Repository: Neuralearn/deep-learning-with-tensorflow-2
Branch: main
Commit: 3dca0ac8c812
Files: 83
Total size: 20.4 MB

Directory structure:
gitextract_a2eje9ii/

├── README.md
├── deep learning for computer vision/
│   ├── 1-Linear Regression for Predicting Price of second hand Cars by Neuralearn.ai-.ipynb
│   ├── 2-Malaria Detection by Neuralearn.ai-.ipynb
│   ├── 3-Malaria Detection by Neuralearn.ai [PyTorch Version]-.ipynb
│   ├── 4-Human Emotions Detection by Neuralearn.ai-.ipynb
│   ├── 5-YOLO Object Detection from Scratch by Neuralearn.ai-.ipynb
│   ├── 6-Image segmentation by Neuralearn.ai-.ipynb
│   ├── 7-People Counting by Neuralearn.ai-.ipynb
│   ├── 8-VAE by Neuralearn.ai-.ipynb
│   ├── 9-DCGAN By Neuralearn.ai-.ipynb
│   └── emotions-detection/
│       ├── Procfile
│       ├── locusts.py
│       ├── requirements.txt
│       ├── runtime.txt
│       └── service/
│           ├── __init__.py
│           ├── api/
│           │   ├── __init__.py
│           │   ├── api.py
│           │   └── endpoints/
│           │       ├── __init__.py
│           │       ├── detect.py
│           │       └── test.py
│           ├── core/
│           │   ├── __init__.py
│           │   ├── logic/
│           │   │   ├── __init__.py
│           │   │   └── onnx_inference.py
│           │   └── schemas/
│           │       ├── __init__.py
│           │       ├── input.py
│           │       └── output.py
│           └── main.py
├── deep learning for image generation/
│   ├── 1-VAE by Neuralearn.ai-.ipynb
│   ├── 2-DCGAN By Neuralearn.ai-.ipynb
│   ├── 3-WGAN By Neuralearn.ai-.ipynb
│   ├── 4-ProGAN by Neuralearn.ai-.ipynb
│   ├── 5-SRGAN By Neuralearn.ai-.ipynb
│   ├── 6-CycleGAN By Neuralearn.ai-.ipynb
│   └── 7-Diffusion Models Neuralearn.ai-.ipynb
├── deep learning for linear algebra/
│   └── Complete Linear Algebra Course by Neuralearn.ai-.ipynb
├── deep learning for natural language processing/
│   ├── 1-Linear Regression for Predicting Price of second hand Cars by Neuralearn.ai-.ipynb
│   ├── 10-Neural Machine Translation using HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
│   ├── 11-Extractive Question Answering using HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
│   ├── 12-Search Engine for e-commerce using Sentence Transformers in HuggingFace 🤗 by Neuralearn.ai-.ipynb
│   ├── 13-Lyrics Generator using GPT2 in HuggingFace 🤗 by Neuralearn.ai-.ipynb
│   ├── 14-Grammatical Error Correction using T5 in HuggingFace 🤗 by Neuralearn.ai-.ipynb
│   ├── 15-Chat With Elon Musk using BlenderBot in HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
│   ├── 2-Sentiment Analysis with RNNs by Neuralearn.ai-.ipynb
│   ├── 3-Sentiment Analysis with Transformers by Neuralearn.ai-.ipynb
│   ├── 4-Neural Machine Translation with RNNs by Neuralearn.ai-.ipynb
│   ├── 5-Neural Machine Translation with Bahdanau Attention by Neuralearn.ai-.ipynb
│   ├── 6-Neural Machine Translation with Transformers by Neuralearn.ai-.ipynb
│   ├── 7-Sentiment Analysis with BERT in HuggingFace 🤗 by Neuralearn.ai-.ipynb
│   ├── 8-Intent Classification for Customer Service using HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
│   └── 9-Named Entity Recognition using HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
├── deep learning for object detection/
│   └── Deploying YoloX to Cloud by Neuralearn-.ipynb
├── deep learning for optical character recognition/
│   └── PDF TO EXCEL By Neuralearn-.ipynb
├── deprecated/
│   ├── .ipynb_checkpoints/
│   │   └── 21-dcgan_By_Neuralearn-checkpoint.ipynb
│   ├── 1-Linear Regression by Neuralearn.ai.ipynb
│   ├── 10-Breast Cancer detection by Neuralearn.ai.ipynb
│   ├── 11-YOLO by Neuralearn.ai.ipynb
│   ├── 12-YOLO v3 by Neuralearn.ai.ipynb
│   ├── 13-Semantic Segmentation by Neuralearn.ai.ipynb
│   ├── 14-People Counting by Neuralearn.ai.ipynb
│   ├── 15-Machine Translation with Sequence to Sequence Models by Neuralearn.ai.ipynb
│   ├── 16-Machine Translation with Attention Models by Neuralearn.ai.ipynb
│   ├── 17-Machine Translation with Transformers by Neuralearn.ai.ipynb
│   ├── 18-Question Answering by Neuralearn.ai.ipynb
│   ├── 19-Automatic Speech Recognition by Neuralearn.ai.ipynb
│   ├── 2-Logistic Regression by Neuralearn.ai.ipynb
│   ├── 20-Image Captioning by Neuralearn.ai.ipynb
│   ├── 21-dcgan_By_Neuralearn.ipynb
│   ├── 3-Softmax Regression by Neuralearn.ai.ipynb
│   ├── 4-Artificial Neural Networks from scratch Neuralearn.ai.ipynb
│   ├── 5-Lenet Model by Neuralearn.ai.ipynb
│   ├── 5-Transfer Learning by Neuralearn.ai.ipynb
│   ├── 6-Callbacks by Neuralearn.ai.ipynb
│   ├── 7-Transfer Learning and Finetuning by Neuralearn.ai.ipynb
│   ├── 8-Sentiment Analysis by Neuralearn.ai.ipynb
│   ├── 9-Word2Vec by Neuralearn.ai.ipynb
│   └── files/
│       ├── exam.csv
│       └── health.csv
└── neural networks from scratch/
    ├── 1-Linear Regression by Neuralearn.ai.ipynb
    ├── 2-Logistic Regression by Neuralearn.ai.ipynb
    ├── 3-Softmax Regression by Neuralearn.ai.ipynb
    ├── 4-Artificial Neural Networks from scratch Neuralearn.ai.ipynb
    └── files/
        ├── exam.csv
        └── health.csv

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FILE CONTENTS
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FILE: README.md
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# deep-learning-with-tensorflow-2
Jupyter notebooks for Deep Learning with TensorFlow 2 Course


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FILE: deep learning for computer vision/1-Linear Regression for Predicting Price of second hand Cars by Neuralearn.ai-.ipynb
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1MpOO7MxtkDaf3S0rIK8SDCfnpI6TmU_G","timestamp":1673816133355}],"collapsed_sections":["j6EtG3kpNKiT","CxlimoToLc4C","TSjCxq__LQkw"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"Q_Wi6cQRp4Rv"},"source":["import tensorflow as tf### models\n","import pandas as pd ### reading and processing data\n","import seaborn as sns ### visualization\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","from tensorflow.keras.layers import Normalization, Dense, InputLayer\n","from tensorflow.keras.losses import MeanSquaredError, Huber, MeanAbsoluteError\n","from tensorflow.keras.metrics import RootMeanSquaredError\n","from tensorflow.keras.optimizers import Adam"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"j6EtG3kpNKiT"},"source":["## **Data Preparation**"]},{"cell_type":"code","metadata":{"id":"_wKxrpeci_gT","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635922384406,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"de7b09f9-1986-429b-f069-e27766c2c2fc"},"source":["data = pd.read_csv(\"train.csv\", \",\")\n","data.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>v.id</th>\n","      <th>on road old</th>\n","      <th>on road now</th>\n","      <th>years</th>\n","      <th>km</th>\n","      <th>rating</th>\n","      <th>condition</th>\n","      <th>economy</th>\n","      <th>top speed</th>\n","      <th>hp</th>\n","      <th>torque</th>\n","      <th>current price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>535651</td>\n","      <td>798186</td>\n","      <td>3</td>\n","      <td>78945</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>14</td>\n","      <td>177</td>\n","      <td>73</td>\n","      <td>123</td>\n","      <td>351318.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>591911</td>\n","      <td>861056</td>\n","      <td>6</td>\n","      <td>117220</td>\n","      <td>5</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>148</td>\n","      <td>74</td>\n","      <td>95</td>\n","      <td>285001.5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>686990</td>\n","      <td>770762</td>\n","      <td>2</td>\n","      <td>132538</td>\n","      <td>2</td>\n","      <td>8</td>\n","      <td>15</td>\n","      <td>181</td>\n","      <td>53</td>\n","      <td>97</td>\n","      <td>215386.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>573999</td>\n","      <td>722381</td>\n","      <td>4</td>\n","      <td>101065</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>11</td>\n","      <td>197</td>\n","      <td>54</td>\n","      <td>116</td>\n","      <td>244295.5</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>691388</td>\n","      <td>811335</td>\n","      <td>6</td>\n","      <td>61559</td>\n","      <td>3</td>\n","      <td>9</td>\n","      <td>12</td>\n","      <td>160</td>\n","      <td>53</td>\n","      <td>105</td>\n","      <td>531114.5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   v.id  on road old  on road now  years  ...  top speed  hp  torque  current price\n","0     1       535651       798186      3  ...        177  73     123       351318.0\n","1     2       591911       861056      6  ...        148  74      95       285001.5\n","2     3       686990       770762      2  ...        181  53      97       215386.0\n","3     4       573999       722381      4  ...        197  54     116       244295.5\n","4     5       691388       811335      6  ...        160  53     105       531114.5\n","\n","[5 rows x 12 columns]"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"txHuR6vUXK7-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922384407,"user_tz":-60,"elapsed":16,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"4f749ca2-75f0-4834-845e-eed50181660b"},"source":["data.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1000, 12)"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"U_l14YAjXLDD"},"source":["# data = pd.read_csv(\"train_semi.csv\", \",\")\n","# data.head()\n","# data.shape"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ssm3ewabXLFx","colab":{"base_uri":"https://localhost:8080/","height":999},"executionInfo":{"status":"ok","timestamp":1635922431466,"user_tz":-60,"elapsed":46423,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"edfa5533-543d-4013-9b89-8a81de77b091"},"source":["sns.pairplot(data[['years', 'km', 'rating', 'condition', 'economy', 'top speed', 'hp', 'torque', 'current price']], diag_kind='kde')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.PairGrid at 0x7f9735927510>"]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1620x1620 with 90 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"kbKG64L3XLIh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432304,"user_tz":-60,"elapsed":869,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"8140c178-3490-4431-a963-f3a9a6ca700d"},"source":["tensor_data = tf.constant(data)\n","tensor_data = tf.cast(tensor_data, tf.float32)\n","print(tensor_data)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[1.000000e+00 5.356510e+05 7.981860e+05 ... 7.300000e+01 1.230000e+02\n","  3.513180e+05]\n"," [2.000000e+00 5.919110e+05 8.610560e+05 ... 7.400000e+01 9.500000e+01\n","  2.850015e+05]\n"," [3.000000e+00 6.869900e+05 7.707620e+05 ... 5.300000e+01 9.700000e+01\n","  2.153860e+05]\n"," ...\n"," [9.980000e+02 6.463440e+05 8.427330e+05 ... 1.130000e+02 8.900000e+01\n","  4.058710e+05]\n"," [9.990000e+02 5.355590e+05 7.324390e+05 ... 1.120000e+02 1.280000e+02\n","  7.439800e+04]\n"," [1.000000e+03 5.901050e+05 7.797430e+05 ... 9.900000e+01 9.600000e+01\n","  4.149385e+05]], shape=(1000, 12), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"7xxpRZBmXLLM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432306,"user_tz":-60,"elapsed":45,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"4f8000c2-48ab-478b-d589-16d648c8c6f4"},"source":["tensor_data = tf.random.shuffle(tensor_data)\n","print(tensor_data[:5])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[2.790000e+02 6.405200e+05 7.510240e+05 4.000000e+00 1.292510e+05\n","  2.000000e+00 3.000000e+00 1.300000e+01 1.420000e+02 7.700000e+01\n","  1.100000e+02 1.786425e+05]\n"," [6.200000e+02 5.112580e+05 7.948000e+05 6.000000e+00 1.043080e+05\n","  2.000000e+00 9.000000e+00 1.400000e+01 1.770000e+02 9.500000e+01\n","  9.700000e+01 2.617320e+05]\n"," [7.250000e+02 6.785350e+05 7.088160e+05 6.000000e+00 9.693300e+04\n","  3.000000e+00 1.000000e+00 1.100000e+01 1.460000e+02 1.200000e+02\n","  1.040000e+02 3.024230e+05]\n"," [6.560000e+02 6.146710e+05 8.504940e+05 4.000000e+00 5.244300e+04\n","  2.000000e+00 6.000000e+00 1.500000e+01 1.630000e+02 8.800000e+01\n","  1.240000e+02 5.265025e+05]\n"," [6.660000e+02 6.357630e+05 8.223260e+05 3.000000e+00 1.305400e+05\n","  1.000000e+00 1.000000e+00 1.200000e+01 1.590000e+02 9.300000e+01\n","  7.500000e+01 2.069995e+05]], shape=(5, 12), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"Q5kVK2udXLOE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432307,"user_tz":-60,"elapsed":43,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"eb66ced6-9aa9-466f-b54b-16f4d5cc4e75"},"source":["X = tensor_data[:,3:-1]\n","print(X[:5])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[4.00000e+00 1.29251e+05 2.00000e+00 3.00000e+00 1.30000e+01 1.42000e+02\n","  7.70000e+01 1.10000e+02]\n"," [6.00000e+00 1.04308e+05 2.00000e+00 9.00000e+00 1.40000e+01 1.77000e+02\n","  9.50000e+01 9.70000e+01]\n"," [6.00000e+00 9.69330e+04 3.00000e+00 1.00000e+00 1.10000e+01 1.46000e+02\n","  1.20000e+02 1.04000e+02]\n"," [4.00000e+00 5.24430e+04 2.00000e+00 6.00000e+00 1.50000e+01 1.63000e+02\n","  8.80000e+01 1.24000e+02]\n"," [3.00000e+00 1.30540e+05 1.00000e+00 1.00000e+00 1.20000e+01 1.59000e+02\n","  9.30000e+01 7.50000e+01]], shape=(5, 8), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"KOy9jxS-XLQu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432308,"user_tz":-60,"elapsed":40,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"a0f50e86-5ad7-492d-a305-261fc9b6da00"},"source":["y = tensor_data[:,-1]\n","print(y[:5].shape)\n","y = tf.expand_dims(y, axis = -1)\n","print(y[:5])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(5,)\n","tf.Tensor(\n","[[178642.5]\n"," [261732. ]\n"," [302423. ]\n"," [526502.5]\n"," [206999.5]], shape=(5, 1), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"1tGN1KNQXLTl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432308,"user_tz":-60,"elapsed":38,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"73e4a3f9-faa7-4fd2-d7d2-07b86d7eae56"},"source":["normalizer = Normalization(axis = -1, mean = 5, variance = 4)\n","x_normalized = tf.constant([[3,4,5,6,7],\n","                            [4,5,6,7,8]])\n","normalizer(x_normalized)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(2, 5), dtype=float32, numpy=\n","array([[-1. , -0.5,  0. ,  0.5,  1. ],\n","       [-0.5,  0. ,  0.5,  1. ,  1.5]], dtype=float32)>"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"ybWIORDvXLWq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432309,"user_tz":-60,"elapsed":37,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"84fb26f2-2c1a-4f9c-cc31-f9cb510f5279"},"source":["normalizer = Normalization()\n","x_normalized = tf.constant([[3,4,5,6,7],\n","                            [4,10,6,7,8],\n","                            [32,1,56,3,5]])\n","normalizer.adapt(x_normalized)\n","normalizer(x_normalized)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(3, 5), dtype=float32, numpy=\n","array([[-0.7439795 , -0.26726124, -0.72800297,  0.3922322 ,  0.2672614 ],\n","       [-0.6695816 ,  1.3363062 , -0.6860028 ,  0.9805806 ,  1.0690452 ],\n","       [ 1.4135611 , -1.069045  ,  1.4140056 , -1.3728131 , -1.3363061 ]],\n","      dtype=float32)>"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"0JiTE-U_XLZK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432310,"user_tz":-60,"elapsed":35,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"e59fc7b1-3c25-41fe-d903-039dc1f92bfd"},"source":["print(X.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1000, 8)\n"]}]},{"cell_type":"code","metadata":{"id":"NjehtcxAW6av"},"source":["TRAIN_RATIO = 0.8\n","VAL_RATIO = 0.1\n","TEST_RATIO = 0.1\n","DATASET_SIZE = len(X)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"27pin8soXEue","executionInfo":{"status":"ok","timestamp":1635922432312,"user_tz":-60,"elapsed":34,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"3e2fc8f4-4d68-49e0-fbe9-daa41dae7b5d"},"source":["X_train = X[:int(DATASET_SIZE*TRAIN_RATIO)]\n","y_train = y[:int(DATASET_SIZE*TRAIN_RATIO)]\n","print(X_train.shape)\n","print(y_train.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(800, 8)\n","(800, 1)\n"]}]},{"cell_type":"code","metadata":{"id":"-9wd1j166mMG"},"source":["train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n","train_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"woglsImt-fu1","executionInfo":{"status":"ok","timestamp":1635929187855,"user_tz":-60,"elapsed":570,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"43c60638-25a3-4e75-afba-c1372c0bd0ad"},"source":["for x,y in train_dataset:\n","  print(x,y)\n","  break"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[6.00000e+00 9.69330e+04 3.00000e+00 1.00000e+00 1.10000e+01 1.46000e+02\n","  1.20000e+02 1.04000e+02]\n"," [5.00000e+00 7.80250e+04 1.00000e+00 9.00000e+00 1.50000e+01 1.71000e+02\n","  9.40000e+01 1.32000e+02]\n"," [7.00000e+00 1.18659e+05 2.00000e+00 7.00000e+00 1.20000e+01 1.80000e+02\n","  6.60000e+01 1.20000e+02]\n"," [5.00000e+00 1.43526e+05 3.00000e+00 7.00000e+00 8.00000e+00 1.75000e+02\n","  1.10000e+02 1.37000e+02]\n"," [6.00000e+00 1.04308e+05 2.00000e+00 9.00000e+00 1.40000e+01 1.77000e+02\n","  9.50000e+01 9.70000e+01]\n"," [3.00000e+00 1.30540e+05 1.00000e+00 1.00000e+00 1.20000e+01 1.59000e+02\n","  9.30000e+01 7.50000e+01]\n"," [4.00000e+00 5.24430e+04 2.00000e+00 6.00000e+00 1.50000e+01 1.63000e+02\n","  8.80000e+01 1.24000e+02]\n"," [5.00000e+00 1.00534e+05 1.00000e+00 9.00000e+00 1.50000e+01 1.37000e+02\n","  7.20000e+01 1.19000e+02]\n"," [6.00000e+00 8.93540e+04 1.00000e+00 1.00000e+01 1.20000e+01 1.40000e+02\n","  5.60000e+01 1.27000e+02]\n"," [4.00000e+00 1.29251e+05 2.00000e+00 3.00000e+00 1.30000e+01 1.42000e+02\n","  7.70000e+01 1.10000e+02]\n"," [5.00000e+00 1.22976e+05 5.00000e+00 4.00000e+00 9.00000e+00 1.79000e+02\n","  1.05000e+02 7.50000e+01]\n"," [7.00000e+00 1.26075e+05 2.00000e+00 9.00000e+00 1.40000e+01 1.47000e+02\n","  1.02000e+02 1.37000e+02]\n"," [2.00000e+00 1.01069e+05 1.00000e+00 1.00000e+01 1.30000e+01 1.55000e+02\n","  7.70000e+01 1.24000e+02]\n"," [7.00000e+00 1.31174e+05 2.00000e+00 5.00000e+00 1.00000e+01 1.95000e+02\n","  5.30000e+01 1.00000e+02]\n"," [7.00000e+00 1.44949e+05 5.00000e+00 6.00000e+00 1.20000e+01 1.51000e+02\n","  6.80000e+01 1.08000e+02]\n"," [6.00000e+00 9.44370e+04 5.00000e+00 9.00000e+00 1.50000e+01 1.76000e+02\n","  8.40000e+01 1.26000e+02]\n"," [3.00000e+00 1.39295e+05 5.00000e+00 1.00000e+01 1.20000e+01 1.50000e+02\n","  7.90000e+01 1.12000e+02]\n"," [2.00000e+00 1.09453e+05 5.00000e+00 4.00000e+00 1.30000e+01 1.59000e+02\n","  9.90000e+01 1.05000e+02]\n"," [5.00000e+00 1.45711e+05 3.00000e+00 8.00000e+00 9.00000e+00 1.94000e+02\n","  5.80000e+01 9.50000e+01]\n"," [5.00000e+00 1.03827e+05 5.00000e+00 6.00000e+00 1.10000e+01 1.51000e+02\n","  7.20000e+01 1.02000e+02]\n"," [7.00000e+00 8.19410e+04 3.00000e+00 1.00000e+01 1.10000e+01 1.44000e+02\n","  6.00000e+01 1.29000e+02]\n"," [5.00000e+00 1.16435e+05 2.00000e+00 5.00000e+00 1.30000e+01 1.75000e+02\n","  1.04000e+02 1.25000e+02]\n"," [6.00000e+00 5.29460e+04 2.00000e+00 8.00000e+00 1.20000e+01 1.98000e+02\n","  5.30000e+01 8.60000e+01]\n"," [4.00000e+00 1.14468e+05 2.00000e+00 8.00000e+00 1.10000e+01 1.37000e+02\n","  5.50000e+01 1.40000e+02]\n"," [2.00000e+00 8.71990e+04 1.00000e+00 7.00000e+00 8.00000e+00 1.49000e+02\n","  7.80000e+01 1.21000e+02]\n"," [5.00000e+00 8.25800e+04 4.00000e+00 3.00000e+00 1.20000e+01 1.87000e+02\n","  9.80000e+01 1.00000e+02]\n"," [3.00000e+00 1.11289e+05 2.00000e+00 1.00000e+00 1.20000e+01 1.53000e+02\n","  8.00000e+01 9.70000e+01]\n"," [6.00000e+00 1.33023e+05 1.00000e+00 3.00000e+00 8.00000e+00 1.86000e+02\n","  6.00000e+01 9.90000e+01]\n"," [6.00000e+00 5.86040e+04 3.00000e+00 4.00000e+00 1.10000e+01 1.53000e+02\n","  1.17000e+02 1.05000e+02]\n"," [4.00000e+00 9.92110e+04 5.00000e+00 5.00000e+00 1.10000e+01 1.95000e+02\n","  7.40000e+01 1.05000e+02]\n"," [2.00000e+00 1.08202e+05 4.00000e+00 1.00000e+01 9.00000e+00 1.99000e+02\n","  7.50000e+01 1.30000e+02]\n"," [5.00000e+00 5.85480e+04 3.00000e+00 9.00000e+00 1.20000e+01 1.82000e+02\n","  1.08000e+02 8.50000e+01]], shape=(32, 8), dtype=float32) tf.Tensor(\n","[[302423. ]\n"," [410877. ]\n"," [239214. ]\n"," [ 64531. ]\n"," [261732. ]\n"," [206999.5]\n"," [526502.5]\n"," [253853.5]\n"," [408452.5]\n"," [178642.5]\n"," [175012.5]\n"," [209639. ]\n"," [341328. ]\n"," [ 90220. ]\n"," [ 94467.5]\n"," [337327. ]\n"," [243097. ]\n"," [274075. ]\n"," [170960.5]\n"," [284462.5]\n"," [498148. ]\n"," [248738. ]\n"," [539377. ]\n"," [221412.5]\n"," [375097.5]\n"," [299609. ]\n"," [306660. ]\n"," [130112.5]\n"," [400257. ]\n"," [369149. ]\n"," [336038. ]\n"," [502708.5]], shape=(32, 1), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NCRgNb3dXge1","executionInfo":{"status":"ok","timestamp":1635922432313,"user_tz":-60,"elapsed":33,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"32d14640-95c0-4b45-b3e7-3f4c28e3121c"},"source":["X_val = X[int(DATASET_SIZE*TRAIN_RATIO):int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO))]\n","y_val = y[int(DATASET_SIZE*TRAIN_RATIO):int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO))]\n","print(X_val.shape)\n","print(y_val.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(100, 8)\n","(100, 1)\n"]}]},{"cell_type":"code","metadata":{"id":"aBwRpaG0-urM"},"source":["val_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n","val_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A3ba-zUTX2Xv","executionInfo":{"status":"ok","timestamp":1635922432314,"user_tz":-60,"elapsed":31,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"4ee76225-a123-4186-9263-15272bf35a94"},"source":["X_test = X[int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO)):]\n","y_test = y[int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO)):]\n","print(X_test.shape)\n","print(y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(100, 8)\n","(100, 1)\n"]}]},{"cell_type":"code","metadata":{"id":"bd4gJi1n-1mW"},"source":["test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))\n","test_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"c1k0dBjFXLcC","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432315,"user_tz":-60,"elapsed":30,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"c2f0a40b-defd-486e-caf4-9c5b7e7ea338"},"source":["normalizer = Normalization()\n","normalizer.adapt(X_train)\n","normalizer(X)[:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(5, 8), dtype=float32, numpy=\n","array([[-0.31518766,  1.0132637 , -0.71715826, -0.91459674,  0.6136699 ,\n","        -1.301998  , -0.35419205,  0.3123971 ],\n","       [ 0.8309489 ,  0.15622592, -0.71715826,  1.1808989 ,  1.058761  ,\n","         0.5187977 ,  0.5274618 , -0.30444944],\n","       [ 0.8309489 , -0.09717798, -0.0044545 , -1.6130953 , -0.27651256,\n","        -1.0939071 ,  1.751981  ,  0.02769868],\n","       [-0.31518766, -1.6258478 , -0.71715826,  0.13315108,  1.5038522 ,\n","        -0.2095206 ,  0.18459642,  0.97669333],\n","       [-0.88825595,  1.0575535 , -1.429862  , -1.6130953 ,  0.16857865,\n","        -0.41761154,  0.42950028, -1.3483436 ]], dtype=float32)>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"ib01hpAGXB9O"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"eQdxWFy6XLej","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432316,"user_tz":-60,"elapsed":28,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"a94472ee-80d0-4aaa-bfab-0305304ffeb9"},"source":["print(X[:5])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[4.00000e+00 1.29251e+05 2.00000e+00 3.00000e+00 1.30000e+01 1.42000e+02\n","  7.70000e+01 1.10000e+02]\n"," [6.00000e+00 1.04308e+05 2.00000e+00 9.00000e+00 1.40000e+01 1.77000e+02\n","  9.50000e+01 9.70000e+01]\n"," [6.00000e+00 9.69330e+04 3.00000e+00 1.00000e+00 1.10000e+01 1.46000e+02\n","  1.20000e+02 1.04000e+02]\n"," [4.00000e+00 5.24430e+04 2.00000e+00 6.00000e+00 1.50000e+01 1.63000e+02\n","  8.80000e+01 1.24000e+02]\n"," [3.00000e+00 1.30540e+05 1.00000e+00 1.00000e+00 1.20000e+01 1.59000e+02\n","  9.30000e+01 7.50000e+01]], shape=(5, 8), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"S_IP_m7IWh1V"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CxlimoToLc4C"},"source":["## **Model Creation and Training**"]},{"cell_type":"code","metadata":{"id":"-6u-a70uXLhR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635929376570,"user_tz":-60,"elapsed":515,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"493dda59-a47e-447a-97aa-6bf788dd537b"},"source":["model = tf.keras.Sequential([\n","                             InputLayer(input_shape = (8,)),\n","                             normalizer,\n","                             Dense(128, activation = \"relu\"),\n","                             Dense(128, activation = \"relu\"),\n","                             Dense(128, activation = \"relu\"),\n","                             Dense(1),\n","])\n","model.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_7\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","normalization_2 (Normalizati (None, 8)                 17        \n","_________________________________________________________________\n","dense_19 (Dense)             (None, 128)               1152      \n","_________________________________________________________________\n","dense_20 (Dense)             (None, 128)               16512     \n","_________________________________________________________________\n","dense_21 (Dense)             (None, 128)               16512     \n","_________________________________________________________________\n","dense_22 (Dense)             (None, 1)                 129       \n","=================================================================\n","Total params: 34,322\n","Trainable params: 34,305\n","Non-trainable params: 17\n","_________________________________________________________________\n"]}]},{"cell_type":"code","metadata":{"id":"11E2rU0rV0pM"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":644},"id":"P0-MdiccV0r9","executionInfo":{"status":"ok","timestamp":1635929378075,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"c292de82-e496-48c5-f9d7-a87073fced6d"},"source":["tf.keras.utils.plot_model(model, to_file = \"model.png\", show_shapes=True)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"image/png":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{},"execution_count":97}]},{"cell_type":"code","metadata":{"id":"KckNWiWJV0um"},"source":["model.compile(optimizer = Adam(learning_rate = 0.1),\n","              loss = MeanAbsoluteError(),\n","              metrics = RootMeanSquaredError())"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-nQSZ1j0V0xG","executionInfo":{"status":"ok","timestamp":1635929394500,"user_tz":-60,"elapsed":14129,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"bb353c6e-5235-48c5-ce68-8dc17c423a32"},"source":["history = model.fit(train_dataset, validation_data=val_dataset, epochs = 100, verbose = 1)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","18/25 [====================>.........] - ETA: 0s - loss: 183443.8125 - root_mean_squared_error: 230505.0938 WARNING:tensorflow:Model was constructed with shape (None, 8) for input KerasTensor(type_spec=TensorSpec(shape=(None, 8), dtype=tf.float32, name='input_8'), name='input_8', description=\"created by layer 'input_8'\"), but it was called on an input with incompatible shape (None, None, 8).\n","25/25 [==============================] - 1s 12ms/step - loss: 153433.3906 - root_mean_squared_error: 202123.1406 - val_loss: 60263.9297 - val_root_mean_squared_error: 72829.0625\n","Epoch 2/100\n","25/25 [==============================] - 0s 4ms/step - loss: 56026.2656 - root_mean_squared_error: 71011.3516 - val_loss: 45472.1758 - val_root_mean_squared_error: 56745.3438\n","Epoch 3/100\n","25/25 [==============================] - 0s 4ms/step - loss: 53667.3594 - root_mean_squared_error: 69709.6016 - val_loss: 42714.7695 - val_root_mean_squared_error: 53672.2227\n","Epoch 4/100\n","25/25 [==============================] - 0s 4ms/step - loss: 50040.4531 - root_mean_squared_error: 65046.1055 - val_loss: 38227.3203 - val_root_mean_squared_error: 47598.3086\n","Epoch 5/100\n","25/25 [==============================] - 0s 4ms/step - loss: 42923.9805 - root_mean_squared_error: 53611.3828 - val_loss: 40198.9961 - val_root_mean_squared_error: 49557.1953\n","Epoch 6/100\n","25/25 [==============================] - 0s 4ms/step - loss: 43780.4062 - root_mean_squared_error: 54295.3242 - val_loss: 47681.0117 - val_root_mean_squared_error: 58350.3594\n","Epoch 7/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41617.0156 - root_mean_squared_error: 51874.2031 - val_loss: 42206.4141 - val_root_mean_squared_error: 53217.7852\n","Epoch 8/100\n","25/25 [==============================] - 0s 4ms/step - loss: 44758.0469 - root_mean_squared_error: 56010.7852 - val_loss: 48947.8008 - val_root_mean_squared_error: 59752.2383\n","Epoch 9/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41648.5547 - root_mean_squared_error: 51759.3359 - val_loss: 43351.2031 - val_root_mean_squared_error: 54776.7188\n","Epoch 10/100\n","25/25 [==============================] - 0s 17ms/step - loss: 44575.9102 - root_mean_squared_error: 55635.6953 - val_loss: 41305.5859 - val_root_mean_squared_error: 51274.1133\n","Epoch 11/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41709.0391 - root_mean_squared_error: 52156.1992 - val_loss: 45903.7031 - val_root_mean_squared_error: 56568.1367\n","Epoch 12/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41540.7852 - root_mean_squared_error: 51719.0195 - val_loss: 38613.2109 - val_root_mean_squared_error: 47886.9609\n","Epoch 13/100\n","25/25 [==============================] - 0s 5ms/step - loss: 42226.4648 - root_mean_squared_error: 52758.3633 - val_loss: 44199.9609 - val_root_mean_squared_error: 54273.1133\n","Epoch 14/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40767.7461 - root_mean_squared_error: 50688.4805 - val_loss: 38259.7344 - val_root_mean_squared_error: 47667.7383\n","Epoch 15/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41690.9062 - root_mean_squared_error: 51740.9844 - val_loss: 37318.7109 - val_root_mean_squared_error: 46564.6992\n","Epoch 16/100\n","25/25 [==============================] - 0s 4ms/step - loss: 42122.0547 - root_mean_squared_error: 52942.2109 - val_loss: 39383.1211 - val_root_mean_squared_error: 48752.6836\n","Epoch 17/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38069.0742 - root_mean_squared_error: 47936.5898 - val_loss: 36968.2383 - val_root_mean_squared_error: 45938.2070\n","Epoch 18/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40485.8984 - root_mean_squared_error: 50408.5469 - val_loss: 55395.7344 - val_root_mean_squared_error: 66576.9844\n","Epoch 19/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40614.3906 - root_mean_squared_error: 51430.3477 - val_loss: 37169.1289 - val_root_mean_squared_error: 46689.2344\n","Epoch 20/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39690.5312 - root_mean_squared_error: 49889.6250 - val_loss: 37958.2539 - val_root_mean_squared_error: 47514.6016\n","Epoch 21/100\n","25/25 [==============================] - 0s 4ms/step - loss: 45334.5156 - root_mean_squared_error: 57302.3008 - val_loss: 50117.3789 - val_root_mean_squared_error: 61919.8203\n","Epoch 22/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41397.3984 - root_mean_squared_error: 51327.0195 - val_loss: 34763.5586 - val_root_mean_squared_error: 43359.3945\n","Epoch 23/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41823.0039 - root_mean_squared_error: 52517.4922 - val_loss: 42227.3633 - val_root_mean_squared_error: 52194.2656\n","Epoch 24/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37728.0625 - root_mean_squared_error: 47307.5430 - val_loss: 37185.4531 - val_root_mean_squared_error: 47250.6680\n","Epoch 25/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39189.0078 - root_mean_squared_error: 48781.3906 - val_loss: 38032.8555 - val_root_mean_squared_error: 47263.8164\n","Epoch 26/100\n","25/25 [==============================] - 0s 5ms/step - loss: 40774.3516 - root_mean_squared_error: 50963.6680 - val_loss: 43680.4688 - val_root_mean_squared_error: 54177.1719\n","Epoch 27/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38223.0547 - root_mean_squared_error: 47434.3555 - val_loss: 36712.6094 - val_root_mean_squared_error: 45607.6250\n","Epoch 28/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40351.8398 - root_mean_squared_error: 50293.8945 - val_loss: 46028.4414 - val_root_mean_squared_error: 56488.2109\n","Epoch 29/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37378.0430 - root_mean_squared_error: 46608.9688 - val_loss: 34020.2266 - val_root_mean_squared_error: 42543.3086\n","Epoch 30/100\n","25/25 [==============================] - 0s 4ms/step - loss: 42107.9297 - root_mean_squared_error: 52700.3242 - val_loss: 57367.4258 - val_root_mean_squared_error: 69031.1016\n","Epoch 31/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40386.8438 - root_mean_squared_error: 50549.6172 - val_loss: 35117.1367 - val_root_mean_squared_error: 43863.0742\n","Epoch 32/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39437.0469 - root_mean_squared_error: 48874.4219 - val_loss: 36209.7539 - val_root_mean_squared_error: 44859.3945\n","Epoch 33/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37666.4180 - root_mean_squared_error: 47192.3555 - val_loss: 37115.8242 - val_root_mean_squared_error: 46212.6484\n","Epoch 34/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37298.2969 - root_mean_squared_error: 46440.7891 - val_loss: 36064.3945 - val_root_mean_squared_error: 45013.4180\n","Epoch 35/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36239.5352 - root_mean_squared_error: 45847.5859 - val_loss: 35581.3633 - val_root_mean_squared_error: 44543.2969\n","Epoch 36/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37538.1367 - root_mean_squared_error: 47149.9297 - val_loss: 35826.7383 - val_root_mean_squared_error: 44622.6406\n","Epoch 37/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40046.1016 - root_mean_squared_error: 50076.9883 - val_loss: 46954.7734 - val_root_mean_squared_error: 57823.3672\n","Epoch 38/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36624.6758 - root_mean_squared_error: 45689.2070 - val_loss: 33482.0703 - val_root_mean_squared_error: 42136.8164\n","Epoch 39/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38041.0508 - root_mean_squared_error: 47904.8984 - val_loss: 39566.6055 - val_root_mean_squared_error: 49108.8516\n","Epoch 40/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36461.2539 - root_mean_squared_error: 46020.3359 - val_loss: 33832.3203 - val_root_mean_squared_error: 42399.4727\n","Epoch 41/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37436.7539 - root_mean_squared_error: 47116.0820 - val_loss: 33855.7969 - val_root_mean_squared_error: 42320.5430\n","Epoch 42/100\n","25/25 [==============================] - 0s 5ms/step - loss: 42520.7539 - root_mean_squared_error: 52917.1602 - val_loss: 46673.4336 - val_root_mean_squared_error: 57544.7656\n","Epoch 43/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36878.1172 - root_mean_squared_error: 46548.4727 - val_loss: 36253.9336 - val_root_mean_squared_error: 44613.2656\n","Epoch 44/100\n","25/25 [==============================] - 0s 4ms/step - loss: 45127.1016 - root_mean_squared_error: 56809.5039 - val_loss: 40591.6562 - val_root_mean_squared_error: 50129.6289\n","Epoch 45/100\n","25/25 [==============================] - 0s 4ms/step - loss: 43439.3555 - root_mean_squared_error: 54211.9258 - val_loss: 39119.6641 - val_root_mean_squared_error: 48735.2109\n","Epoch 46/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37144.3359 - root_mean_squared_error: 46490.9453 - val_loss: 34264.2969 - val_root_mean_squared_error: 42994.8594\n","Epoch 47/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40094.2578 - root_mean_squared_error: 50919.8008 - val_loss: 43557.8555 - val_root_mean_squared_error: 53852.1406\n","Epoch 48/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39449.2578 - root_mean_squared_error: 49006.6914 - val_loss: 38318.1914 - val_root_mean_squared_error: 47533.4883\n","Epoch 49/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35235.9297 - root_mean_squared_error: 43911.5000 - val_loss: 33872.1836 - val_root_mean_squared_error: 42097.6250\n","Epoch 50/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36223.1836 - root_mean_squared_error: 45369.5078 - val_loss: 37482.6289 - val_root_mean_squared_error: 46426.5703\n","Epoch 51/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38728.5859 - root_mean_squared_error: 48386.0430 - val_loss: 42836.5352 - val_root_mean_squared_error: 52722.1250\n","Epoch 52/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38372.0859 - root_mean_squared_error: 47950.6055 - val_loss: 36639.0273 - val_root_mean_squared_error: 45573.0781\n","Epoch 53/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36378.2031 - root_mean_squared_error: 45250.8086 - val_loss: 34652.2891 - val_root_mean_squared_error: 43365.8438\n","Epoch 54/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35747.6836 - root_mean_squared_error: 44717.0000 - val_loss: 34828.4219 - val_root_mean_squared_error: 43621.8594\n","Epoch 55/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35912.6797 - root_mean_squared_error: 44972.3945 - val_loss: 34019.6641 - val_root_mean_squared_error: 42595.9648\n","Epoch 56/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38367.9766 - root_mean_squared_error: 48917.6797 - val_loss: 37332.0430 - val_root_mean_squared_error: 46409.3984\n","Epoch 57/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36671.1445 - root_mean_squared_error: 45700.1523 - val_loss: 34065.7266 - val_root_mean_squared_error: 42134.2656\n","Epoch 58/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35391.8828 - root_mean_squared_error: 44498.2109 - val_loss: 35059.3750 - val_root_mean_squared_error: 43381.7070\n","Epoch 59/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37295.8945 - root_mean_squared_error: 47479.5195 - val_loss: 34208.7305 - val_root_mean_squared_error: 42558.4922\n","Epoch 60/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38737.8828 - root_mean_squared_error: 49007.2852 - val_loss: 37675.8594 - val_root_mean_squared_error: 46528.0000\n","Epoch 61/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37275.9922 - root_mean_squared_error: 46766.8633 - val_loss: 36297.2383 - val_root_mean_squared_error: 44886.0352\n","Epoch 62/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36066.3711 - root_mean_squared_error: 44733.1875 - val_loss: 33840.5859 - val_root_mean_squared_error: 42432.3789\n","Epoch 63/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36461.5859 - root_mean_squared_error: 45768.5156 - val_loss: 35501.4219 - val_root_mean_squared_error: 44325.0234\n","Epoch 64/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35266.5508 - root_mean_squared_error: 44279.4492 - val_loss: 33292.7148 - val_root_mean_squared_error: 41521.0352\n","Epoch 65/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35328.8984 - root_mean_squared_error: 44795.5586 - val_loss: 32958.8477 - val_root_mean_squared_error: 41033.3633\n","Epoch 66/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35405.6641 - root_mean_squared_error: 44796.9297 - val_loss: 33378.3203 - val_root_mean_squared_error: 41350.6719\n","Epoch 67/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35030.9414 - root_mean_squared_error: 43748.3984 - val_loss: 32991.1992 - val_root_mean_squared_error: 41132.3242\n","Epoch 68/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34800.5664 - root_mean_squared_error: 43631.7188 - val_loss: 34250.6211 - val_root_mean_squared_error: 42634.0039\n","Epoch 69/100\n","25/25 [==============================] - 0s 4ms/step - loss: 33978.9844 - root_mean_squared_error: 42666.3711 - val_loss: 32581.5645 - val_root_mean_squared_error: 40717.6484\n","Epoch 70/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38386.7734 - root_mean_squared_error: 49061.3320 - val_loss: 32995.2812 - val_root_mean_squared_error: 41220.0156\n","Epoch 71/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35412.3398 - root_mean_squared_error: 44507.3164 - val_loss: 34876.0469 - val_root_mean_squared_error: 43161.8320\n","Epoch 72/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34612.6992 - root_mean_squared_error: 43434.4922 - val_loss: 35430.1680 - val_root_mean_squared_error: 43927.1484\n","Epoch 73/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35458.6133 - root_mean_squared_error: 44698.3477 - val_loss: 32574.8203 - val_root_mean_squared_error: 40858.1367\n","Epoch 74/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36318.4258 - root_mean_squared_error: 45952.9766 - val_loss: 40543.6211 - val_root_mean_squared_error: 50238.3828\n","Epoch 75/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35710.8945 - root_mean_squared_error: 44680.3711 - val_loss: 34661.6016 - val_root_mean_squared_error: 43508.1445\n","Epoch 76/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35520.9844 - root_mean_squared_error: 44945.1016 - val_loss: 34319.5703 - val_root_mean_squared_error: 42934.1836\n","Epoch 77/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35935.3281 - root_mean_squared_error: 45561.6992 - val_loss: 32581.1348 - val_root_mean_squared_error: 40705.1406\n","Epoch 78/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36242.0898 - root_mean_squared_error: 45991.8516 - val_loss: 33404.2344 - val_root_mean_squared_error: 41946.7461\n","Epoch 79/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34642.0625 - root_mean_squared_error: 43895.9062 - val_loss: 32456.9004 - val_root_mean_squared_error: 40770.3359\n","Epoch 80/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35546.0195 - root_mean_squared_error: 45262.3906 - val_loss: 35322.0156 - val_root_mean_squared_error: 44069.5039\n","Epoch 81/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35978.3945 - root_mean_squared_error: 45280.6211 - val_loss: 35185.4648 - val_root_mean_squared_error: 44014.6172\n","Epoch 82/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34663.4219 - root_mean_squared_error: 44022.8203 - val_loss: 32813.8477 - val_root_mean_squared_error: 41324.1719\n","Epoch 83/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36449.5664 - root_mean_squared_error: 46169.6953 - val_loss: 34012.1367 - val_root_mean_squared_error: 42522.8906\n","Epoch 84/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34057.7734 - root_mean_squared_error: 43135.2695 - val_loss: 33329.9844 - val_root_mean_squared_error: 41505.4531\n","Epoch 85/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36582.1641 - root_mean_squared_error: 46742.3906 - val_loss: 34058.7930 - val_root_mean_squared_error: 42399.1016\n","Epoch 86/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36244.8281 - root_mean_squared_error: 46020.2578 - val_loss: 32478.8809 - val_root_mean_squared_error: 40554.2695\n","Epoch 87/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35911.9102 - root_mean_squared_error: 45986.1953 - val_loss: 33365.6289 - val_root_mean_squared_error: 41517.8516\n","Epoch 88/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39517.6562 - root_mean_squared_error: 49955.6602 - val_loss: 49954.6406 - val_root_mean_squared_error: 61718.5781\n","Epoch 89/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40796.4688 - root_mean_squared_error: 51235.3359 - val_loss: 40422.7969 - val_root_mean_squared_error: 50132.1914\n","Epoch 90/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40360.6641 - root_mean_squared_error: 50773.8359 - val_loss: 38809.0117 - val_root_mean_squared_error: 48129.4922\n","Epoch 91/100\n","25/25 [==============================] - 0s 5ms/step - loss: 36151.8906 - root_mean_squared_error: 45569.1367 - val_loss: 36910.4023 - val_root_mean_squared_error: 46204.5117\n","Epoch 92/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35665.6602 - root_mean_squared_error: 45271.9883 - val_loss: 34275.9141 - val_root_mean_squared_error: 42862.3633\n","Epoch 93/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34847.2461 - root_mean_squared_error: 44639.5156 - val_loss: 32523.6406 - val_root_mean_squared_error: 41008.9844\n","Epoch 94/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34289.5000 - root_mean_squared_error: 43564.6797 - val_loss: 33046.0508 - val_root_mean_squared_error: 41598.3047\n","Epoch 95/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34079.6094 - root_mean_squared_error: 43665.2969 - val_loss: 33661.4844 - val_root_mean_squared_error: 42329.4414\n","Epoch 96/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34789.6133 - root_mean_squared_error: 44418.9375 - val_loss: 33264.1484 - val_root_mean_squared_error: 41816.4141\n","Epoch 97/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35060.4062 - root_mean_squared_error: 44998.0234 - val_loss: 34415.6406 - val_root_mean_squared_error: 43326.4883\n","Epoch 98/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35818.2812 - root_mean_squared_error: 45832.2617 - val_loss: 33805.2031 - val_root_mean_squared_error: 42436.7656\n","Epoch 99/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37061.6758 - root_mean_squared_error: 47643.8867 - val_loss: 34193.2461 - val_root_mean_squared_error: 42820.9141\n","Epoch 100/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36433.3633 - root_mean_squared_error: 46644.2148 - val_loss: 37564.8867 - val_root_mean_squared_error: 47129.3828\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"rclKXms6V0zg","executionInfo":{"status":"ok","timestamp":1635929419718,"user_tz":-60,"elapsed":640,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"753fd3f9-6bc6-487e-84ed-54f123e0f75f"},"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val_loss'])\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZcAAAEWCAYAAACqitpwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3xUVfr48c8zJZUUCC0kQOhdaVJEXRWVYsGOHV1Wdi2ra1tZ3f3p11133dVdXXtFsYHYcS2ICIL03ktCSQg1kE5InfP749xJJiFAQiYE5Hm/XnnNzLll7g1hnnnOee65YoxBKaWUCiZXQx+AUkqpXx4NLkoppYJOg4tSSqmg0+CilFIq6DS4KKWUCjoNLkoppYJOg4tSDUxE3hGRv9Vw3W0ickFd96NUfdPgopRSKug0uCillAo6DS5K1YDTHfWQiKwSkQMi8paItBCRb0UkT0R+EJHGAetfJiJrRSRbRGaJSLeAZX1EZJmz3UdAWJX3ukREVjjbzhOR047xmG8XkRQRyRSRqSLSymkXEXlWRPaKSK6IrBaRns6ykSKyzjm2HSLy4DH9wtQpT4OLUjV3FXAh0Bm4FPgWeARohv2/dA+AiHQGJgF/cJZ9A3wlIiEiEgJ8AbwHNAE+dvaLs20fYALwWyAOeA2YKiKhtTlQETkf+AdwLRAPpAKTncUXAec45xHjrLPfWfYW8FtjTBTQE/ixNu+rlJ8GF6Vq7gVjzB5jzA5gDrDQGLPcGFMIfA70cdYbDXxtjJlujCkBngHCgTOBQYAXeM4YU2KM+QRYHPAe44DXjDELjTFlxpiJQJGzXW3cCEwwxiwzxhQBfwIGi0gSUAJEAV0BMcasN8bscrYrAbqLSLQxJssYs6yW76sUoMFFqdrYE/D8YDWvGznPW2EzBQCMMT5gO5DgLNthKs8YmxrwvC3wgNMlli0i2UBrZ7vaqHoM+djsJMEY8yPwIvASsFdEXheRaGfVq4CRQKqI/CQig2v5vkoBGlyUqg87sUECsGMc2ACxA9gFJDhtfm0Cnm8HnjTGxAb8RBhjJtXxGCKx3Ww7AIwxzxtj+gHdsd1jDznti40xo4Dm2O67KbV8X6UADS5K1YcpwMUiMlREvMAD2K6tecB8oBS4R0S8InIlMCBg2zeA34nIQGfgPVJELhaRqFoewyTgNhHp7YzX/B3bjbdNRM5w9u8FDgCFgM8ZE7pRRGKc7rxcwFeH34M6hWlwUSrIjDEbgZuAF4B92MH/S40xxcaYYuBK4FYgEzs+81nAtkuA27HdVllAirNubY/hB+AvwKfYbKkDcJ2zOBobxLKwXWf7gaedZTcD20QkF/gdduxGqVoTvVmYUkqpYNPMRSmlVNBpcFFKKRV0GlyUUkoFnQYXpZRSQedp6AM4UTRt2tQkJSU19GEopdRJZenSpfuMMc2qtmtwcSQlJbFkyZKGPgyllDqpiEhqde3aLaaUUiroNLgopZQKOg0uSimlgk7HXJRSp7SSkhLS09MpLCxs6EM5oYWFhZGYmIjX663R+hpclFKntPT0dKKiokhKSqLyZNXKzxjD/v37SU9Pp127djXaRrvFlFKntMLCQuLi4jSwHIGIEBcXV6vsToOLUuqUp4Hl6Gr7O9LgUkcz1u/hlVmbG/owlFLqhKLBpY5mbczg9dkaXJRSxyY7O5uXX3651tuNHDmS7Ozsejii4NDgUkcet1BapvfEUUodm8MFl9LS0iNu98033xAbG1tfh1VnWi1WR163ixKf3glWKXVsxo8fz+bNm+nduzder5ewsDAaN27Mhg0b2LRpE5dffjnbt2+nsLCQe++9l3HjxgEVU1bl5+czYsQIzjrrLObNm0dCQgJffvkl4eHhDXpeGlzqyOPSzEWpX4r/+2ot63bmBnWf3VtF89ilPQ67/KmnnmLNmjWsWLGCWbNmcfHFF7NmzZrykt8JEybQpEkTDh48yBlnnMFVV11FXFxcpX0kJyczadIk3njjDa699lo+/fRTbrrppqCeR21pcKkjj9tFqc9gjNGKE6VUnQ0YMKDStSTPP/88n3/+OQDbt28nOTn5kODSrl07evfuDUC/fv3Ytm3bcTvew9HgUkdelw0opT6D163BRamT2ZEyjOMlMjKy/PmsWbP44YcfmD9/PhEREZx77rnVXmsSGhpa/tztdnPw4MHjcqxHogP6deRx21+hdo0ppY5FVFQUeXl51S7LycmhcePGREREsGHDBhYsWHCcj+7YaeZSR/5spcTnIxx3Ax+NUupkExcXx5AhQ+jZsyfh4eG0aNGifNnw4cN59dVX6datG126dGHQoEENeKS1o8Gljjz+bjHNXJRSx+jDDz+stj00NJRvv/222mX+cZWmTZuyZs2a8vYHH3ww6Md3LLRbrI7c5d1iWo6slFJ+GlzqyD+gX+LTzEUppfw0uNSRf0C/TLvFlFKqnAaXOgoc0FdKKWVpcKkjj0tLkZVSqioNLnXk8WcuOqCvlFLlNLjUkb9brFQH9JVSqpwGlzqq6BbTzEUpVf8aNWp02GXbtm2jZ8+ex/FoDk+DSx1VdItp5qKUUn56hX4def0XUWq1mFInv2/Hw+7Vwd1ny14w4qnDLh4/fjytW7fmrrvuAuDxxx/H4/Ewc+ZMsrKyKCkp4W9/+xujRo2q1dsWFhZyxx13sGTJEjweD//5z38477zzWLt2LbfddhvFxcX4fD4+/fRTWrVqxbXXXkt6ejplZWX85S9/YfTo0XU67XrLXERkgojsFZE11Sx7QESMiDR1XouIPC8iKSKySkT6Bqw7RkSSnZ8xAe39RGS1s83z4sx3LyJNRGS6s/50EWlcX+cIOv2LUqpuRo8ezZQpU8pfT5kyhTFjxvD555+zbNkyZs6cyQMPPIAxtfuMeemllxARVq9ezaRJkxgzZgyFhYW8+uqr3HvvvaxYsYIlS5aQmJjId999R6tWrVi5ciVr1qxh+PDhdT6v+sxc3gFeBN4NbBSR1sBFQFpA8wigk/MzEHgFGCgiTYDHgP6AAZaKyFRjTJazzu3AQuAbYDjwLTAemGGMeUpExjuvH66ncyzPXLRaTKlfgCNkGPWlT58+7N27l507d5KRkUHjxo1p2bIl9913H7Nnz8blcrFjxw727NlDy5Yta7zfn3/+md///vcAdO3albZt27Jp0yYGDx7Mk08+SXp6OldeeSWdOnWiV69ePPDAAzz88MNccsklnH322XU+r3rLXIwxs4HMahY9C/wRGyz8RgHvGmsBECsi8cAwYLoxJtMJKNOB4c6yaGPMAmPD+bvA5QH7mug8nxjQXi88Wi2mlKqja665hk8++YSPPvqI0aNH88EHH5CRkcHSpUtZsWIFLVq0qPY+LsfihhtuYOrUqYSHhzNy5Eh+/PFHOnfuzLJly+jVqxd//vOfeeKJJ+r8Psd1zEVERgE7jDErq9y1MQHYHvA63Wk7Unt6Ne0ALYwxu5znu4EWHIaIjAPGAbRp06a2pwNUVItp5qKUOlajR4/m9ttvZ9++ffz0009MmTKF5s2b4/V6mTlzJqmpqbXe59lnn80HH3zA+eefz6ZNm0hLS6NLly5s2bKF9u3bc88995CWlsaqVavo2rUrTZo04aabbiI2NpY333yzzud03IKLiEQAj2C7xI4LY4wRkcOmFMaY14HXAfr3739MqUf5dS465qKUOkY9evQgLy+PhIQE4uPjufHGG7n00kvp1asX/fv3p2vXrrXe55133skdd9xBr1698Hg8vPPOO4SGhjJlyhTee+89vF4vLVu25JFHHmHx4sU89NBDuFwuvF4vr7zySp3P6XhmLh2AdoA/a0kElonIAGAH0Dpg3USnbQdwbpX2WU57YjXrA+wRkXhjzC6n+2xv0M8kgEerxZRSQbB6dUWVWtOmTZk/f3616+Xn5x92H0lJSeX3dgkLC+Ptt98+ZJ3x48czfvz4Sm3Dhg1j2LBhx3LYh3XcrnMxxqw2xjQ3xiQZY5KwXVl9jTG7ganALU7V2CAgx+namgZcJCKNnaqvi4BpzrJcERnkVIndAnzpvNVUwF9VNiagvV6UT7mvmYtSSpWrt8xFRCZhs46mIpIOPGaMeeswq38DjARSgALgNgBjTKaI/BVY7Kz3hDHGXyRwJ7YiLRxbJea/XdtTwBQRGQukAtcG8bQO4dGbhSmljrPVq1dz8803V2oLDQ1l4cKFDXREh6q34GKMuf4oy5MCnhvgrsOsNwGYUE37EuCQeQ6MMfuBobU83GPmdmm1mFInO2MMVYqMTmi9evVixYoVx/U9a3udjU7/Ukdenf5FqZNaWFgY+/fvr/WH56nEGMP+/fsJCwur8TY6/Usd6cSVSp3cEhMTSU9PJyMjo6EP5YQWFhZGYmLi0Vd0aHCpI51yX6mTm9frpV27dg19GL842i1WRyKC2yVaiqyUUgE0uASBxyV6EaVSSgXQ4BIEXrdLB/SVUiqABpcg8Li1W0wppQJpcAkCj0szF6WUCqTBJQi8btFSZKWUCqDBJQhst5hmLkop5afBJQi8Lpfez0UppQJocAkCj1tLkZVSKpAGlyDwuFxaLaaUUgE0uASB1y1aLaaUUgE0uASBx62Zi1JKBdLgEgQel2YuSikVSINLEHjdLr3ORSmlAmhwCQK9zkUppSrT4BIE2i2mlFKVaXAJAo9Lu8WUUiqQBpcg0G4xpZSqTINLEHi1FFkppSrR4BIEeidKpZSqTINLEHj0TpRKKVVJvQUXEZkgIntFZE1A29MiskFEVonI5yISG7DsTyKSIiIbRWRYQPtwpy1FRMYHtLcTkYVO+0ciEuK0hzqvU5zlSfV1jn5evROlUkpVUp+ZyzvA8Cpt04GexpjTgE3AnwBEpDtwHdDD2eZlEXGLiBt4CRgBdAeud9YF+CfwrDGmI5AFjHXaxwJZTvuzznr1ylaLaeailFJ+9RZcjDGzgcwqbd8bY0qdlwuAROf5KGCyMabIGLMVSAEGOD8pxpgtxphiYDIwSkQEOB/4xNl+InB5wL4mOs8/AYY669cbO3GlZi5KKeXXkGMuvwa+dZ4nANsDlqU7bYdrjwOyAwKVv73SvpzlOc76hxCRcSKyRESWZGRkHPOJaCmyUkpV1iDBRUQeBUqBDxri/f2MMa8bY/obY/o3a9bsmPfjcbko8xmM0QCjlFIAnuP9hiJyK3AJMNRUfBrvAFoHrJbotHGY9v1ArIh4nOwkcH3/vtJFxAPEOOvXG6/b9rqVlBlCPPXaA6eUUieF45q5iMhw4I/AZcaYgoBFU4HrnEqvdkAnYBGwGOjkVIaFYAf9pzpBaSZwtbP9GODLgH2NcZ5fDfxo6jml8Ljtr1ErxpRSyqq3zEVEJgHnAk1FJB14DFsdFgpMd8bYFxhjfmeMWSsiU4B12O6yu4wxZc5+7gamAW5ggjFmrfMWDwOTReRvwHLgLaf9LeA9EUnBFhRcV1/n6OdxVWQuSiml6jG4GGOur6b5rWra/Os/CTxZTfs3wDfVtG/BVpNVbS8ErqnVwdaR15+5aMWYUkoBeoV+UHicMRetGFNKKUuDSxB4XfbXqNe6KKWUpcElCMozFx1zUUopQINLUGi1mFJKVabBJQi0WkwppSrT4BIE/uCi3WJKKWVpcAkCfylyiXaLKaUUoMElKPwD+mVaiqyUUoAGl6DwaCmyUkpVosElCLxaiqyUUpVocAkCLUVWSqnKNLgEgZYiK6VUZRpcgqBi4koNLkopBRpcgqJi4krtFlNKKdDgEhQVE1dq5qKUUqDBJSgqJq7UzEUppUCDS1D4g0uJXkSplFKABpeg8HeLaeailFKWBpcg0Pu5KKVUZRpcgkAnrlRKqco0uASBTrmvlFKVaXAJArdLq8WUUiqQBpcgEBE8LtFqMaWUctRbcBGRCSKyV0TWBLQ1EZHpIpLsPDZ22kVEnheRFBFZJSJ9A7YZ46yfLCJjAtr7ichqZ5vnRUSO9B71zeMWzVyUUspRn5nLO8DwKm3jgRnGmE7ADOc1wAigk/MzDngFbKAAHgMGAgOAxwKCxSvA7QHbDT/Ke9Qrr8ulV+grpZSj3oKLMWY2kFmleRQw0Xk+Ebg8oP1dYy0AYkUkHhgGTDfGZBpjsoDpwHBnWbQxZoExxgDvVtlXde9Rrzxu0bnFlFLKcbzHXFoYY3Y5z3cDLZznCcD2gPXSnbYjtadX036k96hXHrdLq8WUUsrRYAP6TsZRr5/GR3sPERknIktEZElGRkad3svrEkp1QF8ppYDjH1z2OF1aOI97nfYdQOuA9RKdtiO1J1bTfqT3OIQx5nVjTH9jTP9mzZod80mBP3PRbjGllILjH1ymAv6KrzHAlwHttzhVY4OAHKdraxpwkYg0dgbyLwKmOctyRWSQUyV2S5V9Vfce9crj1lJkpZTy89TXjkVkEnAu0FRE0rFVX08BU0RkLJAKXOus/g0wEkgBCoDbAIwxmSLyV2Cxs94Txhh/kcCd2Iq0cOBb54cjvEe98ro0c1FKKb96Cy7GmOsPs2hoNesa4K7D7GcCMKGa9iVAz2ra91f3HvXNXueimYtSSkENu8VE5F4RiXa6rd4SkWUiclF9H9zJxON2abeYUko5ajrm8mtjTC52zKMxcDO2+0k5vC69Ql8ppfxqGlzEeRwJvGeMWRvQptBuMaWUClTT4LJURL7HBpdpIhIF6Nf0AF63S+/nopRSjpoO6I8FegNbjDEFzpxft9XfYZ18PC7NXJRSyq+mmctgYKMxJltEbgL+DOTU32GdfDxuFyU65qKUUkDNg8srQIGInA48AGzGThapHF63Tv+ilFJ+NQ0upc61KKOAF40xLwFR9XdYJx+PXkSplFLlajrmkicif8KWIJ8tIi7AW3+HdfLxuEXv56KUUo6aZi6jgSLs9S67sRNFPl1vR3US8rj0fi5KKeVXo+DiBJQPgBgRuQQoNMbomEsAvZ+LUkpVqOn0L9cCi4BrsBNBLhSRq+vzwE42XpdotZhSSjlqOubyKHCGMWYvgIg0A34APqmvAzvZeNwurRZTSilHTcdcXP7A4thfi21PCTr9i1JKVahp5vKdiEwDJjmvR2PvwaIcXpdLB/SVUspRo+BijHlIRK4ChjhNrxtjPq+/wzr5eNyCz4DPZ3C5dE5PpdSprcY3CzPGfAp8Wo/HclLzum0vYYnPR6jL3cBHo5RSDeuIwUVE8oDqBhIEewPJ6Ho5qpOQx8lWSssMofV2f0+llDo5HPFj0BijU7zUkMfJXHRQXymltOIraLxum7noPV2UUkqDS9B4XJq5KKWUnwaXIPH4Mxe9Sl8ppTS4BIu/W0yv0ldKKQ0uQVPRLaaZi1JKNUhwEZH7RGStiKwRkUkiEiYi7URkoYikiMhHIhLirBvqvE5xlicF7OdPTvtGERkW0D7caUsRkfHH45zKB/R1zEUppY5/cBGRBOAeoL8xpifgBq4D/gk8a4zpCGQBY51NxgJZTvuzznqISHdnux7AcOBlEXGLiBt4CRgBdAeud9atV+WZi1aLKaVUg3WLeYBwEfEAEcAu4HwqZlmeCFzuPB/lvMZZPlRExGmfbIwpMsZsBVKAAc5PijFmizGmGJjsrFu/J6SZi1JKlTvuwcUYswN4BkjDBpUcYCmQbYwpdVZLBxKc5wnAdmfbUmf9uMD2Ktscrv0QIjJORJaIyJKMjIw6nZfXrWMuSinl1xDdYo2xmUQ7oBUQie3WOu6MMa8bY/obY/o3a9asTvtyu7RaTCml/BqiW+wCYKsxJsMYUwJ8hp1tOdbpJgNIBHY4z3cArQGc5THY+8mUt1fZ5nDt9cqr17kopVS5hgguacAgEYlwxk6GAuuAmYD/1sljgC+d51Od1zjLfzTGGKf9OqearB3QCXsr5sVAJ6f6LAQ76D+1vk9Kr9BXSqkKx33+XmPMQhH5BFgGlALLgdeBr4HJIvI3p+0tZ5O3gPdEJAXIxAYLjDFrRWQKNjCVAncZY8oARORuYBq2Em2CMWZtfZ+Xp/wiSs1clFKqQSaHN8Y8BjxWpXkLttKr6rqFwDWH2c+TwJPVtH/Dcb5TZvn9XDRzUUopvUI/WPz3cynTAX2llNLgEiwVmYt2iymllAaXIPHoxJVKKVVOg0uQ6MSVSilVQYNLkOjElUopVUGDS5B43DpxpVJK+WlwCRJ/tZhmLkoppcElaComrtTgopRSGlyCxO0SRLRbTCmlQINLUHldLu0WU0opNLgElcctWoqslFJocAkqj0v0IkqllEKDS1B53S6d/kUppdDgElS2W0wzF6WU0uASRB6XixKtFlNKKQ0uwaSZi1JKWRpcgsgO6GvmopRSGlyCyA7oa+ailFIaXIJIr3NRSilLg0sQeVwuvc5FKaXQ4BJUXh3QV0opQINLUNnMRbvFlFJKg0sQedyiA/pKKYUGl6DyujVzUUopaKDgIiKxIvKJiGwQkfUiMlhEmojIdBFJdh4bO+uKiDwvIikiskpE+gbsZ4yzfrKIjAlo7yciq51tnhcROR7n5XHpmItSSkHDZS7/Bb4zxnQFTgfWA+OBGcaYTsAM5zXACKCT8zMOeAVARJoAjwEDgQHAY/6A5Kxze8B2w+vtTPZvhi2zAJ24Uiml/I57cBGRGOAc4C0AY0yxMSYbGAVMdFabCFzuPB8FvGusBUCsiMQDw4DpxphMY0wWMB0Y7iyLNsYsMMYY4N2AfQXf/Bfh41sB5zoXLUVWSqkGyVzaARnA2yKyXETeFJFIoIUxZpezzm6ghfM8AdgesH2603ak9vRq2g8hIuNEZImILMnIyDi2s4ltCwezoDDXVotpt5hSSjVIcPEAfYFXjDF9gANUdIEB4GQc9f4pbYx53RjT3xjTv1mzZse2k9g29jE7Da9btFtMKaVomOCSDqQbYxY6rz/BBps9TpcWzuNeZ/kOoHXA9olO25HaE6tprx+N29rH7FTtFlNKKcdxDy7GmN3AdhHp4jQNBdYBUwF/xdcY4Evn+VTgFqdqbBCQ43SfTQMuEpHGzkD+RcA0Z1muiAxyqsRuCdhX8MX6g0uavZ+LZi7Bd2AfrPiwoY9CKVULngZ6398DH4hICLAFuA0b6KaIyFggFbjWWfcbYCSQAhQ462KMyRSRvwKLnfWeMMZkOs/vBN4BwoFvnZ/6EREH3kjIStXpX+rLysnw/aPQ4XyIatnQR6OUqoEGCS7GmBVA/2oWDa1mXQPcdZj9TAAmVNO+BOhZx8OsGRHbNZadhjtWL6KsFwecHtL8vRpclDpJ6BX6wRDbBrJTiYsMoaTMMHPD3qNvc6Ly+WDXyoY+isoK9tvHA8dY0aeUOu40uARDrM1cbhrYhu7x0dwzeTnb9h1o6KM6NinT4bVzIGNjQx9JhQNOcPEHGaXUCU+DSzDEtoGiXMLLcnnt5n64XcK495ZwoKi0oY+s9jK3Vn48ERTss4+auSh10tDgEgwB5citm0Tw4vV9Sdmbz0OfrMQOGZ1E8nY6j7uOvN7xVN4ttq9hj0MpVWMaXIIh4EJKgLM6NeXBYV34ZvVuZm08yb5t5+12Hk+g4HJAx1yUOtlocAkG/7UuWanlTb85qz2tm4TzzPcb8Z1MF1b6g8qJElzKSqAoxz7XzEWpk4YGl2AIj4WwmPLMBSDE4+LeoZ1ZuzOXaWt3N+DB1VJ55nKCHHPgIH6BBhelThYaXILFKUcOdEWfBDo0i+Tf0zdRdrJkL/6gknuCZC7+bMUTpt1iSp1ENLgES2zbSt1iAG6XcP+FXUjZm8+XK+pverOgKcqHolz7/ETpFvNnLk07abeYUicRDS7B4lzrQpXqsBE9W9I9Pprnfkg+8ecd82ctTdrbLqjSourX85XB938+PuXK/q6wZt2gOB9KDtb/eypVX5KnnzJ/wxpcgqVxWyg9eEjXjcsl3HVeR9IyC1iyLauBDq6G/NlKK+dO0vl7ql8vYwPMewHW1d98oOX8lWLNnHlONXtRJ6usVPjgalg68ejr/gJocAmWgNmRqzq7c1PcLmFuSg0/GMtK4LPfwo6lQTzAGvBnLglOcDncuMu+ZPtYzbkGXXm3WGf7qOMu6mTlH5Pdubxhj+M40eASLP5rXbK2HbIoOszL6YkxzN1cw+CyfRGsmgzzXgze8dVE1czlcOMu/uCSs7365cFUsA/CG1dMWKlTwKiTVY4z7rprRcMex3GiwSVYyi+kTK128ZCOTVm5PZvcwpKKxsJcmHoP5FXpfto8wz5u+g6KC+rhYA8jb7e9fYA/SzhcOfL+45i5HNhnb2sQ2dR5rZmLOknlOndf37cJik/SuQdrQYNLsIQ2sh+Ch/nAHdKxKT4DCzYHfPPeNA2WTbQ/gVJ+gLBYKCmA5O/r8aCryNtlM4SIJuAOqZgKpqp9m+xj9vZDChiCrmA/RDSFSOc21MEcc9k0DTI2BW9/Sh2JP3MxPti9pmGP5TjQ4BJM1ZQj+/VpE0u41828wOCybY59XP1JxYd0foad8n7QnfYDde3n9XzQAfJ2Q1S8vUdNVMvqMxdjbLeYOwRKDsDBei5SKNhvs5aQRuAODV7m4vPBx7fB7KePbftFb8AL/eo/uKpfjtwdENncPj8FusY0uASTc9Ow6oR63Axo14SfAwf1t82xH5j7NsLedbZt84/2sdOF0H2U/XZ9uBT6h/+D5e8H7/jzdlaMbUTFQ241mUveblsS3PZM+/ow3YDHZMErMGFE5baC/TaTErHBNliZS3aqDY6ZW45t+9S5sD/l+HQNql+GnB2Q0M/+HZ9o90yqBxpcgqlxkv2wyUmvdvGQjnGk7M1nd06h/UPL3AKD7wRxw5pP7UqbZ9jutfje0OMKW968adqhO8vcAj//J3iD/sY4mUtAcKkuc3G6xN7L6GhfZwdxUH/D15A2DwpzKo7J3y0GEBkXvClg9q63j8caXPan2Mc9ays15xaW8P6CVApLyupwcOoXKTcdYhLs/+2dmrmo2uh7C7i98OVd1XaXDOloPyTnpuyDbT/bxh5XQPtf2eDi80HKDIransuVr87nu7wkaNSiUtfYu/O3sTQ1E5a8bRsy1tvb/9ZVYTaUFkJ0K/v6cMHFGcx/f58NLiZYmUvgHTD3pVQck6+0YjA/slnwusUynOByMBMOZtduW2NgvxOU9rHu3e4AACAASURBVFTuO39/QSp//mINYyYsIudgSTUbq1NSUb790hSdAPGn22vFfuEXU2pwCaYm7eGiv8KWWbD4zUMWd2sZTZPIEFuSvG0OJiyWufnxlHW/0pYwL3sHCvbxZX43lqVl8+gX6ynqfKkd1C/K5/u1u/l/X67l9+/Ox7f8fYhzsoetsyu/0dcPwqTra3fs/kDiz1yi46E4D4ryKq+3L5lCCWOjaU2eCWdXanLt3gfsf6qqXX2ZWyqmnvEXDBRk2seIOPsYzG6xvRsqnmfVcqaBvN22Sw0OCS5Lt2URE+5lWVoWo1+bb7NUpXKdwfyY1tCqN5iyQ7LeXxoNLsHWfyx0OB+m/z/Yv7nSIpdLOLNDHD8n7yN7/Ux+KurEjW8t5omU9hiXF6Y/DsDTKQlc0K05WQXFvJfbB0oLObjqC/7y5RraxkUwuGguroOZMOKfEBpTObgUF8CKD2DjN7WbfNJ/TUtUfOXHKtlL6d5NpPjiuX5AW/ZIM3amHkO11ZRb4MPRldsCLyzzBxd/IPF3i0XE2bZgDKJnrIcoJ0urbdeYv0ssLLbSB4QxhqVpWQzv0ZK3bx3A9swCrnplHtkFxZW3z8+A5/tA2oI6nIA6qfi7ymOczAV+8YP6GlyCTQQuexFcXvjiTtvdE2BIx6a483YSW5jOztj+XHdGayYuz2Zr48FQlMNmdwdo1JxnR/fmlsFJ/H1tDAcbd0W+fYhm+Rv573V9uL/Jz2z1tWB6UQ9IOqtycEn+3pYwA2z8uubHXTVz8T9WGdQv3r2BFF88V/RJIKRpEhEFO1mWVouKsfwMW2qdOrdypdmuFXbm4ybtAzIXf3BpYh8jm9kxqLpeI+ArsyXIXZzigdoGl0z7pSG/3TD7BcK5FmnLvgNkF5TQr21jzurUlDfHnMGO7IN8v7bKdUxp8+x7VpPdHtYPj8PKj2p3nOrE4c9cohNs9hLe5Bc/7qLBpT7EJNjuse0LYMvMSotG9W7FE73th+oNo2/kH1f24vLerXhu12kATCvqwaMjuxEV5uX+izrTJDKcmw4+yL6yCD6KeJreebNJyF3J9IiLefSLtRxMHGK7dbLTKC3zsXfhZPLcjdkfmkjZ2q8OObTCkjIWbc3k5VkpPPfDporbMPuDSCN/cHG+1QdmLsUFRBzcyW5vG/q3bUx8m04kuvbx8syU8lWKvvwDZeuPENTWT7V1/sYHW+dUtO9cDi17QfPuFTMAOFfj7ylrxEszUygJc7rH6jqon7kVyops5U6jlpC5rXbb70+hREJ4dG0CYMqLA5Y6c8f1bdsYgEHtmxAfE8aMDVWCiz9LW/+/Q7sdq5OTDj8/B3P+XbvjVCeOnB2A2DFNEZu9/MIrxhosuIiIW0SWi8j/nNftRGShiKSIyEciEuK0hzqvU5zlSQH7+JPTvlFEhgW0D3faUkRk/PE+NwBOv85+067y7TQixMOFYZvslCbNeyAi/PPq08hqcwEflZ7L+vjLGdXbfrBHh3l59OKuLM2O4KGwx4nwumDKzeAO5ayr72X/gWJu+jEUgNffncj5//iaqNQZfF12Bp8c6IPZNoctabaaa8PuXO76cBm9Hp/Gta/N51/fbeS5H5KZtckZIM/bbW94FhJhX/szl4ALKQt224wirm0PXC7BG9eWaA6wcP027vpgGVc99TGhy98mY+pjh/+9rPsCmnSAkKiKsmtfmf2P1qqPnVo/c4udX83pFnt2XhZPT9vIMz/7b3dcx+DiH8xv3hWatKt15lK2bzOppgXLS1rbBmfcZWlqFrERXjo0iwRARDi/a3PmJO+rXD22c4W9bqf0oK2QO5o1nwHGlqxX6WpVJ4ncdFuc4/ba16162y8lh5t5/BegITOXe4H1Aa//CTxrjOkIZAFjnfaxQJbT/qyzHiLSHbgO6AEMB152ApYbeAkYAXQHrnfWPb48odB3DGz89tALK7fNgbZDwGV//aEeNy/echbrB/yd+0cPR0TKV728dwIPD+/Ko7dchtz8qf1QPn003Tsm8dINfenU4wxyXbF0OrCMm+M2EC7FXHnzPQy+eAweynjtzVe59e1FDH9uDj9tzODmQUm8eUt/Fj4ylPiYMF6d5XxY5e2qGGcBCl3h+EKiyNu3vTy7WbtqCQBde/azKzlT3vSIzGbRtkwuibYZTMuDyeRsXXbo7yQ/A7b9TEbbi8mNH1SR1e1PsdfOtOpjp57xldjfWcF+jCecL9ZmcnpiDIv3uQHIzqjjvXH8g/lNu9huuFoGl4JdG0kpa8luV3MKJbx83GVJaib92jRGAj4wLujWgoLiMhZudYoTjLGZS88r7e9vVQ26ulZ/XDG90KbvanWs6gSRs8P2aDjKWpwGvhIWLviZrAPFR9jw5NUgwUVEEoGLgTed1wKcD3zirDIRuNx5Psp5jbN8qLP+KGCyMabIGLMVSAEGOD8pxpgtxphiYLKz7vHX/zabAi+ZUNGWlWov4Es6u9KqMRFeHr+sB+2aRlZqFxHuOLcDPRNi7Ifvfavh4v8AMLxnS566+nSiu53PeSHrub3xCmjUkpB2Z3LawKGURbbgivDlLE3N4t6hnZj78Pn8v0u7c0H3FrSIDmPsWe1YuDWT5WlZ5de4zNywl95PfE/Xv3zHlsIoZi9dzbWvzWdOcga7Nq/Ch9CjZx/noO0H3gdXJ7DokaHclpBOWUgUxcbN5ulvHPr7cLrEblvUihe2JtoKucyt5X3PPx9I5N3kELvuvk1QsJ8DnlgKS3w8eUUv/nTVOQC8/PVCvlm9C9+x3t1z7zqIbcv+Ei+lsUmQv7vm4zi+MsLy0sgISeC6AUms8yVSums1WQeK2ZxxgOvC5sE/EmD+S2AMgzvEEeZ1MWO90zWWtQ0Ks0kN60pht6ttZeGRbimdsRF2r4KBd9guw43fHts5/1Ll7oKn2sLmmUdftyHl7rDjLUDK3nzumGnHYhd/N5E+f53O0H/PYl5NZ00/STRU5vIc8EfAP9odB2QbY0qd1+mAP8wnANsBnOU5zvrl7VW2OVz78ReTCF0vhmXvQkmh/QD7/Lfg8tgr8I9FeOOK1Nqv3Tk289j4jb2q3+UGlwt390sZ6FvO0ofP4r4LOxMTEbCdMVx3emOiwzy89tMWyNtNfkhz7pm8nBZRYTw0rAuRzVrTr3Eh6VkHufmtRZh9yeSEtMQV6nSdxdpuIXfudpttbZ2Du/2vWBV5Jkk7/8fBgxVluD6fYducD9nsi6dRm9NZ6LIVMyXJP8LO5ZS5w/n117k8vdQJGPs2Yg7sY2dJJL0SYuiZEMMZPToB0MyVy50fLGPk83P4bs3uinGjmsrYQEFsJ859ehZvr3eyxMDZrHetgt2rq910384UvJTQtG0PLj29FevL2uDbvYZlqZmAYciu90BcMO0R+OQ2wnwHOatjM2as32uP0xlvuXOm4bKfWoHxsWraW4e/DfbqT+z+el5pCxBS51WUaCvY+pO9HmrFhw19JIdnjJO5JPLO3K1c/PwcFmVFsTP+Qu72fMkXHb6muLiEx79aW/u/5RPYcQ8uInIJsNcYc5xvVlLtsYwTkSUisiQjo55m2z3jdnuh3spJMPlG2L4QrnwD4joE7z3a2W/0GJ+9KNOv6yVISQEhqbMqr+/zwee/o9EL3XmwVyHfr9uJydvN/7YZXCK8OaY/d53XkfiEdrSUbGY9dC5PXtGTXqF7CGnRpWI/kc1shVd2mvOTCklnET3oFpqQy4LvJwP2qvUHJ/5A65ylpLa8iPd+M4jfXzOCHSaO5PlTObBtMStL29ChRQwd27Qig8YU7tpAQfZedhVHcP0Ap0soJAK8kYztE8Vzo3tTXOrjd+8v5W9fr6/5f8qyEsy+ZL7eHUteUSlT08Jsu79rzBj4eAx8fGu1Jc+LlywGoNdpfenXpjE7QtsTUpLLpuSN/Mq9hoicZLjkObjgcXsztTcvYHjHMHZkH2TjnjwK05ZSjAdvfHfOPess1ksHZNUUXpmVcsh7YYztEmt3DiURzSntNNxeH5HyQ83OtQ5OmhkGUufZx03ToPQE7V4qzIaSA2wuiuHxr9ZxZoc4vr/vV7S6/SMYeAe9d3zAR7EvsX3PPmYn/3Kyl4bIXIYAl4nINmyX1fnAf4FYEfE46yQC/o71HUBrAGd5DLA/sL3KNodrP4Qx5nVjTH9jTP9mzZrV/cyq0+4c27f/9f12jOGyF+230GBq0t6WN0a1gtYDK9qTzrLXYqybWtFmjL1F8arJgHBj6p/p4t6DmFLW50Xw3HW9ad0kYFA/bxehLuHGM1rTXnYR2apbxb5E7PvmbK+YcSDpbDqdeTk5EoNr1STW7sxh1Itzidj8DW4xnHfl7XjdLi7s0ZI9TQeTkLUI157VJHs68s5tZ/DPq04jxRfP7i2rKMrZQ44rhsucAgcAIpviOrify/sk8P1953DrmUm89fNWxn+6+vDf/gPt34z4SpiX24yXbuhLUZQNXD7/FfcZG22g2Z9SMd9bgC0bVwGQ2KEXLpfQtIO9982elKXcHfGDDbi9roaz7oPrJ0PGei4qsV02M9bvJX3tXNb72vL3q/vzyMhudLlwLL1c25g19+dDP9B3LIOsrRR0uZKLnp1Nz9czyJJYVs6YxOJtNc9edmQf5IvlO2rUjejzGV6YkUyPx6bx+NS1FJce31tz78o5yORFaTX/spC2wBaiFOXAttlHX78hOLMhf5JiSIgN59Wb+9E8Osz2MIx4CkY8Tfzen5gU/jTv/fTLubDyuAcXY8yfjDGJxpgk7ID8j8aYG4GZwNXOamMA/z10pzqvcZb/aOxf3lTgOqearB3QCVgELAY6OdVnIc57BHy6HmciMOgOm1WMfAb63Fg/7zHyabj0v+VFAoDtPus+ygaS966wNyGb+xwseAkG/g7GTMWdv4uJkc8D0L9Xd87r0rxi++hWzuD6Vpj3vL1+pmmnyu8d29pmLVvn2Nr95t0RTwhZHa9gcOlifv3CV5xz8Af+EjMN4johLXqWb9rrnFHESAHhFHHOry6kRXQYnVtEER7fjSYF2wgtzqJJ05Y0CvVUvF9k0/IpYDxuF49d2p17zu/IR0u2c8+k5WzYnUtp2eE/EJcusd90e/YZxMWnxTNuWD8yTSO2p9iKr9yV9s/Oh4vClZ9V2nbtzhwi8rZR4o6ARvb31Ke/ncCze9YszihZDP1/bYs5ADoPg/jeRK+fTK9W0Uycu4Xm+Rsoi+9N91bRALh6XY0RNxcVTWfqiioTha7+GOMO5ZH1SWzPLOCa/m1ZET6I9jnzufXNuTW6s+mBolLGTFjEHz5awYMfr6TkCL+brAPF/HriYv49fSPXNt3GpHmbuP6NBcd1loF/fbeR8Z+t5osVNSjaOLDfVtANvMNW360/tPT+hOBc47JgXzj3XtCJUI+78vKB45CrJ3Ca2cTt2x9m4/YjjMEFmzE2+6uH7rgT6TqXh4H7RSQFO6byltP+FhDntN8PjAcwxqwFpgDrgO+Au4wxZc64zN3ANGw12hRn3YbT71a4by0MuL3+3qPLCOh80aHtw5+CC/9qxxHeutBejNfzahj2D0jsDyOeonmRrWa7+My+lbf1lyO/NAB+eAxaD7LBKlBsGzt55bafIamiAq71eWMJkTJmh93P/5W9QGhENFzyHxsIHd6OQzHY1/Hdzixv73F6f6KlgEgpokNSUuX3qzK/mIhw/0VdeOLCeNxrP+HG5/5Hj8emcflLc/lsWXr5t3WfzzBx3jbmLfiZMlzccqkd87qiTwJ7PAlkpG1g+ro9pM6dwirTgUW+ruya/xGbM/IBWJaWxSOfr6G9azfStGP5eZzesS07ac61np/widfO0BCo782wZw3Xt95PxIE0ouUgvfqfE/A7bgHdLuV67yzemxPQvVdyENZ8QlrcEL7YkM/Dw7vy18t7ct5lY4jiIJfGbOX2d5cc8QJWYwyPfr6alvvmM6XFu3y1PJXfTFzCgaLSQ9ZN3pPHJS/8zLyU/XwwIJV/5P6Jxc3/TtGudVzywhx+rHqtTj3IyCvif6t24hL46//Wk3m0KqrtzgwH7c+FThfZsm7fided58u2V+d7m7Tmyj6HGf7tcQUHL32F/rIR96Tr6vVmYoUlZczelGGr1JK/h7dH1MutPRo0uBhjZhljLnGebzHGDDDGdDTGXGOMKXLaC53XHZ3lWwK2f9IY08EY08UY821A+zfGmM7OsieP/5lVIWIH9xtCSAQMuQf+sAoufAIG/BYuf6Uiw+k/Fk67DhBcjdtU3rZVHxs8Tr8Oxv0EY6dVTCLpF9PaXtSYkwZJFR+a7lanYbpeQmi7M+HGT+HO+RVjQ36RcUj8afZbp3+eNMDbomv585bxVX5vkU3tN1a/vN0w7VFuWXAJz4e8xIJGD/JK0mzKig9y/5SVjHppLtPW7uaWCYt4bOpaBjXai4ltizfMVuW5XUJcYhdalu3k0Xen04sUEgZdRfzg0bQz23ngpY+4ZcIirnx5HumZBfRrlImnWUX25nIJuTH2zp1FXUbZYBGo59XgCePi0h8YEm7rTLyt+1VaRQb+lihzgO77v2eOv8990RtwIIM/7TyLC7o15zdnt7Pt7c8FTxiPtd9E86hQbp2wiFXp1U+8+cHCNL5bsZVXGk1gQM53fNRnNXOSM7j+jQWk7a+4w2nK3nyuf2MhxWU+Pr29D0NSX4UmHYguzWRqyJ+5wfsTv35nMfdMWs7+/CKMMWzPLOD7tbuDmtVMWpRGSZnhpRv6knuwhCe/Xn/kDVLngTuE7MY9MN0utV86ti886vvUZdA852CJra6sYm9eIf/8bgOTFqWxPC2LguKKAL4peQMlxs0tFw3A4z78R25kv9F8lvQY7Q6soHDKb+olm1iamsnI/87hlgmLGPTkNHZMeZC8yLbktB129I1ryXP0VdQvQkgkDLn30HYRGPUinPn7ikzFL7YN/KH6qqlK6/glnVV519d9cPTj+tV4263mCugq8N9mGZDIuMrrRzjdYkX5Fbcc8JXYD/HTR+Nd9Cbnb3qZ82KmktxzGC+lJvCH9/YQ6RVeGtqI/uu2I817VNpl86Ru+FK/4vEOybAD4vpeQVx4Y8zCx7kidCn/TkvkoWFduHVgKyKf3mEvAg2Q1GMQzPuZ8LPvOvT8wmOh++XEbPySJ/teDSvCoFnXyuu0GYyveQ/G7p3OX2dfzeBEL2Wz/s0y6U1qoz58fc3pFdc+hUTAaaMJXzaRz4eexsifO3DZi3M5LTGGkb3iOS0hhv0HitmVc5Bnpm3iqRaziMrZA0270HfL67x9zTTu/jKN4f+dzSMjuzGofRzXv2EzgEm3D6RjykT7ReGWL6FZV1yf3c79W19gQFfDbWuGMGvjXrxuF/udrCIyxM1Dw7pw8+Ak3C7hWBWX+nh/QSrndG7GiF7x/HZnDi/N3MyVfRPKZxMPZIwhb9PP7PJ0Ztjf59AhOoRpEsLe+VMIb9qf2AgvIkJBcSmr0nNYnpbNxt25JO/NZ+u+AwxuH8d/RvcmJrxy5WVhSRkrt2ezNC2LqFAP1w9oUx4QducUcuObC9iccYDXb+7HRT3s/5fSMh93f7icRVsrxsFEoE2TCLq2jOLyrRtp4m7CyF5H/4LZ/9Jx/OO5ZP6c8gEzP3yamLNv57SEmCMGpZrIKSjh+R+TmTB3K61iwnludG/CVk4kYVsa4wru47od+ZzfNfLoO6oFDS7Kjs207Hn09arjDy4RcdC825HXrU7XkYe2RbUCb6SdeTiiygdLZDMbTJ7vAwf2Qq9r4dzxFdV3HS+ArbORWf+k85aJ/NdXii/MhQsfzHX20eemyvts0h4XhpEHp9p78jTvBiJIm0HcUriC0be/QJjXbecjM75KWRZA2JA7oU1fSKjSrejX92Y77rXsPXtldtVSchFcA8fR+at7Kdw8l4n/eZvflGbzRZPbeOva/sRGhFRef+QzkLuTxjP+yLSRrzC5cABfr9rFU99uqLTagLhCrij4GLpdBuf/GV4ezK92vMZ39/2Dhz9ZxZ+/WEOI2xAVFsrkcYPoGFUGc56BDkNthgRw8xfw6VjOWvcyM686h3+sb0ZEiJsBLXyct/lfTMk/nce/KuPzFTu5uFdLikp8FJX6OFhSZn+Ky4gJ99IzIYZeCTF0aBZZ7Qfld2t3szeviH9elQTA78/vxNerdjH+05X898Io+hQuRDI2UDL0Cb5OLuTd2ev4KHM181yXcce5Hdi8N5/ZKT3pvP4r+qy4gBC3m7hGIezNKyov9EiIDadD80b0aBXNZ8t2cNUr83j71jNo3SSCpamZfP3dt0Slz2S/rxHZphErTXs+W96F50b3xiXCDW8uIDO/mM4tGnH/lJV8eXcjOjRrxH9nJLNoaybPXHM6A9s1Yf2uXDbszmPj7jzW786lSWkGoS3a4KpB8G3XNJI2Ix9k6Y9rGbTpaS5ZE0tBdAduHtyW689oQ+PIkKPuw6/MZ1ixPYsPFqbx9apdFJX6uHlQWx4e0ZVGpgCmv4Vpeya/v+APdI6PqvF+a0p+SXXVddG/f3+zZMmShj6Mk0/ODni2ux2Lufbd4O33tXPslDB3L6lcRLDuSzurcqs+MOJf0HrA4fdRlG/75dMW2sytcVsbPFqeVjlT2r7IjkcBDL4bhjk9qQtege/Gw91LoWlH2PANTL4efjPDjlfVlDHwQl9bhTZgnC2+qKq4AN+/u7KgKInTSeZA4hCajf240mwNlZQchPevhrT5cObdUFpEQeZOcqURhb1vJapNb5pMvxdZ8ynctchOc/Ptw7DodfjtHHy+MnZ99ggt982joP0Ios6/3w6Iz/0v/G6Oneet/PeYB6+fZ+9H8rs5trDj/asgcwvG5WHe4Ne4Z0FMeTbjEgj3ugkPcRPmdZN5oJiCYjsW4nYJLaPDSGwcTvdW0dw0qC0dmjXiqlfmsT+/iB8fOLf8Q3jj/P8RPu0B2lAxwP2t61fcUfBbrmq8mX8f/AvFoz8ipNtwe5iLJxL69T383O3/sTK0L5uLYklsHEGfNo3p3Tq20gfzvM37+N17S/G6XXRs3ogtWzczI+yPRFMx1lHqieCq0r+R7EsgMtRDcamPib8eQLOoUC594WfiIkN4cFgXfvf+Uq7qm8gz15xe/T//f3sjCX3h6gnVLq9W7i58r5xJbkgL7m30L37anEeY18XInvEM7daCszs3JTqs8peUrAPFTF25kznJ+9i6L5/tmQcpLvPRKNTDqN6tuHFg2/JCEn74P5v53z7z8F+KakhElhpjDvkPocHFocHlGPl88OE1diynuoKCY/Xpb+w1Hn/cWjErMtgB250rbHBxBWnIMD8DnnGykVu/sYUJYCeMfLYHnPNHWwW29G346Z+HHlNNzPk3zHjCjnf1vqH6daY9CvNfxCDInfOPngkW5dkAs30BhEbbrC53p52zrPVAO/4w5F471gZ2Furn+9rAeiDDlql3vdiZQDMHEDhtNFz52qHvtWcdvHG+PabsNJvBXfUGfP8XyNlByW3TKGrciVCPC49LKgXFMp9h6758VqXnsDkjn12ZefTb8QHunDQeL7mJ09vFs3BrJn+5pDtjz3LGlrbMgg9H44tpzfKWo3lhe3t6Z/6PP3g+Y+XZr9HLtQ3XT0/Bw9ts1yPYi0tfHgT5TvFBWCz86o8wuJruSmBzRj5j31nMweJSPop5kbbZC5Bxs2zVY046TBpNaUg0vwt/mtX7DO/cNoBuUYWwezULSrtww8SV+Ax0at6Iqdc1IzxzA3S9BDwB2YXPB0+2hIG/tZPZ1ob/y0y/29jQ/3HemZfGd2t3k11QgscldIuPplVsGPEx4ezNK+SHdXspLvPRvmkkA+IKuDX3dTrkLkTa/wpPj1HQdrCdFHbHMhtYul1m/w3rSIPLUWhwOcEsfhNmPwP3rQteEDkcY+AfrW131YPJ4A7oLX7zAkhfXPE6shk8VM0Fj0dTkGkr7i74v8MHpsytNsPpdQ1c+XrNj720ELzhFe+z/H1bEOArhbuc60D8ln9gs7EBt8OZ99gP5qI8u03KD7ac/XDFJysn2xkmYtvCTZ/ZbC47zQadkEg7ThPbtqIisPiAnZHA+OwswGExNkh9cUf5vUx2RfXk5oL7yfBFMefh8+y3cSew0KQDjJkKkU0xxpCbX0DMexfai5KjE+x53zG38jEW5dvrk3avttVjm2fAkD/Yi1pF7Id92nynG7crJWU+XGs+xf35b2xV5ZB7Kva1bS68exl0uoiya97DvfJ9+P7/2UAcEsWmuPP5Yk9Tft98JeG7nc+OtmfB6Pcq/o3z9sC/O8OIp2HguJr9mwaa/pi9fKD3jXDp85TiYvn2bGas38umHRm02r+AMwpmEy+ZlLY4nda9zqa1ZMCsp+wFt90utYUPuYGl3c6szNd9EJRCIw0uR6HB5QRjjM1S3MdpWPCTX9sPxguqzOi8L9l+2Lk89ht/8x6Q2K/aXQRF+hJb0BAWXbf9+MrszNLesEOXGVOpJLxWNs+03YqBhRbpS+Cdi+2HfXgTaNHDdqHtWWs/4PzinGAU6i9Ld8OnY/FFteLAyBeJKtpjA/mSCZUCSyU7l8MbQ+1+z/gNXHyE2xD4yuDrB2zG2fcWe+/6ha9W3C+o+yg7g8bHY2x36djplbtLARa8Ct89bP82slNt8Bg4zs4IsO5LO+FqXCc7rhYabbseo1vBNW/b7th5L9gZkQMz4towBn76F8z6u82KLvqrLftP+QFSZtj3D4vBNG6H7F0HZU75dqdhMPJf9rx8Pti5zP7umnV1An0d/74CaHA5Cg0uStVBxkYbhPessdlJSAQkDrBjYuK2H2w7l9mM4YLHK4JG2kKYNLrixnGecGh3tu0+rBpY/PzjBVe9ZWdDOBJj4Me/2UIFsB+sA++wN3xb8Kq9lbfLa8eSquuGNAa+vBs2fGUzmz43V2TSxQV2doqmnSuC9fZFMPmGimux2gyGsx84H7a+cQAABptJREFU9rkE/fxBzi8q3u6z+yh7CYAnxE7fv2cNlJU6v/djr96rDQ0uR6HBRakGkrXN3k21ZS9o0fPQarqqSovshJ69rqk8vnEkG762WVWbQRUfugWZtvuwSXs47ZrDb2uM7WI82nH5ZafZYpBul9lxjmBJ+cEG8fbnlVc0ngg0uByFBhellKq9wwWXE2n6F6WUUr8QGlyUUkoFnQYXpZRSQafBRSmlVNBpcFFKKRV0GlyUUkoFnQYXpZRSQafBRSmlVNDpRZQOEckAUo9x86bA0W9o/stzKp73qXjOcGqe96l4zlD7825rjGlWtVGDSxCIyJLqrlD9pTsVz/tUPGc4Nc/7VDxnCN55a7eYUkqpoNPgopRSKug0uARHDe/s9ItzKp73qXjOcGqe96l4zhCk89YxF6WUUkGnmYtSSqmg0+CilFIq6DS41JGIDBeRjSKSIiLjG/p46oOItBaRmSKyTkT+f3t3FmvXFMdx/PvTmqqiJQgtWjQooYZIjWnwYAoezGOEeJEYQkwhQuJBIqYQJFraaExVNB4EJcWDmmquhCDcplSCmmL+eVjrclxuXO6+5+j2+yQ396y1d85ZK/9zzv/stfde601JZ9f69SU9Lumd+n98r9vaNEmjJC2R9EgtT5a0uMb7XklDXApx1SFpnKR5kt6WtFTSHm2PtaRz63v7DUl3S1qrjbGWNEvSCklvdNT9ZWxV3Fj7/5qkXf7JayW5DIOkUcDNwEHAVOA4SVN726oR8RNwnu2pwHTgzNrPi4CFtqcAC2u5bc4GlnaUrwaus7018DlwWk9aNbJuAB61vS2wE6X/rY21pAnAWcButncARgHH0s5Y3wkcOKBusNgeBEypf2cAt/yTF0pyGZ7dgXdtv2f7B+Ae4PAet6lxtpfbfrk+/oryZTOB0tfZdbfZwBG9aeHIkDQROAS4vZYF7AfMq7u0sc/rAfsCMwFs/2D7C1oea2A0sLak0cAYYDktjLXtp4HPBlQPFtvDgTkungPGSdpkqK+V5DI8E4CPOsp9ta61JE0CdgYWAxvbXl43fQxs3KNmjZTrgQuAX2p5A+AL2z/VchvjPRn4FLijDgfeLmkdWhxr28uAa4APKUllJfAS7Y91v8FiO6zvtySXGDJJY4EHgHNsf9m5zeWa9tZc1y7pUGCF7Zd63ZYuGw3sAtxie2fgGwYMgbUw1uMpv9InA5sC6/DnoaP/hSZjm+QyPMuAzTrKE2td60hanZJY5tqeX6s/6T9Mrv9X9Kp9I2Av4DBJH1CGO/ejnIsYV4dOoJ3x7gP6bC+u5XmUZNPmWB8AvG/7U9s/AvMp8W97rPsNFtthfb8luQzPC8CUelXJGpSTgAt63KbG1XMNM4Gltq/t2LQAOKU+PgV4uNttGym2L7Y90fYkSlyftH0C8BRwZN2tVX0GsP0x8JGkbWrV/sBbtDjWlOGw6ZLG1Pd6f59bHesOg8V2AXByvWpsOrCyY/jsb+UO/WGSdDBlbH4UMMv2VT1uUuMk7Q08A7zO7+cfLqGcd7kP2JyyXMHRtgeeLFzlSZoBnG/7UElbUo5k1geWACfa/r6X7WuapGmUixjWAN4DTqX8EG1trCVdARxDuTJyCXA65fxCq2It6W5gBmVa/U+Ay4GH+IvY1kR7E2WI8FvgVNsvDvm1klwiIqJpGRaLiIjGJblERETjklwiIqJxSS4REdG4JJeIiGhckktEC0ia0T9zc8R/QZJLREQ0LskloosknSjpeUmvSLqtrhfztaTr6noiCyVtWPedJum5upbGgx3rbGwt6QlJr0p6WdJW9enHdqzDMrfeBBfRE0kuEV0iaTvKXeB72Z4G/AycQJko8UXb2wOLKHdNA8wBLrS9I2V2hP76ucDNtncC9qTM5AtltupzKGsLbUmZHyuiJ0b//S4R0ZD9gV2BF+pBxdqUSQJ/Ae6t+9wFzK/rqoyzvajWzwbul7QuMMH2gwC2vwOoz/e87b5afgWYBDw78t2K+LMkl4juETDb9sV/qJQuG7Dfv52TqXPeq5/J5zt6KMNiEd2zEDhS0kbw29rlW1A+h/2z7x4PPGt7JfC5pH1q/UnAoroSaJ+kI+pzrClpTFd7ETEE+WUT0SW235J0KfCYpNWAH4EzKQty7V63raCcl4Ey/fmtNXn0z04MJdHcJunK+hxHdbEbEUOSWZEjekzS17bH9rodEU3KsFhERDQuRy4REdG4HLlERETjklwiIqJxSS4REdG4JJeIiGhckktERDTuVwWvUvdrlNvYAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Mklv2mvHZFtu"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"FcqUS-lcV010","executionInfo":{"status":"ok","timestamp":1635929426629,"user_tz":-60,"elapsed":615,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"71bee4df-9e86-4e7f-abd8-134df1635b76"},"source":["plt.plot(history.history['root_mean_squared_error'])\n","plt.plot(history.history['val_root_mean_squared_error'])\n","plt.title('model performance')\n","plt.ylabel('rmse')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'])\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"TSjCxq__LQkw"},"source":["## **Model Evaluation and Testing**"]},{"cell_type":"code","metadata":{"id":"kuiLjiUxXLj4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635929432319,"user_tz":-60,"elapsed":553,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"23aa1fcc-2770-4467-98d1-7c9a7e82506f"},"source":["model.evaluate(X_test,y_test)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["4/4 [==============================] - 0s 4ms/step - loss: 35082.9219 - root_mean_squared_error: 45458.8477\n"]},{"output_type":"execute_result","data":{"text/plain":["[35082.921875, 45458.84765625]"]},"metadata":{},"execution_count":102}]},{"cell_type":"code","metadata":{"id":"nBrLEVAyXLmT"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"53c0mlV2WGI2","executionInfo":{"status":"ok","timestamp":1635923777762,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"baa2d4ca-fd08-4d15-8256-b95b98340c78"},"source":["X_test.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TensorShape([100, 8])"]},"metadata":{},"execution_count":57}]},{"cell_type":"code","metadata":{"id":"HeN7SJylXLpD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635929436669,"user_tz":-60,"elapsed":6,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"216ec721-9cee-4e6b-9c44-f813ffe214b8"},"source":["model.predict(tf.expand_dims(X_test[0], axis = 0 ))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[465634.7]], dtype=float32)"]},"metadata":{},"execution_count":103}]},{"cell_type":"code","metadata":{"id":"5HbPIpC2XLrq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635923777763,"user_tz":-60,"elapsed":15,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"41238d72-92df-4bb8-ddda-a38787fe3d7b"},"source":["y_test[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(1,), dtype=float32, numpy=array([465338.5], dtype=float32)>"]},"metadata":{},"execution_count":59}]},{"cell_type":"code","metadata":{"id":"jscJH2wLXLuK"},"source":["y_true = list(y_test[:,0].numpy())"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qWCd5NDZie1L","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635929443529,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"b5ee2ef1-0cb7-4514-8141-f292267ad8ff"},"source":["y_pred = list(model.predict(X_test)[:,0])\n","print(y_pred)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[465634.7, 346877.6, 316944.53, 541506.4, 177605.89, 389100.53, 407023.3, 215957.14, 242833.12, 316479.53, 283034.28, 523835.53, 235925.12, 169588.38, 246495.66, 424280.38, 444110.28, 253352.94, 513701.28, 460727.66, 276458.38, 214403.03, 220393.1, 129725.375, 274199.8, 336481.06, 316167.84, 176357.14, 176709.47, 405825.3, 221231.02, 321881.72, 127336.555, 208712.95, 467812.22, 380431.0, 361381.9, 239841.12, 464587.47, 369859.16, 224781.88, 238386.03, 266883.12, 335518.03, 312324.72, 240562.16, 132587.97, 422667.72, 228218.1, 156495.84, 365251.53, 194797.8, 401961.06, 387540.25, 478644.16, 227816.22, 383970.72, 210779.62, 291278.78, 175252.11, 266432.1, 148648.31, 415452.88, 305756.9, 166695.77, 475147.22, 512314.84, 143468.97, 374273.22, 158544.81, 360469.16, 567642.06, 540250.2, 481704.6, 80303.1, 459162.03, 461067.16, 429522.4, 455283.94, 331594.3, 284699.8, 276452.72, 209741.05, 270829.62, 190786.22, 299014.84, 246887.86, 353629.97, 380103.03, 175247.42, 199458.3, 491509.22, 252743.64, 209619.28, 527794.8, 522028.75, 525645.06, 199967.36, 133078.62, 146573.19]\n"]}]},{"cell_type":"code","metadata":{"id":"FJceF2FrXLxB","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635929453509,"user_tz":-60,"elapsed":756,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"0745e271-4261-4f13-d87e-7d4bc9586ab7"},"source":["ind = np.arange(100)\n","plt.figure(figsize=(40,20))\n","\n","width = 0.1\n","\n","plt.bar(ind, y_pred, width, label='Predicted Car Price')\n","plt.bar(ind + width, y_true, width, label='Actual Car Price')\n","\n","plt.xlabel('Actual vs Predicted Prices')\n","plt.ylabel('Car Price 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size 2880x1440 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Pj6Qk_wOXLz6"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"U8aJm7HoXL2d"},"source":[],"execution_count":null,"outputs":[]}]}

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FILE: deep learning for computer vision/2-Malaria Detection by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"rR4jSKq1m4WT"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plots\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import io\n","import os\n","import random\n","from google.colab import files\n","from PIL import Image\n","import albumentations as A\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, InputLayer, BatchNormalization, Input, Dropout, RandomFlip, RandomRotation, Resizing, Rescaling\n","from tensorflow.keras.losses import BinaryCrossentropy\n","from tensorflow.keras.metrics import BinaryAccuracy, FalsePositives, FalseNegatives, TruePositives, TrueNegatives, Precision, Recall, AUC, binary_accuracy\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import Callback, CSVLogger, EarlyStopping, LearningRateScheduler, ModelCheckpoint, ReduceLROnPlateau\n","from tensorflow.keras.regularizers  import L2, L1\n","from tensorboard.plugins.hparams import api as hp\n","from google.colab import drive"]},{"cell_type":"markdown","metadata":{"id":"iFJwm1uxmRez"},"source":["## Wandb Install, Login, Initialization and Configuration"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"SGRTsq6Nqzrm","outputId":"b431d52f-09da-44f2-e630-5093aea605e2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17486,"status":"ok","timestamp":1663064983236,"user":{"displayName":"Md. Asif Bin Khaled","userId":"12341681592563429881"},"user_tz":-360},"id":"Fy2mEFDzpKwL","outputId":"c2ddf590-87fd-428d-f308-48839220704c"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting wandb\n","  Downloading wandb-0.13.3-py2.py3-none-any.whl (1.8 MB)\n","\u001b[K     |████████████████████████████████| 1.8 MB 27.1 MB/s \n","\u001b[?25hRequirement already satisfied: protobuf<4.0dev,>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (3.17.3)\n","Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (5.4.8)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from wandb) (6.0)\n","Collecting pathtools\n","  Downloading pathtools-0.1.2.tar.gz (11 kB)\n","Requirement 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docker-pycreds-0.4.0 gitdb-4.0.9 pathtools-0.1.2 sentry-sdk-1.9.0 setproctitle-1.3.2 shortuuid-1.0.9 smmap-5.0.0 wandb-0.13.3\n"]}],"source":["!pip install wandb"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o7M99wtkhAT9"},"outputs":[],"source":["import wandb\n","from wandb.keras import WandbCallback"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"_-sLU81XmSBx","outputId":"5addff8c-aec5-4203-8b58-339fd257cca9"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n","\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n","\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: "]}],"source":["!wandb login"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Yn6EL3iOtSbq"},"outputs":[],"source":["wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\")\n","\n","###wandb.tensorboard.patch(root_logdir=\"./logs\")\n","#wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\", sync_tensorboard=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KKjsIPdavgSE"},"outputs":[],"source":["wandb.config = {\n","  \"LEARNING_RATE\": 0.001,\n","  \"N_EPOCHS\": 5,\n","  \"BATCH_SIZE\": 128,\n","  \"DROPOUT_RATE\": 0.0,\n","  \"IM_SIZE\": 224,\n","  \"REGULARIZATION_RATE\": 0.0,\n","  \"N_FILTERS\": 6,\n","  \"KERNEL_SIZE\": 3,\n","  \"N_STRIDES\": 1,\n","  \"POOL_SIZE\": 2,\n","  \"N_DENSE_1\": 100,\n","  \"N_DENSE_2\": 10,\n","}\n","CONFIGURATION = wandb.config"]},{"cell_type":"markdown","metadata":{"id":"HrGMGJ7Nb3Bh"},"source":["# Data Preparation"]},{"cell_type":"markdown","metadata":{"id":"DK7_p02_b6_m"},"source":["## Data Loading"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"899Bnj-olJGx"},"outputs":[],"source":["dataset, dataset_info = tfds.load('malaria', with_info=True,\n","                                  as_supervised=True, \n","                                  shuffle_files = True, \n","                                  split=['train'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XixoxMwuKG_-"},"outputs":[],"source":["for data in dataset[0].take(1):\n","  print(data)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"syf8XttCCWzX"},"outputs":[],"source":["for i in d.take(1):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WPBOQ3B_cIpx"},"outputs":[],"source":["#dataset_info"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZF9ACbZwHhm3"},"outputs":[],"source":["def splits(dataset, TRAIN_RATIO, VAL_RATIO, TEST_RATIO):\n","  DATASET_SIZE = len(dataset)\n","\n","  train_dataset = dataset.take(int(TRAIN_RATIO*DATASET_SIZE))\n","\n","  val_test_dataset = dataset.skip(int(TRAIN_RATIO*DATASET_SIZE))\n","  val_dataset = val_test_dataset.take(int(VAL_RATIO*DATASET_SIZE))\n","\n","  test_dataset = val_test_dataset.skip(int(VAL_RATIO*DATASET_SIZE))\n","  return train_dataset, val_dataset, test_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sUiEq8Ew9AwN"},"outputs":[],"source":["TRAIN_RATIO = 0.8\n","VAL_RATIO = 0.1\n","TEST_RATIO = 0.1\n","\n","train_dataset, val_dataset, test_dataset = splits(dataset[0], TRAIN_RATIO, VAL_RATIO, TEST_RATIO )\n","#print(list(train_dataset.take(1).as_numpy_iterator()),\n"," #     list(val_dataset.take(1).as_numpy_iterator()), list(test_dataset.take(1).as_numpy_iterator()))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f5otp5EUU2cy"},"outputs":[],"source":["train_datasets"]},{"cell_type":"markdown","metadata":{"id":"-1QWh3KjbsuJ"},"source":["## Dataset Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7l7Lre1oUzx2"},"outputs":[],"source":["for i, (image, label) in enumerate(train_dataset.take(16)):\n","  ax = plt.subplot(4, 4, i + 1)\n","  \n","  plt.imshow(image)\n","  plt.title(dataset_info.features['label'].int2str(label))\n","  plt.axis('off')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8btu7qeen1hX"},"outputs":[],"source":["# for i, (image, label) in enumerate(train_dataset.take(2)):\n","#   plt.subplot(1, 4, 2*i + 1)\n","#   plt.imshow(image)\n","\n","#   plt.subplot(1, 4, 2*i + 2)\n","#   plt.imshow(tf.image.adjust_saturation(image, 0.3))\n","\n","\n","#   plt.title(dataset_info.features['label'].int2str(label))\n","#   plt.axis('off')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NT8DiQxbVMz7"},"outputs":[],"source":["dataset_info.features['label'].int2str(1)"]},{"cell_type":"markdown","metadata":{"id":"82Gqdfs9kjv3"},"source":["## Data Preprocessing"]},{"cell_type":"markdown","metadata":{"id":"3BUZMRuhV-HL"},"source":["### Data Augmentation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gKFOnzZQ7Fya"},"outputs":[],"source":["def visualize(original, augmented):\n","  plt.subplot(1,2,1)\n","  plt.imshow(original)\n","\n","  plt.subplot(1,2,2)\n","  plt.imshow(augmented)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pMWtOY6e7OH-"},"outputs":[],"source":["original_image, label = next(iter(train_dataset))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9aKifNjs7OSe"},"outputs":[],"source":["augmented_image = tf.image.adjust_saturation(original_image, saturation_factor = 0.3)#central_crop(original_image, 0.8)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Uo80zT197OT-"},"outputs":[],"source":["visualize(original_image, augmented_image)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rIXxlS6LVKmU"},"outputs":[],"source":["IM_SIZE = 224"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4BKlhCPaR61p"},"outputs":[],"source":["original_image, label = next(iter(train_dataset))\n","@tf.function\n","def resize_rescale(image, label):\n","  #print(\"I was here\")\n","  #tf.print(\"I was here\")\n","  return tf.image.resize(image, (IM_SIZE, IM_SIZE))/255.0, label\n","\n","_, _ = resize_rescale(original_image, label)\n","_, _ = resize_rescale(original_image, label)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R7_OVJEtZQWV"},"outputs":[],"source":["#tf.config.run_functions_eagerly(False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9dyO-k9mMYTc"},"outputs":[],"source":["### tf.keras.layer resizing and rescaling\n","resize_rescale_layers = tf.keras.Sequential([\n","       Resizing(IM_SIZE, IM_SIZE),\n","       Rescaling(1./255),                               \n","])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oBXnNGAcVgXa"},"outputs":[],"source":["### tf.image augment\n","@tf.function\n","def augment(image, label):\n","  image, label = resize_rescale(image, label)\n","\n","  image = tf.image.rot90(image)\n","  #image = tf.image.adjust_saturation(image, saturation_factor = 0.3)\n","  image = tf.image.flip_left_right(image)\n","\n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9Ck5ElZT53qd"},"outputs":[],"source":["class RotNinety(Layer):\n","  def __init__(self):\n","    super().__init__()\n","    \n","  @tf.function\n","  def call(self, image):\n","    return tf.image.rot90(image)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ii6zZKmX9cur"},"outputs":[],"source":["### tf.keras.layer augment\n","augment_layers = tf.keras.Sequential([\n","       RandomRotation(factor = (0.25, 0.2501),),\n","       RandomFlip(mode='horizontal',),\n","       RandomContrast(factor=0.1),\n","                                  \n","])\n","\n","@tf.function\n","def augment_layer(image, label):\n","  return augment_layers(resize_rescale_layers(image), training = True), label"]},{"cell_type":"markdown","metadata":{"id":"z_RZU3vxG2Aj"},"source":["### Data Loading"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LaDqyvwZ7D6a"},"outputs":[],"source":["BATCH_SIZE = 32"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PLkx-Ct8m8Ak"},"outputs":[],"source":["test_dataset = test_dataset.map(resize_rescale, num_parallel_calls = tf.data.AUTOTUNE)\n","#train_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5XbLitL4m9DA"},"outputs":[],"source":["# for image,label in train_dataset.take(1):\n","#   print(image, label)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"t5ydz6dhm9Fc"},"outputs":[],"source":["\n","train_dataset = (\n","    train_dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .map(augment_layer, num_parallel_calls = tf.data.AUTOTUNE)\n","    .batch(BATCH_SIZE)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cF4UBrf5eNmQ"},"outputs":[],"source":["val_dataset = (\n","    val_dataset\n","    .shuffle(buffer_size = 32)\n","    .map(resize_rescale, num_parallel_calls = tf.data.AUTOTUNE)\n","    .batch(BATCH_SIZE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OOK_U7-neNuy"},"outputs":[],"source":["val_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CDlvxwC1m9Hl"},"outputs":[],"source":["train_dataset"]},{"cell_type":"markdown","metadata":{"id":"im-Eo6tvHgmM"},"source":["### MIxup Data Augmentation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PI_b_cBzJeRv"},"outputs":[],"source":["train_dataset_1 = train_dataset.shuffle(buffer_size = 4096, ).map(resize_rescale, num_parallel_calls = tf.data.AUTOTUNE)\n","train_dataset_2 = train_dataset.shuffle(buffer_size = 4096, ).map(resize_rescale, num_parallel_calls = tf.data.AUTOTUNE)\n","\n","mixed_dataset = tf.data.Dataset.zip((train_dataset_1, train_dataset_2))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7Kaku3IHHkVN"},"outputs":[],"source":["def mixup(train_dataset_1, train_dataset_2):\n","  (image_1,label_1), (image_2, label_2) = train_dataset_1, train_dataset_2\n","\n","  lamda = tfp.distributions.Beta(0.2,0.2)\n","  lamda = lamda.sample(1)[0]\n","  \n","  image = lamda*image_1 + (1-lamda)*image_2\n","  label = lamda*tf.cast(label_1, dtype = tf.float32) + (1-lamda)*tf.cast(label_2, dtype = tf.float32)\n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4Uwj65QhL5ST"},"outputs":[],"source":["BATCH_SIZE = 32\n","train_dataset = (\n","    mixed_dataset\n","    .shuffle(buffer_size = 4096, reshuffle_each_iteration = True)\n","    .map(mixup, num_parallel_calls = tf.data.AUTOTUNE)\n","    #map(cutmix, num_parallel_calls = tf.data.AUTOTUNE)\n","    .batch(BATCH_SIZE)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cKYfVCHsMV41"},"outputs":[],"source":["train_dataset"]},{"cell_type":"markdown","metadata":{"id":"IylKvmx5LUCR"},"source":["### CutMix Data *Augmentation*"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s4mfRVpjVKkz"},"outputs":[],"source":["def box(lamda):\n","  \n","  r_x = tf.cast(tfp.distributions.Uniform(0, IM_SIZE).sample(1)[0], dtype = tf.int32)\n","  r_y = tf.cast(tfp.distributions.Uniform(0, IM_SIZE).sample(1)[0], dtype = tf.int32)\n","\n","  r_w = tf.cast(IM_SIZE*tf.math.sqrt(1-lamda), dtype = tf.int32)\n","  r_h = tf.cast(IM_SIZE*tf.math.sqrt(1-lamda), dtype = tf.int32)\n","\n","  r_x = tf.clip_by_value(r_x - r_w//2, 0, IM_SIZE)\n","  r_y = tf.clip_by_value(r_y - r_h//2, 0, IM_SIZE)\n","\n","  x_b_r = tf.clip_by_value(r_x + r_w//2, 0, IM_SIZE)\n","  y_b_r = tf.clip_by_value(r_y + r_h//2, 0, IM_SIZE)\n","\n","  r_w = x_b_r - r_x\n","  if(r_w == 0):\n","    r_w  = 1\n","\n","  r_h = y_b_r - r_y\n","  if(r_h == 0):\n","    r_h = 1\n","\n","  return r_y, r_x, r_h, r_w"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QDH9vwpMLWBd"},"outputs":[],"source":["def cutmix(train_dataset_1, train_dataset_2):\n","  (image_1,label_1), (image_2, label_2) = train_dataset_1, train_dataset_2\n","\n","  lamda = tfp.distributions.Beta(0.2,0.2)\n","  lamda = lamda.sample(1)[0]\n","  \n","  r_y, r_x, r_h, r_w = box(lamda)\n","  crop_2 = tf.image.crop_to_bounding_box(image_2, r_y, r_x, r_h, r_w)\n","  pad_2 = tf.image.pad_to_bounding_box(crop_2, r_y, r_x, IM_SIZE, IM_SIZE)\n","\n","  crop_1 = tf.image.crop_to_bounding_box(image_1, r_y, r_x, r_h, r_w)\n","  pad_1 = tf.image.pad_to_bounding_box(crop_1, r_y, r_x, IM_SIZE, IM_SIZE)\n","\n","  image = image_1 - pad_1 + pad_2\n","\n","  lamda = tf.cast(1- (r_w*r_h)/(IM_SIZE*IM_SIZE), dtype = tf.float32)\n","  label = lamda*tf.cast(label_1, dtype = tf.float32) + (1-lamda)*tf.cast(label_2, dtype = tf.float32)\n","\n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uOKOaUrPwydO"},"outputs":[],"source":["original_image, label = next(iter(train_dataset))\n","print(label)\n","plt.imshow(original_image[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X_SDW1m61zA3"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"PTW0fKw313gt"},"source":["### Albumentations"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mFgRO86M87ME"},"outputs":[],"source":["!pip install -U git+https://github.com/albu/albumentations --no-cache-dir"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LWO057_PshWr"},"outputs":[],"source":["transforms = A.Compose(\n","    [\n","      A.Resize(IM_SIZE, IM_SIZE),\n","\n","      A.OneOf([A.HorizontalFlip(),\n","                A.VerticalFlip(),], p = 0.3),\n","      \n","      A.RandomRotate90(),   \n","      #A.RandomGridShuffle(grid=(3, 3), always_apply=False, p=0.5),\n","      A.RandomBrightnessContrast(brightness_limit=0.2,\n","                                contrast_limit=0.2,\n","                                always_apply=False, p=0.5),\n","      #A.Cutout(num_holes=8, max_h_size=8, max_w_size=8, fill_value=0, always_apply=False, p=0.5),\n","      A.Sharpen(alpha=(0.2, 0.5), lightness=(0.5, 1.0), always_apply=False, p=0.5),\n","])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rlv9wdoRshZq"},"outputs":[],"source":["def aug_albument(image):\n","  data = {\"image\":image}\n","  image = transforms(**data)\n","  image = image[\"image\"]\n","  image = tf.cast(image/255., tf.float32)\n","  return image"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"99IrNqzqEx9R"},"outputs":[],"source":["def process_data(image, label):\n","    aug_img = tf.numpy_function(func=aug_albument, inp=[image], Tout=tf.float32)\n","    return aug_img, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XR1XQju8ELjx"},"outputs":[],"source":["train_dataset = (\n","    train_dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .map(process_data)\n","    .batch(BATCH_SIZE)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GyDZGZWPELm3"},"outputs":[],"source":["train_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jAh74s-oELp_"},"outputs":[],"source":["im, _ = next(iter(train_dataset))\n","plt.imshow(im[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uAC1aaCGELuJ"},"outputs":[],"source":["plt.figure(figsize=(15,15))\n","\n","for i in range(1,32):\n","  plt.subplot(8,4,i)\n","  plt.imshow(im[i])"]},{"cell_type":"markdown","metadata":{"id":"TeVM_g7JsaQr"},"source":["### Repeating the dataset (x5)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mP8axz4dzc3d"},"outputs":[],"source":["def augment_1(image, label):\n","  image, label = resize_rescale(image, label)\n","\n","  image = tf.image.random_brightness(image, 0.2)\n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QZE2UFX9zgI5"},"outputs":[],"source":["def augment_2(image, label):\n","  image, label = resize_rescale(image, label)\n","\n","  image = tf.image.random_flip_up_down(image)\n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jMnehRtxzmRA"},"outputs":[],"source":["def augment_3(image, label):\n","  image, label = resize_rescale(image, label)\n","\n","  image = tf.image.flip_left_right(image)\n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4Cq-vZyzzr1K"},"outputs":[],"source":["def augment_4(image, label):\n","  image, label = resize_rescale(image, label)\n","\n","  image = tf.image.rot90(image)\n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xz176pKqEp0c"},"outputs":[],"source":["def augment_5(image, label):\n","  image, label = resize_rescale(image, label)\n","  \n","  return image, label"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LidXL2P7tuAt"},"outputs":[],"source":["train_dataset_1 = (\n","    train_dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .map(augment_1)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OBFQt8Q58QCU"},"outputs":[],"source":["train_dataset_2 = (\n","    train_dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .map(augment_2)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JsTYAvRTEwvr"},"outputs":[],"source":["train_dataset_3 = (\n","    train_dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .map(augment_3)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0kUetjtmEw25"},"outputs":[],"source":["train_dataset_4 = (\n","    train_dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .map(augment_4)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N2LrVC7SEw7f"},"outputs":[],"source":["train_dataset_5 = (\n","    train_dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .map(augment_5)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3yT3s-zBuS5l"},"outputs":[],"source":["full_dataset = train_dataset_1.concatenate(train_dataset_2).concatenate(train_dataset_3).concatenate(train_dataset_4).concatenate(train_dataset_5)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bZti4FnyzTce"},"outputs":[],"source":["full_dataset = (\n","    full_dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LdIY_Vgv0DLt"},"outputs":[],"source":["full_dataset"]},{"cell_type":"markdown","metadata":{"id":"Tn6iFj4SX3Sd"},"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SSX0Fu-vYFAk"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"YM5OVNH9bHaW"},"source":["## WandB Dataset Versioning"]},{"cell_type":"markdown","metadata":{"id":"AsB9YnCuPITj"},"source":["### Data Loading"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DXo1OjaOPMNj"},"outputs":[],"source":["dataset, dataset_info = tfds.load('malaria', with_info=True, as_supervised=True, shuffle_files = True, split=['train'])\n","\n","print(dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WQCKKSWI6HX5"},"outputs":[],"source":["k = 0\n","for data in dataset[0]:\n","  \n","  with open('dataset/malaria_dataset_'+str(k) + '.npz', mode = 'wb') as file:\n","      np.savez(file, data)\n","  k += 1\n","  if(k%1000 == 0):\n","    print(k)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mpFw5pGy57Tj"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iWKy1DX9b5Sm"},"outputs":[],"source":["def load_original_data():\n","  with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:\n","    \n","    original_data = wandb.Artifact(\n","        name = \"new_dataset\", \n","        type=\"raw_data\",\n","        description = \"The Malaria dataset contains a total of 27,558 cell images with equal instances of parasitized and uninfected cells from the thin blood smear slide images of segmented cells.\",\n","        metadata = {\"source\": \"TFDS\",\n","                    \"homepage\": \"https://lhncbc.nlm.nih.gov/publication/pub9932\",\n","                    \"source_code\": \"tfds.image_classification.Malaria\",\n","                    \"version\": \"1.0.0\",\n","                    \"download_size\": \"337.08 MiB\",\n","                    }\n","    )\n","    \n","    original_data.add_dir('dataset/')\n","\n","    run.log_artifact(original_data)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0k4w6y8-uKAL"},"outputs":[],"source":["load_original_data()"]},{"cell_type":"markdown","metadata":{"id":"peB3_ExnPQl0"},"source":["### Data Preprocessing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TrsaL8Ew-ctL"},"outputs":[],"source":["with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:    \n","  artifact = run.use_artifact('neuralearn/Malaria-Detection/new_dataset:v1', type='raw_data')\n","  artifact_dir = artifact.download()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oTgqIEHK-c1d"},"outputs":[],"source":["IM_SIZE = 224\n","def resize_rescale(image):\n","  return tf.image.resize(image, (IM_SIZE, IM_SIZE))/255.0"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9-2IapMA-c6c"},"outputs":[],"source":["def preprocess_data():\n","  with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:\n","\n","    artifact = run.use_artifact('neuralearn/Malaria-Detection/new_dataset:v1', type='raw_data')\n","    artifact_dir = artifact.download()\n","\n","    preprocessed_data = wandb.Artifact(\n","        name = \"preprocessed_dataset\", \n","        type=\"preprocessed_data\",\n","        description = \"A Preprocessed version of the Malaria dataset\",\n","        \n","    )\n","\n","    artifact_directory = \"artifacts/new_dataset:v1/\"\n","\n","    dataset_x = []\n","    dataset_y = []\n","    \n","    for f in os.listdir(artifact_directory)[:1000]:\n","      with open(artifact_directory + f, 'rb') as file:\n","        npz_array = np.load(file, allow_pickle = True)\n","        \n","        x,y = npz_array.f.arr_0\n","\n","        dataset_x.append(resize_rescale(x))\n","        dataset_y.append(y)\n","    \n","    #dataset = tf.data.Dataset.from_tensor_slices((dataset_x, dataset_y))\n","\n","    with preprocessed_data.new_file(\"prep_dataset.npz\", mode = \"wb\") as file:\n","        np.savez(file, [dataset_x, dataset_y])\n","    run.log_artifact(preprocessed_data)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e-qBgAOMmFk8"},"outputs":[],"source":["preprocess_data()"]},{"cell_type":"markdown","metadata":{"id":"hNqYhbE3pCzJ"},"source":["### Data Splitting"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eFTaqcEjuGkG"},"outputs":[],"source":["run = wandb.init()\n","artifact = run.use_artifact('neuralearn/Malaria-Detection/preprocessed_dataset:v2', type='preprocessed_data')\n","artifact_dir = artifact.download()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yBq_XWI5uGp3"},"outputs":[],"source":["def split_data():\n","  with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:\n","\n","    artifact = run.use_artifact('neuralearn/Malaria-Detection/preprocessed_dataset:v2', type='preprocessed_data')\n","    artifact_dir = artifact.download()\n","\n","    train_data = wandb.Artifact(\n","        name = \"train_dataset\", \n","        type=\"preprocessed_data\",\n","        description = \"Training dataset\",\n","        \n","    )\n","    val_data = wandb.Artifact(\n","        \n","        name = \"val_dataset\", \n","        type=\"preprocessed_data\",\n","        description = \"Validation dataset\",\n","            \n","        )\n","    test_data = wandb.Artifact(\n","        name = \"test_dataset\", \n","        type=\"preprocessed_data\",\n","        description = \"Test dataset\",\n","            \n","        )\n","    \n","    artifact_file = \"artifacts/preprocessed_dataset:v2/prep_dataset.npz\"\n","\n","    with open(artifact_file, 'rb') as file:\n","      npz_arr = np.load(file, allow_pickle = True)\n","      arr = npz_arr.f.arr_0\n","\n","    train_split = 0.8\n","    val_split = 0.1\n","    test_split = 0.1\n","    \n","    data_len = len(arr[0])\n","\n","    train_arr = [arr[0][0:int(train_split*data_len)], arr[1][0:int(train_split*data_len)]]\n","    val_arr = [arr[0][int(train_split*data_len):int((train_split+val_split)*data_len)], arr[1][int(train_split*data_len):int((train_split+val_split)*data_len)] ]\n","    test_arr = [arr[0][int((train_split+val_split)*data_len):], arr[1][int((train_split+val_split)*data_len):] ]\n","    \n","      \n","    with train_data.new_file(\"train_dataset.npz\", mode = \"wb\") as file:\n","        np.savez(file, train_arr)\n","        \n","    with val_data.new_file(\"val_dataset.npz\", mode = \"wb\") as file:\n","        np.savez(file, val_arr)\n","        \n","    with test_data.new_file(\"test_dataset.npz\", mode = \"wb\") as file:\n","        np.savez(file, test_arr)\n","        \n","\n","    run.log_artifact(train_data)  \n","    run.log_artifact(val_data)      \n","    run.log_artifact(test_data)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"j8JbsekwuT3X"},"outputs":[],"source":["split_data()"]},{"cell_type":"markdown","metadata":{"id":"jYBajAUcpH9q"},"source":["### Data augmentation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FlyK2BQZpKE4"},"outputs":[],"source":["### tf.image augment\n","def augment(image):\n","  image = tf.image.rot90(image)\n","  image = tf.image.flip_left_right(image)\n","\n","  return image"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"llwsknTp7LYb"},"outputs":[],"source":["/contertifant/acts/train_dataset:v0/train_dataset.npz"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"l45yjHt27vkD"},"outputs":[],"source":["wandb.finish()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qO5J39tfFeef"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ee5e2tQw7vmk"},"outputs":[],"source":["def augment_data():\n","  with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:\n","\n","    artifact = run.use_artifact('neuralearn/Malaria-Detection/train_dataset:v0', type='preprocessed_data')\n","    artifact_dir = artifact.download()\n","\n","    augmented_data = wandb.Artifact(\n","        name = \"Augmented_dataset\", \n","        type=\"preprocessed_data\",\n","        description = \"An Augmented version of the Malaria train dataset\",\n","    )\n","\n","    artifact_file = \"artifacts/train_dataset:v0/train_dataset.npz\"\n","    \n","    dataset_x = []\n","\n","    with open(artifact_file, 'rb') as file:\n","        npz_array = np.load(file, allow_pickle = True)\n","        \n","        arr = npz_array.f.arr_0\n","\n","        for im in arr[0]:\n","          dataset_x.append(augment(im))\n","        dataset_y = arr[1]\n","\n","    with augmented_data.new_file(\"aug_dataset.npz\", mode = \"wb\") as file:\n","        np.savez(file, [dataset_x, dataset_y])\n","    run.log_artifact(augmented_data)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"7t7hU_qh_Wgs"},"outputs":[],"source":["augment_data()"]},{"cell_type":"markdown","metadata":{"id":"FUQgOMJGIZcU"},"source":["# Model Creation and Training"]},{"cell_type":"markdown","metadata":{"id":"iYaYGqawya5T"},"source":["## Wandb Model Versioning"]},{"cell_type":"markdown","metadata":{"id":"SRS_ONJC_dy9"},"source":["### Untrained Model Versioning"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JH9h6T0XyfDi"},"outputs":[],"source":["def log_model():\n","  with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:\n","\n","    \n","    untrained_model = wandb.Artifact(\n","        name = \"Untrained_model\", \n","        type=\"model\",\n","        description = \"The initial version of our lenet model\",\n","        metadata = CONFIGURATION\n","    )\n","    filename = 'lenet.h5'\n","    lenet_model.save(filename)\n","\n","    untrained_model.add_file(filename)\n","    wandb.save(filename)\n","    run.log_artifact(untrained_model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nSGomNAD60oM"},"outputs":[],"source":["log_model()"]},{"cell_type":"markdown","metadata":{"id":"L5HIIvUC_hsN"},"source":["### Trained Model versioning"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CpI1hw4i_jts"},"outputs":[],"source":["def train_and_log():\n","  with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:\n","\n","    artifact = run.use_artifact('neuralearn/Malaria-Detection/Augmented_dataset:v0', type='preprocessed_data')\n","    artifact_dir = artifact.download()\n","\n","    trained_sequential_model = wandb.Artifact(\n","        name = \"Trained_Sequential_model\", \n","        type=\"model\",\n","        description = \"A trained version of our model\",\n","        metadata = CONFIGURATION,\n","    )\n","\n","    artifact_file = \"artifacts/Augmented_dataset:v0/aug_dataset.npz\"\n","        \n","    dataset_x = []\n","\n","    with open(artifact_file, 'rb') as file:\n","        npz_array = np.load(file, allow_pickle = True)\n","        \n","        arr = npz_array.f.arr_0\n","\n","        for im in arr[0]:\n","          dataset_x.append(im)\n","        dataset_y = arr[1]\n","    \n","\n","    d_x = tf.convert_to_tensor(dataset_x, dtype = tf.float32)\n","    d_y = tf.convert_to_tensor(dataset_y, dtype = tf.float32)\n","\n","    d = tf.data.Dataset.from_tensor_slices((d_x,d_y))\n","\n","    train_d = (\n","        d\n","        .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","        .batch(BATCH_SIZE)\n","        .prefetch(tf.data.AUTOTUNE)\n","    )\n","\n","\n","    artifact = run.use_artifact('neuralearn/Malaria-Detection/Untrained_model:v0', type='model')\n","    artifact_dir = artifact.download()\n","\n","    artifact_file = \"artifacts/Untrained_model:v0/lenet.h5\"\n","\n","    lenet_model = tf.keras.models.load_model(artifact_file)\n","\n","    metrics = [TruePositives(name='tp'),FalsePositives(name='fp'), TrueNegatives(name='tn'), FalseNegatives(name='fn'), \n","                BinaryAccuracy(name='accuracy'), Precision(name='precision'), Recall(name='recall'), AUC(name='auc')]\n","\n","    lenet_model.compile(optimizer = Adam(learning_rate = CONFIGURATION['LEARNING_RATE']),\n","          loss = BinaryCrossentropy(),\n","          metrics = metrics)\n","\n","    lenet_model.fit(\n","        train_d,\n","        epochs = 3,\n","        verbose = 1,\n","        callbacks=[WandbCallback()],\n","    )\n","    \n","    filename = 'lenet_trained.h5'\n","    lenet_model.save(filename)\n","\n","    trained_sequential_model.add_file(filename)\n","    wandb.save(filename)\n","    run.log_artifact(trained_sequential_model)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"DywVLvth_jyL"},"outputs":[],"source":["train_and_log()"]},{"cell_type":"markdown","metadata":{"id":"lcZooLJ_fLsm"},"source":["## Sequential API"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2QwVD7qdm9KK"},"outputs":[],"source":["IM_SIZE = CONFIGURATION['IM_SIZE']\n","DROPOUT_RATE = CONFIGURATION['DROPOUT_RATE']\n","REGULARIZATION_RATE = CONFIGURATION['REGULARIZATION_RATE']\n","N_FILTERS = CONFIGURATION['N_FILTERS']\n","KERNEL_SIZE = CONFIGURATION['KERNEL_SIZE']\n","POOL_SIZE = CONFIGURATION['POOL_SIZE']\n","N_STRIDES = CONFIGURATION['N_STRIDES']\n","\n","lenet_model = tf.keras.Sequential([\n","    InputLayer(input_shape = (IM_SIZE, IM_SIZE, 3)),\n","\n","    Conv2D(filters = N_FILTERS , kernel_size = KERNEL_SIZE, strides = N_STRIDES , padding='valid',\n","          activation = 'relu',kernel_regularizer = L2(REGULARIZATION_RATE)),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = POOL_SIZE, strides= N_STRIDES*2),\n","    Dropout(rate = DROPOUT_RATE ),\n","\n","    Conv2D(filters = N_FILTERS*2 + 4, kernel_size = KERNEL_SIZE, strides=N_STRIDES, padding='valid',\n","          activation = 'relu', kernel_regularizer = L2(REGULARIZATION_RATE)),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = POOL_SIZE, strides= N_STRIDES*2),\n","\n","    Flatten(),\n","    \n","    Dense( CONFIGURATION['N_DENSE_1'], activation = \"relu\", kernel_regularizer = L2(REGULARIZATION_RATE)),\n","    BatchNormalization(),\n","    Dropout(rate = DROPOUT_RATE),\n","    \n","    Dense( CONFIGURATION['N_DENSE_2'], activation = \"relu\", kernel_regularizer = L2(REGULARIZATION_RATE)),\n","    BatchNormalization(),\n","\n","    Dense(1, activation = \"sigmoid\"),\n","\n","])\n","\n","lenet_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-snscgqO4u36"},"outputs":[],"source":["def train_and_log():\n","  with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:\n","\n","    artifact = run.use_artifact('neuralearn/Malaria-Detection/Augmented_dataset:v0', type='preprocessed_data')\n","    artifact_dir = artifact.download()\n","\n","    sequential_model = wandb.Artifact(\n","        name = \"Sequential_model\", \n","        type=\"model\",\n","        description = \"A trained version of our model\",\n","        \n","    )\n","\n","    artifact_file = \"artifacts/train_dataset:v0/aug_dataset.npz\"\n","    \n","    dataset_x = []\n","\n","    with open(artifact_file, 'rb') as file:\n","        npz_array = np.load(file, allow_pickle = True)\n","        \n","        arr = npz_array.f.arr_0\n","\n","        for im in arr[0]:\n","          dataset_x.append(augment(im))\n","        dataset_y = arr[1]\n","\n","        # metrics = [TruePositives(name='tp'),FalsePositives(name='fp'), TrueNegatives(name='tn'), FalseNegatives(name='fn'), \n","        #             BinaryAccuracy(name='accuracy'), Precision(name='precision'), Recall(name='recall'), AUC(name='auc')]\n","        # FACTOR = 1\n","        # LABELS = ['Parasitized', 'Uninfected']\n","\n","\n","        # lenet_model.compile(optimizer = Adam(learning_rate = CONFIGURATION['LEARNING_RATE']),\n","        #       loss = BinaryCrossentropy(),\n","        #       metrics = metrics)\n","\n","        # history = lenet_model.fit(\n","        #     train_dataset,\n","        #     validation_data = val_dataset,\n","        #     epochs = 23,#CONFIGURATION['N_EPOCHS'],\n","        #     verbose = 1,\n","        #     #callbacks=[LogImagesCallbackWandB()]\n","        #     )\n","\n","    # with preprocessed_data.new_file(\"aug_dataset.npz\", mode = \"wb\") as file:\n","    #     np.savez(file, [dataset_x, dataset_y])\n","\n","    # run.log_artifact(preprocessed_data)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fc6sCUD84u6I"},"outputs":[],"source":["train_and_log()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pAoTZNps4u8r"},"outputs":[],"source":["artifact_file = \"artifacts/Augmented_dataset:v0/aug_dataset.npz\"\n","    \n","dataset_x = []\n","\n","with open(artifact_file, 'rb') as file:\n","    npz_array = np.load(file, allow_pickle = True)\n","    \n","    arr = npz_array.f.arr_0\n","\n","    for im in arr[0]:\n","      dataset_x.append(im)\n","    dataset_y = arr[1]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tRbHFk7m7fdi"},"outputs":[],"source":["print(dataset_y)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9vyPzaNb8xQF"},"outputs":[],"source":["d_x = tf.convert_to_tensor(dataset_x, dtype = tf.float32)\n","d_y = tf.convert_to_tensor(dataset_y, dtype = tf.float32)\n","\n","print(d_x.shape, d_y.shape)\n","\n","d = tf.data.Dataset.from_tensor_slices((d_x,d_y))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5-hCCnHS_pOX"},"outputs":[],"source":["for i in d.take(2):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WWgnfnR2DRzO"},"outputs":[],"source":["BATCH_SIZE = 32\n","\n","train_d = (\n","    d\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE)\n","    .prefetch(tf.data.AUTOTUNE)\n",")\n","print(train_d)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9GbecTJ47uyr"},"outputs":[],"source":["metrics = [TruePositives(name='tp'),FalsePositives(name='fp'), TrueNegatives(name='tn'), FalseNegatives(name='fn'), \n","            BinaryAccuracy(name='accuracy'), Precision(name='precision'), Recall(name='recall'), AUC(name='auc')]\n","FACTOR = 1\n","LABELS = ['Parasitized', 'Uninfected']\n","\n","\n","lenet_model.compile(optimizer = Adam(learning_rate = CONFIGURATION['LEARNING_RATE']),\n","      loss = BinaryCrossentropy(),\n","      metrics = metrics)\n","\n","history = lenet_model.fit(\n","    train_d,\n","    #validation_data = val_dataset,\n","    epochs = 2,#CONFIGURATION['N_EPOCHS'],\n","    verbose = 1,\n","    #callbacks=[LogImagesCallbackWandB()]\n","    )"]},{"cell_type":"markdown","metadata":{"id":"kbm2WoMpfXod"},"source":["## Functional API"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NeMyO6e1C4u7"},"outputs":[],"source":["func_input = Input(shape = (IM_SIZE, IM_SIZE, 3), name = \"Input Image\")\n","\n","x = Conv2D(filters = 6, kernel_size = 3, strides=1, padding='valid', activation = 'relu')(func_input)\n","x = BatchNormalization()(x)\n","x = MaxPool2D (pool_size = 2, strides= 2)(x)\n","\n","x = Conv2D(filters = 16, kernel_size = 3, strides=1, padding='valid', activation = 'relu')(x)\n","x = BatchNormalization()(x)\n","output = MaxPool2D (pool_size = 2, strides= 2)(x)\n","\n","feature_extractor_model = Model(func_input, output, name = \"Feature_Extractor\")\n","feature_extractor_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CMYcqj4Kdnpt"},"outputs":[],"source":["feature_extractor_seq_model = tf.keras.Sequential([\n","                             InputLayer(input_shape = (IM_SIZE, IM_SIZE, 3)),\n","\n","                             Conv2D(filters = 6, kernel_size = 3, strides=1, padding='valid', activation = 'relu'),\n","                             BatchNormalization(),\n","                             MaxPool2D (pool_size = 2, strides= 2),\n","\n","                             Conv2D(filters = 16, kernel_size = 3, strides=1, padding='valid', activation = 'relu'),\n","                             BatchNormalization(),\n","                             MaxPool2D (pool_size = 2, strides= 2),\n","\n","                             \n","\n","])\n","feature_extractor_seq_model.summary()"]},{"cell_type":"markdown","metadata":{"id":"11Wz41MHkzTu"},"source":["## Callable Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zrkk0npZfYBJ"},"outputs":[],"source":["func_input = Input(shape = (IM_SIZE, IM_SIZE, 3), name = \"Input Image\")\n","\n","x = feature_extractor_seq_model(func_input)\n","\n","x = Flatten()(x)\n","\n","x = Dense(100, activation = \"relu\")(x)\n","x = BatchNormalization()(x)\n","\n","x = Dense(10, activation = \"relu\")(x)\n","x = BatchNormalization()(x)\n","\n","func_output = Dense(1, activation = \"sigmoid\")(x)\n","\n","lenet_model_func = Model(func_input, func_output, name = \"Lenet_Model\")\n","lenet_model_func.summary()"]},{"cell_type":"markdown","metadata":{"id":"3arkQ973eIjM"},"source":["## Model Subclassing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u4JaTaX9lEPK"},"outputs":[],"source":["class FeatureExtractor(Layer):\n","  def __init__(self, filters, kernel_size, strides, padding, activation, pool_size,):\n","    super(FeatureExtractor, self).__init__()\n","\n","    self.conv_1 = Conv2D(filters = filters, kernel_size = kernel_size, strides = strides, padding = padding, activation = activation)\n","    self.batch_1 = BatchNormalization()\n","    self.pool_1 = MaxPool2D (pool_size = pool_size, strides= 2*strides)\n","\n","    self.conv_2 = Conv2D(filters = filters*2, kernel_size = kernel_size, strides = strides, padding = padding, activation = activation)\n","    self.batch_2 = BatchNormalization()\n","    self.pool_2 = MaxPool2D (pool_size = pool_size, strides= 2*strides)\n","\n","  def call(self, x, training):\n","\n","    x = self.conv_1(x)\n","    x = self.batch_1(x)\n","    x = self.pool_1(x)\n","\n","    x = self.conv_2(x)\n","    x = self.batch_2(x)\n","    x = self.pool_2(x)\n","\n","    return x\n","feature_sub_classed = FeatureExtractor(8, 3, 1, \"valid\", \"relu\", 2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SDPkCi9ZhQ70"},"outputs":[],"source":["func_input = Input(shape = (IM_SIZE, IM_SIZE, 3), name = \"Input Image\")\n","\n","x = feature_sub_classed(func_input)\n","\n","x = Flatten()(x)\n","\n","x = Dense(100, activation = \"relu\")(x)\n","x = BatchNormalization()(x)\n","\n","x = Dense(10, activation = \"relu\")(x)\n","x = BatchNormalization()(x)\n","\n","func_output = Dense(1, activation = \"sigmoid\")(x)\n","\n","lenet_model_func = Model(func_input, func_output, name = \"Lenet_Model\")\n","lenet_model_func.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k8E1KU0QndJX"},"outputs":[],"source":["class LenetModel(Model):\n","  def __init__(self):\n","    super(LenetModel, self).__init__()\n","\n","    self.feature_extractor = FeatureExtractor(8, 3, 1, \"valid\", \"relu\", 2)\n","\n","    self.flatten = Flatten()\n","\n","    self.dense_1 = Dense(100, activation = \"relu\")\n","    self.batch_1 = BatchNormalization()\n","\n","    self.dense_2 = Dense(10, activation = \"relu\")\n","    self.batch_2 = BatchNormalization()\n","\n","    self.dense_3 = Dense(1, activation = \"sigmoid\")\n","    \n","  def call(self, x, training):\n","\n","    x = self.feature_extractor(x)\n","    x = self.flatten(x)\n","    x = self.dense_1(x)\n","    x = self.batch_1(x)\n","    x = self.dense_2(x)\n","    x = self.batch_2(x)\n","    x = self.dense_3(x)\n","\n","    return x\n","    \n","lenet_sub_classed = LenetModel()\n","lenet_sub_classed(tf.zeros([1,224,224,3]))\n","lenet_sub_classed.summary()"]},{"cell_type":"markdown","metadata":{"id":"xgF_fkT-qngT"},"source":["## Custom Layers"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BKLHl1obqoVJ"},"outputs":[],"source":["class NeuralearnDense(Layer):\n","  def __init__(self, output_units, activation):\n","    super(NeuralearnDense, self).__init__()\n","    self.output_units = output_units\n","    self.activation = activation\n","  \n","  def build(self, input_features_shape):\n","    self.w = self.add_weight(shape = (input_features_shape[-1], self.output_units), initializer = \"random_normal\", trainable = True)\n","    self.b = self.add_weight(shape = (self.output_units,), initializer = \"random_normal\", trainable = True)\n","  \n","  def call(self, input_features):\n","\n","    pre_output = tf.matmul(input_features, self.w) + self.b\n","\n","    if(self.activation == \"relu\"):\n","      return tf.nn.relu(pre_output)\n","\n","    elif(self.activation == \"sigmoid\"):\n","      return tf.math.sigmoid(pre_output)\n","\n","    else:\n","      return pre_output"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jz30O0yV2rGk"},"outputs":[],"source":["IM_SIZE = 224\n","lenet_custom_model = tf.keras.Sequential([\n","                             InputLayer(input_shape = (IM_SIZE, IM_SIZE, 3)),\n","\n","                             Conv2D(filters = 6, kernel_size = 3, strides=1, padding='valid', activation = 'relu'),\n","                             BatchNormalization(),\n","                             MaxPool2D (pool_size = 2, strides= 2),\n","\n","                             Conv2D(filters = 16, kernel_size = 3, strides=1, padding='valid', activation = 'relu'),\n","                             BatchNormalization(),\n","                             MaxPool2D (pool_size = 2, strides= 2),\n","\n","                             Flatten(),\n","                             \n","                             NeuralearnDense(100, activation = \"relu\"),\n","                             BatchNormalization(),\n","                             \n","                             NeuralearnDense(10, activation = \"relu\"),\n","                             BatchNormalization(),\n","\n","                             NeuralearnDense(1, activation = \"sigmoid\"),\n","\n","])\n","lenet_custom_model.summary()"]},{"cell_type":"markdown","metadata":{"id":"20ultY2FdfyX"},"source":["\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","## Callbacks"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LG6nVKSXdfcu"},"outputs":[],"source":["class LossCallback(Callback):\n","  def on_epoch_end(self, epoch, logs):\n","    print(\"\\n For Epoch Number {} the model has a loss of {} \".format(epoch+1, logs[\"loss\"]))\n","    \n","  def on_batch_end(self, batch, logs):\n","    print(\"\\n For Batch Number {} the model has a loss of {} \".format(batch+1, logs))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"F9PSIylC4dAI"},"outputs":[],"source":["test_dataset = test_dataset.batch(1)\n","\n","# images = wandb.Image(image_array, caption=\"Top: Output, Bottom: Input\")\n","\n","# wandb.log({\"examples\": images})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uSzBOy-m4Fnh"},"outputs":[],"source":["class LogImagesCallbackTensorBoard(Callback):\n","  def on_epoch_end(self, epoch, logs):\n","    labels = []\n","    inp = []\n","\n","    for x,y in test_dataset.as_numpy_iterator():\n","      labels.append(y)\n","      inp.append(x)\n","    labels = np.array([i[0] for i in labels])\n","    predicted = lenet_model.predict(np.array(inp)[:,0,...])\n","\n","    threshold = 0.5\n","\n","    cm = confusion_matrix(labels, predicted > threshold)\n","    \n","    plt.figure(figsize=(8,8))\n","\n","    sns.heatmap(cm, annot=True,)\n","    plt.title('Confusion matrix - {}'.format(threshold))\n","    plt.ylabel('Actual')\n","    plt.xlabel('Predicted')\n","    plt.axis('off')\n","\n","    buffer = io.BytesIO()\n","    plt.savefig(buffer, format = 'png')\n","\n","    image = tf.image.decode_png(buffer.getvalue(), channels=3)\n","    image = tf.expand_dims(image, axis = 0)\n","\n","    CURRENT_TIME = datetime.datetime.now().strftime('%d%m%y - %h%m%s')\n","    IMAGE_DIR = './logs/' + CURRENT_TIME + '/images'\n","    image_writer = tf.summary.create_file_writer(IMAGE_DIR)\n","    \n","    with image_writer.as_default():\n","      tf.summary.image(\"Training data\", image, step = epoch)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YTbdFZ93Tnzr"},"outputs":[],"source":["class LogImagesCallbackWandBPlot(Callback):\n","  def on_epoch_end(self, epoch, logs):\n","    labels = []\n","    inp = []\n","\n","    for x,y in test_dataset.as_numpy_iterator():\n","      labels.append(y)\n","      inp.append(x)\n","    labels = np.array([i[0] for i in labels])\n","    predicted = lenet_model.predict(np.array(inp)[:,0,...])\n","\n","    print(\"labels\", labels, labels.dtype)\n","    print(\"predicted\", predicted, predicted.dtype)\n","\n","    pred = []\n","\n","    for i in range(len(predicted)):\n","      if(predicted[i][0] < 0.5):\n","        pred.append([1,0])\n","      else:\n","        pred.append([0,1])\n","\n","    pred = np.array(pred)\n","\n","    # wandb.log({\"Confusion Matrix\" : wandb.plot.confusion_matrix(\n","    #     probs = pred,\n","    #     y_true=labels,\n","    #     class_names=[\"Parasitized\", \"Uninfected\"])})\n","\n","    wandb.log({\"ROC Curve\" : wandb.plot.roc_curve(\n","        y_true = labels,\n","        y_probas = pred,\n","        labels = ['Parasitized', 'Uninfected'], \n","    )})\n","\n","    wandb.log({'loss':logs['loss']})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ng6lfPud-i7v"},"outputs":[],"source":["class LogImagesCallbackWandB(Callback):\n","  def on_epoch_end(self, epoch, logs):\n","    labels = []\n","    inp = []\n","\n","    for x,y in test_dataset.as_numpy_iterator():\n","      labels.append(y)\n","      inp.append(x)\n","    labels = np.array([i[0] for i in labels])\n","    predicted = lenet_model.predict(np.array(inp)[:,0,...])\n","\n","    threshold = 0.5\n","\n","    cm = confusion_matrix(labels, predicted > threshold)\n","    \n","    plt.figure(figsize=(8,8))\n","\n","    sns.heatmap(cm, annot=True,)\n","    plt.title('Confusion matrix - {}'.format(threshold))\n","    plt.ylabel('Actual')\n","    plt.xlabel('Predicted')\n","    plt.axis('off')\n","\n","    buffer = io.BytesIO()\n","    plt.savefig(buffer, format = 'png')\n","\n","    image_array = tf.image.decode_png(buffer.getvalue(), channels=3)\n","\n","    images = wandb.Image(image_array, caption=\"Confusion Matrix for epoch: {}\".format(epoch))\n","          \n","    wandb.log(\n","        {\"Confusion Matrix\": images})\n","    "]},{"cell_type":"markdown","metadata":{"id":"413m-l0pttrt"},"source":["### CSVLogger"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aZKjzj32tsHN"},"outputs":[],"source":["csv_callback = CSVLogger(\n","    'logs.csv', separator=',', append=True\n",")"]},{"cell_type":"markdown","metadata":{"id":"lLaHf-B12OH8"},"source":["### EarlyStopping"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Sr-2Nzup2OcU"},"outputs":[],"source":["es_callback = EarlyStopping(\n","    monitor='val_loss', min_delta=0, patience=2, verbose=1,\n","    mode='auto', baseline=None, restore_best_weights=False\n",")"]},{"cell_type":"markdown","metadata":{"id":"P04zuKYv_hex"},"source":["### Tensorboard"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nKAAjue_KM8G"},"outputs":[],"source":["pip install -U tensorboard_plugin_profile"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VhhaW037s-9V"},"outputs":[],"source":["!rm -rf ./logs/"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"15BqZlHGKtW5"},"outputs":[],"source":["CURRENT_TIME = datetime.datetime.now().strftime('%d%m%y - %h%m%s')\n","METRIC_DIR = './logs/' + CURRENT_TIME + '/metrics'\n","train_writer = tf.summary.create_file_writer(METRIC_DIR)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CV7CC2YI_ht3"},"outputs":[],"source":["LOG_DIR = './logs/'+ CURRENT_TIME\n","tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=LOG_DIR, histogram_freq = 1, profile_batch = '100,132')"]},{"cell_type":"markdown","metadata":{"id":"yvoXsM8zC6bD"},"source":["### LearningRateScheduler"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_b3eU9yTC69J"},"outputs":[],"source":["def scheduler(epoch, lr):\n","\n","  if epoch <= 1:\n","    learning_rate = lr\n","  else:\n","    learning_rate = lr * tf.math.exp(-0.1)\n","    learning_rate = learning_rate.numpy()\n","\n","  with train_writer.as_default():\n","    tf.summary.scalar('Learning Rate', data = learning_rate, step = epoch)\n","  return learning_rate\n","scheduler_callback = LearningRateScheduler(scheduler, verbose = 1)"]},{"cell_type":"markdown","metadata":{"id":"ZEau-uvfRWA5"},"source":["### ModelCheckpointing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5dBXgkarRZT6"},"outputs":[],"source":["checkpoint_callback = ModelCheckpoint(\n","    'weights.{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_precision', verbose=0, save_best_only=True,\n","    save_weights_only=True, mode='auto', save_freq='epoch',\n",")"]},{"cell_type":"markdown","metadata":{"id":"-t5zQwWckCS9"},"source":["### ReduceLearningRateOnPlateau"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NGkKibvGkC6A"},"outputs":[],"source":["plateau_callback = ReduceLROnPlateau(\n","    monitor='val_accuracy', factor=0.1, patience=5, verbose=1\n",")"]},{"cell_type":"markdown","metadata":{"id":"Rrm7NNzuDd5k"},"source":["## Custom Metric Class"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AhN8td6XDeLC"},"outputs":[],"source":["class CustomAccuracy(tf.keras.metrics.Metric):\n","  def __init__(self, name = 'Custom_Accuracy', FACTOR = 1):\n","    super(CustomAccuracy, self).__init__()\n","    self.FACTOR = FACTOR\n","    self.accuracy = self.add_weight(name = name, initializer = 'zeros')\n","\n","\n","  def update_state(self, y_true, y_pred, sample_weight = None):\n","    output = binary_accuracy(tf.cast(y_true, dtype = tf.float32), y_pred)*self.FACTOR\n","    self.accuracy.assign(tf.math.count_nonzero(output, dtype = tf.float32)/tf.cast(len(output), dtype = tf.float32))\n","\n","  def result(self):\n","    return self.accuracy\n","\n","  def reset_states(self):\n","    self.accuracy.assign(0.)"]},{"cell_type":"markdown","metadata":{"id":"_AcriqBA2IU4"},"source":["## Custom Metric Method (without parametres)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7JvNwoUw2IfR"},"outputs":[],"source":["def custom_accuracy(y_true, y_pred):\n","  print(binary_accuracy(y_true, y_pred))\n","  return binary_accuracy(y_true, y_pred)"]},{"cell_type":"markdown","metadata":{"id":"2bEezFeL_8tr"},"source":["## Custom Metric Method (with Parametres)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TU1emg1I_9CB"},"outputs":[],"source":["def custom_accuracy(FACTOR):\n","  def metric(y_true, y_pred):\n","    return binary_accuracy(y_true, y_pred)* FACTOR\n","  return metric"]},{"cell_type":"markdown","metadata":{"id":"-0mircHogrvl"},"source":["## Custom Loss Class"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qlzFZ7wzgyec"},"outputs":[],"source":["class CustomBCE(tf.keras.losses.Loss):\n","  def __init__(self, FACTOR):\n","    super(CustomBCE, self).__init__()\n","    self.FACTOR = FACTOR\n","  def call(self, y_true, y_pred):\n","    bce = BinaryCrossentropy()\n","    return bce(y_true, y_pred)* self.FACTOR"]},{"cell_type":"markdown","metadata":{"id":"JhawCc8jf8JB"},"source":["## Custom Loss Method (with parametres)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GcmUelUKUShm"},"outputs":[],"source":["def custom_bce(FACTOR):\n","  def loss(y_true, y_pred):\n","    bce = BinaryCrossentropy()\n","    return bce(y_true, y_pred)* FACTOR\n","  return loss"]},{"cell_type":"markdown","metadata":{"id":"J3I7Zhyufjsf"},"source":["## Custom Loss Method (without parametres)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NTNBj8yqfsq-"},"outputs":[],"source":["def custom_bce(y_true, y_pred):\n","  bce = BinaryCrossentropy()\n","  return bce(y_true, y_pred)"]},{"cell_type":"markdown","metadata":{"id":"1AicRPfDS79d"},"source":["## Training"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hV-cIEXu6aZo"},"outputs":[],"source":["metrics = [TruePositives(name='tp'),FalsePositives(name='fp'), TrueNegatives(name='tn'), FalseNegatives(name='fn'), \n","            BinaryAccuracy(name='accuracy'), Precision(name='precision'), Recall(name='recall'), AUC(name='auc')]\n","FACTOR = 1\n","LABELS = ['Parasitized', 'Uninfected']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kXa7OBPBm9O2"},"outputs":[],"source":["lenet_model.compile(optimizer = Adam(learning_rate = CONFIGURATION['LEARNING_RATE']),\n","      loss = BinaryCrossentropy(),\n","      metrics = metrics)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pLWDksoJ0hU0"},"outputs":[],"source":["history = lenet_model.fit(\n","    train_dataset,\n","    validation_data = val_dataset,\n","    epochs = 23,#CONFIGURATION['N_EPOCHS'],\n","    verbose = 1,\n","    #callbacks=[LogImagesCallbackWandB()]\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TDlEZcNtAO2J"},"outputs":[],"source":["wandb.finish()"]},{"cell_type":"markdown","metadata":{"id":"DxMyYeM-DmlM"},"source":["## Hyperparameter Tuning with WandB"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fU9usxE4UcEo"},"outputs":[],"source":["sweep_config = {\n","  \"name\" : \"Malaria-Prediction-Sweep\",\n","  \"method\" : \"random\",\n","  \"metric\": {\n","      \"name\" : \"accuracy\",\n","      \"goal\" : \"maximize\",\n","  },\n","  \"parameters\" : {\n","    \n","    \"IM_SIZE\": {\n","        \"value\" : 224,\n","    },\n","\n","    \"N_EPOCHS\": {\n","        \"value\" : 1,\n","    },\n","    \n","    \"KERNEL_SIZE\": {\n","        \"value\" : 3,\n","    },\n","\n","    \"N_STRIDES\": {\n","        \"value\" : 1,\n","    },\n","\n","    \"POOL_SIZE\": {\n","        \"value\" : 224,\n","    },\n","  \n","    \"N_FILTERS\" : {\n","        \"value\" : 6,\n","    },\n","      \n","    \"N_DENSE_1\" : {\n","      \"values\" : [16, 32, 64, 128]\n","    },\n","\n","    \"N_DENSE_2\" : {\n","      \"values\" : [16, 32, 64, 128]\n","    },\n","\n","    \"DROPOUT_RATE\":{\n","      \"min\": 0.1,\n","      \"max\": 0.4\n","    },\n","\n","    \"REGULARIZATION_RATE\" :{\n","      \"distribution\": \"uniform\",\n","      \"min\": 0.001,\n","      \"max\": 0.1\n","    },\n","\n","    \"LEARNING_RATE\" :{\n","      \"distribution\": \"uniform\",\n","      \"min\": 1e-4,\n","      \"max\": 1e-2\n","    }\n","  },\n","}\n","\n","sweep_id = wandb.sweep(sweep_config)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DDI4jVQkMZpU"},"outputs":[],"source":["IM_SIZE = 224\n","def model_tune(config):\n","  lenet_model = tf.keras.Sequential([\n","    InputLayer(input_shape = (224, 224, 3)),\n","\n","    Conv2D(filters = 6 , kernel_size = 3, strides = 1 , padding='valid',\n","          activation = 'relu',kernel_regularizer = L2(config['REGULARIZATION_RATE'])),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = 1, strides= config['N_STRIDES']*2),\n","    Dropout(rate = config['DROPOUT_RATE'] ),\n","\n","    Conv2D(filters = 16, kernel_size = 3, strides = 1, padding='valid',\n","          activation = 'relu', kernel_regularizer = L2(config['REGULARIZATION_RATE'])),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = 1, strides= 2),\n","\n","    Flatten(),\n","    \n","    Dense( config['N_DENSE_1'], activation = \"relu\", kernel_regularizer = L2(config['REGULARIZATION_RATE'])),\n","    BatchNormalization(),\n","    Dropout(rate = DROPOUT_RATE),\n","    \n","    Dense( config['N_DENSE_2'], activation = \"relu\", kernel_regularizer = L2(config['REGULARIZATION_RATE'])),\n","    BatchNormalization(),\n","\n","    Dense(1, activation = \"sigmoid\"),\n","\n","  ])\n","\n","\n","  return lenet_model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ayj_1UaJM9Nk"},"outputs":[],"source":["wandb.config = {\n","  \"LEARNING_RATE\": 0.001,\n","  \"N_EPOCHS\": 1,\n","  \"BATCH_SIZE\": 128,\n","  \"DROPOUT_RATE\": 0.0,\n","  \"IM_SIZE\": 224,\n","  \"REGULARIZATION_RATE\": 0.0,\n","  \"N_FILTERS\": 6,\n","  \"KERNEL_SIZE\": 3,\n","  \"N_STRIDES\": 1,\n","  \"POOL_SIZE\": 2,\n","  \"N_DENSE_1\": 100,\n","  \"N_DENSE_2\": 10,\n","}\n","CONFIGURATION = wandb.config"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2Xb32xrsDsOA"},"outputs":[],"source":["def train():\n","    with wandb.init(project=\"Malaria-Detection\", entity=\"neuralearn\") as run:\n","        config = wandb.config\n","        model = model_tune(config)\n","        model.compile(\n","              optimizer= Adam(\n","                  learning_rate = config['LEARNING_RATE']),\n","              loss='binary_crossentropy',\n","              metrics=['accuracy'],\n","              )\n","        model.fit(val_dataset, epochs=2, callbacks = [WandbCallback()])\n","        #wandb.log({\"loss\": loss, \"epoch\": epoch})\n","\n","count = 5 # number of runs to execute\n","wandb.agent(sweep_id, function=train, count=count)"]},{"cell_type":"markdown","metadata":{"id":"zzS9pcnOPRva"},"source":["## Hyperparameter Tuning"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GAUfFaBvPT7c"},"outputs":[],"source":["IM_SIZE = 224\n","def model_tune(hparams):\n","  lenet_model = tf.keras.Sequential([\n","    InputLayer(input_shape = (IM_SIZE, IM_SIZE, 3)),\n","\n","    Conv2D(filters = 6, kernel_size = 3, strides=1, padding='valid',\n","          activation = 'relu',kernel_regularizer = L2(hparams[HP_REGULARIZATION_RATE])),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = 2, strides= 2),\n","    Dropout(rate = hparams[HP_DROPOUT]),\n","\n","    Conv2D(filters = 16, kernel_size = 3, strides=1, padding='valid',\n","          activation = 'relu', kernel_regularizer = L2(hparams[HP_REGULARIZATION_RATE])),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = 2, strides= 2),\n","\n","    Flatten(),\n","    \n","    Dense( hparams[HP_NUM_UNITS_1], activation = \"relu\", kernel_regularizer = L2(hparams[HP_REGULARIZATION_RATE])),\n","    BatchNormalization(),\n","    Dropout(rate = hparams[HP_DROPOUT]),\n","    \n","    Dense(hparams[HP_NUM_UNITS_2], activation = \"relu\", kernel_regularizer = L2(hparams[HP_REGULARIZATION_RATE])),\n","    BatchNormalization(),\n","\n","    Dense(1, activation = \"sigmoid\"),\n","  ])\n","\n","  lenet_model.compile(\n","        optimizer= Adam(learning_rate = hparams[HP_LEARNING_RATE]),\n","        loss='binary_crossentropy',\n","        metrics=['accuracy'],\n","    )\n","\n","  lenet_model.fit(val_dataset, epochs=1)\n","  _, accuracy = lenet_model.evaluate(val_dataset)\n","  return accuracy"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fG9GX1iNdAfB"},"outputs":[],"source":["HP_NUM_UNITS_1 = hp.HParam('num_units_1', hp.Discrete([16,32,64,128]))\n","HP_NUM_UNITS_2 = hp.HParam('num_units_2', hp.Discrete([16,32,64,128]))\n","HP_DROPOUT = hp.HParam('dropout_rate', hp.Discrete([0.1,0.2,0.3]))\n","HP_REGULARIZATION_RATE = hp.HParam('regularization_rate', hp.Discrete([0.001,0.01,0.1]))\n","HP_LEARNING_RATE = hp.HParam('learning_rate', hp.Discrete([1e-4, 1e-3]))\n","\n","fixed range of values is very large\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TH58PrBWuE68"},"outputs":[],"source":["run_number = 0\n","for num_units_1 in HP_NUM_UNITS_1.domain.values:\n","  for num_units_2 in HP_NUM_UNITS_2.domain.values:\n","    for dropout_rate in HP_DROPOUT.domain.values:\n","      for regularization_rate in HP_REGULARIZATION_RATE.domain.values:\n","        for learning_rate in HP_LEARNING_RATE.domain.values:\n","\n","          hparams = {\n","              HP_NUM_UNITS_1: num_units_1,\n","              HP_NUM_UNITS_2: num_units_2,\n","              HP_DROPOUT: dropout_rate,\n","              HP_REGULARIZATION_RATE: regularization_rate,\n","              HP_LEARNING_RATE: learning_rate,\n","              \n","          }\n","          file_writer = tf.summary.create_file_writer('logs/hparams-' + str(run_number))\n","\n","          with file_writer.as_default():\n","              hp.hparams(hparams)\n","              accuracy = model_tune(hparams)\n","              tf.summary.scalar('accuracy', accuracy, step = 0)\n","          print(\"For the run {}, hparams num_units_1:{}, num_units_2:{}, dropout:{}, regularization_rate:{}, learning_rate:{}\".format(run_number, hparams[HP_NUM_UNITS_1], hparams[HP_NUM_UNITS_2],\n","                                                             hparams[HP_DROPOUT], hparams[HP_REGULARIZATION_RATE],\n","                                                             hparams[HP_LEARNING_RATE]))\n","          run_number += 1"]},{"cell_type":"markdown","metadata":{"id":"HQBpIvpDSyAD"},"source":["## Custom Training Loop"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_fgzFUyY_Jh0"},"outputs":[],"source":["OPTIMIZER = Adam(learning_rate = 0.01)\n","METRIC = BinaryAccuracy()\n","METRIC_VAL = BinaryAccuracy()\n","EPOCHS = CONFIGURATION['N_EPOCHS']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aO_TeQKoqBkf"},"outputs":[],"source":["CURRENT_TIME = datetime.datetime.now().strftime('%d%m%y - %h%m%s')\n","CUSTOM_TRAIN_DIR = './logs/' + CURRENT_TIME + '/custom/train'\n","CUSTOM_VAL_DIR = './logs/' + CURRENT_TIME + '/custom/val'\n","\n","custom_train_writer = tf.summary.create_file_writer(CUSTOM_TRAIN_DIR)\n","custom_val_writer = tf.summary.create_file_writer(CUSTOM_VAL_DIR)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3toQq0HNQofo"},"outputs":[],"source":["@tf.function\n","def training_block(x_batch, y_batch):\n","  with tf.GradientTape() as recorder:\n","      y_pred = lenet_model(x_batch, training = True)\n","      loss = custom_bce(y_batch, y_pred)\n","\n","  #wandb.log({'loss':loss.numpy()})\n","  partial_derivatives = recorder.gradient(loss, lenet_model.trainable_weights)\n","  OPTIMIZER.apply_gradients(zip(partial_derivatives, lenet_model.trainable_weights))\n","  METRIC.update_state(y_batch, y_pred)\n","  return loss\n","\n","@tf.function\n","def val_block(x_batch_val, y_batch_val):\n","    y_pred_val = lenet_model(x_batch_val, training = False)\n","    loss_val = custom_bce(y_batch_val, y_pred_val)\n","    METRIC_VAL.update_state(y_batch_val, y_pred_val)\n","    return loss_val"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"A8UMV43MXZyF"},"outputs":[],"source":["def neuralearn(model, loss_function, METRIC, VAL_METRIC, OPTIMIZER, train_dataset, val_dataset, EPOCHS):\n","  for epoch in range(EPOCHS):\n","    print(\"Training starts for epoch number {}\".format(epoch+1))\n","    for step, (x_batch, y_batch) in enumerate(train_dataset):\n","      loss = training_block(x_batch, y_batch)\n","      \n","    print(\"Training Loss\", loss)\n","    print(\"The accuracy is: \", METRIC.result())\n","\n","    with custom_train_writer.as_default():\n","      tf.summary.scalar('Training Loss', data = loss, step = epoch)\n","    with custom_train_writer.as_default():\n","      tf.summary.scalar('Training Accuracy', data = METRIC.result(), step = epoch)\n","      \n","    METRIC.reset_states()\n","\n","    for (x_batch_val, y_batch_val) in val_dataset:\n","      loss_val = val_block(x_batch_val, y_batch_val)\n","\n","    print(\"The Validation loss\", loss_val)\n","    print(\"The Validation accuracy is: \", METRIC_VAL.result())\n","\n","    with custom_val_writer.as_default():\n","      tf.summary.scalar('Validation Loss', data = loss_val, step = epoch)\n","    with custom_val_writer.as_default():\n","      tf.summary.scalar('Validation Accuracy', data = METRIC_VAL.result(), step = epoch)\n","      \n","    METRIC_VAL.reset_states()\n","  print(\"Training Complete!!!!\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CkAe8ZV0YO9d"},"outputs":[],"source":["neuralearn(lenet_model, custom_bce, METRIC, METRIC_VAL, OPTIMIZER, train_dataset, val_dataset, EPOCHS)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EqTbzoj2266w"},"outputs":[],"source":["# image = cv2.imread('cell.jpg')\n","# print(image.shape)\n","# image = tf.expand_dims(image, axis = 0)\n","# print(image.shape)\n","\n","# lenet_model.predict(image)"]},{"cell_type":"markdown","metadata":{"id":"qdq-5nzgBgC1"},"source":["## Visualizations"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nURZ0KN8SAAK"},"outputs":[],"source":["%load_ext tensorboard"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qmgGVhNwJJ4F"},"outputs":[],"source":["tensorboard --logdir=logs"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lKt3lx4N0hfv"},"outputs":[],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('Model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train_loss', 'val_loss'])\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N4FAk3gY0hg8"},"outputs":[],"source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","plt.title('Model Accuracy')\n","plt.ylabel('Accuracy')\n","plt.xlabel('Epoch')\n","plt.legend(['train_accuracy', 'val_accuracy'])\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"1h_6C48b86CN"},"source":["# **Model Evaluation and Testing**"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yRGfTlSA-5Gp"},"outputs":[],"source":["test_dataset = test_dataset.batch(1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H1lUnFwwm9bc"},"outputs":[],"source":["lenet_model.evaluate(test_dataset)"]},{"cell_type":"markdown","metadata":{"id":"dyUnLjaOJRZN"},"source":["## Visualizing Confusion Matrix"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QAbnEi-StSqV"},"outputs":[],"source":["labels = []\n","inp = []\n","# for t in test_dataset:\n","#   print(t)\n","#   break\n","for x,y in test_dataset.as_numpy_iterator():\n","  labels.append(y)\n","  inp.append(x)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Kzk8ziZM06PP"},"outputs":[],"source":["print(np.array(inp).shape)\n","print(np.array(inp)[:,0,...].shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9zz5CtEZLigm"},"outputs":[],"source":["labels = np.array([i[0] for i in labels])\n","print(labels)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4wk_Vr8iOQN7"},"outputs":[],"source":["predicted = lenet_model.predict(np.array(inp)[:,0,...])\n","print(predicted[:,0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JdBB3_-RO2nf"},"outputs":[],"source":["threshold = 0.5\n","\n","cm = confusion_matrix(labels, predicted > threshold)\n","print(cm)\n","plt.figure(figsize=(8,8))\n","\n","sns.heatmap(cm, annot=True,)\n","plt.title('Confusion matrix - {}'.format(threshold))\n","plt.ylabel('Actual')\n","plt.xlabel('Predicted')\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XFbX78RpN3iC"},"outputs":[],"source":["#tp: 1267.0000 - fp: 99.0000 - tn: 1298.0000 - fn: 93.0000"]},{"cell_type":"markdown","metadata":{"id":"X6iDg1tvHFkz"},"source":["## ROC Plots"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-ZUiZnMGN-7-"},"outputs":[],"source":["fp, tp, thresholds = roc_curve(labels, predicted)\n","plt.plot(fp, tp)\n","plt.xlabel(\"False Positive rate\")\n","plt.ylabel(\"True Positive rate\")\n","\n","plt.grid()\n","\n","skip = 20\n","\n","for i in range(0, len(thresholds), skip):\n","  plt.text(fp[i], tp[i], thresholds[i])\n","  \n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8BNXfkonHEeT"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k5Phk0rRm9dy"},"outputs":[],"source":["parasite_or_not(lenet_model.predict(test_dataset.take(1))[0][0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0aFmzOJCOnOt"},"outputs":[],"source":["def parasite_or_not(x):\n","  if(x<0.5):\n","    return str('P')\n","  else:\n","    return str('U')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y9vuhe3wm9gk"},"outputs":[],"source":["for i, (image, label) in enumerate(test_dataset.take(9)):\n","\n","  ax = plt.subplot(3, 3, i + 1)\n","  plt.imshow(image[0])\n","  plt.title(str(parasite_or_not(label.numpy()[0])) + \":\" + str(parasite_or_not(lenet_loaded_model.predict(image)[0][0])))\n","  \n","  plt.axis('off')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VXKMU54Im9jv"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"utvqGMT1Xvtv"},"source":["# Loading and Saving"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BkeTbJxZm9mS"},"outputs":[],"source":["lenet_model.save(\"lenet\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Yykivbhcm9o-"},"outputs":[],"source":["lenet_loaded_model = tf.keras.models.load_model(\"lenets\")\n","lenet_loaded_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3vKaCNEYm9rW"},"outputs":[],"source":["lenet_model.save(\"lenet.hdf5\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hlCI1ibKm9t4"},"outputs":[],"source":["lenet_loaded_model = tf.keras.models.load_model(\"lenet.hdf5\")\n","lenet_loaded_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eVam3ZQ2m9wQ"},"outputs":[],"source":["lenet_model.save_weights(\"weights/lenet_weights\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ff3EohH2m9yi"},"outputs":[],"source":["lenet_weights_model = lenet_model.load_weights(\"weights/lenet_weights\")"]},{"cell_type":"markdown","metadata":{"id":"0Cr7sRFKtYvO"},"source":["## Saving to and Loading from Google Drive"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TVJGZOsMecr_"},"outputs":[],"source":["drive.mount('/content/drive/')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-fgJf4GHecu8"},"outputs":[],"source":["!cp -r /content/lenet/ /content/drive/MyDrive/lenet_colab/ "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N5qR6IMXecxa"},"outputs":[],"source":["!cp -r /content/drive/MyDrive/lenet_colab/ /content/lenet_colab/ "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SJELckJC02YI"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EPcnmqoJcL7W"},"outputs":[],"source":["image_1 = cv2.resize(cv2.imread('car.jpg'), (2560, 1440))/255\n","image_2 = cv2.resize(cv2.imread('train.jpg'), (2560, 1440))/255\n","print(image_1.shape, image_2.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xfTgp71H4uwj"},"outputs":[],"source":["from matplotlib.pyplot import figure\n","\n","figure(figsize=(100, 100), dpi=80)\n","\n","lamda = 0.6\n","image = lamda*image_1 + (1-lamda)*image_2\n","\n","plt.imshow(image)\n","plt.axis('off')\n","plt.savefig('image.jpg')"]}],"metadata":{"accelerator":"TPU","colab":{"collapsed_sections":["DK7_p02_b6_m","-1QWh3KjbsuJ","3BUZMRuhV-HL","z_RZU3vxG2Aj","im-Eo6tvHgmM","IylKvmx5LUCR","PTW0fKw313gt","TeVM_g7JsaQr","AsB9YnCuPITj","peB3_ExnPQl0","hNqYhbE3pCzJ","kbm2WoMpfXod","11Wz41MHkzTu","3arkQ973eIjM","xgF_fkT-qngT","20ultY2FdfyX","413m-l0pttrt","lLaHf-B12OH8","P04zuKYv_hex","yvoXsM8zC6bD","ZEau-uvfRWA5","-t5zQwWckCS9","Rrm7NNzuDd5k","_AcriqBA2IU4","2bEezFeL_8tr","-0mircHogrvl","JhawCc8jf8JB","J3I7Zhyufjsf","DxMyYeM-DmlM","zzS9pcnOPRva","HQBpIvpDSyAD","qdq-5nzgBgC1","1h_6C48b86CN","dyUnLjaOJRZN","X6iDg1tvHFkz","utvqGMT1Xvtv","0Cr7sRFKtYvO"],"machine_shape":"hm","provenance":[{"file_id":"1uUH-asz3CFxlvld8uGx-m3crr4Iepp3u","timestamp":1673770117165}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

================================================
FILE: deep learning for computer vision/3-Malaria Detection by Neuralearn.ai [PyTorch Version]-.ipynb
================================================
{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"t7rPu5mRadux"},"outputs":[],"source":["import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import cv2## image processing\n","import os## operating system\n","import random\n","import copy\n","from google.colab import files\n","import torch, torchvision\n","from torchvision import transforms\n","from torch.utils.data import Dataset, DataLoader\n","from torch.optim import Adam\n","import albumentations as A\n","import torch.nn as nn\n","import torch.nn.functional as F"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yDSk5fM0ZTvi"},"outputs":[],"source":["! pip install -q kaggle"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"executionInfo":{"elapsed":361,"status":"ok","timestamp":1638671019530,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"YwShLu3GFUke","outputId":"12f94bd7-898e-4e3d-aa37-48191547cfcc"},"outputs":[{"ename":"NameError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-63b259460c61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'files' is not defined"]}],"source":["files.upload()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SXg3D5fqDJ_1"},"outputs":[],"source":["! mkdir ~/.kaggle\n","! cp kaggle.json ~/.kaggle/"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c4mQMvXCEEIO"},"outputs":[],"source":["!chmod 600 /root/.kaggle/kaggle.json"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6238,"status":"ok","timestamp":1638671027665,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"q7LIIlzWDwOt","outputId":"47aeceb2-011b-41fd-c629-b12ea5ba8188"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading cell-images-for-detecting-malaria.zip to /content\n"," 98% 663M/675M [00:05<00:00, 114MB/s] \n","100% 675M/675M [00:05<00:00, 132MB/s]\n"]}],"source":["!kaggle datasets download -d iarunava/cell-images-for-detecting-malaria"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22572,"status":"ok","timestamp":1638671050226,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"OC1fzx8QErbt","outputId":"6da3d296-3ca5-42d0-924d-e0da08b0b66f"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: cell_images/cell_images/Uninfected/C236ThinF_IMG_20151127_102428_cell_116.png  \n","  inflating: cell_images/cell_images/Uninfected/C236ThinF_IMG_20151127_102428_cell_118.png  \n","  inflating: cell_images/cell_images/Uninfected/C236ThinF_IMG_20151127_102428_cell_126.png  \n","  inflating: cell_images/cell_images/Uninfected/C236ThinF_IMG_20151127_102428_cell_134.png  \n","  inflating: 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cell_images/cell_images/Uninfected/C99P60ThinF_IMG_20150918_142128_cell_55.png  \n","  inflating: cell_images/cell_images/Uninfected/C99P60ThinF_IMG_20150918_142128_cell_56.png  \n","  inflating: cell_images/cell_images/Uninfected/Thumbs.db  \n"]}],"source":["!unzip /content/cell-images-for-detecting-malaria.zip"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":124,"status":"ok","timestamp":1638671050226,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"EQGKSG8SEN4M","outputId":"09825090-5096-4a75-ebaf-53a628627fa4"},"outputs":[{"name":"stdout","output_type":"stream","text":["5512 22046\n"]}],"source":["train_paths_parasitized = []\n","train_paths_uninfected = []\n","\n","train_paths_parasitized += os.listdir('cell_images/Parasitized')\n","train_paths_parasitized = ['cell_images/Parasitized/' + i for i in train_paths_parasitized]\n","\n","train_paths_uninfected += os.listdir('cell_images/Uninfected')\n","train_paths_uninfected = ['cell_images/Uninfected/' + i for i in train_paths_uninfected]\n","\n","paths = train_paths_parasitized + train_paths_uninfected\n","\n","paths.remove(\"cell_images/Parasitized/Thumbs.db\")\n","paths.remove(\"cell_images/Uninfected/Thumbs.db\")\n","\n","random.shuffle(paths)\n","\n","FRACTION = 0.8\n","train_paths = paths[0:int(FRACTION*len(paths))]\n","val_paths = paths[int(FRACTION*len(paths)):]\n","print(len(val_paths), len(train_paths))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qorCH6dYGi4D"},"outputs":[],"source":["class Malaria(Dataset):\n","  def __init__(self, image_filepaths, transform = None):\n","    self.image_filepaths = image_filepaths\n","    self.transform = transform\n","\n","  def __len__(self):\n","    return len(self.image_filepaths)\n","\n","  def __getitem__(self, index):\n","\n","    image = cv2.imread(self.image_filepaths[index])\n","    \n","    if(self.image_filepaths[index].split('/')[1] == 'Parasitized'):\n","      label = 0.0\n","    else:\n","      label = 1.0\n","\n","    \n","    if self.transform:\n","      image = self.transform(image = image)['image']\n","    \n","    return image, label\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"peMOaW12XNAR"},"outputs":[],"source":["#!pip install -U git+https://github.com/albu/albumentations --no-cache-dir"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J-RbuB4LHgXu"},"outputs":[],"source":["IM_SIZE = 224\n","transform = A.Compose([\n","                        A.Resize(IM_SIZE, IM_SIZE),\n","\n","                        A.OneOf([A.HorizontalFlip(),\n","                                 A.VerticalFlip(),], p = 0.3),\n","                        \n","                        A.RandomRotate90(),   \n","                        A.RandomBrightnessContrast(brightness_limit=0.2,\n","                                                  contrast_limit=0.2,\n","                                                  always_apply=False, p=0.5),\n","                        A.Normalize(),\n","                       \n","                        \n","])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UpRfnoPTWTBK"},"outputs":[],"source":["train_dataset = Malaria(train_paths, transform)\n","val_dataset = Malaria(val_paths, transform)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bkqYF2wBWTg9"},"outputs":[],"source":["BATCH_SIZE = 32\n","train_loader = DataLoader(train_dataset, batch_size = BATCH_SIZE, shuffle = True)\n","val_loader = DataLoader(val_dataset, batch_size = BATCH_SIZE)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1638676414473,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"gLDdwLaaY1ql","outputId":"f19a515f-820e-47e8-c75f-5e44931a99c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["tensor([[0.5536, 0.5364, 0.5364,  ..., 0.5707, 0.5707, 0.5536],\n","        [0.5707, 0.5536, 0.5536,  ..., 0.5536, 0.5707, 0.5707],\n","        [0.5536, 0.5536, 0.5707,  ..., 0.5707, 0.5707, 0.5707],\n","        ...,\n","        [0.4508, 0.3652, 0.2624,  ..., 0.6049, 0.6221, 0.6221],\n","        [0.4337, 0.3309, 0.2282,  ..., 0.6221, 0.6221, 0.6392],\n","        [0.3994, 0.3138, 0.2282,  ..., 0.6221, 0.6221, 0.6221]])\n"]}],"source":["image, label = next(iter(train_loader))\n","print(image[2][100:150,100:150,0])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":304},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1638676415076,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"x5B_eBXIY1tN","outputId":"e56ed392-b954-44d2-cd89-f199d68662dc"},"outputs":[{"name":"stderr","output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"name":"stdout","output_type":"stream","text":["tensor(1., dtype=torch.float64)\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.imshow(image[0])\n","print(label[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":345,"status":"ok","timestamp":1638678331484,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"XKQ4gII7gwis","outputId":"58b4e6ba-e5f6-41bd-f8a3-c7154ef56611"},"outputs":[{"data":{"text/plain":["tensor([[0.4654],\n","        [0.4730],\n","        [0.4654]], grad_fn=<SigmoidBackward0>)"]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["class LeNet(nn.Module):\n","    def __init__(self):\n","      super(LeNet, self).__init__()\n","      \n","      self.conv1 = nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=1)\n","      self.bn1 = nn.BatchNorm2d(6)\n","      self.pool1 = nn.MaxPool2d(2,2)\n","      self.dropout1 = nn.Dropout2d(0.2)\n","\n","      self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=3, stride=1, padding=1)\n","      self.bn2 = nn.BatchNorm2d(16)\n","      self.pool2 = nn.MaxPool2d(2,2)\n","\n","      self.fc3 = nn.Linear(50176, 100)\n","      self.bn3 = nn.BatchNorm1d(100)\n","      \n","      self.fc4 = nn.Linear(100, 10)\n","      self.bn4 = nn.BatchNorm1d(10)\n","      \n","      self.fc5 = nn.Linear(10, 1)\n","        \n","    def forward(self, input):\n","\n","      output = self.dropout1(self.pool1(self.bn1(F.relu(self.conv1(input)))))\n","      output = self.pool2(self.bn2(F.relu(self.conv2(output))))\n","      \n","      output = output.reshape(-1, 16*56*56)\n","      output = self.bn3(F.relu(self.fc3(output)))\n","      output = self.bn4(F.relu(self.fc4(output)))\n","\n","      output = torch.sigmoid(self.fc5(output))\n","        \n","      return output\n","\n","\n","model = LeNet()\n","\n","model(torch.zeros((3,3,224,224)))                   "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"07fa5B_shFjJ"},"outputs":[],"source":["device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","loss_fn = torch.nn.BCELoss()\n","optimizer = Adam(model.parameters(), lr=1e-3, )"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1638678332191,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"j8zf0w2RuGj0","outputId":"fcbfe8ce-af17-47dd-f7d9-af50292dc263"},"outputs":[{"name":"stdout","output_type":"stream","text":["cuda:0\n"]}],"source":["print(device)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jJInwi2tqrbU"},"outputs":[],"source":["def round(x):\n","  if(x>= 0.5):\n","    return 1.\n","  else:\n","    return 0."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"61YgLn5HpcbW"},"outputs":[],"source":["def get_accuracy(epoch):\n","  model.eval()\n","  with torch.no_grad():\n","    epoch_accuracy = 0 \n","    for i, (image, label) in enumerate(val_loader):\n","      image = torch.permute(image, (0,3,1,2))\n","      image = image.to(device)\n","\n","      output = model(image)\n","\n","      for i in range(len(output)):\n","        if(round(output[i].item()) == label[i].item()):\n","          epoch_accuracy += 1\n","\n","  print(\"The Validation Accuracy for this epoch:{} is:{} \".format(epoch, 100*epoch_accuracy/len(val_dataset)))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8jWXAI27kQUL"},"outputs":[],"source":["def train(EPOCHS):\n","\n","  model.to(device)\n","  \n","  for epoch in range(EPOCHS):\n","    epoch_loss = 0.0\n","\n","    for i, (image, label) in enumerate(train_loader):\n","\n","      image = torch.permute(image, (0,3,1,2))\n","      image = image.to(device)\n","      \n","      label = torch.unsqueeze(label, -1)\n","      label = label.float()\n","      label = label.to(device)\n","\n","      optimizer.zero_grad()\n","\n","      output = model(image)\n","\n","      loss = loss_fn(output, label)\n","\n","      loss.backward()\n","      optimizer.step()\n","\n","      epoch_loss += loss.item()\n","      \n","      step_length = int(len(train_loader)/2)\n","\n","      if(i%step_length) == 0:\n","              print('Epoch Number: {}, step: [{}|{}] ----> Loss: {}' .format(epoch+1, i, len(train_loader), loss.item()))\n","    print(\"Loss for epoch Number {} is :{}\".format(epoch+1, epoch_loss/len(train_loader)))\n","    \n","    get_accuracy(epoch)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1575443,"status":"ok","timestamp":1638680063140,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"},"user_tz":-60},"id":"JMLWJC2LkQWi","outputId":"59d4331c-aaed-4e27-b029-c79309abf4b3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch Number: 1, step: [0|689] ----> Loss: 0.7978537678718567\n","Epoch Number: 1, step: [344|689] ----> Loss: 0.443876177072525\n","Epoch Number: 1, step: [688|689] ----> Loss: 0.44570982456207275\n","Loss for epoch Number 1 is :0.5038634987199254\n","The Validation Accuracy for this epoch:0 is:92.48911465892598 \n","Epoch Number: 2, step: [0|689] ----> Loss: 0.2583308219909668\n","Epoch Number: 2, step: [344|689] ----> Loss: 0.2292211651802063\n","Epoch Number: 2, step: [688|689] ----> Loss: 0.23481419682502747\n","Loss for epoch Number 2 is :0.2619059797111461\n","The Validation Accuracy for this epoch:1 is:91.76342525399129 \n","Epoch Number: 3, step: [0|689] ----> Loss: 0.050250470638275146\n","Epoch Number: 3, step: [344|689] ----> Loss: 0.20530696213245392\n","Epoch Number: 3, step: [688|689] ----> Loss: 0.06257139891386032\n","Loss for epoch Number 3 is :0.19573745847894594\n","The Validation Accuracy for this epoch:2 is:93.86792452830188 \n","Epoch Number: 4, step: [0|689] ----> Loss: 0.18901333212852478\n","Epoch Number: 4, step: [344|689] ----> Loss: 0.1702648103237152\n","Epoch Number: 4, step: [688|689] ----> Loss: 0.07769569009542465\n","Loss for epoch Number 4 is :0.16940246861433428\n","The Validation Accuracy for this epoch:3 is:94.5754716981132 \n","Epoch Number: 5, step: [0|689] ----> Loss: 0.09062948822975159\n","Epoch Number: 5, step: [344|689] ----> Loss: 0.05602896958589554\n","Epoch Number: 5, step: [688|689] ----> Loss: 0.06608887016773224\n","Loss for epoch Number 5 is :0.15984227066029658\n","The Validation Accuracy for this epoch:4 is:94.68432510885341 \n","Epoch Number: 6, step: [0|689] ----> Loss: 0.25731274485588074\n","Epoch Number: 6, step: [344|689] ----> Loss: 0.1631825864315033\n","Epoch Number: 6, step: [688|689] ----> Loss: 0.18943215906620026\n","Loss for epoch Number 6 is :0.19078102029038185\n","The Validation Accuracy for this epoch:5 is:94.06748911465893 \n","Epoch Number: 7, step: [0|689] ----> Loss: 0.06417269259691238\n","Epoch Number: 7, step: [344|689] ----> Loss: 0.04551408439874649\n","Epoch Number: 7, step: [688|689] ----> Loss: 0.06926281005144119\n","Loss for epoch Number 7 is :0.1524542953916359\n","The Validation Accuracy for this epoch:6 is:94.26705370101597 \n","Epoch Number: 8, step: [0|689] ----> Loss: 0.05567912012338638\n","Epoch Number: 8, step: [344|689] ----> Loss: 0.4253551661968231\n","Epoch Number: 8, step: [688|689] ----> Loss: 0.1601109653711319\n","Loss for epoch Number 8 is :0.1410199522753131\n","The Validation Accuracy for this epoch:7 is:94.5210449927431 \n","Epoch Number: 9, step: [0|689] ----> Loss: 0.28627538681030273\n","Epoch Number: 9, step: [344|689] ----> Loss: 0.2703437805175781\n","Epoch Number: 9, step: [688|689] ----> Loss: 0.07228473573923111\n","Loss for epoch Number 9 is :0.1352588515485406\n","The Validation Accuracy for this epoch:8 is:94.73875181422352 \n","Epoch Number: 10, step: [0|689] ----> Loss: 0.08150535076856613\n","Epoch Number: 10, step: [344|689] ----> Loss: 0.01961003616452217\n","Epoch Number: 10, step: [688|689] ----> Loss: 0.17705705761909485\n","Loss for epoch Number 10 is :0.13057901210412493\n","The Validation Accuracy for this epoch:9 is:94.77503628447025 \n","Epoch Number: 11, step: [0|689] ----> Loss: 0.08929773420095444\n","Epoch Number: 11, step: [344|689] ----> Loss: 0.1760057508945465\n","Epoch Number: 11, step: [688|689] ----> Loss: 0.2690262496471405\n","Loss for epoch Number 11 is :0.12627804922940228\n","The Validation Accuracy for this epoch:10 is:95.2467343976778 \n","Epoch Number: 12, step: [0|689] ----> Loss: 0.28335022926330566\n","Epoch Number: 12, step: [344|689] ----> Loss: 0.13337093591690063\n","Epoch Number: 12, step: [688|689] ----> Loss: 0.09370359033346176\n","Loss for epoch Number 12 is :0.11307342310878077\n","The Validation Accuracy for this epoch:11 is:95.01088534107402 \n","Epoch Number: 13, step: [0|689] ----> Loss: 0.09644030034542084\n","Epoch Number: 13, step: [344|689] ----> Loss: 0.12287657707929611\n","Epoch Number: 13, step: [688|689] ----> Loss: 0.05549098923802376\n","Loss for epoch Number 13 is :0.11319551038919409\n","The Validation Accuracy for this epoch:12 is:94.5210449927431 \n","Epoch Number: 14, step: [0|689] ----> Loss: 0.03809714317321777\n","Epoch Number: 14, step: [344|689] ----> Loss: 0.16326573491096497\n","Epoch Number: 14, step: [688|689] ----> Loss: 0.07199307531118393\n","Loss for epoch Number 14 is :0.1092087013548286\n","The Validation Accuracy for this epoch:13 is:94.73875181422352 \n","Epoch Number: 15, step: [0|689] ----> Loss: 0.06696029752492905\n","Epoch Number: 15, step: [344|689] ----> Loss: 0.018146440386772156\n","Epoch Number: 15, step: [688|689] ----> Loss: 0.1607200652360916\n","Loss for epoch Number 15 is :0.10480997597674349\n","The Validation Accuracy for this epoch:14 is:95.22859216255442 \n","Epoch Number: 16, step: [0|689] ----> Loss: 0.0487273745238781\n","Epoch Number: 16, step: [344|689] ----> Loss: 0.226509690284729\n","Epoch Number: 16, step: [688|689] ----> Loss: 0.12734857201576233\n","Loss for epoch Number 16 is :0.1198067831541905\n","The Validation Accuracy for this epoch:15 is:95.06531204644412 \n","Epoch Number: 17, step: [0|689] ----> Loss: 0.12021400034427643\n","Epoch Number: 17, step: [344|689] ----> Loss: 0.07206504046916962\n","Epoch Number: 17, step: [688|689] ----> Loss: 0.22431977093219757\n","Loss for epoch Number 17 is :0.10368522169698349\n","The Validation Accuracy for this epoch:16 is:95.1923076923077 \n","Epoch Number: 18, step: [0|689] ----> Loss: 0.07299856841564178\n","Epoch Number: 18, step: [344|689] ----> Loss: 0.008264068514108658\n","Epoch Number: 18, step: [688|689] ----> Loss: 0.2893926501274109\n","Loss for epoch Number 18 is :0.10398396185811484\n","The Validation Accuracy for this epoch:17 is:95.08345428156748 \n","Epoch Number: 19, step: [0|689] ----> Loss: 0.2715624272823334\n","Epoch Number: 19, step: [344|689] ----> Loss: 0.013088630512356758\n","Epoch Number: 19, step: [688|689] ----> Loss: 0.01938236691057682\n","Loss for epoch Number 19 is :0.11086055970715605\n","The Validation Accuracy for this epoch:18 is:95.1923076923077 \n","Epoch Number: 20, step: [0|689] ----> Loss: 0.2551685571670532\n","Epoch Number: 20, step: [344|689] ----> Loss: 0.12322147935628891\n","Epoch Number: 20, step: [688|689] ----> Loss: 0.034166980534791946\n","Loss for epoch Number 20 is :0.09190776281421044\n","The Validation Accuracy for this epoch:19 is:94.99274310595065 \n"]}],"source":["train(20)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vzNB0o94kQY9"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BeAhqLSUkQf5"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"machine_shape":"hm","provenance":[{"file_id":"1fR4rnLD-0r72BN67eDO4yE5Zyf_v3Vlw","timestamp":1673770111968}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

