\\"accuracy\\\"]})\\n\",\n    \"model.fit({\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data},\\n\",\n    \"          {\\\"priority\\\": priority_data, \\\"department\\\": department_data},\\n\",\n    \"          epochs=1)\\n\",\n    \"model.evaluate({\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data},\\n\",\n    \"               {\\\"priority\\\": priority_data, \\\"department\\\": department_data})\\n\",\n    \"priority_preds, department_preds = model.predict(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The power of the Functional API: Access to layer connectivity\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.utils.plot_model(model, \\\"ticket_classifier.png\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.utils.plot_model(model, \\\"ticket_classifier_with_shape_info.png\\\", show_shapes=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Retrieving the inputs or outputs of a layer in a Functional model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.layers[3].input\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.layers[3].output\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating a new model by reusing intermediate layer outputs**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"features = model.layers[4].output\\n\",\n    \"difficulty = layers.Dense(3, activation=\\\"softmax\\\", name=\\\"difficulty\\\")(features)\\n\",\n    \"\\n\",\n    \"new_model = keras.Model(\\n\",\n    \"    inputs=[title, text_body, tags],\\n\",\n    \"    outputs=[priority, department, difficulty])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"keras.utils.plot_model(new_model, \\\"updated_ticket_classifier.png\\\", show_shapes=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Subclassing the Model class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Rewriting our previous example as a subclassed model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A simple subclassed model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class CustomerTicketModel(keras.Model):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_departments):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.concat_layer = layers.Concatenate()\\n\",\n    \"        self.mixing_layer = layers.Dense(64, activation=\\\"relu\\\")\\n\",\n    \"        self.priority_scorer = layers.Dense(1, activation=\\\"sigmoid\\\")\\n\",\n    \"        self.department_classifier = layers.Dense(\\n\",\n    \"            num_departments, activation=\\\"softmax\\\")\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        title = inputs[\\\"title\\\"]\\n\",\n    \"        text_body = inputs[\\\"text_body\\\"]\\n\",\n    \"        tags = inputs[\\\"tags\\\"]\\n\",\n    \"\\n\",\n    \"        features = self.concat_layer([title, text_body, tags])\\n\",\n    \"        features = self.mixing_layer(features)\\n\",\n    \"        priority = self.priority_scorer(features)\\n\",\n    \"        department = self.department_classifier(features)\\n\",\n    \"        return priority, department\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = CustomerTicketModel(num_departments=4)\\n\",\n    \"\\n\",\n    \"priority, department = model(\\n\",\n    \"    {\\\"title\\\": title_data, \\\"text_body\\\": text_body_data, \\\"tags\\\": tags_data})\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=[\\\"mean_squared_error\\\", \\\"categorical_crossentropy\\\"],\\n\",\n    \"              metrics=[[\\\"mean_absolute_error\\\"], [\\\"accuracy\\\"]])\\n\",\n    \"model.fit({\\\"title\\\": title_data,\\n\",\n    \"           \\\"text_body\\\": text_body_data,\\n\",\n    \"           \\\"tags\\\": tags_data},\\n\",\n    \"          [priority_data, department_data],\\n\",\n    \"          epochs=1)\\n\",\n    \"model.evaluate({\\\"title\\\": title_data,\\n\",\n    \"                \\\"text_body\\\": text_body_data,\\n\",\n    \"                \\\"tags\\\": tags_data},\\n\",\n    \"               [priority_data, department_data])\\n\",\n    \"priority_preds, department_preds = model.predict({\\\"title\\\": title_data,\\n\",\n    \"                                                  \\\"text_body\\\": text_body_data,\\n\",\n    \"                                                  \\\"tags\\\": tags_data})\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Beware: What subclassed models don't support\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Mixing and matching different components\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating a Functional model that includes a subclassed model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class Classifier(keras.Model):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes=2):\\n\",\n    \"        super().__init__()\\n\",\n    \"        if num_classes == 2:\\n\",\n    \"            num_units = 1\\n\",\n    \"            activation = \\\"sigmoid\\\"\\n\",\n    \"        else:\\n\",\n    \"            num_units = num_classes\\n\",\n    \"            activation = \\\"softmax\\\"\\n\",\n    \"        self.dense = layers.Dense(num_units, activation=activation)\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        return self.dense(inputs)\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(3,))\\n\",\n    \"features = layers.Dense(64, activation=\\\"relu\\\")(inputs)\\n\",\n    \"outputs = Classifier(num_classes=10)(features)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating a subclassed model that includes a Functional model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(64,))\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(inputs)\\n\",\n    \"binary_classifier = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n    \"\\n\",\n    \"class MyModel(keras.Model):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes=2):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.dense = layers.Dense(64, activation=\\\"relu\\\")\\n\",\n    \"        self.classifier = binary_classifier\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        features = self.dense(inputs)\\n\",\n    \"        return self.classifier(features)\\n\",\n    \"\\n\",\n    \"model = MyModel()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Remember: Use the right tool for the job\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Using built-in training and evaluation loops\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The standard workflow: `compile()`, `fit()`, `evaluate()`, `predict()`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"def get_mnist_model():\\n\",\n    \"    inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"    features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"    features = layers.Dropout(0.5)(features)\\n\",\n    \"    outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"    model = keras.Model(inputs, outputs)\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"(images, labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"images = images.reshape((60000, 28 * 28)).astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28 * 28)).astype(\\\"float32\\\") / 255\\n\",\n    \"train_images, val_images = images[10000:], images[:10000]\\n\",\n    \"train_labels, val_labels = labels[10000:], labels[:10000]\\n\",\n    \"\\n\",\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(train_images, train_labels,\\n\",\n    \"          epochs=3,\\n\",\n    \"          validation_data=(val_images, val_labels))\\n\",\n    \"test_metrics = model.evaluate(test_images, test_labels)\\n\",\n    \"predictions = model.predict(test_images)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Writing your own metrics\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Implementing a custom metric by subclassing the `Metric` class**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"class RootMeanSquaredError(keras.metrics.Metric):\\n\",\n    \"\\n\",\n    \"    def __init__(self, name=\\\"rmse\\\", **kwargs):\\n\",\n    \"        super().__init__(name=name, **kwargs)\\n\",\n    \"        self.mse_sum = self.add_weight(name=\\\"mse_sum\\\", initializer=\\\"zeros\\\")\\n\",\n    \"        self.total_samples = self.add_weight(\\n\",\n    \"            name=\\\"total_samples\\\", initializer=\\\"zeros\\\", dtype=\\\"int32\\\")\\n\",\n    \"\\n\",\n    \"    def update_state(self, y_true, y_pred, sample_weight=None):\\n\",\n    \"        y_true = tf.one_hot(y_true, depth=tf.shape(y_pred)[1])\\n\",\n    \"        mse = tf.reduce_sum(tf.square(y_true - y_pred))\\n\",\n    \"        self.mse_sum.assign_add(mse)\\n\",\n    \"        num_samples = tf.shape(y_pred)[0]\\n\",\n    \"        self.total_samples.assign_add(num_samples)\\n\",\n    \"\\n\",\n    \"    def result(self):\\n\",\n    \"        return tf.sqrt(self.mse_sum / tf.cast(self.total_samples, tf.float32))\\n\",\n    \"\\n\",\n    \"    def reset_state(self):\\n\",\n    \"        self.mse_sum.assign(0.)\\n\",\n    \"        self.total_samples.assign(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\", RootMeanSquaredError()])\\n\",\n    \"model.fit(train_images, train_labels,\\n\",\n    \"          epochs=3,\\n\",\n    \"          validation_data=(val_images, val_labels))\\n\",\n    \"test_metrics = model.evaluate(test_images, test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using callbacks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The EarlyStopping and ModelCheckpoint callbacks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using the `callbacks` argument in the `fit()` method**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks_list = [\\n\",\n    \"    keras.callbacks.EarlyStopping(\\n\",\n    \"        monitor=\\\"val_accuracy\\\",\\n\",\n    \"        patience=2,\\n\",\n    \"    ),\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"checkpoint_path.keras\\\",\\n\",\n    \"        monitor=\\\"val_loss\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"    )\\n\",\n    \"]\\n\",\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(train_images, train_labels,\\n\",\n    \"          epochs=10,\\n\",\n    \"          callbacks=callbacks_list,\\n\",\n    \"          validation_data=(val_images, val_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.models.load_model(\\\"checkpoint_path.keras\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Writing your own callbacks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating a custom callback by subclassing the `Callback` class**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"\\n\",\n    \"class LossHistory(keras.callbacks.Callback):\\n\",\n    \"    def on_train_begin(self, logs):\\n\",\n    \"        self.per_batch_losses = []\\n\",\n    \"\\n\",\n    \"    def on_batch_end(self, batch, logs):\\n\",\n    \"        self.per_batch_losses.append(logs.get(\\\"loss\\\"))\\n\",\n    \"\\n\",\n    \"    def on_epoch_end(self, epoch, logs):\\n\",\n    \"        plt.clf()\\n\",\n    \"        plt.plot(range(len(self.per_batch_losses)), self.per_batch_losses,\\n\",\n    \"                 label=\\\"Training loss for each batch\\\")\\n\",\n    \"        plt.xlabel(f\\\"Batch (epoch {epoch})\\\")\\n\",\n    \"        plt.ylabel(\\\"Loss\\\")\\n\",\n    \"        plt.legend()\\n\",\n    \"        plt.savefig(f\\\"plot_at_epoch_{epoch}\\\")\\n\",\n    \"        self.per_batch_losses = []\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(train_images, train_labels,\\n\",\n    \"          epochs=10,\\n\",\n    \"          callbacks=[LossHistory()],\\n\",\n    \"          validation_data=(val_images, val_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Monitoring and visualization with TensorBoard\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_mnist_model()\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"\\n\",\n    \"tensorboard = keras.callbacks.TensorBoard(\\n\",\n    \"    log_dir=\\\"/full_path_to_your_log_dir\\\",\\n\",\n    \")\\n\",\n    \"model.fit(train_images, train_labels,\\n\",\n    \"          epochs=10,\\n\",\n    \"          validation_data=(val_images, val_labels),\\n\",\n    \"          callbacks=[tensorboard])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext tensorboard\\n\",\n    \"%tensorboard --logdir /full_path_to_your_log_dir\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Writing your own training and evaluation loops\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Training versus inference\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Low-level usage of metrics\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"metric = keras.metrics.SparseCategoricalAccuracy()\\n\",\n    \"targets = [0, 1, 2]\\n\",\n    \"predictions = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]\\n\",\n    \"metric.update_state(targets, predictions)\\n\",\n    \"current_result = metric.result()\\n\",\n    \"print(f\\\"result: {current_result:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"values = [0, 1, 2, 3, 4]\\n\",\n    \"mean_tracker = keras.metrics.Mean()\\n\",\n    \"for value in values:\\n\",\n    \"    mean_tracker.update_state(value)\\n\",\n    \"print(f\\\"Mean of values: {mean_tracker.result():.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A complete training and evaluation loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Writing a step-by-step training loop: the training step function**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_mnist_model()\\n\",\n    \"\\n\",\n    \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n    \"optimizer = keras.optimizers.RMSprop()\\n\",\n    \"metrics = [keras.metrics.SparseCategoricalAccuracy()]\\n\",\n    \"loss_tracking_metric = keras.metrics.Mean()\\n\",\n    \"\\n\",\n    \"def train_step(inputs, targets):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        predictions = model(inputs, training=True)\\n\",\n    \"        loss = loss_fn(targets, predictions)\\n\",\n    \"    gradients = tape.gradient(loss, model.trainable_weights)\\n\",\n    \"    optimizer.apply_gradients(zip(gradients, model.trainable_weights))\\n\",\n    \"\\n\",\n    \"    logs = {}\\n\",\n    \"    for metric in metrics:\\n\",\n    \"        metric.update_state(targets, predictions)\\n\",\n    \"        logs[metric.name] = metric.result()\\n\",\n    \"\\n\",\n    \"    loss_tracking_metric.update_state(loss)\\n\",\n    \"    logs[\\\"loss\\\"] = loss_tracking_metric.result()\\n\",\n    \"    return logs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Writing a step-by-step training loop: resetting the metrics**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def reset_metrics():\\n\",\n    \"    for metric in metrics:\\n\",\n    \"        metric.reset_state()\\n\",\n    \"    loss_tracking_metric.reset_state()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Writing a step-by-step training loop: the loop itself**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"training_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\\n\",\n    \"training_dataset = training_dataset.batch(32)\\n\",\n    \"epochs = 3\\n\",\n    \"for epoch in range(epochs):\\n\",\n    \"    reset_metrics()\\n\",\n    \"    for inputs_batch, targets_batch in training_dataset:\\n\",\n    \"        logs = train_step(inputs_batch, targets_batch)\\n\",\n    \"    print(f\\\"Results at the end of epoch {epoch}\\\")\\n\",\n    \"    for key, value in logs.items():\\n\",\n    \"        print(f\\\"...{key}: {value:.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Writing a step-by-step evaluation loop**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def test_step(inputs, targets):\\n\",\n    \"    predictions = model(inputs, training=False)\\n\",\n    \"    loss = loss_fn(targets, predictions)\\n\",\n    \"\\n\",\n    \"    logs = {}\\n\",\n    \"    for metric in metrics:\\n\",\n    \"        metric.update_state(targets, predictions)\\n\",\n    \"        logs[\\\"val_\\\" + metric.name] = metric.result()\\n\",\n    \"\\n\",\n    \"    loss_tracking_metric.update_state(loss)\\n\",\n    \"    logs[\\\"val_loss\\\"] = loss_tracking_metric.result()\\n\",\n    \"    return logs\\n\",\n    \"\\n\",\n    \"val_dataset = tf.data.Dataset.from_tensor_slices((val_images, val_labels))\\n\",\n    \"val_dataset = val_dataset.batch(32)\\n\",\n    \"reset_metrics()\\n\",\n    \"for inputs_batch, targets_batch in val_dataset:\\n\",\n    \"    logs = test_step(inputs_batch, targets_batch)\\n\",\n    \"print(\\\"Evaluation results:\\\")\\n\",\n    \"for key, value in logs.items():\\n\",\n    \"    print(f\\\"...{key}: {value:.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Make it fast with tf.function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Adding a `tf.function` decorator to our evaluation-step function**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"@tf.function\\n\",\n    \"def test_step(inputs, targets):\\n\",\n    \"    predictions = model(inputs, training=False)\\n\",\n    \"    loss = loss_fn(targets, predictions)\\n\",\n    \"\\n\",\n    \"    logs = {}\\n\",\n    \"    for metric in metrics:\\n\",\n    \"        metric.update_state(targets, predictions)\\n\",\n    \"        logs[\\\"val_\\\" + metric.name] = metric.result()\\n\",\n    \"\\n\",\n    \"    loss_tracking_metric.update_state(loss)\\n\",\n    \"    logs[\\\"val_loss\\\"] = loss_tracking_metric.result()\\n\",\n    \"    return logs\\n\",\n    \"\\n\",\n    \"val_dataset = tf.data.Dataset.from_tensor_slices((val_images, val_labels))\\n\",\n    \"val_dataset = val_dataset.batch(32)\\n\",\n    \"reset_metrics()\\n\",\n    \"for inputs_batch, targets_batch in val_dataset:\\n\",\n    \"    logs = test_step(inputs_batch, targets_batch)\\n\",\n    \"print(\\\"Evaluation results:\\\")\\n\",\n    \"for key, value in logs.items():\\n\",\n    \"    print(f\\\"...{key}: {value:.4f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Leveraging fit() with a custom training loop\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Implementing a custom training step to use with `fit()`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"loss_fn = keras.losses.SparseCategoricalCrossentropy()\\n\",\n    \"loss_tracker = keras.metrics.Mean(name=\\\"loss\\\")\\n\",\n    \"\\n\",\n    \"class CustomModel(keras.Model):\\n\",\n    \"    def train_step(self, data):\\n\",\n    \"        inputs, targets = data\\n\",\n    \"        with tf.GradientTape() as tape:\\n\",\n    \"            predictions = self(inputs, training=True)\\n\",\n    \"            loss = loss_fn(targets, predictions)\\n\",\n    \"        gradients = tape.gradient(loss, self.trainable_weights)\\n\",\n    \"        self.optimizer.apply_gradients(zip(gradients, self.trainable_weights))\\n\",\n    \"\\n\",\n    \"        loss_tracker.update_state(loss)\\n\",\n    \"        return {\\\"loss\\\": loss_tracker.result()}\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def metrics(self):\\n\",\n    \"        return [loss_tracker]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"features = layers.Dropout(0.5)(features)\\n\",\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"model = CustomModel(inputs, outputs)\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=keras.optimizers.RMSprop())\\n\",\n    \"model.fit(train_images, train_labels, epochs=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class CustomModel(keras.Model):\\n\",\n    \"    def train_step(self, data):\\n\",\n    \"        inputs, targets = data\\n\",\n    \"        with tf.GradientTape() as tape:\\n\",\n    \"            predictions = self(inputs, training=True)\\n\",\n    \"            loss = self.compiled_loss(targets, predictions)\\n\",\n    \"        gradients = tape.gradient(loss, self.trainable_weights)\\n\",\n    \"        self.optimizer.apply_gradients(zip(gradients, self.trainable_weights))\\n\",\n    \"        self.compiled_metrics.update_state(targets, predictions)\\n\",\n    \"        return {m.name: m.result() for m in self.metrics}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(28 * 28,))\\n\",\n    \"features = layers.Dense(512, activation=\\\"relu\\\")(inputs)\\n\",\n    \"features = layers.Dropout(0.5)(features)\\n\",\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(features)\\n\",\n    \"model = CustomModel(inputs, outputs)\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=keras.optimizers.RMSprop(),\\n\",\n    \"              loss=keras.losses.SparseCategoricalCrossentropy(),\\n\",\n    \"              metrics=[keras.metrics.SparseCategoricalAccuracy()])\\n\",\n    \"model.fit(train_images, train_labels, epochs=3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter07_working-with-keras.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter08_intro-to-dl-for-computer-vision.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Introduction to deep learning for computer vision\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Introduction to convnets\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating a small convnet**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"inputs = keras.Input(shape=(28, 28, 1))\\n\",\n    \"x = layers.Conv2D(filters=32, kernel_size=3, activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=64, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=128, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying the model's summary**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training the convnet on MNIST images**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.datasets import mnist\\n\",\n    \"\\n\",\n    \"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\\n\",\n    \"train_images = train_images.reshape((60000, 28, 28, 1))\\n\",\n    \"train_images = train_images.astype(\\\"float32\\\") / 255\\n\",\n    \"test_images = test_images.reshape((10000, 28, 28, 1))\\n\",\n    \"test_images = test_images.astype(\\\"float32\\\") / 255\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.fit(train_images, train_labels, epochs=5, batch_size=64)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Evaluating the convnet**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_loss, test_acc = model.evaluate(test_images, test_labels)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The convolution operation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Understanding border effects and padding\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Understanding convolution strides\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The max-pooling operation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**An incorrectly structured convnet missing its max-pooling layers**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(28, 28, 1))\\n\",\n    \"x = layers.Conv2D(filters=32, kernel_size=3, activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.Conv2D(filters=64, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.Conv2D(filters=128, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"outputs = layers.Dense(10, activation=\\\"softmax\\\")(x)\\n\",\n    \"model_no_max_pool = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model_no_max_pool.