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FILE: deep learning for computer vision/4-Human Emotions Detection by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"yIQNyieqUSN2"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import time\n","import random\n","from google.colab import files\n","from PIL import Image\n","import albumentations as A\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","import matplotlib.cm as cm\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n","                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n","                                     RandomContrast, Rescaling, Resizing, Reshape)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n","                                        ModelCheckpoint, ReduceLROnPlateau)\n","from tensorflow.keras.regularizers  import L2, L1\n","from tensorflow.train import BytesList, FloatList, Int64List\n","from tensorflow.train import Example, Features, Feature\n","from google.colab import drive"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yQADkLGk19n3"},"outputs":[],"source":["CONFIGURATION = {\n","    \"BATCH_SIZE\": 32,\n","    \"IM_SIZE\": 256,\n","    \"LEARNING_RATE\": 1e-3,\n","    \"N_EPOCHS\": 20,\n","    \"DROPOUT_RATE\": 0.0,\n","    \"REGULARIZATION_RATE\": 0.0,\n","    \"N_FILTERS\": 6,\n","    \"KERNEL_SIZE\": 3,\n","    \"N_STRIDES\": 1,\n","    \"POOL_SIZE\": 2,\n","    \"N_DENSE_1\": 1024,\n","    \"N_DENSE_2\": 128,\n","    \"NUM_CLASSES\": 3,\n","    \"PATCH_SIZE\": 16,\n","    \"PROJ_DIM\": 768,\n","    \"CLASS_NAMES\": [\"angry\", \"happy\", \"sad\"],\n","}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DWcp1Eyb2HiQ"},"outputs":[],"source":["train_directory = \"/content/dataset/Emotions Dataset/Emotions Dataset/train\"\n","val_directory = \"/content/dataset/Emotions Dataset/Emotions Dataset/test\""]},{"cell_type":"markdown","metadata":{"id":"jWFYC4TEJUIZ"},"source":["# Wandb Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7875,"status":"ok","timestamp":1653321087396,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"8dAxqjgsIc9g","outputId":"7450b16d-1f7d-4419-f740-b6fcfe9849b4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting wandb\n","  Downloading wandb-0.12.16-py2.py3-none-any.whl (1.8 MB)\n","\u001b[?25l\r\u001b[K     |▏                               | 10 kB 32.4 MB/s eta 0:00:01\r\u001b[K     |▍                               | 20 kB 8.0 MB/s eta 0:00:01\r\u001b[K     |▌                               | 30 kB 6.9 MB/s eta 0:00:01\r\u001b[K     |▊                               | 40 kB 3.4 MB/s eta 0:00:01\r\u001b[K     |█                               | 51 kB 3.4 MB/s eta 0:00:01\r\u001b[K     |█                               | 61 kB 4.0 MB/s eta 0:00:01\r\u001b[K     |█▎                       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filename=pathtools-0.1.2-py3-none-any.whl size=8806 sha256=f2376b6a40c79937e61f087c8729fe99cedfeaadc2e4a3db11cef68035dcc1a5\n","  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n","Successfully built pathtools\n","Installing collected packages: smmap, gitdb, shortuuid, setproctitle, sentry-sdk, pathtools, GitPython, docker-pycreds, wandb\n","Successfully installed GitPython-3.1.27 docker-pycreds-0.4.0 gitdb-4.0.9 pathtools-0.1.2 sentry-sdk-1.5.12 setproctitle-1.2.3 shortuuid-1.0.9 smmap-5.0.0 wandb-0.12.16\n"]}],"source":["!pip install wandb"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4Kp5Be8kIdn-"},"outputs":[],"source":["import wandb\n","from wandb.keras import WandbCallback"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39697,"status":"ok","timestamp":1653321131795,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"xYxm95YAIdqz","outputId":"cfa567d2-3b80-47a6-b083-d215f063368b"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n","\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit: \n","\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]}],"source":["!wandb login"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":108},"executionInfo":{"elapsed":14968,"status":"ok","timestamp":1653321156023,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"c3JI0X4eIds-","outputId":"eb49f258-f463-426e-8852-b45b1f2135a7"},"outputs":[{"name":"stderr","output_type":"stream","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mneuralearn\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"data":{"text/html":["Tracking run with wandb version 0.12.16"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Run data is saved locally in <code>/content/wandb/run-20220523_155231-1ttqi46q</code>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["Syncing run <strong><a href=\"https://wandb.ai/neuralearn/Emotion-Detection/runs/1ttqi46q\" target=\"_blank\">hopeful-plant-2</a></strong> to <a href=\"https://wandb.ai/neuralearn/Emotion-Detection\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src=\"https://wandb.ai/neuralearn/Emotion-Detection/runs/1ttqi46q?jupyter=true\" style=\"border:none;width:100%;height:420px;display:none;\"></iframe>"],"text/plain":["<wandb.sdk.wandb_run.Run at 0x7fa72caf5e90>"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["wandb.init(project=\"Emotion-Detection\", entity=\"neuralearn\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SmrdMc9h1dJg"},"outputs":[],"source":["wandb.config = CONFIGURATION"]},{"cell_type":"markdown","metadata":{"id":"SGMUuH-qtVxn"},"source":["# Data Management"]},{"cell_type":"markdown","metadata":{"id":"NFmFTOf4tXu-"},"source":["## Data Downloading"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pqPNSEQPdFDv"},"outputs":[],"source":["!pip install -q kaggle"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":23,"status":"ok","timestamp":1668544112542,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"TJZX6C11dFGI","colab":{"base_uri":"https://localhost:8080/"},"outputId":"f8f891b1-868a-4232-8f1d-0576734dec4b"},"outputs":[{"output_type":"stream","name":"stdout","text":["mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"]}],"source":["! mkdir ~/.kaggle\n","! cp kaggle.json ~/.kaggle/"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2f9SxjmgdFIg"},"outputs":[],"source":["!chmod 600 /root/.kaggle/kaggle.json"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23855,"status":"ok","timestamp":1668544136385,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"PB-U2h6ORTEX","outputId":"cc40bced-9efb-42ac-8221-22804547c065"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading human-emotions-datasethes.zip to /content\n","100% 308M/309M [00:21<00:00, 18.2MB/s]\n","100% 309M/309M [00:21<00:00, 14.8MB/s]\n"]}],"source":["!kaggle datasets download -d muhammadhananasghar/human-emotions-datasethes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8545,"status":"ok","timestamp":1668544144895,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"83kapvOERTMl","outputId":"4fb0dc5c-6bfc-4f28-a2f7-848e9c3f3f35"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/EmotionsDataset/data/nothing/720.jpg  \n","  inflating: /content/dataset/EmotionsDataset/data/nothing/721.jpg  \n","  inflating: /content/dataset/EmotionsDataset/data/nothing/722.jpg  \n","  inflating: /content/dataset/EmotionsDataset/data/nothing/723.jpg  \n","  inflating: /content/dataset/EmotionsDataset/data/nothing/724.jpg  \n","  inflating: 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Loading"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2005,"status":"ok","timestamp":1668506246463,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"QLS_pAJrZ5ZH","outputId":"cc0e4fd0-5d8f-491a-fcac-f5568bdd396d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 6799 files belonging to 3 classes.\n"]}],"source":["train_dataset = tf.keras.utils.image_dataset_from_directory(\n","    train_directory,\n","    labels='inferred',\n","    label_mode='categorical',\n","    class_names=CONFIGURATION[\"CLASS_NAMES\"],\n","    color_mode='rgb',\n","    batch_size=CONFIGURATION[\"BATCH_SIZE\"],\n","    image_size=(CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"]),\n","    shuffle=True,\n","    seed=99,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":442,"status":"ok","timestamp":1668410860951,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"dSJCmYW2Z5bY","outputId":"ee4d3c47-90ee-4606-f554-af3f769e5d5e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 2278 files belonging to 3 classes.\n"]}],"source":["val_dataset = tf.keras.utils.image_dataset_from_directory(\n","    val_directory,\n","    labels='inferred',\n","    label_mode='categorical',\n","    class_names=CONFIGURATION[\"CLASS_NAMES\"],\n","    color_mode='rgb',\n","    batch_size=1,#CONFIGURATION[\"BATCH_SIZE\"],\n","    image_size=(CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"]),\n","    shuffle=True,\n","    seed=99,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Pfx_7tnQZ5d9"},"outputs":[],"source":["# for i in val_dataset.take(1):\n","#   print(i)"]},{"cell_type":"markdown","metadata":{"id":"BPLxaV40t01K"},"source":["## Dataset Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":699},"executionInfo":{"elapsed":7030,"status":"ok","timestamp":1668410867980,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"8KT50MscfMQK","outputId":"6e45bf20-3a71-49e5-a431-d3c39dcba83c"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x864 with 16 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize = (12,12))\n","\n","for images, labels in train_dataset.take(1):\n","  for i in range(16):\n","    ax = plt.subplot(4,4, i+1)\n","    plt.imshow(images[i]/255.)\n","    plt.title(CONFIGURATION[\"CLASS_NAMES\"][tf.argmax(labels[i], axis = 0).numpy()])\n","    plt.axis(\"off\")"]},{"cell_type":"markdown","metadata":{"id":"9AT7j9A8_OZr"},"source":["## Data Augmentation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YHQWJZkx_UCT"},"outputs":[],"source":["### tf.keras.layer augment\n","augment_layers = tf.keras.Sequential([\n","  RandomRotation(factor = (-0.025, 0.025)),\n","  RandomFlip(mode='horizontal',),\n","  RandomContrast(factor=0.1),                   \n","])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Oaydf4xUgxw5"},"outputs":[],"source":["def augment_layer(image, label):\n","  return augment_layers(image, training = True), label"]},{"cell_type":"markdown","metadata":{"id":"dgJR73ZlBHog"},"source":["### Cutmix Augmentation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FGb8MrjpBJUt"},"outputs":[],"source":["def box(lamda):\n","  \n","  r_x = tf.cast(tfp.distributions.Uniform(0, CONFIGURATION[\"IM_SIZE\"]).sample(1)[0], dtype = tf.int32)\n","  r_y = tf.cast(tfp.distributions.Uniform(0, CONFIGURATION[\"IM_SIZE\"]).sample(1)[0], dtype = tf.int32)\n","\n","  r_w = tf.cast(CONFIGURATION[\"IM_SIZE\"]*tf.math.sqrt(1-lamda), dtype = tf.int32)\n","  r_h = tf.cast(CONFIGURATION[\"IM_SIZE\"]*tf.math.sqrt(1-lamda), dtype = tf.int32)\n","\n","  r_x = tf.clip_by_value(r_x - r_w//2, 0, CONFIGURATION[\"IM_SIZE\"])\n","  r_y = tf.clip_by_value(r_y - r_h//2, 0, CONFIGURATION[\"IM_SIZE\"])\n","\n","  x_b_r = tf.clip_by_value(r_x + r_w//2, 0, CONFIGURATION[\"IM_SIZE\"])\n","  y_b_r = tf.clip_by_value(r_y + r_h//2, 0, CONFIGURATION[\"IM_SIZE\"])\n","\n","  r_w = x_b_r - r_x\n","  if(r_w == 0):\n","    r_w  = 1\n","\n","  r_h = y_b_r - r_y\n","  if(r_h == 0):\n","    r_h = 1\n","\n","  return r_y, r_x, r_h, r_w"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7-VnA5E-BSP2"},"outputs":[],"source":["def cutmix(train_dataset_1, train_dataset_2):\n","  (image_1,label_1), (image_2, label_2) = train_dataset_1, train_dataset_2\n","\n","  lamda = tfp.distributions.Beta(2,2)\n","  lamda = lamda.sample(1)[0]\n","  \n","  r_y, r_x, r_h, r_w = box(lamda)\n","  crop_2 = tf.image.crop_to_bounding_box(image_2, r_y, r_x, r_h, r_w)\n","  pad_2 = tf.image.pad_to_bounding_box(crop_2, r_y, r_x, CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"])\n","\n","  crop_1 = tf.image.crop_to_bounding_box(image_1, r_y, r_x, r_h, r_w)\n","  pad_1 = tf.image.pad_to_bounding_box(crop_1, r_y, r_x, CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"])\n","\n","  image = image_1 - pad_1 + pad_2\n","\n","  lamda = tf.cast(1- (r_w*r_h)/(CONFIGURATION[\"IM_SIZE\"]*CONFIGURATION[\"IM_SIZE\"]), dtype = tf.float32)\n","  label = lamda*tf.cast(label_1, dtype = tf.float32) + (1-lamda)*tf.cast(label_2, dtype = tf.float32)\n","\n","  return image, label"]},{"cell_type":"markdown","metadata":{"id":"hjeXRtwQ16qd"},"source":["## Dataset Preparation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MtSK-eIdDv5e"},"outputs":[],"source":["# train_dataset_1 = train_dataset.map(augment_layer, num_parallel_calls = tf.data.AUTOTUNE)\n","# train_dataset_2 = train_dataset.map(augment_layer, num_parallel_calls = tf.data.AUTOTUNE)\n","\n","# mixed_dataset = tf.data.Dataset.zip((train_dataset_1, train_dataset_2))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"29PHr5k0Dy8r"},"outputs":[],"source":["\n","# training_dataset = (\n","#     mixed_dataset\n","#     .map(cutmix, num_parallel_calls = tf.data.AUTOTUNE)\n","#     .prefetch(tf.data.AUTOTUNE)\n","# )"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2829,"status":"ok","timestamp":1668410870801,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"AUbaAaWffPZx","outputId":"7f606b17-ac6e-41e3-9283-08389abe8266"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n","WARNING:tensorflow:Using a while_loop for converting Bitcast\n","WARNING:tensorflow:Using a while_loop for converting Bitcast\n","WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n","WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n","WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n","WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n","WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip\n","WARNING:tensorflow:Using a while_loop for converting Bitcast\n","WARNING:tensorflow:Using a while_loop for converting Bitcast\n","WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2\n","WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter\n","WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2\n","WARNING:tensorflow:Using a while_loop for converting AdjustContrastv2\n"]}],"source":["training_dataset = (\n","    train_dataset\n","    .map(augment_layer, num_parallel_calls = tf.data.AUTOTUNE)\n","    .prefetch(tf.data.AUTOTUNE)\n",")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IJvXvR0Tu3ob"},"outputs":[],"source":["validation_dataset = (\n","    val_dataset\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Kxwl9l1Pu3qy"},"outputs":[],"source":["resize_rescale_layers = tf.keras.Sequential([\n","       Resizing(CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"]),\n","       Rescaling(1./255),                 \n","])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1668410870802,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ie9e9QLxfP0S","outputId":"993dadf5-a72c-4b81-fee7-53cd75908b67"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=(TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 3), dtype=tf.float32, name=None))>"]},"metadata":{},"execution_count":20}],"source":["training_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1668410870802,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ZCtC1V3yZv_b","outputId":"97be86d9-0712-4284-b2db-d492c88c7203"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=(TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 3), dtype=tf.float32, name=None))>"]},"metadata":{},"execution_count":21}],"source":["validation_dataset"]},{"cell_type":"markdown","metadata":{"id":"UAxh8kasx0C_"},"source":["# Modeling "]},{"cell_type":"markdown","metadata":{"id":"9uPF-UuxLnOI"},"source":["## Lenet"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":718,"status":"ok","timestamp":1663237489218,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"MUAOxeMtxu3W","outputId":"9f1cfa61-a80b-4a34-9625-f275245f0305"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," sequential_1 (Sequential)   (None, 256, 256, 3)       0         \n","                                                                 \n"," conv2d (Conv2D)             (None, 254, 254, 6)       168       \n","                                                                 \n"," batch_normalization (BatchN  (None, 254, 254, 6)      24        \n"," ormalization)                                                   \n","                                                                 \n"," max_pooling2d (MaxPooling2D  (None, 127, 127, 6)      0         \n"," )                                                               \n","                                                                 \n"," dropout (Dropout)           (None, 127, 127, 6)       0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 125, 125, 16)      880       \n","                                                                 \n"," batch_normalization_1 (Batc  (None, 125, 125, 16)     64        \n"," hNormalization)                                                 \n","                                                                 \n"," max_pooling2d_1 (MaxPooling  (None, 62, 62, 16)       0         \n"," 2D)                                                             \n","                                                                 \n"," flatten (Flatten)           (None, 61504)             0         \n","                                                                 \n"," dense (Dense)               (None, 1024)              62981120  \n","                                                                 \n"," batch_normalization_2 (Batc  (None, 1024)             4096      \n"," hNormalization)                                                 \n","                                                                 \n"," dropout_1 (Dropout)         (None, 1024)              0         \n","                                                                 \n"," dense_1 (Dense)             (None, 128)               131200    \n","                                                                 \n"," batch_normalization_3 (Batc  (None, 128)              512       \n"," hNormalization)                                                 \n","                                                                 \n"," dense_2 (Dense)             (None, 3)                 387       \n","                                                                 \n","=================================================================\n","Total params: 63,118,451\n","Trainable params: 63,116,103\n","Non-trainable params: 2,348\n","_________________________________________________________________\n"]}],"source":["lenet_model = tf.keras.Sequential(\n","    [\n","    InputLayer(input_shape = (None, None, 3), ),\n","    \n","    resize_rescale_layers,\n","    \n","    Conv2D(filters = CONFIGURATION[\"N_FILTERS\"] , kernel_size = CONFIGURATION[\"KERNEL_SIZE\"], strides = CONFIGURATION[\"N_STRIDES\"] , padding='valid',\n","          activation = 'relu',kernel_regularizer = L2(CONFIGURATION[\"REGULARIZATION_RATE\"])),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = CONFIGURATION[\"POOL_SIZE\"], strides= CONFIGURATION[\"N_STRIDES\"]*2),\n","    Dropout(rate = CONFIGURATION[\"DROPOUT_RATE\"] ),\n","\n","    Conv2D(filters = CONFIGURATION[\"N_FILTERS\"]*2 + 4, kernel_size = CONFIGURATION[\"KERNEL_SIZE\"], strides=CONFIGURATION[\"N_STRIDES\"], padding='valid',\n","          activation = 'relu', kernel_regularizer = L2(CONFIGURATION[\"REGULARIZATION_RATE\"])),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = CONFIGURATION[\"POOL_SIZE\"], strides= CONFIGURATION[\"N_STRIDES\"]*2),\n","\n","    Flatten(),\n","    \n","    Dense( CONFIGURATION[\"N_DENSE_1\"], activation = \"relu\", kernel_regularizer = L2(CONFIGURATION[\"REGULARIZATION_RATE\"])),\n","    BatchNormalization(),\n","    Dropout(rate = CONFIGURATION[\"DROPOUT_RATE\"]),\n","    \n","    Dense( CONFIGURATION['N_DENSE_2'], activation = \"relu\", kernel_regularizer = L2(CONFIGURATION[\"REGULARIZATION_RATE\"])),\n","    BatchNormalization(),\n","\n","    Dense(CONFIGURATION[\"NUM_CLASSES\"], activation = \"softmax\"),\n","\n","])\n","\n","lenet_model.summary()"]},{"cell_type":"markdown","metadata":{"id":"offbwo3oLtkw"},"source":["## ResNet34"]},{"cell_type":"markdown","metadata":{"id":"ZzEMtSW6Bj-e"},"source":["### CustomConv2D"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zdqXd0X7LyX5"},"outputs":[],"source":["class CustomConv2D(Layer):\n","  def __init__(self, n_filters, kernel_size, n_strides, padding = 'valid'):\n","    super(CustomConv2D, self).__init__(name = 'custom_conv2d')\n","\n","    self.conv = Conv2D(\n","        filters = n_filters,\n","        kernel_size = kernel_size,\n","        activation = 'relu',\n","        strides = n_strides,\n","        padding = padding)\n","    \n","    self.batch_norm = BatchNormalization()\n","\n","  def call(self, x, training = True):\n","\n","    x = self.conv(x)\n","    x = self.batch_norm(x, training)\n","\n","    return x"]},{"cell_type":"markdown","metadata":{"id":"QPDIkDLCBxEU"},"source":["### Residual Block"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hF5YZKUaBxbG"},"outputs":[],"source":["class ResidualBlock(Layer):\n","  def __init__(self, n_channels, n_strides = 1):\n","    super(ResidualBlock, self).__init__(name = 'res_block')\n","\n","    self.dotted = (n_strides != 1)\n","\n","    self.custom_conv_1 = CustomConv2D(n_channels, 3, n_strides, padding = \"same\")\n","    self.custom_conv_2 = CustomConv2D(n_channels, 3, 1, padding = \"same\")\n","\n","    self.activation = Activation('relu')\n","\n","    if self.dotted:\n","      self.custom_conv_3 = CustomConv2D(n_channels, 1, n_strides)\n","    \n","  def call(self, input, training):\n","\n","    x = self.custom_conv_1(input, training)\n","    x = self.custom_conv_2(x, training)\n","\n","    if self.dotted:\n","      x_add = self.custom_conv_3(input, training)\n","      x_add = Add()([x, x_add])\n","    else:\n","      x_add = Add()([x, input])\n","\n","    return self.activation(x_add)\n"]},{"cell_type":"markdown","metadata":{"id":"WoUIuhBeBxuL"},"source":["### Complete Network"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hjijTufpBx7s"},"outputs":[],"source":["class ResNet34(Model):\n","  def __init__(self,):\n","    super(ResNet34, self).__init__(name = 'resnet_34')\n","    \n","    self.conv_1 = CustomConv2D(64, 7, 2, padding = 'same')\n","    self.max_pool = MaxPooling2D(3,2)\n","    \n","    self.conv_2_1 = ResidualBlock(64)\n","    self.conv_2_2 = ResidualBlock(64)\n","    self.conv_2_3 = ResidualBlock(64)\n","    \n","    self.conv_3_1 = ResidualBlock(128, 2)\n","    self.conv_3_2 = ResidualBlock(128)\n","    self.conv_3_3 = ResidualBlock(128)\n","    self.conv_3_4 = ResidualBlock(128)\n","\n","    self.conv_4_1 = ResidualBlock(256, 2)\n","    self.conv_4_2 = ResidualBlock(256)\n","    self.conv_4_3 = ResidualBlock(256)\n","    self.conv_4_4 = ResidualBlock(256)\n","    self.conv_4_5 = ResidualBlock(256)\n","    self.conv_4_6 = ResidualBlock(256)\n","    \n","    self.conv_5_1 = ResidualBlock(512, 2)\n","    self.conv_5_2 = ResidualBlock(512)\n","    self.conv_5_3 = ResidualBlock(512)\n","\n","    self.global_pool = GlobalAveragePooling2D()\n","\n","    self.fc_3 = Dense(CONFIGURATION[\"NUM_CLASSES\"], activation = 'softmax')\n","    \n","  def call(self, x, training = True):\n","    x = self.conv_1(x)\n","    x = self.max_pool(x)\n","\n","    x = self.conv_2_1(x, training)\n","    x = self.conv_2_2(x, training)\n","    x = self.conv_2_3(x, training)\n","    \n","    x = self.conv_3_1(x, training)\n","    x = self.conv_3_2(x, training)\n","    x = self.conv_3_3(x, training)\n","    x = self.conv_3_4(x, training)\n","    \n","    x = self.conv_4_1(x, training)\n","    x = self.conv_4_2(x, training)\n","    x = self.conv_4_3(x, training)\n","    x = self.conv_4_4(x, training)\n","    x = self.conv_4_5(x, training)\n","    x = self.conv_4_6(x, training)\n","    \n","    x = self.conv_5_1(x, training)\n","    x = self.conv_5_2(x, training)\n","    x = self.conv_5_3(x, training)\n","\n","    x = self.global_pool(x)\n","    \n","    return self.fc_3(x)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8748,"status":"ok","timestamp":1652466935535,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"tTwbOSd0G3Qi","outputId":"44c87fdb-4ab1-4fbe-f3ca-0424576463ba"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"resnet_34\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," custom_conv2d (CustomConv2D  multiple                 9728      \n"," )                                                               \n","                                                                 \n"," max_pooling2d (MaxPooling2D  multiple                 0         \n"," )                                                               \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  74368     \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  74368     \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  74368     \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  231296    \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  296192    \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  296192    \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  296192    \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  921344    \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  1182208   \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  1182208   \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  1182208   \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  1182208   \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  1182208   \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  3677696   \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  4723712   \n","                                                                 \n"," res_block (ResidualBlock)   multiple                  4723712   \n","                                                                 \n"," global_average_pooling2d (G  multiple                 0         \n"," lobalAveragePooling2D)                                          \n","                                                                 \n"," dense (Dense)               multiple                  1539      \n","                                                                 \n","=================================================================\n","Total params: 21,311,747\n","Trainable params: 21,294,723\n","Non-trainable params: 17,024\n","_________________________________________________________________\n"]}],"source":["resnet_34 = ResNet34()\n","resnet_34(tf.zeros([1,256,256,3]), training = False)\n","resnet_34.summary()"]},{"cell_type":"markdown","metadata":{"id":"58KFNBoijOwU"},"source":["## Transfer Learning with EfficientNet"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":170,"status":"ok","timestamp":1668178419552,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"H7jBLyR4qbGh","outputId":"8206f856-99ee-4238-a03c-2f06ed6708aa"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb4_notop.h5\n","71686520/71686520 [==============================] - 1s 0us/step\n"]}],"source":["backbone = tf.keras.applications.efficientnet.EfficientNetB4(\n","    include_top = False,\n","    weights='imagenet',\n","    input_shape=(CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"], 3),\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W-4oY6p_jZBU"},"outputs":[],"source":["backbone.trainable = False"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1419,"status":"ok","timestamp":1668174420394,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"iqp-SwQHqwKx","outputId":"a457bb1b-8a52-4f26-8bcc-569fc12ed64e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," efficientnetb4 (Functional)  (None, 8, 8, 1792)       17673823  \n","                                                                 \n"," global_average_pooling2d (G  (None, 1792)             0         \n"," lobalAveragePooling2D)                                          \n","                                                                 \n"," dense (Dense)               (None, 1024)              1836032   \n","                                                                 \n"," batch_normalization (BatchN  (None, 1024)             4096      \n"," ormalization)                                                   \n","                                                                 \n"," dense_1 (Dense)             (None, 128)               131200    \n","                                                                 \n"," dense_2 (Dense)             (None, 3)                 387       \n","                                                                 \n","=================================================================\n","Total params: 19,645,538\n","Trainable params: 1,969,667\n","Non-trainable params: 17,675,871\n","_________________________________________________________________\n"]}],"source":["pretrained_model = tf.keras.Sequential([\n","    Input(shape = (CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"],3)),\n","    backbone,\n","    GlobalAveragePooling2D(),\n","    Dense( CONFIGURATION[\"N_DENSE_1\"], activation = \"relu\"),\n","    BatchNormalization(),\n","    Dense( CONFIGURATION[\"N_DENSE_2\"], activation = \"relu\"),\n","    Dense( CONFIGURATION[\"NUM_CLASSES\"], activation = \"softmax\"),\n","    \n","    ])\n","pretrained_model.summary()"]},{"cell_type":"markdown","metadata":{"id":"PcqeUKcOso2J"},"source":["## Transfer Learning with MobileNetV2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1435,"status":"ok","timestamp":1653569672799,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"TtcLo5K1sren","outputId":"2c2e014e-b533-4cf0-f519-c33df9df7d43"},"outputs":[{"name":"stdout","output_type":"stream","text":["WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"]},{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n","9412608/9406464 [==============================] - 0s 0us/step\n","9420800/9406464 [==============================] - 0s 0us/step\n"]}],"source":["backbone = tf.keras.applications.mobilenet_v2.MobileNetV2(\n","    include_top = False,\n","    weights='imagenet',\n","    input_shape=(CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"], 3),\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BKN0TknGtQdy"},"outputs":[],"source":["backbone.trainable = False"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":339,"status":"ok","timestamp":1653459178666,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"CjMHv91RtQgL","outputId":"9f0615eb-314d-4218-8335-c43863ca3f39"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential_3\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," mobilenetv2_1.00_224 (Funct  (None, 8, 8, 1280)       2257984   \n"," ional)                                                          \n","                                                                 \n"," global_average_pooling2d (G  (None, 1280)             0         \n"," lobalAveragePooling2D)                                          \n","                                                                 \n"," dense (Dense)               (None, 1024)              1311744   \n","                                                                 \n"," batch_normalization (BatchN  (None, 1024)             4096      \n"," ormalization)                                                   \n","                                                                 \n"," dense_1 (Dense)             (None, 128)               131200    \n","                                                                 \n"," dense_2 (Dense)             (None, 3)                 387       \n","                                                                 \n","=================================================================\n","Total params: 3,705,411\n","Trainable params: 1,445,379\n","Non-trainable params: 2,260,032\n","_________________________________________________________________\n"]}],"source":["pretrained_model = tf.keras.Sequential([\n","    Input(shape = (CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"],3)),\n","    backbone,\n","    GlobalAveragePooling2D(),\n","    Dense( CONFIGURATION[\"N_DENSE_1\"], activation = \"relu\"),\n","    BatchNormalization(),\n","    Dense( CONFIGURATION[\"N_DENSE_2\"], activation = \"relu\"),\n","    Dense( CONFIGURATION[\"NUM_CLASSES\"], activation = \"softmax\"),\n","    \n","    ])\n","pretrained_model.summary()"]},{"cell_type":"markdown","metadata":{"id":"VXvsxcNHBmrb"},"source":["## FineTuning EficientNet"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AH3VfsuJKmoJ"},"outputs":[],"source":["backbone.trainable = True"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x9NKJUrW3ULh"},"outputs":[],"source":["input = Input(shape = (CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"],3))\n","\n","x = backbone(input, training = False)\n","x = GlobalAveragePooling2D()(x)\n","x = Dense( CONFIGURATION[\"N_DENSE_1\"], activation = \"relu\")(x)\n","x = BatchNormalization()(x)\n","x = Dense( CONFIGURATION[\"N_DENSE_2\"], activation = \"relu\")(x)\n","output = Dense( CONFIGURATION[\"NUM_CLASSES\"], activation = \"softmax\")(x)\n","\n","finetuned_model = Model(input, output)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1668179057680,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"nof71YUr8yT3","outputId":"fdb5a282-b919-4ee9-dd7f-ad7e9d2069d3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_3\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_5 (InputLayer)        [(None, 256, 256, 3)]     0         \n","                                                                 \n"," efficientnetb4 (Functional)  (None, 8, 8, 1792)       17673823  \n","                                                                 \n"," global_average_pooling2d_3   (None, 1792)             0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dense_9 (Dense)             (None, 2048)              3672064   \n","                                                                 \n"," batch_normalization_3 (Batc  (None, 2048)             8192      \n"," hNormalization)                                                 \n","                                                                 \n"," dense_10 (Dense)            (None, 128)               262272    \n","                                                                 \n"," dense_11 (Dense)            (None, 3)                 387       \n","                                                                 \n","=================================================================\n","Total params: 21,616,738\n","Trainable params: 3,938,819\n","Non-trainable params: 17,677,919\n","_________________________________________________________________\n"]}],"source":["finetuned_model.summary()"]},{"cell_type":"markdown","metadata":{"id":"-FY0CxZBn6mk"},"source":["## Vision Transformers"]},{"cell_type":"markdown","metadata":{"id":"ndQG-N_UoFX3"},"source":["### Patch Encoder"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qbxiq8r4fBKk"},"outputs":[],"source":["test_image = cv2.imread(\"/content/dataset/Emotions Dataset/Emotions Dataset/train/happy/387249.jpg\")\n","test_image = cv2.resize(test_image, (CONFIGURATION[\"IM_SIZE\"] ,CONFIGURATION[\"IM_SIZE\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g8XY8Ks7fClk"},"outputs":[],"source":["patches = tf.image.extract_patches(images=tf.expand_dims(test_image, axis = 0),\n","                           sizes=[1, CONFIGURATION[\"PATCH_SIZE\"], CONFIGURATION[\"PATCH_SIZE\"], 1],\n","                           strides=[1, CONFIGURATION[\"PATCH_SIZE\"], CONFIGURATION[\"PATCH_SIZE\"], 1],\n","                           rates=[1, 1, 1, 1],\n","                           padding='VALID')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":29,"status":"ok","timestamp":1653233461600,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ZNfbWM_pfCnw","outputId":"d3a18453-7071-4413-dc89-7296248eb5f3"},"outputs":[{"name":"stdout","output_type":"stream","text":["(1, 16, 16, 768)\n","(1, 256, 768)\n"]}],"source":["print(patches.shape)\n","patches = tf.reshape(patches, (patches.shape[0], -1, 768))\n","print(patches.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"executionInfo":{"elapsed":7347,"status":"ok","timestamp":1653233468921,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"3DceVLPzfCp8","outputId":"95aa74b1-3e44-4245-e156-7e8ea434b045"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 256 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize = (8,8))\n","\n","for i in range(patches.shape[1]):\n","\n","    ax = plt.subplot(16,16, i+1)\n","    plt.imshow(tf.reshape(patches[0,i,:], (16,16,3)))\n","    plt.axis(\"off\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FpsbFk5Lv2Jp"},"outputs":[],"source":["class PatchEncoder(Layer):\n","  def __init__(self, N_PATCHES, HIDDEN_SIZE):\n","    super(PatchEncoder, self).__init__(name = 'patch_encoder')\n","\n","    self.linear_projection = Dense(HIDDEN_SIZE)\n","    self.positional_embedding = Embedding(N_PATCHES, HIDDEN_SIZE )\n","    self.N_PATCHES = N_PATCHES\n","\n","  def call(self, x):\n","    patches = tf.image.extract_patches(\n","        images=x,\n","        sizes=[1, CONFIGURATION[\"PATCH_SIZE\"], CONFIGURATION[\"PATCH_SIZE\"], 1],\n","        strides=[1, CONFIGURATION[\"PATCH_SIZE\"], CONFIGURATION[\"PATCH_SIZE\"], 1],\n","        rates=[1, 1, 1, 1],\n","        padding='VALID')\n","    \n","    patches = tf.reshape(patches, (tf.shape(patches)[0], 256, patches.shape[-1]))\n","    \n","    embedding_input = tf.range(start = 0, limit = self.N_PATCHES, delta = 1 )\n","    output = self.linear_projection(patches) + self.positional_embedding(embedding_input)\n","    \n","    return output"]},{"cell_type":"markdown","metadata":{"id":"F8jUDzpGuoRF"},"source":["### TransformerEncoder"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6i526cssxMea"},"outputs":[],"source":["class TransformerEncoder(Layer):\n","  def __init__(self, N_HEADS, HIDDEN_SIZE):\n","    super(TransformerEncoder, self).__init__(name = 'transformer_encoder')\n","\n","    self.layer_norm_1 = LayerNormalization()\n","    self.layer_norm_2 = LayerNormalization()\n","    \n","    self.multi_head_att = MultiHeadAttention(N_HEADS, HIDDEN_SIZE )\n","    \n","    self.dense_1 = Dense(HIDDEN_SIZE, activation = tf.nn.gelu)\n","    self.dense_2 = Dense(HIDDEN_SIZE, activation = tf.nn.gelu)\n","    \n","  def call(self, input):\n","    x_1 = self.layer_norm_1(input)\n","    x_1 = self.multi_head_att(x_1, x_1)\n","\n","    x_1 = Add()([x_1, input])\n","\n","    x_2 = self.layer_norm_2(x_1)\n","    x_2 = self.dense_1(x_2)\n","    output = self.dense_2(x_2)\n","    output = Add()([output, x_1])\n","\n","    return output"]},{"cell_type":"markdown","metadata":{"id":"d3WiHKq-urUU"},"source":["### ViT Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rQ0csggeyTxh"},"outputs":[],"source":["class ViT(Model):\n","  def __init__(self, N_HEADS, HIDDEN_SIZE, N_PATCHES, N_LAYERS, N_DENSE_UNITS):\n","    super(ViT, self).__init__(name = 'vision_transformer')\n","    self.N_LAYERS = N_LAYERS\n","    self.patch_encoder = PatchEncoder(N_PATCHES, HIDDEN_SIZE)\n","    self.trans_encoders = [TransformerEncoder(N_HEADS, HIDDEN_SIZE) for _ in range(N_LAYERS)]\n","    self.dense_1 = Dense(N_DENSE_UNITS, tf.nn.gelu)\n","    self.dense_2 = Dense(N_DENSE_UNITS, tf.nn.gelu)\n","    self.dense_3 = Dense(CONFIGURATION[\"NUM_CLASSES\"], activation = 'softmax')\n","  def call(self, input, training = True):\n","\n","    x = self.patch_encoder(input)\n","\n","    for i in range(self.N_LAYERS):\n","      x = self.trans_encoders[i](x)\n","    x = Flatten()(x)\n","    x = self.dense_1(x)\n","    x = self.dense_2(x)\n","    \n","    return self.dense_3(x)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1653233753299,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"M9V1e6z9yT2W","outputId":"b67f7b30-e256-4811-a892-b3eb0bec9ba4"},"outputs":[{"data":{"text/plain":["<tf.Tensor: shape=(2, 3), dtype=float32, numpy=\n","array([[0.17217974, 0.352223  , 0.4755972 ],\n","       [0.17217974, 0.352223  , 0.4755972 ]], dtype=float32)>"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["vit = ViT(\n","    N_HEADS = 4, HIDDEN_SIZE = 768, N_PATCHES = 256,\n","    N_LAYERS = 2, N_DENSE_UNITS = 128)\n","vit(tf.zeros([2,256,256,3]))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1653233753299,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"gbNhFtW1yT4d","outputId":"76b2caa2-e7bc-4b64-a7fe-ae9e3ac3cf18"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"vision_transformer\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," patch_encoder (PatchEncoder  multiple                 787200    \n"," )                                                               \n","                                                                 \n"," transformer_encoder (Transf  multiple                 10631424  \n"," ormerEncoder)                                                   \n","                                                                 \n"," transformer_encoder (Transf  multiple                 10631424  \n"," ormerEncoder)                                                   \n","                                                                 \n"," dense_13 (Dense)            multiple                  25165952  \n","                                                                 \n"," dense_14 (Dense)            multiple                  16512     \n","                                                                 \n"," dense_15 (Dense)            multiple                  387       \n","                                                                 \n"," layer_normalization_9 (Laye  multiple                 1536      \n"," rNormalization)                                                 \n","                                                                 \n","=================================================================\n","Total params: 47,234,435\n","Trainable params: 47,234,435\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["vit.summary()"]},{"cell_type":"markdown","metadata":{"id":"4urZ7eIU72PN"},"source":["## HuggingFace ViT"]},{"cell_type":"markdown","metadata":{"id":"mL456x7iRLdX"},"source":["### Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7293,"status":"ok","timestamp":1653652133836,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"8p4wCX54u_BL","outputId":"5791763b-d6a0-4235-ce2b-8b906b6bf338"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.19.2-py3-none-any.whl (4.2 MB)\n","\u001b[K     |████████████████████████████████| 4.2 MB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n","Collecting pyyaml>=5.1\n","  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n","\u001b[K     |████████████████████████████████| 596 kB 61.5 MB/s \n","\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.3)\n","Collecting tokenizers!=0.11.3,<0.13,>=0.11.1\n","  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n","\u001b[K     |████████████████████████████████| 6.6 MB 59.1 MB/s \n","\u001b[?25hCollecting huggingface-hub<1.0,>=0.1.0\n","  Downloading huggingface_hub-0.7.0-py3-none-any.whl (86 kB)\n","\u001b[K     |████████████████████████████████| 86 kB 6.8 MB/s \n","\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.0)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.2.0)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.0)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.5.18.1)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n","Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers\n","  Attempting uninstall: pyyaml\n","    Found existing installation: PyYAML 3.13\n","    Uninstalling PyYAML-3.13:\n","      Successfully uninstalled PyYAML-3.13\n","Successfully installed huggingface-hub-0.7.0 pyyaml-6.0 tokenizers-0.12.1 transformers-4.19.2\n"]},{"data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["yaml"]}}},"metadata":{},"output_type":"display_data"}],"source":["!pip install transformers"]},{"cell_type":"markdown","metadata":{"id":"bi3VIZe6HRsh"},"source":["### Training"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y3pf--30_oAg"},"outputs":[],"source":["resize_rescale_hf = tf.keras.Sequential([\n","       Resizing(224, 224),\n","       Rescaling(1./255),\n","       Permute((3,1,2))                \n","])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":170},"executionInfo":{"elapsed":25255,"status":"ok","timestamp":1653652159480,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"17laQAjzu_Dz","outputId":"6e910cbe-337e-435a-bcc8-6441fb4666e8"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a0cc1c8ce0ba45b7bf52ff8bc0b50b87","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/502 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"040e7ad16f3143209c8e00bd593e4490","version_major":2,"version_minor":0},"text/plain":["Downloading:   0%|          | 0.00/330M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["All model checkpoint layers were used when initializing TFViTModel.\n","\n","All the layers of TFViTModel were initialized from the model checkpoint at google/vit-base-patch16-224-in21k.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTModel for predictions without further training.\n"]}],"source":["from transformers import ViTFeatureExtractor, TFViTModel\n","\n","\n","base_model = TFViTModel.from_pretrained(\"google/vit-base-patch16-224-in21k\")\n","\n","inputs = Input(shape = (256,256,3))\n","x = resize_rescale_hf(inputs)\n","x = base_model.vit(x)[0][:,0,:]\n","#print(x)\n","output = Dense(CONFIGURATION[\"NUM_CLASSES\"], activation = 'softmax')(x)\n","\n","hf_model = tf.keras.Model(inputs=inputs, outputs=output)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yhq0pF1R7hCA"},"outputs":[],"source":["test_image = cv2.imread(\"/content/dataset/Emotions Dataset/Emotions Dataset/train/happy/387249.jpg\")\n","test_image = cv2.resize(test_image, (CONFIGURATION[\"IM_SIZE\"] ,CONFIGURATION[\"IM_SIZE\"]))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1653562561447,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"19lSGStT7hEk","outputId":"c022871d-374c-4234-a9b3-0476d071a787"},"outputs":[{"data":{"text/plain":["<tf.Tensor: shape=(1, 3), dtype=float32, numpy=array([[0.32100818, 0.3514786 , 0.32751322]], dtype=float32)>"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["hf_model(tf.expand_dims(test_image, axis = 0))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":28,"status":"ok","timestamp":1653652159480,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"hT9DwILZ7hKC","outputId":"288114e1-112e-4f65-9c99-13d8a86fc63b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_3 (InputLayer)        [(None, 256, 256, 3)]     0         \n","                                                                 \n"," sequential_3 (Sequential)   (None, 3, 224, 224)       0         \n","                                                                 \n"," vit (TFViTMainLayer)        TFBaseModelOutputWithPoo  86389248  \n","                             ling(last_hidden_state=(            \n","                             None, 197, 768),                    \n","                              pooler_output=(None, 76            \n","                             8),                                 \n","                              hidden_states=None, att            \n","                             entions=None)                       \n","                                                                 \n"," tf.__operators__.getitem (S  (None, 768)              0         \n"," licingOpLambda)                                                 \n","                                                                 \n"," dense_3 (Dense)             (None, 3)                 2307      \n","                                                                 \n","=================================================================\n","Total params: 86,391,555\n","Trainable params: 86,391,555\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["hf_model.summary()"]},{"cell_type":"markdown","metadata":{"id":"kRVq7Fjplcv_"},"source":["### Get Attention Maps"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4600,"status":"ok","timestamp":1653334482814,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"JS_-4nHISvIv","outputId":"73012820-dd64-4c6e-df68-1faa7844a3dc"},"outputs":[{"name":"stderr","output_type":"stream","text":["All model checkpoint layers were used when initializing TFViTModel.\n","\n","All the layers of TFViTModel were initialized from the model checkpoint at google/vit-base-patch16-224-in21k.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTModel for predictions without further training.\n"]}],"source":["from transformers import ViTFeatureExtractor, TFViTModel, ViTConfig\n","\n","configuration = ViTConfig()\n","configuration.output_attentions = True\n","\n","base_model = TFViTModel.from_pretrained(\n","    pretrained_model_name_or_path = \"google/vit-base-patch16-224-in21k\",\n","    config = configuration,\n","    )\n","inputs = Input(shape = (256,256,3))\n","x = resize_rescale_hf(inputs)\n","x = base_model.vit(x)['attentions']\n","\n","model = tf.keras.Model(inputs=inputs, outputs=x)"]},{"cell_type":"markdown","metadata":{"id":"Dc0tPTDDJVTZ"},"source":["# Training"]},{"cell_type":"markdown","metadata":{"id":"FmB4mUqRKHUh"},"source":["## Class Weighting"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ltY7eo43zZ60"},"outputs":[],"source":["n_sample_0 = 1525 # angry\n","n_sample_1 = 3019 # happy\n","n_sample_2 = 2255 # sad\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"r5BiOkRMzQJe"},"outputs":[],"source":["class_weights = {0:6799/n_sample_0, 1: 6799/n_sample_1, 2: 6799/n_sample_2}"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1653296614142,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"GUEuM_bg0o-H","outputId":"4520b127-340a-4417-a255-bbf2b1fdb4e7"},"outputs":[{"name":"stdout","output_type":"stream","text":["{0: 4.458360655737705, 1: 2.2520702219277906, 2: 3.015077605321508}\n"]}],"source":["print(class_weights)"]},{"cell_type":"markdown","metadata":{"id":"GFmj8wuTJ6Zn"},"source":["## Callbacks"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hl2N9uHg7ROz"},"outputs":[],"source":["checkpoint_callback = ModelCheckpoint(\n","    'best_weights', \n","    monitor='val_accuracy',\n","    mode = 'max',\n","    verbose=1, \n","    save_best_only=True,\n","    \n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fisfirRoL09X"},"outputs":[],"source":["class LogConfMatrix(Callback):\n","  def on_epoch_end(self, epoch, logs):\n","    predicted = []\n","    labels = []\n","\n","    for im, label in validation_dataset:\n","      predicted.append(hf_model(im))\n","      labels.append(label.numpy())\n","\n","    pred = np.concatenate([np.argmax(predicted[:-1], axis = -1).flatten(), np.argmax(predicted[-1], axis = -1).flatten()])\n","    lab = np.concatenate([np.argmax(labels[:-1], axis = -1).flatten(), np.argmax(labels[-1], axis = -1).flatten()])\n","    \n","    cm = wandb.plot.confusion_matrix(\n","        y_true=lab,\n","        preds=pred,\n","        class_names=CONFIGURATION[\"CLASS_NAMES\"])\n","        \n","    wandb.log({\"conf_mat\": cm})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OAuopKKFQfuh"},"outputs":[],"source":["class LogResultsTable(Callback):\n","  def on_epoch_end(self, epoch, logs):\n","    \n","    columns=[\"image\", \"Predicted\", \"Label\"]\n","    \n","    val_table = wandb.Table(columns = columns)\n","\n","    \n","    for im, label in validation_dataset.take(25):\n","\n","      pred = CONFIGURATION[\"CLASS_NAMES\"][tf.argmax(hf_model(im), axis = -1).numpy()[0]]\n","      label = CONFIGURATION[\"CLASS_NAMES\"][tf.argmax(label, axis = -1).numpy()[0]]\n","\n","      row = [wandb.Image(im), pred, label]\n","      \n","      val_table.add_data(*row)\n","\n","      \n","    wandb.log({\"Model Results\" : val_table})\n"]},{"cell_type":"markdown","metadata":{"id":"mm9mW9ngJ_G4"},"source":["## Train"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oJ0TDmwrxu6i"},"outputs":[],"source":["loss_function = CategoricalCrossentropy()\n","#loss_function = SparseCategoricalCrossentropy()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5Z1HBMsnNWWY"},"outputs":[],"source":["\n","metrics = [CategoricalAccuracy(name = \"accuracy\"), TopKCategoricalAccuracy(k=2, name = \"top_k_accuracy\")]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rLR6HnUWU-7Z"},"outputs":[],"source":["backbone.trainable=False"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8ADlxapfNWYt"},"outputs":[],"source":["pretrained_model.compile(\n","  optimizer = Adam(learning_rate = CONFIGURATION[\"LEARNING_RATE\"]),\n","  loss = loss_function,\n","  metrics = metrics,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":471},"id":"pNbjpqK_NWbC","executionInfo":{"status":"error","timestamp":1668356434976,"user_tz":-60,"elapsed":46683,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e79ab0d0-4e8e-4594-e4c4-23c074de2578"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","130/213 [=================>............] - ETA: 27s - loss: 0.1770 - accuracy: 0.9260 - top_k_accuracy: 0.9858"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-70-3fba6bd800fe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0;31m#validation_data = validation_dataset,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCONFIGURATION\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"N_EPOCHS\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m   \u001b[0;31m#class_weight = class_weights,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0;31m#callbacks = [WandbCallback(), LogConfMatrix(), LogResultsTable()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1407\u001b[0m                 _r=1):\n\u001b[1;32m   1408\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1410\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m 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variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2452\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2453\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2454\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2456\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1859\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1860\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1861\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1862\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1863\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    500\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    503\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["history = pretrained_model.fit(\n","  training_dataset,\n","  #validation_data = validation_dataset,\n","  epochs = CONFIGURATION[\"N_EPOCHS\"],\n","  verbose = 1,\n","  #class_weight = class_weights,\n","  #callbacks = [WandbCallback(), LogConfMatrix(), LogResultsTable()]\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rOU31ni0AAma"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1653618626131,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"3hNRK2vpNWdm","outputId":"cb7616d5-3e24-4aff-9f0c-dce007c58df5"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('Model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train_loss', 'val_loss'])\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"elapsed":505,"status":"ok","timestamp":1653618626633,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"6Jrf3_AyNWfw","outputId":"80300ff3-5076-4d5e-8a04-f319263d8e52"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","plt.title('Model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train_accuracy', 'val_accuracy'])\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3AV0lwDnIBwd"},"outputs":[],"source":["pretrained_model.save(\"/content/drive/MyDrive/Bang/eff_keras.h5\")"]},{"cell_type":"markdown","metadata":{"id":"kqgsVzHwjS9Y"},"source":["## Ensembling"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E1ZRNQYOjXoW"},"outputs":[],"source":["inputs = Input(shape = (CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"], 3))\n","\n","y_1 = resnet_34(inputs)\n","y_2 = pretrained_model(inputs)\n","\n","output = 0.5*y_1 + 0.5*y_1\n","\n","ensemble_model = Model(inputs = inputs, outputs = output)"]},{"cell_type":"markdown","metadata":{"id":"dm0oxOOI6xwE"},"source":["# Evaluation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":908,"status":"ok","timestamp":1652284646316,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"of0lz1HE1RaG","outputId":"9b41b80a-7156-4509-898d-bb5f5e97e48c"},"outputs":[{"data":{"text/plain":["<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7fb65339b3d0>"]},"execution_count":236,"metadata":{},"output_type":"execute_result"}],"source":["model.load_weights('best_weights')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":62345,"status":"ok","timestamp":1653564833979,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"5ewegQCf3duB","outputId":"7b00293f-0b00-40d6-95c5-d2fd3b43c925"},"outputs":[{"name":"stdout","output_type":"stream","text":["2278/2278 [==============================] - 62s 27ms/step - loss: 0.3582 - accuracy: 0.8946 - top_k_accuracy: 0.9715\n"]},{"data":{"text/plain":["[0.358234167098999, 0.8946444392204285, 0.9714661836624146]"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["hf_model.evaluate(validation_dataset)"]},{"cell_type":"markdown","metadata":{"id":"cx6VV204tCPI"},"source":["# Testing"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1220,"status":"ok","timestamp":1653660120635,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"2-LfdgGGrs3e","outputId":"c6e805b8-2505-462b-d96b-6d7e15d0fdf5"},"outputs":[{"name":"stdout","output_type":"stream","text":["tf.Tensor([[3.1441802e-01 1.5602846e-04 6.8542594e-01]], shape=(1, 3), dtype=float32)\n","sad\n"]}],"source":["test_image = cv2.imread(\"/content/dataset/Emotions Dataset/Emotions Dataset/test/sad/123731.jpg\")\n","test_image = cv2.resize(test_image, (CONFIGURATION[\"IM_SIZE\"] ,CONFIGURATION[\"IM_SIZE\"]))\n","im = tf.constant(test_image, dtype = tf.float32)\n","\n","im = tf.expand_dims(im, axis = 0)\n","print(pretrained_model(im))\n","print(CONFIGURATION['CLASS_NAMES'][tf.argmax(pretrained_model(im), axis = -1).numpy()[0]])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":714},"executionInfo":{"elapsed":2738,"status":"ok","timestamp":1653459915621,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"WqUIqwQTrtDm","outputId":"1044e7e6-4197-4703-fb14-dcda713f7bbb"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 864x864 with 16 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize = (12,12))\n","\n","for images, labels in validation_dataset.take(1):\n","  for i in range(16):\n","    ax = plt.subplot(4,4, i+1)\n","    plt.imshow(images[i]/255.)\n","\n","    plt.title(\"True Label - : \" + CONFIGURATION[\"CLASS_NAMES\"][tf.argmax(labels[i], axis = -1).numpy()] \n","              + \"\\n\" + \"Predicted Label - : \" \n","              + CONFIGURATION[\"CLASS_NAMES\"][int(tf.argmax(pretrained_model(tf.expand_dims(images[i], axis = 0)), axis =-1).numpy()[0])] )\n","    plt.axis(\"off\")"]},{"cell_type":"markdown","metadata":{"id":"UfxIFPOgNak8"},"source":["## Confusion Matrix"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-oNfyKMAYnwT"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"omKYrTxPNHoR"},"outputs":[],"source":["predicted = []\n","labels = []\n","\n","for im, label in validation_dataset:\n","  predicted.append(pretrained_model(im))\n","  labels.append(label.numpy())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1652514507469,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"fl3LrfS0NHqq","outputId":"ace31be4-3393-460d-a280-18822c870cfa"},"outputs":[{"name":"stdout","output_type":"stream","text":["[1 1 2 ... 2 1 0]\n","[1 1 2 ... 2 1 0]\n"]}],"source":["print(np.concatenate([np.argmax(labels[:-1], axis = -1).flatten(), np.argmax(labels[-1], axis = -1).flatten()]))\n","print(np.concatenate([np.argmax(predicted[:-1], axis = -1).flatten(), np.argmax(predicted[-1], axis = -1).flatten()]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gQkoPw5VNHyc"},"outputs":[],"source":["pred = np.concatenate([np.argmax(predicted[:-1], axis = -1).flatten(), np.argmax(predicted[-1], axis = -1).flatten()])\n","lab = np.concatenate([np.argmax(labels[:-1], axis = -1).flatten(), np.argmax(labels[-1], axis = -1).flatten()])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":586},"executionInfo":{"elapsed":923,"status":"ok","timestamp":1652514508375,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"lp5I9AucNH1Z","outputId":"895f9730-4926-4bd7-9fdc-09d1e0171e53"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[397  48  70]\n"," [ 27 909  70]\n"," [ 61  87 609]]\n"]},{"data":{"text/plain":["Text(0.5, 51.0, 'Predicted')"]},"execution_count":44,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["cm = confusion_matrix(lab, pred)\n","print(cm)\n","plt.figure(figsize=(8,8))\n","\n","sns.heatmap(cm, annot=True,)\n","plt.title('Confusion matrix')\n","plt.ylabel('Actual')\n","plt.xlabel('Predicted')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M9z9wP4DNH4F"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"ntKb4yTSpJh-"},"source":["# Visualization"]},{"cell_type":"markdown","metadata":{"id":"jmrfCgsvgNJ-"},"source":["## Feature Map Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bRpJXvLcQL2O"},"outputs":[],"source":["vgg_backbone = tf.keras.applications.vgg16.VGG16(\n","    include_top=False,\n","    weights= None,\n","    input_shape=(CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"], 3),\n","    \n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":68,"status":"ok","timestamp":1652432221601,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"I1jUi3m_QL41","outputId":"9a6a7718-f750-4bca-8373-1736245abdc7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"vgg16\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_1 (InputLayer)        [(None, 256, 256, 3)]     0         \n","                                                                 \n"," block1_conv1 (Conv2D)       (None, 256, 256, 64)      1792      \n","                                                                 \n"," block1_conv2 (Conv2D)       (None, 256, 256, 64)      36928     \n","                                                                 \n"," block1_pool (MaxPooling2D)  (None, 128, 128, 64)      0         \n","                                                                 \n"," block2_conv1 (Conv2D)       (None, 128, 128, 128)     73856     \n","                                                                 \n"," block2_conv2 (Conv2D)       (None, 128, 128, 128)     147584    \n","                                                                 \n"," block2_pool (MaxPooling2D)  (None, 64, 64, 128)       0         \n","                                                                 \n"," block3_conv1 (Conv2D)       (None, 64, 64, 256)       295168    \n","                                                                 \n"," block3_conv2 (Conv2D)       (None, 64, 64, 256)       590080    \n","                                                                 \n"," block3_conv3 (Conv2D)       (None, 64, 64, 256)       590080    \n","                                                                 \n"," block3_pool (MaxPooling2D)  (None, 32, 32, 256)       0         \n","                                                                 \n"," block4_conv1 (Conv2D)       (None, 32, 32, 512)       1180160   \n","                                                                 \n"," block4_conv2 (Conv2D)       (None, 32, 32, 512)       2359808   \n","                                                                 \n"," block4_conv3 (Conv2D)       (None, 32, 32, 512)       2359808   \n","                                                                 \n"," block4_pool (MaxPooling2D)  (None, 16, 16, 512)       0         \n","                                                                 \n"," block5_conv1 (Conv2D)       (None, 16, 16, 512)       2359808   \n","                                                                 \n"," block5_conv2 (Conv2D)       (None, 16, 16, 512)       2359808   \n","                                                                 \n"," block5_conv3 (Conv2D)       (None, 16, 16, 512)       2359808   \n","                                                                 \n"," block5_pool (MaxPooling2D)  (None, 8, 8, 512)         0         \n","                                                                 \n","=================================================================\n","Total params: 14,714,688\n","Trainable params: 14,714,688\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["vgg_backbone.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-lBjdh1vdScW"},"outputs":[],"source":["def is_conv(layer_name):\n","  if 'conv' in layer_name:\n","    return True\n","  else:\n","    return False"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":45,"status":"ok","timestamp":1652432221603,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"V8_CKffvQL7e","outputId":"25260328-fdd6-46e3-99f3-e3f392963473"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_1 (InputLayer)        [(None, 256, 256, 3)]     0         \n","                                                                 \n"," block1_conv1 (Conv2D)       (None, 256, 256, 64)      1792      \n","                                                                 \n"," block1_conv2 (Conv2D)       (None, 256, 256, 64)      36928     \n","                                                                 \n"," block1_pool (MaxPooling2D)  (None, 128, 128, 64)      0         \n","                                                                 \n"," block2_conv1 (Conv2D)       (None, 128, 128, 128)     73856     \n","                                                                 \n"," block2_conv2 (Conv2D)       (None, 128, 128, 128)     147584    \n","                                                                 \n"," block2_pool (MaxPooling2D)  (None, 64, 64, 128)       0         \n","                                                                 \n"," block3_conv1 (Conv2D)       (None, 64, 64, 256)       295168    \n","                                                                 \n"," block3_conv2 (Conv2D)       (None, 64, 64, 256)       590080    \n","                                                                 \n"," block3_conv3 (Conv2D)       (None, 64, 64, 256)       590080    \n","                                                                 \n"," block3_pool (MaxPooling2D)  (None, 32, 32, 256)       0         \n","                                                                 \n"," block4_conv1 (Conv2D)       (None, 32, 32, 512)       1180160   \n","                                                                 \n"," block4_conv2 (Conv2D)       (None, 32, 32, 512)       2359808   \n","                                                                 \n"," block4_conv3 (Conv2D)       (None, 32, 32, 512)       2359808   \n","                                                                 \n"," block4_pool (MaxPooling2D)  (None, 16, 16, 512)       0         \n","                                                                 \n"," block5_conv1 (Conv2D)       (None, 16, 16, 512)       2359808   \n","                                                                 \n"," block5_conv2 (Conv2D)       (None, 16, 16, 512)       2359808   \n","                                                                 \n"," block5_conv3 (Conv2D)       (None, 16, 16, 512)       2359808   \n","                                                                 \n","=================================================================\n","Total params: 14,714,688\n","Trainable params: 14,714,688\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["feature_maps = [layer.output for layer in vgg_backbone.layers[1:] if is_conv(layer.name)]\n","feature_map_model = Model(\n","    inputs = vgg_backbone.input, \n","    outputs = feature_maps\n",")\n","\n","feature_map_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iVQisXXYQL-F"},"outputs":[],"source":["test_image = cv2.imread(\"/content/dataset/Emotions Dataset/Emotions Dataset/test/happy/111073.jpg\")\n","test_image = cv2.resize(test_image, (CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"]))\n","\n","im = tf.constant(test_image, dtype = tf.float32)\n","im = tf.expand_dims(im, axis = 0)\n","\n","f_maps = feature_map_model.predict(im)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1652432229621,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"MreZWZh7dE41","outputId":"6c0293c0-928a-494d-abbc-ef63df6c65b8"},"outputs":[{"name":"stdout","output_type":"stream","text":["13\n"]}],"source":["print(len(f_maps))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1652432229622,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"NFis2xsxQMAg","outputId":"7ab33734-1793-48e9-f8ae-da961eb1d135"},"outputs":[{"name":"stdout","output_type":"stream","text":["(1, 256, 256, 64)\n","(1, 256, 256, 64)\n","(1, 128, 128, 128)\n","(1, 128, 128, 128)\n","(1, 64, 64, 256)\n","(1, 64, 64, 256)\n","(1, 64, 64, 256)\n","(1, 32, 32, 512)\n","(1, 32, 32, 512)\n","(1, 32, 32, 512)\n","(1, 16, 16, 512)\n","(1, 16, 16, 512)\n","(1, 16, 16, 512)\n"]}],"source":["for i in range(len(f_maps)):\n","  print(f_maps[i].shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":41452,"status":"ok","timestamp":1652432271068,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"IdgilL61iv05","outputId":"4a99af6e-b712-45cc-82c0-2f2373009d11"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure 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size 18432x18432 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["for i in range(len(f_maps)):\n","  plt.figure(figsize = (256,256))\n","  f_size = f_maps[i].shape[1]\n","  n_channels = f_maps[i].shape[3]\n","  joint_maps = np.ones((f_size, f_size*n_channels ))\n","\n","  axs = plt.subplot(len(f_maps), 1, i+1)\n","  \n","  for j in range(n_channels):\n","    joint_maps[:, f_size*j:f_size*(j+1)] = f_maps[i][..., j]\n","\n","  plt.imshow(joint_maps[:,0:512])\n","  plt.axis(\"off\")\n","  "]},{"cell_type":"markdown","metadata":{"id":"QAGcpFzhgUmN"},"source":["## GradCam"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12484,"status":"ok","timestamp":1668410883280,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"D6ust7-G9AUb","outputId":"eeb27c5d-2cf7-42d6-d790-019c6014db21"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb5_notop.h5\n","115263384/115263384 [==============================] - 1s 0us/step\n"]}],"source":["backbone = tf.keras.applications.efficientnet.EfficientNetB5(\n","    include_top = False,\n","    weights='imagenet',\n","    input_shape=(CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"], 3),\n","    )\n","backbone.trainable=False"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15390,"status":"ok","timestamp":1668410898627,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"DpafE9ZT8hn6","outputId":"277e9b48-9243-4450-81b3-098ed7d34584"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 256, 256, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," rescaling_1 (Rescaling)        (None, 256, 256, 3)  0           ['input_1[0][0]']                \n","                                                                                                  \n"," normalization (Normalization)  (None, 256, 256, 3)  7           ['rescaling_1[0][0]']            \n","                                                                                                  \n"," tf.math.truediv (TFOpLambda)   (None, 256, 256, 3)  0           ['normalization[0][0]']          \n","                                                                                                  \n"," stem_conv_pad (ZeroPadding2D)  (None, 257, 257, 3)  0           ['tf.math.truediv[0][0]']        \n","                                                                                                  \n"," stem_conv (Conv2D)             (None, 128, 128, 48  1296        ['stem_conv_pad[0][0]']          \n","                                )                                                                 \n","                                                                                                  \n"," stem_bn (BatchNormalization)   (None, 128, 128, 48  192         ['stem_conv[0][0]']              \n","                                )                                                                 \n","                                                                                                  \n"," stem_activation (Activation)   (None, 128, 128, 48  0           ['stem_bn[0][0]']                \n","                                )                                                                 \n","                                                                                                  \n"," block1a_dwconv (DepthwiseConv2  (None, 128, 128, 48  432        ['stem_activation[0][0]']        \n"," D)                             )                                                                 \n","                                                                                                  \n"," block1a_bn (BatchNormalization  (None, 128, 128, 48  192        ['block1a_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block1a_activation (Activation  (None, 128, 128, 48  0          ['block1a_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1a_se_squeeze (GlobalAver  (None, 48)          0           ['block1a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1a_se_reshape (Reshape)   (None, 1, 1, 48)     0           ['block1a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block1a_se_reduce (Conv2D)     (None, 1, 1, 12)     588         ['block1a_se_reshape[0][0]']     \n","                                                                                                  \n"," block1a_se_expand (Conv2D)     (None, 1, 1, 48)     624         ['block1a_se_reduce[0][0]']      \n","                                                                                                  \n"," block1a_se_excite (Multiply)   (None, 128, 128, 48  0           ['block1a_activation[0][0]',     \n","                                )                                 'block1a_se_expand[0][0]']      \n","                                                                                                  \n"," block1a_project_conv (Conv2D)  (None, 128, 128, 24  1152        ['block1a_se_excite[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," block1a_project_bn (BatchNorma  (None, 128, 128, 24  96         ['block1a_project_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," block1b_dwconv (DepthwiseConv2  (None, 128, 128, 24  216        ['block1a_project_bn[0][0]']     \n"," D)                             )                                                                 \n","                                                                                                  \n"," block1b_bn (BatchNormalization  (None, 128, 128, 24  96         ['block1b_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block1b_activation (Activation  (None, 128, 128, 24  0          ['block1b_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1b_se_squeeze (GlobalAver  (None, 24)          0           ['block1b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1b_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block1b_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1b_se_reshape[0][0]']     \n","                                                                                                  \n"," block1b_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1b_se_reduce[0][0]']      \n","                                                                                                  \n"," block1b_se_excite (Multiply)   (None, 128, 128, 24  0           ['block1b_activation[0][0]',     \n","                                )                                 'block1b_se_expand[0][0]']      \n","                                                                                                  \n"," block1b_project_conv (Conv2D)  (None, 128, 128, 24  576         ['block1b_se_excite[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," block1b_project_bn (BatchNorma  (None, 128, 128, 24  96         ['block1b_project_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," block1b_drop (Dropout)         (None, 128, 128, 24  0           ['block1b_project_bn[0][0]']     \n","                                )                                                                 \n","                                                                                                  \n"," block1b_add (Add)              (None, 128, 128, 24  0           ['block1b_drop[0][0]',           \n","                                )                                 'block1a_project_bn[0][0]']     \n","                                                                                                  \n"," block1c_dwconv (DepthwiseConv2  (None, 128, 128, 24  216        ['block1b_add[0][0]']            \n"," D)                             )                                                                 \n","                                                                                                  \n"," block1c_bn (BatchNormalization  (None, 128, 128, 24  96         ['block1c_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block1c_activation (Activation  (None, 128, 128, 24  0          ['block1c_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1c_se_squeeze (GlobalAver  (None, 24)          0           ['block1c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1c_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block1c_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1c_se_reshape[0][0]']     \n","                                                                                                  \n"," block1c_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1c_se_reduce[0][0]']      \n","                                                                                                  \n"," block1c_se_excite (Multiply)   (None, 128, 128, 24  0           ['block1c_activation[0][0]',     \n","                                )                                 'block1c_se_expand[0][0]']      \n","                                                                                                  \n"," block1c_project_conv (Conv2D)  (None, 128, 128, 24  576         ['block1c_se_excite[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," block1c_project_bn (BatchNorma  (None, 128, 128, 24  96         ['block1c_project_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," block1c_drop (Dropout)         (None, 128, 128, 24  0           ['block1c_project_bn[0][0]']     \n","                                )                                                                 \n","                                                                                                  \n"," block1c_add (Add)              (None, 128, 128, 24  0           ['block1c_drop[0][0]',           \n","                                )                                 'block1b_add[0][0]']            \n","                                                                                                  \n"," block2a_expand_conv (Conv2D)   (None, 128, 128, 14  3456        ['block1c_add[0][0]']            \n","                                4)                                                                \n","                                                                                                  \n"," block2a_expand_bn (BatchNormal  (None, 128, 128, 14  576        ['block2a_expand_conv[0][0]']    \n"," ization)                       4)                                                                \n","                                                                                                  \n"," block2a_expand_activation (Act  (None, 128, 128, 14  0          ['block2a_expand_bn[0][0]']      \n"," ivation)                       4)                                                                \n","                                                                                                  \n"," block2a_dwconv_pad (ZeroPaddin  (None, 129, 129, 14  0          ['block2a_expand_activation[0][0]\n"," g2D)                           4)                               ']                               \n","                                                                                                  \n"," block2a_dwconv (DepthwiseConv2  (None, 64, 64, 144)  1296       ['block2a_dwconv_pad[0][0]']     \n"," D)                                                                                               \n","                                                                                                  \n"," block2a_bn (BatchNormalization  (None, 64, 64, 144)  576        ['block2a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2a_activation (Activation  (None, 64, 64, 144)  0          ['block2a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2a_se_squeeze (GlobalAver  (None, 144)         0           ['block2a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2a_se_reshape[0][0]']     \n","                                                                                                  \n"," block2a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2a_se_reduce[0][0]']      \n","                                                                                                  \n"," block2a_se_excite (Multiply)   (None, 64, 64, 144)  0           ['block2a_activation[0][0]',     \n","                                                                  'block2a_se_expand[0][0]']      \n","                                                                                                  \n"," block2a_project_conv (Conv2D)  (None, 64, 64, 40)   5760        ['block2a_se_excite[0][0]']      \n","                                                                                                  \n"," block2a_project_bn (BatchNorma  (None, 64, 64, 40)  160         ['block2a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2b_expand_conv (Conv2D)   (None, 64, 64, 240)  9600        ['block2a_project_bn[0][0]']     \n","                                                                                                  \n"," block2b_expand_bn (BatchNormal  (None, 64, 64, 240)  960        ['block2b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block2b_expand_activation (Act  (None, 64, 64, 240)  0          ['block2b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block2b_dwconv (DepthwiseConv2  (None, 64, 64, 240)  2160       ['block2b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block2b_bn (BatchNormalization  (None, 64, 64, 240)  960        ['block2b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2b_activation (Activation  (None, 64, 64, 240)  0          ['block2b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2b_se_squeeze (GlobalAver  (None, 240)         0           ['block2b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2b_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block2b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2b_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block2b_se_reshape[0][0]']     \n","                                                                                                  \n"," block2b_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block2b_se_reduce[0][0]']      \n","                                                                                                  \n"," block2b_se_excite (Multiply)   (None, 64, 64, 240)  0           ['block2b_activation[0][0]',     \n","                                                                  'block2b_se_expand[0][0]']      \n","                                                                                                  \n"," block2b_project_conv (Conv2D)  (None, 64, 64, 40)   9600        ['block2b_se_excite[0][0]']      \n","                                                                                                  \n"," block2b_project_bn (BatchNorma  (None, 64, 64, 40)  160         ['block2b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2b_drop (Dropout)         (None, 64, 64, 40)   0           ['block2b_project_bn[0][0]']     \n","                                                                                                  \n"," block2b_add (Add)              (None, 64, 64, 40)   0           ['block2b_drop[0][0]',           \n","                                                                  'block2a_project_bn[0][0]']     \n","                                                                                                  \n"," block2c_expand_conv (Conv2D)   (None, 64, 64, 240)  9600        ['block2b_add[0][0]']            \n","                                                                                                  \n"," block2c_expand_bn (BatchNormal  (None, 64, 64, 240)  960        ['block2c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block2c_expand_activation (Act  (None, 64, 64, 240)  0          ['block2c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block2c_dwconv (DepthwiseConv2  (None, 64, 64, 240)  2160       ['block2c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block2c_bn (BatchNormalization  (None, 64, 64, 240)  960        ['block2c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2c_activation (Activation  (None, 64, 64, 240)  0          ['block2c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2c_se_squeeze (GlobalAver  (None, 240)         0           ['block2c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2c_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block2c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2c_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block2c_se_reshape[0][0]']     \n","                                                                                                  \n"," block2c_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block2c_se_reduce[0][0]']      \n","                                                                                                  \n"," block2c_se_excite (Multiply)   (None, 64, 64, 240)  0           ['block2c_activation[0][0]',     \n","                                                                  'block2c_se_expand[0][0]']      \n","                                                                                                  \n"," block2c_project_conv (Conv2D)  (None, 64, 64, 40)   9600        ['block2c_se_excite[0][0]']      \n","                                                                                                  \n"," block2c_project_bn (BatchNorma  (None, 64, 64, 40)  160         ['block2c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2c_drop (Dropout)         (None, 64, 64, 40)   0           ['block2c_project_bn[0][0]']     \n","                                                                                                  \n"," block2c_add (Add)              (None, 64, 64, 40)   0           ['block2c_drop[0][0]',           \n","                                                                  'block2b_add[0][0]']            \n","                                                                                                  \n"," block2d_expand_conv (Conv2D)   (None, 64, 64, 240)  9600        ['block2c_add[0][0]']            \n","                                                                                                  \n"," block2d_expand_bn (BatchNormal  (None, 64, 64, 240)  960        ['block2d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block2d_expand_activation (Act  (None, 64, 64, 240)  0          ['block2d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block2d_dwconv (DepthwiseConv2  (None, 64, 64, 240)  2160       ['block2d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block2d_bn (BatchNormalization  (None, 64, 64, 240)  960        ['block2d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2d_activation (Activation  (None, 64, 64, 240)  0          ['block2d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2d_se_squeeze (GlobalAver  (None, 240)         0           ['block2d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2d_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block2d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2d_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block2d_se_reshape[0][0]']     \n","                                                                                                  \n"," block2d_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block2d_se_reduce[0][0]']      \n","                                                                                                  \n"," block2d_se_excite (Multiply)   (None, 64, 64, 240)  0           ['block2d_activation[0][0]',     \n","                                                                  'block2d_se_expand[0][0]']      \n","                                                                                                  \n"," block2d_project_conv (Conv2D)  (None, 64, 64, 40)   9600        ['block2d_se_excite[0][0]']      \n","                                                                                                  \n"," block2d_project_bn (BatchNorma  (None, 64, 64, 40)  160         ['block2d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2d_drop (Dropout)         (None, 64, 64, 40)   0           ['block2d_project_bn[0][0]']     \n","                                                                                                  \n"," block2d_add (Add)              (None, 64, 64, 40)   0           ['block2d_drop[0][0]',           \n","                                                                  'block2c_add[0][0]']            \n","                                                                                                  \n"," block2e_expand_conv (Conv2D)   (None, 64, 64, 240)  9600        ['block2d_add[0][0]']            \n","                                                                                                  \n"," block2e_expand_bn (BatchNormal  (None, 64, 64, 240)  960        ['block2e_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block2e_expand_activation (Act  (None, 64, 64, 240)  0          ['block2e_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block2e_dwconv (DepthwiseConv2  (None, 64, 64, 240)  2160       ['block2e_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block2e_bn (BatchNormalization  (None, 64, 64, 240)  960        ['block2e_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2e_activation (Activation  (None, 64, 64, 240)  0          ['block2e_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2e_se_squeeze (GlobalAver  (None, 240)         0           ['block2e_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2e_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block2e_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2e_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block2e_se_reshape[0][0]']     \n","                                                                                                  \n"," block2e_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block2e_se_reduce[0][0]']      \n","                                                                                                  \n"," block2e_se_excite (Multiply)   (None, 64, 64, 240)  0           ['block2e_activation[0][0]',     \n","                                                                  'block2e_se_expand[0][0]']      \n","                                                                                                  \n"," block2e_project_conv (Conv2D)  (None, 64, 64, 40)   9600        ['block2e_se_excite[0][0]']      \n","                                                                                                  \n"," block2e_project_bn (BatchNorma  (None, 64, 64, 40)  160         ['block2e_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2e_drop (Dropout)         (None, 64, 64, 40)   0           ['block2e_project_bn[0][0]']     \n","                                                                                                  \n"," block2e_add (Add)              (None, 64, 64, 40)   0           ['block2e_drop[0][0]',           \n","                                                                  'block2d_add[0][0]']            \n","                                                                                                  \n"," block3a_expand_conv (Conv2D)   (None, 64, 64, 240)  9600        ['block2e_add[0][0]']            \n","                                                                                                  \n"," block3a_expand_bn (BatchNormal  (None, 64, 64, 240)  960        ['block3a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3a_expand_activation (Act  (None, 64, 64, 240)  0          ['block3a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3a_dwconv_pad (ZeroPaddin  (None, 67, 67, 240)  0          ['block3a_expand_activation[0][0]\n"," g2D)                                                            ']                               \n","                                                                                                  \n"," block3a_dwconv (DepthwiseConv2  (None, 32, 32, 240)  6000       ['block3a_dwconv_pad[0][0]']     \n"," D)                                                                                               \n","                                                                                                  \n"," block3a_bn (BatchNormalization  (None, 32, 32, 240)  960        ['block3a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3a_activation (Activation  (None, 32, 32, 240)  0          ['block3a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3a_se_squeeze (GlobalAver  (None, 240)         0           ['block3a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3a_se_reshape (Reshape)   (None, 1, 1, 240)    0           ['block3a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3a_se_reduce (Conv2D)     (None, 1, 1, 10)     2410        ['block3a_se_reshape[0][0]']     \n","                                                                                                  \n"," block3a_se_expand (Conv2D)     (None, 1, 1, 240)    2640        ['block3a_se_reduce[0][0]']      \n","                                                                                                  \n"," block3a_se_excite (Multiply)   (None, 32, 32, 240)  0           ['block3a_activation[0][0]',     \n","                                                                  'block3a_se_expand[0][0]']      \n","                                                                                                  \n"," block3a_project_conv (Conv2D)  (None, 32, 32, 64)   15360       ['block3a_se_excite[0][0]']      \n","                                                                                                  \n"," block3a_project_bn (BatchNorma  (None, 32, 32, 64)  256         ['block3a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3b_expand_conv (Conv2D)   (None, 32, 32, 384)  24576       ['block3a_project_bn[0][0]']     \n","                                                                                                  \n"," block3b_expand_bn (BatchNormal  (None, 32, 32, 384)  1536       ['block3b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3b_expand_activation (Act  (None, 32, 32, 384)  0          ['block3b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3b_dwconv (DepthwiseConv2  (None, 32, 32, 384)  9600       ['block3b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block3b_bn (BatchNormalization  (None, 32, 32, 384)  1536       ['block3b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3b_activation (Activation  (None, 32, 32, 384)  0          ['block3b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3b_se_squeeze (GlobalAver  (None, 384)         0           ['block3b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3b_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block3b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3b_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block3b_se_reshape[0][0]']     \n","                                                                                                  \n"," block3b_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block3b_se_reduce[0][0]']      \n","                                                                                                  \n"," block3b_se_excite (Multiply)   (None, 32, 32, 384)  0           ['block3b_activation[0][0]',     \n","                                                                  'block3b_se_expand[0][0]']      \n","                                                                                                  \n"," block3b_project_conv (Conv2D)  (None, 32, 32, 64)   24576       ['block3b_se_excite[0][0]']      \n","                                                                                                  \n"," block3b_project_bn (BatchNorma  (None, 32, 32, 64)  256         ['block3b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3b_drop (Dropout)         (None, 32, 32, 64)   0           ['block3b_project_bn[0][0]']     \n","                                                                                                  \n"," block3b_add (Add)              (None, 32, 32, 64)   0           ['block3b_drop[0][0]',           \n","                                                                  'block3a_project_bn[0][0]']     \n","                                                                                                  \n"," block3c_expand_conv (Conv2D)   (None, 32, 32, 384)  24576       ['block3b_add[0][0]']            \n","                                                                                                  \n"," block3c_expand_bn (BatchNormal  (None, 32, 32, 384)  1536       ['block3c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3c_expand_activation (Act  (None, 32, 32, 384)  0          ['block3c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3c_dwconv (DepthwiseConv2  (None, 32, 32, 384)  9600       ['block3c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block3c_bn (BatchNormalization  (None, 32, 32, 384)  1536       ['block3c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3c_activation (Activation  (None, 32, 32, 384)  0          ['block3c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3c_se_squeeze (GlobalAver  (None, 384)         0           ['block3c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3c_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block3c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3c_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block3c_se_reshape[0][0]']     \n","                                                                                                  \n"," block3c_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block3c_se_reduce[0][0]']      \n","                                                                                                  \n"," block3c_se_excite (Multiply)   (None, 32, 32, 384)  0           ['block3c_activation[0][0]',     \n","                                                                  'block3c_se_expand[0][0]']      \n","                                                                                                  \n"," block3c_project_conv (Conv2D)  (None, 32, 32, 64)   24576       ['block3c_se_excite[0][0]']      \n","                                                                                                  \n"," block3c_project_bn (BatchNorma  (None, 32, 32, 64)  256         ['block3c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3c_drop (Dropout)         (None, 32, 32, 64)   0           ['block3c_project_bn[0][0]']     \n","                                                                                                  \n"," block3c_add (Add)              (None, 32, 32, 64)   0           ['block3c_drop[0][0]',           \n","                                                                  'block3b_add[0][0]']            \n","                                                                                                  \n"," block3d_expand_conv (Conv2D)   (None, 32, 32, 384)  24576       ['block3c_add[0][0]']            \n","                                                                                                  \n"," block3d_expand_bn (BatchNormal  (None, 32, 32, 384)  1536       ['block3d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3d_expand_activation (Act  (None, 32, 32, 384)  0          ['block3d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3d_dwconv (DepthwiseConv2  (None, 32, 32, 384)  9600       ['block3d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block3d_bn (BatchNormalization  (None, 32, 32, 384)  1536       ['block3d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3d_activation (Activation  (None, 32, 32, 384)  0          ['block3d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3d_se_squeeze (GlobalAver  (None, 384)         0           ['block3d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3d_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block3d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3d_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block3d_se_reshape[0][0]']     \n","                                                                                                  \n"," block3d_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block3d_se_reduce[0][0]']      \n","                                                                                                  \n"," block3d_se_excite (Multiply)   (None, 32, 32, 384)  0           ['block3d_activation[0][0]',     \n","                                                                  'block3d_se_expand[0][0]']      \n","                                                                                                  \n"," block3d_project_conv (Conv2D)  (None, 32, 32, 64)   24576       ['block3d_se_excite[0][0]']      \n","                                                                                                  \n"," block3d_project_bn (BatchNorma  (None, 32, 32, 64)  256         ['block3d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3d_drop (Dropout)         (None, 32, 32, 64)   0           ['block3d_project_bn[0][0]']     \n","                                                                                                  \n"," block3d_add (Add)              (None, 32, 32, 64)   0           ['block3d_drop[0][0]',           \n","                                                                  'block3c_add[0][0]']            \n","                                                                                                  \n"," block3e_expand_conv (Conv2D)   (None, 32, 32, 384)  24576       ['block3d_add[0][0]']            \n","                                                                                                  \n"," block3e_expand_bn (BatchNormal  (None, 32, 32, 384)  1536       ['block3e_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3e_expand_activation (Act  (None, 32, 32, 384)  0          ['block3e_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3e_dwconv (DepthwiseConv2  (None, 32, 32, 384)  9600       ['block3e_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block3e_bn (BatchNormalization  (None, 32, 32, 384)  1536       ['block3e_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3e_activation (Activation  (None, 32, 32, 384)  0          ['block3e_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3e_se_squeeze (GlobalAver  (None, 384)         0           ['block3e_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3e_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block3e_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3e_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block3e_se_reshape[0][0]']     \n","                                                                                                  \n"," block3e_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block3e_se_reduce[0][0]']      \n","                                                                                                  \n"," block3e_se_excite (Multiply)   (None, 32, 32, 384)  0           ['block3e_activation[0][0]',     \n","                                                                  'block3e_se_expand[0][0]']      \n","                                                                                                  \n"," block3e_project_conv (Conv2D)  (None, 32, 32, 64)   24576       ['block3e_se_excite[0][0]']      \n","                                                                                                  \n"," block3e_project_bn (BatchNorma  (None, 32, 32, 64)  256         ['block3e_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3e_drop (Dropout)         (None, 32, 32, 64)   0           ['block3e_project_bn[0][0]']     \n","                                                                                                  \n"," block3e_add (Add)              (None, 32, 32, 64)   0           ['block3e_drop[0][0]',           \n","                                                                  'block3d_add[0][0]']            \n","                                                                                                  \n"," block4a_expand_conv (Conv2D)   (None, 32, 32, 384)  24576       ['block3e_add[0][0]']            \n","                                                                                                  \n"," block4a_expand_bn (BatchNormal  (None, 32, 32, 384)  1536       ['block4a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4a_expand_activation (Act  (None, 32, 32, 384)  0          ['block4a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4a_dwconv_pad (ZeroPaddin  (None, 33, 33, 384)  0          ['block4a_expand_activation[0][0]\n"," g2D)                                                            ']                               \n","                                                                                                  \n"," block4a_dwconv (DepthwiseConv2  (None, 16, 16, 384)  3456       ['block4a_dwconv_pad[0][0]']     \n"," D)                                                                                               \n","                                                                                                  \n"," block4a_bn (BatchNormalization  (None, 16, 16, 384)  1536       ['block4a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4a_activation (Activation  (None, 16, 16, 384)  0          ['block4a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4a_se_squeeze (GlobalAver  (None, 384)         0           ['block4a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4a_se_reshape (Reshape)   (None, 1, 1, 384)    0           ['block4a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4a_se_reduce (Conv2D)     (None, 1, 1, 16)     6160        ['block4a_se_reshape[0][0]']     \n","                                                                                                  \n"," block4a_se_expand (Conv2D)     (None, 1, 1, 384)    6528        ['block4a_se_reduce[0][0]']      \n","                                                                                                  \n"," block4a_se_excite (Multiply)   (None, 16, 16, 384)  0           ['block4a_activation[0][0]',     \n","                                                                  'block4a_se_expand[0][0]']      \n","                                                                                                  \n"," block4a_project_conv (Conv2D)  (None, 16, 16, 128)  49152       ['block4a_se_excite[0][0]']      \n","                                                                                                  \n"," block4a_project_bn (BatchNorma  (None, 16, 16, 128)  512        ['block4a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4b_expand_conv (Conv2D)   (None, 16, 16, 768)  98304       ['block4a_project_bn[0][0]']     \n","                                                                                                  \n"," block4b_expand_bn (BatchNormal  (None, 16, 16, 768)  3072       ['block4b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4b_expand_activation (Act  (None, 16, 16, 768)  0          ['block4b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4b_dwconv (DepthwiseConv2  (None, 16, 16, 768)  6912       ['block4b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4b_bn (BatchNormalization  (None, 16, 16, 768)  3072       ['block4b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4b_activation (Activation  (None, 16, 16, 768)  0          ['block4b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4b_se_squeeze (GlobalAver  (None, 768)         0           ['block4b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4b_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4b_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4b_se_reshape[0][0]']     \n","                                                                                                  \n"," block4b_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4b_se_reduce[0][0]']      \n","                                                                                                  \n"," block4b_se_excite (Multiply)   (None, 16, 16, 768)  0           ['block4b_activation[0][0]',     \n","                                                                  'block4b_se_expand[0][0]']      \n","                                                                                                  \n"," block4b_project_conv (Conv2D)  (None, 16, 16, 128)  98304       ['block4b_se_excite[0][0]']      \n","                                                                                                  \n"," block4b_project_bn (BatchNorma  (None, 16, 16, 128)  512        ['block4b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4b_drop (Dropout)         (None, 16, 16, 128)  0           ['block4b_project_bn[0][0]']     \n","                                                                                                  \n"," block4b_add (Add)              (None, 16, 16, 128)  0           ['block4b_drop[0][0]',           \n","                                                                  'block4a_project_bn[0][0]']     \n","                                                                                                  \n"," block4c_expand_conv (Conv2D)   (None, 16, 16, 768)  98304       ['block4b_add[0][0]']            \n","                                                                                                  \n"," block4c_expand_bn (BatchNormal  (None, 16, 16, 768)  3072       ['block4c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4c_expand_activation (Act  (None, 16, 16, 768)  0          ['block4c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4c_dwconv (DepthwiseConv2  (None, 16, 16, 768)  6912       ['block4c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4c_bn (BatchNormalization  (None, 16, 16, 768)  3072       ['block4c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4c_activation (Activation  (None, 16, 16, 768)  0          ['block4c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4c_se_squeeze (GlobalAver  (None, 768)         0           ['block4c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4c_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4c_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4c_se_reshape[0][0]']     \n","                                                                                                  \n"," block4c_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4c_se_reduce[0][0]']      \n","                                                                                                  \n"," block4c_se_excite (Multiply)   (None, 16, 16, 768)  0           ['block4c_activation[0][0]',     \n","                                                                  'block4c_se_expand[0][0]']      \n","                                                                                                  \n"," block4c_project_conv (Conv2D)  (None, 16, 16, 128)  98304       ['block4c_se_excite[0][0]']      \n","                                                                                                  \n"," block4c_project_bn (BatchNorma  (None, 16, 16, 128)  512        ['block4c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4c_drop (Dropout)         (None, 16, 16, 128)  0           ['block4c_project_bn[0][0]']     \n","                                                                                                  \n"," block4c_add (Add)              (None, 16, 16, 128)  0           ['block4c_drop[0][0]',           \n","                                                                  'block4b_add[0][0]']            \n","                                                                                                  \n"," block4d_expand_conv (Conv2D)   (None, 16, 16, 768)  98304       ['block4c_add[0][0]']            \n","                                                                                                  \n"," block4d_expand_bn (BatchNormal  (None, 16, 16, 768)  3072       ['block4d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4d_expand_activation (Act  (None, 16, 16, 768)  0          ['block4d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4d_dwconv (DepthwiseConv2  (None, 16, 16, 768)  6912       ['block4d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4d_bn (BatchNormalization  (None, 16, 16, 768)  3072       ['block4d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4d_activation (Activation  (None, 16, 16, 768)  0          ['block4d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4d_se_squeeze (GlobalAver  (None, 768)         0           ['block4d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4d_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4d_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4d_se_reshape[0][0]']     \n","                                                                                                  \n"," block4d_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4d_se_reduce[0][0]']      \n","                                                                                                  \n"," block4d_se_excite (Multiply)   (None, 16, 16, 768)  0           ['block4d_activation[0][0]',     \n","                                                                  'block4d_se_expand[0][0]']      \n","                                                                                                  \n"," block4d_project_conv (Conv2D)  (None, 16, 16, 128)  98304       ['block4d_se_excite[0][0]']      \n","                                                                                                  \n"," block4d_project_bn (BatchNorma  (None, 16, 16, 128)  512        ['block4d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4d_drop (Dropout)         (None, 16, 16, 128)  0           ['block4d_project_bn[0][0]']     \n","                                                                                                  \n"," block4d_add (Add)              (None, 16, 16, 128)  0           ['block4d_drop[0][0]',           \n","                                                                  'block4c_add[0][0]']            \n","                                                                                                  \n"," block4e_expand_conv (Conv2D)   (None, 16, 16, 768)  98304       ['block4d_add[0][0]']            \n","                                                                                                  \n"," block4e_expand_bn (BatchNormal  (None, 16, 16, 768)  3072       ['block4e_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4e_expand_activation (Act  (None, 16, 16, 768)  0          ['block4e_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4e_dwconv (DepthwiseConv2  (None, 16, 16, 768)  6912       ['block4e_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4e_bn (BatchNormalization  (None, 16, 16, 768)  3072       ['block4e_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4e_activation (Activation  (None, 16, 16, 768)  0          ['block4e_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4e_se_squeeze (GlobalAver  (None, 768)         0           ['block4e_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4e_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4e_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4e_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4e_se_reshape[0][0]']     \n","                                                                                                  \n"," block4e_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4e_se_reduce[0][0]']      \n","                                                                                                  \n"," block4e_se_excite (Multiply)   (None, 16, 16, 768)  0           ['block4e_activation[0][0]',     \n","                                                                  'block4e_se_expand[0][0]']      \n","                                                                                                  \n"," block4e_project_conv (Conv2D)  (None, 16, 16, 128)  98304       ['block4e_se_excite[0][0]']      \n","                                                                                                  \n"," block4e_project_bn (BatchNorma  (None, 16, 16, 128)  512        ['block4e_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4e_drop (Dropout)         (None, 16, 16, 128)  0           ['block4e_project_bn[0][0]']     \n","                                                                                                  \n"," block4e_add (Add)              (None, 16, 16, 128)  0           ['block4e_drop[0][0]',           \n","                                                                  'block4d_add[0][0]']            \n","                                                                                                  \n"," block4f_expand_conv (Conv2D)   (None, 16, 16, 768)  98304       ['block4e_add[0][0]']            \n","                                                                                                  \n"," block4f_expand_bn (BatchNormal  (None, 16, 16, 768)  3072       ['block4f_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4f_expand_activation (Act  (None, 16, 16, 768)  0          ['block4f_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4f_dwconv (DepthwiseConv2  (None, 16, 16, 768)  6912       ['block4f_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4f_bn (BatchNormalization  (None, 16, 16, 768)  3072       ['block4f_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4f_activation (Activation  (None, 16, 16, 768)  0          ['block4f_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4f_se_squeeze (GlobalAver  (None, 768)         0           ['block4f_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4f_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4f_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4f_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4f_se_reshape[0][0]']     \n","                                                                                                  \n"," block4f_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4f_se_reduce[0][0]']      \n","                                                                                                  \n"," block4f_se_excite (Multiply)   (None, 16, 16, 768)  0           ['block4f_activation[0][0]',     \n","                                                                  'block4f_se_expand[0][0]']      \n","                                                                                                  \n"," block4f_project_conv (Conv2D)  (None, 16, 16, 128)  98304       ['block4f_se_excite[0][0]']      \n","                                                                                                  \n"," block4f_project_bn (BatchNorma  (None, 16, 16, 128)  512        ['block4f_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4f_drop (Dropout)         (None, 16, 16, 128)  0           ['block4f_project_bn[0][0]']     \n","                                                                                                  \n"," block4f_add (Add)              (None, 16, 16, 128)  0           ['block4f_drop[0][0]',           \n","                                                                  'block4e_add[0][0]']            \n","                                                                                                  \n"," block4g_expand_conv (Conv2D)   (None, 16, 16, 768)  98304       ['block4f_add[0][0]']            \n","                                                                                                  \n"," block4g_expand_bn (BatchNormal  (None, 16, 16, 768)  3072       ['block4g_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4g_expand_activation (Act  (None, 16, 16, 768)  0          ['block4g_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4g_dwconv (DepthwiseConv2  (None, 16, 16, 768)  6912       ['block4g_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4g_bn (BatchNormalization  (None, 16, 16, 768)  3072       ['block4g_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4g_activation (Activation  (None, 16, 16, 768)  0          ['block4g_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4g_se_squeeze (GlobalAver  (None, 768)         0           ['block4g_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4g_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block4g_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4g_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block4g_se_reshape[0][0]']     \n","                                                                                                  \n"," block4g_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block4g_se_reduce[0][0]']      \n","                                                                                                  \n"," block4g_se_excite (Multiply)   (None, 16, 16, 768)  0           ['block4g_activation[0][0]',     \n","                                                                  'block4g_se_expand[0][0]']      \n","                                                                                                  \n"," block4g_project_conv (Conv2D)  (None, 16, 16, 128)  98304       ['block4g_se_excite[0][0]']      \n","                                                                                                  \n"," block4g_project_bn (BatchNorma  (None, 16, 16, 128)  512        ['block4g_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4g_drop (Dropout)         (None, 16, 16, 128)  0           ['block4g_project_bn[0][0]']     \n","                                                                                                  \n"," block4g_add (Add)              (None, 16, 16, 128)  0           ['block4g_drop[0][0]',           \n","                                                                  'block4f_add[0][0]']            \n","                                                                                                  \n"," block5a_expand_conv (Conv2D)   (None, 16, 16, 768)  98304       ['block4g_add[0][0]']            \n","                                                                                                  \n"," block5a_expand_bn (BatchNormal  (None, 16, 16, 768)  3072       ['block5a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block5a_expand_activation (Act  (None, 16, 16, 768)  0          ['block5a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block5a_dwconv (DepthwiseConv2  (None, 16, 16, 768)  19200      ['block5a_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block5a_bn (BatchNormalization  (None, 16, 16, 768)  3072       ['block5a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block5a_activation (Activation  (None, 16, 16, 768)  0          ['block5a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5a_se_squeeze (GlobalAver  (None, 768)         0           ['block5a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5a_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block5a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5a_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block5a_se_reshape[0][0]']     \n","                                                                                                  \n"," block5a_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block5a_se_reduce[0][0]']      \n","                                                                                                  \n"," block5a_se_excite (Multiply)   (None, 16, 16, 768)  0           ['block5a_activation[0][0]',     \n","                                                                  'block5a_se_expand[0][0]']      \n","                                                                                                  \n"," block5a_project_conv (Conv2D)  (None, 16, 16, 176)  135168      ['block5a_se_excite[0][0]']      \n","                                                                                                  \n"," block5a_project_bn (BatchNorma  (None, 16, 16, 176)  704        ['block5a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5b_expand_conv (Conv2D)   (None, 16, 16, 1056  185856      ['block5a_project_bn[0][0]']     \n","                                )                                                                 \n","                                                                                                  \n"," block5b_expand_bn (BatchNormal  (None, 16, 16, 1056  4224       ['block5b_expand_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block5b_expand_activation (Act  (None, 16, 16, 1056  0          ['block5b_expand_bn[0][0]']      \n"," ivation)                       )                                                                 \n","                                                                                                  \n"," block5b_dwconv (DepthwiseConv2  (None, 16, 16, 1056  26400      ['block5b_expand_activation[0][0]\n"," D)                             )                                ']                               \n","                                                                                                  \n"," block5b_bn (BatchNormalization  (None, 16, 16, 1056  4224       ['block5b_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block5b_activation (Activation  (None, 16, 16, 1056  0          ['block5b_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block5b_se_squeeze (GlobalAver  (None, 1056)        0           ['block5b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5b_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5b_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5b_se_reshape[0][0]']     \n","                                                                                                  \n"," block5b_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5b_se_reduce[0][0]']      \n","                                                                                                  \n"," block5b_se_excite (Multiply)   (None, 16, 16, 1056  0           ['block5b_activation[0][0]',     \n","                                )                                 'block5b_se_expand[0][0]']      \n","                                                                                                  \n"," block5b_project_conv (Conv2D)  (None, 16, 16, 176)  185856      ['block5b_se_excite[0][0]']      \n","                                                                                                  \n"," block5b_project_bn (BatchNorma  (None, 16, 16, 176)  704        ['block5b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5b_drop (Dropout)         (None, 16, 16, 176)  0           ['block5b_project_bn[0][0]']     \n","                                                                                                  \n"," block5b_add (Add)              (None, 16, 16, 176)  0           ['block5b_drop[0][0]',           \n","                                                                  'block5a_project_bn[0][0]']     \n","                                                                                                  \n"," block5c_expand_conv (Conv2D)   (None, 16, 16, 1056  185856      ['block5b_add[0][0]']            \n","                                )                                                                 \n","                                                                                                  \n"," block5c_expand_bn (BatchNormal  (None, 16, 16, 1056  4224       ['block5c_expand_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block5c_expand_activation (Act  (None, 16, 16, 1056  0          ['block5c_expand_bn[0][0]']      \n"," ivation)                       )                                                                 \n","                                                                                                  \n"," block5c_dwconv (DepthwiseConv2  (None, 16, 16, 1056  26400      ['block5c_expand_activation[0][0]\n"," D)                             )                                ']                               \n","                                                                                                  \n"," block5c_bn (BatchNormalization  (None, 16, 16, 1056  4224       ['block5c_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block5c_activation (Activation  (None, 16, 16, 1056  0          ['block5c_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block5c_se_squeeze (GlobalAver  (None, 1056)        0           ['block5c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5c_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5c_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5c_se_reshape[0][0]']     \n","                                                                                                  \n"," block5c_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5c_se_reduce[0][0]']      \n","                                                                                                  \n"," block5c_se_excite (Multiply)   (None, 16, 16, 1056  0           ['block5c_activation[0][0]',     \n","                                )                                 'block5c_se_expand[0][0]']      \n","                                                                                                  \n"," block5c_project_conv (Conv2D)  (None, 16, 16, 176)  185856      ['block5c_se_excite[0][0]']      \n","                                                                                                  \n"," block5c_project_bn (BatchNorma  (None, 16, 16, 176)  704        ['block5c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5c_drop (Dropout)         (None, 16, 16, 176)  0           ['block5c_project_bn[0][0]']     \n","                                                                                                  \n"," block5c_add (Add)              (None, 16, 16, 176)  0           ['block5c_drop[0][0]',           \n","                                                                  'block5b_add[0][0]']            \n","                                                                                                  \n"," block5d_expand_conv (Conv2D)   (None, 16, 16, 1056  185856      ['block5c_add[0][0]']            \n","                                )                                                                 \n","                                                                                                  \n"," block5d_expand_bn (BatchNormal  (None, 16, 16, 1056  4224       ['block5d_expand_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block5d_expand_activation (Act  (None, 16, 16, 1056  0          ['block5d_expand_bn[0][0]']      \n"," ivation)                       )                                                                 \n","                                                                                                  \n"," block5d_dwconv (DepthwiseConv2  (None, 16, 16, 1056  26400      ['block5d_expand_activation[0][0]\n"," D)                             )                                ']                               \n","                                                                                                  \n"," block5d_bn (BatchNormalization  (None, 16, 16, 1056  4224       ['block5d_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block5d_activation (Activation  (None, 16, 16, 1056  0          ['block5d_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block5d_se_squeeze (GlobalAver  (None, 1056)        0           ['block5d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5d_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5d_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5d_se_reshape[0][0]']     \n","                                                                                                  \n"," block5d_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5d_se_reduce[0][0]']      \n","                                                                                                  \n"," block5d_se_excite (Multiply)   (None, 16, 16, 1056  0           ['block5d_activation[0][0]',     \n","                                )                                 'block5d_se_expand[0][0]']      \n","                                                                                                  \n"," block5d_project_conv (Conv2D)  (None, 16, 16, 176)  185856      ['block5d_se_excite[0][0]']      \n","                                                                                                  \n"," block5d_project_bn (BatchNorma  (None, 16, 16, 176)  704        ['block5d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5d_drop (Dropout)         (None, 16, 16, 176)  0           ['block5d_project_bn[0][0]']     \n","                                                                                                  \n"," block5d_add (Add)              (None, 16, 16, 176)  0           ['block5d_drop[0][0]',           \n","                                                                  'block5c_add[0][0]']            \n","                                                                                                  \n"," block5e_expand_conv (Conv2D)   (None, 16, 16, 1056  185856      ['block5d_add[0][0]']            \n","                                )                                                                 \n","                                                                                                  \n"," block5e_expand_bn (BatchNormal  (None, 16, 16, 1056  4224       ['block5e_expand_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block5e_expand_activation (Act  (None, 16, 16, 1056  0          ['block5e_expand_bn[0][0]']      \n"," ivation)                       )                                                                 \n","                                                                                                  \n"," block5e_dwconv (DepthwiseConv2  (None, 16, 16, 1056  26400      ['block5e_expand_activation[0][0]\n"," D)                             )                                ']                               \n","                                                                                                  \n"," block5e_bn (BatchNormalization  (None, 16, 16, 1056  4224       ['block5e_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block5e_activation (Activation  (None, 16, 16, 1056  0          ['block5e_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block5e_se_squeeze (GlobalAver  (None, 1056)        0           ['block5e_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5e_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5e_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5e_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5e_se_reshape[0][0]']     \n","                                                                                                  \n"," block5e_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5e_se_reduce[0][0]']      \n","                                                                                                  \n"," block5e_se_excite (Multiply)   (None, 16, 16, 1056  0           ['block5e_activation[0][0]',     \n","                                )                                 'block5e_se_expand[0][0]']      \n","                                                                                                  \n"," block5e_project_conv (Conv2D)  (None, 16, 16, 176)  185856      ['block5e_se_excite[0][0]']      \n","                                                                                                  \n"," block5e_project_bn (BatchNorma  (None, 16, 16, 176)  704        ['block5e_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5e_drop (Dropout)         (None, 16, 16, 176)  0           ['block5e_project_bn[0][0]']     \n","                                                                                                  \n"," block5e_add (Add)              (None, 16, 16, 176)  0           ['block5e_drop[0][0]',           \n","                                                                  'block5d_add[0][0]']            \n","                                                                                                  \n"," block5f_expand_conv (Conv2D)   (None, 16, 16, 1056  185856      ['block5e_add[0][0]']            \n","                                )                                                                 \n","                                                                                                  \n"," block5f_expand_bn (BatchNormal  (None, 16, 16, 1056  4224       ['block5f_expand_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block5f_expand_activation (Act  (None, 16, 16, 1056  0          ['block5f_expand_bn[0][0]']      \n"," ivation)                       )                                                                 \n","                                                                                                  \n"," block5f_dwconv (DepthwiseConv2  (None, 16, 16, 1056  26400      ['block5f_expand_activation[0][0]\n"," D)                             )                                ']                               \n","                                                                                                  \n"," block5f_bn (BatchNormalization  (None, 16, 16, 1056  4224       ['block5f_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block5f_activation (Activation  (None, 16, 16, 1056  0          ['block5f_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block5f_se_squeeze (GlobalAver  (None, 1056)        0           ['block5f_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5f_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5f_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5f_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5f_se_reshape[0][0]']     \n","                                                                                                  \n"," block5f_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5f_se_reduce[0][0]']      \n","                                                                                                  \n"," block5f_se_excite (Multiply)   (None, 16, 16, 1056  0           ['block5f_activation[0][0]',     \n","                                )                                 'block5f_se_expand[0][0]']      \n","                                                                                                  \n"," block5f_project_conv (Conv2D)  (None, 16, 16, 176)  185856      ['block5f_se_excite[0][0]']      \n","                                                                                                  \n"," block5f_project_bn (BatchNorma  (None, 16, 16, 176)  704        ['block5f_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5f_drop (Dropout)         (None, 16, 16, 176)  0           ['block5f_project_bn[0][0]']     \n","                                                                                                  \n"," block5f_add (Add)              (None, 16, 16, 176)  0           ['block5f_drop[0][0]',           \n","                                                                  'block5e_add[0][0]']            \n","                                                                                                  \n"," block5g_expand_conv (Conv2D)   (None, 16, 16, 1056  185856      ['block5f_add[0][0]']            \n","                                )                                                                 \n","                                                                                                  \n"," block5g_expand_bn (BatchNormal  (None, 16, 16, 1056  4224       ['block5g_expand_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block5g_expand_activation (Act  (None, 16, 16, 1056  0          ['block5g_expand_bn[0][0]']      \n"," ivation)                       )                                                                 \n","                                                                                                  \n"," block5g_dwconv (DepthwiseConv2  (None, 16, 16, 1056  26400      ['block5g_expand_activation[0][0]\n"," D)                             )                                ']                               \n","                                                                                                  \n"," block5g_bn (BatchNormalization  (None, 16, 16, 1056  4224       ['block5g_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block5g_activation (Activation  (None, 16, 16, 1056  0          ['block5g_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block5g_se_squeeze (GlobalAver  (None, 1056)        0           ['block5g_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5g_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block5g_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5g_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block5g_se_reshape[0][0]']     \n","                                                                                                  \n"," block5g_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block5g_se_reduce[0][0]']      \n","                                                                                                  \n"," block5g_se_excite (Multiply)   (None, 16, 16, 1056  0           ['block5g_activation[0][0]',     \n","                                )                                 'block5g_se_expand[0][0]']      \n","                                                                                                  \n"," block5g_project_conv (Conv2D)  (None, 16, 16, 176)  185856      ['block5g_se_excite[0][0]']      \n","                                                                                                  \n"," block5g_project_bn (BatchNorma  (None, 16, 16, 176)  704        ['block5g_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5g_drop (Dropout)         (None, 16, 16, 176)  0           ['block5g_project_bn[0][0]']     \n","                                                                                                  \n"," block5g_add (Add)              (None, 16, 16, 176)  0           ['block5g_drop[0][0]',           \n","                                                                  'block5f_add[0][0]']            \n","                                                                                                  \n"," block6a_expand_conv (Conv2D)   (None, 16, 16, 1056  185856      ['block5g_add[0][0]']            \n","                                )                                                                 \n","                                                                                                  \n"," block6a_expand_bn (BatchNormal  (None, 16, 16, 1056  4224       ['block6a_expand_conv[0][0]']    \n"," ization)                       )                                                                 \n","                                                                                                  \n"," block6a_expand_activation (Act  (None, 16, 16, 1056  0          ['block6a_expand_bn[0][0]']      \n"," ivation)                       )                                                                 \n","                                                                                                  \n"," block6a_dwconv_pad (ZeroPaddin  (None, 19, 19, 1056  0          ['block6a_expand_activation[0][0]\n"," g2D)                           )                                ']                               \n","                                                                                                  \n"," block6a_dwconv (DepthwiseConv2  (None, 8, 8, 1056)  26400       ['block6a_dwconv_pad[0][0]']     \n"," D)                                                                                               \n","                                                                                                  \n"," block6a_bn (BatchNormalization  (None, 8, 8, 1056)  4224        ['block6a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6a_activation (Activation  (None, 8, 8, 1056)  0           ['block6a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6a_se_squeeze (GlobalAver  (None, 1056)        0           ['block6a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6a_se_reshape (Reshape)   (None, 1, 1, 1056)   0           ['block6a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6a_se_reduce (Conv2D)     (None, 1, 1, 44)     46508       ['block6a_se_reshape[0][0]']     \n","                                                                                                  \n"," block6a_se_expand (Conv2D)     (None, 1, 1, 1056)   47520       ['block6a_se_reduce[0][0]']      \n","                                                                                                  \n"," block6a_se_excite (Multiply)   (None, 8, 8, 1056)   0           ['block6a_activation[0][0]',     \n","                                                                  'block6a_se_expand[0][0]']      \n","                                                                                                  \n"," block6a_project_conv (Conv2D)  (None, 8, 8, 304)    321024      ['block6a_se_excite[0][0]']      \n","                                                                                                  \n"," block6a_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6b_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6a_project_bn[0][0]']     \n","                                                                                                  \n"," block6b_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block6b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6b_expand_activation (Act  (None, 8, 8, 1824)  0           ['block6b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6b_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  45600       ['block6b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6b_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block6b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6b_activation (Activation  (None, 8, 8, 1824)  0           ['block6b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6b_se_squeeze (GlobalAver  (None, 1824)        0           ['block6b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6b_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6b_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6b_se_reshape[0][0]']     \n","                                                                                                  \n"," block6b_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6b_se_reduce[0][0]']      \n","                                                                                                  \n"," block6b_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block6b_activation[0][0]',     \n","                                                                  'block6b_se_expand[0][0]']      \n","                                                                                                  \n"," block6b_project_conv (Conv2D)  (None, 8, 8, 304)    554496      ['block6b_se_excite[0][0]']      \n","                                                                                                  \n"," block6b_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6b_drop (Dropout)         (None, 8, 8, 304)    0           ['block6b_project_bn[0][0]']     \n","                                                                                                  \n"," block6b_add (Add)              (None, 8, 8, 304)    0           ['block6b_drop[0][0]',           \n","                                                                  'block6a_project_bn[0][0]']     \n","                                                                                                  \n"," block6c_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6b_add[0][0]']            \n","                                                                                                  \n"," block6c_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block6c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6c_expand_activation (Act  (None, 8, 8, 1824)  0           ['block6c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6c_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  45600       ['block6c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6c_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block6c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6c_activation (Activation  (None, 8, 8, 1824)  0           ['block6c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6c_se_squeeze (GlobalAver  (None, 1824)        0           ['block6c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6c_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6c_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6c_se_reshape[0][0]']     \n","                                                                                                  \n"," block6c_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6c_se_reduce[0][0]']      \n","                                                                                                  \n"," block6c_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block6c_activation[0][0]',     \n","                                                                  'block6c_se_expand[0][0]']      \n","                                                                                                  \n"," block6c_project_conv (Conv2D)  (None, 8, 8, 304)    554496      ['block6c_se_excite[0][0]']      \n","                                                                                                  \n"," block6c_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6c_drop (Dropout)         (None, 8, 8, 304)    0           ['block6c_project_bn[0][0]']     \n","                                                                                                  \n"," block6c_add (Add)              (None, 8, 8, 304)    0           ['block6c_drop[0][0]',           \n","                                                                  'block6b_add[0][0]']            \n","                                                                                                  \n"," block6d_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6c_add[0][0]']            \n","                                                                                                  \n"," block6d_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block6d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6d_expand_activation (Act  (None, 8, 8, 1824)  0           ['block6d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6d_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  45600       ['block6d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6d_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block6d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6d_activation (Activation  (None, 8, 8, 1824)  0           ['block6d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6d_se_squeeze (GlobalAver  (None, 1824)        0           ['block6d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6d_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6d_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6d_se_reshape[0][0]']     \n","                                                                                                  \n"," block6d_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6d_se_reduce[0][0]']      \n","                                                                                                  \n"," block6d_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block6d_activation[0][0]',     \n","                                                                  'block6d_se_expand[0][0]']      \n","                                                                                                  \n"," block6d_project_conv (Conv2D)  (None, 8, 8, 304)    554496      ['block6d_se_excite[0][0]']      \n","                                                                                                  \n"," block6d_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6d_drop (Dropout)         (None, 8, 8, 304)    0           ['block6d_project_bn[0][0]']     \n","                                                                                                  \n"," block6d_add (Add)              (None, 8, 8, 304)    0           ['block6d_drop[0][0]',           \n","                                                                  'block6c_add[0][0]']            \n","                                                                                                  \n"," block6e_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6d_add[0][0]']            \n","                                                                                                  \n"," block6e_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block6e_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6e_expand_activation (Act  (None, 8, 8, 1824)  0           ['block6e_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6e_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  45600       ['block6e_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6e_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block6e_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6e_activation (Activation  (None, 8, 8, 1824)  0           ['block6e_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6e_se_squeeze (GlobalAver  (None, 1824)        0           ['block6e_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6e_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6e_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6e_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6e_se_reshape[0][0]']     \n","                                                                                                  \n"," block6e_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6e_se_reduce[0][0]']      \n","                                                                                                  \n"," block6e_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block6e_activation[0][0]',     \n","                                                                  'block6e_se_expand[0][0]']      \n","                                                                                                  \n"," block6e_project_conv (Conv2D)  (None, 8, 8, 304)    554496      ['block6e_se_excite[0][0]']      \n","                                                                                                  \n"," block6e_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6e_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6e_drop (Dropout)         (None, 8, 8, 304)    0           ['block6e_project_bn[0][0]']     \n","                                                                                                  \n"," block6e_add (Add)              (None, 8, 8, 304)    0           ['block6e_drop[0][0]',           \n","                                                                  'block6d_add[0][0]']            \n","                                                                                                  \n"," block6f_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6e_add[0][0]']            \n","                                                                                                  \n"," block6f_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block6f_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6f_expand_activation (Act  (None, 8, 8, 1824)  0           ['block6f_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6f_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  45600       ['block6f_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6f_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block6f_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6f_activation (Activation  (None, 8, 8, 1824)  0           ['block6f_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6f_se_squeeze (GlobalAver  (None, 1824)        0           ['block6f_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6f_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6f_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6f_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6f_se_reshape[0][0]']     \n","                                                                                                  \n"," block6f_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6f_se_reduce[0][0]']      \n","                                                                                                  \n"," block6f_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block6f_activation[0][0]',     \n","                                                                  'block6f_se_expand[0][0]']      \n","                                                                                                  \n"," block6f_project_conv (Conv2D)  (None, 8, 8, 304)    554496      ['block6f_se_excite[0][0]']      \n","                                                                                                  \n"," block6f_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6f_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6f_drop (Dropout)         (None, 8, 8, 304)    0           ['block6f_project_bn[0][0]']     \n","                                                                                                  \n"," block6f_add (Add)              (None, 8, 8, 304)    0           ['block6f_drop[0][0]',           \n","                                                                  'block6e_add[0][0]']            \n","                                                                                                  \n"," block6g_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6f_add[0][0]']            \n","                                                                                                  \n"," block6g_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block6g_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6g_expand_activation (Act  (None, 8, 8, 1824)  0           ['block6g_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6g_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  45600       ['block6g_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6g_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block6g_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6g_activation (Activation  (None, 8, 8, 1824)  0           ['block6g_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6g_se_squeeze (GlobalAver  (None, 1824)        0           ['block6g_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6g_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6g_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6g_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6g_se_reshape[0][0]']     \n","                                                                                                  \n"," block6g_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6g_se_reduce[0][0]']      \n","                                                                                                  \n"," block6g_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block6g_activation[0][0]',     \n","                                                                  'block6g_se_expand[0][0]']      \n","                                                                                                  \n"," block6g_project_conv (Conv2D)  (None, 8, 8, 304)    554496      ['block6g_se_excite[0][0]']      \n","                                                                                                  \n"," block6g_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6g_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6g_drop (Dropout)         (None, 8, 8, 304)    0           ['block6g_project_bn[0][0]']     \n","                                                                                                  \n"," block6g_add (Add)              (None, 8, 8, 304)    0           ['block6g_drop[0][0]',           \n","                                                                  'block6f_add[0][0]']            \n","                                                                                                  \n"," block6h_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6g_add[0][0]']            \n","                                                                                                  \n"," block6h_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block6h_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6h_expand_activation (Act  (None, 8, 8, 1824)  0           ['block6h_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6h_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  45600       ['block6h_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6h_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block6h_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6h_activation (Activation  (None, 8, 8, 1824)  0           ['block6h_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6h_se_squeeze (GlobalAver  (None, 1824)        0           ['block6h_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6h_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6h_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6h_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6h_se_reshape[0][0]']     \n","                                                                                                  \n"," block6h_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6h_se_reduce[0][0]']      \n","                                                                                                  \n"," block6h_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block6h_activation[0][0]',     \n","                                                                  'block6h_se_expand[0][0]']      \n","                                                                                                  \n"," block6h_project_conv (Conv2D)  (None, 8, 8, 304)    554496      ['block6h_se_excite[0][0]']      \n","                                                                                                  \n"," block6h_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6h_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6h_drop (Dropout)         (None, 8, 8, 304)    0           ['block6h_project_bn[0][0]']     \n","                                                                                                  \n"," block6h_add (Add)              (None, 8, 8, 304)    0           ['block6h_drop[0][0]',           \n","                                                                  'block6g_add[0][0]']            \n","                                                                                                  \n"," block6i_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6h_add[0][0]']            \n","                                                                                                  \n"," block6i_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block6i_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6i_expand_activation (Act  (None, 8, 8, 1824)  0           ['block6i_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6i_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  45600       ['block6i_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6i_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block6i_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6i_activation (Activation  (None, 8, 8, 1824)  0           ['block6i_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6i_se_squeeze (GlobalAver  (None, 1824)        0           ['block6i_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6i_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block6i_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6i_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block6i_se_reshape[0][0]']     \n","                                                                                                  \n"," block6i_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block6i_se_reduce[0][0]']      \n","                                                                                                  \n"," block6i_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block6i_activation[0][0]',     \n","                                                                  'block6i_se_expand[0][0]']      \n","                                                                                                  \n"," block6i_project_conv (Conv2D)  (None, 8, 8, 304)    554496      ['block6i_se_excite[0][0]']      \n","                                                                                                  \n"," block6i_project_bn (BatchNorma  (None, 8, 8, 304)   1216        ['block6i_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6i_drop (Dropout)         (None, 8, 8, 304)    0           ['block6i_project_bn[0][0]']     \n","                                                                                                  \n"," block6i_add (Add)              (None, 8, 8, 304)    0           ['block6i_drop[0][0]',           \n","                                                                  'block6h_add[0][0]']            \n","                                                                                                  \n"," block7a_expand_conv (Conv2D)   (None, 8, 8, 1824)   554496      ['block6i_add[0][0]']            \n","                                                                                                  \n"," block7a_expand_bn (BatchNormal  (None, 8, 8, 1824)  7296        ['block7a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block7a_expand_activation (Act  (None, 8, 8, 1824)  0           ['block7a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block7a_dwconv (DepthwiseConv2  (None, 8, 8, 1824)  16416       ['block7a_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block7a_bn (BatchNormalization  (None, 8, 8, 1824)  7296        ['block7a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block7a_activation (Activation  (None, 8, 8, 1824)  0           ['block7a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7a_se_squeeze (GlobalAver  (None, 1824)        0           ['block7a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7a_se_reshape (Reshape)   (None, 1, 1, 1824)   0           ['block7a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block7a_se_reduce (Conv2D)     (None, 1, 1, 76)     138700      ['block7a_se_reshape[0][0]']     \n","                                                                                                  \n"," block7a_se_expand (Conv2D)     (None, 1, 1, 1824)   140448      ['block7a_se_reduce[0][0]']      \n","                                                                                                  \n"," block7a_se_excite (Multiply)   (None, 8, 8, 1824)   0           ['block7a_activation[0][0]',     \n","                                                                  'block7a_se_expand[0][0]']      \n","                                                                                                  \n"," block7a_project_conv (Conv2D)  (None, 8, 8, 512)    933888      ['block7a_se_excite[0][0]']      \n","                                                                                                  \n"," block7a_project_bn (BatchNorma  (None, 8, 8, 512)   2048        ['block7a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block7b_expand_conv (Conv2D)   (None, 8, 8, 3072)   1572864     ['block7a_project_bn[0][0]']     \n","                                                                                                  \n"," block7b_expand_bn (BatchNormal  (None, 8, 8, 3072)  12288       ['block7b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block7b_expand_activation (Act  (None, 8, 8, 3072)  0           ['block7b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block7b_dwconv (DepthwiseConv2  (None, 8, 8, 3072)  27648       ['block7b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block7b_bn (BatchNormalization  (None, 8, 8, 3072)  12288       ['block7b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block7b_activation (Activation  (None, 8, 8, 3072)  0           ['block7b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7b_se_squeeze (GlobalAver  (None, 3072)        0           ['block7b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7b_se_reshape (Reshape)   (None, 1, 1, 3072)   0           ['block7b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block7b_se_reduce (Conv2D)     (None, 1, 1, 128)    393344      ['block7b_se_reshape[0][0]']     \n","                                                                                                  \n"," block7b_se_expand (Conv2D)     (None, 1, 1, 3072)   396288      ['block7b_se_reduce[0][0]']      \n","                                                                                                  \n"," block7b_se_excite (Multiply)   (None, 8, 8, 3072)   0           ['block7b_activation[0][0]',     \n","                                                                  'block7b_se_expand[0][0]']      \n","                                                                                                  \n"," block7b_project_conv (Conv2D)  (None, 8, 8, 512)    1572864     ['block7b_se_excite[0][0]']      \n","                                                                                                  \n"," block7b_project_bn (BatchNorma  (None, 8, 8, 512)   2048        ['block7b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block7b_drop (Dropout)         (None, 8, 8, 512)    0           ['block7b_project_bn[0][0]']     \n","                                                                                                  \n"," block7b_add (Add)              (None, 8, 8, 512)    0           ['block7b_drop[0][0]',           \n","                                                                  'block7a_project_bn[0][0]']     \n","                                                                                                  \n"," block7c_expand_conv (Conv2D)   (None, 8, 8, 3072)   1572864     ['block7b_add[0][0]']            \n","                                                                                                  \n"," block7c_expand_bn (BatchNormal  (None, 8, 8, 3072)  12288       ['block7c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block7c_expand_activation (Act  (None, 8, 8, 3072)  0           ['block7c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block7c_dwconv (DepthwiseConv2  (None, 8, 8, 3072)  27648       ['block7c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block7c_bn (BatchNormalization  (None, 8, 8, 3072)  12288       ['block7c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block7c_activation (Activation  (None, 8, 8, 3072)  0           ['block7c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7c_se_squeeze (GlobalAver  (None, 3072)        0           ['block7c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7c_se_reshape (Reshape)   (None, 1, 1, 3072)   0           ['block7c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block7c_se_reduce (Conv2D)     (None, 1, 1, 128)    393344      ['block7c_se_reshape[0][0]']     \n","                                                                                                  \n"," block7c_se_expand (Conv2D)     (None, 1, 1, 3072)   396288      ['block7c_se_reduce[0][0]']      \n","                                                                                                  \n"," block7c_se_excite (Multiply)   (None, 8, 8, 3072)   0           ['block7c_activation[0][0]',     \n","                                                                  'block7c_se_expand[0][0]']      \n","                                                                                                  \n"," block7c_project_conv (Conv2D)  (None, 8, 8, 512)    1572864     ['block7c_se_excite[0][0]']      \n","                                                                                                  \n"," block7c_project_bn (BatchNorma  (None, 8, 8, 512)   2048        ['block7c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block7c_drop (Dropout)         (None, 8, 8, 512)    0           ['block7c_project_bn[0][0]']     \n","                                                                                                  \n"," block7c_add (Add)              (None, 8, 8, 512)    0           ['block7c_drop[0][0]',           \n","                                                                  'block7b_add[0][0]']            \n","                                                                                                  \n"," top_conv (Conv2D)              (None, 8, 8, 2048)   1048576     ['block7c_add[0][0]']            \n","                                                                                                  \n"," top_bn (BatchNormalization)    (None, 8, 8, 2048)   8192        ['top_conv[0][0]']               \n","                                                                                                  \n"," top_activation (Activation)    (None, 8, 8, 2048)   0           ['top_bn[0][0]']                 \n","                                                                                                  \n"," global_average_pooling2d (Glob  (None, 2048)        0           ['top_activation[0][0]']         \n"," alAveragePooling2D)                                                                              \n","                                                                                                  \n"," dense (Dense)                  (None, 1024)         2098176     ['global_average_pooling2d[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," dense_1 (Dense)                (None, 128)          131200      ['dense[0][0]']                  \n","                                                                                                  \n"," dense_2 (Dense)                (None, 3)            387         ['dense_1[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 30,743,290\n","Trainable params: 2,229,763\n","Non-trainable params: 28,513,527\n","__________________________________________________________________________________________________\n"]}],"source":["x = backbone.output\n","\n","x = GlobalAveragePooling2D()(x)\n","x = Dense( CONFIGURATION[\"N_DENSE_1\"], activation = \"relu\")(x)\n","x = Dense( CONFIGURATION[\"N_DENSE_2\"], activation = \"relu\")(x)\n","output = Dense( CONFIGURATION[\"NUM_CLASSES\"], activation = \"softmax\")(x)\n","\n","pretrained_model = Model(backbone.inputs, output)\n","pretrained_model.summary()"]},{"cell_type":"code","source":["pretrained_model.load_weights('/content/drive/MyDrive/Bang/mobilenet_human_emotions.h5')"],"metadata":{"id":"Sh6VbRrOCTRv"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LVt9O6r021Gy"},"outputs":[],"source":["img_path = \"/content/dataset/Emotions Dataset/Emotions Dataset/train/happy/202291.jpg\""]},{"cell_type":"code","source":["test_image = cv2.imread(img_path)\n","test_image = cv2.resize(test_image, (CONFIGURATION[\"IM_SIZE\"] ,CONFIGURATION[\"IM_SIZE\"]))\n","im = tf.constant(test_image, dtype = tf.float32)\n","img_array = tf.expand_dims(im, axis = 0)\n","print(img_array.shape)"],"metadata":{"id":"qdXp580Fy9eb","executionInfo":{"status":"ok","timestamp":1668413294672,"user_tz":-60,"elapsed":39,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"28f021ab-a451-4039-ea28-f64e93442dd8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 256, 256, 3)\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":29,"status":"ok","timestamp":1668413294672,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"bx3YMcZz2AYQ","outputId":"8cbdd2a5-42c1-47aa-f762-4382c314b11c"},"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 35ms/step\n"]}],"source":["preds = pretrained_model.predict(img_array)"]},{"cell_type":"code","source":["print(preds)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xMBx17GlycCi","executionInfo":{"status":"ok","timestamp":1668413294673,"user_tz":-60,"elapsed":22,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"cf9c338a-c067-4d75-af35-e55bd3bdb558"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[1.1035408e-02 9.8881429e-01 1.5026570e-04]]\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1668413294673,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"U-HKGr8V2Dxu","outputId":"9e4db2d5-057f-4073-9ee9-918482c12769"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["1"]},"metadata":{},"execution_count":127}],"source":["np.argmax(preds[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4nzOjJub2D0C"},"outputs":[],"source":["last_conv_layer_name = \"top_activation\"\n","last_conv_layer = pretrained_model.get_layer(last_conv_layer_name)\n","last_conv_layer_model = Model(pretrained_model.inputs, last_conv_layer.output)"]},{"cell_type":"code","source":["classifier_layer_names = [\n"," \"global_average_pooling2d\",\n"," \"dense\",\n"," \"dense_1\",\n"," \"dense_2\"\n","]"],"metadata":{"id":"PZ74HdeEk6ee"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["classifier_input = Input(shape=(8,8,2048))\n","x = classifier_input \n","for layer_name in classifier_layer_names:\n"," x = pretrained_model.get_layer(layer_name)(x)\n","classifier_model = Model(classifier_input, x)"],"metadata":{"id":"15DTUsJ-k6bu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["with tf.GradientTape() as tape:\n","  last_conv_layer_output = last_conv_layer_model(img_array)\n","  preds = classifier_model(last_conv_layer_output)\n","  top_pred_index = tf.argmax(preds[0])\n","  print(top_pred_index)\n","  top_class_channel = preds[:, top_pred_index]\n","\n","grads = tape.gradient(top_class_channel, last_conv_layer_output)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g_1DodvJk6Wh","executionInfo":{"status":"ok","timestamp":1668413296745,"user_tz":-60,"elapsed":593,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ec3f48ab-11c0-4b61-a8bf-50d4d9553d56"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(1, shape=(), dtype=int64)\n"]}]},{"cell_type":"code","source":["grads.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"owYsDj6yk6US","executionInfo":{"status":"ok","timestamp":1668413296746,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"53e8839c-b4bb-4c20-d0c4-4dcbe9de3477"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TensorShape([1, 8, 8, 2048])"]},"metadata":{},"execution_count":132}]},{"cell_type":"code","source":["pooled_grads = tf.reduce_mean(grads, axis=(0,1,2)).numpy()"],"metadata":{"id":"QPyauQWLk6R0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(pooled_grads.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GTOCGxrAk6Pf","executionInfo":{"status":"ok","timestamp":1668413297489,"user_tz":-60,"elapsed":2,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a8129a20-9b59-4831-b226-f3d521e56d43"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(2048,)\n"]}]},{"cell_type":"code","source":["last_conv_layer_output = last_conv_layer_output.numpy()[0]\n","for i in range(2048):\n","  last_conv_layer_output[:, :, i] *= pooled_grads[i]"],"metadata":{"id":"f64qmzadk6NG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(last_conv_layer_output.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DcnlIj6Yk6Kp","executionInfo":{"status":"ok","timestamp":1668413298724,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"20d675cb-08de-42f9-b5f6-a17f3f5f82dc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(8, 8, 2048)\n"]}]},{"cell_type":"code","source":["heatmap = np.sum(last_conv_layer_output, axis=-1)"],"metadata":{"id":"UVMuMFM4k6EX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["heatmap=tf.nn.relu(heatmap)\n","plt.matshow(heatmap)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":292},"id":"itHRidPPknxo","executionInfo":{"status":"ok","timestamp":1668413301288,"user_tz":-60,"elapsed":789,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e676ce94-a0fd-4dcf-e634-cd6c8dceb20e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f089cc74f50>"]},"metadata":{},"execution_count":138},{"output_type":"display_data","data":{"text/plain":["<Figure size 288x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["resized_heatmap=cv2.resize(np.array(heatmap),(256,256))\n","plt.matshow(resized_heatmap*255+img_array[0,:,:,0]/255)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":292},"id":"Hf_4wbokknz0","executionInfo":{"status":"ok","timestamp":1668413353322,"user_tz":-60,"elapsed":521,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"14da0a11-10eb-4dd2-c424-81b3bfaad8a9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f089ca46890>"]},"metadata":{},"execution_count":141},{"output_type":"display_data","data":{"text/plain":["<Figure size 288x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wigj1LEK2LWp"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"yui_ezOW0Hlh"},"source":["# Exporting to Onnx format"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":49826,"status":"ok","timestamp":1653484076144,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"1a05UhwoU5MJ","outputId":"6ad01a90-2ef2-4066-f2d7-3c71b12451bd"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, layernorm_layer_call_fn while saving (showing 5 of 424). These functions will not be directly callable after loading.\n"]},{"name":"stdout","output_type":"stream","text":["INFO:tensorflow:Assets written to: vit_finetuned/assets\n"]},{"name":"stderr","output_type":"stream","text":["INFO:tensorflow:Assets written to: vit_finetuned/assets\n"]}],"source":["hf_model.save('vit_finetuned')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FoPQjVTVaWgU"},"outputs":[],"source":["hf_model.save('vit_finetuned.h5')"]},{"cell_type":"markdown","metadata":{"id":"ys34NNwc0kzg"},"source":["## Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":26787,"status":"ok","timestamp":1668447308352,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"IebY4MsgU22r","outputId":"49926b93-97da-4be1-9f22-3ad55737c206"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting tf2onnx\n","  Downloading tf2onnx-1.13.0-py3-none-any.whl (442 kB)\n","\u001b[K     |████████████████████████████████| 442 kB 12.6 MB/s \n","\u001b[?25hCollecting onnx>=1.4.1\n","  Downloading onnx-1.12.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n","\u001b[K     |████████████████████████████████| 13.1 MB 43.9 MB/s \n","\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from tf2onnx) (1.15.0)\n","Requirement already satisfied: numpy>=1.14.1 in /usr/local/lib/python3.7/dist-packages (from tf2onnx) (1.21.6)\n","Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from tf2onnx) (1.12)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from tf2onnx) (2.23.0)\n","Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.4.1->tf2onnx) (4.1.1)\n","Requirement already satisfied: protobuf<=3.20.1,>=3.12.2 in /usr/local/lib/python3.7/dist-packages (from onnx>=1.4.1->tf2onnx) (3.19.6)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->tf2onnx) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->tf2onnx) (1.24.3)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->tf2onnx) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->tf2onnx) (3.0.4)\n","Installing collected packages: onnx, tf2onnx\n","Successfully installed onnx-1.12.0 tf2onnx-1.13.0\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting onnxruntime-gpu\n","  Downloading onnxruntime_gpu-1.13.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (115.3 MB)\n","\u001b[K     |████████████████████████████████| 115.3 MB 2.8 MB/s \n","\u001b[?25hCollecting coloredlogs\n","  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n","\u001b[K     |████████████████████████████████| 46 kB 3.4 MB/s \n","\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from onnxruntime-gpu) (21.3)\n","Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.7/dist-packages (from onnxruntime-gpu) (1.21.6)\n","Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime-gpu) (3.19.6)\n","Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime-gpu) (1.12)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.7/dist-packages (from onnxruntime-gpu) (1.7.1)\n","Collecting humanfriendly>=9.1\n","  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n","\u001b[K     |████████████████████████████████| 86 kB 5.6 MB/s \n","\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->onnxruntime-gpu) (3.0.9)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy->onnxruntime-gpu) (1.2.1)\n","Installing collected packages: humanfriendly, coloredlogs, onnxruntime-gpu\n","Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnxruntime-gpu-1.13.1\n"]}],"source":["!pip install -U tf2onnx\n","!pip install onnxruntime-gpu"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1668447308353,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"THNUgBb8f_to","outputId":"3d41f3eb-ab46-4416-f422-a097d3565448"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'GPU'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":2}],"source":["import onnxruntime as rt\n","rt.get_device()"]},{"cell_type":"markdown","metadata":{"id":"6ToRBGkYVxeD"},"source":["## Conversion"]},{"cell_type":"markdown","metadata":{"id":"jjoRveh_aHtT"},"source":["### From TensorFlow SavedModel"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":73265,"status":"ok","timestamp":1653484356691,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"pQ6g3ANLU28A","outputId":"c73adaf6-f259-42ac-fa4a-5da48ac769f6"},"outputs":[{"name":"stdout","output_type":"stream","text":["/usr/lib/python3.7/runpy.py:125: RuntimeWarning: 'tf2onnx.convert' found in sys.modules after import of package 'tf2onnx', but prior to execution of 'tf2onnx.convert'; this may result in unpredictable behaviour\n","  warn(RuntimeWarning(msg))\n","2022-05-25 13:11:26,360 - WARNING - '--tag' not specified for saved_model. Using --tag serve\n","2022-05-25 13:11:34,252 - INFO - Signatures found in model: [serving_default].\n","2022-05-25 13:11:34,252 - WARNING - '--signature_def' not specified, using first signature: serving_default\n","2022-05-25 13:11:34,253 - INFO - Output names: ['dense']\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tf2onnx/tf_loader.py:711: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.compat.v1.graph_util.extract_sub_graph`\n","2022-05-25 13:11:45,495 - WARNING - From /usr/local/lib/python3.7/dist-packages/tf2onnx/tf_loader.py:711: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use `tf.compat.v1.graph_util.extract_sub_graph`\n","2022-05-25 13:11:50,911 - INFO - Using tensorflow=2.8.0, onnx=1.11.0, tf2onnx=1.10.1/a37f29\n","2022-05-25 13:11:50,911 - INFO - Using opset <onnx, 9>\n","2022-05-25 13:12:10,547 - INFO - Computed 0 values for constant folding\n","2022-05-25 13:12:23,436 - INFO - Optimizing ONNX model\n","2022-05-25 13:12:33,900 - INFO - After optimization: Cast -157 (282->125), Concat -120 (196->76), Const -932 (1151->219), Gather -96 (144->48), GlobalAveragePool +50 (0->50), Identity -142 (142->0), ReduceMean -50 (50->0), ReduceProd -144 (144->0), Reshape -24 (194->170), Shape -37 (86->49), Slice -1 (15->14), Squeeze -13 (15->2), Transpose -16 (66->50), Unsqueeze -330 (332->2)\n","2022-05-25 13:12:34,295 - INFO - \n","2022-05-25 13:12:34,295 - INFO - Successfully converted TensorFlow model vit_finetuned/ to ONNX\n","2022-05-25 13:12:34,295 - INFO - Model inputs: ['input_1']\n","2022-05-25 13:12:34,295 - INFO - Model outputs: ['dense']\n","2022-05-25 13:12:34,295 - INFO - ONNX model is saved at vit_onnx.onnx\n"]}],"source":["!python -m tf2onnx.convert --saved-model vit_finetuned/ --output vit_onnx.onnx"]},{"cell_type":"markdown","metadata":{"id":"fvt_KnCKaML6"},"source":["### From Keras Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PGsyiQPAU2-H"},"outputs":[],"source":["import tf2onnx\n","import onnxruntime as rt\n","\n","spec = (tf.TensorSpec(\n","    (None, CONFIGURATION[\"IM_SIZE\"], CONFIGURATION[\"IM_SIZE\"], 3),\n","    tf.float32, name=\"input\"),)\n","\n","output_path = \"lenet_keras.onnx\"\n","\n","model_proto, _ = tf2onnx.convert.from_keras(model_for_export, input_signature=spec, opset=13, output_path=output_path)\n","output_names = [n.name for n in model_proto.graph.output]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1653604704977,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"z76gaGoacDDC","outputId":"f321ea65-33fe-4a5d-cb3f-cdf576b1684f"},"outputs":[{"name":"stdout","output_type":"stream","text":["['dense_2']\n"]}],"source":["print(output_names)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1Bfzdnt-hKYy"},"outputs":[],"source":["output_names = ['dense']"]},{"cell_type":"markdown","metadata":{"id":"1mOc0fOg6YaH"},"source":["## Inference"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OtRfORr8cRty"},"outputs":[],"source":["test_image = cv2.imread(\"/content/dataset/Emotions Dataset/Emotions Dataset/test/sad/123731.jpg\")\n","test_image = cv2.resize(test_image, (CONFIGURATION[\"IM_SIZE\"] ,CONFIGURATION[\"IM_SIZE\"]))\n","im = test_image.astype(np.float32)\n","\n","im = np.expand_dims(im, axis = 0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Baby62KUkgjB"},"outputs":[],"source":["N_PREDICTIONS = 10"]},{"cell_type":"markdown","metadata":{"id":"Mq38t4PCqcLd"},"source":["### Benchmarking Onnx"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3713,"status":"ok","timestamp":1653605235888,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"lbUVMIy_WmMR","outputId":"90b57ea2-2138-41b7-9ddb-f65acbc3c4ad"},"outputs":[{"name":"stdout","output_type":"stream","text":["Time for a single Prediction 0.2902926683425903\n"]}],"source":["providers=['CPUExecutionProvider' ]\n","m = rt.InferenceSession(\"/content/drive/MyDrive/Bang/vit_keras.onnx\", providers=providers)\n","t1 = time.time()\n","\n","for _ in range(N_PREDICTIONS):\n","  onnx_pred = m.run(['dense'], {\"input\": im})\n","print(\"Time for a single Prediction\", (time.time() - t1)/N_PREDICTIONS)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":426,"status":"ok","timestamp":1653497243026,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"8eEbhYcJWmOy","outputId":"92fc78a0-9539-4e87-9bfa-271adfc07964"},"outputs":[{"name":"stdout","output_type":"stream","text":["[array([[0.00484342, 0.00301275, 0.99214387]], dtype=float32)]\n"]}],"source":["print(onnx_pred)"]},{"cell_type":"markdown","metadata":{"id":"N6tKJ2EAqex4"},"source":["### Benchmarking TF"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7379,"status":"ok","timestamp":1653488151593,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"RwGdpDd0Wmp2","outputId":"ab70eed9-0432-4018-b554-7f2e679e6d58"},"outputs":[{"name":"stdout","output_type":"stream","text":["Time for a single Prediction 0.7937646389007569\n"]}],"source":["t1 = time.time()\n","for _ in range(N_PREDICTIONS):\n","  hf_model(im)\n","print(\"Time for a single Prediction\", (time.time() - t1)/N_PREDICTIONS)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CM8wGLmkdiAN"},"outputs":[],"source":["tf, gpu = 0.15s\n","tf, cpu = 0.8s\n","tf_size = 1000MB\n","\n","onnx, cpu = 0.5s\n","onnx, gpu = 0.025s\n","onnx_size = 328MB\n","\n","onnx_quantized, cpu = 0.4s\n","onnx_quantized, gpu = 0.3s\n","onnx_quantized_size = 83MB"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":489,"status":"ok","timestamp":1653488170269,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"9rDsz0oudiEj","outputId":"6440c8d9-a1d7-4d7e-c2d6-8d4c622235f4"},"outputs":[{"data":{"text/plain":["2.285714285714286"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["0.8/0.35"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Zky6IZtodiG7"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"8mubq_HBEYeo"},"source":["## Quantization with Onnx"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Fb1KzDIwnfNi"},"outputs":[],"source":["import onnx\n","from onnxruntime.quantization import quantize_dynamic, QuantType"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"p0nLb8znnfQD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668447376110,"user_tz":-60,"elapsed":22409,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"cd65dc51-5ceb-4e3a-aa85-d3a80e345880"},"outputs":[{"output_type":"stream","name":"stdout","text":["Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._0/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._0/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._1/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._1/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._2/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._2/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._3/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._3/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._4/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._4/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._5/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._5/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._6/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._6/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._7/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._7/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._8/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._8/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._9/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._9/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._10/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._10/attention/attention/MatMul_1]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._11/attention/attention/MatMul]\n","Ignore MatMul due to non constant B: /[model/vit/encoder/layer_._11/attention/attention/MatMul_1]\n"]}],"source":["model_fp32 = '/content/eff_keras.onnx'\n","model_quant = '/content/eff_quantized.onnx'\n","\n","quantized_model = quantize_dynamic(model_fp32, model_quant, weight_type = QuantType.QUInt8)"]},{"cell_type":"markdown","metadata":{"id":"TU2chDY_MnRK"},"source":["### Accuracy Drop due to Quantization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iKNpzfier5yS"},"outputs":[],"source":["def accuracy(model):\n","  total, acc = 0,0\n","  for im, label in validation_dataset:\n","    onnx_pred = model.run(output_names, {\"input\": np.array(im)})\n","\n","    if(int(np.argmax(onnx_pred, axis = -1)[0][0]) == int(np.argmax(label, axis = -1)[0])):\n","      acc += 1\n","\n","    total += 1\n","  return acc/total"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":670295,"status":"ok","timestamp":1653500107832,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"85LXFH1xr5_n","outputId":"9628d7b9-9387-4b5a-b323-e0b041b36ec4"},"outputs":[{"name":"stdout","output_type":"stream","text":["0.9051799824407375\n","0.9051799824407375\n"]}],"source":["providers=['CUDAExecutionProvider']\n","m = rt.InferenceSession(\"/content/drive/MyDrive/Bang/vit_keras.onnx\", providers=providers)\n","m_q = rt.InferenceSession(\"/content/vit_quantized.onnx\", providers=providers)\n","print(accuracy(m_q))\n","print(accuracy(m))"]},{"cell_type":"code","source":["!pip install onnxruntime"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oLzi1NPvup4j","executionInfo":{"status":"ok","timestamp":1668544051665,"user_tz":-60,"elapsed":6218,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6f45ed94-ff95-4672-ff11-e604db4eea33"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting onnxruntime\n","  Downloading onnxruntime-1.13.1-cp37-cp37m-manylinux_2_27_x86_64.whl (4.5 MB)\n","\u001b[K     |████████████████████████████████| 4.5 MB 27.0 MB/s \n","\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (21.3)\n","Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.12)\n","Collecting coloredlogs\n","  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n","\u001b[K     |████████████████████████████████| 46 kB 4.0 MB/s \n","\u001b[?25hRequirement already satisfied: sympy in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.7.1)\n","Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (1.21.6)\n","Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime) (3.19.6)\n","Collecting humanfriendly>=9.1\n","  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n","\u001b[K     |████████████████████████████████| 86 kB 8.0 MB/s \n","\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->onnxruntime) (3.0.9)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy->onnxruntime) (1.2.1)\n","Installing collected packages: humanfriendly, coloredlogs, onnxruntime\n","Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnxruntime-1.13.1\n"]}]},{"cell_type":"code","source":["import onnxruntime as rt"],"metadata":{"id":"GSWrB4kXunAQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["providers=['CPUExecutionProvider' ]\n","m_q = rt.InferenceSession(\"/content/drive/MyDrive/Bang/eff_quantized.onnx\", providers=providers)"],"metadata":{"id":"ivyeOQT0OyM5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["img_path = \"/content/dataset/Emotions Dataset/Emotions Dataset/test/happy/553112.jpg\"\n","test_image = cv2.imread(img_path)\n","print(test_image.shape)\n","test_image = cv2.resize(test_image, (CONFIGURATION[\"IM_SIZE\"] ,CONFIGURATION[\"IM_SIZE\"]))\n","im = np.float32(test_image)\n","img_array = np.expand_dims(im, axis = 0)\n","print(img_array.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FzTi0QuhP1CS","executionInfo":{"status":"ok","timestamp":1668544144897,"user_tz":-60,"elapsed":136,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0b444781-93bc-4373-9c7e-ff637299e56f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(90, 90, 3)\n","(1, 256, 256, 3)\n"]}]},{"cell_type":"code","source":["onnx_pred = m_q.run(['dense'], {\"input\":img_array})\n","print(np.argmax(onnx_pred[0][0]))\n","\n","fastapi==0.87.0\n","numpy==1.23.4\n","onnxruntime==1.13.1\n","Pillow==9.3.0"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i2XXXCljuzXu","executionInfo":{"status":"ok","timestamp":1668544144899,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"86392489-0872-477f-b180-edca865874a1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1\n"]}]},{"cell_type":"code","source":["!pip install onnxruntime==1.13.1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S4BiAlUQRbqS","executionInfo":{"status":"ok","timestamp":1668582830200,"user_tz":-60,"elapsed":6314,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b4a1b1b6-1c58-4305-8614-9e974710963d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting onnxruntime==1.13.1\n","  Downloading onnxruntime-1.13.1-cp37-cp37m-manylinux_2_27_x86_64.whl (4.5 MB)\n","\u001b[K     |████████████████████████████████| 4.5 MB 5.7 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.7/dist-packages (from onnxruntime==1.13.1) (1.21.6)\n","Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from onnxruntime==1.13.1) (1.12)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from onnxruntime==1.13.1) (21.3)\n","Collecting coloredlogs\n","  Downloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n","\u001b[K     |████████████████████████████████| 46 kB 4.1 MB/s \n","\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxruntime==1.13.1) (3.19.6)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.7/dist-packages (from onnxruntime==1.13.1) (1.7.1)\n","Collecting humanfriendly>=9.1\n","  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n","\u001b[K     |████████████████████████████████| 86 kB 5.5 MB/s \n","\u001b[?25hRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->onnxruntime==1.13.1) (3.0.9)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy->onnxruntime==1.13.1) (1.2.1)\n","Installing collected packages: humanfriendly, coloredlogs, onnxruntime\n","Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnxruntime-1.13.1\n"]}]},{"cell_type":"markdown","metadata":{"id":"I3KhYytNAidL"},"source":["# Quantization in TensorFlow"]},{"cell_type":"markdown","metadata":{"id":"0DEOSSUEBAaJ"},"source":["### Installation and Import"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3636,"status":"ok","timestamp":1653656277319,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"KBE5E2jEA4j9","outputId":"084ae210-b40a-48db-f1e0-15c9789f91a9"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[?25l\r\u001b[K     |█▍                              | 10 kB 29.8 MB/s eta 0:00:01\r\u001b[K     |██▊                             | 20 kB 8.3 MB/s eta 0:00:01\r\u001b[K     |████▏                           | 30 kB 7.1 MB/s eta 0:00:01\r\u001b[K     |█████▌                          | 40 kB 3.4 MB/s eta 0:00:01\r\u001b[K     |███████                         | 51 kB 3.5 MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 61 kB 4.2 MB/s eta 0:00:01\r\u001b[K     |█████████▋                      | 71 kB 4.3 MB/s eta 0:00:01\r\u001b[K     |███████████                     | 81 kB 3.3 MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 92 kB 3.7 MB/s eta 0:00:01\r\u001b[K     |█████████████▉                  | 102 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████▏                | 112 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 122 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 133 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████▎            | 143 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████▊           | 153 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 163 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 174 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▉       | 184 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▏     | 194 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▋    | 204 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 215 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▍ | 225 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 235 kB 4.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 237 kB 4.1 MB/s \n","\u001b[?25h"]}],"source":["!pip install -q tensorflow-model-optimization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4u3ndmHkJcYL"},"outputs":[],"source":["import tensorflow_model_optimization as tfmot"]},{"cell_type":"markdown","metadata":{"id":"HndZBX7GAwTi"},"source":["## Quantization Aware Training"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5270,"status":"ok","timestamp":1653656290965,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"EhgLKnF9YXWq","outputId":"c5174661-06fe-4000-ec1c-3b8498de5d53"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 256, 256, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," rescaling_1 (Rescaling)        (None, 256, 256, 3)  0           ['input_1[0][0]']                \n","                                                                                                  \n"," normalization (Normalization)  (None, 256, 256, 3)  7           ['rescaling_1[0][0]']            \n","                                                                                                  \n"," stem_conv_pad (ZeroPadding2D)  (None, 257, 257, 3)  0           ['normalization[0][0]']          \n","                                                                                                  \n"," stem_conv (Conv2D)             (None, 128, 128, 48  1296        ['stem_conv_pad[0][0]']          \n","                                )                                                                 \n","                                                                                                  \n"," stem_bn (BatchNormalization)   (None, 128, 128, 48  192         ['stem_conv[0][0]']              \n","                                )                                                                 \n","                                                                                                  \n"," stem_activation (Activation)   (None, 128, 128, 48  0           ['stem_bn[0][0]']                \n","                                )                                                                 \n","                                                                                                  \n"," block1a_dwconv (DepthwiseConv2  (None, 128, 128, 48  432        ['stem_activation[0][0]']        \n"," D)                             )                                                                 \n","                                                                                                  \n"," block1a_bn (BatchNormalization  (None, 128, 128, 48  192        ['block1a_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block1a_activation (Activation  (None, 128, 128, 48  0          ['block1a_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1a_se_squeeze (GlobalAver  (None, 48)          0           ['block1a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1a_se_reshape (Reshape)   (None, 1, 1, 48)     0           ['block1a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block1a_se_reduce (Conv2D)     (None, 1, 1, 12)     588         ['block1a_se_reshape[0][0]']     \n","                                                                                                  \n"," block1a_se_expand (Conv2D)     (None, 1, 1, 48)     624         ['block1a_se_reduce[0][0]']      \n","                                                                                                  \n"," block1a_se_excite (Multiply)   (None, 128, 128, 48  0           ['block1a_activation[0][0]',     \n","                                )                                 'block1a_se_expand[0][0]']      \n","                                                                                                  \n"," block1a_project_conv (Conv2D)  (None, 128, 128, 24  1152        ['block1a_se_excite[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," block1a_project_bn (BatchNorma  (None, 128, 128, 24  96         ['block1a_project_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," block1b_dwconv (DepthwiseConv2  (None, 128, 128, 24  216        ['block1a_project_bn[0][0]']     \n"," D)                             )                                                                 \n","                                                                                                  \n"," block1b_bn (BatchNormalization  (None, 128, 128, 24  96         ['block1b_dwconv[0][0]']         \n"," )                              )                                                                 \n","                                                                                                  \n"," block1b_activation (Activation  (None, 128, 128, 24  0          ['block1b_bn[0][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1b_se_squeeze (GlobalAver  (None, 24)          0           ['block1b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1b_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block1b_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1b_se_reshape[0][0]']     \n","                                                                                                  \n"," block1b_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1b_se_reduce[0][0]']      \n","                                                                                                  \n"," block1b_se_excite (Multiply)   (None, 128, 128, 24  0           ['block1b_activation[0][0]',     \n","                                )                                 'block1b_se_expand[0][0]']      \n","                                                                                                  \n"," block1b_project_conv (Conv2D)  (None, 128, 128, 24  576         ['block1b_se_excite[0][0]']      \n","                                )                                                                 \n","                                                                                                  \n"," block1b_project_bn (BatchNorma  (None, 128, 128, 24  96         ['block1b_project_conv[0][0]']   \n"," lization)                      )                                                                 \n","                                                                                                  \n"," block1b_drop (Dropout)         (None, 128, 128, 24  0           ['block1b_project_bn[0][0]']     \n","                                )                                                                 \n","                                                                                                  \n"," block1b_add (Add)              (None, 128, 128, 24  0           ['block1b_drop[0][0]',           \n","                                )                                 'block1a_project_bn[0][0]']     \n","                                                                                                  \n"," block2a_expand_conv (Conv2D)   (None, 128, 128, 14  3456        ['block1b_add[0][0]']            \n","                                4)                                                                \n","                                                                                                  \n"," block2a_expand_bn (BatchNormal  (None, 128, 128, 14  576        ['block2a_expand_conv[0][0]']    \n"," ization)                       4)                                                                \n","                                                                                                  \n"," block2a_expand_activation (Act  (None, 128, 128, 14  0          ['block2a_expand_bn[0][0]']      \n"," ivation)                       4)                                                                \n","                                                                                                  \n"," block2a_dwconv_pad (ZeroPaddin  (None, 129, 129, 14  0          ['block2a_expand_activation[0][0]\n"," g2D)                           4)                               ']                               \n","                                                                                                  \n"," block2a_dwconv (DepthwiseConv2  (None, 64, 64, 144)  1296       ['block2a_dwconv_pad[0][0]']     \n"," D)                                                                                               \n","                                                                                                  \n"," block2a_bn (BatchNormalization  (None, 64, 64, 144)  576        ['block2a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2a_activation (Activation  (None, 64, 64, 144)  0          ['block2a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2a_se_squeeze (GlobalAver  (None, 144)         0           ['block2a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2a_se_reshape[0][0]']     \n","                                                                                                  \n"," block2a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2a_se_reduce[0][0]']      \n","                                                                                                  \n"," block2a_se_excite (Multiply)   (None, 64, 64, 144)  0           ['block2a_activation[0][0]',     \n","                                                                  'block2a_se_expand[0][0]']      \n","                                                                                                  \n"," block2a_project_conv (Conv2D)  (None, 64, 64, 32)   4608        ['block2a_se_excite[0][0]']      \n","                                                                                                  \n"," block2a_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['block2a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2b_expand_conv (Conv2D)   (None, 64, 64, 192)  6144        ['block2a_project_bn[0][0]']     \n","                                                                                                  \n"," block2b_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['block2b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block2b_expand_activation (Act  (None, 64, 64, 192)  0          ['block2b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block2b_dwconv (DepthwiseConv2  (None, 64, 64, 192)  1728       ['block2b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block2b_bn (BatchNormalization  (None, 64, 64, 192)  768        ['block2b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2b_activation (Activation  (None, 64, 64, 192)  0          ['block2b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2b_se_squeeze (GlobalAver  (None, 192)         0           ['block2b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2b_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2b_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2b_se_reshape[0][0]']     \n","                                                                                                  \n"," block2b_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2b_se_reduce[0][0]']      \n","                                                                                                  \n"," block2b_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2b_activation[0][0]',     \n","                                                                  'block2b_se_expand[0][0]']      \n","                                                                                                  \n"," block2b_project_conv (Conv2D)  (None, 64, 64, 32)   6144        ['block2b_se_excite[0][0]']      \n","                                                                                                  \n"," block2b_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['block2b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2b_drop (Dropout)         (None, 64, 64, 32)   0           ['block2b_project_bn[0][0]']     \n","                                                                                                  \n"," block2b_add (Add)              (None, 64, 64, 32)   0           ['block2b_drop[0][0]',           \n","                                                                  'block2a_project_bn[0][0]']     \n","                                                                                                  \n"," block2c_expand_conv (Conv2D)   (None, 64, 64, 192)  6144        ['block2b_add[0][0]']            \n","                                                                                                  \n"," block2c_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['block2c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block2c_expand_activation (Act  (None, 64, 64, 192)  0          ['block2c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block2c_dwconv (DepthwiseConv2  (None, 64, 64, 192)  1728       ['block2c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block2c_bn (BatchNormalization  (None, 64, 64, 192)  768        ['block2c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2c_activation (Activation  (None, 64, 64, 192)  0          ['block2c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2c_se_squeeze (GlobalAver  (None, 192)         0           ['block2c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2c_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2c_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2c_se_reshape[0][0]']     \n","                                                                                                  \n"," block2c_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2c_se_reduce[0][0]']      \n","                                                                                                  \n"," block2c_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2c_activation[0][0]',     \n","                                                                  'block2c_se_expand[0][0]']      \n","                                                                                                  \n"," block2c_project_conv (Conv2D)  (None, 64, 64, 32)   6144        ['block2c_se_excite[0][0]']      \n","                                                                                                  \n"," block2c_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['block2c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2c_drop (Dropout)         (None, 64, 64, 32)   0           ['block2c_project_bn[0][0]']     \n","                                                                                                  \n"," block2c_add (Add)              (None, 64, 64, 32)   0           ['block2c_drop[0][0]',           \n","                                                                  'block2b_add[0][0]']            \n","                                                                                                  \n"," block2d_expand_conv (Conv2D)   (None, 64, 64, 192)  6144        ['block2c_add[0][0]']            \n","                                                                                                  \n"," block2d_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['block2d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block2d_expand_activation (Act  (None, 64, 64, 192)  0          ['block2d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block2d_dwconv (DepthwiseConv2  (None, 64, 64, 192)  1728       ['block2d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block2d_bn (BatchNormalization  (None, 64, 64, 192)  768        ['block2d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block2d_activation (Activation  (None, 64, 64, 192)  0          ['block2d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2d_se_squeeze (GlobalAver  (None, 192)         0           ['block2d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2d_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block2d_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2d_se_reshape[0][0]']     \n","                                                                                                  \n"," block2d_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2d_se_reduce[0][0]']      \n","                                                                                                  \n"," block2d_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2d_activation[0][0]',     \n","                                                                  'block2d_se_expand[0][0]']      \n","                                                                                                  \n"," block2d_project_conv (Conv2D)  (None, 64, 64, 32)   6144        ['block2d_se_excite[0][0]']      \n","                                                                                                  \n"," block2d_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['block2d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2d_drop (Dropout)         (None, 64, 64, 32)   0           ['block2d_project_bn[0][0]']     \n","                                                                                                  \n"," block2d_add (Add)              (None, 64, 64, 32)   0           ['block2d_drop[0][0]',           \n","                                                                  'block2c_add[0][0]']            \n","                                                                                                  \n"," block3a_expand_conv (Conv2D)   (None, 64, 64, 192)  6144        ['block2d_add[0][0]']            \n","                                                                                                  \n"," block3a_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['block3a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3a_expand_activation (Act  (None, 64, 64, 192)  0          ['block3a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3a_dwconv_pad (ZeroPaddin  (None, 67, 67, 192)  0          ['block3a_expand_activation[0][0]\n"," g2D)                                                            ']                               \n","                                                                                                  \n"," block3a_dwconv (DepthwiseConv2  (None, 32, 32, 192)  4800       ['block3a_dwconv_pad[0][0]']     \n"," D)                                                                                               \n","                                                                                                  \n"," block3a_bn (BatchNormalization  (None, 32, 32, 192)  768        ['block3a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3a_activation (Activation  (None, 32, 32, 192)  0          ['block3a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3a_se_squeeze (GlobalAver  (None, 192)         0           ['block3a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3a_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block3a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3a_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block3a_se_reshape[0][0]']     \n","                                                                                                  \n"," block3a_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block3a_se_reduce[0][0]']      \n","                                                                                                  \n"," block3a_se_excite (Multiply)   (None, 32, 32, 192)  0           ['block3a_activation[0][0]',     \n","                                                                  'block3a_se_expand[0][0]']      \n","                                                                                                  \n"," block3a_project_conv (Conv2D)  (None, 32, 32, 56)   10752       ['block3a_se_excite[0][0]']      \n","                                                                                                  \n"," block3a_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['block3a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3b_expand_conv (Conv2D)   (None, 32, 32, 336)  18816       ['block3a_project_bn[0][0]']     \n","                                                                                                  \n"," block3b_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['block3b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3b_expand_activation (Act  (None, 32, 32, 336)  0          ['block3b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3b_dwconv (DepthwiseConv2  (None, 32, 32, 336)  8400       ['block3b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block3b_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['block3b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3b_activation (Activation  (None, 32, 32, 336)  0          ['block3b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3b_se_squeeze (GlobalAver  (None, 336)         0           ['block3b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3b_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3b_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3b_se_reshape[0][0]']     \n","                                                                                                  \n"," block3b_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3b_se_reduce[0][0]']      \n","                                                                                                  \n"," block3b_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3b_activation[0][0]',     \n","                                                                  'block3b_se_expand[0][0]']      \n","                                                                                                  \n"," block3b_project_conv (Conv2D)  (None, 32, 32, 56)   18816       ['block3b_se_excite[0][0]']      \n","                                                                                                  \n"," block3b_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['block3b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3b_drop (Dropout)         (None, 32, 32, 56)   0           ['block3b_project_bn[0][0]']     \n","                                                                                                  \n"," block3b_add (Add)              (None, 32, 32, 56)   0           ['block3b_drop[0][0]',           \n","                                                                  'block3a_project_bn[0][0]']     \n","                                                                                                  \n"," block3c_expand_conv (Conv2D)   (None, 32, 32, 336)  18816       ['block3b_add[0][0]']            \n","                                                                                                  \n"," block3c_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['block3c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3c_expand_activation (Act  (None, 32, 32, 336)  0          ['block3c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3c_dwconv (DepthwiseConv2  (None, 32, 32, 336)  8400       ['block3c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block3c_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['block3c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3c_activation (Activation  (None, 32, 32, 336)  0          ['block3c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3c_se_squeeze (GlobalAver  (None, 336)         0           ['block3c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3c_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3c_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3c_se_reshape[0][0]']     \n","                                                                                                  \n"," block3c_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3c_se_reduce[0][0]']      \n","                                                                                                  \n"," block3c_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3c_activation[0][0]',     \n","                                                                  'block3c_se_expand[0][0]']      \n","                                                                                                  \n"," block3c_project_conv (Conv2D)  (None, 32, 32, 56)   18816       ['block3c_se_excite[0][0]']      \n","                                                                                                  \n"," block3c_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['block3c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3c_drop (Dropout)         (None, 32, 32, 56)   0           ['block3c_project_bn[0][0]']     \n","                                                                                                  \n"," block3c_add (Add)              (None, 32, 32, 56)   0           ['block3c_drop[0][0]',           \n","                                                                  'block3b_add[0][0]']            \n","                                                                                                  \n"," block3d_expand_conv (Conv2D)   (None, 32, 32, 336)  18816       ['block3c_add[0][0]']            \n","                                                                                                  \n"," block3d_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['block3d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block3d_expand_activation (Act  (None, 32, 32, 336)  0          ['block3d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block3d_dwconv (DepthwiseConv2  (None, 32, 32, 336)  8400       ['block3d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block3d_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['block3d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block3d_activation (Activation  (None, 32, 32, 336)  0          ['block3d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3d_se_squeeze (GlobalAver  (None, 336)         0           ['block3d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3d_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block3d_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3d_se_reshape[0][0]']     \n","                                                                                                  \n"," block3d_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3d_se_reduce[0][0]']      \n","                                                                                                  \n"," block3d_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3d_activation[0][0]',     \n","                                                                  'block3d_se_expand[0][0]']      \n","                                                                                                  \n"," block3d_project_conv (Conv2D)  (None, 32, 32, 56)   18816       ['block3d_se_excite[0][0]']      \n","                                                                                                  \n"," block3d_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['block3d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3d_drop (Dropout)         (None, 32, 32, 56)   0           ['block3d_project_bn[0][0]']     \n","                                                                                                  \n"," block3d_add (Add)              (None, 32, 32, 56)   0           ['block3d_drop[0][0]',           \n","                                                                  'block3c_add[0][0]']            \n","                                                                                                  \n"," block4a_expand_conv (Conv2D)   (None, 32, 32, 336)  18816       ['block3d_add[0][0]']            \n","                                                                                                  \n"," block4a_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['block4a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4a_expand_activation (Act  (None, 32, 32, 336)  0          ['block4a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4a_dwconv_pad (ZeroPaddin  (None, 33, 33, 336)  0          ['block4a_expand_activation[0][0]\n"," g2D)                                                            ']                               \n","                                                                                                  \n"," block4a_dwconv (DepthwiseConv2  (None, 16, 16, 336)  3024       ['block4a_dwconv_pad[0][0]']     \n"," D)                                                                                               \n","                                                                                                  \n"," block4a_bn (BatchNormalization  (None, 16, 16, 336)  1344       ['block4a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4a_activation (Activation  (None, 16, 16, 336)  0          ['block4a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4a_se_squeeze (GlobalAver  (None, 336)         0           ['block4a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4a_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block4a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4a_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block4a_se_reshape[0][0]']     \n","                                                                                                  \n"," block4a_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block4a_se_reduce[0][0]']      \n","                                                                                                  \n"," block4a_se_excite (Multiply)   (None, 16, 16, 336)  0           ['block4a_activation[0][0]',     \n","                                                                  'block4a_se_expand[0][0]']      \n","                                                                                                  \n"," block4a_project_conv (Conv2D)  (None, 16, 16, 112)  37632       ['block4a_se_excite[0][0]']      \n","                                                                                                  \n"," block4a_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['block4a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4b_expand_conv (Conv2D)   (None, 16, 16, 672)  75264       ['block4a_project_bn[0][0]']     \n","                                                                                                  \n"," block4b_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['block4b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4b_expand_activation (Act  (None, 16, 16, 672)  0          ['block4b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4b_dwconv (DepthwiseConv2  (None, 16, 16, 672)  6048       ['block4b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4b_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['block4b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4b_activation (Activation  (None, 16, 16, 672)  0          ['block4b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4b_se_squeeze (GlobalAver  (None, 672)         0           ['block4b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4b_se_reshape[0][0]']     \n","                                                                                                  \n"," block4b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4b_se_reduce[0][0]']      \n","                                                                                                  \n"," block4b_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4b_activation[0][0]',     \n","                                                                  'block4b_se_expand[0][0]']      \n","                                                                                                  \n"," block4b_project_conv (Conv2D)  (None, 16, 16, 112)  75264       ['block4b_se_excite[0][0]']      \n","                                                                                                  \n"," block4b_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['block4b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4b_drop (Dropout)         (None, 16, 16, 112)  0           ['block4b_project_bn[0][0]']     \n","                                                                                                  \n"," block4b_add (Add)              (None, 16, 16, 112)  0           ['block4b_drop[0][0]',           \n","                                                                  'block4a_project_bn[0][0]']     \n","                                                                                                  \n"," block4c_expand_conv (Conv2D)   (None, 16, 16, 672)  75264       ['block4b_add[0][0]']            \n","                                                                                                  \n"," block4c_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['block4c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4c_expand_activation (Act  (None, 16, 16, 672)  0          ['block4c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4c_dwconv (DepthwiseConv2  (None, 16, 16, 672)  6048       ['block4c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4c_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['block4c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4c_activation (Activation  (None, 16, 16, 672)  0          ['block4c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4c_se_squeeze (GlobalAver  (None, 672)         0           ['block4c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4c_se_reshape[0][0]']     \n","                                                                                                  \n"," block4c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4c_se_reduce[0][0]']      \n","                                                                                                  \n"," block4c_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4c_activation[0][0]',     \n","                                                                  'block4c_se_expand[0][0]']      \n","                                                                                                  \n"," block4c_project_conv (Conv2D)  (None, 16, 16, 112)  75264       ['block4c_se_excite[0][0]']      \n","                                                                                                  \n"," block4c_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['block4c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4c_drop (Dropout)         (None, 16, 16, 112)  0           ['block4c_project_bn[0][0]']     \n","                                                                                                  \n"," block4c_add (Add)              (None, 16, 16, 112)  0           ['block4c_drop[0][0]',           \n","                                                                  'block4b_add[0][0]']            \n","                                                                                                  \n"," block4d_expand_conv (Conv2D)   (None, 16, 16, 672)  75264       ['block4c_add[0][0]']            \n","                                                                                                  \n"," block4d_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['block4d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4d_expand_activation (Act  (None, 16, 16, 672)  0          ['block4d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4d_dwconv (DepthwiseConv2  (None, 16, 16, 672)  6048       ['block4d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4d_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['block4d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4d_activation (Activation  (None, 16, 16, 672)  0          ['block4d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4d_se_squeeze (GlobalAver  (None, 672)         0           ['block4d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4d_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4d_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4d_se_reshape[0][0]']     \n","                                                                                                  \n"," block4d_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4d_se_reduce[0][0]']      \n","                                                                                                  \n"," block4d_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4d_activation[0][0]',     \n","                                                                  'block4d_se_expand[0][0]']      \n","                                                                                                  \n"," block4d_project_conv (Conv2D)  (None, 16, 16, 112)  75264       ['block4d_se_excite[0][0]']      \n","                                                                                                  \n"," block4d_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['block4d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4d_drop (Dropout)         (None, 16, 16, 112)  0           ['block4d_project_bn[0][0]']     \n","                                                                                                  \n"," block4d_add (Add)              (None, 16, 16, 112)  0           ['block4d_drop[0][0]',           \n","                                                                  'block4c_add[0][0]']            \n","                                                                                                  \n"," block4e_expand_conv (Conv2D)   (None, 16, 16, 672)  75264       ['block4d_add[0][0]']            \n","                                                                                                  \n"," block4e_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['block4e_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4e_expand_activation (Act  (None, 16, 16, 672)  0          ['block4e_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4e_dwconv (DepthwiseConv2  (None, 16, 16, 672)  6048       ['block4e_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4e_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['block4e_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4e_activation (Activation  (None, 16, 16, 672)  0          ['block4e_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4e_se_squeeze (GlobalAver  (None, 672)         0           ['block4e_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4e_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4e_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4e_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4e_se_reshape[0][0]']     \n","                                                                                                  \n"," block4e_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4e_se_reduce[0][0]']      \n","                                                                                                  \n"," block4e_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4e_activation[0][0]',     \n","                                                                  'block4e_se_expand[0][0]']      \n","                                                                                                  \n"," block4e_project_conv (Conv2D)  (None, 16, 16, 112)  75264       ['block4e_se_excite[0][0]']      \n","                                                                                                  \n"," block4e_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['block4e_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4e_drop (Dropout)         (None, 16, 16, 112)  0           ['block4e_project_bn[0][0]']     \n","                                                                                                  \n"," block4e_add (Add)              (None, 16, 16, 112)  0           ['block4e_drop[0][0]',           \n","                                                                  'block4d_add[0][0]']            \n","                                                                                                  \n"," block4f_expand_conv (Conv2D)   (None, 16, 16, 672)  75264       ['block4e_add[0][0]']            \n","                                                                                                  \n"," block4f_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['block4f_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block4f_expand_activation (Act  (None, 16, 16, 672)  0          ['block4f_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block4f_dwconv (DepthwiseConv2  (None, 16, 16, 672)  6048       ['block4f_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block4f_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['block4f_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block4f_activation (Activation  (None, 16, 16, 672)  0          ['block4f_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4f_se_squeeze (GlobalAver  (None, 672)         0           ['block4f_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4f_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4f_se_squeeze[0][0]']     \n","                                                                                                  \n"," block4f_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4f_se_reshape[0][0]']     \n","                                                                                                  \n"," block4f_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4f_se_reduce[0][0]']      \n","                                                                                                  \n"," block4f_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4f_activation[0][0]',     \n","                                                                  'block4f_se_expand[0][0]']      \n","                                                                                                  \n"," block4f_project_conv (Conv2D)  (None, 16, 16, 112)  75264       ['block4f_se_excite[0][0]']      \n","                                                                                                  \n"," block4f_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['block4f_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4f_drop (Dropout)         (None, 16, 16, 112)  0           ['block4f_project_bn[0][0]']     \n","                                                                                                  \n"," block4f_add (Add)              (None, 16, 16, 112)  0           ['block4f_drop[0][0]',           \n","                                                                  'block4e_add[0][0]']            \n","                                                                                                  \n"," block5a_expand_conv (Conv2D)   (None, 16, 16, 672)  75264       ['block4f_add[0][0]']            \n","                                                                                                  \n"," block5a_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['block5a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block5a_expand_activation (Act  (None, 16, 16, 672)  0          ['block5a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block5a_dwconv (DepthwiseConv2  (None, 16, 16, 672)  16800      ['block5a_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block5a_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['block5a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block5a_activation (Activation  (None, 16, 16, 672)  0          ['block5a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5a_se_squeeze (GlobalAver  (None, 672)         0           ['block5a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5a_se_reshape[0][0]']     \n","                                                                                                  \n"," block5a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5a_se_reduce[0][0]']      \n","                                                                                                  \n"," block5a_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block5a_activation[0][0]',     \n","                                                                  'block5a_se_expand[0][0]']      \n","                                                                                                  \n"," block5a_project_conv (Conv2D)  (None, 16, 16, 160)  107520      ['block5a_se_excite[0][0]']      \n","                                                                                                  \n"," block5a_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['block5a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5b_expand_conv (Conv2D)   (None, 16, 16, 960)  153600      ['block5a_project_bn[0][0]']     \n","                                                                                                  \n"," block5b_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['block5b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block5b_expand_activation (Act  (None, 16, 16, 960)  0          ['block5b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block5b_dwconv (DepthwiseConv2  (None, 16, 16, 960)  24000      ['block5b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block5b_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['block5b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block5b_activation (Activation  (None, 16, 16, 960)  0          ['block5b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5b_se_squeeze (GlobalAver  (None, 960)         0           ['block5b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5b_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5b_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5b_se_reshape[0][0]']     \n","                                                                                                  \n"," block5b_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5b_se_reduce[0][0]']      \n","                                                                                                  \n"," block5b_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5b_activation[0][0]',     \n","                                                                  'block5b_se_expand[0][0]']      \n","                                                                                                  \n"," block5b_project_conv (Conv2D)  (None, 16, 16, 160)  153600      ['block5b_se_excite[0][0]']      \n","                                                                                                  \n"," block5b_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['block5b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5b_drop (Dropout)         (None, 16, 16, 160)  0           ['block5b_project_bn[0][0]']     \n","                                                                                                  \n"," block5b_add (Add)              (None, 16, 16, 160)  0           ['block5b_drop[0][0]',           \n","                                                                  'block5a_project_bn[0][0]']     \n","                                                                                                  \n"," block5c_expand_conv (Conv2D)   (None, 16, 16, 960)  153600      ['block5b_add[0][0]']            \n","                                                                                                  \n"," block5c_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['block5c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block5c_expand_activation (Act  (None, 16, 16, 960)  0          ['block5c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block5c_dwconv (DepthwiseConv2  (None, 16, 16, 960)  24000      ['block5c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block5c_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['block5c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block5c_activation (Activation  (None, 16, 16, 960)  0          ['block5c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5c_se_squeeze (GlobalAver  (None, 960)         0           ['block5c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5c_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5c_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5c_se_reshape[0][0]']     \n","                                                                                                  \n"," block5c_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5c_se_reduce[0][0]']      \n","                                                                                                  \n"," block5c_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5c_activation[0][0]',     \n","                                                                  'block5c_se_expand[0][0]']      \n","                                                                                                  \n"," block5c_project_conv (Conv2D)  (None, 16, 16, 160)  153600      ['block5c_se_excite[0][0]']      \n","                                                                                                  \n"," block5c_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['block5c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5c_drop (Dropout)         (None, 16, 16, 160)  0           ['block5c_project_bn[0][0]']     \n","                                                                                                  \n"," block5c_add (Add)              (None, 16, 16, 160)  0           ['block5c_drop[0][0]',           \n","                                                                  'block5b_add[0][0]']            \n","                                                                                                  \n"," block5d_expand_conv (Conv2D)   (None, 16, 16, 960)  153600      ['block5c_add[0][0]']            \n","                                                                                                  \n"," block5d_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['block5d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block5d_expand_activation (Act  (None, 16, 16, 960)  0          ['block5d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block5d_dwconv (DepthwiseConv2  (None, 16, 16, 960)  24000      ['block5d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block5d_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['block5d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block5d_activation (Activation  (None, 16, 16, 960)  0          ['block5d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5d_se_squeeze (GlobalAver  (None, 960)         0           ['block5d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5d_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5d_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5d_se_reshape[0][0]']     \n","                                                                                                  \n"," block5d_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5d_se_reduce[0][0]']      \n","                                                                                                  \n"," block5d_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5d_activation[0][0]',     \n","                                                                  'block5d_se_expand[0][0]']      \n","                                                                                                  \n"," block5d_project_conv (Conv2D)  (None, 16, 16, 160)  153600      ['block5d_se_excite[0][0]']      \n","                                                                                                  \n"," block5d_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['block5d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5d_drop (Dropout)         (None, 16, 16, 160)  0           ['block5d_project_bn[0][0]']     \n","                                                                                                  \n"," block5d_add (Add)              (None, 16, 16, 160)  0           ['block5d_drop[0][0]',           \n","                                                                  'block5c_add[0][0]']            \n","                                                                                                  \n"," block5e_expand_conv (Conv2D)   (None, 16, 16, 960)  153600      ['block5d_add[0][0]']            \n","                                                                                                  \n"," block5e_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['block5e_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block5e_expand_activation (Act  (None, 16, 16, 960)  0          ['block5e_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block5e_dwconv (DepthwiseConv2  (None, 16, 16, 960)  24000      ['block5e_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block5e_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['block5e_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block5e_activation (Activation  (None, 16, 16, 960)  0          ['block5e_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5e_se_squeeze (GlobalAver  (None, 960)         0           ['block5e_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5e_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5e_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5e_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5e_se_reshape[0][0]']     \n","                                                                                                  \n"," block5e_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5e_se_reduce[0][0]']      \n","                                                                                                  \n"," block5e_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5e_activation[0][0]',     \n","                                                                  'block5e_se_expand[0][0]']      \n","                                                                                                  \n"," block5e_project_conv (Conv2D)  (None, 16, 16, 160)  153600      ['block5e_se_excite[0][0]']      \n","                                                                                                  \n"," block5e_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['block5e_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5e_drop (Dropout)         (None, 16, 16, 160)  0           ['block5e_project_bn[0][0]']     \n","                                                                                                  \n"," block5e_add (Add)              (None, 16, 16, 160)  0           ['block5e_drop[0][0]',           \n","                                                                  'block5d_add[0][0]']            \n","                                                                                                  \n"," block5f_expand_conv (Conv2D)   (None, 16, 16, 960)  153600      ['block5e_add[0][0]']            \n","                                                                                                  \n"," block5f_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['block5f_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block5f_expand_activation (Act  (None, 16, 16, 960)  0          ['block5f_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block5f_dwconv (DepthwiseConv2  (None, 16, 16, 960)  24000      ['block5f_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block5f_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['block5f_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block5f_activation (Activation  (None, 16, 16, 960)  0          ['block5f_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5f_se_squeeze (GlobalAver  (None, 960)         0           ['block5f_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5f_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5f_se_squeeze[0][0]']     \n","                                                                                                  \n"," block5f_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5f_se_reshape[0][0]']     \n","                                                                                                  \n"," block5f_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5f_se_reduce[0][0]']      \n","                                                                                                  \n"," block5f_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5f_activation[0][0]',     \n","                                                                  'block5f_se_expand[0][0]']      \n","                                                                                                  \n"," block5f_project_conv (Conv2D)  (None, 16, 16, 160)  153600      ['block5f_se_excite[0][0]']      \n","                                                                                                  \n"," block5f_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['block5f_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5f_drop (Dropout)         (None, 16, 16, 160)  0           ['block5f_project_bn[0][0]']     \n","                                                                                                  \n"," block5f_add (Add)              (None, 16, 16, 160)  0           ['block5f_drop[0][0]',           \n","                                                                  'block5e_add[0][0]']            \n","                                                                                                  \n"," block6a_expand_conv (Conv2D)   (None, 16, 16, 960)  153600      ['block5f_add[0][0]']            \n","                                                                                                  \n"," block6a_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['block6a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6a_expand_activation (Act  (None, 16, 16, 960)  0          ['block6a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6a_dwconv_pad (ZeroPaddin  (None, 19, 19, 960)  0          ['block6a_expand_activation[0][0]\n"," g2D)                                                            ']                               \n","                                                                                                  \n"," block6a_dwconv (DepthwiseConv2  (None, 8, 8, 960)   24000       ['block6a_dwconv_pad[0][0]']     \n"," D)                                                                                               \n","                                                                                                  \n"," block6a_bn (BatchNormalization  (None, 8, 8, 960)   3840        ['block6a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6a_activation (Activation  (None, 8, 8, 960)   0           ['block6a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6a_se_squeeze (GlobalAver  (None, 960)         0           ['block6a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6a_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block6a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6a_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block6a_se_reshape[0][0]']     \n","                                                                                                  \n"," block6a_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block6a_se_reduce[0][0]']      \n","                                                                                                  \n"," block6a_se_excite (Multiply)   (None, 8, 8, 960)    0           ['block6a_activation[0][0]',     \n","                                                                  'block6a_se_expand[0][0]']      \n","                                                                                                  \n"," block6a_project_conv (Conv2D)  (None, 8, 8, 272)    261120      ['block6a_se_excite[0][0]']      \n","                                                                                                  \n"," block6a_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['block6a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6b_expand_conv (Conv2D)   (None, 8, 8, 1632)   443904      ['block6a_project_bn[0][0]']     \n","                                                                                                  \n"," block6b_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['block6b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6b_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6b_dwconv (DepthwiseConv2  (None, 8, 8, 1632)  40800       ['block6b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6b_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['block6b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6b_activation (Activation  (None, 8, 8, 1632)  0           ['block6b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6b_se_squeeze (GlobalAver  (None, 1632)        0           ['block6b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6b_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6b_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6b_se_reshape[0][0]']     \n","                                                                                                  \n"," block6b_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6b_se_reduce[0][0]']      \n","                                                                                                  \n"," block6b_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6b_activation[0][0]',     \n","                                                                  'block6b_se_expand[0][0]']      \n","                                                                                                  \n"," block6b_project_conv (Conv2D)  (None, 8, 8, 272)    443904      ['block6b_se_excite[0][0]']      \n","                                                                                                  \n"," block6b_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['block6b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6b_drop (Dropout)         (None, 8, 8, 272)    0           ['block6b_project_bn[0][0]']     \n","                                                                                                  \n"," block6b_add (Add)              (None, 8, 8, 272)    0           ['block6b_drop[0][0]',           \n","                                                                  'block6a_project_bn[0][0]']     \n","                                                                                                  \n"," block6c_expand_conv (Conv2D)   (None, 8, 8, 1632)   443904      ['block6b_add[0][0]']            \n","                                                                                                  \n"," block6c_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['block6c_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6c_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6c_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6c_dwconv (DepthwiseConv2  (None, 8, 8, 1632)  40800       ['block6c_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6c_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['block6c_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6c_activation (Activation  (None, 8, 8, 1632)  0           ['block6c_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6c_se_squeeze (GlobalAver  (None, 1632)        0           ['block6c_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6c_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6c_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6c_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6c_se_reshape[0][0]']     \n","                                                                                                  \n"," block6c_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6c_se_reduce[0][0]']      \n","                                                                                                  \n"," block6c_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6c_activation[0][0]',     \n","                                                                  'block6c_se_expand[0][0]']      \n","                                                                                                  \n"," block6c_project_conv (Conv2D)  (None, 8, 8, 272)    443904      ['block6c_se_excite[0][0]']      \n","                                                                                                  \n"," block6c_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['block6c_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6c_drop (Dropout)         (None, 8, 8, 272)    0           ['block6c_project_bn[0][0]']     \n","                                                                                                  \n"," block6c_add (Add)              (None, 8, 8, 272)    0           ['block6c_drop[0][0]',           \n","                                                                  'block6b_add[0][0]']            \n","                                                                                                  \n"," block6d_expand_conv (Conv2D)   (None, 8, 8, 1632)   443904      ['block6c_add[0][0]']            \n","                                                                                                  \n"," block6d_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['block6d_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6d_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6d_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6d_dwconv (DepthwiseConv2  (None, 8, 8, 1632)  40800       ['block6d_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6d_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['block6d_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6d_activation (Activation  (None, 8, 8, 1632)  0           ['block6d_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6d_se_squeeze (GlobalAver  (None, 1632)        0           ['block6d_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6d_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6d_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6d_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6d_se_reshape[0][0]']     \n","                                                                                                  \n"," block6d_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6d_se_reduce[0][0]']      \n","                                                                                                  \n"," block6d_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6d_activation[0][0]',     \n","                                                                  'block6d_se_expand[0][0]']      \n","                                                                                                  \n"," block6d_project_conv (Conv2D)  (None, 8, 8, 272)    443904      ['block6d_se_excite[0][0]']      \n","                                                                                                  \n"," block6d_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['block6d_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6d_drop (Dropout)         (None, 8, 8, 272)    0           ['block6d_project_bn[0][0]']     \n","                                                                                                  \n"," block6d_add (Add)              (None, 8, 8, 272)    0           ['block6d_drop[0][0]',           \n","                                                                  'block6c_add[0][0]']            \n","                                                                                                  \n"," block6e_expand_conv (Conv2D)   (None, 8, 8, 1632)   443904      ['block6d_add[0][0]']            \n","                                                                                                  \n"," block6e_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['block6e_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6e_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6e_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6e_dwconv (DepthwiseConv2  (None, 8, 8, 1632)  40800       ['block6e_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6e_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['block6e_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6e_activation (Activation  (None, 8, 8, 1632)  0           ['block6e_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6e_se_squeeze (GlobalAver  (None, 1632)        0           ['block6e_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6e_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6e_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6e_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6e_se_reshape[0][0]']     \n","                                                                                                  \n"," block6e_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6e_se_reduce[0][0]']      \n","                                                                                                  \n"," block6e_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6e_activation[0][0]',     \n","                                                                  'block6e_se_expand[0][0]']      \n","                                                                                                  \n"," block6e_project_conv (Conv2D)  (None, 8, 8, 272)    443904      ['block6e_se_excite[0][0]']      \n","                                                                                                  \n"," block6e_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['block6e_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6e_drop (Dropout)         (None, 8, 8, 272)    0           ['block6e_project_bn[0][0]']     \n","                                                                                                  \n"," block6e_add (Add)              (None, 8, 8, 272)    0           ['block6e_drop[0][0]',           \n","                                                                  'block6d_add[0][0]']            \n","                                                                                                  \n"," block6f_expand_conv (Conv2D)   (None, 8, 8, 1632)   443904      ['block6e_add[0][0]']            \n","                                                                                                  \n"," block6f_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['block6f_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6f_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6f_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6f_dwconv (DepthwiseConv2  (None, 8, 8, 1632)  40800       ['block6f_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6f_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['block6f_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6f_activation (Activation  (None, 8, 8, 1632)  0           ['block6f_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6f_se_squeeze (GlobalAver  (None, 1632)        0           ['block6f_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6f_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6f_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6f_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6f_se_reshape[0][0]']     \n","                                                                                                  \n"," block6f_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6f_se_reduce[0][0]']      \n","                                                                                                  \n"," block6f_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6f_activation[0][0]',     \n","                                                                  'block6f_se_expand[0][0]']      \n","                                                                                                  \n"," block6f_project_conv (Conv2D)  (None, 8, 8, 272)    443904      ['block6f_se_excite[0][0]']      \n","                                                                                                  \n"," block6f_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['block6f_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6f_drop (Dropout)         (None, 8, 8, 272)    0           ['block6f_project_bn[0][0]']     \n","                                                                                                  \n"," block6f_add (Add)              (None, 8, 8, 272)    0           ['block6f_drop[0][0]',           \n","                                                                  'block6e_add[0][0]']            \n","                                                                                                  \n"," block6g_expand_conv (Conv2D)   (None, 8, 8, 1632)   443904      ['block6f_add[0][0]']            \n","                                                                                                  \n"," block6g_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['block6g_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6g_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6g_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6g_dwconv (DepthwiseConv2  (None, 8, 8, 1632)  40800       ['block6g_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6g_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['block6g_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6g_activation (Activation  (None, 8, 8, 1632)  0           ['block6g_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6g_se_squeeze (GlobalAver  (None, 1632)        0           ['block6g_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6g_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6g_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6g_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6g_se_reshape[0][0]']     \n","                                                                                                  \n"," block6g_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6g_se_reduce[0][0]']      \n","                                                                                                  \n"," block6g_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6g_activation[0][0]',     \n","                                                                  'block6g_se_expand[0][0]']      \n","                                                                                                  \n"," block6g_project_conv (Conv2D)  (None, 8, 8, 272)    443904      ['block6g_se_excite[0][0]']      \n","                                                                                                  \n"," block6g_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['block6g_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6g_drop (Dropout)         (None, 8, 8, 272)    0           ['block6g_project_bn[0][0]']     \n","                                                                                                  \n"," block6g_add (Add)              (None, 8, 8, 272)    0           ['block6g_drop[0][0]',           \n","                                                                  'block6f_add[0][0]']            \n","                                                                                                  \n"," block6h_expand_conv (Conv2D)   (None, 8, 8, 1632)   443904      ['block6g_add[0][0]']            \n","                                                                                                  \n"," block6h_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['block6h_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block6h_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6h_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block6h_dwconv (DepthwiseConv2  (None, 8, 8, 1632)  40800       ['block6h_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block6h_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['block6h_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block6h_activation (Activation  (None, 8, 8, 1632)  0           ['block6h_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6h_se_squeeze (GlobalAver  (None, 1632)        0           ['block6h_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6h_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6h_se_squeeze[0][0]']     \n","                                                                                                  \n"," block6h_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6h_se_reshape[0][0]']     \n","                                                                                                  \n"," block6h_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6h_se_reduce[0][0]']      \n","                                                                                                  \n"," block6h_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6h_activation[0][0]',     \n","                                                                  'block6h_se_expand[0][0]']      \n","                                                                                                  \n"," block6h_project_conv (Conv2D)  (None, 8, 8, 272)    443904      ['block6h_se_excite[0][0]']      \n","                                                                                                  \n"," block6h_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['block6h_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6h_drop (Dropout)         (None, 8, 8, 272)    0           ['block6h_project_bn[0][0]']     \n","                                                                                                  \n"," block6h_add (Add)              (None, 8, 8, 272)    0           ['block6h_drop[0][0]',           \n","                                                                  'block6g_add[0][0]']            \n","                                                                                                  \n"," block7a_expand_conv (Conv2D)   (None, 8, 8, 1632)   443904      ['block6h_add[0][0]']            \n","                                                                                                  \n"," block7a_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['block7a_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block7a_expand_activation (Act  (None, 8, 8, 1632)  0           ['block7a_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block7a_dwconv (DepthwiseConv2  (None, 8, 8, 1632)  14688       ['block7a_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block7a_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['block7a_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block7a_activation (Activation  (None, 8, 8, 1632)  0           ['block7a_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7a_se_squeeze (GlobalAver  (None, 1632)        0           ['block7a_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7a_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block7a_se_squeeze[0][0]']     \n","                                                                                                  \n"," block7a_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block7a_se_reshape[0][0]']     \n","                                                                                                  \n"," block7a_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block7a_se_reduce[0][0]']      \n","                                                                                                  \n"," block7a_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block7a_activation[0][0]',     \n","                                                                  'block7a_se_expand[0][0]']      \n","                                                                                                  \n"," block7a_project_conv (Conv2D)  (None, 8, 8, 448)    731136      ['block7a_se_excite[0][0]']      \n","                                                                                                  \n"," block7a_project_bn (BatchNorma  (None, 8, 8, 448)   1792        ['block7a_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block7b_expand_conv (Conv2D)   (None, 8, 8, 2688)   1204224     ['block7a_project_bn[0][0]']     \n","                                                                                                  \n"," block7b_expand_bn (BatchNormal  (None, 8, 8, 2688)  10752       ['block7b_expand_conv[0][0]']    \n"," ization)                                                                                         \n","                                                                                                  \n"," block7b_expand_activation (Act  (None, 8, 8, 2688)  0           ['block7b_expand_bn[0][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," block7b_dwconv (DepthwiseConv2  (None, 8, 8, 2688)  24192       ['block7b_expand_activation[0][0]\n"," D)                                                              ']                               \n","                                                                                                  \n"," block7b_bn (BatchNormalization  (None, 8, 8, 2688)  10752       ['block7b_dwconv[0][0]']         \n"," )                                                                                                \n","                                                                                                  \n"," block7b_activation (Activation  (None, 8, 8, 2688)  0           ['block7b_bn[0][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7b_se_squeeze (GlobalAver  (None, 2688)        0           ['block7b_activation[0][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7b_se_reshape (Reshape)   (None, 1, 1, 2688)   0           ['block7b_se_squeeze[0][0]']     \n","                                                                                                  \n"," block7b_se_reduce (Conv2D)     (None, 1, 1, 112)    301168      ['block7b_se_reshape[0][0]']     \n","                                                                                                  \n"," block7b_se_expand (Conv2D)     (None, 1, 1, 2688)   303744      ['block7b_se_reduce[0][0]']      \n","                                                                                                  \n"," block7b_se_excite (Multiply)   (None, 8, 8, 2688)   0           ['block7b_activation[0][0]',     \n","                                                                  'block7b_se_expand[0][0]']      \n","                                                                                                  \n"," block7b_project_conv (Conv2D)  (None, 8, 8, 448)    1204224     ['block7b_se_excite[0][0]']      \n","                                                                                                  \n"," block7b_project_bn (BatchNorma  (None, 8, 8, 448)   1792        ['block7b_project_conv[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block7b_drop (Dropout)         (None, 8, 8, 448)    0           ['block7b_project_bn[0][0]']     \n","                                                                                                  \n"," block7b_add (Add)              (None, 8, 8, 448)    0           ['block7b_drop[0][0]',           \n","                                                                  'block7a_project_bn[0][0]']     \n","                                                                                                  \n"," top_conv (Conv2D)              (None, 8, 8, 1792)   802816      ['block7b_add[0][0]']            \n","                                                                                                  \n"," top_bn (BatchNormalization)    (None, 8, 8, 1792)   7168        ['top_conv[0][0]']               \n","                                                                                                  \n"," top_activation (Activation)    (None, 8, 8, 1792)   0           ['top_bn[0][0]']                 \n","                                                                                                  \n"," global_average_pooling2d_1 (Gl  (None, 1792)        0           ['top_activation[0][0]']         \n"," obalAveragePooling2D)                                                                            \n","                                                                                                  \n"," dense_3 (Dense)                (None, 1024)         1836032     ['global_average_pooling2d_1[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," batch_normalization_1 (BatchNo  (None, 1024)        4096        ['dense_3[0][0]']                \n"," rmalization)                                                                                     \n","                                                                                                  \n"," dense_4 (Dense)                (None, 128)          131200      ['batch_normalization_1[0][0]']  \n","                                                                                                  \n"," dense_5 (Dense)                (None, 3)            387         ['dense_4[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 19,645,538\n","Trainable params: 1,969,667\n","Non-trainable params: 17,675,871\n","__________________________________________________________________________________________________\n"]}],"source":["backbone,\n","\n","x = GlobalAveragePooling2D()(backbone.output)\n","x = Dense( CONFIGURATION[\"N_DENSE_1\"], activation = \"relu\")(x)\n","x = BatchNormalization()(x)\n","x = Dense( CONFIGURATION[\"N_DENSE_2\"], activation = \"relu\")(x)\n","output = Dense( CONFIGURATION[\"NUM_CLASSES\"], activation = \"softmax\")(x)\n","\n","pretrained_func_model = tf.keras.Model(inputs = backbone.input, outputs = output )\n","\n","pretrained_func_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1653653017676,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Tbcs57YdZnUt","outputId":"b09b0ded-9b09-47a9-b62a-8d058ec1f84d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential_4\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," efficientnetb4 (Functional)  (None, 8, 8, 1792)       17673823  \n","                                                                 \n"," global_average_pooling2d_2   (None, 1792)             0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dense_7 (Dense)             (None, 1024)              1836032   \n","                                                                 \n"," batch_normalization_2 (Batc  (None, 1024)             4096      \n"," hNormalization)                                                 \n","                                                                 \n"," dense_8 (Dense)             (None, 128)               131200    \n","                                                                 \n"," dense_9 (Dense)             (None, 3)                 387       \n","                                                                 \n","=================================================================\n","Total params: 19,645,538\n","Trainable params: 1,969,667\n","Non-trainable params: 17,675,871\n","_________________________________________________________________\n"]}],"source":["pretrained_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J_4t4u0S6ND7"},"outputs":[],"source":["#quant_aware = tfmot.quantization.keras.quantize_model(lenet_model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EKFFCCjD6NGv"},"outputs":[],"source":["def apply_quantization_to_conv(layer):\n","  if \"conv\" in layer.name:\n","    return tfmot.quantization.keras.quantize_annotate_layer(layer)\n","  return layer"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VrFVXKB_h0VV"},"outputs":[],"source":["quant_aware_eff = tf.keras.models.clone_model(\n","    pretrained_func_model, clone_function=apply_quantization_to_conv\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5868,"status":"ok","timestamp":1653656301645,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"3v1mr03Ih0Xm","outputId":"10469937-41e6-4704-9be0-79f0a7259fc1"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 256, 256, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," rescaling_1 (Rescaling)        (None, 256, 256, 3)  0           ['input_1[0][0]']                \n","                                                                                                  \n"," normalization (Normalization)  (None, 256, 256, 3)  7           ['rescaling_1[1][0]']            \n","                                                                                                  \n"," quantize_annotate (QuantizeAnn  (None, 257, 257, 3)  0          ['normalization[1][0]']          \n"," otate)                                                                                           \n","                                                                                                  \n"," quantize_annotate_1 (QuantizeA  (None, 128, 128, 48  1296       ['quantize_annotate[0][0]']      \n"," nnotate)                       )                                                                 \n","                                                                                                  \n"," stem_bn (BatchNormalization)   (None, 128, 128, 48  192         ['quantize_annotate_1[0][0]']    \n","                                )                                                                 \n","                                                                                                  \n"," stem_activation (Activation)   (None, 128, 128, 48  0           ['stem_bn[1][0]']                \n","                                )                                                                 \n","                                                                                                  \n"," quantize_annotate_2 (QuantizeA  (None, 128, 128, 48  432        ['stem_activation[1][0]']        \n"," nnotate)                       )                                                                 \n","                                                                                                  \n"," block1a_bn (BatchNormalization  (None, 128, 128, 48  192        ['quantize_annotate_2[0][0]']    \n"," )                              )                                                                 \n","                                                                                                  \n"," block1a_activation (Activation  (None, 128, 128, 48  0          ['block1a_bn[1][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1a_se_squeeze (GlobalAver  (None, 48)          0           ['block1a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1a_se_reshape (Reshape)   (None, 1, 1, 48)     0           ['block1a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block1a_se_reduce (Conv2D)     (None, 1, 1, 12)     588         ['block1a_se_reshape[1][0]']     \n","                                                                                                  \n"," block1a_se_expand (Conv2D)     (None, 1, 1, 48)     624         ['block1a_se_reduce[1][0]']      \n","                                                                                                  \n"," block1a_se_excite (Multiply)   (None, 128, 128, 48  0           ['block1a_activation[1][0]',     \n","                                )                                 'block1a_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_3 (QuantizeA  (None, 128, 128, 24  1152       ['block1a_se_excite[1][0]']      \n"," nnotate)                       )                                                                 \n","                                                                                                  \n"," block1a_project_bn (BatchNorma  (None, 128, 128, 24  96         ['quantize_annotate_3[0][0]']    \n"," lization)                      )                                                                 \n","                                                                                                  \n"," quantize_annotate_4 (QuantizeA  (None, 128, 128, 24  216        ['block1a_project_bn[1][0]']     \n"," nnotate)                       )                                                                 \n","                                                                                                  \n"," block1b_bn (BatchNormalization  (None, 128, 128, 24  96         ['quantize_annotate_4[0][0]']    \n"," )                              )                                                                 \n","                                                                                                  \n"," block1b_activation (Activation  (None, 128, 128, 24  0          ['block1b_bn[1][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1b_se_squeeze (GlobalAver  (None, 24)          0           ['block1b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1b_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block1b_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1b_se_reshape[1][0]']     \n","                                                                                                  \n"," block1b_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1b_se_reduce[1][0]']      \n","                                                                                                  \n"," block1b_se_excite (Multiply)   (None, 128, 128, 24  0           ['block1b_activation[1][0]',     \n","                                )                                 'block1b_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_5 (QuantizeA  (None, 128, 128, 24  576        ['block1b_se_excite[1][0]']      \n"," nnotate)                       )                                                                 \n","                                                                                                  \n"," block1b_project_bn (BatchNorma  (None, 128, 128, 24  96         ['quantize_annotate_5[0][0]']    \n"," lization)                      )                                                                 \n","                                                                                                  \n"," block1b_drop (Dropout)         (None, 128, 128, 24  0           ['block1b_project_bn[1][0]']     \n","                                )                                                                 \n","                                                                                                  \n"," block1b_add (Add)              (None, 128, 128, 24  0           ['block1b_drop[1][0]',           \n","                                )                                 'block1a_project_bn[1][0]']     \n","                                                                                                  \n"," quantize_annotate_6 (QuantizeA  (None, 128, 128, 14  3456       ['block1b_add[1][0]']            \n"," nnotate)                       4)                                                                \n","                                                                                                  \n"," block2a_expand_bn (BatchNormal  (None, 128, 128, 14  576        ['quantize_annotate_6[0][0]']    \n"," ization)                       4)                                                                \n","                                                                                                  \n"," block2a_expand_activation (Act  (None, 128, 128, 14  0          ['block2a_expand_bn[1][0]']      \n"," ivation)                       4)                                                                \n","                                                                                                  \n"," quantize_annotate_7 (QuantizeA  (None, 129, 129, 14  0          ['block2a_expand_activation[1][0]\n"," nnotate)                       4)                               ']                               \n","                                                                                                  \n"," quantize_annotate_8 (QuantizeA  (None, 64, 64, 144)  1296       ['quantize_annotate_7[0][0]']    \n"," nnotate)                                                                                         \n","                                                                                                  \n"," block2a_bn (BatchNormalization  (None, 64, 64, 144)  576        ['quantize_annotate_8[0][0]']    \n"," )                                                                                                \n","                                                                                                  \n"," block2a_activation (Activation  (None, 64, 64, 144)  0          ['block2a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2a_se_squeeze (GlobalAver  (None, 144)         0           ['block2a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block2a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2a_se_reshape[1][0]']     \n","                                                                                                  \n"," block2a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2a_se_reduce[1][0]']      \n","                                                                                                  \n"," block2a_se_excite (Multiply)   (None, 64, 64, 144)  0           ['block2a_activation[1][0]',     \n","                                                                  'block2a_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_9 (QuantizeA  (None, 64, 64, 32)  4608        ['block2a_se_excite[1][0]']      \n"," nnotate)                                                                                         \n","                                                                                                  \n"," block2a_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['quantize_annotate_9[0][0]']    \n"," lization)                                                                                        \n","                                                                                                  \n"," quantize_annotate_10 (Quantize  (None, 64, 64, 192)  6144       ['block2a_project_bn[1][0]']     \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block2b_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['quantize_annotate_10[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block2b_expand_activation (Act  (None, 64, 64, 192)  0          ['block2b_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_11 (Quantize  (None, 64, 64, 192)  1728       ['block2b_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block2b_bn (BatchNormalization  (None, 64, 64, 192)  768        ['quantize_annotate_11[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block2b_activation (Activation  (None, 64, 64, 192)  0          ['block2b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2b_se_squeeze (GlobalAver  (None, 192)         0           ['block2b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2b_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block2b_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2b_se_reshape[1][0]']     \n","                                                                                                  \n"," block2b_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2b_se_reduce[1][0]']      \n","                                                                                                  \n"," block2b_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2b_activation[1][0]',     \n","                                                                  'block2b_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_12 (Quantize  (None, 64, 64, 32)  6144        ['block2b_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block2b_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['quantize_annotate_12[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2b_drop (Dropout)         (None, 64, 64, 32)   0           ['block2b_project_bn[1][0]']     \n","                                                                                                  \n"," block2b_add (Add)              (None, 64, 64, 32)   0           ['block2b_drop[1][0]',           \n","                                                                  'block2a_project_bn[1][0]']     \n","                                                                                                  \n"," quantize_annotate_13 (Quantize  (None, 64, 64, 192)  6144       ['block2b_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block2c_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['quantize_annotate_13[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block2c_expand_activation (Act  (None, 64, 64, 192)  0          ['block2c_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_14 (Quantize  (None, 64, 64, 192)  1728       ['block2c_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block2c_bn (BatchNormalization  (None, 64, 64, 192)  768        ['quantize_annotate_14[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block2c_activation (Activation  (None, 64, 64, 192)  0          ['block2c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2c_se_squeeze (GlobalAver  (None, 192)         0           ['block2c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2c_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block2c_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2c_se_reshape[1][0]']     \n","                                                                                                  \n"," block2c_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2c_se_reduce[1][0]']      \n","                                                                                                  \n"," block2c_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2c_activation[1][0]',     \n","                                                                  'block2c_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_15 (Quantize  (None, 64, 64, 32)  6144        ['block2c_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block2c_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['quantize_annotate_15[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2c_drop (Dropout)         (None, 64, 64, 32)   0           ['block2c_project_bn[1][0]']     \n","                                                                                                  \n"," block2c_add (Add)              (None, 64, 64, 32)   0           ['block2c_drop[1][0]',           \n","                                                                  'block2b_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_16 (Quantize  (None, 64, 64, 192)  6144       ['block2c_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block2d_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['quantize_annotate_16[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block2d_expand_activation (Act  (None, 64, 64, 192)  0          ['block2d_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_17 (Quantize  (None, 64, 64, 192)  1728       ['block2d_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block2d_bn (BatchNormalization  (None, 64, 64, 192)  768        ['quantize_annotate_17[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block2d_activation (Activation  (None, 64, 64, 192)  0          ['block2d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2d_se_squeeze (GlobalAver  (None, 192)         0           ['block2d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2d_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block2d_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2d_se_reshape[1][0]']     \n","                                                                                                  \n"," block2d_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2d_se_reduce[1][0]']      \n","                                                                                                  \n"," block2d_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2d_activation[1][0]',     \n","                                                                  'block2d_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_18 (Quantize  (None, 64, 64, 32)  6144        ['block2d_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block2d_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['quantize_annotate_18[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block2d_drop (Dropout)         (None, 64, 64, 32)   0           ['block2d_project_bn[1][0]']     \n","                                                                                                  \n"," block2d_add (Add)              (None, 64, 64, 32)   0           ['block2d_drop[1][0]',           \n","                                                                  'block2c_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_19 (Quantize  (None, 64, 64, 192)  6144       ['block2d_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3a_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['quantize_annotate_19[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block3a_expand_activation (Act  (None, 64, 64, 192)  0          ['block3a_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_20 (Quantize  (None, 67, 67, 192)  0          ['block3a_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," quantize_annotate_21 (Quantize  (None, 32, 32, 192)  4800       ['quantize_annotate_20[0][0]']   \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3a_bn (BatchNormalization  (None, 32, 32, 192)  768        ['quantize_annotate_21[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block3a_activation (Activation  (None, 32, 32, 192)  0          ['block3a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3a_se_squeeze (GlobalAver  (None, 192)         0           ['block3a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3a_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block3a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block3a_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block3a_se_reshape[1][0]']     \n","                                                                                                  \n"," block3a_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block3a_se_reduce[1][0]']      \n","                                                                                                  \n"," block3a_se_excite (Multiply)   (None, 32, 32, 192)  0           ['block3a_activation[1][0]',     \n","                                                                  'block3a_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_22 (Quantize  (None, 32, 32, 56)  10752       ['block3a_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3a_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['quantize_annotate_22[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," quantize_annotate_23 (Quantize  (None, 32, 32, 336)  18816      ['block3a_project_bn[1][0]']     \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3b_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['quantize_annotate_23[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block3b_expand_activation (Act  (None, 32, 32, 336)  0          ['block3b_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_24 (Quantize  (None, 32, 32, 336)  8400       ['block3b_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block3b_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['quantize_annotate_24[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block3b_activation (Activation  (None, 32, 32, 336)  0          ['block3b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3b_se_squeeze (GlobalAver  (None, 336)         0           ['block3b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3b_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block3b_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3b_se_reshape[1][0]']     \n","                                                                                                  \n"," block3b_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3b_se_reduce[1][0]']      \n","                                                                                                  \n"," block3b_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3b_activation[1][0]',     \n","                                                                  'block3b_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_25 (Quantize  (None, 32, 32, 56)  18816       ['block3b_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3b_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['quantize_annotate_25[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3b_drop (Dropout)         (None, 32, 32, 56)   0           ['block3b_project_bn[1][0]']     \n","                                                                                                  \n"," block3b_add (Add)              (None, 32, 32, 56)   0           ['block3b_drop[1][0]',           \n","                                                                  'block3a_project_bn[1][0]']     \n","                                                                                                  \n"," quantize_annotate_26 (Quantize  (None, 32, 32, 336)  18816      ['block3b_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3c_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['quantize_annotate_26[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block3c_expand_activation (Act  (None, 32, 32, 336)  0          ['block3c_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_27 (Quantize  (None, 32, 32, 336)  8400       ['block3c_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block3c_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['quantize_annotate_27[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block3c_activation (Activation  (None, 32, 32, 336)  0          ['block3c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3c_se_squeeze (GlobalAver  (None, 336)         0           ['block3c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3c_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block3c_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3c_se_reshape[1][0]']     \n","                                                                                                  \n"," block3c_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3c_se_reduce[1][0]']      \n","                                                                                                  \n"," block3c_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3c_activation[1][0]',     \n","                                                                  'block3c_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_28 (Quantize  (None, 32, 32, 56)  18816       ['block3c_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3c_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['quantize_annotate_28[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3c_drop (Dropout)         (None, 32, 32, 56)   0           ['block3c_project_bn[1][0]']     \n","                                                                                                  \n"," block3c_add (Add)              (None, 32, 32, 56)   0           ['block3c_drop[1][0]',           \n","                                                                  'block3b_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_29 (Quantize  (None, 32, 32, 336)  18816      ['block3c_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3d_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['quantize_annotate_29[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block3d_expand_activation (Act  (None, 32, 32, 336)  0          ['block3d_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_30 (Quantize  (None, 32, 32, 336)  8400       ['block3d_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block3d_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['quantize_annotate_30[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block3d_activation (Activation  (None, 32, 32, 336)  0          ['block3d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3d_se_squeeze (GlobalAver  (None, 336)         0           ['block3d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3d_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block3d_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3d_se_reshape[1][0]']     \n","                                                                                                  \n"," block3d_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3d_se_reduce[1][0]']      \n","                                                                                                  \n"," block3d_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3d_activation[1][0]',     \n","                                                                  'block3d_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_31 (Quantize  (None, 32, 32, 56)  18816       ['block3d_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block3d_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['quantize_annotate_31[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block3d_drop (Dropout)         (None, 32, 32, 56)   0           ['block3d_project_bn[1][0]']     \n","                                                                                                  \n"," block3d_add (Add)              (None, 32, 32, 56)   0           ['block3d_drop[1][0]',           \n","                                                                  'block3c_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_32 (Quantize  (None, 32, 32, 336)  18816      ['block3d_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4a_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['quantize_annotate_32[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block4a_expand_activation (Act  (None, 32, 32, 336)  0          ['block4a_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_33 (Quantize  (None, 33, 33, 336)  0          ['block4a_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," quantize_annotate_34 (Quantize  (None, 16, 16, 336)  3024       ['quantize_annotate_33[0][0]']   \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4a_bn (BatchNormalization  (None, 16, 16, 336)  1344       ['quantize_annotate_34[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4a_activation (Activation  (None, 16, 16, 336)  0          ['block4a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4a_se_squeeze (GlobalAver  (None, 336)         0           ['block4a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4a_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block4a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4a_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block4a_se_reshape[1][0]']     \n","                                                                                                  \n"," block4a_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block4a_se_reduce[1][0]']      \n","                                                                                                  \n"," block4a_se_excite (Multiply)   (None, 16, 16, 336)  0           ['block4a_activation[1][0]',     \n","                                                                  'block4a_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_35 (Quantize  (None, 16, 16, 112)  37632      ['block4a_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4a_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quantize_annotate_35[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," quantize_annotate_36 (Quantize  (None, 16, 16, 672)  75264      ['block4a_project_bn[1][0]']     \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4b_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quantize_annotate_36[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block4b_expand_activation (Act  (None, 16, 16, 672)  0          ['block4b_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_37 (Quantize  (None, 16, 16, 672)  6048       ['block4b_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block4b_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quantize_annotate_37[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4b_activation (Activation  (None, 16, 16, 672)  0          ['block4b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4b_se_squeeze (GlobalAver  (None, 672)         0           ['block4b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4b_se_reshape[1][0]']     \n","                                                                                                  \n"," block4b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4b_se_reduce[1][0]']      \n","                                                                                                  \n"," block4b_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4b_activation[1][0]',     \n","                                                                  'block4b_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_38 (Quantize  (None, 16, 16, 112)  75264      ['block4b_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4b_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quantize_annotate_38[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4b_drop (Dropout)         (None, 16, 16, 112)  0           ['block4b_project_bn[1][0]']     \n","                                                                                                  \n"," block4b_add (Add)              (None, 16, 16, 112)  0           ['block4b_drop[1][0]',           \n","                                                                  'block4a_project_bn[1][0]']     \n","                                                                                                  \n"," quantize_annotate_39 (Quantize  (None, 16, 16, 672)  75264      ['block4b_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4c_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quantize_annotate_39[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block4c_expand_activation (Act  (None, 16, 16, 672)  0          ['block4c_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_40 (Quantize  (None, 16, 16, 672)  6048       ['block4c_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block4c_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quantize_annotate_40[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4c_activation (Activation  (None, 16, 16, 672)  0          ['block4c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4c_se_squeeze (GlobalAver  (None, 672)         0           ['block4c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4c_se_reshape[1][0]']     \n","                                                                                                  \n"," block4c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4c_se_reduce[1][0]']      \n","                                                                                                  \n"," block4c_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4c_activation[1][0]',     \n","                                                                  'block4c_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_41 (Quantize  (None, 16, 16, 112)  75264      ['block4c_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4c_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quantize_annotate_41[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4c_drop (Dropout)         (None, 16, 16, 112)  0           ['block4c_project_bn[1][0]']     \n","                                                                                                  \n"," block4c_add (Add)              (None, 16, 16, 112)  0           ['block4c_drop[1][0]',           \n","                                                                  'block4b_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_42 (Quantize  (None, 16, 16, 672)  75264      ['block4c_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4d_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quantize_annotate_42[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block4d_expand_activation (Act  (None, 16, 16, 672)  0          ['block4d_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_43 (Quantize  (None, 16, 16, 672)  6048       ['block4d_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block4d_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quantize_annotate_43[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4d_activation (Activation  (None, 16, 16, 672)  0          ['block4d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4d_se_squeeze (GlobalAver  (None, 672)         0           ['block4d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4d_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4d_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4d_se_reshape[1][0]']     \n","                                                                                                  \n"," block4d_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4d_se_reduce[1][0]']      \n","                                                                                                  \n"," block4d_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4d_activation[1][0]',     \n","                                                                  'block4d_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_44 (Quantize  (None, 16, 16, 112)  75264      ['block4d_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4d_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quantize_annotate_44[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4d_drop (Dropout)         (None, 16, 16, 112)  0           ['block4d_project_bn[1][0]']     \n","                                                                                                  \n"," block4d_add (Add)              (None, 16, 16, 112)  0           ['block4d_drop[1][0]',           \n","                                                                  'block4c_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_45 (Quantize  (None, 16, 16, 672)  75264      ['block4d_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4e_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quantize_annotate_45[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block4e_expand_activation (Act  (None, 16, 16, 672)  0          ['block4e_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_46 (Quantize  (None, 16, 16, 672)  6048       ['block4e_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block4e_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quantize_annotate_46[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4e_activation (Activation  (None, 16, 16, 672)  0          ['block4e_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4e_se_squeeze (GlobalAver  (None, 672)         0           ['block4e_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4e_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4e_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4e_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4e_se_reshape[1][0]']     \n","                                                                                                  \n"," block4e_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4e_se_reduce[1][0]']      \n","                                                                                                  \n"," block4e_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4e_activation[1][0]',     \n","                                                                  'block4e_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_47 (Quantize  (None, 16, 16, 112)  75264      ['block4e_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4e_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quantize_annotate_47[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4e_drop (Dropout)         (None, 16, 16, 112)  0           ['block4e_project_bn[1][0]']     \n","                                                                                                  \n"," block4e_add (Add)              (None, 16, 16, 112)  0           ['block4e_drop[1][0]',           \n","                                                                  'block4d_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_48 (Quantize  (None, 16, 16, 672)  75264      ['block4e_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4f_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quantize_annotate_48[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block4f_expand_activation (Act  (None, 16, 16, 672)  0          ['block4f_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_49 (Quantize  (None, 16, 16, 672)  6048       ['block4f_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block4f_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quantize_annotate_49[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4f_activation (Activation  (None, 16, 16, 672)  0          ['block4f_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4f_se_squeeze (GlobalAver  (None, 672)         0           ['block4f_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4f_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4f_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4f_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4f_se_reshape[1][0]']     \n","                                                                                                  \n"," block4f_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4f_se_reduce[1][0]']      \n","                                                                                                  \n"," block4f_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4f_activation[1][0]',     \n","                                                                  'block4f_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_50 (Quantize  (None, 16, 16, 112)  75264      ['block4f_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block4f_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quantize_annotate_50[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block4f_drop (Dropout)         (None, 16, 16, 112)  0           ['block4f_project_bn[1][0]']     \n","                                                                                                  \n"," block4f_add (Add)              (None, 16, 16, 112)  0           ['block4f_drop[1][0]',           \n","                                                                  'block4e_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_51 (Quantize  (None, 16, 16, 672)  75264      ['block4f_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5a_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quantize_annotate_51[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block5a_expand_activation (Act  (None, 16, 16, 672)  0          ['block5a_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_52 (Quantize  (None, 16, 16, 672)  16800      ['block5a_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block5a_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quantize_annotate_52[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5a_activation (Activation  (None, 16, 16, 672)  0          ['block5a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5a_se_squeeze (GlobalAver  (None, 672)         0           ['block5a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5a_se_reshape[1][0]']     \n","                                                                                                  \n"," block5a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5a_se_reduce[1][0]']      \n","                                                                                                  \n"," block5a_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block5a_activation[1][0]',     \n","                                                                  'block5a_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_53 (Quantize  (None, 16, 16, 160)  107520     ['block5a_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5a_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quantize_annotate_53[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," quantize_annotate_54 (Quantize  (None, 16, 16, 960)  153600     ['block5a_project_bn[1][0]']     \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5b_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quantize_annotate_54[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block5b_expand_activation (Act  (None, 16, 16, 960)  0          ['block5b_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_55 (Quantize  (None, 16, 16, 960)  24000      ['block5b_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block5b_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quantize_annotate_55[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5b_activation (Activation  (None, 16, 16, 960)  0          ['block5b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5b_se_squeeze (GlobalAver  (None, 960)         0           ['block5b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5b_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5b_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5b_se_reshape[1][0]']     \n","                                                                                                  \n"," block5b_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5b_se_reduce[1][0]']      \n","                                                                                                  \n"," block5b_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5b_activation[1][0]',     \n","                                                                  'block5b_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_56 (Quantize  (None, 16, 16, 160)  153600     ['block5b_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5b_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quantize_annotate_56[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5b_drop (Dropout)         (None, 16, 16, 160)  0           ['block5b_project_bn[1][0]']     \n","                                                                                                  \n"," block5b_add (Add)              (None, 16, 16, 160)  0           ['block5b_drop[1][0]',           \n","                                                                  'block5a_project_bn[1][0]']     \n","                                                                                                  \n"," quantize_annotate_57 (Quantize  (None, 16, 16, 960)  153600     ['block5b_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5c_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quantize_annotate_57[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block5c_expand_activation (Act  (None, 16, 16, 960)  0          ['block5c_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_58 (Quantize  (None, 16, 16, 960)  24000      ['block5c_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block5c_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quantize_annotate_58[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5c_activation (Activation  (None, 16, 16, 960)  0          ['block5c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5c_se_squeeze (GlobalAver  (None, 960)         0           ['block5c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5c_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5c_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5c_se_reshape[1][0]']     \n","                                                                                                  \n"," block5c_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5c_se_reduce[1][0]']      \n","                                                                                                  \n"," block5c_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5c_activation[1][0]',     \n","                                                                  'block5c_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_59 (Quantize  (None, 16, 16, 160)  153600     ['block5c_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5c_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quantize_annotate_59[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5c_drop (Dropout)         (None, 16, 16, 160)  0           ['block5c_project_bn[1][0]']     \n","                                                                                                  \n"," block5c_add (Add)              (None, 16, 16, 160)  0           ['block5c_drop[1][0]',           \n","                                                                  'block5b_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_60 (Quantize  (None, 16, 16, 960)  153600     ['block5c_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5d_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quantize_annotate_60[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block5d_expand_activation (Act  (None, 16, 16, 960)  0          ['block5d_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_61 (Quantize  (None, 16, 16, 960)  24000      ['block5d_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block5d_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quantize_annotate_61[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5d_activation (Activation  (None, 16, 16, 960)  0          ['block5d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5d_se_squeeze (GlobalAver  (None, 960)         0           ['block5d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5d_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5d_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5d_se_reshape[1][0]']     \n","                                                                                                  \n"," block5d_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5d_se_reduce[1][0]']      \n","                                                                                                  \n"," block5d_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5d_activation[1][0]',     \n","                                                                  'block5d_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_62 (Quantize  (None, 16, 16, 160)  153600     ['block5d_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5d_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quantize_annotate_62[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5d_drop (Dropout)         (None, 16, 16, 160)  0           ['block5d_project_bn[1][0]']     \n","                                                                                                  \n"," block5d_add (Add)              (None, 16, 16, 160)  0           ['block5d_drop[1][0]',           \n","                                                                  'block5c_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_63 (Quantize  (None, 16, 16, 960)  153600     ['block5d_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5e_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quantize_annotate_63[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block5e_expand_activation (Act  (None, 16, 16, 960)  0          ['block5e_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_64 (Quantize  (None, 16, 16, 960)  24000      ['block5e_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block5e_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quantize_annotate_64[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5e_activation (Activation  (None, 16, 16, 960)  0          ['block5e_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5e_se_squeeze (GlobalAver  (None, 960)         0           ['block5e_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5e_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5e_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5e_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5e_se_reshape[1][0]']     \n","                                                                                                  \n"," block5e_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5e_se_reduce[1][0]']      \n","                                                                                                  \n"," block5e_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5e_activation[1][0]',     \n","                                                                  'block5e_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_65 (Quantize  (None, 16, 16, 160)  153600     ['block5e_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5e_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quantize_annotate_65[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5e_drop (Dropout)         (None, 16, 16, 160)  0           ['block5e_project_bn[1][0]']     \n","                                                                                                  \n"," block5e_add (Add)              (None, 16, 16, 160)  0           ['block5e_drop[1][0]',           \n","                                                                  'block5d_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_66 (Quantize  (None, 16, 16, 960)  153600     ['block5e_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5f_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quantize_annotate_66[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block5f_expand_activation (Act  (None, 16, 16, 960)  0          ['block5f_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_67 (Quantize  (None, 16, 16, 960)  24000      ['block5f_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block5f_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quantize_annotate_67[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5f_activation (Activation  (None, 16, 16, 960)  0          ['block5f_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5f_se_squeeze (GlobalAver  (None, 960)         0           ['block5f_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5f_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5f_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5f_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5f_se_reshape[1][0]']     \n","                                                                                                  \n"," block5f_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5f_se_reduce[1][0]']      \n","                                                                                                  \n"," block5f_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5f_activation[1][0]',     \n","                                                                  'block5f_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_68 (Quantize  (None, 16, 16, 160)  153600     ['block5f_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block5f_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quantize_annotate_68[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block5f_drop (Dropout)         (None, 16, 16, 160)  0           ['block5f_project_bn[1][0]']     \n","                                                                                                  \n"," block5f_add (Add)              (None, 16, 16, 160)  0           ['block5f_drop[1][0]',           \n","                                                                  'block5e_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_69 (Quantize  (None, 16, 16, 960)  153600     ['block5f_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6a_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quantize_annotate_69[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block6a_expand_activation (Act  (None, 16, 16, 960)  0          ['block6a_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_70 (Quantize  (None, 19, 19, 960)  0          ['block6a_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," quantize_annotate_71 (Quantize  (None, 8, 8, 960)   24000       ['quantize_annotate_70[0][0]']   \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6a_bn (BatchNormalization  (None, 8, 8, 960)   3840        ['quantize_annotate_71[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6a_activation (Activation  (None, 8, 8, 960)   0           ['block6a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6a_se_squeeze (GlobalAver  (None, 960)         0           ['block6a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6a_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block6a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6a_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block6a_se_reshape[1][0]']     \n","                                                                                                  \n"," block6a_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block6a_se_reduce[1][0]']      \n","                                                                                                  \n"," block6a_se_excite (Multiply)   (None, 8, 8, 960)    0           ['block6a_activation[1][0]',     \n","                                                                  'block6a_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_72 (Quantize  (None, 8, 8, 272)   261120      ['block6a_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6a_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quantize_annotate_72[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," quantize_annotate_73 (Quantize  (None, 8, 8, 1632)  443904      ['block6a_project_bn[1][0]']     \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6b_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quantize_annotate_73[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block6b_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6b_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_74 (Quantize  (None, 8, 8, 1632)  40800       ['block6b_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block6b_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quantize_annotate_74[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6b_activation (Activation  (None, 8, 8, 1632)  0           ['block6b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6b_se_squeeze (GlobalAver  (None, 1632)        0           ['block6b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6b_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6b_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6b_se_reshape[1][0]']     \n","                                                                                                  \n"," block6b_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6b_se_reduce[1][0]']      \n","                                                                                                  \n"," block6b_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6b_activation[1][0]',     \n","                                                                  'block6b_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_75 (Quantize  (None, 8, 8, 272)   443904      ['block6b_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6b_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quantize_annotate_75[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6b_drop (Dropout)         (None, 8, 8, 272)    0           ['block6b_project_bn[1][0]']     \n","                                                                                                  \n"," block6b_add (Add)              (None, 8, 8, 272)    0           ['block6b_drop[1][0]',           \n","                                                                  'block6a_project_bn[1][0]']     \n","                                                                                                  \n"," quantize_annotate_76 (Quantize  (None, 8, 8, 1632)  443904      ['block6b_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6c_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quantize_annotate_76[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block6c_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6c_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_77 (Quantize  (None, 8, 8, 1632)  40800       ['block6c_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block6c_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quantize_annotate_77[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6c_activation (Activation  (None, 8, 8, 1632)  0           ['block6c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6c_se_squeeze (GlobalAver  (None, 1632)        0           ['block6c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6c_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6c_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6c_se_reshape[1][0]']     \n","                                                                                                  \n"," block6c_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6c_se_reduce[1][0]']      \n","                                                                                                  \n"," block6c_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6c_activation[1][0]',     \n","                                                                  'block6c_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_78 (Quantize  (None, 8, 8, 272)   443904      ['block6c_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6c_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quantize_annotate_78[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6c_drop (Dropout)         (None, 8, 8, 272)    0           ['block6c_project_bn[1][0]']     \n","                                                                                                  \n"," block6c_add (Add)              (None, 8, 8, 272)    0           ['block6c_drop[1][0]',           \n","                                                                  'block6b_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_79 (Quantize  (None, 8, 8, 1632)  443904      ['block6c_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6d_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quantize_annotate_79[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block6d_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6d_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_80 (Quantize  (None, 8, 8, 1632)  40800       ['block6d_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block6d_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quantize_annotate_80[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6d_activation (Activation  (None, 8, 8, 1632)  0           ['block6d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6d_se_squeeze (GlobalAver  (None, 1632)        0           ['block6d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6d_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6d_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6d_se_reshape[1][0]']     \n","                                                                                                  \n"," block6d_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6d_se_reduce[1][0]']      \n","                                                                                                  \n"," block6d_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6d_activation[1][0]',     \n","                                                                  'block6d_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_81 (Quantize  (None, 8, 8, 272)   443904      ['block6d_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6d_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quantize_annotate_81[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6d_drop (Dropout)         (None, 8, 8, 272)    0           ['block6d_project_bn[1][0]']     \n","                                                                                                  \n"," block6d_add (Add)              (None, 8, 8, 272)    0           ['block6d_drop[1][0]',           \n","                                                                  'block6c_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_82 (Quantize  (None, 8, 8, 1632)  443904      ['block6d_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6e_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quantize_annotate_82[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block6e_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6e_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_83 (Quantize  (None, 8, 8, 1632)  40800       ['block6e_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block6e_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quantize_annotate_83[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6e_activation (Activation  (None, 8, 8, 1632)  0           ['block6e_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6e_se_squeeze (GlobalAver  (None, 1632)        0           ['block6e_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6e_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6e_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6e_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6e_se_reshape[1][0]']     \n","                                                                                                  \n"," block6e_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6e_se_reduce[1][0]']      \n","                                                                                                  \n"," block6e_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6e_activation[1][0]',     \n","                                                                  'block6e_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_84 (Quantize  (None, 8, 8, 272)   443904      ['block6e_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6e_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quantize_annotate_84[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6e_drop (Dropout)         (None, 8, 8, 272)    0           ['block6e_project_bn[1][0]']     \n","                                                                                                  \n"," block6e_add (Add)              (None, 8, 8, 272)    0           ['block6e_drop[1][0]',           \n","                                                                  'block6d_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_85 (Quantize  (None, 8, 8, 1632)  443904      ['block6e_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6f_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quantize_annotate_85[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block6f_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6f_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_86 (Quantize  (None, 8, 8, 1632)  40800       ['block6f_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block6f_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quantize_annotate_86[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6f_activation (Activation  (None, 8, 8, 1632)  0           ['block6f_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6f_se_squeeze (GlobalAver  (None, 1632)        0           ['block6f_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6f_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6f_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6f_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6f_se_reshape[1][0]']     \n","                                                                                                  \n"," block6f_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6f_se_reduce[1][0]']      \n","                                                                                                  \n"," block6f_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6f_activation[1][0]',     \n","                                                                  'block6f_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_87 (Quantize  (None, 8, 8, 272)   443904      ['block6f_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6f_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quantize_annotate_87[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6f_drop (Dropout)         (None, 8, 8, 272)    0           ['block6f_project_bn[1][0]']     \n","                                                                                                  \n"," block6f_add (Add)              (None, 8, 8, 272)    0           ['block6f_drop[1][0]',           \n","                                                                  'block6e_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_88 (Quantize  (None, 8, 8, 1632)  443904      ['block6f_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6g_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quantize_annotate_88[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block6g_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6g_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_89 (Quantize  (None, 8, 8, 1632)  40800       ['block6g_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block6g_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quantize_annotate_89[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6g_activation (Activation  (None, 8, 8, 1632)  0           ['block6g_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6g_se_squeeze (GlobalAver  (None, 1632)        0           ['block6g_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6g_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6g_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6g_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6g_se_reshape[1][0]']     \n","                                                                                                  \n"," block6g_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6g_se_reduce[1][0]']      \n","                                                                                                  \n"," block6g_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6g_activation[1][0]',     \n","                                                                  'block6g_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_90 (Quantize  (None, 8, 8, 272)   443904      ['block6g_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6g_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quantize_annotate_90[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6g_drop (Dropout)         (None, 8, 8, 272)    0           ['block6g_project_bn[1][0]']     \n","                                                                                                  \n"," block6g_add (Add)              (None, 8, 8, 272)    0           ['block6g_drop[1][0]',           \n","                                                                  'block6f_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_91 (Quantize  (None, 8, 8, 1632)  443904      ['block6g_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6h_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quantize_annotate_91[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block6h_expand_activation (Act  (None, 8, 8, 1632)  0           ['block6h_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_92 (Quantize  (None, 8, 8, 1632)  40800       ['block6h_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block6h_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quantize_annotate_92[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6h_activation (Activation  (None, 8, 8, 1632)  0           ['block6h_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6h_se_squeeze (GlobalAver  (None, 1632)        0           ['block6h_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6h_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6h_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6h_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6h_se_reshape[1][0]']     \n","                                                                                                  \n"," block6h_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6h_se_reduce[1][0]']      \n","                                                                                                  \n"," block6h_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6h_activation[1][0]',     \n","                                                                  'block6h_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_93 (Quantize  (None, 8, 8, 272)   443904      ['block6h_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block6h_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quantize_annotate_93[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block6h_drop (Dropout)         (None, 8, 8, 272)    0           ['block6h_project_bn[1][0]']     \n","                                                                                                  \n"," block6h_add (Add)              (None, 8, 8, 272)    0           ['block6h_drop[1][0]',           \n","                                                                  'block6g_add[1][0]']            \n","                                                                                                  \n"," quantize_annotate_94 (Quantize  (None, 8, 8, 1632)  443904      ['block6h_add[1][0]']            \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block7a_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quantize_annotate_94[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block7a_expand_activation (Act  (None, 8, 8, 1632)  0           ['block7a_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_95 (Quantize  (None, 8, 8, 1632)  14688       ['block7a_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block7a_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quantize_annotate_95[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block7a_activation (Activation  (None, 8, 8, 1632)  0           ['block7a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7a_se_squeeze (GlobalAver  (None, 1632)        0           ['block7a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7a_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block7a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block7a_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block7a_se_reshape[1][0]']     \n","                                                                                                  \n"," block7a_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block7a_se_reduce[1][0]']      \n","                                                                                                  \n"," block7a_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block7a_activation[1][0]',     \n","                                                                  'block7a_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_96 (Quantize  (None, 8, 8, 448)   731136      ['block7a_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block7a_project_bn (BatchNorma  (None, 8, 8, 448)   1792        ['quantize_annotate_96[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," quantize_annotate_97 (Quantize  (None, 8, 8, 2688)  1204224     ['block7a_project_bn[1][0]']     \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block7b_expand_bn (BatchNormal  (None, 8, 8, 2688)  10752       ['quantize_annotate_97[0][0]']   \n"," ization)                                                                                         \n","                                                                                                  \n"," block7b_expand_activation (Act  (None, 8, 8, 2688)  0           ['block7b_expand_bn[1][0]']      \n"," ivation)                                                                                         \n","                                                                                                  \n"," quantize_annotate_98 (Quantize  (None, 8, 8, 2688)  24192       ['block7b_expand_activation[1][0]\n"," Annotate)                                                       ']                               \n","                                                                                                  \n"," block7b_bn (BatchNormalization  (None, 8, 8, 2688)  10752       ['quantize_annotate_98[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block7b_activation (Activation  (None, 8, 8, 2688)  0           ['block7b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7b_se_squeeze (GlobalAver  (None, 2688)        0           ['block7b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7b_se_reshape (Reshape)   (None, 1, 1, 2688)   0           ['block7b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block7b_se_reduce (Conv2D)     (None, 1, 1, 112)    301168      ['block7b_se_reshape[1][0]']     \n","                                                                                                  \n"," block7b_se_expand (Conv2D)     (None, 1, 1, 2688)   303744      ['block7b_se_reduce[1][0]']      \n","                                                                                                  \n"," block7b_se_excite (Multiply)   (None, 8, 8, 2688)   0           ['block7b_activation[1][0]',     \n","                                                                  'block7b_se_expand[1][0]']      \n","                                                                                                  \n"," quantize_annotate_99 (Quantize  (None, 8, 8, 448)   1204224     ['block7b_se_excite[1][0]']      \n"," Annotate)                                                                                        \n","                                                                                                  \n"," block7b_project_bn (BatchNorma  (None, 8, 8, 448)   1792        ['quantize_annotate_99[0][0]']   \n"," lization)                                                                                        \n","                                                                                                  \n"," block7b_drop (Dropout)         (None, 8, 8, 448)    0           ['block7b_project_bn[1][0]']     \n","                                                                                                  \n"," block7b_add (Add)              (None, 8, 8, 448)    0           ['block7b_drop[1][0]',           \n","                                                                  'block7a_project_bn[1][0]']     \n","                                                                                                  \n"," quantize_annotate_100 (Quantiz  (None, 8, 8, 1792)  802816      ['block7b_add[1][0]']            \n"," eAnnotate)                                                                                       \n","                                                                                                  \n"," top_bn (BatchNormalization)    (None, 8, 8, 1792)   7168        ['quantize_annotate_100[0][0]']  \n","                                                                                                  \n"," top_activation (Activation)    (None, 8, 8, 1792)   0           ['top_bn[1][0]']                 \n","                                                                                                  \n"," global_average_pooling2d_1 (Gl  (None, 1792)        0           ['top_activation[1][0]']         \n"," obalAveragePooling2D)                                                                            \n","                                                                                                  \n"," dense_3 (Dense)                (None, 1024)         1836032     ['global_average_pooling2d_1[1][0\n","                                                                 ]']                              \n","                                                                                                  \n"," batch_normalization_1 (BatchNo  (None, 1024)        4096        ['dense_3[1][0]']                \n"," rmalization)                                                                                     \n","                                                                                                  \n"," dense_4 (Dense)                (None, 128)          131200      ['batch_normalization_1[1][0]']  \n","                                                                                                  \n"," dense_5 (Dense)                (None, 3)            387         ['dense_4[1][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 19,645,538\n","Trainable params: 1,969,667\n","Non-trainable params: 17,675,871\n","__________________________________________________________________________________________________\n"]}],"source":["quant_aware_eff.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19044,"status":"ok","timestamp":1653656320676,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"6EzaaHJch0aT","outputId":"412e4562-447d-4bd7-a009-c7263209d543"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 256, 256, 3  0           []                               \n","                                )]                                                                \n","                                                                                                  \n"," rescaling_1 (Rescaling)        (None, 256, 256, 3)  0           ['input_1[0][0]']                \n","                                                                                                  \n"," normalization (Normalization)  (None, 256, 256, 3)  7           ['rescaling_1[1][0]']            \n","                                                                                                  \n"," quant_stem_conv_pad (QuantizeW  (None, 257, 257, 3)  1          ['normalization[1][0]']          \n"," rapperV2)                                                                                        \n","                                                                                                  \n"," quant_stem_conv (QuantizeWrapp  (None, 128, 128, 48  1395       ['quant_stem_conv_pad[0][0]']    \n"," erV2)                          )                                                                 \n","                                                                                                  \n"," stem_bn (BatchNormalization)   (None, 128, 128, 48  192         ['quant_stem_conv[0][0]']        \n","                                )                                                                 \n","                                                                                                  \n"," quant_stem_activation (Quantiz  (None, 128, 128, 48  3          ['stem_bn[1][0]']                \n"," eWrapperV2)                    )                                                                 \n","                                                                                                  \n"," quant_block1a_dwconv (Quantize  (None, 128, 128, 48  437        ['quant_stem_activation[0][0]']  \n"," WrapperV2)                     )                                                                 \n","                                                                                                  \n"," block1a_bn (BatchNormalization  (None, 128, 128, 48  192        ['quant_block1a_dwconv[0][0]']   \n"," )                              )                                                                 \n","                                                                                                  \n"," block1a_activation (Activation  (None, 128, 128, 48  0          ['block1a_bn[1][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1a_se_squeeze (GlobalAver  (None, 48)          0           ['block1a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1a_se_reshape (Reshape)   (None, 1, 1, 48)     0           ['block1a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block1a_se_reduce (Conv2D)     (None, 1, 1, 12)     588         ['block1a_se_reshape[1][0]']     \n","                                                                                                  \n"," block1a_se_expand (Conv2D)     (None, 1, 1, 48)     624         ['block1a_se_reduce[1][0]']      \n","                                                                                                  \n"," block1a_se_excite (Multiply)   (None, 128, 128, 48  0           ['block1a_activation[1][0]',     \n","                                )                                 'block1a_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block1a_project_conv (Qu  (None, 128, 128, 24  1203       ['block1a_se_excite[1][0]']      \n"," antizeWrapperV2)               )                                                                 \n","                                                                                                  \n"," block1a_project_bn (BatchNorma  (None, 128, 128, 24  96         ['quant_block1a_project_conv[0][0\n"," lization)                      )                                ]']                              \n","                                                                                                  \n"," quant_block1b_dwconv (Quantize  (None, 128, 128, 24  221        ['block1a_project_bn[1][0]']     \n"," WrapperV2)                     )                                                                 \n","                                                                                                  \n"," block1b_bn (BatchNormalization  (None, 128, 128, 24  96         ['quant_block1b_dwconv[0][0]']   \n"," )                              )                                                                 \n","                                                                                                  \n"," block1b_activation (Activation  (None, 128, 128, 24  0          ['block1b_bn[1][0]']             \n"," )                              )                                                                 \n","                                                                                                  \n"," block1b_se_squeeze (GlobalAver  (None, 24)          0           ['block1b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block1b_se_reshape (Reshape)   (None, 1, 1, 24)     0           ['block1b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block1b_se_reduce (Conv2D)     (None, 1, 1, 6)      150         ['block1b_se_reshape[1][0]']     \n","                                                                                                  \n"," block1b_se_expand (Conv2D)     (None, 1, 1, 24)     168         ['block1b_se_reduce[1][0]']      \n","                                                                                                  \n"," block1b_se_excite (Multiply)   (None, 128, 128, 24  0           ['block1b_activation[1][0]',     \n","                                )                                 'block1b_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block1b_project_conv (Qu  (None, 128, 128, 24  627        ['block1b_se_excite[1][0]']      \n"," antizeWrapperV2)               )                                                                 \n","                                                                                                  \n"," block1b_project_bn (BatchNorma  (None, 128, 128, 24  96         ['quant_block1b_project_conv[0][0\n"," lization)                      )                                ]']                              \n","                                                                                                  \n"," block1b_drop (Dropout)         (None, 128, 128, 24  0           ['block1b_project_bn[1][0]']     \n","                                )                                                                 \n","                                                                                                  \n"," quant_block1b_add (QuantizeWra  (None, 128, 128, 24  3          ['block1b_drop[1][0]',           \n"," pperV2)                        )                                 'block1a_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block2a_expand_conv (Qua  (None, 128, 128, 14  3747       ['quant_block1b_add[0][0]']      \n"," ntizeWrapperV2)                4)                                                                \n","                                                                                                  \n"," block2a_expand_bn (BatchNormal  (None, 128, 128, 14  576        ['quant_block2a_expand_conv[0][0]\n"," ization)                       4)                               ']                               \n","                                                                                                  \n"," quant_block2a_expand_activatio  (None, 128, 128, 14  3          ['block2a_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)          4)                                                                \n","                                                                                                  \n"," quant_block2a_dwconv_pad (Quan  (None, 129, 129, 14  1          ['quant_block2a_expand_activation\n"," tizeWrapperV2)                 4)                               [0][0]']                         \n","                                                                                                  \n"," quant_block2a_dwconv (Quantize  (None, 64, 64, 144)  1301       ['quant_block2a_dwconv_pad[0][0]'\n"," WrapperV2)                                                      ]                                \n","                                                                                                  \n"," block2a_bn (BatchNormalization  (None, 64, 64, 144)  576        ['quant_block2a_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block2a_activation (Activation  (None, 64, 64, 144)  0          ['block2a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2a_se_squeeze (GlobalAver  (None, 144)         0           ['block2a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2a_se_reshape (Reshape)   (None, 1, 1, 144)    0           ['block2a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block2a_se_reduce (Conv2D)     (None, 1, 1, 6)      870         ['block2a_se_reshape[1][0]']     \n","                                                                                                  \n"," block2a_se_expand (Conv2D)     (None, 1, 1, 144)    1008        ['block2a_se_reduce[1][0]']      \n","                                                                                                  \n"," block2a_se_excite (Multiply)   (None, 64, 64, 144)  0           ['block2a_activation[1][0]',     \n","                                                                  'block2a_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block2a_project_conv (Qu  (None, 64, 64, 32)  4675        ['block2a_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block2a_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['quant_block2a_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," quant_block2b_expand_conv (Qua  (None, 64, 64, 192)  6531       ['block2a_project_bn[1][0]']     \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block2b_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['quant_block2b_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block2b_expand_activatio  (None, 64, 64, 192)  3          ['block2b_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block2b_dwconv (Quantize  (None, 64, 64, 192)  1733       ['quant_block2b_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block2b_bn (BatchNormalization  (None, 64, 64, 192)  768        ['quant_block2b_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block2b_activation (Activation  (None, 64, 64, 192)  0          ['block2b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2b_se_squeeze (GlobalAver  (None, 192)         0           ['block2b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2b_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block2b_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2b_se_reshape[1][0]']     \n","                                                                                                  \n"," block2b_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2b_se_reduce[1][0]']      \n","                                                                                                  \n"," block2b_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2b_activation[1][0]',     \n","                                                                  'block2b_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block2b_project_conv (Qu  (None, 64, 64, 32)  6211        ['block2b_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block2b_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['quant_block2b_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block2b_drop (Dropout)         (None, 64, 64, 32)   0           ['block2b_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block2b_add (QuantizeWra  (None, 64, 64, 32)  3           ['block2b_drop[1][0]',           \n"," pperV2)                                                          'block2a_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block2c_expand_conv (Qua  (None, 64, 64, 192)  6531       ['quant_block2b_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block2c_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['quant_block2c_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block2c_expand_activatio  (None, 64, 64, 192)  3          ['block2c_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block2c_dwconv (Quantize  (None, 64, 64, 192)  1733       ['quant_block2c_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block2c_bn (BatchNormalization  (None, 64, 64, 192)  768        ['quant_block2c_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block2c_activation (Activation  (None, 64, 64, 192)  0          ['block2c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2c_se_squeeze (GlobalAver  (None, 192)         0           ['block2c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2c_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block2c_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2c_se_reshape[1][0]']     \n","                                                                                                  \n"," block2c_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2c_se_reduce[1][0]']      \n","                                                                                                  \n"," block2c_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2c_activation[1][0]',     \n","                                                                  'block2c_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block2c_project_conv (Qu  (None, 64, 64, 32)  6211        ['block2c_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block2c_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['quant_block2c_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block2c_drop (Dropout)         (None, 64, 64, 32)   0           ['block2c_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block2c_add (QuantizeWra  (None, 64, 64, 32)  3           ['block2c_drop[1][0]',           \n"," pperV2)                                                          'quant_block2b_add[0][0]']      \n","                                                                                                  \n"," quant_block2d_expand_conv (Qua  (None, 64, 64, 192)  6531       ['quant_block2c_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block2d_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['quant_block2d_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block2d_expand_activatio  (None, 64, 64, 192)  3          ['block2d_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block2d_dwconv (Quantize  (None, 64, 64, 192)  1733       ['quant_block2d_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block2d_bn (BatchNormalization  (None, 64, 64, 192)  768        ['quant_block2d_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block2d_activation (Activation  (None, 64, 64, 192)  0          ['block2d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block2d_se_squeeze (GlobalAver  (None, 192)         0           ['block2d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block2d_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block2d_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2d_se_reshape[1][0]']     \n","                                                                                                  \n"," block2d_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2d_se_reduce[1][0]']      \n","                                                                                                  \n"," block2d_se_excite (Multiply)   (None, 64, 64, 192)  0           ['block2d_activation[1][0]',     \n","                                                                  'block2d_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block2d_project_conv (Qu  (None, 64, 64, 32)  6211        ['block2d_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block2d_project_bn (BatchNorma  (None, 64, 64, 32)  128         ['quant_block2d_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block2d_drop (Dropout)         (None, 64, 64, 32)   0           ['block2d_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block2d_add (QuantizeWra  (None, 64, 64, 32)  3           ['block2d_drop[1][0]',           \n"," pperV2)                                                          'quant_block2c_add[0][0]']      \n","                                                                                                  \n"," quant_block3a_expand_conv (Qua  (None, 64, 64, 192)  6531       ['quant_block2d_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block3a_expand_bn (BatchNormal  (None, 64, 64, 192)  768        ['quant_block3a_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block3a_expand_activatio  (None, 64, 64, 192)  3          ['block3a_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block3a_dwconv_pad (Quan  (None, 67, 67, 192)  1          ['quant_block3a_expand_activation\n"," tizeWrapperV2)                                                  [0][0]']                         \n","                                                                                                  \n"," quant_block3a_dwconv (Quantize  (None, 32, 32, 192)  4805       ['quant_block3a_dwconv_pad[0][0]'\n"," WrapperV2)                                                      ]                                \n","                                                                                                  \n"," block3a_bn (BatchNormalization  (None, 32, 32, 192)  768        ['quant_block3a_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block3a_activation (Activation  (None, 32, 32, 192)  0          ['block3a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3a_se_squeeze (GlobalAver  (None, 192)         0           ['block3a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3a_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block3a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block3a_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block3a_se_reshape[1][0]']     \n","                                                                                                  \n"," block3a_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block3a_se_reduce[1][0]']      \n","                                                                                                  \n"," block3a_se_excite (Multiply)   (None, 32, 32, 192)  0           ['block3a_activation[1][0]',     \n","                                                                  'block3a_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block3a_project_conv (Qu  (None, 32, 32, 56)  10867       ['block3a_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block3a_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['quant_block3a_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," quant_block3b_expand_conv (Qua  (None, 32, 32, 336)  19491      ['block3a_project_bn[1][0]']     \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block3b_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['quant_block3b_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block3b_expand_activatio  (None, 32, 32, 336)  3          ['block3b_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block3b_dwconv (Quantize  (None, 32, 32, 336)  8405       ['quant_block3b_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block3b_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['quant_block3b_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block3b_activation (Activation  (None, 32, 32, 336)  0          ['block3b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3b_se_squeeze (GlobalAver  (None, 336)         0           ['block3b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3b_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block3b_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3b_se_reshape[1][0]']     \n","                                                                                                  \n"," block3b_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3b_se_reduce[1][0]']      \n","                                                                                                  \n"," block3b_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3b_activation[1][0]',     \n","                                                                  'block3b_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block3b_project_conv (Qu  (None, 32, 32, 56)  18931       ['block3b_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block3b_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['quant_block3b_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block3b_drop (Dropout)         (None, 32, 32, 56)   0           ['block3b_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block3b_add (QuantizeWra  (None, 32, 32, 56)  3           ['block3b_drop[1][0]',           \n"," pperV2)                                                          'block3a_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block3c_expand_conv (Qua  (None, 32, 32, 336)  19491      ['quant_block3b_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block3c_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['quant_block3c_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block3c_expand_activatio  (None, 32, 32, 336)  3          ['block3c_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block3c_dwconv (Quantize  (None, 32, 32, 336)  8405       ['quant_block3c_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block3c_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['quant_block3c_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block3c_activation (Activation  (None, 32, 32, 336)  0          ['block3c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3c_se_squeeze (GlobalAver  (None, 336)         0           ['block3c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3c_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block3c_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3c_se_reshape[1][0]']     \n","                                                                                                  \n"," block3c_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3c_se_reduce[1][0]']      \n","                                                                                                  \n"," block3c_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3c_activation[1][0]',     \n","                                                                  'block3c_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block3c_project_conv (Qu  (None, 32, 32, 56)  18931       ['block3c_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block3c_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['quant_block3c_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block3c_drop (Dropout)         (None, 32, 32, 56)   0           ['block3c_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block3c_add (QuantizeWra  (None, 32, 32, 56)  3           ['block3c_drop[1][0]',           \n"," pperV2)                                                          'quant_block3b_add[0][0]']      \n","                                                                                                  \n"," quant_block3d_expand_conv (Qua  (None, 32, 32, 336)  19491      ['quant_block3c_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block3d_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['quant_block3d_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block3d_expand_activatio  (None, 32, 32, 336)  3          ['block3d_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block3d_dwconv (Quantize  (None, 32, 32, 336)  8405       ['quant_block3d_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block3d_bn (BatchNormalization  (None, 32, 32, 336)  1344       ['quant_block3d_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block3d_activation (Activation  (None, 32, 32, 336)  0          ['block3d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block3d_se_squeeze (GlobalAver  (None, 336)         0           ['block3d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block3d_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block3d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block3d_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block3d_se_reshape[1][0]']     \n","                                                                                                  \n"," block3d_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block3d_se_reduce[1][0]']      \n","                                                                                                  \n"," block3d_se_excite (Multiply)   (None, 32, 32, 336)  0           ['block3d_activation[1][0]',     \n","                                                                  'block3d_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block3d_project_conv (Qu  (None, 32, 32, 56)  18931       ['block3d_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block3d_project_bn (BatchNorma  (None, 32, 32, 56)  224         ['quant_block3d_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block3d_drop (Dropout)         (None, 32, 32, 56)   0           ['block3d_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block3d_add (QuantizeWra  (None, 32, 32, 56)  3           ['block3d_drop[1][0]',           \n"," pperV2)                                                          'quant_block3c_add[0][0]']      \n","                                                                                                  \n"," quant_block4a_expand_conv (Qua  (None, 32, 32, 336)  19491      ['quant_block3d_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block4a_expand_bn (BatchNormal  (None, 32, 32, 336)  1344       ['quant_block4a_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block4a_expand_activatio  (None, 32, 32, 336)  3          ['block4a_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block4a_dwconv_pad (Quan  (None, 33, 33, 336)  1          ['quant_block4a_expand_activation\n"," tizeWrapperV2)                                                  [0][0]']                         \n","                                                                                                  \n"," quant_block4a_dwconv (Quantize  (None, 16, 16, 336)  3029       ['quant_block4a_dwconv_pad[0][0]'\n"," WrapperV2)                                                      ]                                \n","                                                                                                  \n"," block4a_bn (BatchNormalization  (None, 16, 16, 336)  1344       ['quant_block4a_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4a_activation (Activation  (None, 16, 16, 336)  0          ['block4a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4a_se_squeeze (GlobalAver  (None, 336)         0           ['block4a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4a_se_reshape (Reshape)   (None, 1, 1, 336)    0           ['block4a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4a_se_reduce (Conv2D)     (None, 1, 1, 14)     4718        ['block4a_se_reshape[1][0]']     \n","                                                                                                  \n"," block4a_se_expand (Conv2D)     (None, 1, 1, 336)    5040        ['block4a_se_reduce[1][0]']      \n","                                                                                                  \n"," block4a_se_excite (Multiply)   (None, 16, 16, 336)  0           ['block4a_activation[1][0]',     \n","                                                                  'block4a_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block4a_project_conv (Qu  (None, 16, 16, 112)  37859      ['block4a_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block4a_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quant_block4a_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," quant_block4b_expand_conv (Qua  (None, 16, 16, 672)  76611      ['block4a_project_bn[1][0]']     \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block4b_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quant_block4b_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block4b_expand_activatio  (None, 16, 16, 672)  3          ['block4b_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block4b_dwconv (Quantize  (None, 16, 16, 672)  6053       ['quant_block4b_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block4b_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quant_block4b_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4b_activation (Activation  (None, 16, 16, 672)  0          ['block4b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4b_se_squeeze (GlobalAver  (None, 672)         0           ['block4b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4b_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4b_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4b_se_reshape[1][0]']     \n","                                                                                                  \n"," block4b_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4b_se_reduce[1][0]']      \n","                                                                                                  \n"," block4b_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4b_activation[1][0]',     \n","                                                                  'block4b_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block4b_project_conv (Qu  (None, 16, 16, 112)  75491      ['block4b_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block4b_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quant_block4b_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block4b_drop (Dropout)         (None, 16, 16, 112)  0           ['block4b_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block4b_add (QuantizeWra  (None, 16, 16, 112)  3          ['block4b_drop[1][0]',           \n"," pperV2)                                                          'block4a_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block4c_expand_conv (Qua  (None, 16, 16, 672)  76611      ['quant_block4b_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block4c_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quant_block4c_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block4c_expand_activatio  (None, 16, 16, 672)  3          ['block4c_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block4c_dwconv (Quantize  (None, 16, 16, 672)  6053       ['quant_block4c_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block4c_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quant_block4c_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4c_activation (Activation  (None, 16, 16, 672)  0          ['block4c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4c_se_squeeze (GlobalAver  (None, 672)         0           ['block4c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4c_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4c_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4c_se_reshape[1][0]']     \n","                                                                                                  \n"," block4c_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4c_se_reduce[1][0]']      \n","                                                                                                  \n"," block4c_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4c_activation[1][0]',     \n","                                                                  'block4c_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block4c_project_conv (Qu  (None, 16, 16, 112)  75491      ['block4c_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block4c_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quant_block4c_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block4c_drop (Dropout)         (None, 16, 16, 112)  0           ['block4c_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block4c_add (QuantizeWra  (None, 16, 16, 112)  3          ['block4c_drop[1][0]',           \n"," pperV2)                                                          'quant_block4b_add[0][0]']      \n","                                                                                                  \n"," quant_block4d_expand_conv (Qua  (None, 16, 16, 672)  76611      ['quant_block4c_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block4d_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quant_block4d_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block4d_expand_activatio  (None, 16, 16, 672)  3          ['block4d_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block4d_dwconv (Quantize  (None, 16, 16, 672)  6053       ['quant_block4d_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block4d_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quant_block4d_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4d_activation (Activation  (None, 16, 16, 672)  0          ['block4d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4d_se_squeeze (GlobalAver  (None, 672)         0           ['block4d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4d_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4d_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4d_se_reshape[1][0]']     \n","                                                                                                  \n"," block4d_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4d_se_reduce[1][0]']      \n","                                                                                                  \n"," block4d_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4d_activation[1][0]',     \n","                                                                  'block4d_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block4d_project_conv (Qu  (None, 16, 16, 112)  75491      ['block4d_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block4d_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quant_block4d_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block4d_drop (Dropout)         (None, 16, 16, 112)  0           ['block4d_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block4d_add (QuantizeWra  (None, 16, 16, 112)  3          ['block4d_drop[1][0]',           \n"," pperV2)                                                          'quant_block4c_add[0][0]']      \n","                                                                                                  \n"," quant_block4e_expand_conv (Qua  (None, 16, 16, 672)  76611      ['quant_block4d_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block4e_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quant_block4e_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block4e_expand_activatio  (None, 16, 16, 672)  3          ['block4e_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block4e_dwconv (Quantize  (None, 16, 16, 672)  6053       ['quant_block4e_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block4e_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quant_block4e_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4e_activation (Activation  (None, 16, 16, 672)  0          ['block4e_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4e_se_squeeze (GlobalAver  (None, 672)         0           ['block4e_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4e_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4e_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4e_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4e_se_reshape[1][0]']     \n","                                                                                                  \n"," block4e_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4e_se_reduce[1][0]']      \n","                                                                                                  \n"," block4e_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4e_activation[1][0]',     \n","                                                                  'block4e_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block4e_project_conv (Qu  (None, 16, 16, 112)  75491      ['block4e_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block4e_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quant_block4e_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block4e_drop (Dropout)         (None, 16, 16, 112)  0           ['block4e_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block4e_add (QuantizeWra  (None, 16, 16, 112)  3          ['block4e_drop[1][0]',           \n"," pperV2)                                                          'quant_block4d_add[0][0]']      \n","                                                                                                  \n"," quant_block4f_expand_conv (Qua  (None, 16, 16, 672)  76611      ['quant_block4e_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block4f_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quant_block4f_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block4f_expand_activatio  (None, 16, 16, 672)  3          ['block4f_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block4f_dwconv (Quantize  (None, 16, 16, 672)  6053       ['quant_block4f_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block4f_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quant_block4f_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block4f_activation (Activation  (None, 16, 16, 672)  0          ['block4f_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block4f_se_squeeze (GlobalAver  (None, 672)         0           ['block4f_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block4f_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block4f_se_squeeze[1][0]']     \n","                                                                                                  \n"," block4f_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block4f_se_reshape[1][0]']     \n","                                                                                                  \n"," block4f_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block4f_se_reduce[1][0]']      \n","                                                                                                  \n"," block4f_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block4f_activation[1][0]',     \n","                                                                  'block4f_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block4f_project_conv (Qu  (None, 16, 16, 112)  75491      ['block4f_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block4f_project_bn (BatchNorma  (None, 16, 16, 112)  448        ['quant_block4f_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block4f_drop (Dropout)         (None, 16, 16, 112)  0           ['block4f_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block4f_add (QuantizeWra  (None, 16, 16, 112)  3          ['block4f_drop[1][0]',           \n"," pperV2)                                                          'quant_block4e_add[0][0]']      \n","                                                                                                  \n"," quant_block5a_expand_conv (Qua  (None, 16, 16, 672)  76611      ['quant_block4f_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block5a_expand_bn (BatchNormal  (None, 16, 16, 672)  2688       ['quant_block5a_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block5a_expand_activatio  (None, 16, 16, 672)  3          ['block5a_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block5a_dwconv (Quantize  (None, 16, 16, 672)  16805      ['quant_block5a_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block5a_bn (BatchNormalization  (None, 16, 16, 672)  2688       ['quant_block5a_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5a_activation (Activation  (None, 16, 16, 672)  0          ['block5a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5a_se_squeeze (GlobalAver  (None, 672)         0           ['block5a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5a_se_reshape (Reshape)   (None, 1, 1, 672)    0           ['block5a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5a_se_reduce (Conv2D)     (None, 1, 1, 28)     18844       ['block5a_se_reshape[1][0]']     \n","                                                                                                  \n"," block5a_se_expand (Conv2D)     (None, 1, 1, 672)    19488       ['block5a_se_reduce[1][0]']      \n","                                                                                                  \n"," block5a_se_excite (Multiply)   (None, 16, 16, 672)  0           ['block5a_activation[1][0]',     \n","                                                                  'block5a_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block5a_project_conv (Qu  (None, 16, 16, 160)  107843     ['block5a_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block5a_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quant_block5a_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," quant_block5b_expand_conv (Qua  (None, 16, 16, 960)  155523     ['block5a_project_bn[1][0]']     \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block5b_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quant_block5b_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block5b_expand_activatio  (None, 16, 16, 960)  3          ['block5b_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block5b_dwconv (Quantize  (None, 16, 16, 960)  24005      ['quant_block5b_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block5b_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quant_block5b_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5b_activation (Activation  (None, 16, 16, 960)  0          ['block5b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5b_se_squeeze (GlobalAver  (None, 960)         0           ['block5b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5b_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5b_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5b_se_reshape[1][0]']     \n","                                                                                                  \n"," block5b_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5b_se_reduce[1][0]']      \n","                                                                                                  \n"," block5b_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5b_activation[1][0]',     \n","                                                                  'block5b_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block5b_project_conv (Qu  (None, 16, 16, 160)  153923     ['block5b_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block5b_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quant_block5b_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block5b_drop (Dropout)         (None, 16, 16, 160)  0           ['block5b_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block5b_add (QuantizeWra  (None, 16, 16, 160)  3          ['block5b_drop[1][0]',           \n"," pperV2)                                                          'block5a_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block5c_expand_conv (Qua  (None, 16, 16, 960)  155523     ['quant_block5b_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block5c_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quant_block5c_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block5c_expand_activatio  (None, 16, 16, 960)  3          ['block5c_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block5c_dwconv (Quantize  (None, 16, 16, 960)  24005      ['quant_block5c_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block5c_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quant_block5c_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5c_activation (Activation  (None, 16, 16, 960)  0          ['block5c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5c_se_squeeze (GlobalAver  (None, 960)         0           ['block5c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5c_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5c_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5c_se_reshape[1][0]']     \n","                                                                                                  \n"," block5c_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5c_se_reduce[1][0]']      \n","                                                                                                  \n"," block5c_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5c_activation[1][0]',     \n","                                                                  'block5c_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block5c_project_conv (Qu  (None, 16, 16, 160)  153923     ['block5c_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block5c_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quant_block5c_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block5c_drop (Dropout)         (None, 16, 16, 160)  0           ['block5c_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block5c_add (QuantizeWra  (None, 16, 16, 160)  3          ['block5c_drop[1][0]',           \n"," pperV2)                                                          'quant_block5b_add[0][0]']      \n","                                                                                                  \n"," quant_block5d_expand_conv (Qua  (None, 16, 16, 960)  155523     ['quant_block5c_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block5d_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quant_block5d_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block5d_expand_activatio  (None, 16, 16, 960)  3          ['block5d_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block5d_dwconv (Quantize  (None, 16, 16, 960)  24005      ['quant_block5d_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block5d_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quant_block5d_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5d_activation (Activation  (None, 16, 16, 960)  0          ['block5d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5d_se_squeeze (GlobalAver  (None, 960)         0           ['block5d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5d_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5d_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5d_se_reshape[1][0]']     \n","                                                                                                  \n"," block5d_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5d_se_reduce[1][0]']      \n","                                                                                                  \n"," block5d_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5d_activation[1][0]',     \n","                                                                  'block5d_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block5d_project_conv (Qu  (None, 16, 16, 160)  153923     ['block5d_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block5d_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quant_block5d_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block5d_drop (Dropout)         (None, 16, 16, 160)  0           ['block5d_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block5d_add (QuantizeWra  (None, 16, 16, 160)  3          ['block5d_drop[1][0]',           \n"," pperV2)                                                          'quant_block5c_add[0][0]']      \n","                                                                                                  \n"," quant_block5e_expand_conv (Qua  (None, 16, 16, 960)  155523     ['quant_block5d_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block5e_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quant_block5e_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block5e_expand_activatio  (None, 16, 16, 960)  3          ['block5e_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block5e_dwconv (Quantize  (None, 16, 16, 960)  24005      ['quant_block5e_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block5e_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quant_block5e_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5e_activation (Activation  (None, 16, 16, 960)  0          ['block5e_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5e_se_squeeze (GlobalAver  (None, 960)         0           ['block5e_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5e_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5e_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5e_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5e_se_reshape[1][0]']     \n","                                                                                                  \n"," block5e_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5e_se_reduce[1][0]']      \n","                                                                                                  \n"," block5e_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5e_activation[1][0]',     \n","                                                                  'block5e_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block5e_project_conv (Qu  (None, 16, 16, 160)  153923     ['block5e_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block5e_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quant_block5e_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block5e_drop (Dropout)         (None, 16, 16, 160)  0           ['block5e_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block5e_add (QuantizeWra  (None, 16, 16, 160)  3          ['block5e_drop[1][0]',           \n"," pperV2)                                                          'quant_block5d_add[0][0]']      \n","                                                                                                  \n"," quant_block5f_expand_conv (Qua  (None, 16, 16, 960)  155523     ['quant_block5e_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block5f_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quant_block5f_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block5f_expand_activatio  (None, 16, 16, 960)  3          ['block5f_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block5f_dwconv (Quantize  (None, 16, 16, 960)  24005      ['quant_block5f_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block5f_bn (BatchNormalization  (None, 16, 16, 960)  3840       ['quant_block5f_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block5f_activation (Activation  (None, 16, 16, 960)  0          ['block5f_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block5f_se_squeeze (GlobalAver  (None, 960)         0           ['block5f_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block5f_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5f_se_squeeze[1][0]']     \n","                                                                                                  \n"," block5f_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5f_se_reshape[1][0]']     \n","                                                                                                  \n"," block5f_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5f_se_reduce[1][0]']      \n","                                                                                                  \n"," block5f_se_excite (Multiply)   (None, 16, 16, 960)  0           ['block5f_activation[1][0]',     \n","                                                                  'block5f_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block5f_project_conv (Qu  (None, 16, 16, 160)  153923     ['block5f_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block5f_project_bn (BatchNorma  (None, 16, 16, 160)  640        ['quant_block5f_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block5f_drop (Dropout)         (None, 16, 16, 160)  0           ['block5f_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block5f_add (QuantizeWra  (None, 16, 16, 160)  3          ['block5f_drop[1][0]',           \n"," pperV2)                                                          'quant_block5e_add[0][0]']      \n","                                                                                                  \n"," quant_block6a_expand_conv (Qua  (None, 16, 16, 960)  155523     ['quant_block5f_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block6a_expand_bn (BatchNormal  (None, 16, 16, 960)  3840       ['quant_block6a_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block6a_expand_activatio  (None, 16, 16, 960)  3          ['block6a_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block6a_dwconv_pad (Quan  (None, 19, 19, 960)  1          ['quant_block6a_expand_activation\n"," tizeWrapperV2)                                                  [0][0]']                         \n","                                                                                                  \n"," quant_block6a_dwconv (Quantize  (None, 8, 8, 960)   24005       ['quant_block6a_dwconv_pad[0][0]'\n"," WrapperV2)                                                      ]                                \n","                                                                                                  \n"," block6a_bn (BatchNormalization  (None, 8, 8, 960)   3840        ['quant_block6a_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6a_activation (Activation  (None, 8, 8, 960)   0           ['block6a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6a_se_squeeze (GlobalAver  (None, 960)         0           ['block6a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6a_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block6a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6a_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block6a_se_reshape[1][0]']     \n","                                                                                                  \n"," block6a_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block6a_se_reduce[1][0]']      \n","                                                                                                  \n"," block6a_se_excite (Multiply)   (None, 8, 8, 960)    0           ['block6a_activation[1][0]',     \n","                                                                  'block6a_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block6a_project_conv (Qu  (None, 8, 8, 272)   261667      ['block6a_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block6a_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quant_block6a_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," quant_block6b_expand_conv (Qua  (None, 8, 8, 1632)  447171      ['block6a_project_bn[1][0]']     \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block6b_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quant_block6b_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block6b_expand_activatio  (None, 8, 8, 1632)  3           ['block6b_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block6b_dwconv (Quantize  (None, 8, 8, 1632)  40805       ['quant_block6b_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block6b_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quant_block6b_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6b_activation (Activation  (None, 8, 8, 1632)  0           ['block6b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6b_se_squeeze (GlobalAver  (None, 1632)        0           ['block6b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6b_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6b_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6b_se_reshape[1][0]']     \n","                                                                                                  \n"," block6b_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6b_se_reduce[1][0]']      \n","                                                                                                  \n"," block6b_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6b_activation[1][0]',     \n","                                                                  'block6b_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block6b_project_conv (Qu  (None, 8, 8, 272)   444451      ['block6b_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block6b_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quant_block6b_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block6b_drop (Dropout)         (None, 8, 8, 272)    0           ['block6b_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block6b_add (QuantizeWra  (None, 8, 8, 272)   3           ['block6b_drop[1][0]',           \n"," pperV2)                                                          'block6a_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block6c_expand_conv (Qua  (None, 8, 8, 1632)  447171      ['quant_block6b_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block6c_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quant_block6c_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block6c_expand_activatio  (None, 8, 8, 1632)  3           ['block6c_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block6c_dwconv (Quantize  (None, 8, 8, 1632)  40805       ['quant_block6c_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block6c_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quant_block6c_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6c_activation (Activation  (None, 8, 8, 1632)  0           ['block6c_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6c_se_squeeze (GlobalAver  (None, 1632)        0           ['block6c_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6c_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6c_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6c_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6c_se_reshape[1][0]']     \n","                                                                                                  \n"," block6c_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6c_se_reduce[1][0]']      \n","                                                                                                  \n"," block6c_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6c_activation[1][0]',     \n","                                                                  'block6c_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block6c_project_conv (Qu  (None, 8, 8, 272)   444451      ['block6c_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block6c_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quant_block6c_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block6c_drop (Dropout)         (None, 8, 8, 272)    0           ['block6c_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block6c_add (QuantizeWra  (None, 8, 8, 272)   3           ['block6c_drop[1][0]',           \n"," pperV2)                                                          'quant_block6b_add[0][0]']      \n","                                                                                                  \n"," quant_block6d_expand_conv (Qua  (None, 8, 8, 1632)  447171      ['quant_block6c_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block6d_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quant_block6d_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block6d_expand_activatio  (None, 8, 8, 1632)  3           ['block6d_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block6d_dwconv (Quantize  (None, 8, 8, 1632)  40805       ['quant_block6d_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block6d_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quant_block6d_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6d_activation (Activation  (None, 8, 8, 1632)  0           ['block6d_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6d_se_squeeze (GlobalAver  (None, 1632)        0           ['block6d_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6d_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6d_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6d_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6d_se_reshape[1][0]']     \n","                                                                                                  \n"," block6d_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6d_se_reduce[1][0]']      \n","                                                                                                  \n"," block6d_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6d_activation[1][0]',     \n","                                                                  'block6d_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block6d_project_conv (Qu  (None, 8, 8, 272)   444451      ['block6d_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block6d_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quant_block6d_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block6d_drop (Dropout)         (None, 8, 8, 272)    0           ['block6d_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block6d_add (QuantizeWra  (None, 8, 8, 272)   3           ['block6d_drop[1][0]',           \n"," pperV2)                                                          'quant_block6c_add[0][0]']      \n","                                                                                                  \n"," quant_block6e_expand_conv (Qua  (None, 8, 8, 1632)  447171      ['quant_block6d_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block6e_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quant_block6e_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block6e_expand_activatio  (None, 8, 8, 1632)  3           ['block6e_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block6e_dwconv (Quantize  (None, 8, 8, 1632)  40805       ['quant_block6e_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block6e_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quant_block6e_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6e_activation (Activation  (None, 8, 8, 1632)  0           ['block6e_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6e_se_squeeze (GlobalAver  (None, 1632)        0           ['block6e_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6e_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6e_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6e_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6e_se_reshape[1][0]']     \n","                                                                                                  \n"," block6e_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6e_se_reduce[1][0]']      \n","                                                                                                  \n"," block6e_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6e_activation[1][0]',     \n","                                                                  'block6e_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block6e_project_conv (Qu  (None, 8, 8, 272)   444451      ['block6e_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block6e_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quant_block6e_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block6e_drop (Dropout)         (None, 8, 8, 272)    0           ['block6e_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block6e_add (QuantizeWra  (None, 8, 8, 272)   3           ['block6e_drop[1][0]',           \n"," pperV2)                                                          'quant_block6d_add[0][0]']      \n","                                                                                                  \n"," quant_block6f_expand_conv (Qua  (None, 8, 8, 1632)  447171      ['quant_block6e_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block6f_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quant_block6f_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block6f_expand_activatio  (None, 8, 8, 1632)  3           ['block6f_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block6f_dwconv (Quantize  (None, 8, 8, 1632)  40805       ['quant_block6f_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block6f_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quant_block6f_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6f_activation (Activation  (None, 8, 8, 1632)  0           ['block6f_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6f_se_squeeze (GlobalAver  (None, 1632)        0           ['block6f_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6f_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6f_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6f_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6f_se_reshape[1][0]']     \n","                                                                                                  \n"," block6f_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6f_se_reduce[1][0]']      \n","                                                                                                  \n"," block6f_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6f_activation[1][0]',     \n","                                                                  'block6f_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block6f_project_conv (Qu  (None, 8, 8, 272)   444451      ['block6f_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block6f_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quant_block6f_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block6f_drop (Dropout)         (None, 8, 8, 272)    0           ['block6f_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block6f_add (QuantizeWra  (None, 8, 8, 272)   3           ['block6f_drop[1][0]',           \n"," pperV2)                                                          'quant_block6e_add[0][0]']      \n","                                                                                                  \n"," quant_block6g_expand_conv (Qua  (None, 8, 8, 1632)  447171      ['quant_block6f_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block6g_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quant_block6g_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block6g_expand_activatio  (None, 8, 8, 1632)  3           ['block6g_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block6g_dwconv (Quantize  (None, 8, 8, 1632)  40805       ['quant_block6g_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block6g_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quant_block6g_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6g_activation (Activation  (None, 8, 8, 1632)  0           ['block6g_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6g_se_squeeze (GlobalAver  (None, 1632)        0           ['block6g_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6g_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6g_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6g_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6g_se_reshape[1][0]']     \n","                                                                                                  \n"," block6g_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6g_se_reduce[1][0]']      \n","                                                                                                  \n"," block6g_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6g_activation[1][0]',     \n","                                                                  'block6g_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block6g_project_conv (Qu  (None, 8, 8, 272)   444451      ['block6g_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block6g_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quant_block6g_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block6g_drop (Dropout)         (None, 8, 8, 272)    0           ['block6g_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block6g_add (QuantizeWra  (None, 8, 8, 272)   3           ['block6g_drop[1][0]',           \n"," pperV2)                                                          'quant_block6f_add[0][0]']      \n","                                                                                                  \n"," quant_block6h_expand_conv (Qua  (None, 8, 8, 1632)  447171      ['quant_block6g_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block6h_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quant_block6h_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block6h_expand_activatio  (None, 8, 8, 1632)  3           ['block6h_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block6h_dwconv (Quantize  (None, 8, 8, 1632)  40805       ['quant_block6h_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block6h_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quant_block6h_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block6h_activation (Activation  (None, 8, 8, 1632)  0           ['block6h_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block6h_se_squeeze (GlobalAver  (None, 1632)        0           ['block6h_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block6h_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block6h_se_squeeze[1][0]']     \n","                                                                                                  \n"," block6h_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block6h_se_reshape[1][0]']     \n","                                                                                                  \n"," block6h_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block6h_se_reduce[1][0]']      \n","                                                                                                  \n"," block6h_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block6h_activation[1][0]',     \n","                                                                  'block6h_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block6h_project_conv (Qu  (None, 8, 8, 272)   444451      ['block6h_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block6h_project_bn (BatchNorma  (None, 8, 8, 272)   1088        ['quant_block6h_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block6h_drop (Dropout)         (None, 8, 8, 272)    0           ['block6h_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block6h_add (QuantizeWra  (None, 8, 8, 272)   3           ['block6h_drop[1][0]',           \n"," pperV2)                                                          'quant_block6g_add[0][0]']      \n","                                                                                                  \n"," quant_block7a_expand_conv (Qua  (None, 8, 8, 1632)  447171      ['quant_block6h_add[0][0]']      \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block7a_expand_bn (BatchNormal  (None, 8, 8, 1632)  6528        ['quant_block7a_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block7a_expand_activatio  (None, 8, 8, 1632)  3           ['block7a_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block7a_dwconv (Quantize  (None, 8, 8, 1632)  14693       ['quant_block7a_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block7a_bn (BatchNormalization  (None, 8, 8, 1632)  6528        ['quant_block7a_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block7a_activation (Activation  (None, 8, 8, 1632)  0           ['block7a_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7a_se_squeeze (GlobalAver  (None, 1632)        0           ['block7a_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7a_se_reshape (Reshape)   (None, 1, 1, 1632)   0           ['block7a_se_squeeze[1][0]']     \n","                                                                                                  \n"," block7a_se_reduce (Conv2D)     (None, 1, 1, 68)     111044      ['block7a_se_reshape[1][0]']     \n","                                                                                                  \n"," block7a_se_expand (Conv2D)     (None, 1, 1, 1632)   112608      ['block7a_se_reduce[1][0]']      \n","                                                                                                  \n"," block7a_se_excite (Multiply)   (None, 8, 8, 1632)   0           ['block7a_activation[1][0]',     \n","                                                                  'block7a_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block7a_project_conv (Qu  (None, 8, 8, 448)   732035      ['block7a_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block7a_project_bn (BatchNorma  (None, 8, 8, 448)   1792        ['quant_block7a_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," quant_block7b_expand_conv (Qua  (None, 8, 8, 2688)  1209603     ['block7a_project_bn[1][0]']     \n"," ntizeWrapperV2)                                                                                  \n","                                                                                                  \n"," block7b_expand_bn (BatchNormal  (None, 8, 8, 2688)  10752       ['quant_block7b_expand_conv[0][0]\n"," ization)                                                        ']                               \n","                                                                                                  \n"," quant_block7b_expand_activatio  (None, 8, 8, 2688)  3           ['block7b_expand_bn[1][0]']      \n"," n (QuantizeWrapperV2)                                                                            \n","                                                                                                  \n"," quant_block7b_dwconv (Quantize  (None, 8, 8, 2688)  24197       ['quant_block7b_expand_activation\n"," WrapperV2)                                                      [0][0]']                         \n","                                                                                                  \n"," block7b_bn (BatchNormalization  (None, 8, 8, 2688)  10752       ['quant_block7b_dwconv[0][0]']   \n"," )                                                                                                \n","                                                                                                  \n"," block7b_activation (Activation  (None, 8, 8, 2688)  0           ['block7b_bn[1][0]']             \n"," )                                                                                                \n","                                                                                                  \n"," block7b_se_squeeze (GlobalAver  (None, 2688)        0           ['block7b_activation[1][0]']     \n"," agePooling2D)                                                                                    \n","                                                                                                  \n"," block7b_se_reshape (Reshape)   (None, 1, 1, 2688)   0           ['block7b_se_squeeze[1][0]']     \n","                                                                                                  \n"," block7b_se_reduce (Conv2D)     (None, 1, 1, 112)    301168      ['block7b_se_reshape[1][0]']     \n","                                                                                                  \n"," block7b_se_expand (Conv2D)     (None, 1, 1, 2688)   303744      ['block7b_se_reduce[1][0]']      \n","                                                                                                  \n"," block7b_se_excite (Multiply)   (None, 8, 8, 2688)   0           ['block7b_activation[1][0]',     \n","                                                                  'block7b_se_expand[1][0]']      \n","                                                                                                  \n"," quant_block7b_project_conv (Qu  (None, 8, 8, 448)   1205123     ['block7b_se_excite[1][0]']      \n"," antizeWrapperV2)                                                                                 \n","                                                                                                  \n"," block7b_project_bn (BatchNorma  (None, 8, 8, 448)   1792        ['quant_block7b_project_conv[0][0\n"," lization)                                                       ]']                              \n","                                                                                                  \n"," block7b_drop (Dropout)         (None, 8, 8, 448)    0           ['block7b_project_bn[1][0]']     \n","                                                                                                  \n"," quant_block7b_add (QuantizeWra  (None, 8, 8, 448)   3           ['block7b_drop[1][0]',           \n"," pperV2)                                                          'block7a_project_bn[1][0]']     \n","                                                                                                  \n"," quant_top_conv (QuantizeWrappe  (None, 8, 8, 1792)  806403      ['quant_block7b_add[0][0]']      \n"," rV2)                                                                                             \n","                                                                                                  \n"," top_bn (BatchNormalization)    (None, 8, 8, 1792)   7168        ['quant_top_conv[0][0]']         \n","                                                                                                  \n"," top_activation (Activation)    (None, 8, 8, 1792)   0           ['top_bn[1][0]']                 \n","                                                                                                  \n"," global_average_pooling2d_1 (Gl  (None, 1792)        0           ['top_activation[1][0]']         \n"," obalAveragePooling2D)                                                                            \n","                                                                                                  \n"," dense_3 (Dense)                (None, 1024)         1836032     ['global_average_pooling2d_1[1][0\n","                                                                 ]']                              \n","                                                                                                  \n"," batch_normalization_1 (BatchNo  (None, 1024)        4096        ['dense_3[1][0]']                \n"," rmalization)                                                                                     \n","                                                                                                  \n"," dense_4 (Dense)                (None, 128)          131200      ['batch_normalization_1[1][0]']  \n","                                                                                                  \n"," dense_5 (Dense)                (None, 3)            387         ['dense_4[1][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 19,715,535\n","Trainable params: 16,246,635\n","Non-trainable params: 3,468,900\n","__________________________________________________________________________________________________\n"]}],"source":["quant_aware_model = tfmot.quantization.keras.quantize_apply(quant_aware_eff)\n","quant_aware_model.summary()"]},{"cell_type":"markdown","metadata":{"id":"tPOOkLWmYyKR"},"source":["## Post Training Quantization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YD4JhnozxiZ8"},"outputs":[],"source":["pretrained_model.load_weights(\"/content/drive/MyDrive/Bang/eff_keras.h5\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":56238,"status":"ok","timestamp":1653666977965,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Fv7e_Yabxo2s","outputId":"b3a53152-320b-4577-ba97-0643d3fe204a"},"outputs":[{"name":"stdout","output_type":"stream","text":["72/72 [==============================] - 202s 3s/step - loss: 0.6804 - accuracy: 0.8406 - top_k_accuracy: 0.9565\n"]},{"data":{"text/plain":["[0.6804420351982117, 0.8406496644020081, 0.9565408229827881]"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["pretrained_model.evaluate(validation_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Q4c9lTwJg4Ex"},"outputs":[],"source":["def representative_data_gen():\n","  for input_value,j in training_dataset.take(20):\n","    yield [input_value]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jQt58BQgdzjO"},"outputs":[],"source":["converter = tf.lite.TFLiteConverter.from_keras_model(pretrained_model)\n","converter.optimizations = [tf.lite.Optimize.DEFAULT]\n","converter.inference_input_type = tf.uint8\n","converter.inference_output_type = tf.uint8\n","converter.representative_dataset = representative_data_gen"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":835964,"status":"ok","timestamp":1653672686421,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"R08XLsxHdzmM","outputId":"5d1db673-44d7-48b5-9383-049e6579ea77"},"outputs":[{"name":"stdout","output_type":"stream","text":["INFO:tensorflow:Assets written to: /tmp/tmpvxl2_5_5/assets\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow/lite/python/convert.py:746: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n","  warnings.warn(\"Statistics for quantized inputs were expected, but not \"\n","WARNING:absl:Buffer deduplication procedure will be skipped when flatbuffer library is not properly loaded\n"]}],"source":["tflite_model = converter.convert() "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":685,"status":"ok","timestamp":1653672811288,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ysuhwR-Ddzra","outputId":"3e066f43-2813-4011-af07-319814c7acfd"},"outputs":[{"data":{"text/plain":["22148664"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["import pathlib\n","\n","tflite_models_dir = pathlib.Path(\"/content/quantized_models/\")\n","tflite_models_dir.mkdir(exist_ok=True, parents=True)\n","\n","tflite_model_file = tflite_models_dir/\"eff_model.tflite\"\n","tflite_model_file.write_bytes(tflite_model)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"idBvkEAVdzv0"},"outputs":[],"source":["pretrained_model.save(\"eff_model.h5\")"]},{"cell_type":"markdown","metadata":{"id":"YRur1ewVlctZ"},"source":["### TFLIte Runtime"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4145,"status":"ok","timestamp":1653674442981,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"u0V3u2dmdzyU","outputId":"61988639-ad17-46d1-fa13-113f4248f850"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://google-coral.github.io/py-repo/\n","Collecting tflite_runtime\n","  Downloading tflite_runtime-2.8.0-cp37-cp37m-manylinux2014_x86_64.whl (2.2 MB)\n","\u001b[K     |████████████████████████████████| 2.2 MB 4.4 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.19.2 in /usr/local/lib/python3.7/dist-packages (from tflite_runtime) (1.21.6)\n","Installing collected packages: tflite-runtime\n","Successfully installed tflite-runtime-2.8.0\n"]}],"source":["!pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"O-W-V-2Wdz5_"},"outputs":[],"source":["import tflite_runtime as tflite\n","import numpy as np\n","import cv2"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BOxrWw-mlmgz"},"outputs":[],"source":["test_image = cv2.imread(\"/content/dataset/Emotions Dataset/Emotions Dataset/train/happy/148266.jpg\")\n","test_image = cv2.resize(test_image, (CONFIGURATION[\"IM_SIZE\"] ,CONFIGURATION[\"IM_SIZE\"]))\n","test_image = np.expand_dims(test_image, axis = 0)\n","\n","#print(CONFIGURATION['CLASS_NAMES'][tf.argmax(pretrained_model(im), axis = -1).numpy()[0]])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZgA0h6JqlmjR"},"outputs":[],"source":["interpreter = tflite.Interpreter(model_path=\"/content/drive/MyDrive/Bang/eff_model.tflite\")\n","interpreter.allocate_tensors()\n","\n","input_details = interpreter.get_input_details()[0]\n","output_details = interpreter.get_output_details()[0]\n","# test_image = im.numpy().astype(input_details[\"dtype\"])\n","\n","interpreter.set_tensor(input_details[\"index\"], test_image)\n","interpreter.invoke()\n","\n","output = interpreter.get_tensor(output_details[\"index\"])[0]\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":364,"status":"ok","timestamp":1653674172553,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"CmNvZJ5Zlml3","outputId":"fb47ba25-3460-4643-9e27-d17388ccba8d"},"outputs":[{"name":"stdout","output_type":"stream","text":["happy\n"]}],"source":["print(CONFIGURATION['CLASS_NAMES'][np.argmax(output)])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lSJLQ0z1lmqN"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"Rtdd0-evqrWQ"},"source":["### Accuracy of Quantized Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LQEJ84o8dz8b"},"outputs":[],"source":["def accuracy(model_path):\n","  total, correct = 0,0\n","  interpreter = tf.lite.Interpreter(model_path=model_path)\n","  interpreter.allocate_tensors()\n","\n","  input_details = interpreter.get_input_details()[0]\n","  output_details = interpreter.get_output_details()[0]\n","\n","\n","  for im, label in validation_dataset:\n","        \n","    test_image = im.numpy().astype(input_details[\"dtype\"])\n","\n","    interpreter.set_tensor(input_details[\"index\"], test_image)\n","    interpreter.invoke()\n","    output = interpreter.get_tensor(output_details[\"index\"])[0]\n","\n","    if(int(np.argmax(output)) == int(np.argmax(label, axis = -1)[0])):\n","      correct += 1\n","\n","    total += 1\n","  return correct/total"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":562340,"status":"ok","timestamp":1653675136813,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"mcYn2sgrquvv","outputId":"41b03f83-7722-4bf1-e837-859c4ff80b7f"},"outputs":[{"data":{"text/plain":["0.82"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["accuracy(\"/content/drive/MyDrive/Bang/eff_model.tflite\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1i1lWTIRquyZ"},"outputs":[],"source":["84%"]},{"cell_type":"markdown","metadata":{"id":"mWJKBMHSr1Tk"},"source":["# Pruning"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6H8PPF4ru5bn"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tMmNQonIu5eJ"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vSYQPPU7u5gk"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GmUxMro1u5i7"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aZjeIEkru5lY"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oGlwAVHCu5qS"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jTjSyrF3u5uB"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":423,"status":"ok","timestamp":1653611491088,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"yNIHANLnt61U","outputId":"92ef50b1-83f7-4d4d-bc87-e6e0d35fcecf"},"outputs":[{"name":"stdout","output_type":"stream","text":["sequential_1\n","conv2d\n","batch_normalization_1\n","max_pooling2d\n","dropout_37\n","conv2d_1\n","batch_normalization_2\n","max_pooling2d_1\n","flatten\n","dense_4\n","batch_normalization_3\n","dropout_38\n","dense_5\n","batch_normalization_4\n","dense_6\n","Model: \"sequential_3\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," sequential_1 (Sequential)   (None, 256, 256, 3)       0         \n","                                                                 \n"," prune_low_magnitude_conv2d   (None, 254, 254, 6)      332       \n"," (PruneLowMagnitude)                                             \n","                                                                 \n"," prune_low_magnitude_batch_n  (None, 254, 254, 6)      25        \n"," ormalization_1 (PruneLowMag                                     \n"," nitude)                                                         \n","                                                                 \n"," prune_low_magnitude_max_poo  (None, 127, 127, 6)      1         \n"," ling2d (PruneLowMagnitude)                                      \n","                                                                 \n"," prune_low_magnitude_dropout  (None, 127, 127, 6)      1         \n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/sparsity/keras/pruning_wrapper.py:218: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n","  aggregation=tf.VariableAggregation.MEAN)\n","/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/sparsity/keras/pruning_wrapper.py:225: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n","  aggregation=tf.VariableAggregation.MEAN)\n","/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/sparsity/keras/pruning_wrapper.py:238: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n","  trainable=False)\n"]},{"name":"stdout","output_type":"stream","text":[" _37 (PruneLowMagnitude)                                         \n","                                                                 \n"," prune_low_magnitude_conv2d_  (None, 125, 125, 16)     1746      \n"," 1 (PruneLowMagnitude)                                           \n","                                                                 \n"," prune_low_magnitude_batch_n  (None, 125, 125, 16)     65        \n"," ormalization_2 (PruneLowMag                                     \n"," nitude)                                                         \n","                                                                 \n"," prune_low_magnitude_max_poo  (None, 62, 62, 16)       1         \n"," ling2d_1 (PruneLowMagnitude                                     \n"," )                                                               \n","                                                                 \n"," prune_low_magnitude_flatten  (None, 61504)            1         \n","  (PruneLowMagnitude)                                            \n","                                                                 \n"," prune_low_magnitude_dense_4  (None, 1024)             125961218 \n","  (PruneLowMagnitude)                                            \n","                                                                 \n"," prune_low_magnitude_batch_n  (None, 1024)             4097      \n"," ormalization_3 (PruneLowMag                                     \n"," nitude)                                                         \n","                                                                 \n"," prune_low_magnitude_dropout  (None, 1024)             1         \n"," _38 (PruneLowMagnitude)                                         \n","                                                                 \n"," prune_low_magnitude_dense_5  (None, 128)              262274    \n","  (PruneLowMagnitude)                                            \n","                                                                 \n"," prune_low_magnitude_batch_n  (None, 128)              513       \n"," ormalization_4 (PruneLowMag                                     \n"," nitude)                                                         \n","                                                                 \n"," prune_low_magnitude_dense_6  (None, 3)                773       \n","  (PruneLowMagnitude)                                            \n","                                                                 \n","=================================================================\n","Total params: 126,231,048\n","Trainable params: 63,116,103\n","Non-trainable params: 63,114,945\n","_________________________________________________________________\n"]}],"source":["\n","# Helper function uses `prune_low_magnitude` to make only the \n","# Dense layers train with pruning.\n","def apply_pruning_to_dense(layer):\n","  print(layer.name)\n","  if layer.name != \"rescaling_1\" and layer.name != \"normalization\" and layer.name != \"sequential_1\":\n","    \n","    pruning_params = {\n","              'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(\n","                  initial_sparsity=0.2,\n","                  final_sparsity=0.8, begin_step=0, end_step=2000),\n","              \n","          }\n","\n","    return tfmot.sparsity.keras.prune_low_magnitude(layer, **pruning_params)\n","  return layer\n","\n","# Use `tf.keras.models.clone_model` to apply `apply_pruning_to_dense` \n","# to the layers of the model.\n","model_for_pruning = tf.keras.models.clone_model(\n","    lenet_model,\n","    clone_function=apply_pruning_to_dense,\n",")\n","\n","model_for_pruning.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2356,"status":"error","timestamp":1653608797207,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"W-yXh6wqr24q","outputId":"43bcb764-9199-4c73-94ca-7b802fd3468a"},"outputs":[{"ename":"ValueError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-124-e84c861225a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m }\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mmodel_for_pruning\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprune_low_magnitude\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_for_pruning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpruning_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# `prune_low_magnitude` requires a recompile.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/keras/metrics.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_gauge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMonitorBoolGauge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_FAILURE_LABEL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_gauge\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/keras/metrics.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_gauge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMonitorBoolGauge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_SUCCESS_LABEL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/sparsity/keras/prune.py\u001b[0m in \u001b[0;36mprune_low_magnitude\u001b[0;34m(to_prune, pruning_schedule, block_size, block_pooling_type, pruning_policy, sparsity_m_by_n, **kwargs)\u001b[0m\n\u001b[1;32m    208\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpruning_policy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m       \u001b[0mpruning_policy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_model_supports_pruning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_prune\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_add_pruning_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_prune\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    211\u001b[0m   \u001b[0;32melif\u001b[0m \u001b[0mis_keras_layer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m     \u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/sparsity/keras/prune.py\u001b[0m in \u001b[0;36m_add_pruning_wrapper\u001b[0;34m(layer)\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m       return keras.models.clone_model(\n\u001b[0;32m--> 182\u001b[0;31m           layer, input_tensors=None, clone_function=_add_pruning_wrapper)\n\u001b[0m\u001b[1;32m    183\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpruning_wrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPruneLowMagnitude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/models.py\u001b[0m in \u001b[0;36mclone_model\u001b[0;34m(model, input_tensors, clone_function)\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m       return _clone_functional_model(\n\u001b[0;32m--> 457\u001b[0;31m           model, input_tensors=input_tensors, layer_fn=clone_function)\n\u001b[0m\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/models.py\u001b[0m in \u001b[0;36m_clone_functional_model\u001b[0;34m(model, input_tensors, layer_fn)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m   model_configs, created_layers = _clone_layers_and_model_config(\n\u001b[0;32m--> 194\u001b[0;31m       model, new_input_layers, layer_fn)\n\u001b[0m\u001b[1;32m    195\u001b[0m   \u001b[0;31m# Reconstruct model from the config, using the cloned layers.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m   input_tensors, output_tensors, created_layers = (\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/models.py\u001b[0m in \u001b[0;36m_clone_layers_and_model_config\u001b[0;34m(model, input_layers, layer_fn)\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m   config = functional.get_network_config(\n\u001b[0;32m--> 247\u001b[0;31m       model, serialize_layer_fn=_copy_layer)\n\u001b[0m\u001b[1;32m    248\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mlayer_config\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1412\u001b[0m       \u001b[0mlayer_config\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inbound_nodes'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_inbound_nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/models.py\u001b[0m in \u001b[0;36m_copy_layer\u001b[0;34m(layer)\u001b[0m\n\u001b[1;32m    241\u001b[0m       \u001b[0mcreated_layers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/sparsity/keras/prune.py\u001b[0m in \u001b[0;36m_add_pruning_wrapper\u001b[0;34m(layer)\u001b[0m\n\u001b[1;32m    186\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mpruning_wrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPruneLowMagnitude\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m   params = {\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_model_optimization/python/core/sparsity/keras/pruning_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, layer, pruning_schedule, block_size, block_pooling_type, sparsity_m_by_n, **kwargs)\u001b[0m\n\u001b[1;32m    170\u001b[0m           \u001b[0;34m'should be a `PrunableLayer` instance, or should has a customer '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m           \u001b[0;34m'defined `get_prunable_weights` method. You passed: '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m           '{input}'.format(input=layer.__class__))\n\u001b[0m\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_track_trackable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'layer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Please initialize `Prune` with a supported layer. Layers should either be supported by the PruneRegistry (built-in keras layers) or should be a `PrunableLayer` instance, or should has a customer defined `get_prunable_weights` method. You passed: <class 'keras.layers.preprocessing.image_preprocessing.Rescaling'>"]}],"source":["import tensorflow_model_optimization as tfmot\n","\n","prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude\n","\n","\n","# Define model for pruning.\n","pruning_params = {\n","      'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay(initial_sparsity=0.50,\n","                                                               final_sparsity=0.80,\n","                                                               begin_step=0,\n","                                                               end_step=1000)\n","}\n","\n","model_for_pruning = prune_low_magnitude(model_for_pruning, **pruning_params)\n","\n","# `prune_low_magnitude` requires a recompile.\n","model_for_pruning.compile(optimizer='adam',\n","              loss=loss_function,#tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n","              metrics=['accuracy'])\n","\n","model_for_pruning.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ocV7S1BmxEWL"},"outputs":[],"source":["model_for_pruning.compile(optimizer='adam',\n","              loss=loss_function,#tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n","              metrics=['accuracy'])\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":109762,"status":"ok","timestamp":1653611627596,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"LrrNAw2RsaYz","outputId":"b4b358ef-63c4-40a7-a321-11cb65fd7a84"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/3\n","213/213 [==============================] - 37s 149ms/step - loss: 0.9830 - accuracy: 0.5563 - val_loss: 1.0246 - val_accuracy: 0.5215\n","Epoch 2/3\n","213/213 [==============================] - 31s 142ms/step - loss: 0.8018 - accuracy: 0.6491 - val_loss: 1.0799 - val_accuracy: 0.5430\n","Epoch 3/3\n","213/213 [==============================] - 28s 131ms/step - loss: 0.7204 - accuracy: 0.6869 - val_loss: 1.0230 - val_accuracy: 0.5079\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7f9546795610>"]},"execution_count":149,"metadata":{},"output_type":"execute_result"}],"source":["logdir = tempfile.mkdtemp()\n","\n","callbacks = [\n","  tfmot.sparsity.keras.UpdatePruningStep(),\n","  tfmot.sparsity.keras.PruningSummaries(log_dir=logdir),\n","]\n","\n","model_for_pruning.fit(training_dataset,\n","                      validation_data = validation_dataset,\n","                      epochs=3,\n","                      callbacks=callbacks)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":466,"status":"ok","timestamp":1653611629246,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"AX_T534bycsF","outputId":"29d2d273-3e06-4b60-8b12-b9e551cd1a12"},"outputs":[{"name":"stdout","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]}],"source":["lenet_model.save(\"lenet.h5\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":482,"status":"ok","timestamp":1653611629724,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"d4c19Yg9r267","outputId":"9617b8d0-7635-4c3f-da2e-1959230e0342"},"outputs":[{"name":"stdout","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]}],"source":["model_for_export = tfmot.sparsity.keras.strip_pruning(model_for_pruning)\n","\n","#_, pruned_keras_file = tempfile.mkstemp('eff_pruned.h5')\n","tf.keras.models.save_model(model_for_export, \"lenet_pruned_1.h5\", include_optimizer=False)\n","#print('Saved pruned Keras model to:', pruned_keras_file)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1383,"status":"ok","timestamp":1653609975651,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"qoDgbC7_yfrJ","outputId":"28329f16-1dda-4ec0-b2f2-9291075069db"},"outputs":[{"name":"stdout","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]}],"source":["hf_model.save('hf.h5')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"96AQQv8T1cgt"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D6KNFrZB8rqn"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CFm5mCE-8rs2"},"outputs":[],"source":["def get_gzipped_model_size(file):\n","  # Returns size of gzipped model, in bytes.\n","  import os\n","  import zipfile\n","\n","  with zipfile.ZipFile(\"lenet_keras.zip\", 'w', compression=zipfile.ZIP_DEFLATED) as f:\n","    f.write(file)\n","\n","  return \"good\"#os.path.getsize(zipped_file)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20484,"status":"ok","timestamp":1653612979670,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"dK-G4SIh8rvV","outputId":"e7a22f20-3f39-455e-963e-a53fc2fb0c04"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'good'"]},"execution_count":159,"metadata":{},"output_type":"execute_result"}],"source":["get_gzipped_model_size(\"lenet_pruned_1.h5\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0lzxYG749cP8"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"peXqQuW_vEr-"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"yDD1pOb5LbtX"},"source":["#TFRecords"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_z7cpDJZ_raf"},"outputs":[],"source":["train_dataset = (\n","    training_dataset\n","    .unbatch()\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KPaSOMda_rcw"},"outputs":[],"source":["# val_dataset = (\n","#     validation_dataset\n","#     .unbatch()\n","# )"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":640,"status":"ok","timestamp":1663316396271,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"WejEKkrB_rfV","outputId":"58e8a72e-5253-4e55-9512-8184bbad77be"},"outputs":[{"data":{"text/plain":["<_UnbatchDataset element_spec=(TensorSpec(shape=(256, 256, 3), dtype=tf.float32, name=None), TensorSpec(shape=(3,), dtype=tf.float32, name=None))>"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["train_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n70j9xRE_rhv"},"outputs":[],"source":["# val_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WQwn5FMV_rkP"},"outputs":[],"source":["def create_example(image, label):\n","    \n","  bytes_feature = Feature(\n","      bytes_list=BytesList(value=[image]))\n","\n","  int_feature = Feature(\n","      int64_list=Int64List(value=[label]))\n","\n","  example = Example(\n","      features=Features(feature={\n","          'images': bytes_feature,\n","          'labels': int_feature,\n","      }))\n","  \n","  return example.SerializeToString()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rCLA9D73JFL7"},"outputs":[],"source":["NUM_SHARDS = 10\n","PATH = 'tfrecords/shard_{:02d}.tfrecord'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xHCzLCHlm0WL"},"outputs":[],"source":["def encode_image(image, label):\n","  image = tf.image.convert_image_dtype(image, dtype=tf.uint8)\n","  image = tf.io.encode_jpeg(image)\n","  return image,tf.argmax(label)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dUPviJXho8KB"},"outputs":[],"source":["encoded_dataset = (\n","  train_dataset\n","  .map(encode_image)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0SICB8uWJFOK"},"outputs":[],"source":["for shard_number in range(NUM_SHARDS):\n","\n","  sharded_dataset = (\n","      encoded_dataset\n","      .shard(NUM_SHARDS, shard_number)\n","      .as_numpy_iterator()\n","  )\n","\n","  with tf.io.TFRecordWriter(PATH.format(shard_number)) as file_writer:\n","    for encoded_image, encoded_label in sharded_dataset:\n","      \n","      example = create_example(encoded_image, encoded_label)\n","      file_writer.write(example)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1663316990752,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"vjp3TiEfqGzc","outputId":"0e8f29fd-7c70-4e17-b9c7-65618d824538"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'2.8.2'"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["tf.__version__"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hBPJel4tJFQW"},"outputs":[],"source":["recons_dataset = tf.data.TFRecordDataset(\n","    filenames =[PATH.format(p) for p in range(NUM_SHARDS-2)] )\n","# val_recons_dataset = tf.data.TFRecordDataset(\n","#     filenames =[PATH.format(p) for p in range(NUM_SHARDS-2,NUM_SHARDS)] )\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s7bblv1eJFXO"},"outputs":[],"source":["def parse_tfrecords(example):\n","  \n","    feature_description = {\n","          \"images\": tf.io.FixedLenFeature([], tf.string),\n","          \"labels\": tf.io.FixedLenFeature([], tf.int64),\n","      }\n","    \n","    example = tf.io.parse_single_example(example, feature_description)\n","    example[\"images\"] = tf.image.convert_image_dtype(\n","        tf.io.decode_jpeg(\n","        example[\"images\"], channels = 3), dtype = tf.float32)\n","\n","    return example[\"images\"], example[\"labels\"]\n","    "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qT9w5Ckl_rpR"},"outputs":[],"source":["parsed_dataset = (\n","    recons_dataset\n","    .map(parse_tfrecords)\n","    .batch(CONFIGURATION[\"BATCH_SIZE\"])\n","    .prefetch(tf.data.AUTOTUNE)\n",")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qiXfvL8gaQjI"},"outputs":[],"source":["# val_parsed_dataset = (\n","#     val_recons_dataset\n","#     .map(parse_tfrecords)\n","#     .batch(CONFIGURATION[\"BATCH_SIZE\"])\n","#     .prefetch(tf.data.AUTOTUNE)\n","# )\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1663318009347,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"kaxNamzj_rr_","outputId":"bd29b278-4af2-48d4-b508-3c59f5436916"},"outputs":[{"data":{"text/plain":["<PrefetchDataset element_spec=(TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["parsed_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1663318010944,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"-ndPl1dosLcM","outputId":"166d4d94-0b5b-48c2-9913-d6b27e533146"},"outputs":[{"name":"stdout","output_type":"stream","text":["(<tf.Tensor: shape=(32, 256, 256, 3), dtype=float32, numpy=\n","array([[[[0.41960788, 0.41960788, 0.41960788],\n","         [0.6       , 0.6       , 0.6       ],\n","         [0.07843138, 0.07843138, 0.07843138],\n","         ...,\n","         [0.6627451 , 0.6627451 , 0.6627451 ],\n","         [0.5019608 , 0.5019608 , 0.5019608 ],\n","         [0.97647065, 0.97647065, 0.97647065]],\n","\n","        [[0.97647065, 0.97647065, 0.97647065],\n","         [0.3529412 , 0.3529412 , 0.3529412 ],\n","         [0.16470589, 0.16470589, 0.16470589],\n","         ...,\n","         [0.36862746, 0.36862746, 0.36862746],\n","         [0.6862745 , 0.6862745 , 0.6862745 ],\n","         [0.8117648 , 0.8117648 , 0.8117648 ]],\n","\n","        [[0.4901961 , 0.4901961 , 0.4901961 ],\n","         [0.8745099 , 0.8745099 , 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[0.1764706 , 0.1764706 , 0.1764706 ]],\n","\n","        [[0.94117653, 0.94117653, 0.94117653],\n","         [0.6392157 , 0.6392157 , 0.6392157 ],\n","         [0.8980393 , 0.8980393 , 0.8980393 ],\n","         ...,\n","         [0.34117648, 0.34117648, 0.34117648],\n","         [0.40000004, 0.40000004, 0.40000004],\n","         [0.41960788, 0.41960788, 0.41960788]]]], dtype=float32)>, <tf.Tensor: shape=(32,), dtype=int64, numpy=\n","array([1, 1, 1, 1, 0, 1, 2, 1, 1, 2, 1, 1, 0, 1, 2, 0, 1, 0, 2, 1, 2, 1,\n","       1, 1, 1, 0, 1, 0, 0, 1, 0, 2])>)\n"]}],"source":["for i in parsed_dataset.take(1):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1651992638582,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-180},"id":"pSaccp8I_rt1","outputId":"abe1ac2b-5104-46e6-814e-4ad9e1218066"},"outputs":[{"data":{"text/plain":["<PrefetchDataset element_spec=(TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"]},"execution_count":176,"metadata":{},"output_type":"execute_result"}],"source":["# val_parsed_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":787,"status":"ok","timestamp":1663318033305,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"3NMmny21_rv0","outputId":"db86e689-c32e-4bf1-8c27-ff90e9ee3240"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," sequential_1 (Sequential)   (None, 256, 256, 3)       0         \n","                                                                 \n"," conv2d (Conv2D)             (None, 254, 254, 6)       168       \n","                                                                 \n"," batch_normalization (BatchN  (None, 254, 254, 6)      24        \n"," ormalization)                                                   \n","                                                                 \n"," max_pooling2d (MaxPooling2D  (None, 127, 127, 6)      0         \n"," )                                                               \n","                                                                 \n"," dropout (Dropout)           (None, 127, 127, 6)       0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 125, 125, 16)      880       \n","                                                                 \n"," batch_normalization_1 (Batc  (None, 125, 125, 16)     64        \n"," hNormalization)                                                 \n","                                                                 \n"," max_pooling2d_1 (MaxPooling  (None, 62, 62, 16)       0         \n"," 2D)                                                             \n","                                                                 \n"," flatten (Flatten)           (None, 61504)             0         \n","                                                                 \n"," dense (Dense)               (None, 1024)              62981120  \n","                                                                 \n"," batch_normalization_2 (Batc  (None, 1024)             4096      \n"," hNormalization)                                                 \n","                                                                 \n"," dropout_1 (Dropout)         (None, 1024)              0         \n","                                                                 \n"," dense_1 (Dense)             (None, 128)               131200    \n","                                                                 \n"," batch_normalization_3 (Batc  (None, 128)              512       \n"," hNormalization)                                                 \n","                                                                 \n"," dense_2 (Dense)             (None, 3)                 387       \n","                                                                 \n","=================================================================\n","Total params: 63,118,451\n","Trainable params: 63,116,103\n","Non-trainable params: 2,348\n","_________________________________________________________________\n"]}],"source":["lenet_model = tf.keras.Sequential(\n","    [\n","    InputLayer(input_shape = (None, None, 3), ),\n","    \n","    resize_rescale_layers,\n","    \n","    Conv2D(filters = CONFIGURATION[\"N_FILTERS\"] , kernel_size = CONFIGURATION[\"KERNEL_SIZE\"], strides = CONFIGURATION[\"N_STRIDES\"] , padding='valid',\n","          activation = 'relu',kernel_regularizer = L2(CONFIGURATION[\"REGULARIZATION_RATE\"])),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = CONFIGURATION[\"POOL_SIZE\"], strides= CONFIGURATION[\"N_STRIDES\"]*2),\n","    Dropout(rate = CONFIGURATION[\"DROPOUT_RATE\"] ),\n","\n","    Conv2D(filters = CONFIGURATION[\"N_FILTERS\"]*2 + 4, kernel_size = CONFIGURATION[\"KERNEL_SIZE\"], strides=CONFIGURATION[\"N_STRIDES\"], padding='valid',\n","          activation = 'relu', kernel_regularizer = L2(CONFIGURATION[\"REGULARIZATION_RATE\"])),\n","    BatchNormalization(),\n","    MaxPool2D (pool_size = CONFIGURATION[\"POOL_SIZE\"], strides= CONFIGURATION[\"N_STRIDES\"]*2),\n","\n","    Flatten(),\n","    \n","    Dense( CONFIGURATION[\"N_DENSE_1\"], activation = \"relu\", kernel_regularizer = L2(CONFIGURATION[\"REGULARIZATION_RATE\"])),\n","    BatchNormalization(),\n","    Dropout(rate = CONFIGURATION[\"DROPOUT_RATE\"]),\n","    \n","    Dense( CONFIGURATION['N_DENSE_2'], activation = \"relu\", kernel_regularizer = L2(CONFIGURATION[\"REGULARIZATION_RATE\"])),\n","    BatchNormalization(),\n","\n","    Dense(CONFIGURATION[\"NUM_CLASSES\"], activation = \"softmax\"),\n","\n","])\n","\n","lenet_model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EEPmQlKu_ryB"},"outputs":[],"source":["loss_function = SparseCategoricalCrossentropy()\n","\n","metrics = [SparseCategoricalAccuracy(name = \"accuracy\")]\n","\n","lenet_model.compile(\n","    optimizer = Adam(learning_rate = CONFIGURATION[\"LEARNING_RATE\"]),\n","    loss = loss_function,\n","    metrics = metrics,)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572},"executionInfo":{"elapsed":54274,"status":"error","timestamp":1663318142690,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"w0066LjK_r0Y","outputId":"1318a807-9c5e-45b9-8ee3-b4570800f15b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/20\n","170/170 [==============================] - 24s 54ms/step - loss: 1.3162 - accuracy: 0.3846\n","Epoch 2/20\n","170/170 [==============================] - 9s 54ms/step - loss: 0.2676 - accuracy: 0.9180\n","Epoch 3/20\n","170/170 [==============================] - 9s 54ms/step - loss: 0.0477 - accuracy: 0.9915\n","Epoch 4/20\n","170/170 [==============================] - 9s 54ms/step - loss: 0.0101 - accuracy: 0.9996\n"]},{"ename":"KeyboardInterrupt","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-46-a3262ee21342>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m#validation_data = validation_dataset,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCONFIGURATION\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"N_EPOCHS\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0;31m#class_weight = class_weights,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m#callbacks = [WandbCallback(), LogConfMatrix(), LogResultsTable()]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1371\u001b[0m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterator\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menumerate_epochs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1373\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1374\u001b[0m         \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_stop_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["history = lenet_model.fit(\n","    parsed_dataset,\n","    #validation_data = validation_dataset,\n","    epochs = CONFIGURATION[\"N_EPOCHS\"],\n","    verbose = 1,\n","    #class_weight = class_weights,\n","    #callbacks = [WandbCallback(), LogConfMatrix(), LogResultsTable()]\n","    )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6C1G3Q6Q_r2o"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dWbDLcFy_r5K"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iBIIBLub_r7f"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GZJ2bQSR_r-N"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_NT0TNn5_sAx"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a1VgVj2m_sCg"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ecubv4F9_sEt"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e5v7z9SuLg77"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2DVwwpDhb1Pi"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xvTqq7HhUyV6"},"outputs":[],"source":["def make_example(encoded_image, label):\n","    image_feature = Feature(\n","        bytes_list=BytesList(value=[\n","            encoded_image,\n","        ]),\n","    )\n","    label_feature = Feature(\n","        int64_list=Int64List(value=[\n","            label,\n","        ])\n","    )\n","\n","    features = Features(feature={\n","        'image': image_feature,\n","        'label': label_feature,\n","    })\n","    \n","    example = Example(features=features)\n","    \n","    return example.SerializeToString()\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CScZE6dtoLRc"},"outputs":[],"source":["train_processed = (\n","    training_dataset\n","    .map(process_image)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":823,"status":"ok","timestamp":1651738305999,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-180},"id":"nZfy9YdSoUqx","outputId":"b0e7925f-78fa-432d-d00b-11a76513c78b"},"outputs":[{"name":"stdout","output_type":"stream","text":["tf.Tensor(\n","[[[ 37  37  37]\n","  [ 37  37  37]\n","  [162 162 162]\n","  ...\n","  [ 93  93  93]\n","  [140 140 140]\n","  [143 143 143]]\n","\n"," [[180 180 180]\n","  [  0   0   0]\n","  [219 219 219]\n","  ...\n","  [ 12  12  12]\n","  [ 46  46  46]\n","  [ 63  63  63]]\n","\n"," [[220 220 220]\n","  [253 253 253]\n","  [ 36  36  36]\n","  ...\n","  [ 35  35  35]\n","  [149 149 149]\n","  [ 83  83  83]]\n","\n"," ...\n","\n"," [[161 161 161]\n","  [160 160 160]\n","  [215 215 215]\n","  ...\n","  [106 106 106]\n","  [ 18  18  18]\n","  [ 90  90  90]]\n","\n"," [[207 207 207]\n","  [205 205 205]\n","  [ 26  26  26]\n","  ...\n","  [209 209 209]\n","  [ 44  44  44]\n","  [140 140 140]]\n","\n"," [[  6   6   6]\n","  [124 124 124]\n","  [250 250 250]\n","  ...\n","  [235 235 235]\n","  [ 70  70  70]\n","  [ 21  21  21]]], shape=(256, 256, 3), dtype=uint8)\n","\n"," j tf.Tensor(1, shape=(), dtype=int32)\n"]}],"source":["for i,j in train_processed.take(1):\n","  print(i)\n","  print(\"\\n j\", j)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RuwWeocCV4AK"},"outputs":[],"source":["# filename = 'test.tfrecord'\n","# writer = tf.data.experimental.TFRecordWriter(filename)\n","# writer.write(serialized_features_dataset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rNeCxanddJAx"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iim63o4BdJDL"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":612,"status":"ok","timestamp":1651731702489,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-180},"id":"_SwoJHwG3qCm","outputId":"230b63e5-6fbb-472a-d93e-81563a56841f"},"outputs":[{"name":"stdout","output_type":"stream","text":["tf.Tensor(\n","[[[[ 6.          6.          6.        ]\n","   [ 7.625       7.625       7.625     ]\n","   [ 8.6875      8.6875      8.6875    ]\n","   ...\n","   [40.9375     40.9375     40.9375    ]\n","   [43.         43.         43.        ]\n","   [43.         43.         43.        ]]\n","\n","  [[ 6.8125      6.8125      6.8125    ]\n","   [ 7.7773438   7.7773438   7.7773438 ]\n","   [ 8.128906    8.128906    8.128906  ]\n","   ...\n","   [42.308594   42.308594   42.308594  ]\n","   [43.8125     43.8125     43.8125    ]\n","   [43.8125     43.8125     43.8125    ]]\n","\n","  [[ 7.6875      7.6875      7.6875    ]\n","   [ 8.5         8.5         8.5       ]\n","   [ 8.214844    8.214844    8.214844  ]\n","   ...\n","   [46.535156   46.535156   46.535156  ]\n","   [47.308594   47.308594   47.308594  ]\n","   [46.75       46.75       46.75      ]]\n","\n","  ...\n","\n","  [[ 1.          1.          1.        ]\n","   [ 0.44140625  0.44140625  0.44140625]\n","   [ 0.3125      0.3125      0.3125    ]\n","   ...\n","   [41.839844   41.839844   41.839844  ]\n","   [42.3125     42.3125     42.3125    ]\n","   [42.3125     42.3125     42.3125    ]]\n","\n","  [[ 0.8125      0.8125      0.8125    ]\n","   [ 0.8125      0.8125      0.8125    ]\n","   [ 0.94140625  0.94140625  0.94140625]\n","   ...\n","   [43.058594   43.058594   43.058594  ]\n","   [43.222656   43.222656   43.222656  ]\n","   [43.375      43.375      43.375     ]]\n","\n","  [[ 0.          0.          0.        ]\n","   [ 0.          0.          0.        ]\n","   [ 0.6875      0.6875      0.6875    ]\n","   ...\n","   [43.3125     43.3125     43.3125    ]\n","   [44.1875     44.1875     44.1875    ]\n","   [45.         45.         45.        ]]]], shape=(1, 256, 256, 3), dtype=float32)\n"]}],"source":["for i,j in train_dataset.take(1):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1EaRbn94dJKR"},"outputs":[],"source":["from tensorflow.train import BytesList, FloatList, Int64List\n","from tensorflow.train import Example, Features, Feature\n","\n","def process_image(image, label):\n","    image = tf.image.convert_image_dtype(image, dtype=tf.uint8)\n","    #image = tf.io.encode_jpeg(image)\n","    return image, label\n","\n","ds_train_encoded = (\n","    train_dataset\n","    .unbatch()\n","    .map(process_image)\n",")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1651738336949,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-180},"id":"td26PnO18Q6X","outputId":"5ef3eccf-14fc-4669-9a2d-12d1995aa0b2"},"outputs":[{"name":"stdout","output_type":"stream","text":["(<tf.Tensor: shape=(1, 256, 256, 3), dtype=float32, numpy=\n","array([[[[140.      , 140.      , 140.      ],\n","         [140.      , 140.      , 140.      ],\n","         [140.      , 140.      , 140.      ],\n","         ...,\n","         [ 38.1875  ,  38.1875  ,  38.1875  ],\n","         [ 32.      ,  32.      ,  32.      ],\n","         [ 32.      ,  32.      ,  32.      ]],\n","\n","        [[139.1875  , 139.1875  , 139.1875  ],\n","         [139.1875  , 139.1875  , 139.1875  ],\n","         [139.1875  , 139.1875  , 139.1875  ],\n","         ...,\n","         [ 37.32422 ,  37.32422 ,  37.32422 ],\n","         [ 32.660156,  32.660156,  32.660156],\n","         [ 32.      ,  32.      ,  32.      ]],\n","\n","        [[139.      , 139.      , 139.      ],\n","         [139.      , 139.      , 139.      ],\n","         [139.      , 139.      , 139.      ],\n","         ...,\n","         [ 39.960938,  39.960938,  39.960938],\n","         [ 40.375   ,  40.375   ,  40.375   ],\n","         [ 39.5625  ,  39.5625  ,  39.5625  ]],\n","\n","        ...,\n","\n","        [[ 36.6875  ,  36.6875  ,  36.6875  ],\n","         [ 36.941406,  36.941406,  36.941406],\n","         [ 34.9375  ,  34.9375  ,  34.9375  ],\n","         ...,\n","         [ 15.277344,  15.277344,  15.277344],\n","         [ 14.128906,  14.128906,  14.128906],\n","         [ 13.0625  ,  13.0625  ,  13.0625  ]],\n","\n","        [[ 36.      ,  36.      ,  36.      ],\n","         [ 36.8125  ,  36.8125  ,  36.8125  ],\n","         [ 34.808594,  34.808594,  34.808594],\n","         ...,\n","         [ 14.375   ,  14.375   ,  14.375   ],\n","         [ 12.625   ,  12.625   ,  12.625   ],\n","         [ 11.      ,  11.      ,  11.      ]],\n","\n","        [[ 36.      ,  36.      ,  36.      ],\n","         [ 36.8125  ,  36.8125  ,  36.8125  ],\n","         [ 34.25    ,  34.25    ,  34.25    ],\n","         ...,\n","         [ 14.375   ,  14.375   ,  14.375   ],\n","         [ 12.625   ,  12.625   ,  12.625   ],\n","         [ 11.      ,  11.      ,  11.      ]]]], dtype=float32)>, <tf.Tensor: shape=(1,), dtype=int32, numpy=array([1], dtype=int32)>)\n"]}],"source":["for i in train_dataset.take(1):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":529,"status":"ok","timestamp":1651738217354,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-180},"id":"D6p--dgH434f","outputId":"4ee5f6d3-f689-49f6-c296-21a725fefae1"},"outputs":[{"data":{"text/plain":["<TFRecordDatasetV2 element_spec=TensorSpec(shape=(), dtype=tf.string, name=None)>"]},"execution_count":205,"metadata":{},"output_type":"execute_result"}],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b8689CWo9ZDo"},"outputs":[],"source":["dataset = tf.data.TFRecordDataset(filenames = [\"/content/tfrecordss/shard_00.tfrecord\",\"/content/tfrecordss/shard_01.tfrecord\"])\n","dataset\n","\n","def parse_tfrecord_fn(example):\n","    feature_description = {\n","        \"image\": tf.io.FixedLenFeature([], tf.string),\n","        \"label\": tf.io.FixedLenFeature([], tf.int64),\n","    }\n","    example = tf.io.parse_single_example(example, feature_description)\n","    example[\"image\"] = tf.io.decode_jpeg(example[\"image\"], channels=3)\n","    example[\"label\"] = example[\"label\"]\n","    return example\n","\n","def prepare_sample(sample):\n","  return sample['image'], sample['label']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kBywPb5rP4Gd"},"outputs":[],"source":["def prepare_sample(sample):\n","  return sample['image'], sample['label']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8Ftsqaz8IQFX"},"outputs":[],"source":["new_dataset = (\n","    dataset\n","    .map(parse_tfrecord_fn)\n","    .map(prepare_sample)\n","    .batch(32)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"S6DZs6bMuSld"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["jWFYC4TEJUIZ","dgJR73ZlBHog","UAxh8kasx0C_","9uPF-UuxLnOI","offbwo3oLtkw","ZzEMtSW6Bj-e","QPDIkDLCBxEU","WoUIuhBeBxuL","PcqeUKcOso2J","VXvsxcNHBmrb","-FY0CxZBn6mk","ndQG-N_UoFX3","F8jUDzpGuoRF","d3WiHKq-urUU","4urZ7eIU72PN","mL456x7iRLdX","bi3VIZe6HRsh","kRVq7Fjplcv_","FmB4mUqRKHUh","GFmj8wuTJ6Zn","kqgsVzHwjS9Y","dm0oxOOI6xwE","cx6VV204tCPI","UfxIFPOgNak8","jmrfCgsvgNJ-","yui_ezOW0Hlh","ys34NNwc0kzg","6ToRBGkYVxeD","jjoRveh_aHtT","fvt_KnCKaML6","1mOc0fOg6YaH","Mq38t4PCqcLd","N6tKJ2EAqex4","8mubq_HBEYeo","TU2chDY_MnRK","I3KhYytNAidL","0DEOSSUEBAaJ","HndZBX7GAwTi","tPOOkLWmYyKR","YRur1ewVlctZ","Rtdd0-evqrWQ","mWJKBMHSr1Tk","yDD1pOb5LbtX"],"provenance":[{"file_id":"1ck9EfFJijArBSqbrpWo-s8u9YJT5FgKr","timestamp":1673770023531}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