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Training a convnet from scratch on a small dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The relevance of deep learning for small-data problems\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Downloading the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from google.colab import files\\n\",\n    \"files.upload()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!mkdir ~/.kaggle\\n\",\n    \"!cp kaggle.json ~/.kaggle/\\n\",\n    \"!chmod 600 ~/.kaggle/kaggle.json\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!kaggle competitions download -c dogs-vs-cats\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!unzip -qq dogs-vs-cats.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!unzip -qq train.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Copying images to training, validation, and test directories**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, shutil, pathlib\\n\",\n    \"\\n\",\n    \"original_dir = pathlib.Path(\\\"train\\\")\\n\",\n    \"new_base_dir = pathlib.Path(\\\"cats_vs_dogs_small\\\")\\n\",\n    \"\\n\",\n    \"def make_subset(subset_name, start_index, end_index):\\n\",\n    \"    for category in (\\\"cat\\\", \\\"dog\\\"):\\n\",\n    \"        dir = new_base_dir / subset_name / category\\n\",\n    \"        os.makedirs(dir)\\n\",\n    \"        fnames = [f\\\"{category}.{i}.jpg\\\" for i in range(start_index, end_index)]\\n\",\n    \"        for fname in fnames:\\n\",\n    \"            shutil.copyfile(src=original_dir / fname,\\n\",\n    \"                            dst=dir / fname)\\n\",\n    \"\\n\",\n    \"make_subset(\\\"train\\\", start_index=0, end_index=1000)\\n\",\n    \"make_subset(\\\"validation\\\", start_index=1000, end_index=1500)\\n\",\n    \"make_subset(\\\"test\\\", start_index=1500, end_index=2500)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Building the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating a small convnet for dogs vs. cats classification**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(180, 180, 3))\\n\",\n    \"x = layers.Rescaling(1./255)(inputs)\\n\",\n    \"x = layers.Conv2D(filters=32, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=64, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=128, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=256, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=256, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Configuring the model for training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              optimizer=\\\"rmsprop\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Data preprocessing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using `image_dataset_from_directory` to read images**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.utils import image_dataset_from_directory\\n\",\n    \"\\n\",\n    \"train_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"train\\\",\\n\",\n    \"    image_size=(180, 180),\\n\",\n    \"    batch_size=32)\\n\",\n    \"validation_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"validation\\\",\\n\",\n    \"    image_size=(180, 180),\\n\",\n    \"    batch_size=32)\\n\",\n    \"test_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"test\\\",\\n\",\n    \"    image_size=(180, 180),\\n\",\n    \"    batch_size=32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import tensorflow as tf\\n\",\n    \"random_numbers = np.random.normal(size=(1000, 16))\\n\",\n    \"dataset = tf.data.Dataset.from_tensor_slices(random_numbers)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for i, element in enumerate(dataset):\\n\",\n    \"    print(element.shape)\\n\",\n    \"    if i >= 2:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batched_dataset = dataset.batch(32)\\n\",\n    \"for i, element in enumerate(batched_dataset):\\n\",\n    \"    print(element.shape)\\n\",\n    \"    if i >= 2:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"reshaped_dataset = dataset.map(lambda x: tf.reshape(x, (4, 4)))\\n\",\n    \"for i, element in enumerate(reshaped_dataset):\\n\",\n    \"    print(element.shape)\\n\",\n    \"    if i >= 2:\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying the shapes of the data and labels yielded by the `Dataset`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for data_batch, labels_batch in train_dataset:\\n\",\n    \"    print(\\\"data batch shape:\\\", data_batch.shape)\\n\",\n    \"    print(\\\"labels batch shape:\\\", labels_batch.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Fitting the model using a `Dataset`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"convnet_from_scratch.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\")\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=30,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \"    callbacks=callbacks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying curves of loss and accuracy during training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"accuracy = history.history[\\\"accuracy\\\"]\\n\",\n    \"val_accuracy = history.history[\\\"val_accuracy\\\"]\\n\",\n    \"loss = history.history[\\\"loss\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, len(accuracy) + 1)\\n\",\n    \"plt.plot(epochs, accuracy, \\\"bo\\\", label=\\\"Training accuracy\\\")\\n\",\n    \"plt.plot(epochs, val_accuracy, \\\"b\\\", label=\\\"Validation accuracy\\\")\\n\",\n    \"plt.title(\\\"Training and validation accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(epochs, loss, \\\"bo\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Evaluating the model on the test set**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_model = keras.models.load_model(\\\"convnet_from_scratch.keras\\\")\\n\",\n    \"test_loss, test_acc = test_model.evaluate(test_dataset)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using data augmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Define a data augmentation stage to add to an image model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_augmentation = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.RandomFlip(\\\"horizontal\\\"),\\n\",\n    \"        layers.RandomRotation(0.1),\\n\",\n    \"        layers.RandomZoom(0.2),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying some randomly augmented training images**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"for images, _ in train_dataset.take(1):\\n\",\n    \"    for i in range(9):\\n\",\n    \"        augmented_images = data_augmentation(images)\\n\",\n    \"        ax = plt.subplot(3, 3, i + 1)\\n\",\n    \"        plt.imshow(augmented_images[0].numpy().astype(\\\"uint8\\\"))\\n\",\n    \"        plt.axis(\\\"off\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Defining a new convnet that includes image augmentation and dropout**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(180, 180, 3))\\n\",\n    \"x = data_augmentation(inputs)\\n\",\n    \"x = layers.Rescaling(1./255)(x)\\n\",\n    \"x = layers.Conv2D(filters=32, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=64, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=128, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=256, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(pool_size=2)(x)\\n\",\n    \"x = layers.Conv2D(filters=256, kernel_size=3, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n    \"\\n\",\n    \"model.compile(loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              optimizer=\\\"rmsprop\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training the regularized convnet**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"convnet_from_scratch_with_augmentation.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\")\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=100,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \"    callbacks=callbacks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Evaluating the model on the test set**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_model = keras.models.load_model(\\n\",\n    \"    \\\"convnet_from_scratch_with_augmentation.keras\\\")\\n\",\n    \"test_loss, test_acc = test_model.evaluate(test_dataset)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Leveraging a pretrained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Feature extraction with a pretrained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating the VGG16 convolutional base**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base = keras.applications.vgg16.VGG16(\\n\",\n    \"    weights=\\\"imagenet\\\",\\n\",\n    \"    include_top=False,\\n\",\n    \"    input_shape=(180, 180, 3))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Fast feature extraction without data augmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Extracting the VGG16 features and corresponding labels**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def get_features_and_labels(dataset):\\n\",\n    \"    all_features = []\\n\",\n    \"    all_labels = []\\n\",\n    \"    for images, labels in dataset:\\n\",\n    \"        preprocessed_images = keras.applications.vgg16.preprocess_input(images)\\n\",\n    \"        features = conv_base.predict(preprocessed_images)\\n\",\n    \"        all_features.append(features)\\n\",\n    \"        all_labels.append(labels)\\n\",\n    \"    return np.concatenate(all_features), np.concatenate(all_labels)\\n\",\n    \"\\n\",\n    \"train_features, train_labels =  get_features_and_labels(train_dataset)\\n\",\n    \"val_features, val_labels =  get_features_and_labels(validation_dataset)\\n\",\n    \"test_features, test_labels =  get_features_and_labels(test_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"train_features.shape\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Defining and training the densely connected classifier**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(5, 5, 512))\\n\",\n    \"x = layers.Flatten()(inputs)\\n\",\n    \"x = layers.Dense(256)(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              optimizer=\\\"rmsprop\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"      filepath=\\\"feature_extraction.keras\\\",\\n\",\n    \"      save_best_only=True,\\n\",\n    \"      monitor=\\\"val_loss\\\")\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_features, train_labels,\\n\",\n    \"    epochs=20,\\n\",\n    \"    validation_data=(val_features, val_labels),\\n\",\n    \"    callbacks=callbacks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting the results**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"acc = history.history[\\\"accuracy\\\"]\\n\",\n    \"val_acc = history.history[\\\"val_accuracy\\\"]\\n\",\n    \"loss = history.history[\\\"loss\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_loss\\\"]\\n\",\n    \"epochs = range(1, len(acc) + 1)\\n\",\n    \"plt.plot(epochs, acc, \\\"bo\\\", label=\\\"Training accuracy\\\")\\n\",\n    \"plt.plot(epochs, val_acc, \\\"b\\\", label=\\\"Validation accuracy\\\")\\n\",\n    \"plt.title(\\\"Training and validation accuracy\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(epochs, loss, \\\"bo\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Feature extraction together with data augmentation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating and freezing the VGG16 convolutional base**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base  = keras.applications.vgg16.VGG16(\\n\",\n    \"    weights=\\\"imagenet\\\",\\n\",\n    \"    include_top=False)\\n\",\n    \"conv_base.trainable = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Printing the list of trainable weights before and after freezing**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.trainable = True\\n\",\n    \"print(\\\"This is the number of trainable weights \\\"\\n\",\n    \"      \\\"before freezing the conv base:\\\", len(conv_base.trainable_weights))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.trainable = False\\n\",\n    \"print(\\\"This is the number of trainable weights \\\"\\n\",\n    \"      \\\"after freezing the conv base:\\\", len(conv_base.trainable_weights))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Adding a data augmentation stage and a classifier to the convolutional base**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_augmentation = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.RandomFlip(\\\"horizontal\\\"),\\n\",\n    \"        layers.RandomRotation(0.1),\\n\",\n    \"        layers.RandomZoom(0.2),\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(180, 180, 3))\\n\",\n    \"x = data_augmentation(inputs)\\n\",\n    \"x = keras.applications.vgg16.preprocess_input(x)\\n\",\n    \"x = conv_base(x)\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"x = layers.Dense(256)(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              optimizer=\\\"rmsprop\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"feature_extraction_with_data_augmentation.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\")\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=50,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \"    callbacks=callbacks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Evaluating the model on the test set**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_model = keras.models.load_model(\\n\",\n    \"    \\\"feature_extraction_with_data_augmentation.keras\\\")\\n\",\n    \"test_loss, test_acc = test_model.evaluate(test_dataset)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Fine-tuning a pretrained model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Freezing all layers until the fourth from the last**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"conv_base.trainable = True\\n\",\n    \"for layer in conv_base.layers[:-4]:\\n\",\n    \"    layer.trainable = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Fine-tuning the model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\n\",\n    \"        filepath=\\\"fine_tuning.keras\\\",\\n\",\n    \"        save_best_only=True,\\n\",\n    \"        monitor=\\\"val_loss\\\")\\n\",\n    \"]\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=30,\\n\",\n    \"    validation_data=validation_dataset,\\n\",\n    \"    callbacks=callbacks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.models.load_model(\\\"fine_tuning.keras\\\")\\n\",\n    \"test_loss, test_acc = model.evaluate(test_dataset)\\n\",\n    \"print(f\\\"Test accuracy: {test_acc:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter08_intro-to-dl-for-computer-vision.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "second_edition/chapter09_part01_image-segmentation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Advanced deep learning for computer vision\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Three essential computer vision tasks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## An image segmentation example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\\n\",\n    \"!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\\n\",\n    \"!tar -xf images.tar.gz\\n\",\n    \"!tar -xf annotations.tar.gz\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"input_dir = \\\"images/\\\"\\n\",\n    \"target_dir = \\\"annotations/trimaps/\\\"\\n\",\n    \"\\n\",\n    \"input_img_paths = sorted(\\n\",\n    \"    [os.path.join(input_dir, fname)\\n\",\n    \"     for fname in os.listdir(input_dir)\\n\",\n    \"     if fname.endswith(\\\".jpg\\\")])\\n\",\n    \"target_paths = sorted(\\n\",\n    \"    [os.path.join(target_dir, fname)\\n\",\n    \"     for fname in os.listdir(target_dir)\\n\",\n    \"     if fname.endswith(\\\".png\\\") and not fname.startswith(\\\".\\\")])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from tensorflow.keras.utils import load_img, img_to_array\\n\",\n    \"\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(load_img(input_img_paths[9]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def display_target(target_array):\\n\",\n    \"    normalized_array = (target_array.astype(\\\"uint8\\\") - 1) * 127\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"    plt.imshow(normalized_array[:, :, 0])\\n\",\n    \"\\n\",\n    \"img = img_to_array(load_img(target_paths[9], color_mode=\\\"grayscale\\\"))\\n\",\n    \"display_target(img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"img_size = (200, 200)\\n\",\n    \"num_imgs = len(input_img_paths)\\n\",\n    \"\\n\",\n    \"random.Random(1337).shuffle(input_img_paths)\\n\",\n    \"random.Random(1337).shuffle(target_paths)\\n\",\n    \"\\n\",\n    \"def path_to_input_image(path):\\n\",\n    \"    return img_to_array(load_img(path, target_size=img_size))\\n\",\n    \"\\n\",\n    \"def path_to_target(path):\\n\",\n    \"    img = img_to_array(\\n\",\n    \"        load_img(path, target_size=img_size, color_mode=\\\"grayscale\\\"))\\n\",\n    \"    img = img.astype(\\\"uint8\\\") - 1\\n\",\n    \"    return img\\n\",\n    \"\\n\",\n    \"input_imgs = np.zeros((num_imgs,) + img_size + (3,), dtype=\\\"float32\\\")\\n\",\n    \"targets = np.zeros((num_imgs,) + img_size + (1,), dtype=\\\"uint8\\\")\\n\",\n    \"for i in range(num_imgs):\\n\",\n    \"    input_imgs[i] = path_to_input_image(input_img_paths[i])\\n\",\n    \"    targets[i] = path_to_target(target_paths[i])\\n\",\n    \"\\n\",\n    \"num_val_samples = 1000\\n\",\n    \"train_input_imgs = input_imgs[:-num_val_samples]\\n\",\n    \"train_targets = targets[:-num_val_samples]\\n\",\n    \"val_input_imgs = input_imgs[-num_val_samples:]\\n\",\n    \"val_targets = targets[-num_val_samples:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"def get_model(img_size, num_classes):\\n\",\n    \"    inputs = keras.Input(shape=img_size + (3,))\\n\",\n    \"    x = layers.Rescaling(1./255)(inputs)\\n\",\n    \"\\n\",\n    \"    x = layers.Conv2D(64, 3, strides=2, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2D(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2D(128, 3, strides=2, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2D(128, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2D(256, 3, strides=2, padding=\\\"same\\\", activation=\\\"relu\\\")(x)\\n\",\n    \"    x = layers.Conv2D(256, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"\\n\",\n    \"    x = layers.Conv2DTranspose(256, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2DTranspose(256, 3, activation=\\\"relu\\\", padding=\\\"same\\\", strides=2)(x)\\n\",\n    \"    x = layers.Conv2DTranspose(128, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2DTranspose(128, 3, activation=\\\"relu\\\", padding=\\\"same\\\", strides=2)(x)\\n\",\n    \"    x = layers.Conv2DTranspose(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2DTranspose(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\", strides=2)(x)\\n\",\n    \"\\n\",\n    \"    outputs = layers.Conv2D(num_classes, 3, activation=\\\"softmax\\\", padding=\\\"same\\\")(x)\\n\",\n    \"\\n\",\n    \"    model = keras.Model(inputs, outputs)\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"model = get_model(img_size=img_size, num_classes=3)\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"sparse_categorical_crossentropy\\\")\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"oxford_segmentation.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"history = model.fit(train_input_imgs, train_targets,\\n\",\n    \"                    epochs=50,\\n\",\n    \"                    callbacks=callbacks,\\n\",\n    \"                    batch_size=64,\\n\",\n    \"                    validation_data=(val_input_imgs, val_targets))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"epochs = range(1, len(history.history[\\\"loss\\\"]) + 1)\\n\",\n    \"loss = history.history[\\\"loss\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_loss\\\"]\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(epochs, loss, \\\"bo\\\", label=\\\"Training loss\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation loss\\\")\\n\",\n    \"plt.title(\\\"Training and validation loss\\\")\\n\",\n    \"plt.legend()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.utils import array_to_img\\n\",\n    \"\\n\",\n    \"model = keras.models.load_model(\\\"oxford_segmentation.keras\\\")\\n\",\n    \"\\n\",\n    \"i = 4\\n\",\n    \"test_image = val_input_imgs[i]\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(array_to_img(test_image))\\n\",\n    \"\\n\",\n    \"mask = model.predict(np.expand_dims(test_image, 0))[0]\\n\",\n    \"\\n\",\n    \"def display_mask(pred):\\n\",\n    \"    mask = np.argmax(pred, axis=-1)\\n\",\n    \"    mask *= 127\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"    plt.imshow(mask)\\n\",\n    \"\\n\",\n    \"display_mask(mask)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter09_part01_image-segmentation.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter09_part02_modern-convnet-architecture-patterns.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Modern convnet architecture patterns\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Modularity, hierarchy, and reuse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Residual connections\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Residual block where the number of filters changes**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(32, 32, 3))\\n\",\n    \"x = layers.Conv2D(32, 3, activation=\\\"relu\\\")(inputs)\\n\",\n    \"residual = x\\n\",\n    \"x = layers.Conv2D(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"residual = layers.Conv2D(64, 1)(residual)\\n\",\n    \"x = layers.add([x, residual])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Case where target block includes a max pooling layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(32, 32, 3))\\n\",\n    \"x = layers.Conv2D(32, 3, activation=\\\"relu\\\")(inputs)\\n\",\n    \"residual = x\\n\",\n    \"x = layers.Conv2D(64, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(2, padding=\\\"same\\\")(x)\\n\",\n    \"residual = layers.Conv2D(64, 1, strides=2)(residual)\\n\",\n    \"x = layers.add([x, residual])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(32, 32, 3))\\n\",\n    \"x = layers.Rescaling(1./255)(inputs)\\n\",\n    \"\\n\",\n    \"def residual_block(x, filters, pooling=False):\\n\",\n    \"    residual = x\\n\",\n    \"    x = layers.Conv2D(filters, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    x = layers.Conv2D(filters, 3, activation=\\\"relu\\\", padding=\\\"same\\\")(x)\\n\",\n    \"    if pooling:\\n\",\n    \"        x = layers.MaxPooling2D(2, padding=\\\"same\\\")(x)\\n\",\n    \"        residual = layers.Conv2D(filters, 1, strides=2)(residual)\\n\",\n    \"    elif filters != residual.shape[-1]:\\n\",\n    \"        residual = layers.Conv2D(filters, 1)(residual)\\n\",\n    \"    x = layers.add([x, residual])\\n\",\n    \"    return x\\n\",\n    \"\\n\",\n    \"x = residual_block(x, filters=32, pooling=True)\\n\",\n    \"x = residual_block(x, filters=64, pooling=True)\\n\",\n    \"x = residual_block(x, filters=128, pooling=False)\\n\",\n    \"\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Batch normalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Depthwise separable convolutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Putting it together: A mini Xception-like model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from google.colab import files\\n\",\n    \"files.upload()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!mkdir ~/.kaggle\\n\",\n    \"!cp kaggle.json ~/.kaggle/\\n\",\n    \"!chmod 600 ~/.kaggle/kaggle.json\\n\",\n    \"!kaggle competitions download -c dogs-vs-cats\\n\",\n    \"!unzip -qq train.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, shutil, pathlib\\n\",\n    \"from tensorflow.keras.utils import image_dataset_from_directory\\n\",\n    \"\\n\",\n    \"original_dir = pathlib.Path(\\\"train\\\")\\n\",\n    \"new_base_dir = pathlib.Path(\\\"cats_vs_dogs_small\\\")\\n\",\n    \"\\n\",\n    \"def make_subset(subset_name, start_index, end_index):\\n\",\n    \"    for category in (\\\"cat\\\", \\\"dog\\\"):\\n\",\n    \"        dir = new_base_dir / subset_name / category\\n\",\n    \"        os.makedirs(dir)\\n\",\n    \"        fnames = [f\\\"{category}.{i}.jpg\\\" for i in range(start_index, end_index)]\\n\",\n    \"        for fname in fnames:\\n\",\n    \"            shutil.copyfile(src=original_dir / fname,\\n\",\n    \"                            dst=dir / fname)\\n\",\n    \"\\n\",\n    \"make_subset(\\\"train\\\", start_index=0, end_index=1000)\\n\",\n    \"make_subset(\\\"validation\\\", start_index=1000, end_index=1500)\\n\",\n    \"make_subset(\\\"test\\\", start_index=1500, end_index=2500)\\n\",\n    \"\\n\",\n    \"train_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"train\\\",\\n\",\n    \"    image_size=(180, 180),\\n\",\n    \"    batch_size=32)\\n\",\n    \"validation_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"validation\\\",\\n\",\n    \"    image_size=(180, 180),\\n\",\n    \"    batch_size=32)\\n\",\n    \"test_dataset = image_dataset_from_directory(\\n\",\n    \"    new_base_dir / \\\"test\\\",\\n\",\n    \"    image_size=(180, 180),\\n\",\n    \"    batch_size=32)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"data_augmentation = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        layers.RandomFlip(\\\"horizontal\\\"),\\n\",\n    \"        layers.RandomRotation(0.1),\\n\",\n    \"        layers.RandomZoom(0.2),\\n\",\n    \"    ]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(180, 180, 3))\\n\",\n    \"x = data_augmentation(inputs)\\n\",\n    \"\\n\",\n    \"x = layers.Rescaling(1./255)(x)\\n\",\n    \"x = layers.Conv2D(filters=32, kernel_size=5, use_bias=False)(x)\\n\",\n    \"\\n\",\n    \"for size in [32, 64, 128, 256, 512]:\\n\",\n    \"    residual = x\\n\",\n    \"\\n\",\n    \"    x = layers.BatchNormalization()(x)\\n\",\n    \"    x = layers.Activation(\\\"relu\\\")(x)\\n\",\n    \"    x = layers.SeparableConv2D(size, 3, padding=\\\"same\\\", use_bias=False)(x)\\n\",\n    \"\\n\",\n    \"    x = layers.BatchNormalization()(x)\\n\",\n    \"    x = layers.Activation(\\\"relu\\\")(x)\\n\",\n    \"    x = layers.SeparableConv2D(size, 3, padding=\\\"same\\\", use_bias=False)(x)\\n\",\n    \"\\n\",\n    \"    x = layers.MaxPooling2D(3, strides=2, padding=\\\"same\\\")(x)\\n\",\n    \"\\n\",\n    \"    residual = layers.Conv2D(\\n\",\n    \"        size, 1, strides=2, padding=\\\"same\\\", use_bias=False)(residual)\\n\",\n    \"    x = layers.add([x, residual])\\n\",\n    \"\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs=inputs, outputs=outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.compile(loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              optimizer=\\\"rmsprop\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"history = model.fit(\\n\",\n    \"    train_dataset,\\n\",\n    \"    epochs=100,\\n\",\n    \"    validation_data=validation_dataset)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter09_part02_modern-convnet-architecture-patterns.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter09_part03_interpreting-what-convnets-learn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Interpreting what convnets learn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Visualizing intermediate activations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# You can use this to load the file \\\"convnet_from_scratch_with_augmentation.keras\\\"\\n\",\n    \"# you obtained in the last chapter.\\n\",\n    \"from google.colab import files\\n\",\n    \"files.upload()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"model = keras.models.load_model(\\\"convnet_from_scratch_with_augmentation.keras\\\")\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preprocessing a single image**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"img_path = keras.utils.get_file(\\n\",\n    \"    fname=\\\"cat.jpg\\\",\\n\",\n    \"    origin=\\\"https://img-datasets.s3.amazonaws.com/cat.jpg\\\")\\n\",\n    \"\\n\",\n    \"def get_img_array(img_path, target_size):\\n\",\n    \"    img = keras.utils.load_img(\\n\",\n    \"        img_path, target_size=target_size)\\n\",\n    \"    array = keras.utils.img_to_array(img)\\n\",\n    \"    array = np.expand_dims(array, axis=0)\\n\",\n    \"    return array\\n\",\n    \"\\n\",\n    \"img_tensor = get_img_array(img_path, target_size=(180, 180))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying the test picture**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(img_tensor[0].astype(\\\"uint8\\\"))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating a model that returns layer activations**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"layer_outputs = []\\n\",\n    \"layer_names = []\\n\",\n    \"for layer in model.layers:\\n\",\n    \"    if isinstance(layer, (layers.Conv2D, layers.MaxPooling2D)):\\n\",\n    \"        layer_outputs.append(layer.output)\\n\",\n    \"        layer_names.append(layer.name)\\n\",\n    \"activation_model = keras.Model(inputs=model.input, outputs=layer_outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using the model to compute layer activations**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"activations = activation_model.predict(img_tensor)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"first_layer_activation = activations[0]\\n\",\n    \"print(first_layer_activation.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Visualizing the fifth channel**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.matshow(first_layer_activation[0, :, :, 5], cmap=\\\"viridis\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Visualizing every channel in every intermediate activation**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"images_per_row = 16\\n\",\n    \"for layer_name, layer_activation in zip(layer_names, activations):\\n\",\n    \"    n_features = layer_activation.shape[-1]\\n\",\n    \"    size = layer_activation.shape[1]\\n\",\n    \"    n_cols = n_features // images_per_row\\n\",\n    \"    display_grid = np.zeros(((size + 1) * n_cols - 1,\\n\",\n    \"                             images_per_row * (size + 1) - 1))\\n\",\n    \"    for col in range(n_cols):\\n\",\n    \"        for row in range(images_per_row):\\n\",\n    \"            channel_index = col * images_per_row + row\\n\",\n    \"            channel_image = layer_activation[0, :, :, channel_index].copy()\\n\",\n    \"            if channel_image.sum() != 0:\\n\",\n    \"                channel_image -= channel_image.mean()\\n\",\n    \"                channel_image /= channel_image.std()\\n\",\n    \"                channel_image *= 64\\n\",\n    \"                channel_image += 128\\n\",\n    \"            channel_image = np.clip(channel_image, 0, 255).astype(\\\"uint8\\\")\\n\",\n    \"            display_grid[\\n\",\n    \"                col * (size + 1): (col + 1) * size + col,\\n\",\n    \"                row * (size + 1) : (row + 1) * size + row] = channel_image\\n\",\n    \"    scale = 1. / size\\n\",\n    \"    plt.figure(figsize=(scale * display_grid.shape[1],\\n\",\n    \"                        scale * display_grid.shape[0]))\\n\",\n    \"    plt.title(layer_name)\\n\",\n    \"    plt.grid(False)\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"    plt.imshow(display_grid, aspect=\\\"auto\\\", cmap=\\\"viridis\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Visualizing convnet filters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating the Xception convolutional base**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.applications.xception.Xception(\\n\",\n    \"    weights=\\\"imagenet\\\",\\n\",\n    \"    include_top=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Printing the names of all convolutional layers in Xception**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for layer in model.layers:\\n\",\n    \"    if isinstance(layer, (keras.layers.Conv2D, keras.layers.SeparableConv2D)):\\n\",\n    \"        print(layer.name)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating a feature extractor model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"layer_name = \\\"block3_sepconv1\\\"\\n\",\n    \"layer = model.get_layer(name=layer_name)\\n\",\n    \"feature_extractor = keras.Model(inputs=model.input, outputs=layer.output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using the feature extractor**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"activation = feature_extractor(\\n\",\n    \"    keras.applications.xception.preprocess_input(img_tensor)\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"def compute_loss(image, filter_index):\\n\",\n    \"    activation = feature_extractor(image)\\n\",\n    \"    filter_activation = activation[:, 2:-2, 2:-2, filter_index]\\n\",\n    \"    return tf.reduce_mean(filter_activation)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Loss maximization via stochastic gradient ascent**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"@tf.function\\n\",\n    \"def gradient_ascent_step(image, filter_index, learning_rate):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        tape.watch(image)\\n\",\n    \"        loss = compute_loss(image, filter_index)\\n\",\n    \"    grads = tape.gradient(loss, image)\\n\",\n    \"    grads = tf.math.l2_normalize(grads)\\n\",\n    \"    image += learning_rate * grads\\n\",\n    \"    return image\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Function to generate filter visualizations**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"img_width = 200\\n\",\n    \"img_height = 200\\n\",\n    \"\\n\",\n    \"def generate_filter_pattern(filter_index):\\n\",\n    \"    iterations = 30\\n\",\n    \"    learning_rate = 10.\\n\",\n    \"    image = tf.random.uniform(\\n\",\n    \"        minval=0.4,\\n\",\n    \"        maxval=0.6,\\n\",\n    \"        shape=(1, img_width, img_height, 3))\\n\",\n    \"    for i in range(iterations):\\n\",\n    \"        image = gradient_ascent_step(image, filter_index, learning_rate)\\n\",\n    \"    return image[0].numpy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Utility function to convert a tensor into a valid image**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def deprocess_image(image):\\n\",\n    \"    image -= image.mean()\\n\",\n    \"    image /= image.std()\\n\",\n    \"    image *= 64\\n\",\n    \"    image += 128\\n\",\n    \"    image = np.clip(image, 0, 255).astype(\\\"uint8\\\")\\n\",\n    \"    image = image[25:-25, 25:-25, :]\\n\",\n    \"    return image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(deprocess_image(generate_filter_pattern(filter_index=2)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Generating a grid of all filter response patterns in a layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"all_images = []\\n\",\n    \"for filter_index in range(64):\\n\",\n    \"    print(f\\\"Processing filter {filter_index}\\\")\\n\",\n    \"    image = deprocess_image(\\n\",\n    \"        generate_filter_pattern(filter_index)\\n\",\n    \"    )\\n\",\n    \"    all_images.append(image)\\n\",\n    \"\\n\",\n    \"margin = 5\\n\",\n    \"n = 8\\n\",\n    \"cropped_width = img_width - 25 * 2\\n\",\n    \"cropped_height = img_height - 25 * 2\\n\",\n    \"width = n * cropped_width + (n - 1) * margin\\n\",\n    \"height = n * cropped_height + (n - 1) * margin\\n\",\n    \"stitched_filters = np.zeros((width, height, 3))\\n\",\n    \"\\n\",\n    \"for i in range(n):\\n\",\n    \"    for j in range(n):\\n\",\n    \"        image = all_images[i * n + j]\\n\",\n    \"        stitched_filters[\\n\",\n    \"            (cropped_width + margin) * i : (cropped_width + margin) * i + cropped_width,\\n\",\n    \"            (cropped_height + margin) * j : (cropped_height + margin) * j\\n\",\n    \"            + cropped_height,\\n\",\n    \"            :,\\n\",\n    \"        ] = image\\n\",\n    \"\\n\",\n    \"keras.utils.save_img(\\n\",\n    \"    f\\\"filters_for_layer_{layer_name}.png\\\", stitched_filters)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Visualizing heatmaps of class activation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Loading the Xception network with pretrained weights**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.applications.xception.Xception(weights=\\\"imagenet\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preprocessing an input image for Xception**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"img_path = keras.utils.get_file(\\n\",\n    \"    fname=\\\"elephant.jpg\\\",\\n\",\n    \"    origin=\\\"https://img-datasets.s3.amazonaws.com/elephant.jpg\\\")\\n\",\n    \"\\n\",\n    \"def get_img_array(img_path, target_size):\\n\",\n    \"    img = keras.utils.load_img(img_path, target_size=target_size)\\n\",\n    \"    array = keras.utils.img_to_array(img)\\n\",\n    \"    array = np.expand_dims(array, axis=0)\\n\",\n    \"    array = keras.applications.xception.preprocess_input(array)\\n\",\n    \"    return array\\n\",\n    \"\\n\",\n    \"img_array = get_img_array(img_path, target_size=(299, 299))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"preds = model.predict(img_array)\\n\",\n    \"print(keras.applications.xception.decode_predictions(preds, top=3)[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np.argmax(preds[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Setting up a model that returns the last convolutional output**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"last_conv_layer_name = \\\"block14_sepconv2_act\\\"\\n\",\n    \"classifier_layer_names = [\\n\",\n    \"    \\\"avg_pool\\\",\\n\",\n    \"    \\\"predictions\\\",\\n\",\n    \"]\\n\",\n    \"last_conv_layer = model.get_layer(last_conv_layer_name)\\n\",\n    \"last_conv_layer_model = keras.Model(model.inputs, last_conv_layer.output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Reapplying the classifier on top of the last convolutional output**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"classifier_input = keras.Input(shape=last_conv_layer.output.shape[1:])\\n\",\n    \"x = classifier_input\\n\",\n    \"for layer_name in classifier_layer_names:\\n\",\n    \"    x = model.get_layer(layer_name)(x)\\n\",\n    \"classifier_model = keras.Model(classifier_input, x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Retrieving the gradients of the top predicted class**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"with tf.GradientTape() as tape:\\n\",\n    \"    last_conv_layer_output = last_conv_layer_model(img_array)\\n\",\n    \"    tape.watch(last_conv_layer_output)\\n\",\n    \"    preds = classifier_model(last_conv_layer_output)\\n\",\n    \"    top_pred_index = tf.argmax(preds[0])\\n\",\n    \"    top_class_channel = preds[:, top_pred_index]\\n\",\n    \"\\n\",\n    \"grads = tape.gradient(top_class_channel, last_conv_layer_output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Gradient pooling and channel-importance weighting**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2)).numpy()\\n\",\n    \"last_conv_layer_output = last_conv_layer_output.numpy()[0]\\n\",\n    \"for i in range(pooled_grads.shape[-1]):\\n\",\n    \"    last_conv_layer_output[:, :, i] *= pooled_grads[i]\\n\",\n    \"heatmap = np.mean(last_conv_layer_output, axis=-1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Heatmap post-processing**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"heatmap = np.maximum(heatmap, 0)\\n\",\n    \"heatmap /= np.max(heatmap)\\n\",\n    \"plt.matshow(heatmap)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Superimposing the heatmap on the original picture**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.cm as cm\\n\",\n    \"\\n\",\n    \"img = keras.utils.load_img(img_path)\\n\",\n    \"img = keras.utils.img_to_array(img)\\n\",\n    \"\\n\",\n    \"heatmap = np.uint8(255 * heatmap)\\n\",\n    \"\\n\",\n    \"jet = cm.get_cmap(\\\"jet\\\")\\n\",\n    \"jet_colors = jet(np.arange(256))[:, :3]\\n\",\n    \"jet_heatmap = jet_colors[heatmap]\\n\",\n    \"\\n\",\n    \"jet_heatmap = keras.utils.array_to_img(jet_heatmap)\\n\",\n    \"jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\\n\",\n    \"jet_heatmap = keras.utils.img_to_array(jet_heatmap)\\n\",\n    \"\\n\",\n    \"superimposed_img = jet_heatmap * 0.4 + img\\n\",\n    \"superimposed_img = keras.utils.array_to_img(superimposed_img)\\n\",\n    \"\\n\",\n    \"save_path = \\\"elephant_cam.jpg\\\"\\n\",\n    \"superimposed_img.save(save_path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter09_part03_interpreting-what-convnets-learn.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter10_dl-for-timeseries.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Deep learning for timeseries\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Different kinds of timeseries tasks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## A temperature-forecasting example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wget https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\\n\",\n    \"!unzip jena_climate_2009_2016.csv.