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FILE: deep learning for computer vision/5-YOLO Object Detection from Scratch by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"O9Jf4tDsfe1g"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import seaborn as sns### visualizations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import datetime\n","import pathlib\n","import io\n","from datetime import datetime\n","import json\n","import xml.etree.ElementTree as ET\n","import os\n","import shutil\n","import cv2\n","import time\n","import random\n","from PIL import Image\n","import albumentations as A\n","import tensorflow_datasets as tfds\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n","                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n","                                     RandomContrast, Rescaling, Resizing, Reshape, LeakyReLU)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n","                                        ModelCheckpoint, ReduceLROnPlateau)\n","from tensorflow.keras.regularizers import L2, L1\n","from tensorflow.keras.initializers import RandomNormal"]},{"cell_type":"markdown","metadata":{"id":"mjIGOKcUikaF"},"source":["# Dataset Download"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":24262,"status":"ok","timestamp":1672551875001,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"VnodE7S2DgJT","outputId":"3849eb06-f70d-49b5-dcf8-983e79ebf04d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading pascal-voc-2012.zip to /content\n"," 99% 3.61G/3.63G [00:22<00:00, 145MB/s]\n","100% 3.63G/3.63G [00:22<00:00, 175MB/s]\n"]}],"source":["!pip install -q kaggle\n","!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d huanghanchina/pascal-voc-2012"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Iu237VY7edR3"},"outputs":[],"source":["val_list=['2007_000027.jpg','2007_000032.jpg','2007_000033.jpg','2007_000039.jpg','2007_000042.jpg','2007_000061.jpg',\n","          '2007_000063.jpg','2007_000068.jpg','2007_000121.jpg','2007_000123.jpg','2007_000129.jpg','2007_000170.jpg',\n","          '2007_000175.jpg','2007_000187.jpg','2007_000241.jpg','2007_000243.jpg','2007_000250.jpg','2007_000256.jpg',\n","          '2007_000272.jpg','2007_000323.jpg','2007_000332.jpg','2007_000333.jpg','2007_000346.jpg','2007_000363.jpg',\n","          '2007_000364.jpg','2007_000392.jpg','2007_000423.jpg','2007_000452.jpg','2007_000464.jpg','2007_000480.jpg',\n","          '2007_000491.jpg','2007_000504.jpg','2007_000515.jpg','2007_000528.jpg','2007_000529.jpg','2007_000549.jpg',\n","          '2007_000559.jpg','2007_000572.jpg','2007_000584.jpg','2007_000629.jpg','2007_000636.jpg','2007_000645.jpg',\n","          '2007_000648.jpg','2007_000661.jpg','2007_000663.jpg','2007_000664.jpg','2007_000676.jpg','2007_000713.jpg',\n","          '2007_000720.jpg','2007_000727.jpg','2007_000733.jpg','2007_000738.jpg','2007_000762.jpg','2007_000768.jpg',\n","          '2007_000783.jpg','2007_000793.jpg','2007_000799.jpg','2007_000804.jpg','2007_000807.jpg','2007_000822.jpg',\n","          '2007_001299.jpg','2007_001311.jpg','2007_001321.jpg','2007_001340.jpg']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":50191,"status":"ok","timestamp":1672551925177,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Ty3MspnrKXab","outputId":"4c2f0b6b-1f08-4a14-fb5e-d7d11936666e"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/voc2012/VOC2012/SegmentationClass/2008_001829.png  \n","  inflating: /content/dataset/voc2012/VOC2012/SegmentationClass/2008_001874.png  \n","  inflating: /content/dataset/voc2012/VOC2012/SegmentationClass/2008_001876.png  \n","  inflating: 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folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"eULXDi3TElxK","outputId":"062171d1-bc9c-46fb-a3ce-74e13b26dc26"},"outputs":[{"output_type":"stream","name":"stdout","text":["7\n"]}],"source":["train_images='/content/dataset/VOC2012/JPEGImages/'\n","train_maps='/content/dataset/VOC2012/Annotations/'\n","\n","val_images='/content/dataset/VOC2012/ValJPEGImages/'\n","val_maps='/content/dataset/VOC2012/ValAnnotations/'\n","\n","classes=['aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable',\n","         'dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']\n","\n","B=2\n","N_CLASSES=len(classes)\n","H,W =224,224\n","SPLIT_SIZE=H//32\n","print(SPLIT_SIZE)\n","N_EPOCHS=135\n","BATCH_SIZE=32"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i_UCA8Wk1Y-w"},"outputs":[],"source":["!mkdir /content/dataset/VOC2012/ValJPEGImages/\n","!mkdir /content/dataset/VOC2012/ValAnnotations/"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FoWS5hUs1XkJ"},"outputs":[],"source":["for name in val_list:\n","  shutil.move(train_maps+name[:-3]+\"xml\", val_maps+name[:-3]+\"xml\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u0xG46Yl9-8U"},"outputs":[],"source":["for name in val_list:\n","  shutil.move(train_images+name, val_images+name)"]},{"cell_type":"markdown","metadata":{"id":"iZSKZSsxioCm"},"source":["# Data Preparation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Fz3rHnO2g8b7"},"outputs":[],"source":["def preprocess_xml(filename):\n","  tree = ET.parse(filename)\n","  root = tree.getroot()\n","  size_tree = root.find('size')\n","  height = float(size_tree.find('height').text)\n","  width = float(size_tree.find('width').text)\n","  bounding_boxes=[]\n","  for object_tree in root.findall('object'):\n","    for bounding_box in object_tree.iter('bndbox'):\n","      xmin = (float(bounding_box.find('xmin').text))\n","      ymin = (float(bounding_box.find('ymin').text))\n","      xmax = (float(bounding_box.find('xmax').text))\n","      ymax = (float(bounding_box.find('ymax').text))\n","      break\n","    class_name = object_tree.find('name').text\n","    class_dict={classes[i]:i for i in range(len(classes))}\n","    bounding_box = [\n","        (xmin+xmax)/(2*width),(ymin+ymax)/(2*height),(xmax-xmin)/width,\n","        (ymax-ymin)/height,class_dict[class_name]]\n","    bounding_boxes.append(bounding_box)\n","  return tf.convert_to_tensor(bounding_boxes)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1672551925697,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"XLv4PTY2g_2b","outputId":"2f43f044-e289-4a8c-aa7c-3df4a9b6b4e4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(4, 5), dtype=float32, numpy=\n","array([[ 0.479     ,  0.4644128 ,  0.542     ,  0.37366548,  0.        ],\n","       [ 0.33      ,  0.37544483,  0.128     ,  0.12455516,  0.        ],\n","       [ 0.408     ,  0.727758  ,  0.036     ,  0.17437722, 14.        ],\n","       [ 0.07      ,  0.7597865 ,  0.036     ,  0.17437722, 14.        ]],\n","      dtype=float32)>"]},"metadata":{},"execution_count":10}],"source":["preprocess_xml(val_maps+\"2007_000032.xml\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sTFXxcoeg__c"},"outputs":[],"source":["def generate_output(bounding_boxes):\n","  output_label=np.zeros((SPLIT_SIZE,SPLIT_SIZE,N_CLASSES+5))\n","  for b in range(len(bounding_boxes)):\n","    grid_x=bounding_boxes[...,b,0]*SPLIT_SIZE\n","    grid_y=bounding_boxes[...,b,1]*SPLIT_SIZE\n","    i=int(grid_x)\n","    j=int(grid_y)\n","\n","    output_label[i,j,0:5]=[1.,grid_x%1,grid_y%1,bounding_boxes[...,b,2],bounding_boxes[...,b,3]]\n","    output_label[i,j,5+int(bounding_boxes[...,b,4])]=1.\n","\n","  return tf.convert_to_tensor(output_label,tf.float32)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1672551925698,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Muqtk2qwhABY","outputId":"4e810e5a-52e2-4beb-f92e-e34bc491cc74"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(25,), dtype=float32, numpy=\n","array([1.        , 0.35299993, 0.25088978, 0.542     , 0.37366548,\n","       1.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ,\n","       0.        , 0.        , 0.        , 0.        , 0.        ],\n","      dtype=float32)>"]},"metadata":{},"execution_count":12}],"source":["generate_output(preprocess_xml(val_maps+\"2007_000032.xml\"),)[3][3]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1672551925698,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"DQjSB7Zn7FlH","outputId":"87f52968-728d-41fa-b11d-6140d9f710e2"},"outputs":[{"output_type":"stream","name":"stdout","text":["17061 17061\n","64 64\n"]}],"source":["im_paths=[]\n","xml_paths=[]\n","\n","val_im_paths=[]\n","val_xml_paths=[]\n","\n","\n","for i in os.listdir(train_maps):\n","  \n","  im_paths.append(train_images+i[:-3]+'jpg')\n","  xml_paths.append(train_maps+i)\n","  \n","for i in os.listdir(val_maps):\n","  \n","  val_im_paths.append(val_images+i[:-3]+'jpg')\n","  val_xml_paths.append(val_maps+i)\n","  \n","print(len(im_paths),len(xml_paths))\n","print(len(val_im_paths),len(val_xml_paths))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-sl3BwUjAwpU"},"outputs":[],"source":["train_dataset=tf.data.Dataset.from_tensor_slices((im_paths,xml_paths))\n","val_dataset=tf.data.Dataset.from_tensor_slices((val_im_paths,val_xml_paths))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1672551925699,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"5CdChkN4AwsM","outputId":"261c3eac-faac-4616-f7ac-edf3083151b4"},"outputs":[{"output_type":"stream","name":"stdout","text":["(<tf.Tensor: shape=(), dtype=string, numpy=b'/content/dataset/VOC2012/ValJPEGImages/2007_000629.jpg'>, <tf.Tensor: shape=(), dtype=string, numpy=b'/content/dataset/VOC2012/ValAnnotations/2007_000629.xml'>)\n"]}],"source":["for i in val_dataset.take(1):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Lrg46DOlOZTH"},"outputs":[],"source":["def get_imbboxes(im_path,xml_path):\n","  img=tf.io.decode_jpeg(tf.io.read_file(im_path))\n","  img=tf.cast(tf.image.resize(img, [H,W]),dtype=tf.float32)\n","\n","  bboxes=tf.numpy_function(func=preprocess_xml, inp=[xml_path], Tout=tf.float32)\n","  return img,bboxes"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EOLIwSITBxmJ"},"outputs":[],"source":["train_dataset=train_dataset.map(get_imbboxes)\n","val_dataset=val_dataset.map(get_imbboxes)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1672551926467,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"6dKTo0JYBxom","outputId":"18bab4b8-862e-47ed-8af7-647dc94813c8"},"outputs":[{"output_type":"stream","name":"stdout","text":["(224, 224, 3) tf.Tensor([[ 0.273      0.3602941  0.542      0.7147059 18.       ]], shape=(1, 5), dtype=float32)\n"]}],"source":["for i,j in train_dataset.skip(2):\n","  print(i.shape,j)\n","  break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1672551926468,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"8Ec-he4DBxq5","outputId":"a3c0d437-195d-4b1b-c892-4e8e13456e8d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":19}],"source":["cv2.imwrite('out_1.jpg',np.array(i))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SMVz42ht7XUv"},"outputs":[],"source":["transforms = A.Compose([\n","    A.Resize(H,W),\n","    A.RandomCrop(\n","         width=np.random.randint(int(0.9*W),W),\n","         height=np.random.randint(int(0.9*H),H), p=0.5),\n","    A.RandomScale(scale_limit=0.1, interpolation=cv2.INTER_LANCZOS4,p=0.5),\n","    A.HorizontalFlip(p=0.5,),\n","    A.Resize(H,W),\n","    \n","], bbox_params=A.BboxParams(format='yolo', ))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uVHsFUUNeZGV"},"outputs":[],"source":["def aug_albument(image,bboxes):\n","  augmented=transforms(image=image,bboxes=bboxes)\n","  return [tf.convert_to_tensor(augmented[\"image\"],dtype=tf.float32),\n","          tf.convert_to_tensor(augmented[\"bboxes\"],dtype=tf.float32)]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R0U_N4rMS1C-"},"outputs":[],"source":["def process_data(image,bboxes):\n","    aug= tf.numpy_function(func=aug_albument, inp=[image,bboxes], Tout=(tf.float32,tf.float32))\n","    return aug[0],aug[1]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZNjL4DAzgpGo"},"outputs":[],"source":["train_dataset=train_dataset.map(process_data)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1672551926470,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"JwVETxojgpJh","outputId":"e56dd164-ffb0-4f5c-d6e6-e71168f51663"},"outputs":[{"output_type":"stream","name":"stdout","text":["(224, 224, 3) tf.Tensor([[ 0.727      0.3602941  0.542      0.7147059 18.       ]], shape=(1, 5), dtype=float32)\n"]}],"source":["for i,j in train_dataset.skip(2):\n","  print(i.shape,j)\n","  break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1672551926470,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"pX4ojxfQgpLy","outputId":"167d87cc-fbab-479c-b7f8-d39a2636eff5"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":25}],"source":["cv2.imwrite('out_2.jpg',np.array(i))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aUsA7y7KxwNi"},"outputs":[],"source":["def preprocess_augment(img,y):\n","  img = tf.image.random_brightness(img, max_delta=50.)\n","  img = tf.image.random_saturation(img, lower=0.5, upper=1.5)\n","  img = tf.image.random_contrast(img, lower=0.5, upper=1.5)\n","  #img = tf.image.random_hue(img, max_delta=0.5 )\n","  img = tf.clip_by_value(img, 0, 255)\n","  labels=tf.numpy_function(func=generate_output, inp=[y], Tout=(tf.float32))\n","  return img,labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D8ZsLcjfgih2"},"outputs":[],"source":["def preprocess(img,y):\n","  img = tf.cast(tf.image.resize(img, size=[H, W]), dtype=tf.float32)\n","\n","  labels=tf.numpy_function(func=generate_output, inp=[y], Tout=(tf.float32))\n","  return img,labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C3ViaB8lgikP"},"outputs":[],"source":["train_dataset=train_dataset.map(preprocess_augment)\n","val_dataset=val_dataset.map(preprocess)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1GptrY4-gimh"},"outputs":[],"source":["train_dataset=(\n","  train_dataset.\n","  batch(BATCH_SIZE).\n","  prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7qEQp4RPgipK"},"outputs":[],"source":["val_dataset=(\n","  val_dataset.\n","  batch(BATCH_SIZE).\n","  prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1672551927073,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"8w54dj2Fgirq","outputId":"e3b466ae-dba2-4413-ded6-5b8499c661a7"},"outputs":[{"output_type":"stream","name":"stdout","text":["(32, 224, 224, 3) tf.Tensor(\n","[[[[0.         0.         0.         ... 0.         0.\n","    0.        ]\n","   [0.         0.         0.         ... 0.         0.\n","    0.        ]\n","   [0.         0.         0.         ... 0.         0.\n","    0.        ]\n","   ...\n","   [0.         0.         0.         ... 0.         0.\n","    0.        ]\n","   [0.         0.         0.         ... 0.         0.\n","    0.        ]\n","   [0.         0.         0.         ... 0.         0.\n","    0.        ]]\n","\n","  [[0.         0.         0.         ... 0.         0.\n","    0.        ]\n","   [0.         0.         0.         ... 0.         0.\n","    0.        ]\n","   [0.         0.         0.         ... 0.         0.\n","    0.        ]\n","   ...\n","   [0.         0.         0.         ... 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   0.\n","    0.        ]]]], shape=(32, 7, 7, 25), dtype=float32)\n"]}],"source":["for i,j in train_dataset.take(1):\n","  print(i.shape,j)\n","  break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1672551927073,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"w7Tol9jkULK5","outputId":"9c26b8b1-e6df-45e0-a8af-93b428245da2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":32}],"source":["cv2.imwrite('out_3.jpg',np.array(i[2]))"]},{"cell_type":"markdown","metadata":{"id":"S1ujUNruGtmi"},"source":["# Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xKwVhqkfi51K"},"outputs":[],"source":["NUM_FILTERS=512\n","OUTPUT_DIM=N_CLASSES+5*B"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2887,"status":"ok","timestamp":1672551929955,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"9QurGIeli9Cp","outputId":"cc0cfc58-fb54-4e36-9732-e5b0bae671b8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb1_notop.h5\n","27018416/27018416 [==============================] - 0s 0us/step\n"]}],"source":["#base_model = tf.keras.applications.resnet50.ResNet50(\n","base_model=tf.keras.applications.efficientnet.EfficientNetB1(\n","    weights='imagenet',\n","    input_shape=(H,W,3),\n","    include_top=False,\n",")\n","base_model.trainable=False"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1155,"status":"ok","timestamp":1672551931105,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"tqrvjReCildh","outputId":"fce61b17-08de-45c1-84ce-9684c2f1464c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," efficientnetb1 (Functional)  (None, 7, 7, 1280)       6575239   \n","                                                                 \n"," conv2d (Conv2D)             (None, 7, 7, 512)         5898752   \n","                                                                 \n"," batch_normalization (BatchN  (None, 7, 7, 512)        2048      \n"," ormalization)                                                   \n","                                                                 \n"," leaky_re_lu (LeakyReLU)     (None, 7, 7, 512)         0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 7, 7, 512)         2359808   \n","                                                                 \n"," batch_normalization_1 (Batc  (None, 7, 7, 512)        2048      \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_1 (LeakyReLU)   (None, 7, 7, 512)         0         \n","                                                                 \n"," conv2d_2 (Conv2D)           (None, 7, 7, 512)         2359808   \n","                                                                 \n"," batch_normalization_2 (Batc  (None, 7, 7, 512)        2048      \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_2 (LeakyReLU)   (None, 7, 7, 512)         0         \n","                                                                 \n"," conv2d_3 (Conv2D)           (None, 7, 7, 512)         2359808   \n","                                                                 \n"," leaky_re_lu_3 (LeakyReLU)   (None, 7, 7, 512)         0         \n","                                                                 \n"," flatten (Flatten)           (None, 25088)             0         \n","                                                                 \n"," dense (Dense)               (None, 512)               12845568  \n","                                                                 \n"," batch_normalization_3 (Batc  (None, 512)              2048      \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 512)               0         \n","                                                                 \n"," dropout (Dropout)           (None, 512)               0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1470)              754110    \n","                                                                 \n"," reshape (Reshape)           (None, 7, 7, 30)          0         \n","                                                                 \n","=================================================================\n","Total params: 33,161,285\n","Trainable params: 26,581,950\n","Non-trainable params: 6,579,335\n","_________________________________________________________________\n"]}],"source":["model=tf.keras.Sequential([    \n","  base_model,\n","  Conv2D(NUM_FILTERS,(3,3), padding = 'same',kernel_initializer='he_normal',),\n","  BatchNormalization(),\n","  LeakyReLU(alpha=0.1),\n","  \n","  Conv2D(NUM_FILTERS,(3,3),padding = 'same',kernel_initializer='he_normal',),\n","  BatchNormalization(),\n","  LeakyReLU(alpha=0.1),\n","  \n","  Conv2D(NUM_FILTERS,(3,3),padding = 'same',kernel_initializer='he_normal',),\n","  BatchNormalization(),\n","  LeakyReLU(alpha=0.1),\n","  \n","  Conv2D(NUM_FILTERS,(3,3),padding = 'same',kernel_initializer='he_normal',),\n","  LeakyReLU(alpha=0.1),\n","\n","  Flatten(),\n","  \n","  Dense(NUM_FILTERS,kernel_initializer='he_normal',),\n","  BatchNormalization(),\n","  LeakyReLU(alpha=0.1),\n","  \n","  Dropout(0.5),\n","  \n","  Dense(SPLIT_SIZE*SPLIT_SIZE*OUTPUT_DIM,activation='sigmoid'),\n","  \n","  Reshape((SPLIT_SIZE,SPLIT_SIZE,OUTPUT_DIM)),\n","])\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"87oWv0sYtRCz"},"outputs":[],"source":["def compute_iou(boxes1, boxes2):\n","    boxes1_t = tf.stack([boxes1[..., 0] - boxes1[..., 2] / 2.0,\n","                         boxes1[..., 1] - boxes1[..., 3] / 2.0,\n","                         boxes1[..., 0] + boxes1[..., 2] / 2.0,\n","                         boxes1[..., 1] + boxes1[..., 3] / 2.0],\n","                        axis=-1)\n","\n","    boxes2_t = tf.stack([boxes2[..., 0] - boxes2[..., 2] / 2.0,\n","                         boxes2[..., 1] - boxes2[..., 3] / 2.0,\n","                         boxes2[..., 0] + boxes2[..., 2] / 2.0,\n","                         boxes2[..., 1] + boxes2[..., 3] / 2.0],\n","                        axis=-1)\n","    lu = tf.maximum(boxes1_t[..., :2], boxes2_t[..., :2])\n","    rd = tf.minimum(boxes1_t[..., 2:], boxes2_t[..., 2:])\n","\n","    intersection = tf.maximum(0.0, rd - lu)\n","    inter_square = intersection[..., 0] * intersection[..., 1]\n","\n","    square1 = boxes1[..., 2] * boxes1[..., 3]\n","    square2 = boxes2[..., 2] * boxes2[..., 3]\n","\n","    union_square = tf.maximum(square1 + square2 - inter_square, 1e-10)\n","    return tf.clip_by_value(inter_square / union_square, 0.0, 1.0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-1L5Y0JStrtk"},"outputs":[],"source":["def difference(x,y):\n","  return tf.reduce_sum(tf.square(y-x))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZJRN0DfZWbx0"},"outputs":[],"source":["def yolo_loss(y_true, y_pred):\n","  target = y_true[...,0]\n","\n","  ###################### OBject Loss\n","  y_pred_extract = tf.gather_nd(y_pred, tf.where(target[:]==1))\n","  y_target_extract = tf.gather_nd(y_true, tf.where(target[:]==1))\n","  \n","  rescaler = tf.where(target[:]==1)*SPLIT_SIZE\n","  upscaler_1 = tf.concat([rescaler[:,1:],tf.zeros([len(rescaler),2], dtype=tf.int64)],axis=-1)\n","  \n","  target_upscaler_2 = tf.repeat([[float(SPLIT_SIZE),float(SPLIT_SIZE),H,W]],\n","                       repeats=[len(rescaler)], axis=0)*tf.cast(y_target_extract[...,1:5], dtype = tf.float32)\n","  pred_1_upscaler_2 = tf.repeat([[float(SPLIT_SIZE),float(SPLIT_SIZE),H,W]],\n","                      repeats=[len(rescaler)], axis=0)*tf.cast(y_pred_extract[...,1:5], dtype = tf.float32)\n","  pred_2_upscaler_2 = tf.repeat([[float(SPLIT_SIZE),float(SPLIT_SIZE),H,W]],\n","                      repeats=[len(rescaler)], axis=0)*tf.cast(y_pred_extract[...,6:10], dtype = tf.float32)\n","  \n","  target_orig = tf.cast(upscaler_1, dtype = tf.float32)+target_upscaler_2\n","  pred_1_orig = tf.cast(upscaler_1, dtype = tf.float32)+pred_1_upscaler_2\n","  pred_2_orig = tf.cast(upscaler_1, dtype = tf.float32)+pred_2_upscaler_2\n","  \n","  mask =tf.cast(tf.math.greater(compute_iou(target_orig,pred_2_orig),\n","                                         compute_iou(target_orig,pred_1_orig)),dtype=tf.int32)\n","  \n","  y_pred_joined=tf.transpose(tf.concat([tf.expand_dims(y_pred_extract[...,0],axis=0),\n","                        tf.expand_dims(y_pred_extract[...,5],axis=0)],axis=0))\n","  \n","  obj_pred = tf.gather_nd(y_pred_joined,tf.stack([tf.range(len(rescaler)),mask],axis=-1))\n","  \n","  object_loss = difference(tf.cast(obj_pred,dtype =tf.float32)\n","                            ,tf.cast(tf.ones([len(rescaler)]),dtype=tf.float32))\n","\n","  ####################### For No object\n","  y_pred_extract = tf.gather_nd(y_pred[...,0:B*5], tf.where(target[:]==0))\n","  y_target_extract = tf.zeros(len(y_pred_extract))\n","\n","  no_object_loss_1 = difference(tf.cast(y_pred_extract[...,0],dtype =tf.float32)\n","                            ,tf.cast(y_target_extract,dtype=tf.float32))\n","  \n","  no_object_loss_2 = difference(tf.cast(y_pred_extract[...,5],dtype =tf.float32)\n","                            ,tf.cast(y_target_extract,dtype=tf.float32))\n","  \n","  no_object_loss = no_object_loss_1+no_object_loss_2\n","\n","  ######################## For OBject class loss\n","  y_pred_extract = tf.gather_nd(y_pred[...,10:],tf.where(target[:]==1))\n","  class_extract = tf.gather_nd(y_true[...,5:],tf.where(target[:]==1))\n","\n","  class_loss = difference(tf.cast(y_pred_extract,dtype =tf.float32)\n","                                ,tf.cast(class_extract,dtype=tf.float32))\n","\n","  ######################### For object bounding box loss\n","  y_pred_extract = tf.gather_nd(y_pred[...,0:B*5], tf.where(target[:]==1))\n","  centre_joined=tf.stack([y_pred_extract[...,1:3],y_pred_extract[...,6:8]],axis=1)\n","  centre_pred = tf.gather_nd(centre_joined,tf.stack([tf.range(len(rescaler)),mask],axis=-1))\n","  centre_target = tf.gather_nd(y_true[...,1:3], tf.where(target[:]==1))\n","  \n","  centre_loss = difference(centre_pred,centre_target)\n","    \n","  size_joined=tf.stack([y_pred_extract[...,3:5],y_pred_extract[...,8:10]],axis=1)\n","\n","  size_pred = tf.gather_nd(size_joined,tf.stack([tf.range(len(rescaler)),mask],axis=-1))\n","  size_target = tf.gather_nd(y_true[...,3:5], tf.where(target[:]==1))\n","  \n","  size_loss = difference(tf.math.sqrt(tf.math.abs(size_pred)),tf.math.sqrt(tf.math.abs(size_target)))\n","  box_loss = centre_loss+size_loss\n","  \n","  lambda_coord = 5.0\n","  lambda_no_obj = 0.5\n","\n","  loss = object_loss + (lambda_no_obj*no_object_loss)+ tf.cast(lambda_coord*box_loss,dtype=tf.float32)+ tf.cast(class_loss,dtype=tf.float32) \n","  return loss"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0IEYhYvfWb0j"},"outputs":[],"source":["# #y_true=generate_output([[0.210784,0.616422,0.127451,0.232843,2]])\n","# y_true=generate_output([[0.509804,0.411765,0.107843,0.245098,3],\n","#                         [0.210784,0.616422,0.127451,0.232843,2]])\n","# y_true=np.expand_dims(y_true,axis=0)\n","# y_pred=np.random.normal(size = (1,7,7,N_CLASSES+5*B))\n","\n","# y_pred[0][1][4] = [0.9,0.47,0.31,0.12,0.23,   1.0,0.2,0.6,0.1,0.95,    0.9,0.8,0.2,0.6,0.1,0.5,0.9,0.35]\n","# y_pred[0][3][2] = [0.3,0.01,0.08,0.11,0.54,   0.98,0.56,0.88,0.1,0.24, 0.09,0.018,0.22,0.16,0.01,0.05,0.99,0.3]\n","\n","# yolo_loss(y_true,y_pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GfFbC7820Yob"},"outputs":[],"source":["checkpoint_filepath='/content/drive/MyDrive/Bang/yolo_efficientnet_b1_new.h5'\n","callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath = checkpoint_filepath,\n","    save_weights_only=True,\n","    monitor='val_loss',\n","    mode='min',\n","    save_best_only=True\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tcrbbUDA3nAz"},"outputs":[],"source":["def scheduler(epoch, lr):\n","  if epoch < 40:\n","    return 1e-3\n","  elif epoch>=40 and epoch<80:\n","    return 5e-4\n","  else:\n","    return 1e-4"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-nKYwrhu8qIr"},"outputs":[],"source":["lr_callback = tf.keras.callbacks.LearningRateScheduler(scheduler)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mvwcdzQhWj7u"},"outputs":[],"source":["model.compile(\n","  loss=yolo_loss,\n","  optimizer=Adam(1e-3),\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":87193,"status":"ok","timestamp":1672552019281,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"DWf8c2MTHIv8","outputId":"0eaaaa84-f627-447d-e0a9-90291314ab95"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IiUElFgPmwfF"},"outputs":[],"source":["model.load_weights(checkpoint_filepath)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":5449646,"status":"error","timestamp":1672186203882,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"pyPV4BiaQhco","outputId":"ca25d8f4-c15f-4a68-ecc3-6330315f2fbe"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/135\n","534/534 [==============================] - 233s 396ms/step - loss: 192.8934 - val_loss: 193.3571 - lr: 0.0010\n","Epoch 2/135\n","534/534 [==============================] - 199s 373ms/step - loss: 156.1261 - val_loss: 178.0750 - lr: 0.0010\n","Epoch 3/135\n","534/534 [==============================] - 196s 368ms/step - loss: 145.7912 - val_loss: 165.4364 - lr: 0.0010\n","Epoch 4/135\n","534/534 [==============================] - 196s 366ms/step - loss: 138.7322 - val_loss: 162.8688 - lr: 0.0010\n","Epoch 5/135\n","534/534 [==============================] - 194s 363ms/step - loss: 133.2612 - val_loss: 153.6667 - lr: 0.0010\n","Epoch 6/135\n","534/534 [==============================] - 193s 361ms/step - loss: 128.1001 - val_loss: 150.9135 - lr: 0.0010\n","Epoch 7/135\n","534/534 [==============================] - 194s 364ms/step - loss: 123.8007 - val_loss: 145.6293 - lr: 0.0010\n","Epoch 8/135\n","534/534 [==============================] - 195s 366ms/step - loss: 119.7570 - val_loss: 143.5626 - lr: 0.0010\n","Epoch 9/135\n","534/534 [==============================] - 195s 365ms/step - loss: 116.5913 - val_loss: 138.0385 - lr: 0.0010\n","Epoch 10/135\n","534/534 [==============================] - 195s 364ms/step - loss: 113.6329 - val_loss: 135.7628 - lr: 0.0010\n","Epoch 11/135\n","534/534 [==============================] - 192s 360ms/step - loss: 110.9062 - val_loss: 135.7080 - lr: 0.0010\n","Epoch 12/135\n","534/534 [==============================] - 193s 362ms/step - loss: 108.4463 - val_loss: 137.2339 - lr: 0.0010\n","Epoch 13/135\n","534/534 [==============================] - 195s 365ms/step - loss: 106.0132 - val_loss: 131.9100 - lr: 0.0010\n","Epoch 14/135\n","534/534 [==============================] - 194s 364ms/step - loss: 104.4623 - val_loss: 129.7038 - lr: 0.0010\n","Epoch 15/135\n","534/534 [==============================] - 192s 360ms/step - loss: 101.6237 - val_loss: 128.3577 - lr: 0.0010\n","Epoch 16/135\n","534/534 [==============================] - 192s 359ms/step - loss: 100.0281 - val_loss: 127.3470 - lr: 0.0010\n","Epoch 17/135\n","534/534 [==============================] - 191s 357ms/step - loss: 97.7595 - val_loss: 127.4370 - lr: 0.0010\n","Epoch 18/135\n","534/534 [==============================] - 190s 355ms/step - loss: 96.2589 - val_loss: 128.8158 - lr: 0.0010\n","Epoch 19/135\n","534/534 [==============================] - 190s 356ms/step - loss: 94.3134 - val_loss: 127.5248 - lr: 0.0010\n","Epoch 20/135\n","534/534 [==============================] - 190s 356ms/step - loss: 92.9541 - val_loss: 130.8616 - lr: 0.0010\n","Epoch 21/135\n","534/534 [==============================] - 194s 363ms/step - loss: 90.9427 - val_loss: 130.3878 - lr: 0.0010\n","Epoch 22/135\n","534/534 [==============================] - 193s 362ms/step - loss: 89.4566 - val_loss: 131.1455 - lr: 0.0010\n","Epoch 23/135\n","534/534 [==============================] - 193s 361ms/step - loss: 88.2205 - val_loss: 130.8946 - lr: 0.0010\n","Epoch 24/135\n","534/534 [==============================] - 191s 358ms/step - loss: 86.4512 - val_loss: 133.0307 - lr: 0.0010\n","Epoch 25/135\n","534/534 [==============================] - 192s 358ms/step - loss: 85.1557 - val_loss: 129.3930 - lr: 0.0010\n","Epoch 26/135\n","534/534 [==============================] - 192s 360ms/step - loss: 83.4524 - val_loss: 127.9370 - lr: 0.0010\n","Epoch 27/135\n","534/534 [==============================] - 193s 362ms/step - loss: 82.5288 - val_loss: 124.9810 - lr: 0.0010\n","Epoch 28/135\n","534/534 [==============================] - 191s 358ms/step - loss: 81.5512 - val_loss: 130.5348 - lr: 0.0010\n","Epoch 29/135\n","534/534 [==============================] - 191s 357ms/step - loss: 80.4154 - val_loss: 130.0438 - lr: 0.0010\n","Epoch 30/135\n","534/534 [==============================] - 190s 356ms/step - loss: 79.5193 - val_loss: 128.9287 - lr: 0.0010\n","Epoch 31/135\n","534/534 [==============================] - 191s 357ms/step - loss: 77.8242 - val_loss: 129.2577 - lr: 0.0010\n","Epoch 32/135\n","534/534 [==============================] - 192s 359ms/step - loss: 76.8253 - val_loss: 132.1908 - lr: 0.0010\n","Epoch 33/135\n","534/534 [==============================] - 190s 356ms/step - loss: 75.8465 - val_loss: 131.8864 - lr: 0.0010\n","Epoch 34/135\n","534/534 [==============================] - 189s 354ms/step - loss: 75.2081 - val_loss: 130.5032 - lr: 0.0010\n","Epoch 35/135\n","534/534 [==============================] - 192s 359ms/step - loss: 74.1783 - val_loss: 131.8551 - lr: 0.0010\n","Epoch 36/135\n","534/534 [==============================] - 191s 357ms/step - loss: 73.2929 - val_loss: 127.5633 - lr: 0.0010\n","Epoch 37/135\n","534/534 [==============================] - 191s 358ms/step - loss: 72.2864 - val_loss: 127.3576 - lr: 0.0010\n","Epoch 38/135\n","534/534 [==============================] - 193s 361ms/step - loss: 71.1323 - val_loss: 128.2985 - lr: 0.0010\n","Epoch 39/135\n","534/534 [==============================] - 190s 355ms/step - loss: 70.5746 - val_loss: 129.4332 - lr: 0.0010\n","Epoch 40/135\n","534/534 [==============================] - 191s 357ms/step - loss: 69.7099 - val_loss: 129.5595 - lr: 0.0010\n","Epoch 41/135\n","534/534 [==============================] - 191s 358ms/step - loss: 66.5892 - val_loss: 127.8755 - lr: 5.0000e-04\n","Epoch 42/135\n","534/534 [==============================] - 196s 368ms/step - loss: 64.4563 - val_loss: 124.3320 - lr: 5.0000e-04\n","Epoch 43/135\n","534/534 [==============================] - 193s 362ms/step - loss: 63.6078 - val_loss: 123.1611 - lr: 5.0000e-04\n","Epoch 44/135\n","534/534 [==============================] - 190s 356ms/step - loss: 62.2588 - val_loss: 123.6474 - lr: 5.0000e-04\n","Epoch 45/135\n","534/534 [==============================] - 194s 363ms/step - loss: 61.4806 - val_loss: 123.0352 - lr: 5.0000e-04\n","Epoch 46/135\n","534/534 [==============================] - 194s 363ms/step - loss: 60.8901 - val_loss: 125.6343 - lr: 5.0000e-04\n","Epoch 47/135\n","534/534 [==============================] - 193s 362ms/step - loss: 60.1063 - val_loss: 123.8544 - lr: 5.0000e-04\n","Epoch 48/135\n","534/534 [==============================] - 191s 358ms/step - loss: 59.3607 - val_loss: 123.7684 - lr: 5.0000e-04\n","Epoch 49/135\n","534/534 [==============================] - 194s 363ms/step - loss: 58.9480 - val_loss: 124.3319 - lr: 5.0000e-04\n","Epoch 50/135\n","534/534 [==============================] - 194s 363ms/step - loss: 58.9550 - val_loss: 126.2013 - lr: 5.0000e-04\n","Epoch 51/135\n","534/534 [==============================] - 194s 362ms/step - loss: 57.9545 - val_loss: 128.5538 - lr: 5.0000e-04\n","Epoch 52/135\n","534/534 [==============================] - 192s 360ms/step - loss: 57.6307 - val_loss: 128.1110 - lr: 5.0000e-04\n","Epoch 53/135\n","534/534 [==============================] - 193s 361ms/step - loss: 57.4561 - val_loss: 128.1008 - lr: 5.0000e-04\n","Epoch 54/135\n","534/534 [==============================] - 193s 362ms/step - loss: 56.7609 - val_loss: 129.0702 - lr: 5.0000e-04\n","Epoch 55/135\n","534/534 [==============================] - 193s 361ms/step - loss: 56.8141 - val_loss: 127.3161 - lr: 5.0000e-04\n","Epoch 56/135\n","534/534 [==============================] - 191s 357ms/step - loss: 55.8587 - val_loss: 128.2553 - lr: 5.0000e-04\n","Epoch 57/135\n","534/534 [==============================] - 191s 357ms/step - loss: 55.4541 - val_loss: 127.6328 - lr: 5.0000e-04\n","Epoch 58/135\n","534/534 [==============================] - 190s 357ms/step - loss: 55.2082 - val_loss: 128.1843 - lr: 5.0000e-04\n","Epoch 59/135\n","534/534 [==============================] - 191s 358ms/step - loss: 54.9549 - val_loss: 127.1459 - lr: 5.0000e-04\n","Epoch 60/135\n","534/534 [==============================] - 190s 356ms/step - loss: 54.3071 - val_loss: 128.0587 - lr: 5.0000e-04\n","Epoch 61/135\n","534/534 [==============================] - 190s 355ms/step - loss: 53.9704 - val_loss: 132.5642 - lr: 5.0000e-04\n","Epoch 62/135\n","534/534 [==============================] - 191s 357ms/step - loss: 53.4008 - val_loss: 129.9045 - lr: 5.0000e-04\n","Epoch 63/135\n","534/534 [==============================] - 192s 359ms/step - loss: 53.3940 - val_loss: 129.0728 - lr: 5.0000e-04\n","Epoch 64/135\n","534/534 [==============================] - 192s 359ms/step - loss: 52.6483 - val_loss: 130.8600 - lr: 5.0000e-04\n","Epoch 65/135\n","534/534 [==============================] - 191s 358ms/step - loss: 52.4104 - val_loss: 128.4250 - lr: 5.0000e-04\n","Epoch 66/135\n","534/534 [==============================] - 191s 358ms/step - loss: 52.0027 - val_loss: 132.2194 - lr: 5.0000e-04\n","Epoch 67/135\n","534/534 [==============================] - 194s 363ms/step - loss: 52.1253 - val_loss: 128.9898 - lr: 5.0000e-04\n"]},{"ename":"KeyboardInterrupt","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-46-aff0f6093294>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m      2\u001b[0m   \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m135\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1395\u001b[0m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1396\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterator\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menumerate_epochs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1397\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1398\u001b[0m         \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1399\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_stop_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["history = model.fit(\n","  train_dataset,\n","  validation_data=val_dataset,\n","  verbose=1,\n","  epochs=135,\n","  callbacks = [lr_callback,callback]\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c_VryK6S8WKH"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"wT-H3ar0f0E5"},"source":["# Testing"]},{"cell_type":"code","source":["model.load_weights(checkpoint_filepath)"],"metadata":{"id":"3n4Ptr4k6FP3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!mkdir outputs/"],"metadata":{"id":"tMMD1KU95O__"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TK9oWfBGdINX"},"outputs":[],"source":["COCO_PATH='/content/drive/MyDrive/Bang/coco_images/'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_Ai557nT7uxa"},"outputs":[],"source":["def model_test(filename):\n","  try:\n","    test_path=COCO_PATH+filename\n","\n","    print(test_path)\n","    \n","    img=cv2.resize(cv2.imread(test_path),(H,W))\n","\n","    image=tf.io.decode_jpeg(tf.io.read_file(test_path))\n","    image=tf.image.resize(image, [H,W])\n","\n","    output=model.predict(np.expand_dims(image, axis = 0))\n","\n","    THRESH=.25\n","\n","    object_positions=tf.concat(\n","        [tf.where(output[...,0]>=THRESH),tf.where(output[...,5]>=THRESH)],axis=0)\n","    print(object_positions)\n","    selected_output=tf.gather_nd(output,object_positions)\n","    print(selected_output)\n","    final_boxes=[]\n","    final_scores=[]\n","\n","    for i,pos in enumerate(object_positions):\n","      for j in range(2):      \n","        if selected_output[i][j*5]>THRESH:\n","          output_box=tf.cast(output[pos[0]][pos[1]][pos[2]][(j*5)+1:(j*5)+5],dtype=tf.float32)\n","          \n","          x_centre=(tf.cast(pos[1],dtype=tf.float32)+output_box[0])*32\n","          y_centre=(tf.cast(pos[2],dtype=tf.float32)+output_box[1])*32\n","\n","          x_width,y_height=tf.math.abs(H*output_box[2]),tf.math.abs(W*output_box[3])\n","          \n","          x_min,y_min=int(x_centre-(x_width/2)),int(y_centre-(y_height/2))\n","          x_max,y_max=int(x_centre+(x_width/2)),int(y_centre+(y_height/2))\n","\n","          if(x_min<=0):x_min=0\n","          if(y_min<=0):y_min=0\n","          if(x_max>=W):x_max=W\n","          if(y_max>=H):y_max=H\n","          final_boxes.append(\n","              [x_min,y_min,x_max,y_max,\n","              str(classes[tf.argmax(selected_output[...,10:],axis=-1)[i]])])\n","          final_scores.append(selected_output[i][j*5])\n","    print(final_scores)\n","    print('finalboxes',final_boxes)\n","    final_boxes=np.array(final_boxes)\n","    \n","    object_classes=final_boxes[...,4]\n","    nms_boxes=final_boxes[...,0:4]\n","\n","    nms_output=tf.image.non_max_suppression(\n","        nms_boxes,final_scores,max_output_size=100,iou_threshold=0.2,\n","        score_threshold=float('-inf')\n","    )\n","    print(nms_output)\n","\n","    for i in nms_output:\n","      cv2.rectangle(\n","          img,\n","          (int(final_boxes[i][0]),int(final_boxes[i][1])),\n","          (int(final_boxes[i][2]),int(final_boxes[i][3])),(0,0,255),1)\n","      cv2.putText(\n","          img,\n","          final_boxes[i][-1],\n","          (int(final_boxes[i][0]),int(final_boxes[i][1])+15),\n","          cv2.FONT_HERSHEY_COMPLEX_SMALL,1,(2,225,155),1\n","          )\n","        \n","    cv2.imwrite('/content/outputs/'+filename[:-4]+'_det'+'.jpg',cv2.resize(img,(384,384)))\n","  except:\n","    print(\"NO object found !!!\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11467,"status":"ok","timestamp":1672565048951,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"09RvCO_XpusV","outputId":"3e3afa0e-6e58-4519-a9aa-4c02436a1008"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Bang/coco_images/coco_40.jpg\n","1/1 [==============================] - 0s 157ms/step\n","tf.Tensor(\n","[[0 4 3]\n"," [0 4 3]], shape=(2, 3), dtype=int64)\n","tf.Tensor(\n","[[9.6506602e-01 5.3086090e-01 1.7037451e-01 3.7535757e-01 7.7782160e-01\n","  9.8582762e-01 5.1559103e-01 1.9365844e-01 4.4615015e-01 8.6542726e-01\n","  8.3685671e-05 9.1796201e-06 1.2702014e-03 1.6244169e-04 6.1948213e-06\n","  2.5391690e-05 4.9546943e-05 7.6931395e-04 4.7511393e-03 6.1404644e-05\n","  2.9003315e-05 7.4357847e-03 5.5518249e-05 8.4421961e-05 9.9722672e-01\n","  6.4012071e-05 1.1153784e-04 1.1768552e-03 7.8674138e-06 8.1362450e-05]\n"," [9.6506602e-01 5.3086090e-01 1.7037451e-01 3.7535757e-01 7.7782160e-01\n","  9.8582762e-01 5.1559103e-01 1.9365844e-01 4.4615015e-01 8.6542726e-01\n","  8.3685671e-05 9.1796201e-06 1.2702014e-03 1.6244169e-04 6.1948213e-06\n","  2.5391690e-05 4.9546943e-05 7.6931395e-04 4.7511393e-03 6.1404644e-05\n","  2.9003315e-05 7.4357847e-03 5.5518249e-05 8.4421961e-05 9.9722672e-01\n","  6.4012071e-05 1.1153784e-04 1.1768552e-03 7.8674138e-06 8.1362450e-05]], shape=(2, 30), dtype=float32)\n","[<tf.Tensor: shape=(), dtype=float32, numpy=0.965066>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9858276>, <tf.Tensor: shape=(), dtype=float32, numpy=0.965066>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9858276>]\n","finalboxes [[102, 14, 187, 188, 'person'], [94, 5, 194, 199, 'person'], [102, 14, 187, 188, 'person'], [94, 5, 194, 199, 'person']]\n","tf.Tensor([1], shape=(1,), dtype=int32)\n","/content/drive/MyDrive/Bang/coco_images/coco_32.jpg\n","1/1 [==============================] - 0s 158ms/step\n","tf.Tensor(\n","[[0 5 2]\n"," [0 5 2]], shape=(2, 3), dtype=int64)\n","tf.Tensor(\n","[[7.0675433e-01 3.3882758e-01 6.8008643e-01 2.9932359e-01 5.4331344e-01\n","  3.6954251e-01 2.4831411e-01 6.8809551e-01 1.7649466e-01 4.4539043e-01\n","  4.6973396e-04 1.2022486e-03 9.0340273e-03 5.3767436e-03 1.5105818e-03\n","  6.9374719e-04 6.1293883e-04 2.7464856e-03 2.0839961e-03 6.1602407e-04\n","  3.6038729e-04 1.6369116e-03 1.5843622e-03 6.3700631e-04 9.9400985e-01\n","  1.6557091e-03 1.1474923e-03 2.3364034e-04 4.4364942e-04 7.0041241e-03]\n"," [7.0675433e-01 3.3882758e-01 6.8008643e-01 2.9932359e-01 5.4331344e-01\n","  3.6954251e-01 2.4831411e-01 6.8809551e-01 1.7649466e-01 4.4539043e-01\n","  4.6973396e-04 1.2022486e-03 9.0340273e-03 5.3767436e-03 1.5105818e-03\n","  6.9374719e-04 6.1293883e-04 2.7464856e-03 2.0839961e-03 6.1602407e-04\n","  3.6038729e-04 1.6369116e-03 1.5843622e-03 6.3700631e-04 9.9400985e-01\n","  1.6557091e-03 1.1474923e-03 2.3364034e-04 4.4364942e-04 7.0041241e-03]], shape=(2, 30), dtype=float32)\n","[<tf.Tensor: shape=(), dtype=float32, numpy=0.7067543>, <tf.Tensor: shape=(), dtype=float32, numpy=0.3695425>, <tf.Tensor: shape=(), dtype=float32, numpy=0.7067543>, <tf.Tensor: shape=(), dtype=float32, numpy=0.3695425>]\n","finalboxes [[137, 24, 204, 146, 'person'], [148, 36, 187, 135, 'person'], [137, 24, 204, 146, 'person'], [148, 36, 187, 135, 'person']]\n","tf.Tensor([0], shape=(1,), dtype=int32)\n","/content/drive/MyDrive/Bang/coco_images/coco_38.jpg\n","1/1 [==============================] - 0s 176ms/step\n","tf.Tensor(\n","[[0 3 5]\n"," [0 3 3]\n"," [0 3 5]], shape=(3, 3), dtype=int64)\n","tf.Tensor(\n","[[3.14616263e-01 5.51411450e-01 5.66868305e-01 3.60477030e-01\n","  3.95780951e-01 4.93250012e-01 4.72654909e-01 3.48782003e-01\n","  7.78340518e-01 4.77825999e-01 2.95038731e-03 1.13590613e-04\n","  4.04550415e-03 1.25056307e-03 2.62741349e-03 1.94636203e-04\n","  6.10268256e-03 4.08354104e-01 2.22946820e-03 4.84614371e-04\n","  9.85793304e-03 1.20498545e-01 5.54762722e-04 4.73096356e-04\n","  2.16295966e-03 5.47326729e-03 2.81813499e-02 4.55676671e-03\n","  1.23569072e-04 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1.79498503e-03]], shape=(3, 30), dtype=float32)\n","[<tf.Tensor: shape=(), dtype=float32, numpy=0.31461626>, <tf.Tensor: shape=(), dtype=float32, numpy=0.49325>, <tf.Tensor: shape=(), dtype=float32, numpy=0.26216874>, <tf.Tensor: shape=(), dtype=float32, numpy=0.31461626>, <tf.Tensor: shape=(), dtype=float32, numpy=0.49325>]\n","finalboxes [[73, 133, 154, 222, 'cat'], [23, 117, 198, 224, 'cat'], [22, 12, 203, 222, 'dog'], [73, 133, 154, 222, 'cat'], [23, 117, 198, 224, 'cat']]\n","tf.Tensor([1], shape=(1,), dtype=int32)\n","/content/drive/MyDrive/Bang/coco_images/coco_34.jpg\n","1/1 [==============================] - 0s 179ms/step\n","tf.Tensor(\n","[[0 3 3]\n"," [0 4 3]\n"," [0 3 3]\n"," [0 4 3]], shape=(4, 3), dtype=int64)\n","tf.Tensor(\n","[[4.40516204e-01 5.90376496e-01 4.63306904e-01 2.26402417e-01\n","  4.25642252e-01 3.79184008e-01 4.63843405e-01 4.55334187e-01\n","  5.02563655e-01 5.82069576e-01 1.17558357e-03 1.34797324e-03\n","  5.14854444e-03 5.17882945e-05 5.81924614e-05 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7.40428388e-01\n","  8.85287672e-02 2.24752232e-01 7.13322172e-03 4.53639962e-02\n","  5.34895854e-03 1.68148277e-03 1.29031697e-02 1.28195249e-02\n","  3.78605127e-02 2.01354176e-03 1.31082926e-02 2.48042960e-02\n","  1.43425236e-03 6.93494920e-03 9.56237596e-03 2.29043793e-02\n","  8.79490852e-01 3.26039712e-03 1.29565177e-02 4.39122086e-03\n","  1.68636220e-03 1.25666056e-02]\n"," [2.97271073e-01 2.79405713e-01 2.10693166e-01 2.03924462e-01\n","  4.21683222e-01 3.55998486e-01 3.18360418e-01 1.62996054e-01\n","  1.01651214e-01 2.70663351e-01 2.98312539e-03 2.71943342e-02\n","  4.88797016e-03 1.17386191e-03 9.45033971e-03 1.39128827e-02\n","  8.16126168e-03 7.75303110e-04 1.32866837e-02 1.39796557e-02\n","  3.10029974e-03 6.59305602e-03 2.24018432e-02 8.15690868e-03\n","  9.41463590e-01 4.16150084e-03 3.00765429e-02 7.91585480e-04\n","  6.73246046e-04 4.17028787e-03]\n"," [3.59775424e-01 6.11956060e-01 4.45285678e-01 1.10592507e-01\n","  4.14919972e-01 4.47159767e-01 6.75911546e-01 5.13882518e-01\n","  2.31769770e-01 5.69602847e-01 1.17103744e-03 1.06516862e-02\n","  4.98058228e-03 6.50868053e-04 3.61177744e-03 1.26122059e-02\n","  1.47635993e-02 9.93501046e-04 2.10404489e-02 2.51170453e-02\n","  9.17583331e-03 3.46803316e-03 1.18751796e-02 3.95720173e-03\n","  9.21872318e-01 9.80340503e-03 2.99300086e-02 2.66621867e-03\n","  2.03119405e-03 1.96898403e-03]\n"," [5.22389412e-01 3.16906184e-01 6.63734078e-01 2.67747760e-01\n","  6.97901785e-01 4.16315317e-01 3.53737563e-01 6.75064325e-01\n","  1.58780187e-01 4.66573656e-01 1.10978587e-03 2.46331166e-03\n","  3.03258863e-03 1.94495695e-03 1.81297644e-03 6.23741513e-03\n","  1.33356163e-02 1.48608384e-03 5.68162510e-03 3.73858912e-03\n","  5.55993756e-04 5.96360397e-03 1.51214236e-02 5.08089038e-03\n","  9.58890080e-01 5.20738447e-03 2.03689281e-02 5.98104671e-03\n","  2.39446317e-03 1.76882406e-03]\n"," [1.56593874e-01 4.18432564e-01 6.82104647e-01 5.51494472e-02\n","  1.86134845e-01 3.07122797e-01 4.79927182e-01 7.22417295e-01\n","  1.14706479e-01 3.73124152e-01 2.28761067e-03 7.11314240e-03\n","  6.74544647e-03 7.84053875e-04 2.22426783e-02 6.16841577e-03\n","  1.47497272e-02 4.98858397e-04 1.40013099e-02 8.98342859e-03\n","  4.89794463e-03 3.19931423e-03 5.06300945e-03 1.75907509e-03\n","  8.90142202e-01 3.04675149e-03 1.44489398e-02 1.07111782e-03\n","  2.79315980e-03 1.03412131e-02]\n"," [3.97385836e-01 6.68884218e-01 2.88079143e-01 2.00996861e-01\n","  4.53957140e-01 3.49418223e-01 6.10587239e-01 2.45216265e-01\n","  1.05204903e-01 3.23366433e-01 3.63374688e-03 2.61584427e-02\n","  2.65519135e-03 1.60626031e-03 6.28567720e-03 9.96762980e-03\n","  3.48519767e-03 1.84419216e-03 9.12925228e-03 5.53329103e-03\n","  1.65075960e-03 3.90679901e-03 1.08355973e-02 1.27336020e-02\n","  9.80548263e-01 1.88386906e-03 8.93756188e-03 1.44482590e-03\n","  1.46068004e-03 4.56239842e-03]\n"," [5.65317214e-01 4.20420468e-01 7.48603046e-01 1.94869950e-01\n","  4.18210715e-01 5.10028183e-01 4.79069293e-01 7.40428388e-01\n","  8.85287672e-02 2.24752232e-01 7.13322172e-03 4.53639962e-02\n","  5.34895854e-03 1.68148277e-03 1.29031697e-02 1.28195249e-02\n","  3.78605127e-02 2.01354176e-03 1.31082926e-02 2.48042960e-02\n","  1.43425236e-03 6.93494920e-03 9.56237596e-03 2.29043793e-02\n","  8.79490852e-01 3.26039712e-03 1.29565177e-02 4.39122086e-03\n","  1.68636220e-03 1.25666056e-02]\n"," [2.97271073e-01 2.79405713e-01 2.10693166e-01 2.03924462e-01\n","  4.21683222e-01 3.55998486e-01 3.18360418e-01 1.62996054e-01\n","  1.01651214e-01 2.70663351e-01 2.98312539e-03 2.71943342e-02\n","  4.88797016e-03 1.17386191e-03 9.45033971e-03 1.39128827e-02\n","  8.16126168e-03 7.75303110e-04 1.32866837e-02 1.39796557e-02\n","  3.10029974e-03 6.59305602e-03 2.24018432e-02 8.15690868e-03\n","  9.41463590e-01 4.16150084e-03 3.00765429e-02 7.91585480e-04\n","  6.73246046e-04 4.17028787e-03]\n"," [3.59775424e-01 6.11956060e-01 4.45285678e-01 1.10592507e-01\n","  4.14919972e-01 4.47159767e-01 6.75911546e-01 5.13882518e-01\n","  2.31769770e-01 5.69602847e-01 1.17103744e-03 1.06516862e-02\n","  4.98058228e-03 6.50868053e-04 3.61177744e-03 1.26122059e-02\n","  1.47635993e-02 9.93501046e-04 2.10404489e-02 2.51170453e-02\n","  9.17583331e-03 3.46803316e-03 1.18751796e-02 3.95720173e-03\n","  9.21872318e-01 9.80340503e-03 2.99300086e-02 2.66621867e-03\n","  2.03119405e-03 1.96898403e-03]\n"," [5.22389412e-01 3.16906184e-01 6.63734078e-01 2.67747760e-01\n","  6.97901785e-01 4.16315317e-01 3.53737563e-01 6.75064325e-01\n","  1.58780187e-01 4.66573656e-01 1.10978587e-03 2.46331166e-03\n","  3.03258863e-03 1.94495695e-03 1.81297644e-03 6.23741513e-03\n","  1.33356163e-02 1.48608384e-03 5.68162510e-03 3.73858912e-03\n","  5.55993756e-04 5.96360397e-03 1.51214236e-02 5.08089038e-03\n","  9.58890080e-01 5.20738447e-03 2.03689281e-02 5.98104671e-03\n","  2.39446317e-03 1.76882406e-03]], shape=(12, 30), dtype=float32)\n","[<tf.Tensor: shape=(), dtype=float32, numpy=0.35404018>, <tf.Tensor: shape=(), dtype=float32, numpy=0.39738584>, <tf.Tensor: shape=(), dtype=float32, numpy=0.34941822>, <tf.Tensor: shape=(), dtype=float32, numpy=0.5653172>, <tf.Tensor: shape=(), dtype=float32, numpy=0.5100282>, <tf.Tensor: shape=(), dtype=float32, numpy=0.29727107>, <tf.Tensor: shape=(), dtype=float32, numpy=0.3559985>, <tf.Tensor: shape=(), dtype=float32, numpy=0.35977542>, <tf.Tensor: shape=(), dtype=float32, numpy=0.44715977>, <tf.Tensor: shape=(), dtype=float32, numpy=0.5223894>, <tf.Tensor: shape=(), dtype=float32, numpy=0.41631532>, <tf.Tensor: shape=(), dtype=float32, numpy=0.3071228>, <tf.Tensor: shape=(), dtype=float32, numpy=0.39738584>, <tf.Tensor: shape=(), dtype=float32, numpy=0.34941822>, <tf.Tensor: shape=(), dtype=float32, numpy=0.5653172>, <tf.Tensor: shape=(), dtype=float32, numpy=0.5100282>, <tf.Tensor: shape=(), dtype=float32, numpy=0.29727107>, <tf.Tensor: shape=(), dtype=float32, numpy=0.3559985>, <tf.Tensor: shape=(), dtype=float32, numpy=0.35977542>, <tf.Tensor: shape=(), dtype=float32, numpy=0.44715977>, <tf.Tensor: shape=(), dtype=float32, numpy=0.5223894>, <tf.Tensor: shape=(), dtype=float32, numpy=0.41631532>]\n","finalboxes [[0, 52, 28, 160, 'person'], [30, 54, 75, 156, 'person'], [39, 67, 63, 140, 'person'], [55, 41, 99, 134, 'person'], [69, 62, 89, 112, 'person'], [50, 55, 95, 149, 'person'], [62, 70, 85, 131, 'person'], [135, 63, 159, 156, 'person'], [123, 48, 175, 176, 'person'], [140, 39, 200, 195, 'person'], [153, 65, 189, 169, 'person'], [2, 45, 28, 128, 'person'], [30, 54, 75, 156, 'person'], [39, 67, 63, 140, 'person'], [55, 41, 99, 134, 'person'], [69, 62, 89, 112, 'person'], [50, 55, 95, 149, 'person'], [62, 70, 85, 131, 'person'], [135, 63, 159, 156, 'person'], [123, 48, 175, 176, 'person'], [140, 39, 200, 195, 'person'], [153, 65, 189, 169, 'person']]\n","tf.Tensor([3 9 7 0 2], shape=(5,), dtype=int32)\n"]}],"source":["for filename in os.listdir(COCO_PATH):\n","  model_test(filename)"]},{"cell_type":"code","source":[],"metadata":{"id":"NlM-UhNaLUW4"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"11A5FdjrbGJ89HzuP3sKeRdxhnuQAoqIL","timestamp":1673816134679}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

================================================
FILE: deep learning for computer vision/6-Image segmentation by Neuralearn.ai-.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPXRcvdfpG8Ah9S3v/Q6Z/3"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"IdV7TJbUP-hy"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from google.colab import files\n","from PIL import Image\n","import albumentations as A\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n","                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n","                                     RandomContrast, Rescaling, Resizing, Reshape,LeakyReLU)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n","                                        ModelCheckpoint, ReduceLROnPlateau)\n","from tensorflow.keras.regularizers import L2, L1\n","from tensorflow.keras.initializers import RandomNormal\n","from tensorflow.train import BytesList, FloatList, Int64List\n","from tensorflow.train import Example, Features, Feature\n","from google.colab import drive"]},{"cell_type":"code","source":[],"metadata":{"id":"d2IMnk1bUIe8"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# DATA PREPARATION"],"metadata":{"id":"hy68yrYIULL7"}},{"cell_type":"code","source":["class DataGenerator(tf.keras.utils.Sequence):\n","    def __init__ (self, images, maps, batch_size, INPUT_DIM, shuffle = False):\n","\n","        self.images = images\n","        self.maps = maps\n","        self.batch_size = batch_size\n","        self.train_image_list=os.listdir(images)\n","        self.INPUT_DIM=INPUT_DIM\n","\n","    def __len__(self):\n","        return int(np.floor(len(self.train_image_list)/self.batch_size))\n","  \n","    def __getitem__(self, idx):\n","        x,y = self.__data_generation(idx)\n","        y-=1\n","        return np.array(x), np.array(y)\n","  \n","    def __data_generation(self, idx):\n","        x = []\n","        y = []\n","\n","        for j in range(idx*self.batch_size, (idx+1)*self.batch_size):\n","            x.append(img_to_array(load_img(self.images+os.listdir(self.images)[j],target_size=(self.INPUT_DIM,self.INPUT_DIM))))\n","            y.append(img_to_array(load_img(self.maps+os.listdir(self.maps)[j], color_mode='grayscale', target_size=(self.INPUT_DIM//2,self.INPUT_DIM//2))))\n","            \n","    return tf.convert_to_tensor(x),tf.convert_to_tensor(y)"],"metadata":{"id":"jjup7DNIUIhr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_images='...'\n","train_maps='...'\n","val_images='...'\n","val_maps='...'\n","\n","LR=1e-3\n","BATCH_SIZE=4\n","EPOCH=100"],"metadata":{"id":"Zu2qO_-nUIjr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_gen = DataGenerator(images, maps,BATCH_SIZE,INPUT_DIM)\n","val_gen = DataGenerator(val_images, val_maps,BATCH_SIZE,INPUT_DIM)"],"metadata":{"id":"Aoz9frgoUImT"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# MODELING"],"metadata":{"id":"I2SBs5aLUPT8"}},{"cell_type":"code","source":["def get_base_model():\n","    base_model = tf.keras.applications.ResNet50(\n","        weights='imagenet',\n","        input_shape=(INPUT_DIM,INPUT_DIM,3),\n","        include_top=False,)\n","    base_model.trainable=False\n","    \n","    conv1_relu,conv2_block3_out,conv3_block4_out,conv4_block6_out,conv5_block3_out=[base_model.get_layer(layer_name).output for layer_name in [\"conv1_relu\",\"conv2_block3_out\",\"conv3_block4_out\",\"conv4_block6_out\",\"conv5_block3_out\"]]\n","    \n","    return Model(\n","        inputs=[base_model.inputs],outputs=[conv1_relu,conv2_block3_out,conv3_block4_out,conv4_block6_out,conv5_block3_out]\n","    )\n","\n","get_base_model().summary()"],"metadata":{"id":"NN-LGUw5UIo2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class Upsample(tf.keras.layers.Layer):\n","    def __init__(self,NUM_FILTERS):\n","        super(Upsample,self).__init__()\n","        self.conv_t_1=Conv2DTranspose(NUM_FILTERS,1,strides=2,activation='relu')\n","        self.norm_1=BatchNormalization()\n","    def call(self,x):\n","        x=self.norm_1(self.conv_t_1(x))\n","        return x"],"metadata":{"id":"YOI1J0PSUItT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class ConvLayers(tf.keras.layers.Layer):\n","    def __init__(self,NUM_FILTERS):\n","        super(ConvLayers,self).__init__()\n","        self.conv_1=Conv2D(NUM_FILTERS*2,3,padding='same',activation='relu')\n","        self.norm_1=BatchNormalization()\n","        \n","        self.conv_2=Conv2D(NUM_FILTERS*4,3,padding='same',activation='relu')\n","        self.norm_2=BatchNormalization()\n","    def call(self,x):\n","        x=self.norm_1(self.conv_1(x))\n","        x=self.norm_2(self.conv_2(x))\n","        return x"],"metadata":{"id":"kKLEmdqSUIvm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["inputs=tf.keras.Input(shape=(INPUT_DIM,INPUT_DIM,3))\n","x=Rescaling(1/255.)(inputs)\n","x_112,x_56,x_28,x_7=get_base_model()(x)\n","\n","x=Upsample(NUM_FILTERS)(x_7)\n","x=tf.concat([x,x_14],axis=-1)\n","x=ConvLayers(NUM_FILTERS)(x)\n","\n","\n","x=Upsample(NUM_FILTERS)(x)\n","x=tf.concat([x,x_28],axis=-1)\n","x=ConvLayers(NUM_FILTERS)(x)\n","\n","\n","x=Upsample(NUM_FILTERS)(x)\n","x=tf.concat([x,x_56],axis=-1)\n","x=ConvLayers(NUM_FILTERS)(x)\n","\n","\n","x=Upsample(NUM_FILTERS)(x)\n","x=tf.concat([x,x_112],axis=-1)\n","x=ConvLayers(NUM_FILTERS)(x)\n","\n","out=Conv2D(3,3,padding='same',activation='softmax')(x)\n","model=tf.keras.Model(inputs=inputs,outputs=out)\n","model.summary()"],"metadata":{"id":"WaVhwW9BUV11"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# TRAINING"],"metadata":{"id":"qiHbxjJqUZpD"}},{"cell_type":"code","source":["model.compile(\n","    loss = tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer = Adam(learning_rate = LR),\n","    metrics='accuracy',\n","    run_eagerly = True,\n",")"],"metadata":{"id":"j4y4mE1fUV4f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["checkpoint_filepath='...'\n","callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath = checkpoint_filepath,\n","    save_weights_only=True,\n","    monitor='loss',\n","    mode='min',\n","    save_best_only=True\n",")"],"metadata":{"id":"Nu53xe4PUV6_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history = model.fit(\n","    train_gen,\n","    verbose=1,\n","    shuffle=True,\n","    epochs=EPOCH,\n","    callbacks=[callback])"],"metadata":{"id":"a3LCC5dcUdey"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# TESTING"],"metadata":{"id":"AfWOZFeIUe6r"}},{"cell_type":"code","source":["test_image ='...'\n","test_image_map='...'\n","\n","X=[]\n","X.append(img_to_array(load_img(test_image,target_size=(224,224))))\n","image_output=tf.argmax(model.predict(tf.constant(X)),axis=-1)[0]\n","image_output=tf.expand_dims(image_output,axis=-1)"],"metadata":{"id":"yGiqkCrUUfn7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["image=tf.keras.preprocessing.image.load_img(test_image,color_mode='rgb',target_size=(224,224))\n","plt.imshow(image)\n","plt.show()"],"metadata":{"id":"SelZupaeUfqT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.imshow(image_output[...,0])\n","plt.show()"],"metadata":{"id":"3jf35O9QUfs7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"wz_yrr2TUfvz"},"execution_count":null,"outputs":[]}]}

================================================
FILE: deep learning for computer vision/7-People Counting by Neuralearn.ai-.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOEYsHlbQ9MSIK58KUUGle3"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"g2lR_wjyQBmp"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","import scipy.io\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.preprocessing.image import load_img, img_to_array\n","from tensorflow.keras.applications import VGG16\n","from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n","                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n","                                     RandomContrast, Rescaling, Resizing, Reshape,LeakyReLU)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n","                                        ModelCheckpoint, ReduceLROnPlateau)\n","from tensorflow.keras.regularizers import L2, L1\n","from tensorflow.keras.initializers import RandomNormal"]},{"cell_type":"markdown","source":["# DATA PREPARATION"],"metadata":{"id":"kOZ4KwY3TbFa"}},{"cell_type":"code","source":["IN_X,IN_Y=768,1024\n","OUT_X,OUT_Y=96,128\n","SUBSAMPLING_FACTOR=IN_X//OUT_X"],"metadata":{"id":"lxmP8WGGTZBU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def gauss_distribution(x,u=0,sigma=10):\n","    return np.expand_dims(1/(np.sqrt(2*np.pi*(sigma**2)))*np.exp(-(0.5)*(((x-u)/sigma)**2)),axis=0)"],"metadata":{"id":"ACZxPhNSTiyT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def get_density_map_gaussian(im,points,gaussian_radius=4):\n","    density_map=np.zeros((OUT_X,OUT_Y))\n","    w,h=OUT_Y,OUT_X\n","    num_gt=len(points)\n","    \n","    for point in points:\n","        point=np.round(point).astype(int)\n","        point[0],point[1]=min(h-1,point[1]),min(w-1,point[0])\n","        x=np.linspace(-gaussian_radius,gaussian_radius,(gaussian_radius*2)+1)\n","        gaussian_map=np.multiply(gauss_distribution(x),gauss_distribution(x).T)\n","        gaussian_map/=np.sum(gaussian_map)\n","        \n","        x_left,x_right,y_up,y_down=0,gaussian_map.shape[1],0,gaussian_map.shape[0]\n","        if point[1]<gaussian_radius:\n","            x_left=gaussian_radius-point[1]\n","        if point[0]<gaussian_radius:\n","            y_up=gaussian_radius-point[0]\n","        if point[1]+gaussian_radius>=w:\n","            x_right=gaussian_map.shape[1]-(gaussian_radius+point[1]-w)-1\n","        if point[0]+gaussian_radius>=h:\n","            y_down=gaussian_map.shape[0]-(gaussian_radius+point[0]-h)-1\n","        density_map[\n","            max(0,point[0]-gaussian_radius):min(density_map.shape[0],point[0]+gaussian_radius+1),\n","            max(0,point[1]-gaussian_radius):min(density_map.shape[1],point[1]+gaussian_radius+1),\n","        ]+=gaussian_map[y_up:y_down,x_left:x_rigtht]\n","    density_map/=np.sum(density_map/len(points))\n","    return density_map"],"metadata":{"id":"lfQg3N0eTZD1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class DataGenerator(tf.keras.utils.Sequence):\n","    def __init__ (self, images, maps, batch_size,SUBSAMPLING_FACTOR=8):\n","\n","        self.images = images\n","        self.maps = maps\n","        self.batch_size = batch_size\n","        self.train_image_list=os.listdir(images)\n","        self.SUBSAMPLING_FACTOR=SUBSAMPLING_FACTOR\n","\n","    def __len__(self):\n","        return int(np.floor(len(self.train_image_list)/self.batch_size))\n","  \n","    def __getitem__(self, idx):\n","        x,y = self.__data_generation(idx)\n","        return x,y\n","  \n","    def __data_generation(self, idx):\n","        x = []\n","        y = []\n","\n","        for j in range(idx*self.batch_size, (idx+1)*self.batch_size):\n","            \n","            im_array=img_to_array(load_img(self.images+os.listdir(self.images)[j],target_size=(IN_X,IN_Y)))\n","            im_array/=255.\n","            im_array[:,:,0]=(im_array[:,:,0]-np.mean(im_array[:,:,0]))/np.std(im_array[:,:,0])\n","            im_array[:,:,1]=(im_array[:,:,1]-np.mean(im_array[:,:,1]))/np.std(im_array[:,:,1])\n","            im_array[:,:,2]=(im_array[:,:,2]-np.mean(im_array[:,:,2]))/np.std(im_array[:,:,2])\n","            x.append(im_array)\n","            mat=scipy.io.loadmat(self.maps+os.listdir(self.maps)[j])\n","            points=mat['image_info'][0][0][0][0][0]\n","            points/=self.SUBSAMPLING_FACTOR\n","            \n","            density_map_present=get_density_map_gaussian(im_array,points,sigma=5)\n","            y.append(density_map_present)\n","    return tf.convert_to_tensor(x),tf.convert_to_tensor(y)"],"metadata":{"id":"IyICjrsjTZGl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_images='...'\n","train_maps='...'\n","val_images='...'\n","val_maps='...'\n","\n","LR=1e-4\n","BATCH_SIZE=1\n","EPOCH=1000"],"metadata":{"id":"MEmDHPHrTnrr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_gen = DataGenerator(images, maps,BATCH_SIZE,INPUT_DIM)\n","#val_gen = DataGenerator(val_images, val_maps,BATCH_SIZE,INPUT_DIM)"],"metadata":{"id":"GLCoJNJrTnuN"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# MODELING"],"metadata":{"id":"E5Hzk0j4TqLM"}},{"cell_type":"code","source":["def get_base_model():\n","    base_model = VGG16(\n","        weights='imagenet',\n","        input_shape=(INPUT_DIM,INPUT_DIM,3),\n","        include_top=False,)\n","    \n","    block4_conv3=[base_model.get_layer(layer_name).output for layer_name in [\"block4_conv3\"]]\n","    \n","    return Model(\n","        inputs=[base_model.inputs],outputs=[block4_conv3]\n","    )\n","\n","get_base_model().summary()"],"metadata":{"id":"jfGjAqBVTnwV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["inputs=tf.keras.Input(shape=(IN_X,IN_Y,3))\n","x=get_base_model()(inputs)\n","init=RandomNormal(0.01)\n","\n","x=Conv2D(512, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n","x=Conv2D(512, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n","x=Conv2D(512, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n","x=Conv2D(256, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n","x=Conv2D(128, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n","x=Conv2D(64, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n","x=Conv2D(1, (1,1), activation = 'relu', dilation_rate=1,kernel_iniitalizer=init,padding='same')(x)\n","\n","out=Reshape((96,128))(x)\n","model=tf.keras.Model(inputs=inputs,outputs=out)\n","model.summary()"],"metadata":{"id":"DPmTIVQ9Tnys"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(\n","    loss = tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer = Adam(learning_rate = LR),\n","    metrics='accuracy',\n","    run_eagerly = True,\n",")"],"metadata":{"id":"YT-kjG1JTZI-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["checkpoint_filepath='...'\n","callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath = checkpoint_filepath,\n","    save_weights_only=True,\n","    monitor='loss',\n","    mode='min',\n","    save_best_only=True\n",")"],"metadata":{"id":"1QaRkOB0TyNl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history = model.fit(\n","    train_gen,\n","    verbose=1,\n","    shuffle=True,\n","    epochs=EPOCH,\n","    callbacks=[callback])"],"metadata":{"id":"aCJsE-CCTyQC"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# TESTING"],"metadata":{"id":"W8c53yULT1D7"}},{"cell_type":"code","source":["IN ='...'\n","\n","im_array=img_to_array(load_img(train_image+IN_'.jpg',target_size=(IN_X,IN_Y)))\n","im_array/=255.\n","im_array[:,:,0]=(im_array[:,:,0]-np.mean(im_array[:,:,0]))/np.std(im_array[:,:,0])\n","im_array[:,:,1]=(im_array[:,:,1]-np.mean(im_array[:,:,1]))/np.std(im_array[:,:,1])\n","im_array[:,:,2]=(im_array[:,:,2]-np.mean(im_array[:,:,2]))/np.std(im_array[:,:,2])\n","\n","plt.figure(figsize=(20,12))\n","plt.imhow(im_array)\n","\n","output=mode.predict(tf.expand_dims(im_array,axis=0))\n","output=np.reshape(output,(OUT_X,OUT_Y))\n","\n","n_people=np.sum(output)\n","mat=scipy.io.loadmat(train_maps+'GT_'+IN+'.mat')\n","points=mat['image_info'][0][0][0][0][0]\n","points/=SUBSAMPLING_FACTOR\n","\n","num_gt=np.squeeze(points).shape[0]\n","print(\"The actual number of people is=\",num_gt)\n","print(\"The predicted number of people is =\",n_people)\n","\n","plt.figure(figsize=(20,12))\n","plt.imshow(output)"],"metadata":{"id":"GrVfcr8OT0YF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"jOIrzCOgT0aj"},"execution_count":null,"outputs":[]}]}

================================================
FILE: deep learning for computer vision/8-VAE by Neuralearn.ai-.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"14eFua9pLCV6AOYqGdyq_R-eWWUfZVtYs","timestamp":1673808986067}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"vPKrQB5FrFnW"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape, Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, )\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"markdown","source":["# Data Preparation"],"metadata":{"id":"4lNrm79MlgpS"}},{"cell_type":"code","source":["(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()\n","mnist_digits = np.concatenate([x_train, x_test], axis=0)\n","mnist_digits = np.expand_dims(mnist_digits, -1).astype(\"float32\") / 255"],"metadata":{"id":"wZ3wCXMrRQDZ","executionInfo":{"status":"ok","timestamp":1657285544409,"user_tz":-60,"elapsed":1072,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"26f66499-c049-4fbd-d919-a9d4ed6f3008"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","11493376/11490434 [==============================] - 0s 0us/step\n","11501568/11490434 [==============================] - 0s 0us/step\n"]}]},{"cell_type":"code","source":["dataset = tf.data.Dataset.from_tensor_slices(mnist_digits)"],"metadata":{"id":"TuiM9At8RQFi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["BATCH_SIZE = 128\n","LATENT_DIM = 2"],"metadata":{"id":"rvK_VfBSRQJv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset = (\n","    dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"],"metadata":{"id":"-IxW9wxQRQMM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2ne293QuRQOL","executionInfo":{"status":"ok","timestamp":1657285546248,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3ca219cd-4806-4a7f-f8f0-875053ae47c3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name=None)>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":[],"metadata":{"id":"Kp4aWbGDRQQO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"CrM0hUSmThBQ"}},{"cell_type":"markdown","source":["## Sampling"],"metadata":{"id":"9JBa79Pxd4gi"}},{"cell_type":"code","source":["class Sampling(Layer):\n","  def call(self, inputs):\n","    mean, log_var = inputs\n","    return mean + tf.math.exp(0.5*log_var)*tf.random.normal(shape = (tf.shape(mean)[0], tf.shape(mean)[1])) "],"metadata":{"id":"JfVZjHFWd49Q"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Encoder"],"metadata":{"id":"0U5XGop-TjGX"}},{"cell_type":"code","source":["encoder_inputs = Input(shape=(28,28,1))\n","\n","x = Conv2D(32, 3, activation='relu', strides=2, padding='same')(encoder_inputs)\n","x = Conv2D(64, 3, activation='relu', strides=2, padding='same')(x)\n","\n","x = Flatten()(x)\n","x = Dense(16, activation='relu')(x)\n","\n","mean = Dense(LATENT_DIM,)(x)\n","log_var = Dense(LATENT_DIM,)(x)\n","\n","z = Sampling()([mean,log_var])\n","\n","encoder_model = Model(encoder_inputs,[z,mean,log_var], name='encoder')\n","encoder_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6zfqKfkMRQUe","executionInfo":{"status":"ok","timestamp":1657285548993,"user_tz":-60,"elapsed":8,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9eb4170c-84b4-42f8-dc46-eb22e2f3b602"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"encoder\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_5 (InputLayer)           [(None, 28, 28, 1)]  0           []                               \n","                                                                                                  \n"," conv2d_4 (Conv2D)              (None, 14, 14, 32)   320         ['input_5[0][0]']                \n","                                                                                                  \n"," conv2d_5 (Conv2D)              (None, 7, 7, 64)     18496       ['conv2d_4[0][0]']               \n","                                                                                                  \n"," flatten_2 (Flatten)            (None, 3136)         0           ['conv2d_5[0][0]']               \n","                                                                                                  \n"," dense_6 (Dense)                (None, 16)           50192       ['flatten_2[0][0]']              \n","                                                                                                  \n"," dense_7 (Dense)                (None, 2)            34          ['dense_6[0][0]']                \n","                                                                                                  \n"," dense_8 (Dense)                (None, 2)            34          ['dense_6[0][0]']                \n","                                                                                                  \n"," sampling_1 (Sampling)          (None, 2)            0           ['dense_7[0][0]',                \n","                                                                  'dense_8[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 69,076\n","Trainable params: 69,076\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["## Decoder"],"metadata":{"id":"mhGm9aq4j1NJ"}},{"cell_type":"code","source":["latent_inputs = Input(shape=(LATENT_DIM,))\n","\n","\n","x = Dense(7*7*64, activation='relu')(latent_inputs)\n","x = Reshape((7,7,64))(x)\n","\n","x = Conv2DTranspose(64, 3, activation='relu', strides=2, padding='same')(x)\n","x = Conv2DTranspose(32, 3, activation='relu', strides=2, padding='same')(x)\n","\n","decoder_output = Conv2DTranspose(1, 3, activation='sigmoid', padding='same')(x)\n","decoder_model = Model(latent_inputs,decoder_output,name='decoder')\n","decoder_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NIz8pfv6RQYz","executionInfo":{"status":"ok","timestamp":1657285549853,"user_tz":-60,"elapsed":9,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"04c391fc-9233-4fc3-804c-59ca1a1e0479"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"decoder\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_6 (InputLayer)        [(None, 2)]               0         \n","                                                                 \n"," dense_9 (Dense)             (None, 3136)              9408      \n","                                                                 \n"," reshape_2 (Reshape)         (None, 7, 7, 64)          0         \n","                                                                 \n"," conv2d_transpose_7 (Conv2DT  (None, 14, 14, 64)       36928     \n"," ranspose)                                                       \n","                                                                 \n"," conv2d_transpose_8 (Conv2DT  (None, 28, 28, 32)       18464     \n"," ranspose)                                                       \n","                                                                 \n"," conv2d_transpose_9 (Conv2DT  (None, 28, 28, 1)        289       \n"," ranspose)                                                       \n","                                                                 \n","=================================================================\n","Total params: 65,089\n","Trainable params: 65,089\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["## Overall VAE Model"],"metadata":{"id":"_e6nxyt5FwXD"}},{"cell_type":"code","source":["vae_input = Input(shape=(28,28,1), name=\"vae_input\")\n","z,_,_ = encoder_model(vae_input)\n","output = decoder_model(z)\n","vae = Model(vae_input, output, name=\"vae\")\n","vae.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HQBtlvo2FzAq","executionInfo":{"status":"ok","timestamp":1657199486413,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6253537a-301f-472f-c2b3-afcf9e03a4dc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"vae\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," vae_input (InputLayer)      [(None, 28, 28, 1)]       0         \n","                                                                 \n"," encoder (Functional)        [(None, 2),               69076     \n","                              (None, 2),                         \n","                              (None, 2)]                         \n","                                                                 \n"," decoder (Functional)        (None, 28, 28, 1)         65089     \n","                                                                 \n","=================================================================\n","Total params: 134,165\n","Trainable params: 134,165\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["for i in range(3):\n","  print(vae.layers[i])"],"metadata":{"id":"pm3WXvS0FzC5","executionInfo":{"status":"ok","timestamp":1657199486414,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"860aad02-2617-49c7-ecf7-e177d8f887c8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<keras.engine.input_layer.InputLayer object at 0x7f3504578550>\n","<keras.engine.functional.Functional object at 0x7f35045df190>\n","<keras.engine.functional.Functional object at 0x7f357004a390>\n"]}]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"uOhXdaz_-fY7"}},{"cell_type":"code","source":["OPTIMIZER = Adam(learning_rate = 1e-3)\n","EPOCHS = 20"],"metadata":{"id":"vT02JszjTBSw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def custom_loss(y_true,y_pred,mean,log_var):\n","\n","  loss_rec = tf.reduce_mean(tf.reduce_sum(tf.keras.losses.binary_crossentropy(y_true,y_pred), axis = (1,2)))\n","\n","  loss_reg = -0.5 * (1 + log_var - tf.square(mean) - tf.exp(log_var))\n","  \n","  return loss_rec+tf.reduce_mean(tf.reduce_sum(loss_reg, axis=1))"],"metadata":{"id":"A6b9RgTDTBX1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["@tf.function\n","def training_block(x_batch):\n","  with tf.GradientTape() as recorder:\n","    z,mean,log_var = encoder_model(x_batch)\n","    y_pred = decoder_model(z)\n","    y_true = x_batch\n","    loss = custom_loss(y_true,y_pred, mean, log_var)\n","  \n","  partial_derivatives = recorder.gradient(loss,vae.trainable_weights)\n","  OPTIMIZER.apply_gradients(zip(partial_derivatives, vae.trainable_weights))\n","  return loss"],"metadata":{"id":"ypKgOLjmTBaC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def neuralearn(epochs):\n","  for epoch in range(1,epochs+1):\n","    print('Training starts for epoch number {}'.format(epoch))\n","\n","    for step, x_batch in enumerate(train_dataset):\n","      loss = training_block(x_batch)\n","    print('Training Loss is: ', loss)\n","  print('Training Complete!!!')"],"metadata":{"id":"7-J779KKTBb8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["neuralearn(EPOCHS)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G9-VlnMjTBgQ","executionInfo":{"status":"ok","timestamp":1657140963469,"user_tz":-60,"elapsed":69639,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6b2b04cf-ad05-4514-e93d-6f1ccee0e845"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Training starts for epoch number 1\n","Training Loss is:  tf.Tensor(193.6552, shape=(), dtype=float32)\n","Training starts for epoch number 2\n","Training Loss is:  tf.Tensor(165.99855, shape=(), dtype=float32)\n","Training starts for epoch number 3\n","Training Loss is:  tf.Tensor(167.46562, shape=(), dtype=float32)\n","Training starts for epoch number 4\n","Training Loss is:  tf.Tensor(152.56444, shape=(), dtype=float32)\n","Training starts for epoch number 5\n","Training Loss is:  tf.Tensor(153.63637, shape=(), dtype=float32)\n","Training starts for epoch number 6\n","Training Loss is:  tf.Tensor(148.2903, shape=(), dtype=float32)\n","Training starts for epoch number 7\n","Training Loss is:  tf.Tensor(161.3202, shape=(), dtype=float32)\n","Training starts for epoch number 8\n","Training Loss is:  tf.Tensor(144.28664, shape=(), dtype=float32)\n","Training starts for epoch number 9\n","Training Loss is:  tf.Tensor(147.50954, shape=(), dtype=float32)\n","Training starts for epoch number 10\n","Training Loss is:  tf.Tensor(146.60031, shape=(), dtype=float32)\n","Training starts for epoch number 11\n","Training Loss is:  tf.Tensor(149.86223, shape=(), dtype=float32)\n","Training starts for epoch number 12\n","Training Loss is:  tf.Tensor(153.46059, shape=(), dtype=float32)\n","Training starts for epoch number 13\n","Training Loss is:  tf.Tensor(149.3595, shape=(), dtype=float32)\n","Training starts for epoch number 14\n","Training Loss is:  tf.Tensor(153.71486, shape=(), dtype=float32)\n","Training starts for epoch number 15\n","Training Loss is:  tf.Tensor(148.0849, shape=(), dtype=float32)\n","Training starts for epoch number 16\n","Training Loss is:  tf.Tensor(148.9141, shape=(), dtype=float32)\n","Training starts for epoch number 17\n","Training Loss is:  tf.Tensor(145.43872, shape=(), dtype=float32)\n","Training starts for epoch number 18\n","Training Loss is:  tf.Tensor(152.83977, shape=(), dtype=float32)\n","Training starts for epoch number 19\n","Training Loss is:  tf.Tensor(150.6617, shape=(), dtype=float32)\n","Training starts for epoch number 20\n","Training Loss is:  tf.Tensor(148.25497, shape=(), dtype=float32)\n","Training Complete!!!\n"]}]},{"cell_type":"markdown","source":["## Overriding train_step method"],"metadata":{"id":"asPy7WeB00pD"}},{"cell_type":"code","source":["class VAE(tf.keras.Model):\n","  def __init__(self,encoder_model, decoder_model):\n","    super(VAE,self).__init__()\n","    self.encoder=encoder_model\n","    self.decoder=decoder_model\n","    self.loss_tracker=tf.keras.metrics.Mean(name='loss')\n","  @property\n","  def metrics(self):\n","    return [self.loss_tracker]\n","  \n","  def train_step(self,x_batch):\n","    with tf.GradientTape() as recorder:\n","      z,mean,log_var = encoder_model(x_batch)\n","      y_pred = decoder_model(z)\n","      y_true = x_batch\n","      loss = custom_loss(y_true,y_pred, mean, log_var)\n","    \n","    partial_derivatives = recorder.gradient(loss,self.trainable_weights)\n","    OPTIMIZER.apply_gradients(zip(partial_derivatives, self.trainable_weights))\n","\n","    self.loss_tracker.update_state(loss)\n","    return {'loss':self.loss_tracker.result()}"],"metadata":{"id":"TU1YaYILZGo9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model=VAE(encoder_model,decoder_model)\n","model.compile(optimizer=OPTIMIZER)\n","model.fit(train_dataset, epochs=20,batch_size=128,)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GgfPHSShyP1z","outputId":"6b3a3b44-085a-4ad5-c271-296d02ca89fd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","547/547 [==============================] - 5s 7ms/step - loss: 217.7729\n","Epoch 2/20\n","547/547 [==============================] - 4s 7ms/step - loss: 191.9316\n","Epoch 3/20\n","547/547 [==============================] - 4s 7ms/step - loss: 182.5241\n","Epoch 4/20\n","547/547 [==============================] - 4s 7ms/step - loss: 167.4643\n","Epoch 5/20\n","189/547 [=========>....................] - ETA: 2s - loss: 165.4101"]}]},{"cell_type":"markdown","source":["# Testing"],"metadata":{"id":"KZw5haj0l2EB"}},{"cell_type":"code","source":["scale=1\n","n=16"],"metadata":{"id":"EVcJC-3N-kbv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["grid_x = np.linspace(-scale,scale,16)\n","grid_y = np.linspace(-scale,scale,16)"],"metadata":{"id":"GpJb0aKI8-00"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(grid_x,grid_y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RicXspLx8-3T","executionInfo":{"status":"ok","timestamp":1657200357273,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8d7b4b12-0d62-4ab5-82cb-99cb52a478e1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[-1.         -0.86666667 -0.73333333 -0.6        -0.46666667 -0.33333333\n"," -0.2        -0.06666667  0.06666667  0.2         0.33333333  0.46666667\n","  0.6         0.73333333  0.86666667  1.        ] [-1.         -0.86666667 -0.73333333 -0.6        -0.46666667 -0.33333333\n"," -0.2        -0.06666667  0.06666667  0.2         0.33333333  0.46666667\n","  0.6         0.73333333  0.86666667  1.        ]\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(12,12))\n","k=0\n","for i in grid_x:\n","  for j in grid_y:\n","    ax=plt.subplot(n,n,k+1)\n","\n","    input=tf.constant([[i,j]])\n","    out=model.decoder.predict(input)[0][...,0]\n","    plt.imshow(out,cmap=\"Greys_r\")\n","    plt.axis('off')\n","    k+=1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":683},"id":"EVOLV2Mr8-5p","executionInfo":{"status":"ok","timestamp":1657200420409,"user_tz":-60,"elapsed":18730,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"40367d08-346f-4942-d51c-f676821b74c4"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x864 with 256 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["print(vae.layers[2].predict(tf.constant([[-1,1]]))[0][...,0].shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TSKnoRkB8-7v","executionInfo":{"status":"ok","timestamp":1657135994401,"user_tz":-60,"elapsed":2,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b6ce9cfd-3a04-4f9e-e1a6-89bac6611fc5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(28, 28)\n"]}]},{"cell_type":"code","source":["(x_train, y_train), _ = tf.keras.datasets.mnist.load_data()\n","mnist_digits = np.expand_dims(x_train,-1).astype(\"float32\") / 255"],"metadata":{"id":"AZ-iZ4ji8--H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["z,_,_=vae.layers[1].predict(x_train)\n","plt.figure(figsize=(12,12))\n","plt.scatter(z[:,0],z[:,1],c=y_train)\n","plt.colorbar()\n","plt.show()"],"metadata":{"id":"v_Cs866W8_AU","colab":{"base_uri":"https://localhost:8080/","height":704},"executionInfo":{"status":"ok","timestamp":1657142026560,"user_tz":-60,"elapsed":13499,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"93971b27-7261-4738-ed04-9d01f11fef06"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x864 with 2 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FILE: deep learning for computer vision/9-DCGAN By Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"zGcCsOilc8b1"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, )\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"markdown","metadata":{"id":"gPLHB2s5gBJu"},"source":["# Data"]},{"cell_type":"markdown","metadata":{"id":"QhKsQaul6le_"},"source":["## Download Data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":102810,"status":"ok","timestamp":1662631876305,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"DnE86Hpn6GGQ","outputId":"58dc5f13-0e5d-45b3-825e-c1374abf3537"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197604.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197605.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197606.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197607.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197608.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197609.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197610.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197611.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197612.jpg  \n","  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\"/content/celeba-dataset.zip\" -d \"/content/dataset/\""]},{"cell_type":"markdown","metadata":{"id":"IV3KzHNq6w0g"},"source":["## Prepare Data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P7pb2bpv6GLf"},"outputs":[],"source":["BATCH_SIZE = 128\n","IM_SHAPE = (64,64,3)\n","LEARNING_RATE = 2e-4\n","LATENT_DIM=100\n","EPOCHS=20"]},{"cell_type":"code","source":[],"metadata":{"id":"oLcX0RFw4leA"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15348,"status":"ok","timestamp":1662632042787,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"pNiSHjva6GN5","outputId":"f832fda6-938a-4e54-e04f-19fab2f79b9e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 202599 files belonging to 1 classes.\n"]}],"source":["dataset = tf.keras.preprocessing.image_dataset_from_directory(\n","    \"/content/dataset/img_align_celeba/img_align_celeba\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",")"]},{"cell_type":"code","source":[],"metadata":{"id":"Nrd47dQJ4muu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V98GV8FA4eYO","executionInfo":{"status":"ok","timestamp":1669784102766,"user_tz":-60,"elapsed":2798,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b8df7e21-7cdc-4810-e303-408af37b12bf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 5864 files belonging to 1 classes.\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1669784104169,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"CT7fSwhF6GQj","outputId":"38a594d4-39e8-443e-f5ec-73b13f0cc413"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BatchDataset element_spec=TensorSpec(shape=(None, 64, 64, 3), dtype=tf.float32, name=None)>"]},"metadata":{},"execution_count":6}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dBO55qltL0OS"},"outputs":[],"source":["def preprocess(image):\n","  return tf.cast(image, tf.float32) / 127.5 - 1.0"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MfOhaJz-6GSw"},"outputs":[],"source":["train_dataset = (\n","    dataset\n","    .map(preprocess)\n","    .unbatch()\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE,drop_remainder=True)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1326,"status":"ok","timestamp":1669784110043,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"AU4bl2Hd6GYE","outputId":"7ff241a5-38d7-43e7-b7e7-8fc16f0f43be"},"outputs":[{"output_type":"stream","name":"stdout","text":["(128, 64, 64, 3)\n"]}],"source":["for d in train_dataset.take(1):\n","  print(d.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":357},"executionInfo":{"elapsed":488,"status":"ok","timestamp":1669784110910,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"RcTlClDN6Gaq","outputId":"6008df44-8752-443e-b420-5332819b1f68"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x432 with 4 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize = (6,6))\n","k=0\n","n = 4\n","for i in range(n):\n","  ax = plt.subplot(2,2, k+1)\n","  plt.imshow((d[i]+1)/2)\n","  plt.axis(\"off\")\n","  k+=1"]},{"cell_type":"markdown","metadata":{"id":"tdtWfpUGgC4E"},"source":["# MOdeling"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ni3A9hDmgKKU"},"outputs":[],"source":["generator=tf.keras.Sequential([\n","  Input(shape=(LATENT_DIM,)),\n","  Dense(4*4*LATENT_DIM),\n","  Reshape((4,4,LATENT_DIM)),\n","\n","  Conv2DTranspose(512,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(256,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(128,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(3,kernel_size=4,strides=2, activation=tf.keras.activations.tanh, padding='same'),\n","\n","],name='generator')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1669784117143,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"qfYErd5YuEjg","outputId":"1c10c8ba-0240-4e2b-ab91-efe6dd2fa2f3"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"generator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," dense (Dense)               (None, 1600)              161600    \n","                                                                 \n"," reshape (Reshape)           (None, 4, 4, 100)         0         \n","                                                                 \n"," conv2d_transpose (Conv2DTra  (None, 8, 8, 512)        819712    \n"," nspose)                                                         \n","                                                                 \n"," batch_normalization (BatchN  (None, 8, 8, 512)        2048      \n"," ormalization)                                                   \n","                                                                 \n"," leaky_re_lu (LeakyReLU)     (None, 8, 8, 512)         0         \n","                                                                 \n"," conv2d_transpose_1 (Conv2DT  (None, 16, 16, 256)      2097408   \n"," ranspose)                                                       \n","                                                                 \n"," batch_normalization_1 (Batc  (None, 16, 16, 256)      1024      \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_1 (LeakyReLU)   (None, 16, 16, 256)       0         \n","                                                                 \n"," conv2d_transpose_2 (Conv2DT  (None, 32, 32, 128)      524416    \n"," ranspose)                                                       \n","                                                                 \n"," batch_normalization_2 (Batc  (None, 32, 32, 128)      512       \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_2 (LeakyReLU)   (None, 32, 32, 128)       0         \n","                                                                 \n"," conv2d_transpose_3 (Conv2DT  (None, 64, 64, 3)        6147      \n"," ranspose)                                                       \n","                                                                 \n","=================================================================\n","Total params: 3,612,867\n","Trainable params: 3,611,075\n","Non-trainable params: 1,792\n","_________________________________________________________________\n"]}],"source":["generator.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fXiiUmhZuEmJ"},"outputs":[],"source":["discriminator=tf.keras.Sequential([\n","  Input(shape=(IM_SHAPE[0],IM_SHAPE[1],3)),\n","\n","  Conv2D(64,kernel_size=4,strides=2, padding='same'),\n","  LeakyReLU(0.2),\n","\n","  Conv2D(128,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","  \n","  Conv2D(256,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2D(1,kernel_size=4,strides=2, padding='same'),\n","\n","  Flatten(),\n","  Dense(1,activation='sigmoid')\n","  \n","\n","],name='discriminator')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":419,"status":"ok","timestamp":1669784117954,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"QxJiYAOquEon","outputId":"d869bff7-6332-43be-b841-b6d5d451b97b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"discriminator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 32, 32, 64)        3136      \n","                                                                 \n"," leaky_re_lu_3 (LeakyReLU)   (None, 32, 32, 64)        0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 16, 16, 128)       131200    \n","                                                                 \n"," batch_normalization_3 (Batc  (None, 16, 16, 128)      512       \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 16, 16, 128)       0         \n","                                                                 \n"," conv2d_2 (Conv2D)           (None, 8, 8, 256)         524544    \n","                                                                 \n"," batch_normalization_4 (Batc  (None, 8, 8, 256)        1024      \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_5 (LeakyReLU)   (None, 8, 8, 256)         0         \n","                                                                 \n"," conv2d_3 (Conv2D)           (None, 4, 4, 1)           4097      \n","                                                                 \n"," flatten (Flatten)           (None, 16)                0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 17        \n","                                                                 \n","=================================================================\n","Total params: 664,530\n","Trainable params: 663,762\n","Non-trainable params: 768\n","_________________________________________________________________\n"]}],"source":["discriminator.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BsC-wtWGOU2X"},"outputs":[],"source":["class ShowImage(tf.keras.callbacks.Callback):\n","    def __init__(self, latent_dim=100):\n","        self.latent_dim = latent_dim\n","\n","    def on_epoch_end(self, epoch, logs=None):\n","        n=6\n","        k=0\n","        out=self.model.generator(tf.random.normal(shape=(36, self.latent_dim)))\n","        plt.figure(figsize=(16,16))\n","        for i in range(n):\n","          for j in range(n):\n","            ax=plt.subplot(n,n,k+1)\n","            plt.imshow((out[k]+1)/2,)\n","            plt.axis('off')\n","            k+=1\n","        plt.savefig(\"generated/gen_images_epoch_{}.png\".format(epoch+1))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lcPT8TgBuErO"},"outputs":[],"source":["class GAN(tf.keras.Model):\n","  def __init__(self,discriminator,generator):\n","    super(GAN,self).__init__()\n","    self.discriminator=discriminator\n","    self.generator=generator\n","\n","  def compile(self,d_optimizer,g_optimizer,loss_fn):\n","    super(GAN,self).compile()\n","    self.d_optimizer=d_optimizer\n","    self.g_optimizer=g_optimizer\n","    self.loss_fn=loss_fn\n","    self.d_loss_metric=tf.keras.metrics.Mean(name='d_loss')\n","    self.g_loss_metric=tf.keras.metrics.Mean(name='g_loss')\n","    \n","  @property\n","  def metrics(self):\n","    return [self.d_loss_metric,self.g_loss_metric]\n","  \n","  def train_step(self,real_images):\n","    batch_size=tf.shape(real_images)[0]\n","\n","    ######## Discriminator\n","    random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n","    fake_images=self.generator(random_noise)\n","\n","    real_labels=tf.ones((batch_size,1))+0.25*tf.random.uniform((batch_size,1),minval=-1,maxval=1)\n","    fake_labels=tf.zeros((batch_size,1))+0.25*tf.random.uniform((batch_size,1),)\n","\n","    with tf.GradientTape() as recorder:\n","      real_predictions=self.discriminator(real_images)\n","      d_loss_real=self.loss_fn(real_labels,real_predictions)\n","\n","      fake_predictions=self.discriminator(fake_images)\n","      d_loss_fake=self.loss_fn(fake_labels,fake_predictions)\n","\n","      d_loss=d_loss_real+d_loss_fake\n","      \n","    partial_derivatives = recorder.gradient(d_loss,self.discriminator.trainable_weights)\n","    self.d_optimizer.apply_gradients(zip(partial_derivatives, self.discriminator.trainable_weights))\n","\n","    ############# Generator\n","    random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n","    flipped_fake_labels=tf.ones((batch_size,1))\n","\n","    with tf.GradientTape() as recorder:\n","\n","      fake_predictions=self.discriminator(self.generator(random_noise))\n","      g_loss=self.loss_fn(flipped_fake_labels,fake_predictions)\n","      \n","    partial_derivatives = recorder.gradient(g_loss,self.generator.trainable_weights)\n","    self.g_optimizer.apply_gradients(zip(partial_derivatives, self.generator.trainable_weights))\n","\n","    self.d_loss_metric.update_state(d_loss)\n","    self.g_loss_metric.update_state(g_loss)\n","    \n","    return {'d_loss':self.d_loss_metric.result(),\n","            'g_loss':self.g_loss_metric.result()}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5aoneKPouEtr"},"outputs":[],"source":["gan=GAN(discriminator,generator)\n","gan.compile(\n","    d_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n","    g_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n","    loss_fn=tf.keras.losses.BinaryCrossentropy(),\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LHcLh4YeRF3I"},"outputs":[],"source":["!mkdir generated"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hFfDNRj8uEwi","outputId":"de8fd0c8-f774-46b3-df3b-50d6a453ad78"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/1000\n","      6/Unknown - 2s 188ms/step - d_loss: 1.3566 - g_loss: 0.6954"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0775s vs `on_train_batch_end` time: 0.1039s). Check your callbacks.\n"]},{"output_type":"stream","name":"stdout","text":["45/45 [==============================] - 10s 214ms/step - d_loss: 1.3085 - g_loss: 0.7376\n","Epoch 2/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.2950 - g_loss: 0.7520\n","Epoch 3/1000\n","45/45 [==============================] - 11s 224ms/step - d_loss: 1.3033 - g_loss: 0.7382\n","Epoch 4/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.3086 - g_loss: 0.7341\n","Epoch 5/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2939 - g_loss: 0.7332\n","Epoch 6/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.3010 - g_loss: 0.7389\n","Epoch 7/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2927 - g_loss: 0.7363\n","Epoch 8/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.3005 - g_loss: 0.7373\n","Epoch 9/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2940 - g_loss: 0.7332\n","Epoch 10/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.3000 - g_loss: 0.7235\n","Epoch 11/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2972 - g_loss: 0.7277\n","Epoch 12/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2994 - g_loss: 0.7441\n","Epoch 13/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2981 - g_loss: 0.7267\n","Epoch 14/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.3014 - g_loss: 0.7359\n","Epoch 15/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.3064 - g_loss: 0.7338\n","Epoch 16/1000\n","45/45 [==============================] - 16s 348ms/step - d_loss: 1.3008 - g_loss: 0.7522\n","Epoch 17/1000\n","45/45 [==============================] - 10s 212ms/step - d_loss: 1.3083 - g_loss: 0.7317\n","Epoch 18/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.3012 - g_loss: 0.7263\n","Epoch 19/1000\n","45/45 [==============================] - 10s 221ms/step - d_loss: 1.2926 - g_loss: 0.7280\n","Epoch 20/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2960 - g_loss: 0.7322\n","Epoch 21/1000\n","45/45 [==============================] - ETA: 0s - d_loss: 1.2846 - g_loss: 0.7297"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n","  if __name__ == '__main__':\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r45/45 [==============================] - 11s 234ms/step - d_loss: 1.2846 - g_loss: 0.7297\n","Epoch 22/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.2928 - g_loss: 0.7370\n","Epoch 23/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2993 - g_loss: 0.7454\n","Epoch 24/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2898 - g_loss: 0.7301\n","Epoch 25/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.3017 - g_loss: 0.7567\n","Epoch 26/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2881 - g_loss: 0.7572\n","Epoch 27/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.3091 - g_loss: 0.7567\n","Epoch 28/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2915 - g_loss: 0.7555\n","Epoch 29/1000\n","45/45 [==============================] - 12s 251ms/step - d_loss: 1.2786 - g_loss: 0.7506\n","Epoch 30/1000\n","45/45 [==============================] - 10s 211ms/step - d_loss: 1.2879 - g_loss: 0.7450\n","Epoch 31/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2933 - g_loss: 0.7492\n","Epoch 32/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2861 - g_loss: 0.7318\n","Epoch 33/1000\n","45/45 [==============================] - 11s 223ms/step - d_loss: 1.2833 - g_loss: 0.7530\n","Epoch 34/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2913 - g_loss: 0.7569\n","Epoch 35/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2764 - g_loss: 0.7642\n","Epoch 36/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2866 - g_loss: 0.7685\n","Epoch 37/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2785 - g_loss: 0.7618\n","Epoch 38/1000\n","45/45 [==============================] - 11s 245ms/step - d_loss: 1.2762 - g_loss: 0.7496\n","Epoch 39/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2718 - g_loss: 0.7386\n","Epoch 40/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2714 - g_loss: 0.7571\n","Epoch 41/1000\n","45/45 [==============================] - 10s 220ms/step - d_loss: 1.2723 - g_loss: 0.7628\n","Epoch 42/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2810 - g_loss: 0.7735\n","Epoch 43/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2689 - g_loss: 0.7753\n","Epoch 44/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2692 - g_loss: 0.7858\n","Epoch 45/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2699 - g_loss: 0.7854\n","Epoch 46/1000\n","45/45 [==============================] - 10s 221ms/step - d_loss: 1.2682 - g_loss: 0.7780\n","Epoch 47/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2561 - g_loss: 0.7824\n","Epoch 48/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2569 - g_loss: 0.7770\n","Epoch 49/1000\n","45/45 [==============================] - 12s 255ms/step - d_loss: 1.2546 - g_loss: 0.7780\n","Epoch 50/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2505 - g_loss: 0.7928\n","Epoch 51/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2646 - g_loss: 0.7942\n","Epoch 52/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2527 - g_loss: 0.7960\n","Epoch 53/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2485 - g_loss: 0.8067\n","Epoch 54/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2571 - g_loss: 0.7993\n","Epoch 55/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2415 - g_loss: 0.7876\n","Epoch 56/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2404 - g_loss: 0.7822\n","Epoch 57/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2420 - g_loss: 0.7913\n","Epoch 58/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2424 - g_loss: 0.8121\n","Epoch 59/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2387 - g_loss: 0.8108\n","Epoch 60/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2520 - g_loss: 0.8210\n","Epoch 61/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2320 - g_loss: 0.8103\n","Epoch 62/1000\n","45/45 [==============================] - 11s 219ms/step - d_loss: 1.2347 - g_loss: 0.8239\n","Epoch 63/1000\n","45/45 [==============================] - 10s 212ms/step - d_loss: 1.2365 - g_loss: 0.8413\n","Epoch 64/1000\n","45/45 [==============================] - 12s 266ms/step - d_loss: 1.2202 - g_loss: 0.8320\n","Epoch 65/1000\n","45/45 [==============================] - 10s 221ms/step - d_loss: 1.3631 - g_loss: 0.8417\n","Epoch 66/1000\n","45/45 [==============================] - 10s 222ms/step - d_loss: 1.2443 - g_loss: 0.8049\n","Epoch 67/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2180 - g_loss: 0.8118\n","Epoch 68/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2110 - g_loss: 0.8006\n","Epoch 69/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2124 - g_loss: 0.8063\n","Epoch 70/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2113 - g_loss: 0.8094\n","Epoch 71/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2103 - g_loss: 0.8250\n","Epoch 72/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2199 - g_loss: 0.8525\n","Epoch 73/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2099 - g_loss: 0.8548\n","Epoch 74/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2025 - g_loss: 0.8236\n","Epoch 75/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2101 - g_loss: 0.8299\n","Epoch 76/1000\n","45/45 [==============================] - 10s 211ms/step - d_loss: 1.2076 - g_loss: 0.8444\n","Epoch 77/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2022 - g_loss: 0.8375\n","Epoch 78/1000\n","45/45 [==============================] - 10s 220ms/step - d_loss: 1.2056 - g_loss: 0.8431\n","Epoch 79/1000\n","45/45 [==============================] - 11s 224ms/step - d_loss: 1.1993 - g_loss: 0.8489\n","Epoch 80/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2036 - g_loss: 0.8594\n","Epoch 81/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.2024 - g_loss: 0.8502\n","Epoch 82/1000\n","45/45 [==============================] - 13s 279ms/step - d_loss: 1.1925 - g_loss: 0.8659\n","Epoch 83/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.1946 - g_loss: 0.8562\n","Epoch 84/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1829 - g_loss: 0.8692\n","Epoch 85/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.1922 - g_loss: 0.8717\n","Epoch 86/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.1965 - g_loss: 0.8674\n","Epoch 87/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.1903 - g_loss: 0.8935\n","Epoch 88/1000\n","45/45 [==============================] - 10s 220ms/step - d_loss: 1.1868 - g_loss: 0.8777\n","Epoch 89/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1811 - g_loss: 0.8727\n","Epoch 90/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1800 - g_loss: 0.8802\n","Epoch 91/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.1718 - g_loss: 0.8918\n","Epoch 92/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.1860 - g_loss: 0.8764\n","Epoch 93/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1814 - g_loss: 0.8832\n","Epoch 94/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1603 - g_loss: 0.8834\n","Epoch 95/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1646 - g_loss: 0.9053\n","Epoch 96/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1565 - g_loss: 0.8929\n","Epoch 97/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1782 - g_loss: 0.9001\n","Epoch 98/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1629 - g_loss: 0.8972\n","Epoch 99/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1510 - g_loss: 0.8854\n","Epoch 100/1000\n","45/45 [==============================] - 11s 225ms/step - d_loss: 1.1576 - g_loss: 0.8853\n","Epoch 101/1000\n","45/45 [==============================] - 11s 214ms/step - d_loss: 1.1737 - g_loss: 0.9095\n","Epoch 102/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.1890 - g_loss: 0.9207\n","Epoch 103/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1544 - g_loss: 0.9127\n","Epoch 104/1000\n"]}],"source":["EPOCHS=1000\n","history=gan.fit(train_dataset,epochs=EPOCHS,callbacks=[ShowImage(LATENT_DIM)])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"ZPxKc48PuEzD","outputId":"f024d849-5392-4ebd-a105-9808cf73ea5a"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(history.history['d_loss'])\n","plt.plot(history.history['g_loss'])\n","plt.title('GAN Loss')\n","plt.ylabel('Loss')\n","plt.xlabel('epoch')\n","plt.legend(['d_loss', 'g_loss'], loc='upper left')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"j-bpKlbwuE1j"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SLGNQSpXuE9N"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FjePWMBQuE_5"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1ZO02cStWUyA7R_QjbaO1Bw0QbJtFzsjY","timestamp":1673808973947}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

================================================
FILE: deep learning for computer vision/emotions-detection/Procfile
================================================
web: gunicorn service.main:app --workers 2 --worker-class uvicorn.workers.UvicornWorker

================================================
FILE: deep learning for computer vision/emotions-detection/locusts.py
================================================
from locust import SequentialTaskSet, HttpUser, task

class DetectorTask(SequentialTaskSet):
    @task
    def detection(self):
        with open("test_image.jpg","rb") as image:
            self.client.post(
                "/detect",
                files={"im":image}
            )


class LoadTester(HttpUser):
    host="https://emotions-detection-neuralearn.herokuapp.com"
    tasks=[DetectorTask]

================================================
FILE: deep learning for computer vision/emotions-detection/requirements.txt
================================================
anyio==3.6.2
certifi==2022.9.24
click==8.1.3
coloredlogs==15.0.1
dnspython==2.2.1
email-validator==1.3.0
fastapi==0.87.0
flatbuffers==22.10.26
gunicorn==20.1.0
h11==0.12.0
httpcore==0.15.0
httptools==0.5.0
httpx==0.23.0
humanfriendly==10.0
idna==3.4
itsdangerous==2.1.2
Jinja2==3.1.2
MarkupSafe==2.1.1
mpmath==1.2.1
numpy==1.23.4
onnxruntime==1.13.1
opencv-python-headless
orjson==3.8.1
packaging==21.3
Pillow==9.3.0
protobuf==4.21.9
pydantic==1.10.2
pyparsing==3.0.9
python-dotenv==0.21.0
python-multipart==0.0.5
PyYAML==6.0
rfc3986==1.5.0
six==1.16.0
sniffio==1.3.0
starlette==0.21.0
sympy==1.11.1
typing_extensions==4.4.0
ujson==5.5.0
uvicorn==0.19.0
uvloop==0.17.0
watchfiles==0.18.1
websockets==10.4


================================================
FILE: deep learning for computer vision/emotions-detection/runtime.txt
================================================
python-3.9.15

================================================
FILE: deep learning for computer vision/emotions-detection/service/__init__.py
================================================


================================================
FILE: deep learning for computer vision/emotions-detection/service/api/__init__.py
================================================


================================================
FILE: deep learning for computer vision/emotions-detection/service/api/api.py
================================================
from fastapi import APIRouter
from service.api.endpoints.detect import emo_router
from service.api.endpoints.test import test_router

main_router=APIRouter()

main_router.include_router(emo_router)
main_router.include_router(test_router)



================================================
FILE: deep learning for computer vision/emotions-detection/service/api/endpoints/__init__.py
================================================


================================================
FILE: deep learning for computer vision/emotions-detection/service/api/endpoints/detect.py
================================================
from fastapi import APIRouter, UploadFile,HTTPException
from PIL import Image
from io import BytesIO
import numpy as np
from service.core.logic.onnx_inference import emotions_detector
from service.core.schemas.output import APIOutput
emo_router = APIRouter()

@emo_router.post("/detect",response_model=APIOutput)
async def detect(im: UploadFile):

    if im.filename.split(".")[-1] in ("jpg","jpeg","png"):
        pass
    else:
        raise HTTPException(
            status_code=415,detail="Not an image")

    image = Image.open(BytesIO(im.file.read()))
    image = np.array(image)


    return emotions_detector(image)


================================================
FILE: deep learning for computer vision/emotions-detection/service/api/endpoints/test.py
================================================
from fastapi import APIRouter

test_router = APIRouter()

@test_router.get("/test")
async def testing():

    return {"testing":"testing"}


================================================
FILE: deep learning for computer vision/emotions-detection/service/core/__init__.py
================================================


================================================
FILE: deep learning for computer vision/emotions-detection/service/core/logic/__init__.py
================================================


================================================
FILE: deep learning for computer vision/emotions-detection/service/core/logic/onnx_inference.py
================================================
import onnxruntime as rt
import cv2
import numpy as np
import time
import service.main as s

def emotions_detector(img_array):
    
    if len(img_array.shape)==2:
        img_array=cv2.cvtColor(img_array,cv2.COLOR_GRAY2RGB)
    
    time_init=time.time()

    test_image = cv2.resize(img_array, (256,256))
    im = np.float32(test_image)
    img_array = np.expand_dims(im, axis = 0)

    onnx_pred = s.m_q.run(['dense'], {"input":img_array})
    
    time_elapsed=time.time()-time_init

    emotion=""
    if np.argmax(onnx_pred[0][0])==0:
        emotion="angry"
    elif np.argmax(onnx_pred[0][0])==1:
        emotion="happy"
    else:
        emotion="sad"

    return {
        "emotion":emotion,
        "time_elapsed":str(time_elapsed),        
    }

================================================
FILE: deep learning for computer vision/emotions-detection/service/core/schemas/__init__.py
================================================


================================================
FILE: deep learning for computer vision/emotions-detection/service/core/schemas/input.py
================================================


================================================
FILE: deep learning for computer vision/emotions-detection/service/core/schemas/output.py
================================================
from pydantic import BaseModel

class APIOutput(BaseModel):
    emotion: str
    time_elapsed: str

================================================
FILE: deep learning for computer vision/emotions-detection/service/main.py
================================================
from fastapi import FastAPI
from service.api.api import main_router
import onnxruntime as rt

app = FastAPI(project_name="Emotions Detection")
app.include_router(main_router)

providers=['CPUExecutionProvider']    
m_q = rt.InferenceSession(
    "service/eff_quantized.onnx",
    providers=providers
)