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Inspecting the data of the Jena weather dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"fname = os.path.join(\\\"jena_climate_2009_2016.csv\\\")\\n\",\n    \"\\n\",\n    \"with open(fname) as f:\\n\",\n    \"    data = f.read()\\n\",\n    \"\\n\",\n    \"lines = data.split(\\\"\\\\n\\\")\\n\",\n    \"header = lines[0].split(\\\",\\\")\\n\",\n    \"lines = lines[1:]\\n\",\n    \"print(header)\\n\",\n    \"print(len(lines))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Parsing the data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"temperature = np.zeros((len(lines),))\\n\",\n    \"raw_data = np.zeros((len(lines), len(header) - 1))\\n\",\n    \"for i, line in enumerate(lines):\\n\",\n    \"    values = [float(x) for x in line.split(\\\",\\\")[1:]]\\n\",\n    \"    temperature[i] = values[1]\\n\",\n    \"    raw_data[i, :] = values[:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting the temperature timeseries**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from matplotlib import pyplot as plt\\n\",\n    \"plt.plot(range(len(temperature)), temperature)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting the first 10 days of the temperature timeseries**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"plt.plot(range(1440), temperature[:1440])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Computing the number of samples we'll use for each data split**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_train_samples = int(0.5 * len(raw_data))\\n\",\n    \"num_val_samples = int(0.25 * len(raw_data))\\n\",\n    \"num_test_samples = len(raw_data) - num_train_samples - num_val_samples\\n\",\n    \"print(\\\"num_train_samples:\\\", num_train_samples)\\n\",\n    \"print(\\\"num_val_samples:\\\", num_val_samples)\\n\",\n    \"print(\\\"num_test_samples:\\\", num_test_samples)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Normalizing the data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"mean = raw_data[:num_train_samples].mean(axis=0)\\n\",\n    \"raw_data -= mean\\n\",\n    \"std = raw_data[:num_train_samples].std(axis=0)\\n\",\n    \"raw_data /= std\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from tensorflow import keras\\n\",\n    \"int_sequence = np.arange(10)\\n\",\n    \"dummy_dataset = keras.utils.timeseries_dataset_from_array(\\n\",\n    \"    data=int_sequence[:-3],\\n\",\n    \"    targets=int_sequence[3:],\\n\",\n    \"    sequence_length=3,\\n\",\n    \"    batch_size=2,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"for inputs, targets in dummy_dataset:\\n\",\n    \"    for i in range(inputs.shape[0]):\\n\",\n    \"        print([int(x) for x in inputs[i]], int(targets[i]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating datasets for training, validation, and testing**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sampling_rate = 6\\n\",\n    \"sequence_length = 120\\n\",\n    \"delay = sampling_rate * (sequence_length + 24 - 1)\\n\",\n    \"batch_size = 256\\n\",\n    \"\\n\",\n    \"train_dataset = keras.utils.timeseries_dataset_from_array(\\n\",\n    \"    raw_data[:-delay],\\n\",\n    \"    targets=temperature[delay:],\\n\",\n    \"    sampling_rate=sampling_rate,\\n\",\n    \"    sequence_length=sequence_length,\\n\",\n    \"    shuffle=True,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \"    start_index=0,\\n\",\n    \"    end_index=num_train_samples)\\n\",\n    \"\\n\",\n    \"val_dataset = keras.utils.timeseries_dataset_from_array(\\n\",\n    \"    raw_data[:-delay],\\n\",\n    \"    targets=temperature[delay:],\\n\",\n    \"    sampling_rate=sampling_rate,\\n\",\n    \"    sequence_length=sequence_length,\\n\",\n    \"    shuffle=True,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \"    start_index=num_train_samples,\\n\",\n    \"    end_index=num_train_samples + num_val_samples)\\n\",\n    \"\\n\",\n    \"test_dataset = keras.utils.timeseries_dataset_from_array(\\n\",\n    \"    raw_data[:-delay],\\n\",\n    \"    targets=temperature[delay:],\\n\",\n    \"    sampling_rate=sampling_rate,\\n\",\n    \"    sequence_length=sequence_length,\\n\",\n    \"    shuffle=True,\\n\",\n    \"    batch_size=batch_size,\\n\",\n    \"    start_index=num_train_samples + num_val_samples)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Inspecting the output of one of our datasets**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for samples, targets in train_dataset:\\n\",\n    \"    print(\\\"samples shape:\\\", samples.shape)\\n\",\n    \"    print(\\\"targets shape:\\\", targets.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A common-sense, non-machine-learning baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Computing the common-sense baseline MAE**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def evaluate_naive_method(dataset):\\n\",\n    \"    total_abs_err = 0.\\n\",\n    \"    samples_seen = 0\\n\",\n    \"    for samples, targets in dataset:\\n\",\n    \"        preds = samples[:, -1, 1] * std[1] + mean[1]\\n\",\n    \"        total_abs_err += np.sum(np.abs(preds - targets))\\n\",\n    \"        samples_seen += samples.shape[0]\\n\",\n    \"    return total_abs_err / samples_seen\\n\",\n    \"\\n\",\n    \"print(f\\\"Validation MAE: {evaluate_naive_method(val_dataset):.2f}\\\")\\n\",\n    \"print(f\\\"Test MAE: {evaluate_naive_method(test_dataset):.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Let's try a basic machine-learning model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training and evaluating a densely connected model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.Flatten()(inputs)\\n\",\n    \"x = layers.Dense(16, activation=\\\"relu\\\")(x)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"jena_dense.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(train_dataset,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    validation_data=val_dataset,\\n\",\n    \"                    callbacks=callbacks)\\n\",\n    \"\\n\",\n    \"model = keras.models.load_model(\\\"jena_dense.keras\\\")\\n\",\n    \"print(f\\\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Plotting results**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"loss = history.history[\\\"mae\\\"]\\n\",\n    \"val_loss = history.history[\\\"val_mae\\\"]\\n\",\n    \"epochs = range(1, len(loss) + 1)\\n\",\n    \"plt.figure()\\n\",\n    \"plt.plot(epochs, loss, \\\"bo\\\", label=\\\"Training MAE\\\")\\n\",\n    \"plt.plot(epochs, val_loss, \\\"b\\\", label=\\\"Validation MAE\\\")\\n\",\n    \"plt.title(\\\"Training and validation MAE\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Let's try a 1D convolutional model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.Conv1D(8, 24, activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.MaxPooling1D(2)(x)\\n\",\n    \"x = layers.Conv1D(8, 12, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling1D(2)(x)\\n\",\n    \"x = layers.Conv1D(8, 6, activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.GlobalAveragePooling1D()(x)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"jena_conv.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(train_dataset,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    validation_data=val_dataset,\\n\",\n    \"                    callbacks=callbacks)\\n\",\n    \"\\n\",\n    \"model = keras.models.load_model(\\\"jena_conv.keras\\\")\\n\",\n    \"print(f\\\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A first recurrent baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A simple LSTM-based model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.LSTM(16)(inputs)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"jena_lstm.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(train_dataset,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    validation_data=val_dataset,\\n\",\n    \"                    callbacks=callbacks)\\n\",\n    \"\\n\",\n    \"model = keras.models.load_model(\\\"jena_lstm.keras\\\")\\n\",\n    \"print(f\\\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Understanding recurrent neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**NumPy implementation of a simple RNN**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"timesteps = 100\\n\",\n    \"input_features = 32\\n\",\n    \"output_features = 64\\n\",\n    \"inputs = np.random.random((timesteps, input_features))\\n\",\n    \"state_t = np.zeros((output_features,))\\n\",\n    \"W = np.random.random((output_features, input_features))\\n\",\n    \"U = np.random.random((output_features, output_features))\\n\",\n    \"b = np.random.random((output_features,))\\n\",\n    \"successive_outputs = []\\n\",\n    \"for input_t in inputs:\\n\",\n    \"    output_t = np.tanh(np.dot(W, input_t) + np.dot(U, state_t) + b)\\n\",\n    \"    successive_outputs.append(output_t)\\n\",\n    \"    state_t = output_t\\n\",\n    \"final_output_sequence = np.stack(successive_outputs, axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A recurrent layer in Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**An RNN layer that can process sequences of any length**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_features = 14\\n\",\n    \"inputs = keras.Input(shape=(None, num_features))\\n\",\n    \"outputs = layers.SimpleRNN(16)(inputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**An RNN layer that returns only its last output step**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_features = 14\\n\",\n    \"steps = 120\\n\",\n    \"inputs = keras.Input(shape=(steps, num_features))\\n\",\n    \"outputs = layers.SimpleRNN(16, return_sequences=False)(inputs)\\n\",\n    \"print(outputs.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**An RNN layer that returns its full output sequence**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"num_features = 14\\n\",\n    \"steps = 120\\n\",\n    \"inputs = keras.Input(shape=(steps, num_features))\\n\",\n    \"outputs = layers.SimpleRNN(16, return_sequences=True)(inputs)\\n\",\n    \"print(outputs.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Stacking RNN layers**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(steps, num_features))\\n\",\n    \"x = layers.SimpleRNN(16, return_sequences=True)(inputs)\\n\",\n    \"x = layers.SimpleRNN(16, return_sequences=True)(x)\\n\",\n    \"outputs = layers.SimpleRNN(16)(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Advanced use of recurrent neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using recurrent dropout to fight overfitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training and evaluating a dropout-regularized LSTM**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.LSTM(32, recurrent_dropout=0.25)(inputs)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"jena_lstm_dropout.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(train_dataset,\\n\",\n    \"                    epochs=50,\\n\",\n    \"                    validation_data=val_dataset,\\n\",\n    \"                    callbacks=callbacks)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, num_features))\\n\",\n    \"x = layers.LSTM(32, recurrent_dropout=0.2, unroll=True)(inputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Stacking recurrent layers\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training and evaluating a dropout-regularized, stacked GRU model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.GRU(32, recurrent_dropout=0.5, return_sequences=True)(inputs)\\n\",\n    \"x = layers.GRU(32, recurrent_dropout=0.5)(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"jena_stacked_gru_dropout.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(train_dataset,\\n\",\n    \"                    epochs=50,\\n\",\n    \"                    validation_data=val_dataset,\\n\",\n    \"                    callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\\"jena_stacked_gru_dropout.keras\\\")\\n\",\n    \"print(f\\\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using bidirectional RNNs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training and evaluating a bidirectional LSTM**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\\n\",\n    \"x = layers.Bidirectional(layers.LSTM(16))(inputs)\\n\",\n    \"outputs = layers.Dense(1)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"mse\\\", metrics=[\\\"mae\\\"])\\n\",\n    \"history = model.fit(train_dataset,\\n\",\n    \"                    epochs=10,\\n\",\n    \"                    validation_data=val_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Going even further\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter10_dl-for-timeseries.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter11_part01_introduction.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Deep learning for text\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Natural-language processing: The bird's eye view\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Preparing text data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Text standardization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Text splitting (tokenization)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Vocabulary indexing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Using the TextVectorization layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import string\\n\",\n    \"\\n\",\n    \"class Vectorizer:\\n\",\n    \"    def standardize(self, text):\\n\",\n    \"        text = text.lower()\\n\",\n    \"        return \\\"\\\".join(char for char in text if char not in string.punctuation)\\n\",\n    \"\\n\",\n    \"    def tokenize(self, text):\\n\",\n    \"        text = self.standardize(text)\\n\",\n    \"        return text.split()\\n\",\n    \"\\n\",\n    \"    def make_vocabulary(self, dataset):\\n\",\n    \"        self.vocabulary = {\\\"\\\": 0, \\\"[UNK]\\\": 1}\\n\",\n    \"        for text in dataset:\\n\",\n    \"            text = self.standardize(text)\\n\",\n    \"            tokens = self.tokenize(text)\\n\",\n    \"            for token in tokens:\\n\",\n    \"                if token not in self.vocabulary:\\n\",\n    \"                    self.vocabulary[token] = len(self.vocabulary)\\n\",\n    \"        self.inverse_vocabulary = dict(\\n\",\n    \"            (v, k) for k, v in self.vocabulary.items())\\n\",\n    \"\\n\",\n    \"    def encode(self, text):\\n\",\n    \"        text = self.standardize(text)\\n\",\n    \"        tokens = self.tokenize(text)\\n\",\n    \"        return [self.vocabulary.get(token, 1) for token in tokens]\\n\",\n    \"\\n\",\n    \"    def decode(self, int_sequence):\\n\",\n    \"        return \\\" \\\".join(\\n\",\n    \"            self.inverse_vocabulary.get(i, \\\"[UNK]\\\") for i in int_sequence)\\n\",\n    \"\\n\",\n    \"vectorizer = Vectorizer()\\n\",\n    \"dataset = [\\n\",\n    \"    \\\"I write, erase, rewrite\\\",\\n\",\n    \"    \\\"Erase again, and then\\\",\\n\",\n    \"    \\\"A poppy blooms.\\\",\\n\",\n    \"]\\n\",\n    \"vectorizer.make_vocabulary(dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"test_sentence = \\\"I write, rewrite, and still rewrite again\\\"\\n\",\n    \"encoded_sentence = vectorizer.encode(test_sentence)\\n\",\n    \"print(encoded_sentence)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"decoded_sentence = vectorizer.decode(encoded_sentence)\\n\",\n    \"print(decoded_sentence)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.layers import TextVectorization\\n\",\n    \"text_vectorization = TextVectorization(\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import re\\n\",\n    \"import string\\n\",\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"def custom_standardization_fn(string_tensor):\\n\",\n    \"    lowercase_string = tf.strings.lower(string_tensor)\\n\",\n    \"    return tf.strings.regex_replace(\\n\",\n    \"        lowercase_string, f\\\"[{re.escape(string.punctuation)}]\\\", \\\"\\\")\\n\",\n    \"\\n\",\n    \"def custom_split_fn(string_tensor):\\n\",\n    \"    return tf.strings.split(string_tensor)\\n\",\n    \"\\n\",\n    \"text_vectorization = TextVectorization(\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    standardize=custom_standardization_fn,\\n\",\n    \"    split=custom_split_fn,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = [\\n\",\n    \"    \\\"I write, erase, rewrite\\\",\\n\",\n    \"    \\\"Erase again, and then\\\",\\n\",\n    \"    \\\"A poppy blooms.\\\",\\n\",\n    \"]\\n\",\n    \"text_vectorization.adapt(dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying the vocabulary**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_vectorization.get_vocabulary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocabulary = text_vectorization.get_vocabulary()\\n\",\n    \"test_sentence = \\\"I write, rewrite, and still rewrite again\\\"\\n\",\n    \"encoded_sentence = text_vectorization(test_sentence)\\n\",\n    \"print(encoded_sentence)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inverse_vocab = dict(enumerate(vocabulary))\\n\",\n    \"decoded_sentence = \\\" \\\".join(inverse_vocab[int(i)] for i in encoded_sentence)\\n\",\n    \"print(decoded_sentence)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Two approaches for representing groups of words: Sets and sequences\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Preparing the IMDB movie reviews data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!curl -O https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\n\",\n    \"!tar -xf aclImdb_v1.tar.gz\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!rm -r aclImdb/train/unsup\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!cat aclImdb/train/pos/4077_10.txt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, pathlib, shutil, random\\n\",\n    \"\\n\",\n    \"base_dir = pathlib.Path(\\\"aclImdb\\\")\\n\",\n    \"val_dir = base_dir / \\\"val\\\"\\n\",\n    \"train_dir = base_dir / \\\"train\\\"\\n\",\n    \"for category in (\\\"neg\\\", \\\"pos\\\"):\\n\",\n    \"    os.makedirs(val_dir / category)\\n\",\n    \"    files = os.listdir(train_dir / category)\\n\",\n    \"    random.Random(1337).shuffle(files)\\n\",\n    \"    num_val_samples = int(0.2 * len(files))\\n\",\n    \"    val_files = files[-num_val_samples:]\\n\",\n    \"    for fname in val_files:\\n\",\n    \"        shutil.move(train_dir / category / fname,\\n\",\n    \"                    val_dir / category / fname)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"batch_size = 32\\n\",\n    \"\\n\",\n    \"train_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/train\\\", batch_size=batch_size\\n\",\n    \")\\n\",\n    \"val_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/val\\\", batch_size=batch_size\\n\",\n    \")\\n\",\n    \"test_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/test\\\", batch_size=batch_size\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying the shapes and dtypes of the first batch**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for inputs, targets in train_ds:\\n\",\n    \"    print(\\\"inputs.shape:\\\", inputs.shape)\\n\",\n    \"    print(\\\"inputs.dtype:\\\", inputs.dtype)\\n\",\n    \"    print(\\\"targets.shape:\\\", targets.shape)\\n\",\n    \"    print(\\\"targets.dtype:\\\", targets.dtype)\\n\",\n    \"    print(\\\"inputs[0]:\\\", inputs[0])\\n\",\n    \"    print(\\\"targets[0]:\\\", targets[0])\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Processing words as a set: The bag-of-words approach\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Single words (unigrams) with binary encoding\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preprocessing our datasets with a `TextVectorization` layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_vectorization = TextVectorization(\\n\",\n    \"    max_tokens=20000,\\n\",\n    \"    output_mode=\\\"multi_hot\\\",\\n\",\n    \")\\n\",\n    \"text_only_train_ds = train_ds.map(lambda x, y: x)\\n\",\n    \"text_vectorization.adapt(text_only_train_ds)\\n\",\n    \"\\n\",\n    \"binary_1gram_train_ds = train_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"binary_1gram_val_ds = val_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"binary_1gram_test_ds = test_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Inspecting the output of our binary unigram dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for inputs, targets in binary_1gram_train_ds:\\n\",\n    \"    print(\\\"inputs.shape:\\\", inputs.shape)\\n\",\n    \"    print(\\\"inputs.dtype:\\\", inputs.dtype)\\n\",\n    \"    print(\\\"targets.shape:\\\", targets.shape)\\n\",\n    \"    print(\\\"targets.dtype:\\\", targets.dtype)\\n\",\n    \"    print(\\\"inputs[0]:\\\", inputs[0])\\n\",\n    \"    print(\\\"targets[0]:\\\", targets[0])\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Our model-building utility**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"def get_model(max_tokens=20000, hidden_dim=16):\\n\",\n    \"    inputs = keras.Input(shape=(max_tokens,))\\n\",\n    \"    x = layers.Dense(hidden_dim, activation=\\\"relu\\\")(inputs)\\n\",\n    \"    x = layers.Dropout(0.5)(x)\\n\",\n    \"    outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"    model = keras.Model(inputs, outputs)\\n\",\n    \"    model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"                  loss=\\\"binary_crossentropy\\\",\\n\",\n    \"                  metrics=[\\\"accuracy\\\"])\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training and testing the binary unigram model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = get_model()\\n\",\n    \"model.summary()\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"binary_1gram.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(binary_1gram_train_ds.cache(),\\n\",\n    \"          validation_data=binary_1gram_val_ds.cache(),\\n\",\n    \"          epochs=10,\\n\",\n    \"          callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\\"binary_1gram.keras\\\")\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(binary_1gram_test_ds)[1]:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Bigrams with binary encoding\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Configuring the `TextVectorization` layer to return bigrams**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_vectorization = TextVectorization(\\n\",\n    \"    ngrams=2,\\n\",\n    \"    max_tokens=20000,\\n\",\n    \"    output_mode=\\\"multi_hot\\\",\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training and testing the binary bigram model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_vectorization.adapt(text_only_train_ds)\\n\",\n    \"binary_2gram_train_ds = train_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"binary_2gram_val_ds = val_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"binary_2gram_test_ds = test_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"\\n\",\n    \"model = get_model()\\n\",\n    \"model.summary()\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"binary_2gram.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(binary_2gram_train_ds.cache(),\\n\",\n    \"          validation_data=binary_2gram_val_ds.cache(),\\n\",\n    \"          epochs=10,\\n\",\n    \"          callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\\"binary_2gram.keras\\\")\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(binary_2gram_test_ds)[1]:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Bigrams with TF-IDF encoding\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Configuring the `TextVectorization` layer to return token counts**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_vectorization = TextVectorization(\\n\",\n    \"    ngrams=2,\\n\",\n    \"    max_tokens=20000,\\n\",\n    \"    output_mode=\\\"count\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Configuring `TextVectorization` to return TF-IDF-weighted outputs**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_vectorization = TextVectorization(\\n\",\n    \"    ngrams=2,\\n\",\n    \"    max_tokens=20000,\\n\",\n    \"    output_mode=\\\"tf_idf\\\",\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training and testing the TF-IDF bigram model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_vectorization.adapt(text_only_train_ds)\\n\",\n    \"\\n\",\n    \"tfidf_2gram_train_ds = train_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"tfidf_2gram_val_ds = val_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"tfidf_2gram_test_ds = test_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"\\n\",\n    \"model = get_model()\\n\",\n    \"model.summary()\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"tfidf_2gram.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(tfidf_2gram_train_ds.cache(),\\n\",\n    \"          validation_data=tfidf_2gram_val_ds.cache(),\\n\",\n    \"          epochs=10,\\n\",\n    \"          callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\\"tfidf_2gram.keras\\\")\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(tfidf_2gram_test_ds)[1]:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(1,), dtype=\\\"string\\\")\\n\",\n    \"processed_inputs = text_vectorization(inputs)\\n\",\n    \"outputs = model(processed_inputs)\\n\",\n    \"inference_model = keras.Model(inputs, outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"raw_text_data = tf.convert_to_tensor([\\n\",\n    \"    [\\\"That was an excellent movie, I loved it.\\\"],\\n\",\n    \"])\\n\",\n    \"predictions = inference_model(raw_text_data)\\n\",\n    \"print(f\\\"{float(predictions[0] * 100):.2f} percent positive\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter11_part01_introduction.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter11_part02_sequence-models.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Processing words as a sequence: The sequence model approach\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A first practical example\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Downloading the data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!curl -O https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\n\",\n    \"!tar -xf aclImdb_v1.tar.gz\\n\",\n    \"!rm -r aclImdb/train/unsup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preparing the data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, pathlib, shutil, random\\n\",\n    \"from tensorflow import keras\\n\",\n    \"batch_size = 32\\n\",\n    \"base_dir = pathlib.Path(\\\"aclImdb\\\")\\n\",\n    \"val_dir = base_dir / \\\"val\\\"\\n\",\n    \"train_dir = base_dir / \\\"train\\\"\\n\",\n    \"for category in (\\\"neg\\\", \\\"pos\\\"):\\n\",\n    \"    os.makedirs(val_dir / category)\\n\",\n    \"    files = os.listdir(train_dir / category)\\n\",\n    \"    random.Random(1337).shuffle(files)\\n\",\n    \"    num_val_samples = int(0.2 * len(files))\\n\",\n    \"    val_files = files[-num_val_samples:]\\n\",\n    \"    for fname in val_files:\\n\",\n    \"        shutil.move(train_dir / category / fname,\\n\",\n    \"                    val_dir / category / fname)\\n\",\n    \"\\n\",\n    \"train_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/train\\\", batch_size=batch_size\\n\",\n    \")\\n\",\n    \"val_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/val\\\", batch_size=batch_size\\n\",\n    \")\\n\",\n    \"test_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/test\\\", batch_size=batch_size\\n\",\n    \")\\n\",\n    \"text_only_train_ds = train_ds.map(lambda x, y: x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preparing integer sequence datasets**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"max_length = 600\\n\",\n    \"max_tokens = 20000\\n\",\n    \"text_vectorization = layers.TextVectorization(\\n\",\n    \"    max_tokens=max_tokens,\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    output_sequence_length=max_length,\\n\",\n    \")\\n\",\n    \"text_vectorization.adapt(text_only_train_ds)\\n\",\n    \"\\n\",\n    \"int_train_ds = train_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"int_val_ds = val_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"int_test_ds = test_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A sequence model built on one-hot encoded vector sequences**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\")\\n\",\n    \"embedded = tf.one_hot(inputs, depth=max_tokens)\\n\",\n    \"x = layers.Bidirectional(layers.LSTM(32))(embedded)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training a first basic sequence model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"one_hot_bidir_lstm.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(int_train_ds, validation_data=int_val_ds, epochs=10, callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\\"one_hot_bidir_lstm.keras\\\")\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Understanding word embeddings\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Learning word embeddings with the Embedding layer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating an `Embedding` layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"embedding_layer = layers.Embedding(input_dim=max_tokens, output_dim=256)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Model that uses an `Embedding` layer trained from scratch**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\")\\n\",\n    \"embedded = layers.Embedding(input_dim=max_tokens, output_dim=256)(inputs)\\n\",\n    \"x = layers.Bidirectional(layers.LSTM(32))(embedded)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"embeddings_bidir_gru.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(int_train_ds, validation_data=int_val_ds, epochs=10, callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\\"embeddings_bidir_gru.keras\\\")\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Understanding padding and masking\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using an `Embedding` layer with masking enabled**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\")\\n\",\n    \"embedded = layers.Embedding(\\n\",\n    \"    input_dim=max_tokens, output_dim=256, mask_zero=True)(inputs)\\n\",\n    \"x = layers.Bidirectional(layers.LSTM(32))(embedded)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"embeddings_bidir_gru_with_masking.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(int_train_ds, validation_data=int_val_ds, epochs=10, callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\\"embeddings_bidir_gru_with_masking.keras\\\")\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using pretrained word embeddings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wget http://nlp.stanford.edu/data/glove.6B.zip\\n\",\n    \"!unzip -q glove.6B.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Parsing the GloVe word-embeddings file**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"path_to_glove_file = \\\"glove.6B.100d.txt\\\"\\n\",\n    \"\\n\",\n    \"embeddings_index = {}\\n\",\n    \"with open(path_to_glove_file) as f:\\n\",\n    \"    for line in f:\\n\",\n    \"        word, coefs = line.split(maxsplit=1)\\n\",\n    \"        coefs = np.fromstring(coefs, \\\"f\\\", sep=\\\" \\\")\\n\",\n    \"        embeddings_index[word] = coefs\\n\",\n    \"\\n\",\n    \"print(f\\\"Found {len(embeddings_index)} word vectors.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preparing the GloVe word-embeddings matrix**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"embedding_dim = 100\\n\",\n    \"\\n\",\n    \"vocabulary = text_vectorization.get_vocabulary()\\n\",\n    \"word_index = dict(zip(vocabulary, range(len(vocabulary))))\\n\",\n    \"\\n\",\n    \"embedding_matrix = np.zeros((max_tokens, embedding_dim))\\n\",\n    \"for word, i in word_index.items():\\n\",\n    \"    if i < max_tokens:\\n\",\n    \"        embedding_vector = embeddings_index.get(word)\\n\",\n    \"    if embedding_vector is not None:\\n\",\n    \"        embedding_matrix[i] = embedding_vector\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"embedding_layer = layers.Embedding(\\n\",\n    \"    max_tokens,\\n\",\n    \"    embedding_dim,\\n\",\n    \"    embeddings_initializer=keras.initializers.Constant(embedding_matrix),\\n\",\n    \"    trainable=False,\\n\",\n    \"    mask_zero=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Model that uses a pretrained Embedding layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\")\\n\",\n    \"embedded = embedding_layer(inputs)\\n\",\n    \"x = layers.Bidirectional(layers.LSTM(32))(embedded)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"glove_embeddings_sequence_model.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(int_train_ds, validation_data=int_val_ds, epochs=10, callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\\"glove_embeddings_sequence_model.keras\\\")\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter11_part02_sequence-models.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "second_edition/chapter11_part03_transformer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## The Transformer architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Understanding self-attention\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Generalized self-attention: the query-key-value model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Multi-head attention\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The Transformer encoder\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Getting the data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!curl -O https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\n\",\n    \"!tar -xf aclImdb_v1.tar.gz\\n\",\n    \"!rm -r aclImdb/train/unsup\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preparing the data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os, pathlib, shutil, random\\n\",\n    \"from tensorflow import keras\\n\",\n    \"batch_size = 32\\n\",\n    \"base_dir = pathlib.Path(\\\"aclImdb\\\")\\n\",\n    \"val_dir = base_dir / \\\"val\\\"\\n\",\n    \"train_dir = base_dir / \\\"train\\\"\\n\",\n    \"for category in (\\\"neg\\\", \\\"pos\\\"):\\n\",\n    \"    os.makedirs(val_dir / category)\\n\",\n    \"    files = os.listdir(train_dir / category)\\n\",\n    \"    random.Random(1337).shuffle(files)\\n\",\n    \"    num_val_samples = int(0.2 * len(files))\\n\",\n    \"    val_files = files[-num_val_samples:]\\n\",\n    \"    for fname in val_files:\\n\",\n    \"        shutil.move(train_dir / category / fname,\\n\",\n    \"                    val_dir / category / fname)\\n\",\n    \"\\n\",\n    \"train_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/train\\\", batch_size=batch_size\\n\",\n    \")\\n\",\n    \"val_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/val\\\", batch_size=batch_size\\n\",\n    \")\\n\",\n    \"test_ds = keras.utils.text_dataset_from_directory(\\n\",\n    \"    \\\"aclImdb/test\\\", batch_size=batch_size\\n\",\n    \")\\n\",\n    \"text_only_train_ds = train_ds.map(lambda x, y: x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Vectorizing the data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"max_length = 600\\n\",\n    \"max_tokens = 20000\\n\",\n    \"text_vectorization = layers.TextVectorization(\\n\",\n    \"    max_tokens=max_tokens,\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    output_sequence_length=max_length,\\n\",\n    \")\\n\",\n    \"text_vectorization.adapt(text_only_train_ds)\\n\",\n    \"\\n\",\n    \"int_train_ds = train_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"int_val_ds = val_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\\n\",\n    \"int_test_ds = test_ds.map(\\n\",\n    \"    lambda x, y: (text_vectorization(x), y),\\n\",\n    \"    num_parallel_calls=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Transformer encoder implemented as a subclassed `Layer`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"class TransformerEncoder(layers.Layer):\\n\",\n    \"    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.embed_dim = embed_dim\\n\",\n    \"        self.dense_dim = dense_dim\\n\",\n    \"        self.num_heads = num_heads\\n\",\n    \"        self.attention = layers.MultiHeadAttention(\\n\",\n    \"            num_heads=num_heads, key_dim=embed_dim)\\n\",\n    \"        self.dense_proj = keras.Sequential(\\n\",\n    \"            [layers.Dense(dense_dim, activation=\\\"relu\\\"),\\n\",\n    \"             layers.Dense(embed_dim),]\\n\",\n    \"        )\\n\",\n    \"        self.layernorm_1 = layers.LayerNormalization()\\n\",\n    \"        self.layernorm_2 = layers.LayerNormalization()\\n\",\n    \"\\n\",\n    \"    def call(self, inputs, mask=None):\\n\",\n    \"        if mask is not None:\\n\",\n    \"            mask = mask[:, tf.newaxis, :]\\n\",\n    \"        attention_output = self.attention(\\n\",\n    \"            inputs, inputs, attention_mask=mask)\\n\",\n    \"        proj_input = self.layernorm_1(inputs + attention_output)\\n\",\n    \"        proj_output = self.dense_proj(proj_input)\\n\",\n    \"        return self.layernorm_2(proj_input + proj_output)\\n\",\n    \"\\n\",\n    \"    def get_config(self):\\n\",\n    \"        config = super().get_config()\\n\",\n    \"        config.update({\\n\",\n    \"            \\\"embed_dim\\\": self.embed_dim,\\n\",\n    \"            \\\"num_heads\\\": self.num_heads,\\n\",\n    \"            \\\"dense_dim\\\": self.dense_dim,\\n\",\n    \"        })\\n\",\n    \"        return config\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using the Transformer encoder for text classification**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocab_size = 20000\\n\",\n    \"embed_dim = 256\\n\",\n    \"num_heads = 2\\n\",\n    \"dense_dim = 32\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\")\\n\",\n    \"x = layers.Embedding(vocab_size, embed_dim)(inputs)\\n\",\n    \"x = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\\n\",\n    \"x = layers.GlobalMaxPooling1D()(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training and evaluating the Transformer encoder based model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"transformer_encoder.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(int_train_ds, validation_data=int_val_ds, epochs=20, callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\n\",\n    \"    \\\"transformer_encoder.keras\\\",\\n\",\n    \"    custom_objects={\\\"TransformerEncoder\\\": TransformerEncoder})\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using positional encoding to re-inject order information\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Implementing positional embedding as a subclassed layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class PositionalEmbedding(layers.Layer):\\n\",\n    \"    def __init__(self, sequence_length, input_dim, output_dim, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.token_embeddings = layers.Embedding(\\n\",\n    \"            input_dim=input_dim, output_dim=output_dim)\\n\",\n    \"        self.position_embeddings = layers.Embedding(\\n\",\n    \"            input_dim=sequence_length, output_dim=output_dim)\\n\",\n    \"        self.sequence_length = sequence_length\\n\",\n    \"        self.input_dim = input_dim\\n\",\n    \"        self.output_dim = output_dim\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        length = tf.shape(inputs)[-1]\\n\",\n    \"        positions = tf.range(start=0, limit=length, delta=1)\\n\",\n    \"        embedded_tokens = self.token_embeddings(inputs)\\n\",\n    \"        embedded_positions = self.position_embeddings(positions)\\n\",\n    \"        return embedded_tokens + embedded_positions\\n\",\n    \"\\n\",\n    \"    def compute_mask(self, inputs, mask=None):\\n\",\n    \"        return tf.math.not_equal(inputs, 0)\\n\",\n    \"\\n\",\n    \"    def get_config(self):\\n\",\n    \"        config = super().get_config()\\n\",\n    \"        config.update({\\n\",\n    \"            \\\"output_dim\\\": self.output_dim,\\n\",\n    \"            \\\"sequence_length\\\": self.sequence_length,\\n\",\n    \"            \\\"input_dim\\\": self.input_dim,\\n\",\n    \"        })\\n\",\n    \"        return config\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Putting it all together: A text-classification Transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Combining the Transformer encoder with positional embedding**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"vocab_size = 20000\\n\",\n    \"sequence_length = 600\\n\",\n    \"embed_dim = 256\\n\",\n    \"num_heads = 2\\n\",\n    \"dense_dim = 32\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(inputs)\\n\",\n    \"x = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\\n\",\n    \"x = layers.GlobalMaxPooling1D()(x)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\n\",\n    \"              loss=\\\"binary_crossentropy\\\",\\n\",\n    \"              metrics=[\\\"accuracy\\\"])\\n\",\n    \"model.summary()\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.ModelCheckpoint(\\\"full_transformer_encoder.keras\\\",\\n\",\n    \"                                    save_best_only=True)\\n\",\n    \"]\\n\",\n    \"model.fit(int_train_ds, validation_data=int_val_ds, epochs=20, callbacks=callbacks)\\n\",\n    \"model = keras.models.load_model(\\n\",\n    \"    \\\"full_transformer_encoder.keras\\\",\\n\",\n    \"    custom_objects={\\\"TransformerEncoder\\\": TransformerEncoder,\\n\",\n    \"                    \\\"PositionalEmbedding\\\": PositionalEmbedding})\\n\",\n    \"print(f\\\"Test acc: {model.evaluate(int_test_ds)[1]:.3f}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### When to use sequence models over bag-of-words models?\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter11_part03_transformer.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter11_part04_sequence-to-sequence-learning.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Beyond text classification: Sequence-to-sequence learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A machine translation example\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wget http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\\n\",\n    \"!unzip -q spa-eng.zip\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"text_file = \\\"spa-eng/spa.txt\\\"\\n\",\n    \"with open(text_file) as f:\\n\",\n    \"    lines = f.read().split(\\\"\\\\n\\\")[:-1]\\n\",\n    \"text_pairs = []\\n\",\n    \"for line in lines:\\n\",\n    \"    english, spanish = line.split(\\\"\\\\t\\\")\\n\",\n    \"    spanish = \\\"[start] \\\" + spanish + \\\" [end]\\\"\\n\",\n    \"    text_pairs.append((english, spanish))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"print(random.choice(text_pairs))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"random.shuffle(text_pairs)\\n\",\n    \"num_val_samples = int(0.15 * len(text_pairs))\\n\",\n    \"num_train_samples = len(text_pairs) - 2 * num_val_samples\\n\",\n    \"train_pairs = text_pairs[:num_train_samples]\\n\",\n    \"val_pairs = text_pairs[num_train_samples:num_train_samples + num_val_samples]\\n\",\n    \"test_pairs = text_pairs[num_train_samples + num_val_samples:]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Vectorizing the English and Spanish text pairs**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import string\\n\",\n    \"import re\\n\",\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"strip_chars = string.punctuation + \\\"\\u00bf\\\"\\n\",\n    \"strip_chars = strip_chars.replace(\\\"[\\\", \\\"\\\")\\n\",\n    \"strip_chars = strip_chars.replace(\\\"]\\\", \\\"\\\")\\n\",\n    \"\\n\",\n    \"def custom_standardization(input_string):\\n\",\n    \"    lowercase = tf.strings.lower(input_string)\\n\",\n    \"    return tf.strings.regex_replace(\\n\",\n    \"        lowercase, f\\\"[{re.escape(strip_chars)}]\\\", \\\"\\\")\\n\",\n    \"\\n\",\n    \"vocab_size = 15000\\n\",\n    \"sequence_length = 20\\n\",\n    \"\\n\",\n    \"source_vectorization = layers.TextVectorization(\\n\",\n    \"    max_tokens=vocab_size,\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    output_sequence_length=sequence_length,\\n\",\n    \")\\n\",\n    \"target_vectorization = layers.TextVectorization(\\n\",\n    \"    max_tokens=vocab_size,\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    output_sequence_length=sequence_length + 1,\\n\",\n    \"    standardize=custom_standardization,\\n\",\n    \")\\n\",\n    \"train_english_texts = [pair[0] for pair in train_pairs]\\n\",\n    \"train_spanish_texts = [pair[1] for pair in train_pairs]\\n\",\n    \"source_vectorization.adapt(train_english_texts)\\n\",\n    \"target_vectorization.adapt(train_spanish_texts)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preparing datasets for the translation task**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batch_size = 64\\n\",\n    \"\\n\",\n    \"def format_dataset(eng, spa):\\n\",\n    \"    eng = source_vectorization(eng)\\n\",\n    \"    spa = target_vectorization(spa)\\n\",\n    \"    return ({\\n\",\n    \"        \\\"english\\\": eng,\\n\",\n    \"        \\\"spanish\\\": spa[:, :-1],\\n\",\n    \"    }, spa[:, 1:])\\n\",\n    \"\\n\",\n    \"def make_dataset(pairs):\\n\",\n    \"    eng_texts, spa_texts = zip(*pairs)\\n\",\n    \"    eng_texts = list(eng_texts)\\n\",\n    \"    spa_texts = list(spa_texts)\\n\",\n    \"    dataset = tf.data.Dataset.from_tensor_slices((eng_texts, spa_texts))\\n\",\n    \"    dataset = dataset.batch(batch_size)\\n\",\n    \"    dataset = dataset.map(format_dataset, num_parallel_calls=4)\\n\",\n    \"    return dataset.shuffle(2048).prefetch(16).cache()\\n\",\n    \"\\n\",\n    \"train_ds = make_dataset(train_pairs)\\n\",\n    \"val_ds = make_dataset(val_pairs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"for inputs, targets in train_ds.take(1):\\n\",\n    \"    print(f\\\"inputs['english'].shape: {inputs['english'].shape}\\\")\\n\",\n    \"    print(f\\\"inputs['spanish'].shape: {inputs['spanish'].shape}\\\")\\n\",\n    \"    print(f\\\"targets.shape: {targets.shape}\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Sequence-to-sequence learning with RNNs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**GRU-based encoder**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"embed_dim = 256\\n\",\n    \"latent_dim = 1024\\n\",\n    \"\\n\",\n    \"source = keras.Input(shape=(None,), dtype=\\\"int64\\\", name=\\\"english\\\")\\n\",\n    \"x = layers.Embedding(vocab_size, embed_dim, mask_zero=True)(source)\\n\",\n    \"encoded_source = layers.Bidirectional(\\n\",\n    \"    layers.GRU(latent_dim), merge_mode=\\\"sum\\\")(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**GRU-based decoder and the end-to-end model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"past_target = keras.Input(shape=(None,), dtype=\\\"int64\\\", name=\\\"spanish\\\")\\n\",\n    \"x = layers.Embedding(vocab_size, embed_dim, mask_zero=True)(past_target)\\n\",\n    \"decoder_gru = layers.GRU(latent_dim, return_sequences=True)\\n\",\n    \"x = decoder_gru(x, initial_state=encoded_source)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"target_next_step = layers.Dense(vocab_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"seq2seq_rnn = keras.Model([source, past_target], target_next_step)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training our recurrent sequence-to-sequence model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"seq2seq_rnn.