@app.get("/")
async def root():
    return {"hello": "world"}

================================================
FILE: deep learning for image generation/1-VAE by Neuralearn.ai-.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"14eFua9pLCV6AOYqGdyq_R-eWWUfZVtYs","timestamp":1673808986067}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"vPKrQB5FrFnW"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape, Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, )\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"markdown","source":["# Data Preparation"],"metadata":{"id":"4lNrm79MlgpS"}},{"cell_type":"code","source":["(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()\n","mnist_digits = np.concatenate([x_train, x_test], axis=0)\n","mnist_digits = np.expand_dims(mnist_digits, -1).astype(\"float32\") / 255"],"metadata":{"id":"wZ3wCXMrRQDZ","executionInfo":{"status":"ok","timestamp":1657285544409,"user_tz":-60,"elapsed":1072,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"26f66499-c049-4fbd-d919-a9d4ed6f3008"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","11493376/11490434 [==============================] - 0s 0us/step\n","11501568/11490434 [==============================] - 0s 0us/step\n"]}]},{"cell_type":"code","source":["dataset = tf.data.Dataset.from_tensor_slices(mnist_digits)"],"metadata":{"id":"TuiM9At8RQFi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["BATCH_SIZE = 128\n","LATENT_DIM = 2"],"metadata":{"id":"rvK_VfBSRQJv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset = (\n","    dataset\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"],"metadata":{"id":"-IxW9wxQRQMM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2ne293QuRQOL","executionInfo":{"status":"ok","timestamp":1657285546248,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3ca219cd-4806-4a7f-f8f0-875053ae47c3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=TensorSpec(shape=(None, 28, 28, 1), dtype=tf.float32, name=None)>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":[],"metadata":{"id":"Kp4aWbGDRQQO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"CrM0hUSmThBQ"}},{"cell_type":"markdown","source":["## Sampling"],"metadata":{"id":"9JBa79Pxd4gi"}},{"cell_type":"code","source":["class Sampling(Layer):\n","  def call(self, inputs):\n","    mean, log_var = inputs\n","    return mean + tf.math.exp(0.5*log_var)*tf.random.normal(shape = (tf.shape(mean)[0], tf.shape(mean)[1])) "],"metadata":{"id":"JfVZjHFWd49Q"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Encoder"],"metadata":{"id":"0U5XGop-TjGX"}},{"cell_type":"code","source":["encoder_inputs = Input(shape=(28,28,1))\n","\n","x = Conv2D(32, 3, activation='relu', strides=2, padding='same')(encoder_inputs)\n","x = Conv2D(64, 3, activation='relu', strides=2, padding='same')(x)\n","\n","x = Flatten()(x)\n","x = Dense(16, activation='relu')(x)\n","\n","mean = Dense(LATENT_DIM,)(x)\n","log_var = Dense(LATENT_DIM,)(x)\n","\n","z = Sampling()([mean,log_var])\n","\n","encoder_model = Model(encoder_inputs,[z,mean,log_var], name='encoder')\n","encoder_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6zfqKfkMRQUe","executionInfo":{"status":"ok","timestamp":1657285548993,"user_tz":-60,"elapsed":8,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9eb4170c-84b4-42f8-dc46-eb22e2f3b602"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"encoder\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_5 (InputLayer)           [(None, 28, 28, 1)]  0           []                               \n","                                                                                                  \n"," conv2d_4 (Conv2D)              (None, 14, 14, 32)   320         ['input_5[0][0]']                \n","                                                                                                  \n"," conv2d_5 (Conv2D)              (None, 7, 7, 64)     18496       ['conv2d_4[0][0]']               \n","                                                                                                  \n"," flatten_2 (Flatten)            (None, 3136)         0           ['conv2d_5[0][0]']               \n","                                                                                                  \n"," dense_6 (Dense)                (None, 16)           50192       ['flatten_2[0][0]']              \n","                                                                                                  \n"," dense_7 (Dense)                (None, 2)            34          ['dense_6[0][0]']                \n","                                                                                                  \n"," dense_8 (Dense)                (None, 2)            34          ['dense_6[0][0]']                \n","                                                                                                  \n"," sampling_1 (Sampling)          (None, 2)            0           ['dense_7[0][0]',                \n","                                                                  'dense_8[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 69,076\n","Trainable params: 69,076\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["## Decoder"],"metadata":{"id":"mhGm9aq4j1NJ"}},{"cell_type":"code","source":["latent_inputs = Input(shape=(LATENT_DIM,))\n","\n","\n","x = Dense(7*7*64, activation='relu')(latent_inputs)\n","x = Reshape((7,7,64))(x)\n","\n","x = Conv2DTranspose(64, 3, activation='relu', strides=2, padding='same')(x)\n","x = Conv2DTranspose(32, 3, activation='relu', strides=2, padding='same')(x)\n","\n","decoder_output = Conv2DTranspose(1, 3, activation='sigmoid', padding='same')(x)\n","decoder_model = Model(latent_inputs,decoder_output,name='decoder')\n","decoder_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NIz8pfv6RQYz","executionInfo":{"status":"ok","timestamp":1657285549853,"user_tz":-60,"elapsed":9,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"04c391fc-9233-4fc3-804c-59ca1a1e0479"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"decoder\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_6 (InputLayer)        [(None, 2)]               0         \n","                                                                 \n"," dense_9 (Dense)             (None, 3136)              9408      \n","                                                                 \n"," reshape_2 (Reshape)         (None, 7, 7, 64)          0         \n","                                                                 \n"," conv2d_transpose_7 (Conv2DT  (None, 14, 14, 64)       36928     \n"," ranspose)                                                       \n","                                                                 \n"," conv2d_transpose_8 (Conv2DT  (None, 28, 28, 32)       18464     \n"," ranspose)                                                       \n","                                                                 \n"," conv2d_transpose_9 (Conv2DT  (None, 28, 28, 1)        289       \n"," ranspose)                                                       \n","                                                                 \n","=================================================================\n","Total params: 65,089\n","Trainable params: 65,089\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["## Overall VAE Model"],"metadata":{"id":"_e6nxyt5FwXD"}},{"cell_type":"code","source":["vae_input = Input(shape=(28,28,1), name=\"vae_input\")\n","z,_,_ = encoder_model(vae_input)\n","output = decoder_model(z)\n","vae = Model(vae_input, output, name=\"vae\")\n","vae.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HQBtlvo2FzAq","executionInfo":{"status":"ok","timestamp":1657199486413,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6253537a-301f-472f-c2b3-afcf9e03a4dc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"vae\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," vae_input (InputLayer)      [(None, 28, 28, 1)]       0         \n","                                                                 \n"," encoder (Functional)        [(None, 2),               69076     \n","                              (None, 2),                         \n","                              (None, 2)]                         \n","                                                                 \n"," decoder (Functional)        (None, 28, 28, 1)         65089     \n","                                                                 \n","=================================================================\n","Total params: 134,165\n","Trainable params: 134,165\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["for i in range(3):\n","  print(vae.layers[i])"],"metadata":{"id":"pm3WXvS0FzC5","executionInfo":{"status":"ok","timestamp":1657199486414,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"860aad02-2617-49c7-ecf7-e177d8f887c8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<keras.engine.input_layer.InputLayer object at 0x7f3504578550>\n","<keras.engine.functional.Functional object at 0x7f35045df190>\n","<keras.engine.functional.Functional object at 0x7f357004a390>\n"]}]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"uOhXdaz_-fY7"}},{"cell_type":"code","source":["OPTIMIZER = Adam(learning_rate = 1e-3)\n","EPOCHS = 20"],"metadata":{"id":"vT02JszjTBSw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def custom_loss(y_true,y_pred,mean,log_var):\n","\n","  loss_rec = tf.reduce_mean(tf.reduce_sum(tf.keras.losses.binary_crossentropy(y_true,y_pred), axis = (1,2)))\n","\n","  loss_reg = -0.5 * (1 + log_var - tf.square(mean) - tf.exp(log_var))\n","  \n","  return loss_rec+tf.reduce_mean(tf.reduce_sum(loss_reg, axis=1))"],"metadata":{"id":"A6b9RgTDTBX1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["@tf.function\n","def training_block(x_batch):\n","  with tf.GradientTape() as recorder:\n","    z,mean,log_var = encoder_model(x_batch)\n","    y_pred = decoder_model(z)\n","    y_true = x_batch\n","    loss = custom_loss(y_true,y_pred, mean, log_var)\n","  \n","  partial_derivatives = recorder.gradient(loss,vae.trainable_weights)\n","  OPTIMIZER.apply_gradients(zip(partial_derivatives, vae.trainable_weights))\n","  return loss"],"metadata":{"id":"ypKgOLjmTBaC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def neuralearn(epochs):\n","  for epoch in range(1,epochs+1):\n","    print('Training starts for epoch number {}'.format(epoch))\n","\n","    for step, x_batch in enumerate(train_dataset):\n","      loss = training_block(x_batch)\n","    print('Training Loss is: ', loss)\n","  print('Training Complete!!!')"],"metadata":{"id":"7-J779KKTBb8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["neuralearn(EPOCHS)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G9-VlnMjTBgQ","executionInfo":{"status":"ok","timestamp":1657140963469,"user_tz":-60,"elapsed":69639,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6b2b04cf-ad05-4514-e93d-6f1ccee0e845"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Training starts for epoch number 1\n","Training Loss is:  tf.Tensor(193.6552, shape=(), dtype=float32)\n","Training starts for epoch number 2\n","Training Loss is:  tf.Tensor(165.99855, shape=(), dtype=float32)\n","Training starts for epoch number 3\n","Training Loss is:  tf.Tensor(167.46562, shape=(), dtype=float32)\n","Training starts for epoch number 4\n","Training Loss is:  tf.Tensor(152.56444, shape=(), dtype=float32)\n","Training starts for epoch number 5\n","Training Loss is:  tf.Tensor(153.63637, shape=(), dtype=float32)\n","Training starts for epoch number 6\n","Training Loss is:  tf.Tensor(148.2903, shape=(), dtype=float32)\n","Training starts for epoch number 7\n","Training Loss is:  tf.Tensor(161.3202, shape=(), dtype=float32)\n","Training starts for epoch number 8\n","Training Loss is:  tf.Tensor(144.28664, shape=(), dtype=float32)\n","Training starts for epoch number 9\n","Training Loss is:  tf.Tensor(147.50954, shape=(), dtype=float32)\n","Training starts for epoch number 10\n","Training Loss is:  tf.Tensor(146.60031, shape=(), dtype=float32)\n","Training starts for epoch number 11\n","Training Loss is:  tf.Tensor(149.86223, shape=(), dtype=float32)\n","Training starts for epoch number 12\n","Training Loss is:  tf.Tensor(153.46059, shape=(), dtype=float32)\n","Training starts for epoch number 13\n","Training Loss is:  tf.Tensor(149.3595, shape=(), dtype=float32)\n","Training starts for epoch number 14\n","Training Loss is:  tf.Tensor(153.71486, shape=(), dtype=float32)\n","Training starts for epoch number 15\n","Training Loss is:  tf.Tensor(148.0849, shape=(), dtype=float32)\n","Training starts for epoch number 16\n","Training Loss is:  tf.Tensor(148.9141, shape=(), dtype=float32)\n","Training starts for epoch number 17\n","Training Loss is:  tf.Tensor(145.43872, shape=(), dtype=float32)\n","Training starts for epoch number 18\n","Training Loss is:  tf.Tensor(152.83977, shape=(), dtype=float32)\n","Training starts for epoch number 19\n","Training Loss is:  tf.Tensor(150.6617, shape=(), dtype=float32)\n","Training starts for epoch number 20\n","Training Loss is:  tf.Tensor(148.25497, shape=(), dtype=float32)\n","Training Complete!!!\n"]}]},{"cell_type":"markdown","source":["## Overriding train_step method"],"metadata":{"id":"asPy7WeB00pD"}},{"cell_type":"code","source":["class VAE(tf.keras.Model):\n","  def __init__(self,encoder_model, decoder_model):\n","    super(VAE,self).__init__()\n","    self.encoder=encoder_model\n","    self.decoder=decoder_model\n","    self.loss_tracker=tf.keras.metrics.Mean(name='loss')\n","  @property\n","  def metrics(self):\n","    return [self.loss_tracker]\n","  \n","  def train_step(self,x_batch):\n","    with tf.GradientTape() as recorder:\n","      z,mean,log_var = encoder_model(x_batch)\n","      y_pred = decoder_model(z)\n","      y_true = x_batch\n","      loss = custom_loss(y_true,y_pred, mean, log_var)\n","    \n","    partial_derivatives = recorder.gradient(loss,self.trainable_weights)\n","    OPTIMIZER.apply_gradients(zip(partial_derivatives, self.trainable_weights))\n","\n","    self.loss_tracker.update_state(loss)\n","    return {'loss':self.loss_tracker.result()}"],"metadata":{"id":"TU1YaYILZGo9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model=VAE(encoder_model,decoder_model)\n","model.compile(optimizer=OPTIMIZER)\n","model.fit(train_dataset, epochs=20,batch_size=128,)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GgfPHSShyP1z","outputId":"6b3a3b44-085a-4ad5-c271-296d02ca89fd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","547/547 [==============================] - 5s 7ms/step - loss: 217.7729\n","Epoch 2/20\n","547/547 [==============================] - 4s 7ms/step - loss: 191.9316\n","Epoch 3/20\n","547/547 [==============================] - 4s 7ms/step - loss: 182.5241\n","Epoch 4/20\n","547/547 [==============================] - 4s 7ms/step - loss: 167.4643\n","Epoch 5/20\n","189/547 [=========>....................] - ETA: 2s - loss: 165.4101"]}]},{"cell_type":"markdown","source":["# Testing"],"metadata":{"id":"KZw5haj0l2EB"}},{"cell_type":"code","source":["scale=1\n","n=16"],"metadata":{"id":"EVcJC-3N-kbv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["grid_x = np.linspace(-scale,scale,16)\n","grid_y = np.linspace(-scale,scale,16)"],"metadata":{"id":"GpJb0aKI8-00"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(grid_x,grid_y)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RicXspLx8-3T","executionInfo":{"status":"ok","timestamp":1657200357273,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8d7b4b12-0d62-4ab5-82cb-99cb52a478e1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[-1.         -0.86666667 -0.73333333 -0.6        -0.46666667 -0.33333333\n"," -0.2        -0.06666667  0.06666667  0.2         0.33333333  0.46666667\n","  0.6         0.73333333  0.86666667  1.        ] [-1.         -0.86666667 -0.73333333 -0.6        -0.46666667 -0.33333333\n"," -0.2        -0.06666667  0.06666667  0.2         0.33333333  0.46666667\n","  0.6         0.73333333  0.86666667  1.        ]\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(12,12))\n","k=0\n","for i in grid_x:\n","  for j in grid_y:\n","    ax=plt.subplot(n,n,k+1)\n","\n","    input=tf.constant([[i,j]])\n","    out=model.decoder.predict(input)[0][...,0]\n","    plt.imshow(out,cmap=\"Greys_r\")\n","    plt.axis('off')\n","    k+=1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":683},"id":"EVOLV2Mr8-5p","executionInfo":{"status":"ok","timestamp":1657200420409,"user_tz":-60,"elapsed":18730,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"40367d08-346f-4942-d51c-f676821b74c4"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x864 with 256 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAqsAAAKaCAYAAAAZPRD5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOx9Z2+cV5L16ZxzYneTzZxJSaYS5Sjb4/HYA88G7ARgZ78tsNg/tD9ggMUE7E7aXTnAtixTgZJJJebMZpPd7Jxzej94qvw0HcaSu5vyvF0AISqQKt7nPveeqjp1SlSv19GxjnWsYx3rWMc61rGOPYsmPm0HOtaxjnWsYx3rWMc61rGvsw5Y7VjHOtaxjnWsYx3r2DNrHbDasY51rGMd61jHOtaxZ9Y6YLVjHetYxzrWsY51rGPPrHXAasc61rGOdaxjHetYx55Zk37TX4pEomdGKqBer4u+7u++L34C3x9fO34+uXWeffPtb8FP4Pvja8fPJ7e/BT+B74+vHT+f3P4Wnv03gtWOdaxjHetYx5phItHnd9CzLJcoEoka/HxWff2++Ak0+lqr1U7Zm282kUj0TK/l/8/WAasd61jHOnbCnnVgRQDgq0DLswJeRCIRJBIJlEolFAoFpFIpqtUqisUiyuUyKpUKarXaqQIYWj+FQgGTyQSz2QyNRoNCoYB4PI5kMolisYhSqXTqforFYigUCtjtdlitVuh0OpRKJRwfHyMWiyGfz5+6nwAgFovZV4PBAJVKBYVCgXq9jng8jlQqhVKphGq1eqr7lNZUIpFALpdDKpVCLBajXq+jUCjwWp72u3TyXaePer3Oa3jaPgqNzqOTn39XPztgtWMd61hbTHhwkT2Lh6wQAAKNh+yz4C+BQKlUyh9isRi1Wg2lUolB4GmCAQICWq0WXV1d6OrqgsFgQLFYRCAQQCgUQj6fZyBYrVZPzU+5XA6bzYaZmRn09vbCZDIhlUrB6/ViZWUFkUgE2WwWxWLxVICgEPS7XC5cunQJHo8HRqMRmUwG29vbePDgAUKhEGq1Gsrl8qk9d7FYDKlUys99ZGQEBoMBMpkM5XIZu7u72N7e5iDgNMAgvd9SqRQqlQp6vR5arZbfo3K5jEgkgkwmc6qgmt4hsVgMuVwOuVz+pcC0UCigXC6fOqgW+iqRSBj01+t19o0C06f1s+1gVXghAM92CePk5fqs+kl28oLt2NPbVwEr4Nla15Pv0kl7Vt4tsfjzPs6TIBB4drKBJw9bYQZDCPy+64HbLD8VCgU0Gg00Gg20Wi10Oh1yuRxisRgKhQKKxSLy+TwD19PwUy6Xo6urCy+99BLGx8fh8XhQqVQwPz+Px48fw+/3I5lMIpFInApgpWetVqtx8eJFvPnmm5iamoLBYEAqlcK9e/cAAGtrawiFQqhWq21fT+HztlqtePPNN3H16lW43W4oFApEo1HodDpEo9GGzOppACwCVVqtFgMDA/jBD36AwcFBqFQqDlKkUinC4TAKhQIHV+30k9ZTLpdDp9Ohp6cHU1NTUKvVKJVKyGazSCQSqNfrDZnVdr/ztDfpPbdYLOjq6kKhUEA2m2UgTb4BaPi8XUbrKZPJoFAooFQqoVaroVQqkcvlOENdqVRQLBYBoMHfJ7G2gFXhD0SHq1qthlQqxdHREXK5HB8Cp3lp0eVEm1mhUEChUEAul3PpolKpPDPpd2EZQyaTQSaTcWQoXMtyuXzqWRbyVSqVfqlMQL+n0uBprevXRYdCv+kQO80SEa0ZPXeJRMIAiz4HgEqlwofFaexX8lMul/P+pOwFZQnowsrlciiXy6eSJaDnrlKpoFQq2VeFQgG1Wo1arcbgL5PJcDaj3e+UcD2tVisGBwdhtVqh1+thsViQSqUQCoUQCoUQiUQQjUaRTqfbvlfpfdHpdLhw4QL+/u//HgMDA1CpVMjn89jf30cwGOR1FO7Pdl229K5oNBr09PTglVdewcsvvwyz2cw/Q6lU4s8pk12tVtvKa6R9abPZcOXKFfzTP/0Tent7IZfLUSqVkMlkkM/nGeiLxeKvDV5b7ScB6tHRUVy9ehXvvPMOtFotyuUyYrEYstks5HI5v2OFQqGtvlI2VQhSX3jhBZw7dw7lchnhcBh7e3tYW1tDoVBAJpNBtVpt69kprEi4XC6Mj49jYGAAU1NTsNvt8Hq98Pl8ODg4wPb2Np/7RK1pp5/CqsSZM2fQ29sLp9MJu90OhUKBzc1NHBwc4ODgAIeHh0gmk8jn8099drYUrAovVYVCAYvFgvHxcTidThgMBtTrddy9exderxfpdBrFYvFUQJUQTMlkMqhUKlgsFtjtdthsNqhUKuzu7iIYDHLpij6A9mbb6OWmA1SlUkGtVsNgMDDfKpVKIZ1OMxDIZrNIJpNtBQLCrB89f7lc3gAIAPB6i0QixGIxHB8fo1gsti2D8VXrKZVKOVAh/8jvWq2GSCTCe6GdZSKhr5QZUCgUUKlU0Gg00Ol0UKvVkMvlqFaryOfzCIfDXNIqFAocDLTDV7rATCYTlEol71Vh2a1UKiGVSiEYDCKZTCKdTrfdTyqxejwe6HQ6/jOdTgej0QixWIxisYh4PI5AIIBEIoFUKtX284ouB5PJhNdeew19fX0Qi8WoVCqQSqUol8swGAwMFikTeFqX7cTEBH76059icnISMpkM2WwWPp8POzs7CAaDSKVSp1KyJh+VSiW6u7vx5ptv4kc/+hHsdjuq1SpisRiWl5fx4MEDbG5uIhqNciawnQEf+Uil/5/+9KcYHR2FXC5HPp9HPB7H2toaVldX4fV6T3U9FQoFuru7MTs7i9nZWVy9ehUulwuVSgWpVAqVSgWZTAbhcBjJZLLtGVV6J0wmE2ZnZ3Hx4kW88MILGBwchFqtRi6Xg16vh0QiQSgUwu7ubtvfbQLTer2eAf/FixcxNDQEi8UCsViMwcFBBINBbGxsQKVSNWRZ22V0VyqVStjtdly+fBmvvvoqxsfHYbfb+Z6cmpqC3+/H1tYWHjx4gDt37qBarXJ16kmDvqaDVWH5jMCfUqmEwWDA6OgoZmZm4Ha7YTabObMCAAcHB0gmkxxttXqTfJWfcrkcGo0GXV1dGB4ehsPhYABotVqxu7uLo6MjxGIxJJPJhougXb5ShE8gSq/Xw+Vywel0wmKxQK/XI5PJIJFIIJ/PI51OIxAIoFKpIJvNtvSQOLmmxKcjMGUwGGCz2WAymZjQTr+WSiWEQiGUy2VEo9GWl4apNE3rKZfLoVarodVqodFoYDKZuDmAQBZlrcPhMCQSCYLBIAcFrdwDX/U+6fV62Gw2GI1GmM1mrlYolUpIJBKUy2XkcjmYTCZoNBocHR0hkUggl8u1rNwqBNPkp9FohMfjgclk4kYLWlO5XI5CoYBkMgmNRoNwOIzDw8O2lDGFl4NarUZXVxcmJyeh0Wg4wyeXy6HX66FWq5HP53k/+P1+zri0o3QtzGIYjUZMT0/jzJkz8Hg8yGQyCIVCHIwqlUp+b6j0RqC6HX5SwOdyuXD58mWMjY3xmRQMBnHv3j0cHBxw2ZoqFxKJBJVKpeU+Al+AwK6uLpw7dw5vvPEGbDYbpFIpMpkM/H4/FhYWGoAq+dauTCA9b7vdjpmZGVy+fBlTU1PQaDSoVqvI5XIIBALY3NzE7u7ulyp/7TI6541GIy5duoTZ2VlcuXIFTqcTMpkMlUoFpVIJ6XQaiUQCoVCI17NdyQhaS61Wi/Hxcbzwwgt44YUX0N/fD71ez+eNSqXigL/dSTNhAqK/vx+XLl3Cq6++ioGBAZjNZk5A0L8FgGAwiNXVVYTD4YbkUKvPTapIWCwWPPfcczh//jzOnz+Prq4uaDQaDqBlMhkH/KVSCXt7e4xBnuY9agpYPdmYICxN08Xf29uLyclJzMzMwOVyQaPRNGRWqARM2cpW2UngRxECZXy6urrg8XgwPj4Ol8sFq9XKAJYAzd7eHvPCniZCeBJfgS8uf/KVqBR2ux0WiwVut5vT7waDAdlsliPYWCwGiUTChPZWHRJCMEWlK/KTOlctFgssFgtMJhMDK7lcjnq9jkwmA4VCweVBYfml2X4SSKGLVafTwWQyweFwMBA0Go0wGAz8e6VSCQDIZrPcLUxlTMpYt8KIlkCg32AwwOFwwOPx8LtlMplgNBohlUqh0WggkUiQz+eRTCah1WqhUqlQqVRQLpdbBlyEFAp6l6xWK4aGhrhUTWCV1l2pVHLXNWWGKVvdymYWYbcyBXsTExOcWaXSNB3KBoOB/aTMOwWDrc4QCbOAFosFIyMjuHLlCqampqDT6RAOhxs4n9R1T1SQVCrFmaxWBlXkJ5XVL168iNdff53BQCwWw+rqKlZWVuDz+Rr2IT0PYZdzq0wkEkEmk8HhcODixYt46aWXMDExAYlEgmKxiOPjYywuLmJ5eRl+v5/LlkLVgnYBAr1ej5mZGVy6dAnPP/88rFYrn5XHx8dYWVnB3t4egsHgqVQlyU+1Wo2xsTEGqh6PB0qlkqt6sVgMh4eHSCQSiMfjXypXt3I9hQFUd3c3Lly4gJdffhl9fX3Q6XRM96Ayeq1Wg1Qq5a9tl9E7brfbcfbsWbzyyisYHR3lcx0AU1CoWtXT0wOLxYLDw0Nks9m2+UtB88jICM6dO4crV67A7XZzQofeE6VSyQmrM2fOYGlpCcfHx3xutjWzKgSpdNgQAJTJZDAYDHA6nejv78fw8DDOnDmDiYkJ6HQ6yGQyiEQinD9/ngnNxWIRiUSiZZfpyawf8WdsNhu6u7vhcrng8XjgdDoxPj7OYIUiR5FIBK1WC5lMhmAw+JUNI830lw5xylJptVp0d3eju7sbDocDLpcLUqkUQ0NDMJvNUKlUHM1S48Lx8TEKhQKOjo6QyWRakg0WBicEUnt6engtnU4nisUiHA4HTCYTtFotUwJEIhGq1SrzWRwOB+LxeAPNoll2MjupVqvh8XgwPDyMvr4+9PT0IB6Pw2w2Q6fTMTDR6XSQSCS8RzUaDZfVS6VSA2esmXaSZN/X14fp6WmMj49jZGQEsViMs6pSqRT1eh0qlQpisZhpAMFgEOvr66hWq8zDKpfLTfVTyPNWqVRwu904c+YMxsbGMDU1hVwuB61WyxkB8lOlUrGfx8fH2NnZQT6fRzabRTabbemaUmZtZmYGZ8+exaVLl5DL5SCVSpHNZpHL5Rj4GQwGVKtVZLNZHB8fAwCOjo6QTqeRz+dbBqqFQNXlcuGll17CK6+8gitXriCbzTL3CwBMJhPTV2h96bySy+XI5XIt8VHoq0KhgMfjwTvvvIN33nkHIyMjXNmZn5/H/Pw89vb2UKlUGEyfRrOSXq/Ha6+9hp///Oc4f/48VCoVwuEwdnZ28PHHH+PWrVvY2dn5UiDSrkZAygQODw/jH/7hH3DlyhU4HA6IxWIGqXfu3MH9+/exv7+PYrF4Ktx5CvicTid+9KMf4e2334bdbodMJkO1WkU0GsXu7i6Wlpaws7OD4+NjlMvlhsCk1Ub70mq1YnZ2Fj/72c8wMjIChUIBACgWi4hEIojFYsz5rtVqTFWjhForAylKnhgMBoyPj+Ott97CCy+8AK1WC+DzXo5sNstnTb1eh0QigdvthsPhgF6v5/OglckzAByQDg0N4fz58/jxj3+MgYEBxh10dlJlihIoQ0NDGB8fh8/nY2WNJ72HvnNmVbjpCKgS8j5z5gyGh4cxODgIj8fTIA9BTQxGoxFutxsbGxstz1AKwQqVI4eGhjAzM4ORkRH09fVBr9ejUqlAr9czz1Kj0UCtVmNkZASZTAYHBwdP3dH2bf0Vcv7UajV0Oh2XWcbHx2G1WiGTyRCJRBhUSSQSzqRRQBCPx7lZjModzfaV/NRqtTAajZidneUOYKvVColEAq/Xywe9SCSCWq2GzWZDvV7n7A/RRaj5ptmHGR1cxPF98cUX8dprr+HMmTOw2+2QSCTY399nQF+tViGRSGCz2bihIZVKwe12s7ahRqOBTCZrOrAm8Ee+vvzyy/jxj3+Mc+fOoaurCxKJBAcHBwDAmcBarQar1QqlUolKpYJ8Pg+dTodKpYJIJAKTyQS/399U4EIHrVKp5IaaV199FbOzs+ju7oZcLofP52MKBWVViMdKJU3qxvX5fNDpdAiFQk3zkfwUrunk5CTefvttvPTSS8z93NjY4GwqZVioUkCZXo1Gw/xbyni0woSgemhoCD//+c/x1ltvob+/H6VSCZ999hny+TwHSgaDgaWhUqkU85Xj8XhLy+vCdXW73fjlL3+Jn/zkJ/B4PEilUvjVr36F7e1tHB4eclaFqkTEqW01PUnop1qtxssvv4x/+7d/w+joKJRKJWKxGP7jP/4DDx8+xO7uLmKxGIrFIgdXZO3IXAoB4L//+7/jjTfegF6vR71eRygUwm9/+1vcv38f29vbiEQiTKWgnxFAW/oShH6+8847+Jd/+RfmVRaLRRwdHeHGjRtYWVlBMBhEPB5HOp1GtVptaGBtJW2BsujUnPav//qvmJiYYGCVSqWwvb2N/f19HB8fN2T61Wo1039aTa+gAOr8+fP4wQ9+gKtXr0Kr1aJeryOdTnMgXy6XIZFIYDKZuKmyp6cHh4eHyGQyDU1WrTCRSASVSoWxsTG89tpreOuttzA2NgaRSIRSqcQ0Lr/fD5lMxn0/ZrMZFosFly9fZm41VSyexJpy2lKkRJeW2WzGxYsXMT4+jr6+PlitVib8kxFwqVarLBScyWSa4c5XGmV9hVy6kZERvPLKKxgfH2ceZblcbmjwoA0PgC/WQqHwpYOsFb5SCdrtdmN6epovASpJ53I55PN59oWAtVwubzhsJRJJSwjYQj/NZjN6enpw5swZ/PSnP2WgUq/XkcvlkMlkOOrTarXQ6/XQaDQMqugyEUaHzQSrlKEiSsrZs2fxi1/8Av39/VCpVADABxNlrMRiMYMAiUSCXC6HbDbLXfa1Wo0P3mYagWqz2Yy+vj7MzMzgn//5n/lyBcAvOgHVSqXCtAt6/kRVCAaDTMJv5prSu2E0GuFyuTA9PY0f/ehHuHjxIoxGI4N4yp7T2lIQSNzaUqmEeDzOpUKKzJtpFMiZzWZMTEzgtddeww9/+EPY7XbI5XLO5FJTIq0VAf9cLodUKoVAIIBgMIhoNNqyjJZQmsrpdOL111/H22+/jb6+PsjlcqTTaQSDQd6LlGkRiURIp9Msv0PNYK3SsxQGqhRMU2ONWCxGIpHA3t4e9vb2EA6HGZTK5XIuq9O71srmJWEpuK+vD7/85S8xPDwMlUqFcrmM7e1trK6uYnNzk+lSJ7v+28H5pbV0uVz4u7/7O7z++uvciJxMJvHpp59icXERq6ur/FwJQLeSinTS6K53OBy4cuUKfvazn8FisfAZeXR0hGvXrmF9fR2BQIAbrOhrT4LrVhmd37Ozs/jhD3+I4eFhKBQKlEolBINBrKys4MaNG4xDVCoVtFotn/lUVaV3qxVGZ8zo6ChmZ2fx8ssvQ6vVQiQSIZlMYnNzE/Pz89je3obJZILVagUAqNVqmM1mjIyMwOv1IhwOc3Nqq951qVQKi8XCzWl9fX2s6nB0dIRHjx7h8ePHCIVCsFgsnASge2lwcBBnzpxBIpFAOBxuyBR/G2sKWKXDkkqWVqsVNputoURJh7+wM7VWq7H4MpXVWs1dIaK1w+FAX18fPB4P9Ho9RCIR8vk8HwIEpoRCweFwGMfHxwgGgw0luGb7SWUgnU4Ht9uNkZERnDlzBj09PVCpVCxZEgqFkEqlGIjRB02JicfjCAaDSCQSSCaT7G+zMtgEVggAjo2N4fz58+ju7mY+YiwWQzgcRigU4ow7NTIRXyiZTMLv9yMUCjV03jbLhGWWvr4+TE5O4sqVK+jv72eeZDKZRDQaRTgcBgCmXVDDDXXf+v1+lt4h3mIz94GQr0Yv92uvvYbh4WFoNBqUy2WkUikkEglEIpGGshDtYQoOwuEwtra24PV6EY1G+dJo1jtGXd8UTF26dAnnzp2D1WpFrVZjznQ4HOZyf6VSgdlshlQqRa1WQyaTQSAQYFATCASQzWabeuAK+WBTU1M4e/Ysnn/+eS5ZZrNZfq7A55JflUoFItEX0lrFYhHpdBqHh4fY29trWUcz7VWdTgen04mpqSm8+uqrcLlcDP69Xi8ODg5QKBT456NkAFEThBcCgcFmG51TarUaLpcLL774Irq7uyGVSlEoFLCysgK/349IJMLyhAC4ckGVnlZnAgkE2mw2zM7Ocum/Wq0iEong5s2b8Hq9DeCfwEk7u8GJbjY9PY23334bJpMJEokE8Xgcm5ubuH37NnZ2dnjvAZ8HYSefbas5tZQJnJycxIULF9Df38+g5fDwEDdu3MDi4iKi0Sji8TiXhDUaDcu/CWl5rfCXnnl/fz/OnDmD5557jp95OBzGo0ePMDc3h83NTW72pOEFNpsNVqsVBoMB8Xics8DN3qf0zM1mM86ePYsLFy7A6XRCLBYjm81ie3sbt2/fZk1ih8OBQqHAjZ82mw09PT1wOBywWCw4OjpqWaMirefY2Biee+45TprVajWEQiE8ePAA9+/f58oEYTmqCpvNZhiNRgwNDWFtba2hEfjbWtMarKirmkq5arUaIpGImzry+TzzRGhz5vN5eL1ebG9vw+v1IpfLtbSsTtG1yWRCV1cXl3dJTy2bzSIajUKhUDCvSiaT8cW7tbUFn8+HQCDQsrF2Qp6a3W7HwMAAc34pc5JIJHB0dIRAIMAj7ejSoOiWNOMoECC+arP9pItqeHgYY2NjmJiYYN5fMBjEzs4OlwWoq1Gn00GpVKJYLCIWi2F/fx87Ozs4PDxEIBBgzc1mAWqhnyMjI5icnMTU1BQD6lAohL29PRweHnIkSA1AlHWLxWI4ODjA7u4ufD4flzWb2V1P+1SlUsHpdHKQMjk5yR2qiUQCXq+XaRVE8SA9w0gkgnQ6jVAoBJ/Ph729Pda6E15yzfCVZJTo2Z87d45VPlKpFI6OjrCzs8MVE8qYU8kom80iHo8jFAphf38fPp+vIWPYLD8J/A8MDDDth8BfLpdDMBjEo0ePsL29zZcSXUwEsIvFIjKZDLa2thAOh7n7uhWXrEqlgtlsRn9/P0ZHRzE2NsYZ3mg0ioWFBRweHjacUVKplPm+uVwOyWSS5bVaUcKkdaXzZ2RkBM8//zwHVMFgEJ999hk3TAKfy9SRSgGtcTuUNCirPjg4iJdeegl2ux1isRjxeBxbW1tYWFjgYJ4oDZRtA8CKBa3mAqrVagZX09PTLFF1cHCAjz/+GAsLCwz+KIlCiSDKUgNfvGetzPoTL312dhY6nQ61Wo335s2bN7G0tMTvEvUxWK1WiMViVtKhgLDZRme+wWDA9PQ0Ll68iO7ubkgkEqTTaayvr+PmzZtYXFxEMBjkpl+SttJoNKwEFAqFGFy3wk+5XI6+vj6cP38e/f39fM77/X7cvHkTDx8+xObmZkN2mnR3e3t7YTabGVgrFIqW7FPhOzQ2NobJyUmYzWZIJBIUCgWsra1hfn4eW1tbTDOj6rNSqYTVakVvby+PCiaFJUqwtDyzKoyKKGNJHb0UwdChSS+RxWKBWq1mMBgIBPDhhx9ieXkZsVisAaA0M/snLFdR9zw1JNE0DcqeFAoFOJ1OnsYglUqRTqfx8OFD3L9/H6urq8xpApobeQv5X6RJOzg4iL6+PiiVSvj9fhwdHSEUCjEhvKenhzvFqcRxfHyMhYUFLC8vY3NzkzfQya7W7+oncZamp6cxOTnJ0j+BQAAHBwfwer0IBAJIp9Po6upiVQCSX4lGo3j06BFWVlawu7sLv9+PaDTKBOxmgEBaT5vNxpI/Z8+e5U7qSCSCra0t7O/vI5VKsfwG7eVqtYrj42Osra2xdNnx8TG8Xi/i8ThyuVzTysHCUsvU1BSvqU6nQyaTQSqVwv7+PpaXlxEOh2EymWCz2bgLM5VKMYgOhUIIBoM4OjqCz+fj7FYzfKV3XqfTYWhoCH19fRgeHobFYuHM78HBAZaXl5mvSvQEkUjEWV5qqAyFQvB6vUgmk8y9amYAoFar0d3dzUGf8OKihpXHjx8jGo0y5adcLiOdTiOVSnHWKJFIMJhuhb4uVSoMBgO6u7vh8XgwNjbGvGPKsK2vryMajTJdhEZZJpNJ1Go1ZLNZpFKphlGMzTbi/ms0Gng8Hpw7dw5OpxOVSgXBYBCLi4vY2NhAsVjkxi/KphNnulXVKTLap/T8p6en8corr3A2fW1tDX/+85+xvr4OAPweUUad7gMC160qBdNZ2tXVheeffx5vvfUWjEYjKpUKDg8P8fHHH+P69esIBAKQyWTQarV8P0kkEqRSKYhEoobRta1MpBgMBly6dAk//OEPOTmRSqUwNzeH69ev48GDB0gkEqz9bbVaYTKZYLfboVKpeJ8KJ4I1u+GXsqqvv/46xsfH+c7Z2NjAtWvX8OjRI06QAWDJJdrTTqeTuZaZTIaTEs3yVVhBOXfuHC5fvgybzcbn+AcffMDlf2o4pvddr9cjHA6jUqlApVIxWNXpdIjH4xxEN7MyRYNIaNQvBc9HR0d477338PDhQ27mpgAZAAcA09PTXPk1GAycCab76Nv4+p0zq8TZEwqTU0mK+J/Uzep2u6HValGtVhEMBvGb3/wGf/rTn1hzrxW8pZNNVRaLBQaDgTMA4XCYD6VarQaj0YixsTGWMcpms1haWsJ///d/Y2lpCbFYjAcDtOKyou45q9XKUYhKpUI6ncbW1lYDD42yBb29vXyhhUIhvPvuu1haWsLu7i7/+2ZyWehS1el06O7uRl9fHwYGBrjBY3l5Gbu7u9wAQJ3sNCe6XC4jkUjg1q1bWF5e5kwh6ZY2a4oVPXdhVm1qagoOhwO1Wg2rq6vY2trC8fExMpkMqy309vZCpVIxR/X27dvY3d1FOByG3+9nbmUzAQtdrLRWg4ODLLIsEolYXHlrawuhUIgzWgaDAZVKBX6/H8fHx8y7SyaTCIfD8Pl8nFFthq/CQMXhcKC3txf9/f1wuVyQy+VIJpPY39/H+vo6fD4fyuUyZ4hoWEU0GmUqTTqdZl1gkixr1hkgDPyoQjE0NMS6fySuvb29zQCUpmkRJaRUKnEjCKr5hUUAACAASURBVI06bMVwDeHzt9lscLvd6O3txeDgIAN8mlVfKpWgVqsbGpWy2SwikUhDJUt4RrXCV7VazcopV69ehVwux+HhIRYWFrC4uAgAPBaUVCqISkFr2MrJhZS5oh6KN954A1arFaVSCSsrK/jwww+xurrK7z1VBAl87e/vIxAIcADVChNm/p9//nm8+eabmJiYgEgkQigUwu9//3vcvXsXkUgEer0evb29cLlcsNls0Ol07OfW1haXX1vVn0BZ/7GxMfzjP/4jJicnWaLqk08+wbVr17CysoJcLgelUgm3243h4WEMDAyg7y9SUaurqwxQKKHVTKMz32g04vLlyzh//jzMZjPq9Tqi0Sh+/etfY35+nofP0M+l0WjQ3d2Ns2fPoqenhydIET0sFos1bY8K3x+Px4OXX34ZXV1dkEqlyOVyWFxcxIcffoi1tbUG9RZK/pH0GmVSSWbRaDTi8PDwO/snNDpDTSYTpqamMDMzA5VKxZn0d999F3fv3sXR0VEDfS+bzSIUCsHj8bA6DTUsazQapoI9iX1n6SrhBzVRyGQy2O12GI1Gjqjcbjd3Nfp8PnzyySe4e/cu4vE486papalJpTKK7umSpCxfrVbjiHViYoJHA6bTaezv7+Pjjz/G0tISZ2FboVUqbFbSarXo6upiABoOhzmyI/Alk8m4ZGSxWBq4OI8fP+ZstbDTtpkRoUqlYsF3t9vNkSuZWq2G1WqFSCTi5huTycQZVZ/Ph9XVVayuruLw8BCpVIp/xmb4KlxPg8EAl8uFrq6uBkUKAnwikQiVSoVpFyT1EwqFcHh4iNXVVezs7DCJnS6EZu5ZOmTVajWXoCiDQgoGVqsVxWIRBoMBer0edrsd5XIZh4eHPNLO6/XyxCrih7UCVBNYJY1X0s0FPpdRogY7aqCiQ59oNEJwSFWNZgIruoSIE2axWGCz2VjYv1qtMtgqFotQqVSIRqNIJpPM+RVmoqls2aopcPRekX4yceq7urpYOYX8zWazXMEihQphdYgybK0M/omqMD4+jpmZGfT39zPti9acBi7odDoAnzcwBgKBhgx6q4ySKGq1GmfOnMGFCxcwPj7OgCAQCKBcLsPpdLJ0ndls5oxRMBiEWCzm875cLjc9syp8l5xOJ65cucIyQMViEWtrazg6OkK9Xkdvby8cDgdGRkbg8XhgsVg4oF5eXoZKpcL29jZ8Pt9TdVp/G1+JK3n+/Hnm0JOG7vz8PPx+P2q1GmtWDw8PY2JiAqOjo1wB0uv1vJaFQoErLM3ykUrPlKU2m838HLe2trC3t8dVXOEdYLfbuYfFbrdDo9FgZGSEg36aWNiMiq8w2TM4OIizZ88y/zORSOD+/ftcDaW7WywWM+2L+KrUiEUcW4vFAqVS2dRGdWEpf2hoCE6nk98hSo7E4/Ev4SLCg/SzUmBN5wYFhqQQ0fLMqvAgpJQ+AG66oQWkqUVisRiRSAQPHz7EvXv3WFxfeEm1kr9EHDSlUslUBNJTow3e/5epFsViEQcHB5ibm8Pjx48Ri8W4rNYqX4VDCmQyGTdSEf9DpVLx/6nX63Hu3DnYbDaIRCJEIhGsrKxgcXGRSc4ECJp5udKlSgoAJJFFBzo1MlHWhOTBqEQcCoW47E58WqGURbNBNZVNKFAR/h3NASdB5a6uLiiVSub7Ukl9Z2eHeb8ngUAzjA5ZotOc5E0SX8hms0GhUKBQKHDpd29vjznfBwcHCIVCDTp2zfRVGPxJpdKGJhQ6pKiMLRKJYDKZkM/nWc4kEonA7/dzqUqYXWt2BpB8pW5eUsag8mO1WuXRhn19fSxPR5cvAK4OCb+mVRlAoaauTqeDXq/nBhtaHxI2p8uC+NTHx8dIJpOcrWpVd73w+avVarjdbvT392NkZIQHT9RqNe5SrlarPFSF+NRisRh7e3tQKBTI5XItaWQS0qmsVivz7IxGIz/fXC4Hq9UKq9UKp9PJ0ooAUCqVYLFY4Pf7WUmDlDWa7SOBFqL8UPd/IpHA8vIyarUaXC4X79PBwUGm0wn3BjXWxeNxRKPRpvlJvtK95HQ68dxzzzWMIT44OIDf70e9XufJf6RZPjQ0xJP2CJANDAxws7LP52uqnxTU9fX1YWhoCAqFgnW8V1ZWEIvFmOtNWT4aYGK323mClUgkgtvtxv7+PsxmM9ODmlnxoWQPcXlJb5qa/b4KxFEFm6TzqNnNYrFAp9NBLpd/Z/9O+qpUKjlQooQEyX4FAgFWGyF/iUZDXy9UyyGwTbSwJ7HvDFaFEiR0sVJnHSF+o9EIhUKBfD6PnZ0dLC4u4t69ew081VaCVOFlk8vlOLMCgC8pi8UCh8PB5Hufz4d79+5hfn4eq6ur3M3aSnkVOhQIrNCGpWjfZrNx9GS1WuHxeKBSqRAKhbC2toY7d+7g0aNHHJW1osQmvAhoNj1d6nK5HHK5nPmp1CzkcDggEolwdHSEBw8eYH19HUdHR9jd3WW5jVbsAwJVJJAujPyIG6rVapnor1aredIOZSkCgQCT7IVTTlpRVqVpH9TQI+Qb0uWk1+s5U7q1tYWdnR3s7u5y5pe4yRQ8toKqQgcQVVKoqYdAtFqt5jMgk8kwOCCuKu1r8q1Ve5SAar1e527+ZDLJgSqVSyng0ul0zKOlQ7nVXetCXymDTuN9pVIpisUiz/+u1WrsKwGdbDYLtVrNlz6dx63QUxY2qRqNRvT09GBgYABdXV2cKatUKjxikQIXyrAbDAak02nWJm625Bv5SetJYuRnz56F0+lkP6jDu6+vj6fq0VkGgM+yrq4uBAIBxGKxpssqCgEgTS1yuVy8N0OhEI6Pj6HT6TjrRxUs4R4AgO7ubgQCAUSjUej1+qbLKtJe02q1GBgYwMTEBAPPdDrN5WryzWw2w+12N4A/qhBR5YASWM3UKqZ7yWAwcJc8TSYLh8PY3t5GOp1uGFyk0+m4UkUAStgoTs1ABFab5Sfd4YODg3w/ZbNZ+Hw+HkkrVKUQ+kvrKaRgErWR9nAzTAj+yVdKksTjcezs7CAYDDY0mpLPlCigMfb0/eg+Jq3qJ6lWPPVOIafq9Tq/3EQYpgOXSMq0gMFgENeuXcPc3Bx31AsP/2ZfBHQBUvaGdElrtRqDQq1Wy2UKkq4IhUL44IMP8NFHH2FhYYHVAoQ+tsJXAn3E76OOObr83W430wNow8ZiMdy4cQM3btzA7du3mYMr/NlbccHSr8JSFpH+jUYjZzPpQlpZWcH777+P1dVVrK+vN+hpCqVsmglYCACSD8LmPQBc9iNOTSaTwUcffYTPPvuMu74JTNPzaUUHsxAAUlRKWVEaQ0wABvhc+ieRSGB3d5eVFIjvSwBM6Gczy+pU0qFmGeroDwaDANDw/pPmr16vZ4kdKqdTFabZVYqTtB9qnKBhHsVikcW+JRIJC1frdDqMjo5yZisajXIJVriOrQhSaPIXNaOQZF4ul4Pf74ff7+fSP2WorFYr1Go1isUiJBIJVldXGZg3e48K9yddjAMDAxgZGcHg4CB0Oh3q9ToHnVQyJq4qcdwkEglcLhe0Wi3UajVPLmxFIE2NJ+fOncPExAQ/V8oCORyOhuEptF7kNwD09vbC7/ez9B7QnHNfyPs2mUwYHx/Hq6++CpPJBAC8V0kXtr+/n8vZRPWgn5PE1z0eD7xeLzeJNcuEAYrD4cDo6Ch6enoYBIZCIayvr0MsFsPhcMBms3GJmv6NsJRNw0NIEaZZYJWAEDXTjY+PsyQlSdP5fD6USqUGahWNqxaCPLovaMolTbpslp8E/J1OJ0ZHR3niF/lJ43Pp3wqbhAlYU6VQLBbzu0nNYc2iq9B7TIk8CkrprozFYl/SJaY7V6vVcpZdeM+S0V5oC1gFvnhxqVNSr9fz3PKuri7OAgKf85V++9vf4oMPPuBN0w4RYyFYpYOKIjyz2QyPx4Pe3l5+yIVCAf/zP/+D//qv/8LW1tbXpuNbYbVajUdiRqNRngVNl2p3dzecTidHefl8Hu+99x5+9atfYW1t7St1NFvhN43ujMViDI4oU0aHJwnpl8tlxGIx/PnPf8a1a9dwdHTUMG9baM2+tABw9zEFJzThh14oeu6Uebtz5w7u3LnDig9f1VnbCsBCIJAkbEiwnBq/KJImy+VyLGZOM8xPRrjN8lV4udIHZYVqtRrrvYbDYb4srFYrg1SDwcD8NJKCojVtBVClKW7UfUqNX+VyGX6/H9VqFbFYDAqFAj09PRgfH8eVK1cYSIVCIayurjaU1FulsajT6RgwU9e0VqtFqVTCwcEB8vk81tfXuTTt8XgwMjLCDW0EsIRT15rdtSzskjYYDKyre/78eaYhEcc7HA7zM6BudQANtBHKalMFoFVVn97eXrz22mus+0vDEkQiEQMl6l/I5/OwWq0wm82c1aZAkXjAzVSoIFqXzWZjzixJ/pHaS19fH3p7ezlbGo/H4fP54HK54HQ6eZgJydlRYNNMCciTPNCxsTEOmmOxGLa2thCNRrkhyWaz8TjqWCzGz4Ledxq/S021pAzwXU0YTNlsNgwNDXGjdygUYiqfRCJhjj0NJwG+GPpACRhS16A9HYvFmuKnEMxRTw/9fyShSBqlVH2gn4vOCAJ/lAAgTfVYLIZIJNI0qTq6l4iSRDQpoisIM8CUaKGvoabWnp4eptdRcrNQKCCdTiMSiTyROk1TGqxoM5NI7czMDIaGhvhlog63jz76iLvw2gH+hH4Ksy2U9env78fg4CA0Gg2Az1+kxcVF/OEPf8Du7m5Dw0+7TMhdpMOdpoWQLizpbc7Pz+PXv/41VldXkUqlWiZZctKEpTYCgxTpUemf+FSJRAI3btzAp59+yrzPVnH/TppQV5ea0qirO5lMQq/X83rl83ksLS1he3ubSf/tECwXZgSsViu6u7vR09PD3cn1eh3BYLBBTJ/KhJFIhH1tVeMPGYFVo9EIs9kMvV7PFyY1AXq9Xqas6HQ6PsCoYURIUQBaw1NUqVSsoUzagxKJhOWcSForl8vBYrHwz0CqG6QGQNnMVvDp6TzS6XQ8jpqaOoSVFJoME4vFWE+TeOFCWT0aqnCSUtNMXykjRrz+0dFR3qMKhQLpdJovTODz7KDRaIROp2uoXJBEHTUBNlNPWZjZIQWQ7u5uDkiFgJ5UE6iSRQEhJQJo8htNLKNLuRkmvI9sNhvGxsb4vCcgTwkVyk5ms1mEw2HU63VuZiTd2kwmw5J6wuEvzfKVAmiLxcLqFASSKPCnci8BqFKpxNlDi8XCzyCbzWJvb4/VS5qp+ywWizn4czqd7Cc1cdK7RXuZsn12ux2jo6NwuVwwGAwAPg8AaBQrNa01s5eCeKaUTRdyPimjS/6SlGJvby+mp6cxNDTEmfZMJoPt7W2sra2x/GKz9im9S5QdpQoJ7S8hdU1YldDpdKxnTkOC6O49OjrC0tISD1Z5kjurKTl4WlQSqh0YGGgQqPd6vfjjH/+InZ2dloxSfFI/qdt+YmICRqORNRe3trbwxz/+Eaurqw2l/3abkKtCm6W7u5vBP5HF//SnP2FlZeWJOuqa5Rtl+SgCpWwFSepQJoPUFPb39790+bfShN2r1Flfr38+a5lKksSxpIaGpaUlLsG0A6iSnyS10t/fz5l+g8HAGcBsNotCocCc1Uqlglgs1tD012pfKRtAYJpAK8kkkfwUHfbESSqVSiz5JNTfaxXvW6vVoqenBz09PdygRMCDqCeUaXc6ndwgRIc/NSxRt3gryv8A+MLq7+9H31+6/qmMT9lIUkmh/eFwODhTQRdHLBbD2toavF4vr28rGiopQ0aZHaIjUfmUJpQJzwfhpDrStN3f32eg2uyhCsLMKilSCMv8BGgJwAjXl6SgaL9QA9Dh4SHS6XTTQbVIJOKLvauri89TOg9o3Cf1WRCv3u12sxJAvV7nwIrUQJo9WEeYjNJqtdDpdJwhA8Cgmu5KOt8pGUTNYRLJ5yO/A4EAdnZ2OKvazDuWgiqingBfDHSgtS6Xy6xJTpW18fFxDAwMwGq1Mifz8PAQa2trODg4QCQSaSqoFt6VCoWC15LodGq1mpu8SOaJdMKnp6fR1dUFhUKBWq0Gv9+PxcVFHr7T7ESgMLASNk6Rrr5arWbJKmpmdjqdmJiYYHULarhNJBJ49OgRNjY24PV6nzgA+M5glX4Y4jUMDQ3xQyeZotu3b+Pdd9/lzdnOTKXQT1IpoDIBaQCSkP4nn3yC9957jzsG2wGqvs5X2iAErGk0ZD6fh8/nw/Xr1/Hxxx8jFou1DQCSb8ILjLjA9PJQVpX4TA8ePMDdu3d5akm7/KSDizhJNDaVGqXUanVDdiUej3Nk2szM1Lcx0s3r7u7G8PAwenp6IJfLkUqleCJVtVrlDAWBa5pP3w5QTVmLnp4eDA4OwuFwQC6XcwaSGqzsdjvsdnuDRBQNe3gSAeinMboEiIrkdrshk8kaxtKSbqrZbObRgaSrSyLsXq+Xy36t8JcAAFGRurq64Ha7+eIWi8WcQaUyYVdXFw8IsdvtkEql3OSwvLzM4LrZQJX8oQYw4k5TUCoEWRQcEtiixiUaaLC6ugqv18uaus2WAKSzifyge4h8FHIQaa2E9DWlUol8Ps/KKtTs0uzpf8AXzUAEWsgoKLTZbAC+GE1LnetOpxNGo5GpF4FAgKX1QqFQ0zKAQGNvAq0dZdEAsNqCy+VCoVDgiiXxKvv6+mC1WrkaRENgNjc34fV6mzpe/eS9RFJPAFhf3eFwMN2K9qfL5cLU1BQDQDpbFxcXsbm5ia2tLSQSiabuU6qenpzgpFQq2Sfas0ajEVarFX19fbhw4QI8Hg/zknO5HB48eICVlRWsr68jHo83XbJMGKzT2UJ3gdls5gw2SQGS9vbs7CzGxsZgMpm4Gry3t4fFxUWsrq4+MQUAaBJYlcvl6O3txejoKHp7exmFF4tFLCws4A9/+ANCodCXuGrtMmEpy+VyYWJiAmNjY5wVSKfTuHXrFv73f/8X0Wi0reDvpJ8ko0WZ6qGhIQwODjI/LRwO4+bNm/j0008bwH+7wRVFfAA4aiWeKr3wm5ubrPfa7Kka32TCF4o0K0nqI5/PM2dGJBJxiYjKxK2cpf51RqXrgYEBnrZCIu/Hx8col8uYnp7mGdaVSoU1K9sFVuv1z2Vp3G43JiYm4Ha7uSxdKpW49H/lyhVMTk4yUCgWi7h79y78fn+DaHSrrFarsR4pSSolk0nerw6HAwAwPDzMYxiNRiO/W++99x4WFhaQSqXaQgGhd56oSJRZIbUFGqFKUoAmk4kz1nt7e5ifn2dllVboPwOfP/tKpcKqBCeVKiiL2t/fj3K5zKCWuqtTqRQ2Njbw2Wef4dGjRzzUpJV7gS57YQBAneJKpZIvdaLgEEc8GAzi008/xdzcHO7du4dIJNLSfUtrSxlpkgQkTmK1WmVwQ/zrer2OWCyG3d1d3LhxA5988gn3LDQTrJzc+0JfaZLV4OAgjEYjN/rRPUDrTH0gx8fHuHnzJj755BM8fPiQtTmb9X7R3UIqKJRwEIvFPEFJpVIhGAxCJBJxQOvxeGCz2ZijTJqx169fx8OHD3kIULOev9BHOj+pmmI2mzE9Pc0ZU5KBGxgYwHPPPYeBgQHGLKSs9P777+Ozzz7jyY/NzFTTPUhJHQruqOlqbGwM5XIZDocD5XKZz91Lly7h3LlznFGnCiH5ure3h0Qi8cS+fmfOqkwmg9PpxMzMDC5cuAC73c7liXfffRe///3vcf/+/QYU3W4OKMkvjI6OYnZ2FpcvX4bL5UKtVkMymcRvfvMbvP/++zwe8LQyqpQR8Hg8GB0dxYULFzhTnc1mEQwG8etf/xq3bt3C7u5uS0a+/jWjiRnEtevq6kLfXyRg1Go1QqEQ4vE4PvvsM9y/fx87OzvIZrNtXVN6odxuN4aGhlgXkLK9sVgMh4eH6O3tZaqKz+fD/v5+27OqVLp2uVw4c+YMhoeHIZFIEAgEoFQqUa/XoVQq4fF4uKydSqWwvLyMRCLRFn+FVAVq9Ovp6YFYLIbVakU4HEY8HofRaGw4pLLZLG7duoW5uTkGU632kzIWwo5wh8OBgYEB7kxWKBSsUkLTqQ4PD/G73/0O77//PnZ2dprWpPBNvlKJN5lMIpFIsL6qw+Hg4RXUdEnlarFYjGg0inv37uH//u//8ODBA6auNNtfemeJ15lMJuH3+6HX63F4eMhjqSnT2tPT00BhKhaL2NrawvXr17G4uIjFxUXuWWgF+KOLNZvN4ujoCI8ePcLFixc5K0ycZuIG09fUajWkUimsra3hww8/xNzcHCuWNLsRmDJUNCjj4OAAt2/fxsDAAHevE3eWnjetJ/FEDw8PcevWLdy8eRMLCwtc/m9FcEVrGovFcHBwgHv37sFut7NWOY3QFqqEUId6uVxGNBrF0tISrl+/jrm5Oe4JaPb7RWtD3PmHDx8yx1+r1XJFAgBzvoUNVqlUCjs7O5ibm8O1a9f4fG12AEjNyX6/nyePEbXCYDBgYmICvb29KBQKEIlETKUhpSLKUK+uruI///M/MTc3x1nKZr9TROWjhrijoyPmLFutVly4cAHDw8Pc3Es9DU6nkweB0N36xz/+EdeuXcPe3t5Tc9WfGqwKicJXr17FSy+9xNybQqHAnMr5+Xl+kdqRATrpI/Ftenp68OKLL2JmZgYmk4mbf65fv45r167h8ePHLXvhv40Jpy319/dzkxpN/Njd3cWdO3fw6aefsq5duykVdJBSCaXvLyNULRYLX/qhUAh+vx+3bt1iDdBmc9P+mlGJlQZS0DS1YrHIJPByucyH8N7eHh4+fIhQKNTW50/vEEl8EOmf5oSLxWJYLBburqSO0cePH2N+fr5tWVWhr5SxIkI9ydQ4nU4GAKlUisu+v/vd7xoASit9pWa/eDzOHbxEpaGAVZiZoClVgUAAH374IW7duoX9/f2WN1YSAIxEIlhbW+MsOY00lMvlqNVqkMvlzK2kny+ZTGJubo71n2kIRKv8pUwQBcZ0jhOnemRkBDabjTUTqeSbTqfx4MED3LlzB5ubmw3jl1uV/RU2S/r9fiwsLMDlcuHChQuwWq0NE+GItpTL5XB0dITbt29jZWUFa2tr2NnZ4RHFrfKVApX9/X3cuXMH/f39GB4e5r1K1AsKWEulEmKxGB4+fIiHDx9iY2MDy8vLrK3cqneLsqmZTIbB3ODgINxuN4/QFdJrKBtH//7evXtYXl7G0tISfD5fwzjtZvtJyg67u7uYm5vD8PAwurq6+MynvUkBADVTxmIxPH78GIuLi3j06BFWVlYYUDf7+dNaJRIJ7O/v49NPP0Vvby+Xy4V6z5QZJskyCsLu37+P+fl53L59m6uWrQxUQqEQvF4v7t69y2oP1ARqMplYNYPefwLVFAB8+umn+PDDD7G/v8+Umqfx9anBKpGW7XY7ZmZm4HQ6ubwSCARw7do1PHr0CPF4vO1lVTLKCOn1egwODuLy5cuw2WyQyWRIp9N4+PAhPvroI6ytrZ0qnxb4Yj2JszI2NgaLxQKRSIRMJoO1tTXOVDaT8P+kJpfLOdqjhg8atZpKpRCNRplALTyc2mn03Eljlcp/ADgLRCMf19bWsLGxgfX19aY3JzyJFYtFbjohWROSNqIOTJ/Ph+XlZdy5cwder7dtDYB0YSWTSRwfH2N3dxdSqZRl1IQHajgcht/vx+rqKh4/foz79++3bJzySR9JVuXg4AAajQalUglutxt9fX3ccEFBSr1e58DK6/ViYWGBh1S0I7giXUWfz4dMJgO/3w+bzYZ8Po9oNMqXF81OJ8AQDAaxuLiIpaUlJJPJlo1/JhOCQGHV6fHjxzzC0m638whFANykRqOfhXrF7dgHJPl2cHCA+fl5BAIBnrZHASHx6JLJJHZ3d7G6uor9/X0eXNFKaUVaQxpAsbKygg8++ACbm5swGo1QqVQN+prCvbK5uYmdnR0eVkAUhVavaaFQ4ED5/fffx+joKOx2OwNW0tclXw8ODrC2tsbTCsPhMLLZbEsrFvV6nQO/x48fY25uDhcuXIDZbGb6BPlAVCqaqCj0lQKVVvt5cHCAx48f49GjR5iamuIMNfAFUCS6AEma3b9/H2tra1heXsbx8XHL3ykKPLe3t2EwGDA8PIz+/n6WdyN+MJX7CQfkcjmsr69jYWEBDx8+xObmJmOBp32vngqsUrlNo9HwRAsaVZdKpfDo0SPcuHGDOXVCkm47Tahl19vbyxOfyuUywuEw5ubmcPfuXcTj8aZ30T6JCXlrNpsNLpeLeZakT0mgikR4T8NXKq8Tx4u4SXK5nEWMw+Ewd6bG4/GWZVL+mp9UQiVuHcmUEKmdGtWWlpawvr6O7e1tzmC1y+j5UXR/cHDAE9+IS0cALBgMYm5uDgsLC1hcXGy6oPpfs0qlAr/fD6PRCOCLEbXUoEjl7FQqhcXFRQ4AwuFwW6bUAV9cAl6vF6VSCZFIBHt7ezg+PuaAimSA6vU6Dg8PcXR0BJ/Pxwd/O5QVCACSPmImk0EoFILP5+NGPypbl0olqFQq1gEOBoMMqsnXdulV0/rVajUeREEDQMxmM0wmE++FSCSCnZ0d5qe2Q12DvjeVWmu1GhYWFrC5ucnZIGpOEWY3A4EAEokE6z+3K2lBzanVahU3btzA2toaD9WgLCDxGwmE0TQtmmLXrmdfKpUYxH/00Ufw+XwcAEil0oZR1JlMhsc/U0AllKxrpZ+kTrOxsYGbN2+iXC7D4/FwU1ooFOJERSKRwMHBAU8rpOl6rQbUBP6p4YyknVwuFytSZDIZZLNZ7l3Y39/H7u4uZ6iFXNpW+0p7b3FxEW63GwBYao+qQEQVymazSCaT2NzcxMLCAlZWVrC3t9fQtP609lRglQj1XV1dGB0dhdlsRq32+ei1o6Mj3Lp1C3t7ew3i76cFrIxGIwYHBzE2NgalUolSqYR4PI7NzU2srq6y+Ptp+Um+1+hRAgAAIABJREFU0sQvygRRtx+VC/b29hAIBFgC6DQaq4iqQGPdaCOTfBGBA7rISAO03etKU1EIDMTjcSSTSc4ApNNp5l9RllKoVtAuIymQaDQKn88HnU7XMIqyVCohnU7D5/NhbW0N169f5+7UdmoVU2aVhg/4/X6EQiEEAgH+O8oIBoNBbGxscANFO9eUfAmFQvzeGAwGrK+vM0Ck0m+xWOQDXwhQ2ukrgSrSoM3lcshms5ypBsANNvTvTstX4qLS56lUikE2DdwQasFSVqjdZxQ9Z8pcxuNxpFIpDlKBLxpHqGLRCi3db+OncJRvIBBAJBJp6BIXglUabdwqhYpv8hP4IgCoVqvY3t5GKBTiLLVQCzafzzPFpp3+0vcnEF+tVnH//n0UCgXm1YrFYqRSKaTTaUSjURa2F4L/dvhKnGX6vxYWFlCr1dDd3c20KtItJvm0jY0N+P1+pNPprx1W0ypf6Q6v1WqYn59HPp9Hb28v3G43bDYbP38aoU66rwcHB0ilUk3L/ou+6RuIRKIv/SVpf9ntdkxPT+MHP/gBzp49i2AwCJ/Ph52dHXzwwQc8TrVZJap6vf61c7m+yk8qU46OjmJqagpXr16FwWBgSRKfz4ebN29+46SiZvv5Vb4SiXpkZARTU1MYHR3FpUuXUK1WsbS0xDOiiZ/SzCj1SdaUJF4GBgYwODgIl8uF/v5+SKVSbvRJJBK4e/cuDg8PGzIq33WTPqmfJLDvdDrR09MDt9sNg8HAdIR8Po+NjQ1sbW0hGAyyjmEr/fwqXwFw84fRaITdbkdPTw90Oh1HzbVaDcFgEMFgkDuT272mf/mzBh1Lki4ql8sMrKhkJTyc2u2n0F/KpIvFYtRqtYYJYLVaralg6mme/Ul/6UP4Z2TNHKTwtGv6VX6d/LyZai+t8vMv3/srP38aa4afQpkooa9CEH0a59NX+Soc8Snct82c+PZd1pT8IflHEtgn3wmXCFUDnvbub4afNKac6ClCWhVNTyMN5ac9+5txPhFVkbRsqb9CpVJxkELZVQpYn2YffJ2vTwxWSZjcbDajp6cHfX19kMlkHKkEAgEew9XMjNqTbgrSW+zu7ubxj8lkEkdHRwiHw0gmky3RqXwasEpC5r29vSxOHQwG4ff7kUqlmAva7NLP04BA0vrUaDS8SYUcoGg02vRy6pM+e5L/Ik1FmmJEDUDEv6SscDv8/DpfhaDq5BQYMjpcmyn/9V1AIPks1Aj8y/dseoaqmYDlK773U3r1ld/rOwOBdtl3XdN2WcfP5lqz9+jJYIDep2chUCGf6NeTAQD9etqBitA/ofIDBX7NOlOb9eyFfgqDAqoUCCsbT+tvU8EqjSsjfg3xWQhZt6I77WkAi16vh9Fo5ClVNDmFSn+tKP0/zaZQqVTsJzXUhEIhLlcSUDntNSU5IGpcohKBsAuU/Gyy3MsT+UmR6clDisBq/S/cpu8SUT+pn1/n61/+/Cs/F3zfhl+bYX8LF+z3xU/g++Nrx88nt78FP4Hvj68dP5/c/hae/VPRAKjDmiQKhET/VnE+ngawSKVSBleU+TtNP7/OV8qqCeU1hOTpZpbWvq2v35QFJCB4EpS2AlQ9jZ+Cv/u679cEr77y+37vDwSg4+fTWOfZN986fjbXOnu0+fa34Cfw/fD1icHqadn/T5viZHm1Vfa3sKbfFz+B74+vHT+f3DrPvvnW8bO51tmjzbe/BT+B74ev3whWO9axjnWsYx3rWMc61rHTNPFf/ycd61jHOtaxjnWsYx3r2OlYB6x2rGMd61jHOtaxjnXsmbUOWO1YxzrWsY51rGMd69gzax2w2rGOdaxjHetYxzrWsWfWOmC1Yx3rWMc61rGOdaxjz6x1wGrHOtaxjnWsYx3rWMeeWZN+019+H7S3gO+Pn8D3x9eOn09unWfffPtb8BP4/vjaSj+fVD+6nX4Kx1z+5f/+1r6220+hr08yhe809ujJKYLN8LVVfp74/7/V13XOp+bb1/n6jWC1Yx3rWMc69mzZSQBAn7dq0t3TGE27k8vlkMvlPD+8VCo1jI5u1STBJ/FRoVDAYDDAYrFAKpWiVCohHo8jkUg0jOQ+LT/FYjEkEgk0Gg1MJhP6+vqgUChQLBYRiURwdHSEXC7XkvHhT+OrVCqFWq2GXq+H3W6HXC5HtVpFLpeD3+9HNps9dV9pTeVyORQKBbRaLcRiMer1OiqVCuLxOIrF4qmvp3BqpFQq5c8B8LjzarX6zLzz9OvJM4qCqu/iZwesdqxjHWupCTNqwszVs2QnD1ehCX09bb/JT6lUColE0nAxVCoVVKvVUweC5I9cLofdbofD4YBCoUC1WkUsFkM4HEahUEC1WkWlUvnS2OZ2+imTyWAwGHD+/HkMDAxAJpMhl8thd3cXq6urSKVSKBaLvLbtXk8ab67RaNDT04Pz589jcnISSqUSmUwGe3t7uHfvHg4PD5HL5U4VYFFwotFo4PF4MDo6ir6+PshkMhQKBUSjUdy/fx8HBwfI5XIolUqnsp60N9VqNSwWC5xOJ+x2O0QiEYrFIlKpFDY2NpBIJBoCq3b7SYBaoVBArVZDpVJx0FetVlEul5FMJk8dsJ4E1PRB7zWtX6lU+k5+thWsPm2qvWMda5cRsHrW9+pJ/05GsV/1+WkYHWQn/wxoLLWedoaNQAH5K1xPOmCF2YHTBIFSqRQ6nQ5KpZKzLVKpFIVCAblcDpVKBeVyGeVymcFru/2USqXQaDS4ePEipqenodPpUC6XcXh4iIcPHzaAK/K5nX7S81ar1ejt7cWPf/xjTE5OQiwWIx6P4/79+xCJRNjY2EAqlUKhUGg7KBBmfm02Gy5evIhf/OIXcDqdAIBkMgmtVotcLodCoYBwOIx6vY5isXiqoNrj8eCVV17BK6+8ApvNhnK5jGAwiPX1daRSKcTjcd6b7X6XCFDr9Xp0d3djZmYGo6OjUKlUSKfTCIVCOD4+RjQaRT6fR7VaBYC2AlZ6fxQKBfR6PSwWC3p7e2G1WlEqlZBKpRCJRJBIJFAsFlEul5uSuXwao/0pl8uhUqk4o65UKpFKpZDNZlEoFNjH7+Jny8Gq8CJQKBRQKBSQSqUQiUQolUrIZDINB+qzknKXyWQc2VC6XbjIT8IVapWvQn/pogWASqXSsI7C6OY0/ATAfpLPtHbCskalUmn7pSX0U7iWJwGWEMAQEDitiFtYYqV9etJfAHxxCSPadh64QkCgUCi+tL6UIahUKlzGbPe6CtfSYDBArVbzuySXy7ksTOuYyWT48G13NkMIVEdHR9Hd3c1raTabkclkEAqFEIlEuIydyWTamm0Tgpa+vj68+uqruHz5MiQSCVKpFLxeL4rFIhQKBYLBIJLJJEQiETKZDIBvz2n8rj6KxWIolUo4HA5cunQJV69ehV6vRzabRa1Wg8lkgsFggF6v5/dHmLVul8lkMtjtdpw/fx5vvPEGxsbGOPtLILpWqzW8V/RutfMdkkgk0Ol0mJycxOzsLH7yk5/A4/EAAFKpFPL5PPsqkUi4MtCud4jOI6VSCZfLhQsXLmBmZgYvvvgi9Ho9CoUCA+pIJAKNRgOlUsnnUTtMGIzabDZcuHABo6OjGBkZQW9vL+r1Og4ODnBwcID9/X2sr69zhrpYLAJoH6imZ65UKjE2NoZLly6hp6cHVqsVCoUChUIBW1tb8Pl82N/fx/HxMX8tremTrmtLwaoQ+KnVajidTrjdbshkMuaF7O/vI5PJNGQB2n0BCIGKQqGARqPhzIVEIkGlUkE0GkWhUOByEAGr0wAAtFFkMhkUCgWUSiUUCgVkMhnq9TqX2MgKhUJDUNBqf4V+0vMn7powWAHAfyYWi5FMJhEOhzmDIfwZWukn+UjlFgqqZDIZH6r0+1qthkgkglAoxAdFOw7ck89drVbDarVCpVJBq9VCrVZDo9FAJBLxc87n8wiFQgiFQkin01zObKWvwjVVKBTQ6XTo6+uDVqvlNdZoNMxjKxQKSKVSCAQCiMVifLFRJN4OP5VKJSwWC5577jmo1Wp+RzQaDdRqNSqVCrLZLJLJJHw+H2c16Dxox/tPYESr1WJoaAhvvvkmrFYrcrkccrkcTCYTCoUCrFYr70+v18uXQjtKriczgbOzs3j++edhNpsZPIfDYeRyOQYtBLD+H3tf9tzmlVx/sO/7SgLgvoikSIqUSS2WbTnekmgST2VekpdUpSqv+Y9SU3lIpZLUTNmTcUbOeOJV+0ZSoriDAEgAxL5vBAjg96Dpng+0ZuIFAD3zQ1epVKK4NO93v3tPd58+/fsoGJ3yUyaTwWazYXFxEe+//z76+vpQLpeRSqWwtraG9fV1+Hw+pNPpr52n3fJRKpXCZDLh0qVLeOutt7C8vAydTod8Po9oNIrV1VWsr69je3sbqVSqK+/Ny/wUi8XQarWYm5vDW2+9hXfeeQcjIyOQy+XI5XIoFAoIhUI4OjpCIBBAoVDgc7Mb/gFgYDUwMIB3330Xr732GmZnZ2Gz2ZieUigUoFAo0Gg0Wni19H06ua6EP+RyOaxWK65du4Z3330Xs7OzsNvtUKlU/J4bjUbIZDJkMhnEYrGXJio6ZcL7UqPRYHh4GG+99RbefPNNeDwe6HQ6SCQSlMtlnDt3Dru7u9jZ2cHq6iqePXuG4+PjrzUIflNrG1gVOkCZCZlMBqVSCa1WC5vNhomJCfT390Mul+Pk5ASVSgU6nQ57e3vIZrP8snUaUAkvfiHoo4vf4XDA4XBAqVRylHp0dMSZgFKpxBH4WflKwEqr1cJut0On00EulwMAZ6moxJZKpdBsNlEsFjsGrl/mJwFRAtMGgwEmk4k3tDCDTQFBo9FAOp3mC61TvtLPV6lUUKlU0Gg0sFqt0Ov1XMag8gYBbMpUxmIxSCQSRKNR5PP5ju4B4fukVCqh0WhgNBrR398Ph8MBnU4HjUYDlUrFgJqefaFQgNlshlKpxOHhIXK5HBqNRkcuidMglfwcHx/H4OAgA1SpVMq+1ut15HI5JJNJaLVahMNhBAIBDlQ6dZkROBLy1s6fP4+pqSloNBoOSOn9ajabyOVyiMfjvC8ouO50oHL6cvB4PFheXsbCwgIMBgPS6TTvR4VC0UK7oPefQHUnM0RC4G+32zE3N4dXX30VFosFIpEI5XIZoVAIBwcHHPjX6/UW/m23wItUKoXZbMbCwgKWl5cxPj4OiUSCYrEIv9+P7e1t7O/vc0DabV4traVGo8H58+cxPz+P+fl5mM1mPh/39vaws7MDn8+HeDyOcrnctcDptJ+UrVxeXsaVK1fg8XigVqtRrVZRKpUQj8cRCoWQSCSQSqW6FuCTj/RuEJWCgCo1fxWLReaAkm8vq6R20kdaR5PJhJmZGSwtLWFxcRFOpxNqtRoikQi1Wo3vKYfDwaCVvr5bARXRKPr7+7G0tMTvkNFohFwu57OV/lYqlUin0/D5fEyt+C535vcGq6ezaFKplMuTGo0GZrMZ/f39cDqdGB0dxdDQEORyOb/8Wq0WCoUCXq8XsVgMuVzu+7r0jf2kS1On08FsNsNiscBkMsFkMqG/v58zq2KxGEdHRwgGg4hEIgiFQpxdrdVqHYm6Tmf9lEollEoljEYj+0jZX5vNBq1WC6VSCZFIxPyqQqGATCaDcDjMWcBOHLov85N8NJlM0Gq1AACDwQCj0cjAmgBtvV5HPp/H0dERX2SdKLMKAbVSqYRKpYLVaoXL5YLD4YDVauVyFhHaaX9IJBJUKhVks1mo1WouawlL1+220/QZu92OwcFBuFwuuN1uqFQqmM1mfofo9zo5OUGhUEAymeSDTlguavehJnz+MpkMFosFQ0NDGBoawsTEBCwWCwwGQwu1Rq1Wo9lsIpFI4OjoiEFgsVjE8fFxx/wUZqeNRiMGBgYwPj6Oubk5vmTp0qJDWSKRIJ1OQ6/XQyp9cWQSp43Kb52w076OjIxgbm4Of/Znf4aBgQHusqfAXywWw2g0Mr3i+PgYyWSSuW2dyg4JqRQOhwOvvPIKlpeXsbi4CLFYzIAlHA4jHA4jlUrxsxWWrul7dTLwIxA4OzuL5eVlvPbaa7BYLCgWiwiHw9je3obX64XP50OhUGCgerpBsJNGiZ6+vj4sLy/j8uXLcLlckEgkSKVS3Py1s7ODYDDIfnaT6nOaPnPx4kVcv34dY2Nj0Ol0AF6U/yORCPb29nB4eIhoNNr1LDWtpV6vx7lz53D16lXMzc3BarVyhTefzyOTySCRSLSsJX09/d3JdSV6z8DAABYXF3HlyhW4XC4olUoA4He8VqtBKpVCr9fDaDQyRunWviRQ3dfXh9nZWVy+fBnT09Mwm82QyWQAfkdFUCgUsFgsaDQaCAQCsNls3LD4Xew7g1UhF5E4KJSF0mg0MBgMGBkZ4dL/0NAQZywpGgCAoaEh6PV6GAwGrK6ucrm6nSa8pIi4TBkVp9OJgYEBOJ1O2O122O12yGQyOJ3OlqilUqlga2sL+/v7MJlMyOfzHM2221cALZc/RVNutxtDQ0PcvWixWJDNZluiL/qTz+eRTqeZKxKJRJhu0c4XTwioKDhxu90YGRmB2+1GX18fjEYjjo6OYDaboVKpoFQquZQtFotRq9WQTqcBAHa7nQFru4GAMNIzGo1wuVyYn5/HuXPnMDExAZPJhEgkAo1G09JsQ4dvrVZDPp+HRCJBMplENptFpVJpIeK301chyd7hcGBpaQkLCwuYnZ2FxWJBLBbjwwp4Ef1TwEL8SpPJhFqthkKhwCXjdu5ZIVCld+rSpUtYWlrCxYsXYbfbkclkmEJRr9c5+yqRSGA2mzlQUCgUyGQyTMyv1Wpt85N8JUBvMpmwtLSEpaUlvhjy+TwAMD+VMutUfjcYDJDL5SiVSpzV7NQlIQQDRqMRly9fxvXr1/HGG29gYGAAqVQKJycnLL9DPEb6OpHoRWezVqttod10yleJRAKDwYDr16/jxz/+MZaWlqDT6bC/v49IJILNzU0cHBwgk8lAJBJBpVJxRaVbRsDF5XLh/fffx7vvvgu3241ms4mdnR189tlnePr0KbxeL3No6d3qVk+FkPP7xhtv4Mc//jGGhoagUqlQLpexsrKCzz77DM+fP2dALeT/d5PvLxaLoVarMTk5ib/927/FK6+8Ao1Gg2aziXw+j52dHdy7dw87OzvY29tDKpVifm23aB9UkXK73Xj11VeZPkNnZCqVQiAQwN7eHiKRCKLRKKrValcpKrQvqSJx48YNTE9Pc5WsUCggm822NFRJJBIYjUYYjUbOVneDligWi2EwGHD+/Hlcu3YN169fh9VqBfA7uiElGujcMRgMmJ6exvr6OmKxWAsdrWs0gNOcRGHJf3JykuVAzGYzpFIpyuVyS0aTynAzMzOIxWKsddZOO51NVSqV0Ov1sFqtGBwcxPz8PJaWlmC321uyPcRdJCkOAFCr1ZDJZMhms5zm7oQJy6kGgwF2ux3Dw8O4fPkylpaWYLPZoFKpUK/X4fP5uLwqlUqZv1gqlRCJRJDP5zmIaHdTmPBCJU294eFhXLt2DVeuXIHD4YBarUa9XsfW1hZvUIoibTYbms0mMpkM0uk0dxALo9l2+kqXgMViYa7N9evX0d/fD41Gw6UgypoT+NPpdBCJRCgWiyiVStBqtbBYLIhGo7z27QbW5Ctl//78z/8c7733HjweD/taKBTQbDbZZwK3MpkMEokE1WqV9/rR0RF0Oh1nBNtlRKXQ6XRwOp14++238f7772N4eBh6vZ656UJZJSEHGACq1SosFguMRiNnidt9SdA+JUD99ttv4yc/+QnGxsZgMplwcnKCRCLBByyBVPo3cYBPl647YfT9CVS//fbb+Md//EdMTU1Bq9WiXC5ja2urhS5BweLx8THK5TISiQQikQji8XjbQb/QT/JVp9Ph1VdfxT/8wz9gdnYWarUa8Xgc//Zv/4ZoNIp0Oo1UKoVSqcRNbBQUEG2p05esXC6HyWTCT37yE/zoRz+C3W4HAHi9XvzzP/8zdnZ2EI1GmVNJgIXUQbpF+dBqtVhcXMQ//dM/YWBgAFKpFMViESsrK/jwww+xvr7O3N9qtfq187Ib2VXKUE9OTuLv//7v8frrr3NDUiaTwYMHD/Dll19ia2sL4XCYOd60lpRN72SWVSQSQaFQwOPx4NVXX8Xf/d3fweFwQCR60dB3eHiIR48eIZFI4PDwkHn9x8fHXNGgc6uTfkokElitVly9ehXvv/8+5ubmuLEzEonA7/fD7/dDJpNxEC2TyVoqrEJaUqdMJBJBo9HglVdewTvvvIM333wTTqcTJycnyOVyODg4wO7uLrLZLCcg7XY79Ho9BgcH4fF4EA6HGXB/W1+/N1ilqEAul0Or1aKvrw/nzp3DwsICRkdHodVq+aIvl8tcXqNyGmUAyuUyR7OdMEpfE0gaHR3F7Owsrl27xtwPimJSqRS/+PS70feg7BBxGDtxKFDEajQa4Xa7MTY2hvn5eVy/fh0GgwESiYQPhWg0Co1Gw01hxF8kHynjTZdBuwAAPXsqA3k8HoyPj+PChQt45513YDabIRaLWWA7GAyiVqvxhUHBQb1eZ2AAoGNZSirv9/f3Y2pqCouLi/iLv/gLOByOlhJ/IpHgC4CCLwJP9PFardaSoWx30EIXQV9fH86fP4+FhQXcuHGDL65qtYpsNtvSNEcZYMq00mGQz+dbmgPbHQAQDWVychLz8/O4ceMGRkdHoVKpcHJywtlnEttuNpvQarUtKhvFYhGRSISbrKhruJ3Zf9p3AwMD3AhCJUsqBdIhSvQeAFAoFDg5OUEqlUI0GmUqUCqV6phEEAUqNpsN8/PzePfddzE+Ps5ZK6L20PtMslUikYibLogn2EmtSPrZ5OvMzAxcLhev2eHhIYLBIMLhMAqFAvckUEKC3qFOl7DJT71ej/n5efzVX/0VLBYLACCdTuPjjz/G1tYWi+uTTxRMdUv+i8790dFR/M3f/A0GBwchk8lQLBaxt7eHn/3sZ3j27BkikQiXhBuNBlfKusmpVSgUGBoawpUrV/Dmm29CpVJx8+mDBw/w4YcfIhwOIxgMtjR+CRuT6Xt1si+BKj3vvfcenE4npFIpCoUCdnZ28MUXX2BlZQVyuZwDf6q8yOXylyrttNvomS8uLuLVV1/F7OwslEolqtUqQqEQHj58iCdPniAWi6Gvr48TJWazGU6nE1arFdFolLEVvevtNnqHbDYbLl68iIWFBabNURb9/v372N7eRqlUQn9/PwYHB7mKbTKZMD09zcGgUArum1pbGqyazWZL96/BYOAon8AdSZRYrVao1Wr+unK5jMPDQ8RiMWSz2Y4ttLBJweFwwGazoa+vD0qlkoWAC4UCP3hquKIMSrVaRbFYRDwe54uqE9xPIS/E4XBgaGgIw8PDOHfuHLRaLTelpNNpHB4eIh6Pw2q1Mkil8hqVrOPxOLLZLHK5XNtBIGXVHA4HRkdHGVSbTCYA4IvT7/cjFApBLpfDbDZDrVZDp9MxvSKfz3MWKJlMtjXLQmuqUChgtVoxPj6O8fFxXLx4EVarFWKxmEF/MBjkZhWdTgedTsfAv1wuM+gOhUJMA2h3UwMBK7PZjImJCUxMTGB5eRkOhwNisRi5XA6xWAxHR0eIx+NcBtZoNCzBQs1qBwcH8Hq9iEajSCQSKBaLbSu70uFlMBgwPDyMyclJvPLKK3C73VAqlSgWi0ilUjg6OkIsFgPwopRKmUqaEJNIJFiKhc6BdgcsBP5dLhfOnTuH+fl5jI2NQaFQoFQqIZvN4ujoCKFQiLM9dOiLxWI+H5LJJILBIHw+H3K5XEc67IWNFmNjY5iZmcHc3BwHdvl8Hru7uzg4OGhJFFAWiLh3iUSCOYKd4FQLqz86nY6rVCqVis+eR48ecVaVgicK9k5OTrgc2OnGL+JI9/X1YWlpCSMjIy2X7JMnT1iFhMAfJVO6xa+k995qtWJmZgbXrl3jTGU4HMZXX32Fp0+f8t1Da0kNwMKu9U77SSXoubk5XLlyBTabjalnW1tb+OKLL7C+vt5CkaJMpTBDTXzrTvmpVCoxOTmJCxcusORXtVqFz+fD3bt3sbq6ip2dHW5cowZrmhSWz+e5J6ET60r7zGq1YnZ2FjMzM8w5TyQSWFlZwZMnT7C1tcU8b4vF0rKfXS4X9/oUi0W+79p9J9HPnJ6exsLCAtM5a7UaAoEA7t27h42NDQQCAZRKJX7mWq2WMeHo6Cj29vYQj8dxdHTEz/+b+vq9wSr9ICpBEh+Rmg8oe1IqlWCxWFqIy7VaDbFYDPv7+wgEAtxhT//f7gWXyWQwm838R6PRoFgssshuKpVCMpmETqeDw+HgrKRIJGJQTWClE3xV8pPK+R6PBx6PB0NDQyxNk8vlEAwGcXBwgEgkArlczg1LWq0Wcrkc5XIZmUwGgUAAoVAIkUiEOx7pZ3yftRWCf+ImDw4OYmJiAjabjTNqgUCAy0ASiQQmk4mz20qlEvV6HZlMBgcHB/D5fAiHwzzdpp2gisDn4OAgBgcHMT4+DqfTCeCFsLbf78fa2hrC4TDUajU3gpHWHpVVfT4f9vf3cXBwwI0i7QRWwkPB7XZjYGAAY2Nj6O/v54zA4eEhnj17hsPDQ5arouCw0WgglUpxIEMadwQC28kDpQDAZrPxHiVunVCz0Ov1tpTVSWEjm80ilUpxRtXr9SIcDiOZTLbs1Xb4Sdl/YeBnNBpRr9eRSCSwv7+PnZ0dlEolzvhSdrVarXImPZ1OY3d3F+l0mvnfnbgUKKs+ODiIqakpLleTBNDDhw8RDAa5oZHKgoVCAblcDplMBpFIpKPTbeicUqlUMJlMcLlcGBkZQaPRQCaTQTAYxPr6OgclVIanyTbdyKiSn9RgMzY2hldffRU6nQ7VahXBYBBffvklNjc3GZBQFQ34HVDtdNZS+N6PjY3h/PnzGBgYYAD47Nkz3L9/H6FQiDPTdC+SVFMwAAAgAElEQVRRMx3tRQKxnVxPyqrOzs5ibm6O1TEODg5w584dPHz4EPF4nBsqScFGLpcjn89zRYB+l3Y/f1pPk8mE2dlZzgKKRCIkk0ncvXsXKysr2NjYQDabBQC+QykLSJVLIVhtd68HBXvj4+NYWlpipaRarYa9vT3cv38fW1tbODg4QKVS4bOTqEwqlQoej4eTVplMpiMBgLCCMjU1hZGREVZJKRQKuHfvHtbW1uD1epFKpbgJjO56q9XKfTYejweBQKClMazjYJUeHi26SqWCXq/nmcXRaLRlXrFYLIbT6WROJWW07ty5g+fPnzOXoROHKvFVNRoN+vv7YbPZoNPpUKlUsL29jVAohHQ6jWKxiGq1itHRUWg0Gmi1WqhUKj7YHj9+jM3NTZ6+0omXTCaTQaVSYXBwECMjIxgZGYHdbke1WoXf78fOzg6XISuVCoaGhpiHq9Vq0Wg0kMvlsL6+jvX1dWxubraA63aUA+kwUKlUGB4exsTEBKampuB2u9FoNLC3t4fNzU0WAy6VSty9brfbmSKQyWSwtbWF1dVVeL1eHB4eIpPJtOiXfl8/KVPlcrkwOTmJc+fOYXh4GGKxGIFAAPv7+3wglMtlDA8PQ61Ww263w2QyQSwWIx6PY3NzE16vF6FQCKFQiKWg2jU2UBgAWK1WjI6Ocue/SCTiaJQOhVKphKGhIZjNZlZYqNfrODg4QDAYZKk1+jdJw7XLV4lEwlykvr4+bkgkGs3m5iY2NzeRSqVYwYKarKLRKJLJJAeJ4XAYfr+/xcd2gAPh2UTNfv39/dBqtUxP8fv92NjYQCgUQqPRYG53uVxGNptFPp+HWCxmdYXT51onsuoE/qghFQCKxSJisRifV4VCgTuFqaGF+Miks9op3U3hXlWr1bDZbFz+p2rK06dPEYvF0Gw2uUeBEhnUcS3kW3fChOdUf38/JicneUpVPB7Hw4cPualXOLCGKF5C6hqBwE75qVAouInyzTff5FLwxsYG7t69i42NDdRqNSiVSqjVai5TU5c4VU2E+7ITz50a6UgCyuPxMAj8n//5Hzx58gTRaBQnJyecodTpdEynIlUIalDuxPMnEDg2Noa3334bY2NjfI/fv38ft2/fxubmJlNk6vU6DAYDLBYLbDYbHA4HN1JSAEAqBu0EqzKZDDqdDnNzc5ienoZer0ez2UQymcTNmzextraGUCjEwXs2m+VkUKPRgEwmg8fjgcPhQDqdRjqd5mxwu84mIdXTZrPhtddeg9PphFwuR7Vaxd7eHm7duoWNjQ2k02lez2QyCb1ez7TLyclJrm5ZrVZYLBYkk0nGfN/E1++dWaUyuUql4qiUgBEtGnVgTk1NscRBNpvFZ599hv/8z/9sGQzQbl0zYQaASuVE7qdpL1Qirdfr0Ov1mJiYwNDQEAwGAxqNBiKRCD788EM8ePAAgUCAAVW7Ny81nlgsFu6QJpmkVCqFlZUVBh71ep11LGdmZpg/kslkcPfuXe7CDAQCLRnAdoEqKqu73W4MDg5y+TeXy+HOnTsIBAKcVaGsBokw0zSb9fV1PHnyBPv7+/B6vTg6OkImk2lbV6Owm5qyqtT4Uy6X8eTJE+zt7fHFrtfrMT4+zlzGZrOJdDqNlZUVDhIODw+5q7mdeoHCrNrAwAD6+/uZo0TjCtfX13F4eIhyuQyj0YjR0VHmYdEoy8ePH3PplagN2Wy2bVJg5CfJlzgcDtjtdhiNRojFYuTzeRweHuLo6AjHx8c8BYg4rKQLGgwGkU6nkc1m+W8hV7Vda0qlVZfLBZvNBpvNxuXVbDaLZDLJFytd+Pl8HolEgkFhs/lirjV1u3ai65bWlRpUqdOXpLQKhQJPpiJ5OKqkNBoN5PN5BAIB5PN5pjQJOWGdCqqNRiMcDgf6+/tRLpcRiUTg9XoRCAQ480P0JIVCgWq12qK12u5nLvSR/DQYDJiamsLCwgJ0Oh1KpRKePXuGra0tFItFeDweWCwWaLVaHl6Ry+Wwv78Pv9/f0S5r4XMnyaLh4WE0my+mFd28eRM7OzuQSCRwuVwYHR2FzWaDwWCAVCpFJpPhJEYmk2EaQyf8pMB/cnISN27cwNjYGGQyGSqVCj766CO+H6VSKex2O2fh3G43c4Q3NzexsbEBn8+Ho6MjVrBo19pSEGUymbC8vIzp6WnmpYfDYXz44Yd49uwZS2QSLZCoTBMTEzAYDAgEApyhpMC1XSYMoj0eD65duwaTycSZ9P/93//F7du3EQwGGcwRPanZbMJoNGJsbAw2m43HBQspV+3uS1AoFMw5nZqa4uxvJBLBBx98gJWVFc6o0teUSiXEYjF+/6lZ0WQycWX723KBvxNYfdkPoO5kodamsFR48eJFTnOnUilsbGzg1q1bODg4QD6fb2kCaHeqXSqV8uEKAPl8HpVKhaMtak7RarWYmJjAwsIClwnT6TTu37+PJ0+ewOv1sk5YJ5qAiHtIAICyv5TBq9VqDBKVSiWGhobwyiuvoK+vr6VM/OjRIzx79gzhcJj9bWekRdSD/v5+DAwMcFaFuJ3N5ovpP7TuTqeTBY6p2S6ZTPKFQeUDahhqxx4Qgiq6+CnzT81clMWiRgqn04nJyUmo1WocHx8jEokgGAxiY2MD29vbiEQiLK3UbqAqBAAGg4FLu3T46vV6eDwe1lG12WycyY7H4wgEAtyNGQqFeHiFcDpcu3wVdlYTVYIyZxKJBG63GwqFAi6XizmfNAo0FAoxN5gyV0S2bzdQpXefMs/0h1Q9iBdqMBh4ihZlKFKpFDKZDAd5nSxbn67+ULacAlZS8zAYDDx2Ua/Xt4jEU1ZF2MjUCbml01zVgYEBDA0NYW5ujgEplVKpR8BoNDLvP5FIIB6Pt6i+dBIEqlQqDA0NYXp6GjMzM1AoFCgUCigUCpDL5XC73dDr9RgZGWH5NLqIKftOe7TdIFCYoTabzZibm8PQ0BCDeq/Xi3w+z+VXt9uN6elpVoMBwJ3XALC7u8tJok6UgmUyGbRaLWZnZzE4OMgBaDKZxObmJvL5PPR6PVOYpqenWW2D3jkatkO/I1Uz2+UjAWqr1YpXXnmF16ZcLmNjYwPRaJS7/YVAdXx8HJOTk+jv74der4dGo+FsKvHVaQ+0426itRz6rRY1DUpKJpPY2NhALpfjIIneN2FZXaieMjU1hVQqhXg8jsPDQxSLxXYsJ4BWFY2hoSFYLBbWTg6Hw5w8oWwv3WXUlEh0ANI1t1gscLvdcDgc0Gq13yrp970zqyQyXy6X+bKt1+usEWo2mzEyMsKSO+VyGYFAALdu3cL6+jqnrekC6ER0DYA7ESl7QgCFeCoqlQpOpxMLCwuc/aNy1oMHD1jTTnjxt9tXerAE9MrlMkqlEgNAok9IpVJYLBZcvHiRM5qFQgHhcBiPHj2C1+tl2ap2TwuhAESv18Nms7FQupA3ZTQauZSm1Wr5EJBIJNzMQlNihKV/4T5oh5/C8Z4UmdJLJZVKucGuUqlw9l+n0/FYTeL8kqi1sOzfbg6bsPNUOHCCfKUL3+l0cjNAo9HAwcEB9vb2mEsbCAR4ZjhJA7W7JERRMh1IQskUUgfQ6XTMWyeAGo1GEQgEEA6H+ZISDlVo57Oni0soL0VcSVKgoPdJpVKhWCxyw5+wyYoCU/o9O1VepXUlYEp8ZGETgkqlgs1mg1QqhdvthsFg4ExlIBDgbmBh93qnQDUBAofDgYGBAab2EI1FpVKh2WzC4XCw1BrxFakaJwR/7TyfALQEeSMjI5icnITZbAbwglJBa0j0EI/Hw8o1NFnR5/Oxnm6pVGqLf0I/hVSKiYkJzMzMcHNqoVCA3+9nfqjNZsO5c+d4cAXwYj9brVaUy2Wm1RQKhbaCFaGvNJhkZmYGBoOBFVKCwSBSqRQ0Gg2cTif6+vowMTGB8fFx1tYWquqMjY1x5ScUCrXVT6qiDg4OcuaXKjrb29s4Pj5uaQSngUV9fX0wGAxQq9W8voODgzzIQqFQtM1PAoA6nY7L4mKxGMVikelbpNwiHPdNdxkNAKKPUbM49YS0K6gSgn+Se6R1yGaz3CwlTNzRe/yy+5ECBKLYkTb8N7XvBFaFDhFIKZfLzGM4OTnhC5bKr2azGSKRCLFYDA8fPsT9+/dxcHDQ1qzfy/ykP/V6HaVSCclkkqWeTo8snZiYwMDAAFQqFVKpFLa2tnD79m08ePCAS9qdHGlHF4FUKmXtMlpfmlZEzUJutxvj4+PQ6/Wsqfr48WM8fvwYe3t7DFTb7S+9aFQyo0Ypio5lMhkLbVMJzu12Q6PRIJVK4fDwEM+fP8fOzg52d3dbZlq3W66IGuSUSiXz4wgQE1glrVcqxZXLZfh8Pmxvb3NzEpWDhRyrdlcACKyIRC+k3KhESiL6Wq0Wer2eg8N6vc5i1gT8hVUKoWZsJwJAkUjEpfFyucxrQ1xWoSQUZaTj8TirfgiVQr7r+L0/ZARUScaLmjzT6TSMRiPz6AnQEJ+WlCGIuyrU3O3EOUVBinCMskwm42504kxSOZBoLXSpkr7v2tpaCxjvBJ+W1pQuTafTydUV4lhSp7jZbGYKBklZ0R6QSqVt19MW+ikELaRU4na7WVGhXC63ZNrNZnPLuGpaU5q8Jhy80U4/CQCS8sfAwAADe+JLE4eSKD9KpZL3Bp1BIyMjCAQCSCaT3M/QThMq6QwODmJ2dpaDZVJUODk5gdVqhcfjwdjYGDfhqNVqrrAplUoYDAZutiH99Xb7SXeO3W6HRCJhHncwGAQArrKRhCK9T1TJkkql3GhLlAthQ9v3MeFzt1gsGBwcZHBcLBY5eQOA9yOtPfF+yU96H4k6ZDAYOOPeLqPAlOQ+SQGA6CdESaTfCwDrUwuHPwG/4xILK1wdB6tAK2Cly4AuSbrEqPxChNx4PI5PPvkEn376KdbW1tra9fv7jA7w4+NjZLNZPnSoEQwAxsfHce7cObhcLuj1ehSLRTx8+BA3b97Ep59+ilgsxmCsExlgMiLLFwoF5PN5BjAEYJ1OJ4aHh2G1Wrm0eXx8jP39fXz++ee4ffs2VlZWmLskvLTaDayEYAD4HdAWi8VcohDOq49EIlhbW2P+59bWFhKJBO+XTkhAkU/CrlnyVXjpk35upVLB+vo6Hj58yA1f8XgcxWKxBbh0AgjQtBQAXHImQEUAkA4pCmS2trawubmJvb091tSkPd2JZy+k1FBmlUBgPp9HNptt4f0J1/zk5ATlchnFYpHllAB0ZH+STI6QmkAcKuCFtiY1qVitVgYkVNoUqpQIM5Sd4qnK5XIORs1mMzdOZbNZ+Hw+Bi50gdJlTI1ixAsHflfpavfwD1pTAhwejwcDAwMMokirmGhV9DtR+Y/2I9EDhO9lO/0kwELrOT4+zuMgac+KRCKYTCamXVFliPoriMbQ19fH44xJe7udFBVay9HRUVy7do37DkhHWa/Xc9aP/C+VSry+tLbUPEp0oHZnK4lOQYBlYGCAZaAymQwODw+h1WoxOjqKc+fOob+/nxvpaIgKjaymtbVardDpdG3LWFKAotVquZlOr9dDJHqh5EP9KcS/FJbRRaIX+sQEok5Tsij4bpcR+Ozr68Pk5CTvP2GGnKpnwoSacA/TPqbzg85dkoRshxHlz2g0wmq1Mo2vXq+zipJQporuMMoaE+2O7gu6y2ht6e79poD1e4U1lG0gjgxdPI1Gg2fx9vX1MT/lv//7v/Gzn/0MOzs7PCauU1nK0/7Rv0lii7KrNpsNFy5c4Dm8JycnuHPnDn76059idXWVicOd9JN8Iz3HQCAAkUjEHfQkVTE0NITR0VHodDre4Kurq/iXf/kXPHr0CEdHRy8dV9tO35vNJiqVCouOU9a80Wgwt8Vms8FisTBgKZfL+Pzzz/HRRx/h+fPnLP0j5FV1gqtMjTHFYpFfatoPdLCRLi3x07744gt88cUX3PV9mp/cKb4i/RGJRAxIaNIPDSUQSn3k83k8ffoUKysriEajKBaLX9P+bXdZXS6Xs6YnXTyUkcxms6yTTAc8ZbSpESSRSHyNRtFuME2lJmGWUqVS4fj4GPF4HPl8Hn6/n7v83W43ZmZmsLS0BKfTCY1Gg0Qiga2tLQCt4K+dz532I70vlOGhc6lSqeDg4ICpMuVyGSaTiXmLwq51KrOSn+0MqGhNaUgJNamNjo6yXq1Op2PZNJrqZjAYOBNMlQv6vYWTt9pJURFSFAjozc7OwuPxcFOIUD8XeJFFJW4yBQu0r4UKAMJ58e3yldbJ7XZjamqKG/9yuRzK5TKGhoYY0JGfXq+X+epCTe1SqcQAop2qOsJMoM1mw9Bvx6MDQC6XQzgcRrlchsvlwuDgIPd6hMNh7OzssLY1gUI6Z0OhEPdUtGufCvmqQ0NDfEemUins7e2hUqlwFdVoNPIwABoDazabmSN6cnKCUqmEeDzO+t/tCADJT5r6ZzQaIRKJePAIKffQEAuapEn7VbjPKStNUpGpVAqxWKxteEV4npLcJCV1iNNPSTxKqhEYpcmJ9E7RXqXKYSaTYTWAb7qubZlgRS+0SPRCiLe/vx83btyA2+2GXC7H8fExdnZ28NFHH2F/f5/1VDsNAF9mlOE5OTmB0WjEa6+9hr6+PshkMhwfH2N3dxf/+q//irW1tZbO9E6bkK5QrVa5I5kiOrVaDY/HA71ez9y63d1d/PSnP8Xt27eRSCTaLq/xMqOMLa1jJpNBKBRq0SfV6/UtyhDr6+v49a9/jdXVVaTT6RY/O+Vrs9lkgEWj36h7mgA0/WyiCAQCAezu7rI6BGV8hd+z3UbvD2VZBgYGeBrY4OAgtFotg22a895oNJBMJpFKpVgC5rTUV7t9JXBFa0nNPk6nk8f9ra6uQiwWY3JyEuPj4wyiiL4glKUSrn07jA5xCuyo8kCXAsnPUHNNPp+HTqdjkW2hYgFlYqjy0+6sqlBNY3FxES6XCxaLhQeU0J4jOapEIgGRSMRlbNKsJFWTdDqNg4MDDlja3UxHAuQej4eft8vlwtBvpdNUKhVKpRIymQzy+TyAFyVN4tCRpB41LSaTSeaBtvN8FZZGqXTpdru5gkKjUwm0Uv9CsVjkvSqsXqTTaX7P2gUAhZUpaqijpiPyj0AXZaDpPqCmLyFvkS5/Gv7ysmTF9/X3tKoKBR4UGJGQPvGUS6USEokEAPC0Jcr4lUol5mVGIpG2AmsCgQaDgbOAdKeSLJ1Q81Umk3Hml9RXKHuZy+W4wfbo6Iira9/XhBlgyvAKzxeirxD1j86lZrPJwQIpAdD7Tz0g1K/QrqoK7VGdTsccUzq7KSgkXW1ae41GA7lcztKbc3NzPMmKJpxtb2/D5/Mhk8l8q7OqbYQResH6+vrw+uuvY3h4mDOVsVgMv/zlL7G+vt6V0v/LTMj1E4vFMJlMuHTpEoaHh3lzkLTFw4cPOWroJqCmF4syrHQhkU4ZcXwqlQqCwSB+8Ytf4O7duyyk340AgLLVJycnLKcDgCNnymoBQKVSQTgcxs2bN1negvzspMg2mTAjRIcljfYljh012hUKBayuruLg4KAl+9eN5096ewMDAxgZGcHQ0BAPpSgWi8hkMkin00xToWEKNG+7E9SE00alQBJ2ptGllFVLJpM8lYSaKoWcJQIInZYBUqvVcLlccLlcMBgMLVxUAqp0SBoMBjgcDgwODkKv13OZNRqN4ujoqKONn9SJPjo6iuHhYZjNZs6q5fN53oPVapUnhZHw/sDAABQKBUvd+P1+hMPhr1W42mHCcaqUkaIyJgE8ABzAUvaEPken07G8EQW2pPzRzslaQhBIgIQGFtAkQvocqq5QQEq0AOKtEhczHo/zQJV2DdMQ+krvFI0fFvqv1Wo5cCEOOwDW36WmUWFGjhoX292sdrrcTN+f+if6+vrYd3rHZTIZ96sQ9YP0N4m2lEql2jr8RdhUS0kJAMwLtlqt3FwnBGJutxvnzp3joKHZbLLCAU1fbHewQn5S8qHZbEKhUHDJvV6vcxc9ZVdJqUjYDJhKpfDs2TOWqaR90i4TNnkB4I5/Sk6ZTCaUy2WIxWKeomgymTA5OYnFxUWcO3eOA4BisQiv14vNzU34fL6WIVDfxL43DUBYIrTb7RgeHsbS0hKXCtLpNJ4/f45f/epXSCaTXQeA5KcwcjGZTPB4PFheXubxZvF4HI8ePcJHH33EHNVuA1UhbYGyUFQqJLF6OpweP36MX/3qVzg6OuoqsCJfqRyQyWS4HEH0D+pgpmf/+eefcxTdLT9pT9KFqVar0Ww2+QWh5hSiKmSzWezu7raUJroBqAGwjJLb7cbQ0BDcbjdH/SSbQg01lE2haWudbvojowybx+PB8PAwjyw+Pj5GMplEMplEPB6HRqMBAM5cAOBMZqfmVpMJKzs0rEAsfjF8otn8nRoI8cCGh4cxMzPDDUInJyc8SIH41J0A1nQBUIMSaf/m83nO7AEvqkAko0M8vImJCZYAJK3ojY0NHB4etv0cEJaAieNLlysZvSd0WdHvRllr6hpPp9MIBAKs+0y85Xb7KmywotKk8OPUlU7Nl1Sx6Ovr48Arn8/zlDqi2HSqUZF8JKOsMGlBUwWo2WzCbreznJlwvC41g1JJvt1+Cv0FXjxzoSY4VQNprYkrbjab0dfXx00/hUIBOzs72N7ehtfrbdt49ZdxHoXnt1qtxsDAALLZLOLxOGcAKeCanJzkJmCxWIxyuYzt7W08f/4c29vbiMfjbQ1WTvtJOIqmVlLQTxlYGliwuLiIkZER7l+oVqsIBAJYW1vD1tYWYrFY2wcrUQJNSNshqU+XywW3280fJ16tw+HAxYsXWf+dMsOxWAyPHj3Czs7Od9qrbcmsEgCkmcaUFaLy/wcffAC/398R0f9vY5SlPH/+PK5duwaXy8XZq7W1NfzXf/0XDg4OOqZR+E2NDgar1Yrh4eEWOa1CoYBnz57hk08+QSgU6jpQJf8oMq1Wq0xadzqdnFXN5XLY3d3FvXv3WNy40xQFMiqlkV8koE1ghUppTqeTwV4ul2sZo9nNNZXL5ejv72cgqNfrkclkEA6HeWzm1NQUHxTUEEYZ9W6sa7PZhMlkQn9/P86dO8cUH7/fz01WANDX18eSNaS9u7u7y5PMOuWnsBvVaDSydqJEIkE8HmdJOL1ej1qtBrvdjmvXrmFmZoa7hvP5PO7du4etrS1uUuwEUD3dCESNEcIGJpL9opGUHo+HBxsQHSCRSODx48d48OABl4g7sb4E4AhcSKVSpNNpZDIZ7kCmjDZVK9RqNZcwaWDB/fv3sba2hkQi0bHAlao+tH7xeBzlcpmH1sjlcthsNu66F3LyKGnx/Plz/PrXv8bKygpPBWt3kCWsopVKJZbLogymwWBgRQ16v2m/iES/E11/+vQpPv30Uzx58oTXtd1gRegrdX9T5pyqQuQjcSkpc0hqHJlMBru7u/jkk09w//59RCIR7ltpl49E8SiXy9wTIZPJeICK0WhEMplEs9nks0Cv1zN/lSgMu7u7+Pjjj3H//n2ettWuajAleui5UyAkkUhgsVhw/vx5qFQq7O7u8pAiCr6pWk0Z7Fgshps3b+LBgwfw+/2sHtQuowCf1rNSqfAztlqtuHDhAt8L5XKZp9nNzs5icXGRJSwbjQZKpRJu376NR48e4fnz5zzK9tvY9+asisVi6PV6XL16FTdu3MDS0hJP1NnY2MAHH3yA3/zmNy3jSbsNACmitlqteOONN3Djxg3Mzs5yB+tnn32Gn//857h169bXuIzd9JEyAhqNBmNjY3j99ddx+fJljIyMQCQSIZfL4Te/+Q0+/vhj3Lt3j3ULu+2nUqlkkeDBwUFMT0+z1iKVW1dWVng8LV1y3fKTMhZ2ux0jIyOYmZmB2+1GrVbjEuv+/j4DquPjYxweHsLr9XYd/ItEItb4PXfuHKampqBSqeDz+ZDL5RCLxVCpVOB2u1kO6Pj4uGUyVad9pX1pMBhgt9sxNjbGgR5xkwlQ/ehHP8Lo6CjzFOPxOB48eIBYLNbxDDDxptRqNesokpbi1NQUgwKJRAKHwwGj0cjNC9lsFl988QU+//xzbGxsdLSyQutJWX0Kjqi0Rl3AdIEeHx/zPpHJZCiXyzg8PMTNmzdx//59bG5udqwPgLjG8XgcJycnzEunjD8NBKFMOlEBqJEqFArh0aNHuHv3LutVt2ua3mk/iQJxfHyMfD6PcDiM58+fw+FwQCQScdMnjf8l4EBVokAggNu3b+P27dt4+PAhQqEQB4PtMiGNqlKpIBKJYGNjA7u7u1xWp6wwARPhvUkA/OnTp+znaRH5dvpK+zOfz8Pr9eLWrVuYnZ3lhBSNfT2tnUtfl0wm4fP5cP/+fdy6dQsPHz5kDnA7A4Bms8nNW16vF59++ilPzqLKlVqthtvt5s516lCnHoZoNIqnT5/il7/8Jb766qsWel27jPZnIpHA/v4+vvzyS1ZGIGUHk8mE2dlZ1Ov1Fu11oqhks1n4/X588MEHuHnzJg4ODlom67XLiOcfiUTg8/mwsrKCN998k/sQaJrapUuXuK9Cp9PBbrfDYDCwEkMikcC9e/fw85//HCsrK9y38m19/V5glS6HS5cu4Z133sHk5CRr6iUSCfz7v/87vvjiC2Sz2a7w6l5mVA42mUx488038dZbb2FoaIjLrBsbG/jZz36G27dvc2RyloBaq9VifHwc169fx9WrV9HX1weJRIJEIoHNzU388pe/xJMnT5BKpbqWqRQa8UCdTifGxsawuLjIWnEk+h8KhXDnzh0Wq+8GoBIaXew02s3pdMJut6NarUKtVvMIzWazyZ2pq6uriEajXSv9k590kBIIEOr+jo6OMkCZnp5mHlsgEMDjx49bMj6dXl8qSVHjB2XPSO2DGi8IxFKTxYcffognT560AOtO+UoXAf0hYW3KXhqNRtaepYZK6qB+/PgxPvnkEzx9+hTpdLpje1ZIofH5fLDb7QxadTodS4gCnhoAACAASURBVCkRCCAdWzr4qVr11VdfYWNjAzs7O0ilUh0B15RZoaw5cenT6TT7S5cS6W6SQkGpVMLOzg5WVlawv7+P3d1dnq7TqXOL7hfyk0T1j4+Pcf78eUxOTsJut/PUNZILikQiLWXfp0+fIhKJtB2okAmza6lUCqurq/jwww8xPz+Pod9OCaKGUPKTspq7u7tYW1vD9vY2NjY2EAwGOwJUT/taqVQQjUbx4MEDjIyMYHl5GSaTibnTVBUAwOA2EAhgdXUVW1tbLbrandqrlME7ODjAgwcPMDExgcXFRW74pZ4VyhQToKYq4NraGjY2NvDgwYOOZf+F4H9/fx/37t3D7OwsxsfH+d2njLWQz0tnWjwex+bmJh48eIAvv/wSwWCwY+8UBQCZTIaBNWkW01AV0k0VZl1pGlehUEAwGMTKygo+//xzPH36lOUVv4uv3xms0gY1Go2YnZ3F5OQkl1Ly+Ty+/PJLrKysMPG/myBA6CM1frndbiwuLmJ4eBgajYYn6/zqV7/icg8B6rMwoabZyMgILl++zOM1a7UafD4fPvvsMzx79oy5Kd32VXgoWSwWeDweTE1NwWQycfNSMpmE1+tFKBRiKa2z8JN0CCkAoOk11GBFE7MCgQC2trawu7t7ZplqklihF16j0cDlckGr1WJgYIBlTGj4w+3bt7GxsdHRZqXTRhdBJpNBLBZjHjBlgujdl0gkSCaTiEajWF1dxa1bt5BIJDqesaYLiBqkfD4f5HI5KpUK7HY7a33WajVuQsjlcvy5pAFMmexOBoJ0+dN0tEqlgqOjI1gsFvT19X2NTkOXUbFYRCKRwPr6ess45U6CKrqwALCetrB0WSgUsLu7y0EWNS5SOZ3GFKfTaabgdApUkdElTyCEuMgbGxtwOBxwOBy8H0jcnKaA0TQmCgQ7uV+pxBqPx3H//n2kUinW0CYNUAoCSC7Q7/ezv6lUihUNOnkGEMAqFovw+/346quvEIvFeFIdVSdoz5bLZRwdHcHv92N/fx+BQADxeLxFVqtTe6BWqyGbzWJrawufffYZCoUCBgcHmWJBZ6ww8xsOh7G1tQWfz4f9/f2OnwEUUNG7/L//+78AXoz7Jn4v9awQDYTWn/ipW1tb2N/fRz6f7+jzp6xzOBzGs2fP8OWXX7ImMMlRAeDGRWoKrdVq2N/fx/r6Op4/f44nT5609IN0FaxSJ6PH48GFCxdgsVh4glUgEMDHH3/MeqpnWf4n/s/4+DguXLjAhP9sNot79+7h888/x9HRUctLdFZ8WrVajf7+foyMjGB4eBgGg4EP1JWVFTx58oS5f2eVpSbwR+l+u93OkhY0eYW4ljRXu9vqD+QnlXioAkAcynK5zA1K29vb2NrawsbGBsrlclf9JKNIm+SSSEqJgioitx8cHODOnTu4e/cuQqFQ14IAOtgTiQRCoRBnKkhLlcqAlCna2dnBkydPsLa2hvv373ekRPUyH+v1OoMn4IWofjAYxPT0NPPqSF2BApWDgwP4/X54vV7Wt+yGr9VqFclkEk+fPkUoFGJNQqGKQa1WQzqd5qxqLpdDIpHAzs4Oa4N2YkqZ0GiP0cVJc9UJaPn9fuam0pRC4T4gxQrysxt7loAgnUk0lU6j0bAgvZDDHgqFOCgQ+tpJ/wDw5d5oNODz+RCNRrn7m7ReqVRNe4HkqYjP3Ol7oNlsMhChd+Pu3bvY39+HyWRi4XehFnG1WkU0GuVpdUJpvW6Aanr+t27dQi6Xg9PpZFUIUtmgwCuVSnGZm8Z+dxpQ0xrRqPk7d+5AIpFgYmICTqeTdampwY7OrFgsxiA1Ho93/FwVUk+oke/WrVuQSqWYnp5mwEprSaoVJPu1traGvb097OzscKXi+6zrdwKrIpGIRYKXl5dx/vx5qNVqvtA+/vjjFvmnsyqt06U/NjaG5eVluFwuLv8Hg0HcuXMHfr+fs2z0sp0FCFQoFHA4HBgZGcH58+dZIoTmBT99+hQ+n48PqrNYU8pSkxQUlVZojn2hUODsBEmTtFtL8f8yylSSrAu96NT5T1Yul7G5uYlnz55hdXW1o2W//8uy2Syi0Sj8fj+cTidL0wDgmeqhUAi/+MUv8PDhQ2xtbbGeXrfW9eTkBMFgkCNtemeog5q4SZFIBA8fPsTm5iYfqN1srCuXy9jb28PR0RGMRiMGBgawvb3NTWl0QRUKBR5Pe3rcb6d9FWaBK5UKYrEYK1eYzWbOVtDnAGAN1dMgpRu+EhARNodRYBCJRLjbnoT3SaaMsqjdOqfo5wi71gm4kMxeKBTivUBB4GmJsm75KqTwUKlfqGog9EXIye12UoX8o32QTCaRz+e5mYr0YYVd48J92i1JReB3k/vq9Trro+p0Os5Sn5yc8IRNkrITDn/plq/0LBuNBnZ2dtBoNLC3t8e6xRQ0kdTe0dER4vF4S5DaDV9JnYik6dbW1gAAXq+XZdREIhEHU3QHhEIh7OzscEWlHVXrbw1WqcGGJj+9++67MBqNLEx969Yt/Md//AdnK88KAJKW6vj4OObm5nDp0iVIJBL4/X48f/4cjx8/xu3bt1vS6GcJVN1uNyYnJzE0NASXy4VisQifz8cp/3v37n2N+9vtcjXJkVBgkkwmsbW1xRH/0dER7t+/j/39/ZaMylkEKY1GA5FIhGc602QdkUiEQqGAra0t5lR1quP3mxjJzzx//pxne3u9XgYslUoFh4eH8Pl83EV5eqpWp40OVzp4Dg8P8ejRI9y8eZPLwcCLACCfzyOdTrcc/t32k7JpxEd++PBhy3oJM0Bn9d4LL3fhmOVYLMa+EGARiURnFvCTr8K/CTiRj2RCgHUWfpIJ/aU9Qf6d9lP4+WflJ70nL1tT4eeetZ8EsImydHog0OnPE35tt/wU7lEaUyycDihsWKP37yzAv3Atw+Ew4vE4g39qUCQwS9JgQmWIbgcqdE75fD6EQqGWwSTUZCukWdEY6HZKQH5nsEqlqkAgALlcjrW1NTx//hxPnz5FMBhs4fycxUsmlUqhVquh0WjQaDSwtbUFr9eLJ0+eYH9/H1tbW4jH413X1DxtzWaTo1Oa8PLFF1+gXC7D6/Xi6OiINf86Lar+TXwtFArY399HIpHAwcEBd6eXSiWk02kEg0FUKpWuSSq9zMdarcbZs2AwiLW1tZbxn0RULxaLzKM7i+cvLF0T33d/fx93795lAEgNLpVKhbmCZ+ErZdaOj49RrVYZWJ3+nNMXwFmY8PJ5Gag/a5By2oSXl/DSJ3vZx87ShL78kPx6mf2x+PrH4ifwcuD8Q9yjwneK7LSfZ+2zMMAWAv/Tn/NDOLPIV6IAUbAi9Pd0oNJOf0V/6JuJRKKv/SeJg9tsNp5QodFo4PP54PP5WD+t3UCl2Wx+Pdz8A35Sl7XH4+H59ZVKhUuEJLHUgW6/3+vn7/OV+F7EAdVqtUin0wiHw8xRIvDfZrmPb7Wmcrmcu9MJYAtLbcRZaXfp59v6SZGpsKRGupbCkls3/fx9vv724/y30Offfk/28Syf/e/5POH3a4NXX7d2+NkN+67P/izsT2FNe35+e+vt0fbbn4KfwB+Hr98arJKGpVarhVqtbiGqC5sT2n15fRfAQrIKxP8jEd5OZii/y6agVDpNKKIyZqdT/99lTQlMCcsUp8soZ+3nbz/+h75fG7x66ff9oz8QgJ6f38V6z7791vOzvdbbo+23PwU/gT8OX781WD0r+z6bQpil6rR9303xQ/H1T+XZd9P+FA4EoOfnd7Hes2+/9fxsr/X2aPvtT8FP4I/D1z8IVnvWs571rGc961nPetazszTxWTvQs571rGc961nPetaznv0+64HVnvWsZz3rWc961rOe/WCtB1Z71rOe9axnPetZz3r2g7UeWO1Zz3rWs571rGc969kP1npgtWc961nPetaznvWsZz9Y64HVnvWsZz3rWc961rOe/WDtD45b/WPQ3gL+ePwE/nh87fn57a337Ntvfwp+An88vv7/6OfvGyDyTWUdu+Hn6bGWgp/dFj9/+zPa7utp3fBvOoWv28/+9DS+H9KzP/U9hT/7G3/dn8L59AfBas961rOe9eyHZQQGTl9WPyTNbJFIBKlUCplMxr4K56D/EGadi8ViSKVSaLVa6PV6yGQy1Go1VCoVFItFlEqlljnnZ+UjTTe02+0wGAxQqVQolUpIp9PIZrPIZDIdm8j4bX1VKBTQaDRwOp3QarUwGAw4Pj5GJpNBJpNBLBbjUddnuaZSqRRqtRp6vR5KpRJ6vR4nJyeoVCool8uIx+M/CD9pTLhcLodEIuGR4Y1GgyeH0rM/Szs9KpzGnQNo8ff7jAzvgdWe9axn/9/bywDg6X+f9YUAoGXcMVmz2YRIJOLxx/Qx4d/d9I8uK41GA7PZDIVCgXq9jmq1inK5jHw+zxcXXWTd9pNAoFarxdDQEKampiCTyVAul5FIJOD3+xGJRBiwCMdKd8uE6+jxeLC4uAi32w25XI5UKoWjoyN4vV54vV4GLcI90E2j4MRoNGJ4eBgLCwuwWCwwGAzI5/OIRqM4PDzE48ePkc1mzwwI0nM3GAzo6+vD5OQk9Ho99Ho9KpVKC6DOZDKoVqsAuvse0Tsuk8mgVquh1WphNpuhVCohk8lQrVZRqVSQz+d5NLswAOym0R6VSqUcnBKwFoLUer2OUqn0vQBrD6z2rGd/hPb7Spen7SwBFgEXAn2nfT4NqM7qsKXLQSz+HYWfMgTNZpMvgtN/zspPAlmn17NaraJWqzFgOZ3F7JaPEokEarUaNpsNV69ehdVqRaPRQDabRTgcxs7ODgqFAvt7fHzcdT/FYjHUajXcbjcuX76M9957D2KxGJFIBH6/HwaDAQCQSqVwfHyMk5MTXttu+imTyeBwODA3N4fr169jcnISx8fHCAaD2N3dRaPRQKlUwtHREcrlMmq12pmC6rGxMSwtLeHdd9+FyWRCo9FALBaD1+tFtVqF0+lEtVo9k/0pBKrT09OYm5vD5cuXodFoUK/XEQ6HEQgEcHJyAqPRyCDwLPamSqWCwWCAx+PByMgIXC4XRCIRqtUqUqkUotEoRCIRCoUC70ug+6BaIpFAp9NBq9VyJt1oNEIikaBYLHL2//j4GLVaDfV6/aVVoW9iHQerwkNWJpN97RI4q4X+v3wVXl60WYURwVmXXIRAQMgReplPZ5W9AH6/n8L/pywRHV7dzgwIn7tUKv0aL0j4/+Rnty+ul/kpk8kglUpbQBb5TH8LD4lu7VnhO0TASqFQQCwW83o2m02+WCk7UKvVuraup88llUoFq9UKnU7HP1+hUAAAisUiyuUyqtUqisUiKpUKqtVq1y4xYTZVpVLBbDZjZmYGOp2O96Jarcbx8TGSySSy2SxyuRzy+TwKhUJXs1gEWmw2G5aWlvDXf/3XsNvtKBaLiEQiSCaTGBgYwMHBASKRCDKZDPvarXefytX9/f1YXFzEe++9h4WFBeRyOQBAIpGA2WyGx+MBAORyOVQqFQAvAoJuPXMCArOzs7hy5QqWlpag0WgQDodRKpWQy+X42SsUipbMardBtUKhgNvtxpUrV3Djxg2MjY2hVqshnU4DACqVCrLZLABAKpVCIpF8L+DybY3eH5PJhCtXruC1117Da6+9Brvdjnw+j1QqhXw+D5lMhkKh0JIx7FaASj9TqVRifHwcr7/+OhYXFzExMQGFQoGjoyP4/X4cHBygWq0iEonwWtJ6Ap3HUXR2SqVS6PV6vPHGG5ifn4fL5YLRaES9Xkc8HofP50MwGITf70c4HOYKy3c94zsCVumypwtLKpVCqVQy3wYAp7Kz2SxHWt0utQj9pMWXSqVQKBRQKBQMoshXOkxPc6+6HXUJAYtcLme/qRRYr9d57QmwlEqlFv876SPwu2dPLxIBK/q70WjwYUBrTYcGZTI6eXEJs0ByuRwKhQI6nQ5qtZrXk9aafh/yO5PJIB6Po1QqtQDBTvtKz5vKq319fdDpdLwH6MIigFoulxGNRhGNRpHP53ldO+Wr0E+FQgGtVguXy4Xx8XGo1WpeT6VSyRkhKgsfHR0hFoshk8mgUql03E8h+NPr9RgdHcX8/Dw0Gg0/U61WC6lUimw2y9zAg4MDRKNRZDIZLmt1+tmTrxqNBlarFUtLS7h8+TKUSiXy+TxKpRL0ej0ajQaSySQSiQRisRj29/dxdHSEZrPJ4LrTflLmamJiApcuXcL58+chFouRSCQYBORyOZycnEAkEqFer6NSqbRctp00eueNRiPm5+fx2muvYX5+HjqdDrFYDKFQCIeHh0in00gmk10Dpy/zU61WY3p6GktLS7h27RrsdjtKpRL8fj+2t7fh9XpxeHj4tbOomz6KRCLI5XI4nU68/fbbeOeddzA+Pg6tVotMJoNUKgWv1wu/34/Dw0PEYjG+i7oZ6MlkMuj1ely9ehU/+tGPsLS0BJfLxWd6IpFgvmoul+PMfzf8PF2RcLlceOutt/CXf/mXGBoagslkAgCo1WrOrhKw7jZflfxUKBQwGAyYm5vD66+/jkuXLsFms0GlUqHZbCKVSqG/vx/b29tQq9XMAz9dbfs21hawKnSANgb9USgUTGR2u93Q6XScVTk+Psbu7i5SqRSKxSJfpkBnogNh5x8drARWCLAQqDaZTBxVHR8fI5FI8OVaKpVQqVQ6nrE4nUkjECCXy3l91Wo1jEYjlEolJBIJTk5OcHJyAqlUimq1ykR8kUiEYrHYEXAtBHTkl0qlgkKhYA6LTCaD0WhkQnuz2WxpwKjVakilUtjd3UU6nUapVOqYr/TsFQoFjEYjLBYL7HY7LBYLf5zAKhHFqWxFnDaxWIxYLIZisdixPSB8n8hXh8OB/v5+uFwumEwmBqv0OY1GA4VCAfl8HplMBiaTCXK5HIeHh5xhb/eFJgSpMpkMBoMBDocDg4ODGB4ehsvlgl6v5wwwvXeFQgGpVArxeJyzr8LMUCcuXnqmMpkMOp0OdrsdIyMjmJqawvT0NHQ6Her1Ok5OTqBWq6FUKpHJZBCJRBAKhXh/UBa4k5cZXQzka19fH6anp3HlyhXMzc1BKpWiVCqhUChAKn1xlNNZIBaLORAgINMpo32qVCphs9lw/vx5LCws4NKlS9BoNDg+PuYznzKUtI4UqFIg08lMG62nVqvF+fPnceHCBSwsLMBsNqNUKiEWiyEYDCIajSISiSCfz7dU/rpldN739fVhaWkJFy9ehNVqhVQqRTKZxO7uLg4PD3F4eMhBwFlQFAio6vV6LC0t4erVqxgcHIRarQYAZDIZ7O3tcRY9kUh8rbTejUBPJpNBq9ViZGQEy8vLWFhYgN1uh0wm46A5kUgglUq1ANVuJqKo4ctms/G7Mzw8DLPZDJlMxllo4d0ll8u/MR2sHUbPXKFQwGw2Y3x8HMvLy8yl1ul0kEgkaDQaqNVqcDqdHADQHhAmgL6tfS+wKowIhFky4jDo9XoYDAYYDAbodDo4nU6YzWY+uOr1OsxmM/b29hAMBpFOp1EoFDp2mQq768hPjUbDIEqj0UClUkGtVvPhQCCFNjIdZLFYjJsG2n3ACtdVmE3T6/Uwm83sN136FouFy6x0YIlEIgaq4XAY1Wq1I9kVYeaH1pTACmXSqYORfFcqlQAApVIJqVSKWq2GXC6HSCSCXC7HJdd2AwFhVKjT6eBwODA2NgaXy4X+/n5YLBacnJxApVIxWKX1r1QqKBQKSKfT0Gg0KJfLXytdt9OEB5NOp4PZbMb09DQGBgYYBNIhLATWBFJSqRSDq5OTE86stnsPnM786fV6TExMYGxsDCMjI5icnIRWq4VKpWrhrsrlcg4CDw4OGMhmMhmUy2UcHx+3zUcy8lOpVEKn02FsbAzj4+OYmJjAwsICDAYDFAoFX1IEvm02G8xmM2cNGo0GotEoSqVSR/wEvr5XR0dHMT09jfn5eVy9epXXU6vVcgKgXq9zwFqtVpHL5aDX65FOp1GpVDoCBE9nVC9cuIDFxUVcu3YNw8PDvOcIOKdSKZTLZQDgAFzYOdwpEwIXp9OJ5eVlvPrqqxgcHIRUKkUikcDe3h5CoRAikQji8TjTVOjruwEMhPzPixcv4vr16xgeHoZarUa1WsXOzg729/fh9/tbgKqQptZNmoJKpcLAwAD+H3tf9t3WeV2/ARDzPAMkQHAeRZGabUkeMnlacbPsrpX0pU/tn9TXtk9Nu7qaJk0aJ7Zr2bJsWRY1URLnAQCJgZhxMU8kfg/qOb6glcSWLki7P5y1tGiLFHl4h+/b3z777HP58mWcPn0adrsdAFAsFrGzs4PNzU3s7u5id3cX5XL52BuBqJLjdDqxsLCAq1evor+/HxqNhrvp4/E4V0wEQUCtVjv2HInMmZ6exvnz53Hq1ClYrVZ+n6kKXSqV0Gq1WMpyXAc9CrlcDr1ej+HhYczPz+PVV19FIBBgMkJcdVapVHA6nSgUCnA6ncywUoX32+b7zGBVDKg0Gg10Oh1rlQKBAFwuF5xOJ0wmE6xWK1PEdAPoxcpms7hx4wZWVlbw8OFDBINBSU+IYqCq0Wig0WhgMBjgcDgwMjICj8cDh8MBs9kMk8kEi8WCWq0Gt9vNHW3E/NEisbOzgy+//JKZYKk2/6MMJV1Xi8WCkZERBAIBuN1uZtT0ej3S6TSfEimIzUilUohGo8wCiksbUuZK7LnVasXo6CjGxsbg9/u5G1Sj0SAWi8HhcHQAQYPBAACoVCrY39+HQqFAPB5HKpViXaBUIV5cqWN1fn4ec3NzGB8f55cpmUzy80n3XqvVsq4yk8lAJpMhlUrBZrMxw04VAamCrqvRaITP58P8/DwuXLiAU6dOwev1Qq/XI5/Pd+RKz83BwQGMRiNMJhP6+vp4sSDtpZS50kJLB7xTp07h8uXLOHv2LAYGBqDX61EoFLjke3h4yGz74eEhNBoNtFot5HI5arUaHA4HBEHgErtUIQaqFosFMzMzeOWVV3Dp0iUMDg7CaDR25Nlut1mypFKpADzxiKQuYaPRiGw22xUAI5anGI1GTE1N4fXXX8eVK1cwOjoKg8GAZDLJa6j4kEJrbKlUYta9W0DraPlyZmYGP/vZz3D58mX09/dDLpcjHo8jl8thd3cX8XgchUIB9XqdgTiB7uMAqwQCX3zxRbz77rsYGxtjG6jbt29jZWUF0WiUwT2RBbQJH0fQQc7r9eJv//Zvcf78eWi1WjSbTcRiMdy4cQPr6+uIx+MdwIUOV8dpYaVUKmGz2fh6UkNVoVDAxsYGPvvsMzx69Ai7u7vI5/N8sBNXYrsNsAhcTUxM4I033sCpU6egVCr5oBwMBvHgwQMEg0Ekk0l2gBBXNJvNZtfzVKvVGB4exsLCAt588034fL4OwimRSCASiSCZTEIQBLRaLcYztVqtQ0LZ7evpcDhw5swZvPnmmzhz5gw0Gg0ODg5QLpeZmaacVCoV3G43/H4/tre3ef95Fmb9mcCqeJFSq9XM+Hk8HvT392NkZATz8/Pw+/3Q6XQM+oiVEpeInU4n6xlIjCtViIEqLfx2ux0DAwMYGBhgXZXb7eYNs9VqIRqNwmAwsDyAtCJ2ux0mkwmNRgOrq6ssHpcyjmpCBgYGMDQ0hPHxcfzwhz+E1+vlXOv1OtbX11mzSEybyWRCtVqFRqNBoVDg30PqMpHYXsPn82F8fByTk5N488034fF4+N7XajVotVpeWClP6hLOZDLcfEcSATp5SRHikpXD4cDExASmp6fxzjvvYHBwEHq9HgqFAvV6HbVajVl/Wuj0en1HVzhtemQlIjUzRADQbrdjeHgYMzMz+MUvfoHh4WEutdBiSyVz6m622+38vlH5VavVQq/Xs7xByjz7+vrYBmZmZgY///nPMTc3x3lWKhUkEgn+N3SPrVYrbwBi0EpMvNTXk8q/NpsNU1NT+PnPf44rV67w4ZkYFrqX9NzSQlwsFpnJInagW0Gg2mw2Y3R0FL/4xS/w5ptvwuFwQKFQIJ/PY2Njo2MDpXveaDSQSCRYe5lIJLqma6N3iqyV/uqv/go/+clP4HA4IJPJEI/H8Yc//AGZTAaFQoHlCHq9HgDYfqdcLndVGyoGqmNjY/j7v/97TE1NQa1WM1D94IMPEIvFkMvlUK1WcXh4yDrrdrvddR018FW3utvtxrvvvouXXnoJarUatVoNyWQSv/nNb/Do0aMOS62DgwNeO0nnL2aEuxWk+z1//jz+7u/+Dm63G8CT5rS1tTX8+te/xvb2NsLhcEc3OB2sCAR2Wwag1WoxMTGBF198EVeuXIFGo0Gj0UAsFsPi4iIePHiASqWCcDiMTCbDTDWtwbTud+t9pzXU7/fj5ZdfxjvvvINAIAAAEAQBoVAIjx49QjQahUKhQDab5aZKvV7PjZUE/Lq5LsnlchiNRm6iu3jxIoxGI/vS7uzsYG1tjTX0dHCm9259fR2FQoFJi2+b63PLAEhrYTQa4XA4MDAwgOnpaXi9XgZUhLoFQeCN6CiNTUJ7KTcpcSe3UqmE1WqF1+vFwMAAA+r+/n4+kTYaDQiCgGg0yp3BJpMJKpWKN1nKn0CV1CEGAJTn2NgYLl68iMHBQWbOKpUKUqkUtre3eWNzuVz8ABODRQyNmKWU4pRI91Cn08Hj8TBQvXz5MoaGhtDX18csVDKZxOrqKgCwLsdutzNjSSCq3W5LLgMRA1W73Y6xsTHMzMzg6tWrGBkZYdZCEASkUilsbW3xtSM5C7HWVLJsNpvsa0g/Q8p8ya9wdHQUc3NzeOmllzA6Osrd34VCAel0GpFIBMBX4IbeJ7q3JMSnpqVulP+NRiP8fj/m5uZw+fJlzM7OwmKx8DVNp9NIJBIMAonpowWrXC4jk8kgkUggmUwil8vxmiFlrhqNBm63G7Ozs7h48SLm5+dhNptxcHCAQqHAP1sMlIkBJuY/EokgGo0iFosxU9QN+QfpfkdHR3Hu3DkG/wSaQ6EQ4vE4JTq8BQAAIABJREFUH1BJ1yqXy/l9o3yLxSI3sEotqaE10WKxYHx8HMPDw9zkV6lUsLS0hGAwyN30rVaLDw20WZHGvpvNtQQCbTYbzpw5A7/fD5nsSaPX9vY2fv3rX2NjY6Ojoerw8JCbVo+jy5qup8lkwtzcHH76059y93QsFsP777+PDz74AJFIhF0Ajrq8iCUL3QbVOp2O3/nh4WE+8G1sbOC//uu/cP36dd7zxS4v4vvcTTadDihutxuXLl3Cj3/8Y5bL7O/v49atW7h+/TpWVlag1WqRTqf5PaHrRzlSKb5bEhqDwYDz58/j5ZdfxvDwMFQqFYrFIjY3N3Hz5k08fvwYyWQSLpcL7Xab+0JIuij22JWyGikOIpn6+/tx8eJFjI6OsjY5l8vhwYMHuHv3LoLBIOr1Onw+H/r7+5nMGhoags/nY9cPklB+m/VTMpqF2DvSUZLGolqtolwuc8nSZDLxL0na1WQyiUwmg3w+L+mCRS8DPRSE9KmhhmxqBEFg5iSRSKBUKrF1DemqxCVAOilKXfqlXGlB7+/vR39/PwKBAJxOJ2SyJ75q+XweiUQC4XAYsVgMNpuNmTNqtDo4OOCvpSYLKeUKBKqMRiMCgQD8fj9GR0fh9Xp54Uomk2y1kU6nudOemD4q/ZJeNZPJIJfLSX7ipsW1v78fg4ODGB4eRiAQYNYilUohFAohGAxCEATegK1WK2++tVoNuVyOSzHUBCblIiYGAG63G4ODgxgaGsLw8DC0Wi2zqeFwGKFQCOVymSUY5FxBzg/0fEQiEdZbSwmuCHhaLBYMDQ1haGgIU1NTMBgMaLVaXGLb3d1FtVrlJjCdTseLba1WQzQaRSgUQigUQjQaRSaT4RKSVHmSxQppfU+dOgWLxYJWq8WH052dHVSrVQZ9xFYDT6yryNtwb28P0Wi0QysoVYhL6i6XC4FAAKOjo6ylLhaL2N/fx4MHD5BKpbgMSNez0Wggk8kgmUwinU4z8O/WRiu2/HK73XA4HKjX6+xCce/ePWarAHSspeSwQmD1OFjV/v5+TExMwGg0olarIRwO47PPPsPKygpfKwBc/j8uhwLKU6PRwOv1YnR0FH6/H3K5HIIgYHl5GV9++SW/S7TviJtVxEC123n29fXBbrezvpLWyEgkglu3buHhw4dIJpMAwI1BfX19XJkSNwt1U0et1+sxPz+Ps2fPwu/3Q6FQoFAo4PHjx3jw4AHW19eRSCRgMBhQr9e554byocOVlFW+o3mSk8LCwgICgQBXcuhaPn78GJubm8jn8zg8POS+AL1eD4PBwE1MZAlFxJ/UedK6dOrUKZw6dQo2m433783NTSwuLnLjX71eZ/bcYDDAYrHAbDZjZGSEsUi5XO54Jr5JPJdmFQCfjsX2SeTtR5Y5pJXzeDzclUobgpgpINRN31+Kh5geXAIBpKElpiKVSiEWiyEWi7F1jl6v5/Igsaq1Wo2bVpLJJNPZ3WArqATsdrvh8/ng9XrZQicej2NnZwe7u7tIJpP8UpIFDz3shUKBmSCyC5ESABBb4XQ64ff7MTQ0xCX1UqmEaDSKx48fY2trizdWAqriPEnPRhNjiN2QIlfx9TSbzRgaGoLf78fg4CAMBgOXL5aXl7G8vIxkMsnNTNQwotFoWP+7vb2NYDCIWCzGm7CULKD4pO3z+VhWYzQa0Wg0kM/nsba2hqWlJaRSKc6VACDwpHREJtzBYBB7e3uIx+PIZrMscZAiTzFb6XK54PV62UhdEAQEg0Hcv38fqVSKpRTUoCQIAjOq0WiUDc6pA7sboNrpdPJhxeVyoa+vD+VyGdFoFEtLS4hGo9x4CYDLqeJpMalUCpubm7zgduOwShIZj8cDj8cDn8/HuspEIoG1tTU2WKfGNcpZEATkcjmkUimkUqmus6okkXI4HFwpIZuv9fV17OzsQBAErp6Q1IPkCtQp3M2mG7EEzOfzYXp6Gn19fQz67927h3g8zuV0kmCRVrVb91kcYkA9Pj6OmZkZZv339vZw584dLC0toVKp8J5EFUlqaBGzl91mLLVaLcbHx3Hq1CmuopXLZdy7dw+PHj1CKBRCo9HocHsg4H90cEU3Nd82mw1zc3OYnp5mazcCgaurq0gkEqjX61CpVNzLQpKwdDoNAOxeQTrWbhATMzMzOHv2LMt8KpUK7t69i6WlJWxubiKZTKLRaCCXy/G9J79lAn7UQN0NFpgOpsT6+/1+3r+z2Sw+++wzPH78GLu7u4yL4vE4y67K5TLL78gHOpVKcbWt62AVADMPtHGpVCq02232fQuHw0xRKxQKnhdMNjXVahXr6+tYXV1FMBhEtVrtyimLXnCLxQKbzQaTycSaqmg0ivX1dWSzWQiCgHa7jcnJSej1enYxaLfbyOfzuH//PlZXVzu0F1LmKV5YiQH0+/3QarVsA7K8vIx4PM6gjhouaG60XC7nEsLy8jLW19c7TuRSHQDIsmR8fBzj4+Ncpi4UCgiFQrh//z5CoRBr5sbGxmCxWOD3++FwOKBUKlEsFrG9vY3l5WVsb29jd3eXAaAUnoHiE+HQ0BAzazabDdVqFdFoFA8ePMDW1hZisRjq9TrMZjMbgjudTqjVaqTTaWxubrJrRSwWQyKR4PKmFHo7sTSGwB85KlSrVZRKJayvr2N5eRnhcBjNZhM2mw0Wi4WdDPr6+hCNRjnPeDzOH4vFomS6O/Hi1d/fD5vNxuxENpvF3t4eVldX+ZpSNYV04KVSCalUiisZkUgEu7u73IkrFWMprlJ4vV7u6gfAdjXE+pNOEQA30+VyOV6/CARSN7vUVkHi999qtcLhcMBqtUKn03HXcjabRS6X+1pJlWRWsVgMxWIRsVgMqVSKgWA3LI3IO5tYE5PJhGazycw/ref0/tG6r9VqmZQolUod1Ylurf0ajYYrVIFAAK1WC2tra6wFBACj0cjewHQAKJfLSKVSkh2e/1yedKCamZnBxYsXuepz48YNLC8vQxAEyGSyjgoaMVOlUonXTXKn6RZjqVAoYDabcf78eZw/f567/8PhMG7evImNjQ0GTSSj0ul0jA0ymQwfSGnt7MZ7pNVqMTMzgx/84Afw+/0s5/mf//kfLC0tIRwO86FYp9NhcHCwwwowkUhgd3eXcYG4iUmqPElqtrCwwI4PrVYL8Xgcn3zyCZaXl9l3nGQzRqMRFouFD98ktyPXomKx2OEKJEWetC+53W689NJLvNeUy2XcuXMHX3zxBY/8pfWGNOrUcKVQKDAwMIBAIIBsNov9/X3+3b7pu//cMgDxxB+arUsvDz2UfX19sNlsWFhYQH9/PwwGA5rNJra3t/Hv//7vuHPnDtLpdAej0o2HguyeCKjk83k2zibDWjoBzM7OMgMjCAKuX7+OL7/8kvUj4hsjVRCrEggE4PF4+OfncjmEw2HcvXsXe3t7KBaLaLfbcDgcmJycxIULF9iOo1qtYmNjA7dv38bq6iq2t7d5g5WCwaDraTAYMDg4yGyVxWJBu91GNBrFjRs3sLe3h2w2i2azCbvdzqdHj8fDHYzBYBBLS0s835oaHKTYvMQvmcViYabK5XJBpVJBEAQsLi5ia2sLuVwOjUaDJwNNTk6yP2mtVuMDFTWthEIhLhtKuSj09fVBr9fD6/WykwLlkEwm+d5TEwZZWVEDoCAIePDgAUKhEFKpFINVQRAkA1fi6+rxeGC1WlnX22w2WSMLAHa7nbXsJA8QBAH7+/vY2trC/v4+G+wfnRcuxTUlVt1ms8HhcDCjDzzR85JNGTmCEDAl79e9vT3W+5JVmdh+R2owQHIVi8XSwe4RA0XPRl9fH5LJJLs9NJtNVCoVbqgi2Uc3WVUqmRJJQXpZYvEbjQbr/i0WCywWC/R6PQN/qmQVi8WuAEFxVYVYVZ/Px9dua2sLgiBAr9fz+07Am9bRnZ0dpNNpZti71aNATNn09DROnz4Nr9eLVquFzc1NrK6uIpvNwmw2s4zB6XTCbDZDqVQil8thZ2cHy8vLyGQyXbdS02g0GBkZwauvvsql9XK5zLrfSqXCPuW0Ptntdr73Gxsb2N7exs7ODvb397vmTkLNX0NDQ9xUtbW1hWvXrmFnZweNRgNarRZutxunTp3C5OQkhoeH4fV6oVarsb6+znvTxsYGTzeTKke6536/H1euXGFSTBAE/Pd//zfu3LmDTCbDdnTkYmOz2TA2NobZ2VnY7XaWi1BFO5lMSspW01pvNpv5GvX19bGM5ne/+x1WV1dRKpV4nSJJkiAIbK1JBOHIyAji8TgcDgeCweC3AtbPDVZJ5E0omh4Cushmsxl2ux3z8/MYHx+HXq9HrVbD/v4+PvnkEywtLXWYBUt9eiGfTK1Wy56TpDekQQRUzjKbzRgbG8MLL7wAl8vFxuU7Ozu4efMmHj582DEtRGpdJTEV5FZgsVjQaDSQTCbZ9490atTY8uKLLyIQCECr1fLX3rx5E48ePcLW1lZHI4hUAIBKEH6/Hz6fD06ns2PTPDg4gFar5Rff4/Hg3Llz8Pl8UKlUaDabyGQyePjwITY2Nlg7RA+8VECAZAcWi4XvP/DVqFxquqLSj9vtxuTkJMxmM5rNJpvBP3r0COvr64hEItzZLCUQeBoAIA0qaSepOcxms6HZbHacrEnzu7Ozg9XVVYRCIWYwpDYMF4MAkh/QYklyC7LeoXdLrEve3d1ljSgNAzk6YEPqPI1GI7RaLTehUUOouDxMLiAkmyGNKq0RzWaza1Prjl5Tk8nEIM9utzOLJpa0WK1W9qymgwnN4e7mZC1x+dJkMvEhUFyNIgAinm9O89fj8TgAdLhrdIMBFDNsNKCCtMrpdJr7J6hCMDk5CbfbzdPVSEZhNpu7alFG95SqVIODg6wBjUajaLVacDqdGBgYgN/vx/z8PB+4yWGHqhVUCu7GJCvxYXpsbAz9/f28n9IwBblczhWp8fFxzM/Pw+FwcOlaoVAwg6nRaPhwK9VBQHyQdjgcPJGOGncfPnyIfD7PvRZOp5M17HRgIR9wg8HA7x1VWaTyKhZfS5/Ph4GBASgUCtac7+zssOyA9gOHw8E9LD6fD3a7nckAAMyqRiIRVCqV576W4lwJ/Pv9fv55pVKJvWkJC4nxFslpSNdOEiByDYrFYlhbW2P7tW8SzwVWSa9Keop2u80IWjwhaGhoiEW5wBN7C9Jk0JizbjUB0B9xg9TBwQHTzyQPIAH+Cy+8wDoxstP67LPPWCpAJUCpF1nxCUav10OpVKJSqaBYLPKLQuUU0rSSKJuY6nw+j9XVVWxubnInsNQNFrQBUEc/eaWK2QcyVydXg/Hxcfh8PjYFpklVOzs7zMBKXVqlF4d0iGKdXKPRgFwuh9vthsFgwMHBATQaDTOF5LRAZtY7OzsIh8PIZrN8UOlGFzgttlQiI3N82lj1ej0GBwdxeHjI055oBnMoFEI4HMbm5ibS6XTHDHspASCxAgB4c6TyIwDWpzocDh6YQGXpaDSK3d1dBINBLlOLNWxS50nXk7pPxbo+sf0YNdxQsyc1ftI9IKDajY71o7kenahHaxcAlt4QCUDuEEqlEisrK/y8dANUi5tV6VBFvtRUCRBb0un1eshkMvh8PjgcDtZ/dmuKnjhPMQikCtDw8DDcbjfbEVmtVrTbT2zo+vv7MTQ0BKPRCLlcjkajAY1Gg7W1NfaF7oY9nZitHBwcxPj4ODeukL8mNXo6nU5MT0/z3iSTydBqtWAymTrkLGStJmWIr6fVasXk5CSMRiNkMhlqtRoikQiq1SocDgdPhDt9+jQ8Hk9HwxJZLObzeWQyGcRiMZZhSJUnERQ+n4/1tM1mE7lcDqFQCAqFAi6XC263GwMDA5icnMTExAQP/aDDLAAEAgEkk0kkEglucpIixM1HXq+XbfwqlQr3QxCYpR4Pl8vVUaGgfY1kY36/H+FwGHq9HtlsVrIqKuESm83GByligIkMIzcSoLM5UUy4iKVjJMkymUxIJBLf+L16ZrBKCw5ZTgmCwL8gzX+njvbp6WkMDAxAqVQim81idXUVN2/exL17977G/HRjESNwSnovujjEuNLpe2JiApOTk+ylmkwmsbi4iFu3bmFvb+9Y9FUE8hqNBrLZLIrFIgqFAi+w1PhDRvE2m40b1cLhMBYXF7G+vt61TmBaDMT6HjrFt1otqNVqeL1e3ijoRaJhC/l8HsFgkJlfmgTUDe0SgVWyJqvVaqw90mg0CAQC3HxHrBZNJ1pfX2cAGAwGOyYrSf0MiDeudrvNJWfq8iQrK7L3ouafaDTKZapQKITNzU0uBYona3UDXAHg8cNiZpSaFWQyGevpDg8PkUwmEY/HEQ6Hsb+/z+X0bo1eJOAnk8l4tDNVf8rlMh/8xECENgOxHlAMVLtdrqZ8iKkmay+6LvSsEiNMh1QCrGKtd7c6rMVuHg6HA06nk4dUkOUbASzSipLrSrFYZDKDriU9S1JrFonRsdvt8Pl8GB0dhcVi4Z/ncrnY6s9ms3G+dKg9PDz82qRAKUO83ptMJgwPD2NsbIzLwVShIhbN7/fD4/EweKa9V6vVYnh4mN99qqhIGXQ9iQmcm5vj+02VR6pMTU9PY3p6Gh6Ph9lf2nNJgtPf38/SG/EwG6nyNBqNGBgY4OpouVxGOp1GOp3mptTBwUG2hTQajZwHHWB0Oh1sNht8Ph92d3d5QMjzxlEASF70VMYPh8MoFovsN202m2GxWPhZpD2CKm6kDabpoORhLFWQNp08vwn8C4KAWCyGSqXCv4/4mabrSftuu91mooua3A0Gw7c6AD4TWKWFhTbNWq3GbgDiaS8DAwM4ffo0pqam2Dx2cXERv//97/Hhhx8inU531baEOiWpDBmNRlEoFHjmNwA+hZ05cwbj4+NwuVw4PDxENBrFRx99hPfffx937txhO6tusgJE5RcKBR49S4CDNgEat0n6S5lMhlwuh/X1dXzyySe4du0aQqEQb1rdYKzo1CQ+UdGDSLYmpBGkZjoyXafGhqWlJTx69IhZRAIEUgVt7GJmjTrhVSoVdyrSAgA8YQqXlpZw7949Zn3D4TBXDciMuRsNK9SseHh42MGIiiUq5FHaarVQKpUQCoWwsrKCra0t1tcRq0K/czeaF8S5ikuP9C7TIkqnbHqmM5kMstkslyzpe3RLU0kMJTFA+XwesVgMwJOxrsSq0shaanDJ5/PM+BydDNMNQE0AnzYmpVKJVquFTCaDlZUVZq+pdGm1WrlZldYMei7EzVdS5knXkzZxr9cLn8+HwcFBPpzSfaXyJXVXUxWAGEvqZAek9S4VX0+9Xg+73Y6RkRGMj4/D4/Ew20trFAFv8YhdOgyQIwz5Vottop43xJs6AatTp05xeZ+ulc1mw8DAAKxWK7//VOIlZo2agyYmJlgLTJ3sUuZK0q/h4WGMjo4ySVGpVCAIAlwuF06fPo2JiQkGiSRBoyoG7Qd2u50ZRWKJpciT2Mj+/n4GoXK5nMFVtVqF3W7H6OgoJicn4ff7oVQqIQgCstksNBoNbDYbX2siW8iWLRgMSnBFv+pNIQ0y2bkRTmk0GnwYpCZaOlSTj7LJZILb7WaZAIFw8YHseUPMhpI7kdgTnwgKemdIokhVIapSEEFEe4Jareb36ttULJ6LWQW+GkFICwXwxJfQYDBgdnYWExMTPKlocXER//RP/4R79+7xJIZuAT/KUcyCNRoNVCoV9iir1WpwOp0MqKlctbm5iX/8x3/E559/jmAw+LUmgG7kTKfUWCzGGmCLxcLgwGAwwGw2Y2JiAgMDAzyLd3d3F7/97W+xuLiI+/fvIxKJdIjspd5gDw8PeYIOaT5p0aTFn15wrVbLJvrLy8v43e9+x5Y2sVgM5XK5I0cp7YoA8EGqXC7zgkssiV6vZy0jvYDlchkff/wxbty4wSdcsgUhrZKUeVKuRzdCmkJGDR8Wi+VrG3ypVMLi4iIWFxcRiURQLpe/5qUr5b2n91tc4hEvTrTokE8qgRJagBOJBLsSiFl0yk8qPTUdmsS+z9RlS817NM633W7z5K25uTk4nU6oVCpMTExgY2MDADq67qUKMVihDUbcVU+gZHd3l0ErjaIdGhrC6dOn+R602+2ORiwpmWoxC2S1WjEwMID+/n7W0k5OTmJ8fJw3yEKhgHw+30EG1Ov1DvZMPLSC3iWp7r1Yo+50OnlYycTEBAwGA7N84veXBi00m02YTCbWsdKzSyHVcyCWexCgJ0ZSrVYzyVCr1XgaHekmE4kEdnZ2WAfs8Xg6tMwAmF2X8r0nMEIstd1uh0wmYy2nQqHA5OQkBgYGoFKp2GP73r170Ol0GBsbQyAQ4GoLja2mZmwp1lOxPSXlqVQqmdHf398H8IRR7+/v5+ppMBjExsYGz7unA0273WZ3k/39fezv70tSsRCvUXQgouoPHejb7TaPL6dmS+o/oIMLgT8AfGggdxWp7j89ezqdjt9pIijJyUMsqaKDKWlQSS5E1Wva40iOebSp/i+FZEMByEONTgVXrlxhawsS1v/Lv/wLHj58yN573QSqwFcTrMSl03K5zAuPwWDAuXPnMDU1xRqmRCKBf/7nf8a1a9cYUHWDSTsatNHQjdzb20Mmk+EFTa1WY2JioqPklk6n8ctf/hLXr1/nrv9uTNWhIPDfaDR40AN5FFJzhc/n6yilUrn6N7/5DW7duoVIJIJSqcRlYPH3ljpXACymt1qtsFqt3EHdarU69IL0jJLVF53Ej9rWdKtphUq7brcbw8PDfNCjhiqydaNxqdVqFdlslrvVG41GxzvVjdI/gQGDwQCr1Yrh4WGMjIxweXJzcxOHh4cYGhpiRoj0tsBXGlcx8yc160tNisTq0cQ0MVtJvqkkuaHRxNQYSg2AZGclNatK3sherxdzc3Pc5KdWq9FoNFgnS7IFAgS0SZFWjZ5jKnOSREVK3bdGo4HT6cS5c+cQCARY96lWq3lggVarZYkN8NU6Qb8HfS86kBeLRWQyGckaVene08GEDnpkQ0XXlrw+ibmkpl4qp9tsNpYNAU9IF6oKSGlfdVT/K54+R4c+8ncmcNdoNLC/v49KpQKXywWdTselV2IOpfb/FedJbKDT6ew4XJMziU6ng1ar7QCHrVaL117SWtNUKyrLU++IVPkSI0rjX8V6WSqnU9m9Wq2y5pJYbForSNq4u7uLWCzGUjwpcqR1ihol6ftSRYLWS3rPqYnVbDbD7XZjfHwcXq+XZRaCIGBzc5PlalJX0+idovsLgJvQqOGKqgQ6nY7xgMvlwvj4OE+KlMlkKJfL3FtBbkHfNFdJwKp4MdfpdDh79ixee+01uN1uFlS///77WFxcRD6fP5bZxUdzI3DSbDYZ8U9PT+PSpUvMqObzeXzwwQe4ceMGA9XjANWUJwFBmvhC5SjSgQYCARa2C4KAjz/+GNevX8fq6qrkC+qfy5MYS2qaqVarMJlMaLVamJiY6GBUc7kcPv74YywuLiIUCnGpsJvXlb4vnQyJRSV7JdoEyEybxO0PHz7E9vY2+6d2q+R/NIilpI7/sbExDA0NcQc1+Wu6XC5miAqFQofPa7e01BTiBkBi2IaGhtDf3w+tVsvdoeTnSowwMV4koeiW7yfw1cIqHlKgVqvZkUDcsFiv1+HxeNjUnuQgjUYD6XQayWSya1p6YlYmJycxNTUFt9sNvV7PjapkAk7lf7J/oelG1OhAZTbygBW/V1KxlWq1GgMDAzxNzel0olarcdmPJvyQttpkMnGlhdgYsWacGkhIpy7lAYB0v8T002FYLF0Ra2RJvkJ50xrRbrdZNpJKpb5Vx/JfCjEApNzE/00VFLL3I0BPLBa5L5B0hdYHcrGQ2qtcfKCn60vvBAFYv9/PDFur1eLKntfrxdjYGJxOJw+DKBQKXFlLJpOSXFdxjmKJGuVJgHpwcJDzJsmMSqVCIBDAxMQE66tlMhmKxSKCwSA310p9XcWVMtqzScKwv7+PQqHAzzERLi6Xi+3AiAgol8vY29vDxsYGgsEgKpWKpECVPhLpR/pjag7r7+9HLBbjd532/kAg0LG+0TWPxWJYWVnhMczfZi94LrAqPhHQCzY4OIhXXnkFExMTXBIIh8N477332CS824DqaNDPo0YElUoFn8+Hq1ev8izeUqmEYDCI3//+99ja2upgVY4zT2IiDg8P+QEwmUw4c+YMXC4XlEolyuUyQqEQ/vCHP2BpaQmlUulYQbW4YS0SifCLTydoWtBoMMTHH3+M9fV1lMvlrjgp/KkQsy2kSyuXy8xs1Wo1LqXk83k8evSI2YtueFT+uTyJafP5fBgZGWE9IjERtLASSKlUKjyVqpsAkILYHq/Xi5GREfh8PgQCAZhMpo7RupVKBQ6HA41GgxkqKqkRmOpmjmSZFggEWANIZvXiyTlkwj44OMgd1sRSiDfSbjyrxAC53W4eranRaCAIAnsYEuNHX+/z+TA7O4vZ2Vn09/dzuU0QBPYo7sagAhoH6XQ64XQ6YbFYkEqluMRKA1+oFEjlbWLaDAYDO1xkMhmEQiGeqCfl4VrcUEfrk9goX6ynJraKxlOSPpAaA6vVKpLJJPsAd2OsMgUdmumeA18dXqmCRg2hLpeLR4XTBCEaAEMMoJRg5WiIq6fi+fQKhYL3dVrfSSM8MDDQMVp7b28PKysrHR62UgbtT3TPybvY6/VCLpezty7JUWgd8Pl8MBgMTLLQZLuNjY2vSeueNZ5236kaIpPJYDKZMDExgWq1ir29Pa4KkNRqenoaQ0NDsFgsvMem02ncu3cPW1tbTBxJdf/FRJ+Y5JHL5bBarZiYmEA6ne7QNRuNRsYrpF8mzXexWMTjx4+fGVhLwqwSfT0yMoJLly7hjTfe4JJaJBLBH/7wBzx48KBjcTpOZlX8kGi1WoyNjeHixYv40Y9+BL1ej2aziVAoxGa84mkVx52nWBdJBsynT5/GmTNneErQ7u4uPvjgA9y+fZtP/ceZJy2grVYLlUpZuVSmAAAgAElEQVSFLaq8Xi+LpqvVKiKRCL788kusra2x9vO4rqlYb0dNfyRfIA9ImmRFY19phGq3QdXRIEsYt9sNj8cDo9HIpdJ4PI50Oo2BgQEMDw/zhiuTyb5m+dXtoMlqo6OjGBoagsPh4HHF4XAYsViMhxmYzWZotVrWYhUKha4eVGnBlMvlcDqdGB0dxfj4ODQaDQqFAra2tjpYK1pQX3jhBWYq6/U6dnZ2EAwGkc1muwZUiS0RlwLFsgWdTsfuE8RYjo2NYXR0lGVAdPDa2trC7du3eUOVUgIgfodIn0wHELL1Ij0aldwPDw87mrHI2SQWi+HmzZtYWlpiAChlrhT1eh2CIKDRaMBkMrG3L9nskfaSwCoA7k4mRi0UCuH69et4/Pgx0un017TgzxtiEFCr1bgqMTIywgdr+kijluk5EIP/bDbLjbV37txhZlXKd4zW+0ajwWtSqVRij2WSBh2V9tD+Rax6sVjE7u4ufv/73+PmzZsIhUJ8CJcyz3q9zofnarXKDZTUPCVu9hFbSBGjXq/XEYlE8Mc//hGff/45D42RIk+67yTtIIkPNR6S24/VakUkEmFLRfKF9nq9HbZlgiDgxo0b+PLLL7G0tMRldamCcqVBL9RIR9aZs7Oz0Gq1WFpa4j4F8mOdmZlhUA2AhxWRXz1Vt44VrBLjcurUKbzzzjt4++23eZpRPB7H7373O/zbv/0bBEE4dgAozlEul8NoNOLixYt444038Nprr8FkMuHg4ACbm5v4z//8T/zqV79CLpc7sTwpV5r4deXKFbz88su4cuUKzGYzGo0GVlZW8Jvf/AbvvfceksnksTGq4qCOW5PJhNHRUczPz7PAvtlsIpvNYnt7G7dv38bi4iL29vaOlVEFwKWK/v5++P1+jI6O8iSTWq2Gzc1NNiumGdyhUKhjsT8uUK1UKjEwMICRkRHMzMxwKSiZTGJ3dxf5fB7j4+Nc+js8POT578cFVAlQe71eTE1NYWhoCFqtlks7MpkMLpcLL730El599VW2qqvX61hdXeVcuxUEAmlcLpWozGYzZLInk1Oy2SyX2KempnjwhkajQavVQjAYxLVr17C+vs4NWN3Ik9YiAh50ALHb7bDZbBgfH+evp+Y/YtsIZGWzWdy+fRsfffQR7t+/L7lWjX42VR6I5aeNnoAU2T4RKBEzO9Qpvra2xlaFS0tLPG5VqudWXO0RH/blcjmSySRWVla4+5yqE3a7nb+W2NR4PI579+7hiy++wK1bt7C1tYVisSgpGUC5kTa6UqkgEolgaWkJZrMZs7OzrAGnsioBQfo3iUQCW1tbuHPnDu7cuYO7d+92pV+BcqX7SnnOzc0xQUENXhqNpqMBlNjNXC7HY62vX7+OL774AslksoNJlipPOhhvbW3h008/xeTkJLOSNGWNGpPFQSA3lUphY2MD7733Hq5fv45oNMrkhpT3v9VqIZ/PY3NzEx988AGb/RNTrdFo4Ha7O2QsZGsnto26fv06rl27hkePHnHvgpTvP12XTCaDra0tfPTRR+z0Q+NXDQYDRkZG2GKPrMPMZjMAsEY9GAziP/7jP/DZZ58hFos9U2Pdc4FVKhONj4/j7bffxquvvsp2SqVSCb/+9a9x7do11n+dFPijUs+FCxfwxhtv4PLly7DZbDg4OEAikcAvf/lLfPTRR10t/X3TPNVqNZxOJ37wgx/ghz/8IWZmZmAymXhE7L/+67/yyfQkrim9OGazGadOncLVq1cxNzfHTBp1WN64cQOPHz/G6urqt5r/K0WIGwLsdjuXegEwWKVNqlgsIplM4tGjR0gkEsd+UBHbulCnJ2ls+/v7ubR56dIlnsNNQEW8kHYzX7rnJPMgsT2d9gHA4XBAJpPh5ZdfZo1StVrF+vo6bt++jVQqJSnz97Sggx79IR0jbfo0svLw8JBtcw4ODnjx/9WvfoV79+4hHA5LupkejcPDJ7Oz9/b2WCdHFkrEYBKIpU5farKr1+v8vN6/fx/Ly8vsAS31dSUGKBKJMCtFdkCkWaOvoQ520t3T+NdgMIitrS3+SCyV1Acs0tITuBIPqyDQXSwW4fP5YDQaO+y0crkc0uk0Hj16hLW1NZ4C1w15FX0vAp5k6ff5559zSX96ehrDw8NwOp1Qq9V8banx9tGjR9jZ2cH29jaPfu7WOFjgK9CSz+fx4MED6HQ67O3tYWRkhEdp0ppAWmCyf1taWmJrvY2NDXbi6AbJQvZPiUQCd+7cgdfrxQ9/+EM4nU5+p8h7m8A/Vdu2trawvLyM1dVVLC0tcc+K1FVLAqvVahWxWAyLi4sIBAL4wQ9+AKvVys+l+A8xvoeHh4jH49ja2sLKygru3r2Lx48fs0WY1NeT3iny0tXpdBgdHcXFixcZ9JP/LGmEqcpBo6kzmQy2t7dx8+ZNfP7554jH41xV+bbxXGCVdAqnT5/G+fPnuaGKGMDbt29jc3OzA6ycBAgkj9KFhQWcOXMGTqcTMpkM+Xwen3zyCRYXFxEOh4+toeZPBTUDDQ0N4dKlS5idneWpX5FIBB9++CHu3LnzNQbwOINO0U6nE4FAAOfPn+dJJTRXfWdnp2M6lZSzn79J0D3X6XQ8UYe8/2iBIrs1YoHJouy4gw58JAEhBs3j8bBZuFwuh8vlAgAu/ZH/5nE+r7QZENtEuksa/UvTYeRyOcrlMmKxGN5//31sbW11ZeEXBwG7UqmEbDaLSCTCptMOh4ObKompBABBECAIAhKJBO7evYtbt25hc3MThUKhawdBcWktFArBarXi4OAAhUKBPVSJcaVNjXTLqVSK2XbqqE0kEigUCl0BVXS/I5EIdDodMpkM24JRpcflckGr1XKzBDWI5nI57O3tYX9/n51NaBJfN4AqXVfgqxHF5P27vr6OVquFRCLB2lsaAEL61Ewmg0gkwjkXCgXJvZ+PBl0vMWuaTCaxs7PDOmECgLVaDblcjmUNkUiEh8Z0+xAIgAFzOp3G4uIiisUilpaWeOwvHbKBJ1KMdDqNSCSCUCjE07Wo+bJb1UAxuNrb28PNmzdRKpXg8Xg6Jj+RXpXGvWazWWxtbfG0Qhr93S0igH42NZt9/vnnaLfbGB8fZ49XsiCj+0rrwNraGsLhMEKhENbW1iQfp/60XElas7Kygk8++QRqtRqBQABms5m1yOJhOfTeRKNRHqqzuLjYwVQ/SzwzWCW2xeFw4PTp0/D5fCwIz+fz+OMf/8izeKXyqPu2QaVBmmd86dIleL1etgXZ3t7G+++/j/X1dS5NnRRQFfsZjo6OYmFhAW63mxmBhw8f4saNG7yZHqdOlYJAII1b9fv98Pv9bLVBL1QwGEQsFmNt2nHqPylPAieknTMYDDxGj/RX+XwekUgEwWAQ29vbPPjhOPMk/SmxLNSdTPZQdFolQ+iNjQ188sknWF5ePrZngBYg0qzt7e3B6XTyBCByLaDfiSxfbt26hWvXrnWUKLsJVmnk8O7uLjcqFYtFtNtt1nnSQaVQKPB86q2tLTx+/BhbW1td19aK2dJQKASlUolcLodwOMzvFE15ohJcrVbjbu9UKoVIJIJcLtfhBtGNfOl6ZbNZrKyssJWOXq+H0+nE/v4+MyulUgkqlYrBKvlTEkAhd41uAhV6vsRdzAcHB9jf34cgCFhdXYXBYIDH44FSqWQARoMqyDtSbAHWzaB8aX+kUvTKygq/V2RbRLZf5XKZR6pK7f7wl3KlpqW9vT2k02nWK1PpmjyYqVqRy+UgCMKx5krX8eDgAPfv32cdPYFVqljQ11YqFWasC4UCN+V18zmltQp44gV+8+ZN1Ot1bGxs8BhSqlJQI1apVEKhUEAoFOI+BkEQulq1FOdKvRyff/45+vr6MDMzg0AgAJ1Ox8CaKhr5fJ41yvv7+wiHw7y2Ps91fSawSpunxWLBpUuX8Morr7BAvVAo4ObNm/jwww8Ri8U6TlInAa50Oh38fj9eeuklnD59mr0KM5kM3nvvPSwuLnIZ5STzpNFqY2NjWFhYYFBdq9WQyWRw69YtrK6udjQAHfcBQGy67HA4eCAAWag0Go2nnqaPW65ABykSohMIFY+yLBQKSKfTWFlZwYMHD7C5uck6muMGrARKYrEYdnd3udubmlnI0P7mzZu4fv06Pv30U/YxPE6wGo1G2eydxi/a7faOBrZIJIIvvviC9XTBYPBYnDUIBAqCgPv37yMcDvO87GAwyGwlga9CocAsBc1Uf9qwgm4E6SvT6TQKhQKWl5cZAFLXMgEpWpuy2SzK5TKXtsUEQDfLv7RR0WANerfoQEDRarXQ19fH+dEfcUWt2yDl6CQc8nalRiZBECCXyxGJRPjvCQyIwW63cxVrZemjeLMvl8vI5XL8efo3tD8dV570c8UfxcC6WCx2TAokRlt8bcV7arevKQDeFw8ODpDP59naSaVSMfAXM/EETI8ymcexVtH1SqfT+OKLL2AymWA2m9m7lNYBkmCUy2UUCgX23T2O9V/MlFLD/PXr1/lwTT6xZE1FaxbpVFOpFOf8vHvAM4FVhUIBq9WKubk5vPPOOxgdHUW73cb+/j7u3LmDf/iHf8Da2lrHGM2TAIBUUr98+TJ+9rOfweVyoVqtcrfnb3/7W6RSqQ66/ySYVfJRnJycxOzsLM6fPw+NRoNsNotgMIjPPvsMN27cQC6X67DfOW5QRUy62OZFEASeSb26uor/+Z//QTAYRCKRYAeA42RWiVVtt9tsrfXo0SPWWxLzv7y8jN3dXdy+fRvhcBiCIJwIW01i+/X1dQiCgHw+j3w+z/6lBwcHXPIVd9F2Q6P0p4IYNvKc3NnZ4bG05APcbreRyWRYm0iM2nFeUypbiwdr3Lt3jyev0O8inhIkXp+O896T1pPYXzqsPHz4kAEJ5Uu/23E7qdDPorIdfRQz1mKASODwKMA5zlyP/j8BA/FwAtL3nlSe4vzEH4kJphCDVfHXHXcczYEOBs1m82sHhKP/5rifVfFHYoOBTi9WcRwn8BfnKQbXVGlJJpNcbTs6hpS+7rhJKspVLFUpFArY2NjoGKlNRBCtrwRcpZRWfmuwSt3/NpuNx6jm83kkEgl88cUX+Pzzz7G+vs7i9JPSgFITAOlVKpUKi30//fRT3Lx5E9FolMvUJwVUAfBIM2pQ2tzc5LLV2toaHj9+jL29vWM1qj8a7XabLVNisRiq1SoEQcC9e/fY/D8SiWBlZYXlAMdRSntansSmEnu1ubmJu3fvsvsDlYCJweimjuov5UoLFfnWhsNh3L9/n8f+tVotFAoFnhjWzRLVnwtiU0iukMlksLOzg76+Pl5UqXlNXPI/iWt6dME8ukEdF9v3TeLoZvC0zx8FLScVJw3uniWeBmC/q/F9zPW7nuPR/J62Fpx00B5JhvsU4v/+LlxvMSMsPqgcPQR0a319JmaVGIx0Oo0vv/wSqVQKOzs7WFtbw8OHD7kB4yQagMQhts749NNPYTabsby8jJWVFayurjJQPek8SY8UjUYhl8tRKpXQ19fHAuV4PN4xTeukHlg6VVFnajweZ/N6sqigRouT1P8Sa1av13lkZSKRYCNlAlXik+pJBYE6cRe1IAjcBECfE5cBTyKOMgHUnX70a74Liz/w3crlm8afyvf79nv0ohff1fiuv0vfBSb9L8VJAWfZn/uBMpnsa58kvSp5aVmtVhiNRuzu7iKXy3VtRGm73X56veFP5Cm223G5XLDZbJDJZNje3mYz9ePO80/lSr6lNFuXOlnFQvpusKnPck3pD4Ep4KsSZbfYtG+b5//+/VP/v5sv2rPc+5OKZ7mmJxH/F/IEvj+59vL89vF/IU/g+5NrL89vH/8X7v23BqtHPi/+Ac+a2zeKZ30ojpv2f56H4ruU6/fh4QW+P3kC359ce3l+++jde+mjl6e00XtGpY//C3kC349c/yxY7UUvetGLXvSiF73oRS9OMuR/+Ut60Yte9KIXvehFL3rRi5OJHljtRS960Yte9KIXvejFdzZ6YLUXvehFL3rRi170ohff2eiB1V70ohe96EUvetGLXnxnowdWe9GLXvSiF73oRS968Z2NHljtRS960Yte9KIXvejFdzb+7ASr74P3FvD9yRP4/uTay/PbR+/eSx//F/IEvj+5/v+Y5/OOsz2OPJ820vJp//3n4jieUcqT5sRTft92GEu3rynl+edGMX+TOI486SONXqY8xR+fJ8///f6SPaf/+/Oe+Xv8qVyfadxqL3rRi1704mRCig2hmyEGLLTJijfXkx5vDXw1jU+lUkGr1UKpVKLZbPIo4UajgVarBeDkrrNMJkNfXx8MBgNMJhNsNhsAoFqtolwuo1wuo1aroVarnfh4YblcDo1GA7PZDIfDAYfDgVarhXq9jkKhAEEQUKlUujbh8tvkqVarYTabYbPZ4HA4OJ9yuQxBEFAqlSAIAo+5Pqk86dnU6XRQq9X8OXpG6dqeZJ6Uq0KhQF9fH38Uv/c0orterz/Xu98Dq73oRS/+v4+jDMvROGlgKGZYnsYIAeCN4MRmd4tAal9fHzQaDTQaTceGRZuseHz0cQMtAoE6nQ5msxkDAwOw2WwolUooFovI5/MMWggInATIlsvl0Ov1GBwcxOTkJPx+P2QyGTKZDOLxOCKRCNLpNNrtNprNZtfGXf+loOtpsVgwOzuLqakp+Hw+1Go1CIKA/f19hMNhxGIxBrAnkat4BPvs7CwmJibg9XrRaDRQr9eRTqcRi8UQiURQr9e7NpL9m+Sp0WhgMpkwMDAAn88Hk8nE97hSqSCXyyGbzaLRaJxYnvS+azQaBtVarRZarRYA+Lo2m03OsdVqPfN71AOrvejF9zD+ErgCvhsA62l5ipnBkwRW4o/EAoo/L5fLcXh42AGsxP99nLkSE0jMhUql6vi8TCZDvV5Hq9XijYs+Hle+lJ9arYZer4fD4cDIyAhUKhWazSaKxSKy2Szi8ThqtRpvXI1G49iuKwErvV4Pr9eL6elpTE9PY2hoCNlsFqlUColEAsFgENvb2yiXy2g2m2i1Ws+10T5LniqVCna7HWfOnMHFixcxOzuLg4MDrKysYHNzE2q1mp/ZYrHIzPBxPqP0bGq1WgwNDeHChQt46aWX4PV6IQgCtra2oFKpcHBwwCywIAic63E/m3q9HpOTk7h8+TKuXr0Ku92ObDaLRCKBnZ0dtNttlEolGAyGYz+o0HusUqlgsVgwNTWFubk5TExMwGKxYH9/H8ViEclkEn19fajVatBqtR2M5XHfd7VaDbfbDa/XC5fLBbPZDL1ez+w/vU8AUKvVcHBw8Myym66DVTETIN68xNqQky5hUBzN8U/pQ05qgxXHUablz33NSZ646ePT8jxaLqSN9jgZjKPPZl/f118JMaABcCKbAuVBoKWvrw9qtZpzEn+NeOGiky2BruPKkUCVWq2G0WiEUqnsyJUYFmLcarUal167/ayKr6NSqYRKpYJOp4Pb7YbBYIBCoQAAKJVKlMtl5HI5VKtVNBoNVCoV1Ov1YwNYMpkMCoUCKpUKGo0GBoMBdrsdExMT0Gq1vJGqVCoIgoB4PM6sYLFYRKVSOZZcZTIZlEol9Ho9nE4nhoeHMT09jfPnz0Ov10MQBGQyGRSLRYTDYUSjUSQSCeRyORQKBRSLxWMpZ1KeVqsVCwsLOHv2LC5dugSbzYZEIoFwOAyTyQSLxQKz2YydnR3kcrmv3fdu50jsGrGqZ8+ehcfjQSKRQLVaRbFYRLlcZoa42WwC+PaaS6lytVgsmJmZwdmzZzE5OQmtVsvANJ/PI5/Po1Kp8NpA69Hz6oW/aY4ymQxqtRpDQ0N48cUX8corr2BiYgJ9fX1oNpuIRCKoVquoVCrI5/P83snl8ucCWN82R5J9XLx4ES+//DLOnz+P/v5+HB4eYnNzEysrKwDAEgAA6Ovr+9qhuptB10atVsPhcODtt9/GuXPnMDAwAI1Gg3q9jq2tLWxsbKDdbqNQKCCfz0OpVKLRaAB4tkN/V8DqURDS19fHbABtBLTZizfTk3jRnparWHcBPLmwtJBSnrTJHle+TwP9Yp0I5U8LAP0dAN5kjwNgia8nLUwKhQJKpRJKpZLzarfbHYBGJpOhXC4jm80yiOnmpiDWrKnVamg0GhiNRuj1eqhUqg5gTfee/o42XgIw3bym4ntNYMVisSAQCMDlckGn03WA6XK5jGKxiFqthkqlglQqhVQqhVKpxNe1G7mKQaper4fRaITL5cLY2BgGBweh1+t5wVcoFCgUCkin0yiXyyiVSojFYkgmkxAEgRnCbuVJz6O4DDw+Ps4AkEKpVKJSqWB/fx/ZbBb5fB67u7tIJpPI5XLMDnbz3hNQNZlMcLvdGB4extTUFKanp6HT6fiAp1KpUCgUEIlEkEgkEI/HEQqFEIvFUCgU0Gg0up6nWq2G0+nEzMwMpqamcOHCBUxOTkImk6FUKnFZfWhoCFtbWwiFQtja2kKz2eT16TgOKQaDATMzM5ibm8PFixfh8/l47Vcqlay91Ov1MBgM/J7TRtvNEL/vHo8Hly5dwqVLl9Df3w+FQoFMJoPd3V3E43FkMhmUSiWUy+UOEE3f4zjWeYVCAYPBgBdeeAGvv/46Tp8+DYvFgkajgXg8zveZnsNyuczPbLdDvBdpNBp4vV68/vrrePPNNzE0NASj0YhWq4Vms4lYLIb9/X2k02k+5B2XFpRyVCqVMJlMmJmZwY9+9CNcuXIFbrcber0ezWYTFouF93iSfogP+MeRJwA+IDkcDrz44ov48Y9/jPHxcZhMJiiVSrRaLeh0OtTrddTrdZRKJWQyGZTL5ef6+c8NVo8CPgIgxFio1WoolUpotVqYzWao1WocHh4yO5VMJlEoFFCr1XgzBaRnLZ/GoIlBtFKp5P8n7QWBqoODA1QqFTSbTQYBlG+3gMpR0EdgT6VScZ50WtTr9cxctVotKBQK3shILA4AlUpFcnAtBkpi8EegT61WQ6FQQKvVwmq1QqPRoK+vD61Wi4Fqu91Go9FAJpMBAAYC3chVDPyMRiPcbjc8Hg/sdjtMJhPr7Gixp2eV9DfVahXpdBoymYz1Yt0AAuJcdTodTCYThoaGMDg4iP7+fvh8PlitVhiNxo6NSRAEpFIp1jSZTCYoFArE43G0222+rlIGvfc6nQ4GgwGBQADDw8Pw+/0YHR3FwMAA9Ho9gK9O1MViEYlEAslkEqlUChqNBkqlkhkCqYGL+Hrq9XpotVoMDg5idHSUWUCfz8cHFTqc0KYbi8UQjUZ5vaD1i5pwpIynPaejo6OYnJzExMQEzpw5A6vVCpVKxc8osdMmkwlarRYymQzVapUbW4h560aeBAbsdjvm5+cxOzuLhYUFjI+P832nfaHZbEKpVKJUKiGfz3ew7n+qQiRFnpSDRqOB3+/HwsICzp07B5/PB71ez+xpuVxmhg14sjHT892t/MR5igHgwsICLl26BJ/PB41Gg2KxiPX1dSQSCWQyGd43m83miVTN6J0fGBjAhQsXMDMzA4vFwmza8vIyIpEI4vF4R67HSfJQqdput+Ps2bN4+eWXEQgEYDAYuKlqb2+PwX8ul+PD8nFVTojtpzX+woULuHDhArxeL5MRrVaLn0/xYf4k9LTEqC4sLODy5cuYnJyEw+FgaVKj0WAygKoUGo2GMcKzMtXPBVbFJwJaxOlUajKZYDQaYTAYGPzp9XrodDpmKgEgHo8jGAxif38f+XwepVJJ8lPX0/Kk8qTRaITJZOINjIC2GAS0221Uq1VUq1Ukk0nE43GkUinWW0l5ihUvWARQKVe68TqdjllKhULBCz7lQf+mUqkgk8mwYLzRaEh6bcW5iu+9y+XiEyGVqhUKBWw2G3Q6HW9aKpUKKpUK1WoV+XweOp2Oy0VSa5rEz4BWq4XD4cDExARGRkYwMDAAj8cDs9kMAHxQIQag1WpxOTiTyUCr1aJUKjHj0i0gQAcnp9OJyclJbl4YGRmBzWbj606A5fDwkEtvsVgMu7u7AL4qGRE7JCVjLQaqdrsdIyMjmJubw9TUFMbHx+FyufidAr6qqLRaLTidTsTjceh0Or7OVHKv1+uS5Qh0bq5WqxX9/f1YWFjA9PQ0ZmZm4PV6WV4hlv8cHBxAr9dDr9dDoVCwfnF/fx+lUqkrDJaYqbRYLBgaGsL58+cxPz+P6elpeL1eBn0AeB1Sq9V8sCY2g4BYN4AWvVPUXT09PY1z587hwoULGBwchNlsRqVS4eeNrh/dBzoYioFqtxhBMXO1sLCAq1evYmxsDFarFa1WC+l0GolEghl0QRBweHjI+8VRuU03Qgz8XS4XXnzxRZw6dQpWqxUHBwfIZDLY3t7mvZIIEzpYPU1q1c1clUolzGYzTp8+jZdffhkejwdyuRzFYhGhUAjr6+vY3d09lirEnwpi0gOBAANqs9mMw8NDCIKAaDSK9fV1xONxvq5iBv04ZArAkyqOy+XC7OwsA2q9Xs8NVdlsFpFIhO877eNiWQU9B93MVy6Xw2g0Ynp6mt8jt9sNpVLJbC8d+GQyGZOURF4RBnkWwPrMYFXM+JlMJhgMBjgcDng8Hvh8PrjdblgsFgasNpsNjUYDZrOZGUuZTIZCoYCbN29idXUVDx8+RDAYlPREI86TwKndbofT6UR/fz88Hg9sNlsHsM7lcnA4HB1lS4VCgd3dXYTDYWxvb+P27dssYZAqxOy0VquFwWBg8Dc4OAi/3w+r1crAWqVSIZPJwGw2s9ayr68PNpsNrVYLiUQCoVAIcrmcaXgCgc+b59OYP4/Hg4mJCUxMTMDtdsNkMjGwTqfTsNvtfPpqt9vQ6/Vot9sQBAGRSATtdpuZITqFS3FN6aNKpWI2dW5uDi+88AJmZ2dhs9lgMBigVCqRz+f59EfPIW2yxWKRGUoqGRFgleoQIC61GAwGuFwuzM/P48qVKzh//jycTicMBgNUKhWKxSIAMKgn5l3MvtfrdeRyOSBHODQAACAASURBVAYOtGlIlSvdf5vNhpmZGbzwwgu4evUqPB4P6z/pAErXUywJAZ48C6QVMxgMEASB74FUeYqtdYaHh3HhwgW89tpr8Pv9fDAVBKFjEyCWVa/Xw2q1wuVyQRAEpNNpqNXqDmArVVCu9Kz6/X5cuXIFb7/9NufabreRTqc7/p24qkLla7HdTTdKwwRY9Ho9fD4frl69irfeegsej4c3plQqxfpkAlfEDNHz2u2g55SagN59911cuHABWq0WBwcHSKfTePDgAaLRKLLZLIrFIudoNBq5mnYcYJDAwNmzZ/HXf/3XsNvtaLVaSKVSuH//PoLBIJLJJDNsh4eH/D6J9YDHEWq1Gj6fDz/60Y9w6tQpKBQKXss//PBDbG5uIpPJcEm91WrxgZDWoG7LARQKBcxmM6ampvDWW2/B7Xbj4OAAiUQCq6ur+OKLL7C5uYlQKMTSLgJS4vWp25IKvV6P2dlZXL16FZcuXYLJZOL3JxwOY2Njg59P0qETriFmvZvyCnFFemBgAOfPn8dbb72FsbExKBQK1Go1blTLZrPI5XJoNBrQarWwWCxwuVzsXkDX89vm+kxgVQwAaaEKBAIMVAmwkIaBaOxUKgWz2QyNRsOldxJdt1otJJNJZoOkCDHFrtfr4ff7Wevn9XoxPj6OmZkZ2Gw21k3W63Xs7e0xGCQmTi6Xw+12w2az4eDgANvb2/xwS5krbVQWiwU+nw+Dg4MYGBjA5OQkzp8/z+U/4Inf3vr6OutWSdtmMplQq9WgVCqRzWa5zCa1DpTYH6vViomJCYyPj+P06dO4cuUKDAYD+vr6GIgAYPaMtJcEqtvtNn9tX19fV0rABKqGhoYwOzuLc+fO4bXXXoPRaOTnj0pCrVaLJRYWiwUmkwkHBwe8GbTbbQaEdJCRchOTy+XQ6XQIBAKYm5vDuXPn8NZbb8FkMgF4wpRmMhlsbW0xQNFoNLBarXA4HFy6pEWX2CH6eylypeeVtGCnTp3CwsIC3n33XVitVq5GZDIZ7OzsAAC/S1arFRaLhZ+Her3OzBXlJ+X1pHtJbPr8/Dz+5m/+Bh6PhxfadDqNnZ2djsoLyWuazSbb2mSzWdZVd6NMSIcq0iWfO3cOv/jFLzA4OAiFQoF6vY5kMont7e2O8rRcLmeZEh2qQ6EQ0ul0xwYhZZ60rnq9XszPz+OnP/0pfD4f5HI5qtUqgsEgPvvsM25OajabkMvlMJvNvBGn02mupHRTS63VauHxeHDlyhVen5rNJgqFAm7fvo27d+8in88zq99sNmGz2fidEVcvuhGUp16vx8TEBH7+85/D4/EAAHfVf/nll0in03wdCfSRbIn22eMAV0qlEk6nE2fOnMHrr78OjUaDWq2GaDSKa9eu4e7du+wBStFut/mZBdCVitTRPPV6Pc6dO4ef/OQnGB4ehlwuRy6Xw9LSEj744ANsbm6i1WqhUPh/7H3Jd5vXkf0FQQIEMYMACIAgwVEiRc2TpzjtOD5RptOL7k2vuv+nXnb3ole96ukk+TlOPEm2LMuSLFGkOI8g5nkeCBLEb6G+5Q+0kljSB8rOQZ2jQ9qkxOL7vvferapbt4ptahrKKkun1QB0Oh1mZmbw7rvv4saNG7Db7Wg0GohEIrh37x4ePnyIXC4nVBA2IyrVN3hedhL8c+++/fbbuHHjBmZmZsSn7e1tzM/PY2NjA41GQ3R2ee96PB5Eo9G2O/R5z8+XogHwEHA4HLBYLHA6nfD7/RgZGYHRaJQLnmXIVColRGGz2Sx8VpZZeLmqZUoAqNfr4Xa74XA44PV6MTk5KWUgAHJIZbNZbG9vw2q1wmazwW63S+bSYDAI59JgMKj+AtNfnU4Hl8uF4eFh+Hw+TE9P4+zZsxgcHBTwVy6XkUwmsbi4KOUYn88Hl8slTSIEKAThSgL+yx5mBEG8VCcmJnD69GlcuHABDodDLoJ8Po9IJILV1VX09vbCYrHA6/UKj+Xw8FDeg6OjI8nCqXnYEvx5vV6cOnUKZ86cEeDfbDal2ScUCmF7exsajUYI5FarVbKAyrUsl8uqH7Z8/mxUoa+vvfaaAMBcLifNM4lEQn43vqfKDNr+/r68K53QNuTzn5qawuzsLN5++204HA7J5IdCIezs7KBUKonAtdlsFvJ9o9GQTGUymUQ6nRa6glp7i/vfZDJhZGQEp06dwvXr16VZIZ/PIxqNYmtrC9VqFf39/dDr9ZLdZzPY3t4eQqEQIpEIYrGYNLV0glfLADAQCGB6elqoMtVqVSR2qtWqiIWT3nR0dIRcLodEIoF4PI54PC7VlE6cVcziulwuuN1uCU4ajQYSiQTu3buHUCgkwQjBLXmsfEeVjX+dAFmk0/h8PoyNjaHZbKJQKCCRSODRo0f48MMPsbW1JZWK483ADPZPAgA6nU5JojDb//DhQ/z3f/837ty5g1KphFqt1lbyZZZN2QTaSSMIvHz5sux5Vps+//xz3Lp1C8vLyzg8PGzrPTgOTjrpK++nQCCA69ev4/r169DpdNjf38fq6ipu376Nr7/+GuFwGL29vW3DCpj1o69arbYjzX/cQ2azGdeuXcOVK1cwODiInp4eFItF3L9/H3fv3sXKygoymQycTicqlYqso16vl6oekz1qVE6fZUyi+f1+vPbaaxgdHRXaUTwex507d7C4uCj7nbxgi8Ui8lYej0cCAY1G89zn0guD1ePZD2Wj0tHRkciRFAoF4fodHR3JRWAymaDRPO1s4/cVCoWONarwwOIkkMHBQZFZKJfLAqaj0ShyuRyGh4cxMDAgIIXZN0Y2jMLUNnJVbTYbBgcH4fF44PV6YTabBQCSh7q3t4d4PC6NQexq12q10lxFDqiajTVK/iczwD6fD6Ojo7Db7Tg6OkI2m0UkEsHW1hYikQgKhYKUJikV1NPTg0ajgXK5jFQqJZI7apZ/+ezJW/J6vfD7/XLAssN7ZWUFkUgEtVpNeLV8p4+OjoSzys5wgiq1eVgEn8PDw6Jfx4CqUChgd3cXCwsLQkdgBpt/l+sZj8cRi8Wkc13N7JUyAHQ6neInD1rKE83PzyORSECn08FkMsmhWqvVoNVqUSqVBABGo1EkEgkBV2q+qzqdDlarFV6vF16vFx6PRyhIoVAIT548QTQahU6na2sMZGdrsVhEIpFAIpHA3t6elOI6tf/JqWYGmpzKdDot+4klbHK/qQRBekoqlUKxWGxTWlHLlBUgnqmsku3v7yOfzyMYDEqgAkD4qWyoZHKgk93M9JPJBZ6T1H9cWVnBo0ePsLW1hVwuJ1lK9giwkY7/VqezqgaDAcPDwxgZGZEy8Pb2Nu7evYvFxUWhfjBDyeBZq9UKUCXA7pRxP7ndbkxPT2NmZgZarRa1Wg2Li4tYWFgQfVqlXwQoyoBE+f/U9pF0iosXL+LixYty3qfTady7dw+bm5sSzPGdZJWM4JQVNv6bnfBRp9NhdHQUV65cgdfrRW9vLxqNBjY2NjA/P4/t7W1EIhFUKhVZL4rwM+jjv9epYIUZXLPZjPPnz+P06dNS5atWq3j8+DGePHkivUeNRgP9/f2C90wmE1wuF0ZGRmQPscH6eYKA5warxxeDD5WA7uDgQPgKwWAQqVQK+XwepVIJLpcLo6OjT3/w/0UCtVpN5GDy+by8zGq9xNzALJOzUYlli1wuh93dXUQiESSTSWSzWTmAyXNjQxAJ2fS1ExlAZWnd5XJJFpIbbXt7Gzs7OwiHw0ilUuKj8jButZ4KGzMLlEqlJBp/WVMCwP7+fgHTfr8fg4ODkq3a3NzE0tISdnd3kc1mJSs9MDAAm80mmelCoYC9vT2Ew2Hh1apJVyCoJp/a4/HA6XQKqNrb28PDhw+xvr4unEmuJ9ULqFSws7MjvCGWjZQSZi+7rkpuNSkn5HhXKhWEw2E8fvwYS0tLKJfLcDqdAq7YAESppd3dXenEJbhic5Ba60ptUofDAbvdLkLV8XgcKysr2NzcRKVSgdvtlufNbAuzhHt7exLUsCSsVsZSmal0Op0YHBzE4OCgZCSSySTW19exu7uLQqEAq9UqVBSCE3bgZrNZkbehAHsnQGBfX59QZMjfPjw8RD6fFwmgfD4PAMKp5XNNpVKSoWajWqfKwlTyMBqN0iRVKpWQy+UkU57L5dBsNgWoMqvKBAGD6E4pwADf7H8292q1WuRyOSwsLMj5RD/Zf8Eehr6+PhSLxRPpCuegguHhYYyNjcn99PXXX2NlZQXRaFQkypRKOwSE5AN3klerBNWnTp2SZj+NRiMgcG1tDeVyWQA1VT5YOVVqVFNmsRN+smHp0qVLkgU8ODjA2toaFhcXsbW1JQkc0i/IQ2fnPatRSgqYmsa1nJubw+zsrDT4lstlfPHFF1hZWcHe3p4Exuz5GRwchNlsFqxARSXgG+F9tRu+Ofnr0qVL8Hq9EshFo1HcuXMHq6uriMfj0lBJJRpS5lwulyT4uL7FYvG5cN5zg9Xj6J0p856eHsmS5vN5xONxbG5uIp1Oo16vyy9LsWVeXrzYtre3US6XVRcKZgTKw5Ld9Pv7+0gkElhbW8Pa2pp09wLA7OysfK/FYoFG81QDdHV1FcvLy1hZWUE2m1U1s0JQ3d/fL1SF0dFRDA0NQavVIpFIYGlpCQsLC8KdazabmJychNVqxcjICFwuF3Q6HcrlMnZ3d7GysiLEbDUF15V+jo+PY2JiAn6/H3q9HtlsFhsbG7h//z5CoRASiQSazaZ0ho+Pj8Pj8QjXZXd3F+vr6wJalNIcLwtYjzeqMKvW398v2bK7d+9ia2sL0WgUrVYLbrcbHo8HExMT8Hg8MBgMyGQyAv4ot5TL5UQRQK1MsLITmLJUzEBmMhksLCxge3sbmUwGGo0GJpMJXq8X4+PjcDqd0Ov1UiYOh8NIJpMCXsrlsirldSWoNplMsNvtkt2l5iM7lmu1Gnp7e2G1WuFyuSRbVa/XEYvFEIvFEIlEsLu7i3g8jkKhIGUtNQNVKioQ/LFZjjqVR0dHGBgYEOB1cHCAcrksgvW1Wk0msbCzuRO8ar6vVFKxWCxtgt9cc41GI+MN2URH2kUul0MkEkEmk+kIr5aXFy9KBnd8T5mc4LPnuT84OAiHwwGdTodEIiENdblcrqN8VfrJNe3t7UU4HJaSZSaTEXoSqWwMFKrVqgR5tVqtI5lV5X5yOByYmJjA3NycrBMBNRvSqLTD30er1aJSqbS9m50yrqfFYsH58+cxNzcnAd7y8rLcowCEd80mW+77YrEoQYoSYKtd9evv78fZs2fxxhtvwO12Q6PRoFqt4v3338fKyookxnp7e0UdxGq1yrQt5RlP4KrmuaRsTqdMFZvkNjc38eWXX0qwz/ulv79fem7sdjsGBgaQSCQQiUSeSftR8wzV6/Xwer340Y9+JKA6l8vh97//Pe7evYtYLCb3i0ajEfkqJtF8Pp/0fgAQBSCqWXwXX1+IBqD8h1mCbDQabdNIeBnt7+8Lsr5w4QImJiZgt9uF1/a73/0ODx48wN7eXkcyASxXUppIOeklGAxidXVVplf09PSITNDVq1dlIkO9XsfKygru3r2LhYUFKWurmQFkNMjORepparVapNNprK2tYX5+Hru7u1Jas9vtwhUcGxuT+cHBYBAPHjzA2toatre3kc1m5bBVIwPIC/X06dMYHh4WysTh4SHC4TDu378vGdVmswm73Y7z58/j2rVr8Pl8MnGFJdjt7W1sb2+LcLQa74AS/POiJLBilvrx48cIh8Mol8uSzT5//jzOnj0Lp9MpwGV7exsrKysIhULY29uT303NgQDKcqXT6YTZbJZSSrFYRC6XQ6FQgEajgcPhgNPpxOXLlzE6OipgsVQq4dGjR1hdXUU0GpX54cViUdWBEARVQ0NDAkAbjQby+bxkehwOB/r7++F0OuH1eqWhhXvvyZMnQmNRdmCrechyTymzlKz+MIM9Pj4Ol8sllKBKpYJYLIZ0Oi3BCS+sSqXSsUygMmtFnpcye8neAJbPU6kUDAaDXPzFYlHmr+dyOcn+diKryufPahq1iNkxzwCRWTWv1ytc+lKphIWFBayvr2NnZ6dNgUFNU1aACKq1Wq1IADGbSoDqcrnko9VqxeHhIdbW1pDJZJBMJiWo6YTxjmJDLZVzWPpnc6/NZsPk5KToQhuNRhSLRayvr6NQKIjAvZqVSZqS+uP3+3H16lUMDQ0Jneb3v/89otEoent74Xa74XK5MDMzg+HhYTgcDhgMBtRqNWxsbMizTyaTqveoKBuUr1y5Ao/Hg76+Puzv72NhYQFfffUVMpkMtFqtqAKdP38e09PTUsUEgLW1NfFzfX39ubOAf8lPZcPv1atXhdaTSqXwP//zP3jy5InsJ3bUz87OiiTgyMgITCYTUqkUFhYWsLq6KtUVNQMWJY1qamoKw8PD6OnpQbVaxdbWFm7duoW9vT3U63X5XlYme3t7MTg4iKmpKZHbAyAShcFgUM6PjoFVvmCUS+Ahz8iDpHlGtC6XC7Ozs7h8+bK8DMViUV6enZ2dtsYKNYGqcpxif3+/6NWR17W/vy9ZV7vdjvHxcbz11lvw+XwCVpLJJD755BPMz89jb2+v7SJQy1iKGBoawtDQkIAQRs3pdFp8ZRbI5/PhzTffxOTkJMxmszRY3Lt3D0tLS1LaVlNgn36y/M+IlJ3KBBxmsxkGg0G6Ri9fvoyRkRFZ02w2i6WlJWxsbGBzc1P4imo1ASkvKgIqbgxmbQ0Gg2T8GKjMzs4K6C+VSkin01haWsLW1pbQWthcpTawIlWFnCnqomo0GvHN6/Wi2WzC5XJhaGgIvb29AmaDwaCUuFgaUntyGf0kp5ekfupncj709PS0cAC5ltFoVCgs6+vrAgQ5alXtLCD3P4EfaTZUTuDZxM7afD6PnZ0d4V1GIhHJVlFPtxMzuJXAinx+o9EoJT8Kgw8MDMBsNss5oNPpZNAH1Uk4BKATc+yPU4AsFotc+GNjYwII+/r6UK/X0dPTA4/Hg7GxMQwMDKDVaiEWiwmo6uQ0NSUgYKVqbGwM4+PjWF9fx8DAgHwvgQCbQkinicViMBgMorjQKQBITv3o6CgCgYAE85lMBn19fTIUwO/349y5c/D5fDL4gRUTSgaR26jmsz/+fpL7zwbZRCKBQqEgU+uUzaEOh6Nt1PLQ0JDQV5i5VvM9IFi12+3Src6K7+LiopTSnU4nRkZGcObMGVy8eBF2u1146gxwnU6nVGJ4j6r1DnAt3W630NJIN4vFYgAgA0E8Ho80h54+fRoej0cqb6xskILF6qSa5z0zwKxKtlpP5Sa3t7eRSqXQan0jm8izlRQ2UhNJwdFonjYoFwoFbG1tSSLlO63Zi/4S7O5rNBoolUrCS6RDlH6yWCwIBAJ48803JdXN8u/nn3+Ora0t2XCdyALwQKBYNnmR5NXyYRuNRrjdbly9ehXj4+MiyJvNZvHw4UOsrq4iFotJWUjti1UpAM8MMDcJfWU5RafTweFwYHZ2VmgAACS7sra2hmAw2CbHoWYHuMlkEn1akrw55eno6EgyQ5zEEggEMDo6Kocw+Xcsv1MCTM0LlpuMDV0Ef+z41mq1cLlcovXK7D+zGtQLDAaD2NzcRDAYbCund6ozFMAzy2QEV0pNVTbesFOdz54gkBI8au4rZSaEPlarValMmM1maWYgSGawpdQpjkQibZJGnQJWAIQnRVkarh9VM+jnwcEBNBqNTIPi70UKjdpqCko/eU4RvCin1DEQACABDSXsWNEgwDperlSbu0bAr+T/O51OuN1uGVLA/UZgw68xCFBmANU2pZ98xh6PB8PDw5JFJzf68PAQBoMBQ0NDmJubw+DgIPr6+tBsNpHP5yXIYc+D2n4qgye3241AIIDBwUFpQjw8PMTQ0BB8Ph+cTqeMLmb5n3xgUlzIX6dUoNq+svI3MTEhdw4pPQDg8/ng8/kwNTWFmZkZ0dUmVmDVJZlMIpPJtAWDavmpnFIWCASktE7OOVVeAoEAZmZmcO7cORlcwveROIZc8XQ6jdXVVdWywHzuvEeVfQmRSATFYlESUlQEGh0dlaw7B+1QpWZoaAhjY2MIh8Mwm81IJpOqUBSVmWqHwyET/tiYvLu7i1qtJv6w10epr07OLxvDqFrk9XpFZem7BoIvTAPgZaTsPqaT7KhkV/OZM2cwNTUFo9GIRqMhUgdffvmljDfrJImd3dzkqDUaDck2coNz+s7ly5fhcDikvEFQvbS01JHsL9BeYiGQonh7NpsVLi1BrM1mw/DwMC5dugS32y0vejqdxtdff4319XVkMpmOkK25yZQcMHLqDg8PodPpJChhowhLQZxuEY/Hsby8LPJLtVqtTXJD7cY6XvicQsZLKhAItI0HZhSunGkdCoWEe93JUXc8CJvNJkqlkkwBYfOicmQdg8RwOIzt7W2sr68LqI7H4wIA1R4ZqOyMpcwYgTVLPORSMkCs1+s4OjpCNBoVf0OhEEqlkjRnqg0ClYCF/CmOyuUzbDQa8tx5ofHcUoLETqzjcV8JUKlJzYN7f39f1omXE30zmUwwGAwyqU6r1Qrft5M8VQJANiCSj2o2mwFA1AmUQS1lwqrVqky6oX9ql4GVfrJa5vV6EQgEZKgCzycKxjudTgwPDwsfmM+dQJUf1fRV2UxrNpsxOjqKiYkJ2Gw2GUJCrWWHwwGfzydTAQkYWBFg4mBwcFBK3GoPq1EC/5mZGUlS1Go1JBIJedZnzpzB5OSk3EtKPxkcjo+PIxqNYmhoCKFQSFUQSHqPz+eD2+0WpQJSjVwuF8bHx3Hu3DmcOXNGtJYZQAFoy8gHAgFEIhEZHPKyprzr7XY7/H6/rAvB6v7+vlQDCLp9Pp+sOc+zVqslNCFOjWSPjVrGDPDg4CACgYCoFbDqeHh4KGO2rVarYBSq6Ciz51xXm80Gp9MpTezf2ZcX+QUYKQGQEh8AQdEHBwfY39/H+Pg4zpw5gytXrsDpdKLVaiEajeKjjz7Cb37zGywsLLR1qasNAFqtliwspX5YPlOCFPJWzp8/LwCmXC5jfX0dv/vd7/DBBx9Io1AnhaGZHs9mszJ/vlKpQKPRyKHm9/sxPT0tU8LYYJFOp7G4uIi7d+9ieXlZAIvapTalsPzR0ZEcSHwRmbGkRimbVigBFI1GsbS0hJWVFTx+/Fg4eGoCAmW2CoCUcJk5Y4nV4/FIFz2/b2VlBYuLi9L5v7u7i1gsJoFZJ4ALDzAl948gj7JPNptNIthms4lyuYxgMChUip2dnTbVB6oUqA1WlRQg+sooXnmx8x2hTik7xNlNryTWdwqo0g8AbSNIw+EwAMghSy6ryWTC2NgYotGo6EQrgbTa5xN9VFKVjoMA8mQPDg6EtuD1enH69Gmh2bAKQ2DdqTHFTEAoJfW8Xq+MW2QlgJcxAxeCKz6PZ2Uq1dr3LEVSUYNatadPnxY/KQdH7V/eCcp3ltU2Jf9O7cwaKR9+vx+Tk5Pw+/1C+dBoNJIR5uRFcsOVgvBGoxGBQABjY2MyQSiXy6nip9LX/v5+0dQmWGUVoqenB6Ojo5ibm5Psn0bzVCOWQ3OMRqOMC/f7/RgbG0MoFJKJkS9rBNSkqE1MTMBkMrWpEPT29sLv9+PSpUuYnZ2Fy+WS3pl8Po+joyOZ/kaaC5+B0+nE+vq6an6aTCb4fD6cOnVKEj5Moik5ouSnApBue1IA2WTFkaakYKn1ntJXs9ksajpcT+UwH6rCsCrcbDZRq9VEkpKgX1l1Z6M799x3sZcaCsCoqVKptH2u0WikfHH27FkMDw9Dq9UiGAzi3/7t3/DZZ59hfX29bW50J4wvAC/vUqmEgYEBEdPWarUYHR2F3+/H3NwcAoEAdDod8vk8/vjHP+Ljjz/GZ599hkgk8q20utqgmpJfq6urqFarKBQKQkVgmY/TwSYmJqRklc1msbi4iNu3b4uIcKlUagsA1FxjcnjX1tZEew34RkaHDU1sqmFZbWNjA5988gnW1taws7ODpaUl5HI5AQMMAtRc10ajgUqlIuR4Zq4Z1bIJREkT+eyzz3Dnzh0Eg0Hk83nRqSU457uklvFiUoIiRssEJ8qpVAAkYr1//74IW5dKpTaN2k6BQCVg5eVAzb+BgQHhc7MkzIoGdV8ppn8cBKrlo7I8xkwkx46yAsSGyVarhaGhIUxPT2N6elqk18bHx7GysgIAHfOT/pHbTeDicrkAQOTR2IxQqVREq/Dy5cuYnZ2Vsqry3aCpxftmRo3Aj5lSKpD4fD4BJ1SD4MAXBgH0h8GtMkOsVtMns5ROpxPj4+Pw+/2iQnHhwgUJTMn9VUpAAU8TBcA3kopHR0dCwQGgWuCnzKhaLBa43W74fD4EAgFRyGHQwdG6zJRSwYLVNdKwlDJ79FUNU2b9eWaSY8kM9P7+PsxmMy5cuCCN08y6PX78GBqNRobGKM8v0mwYlL+skUKj1+vhcDgkkdNqPZX1KpVKMm3N6/WKekU8Hsfdu3fRbDZlXDgrG6SDxWIxxONxVYJA+skzkz08lHMiWFVqwZfLZezt7WFjYwNarVZK6FxPJi/i8TiSyaRqFBsmHwiIWXlknxKzpWwKZaNvsVhEoVCQBBtpdkzIUL3keRVLXhqs8g91yJrNJvR6PU6fPo13330XIyMj8gL/x3/8B27evClch04CVaWPjESV2SBmCfx+P65fv47h4WHhCn3wwQf43e9+h4cPHwr/o1MUBVqz2ZSyXzgcFhUFHhQ2mw2vv/66CO9TsuTjjz/GJ598gidPniAYDErZUPn7q2VcP5ZVUqkUdnZ2kMvlJAhwu92YnJyUUZUkY3/00Uf46quvsLm5KbIgytFrnfC11WrJwWAymYSQTjFiZUTHcb8rKyvY2dkRXhtLGGr7d9zYtOJwODA5OYkzZ85gbm4OQ0NDaLVasl8IAhncFItFyWgdV6foRPaX2SiLxYLR0VFMTU1J2Y/ySZwFTV4byz3Ke/kNYQAAIABJREFUxppONSqRzsHAhA0KJpMJwNPBCuSts4JiNptx6tQpOYzZ7cqDVO1zitI6bPJQ0qdIAeI5VSgUZE8bjUahhTDbx6xRpVKRrL9aQZ9Wq4XNZsPExASuXLkiYJV8NHZ59/T0CG2JAJBAutVqtQU49JWNYGpcrMyoulwu6Tnwer0AvpFQYlWCdwE/57tIcM2Mr/JiVVv7lxU9nu0M6JXUCK4js21Kyg3PWgbaTBKRQ96J+0q597kfmHHlZCLun2q1KqDJarW2yfCxGTuXywkF8GVN+X4RXDmdTvk6aVQcBsPmumq1inQ6jWazKZlOt9stAQz5uFRUUWtNCaqZMec+odau3W6XIIQa7wSxlGGkAg8blqitzeFLaj5/3p98vsyOsgFU+U7r9fo2qTjSW0ijYLJrc3MTmUzmufpUXqrBihEI0TYfgtfrxY0bNzAxMSGapl9//TU+++wz7O3tSbdap40vATOspCnw0h8eHsY777wjHZiUC/nDH/6ABw8eiEZsp4EqgLYGD+BppM/DzOl0YmxsTLorWfp/8uSJ+JpMJuVS66S/PMA5dIBlamaFlB21rdbT0bCLi4u4f/8+FhYWRKBezZGax+04J44Ahtwk/mzO2AaeZgCXlpaws7ODTCbT1qzS6XeVly2bESm47XK50NfXJ9lhHvoABEhzLTvBVTzuI3mI5FpNTU1hYmICbrcbrVYLGxsb2N3dhdvtxuXLl0W6iB2kbHJSu5xO/5Sdq+xIpbYzOZ1ssiyXy1K+4rml1WqF3875250C1QMDAxgfH5cGSf78Wq0mFz1VCDSapx3rQ0ND8Pv9GB8fF2DFQIZZdTXHlvJ58308deqU6GoC31RT+O5xOAX5a2yy5boywCqXyygUCm0joF/GWDUhb4+BUrlcluCOZz8A8Yfrxb9PXxlUMVhQq2Ndyanl56QXMUChj7z4eV7xzOJkQyYDms1mW/lfrUwl/T1u9JeJANJC2CRFSpdGo4HX65VJfGxk5b2RSCRk/LralRWuKzN/vENnZmYAQBp/iFlGRkbg9/uFHsJ9mEqlsLW1hVAoJKobL+sfgLbnQz+Bp1SJsbEx0e42GAxSyeOkK3JY2bRIuStWLEulkur3lbKiyP3CIJYN4Lxf+U4wg33q1CkMDw/LlLBMJiO+Pq9W/UtnVoH2xguLxYIrV67gxz/+sYzeTKVS+O1vf4uFhQWRKjgJAEgfjx/ejLYvXryIa9euwWazodlsIp1O4//9v/+Hmzdvilj1SYBqGrOr/Eiul16vx6lTp+SSYhbwgw8+wK1bt5BOpzvS+PUsU2aneamy7EJupdFoFCmOVCqFTz75BA8ePBCpsE7yfp9lLL2RS8OGLqWkF+kUbP45KaBK/1hm8/l8mJ6eFqBKIJLP5zExMSFC6/v7+wJmOjmyUmkE1CMjIxgbG5PBCRrN0wk26+vr2NjYQLFYhN/vb2tg4yXXaUCt1+ulGYAcKqPRKNkcCv5Xq1VYrVZpuGGWglUD6nACnclQ9/f3Y3h4GBMTE3C5XG1yRXz3eJCTvzg1NYWLFy/KRB4AcqlyXKjaEmXMTI+PjwvoIN+Y5+PR0ZEI6zObzcwQwSI568FgsE2tQi1fmelliZ9BB4MUltYJAoFvnivpLKwEUVovlUohlUqJ0LoapswCsgGxUCjI8AlyQEkFUp6VzMbbbDYBXKVSSQAVaTZqv6/AN/0f1WoVpVJJStR6vV6aahhYHR0dyTvtcrmkmsVM5fLyMvb29pDJZFRtBCOg4lhyjs5mxUqv16NerwtHFADcbjf8fr9UX7iu+Xwey8vL2NjYwPb2tipgVWmHh4eo1Wpy1/T29orus8FgQDKZlAbmZrMpWsWUimR2uFQqiXbt5uam6n4y0UfOPxM8drsdc3NzMhKYVCZ2/LOvho1UrBhxtPnq6upzT4d7KbBKYwrbZrPh0qVL+Kd/+icMDQ0BABKJBB48eIAPP/ywLUI9SbCq3HAUA75y5Qr+8R//EV6vF63WU/2/O3fu4P3330c+n++YlNaf8xNozwBws585cwY3btzA4OAgms0mEokE7ty5g5s3b3ZsUs2f85NZEnKBnE4nTCYThoaGZJJSs9lEoVDAxsYGVldXJUvdCQHw72I89Em1yGazogF6dPR07CvHAqst9/WXjLwfipK7XC4cHT3VzE0mk9jd3ZVJKxwXqtfrpbO900EVL1h2A4+OjmJ8fBwjIyNSgtrZ2cHjx4+FW0kZE5Yqlc0/nfKRfEjKFI2NjUkJOBgMCi+MzTNjY2O4cuWKNIxQgzkajYp+YCcywEr936GhIUxNTcks+Fwuh76+vrbRw3q9HuPj4zh79izGxsbgcDik9B6NRvHw4UMZqqImWCVfUdmIxgwJQQbLw0oVBTY5kcZ0eHiITCaD5eVlPH78GLu7u220hZf1k3/4s0iN4QjKdDrdpvmozAIdHX0zvQx4ymsOh8MyOpQAUM0EC6sMBJYajQahUAjhcBgul0vGUSubxZhdYwNNvV5HJpPB5uYmbt++jfn5eUQiEdWHFyj5u8ViEZlMBsFgEENDQ0JhIC2FmTc+V64zOZXRaBR/+MMfcPfuXayurqo6BZJ+1ut1pNNpmebG6o7VaoXJZJK1UVZZyV/m389ms/jss8/w6aefYn5+XpIyavhIQM0JeXt7e5iYmJARv6QwHK8+MxHY398vmeNSqYTV1VV89tlnePz4sSSD1HxPOciFDbJerxdGo1GGvpC21mg02ri4Pp9PghS+r5FIBLdv38by8rJMsXued1UVsEoA+POf/xx///d/j/Pnz6OnpweFQgEffvgh/uVf/gXRaLRjE1W+q1Fg+Ve/+hX+7u/+DtPT09BoNMhkMvj973+Pf//3f8fOzs6JA1WlKYHB9PQ03n33XfziF79AIBAQUP3+++/jf//3f7GysnKiQPW4n+ysPX/+PM6dO4e5uTkYjUbJ9iwtLeHmzZt49OhRR0ZU/iUjB8jtdsu4VWYANjY20Gg0pOkiFotJueek/ezp6RE5srGxMXi9XtTrdWlk29vbw9TUlAAC4CkNgDJVJ/H8mQ1U6igODw/LpZBMJrG/v49Tp07hnXfewdzcHCwWC46OjkQ0vBP6tEoj6KfUD5umBgYG4PP5EA6HEQqFRGD7woULCAQCsNls6O3tRTqdxoMHD0SsulPryiwgs77kAjocDrhcLkxMTAi1g19TfiSndmlpCX/84x9x+/ZtRKNR1XWKqfDBC5LrYTKZRFyd3EqWJLl/lBWi7e1t3Lt3TxpAM5mM6u9Cq9WSagMnvfHnkxdJPiqTFkodyGKxiHA4jOXlZTx69AgLCwtYXFxsk1ZUw1h+Jkilv7u7u3jw4IEoEDAoJYBRagGn02kEg0HMz8/j4cOHePDggVz+agbZBICsiuRyOaytreHzzz+HTqeT/UXFBHIalaC1VCohkUhgZWUFn3/+Ob744guEQiFptFTDV97VDAB2d3dx//59jI2NScWKgwnIXWbygs+DjaAE/3fu3MHm5qbQVdRaUz7DYrGI1dVV0a1lgyqVMshlVZ5BfMcLhQLi8TgWFxfxxRdf4Ouvv5Zx8WoGKlybbDaLtbU1fPDBByL9Rf4s6TeUWQQg1C+C8nK5jHQ6jVu3buHmzZtYXV0V9YXnsZcCq4y+HQ4H3nvvPfzqV7/CxYsXZQ7wrVu38Mc//hFra2sdv6j+kp86nQ6BQAC//vWv8Ytf/AJTU1PSEfib3/wGH3zwAVZXV59rVm0n/KSEzsWLF/Huu+/iRz/6kUhXJJNJ/Od//idu3bqFhYUF4SedtK8ELn6/Hz/+8Y9x9epVafw6OnqqU/rgwQM8ePAACwsLHeHR/DnjOrIzlOCEB8D+/r5w/rj52fjVKU7lnzL6abFYRE5Lr9djf39fIlSr1YpLly7B5/OJ6Pfq6qoAxE6/A9w/zE5ZrVbpRHY4HBgdHUVvby/Gx8fxzjvv4PTp05L9K5VKuHnzJra3t1WdrPIsHynlw4OffzhqlU1hh4eH8Pv90kHdbDZRqVTw0Ucfyd5iJ34njE0oLP/ybGRwpexIJ6f68PBQKhnVahUbGxv4/PPPsbi4iPX1ddXLv8wA5fN5GTvLTBkACagZPJHHXqlUZJhCrVZDLpcTebWVlRWkUilV6SDcq2zc4LOnwgcrKCaTSUrsyj4LVloqlYoMU9nd3ZVRsGpyKpWgikCJkmME2tVqFaFQCLOzszIx6OjoCLVaTbqsqVEdCoVkihApBGoaf2+lnnooFMLNmzeRTCYxMTGBiYkJnDp1CiMjI9J8RQDOUvrOzg6CwSDW19ext7enWmPdcV8ZnOTzeczPz0Ov1yMej2Nqakp4zDabrW1IDOlBW1tboq1Nn4vFYkeqVlyjTCaDR48ewel0ihoBASvPAIJ+8prZSLW7u4u1tTUsLCwglUp1RFlJSfmLRqP4+uuv4fV68fOf/1wqqNxL3M9K8J/L5UQqcG1tDY8ePcLa2pqMBn7e5//SYJXZyitXruD06dMy9jMWi+Gzzz6TGbcnXf5X+kjS74ULF/D6669jbGxMxqgtLy/j888/x8LCgjzwVw2q7XY7Ll++jDfeeAMjIyPQ6/UolUq4e/cuvvrqKzx58kQaQF6Fj9SJGx8fx9WrVzE7O9vW7bm7u4uFhQVsb28jEomoykv6rkZivc1mg8fjgd/vFw4YeZ7A07JfMpmUmepAZ7v+j5uy85cyQeRdklqhPMhYdp+fn5cL6iT8ZZmVBygA6PV6eDweWCwWTE1NQaPRSMOQRvNUI3h5eRlLS0vI5/MdpSso6SnValVG0FJrkRkrm82Go6MjmZ7Ci3VzcxMff/wxFhcXVS+nHTdm8mKxGJxOp2Ql2T2tnKzFM5MHfyKRQCQSEfC3u7srXFy1/T08PJSsn8PhQKFQgNvthslkEq4iOaH5fB7FYhHJZFJAeLFYlDG7e3t7bZeq2kCFU4qOZ8/IX81ms7Db7W3TwMgbJMBOJBKIx+NIpVKSUevE/lJmzJixPjg4QDAYFM3s5eVleDweabIhqGY5PRqNIp1OCzez00G2skmZMloEoPPz823d6ZR84sjiSCSCZDKJfD4vjYCdumcJWHO5HB48eIBKpYInT56IFJTL5ZKGTyYqcrkcQqGQyOvF4/G2AUBqGwMWjvS9f/8+ms2maKey3M81Yim+Wq0iHA4jlUohHo9jfX1dVGs6eQ8cHh6iXC5je3sbd+/eRV9fH6anp4UHzLVkRp37KRaLIZPJIJlMYnl5Wca/v2ji8oXBqjILePbsWVy6dElm3NZqNdy7dw+PHj2SWdCvMlup1+sxNDSEixcvYnZ2VrTNMpkMPvjgAzx69AipVOpEeYrP8pMgcGpqCtevX8fY2JiM2AyFQvjwww/x6NGj55Z8UNPIUSMInJqaErHtRqOBdDot8k/BYFCyEydtLPNx2payk56XFC/UWCyGaDSqqjzJdzXuI+CbzlCtVvutSSTUWkylUnj8+LHMuT6pd4AHLLNl1J6l3iPBrHIay97eHj744AOsr68LOb+Txog+nU4jEolIOUopWabsTC8UCkK1+PLLL/HFF18gmUx29PDn5VMsFrG1tSU6islkEqOjozJJCfhmhHGz2cT6+jp2d3cRiUQQjUYRiURkup2aHeBKP8n/XFtbQ09PD8LhsDR3KGV32GxJkFAsFgW8KrPBHF3bqQwQNX65n5htz+Vy0qxC6SeCL0op8d1WNi52CqgqPydYJZ+7Wq0iEolIowo1dJmNY7OLsrnyJBJBBNhsViPVa319XSoYDLopp8SmHFIxlGPKOwmqyY9nAMPZ9Ha7XQIspfIHFUK4ricF/vnOPnnyBMlkEk6nU+hBer1eKiv0k9M/GQxms9k2CdBOvKvKc7/RaODevXuo1+tYX1/H0NAQ7Ha7UG74kYo/mUwGpVIJyWQSGxsbwlF+0XV9KbBqNBoxNTWFX/3qV9LFxu6w//qv/8La2ppEUq8qq6rT6eB0OvHWW2/hxo0b8Hg86O3tRalUwhdffIEPP/wQ4XC4raT+KgArs78jIyO4evUqzp8/D4fDAQDI5/O4desWHjx4IGNUmUk56fI6hcw5wcZqtYoY9eHhIcLhsADVRCLRVko5ydI6u34pRQNA/ltZWtnc3MTy8jLm5+dRKBROlK9KfnKr1RLB793dXZH/or+MXMPhML744gt8/vnnmJ+fP1EaCLNToVBILlKbzSad9ORS1ut1AdSffvop3n///TaOYicPf5atmclVyiR5vV6hfrAE9/jxY6yvr2NtbU1oCp1WLODhz5JqJpOB1WrF0NAQhoeHZdgHm1rI9d3e3hbJLWbUOzGq9ll+UtWDjX2kWgCQUnqtVhMurXJinLJE2KnzimCP/DgGeQQl5LDSlGeAkkP6rD9qm5J/SFNKGhEIcrKS8vtZ0VCu50mAVOXH476ykU1JD+H30+fj/nbaz6OjozZZsEqlgmw2i3g8LrQVpXQY3wV+3un9D3yTqdZoNAIGo9GoBPwcbMJ1ZeaSkorcX51uWFY+Q76T9+/fx8rKiiQqqFHMyhsDU+qpsyLwssnAFwKr7PoaHh7Gz372M7z99tswGo2oVqvY2dnBP//zP+P27dttDr4KANjX1we3243r16/jH/7hHzA5OYlWq4V0Oo1Hjx7hX//1X7GxsdHW/POqgKrZbEYgEMDly5fxzjvvwG63SxPNvXv38Nvf/hbhcLjtMj3pzCpnabtcLtH6Y7o/n89jY2MDH3/8MYLBIGKxGEql0ok3Vim19jiycnNzE0+ePIHX64VGoxHOXyQSwb1797C8vIxkMtk2+vekjPIzwWAQ1WpVANPs7KyU07mvPvnkEywsLCASiaBQKJyoAgTLk8z87OzsIBwOw+/3SxY4m81ifX0djx49EiHtk6TWsATFjmBmTB0OR9vBXyqVJDtB5YeT3P/K0l6xWEQqlRLFBwZXSnBHMe3jF36nfSVgpeSbsvOeQZbSB/JE+XdPci8pJZ4ACAhQmlJi8STX8bgd/3nH//u4n3/p73fajmeDjxvfT36u/L6T9PVZfjIYoSnfX37fSYD+434qfxaBv/K5K0f/MvvOgPwk736lrwT1DPb39vbaBgIoZQpZCVBz5PsLgVXqLbL5g5MzdnZ28PHHH+POnTuSETh+iJyUkaLg9/vh9/tF6iebzeLBgwe4desWlpeXX0nm71m+WiwW0TGkGHE0GsXi4iLm5+exuroqWY6T7lZX+kkuYigUAvBUmgyAZN2ePHkiOpYnpf2qNG5kliQJ+NfW1mC329FqtVCpVETmheWfkyypK32l1iJLQolEAktLS20ZNpbTWKJmlH2SfnJNOYGIYFDZdMOSKhsoTkqnVml83wiwisUi4vH4t75HKaDP3/GkTXlJUkhd+bXj3/uq7Luu0auSpXuWfR9A3ovaq3wnX8T+Eph9lfZ92kd/yp71vHluHqdOvWr/lRl+nlfMsB8fIqE2PeGFwCovoWKxiN3dXXz55ZfIZrNYXV3Fw4cP28TfX+Xistkik8ng4cOHwqdcWlrCw4cPhU/5qv0EIOVTs9mMg4MDPH78WPQrd3Z2kM/nO16i+EtG4MSPqVQK29vbUoLL5/PI5/NtmapXYa1WS0qR9XodlUpFypj8GnlJrzLzT1+VEiosqzFqVQKrV5n9V2b6WPpVllxfRYbiL/kKfNPJ/H22Hxo46VrXutZZ+yGcBScdpGj+3A/RaDTP/CL5lVarVSbVhMNh6VRTU5eO1mq1vj377c/4SX7l4OAgfD4fRkZGYDQasbq6inA43Dbx5aT8/FO+KuWLTCaTSGsUCgUhqHeI7P/ca8ppUEqO0vGZ5K/aT6W/ys87vble5Nm/KnvRNT1p+2vwE/jh+Nr18/ntr8FP4Ifja9fP57e/hmf/QmBV8fXjP+RFfPtO9rKARQlWXpWf/+fHdwJX//dvqeTVs+2vYaP9UPwEfji+dv18fus+e/Wt66e61n1H1be/Bj+BH4avfxasdq1rXeta17rWta51rWuv0npetQNd61rXuta1rnWta13r2p+yLljtWte61rWuda1rXeva99a6YLVrXeta17rWta51rWvfW+uC1a51rWtd61rXuta1rn1vrQtWu9a1rnWta13rWte69r21LljtWte61rWuda1rXeva99b+7ASrH4L2FvDD8RP44fja9fP5rfvs1be/Bj+BH46vXT+f3/4a/AR+OL52/Xx++2t49i80brVrXeta17rWta513o4PiwG+n+M4NRqN/AG+PUb4++IzJzHSV+UM++/DuGga/evpeVoAV/r2ffKT9qxnT1PD1y5Y7VrXuta1rqlmyqmBQGcurpcxJQjo7e2FVquFRqNBs9lEs9mUsdH8+KpMq9VCr9djYGAAAwMD6Ovrw+HhIWq1Gvb399FoNHB4eIjDw8NX6qdGo4Fer4fNZoPT6YTdbgcAFItFFItFVCoVVKtVNBoNHBwcvDJfNRoN+vv74XA44PF44HK5oNFoUCgUxFeON6/Vaq8MENJPq9UKh8MBm80GjUYj61ir1eQdqFarr/Q95Vj7gYEBGAwG6PV6GcHO97PZbOLg4AC1Wu2lxtt3wWrXuta1rj3DlCOaX7UdB4DP+vrxzMtJGwFgT08PtFottFotenp62vw6OjrCwcHBt7JZJ+mjVquFTqdDf38/jEYjTCYTdDodyuUyGo0GGo0G9vf3UalUBAictJ89PT3Q6/Vwu90YGRmBz+eDwWBArVZDLBZDMplEoVBArVZDtVpFs9l8JX7ymRuNRszMzGBmZgY+nw9HR0dIpVKIRCIIhUJIJpMol8sol8ttvp6kn729vbBarTh37hzOnDmDQCCAo6MjxONxxONxRKNRhEIh5PN5NJtNNBoNACe7l7h3bDYbZmdnMTU1BY/Hg1arhXQ6jWw2i1QqhVQqhUKhgMPDQ+zv75+4n0pf7XY7PB4PPB4PrFYrgG8ClVKpJCD74OAAh4eHLwxYu2C1a137gdpxMKUEMt8HkKUEWPTnT/l40v4eB3/MtrVarW/5/SrLb0oASKDFTCB95deVmStldvAkjFlKvV4PvV4Pg8EAk8kEs9kM4ClIPTw8RL1eRyqVwv7+PprNpmReTsJPrpXBYMDg4CCGh4cxMjICj8cDi8WCXC6HYrGIQqGAVCqF7e1tlMtlyV6eVAZTCViuXr2Ks2fP4vTp0zCZTEgmk1hbW0MoFEI4HEYqlUI6nUatVkOj0cDR0dGJZdq4njqdDkNDQ7h8+TKuXbuG8fFx1Ot1bG5uore3F61WC4eHhwCe7h8luD6p584s9fDwMC5fvow33ngDgUAAtVoNm5ub0Ov1ODo6Qr1el2CF7yz97rTxuRuNRkxPT+ONN97AhQsXMDw8jHq9juXlZYRCIfT19Ymver1e9tBJg+re3l6YTCacOXMGs7Oz8Hq9sNvtaDQaCIfDAqwjkQiazSZ0Oh2azeYLJwE6Dlb/UkYA+P7wL76Lr8D3h4Pzp/w9/v+V2YFX4R8//1MAgZycV7HpjvOsCAr4+XF/AeDg4ODEswPKterp6UFfXx90Ot231hiA+EYwwExWp32lj1qtFr29vQJeTCYTtFpt2/cSuLD0ur+/L+va6QuMl4JOp5MM28DAADweD/R6fdv35XI55HI58Y/lNwKXTq0pn6VWq0V/fz/MZjNMJhOsViuGhobg9XrbfNVqtZLFyufzqFQqktF4mWzGd/GTz9xms8Hn8yEQCMDn82FkZASBQAA6nQ7VahXlchmVSgWbm5vY2dlBPB5HNpuV/9/Jva/0c3BwENeuXcPc3BzOnTuH0dFR6PV65HI5yV4VCgWsra1heXkZkUhEspgEhJ3ykR91Oh1GRkYwOzuL69evY3x8XPZTNBqF0WjE4OCgZIej0aiUsE/iXFL6qtfrcerUKUxNTSEQCMBut8v7l06nkUqlUC6XAQA6na4toOp09YLPvbe3F0ajEXNzc5ibm4Pf74fFYoFWq8XBwQHy+bzQAQ4ODiQoPCnwz3Ndr9djcHAQP/rRj/DOO+9geHgYAwMDKJfLiEQi2NnZQa1WQ6lUQr1el7PsJNZS6SvXMxAI4Ne//jUuX74Mm80GrVaLUqkEvV6PWq0m53y9Xm+rtADPj59UB6vPuviVGQF+D4C2C/Wkyy3HwZPyT29vb9t/H8+qMFI8KcDyLF+Bby5dJWEcAPr6+tDb2ysvRqPRQKVSOZELVgmqlL719fUJiCEIpJ86nQ4AUK1Wkc1mJevSyUuBG0ev17eBFpYF+/r6AEDKQc1mEz09PWg2mygUCsjlcqjX6x0HgkpgZTabYbPZ4Ha7MTo6CqfTif7+fhwdHcm7mMvlkM1mUalUUC6XkclkkM1mpQzTCUDAZ841dDgccLvdGB4elj8DAwOSqWg2m0gmk1IazOVySCQSyGQycuF2yk8+c5PJBK/XC5/Ph6GhIYyMjGBsbAz9/f0AIGdSJBJBLBaTdY3FYlKCI2hV25T7x2AwwG63Y2pqCiMjIxgeHsbY2BgmJyfb3tFms4mdnR3s7OyIz8FgEIlEQrKDau4n5V7XarUYGBiA2+3GhQsXcPr0aUxPT0vWstVq4eDgQC6tmZkZPH78GOvr61hfX8fe3p7w2dR85s8Kio1GI0ZHR3Hu3Dlcv34dY2NjMJvNkiVyOp2SXXO5XBIUbm5uSllYbTvup06ng8Viwfnz5/HGG29gYmICAwMDAv7q9To0Gg0GBgYkoCL3VnnnduJMUvra09OD/v5+eDwe/OhHP8Ibb7wBm82GZrOJbDaLjY0NRCIRZLPZNnrFSdybSj8JrM6cOYNf/vKXuHTpEoxGIw4PD1EoFLC5uYm9vT1Eo1GUSiXs7++jXq93/L5U+sm1dDqdePPNN3Hjxg0EAgHJ+B4dHaFcLqNQKMgZeXBwIHfTSZgSH5nNZoyMjOAnP/kJ3n77bQwNDUnmtK+vD1arVd5H4qmX9fOlwepTUi+LAAAgAElEQVRxnhI/EoQww9Lb2ysEXCUw5eLX63V5kQH1s5bPKqfRL35OQM0DimALgIAnkptrtZpkhjp1+R/3ta+vT0Dfn1rnVqsloIsveC6XQ6vV6kj2QukrI//+/n4YDAbxi5euxWJBf38/tFotDg8P5XtZ0kilUmg2m8jn86pnCJR+khDOTNDg4CDMZrOsGyNEAlUCvXq9jkqlgkQiAQDI5XIdy1rzXeXF5fV6MTs7i+HhYfh8Pvj9fjgcDuj1egn4Go0GEokEotEoUqkUMpkMwuFwWzm7E4CAJTabzYaxsTGcOXNGgNXIyAhsNht0Op2AloODA6TTaezt7SGRSCCRSMh7QR/V9pP7pb+/H4ODgxgdHcXZs2cRCATEV5vNJgCQ6zk8PIzd3V3EYjHE43HodDr09PQI6O7EfuI7ajAY4HQ6MTU1hfPnz2NqagoTExNwuVySFdJoNLJ/+vv7odfr0dvbi6OjI1QqFRSLRbl41fQR+CbDwgDlzJkzuHjxIs6fPw+fzwez2Qyj0diWlKjVatDpdCgWi8jlcojH4+jr65MAVm07fjY5HA7MzMzgypUrGB8fh91ul+wafxfeAaOjo9jd3UUikUBfX99frLi9rNHPgYEBjIyM4Ny5cxgfH4fVapWzPBaLoVQqSZDcbDbl3lLeV50yUk8Iqh0OBy5cuIDr169jaGgIGo0G2WwWOzs7iMViQlEgjeKkaCk0ltX9fj+uXr2KCxcuYHBwEIeHhygWi9je3kYwGEQ0Gv1WAuKkTLmWc3NzePPNNzE2NgaLxSJ+ptNpxGIxyQCTpnDS1tPTg4GBAfh8Ply8eBF/8zd/g+HhYdnnrD6wosO7QRlEnTgNQBlVMztFvtLAwADMZjMGBgZgNBoxMDAAnU4nIEGJsrPZrGQAcrmcELDVMiVIUWbR6KfJZJJuS71eL2BQr9d/a/Pv7+8jk8kIwZ0AVq0oVnkJEJj29fVJJshqtcJkMsmlpPy96DcAmEwmGAwGVKtVASwsX6r1gh8HfzqdDgaDATabDV6vV0pUvIgYbdHvZrMp3LZKpYJsNov+/n7kcrm2LKCavvK5ms1meDwezM7OYnR0FG63Gw6HQ9bV4XAAeApYWL6sVqvI5XJIpVLo7e2VMoyafir9ZaRtsVgwNjaGS5cu4dKlS5ienobb7YbRaBQApiz3Dw0NwePxIBgMIhQKSYm9UqkIp1FNP7mmVqsVk5OTuHbtGl5//XWMjo7KmhJ483I9PDyE1WqFzWZDMBiU0uD+/j6KxaIceGr6yX1is9kwNTWFs2fP4rXXXsP4+Hibn8qqz8HBQdvZBkA6hdPpdBt3VE0jqLZYLJiYmGi7wBig8PtYPenp6YHVaoXX60U+n0cul5PGIWU1Ri1TrqnZbMbY2Bjm5uZw7do1DA8Pw2w2yzOnrxqNBgcHBzAYDHKWMTvYSZDF88dkMmF8fFz20eDgILRaLer1OorFopznDFT4d5Rdzp00jUYj5+SpU6dw/fp1uN1uaDQa5PN5xGIxoSQwuVOtViVhwTug034CEO6v1+vFxYsXMTk5CbPZjFKphFwuh83NTYTDYWla49ryLDjeeKe2EVT39vbCYrHg1KlTePvtt+H1eqHValGpVBCLxbC4uIhgMIhUKtUGVI9XMzudXdXr9QgEAjh37hzeeustuYPK5TKSyaTQUVh94r1zUn4q73uLxSJ7/cKFC9JUVa1WUSqVkEqlkM1m2wJAVlEZsLyIry8EVpXpYF7uTqdTPrpcLrhcLlitVpjNZpjNZlitVhSLRTgcjrYNVavV8OWXX2JlZQVPnjzB1taWalkVZTmNF5XD4YDD4cDg4OC3/OTBlMlkpBTMiJW8oO3tbWxvb+Prr79GrVZTLQpTAiqCeovFArvdDpfLBZ/Ph+HhYVitVgkANBoN0um0HFR8MbxeL5rNJmKxGLa3t3F0dIRwOKzaQabMUut0OslG+nw+TE5OYmZmBg6HQ+RWNBoNyuUyjEZjGzXAZrOh1Wohm81id3e3DRwoeYEv4yfwTZaS2VSWAd966y0EAgGYTCaJ/srlchuwMplMGBoawuHhITKZDAwGA/b39yVoYMlQDcCqjDy5r8bGxnDlyhX88pe/xPj4OIxGowAQZfMHaQpsHmE2i4ddf3+/BCxq+qrT6WCz2TAxMYFr167hb//2bzE2NgaDwSBBSaVSaWumIK3BYrFgeHgYjUYDpVIJ2WxWMoNqXbj0s6+vDxaLBSMjIzhz5gx+9rOfSdMKm5PIq1Oeb6wK2Gw2WCwW4d92AmApfy45tFNTU/jpT3+K2dlZAYD1eh2lUqntfCLIor8MtjuZfSGodjqdOHXqFH76059icnISBoMBzWZTmpXoC2k0Sv6iXq/vKBAgkGfmamZmBu+99x68Xi80mqdyQJFIBFtbW233RU9PDw4ODqDRaKTi0ikgeDxb6Xa7cf78eZw9exY6nQ6lUgnJZBIPHjzA3t4e8vm8nDmtVkv2ICtB7A7vpGk0GhiNRkxMTOC9996TM7JQKGB+fh4LCwuSVSVHtdVqtVHs+Lt38vlrtVp4vV7MzMzg+vXrMJlMqFarCIfD+OSTT3D//n3EYjGR1uK5z7sYwEs1Bf05U4JM0j5u3LiBqakpSYjs7Ozg1q1b2NjYEC618gxnFeWk/NTpdJiYmMBrr72Gn/3sZ/B6vQCedv/v7e1heXkZe3t7KBQKQlWx2WxCBeGd/iIUixcCq0oAODg4iJmZGfj9fng8Hni9XkxPT2N0dFQiewBSomS5ldm4arUq5b9cLodgMKjaZmNETaAaCAQQCATgdrvh9XoxPj6OU6dOwWq1or+/HxqNBrVaDaFQSDKwOp0OAwMD0Gq1cDqdMJvNODo6wt7eHmKxmKplamUZyO12Y3x8HB6PB36/H6dOncLc3BysVqusablcxsrKihwCJpMJNpsNVqtVdOISiURbJPuyl5cSUOl0OuH+TU9PY3x8HGfPnsXly5clY9JoNJDP5xEMBtFoNOTvUD+OeoG8HJgpVDMDzAwQS5UzMzO4fPkyXnvtNblYa7UaEokEdnd3xU+r1Qq32y0djtVqFQAEGCp/hlpGYGWz2aQB5I033sDs7Cz0ej329/el1E+ATzDl8Xhgt9vbwIqS2qImwOJhbrVapfz7zjvv4PTp09DpdJItDwaDCIfDaLVaGBgYEJkTi8UiQQEvXrWA9HEjV3FsbAznz5/H66+/jnPnzsFgMKBeryOfz0t3tUajgcFggNlsht1uh0ajQb1eRzKZRDweRzKZRCwWQ71e7whXmQGH2+3G1NSUvK8DAwPSWBGNRhEOh+Vs43m6v7+PQqEgnNVQKIRCodARbUvuf4vFgtHRUUxOTmJkZESygLlcDru7u/Ls+Q6ymS2TySCTyQgHs1P6mzxTzWYzxsfHceHCBXg8HgGq0WgUX3/9NdbW1uQc4p5xOp0ol8tC+eqkGoAyoBofH8frr78uzzyZTGJpaQmhUEikqpihOjg4QH9/P0wmk7wfJ8GzNBgMCAQCmJubw/T0NLRaLbLZLFZXV/Ho0SMkk0lJoJCqcnBwIHcX934nON80nk9Xr17FjRs34HA40Gw2sbW1hU8//RT37t1DOByW30mZ7VVyLTulWsCfo9frceXKFbz33nu4dOkS+vv7UalUsLS0hI8++gjz8/PI5/MwGo3f+jf43tA65SfXx+Fw4Cc/+Ql++tOfYmRkBH19fchkMlhcXMS9e/ewubmJg4MDOBwOCRJNJhMsFguq1SoMBoPcnc971j83WD1eqmZJx2KxCMDyer0YGBgQjlK9Xkc2m0U0GoXP54PdbheAyHK8sqythh3nUhIAUrJkcnISgUBAUtgkLSeTSayursJiscDpdGJwcBAWi6VNnJlll06UfxkEsPFjdHQU09PTmJmZgc1mw+HhIfL5PIrFInZ3d/HkyRPROhsZGYHT6ZRsBbOFBARqvsh8/sz+BQIBzM7O4uzZs7BYLKhUKsjlcohGowgGg0gmk+jt7YXdboff74fL5ZLSK7NpzMKpTQPhZTX2f40pZ86cwdzcHIxGI6rVKhKJBHZ2drC9vY1CoSDrCQButxt9fX0SEABP1QCU/F+1AxZeBBMTE5idncXMzIzQJSKRCFZXV6UrlFkV8pW5P9mFydIMKSBqVSyUWaBAICDBSl9fH8rlMkKhEJ48eYJgMIhmsyki15RaaTQaqNVqyGQywq/NZDJtmRg1TFkKZHViZGRE+PLxeBzr6+sIBoNotVoiuUSAdXR0hGQyKaB7b2+vjaqiph2nK9jtdjgcDpRKJWnqYyNIT08PzGazgH7u80KhgEwmg3g8LioGneDVE9SR6tXb24toNIpGo4F0Oo1oNIpYLCZ0H6PRCIvFArPZDIfDIfunUql0lH/HM5V+ajQaKaVHo1FsbGxgeXlZQB4zQW63Gx6PB+l0Wu6mTjVS8rlTAornY61Ww/r6Om7evImvvvoKS0tLsq/5/eytANDWxNwpo68mkwlnz57FW2+9Bb1ej3q9jsXFRXz66ad48OABIpGIJCt4LrHZl593iqdMP/v6+nD69Glcu3YNMzMzAqhv376N+fl5bGxsoFAotPn5LF4/EyidCPi0Wi3MZjMuXbokQWmr1UIymcQXX3yBpaUlbGxsoFwuS3D9LPohcU4nAiolLW1iYgLXr1+Hx+NBb28vGo0GdnZ2cPv2bZFUq9fr8Pv9ACDvo8ViQalUEl9JCXqeu/6laADKP0qOJUtAlH1JpVJIJpPyopKPxYt1f39f5EvUNGVpnSVyltZtNptsMpar4vE49vb2kE6n4fV6YTAYpCOUl0GlUhGhW7Uu1eN+EoA4nU4R2zUajXIRxGIxhMNhAYEWi0XKw8yokZit7B6kry9bKlD6Sh8JBMxmMw4ODpBIJLC2tobt7W0kk0nhKVosFhgMBikTK0vAlUoF9Xpd1UiWYIUg2ev1wuv1ip/xeBxPnjzB2toaUqkUAAifsr+/XxqD6vU6crkcMpmM8FXVPBi4F7RarZTH3W433G63UA9isRgWFhawtLSETCYjAFDZeckyazqdRjqdRi6XEyUINQ9cZlc8Ho9Qa7if4vE4lpaWsLS0hFwuB7PZLJQANljVajVRAUilUkgkEm0lLjX3FbPqfK46nQ61Wg2pVAqrq6tYX18XjicvUY3mmyksiUQCyWRSZHi4nmqCFz5/JgDYjKjRaOQsDYfDiMfjKBQK8vX9/X25TJV8MeVzVzvTxv1P9QetVotGo4FoNCrSTzz7+/v7RQGkv78f/f394mu1WpWuZqAzUoC8xJnRy+fz2N7exs7ODhKJhAjCNxoNyagbjUY4nU5YLBZks1m5rDsJqHk/DQ4Owm63o6enB9lsFg8ePMDCwoKUgRkwkytMLdtarSZNtp3krHI/cViBx+MB8LT35P79+9jY2BAdXfpKv9gMxvO9U1xlZdXn/PnzAgIPDw+xtbWFpaUl7OzsSOmfmUmunXLPKEvgavvIwHRiYkIav0jzWVhYwOrqKoLBoPRM0Ac2LlNh4eDgQAJWtZNSysSExWLBuXPnMDY2hoGBATSbTeRyOXz11Vei6pFOp7G/v9/WZ8OkZKv1dLBBsVgEgOeuTr1wZpUf2bnLy7Jer6NQKIgkRCwWkwuevEYAwmXc399HPB6XzAoXWg1AxY+MQqlXSKkScr+2trYQCoUQj8eRTqclE8eInCK8LMGxGUztDltuMmarXS4X3G5324uxvLyMra0tRCIRpFIp+X6DwQCHwwGDwYBWq4VSqSSNYJlMRl5iNdaVF4DRaBQw7fF4RAA8m81ieXkZKysr2NvbQ7FYlACBmRWDwSCZIHaFK2Wr1FpT8qqHhobgdrvhdDqlnFIoFLC6uoqlpSVsb2+jUqnA5XLJ8x8cHITBYMDBwYGAhWQyiXw+L6U4NQErs9XMqhEwt1otFItFbGxsYGVlBdvb29jf38fw8LCUAEkP2d/flyoGQRYzq2oGV3z+drsdZrNZVAmY8d/a2pLMGqk/DKQoDRMOhxGNRpFMJhGJRMRPtdeUdCUGStwfoVAIoVAI6XQaBwcHknk7PDyUaTvValUyvzxoO1W2ZtDPxiP6ShoC3zsCamYtKE2XTCZlHxWLRVWz6UoflWBV+UyZ0VVm8gFIVcpoNKKvr0+yv1SB6aSiCukHzJLzbNrd3RXt1Eql8q1z1+v1QqfTSeNipzrElUEqkykmkwmHh4eIRCL4/+x9WXNbV3b1IgkQ8zwQIDiPEkWJkjV0ZLvdneok5VTlKQ95TOWHJVV5SR6SVKb+ep4t2S1LsihR4gySIAkQIOYZxEDge1Cv7QO23GlJF5TdxV2lUrclkZsX556zztprr/38+XPs7e0hm80KS01Q6/P5YLVaBRT2urlKZdgmJycxNjYGu92OdruNSCSC9fX1LhkfWV+++2ojKJnqXoHAwcFBBINB3LhxQ5qqTk5O8NlnnyEcDiOTyaDVagkQ43qmM4Sqr2RlUus8iS8uX76M6elpWCwWsf26f/++5Mm9how2K71sSOWl9Kw1mFbB5+n1enHz5k14vV7odDrU63WEw2E8fvwY29vbUslhY7Lb7ZZ+Fl4YjUajTOBi813PwCq/MG8eFPUS0BUKBUQiEUQiEWxvb+P4+Fg6Ftk5xgMOeAkY1tfXEYlEZMyZFqHejPjCcNau2WxGvV5HLBbD+vo6NjY2xBYCAGZnZ2GxWODz+eB0OtHf349qtYqdnR2sra1hbW1NbJa0XBSUKwwNDSEUCmF8fBxutxudTgfRaBQrKytYWVmR6RCdTgcTv+sQnpiYEDa4Wq3i8PAQW1tbCIfDOD4+FrCqBcuiNoCEQiGMjIzA7/djYGAA6XQaa2trePr0Kfb395FOp9HX14fh4WHRtgaDQRiNRhSLRezv7yMSiYjdEhnLt3223LSoAxsaGoLT6YTVakVfX58AurW1NUSjUWlUczgcGBsbw/z8vOSZTqexv78vnoG5XE6YIS2bq2jv5nK5ui5J+Xwe+Xwe4XAYqVQK1WoVZrMZPp8Pk5OTmJqako724+Nj7OzsIBqNygWwUCh0NRC8bZ58rhaLpevWzC7lWCyGcrksjQO02qKGnX9nf38fBwcH2N3dFSColReseqliad9kMkGv10sZlUbVDocDer0ePp9PWGx2NWcyGZGz0LO0VyBQvQTyFwEhKzxspKEDA6s9qVQKBwcHSCQSUuZ+nYPgdXNVhz6w8sT9lrIek8kEi8WCYDAIv98Pm82GTqcj5EQ6nZaRkb0IPlMCe75LxWJRypXAy/3M7XaL7Gp6ehp+v1/8iVmh6tXQAvXyxybgRqOB1dVVHBwcIJlMiqyL1RZevKlVZdWyl5+5KqmamprCzMyM2Cs9ePAA+/v7Avx5QSRTTIvCUqkkFR9+7m9LoJzNk4CaWnp21mcyGcmTewDzHBoagt1ul/2Jlb5qtSq/a5WnWo222Wy4desWvF4v+vv7UavV8OTJEzx8+BCHh4cC/PV6PbxeL6anp7tIqVQqJVUM5qzlmFiV9R8eHsa3vvUtWK1WnJ6e4vj4GN///vfx9OlT6f7nv+GeFQqFBEexh4HECi/UwB+ntX0jsKqWk1XfTGqVjo6OZEJJvV7H4OAgPB4PFhcXcfXqVdEBlstlfPrpp1heXsbm5iZKpVKXduRtgv+enfwTExMYGhqCxWIR9odMFa2d+vv74XK5MDc3J53iZrMZzWYTe3t7ePDgAV68eIHDw8Muq5O3DebK53Tp0iVMTU1haGgIAERgv7KygkgkgnK5jHa7LfODP/roI8zMzMDhcOD09BTRaBTLy8vY3t7G7u6uMJZaHLBciA6HQ/TJQ0NDctMjG5BIJFAul6HX6+HxeHDjxg3cvn1bAHWz2ZTuwUgkgt3dXTFl1uq58sB0u92wWq3C7LRaLaRSKWxubiKXy+H09BQulwterxd37tzB5cuXu8raGxsbePHiBaLRKA4PD+VipeVAgLOXKurkaD+WTCbRbrdhs9lE23br1i2MjIxIM1s+n8eDBw+wvr6OWCwmAItrVYv3SgWrHo9HXCmq1ap0gLKZkfIQTgiq1+tIpVKIRqN48eIF9vb2cHR0hEKhINpFrTXAAwMvJyuZTKYu1oSNLIFAAO12WxiiQqGAnZ0dpNNpRCIRHB4eSkldtX/rBSBQtZUEgLyUsHud3crZbBbtdlsOqVQqha2tLUSjUeTzeZRKpZ5JAHjQUiLTaDQE+Pt8PtH70tljeHhYhi1kMhl5n3Z2dlAsFnuqBVVL0NSlEuD39b3sVvZ4PBgdHcX09DSmp6fhcrlQr9extbWF1dVVbGxsoFAo9FSvSrDKil8kEsHy8jIKhYJctjweD+bm5jA6OirWcOVyGU+fPkWxWEQmkxHnDS0BIEN97ycmJuS8SSaTePr0KUqlEiwWC9xuN4aHhzE3Nyf6Ww41WFtbw8bGBmq1muwXWoVasnY6nbh+/Tp8Ph8GBgZQq9Xw+PFjrK+vo16vi9RmeHgYV65ckfOWJFo4HMb29jb29vawtrbWdanSYg/lZX9qago3b96EyWRCq9VCIpHAj3/8Y2GoqbcPBoNYWlrClStXhNEeGHg5tW5rawvb29siZWO1QotQbSenp6fh8/lEmrSxsYEnT56IxJOg1uVyYWxsTJxXxsfHYbPZUCwWEQgE8OLFC9TrdcEyf2yubywD4Etfr9eFiWDZrFgsotVqSZey2+3G9PQ07t69KxTyyckJ9vf38cknn+DFixeaAirmqTIrqgF9oVBAPB4XOwUeEDabDSMjI3j//fcxNjYm5fdsNotf/epXePr0KXZ3d8WUWctDleW/QCAAv98vIISNSgRHbPJix/Cf/dmfYX5+Hg6HA319fSgUCnjy5Ak2NjZEQK4lCOCBGggExNOTLw7XQqfTgd1uF3bQ5/Phxo0bCIVCMBgMaDabMuGELDwZQy0/f3XcJ0tlLOmwmSYQCIjmMxAIYG5uDjabDY1GQ2aEb29vCwuYTCa7unG1XKuqb+rJyYmUojnTenBwEOVyGZ1OB0NDQ9JxyTGR+/v7WFlZwc7OjpRZ1MlAWpbWqe+i1yMve4FAAB6PR1hcbsKsuHBazNbWlkywov1XL4AVwQrw5e2dFxg2ePD9I2tKKQVlH2Sle1kKJrCmVzWbklwuFywWizRLdjpfzlmn7o/AlJO1uD57BVT5XrFpiv6uDoejS49oNBrhdDrh8/mksY6VE63lHmfz5Bql7ZTb7YbP54PL5UI2m5VmJObIRkav1yt2Ua8CUlqzgCqryos/SYp2uy06f5vNhmAwiPfee08ag7lmj46OxEqvF1KAs587K2pGoxHNZlN6Enw+H2w2G3w+nzhZ8NLf3/9ykAa1o5Tg1Wo1TTXqKriampoS+QHlfrxQhUIhjI2NSbMtS+vsxeAF0Ww2i2OIlrZ/fD88Ho/sRfRvpoezw+HA8PAwJn43aOX69evwer0wmUxSVufapil/LBZDpVLRjJgCIGw63412uy2EXz6fl0sfbR45ZW98fFwuKhaLRSotlNfw3/+xe9UbM6u0najX6yKk54Kk/ou3l2AwiG9961sYHx+H0WiUjfXTTz/F2tpa10LQevPi4uP0KZZx+ZCoY+ON4OrVq5ifn5emkFKphLW1NbEO6RVjwcYar9crG1G73ZZubh78ZDT4MpIFBCBd2Jubm+J1pnXDik6nk8PJ4/GIOT2thwDIi0+5AG9ZZrO5y8oqEokgmUzK5BAtXQB4WLHpj5qearUq2iSv1yuspNFoFADYaDSQy+VE00gLnnQ6LcClV80gnU5HQCovgD6fT0pqBEu0KkokEgKkDw4O5HZdrVZ7MjKQBxfXJjV/1WpVDKPZzMJ1xwaqSCSCvb090bOqgwp6pVkEIHuS+r3U5iD+PNT1cixosViUJiB1+k6vum0JnpkT82eJV/V9VE3q/X4/dDqdsNO9nqrH94o2VPTRJhBRAZjH45GmNjZeqJZFam+BliVW1a2GJf7h4WEZUUwmyGq1wufzyXQ4Hvps/qCuUusBEMyRgNnlcgkw8fv9qFQqIkHjJLOpqSnMz8/LpDXmog6K4YQ1rR1VVAswghA2+aTTaRiNRoyNjYnTzszMDDwej0iE+BxHR0dxeHiIVCqFWCwm0jut8lRL1uPj49DpdNJzkE6nZWgNXWEuX74sDW0EeLSCOz09RTqdRiKRwPr6umaXAJVZJdnT1/ellVq1WhXWl77lV69exdDQkOACALJ++/v7MTU1hWQyCZfLhXg8rkmewJeaf65PYikOqVCtE9XLFgfXdDodkQcQZLMZ2+v1Ynd3V+Q4/1e88QQr6lVpP8Nk+FKTVfP7/Zifn8eNGzfgcrnQ6XSQz+exvr6O+/fvIxaL9cTDTv1aFPymUikUi0UBAwDE7Jlm8RwbNzAwgGq1ikQigfv37+PFixfI5XJdZsxaBoEdx5R2Oh2ZPMSOX4JYh8OBQCCApaUlWcBsbGPnqFoO0DJXakB52+fi5YY0ODiIQCAgjCYPCrfbLVZm6XQa29vbsmmd7VbXsqxOVo1WSbVaTRpYXC5X10Qz2pElk0lsbW0hEokgFothb29PAKDK/msNAIGXtlh0cOBnr9Pp4HQ65WLA0uvh4SF2d3exubkpTU3sxKX2sxcd6wBk/COBar1el/eeXnoEgO12W7xBd3d3RdOqNqj16nkCELaU5Wq+wypD3Gq1BCyqVaNms9nTGeEqq6raDnGEKmUqXKdsTGW1iBcEShy0dn04myMBIIkIdqOr/oksxXIP4B5LAKnmpnUZWPUAp1cuD0dqUembTDkTgSzHLHO4gip305KxVME0cyC4Gh8fh9fr7dL5s6xOH2VKKjhEhdPAVHZQq/NUbVhiPwWBKJtsarUa3G43AoEALl26hImJCZkOpoIVytwmJyeRSCTg9/vFkF+rPAmoqZOmBIAablbPrl+/jtnZWdGKqhdSyjHcbjcmJydxeL0eFs4AACAASURBVHgo5JVWedKhhBXUvr4+cShpt9vS37G4uIjFxUXBJNzD2u121+XE7/fLGtbSZYGVaY6o5mfOiY4AREbDUeDEKfV6Hfl8XvYoypvoCU7bvT86lzf5AVRmlQ+4Xq9LQpz8MjQ0hMnJSRnBNzAwgGKxiOfPn+N//ud/8Ktf/Qr5fL7rINByk1U3/IODA3Q6na4Zz9T2uFwuzM/P47333sPc3Bz0ej0ajQbi8Tg+++wz/PznP0ckEpFuxl5prAhO2c3LxckDgqXqmZkZTExMIBAICKVeKBSwv7+P58+fY3V1VQCE1gcXAWBfX58017HsZzKZAABDQ0Ni/WI2m4VNr1aryGazODw8xMHBAdbX14X91Vr/yV+np6fyLGiLRaDKph8V0G5tbWF9fR07Ozs4OjoSs3iOA1W7RLUOstO8TLVara5hDwQE9Kc8OjrC5uYmNjc3sbOzg2w2K8MgCFR70QjEr39yctIl6OfaoFURjcCr1Sry+bx4qp7VpveqY101nyYDTHsnSj04Cpr5syzHsvVZoKp1nnxmBC78vqVSSUp6ZIXJAgaDQZloxS72Xu1NZ4EVNZ5+v1/ec8po2IxCuyruC+rEL/VnVhlkrRr/WPYnA8jhCtQl2u12jI+PA4Cw6/w3tDAi88W9ixUtLRhLFfxR5kHGdGFhQXzI+/r6cPXqVbEMouMG9zR+LYvFglAohFAohFQqhUQiIZVKLYLPggz15O8GK9CSqN1ui5Z2ZmZG5CADAwNdMiReHoxGI2ZnZ5HNZpFMJrGzsyPa1bdZt2eldBz/yjXW398vkoqbN29KUyoAaUTlgAWLxSLgPxAIYHJyEj6fD+FwWLM8LRYLhoeHMTMzI81/nc7Ljn+n0wmn0ylTzIaGhtDX1ydWa5ygqOrb6bTkdDo1vVSxiuv1ehEMBmVfpwMU5Yj02h4bGxOJZzQahcFgQDAYhNPplAsMKy6vGmn/h+KtwCoAAUQsm3FuNUcwLi4uYmZmBgaDAZVKBb/4xS/wox/9CL/61a+Qy+W6NimtAQBL/zw0k8mkvPRkqNhJf+XKFczMzIhGZW1tDT/5yU/wy1/+Euvr611UdS9YFpZ0OTKP0x542NdqNdhsNlngtFYpFArY29vD06dP8fDhQ/zkJz+R2xmfgZb50k+RpXSLxSLMLy8DQ0NDol+ixmV/fx9ffPGFiNYfP36Mo6MjWfhaM0Jkptn4EwwGRefDA5eNTJSv1Go1PHz4EA8ePBDPuHQ6LTpRAsBeMFd8p3gB4KbJzt+z01Rowr22tibDDAhs+PMD2k40UTcV1QWEWiQOLiBIASAAn41A6ojiXjGqLKkTIBE4nZycIJPJQK/X4+joCACE3RgfHxd9PQ9c9VKrdZ4Ecaq0h+CFw0lyuRw6nY40eHFc7NWrVzE3N9flH80RvGdzfts8CdrI3Pj9fnlOtAOjBzQrAhxNy0uLmpNqeUWQqlWeNpsN09PTmJubkwYks9mM+fl5Ocg5Irmv70vXAuan7kUqO8xctXimqlZxYmJCSIf5+Xlh2cgM+3w+IQFISrArnfpRAhbVQUTL6pSqjeQlhU1L3AftdjsWFha6mi1LpRI2NjbQaDTg9/sRCoVkLfCsoDznbdeAWpmgpIK9MarkhOcSmWv2JDx8+BDVahUulwszMzMYGRkRRrhcLkvHvRb7vlqd4Eh1AOJBDbz0+R4fHxdwmM/nEY1G8eDBA5ycnIgEEICcXRxuk06nNSNT1PNS9Rzn50XWld71nLi2u7uLjY0NwVdkWnkmNBoNsTNVXYr+z3ze5odRGVaWnOv1OvR6PWZmZvDtb38bs7OzIsR+8OABfvCDH+Dzzz8XT9VeMFSvyo8TZ2itQQZoYmICd+7cwcTvjG5brRaeP3+O//7v/8ajR4+ke7BXhtDMk7lx4+QkJfVgu3v3LmZnZ+Hz+WTzevHiBX75y19ibW0Nz58/Rzab/b3FquUzpon/8fGxgD2KptmsQNDPA/Tk5ASPHz/Gb3/7W+zu7mJ/f19GVqpAQGsZiPpSscGOE8hUpoxANJfLibkxfT/5Qql/V2ugCny5iVmtVilZcowxbZZ42HNN00Lr5OSki51Wn4GWefLwYp5krWZnZzEyMoJyuYy1tTVhrsmwcH0AEPavV/pPsoBqcx1H++p0OtlM2YTk8XiQz+cxNDQklQF2BPeqikIgRFN3i8Uiz8xisYjzAO3RVC2qy+USnRj1n6rRupZsOkuhE79r8OCgCjZ0sBzNBltV56lO2SFIIUFAf0it/Gop47py5Qref/99LC4uYmxsTCpT6sUZ+NJUnYwvwQzBtSrJ4CVWK9s3MlV0HhgZGekaAMH9hYBVvcgTLFEvzD4RVn1YktfSp1plv/m/qdsnM+ZwOHByciLgsFKpoFAooN1uCyCz2WzCTLPSQgZYy/eLlwHapPFS4na7cenSJWE1eS6RMKPEwe/3y3Ol1RYdVbQA1Qyem/T8JkHh9/sxNjYGj8cjMrtms4lisSg+q4FAAKOjoyKvYd/Q/v4+MpmMpmQKWXHqovlMSfgEAoGu8d5891mxJLmm/iy5XA67u7tIpVKv9V5pAlYBCEDq6+uD1WrFBx98gBs3bsDtdqPdbuPw8BDf//738fjxYySTSc0X6VcFWVXe5PjS0V/t5s2bIrJut9uIx+P43//9XwFWtP05jzxZuqG5Og9eda41vT9PT08Rj8fx4x//GI8fP8bu7i4ymUxPgbVa/uXs75OTEzls3W43XC4XrFarlNQajQai0ahMYqFVERlvfl2t8zz7iweBqkEkCKVe6ODgANFoFPF4XHJUX6ZerleyAl6vF/Pz87h8+TLGx8eFtUokEsJisXOUjHuvdZVAt9aKQHVubg6zs7MYHR2FwWBAPB7HxsYGbDYbFhYWMDU1JWyWWj7qRUmdOapMLztYh4eH5VCgfu34+FjWM+2WCGAA/B7TrzXwp2cmwQp1oPxzdvVTe6yOYFXlK/zsKf/QmlVzOp2Ym5uT9cgxyXq9XkCSqv+lvk0dqa1esNg0SLCqxcHKZq7x8XHMzc2JFyUn/BDMcx9XpSoAuhg/vutk1SqVimbTCvlMrVarMFE0q+dYcl44edni58/cXyVLoCcw3Sy0ngAHQPbLcrmMXC4nhAkvXWxI4nrs6+sTiQgZbuDl+5dMJpFIJMTySKtQ31VWdEgCcKw6S/0q8B8eHobb7ZZx7GQQS6US9vb2cHh4KJZgWuaq6uKBlz0/Y2NjUrXklMd2uy15ejweaWSi12qxWMTOzg52d3eRz+c1q1ao+wnPfX62LpcLly9fFokVc+VexUrV+Pg4QqEQrFYrAKBYLGJvb0/cYF7nvXqrBisGX3BuWNPT0/jrv/5rjIyMQKfToVgs4t69e/j5z3+Oo6MjuYH3OtRSKF96blwWiwWzs7P43ve+JyXiUqmER48e4ac//akYHJ8HUGWuZHK4UarlgpGRESwsLHS5FDx58gS/+c1vEA6HZUPt9XPlpkWHgnw+L6CA2hlqhbiRrqysYHl5ucv2q9fPVX3JuDHRvYKHgspCFAoFafo6a6Lf60sVDzFaaU1PT2NyclJKRLlcDslksmuToo6RgLpXhuVqqL61k5OTop222Wwol8sIh8NYWVkR8BUMBuFyubpKvlo20amhsj8E09T/eb1eAJDJSslkEqlUSqoTfM8IqHkJ6NW7RPZnZGQEk5OT4gNMloQXDz4rjgseHR0VbSNLrmzIo7uJls+XrFkwGMTU1JQAa7KPvMir9nAsF6uTzXiIVSoVHB4eIpvNdumW3yZ4ieIQFzZ4EJAQeHAf4DQrtbFDZVMpw6JsJZ1Od42tfdtc+Yz4vTjFh7Z01CMyT3r7ElhxsAYA+fecApfJZDQ7W88CVYKVQqGAWCyG4eFhqVKpwz5UTSdlIhwcQeeAjY0NsYXT6hLAIHObTqdRKpUE+FmtVvHNJmnBMrXX65XLA90LqtWquKtsb2+LFOxtg8+SUj9641LOQQkSZYD8LP1+v9gCUnLFihuHG21ubgoDrFVwPywWi8jn8wgEAlLJuHTpEqxWKw4PD6XKCgDBYFBs4jiIhxdrjuLe3NyUqWw9B6uvCr1ej2AwiI8//hhLS0vQ6/WoVCrY3d3Fv/3bvyGRSLz2PNi3jbPMCBmsiYkJ/M3f/A2uXLkifnW7u7v4j//4D5nHex4A4GyeBKuq9crs7Cw+/vhjKQkXi0WEw2H8v//3/7Czs9Nlp3UeefJlA14yUNRX+nw+YQPJ/sXjcXzxxReIRCIyoeg8Lipnc1UBQL1eF5mFar7P6SpsGjqPdUrWl/61Xq8XIyMjYq+Ty+Wws7MjnZdkV8nCcRTkeaxVsijUeM7OzsJkMiGfz2Nvbw/Pnz/H/v4+QqGQdNWazWbR2akMUS+CzKrH48H09LTYozidThk5DLx8ZrSKGxsbw3vvvYehoSHZ/Dm2tBcNaioD6Xa7MTY2JowlWQq9Xo9kMgmn0ylm+4FAAIuLi1haWsLExISwbul0Gs+ePUMkEuny1NUiT2ppeRll1US1+Onr6xM2FfjS1YSfPTXrZKkeP36Mra2tLr3a24R6SbHZbOJJ+6rmLeplyQJSqsDLA3scjo6OsLy8LGOtX3cs5B/Klc+WNoqqZ7fX65VfZKoou2HZnRUCtfT77Nkz7O3tSa5av2NqNS0Wi2FtbU2M6QmseQbwEs31QZkCG4c/++wzfPHFF1hdXUU+n9eEWeXljHK/RCKBSCSC/f19GahAFthsNnddXtgPoPpb5/N5bG9v42c/+xmWl5fFsUgrsMqGaDbIUg5iMBjEBorvGBsn6frDtUuNeDwexyeffILV1dUuj1Ut8my323LBiMfj2N7eFstKnkOcqqVKu85aGHY6HbnoPHz4UMbyvi6wfmuwysNWr9djYmICf/VXf4V/+Id/EF+9R48e4Z/+6Z/w+PFjTTfTN8mRWos7d+7g448/xt/93d/BYDCgWq3i4cOH+Od//mf85je/EUb1XeQJfHnout1ufOc738HHH3+MP/uzPxOg+uDBA/zHf/wHfvGLX8gAhl6WgM/mqJbWrVYrFhYWsLS0hLt37yIYDMrmFIlEcP/+fdy7d09sv84zT+DLCWaBQEC6EhuNBsLhMPx+v4x+4+Qi5nne69RgMMDr9XY1IxSLRezu7mJ1dVXGxKrdk2QKz2Ot8nt7PB5hBKempsQNhAMTfD6fGFhzshln17NU3av81PGJZKg5CjidTmNvb08aahwOBy5fvoy7d++KrzJnXa+urmJ/f1/Kc1oHvYrJPFBTywaZqakp6aTu6+uDz+cTuzjqw1qtFnZ3d/Gzn/0Mv/jFL7C5uakJ+GOoLDW7uHlYkn0icCUzyUOJWlU2tcViMTx58gQPHjzAvXv3cHx8rGl1jU1INptNfJN5YDabTdHTq6VzWq7x4mKxWBCNRmWk9sbGBp4+fdo1rORtQ63utFotVCoV+bNkMom9vT0EAgHxUaVzDZum2u22lOGPjo6wvb2NtbU1vHjxAhsbGzJYRytQRekG/VTJ4j558gSdzksLymvXrslkSMorKBHhv0mlUohEIlhZWcEXX3yB9fV15HI5TZ8rqzbVahVHR0d49OiRvCcTvxtJznXACgqBICuZmUwG+/v7ePz4sfSrZLNZzS4A6jNlJYrNdtSA0oqKQE/Vf7PZmqPCt7a2sLKygqdPn+Lg4EDTcfXMt9lsIp/PY3V1FWazGaOjo/KesRrBkbu8APB8opadI46Xl5dx//59PH/+HKlU6rUJtrcGq7z9TUxM4K//+q/xt3/7tzKGbX19HT/4wQ9w//59VKvVcwMqr8qxv78fNpsN3/72t/FXf/VX+Mu//EuYzWYB1P/5n/+Je/fuaXYzeZs8BwcHEQwG8b3vfQ/f/e53cevWLRgMBhSLRfz617/GD3/4Q3zyySciYj/P50rQTy3tzZs3cfv2bVy7dk3kFLQkefToEZ4+fYp4PH6ugJp5kq0cGxvD3NwcgsGg6NLYZMFDY3d3F4lEoguonkeuamev3W6H3+8XNgqAjAomQCRA4NCCWq3Wc1adnzcb6GgAzVKr2+2WgR/T09O4desWxsbGxLLs008/xebmJjKZTE9zZYmVHaiUAHg8HulaHR0dRbFYFEcN+lbW63WsrKzgf//3f/H48eNXNipqEdzIecHg58yufpvNBrfb3cX6qR6mBDs7Ozv42c9+hidPnuDFixcyglnLfAmOMpmMALtOp9NlR8WfgawkpxjyYlqv17G8vIy1tTWsr6/j+PhY85G1nc7LQRqxWAyZTEYak4AvByiwnFkoFEQ2USwWhZgwGo0Ih8OIx+M4Pj6WZhUtewDIlLGqQxDKA54ysEwmI+wVwTWBHwAZU8xhG+FwWCwAtTy7VCkKwQj10WRZnz59isXFRSwsLMjexOecz+dlXz0+PsbBwQF2dnaQz+c1bQRTc6VEbW9vTyQ/vFhPT0/LvqRW2LLZLMLhsNgUhsNh7O3taTq1Sg1+b4JAo9GIUqmE2dlZIVDcbresCV72s9ks9vf3cXh4iFgshoODA2xvbyOZTGqmq1ZDlX+wgvODH/wAAwMDMjad1QoOXKFuuVKp4Pj4GPF4HIlEArFYDOFwWC4Ab1KpeGuwSr3Q7du38f7772NychIDAwMoFAr4zW9+Ix3q74KpZPCwDQQCuHnzJm7dugW/3w8ASCQS+PnPf47l5eWuA+pd5crD6erVq7h9+zYWFhbgdDrRarWwubmJX/3qV3j27JmmFhVvkuPg4KB0WF6/fh0jIyOw2WxoNps4ODiQmyk7r99FjmdnFXOusfr5siTDDVVljs8jeEGh5MNqtYpVDdmisbExYeA4qYi+qr2Y/vSqHNnJzJIZJ+bQPicUCqHRaIiulmMOj4+P8fnnn4sGvJeMNT83lq/q9bqsVaPRKNP02u22ANi+vj451H74wx8KS9ErXT3z4xjldDotZUp217P8rkpECFa4Vj/99FM8e/YMGxsbXVULLfOkQ0Y0GsXW1pYwp06nUwzJCZ7Z4R2Px5FMJsWEvVKpyIQ1TgDUcs0yz3K5LPZ4qVRKrMfa7TZcLhcAoFKpYGtrC5lMBsViUZqa2MuQy+WQyWREr1oulzX1fwa+1ACqFk6qJ3K1WsXh4SEGBwelxE6wSPvETCYj3fTMtRcDYNScqY/mu9VoNFAsFhGNRrG3t4fl5WWp/jSbTXGySKfTMjUym832dFolc+W7QDnf3t4e1tfXZUws3QBYSs/n80gkEkin00ilUkgmkygWiz2tsKna6OXlZTQaDayuroqGltU1Vif47I6Pj5HP55FKpXB4eCjTH3slr1PlFfF4HI8fP0Z/fz9CoRBcLpdUfMjos9ufTayFQkHkKgcHB8jlcm98qXorsMpDNhQK4c6dOyK4bTabwqxtbW29UwDITcFiseDatWt4//33MTIyIjKFL774Ao8ePcLe3t47Kf+qwYPV5XJJaX1oaAg6nQ75fB6ffPIJnjx5gv39/Z6M0vxjgs/TaDQiGAxifn4eExMTcjjk83kpo+3s7CCdTmva8fnH5sg8CfhcLhfcbnfX5s+SCjWK2Wz23D971ZqMXclkrdWJRgSKtVoNx8fHePLkyblLQKg75WHV19cnTKDaJEbGOpfL4dGjR3j8+LFoRnuZJzfLUqmEQqHQ1RxDiyiWiGlfViqVZErdL3/5SzEp79UlgDlWKhUcHBx0eSlzLbAhQdXWZTIZRKNRbG9vY3t7GysrK4jFYuJaofXh3+m8bKQk62Q0GpFKpRAIBDA8PCxAStXO5fN5xGIxpNNp5HI5mRhH4KeaxGuZK10GVldXAQC7u7tSpqSNTqvVkjHPHA6hDvnghYAOB2xs0poBJrgnAAS+LPXW63Wk02nZo3iZ4t9rNpsCspi3Crh7sQ/wwqRe4lXwnE6nsbW1JTIASi0IntgAyp9ba/D/qnxVQF2r1WRcKufXU79K7bA6KZITonrdW8GyPmUI+XxerBXdbrd47NIOjBcS1apQZdN7tVfxwkds9OLFC2QyGfh8PrjdbgGrnU5HLl+Uo+TzeXnX4vG4XADedJ2+MVglGHA4HPjud7+L73znOwgEAgCAfD6Pf//3f/9asJX9/f0wm83iUDA3NwebzYZWq4VoNIr/+q//wvr6unS3v0tQTYuNGzdu4KOPPsLIyAhMJhNqtRrW19fx29/+FpFIpIudOO9c2YzgcrlkPKBqtp1Op4VFOTo6Qj6fP7cGIDVYZqOFFjd/atq4IR0fH2N7exsbGxs9L1OfDR5EahNCMplEqVQSz011GhSbPz777DPcu3dPnu155csmi3Q6jZ2dHfj9fnmm7LoFII11n3/+Of7lX/5F3CrIIPUieICSYWPZnJt9MBiU7vSBgQFhAZ8/fy6uGrFYTIBfLw+AVqsleuRsNotgMIjh4WEcHR1hZGRE9HXtdhupVArZbBabm5s4OjpCLBZDMpkUn9izvrVaBW11stks1tbWcHh4KKyPx+MR72wOg0mn08LEcqQt16bazKT1c+XzLBQKODk5QSKRkCYqdVohy6m0ByQYIVBUv54qbdAy+CwIMBkqGC2Xy13ey6ptngoc+Rx78UzVfNXfz/4ZQUytVhOrRdVBgLmqkpdevv/q7+pFQM1R9S5Xm+9Ubeh55cn/rcoXBgYGkEgkBPRz/+d7zjI7/02vyTWV+VdlIIeHh9JMqVbZ6GjS6XTEno42i2+LA98IrKrdl5cvX8bf//3fY2RkBHq9HsViEf/93/+N//mf/0EqlXpnoAqAMGuhUAh/8Rd/ge9973uw2+04PT3F0dER/vEf/xH37t1DoVDo6RjV/ysIVJ1OJ6anp3H16lWMj4+j1WohHo9ja2sL//qv/4rl5WURe/dyk/qqIFC12+3SsVqpVBCLxdBqtRCLxfDJJ59gZ2cHBwcHYgF13nIFgkCWB+PxOB49eoRarSbdoOl0GkdHR/j888+xtrYmJYrzdoAgCM1ms1hdXcXp6SnMZjNu3boFl8sloKVSqeDevXt49uwZnj59it3d3Z7eqs/m2Wg0BBxnMhnE43Fks1ksLCzA5/PBZDKh1Wphe3sbDx8+lOEKZNbPa1MlY5ZKpbC+vo4vvvhCJCrUWFHTdnBwIH9ffZa9fp4s7SWTSbncUfYxODgoe6bKaKjs2VkA0+s8Wa6MRqO/N/FJfV94+J8FDuexPqmpJEOt2hmd/f4qSDmP/M7m+ioNJPWJZ//bVwHF84qv+l6veqav+zW0DvX7fNX3ZJ5cI+e9Vr/qe1G3zKZOlch4FbN9XpjqbK4Ey3yOuVxOqmqqjh2A7F1agf83AqvsurXb7RgeHhY0XSqVEA6H8aMf/UhEv71kU/6vYEmdo8sIXpLJJB4+fIhf//rXIqJ/l3nSpYC6xNPTU0SjUTn8l5eX8eDBg65c38UFQNVZ5fN5hMNh/PCHP4TZbBZWcGVlBYVCQaxp3pWrgjqCjozk/fv3pbM1lUqhWCwilUqJv+G70AAzV4KmYrGI/f19/PSnPxXfOpaqWEqpVqvn5lXMHMm0kQFOpVLY3NyUsho32kqlIjdq1TLsvPJkQwD3o2Qyia2tLSn/A182OKglyfN+988yFizxnT1AX/W/zztU9uksoHpXOb0qzhNwaB3vGpS+bXxTcv06r5FXge2z+9LXIe9X7UW8gJ29JGqZ7xuBVSZxevpy0tLTp0+RSCRwdHQkVgrvGqgCENqa7N/a2hoqlQo2NzexsrIijRTvsvkL+FLHRnC1tbUlk1NoUcHRZO8yVzJs5XJZSq7xeBwDAwNiHJzL5brK/u8q17ODAFh2JePKLvp3rVNWb8kEeNVqFbFYTICLOiziXUlV1PIowZ5asgTQldu7fp7Al+W1Wq3W9edfp3iXz+p145uS50VcxJ9KfFPeufO4BPT9oS/e19f3yj9k2Zr+lTdu3IBOpxNglUgkemGj8JV1hq/Kkz6Bbrcb8/PzmJqaQiaTQTgcRiQSQaFQ6EVjwlfXQ74iV7UZiIbvRqMRhUJB/AB7wVC9yTNVG1SY+1lZwtchT+b2B77mW2b1yq/52p/9u4o3fabnHX8KeQLfnFwv8nz9+FPIE/jm5HqR5+vHn8Jn/0ZgVfnzV32j183tj4q3WRSqhqnXN4C3XRR/SGuldfwpvGjflDyBb06uF3m+flx89trHRZ7axsUa1T7+FPIEvhm5/kGwehEXcREXcREXcREXcREX8S6j/10ncBEXcREXcREXcREXcREX8VVxAVYv4iIu4iIu4iIu4iIu4msbF2D1Ii7iIi7iIi7iIi7iIr62cQFWL+IiLuIiLuIiLuIiLuJrGxdg9SIu4iIu4iIu4iIu4iK+tvEHhwJ8E+wMgG9OnsA3J9eLPF8/Lj577eNPIU/gm5PrRZ6vH38KeQLfnFwv8nz9+FP47N9ogtVFXMRFXMRFXMRFnF+oo3i/bkF/8Ff5hH+dcu7v7+/yXQfQNcHw65Irc2S+AH5v6M7XKVfgy2f7qimGWuR6AVYv4iIu4iIuQtM4z+EmrxsqWHkVGADe/RhcTjXU6/Xye39/P9rtNhqNhoxe5q93Gf39/TCbzXA4HDKBsdFoyMhwjpFWpxyed/B5ejweBAIBuN1uGI1GVCoVZLNZFItFlMtlnJycoNls9mRi5Ovk6XQ6EQwG4fP5YLVaUavVkEqlkMvl5JlylPi7WqcE0zabDQ6HAy6XCxaLBc1mU/Ks1+toNBqS69us1QuwehEXcREX8TWOs5MCX8Wwqf/tXR2y6q/+/n4MDAwAAE5PT+XvdTqd3xtxfZ75Mi+9Xg+9Xg+dTgej0Yj+/n4BgK1WC81mE7Va7Z2wbgQsVqsVdrtdfhkMBtTrdaTTaZTLZdRqNTSbTZycnPRs1PX/lWdfXx8GBwfh8/lw5coVDA0NwWKxCLg6PDxEKpVCqVRCo9EQwHKeufJ5Go1GTE5O4tatWxgeHobFYkE6nUYsFsPhvkQyqQAAIABJREFU4SGi0Siy2Swqlcor1+l55WkymTA9PY333nsP4+PjcDgcyOfz2Nvbw+HhIY6OjpBOp2UM+3nnqeZqNBoxOjqKy5cvIxgMwuFwoFqtIhqN4ujoSC4ClUpFLgBvmusFWL2Ii/gTiq9bqfBVI5nPxrsCV+r/flWeakkLOH+27WwpkAdEf//LvliyFPwzgi21DNfrfNUcBwcH5ZfBYIDJZBIQ2Ol00G63cXp6imKxiEajgdPTU/lv55Fnf38/9Ho9rFYrHA4HPB4PfD4fvF4vDAaDMJa1Wg2JRAJbW1sCCM8rT4JpgoDFxUWEQiH4fD5YLBaUy2Xs7+/j+PgYiUQC+XweyWQS1WoVzWZTnmmvQwWqVqsVS0tL+OCDDzAxMQGr1YpsNovd3V1YrVYYjUbE43HUajVUq1XUajXJ87w+98HBQdjtdly9ehUffvghRkdHodPpcHR0BLPZjHa7jXq9jpOTE3lvzhNYq+DP4/Hg5s2b+OijjzAxMQGDwYBUKgUAwk6TqW40Gl2XlfMINVev14tvfetbeO+99zAxMSEXgOXlZdkLTk9P0Ww2odfr34pd7zlY/apDQI13XXJR41UsBn8/ywZ8HXJ+1aF79r+9q/LL2TzO/s5Dl7+f9y1RzUUtC579czJEDLUEd155nmWt9Ho9BgcHu54V8yU46HQ6coD1er2qufX390On08lmZbFY5O+peXGT5Wamgq1e56jX62EwGGA0GmE0GmGz2eDxeOQ9Z07ZbBblcrmrlKU+017mOTAwgMHBQTidTni9XrhcLrjdbrhcLjgcDgAv35lGo4FGo4Hd3V0kEgkpv/H3XuSqPkudTgeTyYSJiQnMzs4iGAxieHgYY2Nj8Hg8aDabKBQKyOVyyGQy2N3dxc7ODhKJRFf5VevP/uxeMzg4CJvNhhs3bmBhYQHT09OYnZ3F0NAQTCaTfL6lUgmRSASPHj3C8+fPsb+/j3w+j0qlIu+W1nEW9DudTty5cwd37tzB7OwsPB4PTCYTcrkctre3cXh4iHQ6jUajgUQigWfPngnbRiDTqzj72TscDiwuLmJpaQkulwv9/f0wGAyIxWICvgcHB9HX1we9Xg8AqNVqAHBu7/vg4CA8Hg8uXbqE0dFRWK1WnJ6eYmBgAPl8Hul0GtlsVgCqCrLOC1DrdDqYzWZMTEzg9u3bmJ2dhc1mQ6fTgcFgwOnpKWq1GkqlklyidDod+vv7z439575kMBjgcDhw7do1fPzxx5ienobVagUA6PV67O3tYX9/H9VqVdakeul+52D17OGvgoCBgYHf++88uFqtVhegOo8H/qp8Achhy/+tHhzMj6Wit6W1Xyffr8qZQIqMCzcKPm8AsgH38jZ7VgPGXJgjf/Hl6uvrg8FgkFJcp9MR/RA3jF5suOpzI6Ai8CN4MZvN0Olevhr8nJlLq9VCuVxGPp9HvV7v+rNe5MqN3mg0wm63w+l0YmhoCKFQCIFAAIODgwL8Go0GUqkUksmk6JpyuRyKxaJosXrx+fNAom4tGAwiEAjA6/VieHgYwWAQJpNJ8qzX69jf30cikZAyUSaTQbFYlI1NLR1rEWcZlkAggNnZWfh8Png8HgwNDWFkZAQDAwOSZ7lcRjgcxtHREfL5PDKZDFKpFAqFggBYrT97VatoNpvh9/tx9epVzMzMYGhoCD6fD8FgEE6nE8DL9XhycoJisYjnz5/j4OAAx8fHiEajOD4+RiaTESZLq8+d7xDfH4vFgpGREdy9exfXr1/H9PQ0vF4vHA4HbDYbms0mGo0GarWalDOfPXuG7e1tbG5uIh6P9+xZEiDpdDpYrVaMjIxgaWkJd+/exfj4ODweD+x2u7A+p6enaDQaCAaDCAaDsFgssFgsWF1d7ZmWUd0zCainp6dx/fp1AYAGgwF9fX2wWCwYHx+H1+uVd351dRXlclnkC73ek5grAcv169eFBex0OsjlcohGozg8PESxWESn0xGW/eTkRH5mrd/xr8pzcHAQLpcLd+7cwUcffQS/3496vY7j42Nsb29jZ2cH0Wi064zkhbTXoZ5HFosFo6Oj+PDDD3H79m24XC7Rf1KmEI/Hkc/ncXp6em5gWs1VvUwtLCzgu9/9Lq5evSpAtVarodFoyAW01Wp1acLVr/W6eb8VWD3Lpqi/KAzX6XSyWRAUEGC1222Uy2VUKhXUajVhAXpxoHJBqL/zNsM8+YvAj3nz37RaLSljnJycdOkwtM71LOhTxfbMUc2bwI+ggeCgUqkgl8vJs9YSXJ9l+3h4mUwmGI1GAYD83WKxCJA+PT2V/95ut1GpVJBKpdBqtVAoFNBoNDTPVb3lW61WuFwuDA0Nwe12w2KxwGg0wmKxwGazyWZaqVTkICBblUgkAAD5fL4n5VaV/TMajXC73ZidncX4+DgCgQCGhoYwOjoKn88HvV4v4KpUKmFnZwexWAzZbBaFQgHRaBT9/f3I5/OaXwDUzctisSAUCmFxcRGzs7MYHh4WwOpyuaDT6QQI1Go1hMNh7Ozs4Pj4GOl0GgaDATqdTgCDlnuAygbY7XZMTk5iaWkJly9fRigUgtfrFV1gX1+fHALlchl2ux0ulwuJRAJ2ux2Dg4Po7+/vqgJovUb5Dnk8HiwsLODatWu4du0a/H4/7HY7zGYzzGYzAEgpsFgsotVqyd7AvapUKsm+qlWoYMViscDr9WJ+fh5LS0u4ceMGPB6PvOtk181mM6xWq7z/xWIRhUIB8XgcqVTqj5KLvG6OACRPs9kMr9eLubk5XL16FbOzs3A6nV0XU3XPPT09xdjYGCYnJxGPx2E0GjXP8Wy+3JeCwSAuXbqExcVF+Hw+Yfnq9TrK5bKAVjYyUSZAjWCvgxcAm82G8fFxXLlyBRMTE7Db7QJSqKssFotoNptCAPFsPVsB7FWQ+Z2fn8edO3cwPDws2t98Po+DgwNEo1Hk8/kuacp5y30GBwflYvrhhx/C5/NBp9Ph5OQE2WwW29vb8q5Q8nGejXXqOW82mzE7O4sbN27g/fffh9vthk6nQ7VaRaVSEe1vuVzG6empZp/3G4NV9fA3GAwCllhes1qtskFZLBYBqUajURZsu91GsVjEwcGB3Bh6BagIUsikMW+bzSabv9lslhusCrIJFGu1GnK5HGKxGJLJpNy+3pTW/qpcVbBPgG80GuFyuYT5M5lMAqQJ+njgm81m6PV6VCoVZDIZxGIx2US0OLTOstDM0WQywel0IhQKweFwwGQyda0LHmAEBATZ5XIZmUwGg4ODyGazUuLQIs7esE0mE2w2G0KhEGZmZhAKheDxeOB0OmEymQSsAl8y0mSnWC4aGBiQC5bW0oWzANDr9WJhYQFLS0tYXFzE8PAwnE6ngGsCJ2qXyL7GYjEkEgkB2bzxanW5Ulkrm82GoaEh0a1du3YNbrcbJpNJ1gZLVcyHPyPXqgoQyVhrmScZq+HhYSwsLOBb3/oWrly5AqfTKQCK7xPwkrE0GAzCDAwMDKDT6UiXc6FQEOZAyzib6/j4OJaWlqTMxn1UrQDp9Xq0Wi14vV6Uy2UUi0Vks1mYTKYucKDlAcz33mKxIBgMYmJiAouLiwgEAjCbzZJbs9nsWit8/4aGhgR4q9WiXlz8yaoODw9jcnISly5dgtfrxeDgINrtNqrVqpSEWabu6+uDyWSC1+uFzWaD0WjUNC81P/5OPe3o6ChmZ2cxMjICm80mF7x0Oo1EIiH7GKsAaiOWuoZ7GTqdTi5+t27dgs/nQ19fH3K5HBKJBGKxmDSCkQVst9tCaLBi1YvP/Gyefr8fc3NzuHPnDux2O1qtFiqVCvb39xGNRpHJZORdJvYgtul0Oj3LUSV7zGYz5ubm5BJtNpvRaDRQKBSwubmJcDiMaDSKYrHYVdHjedHLCu/ZNep2u3H58mV88MEHmJqa6iLG4vE4wuGwnOPtdrtrv3qbXN8IrKqAymQyweVyweVywW63i3Cdm5HD4ZDDtVgswm63y4sGvAQEn3/+OTY2NrCysoJIJKIZAFRLamQBbDYbrFYrnE4n/H4/gsEgbDab5K/X65HJZKQMRwBLAEC91dOnT+Vg1eKGc5b5I1ixWq1wu90YGRnB6Ogo7Ha7AOzT01OUSiW57VgsFpjNZvh8PtRqNcRiMUQiETSbTUQikS6blrfJ8yygtlgscDgcGB4exvT0NC5duiSsBbVKjUbj95hqm82GVquFRCKBSCSCWq0mX5Ps69usAZWZJqjy+/2YmprC5cuXcfv2bdGrUZ5Qr9cBvGSszGYzXC4XgsEgyuUyEomE3HaNRqPc0LW45aobF3NVgdWHH34It9stFjbMlfpKgiaz2YxgMCgsKplgFeRolSs/+1AohKtXr+Kjjz7Ct7/9bTidTrmQqGsUgGz+er0eXq9XrE1KpZKsDy3WqZpnf38/TCYTAoEALl26hBs3buC9996D2+2WdcaS6llwNzAwAIfDAbvdLpfuXspTKI/xeDwYGRnB1NQUJiYmYLPZMDAwIB3VrVYLg4OD8j7xgDUajXJJUFlqLfMEIN/ParXC6/VifHwcoVAIRqOxS/sJQC5WBILqRbVXIJDB/cNgMMDlcsnzJPlQKBSQSCRQr9dhs9m6zqdqtSqXfz5/LUNd43wvzGYz3G43Ll26hEAggFarhWw2i3A4jGfPnqFQKIjGmpeXer0Oo9EIh8MBs9kslaBe6H8ZOp1OzqWFhQVYrVaUy2Ukk0msrKxgY2NDWED+bLQ5UsvClAVoGWquBoMB09PTWFpawtTUFAYHB1EsFrGxsYHPP/8cq6urqFQqvydDUS8tag9AL3Ls7++H2+3G4uIivvvd72JoaAidTgfpdBrPnj3Dp59+ipWVlS5rLeZDCR2j17pvg8GAy5cv4+7du7h165ZIFQqFAjY2NvDgwQPs7e2hWq0KUOW+zh6LRqPxRrm+Nlg9q6ny+XyYnJzE8PCwgNTp6WlMT09L2ayvrw8nJydIJBLSHUg2sFarSRmmUCjg6OhIEwCosqkmkwkOhwNerxcjIyNwu90IBAKYmZnpYlf6+/tRLpcRiUS69IsOhwP9/f0CZk9OTgT8abGIVfDH52qxWBAIBDAyMoJQKIRLly5haWlJbs/UBu3t7YkmSPU6o/4vFosJkNFqIat6NbPZLPq/qakpLC0t4cqVKzAajWi326jVashkMkin0wIEHQ6HlN6p/zy7iLU6YFXwNzExgStXrmBhYUG6F3U6Her1OorFIo6OjpDJZNBqtQSoUtPGUjpBmCpq1zJoXTI2NoYbN27gxo0buHnzJoaHh6USkUwmcXh4KCCQkgav1yvsjMViEWDLPLUCgfxaBoMBwWAQ169fx+3bt3Hz5k14vV40Gg2pQOzt7Qmwstvt8Hg8cLvdACAXE2rteiGrIVPpdDoxOzuL+fl5LCwswG63o16vo1Ao4Pj4GIeHh2g0GqINttvtsFqt8nfy+TxSqRTi8TgymYzmpXUGCQC/34+hoSEMDQ1Br9eL9jidTiOZTOL09FQu3wRXBF7JZBLpdFoabnrRYKU2WTidTthsNpyeniIejyObzYq8g2SG3W6XUjrPglwuh1wuJxWfXr1PfCcIRpvNpmi79/f3EYlEpKmGsg86BpRKJZycnEgVRcsc1a/F98nlcslFv16v4/DwENvb29jY2MDu7q5cTHl+EjRSNgX0pmmJX08tBU9MTGBmZkYAy/b2Nh4+fIi9vT1UKhWpTKh6f15aegVUmSvPqKGhIVy7dg13796FyWRCtVrFgwcPcO/ePWxubqJWq8FgMMiln/0oBFh8z7V+15kjGfzbt2/jz//8zzE7O4vBwUGkUin8+te/xr1797C2toZSqSQkDhtUGQTW7KfpRZ7Ay3fJ5/PhL/7iL/DBBx/A5/NhYGAA8Xgc9+/fx+PHj4V0YtUEgFQtVGD9Jhr1N2ZWVe2f0+mUW9bExATGx8dht9vRbrdRKpVEjxiJRERfQ12T2WzuYi3Ubuy3CRUAUqtGvV8oFMLU1BTm5+fhcrlEz1kqlbC/v4/NzU3Y7XYEg0GMjo7C7XYLM2u1Wruab7Qu/5JxCAaDGB8fl5LQ4uIi3G43arWaHAZ7e3s4OjoSlooNA0ajEaenp1JioyWHVhIA9dkODQ1hdnZWgP/i4iJsNhtyuRzi8Th2d3cRj8dRr9eh0+mEMaIpc6fTgcVigU6nQ7PZFO84rS4Baulienpa8hwfH4fRaEQul5PP/OjoCKenp/L3O52ONDe0Wi0BBdQDciPWWltps9kwNTWFyclJ6VbudDpIpVJycKVSKdG0ulwuAIDL5ZIcO52OfO6qrY2WpXWj0YiRkRGMjIxgcnISTqdTGhfW19exsbGBbDYrwI/2JTabTbS2+Xwe2WwW+XxeSlxayyr0ej2cTiecTqd0VRcKBWSzWezs7ODw8BCFQkGqFioTVCqVcHR0hGg0KtIKtbmqF1plVR7RarWkWer4+FjKax6PBwDkUAUgl5lsNit59qIJVK1YcK3l83lsbGxI+Tefz6PZbCIUCokbBIE4ZRfU2mqtTz+bq6pDjcfjWF5eFjcCarsJ/AlwxsbG4HK5hAjolSWUmh/7DQYHB1EqlYSp2t3dlbU3ODgIAHC73eIMEQgEpMqhNfv7qlypAx0ZGcHc3BwGBgaQSCTw61//Go8fP8b6+jqy2azs6Wo/CAA55/j/e5Xn4OAgLl++jGvXriEYDKLT6eDw8BCfffYZnj9/jmg0imq12tXsRTJCBYM6na4nl2ju97TUGhsbk67/vb09fPHFF9jc3BRrMp49wJeuKlyTPI97eekzGo2Ym5sTXTob5jY2NvDo0SNsbW3h6OgI5XIZLpdLcqWW/qzV3uteAl57tfClYrmM2k6WSqxWqzCUuVwO6XQa8Xgc8XgclUoFAKSRgSU4drRWq1UA2tgv8OurANBut8Pr9UqnJ3UhuVxO2KpIJIJsNisdt3ypWD6tVqtiyMwP420lC2oJkIxFIBAQsDw2NgaHw4Fms4lkMolwOCzmwNVqFQ6HAw6HQ3Sr1DESgLPMoTWwNplMCIVCCIVCGB0d7dJYHR0d4fnz59jd3UU+nxfAQEBFlkVlrrQEAeptUK/Xw+fzCVPFHBqNBg4PD/Hs2TOEw2EUi0UYjUbJk/pm9XkWCoWuZkCtNzCdTieMLnW0KuP/4sUL7O7uolarwel0CnOqaoJYVi8UCtJgoxWwVp+rxWKB2+2GzWaDwWAA8LI6srOzg42NDezt7clhxfKwqvNmjvSKZIlLa7BKxkoFgPl8HuFwWFwJms2mgEQy7a1WC8ViUbrq2TCipab2bK4EdKpDRrPZlKY57pFqtYTWVcxRBf69ahbhOcAyaaFQwMHBAY6OjlAqldBsNgXIqtIers9CoSCOFb06YNV9lbY/1O+r+j9qqKlN5cWGEgV1UlSv8lQ10/y89/f3sba21mWubjKZRJpmt9tlP8vn87/HXvUiXsUAs2Fyc3MTkUgE+XxepFI8z9ivwL/Pc1Vrfa0KqCn7CIVC0Ov1aDQaIjWk9IPvvfoZEECp2tVe6ID5LCcnJ7G4uCikQ6VSwaNHjxCJRKQ3htpv4hlVnsBQ/Ve1CvUC7XQ6pW+CBM7x8TF++9vfIhKJIB6Pi/MMpYyUJBkMBlSrVdk7BwYGXnt/emNmVQ2WLtUDK5fLIRwOSzNSPp+H1WpFKBSSD4mbCNmVXC7XdcPRSq9IYOVwOOD3++H3+2EymQQo8SVjrgMDAwJoqAsi+3p8fCz2Nb1orNHpdLBYLPB4PAiFQhgZGYHdbsfp6SkymQxevHghYutsNivMNLWtLL8zV1oCadEIogJqsup+v18Aq9VqRbvdRjabxfr6ujzXer0uneG8MJCtIBhIp9NdLgBaBDcallVZgqbAP5fLYW1tDVtbW4hEImi1WgiFQlLeJAtH0MBxdyoQ0FJawU2Blj8WiwX9/f3i96mu0/7+fgGz1IbTxqpQKCCTySCbzXY1D2iVK58rbX3IkNfrdWSzWWH8c7kcjEaj+HByfyC4Yrk6m80im8125alFqJc/TgACIF3VBHZk/clSUutbLpeRzWYlz0Kh0NWJ2wumhWw5DyS+D5TI8AJlMpm67ICq1WqXBOBsw4iWQTBAEMpLPPWTAASYUNJEQEC3D0416kWTmpon1+rp6Smq1arsialUqsv7kXIat9sNh8Mh1alisSjNib2yMlLPKu7d0WgUuVwOkUgEuVxOLsd6vV78doPBIEZGRmRd9xKkntV/+3w+IXNKpRKePn2Kg4MD5HI5aZxi3wUZYLLCuVwOtVqtpyBwcHBQXBXY/FUoFPDkyRPx/yQYpWsF5YlsROXlmpcZrT7/s2f9pUuXMD4+Ll6/iUQCX3zxheRJ7SeliSaTCYODg102f5Qv9KIyxe/t9/tx+/ZtuN1u9PX1oVar4cWLF1hZWcHu7m4X2dTX1yeki9FoRLPZRLFYFPKMMsXXufS/NlhVtR18QOq0kmKxiHg8ju3tbWxtbQmjqtPpcOXKFdEr0h6oWq1iY2NDxrJp1QWuThxRAUswGJSOwHA4jBcvXmBtbU3sKwBgampKZAO03eHkkM3NTWxuboq2UatQ8wwEAmKbQn1fNBrFysoKnj59iv39feRyORFme71eTExMIBAIyLzjaDQqxrzZbFbTg4svGdnfQCAgJuXJZBLr6+tYX18XlpqasVAohPn5eQSDQSnB0z9OtTnRirUio+d0OkVDQ6CayWTEPimRSKBUKklzw/j4OObm5hAIBGAwGASA0cScL5rWXetqhYIlMjJmsVgM8XgchUIBrVZLbLemp6cxOjoKp9MpWideZggEzwrz3zZP1Q6IeqlGo4F8Pi+M2enpqegVR0dH5TbOQ+P4+Bibm5vY29sT9l2dFqRVnlwDvOkT3AGQtcGGFb/fD7PZjJOTE2F7E4kEjo6OEI/Hu/SVWss/AAizQyDKy4DP55NmRK4TshW8IMTjcWGJU6kUyuWy5h6MZytA6mAFXggYfLYEKrzQVCoVhMNhJBIJkSr0amgB8KW/N8Eq8FJDl06nRXvJiykrRG63G/39/chms/IeFQqFnvnAAl8OI2FjGseV0oOW75zf78fo6CimpqZEIlQul0UDShDWi+DnbjQapVLFfZzl6nq9LjrM4eFhsYbj/lQqleQ8Vb+ulvIkNhrOzs7i6tWrIudIJBJYW1tDOp2W0rRer5dzjD02vKSwOsyKRi+cVJxOJ27fvi1l9XK5jE8//RTPnz8XjMGfJxAIYGpqCjabDYODg2JrRUvFXC6n+WVa/cxHR0extLQEg8GAZrOJaDSK73//+1hdXUWhUOjSCnu9XrkosKmOZ3w+nxdcqDY0/1/xRmBVtXeghpMbQKlUQjgcxtbWFhKJhHTYOhwOLCws4NatW2JmfnJygufPn2N5eRlra2ua+mtSz8HGqImJCYyNjYmuLh6PY2VlBaurqzg4OMDJyQn6+vrgdDpx6dIlMTk2m81otVo4ODiQ7sGDgwMUi0XNGoH4IXPKBhtBVK2ieoNhl63D4cDVq1fx/vvvY3JyUg60aDSKp0+fIhwOy81ciwWs5ulwOMSY3u/3S3ctQTWZC4vFIjey9957D36/HwaDAScnJ9jb28P6+ro0OVCbo4U+TAVVLFepzFomk8H29rYI14eGhuD3+3Hnzh1cunQJDocDAwMDKJVKePbsGV68eNE1Q5pstZbaWhVcARCbJBrUs6HR7XYjFArJu0Q2PZ1O4969e1hfX8fh4aEw1qVSSdOBG8zTbreLfU65XBbwMvG7kYutVgtjY2My2rBcLuPo6Ah7e3vY3NzE9vY2UqmUZrOjXxUDAy9nrFPvRSaPzBBZAI5cLJfL2N7eRiKRwM7ODsLhsBwCqvNDL8AVDyVakul0OjidTkxOTkpjCplMylGSyaQAG+pveZnqVdmalRVKjngW+Hw+DA8Pi8yHmnmLxYJOp4NqtYpkMonnz59jZWUFBwcHModdyyAA5DMFIFU07p3NZlNy9Hg8mJmZwaVLl6ShLZVK4Re/+AU+++wz6cLvJQjkcyyVSgKueR6yihEIBLCwsIC5uTnMzs7C5XIJ2bOxsYFwOCzWj1oBQOan7k9WqxVWq1W0ytQqtttt+TO6BExMTEjV7eTkBMvLyzg6OpLhIFqHWp2iRVl/fz8qlQqePHmCaDSKTqcjTakTExO4fPkypqam4PF4xNx+b28P29vbCIfDWFlZ6WKBtdjvafs2MzODmzdviiQxHo/js88+QyqVEr0087x27RoWFha69vy9vT2RL25tbUklTcvGb4PBALfbLc8IALLZLJ49eyZAlay/3W7HyMgIrl69iqWlJUxMTMDlcmFgYECkljs7OwCASCQilaw/Zr2+lcKZGgn6lpVKJdEesluV3aLj4+N4//33ZTNoNps4Pj7Gz372Mzx79gzHx8eaj9zjpspyFNlcsibcDPiCWSwWDA8P4+7du5icnITdbken87Lr/v79+3j+/Dl2dnZQKpU016yRreTEn1AoBJvNJrQ5QZxqXeX1enHnzh3Mz8/L5lEoFLCysoJwOIzd3V0UCgXNrDd4a7JarQgEAjJNh93yascsm+YMBgOGh4dx/fp1hEIh0aqk02kcHBwgFouJpIG+bFoylWQAWWKjPRaBdDAYhMfjweDgIEZHRzE3NwebzSalYjaycSILAbXW09bUUiAnEtFCjWwv7WEAyHMHgHQ6LR3tlIlkMpnfG2OpNXuh6njz+byA6KGhISkHWSwWkTFQ13ZwcCBA9az+txcHLACZosQD1ePxdNmADQwMiBa9XC4jlUphf38fmUxGNN+9ngDHg5YMKyfvkGGlNIANIHyX2InPi4kqAdA6+Kyoj6eGkg2zKotNyQJBq06nkwu16qbQq+fJi99Z68JGoyEg2m63i51dIBCQCxjBFH2Ue1GuPpsnJRNqZcVqtYp/td/vx5UrVzA/Pw+v1yse1vwZtbR9e1WePE+rAr90AAAgAElEQVQpQ2DvSblclv4FyrwmJydx/fp1+Hw+afxqNptywaJEScvpaqpelcMVyJSWy2XE43Hx+R0eHsbo6CiuXLki5ITqqe7xeMSYn82YWjqAqEQK3YbYPF0sFqHX6+FwODA2NoapqSlcv35dKtOqvZ7JZJKG5f7+fsTjcSEntApelrxer4DLYrGIWCzWZe9GX2j6xQaDQZFR6nQ6DA8Pw+12w+PxYGBgALFYDPl8/o+Wfr0RWCVryQk/pVIJ2WxWtF6dTgcOh6OrjHX9+nXMzMzILTuXy+Hx48dYXV2VQ6sXHXdsrgIgICCXy8kHyvIwF878/DwWFxeleaVSqWBnZwcvXrzA/v9n70uf2zyv6w+4YCP2hQRJEAR3iou1WEscJ7EzjTvTab90+qFfOv0v8t/0a2Y6bZrOuD9n8Th2YlneZNGUJZEUF3ABsRH7TgAE8PugnKsHtJxI4gvKyfDOcGRrIS/e5XnOc+655+7vdwWoApCSgMfjgdfrlSYLHgh6enoE/PEEMz4+jrm5ORm9WCwWEYlEsLOzg2g0Kp22WpbZqJkiE0mtJDdyaik5GcZqtWJiYgJ+vx8mk0nuARvE2AnejSk73FTZzcmpYzwle71eWSisViu8Xi+cTqcwrwcHBwiHw8JSshzUjRF33GBYguJzypO12+3G4OCggCW9Xo9qtYpoNIqdnR0B/pSoqDIFQHtAwHed+iNO1mGTCteIdruNdDqNWCyGvb097O3tYXt7G8lkUtjUbgErXlMCVX4BEGaDGzy9VjlAgRPBVF1YN5k1tRGIQfDO/MmYsTTIoSacZMY1tFvNXyq7RuBCsMQuZjbMsjuZWkAeCsgIqflpzQISUJP95eHE7XbD6XSKlY7ZbIbD4cDY2BiGh4dlkAk/Bw9S/L5a5qrmyRzpVON2u+VdAp741DqdTrEwZPn99DXkfelG0xLvO6dWqdKjnp4e8aMeGhrC+Pg4pqamZECETqeTgQBer1f6BywWi9gBanFd1f2elpokxqiXdLlcGBoawuTkJBYWFsR6i9XhdrstbhWtVktGFz9+/BiFQkEzRx3V95ugv1qtSuOX2+1GIBDA9PQ0lpeXsbi4KFpRvuOsblFfnc1m4fF4EA6Hz5yjmiuZVZ/PJ9eJB3oe/umwNDMzg4WFBYyMjHT4qlLiyApWOp3G0NCQaG+fJ84kA2g0GigWi0ilUiICJkKmK4DL5cLExARu3LgBr9crutb9/X18+OGH2Nra6oqJMYMvwfHxsbAk1MfRJqinp0fK2rdu3YLP50N/f78wgJ999hkePXokmrVulSvZ1a+erFlio0WROjFoYWEBQ0ND6OvrQ7VaFbPj3d1dJJPJrnTaEiizG5Q6RABy0hsdHZUTLBkBh8MhIwNpF0WbGxUAal2qJqNKPRf9/6xWKwKBgBxUKLI/OTlBKpXC3t4eQqGQMKoEgKpbgdaaxVarJdeHizmZYHo/8vM0Gg0cHBwgFAqJB+Pe3h7S6XRHg0A3tJXAk270YrEozD+bEDhdjXnyesXjcdFVHh0dSZe72qSmNasKQNh0AlGa6qudvmqouZx2UehWs5KqAyUJQBaKmm/V8okbKgE314Vuza5X8yMYYMWCeXCNpVctAaH67wB0jC1VmW+twIqaI9cpNtXyS2V52ahE32c+t5wKR3CiHmrOGqc1v5z6FwwGxbMcgEzLY2MwtZXqhD1eQ/UZUYGXFrmS8WdFbXJyEpOTk2LvRxsrr9eLqakpAdyUidDhg4SM3+9HNBqF1+sVGYuWebJiRma1Wq3Kz/D5fDJyd2pqShhJvjvNZlOkOA6HA8FgEPv7+7L2nvU5VQE17T5NJhN0uif+w+xFGRkZwfT0NK5duybaT7KvXDupsaXTytjYmJTctToAUEPPXEn8MA/VdWFxcRGLi4siC6xUKiJJoLsNmXlWjvlePVcuL/MB2u22vMwcW5bL5aQ8zReedPv169cxNTUl039CoRDee+89vP/++zg6OurKRkBmh3queDwugEQ1/uVNn56expUrVzA/Py+2DOl0Gvfv38ft27exvb2tua2OGmp5jw0qPIHZ7XaUy2X09PTA7XZjfHwcIyMjcjIhC5BKpbC1tYWHDx8KgNAarFCUTo0VAGFUCajVxZ/WFdSslctlZDIZJJNJhEIhZLNZAVdad1sCEIkKwSoXI7fbjYmJCWHXgCcMHJlUjgA+OjpCLBaT5h8tR5aq0W63hd2jnMbj8YgPn2pjxWaRdDotukpqK9k8pE4v0jrXZrMp41150AQ6/RNPAy/KBTKZTIeVmtZA9XQQDJNp5qGO2kWWqMha0raMDGC3GVUyjmoJkvKKeDwu056AJwwbbZWmpqbEVYOLvrqOag381SEgrAC5XC5YLBYZlJJMJnFyciLldQ7XoEyILCJlWf39/ZqyamwEIYCnXo6OJWNjY/D5fB2ghoBbnarIZ4FgkoBcC7Cq5mm1WuHxeDAzM4PR0VEsLCxgYWFBKij5fF4cadgcqr5b1FqzbyCTyXTYq501VB019/JLly7h9ddfl2mKzWZTRm4ODw/D6/XK4BoeXtgYSJ/jS5cuyXrA/o+zxmmgOjExIYwpnzvafF27dg3j4+NSPc3lcrJ2slLAARIcyz00NIS9vT1N8mTD8fDwMKanp+UAyuqpx+OB3W7HtWvXMDc3J0x7KpVCOBwWK8jJyUkMDw/DYDCg1WoJAHxe8Pc8uXKaHhvqiO3IuPKgsri4iCtXroifLSu8lUoFZrNZmGHKgujO8iLA+kwygHq9Lrq4bDYrLzXwhFldWFgQ832LxYJ6vY779+/j3XffxW9+8xuZXX76e2sVBCpHR0diCcFOOrJtPO1NTU1hZmZGFtmDgwN89NFH+PDDD3H37l0RrjO07gyt1WqIRCKiP2I3cKVSER9YnU4npvrsqOc0mI2NDdy5cwe/+93vxNye90lLwEIPVS6e1KAAkAMAwRWnKLXbbSQSCYRCITx+/Bibm5v48ssvsb6+Lh5yKhOoRZClpP2Qx+MR9p8LAv1BuQnV63UcHBzg66+/xuHhoYyBpR0LS5zdqgLwEMWSGWeTDw0NdWykfPe2t7fF8YEdxKpEgX9X61BlQASn1CiSweDiw/e70WjI5qWCaK2BqspUqr9H9oRaK77P9IwMBALyLI+Pj2PvTxPq+Hm7kSfBqcqymUwmVKtVJBIJqUCQoaCkamJiQgCK0WiUZhFqWrVsBCEL5HQ6ZSz18PAwBgcHZRoh8ESCxEMUB0DodDo5eKubsdPphN1uRywW0+Taqk01ly5dwvT0NAKBgMgjqEunPKnVanUMXuBBkc9rtVqVjZrNYloY2BMA22w2maTGcrnVasXU1BS8Xq80J5KJUtlrViNYsaCEgPaFJIu0CNpUuVwujI2NyShtjtWldpKHO1oqtdtt0YjW63W5NzywqM89oF3DEnXJbre7Q19Jlp+6f4/HI1rbXC6H1dVV8bAlEcRrTJmTFv0Uqu6XFShOeeL3phvQ+Pi4gENOLbxz546M2+Voc+5dJOVUmeJZr6va80HGnt+XzfXqetDX14dCoYC9vT18+umnqFQqkisP5MDTxmHuV89LCLz0G6hKAbjJHh8fywNss9lw9epVLC8vw263o9VqYXt7G++99x4+//xzRKPRrm36DJZLWU7jtArm2Gq1pCRAoAoAkUgEH3zwAe7cuYO7d++iXC7Lpt+NfKnryOfzcnrL5/MdJd96vY6FhQX4/X54PB7RLHEix71797C6uopoNPotRwWtcmZpNJfL4fDwUITzXLg4rpCnPbIR9Xodu7u7+OSTT7C5uYnNzU0x4VfZNS2BFRcp1WZNBVmnZQcEt5xkRRsgPjfdYK2AztI6GQCHw4FAIIDgn6bBkaVot9vCqtCvkgx6vV7vWqc68zxdclQbAMbGxnBycoLd3V05PbNhhONL+cyqzVRaX0uVqWTTIn1oqasMh8PY2NjoWEzZwUx7IJZau1X6Z1MN3xPed4fDIRPSaD9FCQPL62wIJDhgU5P6LGuRM0vQ3DydTqfo1eldSYadmw8ZPW7I6oGGFRiy26cPV2fJk4zZrVu38Nprr2FsbAxGo1FkCWQlyRSyysbNnrmpLDf3CQCaVCkI1ufm5vDTn/4Ur7/+OsbHx6UErE5HpMaPuQBPJWHcw7gO8L7zwK1FNYDAij6pIyMj8Hg8MJvNUqlSr6cqYzk+Psbx8bF8XjaN0e8WeHp41aL5Vz2gEbhyQhpt3ux2O8bHx9Fut0VHS7KN+mDKREgY0WNVtazTItrtdkfjN/D02fD7/ajX62K32NfXJ5PeKFtkVZBrGiVZZF21uv983ik3IsnH6zU8PCzTqtiYyLWLmMDj8YjcggxwsVjE7u6uVFWf9716aWYV6NR38cNRuL68vIwf/ehH0gFeLBbx/vvv4+7du9jZ2emKZcmz8qROkmBEFYubzWYZH0ZNSLlcxh//+Ed88skn+Prrr5FOp7taCmTwdJROp2VSFV9CMpjBYBBDQ0Mwm81ot5/YnHzyySe4c+eOWGqxu7IbwcMJDfLZQc3NwG63Y3p6Wk5hFIPncjl8+eWXuH//vliaqdZP/N5a50omlIsOdTYsXbMrmHlmMhmRAdDT9PRs9W4BQWrtBgYGMDU1hYmJCQSDQSm1JRIJKVuyW5lfqjShmyV1Fag6HA7Mzs5iampKLOHy+Tw2NzdFcxUMBgU0qJ6s3cyTObIc7HA4ZBody9XlchmxWAylUklAKa231ElC6iKqNagmw0Z3D7JjBoMBuVxOuvo5MpdgSu0gZhm93W53MD9a5EpAbbPZMDs7i7GxMRmqMTQ0BJfLJRPI1J+vlvlYvVBL6ATh+XxeM5kSLb6CwSAWFxdlcwSegkzg6XPHe0wwyt9Ty+s84NK5QAtQTaZybGwMwWAQIyMjsNvt0lHNtUqVhBCc8p4wT7LAbBok+85D7VlDZQHVqYgcqEDWlwwhWTdeN51OJ04GPFRxnaXPMvc6LYAV0EmgFYtFlEolOaQMDAxAp3sy9YkkEN8pTrNkRZAN2ZVKBYeHhzg4OEAul9OU9CEJRcCuesET1HFMMfBkyAZHF9NbnZ+JvupsXNWSBFT3UB4uKelYWloSf3X1MMIx3GazWbztHQ6HNJFFo1Gsra0hEom80BCgMzGr/JVglS+91WrFT37yE8zOzop1zfb2Nj744AM8ePBAs4lKz5MjF8Nms9nBqlJATT9Vk8kkTSu///3v8dlnn3V4lnU7CKb5AKfTadGHWq1WBP/ks+ZyudDX14darYZwOIw7d+7gzp07HQ1V55EnzemTyaS86MFgUIT4XFA50nR1dRUrKyvS5NZNVl1lGrigHx8fo1qtolKpSEMXS9a0i6IJdywWkya8brP//N5q04ff78fExIQI1UulEiKRCBwOh1jBsKtdbQzoFqvKUBst2P05NTWFoaEh6HQ6HBwcYGVlBe12G5OTk8IUsJyqNg12E/QbjUYxevf5fBgaGsLAwICMT43H4zg4OOiQdrDUy7KU1jZ6avA6Op1OsZ2h7RMPTwRKp83Lx8fHcenSJSn7U996dHTUMWtbi7zZPDE8PIypqSnRopMxJ6gncGq32zAajWJcTzN4Apl6vS7VCnUK3FmC95xDXEZHR6Xcz0Mm32OVqFBL5fx9fhYeyGljppUDiEo8eL1eYdUI9ClLo8uCKj3gz+Yepn62fD6PQqGAfD6v6R6glm45KKNarcLpdMpBhH0JbPJhhYeHRlWWQAaQzbXRaBTValVTEEipD6e5UdPN5jO+32yytNvtYg9HFpafN5FIYHNzE6FQSHzVz5orMQkPzdFoFKVSSSye3G63WCeSqQQgDc10W+EzQmJrfX1dfFa1qlByD+XAgWQyKWCaVl+Dg4MoFAqwWCzCVg8NDYm0RdWF017v0aNH2NraEpnI8+Z7JiHO6RvH09T169fxL//yL+Kpd3R0hF/84hf45ptvvsWodTtOg2lS8FarFTdu3MDPfvYzAdSpVAr/8z//g88//xypVEoe6PPKUy1Vc0Pr7++Hx+PBtWvXMDs7C6PRiFqthmQyiffffx+fffaZzFXvhj7xWXny/jWbTSk/0qqM5Ql6mWazWayuruKbb76Rzs9ugyrmqTIjtMfgYYUdotzsyLzSf5cjFs/r/qts2+DgIFwuF3p7e1EulxGJRLCxsYHBwUEsLS11WPIAEFDT7fwIBJ1OJ8bHxzE9PS3lYHXB1Ov1ogtjUxg7rbt1PVWZAm2AAoEAhoeH4fF4ZMJWo9GQX6kB57Q4ltyKxWIHQ6E1q6p22HK6F70HqUPkATCfzwMAXC4XZmdncePGDVy/fl2mwhSLRezs7CAUCnXoQ88aKkPNqg4bONhcwV8dDgd8Ph/q9TrMZrPIBei13Nvbi2q1ilgshs8++0wkQFq8XyrbaLPZOlwT+EzwYEdm87T+k6VOgphCoYDDw0Mkk0mZBnZWYK1KVFgaV/WGbKilFIAyBVXTT7DLPHO5HKLRKA4ODpBKpZBKpaRTXMtntlwuIx6PI5/PC5hm0w1HUtNHlWCV6xkZWYLqjY0N3L9/H7u7uzIWWAsAyPvJwSN2ux13794VXbXqYEFWk4dV7gmqZDCZTOKTTz7B2toa9vb2vtWzcpZcuQbt7e3hwYMHmJ6extTUlFSfTCaT/F0eprk+8Hpy0lkmk8Fnn30mHvC019Iq13q9jkQigb29Pdy/fx9er1fcfuj1rvbHnJyciJsSD1Wqe8DXX3+NBw8eYHNzU3pBus6sng6d7onJ8tWrV/Hzn/8cNpsNrVYLW1tb+K//+i/86le/EjbuvACAGgSs9Hp766238POf/1zE7Ds7O/jVr36FX/ziFzI2rpsl1e/KUQ2j0Yj5+Xm89dZb+Nd//VcZr7izs4Nf//rX+M///E/E4/FzA6pqjlzke3t7MTg4iMuXL+ONN97AwsKCdF8fHR1hdXUV77//PsLh8LkB1dPBJgBav9jtdvHZdblcMk0tFApJQ8t5MP+nczQajfB4PDIF6Pj4GKlUCqurq8jlcjAYDLLxcvEFusdUng6dTgej0Sgn55mZGej1erH6ikQi0hU8PT2N4J8mwHED09Lx4bvyowyJpt/j4+MIBALI5XIyMYnM0MjICG7evIm3334bgUBAQPfKygoePXrUlaoKQQvnqw8ODsogEIKt5eVlJJNJpFIpVKtV+Qwsv1MGdHR0hNu3b+P//b//h6+//lpTNpjX0mq1ymAXs9kskg4ar3Oj4uci6FL9rY+OjrC5uYnbt2/jj3/8Iw4ODjSbsMWfS3BPdwKy0SQn+LPoulIqlcSVghq8TCaDo6Mj7O7uyhTGw8NDaVrR4ppSLmWz2dDf349WqwWTyST9FAA6Jq3V63XRAhMEcgQoxwFztHk8Hpc19qyhAiWya+VyuUPKw2ZQlqdV3SqBH1k55rm2tibTAGkIr0Wu/D4cP7y+vg7gSePfzZs35WDN54MMNa3s6K4SDofx+PFjrK2tYWNjQ7zAtcAtavmfuk2+K//wD/+AsbEx8S1nlYVgmgfpcrmMdDotZX9eT9W2UCugyiazXC6Hhw8fiob5Zz/7mejmAXQ4p7DCx2c4m80ilUohnU5jbW0NX331FdbW1hCLxV4Yt2gCVvmyv/nmm/jHf/xHzM3NAQASiQTef/99vPvuux1A9VWAVb5IPp8P77zzDv75n/8Zfr8frVYLBwcH+L//+z/87//+L5LJpGYTn142yA5dvnwZb731Ft555x14PB4cHx9jc3MTv/71r/HBBx/g8PDwhQTKWgZPzqOjo7h27RquXLmCS5cuwWq1olAoIJFIYHV1FV988QXW19c79GnnyVb29fXBbrdjcnISIyMjokkyGAxot9vCqGYyGezv74ue9byuqWoNRNbSYrHIotZsNuFyuWC327G0tCRG2+32k8Ea3HC7ma/aWEXrIp/PJwwAgdelS5fQ39+P1157DZcuXZIuUJZ+9vb2NC37PStHznq32Wwy753la+Y9NzcHv9+PqakpjIyMwOl0ClD94IMPcO/ePYTDYTQaDc3zZK7UqBkMBtHXUroAAIFAQNg0glhuspz+d+fOHdy+fRsPHjzQfGoN8GTDOj4+lkY+6s7IrlHqw/uploGpTS2VStjc3MT9+/extraG/f19TRlg5snu80KhICVdAMJmMrdisYhEIiFDPmgJ6PF4xPc5nU4jkUjI4UYrppJl+1gshng8LtpObug+n0+082RzqUVVK0Fke2nHx6ElWs6vByAglc8hI51OI5PJIJvNYmtrC+Pj45iZmZF1iYCRms9sNot8Po9UKoXd3V3Ri2v5fqlOKgRNrEqtra0hGAzKCFBKRCqVCvL5PLLZLHZ3d5FIJJBMJhGJRLC3tyefQcsKGw8BfBZ3dnZETjc5OYlgMChaf/p+k+0/OjoS7+9YLIbDw8OOKXtaV4IJWMniP3jwACaTCa1WCzMzM3C5XOIQQsa4Vqt9a6gOJRkHBwfY3d0VOcmL5npmsMqNdnZ2Frdu3cKPfvQjKVXfv38fq6ursvC/KvAHPO22u3nzJt58800sLCygv78f5XIZd+7cwcrKCg4ODs619P+sUNnfq1ev4ubNmxgbG0NPz5NRardv38b9+/exs7PTtY3/eXIkO8SJFYuLi/B6vQCAeDyOb775Bvfv38f29naHOP08garaJEI9m9vtlm5PTs2idigWi3XYZ51nrioYpA6Meh/qE/1+v1jA5PN5mQJ1Hqy62nBB3SobJyj6p06VYxc5VenBgwfY3t5GIpHo+jqgahJ1Op2MWSabGgwG0W635QDAxo9CoYDbt2/j888/x/r6uoyM7kau6sKuTgCjDyEnPgFPD4VkWSqVClKpFG7fvi32b90YVc1NNZfLIRaLIRQKCWvG7nTqGVUvW47e5RcHQUQiEYRCIWQymW+5lZw1T2qR9/b2sLGxgUajIdo/ANLYQVCytbWFeDyOYrEowyx4yKZTAbW1WuhqGSzfh8NhrKysiI8yRysfHh6KfCoejwvrpza5sFmZAytqtZo0rL6I/u95gmVgVUYHPO1bYGOP1+vF3bt3pflTBbmcFlmpVOSZUAeCaJUn81MdEkhEECSHQiF8/fXXoqOlhpaNzblcTtZVdWxxN9ZXspZkWMvlMkKhEAYHB0XCxMoEwSLZ9EwmI1IFar+75QTD94uuCCsrK2i1Wrh//75MVgwEAh1a3FwuJwc+ethGo1Gk02l5dl+mwnZmsEoW8MaNG/jxj3+MsbExAE+mb/zhD3/Aw4cPNZ9P/jI59vf3Y3BwENevX8fVq1fhdruFVSUA1Lqj9kWDoMVoNGJmZgbXrl3D1NQUrFYrGo0GNjY2cPfuXTx8+LBrY1+fJ0duoGygWVxcxPDwsDSxhMNhfPPNNwiFQohEIpqVpl402FFLMMVxqqo3JF8ush2vgv1XGz/UUjYXfxV0A0+7VKPRqOZsyp8LtQmF5V52e1NPRbYQeNKkEIlE8OGHH2J9fV2aFLoR6kaq6pRppaSaqtMXlhtWPp/H1tYW3nvvPXz11Vcd2upu5MlFPZlMSjNib2+vDCRQbZa4WbAcF41GcffuXXz88cfY3t6WDUDrQwCBSiaTwd7eHoxGI0qlEgKBAI6Pj8VdgcC7UqnIxhuJRDrmvyeTSeTzeenQVjv0tYhmsylOFGazGdFoVAzqeb0zmYxov+PxuLCTLK2bzWYpeXMktJaelfwe1WoVGxsbaDabHdZEuVwOAwMDaLVa0lBJgKoenlX7J+CpZ6XWI4GZL8Ga2nGvDgSKx+OyZ6kOC1wfVF9lWuyplSutn1ng6eFJfTaTySS2trYkT37xWrIxjfpKFfx1Yz9Qy+xs6Ds8PBRLOjYmsprCwwibBfm51ANKt/YA3nN28hcKBdH/ejwe+Hw+2cPa7TYymYy8Q3QzKRQKsle97L0/E1hlE9Ds7Cz+6Z/+CXNzc6Kr++STTzrKaa+6/G+z2fDjH/8YP/3pT+H3+9Hb24tKpYLf/OY3WFlZwdHR0Ssv/6vdosvLy1hYWIDD4RB92ocffogHDx50NFS9inzZaMNZ1tQCkSXY2dnB3t4etra2kEqlzl2qoDbbULBOAMgORUY+n8fh4aHkrCWT8ry5kkVjJ2sqlYLf78fAwIB0idJupVKpIJFI4IMPPhAz6/MCq9xwWHI9Ojrq6AhmaZgb7t7eHn75y1/ivffekylg3bSBY36pVEo6gLe3t2E2m2XijlpK5+J77949vPfee/jss89kU+gmq6oyKtVqVUAcJ4INDQ2Jto6f59GjR/KM3r9/XzYEdWStlkFAnU6nsb6+jmQyKbPffT4f7Ha7+BWT7a1UKtjf30c2mxXnDQIA5qg1+8d7nk6nhbWktZfD4UCr1RL2Op/PI5/PC8OpAlHVHYAgR+v3is4NjUZDrP94GOU1UQePkE1XQZjKuPN7dstaT9UjP+vP+Ps6na5jvjsPtCrA5f3vlh3c6e+pNlSrgLRUKgGAXPfTf5cygm4BwGflCTxl3YEnuIrWlXxG+BnUJuxu+mqfzpXXhQcVEhcE2OoBgPphgmo6HKn+2i8TLw1WyfY4HA7827/9G65cuSLz3zc3N/Ef//EfWF9f/5ah+nmG2iQwNzeHf//3f0cwGITBYEClUsGdO3fw3//93wiHw69EU6kGPRRtNhvGx8dx+fJlGAwGFItFpNNpvPvuu3j//fdl0z+9SJ1XqCMTOUErHA4Lu3Pv3j189dVXWF9fRyqV6uikPe9cCQzo62Y0GjE2NiZNP/F4HCsrK9jc3MTGxsa3noPzypEbfjwex/3798VbNRgMil1JT08PwuEw7t27h5WVFXz44YdSUu02uOaCT5DKcm+xWMTi4iJGRkakuaVSqUiDEr9UHfB55MgBG7u7u9jZ2cHGxgZ8Pp8AwHq9js3NTaytrSGRSIi28rzeKR46otGoMD7sELfb7XKAoUcxwexpi7JuMyrMk4LrIJsAACAASURBVCzw1taWgH2yZqetAZ/FRHZbAkRgTfcR5qf+uZqTCg6/K7qVK6Uc1J/+uZ+plt755+q/Oa8D6vPEX2qU6naup7//dz1z/Dw8DJyWOJzHNX3Wu3E6SLjwS83vVebK66c+hyo5RLJCHbah/ruzxEuBVbVBYGhoSEZZUh/yy1/+Eo8fP9bU+PllguUJu90uIJU6oHA4jF/96lc4ODiQ0+6ryhOAdM/SEiKVSskYzbW1Nfz2t79FOBwWtuJVNVWpDQtkfFKplOj+1tbWcHh4KAzVq2yqI5MRiURw+/Zt7O/vS1MQ9VOJREJ0S6/iWeWLrGrQSqUSVldXZWoI8GSRTafTSKfTHQzRebHALK/zWmWzWWxvb+N3v/udWAWxuzaXy6FSqcikm/O6pgRNBC70H3306JGc+oGnAzh46FOZyfM8pBB80oZGnZikfp5uMlLPkydzIENy+s//3P+fZ6js5HmDuheN51kTn/Xn38fPAnx/8zod53mIOku8ygrv88SzriP3iO/6e2eNM2tWm80mQqEQ3G436vU6NjY28Mc//hG5XO6VAlWg0xy+UqlgY2MDyWRSbBS++OKLDr+/V73Q0u4hHo9jdXUVW1tbYrnDqV9npdLPEqrOhjkTVLVaLfF9e5WAWs2TObZaLezv7yORSEg5nYCG7NCrfk6ZQ7PZRCwWQyqVkkYWdW55t/RezxNqjtSsPevk/6qAFX8ec1DB/7P+3qsO9R6ypP59je/zxv6s+GvJ8yIu4m8luv3O6f7cD9DpdN/5h2Qt3W43bty4IZMMdnZ28OjRI/HR0yra7fa3hTN/IU82AlksFgQCAVy9ehXtdhuJRALb29tS9tVYO/Odef6lXMmums1muFwu6biknk1rgPKy15QMq+qrp9L9XdD5vHCear789XnKL2eJl733ryLOck3PM/4W8gT+enK9yPPF428hT+CvJ9eLPF88/hbu/UuDVeXvvHIg8DyA5Vlloe8rYDmvEtbfwov215In8NeT60WeLx4X9177uMhT27h4RrWPv4U8gb+OXP8sWL2Ii7iIi7iIi7iIi7iIi3iV0fOX/8pFXMRFXMRFXMRFXMRFXMSriQuwehEXcREXcREXcREXcRHf27gAqxdxERdxERdxERdxERfxvY0LsHoRF3ERF3ERF3ERF3ER39u4AKsXcREXcREXcREXcREX8b2NC7B6ERdxERdxERdxERdxEd/b+LMTrP4avLeAv548gb+eXC/yfPG4uPfax99CnsBfT64Xeb54/C3kCZwtV9UbXPl5L/vtunZNOcxG+Tln8l3vRp4ctsP/BtAxtVDrPP/0c858TfnrWcdDf1euZx63ehEXcREXcREX8dcSKlg5r2ExLxrq5t/T0yNfHHd8VvCidZ5GoxEGgwH9/f3o6elBu91GrVZDrVZDo9F45SOte3p6oNfr4Xa74XK5YDabAQDlchmFQkGmRKrjt887eC0NBgNGR0fh9Xolz0KhgFQqhVKphGq1ilqthmaziWazee55MledTge9Xg+v1wu32w2Hw4H+/n6Uy2UkEgkUi0VUq1U0Gg2cnJzI2PCXjQuwehEXcREX8T2OZ7FWQCeoOj1F8DxDHWmsTgskaDkNqFQgcJ45Ewz09vait7cXPT096OvrQ1/fk22Qmz8BYb1eP3cAy+vH0dvql9FoxMnJCarVKur1unwdHx/LNT3v69nb2wuj0YjBwUEMDg7CbrfDYDCg0WggnU7j6OhIgODJyYkA1/PMk0DVZrNheXkZU1NTsFgsAIBMJoNoNIr9/X2k02kUi0WcnJzIvT/P+06g6nQ6cf36dUxOTsJqtaLVauHo6AjhcBihUAjpdBqFQkEOA+d9YGGuer0edrsdV69eRTAYhMvlgsFgQCaTQSgUklxLpRJqtRqq1SqAl39GL8DqRVzERXQtvgtoMV41wDr938+Kbo+SflaowK+np0d+j2xbu93uYFVYflPBYbdzVfPr7+8X4Nfb24v+/n4YjUYBfWSrWq0WqtVqBzvYbfCi5mgwGGA2m2EymWC1WmG32zE4OAjgCctWq9VQqVSQy+UQi8VQq9VwcnLSdbaNz2Bvby/0ej2sVit8Ph+Gh4eFuXK73ahWq8jlcsjn88jlcohEItjb2xMweB6soAqoTSYT3G43bt26hZmZGbjdbuj1etTrdYTDYRwcHODw8BCJRALHx8fI5/PCCnb7GVXZv4GBAYyMjOD69eu4evUq7HY7arUa9vb2MDAwIGX3druN4+Nj6HQ6eW7P6z0ioJ6amsKNGzewvLwMg8GAWq2Gra0tNJtNeR4bjYZcv3q9Lrl3O56V6w9+8ANcunQJDocD7XYbu7u7wqhynWq1WsKwfq/B6l/aDIDvR+mFcTpf3qDTm9b3IefTm666uTFeVQnmuwABcySjwWg0GmcuFbxMfiobdPq/WX5rt9vyDLyKPE+DF4KCZ+Wggpbz3MB4rchc6fV6YYXUZ5ALl7oZqNe0G9f19LVTgYvZbIbNZoPZbEa9XpfNoNFoIJ/Po1KpSBmrXq93AK1u5EnWwmAwYGBgQACL1WrFwMCAlNsqlQpKpRIqlQrK5TL29/eRzWal9KqWYLXMVd2wjEYjBgYG4PF4sLCwgMHBQXg8Hvh8PkxNTUGv16NYLOLo6AixWAzhcBg7Ozs4PDxEPp+XsiafBS3jNEi1WCwYHh7GtWvXMDY2hkAggPHxcYyMjKC3txfHx8eoVqvI5/NYW1vD3bt38ejRI8TjcRQKBQGEWt939d0xGAywWq0IBAL4yU9+grm5OQSDQXg8HjidTtTrdbnf6XQajx8/xsrKCr7++mscHR2hXC535VqquZJR1ev1sFgsmJycxBtvvIGFhQWYzWbodDqcnJwIy2o2m+FyudBoNIQhVPPs5vve29sLg8EAm82GmZkZXL16FYFAAO12G8ViEf39/QICm82mPNdc87vNBKvA32g0wul0Ynp6GpcvX4bb7RaWH4CA/VKpJAC1r69P1vjzBNVmsxmDg4NYWFjAG2+8AZfLhd7eXlQqFRiNRvT29soaxHemr69PrvHL5KopWP2uctBpEEV2QN38z5MVUPP6c3mzVMQNjnmxXHQeJ6+/lCtPhGQzWJZh7q1WC8fHxygUCl198U6DKfVXteymMjBGoxF9fX3o6elBs9lEoVBALpdDrVbrCsBSc+T16u/vh16vlw3NaDTCZDIJkFZPrTzFFgqFDoagGxvD6cXWbDbDarXC7XZjcHAQQ0NDcDqdkmO1WkWlUkEqlUI2m0W1WsXx8TFKpZKU4bqRq3otmd/4+DiGhobg8XgwODgIl8uFvr4+VKtVlMtllEol7O7uCmChbqxYLHYlTxUAms1meL1eTE9PY2JiAkNDQ8Ja2Ww2tNttVCoVFItFZDIZrK+vIxqNSuktl8tJnloDF7WsarPZMDg4iLm5OVy+fFkAi9lsxsDAAEwmUwcLGI1G8eDBA0QiEcTjcSSTSWSzWRQKBQHYWuXI99hms8HlcsHv92N+fh4/+tGPEAwG4XA4YLVaYbFY0N/fj3q9Lvf48PAQKysr2Nrawt7eHvb395FKpeRaanU91Tx5GBkZGcHCwgLeeustTE1NweVywW63w2q1ore3t2NdHxkZgcfjgc1mw6NHj7C5uYlms4mTkxNN8judJ99zi8WCkZERLC0t4datWxgbG4PD4cDAwAAsFosw6CcnJwgEAggEAvB4POjp6cH9+/cRDocFvGgdp4Gq1WrF2NgYrl27hps3b8JmswmY5nvsdDqh1+vh8/lQq9VgMBiQy+WEZetmnjqdDv39/bBYLMIALi8vo7e3VyQKoVAI4XAY2WwW7XZbWGGu+/x+3d4z+/v74XA4cOnSJbz99tuYmJgAAKTTacTjcYRCIRwcHCCZTKJWqwFAB5DtZp5qrjxQDQ0N4fLly3jnnXcwNjaGvr4+WdvD4bCsP7yOp/fzl8n1TGBV/QB84VT0rWqCCKT0er2wQjqdDoVCAeVyWUTDZDC0jNMgSv1v5skNl7nxphgMBuj1ejktlstllMtlVCoVAS9ab1hAZ4cd/9tgMHQwQxSz6/V6mEwmmM1mGAwGmEwm9Pf34/j4GMViEdlsFicnJyiVSpptCM/Kk3kNDAwICCQA5EI8MDAgQJD/ptFooFQqIR6PC7vGF1KLUBfZvr4+AS0ej0cE98zNYrHAZDIJgC6VSigWi2g0GgL64/E42u226Ia03mTVDWFgYAButxuzs7Pw+/0YGhrC0NAQfD4f3G63PIOlUgmpVAqhUEiYoGw2i0QiIQuD1rny/plMJikJLS8vS65ut1vYwJ6eHjQaDVSrVRSLRayursJmsyGVSsmCDDwtw2n5nKqM1eDgIJaWlnD58mVhAi0Wi7zrAOReZ7PZjs+XSqXkcKhqG7UIlWFhnlNTU5idncWNGzfg8/kwMDAg66fBYBBtncfjgd1uR6VSkc/bbDblANNoNDTdyJjnwMAABgcH4ff7MTc3h8XFRXg8Hjnsqetpf38/TCYTAKBSqcgBi4erboSap8fjwdjYmFxTr9cLk8kEg8HQ8c5xjXA6nZifn0csFkMqlcL+/v5zVQhfND81T5bUx8fHMTExgUAgAIfDgd7eXpycnKBWq8nhns9Bq9XC/Pw8Njc3EYlEEIvFNM/zdM4E1m63GxMTE5idnYXH44FOp0OtVkO5XEYymUQ+n0e73cbAwADMZjPK5TI8Hg8cDofs+d0MXqfBwUFMT0/j2rVrcDgcOD4+RqPRQCqVQiKRQDqdRqVSQavVemZTW7ebmLg+jY2NYX5+HsvLy7BYLKhUKqhWq4jH44hGo0gkEqhUKt8CqOcVfE4tFgvm5+dx+fJlLC4uYmBgQNZ2Vk+SySRKpZIwqae/z7kyq6dLLKpeSa/Xw+FwSBnLYrHAaDSip6dHgBSZynw+j3A4jGg0imw2Kw+N1idslUnjotTf3y+lCoIVblgEgEajUQBtpVJBNpsVDQ5LhlpvrDxpceHk5kQBs5orATevM0tzvLapVAr9/f3I5XKoVquaMAOnDykEpkajEQ6HA4FAACaTSb54Da1WK8xmM/r7+zvKwvl8HslkEgCEFdSCBT7NpBKk2u12+P1+TE9Pw+v1wul0ChvEaww8AS3UrrHslkql0G63v9WRqdX9Z65msxkWiwU+nw+XLl3Ca6+9hrm5OWlioO6OLEutVkOxWJTnIJFIwGAwSIlLy1xP5+l0OjExMYErV67g7bffxtTUFKxWq7w3er0ewJPTNZmXer2ORqMhawEBIkvCWmwQap4Wi0WYyuXlZbz55psYGRmB2WzuqE4wT65TwWCwozHg+PgY5XIZ/f39mlcq1M2AesVgMIjh4WHY7XY5NDPIwvb09KBer2N4eBjFYhHFYhFms1mATTeC4MrlcsHj8WB8fBwul0sOerzXvI9cp6xWqxy4otGo5K/lpqu+96zguN1ueDweBAIBuN1ukabwWVQrPgBgMBjgcDgwPDwMm83WAWq1POyrh36j0SjVCBWosomqUCgI+GZVisDa6/XC5XKhv7//zLl9V67q/7OpamRkBFNTUzCZTPJuEDTncjlZc/V6PXp7e2G32+F0OpFOp6WEreX7c/r/CQKnp6cxPj4Og8GASqWCfD6PeDyOo6MjFAoFecdVso0H0larpTlrqeaq0+lETnHlyhWMjIxIRYKyiXA4LNU87uGUKnRTCnC6ktvX14fBwUHMzMzg5s2b8Pl8aLfbKJfLSKVSIvFJpVIol8vy/p92tDg3sKpu/mQdLBaLlKiGhoYwMjICq9UqQMBsNqNQKMButwtDwJLbV199hc3NTTx69Ah7e3uaMQG8uGqJl2VeCtgDgQAGBgbgdDphs9nQ29uLXC4nZS6bzSZluUgkglAoBJvNhpOTE+RyuTPldzpXld0lQDWZTHA4HBgbG8Pk5CQGBgZgs9kwMDAgoKmvrw82m02us9VqRS6Xw+HhIfb29lCpVDp+zlnzJPjn4YTXc2RkBLOzs1hYWBDWgmwwAPk83KB6enpQLBZxeHiInp4elMvlDnb+rHmqByoyVsPDw5iensby8jKuXbsGi8UimzqZaka73YbdbofX60UqlRIbkWq12sFsa7HRni4HUrs0Pz+PN998EwsLCxgYGOj4WewC5pfBYIDP50O9Xpf35/j4GKlUSj6jlteV7MrS0hKWlpbwzjvvYGpqCkajsQOsVKtV+bl8p00mEwYHBwW0FotFxOPxjkqCFgcV1bJmfn5eNFaBQAB6vR6tVgu1Wg3Hx8cAgP7+fvnZBK0OhwPFYlHAgtYSldOHKq5HbrcbPp9PSpM8bB4fH8uapgJskgNkNk83YWmZZ7vdlmeAByQAHV3K9XodJpNJ9gaubzxwm0wmOchomaMa3MyZBwFduVyWe1oul0V24XA4OgA3K1an3z2tg3kODAzAarXC6/WK3jeZTAoAGBgYQCAQwNDQEOx2O3p7e1GtVuVwy+vcLW0tr4FerxeQ7HQ60d/fL2X1hw8f4vDwUJ5Ju90Oi8UiJBHlF8ViUdMKmponw2w2IxAI4OrVq9JIl8/nsbGxgfX1dYRCIZH0qO+geojqxvvOX/nzqP+8fPkybDYbKpUKYrEYvv76a6ysrGB/f1/WSt5XgnF+H7XpSstQ11Kz2YzFxUX84Ac/wPz8vOC6aDSKu3fvisTndJWcBwAAArZfFLS+MFglAOSC4/F44PF4MDw8LA/v3NwcLl26BJvN1sFKHh0dSUmAOsFKpSIApVKpIBqNalIeUE/VXKgIpL1eL4aGhjA3N4crV67A4XDIhSwUCtjb2xO2khsHH4x6vY5MJiObGnD25jDmqtfrpXSu1+tFDxYIBLCwsICrV692lFTz+TwSiYRscNSQ8XqmUik0m02Uy+WOrryz5sryL8H/0NAQZmdnpdQyNTWFvr4+1Go1lEolZDIZafbgCdvhcMiGSvZAbWTRYnFQwYrdbsfs7CwWFxexvLyMy5cvw+v1ClDKZDKIx+PyYlosFtjtdtjtdthsNrFi0el0wk5qzaqRUXc6nXjttddw+fJlXL9+HbOzs7KAHR0d4fDwELFYTBhrm80Gp9MJu90u2lECFp1O18GmawWsCf5nZ2dx+fJl/PCHP8T4+HjH4rW9vY14PA6bzSasNRlsAHJoBfAtC56zBhviyFgNDQ1hdHQUU1NT0rWaTCaRSCTEtoZAgTnq9XppsCqVSsjlckgmk2Jvo9X9Z67q2kqQxKaUZDIppdXj42N5NsmyN5tNHB0dIZ1OI5fLIZPJiGWMlteUv3INILtzdHQEvV6P/f19afTp6enB8PAwhoeH5SB9cnKCbDYrGmA22mgFqnktGXz/yUrzEL+5uYlYLCZa/sHBQYyOjmJ0dBQul0v2LLJZvI5ave+nvw+1tWazGT09Pchmszg4OBDNdDabRT6fh9lsxtbWljDv3E/V/LRm2E43Fvf19cm6ODAwgFqthsPDQ3z22WfY2tpCLBZDMpmEwWAQfXc+n0er1YLVapUDDg9a3ciTB6np6WksLi7KnhSPx/Hxxx/jm2++QTQaxcnJCUwmExqNBoAnaxsPWf39/dJToyX4V5t1CQBv3LiBH/zgBxgZGYFOp8Pu7i5+/etfd+RJ4ofgudFoyBoPPLVe0zJUYNzX14exsTH8/d//vTgqtNtt7Ozs4Le//S3W1tawt7cn2mRWfKhbJWBlvi+6Lr0UWCWrSsbR7XbD6/XC7/cjGAxiZmYGTqdTNH+lUgnRaBSRSAQjIyPw+XzweDwCvKi5IpDR4nSgaiiNRqOcVsfGxuD3+zE5OYnFxUU4nU6cnJwgn88jn89jZ2cHu7u7AmwnJyfhdrvle/CEyIdFq5KQ2oBkMBjg9XoRDAYRCAQ69DbqSTsWi6FYLMJgMMDj8SD4p+YGMh1kA46Pj0UHeNY8TzNrLKdPTU1hYWEBc3NzMBqNcsre3d1FLpcTME4AzoMA2YCenp6OcrVWCwPZnOHhYczMzGB2dhbz8/Pw+XziX7exsYG9vT3ZDCi5YJmY19JoNMoJViupghoEqy6XC4FAAMFgUCoUx8fH2N/fx4MHD7C/v49SqQSXy4WBgQG4XC7RhvGwA6Cj2UE15tYiKOnhpulyuaDX65HL5bC5uYkHDx6IpY7f7xfmkmVBdoaWy2Xkcjlks1nJUyupAvNkkwXvYbPZRCQSwdbWFvb395FMJlGv1zE6OiqHJ51OB7PZjOPjY2QyGSSTSQEN3ewK5xcbI6mri0ajApzI+BiNRtTr9Q6NajabRSqVQiqVQrVa7Whc1TrHZrMpevNIJIJEIoHDw0OUy2WcnJxIlYdlVK4ZKlPMBrBuukCoevPt7W3s7e3h4OAAhUIBJycnUiVxuVxyXe12O5rNJrLZLPR6vSam5s/KTWWq+b4WCgVsbGxgbW0NBwcHcmCq1Wpy4OM+ND4+DpvNhlqths3NTc0B4OlcVUkdWfx8Po+trS18+eWXCIfD4q1psVjQ09MjJBbJFAAiWdOarT69P1ECYLfbcXJyggcPHuCrr77Co0ePRAPKCgvxAvAUSLHK2Q1WnRiFTgXDw8NS/r937x4eP36M3d1dOaCyqsPPqVZO+GfdaPokPjGbzUJEOhwOAECxWMTnn38uuWYyGVQqFRgMho5mefVZJ9H3ouD6hcCq+rCyXMfNx+VyiTGwXq+XhZNMkMqYOp3Ob1kXsbylxeavsn9qqdrj8WB0dBTBYBATExNSzif4oydcLpeD1+sV9o/6US4oql5RiwdDbVAjCBgdHUUgEMDk5CRmZmbgcDjQaDQQi8WwsbEhNjUAhFUjW83TF5vBSqWS5sCaTEAgEMDExAQmJycxNjYGi8WCarWK/f19rK+vC2AhY0UtMxn3k5MTVCoVYa+0bFjjc2AymeD3+zE6OoqxsTFhIDOZDLa3t7GxsYFwOIx6vS6TOOx2uzw/ZICpqVSBqpblIUor2PhFz8J2u410Oo319XXs7OwgFouh2WyKzILXi898s9kUJwCygFqeuvkMWK1WsXwCnoDjRCKBtbU1hEIhHB0dobe3Vxq7qK3lglqtViVHMlxaOgGo+jOj0QgA0umfSCSwu7uLRCIh5X2+zwRgtVoN6XQa6XQamUwGuVyuw61Aa3ClspUq41+v1ztcMlTdPUEtD7FsWFMBdTdANeUybO7LZrNSkm61WnLNKRXj+tlsNqXps1gsnovNEptLs9msbOi8rny/W62W5MsDH6tBWlZ7/lyujUYDhUJBwN7x8THi8XgHa2oymeB0OqW87nA4YDQaZd3vFlhljmqTNN/hcDiMzc1NhMNhxONx0Z2rvRSUYFgsFrGM6hYIVCtU1HvrdE+auVdXV3FwcIB0Ot2xh7NSyLWMOlDu/1rmqQJqo9GIqakpzM3NiRtJNpvFo0ePEA6Hpbm32WwKPuDeqdpA8X3UspLC4M90Op1YWlrC4OCgaPYPDg7w6NEj7O/vI5/Pd1j78Vnp6ekRsoLMP5tDX2QdfSlm9XSZhaVyale4MGxtbeHg4ADxeBzZbFaMeXnqYflXZVe02lBVuyRS7QSr4+PjsNvt0hH46NEjhEIhRKNRKWep+k+WKlVrIIIAXhOtADZZIDZXTE5Owul0ot1ui5XO48ePcXBwgEqlApfLBafTCYvFIgsXAGkIymazInTWsguc18jn88Hv92NsbAw2mw3AE7uNra0thEIh7O/vo9VqIRgMSkOb2+2W5qByuYxMJiMWRlqdDNXrSfaRmjQyJclkEjs7O3JIYX5kVpxOJ0wmk2irc7mcjObjS6kFGDjNBqhlaIIRlqtjsRiOjo7Ez5Cla2pv2+02SqUS8vm8gMBu5MpGIGqm2AUcjUYRi8WQyWRQLBbhdDrFi7O/v79jAg9L1hwjqGWeDF5Tbj6UpXBaTa1WExaVrhV8Lmu1mnSDJ5NJ+b1u2CyprANtlGhNxY2Uh1FqxFmZqlarSKfTiMViSCQSAmy7wQQC3wbVnE7DBkpqRB0Oh4y2ZEn1+PhYrimtbbrdbU0Gn82brVYLhUKho5eB9laqfKZWq8n6qdVh/1mhlnELhYJ0pfNgxWdY3R/IsFIfrlZPupmn2lDbbDaRz+eRzWYRjUaRTCZl4AN1n16vF16vV7yCe3p65BCrNbBW11HaZY2MjMBisYhMZmtrq8P+iWQGpVQWi0UO1sViUd5DrRsV+a6bzWZxTzEajQIAt7a2kMlkREvL68mKpMFgkPGwrHCwgqrle6/KKYeHh3H9+nUB/6VSCffu3cPGxgZSqZSQEgThqjSN7g8nJydCTPJADTyflPKFwCpRsdol1263hTWj9x9LGI8fP0YkEkGpVEJfXx8WFhYEVLFEUKvVsL29LaCWD9FZACAfNm4qbJaiJYjNZkOr1UIoFMLKygoePnyIcDiMXC6H3t5eBINB6Rgnu0U9bSgUws7OjthyaBG8rixB+Xw+TExMiBdgT08PotGomD/v7u4im83KDGGPxyOekUajEeVyGbFYDNFoFPF4XCwktAj1JfP5fMKm834mk0msra1JiTWXy8nJ2u/3Y2ZmRppGMpkMDg4OkEgkOjYFrQCLagdDRwo2HySTSXnuOF2FjU2UsrDJgV2OlF2ooEXLsjqvKzcC6jhrtZqAkHK5DADSVR0MBuHz+aQseHR0hN3dXTl4FQoF6XjWqmqhjoJktaFWqyGTyaBcLgv7x8PM2NgYhoeHBdyw2rKxsYGdnR1Eo1EBjlo6K6iyGrVpq1qtwmg0wuVyyeGOUiaz2YxKpYJ0Oo1IJIL9/X2RCvC+d6vrVq0GUb40MDAgPoYECXq9XpwUSAwcHR0hHo93jDfsxuAKtSyo6msHBgYwOjoqem9WJ+gAAKDjQJPJZDrAYzfZXzbR8d0lACHYt9lsMotd9YY9OjoSb0s+11oHn8l2+4m1XC6Xk/WaDBRL0yyjs+rG/gQOXeC/7ZY/Nf+b950yv1wuh3g8LteW/R1j3s+gwQAAIABJREFUY2MyfIF7KaduqZZQWpA9aq4EV36/H7Ozs7BYLKjX69jf30c4HJZrRAAYDAYxNjYmsioAHSD8+PhYpEFar00ulwvXrl0T6698Po9PPvlEGqObzaZc77GxMUxMTIjul880vb/j8bjmaz3ZerPZLJI/vV6PWq2G/f19fPTRRzg8PJSqOcF/IBDA3NycuOvwQE0JHWVKL7JOvTCzSrBKBO10OkUszRLUgwcP8PDhQ8TjcTQaDfT29sJms2F+fh7Xr1/HyMiI6JY2NzdlUghLM1oAFeap0+lgs9lE++lwOHBycoJIJIJPP/0Uq6urODw8lBFrVqsVCwsL+PGPfywPxsnJCcLhML788kusr6/j4OBAU7aSDxYfXuo/h4aGADyxc7p37x5WVlYQCoVQKpWkw3lpaQk//OEPxdWAY+7u378v+ixqs7Rig8iqDg4Oiv6YpsChUAjffPMNjo6O0Gq14HA4MDIyghs3bkhTE2Ui29vbWF9fl0WEjVhaaRZVo20CFrUkGIlE0Gg0pDnN5/Ph5s2bmJmZEQPuTCYjz2c0GsXh4WHHAUDrkjUXRNqkUX+q0+ngdrtl0/D7/bh+/bpc+0ajgXg8jtu3b2NtbU0AFi3LusGycQY4y7psuFxYWMDo6CgAIBgMCojJZDKIRCLY3t7G1tYWtra2kM1mBVy9yCn7RYLsLxmIvr4+WXgJushqlMtlAdAbGxvY3t4WHVY3h0AAT68pDyxmsxmjo6NYWlqSDYpyBeoYyWzm83lEIhFpAOtGOVDNk2V+1WljZmYGVqtVGFZWMNi0Qu/a/f197OzsIJlMatpU96yg/EAdR8qcCbLpacvyer1eRzwex3vvvYe7d+9ifX1dmlS1DvV7knGijEutYNKiigdpn88Hg8GAWCyG27dv4/PPP8fDhw/lMNuNUA8qXKPa7bYciNmXwAPWa6+9JpZR1NXu7++LBlvrXNUDHwcWsJkyn89jc3NTJAiq5d7y8jKmpqbg8XhgNBpxcnKCra0trK2toVwuy/c9i+3S6TxZ8ZuenpbG6VqthkgkgsePH4vLD9fUqakpsS8kAGy322JrFQqFpAeD90UriSJ7TSYnJzuaU1dWVqShSqfTCTE0NTWF119/HZcvX5ZrWqvVcHBwIEQFhzGwkvA8B4GXkgGolDhvHhfzo6MjObnQAN5ut2NsbAy3bt0SoHpycoJUKoXf/e53uH//vpxgtDxl8+Rkt9vhcrnkJtPXk4CD2juONnzjjTcwNzcnIuJ8Po8vvvgCjx49wvb2tpSrtQy+YH6/X/SVFotF2Bx21JFaHxgYgM/nww9/+EPMzMxIR3A+nxdZw/7+fodcQYvrypeME4pGR0el/M+yhE6nkzKV2WzG2NgYLl++LGMNebJiSTsWi0lJUOsTtqpXpBaRzIrdbsfo6CiGhoZgMpkQDAbFI5QAIBwOY39/X4yZU6mUpqb1p3MGnko4OAqQE5dcLheKxSLa7TZGRkbgcDjQbDaFBYxEIlhbWxOA1a3Ri8yTc8rZuGS322WqDp85PpeJREI2gJ2dHezt7SGTyXSUsgBtgSrXKmp4WSKz2+0YGhrq8CKlpo1MUSKRkAPUeY6G5IbIUtrg4KAAQLUxge8KG8fa7bYcTFgO7kaeaoONyq6y3Ee5Aku87BkgwPF4PCJv6Wa5WtXW83mlawoHRHg8HnGHoasCqxoAZE/r9pQlXkfVB5x5c6Sp0+kUT9NAICCSO1YneRhVGdBuNNpw/acbgMlkQr1el32JVVa/3y9jbSlpqtVqMnmPvQuqVlyrPHn4VEEddd200/R6vRgdHcXCwgLm5+dFHkZsQxehVqslenUtbLZOS75sNhvsdrvcR1bP2GMzPj6OYDCIa9euYXZ2Vqat8fuYzWaMjIxItZhEipbesKqdHiVK1MgfHx/LAYX75/Xr17G0tASbzSbStP7+fgQCAYyOjmJ4eBgmk0kqqs+7Xr0wWFU/vPoQpNNp1Ot1FItFAE9AFU/YLpcLi4uLmJ+fl5JQsVjEN998g7W1NTFd1xoAqqMLqUVjKapUKkGn00meXHCnpqawtLQkTWDlchm7u7t4+PChdGF3Y2qVXq8XSy2/3y9sIIXJ1DJyQbVardLY5HA4xOCaXrDxeBz5fF5zHRPLu5yiRI0nNV1kh7kpOBwOTE1NSWc9u105WCGfz4s3o9ZedurACnZLU4cEQOzWKBOhsTbtyfb29rC3tydWLMyzG53gAKSkns1mZYGnlynNwsns8dk4ODgQOUMkEsHm5iYymYzoQrU8qKhMT6vVEl1sLpeD3W4HAPEl5s9kw0AsFsP+/r5cU+aoWkB165rSR5W6PlX/RX0idXbUifJZ6Ybrw58LtQLCe02ZBatZfNf6+vpkfaU0gDIarUMFVwT4BKHcPKvVqshs6KFqMBg6GlSprVY3XK3LwKe1ldSkEkhZLBYZDUzNPder3t6nM80pwekWCFSrP7Sgorc3nVzIsBMAUhrGg6zaQAs8nSioZajXk2w/v/gOqU04Q0NDmJiYwMTEhAAxXkN61qqyLK2eAV5PVh3Hx8elrM93eHBwEG63G4FAADMzM5icnOy4nmTenU4nAoGAsMC7u7soFouaMqsWi0VcEqiRTqfTaDabMnFtenoaS0tL0oGv0+mk6Y/MK11B0uk0PB4PIpFIx7jYswRBNe+rqv2nz7zNZpNG8OXlZbz22mvwer2yn5HAInbp6+tDqVQS2SLlDpozq8DTOa8nJycoFApIJpPyslCMTBEu2ZZbt27B5/NJufjw8BAfffQR1tbWNPctZHBhJVjhiYNlcQJU6paGh4dx48YNDA8Py0kwm83i7t27WFtbky7bbrBAfNE9Hg+8Xq+ALDZUECCq03ioq+zr65PNlU1tZAC1PrXyRWauZKeoAbJYLAgEAjIpyuFwCHNBu69CoSBWN8+yAtJq0VK7Y1mGpKE6NZ+cssWNtdlsIpPJyAJFLTVZK1WmouV1BSCMbzabhd1uh8lkElcCp9MpTVW8jpFIRMDqzs6O+IXSPFrrZiVGu92W+8jyP0d90iuSmw+vVyKRQCKRQCQS0XRK2bPiNKhWJ2OpAF7dJLmZqtdLnV+u9T1Xc1U1gdTaU9tF/9dSqSRSFgJVNo5Se6tqqLupqSWjp07IqtVq4gPLZ4CjgrmW8YBA3S3fUX5/rTR2PKSyYqZqaFW3Dzb+sFRJFojleDKWAOSzajVZjewpKxKsVNEJhGw52Te3243R0VH4fD5YrdaOagEP+upQGa3eq9NyquHhYUxMTGBubg6Tk5My/IUgm766brdbmDWuRwA6XAycTqes/93Kk96+AKRJaHJyEpcuXYLf7xe2UH3vKBGhHWQ4HIbFYkEqldKEUFPH1Y6OjsoQCjaf6vV6jI6OYmZmBleuXMH09DQ8Ho80U7LSQ6DKRrZAIACPxyMHAC1CnVI4PDwshznup3q9Hh6PB5OTk1heXha3AGqpKVfigctkMkGn02F0dPRbbPZfipdiVrlZVioVJJNJ0aSy65M/3GAwYGRkBFevXsXs7KzYHYTDYfz+97/Hb37zG7Hh0XojIDPBcm4qlUI0GpUmMLJp9AMcHx/H4uIi5ubmhIXN5XJ4/PgxvvjiCzx+/LgrncAMloJ4clJLBQ6HA6Ojo8JqclEbHBwUgEU/yN3dXTx69KjDFkZrUE22BHgKDDh9paenR+yhOK2Kf5dsBS21OMNezVOrXPkM8hRIv1EAMiucoJqAttlsIplMirA+lUqJfRBZOXUqh5bB55Xek9lsFm63W+45S25kAfn39vb2EAqFsLW1JdZKaiNkN9hAvldcOLkREchwY2cO7FQlq/4sRrUb4IrAg/ZoXGBLpZJM1VLtc9rttjAI+/v78nvdYn1VoKqCNh48OdGLc+lVJ4CJiQkMDg4K2GGDndblXwAdpX82VKnSBBIAHJWtWhZxohnZVR68We3i9dcKqLJ8y4Zaj8cj0/3cbrdUUzixkJ+B158Si0aj0cHOagVWCap4fbxeL2ZnZ2WwDn3AORCCLCuvIXNhSZqNjhx8YzKZ5J0863VVc+UUxUuXLuHmzZtYWlqCx+MRB5VsNiuHAD6LBH+8nnxWp6enhRHmAImz7FPMk4d79qeQjGg0GrBarQgGg3C5XFhaWpLSOUEVn18AUrns7+/H1NQUUqkU7t27h4ODgzOzwHxG2SPBYQU8bNjtdmmYvXLlijDp1PxHo1Gk02lx2KGLAJl3q9WqGbvOXDlSmW4OrKDSu354eBgLCwu4dOkSBgcHodPpEI/HsbW1hUQiIblSlkG8SEs79bD+5+KlmVVuUOl0Gvl8XmxfmIzBYMDExARGRkZEE0Lh8rvvvov33nsP4XD4WzoQrRZablTZbBahUEg0smRSqe9sNBoYGxvrmG7TarWQSCTw6aef4re//S0++eQToby5cWlZsqacIhKJiBcpdam1Wg2FQgGxWEyMy71eLwYHBzEwMCD2W1tbW/j444/x4Ycf4uDgoIP91hK01Ot1xGIxmZ7j9/sFwLOhjYwG2Yqenh7Rf+7u7mJ9fR2rq6tYXV2VJhGWOrW6rpR8FAoFmbnNcgS9CsnykK2kCfjDhw8RCoUQi8Wwu7uLWCzW4bmopQ5UXfx4v1QWgvoq9bTMv3d0dIRoNIpoNCoATLXY4ffUGrzw2eLGrdrEqSwJ/w6Bivo5u1X6P73wqWBTNbBXvVLpw0v94vz8PNLptMgZ1O+ldZ5qKZ1fdB7h5nRycoJyuSwG4l6vF3/3d38nLhd0XVBHRGqRLwEgvVLZ/MXhKhzmUa1WkUwm0dPTIw0sBE89PT3C/JL1om7RZDLJzzorqCL5wI7poaEhDA8Py+Q06iQJkikNoEaV73e5XEahUAAAyXdgYEAmcp31evKdvnbtGi5duoTZ2Vkxg/d4PFJBISigBpj/z+e22WxKdZDTzmw2mwy3YZPNWXJlRWd+fh7z8/OYnp7G5OQkxsfHMTQ0JOTEycmJsHwkrFjJVPsYaF9H1l21NDtLrqp9JmUdfr+/Qxvd398Pv9+P3t5eOJ1OYXwrlQq2t7eRzWah0+ng9Xpl3j2v8elmt7OAaspSOGWQOXLvdDgcCAQC4qDCkjkrvKlUCsAT95JgMCjkBadFajVylbnynVfHULfbbWm68nq9mJychN/vR19fHwqFAhKJBD7++GOkUinpX5mZmRHSijIbdQ1+nnxfCqwCT8Egu+hZ/lfn/05MTMgJDAASiQR+//vf46uvvsLu7q7m2s/TQUYnl8uh0WggkUh0GBrXajXxM6VTQE9PD1KpFD799FN8+OGH+Pzzz1EoFDpO1N3IuVqtyuhUMnzUh3D6yvj4uIw5MxqNUl79/PPP8cUXX0h3ntoApCUgaLfbUpo8ODgQA2WWJ6mhu3Xrltiq8IR9cHCAO3fu4PHjx9jZ2RFZBc3qtTbY50JDgEmbD3ql0jKHzSxcMKLRKA4ODhAOh3FwcCB2VQTkWufJUBsYTCYThoeHMTIyAr/fL1Y6p0vVBM28hqelNFpLFVTNIgHM8PAwfD4ffD4fdDodDg8PRVtJBoENedzcToPoboBAsmLqSFpOIIvH4zKxrN1uw+Fw4NatW6IVbzQaAiC6aatEEEI2msAIgEgXstms6GhZ9udaxOlq3DjUZ0OLnPnzVMaMgMDj8cgeQEcIVjHIclFHz8YhPjcAROagxcGPAGR2dhZLS0uilxwaGuoYtaoCb+4DKkumNmRykz59oD3LdSWj9Prrr+Ptt9/GjRs35P3mJCU+E2rzmqqr5vfhc9JqtSRPgkEtQqd7YqM4MTGBN954A1euXEEwGJT81MMqmWkSVXy/WW1j9YpVQK4JWgwFUCuQbEbje14qlaSLn1MhOfKb6yevO/2ASWJQB8o9T4v3SiUbeD24jxNws9TO9YBOL8yTJJbf75feEBIztGDUas1S7yPfGQJ/AlDgCXC2Wq2SK6dqUh8+OjoqkxgBSG9NLpcTWVbXwKq62fDUwcYF1ZD3jTfewNjYmGy2X3zxhdgAdcu3Tg3eRDYpFItFYXi4SXAGO6cy1Ot1fP311/jDH/6Ae/fu4ejoqGsNNQyyPrlcTkTzh4eHsrEDT/Q2i4uL8oCye3BlZQUfffQRHjx4gN3d3Q5P1W7kzOsYiUSg0+nEXJsPtN/vl8WWC3ylUsHq6iru3buHra0tHB4edpgIdyNPnoy5INFeiVra0dFRKZWpeYbDYcRiMbHZoA1Qt/JUQ313OMbU6XQCAFKplJQB+/v75XCosi2ngXS3QJbaaMGypcvlQqVSwePHj1EsFmUK2+LiImw2Gzwej3hYditHla0k6CCTp+o66UCRyWREPxX809AK/juV9dM6T+Bp8ye/aJ9GFo+yBfWL/4Z6N+rFeSBjnlrlyill3GwsFgtsNhsGBwfh9XpRLBZlAhlHJVNPy/eM153rASVLHCGphQaQjDNJh9nZWdkc+bzxHSHzrA6MYRmWYFWdYqXKA856XQlIAoEALv1/9t7st+0zvR4/FCXuFCnu1L7L+5bYSSf5znQGnRbtoC3aiynQy14UBYr+Ib1sgaLoTS/aQduLAl2QFjOZJJOJnThOvMmydkuiSErcd3GnxN+Fex6/VJzF1ofyZH58AMN2YomPPsv7nvc85znP2bMYGRmRseT8DPV6EPypHsHtdltkS5QGsILFdUyLILMWCATg9/vh8/lgNpulcsJmOjUHVYPMtUwFz1w/Dg8PUalUNLN/VCs73PPj8bgMHyD4JAvMa0nMQicLMtTqoJNMJoNoNCrTz7QIVqbZ/V+v12Xd4Wh3suY8EOr1egGyNptN3BjorpNKpbC9vY1EIqGpVJHXkzIu1Q50bm5OBhYNDg5Knw/ln3SC8vl8oqWl/G91dRXxePyFrOteGqyS5QHQseFzwb927RouXbokc8uTySTee+89fPbZZ0gmk5p1q31dnmSdWIrgQz0wMAC32y0ea3a7He320ylR77//Pm7evIm9vT3RYXU7qENUG5CYq9lsxszMDObn50XHeHR0hFQqhQ8//BC3bt3qmqOCGjyc8KGt1+vY39+XDZdDAtQpMK1WC5lMRsr+HBLBja1befIl53hcdtjbbDYZC6caGbNsRS/VZDIpG+pp3H+VISBYDQQCMJvNqFariMViog9zOByyURwHrN0E1GRVeWqemprC+Pg4hoeHYbVakclkcP/+fbnWBwcHmJubk6YKlfnpFvhXwTQ1VWojRa1WQyqVQigUktI62Ynj2sVu6dPVHIeGhqRMTfCZzWYBoENeQ6aanczBYFA8eLmpqvlqUQYku0OZFJuT3G43fD4fEokEMpmMVKoODw9FN+j3++XryKzxHctms6IDP+kzyzV9cHBQhmQQBBJwqhppAF/QyanNYmS1VPBDzaUWoMpqtcLr9cLj8Yg/OcGIuieyAU1l4573+SzJAs/0tlqsV6q0Qh3gk81mJTeVhVbBMplDfg91H+aULvUZ0OJZ5UGoUqmgUCggHA5ja2tL+iYIQlUSA3hWnSDYZt6tVgv5fF4mHGazWc3kdKozTSgUwsWLF8WRwGaziV4ZgLC7BKeqdplMdT6fl4mRsVhMM3KNoJpuT/F4XDSyrDi43W5pCuNBiusED/08BNBmcX19HRsbG1+oAn9dvLQMAPiijRVf+uHhYfz5n/+5nATy+Tx++tOfio6h2+V/NSe11MiXiWWqyclJ/PjHP4bX6xWW8ObNm/jFL36Bvb09KRWfRqhMNeULbFjh9IjXXntN5gcXCgV89tlnuHXrlhgsd6Px5+vyPDg4EN89vV4vCzGb1Gq1Gra3t7G4uIhoNNpRUu92nlx0OCqVWiQAwgSpbhHNZhOpVOoLjGo3Q31GCWLYqcyBFJlMBnfu3IHRaBS7FbfbLYwAgK4eUhjcfDi9bGJiAqOjo3A6nWg0GtjZ2ZFGr+HhYXHVYJMYmdVu5cZf1BoGAgFMTk4KsKtWqwKWqJU2m83w+Xy4ePGilOQ43rYb9189lNjtdtH/UcPJ8ivLbhwMwUEGZ86cwY9+9CMpydLbenV1FcViUbOxoAQj1MjOzc3JlB8ePuhKkslkkMlkxB9yeHgYIyMjcLlcwgCznLq9vY0nT54gmUxqsr6qDgV0THE6nVJ94jp0nPED0CFBIlvJUMfIlkqlDheYlwkVHBM4MSdu8gTI1KES6KnT8vj8UHLBgz8P36o7wElCfZcASIPq7u6uNMbQqYRSG3VqJPEA9fWUtSSTSWxvbyOZTCKbzYpDz0meWe5HPLRRNseJX9QrE5ASrFKTToCqjiwtlUpYWlrC2toawuGwjII/6bvFzyYQdjgcmJqawuXLl8WCTgX+1APTShOA9HfQWu/hw4d4/Pgxtra2xA1GS1aV9+zevXsYHh7G+Pi49CextK8eSDiNi9e00WhIQ939+/exuLiI9fV16QXpKrN6PAgE9Xo9xsbG8Bd/8ReYnp5GX9/TOcC//OUv8Xd/93ddMf5/kRyBZyL3hYUF/NVf/RVGRkbQ1/d0TOjt27fxt3/7t9ja2jrx4vSyOfJacmGz2+24fPky/vRP/1RKwtlsFvfv38c//dM/YWdn51QkFWqOwLMmH2pvpqenceXKFXz3u9/tENhvbW3hnXfewcbGhqZTv75prtyM2CzAUYV9fX3I5XLCTKjm+qcFqI8HbUJcLhfsdjuOjp4aUi8uLmJ3d1fmV9MZgkycWiI8jRw59WdiYgJutxuHh0/nbodCIRmy4fV6MTExAa/X2+GzSUajW7kSvAwODmJ4eBjDw8OYn5+XEbvUUwWDQVitVpw7dw7f/e538eabb8JgMKBUKmFrawv379/HwcGB5tdVlSm4XC7xVQ4EAqINLRQKUsYsFosCas+dO4fJyUnxO6zValhbW8P//M//4NatW5q/X6ptjc/ng8vlEoaHQIBAicw0mXe1g55NoOvr63jnnXfw+eefixm4VsCaeZKhJuPHA6Da1Hd0dIRyuSzNP0dHR2JuT//IUCgkntXUDWshAzAajXC5XFI2JSvcbrdhtVrl/hGMckQsQSnLvgSllUoF8XhchpbQXUWLYDMqddys3LFxhmDU6XTKeqUCPnXELRu/1MElsVhME2aVVRBOxaQsZnl5WcbmXrt2DePj4+JVSgkI7aJYfUskEohGo9jY2MD29rawldStniT47FHqx+bJRqOBaDSKK1euYGxsTHSzbGriL97vTCaD/f19mXa1vr6OUCikibPC8VxbrRYKhQLW19dF4vPbv/3bCAaDoqkl6Od9YJWA/S2xWAzRaBTLy8tYXV3F9va2jDB+kfVKE7AKPNUNjY2N4fvf/z7+4A/+QPQJn376KX7yk5+IUe1pbarPC758ly5dwu/8zu/gu9/9Lvr6+oRR/dd//Vc8efKkq9NqvmmebFK7ceMGvve97+Hq1asiU/j000/xn//5n3j8+HHHrPLTzpcarOnpaZw/fx6vv/46gsEgms0mcrkctra28Mtf/hKLi4uy0J0WqAaesf3UXvl8PmGwqAvihlWpVDomaXWrBPxloYIsu90OnU4n5UuLxSLTwmZmZuB2uzuaBLTS/n1V8JkkSCazxg3LZDJhfHwcdrsdPp9PrEw4kjUajXaU07oRvIa0cqHdztTUFI6OjuB2u8Xb0G63Y3p6WsrGBCs7Ozu4c+cOHj9+3LXKChnqoaEhMah3Op0IBoNipj0/Py8AidPgWHqjY8D29jZu3ryJBw8eIJVKaX7AImgiw8jBCXx/+vr6YLPZ5IB9vNLGSTeZTAYbGxu4e/culpeXpbqmpQawUql0gHt6/ZKt5EZar9dlLHgul5MhNoODg9J8yQlmHF6hFcPOPMnU8RryIMdrWCgUEIvFBICwytNsNmE0GqUvgYArm83Ku6XFlCXg2YCSWCyG7e1tcZ6h+086nUYikZCJSoODgx3gjwNUMpmMMMONRgOFQgE7OzsCrLQCVSSXCOyoi97f30coFML9+/cxPT2Na9euiYctNaN0CMnn88jn86JT5TQorQ6tKgBU3XrK5TL29vZw9+5dsdC8ePEizGazaLw5MYrgOZVKIZ1OIxqNyjROrldarQGqZILjanU6HdLpNCYmJjA2NiZMK7Xz6gHqyZMniEajiMVi0pTN5/llPOA1Aau0J3nzzTfx/e9/X8ZARiIRfP7551haWuroqHsVwYVrfHwcb775Jn7wgx/AZrOh0WhgfX0dt2/fxsOHDzU7mZwkT5YCFxYWcPnyZbzxxhuw2Wyo1+vY3NzEZ599hsePH4tLwau4rtS0OZ1OzM7O4syZM5iYmIDJZEKpVEI4HMaDBw+wsbGB/f39jgfztHLlCbq/vx9er1csv7jxcwNm2Whvb6+D+T/NPNVuZbXphv+No/e8Xi8sFouwrt2eA6/mqDaksFRJfTIN1w8PDxEIBISFa7efzpGmT+FJLXW+Lqg5ZAe61WqF3W4X4+zp6Wm0Wi0xJieD0Wq1EAqF8P777+PRo0eIx+OiG9M61JIZARQbqI6XiAkO1dJ1qVTCysoKPvnkEywuLnalaqGCzWw2i3g8Lno5FQyq/rAsA9NGp1gsIhKJYGtrCzs7O9jZ2cH29rZo3rUK6iDJiDEX6pSBZ6OWafNHcMdhFupo63K5LLpm+lVrCVZ3dnawtLSEWq0Gl8sFs9ksAKZQKCAajeLBgwcIh8PiXMIBGtSw8z3s7+8XcHtwcKDZgYUHFbq+UHPIa0NmmFpWPp/8ObmOco8iMKMjizpgRYvgs6/6a9PPm0z5xsYGlpeXRStMhr3ZbIrdV6VSQTableuulV5ZDa7V/L4kSyKRCDY2NrC+vo579+7JsABWKUulEtLpNHK5HIrFIjKZjNiDqRIMLUMF2MViEWtrayiXy1hbWxNbxampKalOslE8m83K0J9cLod0Oo1isXiiXE8MVglaFhYWcOPGDVy9elUmKt25cwcrKyvI5/OvFAAyT4vFgmvXruHtt9/zlFWIAAAgAElEQVTG/Py8+H/evHkTS0tLmoqoTxJ6vR5Op1PYyrGxsQ5LreXlZfFdfZWgemBgQJoa5ubm4HK5ADz13l1aWsL6+jp2d3c1OUG/bI7M02azibkxx9YCz3Q5qVRKGupOU/5xvEmRf6cNC22fAMg87Xa7jVKphI2NDSSTyRcSqWuRI/BMBkJdo9FoFG9DlXEtl8tYWlrC4uIitra2NF/4jwc3AgJBMlBsolC7vVVNYDabxbvvvosPP/wQjx8/1my04vHg4t9oNFAsFlEoFKQBIRAIyKhSHgJUf1oOOAmFQviv//ovPHr0CKFQSNgrrVlVNnuyiZI2VSz7cTgI9XVk1PgucbpaLBZDMpkUNkitBmmRJ0uVW1tbsNlsyOfzmJiYwPDwsEgAyuUykskkNjc3sbu7K24QKlhtNBrSOMoGIPpBapErwd7a2hosFgsikQh8Ph8cDocAZbJljx49El2vuh7xmeWzAUAGXaiuECcNgqhoNCr6TgLTXC7XcQhgo6oqYVM1q2oDFDWMPKxp+QwQgDLU4SWZTAa7u7t48OCBgHxaQ5IR5vciSGV+WrOVvD6UTVCDykPH2tqaODzw0M1KBN9ztSrYbXKFn6ceOFT7Qo4spySNrDqBKRvfjleBTx2s9vX1wel04oc//CG+973vwefz4ejoCLu7u/jggw/w8OHDDuufVwFayE6Nj4/jhz/8IS5fvgybzSa6ltu3b2NlZeUL/qSnHWrzxczMDF5//XXMzMzAYrGgVqvh/v37+Oyzz7C0tCQjY0+zrK7mSasqeoESSDUaDezt7WF5eVlsqtQF/zSvK/OksJ76K3am08S4XC4jHA5jd3cXhULh1K+rusDXajVkMhlp/qHlE8uZPLlub2/j008/RSgU6jpbyVBLbrlcDru7u2ILpU6wAyBNBMvLy/jnf/5n3L59G+l0umts5fH8isWiAC1OeiK4IiNEti0cDuOdd97Bf/zHfyASiUjzVTeeAS781WpVhhOoY13JTHM0Iccbcjrdw4cP8emnn2JlZUU2BXW0rlahNqysra0Jk8NZ6Wxao80P2dfNzU2kUimkUinE4/GOTYqsq5abv5onbck43MHtdst4bzbW7e/vd4ys5HXr73+6FaoHAy3lYLzv7NxOJBJySLFYLMKulctlVKtVeaePu3yoFQ6CQXVssVZrLA8fPAioB1a+F/xMlt/VHAnKaLulNgypeWqVK/NgqDkQiFJaBUCuH/+7+jXqlEKtq4HHv9/xwz8AOfSpMhY1P3798XHV3ThY8xqqOdJZCXh6HROJRIejBg8oKtg//i69TK4nAqsEVr/1W7+FP/zDP8Tk5CT6+/uRTqfxD//wDyKkV5M+7VCtTX7/938fb775JjweDw4PDxEOh/H3f//3uHfvHgqFwqnY/3xVnmQBrVYrZmdn4XK50Gq1EIvFcO/ePfzLv/wLVldXUSgUXikA5IvObr/9/X04nU4BB++//z6Wl5exv7+PQqFw6o1VzBOALLZ7e3tYW1tDX18fKpUKjEYj8vk8VldXsbm5iY2NDSwuLopR8WleV77M5XIZkUgES0tLwqTPzc0hGAzKGMVwOIw7d+7g3XffFcDC57ZbcZwN5AZJ5m1iYkKYLJ1Oh/39fTx8+BCLi4u4fft2h/C/W3mqQJW/Z7NZYfRWV1flkKLTPR1esLq6ikgkgkgkgkwmg0qlcipaZZaDOTpxZ2dHzPf9fr90/wOQd+q49k9tGOLPr3WQCazVakgmk1hZWekYAEKwR5BNfdtxIHL8z1oH73ej0UAmk8H29rZs8AC+AJCO5wR0AoFu5UkgVCwWUSqVOg6p6md+2WerrBzXt24+p2RXv+yAefz6fZPv1614kXeWQOqrvlc345s+Z1+WZzffpefF132eWhV83nuk1Xr60mCVrJXdbpexYDSMv3nzJj777DMkEgkpq75KoEp/Qo58ZWfiv//7v+PevXtid/EqJQBqA4vRaEStVsPGxoY0/XzyySd49OiReIR2c+P/ujy5MLFxxmQyIRqNAgB2d3exuroqc+rJGr2K60pQValUsLa2hkajgc3NTRgMBikNZTIZ5PN5MTh/FU1gwDPvvcPDQzx8+BDhcFi6bKldKpfLyOVySKVSyGazp3oIIHPBe1oul5FOp3H37l0pW7ObmazmwcGBaMJO4xDIHMmKsXOWIOt4SZ3G+2QmT/OgqjI4nLKXSCSwtbUFAMJQ8Bl+3sH0tO67mqf6/j8PZL2q9RP44hhgNb4ur9PM+yTX6bSByjf9rFd53180vi25vur36ZvGaeWpiWY1m83i4cOHGBoaQiKRwHvvvYft7W3ZTF8FqFKDC240GsUnn3yCvr4+7O/v44MPPkA6nX7lQJU5qoBge3tbNodcLofl5WWxe3mV+l8yWNTMRKNRmWLVarWk6ec0tDRflyfwDASm02kZ/chuUbIxanfmq8qVv6iXU6eEqeUp/noVhxU1Tz6nnKkNdAKsbrN+XxWqbpX2Ocd/jleR1/HgtXzV6+M3iW/Lxgm8+vvai170QvvQfQ0N/aX/k0yg3W7H/Pw85ufnodPpkEqlpPyvsTD5S+sMX5Un9aoOh0MmQHHk1/b2tuYuBV+V5zfJlfmyAYgbbjeaaF72mqqUvzoKsFuA72XzPJ7v/32vjt+1jJPc+9OOk17T04pfhzyBb0+uvTxfPH4d8gS+Pbn28nzx+HW49y8NVpV/88qBwIvkeQq6JE0eiuO5diN+HV60b0uewLcn116eLx69e6999PLUNnrPqPbx65An8O3I9SvBai960Yte9KIXvehFL3rxKqPv6/9JL3rRi170ohe96EUvevFqogdWe9GLXvSiF73oRS968SsbPbDai170ohe96EUvetGLX9nogdVe9KIXvehFL3rRi178ykYPrPaiF73oRS960Yte9OJXNnpgtRe96EUvetGLXvSiF7+y8ZUTrL4N3lvAtydP4NuTay/PF4/evdc+fh3yBL49ufbyfPH4dcgTOFmu9FpXPutlvxW/XvNrqnrCK59zoly7kWdfXx/0ej2/PwCceBpkt+59X18f+vufwUh1OBD//qLxZbmeeNxqL3rRi170ohffttAaYGkZKrBSJwYSDPyqjAzmJEuOsT4+0ZDjwU8CXrTIsa+vD0ajEYODgzAYDOjr60O73Uaj0UCtVkO9Xkez2ZSx668iTwI/m82GkZERWK1WAECz2USpVEKhUEClUkGz2XzlY9eZq91ux/j4OGw2G3Q6HVqtForFIrLZLMrlMur1umbjwXtgtRe96EUvevHScRxYqf9d3fi7PT3wm+RIMKXX6+Xv/f39ODw8FKDCjfXw8PDUc+XI7f7+/o5fBoMBBoNBxm8TsLRaLTQajVMHWLx2AwMDMJlMsNvtMBgMMBqNAlzL5TIODg5Qr9cl51ardaq56nQ69Pf3w2g0YmhoCLOzs3C73ejr68Ph4SGKxSKSySTS6XQHuGKepxUqUB0eHsaNGzfg8XjQbrdRrVaRSqUQCoUQi8VQKpVQrVY7wPVpBkfCW61WjI2N4Td+4zfgcrmg0+lQrVaRTqextbWFaDSKYrGIWq2myTPaA6u96EUv/n8Xx1m14yOjnxenDQZUgMXPPs6wPS+/02LdmJter0d/fz8GBgYEDHLz5cZ/eHgoTFCz2ZSNq9vsEK8XAZ/RaJRfJpMJVqsVQ0NDqNVqKJVKqNVqqNVqqFarKBaLHaxQt/Lks0cQQPA3NDQEl8sFl8sFp9MJt9uNer2OfD6PUqmEdDqNvb09xONxNBqNU2EFmater5c8/X4/5ubm4PF4YLVaYTKZYDAYkE6nkUql5Fc2m0U6ne5gBbt5TXko4T2en5/HjRs3EAgEBFjl83lEIhHs7u4iFoshn8+jXq+jXC53PLPdCjVPm80Gv9+PCxcu4M0338To6Cjq9TqSySR2d3cBoIOh1ul0Hfe926Hmajab4fP5JNdAIIBarSagulKpoF6vyz3mr5Pkeqpg9fgGocarLmccj+MMATcIxqsqFRyP43lykzg6OkJfX18HS/Aq2Izn/ZnswcDAQEdOp/niqTkdL7epf2auDG62p7lAHM9Hr9dLXipg4QKmgpXTKBepm636DA4MDMBoNEKv13cs/IeHhwIC+B51O0/12g0MDAhTReBitVqh0+lQq9UEYDUaDWEw+B4dL2tqHXq9HgaDARaLRQBLIBCA2WyWfB0OB9rtNvL5PAqFAnK5HPL5PLLZrLAYh4eHaDQamud6HAQ4HA54PB6MjIxgZmYGbrcbbrcbHo8HXq8XR0dHSCQS2N/fx/7+PsLhMDY3N5HJZGRDq9Vqmr9PKkhlnj6fD2NjYzh79izGxsYwNjYGv98Pt9uNRqMh5cvd3V08fvwYi4uL2N3dRbFYRLVaRb1eF7ZV6zwJ/gYHBxEIBDA7O4vXX38dk5OTGB0dhcfjgdPplGfy4OAAmUwGd+7cwUcffYS1tTXk83lUKhV5t7QONVc+hyMjI7h48SJ+8IMfIBAIwGg0wmAwwGw2C2tJQB2NRrGysoLd3V2USiXU6/WuvPPqWsnSP+/7b/7mb8JqtaJeryOXyyGdTuPw8FAOUH19fWg2mzCZTCgUCqeyJ/X19cm9Hx0dxfT0NM6ePQuLxSLvd7vdljWJrC/XitMGrCaTCU6nExMTE5iensbc3BxMJhOKxSJKpZKsOY1GQ3IDILgEeDm8pylY/bJN//jv3Gy5sbIEo25ep7G5Hs9V/TvzZBlGr9d3lIiazWbXXrbn5fpVeRIccAM2m80CsI6OjlCpVFAoFLoGCJ4HqFSgR5DAl8toNErJg6xRvV5HsVhELpfrysun5qiW2VTgwtzMZjP6+/uh0+nQbDYBPL2OrVYL9XodpVIJxWJR7r/WG8Px+2o0GmGxWOB0OuH1euHz+eB0OmG1WtFsNjuYoEwmI+WsRqOBSqWCg4ODrl1T3luDwYDBwUF4vV5MTU3B6/XC7XbD5XLBZrOh1WqhXC4LqAqHw0gmk5JruVxGpVLRPE+1XGk2m+FwODA5OYmpqSmMj4/D7XZjcHAQJpMJZrNZ9GHZbBbJZBJPnjyRchavJYGLlu8S8zSZTPB6vZicnMTk5CRmZ2cxPz+PoaEhAQI8AJTLZWSzWcRiMUQiEayvr2Nvbw/pdBoHBwcolUqagxeui3a7HcPDw5iYmMD4+DjOnj2L69evw+12C7vGcnC1WkWpVEIqlcLa2hru3r2LUCiEaDSKZDIJAKhWq5oBweNA1eVyCThdWFjA9773PQQCATgcDpjNZhiNxg794vT0NCYnJ2G32+HxeLC5uYm9vT3NGdbnAdVgMIiZmRlcvHgRb7/9NrxeL2w2GywWC4xGI3Q6neyVY2Nj8Hg8MJlMsNlsePToEWKxWFfXJB72rFYrfD4fZmdncfXqVVy6dKmj0cZkMsFiscDj8WB6ehrlchn7+/sYHBzE4eEhIpFIV0C1us5z7XS73ZiensaNGzcwMTGBRqOBdDota2a9XofZbEYwGITD4UCxWEQoFMLBwcFziSotc+X+aDAY4PP5MDMzg7feeguBQEAOJbFYDOFwGJFIBKVSSYAqASxJCqB7pJ+61pvNZgwPD2N+fh7f+c534HK5UKvV5B5HIhHs7+8LcGVuJ32/TwRWj7NPBCk6nU5EzKruhicy6loAoFAoSPmFepFunbLV/Phng8HQ8RISVA0MDMBiscBiscBsNgtYKZVKohnhAqflaft5LBX/bDKZoNPpBGAR9LMcY7fbYbFY5Gfiy5hKpYRl0eogcBwsq9fNbrcLgDGbzTCbzbKgWq1WeTa4CNRqNRQKBcTjcQELPMRolafK5hoMBim5DA0Nwel0wmazwWw2y+/cFA4ODmRBq1arKBQKSCQSssiqpQ4tcgUg15Eg1efzYWFhAaOjowgEAvD5fHC5XHA4HGg0Gjg4OEA+n0cymUQoFEImk0GhUEA+n0cmk5FctQSCfGfIArpcLpw5cwbnzp3D3NwchoeHBQxYrVa0Wi0BLXt7e3jw4AGi0ajkmk6nv1Au0iJHvi82mw0ejwdTU1M4f/48rl69iqmpKdjtdgEBBoNBnsdSqYRYLCZfn06nkcvl5B6ROdAarJrNZgQCAUxNTWFubg4XL17E5OQkLBaLrE/ciBuNBtxuN3w+n5Sy+b24NpEN1GLDPQ4CfT4fAoGAgNWRkRHY7XZZ61V9KMvbAJDNZgUY8hCg5q1FqOVKXqNAIICZmRkEg0EMDg7CaDTKoZ73mQRFs9nEuXPn5ICVSqW+sjL4MvmpeZIB9Pv98Pv9cpCyWCyir2y1Wh2H/76+Pvh8Prz22mvY29tDLBYTEKZlHCdJqFf0+/0IBAKYmJiA3W5Hq9VCrVYTUoQgzOFwwGazwWAwIJPJIBwOI5PJoFQqdUUbqgJri8WC4eFhjI+PY3p6GmazWRi/fD6PfD4vezmrF3q9HrlcDuVyWfCI1qGST8xzfHwcs7OzmJychMFgkKYqyih4vfis8hrz+ehWqHs99/fp6WmcP38eExMTGBgYQKlUQi6XQzKZRDKZRLFY7GCr1e9x6jIAVQg+MDCAgYEBWaQGBgYwNDQki5rdbofJZJLFw2w2Swk4nU4jEokgHo+jUCigXC4LGtfiIVYXTFVXxTydTqfkabPZZMGyWCzy34xGozCU2WxWyhp8MbXOVQV/BPpGoxFer1c2XqvVKoutyWSCw+GQ68yHN5fLIZVKScmQJc6Thgr21QYAlgemp6fl7zxhE1DbbDaYTCZhUxuNhrBYrVYLuVxOSq8nva5qngR/ZrNZShhzc3MYGhqCw+GQBZXXmKXpXC6HaDQqjKDRaESr1ZLGAUoCtLimzJcAcGhoCCMjI5ibm8PVq1cxNzcnTCU3DDL81WoVmUwGFosF4XAY6XQaJpMJR0dHHc0NJ81VPUxxI6Jm7fLly/jud7+L4eFhed9ZiuNmW61W4XA4UKlU0G63Zd1gE0a9Xhd5w0nvvcpWejwezM7OYnZ2Fq+99hquXLkCh8MhDPrh4SEMBgMAyCG1v78f4+PjAqYASI7ValUz8KJuWmazGX6/Hz6fDyMjI/B6vXL4U6slZFesViv0ej0ajQb8fr+Av2KxKM++ljkCz+690+nE0NAQPB4PBgcHMTAwIKwf/x3BNddUl8uF4eFhpNNpZDIZOShoFccP0WT8BwcH4fF44PP5ZD0nqFNBqlolGB8fF0aL5IqW8bw8h4aGBFwbDAYBgIeHh9LMREJCPdwEAgG43e4OdrNbuao6UMoTAIietlgsQqfTyVrLA4zNZsPExAQ8Hg9sNhtSqZTmOap/5vXxer0YGxuDy+UC8JTFTyaTiMfjiMfjKJfL0Ol0QqQ1Gg14PB7kcjlZO7Ws9h7PU6/Xw+FwYHR0FBcuXIDL5RLSJh6PCwBU92+uBwSt3dJ/H69IGgwGDA8Py3rvdrul0sgDUyKRENcCsr7ECszvZXJ9qSdbLf3x5TGbzaKz8vl8GB8fl4fV6XTCZDKhXC4LqLJYLGg2mygWi7h//z62t7exsbGBUCgkpdeTxvM0agQtdrsdgUAA09PTUoKx2+3Q6XSo1+uyETN3i8WCaDSKUCgEi8WCer2OTCajSZ5qrioL3d/fD7PZLPYQCwsLoq+zWCxys/v7+zE0NIShoSEpZ6bTaUSjUej1euTzeU3Zv+N5Go1GsdtYWFjAxYsX0d/f3wG6Ca55WOEDnMlksLu7i8PDQxQKhY6X46R5qvdfveczMzO4du0arl692qGbZZ78+na7LQsyNyyyrSoLrwU7oF5Xm82GYDCI+fl5zM/P4+2338bU1JR8PjVWqtaPB6/p6WlZIJhrOp3WLFdVnmC32zE1NYVz587h8uXLePvttzE8PCzv0MHBAY6OjmA0GuXrDw8PYTQa4fP5kM/nRYeVy+U0BVfMlRWSqakpzM/P4+LFizh79iwGBwfRbrdRLpdF52k2mwVcEdQPDg7C6XSKBECv13dFD6ge/lmapsbr4OBAcuTayEM0y4E6nQ5Wq1XeMz6/3ZB98B7xvSITmc/n5XqSAeIaarFYZK1gJYMHmm4xa8zTYrHAZrPBZrOhv79futTVJhCXywW/3y+gm3ua1WoVxq1boJq/SI7w2rAyRoatWq3C5/NhcnISXq8XJpNJDlu896oErJu5qhWzvr4+lMtl7O3tYWdnR0rAXI8mJyfhdrsBPH3OBwcH4XK5EIvFpBqgVZ5qvn19fXA6nXLNLBYLarUawuEwVlZWsLGxgXg8LliG0iqDwSD7KVlYrYGgei37+/sxNjaG+fl5zMzMwGq1Ih6PY2NjA6urq1hZWUE6nRZ5hyodUCuv3XKFUAk/i8UiTVVkqvf397G0tISlpSUsLi4KLuIayn2NP68qrXuRXF8YrKoLqsViEUZqeHgYbrcbfr8fZ8+exaVLl4SpBIBKpYJUKiVAi1IAbgBkV3jSOWmoOk4COIPBAI/Hg0AggOHhYZw7dw5Xr14VdoKAaW9vTxhMh8PR8aIVi0XEYrEOuYJWQIVgmqWIoaEhjI2NYXx8HJcvX8alS5fktF2tVpHL5dBsNtHX1/eFXFutFvb390VIrkVTkFpO58ZjMpkQDAYxOzuLubk5vPnmmxgbGxPJRDqdRj6fl+vLkiRPubVaTcANdbUqO3OSXLkQmEwmuFwunD17FvPz87hw4QIuX74Mp9OJcrksXbW5XA4+n080eXa7HQ6HA0dHR1I25KKgNgidNNSFy2g0wu/349q1a7h06RKuXbuGsbExGAwGYXl3dnZQKBQQDAaF4bDb7fJuqXKQ42BAiw2X19XtduPcuXO4fv06rly5Ao/HIw01Ozs72NraQrFYxMjICFwuF7xeb4fPocFgkEWMOvBugCur1Qqv14uRkRGMjo6ir68PmUwGiUQCu7u7SCaTqNfrGB4ehsvlkkMf2TfaBRWLxY4mlm7ow6jh5kEjHA6jVqtJo0+r1YLRaJSmG2pZy+WyaFQrlQqq1WpHSfakuapNe/wz16FsNovNzU2sr68jFAqJjMdmswlYcblcMJlMqFQqHaC2G8yVKifhetVut1GpVBAKhRCJRLC1tSWaOrPZjOnpaSwsLMDn88FsNkunOCsoWusrj19PtRmx2WxiZ2cHoVAIu7u7KJfLYlU1NDSE/f19yfXo6AjZbBbVarVrTX/MlWsUWWCTyYTDw0OEw2Gsrq5ibW1N/DWr1SoGBweRSqUQjUYRDAYBQKQMPORoeQBQ8ySrygYgv9+PZrOJBw8e4KOPPkI0GkUqlUKxWITVapVDNPB0TzIYDHC5XB32W1oGD5h6vR6Dg4Oy1nu9XhweHuLzzz/HzZs38eTJE2F+mScPI7VaTSrFADrs1rTOlXhqYWEBv/d7v4fz58/DZrOhXq/jo48+klz5TvEgpTbUqoCVB4AXiRcCqyoAYMPC4OAg3G43RkZGMDY2Ji+9y+WSpopisYhIJIJYLIZgMPiF8tbIyAh2d3c1ZQSYJxsSKLSf/L/uytnZWVy6dAlDQ0NS3qeQOZlMSnlldnZWSkcE5ipg0ar8T1qf4MrhcGBmZgaTk5OYm5vDtWvXYLPZkMlksLOzI6Vpljl9Ph8mJibgcrmESWC5vVqtvtTD8WXXlWylyWTCyMgIzpw5g5mZGZw7dw7T09PQ6XTY39/H+vo6wuEwqtUqXC6XnFiHh4dFHsKDC4AvNK5odQiwWCyYnp4WTeXCwgLcbjeq1Sq2t7exurqKaDSKer2OYDAIs9mMoaEhBINBOeiQyaJ84biJtBbBA8DIyMgXNs90Oo1Hjx5JE83h4SFqtRocDgeq1SoCgYAczHg92TlMRk5LvSqlPsFgEMPDw7Db7QCASCSCe/fu4cmTJ1LmI8tOkMqyYLPZFB0wu4O1BgXMlQy6KuXZ2NhAKpVCuVyW55kLqsVigU73zN4mnU5LQ1i33CC4cbMEmEwmRS+ZSqVEImGz2aS8yga1YrEoDYrchLV6558X9KjM5XJSvTk4OEAqlZKNidIplih5wFf3Ba2dANRmEzLk+XweNpsN7XZb7mEmk0Gr1ZLnOBAIyNdwzaBMhI0s3WqmJduUzWZF2hOJRHBwcIBcLofDw0OpZB0eHsLn80nuPKRGo1Gsrq5qJkn7qlyBp2s1r2OxWEQ0GkU0GkWpVBLJEYEXD4tTU1MwmUw4ODhANBoV2Y3W+RGssppGqV8mk8Ht27fx+PFjxONx2W8qlYqsDzqdTizNDg8Pkc1mYTAYOqRAWubZ398Pl8uF6elpuN1u6PV6ZDIZ3L17F+vr65In9efAM6aSwf2YOvZurEus+J09exZTU1Ow2Ww4PDxELBbD4uIinjx5gkwmIxI+yhWO9wmpchqSV980XphZVS9Mf3+/6JAotB4ZGYHZbEatVkM2m0U8Hkc4HEY0GhUWzev1yvfhD8SGBS0WBBVQEVhbrVYEg0ERMc/OzkqDSiqVwubmJnZ3d5FIJFCr1cSChQwwO1u5wbJxQYtcVQkAS1Dj4+OYmZnB7OwsFhYWMDQ0hHK5jN3dXayvryMajaJSqcBisYhVDNljtcOeLItWukq1XMky8OzsLM6cOYPx8XFYLBZkMhlsb2/jyZMn2NvbQ6vVQrPZFM9ANovwvjNPlg+1BNXqRJCpqSlMTEzA6XRCr9cLI7S1tYVEIiHMpsvlwuDgYMc9AdBxPbU2tz6ur/T7/bJZtlotxGIxkclkMhkMDAxIp7B6oiaTSqDKJgEtm+u46FA6Q01qpVLB1tYWtra2sL+/L6yF2uDFjYHvO/WVZCu19IpUKxY63VNbqnQ6jUqlgp2dHTHYJhjg51JL3Wq1pAmDDRfdnHJDIEDHCV5nNitwA6aelrIOVq1SqZRUMrQ89H1ZUBpBFqvRaACAkANOp1P6EwDI2plKpZDL5boGqNWft91ui81Tq9WCXq+XRr/+/n5h+rnOc+0Enj2vXD+7CQIJ/mOxGMrlMtrttjDl1LATQFTRXooAACAASURBVFHSwz2Ozy/XkG6FKgOhzCeVSgm4TiQSHT0RjUajowmTlVbuU1ozq2qOZH8tFguAp+A6EokgEokgmUzi4OBALOooVbLZbLDb7SLDqVQq4hmrdRMTASCJlImJCVgsFhweHgpmymQyX9gTSWaxd4KHftWtRuvDPtcdj8eD8+fPY2hoCHq9HvV6Haurq9jZ2UE+n5d3mfpqtVpARv3o6EjIixfFey8lA1AbgUwmk7BlwWAQNptNSkPr6+uyKWQyGQEN/DqVzqYuR6tSoOpFybIuNaozMzMYGhqSkuXi4qJMXMhms3KqtlqtUv5lnqp7gZZ2MKq+kv51BKoejwc6nU5A9fb2NsLhMI6OjjA8PCzlVepryWgUi8WOMpZWQIUNXy6XS/IcHx/H0NCQsBfb29uIRCJygnY6nfI1BKzU43GMHF9MLQ4AzJXaY0o/HA4HDAYDms0m9vb2sLu7KyJ2LhZkVvlvj46OxAWCeXKB0Cq4eLFaYbPZRDrBQ8r+/r6wZjyFcxNjVeLo6EhKwup4Pq0OgSojYLVahe2pVquoVCrS4U+QTHBFprJWq0Gv14tbAbtItT4AqM8A3182gFQqFemsVT0jqcHjz6NOt2HXcDf0a2rObJaq1WryPNAGiiCQz4fZbJZmP9V8nddSi2bKL8sTeCYDoHyJuknVZo1WVtzc2N3MRpxu2OkxP5YgyabzmpLdpYzJ6XTC5XKJZlltriRj3G2wyveWjbHqwU11UKGriupk0Gg05CDVrRxVdqyvrw/1eh3ZbFb6ThKJhJSrWdHkWsa+Fa6vzFFrjTqDJAPlPJT3bW1ticyQ7wZJl+HhYfh8PrjdbpjNZpHhqA4BWu2hqu7farViYmICfr8fRqMR9Xod6+vrIv9gszmBrXoteajm4ZbPupYSJVVKNzIyIvJONm0/ePAA+/v7cqBT+wS4h5rN5g5bQlaOiEuAb2a59UJglS9/s9kUCp9ly9HRUSlhPHnyBPfv38fq6ir29vZQKpUwMDCAM2fOwGKxYHBwsKOLlToiCq5Jx7/sBWfZhnnyVDA/P4+5uTnpXtze3sadO3fw6NEjhMNhFItF9Pf3Y2ZmBk6nU7R2BoMB1WoV8Xgce3t7wmpq8UCwREYtisViQSAQwNzcHGZnZ+UUs7+/j88//xxLS0tiqswmAFV0bzQa5YSeSCSQSqXkmmoVfMkIAAOBgHimplIpPH78WO5ppVJBf38/HA6H2If4/X709/cjkUggFAohmUyiUChoWhLkxk/rGjKAPCHT/DuRSCCdTqNer8PhcIhUhDq7/v5+xGIxWeiYp5a6VbUSoG7sqv+o6uZgMBhkcszk5KQsCACQTqdlLB8ZBEoWtMxVZSu5qNMChmU0Hk7pB0lgFQ6HEQqFsL29ja2tLeRyOU1BNUOtrqgemUdHR3C73SKTIZvNza1UKqFcLiMSichzzPveLXClVgK4kfHQTKsluoAMDAygWq1K+Z3AIRaLiYF9N/NUN1vea7fbjYmJCelm5xrfbrdhNBrFtaJUKglTzQ2sW1IFAFK54cFPdSdgQ2UgEMDo6GhHj0W1WpWDl8pUdytHXgeWelW2FAAMBgO8Xi+8Xi8CgYAcFMn+JxIJqVB083qydMvKqarrJfuv1z/1jfX7/QgGgxgbGxMQyPdI1SprJV1QwTTlHXa7HUdHR0gmkyJTUE3qTSaT2EXRUcFkMokFWC6X05QBVt916mLZVa/T6ZDNZrG4uCgHFuBZIyPzdDgcMJlMaLVacs/pXU1Pba3WenUtGhkZwfj4OAYGBlAulxEOh/HgwQMhGVVryJmZGXHbYbMgJS3VahWJREJ09d90z39hZlU17+cD4XQ6xWsrHo/j448/xr1795BMJtFsNqXcsrCwgOvXr2NkZEQmL2xtbeHzzz/H8vKy+IECJze3ZZ7sPhwfH8f58+dFSxuLxXDz5k3cvXsX0WhUShaDg4M4d+4c3nrrLczMzMBms6HZbCIajeLu3btYW1tDJBIRQbsWLxlvFvUrk5OTuHjxIrxeL3Q6HdLpND777DMsLi4iHA6jXq8LqL18+TJ+4zd+AyMjIzCZTKjX64hEIlhaWsLW1pZon7QoXfJr2Xnu8XgwOjoqLgqlUgnr6+tYX19HJpOBXq+Xf3P9+nXRCPf396NUKmFjYwObm5vCwJJpOSlg5deqmmU2LnAj39/fRzabRbvdFlubsbExXL9+Xbocybx/8sknWFlZkSk82WxWU7YaeMYG8WVOpVLCRPN+s3Kh1+sxPj6Oq1evwuFwAHjK+sfjcdy6dQvLy8sIh8PI5XIdVQAtdYHctHK5HOLxOICnz8X4+LjIa/r7+zH5f53Ah4eH0tDEZ4QHRHXqktbMEA+BZNN0Op1YGLGcxmYPAu90Oo14PI7NzU08efKkYzJUN4eAtNttYaSosZ+dncW5c+dkgyLDwwoPgUK5XBapAp/xbjKBLJuS7RkdHcXVq1fFWo1d6WwMKpfLAhg5KYobazevJ/AMsPJZYNe8w+GQAQyUUVGnvLe3h0ePHmFra0v0wt3KUdXuqaVmAj/gGUEwNjYmVapGo4FoNIqf/vSnuHfvHlZWVjQjUb4sV/5qNpsiAWF3N6WBdru9ozI4NjYGu92OYrGIxcVFPHr0CDs7OyJ30DKOV3x5iCYwIiFA5nVychKXLl3C/Py8OCyw0kvbMK4bKmN/0hx5UJqcnMS5c+dgsVhQqVSwv7+PZDIph2j6bM/OzuLKlSuYn5+Hw+EQKVUkEkE4HJbmpmg0KlIWrSrUbEynLzXB/4MHD6QvgRU0v9+P+fl5vPbaazh//rxI2dgfks/nEY/HMTAwIHv+N/UqfynrKlUXwhIQT820rQAgDVR2ux2jo6O4ceOGgCqyWx988AEePnwoAFBLjRU7+v1+v3T6ms1m5HI5ZLNZVCoV6brnw+P3+/Hmm2/i/PnzcDqd0Ol0yOVyInje3t7uKCNoEeywdDqdGBsbw+TkJEZGRmC1WoV9IOj3+XzQ6XTiwKBaSPCUvbq6Kjph9SSp1cNLSYXP5xMARa0fR9UFAgG02087gjmRhU0M7MBPJpPIZDJieKzlBssFgVo0gkAusI1GQ9wWms0mzGYz5ubmMDExIeP40uk0dnZ2sLe3h1QqJQxwN3V2PC3H43Fp5BsaGsLZs2dlqolOp8PIyAicTieazaYYbXOU5e7urpStu9W1DkCaf6j1oyaYGyxL8PV6HdFoFBsbG1heXsbm5mbHQqW1pEIN6uvK5bK8t3zP6FdLLRXHl2YyGRkPqfr+drMUzDWVoBp4xqbR6om6eWrTms2mSEDa7Wc2XN2+nmQleT0IDqij5f1X/706oYkHrm6UgNXmKtUJgHkSXHs8Hmn2ZcMf/606ofD4hKBu5MtrpjJZbJak24ff75cKJvdV6pUzmQwODg4ENBJYdQsIct9nc+zxkjobL+fn5zE5OQmn0ylyquN9D1p4Kqv5Ac8aValBHRwclM90u90IBAIybW9mZgYXLlyQSWB8RiYnJ1EoFGQtpr5ayylrZCztdrvsSfwc9gOMjIxgYmICV65cwdzcnPRSqM4MPHjX63Xs7e0JMNfiGaDGlO8N1yjKt5rNpsioZmZmMD09jddffx0LCwtCYvG+j4+PY3JyEqlUSnpb8vn8N64CvbSDsNq5WigU0N/fLyP++vr6pBOQvnoLCwuCtHU6HQ4ODrC6uorl5WVEo1HZSLQuV9NomR2BlAjQS9Xr9cLlcknDyOTkJC5cuAC32y3+cZFIRACg1qCKQYA3OjqKmZkZEf6rzLTf75fStMPhwPT0NKampsRe6eDgAPv7+wiFQqJt1HrUItk+n88nXeDME4CMYnO5XB33nvPN2QVM65CDgwOUy2XNpwGp/qrUqVHbxXLr+Pg4PB6PsO+jo6MCAHO5nNgvJZNJYa20nlimBhmgXC4nQMpgMIglXCAQkM9mUxN1zNRc8/RK0KLlQYVBZoW6U3oBA8DQ0JDoUwkS0um0jAQNh8NyoFXBQLcagajfJuPICgpdHtShAJRfsKmGnfbd7AQ/HmQi6e9I0MSSb19fXwc4YIMrQUO3DifHNYtkAykLY5mPz7AKXtVmXHXEKUGaVr7aaq7HG2xV4Of1egX8BQIBmRJFlpONVZT6ABB9qJYlaxX8sRGNw1MIVtjNzrGlLFNT4xqPx6WCQua4W01LvIcej0f2TR7s2VRDsDo6OorJyUmppKnDS9rtttx7rs0nva6qlp59CsPDwxgdHZWGXhJX7GGgLaTX6xXfcpV9p4bV6/UimUxqVk2lrpPVE4JVak51Op0QbNPT0zh79qyU1Pn+N5tN+R5GoxFTU1NIJBLweDxIJpOaESpq3wf7JOjxTaw3NDSEmZmZDh9rl8slBBZJEx4gWFXf2NiA2WyWZrevixcGq+rGQguSeDwuwt5KpSKAhgxgIBDAG2+8gWAwKHqreDyOX/7yl3j8+HEHW6XlQssGFD6sXFjZRed0OjtO3F6vF5cuXUIwGBRGqFAo4OHDh1hfX5cpEnwItARXtJ8KBoNyUmIjE0vA1DGZzWZ4PB5MTEzI1BKyhpxlTh2mlmCFYJW6NL/fL36eAOSUxxnmtKdiAxgZi4ODAySTSSkFdkMPxoWLYIQvGGUB3BCYKzczAEgkEqJxisVi4h3Y7TLw0dERarUacrmciOir1apcc5YpuajSw3B7e1vcFzKZzBcsoLQGqu12W8YVcioZmyvY8EXGhAe7dDqNdDqNRCIh0oRugUBu1tSCE3xSB6weNvjzqFPzuPERjHXznh8vL7JhgptBOp2WsjqZKDIqOp1ONlW73d6xNnfjmh5nKwF0eNDu7Ox09CSweYl6cfW9U6cwaVleVZ1VWEblOkRNejAYRDAYFK9aTrRSh21wAz06OurQEmvpqsLDNAkVjgQmuUJHBbrtcDz0wMBAx/PBQxXwrLFYS7aSAJDP2uzsrDRUDw0NydAKlt7ZWMe9QH2meeBi46BWDDvz5DNGe8pgMCj7APMJBoPSO8GSOvBsOh2fHRIEiUQC29vbHUNrTponvb/Z78HGvsPDQ8Esc3NzOH/+PKampoQ8Y5WoVqsJs819d2JiAl6vVzOrLVWu4HA4xIOcB+pWqyXv+OTkJM6fP48zZ84IqCX7ymompUwDAwPiy88+km9yXV+KWeWiWigUEIvFcHR0JAifp0RqWkdHR3Hx4kWcOXNGDO0TiQRu3bqFd999F5FIpCvNAMAznz02IJRKJemaZsMH2Ss2q3CiFTvAQ6EQ7t+/j5WVla52A6vDCwigCbbJ/rFUofrbkmGj4J2lYNLrWm9cXLTYOUv2gg+cxWIRGw5KRFgmVj0/qbvjqUvrEiuZEB6gyJTwpaGTgTq16OjoSBwU2LFcq9U6ROAqeNHysEJgR7G82+0WVsJms4kJNzeiWq2GarWKWCyG/f19bG9vi9UJc9RiuMLzgloudtWTFeempm7szIE/33Hbt24xqsAzBpg2O7xmpVJJ5BTHGTO3243R0VGRYRzPr1vAGnims6e/Zn9/P9bX17G/vy+MD91CZmdn4fV6RbrE8Zwq+NUyRzJrBG6sWLTbbRn1WywWcXR0JE0WZrMZU1NTmJubkwYb5uvxeBCPx+WwelIQeBwAElj5/X6RUTidTvEFZtMaWVV2M5NVZcMIQRUdGk669qvlX9oOTk9PyzAKNiURWHE0KS2U+EwSnPIwzn2MJItWbCXXHzYcLyws4Ny5c5ibmxOgxTWSDYAkWoBnzzQJIo60ptk+gfZJrqsKqtxuN2ZmZnD+/HnMzc1J34fRaMT58+dRKpVk5KvVahWNMteFZrMJh8MhPSGNRkNIoFQqdWKJBZ8pPouTk5PyHtGOcHR0FGazGZcvX5amRTqoxONx6ZkYHh6WPczlcmFqakr2DC3cC8gwu1wueDwewSHU1vPg5/f75XqzUSyZTGJzc1OcAtxuN1577TXBX3wnWWHpCljlBgA862IMh8NiWaL66gUCAVy6dElM2NvtNqLRKP73f/8X77zzDjY3N2U+My+qlk0gbDphqaFYLMoFbzQaSKfTKJVK8Hg8cLlcUgYmE/T555/jZz/7GT788EOk0+mOzVXraSb1eh2pVAq7u7vw+XzCrJG9pjUJrWtU+6dcLoeNjQ18+OGHuHXrFra2tuQAQGCo1XVttVqi6eP1I1DlC8dRfAQu9DZMJpPY3d0V+cfi4mKHbZWWjUC8prlcTkTq7JSl5ENld1R5yO7urlha7e/vIxKJyKJKlkBLoMp8uaizDMzDCp0W1H/LAyM1v1/mVar1wUr9vqokgvdffZ/JVJE9Ijv4vNy6AVjVa0qArTYh8P9xMMno6Cj8fj/6+vpQqVTw8ccfI5lMdi2/42wlP4eNJ7TOokYMeCoX8nq9qNVqeOutt+BwOGA0GjEzMwOHwyF+h1rlzEOzOl2PaxBlE3zXuPHzsN3X1ycDLM6fPy+slzofXovrS7CijvfmxDKfzwePx9PBqqrgj2wVRyhzCARL2i6XSyQZnMx1kjxZdTx//jxmZmYwPj6O8fFxcaTg4BkeBtQ1CoC8UwCEJKK8ge8+19KTBEvVrIrOz89jdnYWY2NjQpQwNz4D3Pv1er3sPWo1k2VgMskejweJRAKFQuFEuer1erhcLtHITk5O4tq1a/KMUlNNdprXlz0BmUxGHED4vQwGg9gYMueTetj29fXJ9yL5wM9hZWdwcBDT09PSBEoWvVgsYnl5WaZnqjag3DPIdKqa8ZcNPqsmkwkmk0mIP67ZZrMZfr8fhUJBej0MBgPK5TJSqRQ+/fRTxGIxNBoNWK1WjIyMiDyMe8eL7lEvxaxykQeeddvSZJkPh91ux9mzZ3HmzBnZADKZDG7duoXPP/8c6+vrmnqVPi/oq8jFPhqNdnhDFotFOJ1OTE1NIRAIyAkmk8ngs88+wwcffIDbt2932Eh0a1MtFovY3t4WO69wONxhtp7P5xEIBPCd73xHNF86nU6mcnzyySe4f/8+NjY2xJ9Na+aKG+n+/r6ctrmg8kXW6/WYnZ2V/AgEY7EYPvnkE7G1WllZkYeZTSVaAiu+EGTWeCoFIOVTlivUKBQKCIVCCIfDMiksl8sJo97tSTaq9QulCdR8HT/Vq+CVeR0v/2sJqtVfZNoGBwfhcDikPJlIJETrp9PpRGMXDAYFWAHoGlA9DgDJ/LMk3N/fj1wuh0QiIffVZDLhwoULGBkZkQl1tK9TQ2vmn7o95skDHgA5GB0cHIh+lofBg4MDXL58WTw3AWB4eFjeOa1ypV6N5UaCDQJAVePJhq9CoSD6X6PRKMMCVFkON61Wq6WJto4MkMfjwfj4uLA9/DNLvdSE0rKMn61q6gjGy+Wy6EXptHDS0Ov1sNlsuHjxIq5fv47Lly9jenoadrv9C2b/BN/qWFLeU1aHWIlxuVwCYAmqTxq8pleuXMH/+3//D9euXUMwGJT9ic1+rKjwQKM2/7TbbVgsFjl0Hx0dYXR0FMCzXheymy8bZJUDgQAWFhYwPz8vZfNGo9FR3RscHPwCoONAAJ1OJ3IVrlOctsV1+KRgVb0uPGBR482KgMfjEUkAP5PVYTKoZGC9Xq/0q7B/gKAXOPkawH0FQMfADPb6XLhwQcC33W7HwMCAyGjoszowMACPxyPe9jqdDoVCQbyrX6RS/dIyAJXe54VXNzGXy4ULFy5gfHxcyv+rq6v45JNP8OjRIxQKha4CVTLALOmUy2Ukk8mOWbo6nQ5DQ0MyUnVgYACtVgtra2v4xS9+IbZW3fTYY9RqNbHu4khVjoDjNeagApauG40GlpeXxVFhe3tbmqq6AQTIoOTzeWxvb6O/vx/5fF7KvuoITr6QAERMzUPKzs6OGMd3A1QDzxgInkqz2WxHuSUYDIosgC84rZjIphKokpHVuvz/vKB+Wu1k1el0yOfzHRO1CCTUjUwtuXczP5aB2ajAjvVmsykWX41GAzabDa+//jqmpqakzElwxehGWV0FqWzqIWgGgGw2i2QyiXg8jnq9DqfTKZpqasLtdrswCloHNy0yldwYqa0kiOPvlE7w2SPjo87f5nMBaK+ldzgccDqd8rvL5YLT6RQgTXkFZT7qgYvXUtWmkYGtVCqayH/4zoyMjGByclLK/BMTEwKw1AOeeqBjru12W5xD2u226EY5bhM4+XUliTM6Oor5+XnMzMyIxpCgX/UCVgfbqJIUAjR2knPufa1WeyEN4NflymbZ2dlZBAIBsaRTbeCAZ37GzJfsmepywP2K4J+SppOygHyPaEDPZ41MOXMyGAwy7Y9rpvq+EzRzzVC9rmmvxziJXAF4Rvax8kvtPxunTSaTNFDxUNXf3y8VYPps0xaKh6nd3V3Z/7XQVnOtYZMUvzef47GxMTgcDhweHkrnPw8IlLJwwE4gEMDAwAAqlQrS6XRHD1DXfFb5Q/ClUTd6FawuLCzgxo0b8Hq96OvrQz6fx3vvvYc7d+4gEoloblT/vFDZtWq1Ki8HH3DaP7GERtT//vvv49atW9jd3RWP0m4GgTVL4tlsVl405ur3+4XFolVNoVDAhx9+iI8//lgMgbvtr8jpEzwI0JaEp/35+fmOwws1ePfu3esA1Oys7FaoGklOWOHzSUswGquzbEkbqEQigb29PRGHd3t6DUPViHExstlsaLVa2N/fx9HRkWz+fA6o+VHBardyJdAgU8myGhvoisUiHjx4gHA4jGazKd58CwsLcDqdsqEA2rK+x/NTO5fZFczO5Xa7LYct+uxyEtNxZuu47ELLPMlEsSGJYJDdsZyYRu0xn1Gj0QifzydlNYIAAj+tDlRqad3n82F0dBQOh0NYdJfLhXQ6LX6vfH8IHLlWkd1k9YVVrmKxqIkDjPrODA8PY2JiQsZ+u1wucaVRyRWWp9USMS33uK9xA6blkha6el5PejpT6kXwyTULwBdAKvBM1sL/zmc8EAigVCohm81qUgLmdaWbC43d1cYtglWy5TwcHn/2+D7ydx6w+T1O6gTB70uJESumlUqlg7Hku6LKKVSnBwJDlf3kJM50Oi1OFycBqlxPKNUoFAoymZDyCnWsLoEqWV9W2VRtNntrotEowuEw4vG4Js46vAacUEZ7RHrPG41G6avgVEJKqjiMgT0MrFYcHh7K2kt/dU5t+ybx0tZVQKfOVN0orFYrfvzjH2Nubk46mpeWlvCzn/0Me3t7pwJUmZd6ouYDypfHarXij//4jzE6OipTFlZWVvDzn/9cxp11G6gy1KYY6r+46fJB/c53vgOPxyMTtZ48eYKPPvpILLW6DapUQMTyei6XE2aIrKU6YaVeryOZTGJxcVFslbppBM48CYTJqnNTcjqdAk5osaNqVvf29kRHpbWZ/pflqn5/bvZ0XDAYDOJIcXR0JNPKOAqWJS2V+e1WEMBQz0eBfyAQgNFoRCQSwdbWFnZ2dgBANiWDwSDNIzwUdhNQcyG32WwYHR2VUcDUoqpm+vxZxsfHZbgJ7au68f6rmzanqwUCAXg8Hjn4saTHyVsEVmazGRMTE7h+/TquXr0q/sYHBwfCVGhtWUO7n/HxcZnwMzQ0BJ/Ph0wmg3Q6LbppOpO43W6ZbDcyMoLp6Wk5YJdKJaysrGhGBBAwWa1WeL1emfLjcrnE25XglOs/qy68F8AzU3Pg6XNLkMD146Te2qo0hcMUWDbnZ/LgybVKZdbUBk/2XDSbTej1enEOILOu1bpFtlllIlXfWR64OFyDzyr/Da+X6gRQq9U6/IC/qW3R1wXtEzOZDLLZLIrFIvL5vLDPrBKorkB8dhhkEIGn+/He3h5CoZAQGCftVeDX0ks7Go2iv78fd+7c6aiuqN7P9ABXy/vUJNOTdX19XaaFhkIhTaaYcQ+l3/iTJ0/w4MEDTExMYHZ2tqM5ioMCeO9dLpd8HzYmsqFucXERDx8+xMbGBra2tl6ounIisHr8h2OX1w9/+EP86Ec/EgC4sbGBv/7rv8bGxoZ0XZ52HD/tuVwu/O7v/i5u3LjRkeff/M3fYH19HQcHB10v/T8vx+N6RL1eD5/Ph+9///uYm5uDwWBArVbDzs4O/vEf/xErKyunyv7xMwiqVYnCxMQE3nrrLdGCNhoNpFIpvPvuu3jw4IFMqOp2KZ15qqd3jt51u90YGxuTxgl21ep0OhlqQUB9mtdU1aOxAYHepaFQCFtbW1JmcbvdANCxuJ3Gs6qWINnN6vf7xT+XhxEAYiJNGZDFYpGy+mlcU9p9BQIBTE9Pw2q1ijaxr++pD/TQ0BCGh4dx+fJl/Mmf/IlIAbLZLO7fvy/NQd1ggMmKqX6fbrcbHo9HJEvxeFwmwdED+urVq7h8+bLov9LpNO7fv4+f//znyGazmh4EWconQ8nuerLpY2NjshHRslC1pqKWkQeAdDqN27dv47333sPu7q4mmypBExuXhoeHBbyxpMqDNDdUde1i+Z0TzFRHDo6Dpg+4FmyVylayIYmle8rlmBc3eB6caEfE3Pk11KlylK0WZBDZO7WaxzI1gTu9XhuNBsxmc4cchE2zrE5x/T06OsLm5iZ2dnZkgt1JgXW7/cy3lawqGcGDgwPE43HMz89jZGRE1k6CKDbPFYtF5HI5JJNJlMtlYRLj8Ti2t7fFAP9l81T3I0oM0um0ALxUKoVr167h0qVLGBkZkeYwfh3JITYwcZz69vY2dnd3xRUmnU7L/T/pNVX7kUKhkIDXt99+W3qR7HZ7B2vN95EH/mQyKf7alADu7e291BhjzcAqdQxXr17FX/7lX4pJ9crKCn7yk5/g0aNHHZNVTmPDel6w2++NN97An/3Zn8nc6pWVFfzbv/0b7t6923EyfRV5kqWmJ9wbb7yBP/qjPxJbkidPnuC///u/cfv2bQH/p5mnqkflyWpmZgbnzp3D5cuXOxjVe/fu4c6dOzI/uNvs3/E8gc4uXJYyrVarvHxcePP5fEdX/WkfVsgKUotIGYPBYBAR3chgagAAIABJREFUO70DOQWOi4KWDgVflR+lNJxVT2ZIr9fD6/Vifn4eAOD3+3Hp0iWMjY3JQSAUCuHJkyeam8AzN4JA1b+SXeF2u12kCS6XC0dHR2K0PjU1Bb/fD51Oh3K5jOXlZdy5c0dKf93IldeQetmhoSGMjIxgbGwMAMTrs1arSWcwPUsJvg4ODrC5uYmPPvoIq6urXalYUX5AZodNXeyOPn7QUuVLZOLK5TISiQSWlpbwi1/8Ak+ePNF0hn273RZtIT2KeY3VXPh56pAIAha+b5wIxRGW0WhU2L+TyhXa7TYqlQoikQgymUyHR7X6b2q1GjKZDLa3t7G3tyfysFarJQ12AIQ5rtVqAlrYUHzSODp6OkxjZ2cHqVSqw5KSew4lVtFoFIVCQRhTAkFOVCRAY8NQIpEQD2u1K/xlgkBuZ2enY8hEpVLpGJJz//59Ga9K0MwGxlqthlgsJn+mrCadTiObzYoPvBYAkHsgq6aULezt7WFjYwM3b97E3Nwcbty4AavVKg41lUoFxWJRhtQwL1pZkf3XIk+G6ujQbrcRDodlJGwwGMTY2BjOnDkjTh/cj6jFXV9fRyQSQTKZFIDNiYDUt79InpqBVYPBIKO2ZmZmoNM9HVN679493L17t2Ps56sKne7pkILz58/jxo0bmJiYAABkMhncu3cP9+/fl27WVx19fX1wuVy4cuUKrl+/jpGRERwdHSGdTmNpaQmPHz9GOp3u6jjNrwuybH6/H9PT0zhz5gyGhobkBdzd3cXS0hJCodArz5NMH/V2XHhV6QWnVHVzXOU3yZUbPrupjUaj+Br6fD5h1SqVimgGT+MQwAMKASsbhKhPMpvNor0iwOIYQE4DS6VSXXVTeF5+BK0sr3HTJ3tNFrBUKuHRo0e4efMmlpeXNR//zOD35C9Ka1h2N5vNHSwFbQHVaVvxeBxra2v4+OOP8fDhQ2QyGc3fMTqRHBwcIJ/Pi0USN6/jDUA8zLDMyuEvzHV1dRWLi4vCUmnxjpGtKpVKSCQS0pjK94gHawLnSqWCWCyGdDotlm+cEkg/5lKphGQyiY2NDfEu1qqsWqlUEA6HsbS0hMPDQ5lKSMkBP3tpaQkrKytfMP03m82oVCodXq3NZhNbW1uIx+OaTVniASAUCuHu3bvinKPT6ZBKpVCr1ZBOpxGPx7Gzs9OhQVb1oBwWwupKX18fstks8vm8JocAvj/pdFpkCVzPC4UCEomErKFGoxFer1eeW0osWq0W0um0ADNW3Pi7VmOrmSv/zAMUGXROI1xfX8fy8rKsAyRVms2mPLMHBwfiU052WKvGquM5E2RTOlUsFmWNf/Tokch82LzIaXaJRALpdBrFYlEOXOpAgRd9TjUBqwRW165dw9tvvy0v0JMnT7C0tISdnZ2uj1X8uiDrEgwGce3aNbz11ltiq7K+vo7FxUVsbW115Pmqgkzg5OQkzp07hytXrsBsNqNer2Nra0s0H6el/f2yHClsHx8fx8TEhJxaG40GYrEYVlZWEAqFpNxx2rlyA+WGT99ENjZQl6ZuYlosoC+bp9rBy2eAfnzswlansbFU3G1njeO5As+mQ6lzo9l5zcYUsq7xeBy3bt3C2toaisXiqYBqteTbbv9/7H1Jc1xXcvWpeZ4HoDDP4CxKFMXudtsdboc7wtH2whuHd/bKv8B/xGtHOMIre+cOu1vusBSiWi2JosgWZ4KYh0LN81wFoFDfgn2St0qURBKvQErfywgEKJEAEvfdd+/Jkycze6IDJpvFNkUEf61WC0+ePMF///d/S4/AYRUr8tLi6GG32y3p3m736dhKyibUoRrqgJNPP/0Un332GR49eoStra2+zhpa+cj3Ip1OizaahRXs76l2pFABYblcRiaTwR/+8Adsbm6Kno4pdS0v1W63i3q9jp2dHTidTmGhOp0OfD6f/C4EWOvr69jf35cJetQE8+Jvt9vIZDLI5XISvGqxrkdHR6jX63jy5AlcLhey2az0WWXwmU6npbXfxsZGX9s8ngm9Xk+CQ2bbCoVCX9eIkxoB0urqKtxuN3Z2dqQVUT6fR7PZlM4pOzs7Au4G9fesY1CLm9hHWquAheQIBw+o7zT94TunZoLU4JayChYK8vwYbAd4UuP3IbvKM4qZFA72+eqrr/q6mjDo4t2kZgVV4Ks1duH3Bp5N+Gq1WsjlctjZ2ZGzSi2041AKdfiLKrt5VRx4YrDKF+jHP/4x/uIv/gKXL1+G0WhEqVTC+++/j5s3b0oz5dcNAN1uN37605/i5z//ORYXFwEA2WwW//M//yP9VFVQ9bpANUXzly5dwjvvvIOxsTH0ej0kk0l88MEHuH37dp+e7rTXlsDKYrHA7/djZGREdKBk1B8+fIiVlRXs7u72Rfun5SsPIlXTxmIan88nPfdUzRJ1VOoF9Tr2ANOT7GMaiUTkMGD6qlQq4bPPPsPKygpyudxQUuu0QSDNiu5sNotKpYKJiQmZCc6LiYdVPp/H//7v/+L69et4/Pixpulf1fisVKG/evizJyEZHrJvZGDW1tbw7//+7/j8889lfPQwGGD1omEKlUwwq2enpqZkiArwrHK5VCphZ2cH//d//yeFlWTdtM4GcJ9Vq1Xs7OzIxDIyrTN/nPtOfSr3xMbGBvb29qTYLpVKCXAk60IQoKWfHIxSKpWwubmJJ0+eyJQqglAOJmGQR42q2tGGgIrSGvWMPYkRMDOlXCgU4PF4ZPY8O+aUSiWUy2XUarWvPVe2g+LZS19V4KfVnmUwtb+/j0Kh0NevlPtXHV/8TS3zuLfVKnzVTy30qmT9ms1mHwD9tjvneeQAgexgVw2t8IAKKtWfrRoZaU7YUwvzVF/5bwcBqtZAlcZuT/yz6kutVpP1o5+DfgxKAF/FzxODVaPRiPn5efz93/89rl27Bq/Xi2aziffffx83btxAPB6XyPA09YqqkVU9e/Ys/vIv/xLLy8uwWq3IZDL4t3/7N1y/fh17e3t9RTWvU6tqNpul6MLlcgmQ+o//+A/cuHFDGMDT1oAO+srP1KTF43GkUil89tlnuHfvHnZ2dpBOp0+1sGrQ1EsilUphdXUVNptN2m8VCgVsbm5iY2MDX3zxBba2toRtOS22kuvCKtGtrS3RUtdqNVy4cAFOpxO9Xg/FYhEbGxv46KOPcP36dezv7w9dt6yCpk6nA6PRiGQyKQUh2WwWi4uLfd0/EokE7t27hxs3buCrr75CoVAYetGaqpFU03mdTgeZTEbGkrK4bmNjQ+QJq6urKBaLfdWpw/STbBhB/5MnTxAOh3H37t2+HqpMUW5tbcmY2Far1aclG5avx8dPG+Ynk0kUCgVsbGwI28tnz5/LoE8NTOmfWqcwLD8PDg5Ex7e7uyvAg6yZ6hO/5tt8GZb8g0EUO5SooGVQHvK8YkQGpaxa59cNw/j82+3218DVIOhQfR38sxpID8tXMpQvas8Di/RzmHfUy4I1BlHf9r2GbS/i83f5qYWvJwKrbLExPT0t7Quq1Sr29vbw4YcfYm1tDbVa7bWm1on42d/u8PAQpVIJtVoNt27dwieffCL9vt4ECQCF18fHx0gmk/j8889hsViwtraGzz//HJlMRi6r1wFUByNSarC63S7W1tZQrVbx+PFjZLNZqQh9mSkVWhsj72KxiCdPnqBUKuHRo0eiUaxUKiiVSvLxOgrWgGcXLgDE43FUKhXRJKrTgjj9I5vNolQqnXiu9osavz/BIIsTtre3pak2e6iyQrxarX6tn+aw/eRacO9Vq1XE43Hpw6gyvwR+gwzRaTx7Ps92u41Op4NarYZcLoeNjY0+DSiLHAYbxvN7nJafBC3ValV8U3++yv6pX3uaxvVRG9YP+vE6z3bVBz7Hb+uO8Sb4CrwY2PimNX5TfodBe1P9GrTvk5/D9vVEYJWHVqPRwIMHD6Td061bt/Dll1/29dR8ExY9n8/j9u3b2N3dxeHhIe7du4eVlRUB1K+rqEY1Xg65XA737t3DxsYGut0udnd3pdfb62R/1SiL7UE2NzeRTCZhMplkmgZB1Delh07LT172lUpFGDZWYbJilaLv02RTB30lk0L2sdVqIZ/Piy5osKm5Fk3KX9VPXrRkftVUlfrMB5mi0/RT7avJlJr698/7fNqmrguZ4NNq7fUy9qac3y9ir/uZvqx9X/zUTbfXbYbvoHa/9U3iRJVYLIaLFy/C4/Hg4OAAX375JZLJpKYAsNfrfeP8uBf1MxgMIhwOw2azodVqydQnjQsTvnXO3Xf5qmosKVDnRaY163OSNVX1NLxgCWKGkD57ZT/p6zd83xN49dzvd6Jnf5p20jU9Lfsh+Al8f3zV/Xx5+yH4CXx/fNX9fHn7ITz7E4HVP/6br4GBIRVQaA5YhhHV/hA2BaD7+SqmP3vt7YfgJ/D98VX38+Xth+An8P3xVffz5e2H8Oy/Fazqpptuuummm2666abb6zTjd/8T3XTTTTfddNNNN910ez2mg1XddNNNN91000033d5Y08Gqbrrppptuuummm25vrOlgVTfddNNNN9100023N9Z0sKqbbrrppptuuumm2xtrOljVTTfddNNNN9100+2NtW+dYPV96L0FfH/8BL4/vup+vrzpz157+yH4CXx/fNX9fHn7IfgJnMxXrSevDWNN1Z7wWk0609pP+mgymfomx5102M4wnj2HAtlstr5x2iedWvlNvp5o3Kpuuummm2666aatPW/YDu1NGX+rTjE0Gp8ladWxywQvwOsbLUv/bDYbbDZbH1DlSGZ1fPXr8JPraLVa4fF44Pf7YTQaZbR2u91Gs9mU/1bHWb8OX00mE+x2OwKBAKamptDr9XBwcIBms4lms4lKpYJ2u43Dw0PNfNXBqm666aabbq9sBFXfNSXwdQKswfHQ/DPwDFx1u90+YHXaYEAFfmazGSaTST74/whWVNBCkHWaZjQaYTKZYLFYBASaTCaYzWZZ14ODA3Q6HRweHsoHGbjTMK6nxWKBw+GAz+dDIBCAyWQCAHS7XRweHqJWq6Fer6PT6chY89NcU/ppt9vh9XoxOTmJpaUlmEwmHBwcoNFooFAoIJVKoVqtotVq4fDwUPbrae9Rk8kEh8OBQCCA5eVlXLp0CSaTSUBqsVjE5uYmisUims0mDg4ONPFVB6u66abb/3f2TayVar1eT/P05osaAdXzUpf0adCv0wSGKuAjsDKbzX1/D0CYK/XyH2RahuWr6qPFYoHZbIbNZoPVaoXVaoXFYhEgU6vV0Gq1cHBwgMPDQwFaqq/D9pNslcvlgsvlgtfrRSAQgNfrhdPphMvlEuBSKBRQKpVQq9VQLpdlfYcx6nzQV66ny+WCz+dDLBbD6Ogo3G43HA6HrG+1WkW5XEaxWEQ+n0c2m0U+nz8VkKUylX6/HyMjI1hYWMDk5CQcDocA/m63i2w2i3g8jlwuh1qthna7jXq9fipMq5pKDwaDmJqawrlz5/D222/D4XCgXC4jl8shm83C4XAgkUigXC4Ly6ru0WHb4JrOzMzg7NmzuHbtGqxWKzKZDPb392Gz2VAul3F4eChfe3R0hIODAwCv/r6fOlj9ttTGm2b0ldH44AH7Jvisrqcalau+DV4Wr8M3/jc/zGYzLBZL30XACOw0I9pv+qxeIEajse+585A7rQNi0Cc+Z9Uv1VRQcFoXw6B/fL5cPzIqKmtxWn4O+mU2mwWsWK1WmM1m2O12dLtdYS2YGlTTWKfhJ8ETwYrf70c4HIbNZusDNJ1OB/l8HuVyGdVqFfV6He12W5ghvvNa+8p9Z7fb4fP5EI1GMT4+jlgshtnZWXi9Xng8HjidTpjNZuTzeezu7iKVSiGdTmN/fx/FYhGNRgMHBwd9oEErU99dm80Gn8+H0dFRTE5OYnx8HGfOnEEsFkM0GkUgEIDL5UKlUkE6ncbW1ha2trawurqKtbU1AQb0VUswqPpptVrh8/kwMjKC6elpTE9P48qVK5ienkY4HIbX64XD4UCj0UCxWEQqlcLq6ioePHiAL7/8EtlsFvV6fWhnqPoOWa1WeL1ejI6OYn5+HouLi/jTP/1TBINBOJ1OOBwOWCwW8TWdTmNnZwcrKyu4efMm0um0rOkwwDXva4vFAp/Ph7GxMSwtLeHixYv48Y9/DIvFIoxlt9vF3t4etra2kEqlhL3MZDKoVCpDvZO4niaTCR6PB7FYDHNzczhz5gzeeecdAEAmk8Hx8TFarRa8Xi8KhYKcq7wbTuPeVJ8/fZ2dncX8/LzIANrtNtLpdB/7r7KqJpPpRH5qBlZVYDKotxm8cJnaIAAc1IsM83L9JgD1PF8ZeTMqp8aF6YN2uz3UTfK8NX0esOJhZ7PZYLfb4XQ6YTAYxE/S8wcHB5qD7G8DU+p/k3mhj3a7HW63GwBweHiIVquFSqWCcrksh5jWfg6CFjV1pQIYm80mBy5fNEaxjLprtdrQ/ASegQKLxQK73Q6Px4NgMIhQKAS/3w+XywWLxSIRa7vdRrlcRqVSkYuABzKjcC19HWStmMIaHx+XC5Z+OhwONJtNlMtllEolFAoFxONxVKtVAVitVkuAolZ+qoGRzWaDx+NBKBTC4uIipqenMT4+Dr/fD6fTKetN1iqfzyOTyWBrawulUgmdTgedTgetVkvShVq9S+o6kl2ZmZnBxMQEFhcXMTY2Bq/XC6vVKu/V8fExCoUCEokEEokE9vf3sba2hlwuh2azKRo3Lc8oFaiOjIxgaWkJs7OzmJubw1tvvYXx8XE4nU7YbDZhWTudDorFIjKZDDY2NrC+vo6VlRWkUimUSiXU63U0m01hiE5q6h3jdDoRDAYxOTkpfl6+fBlLS0vw+Xziq8lkwsjICMbHxzE7O4szZ85gdHQUTqcTW1tbyGaz8l5p+cxVQO31egUATE1N4dKlS7hy5YqAaa6p2+1GIBDA6Oio/FuDwYB79+4hkUigUqmg0+loCv7pL4Eq13V6ehpTU1O4ePEi5ufn4XQ6JQC0WCwIBAKIRqOYnp7G2bNnsby8DJvNhhs3bmB/f38ozCXXlee40+lELBbD5OQkLl68iFgsJneiyWTC8fExpqen4fV6MT09jWKxiFwuhy+++ELO92Hgkeedn6Ojo5iZmcE777yDQCCAcrmMer0u7HSj0YDFYoHT6QQAOTv5/YDhkH7P83ViYgLz8/O4fPky3G63nJuZTAbJZBKVSkWYVfrGdXzVbNWJwOrzmB46xoOVQI+pIoIVm80GACgWi8IKdDqdPk2L1herqlmirsZisfQxQSqQcbvd8Hg8cLlcODo6QqfTQa1Wk4Pr+PhY84NhEKyo62u322VNyQjxUCYLY7fbZTMQpOZyOfHz6OhIMz9V3xiAWCwWeL1eAdAEpgSnTBWRFex2u2g2myiVSjAajeKnFofYIKBn4GGz2eByuTAxMQG32w2XywW73Q6HwwGPxwO73S6sb6PREHalVquhVCohm83KPtWKFVJ9JXB2Op1wu90YHx/H8vIyxsbGBKySwWo2m3KgZTIZxONxFAoFAf7U4wHQ1FeDwQCr1QqHwwGv1yvg5dy5c1hYWEAkEoHD4ZALo91uo1arIZ/PY3NzE48fP0Y6nUapVEK5XBYAppUYXwUDLpcL4XBYGKtz587hrbfeQjgcFgaQkT+Dplwuh/X1dZjNZgEBtVpN/GTQquUZZbPZEIlEMDc3J4zV0tISgsEg7HY7zGZz30EfDoeFMXzy5Ak6nQ4MBgOKxSIACLOhxWWrnkv0MxaLYWpqSgB1IBAQ8Mfz1Wq19qW3LRYL6vW6sL8MtLSywQAlFApJmloNUBwOhwDqXq8Ho9EIp9Mp5wTTwzz3G43GN2YFT+orGcBoNCrPdHx8XNYLQN+9aLFY4PF4YLFYMD8/jzNnzqBSqaBer6Ner2vqo+qn+j5Fo1EEg0FEo1GMjo7CarUCeCr7YOChyi4cDgcMBgMuXbqEZDKJUqmERqOh2X006Cffp0AgIGsaiUTkHecz7Xa7MBgMCAaD8Pv9cm4xmKLmVksb9JN35sjICGZmZhCNRmEwGNBqtVAqlVCpVNBoNHB4eAiz2QyXyyX3p4qZhgVUVZ8ZUE9MTOD8+fOIxWI4ODhAvV4XX1WgSlxAEvJVgSrwimD1eWk1VbdkNpsRCARgNpvh8Xjg9XrlEKP2xmq1otfrIZ1Oi16E6SxeBlqYCvhUH3koUXBNnRD1NgSATL+0223RCyWTSaTTadE4neQBqKaC6cH1tNvtiEaj4hsBFiMtn88n68xLilEi/6wVyzK4pgRXrA6cn58XQM2DyuVywePxCFg9Pj6WysFCoSAC7VKpJH5qsZ4q8+x0OuF0OhEIBDA9PY3FxUW4XC5JXbndboyMjEhQcHR0hHw+L/uzUCjAbDaj0+n0pdy0AgP0lcUAkUgEk5OTWFhYwHvvvYexsbG+5+7xeOSyr9frSKfTcDqdiMfjchnz75maOeleVQMUh8Mh4Irs2rvvvotQKCSBn8FggNPpRLfbRbvdxsjICAKBAA4PDwX4GAwGYS65plq8UwzsPB6PgL/l5WW8/fbbmJychN1ul4O02+3CarXC5XLB7XbD5/PBYrEgl8vh6OhIQL9aNKLVGaW+S3zuY2NjmJqags/nk+BOZU8oF+DXHRwcIB6PC6vearUENHJvvep6DmZICK78fr9c8haLRdKVg+DGaDTC5XIBAObm5rC7uyt6xkqloska0k+gXw7F+ycUCiEUCsFut+P4+FjAJ89a9U7w+/2y/qVSSYChWvF+Uj8Hs4wulwt+vx9+vx+BQEBkKY1GA41GAwD6MlMMAILBIBYXF7G9vQ2v14tMJqMpqH6er06nU3wl46+eMwSqlLLwDmBBzh/+8Ae43W7k83nN7s5BX/nuBwIBBINBjIyMyD1eqVSQzWZRKpVwdHQk6+jxeODxeNDpdBCLxbCzsyMaVq395GfuU77z8/Pz8Hg8Qopks1kUi0WUy2UB9jz/WbTGAGGYgFXNAkxPT2NpaQmLi4vwer2Ix+NIpVLI5/MoFAqo1+viB+9e/jf3B/DyZOQrg1UCFKafmTplhDA3NydCXB5mnU5H2jK4XC4cHh4im83i/v372Nvbw8bGBvb29oTaPqmp6WdG+KSxXS4XRkdHsbS0JGJxl8sl8gRqnchWmkwmxONx7O7uwmq1CjjQcgOrQFql3H0+n6RS1GiVbAAvZJ/PJ+ki6sOOj4+Rz+dP3PtM9ZO+qal9t9uN0dFRnDlzBhcuXIDRaBRgbDAYMDIyIutst9uFlUwmk8KoMr3Bn3PSC1Zl9l0uFwKBAMbHxzE1NYXLly/j0qVLACCRtsVikeibBwqZV2YCDg4O4Ha75Tmd1E/VV7L7Pp8PCwsLWFhYwPLyMq5evYrx8XH5+bwUrFarMILcz2T8CazK5bIwrARcJ/GV75TVakUgEMDbb7+N8+fP4/Lly1heXobP5xNgykOeoI6+er1ejI2NCbPSaDRgs9n6gNVJjetptVoRDAaxsLCACxcu4OzZsxgZGYHJZEKj0RBpR6/Xg8fjEea01+vB6XQiFAqhWq1KVoXPWiutnQoC1T1L8EcJhyrlMBqNkr2gvwxwqBdlxbOWLW7UYJqAhIFGsViUgp9OpwMAErySITSZTHC73QIMKL9QL2+tgj4CS4PBAIfDIcCeWTxKULrdLux2OyKRCCKRiPgaCAQwMTGB3d1deL1eYTi1ssGMlNlslmCadyW1nZTHmEwmYdK9Xi/MZrMA8UAgIOe/1uyqmjmlrwzyLRYL2u22gBV1XaemprC0tIRIJCIEC/ep3++HzWaTvXJSGwyo+Jng3+v14vDwELlcDk+ePBHm9ODgAMFgEOPj45iZmUEoFEI4HEYoFEIkEhHypNVqaeInTV1Pm82GyclJzM/Py9mUy+Xw+PFjrK+vY2trC9VqVXCI2+2Wu5IZQAByjmktqxgM/i5duoR3331X7qT9/X08evQIq6ur2NzcRKPRkLOBgSDwLINBgP2ywf5Lg1U6zQPA6XSK3iIWi2FsbAznz5/HxYsXJcXW6/VQq9WQyWSEXaNeaHR0VC7pg4MDZLNZVKvVl3XruX4SnBJQ8+IaGxvD5OQkLly4gEuXLsFms0l0wvQEReQ+nw+hUEg0gIwGW62WpilLlUXlZUWQOjs7K6Dg+PgY1WpV9F4ej0fYOK/Xi2AwiKOjI9RqNbns8vl8X282LfxkZO9wOCQiXFhYwLVr1xCLxdBsNpHJZJBKpdBsNuXy6Ha7ctm2222USiUAT7Vt1WpVmMqT+Dp4cDmdToyMjODChQuSNjt79izcbrdoKPf399FsNrGwsCAaUbJs7XYbTqcTVqsVBoNB2D+tNVdkK6enp/HOO+/g4sWLWF5exsjICAwGg2go9/b20Gq1sLy8DL/fD5/PB7fbLXplXiTMZmhlg6ngmZkZXLhwAe+++y5mZmbgcDhQrVYRj8exubmJVCqFw8NDLC4u9hWzqAEkW90wLaw1M2AymeTyYaFSrVZDIpHA9vY20uk0qtUqjEYjZmZmMDIyAp/PJ4HAYLqagY2WhSGDvzPf20wmg3Q6jXw+j0QiIelyj8eDyclJxGIxYYDJRlNfTQZYS6mCWlPAYjRmbo6OjqRI6eDgQIKZhYUFzMzMwO/3CwPH7zGM/paqj3xGx8fHqFQq2NnZEbYslUrh6OhIgOnS0hJ6vR5CoRCsVqswaoNFIlqtpeob8IwtOz4+RqlUQrVaFTkKmXSr1YqJiQkcHh5ifHwcbrcb9Xr9a+l0rd8hpm+Bp2cAQafJZEK9XsfDhw9F483szfHxMXZ3d5HL5XDx4kVEIhF0Oh3kcjkJWrViqmnqHjKbzQgGgxgdHZVsWSKRwCeffILd3V1Zs8PDQzQaDalDCIfDqNVqODo6gt/vlztCS2Z10M9oNIqzZ89iaWkJXq8XjUYD169fx71795BOpwX8q7UpFosFzWYTfr9f9iXfJS1N9dVqteIxHaNhAAAgAElEQVTy5cv4xS9+gYWFBdhsNuTzeXzwwQe4e/cu8vm8ZJrtdjuAZ23hgH7g2263X/p9eimwqkb+KpMaCAQwOzuLmZkZSbUFAgHRMlSrVWxtbWF/f19E5ASDFMAnk0lh3LSKrgn81BT10tKSpIAvXrwIt9uNcrmMVCol6d5OpwO3241YLIbFxcU+RsDpdPZpVbUsXFD1sn6/H8vLy5idncXS0hIuX74Mq9WKnZ0dbG5uIpFISIWg0+lEJBKB0WiU6NrlcsFsNuPo6Kiv8vKkYFXdAwSqFy5cwMLCApaWljA1NYWjoyPE43E8efIEiUQCh4eHqFarkoIfHR0VDR7T1bz8tKpsVP10uVxYXFzE+fPnsby8jKmpKfj9fpRKJTx+/Bhra2tIpVJy0TNAGRkZEQkGQRaDGvUC08IYCDidTmEk5ufnEQwGYTKZkEgk8Ic//AFbW1vI5XKiYVarqlW5DdeTInwtL1tVtzg6OopAIACr1YrDw0M8fvwYd+/eRSKRQL1elwwFMxvdbldYKqarK5WKPHstgioa15QpXF5MhUJBsjkMkJiGZbrabDaLTKVWq6FYLEqjay19pKlpsnq9jnw+L7q6RCKBarWK4+NjaWpOhpBkQDqdlhZB+XwezWZTMy2bWkPAj0ajITpzsmmFQgG1Wk0yPna7XZh+toSq1WpoNBqo1WrCWA/rgqWfbJdUKBREElWr1SQjwcwOv5bsNYOAYXf/IJmTTqfR6XRgt9tFr1itVmG1WkWa4na75bnyXmKARV3osHwEnr77jUYDuVxO1pRZxnK5DAB9BXYs+DKZTCIRW1tbw97engT/WptKVDDLk8/nsb29jYcPHyKRSPS1UWIAxRqBSCSCcDgsRE82m0Wz2dTUPwByNo2MjCAWi8Hr9eL4+Bj7+/tYWVnBxsaG6GoJrFlU63a7hYDj0AC18FNLI5byeDxCnjDw3NjYkPuz1WoJYCYpRTJisAcvpVdDA6sA+hgRpp/ZbmNxcREzMzNwuVxotVqi79zb20M8Hke9XofZbMbY2Jh8PQDZtFpOZlCZSqYtJiYmpNpzbm4Ofr8fjUYD+/v7WF9fRzwel5RpNBoVjajaKoKRNi/Wk9ogsKavXM/l5WUsLi7C7/dLG5jt7W0kk0kcHBzIixWNRqXHHaMZpjipV9TCV6at2Gplbm4OS0tLOHPmjFQD7+/vY3t7W1rWABB9YzgcFoDKaJAvGrWAWgIVMjxjY2OYnZ3F+Pg4fD6fMJWbm5vY29tDPp+H1WpFuVwWvZgKeAH0+ap1cQ0PLh6WIyMjcLvd0hh6a2tLnnulUoHb7Uar1UK32xUgwAOCgLrRaMi/0RIAsiqYBYgmk0mCorW1NcTjcSlOCAQC8j7TF7vdLh0gms1mX+pQy8I6AOIrAR7w9AKNx+PCBPC9I3tC4FytVlGtVgVYaT2RZdAGCw5ZJc8LiwEi068GgwHNZhONRgOpVEp01QxghgGw6OPBwYHIIpiZ4lpSuqRKqAj6crkc8vm8FNZqzaryM/1k9ubo6Eia6lerVRweHkoA6nA4xE9Vl8xqbO5bLZk1VX9MDW02m5WsHgs7j46OBFB7PB7JagHPGE8VUA/bjo+P0W63kclkhEGnvpJSKsoZ6A9/R1WGx+yhVmCVazpIqB0cHKBSqaDb7SIejyOZTKJarUpKmr8TcQ21tZQFERTyXNXKCOBcLhdGRkYQDAZhNBrRarWwsrKC/f19YXi5lwFIbUUgEJDfle9ivV6XPayV8WdQKnPmzBnp4lOv13H37l0kk0kJjoFn2QLuBT4Hylu47ry3XvS9eiUZgIqUVY3q1NQUvF6v6G1WVlaws7MjB6nT6cT4+LhsWDJ/1GTV63XNwIqqByJg4WSIhYUFhEIhdLtd7O/v4/Hjx9ja2pICBaYMnU4nPB4PrFarHMZkBbTyE+iPBFnBOj09jeXlZSwvLyMcDgOAgMCdnR2k02mJrFmYEQgE4HA4pABosMXSSX1UDwKK0icmJrC0tISJiQnRK8bjcezt7SGRSCCXy0lfSIfDgWAwKH6SdaevWraCYgDAZsuxWEzSplarFZ1OBzs7O0gkEshkMqhWq/B6vej1eqKrYsGduj95eWkZWHFtCVbJoPDgaTQawvqXy2XRfpOZZpESI3CmtchcaQmwCKwpASEQYecJXrhkU3hx2Ww2HB8fS6sipjkpZxkGuFJlK91uV1K7nALDdJXD4ZAWVpQjlctl5PN5lEolAVZaBynP87fX68n7y4paXlDUpQeDQbjdblit1r51Z9GIyv4Oy7jP2EmBZyzbMLFS3OPxyLMnCGfByDD95F6ntKvZbPa1H+S74/V6pQqfQQ0Z+Hw+L/tV6+eufi++P8fHx0KWUGbGc6zX68koTpWQ4PnAwGYYwYkKKEmCFItFeYd4zvCMIfnE7Csr1xkIENQMiwVWA+qjoyMJOlKplARyPG/JGLIQiwWDrVZLAC/JKq1kSio54XK5pENFr9dDuVzGxsYGCoVCX+snygLHx8el7y5ZYb5b3Ada+kl2lO2qlpeXhcwpFotYWVkRPKTeYx6PB5FIRO56BnzU/ptMJulkALyYdOWlwapK89KppaUlYSq73S5WVlbw+9//Ho8ePZKqeYvFgjNnzsiB63a7JTW3v7+PZDIpqW2+ICdZcF7cfGHGxsZw8eJFnD17ViKnra0tfPzxx7h3757Q2DabTdqwjI+PS4qz0WggnU4jm82KVEBrDQurZsfGxnDmzBksLi4iFArBaDRif38fX331FR49eoRUKoV2uw2PxwOHwyFtWSKRiBR/ZTIZSSOpBWuvuq5qao3sL1ttRCIRuN1uGAwG5HK5viDl4OAAdrsdfr9fWgeFw2EcHx8jlUohmUxKKomARStjREhdJyN5VvmnUilp88SCpVAohJmZGYyOjsLr9cJgMKBcLmNvbw+5XE7+rVapYFUHSraSmh7qesiWESypjc7Hx8flQjg+PkYymZTqTF4oWg6FUAEgLwK/3y+sGYEsL6vJyUkJZNrtNorFoozj297eFgaBzKrWDDAv+mazKUUJx8fHiEajArI8Hg9GR0dFV1kqldBqtaRIkb1LtZYpDPqr9p7mO8YuICyicbvdsFgsck5S6lEqlST9P0w/B31mhoUdNigLoYb64OBA2gWxGIuM+rDHb5JZpc6caUhW1Hs8HkSjUSwuLkpWCHiWQWHBGAuchuEnwTHBJp8/AbV6qTscDoyPj0t3Av5eDFIYAAxrPSmT4h5j1TfBH9nHbrcLh8OBcDgsbdf4d7VaTaQgvEO1LKxTCzT5MxngJ5PJvvuFsrnJyUnMzMxgenoasVhMpBZqkZ5WwHqQ9KE0wu/3o9PpYG9vD9vb231AlbI+ZoRDoZCQGfl8Xvqqs9OBllOteC9xDOzExISckdvb21hbW5PnyH/rcrlw7tw5wYQspGOv93K5LGzsy3QoeimwyrSt2kKBE0wsFguq1SoSiQQ++OADfPHFFygWiwJqvV4vlpeXce3aNUxMTMhBtrOzg1u3bgmwPelILn4tF4BaGRaDBAIBHB0dIZ1O4+OPP8YXX3yBTCYjfgYCAZw9exY/+clPMDs7K10L9vf3cffuXaytrSGZTMploYUejBuLOsDZ2VlcvnwZkUgEAKRJ8aNHj0Sg7vF4MD4+jrfeekuKmtiagwUuBIwEPSdlMPj1ZNTD4TAmJyfhcrmEFXj8+LG0pmE0ODMzg6tXr+LixYt9AJDapWQyiUwmIwyGViCQTLVatUztH2UpBoNBGoRPT0/j3XffxfT0tDBs6XQan376KZ48eYLd3V1ks1mJJLUAgOrXk7WoVqvY29uTVGW5XJYK+lAoBJvNhvn5eZw7dw4ejwdHR0eoVqvY3NzEzZs38fDhw7450lo2sOe71Wq1xM+joyOEw2H0ej0sLS1hcnISBoMBbrcb09PT0mKrXC6LHuvJkyfyDAbZX62MlytT6dRRTkxMIBqNSqEI2ZNWq4VarYZkMolsNoudnR3s7u4K6B/GEAga13WwSTzrAKhFJ8PDtk8EqK1WC41GQzTKwwKBPEeYzWHWYmFhARcvXpQAmmCKshkA0jWGjOcw/SRZwc8ERCzuJBmxtLSEmZkZBINBOSMYeFGywIBvWJIKysvoJ43SMA7c8Pv9iEajEgy2223s7OzI+cTU/LD2J4vimPFRfVX7bLtcLoRCIYyPjyMajUqdRzKZxEcffYT79+9ja2tL0/66g8aMKtupUd/Nom9mAmZmZnDu3DksLy+Lr61WSwpZWeAEaNd4fzC1PjMzA7PZjGKxiJ2dHXQ6HTmT3G43otEoFhYWcPnyZSwuLvZlezc3N2EwGFCr1eR3I5N9Ul/VO5TZXt71uVwODx8+lBZ5JC7HxsZw9uxZvPPOO1haWhJQ3W63sbe3h1KphEQiIQQctfcvcj+9NLOqagHZw87lcqHb7aJarYoDbFFDYDM5OYn33nuv7xcul8v49NNP8eDBA+zu7moGAGkESxMTE8IAct4uoy1uGPo5OjqKH/3oR9KGBwBqtRru3r2Lzc1NkQponbpip4KZmRksLCwgFovB6XRKxE3t0ujoKEwmk/xe7733HmZnZwX8l8tlqcQmANS6CIhtqsj4sG8qNX1utxtjY2MAIEHK+fPnhc1ic3gWZLDARmtWVU3jsGchD91ut4toNCqV3i6XC2fPnpXZ0c1mE9lsFuvr68hkMigWi6JbHCYQYIqNhWlM+V64cEEqga1Wq2hvO52OaG85KjKdTvcVXgHaVghz/UqlkmiSAWBqakp67KoV/1zL1dVVPH78GKurq9jf3xft4DCaWquHNSUHvPSj0Sjm5uYklU4JBVPUhUIB6XQaiURCWNZhgOlBMxqNQgYwnRoOh6UlFYOuw8NDkdYQgAMYOlClj7wMuSbUfRKwDLajUntzkmUf7B2rtZHBYsACQPxkkSfHr7ItHQPlwYEPBBdaVoQ/z19+JkglITA2NoaxsTFMTEyIfpUMZyaT6cv4qOzisCQBarstFi9yoIpKYszPz0vbKgYoLG4bpq/MqLArCqVcBwcHfa0eKWM7d+4cYrFYXxEtZSJ8v6h31rq3Mju4qO+OyWSS4S/RaFR8XFhYkP71wNNzmPuX9xvBtRaFi2rGl5IZnpWUmgEQDe3sH8evXrlyBUtLS3C5XJLJNBgM0i0qHA7DbDZLb/0XzQK9Up9V0r2MktlqKpfLoVarwWKxIBaLCVvIvpHnz5+Hz+eD0WhEs9nExsYGHj58iN3d3aHoQG02mxQfsQJdjTo8Hg+mpqZwfHwsG5j9TEOhEAwGA+r1Ovb39792wWppBsPTXoATExPScJe9/VgBSM0tp4X4fD7Mz89jZmYGXq9X9CCUUxSLxT6mUiujBpQvkdfrhd1ul3Qb221RkhAMBnHu3DlpH0IdbSKRkMlQZC60NLVjAbVoLAJiVfXMH/vqAYDP58Ps7Cx8Pp8UYbAwkEB1GJXLNIJoMpY8OI1GoxSIMQBgtXW3+3TKztbWFjY2NrCzs4P9/f2hF9hQe1YsFqUHLdPToVCor88nWViCv3g8LmBaSznFN/nK1CObq5NhYes9ygR4CTNFTaaSgeIw9Z80Bnz8IJvLtlkEqzz8TSaT6O0cDofm7ZW+zegTh6WUy2Xkcrm+Nm9qGtVsNkvlOguvtGy0r5oqAVF7VlObzDGgLPYkkOJnfpA9JEDXEljxe6ntiChRoG59ZGQE4XAYsVgMExMTWFhYEH/ZNmxnZ0fOerVFkJZ+As8AIIvn2LeaTf/VQTqhUAhTU1MCAHu9nnQQ4JlPRlllabVgLAmguddI+rBNWbvdhsvlkjtsfHxcilkZBJCdZkqb2RetAlb6SYDP94H7gEVd0WhUCIDp6WkEg8G+DhUARLoWi8WQTqdlTKtWverVWgr2duaZ1Ol0YDabEQqFMDs7i8XFRekMRKklZT+U4TDYajabIiNQ9fnfZq88bpUauWazKSlHAk6v1ysvt8vlQiQSwdWrVzE2NibFLblcDp9++ikePnw4tHnwHLfGSRv0iaJqRiUstAgGgzIXmjqLWq2Ge/fuYX19XbSqWlcDE6yGw2GMj4/LpBU1JTgzMyNNtHnoTk5OIhgMin6wXq9ja2sLqVRKIm0t0gGqn4MBAIuQjo+PRRvMSkW+jJFIBB6PR9JI9XpdWFWtK23ppzqwgI2TeckbDAYZVsEUBoMqk8kkc+zZ45K632EDAYLpUqkk68qDKxwOS7AFPH2ehUIB2WwW8Xgc8XgcW1tbkjEYxthiGhngSqUizd05EYgFVbyEuAepVWXRyrDAtGpk/xqNBqrVKjwez9f0h0xr8/JUix3JsA2r+n/QWGjDIs5isYhsNivAmsCLwIHnaywWQyAQ6AN+w/BV1e5RXsF3hT112f6HgJV/5mXs8/kQDofh9/uRzWZl+pZWPvOMJwhkRTJbk7HdGiepkTGjD/ydCF55PhDoaGEqmKYekW3nOB6UgHp8fFxkK9RUk9nKZDLIZDIol8t9AyNUllZLAGi32wWYBINB0aSqAFH9//w7FqcymKHchRkYLQgVgipqkaempmRsMWVIfIaBQACxWExqLdjmkYEXiReXyyVtDHlXkex61XXlerKVZjQaFWKKhb2UpLBt5eTkpNxNvD+ZfueZOzo6KqwlZYonPV/VrAiLJtX7h1Ivv9+P2dlZnDt3DouLi3IWMYil9I7yBWZYwuEwPB4PCoXCC2VYXhqs8kGxWCKVSqHb7faJvvlLGY1GjI6O4ty5c7hw4YJUhebzedy6dQsfffQRdnZ2hpa6UlNWRO/cFGQok8mkTLoIh8Oip+XX7Ozs4N69e1hZWRHGahgXAQ9USiTUlDvTV1NTU9I2jAcbUwDq82D/SK3bmRDUqy84fVF1QOxtq3ZjoG6FLBVbbgwLAJBVUdmqbrcrvnNqiTo5CYBEgdTS0LRmVlTj92SFdS6Xk4k0fPbqxB8ym6zIZNN4Bn3DBoAsQOHITHbWANDXkoYpIx7QjLRPo/iHflKvWqlUpFUd2R313/L5U0NWKBRk3vng9x2GqfraUqkkrKPFYkEymZT1UvtVBoNB6W5BfT17XWq5T9U0uPp9GQiYTE9H1iaTSUkb8gygnnVsbEyarM/NzWF1dVUCF63GK/O8J0PldDoFOHF0NtsXjo2NSUEVO5KwKT+ncFEaxmIwdRDMSdaSJASB1fj4OEZHR+HxeBAKhQSkRqNR6b9J8oLZHXanYaaDYI2BF7MKJzGV2GGLSo7ZZHEnASkAGU3OO4pZAAB9xc4+n0/OL+qKGTS+ytrSTwZCMzMzuHz5Mq5evSrtJ9mb9ODgAKFQSNYUeFaIx6wfAAQCAfnex8fHUmx3EnZVzTCEw2FMTU1hcXFR9iG7Jc3NzcFms+HcuXMYHR2VVovsv5zP51Gr1aTIlvLAWq2G+/fvy317UokNW2uxOxJ7uQNPO5REo1EByGfOnOnrC14sFrG9vY14PC491q9cuSJY4ejoSMYKv6ivrwRW2+22VB4Wi0XY7XZByVwoNqzn2MhwOAyDwYB0Oo0PPvgAv/nNb3D//v2+ObJashcq2FSp85GREWkCzcjU4/FIpOX3+wEAxWIRt2/fxm9/+1t8/PHHyGazcgjwQNDKmNYpFovY3d3tm/bT6z1rTwE8a8jOQgbKMDY2NvDJJ5/gyy+/xPr6uhQKaB0I9HpPK6uLxaJEoUz5MVImo0GGipohVgGura1he3tb2GpeWK8y1eJ5xguV6fxarSagigwGq5XVVCQP/Wq1inQ6jUKhgFKpJP0rKdTXstpSFe1TP8uUNQtZ1D6/qrFrAFtBkTVUL6thtNzh9+Yeo86S60M/+f/tdjvGxsZkqAbQPwZ0WKylCljJmlSrVezu7opMgcU/Y2NjUs3O+fG3bt3q0+Rqbapmj/7yPel2u5KB6PV60nOTc8R//OMf42c/+5mMLr18+TKi0ajmnUpUplmdrseeiZz4VyqVJNOmno2RSATxeBzXrl2TAteZmRmMj49L30stdKtqlT8DaupSI5EIgsGgFImwwwoAGRKRzWb7WlcBEP0l2ysOdlZ5WSNQZW0EQSplU5QmcHIiA2sGf3yfGRxQv86eywTrDGZPYsyixWIxXLt2DXNzc5iZmcHc3Jz4x0I6AJKdIvuvgmXuxVgshvHxcZEHkrw6yShTAsDx8XH86Ec/kuFES0tLAuTYXo37jH111Yyk2peamkq2aGq328hms0in0yeSrVDSNTExgYmJCYyMjCAUCol+l7Uoi4uLUgRIHzqdDuLxuJASBoNBmvMTGzA77HQ65d+cZF05DMfhcABA393icDgwOTmJVqsla045YLlcxr1796SAlmPMqbsHIIH3yzDVrwRWyayQCTAajSgUClKJxo1+9epVeShmsxm1Wg1ffvklvvjiC9y/f79PZzMMbR21cp1OB4VCAfF4HB6PR7QptVoNZrMZ77zzDpxOp+hpa7Uabt26hQ8//BA3btyQ5uHDSqv2ej1UKhWsr6/DYHjajiQej8s0HU758fl8uHjxougEDQYDSqUSvvjiC3z66ae4c+cOHj9+LE2QGa1qpbdjT79EIgG324319XWJFHmA9no9aWpP3SX9/Oqrr3Dnzh1sbGxItwJ1pKFWoFrdo9QflkolGbNHNsjv98shwYO+0+kgkUhgd3cX8Xgcu7u7MuqSPg6zgAVAHxDkgU8gqxrZK/UiU/3TWqoy+EEf2AeUh2Q2mxUWim2MRkdHJf2rHlDDYKnVdL46o5pBVaVSwf379yXgMhqNmJ2dlYA7EokIG/jgwQMA2gbS9FPtWa12rgAgjb45RYvvNFmLqakpABBwRgaOX0+fT+qjGnwSkLAyHXgmXeAYWk4E49eTyeL4Z4IrstZayGvUAJ7FHpFIBBMTEwJW2aeWGstWqyXdH6j55Pfi781WjCweVusdXnU9HQ4HRkZGcO7cOczOzmJ2dlaK/Sj14HutDnkBIOtHGVMsFkM2mxXZGgsJmaE5iVEXf/HiRVy9ehXvvPOOsNEq0OA+Zs9Stf0a/y4cDsNisaBer0sHk1QqJT3BS6XSK4Nrgqrl5WVcuXIFb731FqLRaF/GjO8ZuxJQGsbnzO4K6nQoAPD7/bBYLGg0GohEIrDZbK+8rvSDbLrP55MC7na7LTIZv98vfaEppyLwdzqdIgdxOByIxWIIBoNyX7CtIQsGT7pXKfdidkztBBIMBrG0tCQtIdnvnfcUAz1mNc6cOYNIJAKT6emIXrbVfJks2ytpVpmyYsTJFK8qGvf7/Zj5Y+8yiv83Njbw6aef4t69eygWi0O99AdF85xZTtqZkSmFy+ynenx8jI2NDVy/fh23b99GPB7XlEn7Jl+p/eXklK2tLdFacpOwPYzawoa+3r17FxsbG/LiD4utYmU3D6V8Pi8vBy+Na9euSZN1AqlkMokvv/wSjx49wvr6uqQytGyurxoZNaZO1GEJ7FEbi8X6GGHu42QyiWQyKZX1lFTwWQxjXVlVDUAuJ15cDKD4fjHq54VGgDJYsDQsMMi1YoU3+9gCwM7ODvb29lAul2EymfD222/j3Llz8Pl8whwNshNag0BVs8zCD2YimE6jZIYFoQAkiGFfwUHwp6UN+khWkAEe8EzDTMaHzCozRQSQvHDZ6xjQTqOuTiRStWtMj7MQ7PDwUAJPSgHUtDz95PvG91OLO4DkCJumc59NTExI2y+mpk0mk4y0Zb9QBjUEiwAElBGoarEPWG8Qi8UkBcwBNW63W84X9fdSjbIm6lz9fj9GR0dRLBZlhr3R+HT87UnZagKrkZERzM/PS/9cakzVokOVeR/smqAWjRmNRszMzMj+rdVqQnS8qnGPRiIRyd64XC6RFxweHvZ1huGf1fVRM4LHx8cCXnu9nvyZe+Qkplb8A5DWiBz/zUDObrdLizAA0p2A9QsARHLncDgkw1ksFvukISc17n+OnM5ms5ibm5Mzlb2pDw4O+thXi8UiWlwWVTFj1Wq1pG85ZUAvega8MlgFIEBELVwxGAxSoXblyhXEYjGRBXz88cf4/PPPhVE7jYIFpm15EamRs91ux9mzZzE3Nydai0ajgU8++QQ3btzAxsaGAKphGgE1J21ks1kBpMDTF54FVWwUzoq6Tz/9FDdv3sTGxoZUrA87ACgWixIA8CUDnr5Ao6OjWFxclAsLePpSrqys4OHDh7h//76k1AfTRFr6ycvQYDAgm82i1+vB5/PJy8H2RQQqtFarJR0VOG99mMVK/J4qsFQveB5OuVxOmm+zqIZAgkGNmvYfhp8qY8nWdYye2Ubr8ePHWFlZQbValULA8+fPw+12CxOgsvDD8JOXJ1lfFq0wJVir1bC/v49EIiE9DZnuVTWFDLa0tsECGzLTBMjUWROgUifHr6VeXQW21IFruaYq0AiFQlI0wzRwuVyWYjDKUHhuEXgTmLFGAIBIHNRxqyf1k8U/ExMTUkA1MTGB+fl5AamUVLHAj+DRbrej2WxKtTX3OdlAtdhOCz/ZSUVl8z0eTx94V/XBqqaaRvAyMTEhkjaz2SyZoZMa73EGJyz45Lmu1h4wgFaLb1TApOqYg8EgDg8Pkc/n+2RYr/qe8Wez3oOAnkMo+J6xY4F63lMaQLaQz1mVXJEp1KJTEZ8pJVPlchm7u7sYGxuDyWRCMBiUc4frx/ef60fjGUxZJnsusw2kFqSKKqUrFApYW1vDwsKCDHrg3UTZDDPtACSDzffI7XZL4JdIJLC5uYlMJiNnwIvYiUIF9QLnw+Ul8dOf/hRXr14Vmnp7exu//vWvsbOzM5Q+pd/kH30kG6weRA6HA1euXMHy8rI0V9/b28Nvf/tbbG5uolarDa1V0aAxbX9wcCD6I/XidDgcWF5ellTM0dEREokErl+/jo2NDdG0DTMAIAhk2oEj7PjM3W63NDEmm9Hr9UT+sbKygkKh8NypFVoWhPBFZ4qRhxfZIDZgbjabfayNEy4AACAASURBVN0MgKegMJ1OS3slrVt/Pc/XwcMdgPTf46g6lQVUQR/T3GphxTD3gJrOCofDGBkZQTQahdvtRqVSwerqqgROkUhExkeqxS4EDsPwTWUsOb1MHQIAAOVyWYpomOIiYGCadZjgX5UlOJ1OAdNcR0oAuEdVuQAnBv7kJz9BJBIRlrJWq4leVSsAqIJ9ZqCCwaDIfNiXltOzeIay9VI0GsXs7Cx+9KMfScN14Ok7lkqlviavOomfBIFTU1OYnp7G2NiY+Kk+T2YA1TQwQSwD70F9KC/skxAB6r7kniTwp5SD6V+COzJ5BIb0n8WpBDh8v46OjmQQj1ZyKhbR8r8BSJBFkKeCQEqXGHAxlaw21ufzYiHzSc9XgmZVq1sulwVAM5tKwEoQpr7XZBBJcJjNZhQKBeTzeSnKPOle5e9KqQxBoBpYezwe8Ze/z6Csi7IbrnU8Hsf6+joSiQR2dnZE835SU8mp1dVVOJ1ORCIRvPfeeyK14PuiZttVXEi5DTsYPHnyBI8fP8bGxgZWVla+1rXo20yzHBcBq9VqxczMDP75n/8ZkUgEvV4PmUwG//Iv/4IHDx68FJLW0tQFJGNw9uxZ/OM//iOCwSCOj4+RzWbxr//6r7h3754A1WGzv4M+quCah6zX68X58+fxi1/8Ah6PB73e01ZA//mf/4k7d+706cROy0dGdJ1OR140h8Mh7TaYFmg2m7h//z5u376NYrHYxxINfl8tTU2F80WiforAiRXqaop/f3+/b0jBaQZVakUsG9i73W6RhdRqNUlVqg3heVEME6iq35tAkAUCBFgbGxvIZrOi6yKzSmBAEDNsXympYNePmT8OBDEYDNJii5pLs9mMubk5/NVf/RVmZmZEz1gsFpHL5TQPAAfZaabIYrEYRkZGMDIyIhp7pkjJXIyMjGBubg5/8zd/g7feegsOhwNHR0coFAr44IMPsL6+rmnGiud5OByWXo4EhJOTk1JQVa1WkcvlZF0ZlKgdToCnxWypVAq//vWvce/ePZTLZc3eL6PRKI3eyQIzFalqPo+OjqQvNdAvF+OZRVDI946yJS2q61lhHY1GRZrCIEkNkNh9gFp5kgPscdnr9YQgYO9KnnMszjypqTIQameZSldbfrGtE+sBqtWqtKvKZrNot9swmUwYHR1FKBRCIpFAKpVCqVRCJpMRmd2rGM8RFnICkDoaFoFXq1VpsRQIBGTfcm35vmezWVQqFQBP90A+n5f/v729feJe8NxXHOBTKpXg8XhknS5cuIArV65gdnZW6ikYpDBTWK1Wkc1mkUwmUSqVhFipVCrIZrN9xNVJ9gDXFIBkIxkUb21t4b333sPy8nJfbYqKXbgvMpkMdnd3kUwmsb29jUQiIdPhUqmUEEKnClaBpwf/1NQU/umf/kmq/xOJBH7zm9/gd7/7nWbV3icxCrJnZ2fxD//wD9KiIp1O48MPP8RHH30kFa2v00/66nK5MDs7i1/+8pfSwD6Xy+HGjRv48MMPRaYwbEZt0Piz1ArXpaUl/Mmf/IkAAfZ9/eCDD5BMJvumP52Wr+rPYlqLE2zIqPIy4MVPFmXY8o9BP2lcUx5uLBxhL97Z2VlJ/1O7POyWVWqajiCGRRX8uX6/H1NTUwJUZmZm8N5778Fms6HdbiOfzw99zOIgs8pWQHzenAo2Pz8Pg+Fpmz2OMmT3jWw2i1u3buHx48dDmQlPwMpiJZ/PB7/fj5GRESwsLMBgMIhOrNFoSDo9Go0iEokgHA7LQJZCoYA7d+7g888/l36bWvqrsmfU1PHS57PnBwMEtXsF/67RaGB/fx/Xr1/HzZs3RdailfEdoA6VQIuMNH1hZbjRaBQQADyrGyAoTKfTuHPnDtbX16VIV4u0aqfTQTabRTabRSAQgMfjkb/jZ4KCnZ0dZLNZabXG+7Ner8NoNMLtdks7ofX1dWxvb2N3d1d8PSkL2Gq1sL29jWQyKaM8uZbs9coZ8dvb2ygUClJZzzO1Wq1K8B2JRCTbksvlEI/HpXDwVf3lmm5tbWFtbQ2zs7NwOBwolUqIx+N9gI+TCrlXCMD4TJjxYzDJArxSqYRSqfQ1NvZl/eSzU6UGzE6yT/bt27cxOTmJM2fOSDBKgqfZbMoQiGq1KoEBCzEpydECu6gkD0kbEhH7+/t49OgRJicnsbCwgKtXrwqzr7ZUfPz4sYBTBv+VSkUKn18WD2oGVo1GI4LBIC5duoSf/exnMmt7fX0dN2/e1DQ9cRIzm80Ih8O4dOkSrl27BrPZLC/l7du3+1pUvW6z2WyIRCIya5cFQPF4HF999RXi8fippH6/zYxGI1wuF8bHxzE9PY3FxUWYTE+nXBQKBan610pH8zKmylOYKmPTak7jILOqHr6crnTaa8qfR221WsjESSz032azSduVYY6A/TZTO3+w6t/j8UiBJSfI9Ho9lEolrK+vIx6PnwqwZuqcejaybQxQyKQx/U/GJZ/P4+7du7h9+zYKhcJQMhbq764WmzGYYrsi7gGXywWPxyNVzGTWs9ksvvrqK3z88cd48OCB5nUADOTImDH9qGbR1OJEpk/5tUx1lstlxONx3LlzB7du3cLm5qZmUjBeqmSjs9msDCag1pJSGQBS2MPm/wSofP8rlQoKhQJSqRTW19exs7Oj2dRCyqdSqRRWVlakL+rx8XHfyOparYbd3V3cvn0bmUwGtVpNLnY+ezKeZOCSySTi8bgwwFqAlYODA7kXO52OTCw0GAxotVpyvt+/f1+mJlJnTYa42WzKHs5mswiFQtKdhW0LT3p2dbtd7O3tST2Ey+WS0aPUUbbbbZRKJQnuVT1tt/t01HK32/1aP2F+LffrSQMA3ivckwT+LPrc3t6G3++XfqkqSdDtdkXnyXG7vLsGtbVayUCAZxkJdvxoNBp947MfPHjQV5TGd4nsL4NutdXhoGTgRUwTsMqWHGQoJiYmJP1/+/Zt3LlzZ+gX1Iv66XK5sLS0hEuXLmFsbAzHx8fIZDK4desW7t69O9QCpZfxk5Hz/Pw8zpw5g1gshuPjY+RyOTx8+BBra2uavEAn9ZNNoycnJzE7O4tIJCKNtZPJJDY2NpBMJjUfUPCi/vEzgQDTlGy4z8Pi4OAA5XJZNEqvI2BRdd+qNpTV9hwMwJYmnBw0jBHA3+Sf2kORHypY5UhIVreTVd3e3sbq6ip2dnaGug/UZ6b6abPZhBFkJwV1IhgA6Q/4+9//Hrdu3UKtVtM8EByUpvAypLaLvrLPJplKdQpbp9PB3t4ePvvsM3z++ee4ceOGTJDSylf+LDLi1HM6HA4Ui0Vh9NQqcABycZKV2tnZwcOHD7G6uoonT55gdXVVqpa1OrvoZyqVgtfrFf9ZVU1ADTyrwC4UCjL+kyxbpVJBvV6Xy3hvbw/FYlGzoQUEo7u7uwgGgwKoFhcXRUpTqVQEzFKHqI6jJMClxILFOCyuedHRld9lBGrxeBy3b99GLpfD+Pi4TCcsl8vIZDJIJBIi6SCgHgxCLBYLqtUqisUi9vb25Hsz+DnJPuA+zefzuH37Nvb39+HxeHB4eIhcLidBFplHnglA/yhZBgLqeaBqMbXYr/SVgZxaxAVAphfG43E8evSor/MK/6zW3TAoAJ5N4tOaZFEBK/cVsw+5XA6bm5u4efNmX1cdfh2LrYD+Hq1qW81TB6tGoxGTk5P4+c9/jl/+8pdyQf3ud7/D7du3kUqlvnbovw4gYLFYMDMzg5/85Cf48z//c1gsFjSbTXz00Uf47LPPsLGxIQuppmVO28hWjYyMYHl5GW+//TZsNhvK5TK++OILfP7557h//37fsIDT9nWw0CYWi2FmZkZ60cXjcTx48AAbGxsoFAp9Ed9p+8nqZAI96izJslHntb+/j729vb72X6fpp1pIw1QlG4irvXXJeHG62tramjBep2XdblfSk71eT1qBEVzxUCI789vf/ha3bt0StmOYvqqgrlAoIJFIiB6UXQw4vIRsV61Ww40bN/CrX/0Kt2/flk4Bw/BTBYKlUkl6I+7v70v7KQJDvi/UKpKh/q//+i/cvHkTiURCmEItA1c+v1arhXw+L5cPW1RVKhWZXc9ei71eTzR16+vrWF1dxebmJjY3NyX9x8IQrXxVmRym69nAfW9vD7FYDACE8a1Wq9KarlwuC1tEIEkwQS2j6utJgQoZNH5+8OCBZB/YgqhSqQjrSKClrpVaIEwtOPf6YJ/lkxiffS6XwyeffCJdE6gBZvcHrt+gRlL1l0EZwSKBCgPJk2orWQDH4k5mVPievcia8PxV+5ry+6sB5klMDZ4JUAlYB7GRSrTwg6aSLINYRcv7Vf2+g90aqKsGILIulZWmqUHs83x9GTsxWDUYDAiFQvi7v/s7/PVf/zWmp6dxdHSE1dVV/O53v8Pdu3clNTCMfpov42cgEMCf/dmf4cc//jFCoRCKxSI++eQT/OpXv8K9e/ckKn2daXU+bDaP9ng8aDabuHv3Ln7/+9/js88+ey478br8VRmL/f19fPbZZ9jd3cWTJ08Qj8exvb2NdDrd1wHgdaTXmerJZDJYXV2VdHW1WkUqlcLu7i7u3LmDO3fuoFKpaMr8vIyPFKbv7e1J8ZTL5ZLG2u12G4VCAbdu3cL777+Pu3fvolQqnahI4UX9Yzro4OBAJkFZrVY0m00UCgVcvnwZ4XAYJpMJtVoNqVQKN27cwPXr17G6uopKpTJUP+mjOmueH7VaDZubm5icnMTU1BQcDgeq1Sr29vawvb2NjY2NvrZqJy1Q+C4/1ck5XKtgMIi9vT3cvHkTXq9XdMnNZhP7+/vIZDLI5/MolUryta/CULyMn4eHhygWi6LjZAUw23qRVT04OJAUNsGByr6f5JJ6ET9VZjSdTuPBgwcCPtSLkp+f145uEDAMw08CawYB29vbfRraQcDxTT6ogGBYZ2qv96yIlr1bn0c4Ecx8kw+DrPQwfOWeGwRVL/OzCKxf9utexl6UBFPB/qCptRSneT9919+96O/0qnYisMrDim1XeHE1Gg385je/wZ07d0RI/zp1oGTXeMDmcjnY7XYkEgn8+te/xv3794feTP9ljIcsx6jSt/v372N3d7ev7c7rAv5qSrhUKuHJkyfShiMejyOdTotWhenN1+ErL6hOp4N8Po8HDx4gm83i3r17UrFarVZRKpWkQGCYQOWbfASeaYMqlQo2NjaQy+WwtrYmaRaDwSD9eNPpNJLJ5Kn6y8uWIIlA5sGDB/j444+lLRUAAS7lcrmvXdmw/VSfN1nAcrmMlZUVYTDV/oC8jFl5rSXr911+qiweR6tyFKyaklR/H37NaQWpqp9s68PzlCCLPg4yUur3OA1TgejzLvnXmdkb9GOQYXtZn07T/xfZZy8CZk7DTsp+v2n2Jvr0TTZsXw3fQY1/609XU+t/+7d/i/Pnz8Pv92N1dRXvv/8+7t+/LyO1TvqL9Hq9b+wa/CJ+cqb2u+++i4mJCQGrN2/eRDab1SyN9m1+vqivFKSzYIVNq3O5nKZptJOuKXvAsg8s0+pqNf0g/f86/FTHAarNoQkAVNH3CfVTJ372amEQ+++x8psXMX0+SXB10jWlr9wH9JtglGk+XszDWNPv8lP1dTA99U3s1et69vT1Od/3lfz5Njvpmp6W6X5qa1rs0dOyH8Kafl/8BL4fvp4YrJrNZoRCIVy8eBGjo6MwGo24f/8+1tbWNGX/tAAsrJ5kPza2YtCS9dNiU6jtVggAVFbqNHx9WSBA09rHP36vEx8IpwEEfggHAqD7+SqmP3vtTfdTW9P3qPb2Q/AT+H74eiKwSnverO/TBAKvClhOm7X4ow9v/KYAdD9fxfRnr739EPwEvj++6n6+vP0Q/AS+P77qfr68/RCe/beCVd1000033XTTTTfddHudZvzuf6Kbbrrppptuuummm26vx3Swqptuuummm2666abbG2s6WNVNN91000033XTT7Y01Hazqpptuuummm2666fbGmg5WddNNN91000033XR7Y00Hq7rppptuuummm266vbH2reNWvw+9t4Dvj5/A98dX3c+XN/3Za28/BD+B74+vup8vbz8EP4Hvj6+6ny9vP4Rn/61gVTfddNNNN9100+37bpy2qMV45WEZfTSZTH2jvzkZ8k0xdeS2xWLB0dGRDIPSYhT880wHq7rppptuuun2BtnzxkPT3hSApQKW54FA9eN1+mg0GmE0GmGz2WAymWS8OoHV0dGRAKzX5StHrFutVng8HtjtdhwcHKDb7eLw8FA+3hRfrVYrHA4HgsEgQqEQ6vU62u02Dg8P0Wq10Gw2cXBwoKmvOljVTTfddNPtle3bgBXw+sEVQZX6MWgELvyz+vm0fTSZTDCbzQK0TCaT/N3BwQGOjo7Q7XYFCJw266YyamazGTabTfxV2cvDw0N0Oh0cHR0JyOp2u6fqJ/1zuVzwer2w2Wzodruyht1uF61WC61W67X76XK54PP5MDU1hXA4jGaziUajgVqthkajgUqlglarJaC11+udqp/01Wq1wu12IxqN4vz58xgZGRE/i8UiSqUSUqkU6vV635qedJ/qYFU33XT7/94IYF43sAL6wd/zUpe0b/N12L8H2TR+NplMMJlMX2NRePF/ExAcpp8qq2axWGC1WmG1WgVkqf+m1WoJM0S2TWXghuXnYDrV4XDA5XLB7XbD6XTC5/PBZrPJRzabRTqdRrVaRavVQqfTQafTORW2TQXTdrsdfr8foVAIo6OjcLvdcDgcMJvNArBrtRrS6TTy+TzK5TLq9TpqtdrQfVX9dLlciEajmJ2dRSwWg8fjgclkEiDd7XaRy+WQTqdRKpXQbDbRarVQr9fl+Q/LBv0cHx/H0tISZmdnMTo6ik6ng1KphGKxiHq9ju3tbeTzeTSbTXQ6HfkdhpV2/yZfHQ4HxsfHcfbsWZw7dw7z8/Oo1+tIJBJIJBLw+/1otVoAgFarBYPBgKOjIwAnkzO8FrDKw3jwgngTLgrVBqNx1c/Xnd6gqWvJzWSxWPouh8PDw1OPwFTf1P9WD2aLxSIHwvHxsfh5Wus6uA9VH/nBw1fV4vBCOw0/B58v1w+AfB78t8AzpkhlYE7DP15UJpNJ/gygT9OkpuDoqwoMhuHjIHCxWCzyrvBzr9dDu90WoKICFq7hsPykfzabDQ6HA3a7HU6nE16vF16vV9g1+t9ut1EoFCT91mg05P3hc9faV/V88fv9GBkZwejoKCKRCAKBAKLRKOx2OywWC4xGI46Pj1EoFLC7u4tkMol0Oo1yuYxGo9HHEHJvaOUj/VQZoPn5eUxMTGBychJLS0vw+/1wOp1wOBwwGo3IZDJYWVnB5uYm9vb2sLW1hUwm0wcMtD6b1NSvy+XCyMgI5ufnMTU1hZmZGVy8eBGBQABerxculwtmsxmlUglbW1u4f/8+njx5gvX1dWxvb6NWqwn4GsZZz3fHarXC6XQiFothcXER09PTWFpawoULF+DxeOB0OmG1WmEwGFCv17G3t4eVlRVsbGxgfX0djx49QrVaFV+1ZoUHgf/ExATOnj2LhYUFLC8vY25u7v+x913NcWXX1asbnXNGd6MbjQyQBINmqFGw9b1YLpeqVGU/+1f4J/in+MVPepGqZJVKtkbjCeQEpiGYQORuAJ1zzt8DvfacBjnSkLggR6reVSwwgMDGueees/baa+8Ng8GAwWAgz/T09BS7u7s4Pj7G6ekpCoUCUqkU6vX6RCpbaz+5T81mM4LBIC5duoT19XVsbm5ifn4e3W4XqVQKyWQSpVJJ7qNcLie/ByBygbcBWA0GA7xeL1ZWVrC6uoqrV68iGo3K+VMqlZDNZl86LwHIPfqmfmoGVs8Ck2+7SHU6nVwQ1I+cjWIvcuFfBUxe9Tmku00mk2hdeAlQP9Jut98aEAC+WVP173ngWSwWueAsFgvG4zEGgwG63S7q9Tqq1eqFHQ5nAYvRaHyljzzo+MtgMGA0GqHb7UrUXalUJG2gtZ8qG2QwGGCxWOT3ZFt0Op2kjaxWq7AsnU5H2JdGoyEpjotaT/pFP91uNzwej1xeVqsVOp1OUkLdbhe1Wk1SRr1eD91uV/RDWvrKZ8t0pcFggM1mQyAQwMrKCgKBANxuNxwOB0wmEwwGg+zBSqWCbDaLbDYrB1yv10Or1UKn09Fsj6rryOfpcDgQDAaxsrKCRCKBQCAAp9MJq9UK4AWgLhaLKBaLyOfzSKfTODo6QqPRkPXsdDqaX7R81jabDQsLC1hYWEAoFEI0GsXq6iqCwaCkWgkC+/0+MpmMsBnJZBK7u7uoVqviY6fT0ewi4ztsNpvh9Xrxgx/8AAsLC0gkElhaWkI0GoXb7ZaznX62221ks1ns7e3h4OAA+/v7ePLkCcrlsjCDTG2e10cVqFgsFni9XqytrSGRSGB5eRkbGxtYWVlBMBgUhpXBVDwex9LSElKpFB4/foxHjx7hwYMHSKfTEywmGaLzGp+52WyG0+lENBqVfbm6uoorV65gbm4OdrsdZrNZAgCn0wmPx4NoNIrl5WXcvXsXAJBKpVCr1dBqtTSXBpwFqoFAAIlEArFYDEtLS7h8+TLi8TgsFsuErz6fD8FgEAsLCzg+Psa9e/cwGAzw/Plz2adaB1Pcp0ajUXwNBoOYm5vDwsICfD7fBDbR6XSIRqNYW1tDNpvF0dERjo6O8OGHHyKVSmE8HqPX62l6x589400mE1wul6zrwsICzGYzWq2WvBd6vR5+vx9GoxFerxf5fB7VahXpdHriDr6oYJ97wGAwwOl0SgCYSCSg0+lwenqKfD6PfD6PXC6HXq8HAHLHv1MZwFkwwiiBf8foSgUCBoMBVqsVDocDFosFAJDJZIQdUA9WLUHgWVZK/Wg2mwFA/ONhR1bD4/HAbrcLUKnVapIuGA6H6Ha7mm9k4JsDgozKzMwMbDbbBDNEIE2ti6rJGQ6HaDQaEpF1Oh1ND4ezL9zZy4wvISNci8UCp9MJh8MBu90OnU4nF2q9XkehUMB4PJ4IALS4vLie3IdcM6fTiUQiAYvFIv6ZzWZYLBaYTCZYLBbMzMyg2WyiUqmgWq2iXC6jXC4DgBxgF7Ge/P5Wq1U0TKurq/D7/fB6vXC73bDb7bDb7QLyq9UqMpkM0uk08vk8yuUyarUaAEwwV+c9MNSDi8/U5/MhHo9jfX1dIm2HwyEAy2KxoNFooFKp4PT0FHt7e9jb20M6nUalUkGtVpN3nu+TVsDFZDLB7/cjFovJBXvlyhWsr6/D6XROBKI6nU4CppOTEzx79gwWiwWnp6eo1WpoNBoTZ5OWfhqNRjidTiwtLWFxcRGLi4tYWVnB/Py8sGpcdx780WgUiUQCx8fHcDqdGAwGOD09lT3KwPrbZASv46Oq//N4PAiHw4jH41hdXUU8Hoff74fJZJJzgH6SabPb7fB4PDAajahWqxPExKsKdM6zltxzPp8P0WgU8XhcGEu/3w+bzfbSXWA2m+Hz+SSoJiusai57vZ5mfjLQMxqNcDgcmJubQzgcxtzcHGKxGDwej6wnA2b6a7fbEQ6HYTAY0Ov1cHx8LBIGMtZambqmTAEHg0EEg0EEAgEEAgE5zxlEARAyymazyZr2+33s7OygVCqJfEFLO5tddDqd8Pv98Hg88Hg8MJvNAqIATDCwVqsVLpcLPp8PFosFyWQSlUpFWHWt7Www7ff7EQgEEI1GYbfb5U6sVCpyJxI3OZ1OuN1uHBwcoFwuC2a6CEZdxU683xlILy4uwu12I5fLIZfLyZ3Tbrdlf6vZaJ6x/PPr2huB1Vel1biZCfp8Pp+IhnkpWCwW+bPFYsFgMEAqlcLx8THy+bywQlpujrNsmspY8SLjS2iz2WA0GoWF8Xg8ohmq1+uygU5OTpDJZITB0srUA5TrSYBltVoRiUTEP7vdPvF7t9sNp9MJg8GAZrOJZrOJUqkEg8GAVquFcrms2eFwdk3NZvNEdeDq6qr4TpaDTBuZrH6/j0qlglKphGKxiNFoJEBBKx2OukcJ6gmuyGBQC0Zw6PP5ZM0BIJ/PI5lMIp1Oy8tHTZOWYnz1AnM4HPD5fIhEIpibm8Pq6iree+89+Hw+Yc55yZHxrVarSCaTePLkiexvABKkaMWsqsyFx+PB/Pw8lpaWsLy8jA8++ABra2uwWq0TRSF83s1mE+FwGB6PRw4urmmv10Ov19NMYqGCq3A4jI2NDaytrUlKMBAICJMOvAB2vOD43ut0OmH6+TndbldAixbAhWvKS9PlciESiWB5eRlzc3NwuVwCnsgI8fJXn0Wr1cLp6emEjpHv6Xnt7FnvdDrlzHG73cJMk2xQGS4V4AJAvV7H/v6+sICdTkcTH+kn9z7PRgIQv98Pu90OABMMKX8mAmyn04lIJIL19XVsb2+L1rLZbGriI78nwRyfu9vtFj9tNhtGoxFarRZardbE2vPdt9lsCAaDsk8KhQLK5TLq9bpm6wm8zKqR2eM7YrVaMRgM5PuqvlJ3SwJjbm4O8/PzODo6Eh2mVsBa/d70lcQYA+fBYCB3NtlKq9UqGRa+a91uF9FoFF6vF/V6XfboRUhV1ECJchrKUo6Pj5HL5VCv1zEej+WOpz641Wohn8+/VHCnpZ2Vd9lsNglSI5EITCYTcrkcjo+PUSgUUK1WMRgM5MxStewA5HwHXh+wvjFYVZkqpvooCA+Hw1heXhZ62+12y6XkcDiEGer1ejg5OcHjx49xfHyMw8NDpFIpdDqdN3HrlX6qh5HK7jL1sra2Jn/HtDAjGI/HA5fLJRqXZDKJZDIJvV4vbJBWpoIVbmIeZF6vF0tLS1hbW3vpc2ZnZ+VgcDqd0Ov16Ha7SKfTMBqN6Ha7wh5r5ae6pmQB3W43IpEIVlZWcOnSJdF29vt96PV6JBIJmM1mObyGwyFKpZL0aGu1WpLa1spPFTBTuzY3N4d4PC7i8F6vJxe8TqdDOByWF0yn0wmbTTF7H5kVwgAAIABJREFUs9mcqH7l9zovc8W1ZOS6ubkpz/zy5csIBALo9/uiT+TnA4DJZILH44FOp5PKVoJD9SI+L8BSnz1F9h988AGuXbuGS5cuIRqNwmKxiI+9Xg8zMzMCVADAZrMhHA5P6Jzq9fqEJlMrP8mwRSIRbGxs4Nq1a5ibm4PD4cBgMJD0PtlHp9MJ4AUTbTAYRHZRq9XQbDZRq9U0vbRUXwlER6ORpFJHo5FUAdfrdQwGA8kGuVwuYbNmZmYEODqdTpTLZU3TgiqzqtfrRXbS7/fRarVQqVRQqVTQ6XTk8jSZTPB6vfD7/RPZK7JcXq9XWHUtAbW6nmRzuJYkQ7rdLkqlEvr9PkwmE9xuN0KhEFwul+ztQCCASCSCQqGAfD4vGSotTD3r1Ywe14nvTq/XE5DMlkbhcBizs7NylpLhJrvO56CVnT1HedebzWbo9Xq0Wi2k02nZC8yqWiwWxONxxONxkTKQOQwEAnA4HKhUKpqm2M/eoQT3MzMz6HQ6OD09RTKZFEkcMy9ra2tYX18XMiAUCsHv98Pv90tBk1as5auIPovFgtnZWUSjUdhsNjSbTWxtbQlzyj0wNzcHv98Pt9uNYDCITqeDcrk8oafWWk+vAlVqa3/4wx/i2rVrCAQCqNfr2NraEu00338Sf9zTxCW8m95EW/3aYFVl06xWq7Bq0WgUsVgMsVgMm5ubuHz5soC/wWCAarWKXC4nBQNkV4PBoDw4bnwt7CzryxfN6/WKLuTq1avY3NyEXq+XFH+tVpODgwer2+0WEJDL5eQS0SqSOXtJ01+fzyfpwBs3bmBpaQndble0Ie12W9LWNptNAGu1WkU+nxcgVi6XJ/qzndfPs2wlNWHLy8u4ceMG/H4/KpUKjo+PkclkXpIfkD3sdrswGo0AIKBFCwmA+oKRNY9EIrh27RqWlpawsrKClZUVWCwW0SaenJwIGFCjV5fLNSEVMBgMmkex6nN3OBxYX1/He++9h83NTczNzcHtdqPX6yGdTkv6fDgcYnNzE36/XxgMpotZOEJdsJZGP+12O1ZXVwWoBgIByZQkk0mkUikBTRsbGwiFQvLOqxceL0EAmuiaVCOg93q9chENh0Pkcjmk02kkk0mUy2X0ej2YTCYsLS1hdnZW0sQ0pmHJWGrxLgEvF8Tx8O73+3IJlctlpFIpFAoFAdF+vx+JRAKzs7PCatIXpquZEtbCT4I+psNZdFStVrG/vw+9Xo9yuYxcLodKpQIAEwVDBCwARF9P0K9VgRXPDBbO9Ho9qehuNBo4PDyEXq9HsVgUP6m/m52dxaVLl7C0tCRBAACYzWYJCLSuYOfPzedFbR+lRuVyGfl8Hs1mcyJFvbKyIqCfWTPg2+svtPSV76nVahUGkpreXC6Hdrst59DMzAz29/dx8+ZNxGIx2O122dPMajJA1crO3jE+n0+yjfV6Hfl8Hk+ePJFnT3ySzWaRTqexsbEBr9cr4JtnFf3U6tnTT53uRX1HJBJBIpEQacfBwQG+/vprHB0dYTAYyPcnO0zfWq0W/H4/6vX6RB9WLU311WQy4caNG/jxj3+MSCQCvV6PfD6Pr776Cjs7O3LP885VCRVKgpgFepMA4LVuMpVZUfWSTP2urKxgbW0Nq6urcDgcaLVaqFarKBQK2NvbQzabFWEu2QEevOl0GmazWTOwojIBvBi9Xi+uXLmCpaUlbGxs4NKlS7BarVIAcHJyglqtBoPBALfbjVgsJkJ9srEzMzNycWlVxaqmBRjBeDweXL16FcvLy1hfXxe28vDwEHt7ezg9PUWv10OlUhE2my8gf15VF6qFryoIZCoqGo1ic3NTqgPn5uZQr9exu7srz5zPkwxGMBh8iQEhEFDTBFr4SkZiaWlJqi3D4TDsdjtyuRwePHggLUF4eFCUT4BDcEW2WK1k1mqvqnrfWCwmaRaXy4XRaIT9/X3cv38fqVQK1WpVfi5epGSzeKGooIKXopbP3263i2aNgKlWq+HLL7/E7u4uisUier0eHA4HvF6vRNg80EajEXq9nmiW1WevFRDkGcADs9frycX61VdfIZ/PS7pc1bWxgIradOrUyXhpVWXPS0Dtl0l5DH3PZrNIJpOiA6P8p1arCUPcbrdxfHyMUqkkhRcsVtMSYKmgvV6vI5vNSoapWCyiUCig0+nAZrPJ+87iPj7fYrGIer2OcrksaWCtQQClOd1uF5VKBalUSkiGQqGAQqGAXq8Hm82GTqcDq9U6sS/V1lVnW0Np5Se/D4Egq/wtFguGwyEKhQJyuRz6/T6cTicCgQCGwyHm5uYAfCPFoh5YzfJoaSpbx+CiUqng6OhI7utCoYDT01OMRiM4nU54vV4JnMmcMsOyvLyMp0+fwuFwaB5Iq8Z7pVqtylodHR3h8ePHaLVawmiytiYQCKDRaMDr9Uqng2w2i3w+L7pRrUx9VtSr8rmXSiXcu3cP29vboj1XQT3lSkajEcFgEG63e6IAWMvOP6qflH2tr6/D6/VCp9OhVqvh9u3b2NvbQ6lUEmkXzx3KBtiOjXcTg8rXLVJ9bbBK1o+/COpWV1dx6dIliaLa7TYymQyOjo5wfHyM4+NjtFotzMzMIBaLydcZj8cSvWjVPBaYZFaZBmabjcuXL2NxcREejwflchl7e3vY3d3FyckJWq0WbDYbACASiUiahhuAl4NWGkD1UlXbmCwuLmJ9fR0bGxtYWlqC0+nE8fGxMFc8HFR9IvVMjLK63a5UFJ73UlBBlVrFuri4iNXVVWxsbGB2dhYWiwUHBwdIpVJIp9PIZDKYmZmB1+uVSJapGV7OBC3cwFqsqZq6oP6T/etcLhfG47GsZTqdRrlchsViQa1Wg8fjESDBF5XgipM5WKWpVbDCAMDr9SIYDIrGTq/Xo9ls4vnz5zg6OkI+n0er1YLH4xHQzHQ7vxbBwdlnr1VBENlbdkxgsFGpVHBwcCANoQFIZwpq8Gg8YKkH1LJFjHphM7MyHA5RrVYBQDTn9XodACSlCWAiXUxAxS4LahcALVNtBAEMhmq1mvyZgHk8Hovmn9KU8XiMZrOJQqEgbaHK5bIU22jVVUUFKiza4vo0m02Mx2MBpfSRukaCAQbWLMSoVqsTAb8WfvKyJNjs9/uoVqvyfg2HQ9HF8yLleatmAcnKUq9KsKqVqWCVRYUMnCg5y+fzE7ITSlToJ/9vv99Ho9G4sLZ/6rnB71coFCQrNhgMpO8ryREArywIpKkt47QG1zyzdTqdZOu4LsfHx6hUKuj3+9I2ke/z2cwmta52u1304VoWMKmED2WSrDU5OjqSgJP3JN93FoNRh9vtdiX7qxZ+a2VcD8rTFhcXYTQa0ev1UCwW8fTpU1SrVand4fMGXki+vF6vFH9yfdX383W6gLyRDEBlAL1eL+bn57GxsYH5+Xk4nU50u11pUZJMJpHJZJDP54WJU4Xi6hQJLYurVOaOgGVpaQlXrlzBysoKPB4P+v0+9vf38ezZMxweHuLk5ATD4RDRaBTj8VguZPpJ+pp+atkKSNWv+P1+YX/ZE7DX6+Hg4ACHh4dIJpMoFAowmUwIhUIwGo0T0opGoyGyBRUIaO1nIBBAPB7HysoKIpGIsOlHR0dIp9NS7W2z2SR1QR0wIzCuKYGVFgeuCv5YFRyJRCZ0abVaDclkEtlsVnRJ1KGqHSuYnmPkqj57rbVW1M+53W4B9MPhELVaDScnJyiVSqhWq3J4UV+nFikRSHCiiJZMoKq3om5XDThYvUpAT+DCtCF1bbxg2WGBn6/1ZcuDFnjB6LJYirIY7hGyv+y52el0JCOk6jG1Yqj/nPH5qcEx15BN2NnKit0qKLFiOzCCaq0ZSxVktVotWCwW0aTPzMxIoQqLRRi8GgwGaU9HQE29sNbAX+0xS4DMi5JAG3gB+ljIGI1G4fF45H3j82fBktZA8CxT2e12USgUpHvHcDiUPcBAi+eS2+0WP3lv8h3SMoB+lb8qsGYQwACAGtCZmRnJVjgcjolMD/f2RbT8U40AmFkKFkVSGqfuB8pS1P6w6j13djqXlrI/FgEGAgGYzWZprM+sKfXo/FzKmdiNwWQyodFoiLSSmErLM4rP1Gq1SmcN9nnOZDLY39+XbgVni29jsRh8Ph/sdrucSZSmjcff9LQGvpu2/rXAKulbLgYFtzdu3MDi4iKcTif6/T6ePHmCP/zhD3j27Bny+Tw6nQ6MRiPW1tbgdrtFYE02NZ1OC+XOdMx5jYcg9XWJRAI3b97EpUuX4HK5MBwOsbOzgz/96U94+PChCIMZuRDgkB0gg0HAoHXbDdLmDocD8/Pz2NzcxOrqKnw+H4AXffTu37+PJ0+eIJ/PYzgcSoV9OBxGLBaD3+8HAEknVKtVORy08I+/yKxR0hEKheBwOKDT6ZDL5bC7uyti6/F4LEzLwsICYrEY3G63FCuVSiXU63U5bLU4xFSdjSo3USPkfD4/keYBXhQxBINBJBIJ+Hw+WK1WdLtd0bmxCbuWEhD6qx6OKsMzHo+FTWVql6A/Go0iGAzKhdbpdJDNZqWFleqrFi2r+JGHEhvTs69utVoVaRA/xmIx6RfJYhx2WDg6OkIul3spUNFiXRmozszMoNfrCavKnyMcDgOAMBusvNfpdCKfyOfz0gqKKTattYv0if4yLazX66XinlOCeG6ShSFbrLbUY7pdK13tq4z3ALNivCzn5+cRjUYnulZ0Op0JZk0dwXmRAysIAsk0E3Do9Xq5g9jHdmNjQ84HggSm/y9qIADwjayCbBj3pirrIaPG7FQgEBBQxT62bKumVb/av+SrevcxG8AMH88tk8mEcDgs2kUGtcViUYJp+qp10SKzYATR1WpV5DzqOut0OrhcLoTD4Qkig+cvf2ndEko9m1wuF2KxGAKBAHK5HE5OTuTe5OfxXlpaWhJ5GDsxnJ6eolgsij5X6246ah/Yubk5zM3NwWKxoFgsYmdnBycnJ/JuU4bo8Xhw5coVLC8vw+PxSHaNfbbZdYF7iev7l/x9bbDKA4YLzXYUJpMJtVoNR0dH+P3vf4/bt2+jUqnIgeZwOHDp0iX83d/9HRKJBBwOB/r9PpLJJO7evYunT58ik8lo8rIxCgRepAFDoRBWV1exubkJl8slXQj++7//G1999RVKpRIASMHXlStX8NOf/hSJRAJ2ux39fh+np6d49uyZTDZR+2xq4atOp4PFYkE4HMbKygree+89+P1+jEYj5HI53L59G0+ePEGxWAQA2eQ3btzAD3/4Q6kQrdfrODk5kRQ3UzZaFVoAEE0v0wJkTqvVKh48eIBkMinaX+pFP/jgA1y5cgUOh0NSrZQKnJycyKWiBRhQ/WQBIAsC2ONvZ2dHUtVsW7K4uIj3338f0WhU0u+Hh4f4+uuvcXBwIGkkrfykqXqkVqslU39Y9V8ul+H1etHpdBAMBuFyubC2tobl5WXY7XZhgh4/foz79+/j6dOnyOVyaDabmmkXVXaF4C+fz+Pg4ACdTkem7Fy7dk2q+7mmbrcbnU4HhUIB6XQaT58+xfb2NpLJpDCBWrNCZNg4wKFcLos2dXFxEYFAQPRUbF/GFmBkuo6Pj6WAhH2gLwJc8VxVSQAGy7FYDPPz89JDeWZmRhgjBlmssiWDdBFAVWUCyU46HA5pr7a8vIylpaWJQRCUBzAYIdPOHttaT68i8FE1q51OR1KTlIT5fD7Mz8/j8uXLIl0jS8y14zqqU8G0MvVdUnt1qylxAmsSJ4FAQDpZMKhhyvjk5EQKnLQusKG/BJtqylz1Ve1mwvZvwWBQiul6vR6SySTu3LmD7e1tYTkvyleCTGZsCKAZWDPjxo4FHMVqNptRqVSwvb0traN6vd7Ez6zFflUzaZFIRIYAVKtV6aXKgJUtyq5fvy6EHwOrk5MTAN8AS7UORAtfKdvj/rPb7VKkenBwIKyrwWAQTLKxsYEf/OAHWFxcFCkbJ4WVSiXBAMfHx9Jj/7sEBG+kcCZ9zVQPKz1rtZo4YDKZ4PP5hNmKRCL44IMPsLS0BLfbLZ//+eefi1yg3W5rmlol87uwsIC1tTUEg0GhsAk6+CB4KESjUfz4xz+WIrHx+EXxwKNHj0QrqhX7qxqjJ2pVObqQD7Pb7coEHvq8sLCAH/7wh1hYWBAwxmfAilK2MdEqeqWeJxqNYnZ2Vi5RtS2Fw+HA7OysVC6zTRSLBKrVKrLZ7ESUTfCvlZ8srGOrLDVNzubuwWBQLnW3242rV68iEonAarWiVqshm81if38fxWJR0ttap4HPpgTJVlosFjSbTZEjbG5uIhaLAYDor51Op2jDnz17Jox2qVR6qbeqVj4z/UiN6unpqfwbZ1urMgyCwHQ6jZ2dHTx58gTb29s4PT0VoHpRa0qQUa/XJ1LpDLBZncqUIDMDxWIR2WwWp6enwlxe1IhdFaCPx+MJ4E6ZFTMolDTwguh0OtIJQq/XS7uji2ABzxr1Zkyl8sJS26SRHWJ2a3Z2FvV6XeRM7GGppZ39uRkAMmPBOou5uTnJoFCTTiaNgIx1FfyZtDYCK+rN1VSqyWSSM35xcRFra2uIRCISrPCcSKVSyOfzUqymsoZa7AEVRPPrcS24PgQqPp8PoVAIoVAICwsLEhAyW8GRuyQM+LW1ZlaBb94RdhwBXrQE4/3FxvqxWEymmrEwkHcESR51X2vJspI1ZRBK+RlBM1u8hUIhmRRGTAVAspaBQADVahXBYBClUkmm7Wnh69kMJds48j1htxVKAhcWFnD9+nUhUrhmvV5PBkkQV+VyOenY8V3O1tcGqyw44eHPXp4q3W61WrG0tAQA0kR6YWEBV65cEW1Qu92W6rxkMilOawlWrVar9HSMx+NyYPHQYhcDRg8ejwcLCwtYXV2VvpWNRkNaBmUymQuJXnW6F/08Y7EYFhYWsLKyIp0HOp2O9KhbXl6WHmZerxfr6+uYn5+Hw+GQDZHNZpHL5YQh0hKscNMGAgGEQiHMz8/L0ARqltxut7TVIVN9/fp1AdlkutLpNOr1uqznWWbhvH6qrbXUXqkskOMeDYVC0uZkbW0NLpdLDisW4pBZ48vPA1YrX3lYq2k9Fi4Nh0PZw0wHUmdJ1p0jAqnB1XIIgGoqI9RutwVUc53JWKoFS9QmEfwdHx8jm82KZvGigJXKAlcqFTidTgHH1Ppyug7wAnxRb0fJhQqmL1JjR3/ZXJ1DJ6g5ZgEigTWZTWYufD6f7IeL0Cy+yleO8aUOtVAoSIBFZlXdA+xeQR2b0+mUoSVaT1ziRxVg8t4iGGDBJ4uCWJSoDtEgYCVY0RpY0Ue1YwVbQvIu4n1ASRgDmnK5LMw/268xQ0cmWaszXy2uZZtEgj4GUn6/X4YaMDtIfS3PimQyKZImAmuygFrqQdk3PRaLSRtCg8EgbSkp/SE5RcJtNBpNFA4yECMjzzXVAgTyfuJdbrVaZQ3ZLScQCMhUs3g8Lt1KKFOhVpTTNn0+H1wul+Z1FerwIQZL/LrUfnPs8vr6OhYWFmSoCv0ki84JaLFYDJFIBIeHh1Ig+JfO2DdiVgn29Hq9bESj0SjVtYFAQPRfbM3wgx/8ANFoVEaelctlfPHFFzInWqtIgKZS/YzweOBQrLyysgKv1ytNjH0+n2gwyRbW63U8fvwYh4eHEhFqzVbpdDqJkObm5sQn/gxut1taRvCiZQqDo03JIBwdHaFQKEgfWC2NAUAwGEQoFEIkEhFh+nA4hMPhwOLiorxsfBE5fpMXMkeY8tCiETho4SdZPRb3qE3CZ2ZmpDUVLwoOXzCbzSiVSnJoETzwIuChfRHGZ9hoNKT1C30LhULCXtEPpoIzmQwymQyOj4+lIOSiwBWfIZkdMnucYKNWVjN1qNO9aHPCiWUXVVB11k8e6tVqVcYm82AEvmGfVNmQClBUbeZFA0ACa/Z6ttlsoqvmHlABIM8yh8OBWCwmQZfW1dU09etSu0g/ya4xhUlZBS9Rske8kMPhMPx+vwAXLcEV8E1bJ3VCkM1mk/Q0wT0DWFb/ky2mlo7FN5QRaTWuWq06J7AiC0jQQY3ixsYGFhYWpL0RO5KUSiWk02nR/J8NpLUC1moxELWL7JnM5vksVOO9EAgEpH1Vv9+X/uTFYhGtVkvOUnaWAXDudeWZzxR6LBbD4uIiZmdn5VwvFosYj8cS4Pn9ftkTJKao/R4Oh7Jv1JZ15x1fTD/Vgkmy+5ycVS6XYTAYZET07OysyGsIqBuNhshrOMqY47ipsdcCT5Hx5fdQ9baUK5GMZOcifl673ZY7ajAYwOVySbA4NzcnrbdYq/SX7LXBKjcVdXKpVEqEymTWrFarFDSwkTF7mo5GI1QqFTx8+BB/+tOfsLu7eyGaNQAS4QOQaJmaGjKSbGNCXW0kEhFBcLPZxMnJCR48eIDHjx9PpKu19JPAjhc/ARzTK2R92aKIG57aFV7KlUpFmFVGhupFrIXxMCDzq/ar4+QXXl7qpma7C7XwjWkQ9RLW4rLV6XRyQRoMBkmbAZAKVbfbjWg0+tJoUPrIZ8BKUavVOjHpSktQQJ0dtYj5fP6lkboEWxxdRx0Vq6yZ1VBB10UEK0yTsQq9XC7L0AKyk3yelAxQe8d38SKLf1Qji16r1VAsFmGz2aRyPh6PTxS0cK1mZ2exsrKCWq2Gr7/+euI5XySwprSi2Wwik8mIX8CLVltcT06rWlhYEImA1WrF5cuX8eGHH6JWq2muBTybWualXa/XhRnl9CL17OIlxsIMr9cLh8OBzc1NkYuRxdayW4k6HYqsn9vthsfjQSKRQDweh8vlkmE1jUYDqVRKAhVW2TPjwoufH88LAnnuqWdmKBSS58n0/+rqKuLxOJxOpxACLKTiR7UASJUTnBdUcT3Z4stms0mf1MXFRfj9fpm65PF4YLPZ4PP5JoZ+8L3iniH4U3Wsasu4N71Xed6TLWWB8k9+8hNhTY1Go7RZc7lcEyQGALTbben5rNPppH2hynqrxeVvAgQZoPDuWV1dxdWrV4WFHI1GWF1dBfDi/WGmyuFwAIAUUnIoEGUABOOj0QiZTAanp6fSfeU8gJUF30zfz87OynoxE9xsNuH3+3H58mUsLS3B7/dDr9ejWq1Kq03WC1y/fl2YeQAS9FBq85c6Qb02WB2NRpLGpT5ye3tbmB+mABuNBmw2G2ZnZxGJRBAIBKDXv5gg8sknn+A3v/kNvvjiC2kqrf7SwkajEWq1Gg4PD6XrgE6nQygUEn0XLzKj0Qi/3y/IX69/MZFla2sLv/3tb/HRRx8hk8lMtEXRWnDPoo79/X2JthgAABDdDfBNtMOXvtVq4fDwELdu3cLW1haePn06MSNcS00gNxWjOsoUKKVgz1yV3eDL3mq1kMvlZAoTi6uq1aqkFbVi23hQM61bKpXgdrulHZnT6RRWWE1Xcq/UajXppkC2i4yLWrSkhZ9kfZmKLBaLCAaDE22BXlXUwIIVgla1KISH/0UWBFFv3Ol0pNK/3W5PgBp22FhdXZWRysfHx/J1LuLdV7822epqtSrB4N7enrSp4oWvToViQPP1119PaHL5dbUyNejhs+p0OiiVShgMXoyDLZVK0OtfjHcm2+fz+XDz5k38v//3/2SYwfvvv4/l5WXkcrnvXLDwOj6qfSjpM3V9HJvMllYEdEajUaYFXr9+HdevX0ckEhH9XalUQjKZlHfuvH4SWDBQZaV3NBqF3++f6F4CQPrnshaBTCrbxrlcLtkfZOiptXxT4/dgq0EC02g0irm5OdF8qvo+VtPzzGVHBe4H9o1WJxid947ielIeNT8/j7m5OZGesSURW5PxLGXgT8DMwNVmsyGRSOD09FQyMSxoLBaLb8wE089IJIK///u/FzC9vLw8MdSFhBrXn1kI3hFcs/F4LLUBuVwObrdbugRwzd+UqNDr9QgEArh586a8FysrK/JzMGupklGUXDAgrNfrKBQKaDQaiMViMslsPB6LbODk5ERqXd60FShbj9EHABPDcPg8B4MB4vE4YrGYTAis1WrY2dnB4eGhFIFSMkgpEzMe/PN3sTeSATAVTiaAlWHcrMCLxb98+TLsdjv8fj8MBgPa7Tbu3buHjz/+GHfv3hWdKk3Li0AFq2yRlEql4PV6BawyfbK2toYf/ehHwrI1m03cv38fv//973Hr1i2pqL+oi3U0GskEE2rS8vm8pPjZKFyv10tjez7kRqOB+/fv4+OPP8b9+/fx8OFDFItFAQoqa3ReG4/HqNVqSKfTwq4yraKyZkxl8e9nZmbQaDTw/PlzfPHFF6JTfv78uegC+SJoVRTA1B6b/DMSLZfL8Pv9GAwG0mibfYPJBpRKJRweHuLo6Ainp6fY39+Xi5kaQi2DFZVVIGCpVCrwer3C5pB5UwtXmA0g68K2MSpY1RJUn2WUVQaaLDv7u/K58rCnAD8cDmN7e1tYDq3fp1eBK64FfW82m3j06BFKpZK0swmFQvjFL34h7VaWlpawuLiIu3fvauLXn/OTjJ560XP/sr8v56gz9e90OnH9+nXE43FYLBaEQiH4fD65mLVaU5Wp5DNWA2WyTdSnqWCVd4HJZEIkEkGn0xEGLBwOw2KxCAFwXgaQnT+YBWGqkSDQ6/XC7/fD4/HAaDSiXC7LNKtMJiOdN3gGMU3LtWT3kvOsq6qbDYVCiMViCIfDWFxcxMLCgrR6Uoc/cA15NpCZ4tAFl8uFeDwOh8MhAT+AiQEcb+ori343NjawubmJS5cuyYhfTkjkOURdp5oh4x5Q5WyNRgPBYBCVSkU61/BsPY+fDIhu3rwp2VHgm4EfqixEHUjAZ8n3kDU1HBmeTqeRSqUAQAi6N/WTWRH2pV9dXRWphEr0kBVlTRDfQepq2fedDfc5lpn9opmGP68RyKuFyYPBQNjhxcVFeL1eeV/U/r8+nw+dTkfY9oX/a1up1+tF6kapIv/PX7I3kgHwcGHEyQ2gXmhMXbLdxmg0QiqVwkcffYQHDx5IX9OLTAUQE85GAAAgAElEQVSqffYYxbMfKC92pjaYnhgOh0ilUvjjH/+IO3fuIJlMTkyFuKhikGaziePjY2F7k8kkrFYrdDqdABGOt2RVKNf0T3/6E+7duyc9WFVtldZsVavVEmaM00voJ/ACtKyvr+PKlSsTk3aq1Sru3buHr7/+Gk+ePEE2m0WpVJoA1Vr5SrDabDaRz+fl4OJ8aJ/Ph16vJwULZ1NXHB94eHgoaQy10OYiC5fUdjZqxTVZTLK/jPbZ05RBjpqquiitJd9xyj2o+dbr9dKbNJvNYjAY4NKlS7hy5QqcTqeAVVaz8me/CN9UTSCZNgK5VqslIIUsZq1WQyqVwsLCgsxiZ0sZtjjTmlVV9bHUBPIcInCibl6t9ub7xFQvMy2hUEiyGFr6yLUzm83CCrpcronMjTqRRtV3889k+lR5kNoO6bx+8l1gKpgMZSKRQCQSkRQ7dZ+cokX5wtnnwMwLZ5mrwPE8flKawJZk8XgcS0tLWFpaku/B9VElXAxQ2VnB5XIhGAyi0WjIkJN6vY5isSgs8HnYaq4pi2FWVlYQjUal+8vZVDj9Jkhir1KyqgwWb9y4IY3vT09P0e12cXBwcC5wRT/n5uakXoZrRiNg5TNWmVyuLYEv098MyBqNhgzcOY/8S6/Xy76ipIsF6apczmw2T7DS/L8E3hxvTWa72WzKz6JOtDzve6Vm0KrVKg4ODrC2tibZB8oUu92unDuj0UjOCE4oZfcim80mg3VyuZzorb8rDnwjZlVlbdSomg+SE5VWV1cRjUalJ9itW7dw+/Zt7OzsSNHKRRnBNLVG1HqozK/BYMDKyoqIsKmr/Pzzz/HFF1/g2bNnIg6+aH0dNbUEVGxnQbPZbHj//fel8S7nLt+5cwdffvkltra2JnqVXoQRrDK1ytS6yWSSF54Vi6urq3LZjUYjnJ6e4tGjR3j48CFSqdRLm1Tr9SUzRQDYbDYl/U/mOZFISITNy7Pb7UoXAM4U13o2uGoqUOVUJfrBy7/f70+0o+JhQRaG4I//5yL1oKouUO22wFTRwcEBHj16JLpLg8GAy5cvw2azIRgMIhqNiib8op49n6davex2u+WC6HQ60kWBMiQOilCn2nBKjNq3UCv/VIBEEEjdnzqRTG1Kz9ZGACbOAT4TVdOsFZvOy5PvDjWVTqdT0pLNZlO0p/SdAJp6THV8MINzVgmf11f6abPZEA6HEQ6HEQwGEQ6Hsbm5KQM+mIFg43TWCrCdId8pDlohs1UsFoU1Oq+fbPUTDocRj8exuLiI+fl5RCKRCTmSGriSbeeept+UtIXDYZRKJWSzWczMzKBYLL4kG3oTMxgM0jqJxUhkVVW5EUGqCjjVPahqX5ku5l1bLpcnpFhvsqZqhT/vzbPaYlUmorLVZ78OMwjELCowO28BIwEnALk/VcaXDDXfHfVu4HvDYGo0Gk38rCy8IxOqRQAIQIo+c7kcdnZ2cO3aNYTDYQHzDEp4hhJks/UWcYHdbpf3vlAoIJlMyujl79pb/43AKoCXLhr+cNx06+vr+Md//EfMzc1Bp3vRv/DXv/41nj9//lL6/6JMfWB8oDTS6mxYT/FwoVDA7373Ozx9+vStAVUVWHPTqVW/1A5R3+R2u0XE/Ic//AEPHz6U9iUX6auqi6LeiAcoNavxeHyiMIx6yi+//BKPHz+W/ppn2wGdtxhANQI/fmTxGSuSOfUpm81KgQo/t1gs4uDgQAC1WrV+3sP/L/kLQHqtsrcuuxKomjm32y09g1VwqjLUF7kPCA4YMXs8Hpn/TK1SJpOB0WhEPB6XNBfZSq43oD1byY8qC0XgwmkqKsgiaGEq6uz/vQgm/VWAn0xZOBwWmVKtVpOiNTWoYlujWCwGu90u7w41w1qcWfxe9E8Fnfw9JR8cTWswGOTs4vSylZUV/PSnPxWtKJnKVColrQDPq60kCGDhSiQSQTQalZ7VZK4AyPNmwRoA0YOyhRABKz/3+fPn0hP2POvKDCTBKplVvkPAZPDK50rQyrOXDBr7cI7HY5RKJezt7aHf72N3d1fW5k3PVXVdySoSTBFcck0J5NSULlPIvCu4lpRohEIhab+l9rJ9k73ANWNApxZ2qjIW6pnV+5ZBIeswOp2OBKtqw3qSBmelUK/rJ4kHAMhmswBeaL83NjYmutdwPdXpaQSG9JXvmjr5inUg58UCXCNmpYfDIUwmE27duoWf/OQniMfjEzVKZ30Fvnmv1NHfBwcH2N7ext7enkw4vXCw+qofDnjxQnq9Xvz7v/87lpeXYTQaUalU8B//8R9SUPU2gOqr/FOBtU6ng9frxb/9278hkUjIBK5f/epXuHXrFqrV6lv3U/WRF+dwOBSw+q//+q8TqcmPPvpIJoVd1Fzob/NRjfoJXsga/OxnP5OUJKeU3bp1S2QOr9Koae23WmDEl5utV8j+shKR6UpOtmLvwrMv30UyqwAm0nYM6PR6vWhYOdOe8hZG/wAk5XJRe0C9QIHJfoYsAshkMjg5OREWWGUxqNVTu21c1F7l16WmNxaLYXZ2Vtjfdrst4JkV2ZcvX8aNGzdkcgyzHBfVukrV+6kgK5FIyDjVUqkk7WGazaakOzc3N/Ev//IviMfj8o4dHx/j2bNnmp6vBKw+nw+JREIKfyKRCBYXF8XPcrmMbDaLVqslbBwrxTkmlvuz1+vh2bNn+PLLL7G9va1J5wLuSTL8bE3EMZaUVQAvzlVODSIhwHoEFtfyTMtms9jb28PBwQGOjo40IQPG4296zjocDpGcELCpDJWq5WfBnSpJ4wx2MsZqalmLoJVBtKqnJJunyjwoUSKAIrBvNBrIZDLSI3h+fh6hUAj1eh3lcllqCihxeVOgqr6r/GUymQSQEnCy5zt7KLOFHiV4xWJRenA7nU7pCU4CI51Onyu4GgwGyGazsgZkmyuVClKpFObn57G8vIz5+XkZnc4ey8wQF4tFpNNpZDIZtFot0bOzHeSTJ0+QSqXO3bqK7yr343A4xPPnz9HtdrG7u4sbN27g/fffx+LiotR+8P/x/3L4DzOU6XRaxldnMhkcHBygVqt952evGVgFvql2++d//mdcunQJRqMRjUYDW1tb+N3vfqdptfd5/fT7/fj5z3+O9fV1KQx5+vQpfvvb317oJfW6RqDKSkej0Yh2u439/X385je/EVD1NloBnTU1QOEldfXqVSQSCUnzFItFfPrppzLe9G36qn4PMqNqGo4N7MfjsbCZbFh/UaML/5yvahCgXppcWxYxBIPBiZZbBOMXKas5a7z81YuLaT5qu/x+P/7+7/9+ou9eKpVCqVS6cOaXbAr1lWSvDAYDgsEggsEgVlZW5M/vvfcerl69CpvNhl6vh3w+j4cPH17YupJlI1jxeDxShOZyuUS3zDOTPUoJFgnCWIz54YcfShcQLddWTT2SZfX5fFIgxc8hA00dHdvG8TmwSPTg4AC/+c1v8OjRI9TrdU18PcuqARD5gppaVfV/BLgEj5QusEtMs9nE9vY2Hjx4gN3dXZEUvamR4VRH5TLDdLbAhCAxl8uhXq/LPqjX65IRUNlri8WC/f197O7u4vT0VMDfeZm1fr+Pk5MTnJycSIN9SgDInNbrdWQyGRnxzCp/gqtqtYrRaASr1SrDeTqdjgCW4+NjkTe9qb+DwQB7e3vY2dmBy+USbXS9Xhd2L5fL4fT0VPYhK+V59rMHLOttWGdDOVkqlZJuDG8Kqtm2c3d3V9pnGQwGdDod2O12PH78WIrmIpGI/GwE2t1uF6lUaqJHOQty+Szy+bwmsjW+U2R0h8MXI1YrlYqwo7du3UIikZBzk++fyWRCvV4X4qdYLKJarUpXHbX92uvgQc3AKin+xcVF/OIXv4DVasVgMMDp6Sk+++wzHB8fv5WU+nfx02KxYH5+Hv/wD/8Aq9UqD+LOnTvY39+XBXzXvlKLE4vF8JOf/EQKwMrlMp4/f46nT59ODCl4V8aUaSwWw7Vr10SfQm3g9va2jIF7F6w6jcwqhy9wFCCjc7Y40iqV+qZ2tuCDgIZpSqbdVUb4Tatp39TUNL5aOexwONDr9WCz2QR8kQHO5XJy8V80WFUBCVnd2dlZ0YQyECHDPjs7C6fTieFwiGw2KzPMz9uq6NuM66emFNkuhtPfCLSoT6W2lSCR+uovv/wSn332GYrF4hu3qvk2Hwk61FZ4BHhqypIZCqaIVXBWr9eRzWaxvb2NO3fu4O7du5rOhuf7y6lfzEJQF8vMj9ptAcBE9wwOB2ArpWw2OzEauN1un9tH+lkqlXBycgK73S6paQIs4AWbViqV8PTpU2Sz2QnJCtsFsqAsm83CbDZLwdLx8fEEs3oeGwwGyOfzePbsGSwWC7rdrnRTIIjLZDJ49OgRdnd3ZRgNGTmyriwqq1ar0pWl1Wohn88jk8mcS2NJUFUoFLC1tSWN9Y1Go8imKFNh5wFOz1OzmBxoRPaYGRYSAdVq9dxaUN6JpVIJ7XZb9Kvco3zHWftxVm87Ho+RzWZlgBLPXu4rtU5DqyBQfVd4DqgFV16vF1tbW1K8SuKKxdcE1rxT1XdN7bv+XUwzsGowGBAKhbC5uYkbN25Ar9fL9Kf79++j1Wq9c/BHPwOBAC5duoSbN2+Knzs7O+Ln94H95QHLFhGbm5vS9uH4+BhbW1uiJXmXPnJzulwuRKNRmQ3PMZfHx8dy2L/LIIApV06wmZubkwuWTAZ7hXIK1LvaA6q+iuDU4/GIns5ms8FsNotInwD7ooMWFVipuiS14IMXMItVWLlaqVSwvb2NnZ2dibZVF2mqNk2vfzGwJBQKSUUz08NsycRA8Ouvv8bt27ext7d3YUGLyqKrYz57vZ7oEV0ulzQ0Z59lrluz2UQqlcKnn36KTz/9FJ9//rnmFxXTebVaTcapWq1WAYJkXAmqz1b59/t9mV9/7949bG1t4dGjR9JOUItzln6SMWNDdIvFIkVdHA3OC5XvN1vo8JLnxLB8Po9yuSzt66gd1sLXwWCASqWCnZ0djEbfjFdeWFiQYplGo4HDw0Ps7OzI6GyVLWVxDacvzczMoFQqSbN4ngVaMKt8H9rtNk5OTuD3+2EymQSwZLNZPH36VICgKkdTK9QrlQqKxaL0OuUwE7ZkO4+/bFN5584d5HI5BAIBKUButVrSpiyXywlgUr8X/WS2g33WuZdZ6HheP9XgjT1yuSfUs1vt+qEG3mq/eLVQnHLBsz22tTDuN/UjA6ZqtYqTkxM8efJkAgvwHGCrPfrHn5Eyl9e9rzQBqzqdDm63Gz/72c/wy1/+El6vF/1+H8+ePcMnn3yCO3fuvHQwvSvA4nK5cPPmTfzsZz+TxuvPnz/Hhx9+iNu3b7/EpLwrwKI2Uuac5Xa7jcPDQ3z22We4c+eOpPzeJQtMMTpbxdhsNgF9BwcH2NraeinV87Z9JVA1m83So5KHJvWJ7LvL+dXvgl1VK1bZxoQ9GZl2VdmhUqmEe/fu4fnz5ygUChcaZKlAVS3cIBAkOxkIBAQUAN9cJFtbW/j888/x1VdfXTizyu/Li79QKEiRGjWinBeuarJKpRI+/fRT/M///A/+93//V4JBrX3lwa+yNkxZ7+zsSEBCdkfVWxJYPXr0CL/+9a9x9+5dHBwcSKsiLX3lRc1phAT+ZG/n5uYwOzsLv98vuuXRaCQ6v8PDQ9y9exd3797F0dERqtWqpCy1DFzZPYPPi5OIyuUyksmkZFTY0zKXyyGbzU6wPpwcxrnqLC7huXXeJvuqnwRoZJvn5+flziTAYms/dXwy14oAkMHCeDxGu91+ibE6r68MALa2trC3tydTrKxWq7RTI9v2qj7ZBFtMdTebTRSLRQCQd+68Y5fVrBi1xQzuCJjO9p5WGUv197wL1L9XgZpWqXWuGb8n/+1Vd/mrCrrUoFDdE1pjAfVr0xfVzsrk6OvZbBELsNRs3Jv4qglYtVqt+PnPf45f/vKXeP/99wG8mP7wX//1X/jiiy9QKpVeuVnetlksFmxsbOD69eu4cuWKbPD//M//xCeffIJ0Ov298JOsKnvVDodDGaJw+/ZtPH78GHt7exMR9LvwVdUGAi/Ysz/+8Y8wGAw4OjpCOp0WhoD6rHflK19u9l59+PAhMpmMpIDS6TS+/vprfPbZZyiVShMX6tsyNXrlCFVO+uKUGKbQDg8P8atf/QqfffYZMpmM6LMu0jd+ZJEXq7rZ9YHpfkqAms0m0uk0fve73+HTTz+ViSYXxVqraT016Nzb2wMAqZi+dOmS9Crms9/f38cXX3yB+/fvo1QqTezXi/CTWjNV28dWLoeHhwgGgzJYg8xZsVhEPp9HKpXC/v6+pDm1uEi/zU++HyzyOzg4gMViwUcffSTtoljMwgIQVoETJKiM1EW8//ST09T4nNVBH/w8lTFW9wsw2T3monxVNYa1Wg0nJyd49OiRsE9nv++rwB9NBVWv+vzzGteJ6XydTodkMin/dvZ7qf6p//Zt2T8tfVW7eZyVbHzX76N2ZdHaP/Vrfpe1+XM+vA0/v6svZ/9dZYSByZ/vvH6eG6ySVYlEItIgtlAo4OOPP8aXX36Jg4MDOZTfpbZS7WNWq9Wwvb2NTCaDW7du4eOPP8bR0ZFmqanzGiMuVtKPRiMkk0kUi0U8f/5cLlMeuO8KqPIXiwEAIJ/PS9qQ6avzVHtq5SdBQSqVEkE9L1RWNBcKBWE93sW6qpF3sVjEgwcPkE6nsbu7K3uXbBz1n/T3bexbFUwDQLlcRqvVQiaTwcOHD/HRRx9JT0IyU5x4pg5WuEg/VRZEbQWXyWSk4T5lFLyMu92uAEYyVBddBMhnTQar3W5LgQRZM3UUIdk+lT17G7Ia1U++K2xFqJ4B9OPsQIq3+R6pLLnateJV3/+7/t1FGNdE7a7xXb//uzjrX+cZvst1Pe/3etf3/ne177OfFxE00XR/7ovqdLq/+B1nZmYQCATwT//0T7h+/Trm5+eRy+XwySef4LPPPpNRpecFKuPx+Fubm30XP9VipbW1NQQCAQwGAzx58gR7e3uaVFD+JT9fx1d1fCDTv81mUzRWWoD/86wpLyk2UGbqmk3X1Tn15wVS5/WTaRP6ykphtdUJQcA5U1Lnevbqmp4tsGK0qra1YRD4Jv5q8ezVqTDcs6PRSKpHCRrPA/7O+96rfp4FMGcP1nPq0c793r8q5XeWWdPCzrumb8umfmprWuzRt2V/C2v61+In8NfhqyZg1el0YmVlBQv/N6rw8PAQjx49emXz9zc1LS4tpq04mYRVlrxYtbgMtLy01MvrIpgKLdZU/fh/X/OVvz+PaXEgnAUB//d1z+HVy/a3cCAAUz/fxKbPXnub+qmtTfeo9va34Cfw1+HrucHq/33eS4DlbQKB11no1027vK79LWwKYOrnm9j02Wtvfwt+An89vk79fH37W/AT+Ovxdern69vfwrP/s2B1alOb2tSmNrWpTW1qU3uXpn/XDkxtalOb2tSmNrWpTW1q32ZTsDq1qU1talOb2tSmNrXvrU3B6tSmNrWpTW1qU5va1L63NgWrU5va1KY2talNbWpT+97aFKxObWpTm9rUpja1qU3te2t/doLVX0M7A+Cvx0/gr8fXqZ+vb9Nnr739LfgJ/PX4OvXz9e1vwU/gr8fXqZ+vb38Lz/7c41anNrWpTW1qU5va1L7PdtE91rUwdTqgOqjo++YvfeR0SHXM8kWNVZ+C1alNbWpTm9rUvkf2NibundfOTlo8C6wuYjjQ65oKqoxGI3Q6HUajEYbDIQDISOh37SvHgZvNZtjtduj1ehmnPRgM5OO79pXjwC0WC+x2O3w+H0wmE6rVKjqdjowC73a7MgpcK1+nYHVqU5va1KZ2blNHQ3+fTB0LfXaENU29VN+F/yqjptfrYTAYXjkZst/vTwCst+2v6qfRaITBYIDJZHrlBMter4fBYPBOQBb9NBgMMJvNsNlssNvtMBgM6PV6GA6H8qvT6Yiv9POi2MFX+anX62E2m+FwOODxeDA3NweDwYByuYxms4l2u41ut4tWqyUg8G37qfpqtVrh9/sRj8extLQEg8GAQqGAQqGAarWKer2OSqWCbrcrjKsW4+ynYHVqU5va1L6HpoKs1znoLxoQnGXU+GtmZuaVF9PZP3/b7y/CRwK/mZkZ+UiGTQVPvV4P/X5fAMzZn+Mi/WQq1Ww2w2q1wmazweFwwGw2w2KxyOeOx2MBBCrA0prB+jZTGUqbzQaXy4VgMAi/3w+r1Qqj0QgAAqCy2SxOTk7QaDTQarUwGAzQ7XYv3Ff6aTKZ4HQ6EQqFkEgkMDs7C7PZjNFohE6ng263i9FohNPTU2QyGdTrdWEH2+32hfvJd8ZkMsHj8WB5eRnLy8uIx+MwGo2o1WoolUqoVqtoNBo4OjqaYDCHw6H8DBdt9NVoNMLr9eLSpUvY2NjA2toajEYjTk9PZR1LpRJGoxEajcZEYEDg+qb2TsCqeggDeCcR4l8y1Ue9Xi/pA+CbyPH74O9ZP41G44TehSkPpj3ehW/8vXqBmEwmGAwG2cDD4VBewLft37f5ydQMLzZ1Ld+Wn69ihc7qmvR6/cTncG/Sz4s8zM76x4uX7wz//ezFf/ZdIlt0UT7SHxW8nP1FNkh9zmfThBfhp/pMjUYjjEYjTCaTsC02mw3AN+tmMBgwGo1Qr9cn0m4ELvSRIEYrH3m+mM1meDweeL1e+Hw++P1+eL1ehEKhiX03GAxQq9WQyWSQzWZRKpXQaDTQ7XbR7/eFydLST+4/k8kEu92OQCCAubk5zM3NIRwOIxqNYmVlBWazWdjLXq+Hw8NDPH/+HMlkEoeHh8hms6jVahPAQAt26Nv89Pl8WFxcxMLCAhYWFgS4mM1mGI1G0QXmcjk8ePAAjx8/xrNnz3B4eIhqtYp2uz3BumppKlCxWq3wer1YWVnB4uIiFhcXsba2hoWFBVgsFhgMBjmX8vk87t69i8ePH2N7exvJZBLFYlHWVMv1pJ8q8CcAXF1dxerqKi5duoRwOAydTofBYCB78PDwEI8fP8bh4SEODg5QLpeRz+clDX8Rd70K/B0OB5aXl3H16lVcvnwZ6+vrsFqtqNfrOD09RTabRbVahc/nw9HREbLZLBqNBgaDAQDImXXRARUZ4FgshtXVVVy7dg3Ly8vQ6/VwuVwAgFarhWazKXtWvR9mZmbO9a5rBlbPamxeBQQAyAMiqGKaQN0UWh5ef87Ps2kW9e8NBoMczGazGXq9Xg7gfr+PXq+HTqdz4ZtE/XgWlPACNplMsFqtsFqtkupg1NXpdFCv11Gv1y9kXdU1VMHAq8CLxWKBw+GA0+mE2WwGAHS7XbTbbTSbTdRqtQvx81Ugj+khHsImkwkmkwmj0UgicpvNJuvYarXQarXQ6XTQbDbRbDYvxE/gm4OMoIpMi81mg9vthsfjgd1uF4aA70y9Xker1RJA0O120Ww2Jw5drXxULzDuv9nZWSwvL8Nut8PpdMJut8NsNmM8HqNSqcg+ZPTdbrfR6/XkXVJ1Tlr4yb3HFKDb7UY4HMbS0hIikYg8Y+7Ffr+PfD6PcrmMUqmE4+NjpNNptFot9Ho9eZ/IFmjhpwparFYr4vE4wuEwQqEQwuEwNjY2EAgEMDMzAwDyrHu9HnK5HHK5HDKZDFKpFA4ODtBoNMRHrUABQbTVaoXD4UA4HMbVq1cxOzuLaDSK+fl5xGIxuN1u8ZHAv9PpIJPJ4ODgQMDA9va2AEEVaJ93PXnG8D2Zn5/H5cuXEYvFsLy8jEQigUgkArfbPXEejEYjXL16FdlsFnt7e9ja2sL29jbu3buHcrksfvK5n9cYrHM9Z2dnsbq6ikQigZWVFayvr2N+fh5+v38CKABAJBJBJBLB8vIyIpEItra2cO/ePeh0OnnuWoIrvudkfb1eLxKJBBYXF7G0tISNjQ0sLS0hFArBZDJNBPizs7MIh8NYX1/HnTt3sLW1hVu3bsnX1hqwqkwlA4BIJIJoNIqFhQVEIhF4PJ4JHwEgkUjgypUrePr0KR49eoT9/X3cvn0bo9FI03dd9ZPnu8lkgsVigc/nQzAYRCwWg8vlwng8htFolN87nU643W5Eo1F53/P5PI6PjyfenYu631V/3W63sNVutxvVahXlchmVSkWkCpReAJBzvdfrnUsq9MZg9VWMivpRZS8ITCkedjgcsFqtGA6HSKfTqNfrchiojKCWmxjABNvDzWqxWCb8pZ8ulwsejwdutxsmk0mAQLVaFVq+3+9rHnWdBSz0iZcuDzqTySRRmcPhgNfrhc1mg9FolIu1UqmgUCiILkerNX0VOGWajREtf8+0Fp+73W6H1WrFaDRCs9lEo9FAqVRCoVDAYDCYAF9a+HmWsWIA4nQ6MT8/LwDa4XBIyo2+G41G1Ot1ibT5QvLF03Kfno1eKWD3eDyIx+NYWFiA3W4XFsvpdEKv1wur0mw2kclkkM/nkU6nUSqVUKvVvjWteV4/DQYDHA4H3G43AoEA4vE41tfXsb6+DpfLBafTKalB6q9yuRxOT0+xv7+PVCqFTCaDarWKVqs1wbBpAa547lgsFsRiMSwtLQmwunLlCqLRKCwWC0wmE/R6vbzHlUoFpVIJyWQST58+hc1mQyaTQa1WE0ZTqzNK3Zt2ux2hUAiXL19GPB7H/Pw8VlZWEI/HYbfbJ/4ffWCa8ODgADabDYPBQNYUgDBH5zUCAJvNNsEAxuNxLC4uYnZ2Fl6vF2azeeK9pZ9utxs+nw9er1f2rMpU93q9c/uoBu1OpxOzs7OYn58X9m9hYQHhcBhutxtGo1HYR/UZqOBhMBjg5ORE0tbM/LyuJONVxnecvkajUcTjcSQSCQF+TqdT2H7gxeXOsz8YDGI8HgsrfHp6OsFSa82oqwGp1+tFJBIRltrn88FqtQroP5s9CwaD8udOp4O9vT1hqrXYm6qfPJd4xvt8PvnlcrkwMzODXq8Hg8GA8Xgs95bL5eEecv0AACAASURBVILVapVsRr/fx9OnTyXFrnUW7WzmjpIKt9sNp9MJACiXy3K2D4dD+Xn4rhELFItFed4Xle1TcYjVakUwGMTs7CzcbjcMBgOKxaKwv51OB3q9XgqvDAYD2u22yEDOA6rPBVZVBogfuVm8Xq8cwna7HSaTSVg1MmvdbhdHR0dIJpMolUpoNptotVro9/tv6tZLpgJU+qleZKFQCDqdTkCVwWCAx+OBy+USUDAzM4NCoSDg7+TkBKPRSB6AVvYqsM8Xz2azIRaLYWZmRhhUo9EIn88Hp9MpL9zMzIzoXHh5NBoNVKvVV7LI5/WTwJk+BgIBLC0tTbBFjBxdLpf43el0kM1mkc1modPp0O12BRBodSmoBwJZDAL7WCyGlZUVYVHdbjesVivMZrP8XOPxGLlcDkdHR7J2ZFnppxamsoCMpslKkAm8du2a+O90OmXd+c40Gg3s7Oxgd3dXLqx+v49OpyMHzXkDq7Op4GAwiPX1dQEtN2/exNzc3MRZQFam1WpJOpZBAX0he00NnhamatZWV1dx+fJlbGxsYGVlRVhV/kz0ZWZmBm63G36/Hx6PB71eD7VaTQ7ZwWAgZ4iW7xL3ZzAYRDQalbQlwZUql+H3VYNB6tuy2awwG71eb+Lse9PnrmaZyFjxbAyHw/B6vbDb7fIOd7tdAJgADgTbTA0fHh5OMKv087x7k2tCHz0eDwKBAAKBgLwzzEZ0Oh1ZR6YsTSYTfD4fhsMhTk5O4Pf7UalURGqhxTMn+GNAyvfZ7/cjHA7D4XDI2TIajeSM4XvEgNvv92M0GiGXy+Hp06eo1Wry3LXam/RXff48P/1+P1wuFwwGgxAj3Mvq3jQYDLKm2WwWDx8+FF/JuGll6hlKwonZE71eL2QTzwY+A6/XC6vVilAohNFohHQ6jXA4LJpLrUkpFfzx/SBQnZmZQafTkXek3+8LSAyFQhLIkEg5OjoSku8isrxq8MG7KRKJIBaLweFwoNvtIplMIp1OC7MKAC6XS9a4Xq9PZISGw+Ebve9vBFbV1BUjUqaJSFUvLy/DZDLB5XLJC8iUOpnVdruNeDyOZ8+eIZVKIZVKIZ1Oo91uv4lbL9mrmD+2XXC73UgkEtjY2JB/o/B+dnYWFosFHo8HDocDo9EI1WoVyWQSVqsV4/FY2AutTH3R+dFgMEg6gxomfi4PkcXFRTmgLRYLRqMRPB4P0uk0hsMhWq0WMpnMxPfRCrColwMj7qWlJaytrU1Ugc7MzGB1dVUOh5mZGbmkmKq22WwwmUznXkfVT+5RHlzRaBTRaBThcBjLy8tYW1uTg0in02E4HCIYDMrPBwB2ux2dTgfVahXNZlOkA2TmtQTVlHLE43FcuXIFC4p2zePxCDvOS55sgcPhgMlkwvLyMprNJiqVisgquI+0uMRUQOB0OrG+vo4f/ehH2NzcxPz8PLxeL3Q63YSeymAwwOl0YjAYCIuRSCRQKBTQaDTQbrdRr9cl8NViTVU/HQ4H5ubmcP36dSwvL0vETzaaWkQyHGT7CFxcLhcajYasp5o+PK+pDAt/dgagNptN1pKB3HA4FNBCsD0ajWA0GuVSttvtqFQqAL6RDWnB/tLG4zE6nY4wPvSt2+2i0Wggm81iPB5LFsDn8wmTyeCBBAb91MrUZzMejyeY5W63i3K5jHa7jVqthmKxCJ1OB5fLBZ/Ph0AgIAyhxWKB3+8XyQ33p5Z+qsSOmkXr9/sol8uiS6zVapJ6pZ8EK7zDmG3h+6SlqXejGhyZTCb0+30Ui0VkMhk0m00MBgMBsw6HA6FQCH6/HwaDAV6vV6QLuVxO5ElayD+Ab955/qImmcA/n8/j5OQE6XQag8EAVqsVLpcLLpcLa2triEajMJlMCAQC2NjYwJ07d5DL5YQ80wpUvyqtTmbVbDaj0+lIAFKpVDAYDKDX6+V9X1lZkXO2WCzi4OBAQL/a61Qr474kbpqbm8O1a9cQj8dhtVpRLpexu7uLZDKJfD6Pdrs90dqKEjbiGj73N1nT1warKlBlpGexWBAOh7G4uIhEIoGrV69ibW1N9Gr9/8/elzXHdV1XL6DneZ67gcY8ERQ1ULbjSr6UXU6lnD+Qn5EflNe8pexKOUl5iC1ZlmxJlERSoAgCxIxu9DyP6AH4Hui1ebpFSyJwQdmqPlUskRQI7L73DGuvvfY6/T5qtRry+byUEri5BoNBmVTdbheFQkGzDJabgJrle71ezM/PY3FxEW+++SaWlpYwHA5Rq9VQKBRQr9elPOPxeIQN5OGaz+dxcXGhaRfeizSVBoMBfr8fi4uLWFhYwJ07dxCJRETzVygUJAZODjLWvV4PmUwG/X5fSu1aWIeMP1Nm2ouLi5ifn8fS0hI2Nzdht9tRLpdxenqKfD6PwWCAVqslGSJBKzdqACJV0GKxqcCfMSYSCWxuborWKpFIiOUGRevD4RBra2tSriQoUEvzFovlWrG9KFaVvfB4PFhfX8fdu3exsrICn88Hq9WKTqeDfD6P09NTlEolXF5eYnV1FT6fT3TKTKy4UbwIEFwVCI6X95hAra+vIxKJwGg0SuWBMZKBnp+flwSQukIAcgCqXcRavHsVBNrtdni9XtEpNptNnJ2d4fDwENlsFp1OBxcXF7BarZiZmRHtncqmsBQ4GAyEadGyiYWsw2AwEBsg6qJTqRT29vYkOeb+GY/HEQqFJPHjxs/vQ239dQ9ZPgOy9NSnDQYDkUJ1Oh0cHR1J5zeBVCwWQ/LPWkEycCxn30Qj2GAwkMPSZDLB4/EImGaycXh4iLOzM3Q6HZE1sPyeSCREv8xER69/dkRqab1EPS810Cydnp+fS1f68fGxACtqBGOxmOxfBNYApHKpJaOqDr4jvi+SIgT/uVwO2WwWl5eXor2kFvfWrVtwuVyS5LDSpmXSBzzf1/h+uGdPT0+j0+lInCcnJ+j1erBarcK2Hx0d4fXXX0ckEoHBYECr1RIgW6lUhC3Uaqhx6nQ6qY6y4phOp3FwcIBCoSBnu9FoRK/XQ61WE2lFt9uF1+tFrVYbcbPQavCZcq8zmUxYWlrC8vIyPB6PVEoODg5weHgohATPcsZNEiYcDqNUKqFWq12pYfGlwKoKpFiGZCl9Y2MDKysrWFtbQ/LPnYEsP2cyGRwdHaFQKCAQCMhiMxqNwsilUilpDNJCC6Zmq2p34J07d6QsuLy8DJ1Oh5OTEzx9+hSpVAqNRgNOpxPBYBCDwUAOfn5eAF8yvb3OeJHml/qU27dvY3l5GUtLS5ifn0e328XJyQkODw8FXPX7fQQCAcRiMdHf8fsMh0NpYrluBjueEdK+JBaLSYl1eXkZXq8XpVJJOlWLxaL8XDYHsHGAgECN87rdl+NxmkwmYSBWVlawvLwMv98Po9GIk5MTfPHFFzg9PUWlUpESUiwWQzAYhN1uB/CcsWGSonUWq2qSqbVbXFxEIBCA1WpFt9uV7t9CoYBWqyWyFZb3yV4ZDAYpWTOD1dLnUJ2fwWAQLpdLAFOpVMJHH32Es7MzSZDIpFEHSCDGA1uV/mjZecsEgAkGqwyDwQB7e3t4+PAharWaMH7UgRE8dzodOQToG6h2XGvJYIyD4UKhIHY/+/v7ODw8FN0a2fRarSYNDPV6Hfl8Ho1GQzruVUnFdeMcDofSDMuGyEqlgtPTU0kAWBVjMwi1aq1WS+ZgtVqV50gZgFbMmqor5bxqNBpSqRsOh6jX6zg9PRXpEUGUy+USDbraRKsCci3ZKvVds3Ezk8mg2WxKnGzu0+l08Pv96HQ60Ov1SCQSI4QBKz1c4+NM+HUGwQrPZDbMcQ9i38HZ2Rny+Tz0ej0CgQCKxSJCoZBUB1ix9Pl88Pv9sNvtUpnTKlauR/7i+ZLNZlGpVHB+fo50Oo3j42MMh8MRqQiJMpIp1DnncjkhqbSWLADP9lKyqoPBAMViEd1uF48ePcLu7i46nc6IbESVNjDJolSEYJXzWOs4SZ7Nz8/DbrdjMBigVCrhww8/xP7+PqrVqiSiPH/pG2uz2ZBMJuX7MPl9Wfx0ZbCq+oMlk0msra1J44LNZpON4fj4WBYeJ0Q8Hn9ht716qF53qHpFaq3YvXjr1i3Mzs7Cbrcjl8vh6dOn2N/fRyaTQavVgt/vh8lkkkxBBStkGLTsZBwvC9jtdol1bW1NGoFOTk6QSqXE0wyAMGgARE8HQDZD2sRowQSpz5QyidnZWSwvLwvLNzU1hVQqhZOTE6TTaRSLRRgMBgQCAWEs1aSEi4ybgVaNVaou2e/3IxwOI5lMIhgMCgA8OjoSVrVer8u8pQWPCkh4gPF5atm1ruqsaAXkdDqFgWw2m9jf38fp6SlqtZocngT36lzkOmKpVkugqr5/NspRPtHr9ZDL5ZBKpYT1J1gkGCNwIXigs4Jqu6RVnBxknM7Pz6UxkodQu90Wdp8HMwEZGykJrMaTKS2GytzyV6fTQalUEo0dtfzUKrLUz+fe7XZRLpdRKpVQrValJK9VMsX4yNb2ej2RbnBP5HscDodS9qO1ler6weY1SmqotePPue5gMkRQ1Wg0BMRTu18qldBoNKSxlk2qJCKYOHOuMEHREqhwL6E+kfZebDJtt9tIp9Oo1WpCDLEywPnMs4jJw00406hVBYJ3Vva4zxBot1otaVwiMcRzXY2JekVVRqXVUHWbjJWJFomeSqWCy8tLOW/osqHKPPR6vbCB7GPRmgXmOcifMxgMZG2k02k0Go2RZjDgua6ecgyv14tqtQqv1yugms2qWsbK856k2PT0tFR99vf3Zc8hQcB/Q1BN3b26Nrmfvkwi+NIyAOqryFoFAgHMzc3h1q1biMfjovHb29vD1taWlIKLxSJsNhui0ajoW9m5zsOODIuWQmY1TnqDMTtot9t4/Pgxnjx5IqAFAMxmMy4vL2UisRTEckK73dbczoJJAPVSLLHOz8/D4XBICYtAkA1UvV5PwIPNZhuh4Vlm0sprU01WCALJArLMWy6XcXR0hEwmI2yB3W5Hv9+H2WwWWQWBFDdbHrBad6yzbBoKhRAIBKSsT5kCm7z6/b4AF7PZPFJ6GbevuomOWwI7tTxFdpwAi2BErRYQDKhsULvdRqPRkGespS5MZf+B5/INgib153Kds/Gv3++LfRXlKWQtbkJvBUB+brPZxNTUFGq1Gur1urDQlM+oHbYEqGQpVbPwm7B/UwFrp9NBtVqVA5bd9DabDYFAQEzYedgTWJfLZZTLZbHZ0jJOMnYqYCWzxr83GAwIhUJwu90IBALSLc41RU0rdZhae5eqoJogrtVqoVwuS3JMppfaWcYai8WEsWaiR/cXra+NZJz9fn+kOYmgiWQIXUcACKhiczL3BUoc1DV0E4CVTDB1v6pDQq/XE0bYYDCIvZ9Op4PH4xEHDX4t2XStvWDVz0wwSvcW4HmyxAZA7gvT09NSOVVZZAJDai216k8ARs8nVm7JUheLReTzeSEgGA+TKvYAUfrlcDjk7wuFwkgzphaD5z0bQMPhMHQ6HSqVCvb393F0dDQiR1RlYrFYDIFAQBw4+P7HzyfgmyWrLwVWmbGo5fFoNIq7d+9idnYWVqsVvV4Pjx49wv/+7/9ie3tbskV+ALfbjVAoJKWA4XCIfD6PUqmESqWiqRMAJ5zdbkcymcT3vvc9rKyswOFwoNfrYXt7G++99x4ePXokOgpaLAUCAem844IjWCAroNXmxcGyQCKRwJ07d7C4uAi3243hcIiDgwNsbW1he3tb9GvUVEajUfEP5GZNz1IyA1oMbtgEdH6/H0tLSwgGg2JHdXx8jL29PRwcHEiHKJsYZmZmRM8EQOLUsiSoslUARpIVdvr3+32k02nRWrERiV8Xi8Xgdruly1rV6jEj1FrDBjx3rgAwUtrJ5/OSKA2HQ2kQjMVi8Hg8ACBgoFQqoVAooFgs3kiswPPyYLvdRqVSgcPhkE5ki8UiCafNZhMzdrvdjnq9jl6vh3K5LDZW1Wr1Rg4wlbXpdDool8swGAwCrCKRiBxUHo9nxCKKVnqlUglnZ2diyXJT10aOx8pkifIJl8sldkHcj8hmELRwfqrM/00BFwJBNoCyChCLxTA7OysNNnSBIEPE76FaLN0UU93v91GtVnF2diZsJP9fIBBAJBKRBtuFhQXY7XZhq9WhAgatnyWBcbFYRL1eH/l5arJH/WwikZBERU0auE+o1Qkt41Tf+2AwEPmMmiCoSclgMBArNr738YRfZdW0AoEApFpHAE0WmmQYgSITLTqvWCwW+XvGqs5RrQZ/vko4cP3UajWUSiWRSZGhpAVYPB4XAGi328VhgY1MlITwc2hB+pDZd7vdiEajiMVi0hzP3gQ+S4LvUCiEtbU1aWhlFY5NjUwmVA9w4OsB60uDVR4o7Jybn5/H7OyseCnu7e3hF7/4BT766CMpFdEeYmNjAz/84Q+RTCbhdDrFI+7Ro0fY2dlBqVTShFllnAR/sVgMq6ur2NjYgMPhQLfbxeHhIX71q1/h3r17I80/kUgEt27dwttvvy32DO12G2dnZzg6OsLR0RHK5bKmllUEgASq6+vreOONN+B2uzEYDHB2doaPPvoIT548EQsqm80moPb111+H3++HXq9HuVxGJpNBNptFOp0WFuO6B6yqBWLiwa56i8UiWqYHDx7g5OQEzWYT09PTcLvdmJubw9tvv43l5WUx2mcWWS6XUSgUpOyhBRDgHFUtSshCVyoVNBoNHB4ein0NO1iTySTeeOMNRCIRAJAr7tiUQS2hls114yUyMpS5XE6af9rtNjwejyQdbrcb6+vrmJ2dhclkQqvVQq1Wk2Rmf39fWDbGqrUusFKpIJvNipaJ90XfuXNHuqedTicWFhbgdDqFfS0Wi9jb28POzg6y2eyIHkxrHWiv10O1WkWpVIJerxd7mJWVFXg8HtGzEhCQ0SIrQy0ejfZv4tYd9cBXgR0bVdhYwwON3c2sYkxPT49cAEDAchOghXFyXjqdTsRiMfEJjcViYk1HppDa0W63K2wmy4KANk1143ESbDSbTQF6qvVWNBrFysoKNjY2kEgkEAgERHfNPYFWfExwb6q8zuSCMoRxCQs9jJeWljAzMyOOFQRf6jpXEz6tAaBqnaZ+b8aruuxQz07NKuf20dGR2BwRTN7UHKXkUAWg6u1wvNyCFTc2fdH55+joSJJpginVaeI6Q20AJVhV2WfKq0i0+Xw+zM/PSwM7mUq+d+6f/Deq9EKLWGk9yT4OSr4KhYIwvmyq5Fy9ffs2kn/2BqcEgNV09g8QJ3Duft24knUVkTY3UpWRSKVSaLfbI2jf7XYjHA7jrbfeGrFeaLVa+OSTT/D06VOcnZ3JRqHFmJqagslkQjAYxPz8PNbX1+HxeGA0GtFsNoWFYIw0kk4kEvje974nUgGWYff29kRXxDKIlsNoNCIYDGJubg7r6+vw+XwwGo2o1+sij7BYLHC73bBarQiHw1haWsLdu3cxMzMDo9Eo9Houl0OlUpFGCy0HvShpA8UuRmbM/X5fOrBNJhPC4TDW19exvr6OQCCA4XAo1izValWAAPVmWpRb+D1oC0K7MbKMLA35/X4Az0Ctz+fDrVu3EA6H5aq7UqmEVColz1JlArQ+DFRtGIFgt9sVneLq6irC4bAkAIuLiyKnKBQK2NnZwf7+Po6PjyVWrTvWVdaq3W6jVCrJIRsKheSGGGC0aazT6SCTyeDg4ABPnjzB7u6u3AylNQBUS+o8tKrVqiQsZFHJEHAjHQ6HqFaryOfzcoFBJpN5Iauq1eAzJcDiYcWkhAmA1+sd6UxXnysb2GhPRrCq5VDfPX9P6Q4b1Ngwq16uQr0gLQwTiQS63a5ImbRuXHlRnJRSkDljoj03Nyc+tgQpKtijRISWegTfWsUJPNdvqkyYKl0LhUJYWVnBa6+9htdff11uM6JMpVAoyHWcqlZRa3DNmFWAqkrC6LITj8cRj8cxNzeHpaUlkdUwAWfZWNXda8UCqjHyOXAeMsmbmpqC2+2WCi9BIJNBduPn83m5BIbnErWjWsSqrns2mgHPSBO/3492uw2dTid+7z6fT0Cgz+cT/3RKRvR6vcjHKFNTGcvrDp6llKCwYq2er7xxLx6PS7VC/frBYCBkHOcwCZlqtSrn31eNlwarDFy99YcZPRsonE4nVlZWZDLb7XbMzMzg1q1b8Hq9IixPpVJ48uQJ0um05jpQso80Io9GoyL45mcIBAK4deuW2P54PB6xBqGgneb1p6enKBaLml25pw6VAZ6ZmZEMWs2+6P92eXkp5vu8SpCgut/vCwhUr9jUyiOQ2REblmZmZkRfSc0n9bbxeFy629944w1pWiOopY8gwTSzYK0GG5ZUfQ+ZjF6vJx2KBKeBQABra2twOp2ib+OmRfDHZ6C17Yrqh0m7MZbaqPONRqMAIH6sbrcb09PTUqqmh2C5XB5pXOHQ6gAjWG00GqjVasJKciPie1ZZCLLu2Wx2hK28ibXEQfDXbrdRrValManb7QrDRgADQEz0qXdkyXKcsbqJQXBNuYnb7RZ9p2pDxffHNWMwGMT4nkmC1iBFHSqzTplRpVJBsVgU2Yx6P7ya1JF1oxTI5XKJM4BWrgUvGmoZn/u+qqNmEsumDwJoAFKVoZ94t9vVHAgSCNG+jcmpx+PB6uoqNjc3sbm5iUgkArPZLHpaWu5xLZH5VJ87oO0zZaz0MKVXKRk1nl8EgAaDQWwrU6kU0um0kEQE6gS9WlVV+P3Y+0E9OpsSeYEOPWvj8bjMXUp/1DhZoSWgpF/1dWMd71HhmuE8Vdc2gSAbFnlGsEmU7KfD4Rhx39AqCWCPAt87kz6VPOPtgMk/32zndDpFFqLq/em8wktvstmsVJS+blyJWVUnbLfbleaFXq8n2iVVROx0OsUpgA+7Xq/j/v372N7eRrlc1twagsCKL9xqtUp2TMZ1bW0NiURCyoG89zgQCEhzTbPZxPb2Nk5PT+U2E60odjVWmmiHw2HJ8lQJxerqKgKBgOiY/H4/ZmdnZaExm1J1gGQBtRqMh40e4XBYmIfhcAiXy4WlpSUx1memODMz86UOZpb9gecZsFYgkJsqAQpLPHxfbAZxuVzin8oNzGw2i2aRelVuWOzaVzdarUpCwHNdF0unzOS5WfE6XbJXbGZgNzitT7iOGLPWukBa+rBpkpeBAIDVapVEi6V4do/W63W55eQmgSrjVDd1+joz8VBZfIJFFTjQCuxFz+4mysJMkEqlktyoQ2aVEgXuX6rMxW63Ix6PIxwOjyTjNxkr9+9KpTLS8V0sFsUzWz3M1Os6/X6/XMxRLpdFV63lXjr+Z/VGJVb8qF0nSUIGnfIByjH49Wys1WLvV0vnZKgI8tlBHY1GsbGxgTt37iCRSMj64oUGlHrxumJ1zave1VoM1amGty2pDX/sR1hYWEA4HBbvZyZ/tVoNZ2dnKJfLI+cnv+84I36VwffMeRYMBsVKkTfSqRp1epuyMkGpApvB1e52Vo/UvfQ675/aTkomYrGYWCyqyVEwGBSrRzZQUxZWKBSQy+XQ6/WElPH5fLKetOgD4OfnumETIhu62aDocrmQTCaxuLiIeDw+chMXmz+73a5gHEowgsEgvF4vCoXCN/KyvfJ1qzzMT09P5YNMT08jkUggGAxKxmy1WhGJRLC4uCiG6o1GA7u7u3jvvfewt7cnTUBaatYoA2BGxBs2OEkIutrttmzwbBqiG0Cn08HZ2Rk+//xzbG9vj+gVtc5YVWBFmQF1dlarFYFAQA5ZAPL/2MXIbsdCoTBSZtE6Tm5Wqg0VF5jNZkMoFJKskSwk2TfGxO5GVRemZZmNmSBLk1y0PNydTqdcSMHyqqpv4nzg4eFyuUZcAG7iSkMyPKVSSUTpzK4J+u12uzwnxsISJjNXPnO11Kjl4Ocnu8NrfmlBxQ5aFQSycqGyWTc9CKop56A9kc1mQy6XQzwel/fN0vn09DRCoRAWFxfRaDSwtbWFYrGo6bv+qlh5+xivc2Zc1IZx37HZbAiHw1ImNJlMuHXrFt5//325HpbgW+tnfXFxIaxzoVAQ9rpSqQiTwjVEuQpZFDZebW5uir0Zu4O1cishScJ3RhDAsm8sFoPP58P09LQ4U9Trdezv78u+ND09jWazKdphfiatdJZco+oNgAR9BADLy8t4/fXXxWKRLhW8MYqWXOpFC0ximVBc1xaMz5Jg2uFwyMUEPp9PpD8zMzMjOlWeuZT5UNqi+rOr+9h1PZa5V3NvD4fDwkrHYrGRvZN7usoS8t1SB0y/WADSTERigGvrKvOVzL7H45FrlV977TVEo1Ho9c+ueae8cnp6WnysqWFlc1u5XBYva+6r3Ie5Nol3rtpbwzmqNnDzundKAXmjpsfjwfLyMuLxuFSkKUfkzVb9fh8LCwvSjD01NYXj42OxOKOM6avGS4PVi4sLNBoN8SxLp9PY399HKBSSA9ZgMEiDzfz8vLCbbML6+OOP8fOf/xx/+MMfxPdMKz0Ix3D4zFz55ORELEJ0Oh1CoZBs/ABkUfNmDWaEjUYD29vb+MUvfoE//OEPcn3puLZJi0HLGt4G4XQ6pbSn6m6Y2RGIk9Wg59m9e/dEE8jF1e12NWUu2InKA+b8/Fxio92T6ltJ4MpGkEqlguPjY7HaoWxB7XbXIlZu3MAzW6dKpSIL3263i8ZHZaMIsKamnlsCsQGC8gVabWk1B3ggMM5Wq4VsNit3hqudoyoLS92i3++XjBd4bjjOtaSVJZQK2AiOae/kdDrRaDQkQ+bX8hCyWCxYXV1FqVQSL1a1THkT4JVsJbXxxWJRkqqdnR3pqm+1Wuh0OnA6nZibm5MqjNFolIrKi763FmP8maqsCW+rKZVK0Ol0ohG9vLyE0+nE7du38Xd/93dIJBJwOBx4/fXXsba2hnw+L3NWq6HOOw6a/JON4qUPLEMCzw76g4MDuTiELOH8/DwajQaazSZSqRRKpZIml5YQsKlXESXqugAAIABJREFUbrIJjGBqfn4eHo9HLJ9KpZJIvC4vLwXwEETyXvvz83OpAF73WZKxNZvNwozPzs5iZmYGoVBIdJ/RaFS8lim9KRaL4qlLosXpdAJ4PofUpqLrPlOLxSLJRiwWw+LiIlZWVpD8c5M091JWA9W9TNXhUibEhlruDay4XMdekbdAvf3220gmk0gmk9jc3JS9kVpqylHUs4FnGPBsXZtMJqytrUGn06FYLErHeqVSwcnJiQDAqzzb6elpeL1e/PCHP8TCwgIWFxexsbEhF5Gwysf9hySPqqmmDv/y8hLJZFLeE2/e5Nfw/L9qwkrJRCKRkF4EViMuLp5d9jI/Py9Vakot1HM+k8mgVCrh/PwcwWBQHHlYvVKrnt+kUvnSYJUaNGbU1KNwsqoM2ezsrJQw2G356NEjvPvuu7h3755MUq3L6vxetVoN+/v7crPL2dkZfD6faGnY3BONRvHmm2/C7/eLIPzzzz/Hr3/9a3z44YfS7DIu3tcyVvqosnTSaDQQDAZhMpmkQ7Hf7yMSiSAUCo1cJffkyRO89957uHfvHj7//HOUSqWRZgstO9dpYM2NajgcyobAycwsnKbKOp0OvV4P6XQan3zyCT799FMcHx/j0aNH8g5Ur00t4lR9HQkA+/0+PB4PPB4P3G63MOjcvChc53WHR0dHyOfz2N3dletDGavW759zazAYCAikjprgg5sqNU0ErPwc9EIkE6R28WoxVNDCEj87vcngUhPMTV6VWMzPzyOVSon9mtpooNUcVcv7KmhWLZ06nQ62t7dRqVRQKpXQ6XTg8Xjwox/9COvr63A4HJibm8Pa2ho+/PDDET291s+SjD5ZHrVxglIZFShdXl6K1pYlV5fLhUAgIJ3CKgGghbZOjZGHKg//Xq8nFwWoiRzwrJrFxI/uJXNzc2IlyMNLqzjJ2lEqRTaVNyexHKzX60VDTZ9l7j/Ue5O15nvp9Xo4Ojq6VoxqDwIvTmBTbzKZFBtAl8slJWFVrkC7MFV3y+dKRp1zpdPpaBIrZX2Li4uyJuLxuDQAq57p1N2qAFWVD/CKTurqe70eisUinj59KmX3q8RJFn9hYQFvvfWWlP/JCBNAM/FXNdWUylGO4ff7MRgMEIvFkMlkUKlUUKvV5MZINjxfZbCyNzMzg9dff32kCYlAnzpp6jv591wf3Pt5+RJv26tWq6K7rdfrOD4+vnZFiHsU15XKkBPveTweDAaDET/66elp0adTSx8MBoUxVi8EeJmk6kpglZs+N9Vms4lyuSxAlcxfMpkUbQgAZLNZ/O53v8Nnn32GbDb7wkYQrQZBNRcuTeAdDodQ6r1eTxYZs8jLy0vkcjn8/ve/x6effoqDgwO5neUmQDW/H+8sJ6OXyWTE5J/AwGKx4M0334TH45Fry0qlEj744AN88sknePjwIXK5nBh23wSopp0TAeHZ2dlIVzUARKNRLC8vIxKJiFNEt9vF7u4u7t+/j62tLRwfH98oqKb9EOOieN5ms8HhcIhdCc3gKRm4vLxEo9HA2dkZTk5OcHx8LCJwrYXrHGQqufHyVhv+opE9rzDlFYysBnDeshQ/7q2qVeMCMDr31UyYmz+9iFmmisfj2NjYkGbHRCIBj8eDVCqlaXxqTONAUDX3Bp55mdJChze/sES8sLAgUpf5+Xk4nU7xNdZ6jINA6sO4H5ElYbmayQot/yqVimiCrVYrlpaW4PF4hA3Wgk3nQUrQQXkNr4hUmSYCENXqhyCLSZ4K1lRvy+sygGp3stVqFQcaNtfyMgWbzSaWT9R7qk00nDv0XGayV61WRbt4ncF3TF0q/V5v3bolzjqcA6qdEb1Mqa/s9/vCYAcCAbltq9VqAYA0CF1n0DqJJWA2zvICGFb9+EsFR9T3ExjyzwaDAblcTnyBDQaD9FlcJ06XyyXd6D6fT/T9XPME0KxSqfHy/7GCORwOYbfbJfHLZrOYmppCOp0WQPayg/OK9m20zlP3P1UbzH1eTbrV6sFgMBDWlYb71LWyunmdwZ+lyr7y+Tyi0agwvqw+cF2r+y4rV9QKu1wu2O12Aaps0KRc4Zuc/Vd68iprM64RAp7rKdkhxgV2//59fPjhh9je3par0G5qMOtnVkpGUGV+p6efWRb9/d//vehYh8MhHj16hI8//hhbW1tyUcFN6uxUtrrVaqFQKHypKUiv12NhYQG3bt2SjIuXBdy7dw+ffvqpgCqtwRQHy5TsQC0UCpJhAc9dDdbX1xEOh8VqiSz3w4cPsb29jSdPnqBard5YssLnycSKEgseBNTW+v1+DIdDeL1eORwGg4FYwvDGMDVZuYkEQNUVMXOu1WrSbU1XAh5QLB+SNeAzHr9l6SZiVTdObvrqoVAsFrGzsyMOH51OB8vLy9IAkEwmRe/M73kTc1UFWmpjDW+uYVmP2slarYaVlRW5j9tisYgrSCaTubH4eDiRBSKjxufJ5IMsBGUqalMNDyxKArTWfzM2SmhcLpdY7PAXu+k5F9Vn7/F4pJmFn417mxaNtSytj/t8BoNBLC0tSZOK3W6XpI7JIXXUfK68RjqRSGB2dlY0l7ze+rpxshoSDAbFn3ZhYQHLy8virKJqvpkgAxBwwLWj1+vh8/lgsVik8YpkxenpqYCN68TqcDjg9/sRiURETkHrSY4XNcaqSaPa9U7Xm3w+j9PTU3EPyuVy12JWSULYbDYpq6tM77hM5C99H85NJozAcxmZWrK+yhiX89Gqk241qu2bCja5P15cXAjbzuRFBbOsUJFI1GJfZZMUr6WPx+NyKQEZfvXSDDLCtNrk3GViSuDLK69JWH0TzHIlsKp+Uz4g9RCbmpqC1+vFP//zP2NxcREGgwHlchk///nPR26LuulBIEQdnzrJuAnb7Xb83d/9HWZnZ2E0GlGr1fBf//VfuH//vpQrbrohhIBFNbNWJys1Tmtra1hZWUEwGITRaESn08FvfvMbfPbZZ8hkMjdyP/R4nCynqmw6NzYaxCeTSTmcVFD9xRdfYGdnR24yUueAlk0sZKJZFms0GshkMpLp22w2sVehUTwZokqlgkePHgnzy7LmTT7TcXCpJihM/Gi71O12YbfbEQwGYTabRyyObtJmabyqwASVjDTZABp/E6zyZhY2CkYiESkHav1Mx9c3G+3oocxmNbLXZPzITrJrldq1QCAgneM3JQFQGw3pVUnpDy9RIIBR7Ze8Xq9oydiwwvKhVnOA8VHK4fF44PP5EAgEEAqFxC2lXq8jl8sJKGCjksvlwsrKCtbX13H37l0sLy9LcwVvMmOT1nWfJ+Nkd3UwGJRyq9frFVBKX0oAkoROTU2Jm4nT6YTD4ZD1ReszVhGvywCTWaXzS/LPXdSqtSIwelkC5wuBLhuV+Lnor5nL5fDo0SPk83nZO9iIeZVBEEiZCZMONkqrX8fkQ608cT/qdDriZkFSiFKScrk80vB0Va01GXrVKUNN2tQzCsCIHzGTfOrX+bnVZkEAIhm7DmtJn/GTkxMBqz6fD8DzpFN1Ixn3TaaMiQw6tbWFQkG8oTOZjCZE0GAwEPkRb9ay2Wx4/fXXZZ/inB2Ple+i1WqN2Gmdnp7i4OAABwcH4l3/TS/ZubIbwPhQDzGr1Yp/+7d/w5tvvikL6Z133sG7776Lcrn8SoDqi+IbL2GaTCb867/+K9566y24XC70ej3cv39f4rxpRvWrYhy/NUOv1+OnP/0pNjY2pAR3eHiId999VzRXNwlU1RgZm2rkz6wRAN566y0k/3x7xdTUs/vY33nnHezu7sqNWuOTU+u4uVjU7n0mKPwMBKrUzdXrdTx+/FiYwVfhsTmu26Q8hTeueL1e0SQRWJ2fn+P09BQXFxfC/PMAuak5oIJVuhBwc2KZvdVqIZ1Oy41krG7wNhk6cFAbrn5frWMEnlvEUKesdswCkIYGPudoNDrCbPIAvon9ahyABINBxONxJBIJWK1WOTzJZPJ2PzoW/OM//iO+973viQ6s3+9jd3dXLlfRSvrB5I7X5tKuaH5+HjqdDq1WSxolaaLOMn8gEMDGxoZcyMJKAM+DBw8eoFKpaLa+KE+g1pvXU1KfTqax0+kgkUhIwsU5zCtXAYj+L5fL4ejoSC67uW7SMq7PU7vTVUadjC7wHHgxqWIlQ6fTic0Z9c28YUgLuRIbf9U9kM+R7CCTZPawMKGmywOrcDqdDrOzs4hGo7In0P4sn89Lp/3LDu6bZJTpLKRKlJhosNmT0hTKpiqVyshlBU6nE5FIRIA2XY9YEbwKoCaQPzs7w9nZGS4vL5HP52EymXB8fCyMtdfrRSgUgs/nk7lKEqrZbAojXS6X5fPxCvhqtSq3A6o3Ql5l8Oexmkobx3q9jq2tLayuruLOnTtYXl4WXaoqTeGZlclkcHp6KpI26oDJ1BaLRakGft3QDKwCzzY3u92O73//+/jpT38Kq9WKXq+HTCaD//zP/xxpqHqVIPBFcZrNZiwsLOBf/uVfRKaQzWbxH//xHwJUbxKkvEys1KP9v//3/+S60nK5jJ/97Gc4OjqS6w+/jWeqZv9WqxWLi4t48803R0puXEDsDHyVz3W8CjA9/exa3XA4LNdu8s54XgHHxX+TjOpXxas+U+rw4vG4PDsebOq/uaqdylXiU3+vlq1MJhNisRh0Oh1isRiMRiNmZ2cRiURgMplGrKRu8tmqQJAd4R6PB36/X8rkbPqirCIWi+Gf/umfEIlEpGlhd3cX6XT6xpJWPjM2nxBgeTweSQZ4sFNfz8YF2sCQjTo7O8N7772HXC6niRMAD3syjyoD7Pf7MTc3JwwwwYyqm6Pukk2WLF1Wq1U8fPgQ77//PnZ2djS7tpoMoiqNYCzqLVTc+1UHENXyjc+cDhKfffaZJK/XnQdcp5R2UN7B/6qaWQACvBgTZTVkAllWdTgcKJVKckMUpS3XXWNs7K1Wq6jVamLhpILVTqcj16yfnZ3J15FVK5VKGA6HMJvNiMfjWF5exsXFs1sEeUkIrzi+6v5FBvDs7AyZTEauL2UlgnvO3t4eqtXql66ipvSu1+sJ++v3++VcILNYKBSEJbzKc724uEC9XsfR0ZH0fzApZeLMKorb7R65SpWWlplMBo1GQxroaM3IOUvQfl38wrVPK69+v490Oo1sNovt7W08evQIf/rTnxCLxTA/Py+NgJeXl5IwcS7WajWxBmODGsG1Klv7uqEpWNXr9QgGg/jRj34Er9crE2VnZwdbW1vfyuH/l+L0er343ve+h0gkgqmpKTSbTRwdHeHRo0evHFD9pUFmw+Vy4bXXXhOzfWb8jx8//tZAlTrIWDocDiwsLMDr9QrbU6vVsLu7K44K3xaoZpxsbmAJVdWHsQNba+ufbzpUTSj9aa1Wq/jaXl5ejmgw1ZLWq4pXLbXzgOfvKU8IBoMwGAxwOBzw+XyicWs0Gjg9PUU2m72R9TUeGwe1VV6vV/xJefizGSAYDCKRSMBkMuH8/BypVAoffPCBsIU3ORgry4/BYFDsk+iyoXbiE4SxrJ3JZPDb3/4W9+/fR6PR0OTZqjo5JnJk7ACIvEK9BEDVgI7rZsmy7e7u4ne/+x22trZQrVY12Qt4sFLyo2ppCfZUPaA6b7nm1EaSSqWCarWK/f193L9/HwcHB5rNA+4zuVwOTqcTqVRK/J5VVxU2r6oHO9lAWvzRitHlcomnJeVL1/XZ5jPtdDo4PT2V++i73a5Y5bHzfGdnB7u7u8jlciPerwQnTByq1SpKpZLIcKrVKo6Ojq4FABlru93G3t4eTCYTKpWKXE1LIJvNZnF0dIRKpTJyCRHlCizJU07Cm/guL59fb9toNK4dJ6tig8EATqdTnqE6J9lMTdAPQMgJNdHnvsCKAT1WtbrCWt1H+CxZgaxWqzg+PobD4UA4HJavp+MB8Kyhnt70rGZRbscz9mX2Ks3AKju+NjY28OMf/1isDtLpNO7du4dqtfoljeK3AVp4IMzMzOAHP/iBiIBzuZy4FHwbMoUXDZUF3NzcFHaKG+n+/v4rlyqMD5bXeSVsMpmUOGlifXh4eGNX1b5MnOpGxOsLOQ/ZzMAORS3dCV52ULtEUBoMBkWbaDabJQOn7RfdAl51vGoHKNkA6qkZJ5uaOp0OstksPv/8c6RSqVfCrALPy65k/gmmCapUf0OWWnO5HD7++GN8+OGHI84aWg5V+sEyJRkzAn3aJ7GhUpXcEPScnJzgT3/6E375y1/i4OBAEkKtYuQ6rlQqcDgcsNvtwuzQjYJuGmrzCp8ZAcvp6Sk+++wz3Lt3Dx999NGIxl6rONvtNkqlklywcnZ2JkwzbzDjO6aekwC80+mIC8vx8TEKhQJOTk5EWsGSqFbPkxp64PnNSFzjJE/29vbEj5ogkCwsE1feEEZ7xlQqJcD6uvGyBLy7u4vLy2dNsoeHh/D5fPK8y+Uyjo+PcXx8LDfoqWubOk+z2YxCoYBUKiUNOGQ0r+OvrbLVjx49EuaSTDrnXy6XQzqdFhmVuqb5PZjQ0Ceejju0vmOc13mu/X4fxWIRnU5HKhM8F5kgUP6h/hwmWQSE479UH1atvMqB50nL+GemJCCTyWBvbw/AcyygOhYAz+c3pV8kV17WsUgzsGo2m3H79m38wz/8A5LJJC4uLlAsFvH+++/jt7/9LTqdzpdKiK96UKcaj8fx5ptv4u7duwCe2Xzcu3cP77//vhxO32acjJWb0dzcHJaXl8XZYHd3F3/84x9RLBYl1m8zTjYNBAIBBINBcSQol8vY39/H4eHhyJ31r1oGojaz0MCa1xcCzw+RbreLVColGeGrTgTUjlXq/qif4qUb6i01pVIJW1tbePz4MZ48efKlNfYq4qRe1Wq1wufzyfXFZKzJohQKBXz44Yf44x//iLOzM0031PHYVB0wfWvJDPL6Z/owqv+OQPWdd97B//zP/2Bra+tGGizV2Mjmkznd398XeyiXyzVytbXa6dtsNvHw4UP86le/wnvvvYenT59qxqpy8OfRL5Wd/hcXFzCbzYhGo2ILxVIlS+l0Bzg7O8Ovf/1rfP755/jiiy9QqVTEvkarOcADtdVqjbCsdALhJSterxc6nU7ANhNTlolPTk6Qz+fFs7TT6Qgz9DLlyq+Lk9rXTqcjPq/7+/sjGlpaBZVKpZGbk8ZvquPcIHtFragWkgUyq5lMBuVyGVtbWwgEAnA6ncL48r+UHZCMUPd46tkJGlWJiWq1d9VBsHp2dibNZXQEoNyCQE7dG9TPyufOiiBdQfi1lJhoUVrnc1PBHefWixLj8eZj1S2AsaufS6vkevw5jcfxokqDShQw1vEG1fHff9OhCVg1GAzY3NzET37yE/z4xz+GwWBAq9XCu+++iw8++AB7e3sjZYlvC1jRn3JzcxOrq6vQ6XQol8t455138H//93/45JNPpDnh29bVquBqampKDPQfPHiAx48fS3OCmkG/6njH7T5arRY+++wzFItF1Go1FItFuQiAnpCvmv1TG9SYgbZaLRwcHIj2kyDl4cOH+P3vfy8HxLfFrpKdGgwG4r/LG80oA8lkMvjZz36GDz74QJoDXoUcRN0QqcEqFos4OjqS8j/jJGi5d+8efv3rX+PBgwc4Pj7WhKX6S7EBz31rgWfv/+zsTPakXC6H9fV1LC0twel0Cnhot9v44x//iD/84Q/Y2tpCPp8XJuUmhlpeHwdGhUIBjx8/luSP8p9qtYp8Po9sNov79+/j6dOnovdTD2ItBwELmbBsNoudnR189tln4mmq+qXW63XRVapgcJxJ0TpOMs2MM5/P4/Hjx5KQcB/gvOXz4p/VvwPwpTi1ZKoInqjp5GUwLwIbL3pWqlxI/bubiJUAlEwaPUe/7j2qf09wTZ3lTZBWBJMEUM1m86V+hrp3jIMwLefqVeb+i97/+L70Ks7+l3mOHC/a568a69RX/cOpqamv/a7Uqv3kJz/Bj370I/zwhz+E1+vF6ekp/v3f/x0ffPABUqmU6FiuGujl5eVf9Db6pnGaTCaEw2G89tprWFtbw507d1Cr1fCb3/xGfEpvMs6XiZW3h3g8HoRCISwsLECn0+Hg4EAMwa/rqXrdZ0qgSgswNohMTU2JmT0F+td5rteJU3UBYIMIfRSpA2o2m6jVashms19igLWK8+tiHW8KoiaQ3qQ0USe7Tj/TSqVyJWB91Wf6ojhtNpvoVdlxz4Pu/Pwcx8fH0hX8sr6aV4lT1YCqcapXKrPpRi2fFQoFeZ4vm1hd9d2rpX0yQqpPJP8eeK73omWUqrt7mbl61Wc6Xubj36nASY2Hc/KqyfR15yh/vejnfh1guunnqcZ6nZ/9MkOLs+lVjeueTa9qfBfiBP42Yr02WKUG9O7du3jzzTexvLwMAPj888/xzjvv4PT0FM1m89oMhRZglaxPKBRCLBaDz+dDrVaTK0pVQfNNxPkysaoejNSFXVxcSAnrqoDqm8b6MnGOa//U22zUW5++rTjVA5blVovFIs+PHbnX7aC97rtXgSDL69R/sjmEeh+K6a/qrnHdA1YFLmq8AESmwDLydZg/reaoCl7IAvK/HKq59csOrdb9OPj7KiD1bSWpr2pM4tR2fBcACzCJ8yrju/DuNWFWLRYLIpEI5ubm4PV6US6X8cUXX6BWq2lyQwmg3aFFA3NqwFTNjEZif00mxYvYga8qDWkd68vEqf73Jko8Wm0IN81ifBc2BGAS51XG5N1rPyZxajsmc1T78V2IE/jbiPXaYPXPXyf/Zcea1qUMLSfFi4CVVuO7MCmASZxXGZN3r/34LsQJ/O3EOonz5cd3IU7gbyfWSZwvP74L7/4rwepkTMZkTMZkTMZkTMZkTMa3Oa5+ye1kTMZkTMZkTMZkTMZkTMYNjwlYnYzJmIzJmIzJmIzJmIy/2jEBq5MxGZMxGZMxGZMxGZPxVzsmYHUyJmMyJmMyJmMyJmMy/mrHBKxOxmRMxmRMxmRMxmRMxl/tmIDVyZiMyZiMyZiMyZiMyfirHfqv+p9/C95bwN9OnMDfTqyTOF9+TN699uO7ECfwtxPrJM6XH9+FOIG/nVivGudN+KtrHad6EdBVryl+0dD63as3A05PT4/cpHjdeP9SrF8JVidjMiZjMiZjMibj1Y6bvLhGizF+ERDw5WuA/xpiVwGVXq/H5eXll4CVVjdCXnWo16vr9XqYTCYAkNs/Ly8v5cryv4ZYeV251WqFy+XCxcUF6vU6zs/P5Xr1fr+v+eVQE7A6GZMxGZMxGd/ZoV6z/HVXLn8bQIAxEVhNTU1Bp9N96et4Jfi3CVoIrAj+dDodDAYDgOfXgDPGfr+vKeP2MoMxGgwGGI1GmEwmmM1mAECv1xPwNxwOcX5+PgIG+VleZZxmsxl2ux0OhwM+nw8XFxeoVCpyHfz5+Tm63a6AQMb4qp+pTqeD3W6H3+9HPB5HLBbDYDBAOp1GuVxGo9FAq9VCq9X6EmC9bqwTsDoZkzEZk/FXOl4Erl40XuWhpbJq/C9/vehg+ia/v4kYVWCl0+lGfk9QxTguLi5eyF7dJCBQY1RBFX9vMBgk1uFwiOFwiHa7jVarhcFg8KV4b/p58tmZTCbY7XbY7Xa4XC44nU4B14PBAL1eD/1+H7lcDo1GQxg3gkPg5t69GqfFYoHL5UIgEEA4HIbH48HU1BR6vR663S5arRbOz8+RTqdRr9fR7XYxGAwwHA4xGAxeyfzU6/WwWq3wer2Yn59HPB5HMBgUsFqr1VAul9FqtXB6eop2uy1JwKuIczxWi8WCUCiEzc1NLC4uIh6P4/z8HKenp8hkMkin06hUKkin0yIPYMLC/151fCtgVd3k/ppKBupQY2TGO57R/DXErGblzHanp6dlY+AkYezfRmz8vTrpmZWrGS5/fVvxqXGqBxzfPcsxr/J5fhUweBEjA2BkY7jpWF8UF+fii/RXjG98LV13I/u6GNXYuEbIYPH34+W2cRaLf6d1nGp8Op1uhLGyWCzQ6/XydQBkfbNMSICgHgxq7FoMrgcCKpvNBovFApvNBpfLBZ/PB7fbPQKsBoMBOp0OisUiyuUyms0mWq2WMFucm1rGqcbocDjg9/sRCoXg8/ng8XjgcrkQjUZlHwKARqOBbDaLdDqNVCqFbDaLer2OTqczwrppuY4Yp8VigdPpxMzMDGZmZhAOhxGJRBAOhzE/Py9z4eLiAq1WC3t7e9ja2sLe3h729/dRrVbRbDZlLtzE/OSasVqtcDqdSCQSmJ+fRywWQywWQzQaRTweHwGs7XYbjx49wuPHj7G7u4vDw0M0Gg202230er0bYYf5To1GI+x2OyKRCJaWlpBIJJBIJDA3N4dQKISLiwv0ej00m03UajU8evQI+/v7ODg4QDabRafTEVB4U+udCYrZbIbX68Xt27extLSE5eVlzM7OwmAwoFarIZfLIZ/Po1qt4unTpzg5OZG1NBwO0el0BLDe5P7J/chqtWJpaQkrKyvY3NxEKBRCp9OBzWYTmQUAlMtlXF5e4vz8XNYN962rxqkZWB1nAF7ECHBDZuY4PT09kiXwQ930wTUez4u+htoRs9kMk8kkBxk3WlLzNw0EXgQG+Ge11GG1WuXX9PS0ZI+dTgetVgvtdvvGD1oVjI6DPp1OB7PZDJvNBpvNBqPRiOFwiG63i3a7jW63K5uZlnG+KOlQNzVVK2Q0GjE1NSUHndlslhgbjQa63a7E2+l0NH+ejJUAigeVxWKB2WyGTqeD0+mEx+OB2+3GxcUFOp2OfK5Go4Fmsymxnp+fy4GrVazjwMpoNMJoNMJmsyEUCiEajcqf7XY7TCYThsMhqtWqzMVCoYBKpYJut4terydzVT3EtIiRz5CxOBwOBINBzM/Pw+v1wmQywWq1yvputVoolUqo1+toNBpIpVIoFApSfmOcWh1ijJHgymKxIBqNwu/3w+v1IhgMYmlpCT6fT74WeFbG7HQ6KBQKKJVKKBaLODk5QSqVQrvdxvlUIlIxAAAgAElEQVT5ufwi0LrOIKPidrvh9XoRCASwuLgIt9sNn8+HUCiE2dlZeL3eEVDPdZJOp3FycoLT01McHh7i6OgIzWZT3rmqzbvOMBgMcDgccLvdCAaDiMViWFpaQiQSQTQaFdDqdrsBQJ7n+fk5ms0mzs7OsLOzg6dPn2J/fx+PHz8eYQY5P687dDodrFYr3G43/H4/5ufnsbKyIoAqHA4jEAjA7XaP7PmDwQArKyu4ffs2Hjx4gIcPH+LJkyfY3t5Gs9kURlPL83NqagpGo1H0idFoFMvLy0gmk0gmk5idnUUoFEIgEJB9n7Emk0msr6/j448/xsOHD7G7u4uTkxMAkDgB7Ygq7kdMoCKRCCKRCOLxOObm5hCNRhEMBqHT6SSB7vf7WF1dxcHBAR48eIBHjx7h6dOnSKVS8q5vYp8n+DObzXA6nQgEAkgkEkgmk3A4HHIOud1uzMzMoFarIRaLIZfL4fj4GDs7OyiXy7IXadmMNR4r91HG6/f7kUgkEIvFoNfrUalU0Ol0AABGoxEOhwOBQAC1Wg3tdhvtdhvD4RC9Xu9aWuwrg9W/xPSoWQOBCicxM3IVCGQyGaHjOYFvAqwAo2Jr/jKZTAKgyfiZTCY4nU75ZTAYUK1WUa/XUavVBFT1+31NM5oXAVEVuFgsFomZ5SIuTIfDAYvFAoPBgG63i2aziVKphEKhICyMVsBa3UDHmSA+O4JTAmiHwwGr1Srg/+LiAo1GA5VKBaVSCaVSCYPBAOfn55o9UxWgqqU1o9EIp9OJcDgMg8EAp9MJm80Gq9UqmzOBbKvVQrlcRiaTQblcRq1Wk2RFy3evzkmK11nGisViCAQCMBgM8Pv9CAQCI7qmfr+PbreLQqGAXC6HVCqFSqUi2bdWmfd4gkRGLRAIIBKJYHFxEYlEAkajUcqDRqMRAJDL5VAqlZDP53F4eIh0Oo1sNjuSoGgZJ9ex3W7H/Pw8ZmZmEAqFEIvFsLGxAa/XK0DRaDTKwcUY0+k0XC4XDg8PUSgU0Gw2v1TW0iJOsqdutxvxeBzr6+sIh8OIRqPCtlmtVlxcXAi4Ygm4Wq2iUCjg+PgYFosFl5eXyOfzaDabADCyl141Vurp3G43QqEQ4vE44vE4VldXBVQRAFoslpFDk80WwWAQ0WgUXq8XANBqtb70ddetqExPT8Nms8Hv9yMajSISiSAWi2F9fR2xWEyYVSZQFxcXso8xmSFTbLVaMRgMkM1mR5jqbyrJ+KrB/cXpdMLv98s7Xl1dxezsLAKBADweD5xOJ8xm88h74/mk7hW9Xg9nZ2dfqghouS/x3Ha73cKizs7OIh6Pw+12w2azyXnPOarT6eD1er90plUqlZE5qVUlTSUgCAC5V/r9/pG9SD1XCcBsNptggX6/j3K5fGMSCxX8GY1GSfp8Pp/MzfPzczm7rFYrjEYjAoEAqtUqXC4X9Ho9njx5gnq9rtkaetEYxyIulwvBYBChUAhWqxXtdhuZTAalUgndbhc6nQ4OhwPJZBLVahXFYhGVSmVkj78qsL4SWFVBKScJXwAPCa/XK6UDq9UqH8LhcMiG0ev1cHBwIDoHZtv9fn9EInCdMc74qeUsq9UKv98vzCTBHg9ah8MBm80GAMjn8yiXy8jlcrI58PDSaowzQlx83CwikQj0ej3sdrtMYK/XK39mF2G5XEa5XIbBYBB2jSV3LUGgygoRmPp8PszOzsohx/fv9/sl5qmpKXS7XWSzWeh0OilntFotiVOrGAmmLRaLzD+PxyMZt8pW2u12GI3GESlFPp/H0dGRAAT1eQ6HQ03mqZrgGY1GeDweYQH4PBOJBAwGA0KhEPx+P+x2OwCI1qrVauHp06fQ6/XC/p+fn0sioVWsPLysVitmZmawvLyMaDSK2dlZbG5uwu/3A4AcGnz+kUgEtVoN2Wx2RKYCQJoc9Hq9gIKrxqlWbwhe1tbWsLa2JmA6GAzKoaAesADg8/kQDAbh9/vR6XSEQQcg5UOyGdeNU22u8Pv9mJ2dxfLyMlZXVxGJRODxeOBwOKQCpcoVpqamYLfb4XQ6YbVaUS6X5cAgC8ivv87gYWm32+F2u+F2uxEOhzEzM4NgMAiPxyOVksvLS2FP1PnMzzkcDmX/ZAOGeihfZ24yTq/XK8CUTL/X6xXwB0DYXLV6odfr4XQ6pYKWSqXg8XgkTn4uLdYQ2SeCfCZRHo8HVqsVBoNBAMtgMPiStMZmsyEcDmN9fR2ZTAYejwftdlukFVoOrncmztSABgIBOddZ9geekxeM1+12I5lM4uLiAs1mE7u7u8JQa7HPc6hsJStjTqdTwPTU1BTOz89Rq9WEtGCcZrMZPp8PS0tLAlT39vbkvNSyejp+LpEscbvdkpzU63Xkcjno9XqYzWaYzWYYDAZ4vV54PB7o9XqR2ORyuRtJUsbjJRZhMuj1eqHT6dBqtZBOp1EsFtHpdDAcDqHX6+W8Z+ylUkn2zKsC6yuDVU4M/pcMgcfjQSKRwMLCggAEAj6TySQ6HZPJhHa7jXA4jN3dXaRSKaRSKeTzeXS73auE9cI4xzckllUpaF5eXpb4qWEi48ZsazgcolQq4eTkRMrXZC+0GuMgVWWGAoEA5ubmMDMzM6Kz0+l0WFxcFBaToMRut2N6ehrdbhcOhwPFYlGzOPlz1YYAm80m7Bqzbk5GAoelpSV5BwBkYlPHZjKZpDyjxQHLnzvOXIVCIQSDQSQSCczMzKDX68nn4SLj5xwOhzCbzVIetlqtIxuxVkwLD3aLxSJM4MbGhrBY8XgcZrMZ3W5X2IFOpyMMLA+TwWCARqMhDHC1WhUJxnUPWrVkzRLmxsYGvv/972N5eVkA9HA4FABNUE/WjyxCJBJBuVxGp9MRiYUqHdHimfJ5ssz61ltvIRwOw263Y2pqSqQxl5eXIwnscDiUmH0+H1wul3wWsqtaxMg4x1kWgmVWJyjpaDQaIxUCi8UirJ9er5d91uFwoNlsotlsagIAGSMZerUcPjU1hcFgICRDt9uVOce5zJLm1NTUyHlgs9nQaDQ0fY4Gg0HAJoEbD3FVFtPpdFCv12EwGEaahLjvWq1WkdpUq1W0221N1zv3Jf7iGr28vJQ1wS7wTqcj4Iu/WOr2+XyIRCIIhUJoNpsjoFqLocZqtVrlPFT3cJIO3O/JUvPdc++dmZnB/Pw8IpGINIhRZ60FwFLXEckTzn/qpFmBIkPI5CAQCAhgXF5eRjqdxvb2tsQ37hSgRawqiUKSrNfroVAo4OzsDMfHx7i4uJB37XA4sLa2Jmzx+vo6Go0Gjo+PpYKiVcWHY5xVpUyJUoWLiwtks1mcnJygVCqJFMBqtSISicDlckn8FotFpGpTU1NCqLzMeGmwqoIVHvBms1nKgIuLi7hz5w6SyaRs/ufn59LZZjab4XK55AOEQiEBAO12WzNgNd7gwcns8/mwurqKpaUl3L17F/F4HBcXF6jVaigWi7KJ2Ww2Ydu48TYaDRQKBQDPsnOtxriekh2X4XAYKysrWFxcxMbGBhwOh2Rd1WpVXjjLGART1DHy2Xe7Xc02BLWEQiH78vIyFhYWRHtlNBqlnEo9S6vVgsvlkoTFZrMhlUrJuwee6ZiA6+tu+AwJQOx2O+bm5rC+vo5kMimsEACUSiXkcjmUy2X5dzy4DAYDfD6fLFSbzSaboBZxqrESgEQiEWxubuLtt99GPB6H0+kUOQLLKs1mE3q9Hqurq7IhMPOmjIDri2UiLeJU9dEzMzNYX1/HysoKXC4XLi8vkc1mkclkUCgU5EAymUxIJpOSiQ8GA2EL1cNay9I656jZbIbH40EwGITZbMZgMECxWEShUMDOzg4ajQYGg4Gs90gkIslKp9MZacQCIFIBLeUKwDNmud/vo9/vy55ZrVbRarWwu7uLg4MDdDodmcsEKSyr12o1AM81mGqD03VYIX4+sjilUklkPq1WSxKjUqmE/f195HI5nJ+fC0CJxWLy7qlhZRkceG7FdF3AwnfR7/dRqVQECFitVlQqFQwGA5HJnJ6eitZPJQJYgico4Txn05BWjWB856yGtNttkZgxeSdgqVQquLy8lEoQdZdsEONZ6vV6pfv6Jpg1tQGW87RYLKLf70u3eq1Ww9TU1AiADofDmJ2dlb2NDW+ZTEa+Xssxbu0FYASosuJ4eXkp1TSu+7W1NTgcDrFmSiQSqFarkjjwbLruUJ8n55nZbJb4BoMBTk5OcHh4iHq9LpZWJpMJhUIBiUQCTqdTKi1zc3PiYsC9ScvBtUVGPxKJwO12C16j9CybzQq4JvnEs5L769zcHE5PT1GtVqVi8DLz9aXA6jhTySwmGAzitddew9raGjY2NjAzMwOdTod6vY5qtYqzszOcnJyg1WrB7/cjmUzCYrGIti0QCOD09BRTU1OaHFqqTkYFLT6fD2+88QbW1tZw69YtzM3Nod/v4/j4GAcHB8hkMmg2m9JEQGYNgAACZr9aN4OosZpMJvh8PmxubmJ1dRWrq6sIhUIoFos4PDyUjkBOfGrCDAbDCDvFQ0aLDHZce8QNKJFIYG1tDaurq5ibm4PVasXp6Sl2dnaQTqdRq9Xk0I/FYsIaqXq6ca87LZ4nDyx2W8bjcQHVBFcHBwd4+vQp8vk8Go2GLLCZmRnRBambFEvrWmgBXxQrNwPq2LgpVCoVfP7550ilUqhWq+j3+8IEGAwGebfn5+cCCMkiaWEmzXfPOO12O4LBIMLhMCwWC4Bnh8LJyQkePHiAcrksZWhqGZnYMnGh3q7VaqHb7WqiBVUTUzUBsFqtGA6HaDQaqNfr2Nrawvb2tjwvlo+pX728vBS/QAII1dpGK/aC34NrgAxgsVhEt9tFLpfDkydPcHJyIoeox+ORfddoNGIwGAhY6Ha7IqfiPFV/zlVj7Pf70Ol0aLfb8lyojaU2/uTkBIVCAXq9Hi6XCzqdTrqqHQ4Her0eyuWyPMfx56nFsyRrxkPe5XIhlUphamoK9XodpVIJ2WxW9iRaGfn9/hFrJbKaLG9Tn64VuOL6bDQaqNVqqFQqODo6Qq/XQ61Wk0S/Wq1KwtxqtWR/UBMospzc428CAKpNpmxEYzOfSvJQV1kqldDr9RAOh0f6BDwej8jAVIJGq8F1RIa52Wzi8PBQZGaFQgHlclkICb/fD7/fL+w/Y11YWMD29rYkNqxM3UQiQPlhvV4Xn9LDw0McHx/j/Pxc9jDKGUKhkDQSrq6u4uLiQhJbnvU3ESclaDMzM9LDs7+/jwcPHuDg4EAYU1YnyPwzSXG73TI/Kad6Wfefl2ZWX6SvW15exq1bt3Dr1i2Ew2GYTCYUi0UcHBzg9PQU2WwW+XxeFnw0Gh3RY9JUWEu6ffzQcjgcWFhYwMbGBjY3NxGPx2EymZBOp/HkyRMcHR2JBOH8/FzK/wSQAKSjTQUsWg0VBNrtdiwuLmJ9fR0bGxuIRCK4vLwU0H96eopisShg32az4fLy8ktdjupNElrEOq4LouSDjLrb7RbfupOTE2SzWVSrVSm1kAnS6XSSoVO3RvCvRVlIjZN6QC40n88Ho9GIWq2G4+PjkRKG2+2WQ5RSCsbF905QrWVjgFpedzqd8Pl8ogPs9/tS7mEzIvDskOJhD0A2ZMbKQ1YFgdeNU32ulPJQp9hqtZDL5ZDJZIQloraVMfHAI6NJZ4Vx8/CrDvXfcs7TToXrmnsRGTez2SwHPj/L+fm5sF3dbhe1Wk3TBFWNl9o9zqt6vS42RYyTQIVsIbXpfM+VSkUqV7xJRitQzSSIBxGTCz4fNiCyDG02m+WAYnPqcDiUygBjJOOl1R7Kd8z1RPlOLpcTeQxZwE6nI5pvMqg8g1h6JyjX2sKIc4yAndZVh4eHsj7YONftdiUpUbutgednkRoj15CWAJDlejbtVioV0X8yiSuXyyNnptlslj/zPOJ6554EvNgx6KqDa4lgtdVqoVgsyp9ZteUcYcMPSTP1EgZWEEnK8VzVcqiaVQCyx1SrVfF95TPr9/tCRFEeyATc4XAIBqjVamJzptXgns8mS7fbjV6vJ42yR0dH4pjBd01wSzmN3W4X4qder6Ner4trycusqyuDVbJWtFe5ffs2otEoTCYTms0mnjx5gs8//1zKgvV6XcoZBBHUqRGwaJlpq4wV2d+VlRW89tpr0mFbrVbxxRdfYHt7GycnJ6hUKpienhahPZk2Nn7wwFMPAy3GePnf7/cLsJ6ZmYHFYkEulxNWNZ1OywFGNoH2RtRsMU4yBFqxQerkZTfr4uKiNKpVKhWJkWVBbhicwCzJcjGqm5iWjgXUBDGL9nq9sFqtAIBmsykxlstlDIdD0VaqPrAq86sCQK2TKtUqjfpjagIbjQZKpZLo53gIqIeBanJN9nJ8M7hqvPx34xUANRmq1+toNpuiYwMgTKV6fSDLxzysx5/pdYf6Wfn7drstP4uWWQAkYaZbBcE3D+JarSbsoWoYrhUIVMEq12mtVpO5Rl08u8NDoRAikQh8Pp/8WwIrumqoIFArcMWSo16vFyuaWq0munjKF0KhkDQ2xWIxYWGA58Caz1XLCgXjpF5Tr9fLz8nn81Jhor0TpQy0jmJnMz8rWSpKWbROUAiI9Xq9JPMEnq1WS+YcAKk2klnnHk/9Za1Wk7WutWaRc4znM/1oy+WygA1+FsbJZ8y9nucRgfhN+YKq64nyBJ6BKmAlmGcZe2pqSvThXIskUcbleVqxq5TRqe4eTAYKhQIajcZISZ8AUHWJIWE0Tvpp3Uit7pFcMxcXFygWizg7O0Mul5O9i3jOYDDA4/GIE4PD4YBer0ej0YDP50O1WhWHAH7GbxLvS4NVTorp6Wk4HA7MzMzg7t27iMViMJvN6HQ6+Oyzz/DLX/4ST548Qa1Ww8XFBUwmk3S4shmDXW3FYlEsobTShjBOLvJkMonvf//7WFhYgM1mQ6fTwcOHD/H+++9jZ2dHNgdaLFHITIaNWQEPLq2YNXXRsiQZj8fx1ltvYXZ2Fg6HA91uF48fP8bW1haePn0qmwOF12wacrlcwgARFGjtu8fFTg+4xcVF+Hw+yab39vZwdHQksg+WgtlY4/V6YbFYZDHyACN41SpOHjBMWLxer2SnvHGjUChIOZMLn1ZMZF644fF5Uv+k5fNU2U8Col6vJ+uD+mQmSezO9vv9sNlsstl1Oh1UKhUUCgXUarWRawW1LFtz86eVmypFoH3R1NSzbnXaGxmNRomn2WyKdnQcXGmZADJW6uP4M7gXUdLg9XolKVTL8ZVKRfR1BKs3wazy971eTxgV7q90J4nH4wiHw3C73SPehmTUq9XqiBuAVrGqIICHeavVklI77dR0Oh3i8ThmZmbEKojnwXiDkir/0WpvGgcr7XYbuVxO9iv+PJPJhEAgIA4WNI2nbpBDjZffW8s4WVqv1Wro9/uy1rlf9/t9uRCCHqeRSAROp/NLBM/49aFag0BWRprNpnT9qxW74XAorg9c+263W/ZQ9cYoVQPKua8VCFSBMdeQapfE50zyCYCUq9njQQmB1nunOlSpnirdUOUx/HtWsZlUud1uAaYkAUggqJZmWsWtVqb9fj/C4bA8S96sdXFxMeKlTq367Oys6ILNZjPS6bQkYuplFt8UsL4UWOXEpbg7HA5jeXkZMzMz0Ov1KJVK+OKLL/Df//3f+PTTT6UbmFqLtbU1/OAHP0Dyz91k5+fnSKVS2N3dxd7eHiqViiaLbRyozs7OYmNjA2tra7BarWg2m9jZ2cFvf/tbbG1tSZx2u118GN966y1Eo1FYrVY0Gg2cnp7i5OQEJycn4rWp1WCsNpsNMzMz2NjYwK1bt+BwONBut7G/v4+PPvoIBwcHsvE7nU7E43Fsbm7i9u3b0gjEJjACMZZctYhRBaperxeRSATz8/NScstkMnj8+DEymYw8U4/Hg9nZWbzxxhtYXFwUoEqtW6lUkkmvhW+tCgBUHz16J1Ijlk6nhXlk0wqffTgcxuXlJWq1mrDEmUxGjO218tdlrPxeZEtYLlU3TbfbLdm+1+vF4uKiaMKYRO3s7GBvbw+np6fCXmpVwlTtUfj+MpmMgKrp6WkEg0HYbDbU63WxAlpaWoLb7ZYNmJKGg4MDYQLVpqXrDhVYkQ0iaLHZbKJHp+8iu3EtFgt6vZ6I/8ki5fP5ER9orcEq8NwSi8wTm2bYkU6QQscMshjUrfN9qBKVmwDVnJ/1eh29Xk8aaahf5n6pllXVhNXj8cDn80kDI0GK1okfWVwe9i6XS5oOjUYj5ubmsLS0hIWFBYRCIWlMZGOIatNlsVjQaDQ0La2q1S8mRmSg+f95xkajUczNzeHOnTtSuWQlQq2a8d9xaKmxHP9Z49+bbJrL5ZLbopLJJPR6/YhcgRdYjGvptRrjmmNKgFQyiGcB+xioAeW/Z0d+Pp8X2cC4bOG6Mav9HwCkokOmlfuR6rST/HOj4v9n772a5Lyuq+HVcTrnHCZnDAASpGhRtt9S+cay/4T/n2/kKpdFFmVbVkmiSJAgEhE4wMSezjlP5+8CWhunGyAJzDxDUvr6VKEAiUD3niecs/baa6/Nkj815EykR6PRFFi9bDWN8akSAFZ4yIo2Gg0sLCyIjI171fr6Ora2trC8vDxla9dut+H3+1Gv1xEMBpHL5YTtfhOMciHrKuoVo9EolpaWxJqq2Wwik8mI/s/v98PpdMo0lvfffx8rKysymaPdbuP+/fs4OjpCoVAQQ3gtFkFVKBTC8l8maXCT7PV60gRCDZjL5ZJsm6ym3W4XvdXp6amI3rV0AmCsFKevrKxgZ2dHmhR4cA4GAzGtt9vtiMfj2NzcxK1bt8SDk3OjVSNedt1ptQhC2JVKBwLqDwkKyAzE43Fsb29ja2sLgUAAo9GL+daFQkHKrAQBs2zGZRavKZly4CUbQFsfjoekfxyNzpmgqMwaWSwAmvqWzv778fiF0X+pVBJ7EHZSMiHxer3Y2tqCzWYTp42joyOkUimk0+lXmH+tDi5VX8lkg2VJdn+7XC65l7QG6vf7KJfLODs7e2WsoVpa12rx0OIhWavVhDlnnNFoVBoqyLqyS5w6PDbjaN1YNRuraglFWQSZaXq+UrMKvGD7bTYbzs/P4fF4RMLAa3kV5uCqHpBMGfV/1KfygCUQ5QG6sLAAt9uN5eVl0Q1nMhkpJWtZpWKcrNRQm0j9MkuUZIDZAa6ykpRjqXZNquepVkkq9xAmHAQsLL1Go1FsbW3hgw8+wLVr18Qajg4CdN8olUoCfLUsV6uxDofDV7SbaqyxWExcdvb29hAKheSsbbVayOVyODk5QS6XkwSV+4RaXbhMnLNEBa+F6llOiQpdCzY3N+WsZTLNvhAmZfwMrSoBKiPNa8q9MhqNwmKxiB84f+cULo/HA4PBgF6vh0KhINUU4h1iMa2qv0wo2DSlOgHRd3dhYUH8vyORCJaXl7G2tjb1bvV6PQCAz+cTrTP7Bjgo4vvivZBmlSiaQEDtGASAUCgkpT/aFHHqCctYnU4HmUwGz58/F4SttTCY1liRSAShUGhqggUvMA8slgNWVlawvLwMt9sNnU4nXbmZTEaA6lWUBaxWq7AT8XhcKH7qLkOhEHZ2duQQo9daPB6XTWw0GgkIvIpRsHwh6AUZj8floKRljd/vx9raGobDIRwOB8LhMG7duiVSAY5WVU2E+dladV3y5WejB83+gZdTfShhofdmJBLB3t4eXC4XAAjLxnIrN1bKCrRwLmCsalloMnmhmVTLwQsLC1hfX5eGAEoALBaL6Fnz+bxow3lNGa+WwJrsAyUA3MDMZrO896orhU6nE8/XYrGIUqmEUqk0xaRruXhoUQdarVbh9XplchrLaLT2IgvBoQSzwFHV/V5VrAQDHJXbbDbFL1Ut7/LfkGFngyubF7VuApmNk7ESLHm9XkmsaK9DDT2vK98R2oitrKzg6OgIXq9XksC30a29bazq/SR45rQlAFO9CGxWopOFalfHv6MlsObi3ge83O/9fj+2trbwzjvvYHNzUxJVMpT5fF661amt5WepP5uWz6zaYMnrQ4eK7e1tcYVZWlqC2+0WTTXdIgqFwlTDGuNkYqNV4s93g88kbao4fSkYDCIUCsmUM3pU1+t1nJ2dyVlP8kxtttJywiL3dgJQxlmv14Xd55ALDuCgy0a5XJayOmWWbLQGpqfXaXE9CfhZqXS73TLW2O12C3ahXIk6YMo/eF5QehUIBBCPx6XP4U0GBVwIrFIvoU5I4g/m9XqxtrY2ZaButVqlbLmwsCDsxb1797C/v49qtTrVJajFYhmFomCCPwCiq7h27Rp6vZ6UBlwuFxYXF6c0du12G8+fP0c2m0Wz2Zx6qbTaCPR6/dT0FQIAFXATmLJM5ff7sby8LBMtmCwwc33d5qVVnLT8CIVC0k1JsL+5uSmAhZtDIpGQUjG1ebQFUj17tWKs1eYqCtJVyxRKWBKJhExmoZaaemYeYgQGanbOTFErYK1O8KEGkUCFGrtEIiFVAP4ie0RLmUajIbo7brBkW7RaZFbr9Trq9broKumpx4ll6r0mw0IzdrUMqAJ+LRkh3qNKpSKAgwcXG4bU687yJjdktTQ4u+lrGSe/g7ZFhUJButPJAlKiwGuqsnBOpxOJREKqHK1WSxoDtapScKnPZ7ValX2V14NWS+pwE/YmUHLBZzkej0+5LGix96s/p8pecRGosnGR2n82CLI5UAUAdAhRzzktQKBaCmZzJXsW/H4/kskkrl27ht3dXYTDYWlKpQfr8fEx8vm8dGLzPee917oSQE0i2WaeVxy9SZcdSlbot9loNFAoFMTZgucnzyQyb4z/ovFyHyUr7nK5kEwm4fP55FyldI3Aj9PfWBmk9IcjrHlv+Dzz+b/M/VcbqQn0Y7EYnE6nJKzValWm7wWDwSkZC8FfJpNBsViUM4LOG6xqaGEFqZ6jdrtdpEh8j6lfpSvQ8vIyIpGI7LXdbhf1eh2ZTEZ6l1jNCB1yFzcAACAASURBVIVCaLfb0mNBwPpd60IyAADCrpydncHv98tDTC9LHvbAi6alpaUlKce2220cHx/j008/xcHBwZWU2cgCspTOg5z6C7K9qhE9G3EIbDn39vHjx+IlprV9DWPlDaY9zHg8Fo9Qh8OBWCwm4I6lKlLwZIbYZEGgo/UiGOHEF9UVgmWzcDgsCY06+YK2Jlwcx+lwONBoNISNuSywIiDl1BeLxSIbIQ8Fu90OvV4v2Ta7KPmik9FU5zazjEEAplUCAEBYFWb3FKFTx+x2uxEOh1/R+lHGYrFYJEvndSZbobUMRG0+yuVyYpzOOdEqIGDplWw87wfvkWpiz59JKyDIRovJZCLJNa9RLpdDLBabAtT0Eg2FQtJh7/F4UCwWp5pBAO3AHz+LOjuOoyXDSxBXqVTEqoY/D0uE3Mdu3ryJu3fvyruvdeIPvGysY0NXNpudkk2wEZX30+VyYXl5GX6/H+FwWPR2e3t7qNVqsm+pI20vq7HjUkvXPJuoWbZarej1euJnSr09EyudTodWqyXyID6vJAG0SlJV+yJqAsmUczocE+rJZIJWq4Vnz54Jo0qAzbOLZwP3qssCQMbJ94cTKldXV4WwYCl9aWlJfJf1er004tVqNalIsoqpNgbx+WcSdtHnlqQYp+utra2JPzmrq6wGs9JGQoglc34/x6/SxxiAEAME1hepCvGeu1wuhEIhcfzZ29sTVpTNgZQs2e12OUvV6ku9XsdoNJLEgPsdzwJq7C/TC0CMwXHf6+vrcm5Ho1Hs7e3JGcpnwul0AoDsEScnJ0in02g2mwgGg5LosBm8WCzKNLvve1bfGqxS13F0dCSb6/HxMWKxmHSHsszf6/VkZCEp7Gazifv37+PXv/41/vCHP4heQS3faLHG47HoT0jf63Q6RKNRObh4oJJp4SQollsODg7wySef4NNPP0Umk5GHWasSsBorb9zh4aGMiaRRORkKlvpVEKDX69HtdpHNZvHVV19hf38fBwcHspFRbK5ViY1gpdlsotPpoNvtir0XgZOqm2JJRvVZS6VS4g1ZKpWkGYiyAC0OAjI7jKXVasHtdgs7yRIqmSF1Mhc7bKmpUUuFbDLRooStfi8ZBvprqqwwQagK6LnxccytOtKO15DMklZMkApaKI8hC+h0OlEsFqWxhXEALyoZa2trqNfrKBaLODk5ERkAl1bv/yxgoZxDvX4sY5H9oQ1cMpkUdt1isQgwUFl0rZNUlf3k+5HJZKRTvFgswmQyTY05tdls2N3dxa1btxCPx+Hz+fDuu+/iwYMHol8kM6R1vGqslJ0wUVVN9IEXjSKrq6sIBoPY2trC9evXsbS0hKWlJelmp3xFiz1KrVKoiTTZNTbXmUwm5HI5GVpTKpVE388Emu8fAAGBlC5dlq2ibIZWdT6fT+Rn0WgUy8vL2NvbEy9w9k2kUikcHBwIwG61WqIXVzvc+efLEkDcRwnwgsEgNjY2sLu7i42NDUlAvF6vnFXAywlq7P6fTCZSzWSiy88nU8j96iJ7FYHqysoKVldXEY/Hcf36dWxtbUlyosr9VF9latspGwsGg3jvvfekkYhnPn1as9msYICLxOnxePDBBx9gbW0Na2truHnzJoLBoJybxCMEhTzDeDbwHbRardjZ2UGz2RTf8k6nA5/PJywtz9SL3H+9Xg+Hw4Ht7W0hINmLQqKHVVSHwyHuH3SGoVaZ95bNmBxqQjDO8031uf22dSHrKoIKNvOkUik4HI5XJigFg0G8//778t+GwyH29/fx+9//Hl988YVkB1oDVWAarNJfsVAoiB8oGZXz83P4fD7s7u5iZ2cHBoMB/X4f+/v7+N3vfofbt29LM4DaEa01u8LNiEwwu205IYgWMMy8yWAOBgOkUil89tln+Oyzz/D48WNpVqNeS8syIEE1SyjAi/vMB472IOxoJOCiRdnjx4/x+eef4/j4GE+ePJkC1Vp0hPM5Uv0SW60WUqmUdFo7HA5JSsgMEmzzXmQyGRwdHckoSeqDqLnV4hngv1e1dWyYYmMHLUMooudBrNqXqH6rnH1ONvAqtMvAyyEEpVIJXq9X9gR2pjMOAkObzYZkMimaxXK5PFVW0+qdUvcf4CVgVfWIrVYL+/v7YqHVbrfhcDjwwQcf4MaNG7DZbFhaWsLPfvYz3LlzR2bYayn/UTW9BNHAS0aYBxOTTTZMsuLCEjYlOD6fDzdv3sSjR4/Ejk0r8M/4yAKqFQi+a6rukwkepVQcoU2ZFbWrqVQK+/v7miQCTPo4rEZtkGIlyOFwyDuu6rzr9brsbXa7Hb1eD263G4FAQLT2dA3J5/OXvp4E0fT3jUQiCAQCUkpNJBIC/vhOs1yqygfoYGM2m6fuN/WWZFgvEysbf+LxuLy/q6urWFlZEYCiOlUwQaZjDatxLLvTvJ6f3263cXh4eKl4+R4sLi5ic3MTOzs72NjYkO8k60zAyneNcgT617pcLsTjcXS7XSwuLiKfz4s0pFar4fDwUJjNi66FhQXE43HcvHkT29vbQpiQkWZcfH/U7n4+49xLVXlVq9WSCsxwOEQ6nZb39KJLp3vZHxOPx6UZjqw0mXZ6qLNCaTAYpHnZ4XDg/Pxc5JhMUM7Pz6cqa2/y7l8IrNLGgk0IzWbzlQtDI1n6GOp0OpTLZfzhD3/A/fv3cXZ29tYTDN42TmZx3W4XlUoFmUxGRqtR16PT6bC7u4v19XUZrVqtVvGnP/0J9+7dm5ohrrVWVY2V01Yor6DWTh2np9frsbGxgQ8++GCqXHXv3j3cuXMHDx8+RCaTecXMWkuw2mq1cHp6OsWusRTBl4qdv/TeBV504p+enuLu3bt49OgRDg8PUSwWpyyBtAJVvPelUkmuH62WCKI9Hg+AFxIVNgqwhNLpdJBOp5FKpWRqGHVrV+FbSx2oylySAeZEoFAoJAeSyWQSXTU3YmbeaiOI1sMLGC/vEzNolXGmhq5QKKDRaCAQCGBrawtOp1PswYLB4CtNIFq/U+qhPgtez8/PkclkUCgUUKlUUKvVhB1YX18Xfdbe3h5isZiwlfz5tVqq1YxaEqbnIxM4NjSp//vw8BDRaBTlclk8Yjc2NoSl0SpWVX+qJkd0quAvPsOz5vSNRgN6vR6NRkNKfWoSxoRXC1BN7TQrZD6fT9hy2v6QTed9ZwWQjYjcNw0GA5xOpzQR93o96W6+qMSKiSYZPp/PJ4MeVFaQzZNqotXtdmEymeByudDr9eRzWM2i9rff78Nut8tUvsvIwZgUsxmGgx4o7bBYLLIHvU77zaoL9zB6xZbLZdkzOP2qUChcWFpFQOz3+xGLxWRUNcG0yrarWnXgpU0YwZbNZhMnG+oqa7Wa2ECSAb1onEzeWTJXAZ46KADAVCWNcbKhmTI8TjikdKjb7cJqtb4RU/l9i7HxPG02mwL+2TzPd0O9xsDLvc3hcEhj8MLCgpA9JN9mp5p917qQZlXdoIiU1Ruoou7l5WVB2fQLffDggXQtXwVQBaZBNQF1uVx+5SGlzpaM23g8xuHhIW7fvo2vvvpqyhbkqhZ1RmSrK5UKDg8P5YFjrNx0WcqcTCYoFov44osvcO/ePZycnEz5ql4FqCabxnI1NwTqf8xmM5LJJACIWTjwghl++PAhnj59isePH8uGdRXJCtkIvmSNRkMOHpZ7PB4PTCaTjGBl6Ww8fmFhlM1mxQ6KFiZXAawIVtWyPdlpdZOKRCKiWyJjTZaIzzptlrQaXzobJ5lQAFOHk9pxTUF9KpVCvV6XRja+X4lEQky4+e+1erdmgSk3TzJtTKhGoxFKpZJY/zQaDZhMJoRCIdFW2Ww2OaA5NOQqJAA8qMgIkv0jg8myoNrRPhqNUKvVxJcVeHGwRCIR+Hw+2Ru0iJFgVZ2uxslPNKVXbZ3Ue6Dqx1nWZGmTpVkt7r9aWufEOlorJpNJBINB0SvqdLopr2QCQL1eL/IPj8eDSCSCZDIJj8cjBuaXASpcBICcSMb3Y2dnR0Agm9JmmzzdbrfsDQ6HA71eT1w4aFtI7bDD4UCtVpuyhnrba8pzhvKJRCIhUgVWS5loAa8OuCGg4XNgs9kQjUalPExrwFKpJKDyMnHSJpMEBBs91YrULCBkRYrPEP/MxmV6RlMf+qYl62+LVd2HgBcJPyu9szGqbgm8tmpjGn/vdrtTrCz36ctKQAiG6/U60uk0JpOJJGzco7i/qtVxdd/lnsXqH+WD9Nxlc/2bPKMXAqvqBeBmo25Qk8mLKTG/+MUvsLu7K56A//Ef/4E7d+6gVCppOq3o25baZcjyGhcfTrvdjt3dXdFk9Ho9fPTRR7h9+zZyudyVA1XgJWBRDa1nMyxmqLFYTLxAJ5MJHj58iDt37uD58+evMMBaL5aVme1XKpUpXajqxfb+++8LK6HT6ZDL5XD37l08fPgQlUplqpR+2QPg2+KkFKBWqwkDqY7ftdvtsonyWTk/P8eTJ09weHgoQIaxXsUiGFHvPwEAZ5kPBgMB2sPhULopHQ6HDIGg/o7NAlrHO/ueq9ZEKpgiUD05OZHBBvl8Hn6/XxwigsGgfOZVgH91L+J7Q5N9mlRzbCh/GY1GFItFVCoVLC4uwmw2w+/3I5FIaMpUMi71AOUz6XQ6xatW9X2kpo66Th5k1Apz/CJL3SaTSTOdMhlVlqy9Xi8ikQjC4bCwqTx8uKhZtVgsCIfDWF9fx+7uLm7cuAGv1zvltEK/3cvEqoJVddJONBrF9va2gCs2npjNZgwGA7hcLmmmBCCOMJFIRBhOk8mEdDqN09NT0V/y3r1tzIyTvrNsUFpZWcHW1pYkSapOkc1IbKKxWCwIBoNioUTN6Hg8Rr1ex/HxMdrttvjeXkYKoAJrn8+HeDyOUCgkLhAqS0mNOhNuAiZWXNhg5XA4pLmGgNtut8tze9Gldu2T/SeY4n6gNqGpfsQES5QKEb+oOmru0aqM4G0WY+D30E2DDPpshYUVQdXblaw/dfbc40ulEiqVCgqFgowQ12IY0Hg8Rjqdxmg0QrFYxOnpKXq9HnZ3d8WySp0Ipvby8Pnl/sB4eTakUikcHh6KTPPKwOrrlvriGo1G/Ou//iv+5V/+RaYBpVIpfPzxx2K3cNVA9XWxqd/JB+PDDz/Er371K0SjUQBALpfDb3/7WxQKhSvxVP2+OPl9KoPFhqp33nkH/+///T8ZFdloNPDJJ5/g6OgInU7nSpnq2Rj5cNE0WWWJ6FkaCoXE3uz27dvY39+X0r96mF5FzGqioto5MQtnCYubL4cqHB4e4t69ezg8PJTJRVclVQFeivx5nwGIpoffHYlEZCQs48hkMjAYDDKRC8CVTC9S45ztLibTzus7Ho+Ry+VkjCo1jI1GA+PxWEqfsVhMWAytgSrj4OGg6pFZ3rdYLGg0GsL28RDi1CKyJ2rn8lXdfwICp9MpQ0ncbrew6PRc5aQy+sHu7u7iww8/xPb2tmgD6V5SrVY1Bdbsjo5GozLve2lpSUYs026nUqlIAxLB48bGBn7xi18gkUggEAiI5drx8TFu376NR48eaeJcwsSEGlXa5i0uLiIUCgkTxGaZ1dVVOQPIVhJIU1NK2dP5+blM27sMY6UmZ2qntsvlEmcHVftJgKcy7NQ28nyglygbXzjshknOZZ4DSr0ajYa8z+xLIQhUBzBQisDklVOO+P6Hw2GxgGK1M5PJ4OzsTJqZLrLG47E8f5yK5/f7p0Ae99KFhQUBe5TLNZtNHB8fiySBvtusWLVaLaTTaTx48ECaQ992cf+kPy6ruKFQCKVSSRxqjEajJDIkrgjmarUaUqmUSKwIvtkjQIne48ePUavVpLnpote0VqsBgDTShsNhpFIp3L59G4uLi9jZ2RHgSmDKKjt7W9LpNDKZjGhr8/m8NFYTD8xOYvu2pRlY5TKZTIhGo/i3f/s3+P1+6HQ61Go1/Pu///sUAPwhQeDsIrDyeDz41a9+JfYkjUYDv/71r6UD+CpBytss6qf+/u//HsFgUKj/R48eiaTih04A1KXqlBwOB/b29hAMBsUBoFQq4f79+9LlfNVM9WxswEvGn4eay+XC2tqaeMUCkE2pVCpNmev/GHEScLFRZHl5Wbr9CaLIXMzqBH/ImAkGWYKl3i4QCGAwGEjpn6CPbPdVPq9q8sS4OLqUUhpariwuLgqQ/vDDD7G6uiozzVutFo6OjqZYDC0X2VGr1Sr6RdXnmYvTXqhb93g8ePfdd3Ht2jXpru33+7h79y6eP3+ORqOhufyD0i42XGxtbcl7Q0u9brc75QPMPZYx6nQ6Ydo//vhjPHz4UGbNX3aR0eM7ywSF913tOmZDiDppi5IAlXXr9XrIZDL4+uuvp56Dy1xbtZmS30OwN1tKnW0+5i+CAn6O3W6XROX58+eir7ysfy2/i4wjh6SwGUm1U8rlcrJv0lmhXq+jVCqh3++LLdzKygpMJhNarRby+TyOjo7E2ugiPtu8JrRKqlQqqFarsFqtKJVKMJlM0iBFFxpeHzb5sFGclVd6hlNPzSrX8fHx1LCAt12Up52ensLhcGAymeDk5ASVSkXsydi/4XQ6pfl4MBjIwI1isYhWqyV7Aas+qs86x4JfZo9lLOxBIBg+OjqS94oVlmQyiX6/L+eQx+PBaDRCOp1GtVqdGglLnSornurZ9X1LU7DKjfTDDz/ExsYGjEYj+v0+CoUCHjx48IMDlW9bLP2ur6/jvffek2y/VCrh3r17mk/TusyiLcfi4iLeeecdWK1WjEYjNBoNfPnllz+YVOG7FjNtGtizWY2bHSeAcRDAjxknWROaWVNvC0C8F6kT+zEWDyoyezSODoVCwgazYWH2cNVaTvF9i3Eyw7fb7eJnyMOfAxhY+q1Wq0in05qOVn7dUkuUjIPXcnFxEUajEcvLy9LdyrnmbLyr1+t49OgR9vf3L12m/q6lNt0w2aPGkk4aBDgApibAcNpar9fD8fExPvroIxwfH1+6C1xdBFOqY4dOpxNtKDusKfehVzSvPWUE/Ix8Po8//vGP+OKLL/Ds2TNNngO15EzGjPGqkhgyV0xiVHaQHexkitldfffuXXz99dc4PDwUGcBlFhlSNtG63W4BenyHqO+jib7auMafi2CL3rX0tDw4OEA2mxXAeJl4CVg4iICa3Xq9DrfbDeBFZY32lSw/85zn7Ho22ORyOeRyOZjNZvkZWIkhmLkoYz0YDHBycgK3243hcCgTv/R6vYyGpjl9Pp+f8swlGAMgZW36mzPBYvXgMnECL5J1durTFaFWq4nsh2Vzs9ksQJGyhNFohFarNVV15fvG95QMuBYuQGSWeX35sxuNRpRKJZyenorEilIKnU4nMiaa/TOp4s+oyt3epg9EM7DKDSwej+PDDz+Uh7ler2N/fx/7+/s/CQDIJiCv14vr169L40qz2cTJyQn29/d/VJZyNlZuYJwAxg21WCzi8ePHUw/vjxknmyYSiYR0e7O5LZVKIZfLaTav+KJrlsmiWJwgj3OK1TGXP/QieFEnsbCTmaVsAlWOLiTz8UNdW7XETpBF9p+WO2TX+GdayR0dHeHo6OhKwaqqCSVoJkChIbc61ILNQ2QzG40Gjo+P8dvf/hbHx8eagBR1qVIa/mIn93A4hNFonLrvqkes6ss7mbxwETk9PcXHH3+M//mf/0GpVNIs0VI1yWRC3G43Go0GWq2WTLZxOBxwOp0CTGelIoPBALVaDWdnZ/jzn/+M3//+9/jqq69QqVQ0G17Ca0hQ4Xa7xfSfexFB0+zzoWorq9Uq8vk8stksUqkUnjx5gufPn0u17bLVCwIAAjV1OhDjJmNNOQ2dFKhhZif1cDiEzWYTYMPGUOprLwOqVLkCS7ZkL9Pp9JStVqlUkvGflKowwSLrzgoRZTZMXsi8vWmTzbfFSrsmgkEOICHA430lGJz1oCUI47tFwMj/RiA4OyDkbeMcj8dCiJyensrnqyw7k031DFITK9UlgHsbKwuqXaVWSeCsTpvXkM17qVRqCkCrvuHqv+P+xefibauBmoFVg8EgQPWf/umfoNe/MFd/+vQpfvvb36JUKv0kwCpLU9euXcMvf/lL8QBNpVL49NNPkcvlfhJxAi/Hm4bDYaytrYkHbK1Ww/7+Pp49e/aD62pnl8qqUicGQFiYWq0m42p/LGnF6xpZqPNjoxCz2uPjY2Sz2SsvVb8uRv7OMj8nfVBLSYNrivyptTo8PJQGu6u+vmozHVk1drJzVjSbwciwkVE9PDzEH//4R9y+fVszr9o3WWonPTtZ6Q2sdt6Ox2O0Wi0cHBzgN7/5DT766CPxgtZ68eCiLo3NihybyIOd9jDq+Fc2jFarVdy/fx8fffQRPvnkE6RSqUuDlNfF2Ov1ZFgBDxm6fqyuriISiUjZnf+Of6/b7eLg4ACffvopvvrqK3z99dfiA6uVDRzjVBto+e7q9XrxpmZTJRMXAvFut4tCoYAnT55IgwrZ2VKpJLrVy1aw1OtJgEXtJgdX0C2B1nt0IuH15IQ7Agh1tCUZWK2aQlUWmNfo6OhI9JSUI7C0q14f/nzAy1GtJpMJ+Xx+qtmIn3GZvYDfVavVJNHkdaF+VtUIq9dlFrASBKo+oCpw10IGQh2qCuzUXgM10QOmbQMZJ/fh1w0sYQJ82aUm1er5pMbyuqUmoK+zCZuVtbzp0gSsEqj+8pe/xD//8z/D5/NhMBjg2bNn+OSTT/C///u/muh9Lrto6bG4uIi1tTVEIhFhJj766CP85je/ETH4T0FXy/Ilmyu+/PJLMTT/+uuv8fjx4ykA+GPEqzKBANBsNvG73/1OSj0s+2Wz2bem/bVcKiPFZhT6/RoMBtRqNdy/fx+/+93vpCylxeb0NkvVWjLL5yZ8cnKCXq8n8opKpYKPP/4Yf/7zn/Hs2TPJ1n8I5wpVV0ddU7lcxjfffAO32y3zockEHB4e4pNPPsGdO3dw9+5dFIvFK20G5CELQMqpR0dH0kxRrVZx48YNhEKhKRuaVCqFjz76CHfu3MGDBw9QLpc1HaqhLhW4qF2z1NcdHx8jEolgaWkJy8vLcDqdUqKu1Wr48ssvcfv2bRweHoqn5lVcUwJqJnO1Wg2ZTAZPnz6VRIqlayb+/X4f3W4XjUYD6XQahUJB2Cz1ndKarSb4o8YwnU7j888/n7IEYuLB3/nv+HPyoFcP6lnwoEWcvJ5kWL/88kv57+r3qO+zCgSB6Wlts2BFq0UQpTKhh4eHr4Cq73JHYLmdpXY1ZvX3y6zZhlr1u97087/LRUWrZ3X2/r5JIjz73d8m+brKc+pt7pX6d77t718kVk3AKmdV0wqIh9d//dd/4fPPPxf6/YfoVv+uRZTPTrUvv/xSulK/+uorHBwcXNmm/zaLoAWATIB59OgRisUims0mUqkUstksarXajypZUOOkR+zBwQGKxaJYbNBSg13CPwarqpYiBoMBqtUqvvnmG+TzeVgsFgEI2WxWuhN/LFDNOPv9PiqVilzXx48fiyif7MnTp0+lA/syc7XfdKmHIQX95XJZmjuePXuG//7v/4bb7Z6ysuF86Hq9LqW0q7quKvuglnjJQtMwnr676qbPMqraJHKV95/PGIESO69PTk6mJBTUVavNOeyyVjtprxL8q9UHNqQwAVQNwVWQx8ahqxhQ8rrFGPnd6qCN1/3d2T/PxvZDxvqm/0Zds2D1qtab3LfL/netlhYs/V/D+v9jnLrv+jCdTve930SR7/LyMt577z1sbm4iEAggl8vhT3/6E54+faqJXdVkMvnW7pE3jZPdcyxXLy0twWg0Yn9/H9lsFvV6/UrjfNNYgZcNIuroQLKVLCNcVlepxTVVS+xqo4Wqw2Oi8mPGqXpTkrFm/CwT0UP2oofqZe+9yqySWackgM8CD3+ONb2oVdVlrql631XGmvIEtWTNspfKWv3Qcc7Gy/8265dIwK/19XyTWF8X77d8j/x+Fc/pm+5Pf/m7r8Sl5dIqzqtefwtxAn89sc7jfPv1t3DvNQGr7J6jDx999DjPWqPOtEs/FCpgISBgyVC1PbmqON8m1r/83VcOrsscUrNL60PrdXqWn1Kcf/n7r/v8C0T16vpb2BCAeZwXWfN7r/2ax6ntmj+j2q+/hTiBv45YLw1Wlb87VW69Ak2S5oDlh2YD/vLdP/mHApjHeZE1v/far7+FOIG/nljncb79+luIE/jriXUe59uvv4V7/51gdb7ma77ma77ma77ma77m68dcbz/kdr7ma77ma77ma77ma77m6wdac7A6X/M1X/M1X/M1X/M1Xz/ZNQer8zVf8zVf8zVf8zVf8/WTXXOwOl/zNV/zNV/zNV/zNV8/2TUHq/M1X/M1X/M1X/M1X/P1k11zsDpf8zVf8zVf8zVf8zVfP9n1neNW/xq8t4C/njiBv55Y53G+/Zrfe+3X30KcwF9PrPM43379LcQJ/PXEepk46QOv1boK/3fVB/6nOLDmdZP21Kl/VzUA6jvB6nzN13zN13zN13zNl7pmgRWg/eTCy67ZUcsEqhz5fBXDiy4aI0eWc2w5x5Vz/DPB4E8h1oWFBSwsLMBqtcJqtWI4HKLVakm8nAiqdaxzsDpf8zVf8zVff9PrdWOWZ9ePBQRU4DfLWM0CwB8TYKnASq/Xw2AwCAhU4yO4ItCa/Tl+iDgZn8lkgslkgtFohNlsngJT/DUYDH4UMMg4TSaTAD+r1QqHw4Fer4dms4nBYIDBYIB+v49er6cpg3mRWM1mM9xuN3w+H8LhMAKBAJrNJrLZLOr1OrrdLjqdDrrdruaxzsHqfM3XfM3XfL3xmgV+Ktj6tkNp9v+/6oN2llGb/fPsIfo69uqqQQFjMRgMU6ya0WiEwWB4pcQ6mUwEtAyHw2+N96piVYGVzWaDxWKB1WrFwsKCXL/BYCDxNZtNnJ+fvwIGrzpOg8EAs9kMp9MJl8sFn88Hj8cjILDdbqPT6aDT6eD8/ByVSgXdbvdHiXNhYQFOTeGJMgAAIABJREFUpxPhcBiJRAJ+vx9OpxOtVgu1Wg2VSgWNRgPNZhOVSmXq3gPAaDS6shi/LdbV1VWsra0hFovB6/WiUqkgEAggl8shn8+j2WyiVCqh3+9PAdbLxvqjgdXZDe+nUDZQl7oB6/Uv+tBmN4afQsyzpQ6j0QidTieZrZrp/ljxqXEy42WczL6Z5f6QcarP4LfFyQNOZQt+yDhny22zz6V6AHMxVvUZ+CHim2WF1BhfB1a4kalA4apiVH/x2VPvNZcax+z1uyr2ZTYWPnc8IBjfLOM2GAzk+vGgvap3nnuL2WyGyWTCwsKC/JngxeVyyTUajUYYDocCWggMCF544GodJ68ZGSqv1yu/bDYbzGYzfD4fgJfgutvtolaroVAooFAooFqtot1uTwGtq4jTYrHA4XDA5/NhaWkJ4XAYPp8PbrcbVqsVyWRy6qBvNBo4OTnBwcEBDg8PUSgUcH5+jl6vJ8DgKt4jgj+bzQa/34/t7W1EIhEBgU6nE4lEAuPxWMBguVzG06dP8fz5c6RSKdTrdWEJ+dxq/S6R+bNYLPB6vVhbW0MymUQwGEQgEEAwGITf70ev10O9Xke1WkWpVMKTJ09wcnKCYrGIZrP5Silb6+upvttOpxPJZBLb29tIJpOIRqPwer0AIM9ksVhEpVLBo0ePUKvV5Nkcj8dyNl0VHiFQ5Tvv9XoFrC4tLcFut8PlcsFiscBsNsNgMCCXy6HVagEAhsOhXEeeAxeNUzOw+m3Z9uxidkbAwh9mOBxOlRGu8uBS//xtgMVoNIo2g+WD4XCIfr//ykt3lXG+DgzwfxMImM1mOTAWFhYAQDax8/NzdLtdebivIs7XgQEAAqTIFFgsFtjtdtjtdhgMBgyHQ3nxWD7QOs7Z68WXTwXMZDTMZjP0er3EubCwgNFohG63KywBY2VJ5qri5DXjc8h3xmKxwOVyyYY2HA6FhWm322g0Gmi1WnL/u92uHGRaxclfZrMZCwsLMBqNsNlsCIVCEhefR6vVislkgk6nI6xQuVxGtVpFp9NBr9cTNkarOGeTIofDAZvNBofDgUAggOXlZXg8HvkZzGYzAKDdbqNWq6HT6aDZbCKXy6FcLgtomWW1tIhT3WcsFouAAK/XC7/fj5WVFQFXBoMB4/EY5+fnaDQaqFar8vvZ2RkymYzcb8Z62cSKGjWCqkAgAK/Xi1gsBpfLJc9iNBpFNBoVADgcDlGv19FqtZDL5eTX8fExzs7O5N6/rrx50TgtFgs8Hg9CoRBCoRCCwSBWV1cRCoUQCATg8/ngcDgQCoVkLxiNRmi1WqjX6zg9PcXh4SFOTk5weHiI4+Nj0eJx79fivptMJrhcLgSDQSQSCSSTSezs7GB5eRnhcBgulwt2ux3BYFB+tvF4jG63i0qlgqdPn+LBgwfY39/HwcEBUqkU2u22JAdaJqjcD10uFwKBAFZWVrC5uSmx+v1+uFwuhMNhGI1GjMdjDIdDdDod3LhxAw8ePMCjR4/w9OlTFAoFVCoVAJh6h7SIle+S1WqFy+VCKBRCOBwWABiNRgWwGgwGjEYj9Ho9NBoNbG5u4smTJ3j69CmePHmCZrOJZrP5ir5Vq6WCaqvVCrfbjUgkgqWlJcTjcUkCY7EYVlZW0Gg0UC6XEY/HkU6ncXh4iFQqJWfmVWqF1fOICUswGMTS0hKSyaR8P5/pfr8Po9Eo7xXPotFohH6/f6k4LwxWv41RUWn4WbZvYWFBDrCFhQVMJhMUCgW02205tFTQqsV6HWPGGPV6PRYWFqYOLb1eD6vVCqfTCafTCZvNBr1ej3K5jEajgUajIWUEsmxardcBUaPRKL9brVaJ2WKxyCHicDgEXBmNRnQ6Hckaq9WqPChaXdfZe62WrxgTNzmbzSbX0mq1yotIpiCfz6NcLkOn08mBoGWcvN8EVvydh6zBYJDs0G63y0tpMBgAvAAwlUoFZ2dnstEy89b6evI+E1hZLBY4nU5EIhF4PB4YjUZ4PB45eMfjsRxSvV4PpVIJ2WwWZ2dnqFarACAHmBaxqqVAo9EIv98vbBBBYCgUwmg0kmvqdDoBAMViUdiMk5MTpFIpKRl1u91XEtbLxsgEzul0Ym1tDfF4HMFgEJFIBFtbW/B6vZhMJjCbzTAajVJiLRaLKBaLyGaz2N/fx/HxMcrlMjqdjoBFLZgMFai6XC74/X4kEglsbm4iEokgGAwiHo8jHo/DbrdjMpnI/jAYDNDtdlGv15HL5ZBKpSQpKJVKaLfbACBA9bvK89+3DAYDPB4PIpHI1DXc2NhAIBCAx+MR0Op0OqdY6PPzc/T7fVSrVWSzWTx79gw6nQ7dblc+nwzxZUGgXq+H2+3GysoKEomExLm3t4dQKASXyyWla4fDAeAls+rz+dDpdOD1euHz+WC32zEYDGTvZJyzEoKLLJ6DwWAQKysrSCaT2NjYwHvvvYdIJCJ7uclkgs1mm/oum80m+7zNZpNGnEqlgtFohPPzcwDQlOzheej1euV5XF1dxebmJpxOJ+x2OxwOB5xO51QF0m63yzvIc2l/f1/OTeAl66bF0ul0MJlMAqyDwSBCoRASiQSi0ajIACwWi+AREifcZ3nWPn/+XICVKg3QKk4V/Nntdni9XoTDYYRCITgcDnkXSKA4nU74/X74/X6EQiFYrVYAQC6XkzPzqipo6hlvNpslMQ2Hw7BarWg2m2i1WhgMBjAajXC5XDAYDLBarahUKigUCpLwM4m9aAJwIbA6C/xUQMWykM/nk4PXYrHAYDAI+OOL1u/3cXx8jGw2K6WXbreL4XB4kbC+N04yaqS0rVYrAoGAPDR8wZxOJxwOx5QeJ5vNolQqiS6DAEGrB3k2Vj6ozMDICJAp4ovn9XplAzMYDJhMJigWi7DZbNDpdMKu6fV6TTYGFUizlGGxWISN9Pv9wrAwbrfbDb/fD4vFIizB+fk50um0dD0SDGgZJ18yg8Eg18zj8cDr9QrzotPppFzo8XjkurGkVS6XcXp6Kux0r9eTn1/Le88Eb2FhAYFAQPRLbrdb/mwwGBAOhxGNRgUYMHOt1+s4PDyETqcTJpCMpRax8vnku2O327GxsYH19XWEQiFEIhHs7u7C4/FgMBjIgcoEsNlsCrgCIIkpEz4t42RC53Q6EY/Hcf36dWxubmJlZQWRSASBQAAWiwWDwUD2LX5nNBpFpVJBMBgU9o9AZTQawWQyaXKAzYLVeDyO9fV13Lp1C6urq6JdY+I3Go3kHvC72+02fD4fXC4XqtUqqtWqHFzcn76twvU2MXq9XgHQ4XAYq6ur2NnZgdvtlmoOG1mGw6F8J8EUE2sAyOfz8Hq9OD8/l4P2MjEyTr1eD4/Hg3g8jnA4jHA4jOXlZSSTSXg8HlgsFqlOUKbA99hkMsHhcEiC2u/3kclk4PF40Ol0MBwOMRgMLh0nY7VYLAKmyJ7F43F4PB7Zx/V6vRzsaqXAYrEgFArJvlkqleD1eqfkClrEyWU0GgVQ+Xw+BINBJJNJ2c8J/Pr9/pRsisBlaWlJ3h3u+fyz1kQPwaqqVWWiYjKZMJlMcH5+LjiF9557K9+bwWCAWq0mCSnvvVZgUAV/JHOYJAGQCh7PVWKq5eVlOJ1Okf3w7wLQlORRF5874iaeTR6PB3q9XsgcVh1I/rjdbsF7RqMR1WpVns2Lsv9vDVbVUi9vOP/M0sXKygrW19cFwDDrt1qt8lDp9Xp0Oh1EIhEpZaRSKdlkL7tmmT/1ghP4ra2tYXV1VQ5ggu5wOCxxGgwG9Pt9+Hw+nJycyIbc6XQuHePrYlW7FwlGQ6EQkskkIpHIK4B7fX0dwMuSe7/fh9VqlbJRtVpFrVa7kjiZObvdbjkgIpEIwuEwRqOR/D2bzYaNjQ0AL1lJlv9rtZokCepmpwVgmU1KEomEgFR2Mvb7fUkIbDYbAoGAaP8GgwHsdjtarRYKhQIsFosccKrO8bLXlEwgX/Lt7W1sbm5KBktw1el05FqNx2N5n1QLkXq9jkqlglqthnq9LpsNwc5Fr6t6Tfn+XLt2DR988AGSyaQwVwBQr9cxHA7lQOK7xUMvGAyiXC6j2+3KLz7Xl4lT3Zv47sTjcWxubuL9999HIBCQa3Z+fi4MpCoLAQCz2SxsJxsF+v2+JH1a3Ht1D2Xp0uv1IplMIhwOC8vf6XTkfZmVh/BaqRUWl8uFTqeDdrutCWjR6/Ww2+1yT9T3n2XfbreLRqOB8XiMRqMxVea0WCyYTCbCthBIsEKlJuiXeed5SAIQfRyvExNPdimPRiN0Oh15JnlG6fV60WVSikEZAEHBZRfvl1rlIbuvSuIo4ej1epIcOhyOqYR2eXlZ2G6SPGqpVYtYmVDNNlTRroj7JPCShSXRY7FY4Ha7sbi4KKz/8+fPX9H/awGw+Myp11Y9q9vttsg5mCCS8FlYWIDH48HW1pYw/2dnZ68032mxVGaVZyOrt9yTMpkMisWiYCq+16FQCG63GxsbGyJRaDQa8p5rrQOePe/ZCBaJRGC1WtHpdIQ9JXPKimogEJDnwGazIZfLQafTiZzuIjK6C4FVtXuR2V4ikcD29jZ2dnbws5/9DIFAQB4WCtjPz88lm+BGFo/HpRTPjjctlroJqhech+zOzg7ef/99+Hw+OeTJ7nJjpdaNQKbVaqFSqUCv12uqV31drBaLRYTX6+vr2NzchMlkEjDSaDRkQ1NZYL1ej263C5PJBACy8WlZVuf9p0aMeiuW4CaTCRqNBmq1mgjWu92ubA4GgwFutxvpdFo2C2bfWsSosugLCwtwu91YW1vD1tYWlpaWRG+nCu3L5bIAcMbJrHZ/f18OWq2AP2NVQbXdbkcymcTe3h5u3bqFcDgMi8WC0WiEZrOJdruNfD4vWffKyoqUr8i48M+sXmgdJ5OT9fV17O3tYX19HXa7HePxGIVCAdlsFrVaTQ5Nm82GeDwOl8sF4MXG73a75WfmvdeqSYD3nRUSAlaLxYJ+vy/Sk8PDQ3Q6HZGIUIvl8XhEXsF3ikmresBqsZgcc/HdIjBuNBp4/vw5Tk5OhK0mC8NKwHA4RLVanZKv8CCjDOAy13QymYi2j0lRpVKRcm673UYul8PR0RGq1SqGw6EkCtFoFLFYDE6nE/1+H61WS55PleHUCrD0ej3UajXZU+x2O2KxGOr1Omq1GjKZDNLpNFqtFsbjsQCAlZUVhMNhYSgByPtOtlWr55PvEvW6PBt57Wq1GtLpNIrFojyfPp8P8XgcS0tLCAaD8Pl8Agw9Hg+CwSCKxeIUG6jVmt2XB4MB6vU62u32VGLcbrdhNBpFh0sQHQqFYLfbpfqyuLgoDXdaL1X2wjM6n89jPB6j0+mIjE+VMHk8Hvh8PsRiMVit1ikN8ePHjyVh0HKp94iJCq9hp9NBPp9HLpcT4olEAKUCk8kEDocDwWAQN27cwJMnT1AoFETypXWs4/EYer1e5AgA0Gw2US6XpfmrVCpJskccwqq0xWIR5vjo6AilUulCbPVbgVUVrKhliXA4jFu3buH69evY29tDJBLBeDyWhzmfzyOdTmM4HMLv9wtLyIyGZRpAG72NqqlUSz2BQEDivH79OqLRKNrtNk5PT3F6eopSqYRerydC7FgsJuUrljQBCEjUavPi72pDTSgUwt7eHnZ3d7G1tQWHw4F0Oo2joyOk02k0Gg2YTCbodDphM1XmBYC8aFocBrPXkxKPxcVFbGxsYHt7G4lEAnq9HgcHBzg+PhY9MkFfPB6H3++H2WwWAE0di+p9p0WsKvvMzX5zcxNLS0uw2WzodDo4PDxEOp1GtVrF+fm56JcSiYSwSe12eypO1ahZi6U2ybFRIZlMilZxNBqhWCzi66+/RrFYFP86vjP8t2SLWGbhYag+p5dhVXlN+c7y/SBz1mq18Pz5czx+/FjAAEvI/PcA0Gq1JM5+vy96dVW/dln2l4k0GUebzSZJc7FYxKNHj3BwcIDBYDClG+NnjMdj1Go1udatVkuSQy019fwMgjayQJPJBLVaDalUCt988w1yuZyUVQOBgHw/gS2T1/Pzc0m4ee8vu6gtV7XyjUYDxWJRNL7pdBrpdBrNZlMaLcbjseiWFxYW0Ol0UKvVJE6SF7yel128T/V6XZh87pnNZhP5fB75fB6FQgGdTkc6m3U6negBWV6l5GcwGEhjoJbldX4uyZl8Pg+bzYZisYhcLic6P7KqPp8P7XYbbrcbwWBwiokm60emSn3XLrv4HWyEJUA9OTlBs9lEtVpFpVKRZkkyaqVSCUajEdFodKr6qsrAgJfVQC3iBCDSCMoJCaa63a44FNTrdam0hUIhSQC4lw4GA4TDYTgcDpFksIFVqySA7zsAIRRIQjCpotRQrVz6/X4pq5vNZmEyi8WiVCooB9ByET8Fg0EEg0H0ej3R9LPRj1USJtR0tFBdJAaDgUgc1L30TdeFmFWVDfJ4PLh27Rpu3ryJa9euycuUy+Wwv78vWs9KpSI3hh15LHkzy1abQbRYqhbQ6XRia2sL169fx40bN6Sknkql8PjxY2QyGRGqszTMOMl+sNPxKiY0qNeVVP/Ozg52d3fFbiOdTuPk5ETYKz4EzLIIUnkdVYsYrQ5YxmixWOD3+0Vrt7KyArvdjkajgUwmg1QqhWKxiHa7DZfLhWg0Cp/PJ4csy0cssWoJAtRrydJ+IBBAPB6H2+2GXq9HtVpFOp3G2dkZ6vW6SD0ajQa63a5oLVmGVTvBtSq3qGVrtSGIQNRoNKLf76NeryOdTotQnRuoGg9ZJepUGbdWTOAsW83kjQdEvV5HsViUBAWAMFxkkAh8WD4kk6DVNeXhrR7iPIA6nQ5arRay2aywAZQmsaROkE8mkc1fNLvWstmCDV0s9bdarSnZTqlUkqa+ZrMpDSxk/6n7owNEvV5Hs9kUMKjV+zQej9FsNgG8eAZsNhuazaYwf2xIq9fr6PV6sqezSZB6W8ZJ7TJBoFbXknGqDZVkKRuNBkqlEsrlMsrl8lTTGQkX6qXJALdaLbn/WlbRqJuk/IkVnMFggGKxiHK5jEqlImcRnVNarRYmk4loL3nwE0Sqe4FWYJWgqt/vo9lsolarYWFhQd4JVqXIClOKZLVapYoKQD5DTQK0dFLhc85qApM2Jqjn5+dSFe33+zCZTMLs+v3+qX4PtfQPvOrCo8VSP49yCu4xtVoNZ2dnaDQaGI1GMJvNGAwGcLlcsFqt0nxnNBrRbDZFbkOGVq+/nD3UbJxqpYpnEt1HUqkUTk9PpZLG6hMlGGyqJu5rNpvw+/2oVCpotVrSVP+msV6owUplVePxOHZ3d3Hz5k0Eg0EYjUZUKhXcu3cPjx49EruK8/Nz0eCpdjwABLBoabHDOJnRhcNhbG9v49atW5LxFQoFfP3113j69CkymYzcfJfLJQ8K4yRLycaVq7DXYsd/MBjE+vo6rl27JrGWy2UcHx/j5OQEuVwOvV4PLpdLmD/Gyo1BBYFadzPy3vt8PiSTSaysrAhL0Ww2BQSWy+UpITh1wGSEVRCoZTlwNk42fvGFHwwGUg7O5/Oo1WrQ6XTCZKqbiRojdWFaMav8WVV2lXorNfFotVpyKPR6PdGG8jqS+eDhyt+1BIHAqxILMs2ctkJGg9pOdXIMATUPV5blrgIEEqgSrDPrJxCk/nC2K5fgWwWB/J2HrFYVFbWi0Ov1BKxWKhWMx2NhqwDA5XIJqxYKheD3++W95kFMA3GVVdUiRpZQeb1oWF6tVoWxGg6HIp9hZzO7sFWnBfW6ci/VKpFmnKrO0uFwwGg0CiPIvVK1f/P7/aJlJgikhV6r1dI82Ve9SCuVishg6DZTrVYFzLNKpmqTWckgA8zrOXsttWICmcC1Wi2Uy2VJfBuNxpSxPvchq9UqwIXvE/89k5VZeYoWsRJYDwaDKbs0OlJQiga8lAo1m00pUwMvQfWsplIFrFqCQABiQ3d+fi5sNRM/XiM2SrJKQW09nwXuszxHtGxcUz12WSEl8KfbkHqtWFGjBMvtdgvgHg6HolknJnwbqdKFwCozUpfLhcXFRfzd3/0dwuEwTCYTms0m/vznP+OTTz7BwcGBbCBWqxVLS0vwer0ivjUYDNJ5x81aS2YVgHSCr6ys4B/+4R+QTCaxsLCARqOBL774Ap9++imOj4/lhXO73VLOCAaDYr3DF46MlVbaEDUTpjg5Go3iZz/7GRKJBCwWC5rNJh48eICHDx/i9PQU5+fncnDQ35JdwYyPG4kWujXGqf5OUL++vg6v1wuTyYR2u41nz57h5OQEZ2dn0nlJjQ07MxcWFuRBpRes2iGo1aGggkFaavDlT6fTKJVKIg7nocEDjGwHu7/V51NLUM2Dh6w9N1fqpFhiJzNBTaDVahU9GMGXuuGpzKpWwJqfxXJ1rVaTA4nfYbFYhNHksxkKhbCwsCA/J/1MKb/QErTMsiO9Xk++h4corx03VZ/Ph+XlZdFYEUzVajVks1lpCtA68VN1paq2n2VHanvdbjdisZh0jJMQ4PNAfWupVJoC1Vq992oVhIxjtVoVaQD3yng8Lg1iTAzJGqsHvvqea/UuqZW5druNarUKq9Uqn08nB77fsVgMi4uLMonHbDaj2+3Kfswud362ltIPAkA2pbERknshnzU22VGbGgqFYLFYJMlRh0Ko74+We5MK4MfjsQBWEiHcD0nqqCVjNmLRD5RVi6sy22dSNJlMJF51WAafAVZ71H4PleRhEj5bndACrKqgdzAYoNlswmq1Sv8EfZ7Vv6vq71VZBa/vrGOEVu++Si4xwQuFQiLz4r7K7ycxGIlERJrodrvFprTVaknjKu0A1erK98X7VmCVLwYPIjZVUS9ZKpVw//59fPTRR3jw4IFobhwOByKRCDY3N/Gzn/0Mi4uLcLlc0nX3/PlzPH/+HLVaTZOsgA8Y7bJWV1fxzjvviENBtVrFw4cP8cc//hEHBwcCqgi+d3Z28O6774omj2XYdDqN09NTyci03BQIqpeWlnD9+nVsbW3BarWiXq/jm2++wb1793B2diad0y6XC7FYDDs7O7h27Rp8Pt9Uk1q5XBbPRa2ANR9K6r0ikQiSySTMZjNarRaOj4+xv7+PQqEgVkS0Xrpx44YAAgIBljvJ0miRqPAZBTCls7PZbBiNRqjX6yJg54tG38NoNIqtrS2EQiFpXMlkMlJCnC23aQUE1IObJbd2uy2HxGQykSYVAmpOZjGbzXJAHx8fI5VKoVQqTYFVLZhVdQNU2QtumtSo2Ww2NBoN8efc3t4WnZNOp0On00EmkxH5BZlVLe89f15el2KxKLo0uitYrVbZH+x2O2w2myTOBNS879xUtQSrKrCYLZMGg0HR2rJJSGVVhsOhVFuKxaIwrKp9kZYgkJ+pAuN+v49EIiHsCRvZKA9hkkqdos/nQ71elwahYrE4BQa1ipNMNZ9Bk8kkzR0sT66srGB1dRXRaFQqLvwMm80mhApHSFLWotVStZUELKoukmcgiYv19XW89957CIVCYgemVg1ZxtayVM3FeGaTSu6xBPdmsxnhcBibm5vY3d0V+0K1ckDGUiswNbvUaoqqf1fBJsvTTKzYEKxW0HiG8v7MVpa0IlPopKG+XzyP6ATg8XgQi8WQSCREkkh5BafDsROfjDFJGf78F10EwKqdp8/nk3vY7/dFgmCz2WS4AQdxxONxOJ1OmEwmdLvdVybLNZtNAatvglEuxKzycKdHHOnoZrMpXWk+n0+6xWlm++6772JpaQlutxsA0Ol08OjRI5ycnIhFjFaLoIq+cAR/zGBpr+N2u+F2u8X4enFxEbdu3UIymYTdbpcD7+zsDPl8XjZqfodWZQGz2Qy/34+lpSVsb2+LncVoNJJyFMXVdrsd8XgcW1tbAqr5QLC8RCCgtZcdQX0sFpMOSnUiCAA4HA4pCcViMWxsbIiJOIFOqVSScpeqtdLqAOM1JRDhZkD2kkCa2XY4HMba2poYM1OTVSqVZENgOW7WHF7LRWavXq/DZrNJ4xebvvR6vTCBbBxqtVoiymfjC6+nVp6wKgAkA1StVqfG7SUSCWxsbEyVAVV5SC6Xw+npKVKpFHK5nKaeymqcfBYpN6DWjvZGLKfP6rwIVFlWz+fzUpJTzay1WgSVlJgwOen1erIv0WaN7z4AkYKcn5/D6/XK9afuVst3fjZOghYmbhxUwelQ1LUBEC9TugOwVKxWNLTU/zNOdSIaQSvBvt1uF5s9elRT0jKZTKTLnppbSq04uVDLkvVsk6nK6ppMJiQSCezs7ODDDz/E+vq6MICU+vA6lsvlV955LZMqvqOqrRyvFZnKxcVFvPvuu9jb28PNmzfljD8/P0elUkEqlZLEX33vtYpXBfkqi6/2L3CPWv6L5dfq6ipWVlZEutDpdJDNZnF8fIx0Oi1ED++JVom/2vsCvCSBVP08k2omTisrK9KczD1fHawymUyEwTQYDHKuXmapiSqJHzavsheEFQA2gLE3ZHV1Vdw0OBiE76LD4UA4HJb3iTry79u3LtRgZTab4Xa7ZeoPH+jRaASj0YhEIoFEIiFeaw6HQ0CLx+OBwWBAt9tFNpvF4eGhdMJpeRjodLqpcr7X65VGKTYKJBIJBAIBQfwejwdLS0vC/JIJKhQKyOVyog3SImuZjdVqtcomyoOUDx4fBgIPWoHs7OwgFosJGBsMBgKuVHpdy+5Qs9ksh1MkEhHGggdBKBSSw91msyEajeLWrVtSWudhQu0gDzQyBVq5AdBmidOzZjtRnU4nYrEYAoEA7HY7otEorl+/PiUXYAmTJSW6NVDDrMXzqgJKsiME9GQyLBYLlpaWEI1GpTs4mUzCYrFIhzV9VVmi4QbLjVGLxc2L4IpWMPxvBFf8OchscUQtu4fJ/M7ea60SFb4LnOTG+08JADtW+f+R6VIbrdjA8jo3DS07g8kCUa86O2lJ/R6V2VYrHJQBUQenLq1iZZwEgHzmVJ9SMlG8/ypwpr/tyspUz6fEAAAgAElEQVSKNIqqchUt42RSykZD6pK5/6iHMMEykxuW4MnAeTwe0RSq4PIyi8BKBWkEVmxO8/l82NjYwLvvvotlZTwwE3067LBszOus+gBrFSuvl6pX5/Nnt9vh8/nw7rvv4r333pM+C4vFIu98NpuVxkYmKCow10ICpFYq1DI533m32y3e6sQnS0tLCAQC4nHKSYX5fH6qs573xWg0CoupRawAJKGfZcYDgYCAP0qp7Ha7SHFSqZQ42fB5tdlsckap8ofLLPW5YmKiDlygDSX9Vzl0xeVyTQ0NODs7E4kTn2+ex2oV8LvWhcEqu7xInwMv9KHBYBC7u7tiYAy8YGITiYTYVZG+fvjwIQ4PD9FsNjVnLlQbBXalclmtViSTSWHVKBym12IwGJSfrdPp4ODgQLqctXYBAF5qVcmmcFQtb2wsFsPe3h5ardZUw1AymRQdC/VAfNHULFNLA3uyJJxoonoahkIhbG5uwu/3CwPLSTIEgTzEWP5gEwEnmmkVJ0sTZFbJTI3HYzgcDpkVzUYx+gA6HI6pjHc0GkkHPIXk6iGnBbCe3ajYPMEDjTPtmVSR9aHvrtoUwASH2iZVt3XZkhAAYaYJWBqNxtRhq1q+qNdQZbxU/Sc/W8sSG5PnbreLfD4vXdfcE/r9vhxo/H4extRosXlNiwP/+2IlMFZjZfLCSTDD4XCqY53/m89yPB6XioCqY9NSqsTvLpfLIlNQ/Wcpp1LvJ5lWJo/UtsZiMUlYtQKBjFOVBPAXmVEmW9R381qRhWd1ajweyz5L1puaVq1AIBf3KxIT3Ntv3LiBzc1NmbI4GAyQzWalgZV6atWUn24BajKtRawqQ0kvTYKUWCyG9957T7yhSWAxMc1ms+JqoYIfNTnVSrOuxki2n8lcLBbD1taWnJvUVXOvKJVKUyPK+Xl8pvn+MYG96CJI56TPWCw21eWv1+sFqAYCAUms2RdSqVSQy+VQqVSkr4LN6rO658vu+5SacOgI5XTJZFKqf/wZVPLPYrFIIxabwjudDjwej0y+4zNNe7Hvcwa48LhVIvyzszPRBNEeamNjAzqdTkoxRqNRbgjZyrOzM9y+fVuasOgEoCVbqZbNyuWyzLPlg7q6uiovDL+ftkE63YtJFoVCAd988w1OTk6m2C4tDzBuVqrFA5lJr9cLh8OBRCIBAHI4qN2B/BmpAWWmcpWg2uFwCJCbBawAJC7S/ixRcLFJyGazwWq1otvtTskALrr4GcwAOUeZWSHBPr30XC6XjLXlPaBOhy8ipR96vV7sobRgq/kZqj0aD1WWWFlu4Yg7xkf2l8kYG97cbjcmk4l8DkGNVovd9cViUa4vKyqUV6gja0ejkSSB9AlUHTYACGutJbAiO03wTsZar9cjn88jHo9L8kIwYzAYEAgEsLq6imazCa/Xi3w+/1qfTa1jZYJJ6Y86banVask8++FwKAcdK1ccdfv48eMpPb3WewABKQAUCgWpkJExp6UWmXzKgOjLy/1qa2tLvFtZweD7rxVgJVvKPYr3jo1+LEvyXWF5mqCE/tDsW7BarWg0GlNJlhbJH7/P5XJJxSoYDOLmzZt4//33EYlE5LrWajXcvXtX7LjYXQ+8nCMPQDr0tXBY4TnKhN3pdEoTHfsW9vb2hFHlvs+zk0MYgJdWdgCm9jDVfu+iVSCeoQRW8XgcGxsbQj5FIhEsLy8jEonInk/wqVZQ6BPsdDqFNCKAVBMAJi5vu5hgejweLC4uYnd3F7du3RKQR2Bss9mmdOrEU6zCsAeA/47kAF1hSKzwGb/I4nPJQQnb29sipVz+S/9JoVCA2+1GNBqF1+sVpxpWNQqFgjhCkeji4BWCb4/HIw42moLV8fiFn93BwQEajQay2SyOjo6QSCSmDGuZgS8sLIhOlf5mz549w3/+53/iD3/4g9DYWmVWapw0/Ofm0+12sbKyItN9qLUkuKN/pNFoRLfbxenpKf7v//4Pt2/fRjqdlg5CrX3i+MJks1lhK5i1qgbX3IQJQPiQnp+fo1Ao4P79+3j+/DkODg7EquVtvcy+azEDpnVNuVxGq9WSSUTq9AqVrSIIpB0M/SPZEU7PNS0HGFAzSYkCtUdkevT6F7PEuSExg+bUNTZjqQcfN1Mtr6kaK90xyuWylFcdDof8WZ0bznKcOomFna0szQPQpHlplvkcjV5M1Do+PhYgbLfbpTtU7aTmNU8mk+LLST0YEwCVWdFycWMnsCJwJStksVik/EsbPp/PJ1Wj09NTnJ2dvdLBfBVMK5+xcrkMAGK3lM/n8fDhQwGfo9EIVqtVGlnYeX/9+nWkUinUajU8ffpUDjMtAOAsSOceQMaM89gnk4kcpOwQX1xchNfrxerqKnZ3d7G0tIR4PC4/M7XMlx1nquoTZ7WKlCmQnTo/P8fR0ZH4WxKE0l+b75HH45mqYmQyGZGrXYZZU/1ymWQmEgmsrq4iHo9jZWUF77zzDuLxuFT4arUa9vf38fjxY+nv4PPApheCcvqfsgp0mcVzhpKq5eVl7OzsyBAYVqTY/KVWCiibo+vG8vIydDqdTJEyGo3y/qm6xbd9XgkofT4f/H4/EomE2GkuLi6Kaw5tk7hn8SyfTCYib+O0O7/fLyOO9Xq9VJToc34RdpVyPxJ6q6ureP/99xGNRsVmjX+PjUt89yjBUUdJO51O8a/n/kanDp4llIW97dLpXlg57u7uYmdnB9evX5fpWfSgp7yTlUu1mke5B5nf5eVlWK1WLC4uSk+LWqVhJeu71oXAKjUyzWZTBMkc+6jq1ex2u3SrUy+USqXw6aef4s6dO6hWq6IL0foQIFjNZDLodDrSOevz+aYyETYCLS8v49q1ayK2T6VS+Oyzz3D37l3pwieo1vpgnUwmaLfbyGazAqCHw6E8xGShut0urFYr/H4/IpEI/H4/RqMXE44ePHiAL7/8Et988w0KhcJUl7VWwHoymYi2J5/PywvFRIWxU+zNLJfsW6PRwOHhIb788kscHh5if39fGsG0nGajliyZ4R0dHaHRaEwZcZOBJZPK8lW/30ehUMDz58+Ry+XEiouaNi3lIHxBuVEPh0OZEMRyPkEVgSqbQPi/VS1uu90WnS03WK0SALVsx0EAnL7j9XoFzBOw9Ho9sTwxmUzw+/1YX19HNptFJpMB8FILqUWH8GxjBT+fJuxspGw0Gjg4OMBwOJTypMViwd7eHm7cuAGLxYLFxUX84z/+I77++muRWmgJqlVApU4kYpc4DyC+w3xP+v0+FhYWJNm7efMmNjc3EQwG8fOf/1wkS0y0tCirMpGjHo7PG/dE3mt2thNEGY1GNBoNOJ1O6agPhUKIRqPic5nL5QTkXPZ6qoNmuP+oc+3J6LM5sFAoSLJMQoP6RlYNeCgbjUbE43Gcnp5qcj0ppbPZbPB4PCKZWlpawsrKipT+yawXCgWkUinodLqpbne73Q632y1ki9FoRL1ex6NHjyRhucw1JVh1Op0CTIPBIFZXVyVJ9nq9Yv2nSlACgYBMC6zX6wgEAvD5fGi1WgLUu90uUqmU6NovmlwZjUa43W7pOVn+SxMVq6kE3ARFfI/ZIGiz2RCJRLC9vY1Wq4X9/X2USiW5zr1eT5IqOtlc5HrSNWVzcxPvvPOOVJyJn1TZnpp4MU7q1NfX1zGZTKRJWZW2pFIpAbGXqarpdC+cZ5aWlsRJCYCcn3x2CUhJoDB24EWzNSfG8aylvFJtGn2Te34hGQA1U6rJM7+UP6TZbMba2hpu3bolwwI6nQ6++OIL3L9/HwcHB6JbfNNg32Yxy+emWq/XkcvlpPtTjXV5eVkEzAaDAe12G1999RXu3r37ymF1FcwKY2VXJ7vB1YyD1ykWi+HWrVuIx+NSGjg8PMS9e/fw5MkTpFKpKS2WlsCacWYyGQEB1WpVNitKFDinfmVlRco+w+EQuVwO9+/fx9OnT/H8+fMpwb1WDUu8P5yxzA2fGRxfGqfTiWvXrklWSD0O/y1tytLpNFKplHSFa2W4rsarzggHICyT2ilOdgCA+BiqmTc1dTSyV0eYahmrWlnQ6XSi9eP0JE7/oWeg2+3G8vKyWAQlk8mpMa0E6FqDQDVm/s5sns9Go9FAuVwW6yCDwYDlv7gsuFwu7OzsYG1tDel0+sJlv++Kkwy5ClhVrS/ZGzaK8V1hSZINpIuLiwKwE4kEfD4fjo+PL71XqQclqw7qhCo10eLzR+DKDmImuF6vF5VKRXyC9Xo9AoEAPB7PVEPQZWIlSCUwYXXK5XJJaZf7Ox1TaGrOEdBMuAHIhEY24vn9fgG7l42TbDT1frFYDMlkUsZ/UjbV7XYl+WTFjbKJyWQi48onk4mcFZVKBcViUVxMLhsrJyXGYjHZ10OhkEjU1DOKezhBldPpRK/Xg8/nQyAQwPLyskgXAEgVKJVKoVqtXjhONn2z0hSLxaQsTUZd1VPPAkKSadxzbTYbKpWKPM/cI1Kp1KVswsjc+3w+8SBlkjG7F8wm3rwffLYBwOv1ik89YySzepmqymzyx0SE7wYr6ACkaqleV/4stARUJ0JSV09f2zetqL41WOUBw0OGzR2zPygzWpYKdDodcrkcPvvsM3z11VciDr6q5gUeTgRVNIqe7cBk9kzNkE6nQz6fx+eff447d+4gm81qbgH1uli5wfNmZjIZObgYq81mw89//nO888470l3bbrdx584dPHr0CM+ePZPRZ+ohrWWcZNWpR6EjhMqoBwIB/H/svdlz3NdxPX5mw+z7ggEwWIidIilRqx3X10pV3vKShzz434wTl+O4XLZiy6ZEUqQIUcS+Y/Z9x+zze2BO886IlIiZD0hJv+kqFSQRHDTu537uPX36dPf6+jp8Ph8WFxdF43x8fIzd3V08ffpUtLVayz9UP1kUx8IVHqzs/Xt5eYlwOIzZ2dmBQpFyuYxoNIqTkxOcnZ3JnHgtWpcM+0mwSj00I04ywoziPR6PrDMZGbULBC9gXlBaasDppxpYMqXOg1ztSMBWb/l8XthMXvqccKROjbkOCQDfGRVsqQVn1HenUilks1n0+89HrxYKBQSDQdjtdoTDYayuruLrr79GoVDQfJ+qlxFZS45UJFPF4IA6ZhbT5PN5YVBZDezz+WRwgFZGQK3qutn+S9VE8pJXTT2DeX7yM7iP2fVkXKCqtidiOzW2/SIwIEjm5DT6S8BAfb3T6RTQMz8/L4EMW8eNClT4rMmqsqMK2+YRBLJjDrNp1FMHg0F579T+y0tLS3JXUL7GbJDalmsUf9kVIRQKSS/qGzduyBAadVSpeq+aTCaRrqn91ufn5yXoajabyOfzKBQKA2nwUfyknELtjsFnrgJVNTBS371hgMjAhPLBqakplMtlAV2jridBHt8B1Sc106ICYjUoVL+XPpBV5XkBYOCdG9Wo6efdAryQsKjA+mXPTQW71Nnzs5iVy+fzMgntdXwdeYKV+nWYEeMvwbQaBbWq/pMVoNdp9Ivte9RDRt2oCwsL2NzclNn19+/fx/3793FycjLA/l6X8bBn6lpl2dQLl21tGDUaDAYkk0k8fvwY33zzjTAX1+UvL3qmeguFwneiVbPZLACQ6Q29Xo9KpYKHDx/i6dOnIlPgRTdOquplxgCFLDVT6mpTZU772tzchNlsxvT0tBysJycnODw8xOnpqYyFuw5ARV8JQqjpNZlMogVkcR1bnHBi0PT0NJxOp0hx2F6N0660BtZ89mphhE6nk33gdDpleEa5XMb5+TkymQw8Ho/0DGQRHotGgMEhDlqY+nmqtpcMCy8KAilmhvr9vgwrmJ+fh8/ng8fjwfvvv4+//vWviMfj13Ze0U+CQLZ50+l0Ak45956mnmlqYSA1ZFrKVHjBWq1WAYGRSGSgOwGlVrxQ2cJKZeVu3LghbY263a7oK1kIN47xOVOPrBbVsD2dXq+XjEOlUoFOp5NCGkoEuF9XVlaktZFaqawCmlF85uVOTSz7Ur7zzjuYm5uTbjB81hwDq7ZhXFlZEXKDxWtksMgGMrjlNLarGs9zPne2flpcXJT0v8oIAoPto3i3so0YU8VOp1NkC5wKSD0sz4RRfOWzdzgcCAaDktpXdf6qhEXN5PHZ1ut16QRBFpBnM7NGWmiA1foHBqNk0vv9F623eJax6EydAKj6QjlLsViUmgBKLMfxt9/vI5vNCg6iJIDBChv+qx0o1C5EzA6xwK7T6SAajSIWiyGZTOLo6AgnJycolUqvRVyOFsq85JdSTafTYWNjA7/5zW+wsrICvV6ParWK//qv/8LZ2Zn0KntT9jKWkRs8FArh3/7t37CysgKj0YhqtYo//vGP4ud1gZRX+fmyn8WD0eVy4Z/+6Z+w9H+VeO12G1988QV2dnauHagO+6i+6PSRQnSTyYS1tTUsLCyIDvTs7Ax7e3uiT1Iv0+tYXzUlRR+BF5EhD0u73S7aqlarhXw+j4cPH+Lo6EiqKq+jXRlNDVTUgIoXRbVahdFoRKFQkHRks9lENpuF0WhELpcTwT/7XF4XW83Ppa88DFk1Sq1vpVKRojlOg2FqjQ2heThrLVMBBoNUtc2YOhOe68qUcbfbhdvtlr7BBD9sZn8de4Cfx44aLpdLNIHMnLRarYGOC9R+zszM4Pbt27h9+zamp6eFiVN7L2rlL0Enez4Gg0EpmqWulhkMZiEY5HMq3DvvvIONjQ3JXtVqNezs7GBra0vGS45jvV5PnqfD4ZCpWmyVxdZAvV5PBtoAL85WgjFq2tVqcGa7OJlvnD3LPc8OFL1eT7KQDKhUUEWtLc+JUCgEvV4vUjwWjul0OilqKhQKyGQyMkxmHD/ZZaZUKknzefX9IkOqFtUQtFBDSV1lIBCQ94p9hU9OTnBwcIBEIjGyZEEt+JudnUU2m8XCwoLIOvr9vtwBZEvVbhv1eh0XFxdIp9Oo1+sCzvn7sU7j8PAQOzs7ogcfxc9CoYCLiwsZRarTPS84Y9FRt9uVO4lAj8+xWCzi5OREpupxqAllX+xdv7+/LzKQUfFAv99HoVDA9vY20um0tKp8/PixDFVZXFzE7du3ZQoY33sAQhJxoiKDgUwmI1k41oSQWHojYFU1g+H5CMN///d/l/Rqo9HAw4cPsbOzM5D6fZtGncqvf/1rGRnabDaxvb2NJ0+eSIT1tv0EXgCXO3fuYGVlRSYXlctlfPnll9Je5237ygBgeXkZy8vLAlSbzSa2trZwcXEhz/9NGtdFZXCZViNDycpUpoYJ/t9koDL832qUzZQk265xkgmBodqi6jqBNU1llsju2e12rK2toVarIRgMolQqodfrYW5uboAt0moazPcZgydKACgDcbvd0raK89bJ7BH8+Xw+8fH4+Fj2wjgasFf5qALVUCiESCSCmZkZAc0c9xyJRKR4yWaz4fbt2/joo4+kpVm/30cqlcKTJ09wcnKiaQDA95Ugzu/3Y21tTfopc50pZVHZd/Yw5nQrgr/d3V08efIE5+fnmkisVMkBAyhmodj702w2S1EPGSE+Z8oS+PvyrGD3kqdPn+Li4mJsfTVBJ9lGtQ+sKolh8KFqD+kr2WyCFE7iy+fzOD4+xunpqRQWj3OGcU2pR6bW++LiAgBEVsG07nBXF3WYTrfblaE7LLhLp9M4Pz/H8fGxZNtG8ZV+Uo6USqUE1LlcLpHXZbNZlMtl8YkFaOxGoPb+ZhaL+vBqtYqjoyOkUqmx7jC2SUulUjg+PkYul0M+n5cpWkyTO51OFAoFGfDD1oBqVpJ7Vq1bSKfTyGaz8izG3afslFEsFhGNRtHr9UTTz2BwYWEB9Xpd0vlsa8m6AJ5bDH64n/L5vHRiep311BSsEgAuLS3h008/FbBSLpdx7969sZC+1n5yvOkvf/lLOJ1OAM8Ppi+++OLa9bRXMV5oDodDLlIeELFY7DuFam/TT14QnBnOw7VcLuP09BSFQmGA5XxbfjJlpKYyKCCvVqtyYPD736aflH9wEIPar5IFdgxURtVSXcWnl/lIHTDTrywaIcMyMzMjQIBdL3K53LXtheHiBOrnKKsIh8NSfEOQYDQasbCwgHA4LEM2MpkMvvrqKwGr1+Ur15DpUE6vodaXFzFbfXH6GoeX9PvPG7B/9tln2N7eRj6f1xSsElxxDfR6vUh82O+VZz2ZMwIstbcmf4+TkxP8+c9/xrNnz2TE7bj+kj1rNBrSt5sZHACiu2UbK6aH1QDEaDSKDIufEY1G8fDhQ1nXcRl2Ait1sEY2m0U8HofVakW73ZZBJpRSMG2typuo9WTBTrPZRDQaxf7+vujFx00DAxioT2AARbkB+6Y3Gg1kMhkBSQRTZGQJCl0uF1KplAAzFjheXFxIoDPq2qrMIwDxj+tI3WWtVhO5FP1iVopnKDNFDodjoL0layDGIYZ6vR7S6TT29/eFHa3VapIx4/0zNTWFXC4n2V3uAwJ6nm2Ur3AN1L077rMnW86vwIsRymrrNb/fL5IJADKEiT3JuXepC6b04aqDCzQFqxT5f/DBB7hx44Y8gFQqhW+//fY7I0DfFhhkWmt5eRnvv/++TLHI5/PY2tp6I3ra1zWyqn6/H7dv34bdbgcAYScSicRbZ1VVfRMbMhOc8GBlpP82QbVaiMGWL5zHTLCayWTkZX/bfrKnHqd+sFiJuja1mIppweveB2p1qlohzg4LTJ+qOkpq2yqVCtLpNJ48eYJMJnOtgctw2xeyapxYFgwGB3rrUkeoMuwPHjzAl19+qRmgUk2VwPAfAitqJ0OhkMwDB17oMrm+1L8Vi0Xs7e3hD3/4A/b29uTi1cJH+sULnQxKtVqVgJRMi1o5rP6OAKRzyMXFBf785z/j73//O/b29jTRq6rSFPanzefz8Hg8Ao4YFKj7Uv0d6WO5XEY6nZZWh99++y329/fx9OlTYQjH8ZPryeLJZDIJm82Gg4MDXF5eSrN9p9OJVqslumUGVexiwOI6nhG1Wg3ZbFa0gOOCP/qrts5iwVelUsHZ2ZkU1rBANJPJDLRSUnspqzpd9r1mp4NcLjf2VKher4dSqSRsJc9KviOUcvAZqwWoahaL+lb2XuY6kMig5GHU9ez1evK+srMA/Wc9CEHf8JoQM6kFYdzPw0XvWp1XvFdUP4EXrakAIB6Py92j1gKp/w28OL/UosurrKVmYFWv18Pr9eK9997Dv/7rv0qlciqVwpdffont7e23zv7RT05W+fDDD2W+LZuc7+7uXmvl/1WMANDpdEpfO1bX5fN5PH78GNVq9a37S0BgsVikLyGZjlarhXg8juPj45fOg39TxoOIFcMej0f0XnwRu90uDg4OEI/HpZfqm9yz6otOYEUAyDY3bBdjNBqloj2dTiMej0ske53pdfqpVrUyuic7yDG81FSR2chkMnjw4AE+++wzFIvFa90LfG5kp9ViPoJB9vnjIUrJSi6Xw1dffYXf/va3OD4+FnbhOnwky1apVJDJZBCNRmG1WuFwOASoUvah6pn7/ectoeLxOL766iv8/ve/x9///veB3tVa+ag+P7J8ZrMZhUIBa2trko4kw6OCgE6ng0KhgJOTEzx69AhbW1t4/PgxEonE2CyVaqouUWVAOdyBI2lZeKS23gKes1LHx8fY3t7GycmJ9F9lz8paraaJNIzrST01A81ut4ujoyNhfnU6nRRLqj4SqPIsJVihXpDFuVrI7dRApVAooF6vC5inP9y/assy/j3+nny/TCaTDOgAXjTlH7dvNUEgOzxQ30+tN6UW/BlqUM99qhYJq2yr+jO0aFtIbSeDN55PatGX6tvLjMWJ1LSTdeX3a3Wu8jkSBKvvyw99/nAtC03tbMDPeV1fNQGrOp0OHo8HH330EX7961/j1q1b6PefV5L99a9/xe9//3tkMplrL6p5HT+Z/l1eXsbKyor4+fXXX+MPf/gDotHoWwNUw76q7JrZbMbBwYFE10+fPsUf//hHAavXDVB+yE8eRkajEaenp9JPsVQq4YsvvhDG5234+jIQyD5629vbSCQS6PV6ODw8xB//+EeZbqP1CODX9ZVsHy+iXq+Hk5MTKbiamppCvV7H559/jgcPHmB/f18Km96kvzyE2Az++PgYoVAIa2trArAMBgPi8Tg+//xzfPXVV3j06NG1AkBVc0iWol6vI5lMSoUt2aJIJCIFLVNTUygWi7h37x7u3buHL7/8EoeHh9IiSOs1pZ9sV8ZClWaziXK5jFgsht3dXSwvL2N1dRWBQEBSaDrd8/Z6f/nLX/DNN9/g6dOnUqhwHQwwL3cyl5z3PT09Lf1z3W73QKEiWdhEIoHDw0OkUinRVw+zWlr5ycuea8r3m7plBiQEBwCkKBSAgGeV+VHPK618VQsq2U1jf39/4HcBvqsLV9sSDUty1Mtfyz1AUN3tdoVBVTtjvCpTqv4713fYZy3XlZ9DX5vNJkql0nd8oZ/D7D+/qnrl4d9FKz/Vr1zXq34GfeQ597KfoaWN+pzUv/eybPpVPlP3fd+s0+le65NYAPKrX/0Kn3zyCT799FO4XC58+eWX+POf/4zPPvsMmUxmrGr1fr//SvHg6/qp1+uFVbtx4wY++eQT/OIXv0A8HsfW1ha++OILXFxcjEv1f6/I8XV9VbWVTqcTkUgEGxsbsNvtKJVKODk5kTTaqIfTuGuqRqNsceL3+6X6ljqi09NT5HK5kYHquH6q/eBY/e31ehEIBOD1egFAAE00GhXW4qrrOu6zV3WqDFLsdrv46vf7RUPVaDRwcHAggvurdi0YdU2HgT8lADabTRp/cwgI8Hzt4/E4zs7OBvrAvu77NY6fahDFIIW6VU7eYdraZDKhUCjg/Pxc+v9p5ef3+apqa7mm7PvJvUq97f/9HBgMBpRKpQGN4FU0aqOs6bCfBH8MUCmvIeurFgKxSpzA53XPgHGePfBCCqL+P/77ywDLy1it6/Rz2K9x7Yd81epuGtVeJg95lWlx378J+zn4CbwZX19X+vkqXzUDq1ZQT3YAACAASURBVIFAAJubm1hfX8fS0hI6nY60Jjk9PR2bSdEKrFJj5Xa7EYlEsLCwIC00tNBVaglW1XQrq5kpyGbft3EKP7RYU/UCY4qdejpGuGSOxkjzaAaqVXE4L1q1Rck47JQWYFX1lYCA6XQW06j6vFFbVY17waops2GwogIrnU430AJMywDgKuupPn/6zbQ1AClaZKD6pgOVl/mrAq3hVNo4E+q0eu9/6M/HZaR+DkDgp+In8NPxdeLn1e3n8Ow1AassWKKmjnomjgHTIuWnNbDiBUtQRXHzuBS6lpti+MJSI1ONKmg1e9FexmBolT7R+kB42UWrUYrnJ38gABM/R7HJs9feJn5qa5M9qr39HPwEfhq+agJW/+97vwNUNNZOaQ5Yrknb8ZPfFMDEz1Fs8uy1t5+Dn8BPx9eJn1e3n4OfwE/H14mfV7efw7P/XrA6sYlNbGITm9jEJjaxib1N0//wt0xsYhOb2MQmNrGJTWxib8cmYHViE5vYxCY2sYlNbGI/WpuA1YlNbGITm9jEJjaxif1obQJWJzaxiU1sYhOb2MQm9qO1CVid2MQmNrGJTWxiE5vYj9YmYHViE5vYxCY2sYlNbGI/WjN+3x/+FHpvAT8dP4Gfjq8TP69uk2evvf0c/AR+Or5O/Ly6/Rz8BH46vv5c/RweVvOm+tT/38++ck991V8OfbrO4TrfC1YnNrGJTWxiE5vYxFR71bjdH0vf9leNLeb0R0D7wUWjGEcpGwwGGAyGgamaHKms1cTKcY2+mc1mmM1mGVnearVQr9fR6XQG/NbaJmB1YhOb2MQm9v8LU0dW/1hseFT19zFsWo2wHsVU8KfX6we+qoDqZf+8aT85Up3/EGh1Oh0BU71eD91uF91u9634SZBqNpths9lgNpthMplgNBrRaDRQr9dlDLw6Dv5tPHu9Xg+j0Qir1Qqv1wuPxwOXywWr1YpcLodsNovLy0s0m000Gg20Wi3NfZ2A1YlNbGITm9iVbBhQvQoEvmys9Zu4bF/Gqqn/PQz6dDrdAHv1JkChCvyMRqMwa/zKn09gBUCYKwIsfs91ryn9NJlMwqwRWBkMhgG/+LXZbKLdbg+AWPp7XaYCQLfbDZfLBbvdDqvVCoPBgGq1inq9jsvLS7RaLbTbbdRqNbRarYF1vu715DO32WwIBoOYmZmBz+eD3W6HXq9HoVBANptFqVRCrVZDvV5HrVYbYFvfFHClr3a7HeFwGOvr6wiFQnC5XACAVCqFWCyGbDaLcrkMg8Ega6ll0PLWwOp16jO0sOFDDvjuAfZj8Hk43WE0Pn+karT4NlMIw+vIw0T1s9vtSpT7Jv182YWr+sgInWv4NvxUfXzZWpLZUPdov98XX6/72b8KDKhf+axpqo9vYo8OvyNcN/XPgOeHsvpuq89a9fNN+EfAYjKZXroHAMia8R3iM78OX+mTyWQSn/jfTAeaTKaBn0kfOp0OWq0Wms0mWq3WS1OcWpnRaMTU1BTMZjOsVivMZjMcDgdsNhtsNhumpqbg8XjkuXY6HdTrdTSbTVQqFZRKJdTrdWGHCLa03J9cO4vFArvdDrfbjWAwiEAgAIfDAYvFAoPBgGAwiHa7jW63K6Aql8shHo8jk8mgXC7L2r4MFGplBH8OhwN+vx8LCwvwer2w2Wyyxj6fD61WC7VaDbVaDaVSCScnJ0in0yiXy7i8vES/37+W9VT9nJqags1mQygUws2bN+H1emG322WtnU4narUaisUiCoUC8vk89vb2UCwWZR/Qv+taT71ej6mpKVitVvh8PrzzzjuYm5uD0+mEzWaDxWKBTqdDLpdDJpNBKpVCKpVCNBpFs9mUIADAtZ/xfO+npqbgcDgwNzeH+fl5hEIhOJ1OdDodOU+NRqO8z81mc+A9Z9A6znpqBlZfFWkP23AUqQKA6zi8XuXnq0AAvxqNRtFlGI3GASpe/ec6L1h+fZl/6qXLjW+xWGAymdDtduVyUP+5Dl/ph/rvKoBSwanFYoHNZoPdbofBYECz2US1WkWj0ZB/tPZTXTP6NawPUi9d1U9evtVqVfxsNpu4vLxEu92+Vj9NJtOARoipLF7CTqdTADXBQ71eR6lUQqVSGXjuWvqqgir1/TCbzQgEArDb7ej1erBYLLBarbDb7ej3+/JcO50OCoUCcrkc6vX6wD7V6n3iuvGsUcGK1+vF/Pw8pqam0Ov15NkbjUa0Wi1JZdVqNaTTaeTzeQEu9FWrYIX+8ZmaTCYEg0F4vV74fD54vV65xNSLs9lsolQqCUtUqVQQi8WQTqcHfCUwHMdXnU4Hi8UCt9sNn8+HQCAAp9MJr9cLp9MJu90Ol8uFYDCIcDgsrEqj0UC5XEahUEC5XEY+n0c2m8X5+TkSiYSkOOnnuBeuTqeD1WqF3++Hz+eT9QuHw8ICOZ1OOJ1OLC0tAYCsZTabFf+SySQSiQSOj48H/OTZr8XdZDKZ4PF4EAgEMDs7i3A4jOXlZUQiEXi9XtmvkUhEWMtWq4VCoYCzszOcnJzg5OQEp6eniEajyGQyuLy8fCnrOq7p9XoBVeFwGAsLC1haWkIoFILdbofdbofP58PCwgL0er0A1nw+j2+//RZnZ2c4PT3FyckJarUaKpUKWq0WAIwNXlTT6XSYmpqC3W6H1+vFzMwMAoGAvD8OhwOhUAjz8/MAgEajgVKphEQigYWFBcRiMRwdHSGRSEgqm4CQvmphapBntVolle73+xEIBCSg8nq98sxzuRwKhQKOjo4Qj8cRj8dRKBTk/VGxjNZ3/MuwkM/nw8zMDBwOB/L5PBwOh7zDBoMBDocDJpNJpAyNRkOCaX7mKH6ODFZflWLhBTo1NTWgHeHlxoPZaDSi2+0in89LRKMCQK0vV/67yloQ6PErGQI1GrdarQCeU93lchm1Wg2Xl5fyALQGLCrLQ1BP3Y3ZbJZ1JBiw2WziK9e8XC7LAVwulyUy52bRyk8CJfXCp/6GlwejWafTKWCaB3CpVEI0GkUulxMgo7Wf6nNm6spsNkt6iGCGlxn3ANOCZDPOz89RKBSEKVJfPi38VEG03W6Hw+GA2WyG3W7H9PS0gFOn0yl6IZVVazQayOVySCQSiMViKJVKA3+uRfQ9zKb5/X4Eg0G4XC54vV65bHu9HqxWKxwOB+x2u7BCjUYDlUoF5+fnOD8/RzqdRrValbXWgiXgM2fA4XA4sLy8LGm2QCCAGzduwGq1ot1uDwQpzWZTGJdsNoujoyOcnJzIGaXX6zUreOAZabVa4fF4EAqFMDMzgxs3bgjAmp6eFlDAc7HX66HVaqFarcpFFo/HYbVa0e/3USgUcHl5CZ1ON8BejmpGoxGBQABLS0uYm5tDIBCAz+fD/Pw8AoEA3G43bDabpFwByL67vLxEuVxGuVxGNBrF6ekp9Ho9ms0m9Ho96vW6vE/jgkC9Xg+/34+NjQ1MT0/D4/HA5/NhbW0N8/PzwgBaLBb4/X75e51OB5FIRID16empXL61Wg39fl8u1263O/JFS+O5yGdNpuq9995DOByWc9xsNsPr9crf63a7CIVCAnC9Xq88c64jQaCWTBvPnFAohMXFRSwuLmJzcxNzc3MSYDmdTgSDQTnX2+02wuEw3G63sMVmsxnHx8fiIxlWLc9Qsn9+vx/hcBjhcBiLi4vCWDIIpJ88v6xWK/b39+V+zWaz6HQ6AKDJOzRsZCpJOrhcLkxPT8Pv98NisUCv18PpdAIAXC4XQqEQqtUqvF4vzs7OYLPZcHBwgGq1KvemKmHQ0tQzn9KK6elphMNhGI1GVCoVIVHcbjeMRiM8Hg/8fj9yuRzS6TSKxaIE+i+T2ryujQRWhxk0NWWl0tsmk0kuDWoeCFgYhZ2dnSGZTKJcLouWRCsQqLJ7jBDUNBGjQkZkDodDNrzdbpdLrN1uw+PxCB2fz+fRbrdfyR6P6+uwNshqtcLtdsPv9wuYUZkign+DwYBOp4NUKgWLxQIAaLVaaDQa8vlaABaVtbJYLAKcnU4nfD4f/H6/MDJkYXw+H8xms6SCms0motHogCibgY5WfqpAn4DZ5XIJOHC73dDr9XA4HMLGEGjzsioUCjCZTKhUKhJUqcyxFn7S16mpKVgsFkxPT2N2dhYejwdOpxMzMzNwu90wGAzw+/2Ynp6Gy+WSC4pprdPTU/R6PfGTrJUW+1RlfPmsNzY2sLS0JM93bW0Nfr8fnU5H3nuz2Sygv1arIZVKod/vD6QuO52OrOs4B67qo81mk5Tl7du3sbKygrm5Obnw+V5zH6vBSbFYxPn5uZxHfMZcb5VxGdV4wDNluby8jOXlZXzwwQeYn5+Hx+MZ0Nrx8gQgqd96vY5CoQCXy4V8Po9CoSBnZ7fbFUJg1H3KYHRmZgYLCwuYmZmRf19ZWYHT6RyoCiZbTXM4HHC73ajVanA4HDAajchkMkin07KOWpyjfH9CoRAikQimp6cRCAQwMzMjqWA+YwauXBfKF/gsmEVLJpNwuVzCpGt13qtgdXZ2FgsLC9jc3MTKygo8Ho/4yTuVYFk9y3iH9no95PN5JJNJyZ4w5aqV8exk4DQ3N4eVlRUBfTzn+TNVYoWaTP45gywagasWxveJYNXr9Yq/ZrNZZBdcH4PBINmzzc1N0Ypy/zKDBkBzAk2VK7hcLrjdboRCIXg8Hvl5zWZTsIrFYoHH44HH44HVahU9cCwWk5Q7zyStwD/w4jw1Go1ypvp8PiEl+E6QVSVestvt8Pv98Hg88uz53NUCt2sHq+qFQIDExXe5XAiHw1haWsLy8rIwa1arVTYTD11equFwGKenpzg/P0c8Hhdqe1wjCODLo6aiPR4P5ubmsLi4iEgkIgctX7BgMIhutwuDwQAAuLy8FLqehxk3sham+srDkwA6GAxidnYWMzMzwgiooGFtbW1Ao9ZsNqWdRLVaRbFYlDS9Vn7yefNwUNNtTGNy/fg9q6urcui3Wi0Bf2SAeUhrDawImnhwURsWCATg8XjQ6XTk4OBBp2rE7HY7KpWKAIJqtSpgVQtTmUCmsDY3N3Hjxg2EQqEBEN1oNISx5CHWbrflHavX68jn8/B4PJIFYGAxDjugZiQYOEUiEbzzzjt4//33EQ6H4XA45P1gtA1ADjG+f71eDz6fD5lMBoFAQC4F9fmP46eqryJ7defOHdy+fRsej0cyKSpA7na7Ayl5u90ul4Pb7ZZ0eqvVEjmGFutJ0B8IBBAMBrG4uIiFhQVMT0/LIc+ME9lSNaBTLz2n0wm32y2SisvLy7H3KX+G3++X950BstPphNVqhU6nQ7vdlvOQfqpnrnoOMIvBDJXq5zhggEE8z3iyek6nU/YdZR5kynmGMqtms9ng8XgwPT0Nn88Ht9sthThave/qu86fR10lgws1IGq320IA8X4iadHpdBCLxRCNRgUAaBFI0fgcKd3iPuN9zqCpUqkgk8kIcCVAtdlsWFxclAzl5eWlfB8ACVS18pWAipI4ys0o9+B9w+dut9vl31dXV+XvAc/3MfXLWjPAKqnHe8dsNkvWgYVVTKkTe5jNZty4cUPWjPtalahdBwtM0OxyuSTgN5vNKJfLqFaryOfz8o7we5lRJeGWTCZFrkIi5arExJXBqqoH4y9is9mwtLSEW7du4fbt27h7965Qwu12e6ANAy8FHhBzc3NyMFcqFeRyOU0Bi8qqWiwWLCws4M6dO7hz5w7effddTE1Nod1uo1QqCV2tCvQZxRIssNrtOiIY1Ve73Y7V1VVsbGwIg0UNSz6fR6VSEYBKUT7T19VqFclkElNTUwC0i7ZU0ML1DAQCAq4WFxcRCoWkWKFYLMol1mq15NDT6XTwer2IRqPywvJQ0dpHptNWVlawsbExAAYajQZqtRrK5bIUAtAfXsw2mw1HR0fie7FY1CzdooJqXpaRSASbm5t49913MT09DbPZLPuz0WigWCyi3W7DYrEgEonIgWy1WhEKhXB0dCRsN4MtLfwk4LBarQgGg1heXsa7776LjY0N2Gw29Ho9VCoVpFIp1Go1ibrtdjtCoRAcDodcqGQIeACT1dTCT6Z6XS4X/H4/ZmZmsLS0BLvdjm63K0ULZ2dnAlbNZrPIKyhbuLy8HNC6snhAq7Qgnxn3m3q5Xl5eolgsolgs4vj4GMlkEr1eT+QrHo9HgB/fNT5zFrMN1wGMYwyCWZFM5r7ZbKJcLovGs1KpoNvtStFQOByG3++HzWbD5eUl6vW6gAS1GEOrTBoDEEqJ+v2+SBGol41Go6jX6wAgwHRxcVGkNZ1OR/at1+tFPp8HoI2+Us2e9ft9CTb0er1oKLPZLKLRKJLJJJrNphRazc/PIxwOi59Mv1NLWiwWBfBrra/knUSGknKebDaLTCaDUqmEZrOJqakpBINBATQM+FwulzDeCwsLgge0MvrJfydTziyjquWvVqtCXjB7xTvC7/djfX0d2WwW7XYbOzs7wmJq6Su/8uwxmUwoFAooFAoC/CmNczgccl5SI+5wODA7O4tOp4NAIIDd3V2k02lhLbX2FQCmpqbg8/ng8XgkqxOLxZBKpZDL5VCtVgWAc09TmuHz+WA0GhGJRHB8fIxMJiPv2VX26pXA6rC2joBlfn4eH330Ed5//31sbGzA5/Oh3W7LgZvP55HL5dBut+H3+wWZUz/k8/lEq6HFy6ZqKlXGJBwO44MPPsDdu3fxzjvvwO12I5/P4/DwEIlEQtJowWBQUrGMeMhmkYnRUqrArypjGolEcPv2bUlh6vV6HB0d4eDgAMlkEtVqFRaLBWazGXNzcwiHwzCZTOj3+wJaGaVr0UeOfqqtQZxOJxYXF7GxsYHNzU1MT0+j0+kgGo3i7OwMuVwOl5eXosNi+sBkMglQAF50BNBSA0ydjcViEUH46uoq5ufnYbFYUC6XcXZ2hlQqJYctU5WRSAQ2mw0ARPPLtdSyqG6YIWOhCpl0h8OBdruNfD6P3d1dKfbp9/uSMmIRBouCgJdf2lqAKwZtTLHNzs6Kbq5Wq+Hw8BB7e3uo1Wro9XowmUySLpqZmUGv1xsIYBgsaLFHX8b6kyWdmpoSVi2dTmN7exvn5+ciV2B2oNFoIBgMotVqoVwuC1NRrValaE2rQIUgjecJ2VCCukwmg5OTExwfHyOXy0kVO6uuWbDGi5jrWCwWB9ZUCz+z2ay8Mw6HQwqmqtUqzs7OcHZ2hlgshsvLS/Gz2WyKRMBgMEiFONexXC6j0Who9j71+32Uy2Wp6KcEYWpqCul0WoBqKpVCs9mExWJBvV6Xd4nBTLvdHmhtxMBLS7aKel4WIuXzeeh0OiQSCZydnUnRFP0MBoOoVCqS1VCzFgQolCsAry5uHtVIOFEnrdfrRc5BFrBer8vZkEql8PHHH8Pv98NsNoskhX5zPXnnaUWm8D1iUJVKpXB6eopyuYxSqSR71m63D8hE7ty5IzIAtZYBGATsWu0BynQY+NbrdZyenoqsJ51OI5PJAIAEJDMzM/jwww8RCoWEHOh0Okin0+Kv1n6qRtkECZtkMomDgwPs7u7i4uIC9XpdSDa1swXPALfbjV6vJ3LPUd79kTSrql4xGAzi9u3b+Oijj7C6ugqv14t+v494PI79/X1kMhk5QKlfI+tKRolRkVZMAI26H7PZDI/Hg1u3buHu3bu4efOmHPoHBwfY2dlBKpWS9GW73YbZbBbROFOUaiuW66i6Iwj0+XxYWVnBO++8g9XVVbhcLpRKJSlMicViqNVqIr5mKpHpyeFJElpWXKrpdQKr1dVVEdyn02kkEgk5cBuNBnw+H7LZLLxeL9xuN3Q63Xe6K2jZO07VplFnQ9E9i0CazSbi8TgSiYS0gPH5fCiVSqK37vf7qFQqA50KtGwgrerRCASZPmGqjQAvHo9LuoVRKwv99Hq9MMS8bNUCQK2evapHYwqN+lOy08lkEvV6XVKCJpNJQFi73Ua1WhUAwGp2LTRh/Ps8Q9RiTTKlhUJBquZzuRwAyHrabDa56MgcVatVYS7pv1bnU7/fF7BeLBbhdrslu3N5eYlYLCa9C0ulElwul4BbygN4KbNbRbVaFZCt1Zq2221kMhm5DC0WC3K5nDQDTyQSSKfTKBQK6HQ6cLlccuZOTU3JZ9BXFjJVq9WRUoGvsm63i0KhgGQyKT+bLDnPpGw2K4QEuxao7CZBH9e0VCoNPPdxjc+DUgT21TSbzcjlcsJUEax0Oh2RMVBaxdQv9zQDKb7vwIvATSu2mux0sViEyWQS8olAm+eS1WodCAR4PnBdLy8vUalUruX+ZIERA4xsNotKpSLPslwuI5fLodlsimRKp9MhEomILpyfw4JV9V3XUvZFY9CSSqUkICiVSojH4yiXywAg2V632y1BFc97Bg6UZDI7qyW7rtbPMOvDezOZTCIajcrzNxgMaLVaUvPDPetwOFCv11GtVkXSQALhKoHgyJpVIu2FhYUBprLf7yOVSuHhw4fY399HLpeTlHU4HEYwGByovidwof5Fy4o2XrBWqxWzs7O4ffs27ty5I9rPeDyOb7/9Vmh0bmRW3/IhqQcEgaDW2hA1AAiFQlhfX8fGxgYCgQD6/T6y2SyOj49xfHyMbDYrbB8rVrmeauECN4LWHQuYwqTYem5uTi7TQqGAaDSKi4uLgYuh0WgAeCHAZ1BADavWU0RUP5mWZLV/q9VCqVSSS6xcLkvhA/cfD1kCQgKX65giomYAKFLnodZut+WCz+fzskfVAgcermSEeHlp6at6aKs6WDKWaiuler0+0HGDWjweyNQrkmXTyk+VsaAuiuvSarWkOwaDDlXOxBS6upYqABwG/+OCQDIqTAEyUMpms8KwkPlj6o/MKrXWnU5nQMqSz+dFE6ZFloLrWSqVBtYpn8/DarWiWCwK6GRXDa/XK5mpUCgkoJBFlAQ+ww3jx/WTrE06nYbFYpFAlUEK9xoA0Qm63W6ZxkMihXtUZf2HQcs4/vZ6PQGr6XRaWg3q9XpJBedyOak1YI0EAMkYMEDku0YAqHV3Gj5/yo8sFovsLQZWBIQMoCivYsBCtpO9Vocb22sBrNVAlR09iCdqtZpIVig9ILDi3UqdqnreVyqVAf2vlgEAP6vX66FQKMiz5J4j+AMgRZ8ARAPa6/WELOD3qV2OtMImw3U0lKuw80w2m5XgutvtStGfXq+XgnDqcAuFAhqNhhAyLMq8CpYauXWVyWSC2+3G4uIiPvjgA/h8Puh0zxvZ/u1vf8Of/vQnXFxcCHNBcS6rw6nPoh6PF4rWURdbbywtLeFXv/oVwuEw9Ho98vk8vvjiC9y/f18Ku9RigEAgAL/fD4fDMSDQrtfrmrYt4gvLKkWr1YpwOIxPPvlE+hbmcjlsbW3h2bNnSCQS6HQ6ovG02WzSqsXhcKBarcolpmUDZv59fiUbtbi4CJfLBYPBgEqlgr29PRwfHyMWi8madrtdOJ1OYTaNRqOkitV0tZasFSNkHuBOp1OizkajIbqwdDotUgUA0naDUgWTySQXmNrQXEsQyN+dwI8Ann9GcFetVqXdktlslo4Gqi6YDJba3FpLEEggWCqVpAhAbY+lBiPUgoVCIQkEVKaGOlytRgmqmQ/6SUaFIwwJuMieMyVI/S/ZdJUtJlhVZRXjmtoFQWWdyI4bDAbRJrJwlb0jp6ampEUV33WmaLl/tPSToJ/PrFgswuFwSIsyFizOzs4iEolI9woyWGxPBmCA+eHz0uJd4nqqRTQGgwE+n08uUOqPmWWZn5+XLgdkq4AX2ufhgk+tAqpOp4NarYZkMil9h8lacX0pS6IszOfzIRgMCmBkRxXeHWqQrXW6mlIkSre4HxgQdjodYd743gcCAVgsFgGovJeGAxQt73oGAp1OB5lMRogbtf8sCTICUXap4dnLrA+DU5U8417QirGmxrtcLg8EHyw6pBSBHRkIqkn2scBW7XBAYk2rfUDJp1qzwKCAI1bZWYNnK4uXXS6X+Gyz2UROxFZiauEq8MPreiWwysOFVWqLi4uSUtfpnvcne/ToET777DPs7u6KJszlcmFmZgYrKyv44IMPsLCwINQwm/EeHR2hVCppmm7hgc+fOzc3B5PJhGw2i8ePH+P+/fuIxWKir/J4PFhYWMDGxob0vbNYLCgWi0gkEtLDkptMy0OBzMTS0hLeffddLC4uSnpoe3sbz549E/aXF1gkEsHNmzdFJwwApVJJWLhsNiuRrBbGl4JNi3ngm0wmlMtlHBwc4PDwUPRWXP/Z2VncuXMH8/PzMJvNqNfryOVyoiMiaNHCT/Ul5ctDJqXf70vPx2QyKcwJdXahUAhra2uiW2T1aqVSEWZGS7aal4EaAPDApQaoVCqh1WpJAQ3bgrDXncVikQuFwnyCLZVtGRcEqsEEAWc6nR6o+g4EAtDpdCgWizAYDPB6vdLVgIdzs9kUvRsDVK0CFfrGM4RrmcvlpI0KCz+o62ZTe6fTKUWevJDJdKmAWsvMjwrQyeq1Wi34fD6EQiHpGDAzMyNt/xj8kZFlARAZagIXrS4spvEJjrg3e72e9LHkeTQ7OyvafhZQEfSxgIXAlhedlmeoWp1OkEe5ks/nEwAwOzuL+fl5kSUxSGFxGN+v6elpAWlaFdmoafFSqYR2u41sNjtQDc5zkMTFxsYG7t69K0E0K6958VMbyOyVlkYWlwGzWrinvrNc1/X1ddy+fRvhcHhA7kWf1ec9nGIf94xi4Eb2Vz1XSApNTU0hEAhgdXUVa2trWFxclECHe5zPZ5hR11IPSsCn1kPwnGHW2uPxSHHdwsKCZCnJeJLsYbESSQK1eHEcf9U2miwCDQQCA4MyWBvAXtsccKH2MufzVbuCuFyugWEBr4OnRpIBmM1meaF5WJGuLhQK0Ol00sxa7Xn27rvvIhKJSMPby8tL7Ozs4Pz8HLlcTtO+a9yYrE5cWVmR6niyBACkDYjX68X09DTm5+fx/vvvY25uDjabTVoYUU9E5kPrtACbrM/NzWFtbU0ODLAQAwAAIABJREFUebIFatW/0+lEJBLB2toa7ty5I8VVZDE48YIssJZ6G6PRKMFHOByWiJoMDAEA0yuRSASrq6tYXl6WHpxku6hlZpSrZQqD0Sf1M2qrFaapXC6XAMFwOIxIJIJgMAin0ylFK0x1EaQyytSKtaSp+4lpXUbOrF4lwOKEFv5eZD4IrnjZqZH2uL6q7CoP9UKhIG2zeFnNzs6iUqkAgIzns1qtImWg1okBDcGfVu8TLx5VbkCtHSfuzM3NSTsmtRUU3x+1gpzFd1rLVHiZqIwO33W+52T5GZRQV8dLrd1uiyyARRfXwVqp7Cr9rFQqApQ4Lcrr9UqhLADJAHFCjzoMRJ22NU4vWNXUS5/ZhUqlIsGo2+2WSmqfzyf6OWrtgOegi99H2QUBAc99LfYoWVGek2qBFIMU9gj+xS9+IQCAe4RMeqFQkA4hwIs6Da32AJ99v98XvbS6xwhA2CP47t27uHXrlkxd41AVkj0sEuUz1+rMJ1glKFV/d1Vit7S0JLUgN2/eRCAQkAJVap45zrZWq4lEgxjndVnA7/NT3UdqxxbKPKamprC4uCjtIGdnZ7G2tgan0ymsL4vHzs/PUSqVRM9MzKIVSaHWwDCjRz243+8XPTWHGjDDQvykFpDxzGddDokNand/yEaSAVACwCiaLxnTQjdu3JA+q9QMhsNhrK6uSnPzer2ORCKBk5OTgXFxWhlBNQ9zu90OAPLSuVwu3LhxAwsLCwO9Fefm5rCwsCBAgJW5KlBVUy5aGHvv+f1+aabLTczDk0JwpgSCwSA2NjYGKrI7nY60teIBw7XQwgiq2chYHQ/Hij91njWf+3vvvYdAICBpQZVlI1NPzY0WF5eqVXa5XHC5XMJccE0JuH0+HxwOB6anp3H79m2pICa7Rg0g8AL8qVG7Fmuq/v6qxo+Xr81mk6k1DBSXl5dht9vlwqfOkr6+rDWQFhcCD+xmsyl6zm63KxNMOHedLWScTidMJhM6nY4UgzCQIhOodu/QErRwvGOxWBStH3XB7LnKy5eBjArKXzYG9jqqgilXKBQKA5Po2NKP+wJ4UQDC840aUbJuaupaq7Ql/eSalkqlgbZ+1IcShBAsUY6h0+lgs9mg1+uxvLyMVCqF8/NzmceuZeEaswzsM80pS2pBr6rpJ1BlERiDUgYLgUBAgC8vVK0DP0oWAAgj7Pf7sbq6ivfffx/z8/NSGFqtVqVlFO8kFejyjOJaaPXOdzodkfjw81XQ8t577+Hjjz+WrjBTU1Pi68XFBVKplBTc8n1SWcBx9wDXVNX/q+lrnvF37tzBxsYGVldXJeDvdrvSdo8FbqpGnZ/DnzHOunI9eXcYDAbp8MDUvtPpxPz8vGTPZmdnJbimppXZQWbeiCEY0PBOGMdUdpb3EwcpBYNB1Go1GQgzMzODUCgkvcGdTqcQU7FYTN53+snBNjabbeAu+L51HYlZVSdUUJDMiuaZmRl5ABTQGo1GhMNh6RvJi+vZs2c4OTn5DgjUwgis2DNT3cxWqxVLS0uy+IyuKQWgLowH2NHR0UCkpbXx4iFtTlCl1+tht9sRiUTw3nvvodFoDLTamZmZkeks3JzUYBIEkMrXwqhHIgB0uVwCVKlJ3dzcRDAYFJAdDodl2g0Lh3hB8yDgRaJVQ2s+e7VPJgXpvV5PgCr1sz6fT6bJOJ1OeTlpjCgJZtRoc9zLQNUa8YAlC8w9y36Q/J2cTqf0rySLypddvQBU0DBuEKAWBbBYhmCw2+2KFpDvvdrrFoAc9mpBnfr/+VULNoCpy3K5jEwmMzDMg61yeF4BL1qn8RlQ/P+q90ZL7RrXk1XA6jui0+ng8XgGKpPVQIbpQk6WcrlcUrChJbtGP9nBIR6PD7D2qjaeoIvPgUCMZ3EwGMTCwgLOz88HtMBasJaqXIXPn30s+W6ZTCaUSiWRzzDbxi4A3Nc6nU4CckpqCFq12qMqGKB/BAOUTi3934Q4nvHxeFwG6PBOYkCggl6upVbsGjMQKrvGbjTz8/P4+OOP8e6774o+nbrR8/NzJBIJyaLxjleLSNV1GAcE0siiqmNWORTmo48+wsrKCvx+v9TNqKOL2VqJa8jzlKyy2ilgVFO76lB6SLka0/9LS0sIhULwer1yzwIQrWgmk0E+n5dzgOl4ZmNVze24xI86bMFut2NmZkamYlarVclesyaFe4RFhNwDzWZTesVSzmIymZBIJOTd+j4buRsAD9iLiwuZC62yqixoYLU6Wyzp9XppWv/o0SNpJq1ldE0/ydTVajXE43EpmKL4d3l5Wdpr8NDkBmd1Yy6Xw+HhIc7PzyVlrXUnAJVdIzs6NzcnFat2ux0LCwsCAAhkmC7U6XTSYzGXy8nvoh6IWviogkCCNwJtstOhUAj9fl+YTLLYFotl4PJUtaRM1w4XNIzqJ0E1dX79fl+iVr5wLFZQATe/v9PpyH8ziuTLCUAOA63WVK24BCCBINeak7VYtawGi6wM5eEcDAbR6/UGtFvj6u3UZ0JZTKlUgtfrFZ053zUGMwSfPOQDgYA0NXe73SiXywJ0eLFqlQHo9/vCVsZiMTnACTQymQxmZ2cFSPOdNplMMqK1UCggGAwimUwOZCkAbWev8/lwHSmx4IQ3GgvUuF7cs2Ta19bWsLKyIpXQKgDQMnXN58bAQ9Wdn5+fS6DQ6/UkW8T9y24cq6urkp5nYZGWMgtVE8ozhj1gCehVBrjf7wvrD0AkISQ1KHEg+z0uCHzZPmfhMXst37lzB3fv3h0YDVwoFLC1tSWjgDmljiw8mXUCLj6ncQgWlfBgwDEzMyOTCqenp3H37l289957mJmZkZG71WoVR0dHODs7Q6VSGbgnCAB595KkYHHgKOuqkmgcXEJQSsni7du3sbi4KL2+AYjGme+fzWaD2+0WaYU6DIEEEABZ36samUWOsF1bW8PNmzfh9/tFO8+pZsxc8ywl/uB6sY81s61Mu6sZIWY5RzG9Xg+n0ynyruXlZcFONpsNc3NzqNVqUvnPNmwApGsAZXQk2thG0mazoVwuo9/vIxAIiPzq+3y9MlglZX54eIhisYiLiwvs7+8jEokIeCI7yCr76elprK+vw2QyodFo4OzsDH/6059w7949ZLPZgcIALXVWtVoNZ2dn6PefV/jm83lsbm4K2CIwYfsNo9EorGCz2UQikcC9e/fw9ddfIx6PS8pVaxaY4CIajQoL7HA4BlL/1Kypm4+AhUUr33zzDQ4ODnBycoJ8Pi8FRFo23qbmLJvNyiAFriX7p6l9cwEIU8VL+OLiQvr0MSXM1LJWhWsqY0lGVR0Fp9PpJAokaFTnm7NKlykVHgAEF1o1CqefBNFms1nkEZxaQl2gw+EYGMjBSNrtdsulzFSLmrLXIsDipUV2oVQqIRqNDshquD5qhSovOR7M6XRapsqojBwwfiWzGuyo7Orp6ekA83x8fCwMNdOx1Le7XC4sLS1Bp9NJexa1tZpWpvqqspbs9sEK6lwuh52dHdEms2h1aWlJZrRbrVZsbm5K0dLTp08H+sxquaZkltg+L5fLwWazIRaLyZrz/TCZTAiFQnC5XDI8ZGFhAaFQCB9++KFkU5LJ5ECf0FH9fFUWiWCd2adcLod0Oi1rzEKvRqMh5wOlN36/H8FgEA6HA4lEAt9+++1YjNVwmtpkMg2MBubXu3fvYnZ2VmQKhUIBBwcH2Nvbk/oOaoLD4TB6vZ7cBzyb1WKocYA1pQkOhwMLCwtYX1/H+vo65ubmMDs7K5MLeXayO0U6nUar1RK5yurqqqR9eWeQ0aa/o95V1Pk6nU6EQiHcvHkTt27dEl0qJ7+x0I7vHPc0/x41mPPz89LiiqRAt9vF6ekpTk5ORmJXuZYEfouLi/jwww+xtrYm3UmYhVBHxTI7VqvVZL9wKmAulxu4e9lVwGq1ihRmlPeKwQW1vZubm1hdXZW7nIQPayoI6NU6G2bMHA4H1tfX0e12EYlE5Hfl1C2/349kMvmDZMWVwWq/35fCHTKkZ2dnA1WgjPgNBgMWFhak+r/Xez4N5euvv8bW1pa0l1BZAK2Mwl5ejNSiPH78WHRhjAJYEHbr1i154QqFAp49e4atrS2cnp5K2yBVA6iV9ft9qTwnIG21WpidnRVWkqAUgKSxg8GgAIf9/X1sbW3h8PAQqVRKJrBoBarU9GqhUEA2m8XJyYmw6QR+PBx5EDPiI8N9enqKR48eyUtPsDqsDdTCqLOjlKNSqcjoTPYAJFhlSoaRaSKRwM7ODmKxGC4uLgZSFdz/WoMXXrTlclmyF3w/mJXg91ELyoOMz5tMF7W2TBNqIVdQpQDsX8nUlcvlEgBKsNJut4W9JuO6vr4uzfnJqgz32h3HuEY8+Ng8PZ/PC/BnRkidb20ymaS7CcdA/8u//AvOz8/lfVL1ZloGKgRZBIKlUkmCPj5/Pst6vY6pqSlEo1FEo1Fsbm5ifX0ds7Oz+OUvfyk6RnWuuRZBCoMk7lG+z2owR9ad+0Cn00mTfnYmsdvtWF5eRiQSQb/fR7FYxNdff62Zn+oobwJCXrB8X9g/lw3tm80mjEajjFsm+8/pQdSK37hxA3t7eyP7qPrJwSrqDHVKuyKRiGgUGcRkMhmRX6j7hplCtmhk9xruJbW7wKi+klFlT1oWfKpjVdUUNMHR4uIi/H6/nFHMaFCHz56s5+fnAnBHya7QT4vFImtIRnVubk585pkJvKhdYSDt9XqxtrYmQOvw8FAYYfYJJ15Jp9MjS+t0uucFspFIBOvr69L60e12C+s8LEEi6GNgGg6HhYjiaFh+H++ufD4v5Maopvq6sbEhn0f9OY0kAM9eZqhMJhN8Ph9qtZow02oxNoutXtfHkcAqozq1ITVfHnWDz8zMYG1tDbOzsxJF7+/v45tvvsHOzs7AYarlxU8/mfYjo5fJZGCz2SSC4WEWDocHZla3222cnJzgyZMn2NnZQaFQGGhlcR2+kjKnKLlQKODk5EQuLV5mbrcbm5ub0huw03k+3vSbb77B7u6upF207l2q+sm5xWQmjo+PZV2ZXvN4PIhEIlheXpbolW24Dg4OcHR0JBeYln0h+XwYoPB55nI5AU08fJeWlmRaFC8nMvIcGRuNRhGPx6UqXEtf6S8b/6fTadEBcWRkJpORSNRqtQLAQFDIv8/+qoVCQWQVKrumla9qIVK/3xc/rVargD+2N6rVajKOmbILv98vPU0BCLOpJaP+Mi0cU8NqpX+325VqamZfqK1ny7v19XXs7+8PpL+18pMHPA9+NRhgMROzObVaTZ4r8LzQBoBkYCwWC+bm5rC6uorj42O5lLXwUZ00SP0nA2gAAox4H9BnANLCjkW5hUJBsh1MI6sSoVGNfjLVzCCJGQlKZdhPlW2/8vm8tOAhG0QQCUAKiMgk8e4YBwDyXSZI8fl8CAQCCAaDiEQiUjPB85bDNpgN4jnErArBNAFPNpsV9pj7ZFRf+azpJ1v8sZCGBanMuPAdMZvNmJmZkfe60+lgdnZWgmjuIw5kOT8/RzKZHNlXVsKz+1AgEJC+7mrHErKqBKFqxojnRbvdhsPhED0+zw6LxSLv1qhglWCPgRBbEqq6alXPqxpxFSez9Xo9SaEzMOF+UQHgqPIqNTNJdpmfx+CV5xX9U4s7ycCykLHb7UonCxJpBK2v8z6NBFZZkEBh97B+jxQy2xvNzc3BaDSiWCziiy++wNbWFpLJ5EDrGq2NLwjTttT4qA+OC8pCJkoASqUSHj58iKdPnwqrqjWTNuwrmSW21SLLql5kBoMB77zzDpaXlyXKaTabePbsGXZ2drC3t4dsNivz4/nZWvrJyj2OtLu4uBDBNNMWLpdLROMrKysCqmOxGPb396VnrLquWgYBKqvOFBPTvmRZbDYbEokEAoEAFhYWBlI95XIZFxcXOD4+RjQaHZjSoZUGGHgBrBmokLXmqDqCQFaL8mKgjtnhcEjQWCqVpHft8BQjrdaV7zsZ6V6vJ5Nt+F6lUino9Xppn0ZtMnViTGEReFNXpXUQyM9TGWEVsBIAqD1f2+22TLhzuVxwOBy4c+cOHj58iEQiobmPKrBWtdZkWNQejGwYzgC/1+vB6XRibm5O9JhsIeT1egcKbMb1kZcW3x32Lh2+BFVZB/ceC9fUAgpOmOIe1kKnrhb/sEUZ9eZer1eyaQQj7J6hts2jrp0FmSzMcbvdaDabAx0kxvVTBdN+vx8zMzPS8YNZFF7uvF89Hg+A58Eq19Jut2NxcVEkeACQSCRwcHAAm802NrPGLBR95dRCapDVsdDcrzqdTgYZABBQwvWjJKfTeT5ONp/PS7HrqH6qvUkJ/tmthmBwGGDy7wEQxo9kizosgPjB4XB8B/COsp5qUMV3i0GrmmkBBnvRkmHln/N84ztHmRDPDLWQdRTjfU45AdP9lCSqoJU+qmvCP1MLWwlM1Rab7GjwQ2fVSLtjGAgNF0jwQltbW8Pdu3dlatTp6SkePHiA3d1diWKv0wgq+GIMby6CakoAWHSRyWTw8OFDbG1tiW7xOn3loU4/OcFC9ZMVf71eD/Pz88JWZzIZ3L9/Hw8fPhSN0HUFAKpgn6A6l8vJJuXLxip8g8EAj8cDo9GIer2Or776Ct9++y1isZhmQwBeZvSP4nkyrHzReVgcHR1hc3MTOt3zGdEENLFYDMfHxzg9PZWBBVrLVFRfmcKvVCoyfYepTIrS6/W6FChFIhGEw2EpeDk7O5NZ8tfBqvOZk9FhZTJBJttYdTodHB0dCVgtFosSBLKbgU6nkwtE1dZqGahQywdgINhTLyOeUZTfUK94dnYm7JbVasWtW7ekn/B1yH/4eWrhBcEV2VGCbPXMVA9+ALJX2BVAq9ZqwItLlgCTAR7T6tQps0hFfaYEgQQRi4uLcDqdcp4xk6SFr3y3CVQ58pWZCZ3ueSGqWpDI34tFtSxSXFxcxLvvvovV1VUYjUbkcjlpaacySKOsJQEKwRXXc3p6Wuo9mFplgMd2e4FAYGASFLWWDFA4AYmAe1QASGOHDGriWfnNPTpc1c/fka0DmYkhu8Z9SikR9wnZu3H9pIyDBd1kVFWZlSrn4TnA9D87/lCaR/9zuRySyaRI1sbRLPPdJG5SawqYKlfPRr4b/D7KWPhn2WwW1WpVpqJdXFx8hwwaxZg5S6fTODk5EQZXxXlqVxXVV/47s6/8PLbcSiaTOD4+xsHBgbQK+6EzYLydrPxS6ledTgefz4ff/OY3ogNtNpv4j//4D+zs7KBSqVzb5f86/tEoyv7nf/5nbG5uSsHV3/72Nzx79uyNANVhP/mz1AfHDW6xWHDr1i1sbm7KYIXz83Ps7u5eO1B9mY8A5LJUASsDgI2NDQSDQej1z8fbPnv2DEdHRwO99q7L1BeHpvrISHtjY0OqfQGgVqvhyZMnwqhfp/wDGMxUqBcggT/7FbIC0+12S8s4Hk6UKTC98jo9665qaqDCwI/pfh5iVqsV8XgcFotFNFWs9r68vJRIm0wHZQpaMtXqYalmgKjvo7ZPZQPIdrjdbmH9ePmxv6VW2m/VV/Ur9ZZGoxF2u13GlXJkpepvt9vF9PQ0NjY2sLKyIpkrALLWr8NUvK6f9I1AdXp6GsFgcIBp7HQ6SKVS8Hq90qz+8vJS2MnV1VV88sknWFlZEUlYJpPBs2fPRFY0jr8kS1SNJYMigha+82z9pmqrg8EgwuEw3G639IvmCGvKGy4uLiQoG8VX/o5qto/PSdWhEiARCFBLT2kFi1VZ3MJ6EbJph4eHOD09RTabHasTCFm1crmMVCoFp9Mp7D7Zab5b9InMHoNG9gUtFotwu93SaqnRaKBQKGB/fx9PnjzB0dHRyJIFZqfS6bR0JiJjR/ZSDZwIOLn2nKQZj8dRLBZl//BsarVayGQyOD09xe7uruzXUfxkg3yfzydgn+cos8GUM1CnTgBdrVYF3FFiZTKZ5NxvNBpIJBI4PDxENpsdKwikLGp3dxeVSgWxWEzkMGxR5ff7sbKyItO12Ma03+9L8T3vJjLqmUxGNPUHBweS/aRk8/tME7CqGtnKf/7nf8ba2hqsViva7TbS6TTu3bsnvcHetvFwWFpawnvvvSdsRK1Ww1//+ldpWfNjMeqTNjY24PF45CD5+9//jmg0qunowlFM/dmcEuT1egd0tYlEQiLCt+kjLw02356enpbWQKzIzmQybzRQUf1T9VP8h+1BIpEIZmdnpU9ksViUWeLjplNfx79hFkUFGWazGUtLS3A4HFJo1ev1pKoZeMEgquwbf1+t/VTTaQSkTGGvrq6KHjgcDuPy8hLLy8vSK5IFI8ViUTTrWhv9pIbR6XQOtPgiQ6TT6ZBIJLC0tIRqtQq9/nlz/Tt37mB5eRkzMzNy6W1tbeHi4kKzrAUv0H6/L62fPB4P5ufnZZ3YZ5lz7dXeqZSBMYXMJuzFYhG7u7s4Pj7WJMvG85CsIwAp8JienpZAxOFwyIx7/tPr9UTXyv3MzAY7rWxvb0sV+DjnApk0VdqhdhohE8Z9q04EU58FPwOAFGKxzmF/f1/62I4TtKjBKbMnxWIRh4eHEgSosghV0w8A2WwWyWQSuVwOtVoNNptNOtxwstXFxQUODw+FtRz1biDAZ8/U4+NjVKtVzM/PSyqaErZ0Oi1DaQwGg9SzkJxgKygy6SxiPj09FcnQKH7y+fF3j0ajAjI5TZGBgNPpRKFQkMJu9iUlUCXop/RDr9cLqM7lcvK7jPNeURKn1+txeXmJi4sL8Y26bmbOKFfp9XqiU2XfWp6dOp1OApp6vS4DjF63vZamYJUHr8/nw//7f/8PHo9HNK3b29u4uLgYewG1Mr1eD5vNhg8//FBaVrTbbUSjUezu7gr4+zH4qtM976u4traG5eVlYYCr1Sr29/eFqX7bPjLCZpNypns6nQ6Oj4+Ry+U0a/yvha+MwilZ6PV60nOPOrHrBoA/5Keqx6KOjhPZdDqdRKV82d9kxoI+qulKslNqb0IWV/X7fWGD2ST6uvatqlUlQ02dWCAQkKKvGzduSHHQ/Pw85ufnYbfb0ev1UC6Xce/ePUSj0WsBqzQylyyS8fl8CIVCMmeb7z510zqdDrOzszIamC0BDw4O8PDhQxwdHWkCVtUsD4EU8Hxtqbmkftrr9b60DRl1qWpbm0qlgq+//lokYeP6yp/FTh5kqtR2WNyfbF2kttjjnxMQkPEkqNje3sajR49wdnam2aQlsmalUklaAcZiMXQ6HXi93oHuC1xTyptYrJrL5WAwGAQQXFxcYHt7e4BVHff9oq/sSECNMeUUDFZ0Op10oCCgZ/9dptOtVivy+bywsKzNiMViUmg76rqyZRYr9dvtNuLxOE5OTmTf0Y9hEMV6FrVQiGy82qs9l8sNTDkb1c9qtYrT01M0Gg3E43Gpilf3rslkktG0vV5PaiqIS9QsJrMq/B2Z1Rr32ZOx5n3IdRzW2Vqt1oEeqWoho/pu00+Vdb9KwbKmYJWptvX1dfzyl7+UJrWlUgn37t17q6zasJ9sFfLxxx/D7XZLWvPp06fIZDKyIbVmfa5qvGwtFgvW1tYQCAQkjZbNZmXTv21Qrabg2J9QfdFPT09lNOfb9pHgStUGMr1aLBYlKn2bpuqBWPTBXofsA8mDjUJ6rbs//JB/lCoQaLGNDpubU3/HwhZ2PHj06JHIQa4jIFQ1VWqRCIsDCPi5vgQtahFOpVLB+fk5/vd//1culOuw4d9drb6enZ1FOByWynSuN8EiLzAOL/nTn/6ER48eIRqNahYU8vkwbc2OBLycVLaVAJDr3u/3RS/IFmLlchn7+/v4n//5Hzx48ACxWEwzX9XLulQqSVcVauhZuMTOFcPFIMBzuQfZdPa43dnZkaLQceUgquafgISjvglCw+GwNFFn8SIZTjZYj8ViUvRntVpRLpcRj8dxcXEhWnstpCvcXxyYwLR4NpsVuYyaheAeqdfrIgXh+tpsNqRSKUxNTclzYmeGcfrsco/yZ7FjDYs6qakmy0wQOHxmMmjU6/XSC5a/M9Pw44LAfr8vbCmBuzpylkVSAL7Td/xlRVYselQlT/RRi3OVdwuH9gx/Jv+bjL9KEqi+8hxm8KXqrl/XNAOrLJxYWFjAp59+Kq2VqNF48OCBHEpvEwCSpXS5XLh58yY++OAD6QGbz+fxxRdf/CjAH31lIYLb7caNGzcEWDcaDezt7cnghbftJwEgW4BxU3KyFjVJb5r5U30cBoAsQOB+7Ha72NvbGxB88yB8Uz6rIFCtGmZFPat+LRaL9FTlUAseptftr/q8CQLVGezUABMUMAtQLpfx7Nkz/OMf/xCN9XXpgGksoCFTpepm2V1B7cHJjgzHx8f43e9+h3v37knfSq2NhzZb+dTrdQFKfr8fwAtGUJ0Ix7QwWcBYLIZ//OMf+M///E/s7e1pWhPAS5TMGQdBUN5FForAlIGf+vcJps/Pz/HkyRP87W9/w9dffy2t67QoXCOgIGNHEGI0GqVF1dLSEmq1mugFCaxZfJnJZHB0dITt7W3EYjHpJZ1Op1EqlVCpVMaWBvGcASCfBzwHc6lUSpho6hn5nhGQsGCU7zy7LVQqFXnHODVo3HVVNeBk61qt1gBw4TqzAp0/j5pWVepiMBiQTCaleIh/R4te0Eyxc/IbW2oSwKk6fvVnqWclU+nsssMARg3YtFhTglL2H2VnleGiyFed42qRKDXDanZByzaQaj2FCkL55z9k6t/l2jIDc9W7VTOwytF0n3zyCT799FMRMu/s7OB3v/sdDg4O3srlP2yM8MLhMDY2NuB0OiUS+/LLL/Ho0aOBl+5ts6pkWjjGNB6Po1AoIJPJ4Le//a00BX5ba6qyV1NTU7BarWg0Gnjy5AlKpRLsdju2trbw+PHjgcP+TfuqVuFSu9bv95FIJPDo0SNcXFygWCziv//7v3FwcDCgpXmTQFUFgqyctdvtqFQqSCQSwsYYjUbcv38f//hA2RFEAAAgAElEQVTHPwRgq71A34SvaqqSPUsfPXokWQqCrFwuh/v37+Px48d49OgRnj17pknD+peZmrrmQc6zqFAoIJlM4quvvkK325X3n2D78vISu7u7+Pzzz3H//n3cv39fgOp1+UmWjZcMwWu5XEYikcD+/j42NzcxNzcnGjr2BD04OMCDBw+wtbWF+/fvy2QjLd8vXoBkWJi+zuVyiMVi2Nvbk6IrVrSznRoApNNpPHv2DGdnZzg+PkYsFhMtpdaFi3xOlMawKIrTfShNsdvtA0MDgOdAJZlMolwuiyxEnf72soLNUU0tViRYu7i4+I7kiGlTngusIB8Gdipo1NpX9bO5N7PZ7MCf08fhv0cjuTIcxKgASws/h999tQpd9eVVfhJYD/ujNWZRsUWv17sy2URfVF9fBr61tFE/d5htHX7nr/KZmoBVXgiM/pnuS6VS+Mtf/oJ79+4hl8uN1fJBC1MjA/ZYfPr0KZxOJ54+fYoHDx7g8PBQUgRvG6jSCAQeP34s7WFSqRQ+//xzqU59m/IKbkKmC7a3t4VF6/V6ODk5QSwWG+hV+qb9U439C7e3t5HP57GzsyP9dXd3d6Xv29vYq/SVB269Xkc8HpcLTZ0ecnBwgGQyKYzqm1pbHjZq0UKz2UShUMDZ2RkePnwolddsr3Z2diYVqpwAdp3+qYci9WxkBsnusq8yU8PlclkmVmnFTv2QnwDk/SXbksvlsLe3J2MUOS9clQRxnGU+n5cpXNdVEKiuJwEr13J4aiHb69EPgkY2LecdcF3n1TAQ1Ov1KJVKA62LyEqp5wJBoNpL+TqJlWEm7GUgatg//r9hf4YB1nVmK0Z9b1Vpzpsggl712d/3M1UQ+Drf/zZt2Ncfq5+Aduuq+76/pNPpXusT2VR/ZmZG5gZPT08jHo9je3sb33zzDfL5/LhVlK+sdHldP9ViC85j/uCDD6DX6xGPx7G7uyuFH9fh51V8Vf1lep0j5DqdDkqlEuLxuPSq09rXq/qptlqiNtBoNIqQepyeb+P6qR6U1CVxXRlg9fsvmh+Pyv5q8exVdlX1VdWG0a9KpTIAUq/i7zhrqkbK6poSnLJCFYAI7VkpfFWgorWfZIKZsla7BTQaDVxeXmru5+v4+ip/gRfNtWksBh21n65WZ6n6Xqn/f+hnjcykaHU+Xbf9HPwEfjq+Tvy8uv0cnr0mYFWn08nkEM7fZvqPWh8NtB6aAyuj0QiHwyFsgRaMqtabQr281JSrqvu4Dl9H8ZM2PH1jXHZC6wNh+EJVfs5VP2r47//kDwRg4ucoNnn22tvET21tske1t5+Dn8BPw1dNwKry/QO0tJbU9HUAlmtKl/zkNwUw8XMUmzx77e3n4Cfw0/F14ufV7efgJ/DT8XXi59Xt5/DsvxesTmxiE5vYxCY2sYlNbGJv0/Q//C0Tm9jEJjaxiU1sYhOb2NuxCVid2MQmNrGJTWxiE5vYj9YmYHViE5vYxCY2sYlNbGI/WpuA1YlNbGITm9jEJjaxif1obQJWJzax/4+972yO80quPpNzzgEDDCLFKGmltZy+vP4F/o/+Ba79YNfatbJrtaKltRIjCCJjcs555v1An+adISkRmAckpZquYoEJQOM+9+l7+vTpvitb2cpWtrKVreyDtRVYXdnKVrayla1sZStb2QdrP3vd6q9h9hbw6/ET+PX4uvLz8rZ69trbb8FP4Nfj68rPy9tvwU/g1+Prys/L22/h2f8sWF3Zyla2spWtbGUrW9n12uuuKv7QTL1iWb1YSetLoF5nK7C6spWtbGUrW9nK3to+dGClXlOu/t3iFeXv02/6p9frYTQaodfrxd/RaCS+zmYzTKfT9+YnfeU19RaLBSaTCUajETqdDoPBAIPBAOPxGLPZbOkr699kK7C6spWtbGUrW9l7MpWtep0tHvzvC2CpAHARDKog8F2ybW/ykQDQYDBAr9dDr3/RnkMQqP56X37q9XqYTCZYrVbYbDaYTCZhK7vdLgaDASaTCcbjMSaTyStA+137arFY4HQ64fV64XK5YDKZAAClUgmtVgvD4RCj0Uj81trXFVhd2cpWtrKVXcneBLDeZO/qsF0EgIsfF/0gSFgELtfprwqsdDqdMFX88+uAFEELmbZFgHidvpJZM5lMMJvNMBgM4vNwOBS/yLCNx+M5QPiu/CT753A4YLfbhQkEgHa7jeFwiPF4LOBqOBwKuHoXfqpA1eFwIBaLIRaLweFwwGAwoN/vI5/Po16vo9/vo9/vC3u5uCfe1XM3m81wu93Y3NxEOp2Gy+WC0WhEt9vF+fk5crkcWq0Wer0edDod+v2+5iz2ewOrapD70EoIwKvaDADvdJO8rS1mucwg1U39PjLHN/nIgGcwGAC8CL5qAH4fGe6in/SRGTlLG+/Dz0X/1I9cR/Xvp9PpXBZ+3b4uvievAwc80Gj0iweEevi+C//4jvCjup4qaFlcy+sqx72JsSIYUN/pRd/pIxkY1U8tnzvfCbJV9E9lsLh2KpPF9RuPxxiPxxiNRnMlTq39JJgymUyw2WwwGo2wWCwCWMxmMywWi6ylTqcTwDIYDNDtdtHpdKSs+TrAtaxx7cxmM6xWK1wuF1wuF3w+HxwOBywWC3Q6HWw2m3z/0WiEdruNWq2GdruNdruNXq8nPvL/XUesJ/iz2WzweDwIhUJwu92w2+0wm80wGo2w2+3o9XrodDpoNptotVoolUpzYAt4Ge+vw0+j0Qiz2QybzYZAIIDNzU14PB4pWVutVlgsFrRaLTQaDZRKJVSrVZRKJQGDo9FI4uhiQqCVEfzZ7XZEIhHcvn0bwWBQzsTZbIZUKoVKpYJisYhisYhWq4Vmszn3/vD/Xuf5rrK/fr8fGxsbSCQSsNlsmM1msFgsAvYBYDwew2w2/ywbfFVfNQOrb8qwF/9e1Wfo9fo5EPAu9BmvO/zV3/Og4MZnIGag5aHAAHzdfr4JAPDQ5cbni8g1VV8+ZpLXsaHVw1/1Sf2z0WiUQ8PhcMBqtUKv16PX66Hdbouvqu5Fax9VxkIFeSoosFgsspZWqxVmsxnD4RCdTgftdhv9fl8ONa39VJ8t/SNAIIuh0+lgNpthNpvhdDrleTPIdbtdNBoN9Hq9OV+1LMmoa0lfeJh5PB5YLBYJYixvcT8AL8BVrVZDuVxGr9ebe/Za+akmRAaDQfYcy1iJRAJGoxHT6VT2pslkmgNUvV4P5XIZ1WoVvV4Pw+FQfmnpJ8GTxWKB0WiE1+uF1+uF2+2Gz+dDLBaDzWYT5oqJU6fTkefbbDaRy+VQqVTQ6/XknVc/56qm0+lgtVrFH6/XC7vdLsDKbrfDbrfD4/HA7/fPgehOp4NCoYB+v49Wq4VarYaLiwsBMTx4h8Ph0gcuwZ3X64XH44HH44HT6UQ8Hkc0GoXT6YTNZoPb7UY6nYbBYMB0OkWv10O1WkU+nxeAVSgUcHR0JHuUfjLuL2smk0nWMxAIwOfzIZ1OY21tDS6XCw6HA06nE+vr6zAYDBiNRrKWz549Q61WQ6FQwPn5+RwDp5aMtXrfDQaDrJvf70c0GsXGxgaCwSDsdrsA2M3NTUynU9TrdVQqFdRqNezv76NeryOTyaBYLKLb7cq7pCYqWr1LJpNJ9mYikUAikUAkEpG47vf7sbm5idFohHK5jGKxiHK5jCdPnqBQKKBQKKBer8ueBLRnrhnfmaQEAgE4nU6Ew2GYzWbMZjMYDAa4XC40m00B1P1+H4eHh6hWq2g0Gmi324JB1D2p9ZlELGS1WuF0OqHT6WSPErsFAgFJ/oxGI/r9PprNpsQnMsJ83mpj1mXsymB1EUSpBz83ziI7wIzXYrHAYDBgMpnIi0ZApbIDWpjqowpWVKCnHrz0kS+iyWTCbDYTXUav15ND9qqL/ja+qoct144ZN4EAwYDL5YLVahVfq9UqCoUCWq0WOp3OHIullZ/8xaxVBaQEo1arFQ6HAx6PBw6HQ5gOnU6H8XiMcrmMi4sLNBqNOfZSSz/5nFVmhWDPbrfDaDTC4XCIDof/DrzQN3W7XZRKJZydncnz1vpAWATNBADMvnkwmEwmOJ1OuN1uKcPMZjMMBgP0+31Uq1Xkcjlks1nxVUut02IZ0O/3IxgMyjOOxWJwOp0AALvdDqfTCavVKszQcDhEu93G+fk57HY7CoUCms0mAGjGXtFHh8MBh8MBl8uFZDKJUCgEl8sFr9eLtbU1mM1mTCYTea9Y0ur1euh2u6hWqzg5OcHJyYmAFpVx0cJPi8UCl8slICAQCCCRSCAYDMLn8yEYDCIej78CVkejEWq1GjqdjgAXh8MBAJKs6HS6pRksHlTBYBBbW1uIx+MIBAJwu92IRCJy0DqdTtkD3HcEq6VSCfV6HYVCAdlsVsAXY6n6Pi27noFAANvb24hEInC73XC73djb28Pm5iZsNhvMZjMcDgcCgQAAyPdut9soFouoVqs4OzuD0+mUd0oFK5PJZOmYr9PpYLfbkUgkkEwmEQwGEQ6HcevWLWxtbQmzarfbEQgEZM+RWU0kEigUCjg8PITD4cBsNpO9oZbitYpNer0eTqcToVAIyWQS8XgcOzs7WFtbk2SFYIu+9Pt9tNttBAIBZDIZSWKz2awAq9FoBABLP3caz0WXy4VwOIx4PI5kMolUKiWxNBgMIhKJYDabIRaLod1uo16vw+124+zsDIeHhzg6OkKr1ZL1uw6Gleeky+VCKBRCIBBAPB6H2WzGdDqF2WyGx+NBs9mE1+tFp9PBeDyW9Tw6OkIul0O/3weAOcx0HSwwmVWHwyHvPpn08XgMnU4nzLnT6ZT3u9FooFarCXBVKyrA5dfzSmBVBQEqsGLgZ4bLw5aggGwVWaJ+v4+zszMUi0W02210u10BVlrYIqNmMpmEBSLL5/P5BLyQrbLZbAL+qMfJZDLIZDIol8sAMFd209pXFTwTlHo8Hvh8vrmDmIyGzWYTdm04HApYASCC5zcx31f1k8CKa0XQ7Pf74fP55hgZr9cLn88nrNtgMECn04HZbEan0xHwr3ZDauEnfTQajXC73cJYeL1ehMNhOJ1O0Q15vV4BXmR9m80mqtUq9Ho9Go2GlLTopxbJyiKTb7VaEQqFEAqFBEATaBkMBgExgUAAer1emLVisYiLiwtMJhO0220Bh1r5qrKpZFr29vaQTCbh8Xjg9XqRSqUky3a5XHA6ncJgDodDdLtd5HI5ABDmj0CFvi4LrshEu91uxGIxrK2tYX19HalUStYtEonAaDRiPB4L8KZPZAHPzs7Q7/fR6XQkHhE0LJtQcy3tdju8Xi/S6bSU1/b29pBIJF5J8MhGAS8Op36/j263i3q9jsPDQzQaDdTrdQGo4/FYCIFlnr3JZEIikRDmLxaLIRwOy3Nn9YkHmnqwDwYDOWzD4TAsFgsqlQrK5bI89/F4LNWgqxqfezAYxPr6OqLRKEKhkJRYA4GAECaMWaqfZN3JGk+nUxSLRZRKJTlgeSgva3zu8XgciUQC8Xgc6XQad+/elTVSyQmazWYT8iQcDsPlcgEA6vW6rCcATSt+PDMJACORCFKpFHZ3dxGPxyXJY/yfTqcS8/1+PywWCyKRCKxWKwCITpR7keylVr7ynOSzX1tbw+bmpmAOJgKz2Qxms1mYTcYzo/EFHDo9PRWmUK2oauUn3xUmVOFwGIlEAiaTSWQIJFHsdrtUK7xer1SqJpMJKpUKAIifWvmoGuMp1ygUCiGRSMBsNqPRaKDb7aLf7wtB5Xa7JU42m01cXFxAp9Oh3W5LAq1KVi5jlwarDLQEfiqj5vf7kUgksLa2ho2NDdjtdgm4BLRqkOp0OojH4zg5OcHp6SkKhYKUXJY1+qnqrAie/X6/ZF2xWEwYVZYLfD6fHEjT6RTdbhdut3uOESZ7oYWpAFDVW9ntdjkYYrEYfD6faMLIwG1tbc0J2tvtNgCIfqjdbs+VYZf1Uy1NWywWAaPRaBThcBjhcBgej0cOSWaJu7u7mEwmAviovXK73cISqYytFsCKQcFutyMUCskhFgwGEQqFxE8eYAzKfOYEW/V6HU6nE/V6fU4+oNWaMoEikN7a2sL6+rqUCP1+P+x2OwaDgZTd+HONRqM5gF2tVuF2uyUJYPKzTPlSXU8yU6lUCjdv3sSdO3cQiUSkzGowGNDpdOR94jPg+z6bzZDL5VAsFuH3+0WyQMZ9mWfPg8DhcCAUCmFrawubm5vY2dnBjRs34Ha75eBS3xXgRVBWpRXBYFBKyjysRqPRK/rbqxoTqGg0imQyia2tLezs7MhzN5vNwqpxjbhXAMyx/0xqPB6PSFSYVC1jXM9oNAqPxyPMbzgcRigUEjDFeEg2UpWmsIxM5lX1czgcotfrLeUjjRIKJncE/wRLTJg6nQ46nQ76/b7EW8Yzj8eD8XiMSCQCv98vfhJAaGEEVV6vF5FIBOl0WsCf3W4XBrfb7UrSyXeI5WEm2ePxWEgUnlXD4VAzX4EXyQoBYCgUQiwWk2SK4IM6VVX+YzQaEQgEpGRsNBqlUqXX6zWv+LGS5/P5JGYyoWIlj5pfluGZCAYCAXz66adSobRYLHj27JkQaKz4aWF8pxjryVS63W7o9Xr0+31JPAEIwWI2mxEKhSS5NplMODw8RD6fl8kBr2tqWtZXlQX2eDzY2tpCOBwWgmE8HqPVask5yl8A5Nyy2WzI5/NC+PCduuyZdCmwqjJ/fNGNRiNcLhf29vZw69Yt3Lt3Dzdu3IDL5ZJNQu3CaDSa+3yj0ShlLr1ej3a7jWq1qiljpZbRHQ4Htre3cefOHdy5cwd7e3vCWjQaDVl0ZmDcFHxgBIClUknz8r/aqEDW98aNG9jb28ONGzeQSqXQ6/VQqVRQqVQkU9HpdMJiU9hMpo0gRisNsOonpRKhUAi7u7vSJRgMBuU5EpACLw5Vloxmsxn8fj+y2awwnpQCaOHjog6Vmqrt7W2kUilEo1HY7XY5EKgN6nQ60jjAjyaTCaenp3NgTGuZgtoYEIvFsLOzg1u3biEWi8Fut2M8HqPRaECn06Fer6NWq0kiw0BGMHFyciIdsMsylaqfBJ+UJayvr+OTTz7BzZs3pQTdbDYleM5mM4kNPGCn0ykMBgMCgcBcIqsya8uylQRGZPgTiQQ2Nzfh8/kE0FN+QiBA1oAJAPWUHo8Hbrcbg8EAjUYDADQ5XOlrIBBAMBiUNeJ7TLlEvV7H6ekpisUiZrOZMLEej0cSLDYEsYJltVpfkSsss6ZMONXmDybszWYTjUYD+Xwex8fH8q5z/f1+v0hBms0mut2uaPWq1SqA+TLrMuupVidcLpdIKXQ6nVRILi4ucHR0hEajAb1eD5/Ph2g0KppWsqrct8FgUJ67FmVWPndWy3w+H1KpFMLhMEwmk4Aprme9XofFYkE0GhV5CGOozWaD3++X8jz1/1pL6IxGI5xOp7xLqVQKJpNJyry5XA6FQgHtdhs2m01K8Gxscjqd8Hg8SCaTyGQySKfTODg4kLKxVjGfz57aabLUrVYL7XYbpVIJ5XIZrVYLDocDkUgEkUgEDodD9M6JRALtdhudTgcWiwX7+/tCsGhljPc84wlSmUCxqapSqYjUyu/3w2g0YjKZwGw2IxAISFK4sbGB/f195PN5qfpoacRObrcbwWBQyvydTkd08tT5Mn7OZjOYTCbo9XpZa5vNBovFgufPn6NYLKLZbF66Mn0lGQABIJnKGzdu4O/+7u/w6aefIpVKieaHQuB6vS66BQZot9sNm82GSCQCn88nNLIWpjJ0XGybzYaNjQ38/ve/x+9+9ztsbm7CYrGgVCrh8PAQxWJRAlM4HEYsFkM0GpXsixQ8y5paZTCLmlquaTqdxr1793Dnzh2kUinMZjNcXFxgf38fhUIB3W5XSvBkEMjGMLMFcC1+ElC7XC6sr6/j1q1b2N3dRTgcxmAwwNHRkTQo9Pt90Viura3JQcuyL4BXSgNagAEmUzabDcFgELFYTMCq3W5Ho9HA2dkZcrmcvGxkz5PJJKxWqzAcLK2RYdP6QOB6siQUj8eRSqXgcrkwmUxQKpXw7NkzNBoNCfJkfRKJBAAIaAFeSj+0kqmooJqVCfpps9kAAJ1OBycnJ3jy5ImAVYvFIsA2HA5LFk4/mfxp0WBDPwniWDKlXo0sXrlcxuPHj3F+fo7ZbCZANRAISGyi5o5lSlYotGhYUo0HC1nn0WgkzQmlUgknJyc4PDxErVaTylUwGBTQ4vP50G630Wq1pMGm2WzK11qWDSLLV6vV4Pf70Ww2UavV4PV6odPpUKvVcHR0hOPjY2SzWQwGAwFh1HsSiHIN2WBDn7U6XGezmZQkmYCyJMkYf3h4iPPzc/T7fYkLw+FQ9ojFYhH2j2wV15KNIVoZYwp7IACgWCzi6OgIz58/x/n5OTqdDhwOB6LRKBqNBu7duwe73S5+kN3i3mY1S+uKH8u6vV4PrVYLRqMRuVwO5+fnyGazKJfLch4FAgEkk0ncu3dPtOHsC6Hemk2KjCtasZZsLKaUJ5PJSLd/qVRCpVJBq9WC0+mE3+9HPB7HzZs3sbW1BZfLhdFoJKw1vx4rmVoQaDQy4IPBAL1eD/l8HplMRpKqQqGAarUKs9kMr9eLQCCAWCyGW7duCQnY7/flebA6oE7k0MK4j7gGJpNJJJHZbBZHR0eCnYbDoZxhoVAIe3t7wlIz4dHpdCiVSpK4XjZGLSUDsNlsiMfjuHfvHv7mb/4GiUQCDocDw+EQ5+fnODo6QqVSQbPZxGAwgMlkwmQygdvtltIL2UsuspZAQNVVhkIh3L17F5999hk2NzfhdDrRarXw9OlT7O/vSxMFgRQPWlXqoDIWgPadd9T/BINB7O7u4s6dO0in07Db7SiXy9Lwkc/n0ev1BNxwg5Dx4CGxOL5KCx/VZx+JRJBIJLC9vY1EIgGLxSI6lYuLCxSLRQwGA4RCIZTLZfj9frjdbmHcX9dlq6WfbKjgAZ9IJOD1egG8ODyz2Syy2SwajYb402g04Pf750rFPLR4IGjdIUpgzYYgaue4F+v1unT9DgYDYXc7nQ663a4Er263K9pQtbt+WT/5+WQvmKiw4W84HEpHOplVshXUEnLt1DJsq9WSoKXFWlIKw8NAbTjhpISLiwsUCgWUy2XodDppCGNZkECFoJraZa39nE6n0oDgcrlQq9VQr9dhMpnQbDZxdnaG8/NzFItFdDodaWJiYtfr9WA2m8VPMkKMtVqAQK5nLpcTCQVL2KqOn4crDx4ViIzHYzmUWcmg1o3PRws/p9Op6GHJiHNu5enpqSSmtVpNyvpcI7Xpk+86/eQ0APq5LBjge9tqtaQh0mazQafTSXxnl3+v14PX64XJZBJpmgr6mKCqz5ygWgvQwmSFsaXZbCKTych7XigUUCqVJM5TlmQymdDtduekLGrj1eJ6agWwptOpPL98Pi/NfZxQQPLEarUKC51MJqWCxrVldVLdn1pWqQgCB4OBdPrz/SBb3el0YDAY4Ha70e/35Vyw2WwYDAZwOByiceUzZ8Va6/OeuvTRaIRisYh+v49CoYB8Po+Liwshe0wmk/jq9/tFAmI2myWJVufeXjb5Xwqskln7/PPPsbGxAYfDgdFohEKhgPv37+Po6EgCPQDROgGQBVDHV11VePtzvlKovL6+jo8//hi7u7uSRWWzWTx48ADPnj1DpVLBeDyW0iQlC8wKCWa0nlZA40Zjp+hHH32Era0tORiYyTx//hyNRkP0PmwAIeChto7+agUC+fkqe0XtYiKRkHJpPp/H+fk5Tk9PhammhII/J198SkOuY03JWLI8SqBMXziuJpvNotVqiWaR60XmQw0kWoPqRV8XxxgBkMBZrVZRrVbnMlgGPQZpTtXggXAdgntVZsEDnmwgpTTUytI39cAim0pWQCtQzc9XAYfKkAFArVYTAMIGJBWIs1Od4EoFgGQBtWL/yTKXy2WRAZRKJdFIU8fNiQUcHaOOjSEAIAis1WqaJyrj8RilUkkOdMpqnE4nqtWqJHOUYHAkk9/vFwaWe0RtYOO7r1UyNR6PUavVkM1mZVQZ5QulUkmqEjqdTnSAbFrlFBB1TQeDwStsuhZ+qiO9mJQyZhcKBeRyOZTLZdRqNekMpw/UKxJAUlOpAkCtzyUm7GT7qU1mPCqXy6jX6xLPLRaLECdWqxWz2WxuTVutlmbPXTWubb/flwlDvV5P3iMmdbPZTOKT3+8HACldq4wnZ5qqoForo9a32+0K0Od72+v10Gg0JOFjT8JsNpMmW1aODAaDkADASwyxrKRKNcoggRfgmlIKjv6q1+tzul5WyFk9Zx+ATqcTeYU6FvIy02quPA2AjUgUiLtcLpmh+OWXX+I///M/kc1mBXGrQmJKANiRywNOa80FgZXb7RaNndfrFT+//vpr/M///A+KxaJ0BhM0sBubwUR94bSescoXgXqmUCiEe/fuwefzAXjR8fn999/j0aNHKJVKGI/HMraIOkev1yubWu1a1nK6AgMMtYdWqxXpdHpOs/j48WMcHBxIJy3HcfD5u1wuOdh4yHDDaiW2V5llBhyyLOPxGN1uF2dnZ8IKsRSo0+mk1GowGNBut0UrRDG51mPAgJcyCAYtfj8yq81mU+Q04/FYOlcpoWHwVUErwapWQFCVQoxGIwlSBHj8XmqFhGNvOOqEz5tAlUBHKxBIxolBnxow6n3VZhWv1ysJCkdHEVxRmtBut6V0qK6pVqCFIJ+l9UqlIpUp6v3IUFN2EQ6HYbPZ5oA+kwAybFrNBFWZtUqlIjpuNiZOp1MZseTz+UQ+FYvFpLmCoF+NcWq1RysgyPUslUpwOp2SiFBjR3KFmutoNCrTDSKRCHQ6nfQsAJCEUUuQQj8J9rLZLCaTiUyYYRLNMYlkoTi1xu/3w2azyaUFbExcnKKiVWzi82cVqtfrwWAwCKPLGDAcDoV8AiBMsLpPKQtZJCa0ivcExZQkzGYzSTbU2aWlNAcAACAASURBVNis4k4mE+j1+lfeJ8pp+BF49TIWLWIpgWq5XJaYyqbDwWAgutbZbCZNYCQD1TGgbEZXR13ynFvWVJkG4yl9bbVayOfzaLfbQpZxHB9nRRNUEwPk83npzbBarXONq28DWC8FVlWgQg3izZs34fF4MJu9mEX6v//7v/jqq69wcHCA8XgsTQGxWAzpdBp37txBPB6H0+kUvcbx8bGwsFqVhXhY8oqwTz75RDI++vnNN98gl8thPB7LDQ3r6+vY2dnBzZs3EYlEYDKZUK/XpdzBB6RVFrt4sK+vr+POnTsyuLxYLOLBgwd49OgRyuWyBAav14tkMokbN25gZ2dHytvVahX1el0yXzXz0sJXne7FPDt2BrP8X61W8fTpUxlcPBwOpYyRTCZx8+ZNJJNJmM1mtNttlMtlOagZfLVcTwACitkgAACtVgvHx8dyPRz99Hq9CIVC2NnZQTgcFgaoUqmIFk6dA6yFqYf1IlvJYcrVahXdbnduooXf75f1VzVsZA/USyC0YjDUn5sd6uVyWZImnU4n3e21Wg1Go1GaMlhN4RqS4STY0ioBIFhVATonT3CCQigUkqYuJnvUrFqtVulWJiBvNBpzDIBWzYo8XFWgTr8J/tbW1qDT6QTsM/ADkKYlTqhQAar63LVYUwJgfuSBmUqlkEqlhFGJx+MiDyFYZMMXNa+BQAB+v19KslqNL+J6drtdkVaQ1eH0D44jWltbkzIlD1TuHU4EqdVq0jxCwKOVMWEul8uYzWZS2QEgh79er4fL5UI8Hsfu7i5u3bolzTh8pky6PB4PKpWKgB2tTGUbydqrl8yok3s44oy+hsPhuaopAGG6VXCtlsWX3aucRcu5pOrYMVZ4zGYzYrEYbty4gdu3byOZTApgUuOmClTV/pdlz1IC5cWOeLUiqiaoa2tr2Nrawt7enjwPNryqoJSVVU4s0aKvhucR5V/sp1ETePUiBs6L3d7envPRYDCgXq/L12JzLRn2t01YryQDsFgsCIfD0qXIB0DGxG63Y319fa4rl5pRAlXgRVPI06dPcX5+jmq1KuU6LUwFVdFoVG4DYWbb7XZhMpmkMYkBbW1tDffu3ZNOQWqDstks8vm8lAf4PbTShpCppq/MUtl0wcOfml+Ou7l16xZCoZDMLOUQe2aYWs0HpJ/slOUsO1WXNBqNhK3ipkwmk0in00in0wgEAqILJWBhAwO/vlZrSgkHS6xkHhnEOO+QXYxkrjjonAGF19zxEFBlDFo2r7GBSafTSWmPbCDnB/L7k8VSmwH4OWSH1IkbKhC6iqksmBpUq9WqACi9Xo9oNCqjyDhrVb2BiWW5Wq0mCQ2DL9/NZdZUrSqot3ixCYEa5nA4DL/fP3dgksXg5zGRqtVqmmlA3+QrQQDZQUpBWKrmLTyqXhF4kTQEAgFUq1WpbiyCaS00lirrz2Y4rq16oQbHWVECwnjAg5fPuNFooFwuC7jSYti+yqpTF82qHSt7ZFY5t5jlSCYns9lM4rDX6xXGXWWAtFpPjigicGMizDjjdDqxtraG27dvS+OyxWIR1o9ymlarhVarJSQSR1oB2jTXMg4CkJ9f1ceS4dva2sKnn36Ku3fv4saNG3A4HCIhKJfLKJfLKJVKqNVqwloy5mkxI1Rl6FTwO5vN5mbAb21t4eOPP8aNGzdw69YtiautVgu5XE4uVaGfXFfKL5aN+yq7zBjNRIU+Wq1WGWNHsMorTrlvstksTk9PcX5+LtU4Sh61agRmvOd7yr3Khime59FoFLFYTDDBxsaGSCx6vZ7cCEeiT6fTCfPaarUkmfglIuBKMgD1BiAeqAQaXq8XOzs72NnZkYOMDU7pdFp0F91uF/l8HkdHRyiVSrLgWhkBIGeZUc8JQIar008eDOogcY59YmmBQFUVXWvFsPGwZJbM8h/wYgOT8eUICw4z3traEjDAYM1uOzKVWneH8tnzLmuKpel/NBqVQ5Vg5e7duwgGg3In93Q6letLyXbwpdXi4GJWx4YlNluo7KTX60U8HpeDK5FI4Pbt2zLCiAwHgRn9Y0lDy3FgHAWm3p7FA4I6ag4CZwPe9va2yD6Al53BbMJgsgNg6QCrGoEv2aFWqyUHPuebBoNBAJCZrOyyJmBVLwDhz899oIUUgAFWbZRhcqIm0Px+amMQDxMCtEXtr5ZMkKrjbbVaMqqI4/PUBgfeTkeGlyU3gtlgMChNIqqfWjxz+kn9bq1Wk+9BYGSxWOYSY34OQQnPisnkxTBzNjYS9GpVYlX9pLaf/gAvkj1qK1X/CQL5/rhcLkQiEZlgwPnQWugt1YYjjvNi/GbV0uPxYGdnB7dv35ab4Vja5vjCarU6N61AHSvIs0urPaqeR+o4SIL/jz/+GJ9//jl2d3cRDAZlaHyhUEAmk5lrcFrssuczWSZGqZIvdQQkAGEEw+EwPvnkE3z88cfY3t6W8VasEuXzefGTgJyJNAHlsiBwUULIBJpr63K5EI1Gsbu7i7W1NRm1yHmx3W5XtM35fF6eP2UDPEPV5tKrGn1VKzac883eidlshmg0ing8jkgkInIqTtwplUo4PT2VPcDLGVjpbDabAlh/iVC5ErNKxgZ4WXqkEDeZTMr8MAACmnhzDIMxWVXOk9NCY7Xop3q1K6/7YqafTqelUYXdmGQFA4GAZKi9Xg/Hx8cyh1NrvSJ95aZl4GJGR2kAX0R2LXs8HpklSG3jYDCQbj1VdK0VWFXZSvqqzjZcW1tDs9mUcj/LwkxS1IY6MggMrgwwWhyw6iQAAhTqv6bTKYLBoOxTk8kkjWLb29vSuKT6o461AeYD47KgWm2uUgc+Ay9nWjKJ0el08Hg8wgI7nc5XBuqrWicyWQy6V11btRTGZ0fwyWDJJIY3bPEZMKnlNBCy8Kq/AKRszH9bFgyMRiMpkxPUMzniDVYEgGoXNZkiPv/Fn1+rciCNoJ9aS5bdXS4XxuMxvF7vHFOmsm9kUrgffD6fHLQ8qLQssXa7XVQqFdGBMo5zwDtjraoXV99vMqyJRAIXFxeiE2SZXYtRW2RX2RCkXpjCM4ZrS+AFvASrHCXFBhzqH/mzqlWGZdaUJXQCYfUKbY/Hg2g0ilu3bslge8Z4NoVmMhnUajX0ej1pvuL+JfvJ9VzmXOXPyp+XiQfZNZaqP//8c9y5c0fkNGy25dnJRmv6or5fapXhqnvgTfIv9QrWZDKJL774Ant7e6L/JdOfz+dFckWiYHGePN97dR9c1VeV+InH4zJ/2O12I5FIyGUrbAA0Go1S6ePkEMom1UtlbDabVLnVnoWr+gm8lBhwxiubPTlXOZlMityLzYo6nU6mBuRyOamkud1umdZBgiOTyci6aw5WmeFxLhwHFPMquFgsBpPJhE6nI9o0looMBgP6/T7K5TJ++OEHHB4einZQy25GHiyc93p2dibXQlLPRMDKDs1eryeHK0tDtVoNh4eHsqCqzkQrI2ABXoD7XC4n+i8Cw1QqJS84sz4CRv6c1KpqzVLTx8UEgN2VFNRzADBZH1XLSPaXrByzXZvNJo1MWoBq+smvz/KNevUvx1kR/LHsSj/ZhcspAsFgUAIUANG5aiViV8s/lKRQKuPxeITpIevLpgt2aHMdqQ2n4J1d71pet0vWknuQkz4I6BmI1OstyfTwlrNQKIRmsynAlwCW+2WZtQReNtuwYYmBniU/dvczKWBCxypGMplEsVhEMBiUW/XUuETwv2yiArxYz3a7jXw+L4dMvV6H1+sV1o0Jqprk8R1kpYXXytZqtTmmWP2ohZ+5XA6DwQDxeBzNZlOkCgSIOp1OkhnuTfYDEORsbm7O/WwPHz6U53/VM0AF5dQp88/cXwTU+Xxe/p2HPeVJ/P/9fh9OpxO7u7uIRCIIBAIynm2ZpjBVeqLKatRRP4FAAHfv3hVQxVJ5o9HA06dPcXZ2Jh36g8FAhuFTglEoFIRVJgBYJkml9InMH4f+kwX85JNPBFyZTCb5vvv7+7i4uJBbFHmpCPcG4wJJAILZq/qqDtvnLWYej0eqD3fu3MH29raQUTz3eYHNbDYT1o9gn+cA4xmNlberGNfB5/NhY2MDW1tbMq0mEAhgY2NDLqrgGk0mEyEG6RcTG6PRKNUiNgSrzZdXPafUahkbEjc3N+WZ8VY9kilqMxWrZ6xWTCYTuWiDN8SNx2OZHlGv1yXpfZNdGqxy0Tis+vT0FA8ePEAymRRNEA+Is7Mz9Ho9BINBbG1tiQA4m83iz3/+M+7fvy/dcFqPreJGPD09xXA4lJl2u7u7ch8wO2+pTZvNZrh586YEh2KxiL/+9a948OABLi4uZBqA1iwwG1YuLi5kpINer8fOzo40LPDlpnZsPB7LRh2NRqhUKnjw4AGOjo5k9hkHcGvVFETmolaroVQqyexCsibqrV9kiGazmcypZWf70dERisWiNIMxq9JqODzXVP1alKpYLBZhpglm1UyX6881ZfmPa0mwrdXMTQACOHj7CictqM/d6/UKk82gzDEh6lghanNZLubPssy7pWpq+Xw5cw+AgH0eaCybkZngzxGPx/HRRx8hl8tJCVTN/i8zxuRNplYSOHap2+0imUwKyOQtX0wIAEiXO5kNsl7T6RQPHz4Un1S2cllTgSA72TudDqLRqDB87XYbp6enkqSSxebd8hzHtrW1hX/4h3/AbDbD48ePcXZ2NsfCauWnepvXYDCQa4jZLETwRQDCpCoej8s1wuFwGJ9++qmM36lUKjg6OpLPuaqP6rNXm2YYt6j3o8aTGn8ywL1eT6YwOJ1O0eCxMsjGErVp6Cp+qrO71Tvq0+m0MJV3796VKQWctXx8fIznz5/LTNvxeAybzSb9GCQu3G43jo6O5poXr2oEmbwuN51OC8CKRCKIxWLyTAkAqfnmOEir1SqML0cxMsFmU9Tp6ak8p8v6yzWl7CscDsvtj7u7u/D5fDKTnPGVQI77hLPf2TeSyWREB8qkFgBOT0+xv79/pcZl+unz+RCLxZBKpXDv3j3cvHlTLlhRJYlMaIg3RqOREIOcCsOEnFI3voNMblliv6wxeWfT3N7eHj777DOZnkDfAMj5wwSe4xZZXfN4PLh58yZ6vR6SySSi0SgcDockf5FIBJlM5hcvhbo0WCWw4kKQ6levKCXqn0wmiMViCIfD0q3ebrdxcHCAH3/8UW49UYXLWhn9rFQq6PV6KJVKyOfz+Omnn+RFUXWpDocDW1tb+OSTTwC8yPJOT0/x8OFDHB0dod1uSylJaykA2QBeQcggur+/L7PKeO0mx1pwRMxsNhNQ/ujRozkNMA8NrYAqNWHNZhOFQgF6vR5fffWVXLNKjZD6/TjMfDp9Ma7j5OQE3333HU5OTnB6eipC9utIAlgSrNVqePr0KTqdjtxsxKHELD+zgc1sNqPf7yObzeLRo0e4uLiQ4excU4IsrfYAgSCBJku57FQmO6VqPBkIuM85+YGzD9ULArSoBpBdWSyHNRoNYQqY/HF0FBkAdotbrVbEYjF8+umnOD09lQODz39ZjZ0qLSBwIeCsVCrCUNZqNWQyGXg8HpnFyia7tbU16PV6JBIJ/NM//ROazSay2axcZKCWk5cxgiv15hkCy1qtNqc7LBaLolfr9XoCSGKxGDY2NkTf9vnnn8sactzZMn4u+sgEhM+NrDqBE2dwqneqU8+cSqWE9b1z5w6i0aiwQPv7+zg8PASw3HW7i9pvVWfOUVSMl2RfOe3BaDSi0WjI9Bq32w2v14tIJCKJ4snJCb755hs0m82l1pQ+cRIFk2ZKfKj7Y+Lc6XRkruXiFeAEFfTbarWiUChIcxPnWy/rKyU+HPJOUEcpGmM/3w+dToetrS1JAHQ6HSqVCkqlkmiyOdrs5OREmOKrJoKqhjsUCkkVNRaLid9cUwI6EhTpdBrJZFLOuMFggLOzM5kfTBlhPp/HYDBANpsV8uUqfvJWMq4fJX3qTWrcq8DLSys4+nFzc1NiZj6fB/BSSzyZvBiFxlFzjC1XNWp919fXxT/KEhgf1BjD/cim72g0ik6nI2ws4xqrHI1G4xXW+k12pQYrsnVslGKHFw8Iahx4JzA72abTKTKZDH788Uc8fvxYUL9WbJpqBFbcfJxTyUBGQM2MjB2MnAdZLpfx6NEj7O/vo1QqzenArsNXdRAxdSnn5+eSbTFrNZlMSCaTCIVC8nfVahX7+/s4ODjA6enp3DBrLf2lnzx0OB/37OxMkhXgxYa1Wq2IRCIycmMymch4q4ODA7mOlfN1tQJ/3EtkqhhoWq2WMGpkAqkV4qHBWcHdbhcXFxc4PT3F8fExCoWC6OuuI1kZDAaoVCqy9wqFgjDqNptNNHYs/TKYsamFMzYbjQYqlYokO+r4lmVMZRX5vdgUwX8j6+b3+wV49vt9GAwGSVipwY5GowK0qVfUQgMMvBy3wj1H7a56PSEAucGOutbxeIxQKCRzOAlWbt++jYODAynTa/U+qY0bi5ICsoBsQuTP0mw2JUaoeuVQKITt7W34fD7cuHEDJycnwhovG1sJAgmS6LM6nYCxhiCVcUFtuGJzRjwex507d0QaoN4qtwxQVcvAVqsVDodDSrqqZl2NB4yzBKscrs8qBedZk9xgoxNJhWX8ZMwhMOYc1Wg0KpI6NSFk2ZmkBaU+/Fw2vNpsNvh8PukW58igq64tnzW/PkEf5XQqcKFfk8kEFosFa2tr8nPPZjMkEom56gClZABk7vVVbXH6i3q7I8ketaROvEJ2XwVLk8lErgzmz8MZs4eHh6KzvwoIZMJP3Sf3KUGqKo9g3FalGMRRTJhDodDc/FPGOS16P1RMp45MUxNY+snqpCpzoWyEvUIk2/r9vkzrYAXpbUi1S4NVVXBNGnwxw1AdjUQiSKfTMJvN6PV6+OGHH/D48WOcnJxIw8N1GOloHoh6vR6dTmduMelnLBbDxx9/LI1Bk8kEz58/x+PHj/Hs2TNhVbUGqaqvBGwE1mRXFnVD1E95PB7xNZPJ4NmzZ3jy5Il0XGo9sYBfi6CdN4UUCgUB1MwGCQRv376NnZ0dyfhyuRyeP38ufqpXWGqZsBBUlctldDoduS5SLf2bzWZEIhF4PB6Zu0cw0mq1cHFxgaOjI5ydnaHZbApDpzX4n81mMt6Dc4dZkqaGlWJ2lnpCoZCUilhGq1QqKBQKr9xipJW/akc49YtOp1OCD7vZ/X4/HA6HdGRTGsBRJqrmWW0Y0loCpM4fZKBkI+h0OoXX65U5uhzCXq1Wsb29LTo3h8OBjz76CH/+85+F2dbSz8XmRxXA8oDk/2Eyy7mfnL0bCATk52IiEAwGRSOuxYGlNpnwqmXq49ROaQIWHpx8t3U6nUxmYAJDkED94LJGEEigSiaQoIpgRdWdcxIBY7s6MYCAkuAHAILBoFTjrspWqX7yHaaWz+/3w+l0zk0HUC/P4MQVvjsEL5Tg0TcmMBwhuOyaqnp/NiBzTRelR8QEnFqi1+vnRlORTQdevKfNZhPhcFgubbmqn5RWsMLDKz9VOZWaOHFfEhhyIslkMpGkjCCKibTNZpPPvypu4TlOxppJIP+ee2uxoZO+M4nhWhkMBkkOGfcIBpftXeH7zAtS1IubVGaV+1EFqyQJKKnkHiEZ1O/3USwWUSgURG6nOVilI+rHRRqYH9fW1vC73/0O29vbco3gV199hb/+9a9ypdh1GhkJ+qPO86SxHLO7u4vd3V2YzWbU63Xcv38f3333HYrFoiYjQN7Gz8XObdVXvli8EWhzcxMWiwWdTgfffPMNfvjhB2l+uK515UFNUA28uF1r8QUzmV7cDX3z5k0pvYzHYzx48ABPnz4VzaLWsytp1ACSQVGDEg9XdlJvbGzgs88+QzqdFiBTKBRwcnKC4+NjaRBUQYqWe4Egio01LE0RHBCsnp+fIxaLIZFIAADW1tak4/3o6Ej8JfOmJVBl4kdpQafTkbJptVqVQ4yDwKlLo7wmkUig2+0KaCHgBV6WkLVIWumnTqeTg5PaQOoVAQh45WHJETCTyQRnZ2dyAxOrGGTUtNQp01d11BRv12JCpTbekVVVG5FUwMWDj4msVtUKNZEkyPT7/dJMo/4fSr/Y4a6CWJaqWTZ2u91yAxrfr2WN8YdMIK98pbaf5na70W63RbbCNeKQ/UAgIBeucF54p9ORStwyYJUgCYAAf2qlzWazJJ9899kARmDY6/UkSQUgyTYrbSQR+PlXBYA0MmqMS2QD1dFfKhgBIEBM1Y5TjsDmZfXv2ajL9bmqnwRxTATUK91pasKp4hcmgGzy4RlCrSibyavV6lxF+LLG2ARAADKBmvpukxlVgTFjJRM/+kmw1+l0cHZ2Jr8qlcrS2IUkRLFYhNVqBQDplVDBqtrESF9ZbWGDHQDkcjlUKhXkcjns7+/j6dOnuLi4mDsT3mRXT7veYNzYLpcL//zP/4zPPvtMqOu//OUv+PHHH1EsFjUvpb6NX4u/Z0b2t3/7t/jss8/kusVcLoeHDx/i7Oxsbq7qu/LxdetCXzl8ORaLwWAwoNFo4MGDBzg8PNQs6P+Sj6p/6gbjYTGdvrhedW9vT0Bgu93Go0eP8OzZM9EzXufzV18c4CX7r2ar3W5X9NTxeFzGlT158gRnZ2dS+l+m+/eXjKAFwFxXLP1kGa7X68FmsyGRSMgMzvF4jHK5jGw2i0KhIGOktG5WBF6Og2KjGvCiq54Hk8PhkDmhsVhM2AwA0o1PcJjL5QRoaQ0AefCoyROHp7vdbmlMVGUyBCAsc6rlbrvdLt3N18Gqq++PXq8XoKJes6nepU1wysaWVCqFZDIpBwiZ+mazuXQyuFjx4F4km0ZAwLXitbZMBrvdLux2O1wuF9LpNG7duoUvvvhCxrC1Wi0cHBzg8PBwqcrV4kFP1hGArBcZfjZP2Ww2Ka83m01JuLxeL8LhMPb29rCxsQGXyyWSp4cPHyKbzV65Isi1JGPKSzV4K5DNZkO325VKSjAYlCYqAlHuTzYHeTweaRjmJQgPHz7Ew4cPkclkltKsEkjVajV5R1hqt9vtAuDdbjdCodDccHc+D/aNFAoF0ZRSilGpVPD8+XM8evQIT548ubIWWP0+R0dHc9IN6uW5dx0Oh0hUuN84Yuns7Ay1Wk0AGBnV0WiEQqGAw8NDHB0doVKpXPn593o9ZLNZSUQ4haLf7wvApyZ4kfnnGM1cLodmsyl6cM5/73Q6yGQyOD4+RrVaXUqzzu93fn4uF8Ccn58jEAhgfX1dpBW8GIolfpJlzWZTJHTtdluasorFIhqNBur1Op49e4Z8Pi9a92thVn/OWDr45JNP8Pvf/x4+nw+z2Qzdbhd/+MMfcH5+fu1M5WV89Xq9+PzzzxGJRAC8AAz/9m//hqdPn6Ldbn8QftIsFgt2d3cRCoUkkzw4OMDJyYl0uL5Pf5moGI1GJBKJuVEm1WpVLlZ4V+D/TT7yo8FgQDAYFG0Tm1w45eC6mN9f8k01su285IAdytQHl0oluRJ2GSH92/rHjJ8fmd0TyPIaWJY02bim6pXU+aZa7wV1DVWtFX9xesKtW7cwmUykoaXVaiEej2NnZ0cOf4PBIBILzhTU0lStFwEqGX+fzwefz4ft7W2sra2hUCggkUigVqthPB4jlUphY2MDm5ubWFtbE40lGwJVtmoZI6CeTF4O9yf76Pf7Bbz6/X6R3vAqxun0xUzbSCSCeDyOZDKJjY0N0RBfXFzgyZMnqFarmjDAbO4jq6zT6aSRlh3T8Xh8jlnnvuQoJpZiyf5OJhPUajUcHBxgf39frjNeBgSw8ZNMHg95Ms0qG6iOTeK7ptPphLECIKXVWq2Go6MjPHv2DJlMRkZCLrO2lHV0u13RxZ+fn2M6ncLhcMiFP5SsqMkNp0TkcjkBgZR9kAGm3IoNrFcFgSrrWC6XZWRWJpORWMmej3q9LvvZaDSi2WyiWCzKvgVesodc90qlIjdtqmMML2tMpuv1ujRHHR4e4smTJ3MjoSwWy9x4LFYnOZGCOlD1xrLhcDh3Hiz77NXmXTb+ApCGOq6f0+mUBIC+zmYzkeGpa00pAZMLEixvUw3WHKxyJuQXX3whTRXD4VC6q39plta7MmYwd+7cwc7ODux2OyaTCTqdDn766SfZ0B+C8dDlrVUej0cC7Y8//ijjv963kcni/D02eXAMWLVafecA8OeMc+o4F5RlC5Z6PhRjwxpLhhx2PZvNUKvVRKyuMkzXbWo5Uy2/er1eBINBbG5uSqXC6/VKpl4ul3F6eopCoXCtlYA3aUEtFgt8Ph9SqRR0Oh3S6bRM4mDDj3pb0IMHDyQZvC4jQGGZ3efzSVfzzs4ONjY2sL6+PjdCjbdWkSkkqPr6669xcHCgaaJNkKSyZizrczZkPB6HXq+fu6FIp9MhEAjIeC1qlbvdLp4/f46vvvoKDx8+FGC9rL8Enmz6VZs3OR6OSZTaJT2bvWj+slqtcqMY/azX69jf38f9+/dxcHAg2sBlWGDKqCj9oW6Vh/l4PEY4HAaAV5p/VM0tf0Yy7icnJ1IVYml42QoW2T9eCMFLfXq9nkz4KJfLMteV48LY4ErNLZsBq9WqJCutVkumHKj9C1ddV7KLuVxO5noWi8U5Paper0e9Xp+TBLZaLTQaDUlg1HGABKvU4C+7prPZTGai853lmcmfodVqiRaVkgB18ouqrVYvtqBUi/tei9hKrTzHf/LdVy/ToMyKa8d1o7xBlb3wc5hYXEaupClY1ev1sFqtiEaj+Pu//3u4XC7odC9Grvz0008oFosfDKjiwXD79m1hAKlN2d/fXyp70tpXar6SySS2trZEVtFut/H8+fMPggFWy+ssC7EpZTwe4+joSEqTWnQqauEnS3Bq5q2Os3mfpvpJHRYnWHBEHAOb2kz1ruQ19E8d/k/wEgwG5dpVBjYG+kwmI9Mger3etfqqSinYZauOJiIzxGDLRJsxq1Kp4N///d/x7Nmza4sHKvAhE8iSlOPk8AAAIABJREFUNMEzk34Cl9FoJPuBGjjKbL7++ms8ePBAU2aVIIQAS2UXqUMNh8NSblV95d/xa3B6yH/8x3/g22+/xU8//aTJ2hLsEqw2Gg0B/my6UfWsZIbUg5S/p2yk2Wzi4cOH+PHHH/HTTz/h/Pz8rcqVv2Rci06nIwmUzWaTubX9fl/kHzabTcCqKkfhNaasEFUqFZydneH8/BxnZ2doNBpLS2y4ppTV6PV6GTdZqVTknS+VSiiVSlK25lXHLPEScPHiFSYCbAxS+wKW8ZV6X64tJRNMEAic2DCt9okQaDGesWmR666Cq2VBIMc/kg3ne6w2nHJs3esIM3WKCHW5rFSxyqUFacGvSfZ/sZdGlbAtxjH+O9dU7Rvh/uf6v61pClYtFguCwaDcEczSVKlUwh//+Me36vh6F0agGgqF8MUXX8Dv98sGZVPVh8SqMqDxSkUVWD9//vzaD/238VEFBQQAPEh7vR4ePXokI6/eJ1BlaYfghUGOjQKHh4cy55R2XXrVN/m46CebAFU2iCXqarUq7xVLoNftr+ofGzl4XaDZbIbX65XSK0ftcLZloVDAkydPRPx/HUwwv57qH9eQIM9oNMod1vwzg2ir1UIul8Of/vQnfPnllyiVSppLl9TmCQ6lZ3K3eJkGO9zJvqoNKmwcOz4+xr/8y7/g66+/FlJAS7BK1or7j+CPt+tRy8jGGzWRIeNZrVZxenqKf/3Xf8X9+/dxcnIijJYWBytjtiqJYiLC0W+Mn/w3strT6VSe+/HxMQ4PD3F6eorDw0OcnZ2hXC5rci24+swX5zUTVLvdbpRKJZFZ8PmbzWZ0u10pq/MGK4IfJtr1en2pJqDX+UtGUe3sV7XMvJyGcUGdHML3kPGL0y7U6TfL+srnrwJmAmBVS0ut/+LPB7wcyaTT6YQJBl4CMS2YavrJd4pz09W/V2VW6ufR1NFbapMmP1fLM5brxu+jViNe55/67wTg9JfSBdXHy5xVmoFVk8mEYDCIe/fu4R//8R/h9XoxmUxQKBRw//59PH36VDbFuzz8F40icc5Q3NraksaPk5MT/OlPf5ob+fC+QSCBKpsCstmslID/+Mc/4ujo6J00Vv2Sn+q4F7PZjIuLCzx8+BDtdhvn5+f47rvv5g6md72uiwCQt0HV63X89NNPcm3hH/7wBymjXsdM1cv4SqDKm0mazSb29/fR7/cRCoXwww8/4KuvvhLdHyU278pfgimyp7PZDJlMBj6fD6PRCPV6HR6PB41GA1999RV++OEHmQhB8f91AFU1SNIvMkTNZhOnp6f45ptvcPfuXRmkzuaWk5MTfPvtt/juu+/wX//1X3P3y1+Hrzy0VPAKvJxb2+12cevWLWxubsq7ZbPZMBwOkcvl8OjRI3z33Xf47//+b2GrtV5XHi7qRSOcW10ul3F0dIT19XVsbm6K9n86nYoMiBKwJ0+e4Pvvv8fx8bHMAV5shFzG+HXYuEfZCZMmjjTiDTpMqNkMmM/nUS6XRfdH5k8FaVonAGSsS6XSK8wV2V+CqNlsJrcYqk1MKlOl5XQN+sqfWW0IUv990W/+30WZEEvHqlxJBS1a+cp3X20uWwRTrzNVKsD+BRVQaRkDFpPVy5iKo9Tfq1/3uqpAv2SLAHZxf/C5X8U/TcAqs2g1KyGLxqB/eHio+c0/V/UVeHlt7MHBASaTFwPr79+/j/v370tW+iFoa9UN/ezZM3z55Zd49OgRhsMh/vKXv6BcLmt6Temyvo7HY+TzeTx8+BCNRgNOpxPFYhGPHj0Swfj7AH+LfvKZP336FNVqFY8ePcJsNsMPP/wgJa137av6UvO5k5U8Pj6Wa44dDgesViuOj4+RyWRkDuO79pcgjofk0dGRvFNMBtjpfHp6ikqlMnez0nX5yq87mUyEvSXTU6/Xkclk8PTpU0SjUYlZNpsNrVYL2Wx27ipgLacVvM5P9dBSmdJMJiOMGgfGs/zHknG5XEa5XBZfybBfF1OtlhmpN3z8+PEc+8cZoQBkLmWj0ZDnru5V9WtrZQRJKmDh1Ao1CSRwUudb0i/1pkL1nboOZp0+A6/GKd4Qpv692sS0yHJdJ1Dh9/mliuMis6b+vSoBu441XfT1Mn+vGplA9f+/77NVNXXffMh+0hb3+VX3qO7nPkmn073VV1SbK1KpFO7evYtPP/0UnU4HT548wbfffovDw8OlLgGYzWZvTIne1k/gZWnQbrcjEAjg//2//we/349arYbnz5/j+++/n5tnqKWfl/VVZQPJBieTSdjtdgwGAxmjcdUSilZrqpau2dXMeXxGo1EOtqsywFr4yZdanQ/HEivH78xmM9Tr9SuPKdLi2atshHqQqjdX8d/YuKCyLFr4+kt+LjIp9I/7lKVg4EXFhRKAq+iUtPBT/UXAR+kCAycvLWEZ8bJzarV69uovdV3VwfkcrE5mTktfL7tH1b3AvUojsFL9e5d+/pL/r/u7Rd/extfr8vOXTP05lvXz/77etSOdt+1XeF9rell7335qsZ7/93Xeia//58vP/r83+aoZWGUTiMPhkGtWeYc8OxOX1PpoCqyor+QtG71eT7orr8vPy/pKf/mRc9WAl3cwfwhrqnwOgPmbebTI+K8jILypHLRMZvouA8LbBqk32fsOsm9rvwU/gV+Prys/L2+/BT+BX4+vKz8vb7+FZ68JWFX+/7XR0tcJWDQu7/zqNwWw8vMqtnr22ttvwU/g1+Prys/L22/BT+DX4+vKz8vbb+HZ/yxYXdnKVrayla1sZStb2crep+l/+b+sbGUrW9nKVrayla1sZe/HVmB1ZStb2cpWtrKVrWxlH6ytwOrKVrayla1sZStb2co+WFuB1ZWtbGUrW9nKVraylX2wtgKrK1vZyla2spWtbGUr+2DtZ2+w+jWMMwB+PX4Cvx5fV35e3lbPXnv7LfgJ/Hp8Xfl5efst+An8enxd+Xl5+y08e02uW13Zyla2spWtbGUrW9nl7XVXgn+Itnh73XVf8avaCqyubGUrW9nKVrayt7LX3f73IYEr9XIi/pmm3k//vk29UttgMMxdUqTeTvmuwODPGX3kder0dzqdYjAYYDgcynXKy9yq+XO2AqsrW9nKVraylb0nWwR/v3Sz4vsCLgSBKrum+rR4a+X78FO9Tl39SF9Ho9EcCJxOp+/NT4PBALPZDLPZDKvVCr1eLz51Oh1MJhNMJhMBgNcFAn/JCFTtdjscDge8Xq8A1n6/j0qlgl6vh9FohMlkIsBV63VdgdWVrWxlK1vZle11TNub7F0Bg9cBqrfx83Wg67pMBX8qy8Y/j8dj6PX6OZCiAqx3CQrpn8FgkF9Go1F8VQHKbDabA1nvej0NBgMsFouAQJPJJECw2+1iPB5jMpnIx9Fo9E7L2fTRZDLBbrfD7/cjGAzCarWKj4VCAf1+H8PhEKPRSH69Sz/pq16vh8VigcfjwebmJtbW1sTXRqOBg4MD1Go19Ho9DIdDAJCPgHbP/b2B1cWs7EOzN2WPr/v9+7TFgEd/1YD2vn1VgzFfVAaPxaD2rn1dPMwWyzI8LKbTqfg6mUzem3/q73loLD73d5mNL/r0uhIcDwvgJRhQ/eSfr9M/1S/uQ/qkHnIGg+EV/67bz9e9wyo4eN0eIFBgyZAH73W9S6o//N58V/j3fFe4vurn0sfhcCglzuvwk76YTCYBLGazeQ5k8RkbjUYYDAZMp1P0+32Mx2MMBgP5RZ+19pNrZzKZYDKZYLPZYLPZ4HK5YLFYYDKZ5GdQ995gMEC1WhU/e70eptOpMFrXEe8XfXU6nXC5XHA4HLDZbDCbzbIHer0e+v0+BoMBut0ums2mMG2j0QgArnV/cs1sNhu8Xi+i0ShcLtfc/tTpdGi1WqjVamg2m+j3++In3yG+V9fBuqqMqs1mQygUwt7eHjweD3Q6nfgRCoVQrVZRrVbFz16vN/eOvwvNKNfUarUiFAohnU4jHA5Dp9Oh3+9jOp0iEonMJSlGo3Fu3/LnXtZPzcDqz2WtP3fILh4G1w1Y3lRyUX+vBmWTySSbiCUElZ6/bj/fxBDwF320Wq0wm83Q6XQYjUbo9/sYjUYYj8cSzK7D1INpEayoBxl9tNlssFgsmM1m6PV66Ha7onmhv1qbCpRVsKwe/AwgVqsVdrtdDo3BYIBWq4VOpyOH2HA4vJb1XARNXD/1QFADncPhEN8nkwm63S5arZYcGsPhUFNfF8Ge2WyWg9VoNMLhcMBkMgEAzGYzLBbL3L4EXhxWjUZDMnF1TbU6HNQ9p9fr4XA4YLFYYDQaYbfbEQ6H5wCLxWKBxWIBAAm4vV4PpVIJ5XJZ/BwOhxgMBpr6yT1H/zweD+x2O+x2O9xuN0KhEIxGoxxODPoqUGm328jlciiXy3NAS4s11el0sFgscDgccLvd8Hg8sFqt8Pl8cDqd4r/X64Xb7ZaYTgBYLpcxnU7R6/VQr9eRyWRQqVRkPVU/lzHVTwIqu92OVCqFcDgMq9Uqfq+trcFoNGI2m2E4HKLRaODo6AiDwQCNRgOlUgmnp6fiJ1ktVUe4jPFdsdvtAlI3NzeRTCYFCJLFMhgM6PV6aDQaKJfLePr0KQaDAWq1GvL5PIrFIprNJgaDwRzg0uoMJavGeEO/gsEgHA4HrFYr3G43EokEJpMJGo0GGo0G2u02nj17hmaziXK5jHq9jn6/L0mB1qCVSTHfm3A4jFQqhWAwKDHS4XAgFouh1+uhXC6jXC5jOBzi6dOn4nen05EzE8C1YBKuqdPpRDAYhMfjQSQSkWRPp9PBZrOhVquhVCqhUqlgNBrh/PwcnU4H3W5XgOJibNc6AWCcdzqd8Pv9kggAkHMnHo/DaDTKr8lkgna7PZeoLCb6V/HzymD1dQBlUSvCXzw01B/IYDBgOByi0+nMASsCQa1MBXmqbzz8VaBiNpthNBolADMjH4/HqFQq4isp7uvIDvlRXT81s+UBZ7PZJAAz6BmNRgyHQ1SrVRSLRXQ6nblsTEtmQN3IBCs8+AlOrFYrnE4n3G63ZOJG44stNxwOUSwWcX5+Ppe08DDW0k+9Xi/lID5ngmaj0Qin0wmPxwO32z0HpgeDAdrttvgJYC5R0dpPriOfLf30+XxwOBwwm81zh7HZbAYADAYDdDod1Go1ZLNZFItFAHglEdTCT5W1IkCx2WxwOp0Ih8NwOp0wGAxzoJ++DIdDtFotFAoFXFxcoFAoyPNWmaFljP6p4CoajcLn88kBkUgkBFQzgTKZTBKHut0u6vU6jo6OYDQa5d0nQ6iFn9yTBKSRSAQ+nw/hcFiet8vlQjQahcViEcZSr9djPB6jVCrJ/iyXy7BarQCARqMhWrJlwRWTYZ/Ph/X1dSSTSfEtGAzK7x0OB/x+Pzwej6zNeDxGr9fDycmJgMB8Pi9r2Gq1JN5psaZ6vR4ejwfJZFLW0OPxIJ1OY3NzE06nEzabDX6/H6FQSIABfVlbWxMfz8/PBfTTR+7RZeMT4yJLv36/H4FAADs7O9jb20MgEIDD4YDP50MkEpH16ff7aDQaiEQiqFaruLi4gNPpFBIFgIB+LWOTXq+HzWaD2+1GIBBAMplEOp1GKpWC2+2G2+1GMBhEIBCQBKXX66HVasHn86FUKiGbzeLk5ATFYhGz2WyOQdQykWZ89Pv9SCaTiMfjSCQSwgAHg0FEIhH0+33U63U0Gg2MRiP4/X7xMZfLod1uz1UDtGRZVWbV7XYjEokgFAohkUhIAsXYWSqV4PP50Ol0AACRSAQXFxfIZrOo1+uvJPnXQfRRUkGw6vP5EAqFMJvN5s5SMqo+nw/T6RS1Wg3tdluIHuK7ZeQrVwKri0zVIuCz2WzweDwwm80CpMi0qCXgbreLi4sLlMtldLtdYdm03BgqYCHgU0svXq8XJpMJLpcLTqdT2CCLxSJ+9no9ZDIZZDIZ1Go1AJBAp5Ut+kqwwqzW6/XC5XJJVs5fLpdLxNnT6RTdbhfZbFYOf5VZ1XJNCfQJSNSA5nA4BLAQMPh8PpjNZkwmE/T7fbRaLRgMBjQaDWEAR6ORZmBVLVvy+TLr9nq9CAQCsNvtMBgMcDqd8Hq98Pv9cLvdMBqN6HQ6qNfrKJVKACB+DgYD2ftarieTJIvFAr/fD7/fD6/XC4/Hg0AgAJfLJT+Hz+cT8DUajVCr1VAoFGA2mwUkjEYjYa4YJLRYT5W9SqfTiEajcDqdcDqdiEajCAQC0Ol0kqQQaI3HY9TrdeRyOej1egED/Dfu0WXWlQeB1WpFIBDA+vo64vE4otGolAQ9Hg/i8ThMJpMcDDabTcpao9EIrVYLFxcXEpNUSQWZai38dDqdiEQi2N3dxdraGqLRKNbW1hCPxyVhIXs+mUwErJLBYgnz+PgYtVoNjUZDYud4PJbPW8ZPk8mEeDwuPsZiMfh8PvnIhI8HrCqX6Pf78Hg8wrBaLBZUKhXUarU5icXPVeUus55+vx/b29uIRqOSAHz00UeIRqNSBSBrzTWZTqcSV9vtNvx+P3Q6HQqFAqrVqvg5Ho+X9hN4Af7sdjsSiQQSiQSCwSASiQQ+/vhjbGxswG63y7lJP7mePKuq1aqUjWu1Glqtlqy7lmQP4xIT0UQigVQqhY8++gjb29tSsSBLrCbGw+EQVqsVJycnkkgRsOh0Onn3tfTVYrEIqxqNRrGxsYF0Oi3nkcvlgs1mQ7/fh8vlQiKRAAA4HA48e/ZMyKB8Po9msynnEZMBrYyEhM/ng9/vRzweRyqVmgOrPBe8Xi9msxn0er0kXBaLBaenp2g2mzAYDHNEn9bGeOrxeODxeBCLxZBMJjEajYSYogTEbDYjEAgAAILBIGq1GorFouA7Nd5fBeNdGqyqwI9AgEyk3++Xl3BtbQ12u10WmD8UQclwOESz2cTp6SmOj49xdnaGUqkkh+uypmqq6CsBaigUQjKZRCKRkECmduV5PJ45UXO73RY90enpqRxaWgMrFVAzWMXjccTjccRisbmNS2o+lUrJZiWDMZlM0Gw20e120ev1RJunlZ9qWZ/gLx6PIxKJIBqNSuBituvxeLCzsyPyhFarhXK5jFarBY/Hg0ajMZf8aOUnGUC73S5ZNTPZYDAIl8slTQxkChOJBGazGZrNpoDBarUKu90uwUxNLLQAgcxe2WkZj8eRTCbnmBeXy4XBYDBX6nK73RiPx7DZbOIzy3XtdntOd7tM1q2up8pcbG9vY2trC4FAQPaBxWJBu92eq07Y7XaMRiNYrVZMJhOUy2VhsweDAfr9/is6x6sY3wuXy4X19XXs7e0hnU4jkUhga2sLTqdzrlTV7XbnKioOhwPj8RhmsxmDwUASRLUUbDQaX2l6uYoZjUb4/X5sbGxgc3MTH330EVL/v70ra27zyK4HBAGCJPaFWAiCIClSXDTUmnJNTSZ5SKryV/Ocl7zEk3ESeUaWZW2URHEDAWLfd4DElgf5XDZoyRaBj5ZnCl3FkixLYLO//rrPPffcc0MhCVSYgbgKQBgk2u12OYMcDocEhTw/1aBq1H3K9zcYDMLn8w2xlHa7XUAIwRTlEtzTAGCz2YR5YfaCc+90OkPs5bjr6Xa74fP5cPv2bdy6dQsej0fOIwDC5tXrdbTbbTnHOE/+vUqlApfLBYfDIWt/fn4+9hyBS6YyEAhgbW0Nq6urWF9fx9raGqxWKwAMBXcMOphFcblcso/b7Tay2awQKIPBQFNgRbDqdrsRDoextLSEjY0NPHjwAE6nU/S+PCO73a688waDAV6vVwggABKQVqtVyaZpEUgDl0GAz+cTUL27uwuXy4WpqSl5fylLISk1PT2NxcVFuW95X8TjcdRqNTSbTXkHtRg6nQ5zc3NwuVzw+/3w+XzY3NyEy+WSbF6z2ZTAk1lJAnxiKrPZjGg0imKxiFarJe+e1hk/BgC8M7e3t+FwOOR8qdVqcrYzc6rT6VCtVuUdmp+fRyaTQaPREMJnFMeAkcGqyqQ6HA5sb29je3sbu7u7Es2oWk9VvKyCkvfv38NkMkGv16PRaMhG1ooJIus7MzMDi8WCnZ0dbG9v43e/+x3C4TD0ej3Oz89RrVbRaDQwNTUFh8MhWlBe9ATaTBFqOdS5EgRaLBZZ0+3tbQQCAdTrdRSLxSFJAsEgL1HqmYrFIkqlEsrlsuaFATw85+bmRCDOA9flcqFUKiGfzwuLwk1JgMK0UTqdhtPpRKlUQqPR0JSpVD3hVPBCtm1ubg7lchnlchnValWYKrfbjbm5OVnTfr+PRCIBs9ksh9uoaYxPzVV97i6XC7du3cLW1hYWFxdhtVolraLqPSuVCpaWlkReYbVaJZ3FP6vX65qkhtTUFatCA4EAdnZ28Lvf/Q4WiwU6nQ6NRkPS09SD2mw2XFxcYHZ2VgJGh8MhWj0GKlqtJbMNZC2Wl5exsrICj8cj73o+n0cqlcJgMJAAVpUv8MJnapuBNS9YrdZzYWFhiEGnpKLf70sKLZlMSvrcZrPBbrdjfn5eGBhW4FosFlitVrRaraEiMa3S63a7XTSqZrMZU1NTUkRTKBTkctfr9bIXbTabAEIyK8y6VSoVAJcFN1qsJ8FpOBwW1nJ2dnZI4xmJRFCpVDA9PQ2Px4OFhQW4XC7Z2wBgMBhgs9mwsLCAWq0GAJrIVFS2zOv1Yn19HTs7O6JVJUBNp9M4OTlBoVAQFtbr9cJms0nQyPfI7XZjeXkZnU5HzlAtz3sCwFAohO3tbWxubsJut6PX64mmMhqNolQqiSZ0YWEBc3NzEvhxnUulEnQ6HY6Pj4eqxbWYp8FggN1uh9/vRzgcxubmpgRvtVoN2WwWqVQKtVpNskDUg7daLVgsFgQCAZHNORwOHB4eIp1Oo9VqaTZXnoFk/kOhkATJjUYD2WwWmUwG1WpVGG1mJIlNyExyvx4cHCCVSmnOAHNdKQEIBAIwGo0YDAaSLSkWi6jVahgMBpLt7XQ6QlhxrsxcHx8fI51OSyB2nXEtsPoxIGC1WrG7u4s//OEPuHv3rmisCACbzaaIgjudDtxutxzKRqMRi4uLiMVicplpdbmqc6XmYmtrC7///e9x//59BAIB6PV6ZLNZHB8fI5fLoV6vS0QYCAREUDwYDOTgYySu5YHAX9W5rq+v4/79+7h37x4CgQC63S5OTk5wcHCAXC6Hdrst6RdqyQiuzWazsMlaCtj5K4GV3W7HysoK7ty5g62tLXg8HpEhHB8fS8EHU+yhUAgWi0WE2QaD4ScFa1oy1SqgJhMYDocxPz+PYrGIWCyGeDwu2iUWjQSDQTkgLi4uhiLzUVMYnxoM/AiqyQyEw2E4nU4AQCKRwOHhoUTRAEQbFAgERFrRbrcBYKjIRqtBQD03NyeXpN/vx/z8vKSBTk9PcXh4iFarhcFggJmZGbhcLiwtLcHj8YgelGxau92Wc0FLPRhTqbwYyJBT37m/v49oNCrFV9SKkvWj5o7PnmeYFoVAHCpbw7OSwXC1WkWpVMLp6SlOTk6E2Xe5XFhYWIDb7RZApWrCBoOB/DeZi3EHpUVck3q9jrm5OQGAkUgE0WgUyWQSFxcXMk9mWXiB8Q6gjIIFi1ruUV6OatFet9tFPp9HJBKR595qtUSC0Ww2JahiSpVaf+5XrqUWDDDvTjLPPGOq1SqKxSKi0Sjevn2LSCQimadMJoPV1VWpGmdwp8qnWq2WrKUWzD+AobuexBML0tRnH4/HUa/XRcscDAaxs7MDp9M5VMNA4ooMoNaZNFX/fnFxIfKoeDyORCKBbDaLZrMp2SG/349bt27B7/djZmYGjUZjSFJH1leV32ixngBkLXhflstlAaq5XA7VahUWiwV2ux0LCwuiv52ZmUGv1xOSpdPp/MSRQysW+GpGlmdhPp9HLBbDyckJYrGYEFLMtLpcLpE1MCOgFriyePm6hd8jMatqEUMoFML9+/fx8OFDqbpstVo4OzvD6ekpSqWSeJsZDAZ0u125mJmmVa1ttBxq6nJxcRF3797Fo0ePEAgEYDKZUCgUsLe3J0Dg4uICMzMzorfhwQDgo3PUukKQEXMgEMDm5ibu3buHUCiEmZkZJJNJnJycIBKJCFh1uVyiGeRhqx4AWnvcXdWq+nw+LC8vY3NzE36/H9PT08hkMjg7OxNZBw/UXC4nOlG+UEyvalVlqw5VKsH0fygUEilFpVJBIpFAMplEsViUtapUKqJbIwhkqlpLsKIOspbUVDJ9MjMzg3a7LawQq5O5D+v1OprNJgCItIZz1rK6Xp0n9yiB3fT0tKRWM5kMMpmMzIkHP4MAXlScH9OxWgMWXlZcD6bXSqWSPPNMJiNFVSwW4J6hLRBdFarVKur1+pDP4TiDrCftc0qlEqxWK/x+P/r9Pmq1mrxD6XRaKpRNJpMAMUoSGo2GAEECSi2KqzjPTqeDZDIpwINMNACkUilZT6ZXybDwIqL0h18E4/V6XUCLFqPX60nldDabhd1uF/lBKpVCJBJBIpFAPp+XDN/VvcczicFJuVz+STA1btaP8pN6vS7rwJRvIpHA+/fvBfwztUswzWdOIMX1LJfLAla1midwuU/5LjWbTeRyOeRyOSQSCSn2SSaTaDQamJ+fx8XFBaanp7GysiKEEHBZYEnnAq67VnIqnokE8alUSrISBID0Lp2dnUWtVkOv1xOGm2cwC6sqlYqwrFrLvng/X1xcIJPJCFgtl8uyf1utlhQEUmNLSQPJPp1O95MghUBbq/ueenS9Xo9ms4nj42OUSiVZ30gkIpX/RqNRCL6FhQUJVAAI0ULJFeWO15nryDIAo9EIu92OUCiEf/iHfxA2qt1uI5lM4q9//StOT0/lkJ+amoLb7Ybb7cZgMJCITdXTae2/RgBosVgQCoXw4MEDhEIhEVnHYjG8fPkSR0dHki6nxioYDALAEJN6U1V33MAE1oFAQGQKZrMZzWYT0WgUh4eHODw8RLVaBXBpA0TROvV4ahU4/964Qz0ECVZdLpccSmazWQqdCJtAAAAgAElEQVTRYrEYotGoVFXOzc0JKFD9GAlebsLsmJEmi1Q8Ho8U/THdkkqlhBUwGAxwOBxDDAova4IXtZpR6+dPdpVAcGZmBjqdThgC1WqFrLZ6QKuFSmSF+Ny1AljA5bry0OEa0fqFh7zK/gCXOryLiwsBLipgGXeO6sXaarXQaDQEzBMwERiqwFMNlpjm5/MmaKHPoQoGxl1PNeVLSUQ+n4fdbkepVBIgS10qM1nUBPL9YTDVaDTEMuhqhfA4a9rpdJBKpeB2u+X7U+LFFCDfbe5fFteZzWYAGHrP6QhBqyUtLtbB4ENBGYMlm80mus5utytpRzWIolyEWlUSKdyfdKpRAx4tBiUeLN5MJpMAgGaziVgsJsCf5zrJE1W7yvec7DGfOVkqLdhK4PL5810ioUO5Ty6XE8s0zsdkMsHtdkOn+2C/pBZ5djodYQO1DKT57l8FxOVyGcViEfl8Xt6nwWAg8iir1YrBYACLxSIYhXupXC4PnaFagVV+FoOWeDyOQqEg7xLPHACSMaHsh7IGZouoDeUZSyKJ+0CLAIAYjUWdfO6FQgHpdBrFYlHOcJKPOp0Ofr8fBoNB8Ajfe9aHGI1GyVZ+7l4YmVmdmZmB2+0Wfzim2gqFAv77v/8b//M//4N0Oi1VY3a7XcS2c3Nzov+jVoNgQCuWRZUBWK1WhEIhbGxsiD4kn8/jr3/9K77//nsUCgU5GFgwRv+z2dlZudAoDiZ7oyVg4ZwZlWxvb0sBTbFYxIsXL7C3t4dyuYx+vy9p6tnZWSm+Uisy1TlqKQPg5zHFysKVwWCAUqmEvb097O/vi8aSNlCs0qQGk8UZ6tByPXl48cVllT+j5mg0itPTUxQKBfT7fXEGoL6KEbaaEtLaFoaDz0otjGEA0mg0xByaa8pAj8VirVZLAhheGlo2MOAzJ1OmAg61ywq/7/n5uaTjTSaTPHeyGbzwmHFRvSG1AFdknBiUBAIBAJALn5pwVuSbzWYpICCDQFsoVlsTbGnx3vNybbVaKBaLolnNZDKijef6OZ1OGAwG0bj5/X5YLJYh/0I+B0oX1AYB486z1+uhVqshkUjIJUlASs0vC9GcTid8Ph8CgQA8Ho/IBZrNpgABFUhpoVdV51mv1xGPx0VSRs0k2T4CaFoFBYNBeDweeDweABBzeJ6vag90LRl1pn7n5+cBAIVCQdaZDFuz2ZTggDpfAutyuSzPn2BCLVDUEgR2u13J7HAdyQrTMaHVag0xZXq9Xu4jVZLxKc23FiCQ60qN6WAwQD6fF/aacg7aW/F8pbyP66lmYlRWlcVk48yVe4jWc9QYl0olsfwigKPUjncQXXYI+GhryfoA3gm9Xk9kdlrtA95BJCf47OmaAEBkh6y7sNls8jMz20fHGmZneE59bmHYtcAqX7apqSmpnN7a2oLZbEa/30c+n8f333+PJ0+e4Pj4GN1uVyqX/X4/lpaWsLm5KVomthUjE1etVjU7vKg1sdlsCIfDuHfvHhwOB3Q6HXK5HJ49e4YffvgB2WwWvV5PtI0syFhfX8fCwgIMBgNKpRIKhYJEFbSz0WKoc52bm8PS0hK2t7exsLCAqakpZLNZvHr1Cm/evBFtJcH/8vIyNjY2sLq6CpvNhn6/j0KhgEqlIsVNNA/Wcp6MnkOhEHw+H6anp5HL5fDmzRu8f/9e5sk0PPdJMBiEwWAYAgFkrbTSLaqXCm1XrFYrHA4HgA8X0snJCVKplPRfZprN5XJhdXUVHo9H0u/FYlHAi9b+qnyfgMsolk0oaEGVTqclTQhACpycTieWlpZgsVhQq9VkX6rtBLXMVDCI5OVN7ZqqPaJ0BsDQmnq93iH7pUqlIgyLygJoxbCpjDJZVmonvV6vdAbq9Xqynh6PB06nU5gqNU2v6pS1ev60dlJZUF5M1PX7fD50u104HA6xXiOAou6T/rtkrjg3rc4ngmpqTBmU2Gw2LC8vi8n69PQ0AoGApCiZ/iVjRbKiXC6LswovZy0IisFgIE0xyIaXy2U4HA5xM6DrBDX+rJtg9qzZbGJ2dhZutxuVSgXlchmlUmlIr67FPBkI8dKnDp0XO1l0WoStra1hbW1NJFTAZWMBBjpkAgnCtAKu3W5XdIoEnmw8QnN63p90ONjY2JBzgKCJrJvqs66lswrXlQ0ICE5JMPHZTU9Pw+fzia+tz+eTLAXXjrUeBITq2TXuOaXT6cQej4w4iTDVvm9mZkYcONbX17G6ujqko1WdLLimasezcYE1cNn8hWdTu92GTqeTe5ukBN8Z1ltsbGwAGPZopc85Le4osQAuJSKaglUOsiULCwtiXKwWedjtdty+fVtSLfS13NnZEY3lYPChouzw8BDxeFxSDFqOmZkZYSQCgYBEdmSHrFYrVlZWpBDE7XZLCp4VjUyrqt1CWHWnZVqAeo+FhQUEg0F5MThXzhGA2Butra1ha2tLunRQv5ZOp6XzjpZ6QAAiVXC5XFJNqYrnGbkSqAaDQayurootD1NBaqcTHmhaplqYqiZzRisgAjmmI7rdLubn50XETpsoAhOmq9VUMQ8zQBsWQ3VYoI6SmkkWolHzySpgj8cDs9mMubk5NJtNiVAZnPAzCVrGibTVn5Vf/X4f5XJZDkxWObNoCYCAQFoC9ft92aNkWYHLohNe1uOsqVrEwWdWr9eFgeJ5xKwF9wIBFJlfsjFMs11NB6rrMurgJcWKfrKqqico96bqTELGnMC2VCqJm4FKKGhVaKFqjbkfeSmy8cfc3JwUqqhnF7MW7BpG6RLTh1p5RPL7UbvLIj4yu2SpmTWbm5v7SaHTYDCQc7hcLosbALMeaieecZ49Nd6lUkk8vck+ttttMVcPhULiXsNKbIIG/n2CRgCSFSSg0WJNea+TgKBrD98vgpX19XXcvXsXOzs7WF9fh9lslkp8utKQQCGTxnf+qln8KPOkfIeECteZAJRM+a1bt3D37l2sr69jY2NDMn10DMjlckin0xKkkKlkkEjJy6hzJTBTC8yuusLMzs5iY2NDCoLX1tbEKoyd9di8gtlrAFJUSm/rcYss1Xeq0WjA4XBIkEG3B71ej+XlZfGJZnBltVolaIzH4zg9PUUikZC7iu4AzWYTZ2dnn7UHRpIBqKb63CTUsno8HmxtbYmulYvndDqxsrIitietVguZTEY6W/CH0Gpwg5rNZqlYVqMrj8eDzc1N3LlzR1JbFotFTJqpy6K0gbon9eLSimHjXJmm4mYDPhxANLtm5x2CxXA4LO4L3FjZbFYiXy0rLoFhDTC7KKmdq1jxz7WjHcidO3fgdrvF9oKghR52fFEJFLSICHnZX9Wl8Vm7XC5hdugburOzA6vVCpPJhGazKcENU1uqIFyrFPtVPaLKnPHz+bwtFous6a1bt2T9CWT4bwhUOca9YNVUmLqXCOTJBPNdY3BAiyum2YBLMMkDlhcWfx333eJnkFWhVpXvGCUA7LjDA50SILWimJcd56IGKlrJK1i4QmBMsEHfXe5jAj2V4SZrwQJCAsWrjJUW60ldLLWx/HO2qjWZTAI8gMvnTBaIrCCzP7TnIbusBQgksCqXy2KJR2DEwhqLxSIMEYEqL0mCHbLtXq9XLALVFsbjsmucZ7ValcBeXS82hKADgNfrxfz8vEgdaF9Yr9flnOc7z4CLQF2LNSXgByDnk6qxd7lc2N3dxYMHD7C2tibkSaVSQSaTQTabFZaaWTTgsgiW1mbjrCvvDuCSqCCTS0Dt8/lw79497O7uYnV1VYoG6/U60um0aDCLxaKcUWrGi+/9OGvKcwW4dFjhc+N74vf7cefOHayurmJ5eRkLCwsiXaBdGGsYCJ55t/H8YCZw3DUlWcY500lBDfqWl5fFX9/v98Nms2FmZgbFYlEKrjOZjFh+Mgg3mUxDgdAvWYSNxKzyQKQQmV2UDAYDFhcXxTOQelQCAo/HIy9Tq9XC4eEhIpGIpDC01FkQBLDCj4cNgXY4HJYUChmofr8vFXjUN9KShxoTrUyMPzZXvlxkHAhMw+HwEPtG9tLhcEhFNpmPdDotHU1Uuymt5snLk5pjzp1epqx0pbUOO14QWLEQ4KrWRk0LjZtmITBh9zT1q9vtwu/3i48ddaorKysSaavNLvh7Hga8VLUoEmCAx8+mPo2sD9MmHo9HUqv0VVxeXpYCFh746iHNtSXA0aoxAFkLAnmaqwMQFtvv90sRi+oAwbmpmivuIeAyjT8uEOT+YvqaMgpa7fGS4j5h6pUFogRgLCLjfLl+KmOtBXNRq9Wk9zu1d8zqsA83fy6uD98ZMoZqS1kyoFzHcdaT7yMLVmgHRABDE3XWBqjFk2pREOfq9XoRDAZFhsOgQmWBxgHWvPxKpZKA01arJabv5+fn8q6p5yJZK6ZjjUYjFhYWZC2ZDldB9TjzZDqdZykzFDzb3W63uKxYrVYYDIYhVi2bzUoamY0GDAaDBPt8DuOwa2oKmM+T79BgMJAi20AggEePHmF7exsul0uCfbJ/hUJBJAP8XLV4R2VXR8UA6nNRC2u572ih9/vf/x63bt2SBiYko+gKo1rr8exXA1nV5WKcear3psvlEkBtt9sRDAbx4MEDLC4uwuFwyPlJezP6kjOA4M9IUoZk0LiYSs3ScB3cbjfm5+clEDKbzVhdXcXi4qIQKiwArdfrKBQKwqjT2o6tkFmUzWJsAu9PjZGY1V6vh2KxiEQiIRvX6/WKQ4DL5YLBYBCj9Xa7LR5cer0eFxcXKJfLePXqFU5OToaqc7UafNHYLenw8FC0FQQEtIWiuJ0egmRiO52O9AhPJBI/sQfRcq4E/41GA/F4HH6/H06nU9iqcDgswJsRvslkkkIlrmmhUJADVuv15AvGg5DGvhTUz8/PY2lpSYAJvz+jKAIcRsBs16cCVa3mqqZuWGhB8Eo9INMRXEfqAtXKRoIbtZUlI26thgqIDQaDHPjU27Lgi+1gybCyoI77mUbmrVZL9gkvmnEkNleLYoDLjjHcD3z3qbdUU9kE4e12G36/Hx6PB+VyWSpiVU3oOJ2COE8CPharEKTw8iJTQOaSAJQMdavVwuLioljv8fIiI0TwrwW7qhYG6fV61Go1eb4XFxdwOBzSEYzPkWcb30mTyQSv14ulpSXpZsSKZ61S173eB2/H09NTdDodKfjg+6R6PZLRVgkDXqR6vR6rq6uiD52bm8Pr16/l/Rp1Tfnse72epB57vZ68M8yyGI1GpFIpORcoU9DpdLInAUir2Nu3b4u3LV1ZuJajzFV9l9QUKy1/SELs7OxgZWVFGtSQ4Dk9PUUsFhNWlXpRZoQACJNFAmacu5V3EwNLnp0Eg263G7u7u7hz544Y2PP70gCeJNHV1utcdxVQszjqunPkXuO5ye6ULOj2eDzY3d3F+vo6XC6XBJyVSkUq8klW0Xifa8tmGMBlIEUt+yjzJMZwOBxDbjoEchsbG0LwECRT8kGnFe5nZgKouaerBc/VcewWdTqdMKk8X3Z2diTD2O/3xVeVDjbM6NFOjSw/AMlce71ekTY1m034fD4Ui0WpFfjUuDZY5WFwcnIiIHBxcXFIXM8FjsfjOD8/F4sjshj5fB7Pnj3D999/j2w2K/oXLQEWCxCi0ai0pTs9PZXNykuffqvpdBqDwQB3794VTVY+n8fz58+xt7cnYJWsp1YFDMDl5ZpMJiXCb7fbuHPnjhzy1AHyEu50OuJjx7m+fPlSgDVtgbRcV4KJYrGIbDaLo6MjhH9sF8mLnywBCzMIZslUl8tl6QxSKBSk0lrVhGkxVzKKTIkwquchy8ia7CNTKaqFDRtFVCoVEezzM7UIWvgSsyqdkgUCEDJSg8FAADXnyJS2qiEkS6GmqpjOGncPMLq22+1S/c3CGjJY1KhzL6hzZVS+tLSEUCgkxWu8nMhWjAuq1LQdtWiRSAQul0syJplMRiraeRETeDNdSGBK9j0Wi8kFNa7GDhg2Midop3G52kmHhatqFyjqKhnU6PV6BINB/PM//7MEWIeHh5o4QnCeAESDSC9dBv7Mpuzv7wOAgFUycTMzMwgEAtL61uPx4NGjR9Kytdls4s2bN2M9f4IVBsQsYiIoYpqRLBrPJ4Lq+fl5tFotKQAhsGLRrd/vl2p91e5sFGBFAE/Gju9NOBwWppK1CACELU4mkzg7O5NCZKZiuY7UhvNXlREdZ03pRsCsTjAYxPLysmRLl5eXpX5BXftyuSxyMMrbPB4Put2uSJ6YvYxEInJWX/duVVlK1h5sbGxgeXlZGtHQEtLpdArZwrucz52dFYPBILLZLIDLrBWDQ6vVir29PQGD1x109mDa/P79+7h169ZQFzU6PzAg5hlOGyuPxyN1Q8ViUTyvaXEFQFhjptivO5hFVdtq/+M//iO8Xq8EGbwzeY4Cl5rcTqcj9xhbmzcaDWkd73A4cHFxgWKxCK/XKwHDz8312mCVqT+KxHO5HM7OzmTzqenI8/NzSU95PB5MTU2h3W4jlUphb28PZ2dncihrBVI4mBKiTiaTySCRSGBvb08YAWrrGo0GjEYjlpeX8dVXXwH4cOCm02m8e/duiP0d9ZD6pbnStJzRXr1ex9HRkUgquHnJQHg8Hvj9fmHQksmkzJXVkFcrhMedI8FPtVpFIpFAr9fDN998g2g0Kj6mbAHH1N78/Dw2NzeF8o/FYnj+/LkIrunLqa6tVnNlqjKfz8sBwyiZAAWAvFhut1t0NKlUCq9fv0YsFkMqlZKCEFaXarUHVI3hYPChej0ej4spNA9LHgZMu9MzlsxcOp0eSrswpclftQDWZC4IBBkA8P1lByB+8TAj+6vX6+FwOLC1tYW3b98Oea5ynuOwAGqRAqN8zqNarQ79udlsludIhw22iwSAhYUFKShgYFWpVGRfj7NP1TQovwBIN6VisSgyiU6ng1wuJ+cUGVO9Xi/9xcPhMMLhMHZ3dwW0kFQYR2N3dZ4829k2ldXA1CxTc9ZqtVAqlSQQ0el0WFpaQi6Xw87ODra2tuB0OrG9vQ29Xi/n8qh3gKqp5oVPmQfBFruacZ0ZONN702g0ilyAFosEP2Rmt7e38f3336NYLF57jh+bp9lsht1ul4DO4XBIG1CbzSZp/WazKV6cJARU3Shdb6gldDqdAsxV7foo82UhKjNMJKN4p7vdbjgcDtEsq7r5jY0NkVTo9XoB3GQsGahEo1GRwYyaXaMUgtI49fnZbDYhKNS6CeADqNvd3ZXiMbLYuVxO7gXesTxjrVYr0un0yGvKPabWIfDuoQyEwJ/v+uzsLJaWluByuYTcoQUnSQGeF3Qu4t4adU35XlC/zfeKNpBqbQTfXQb/ZN/D4bDIFVqt1pCkguTf587x2juZgIWCZr1eL0UWahEGX6pAICDR32DwwVPs7du3eP/+vaRdtAZ/nCcvafo61ut1nJ2dyeE7Ozsr2i/ag1gsFgAfaP6joyNpxXoT1kVX50pATA0bI0KVWe33+0O2K4PBB5NjdrdKJpPyOaTqtZovASAje6aikskkzGazrCeBldlsxtraGm7fvi2SjOPjY0QiEWkjSfZVS/ZXZYDVtGQsFhM2nUV2lCDwQCOoZgVjNBpFPp8XDRAPCa1ANSP8fD4vnr6ZTEaeMZlgtq/j5UELMQaNNL6m7ooFi1oacBMAMdVMHfD8/LwY2VP3SRAIQFpvAhDbNeCyLSwj8XHmyX9H8M8UK9eJKXYekmQwqGk9Pz+H0+nEV199NVSQt7GxgSdPnkjRo1ZnAM9KlQ1kip8+scyy8LmrXZV0Oh28Xi86nQ6sVis2NjaEtcpms4jFYsIgjTtPMoGqtpyXKautKWGiPIi6OtWajDr2lZUVsbIKhUKwWq1Dz3CUQeZfZUaZEqZtGS9FptT57AmoGo3GUCdABmPUDIZCITgcDiQSiZEBgAqgOTdK5Ag47Xa7yBTUanpqhNWCT/6MNptNwJjBYMDp6al0Gxpn8Lwh08yiPoJBtvbmuqqWcMz8cT1pX8lsm06nkz1zdnYmut1x1pWSI77ffPYkpii3YxA1OzsLr9cr+5rEgdvtlvqKTqcj8yRY578fZ13VWghmytT3DIBIlHhHWa3WoTPI6/UO+ZXy7FczbKMMvosqK0+tLd9/nl/AsNuMWjdit9tFNkcSgwEY7dvoLqHW2XxsjBR2qYvAw0odKsPhdruxtbUlzgHxeBzv3r3D/v7+0GGm9VAfFhePlxfnyAuDVlvUjgBANpvF4eEh3r17Jwea1iBVnSsBEC/Zer0+pAPj7+fn5/Hw4UM5lAeDAYrFIk5PT3FwcCAtObV2LACG/dAIWguFwhCrzo1ssVik7zJT7aVSCdFoFAcHB8hkMkO2VVoy6yqr3mw2kc1mEY1GhTEheGGhDd0WwuGwgP94PC6SCnrKjVtY8bF5DgYD8RtmMEWDdQYk8/PzSKfTAlwXFhZgNpvhcDiE6VYtV5gu1ipYUefJwI2BFLVyamcj6tgJQgFIC1u+l6qcQktADUBSe9ynfKfJBnOu9AvM5XJSQR4Oh+XdohaUBW4M0rUKVHhhc03UQjOylMwADAYD6R9eqVRE/mM2m6WgkYUki4uLQwBwXLmCCqrVIkteVOo+Uy9MVbNWrVbli8VuPHuZ4Rh3kGlUNejcnzyfCPRUD05aQZHlIUHANCaDcMrHCCTGmafKAFN/yKwFgSrrECqVCmq1mhSskTVmCpbnAQFZr9eD2+0eSs+OM1dVU0/tP0GxugdU0oHsq2pJp9PppAsTcNmkg+fwOOvKf6vuVwYoBFZ817hXdTrdUDaY/4+fQSKF+4RrO64G+KqjEN/7q80d+PevOnwwaCYgZPEa9zTPNdUecNRBnSwbN1EKxwCFX1cHzzfVo5YEF63w2K6ZblC/dK6OnCPgw/rYQ+MP4Ha7cf/+fdy7d0/M4L/99lv88MMPyGQymnuAfmyO6vw+Bqr5Mq6trQkIPD8/FyP+ZDIples3PU++1ACGCk3UzTwYDCSFxgCAbVjj8bgUV93UPHlZqy3eVP0dNymbFrAQA/gg/GePbnqX3sRglElGHfgQfKjAny+60+nEo0ePsLi4KJcmRfenp6diY6IeLlruBTJSTJ1yXmrqVdU5hUIhTE1N4fbt2yIBOD09xdHREU5OTiSw4oGmFVvd6XSESSdjTSZATQ8Vi0XRMvLip5Var9dDoVBAJBIZiv61AqtkqQmayFqwcpZAxmq1wmq1Yn5+XmyUWJl+enoq+jrV55RFWlpolbmHyKTS+YMSGlob9ft9YYa4RqolHdeO6XgylwSqWmQACELYWW1qakqAERkmNVVJD2Vewnzf6GLCYiC2NmaR5rhkAOfBM4U6ahZ1EXCQ2W82mwJkKFXhXUA2kTILAiz+vABGAoHqOU/AwvcIgLT8rtVqEiDxnVMBHoMDzon2USaTSaq3+UzGAav8WdWW2AQd7JrHAIFz45nDZw98ADz1eh0ApNCW+54NbCgtHHW/MhDl51AHTiaPzxbARwNO1riwqxTZVP4+kUjg5ORELJjGcQNQXTCYqVJT9iT7rsoj+d/MEvPzKHVkNjgajeLo6EjqgcZ5r+r1umQgzs7O5H5SST81O8Q58f7hv+edm81mUSwWEY/Hpdsl3QB+6b4aXdDyC8NkMuHf/u3f8E//9E9wOp0AgGQyiadPn+L9+/c3DgA/Nq5+P2oy7ty5gz/84Q8I/2gR1Ww28eTJE7x+/Vouh197fh8DnDSLfvjwIe7evQuj0YharYbvvvvuV5vrVbB2dZ7qBbW+vo6HDx+Kvu3169c4PDwUoHqTz5/rwPmpjRz4q+oPu7W1JWA1Ho8jlUqJIbzWzK861AuWg6yayv5XKhXodDqEw2GxgGMaLZfLIZPJoFarDZmsazlf9YBXgz5V02gymVCtVqVLHXVY7GZDBjkSicglrCWrSvBLpkwtYOOzpp0WixIImqempsR9gxcy/5xpeS33LMEGcGm632w2UalURF5Baz0CZaZimZKmvII+jAQp5XJZrKHGGbx0GJySbWLhDJkzMnzUhJKlZHHN/Pw8FhcXEQ6H8ejRI/h8PtnT8XgcR0dHYxWtqZcjLcuYnUomk8KSTk9PY2FhAXa7XQIFg8GAfD4vaVi6b9y6dQs7OzvSnbFUKuGHH37AwcHBWO41XMtyuSxsXq/3oe96KpUS2UowGMT29rYUdgIf2l4SYDOVzvQ/i5XYmpt1AdQLjjKYocrn8yI3arVaqNVqSKfTEtCtrq5ic3NzSBvP85b1IrlcTsgBsoulUglHR0d4+/Yt9vf3BayPsqYsoiYAZNOFWCwmciqPx4NgMChuKSp4LRaLiEajyOVyACDNFxhYpFIpHB0dIZlMSm3JqOuZzWbx8uVL6bIWi8WwvLwsTDAtH4mReI71+33EYjHE43GUy2XJEFJOV6vVEI1GpRvouJ02qdN9/vw5SqUS4vE43rx5g3A4LFILj8eDcDgs8g+eF61WCycnJyJLZHDL+o9CoYCDgwNks1k5r39p3AhYnZqawvLyMv71X/9VOkd1Oh38x3/8B16/fi1FAl96ULPy1VdfYWVlRdLV7969w/v371EsFn81oPo5Y3p6GltbWwj/aGM1GAxQKBREV3kTetrrDr5c9H4ky9PpdHB2doZsNvtF11Rdn6mpKRHhsy98v99HJBJBOp0eAuK/5rpe/V6ch8VikY5ss7Oz0Ol0KBaL4lpw03P82OdfBV20MgoGgwiHwyIVYHvWXC4nz58H203OkXpK/kqQwY5qAMTzMhAIYHFxUVLT09PTcrmwsYCWgwBLlfuozB0DqYcPH6JeryOTyWBxcRHdbhfhcBherxd+vx+Li4tiY1MsFhGJRKSN9LiDzBIvdlUXyhSqyWSCz+eDz+dDqVSSNpHdblcqrNmZj41hLi4ukMlkpNB23OCKoKLZbMJkMv0k1Up21+VyIRQKiRUVU5tms1nS0dQxshqf9oVqy+txtIDU0FOzx46OBKCqLp6SFafTia28iu0AAAwYSURBVEAgIOwbswUOhwMGg0E0+sfHxzg8PJRge1xSiGlwutAwcGKr5dnZWdHzAxAwzeLGcrmMTCaDQqEAAFK7QmY2mUwiFosNFeSNMphlaLfbKBaLEjgnk8khez1653LuMzMz0q6ari+0piNL3e/3kc1mkUqlhMUcdZ6q1I93zMnJCY6Ojob0nyR4VM0wABQKBXmuJApYT3N+fi4NGEgEaPFOVSoVRKNRlEol9Ho9vHnzRvTFvV4PVqt1qMkLiZRCoSB7g5kj+qmyaJDB2OdgF83BKiPDR48eYWlpSex16vU6nj9/LmnVLz0YWYVCIezu7sJms8nB/N133yGVSmne/nWcQb0qbS54+cbjcWQymbFeIC0H0wS0MyEApO3LL3Wp+DUHmWo1pUnLKlYJc1Di8KWG0WgUDR79IgGIQJ3M1JcKWHh5036LTBqlAJQ75PN5ZLNZ5PP5savqPzb4s38KWOp0H2yK2Meadla0gPH5fMLENRoN7O/vS0pNS7b6Kluvyml4OVmtVrHhIWighszr9YphOC+yer2OFy9e4NWrV+IzqhVjzcCCXyy64jyXl5exuro6VCzX7Xbhcrmk8If664uLC0SjUfzlL3+RO2HcfUCwRw0qWz0yNU6drc/nQzAYHCrm6PV6Ircg6DEYDFJZfXh4iKdPn+Lg4ECTFtaUqxCwzczMiLcn9ZWDwWDILYJAhlkC3lV0NajX6zg+PsbBwQHOzs6Qz+eHuhiNuqYEVwzostksGo2G6HgpUaHNklohrjLcTAVTs8w/Z1p9XG9wrgdtvvr9vuw36lWZPWk0GiJfmJ6eFoZTXS811a3Od9w1BS79dZnSHwwGSCaTQ9KeqakpafjAgjwAUugFXNpLcY6UPrBLmBbvFOVflGz0ej1kMhm5DzlHzpl6bGakOE/iAp4L9IK9TmZNU7DKVIvNZsODBw+kV/jFxQUSiQTevXs3VvSk9VyNRiNu376NUCg01Plhb29PXqrfwuDDtlqtWFtbE2Dd7Xbx7t07FIvFG9OpjjJPplzIqtIKpFqt/mYCFb7oqjE8QXWxWJQUKrU4v/aevZpqZ0EDbWB4OfCgJ/DROv3/S3NUpQq8vGgWTisdo9EodmylUgmpVEr0VL/GO0YAwIOUzQv8fj98Pp94AAIQyQKZqq+//hpv3rwRVkHroQJWriFlAFarFX6/X1oVs4Dx4uICRqNRLjBqdCORCL7++ms8e/YMuVxOs/mq2mJqP1ngQaC1vLws1d8EV4PBQIoE1X+fTqfxzTff4OnTp3j69ClqtZomMhuVtaL+mz65ZEzpE6x2fQMu+6qzcJTOC2/evMHLly/x5s0bnJyciJZYCwCotgLmWhIE1mo1tNttCUqBy4CGzCxdFzqdDtLpNE5PT3F6eop0Oi02a+OuqSqt4Rq3Wi3Zi2rHNJXcIXBS9a58Drxnuc6qe804gy4PnIvRaBQ3BK5DIpEYInYoyyCLyrOVtQJ8XpQFaJEB4NqwpkIt/uK9TjKK81KLLzlHvmsMbij/0sIHmoOgUrWpU38WBm7qs1P/jlrcxqyACliv8y5pClbZ4eL27dv46quvxKS2Wq3im2+++c2k1RmtzM3NDXW06HQ6iMfjODg4+E0xlXx5rFYrPB6PRCiNRgMvX75EpVK58WK1z5mnOldWdzL62tvbQzqd/qJstapXZeqVbBWLZxKJhPiUfik2VQWB3Ks8vNhpjWlMVuczKr8Jz+JPzVFNX5NpIwjgYCU+Lzymv6ivvcm5MnhWTdjn5uakqYHT6RRNKNOTZFGKxSIeP36MP/3pT4jH42Prvz42+LPz8KaFDYtuOC8CbLUvO/cDGa9MJoN///d/F99jrWoC1AuU78TU1BSKxaJ4hbKanVX0qgcjvR+peUwkEviv//ov/OUvfxGplRZzVedJ9onsJDsrESiwsYLKZOv1emHl0uk0jo6O8OLFCxwfHyMajUrfeK3mSsCiNm4gWCWhU61WJWghyNbr9dIalNrMQqEg3Qv552xgocVcCeg+drbodDrk83kcHR0Jg83ghMCRe5afxbOKRUZa1DCoWlmeg2oxmqpBV5lJPgv+N8/aqx6i3DdaFYLynefZqc7xlz6f4FV1CVI/V+sMkOqdq4Jm9Xt8KqOlgm3KK/iZ111LzcCqXq8X5u+Pf/yjtN2kt+azZ8806f887uBDZkpoe3sbAKTjxtdff410On3jF+nnzpVgwGQyweFwIJ1OI5fLodvt4vDwEK9evRJm4kvOk+vKw6pUKuHk5AQulwvtdht//vOfkU6nNY36Rp2nepGWSiUcHBxIj+M//elP2N/fFzD1a7Oq6hx5eDKtwrn2ej2srq4im83i8ePH0q5yXNZnlHmq1jbUf2UyGTx//hzZbBbBYBCDwQBPnz7F/v4+3r59i6OjIwF/NzFXXor8vTpn6uVevnyJqakplMtl8Ytkiuvt27d49uwZ/vM//xPJZPKzNVWjDLJsahW3CqK+++47tNttaaxBDeNg8EGvTlD15z//Ge/evZOqWi3ny/VU7dBYLU1P6GKxKD3CeYmyAIs2gPv7+3jx4gXevn0r2k8t56pmFsjk1mo1KY559eoVvv32WymyIlBwOp1wOp3IZDLyFY1GEYvFUKvVhjrXaRGw8Gfl51EXeDVTYTKZJPhn0OVwOCR9yvVTOytq7a3M+fKLwbL6/7hfgcs7i/sZGJa4XJWUaP38gcvAmGyw+v9UwkL9c/4ZQRlZdvVn1/oMYHB1XaKJa07wrWKqzwW8o4zP2fsf+75cU/5eXdPrDE3AqgqqiKAZ2SUSCXz77bd48eLFEJvypQcZv+PjYywtLcFgMCASieD//u//UCwWNUmhaDm63S6SySRevnyJTqcDm82GJ0+e4Pj4+FcFKZ8aasVio9HA8fExHj9+jLOzM1QqFTx58gSlUumLrevViI+ghS11c7kcLBYL3r59i7OzM2E9vtSaqgcS3yf2qn///j0cDgcymYy0AaUTxK81X5XJ4vesVqvY399HLpcTLaXVakWtVpMq2lqtNsR83fQcCQio+VTZ3WfPngkLODc3h3a7LVZW+XxedH83lQ1SU5GDwUDSbZRLkOH77rvvpACQFlHUKubz+aGuZTexZ/msmYJmarBUKuHs7AxGoxGPHz8Wr1C+a2azWbS/1FWySvkmglZ1PQmEVIslNQPAu4pysJmZGcmu0M2A/30TAEAFVgT/V88oVYrEOzaRSPyE0VIB36hA4HPn/KnP/dQ7ooLDq6zcTRJXn/rMX/qeKjD92Fx/C+PqucE/+63NExieq/pno8xV93P/QKfTfdankQEym83irfov//IvmJ6eRiQSwevXr/H48WPpWDXKgg4Gg0+W4n7uPDlXMkFWqxV//OMfsba2hn6/j3Q6jf/93/9FMpkcx0ftZ0uGrzNXYJjyt1qtWF1dhd/vh9FoxP7+Po6Pj0VDpuVcrztPlQ1kizZ2LqvX64hEIkOFQF9inipzwfnSsoa+oOy8M8plqtWzv3rA8/1SDa4NBoP0NlfTXVrM9XPm+bE5qhIQVRfMi38UJk2LeX5MVkFv2B+/B4xGo4BZFgld56zS4tl/bK4EWJRWcK4E32pThc99/jfx7HlGqYBLtQZSbW0+973S+nz61H+rQOrH73utC1XLef7SuPpzXJnHz/5bre+m64yPkQU/N37NNf3E9+A8fvbv/T3M88fP+dXm+uN8Pvn3PjVXTcAqcNmVg76KOzs76PV60qVgHKAK3AywMhgM4rmoMhrj6GduYlOoDACF7dTQ3NRcR928qpZRZVvHsSm6qQPhakrox+/1m3r2n/gcfr+RP+PXOmTJDo86vvRl8Lnjt3AZfO74e1jTyTyvPyZ7VPvx9zBP4G9jrpqB1R///tCvWtL8Nw1Yfvweo36M+hl/85sCmMxzlDF59tqPv4d5An87c53M8/rj72GewN/OXCfzvP74e3j2PwtWJ2MyJmMyJmMyJmMyJmMyvuQYvXHwZEzGZEzGZEzGZEzGZEzGDY8JWJ2MyZiMyZiMyZiMyZiM3+yYgNXJmIzJmIzJmIzJmIzJ+M2OCVidjMmYjMmYjMmYjMmYjN/smIDVyZiMyZiMyZiMyZiMyfjNjglYnYzJmIzJmIzJmIzJmIzf7Ph/fqRYTEK/p0sAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["print(vae.layers[2].predict(tf.constant([[-1,1]]))[0][...,0].shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TSKnoRkB8-7v","executionInfo":{"status":"ok","timestamp":1657135994401,"user_tz":-60,"elapsed":2,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b6ce9cfd-3a04-4f9e-e1a6-89bac6611fc5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(28, 28)\n"]}]},{"cell_type":"code","source":["(x_train, y_train), _ = tf.keras.datasets.mnist.load_data()\n","mnist_digits = np.expand_dims(x_train,-1).astype(\"float32\") / 255"],"metadata":{"id":"AZ-iZ4ji8--H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["z,_,_=vae.layers[1].predict(x_train)\n","plt.figure(figsize=(12,12))\n","plt.scatter(z[:,0],z[:,1],c=y_train)\n","plt.colorbar()\n","plt.show()"],"metadata":{"id":"v_Cs866W8_AU","colab":{"base_uri":"https://localhost:8080/","height":704},"executionInfo":{"status":"ok","timestamp":1657142026560,"user_tz":-60,"elapsed":13499,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"93971b27-7261-4738-ed04-9d01f11fef06"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x864 with 2 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FILE: deep learning for image generation/2-DCGAN By Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"zGcCsOilc8b1"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, )\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"markdown","metadata":{"id":"gPLHB2s5gBJu"},"source":["# Data"]},{"cell_type":"markdown","metadata":{"id":"QhKsQaul6le_"},"source":["## Download Data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":102810,"status":"ok","timestamp":1662631876305,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"DnE86Hpn6GGQ","outputId":"58dc5f13-0e5d-45b3-825e-c1374abf3537"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197604.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197605.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197606.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197607.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197608.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197609.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197610.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197611.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197612.jpg  \n","  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\"/content/celeba-dataset.zip\" -d \"/content/dataset/\""]},{"cell_type":"markdown","metadata":{"id":"IV3KzHNq6w0g"},"source":["## Prepare Data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P7pb2bpv6GLf"},"outputs":[],"source":["BATCH_SIZE = 128\n","IM_SHAPE = (64,64,3)\n","LEARNING_RATE = 2e-4\n","LATENT_DIM=100\n","EPOCHS=20"]},{"cell_type":"code","source":[],"metadata":{"id":"oLcX0RFw4leA"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15348,"status":"ok","timestamp":1662632042787,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"pNiSHjva6GN5","outputId":"f832fda6-938a-4e54-e04f-19fab2f79b9e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 202599 files belonging to 1 classes.\n"]}],"source":["dataset = tf.keras.preprocessing.image_dataset_from_directory(\n","    \"/content/dataset/img_align_celeba/img_align_celeba\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",")"]},{"cell_type":"code","source":[],"metadata":{"id":"Nrd47dQJ4muu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V98GV8FA4eYO","executionInfo":{"status":"ok","timestamp":1669784102766,"user_tz":-60,"elapsed":2798,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b8df7e21-7cdc-4810-e303-408af37b12bf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 5864 files belonging to 1 classes.\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1669784104169,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"CT7fSwhF6GQj","outputId":"38a594d4-39e8-443e-f5ec-73b13f0cc413"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BatchDataset element_spec=TensorSpec(shape=(None, 64, 64, 3), dtype=tf.float32, name=None)>"]},"metadata":{},"execution_count":6}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dBO55qltL0OS"},"outputs":[],"source":["def preprocess(image):\n","  return tf.cast(image, tf.float32) / 127.5 - 1.0"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MfOhaJz-6GSw"},"outputs":[],"source":["train_dataset = (\n","    dataset\n","    .map(preprocess)\n","    .unbatch()\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE,drop_remainder=True)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1326,"status":"ok","timestamp":1669784110043,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"AU4bl2Hd6GYE","outputId":"7ff241a5-38d7-43e7-b7e7-8fc16f0f43be"},"outputs":[{"output_type":"stream","name":"stdout","text":["(128, 64, 64, 3)\n"]}],"source":["for d in train_dataset.take(1):\n","  print(d.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":357},"executionInfo":{"elapsed":488,"status":"ok","timestamp":1669784110910,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"RcTlClDN6Gaq","outputId":"6008df44-8752-443e-b420-5332819b1f68"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x432 with 4 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize = (6,6))\n","k=0\n","n = 4\n","for i in range(n):\n","  ax = plt.subplot(2,2, k+1)\n","  plt.imshow((d[i]+1)/2)\n","  plt.axis(\"off\")\n","  k+=1"]},{"cell_type":"markdown","metadata":{"id":"tdtWfpUGgC4E"},"source":["# MOdeling"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ni3A9hDmgKKU"},"outputs":[],"source":["generator=tf.keras.Sequential([\n","  Input(shape=(LATENT_DIM,)),\n","  Dense(4*4*LATENT_DIM),\n","  Reshape((4,4,LATENT_DIM)),\n","\n","  Conv2DTranspose(512,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(256,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(128,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(3,kernel_size=4,strides=2, activation=tf.keras.activations.tanh, padding='same'),\n","\n","],name='generator')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1669784117143,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"qfYErd5YuEjg","outputId":"1c10c8ba-0240-4e2b-ab91-efe6dd2fa2f3"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"generator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," dense (Dense)               (None, 1600)              161600    \n","                                                                 \n"," reshape (Reshape)           (None, 4, 4, 100)         0         \n","                                                                 \n"," conv2d_transpose (Conv2DTra  (None, 8, 8, 512)        819712    \n"," nspose)                                                         \n","                                                                 \n"," batch_normalization (BatchN  (None, 8, 8, 512)        2048      \n"," ormalization)                                                   \n","                                                                 \n"," leaky_re_lu (LeakyReLU)     (None, 8, 8, 512)         0         \n","                                                                 \n"," conv2d_transpose_1 (Conv2DT  (None, 16, 16, 256)      2097408   \n"," ranspose)                                                       \n","                                                                 \n"," batch_normalization_1 (Batc  (None, 16, 16, 256)      1024      \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_1 (LeakyReLU)   (None, 16, 16, 256)       0         \n","                                                                 \n"," conv2d_transpose_2 (Conv2DT  (None, 32, 32, 128)      524416    \n"," ranspose)                                                       \n","                                                                 \n"," batch_normalization_2 (Batc  (None, 32, 32, 128)      512       \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_2 (LeakyReLU)   (None, 32, 32, 128)       0         \n","                                                                 \n"," conv2d_transpose_3 (Conv2DT  (None, 64, 64, 3)        6147      \n"," ranspose)                                                       \n","                                                                 \n","=================================================================\n","Total params: 3,612,867\n","Trainable params: 3,611,075\n","Non-trainable params: 1,792\n","_________________________________________________________________\n"]}],"source":["generator.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fXiiUmhZuEmJ"},"outputs":[],"source":["discriminator=tf.keras.Sequential([\n","  Input(shape=(IM_SHAPE[0],IM_SHAPE[1],3)),\n","\n","  Conv2D(64,kernel_size=4,strides=2, padding='same'),\n","  LeakyReLU(0.2),\n","\n","  Conv2D(128,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","  \n","  Conv2D(256,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2D(1,kernel_size=4,strides=2, padding='same'),\n","\n","  Flatten(),\n","  Dense(1,activation='sigmoid')\n","  \n","\n","],name='discriminator')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":419,"status":"ok","timestamp":1669784117954,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"QxJiYAOquEon","outputId":"d869bff7-6332-43be-b841-b6d5d451b97b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"discriminator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 32, 32, 64)        3136      \n","                                                                 \n"," leaky_re_lu_3 (LeakyReLU)   (None, 32, 32, 64)        0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 16, 16, 128)       131200    \n","                                                                 \n"," batch_normalization_3 (Batc  (None, 16, 16, 128)      512       \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 16, 16, 128)       0         \n","                                                                 \n"," conv2d_2 (Conv2D)           (None, 8, 8, 256)         524544    \n","                                                                 \n"," batch_normalization_4 (Batc  (None, 8, 8, 256)        1024      \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_5 (LeakyReLU)   (None, 8, 8, 256)         0         \n","                                                                 \n"," conv2d_3 (Conv2D)           (None, 4, 4, 1)           4097      \n","                                                                 \n"," flatten (Flatten)           (None, 16)                0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 17        \n","                                                                 \n","=================================================================\n","Total params: 664,530\n","Trainable params: 663,762\n","Non-trainable params: 768\n","_________________________________________________________________\n"]}],"source":["discriminator.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BsC-wtWGOU2X"},"outputs":[],"source":["class ShowImage(tf.keras.callbacks.Callback):\n","    def __init__(self, latent_dim=100):\n","        self.latent_dim = latent_dim\n","\n","    def on_epoch_end(self, epoch, logs=None):\n","        n=6\n","        k=0\n","        out=self.model.generator(tf.random.normal(shape=(36, self.latent_dim)))\n","        plt.figure(figsize=(16,16))\n","        for i in range(n):\n","          for j in range(n):\n","            ax=plt.subplot(n,n,k+1)\n","            plt.imshow((out[k]+1)/2,)\n","            plt.axis('off')\n","            k+=1\n","        plt.savefig(\"generated/gen_images_epoch_{}.png\".format(epoch+1))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lcPT8TgBuErO"},"outputs":[],"source":["class GAN(tf.keras.Model):\n","  def __init__(self,discriminator,generator):\n","    super(GAN,self).__init__()\n","    self.discriminator=discriminator\n","    self.generator=generator\n","\n","  def compile(self,d_optimizer,g_optimizer,loss_fn):\n","    super(GAN,self).compile()\n","    self.d_optimizer=d_optimizer\n","    self.g_optimizer=g_optimizer\n","    self.loss_fn=loss_fn\n","    self.d_loss_metric=tf.keras.metrics.Mean(name='d_loss')\n","    self.g_loss_metric=tf.keras.metrics.Mean(name='g_loss')\n","    \n","  @property\n","  def metrics(self):\n","    return [self.d_loss_metric,self.g_loss_metric]\n","  \n","  def train_step(self,real_images):\n","    batch_size=tf.shape(real_images)[0]\n","\n","    ######## Discriminator\n","    random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n","    fake_images=self.generator(random_noise)\n","\n","    real_labels=tf.ones((batch_size,1))+0.25*tf.random.uniform((batch_size,1),minval=-1,maxval=1)\n","    fake_labels=tf.zeros((batch_size,1))+0.25*tf.random.uniform((batch_size,1),)\n","\n","    with tf.GradientTape() as recorder:\n","      real_predictions=self.discriminator(real_images)\n","      d_loss_real=self.loss_fn(real_labels,real_predictions)\n","\n","      fake_predictions=self.discriminator(fake_images)\n","      d_loss_fake=self.loss_fn(fake_labels,fake_predictions)\n","\n","      d_loss=d_loss_real+d_loss_fake\n","      \n","    partial_derivatives = recorder.gradient(d_loss,self.discriminator.trainable_weights)\n","    self.d_optimizer.apply_gradients(zip(partial_derivatives, self.discriminator.trainable_weights))\n","\n","    ############# Generator\n","    random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n","    flipped_fake_labels=tf.ones((batch_size,1))\n","\n","    with tf.GradientTape() as recorder:\n","\n","      fake_predictions=self.discriminator(self.generator(random_noise))\n","      g_loss=self.loss_fn(flipped_fake_labels,fake_predictions)\n","      \n","    partial_derivatives = recorder.gradient(g_loss,self.generator.trainable_weights)\n","    self.g_optimizer.apply_gradients(zip(partial_derivatives, self.generator.trainable_weights))\n","\n","    self.d_loss_metric.update_state(d_loss)\n","    self.g_loss_metric.update_state(g_loss)\n","    \n","    return {'d_loss':self.d_loss_metric.result(),\n","            'g_loss':self.g_loss_metric.result()}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5aoneKPouEtr"},"outputs":[],"source":["gan=GAN(discriminator,generator)\n","gan.compile(\n","    d_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n","    g_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n","    loss_fn=tf.keras.losses.BinaryCrossentropy(),\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LHcLh4YeRF3I"},"outputs":[],"source":["!mkdir generated"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hFfDNRj8uEwi","outputId":"de8fd0c8-f774-46b3-df3b-50d6a453ad78"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/1000\n","      6/Unknown - 2s 188ms/step - d_loss: 1.3566 - g_loss: 0.6954"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0775s vs `on_train_batch_end` time: 0.1039s). Check your callbacks.\n"]},{"output_type":"stream","name":"stdout","text":["45/45 [==============================] - 10s 214ms/step - d_loss: 1.3085 - g_loss: 0.7376\n","Epoch 2/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.2950 - g_loss: 0.7520\n","Epoch 3/1000\n","45/45 [==============================] - 11s 224ms/step - d_loss: 1.3033 - g_loss: 0.7382\n","Epoch 4/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.3086 - g_loss: 0.7341\n","Epoch 5/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2939 - g_loss: 0.7332\n","Epoch 6/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.3010 - g_loss: 0.7389\n","Epoch 7/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2927 - g_loss: 0.7363\n","Epoch 8/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.3005 - g_loss: 0.7373\n","Epoch 9/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2940 - g_loss: 0.7332\n","Epoch 10/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.3000 - g_loss: 0.7235\n","Epoch 11/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2972 - g_loss: 0.7277\n","Epoch 12/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2994 - g_loss: 0.7441\n","Epoch 13/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2981 - g_loss: 0.7267\n","Epoch 14/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.3014 - g_loss: 0.7359\n","Epoch 15/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.3064 - g_loss: 0.7338\n","Epoch 16/1000\n","45/45 [==============================] - 16s 348ms/step - d_loss: 1.3008 - g_loss: 0.7522\n","Epoch 17/1000\n","45/45 [==============================] - 10s 212ms/step - d_loss: 1.3083 - g_loss: 0.7317\n","Epoch 18/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.3012 - g_loss: 0.7263\n","Epoch 19/1000\n","45/45 [==============================] - 10s 221ms/step - d_loss: 1.2926 - g_loss: 0.7280\n","Epoch 20/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2960 - g_loss: 0.7322\n","Epoch 21/1000\n","45/45 [==============================] - ETA: 0s - d_loss: 1.2846 - g_loss: 0.7297"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:9: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n","  if __name__ == '__main__':\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r45/45 [==============================] - 11s 234ms/step - d_loss: 1.2846 - g_loss: 0.7297\n","Epoch 22/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.2928 - g_loss: 0.7370\n","Epoch 23/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2993 - g_loss: 0.7454\n","Epoch 24/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2898 - g_loss: 0.7301\n","Epoch 25/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.3017 - g_loss: 0.7567\n","Epoch 26/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2881 - g_loss: 0.7572\n","Epoch 27/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.3091 - g_loss: 0.7567\n","Epoch 28/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2915 - g_loss: 0.7555\n","Epoch 29/1000\n","45/45 [==============================] - 12s 251ms/step - d_loss: 1.2786 - g_loss: 0.7506\n","Epoch 30/1000\n","45/45 [==============================] - 10s 211ms/step - d_loss: 1.2879 - g_loss: 0.7450\n","Epoch 31/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2933 - g_loss: 0.7492\n","Epoch 32/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2861 - g_loss: 0.7318\n","Epoch 33/1000\n","45/45 [==============================] - 11s 223ms/step - d_loss: 1.2833 - g_loss: 0.7530\n","Epoch 34/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2913 - g_loss: 0.7569\n","Epoch 35/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2764 - g_loss: 0.7642\n","Epoch 36/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2866 - g_loss: 0.7685\n","Epoch 37/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2785 - g_loss: 0.7618\n","Epoch 38/1000\n","45/45 [==============================] - 11s 245ms/step - d_loss: 1.2762 - g_loss: 0.7496\n","Epoch 39/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2718 - g_loss: 0.7386\n","Epoch 40/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2714 - g_loss: 0.7571\n","Epoch 41/1000\n","45/45 [==============================] - 10s 220ms/step - d_loss: 1.2723 - g_loss: 0.7628\n","Epoch 42/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2810 - g_loss: 0.7735\n","Epoch 43/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2689 - g_loss: 0.7753\n","Epoch 44/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2692 - g_loss: 0.7858\n","Epoch 45/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2699 - g_loss: 0.7854\n","Epoch 46/1000\n","45/45 [==============================] - 10s 221ms/step - d_loss: 1.2682 - g_loss: 0.7780\n","Epoch 47/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2561 - g_loss: 0.7824\n","Epoch 48/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2569 - g_loss: 0.7770\n","Epoch 49/1000\n","45/45 [==============================] - 12s 255ms/step - d_loss: 1.2546 - g_loss: 0.7780\n","Epoch 50/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2505 - g_loss: 0.7928\n","Epoch 51/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2646 - g_loss: 0.7942\n","Epoch 52/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2527 - g_loss: 0.7960\n","Epoch 53/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2485 - g_loss: 0.8067\n","Epoch 54/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2571 - g_loss: 0.7993\n","Epoch 55/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2415 - g_loss: 0.7876\n","Epoch 56/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2404 - g_loss: 0.7822\n","Epoch 57/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2420 - g_loss: 0.7913\n","Epoch 58/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2424 - g_loss: 0.8121\n","Epoch 59/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2387 - g_loss: 0.8108\n","Epoch 60/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2520 - g_loss: 0.8210\n","Epoch 61/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2320 - g_loss: 0.8103\n","Epoch 62/1000\n","45/45 [==============================] - 11s 219ms/step - d_loss: 1.2347 - g_loss: 0.8239\n","Epoch 63/1000\n","45/45 [==============================] - 10s 212ms/step - d_loss: 1.2365 - g_loss: 0.8413\n","Epoch 64/1000\n","45/45 [==============================] - 12s 266ms/step - d_loss: 1.2202 - g_loss: 0.8320\n","Epoch 65/1000\n","45/45 [==============================] - 10s 221ms/step - d_loss: 1.3631 - g_loss: 0.8417\n","Epoch 66/1000\n","45/45 [==============================] - 10s 222ms/step - d_loss: 1.2443 - g_loss: 0.8049\n","Epoch 67/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2180 - g_loss: 0.8118\n","Epoch 68/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.2110 - g_loss: 0.8006\n","Epoch 69/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2124 - g_loss: 0.8063\n","Epoch 70/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2113 - g_loss: 0.8094\n","Epoch 71/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.2103 - g_loss: 0.8250\n","Epoch 72/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2199 - g_loss: 0.8525\n","Epoch 73/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2099 - g_loss: 0.8548\n","Epoch 74/1000\n","45/45 [==============================] - 10s 218ms/step - d_loss: 1.2025 - g_loss: 0.8236\n","Epoch 75/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.2101 - g_loss: 0.8299\n","Epoch 76/1000\n","45/45 [==============================] - 10s 211ms/step - d_loss: 1.2076 - g_loss: 0.8444\n","Epoch 77/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.2022 - g_loss: 0.8375\n","Epoch 78/1000\n","45/45 [==============================] - 10s 220ms/step - d_loss: 1.2056 - g_loss: 0.8431\n","Epoch 79/1000\n","45/45 [==============================] - 11s 224ms/step - d_loss: 1.1993 - g_loss: 0.8489\n","Epoch 80/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.2036 - g_loss: 0.8594\n","Epoch 81/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.2024 - g_loss: 0.8502\n","Epoch 82/1000\n","45/45 [==============================] - 13s 279ms/step - d_loss: 1.1925 - g_loss: 0.8659\n","Epoch 83/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.1946 - g_loss: 0.8562\n","Epoch 84/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1829 - g_loss: 0.8692\n","Epoch 85/1000\n","45/45 [==============================] - 10s 219ms/step - d_loss: 1.1922 - g_loss: 0.8717\n","Epoch 86/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.1965 - g_loss: 0.8674\n","Epoch 87/1000\n","45/45 [==============================] - 10s 217ms/step - d_loss: 1.1903 - g_loss: 0.8935\n","Epoch 88/1000\n","45/45 [==============================] - 10s 220ms/step - d_loss: 1.1868 - g_loss: 0.8777\n","Epoch 89/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1811 - g_loss: 0.8727\n","Epoch 90/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1800 - g_loss: 0.8802\n","Epoch 91/1000\n","45/45 [==============================] - 10s 214ms/step - d_loss: 1.1718 - g_loss: 0.8918\n","Epoch 92/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.1860 - g_loss: 0.8764\n","Epoch 93/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1814 - g_loss: 0.8832\n","Epoch 94/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1603 - g_loss: 0.8834\n","Epoch 95/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1646 - g_loss: 0.9053\n","Epoch 96/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1565 - g_loss: 0.8929\n","Epoch 97/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1782 - g_loss: 0.9001\n","Epoch 98/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1629 - g_loss: 0.8972\n","Epoch 99/1000\n","45/45 [==============================] - 10s 215ms/step - d_loss: 1.1510 - g_loss: 0.8854\n","Epoch 100/1000\n","45/45 [==============================] - 11s 225ms/step - d_loss: 1.1576 - g_loss: 0.8853\n","Epoch 101/1000\n","45/45 [==============================] - 11s 214ms/step - d_loss: 1.1737 - g_loss: 0.9095\n","Epoch 102/1000\n","45/45 [==============================] - 10s 213ms/step - d_loss: 1.1890 - g_loss: 0.9207\n","Epoch 103/1000\n","45/45 [==============================] - 10s 216ms/step - d_loss: 1.1544 - g_loss: 0.9127\n","Epoch 104/1000\n"]}],"source":["EPOCHS=1000\n","history=gan.fit(train_dataset,epochs=EPOCHS,callbacks=[ShowImage(LATENT_DIM)])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"ZPxKc48PuEzD","outputId":"f024d849-5392-4ebd-a105-9808cf73ea5a"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(history.history['d_loss'])\n","plt.plot(history.history['g_loss'])\n","plt.title('GAN Loss')\n","plt.ylabel('Loss')\n","plt.xlabel('epoch')\n","plt.legend(['d_loss', 'g_loss'], loc='upper left')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"j-bpKlbwuE1j"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SLGNQSpXuE9N"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FjePWMBQuE_5"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1ZO02cStWUyA7R_QjbaO1Bw0QbJtFzsjY","timestamp":1673808973947}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