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"])\\n\",\n    \"seq2seq_rnn.fit(train_ds, epochs=15, validation_data=val_ds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Translating new sentences with our RNN encoder and decoder**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"spa_vocab = target_vectorization.get_vocabulary()\\n\",\n    \"spa_index_lookup = dict(zip(range(len(spa_vocab)), spa_vocab))\\n\",\n    \"max_decoded_sentence_length = 20\\n\",\n    \"\\n\",\n    \"def decode_sequence(input_sentence):\\n\",\n    \"    tokenized_input_sentence = source_vectorization([input_sentence])\\n\",\n    \"    decoded_sentence = \\\"[start]\\\"\\n\",\n    \"    for i in range(max_decoded_sentence_length):\\n\",\n    \"        tokenized_target_sentence = target_vectorization([decoded_sentence])\\n\",\n    \"        next_token_predictions = seq2seq_rnn.predict(\\n\",\n    \"            [tokenized_input_sentence, tokenized_target_sentence])\\n\",\n    \"        sampled_token_index = np.argmax(next_token_predictions[0, i, :])\\n\",\n    \"        sampled_token = spa_index_lookup[sampled_token_index]\\n\",\n    \"        decoded_sentence += \\\" \\\" + sampled_token\\n\",\n    \"        if sampled_token == \\\"[end]\\\":\\n\",\n    \"            break\\n\",\n    \"    return decoded_sentence\\n\",\n    \"\\n\",\n    \"test_eng_texts = [pair[0] for pair in test_pairs]\\n\",\n    \"for _ in range(20):\\n\",\n    \"    input_sentence = random.choice(test_eng_texts)\\n\",\n    \"    print(\\\"-\\\")\\n\",\n    \"    print(input_sentence)\\n\",\n    \"    print(decode_sequence(input_sentence))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Sequence-to-sequence learning with Transformer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The Transformer decoder\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The `TransformerDecoder`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class TransformerDecoder(layers.Layer):\\n\",\n    \"    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.embed_dim = embed_dim\\n\",\n    \"        self.dense_dim = dense_dim\\n\",\n    \"        self.num_heads = num_heads\\n\",\n    \"        self.attention_1 = layers.MultiHeadAttention(\\n\",\n    \"            num_heads=num_heads, key_dim=embed_dim)\\n\",\n    \"        self.attention_2 = layers.MultiHeadAttention(\\n\",\n    \"            num_heads=num_heads, key_dim=embed_dim)\\n\",\n    \"        self.dense_proj = keras.Sequential(\\n\",\n    \"            [layers.Dense(dense_dim, activation=\\\"relu\\\"),\\n\",\n    \"             layers.Dense(embed_dim),]\\n\",\n    \"        )\\n\",\n    \"        self.layernorm_1 = layers.LayerNormalization()\\n\",\n    \"        self.layernorm_2 = layers.LayerNormalization()\\n\",\n    \"        self.layernorm_3 = layers.LayerNormalization()\\n\",\n    \"        self.supports_masking = True\\n\",\n    \"\\n\",\n    \"    def get_config(self):\\n\",\n    \"        config = super().get_config()\\n\",\n    \"        config.update({\\n\",\n    \"            \\\"embed_dim\\\": self.embed_dim,\\n\",\n    \"            \\\"num_heads\\\": self.num_heads,\\n\",\n    \"            \\\"dense_dim\\\": self.dense_dim,\\n\",\n    \"        })\\n\",\n    \"        return config\\n\",\n    \"\\n\",\n    \"    def get_causal_attention_mask(self, inputs):\\n\",\n    \"        input_shape = tf.shape(inputs)\\n\",\n    \"        batch_size, sequence_length = input_shape[0], input_shape[1]\\n\",\n    \"        i = tf.range(sequence_length)[:, tf.newaxis]\\n\",\n    \"        j = tf.range(sequence_length)\\n\",\n    \"        mask = tf.cast(i >= j, dtype=\\\"int32\\\")\\n\",\n    \"        mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))\\n\",\n    \"        mult = tf.concat(\\n\",\n    \"            [tf.expand_dims(batch_size, -1),\\n\",\n    \"             tf.constant([1, 1], dtype=tf.int32)], axis=0)\\n\",\n    \"        return tf.tile(mask, mult)\\n\",\n    \"\\n\",\n    \"    def call(self, inputs, encoder_outputs, mask=None):\\n\",\n    \"        causal_mask = self.get_causal_attention_mask(inputs)\\n\",\n    \"        if mask is not None:\\n\",\n    \"            padding_mask = tf.cast(\\n\",\n    \"                mask[:, tf.newaxis, :], dtype=\\\"int32\\\")\\n\",\n    \"            padding_mask = tf.minimum(padding_mask, causal_mask)\\n\",\n    \"        else:\\n\",\n    \"            padding_mask = mask\\n\",\n    \"        attention_output_1 = self.attention_1(\\n\",\n    \"            query=inputs,\\n\",\n    \"            value=inputs,\\n\",\n    \"            key=inputs,\\n\",\n    \"            attention_mask=causal_mask)\\n\",\n    \"        attention_output_1 = self.layernorm_1(inputs + attention_output_1)\\n\",\n    \"        attention_output_2 = self.attention_2(\\n\",\n    \"            query=attention_output_1,\\n\",\n    \"            value=encoder_outputs,\\n\",\n    \"            key=encoder_outputs,\\n\",\n    \"            attention_mask=padding_mask,\\n\",\n    \"        )\\n\",\n    \"        attention_output_2 = self.layernorm_2(\\n\",\n    \"            attention_output_1 + attention_output_2)\\n\",\n    \"        proj_output = self.dense_proj(attention_output_2)\\n\",\n    \"        return self.layernorm_3(attention_output_2 + proj_output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Putting it all together: A Transformer for machine translation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**PositionalEmbedding layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class PositionalEmbedding(layers.Layer):\\n\",\n    \"    def __init__(self, sequence_length, input_dim, output_dim, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.token_embeddings = layers.Embedding(\\n\",\n    \"            input_dim=input_dim, output_dim=output_dim)\\n\",\n    \"        self.position_embeddings = layers.Embedding(\\n\",\n    \"            input_dim=sequence_length, output_dim=output_dim)\\n\",\n    \"        self.sequence_length = sequence_length\\n\",\n    \"        self.input_dim = input_dim\\n\",\n    \"        self.output_dim = output_dim\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        length = tf.shape(inputs)[-1]\\n\",\n    \"        positions = tf.range(start=0, limit=length, delta=1)\\n\",\n    \"        embedded_tokens = self.token_embeddings(inputs)\\n\",\n    \"        embedded_positions = self.position_embeddings(positions)\\n\",\n    \"        return embedded_tokens + embedded_positions\\n\",\n    \"\\n\",\n    \"    def compute_mask(self, inputs, mask=None):\\n\",\n    \"        return tf.math.not_equal(inputs, 0)\\n\",\n    \"\\n\",\n    \"    def get_config(self):\\n\",\n    \"        config = super(PositionalEmbedding, self).get_config()\\n\",\n    \"        config.update({\\n\",\n    \"            \\\"output_dim\\\": self.output_dim,\\n\",\n    \"            \\\"sequence_length\\\": self.sequence_length,\\n\",\n    \"            \\\"input_dim\\\": self.input_dim,\\n\",\n    \"        })\\n\",\n    \"        return config\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**End-to-end Transformer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"embed_dim = 256\\n\",\n    \"dense_dim = 2048\\n\",\n    \"num_heads = 8\\n\",\n    \"\\n\",\n    \"encoder_inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\", name=\\\"english\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(encoder_inputs)\\n\",\n    \"encoder_outputs = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\\n\",\n    \"\\n\",\n    \"decoder_inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\", name=\\\"spanish\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(decoder_inputs)\\n\",\n    \"x = TransformerDecoder(embed_dim, dense_dim, num_heads)(x, encoder_outputs)\\n\",\n    \"x = layers.Dropout(0.5)(x)\\n\",\n    \"decoder_outputs = layers.Dense(vocab_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"transformer = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training the sequence-to-sequence Transformer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"transformer.compile(\\n\",\n    \"    optimizer=\\\"rmsprop\\\",\\n\",\n    \"    loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"    metrics=[\\\"accuracy\\\"])\\n\",\n    \"transformer.fit(train_ds, epochs=30, validation_data=val_ds)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Translating new sentences with our Transformer model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"spa_vocab = target_vectorization.get_vocabulary()\\n\",\n    \"spa_index_lookup = dict(zip(range(len(spa_vocab)), spa_vocab))\\n\",\n    \"max_decoded_sentence_length = 20\\n\",\n    \"\\n\",\n    \"def decode_sequence(input_sentence):\\n\",\n    \"    tokenized_input_sentence = source_vectorization([input_sentence])\\n\",\n    \"    decoded_sentence = \\\"[start]\\\"\\n\",\n    \"    for i in range(max_decoded_sentence_length):\\n\",\n    \"        tokenized_target_sentence = target_vectorization(\\n\",\n    \"            [decoded_sentence])[:, :-1]\\n\",\n    \"        predictions = transformer(\\n\",\n    \"            [tokenized_input_sentence, tokenized_target_sentence])\\n\",\n    \"        sampled_token_index = np.argmax(predictions[0, i, :])\\n\",\n    \"        sampled_token = spa_index_lookup[sampled_token_index]\\n\",\n    \"        decoded_sentence += \\\" \\\" + sampled_token\\n\",\n    \"        if sampled_token == \\\"[end]\\\":\\n\",\n    \"            break\\n\",\n    \"    return decoded_sentence\\n\",\n    \"\\n\",\n    \"test_eng_texts = [pair[0] for pair in test_pairs]\\n\",\n    \"for _ in range(20):\\n\",\n    \"    input_sentence = random.choice(test_eng_texts)\\n\",\n    \"    print(\\\"-\\\")\\n\",\n    \"    print(input_sentence)\\n\",\n    \"    print(decode_sequence(input_sentence))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter11_part04_sequence-to-sequence-learning.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "second_edition/chapter12_part01_text-generation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Generative deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Text generation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A brief history of generative deep learning for sequence generation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### How do you generate sequence data?\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The importance of the sampling strategy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Reweighting a probability distribution to a different temperature**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"def reweight_distribution(original_distribution, temperature=0.5):\\n\",\n    \"    distribution = np.log(original_distribution) / temperature\\n\",\n    \"    distribution = np.exp(distribution)\\n\",\n    \"    return distribution / np.sum(distribution)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Implementing text generation with Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Preparing the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Downloading and uncompressing the IMDB movie reviews dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!wget https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\n\",\n    \"!tar -xf aclImdb_v1.tar.gz\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating a dataset from text files (one file = one sample)**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow import keras\\n\",\n    \"dataset = keras.utils.text_dataset_from_directory(\\n\",\n    \"    directory=\\\"aclImdb\\\", label_mode=None, batch_size=256)\\n\",\n    \"dataset = dataset.map(lambda x: tf.strings.regex_replace(x, \\\"<br />\\\", \\\" \\\"))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Preparing a `TextVectorization` layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.layers import TextVectorization\\n\",\n    \"\\n\",\n    \"sequence_length = 100\\n\",\n    \"vocab_size = 15000\\n\",\n    \"text_vectorization = TextVectorization(\\n\",\n    \"    max_tokens=vocab_size,\\n\",\n    \"    output_mode=\\\"int\\\",\\n\",\n    \"    output_sequence_length=sequence_length,\\n\",\n    \")\\n\",\n    \"text_vectorization.adapt(dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Setting up a language modeling dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def prepare_lm_dataset(text_batch):\\n\",\n    \"    vectorized_sequences = text_vectorization(text_batch)\\n\",\n    \"    x = vectorized_sequences[:, :-1]\\n\",\n    \"    y = vectorized_sequences[:, 1:]\\n\",\n    \"    return x, y\\n\",\n    \"\\n\",\n    \"lm_dataset = dataset.map(prepare_lm_dataset, num_parallel_calls=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### A Transformer-based sequence-to-sequence model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"class PositionalEmbedding(layers.Layer):\\n\",\n    \"    def __init__(self, sequence_length, input_dim, output_dim, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.token_embeddings = layers.Embedding(\\n\",\n    \"            input_dim=input_dim, output_dim=output_dim)\\n\",\n    \"        self.position_embeddings = layers.Embedding(\\n\",\n    \"            input_dim=sequence_length, output_dim=output_dim)\\n\",\n    \"        self.sequence_length = sequence_length\\n\",\n    \"        self.input_dim = input_dim\\n\",\n    \"        self.output_dim = output_dim\\n\",\n    \"\\n\",\n    \"    def call(self, inputs):\\n\",\n    \"        length = tf.shape(inputs)[-1]\\n\",\n    \"        positions = tf.range(start=0, limit=length, delta=1)\\n\",\n    \"        embedded_tokens = self.token_embeddings(inputs)\\n\",\n    \"        embedded_positions = self.position_embeddings(positions)\\n\",\n    \"        return embedded_tokens + embedded_positions\\n\",\n    \"\\n\",\n    \"    def compute_mask(self, inputs, mask=None):\\n\",\n    \"        return tf.math.not_equal(inputs, 0)\\n\",\n    \"\\n\",\n    \"    def get_config(self):\\n\",\n    \"        config = super(PositionalEmbedding, self).get_config()\\n\",\n    \"        config.update({\\n\",\n    \"            \\\"output_dim\\\": self.output_dim,\\n\",\n    \"            \\\"sequence_length\\\": self.sequence_length,\\n\",\n    \"            \\\"input_dim\\\": self.input_dim,\\n\",\n    \"        })\\n\",\n    \"        return config\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class TransformerDecoder(layers.Layer):\\n\",\n    \"    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.embed_dim = embed_dim\\n\",\n    \"        self.dense_dim = dense_dim\\n\",\n    \"        self.num_heads = num_heads\\n\",\n    \"        self.attention_1 = layers.MultiHeadAttention(\\n\",\n    \"          num_heads=num_heads, key_dim=embed_dim)\\n\",\n    \"        self.attention_2 = layers.MultiHeadAttention(\\n\",\n    \"          num_heads=num_heads, key_dim=embed_dim)\\n\",\n    \"        self.dense_proj = keras.Sequential(\\n\",\n    \"            [layers.Dense(dense_dim, activation=\\\"relu\\\"),\\n\",\n    \"             layers.Dense(embed_dim),]\\n\",\n    \"        )\\n\",\n    \"        self.layernorm_1 = layers.LayerNormalization()\\n\",\n    \"        self.layernorm_2 = layers.LayerNormalization()\\n\",\n    \"        self.layernorm_3 = layers.LayerNormalization()\\n\",\n    \"        self.supports_masking = True\\n\",\n    \"\\n\",\n    \"    def get_config(self):\\n\",\n    \"        config = super(TransformerDecoder, self).get_config()\\n\",\n    \"        config.update({\\n\",\n    \"            \\\"embed_dim\\\": self.embed_dim,\\n\",\n    \"            \\\"num_heads\\\": self.num_heads,\\n\",\n    \"            \\\"dense_dim\\\": self.dense_dim,\\n\",\n    \"        })\\n\",\n    \"        return config\\n\",\n    \"\\n\",\n    \"    def get_causal_attention_mask(self, inputs):\\n\",\n    \"        input_shape = tf.shape(inputs)\\n\",\n    \"        batch_size, sequence_length = input_shape[0], input_shape[1]\\n\",\n    \"        i = tf.range(sequence_length)[:, tf.newaxis]\\n\",\n    \"        j = tf.range(sequence_length)\\n\",\n    \"        mask = tf.cast(i >= j, dtype=\\\"int32\\\")\\n\",\n    \"        mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))\\n\",\n    \"        mult = tf.concat(\\n\",\n    \"            [tf.expand_dims(batch_size, -1),\\n\",\n    \"             tf.constant([1, 1], dtype=tf.int32)], axis=0)\\n\",\n    \"        return tf.tile(mask, mult)\\n\",\n    \"\\n\",\n    \"    def call(self, inputs, encoder_outputs, mask=None):\\n\",\n    \"        causal_mask = self.get_causal_attention_mask(inputs)\\n\",\n    \"        if mask is not None:\\n\",\n    \"            padding_mask = tf.cast(\\n\",\n    \"                mask[:, tf.newaxis, :], dtype=\\\"int32\\\")\\n\",\n    \"            padding_mask = tf.minimum(padding_mask, causal_mask)\\n\",\n    \"        else:\\n\",\n    \"            padding_mask = mask\\n\",\n    \"        attention_output_1 = self.attention_1(\\n\",\n    \"            query=inputs,\\n\",\n    \"            value=inputs,\\n\",\n    \"            key=inputs,\\n\",\n    \"            attention_mask=causal_mask)\\n\",\n    \"        attention_output_1 = self.layernorm_1(inputs + attention_output_1)\\n\",\n    \"        attention_output_2 = self.attention_2(\\n\",\n    \"            query=attention_output_1,\\n\",\n    \"            value=encoder_outputs,\\n\",\n    \"            key=encoder_outputs,\\n\",\n    \"            attention_mask=padding_mask,\\n\",\n    \"        )\\n\",\n    \"        attention_output_2 = self.layernorm_2(\\n\",\n    \"            attention_output_1 + attention_output_2)\\n\",\n    \"        proj_output = self.dense_proj(attention_output_2)\\n\",\n    \"        return self.layernorm_3(attention_output_2 + proj_output)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A simple Transformer-based language model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import layers\\n\",\n    \"embed_dim = 256\\n\",\n    \"latent_dim = 2048\\n\",\n    \"num_heads = 2\\n\",\n    \"\\n\",\n    \"inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(inputs)\\n\",\n    \"x = TransformerDecoder(embed_dim, latent_dim, num_heads)(x, x)\\n\",\n    \"outputs = layers.Dense(vocab_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(loss=\\\"sparse_categorical_crossentropy\\\", optimizer=\\\"rmsprop\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A text-generation callback with variable-temperature sampling\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The text-generation callback**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"tokens_index = dict(enumerate(text_vectorization.get_vocabulary()))\\n\",\n    \"\\n\",\n    \"def sample_next(predictions, temperature=1.0):\\n\",\n    \"    predictions = np.asarray(predictions).astype(\\\"float64\\\")\\n\",\n    \"    predictions = np.log(predictions) / temperature\\n\",\n    \"    exp_preds = np.exp(predictions)\\n\",\n    \"    predictions = exp_preds / np.sum(exp_preds)\\n\",\n    \"    probas = np.random.multinomial(1, predictions, 1)\\n\",\n    \"    return np.argmax(probas)\\n\",\n    \"\\n\",\n    \"class TextGenerator(keras.callbacks.Callback):\\n\",\n    \"    def __init__(self,\\n\",\n    \"                 prompt,\\n\",\n    \"                 generate_length,\\n\",\n    \"                 model_input_length,\\n\",\n    \"                 temperatures=(1.,),\\n\",\n    \"                 print_freq=1):\\n\",\n    \"        self.prompt = prompt\\n\",\n    \"        self.generate_length = generate_length\\n\",\n    \"        self.model_input_length = model_input_length\\n\",\n    \"        self.temperatures = temperatures\\n\",\n    \"        self.print_freq = print_freq\\n\",\n    \"        vectorized_prompt = text_vectorization([prompt])[0].numpy()\\n\",\n    \"        self.prompt_length = np.nonzero(vectorized_prompt == 0)[0][0]\\n\",\n    \"\\n\",\n    \"    def on_epoch_end(self, epoch, logs=None):\\n\",\n    \"        if (epoch + 1) % self.print_freq != 0:\\n\",\n    \"            return\\n\",\n    \"        for temperature in self.temperatures:\\n\",\n    \"            print(\\\"== Generating with temperature\\\", temperature)\\n\",\n    \"            sentence = self.prompt\\n\",\n    \"            for i in range(self.generate_length):\\n\",\n    \"                tokenized_sentence = text_vectorization([sentence])\\n\",\n    \"                predictions = self.model(tokenized_sentence)\\n\",\n    \"                next_token = sample_next(\\n\",\n    \"                    predictions[0, self.prompt_length - 1 + i, :]\\n\",\n    \"                )\\n\",\n    \"                sampled_token = tokens_index[next_token]\\n\",\n    \"                sentence += \\\" \\\" + sampled_token\\n\",\n    \"            print(sentence)\\n\",\n    \"\\n\",\n    \"prompt = \\\"This movie\\\"\\n\",\n    \"text_gen_callback = TextGenerator(\\n\",\n    \"    prompt,\\n\",\n    \"    generate_length=50,\\n\",\n    \"    model_input_length=sequence_length,\\n\",\n    \"    temperatures=(0.2, 0.5, 0.7, 1., 1.5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Fitting the language model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model.fit(lm_dataset, epochs=200, callbacks=[text_gen_callback])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Wrapping up\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter12_part01_text-generation.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter12_part02_deep-dream.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## DeepDream\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Implementing DeepDream in Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Fetching the test image**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"base_image_path = keras.utils.get_file(\\n\",\n    \"    \\\"coast.jpg\\\", origin=\\\"https://img-datasets.s3.amazonaws.com/coast.jpg\\\")\\n\",\n    \"\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(keras.utils.load_img(base_image_path))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Instantiating a pretrained `InceptionV3` model**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras.applications import inception_v3\\n\",\n    \"model = inception_v3.InceptionV3(weights=\\\"imagenet\\\", include_top=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Configuring the contribution of each layer to the DeepDream loss**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"layer_settings = {\\n\",\n    \"    \\\"mixed4\\\": 1.0,\\n\",\n    \"    \\\"mixed5\\\": 1.5,\\n\",\n    \"    \\\"mixed6\\\": 2.0,\\n\",\n    \"    \\\"mixed7\\\": 2.5,\\n\",\n    \"}\\n\",\n    \"outputs_dict = dict(\\n\",\n    \"    [\\n\",\n    \"        (layer.name, layer.output)\\n\",\n    \"        for layer in [model.get_layer(name) for name in layer_settings.keys()]\\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The DeepDream loss**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_loss(input_image):\\n\",\n    \"    features = feature_extractor(input_image)\\n\",\n    \"    loss = tf.zeros(shape=())\\n\",\n    \"    for name in features.keys():\\n\",\n    \"        coeff = layer_settings[name]\\n\",\n    \"        activation = features[name]\\n\",\n    \"        loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :]))\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The DeepDream gradient ascent process**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"@tf.function\\n\",\n    \"def gradient_ascent_step(image, learning_rate):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        tape.watch(image)\\n\",\n    \"        loss = compute_loss(image)\\n\",\n    \"    grads = tape.gradient(loss, image)\\n\",\n    \"    grads = tf.math.l2_normalize(grads)\\n\",\n    \"    image += learning_rate * grads\\n\",\n    \"    return loss, image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def gradient_ascent_loop(image, iterations, learning_rate, max_loss=None):\\n\",\n    \"    for i in range(iterations):\\n\",\n    \"        loss, image = gradient_ascent_step(image, learning_rate)\\n\",\n    \"        if max_loss is not None and loss > max_loss:\\n\",\n    \"            break\\n\",\n    \"        print(f\\\"... Loss value at step {i}: {loss:.2f}\\\")\\n\",\n    \"    return image\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"step = 20.\\n\",\n    \"num_octave = 3\\n\",\n    \"octave_scale = 1.4\\n\",\n    \"iterations = 30\\n\",\n    \"max_loss = 15.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Image processing utilities**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def preprocess_image(image_path):\\n\",\n    \"    img = keras.utils.load_img(image_path)\\n\",\n    \"    img = keras.utils.img_to_array(img)\\n\",\n    \"    img = np.expand_dims(img, axis=0)\\n\",\n    \"    img = keras.applications.inception_v3.preprocess_input(img)\\n\",\n    \"    return img\\n\",\n    \"\\n\",\n    \"def deprocess_image(img):\\n\",\n    \"    img = img.reshape((img.shape[1], img.shape[2], 3))\\n\",\n    \"    img /= 2.0\\n\",\n    \"    img += 0.5\\n\",\n    \"    img *= 255.\\n\",\n    \"    img = np.clip(img, 0, 255).astype(\\\"uint8\\\")\\n\",\n    \"    return img\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Running gradient ascent over multiple successive \\\"octaves\\\"**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"original_img = preprocess_image(base_image_path)\\n\",\n    \"original_shape = original_img.shape[1:3]\\n\",\n    \"\\n\",\n    \"successive_shapes = [original_shape]\\n\",\n    \"for i in range(1, num_octave):\\n\",\n    \"    shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])\\n\",\n    \"    successive_shapes.append(shape)\\n\",\n    \"successive_shapes = successive_shapes[::-1]\\n\",\n    \"\\n\",\n    \"shrunk_original_img = tf.image.resize(original_img, successive_shapes[0])\\n\",\n    \"\\n\",\n    \"img = tf.identity(original_img)\\n\",\n    \"for i, shape in enumerate(successive_shapes):\\n\",\n    \"    print(f\\\"Processing octave {i} with shape {shape}\\\")\\n\",\n    \"    img = tf.image.resize(img, shape)\\n\",\n    \"    img = gradient_ascent_loop(\\n\",\n    \"        img, iterations=iterations, learning_rate=step, max_loss=max_loss\\n\",\n    \"    )\\n\",\n    \"    upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape)\\n\",\n    \"    same_size_original = tf.image.resize(original_img, shape)\\n\",\n    \"    lost_detail = same_size_original - upscaled_shrunk_original_img\\n\",\n    \"    img += lost_detail\\n\",\n    \"    shrunk_original_img = tf.image.resize(original_img, shape)\\n\",\n    \"\\n\",\n    \"keras.utils.save_img(\\\"dream.png\\\", deprocess_image(img.numpy()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Wrapping up\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter12_part02_deep-dream.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter12_part03_neural-style-transfer.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Neural style transfer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The content loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The style loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Neural style transfer in Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Getting the style and content images**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"\\n\",\n    \"base_image_path = keras.utils.get_file(\\n\",\n    \"    \\\"sf.jpg\\\", origin=\\\"https://img-datasets.s3.amazonaws.com/sf.jpg\\\")\\n\",\n    \"style_reference_image_path = keras.utils.get_file(\\n\",\n    \"    \\\"starry_night.jpg\\\", origin=\\\"https://img-datasets.s3.amazonaws.com/starry_night.jpg\\\")\\n\",\n    \"\\n\",\n    \"original_width, original_height = keras.utils.load_img(base_image_path).size\\n\",\n    \"img_height = 400\\n\",\n    \"img_width = round(original_width * img_height / original_height)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Auxiliary functions**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"def preprocess_image(image_path):\\n\",\n    \"    img = keras.utils.load_img(\\n\",\n    \"        image_path, target_size=(img_height, img_width))\\n\",\n    \"    img = keras.utils.img_to_array(img)\\n\",\n    \"    img = np.expand_dims(img, axis=0)\\n\",\n    \"    img = keras.applications.vgg19.preprocess_input(img)\\n\",\n    \"    return img\\n\",\n    \"\\n\",\n    \"def deprocess_image(img):\\n\",\n    \"    img = img.reshape((img_height, img_width, 3))\\n\",\n    \"    img[:, :, 0] += 103.939\\n\",\n    \"    img[:, :, 1] += 116.779\\n\",\n    \"    img[:, :, 2] += 123.68\\n\",\n    \"    img = img[:, :, ::-1]\\n\",\n    \"    img = np.clip(img, 0, 255).astype(\\\"uint8\\\")\\n\",\n    \"    return img\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Using a pretrained VGG19 model to create a feature extractor**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = keras.applications.vgg19.VGG19(weights=\\\"imagenet\\\", include_top=False)\\n\",\n    \"\\n\",\n    \"outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])\\n\",\n    \"feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Content loss**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def content_loss(base_img, combination_img):\\n\",\n    \"    return tf.reduce_sum(tf.square(combination_img - base_img))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Style loss**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def gram_matrix(x):\\n\",\n    \"    x = tf.transpose(x, (2, 0, 1))\\n\",\n    \"    features = tf.reshape(x, (tf.shape(x)[0], -1))\\n\",\n    \"    gram = tf.matmul(features, tf.transpose(features))\\n\",\n    \"    return gram\\n\",\n    \"\\n\",\n    \"def style_loss(style_img, combination_img):\\n\",\n    \"    S = gram_matrix(style_img)\\n\",\n    \"    C = gram_matrix(combination_img)\\n\",\n    \"    channels = 3\\n\",\n    \"    size = img_height * img_width\\n\",\n    \"    return tf.reduce_sum(tf.square(S - C)) / (4.0 * (channels ** 2) * (size ** 2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Total variation loss**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def total_variation_loss(x):\\n\",\n    \"    a = tf.square(\\n\",\n    \"        x[:, : img_height - 1, : img_width - 1, :] - x[:, 1:, : img_width - 1, :]\\n\",\n    \"    )\\n\",\n    \"    b = tf.square(\\n\",\n    \"        x[:, : img_height - 1, : img_width - 1, :] - x[:, : img_height - 1, 1:, :]\\n\",\n    \"    )\\n\",\n    \"    return tf.reduce_sum(tf.pow(a + b, 1.25))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Defining the final loss that you'll minimize**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"style_layer_names = [\\n\",\n    \"    \\\"block1_conv1\\\",\\n\",\n    \"    \\\"block2_conv1\\\",\\n\",\n    \"    \\\"block3_conv1\\\",\\n\",\n    \"    \\\"block4_conv1\\\",\\n\",\n    \"    \\\"block5_conv1\\\",\\n\",\n    \"]\\n\",\n    \"content_layer_name = \\\"block5_conv2\\\"\\n\",\n    \"total_variation_weight = 1e-6\\n\",\n    \"style_weight = 1e-6\\n\",\n    \"content_weight = 2.5e-8\\n\",\n    \"\\n\",\n    \"def compute_loss(combination_image, base_image, style_reference_image):\\n\",\n    \"    input_tensor = tf.concat(\\n\",\n    \"        [base_image, style_reference_image, combination_image], axis=0\\n\",\n    \"    )\\n\",\n    \"    features = feature_extractor(input_tensor)\\n\",\n    \"    loss = tf.zeros(shape=())\\n\",\n    \"    layer_features = features[content_layer_name]\\n\",\n    \"    base_image_features = layer_features[0, :, :, :]\\n\",\n    \"    combination_features = layer_features[2, :, :, :]\\n\",\n    \"    loss = loss + content_weight * content_loss(\\n\",\n    \"        base_image_features, combination_features\\n\",\n    \"    )\\n\",\n    \"    for layer_name in style_layer_names:\\n\",\n    \"        layer_features = features[layer_name]\\n\",\n    \"        style_reference_features = layer_features[1, :, :, :]\\n\",\n    \"        combination_features = layer_features[2, :, :, :]\\n\",\n    \"        style_loss_value = style_loss(\\n\",\n    \"          style_reference_features, combination_features)\\n\",\n    \"        loss += (style_weight / len(style_layer_names)) * style_loss_value\\n\",\n    \"\\n\",\n    \"    loss += total_variation_weight * total_variation_loss(combination_image)\\n\",\n    \"    return loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Setting up the gradient-descent process**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"@tf.function\\n\",\n    \"def compute_loss_and_grads(combination_image, base_image, style_reference_image):\\n\",\n    \"    with tf.GradientTape() as tape:\\n\",\n    \"        loss = compute_loss(combination_image, base_image, style_reference_image)\\n\",\n    \"    grads = tape.gradient(loss, combination_image)\\n\",\n    \"    return loss, grads\\n\",\n    \"\\n\",\n    \"optimizer = keras.optimizers.SGD(\\n\",\n    \"    keras.optimizers.schedules.ExponentialDecay(\\n\",\n    \"        initial_learning_rate=100.0, decay_steps=100, decay_rate=0.96\\n\",\n    \"    )\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"base_image = preprocess_image(base_image_path)\\n\",\n    \"style_reference_image = preprocess_image(style_reference_image_path)\\n\",\n    \"combination_image = tf.Variable(preprocess_image(base_image_path))\\n\",\n    \"\\n\",\n    \"iterations = 4000\\n\",\n    \"for i in range(1, iterations + 1):\\n\",\n    \"    loss, grads = compute_loss_and_grads(\\n\",\n    \"        combination_image, base_image, style_reference_image\\n\",\n    \"    )\\n\",\n    \"    optimizer.apply_gradients([(grads, combination_image)])\\n\",\n    \"    if i % 100 == 0:\\n\",\n    \"        print(f\\\"Iteration {i}: loss={loss:.2f}\\\")\\n\",\n    \"        img = deprocess_image(combination_image.numpy())\\n\",\n    \"        fname = f\\\"combination_image_at_iteration_{i}.png\\\"\\n\",\n    \"        keras.utils.save_img(fname, img)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Wrapping up\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter12_part03_neural-style-transfer.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter12_part04_variational-autoencoders.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Generating images with variational autoencoders\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Sampling from latent spaces of images\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Concept vectors for image editing\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Variational autoencoders\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Implementing a VAE with Keras\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**VAE encoder network**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"latent_dim = 2\\n\",\n    \"\\n\",\n    \"encoder_inputs = keras.Input(shape=(28, 28, 1))\\n\",\n    \"x = layers.Conv2D(32, 3, activation=\\\"relu\\\", strides=2, padding=\\\"same\\\")(encoder_inputs)\\n\",\n    \"x = layers.Conv2D(64, 3, activation=\\\"relu\\\", strides=2, padding=\\\"same\\\")(x)\\n\",\n    \"x = layers.Flatten()(x)\\n\",\n    \"x = layers.Dense(16, activation=\\\"relu\\\")(x)\\n\",\n    \"z_mean = layers.Dense(latent_dim, name=\\\"z_mean\\\")(x)\\n\",\n    \"z_log_var = layers.Dense(latent_dim, name=\\\"z_log_var\\\")(x)\\n\",\n    \"encoder = keras.Model(encoder_inputs, [z_mean, z_log_var], name=\\\"encoder\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"encoder.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Latent-space-sampling layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"\\n\",\n    \"class Sampler(layers.Layer):\\n\",\n    \"    def call(self, z_mean, z_log_var):\\n\",\n    \"        batch_size = tf.shape(z_mean)[0]\\n\",\n    \"        z_size = tf.shape(z_mean)[1]\\n\",\n    \"        epsilon = tf.random.normal(shape=(batch_size, z_size))\\n\",\n    \"        return z_mean + tf.exp(0.5 * z_log_var) * epsilon\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**VAE decoder network, mapping latent space points to images**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"latent_inputs = keras.Input(shape=(latent_dim,))\\n\",\n    \"x = layers.Dense(7 * 7 * 64, activation=\\\"relu\\\")(latent_inputs)\\n\",\n    \"x = layers.Reshape((7, 7, 64))(x)\\n\",\n    \"x = layers.Conv2DTranspose(64, 3, activation=\\\"relu\\\", strides=2, padding=\\\"same\\\")(x)\\n\",\n    \"x = layers.Conv2DTranspose(32, 3, activation=\\\"relu\\\", strides=2, padding=\\\"same\\\")(x)\\n\",\n    \"decoder_outputs = layers.Conv2D(1, 3, activation=\\\"sigmoid\\\", padding=\\\"same\\\")(x)\\n\",\n    \"decoder = keras.Model(latent_inputs, decoder_outputs, name=\\\"decoder\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"decoder.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**VAE model with custom `train_step()`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class VAE(keras.Model):\\n\",\n    \"    def __init__(self, encoder, decoder, **kwargs):\\n\",\n    \"        super().__init__(**kwargs)\\n\",\n    \"        self.encoder = encoder\\n\",\n    \"        self.decoder = decoder\\n\",\n    \"        self.sampler = Sampler()\\n\",\n    \"        self.total_loss_tracker = keras.metrics.Mean(name=\\\"total_loss\\\")\\n\",\n    \"        self.reconstruction_loss_tracker = keras.metrics.Mean(\\n\",\n    \"            name=\\\"reconstruction_loss\\\")\\n\",\n    \"        self.kl_loss_tracker = keras.metrics.Mean(name=\\\"kl_loss\\\")\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def metrics(self):\\n\",\n    \"        return [self.total_loss_tracker,\\n\",\n    \"                self.reconstruction_loss_tracker,\\n\",\n    \"                self.kl_loss_tracker]\\n\",\n    \"\\n\",\n    \"    def train_step(self, data):\\n\",\n    \"        with tf.GradientTape() as tape:\\n\",\n    \"            z_mean, z_log_var = self.encoder(data)\\n\",\n    \"            z = self.sampler(z_mean, z_log_var)\\n\",\n    \"            reconstruction = decoder(z)\\n\",\n    \"            reconstruction_loss = tf.reduce_mean(\\n\",\n    \"                tf.reduce_sum(\\n\",\n    \"                    keras.losses.binary_crossentropy(data, reconstruction),\\n\",\n    \"                    axis=(1, 2)\\n\",\n    \"                )\\n\",\n    \"            )\\n\",\n    \"            kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\\n\",\n    \"            total_loss = reconstruction_loss + tf.reduce_mean(kl_loss)\\n\",\n    \"        grads = tape.gradient(total_loss, self.trainable_weights)\\n\",\n    \"        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\\n\",\n    \"        self.total_loss_tracker.update_state(total_loss)\\n\",\n    \"        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\\n\",\n    \"        self.kl_loss_tracker.update_state(kl_loss)\\n\",\n    \"        return {\\n\",\n    \"            \\\"total_loss\\\": self.total_loss_tracker.result(),\\n\",\n    \"            \\\"reconstruction_loss\\\": self.reconstruction_loss_tracker.result(),\\n\",\n    \"            \\\"kl_loss\\\": self.kl_loss_tracker.result(),\\n\",\n    \"        }\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Training the VAE**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()\\n\",\n    \"mnist_digits = np.concatenate([x_train, x_test], axis=0)\\n\",\n    \"mnist_digits = np.expand_dims(mnist_digits, -1).astype(\\\"float32\\\") / 255\\n\",\n    \"\\n\",\n    \"vae = VAE(encoder, decoder)\\n\",\n    \"vae.compile(optimizer=keras.optimizers.Adam(), run_eagerly=True)\\n\",\n    \"vae.fit(mnist_digits, epochs=30, batch_size=128)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Sampling a grid of images from the 2D latent space**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"n = 30\\n\",\n    \"digit_size = 28\\n\",\n    \"figure = np.zeros((digit_size * n, digit_size * n))\\n\",\n    \"\\n\",\n    \"grid_x = np.linspace(-1, 1, n)\\n\",\n    \"grid_y = np.linspace(-1, 1, n)[::-1]\\n\",\n    \"\\n\",\n    \"for i, yi in enumerate(grid_y):\\n\",\n    \"    for j, xi in enumerate(grid_x):\\n\",\n    \"        z_sample = np.array([[xi, yi]])\\n\",\n    \"        x_decoded = vae.decoder.predict(z_sample)\\n\",\n    \"        digit = x_decoded[0].reshape(digit_size, digit_size)\\n\",\n    \"        figure[\\n\",\n    \"            i * digit_size : (i + 1) * digit_size,\\n\",\n    \"            j * digit_size : (j + 1) * digit_size,\\n\",\n    \"        ] = digit\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(15, 15))\\n\",\n    \"start_range = digit_size // 2\\n\",\n    \"end_range = n * digit_size + start_range\\n\",\n    \"pixel_range = np.arange(start_range, end_range, digit_size)\\n\",\n    \"sample_range_x = np.round(grid_x, 1)\\n\",\n    \"sample_range_y = np.round(grid_y, 1)\\n\",\n    \"plt.xticks(pixel_range, sample_range_x)\\n\",\n    \"plt.yticks(pixel_range, sample_range_y)\\n\",\n    \"plt.xlabel(\\\"z[0]\\\")\\n\",\n    \"plt.ylabel(\\\"z[1]\\\")\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.imshow(figure, cmap=\\\"Greys_r\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Wrapping up\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter12_part04_variational-autoencoders.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter12_part05_gans.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Introduction to generative adversarial networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A schematic GAN implementation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A bag of tricks\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Getting our hands on the CelebA dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Getting the CelebA data**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!mkdir celeba_gan\\n\",\n    \"!gdown --id 1O7m1010EJjLE5QxLZiM9Fpjs7Oj6e684 -O celeba_gan/data.zip\\n\",\n    \"!unzip -qq celeba_gan/data.zip -d celeba_gan\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Creating a dataset from a directory of images**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"dataset = keras.utils.image_dataset_from_directory(\\n\",\n    \"    \\\"celeba_gan\\\",\\n\",\n    \"    label_mode=None,\\n\",\n    \"    image_size=(64, 64),\\n\",\n    \"    batch_size=32,\\n\",\n    \"    smart_resize=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Rescaling the images**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"dataset = dataset.map(lambda x: x / 255.)