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FILE: deep learning for image generation/3-WGAN By Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"zGcCsOilc8b1"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input,UpSampling2D,Activation )\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"markdown","metadata":{"id":"gPLHB2s5gBJu"},"source":["# Data"]},{"cell_type":"markdown","metadata":{"id":"QhKsQaul6le_"},"source":["## Download Data"]},{"cell_type":"code","source":["!pip install -q kaggle\n","! mkdir ~/.kaggle\n","! cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d badasstechie/celebahq-resized-256x256\n","!unzip \"/content/celebahq-resized-256x256.zip\" -d \"/content/dataset/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DFENPqg6RkKG","executionInfo":{"status":"ok","timestamp":1658818444893,"user_tz":-60,"elapsed":12792,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"909ce69d-af59-488f-d323-795e31dbb3ce"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/celeba_hq_256/25000.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25001.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25002.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25003.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25004.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25005.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25006.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25007.jpg  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Data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P7pb2bpv6GLf"},"outputs":[],"source":["BATCH_SIZE = 128\n","IM_SHAPE = (64,64,3)\n","LEARNING_RATE = 1e-4\n","LATENT_DIM=100\n","EPOCHS=20"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5615,"status":"ok","timestamp":1658818473167,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"pNiSHjva6GN5","outputId":"c2710c92-8b33-4c2f-834b-66b20b869309"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 30000 files belonging to 1 classes.\n"]}],"source":["dataset = tf.keras.preprocessing.image_dataset_from_directory(\n","    \"/content/dataset/celeba_hq_256\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1658818473167,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"CT7fSwhF6GQj","outputId":"58270e86-a47b-44d5-e6d6-03375e456af7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BatchDataset element_spec=TensorSpec(shape=(None, 64, 64, 3), dtype=tf.float32, name=None)>"]},"metadata":{},"execution_count":7}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dBO55qltL0OS"},"outputs":[],"source":["def preprocess(image):\n","  return tf.cast(image, tf.float32) / 127.5 - 1.0"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MfOhaJz-6GSw"},"outputs":[],"source":["train_dataset = (\n","    dataset\n","    .map(preprocess)\n","    .unbatch()\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE,drop_remainder=True)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1356,"status":"ok","timestamp":1658818474519,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"AU4bl2Hd6GYE","outputId":"2f107292-6fad-4a0d-fa1b-4536b0fa5457"},"outputs":[{"output_type":"stream","name":"stdout","text":["(128, 64, 64, 3)\n"]}],"source":["for d in train_dataset.take(1):\n","  print(d.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":357},"executionInfo":{"elapsed":948,"status":"ok","timestamp":1658818475464,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"RcTlClDN6Gaq","outputId":"8df4e220-81f9-42af-d5d0-9a0794837f87"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x432 with 4 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize = (6,6))\n","k=0\n","n = 4\n","for i in range(n):\n","  ax = plt.subplot(2,2, k+1)\n","  plt.imshow((d[i]+1)/2)\n","  plt.axis(\"off\")\n","  k+=1"]},{"cell_type":"markdown","metadata":{"id":"tdtWfpUGgC4E"},"source":["# MOdeling"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":718,"status":"ok","timestamp":1658818485540,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ni3A9hDmgKKU","outputId":"ff8dc23b-14da-4ae1-f40a-e7c9d2332910"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"generator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," dense (Dense)               (None, 1600)              161600    \n","                                                                 \n"," reshape (Reshape)           (None, 4, 4, 100)         0         \n","                                                                 \n"," conv2d_transpose (Conv2DTra  (None, 8, 8, 512)        819712    \n"," nspose)                                                         \n","                                                                 \n"," batch_normalization (BatchN  (None, 8, 8, 512)        2048      \n"," ormalization)                                                   \n","                                                                 \n"," leaky_re_lu (LeakyReLU)     (None, 8, 8, 512)         0         \n","                                                                 \n"," conv2d_transpose_1 (Conv2DT  (None, 16, 16, 256)      2097408   \n"," ranspose)                                                       \n","                                                                 \n"," batch_normalization_1 (Batc  (None, 16, 16, 256)      1024      \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_1 (LeakyReLU)   (None, 16, 16, 256)       0         \n","                                                                 \n"," conv2d_transpose_2 (Conv2DT  (None, 32, 32, 128)      524416    \n"," ranspose)                                                       \n","                                                                 \n"," batch_normalization_2 (Batc  (None, 32, 32, 128)      512       \n"," hNormalization)                                                 \n","                                                                 \n"," leaky_re_lu_2 (LeakyReLU)   (None, 32, 32, 128)       0         \n","                                                                 \n"," conv2d_transpose_3 (Conv2DT  (None, 64, 64, 3)        6147      \n"," ranspose)                                                       \n","                                                                 \n","=================================================================\n","Total params: 3,612,867\n","Trainable params: 3,611,075\n","Non-trainable params: 1,792\n","_________________________________________________________________\n"]}],"source":["generator=tf.keras.Sequential([\n","  Input(shape=(LATENT_DIM,)),\n","  Dense(4*4*LATENT_DIM),\n","  Reshape((4,4,LATENT_DIM)),\n","\n","  Conv2DTranspose(512,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(256,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(128,kernel_size=4,strides=2, padding='same'),\n","  BatchNormalization(),\n","  LeakyReLU(0.2),\n","\n","  Conv2DTranspose(3,kernel_size=4,strides=2, activation=tf.keras.activations.tanh, padding='same'),\n","\n","],name='generator')\n","generator.summary()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lrHVyX_bYoSq"},"outputs":[],"source":["discriminator=tf.keras.Sequential([\n","  Input(shape=(IM_SHAPE[0],IM_SHAPE[1],3)),\n","\n","  Conv2D(64,kernel_size=4,strides=2, padding='same'),\n","  LeakyReLU(0.2),\n","\n","  Conv2D(128,kernel_size=4,strides=2, padding='same'),\n","  LeakyReLU(0.2),\n","  \n","  Conv2D(256,kernel_size=4,strides=2, padding='same'),\n","  LeakyReLU(0.2),\n","\n","  Conv2D(512,kernel_size=4,strides=2, padding='same'),\n","  LeakyReLU(0.2),\n","\n","  Conv2D(1,kernel_size=4,strides=2, padding='same'),\n","\n","  Flatten(),\n","  Dense(1,)\n","  \n","\n","],name='discriminator')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":348,"status":"ok","timestamp":1658818486580,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"KtDjnaTF8-de","outputId":"a2258266-c659-4e3b-9a7c-a494d0c1bfc8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"discriminator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 32, 32, 64)        3136      \n","                                                                 \n"," leaky_re_lu_3 (LeakyReLU)   (None, 32, 32, 64)        0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 16, 16, 128)       131200    \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 16, 16, 128)       0         \n","                                                                 \n"," conv2d_2 (Conv2D)           (None, 8, 8, 256)         524544    \n","                                                                 \n"," leaky_re_lu_5 (LeakyReLU)   (None, 8, 8, 256)         0         \n","                                                                 \n"," conv2d_3 (Conv2D)           (None, 4, 4, 512)         2097664   \n","                                                                 \n"," leaky_re_lu_6 (LeakyReLU)   (None, 4, 4, 512)         0         \n","                                                                 \n"," conv2d_4 (Conv2D)           (None, 2, 2, 1)           8193      \n","                                                                 \n"," flatten (Flatten)           (None, 4)                 0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 5         \n","                                                                 \n","=================================================================\n","Total params: 2,764,742\n","Trainable params: 2,764,742\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["discriminator.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BsC-wtWGOU2X"},"outputs":[],"source":["class ShowImage(tf.keras.callbacks.Callback):\n","    def __init__(self, latent_dim=100):\n","        self.latent_dim = latent_dim\n","\n","    def on_epoch_end(self, epoch, logs=None):\n","        n=6\n","        k=0\n","        out=self.model.generator(tf.random.normal(shape=(36, self.latent_dim)))\n","        plt.figure(figsize=(16,16))\n","        for i in range(n):\n","          for j in range(n):\n","            ax=plt.subplot(n,n,k+1)\n","            plt.imshow((out[k]+1)/2,)\n","            plt.axis('off')\n","            k+=1\n","        plt.savefig(\"generated/gen_images_epoch_{}.png\".format(epoch+1))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rzHwQWa4bWBh"},"outputs":[],"source":["def discriminator_loss(real_predictions, fake_predictions):\n","    real_loss = tf.reduce_mean(real_predictions)\n","    fake_loss = tf.reduce_mean(fake_predictions)\n","    return fake_loss - real_loss"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JIvzYPZGbmyo"},"outputs":[],"source":["def generator_loss(fake_predictions):\n","    return -tf.reduce_mean(fake_predictions)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qZ_Swk6_Zemq"},"outputs":[],"source":["class WGAN(tf.keras.Model):\n","  def __init__(self,discriminator,generator):\n","    super(WGAN,self).__init__()\n","    self.discriminator=discriminator\n","    self.generator=generator\n","\n","  def compile(self,d_optimizer,g_optimizer,d_loss_fn,g_loss_fn,n_critic,lamda):\n","    super(WGAN,self).compile()\n","    self.d_optimizer=d_optimizer\n","    self.g_optimizer=g_optimizer\n","    self.d_loss_fn=d_loss_fn\n","    self.g_loss_fn=g_loss_fn\n","    self.n_critic=n_critic\n","    self.lamda=lamda\n","    self.d_loss_metric=tf.keras.metrics.Mean(name='d_loss')\n","    self.g_loss_metric=tf.keras.metrics.Mean(name='g_loss')\n","    \n","  @property\n","  def metrics(self):\n","    return [self.d_loss_metric,self.g_loss_metric]\n","\n","  def gradient_penalty(self,batch_size,real_images,fake_images):\n","    epsilon=tf.random.normal([batch_size,1,1,1],0.0,1.0)\n","    interpolation=epsilon*real_images+(1-epsilon)*fake_images\n","\n","    with tf.GradientTape() as gp_tape:\n","      gp_tape.watch(interpolation)\n","      prediction=self.discriminator(interpolation,training=True)\n","    grads=gp_tape.gradient(prediction,interpolation)\n","    l2_norm=tf.sqrt(tf.reduce_sum(tf.square(grads),axis=[1,2,3]))\n","    return tf.reduce_mean((l2_norm-1)**2)\n","  \n","  def train_step(self,real_images):\n","    batch_size=tf.shape(real_images)[0]\n","\n","    ######## Discriminator\n","    for _ in range(self.n_critic):\n","      random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n","      fake_images=self.generator(random_noise)\n","\n","      with tf.GradientTape() as recorder:\n","        real_predictions=self.discriminator(real_images)\n","        fake_predictions=self.discriminator(fake_images)\n","        d_loss=self.d_loss_fn(real_predictions,fake_predictions)\n","\n","        d_loss+=self.lamda*self.gradient_penalty(batch_size,real_images,fake_images)\n","        \n","      partial_derivatives = recorder.gradient(d_loss,self.discriminator.trainable_weights)\n","      self.d_optimizer.apply_gradients(zip(partial_derivatives, self.discriminator.trainable_weights))\n","\n","    ############# Generator\n","    random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n","    with tf.GradientTape() as recorder:\n","\n","      fake_predictions=self.discriminator(self.generator(random_noise))\n","      g_loss=self.g_loss_fn(fake_predictions)\n","      \n","    partial_derivatives = recorder.gradient(g_loss,self.generator.trainable_weights)\n","    self.g_optimizer.apply_gradients(zip(partial_derivatives, self.generator.trainable_weights))\n","\n","    self.d_loss_metric.update_state(d_loss)\n","    self.g_loss_metric.update_state(g_loss)\n","    \n","    return {'d_loss':self.d_loss_metric.result(),\n","            'g_loss':self.g_loss_metric.result()}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ldt_EvEHZgHR"},"outputs":[],"source":["wgan=WGAN(discriminator,generator)\n","wgan.compile(\n","    d_optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4, beta_1=0.5, beta_2=0.9),\n","    g_optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4, beta_1=0.5, beta_2=0.9),\n","    d_loss_fn=discriminator_loss,\n","    g_loss_fn=generator_loss,\n","    n_critic=5,\n","    lamda=10,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7r5XnJ1sZgJY"},"outputs":[],"source":["!mkdir generated"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hKUcUoL0Uk3w"},"outputs":[],"source":["history=wgan.fit(train_dataset,epochs=EPOCHS,callbacks=[ShowImage(LATENT_DIM)])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"l_0-K4DtUk5_","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1658823588512,"user_tz":-60,"elapsed":15,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c7e4e499-f8d1-4cb5-af05-9e9c75e523b7"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.plot(history.history['d_loss'])\n","plt.plot(history.history['g_loss'])\n","plt.title('GAN Loss')\n","plt.ylabel('Loss')\n","plt.xlabel('epoch')\n","plt.legend(['d_loss', 'g_loss'], loc='upper left')\n","plt.show()"]},{"cell_type":"code","source":["history=wgan.fit(train_dataset,epochs=50,callbacks=[ShowImage(LATENT_DIM)])"],"metadata":{"id":"6HUZExx3vr4H"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":749,"status":"ok","timestamp":1658657883738,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"dXE5JVFxZgL4","outputId":"fce78a7e-afd8-452b-f649-d0eab24d2d04"},"outputs":[{"name":"stdout","output_type":"stream","text":["(32, 64, 64, 3)\n"]}],"source":["x = tf.random.uniform([32,64,64,3],0,1)#constant(3.0)\n","with tf.GradientTape() as g:\n","  g.watch(x)\n","  y = x * x\n","dy_dx = g.gradient(y, x)\n","print(dy_dx.shape)"]},{"cell_type":"code","source":[],"metadata":{"id":"E8AEotBPELVn"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1hhbMLDUAuQLr3bY4N-gGEXRI5rUnOvhG","timestamp":1673808991408},{"file_id":"1ZO02cStWUyA7R_QjbaO1Bw0QbJtFzsjY","timestamp":1658047817968}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

================================================
FILE: deep learning for image generation/4-ProGAN by Neuralearn.ai-.ipynb
================================================
{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5522,"status":"ok","timestamp":1659668397223,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"2VJ3CDxQWBVc","outputId":"03f71bcc-d219-4073-9640-0a5731c78e9d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting tensorflow_addons\n","  Downloading tensorflow_addons-0.17.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n","\u001b[K     |████████████████████████████████| 1.1 MB 8.2 MB/s \n","\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (21.3)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow_addons) (3.0.9)\n","Installing collected packages: tensorflow-addons\n","Successfully installed tensorflow-addons-0.17.1\n"]}],"source":["!pip install tensorflow_addons"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b9OIblpyV3lF"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add,AveragePooling2D,Lambda,\n","                                     UpSampling2D, Conv2D, MaxPool2D, Dense,Activation,\n","                                     Flatten, InputLayer, BatchNormalization, Input, )\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"code","source":[],"metadata":{"id":"e8O4ZJUv5u7U"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Data"],"metadata":{"id":"yNtMOBR_5vI7"}},{"cell_type":"markdown","source":["## Data Download"],"metadata":{"id":"ZtfhWcyJ5wYC"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16278,"status":"ok","timestamp":1659668416767,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"jupba1yQ7SQk","outputId":"f7120684-e1c9-4c66-e890-cae337803f7b"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/celeba_hq_256/25000.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25001.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25002.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25003.jpg  \n","  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\"/content/celebahq-resized-256x256.zip\" -d \"/content/dataset/\""]},{"cell_type":"markdown","source":["## Data Preparation"],"metadata":{"id":"ZyYJLbl25yvj"}},{"cell_type":"code","source":["NOISE_DIM = 512\n","BATCH_SIZE = [16, 16, 16, 16, 16, 16,]\n","EPOCHS = 50"],"metadata":{"id":"mvKbkG4KsSCO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def preprocess(image):\n","  return tf.cast(image, tf.float32) / 127.5 - 1.0"],"metadata":{"id":"QI7LK2D3r2oH"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U8SS-QUEOK5T","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1659668420222,"user_tz":-60,"elapsed":3468,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a23bfff2-41b9-4d46-c817-60f1bc4ccc62"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 30000 files belonging to 1 classes.\n"]}],"source":["def create_dataset(res,BATCH_SIZE):\n","  ds_train = tf.keras.preprocessing.image_dataset_from_directory(\n","      \"/content/dataset/celeba_hq_256\", label_mode=None, image_size=(res,res), batch_size=BATCH_SIZE\n","  )\n","  \n","  train_dataset = (\n","      ds_train\n","      .map(preprocess)\n","      .unbatch()\n","      .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","      .batch(BATCH_SIZE,drop_remainder=True)\n","      .prefetch(tf.data.AUTOTUNE)\n","  )\n","  return train_dataset\n","train_dataset = create_dataset(4,BATCH_SIZE[0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eGwtlH0dSGlM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1659668420222,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"759d7ac0-85c5-480f-c5f5-4f473e5254d6"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=TensorSpec(shape=(16, 4, 4, 3), dtype=tf.float32, name=None)>"]},"metadata":{},"execution_count":7}],"source":["train_dataset"]},{"cell_type":"code","source":["for d in create_dataset(256,32).take(1):\n","  print(d.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vnrSIweosB1W","executionInfo":{"status":"ok","timestamp":1659668424660,"user_tz":-60,"elapsed":4442,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ea6e51f0-e465-4857-c62f-21bed28b3dea"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 30000 files belonging to 1 classes.\n","(32, 256, 256, 3)\n"]}]},{"cell_type":"code","source":["plt.figure(figsize = (16,16))\n","k=0\n","n = 16\n","for i in range(n):\n","    ax = plt.subplot(4,4, k+1)\n","    plt.imshow((d[i]+1)/2)\n","    plt.axis(\"off\")\n","    k+=1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":857},"id":"bfWT_UkBsB4M","executionInfo":{"status":"ok","timestamp":1659668428572,"user_tz":-60,"elapsed":3922,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"10350e79-0cc4-4389-b4d1-50aaeaaf7ef1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x1152 with 16 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"KjtLD8zVsB6b"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"p6Q0RUZ46VyO"}},{"cell_type":"code","source":["class PixelNormalization(Layer):\n","  def __init__(self,):\n","    super(PixelNormalization, self).__init__()\n","  def call(self, inputs):\n","    mean_square = tf.reduce_mean(tf.square(inputs), axis=-1, keepdims=True)\n","    l2 = tf.math.rsqrt(mean_square + 1.0e-8)\n","    normalized = inputs * l2\n","    return normalized"],"metadata":{"id":"kbXjSDiJsB85"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class WeightScaling(Layer):\n","    def __init__(self, shape, gain = np.sqrt(2),):\n","      super(WeightScaling, self).__init__()\n","      #shape = tf.constant(shape, dtype=tf.float32)\n","      fan_in = tf.math.reduce_prod(shape)\n","      self.wscale = gain*tf.math.rsqrt(fan_in)\n","      \n","    def call(self, inputs):\n","      inputs = tf.cast(inputs, tf.float32)\n","      return inputs * self.wscale"],"metadata":{"id":"SrbvFv9ysB_D"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class Bias(Layer):\n","    def __init__(self, ):\n","        super(Bias, self).__init__()\n","\n","    def build(self, input_shape):\n","        b_init = tf.zeros_initializer()\n","        self.bias = tf.Variable(\n","            initial_value = b_init(shape=(input_shape[-1],), dtype='float32'),\n","            trainable=True)  \n","\n","    def call(self, inputs,):\n","        return inputs + self.bias"],"metadata":{"id":"2Bgaqsw-Wh1e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class WeightScalingDense(Layer):\n","    def __init__(self, n_units,gain,use_pixelnorm=False,activate=None):\n","      super(WeightScalingDense, self).__init__()\n","      self.n_units=n_units\n","      self.gain=gain\n","      self.use_pixelnorm=use_pixelnorm\n","      self.activate=activate\n","    def build(self,input_shape):\n","\n","      self.dense=Dense(\n","          self.n_units,\n","          use_bias=False,\n","          kernel_initializer=tf.keras.initializers.RandomNormal(mean=0., stddev=1.),\n","          dtype='float32')\n","      self.bias=Bias()\n","\n","\n","    def call(self,inputs):\n","      in_filters = tf.shape(inputs)[-1]\n","\n","      x=self.dense(inputs)\n","\n","      x=WeightScaling(shape=(tf.cast(in_filters,dtype=tf.float32)), gain=self.gain)(x)\n","      x=self.bias(x)\n","\n","      if self.activate=='LeakyReLU':\n","          x=LeakyReLU(0.2)(x)\n","      elif self.activate=='tanh':\n","          x=Activation('tanh')(x)\n","      \n","      if self.use_pixelnorm:\n","          x=PixelNormalization()(x)\n","      return x"],"metadata":{"id":"Omm7LbMeTbzu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class WeightScalingConv(Layer):\n","    def __init__(self, n_filters,kernel_size,gain,use_pixelnorm=False,activate=None, strides=(1,1)):\n","      super(WeightScalingConv, self).__init__()\n","      self.n_filters=n_filters\n","      self.gain=gain\n","      self.kernel_size=kernel_size\n","      self.use_pixelnorm=use_pixelnorm\n","      self.activate=activate\n","      self.strides=strides\n","    def build(self,input_shape):\n","      self.conv=Conv2D(\n","          self.n_filters,\n","          self.kernel_size,\n","          strides=self.strides,\n","          use_bias=False,\n","          padding=\"same\",\n","          kernel_initializer=tf.keras.initializers.RandomNormal(mean=0., stddev=1.),\n","          dtype='float32')\n","      self.bias=Bias()\n","      \n","    def call(self,inputs):\n","      in_filters = tf.shape(inputs)[-1]\n","\n","      x=self.conv(inputs)\n","      x=WeightScaling(shape=(tf.cast(self.kernel_size[0],dtype=tf.float32),tf.cast(self.kernel_size[1],dtype=tf.float32),tf.cast(in_filters,dtype=tf.float32)), gain=self.gain)(x)\n","      x=self.bias(x)\n","      \n","      if self.activate=='LeakyReLU':\n","          x=LeakyReLU(0.2)(x)\n","      elif self.activate=='tanh':\n","          x=Activation('tanh')(x)\n","      \n","      if self.use_pixelnorm:\n","          x = PixelNormalization()(x)\n","      return x "],"metadata":{"id":"-ZR1V8dMsCD6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class MinibatchStdev(Layer):\n","    def __init__(self, **kwargs):\n","      super(MinibatchStdev, self).__init__(**kwargs)\n","    \n","    def call(self, inputs):\n","      mean = tf.reduce_mean(inputs, axis=0, keepdims=True)\n","      stddev = tf.sqrt(tf.reduce_mean(tf.square(inputs - mean), axis=0, keepdims=True) + 1e-8)\n","      average_stddev = tf.reduce_mean(stddev, keepdims=True)\n","      shape = tf.shape(inputs)\n","      minibatch_stddev = tf.tile(average_stddev, (shape[0], shape[1], shape[2], 1))\n","      combined = tf.concat([inputs, minibatch_stddev], axis=-1)\n","      \n","      return combined"],"metadata":{"id":"kRErk4lZcVEH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["FILTERS = [512, 512, 512, 512, 256, 128, 64]"],"metadata":{"id":"vZI75R0rgew6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class WeightedSum(Layer):\n","    def __init__(self,):\n","      super(WeightedSum, self).__init__()\n","    def build(self,input_shape):\n","      self.alpha = tf.Variable(0., dtype=tf.float32, trainable=False)\n","    def call(self,inputs):\n","      return ((1.0 - self.alpha) * inputs[0] + (self.alpha * inputs[1]))"],"metadata":{"id":"GiHQhCl1I-0A"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class ProGAN(Model):\n","    def __init__(self,latent_dim,d_steps=1,gp_weight=10.0,drift_weight=0.001,):\n","      super(ProGAN, self).__init__()\n","      self.latent_dim = latent_dim\n","      self.d_steps = d_steps\n","      self.gp_weight = gp_weight\n","      self.drift_weight = drift_weight\n","      self.n_depth = 0\n","      self.discriminator = self.init_discriminator()\n","      self.discriminator_wt_fade = None\n","      self.generator = self.init_generator()\n","      self.generator_wt_fade = None\n","\n","    def init_discriminator(self):\n","      img_input = Input(shape = (4,4,3))\n","      img_input = tf.cast(img_input, tf.float32)\n","      \n","      # fromRGB\n","      x=WeightScalingConv(n_filters=FILTERS[0],kernel_size=(1,1),gain=np.sqrt(2),activate='LeakyReLU')(img_input)\n","      \n","      x = MinibatchStdev()(x)\n","\n","      x=WeightScalingConv(n_filters=FILTERS[0],kernel_size=(3,3),gain=np.sqrt(2),activate='LeakyReLU')(x)\n","      x=WeightScalingConv(n_filters=FILTERS[0],kernel_size=(4,4),gain=np.sqrt(2),activate='LeakyReLU', strides=(4,4))(x)\n","      \n","      x = Flatten()(x)\n","      \n","      x= WeightScalingDense(n_units=1,gain=1.)(x)\n","\n","      d_model = Model(img_input, x, name='discriminator')\n","\n","      return d_model\n","\n","    def fade_in_discriminator(self):\n","      input_shape = list(self.discriminator.input.shape)\n","      \n","      input_shape = (input_shape[1]*2, input_shape[2]*2, input_shape[3])\n","      img_input = Input(shape = input_shape)\n","      img_input = tf.cast(img_input, tf.float32)\n","      ################## Left\n","      x1 = AveragePooling2D()(img_input)\n","      x1 = self.discriminator.layers[1](x1)\n","\n","      x2=WeightScalingConv(FILTERS[self.n_depth],(1,1),np.sqrt(2),activate='LeakyReLU')(img_input)      \n","      x2=WeightScalingConv(FILTERS[self.n_depth],(3,3),np.sqrt(2), activate='LeakyReLU')(x2)\n","      x2=WeightScalingConv(FILTERS[self.n_depth-1],(3,3),np.sqrt(2), activate='LeakyReLU')(x2)\n","\n","      x2 = AveragePooling2D()(x2)\n"," \n","      x = WeightedSum()([x1, x2])\n","\n","      for i in range(2, len(self.discriminator.layers)):\n","          x2 = self.discriminator.layers[i](x2)\n","      self.discriminator_stabilize = Model(img_input, x2, name='discriminator')\n","\n","      for i in range(2, len(self.discriminator.layers)):\n","          x = self.discriminator.layers[i](x)\n","      self.discriminator = Model(img_input, x, name='discriminator')\n","      self.discriminator.summary()\n","\n","    def stabilize_discriminator(self):\n","      self.discriminator = self.discriminator_stabilize\n","      self.discriminator.summary()\n","\n","    def init_generator(self):\n","      noise = Input(shape=(self.latent_dim,))\n","      x = PixelNormalization()(noise)      \n","      x=WeightScalingDense(n_units=4*4*FILTERS[0], gain=np.sqrt(2)/4, activate='LeakyReLU', use_pixelnorm=True)(x)\n","\n","      x = Reshape((4, 4, FILTERS[0]))(x)\n","\n","      x=WeightScalingConv(FILTERS[0], kernel_size=(4,4), gain=np.sqrt(2), activate='LeakyReLU', use_pixelnorm=True)(x)\n","      x=WeightScalingConv(FILTERS[0], kernel_size=(3,3), gain=np.sqrt(2), activate='LeakyReLU', use_pixelnorm=True)(x)\n","      x=WeightScalingConv(3, kernel_size=(1,1), gain=1., activate='tanh', use_pixelnorm=False)(x)\n","      \n","      g_model = Model(noise, x, name='generator')\n","      g_model.summary()\n","      return g_model\n","\n","    def fade_in_generator(self):\n","      block_end = self.generator.layers[-2].output\n","      block_end = UpSampling2D((2,2))(block_end)\n"," \n","      x1 = self.generator.layers[-1](block_end)\n","\n","      x2=WeightScalingConv(FILTERS[self.n_depth],(3,3),np.sqrt(2),\n","                           activate='LeakyReLU',use_pixelnorm=True)(block_end)\n","      x2=WeightScalingConv(FILTERS[self.n_depth],(3,3),np.sqrt(2),\n","                           activate='LeakyReLU',use_pixelnorm=True)(x2)      \n","      x2=WeightScalingConv(3,(1,1),1.,activate='tanh',use_pixelnorm=False)(x2)\n","\n","      self.generator_stabilize = Model(self.generator.input, x2, name='generator')\n","\n","      x = WeightedSum()([x1, x2])\n","      self.generator = Model(self.generator.input, x, name='generator')\n","\n","      self.generator.summary()\n","\n","    def stabilize_generator(self):\n","        self.generator = self.generator_stabilize\n","        self.generator.summary()\n","\n","    def compile(self, d_optimizer, g_optimizer):\n","        super(ProGAN, self).compile()\n","        self.d_optimizer = d_optimizer\n","        self.g_optimizer = g_optimizer\n","\n","    def gradient_penalty(self,batch_size,real_images,fake_images):\n","      epsilon=tf.random.normal([batch_size,1,1,1],0.0,1.0)\n","      interpolation=epsilon*real_images+(1-epsilon)*fake_images\n","\n","      with tf.GradientTape() as gp_tape:\n","        gp_tape.watch(interpolation)\n","        prediction=self.discriminator(interpolation,training=True)\n","      grads=gp_tape.gradient(prediction,interpolation)\n","      l2_norm=tf.sqrt(tf.reduce_sum(tf.square(grads),axis=[1,2,3]))\n","      return tf.reduce_mean((l2_norm-1)**2)\n","    def train_step(self, real_images):\n","        if isinstance(real_images, tuple):\n","            real_images = real_images[0]\n","\n","        batch_size = tf.shape(real_images)[0]\n","        \n","        random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))\n","\n","        with tf.GradientTape() as tape:\n","            fake_images = self.generator(random_latent_vectors, training=True)\n","            fake_logits = self.discriminator(fake_images, training=True)\n","            real_logits = self.discriminator(real_images, training=True)\n","\n","            d_cost = tf.reduce_mean(fake_logits) - tf.reduce_mean(real_logits)\n","\n","            gp = self.gradient_penalty(batch_size, real_images, fake_images)\n","\n","            drift = tf.reduce_mean(tf.square(real_logits))\n","            d_loss = d_cost + self.gp_weight * gp + self.drift_weight * drift\n","\n","        d_gradient = tape.gradient(d_loss, self.discriminator.trainable_variables)\n","        self.d_optimizer.apply_gradients(zip(d_gradient, self.discriminator.trainable_variables))\n","\n","        random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim))\n","        with tf.GradientTape() as tape:\n","            generated_images = self.generator(random_latent_vectors, training=True)\n","            gen_img_logits = self.discriminator(generated_images, training=True)\n","            g_loss = -tf.reduce_mean(gen_img_logits)\n","        g_gradient = tape.gradient(g_loss, self.generator.trainable_variables)\n","        self.g_optimizer.apply_gradients(zip(g_gradient, self.generator.trainable_variables))\n","        return {'d_loss': d_loss, 'g_loss': g_loss}"],"metadata":{"id":"JQETwf6LoQEo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class ShowImage(tf.keras.callbacks.Callback):\n","    def __init__(self,prefix,res,latent_dim=512,steps_per_epoch=1000,epochs=50,):\n","        self.latent_dim = latent_dim\n","        self.prefix=prefix\n","        self.steps_per_epoch=steps_per_epoch\n","        self.epochs=epochs\n","        self.res=res\n","\n","    def on_epoch_begin(self, epoch, logs=None):\n","        self.n_epoch = epoch\n","\n","    def on_epoch_end(self, epoch, logs=None):\n","        n=6\n","        k=0\n","        out=self.model.generator(tf.random.normal(shape=(36, self.latent_dim)))\n","        plt.figure(figsize=(16,16))\n","        for i in range(n):\n","          for j in range(n):\n","            ax=plt.subplot(n,n,k+1)\n","            plt.imshow((out[k]+1)/2,)\n","            plt.axis('off')\n","            k+=1\n","        plt.savefig(\"generated/gen_images_{}x{}_{}_epoch_{}.png\".format(self.res,self.res,self.prefix,epoch+1))\n","\n","    def on_batch_begin(self, batch, logs=None):\n","      alpha = ((self.n_epoch * self.steps_per_epoch) + batch)\n","                 / float(self.steps_per_epoch * self.epochs - 1)\n","      for layer in self.model.generator.layers:\n","        if isinstance(layer, WeightedSum):\n","          layer.alpha.assign(alpha)\n","      for layer in self.model.discriminator.layers:\n","        if isinstance(layer, WeightedSum):\n","          layer.alpha.assign(alpha)"],"metadata":{"id":"9vKZV5714wBr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["1/4999"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HpC4lmxDBChW","executionInfo":{"status":"ok","timestamp":1659669596135,"user_tz":-60,"elapsed":818,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"fef8614b-97e6-4bb9-fd27-7c91816bb942"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.00020004000800160032"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["!mkdir generated"],"metadata":{"id":"TNCJVXWi48KF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["STEPS_PER_EPOCH=1000\n","EPOCHS=50\n","NOISE_DIM=512"],"metadata":{"id":"kJfwjLXrDPQu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["50*16*1000"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8kz58eL2EJ5V","executionInfo":{"status":"ok","timestamp":1659669832462,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"57c1a212-6e8e-4102-e34f-d105b4e02ad7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["800000"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["train_dataset = create_dataset(4,BATCH_SIZE[0])\n","generator_optimizer = Adam(learning_rate=0.001, beta_1=0.0, beta_2=0.99, epsilon=1e-8)\n","discriminator_optimizer = Adam(learning_rate=0.001, beta_1=0.0, beta_2=0.99, epsilon=1e-8)\n","\n","pgan = ProGAN(\n","    latent_dim = NOISE_DIM, \n","    d_steps = 1,\n",")\n","\n","pgan.compile(\n","    d_optimizer=discriminator_optimizer,\n","    g_optimizer=generator_optimizer,\n",")\n","\n","cbk=ShowImage('initial',4,latent_dim=512,steps_per_epoch=STEPS_PER_EPOCH,epochs=EPOCHS)\n","pgan.fit(train_dataset.take(STEPS_PER_EPOCH),epochs = EPOCHS, callbacks=[cbk])"],"metadata":{"id":"mQirF3Dx54VF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for n_depth in range(1, 6):\n","\n","  ################ Faded\n","  pgan.n_depth = n_depth\n","  train_dataset=create_dataset(4*(2**n_depth),BATCH_SIZE[n_depth])\n","\n","  cbk=ShowImage('fading',4*(2**n_depth),latent_dim=512,steps_per_epoch=STEPS_PER_EPOCH,epochs=EPOCHS)\n","  pgan.fade_in_generator()\n","  pgan.fade_in_discriminator()\n","\n","  pgan.compile(\n","      d_optimizer=discriminator_optimizer,\n","      g_optimizer=generator_optimizer,\n","  )\n","  \n","  pgan.fit(train_dataset.take(STEPS_PER_EPOCH),epochs = EPOCHS, callbacks=[cbk])\n","  \n","  ################# Stabilized\n","\n","  cbk=ShowImage('stabilize',4*(2**n_depth),latent_dim=512,steps_per_epoch=STEPS_PER_EPOCH,epochs=EPOCHS)\n","  pgan.stabilize_generator()\n","  pgan.stabilize_discriminator()\n","\n","  pgan.compile(\n","      d_optimizer=discriminator_optimizer,\n","      g_optimizer=generator_optimizer,\n","  )\n","  pgan.fit(train_dataset.take(STEPS_PER_EPOCH),epochs=EPOCHS, callbacks=[cbk])"],"metadata":{"id":"wUvMOjlu5yq-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pgan.generator.save_weights('pggan_generator_128.h5')\n","pgan.discriminator.save_weights('pggan_discriminator_128.h5')"],"metadata":{"id":"S0fS4eB11bQZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"HNzyECYl1bXE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"IfjOBwLC1bZe"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"zdx4Mw_K1bb0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def init_discriminator():\n","  img_input = Input(shape = (4,4,3))\n","  img_input = tf.cast(img_input, tf.float32)\n","  \n","  # fromRGB\n","  x=WeightScalingConv(FILTERS[0],(1,1),np.sqrt(2),activate='LeakyReLU')(img_input)\n","  \n","  x = MinibatchStdev()(x)\n","\n","  x=WeightScalingConv(FILTERS[0],(3,3),np.sqrt(2),activate='LeakyReLU')(x)\n","  x=WeightScalingConv(FILTERS[0],(4,4),np.sqrt(2),activate='LeakyReLU', strides=(4,4))(x)\n","  \n","  x = Flatten()(x)\n","  \n","  x= WeightScalingDense(n_units=1,gain=1.)(x)\n","\n","  d_model = Model(img_input, x, name='discriminator')\n","\n","  return d_model"],"metadata":{"id":"__UnwY0VEPbz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["init_discriminator().summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BfDf5vjsEnuU","executionInfo":{"status":"ok","timestamp":1659536252591,"user_tz":-480,"elapsed":859,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"060c23ca-044c-4415-9f71-9be8cc664bb7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"discriminator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_32 (InputLayer)       [(None, 4, 4, 3)]         0         \n","                                                                 \n"," weight_scaling_conv_49 (Wei  (None, 4, 4, 512)        2048      \n"," ghtScalingConv)                                                 \n","                                                                 \n"," minibatch_stdev_4 (Minibatc  (None, 4, 4, 513)        0         \n"," hStdev)                                                         \n","                                                                 \n"," weight_scaling_conv_50 (Wei  (None, 4, 4, 512)        2364416   \n"," ghtScalingConv)                                                 \n","                                                                 \n"," weight_scaling_conv_51 (Wei  (None, 1, 1, 512)        4194816   \n"," ghtScalingConv)                                                 \n","                                                                 \n"," flatten_4 (Flatten)         (None, 512)               0         \n","                                                                 \n"," weight_scaling_dense_8 (Wei  (None, 1)                513       \n"," ghtScalingDense)                                                \n","                                                                 \n","=================================================================\n","Total params: 6,561,793\n","Trainable params: 6,561,793\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["def init_generator():\n","  noise = Input(shape=(512))\n","  x = PixelNormalization()(noise)\n","  \n","  x=WeightScalingDense(n_units=4*4*FILTERS[0], gain=np.sqrt(2)/4, activate='LeakyReLU', use_pixelnorm=True)(x)\n","\n","  x = Reshape((4, 4, FILTERS[0]))(x)\n","\n","  x=WeightScalingConv(FILTERS[0], kernel_size=(4,4), gain=np.sqrt(2), activate='LeakyReLU', use_pixelnorm=True)(x)\n","  x=WeightScalingConv(FILTERS[0], kernel_size=(3,3), gain=np.sqrt(2), activate='LeakyReLU', use_pixelnorm=True)(x)\n","  \n","  x=WeightScalingConv(3, kernel_size=(1,1), gain=1., activate='tanh', use_pixelnorm=False)(x)\n","  \n","  g_model = Model(noise, x, name='generator')\n","  return g_model"],"metadata":{"id":"iLniuAc3nodQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["init_generator().summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IGWT9kmWnqNq","executionInfo":{"status":"ok","timestamp":1659536252592,"user_tz":-480,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7dd353e7-808f-4628-d947-3c1021a776a4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"generator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_33 (InputLayer)       [(None, 512)]             0         \n","                                                                 \n"," pixel_normalization_9 (Pixe  (None, 512)              0         \n"," lNormalization)                                                 \n","                                                                 \n"," weight_scaling_dense_9 (Wei  (None, 8192)             4202496   \n"," ghtScalingDense)                                                \n","                                                                 \n"," reshape_9 (Reshape)         (None, 4, 4, 512)         0         \n","                                                                 \n"," weight_scaling_conv_52 (Wei  (None, 4, 4, 512)        4194816   \n"," ghtScalingConv)                                                 \n","                                                                 \n"," weight_scaling_conv_53 (Wei  (None, 4, 4, 512)        2359808   \n"," ghtScalingConv)                                                 \n","                                                                 \n"," weight_scaling_conv_54 (Wei  (None, 4, 4, 3)          1539      \n"," ghtScalingConv)                                                 \n","                                                                 \n","=================================================================\n","Total params: 10,758,659\n","Trainable params: 10,758,659\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"ZOkC-JPf1bd7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Jc6fNAXI1bgR"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1iE5YXHA-WUO4jC5nRovAJBUAFeKJiVp8","timestamp":1673810453118},{"file_id":"1elGSbDXBiT1kXW7ggwqFWOJhYLsskS7c","timestamp":1658819763574}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