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Displaying the first image**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"for x in dataset:\\n\",\n    \"    plt.axis(\\\"off\\\")\\n\",\n    \"    plt.imshow((x.numpy() * 255).astype(\\\"int32\\\")[0])\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The discriminator\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The GAN discriminator network**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"discriminator = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        keras.Input(shape=(64, 64, 3)),\\n\",\n    \"        layers.Conv2D(64, kernel_size=4, strides=2, padding=\\\"same\\\"),\\n\",\n    \"        layers.LeakyReLU(alpha=0.2),\\n\",\n    \"        layers.Conv2D(128, kernel_size=4, strides=2, padding=\\\"same\\\"),\\n\",\n    \"        layers.LeakyReLU(alpha=0.2),\\n\",\n    \"        layers.Conv2D(128, kernel_size=4, strides=2, padding=\\\"same\\\"),\\n\",\n    \"        layers.LeakyReLU(alpha=0.2),\\n\",\n    \"        layers.Flatten(),\\n\",\n    \"        layers.Dropout(0.2),\\n\",\n    \"        layers.Dense(1, activation=\\\"sigmoid\\\"),\\n\",\n    \"    ],\\n\",\n    \"    name=\\\"discriminator\\\",\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"discriminator.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The generator\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**GAN generator network**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"latent_dim = 128\\n\",\n    \"\\n\",\n    \"generator = keras.Sequential(\\n\",\n    \"    [\\n\",\n    \"        keras.Input(shape=(latent_dim,)),\\n\",\n    \"        layers.Dense(8 * 8 * 128),\\n\",\n    \"        layers.Reshape((8, 8, 128)),\\n\",\n    \"        layers.Conv2DTranspose(128, kernel_size=4, strides=2, padding=\\\"same\\\"),\\n\",\n    \"        layers.LeakyReLU(alpha=0.2),\\n\",\n    \"        layers.Conv2DTranspose(256, kernel_size=4, strides=2, padding=\\\"same\\\"),\\n\",\n    \"        layers.LeakyReLU(alpha=0.2),\\n\",\n    \"        layers.Conv2DTranspose(512, kernel_size=4, strides=2, padding=\\\"same\\\"),\\n\",\n    \"        layers.LeakyReLU(alpha=0.2),\\n\",\n    \"        layers.Conv2D(3, kernel_size=5, padding=\\\"same\\\", activation=\\\"sigmoid\\\"),\\n\",\n    \"    ],\\n\",\n    \"    name=\\\"generator\\\",\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"generator.summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The adversarial network\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**The GAN `Model`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"class GAN(keras.Model):\\n\",\n    \"    def __init__(self, discriminator, generator, latent_dim):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.discriminator = discriminator\\n\",\n    \"        self.generator = generator\\n\",\n    \"        self.latent_dim = latent_dim\\n\",\n    \"        self.d_loss_metric = keras.metrics.Mean(name=\\\"d_loss\\\")\\n\",\n    \"        self.g_loss_metric = keras.metrics.Mean(name=\\\"g_loss\\\")\\n\",\n    \"\\n\",\n    \"    def compile(self, d_optimizer, g_optimizer, loss_fn):\\n\",\n    \"        super(GAN, self).compile()\\n\",\n    \"        self.d_optimizer = d_optimizer\\n\",\n    \"        self.g_optimizer = g_optimizer\\n\",\n    \"        self.loss_fn = loss_fn\\n\",\n    \"\\n\",\n    \"    @property\\n\",\n    \"    def metrics(self):\\n\",\n    \"        return [self.d_loss_metric, self.g_loss_metric]\\n\",\n    \"\\n\",\n    \"    def train_step(self, real_images):\\n\",\n    \"        batch_size = tf.shape(real_images)[0]\\n\",\n    \"        random_latent_vectors = tf.random.normal(\\n\",\n    \"            shape=(batch_size, self.latent_dim))\\n\",\n    \"        generated_images = self.generator(random_latent_vectors)\\n\",\n    \"        combined_images = tf.concat([generated_images, real_images], axis=0)\\n\",\n    \"        labels = tf.concat(\\n\",\n    \"            [tf.ones((batch_size, 1)), tf.zeros((batch_size, 1))],\\n\",\n    \"            axis=0\\n\",\n    \"        )\\n\",\n    \"        labels += 0.05 * tf.random.uniform(tf.shape(labels))\\n\",\n    \"\\n\",\n    \"        with tf.GradientTape() as tape:\\n\",\n    \"            predictions = self.discriminator(combined_images)\\n\",\n    \"            d_loss = self.loss_fn(labels, predictions)\\n\",\n    \"        grads = tape.gradient(d_loss, self.discriminator.trainable_weights)\\n\",\n    \"        self.d_optimizer.apply_gradients(\\n\",\n    \"            zip(grads, self.discriminator.trainable_weights)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        random_latent_vectors = tf.random.normal(\\n\",\n    \"            shape=(batch_size, self.latent_dim))\\n\",\n    \"\\n\",\n    \"        misleading_labels = tf.zeros((batch_size, 1))\\n\",\n    \"\\n\",\n    \"        with tf.GradientTape() as tape:\\n\",\n    \"            predictions = self.discriminator(\\n\",\n    \"                self.generator(random_latent_vectors))\\n\",\n    \"            g_loss = self.loss_fn(misleading_labels, predictions)\\n\",\n    \"        grads = tape.gradient(g_loss, self.generator.trainable_weights)\\n\",\n    \"        self.g_optimizer.apply_gradients(\\n\",\n    \"            zip(grads, self.generator.trainable_weights))\\n\",\n    \"\\n\",\n    \"        self.d_loss_metric.update_state(d_loss)\\n\",\n    \"        self.g_loss_metric.update_state(g_loss)\\n\",\n    \"        return {\\\"d_loss\\\": self.d_loss_metric.result(),\\n\",\n    \"                \\\"g_loss\\\": self.g_loss_metric.result()}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A callback that samples generated images during training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class GANMonitor(keras.callbacks.Callback):\\n\",\n    \"    def __init__(self, num_img=3, latent_dim=128):\\n\",\n    \"        self.num_img = num_img\\n\",\n    \"        self.latent_dim = latent_dim\\n\",\n    \"\\n\",\n    \"    def on_epoch_end(self, epoch, logs=None):\\n\",\n    \"        random_latent_vectors = tf.random.normal(shape=(self.num_img, self.latent_dim))\\n\",\n    \"        generated_images = self.model.generator(random_latent_vectors)\\n\",\n    \"        generated_images *= 255\\n\",\n    \"        generated_images.numpy()\\n\",\n    \"        for i in range(self.num_img):\\n\",\n    \"            img = keras.utils.array_to_img(generated_images[i])\\n\",\n    \"            img.save(f\\\"generated_img_{epoch:03d}_{i}.png\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Compiling and training the GAN**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"epochs = 100\\n\",\n    \"\\n\",\n    \"gan = GAN(discriminator=discriminator, generator=generator, latent_dim=latent_dim)\\n\",\n    \"gan.compile(\\n\",\n    \"    d_optimizer=keras.optimizers.Adam(learning_rate=0.0001),\\n\",\n    \"    g_optimizer=keras.optimizers.Adam(learning_rate=0.0001),\\n\",\n    \"    loss_fn=keras.losses.BinaryCrossentropy(),\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"gan.fit(\\n\",\n    \"    dataset, epochs=epochs, callbacks=[GANMonitor(num_img=10, latent_dim=latent_dim)]\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Wrapping up\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter12_part05_gans.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "second_edition/chapter13_best-practices-for-the-real-world.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Best practices for the real world\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Getting the most out of your models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Hyperparameter optimization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using KerasTuner\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install keras-tuner -q\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A KerasTuner model-building function**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras import layers\\n\",\n    \"\\n\",\n    \"def build_model(hp):\\n\",\n    \"    units = hp.Int(name=\\\"units\\\", min_value=16, max_value=64, step=16)\\n\",\n    \"    model = keras.Sequential([\\n\",\n    \"        layers.Dense(units, activation=\\\"relu\\\"),\\n\",\n    \"        layers.Dense(10, activation=\\\"softmax\\\")\\n\",\n    \"    ])\\n\",\n    \"    optimizer = hp.Choice(name=\\\"optimizer\\\", values=[\\\"rmsprop\\\", \\\"adam\\\"])\\n\",\n    \"    model.compile(\\n\",\n    \"        optimizer=optimizer,\\n\",\n    \"        loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"        metrics=[\\\"accuracy\\\"])\\n\",\n    \"    return model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**A KerasTuner `HyperModel`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import kerastuner as kt\\n\",\n    \"\\n\",\n    \"class SimpleMLP(kt.HyperModel):\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"\\n\",\n    \"    def build(self, hp):\\n\",\n    \"        units = hp.Int(name=\\\"units\\\", min_value=16, max_value=64, step=16)\\n\",\n    \"        model = keras.Sequential([\\n\",\n    \"            layers.Dense(units, activation=\\\"relu\\\"),\\n\",\n    \"            layers.Dense(self.num_classes, activation=\\\"softmax\\\")\\n\",\n    \"        ])\\n\",\n    \"        optimizer = hp.Choice(name=\\\"optimizer\\\", values=[\\\"rmsprop\\\", \\\"adam\\\"])\\n\",\n    \"        model.compile(\\n\",\n    \"            optimizer=optimizer,\\n\",\n    \"            loss=\\\"sparse_categorical_crossentropy\\\",\\n\",\n    \"            metrics=[\\\"accuracy\\\"])\\n\",\n    \"        return model\\n\",\n    \"\\n\",\n    \"hypermodel = SimpleMLP(num_classes=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tuner = kt.BayesianOptimization(\\n\",\n    \"    build_model,\\n\",\n    \"    objective=\\\"val_accuracy\\\",\\n\",\n    \"    max_trials=100,\\n\",\n    \"    executions_per_trial=2,\\n\",\n    \"    directory=\\\"mnist_kt_test\\\",\\n\",\n    \"    overwrite=True,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"tuner.search_space_summary()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\\n\",\n    \"x_train = x_train.reshape((-1, 28 * 28)).astype(\\\"float32\\\") / 255\\n\",\n    \"x_test = x_test.reshape((-1, 28 * 28)).astype(\\\"float32\\\") / 255\\n\",\n    \"x_train_full = x_train[:]\\n\",\n    \"y_train_full = y_train[:]\\n\",\n    \"num_val_samples = 10000\\n\",\n    \"x_train, x_val = x_train[:-num_val_samples], x_train[-num_val_samples:]\\n\",\n    \"y_train, y_val = y_train[:-num_val_samples], y_train[-num_val_samples:]\\n\",\n    \"callbacks = [\\n\",\n    \"    keras.callbacks.EarlyStopping(monitor=\\\"val_loss\\\", patience=5),\\n\",\n    \"]\\n\",\n    \"tuner.search(\\n\",\n    \"    x_train, y_train,\\n\",\n    \"    batch_size=128,\\n\",\n    \"    epochs=100,\\n\",\n    \"    validation_data=(x_val, y_val),\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    verbose=2,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"**Querying the best hyperparameter configurations**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"top_n = 4\\n\",\n    \"best_hps = tuner.get_best_hyperparameters(top_n)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_best_epoch(hp):\\n\",\n    \"    model = build_model(hp)\\n\",\n    \"    callbacks=[\\n\",\n    \"        keras.callbacks.EarlyStopping(\\n\",\n    \"            monitor=\\\"val_loss\\\", mode=\\\"min\\\", patience=10)\\n\",\n    \"    ]\\n\",\n    \"    history = model.fit(\\n\",\n    \"        x_train, y_train,\\n\",\n    \"        validation_data=(x_val, y_val),\\n\",\n    \"        epochs=100,\\n\",\n    \"        batch_size=128,\\n\",\n    \"        callbacks=callbacks)\\n\",\n    \"    val_loss_per_epoch = history.history[\\\"val_loss\\\"]\\n\",\n    \"    best_epoch = val_loss_per_epoch.index(min(val_loss_per_epoch)) + 1\\n\",\n    \"    print(f\\\"Best epoch: {best_epoch}\\\")\\n\",\n    \"    return best_epoch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"def get_best_trained_model(hp):\\n\",\n    \"    best_epoch = get_best_epoch(hp)\\n\",\n    \"    model = build_model(hp)\\n\",\n    \"    model.fit(\\n\",\n    \"        x_train_full, y_train_full,\\n\",\n    \"        batch_size=128, epochs=int(best_epoch * 1.2))\\n\",\n    \"    return model\\n\",\n    \"\\n\",\n    \"best_models = []\\n\",\n    \"for hp in best_hps:\\n\",\n    \"    model = get_best_trained_model(hp)\\n\",\n    \"    model.evaluate(x_test, y_test)\\n\",\n    \"    best_models.append(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"best_models = tuner.get_best_models(top_n)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The art of crafting the right search space\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### The future of hyperparameter tuning: automated machine learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Model ensembling\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Scaling-up model training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Speeding up training on GPU with mixed precision\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Understanding floating-point precision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import tensorflow as tf\\n\",\n    \"import numpy as np\\n\",\n    \"np_array = np.zeros((2, 2))\\n\",\n    \"tf_tensor = tf.convert_to_tensor(np_array)\\n\",\n    \"tf_tensor.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"np_array = np.zeros((2, 2))\\n\",\n    \"tf_tensor = tf.convert_to_tensor(np_array, dtype=\\\"float32\\\")\\n\",\n    \"tf_tensor.dtype\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Mixed-precision training in practice\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"keras.mixed_precision.set_global_policy(\\\"mixed_float16\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Multi-GPU training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Getting your hands on two or more GPUs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Single-host, multi-device synchronous training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### TPU training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using a TPU via Google Colab\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Leveraging step fusing to improve TPU utilization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Summary\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter13_best-practices-for-the-real-world.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  },
  {
    "path": "second_edition/chapter14_conclusions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\\n\\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\\n\\nThis notebook was generated for TensorFlow 2.6.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"# Conclusions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Key concepts in review\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Various approaches to AI\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### What makes deep learning special within the field of machine learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### How to think about deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Key enabling technologies\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The universal machine-learning workflow\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Key network architectures\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Densely connected networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from tensorflow import keras\\n\",\n    \"from tensorflow.keras\\u00a0import\\u00a0layers\\n\",\n    \"inputs = keras.Input(shape=(num_input_features,))\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"outputs = layers.Dense(1,\\u00a0activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\u00a0loss=\\\"binary_crossentropy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(num_input_features,))\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"outputs = layers.Dense(num_classes,\\u00a0activation=\\\"softmax\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\u00a0loss=\\\"categorical_crossentropy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(num_input_features,))\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"outputs = layers.Dense(num_classes,\\u00a0activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\u00a0loss=\\\"binary_crossentropy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(num_input_features,))\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"outputs layers.Dense(num_values)(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\u00a0loss=\\\"mse\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Convnets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(height,\\u00a0width,\\u00a0channels))\\n\",\n    \"x = layers.SeparableConv2D(32,\\u00a03,\\u00a0activation=\\\"relu\\\")(inputs)\\n\",\n    \"x = layers.SeparableConv2D(64,\\u00a03,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(2)(x)\\n\",\n    \"x = layers.SeparableConv2D(64,\\u00a03,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.SeparableConv2D(128,\\u00a03,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.MaxPooling2D(2)(x)\\n\",\n    \"x = layers.SeparableConv2D(64,\\u00a03,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.SeparableConv2D(128,\\u00a03,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"x = layers.GlobalAveragePooling2D()(x)\\n\",\n    \"x = layers.Dense(32,\\u00a0activation=\\\"relu\\\")(x)\\n\",\n    \"outputs = layers.Dense(num_classes,\\u00a0activation=\\\"softmax\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\u00a0loss=\\\"categorical_crossentropy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### RNNs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(num_timesteps,\\u00a0num_features))\\n\",\n    \"x = layers.LSTM(32)(inputs)\\n\",\n    \"outputs = layers.Dense(num_classes,\\u00a0activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\u00a0loss=\\\"binary_crossentropy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(num_timesteps,\\u00a0num_features))\\n\",\n    \"x = layers.LSTM(32,\\u00a0return_sequences=True)(inputs)\\n\",\n    \"x = layers.LSTM(32,\\u00a0return_sequences=True)(x)\\n\",\n    \"x = layers.LSTM(32)(x)\\n\",\n    \"outputs = layers.Dense(num_classes,\\u00a0activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\",\\u00a0loss=\\\"binary_crossentropy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"encoder_inputs = keras.Input(shape=(sequence_length,), dtype=\\\"int64\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(encoder_inputs)\\n\",\n    \"encoder_outputs = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\\n\",\n    \"decoder_inputs = keras.Input(shape=(None,), dtype=\\\"int64\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(decoder_inputs)\\n\",\n    \"x = TransformerDecoder(embed_dim, dense_dim, num_heads)(x, encoder_outputs)\\n\",\n    \"decoder_outputs = layers.Dense(vocab_size, activation=\\\"softmax\\\")(x)\\n\",\n    \"transformer = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)\\n\",\n    \"transformer.compile(optimizer=\\\"rmsprop\\\", loss=\\\"categorical_crossentropy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 0,\n   \"metadata\": {\n    \"colab_type\": \"code\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"inputs = keras.Input(shape=(sequence_length,), dtype=\\\"int64\\\")\\n\",\n    \"x = PositionalEmbedding(sequence_length, vocab_size, embed_dim)(inputs)\\n\",\n    \"x = TransformerEncoder(embed_dim, dense_dim, num_heads)(x)\\n\",\n    \"x = layers.GlobalMaxPooling1D()(x)\\n\",\n    \"outputs = layers.Dense(1, activation=\\\"sigmoid\\\")(x)\\n\",\n    \"model = keras.Model(inputs, outputs)\\n\",\n    \"model.compile(optimizer=\\\"rmsprop\\\", loss=\\\"binary_crossentropy\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The space of possibilities\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## The limitations of deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The risk of anthropomorphizing machine-learning models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Automatons vs. intelligent agents\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Local generalization vs. extreme generalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The purpose of intelligence\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Climbing the spectrum of generalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Setting the course toward greater generality in AI\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### On the importance of setting the right objective: The shortcut rule\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### A new target\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Implementing intelligence: The missing ingredients\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Intelligence as sensitivity to abstract analogies\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The two poles of abstraction\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Value-centric analogy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Program-centric analogy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Cognition as a combination of both kinds of abstraction\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The missing half of the picture\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## The future of deep learning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Models as programs\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Blending together deep learning and program synthesis\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Integrating deep-learning modules and algorithmic modules into hybrid systems\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"#### Using deep learning to guide program search\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Lifelong learning and modular subroutine reuse\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### The long-term vision\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Staying up to date in a fast-moving field\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Practice on real-world problems using Kaggle\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Read about the latest developments on arXiv\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"### Explore the Keras ecosystem\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\"\n   },\n   \"source\": [\n    \"## Final words\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"name\": \"chapter14_conclusions.i\",\n   \"private_outputs\": false,\n   \"provenance\": [],\n   \"toc_visible\": true\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"
  }
]