================================================
FILE: deep learning for image generation/5-SRGAN By Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"zGcCsOilc8b1"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import cv2\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input,GlobalAvgPool2D,PReLU )\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"markdown","metadata":{"id":"gPLHB2s5gBJu"},"source":["# Prepare Data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19171,"status":"ok","timestamp":1659773689345,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Plya5Y4OeOPl","outputId":"69a77bc7-8bca-4817-d300-b54f466ceadf"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/celeba_hq_256/25000.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25001.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25002.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25003.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25004.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25005.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25006.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25007.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25008.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25009.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25010.jpg  \n","  inflating: /content/dataset/celeba_hq_256/25011.jpg  \n","  inflating: 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folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"j9_RtczneOSH","outputId":"9086b382-ba4a-4ded-cd2f-99523478f6e0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 30000 files belonging to 1 classes.\n"]}],"source":["ds = tf.keras.preprocessing.image_dataset_from_directory(\n","    \"/content/dataset/celeba_hq_256\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YZjG7N8geOU_"},"outputs":[],"source":["def preprocess(image):\n","  return tf.image.resize(image,[IM_SHAPE[0]//4,IM_SHAPE[1]//4],method='bicubic')/255,tf.cast(image,tf.float32)/127.5 - 1.0"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1659773694123,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"X2lAK49h977P","outputId":"b5a35f95-e26a-43de-c1a6-0d33cba8f321"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BatchDataset element_spec=TensorSpec(shape=(None, 64, 64, 3), dtype=tf.float32, name=None)>"]},"metadata":{},"execution_count":6}],"source":["ds"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_JBWJWy0e-G9"},"outputs":[],"source":["train_dataset = (\n","    ds.take(12000)\n","    .map(preprocess)\n","    .unbatch()\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE,drop_remainder=True)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1784,"status":"ok","timestamp":1659773695902,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"bssNU2hc0eo3","outputId":"7dbea1a3-1814-4b1b-9707-0ea58f87b323"},"outputs":[{"output_type":"stream","name":"stdout","text":["(128, 16, 16, 3) (128, 64, 64, 3)\n"]}],"source":["for d1,d2 in train_dataset.take(1):\n","  print(d1.shape,d2.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":483},"executionInfo":{"elapsed":838,"status":"ok","timestamp":1659773696735,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"5AhVXUnre-O1","outputId":"a6db6f0b-d595-4339-fa58-dfe93da2e7b5"},"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 6 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYEAAAHBCAYAAACPAQdIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9a7Bt21Ee9nWPOdfa+5z71IUrBEIWCCEZIcCBYHAs28QxAZLCcRzKFcc2pOLErnJSZVflV6qSP/EP/0kqKeeHXakEx86jkjKppAJO/OYVgx8gKwiBBHohAZIQku7r7L3XnGN0fnT3GGOuvffY59wrF75n9Xdqn7nW6jXnmo8x+t09SEQQCAQCgdME/1afQCAQCAR+6xBCIBAIBE4YIQQCgUDghBFCIBAIBE4YIQQCgUDghBFCIBAIBE4Y04j43vd/aJg/yneIEKIx/cXPfGJI/8yvf3RIP6NXhvT7Z+Pff/75rxnS5Q4Z+esf+eUh/eJwGNIL7Yb0c1mH9Le8691D+iGP039lzUP6N7znu+54goHXE77rz/6N4YCgO7LF6Y4Jfe+O0fInfu8nh/T3/f0fGdLz4XJI/7I3femQ/o53/44h/S3vfMeQ/qPvvRrSf+T994b0u+7fnXiN+//Qn//OGw8QlkAgEAicMEIIBAKBwAkjhEAgEAicMEIIBAKBwAkjhEAgEAicMEIIBAKBwAkjhEAgEAicMIZ1Ane3mR7nra55GdI//emPDOlnu7GM2pXx8dc1DenzvaeG9FdeenlMf/BgSP/Upz4zpP/Mz/zMkP5v/uE/NKTv7o/P/+l7Twzpn/zYh4f0wOOFu9LMmcdfIB7Ph//8j4zrTv67//QvDelPPDGe71/2tueH9O/87vcM6fee+pIh/ezeuLDo+37PG4b0H3nvZ4d0PtsP6aWUIT3dUZj1ausQwhIIBAKBE0YIgUAgEDhhhBAIBAKBE0YIgUAgEDhhhBAIBAKBE0YIgUAgEDhhhBAIBAKBE8ZrqhNY7+hH/75/8DeH9F/7+Z8c0v/Ad/1rQ/onPvb5If3r3/0tQ/oLv/TzQ/rTT43z8N/2xqeH9Ld89VuG9N/3B//okP6xf/TDQ/r5E+Pff+WFz92x/5NDeuDxAt+1YEAa64T/8R3rAfzKB8d58r/jd79tSH/XV711SP/SN47rBPZ5XBf00b/3Y0P6N3zfnxzSE43rCP77Pzck40/95ReH9Lvy/OmO5/dqlxsISyAQCAROGCEEAoFA4IQRQiAQCAROGCEEAoFA4IQRQiAQCAROGCEEAoFA4IQRQiAQCAROGMM6AaJx3u1nPz7Os+d13H/8u//t/2BI/39+8C8M6b/7jjqAe+vVkP4lb/mqIZ0wvv7p/KuH9MPl+Pp/9p/83SH92/7wvz+k/8rP/vSQvvD4/M+fenZIDzxe4DTuV//l6UND+nNPj/Pkf+rHf3ZI/9Z3jOfbG54ar3+x35+P6fefG9K//l/9I0N6eeXTQ3p+Ynx8ukOlTnkd0vn8rvUGxnVZaR7P91t/91XtFQgEAoHHAiEEAoFA4IQRQiAQCAROGCEEAoFA4IQRQiAQCAROGCEEAoFA4IQRQiAQCAROGMM6AdzRfnw+H+f1vuVd3z6kl2WcR//5T35qSH/yPeM898S7IZ35jrzcOxJ/p934+gvNQ/pbv+T+kP7hn/6JIf2rvvlbh/TP/MonhvRPfPgXh/Tf/s2/Z0gPvL5w13j/jrd+dEif+V1D+lMYN7RPd9St8G5chzDff2Z8/PMxP6A0vv5XPv3+If3pN47XQ5iWw5D+F//MMqT/ub863p/vqFsijOtAbj9uIBAIBE4WIQQCgUDghBFCIBAIBE4YIQQCgUDghBFCIBAIBE4YIQQCgUDghBFCIBAIBE4YwzoBwbh/9RueH/cH/9gHf3xIP3/uqSH9Vz8zriOY7qhTIB6XQeCO/tt8x3oK5Y686On+vSH9Dc++dUhPL31mSP/gT//tIf2rv+n3DemXD14c0gOPF56ePz+kv+vrv2FI/7s/9MND+jP5wZB+dsd6AIfL8f55HRcucb4rT/5ySD1/4suH9N/8lZ8b0p9+eszP6OpiSC95zE92Z+M6B5E7CrtuQVgCgUAgcMIIIRAIBAInjBACgUAgcMIIIRAIBAInjBACgUAgcMIIIRAIBAInjBACgUAgcMK4I5F+jHxHHcGDl35zSP/sg88O6U8+//yQ/pnPj/PcL+VjQ/rXPD3uP/7L/2Schy93rCfw3Fd93ZD+xPqFIf2pO+ogHnzuA0P63/k/f3BI/47v+aNDeuDxwm+/974h/fzeO4f0X71j/YmveNtXDulXl+N++eWVcV3QXf38D698bkjf3xvzk8ODl4b0Fz/3q0P6E+/+nUP6+fnTQ/r3ftOYX/7ND43XJ5nGZU23IiyBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGM1xO4oz8187j/9f078tw/8g9/ZEh/w9Pj/V88e+uQ/o9/4ieH9De/ebwewvNf8+4h/YO/Ou5P/tf/6/9mSP+P/vSfHNJTWYb0Nz0zrkP46Md/Ykj/9EfHeeNvetv4+gOvL3zNm8Z1NdN4OuNL3/DGIf2JZ8brZzy4GOfh//wHfn1I/9//xv87pL/n68fz+Ru++98a0n/4L/+VIf1/+tGfGtL/x7//14f0Z57+siH9X3r7ONH/7334rvUC7niAtyAsgUAgEDhhhBAIBAKBE0YIgUAgEDhhhBAIBAKBE0YIgUAgEDhhhBAIBAKBE0YIgUAgEDhh3LGewDgv9aXfHPfX3qfx/iuN+2N/47veMaT/9qfWIf1b/tD3Dun37z03pKercX/zN+1+Y0j/U//e9w/p9558Zvz7d9RpvPTZjw3p+cUHQ/pHfmacd/1N/8ofG9IDry/c2++H9CzjPPN3fN14vYGLXx+vN/C2d473/47v/NohfX5q3I9/ffkz4/1pN6T/wf9wPN5/73d/+5D+3FPjuqbLy3EdxJ5fGdIzj3X23R302xCWQCAQCJwwQggEAoHACSOEQCAQCJwwQggEAoHACSOEQCAQCJwwQggEAoHACSOEQCAQCJwwhnUClMYy4oM/+b8O6W98/kuH9C986tND+tPTk0P6s18yPv6vffIjQ/q9s7MhfZ7G/b3f8pXjvGbckbcruQzply98akh/+XPjOoW3vvlNQ/rnPjuugwg8XlgvLob0kq+G9G//N75rSH/vX/vEkP7CL4/pb/qm54f08zvqaj79qx8f0vnLx3UShxfH8+n5d3zjkP5L/9tfHtKf/PZxncGzb/jKIf0eH4Z0xvj6bt8vEAgEAieLEAKBQCBwwgghEAgEAieMEAKBQCBwwgghEAgEAieMEAKBQCBwwgghEAgEAieMYZ1AuRrnpZ499yVD+gc/8N4h/U1vGucF873zIX25GJ/f00+P+4/P98f05cWPDukvr3n8+2948/j4X/jskH548NKQLnfUcUgar9fwzm/+F4b0wOOFdMd6Aam8OKSvu68Y018e15189nI8X5/89IeH9Afr+Pi/9Au/NKR/+uc+MKS//bt//5D+Uz/4F4f08+fG/Opz/3S8fke+Y72FP/aNY/qP/H9jfnIbwhIIBAKBE0YIgUAgEDhhhBAIBAKBE0YIgUAgEDhhhBAIBAKBE0YIgUAgEDhhhBAIBAKBE8awTuALn/mF4c77/RND+r08zgt+9i3jfvyXl+P+58Tjfvy/+KM/OaS/+z3jPPrn3/2tQ/oTZRnSf+2D4zqJXVmHdNyxnkEuY/onPvaxIf2/+p//ryH9r/2BPz6kB15feO6N47qeT318vP7GC5/9tSH9/jNfNqTfe3qscz737t85pL/vr/+tIf13/Ts/MKRnvjek/+Lf+uEh/Xf+wPcP6e/96f97SH/uyfH1f/bz4/n8hvs/P6R/xzvGdUm3ISyBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGEM6wR+41feP9z5s+//sSGdMO5f/qVvGuctf+SXxv38f+FTnxjSn3vzbxvSn//Xv29IPyvj8//Me//pkJ7OhrcXnMcymPJ4/zWP+4d/8rAf0r/mL/y3Q3rg8cJHfuFDQ/rbv+6rh/Tn3zKmf+YwXv9ifd/HhvTPfnTc7/+N3zb+/S/87D8Y0vn8/pD+jd8zXk/gF37s/xjSv/ztXzmkz0+/bbz/s+P5+oP/6Kkh/cue2Q3ptyEsgUAgEDhhhBAIBAKBE0YIgUAgEDhhhBAIBAKBE0YIgUAgEDhhhBAIBAKBE0YIgUAgEDhhDBPR8+U47/fZNz4/pD/xzFcM6Z/62MfH+9+xXsEnX/jCkP71XzauQ/j1H/prQ/q9r/iqIX1/fj6kl904L3l9+TeH9KurB0P6h94/rlN45zPjvOL/4vu/d0jH+39lTA+8rvDRXx6Ptz2N1/9485eMx9NTz43z+H+OxnUAH//b4/UC3vGt3zL+/be+a0g/e/LZIf3i82N+9JFf+MUh/Rue/Zoh/Y1vf/uQ/qlP/OMh/fu/bVy39Jd+fFxncBvCEggEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETxrBO4FDG/eyppCH95TzOa/3Cb3xuSH/7v/g7hvQPfeB9Q/qP/tyY/gN//E8N6fsnnxzSBeuQznmcd/3Cb35qSP/oR8Z5ye9419cN6edPPjOkv+HJHx/SA48XHrwyno/zF14Y0j/yvvcO6V/xte8c0r/p93/PkP5X/pP/ckj/jU+M62aefeInh/TljvVLvvZbvnFIf8NXjff/xx8Y1xn8tvc8PaT/098Yz9e3r+M6j9/1/Dyk34awBAKBQOCEEUIgEAgEThghBAKBQOCEEUIgEAgEThghBAKBQOCEEUIgEAgEThghBAKBQOCEMV5P4IVfG+78wudeHNJ3X3hlSP/cb35+SP+6e+P1BOTF8XoHP/VT/2RI/3f/xPcP6ZcPxnUMH3vfTw3pv/HrHx7Sn3vDc0P6173rm4d08LhO4+ryjvUW3vrW8fEDjxX+wDvG8+nZLx2vj/Hs8+M8+Z/7Oz88pH/r9/+ZIf1P/8X/bEj/mf/hfxnSv/qd7xjSP/DJMT9by7gO4c3v+Noh/cuX8f5pGvf7/7Z3jnXyD3x8XLf0Dz885rf/8i2fhyUQCAQCJ4wQAoFAIHDCCCEQCAQCJ4wQAoFAIHDCCCEQCAQCJ4wQAoFAIHDCCCEQCAQCJwwSkd/qcwgEAoHAbxHCEggEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGGEEAgEAoETRgiBQCAQOGFMI+J73/8hOf6M6Pr3bvrsmCYikFIAKch5gZSCsi4QKcjrAVIK8rqglFy3q32+LgeIFJSSASlgFAAAUwEg9l7AJCAAzLhhS0gpgZgxpR2IE6ZpB+aENO1AxGCeoKer/0v3GgBKziilYD1coeSC5XCl53k4oJSCvK56nSIQv+b+fbsr0DP139HX/ktcP+m3hGlKYE6Yz/bgNGHenyGlhPnsDMwMnib7Nurv672335bucQogqIT6WY9veM93DZ5s4PWG7/qzf+PafK6QfqTfDuomexuvNs/Itvb6fJcxJ8GzTyzYTwXP3F+wnwuevb9g4owJF5Cy4PLll7AuB1y+8hLycsDFyy+hrCuWwyVESh3AIjrvRXyeE4gI05zAxJjmCcyEaZrAzNjtduCUsD87Q5om7M/OkVKq293ZGTglzPs9EuuWU8JuvweYkYWRC+GVi4IlA69cCJZV8MqlYFmBly6BJRNeuiQcMuGlS7YtYS2Eq5U2U45GjPJh8Br3/6E//503HiAsgUAgEDhhhBAIBAKBE8bQHfTFgMjWipHuP4G6SpobwtwXnXvi2H6lO7bDc/Hv0fW9bnp1fFSp59f+H/3ate931yObb24/OX6l5yzHn9x0ikcfq2sIJCABpPnmAIJ99hCXEnj88RDjQIdP/yV1YZK5gah3B7G6hNRF2/4gBdL/lWJuYoEU5wey4QFHL7ZDnvQsiOz3idofU3UZ6R/q1nlAdcrW/dsPkLtoifT86/76mo+O6df/esMjC4Fjpv5w+/SsTyAo5ivXgVAka4ygFEjR1+r/k40fm8xxSd2xelw7LeP6Ih1Rjr9NIDBAbD67Y4nVrltPpzFzFWJHvn8RFCn2uW7be48V9H56oNjvEOmgSzaoUjegQcrQe/nl56v07hqk+47fKdH7p7ez1Gsin25VMAdOFjfF+279ssXgCJiYwARMpIx/YiCR4HwumFLBjldMlEHlAMoZ+eoKRBkil5CyYD1cIS8LynJAWVdIzkDOgMUQnQeQj07SGFliZfQpMTgx5kljAvNuAnPCbjerj9+28zSBp0ljBilpjC2luj8zg5nATaJAoNeDRJiTCrJ1ZjABh6xz8DLp9xJTFX7OSeTonv2zFhNU/3t4/DO3BHrdtzHN64yzaQfGXqs2cDujb2HVI0HRfWt426l7WteOjlu1I2eqlXHaB+1aPBBuV2DbUuweFHTvdaunY5pHYtVCEoMgYGI3BLrT7NUv6oSCbv26pft6U65MUByZAiEAThc3zZHrn3XzzOYNEZBYwATMrBbAjgFmwW4qmLhgooxEGVRWgDJkXZApA+UAKSvKslbmLzlXRRBmIRDdwAPst5nImC+BEyMxI6WE5EyeU32fUlJGb9/j/o+o/rlmL9DjMwMsgmTMfkoa8J2T8oHEBC5u+fS3q3GfakS9Whkw2O9m9vXwP/TPUAhsHR6N8au2X0pGyesmGyhbtlDJ6yabCG5SVjNty7tdCDRL4RZ2ZjxPNWo39hhEDGLdVssBqG4T1dqvC7AiglJM8y+ilowIsm3L0TbngmJbEcGa/fMMETMvmbCbZjATdvOsg3WegMQdw2/avw5YBnGya6DNHamb3hLJWe2x3DKEVI6FGDhFHLOLa+yjMuE2z5T5qwA4m4EpAfdm1ZrPd8BEgn1awciYcQmWFWl9AKwrDpevgGQF8gUkr1ivLlDKiuXqUnnC1ZXO/by2eU9qbYAAYmXS06TMfL+bLOtnB54S9vs90qRZPokT5p1m/eiWMe80K3DezWBOmOYJlFizjJgxJZ1HDEZh4KwQctL7kote45L1vK5WtQiYgYtFrfpEQKbeDYHOhD8SDD2/ueMBXX8ut3/wKLLmVQmBu11Ct/m3qxPlBvdJ/6e+Q3dPVJeFMbNez98KADm6+O6bcu32V21io9ZszndrClxzT7ZL2l5T2W43wqEo0y+lYM0ZUgTrmqvWQWaOJmGUxKaRyNFAcUGAjVCobiGok2tjG5LfBIEwgQo27qKwA04Tw2l8g2Hcu2Fd801JMCVgnlQ7PpuBiQV7LmDJ4JxBWMF5AcmCkg9AWYD1CqWsmgKeM8qiyqBaA2YJQACmOpTNeWtWgKaJpsT6N6m2P89T3TqTZ+bq/plScwMRN1eQu5fcHcR2xcnSZ6akv1kmVSLnSR2rUxJMnSVAlQ8pD+lf+2YzNW07EgZ3M3V6iO/cjKEQ6DXD4xzXXhDcGPztX0nP+DXfv5SMXFaUnJFXzbP3ugHPDZaNPxAbfuZb13t7/Xdz4535+7m2KwJqLKD9gQAUMZdJn+8P8+9vLYAiGssoNabhmv12W0SwritKKTgcdLssC7JtRaSapuXsDNOUVNspxV6Tem46awBuwbBbM6nGD6gTakRs12ECdgWEBCQWn0FYAqeAu2J5GwUJ/XxybbbU90SClJQBnu+A3QQ8da7bZ84FEwt2WIGyIF9coqwH5KuXUdYDlosXUfKCfPVAvQJ5gUiu87+sB5v3xRimauaJlS07898lRprMEpgmnJ/tMc0Tzu+dW12A1s9M807rg+bd9v00g5iR0qTbSesNklvdVoFEIBRRC6AIYTcBqylRu0VwcVDNdDcV5OLBcHTBuMb86x2+4VncKAzohq9eswxuNhUeVig8tCUg4u6YR5A3xzyld6dAagC4dAHiyvz1R1E1D8twqdcrTeun7sf613eea1Uvuj//XPpjDNxLshUS14LEpRMYuSCXgpxVCKzrgpwLlsNBLQFW8zbPE4isOI2ounHq6dQL7yyYIxeRktnIKhSlsLqBKh0Q6e2qwGnjpvnipveR+VszZASJVUveTcB+As5mFwIFyAVXtKLQilIWIB+QlyuUVQPCUjIkr+Ymtm1WC4BQdFyzZeaYssc2npnNEmDClBjTpNr+PKtQmM0CSPOkzH1Sd49bACmp8lS3VZlyJQogIbWcBUBSYUDQGMBuEhQhTKlgSmKxCdlM0XbPXq2efteTun7cEe0mPJI7SIXazQy2CTxTvauTwVxAIihoLh+1BDQu4DGBkpeO8Suj11+ybS8A6qAQ+565hqrk7Ji3UNV0yf1KGzdKZwnY90Cimo+IHXkb9HWLoLmAOoug0/ylCFZz/xwOB+RccHV1iZwzLi+vdHt1CRFBsiAWETDPE6YpKdMuexS/we7ycf8/e0ygWQLEFlg2Jyoxq8BiPT+21LxCDKraXVgCp4pjVnHdYvD553OzWEygYErA2U7jAE+dC852wHNPAjMXzHlBWRe8fHWJJV8hry+jXF1ieekLWA8HHC4eQKSAPBMQzvx1/rGneHICg8ACZeakAmA2xr/fTZjmGefne8zzjPv3zRI4P1emP83q459mMDHYNH9OCdopwLZJ5w/73CKthU42j4qQzX9WSwCCKQle3Ct/2E0ZS3aXkPGvym+clz2EY4dud7lvP6NbLIVjm26M1xAYvi7d+lRQlQNHMQDX/IvnB+fqGlIh0A0Cu4prAxR2jbfxq3r3tufXbkv3mdx2m6R3fbb3fny5YVvvSZ9y2dwsm3tznBVVmpAkJrtP0t2/dux2Ldi4faobqE91Jbb75fYR1YD4NnpSoy6BU8dNLgWzBFwIaMaaZgCpFSCYWOMBuyTYJWBOBROpdT9RQaEMkhUkK2RdIdkygqToaCSpvnQinVN9O4qavcMtGygxWTaQ/k2Ja1ZQmibz/avPX5k9GZOnmll0fdtiAjUFm61xi/pjIWKB6USYJ8Fs1z8x1W1iATO2Tomt62I44+4WALhZAFx/hHfiNdUJOGM7fi+STWL2W1Hff9aeICWvOFg2wHJ1YQGhFYD1BHEJXDVbk65+EtUCsAyiDde24g1n/cYYmdhMyT5moMJJrRjTiov3K/FAQHvv1of2R1HLw7MWfBAJEwoAFgahQCzbIGcNAKekPX7SlAHSrccEkgWtuMv22abISWfIeEGMBbjcnO2tHLiAAKj01k8vPFC/F8LgdEDXXmwZiI/vZAyaqWgsgDISC+7vBLtJcG8WnM/A+c62s2DmgoQFpSy4pAOEDkhyQJYDUA6ABYepFJvvmmpJ1PUEmth8+RM4EaadBXh3E1Ji7M92mKYZZ/sd5nmHs90O027GfrfDNE3Y7eZmJfdjXQoIDCrF6Fp3oELHhQSDKNkd8cYKtiVGKZYhxIJ7e1XV7p/r+yfPBfOkvCkXIJsnwi2JjCO//w3vrz2rY+F8q5B4dLfTFz1FtOXHa1YMRJTBS2sQV9ZV/eJ5QV5X5FVTRaWsyuxFAzNC/lioCtJrvjZx4eNaylY8bioIe8bYhRs8cE3Fi6gskd+25ALAtPdWAdksE9cgimsSDLCouGA7v5SSMX3dTjmDiDHPpQWGEyNNU8tpdteOaWPX0d2ZLrZhOky7F3LL7sDG/qSQASeB6wLgyFHhQwpifm51ffg2sWBOBbuk/n8vEEskYGhTR0IGkEEo+ucNHms8AQATUrLiyKTz07fTxF1WD1tjOKpZPnOakCbN9qkZQtzVAKAxS3cZq4OgaGKEu2lK0q2bHj57aDu3GhfSWFtivQ9TKpgnYDcx1lyw3+mPHlbCWoC1aCxhLWZHFXN8dXPNp+CNz+ohBMBrwatOEQV6CwAAPBWy1Hz/fltKwbqYBXD5wCyAByh5weFSswQgxfzh5rubJ/N1eyGH/lgtLTe3iRR3I6kpmaBle2w+8mS+v5QmNQv9gZZsbhjVA8Qsjuq6cUvHBAVLBklRgUGAsImdxCgEJCQUYeSsAdicJxR4XQCw5B1KEezOziw7yGIFywqB+xIZZ/sJiRln+9nMXb12Mv9pFULG89msASbuXERdfKZmOZUWx4Co5u8akk2aiAs8vngo5kFtw6TpkftJkKhgP6kFsJ9WTFzwxL5gToIn5oL9BMwoSCKQVZCpQNYD8rpAdd+ClAjTTNidTUhJkHgPiCizJw3ukgV5K7PnrQVAzBrwTYzd2d6ygqxL6DQjTTMmYrApelREA85wB4KnWxMoJe0mvAOYExgJSFakSQyiyQRBbwm4cACmSbADcP9M02QPK3BvrwLiahHcuyAsGbg8qAC4WGx70CzDJV8XBK/mGW4/u+6mvwuvyRJoGUPtBnt2TwuQlqrpr8vSAsF5RV4O9b0UZbDKXAnC7Fy207qbZtszuPpH0GwX88X5Psr49c8zZXq3EooxdjlyhlgGkgCWjeTWBkzb0U+8ZbWIa9Ksh6YCFmXsIgIwoRQBMdtWA8g8lXovmQi7OVn+c1L/J/VaWh8b6FJBN37M4+dkd8yfzxG9Ocfoi65lBF5HoO1rH1aJXPMXTFywn4rWAUwqBGbW94lUQelTwcUULQ30trx+CEOyMlcXAvOUVBhMLgRM86/FXFMnFLweYDJLIKnC5LEDt/bd5WseCljGHqCBZrBAUla1StzFrDegDw7TkVtI56pZAhNjloL9zBAU3NszEgtWcxkJgJSBtZj7mAEpGpl7lN5dDz83BXiEufwqhMD2jJsk88rZjLwsyCWb77/fXqgFcPmKbdUSWCxfWE1PBktBSgmilShgsRQv+6W8sQRU2ECk+f3E3ECwPuM8mWtl2riDyALRhdQWyG7w2YotfYYAACAASURBVN1zH5s/evIaBtKHRwwUIpAwhAlJfxrZ5FPxrd2n+rl5m3KhzXuPObCZzgmlvmc+tgTaJGXiGhcgG8yV6dtxvWitCU27JvLsIXHZGTgRXHP/HIGtIng/C2YWPLHPmFPBE/sVMxfc362YWHBvLkgkmFEwiUDWjIICrNoLCLKCUMAJ1RIoKzBPOt9mS93czSoE5nlqW4sJkAkDf98qf7muC7CbZ3CaMHkMUAQoQF5XZf5rtnVLsl4yT5opJAkyCVLaabRPGFqb0FkCG3eQBofnSWMl98/UFQQAS2bsZ+CwCu5fEA6r4AuvCK5WgFhwtQoOWYAVWMwb1RRbHL2wR3OLr5/6nW+kPRweUQjcduBOyxQXBmYJ5KyWQF61SVRemiVggeK8aLEIQwBmSJ5UQpZi1YLug1exaQmbZg2YhHfLwDmZ9L56rtYAEQDPCuqZJFTeq6VourFzWbcHungAzB3Dot8TqKbvOjqg3lDAhQFVy6FYlkGxwVTUd9PiDbKqa6zo5KHSVSDKtjCuBXbdUjINo+8J1NmcdtdwbA/obXmYBLbA6xW3PttjQnUJSe0E6pbALgnOpoLZLQISXSCGPA4g6mZF0W3JdcQxAcVjAGAwJRCg2Tzu5mHCzhaFaXn+qTF/1tgAecO4xK0CuGsAR1AGKyJa/FlKrUSWdVXHQYJOqVwgXMxS6G6CWQLUu4OoqYXaUwiYEgNQSyBxQS6MKQnWogV1l4sebkqCLAQmqU3m+lnoj0Fo+/7GR1aJ1wXBo1rzD10xfMs3Nt91F5AGexcsV5fIecXVxSvIZgHkdcV69co1iwBStBdJYsxWmy1zAmgGS9E8Ybu4Yq4bFHPlZHenEFgYMgEQAiOBKSGl2SoB1ZaQ7K1rPXBd6qUQtGgLtM2zdybsjFrcrCNj4mAUscpc6rbmdxcQhBM0Yjzp4KLJVHnNFipFOyfmw4U21jpcqGV1KIAF1zQbygNsHhPoKodhQsbS7LaFa8eWgAkQtwQiHnB6uFEAaEZatQQmZfxP7DP2U8Gz5yvmVHB/XlRAQHv8JGSwWQJe+Ss5A2IxgUnn0+5sghQGFY3Z7WeNge12GgPbz7NZBiYEps4ioGYRpMkrfef2nhjJYmMuALCuQC4oVwd1S18dTAioJZDSTufKToUBmSXApMdzRbCmW5C6eydo0BwElMLYzbp9clU30P0HwOUiICq4OGiPoXQQvHRJKJ0rHcBGGtyg87dXdzD4V+POvcMSuOUsb3y9LZyqxVNmDZTsGUFr+6y2jtagsEBbI4ho3xBvIKfZOmR/XQxASv3NXqunGg9ofvPqBoI3Uu7OvjJFk6o1k0Athz6rvu5v/nsNASgTdduu+CAkttNWYQBj+mRCAKwpbC4E/F6QLJAM5JxQSHv96Ll4hhPqZD1+RvVMXQvqhEB93cUV/Cji5kTIgccYN/oZGq2+d83d2kN4FgyrW8gtgjmZq9JStFl8vtp8tvqfakGb4pQSV3eqxgDMEpiSNnybmsZfLQDLqquWQGX61k7CEiP6Jop1Xgsaz3CrQNRlItz5bje3yNXxzg1UX7uHQb/iqa07EEqBJaNAl5cEsJsIa7EMKm7KG5qudufce7VB44fBUAhsh0h3lh5c7Nb8VN98c/OoNaDZAetyhbwuNRBc8gop6vJQD5AyOVXASSsIfRBlIC8HIDOQ1H7L69rMulK097iIVsyalaAZX4QEWLDIysL94fc9c2DsUwBYilvLG645AfXBsQV63eMiTBu2KpxM8++FAQGc1ALg2bZNGACoQjFRRskJGdpRNVulNRODUrIU0sm6KTbBqFlSZCm50nUL7ddv2Bau+QisAyj8QY8v6PjlVv0kV4/I0z210GuXMnYp42wqOJ8z7u8KdqngfNL0T7G1tbXi37ICvRdYKTbXswoVJtDMzVJnwn7eWTfQGYmTbblmCdZ2D5NbAJbtZ0KArOK3VcyrxV8EEBLLmtOtpoYmkKWIWgi5SxjZpqP306E6Sz1myEAy4SBQt5BIwV4Ya9GOAlMS3LtQfrmbUSuK3R20eSzX5t5gMlJPPXIJfTHdQTejVQNvev/XKmBvDZE379viEF1rWNZUSikJJGIpad7IjfQ4hVDyCs0U0kGqQkTUgihSP+9XMLqWy+9/7uoTsqCuOlqoe77HdQXs5mDVvvW31HKRKs1dogttmX//vmbwbB6U1GPqOajQksQAEiRNdt807XVTSNY9C/E6h3LM9BvzF9OOtIrbfps2m8Bjipt5Q9+ivY3DmpBAxbZS1w3wFtJMqiBpCaQrbuZ7l26BqNJcmFQnIWomT636tU6e217/uk6AJz94OrQXRbaWL9TeV22dIWQFYaTvmQFOSd26aermEt8wL2EWvs+MLbNV/ctb2MDmvn4tiQfWuzYS6Lbd/b/+o/Qob9txXDjdSL8dDy0ECJ11ZSZe6bZaD2Ca/rqYFrCoFtDVACTSHh5AwjyxabB76wekGSop6co9UgpWKbi80BAr+0VWdw3M/aMMMhGBhZBEkMzHzei2puFrXjw0L5jF0lHNkVIDyajZNuwxgToYYNbQNvBa480eA7DzFRMixR5/Ker6Klhsui12b5vLBgSkaYc0FfWFArVmYpr32k+Fteis5KzprVaRvIkBwOs3/PjtmW4dQroNQ+BxxlbEe68ttp49THWEarYPZcxUMLFmA02ki8SoECBd4kIEWTQIXCzVez1c6ZhbrReYZf4l+50p6XzS9GfGbuMOYqsT4BrodcWQ2av+WwcAmFBAtQjMEiAyywMQYXApAE+QUsDTvsUEOGHen4Ot6Zz2D7LmdWW12iGGq306R0TntFQOUP91N/foXvd/0vhWfS43CKBrAulm+m2WxG1ZRcd4xJhACyp6z39lOC7x26pAtQ1019LBGSmxF13ocUk0pYfdF+nOJ3NzZNGULnfP+Fl55/z+8/5GE1Dz+/2GN2au5hxEtXlfsg7m7nHNYVtpDFR9iZrm1Hf3AdCYPyzG0THZFoOQKjVKu62+l52ftrrmlJoQINK0tloYBouhNCFUvBV3XcmsVOFQn+tmgNykjQQee1DT+n3caUZQNqXJt62/T81s2bgzpPEDiwP6FiImZFCr0T17J5EzeapZPUy9BY4Wy7PXW5W6C9a6crfpnaVnyKzJ1pzUPZQsgYM4gVIC2wpkdb4DaPxOFTbnVfp/s6breygPqbFJ3DKjHsLUPvbtH4cpjh7hNRrd9OEAj+AO6gyYou4edctYLKAUbRGbM0o+aIOovLUEtBcJIbEHdKhuAYDEsgqWS83ntW1ZtMum5shDU8KIMHGyhVjMZygJCYQkBQmMJAVsbRt0MDchsFmLN/kV6uBhG2hUG7B5atiRtD56Wp2nvbmH6mtB7haZgYgFx22FMdMuiKjzfc61Fe6mu2GNJClK1vL8ZhwZ8xcxebMVUm0y2QRyC+fVRpYCrw9sHm/nMq1MfwVZtk8iwUQZMwlmKphZC8UmbxqnjWytCMbid9YCZj0oH5C8qvLVNWxj8jWJSecxe5EYY05tZS/vo6VB1qb5E7eU77aqnr9P+ltmCZAXbfKkHUgnnRPJl3M1V1Da7WqszZd0JWttTdYFTotQrZcQ9XdR/98mXvezv/9mU6RbB2R0HoaNZK0PjI6PJf137OUNfOlhZ/OjxQTM/dIYjAWE+xiAbP2A+hTMGrBrZHNrJGPmaVJNF0WFymItpdXPnbHmVbNmipmrogFVSdDMGWKNKcADwmgZQr4Fqtuo3iLXLux91Stcy/Cb6RpHFzhowrYTjvYs/RmV6i6yYUJF+5G7y8cWoSl5Nb5uvzHpY2Frd+vCgLlrdS3oYgCdvx9aku7fk27r19leETbVKnj4gRN4HNAsAJj/v8YCUMwSAIiKConaSK6zBKox2WUFZssKyl4jYFZr0jHnGr+7gxJRbRu9tQh6S4C289Bp3Wfb7rkagzPPr7Ih13V8VT1ryc7JmL8nWvSNxaRbgm+j1rmFf5PKd/0e97hpjtFdX+iPUs/F+dN2v81zeQiMhUAvXnqGUh+2MueyHGyN4GYJqCawWPC2tYRgIkzzjJQYu91e84H3e72dll0EWbEugFwUrOuKywdaT4C8aMHVbq9xg3mv5hw00yCJ+h1TN5CTpbCx+LJ0li0ADyp1waQ6oJpV0F6jox8NOt7e8Sog0Xz8asmsyFSwigDdcnrr4aB5w5y0PH43gyghzTukecb+3v3aWRQQ5MPBLKUDSi5mcZkZ7oPFBU8dPE2I1RXIeGvdPJr+EHj9oVcFpPq+GRlMxfL9CyY6YKoxAWAm1fwn26ZkMQEiLQGQAoiuB+KLxZScgXXV4OmclNnyhESEnfn6dzXlUzVytwympBp9Tf1MVGMCtfdXtwZIW1nP3KduCZDV5Vh9QvMF8GYOUzramlBEWS2rzwSZW+BVe29lq9eFgd7vpoA2ZRT+XrrZ1thNp2zaUUy7rN91l5MrcI091f0eZRa/ut5BR/nndW1gz9SpFgDQ2y7uZ1et1tf5TJgmy5P3hRdckkOPnS3YDNOYS0ogJEgq5n8/zgSSzY1HPYNO36duLVF395BHGbZ3dZsnDF3QpTJ/qu6ierXemrquk2xrBcAT0kz76lJh0aWctiwq842aAOCUAAEK58099Vvc3EDtXI6Hpbt+xC0Se57aRhpH3w487lDL191Cpjj1WxLrxdPcOb51jbMunuJxKIsPomTlmUVTNOvcrMfYxgH6eMCm4y/a62otuxLm1vyxYoaWZQhrCV1bQ1cl0PYx2VB37bX6Oh8LILyht5nSzZmO4bTzg12zNeUjyxoyd5o0xlUPIJvj2K/c4Aa64We3guUhMBYCN1k29kKMiYmnhdXCLpdSqFq2Mi+x4hA2S2DCvNvpdn8GApAXVN83CLX6eLm6Ql4PwLpoTxFzmUia6iAk6M315lFaYUwu99vi1EAdYDXVjFMdaNdsK3utz8eEBCcbMLblVL8tAISM+XcpmlQYhdV9o6sM2IQopcZMfMk+F6RuDrNpN8wTINpfSS0HbgxcbNCZcuDMffPw3FWlPqla2CJ+hfSQoybwusT28VJl/srsM2ZakZCxowMSF+xTwS4Bu8TYJcLMrAumEFkvKz+WKTQ2liWvkFXdQcqDk809FSiTuYImbllAxL7Qe6fxeyKEafiu8avS5kze+v57TMDmM4hBaQKBrT6H4BX6tVK/TvXtymY6MSwOUBU8n7Xc7XfMK9yhXGzxP82gmifGbi4422lL6ftnWkxWRHS9AagaW2zdAV9vYKNGiz4voHdDOV+6+XQedjq/9vUEblIcXYoz64NnzcvVLoLO0LqmZ9R88dRx0+Z6ytoaIusiLKptuJRuQnTjO6T2mYv4Fg1oWQXwfV2juFGm9iaXn2OnSXQMVNPuTJpLF4D2fGWjt2vWyVHqL3V+2hssLv+4nRdZfyW2Ijs6Yv5ozJ/6D8zyElgX1XZdgccV1L1qljNbgaSvB5AsFVT/OuXKLYC6Ahjq1LARj7bxgdqZqv49Op6r27lUFbJOjaY2SZvG321ver2JFVSBQaaC+3zv42V9HMDR6pvE5yXaqTkrVpo3b2z8jC2ddkqEOeki9ftZ9zushFyAVWy9gTblTRjoXCZ0lkH1bmznKXXvH3UGP5oQqM/TmSjrgGCLqHugV8T65GhOvOZmedk4Ic07EwBTy3hx/3lpLWhLzsg5Iy9qEciqQqBYQLWeVudeagImdf10tkGjzUCxwQG3GW60pVwCNK3g+jG677O1pWVjyJaKSqz5/Im1U6HmSydME6tgs8OwFFDRmAGJYL28AKXJViSD3Z9iVghjmneWdTRhu6iPZSPZyGoBYwAQW9AbTUgEHm8cqYYtW0eZ/54zEq04Tysmzrg3qyVwNk3YTcrE1BJgDRI3frixuH09Lu/q7pn2tfK+U6raETqFrbppuvlp82y7lvZW8wcntQa697pvqvtXodALJ3P31HYXsvoN6n5XAJnsYl2YpLq/p8nWpA8STJP2CLp3JuBEeGYBzpYCQdL1Bs6KrjewakfhiwXIBbhYtf1EKU3r7xm/i63Gqmj7eB9RCrwKS6CTOOQSirqHqxYACjR1k0j1XPGKVzf5Wg6+o5Zb1Mwg+/N1eI1RbjTdgTbQqe+o0nlzHd2tpf7F9Wu9bhVQ+6Pud1z78VngQgO2pB2VIw3IehCxfZ1gPlYNtklhlJzB4j2PqNVguPxnrn3QBUAhr9C0bXHrolQBUIWuWVxbjSjwOOImbbFao94jiKSzAswS4FIZf7UECLVS3rXe3r7eHH9zDjee2FYwUH+UraVQ51unzNW578pZVznUewKOJvoG4jENn3sAUAhWtapWfc98nadQc9fUtCOP/5GvwcyYS8Fu1n3Od+omWgthyrrrmnXBGRDAuWMdOFLQjuXn4MY+rCx4VXUCW7PMfHOslgCKruojXGpjNTYNVgePrRhEGvD0HGJ/CKiN57z53FotAaxrXZDF0yN1AHbBZv/zz7qCr7ZAhJeW9zrM8fDFkebUC5ammfiA9IHhLbAJYgPIfY1mCaBl+UxJex0V6zlS/NaaJSCLrchm2g5PM9zNBhOomg2h9zdZYDhnz97S9F3KFrvJOkA9VlGyrf3sgWkJQfB4gzYv2dwVXgOwTxkTZ5xPC2bOuDdnzIlwltSvrZZAar1vjHkTNU1fA8jbX/T2Elsh0SlCG0Z9pMzVH+rrAXqL4NgS6GIC1rVXEz7c4uf2W7ZKoHQJGiK+1rnOXxIGrK6pZeU4z0um7KlCRaLzn0RjdprvopbAPKnLaFl1HYIlC87PgMMKvHChW7rQdQeWAiD7cpS98O5jAjcw+WMB8ZBS4DXEBDqGaVqvBmFU44WQ8UCp7p72PLeVuHqZXa577wvvLAFs6GiuKbr5zwfV1gZod6p9B+22Vm3h+FqdTk196r/bWRug4/IOswTouiXAxsyl2CAjwOMBUsz9ZUU3bmVpZoYZ2tTK5sX9h6KpbSLQdY5FgKK/Xc/XYy4ed/HtqxwNgdcDaPOqMXHrA8SCtLECirp/zELwtQXa2h7H1vV1VaoZye7Xx/XphfblWsfjJ9gJhva6c+vUedd970hZ6xk/beYsGh/Z8BYLEAtZvobUudVuI9WL82OKNYZx68gXyGrrDagFkM0CWLO+v1rVspoOhCx2j/v7dDQpb2L+1z5/SAEAPKIQaNdO3dalIkBIgKjZqO6FyfzNbkbpxSW2TIGNK92ZvGmwJaOsFhdYVrMEMpDUly2piwmQ5xMnsy66/vrdgNlqHl2amAsLe33MCGvQlHz4b/ON+57j22dQLO2zgDwmADtHaA+VAm0UJwTtwQIAoo3xZGVIzihZNM96yuCUMO12ahnYtaZ5VyeEACBeVGjSYvEBWKGaWCYSLIaQbe3nBXWFtsBji9716u+TWQJz0jWEd5xxb16w44z7c8aUCGeT5vLvOHftkJUpFqozwUNftZ2LC4mtne2BX/vOxp3jc7Fl/9Q6Huo1/6aJH2v+ZDEALwRTxbTVBTUhgRZt9aSLGou0nkdWfSwlmzUh7T4af6mSRNrxXGhMyax1KOOfJ0EuBffPEtZccL7XtYgpES4OgkMGeBG8fGXZg6vUUgDUe9pkwu3C4NFUudcQE2hadJOyvUhqmi2OhEBdFq3mmLmZ4xkFXf1Badpq77KQ7lb0ZummwrCeZX/O2HxStYibVJRjd1B/2cdmq/1m9RqK7k/iV46NMOpfMzEKC7i0x6d+Rq8INncNZ6vSL9sJbc+gpoyy5hvpRLCtnbP0+1UNqOsyGrbASaAatIClbqNmB03cryFgn9fMGJ+jzZKsKS2VGeqI94yZfnbdZCXAP7tRe+34TH17LDR67b8P/G4/v34GjnY97fqsLsA/q9d2+/xwl5HYayINiieTPSKEVPSsExP2q57HnARrIqQkSFkts2ss6+iO3HoO9f+Hn8ePIARcJIkxFc9TB5gnZST+6LuUqmYJdI3RrFshar5TnwZpXS/Np51zRl4z1rWAsmqxJUvrTA1g4y/sLIBrmQXHg6Z37dSBRt0tbLf02sA7+mtuLT0fnQBslgDZ+sCaTeAps8ksAqSEIoQCK4azu+ddQLVyna0JVkaxdRUkr3qXy2xqWHtaOrlJYzRJW1qzx1H6rqhVK2rPLfB4grt1Zwm6KhazYGZgTrr4yT4RznaEHRPOZsKUoJ1DidRCzYK82NKoeYXktVrqsmZbScwSEuQW9tu7gTeKlH9BsR2JRwz9yPWzcQfV150Nchvvl6PXVaDph03QtZb4Go/MrWjUBUSn7AosJEiCSZMjkUgTMXaTLoFSBJgScH6he+4SsCRpltYdvJyOr+f4fj0kXn1MwB+e58KDodFxRb0ndekcGGPcfKOzFvwbW4GgQkGqRcDd59eG1w2D5PhGHZvE9cevqSLXB+V28G1ppvjXN17ABXiebxuozRLQwHUhracQ4rrvVhh1vkqxyVhIffisK5L1o0Us4LW5ZlKhQOICslUkU3+v5Pj+BB5HeNv2WkRJvk6A9/fX+EBdBQvGCAuQs7Z2l3WFWBq3unDLdgnTaxysKVXNJXQLfPeHHI7tqy1ice0L7RS6HziGT+zux4/iBmLuXZTSLOuNhdCEQmvPYR0BIFU4cI2zoPted7K3nB71bwZS4mFn8h1C4KaIhPnWYBqtNWlSn7IGR4ovF2l/vu4AaktagLrgh0foe6af/S9rl82cLXEWrFV24gu4o2P4bgn0GsGRBbC5mCNGT8CNH7iAuOmu9lrMDYKD4AvPtApfbW0r4DxBAKQ0Q0S1FpWJ5tuXfkALaqwgM4oUZAZKZhRRnyUlz9C2PcRPUfOcU5pA5Mv+0dG2mDsrLIHHFYlazEfTF4suID8R5qQLu+8mYL/fYccZ88xIzpgEWA8rCMB6sAaPywLJGcvFhfUMOlgnUW0Zwb2iB9gU7ONzXC30nje3xapKe99bqp3XZjMlxRVNrfYlEcvWEcDHtgC+glpvFzW+pj28IIxOAtYYGoFQ1gNASav9ia6xh3Yt5g0pqyVnGB8U35ati6kmvEi9zIZeaFL3ezeZN9dfjvCI7qD+HKxLnxYE6MesfjRC1i/Vi7KF4OEtk/VwbSsbS8ytgXL0RwUQdmvBzqpvvNHdJNo8lQHzv0UA3KQU12NesxpuwbFQMcsJ3QSogXVIFa4+6dxqaTUAKkxQ2gprJKmmznoDiPo8PDHPhTezfuLFfVZf4J97sUvg8URdPhKuL0t1O/iqft7Cma0+wNfigHjRIWrg04VAXldL4ii2lGlpDLDTlLcabPfqGh8T1HhhJwR8ePfFUu5xOBYG9QPjM+SeAxL0AeGNBu+eDXDtY7bhHTVOqW0hpLgV7b95A0M+jpl0Fcj1HDYcX64d4tr9uUUfrUQ/xkOaAkMhcJNWqAq2xgTMHjA3hmmS1k4axdOsCkpZbRBlG1SWPmpXI0LmwijVEiii+e65FKxrQV6LSV61BPQ7zRLYdBXs4gAbH2HPxOvoo6PvHN29/rPNPvp6a4Zu91GNXpqmIJ0FBUDSpOPRLQGx75emwahFICooJQMoqkgUUo2CGZQPKkxszVVOuoA9J225zTzDM6KE1TIjtwCI28AmemjtIfD6w62WQCKtA5iTWgK7HeZUMLPGsvKyQESwLisk2/tSkA8HXVHscKVZbIcrW2FMxymTt1Ho9bHeCuiSSZS7VwUQngzCTTnZpofLxhKQ+p8fzei20EsTBDfxNbcCrCMAT6aw+nnpuVWPhnse0qr7siqdbR2SreComn91lRV1Y/SrSVVhdyQz6t1zIdYLziOXcXc9jzKPX1VMYNOwDLAICFthhYByn6TkppwFVUw0C2jzEOtX7eHVBVGKbosNBG+FsLlHlbkbI+/Uixu8g0f73CDEjzV43ES7mXz8mVpMaJq9B6zMJ39sCajvsF+JSapPtiotVj/g6aeucbl2D7Bl2Xl/GBu0DA0Ud1lEusaCpviy8Cj5IfA6B21mjPmgvQrY1/ZNmrCQkjZ7JOkampnff123wgDLWvt7wVYVJBHLFd06XYBumvZB3SNsCxe3bLtp/7cPVhcA1Z1qHgcS6qyMo/17ZVB0jvbuIOfMWr/DZtSTKlTkrtvmwq3Mue6rinFdHree//F1+PvrttOxAGi0zgKwtw87lR89JmD1zOyuDf+eS9lCIF60mRks798Wi4H15NCF3S01EmQFTV1MQIrFBQpWW3lrXYu1PygWLzALwh/akSWwyTqoQVm7iCoobr3DUO2g/0KzGI6zGaoQqcffHls/JvgSZm4JgCcdMKlUS0BTgbSSl9jaPFThWUwgWsfzrIKg+HlNM4gT5p1WaTMn7ZFee7BY8z3JICIUKeDOEiid5hZ4/MCdJaArh+lKYd7cbJ4TdjNjtwd2XLAjQPKK9eoAmCWQlxWXDy6Q1xX56gpSCjgv6gkoiy78ZGsKC6Ujl4unbx/PUZ9OxkPEix5t2VQd9J0lIJXn9WnlQNd4sbNqpYtr9DLAfgw+v6nW/Yi5g/R3iu1bcgaooIgqYVRsTnn/M9b4KNf6Ad/R21B0loC7Xnt5YBZOLx96T0N1627NgfZlfy/+7uHm8qNbAgSVplX79B8vW+bYn5tbAqUo0zS3iD5sgQijXxLRTaMmFFwTaLEDP3z/uv/ZakDdQL9+b/S8+/3qiPHXPngIHaNs0fwbbzdR28ePWzOLmjAR0zzItYkaC6hn1207gWunKNBjUCawBbBAJliL5Tr3vlBsf7/WEXDEBB5ncB8TINWUvWhT/8gyhFgLOdEaMKoOopZA9mUk19VWx7NFo4paACxtXoyx5RV1vvbuG+oIziD77/eH68itTsE/9+w3sUw93GwJdAKkpknU39eZLlYhJ7A27nCLQZqZ4683Pn9p53XEqF2H7PRWFSjSTq1uaftZf93b23rdwroJry5FdGOOeHSctN8NVQPMtPtslkCGFO3JUaD9xQt8LVCzGHK2SiHFIQAAIABJREFUNLNcg8GbuAo8nEU1HiBorjVfupGKqFuqCIRNMyEBFa5mKIEte4Hqw/ZXdU3eKmEaHSDrSUK1YlD7maPFIDZWCACvNjSX2DXtyL7L1CwbFxgC1O7Qtd7C/aN2rp4QSmWCWPdUr3LkNNU+Kx4wLm6yA4At8ynM4MK3ibPAY4CZD/U1EzCTFoPNLFY1DExJe3tNTJgkoaBZ9HldsS4Lri6vkJcF69UlUAomy/pLWv8OqpXyXNf66HhXY02V4fr4tdhXgfXhsewgW4ejZQkB6LqYujLZGK5a1sT+XWfonjDRjfJO67ZJh56kh9Xr8XlW/Id9Oco0WVzAF6sH3Psgfp1AW8QJfk5WTFYbzenKbbuka33sEpDZz0nqAlrVRVWPVX9pi4ecyo8sBOj4XW+RVGukF9f+4MwaAIwBeTCYzEJqKaWqDFR9vIlF2Q6ljTXl1oPtW0TAlldfRDWeYoyP3d8n/bn6+cIGpdR21TVVza9XzPtnaZ3E0m1hZmET58Z7t/cCjZHDLaJb7zk1S8TOk2yw+1bvR1bzuVjRWVH3W86ramkekC65Owd0sR0OS+AxBqN3B7W/Pl+9rhzGBCpN1dNhJnUZU/9DKShmUdQ2KZ3KdBM2Q0wsnZOa8qVBWW2vDu69A6iZSs6hq/Vg77wHmR+P6ulTTRVt7qQjs6KeT/9xm6Mti7EpcKquF+ufoTHRaqkf3wH77Ro58N3JOo6S12o0gUBufXSqalt7xE/7ljn7cIbAowuBjtd3P9406LqtNQLb9QEIoj5tc4Fo103WbIN1QVlX/b4JDHX3s61ORJoL7w3ToNp/9vhBztVSIFGdxP3qxKx9zomRCFY0pdp89UECgHh3zVK7bJa8QrrBsbUA9OF7f/HW59zOtb5H01TyCm0TvcJbRjcrwRi7DxLA1ifQbREgiXW5FYGggMXWFs4FoIx8IBROyKWAmHFYNK+Z2VdW0ifIHkuwCuLbp23gccAZX9bXzMCOCTsGZibMDGsWB1vsHaa+e1pkRs4r8rogLwesi9YFQIqv0VLXwyDyAiiYsoReU1OG6gtDEQDPxLESY1WgPDkigX05VS/OqseCH1gVy+JCSFcGq82MrBZBrNuvupqoCZJ6Xt6aRrdFF1BGsZhcsVbsxWwC7+Zb061Jk7LFuos2r4z/litc3kNNkJJgngS7Qjjf6TU/cQbMi57bWoBsimzbSuV9X4wZ+9pXFsMN2jS2mm4vFPS9ukfEBKjmqJdqDWzdJagBpG130N4K6GoJLDOBigZWuQCFi2o1Jauwdg2j0wBUo3D3U+kWtymdELAvF7cArB6CnPlb8Uhqq4iRDbz68H2wlTYhIO1e9XDB1CwBan5DQRMWaPdZ76sVp1jzLPKJwBq34brQvC37V3sLcYiBxxibFFEAicgsAetMS76IO7ZMrCp3TbGr6wi79U2AeMzJY1q4wQVUIY1vSPcdaQa/Bmetxkjcl975+rvzA3k6ts+F5n7xat2aNoqWut3kiCl5/XzsrxsApLnGqvpryRbVWqg8rwVy6/9mhLgB4cJStX+xFcjULVeKpu6qPDTF2ZoAe3z5bjycVf+ahIDfwCqcK8M/HjBZGar1uiFvNVFU41hZryyvi1oDnSUA06ipyMYSEDLvowC5WD1BLpUtahM1Hdjap987bJbqv+OStI1zZwm45l9KRl4OKDljXQ6dkELrGdKtJwCiqmnzNGsB1mT5+pN9bneNsloAVC2B1QRQrnfW1xOlOqztXpAaDlqnUcwSWFWXEr32UjIEjEIHfU9aL0BpBlPCvNuBOWGe99aJVOMGiacvjmoR+OcSe76or5nILAHCTEnXD6akbSNo6tYL0DnhbiCNCxyQDwe1BCBI3iFtsrTJbjU/X+cbQMdsxYxeV2XNqbGRGGatinbxLNDeVwI2fmvunqr8FLX+LbvOgo5VIGl70xqthHsvekvA09h1uVWpdQEiWTMWxWOV2XgJV9cvp4SSkq6l4t6malv7jTSmDv2CN+mbp4IiwPlOLYQn9ioI3BK4yoRcBJSbNdC8L01aP2w20DG+KJaA4sgasAe1vclNSkMEYu4NghWZdZZAn6qoWjZZYPfYGeXau5jQUd88lQIirewjC5IWKbpCVxEwtZRUPxbV45U66Iu1lvXMCECAYu0ZjoSAsJiAsngBkdrd7i80l5BaK537R6QOSKANz2q6mtnjXQqbJYC6X/WVwpWZgmz6TrYFsmmSuugOEiCTpaZahpCbtIHHE9tiMSCBLTBpq1OQvq8tXSoak3TXkPcJ8tYMwmQMiTaN44Dtaz9cPeZNJ2qKZRMYphR1+920jxj3rUVhNXgsde5VY+X4XAC4i0n8N3oPhgnC+llnhfs51vNVM6QKAgdZTMHT7KslQL7MJ2mgPqmjYE56I1Zj+lz03MgDwZ6leXx/H3EKfxGEQGNfN8cEcrUEvHIYxR5y8cwi3BoTgPnt1a9ufT1Y2y0IUC2B1SwBtrFSLYCi+fBE1knT4guChJT0RJolINX9U9YV63KFklcslw9MGKxmznXMH8boYRW63t+fE9K8h6+ERqx1EVoxrZo/W7YUW0Uh+TWjGZH1/tj9Epi/VlSQoRQwMgS2Gpm0DoWHomuVHny/aQ9Oug7xNM1IbqkQI3HCZCuXBR5PHFsCe04WDxBMzNY8zhQFdwlRN5frvFiwLgesyxVIgBkTKJlfFxrZVJe8W79AYxKoTLOUZvH6l1wlqzo0iyorZEoiWQyv6kydULBEEPFYgB7B23maaxTmCm77ujundgstHe+qdU6CUrRyOhdfeUwz8ZgTShKUPJkl4MLnWPy1TEqGWgHFYgIC4Gynp3pYgd2kZ7hkATNhyZoFuBZ9LR1v6A7fxSEefly8KiHQa8030zvJ6t/bWAMmC6y7HghaDGLaRd/bnmCWAJkm3NupcFmsJljf24QgusxjTb+0sm/AmqXx5ncAqlrDxqVV2p+fW6sktK0IQMUCTgngbIPNevvwqlWFFoBlKdD1f0u3alF3r5p9Vz9y89oHUs0+pWoYdvfYrNqsrTXy6vdJawZKzijMTcux39qW8QceN2wtAbJMIAZTqRkqbVx1c9u17CNXb+837zTBI/7jidV+IHRMu/fMm4eg2fdA0XTTkgsAs8TJLfKs88w679b5uFGfjFfUlijqTqKeL/VWjmcoFp2bfYJIEW1rL1KQffnJJOb2zcr8i6iwqrejCbOeY7uL1+MCHpuZWJ0Mc9L9J9umrG6fzbKex6z3VQoA4DVYAh2ruoHSbbsb7pK2FE3VLNDUywK98etyibysKOui63xaIMaXUCRYUJMTQNrBVKzqOIOxFrKFHAVA0QAwEXjVQTAXAacEcEIqWlWboEJBFwA6GuQ1bdU1AluVizwE5WanWQRZQJQtfsGg1XL1p8X8htqobbLJNkEbeE1mOjJdv6euObggMHEAhgXxxLIeBMik50fFFuNZrbp6cZ+qoKQJu90eBEFZF6sS1gXtxSymwOOJfVrqayLCPhVMKWHmhJmBRLpuALNU5Zl6Ju4MvHR/7opEU9Q8xdQFTW2yBj1IjTHY554CWjyF3LtzZrX8C65AnDEJ6ZzNWnXP01rXDwGoujOVP3gCBIPTDBCDLWEDaa5zCqIeCJGMsh4si/Gg/Gg9oIhtS8GyLvZ+UQafEjhN2GdBmmYUSmpdzx4zmKzaiyw5o6upgt7nJIJpUo61n/V+LXtgXtVtvWRdcnJh4FAAmGXANfDgAWjUe/yowuCLGBO4GXTjGTWB4Gprn39cYwPud3MttdOG9cBcNdxqEUDjDIAGnwBB8cwiLmAhTFk18WJxg2oNeFVif+5V3aYaXG7lL6hWSHPPCSgX8wmqy8iD2ilZVbB3qWW3UpvZLHR8p64nbtYgtx8fuj6Alr+VbtJ2hWUireeJxSI2wo59wYzX8LAD/1wjdet9EKtilahYnUDf275+q3k0qE6DNh/JxyDVUbqZnj53juBaslsQugSquocgYsJAGScJgFWz+oQXcBYUqHuYbV6xp497kNaEgKTJlBoyd7Am3gsnVZ5sbrjLOq9a1JoXTU6pzH9RYXBYXBgcdP5NCSll0LTDVIC0WyHEmN3ToBpjlTeb+wq14NW5YQKBCcVWc5PUWQKs8rZ/PjVof3R7X40d/9qFAB2/bszauwWy+dArWy/qy/a1PCUvkJKxXF1ZSfoVij0QiGnpDJPqtrZuShCeAJ5QeEahCRmTpVJ5H3JL6TJzcFoKUlIrYpoKOM2YJmCeVyBZYzVju1qboD6+NO9BJav1YRxflSKvVdCgmP2snrsARVRj8CSIlFR7P9tNSMw42yVMzOB5gjCBkqVzdje0ypZ6g90OAIRmCBVMsCpg1tbeiRYIFV3JCAUJYv1PrCYhrxqUXw8oBOTlCpCMBRq7CDye2M192wjCLhVMEzAlXQw92Upj7nWt6weTxrM8qSDxBElFmSygDeesBXVKrOOc/Y+q4KisXwqKMGDZfOpm8a12D9Zvm5JzZetlTFc2Ly37Lk2t9TUR0pQ0vuXbeQZzwjTvQSlhms9AnDCJegi0j1bBcnmBklccbLtcXaDkjKurS+SccbUcsOaMq8MVcim4PFwpc55npHnCk4eMebfHk0iY9xlpd4ZpBnierYaCO3eQCUyCtXAXTEmF3m5S66kuRF+AtGp2EJEKhQILiYq7hZoVtsENXoXb8EW3BKj7b6O1u7ZQfffS3C15bQHZ7C0mfOEFseN2dQLkUqH9CekKXYQm4TfrE9tRdLCpBeAZDtJpy1VkV+uBar/9kqData0FLMWsAbMEivlOc7ZW2HZsHdSiE4UZCQJJjMwESkCZxJh/sy78AV53u7XrVz8nm6AFiJLdavaVBFrowk601im4NWBBe28tHSuLPb5I3KkThLq8pI4dDdK2UejTgK7/cVPuCGK1BTf9+UH6s+j88FUoWPv4roW8K1mtdxCDsmhwl4u1O9HW6fM0gZkw5Ulbr+SpdsqVZC4hKSg8qWXsvbUsDpkt4J2XBTmvWK4OyOuKw9UVcl5xeXXAWjIur67qVqCaf5pnzOcHFCGcLQsoJeScwSmhFpjaZfT34cjJoJ4Ca/M1dduSWhUxsxYnHxtYdHzwa+/GGAuBPvhw01GvmSJUl1H0gcDkfbYZxcwilGKLUSyQsiIfLtUSuNSbnpeD9iQvWllrnaqrdkE8Wb3ABOEZhWcQzch2OTkb81284dUBBGCaMqaUkNKMeS6Y550JhRWAZUQAVQB4P/40mzBZVWMpRV19OZsvUy8JS1nVj3dYkXPG4aDm4+FwBYgJgcTI984xzzog53nCPE0ABJPQ9un2D0HcDQUQEmDmO0hdXIUEky3oM7Ey+kSaJJoA68OkzL63BAiCvFwCZdLspIgJPLbYTS0wTETYTYwpQdcOMEsgsbuGSN2oQJ3DqsQkTDwBqWjPHOiKdZP1oEpJ17RmZnCy9hMuEIzpF9GArycxLKumnK5L1vmzmkVgbeTFMgHr1rp9usWxm9Wy1i1hN89IzJj3e6Q0oZwt4GmCFD1XNiGmgqdguXiAdVlw+crLun35ZazriosHD7CsKx5cXmLJGa9cXmK1rQCY9nvM+x0yEvb3zsH7M5xlwdmTC8CMncU22H28LhBsjhNrnG5KasHniZAsGNNbAheLzn0tIiMVBm4J1Cd6HIR+eDy8JXBN2GzD0xsnRj2XZgFQ+6TLvNEe5CVrIZlaAtksAcs+ML+Zr2VMdVC1GnWBB4iVgWWrrltN0q9r5+gWYM0ZzNwsAXOlwIMtvfXC6mMsIqAEq0i3kLALNYgt+kIoQlizajOLrbh0uNJis8SMKTGWWZl+zrtarNYbIdvbfVN75xZgQlvaR2sfCBoj6CIX+udWVVcQ01V+FtIiM+r8xoHHC8eWgLp/YC4gj73JZjLXjLQj5Y7IXD11XnI3NzuL3TlVzyA8QxAeEvTW8cqUc26t5IsAxXoeZbNSraGEChsiIM+qHJYJhVnbqLAWgqIU8DQDEOUxBLMEzFNQV0ZbsR4WrMuC5eqAdV1wdXmJdV1xeaFC4OLiAmvOeHBxiQJgzgVzLrh/eQVKCcuyYprXut6y5x1aqPBIv2smuiu5yVJbkyU8TVYvUC2Bo1vaH+m14KFXFrv9hzq2RY1x9ZYAMWuhF6hZAiVbXcCC9cp9cZcouWiGUC62JqcFOhngpJqI1gokgGeI/RWzBEQESyGUDBwOKmCWywVAwZwSpilhng8oRbDfa7ZEzlmDN8lbPOg1eE49kaWVsfbt4LUFnTWQJShZsIgO4MuD5lJfvHKBdV3w4JWXIKUgsZquJIL9boedaSfreQFRqkHupv3L5g675GnLUFoMw5plCRNIMiZedTARQ0g7O0K09wl5g7nSWwJTjb/cFMgLPB7YTU3AMxHmqSAlYEqi6wqwNjHTwjHU7CDmFhNInJCSjlskHcstFqCMNyW2dtRUYwIg1LW2vXGkxu+kWgKHw4pcBIeDzv9l1QrdNRfbqoWwmnuXbSGc892sytVet3k3I00J5XCONM+qgFkNTJp3SJNWyJcsyDljubzAcnXAxcsvY726wssvvIDlsOCll17Csqx48cEFDut2WwDs7p1jd7ZH2p/h/2fv3bYjyXElUQNIDymravecWev8/0fOdFdmhJPAeYABpIdUedndD6e0xapIV4RCfiUBmOH222Pg9Y//ggH4x+MB6VG7C8KIpDIYgTQy6YJEaww68XAMqwjGBKZF34eXI/ypRyM6EGDoW2XwHnn8M+OnkYDjLa+1PtzevzOWBcGvFWe/kjKyTk8Ko5WRh2pFqTtHf2lKsVWu8rQwpFwKmT9gunUm86XkMkRtcwhcYBtUI9OYGcdgJvJWGQtR8Ep43OUTmHNijrg2MApolcxe5bLzTtHN8/beA7vxkF/nt/NeKUSMSCCaYiMjLOJC0/RiAZLlEwCAbJP3OT7meOMTqIgg9hoGsGcLlwMTVwtfuf5cGJWmircIYFs/2MDAE5uQ67xi6+nLM8fyETDkebJ/8ZgxX1WDfp40pWcTSG4R5V9UBT4nXHXJF8tqxolCkpXICgEzfARjVt+EkUlyY+B8nLEme4e2hsHv7j7GuqAfjLwvGfnDXDu4hwJQzazifGbYjO33xnvxhN8fv+QYXjJ/wbn6zRZfn++LitgQoezfr7DQaFIRdBBr3TsdTgp4jzIN7ehorePoYdG3HlZJY9RCdOxyqDTWTovORrXlBA4HKkMrk6zavFjCzEJXLceVu5dFrRYF4FRjcq5mLLIJZv5YmDdn+X5HE2ovB3e1mqw7nA/Vt9Z4sv0e1a7uck0U/kkDia3n4mNEEtl5h9vEYEa2zfGJBD7w6Ju7J+ggUkKShXk3BZDCJoW+RtRN7x2daFbsQPgEgsvOaCBmzSDm5Ba5Uvsm1Yq03Wi8CJvZaIPAyCA4hDQnJIw7YBlusbwYDch+Jm7KJn0TPpXIV4NZsFbfL4PIM0x8Fa9cRI5v63d7AVtZ6/W6sGmCbZ3Sgqv3ifJDATvoj3GHtOAEjgZMD6TWJiu8Fm33FPRxGT+hffZ58Uvfxk4ReW2yKQz2KJtniSeXzeX3TsWy6omDJR7ohGUySPRATaipaKJVdyOdV8GtKcQj7G26ordQAglZM0pHddXLuUQfOd7WGIHQyl4PYMVKL8SS55EJM3lMF0FvQsXVthC6t+0wn9ZM/LyhsKWIBW8n2P5sapXUewcRmNAfg1DC0WsYn0rgA49rRAme5vDbR78cjUntapVJcA0fF+BVevoNPbHPvQ1RoL6byN6grlAiFWXNnCwrYUCUhEdUBE4TNJQXfQO7Qzp9eZd1uizRzdyr9aypxJpCe0OzMDTNHf1oMAD96HAAt6MHPdMjqKOTZl6hsVvhPLncDFBaXnqDLGNvyb+M2NK6b3kfdzs/d/LvrdlfVAKbZiyhvbJszc6AXrYlfLmh0rguUOaCC5AxwRCHtrBilbLNWjyoftyg7Yg4+97xeihaF7x0QWvBrbkDx0uHT8HQKBUx6YjtFMi///4bjt7x+vKK4+jo/UbB3MnNrXISu0WgCsAcjeq+8UGZGEQdt6jLBn/pmA1o+II5DtyaAHQMt97wxx9/4HYc+PLlFcdxQz8OtN7r+HsJiR15lXLdLRkAjgxznXgudQHbnMAeSWzGBuFZNA4e6e+zNfy7E+pz/P936E7PpL9up3FKLOaXQOs88nN67/BpuL3cMFUwWAixt0UtlX/KndSoLguZx2xM7mqikUMjimmAtqBI+xHb9AWEg3iFXA8m5CRVcutR/fSlK7chkG8vUSur325orJXVstMeHdvowO0lAjS+jN8wbjdAgHGeaLcD5xg4/nzFOQZev37BOQZ+//MrHEB/fcXt9QX/7//+B15//w3/9ftv+PLbK15uB46j0zeSiiiNRSsEUzLUWVMN0b9Aapuon8lzfCy72oCEYX5dtb9GCP2CEliWv28QySmUQvjslNDK+N2t1PdPbimEmKds/kDHSULGckCphlUtAUO7LN4MtAJMGsQNboLJu55K4CCkDVSwrHLJ49Z55LmtPIOd1lJWBYxs3XSmCUO+FNYzbjnq+9fxjwPHEROyUEGiAFkqEpejrvsPCDOUr89kLb5rDHahM4asZVq+zRk9ieeIptnun0jgI483UOCd9XiZ92m1b744vpzhzvBUAImUuV6qJ6pfD5lKh0Ugo/CuAIyuEwNcLKZqC+Gv9ONNzuWWjmEK11sPRuBgslXvS3Fpj+RSZbTQc+WBSkA1QzsOQIDjPCAquM0JHQ3DDW10TDiO0cs07C+vOF5ueHm5RaDHEVR1MQ2lYPNu5r1In8SVNRFcxDufwo7w/wr5P6uA/ygd9LSzPOEMf7IQ9svBe1aiV0Wb+F7QAQsKltxfl5up1pqNWDbQJqI46Nl/vYUQfzkUvSteGgIJ9Piu6RHCr4ePwOYLBCgl8vr6gt4aXl9eA8q1g1CSNUd81TQSZCGGsHRMA/4CzgrRDhOHqQENMBE073BTvDSJ+OeXAwAbeLeG3758Qe8dX768kmM9Qhkw8/EaFeQb5UbFa2FZpSIIJPD02lBA5GVk4S0AMiAzkvFMlaGhRAWfOuDDjh8hgT25M2wN0kAtKMx+dMAMt1sggUmk3zw6BmbxlmIKCFirtPRGkWYNMIeALjv0I7bTls3iWL19k93cUj8hEr4OFZQx2DWOdRwHtPXon9E6WjugnYlk2koZHLcb0Qlgc6AfkfB1+/KCOSdev33BGBO/f4sM4q9fmSdwu6HfDvyv//0P3L684r9+/4Lbl0QCrRRBrKlE8dsCqyJ8mRjLasKUO4umY7MnBp+8cTyknL589vPjl+mgsuudj2dzCF9q/ly0XJ7bM2x5OmnZxL4A6eRUOkwTBTSWvY12eHzJcnyFII3IAYdHOQXCUFXFsaOAssTplCUSCYgVqiuSqGjFuKdaWoiA15j1gNA0Wu2hwz2KcgErxPW4HeitX5GAruPvwG+hgISFWA2nL5T/k9Xv+3tcrTIzOJFAVVRUZeeyTy3wUYe8+XmfyX/1/VQQyjDR4M1hDd4a82boK3Ohs3RjCzYm4Lq/4OA9lQ5BqEOys2SEkG57uDLni2JJWrbne4ZZhsBnmRltlekM2YJBaJhBgGaRSOYwtBmUzJyRcNkZRj4nk1fBshG3Ay8vN9xut0r8LN9AGruXkQwKik7/69f2N5cnSDSQbMCbg6S8+Lnx62UjPJ3AWVUze/AavfDRO9f3vrkbEngGLpCgNnyDB1HPXCInAILOut2344bWiARax0tv6F3x2gBtwnZs2RglJHJoUCoB7uc4IrkkLAWtOvrhgEZY1uLwLA+RCV3lCwglZUJKSByuFg4tj8JVvFX7M6nj39jZK7ZxHsLJeLlHOyIgxwq3KH4FBZhE4xdlPC8+AScKcLPIeKayEJFKn69eqZ91gz70EF3hQWFcbXSFPK/ORU9mfZ52dMAdt9ttJWVZg0wqgsl2q1m7S4wGS1rtiQRYg+iIjPxoN7skRGKKzF73PG8GiOwe6OjPETJGPbn0UEpKqrf6e/QjCs9JKzoKcOB2g7uhHxE59GIvVWnApuG83yPfqLaPYCx6R+sHvvw//0C73fDlj9/Qbze83MLHVx3Xkp5NhFTW2/LlgZFKgg0JIJ+Lo0pDiIOex9jFxYeY3/81SugXQ0QT0qw4f6RD0qgUduFTPoP3tFoaqHlD1q92i2H39O9RPUoEkBE5TUAnzKbttwqCiQQSUVR0zhaZI9Suzl6+Tst/1Ty/LpoosiVlGaRCAzIsbqe6stqhovcsfMVmOZcIpbwPXg8U2/NfqtTWvX1GAPWcttvt273P7xiWFcHr/hwfd1xEvOzb7zx3WetHVeH0a8GdUW8APHwDYjk3N4riCQnUK5G1CBWFAEyYZLVIPHfuYxwrUIg5ke0MlEw/hHjmETAcvJD2isDLNRlGZyid6PylEGPPD3F4IyU8Z9ToNUPjetfWob1FcMnRWYRPV4isp+Qgr7VtY6lurMkmW6/IaTmI3zIDqJ/Xs/wP5wnsKGPvw+k2IjGCdbjnVoc7+OdHKAa+h2VF0FkoYlcUZgZLfiyPjaSFGBqqq0JhVyWXH2UYjhY849G0uO144OT4GYOfJWd7a+ypy5A3ZM/gvJmEp2kNsT2jSmQ6huIIC0WBoH9cltXDRu4V+qmrxG06uIXWlbCr11JEQMWDFhJ4ouEyHNcyKiuTvsj7syRvxCCvBBPLPW20EAA6iqlgPvXA/4yRtPJuHHAbDIOXEdZ6CP3jFjV5MCZsTAxFRAM+BLABkwnMGYUVt6CFqz4I40TyJSsEPPsACHt1I6Pl8vOWv2/r5N3h7NXtZCTApi+Zf6O6GVuXxDYthO6boHWw4dQMZDB7CyP3dkTBudeXWF8aXQr76wu0dxwaikR4/ERCGVa/glsXIohbskf7bayJpAxMJAUkKli2G5FAGp88Qrz9ucX83+uxivGBAAAgAElEQVQs5ukIHhT+7ME7znoY8OUcrgbOFGDr/epQlBU/Y/+kXgJTXo6dECkV+rNjS3VB27oHeRPf0ZJSDwSs5Lm0sG//AqkcljWRRbaUDm0v1BoWi2qEXO7v06IS2fMD1rWtm7y2S/g/R/wkEtuzFHdLYim1hOLuKGV1ORQX/i+gyM/xdxs7Il+c4yaAts8W30IUG0gWDrQjhDNmD9+SNfh0YIbwTEv77bHXsbInb74WjbHW14pKCuu/aMuWtnauXfbBIJq2gPF1mct/t/lAtnVXUkFWUKYAsEwaTVqHUXV1zmlE6mr5Kin/EAEjix0AZdoSab4W93bfn1DThtie5ZcIwgcjeX0bQvgFe+4HSkAuP4awnphzwOeJ8fgGnyfOx9dAB+MegskmAvrQ8eijlISzbGv2K60XM4fD7hbAonkEBLCIGwtFoeuE0upvjF5oRAKlb7NTUdYnr85ZDnUl9bIJ49w3hW1E5NTRkDwqNPoVwzuLbwmKkgFickCq49FSAut9Jd8kDbUP26Bh+WDi3mev1+t29XFOiCmIKKbmiqM3NtbhM8zD5OXW9lMDfOixCXoHmDQoNCb0gjJBJAAVtIPF2F4dPiPfxefEPBQ2JuY3wMeJIQYfJyajBINSSWdx/K1jws4TUIvS76LQLoBG2RNomS5VfXh1DEvfVau5DGf0HgU4HFw/vFxZwRGl2lJA7nQrsDh739cfQmaos4e4bzIGa+2bwR7fYENh8xGf90AuaAw4STqr6eYDzc0m3Mv6j9CYShgDE1LhRTnJJuyTKlp38OfG95XAvhfe4aC00vk4YDaiOYnNQgKSDhoPR6TsNNAFAewtHCPPACAq0C3ePa0HB66U0RUFlAVRgi6FOOGYB3RSz1rfV59FWDHr+1ctvR5WWS1Ma6+ORX+hBOI9SuCv833nZm8W03UyrigsuAfttlFCOzKQ7XzTZwIVqO99lbP+KGsrvXngn+OjjWe6NedWYeFLJAOWhanB+TcaE906vAlgRyiH0WGIjHzzBsvqcymO0sB1Zy9xWu+TNbg4j4X9OrCBibLg9/VdtCl3LlJRk7lk05O3Xf328xLAb7/ilzUIHt/rPvD88g/yXGikwSMLHyKRe6OS2a5LKZCmhmZe0ia8t/O63oMNFbzzwjvbnx2/5hjekICNB8bjK+Z44PH1/9KyDyRwKUaFiKCBe9BGtPgXAsi6QRM+JiDR/BruMApXY5RQFX5zPhryetltTJnxGj2MncWcUPHxwg5jQeswikgVZuQNHYE4Ntu4dGoUEwIkahF5On00CmhdFtg2WUtp1OR9O5lLc6fySlRhoUwDAXhxntl4x7ZmPHshrFQEjdC59wY3LSSbTX2yeJ2k0tkm/uf4gGOnfBwhjMWzNgtWhFkIQaHk6b3BWziF4Q6/3UIOHB0+TjxgsLPhtBOmAj976ICxBzcEEjAH/ByRn4KI14e0KBmhUSIzBVtFL2n610KoKvNZQh5hFU53WcZTriVhfS9qCIoOXBSB7zcFa/2lMpAlawBkcE6hbndGR7IRvdMxnVXf5HaEwD8OoCnE+D7bYkrj6TQAm7GIawRXJqSG+PAy4mKUqtr0SGGf747/RtkIwMn3W/UAOOM1HuBTIfqhvhQA2Cx/29DAFk1kGf5o4YBdSCBfuAqpJxSw+wIW3Zi5C+tm5f73h537rxod7w3qgqWeY/olks6Tq1LPUk+MSnHLAyiFULuO7dME3Pn/a0KYLxRlSRltCxgs3uX0WbA+SyiXtDs2yqvMqM/xUcelL0Va0fnZMqEv3xWAOSScJO6AaBgVNmESCEG8YzZ+vs177qxQQBg1EcKJXIdJS21WeBlHSZfW7tY6jySF5EM2YcfdCLD498vc/gvBSEPInwRN0bU7QQ9nUQRbTMVMuvYR1zkDgYuk/wCAt2gjC99KgsY69a2zW8iLMkE3K3+/N7iiknWm37vKN+MHdNC+mxRIWWaVSOC8BxIYJ+z8BhAWrqgeML4XW+OY8+oPKJ9AlD82YTo6Nb6pFRKwkljLOlBdqeEAsNMjizvPiobseezGWOcGU2PPXloT8t5tkNp6FjXKOGZ/Kz1ln5zbw9x3vm5vai0s2LxTZXNwko16b/YXvgFf6TSZJg80uEf2YnVssqzFAiSNlJnRn+ODDtuVAJ2zsmjGokezVo3EPNbGpE0KHuV8nbcOO0+oPTDvDX5+C/ajBz0U6zn1zUK0weErzBXSHKo9pl137C0u94CGjPKpLbZS8VBUG0oinEQCQnp5GXe7IH8Wk2U58u+T0dhCzjdEEOtxwu4nMGd0SJwD4/En/S0eyWhfXiGtQe0V6B0qFu8RyiHK0+da1RIZCw1lYUosRAAigU2GLGWwq4Mfr+df6yxWXPVuxRMR0D8QVAQFDhTmgkzBWkXlnibebunLZgk7IhELC5k9j5yobzl20hvgsbZreP5vzR6syfQdNfrsyHn7+7eWw7MCqO++e74LltaEvNyrPdpqIZncS56/8NjxLFDd06LfKgIZwBnGJpUn/Tk+6vDtp1D4q5cGf7+/v6ytNLqi8BvcgTkgbtFPl7k3tsfjp1GRhhvpJ2dp6Kxh5e6BNOgLzOOvfOHdEt5+qF8/rYNaDx4ZnbluLHqLp4FY0YBOH8XTPsC1gTerYjOWHCs3Kqnu84z1mlUwR9Qxw+wQAQNgHGitEDugdUwkOq8nlahdSAltod8VPfmk5H6SCgJ+2GN4l7pvFYDZbtmfmOPBh6Ew8u4qAliogeT+5ziLx3Y6keP084Kfz+N6mfHlbaIVZLxa2VKXwIcr0cvzMtHfjJwY2O7nX4jGlPHvwsV3lMHlejZBz2PgQutYRQNdtpdkPFs5FraUasFEHjeSYXyheWgUjqPCzdBQ87+8KZ/jI4zn57tbV2lU7EgAgRSUJRea9qoCGr+yqJV1HNHGkSVQRHNNct2ZAxKVuBT0RSWKd4eNMB7tGNCJCDyhkhDwvTrEWI7FGS2Y64QtaueMxjBjDAaZACIGSIc0A9CgLXJ+RBuMQjhl0Npm8UvkgkEmkwVCokE2T9icGI9v0SHx21fYPDG+/Rn+OQlfi8KjAY0A0lsECbWGBo/cB7NyOuvuLHYPOheRDHuoYGhUTQaiy1g0ySKx6+tnfE/EPY3/lk9gU8HbJ8uqrlArA5tV87tbGONyQCX0wxLsxSemkMeTIH1PJKf6WPm966uyLOPa3/Vvty9sf7dbIe/c0t2CeqJSdlX2Vq359e7tTqjNqnnPuikfyXaf3yzu7Rqy0Fz2ggXIYvlapMpz+J475HN8rJFzOqka5Pvk5HefwIVBkaB3PagMJOW47LH4bu3S2VaSBiSEzdLTiElhHs1DnJ8LS6TEFHUIf5f7AeTiS7QyTGc1pgpDyDHniI5+g7SwNvYwSF9hCv33Kx9XqHkJDtKnI0JhK+R9Llo7G9wgfQUCwAZkIhAUHGILCag39kl3qE44FKynU62vsj5a17CrDwUmZHVOzKtwIpifXM8/jwQ2zrocNiKVNafeYDO4dk84MweArcLNGKzJETcrbqsH55gTTCJDUWU1icgm1lWhjRPtimJ3SiSGCONsddWkLh/C9lqZhJk7EDstUf1sGeB6f93TQkBsKw2dWl22JI8dvm6OMvju6N0RAC2T+t1GAy3cjPohISK3vq9iaPGLAIKLhGyP+ecnzuf4mw9/tnnSl5T1vijs8svgtNrm06r1k9MxrP7ly6KPysMYBIWTi4fQ1wF1h44TDofO4MltNLhHd8DqMkYUH/2+47wyQ348WNPncY+ewY8HFUQI2DYMqg3jOKHSMI57lZpeyWhRfwwikJbmZApxVjaYM4TtnHBzGCslTOZJzfNrIJIzAmQ86mRDzwZ4g94V6KOQwPQJ1QY7bhG8YbdQrDbhonDrwFQ0KA4RvLaIkvzHTfFgKf1pQkSAy9Y9upL9zPh+o/knNuiNhEgefrfeM8TSnWnSXskixtIGexha7IZ/q1tY1Du1Pjaie5uIF0PmStFQENbEvfCb65zfRDOQHgk66clCr1vhT3cjoFhyUMKty1sccLmpu0B/j+fPn7G+t1DXwhTI2+LP21yoLBrnSRMtZPAONPocH3C8ecJlQ/jlg/dqd17+eps2O1F6WYfbnL3MVQdcHQaDMiroEiUoc/kKsvS50fGbdJBFKLlZypR4zWlliVsqAQBARMcBApdVQr31yHzWrEUNrb4JC8h49T839gqfYwBmmGeUzbGTymCwgCYrj6aHzWxCJti+1QsJYCjQElEpbCrEG0wHIA1ZfoNnhq4h9I8WV3U2YMpS14FyWKkH4UD+GT3wAySwQivLcYu0euklZ10fZz0cB6qJCoxbvt/j2o0WrUjU2ccmkOLhpKbW7ZXCepuIOeH4KsoHXu0m3TNkM6OJ8pVIYDsOgKylEw9g5wpzUq97kqfAAwR80wanc/zNwhNgpcxTGVbi19y2WyjtTqFtSCAX1fP+dySw+CAg0UqWs1hJba2e6ef4wGO3F2oZLYQbZdKjR4hAyL3zD7eSB1ljymlYrXi0FbiwOtuF70mSm6TQz0gbRdTaaUQCDgtE4C1Av9LyV/rzRJmMKtF4nkhgEgnYHHjc76EEZpx/a4O+jHsgA42S0a0fkKZot6gm3G7xHhJNZXJtjsc9fA2sIjq+RW/ueT54rWdt4dFXBfDqdCWnAMbrmbyO1tBsRO2hOcK5bpOsxQsgCscBd0Xzg0jgQIPAXDBM0EUxzHEfjjGjMf00ABbbKbuD+a/HL0QHPVmcnAg7NZSCPCeE8SYahZllHG0KskICudO3cf/vooFEATl/10GfhCJVwuaofc48LDRQ+3UK84tZU0I7D1xn77s1ziSWWhbxU13e0wMpAHA5xo4AVkTQjhgKCfwU3NtgQSqljRaDsKYRFhL7HP/TxrPVj8v6fDPRLgjz6U9qd2su5wxkt2CEF8ouCsNo+bsI+XRE4mMKU/MLIgBQCZIRLr3QwJzhG/CxrVeayGG4ToahG2Q2+sgU0pWJrX3RWzTKguYJR/Ak/RSFMw3wwfVIozcjg0QgJuXg9hnNd8BtKgQjXe2qMDS4EQnQlo8Cl4omBlfFTeOe3lpECU0LeTNm0BcKh8sqWPej8X0lsMcVlyBaE6EqdrYG8VYOI59x8yYzW+cjNGZGATxz2q3RUsdWbbPKvy4kkHFRO8/t+ZDLOl7hmJVerhsSKC5wHSMLuwkfhhi9GBeaZiVjlSj2nO+Jq1m7qJShblAbmyLYlIo7y3BckUDlCWBHBPH+ggTy36TlHGX9p3PIL0pQq8/qZbvVaPkcH3NcgxXWdtnyTkSwHKayifD8o0ICshTBvg4vyYwWNYIAkBqV6MktMT/VFdpPOAxzKNwbWpQnDftMQ1hjRwQM+Z7slneed8wRdf5nIoG55E2T5ViNbVDO8zigvcHtC7RHXSDvHXo0CEM2zWb4HM4Tjz//BRsn7v/8Z+RKnZkcS2OWW2jcKzFhvSBEpjCjheAD0hTTBhv0TBbnm+wpMqPrmgap0+CANLw2wWT142GKoyEQACz8AGYY0zHUo5wNlcGPxvd9ApsAW7bvk1DbLWtsFfMIDc18hTAai5tRaC5AkSgg6Yknnl62L1+4oDwWtw6spJBNYW2Kq97/nJJ8O0gTPR/d81d113wJZOw/5nlRgJdDe1OOO/X2ZP3Hr/16aU8o43p2Cy7Fs9n+dkMG+Sz/u7flc/wNhlzfvJUPOddWbf6qVZXzVHLeXmblNq3LMnpemJyDzvj87Xg1x/PTbV34UkwRIAFAZqitPVy6QqWfoud8yS7nYnGEo9qaAjMczPLUB6WQeDq6KydqblFAy6itYBwBxIhe6j4Y/e0WhIZNRLLejNa09HEkAoqyN4IIJQIExjDRQBSmQdsNFtvrGufQ2eBKJXoe/EfoIGd0TzxHqxDPFVPM6ZSOXFVIlvl0rFj2zAmwgUB3oTBWHHujX3jVE0+uuiz0RAJv9MBVeALruJWhaHEdYnGOJgPqgLcJg8DIxb2nP5I6uQjaPE5C3fRFUInxD7k4hAtJ6m+j7EZGBT1Z+jnZd4vqgpz++qnWN6gojL4Hs0yWy7STaP4zPdCRWj7DCbwjGj7HxxjZOQ/gEqrG68BaR0x6gpejEsJkJ8n3kdEf5WKym+BciiKPIAiBXwEX6/PLK/MKiPT3iqGQlD0C+IzY+7kjAYeNCAtN9Cuth+DsocgEmwGWm/JlRLCE5Xoxhw1a90QCNhftVD45BmQm9ftsFq4ilnFe4mDUT/hJBArMQELWGuCG2ViTrDW27Mz0zaCNOgQu0YMk0JVhTGB2xylBBzUBxozP9yrI3xs/iA6a28+b0CoqIp+3bNsnS30X0E8TJDlqIRddVTcpeJ9r7+x/mj8/nTEPs2lwt5ggYEq6RxkKIB6uUiDWo9z3WfSK1gGlHjxRxwV97DWD6oy2hbEsqiXoN0RQk3T/m9zL98YTMqrdEI1lvDVvno24x+YCiEEMl3v9OT7m2Kk+QdKD6/2am/scFXLzjHJxB1jo7Jr3s+bsjvBDB8j1+JvgX0J/KYI0+rKPALmnhYSz1/ZMQ6kucPN1adGhilj3VX7ZWUKmdTqC47UrBLFNjiD1Bs+3KQQNaiG0gw6KAzjivSdDgjz9LF0hRaDtTvkw3tIHwc+VMkHoqA8MA0UErjSEbdnEo4aThBO+82KzQOSPxneVwBz3+jk4/jvDpbJT2IqfDypHq+qdSpaLyFClJcja9rCiNkk4JtNRWRZ11erICfKOnqmfl0C1QSfReQand4a3Piv2+YtFbXADVHtk5WkDGDKG7NwjLRRHTvoKHeU18w6rPwnpMjhiEppPyn8qwsmeoln/v6yopWAvjH/6GIgmKts9p9OmY83BMFw2yTaLrbNmEKJPgwPVyDsrvHwfZ3yOv/vo/brcW5OLIkihNwcqPDHi6SOjVecAtKHNQPTz8YCfwY0buwkG9aNQafDWsWp2gWs5lY+gHR3aGvrtBu0d/eUF2jra65eQDVkVeBqXSxh1cybVjELbghY9ggG0Gy4Tuah6rG0anREiGvkCLh0GwThZmC5RORSiHe3lFdqPiuSJzooG8wkHt+5wH9WPHIpQNJpsidRrVQQtERbXSZeMTARtBGOA3wlA0STQQWca2U3jmkaPRDL3yB04roTNX8+L7/3SbCEBlKNn6w3AX1VsjaB45s3mf2Ow56RIOKop7IkEUvvvEUdvd/L02XYXiyNkxEAVkINA1NEakz7aBFpYNA7AbWVABo25rBUgTyW5Ig/EuCkE532K3y5OqaykLTS0oLev/b19Xu88QQGtojcfFmO0WxSWdJB7hNS5Y3hc7yDFyki6SxzA5/h44+L0FxpuabYj582iX9xmRPjJqiIqbmGHO1jIcCB7jOcEjClKGhVOpB/rHVjrvijktgJMtLWw0DUELwAm6PpaOlQKaaWn0aiUqppljHlde+xfUkOrvhGFdJxgrAPjl2hZVd1OGq2Awa1BGiN/bAQ64XlO+j6ryYHk9W5CPxHSTprkliWxl1zIN4EKAIO4R2KdhKPbBejcX6+I+5/z8f00EoBHP4BoKbkhAXdOKFr3wnpBRAMZFJaTA4JKBmsZodMOZFRQcnXAeohLIeD64lcqHNODb5t03FT88LdvyFRz1QaYo7UjJk7raKLw1jkPFEpEkHkQWfc7lUEJXN7ist6Lg8+qn/RJ0PJP3hS5YMpRvnwABcmflQInk7/5EHV3d19ARAoEVzvOE2aGx5iYZngMw3THfRimOe5jwhwYZvipWfM5/paj7UhAcLFEAadzFZiwqLtmM4TxnGG5tszfeYQRdQ74mNFUap6FZEOANyiRgNKXt5RBrLN+dGjvaLcbWuvot1ciglcKXCVwfgDIRDDHuA8aOAg5cwRqaEdk3MrtFgou6/VvymDf1lrLjn1cn3NwnSJzmcKCby/ZHOqF9yvyncYIhkS4tQEayeHwlUbLnz4PbbJsS1kqyl0AE0xbKzvkniGbMkf5l/DdORoEwJSoR2RdMTyM6Ek08G8rAd+RAOjQfMdZGWzVYq2AJ80bfx68GlAPZpWKXaGgV54nj/xXCsCvX6VlvpAAnVysGQIArgYbNwTMZOlqmxSwW1QEeafdQY1yrC2HcN5mcY+OQu7hHKfFlIJ5OYDTnIlfykZjJRr47oN7RkTPz+yCBOg4o1NrzlAC55gYZrifE9McXx8D5o4xfzay+HP8HYfomjyJ3K8uoFikUfCNgTjOaPVKuJT1vTlifRU7cOUGnP02kgqq8vkUiIkANA2tevVSAjCn1ljnZjOjgCgXjmWpa2tMAuN+ah3zvJJPdiyf4TjLMAtlsPGrEs5WSCrBFAMOd4WaRftbEwiiNpCYUjTa9UYXHZZGM67rOZgtigIpJFAGdIbsCuqasv1kg6BpPLSujFKVH8gSjp9HAmDGr2dHq7EpggWvEglkhLHI4syzhk4igewB0PoRkE4bmY10lKyJuRyXqBf1Z51fxtTbHJjnCCRwDjy+fmWCGiORPLIFmyi896iK2GaUwW0ekNQBSAO0QdqtzjfurF/FNd9rLQiEFVBNJlj9s2gpI1rkA94Uau07t/uDzAmSPoI6g1VbPQCGl9C3OXGeA2YT9/sDY07889sDYxr++e2Bcxr+9e2BQaTwMxzi5/h7jmefAPFjzEdkaKJXcpMpxXlb+TsQqSgjHxMYFjV0RlJCmZDl1Zg+/mzRIUrLOHwCRAL9QHt5QesH+uuXqD7gApsOuc8I5ZyADcf4NkgFkVG4SWTeHi9ox4Hjj99jvy8vqDLNSI2XVvfyHZ5f/wyZ9hUwDIwz/BviEdKpL8ynuXVoUxwvnQI2kLbcO+Y44XfBnCeGDAhzfyAO6Xq97qxVtEU7lrxz0B9DG1ccqlbIDbJqfnUoDIKbdkwq6dkCaZgLJuIe/nBefO+Xb5BAafy9Z/Amffi9XZKsypQbyXPRhnLhCIHIdFsUT0o+bNv1Lu3n0BeLC78kq8zMUWBgV0UOZRTE2ldaDKnyhcopIhYa/QV7cg0v0n1FACTEKx1BeifpHmTFzud7t13Zftl5Tm9gwPV9YYknn0CVnDbDtMioHNNwTsM5Jx5zYsxQAp9+gQ88nqbPtWrsZnjkyxAJXQYgqV2RCpCoooabwbKsM9KpshzDixOXNy+Uw5Th4OUXNOzmXiFd+gOS+w5mQSv5UXtHa51me0EQXmu2gxVgCkMynT6MDHEn9bpdSyWYts4oWYGbQAaL3A2GyGfByrzhlHEVCSXr56S/AzldaA7uQRbDIcg0jfXM+E2BQyUUY0t9l8jiB+MHSODbmiLJnZkxXXquBvPsHexjxQ3DBqNeaB3kCXMSqCoaM3fb5ghyoLjyLC+R8DQv232VjzU3OmIAZzjbtKgtPqdh2ixLHIiI28r65cyU1qLBQz8gvUMOwsl+i+1xK6gJLAt+T6SBM+FE9seyzjeTWYIC4pUU3Iut8EEXJiSeE+GEkDVB9pGIKRXapF9gmMOm45wRUfGYhnM67g6cDtwhOKH4hoZTFA999jl8jo80/EnDp8CXpCXBdcqgj1ir6XxdNMQqKUYnJR2gwlo52lt035M4SlmwafwpyyfL1kM3qZoLT0UhKZswk/3cabJtFJMyozZoIVJKVCguCxEs5kqg7Yh11jrUHdI6ILPWXoaShkzQ6glAPRjXKw7tPbazAybMB3C0zmY8PVpMajrCtQOi8HYgitx1QBqcIa6pNZ2hpZOyY9q6+w6Wk8ZSJqJ0jj91If6r8UvRQWaTJQ4GK/dl1byVMLJeiRR4qjs6SGFWSGAViAvxl1p4fQ+4Wv0ORr64V2/isoB9fW+ZC3uoaWr1EP6hAHq8Wgdaf+IoG0T4fSRCiuPFJh7Su/LzZ6TqG6t/+3BXCt/5fqkSwppUQOmXMPeaKM4YaldSaEdMWGhWd/0cH3GY2eW9XGbJdVpdzIxcN9iE9fZ5tbmiEohm8VG5cwcHFS0DYMfxtbLL10jr33Gdj5uSkE2zZMLbhhfC2PJ9/+B1kh563m0yEvRRZHRfyGEebzv3lDFv1qWkEE4aCsj+witEVEsGLWHfFgLKLaOVqvqSJxqK+xjrWTGL1MugGsUq8/1v0kHzsSEBOIW9YZ7fIg7/8Q1RR/tbWOCP3N5DUYyTkUTJFXIykQ9Tjf6loSlD2BY1kxOiRL4UDJw2IVMxbAJT8BgDTSO1GtMx3KqxgtMq8KwayjjkdtzQf/sd7XZD/+MfaMeB/uU3CDlK1Yw2aNDjBRUtBI8G0uXoRdFPNpczdk+eycmblk417d6GUNgLUrEEbolfJt0E1KLNWV+0EgrhTEQt8Xyd5AendMzm8JcbHAJ9FagDPTMa34Sefo6PNB73++V9+okbDeSkEZaxRIHWtnwejc5iAgmjoUWyoWsEGfjMWsYGP1MYUzwln5Olokk/2oiMfjtPwB3z8SClolxbExALOemK9tqJaihQW6AL+IBPgT3uwBwhJ0QDAQgKEYBRRyWXfELFI89AAZFXKqMQr9qFVSCCDTkfgX7cRzAR86TBzKrIvcFdoMyWbukAb0Evt07fSusU+ge3t3ofDWZCmGd/gBHBj4z6EUwPJWDSAChMeigD6QCyUN+Px88jAWRd7VmUj/PibY5qIu9bMlmWR3hjDZcykBKu5RcQwCzxAJYmK+s+QsMy/t2UGbEGhIa3hQIKWjJKgQko2lqEgfYeyR/HEZTP7QbV+Cy+yyiFtiBlSGdGEdE/cInISUv8WZruQlsWihDIUgqEfRsThCImc3EWDF4HcP5bVkIp0gUZzVkvBQrvjftgkl6UqIJC8Xzan+PjDJtXJJAVHcK6B7LiSyLvVXFXC7FrcuPxDQgk6IuGWCdwrhdH9vddDbu8DJpYIwZxLd9dyA6NcMs8x92ap3EtbVFTqlJLkxZZBGTA4apF+abwT3ngeT6bf09F4E3RvGOVenGo8rxTAplRDq2qyNl9bBl7ub5AJqaK9+oAACAASURBVCHZBykEkHQQWgegS85otpEPx2+u56B9gOGR6JlKwKWxLwPpox0JFIX81+P7SOB88glQyI/HV/gcmI+vMBuY96+BAM57KQc359aQ5Sey01YgSKKARAJJz1DAu0V5B7El4pz0T2h4wWkTPgX3MdA0OvCA3wlHikS/g94BB9pxRBTC6xf020sggZdXtD/+gXY70L/8HvHGFQ+9tiV3008BYfMIYVy+r20qBP5JUTrbA5GU+fX7hQ0ukDwVBtIhF9UIr15/r/sTOCEU5cATElCNiIHjFdAG7S9o2nAcr7G4W/9UAh94vEECmRjZNX5mU5Xk6bE5QnVrwtL7EVPZAgnALBCAT9gUOC1yKI2zEWUYADBhkslek1Ft4wyjrp8RdEE6FtI4pyMjV3pEH7aXXvok1qdAWyABmMNOgQwFxiglhl0ZVNOYUCbqiKCOowFo6LdO/TARiWGrX0D4RNk3wKP6qfnA8o8EEgCYxisrvyKT4JShq1LVe3sYxY1b7XARTGuYDjw8msk8IJgOfJ3sKOZKmzIrLbR6bkja7icW9C/6BJYPIJrMn9Vofgl/Y5KUVXJUFVir+75ZthpIQAk93ePz5SmX/RTK2s6CTyYbEhAa6bTOeZDSzFpIgPx/P6C9Q48Detwgx22Vma6HxMXACex8sOW42uirvNZLuNHlurEM/vXDZtwT+9ARvGuDcN5tf8Od+ZunvCMTlobwnKIKV1oerUNuL+EMu32Ba0Nrx09ZDp/j7znmXOs5ok645oy2cNsqfG7rJut4ZUOmVXI8kIC1hkQAkkgAHm2vyNvHexBM+1pPEihAlPLCVq2ipO6zemmiFWlbBjNkBf0wEs5n0EfO69iFPyAZ+xqIgNuiwSDh1K51pLAx41zTEC2WI5STw7CHWlU+RkU9pjJdCEAo7C9JbQzBFfJzJhrKk+eZ1v9pwmQwOoMZ9p55VlqQTn/CI/CLPoFsrDwfEVc773/C5ol5/5NJWQ+s0FEgM4ozWzdKHlEx0yfQNJBAdvhyR0X7iErmiCDmTEC0aQaIYdgIGmNE79CWSIMQMq0A7VGVtN1uaMctkMDLayCB11f0//oH9Lih//Zb+AAKsTA+WgJhYIbVIyNzAeSKBJjEkskmOy0lQEyuJzYnH1IqiFIE9SlRwA4VRLZyvMDyB5AqA8tB+IYEXDC0w1qHv/wGHDfI7/+A9gP993+gtQPz9vKpBD7wOO+Py/uWPbwRfb610SxO40kbawclEjgQmb43+rAcWTnApsJ9RhMVdPhEcNUTAAyYAs8JmUgAA/DGuv/AbCfUHbM9QqDRMs6VJD18ApG95aHEQHkuRAKTCP0vaKC4tKBdpIdQ1uMoOQHN92Bp6onz24RNYM6ggMaDmcF2AjBIL/aH0T+R71TCnXlGokcg8MbjJSKgw1izllFjMTvTUIozaKEHFA8H/pyKYYLHDGGvzONoHsostlJU34/G95GAX5HA2wigd1575mAKY+STQlnQKyRsi52VJQBlE3gXqZk0C9GAuGN6hrcJacMlYYWaHnWzt3oltPqzNETC3zQtZKseut/L3fbOKIK83LTMrxb6hmhI78h2SVKUz/VSd6XwfAJplQALfVwrQF7BSMJfRzjLPSOkWiAi5xafSuDDjmckkI/aWOfezaMJPNZ0vMzbCyoQsAIjjTVy2qSU4Bq1dSBZQXINHsDJ4bsZXNPfKJA5IJ51ehYqDoPKkTH6kkqg7CP6GhDRgmE4CTJ3p9pbMuMXlijCqSPSEU6l4YjWmMzETYPWsq+ARc9gtQhj1ZZGXtwfZRVUueQbtc0nQIe7pnxa0ZLg9e2sw0rtU/oHKOk8jV/K1dziP6EExnl5ckn7hCP45Is/0yHMx7PNn9S+QouiFaSEyCZznvmT9fGSiyymapGUNS3qc8gM777TH9Xo7GmkmwJlERH0VunqpYBqAiV1xXQ1Z1u4FOwZ9eDZryDT15+bUeR5055PRZYFtbDdHgDkwPaV987YBT3zDmwyM/iMJLAxMKtwXvYlWOF/IqmOrkrDzOBCB9enEviw43Ff61lE0GZGrABmsS6bO2ZToMU6A6zoxWXHpTLgdKflq9ZCGFqHN4lwcpGgUxwZt4G9O564wccZhelEIW0wxkOhaZRkuZbqMbCFhdI3UOuOCMFTGHuEQgf1RWPKHNmzOJWhiMfuW8Tzx02J648dG+YcmGPgfDxIgz8AcTRPh2+gFEWjUtGN7mEekjRIf73QQJq+g9114VQkea0ZWkraR0FuLDsIIhzH4kHvXWXr98cPGs0voeTwenBp4T8LbknLtQSr8P/4TLM+kCSnuI6TVTbTyl/VNWvvl+1+KsbzMfeL0VH91Ss7D3XcskKy+cyMF9wv7SEd4cSOr88V/rYLWV8q6tmASlXgdX/+8mY/bVGo5/kZ7LWBLn1WmZ+xN6bJ84oFw0gIs1ig84yogvMRf4+fiyb4HH/PcYkO2oREfC6FCMLIMpaRQMwlk7UuASyzbK33PQ8H7uEL8FAYzmzWvUDmqrzLnhYZFTQGRBnhQpRRMqUs5bqQ3Ns7b+UqNvKCa18otuD9ApULaS/jK7uLxSuUH4As0bDBqHUKWxTk5UW0I3s0/6bM1qMKmkcdzSOk1wE0X/ZjHQ+r5tPPlIwAfthUZg8pS63IDEFQI7kCraVSQkCrTPkmKJHMmDvKQZKQxw2YY4aWloy330JLPfPgEmPIOhs+GLGoeggwsp4wUUVgElsnKnDxCGEdCrt/BWxiMFRUzgeq1y6Enc4QW4AowGEnHeDjrFhjsM4ICnkK7IIA8qx5H/mgdxi7W/t8APx4OaSiamGE5p7nAzYnHo87pk2cj7OQgJtDqRAOYWtvH5BpOL/9C9CG+TgxtOHb//k/mNow9DM66COPx2OjgwSw2aAa+TutxbpudO5abRtDJ1v0H2gGn62oCyclm0aeafTocCi8TTgUUydcHUYn9GSN/LTeZdDpTAtdz0HKltE0jErS3kt+gHk/YeJoKYhEDrF+KYfS4Zqm9m6ha9BWSIoI7IAoqMCXOR4Y5x3j8cA4Hzjv96hUbI9AQ9JZF6hDTWDNoa6RQwFhD56QJ6KdVZPZL0GAQhvlK8nS9gBM0MRxqOC1N3TKzeGCewRDYZqyJLzA02HsqPDSH43vI4Enq/RCV6SSJVSBKSSKjGxKICmXFWdc1QF5gsFtr+ged1Ty1dKoYWPvNcDzlEAkIEQCIJkTiEoYep/qPv/QKorAdcDPRwhZ0lSaCSbKKqOZ+ZgWwSQiKGs70+5RlsVujMQp7CUZqP0vD8ifLP/du7BbJKx8uCGAycJ1k0pir1aadccDjVtUFxwnIBM+ARfFbAMTiiHtp2qNfI6/55gbEqgSxZD63CYLx8153Robw5vFunBDJhUkyqyVKrnuUQigOA65IgFLhGqR/OgyuTbJHHgalEB1HnNfFK4Igm6mUSr7OaxjJn2EjGrSJY+qteW71v+ev5D066xSNHFfPKKrhPkOqox62lmTxdCXPKQiItS6cirl94y3mTXQNO7a0bR6mZgBJxzTr+kYJFZ+avxCAbmNEkqZyraQyOqa1TQikpAq1FLSSx4KIL3iDgDG+t1iyDRrY5eTpIQSTq1y1XGcdAxXA2dsMc4IFLAUQMbtRqirDGDevwLzxAA9+vdvyJoe6cOoUNEdsCVtNNMPELf/Tb5tmAmhVGBInwAwF1e1Qb+d7gFwcfxG1FFMvjkG5oxqh3MMPO7fiAxOVD8BRPXWWIOOBpZksgF5RD34OYHTgPuMBJT7k57/HB9rPCOBPhWTpVrC8jcigdiqOLw19KaANViP0EyfDe7KVGPuD4hyCybwdsBlwtoR2FwfgEYDFBPBkJjrkx33JLN/z0Gq5gGIVE2xfrtxO9mF0JAJayKknbJwo1AegZb+O0hghYDn5ym7QilmhWQbsb7meGCeDyKBO877vULjQw8FgtLmcFe0Dmg4VZBxrolMRCI6KBzEHaAgF6x+BsZktKyU2sR57oC54ugT0wX3GQGLX0+n9R8owDx6Eoz5cxUAfgEJYLN6l3W7tCz5P9JEFw6sGsa39f2LRZ90SDZE94vb4XK87Xycf6ubTwAIBBC3MXm+gHue2j61/JyhPc8HbDZYOl0aLR5joSvz1TPUgaxImry7pOBOuU4e8LLdTOzddlq32a+f5fvNB5PWiT1VBbW5rJNKUuOuaCsBiPuk7pAZ+ef+CNpoPgzDo1H1pxL4uMOekMAkQlxIQDYE4BdEoEQC0ETAQMLGnNnCBbCyjDWEdNaqouFnAFy4XjfKFwwll5y15pcY+1qvkxnv1d1wExQ8/kIkUrlISwloyazlK+RFpGwgPQNfCKCazs9Vpl00lKmJw4zOcrOgnj3jefKeP/sEGCgiUvx9yrQyuOHslGZootFf2DX6C8MxBHhMHktKvMGd0bj/Lh108Qk8CapEAo6oBhoKgMkKrImhaVEzPTqh3aUUBLnuel800T7J0ifAh4qtkBK2EDeJwk2NcNKSt9SAgOETADV9dFGCKsZ5RphWP+LhlO8itpKfZ+jW031aZ5RXR7qHM+sNEvDUbIJsms0bzge4lMzulDIW7pvjrCiFMc7wCcz43N0vnd2ACFWztLzMIN++Qc6B+a87zjFw/9c3PKbh62M13/kcH288I4HIExAoDLMQgEAQ1n8Th/WOo7eI3jlaGFezA2gM/BPOfSmhpnrAxcKYSqbADCaCSeVjiDanMI/8AQelFpBLomnkJ5i9Birw6A3eVKBuDHNG5DYk7aRxvGxXGXIok7CIDKr2URqJ+bqGrdo8Y62dRALnA+fjjvNxxxwDZoEEtEVsfu/hxPapwUJMJpEVmxG9l1s7AgUoRbnPEtyVbwRWHQZrXEJxZHEnjfDQ+wTOKTB3PFRwHwKzeI0JnEP+fSVwxRK7VVpTiTI57c3k7RU777UQgK5dYSGAjLUvrV4me1rS/JnCrcTt9oPDA1YqaSKhRpV1rNCwfMDkIOOzSMwI5LCnljPKQXQ9TCqbPDf+wI1XdYiS89u9ent7l+J4jrzYu7dlA/DIjbBtu4eqrp4JKHQsEPGlDHiOktVgxwM4B+x+jwYbj7PQ1Of4eMPsOhtFwnFpJhCwr7AIE708wkazXpjJZZ65CGC6uO/cb/LrvtDAomRDgHlmKZctSARqTDIddBAq4KpoDDGdMwqjGfMdEhkoy8RnvZ/MoQnV5LXOhPLgLQp/+sxWPsDqpmiVB2UsC+/pyzCLMjesZmxm7C62B3nwrksYy4mW8iyTOnZkwMv1vKP8NtkGDT/fpNHYFGhbTcm6ov8MEnimJ/aRGGrF+1e4WEK4TIrY2rw5wILY5LwcLHEbIU0CIGpgoCZQROdIwcqCl9QcVho3nDSjxVabw0ygzTCZ9dtUETQirQmJFnEQrcYSClAJsAppXmvzjc4ClQPWrZfUFe8UcF3gBxeFyklVSTO++g5kg/iccGNENFD2UN5DQkMBxzHSLxK8bmzdHXNGNFbzCZ0D8rjDHyfsz3/CzoH57f6pBD7wGGOjg4AQOk0i5LAJujpggikKcYU1iSqZZ4+kzDOscTsbYC0MCnkKeEDKAZZkdl29hntEn0VGrkDmjPyUGcaZzQmfjvmIDGLlPszZH9lRdcC0N/gMX54dCJ/BDPrIpkc+0rSQH8X9EyHMzRcAIEtS7CVZ3I2+gBFoYJ4MvJhROM5mCH0A06Lz2RxBsDVa4HpMKBQ6w1HcnPJSG0Q6oD3WvbSgZX1Z8Z7GMYAmHkEdGcklLGBHYuGQQB6dSCu8Jqjtj8YPfAI/ObaJkLxgfl6vjR1LznpZ6LYhASrF3FfCzHTubshg7WvRQqDzSYSoMrlPschgdwaUZeyyOFPlVzLMxkdtZ7zb9bId/a2Ff0EA8vy7FfVTTuCN8qn4/1ICq0qhTavon52qu3CgwEo/11ACsRgB1ckyAam0Gd7nVvkPsJ+ZNp/j7zguEd9SNkj8kDW3MjO28mF2BDDhJuUr8KRYnwhRHg3P/Ly0BnVjc6ZoPxl+OCJdoKoDe9bqUQaOQDDGRHNg9hHzWQbRDdkGaLRiRET0ZXRRRjdJo7GnVkqgSAygIrlFvdafbX3Vk3HI+7dvHauCgZkDwmZXlp8vubcM6Dh4eA4U5rKqgyZrTMMSHohAfacZAvlU0hv3Fi1+F9v1o/F9JLDvwoHV4ufp9dT0xDOLjRdhSaPEL1kl1MMh5WubAl85ecCQ0qi1IYC0C420HKbhIHJEbKyMgQnANCiQKeGZn72jqcJvr2isEdQbYWtr0TFI2opPZimJtCSUPZCXQxw853X9+YzrfqSHexP2sFm+kHBQ00fBHsTzva0Zxhh0Bsf3g49U9OPgRO0QAI0laxuVQWtRjXFA4NLQbzccDvR+oE9Hbw2zGb//E7Pmc/wtx67fRQCjIeCTpsnkXJ00wJJ2Oc9ABHcF5sSpAmsKt3lN/txCwgGKOBFWHwXcXjB75NzYnBBpsHPiYd8AGRh3g8FxjjB4wGrB0wWtDcwRimOchtYa+u2sHuWiGtVNVdGO8OFlXgGYmYss4FY1fZQVBbTWUkQFhkKb4xG+NiIAJ2LQriGLGDruNDiHBaqxB1mINtBMgdtEh6GZ075kbgPpIPOG6Y7hDdMc99lILwEijo4Q6LE1GCMLpwXTAROIBRJwEbyqxXd9R2l/PX5AB/3170rOXUzeFe9ahd9SafHnhQIW7x0NIt5a23t+AWSr6WPXKKJVNZSIgFaJWNzwoCIlPOqqmC3i/4uzK6HOOiia6GOrk1Kxz6nFec07Effmfq0PFjeZ0TtPnL8vnnFZ/pNNK9jW85IRnJZWTF7fejAoE3wKCVAJRBVVW5Ugs+6LXNv8fY4POvzpx7JNnAael8VJsxxglU+fwsx6wOYAwPIvHuGaLsIlstZHGru5hpWd+YzJX60fYcnSdxgUr0RzGjqMBYLZJtyIZA1QHbDpgGuhCJFoQKOM1xdR6JykcXtQP205iKFRMgOqbPoS1TdFBI2xN5boJ9dbohsWthQKYyc9nCXwQZ/JNANmNJmyklVACo3FM7AmEFFAIoJwdUgU5VNAGKWobLebhSszKlMBqES5HBdBl/SBfH/8PBIAFgpwIX+/SUDnU88WaED0891+nxz+9Ix7NyKCuW6FRiIEkEiA2bxM4HIANkZM1AzVnEmfhAPJSHNMoJKlVASz39BbWPvWHbfjFpnK2VauRzSCdJZ47dkMYr1K8IMKoYbUJMk759iEv69XJp9UEsoMnnEyAzkygsPyd1/vKwSU+8nFdRxHvYcsBJAdoZo2mDuGR3na47hhmKP3jj7mQgJNV933z/Hhxp48lD1fBAgqSLyicgIBCEtBO/wMBDpaZA6rZk7ArCqjgZwZUt2TbkTMx96gxgg/0kFBKTWMfmLcJ8wVLneYSyCBMTEfI5TAjOPN06DaMO4DqoreH6xuynneO5FH29avAJ1lPtnRK9/rwTyEo0OaoB9HVDa+HUQuWZtrYvpEUfqdrARp5Ag+IYXmgoEIw7Z2olk4tk0NhyUtJAw5b4EEEEjgtIZBJBAFi5kEC1Y/8OjX7J4Iya5IgPL4tQmm5Psfj5/3CbyDCnY7Nx4wo3LyPZ748bLafZOJS6jll/PvytFcEUZKYRcWSOwz0UQ6WCkgS+061JxIIP4+Yn61ms8QKqCKNOX7RAiZbp7nA1zJtu3nqofyVzdxO689A9htXcNeC8hsFh2UjuLLYYuWWqglfQJtp7GIBLRZFdBTvhIJRNvWTyTwUccbZH9BAthesowswYqCmYHAbbIE9MzoOSkDLqvEedGkIcANICINRB5G1wjeXhtE57KIzTGnV/6CyERTx4BCNaSLiiKirhU+Z4SrzxCONlmYbVJmzEhCw2Rp6jlobAad5TAoewwo/RxCY86ekUCcENGAbPQvdah75DeAmdhKJJBBMBsSyD90svq2oYFpRAKQSM0QpmiIo0VxoKDd/X0kEL6O/wgd9CwQBG99Auve1GceEElToOfvOemMAm/S4TQnMwe1Ak1jPyy7qv1WlJC7Y24mjTsiVMyy4w+RBbn3KLAfyS7zGDh6x+14gXv0LWjhMeVxOikT5jlUnPFKMrmotcvteU60WqtKfKGBa1jnatcZ5x95AON8rNpAZjgf96qTBJDz3yz9zMdomr1L8/NVqtbccRoWEphEAj2RwIz9/lQ8wef4O46gDNdIJJBO2YzTlwkEEgjL3ZvATKPmT1MAFhSkBxLodqsMXtXG+P2wdDMzV1uD98iQjfpgkfSkjwe+3e6YE3BpmD5wDsM4J877CRgwm0NVcLZYx0c7o8MYUcnyfcllG9w926mqxPG5FRXo7YBqw+31BdoUx+sLWms47IVloAHAMS16CUcQSfgE3AFjobw0JmcGdAwP4a8D3RtuY8KbYcwwSEnaRlQQHNPfQQIzk/gin6nRWa0KGsJ8po4LEoj8BzA7+13b/c34AR30tIuUcsUbeh0lqfW0VC8WcZr2ux+AlFC+qDk2L3xMxJ2OAXk/XLjrfX9hUaPKOrAWyZxQCDonZdbYMd8s67SCyz+Q2+3zN/SYX+5TVe6saIItlr8qe74T3//O9y6oYMsToI7l6V4t/yX8GVpbUUKJBJ58AdXXWRhRsPs7PsdHH8uUy4Sv7TOiaUH6CpLK5DrbkMGKl28h4FhbKJmBcp3JVuwNBu0dbQY9VMXcgmsKi50Gl1E2GEO5J0IgCyIUHGrh02L55dxmcqmz70Gifg8HA5oA1gw6FA0NbYY4bBZ0mOheOdWT7Q7/gsdxAhrxhlJu0VCPSKfNbwlsBu7uV8SSL1HCPlEB+GSyr3rs3ORtdvByWXihgpQVPxo/qB2078JLwJWQy0qfJcRZ3UcYisWKo+k7SMFfjk7SHHMyLrjFzTXHpfDbasQgUNkKyeVhjSUgGD1jg/TJGb1B7RwQiXyC2xh4vP4WymBO9iTwBfFKOOZzkQuUKYseKeRB+OvLiUQuP9ttWkb/sPOaJcc/HnW+tiGCLE6V93p7whTqUpaQSERFKKOZqlAfFrUlqhBztNbQmqG3ht46jtYx+sStd7gZjt5gpvgcH3PcMi4eiyJvAhwKNA1HYtf4rAlzacCEXCJayTlpiPkOh+mIvAIRuIZ/T+ljKzpSNGhIZvBa9+DIpeF4/RI1cV6/wCE4vnxBdhl0c7SSIKRPMvuYHLk0rRItGdwQQjKUgIGh49aYqBaKZiIVUPxOmqJ5gw5SpWyTmQEdwnUHYSN6KgnYDIE/o+idpUP68tqjqOJ9rEtwu4XBk2qCaskYRwI1x6jEMCejkQxLiuSkivBTWuAHGcP29v1uFTyroeR+GFUQfT41qgTSoihLwnZrt6Q5ojrhfvZXy9x9TYY4p+v5RFTBvAhTY8PpqQNDiQQy5j5LNFwonoIu23VhfbfKOdgTxTNL6MOpjDzSz5HKYFN+mey1eMeV+LXZ+6gaI0BZ7RlRUby+KidoTtSFFESkqjnK/jlbfCpb2zXVsgg/x8cb7UkJNGG2qXhsVUL4a1ZZWVFjus0lLaHMQQnklEYuGgmPAoiEcojvEwVQyKWPSntHOyb67YBNw3G7AQBsRK4C/a9URhQtWEbakrURoaTpz9sqhAaLvSP3XYaFnAqBuwRvUb/Cn/NYkKCysHqYwD3ob2cXkn1tEmWvagqbUsssYAHXYdx31/AFlBG6iyOG5Adq2G1xr6iiSDL9DyAB2Fg/l9AzuI2iWUDhtxPiIYskyjhkcgawkAAt9YiG8eDywYvrmXbtmyrIB5CJHuuG1F0wg4/g2MdJbv1Obv3xKO5zTsP9foeKYrAaZyaErHrWFIU+633qguAzB8BthnKmsM+aI/t27krAnfdvQw7MG8j7mI6v4Dwz83KL/mk9ohhaRDG1rLeuy9KIeUs6y1c6j4BWnwqO1jBbw8txhO6+EYF8jg85/njZqn4K0CnsX49QBi+HcBuf346gDfsRHHrrnHfJqaevjHTQPGN+zfNBTr5DtKHdXiCtoYtAGtBppLTbDVDF6x+/oR2hAM7fHtCmGI8Tjz+/hYw5w1gCG75nSq1yTfSujBZSvk9fREThTAkTamp4vGaukSbsA5C0L5D1gyrGkYjIPXxxAKKgm68SLukLaFzHnZnKx8sLWn/B7eXAcTvQjx6RUpm5rAJ1iVwlF9x6KNv5wgq/EQq0lX2xRQ/BK5fgnCwpzaqiJ8Xx/Mml/AsF5NJSX5z3UgpLswbvEuUYgg6qaVe89hUJOAslSWXbrSp6a8JyF6zGuWuB2F79DKz0V6WXI+lltAgtm/k7tzoesF8D95ms5M795/lfkruehP44i/Zxn9yGsoBnjZ+AkQ4qsM1XkhyqMqt5X7kZ9XNx/FY+gy4Ka7tLaTfkzwtRLBSwkMDn+Kjj1q5Pl/5SdA3h3zWUgfJ9zAsaFzVPlrGRpWDSAHSPZih7bxBpBmk9Aj48G9CEFRzO5Uj2cnP0lxsggtuX1wjzBOBjwhqNqMeItZLUMw2mFSIa236s0tEpLsLEiwi4rCSwyr+s1/UOLUYgSkwkIs9CkKS4Ja6tcZsGa2sNrWskphKp7+g8aCvf1iGqc1jczzCMQwQkrY6ih4zW/7Sg0IeFMhi2U0M/nhffRwLzvLzNLloRj7+EmmU0Dm9aFDqKhI0w4htPPs5qFBKgb2CM7SkEElDzElyFAkThYgWlImEjybDlFxjniTknHvcH5pi4f/vG84+ohMfjjtYUY5yY84BfkAArftLx4y6AhhLJEM057hHNwyieeT54HfF+5/yzBklGA+Xkktpuyo7/ZGhsa3ttosX1ZyPsFf2TtZaeRbhftlHrxbnIEwl0vPQe8ctjVS78HB9v7EgAAFoLoXjrIfhvB7d83w+9WP69L0SQAtTBqpe2EPFk20VpnRWFBdoP6HErp69oC0TbDC9/GPoZEUZzDNxeblEl989vsZ6/foOPuW2/wskWiADHEfk9nduDyCWcWCnQmAAAIABJREFUwcDwraQDaCgKKlQ6i95JUUZcA7JKYSddz48BLKPQSB8FrQuYT0AU/XhB6zfcXg7048ZovL5RuMEw9Bbr+6ULRnZfM8GpYThblpFgi0+j8jk9Ii0f0zENuI+w/u9nXC+DLn84ftkn4Mn/bc7hvf4N4EEHZdOUzXC/RvEYkzGcFASdKr6QwMWvIbnZBV1yNNi4+S3efk6WWA5aa7aO0SY7cXHiXuqCANiOWlY5454znj8zCcORu4T+PB+R4TuWUqht0j3wLFWC3RApktP3yKQ16ypzGpslVtE/zKQuJRDXUPeuLIntuBvX24gCPpPFPvY4npBAo/XZG61Q3bdMOnxGAJrIE+yTgeoLkMlVNgYcWZKLfQm0LR8ijRyVBsNSKjZmhF9y7SoiMVTdw683R7SoPFtEBTEvQFuDNiXVomgHlRQjgtLnqMwjqjDoZXe+Y0ABy6jN7/G8awGnYRuwRzJtwqSQSZ6TtpWdnzss34AEIlMFGlaPLrcI9cQMTmKyg1oRVukD8EjAnZGgXEhgpHvxB+P7dNAc+zvSOenQ9KJDEglUeKhYwa2kHoClBOaYTAiZhQjSOginkl9pms2pAlnFkson4G+RwBgDj/sDYwx8/fqtjm9meNwf6L2HT4CoxH1S6XF2b2SKUdlYnu95h82J8/EVNifGndvHPZABo34SGaQSSKXa2JEthPkq+NY0Mx33kM/sxLZbLVsJC+wTK5VXRhTRUZc0V/oEJJFAxFDfDnZOm/ZZRfQDj4tPAOlARXDpCvQewr+Tm24Hq9BuPoGFBFBlTmaWOHk8MG3icf8W1E8/wi9w3OAO9NfJkg0U3P1AA8ueTMPt5SXWzX+d8Dlx/vkV8zxx/7//xHw8cO+K+XhAMcL/N2L9HLcj4vtre0O2ozQAMiamh88wLGT64DJEWgXZdTCrtblssocRg1lbqPWsIRb30klR7WVgIIp2vKC1A7eXW9TpekYCEkisN6HfIct6x7YFs46HhKD3QacvwLwCIoBpmCb4NiIX4dsjIol+tknUryGBaoi8/AIl/H1x5/G3eVOltJFnuJMti32Pw31GFGtshN0un+tYiQjW36fFvqKAnL4AduPaUMBGPNXJFyrw7ZNCG3xNKh6+IupnXkJf3YhE+LchqxmTj+ADIycva4uvM7nmAURC27Pjt7jN/b7nvfJ37mKigH3/omjCmkI/25j0c/ztRtfrwkkl0DQ477bVkcow5FVbKiNcAhUASVviisRzTXjObSkf2lpli94EotiaE4KqG5TZv5gTqoJ57xA4xtEAb/QlcD2RHtW2EIF29glnjR/1cKJqc9JISSssSf7WF+CLfdjQwp5bU9VHJUVPyMdwCwS6jjpeiag2f0Duw732r5RtyQJP3mdVMAt5P7vVeyDQQCiEQAULEfzHlcBb4X11aGZeQeQGxN1xbEjAkjujY7W2s3wB4rtiWDItufJrZVM+rJyEm1M4HcJjDpxn+DbGOdBaw5yjKKFszrLxSttkzXMwKrl1vtljdM6xqg3SRzDGg1FK3G5IQARorcNE0BsLb6EBGqF1om9DQTMKKKuZxiBM3bxZQV85YNlrQWqC7pNcgCsNpApvit4a7JMP+rDj2TGsGiGVWUBtz7iN6LPMxGWYY8X5U/qZw1ASkAbRwGTVUXNn3PyAzV5yQ2ICbsmNsQ8/etFKPg1NBfM84ecDsyns24EBxzg6s2GtHMOtKfoRmcn9OIgEglVoggg1hbMAW2ThZgOoN7Q1tvWShqxsdFjl4xQU4HpjXoGHP7T1Bu10DjPSr8rSyFq/Sg1AV1/ISNJBUzLKZ/ktUlJlAtn0yB2Il+Ckj+C8Bm3+5fjpHsNvd+ZrczFgk7dCoqmnP06lwS/4Ag1Ph3wa73F2+zGfjsF/92OtOkXbReW5vUEfz5e6K5w8xo6EllLEs3JMiJiKzQ2ClVEZ1//O8dN0336WTejvlFX+63j+3fPnb4dQwe7U3ef4eGN/tIUKU7g8fbbM4Pjn+t13rOdcZ9t6yGjC3ajLndYediRLR6lEpD1pIjaWSR/Ylsi5Og4uVHuxsmVdyULOvq6pPnvvbl1XzGU1pVFaSqPidvizrHOq/67Xva7f10akTq/su2fr7ekMVzte/pUvPmEXdd8bn+mhn+NzfI7P8T94fCqBz/H/sfdmvZJtyX3fL2KttXcO55yqulV36BZItsSmWiIJTZYNSRAM2C/2mz+KP4W/jv3gB8MDYMAwDEMGaVomRUpkk1SzpzvWmXLYe6+1wg+xdmaeU3Wr7m12W1JVxkXerJPDzj2tGP7xj4iznOUs77GIvYrznOUsZznLWd4TOUcCZznLWc7yHsvZCJzlLGc5y3ssZyNwlrOc5SzvsZyNwFnOcpazvMdyNgJnOctZzvIey9kInOUsZznLeyxnI3CWs5zlLO+xnI3AWc5ylrO8x3I2Amc5y1nO8h7L2Qic5SxnOct7LGcjcJaznOUs77GcjcBZznKWs7zHcjYCZznLWc7yHsvZCJzlLGc5y3ssZyNwlrOc5SzvsZyNwFnOcpazvMdyNgJnOctZzvIey9kInOUsZznLeyxnI3CWs5zlLO+xnI3AWc5ylrO8x3I2Amc5y1nO8h7L2Qic5SxnOct7LGcjcJaznOUs77GcjcBZznKWs7zHcjYCZznLWc7yHkt805t/8Ed/at90QyLWngHmrwmIIIDZcVO1VjCj1ILVSpn21JKZ9ltKntht7iklU8uAWT18N4TQfkMQDLUJsQpUBEOYAEPF90coiICIAkLQiIgQY4eoorFHRUlpgaqi2iEooBhGqSNWC9M0tf3M1GqUnLFaEct+rLWCgSIAqChgZCkg7aSIIBJBBA3Rf197RJUQO0QU0QiibR8MmPz4c3uuE1TDam6nNyCihJRQDYSYEFViTCAKGrB2vs2MWiu1VnLbf/IE7fyKyOGhqogIf/8/+6/km17/s/z7L//lf/0/vbKeReZLbEBBMPymffz8+POPxGD2KU18XRQqIAgJEYgUgmSWekPSkQ8W9/Sh8rSvBDFyHcAqwTK1ZobNNVYySkbF6DsjBGGxTKQUePr0ktRFVqsFMQaWK1/HAFaNcRwwgxC03duGqNL3C4IGYvD1EkJEEEqmHWvHMFX+8tM7toPxcugoBLrugt0Y+L9/CNtRKKnplSEBkHUCebiWvu58nerD112Px+9/nSJ+vP2v+z7Af/vf/Bev3Zk3GoFfhsy30Zvfe/wJQ7BXT0RTVqef+9p/P7x3OX5N2v/dOLlyfvDK8W8RUEVFqYIrbowqAipQjz817x++54BhMr8p/jmpiChV2vFR3EbU6nbCCkhtRgDMsivpUjCr0JS5zOdF5mOTZm/bPovf9CbCg7P16HzOx+lfnrfz9TfuWf5Dl0frTF63Ol9nAI6L6dV76HX3SltH5r+pWlCBTieCFPpQSKHSd9AH6DpBMZjMnZNaECuoGKZGwJdbUEFFEDNX8sNILQUxI8QA1dDgTgwGpRZAiCGgooSoqCpRI6oBbU6hijuXGgyztu8KKSqpVlIx1AxVIwRjvfD3dxhWwRS+Rqf/0uS1V+qXtE5/5Ubg9DZ63buCIUbz5I8PzDBzz3WWxwbA2k0mZifGpP1b7HBlXEfOXq4/Dh6vavN8j88iAcQ9e8MImrBaydNErU2Rl0LNhtVCqRWzSql+LCqCSfPAMQyPFCradia7slV/DmFoJsg9CzMQKlI9sqG6tVEzRDwiElXomuLXQAhzJBBaJOA+XT25O83saEhVEWs3v8zGz42Gqp5azbO8M3KyCl+5vHby/PWRwNduWfD7HEWsRwxC3aFa6OOGFAovlhu6ULhYDPTJ+PCp0MXAKlUsG9efZ6ZxZD/cUmtmHSYkQp/U73t1Z6pMmWk/8tPPvqTW0takRwgxBlbrFSklrq6u6Lue9fMVfey5ePIUjaFF6oIQ2hH7cdYwgBilGJLgww97xqmy3ExMBbZ5ou8K/+C3hP0o/PlPAttRuaWSDaQc19GvQk716C/zN95oBF4XUnzrHz8o6IfbfKi0rSkoV6bYbATs8J3X/u6DbbvXPcNRZtYMwbzjD47i6EW/ZpePnrWbeA2GiaDVw1sPOQ0r7m3PW7IH+wxVqh+hucGqbX9NmpJVA5Sg5cQItOPF0Do1g+i/EAREZ0/HGtIkyGy8mkFD5XBkp8cnIg/OpbTQ2Y3AyQkRPduAd1BOV8spCHT696xqZgfr4aeO8up6nD9T0aZWQzCCVPo40YWJZRhYpMrlwugTXCwCKRrJMlkylnfUcaSMO6wWRAqiYCW0e9fv31oKOWf2ux0lF3Jx2DfnQIoN2ukyfXJjlIcJFWUYRjSHZteEGBIqgdR1vq5iPUTxiLHolRAqUzGGCcbqcJlGJZhytTRUYZONUo+IwuvPz9vO35vff50ae9M2vo2e/hVHAq+GkTM2fYA5aqHmiVIm8jj6v/NEKZlSCnA8oAc420Hh+rbAEGnGhJNIt50Lf/01EBOunHW++Kci2nIdgqgRgRqaQi+ZWgtSBNOClUppmygzBk9x0KcYtRpTLlSD0vYxt9BTm/HS5omLGQokKe11XwSp69AWBQSEqAENkRASIUQ0+s1MC29ry7kcDkeEEMLhHIjFB+f3eG7OFuDdlOO9YAeHqf2NvGII3iSvh2Ynv28ZScF4uproYmbZ3ZB04mncs0zCJ88uWfSBq3XASubLT79iu7nnix/9OcN+T55GwEhJEfX8nwh+77cIvpTC5v6eUsrhWPIkxBiJQchTIk8jKUaG3RYJgbF41F6KEULg+QcfcnFxwfe//1ssFgs0rjFgP1Vqha5Taq2suh37YWIabhiysd/09BL4O7/RsRkDmz+G+wlGhPqW8/aLyi8SBXzTz/3K4aBTOTUEzUVukE+hFn8utVDLq8rrmxzQiclpN+nXf1Zeo+iO3o95fEsLFWcnWQNKRVUxm5NNLZEszc8yT0oZ5p6/GaU4rDVlTyxPxY3E2A5RrbrnpL5XMwaKGtoSyRoCwSpiHsrSYCBpkJY/2v40j/+VYz4xpocT9uBMzMdgv6Jb+Sz/buX0qsojQzCvnrcbgNeJf9NQqSQ1umAs00gXJy76TBcqF0lZJGG9UFIUbByYxoHNzQ3b+3uG7bYlcz1HVjUgtSGoc3Su2taE0PURq+HwXghCiIGYAqpCziNmhf2wR1TYTyO1+nHG2FGKO5/TNBCCEK1nJmao+vqyapSUMYNlF1Er1D1UMWKqFGDdK7VCzhxyCvB6JfyLwji/KCrzTT7z79AIVGqZnBU0DJRpZNjtqHliHAYMQ2NA1K37qSGYE7CIINaeWwgKjsVLO7qHrJeHWXtPCrVkk8xh8DGaqHOSqMEjgmJS0eB3ZigJjxKctVPnR1PyZo5XDuNEKZXtbk8uxZ9rZcgFa1GIAqkhOX2MBBX6XokhsFiuCCQCCRQsBUiJ1PWEGAnRIwRVZx/V+fQgB8jn9Py7HBPhs/GrdTZa5WvZC2d5V+TrsP43Z/FmeYWVAnShkELmg/VAHzPPuhuWqfCdDwKLPvDB0+ekqCwDjLsdf/pH/4rbl9f8xb/+IeMwkvqeEAOr5QoNivszQtclJAiSHIqZ2UCrddc+v2j5vECtlf1+zziOfP7554zZCJM0x60SU+DZsw9Y9EtevLhCNfDjH/8ZKsKTiytSt+DyxXeJqcOCw76qiUUfWKSPmKbC57f37KbCl3c3UJTf+Y0n3O8jv/8jZTO8/vz8MuR0Tf7/lhN42468bmdcP89wQ1OsDadHHhqCUgo1Z/I0kqeRkj3b79CONMWmqIaDx3L8fTt51IPn77BcS3BCo4vOnoQePeUDkwZOvtiO5/g8v34UBVH/TXw7dqJM57yGNYVqzSiUUshTZsqZYRjIpbCf8sEICFACRBG0S1hQYogIR7jKZvaOhlejgPbeg319hP3LK+ta21qvx+tmxwTyWd51eZ0hOI0W33QPeD7L83AFbdh/HwvLLrOIE6s+s0zGepFY9pHVMqEY29trdvf33F1fc3dzw367JedMTBExPZI32kNDcNZPBxqF1CdSiizXS6eGLnvXETjUKaqEFFlt19Ra6BY9KkItmRgDiz7RdQHBWXelDFRRas2YFUoZQYXQ8gsEjz6kCwRVlssEAdLWz1UKBQOiBoIo1UKDpD3UP0bvbzuf3/7aPc7tnIqcfO5N8o2NwDe3Qq74AEJTopmKiVGlJX1rpVpmu90wjXs2Lz+jThN1GhAVFqsLQoiEfuVKT+ohcvDEUAYqIvnwumOIERXnyStGsKEFDG4ACAmRgGkPLYEqB7aQtjoCZebzmx4OqSlHV5aGUgkUU4rNCSuwWrCSnddfjdpyAWXI5Cmzu9syTiN3t/dMObMdB6pVhEoQYdUFYlBsuSDFSNSVX2ZzkEhjj6YO7Xo0JUgRQkRCANWG655QXoM+ujKPr2lLy+fsi2GqznZq5/ks75a8MZF4iAzb/SNzchiY3ZzZqdPs/65LwEjhjkUc+fUntyy7wtNVpU/w/LJn0SkvrpZ0KbLue+5ub/jf/sf/hZuXL6nDiFllue4RWRBUCdFIyQhR0BRRVUIKhCj0V5GYlNVqSYqRxWqJijAVQ0ohxUAIiY8+foqq8r3v/U0AcimUPHHz1TVY5cnFArHKl5/9GFHl2Ycf0S9WPP3gGSKBzf2XiEaePf+YGDsIEVOYakGC8iI+YcyVFDcMY+bmfkctxlWKSIncjc/JJpRwgwBpukQQLEz8dYBWfXz5nEbYrt/JlTxBe2cf903yC8FBX8vWmd8/TTM1D/OUAUQt1OIRwDQO5Gmg5uwesWiz/K7cPAnsG1JpDBsrYF4ghtgJzIMncs2jiWrVja/458SsUdlmj3n29k896hYtnJ5MmzH+I9PnwGaqM6vp+PzA4NvRu661Ukol54mcM9M0Ni+/UgWyJMQ86RVUjrmTFplIi4o06CFBLHOoc4heTvyD14TsD3er7RvH46ktR3M2Au+enK7Z11/fU40ijRTRPmeO+YNRZyi25QAWcWKZJlbdxCpVVr007D/Qp0AKitTKzVdfcfPVS26/fMn9zQ0Bx/YXrcgrNFiU6qQRidqWrpPxg0AQJYh4hH9Aruyw2yqQUiTGRNd17oTm7OttLFAri0XEakY3vsZjjJ5HCI4SeBQC1vZDQzi8jkEnEZHKskuoCPv9nj4aFwsoJmyyIPXBajvkGL9Obb5tuZ0yjx6+9rrPPbiab5VfYU6gFUsBIFCNafQKXBsH8rRnf/cp47Bn2N8jIlxcPfXq134JGpCQfDvZKxBDqwKe9veePFo6JrjulwhGnbbUnBk291itVJzT3y3WntQNICGSRAlETFaYRiQs2vutcldCg4jccOXqCd1xHL1yuGRnB00jtWRKnqg5U7IbJw2hFXS1cFABMSqVYoUpT4zTxDiOzVAZQaETA4uUUijNCBhGCEoIod2skRgjIUQvggmeNznmNWYKK4c7QvVhRDBXEFOKK/12PHlyOG4c9g8S82c5y7z+TGBq67JjwzJk/ubTHes08Ww9sejgxQdrFinwZBWdiT8Vrr96yf/83/0P3F5fEww6UyoFDUq/7giqpObc5ds7h4OXSwiKxIjEgKRLpKuYjVgIlKkSgtL3kRCcbhqSEaUSpBJCQDXQdY4oXDz5CMHotGJlYrW+wqzQ9z2igf1uT4gdH330MSLK3f3ISPHcgyp9dD++VOhCIFyumHImWGbZGz+QBS83yu6Hmc0AY+3cIZbxBFb+5WD534Zi+rbPfus6gdd86jX/foQnWvNUcUil5JEyjS0CGDlk/VPygqcQG3Y/b6YlkmsBcy8ZXDF6SThI4w+7gnasz5pSnA/DnKzPMQE8Z/PniOCIsB29fYevrBZqbdsuBavZHyW7YWsFXdB49yLUas1Tp2GbenxU9+ap4hWSj8/hA6/+cWJbHya5T67AIf/R/pgpdafXdKbYzg9r7STm81dKPhuBd1zmmpH538DXwNJtEToedAIaQdSRLkys08S6yyw76Dth2Sl9CnQhQC3c3d1zd33Dyy+/ZHt7z7OrK4JqiyRaXsEa3GEg1V+o0wRFXeuWwLQbsFIIGDUolpUQFEoipuAVwQJUd6zmynovCg2k2CMiRApWE4ulO6UhuD4p6GG9iHg+0tBDZH1Ya+r7m4IiBBZdxDCuVoFchaTZa3qYUYzKtzEAr1yXk2v2pr/f9PqbDMFfOxKYaWav4IfM7RP834LQx0i2wv3mhnG/YXd3g9VKf3FFTD1peUGIbggAahkb9DNSa2bc3KICz58+JcZASEqphbuba/I0sr2/wWohtmRS160afJLcwyd44qn65cij3wAWK5hg5BNoxMiNvSN4WDjtbj1xXQu1FvKw9WKVYcBqdYqneMGKGVQGrBqxi6DC+nJNt+iowDRNdLuBUjLTsAerRBVCVFJKpBibJzP3EbLW7yQSDlXO8orFFzm+Plf+zp897R8051ZKyW6Uc2bcbyklOzvLzkbgXZdDnx1ryv3ECXKZvbDmwLX7sLNC0Imn6Qsuu5EX65GLXnj2tKfvIh9c9d5yAbi/3fF7/9e/4OUXX0EtLJc9q2Xvua/2OzVP5Ap959RO7bxCf9rtHELNBRTub6/RqCwWHaqAOJW06wKp7/joux+zXK+5SEvUBHSCaKhGVAIx9Q4x407jOi59LU97P9wYAOXubksIkadPniMaGabaEuC+xoJI800zUeHZxYqLYhArq1j44+6OPMJULinWnFmxt4Pzb5BvYgB+UUrqm41Ag7IO2NtbN/o1UYG5lailUnJmGLaMw86VVYjE2DeaY0I04pWzFSyDuQdutTgHWIQYk0cA5gq5TBN5nCi5JZ5DBAm+rblHyIGI6R668/09Mpj7j8yWbKZI5tETpEG98KpOQ6thKJiVQxRAq94NGg7YvZjDQGK454+RkrdzWCyd2llRSnGvwao3zpoV/AMlPzvt83kVOTEAr7vwzHfsaw3FfHWMYyTg0YDXaNQzRfSdlCOu/JAx8uD+eOVL7bndfyIQKCQKy+S5gC5V+k5ZdIG+i6TkuH3NLaq0ijQnSKqxvlwT56p7jNyU+fy9OVINQamlHinjgrdOaYthZhJOVrBq7O+3SIXt7R25n4jZiCkRQ2uoyOxhe1uYkKRFDYBVUGf6lTKrAl9DKtqO/STX1iJqUfEqZa2sOtj3lcuVMFXYbOxQl3A4lfP6ew3L8pvUAvyi0cGb5M1G4JEB+Do59vc5DRtP4juDWip3NzfsNnf8/Cc/wmrho+efkLoeujUaIjEu/SCqK3+dNpQyMW52xBD5ziffJcbANDqldPvV5+RpYLPZUswQ6ZAQ0LRANYD0iARi8DDQe7BV9psJ1crVkxUBGDf3AIfE827nHn4ZnHXUdQpWGfb3Tek7JaxaAIS+sRhi6gFpFcJ+UwlC1yeiRTRFajUWqyWlVoZ9ppZKngZKnthcfwVWScl7oMQWCVRrFNOcCTF6bcNsKE5aRMzXoFZDpDoD6mulwVxWWkQwUXKm5EzOZ3bQuylHA/AYcnhd8eSpmICJEqhcyMAqDvyNJwMXi8zFZcdykbi4vKRPkb6L1JJ5eXvNOE38zt/7HaZx5PrlNVYqSw0ornyMyphbl17LCNDHDlUlhUAthduX19751kZEhb7vwWAY9uSSGfYDtRY+/8ufgwif/ujnhC6xfvaE1cUFP/id32a5vqDEhMQO6S+REAnpwvVbHDwisD0ihoYeM7i5vUMlcPnkAzTERkqpraswnrfEG9IFM57ZRB8S/+B3lnx+V7n/wy13u0yti+ZN1zee5V9Vz6G3ydsjgabEH7cWeLjDJzmAQ9TTipYAzDH1POzJ446aM0Jt0E9HDd5CueSCiCFl9CigZKQWYgykEBtEoozFm7lt7++YhoFhmvzXtTpfWCMhGiGkk2ZRjs2r6MFoWalUgzy5sg/Bo4Q8OjxSxhEBCgE46ebZoBIVz12E4Kyd0DwJV8I03JMjhqjB3wv+HEJ1I5ATZZqo04iVTAhzP6Ajkih4C+laC8c0RoN/5uNr18uL1/VBTcYDZsh8zQ6e4aPLbt80H3SW/6DklOZ9Ggm0e+EhjX1ez7PycvBGxejDwCKMrDpj1Qt9F+kahBlDaCk8IzenIkZFpWN9sXL4F0HNK+XNKjI6k69U35e+a4nYrvN2LHlq7du9JXXfLwAjpkDJmRACecrss+fmpmF0hl1yYzPc3aEGKSwInRG7i4YJtCp77UCKO58NdvXONpkqNEha8HYs3iEAaU0d8ZDfqpBCoEvGk0thskKfYD/C2FKRD3N3r1P4p7TcR9ehXalv9/f82uPtPJS3RgKHe+ExLdSA1oVvpo4xF0e0fMhYPaEb84Y6bNnf/Ixpv+Oy71CNhH4BsaMKlDKx+epTrEws646gxnLRkbqOjz7+rsMnU2bY79lff8Hm7pZ/8yd/wjiOrNdrEGEcJ0RgsVjS9T0vPv6Eru+JIhAiKXWYGCpuye/vbgGh5AkVYbVcOGw17B0Syd7DpNDmEGhqutdD1qTJPfLk52Es2dk+UggBlrrCRJmkqeZGMZtLeksrbvF2EpUXH39EHge++vmPvY+6GdGMZfRmXM6KylR77g3tgrOElOiOfc4YRmldSqFHRLGZaittKbfEumhEA8TUgSgah7Zrwmn5+1neETmF+nkEAx0ifkPICIJVz82ZTEAlYCQdebH6lMvFyEdPI+tlx4snT1mkxHrhxY152DONA9N+z7Dfc/PVS8wqi8WKqE71pBaG7b2z0oa9wyvBiyADgaCBoBWNwvMPn7ZEtEfAqUsgUK1Q2/opubC72ZDHie3mznNcuy11eMlP/+Uf0i+XXP3691hcPeG7379wA1IyRqAPzi4sVVsuzCPpvhfMKrvtZ2hI9KuPEfHeRGYZuHO1Vy4wCZA6FrHw3bRlkTLfuVQ6M362nShV0KYvH597mPWrn/9jPu4x1PP4tTc7am8M+HfIAAAgAElEQVQv+HN5ayTw6o5+c+XgyRPz5nDTyH63ZRr3junP7Y7NyNNAzhPD9g5Kpk+VgLdMSDHRtaEpQ2swt7m75e72hu12R84Tfe8Vg9oSNypzpbDN0DgqEEPATEgpUkvrXW4480cE86kSzlUWnIGDOE4/Y/0nOHvU2KiZ2jxwfwSdk2mKiWLauo0GL0JrtpIqEQNidkzeusg07NmuVtRpJLR9cIapNRZSaQ32pkOiSvHkU21YZW24aW1Yq0irkD5QR1vKQBWp2vImtSXR/fks77bIo+fHrx/zBu7giVSiFDqZawIKfeoc/okeBXg32nrg5df2KA1e1EMH3IpxpFrPDSCDBFQhKF4LoH7/z7m2FPRgBESE2uqPupY8DqbkcUIVyjSx98WN5cI0DOxurrFa2d3d0JVCWHptwBxJS+P/z2weDdERjDL5yq65QbxzLdGc12iQWnA1vxBllZSnF0oulc/2Rq4GjX308Py2835yIeb8wOtU7ZvV7zfA7l8j3zgSeN1788/V1v55ni42wy1BoFhmc3/P5u6WH/3k52CV3/z+b9L1vfPhhx33X/yUcb/j7ualZ+V/42+xXK/58OMPGtOmejGZZYbdPf/vv/x/uL+7JdfqU4FiR98veP7iuTNrUkcISrdYEkJksVgQQ2K9vkJEWK7WlJzZ3Nz4zSMAlTruEREuF93BcxY59i56POHM2s1dqkcAgUwQQ1NoNwkgQu06JER0eQESqK2MXCwgiM9FM6O21hkXqzV52LH50iOjahVqJpiieWD78nPyfs2T9RNi7AgxYibevbSKU+swrEy+3zGAqA/eaEbSxDuKqgi1em1DnzMhN+/vTBF9B8W+XvtzjOjngkprJaqihkrmKn7FZTfxyTPlctHxdL1g2XdcLPrmYI2HyYDDfs9uu2GaJmJT3qvVAlVhd39PySP7cQe1sIju+S+63tdr3zkTLkZUxduniBDawJi+9wictg5nZ3K6uHK2YqN15707l/c3Nwy7LZ/963+FifDVl5+xfvacH/zjf05aPwF6IBDjAoDaJgYGOmcBbqEUYxhfeh+h9ByVHqtPMSrZMiYFXSUE5WK7Ji4L//i3hc9uMj/7F1tyBk4iAVcls6I/NQZyeP7r9Ar6Nq1f3koR/bqstb/JDCAfmCZzODP7wmJzhWwbyHKSmBr3O8Zhx93LL8jj4Ph/ctgmtkcIgWkcqCWz323Ybzfsd1uGYSB2XasV8Bum6zq61NF1XRsbF479h8KRdQMditLFRGkIurU+6HPEMHfklMZGQmbFKM0IGNUcgzR3w1v9ghxaQvtZmPurG0HU8wEtlxAlAXo0AjFQc0LqRO4SZXdHmUbEXJl7RIK3eciTD5tpAzUQp8UebwIOtRJWQKS2Y5KWTIa5FkODYhZap1IjhIjp2Qi8c3KAgeyV1173uaZRESlEySzjxDKOLKLRJ6WLwVs1tEh5KoVSnRE0V50LeJSg2gYwcegVRm2V/xIO0bvKHAX4elLVQy1Q0ONQqLlOBvH2KIYcdGxsc0BK6j1qrhUJSuo7dzy3G6bYMd7eEEyQix4NECS2mMBzgO7oCqIJsUKeRjRUp5SLIt5cHrSNkVVDTAgS6AJcroxdNpYd7AcYyglpXnjw7/l0+9/fXOG/qVbgl2YE3rxT8+SscuCgg1FKBjFSCJBHxu09ddzx4tkVViv7zR3b28JnP/8xm7sb/vJf/yEpRf7JP/1nPHn2nKcffEDXLzFNFJzPvt/e82d//Ifc3d6iVFaLnsV6Reo6Ltcruq5rBV0TKfkMUe8VZ0T1KkK1jFokxQVEWEcP9/a7rRdKZU8Edylx2kAuNHik4PMN5iEsot6zJ0lssFc8VhRbpdTR5weMo0cCoUNTx/KiJ6SO9fIpiDLk1m6iGdA6XFKmgdVqyTjsuP3yU6iZ5aJrRqwSS6YMO1SE0C+8Ejr0jVfd5jG02cRlcAOwYOlGEY8IpNURBOtAsvOo21zicyTw7snjZSwPM8FHnoA6yKE2IRQWcs8yDnznastVn7nsM+sucrHu6buelIKz5zZbpnGgZFfwfUpIiqz6nlIyn3/+GeMwsLu/Q6yy7hNRlWQVNbx3mGXEIkGERZor5eeclitmlRl65ZCrm8fAijiM7DRTX6dXLz6h1sx3/tavkXc7Nn/+E8rdnp/9H/8n6eqKT/75P6O7eELPJSpOKbemxwxluXpCziNffvkjALoPVlhaoHKBCMTeM4fD5BMCF/GSqJl13vJ0MfGbLwJf9MpffiUM5UTZH878Qft/Ewj/5Pq92Vg87hTwdfJGI/C2XiPWPP+TVw7/yaG3TvUOodPo83mtsL2/dYrn3TX7zb1j2Kp0ixX9cuWRQIyNaVMPLQ0293fsthtiCC1fEEmh5QIw9yyquofc8Hx1Nj6Cgvn7Mlv4ELHm6ddWwSjg3QNPLog2z9nsdBIXBwpmaAkYMY8KpHp/ldISPbU0nDFP7pnnsWH07oVH0UPRHebN9kpUFhdXaIrsN7fUPB0WsW/WWUwSEql5XNLYSKIBmbn/rZ21mJBzRmsl+uo5LRrGjZq2Lo7hZGLaWd4Veaz034QfezVvQckkJpKMLEJhEQtd9Pm7cxQu4g5gniby5EyeWot78LSK9NYyftrvmy4ATZEg5p1zZ+9flKDifYJmr7/lsFrXeA55inYEh3Ypom4MNHDaA8xzXIYmyGmHra6ZZM/tfgdbZbq7QUXoLlaekBCdUX7mnmTO/mtFbHmPAV23bvkB1xzaCB9zHi7FwKKrfHCRqFX40bXhk6fm63Fywq3xtWTWNW+vGXjb+980oviFK4aPBuCYfD3SoDz5U/LIOO7Y3L5k2Nwx3X3JsN/yk7/6EeMwUMtASh0/+N1/wPrqKZcf/zr9es1y0RNCoJp51v/ujvuba26++oJpGHj27DkxBqz15sn7e2yKLKJX/U47qCGy6Bc+U7QIas6YMUl4byelqO9w7Ja+34vT4S4GJT/A1mJ4fA6cLTQbgWIRtELwCKkTo9TC9n7nvfrvN2TZsr29JvY9Xb8gLVakftWgmVbQUhSzBd36kjyNxNgx7u65/uwnWCmsFgusGNdffkFabOkun7hBQzBRUuoQYDddU0shJI9U7m6uQYTleoWol/yAHKaqeW2FHNpgn+Vdk8fX9OHf8/wMrKAYC8kkRi64YS0DF3HgohOu1iuWy5606JAYmSyTy8Dd7Q3DsGfYbBCBp5cX1FL46Y9/zLDbMd7fY6UQMaIKsWSSRK763vthLfpW6e8wbxdaHUwbuqSheUq1YGIO0yCHrgza4FiRh0bAnTel6z6ii4XFby+Y9hvKX/0FZcxc//7veSTwT/9z9OKy9XoTQgp4i62Cho6PP/o+Oe/59LM/BRE+/niNpt4b6pmyMCeeTJoxhfXFJ/SLyn/8tyufvsz88Wcv2QzlRDnPZgy8jfvD3MDrGESv+/t1Hv+3gZTe0jvowWaZbxqZqYYzD/1Btsl56tUKw7Bj2N6zvb1mv71jur9lHPaU1hAupZ5useTyyQesr57QLVekbnHAGCteX7Dfb9nvNh5RFKermQllGl1JB4VYmoeNs3wExCpSBakV8BGQIgXT4qFl84BVo1t72oB5aUagdQWdE92n1baYY+pCu/nMWvVL+x5GVFqdQz4Mna/VsJLJwLDbUoFV6pEgh+gFnDXkHGalX60BQ2NPYWyVjVBzQZrXZbVAsLZYFC2tJQDzOE/z3kvimKxowIJPUapzJKWt+6HK6STCs7wz8sgIvKIoTmsDDKUQxGcDLIJPB0shEKJ3+dUWeY7jjmkYGIeBPI6+aVx5lmli3O+ZhoE6jYgZMXr0m06i+RiDF0Kqekvp5oG/osoaAWaOAI59feY37eQ7rmSNud9WBA3oag1BWKzXTLpjutlQRLFxi+WOGhcnrDs7eOgpLg6sOncOM2bxmDtonzdztmGQgATlYlnY7iurBNtoDNUp2Kdas63AR5fnQajw6pU6KGg7XMtTiOmXEglYbcNI5iExh4bW3mvkkNCUdlDWAK9pz7Tf8tN/+6fcvfyCP/uD/53d/T33Q6XrF/ztH/xdlqsLFpcvCF3P+skli0XPBx98QBcjIWcoTknL456f/dWfc3fzFSVvsJK5ffkptcDmK+fpPrm8oO87lmaw6Fn1iqoh04RUwzRhKlTLmBoafW5BNL94wSraGtJ5IshjT/esaRO7wItF5phUEGuXPrSQcOEXplRPCcd5mlFcUUpm2O88XzBk8jDw0x/+GbFf8mt/67folyvC6sJ7l8cVAKGOqML6+Ud0l1eMZuRhR97dOMTTup1O+y1ilT443dN69RzBYgHjyHZzj5nRdwnB2N/dIaosLoI362t3T4jBcxi5Us+J4Xdb5LENcKVvCNUWYAWRG6Lu+PBy4LKfeLqGZQ+alkjq6UKg5Imf/du/YL/dsrndoqK8+PADwPjq808Z93vKbodMI3HaEQQulpf0fc/zFx84m69ztk8XozeSDDOV0okV81yM2qAUjZ64kKbcQ2MP1VbAhWVfG+pFqKqtbYQ2pRmfEtMF3/lez7S95Wd/8gdYvWH84i+owzP46LfQmFiRUDNaZRkWIxqWfPjiN3wq4rhhmvYsLp4hGqjijlYZtxiQxCOVFDPrZebvfFT4bGn80cvAPgup1Gam2vCaNw6dEUTrCQmHgzE8FHkcCB+vs55fL98IDjKTEzy6sXtOLNMMmZRppJaR6f4l++091199zub6K6b9lpJHQlqRFkuWF1es1lcsnzwjxETqO+/xEUPrCohTTs2oNbPbOSNoHjjtHGSvDBRAqqHmQ2yCzLiitoKTcEgYHfnAx0Ds6Cscm2gd2mWJtPPbVkybSjYbATXf5tyobTaS1ozA7DHElBFVcnbqpopHJ3ncY2YMmzusVjqN3km1jzPw78o5JQzoV2tn8uQ9VgqYJ8DKOJJF6ZeFOeErIsQUH7b0wK9TLRmqUkvxpRSO/GU3auFRP/SzvBPylkt6QCMaSUHJBJnoQm6jIyMxaosEguf8ircdsVoOfa8QXwPjsGca/F6VWgmqRPU6nfkRk8/PnluhSMsPnC7SA0mjrcWZp3+cq316bKfMp+O6nRWjiGINLkqLNWKVtFpRa2G4v6YA6YMBDQkaXDpPI6zN805djxVl2G4dWjabQ5MjpMs8XwRUjRTh6TowVojXtA6npxroNYHZ4+szX0N7zWelzXA5RZi+4RL+pcwT0DJSpoGbz3/C9u6aH/7xH3B/d8sXn/8cq5nn6zXPP/yI7/7WP6RfXpAWK0JMLFcXrsTq5ErYmkJW7/OT88g47Lm5fsnu/pZVv0DM2G4mavY2DzFEnj57wnq14vnz53SLntXlJTEllv2lJ4laF1HVmTrqhWqH+6zh8ccOnHqAk/wCz5z/cHKhvVhsvtGkUdUQL+6CZhxrBesJxelkeYpM04gx0U17yrDlr/7sTwip4+rFx/SrNR99928QYmJs9FOfJdzxwUffIU8j911PmUbYbrFaufvyS0Lq6FJP7Hos9hiBi4tLcj81Sp4/++QwT1Rv7u/QEFlfXKD4UG0R4XJ1+a0wxbO8CyLU1s0nsiPKyEpuWMuWvt7RAYv+A5bLJVcXK0IMDNsNJXvF/mqxRJ8HSincXb9k3O/YXd9QxgEdJ6LAkydXpBi5uLz02RghHkkW4mw7bcp9VtiIuEIWQa0VWza6d4g+WAk9bdWsh8aRs7L3LsIzvCOgLfqNkZiWfPKD/4j95oYf/skfICnxW09f0F09x7qPQBPSLbzz7zAgVBbdgloju507cTlPaPD6IFUlmdcXTOMIiNcoLZW//b0rnl5P/PHPbqkFsly6cdEREWuO19etu5NBXY+v3KyDTiOBbyHfwgjYK//3fxqWJ8o0Mu03DNt77m+u2W7unfcbI13f0y/XrK+e0i8vXCnPF1FAayRGz+CfVsqVPLXpY67EQrdCzCh5T8nVawNSou87ur5rhWLJR1Nq8zDmWbyz59BuLDkYgWM9wKmX387m4aTaiUdgLaqYPQxrMwNQfXgBqrWRxKENjolY8MIsrdW7h5oxjXunwd7fUUtme3dB7Hpkbn3bLnDsvA1Et1xRQ6RM2XsOjRMizsxwA9V6GmkgBj9PVit5rM7Eak7VXMlpbXKTexitQvMb0svO8h+OvF05uJupkon4rIAuZFKoJJWj4lZB2n1bpungAKUYEYxxv3cveZp8MhdeI5NialO/Wo+hR7szU01kxqrkODnP1144rF0OxqJF34Jj8oc1Pjt1xyTxvGZnp85aDi6uLom1OhXczHMDwwJSBhxSOk4Sn5mnHjFbm0/iVc8tP9CYhjN5RvCWF6uFsl4oy1DZCpR5qyfJgbddIbPjdXwbeeNXwg6y5qnPe6p4lerm9iXjfsPm+gu2dzcM+y0pKn/v7/8npBS5/vJzRBN0F1h3wcXlJSIwDRtE4fmzZ46fl4lSITQY4+b6JTcvv2Rzd0cednRPrrBi3L68AzN+7bsfsV4tefr0CYvFgn6xIKaEireSri13HTjeFLSbyWbLKfgoy/kmY+6bcxIBqOcKOPTnj/Nyad0Vj60kRGZszsverRpavbgkNHxzkSdiHKnF+fxRlVwrX/38x1Tg05/8WxbrNd//3b/PcnXp5WQSkNgRoxu9Mo7cGLDfExrMtLm5RmMiLp8QYiRdLAgxsl6tGENgt7mjlEKXEmbG/W5AykTNqzauz49TJbjXdJZ3Wh42g5xTm8aSWxa648Vyw2U38fwysl5GLi8u6Loe8siUMy8//7R5wZEgilKZ9ns+/8lfMW63dLUQgVXfkWKk7/uDEQiqrc2yOykI5FJRnYvLAto8fW19/pkjgVYoZs0Jqw0yCvE4IRAJSOz9+TAx0LF3a8SWYtV7/vRPiGHB93/7H1Hznun6Z+TtDVf981ZV7721wsI9/GHaAZXl1RViUEdnEZZ5eJZGp8CKz1tX8/Yt6z4zLjOfrCdigR/vC5NpW9926C006+7HOv4Uln98DTFzO3TwX795NPCLwUGGY1DiRqHmiTw2dsA0sVqtCSGwXK+JIdD1S9BESG1QepibyHqH/xgCUdUHyZ9g1zlPhw6fM55vOG0UoOs7+kXfWi83tsLB6p8OXpcT7+II3zzA+k+wuYOr3EzzqWdy+O6JFT/dljWvxSF9wQtcZnaCexqhtcKYo59gbXZy6+efy4QAw/beq5w7r2j0/3lPE5IR+6UnoIfOvzeNaK1ImnxR5eyRlrYpZhw9iDkCkNpmJdTQCr7tkBQ/y7slb6r7md9RMaJkkk4kmYgykYJ4a/M26Mgn0E2HXIC2ivppGBj3O6b9jjwM9DEcYJs53zfj/gcPft4Xa00rmqcrJ7mtg4s8e8qHNTyvRw6RwTEKmCP19joBaU7e/Mt1RjQaZLxYP6FMHbu7n4DhxaNlQmo+dPVVPeZDfYwsFDJQD/t2RAkUkXrQ5lGFFIWrVWDIRhgruR7yzu345lwBvFrXcbxSXzd97E3X/Ovkr5UT0DY3N4979tsNX3zxEhH4h//kPyXEyMuXn7MdJ9LFM1K35Or5h8TUY+OeUidymcDUcfPmaGOQa2YqI+O4p5SR9WpJiYpVo0zF+/2ocnGx4qoxg1JKBG0wkIYHD4dU5ijg+HDov42fbFdBTu42mcPDOVegc6VtOEBErpQbLBSkRQqu3Jl8Ytn8eRFnQSxZU1JHzZN3EB0GRAuXayi1MuYMtfCX/+aP6Jdrfu37f5d+sSYu26zktgjWn3yXkidCUqb9lusvPgcTLroes8T1uPXfWy4OrTXMCrvdDrM2YkdgHAZKrgRJiAk11Fc8jrO8e3Lap0aAaIUkmaf9yEIGYr1GayGlS7ou0vd+Hw33Lyl5pI8KqWd18YQ8Tfz0h3/K9vaWfL9BamG5uKDrEqv14tDWZe67RWsohwilDXXSFL3Yqzl0s+NSW0VjiLVh/PN6dsWusU0O1BbROwDlEbsGryeQgND78VIA78d1MIYhoZd/g7K/Y/vpn1FlR7y/JuXCIg9OYFld0KADnDiiGJVSM3BsdOd5CfNW2LUw7SdnkMclF6ue3/nNyGe3mR//qz1lVwkWwJQqR5Tl9dfrGB28Agn9qnICr+tkN9ummZUkDU7RENq0MAjBPfOcPQm5XCxJ/eIwGnGy1vOG2dod45hjKGTk0XnHqoqp+mCV4sUWs1Lz/kBt5u4BPzzB+w9qXQ5WWo6/dnKsj1gGc9P+9nRMy5yGZPNv6TFiOLzfOoee5BlEFNTzJAaEmKhAmEbMhBj8M2aBivnEtDAwDXs3LLFDtUKMbthCIojQLZZtUfmcAmvzgudOTXNQoyGgJbSEtXm4Ld5KG/PGf1Kr91o5G4F3Tw7OTssL2fH+BhB8QHsXCr0WohWC1GN03b47tUaHSKupEcCMcbdn3O99vu+8RltV8TwqdTY4x2j7+PsPI3TldH0e1tHJOj+y9cID584O0G9TyIdoYj7Y5py19X2IekOEkBwiwofRW5la995WG6AcWH/VDOqxCdx83DIzhDS0KKdgJqgYKShX68RQhEXcsteKldhQBTucj9ctv1M46PGcl8Npms/aIdh6c94A3hoJ1EN4dtohwk4pUbHn2Xd+ndD1bL/8KeOw5UdXl6S+Zz9tiSnx0Se/Tr9coVZhGiGPiGWWQX2QS5nIbXQLIiRRpmLcfvY597cvoVSswu3dHTVXLtdL+q6jT4nUDJArRUWjeJGYqQ+uqP4QsQbp+0XxNhPOH55Lv/XxqZdwPIUyF3Mcj11mtsF887a35vLxojO/uRkB9cRZ1IiGQr/KxGmAOrjBHEa0Gikt/cbuIxoT+9s7pt1A3E/E1LN++tRL5Cefg3D17BPKxcg4mA/UGEZEKnG9RkMgm9/o/XqFpsiw24NWln2PmXF3d+NFaX3C6MkCNudDzvIOSWt4JnNBl0/Cq5Jbe5WJpANPVztWuueyKH1SNPUQOjAv/np5+5JSJpZ9TxDY328Yd3u217eMux3LfkkMymq5okuRXpO3iZbG6gkeUc/T92LwVvHHho+eC3B2c4M/VSF2oIq1IVTWGjFag3yCzjmA9n5L6tIopaanVkdBkkf6xYtXDZ9lvFi/ACssbEesgoZLCIHcCBQh9lQz9rsdWGUZF55Tq6PD0GnhxAujOWITQkVbS5bvPlmwUOU7y0Islc+GQDZFxcdlHuQ1Cv7477nx5fx3S0ILB6P3Tf24XxwOsiN+F1NPTL3DFGaOpQW3hqLq3T5j19oR1MZG8RmiQY8D0AX3LKoItVT2ux373Y5a6mFikFWj6xKLvvcagNNZvKcn7Ug1ODwfHI6TU+n539NX5PD1+e9DNf3DzXG0u4/jipMIY36WYySBeA1ECA7PhOCTiVr5XZv5Is1ACZYLhYxMXgRTcquAxiMkSRG1RLdYISi7YaRaJbYweobbQohYNEJMPiUNDv2dfK7D5Asq9OdI4J0VObnH9fC3YAQmgkxEzaRQ6EOkT0JMiRCjjyOthdoq6WP02d3DZs9+s3UHZMo+FrUpfBXv6aOnN9Rj5SZymNeh+nDtuFd74tmfYP3Hv/24jpHBaSRwXJ+vzEkS9TV3iErck0+px8znHNgMGc3rCEGT4HVbzRtv+QIf1zrvy+xInTqI/lspVvpkPF1Hxlr5chJKPZ6Lx+fmdTI74g8119GAzFHAXzsncIoZvmY3DgBViErXd1x9+F363T0X6yWx68hhSeqXLFYXhNix3U8HyCEoLBZuOEotzpude4QI3N/f8+nnX7C5u6YXD81uX74kauB7v/Y3Wa9X9P2CGNMB+5t36fRRDcSal3+Abk6SR/NFev0ZOJzoQwLr8GwcCjQOn/VYwswNWzm5KU4nN9GgodgtUFVKWuAtdTNIIYQImilF23yAjJpgU6HYxO5+g4QAXfSkm6wJIjx98ZxxP3C33ZCnCR22hBxQXSIaSN2SEHtWTyCPA3c3X1LL1Oigwu3tDTF1XL1YovEcCbxrIvPkP42AQPHlr7UQxVjJV6xkS8c9faw8e/KE5aLjyUcviK06uOZMlxZoL3z4/COmYeRf/tEfcHd9w/3La4d5lwuERKmVikfoEvTo7LX253NblNDw/9SGR81r7Vi3I4dIXURal1DH/UGOtVpw4hC+5VwcNKhj+Qd4Rb2OoZaJcRyZitEt90ijgs6Ud5tnfDeih2AMecSqEGOb7Fcddgqx9xnGFKBAKCzXyj/63Q/5/Mb48ve33OwKRR4W4b79ep7MiuZt+vrr5a+RGJ5Vo4tqZP3kGSFFL0qyiq7XjufTlHKt3uemTLgi7AnahqHQOo9ilOLDnO+3G7abLdIpUorfYBroUqRL3Qkb6ATpPw7g5XB6DvDjERs84vevP2GHJIw99CCaw+ANrI6vtK3PIcMrLscDA9BCqJbMmkPg1nPd3MvHBGktc60WrKhX+opXCEsMrQy+te5Wb6EbUiR2HYaP35OKc5llNtpCTN2BIVTNiK1Vdi4FxHsRaT0bgXdbju6L4i0jkgwkBp9fYUYIS0IIpBRRUabtlpIn5hqUoJGpTmxu79nc3DGNkzcjPLDP5rofX3OHdgcn9/9Mcjgq+2YAfMdeiRoeoCXtz2Mu7zQiP/WLX1WtsxfvuLovjwotlxEdx58q8yxhX/Sey5yzCKFNSZuX9AEublzNY4DSYGXLviUBCXCxgN0o3pNJqrfrepTwfds0x+N739x4PJZv2Dbi1R05Yk+GlULqen7wD/8pt199xu/9r/89JU9873f/HjH17LcbQnQckpq5u/uSGIQXT1YeZmaHieYpV/thx93dHX/5ox9zf/2STy5XLGLgatGzWixYL3uWi0SKnVf/zmNbmgEImlrFYOsiiOP+xen6hFbpqye49+u69h1uHnM4Z458qlWkHisVzRTMi8Y89DuWjkM9UEOZ6w9arsUawyElh3HWyyvyNLCfdiCV5eISVJnyQC0TUy3eEyiPaEok1lAiOzNCCEM2dRwAACAASURBVD6AW5UPv/NdpnHgy5/9hDxl4lgQNabijeMW6zWp67i97kGERd9hVhnvvK33uN9R4vTt7qSz/Hsv2hRjNa8Yb+OMSLKnkx0X4Zpl2KDlDsvCNHSkoKTmnHz+85+Sc2b15BlIZLeZuH+54Sc//BH319esl+r9qS7WXkNUikezbVrMYUZFg39Sii0SCAevtlajUhBRUpSjjjkJ7S1nrwZukKke4J92oKdOW9umyJGLOW+rtkaW2r5jEjCpThlVr34WfKxlCOr7hUCZfB0t+sZYHJxufShey224DL6ttMTqRBm2mFWqdkBmWb/i0oxni4wV+GLsWq3B2+VNdN9vK984EniAh8vDKACci768uOL/Y+/NfiTJsjO/37mLmbl7RORSWUt39UI2ZwBSFEatgUAB0mgALYBeBOlBf6de9DgYjCgIIwqSAI0EiMKQIrh0s7u6qnKLCF/M7nL0cO4198jKrqUbAtiJvKgoj4j0cDczv3bvOd/5zvct88mwtJKITStkmY+4nPExoDVT8oxoF6frPH5nka9Wci6klDieZo6nmbqdUG9pY/D+7DrUJ8ADbL7HAeeov2cC+ubv4aubW88cvvHC2iuqymWR4CIT6Zfo4ufLA8HiCWtaC3gXiWFEgCX3wt05KzDs3ihpNUcQpebBajDioSollNaJHQyzHcY20WWNyiyasQMIMa6b+UqTVWMlnc2u3493ZQhv3rcVoeIkr7WA4MrZ45cGwbRsdJ6PlFK5bgqid6/vuX11x7w/kU4LOljdr0dO2jL7rx4Hazb+oHypiq7c+F501Af/DtrkTerFYt/xXy4WKdaMw4I5y6rXoP4iwHtwu14cU69d9vetjcpaSzaJG2f9EaXV1DprqWuHGXyFFbPbeod2T2IhSmJ0le2gHJLg0gO7ga9et69d+M/Q9due/3XjW28Cb9lkz6kYCs7ht4+ZUuGTjz5E84nvf/gUwsDf/fLnVIWnzz5ARCnznYmitcjACkwWsydVDscDd/d77u6PnE6JaZq4GgfGFpVY/2DzyG3dvsJFRNDnQ88EmhuFipVePQK/riNWzt906lf//VtTSuo62aU3WbVMoNcGuJjXl3WL2nLR4EeQQLw2vSRRTy6JnEyNsKshUjJaPXkpSI5oUdNF2RSKD9RicJlvDmEfff/H1JK5f/GSmgtTMPhnf/sSFXjy9ANKsc7PkheGEKiq3N2++i2Sy/fjH+roUseWwSowI2Siv2dwBzaDsg2e692OTfTstlfm95sLZZnZ374GHE8ePyGlyv/0p3/Kqy+ec7y9R0rFY/BOKYWSHYqaKm2DR0IvFLdHbdBJKRi9shScamsmFaODVyVGk5fpBAeV2mQiiq0nVSzDDzZrS612nmIbmGUCzdxJznd+v7+loch1RRKaYFy1ptSSZmqt3B/3qDg2xSDVaXcDCvNiDa2bqwlQ5v0BgM20wYlrfoTNWEqhlC2iwqNYCZvCDz+ujPfw+d8J6VvGXm9b4B8Whr99XeDr+wTouPcFnmc/nuPb1e7HmqRCHLh69ATNR0uhtDmLKZSSW0PYmSHUX6+9oUWiOZvngJizUGx8Y2DlytpbXizUcv5SuYgxHlyMC46/6qrL38kB9tiPSdbDe2N75c3ofg0m1sjlHO4LFyjQOQzhMnqxVFaN968QQqPuaaZSG8aoaBWQSsmKuIL3E4q0aB/TcfGWljo8foxmPxlM0lZrMaxTu1eybb7Wh2EWgagZ3tf6fht418b5Vjhj5w4l9M5gp0QvTd7ZmwZXW5Cl3d8iZlpUS+L+7p672ztKqbT2yAaJV2MQ1V4XYI24H8ir0LV7e2BUG6TbX+gibebiDXjjVxf3m17eY3L+t77h9LVMLu5DbRHe+reXOYqC5tSOr933NZ37Bi4Wh07TrDW3ExrPp0LbhLHap933gveO7ahskxKcwU1VW3d/r3ij64f3cG1/M7N7gxYq344m+s19AhcLZ3fy6pCK729aDVYYtRLGid/76T8jzXuOz3/GPJ8oCiqeos70fKqDavi/lrryd6U2KOhwQpfE4+sNOVR2o2eIjjmZ0p5zbVPwQnUCrR0d71vDx2BdfU1q1jp6HV04QbTSzVTsWtk59p8NGtHzJtNqB/2SutVXoN8gHZJqWKsY/dKjFmH01FjVituaoSq+LbprEcgFNAjj7gmxJnyKlJo4HI9QM9SZqko6JMQPDH5AKNRF0OTNlN4JdfTWdVlv8C5w/fQJpRRevXhBQZmmyc4kW/Zyc/XY/A7mPSkt1HSklPItps/78bs0lNI4C5YLB/UMUrkJezb+wHWobKPw6GbLOETCMCEx4uJEcJ6nH3+CE8d22qLLgfk4czqewJveVCmOnCAtGREh50QIDm3FOAl2P/VegA6dmqSDUEtBazXCh+tg1AWE6dvznQnFrc2YPXLXjFSHeFkDK7tPLWN3ms7QKqxBaA++TeKhIGom8oNYrazevUCGic3jT3E+MMgeoZD1ESqecRjbMWbTP1tuLZiKdk/nEltddYd3BS17y8OcowTHx9cZT+XKncgId/oYxRHVaharkT2N1bWuW9BJKH3rehixfrvxLeCgXxcRXoBptG45tahy3N0gXnj9WWZZkjWHdGOWzhrona21tMM//6ytAr+dJrK2ZKoUUgFf267YlTsvsoIepV9q+cj5aefr9/DQvwr1XPzb5V5/TrHaa/fHBxX6M83rbZHXRSrSCDvarsk5dzCTDAgaoYBzC4riaqFHElILJc/2ji4iUlHMUN6FFpHkhAQhOGOB+9Aogb0w1t7XPJVhWXp95mFq+X68I0MulwptDWIVT8ZLNrEFh3X4hu4g5lcMexgnI1Mozfu7UEvFtftHayvsNn/ri0B8zbTPTD7We+jsF3AR+XbMVFqQdBnVvpH9f6VH6PKU+1u9mYEDb6v7WQnCgkbnTKqm5sX6nVrBmFrQ0iL+Bm+h2CZQq0nQYw5j1IsAUnxbT1rG7UxBYIyOzaBsonJMwj5pqw3IxWpyee5f/Xm91m85928a34ki+qbWyPquYjvrpelCTide3R3Iy8zjDz8xkSknbeJkMso8nwCaSqBSUzIbOq0MIfJ7n/6QeX/H4dWXtgkQyOKoMSBD8wgQaZFFK/0Lrenkq2JVbz2ft7KC+nzR9aeeLZwdjVpEctEIsz67TdL+e7d+lAK1UnK2hLQ5sfVNz7xUtRlte5xsCT6j1XopltyawHymaOX29efgAo8ef4TzkbL4ppWyxXtl4YgLGRXLnh49fkQphfvXL8wMXA07DTFQ3DmpjsOEr+8zgXdt2LqiOC04KlH2RDlCuQdOMGRoDnshRDabrcm8pBmthd3VIwTheL9n//qWssxQkpFu1JoNRSu1eLQaiSME12QXBJGu1tt4/83Jr4l4taKwrEFaVbWeAq1WxuiUUe33bLv/Wl3QPLJde+KFmFzbxGrLvH8tXl4D4MFlBCEMEyUvnE53SMk8qhlxsL8/oCIEf4cPI37cgQrzKRtTMg6AUd2dK8TB2IglZShKToYOOIEQhOurDeorP/wItvfK4YvMnMta07Su6IeH+o1rVg+Mv8X4rQTkLpuWz79r/HdpC5IPJu4WAks21cFaK7UXkEoxf101vnv/qlpMgGkZmNVkZk81E1Juk/miiWT9nhXNW39eQ4FzBPLmtXnzgnae8lvihItr+waz4fIy9PSDy43jEqeUNUtdG88umAj9ZZyYC1LHZb3rUQZQK04WoKJ1oaJUDXYTZmMvVG9ey7XpLZk5B6srVCkFVW1c7b55miZTfd8n8O6Ni6hYqASXCLLgNJs1rDTYxknz+jUmXsrGFgtxAIXT6cTpeDC5hXqu7a3KtJyjfguE2ryWi1uDHh3LxeH1YOoyE7C1oQdWfejFX50fpcXO/UmXuAkXQd15nOnvlxmKdU6Is6ZNg3GLfRWhVKsxhJLOqpci6y28Cki+0SfRs5fzumkVEe+Mhno1OU6p4lttwAr5/Vnf7TP+NT++dXztJuCaUNKbo7cs11b37mCHtOlVqiA+8uyTTyl5YdpM1Fo57O9Iy4llnqnec5pPtiiFCYAlzZxOR25vX3G8vzcmo/OoH1mS8vnL1xyWQq60COBcvLI6gQmrucbXv5SUEMEm90VLeUeW3rqrvrUqTPscH0pVaH+adxc7sOCl6Z+LTRotuUE2oaXIFVGTj1aqNcRx3lxFBY9ncAOlSVLXWsnFEYIy7UYqynHeU1VQiSCBY824ENhoMUmPwQyyq1rt5urmhpwSL158YRtuY9ttdztqqW0TeE8RfeeGt2DE10IgcT3eMbBnTAeiLi2rdgQfiCGukuf7/WsQ+OCDH1BS5W//5t/y8osvKKcDkhdqrrZMNVXeIQbGGAjOlIZtdewsujPE2u+hzrLut1pupJAQ5aJu0BZR3DpfpeFQWi2LcK6JNjZZapOiMX2yNfhC13Wtz/G1SN3+rhDtXMJkOYUzyWg93aM+0qTBcPmAk0ytV4Bre0yTzqcyn45Yg1hGuvcI7Xy1oppRbLOdIvzgcWXyhf/3s0TGUV1AWw8UfJXP+CZl9KvL2LeDdH/rTODNiLm79SKOOE6rhrhJF7cpsGYCmVJKg5G06dq3r1a8UREKQlFIuZByM7O/eNPLFnR32Wp+ER18hZPMw4zgsgX7zfH2rOqiYL6GPqzFqhXn5DL0kXUT6RtFi6HOUYrq5W/XY3O0qMoJUuz9rYHFuN79mCyj8ii1dXeK8Zqdo9aKc5aGe63m56ytS1ntuPqk/3YxxPvxOzXazWr6VEqQpTGDCsF1La8Go7pzVF3a/PEhUmvmsN9zuL9v2ajVArogqRNpMhDufN9ckB/We7dnxe3eWZ/Kxb/xllmol4998T4v5ELbCB5sHBfZhrz9+/5ml5tNl6q2L0GTqe065+2f1by6rdDc3rNvbH2TEV3ZeJcbnZ2CXYiuFzZFZTOYH3HwSrqoE56P+eLvL9art/VufdvxtZtAbRofbx9KY7qfF62OgfuA14HN7oaST+T5iKBspgEnlVfVaIqn0xGAYTKqYk6JUhLeO0LwZA+LwD4VjktlyZAzkCqSz8JnPeqPMeCcZQZnE+peG3APPqDLi/eQtoYxY1oa+nWjW9Z15ljfANT3yenWK2WPNrmk0THNoELQ0tPfixZ1dIV+vG+AkjhKUeaaDC/NA4gyBMM7ky5UFfMTzp5aCz6MaJgIQ6Y2qGcaB5wfePTkhpIzx8ORmgtpSSZFbdPya8/9/fjdG01yEK+VIJnB7xnlwG6EMTh225HNZmAYIsHb0qCtu1jEMUw7Sj7yi7//e1598TleYIyB08kokTGYpMs0DkxxaG511QgK6ApL2uLYAA9xDVI5Iwu2yMqFNSztWAARc+CSleu3fvV+oS5D3f0FpCsDiKww6OX93h+da7AO0SigbrDC7XCF1sp8f4fEyNUnnyBeqKcXkDIpJ+tT8BGhUsuJWhLH4wEQfNjifDD/ASd2eLWvmxbEBVEeb4wU8+Q6gne8PEijhXcC7jk7f9NU5lssV792fEMm0PK085b+lqe8bZOQNXKAQjrtqU0WwjfdEUvXisERpbaTsK3PNUnZjp/ZNDxHEktKzMuCP83EonhvvPoVX193WNbHh0dpW5es31+cS7+4vQGsvWf7xv5rvQCqdYWDDDbUdl7t6Q3eqWKdkMag6r2Kli11+ul6XBfc6P7/vsk7ueyBUChl1TAS2huvB1MpLKBCSfOaERSR1WfYJDfEJD0UILUb7Y3Dej/eiSEtC/BS8JKRmnAuEzwE75ruf1gpmvS55axfp8OrJc3kZb6oBVRUDNsOTppyaJunKg3e1xXmXxso1wx6TZUbaiNrgPUwmj/fsW3po6XWD7J8uXytdVxk23T4xD14xa+uZO21XUDJVmvzDt82kJQN3g3VvL2NJWTnWy/C/lU5eT3Opo6wHqdtzsEpQ9MUmgu8Ol4c+/pyffHvp6MPNoLfZHy9iijdBahFqBcHYY/m2dmj7LUwLQ7nInFzA4tnfv1LSppxRRA8u90jqlaWvKBS2R1H8yhNEKpnmq6sey+ABkW9IMExBcWT+eWXL7g7Hpl+9YphGPjw40/YbDc48cRhANpGIp2+1bMWd54HrhJIGFbX+LcdnKytQavRU3s6iFaLxrECWql9ma6meXTaU2qm5MUgr5MVXpMoznuubm4IPjIG4+lXN6DqQRZMqMpqA+cUUM6XGkxjBWETJ0pJnPavUSpuYzWVMptxfBTDEU/LQnYR7wNh2FiWNAyIx9J1HxEXGDeOEhfm+Yi4Qgznjej9eHeGV48XZTvsGfQeTnfgFraPPZvRtKeGOOL92DaDAlIYhw3Ox8a4q4zuwCAH7oqyJGXJmeiFMQhTdHgEp4LURqlUD9UjpQkiBqypHuP61w7frJVj0wRQ7Ilam5+As0i6tmDKIa0PqFlH1osNQWQVe6tNPbXfz6uIkHRIppsoWZHXSUW14AWqcywhUouQ84KvCy4dqQlePb8HEZ4NLwnjBjcZvz8vRsnebK4BEKyALjJhheCpFdBtMxStdugaGEPlH31UebJXPr9V5qTgc3udh6v8edHXN37+buMbpaS/gpOLRcnrDw8OTB88UXwTYqrG+BGNiIgVHtcu2NrSxE6VNMetEMzwodZKafDROI4GZcSAeHPjKRWWxfwLTvNM0UoVxbkAGNvFh9580iN3dxbV7NF5wzRtc9XzxtEonD1xA8hi1Q2prkGcbROY962mkQ0nnQtVIYsi3q8+rW4oTbzONpY+93tB+Nxh3K9xj1Rk3dhUG/F0hZGwTapqn8urf8PxeE8slfHqMa55xAIrRa93DHtvMFUt6TdOLd+Pf7hD1BoZg2SCZHyjivpL3f8H/hxNLXPtHLbZGLwQHJRSKaV1B2vPFqQ1zZ+x/4vSwBnzv8wI2s23eg60e2/Nitf78c3H9rdrw1Q9/+5iaL8h1syei2WrR+uXf9+76vsJyBoAq1brIEbXArbWDDWvi3Bt52V9Odo2n14/NZTExEnV0rN2UZxzBA/boXJM4J1pJHUv8xUVePiprmcJv9lG8J0Lw/LgAra3vyz8wLko6yNZPMfDTFmOXI+Wak6bAa2VrIYVpuVoMEW2F99eXSFOKHNluZ/Z394jqvzwxz/k8aMb/tEf/hGbzQaSSUzc3t9xf7twn6wxbZxGYohc3zwixoGr6xt8CCafIKZt7hBccTj14GqDET299ds2gITWzHw4UHLmtD9QSuaUZ0rOzPcHaqnWEY1AczebNubo5cPY2A9CXZT96y8QYDcEQohsH39ACIGNV8Q1vROATg1bIcCW7rUb1HTXhTqOVM0s1Sh8oguCknPTbKmQsvLlr74gDFv+6OYRITjqMqBO0dAd1RzeDdxcPyWnhZfPv7AJ/n68UyPqLVELozsxSmIzCoNrGS7Fmuw9tph3OEc82+3OPDtqQWrh+vqa+f7A8fiK4/5EaKqaMThi6/npEIhqs17tDDk9Q6k5JaRWQrT3GwFUDSEANObWDVwQPFLEpNclIE6RrOBa46lUijPlW+fDOaOQ5vktdi4d4qEhGKCrh3FtBjKqCescTkAhSKFKD0YLx/tbo2SXpan8LkiIjRwodu9oZbs1c6blZOqhrsFcMUSrTaTUGuzsRt9sN/iiXKUjc67s4kzOwqzTuoX8urHyUH6D8Z03AZWHG8FXK9Tno5K1qaPvqa00KkJ1oNmi8N55Z7pAnhgHUky2kRifChFh2kxst1umzZZps4Fou3GY5yYaZaliSplSFX84EGPGeU+IkaEZZXuxxjX77AVpkYy0CEPaedWSqCWzzCdKWpiP95SSSWUxv+PlZIXU3pTiDcc06mokDOPa4l6rZQtobZz+QkoLqoXYgbcLrLRDQp0q3WII++fVpi+Yx2nJpv/uBKlK0m4LathjbRDVYX+HIlwFc3oTr+smgFTEe7QGvI9rReX9eHfG6IvVA8h4Wi3ACSaX4B5A9CLrVMS5LvNgsyIG8xC3TKAQBfxKvOAimGnRt7S1Ya0NnBUDQFBnNYWSLQLPxXqBHE0Q0TUxuq7eKaYygCtN66UJy2HyCrYUiNXLWl2x9z8YhbRn0XaMRjCp1geAuYmhhVrmll0vVk9LC6rKqb8O1mOj2pQPtG90vSntXMU4r3/nvoR+hWw0YT2U4CA4YwudspC7uugDOF7WdeE8Vjy+/fg2Ddevjm/eBGT9n32gb8kE+njIu20yy3imaUOStrC2qEM1k+cToERy61Kc8MGxvXmE+Mg4bBnjkWna4J1wc3PD9c01MU7EMBG3hv/vHj2GhhGWUnj58iVpWfjFi79HUcZpZBgizz76iHEaefT4kU3iOqDOoy6anpDrRdZqVLi716T5xN2LF+RlYZmPgDJMA8F7pmcf4n0gbnY4HwijPQ6TPfrNFeIDPhpmryVTS+Lw+kvScuLu1XOohRQdwTk2MTa2RJskFwY5l+3zIURUPaI7SllIByvSXU0jRSuv7kzFcBxG4iB8hGdOhT//v/534rjln/z0T7i6ecx22/wcVKnVkYr5Fdw8+eCrMOD78Ts/vvcYqAV3uiVw4nrnGJzHtYxcXDUr3yYZoR02lIB3AQ8EEbbbK6bpipwLOVd2VxPbKeK9LUytkb5RJyvei3mJG0Zpi73rzmGVkqxX5nR/S63FgikB5wUfAo8eP8KFaEKJLhCGYoFW2yScWIev7xF9OfNuBEFDqxmE0ep7FIOSsy3+Oc9AtdfDDHZqyexfP6fkhGbbBA63t+SSOZVCGAY+/vGPiGOgphMZc+tTcQZv900LpdSMqqA548QTVvbgGdYSwTTQqIxUrnzl02eB3cHxN18Ic7ZNZO1/Q8+LPXCJvXlvrI5S6tclD+v4VpnAGZU+ZwLaagMq0mCtN97tAr7rkSv9RFqqWItRy0oAo0wGnIaG40d8GAzCca0PoB1ISYXk80qYqqV5FmPys2lJ7WtpLADT8ZiPB6CS8waHUoOlh06r8X27/V7/cJqnqpbaNMxbE5oP+BAIMeJCIDRqagi+RU1n2QqDhQab8NHOOc0HELGIW2lGGq2D16ltBCtzoX8GF0XitjE78agEWgyHl6bK2Clv4hum6/Glmvezc+TlRF6OlLxgWKRHRHFeQBxe/fuawDs4Bm+FXpWMpxB8628sF1QEYW22XBNT12HD/u++2UDaPAveE71v/QHn5szLTAC5yAK0ohVKLohURLzV/hqDyLUItia77+bjEecTJdiin5dsi39cEO+J49Yo4qHLxLgecxvCIOYQpuIt4+2BbcsAzOmwtpqAmnpvTuSGAKSTOaqd7u/IpTDX2rKSiqMt/tqkp/s6BRaJy3mhR3u5ouf0b4z2AXixTGAzwCm3BV+locQXmcAbr6HnxeHBb79pfOMmIG8+tvf5yuPF4rN2BtYO8bi2sBVzBlKjhi7zyaKNYhPLL4lhLFw/FVyIbB89Zk4LBag5Mx+OHMTz5S+/wDvP/XIg5cz97S1alTEYVm40VMc0RaL3TM7jqvL6xZcMQ2QThTpNuK3gg+JKwbnmGAYrO2jwHhcH6vbKNMm9sRQkdjOJhUplPhkfWI57BEcIxrrxm2tcGJmuP8T7iAvWsTtNO6Y4sYs7Slo4vPyckmcOhyMiwmZrOGbvOH7zQzXSg6A6gDqiG6maKDmBKtGPzdAiUqpSyx4nle9/7wnOB/Z3nzPPdxyXhThOPP3gKTFGps1IrZW7+2XVUn8/3p0xyQGVBdyJwMIgxk+nCfA6Ly369njvSCcLioY4EpzHc+HF0TR7QojsppHdFNlsJoYxEqN5X/eNoNTSoJDWNOoKRSv5cEIQNtMWFwLbx08bjFzQUjjd37HMiV88/zm1VnxpPgPaCBcUfAg8+eApcZqYHj0hxIHt1dUaqCEObY81LK1/yCjlKwuwnFo2rJaZH/eUnEh3r1lOBz772d+xzDNlTiBC3G2RqytkXlCE14c9bpgYHn+fMAohNl6/1BX9aIvkBdR27pYGztRZlMk7alRuNoVcK169FZL912UCZ4Cp1sYmkrcsH28ZX+8nsHI+3/yHy8dfr2yh678+3Eq6AF1JiVoLwTe1vjoj0lgI4ghNc0ixbuKcs3kP31sk/WJ/x5IW7l69RqsyjSPBe7bjhug9qsowBDbbLSKOnBcStHZuZYwb209rpgp8JfYWb4t5HJDazC7EmDRKbTpIpaWV0CWlkwvgPG4puDiiOuDjyDBtGgPANpvgI64aG8rc2JaL63ZxEYWL2gAWYaihppZlNUhHTVvIO0CFUiqrlguAFrRggmBAWo6IQFpmO544XtD03tcE3rVRa0I040XxzqiQTrQ1ZrkVw14Z+g3b7wXNs1fGOfMMImtzpw9mwyirwq9cZABcYOb2Mn0h7IKPMljG7KhoKeRkhVcXZygVh93TmkyDLOeZmjzLfm9QSxioY2YIJoDnGK0eJ70+hnln+2ZQv2YCTf2zFLQYTF3yQllmymJQEK1x1jnPEAdjOOZCdiZ66VSoNaHExkTsGU9ZI3TpuVMvuKyj1yErorTrqQze+gac1HM98I3P9E1A6OE3b/mDt4xvJyXdK/rdT2DdvTqe9ZZ3EowPXLDiTXHm0Okck0Aqyml/R86ZKI9Qqcz7L8ibA/p7Ge8dmyEyxUhNC3lZ2B9PpALPbzNzKvzNr77gdFqY7w0DH66uGceR7z/7mME5JD1nt534p//+95mGgXx4ScqZn//8Z4xj5AefVqbNBg3g48A4XgHGaVZx6LiDkPHqcbWgJRmldZmpNZP2Vnt49eUdpSghTiCORatFKtmglt2jJ4zTxIcff49hnNhsdwbRYBpCm+0jVAu5bFEt7QuktsnR+xGa70L/HKw3xRHGKzOMKSdcLWx0JpXMq8MtVZUpRuaa+Nu/+zmq8L0fCpvtjqvdHcjCqy8KYZj44OPJfAjiCOE9HvSujfv7A8Flnm2E0TuCP+EdOG+LWhBbEJx6PJ7Y2md8P1BsgQAAIABJREFUcDgnFFfJrlLwIIGtd+To2WxHxu3AuBmI0Rsk5Jxlzs1noFYhYlmHFmsGtYjds9kOuOBxcYt4zzCY7Pzu2dMGR2HLUDYV4uPtS9I8c//iBTll0lzJywnyl4QYCKd7/BCZHj1CQqQMk0lBJ48Xj0Tb6ozeWkj1iJZEvXuNZlvUc07s9/dorXzw0TNjPw0jzkem3Q3qHHf7I/n+nsNpT9zMpHSHGwQ/3Jiq6vE1tWZyVcRZYd70jSIqSm2tw8GZOoBRTx1sJyjK02TrTYgzUkAY0Qv664NyLReQcR8PoslfP76Tx/B3Hj0EbX/snG+Fl9wWt9oaOmwXLMtM8QHVjBNrXQ8+tK5DpVQl58qcFk5L5nZ/YlkSUs09a/PoKeM4UvxgC/GScM5xOCUUYUmtBd4JpfT+As9QC+4NMSmDFn2zu/PNfs7YA30nN0KBklIl50oqCRVhroValZwq3geGzYwT4Xh/1zotKz4ExrBptYNWN/HBsMXSrnjHZRU6Z/vBtUUQLBOoOJwaayJ420RcY0tpVbQYC6JU5Xi4B63k6+uWtUbArRpDzrlvE0C8H79jQ8QweO8V700/yDnr2wkhrP0Ca41x1UOU1T8X6dLtHof5vPjgWg+MdR13yZaOYL8dK2gZgJfWu9VvqH7/Ca55+EordKYWXNVGuxbvcUUpllZQs0msL0dHKJm42dg6E6K9ZZdp6TaVF7XJWrI56uVEXhZyzszHmVqVUK3BMmnGeShupgL708G0u3wlDBEtBa2l9QEYc9HqBJ61oHmxMpvkTOurEEWq1RCcN0whiBlTOSl2L7/tM12v7FvyBLlAc75mfDePYelwTnvbN95gdchqmYMXU7voBZRpOyEoeX+kamYMJg4XxdKmdHiN1ASne9ywYTftOG1uCG4gYwYNNcNyzJyWzJIU3MCPf/Ijnn30Ef/pf/1fId7zv/7rP+Pu5XMO6RWHfOLP//IvGYbI1W7DdjPxkz/4IWP0vLy9JR4Xht0TRAbbZcWZ2qIqQ/XmCRFHs8kUT9WKdwHxA/4EriTyGDlJ5vX+YNTRNNt1cgPTNPLp44/w3vHzX/ycWjPbzcgwjnz00fcYhpFpmlpxVhAxLFNU0ZygVnItoBB6Stu8E6R2DrZDXTXpaa0McUMuhUUj8zzz5csvSTnz8QdPOaWZv/mr/8cwU4Td7prNDaCV+XBLHDdM1xahvR/v1nh8nQlS2YyFKNWavrzn+uq6aUqZ7Lvh2EZQENcE4ZzH+UgII5vtFdPmDuulKWw3A1fbkWkaCcHk4wFyUw71NDVdb/aUKiZ3UhUowjJbbUDq3orE0wZxniqBCuQqpFx4/uUL0rJwOryGWhidsZWuNiMC5LSwlMTpdDAY2XnitGHjR3x0+MHOo7ZahUqlaianEyXPLHmmpkw+ZZbTwq9+/prjaeH1/sRSCq/ziaqKVzEvAF/Z7Tb89J/+EcO0RZeEpkwIAa2V/eFIrZnd48f4EEwPVEtrPjXmk1ZB02w1vrZBeA9eK1pOUDKDZAbnWtn6IWT98PuH6/GlPtLXjd9CRfSbdxgBW8ya5rjhjrpGGCtTQG0HpVZrSFH7gopzyjQNCJXNNOJ9tB3RZ7bTBpxjmqx/4NmHzyzN3O1I8xG52kGLblUcuIj4gTjuCNFxWpoEc4u213bzXrvoKL/ziAr4ijRecqmF/X5mTgUZtobhSWRJC/sXR6OJeU+VxPOXr3GivHj+HLRSH+2YcuJ43FO1EGLAq150ZfbCkkUKD/Z4Oe/+smKLpkrqJJgJN4JXIfhI9tX6NTDWhwejqtbKfDwQfGDaZbRmcpqtr0F3F13h78e7MgZf8VIbK6jj/ucu9M5sW5u6vrJ+mE9IiJEQY/tb6zXwrajsvFuFEWWVI7fXWm0jnf1Oa6EipCaXWWdQFfaHGcWR1FHVzNdzrry+vSOlhf3da4TK4+3IGDybEI0eXq0uVrIpEy+nGUSYSrZGznZ/i2t6xr1BWE2MsqZMSZk0Z+ZT4m5/4nBceH1cSKocnafUwvH2HkohusKjOZEKVMxGU0u3jMX6HWpZ2ZHa3tDg3CZs57Td471IbORWsM/KixK9fc0XtuzwtrLdb5a/fzc46MHCcP6+K3lesoPAqF6uVvI8U/OCk2tL/4ZIToHjvJDnmeA8jsrghTE4vGakzMyn59R6y+/9/jMc8KNPf0iME0VHTkvlyc+ec5ozdSlEzUTNDN7z6YdPebYb2P7oCcbXtcjZ+cgwjlw9/cS0zilmp+cmHBEh2ALs/YrDI4KLEVDzUc2FkjN3tzN/9r/8G6p4fvpf/Jc8e/yUnzy+4f72Nf/qv//vuH39GpUj8zzzL/6Hf0lJC09uNtxcbfknf/yHXF/vKFrYbDZ8+ukPGYbR0k7xeN820MbJ65wMeuFu3QjaxFHLXMRHFKWUgnOFaVCcBG6ubjidjnz54jNSXvj+s2eowt3zL5n3e66vrlBXuX/1OXGcGMcBjfE3mlDvxz/cMfkTwcFuG/DiqKfZePJpNmnpMFnBs5zVaxFaI6aiMiBB2eyu2e3uGMdArp4hwhCEEBwhtKasVhS0erCxY4bNiPdGB1WtLGk25YClkJbCF8/vOZ4WfvGr5xznhee3R1JRlgb3fvTJ96ha+NnP/hovyr/z+59ys51ITxKD942eqQRnSqVffvE5wzQybneGRmy21vMwWd9OOuypBbyC5kq+PbAcZl69PHK7P/IXP/+cQ6nUJ0+JV9f86A//XZZ54X/+l/+K+1cvSftXPHmy8Ed5wDPxwSkRgxWTS62clhlBidHgtjIfzWO9aXbFMKIaWMqpNapZs1zJM7VWtgPkrfDsOhCCcNorSz3Teb+y5uvbfvnN4ztlAr3D7m0ZRt8IHuqGtwwADFOvpbFPWPX/cdKE2LT1AkizqaPtosp2M+GdsNttGYYJlQ3jUnlyM3M8LhzvDkRRjq9fkccBlxciyjSNDeccMLEq3+SmPeIUHwe8dD69ZxXCE6EbzYOYcXXHSEVJSyHPZhThfWTaXbO9ueHq6VPUCz4GnHcNu1TuD2azOUSLuF6+uqNUZXe1YRgG81SolSpNXrpFYf1aOUwEr/OCpdUsGpSIpbbniWDw40WU1y0w1Zr9xjigqhyPubEfFmqJpjbq3OpD8H68WyOINhcrE1dMbRKZ5WtZe1s6j5/wBuDgHCKB4CM+dEG5Vj+7qFlJj1YuiCOdBWQ+BUC1/p6cC4f7E6dT4ovnt+yPM5998Yrjkrg9JrIK1Ufi4LlWQdUz44lOiNtr4mYkF2PAjePUrFkLaGVJC5JM/bMWE5BEDNZSaOy/1gPhHCEOlEGpekQR4rRhqFC2W4btlmm3w4WBzXZHOp1I+1u0CkvOLHPieDgiPjAV0xpzjSXU15VaG+HDnQNMq7kY86p3PHfb2eBpwny2yfbr/GA8uNT/P2cC32bknNfNAK1WaEmJ7WYiJzgeDohTplCbquYT5taQoVUZ4og6x2eff47zAyKRabjieveY4B3XV08Yhw3id+RcyXNlOZ3YD45SCv/mT/+FpWKngg+e8PFTNlc7Pv2RRdoVR1XllA9kKtN0RfSOEAxm8s58CFIPt4NHcUgIOCBSKcuRL3/5JXlO/Ed/8icMV9c8+cnvEa+viNc7GITv/eAjrq8iEgbmeaHWmfk4k5dEXjL/+n/7v3l0veO//W/+c8ZxY0XknJviaTWpWueQGBARfG14VZOHODeJ5L4L9FTt4tNQwFyNTBOlMvqBKEblq7Uw6z2UxHy4A82M3iMOlsOBEt5rB71rY5JIcMJmHHBU6nyw/pvjHq2ZED4mxEBaMuKEOLjVv9saOEeojnGcGMeRIUZYPI6Cw0TSAFvQGhwCwjCYEkDlbD2pCOlUOexn/uov/p6Xt3v+j7/+jPtT4pgLLg588L1PmXY7nn3vU4ZpYrgxVc4ff/Q9brYb/vl//B8wauYv/ux/ZC4LP/jBDxiGQDrcktJMfvkCFVhywuXMICPOb4njFYhQUqE4j48HCAPbP3rM6TTzKv8VV0vlP/nHf0wWz6/mhSLCVGdUCv/Zv/fHHO/u+Ot/++c4p6Qvv+TlcqQc77h68oQ//vhHDJuJR48fY9LdgpbSNISEzc0Nbi20g/cDVTKlaYCh1mm8GaCqcL2BpTrzO3ijOvyQLOLOH8Fav/0tawKXXpjwNgzK0Gp94/mqhu+X1GAg73E1gBoftzZV0BCjqW8uizVqNDbMcX+PCwPRDesu7Zt9pA/B+Poo22kgisJpIeXE/WLV/KHxlofoGUJgHCLDMFDFUbSS1JnmTov2pfOgDWyhY5i9a7ebUli0I4b71cowRIYYqGkmnwJ+MyC1stnsKEsi5Yx3ju20IYgn+0TyC/evm2pj67x8aGxjx6Hte4ug2iZQz3Bb510olwX69rtudnqRjQmsXsWomlkF1iC3nI4gQtxmtGTSclqVRt+Pd2c46V89QrU5UGqmlrDOv6qt9rUat/TRGj+9x7vQXsvw/DNS/FCTR8R6B8KF7auIs3pBM4W3OUx7rmcbB8I48vjmmml3xeObG8I0EbYTCGyi49Fuw83NDYMWtjc3aFmYrq8ZhoD3FT9HDoe9bTgX2j7nhhsjgYg07wTAbzcMPjBdXxNS5frxY6o4lv2BVAteFJzj6vE1c3TcP7mh1oSrmZpmSiswU43XH+NI7w3o614v4J7XUrEGVHXr+me/tedYzQWC05VYpHJGBNafRayOd24m4tu6jX1DJnBeCB4ctHQ9b1uGSvsgTWSqUNOJmmb2Lz+jloQMI36IjPlIzQun+xeUnNhtRqKD/avn1JzwXkgl8erv/hIfBj76/k9wzlHqYlr4UQjRoXhCFJ48fULNid1uAlWGaBMtiEk7DNdPCHFkM1onsQQPeDbTDaUk7l5+gWZBdzeme+4iJptQURTnJmNKVFtMTaRN2W4isxY+//wz4/p+/gVxu+OTn/wBKp7HT3+MD0/46z//Pzkd9mzEc7XZ8vjDrRXk/uCHTOPAzdUVIUTiMBGHgdj4zAZHNb1/EYPFtCJRG2bY7CBL+wRa81jfXEstTceprJuFiGMYtqScub0zDZQsJmz3i198xjhN/P7mGq/Cqy9/CeL4x984fd6P36XhO0+9ZkRqc6sT5pzMq8IFRAK57BsldIe4YHIOteJbU1UYNlY7QtFSqYtQgphPvbKaG0lRvBeuNlviMBC2G1v4q1CCMu4ECZEf/uQHfLQsPPvRJ6DC1W5HjJFxZxpi2W3AOzY7j3fC4LZMw8i2HIlD4Kf//J+hAj4GxMHATFlOhE0kzzPUTF4O1HRLDYWSrpBubOWE2hrcXNwyxSt+8sc7aq7UJVNy5mqyDmcNdn7x2Y48X/FkOjLPR+ayx4cTHzzx7B55fE34Utlun9m6kW+pZTZ1BCcgCyoOcVcNtl3MJ8EF2wTqAqoUElWUyQkb5/G6BTXvD4CQoK/H7YIDvyb6/xqix3eAg9Yt6lyU6ASC9gZVC9aVahIGaTlZmjkO0HFpOWOQPUq1nysSjJ42z0d8zlQt6y7avQbOFpHCECPVuxVDm4bQ3I0sWgmbqZlhyLqr2pk0ZRG1aKTbT7bw396ns4UQ1s2wRdRDjNQhc5qxTW85UpySD3fgIzF4pjGymSa8VmQc8E54dDURvGPwwjAM1mY/mD6S95bhiJjJhtUD/Bs4oGvOZJ1OxhqFnZlW7ZHLbIAW/bdegVwpta7NZ6VYsbtk+9yUwHtrsXdv9EZw1bpKpKw885V50G9wLu6JlnM26rep5PpOTTDtq7pOxIv5qC1rbRkAbYFqkXdsHcLXj66YcsaPEyJws9uZEdK4RSUw6wBOGCeHd8IUIjEEYxHimHZbqy3aCooXM4QaxtFUik+nNv8t09WS12NE9XwvtSxl3ExoqSSOOKdo8agKpQlMRjGDnt12JIaKHg94LwzR9Iu6H4FvXt15LpRc1utaNYNmnF7y/uR8/e2CQ7vTx2CGPU4UWU2nLog5l68g7e/eIOl8Xbngu/sJXHy3QlDZqFDL6d7w53Qkp7l13BV2ziICp7bwdwz89vaOeZ45nWYcynCzQ0tmPs2Iy5S6gAvkbM1lUsEphGiSDl5M3nYzDA0CMXGoGKPBR8NoDV+uFX0bc2Z//9K8jBWi94wxEkM0Do5iQvycW1xcNbu43CCSR0+fcJWvuHq6MTkLNZpXnT9DRbjxcH3j+PQ//KmluM6K3Kf9HdTKOBgf++bxE3yMJtfgTHOoi18hYueHYNK57SCa9LWoYJj/OQPIJa8NMGvLei1QCzUl7l+/YkmJdJypVBjAe2Gz2RLiwP72nmXOXD2dcP79JvCujRBMKDClhW6w5Py5WawXhkMIKwy7egI3KXRBGDYTw3aDC8bIMcZcC+RcaZ24anIqFdJikauPAfGVOG4gesbNBhXhg+87alVO+wNUbWqkAl2ra9hYB39tfhrNArPKQioFnbVlLkagOOUDWhaTdwDmJSMq1Hkmq2eJezOlKtXMYLIZwmhJ4E3WGqe4KSBVKDJZFJ8tmj/NJ2pKDIMQXGAIG1wMbKbJAsQyU8qR4LfUWnl5f0/NC1MMiBOW0x5xC0xbxAW7f6upmJouQwsEVYlB+OjpljgK21+dWApkDQ1kaoHxtygIfx0o9C03gUs6KC0baO4Aqrbz1Ww1gJLQslBKorOAaGyDrrXTX7E2fm6HrrrOeK3FsPcGZdR6NpXv0bgT2k7bFusGgwimztkLWuLcqgkk7VhLNp+AofkdX+KCa9p0gXH2hx7JhCHivKB+omrBFdMHMZnszoeGadyYBK8zSMdjH/Y4mB77MJqNX2+ika7B22O0tsuudNCVAiSw9jfow4X/IgJbvU1bOp9zMiOLfryp6ciPphBZip1LXwDej3druKbpY81KtSmWy4rVn5/n3gopdFaaGTS1eesc3X2vZ52rX0C7v3PKVmvIZ6UA1zYgmryEKkiZoOoagNTmxeFHC45qNVWj4HvzmVgJj+6H0GpgtZi/d18vembdBOIMVagP1YLF1qd+pg2qp4qx/dQ5hEIVg4+ojhgD1VUEM5MK4vACtIyD3pGsTdbaeZxYDQYAzedC74rft+NVWa9nDBgN1xcGB0cabLS6knXE4nKdPp+36te7g3yjvWR/wV7s6XykNT3UipYjNc2c7r6k5GRVbODm5pH5Z+YDmhLz6ZZScquIB4Y4gMI02U57PB4pKZFLIThngYCHkjO5guYCpRKw7tghNO/f+DCl6ib1Bq84pFUvSlrQvKDzEdHK9uqGOEw2oV1XGbdsA2hOQgbHOFWC82gQmEZqjbg6oqrEbIVtvVDvQ8zY3fTRDda5vr6xSKZJTsc42vF534pULfJvC3C36ZO2qJu0tZoSa63GJqiV0jIAywi0/VzIeTF4p7ZmsNOJvCTIliXc3+5x3jG5gFfbJJ2Yc1l43yfwzo1hDJYxpiNCbdIRprcfgl+Nj3omsMI50pvJjG4cd1umqx1xO+EPRnHOORvZotGQUasXlJK4ff3a3PBEiTFCUVzwBG3eAd58iKObmjS0NYHmVuj0wWp9/T4V36SiW5yyQlNNMLFSKVo4HfbUlM3zuNnLCrUpGNhmUxFzMtOKOwYLHvv5+2D3w3BtxIp6bRn9Zk9NC2UbyMuR2y/34JQBISrovDe5tHlDFYcfNngdGKIiFMpyj1YP5Yj4EecHY1PVBLVYYCiCLiZcNyBsnPDB1mp7x+OGpA6R1KCZJohHq8W8sYF/E0Pot6aI2qRayMtMzclE1uiV/khvh36g0VEtXew8du8tEaLZu7VeRqsJVEtHte96fbFq+5FrVf5zxf/STq5H1XrGBKvpgIPiu6+udKegftVY3wvapKbj9DYJXfve2uIjrlZqeUvlv8loOyeNIWHMJfudX7F/pKmtilwAUboeO30j4BLu6ZHGZRbQflfP/97rNlpMotdCE0vxFavRlFwoKVNjWTHc9+PdGq5F7dCx4h55YhlC+7fLTMCkn/v9BT3IcsHjvHsjE+CidNixbav5FXHUXKhrRqBQMqijm657R9MTMhjINdTBtYjWt8zeniimDgoXeke1QaVNk6ydozaY92yD29P6/tXOtWXIiloa4C0w86sXt6LVmaqwgJQRKDhvXdbUiuZCWWZwnpoT+O6X7m3R1tKknis1L4BD3LAeP2qeCVZDsRqAI+NFGNbaQLu6D+jhb4f9f2uK6Nkp7PyCl3ULFSi58vLLzynLkcCMR43uhFqnYU/Pqtmz5bxwnE+2SGETLMZIdYKvdnLTNCFOOBzuTUOkVvCtWKuK04rTYjSzZm5tlV8zlxffsfR2HtmMYZbTiVoSUcxkfZgGwjCgwVG9rGbzUnudzJrE3DkzM2+HYBEVrbMyDPYh5tIt5uwjGZ05hYUwrNZ2lgnEdi1bcc4Fax3vGcgKthUrBGlqYnupWUXOJqXbVE3PtYBzJlCbZ4NBa1ZgystMnheCeLzC6CM4WI4na5dXYVoyzz4t3wJlfD9+14b3hr2beKWutMnaZFt6JrnZbL6aCYhY8ddVJDpcDPgYccFbpu6bqYp6vBinPYtrXiiGb6dlgVoNMsmeUIst9s7hQiDeDEgAiRWcI4qZLlFKU1LoQVhcNY1Ulbo0iHOZTQ00n6g5M8VIEcdymDE7YqudVacgLXiiEqcBaoZ5oaIkKuIccWvNYS5uAc9SDBnQOCEhMjjFB08cNtSSSPdHylxIeMJmh5+uCNOO6+sPAMj7zyk5s5z2dp/mAR82TDdGhdeSWl1zwGFSHLZZHonAzTSSVQj3QqY3mJ3h4gdyMt9hfHePYc47Tqcl5uVEWY6IZOO1DhNgOLhqQbP1C5h3ZwPBpHcRtq8qDVLR1WKx1orTziw41wj6Dm+ZwIVGd4/UV17/efevWs6dkT266Foml2enl949XGB1/Tfn1++qqKa3QyfhrDeOb1mAb5FL3wRkZf1cbDZr7K9Nt0cfYJYdX+z4/mWk3zXLtVpZyaCjc+bkpHcO9/e0SKhL/sZg2G5eFlKYySlZFvd+vGNj9dtqgWdjyOka/zao1iLRlNIbf33GnEUEP0R8jDTvw5V9xpqJy7mkpkrNxVrKarSgp9azHXExqMZ1nmmLvtoy2CJx36BSt2ar0qY6KytOV0jE+dC+dxf1y3bfuGb00jB4y8DfqCEg55u6HajSNiF1uDigNePjYFcnFyoJSQmNCc0ZLaYAantZIefSCBtKlQXBU0u6YCia74NqxTWVg2bJiBNjPgnncsDDSgDn7Ob88MYTvjq+vibQXqauqZ0tIL5ZpqVlIc1HyvEl+XTPvBzxPvL449/Hec9pfklOJ5bXv0JzQiggjmmzRYGUjpQMfvZoLdzd7Sk5AwEvHlcbjNKcimot5AYpOSBEKzBJaB9iK3y59gFW1wrNWDfevJzQWsx0wkecNwmJUCzDqG3VrNIWSWeToK7FYodTwxYR8GFqV9puIe/yCiHZXZFaNE+rSwz0aS2YoF2fWn2xt5pEW/Rbcak2Iar+2CN8zRZZ0Ww6182j0W9dcyCL3qMxcHU1sUSxjAjFj4E4DHz64UdQ4W9//hnHOfPqs+cM0+HrZ8778Ts3Smoy5i2lrbnVkUqCEKypK0bGzQQoh+MBa+psuaRmqpr8mXjh+qMPQAvy2ed4WDMCP5r1pPoWqbeAZdmfqN4z+gGJjuIEbZam4gRSAgQ3GtZd4wjOE/wWJ96aR2mF1Vqb/LvdKxUlOZNP8TLi1O6xmjMlWSAYasGVhbrsoUTCMAJKLlbzrA3e2m6sb6g0YcZUEiIZL57gPOK3ALg64cKWzeNX5NOe0/MXyLJw9egRQSsuz2jypOVg3t/3r6x/IbcaI3tEE/kQcGFk3D5BNXP/+m/RmhnDI5xTjjqbND4miU/L4Fqoa+J1QqN8NxxCxeRmeDtMdDm+YRNg9cTsix3aKYmFmhfz4JyP5OWEVFPra/HoClH0xcs39VDvPbXBLBWl5kxOiZwSJWekWMGqNqNkYxrwAPvux2eQ3kVYvWLrFyfRjqg2yqTgm05R6xhULJyQs8qfXF4+vfAGEm30zJYRXFwX1zVA3sDo1ke5gFbfeP014mjY4oNaQD1nAA/w/p4hrJ4MesHMaPhnu87OO8ZpRESpJVHUinRGpzU5DWnXOs9L27Tej3dqrN3jzYi9+3k8YNFYoNdtEW3O2Z/3+atqk2p7fUU5HDh98dxoyLVSSzX+e4Npa+1BDy36VWqpVFfX6d4ZcZ300I+zB1Om7+OREAGB/4+9N22SJLvO9J5zF3ePiNxq6w1AAyQIcmwkkTSTaEaZZCYz/QD9B/1RfRRnZDIOZzSDASlBABrovSqzMiMjwt3vcvThXPfManQXKPGL0FbXurq2rMxID/d7z3nPu2Tr2O11NyQB+5m6PIML07B1Bhhq4FRtE3X+0fPZdhJpHUTbF8pjQy4ej0UWJ4HAkjyoqRVkghVlJZHGIwKE3WhKo0YC8RqNMNPgH83H9Xt92BOW98s6Ge8gihKk4qQ8vOZHvdJa8D+q/B9PGL9rvR0OesOsyF6A1sx8vLEDYB6ZD3e8+uoztMx8+IP3if3AWA5QnV10cYg3A7dlkORCRGslzYlyOnH71ddM48R8OJoda85WpQ+92THPGe1s/lCLfmPjb7RK53A+Nhx/OQGXj7VfLxi6cx3eRXwb1BptVSlqP+sjrH4BakS1DaIEgrWhyya7PjyyDN6W5K/OrlyTxz8+rOwwadBYq2i0YbKssE9pjCDDMrV1AGtHkNJ6KMDDIVDKohtogjxxxNjx4r33SWnm5tUrSq34rVU0t6/vKbky9MZbTqOxtN6t7+9SVXJKrTizDcg7xVHNNqTqyu+vVXEeC3gSRykTPnh++uc/4+7qkp9/+inTcWZOyeZqnWHaMdr9X2fLw3ANwpnGiVIqfW/Ptw+WNFYKVFG6Ys9r1WwWn1LQ4ClxB+LQ0GFU75N1Nl1ANNNN9mykw0yeE6euoYPtAAAgAElEQVTXt9SUcbmYiLS3PUlqxVUlupaZ4QNasBljuy5IoTqPeKHzzeiyWGErNa8dvROhDwMuJg5qosv5/jXzeOB+v8cNG56KEjZbLq+e2+hyviPPR15+9ivLF9EToRvwQ0RcRxefoy4zpa9AK2fdlijwIt7jktK7ieQqRQOKM98mFbT4tSh9OCLg9wyHvrH+wEygnUzrFNpOr+P+tWkC0sh8OrT2pJImy67F9SAOV5KZngEsJyxi2GBJTKcT0/FkNMaSWU9+bFNVjC9big2Z9XFJAg8HQJsDWJpRw9YadPUwQH9gBqzpWQsrYPn/Un1oWUoe+zLLxyxftm3kTaXdXo7x+B/+2UPZL+0QYPmaTh5/1vVrLwfAg/fPo183RtWbzKBHFT+88fsVwVqOMbFWHxFi1+FUiV1vFeFUwSvbjXFyfcubfbe+f0u/8es3ft865ZJzg0D1jQ9eeeeAOMfmbEc67NYc31Irfs3UXVSrgriFNvr4iz10uo//iGrUUmNnlDagNtsUbUUaWhrf3xwKqs4Pz0SbK1IrJWcrKKuyULDFBcP0fUR8D4CEGcTj0howYNhBbSKzjB0+arYzKrntD6V1UG4tALUWs6oohZyNipono2J3uyv7fpxHxbccAbVBtoOcjjhf8bIDL0im+QTZTG/TBbbZ0UdlpjJWePSgrzOYx8gD/EvhoGUHFayyTCOn/Q2/+Pv/lfFwS+cq0TteXJ1RSuBX//QLVIQP/uSnxBDJ44iosukC0dmJWkrh9asvOR0PfPbrX5FTYjsM9F0kto3RI7gQ2Z5dWqjE6bpFLsrKVHCNSuZ9AB/M+8dHLKjaLy8aaZx51YJzhhMO/YYYe8DZzEeWm9IOMyHxcI66Fn/3wELSdi6umNvSIrfLvQqt2qEni24hhEc9NZDLCq8tM4BF+Yyq0W0fzQRKnq0TyHkdgq2Y6zcOBDCLbNCW2KcgVrlsL68AJXQbnDieXLxnuothiyrcjce1u3i3vj9LH23sD4JAbSZwShqPODX3X0RWN9v2D3BtM1oKit3VBXke0SFSJiHlhAj0taL+wRjRBxM31ZxRIHjD1rVUili0axXBuYJUyOOICxnXi83fskdrotbZnspqVEuZT9Sc2N/eGpy52dnrLBXJFhKjuel0XKB0VzBs6c4/xHUDbC8AQfojkifi4TNjMQZPqcp0PFBr4XQz4Zxjs70yDUFnRWIRRWpZ9RNaCmWeOV3P4B3x/AzRxPj1Z5TdOd1wBT5wOM6UXOjPLtE8kk7XpOnIXCHEHVeXP8OHDheuKCVxv5+p6njvvffYJMdHKXNzVL56nZiLkJrnd6gJG6C3wnh5t0XeOH+/uf4ZncDjZZ3A4e6a4/41gy/0MVJ3HbVkxsOBCsyHPXQ9UkwNrFkpTkg1m0/NNFLnuWHZD9WwWc22SjVEo7Kvm+ujqn2F8NpQaWEFtQm74AwrtOOR2rj0lt35kKC0VOvryamLCM5eh50QlUVvJ43y1lqbR+wHfdw4LJ/NXsfCVloYB+tgoHEtFtz/UZW/HgKP2EBvOIOy/N033q1HL8Be2+KI+oAtioAPZp5lbpCeGAacC/TDlqpKKDPlnYvo926t1EF5QJ91nQNAyYm8sFRadsD6bx4geoPjhWZ5Es2Y0TsWZtx6Xz984UZakOU/+9pvFC6LYYJV2FrFvIGqtGKoILTKu5ioSqeRMs8c725RhYgpmX3jdK+zMbB9opnkOd/hfAeus0MjLoxFtyb5LUwhLZl0PBq8JdE2Z2BJfpLlecWgYarpIqQ4SBmVmXF/S67K2TzhuoZWuIAPPYqSKvb9zSMVTy0TIh3iO0SddR4IMToGcVxubC8YT8KUYZ8LVQXv6oqiLGC4YNTzt3UDb7eSbsHHtU3gPQXKxN3Lz7h//TVREtEH9HgHCPlujwKvP/k1m+2GH3/8Mc47rl+9Yp5njod7UGXTR3rn+PjjH5By5vrVNdM8cbi/J4TAsxfPEBf48uUdpSh9CCu++IZR2rIHNwhoUQg7fOsaa4uvmyklt5AZTwhm2uaaXcN6l6+xlo0nX4t5lqhBOBJ84/svrasdIFUfH2YPGcC6BNUsVZFzrUFZHhRrccta6ad1PkDTIWiD4HSZDdS6wm+PbSIevu6jb0fsUBM1kYVzYkZIxay4ad1T3J3btYvBFIspPDaQfbe+JyuE0O6XNitqa7lvTvd35NjR7yo+RLpNbEIoK8ZqsZnVrFaJh74jbgfibkOaT7B2riYOc7XafV/tgakN2lhIGCuxYe1UjR4qOiO1QBGqOrKOzVkXUCUfRoOUDyPzaeKz33wCCO//BIbNhv7iDKpRMmspdH0HwYLbo0Dv7UCwQs7j+y3VC8c9FC2UYgfAED0pw/Wnn1NTYj67JnQd/bMnRo8dTDTmyRRNOHW44tFpBgpJ70kCX3z+GfHsgt2TH7C9fMrFxRNUYBp78nxivLtD84icDmiaOYTf4Loz+u1PUBx+qKhmAifOnPKX752YsuP1xQX7Sfg/vjpxyqDNUyhnY3RWDag6Sn0wAvzW++IP3zqP1KsGBtJvdyY9z0ecSAtCb/78qszjEUdlPh3x3lHSbGriVvnXaubTvouGczcszzcjK5zxehd3wsWDXFkOAVYoRNc6vd1Hj8DsRcmoxW646CNhcUBcffwfbCUWHoNoU1GuD0orfbRCNbm6tN9r6wQet9oP84AW//hGB7B81MrMXiv/Bz3A4nvyjY1+/bM3sdTlc34njr88P9KqkCazN/fSuJrsuWava8rSd0Hz37slD92xYe3Yc9uKk5KTjauaglced68YJGzOBLYXLOrebuhJfY+MzS//8TwL99BBrGeAtvnWA4tHmmZB1LXnzmYBSyG6VOeLB5aWutpc1Gz7x7g/QC5suvjwBVUpyVIAyzTjxPI/nDi0hUiJQE0WblVyovgWR/tw4ahVOe33hBjxmw4tHeLVUgep5qC8NAS14RrF7Cum45GCMO5f40OgO7/AuWAOx77g48Z22bLHsr4POIQQJ1heY7suooWNLwQqdSh4cTzZKkNuRgAKp6mSizCXYuzLte/79vUH2EEmGPJVqRXmDH644K//x/+JeTxw+8UnpPGe0+0XlHlkKzMlJQ73ew77O+5vvibGyLMXL4j9wPsffkBR5cuvvibniXg0v+75cMI54U9+9DEuBO5PiVwyse+JWDi6sRIgZ4PSXVGyzhbQXnscHinawirMqbSkkTxPlNMMKGdXF8TYEYeztvGF1iYuuQGC1IQvc4NjZrsZxEN1dqCIUL0dPVp8w/zfHIQpFt6iwfqwKouwjUdvhlvbZlcXOwc7KFOZ299Ju6maGvjRMMweMreaU1llbz/X5SGtD6+rqjSFpNAPW7wP7LZPV0W2iNCHiGogxw3VvZsJfN/WUij45T5t7DHxHc4LKd0j0tHFp4TOcohFPMF3OPHN5kAQv3S6HS5uePbRDxg2A9Onn0LO+NxQA20ixmIHQa0FFWH21qF7rWgp+NOMeofrHC44tBnK5TKbgRzBguOPFhpVvUO8MJyfEfueZ0+fMR6OfPaLf0K8Y/rpT8ycMXgcyv0XrxCFfHMgbjbkmnB9jzbvMsaM5sR4+Mogqd0GvCd7U/KfffADpv09v/sP/wFq4Udzoj/b4D58gosepVAnK3RrKaRlXkclayGPR1Ia+eTn/xtnT57xJ4PQ7c7RuMEPGy4/+CklHbn96p8oeYTD1zh/Tc4nUxSffYhIT073ZrszWfzuLtyyiZ7/9mJDxVO0Z07Kpy/vOYyVL/fKlOFY41v5Qf+8ZLEVU3I4F9ldPqUbNuTTgbnrKeme7B2aTogTulwpuZCnI2i16ERv84BcK/M8k1M2NVwxwyrvm4Wt85RqhmwLrGIQl6wn/1Id1+YT8riiQJojoD7oAhYXPe/DWvlaiMzCGPCtsvFWvZiu/tGjsx7vKyZqlcqCl77ZAXxDcwyPruMi9VsZPWuJ9FC5rFoItcHu+v19453RN77MgvfKw+dbX6c+vEyh+RYFYt/jxDNNk1HXGlvBLxXgu/W9WgtWvHj7r668C225WUesBDZgnVcJLYNAAL/O45wP7C4uICcm/4XZN7R/s6A/DzuJ3fs2o5O1+NCFX7+4zLTfP3QetbGGzLaBxXDRO1wNdJvBrKyrHSqnw5FcMl3Xobly2h/a9wFdSvT3d/jUo9HyS+rJXIXH4wFE6ftoA3Bv2oPQ9ZRuboVfZT6dcA66eYcS8FRqfqAKLnOFpeOxa11J4z3TwXO8u6ZoJVxExAckREQ7YyzVRKkm6tM6orV1Rg22XsvJhoKYsnh5XwrejgO8FLwB0sb20+9+nv/AIdC44mr0J+9jo1aeEXyPvoCcTmx2PWm85/rLiNbCR+cXUArXv/uEPE3cH07U/YHPP/uKUgrjaQQwX/K+4wc//AHOOW7v9uSqFAIqDr9MudW+7zFNuODIZYcvziTYOLxLiGtjJRFUbBOd82jOfMHweDd0SIxo8KjzuNgZJbK5+AkJqmvsm9y+f8WV3J6HxiZSg6vK0qTWjIgaj9pJy8xokIo+mNstVrUrFW4RgS0DtXaw1VJsIK6LX3j5Bvzj1ocTmqW2tLZ5GW6jbd7xAJ+tkF61m+P88hLnAnrzyvjiUlAqUQrFvRsKfN9WCKF1nhV17oFwgN1eOSVj3jX7hlymNoCNiKtocM0O2e5xkUh/ds6P/+Jfs3/1kutf/4Z5nMil2sCVBYZ80OtAJeeZWp1ZzIRAjNaRp+ogOxhH0w9gCECirLe/ONcSxBxZBPrA7sMX9FfnzGlmOo28bDoYKcYQGm/uUFXC2cCw2YAUuqGH6KgoxzKTcuawPxJi5EdnF8To6YMplGsvhFJ578P3mY9H7l7fcjjcwy4Qh44uelgs2IPHSaRoZawzuRa6TQ8OfB1Jh8w//cPf0Z9f8rO//lu67TlJA7UU+v6C4joOh0IVbdA15HyPOKO0OhG0dOSs3JyUU6p88fqeVAxSK1U5NDgoJ4O6d7HFyn7XffH222aZAzz2ozYc3YmJvlzNhm2FjjhsESqbs3O0FLrtFpyjjJPRH5PZSHe9DXljH4ldJLRA9QXzXxWCjVXj28WtrRqpTZ24qGZrtdPQqhg7BOzjLGR9SVGy4qWVOSLWCTQIpX1r9j/nELU2z3b98qhCt9NYkdU9sLQqwLmCq47Vgc6GB6zj+YZ1sm7KD/OBZY9efvnwB0vX8Ljst9e8vvK1E3joOBYMlfbzOkNRZZ7nVZRskK9d+1yMfno4Hszy+936fq1vzATg4d4A1hmaPV8Zlciiogfrjd16L7Fm23abLf1mSxVL9/qmW+dKovu2r1Ma9q9QWmAS1YgZrph7cFnp+64xmdozIGKbax9RlP78DILFO2oyq5gqQsb2jTTPVCccDwdqzvhNaLGO1UJkYsDFaDTQ1ckTE6IGT7fdoLVwf3ONK5k0JzOyQ6EF6ZhTy+L/qVYotnwR19hE4+EORZlPeyOahDPryFxAGlrRNt72/2J7yepyEFBR5pKZEhxnZc7a5iRm5VQUcqMyumUo+B3r7Z2A2rDUO98wury+gaVW5jST5on98QBl5snzF83rp6OUTPfiOb5kzp1VrnkaEYTdsLF2K9tMYP/6NaUUumFDRCh0aOPwOydsthuzX44OdZDyjAjEvLGGx2WcU1MDClQKVQvzdCSnxDwdCD7ahXBu1RW4YFemzCcbQCWbHQRvhlBVvA1pMdfTnBK1mhuQ857N+RmqlfH1wR4a9QalBKtYNABtoP14artW/jxAQ0s2sLYW2W6AN6miywEsLGyj5e9bF1Db52znQW4Q1gId1VyYponf/OY3xK7n8sWHbHY7Mkquyuu7O46He/7xF79gHE/81//z2+6Od+uPbZVSEFU6seQqoGH1zcitVIpkjoc7ujzTbQPeWfKeuEgRg2ZKHo3p0yQ0fhiQYcOpVMacuWjW6ZZDrhAW+igsppOqwpzs1ylFY7J4cyB2YHO9YoEsczFX0X6zM33QAmPRCrrg8TFw9acfU0rhfJrIKXPc70njxPUXXzNNI3d3d6RS+PSrrxi6jmfvPaPbDly99wzf9/jdJc437ySBOdt+V4Oig+fs/Wf4IfLbX/2SWjLbuwty6qnROqxpnixRLSgZZa5KdkpVc3DtNxsrqO/vSPcTX/zq5wxnVzz70V80uqjH+YHd2RNUi4XxlMSms8OmYrCciz2ilfvTxGGqTLVnqrBPQqmYFxIYXC0OV/1b4d0/0Ak8nLgP7oO6VuMm4LDQEtRoZd7ZJL0UtVNNxNo9Vbwzscmw2djnmiayPFSp5ucTENdR1QypELEJvBOKVnLN5Jzx4qzlaxWF3TfGxilqArElvyDPMxqUkjM+FFxo2Hs1Obvm/IYRW1FBRB98k9oxWpqHkfhlsm+W0uK8zSaa6EUXz6M2MLZN/BFjqO3KKz/6MST0CPlf6/n2cQ8uHouZBQ8n/ON/9nufwd63UgppTozHEyll9vs7UsmcjiNzmrl+9Yrj4cjXN7dM8/T2W+Pd+uNbrSN1zqiMwEOHSKuwFXKecd4R1WJNV5aQ1mZ3Ys9bLUY0CGJhLLHvKV2/frnFwlxbRe3ckob3oM1ZxI5SC0Xbc5TBOJ72uha/z8d3s7TvZyXeeUccenxVXAi2dyik2DFNM2HsbWBbK77vcX1H2GyJm4G42RH6nm57ttK5F0uYN7rw6JHOLLTBInL97Cjy8L0oansAZmipVR7cikOk2b2BFsp0sutV7Tpru2rBR1BHLnllDBqKYLMB76XNUD2hCDE4ClAnKLpQ+jG7mjcYid++3noIOGnc/Grbv/M2OJzTkZxO1PkGSUciI0hBilKS8vr2SMkVj66qWueEYdgiKKl5lsxLW+ZN8FXaBRvOz1EV7l5fU2s2kRmFfBiJ4uhzJXU9vt8SowIB55s/uCgpn2yDT4kyTdy9eoXzgf3Vc4ZtYXs5IK6Sx9HcCFuCT02WkzqljIoQt2fW2QQbAk+pIj5yefWcEPvWIVXOzp5Q0szh7oZSMy480gaIM7Oq5rG+DGqXzmoJd+Ebqt+lO7ABUH18/z/SNjzC7Zeh36P/qzSWwprpfMfpcGQ8nSjHI//+H/4dFeHzr75mvz/yi//rV6RSuXr6HB/+xXlD79b/z5ZzNmfb9h05WHdfi9kriHdsYsQ5GI8Hcs50m0uci62NLJQyAWrh6lo5jSMijvPzJ/RDx49/+qecrq+YfvdbtDyEE0lT3MfQ2z1cmuVzNQV7LsnSwLLdt2WcsTAbi7Dsz85w3q8DV6eCtEExIoS+6YOCsRmLi2ivnG/OUFU++Ogjcinc7vdUVfphQ+w6rp48bdqJNu5echYanV1yWWGrUgsHqeTB8/5PfkQeR47jPVOaELZ475uLsaChUUPEIyqE6ggx4LaXdv2mAyJKdIVIIkjGkZmqzWC7YQAtjONIBdJc8D7jeqPwdl1PFz0/fO+SU4LNXrk/FY7HPVO1eaJKM9aTFrMi330M/LOspJffLT2dlmwqvpKgWuoNsgin7MaqtVh4hLBGzi3e5SXZNL6kuVUT9vm15ezGGM39b9317FcpZxsg5YR3nlIK3jdzNTUaJPqQtCVNf2ChMsp4f0AruLjDBY8wW9RcWg4B+/hczPLa5wLeUdvQGWeeIz6a2EwQqIIPnXUyYoOmWhVHQatrviltkNsMVOwy6GoOt57yrD1zu+bf7Aza2yCPPuTRHODbcT9Zu440J1JK1FopVTkejxQgl8pcMtev70ilsrm4Ir7LGP7erdrgoKV8lka8qKVate0ax6QUs1tZmXhW8eecgEr0dk/Vks0PTBXnPedXT/C1kj77FK15vXudPMwRbVDcMIXaBsa+pXgF8//HOyjtOayKm2bIBc2KOE8Ksx0wzUE0dwPOeXzo2jdqz8TiDxa8DUh7H6konfcE5ww5yEqdDcnI1Qwa0zhSSmGeRuscnMFgJ8ymwgWHj546NufdloUMD12JFWoJDMmyPwsBwToVaIaQxYR35oHUyN6Lu2grGGs2gzh8sqiGaJkoQx9wXjgl+3fnG0f0ldLQ48LS+ekbcPQ319s7AbdcU2/89Vwoc6KeDtT5iE4j1MLQnyGiDUIpxCHisqA6G0wkNrlO+4mSC6fDaOrDBjnUsEHFUduAebPr0Vq5vbaNvOvMe+f+mMgK9wRyUXZpRBx0OrTDqRjmZkb6zCrkKvTqyFPi03/8RyR2DO+/oht6nj09wztHmlqspdpAuusGRDypJKjCWM1qYnPxxA6AbmtqY20CF9nifCBsR8sePVknJL4YVBRCE58tg7HmEdTyAGrTB9hc4UGG/mBRsbTtrX3WgrqWOKaKsZakkYkePEMW2w2plZozx8OB03EkV09RSCkz7Lb8N3/7N9zeH/n5L7/gcDhydrajH7p/1sbybv3xrP3NLSEEukaNdP2AF5iPI8Up2ysjaNRsm3ZtVfBURqrC7evXCEr35NzS9lIxD3utdH3PD//VX3G8fsntr/6R6f5IohDUE6WzDdlbqpnzBkXVBgEP260JRfszQCjZhq6vPv+aeToxf3nDnDLXdydKKoRU8ArbZna46XsTO3ZG7exdwDvD4J33+E2POiGr7VHOQRHHnXaAEGqilMztuGeaE1+/vOV4mvntV9eg8MOrC/oh0r/fE3vP5fmAOCjR8o/D5YXZ448TCAy7DRnleKyIZpzLhOiJg13fLl1R88zx/jUlT6RpT0ApWdCauT9eA0rurgDHvL83iGqYcLFji+BCx8XmDNRx0XvmLDw9G5hS4XDMzKlys0+U0g4p+e6i7g8ayC2JPEql5kTJkzmI5vmBotigg1JS8w+X5u29kGJs4l9SouZiFral4FVtcOGMsikhrpoBbdilikW4LQVvrWaRXNqPWstKFHhcCdsJmqkL3l8LaUqQMtzvqTkxb0w93EYDxngSzELBGRfZZhLBzOq6gRA7xHvUOWtJwTZ6b77igpLHxqFoLIdaLV2oirNr+sgJ9E0F8EMH8DgBisaIWJDbpS3+vXmALh9jf6jt7xa76dqu2QJJzfOMi5Gu7xhyfVBSu3c6ge/jWph0S0hSjBFqYTwczB5GvWnnVVYCyFKcVBUW7UwtbVbXfp/zjPfRMPbtmUUy+tB4/3WFKBeIelEoi2qLlvS40EJenMfXioREGO7NNsJnJCs5KWkqTPdHPNBtNoj3JEl4X4mdBzFfMFm9wZrFvH/EBCzmduyCxVfWmozJo5VcC1NKTPPMdJpYvXfEEUNH7AJxszEfsmrWM34Y8OIsSQyaIr9a9V/NQn/5YRRXmynUWZvr6AgS8H6HiqdOpvQ1K3iHNiKMqw4tulpoOxeBQBAHXjnrofeCV2EOQk4mrK3rZOXb11sPgeLtlK9louSJ8f5L8nTgtP/CqtcQcS4ShoFaK/vrIzlnajEsLIoNWvPphJZMGUdKLsynEcSxOTvDhwjdDvGBsNnayRocJSux7wko280Wrcodr6laKTWRqmOeJ8sEEAu1d8Ew+nFO5Ckzv7wmjxPzZF/XNt1E+fqaOQZu5pHQd5xfPCHESOh7Qoj2unxA4oB4T+jtZnP9BvEO9ZYIprlBUVKNLXRxZbOBopanPB+AQo2+ZQ3HxpdrArYGBdWy6AC+Yf607sMNMBMeDZFNAYyamhtY9RGqpYX62MeUZKE9dmjWVuXNfPHpl3SbDS9+9APGuSAtCjOn9Lbu8d36I10+NtsUsc332dOnjMcjv/ziC7RmLq+sYqd1ldM0UlVw/YjzgcvLLaqVed4jWglNUfb6+lOc7zl/78/QzRmb8w9xpUPSHZREDgkfKr1E0+ss01y1ZMDgvSWaXVziuo6+36JF2Zw9o8yJsj8ynWaG373isD/w+Se/xjnho7/4Kf3QUyQTusjzH39E1/f0YQM4UhacC+yuntq8oO/IeeblZ79GxPHDn/w54oRXX/yONI9084lpntDPX5IOIx99ek0XAh//V3/KcL5j9+LK5n3SguKrQas5J0qaOTYyyhJXH4M3sVaMhC4QN71ZcOuOmiLKiAjcfvkpcXfB+3/2V4iLvJom5mlmfziimunlQPDCxabgamB8fUDxSDhHJCLhDJxncI4hCNszoVbHs21HKbYfPjaX/OZ66yGQl3jDNFHTREkn+5FHqlZ87BEfWquha7LQEggvatzVkjOaEqVtRKpmXyttBmCBMI4Yw9oqgq42tt755jBKmzu0YU0plGLthhPBL2ydlkFqKRUt49O5ZqcgeFXzGK+KVPDi1ug472zDFmdBF+KD4f/emyV0a60WFSWwUjot01RNP6EKs1VUpVjbbBoC+94eOHOPLvij0n+JmrfPzxuYv329BXdUlsDp5WMeZwos7IvFGlraJ6u1DeidY7+/Z0ottlIXetnb+ATv1h/jWpxhp2kieEcXPDHaUFURSlVKgbBk+xbTCyxeQktQ/XE6WTDLEEEhzzMStDHlhOHsAsmZ+fW+ER5sZgeNa9cqDCeLevmBjy8+4DsbIPe7Qo2JUgXnIufnM048+/NzvAj9ZkM/dGRNhKGjv7ig22zoux3gkCSIC3SXz3AxIn2HTzNhf2vOBldPEOfoxgMyj8jcIdPIcHciukB8z9F1gYvnT+nPt2yeXOG8UMuM2bgYjFSO96ZtCM26XQtFa0MopHUBpgGwzscQAw3e9tc8U9NskLoDlUCVlkNclSRmV5OLwWD2NnpEJ3DVDoIaVqv6WsXYldVo4ulR9/dt662HwM31l/Ymjgc0z5TDK0oaSekI4uh7w8JTnqkpU1NGciJgRkc6j9Q0Md3dUVIitxlA7AacD2SMdinVck+3IeC8o+TZrCacfVNezPAsiJBRUplBlCmNZjWBHQDbzQ5tbZRUZ5bUgHcdJReO+YggXOzMc2RzcUXoe85257gQkWgnNdneSFxBVB64+9XIak1lzxJ2YYeegHi8F7bnF5Q0Q5nJKZFSm31oapYNdriF9r5I01FI29zpU7MAACAASURBVKgrtHn4Uo7LylS1wVqbEfhudWRc6HuK0voAVO2hneeZlBK+meelZANicY6UZv7zf/45pylzc/Pa0v20OY6+W9+r9fL6FhHl+tXnDH3kz/7kx2yGjovLK9I8cTplclKeXJ4TQzCWjBM8GY+AevI88sWnn6A188HzpwTvSXPCxYk87fE+8sP/4q+YXt/wyd+/JJ3u0Wzsn5o9rAaO0n5usGqt5qIbIjVExAXCZaAm8xELIfDxxxt0Lnz8/CklJ+o8orl1ExqQ7hw25/ir54iPFDrTN2zOwQW60CE5c1mNyFH754gTrj7oKHnkePyc+XBg+nyP7gYu//zPiX3H2SbiokfOzgyKTZNBvfkECCnP5JKQzQBp5nB3bTnIXnHetTlij+9sRhElUHNHnfdQM+orTmbGwzXqehKe6ge6Xiklcz8mKJXpdSK4zFnf4b3SBxN0lnKk4knZkwp8uU+MGV6Onqk69jlQEP7777gv3t4JtNxMTRPk2UJNitGYpGFtzrebRXWdcgtlnQGUlAw2qQszYakALInIOaVr7qELpWm1OGgVdm2OgUv1XJfTdp0L1LZxmcd3CB0aCq4zOwin4Ch4ZwZYPkabY8jiTeRa79AyAGrjNy/4eckr/CPOuglQwwC14oo1gFIfJvwW2uFxrkVWakWrUFvK0upYysruXCf4oqzWub+3FbfqQqF5jTfG0cIH1oVT9KC/qO0aLZ9t6QycCFWVw+HIaUrNs8Wth8W79f1axrirQKJ4cw0VEYt5FKj5SCmLLsA8a8yFNzfK4eII2phtJbeM8ATiqOmE80p3dgYlIdHBvDDhjA2EW7iArIrcWpttei2NM29wkYvN8jw4qJXQW27BNg+U2TMW8xhTBKcgc4Epo6lh6c66ZNpX1EbXDm0wbq+rzSZUzfiuKH20nIHNxQWhjxZG5V0bsNJmhdoo/EvGt7F+XGMnllpwvnU8LfzKnEODUTZRCNFsgbwpoUuazK/Ix0ZSseuQ6YzxmCB6YYhtvqE2t13xfpWH7l+X2Y6gLV/lu9bbyeCzDSAkTWiamcfRaGFhwMeeoT/DiWeeRoNB0pGaRkQnsx+4uTcdgFZzJOw6+8a8WR4dp0QUzwcvXtD1Panxc30QY7yIo5TK4f5ALZl5nmwo5cAVmMYRh+P+/h5VMf5+iFycO9KwYU4jZZoJt5UyZXJs12K7IwfHNJ4IKRH7LV2FXnpTHouCFEo9oS0Nrbg2CxBn8XAolJPpDFLjE5dsnz+0St9FJMCcjvb3zXOcJWxmrfXb/91iDOUejXJk7RwexGX2INZGPV1gpLqiTNr83yulZKZpMsl8s9tYOoMYI1oKd/cnTnMmtOD5s92OYTP8gS3l3fpjW7EfcFSG4AhOuX71kuADV0+eUnLhqy8+oeRqm5VzrZCbuL+9wXcdu4vneBd58fw9NBu/XXOmzhOaM+PL3xL7M+J7P0M6RS96tHrkZDbL2lejTWsrpJpN9DSOuJzpxxNOnPnwhEDcDpTiqfdQXGX2E5oLdRyBwrDtICtlTLhJCb/8FB8j85NrtItMwxbX9XTPXqB9Tzo7B1GC3hsrb9+ooLdfoeMJ+fJrOoVnL57ihoGzy6dICIxkilRQm3E67LWP9/eUeUTSSBAlnF0w54TefGHsxwouCNvdjm7YEuOZwecRapkp9QB1NgGe89TTHsLMsHtBrnA3zqSiTPI+WR37nAlFcSHTqxKi4B2Iq61edvTB8X7oKdXxXunJ1TEmt7Bmv3W9nR3UKm+hBUI0ipX4aEIOBbA4upomKAmKMYhqNntWg1KsyHWNHrC+HieI88SuI8RILgWV2ni+FlKvslSuzaBJLOZRnANvrKJcMjmnJkc3LN9pIPQ9IIifcU5tZuCEuDtDgt1sznt8P9jgyDtUbA6AE7OVeOQ11AiebYi7WDYU5mlaaZ0I5o6KUqZktM9q2L9xrKzDWaogc1qUB/hnqTR46ALezEgAWv7B74vFlo+RtdLRqg+zk5Yl4DcbOwBQs+6YEkUqEiMSzSTwHRr0/VuWzYF1eg5yNjhhE4y66Jubrj1vTdxYW/B8u5+ccwz9QPVCHWcjNVR7DvJ4bFoZg0LibmdWLKejVfiPHIBlpUu311Wts645Nx1SYAliEh/AZzSrkR88FlQTzO1XMfuJejxCCGYQGTvT45SK5GSMvmQwsmv2LJqKQVVHo7u7aUakCbu65iHk/dqzazsEtBZqSeQ0rXudeCF00QRj3qPF41w1gV6wxDPLNQ4L3oAGe43SVNnaXJG9NyTAvm8oKZLUMxWHd8pYPVRlqkootP3Q9kXvHJ2zMBlfA7U6opP/74dA8OaC6UJHFaXrBVVP7M4RF0jTHSXN3L78FWU6IqfX1DRzf3dASyV4R/CPxAottGQqgHNsLs7pN1tc35lQJDs0Ses4lGHoG6ZtFLbNbmc3YnWE0LF58SExRk5pRI+V0+GGMmzwZ+eIj+zOLyhh4nDzFdVlO2w2G977i/+SsNuRvNHJzmMHtbK/uQaB/urCLDCiSeCnaUIVOtcGzq0irxJJ08irL79Ga+Hyvac470jjSBon7n79FVoL4emARIcEE8x0LdHM1eVhMFBImwrMUo/AtSHtaq5Xm9JSwByS7KHA2c1Dbi13dVAMvqpzYTqYPiN2G/z2gvd/+BOy8+QEx9OJL/7xP1Huj6g7Wl5Cmu2Gf7e+V+v29ZEuOq7OzohBKOlgA8xQ8V44v7iglsz9OOISnO16nCrT9WtCN3D+7H1CCFxcnJOnwJfXX5HTTPQRtHK8+wo/3tFtL3Ah8uFf/CXz3R1f/d2/oZxO1ByAQvZirDs/sFSItSrj4UjIlbDbE3Kl7zY48fTDBd71zMcZzQ7dbKCrJBkps+mOKIUyKh6HP52Q0BGeVvyuEt73eHWk270NuLGpabm5QeYJ2e+tePUg0RnrqRmaiodOIkplngu1TIzHV5T5xHh6SZknxsOR2Pe8OLukoLjdJRIiQxjpumh2FkMP/QZcJBaH0iH9JVpnyA1ib8V1h1J84HTxIXmC07FwTPB6skxw7TYMCKfTTHSVPkzEAFfnoe25EfAYt9IxlH9BxrBz/g07AwMomi9FqZQ8U9JImY6U+YgkOxVzthMzdt5Op0bzz1WpDaMS5yw0pu/XYGRby7QUQgw2Hc/SDqPQMA9plM0eFwJ1mihU5jwj2eMx5lEIAUKxbqKxG7z3SKv2u90ZLnh8g0VkNlm82+5wIeBjbzMFMdjFN0x0DWtxzpwVOxO3ua43sY1OmCW6sXhcY0HpOt1dgKAHKGdd3+BmSvuzN/5U5GEusQBHi86g4ZzLe1RLoeRKKRWVgIYeuTTKnCRBNiPx+Sv6zZGz/kSoBefrtwwj3q0/9jVNM1RPKYoXbWwhG8o65xiGDaUkxnSwGdei7C3ZAlPyTA1uTRQTH5BSViFkLRMkJU33hDoQ+h4dBiR6Y8rVankBpZiZjOobt1nJCZwjTxPigs3kvMOHaBU9rlXIrSjy1g1I58EtRnhijMCSYZ7ROFFOR6Oo54w9t8Z8KqcTNc2WVKWKxNB+GBNQl5mcsDIeqbbvWReQSDlxHCe6anognDMDTc14lx+G3+KblYxHqjNBqwSQArK4HLv1WXdAiB1RBe9nxClFPQXPWCO1CA4luMJcMrEoXe+IwRHx7T2y3IelmPyu9dZDYNhsKSlxe/OSPB/J82iePycDoOt8S80T5XhLSSOn/W2ToJsARM4GQvRsQqSUwsuvbygVuu0lcdhw+eSp+XjE9jIabz602UEczilVyXVGcqK2GcGULcPU77Ym3nKeArw+HOlT4f3tuQVgDwMKjFIpUtn2ES9w88nvCBfnPP/Lf00821G2WxBheP4Up0IvXQuVsEu3aUNpt+6zzShKCz4XftA9MdM4V8nzzH5fwAc+/PEWJ5UpGDti1BGhRXK0wa/NzNqh8o2d981ZwDdXE5Qtw7taGg5ZmkVvoUwjZZpIc2JS4d4P1OESfe9n1M2Wg+8pKB/8+V9BKcRTRu/3jP/u76jH+7fdGu/WH+G6eXVL33me7IQuCpruiTGQt+f4YcOLDz9Ca+XLrz8zfnofQQvZg9SJ0/WnlH6Le/o++IHds48o84Tef01NiVQOlHpi/+q3+Lhhc/mEKolwdQ7BU/b31Fmx/a+FqThvthFaSKc9eT5RfTRTt+0G3w/EzgrFqTNr61KKPS1DR+w7ttsdokqZMzVXyt1o9i/TnpIO3Pz83qwoutjsmA0uXeCwrutwvad7eo7re+LlZcsdaeLLlgFe82gdwJ29zvE4czpN/Oo3XxJi5OKDFwy7gSdX55Q6UA5WOHointDyRiIinpppaX8O74Idaq3jrzUjwXN5NjCUwAdTz91Juc2FKcP1wSD2l4YbodnhRbl6JfTR8eJpR98HnlwEvCi1HFhyI75t/UEXUYWVhWPJPjbBN6XbEc1T8xEqzfpV8dEwsNjHlWVifHlLFPKxI3Q9MdosYKkkFtdBOzlbctBS3ZpszzxCOm/q3IW7H83kai4FafbU3gm08Am8Ay8Gl1DReaaOI/n+ABjf2Khr0fxINFqB7pecgMaKaO6gVZvEeFUA2/fnEROFiUd9IHaCk0oRczZ1eeH9f3O7t/V4gP//qhBfVccPSuRaCjkncs42gBZHjh0l9mi/o/Y7auxREbxucKpstoUaA4fgLbDj3fperVIqpQg5FbtX1SDGeZosQhWzdOiHwUgOreu3+1Up80gWoZSK847QDzgR5nseVPBayZNZs5e8RWslbAYolfm+DWRbAMvCvhP3eN5WTJMUjI6Kk1bRauPoLxnEIMEjCoG2xziPlIImcyCVavBtUQuLsmiERTmMzRbEIX1AuoD0HdJFCMZCWp5Sbey+WlqEZM6UlJnHxOk4cfP6QOgC0zwSB9Ne+ArJP7JwbjO/xUdJpUXDVpvRNN7e+rVE1UhRCJs+kKsyRENkcm1eadWQhVw9Ts2ss1ehG2FQJUTFO0XzMsf89vXWQ+B4Mi+chwAX8wLqfE+phfv9NSWNSLNvUPH46Hn64gWx79jsOrQUbj7/immcGceEix1nT54x7M7ot2dmCbH46Gu1gaS3JJxaKilnxsOJknM7NDounj3D9wMSItV5+otLKJXD7S1zHTnc35L7nuH8wjqKiy0Exzje40vmQgvcH/nsf/k3xN2On/4P/x3x4twqDe+p0U5mckYaHKSq5HGilsJpNjbUNgrpeOKT//if0FL44Y9/QIye59uIVk9Ri3kOYjekzyOgeDHVpKmNl6vd7GuFNzuCR+/dY4dRoGkUrAvQsnQAmTSPpHnm7vY1JVd2XU8XB/bPnlHOnnDYPIF+y66PVC1c379Ga8FFyKHyKt0wTzdvuzXerT/CJa4HEeZk8M/ldkCofPnZZwaJethsd7z/wUdoKXz2u1+R82jePFTm454yT8TdU+Kw5ez8gjLPljU+FaOgaqXuryk+grc0wsuP3qOcJr68f00+ZWqe8CVb3GsI+O1gA9TOqJllPpG1cLz+Ej8MbK6eg5iugC6ixZ4VaWLPUioOoYumyO/Pz21QGiMqQmk5l74Nm10wuESb0ZuL0ezqYwTnqC2O0bGw8Qq5zBz313bATRP5OPHFp694dX3H3//Drxm2PX/9N/+K7a5n2G1BK+Pc9Ag5U1xqh4DHNcPJ02TF22Z7gXOQ5r0hDCW11zvSifLe5RnnG5jGxHGqXI+VXIXkB1SF4LcUVW7UDr9X1wlHov/CBtARM9D7m7/99vvi7aEyb9gb19X3u9Im+O3PbG4AIUZ8DHRDb74kjaOe50Se20VwVsW72FklvWAirRNoNBoUSCmtPkNaCtUpzkPXDfiutxl7s5BVKZgNuYXdiEDfKnfzJvFMza7B0TbQeQYfqPcHqhNq36HOkzrDSo3uqnQ+IorZXtSKlrkReqLR2ZZrUAtUs+tVsZQlVv2BPCh/l+u7Pp3tN4+0YW9+wO9DQovf0PLeLD9qNYV2zplUCloqg1hlE7tI7uKD95GYE2NZDxaDn8xzKb3t1ni3/giX+WE1ZXCtRsNeFO0ox8M9inKRruzjfcBrxEcx8Wex/SDPo3XrnNvMrxsoNVPHgGpGSkXJBh8HRYaNpXZ10WwVxgSKpdcJyDIjoMUgqmV75PlknkZ5WtXxpr+x121ag5azoaz27Sw27r3BymopNbgc7d+3JEPjVz4cCizunw9qUFiYRzW3GajNAvI8c78/sr87cjiZ8j5nQ0PMFsOCdSzoyexlllwG19T+xvSWNgtoj7s+6qpKBueJHmoQNtHsPGIyt4K8OAU82jgqWNQkdjgKQjBS63eut1NE28C2lGLOftOIlsSUZsOYYo+I43S3x4nj+Ytn9H3H7uqcWgvXv/sd0/HE/csbtArb3ZUZTA0biJ1tkrUSW1CFYdxKKkrOhfvbG0rK6JiQqkzTiA6OzXBGHAZmVZwKvh/QWsnek3Pi1d0Nm65jM5zjvKcbemqtHNOIr4J3laHrufjJR+Ach1/90mwlug4VR/JCzoX9zSucKi92lwTnqCmbR9CTC9zQw5MnBAn86Z/8zCxopZBUsfi3SnWgFChLZfFgzFVFmrFT28iXt3JRj+ny45sZA7oa55WaTQiWs71HyRTK4+nAPCfGlHEKQwCC4+qsZ9h1iK/2Wksh1ULJdpAF9bhJGA4Zf//uEPi+LekH1CmnnChacZoJHsvA1crnn/wScZ6b61s2ux0//PiHxOCY59dWFJwO1FzYv/ycEDvOYiB0Pe99/DPmaeSL3/xHynjAywhVOd3dIsHwcEHYfvCEfBq4/vXn6DyZ134wNoyESHHRlMTOUIfx9iW+64ihFXrVBp1dy8x1SwEpFiBDM41T71FxOGwQO8QBXCBvt82ufpFq2p6zJBKK2GHgxQ5LC4ovjGkkzyfm8Z58OnK8vePu9Z7f/N+/5fr2SNEAfmA8JsbjzO6JNATaXJUrM4g5CRv92uOKBVNVIFfXYjsFqM2IU6m6x/mZsNmC85xvbdb6uhZqVu7HiVyhJisyz1tuCbHNPFxLWMzz29CgPzQTaEszWrOpf0ta/fHFGRfWfhZiU/6iiuZCmjN5zutpF7ue2DWLYm3lfm1JOI+7jsUzI2VqKmuc3KJ8dd5bG1mrvXHL7KKpGCcyDgvO9qqrVYM6Z/FvxXzDQ2eq4Wmcm1WDQtMmaCkwtXSt3k5kWdiZahYPmjPiPCF6tDpSq5QQmuJX1tf2xuVc3hF9o9Rf9QKP/vCNH4sC+PF1WnjXqotLaCYnm4uItNreAVqR0xERj+tuCGFA+gAlI7fXZjVdBL27gSkh+bsHSe/WH+dqOngTEVKY5kT1wjD0Zo+ilVqU4/3eZgXjhPTRzBnFWUY4M/V4oKCk+QgCfjMQamcRiT6DtCKxQZUpTfYMdrb5FYSiMKVEqYrrDNUvOZnPDjQFbZsxjCd8LKh09ryHtpE7Z+r6NYOYVkTJGz+c8+CC2cK0OZ5QWxR4+zWslfpSNsvKtitrQmGaZsvlmBNptqJrYf7d3R0YNpGL51PbiB8wfm1OAgs7EGfQUJVCLtWG5WKHpWmiMlJmOxhqRoC+gyLCMNo+5tE3tpClflx/s/AR26+/a739ECjWlkk+oPMd99c3oMrTF+afnSaozhMvLXihC4Krif2Xe3LKpKlQCPiLJzgf2D1/hu96NI0ULUhz1TwdT3ahs23yNVvASynmfWOhNYbNKQWJAdf3DGK5wZoLeZ44Hu4o80wVGGNid/c1fdcThh3R93Tn5+iUubm74RiPuN05sevpz8/asMYqgE27EV5cnSPe051dmj3uZmtso+lEKhl3f2fwU2NsavC2j7er6tSUz1UduiqFFdXUqKbN43u9QVirBWlY5BudQDXctah1ArkYC6gWS0WbDifSNHG63VNr5Ww34J2QqeR8z93f/1tmPHrx74mbDT/4+ANSytz+7z/n9vaO//PzL6g58zRYLOi79f1acy4EKaAnConrdKLrA8+ePOH/ae/NeuzYsvy+39pDRJxzcuJweYeqlrsGWa2WIUiQLQkGZFsfwoC/qAH5QbD9YEF6ESB1q2VVu6u7UcO9t0gmmZlniIg9+WGtiJOsusUW+q1YuYm8SV6SyXMiI/Ze67/+Q98Hhm1PTonbX92yf/uWh9e/Ynex4x/+k3/MsOmZJJLTxDRppvbt7S8IXcfVs68Az8Xlc1K34X2aaSUxDFos7u++wSFcXChLaAwD0yQ6UBX4PoWu85R6UlfgzQ4Rx5yrUh2LqG5ne4mLHcFdKMW560E8vvfQGmWe7CRQmFhcMOuWgISgCIQ4Ws0KnFSDntrZAlphloKI0mg9hZBm8mnk/bfvGY9H5uOR4zjRx8Z2EPoykCr8m3/3H3j+bMP/4meurjf0gxrqLRG8UqvSNc16vgs91Mbt/QPOeT57/hwvlWn/C2gZ11eqdEypR3zPy+cXpKLc/+OpIVNmAk6oB9tUEhU1yzyrj4QheD72NH/cSjopBpbTRE7zelIuMBH2j/ioVCQNS2+UpNNzzeRVNpCPEd91+BBW9kqrOhgtOVvmQNXhkp3sSzwdxm7xAR0km5NnWFhBabQQeAvBMKrpNI/a7nUDoOlHNVTylHElMc+TDpxCWL3OEYXBxAm+VwaS61TMJtHrZjzaLKTov7NgjlpynyuJD0B/yxiW9rg3aOdCH8U3l9iwZlXI8uMMGy1JT5qCdlYEazB1znlNZQvmYY5F8ck0qmuq83Q1cVGeMeeM37+H9+84vfmGUiqXz17Ak3fQJ7eqRV2rwrcxTWll7bUmxNixqFlLyRz3DzrgPJ5MlRrwoSkPvmh+CAlKnnEuEmOnM7jYUZ2od45V0a1hduaN0EVyqhxKIdfK/jjRJ0/XKiEGYhzANVMjO9J0ouRE9AFXC9J3Rq82B4NFv+Oc2cU/ftfyaENcW/kz43D5+VotKySzdNclJ+bTkfFw4LA/Mo0n9TlqTSEfg5ZKrdzfHwlerdub2UboV/0Q6VgsHryLOMmkrILaimYQt6bvp9aMFIE8qREmmrew6wTXhIvBE1Ijj3lFFdQy5rzlL43Nx2q6jx4C79/8kjyP3L7+Fmri2ctXeOdJzdFypc4Tjspmqy9+fzgoJUwCTRzTrMZSz59fEfuBvt/QRDilgtTMPCtvfjod1IOHxTRJA+tjDFQRXFNK2c3ukrjdgmh4xG63o5TC6zffMo3H9XXf3d0RBLa+sRkGbkJAcISowS60Smozv3rzc7rY89mrz4mxU+GaGFU0BORyA84zl0kdUe9vablQDsah327W2DhEPkgC02/iY3qrUsH0jDCRjqm61vGOidJyeazWXQZTVRPFakVJxgVqppbMeDoyTxMP+3tK1hQj5yKx7yxUXLMCnl9dkWsju56+G/je888Z55mNjwTRNjy3wuF4rxTbp/VJrZLMhyp01FK5fbsnBOHdiz35ovLixQUhOm5ubpimidevX5PuEn/2p/+Ri6sr/t4f/wOGzSXBfUFJEw/3b2kV8nQgxJ7t9UtKacxF/fV9nah5wnFHTjNvv36Nc54vvnjOPCbe/+o1+8PIm6/3BO/56strtpseZEsIniaJRuUh3WvRd3jAx56LkgjDBm8QD0MExCznhTkpYuAoOA/edAVuPun+sib4WcB706xiR7AdU7vw03hkPB746U/+M/d3d/zsL/+SVitfvHqFD56+D/Rzos735Fw55czcR7b9jl2/I813arfjlURS8ogLHVEcBEc/XFFbJL/PVITDHAm+4fwFUkfm8YCQ8OmW5rULc6Hjy+trigSubzYcToX/96cHDqUwSQcoeqI2NzrXyFK/A5Q+r7+xE6glWfXvCbHDeU+az9XpyvBtGJW00UwhXGpDXNPouBDWyfuSSLQoFktRXxGFy5xh2HLG4K0674aBOGxUrWgOoM02zVyWUOiq1XCrHI9HWqvs0ox3wbyKTDfQGrVmchVyntQdNGGB1hWh4HJHc9UKfgugzlkhqWXDX7FHvWZt/Y/+5Dy/t67G3P/Of3D5kHVWsM4M7Gsuvi3nRLKyisNq1sCYRRPQatEwi+DNuXDhJle6LqCmpkIQteYOiCWvPc6TrR9UE0/r01jG4DSbKyEEC5YfZ4IX5rlT24EQqbUSY9CA9f0DoG6zrTb6EJEohNB/cD/qUFXoNxeUMMPsyAjeR6rTzbYWfe4alTh0hLlynxMuFQ6HmVpRP//OE7sGrtIkAwJ1otVGGk8AlGmEBj50ivVb7oGsz5zZsth9jWHsrS6b/+L3ZXBsM3GmsYHSPDKNJ/YP9xwe7pnmSR95M3pUHzOIrlGdoh61iir0c7X89IqTsL6Gs2hLMxKcLzQCFZiSun8OrgMatZ3US6jo4LuIepS5NiCuMgRP7Sq7Xr+f4wypQim26yx6JFk3nO9cH6eIUvDB8erLr3RTRDdaXxIOwUc1MppH5b87H8BZmEFtpFxwDsXzQrS6V4iDOlRO80lFZ7N+MzuvtgvOvETKNFuYvFpIXL14Sb+7ZNheIT5wPBxJFlZfa2WcJvI0Mk0TNSf+av+ezTDQDZcMw4Zo4TAE9eXpJKgL6fQAkx1NYvijDwx3O00YG7ZmU9HhNo5wsbXX2UMzlk9b9vXHEm0bDBuM5r3XB9AoeXpjwDr4XWEhPVSKHQxlMbwqygZK85GSM+PhyDzPHPb3pDST8oxzwuZC/eD90OMtPKTVplF8c6Htk8Jh93eM48R8eGA+7JmnkdyapqyF/zrOwNP63VklKStsZKYPwg9/+CNazbx+/Qvevq7k8YHtZuDlq5cMm44Q1TfrzetvOe7vGMfE5eUV/+gf/yM22x0xdJQys394S64jMU+EOPDyi/+GVhv7u1vSeFDW2nSiDyPzOPLTv/4pNPiDH/wBaWycHv6S037k5798j1D567/+mq73fP/7Lxg2HZdXA94LLU+acV4KIXbkKRH7gd3zF4QQVXiKUiKVPaTUU1dnpGVqCdrTXAAAIABJREFUHoGzQn+xxgjRvLwctFaZpwNpnrl794b9/T1vvv45x+MBXCXETtlUpWikZah8/mxgyo37E9Ta88237xjHI9ttJkRhE6KJXjOtJkorQCB0G2Lz+H4kpcw3tyMxOv7OZ58RXCJNCWkzoSktfUpqSzHnveqZwo5OIn//D64Zs+PPbxuHqXJ7n9Fsep1RepcVav4t6+MGciEgNKIHVfKdrFp/hKW1x9WsdQClkk09LOs0fBnWWKXRKvOsroHVcLwmTbHAek4pa7WqR4h1At0waEaneOZ5Ij3isysetnDlC9N4pNbKNFkGat8ptil6mzjLEWhi1EvQDgSzYyjF4ChLKzNHUQlW1Vfd4NUS99ev3vmaCJx9SPj1M/n8q1UEtnJFObOAbN6hfOWklN00k9NskZ4FbzOAsHQCQQO3NaO5EbuoWOSog6/T8YHxNKnWodVV8d3k48HUT+t3c5VaECnknAkipmcx59CmyuHgxH7f03URpBGDI+dKGk+MPnB/90DOld0u4rz65SM6I3Au4IO694ZuS2sQ+i0g1KngQl7vLecdIcJm11NL5Xg6UUtlnmZS8toZFJ1tee/w0ZuVis650ukIpZCGnhojUjc2r1RqKOJ0dpYtl9uer7rOAKA5y/gQoaA5APt7LaoWJEQ/MktKmHYCFXEQvLAdPC7B/VjVAvphj3OZrtekxHWy1x7PBZoljpltRnbMWQvIYsx+MYO91ibNQLZZJzPgC9SAuErfzRA8u6CMpzJALmIew00ZnH/bjOGbl19Qa2Ye31PzRJ0P6orZErXOhsNXNlulfY7jRM6F41E3ptYsnEI0wNxFDaHptjtyztzfvSHPE67oPKBIRaqQ00zNlWTTftd1+Bi4fv6CYXdFG67JuXD37i3zNNpFPe/CajqXeHt7RxdPfPHqjjzD8NkFrusIw0ZrBQvI8cZSik5fZz8MeAufcC7gup1SRF00gRXantlF/o2pyzoEOv++PAp8eDwgWl53e/RlNBKyUdCM4FQU+8/jkZITp/09KSUO9wejg84gwvXNNT54OrPr6HpTYgY9hLYxkHMhO880Tfz0J3/K6TTiWqILjmmcmEsjZ9GH6Gl9Uus0Hghk9nLH7BtFJmJ0vHh5g6PSpiOncuDrb76m7zu++PIFw7BjG76vRdUxMaWJ//Nf/18M2x3/4l/+Cy6vdly++EzdR+9vmXzPcPElIQ5srr+gSxOlzMynB+ZpJLjGH/7wj0gp8fab19RU+MGPbkjjBT/9s8bpMHE6Nua58e0vH/BOCO49ITquPuvph8hnN8+oMdPSr3DBMR5u8SGwubzBx0jsNzgXiN2gMJHTmcEKty7hME4TzpLN6lpVCPlP/+w/0lrl7/34B2yjZ4ieGr3OFYMHp4VZFx0yBLZXlxznxi/uHnh/nPhP/+X/49nNwD+9/hFhCJqq1tTGBZ9w1ayne0cUT+gdrjnyKNTsuR8DQwxcXnyOazPp+K0eRE1hITcaAYWDHibxDSB8SUSGjuGz7wGB/ZhVS1RP8Lc9BJz3sGQBm7pXZdDJ0rb0hHnEdaE2dKPJliPgHnN2neaIiqg9xCPVa8UqXjS4WSEehU1iA1DBiA8dxSb5rdmwtFWjPjQz1FSWUSmVROZ4OBJ9XA8L74NGBBg1U9+nwzn1IgohaiZBCKzuf+4cBMOj9wvnzVvfUnv8i/NP1791ZlX9+u/Ao0OCc0dUrBLJedaYzjRbXnOiNrUAcM4RY7RZgGYqeOe1cvHGSLIs2RAcOQllninTjBNlOdCUR15qxdXC0/q0VogdvjlKduRaSClD89AvHjcmZiwLr13W+yo4tSevrVHSxDwK+/0D3gs3z3rE66xr6Vo1b1ipkKHf0FrBd4O6bDa3qpcRiEbx9FHwwRTBtZGzJosXtDDpTtp1j91MWVh+Xki54GNAJChKUAyaLkVfgxhxw9r1RcPTTDGMGTXWVjkeDtzf3a3PtAseb901ocfHzjJG1Mc/BIfvPJmmcFJunE6JYfBUYE1ps1lEq+ZkCiZOq3TRkbKzvbQxZ413zTHiUffQZvH1ChLos1qKgVo16T5dKy40IhPiKptgotLS1r3oO++Lj900eT7pN4eAGqUFWp45vntnPGC9gOM82yQ6UJswnjI1F0IMitPFjuY7mguAo856iAziSRKYUqE2SEGHR4fjgVILqWQdCJdeB1qypUqv7oA54Z3qE8o0kqeJOmXqnElTJs+FUD2Uxi9+9lc8XF/xva++wEvHENX4aglnWToBcZpx3IUOcYEiHU08DstVWKp2+6hGB13OZUzC3paB1AqZmQvpIkSTCNS1RW1mZ1GtXSxNB21pOlFzYjpZB3B4WClrpVYqGReE3WaL955og/vOPoeoMxAf1JOllowjMfgDEhzX/Y6uBPZx5hQDfb+h5MxxPuDLExz0qa2vfvB3NSnsa09JI36cKFJ4d7onBnh2saHvIjeXl0oiyKzMNCfCzfMLrltjc7lhTpn/8if/nmG75Z/983/OZrNlNzwH52jpQK6zSjZF2D17SSlX4BtpPPLw7g1QuX55Q5kz0/2Jcc60kHB9Ydd6qOCqHSzKfuf0JjFJ5vgrZbv5qBv0ZheJXeDm+ZHYBbYXO7z3hF49gZxlGjfX0XCUol15pqIGF0mf59BxOo28/eYNfd8hplbutxEXHbtXP0RioHCkjIl+6Kge/IVQ58bFJhHmRk0X1LRROLYLxLDD+56cTjTn2TTBEamoEO/L6y3HfuT9wz1Tbrw/eMLomWYhijDUAY8Q3BHvhL4bqM3z9iExpcrd7KilEvKMlyPD/Z8Tgufy4hovgZajHva/ZX3cNmJht5iSNZt9xHrSi7VR2F63fF6GpKL4XamYy50yiKZZsbZWirF0dEBaSjHecqJUjUt3Vp0qXKJYdS2ZUvLK/VXBlHFzlzmCfc1WK6fTka4La2fhFgM3WTx99LR23ms3IAtV1bPmEC+6CJFzXu+iD1g5xudfNuyyfcBHftQgPGIILdW/JqO11Q4iz4tPyaTsnzRTS7brVS1NyKtOwwftANzCCnr0Xpy391D1fYpom+0DPmRlEDlnGc2VnAulPimGP7UV+kG78NCbYWNSHn/VIqyVqpbotUGp5DnhHApDOPO5d47t0OGd8O4uU9JMmhIxJEMLnLoKt6Z0bOcJ3YCXSDdsUV3RHa1WNrsdOczMhxkXPJuLgeA9Yx6pWZ/9heqIdSiVxlyqsg5rszkY1FKZDiM1KZ7uvCMkdREO0Tz9g7Lexilrld5FtXjPJ31e+w1pTooqgNK/bc7mm7C9uERi4HCaECeEGKnS8NETEa6vL+imaqlo5unvbIaArPuS7gOyDm5jUPLGplcxaW6NXCpTEorTmFotLIPuGWu+8DJeUchpLgWhUqZk+QIZ74AaPkYO+vghEIOKL1Jp5Jq5vb2lzCeGEAjiQAoChOAVBjpp1RC8Ny6RJ1XH4TTS1cbOO3Ka+eav/oKaE7vdBhFhnlRMUr2+2VqUCqahCwpP1FbVOtYJ42kk51nxMIF5OjGfTqR5Is0qbMtmPldyYZqOiECaZ0o2mMOuYAOdV3jPMGxWCbjCJmorsdhhP547IJiSWQxaslAJq/zFhj8adrEwgcq5JWyVVpOJvVToltKkYrdppOTE8bjXjOBx1LYuzYDadXgJ9F2vlb/5rSvlzxPjoHS9YIZYdhPSnIVeBGqNuD7iWyEOkThbBF4q7I/pY/fM0/odXZN4CIHNxQ6XHJtxwpeKJMGlwunuHckLMh8I0bPd2uDXgQ8OV5NWt7FjO/T8+A//jnoNvf2Wh/vIs5tnhBCYDvfaEXgVW2anAVWXVy8oG4Uya57ZvGykeaLUv2S4HPjyq88ZDyf+w7/9E9J+IsjGWDteO+tiUL6Yx9AS0J4TrSbu31W8czy832th4/Uw2GwHfIj02x2pFP7q629p3vHVj/4QvPDN7RsajefPXkGDy6srhq0RULyAj4TgefW9L3Eh8MufPSBphmfXGqEboPcd/+Mf/HecxsJ/+pP/TNcJXb8hdsqEPA+Hzy7AIQRaE8okdNHzh1+8ZEyVn785MqXE+6Meuu3qBUEqnAJeEgwZL4ntdmSowqbbMmXPL9mRSlOPs9zY0/C+Mgwq9vtt67+qE1hYKUtrKN58O6SuNgeKxWd7g7Kq4loTshmvKQZfyWlUbLsPmtplWNcyPa82WD3zLpfK2ioDYw6tMwU7JBa+crFMgVX1V1RJOydNAjrj9obMyzn9a1EfNusGxGYYH1yXx0MAu048/vig1DcPlWVuUVWksjh+tlrNy0jViaVk8jSRi2H/1vW0tTLXG9s5VUw7m2GI03CKZa7hlte/zGNg/bXOZJzNC7x1Aueqa87VaHRP65NaThltYk2sp+FRBbmjEb0QvOi+t8zMqvW1SyKYCM0rPBRD0M2+ZKrAPI/UahCw08JQCFDN/sF5CJHYDTTn8U4739j3eO+52OxWFpDOGFS/vNioIHoYOMsbWYwLVkJFVUhVRFk/+hj6czZxVl3NOI6aGeAE8Y68wLG1IKhINdqzpaRGJYx0Q4+zjtt7h+/VfrtJw4XI7uaafsrE3uN9s448GOLQHqECihCc54LgRNh0OpvpolAqHOcMzjFlR3EKITVEN3q3IBoQrdDvO4+ohpTFIA+W6/O3nAlQZlopjMcH0jwRu0hwTdtFqUohdVDQYHUdYhbDtvXmEoT9wwN9mnn+7AIfHJshMlMYj0ecOIb+EsEprbRUakprOI1D1rD3lYZp7apau85WeVeqQSf7hzvSNFGLUT9rZZ4Tb9++pTW4uXqp8IcPupl2vUEhCv+IOf3pN9/9RsLXCg3pdNtaPPX20YNp8fzRzbvkWamdRZPZMBvunGZKLUwnlcWPxwMlZ6bxpK6kZbY2UP9NZ5V/3+vnGDu888RusHyCRY9glrlOD45mr9c57QS891RjEZVaiMHhndpoTbVxe5hJT3DQJ7c6H2klMx/31PlAKCd6qVxfDHTRcXXlidGz3fRqNyJ6X6esHajLyldPSWd1lIKPkcuba1prfPvLn+PE8cXnLwkxUlzAxQ6/u8CJV4NHhN31C0qaObx/Q6qO3YtnSKts+x7pPN3VhlQrbna4hlb8gJdeOwHDt4WGk0YXepwDJyqODFbYhCCKjW+3ahmj+Cy56kxhe3WJ6wL9wxtDDSYERz90bLYbYt/rgdl14IImnYWg+QplgKsL9f6fE7EbePnqhtOU2F4GFWcOW6XJ1kIVkEcUdrfMAGtdoeMhVoKHz5/17MfE7f0dc2qkIsToeH4VwQXeH3pcq2zDjJdC7PZsOvjhZY/goUZahTSZdf4jevp3rY+LxZpCF0s16ryjNRU8VeM06iGjb6gsh0DRQ8AV24RKphS/noLeeOx1zMafN9hiURzXdi6mUfaO8m1ZK9QPODrtQ1y9mHrWrQpAVRHv93v6bjC+LWuVrxX/UlI8ZjN9eD2WWDr4tdPVOo7HnzGdQWvFKvlz/GOzyj5Z5Z/SRE5JW+Oc7RDUPGdFrZwNwha4Zxn8dkYBDcbcWmYc7qw1WN/D0rYs3Y/yxL3zeGutQ4yEWJCoZldP69NaYt2o1Iyrmc5B74WhC3SdZzNEYnT0pqdZfKqcsypWgziMbNj0PnbmLIB6AzkRE38WCB1tsW1uzcaw4L06DUuIuFYNMqk66O0C292WloGjdiALJ8+7oIfAYrxYtTPx3upEr8+zD84OAb9+iFM7hdaa2XiZ51kMdLFbn4ll7rF0G+KEYIeABGXbBR8oIeC7iBSgVqVmd4HqhMvrrb4fr89rqZlzeOy5dfmgqJSFqNToOyFXYegci61FKcoaqk6gejyO0gBJlAxOKt2CzIhmLjePdVPfnWS4rL+RHZRzYRxVodp3HdU7TuM90jKt81Qa45RJqXA8HklT4rjIy/0JFwKdv0aqZ04zzgvD7gIfO1o+0urSMorCOAVqUXftVhouwO7igu3FBblVxAbCXoToPdXboDinVUQ1W65udDoAm+aZlDM/+clPePniHT/+8d9nM2zXuYAOTjXuDcz9U+DxPP3xYHj5BjYb5NacWOTztKZD6lpI84lWMjlN+mcN8y/zRCmZw2lPzonpqNc3jyeojWhWDy6auK7rcC7QbbSi6foBEXNHfERBW8yLmjGU3OK7YZDb44bQAZsQkVjZ9h0X28rLL7/Aj4U2N9ITGvTJrbZ/gDTS55FeEp9fD2yi4+KiJ0TH9iKY2HCBQHUz7bpLg2szjUZuldIqp/nEdJy4v3+HOM/28hnOOd6//kbhnWcviK3SaqY1T2rq4dO5Ht/1XD//nFIS+4fOiCITYQs//uM/Yt6PHF7fk8aZ+/e3amMROpV3FtSCPiWEigaCCb5XWLQL2gkMXSR4Tz+oLcN+PzLmhESP7yOh6+iHjpfPnq+061YbKTVccFQnhBh4/tkrcB7XKYtw0/cECnS27wAxdnSbQOd7/vv/4Y9prbK7uEQEsvH0g8ijA8aG3TRiCLQGCb2+w1AJ0fHHP3rJOBV+/s2ecS68/lYH4V+82OJiIIfAmAPffqPJi71LBN/YbI+EKGw3HcEHgtvZHvHd6+PeQSVbZa9JYuqPURY+5DroqGVxBc3K25fF39+c9NYquZlmQVlDtTVosmJl1ewRWIRUjrXSdd6z5OcCWtEu1S6c5wM2I2i1Gq21re6kh4c9Q79hnlXMFny/bqBn3L+dKZ6tsSjDfhMi/3U/H5tJNOX1Nxvk1pIpdggsn9M8agcwj7r5r6yfov+ceH3v/rFuIayfFe5xOBfO73/lSp1/dS7+l9nK8p6ERfPgjBXlg+KgoenPn7yDPr0V0gnJM9HDgGPTeYbO0UVPiMqFd6bOFXu+FhaMblpOWWm14GphEo1grCmBKN6OcxpJ6x05jbgQdHhqkOqyGWnCV4c4R+y2BpkmtZy/vqKLA3UsuOAJh0ApxbIGrK6VhlSbGXhWpbx4Z0p53cC9V/YctZKqOhksw2Zn9NGu63ACqSmSQTJjR2PydIMaSbZalVVkA2cvEYDm3HkQHR3b3YZWy5qApjNGWPYZxfL9Wb61+qABqHW+eGHbO7w4Np1uoSf9bXJpa45LqY5k1PxgOS9zSVQRQnFUHKb4+O33xcdumvG0VzbNdKClxH46Ak15sxKY6gksPrLkjLQZ7xvD1Q21NE77d9p2lkQrgVCV9jUdM9OUOJw0q1fhDJjng8azUXHiGTaROPS4uMWFAclqJKXXUyjiqKJqXFeFUCDkSpszbc6EjX4zpwy5ZG7HW0oWvnlzS5HA9z97hQtBZxei6lyFifRq15Y1rFr8CgWdp/uNsgZnJMX+04laCtNpr5v66agYbDpR7XMpidPpoFDRXFZ2UGsNb5W/74Ilom1xPtJ3G6N86uzCu07poV4r/LXNtQG7W6EsfVic2KC9Jj3EnVL3qtFkQ7chJocLR6R5pu0zJnnyDvrU1qv9zwkCz1/0dKHjegMhCHGIOO+Inelkus4sUhZ4JVhB1kFruDTrkLg1kk80cwp4+PY1IrDdRVxwvL99TbfZM1xf0A87+uFG2Xe+gnhc6JHQs3Mb8jzxMI0QHC9+8IKSCsSO0/2B+TiSJu2epTVLImzGFoTQRbxzbKN6ZflNh9j78d4Ru442Zx6+fs9xTHQS6X2HCwHXBba7C0pKTBRSapyOe3KCfDzqJvzsS0CY7x9oLVOiPkPDvKUwc3R3iEOvX9AEMfUhyzYQ1gNUbPMP/gIXtjQxJME5WnV6ALVGjyqcg0AX4MvnPVMqXG0d01x5d3ekNmHYXuCd5+LqGu+E3aaHBqfTRKqV24e6Ukk/hgf9DZ2AsoKoRRktVT1nQMNczpW3fihDRXB+Qy2NNB8wSZVWoKXS3LlSPy/9Otm4/t75tQtwxoXXMIhlkPLhj6ULWN7r4wQwUPWd2lEUxmnizds3OO/4/NkNMXo9Zg0y0b/WrIsw9LO59TXqP1VXOIh27oLyPFFKUpFXSdTpdK78zTq7Vq38Wy1IWbQDlp3qtUJZOh8XojJ+Ft6/eaJ8wPwx/v/yumG5CCsC+cE15oM/07TSc6oyXn2e+gFc/Nit8bR+B9fQEsHBJga64Aih4bxYSpfwQe2wdIuPskP0DzS9VwSIEQGiD7oHpIkqUJI+M+IaJThqnqlFh9Ja8QalK1vin3YYsjJpumGgxEp/saXWRr/bIE6YRw2fctWKHZTVFqLHu6WT8To786rk9c4hwUPWONVcihVKrAWdkih0LlbLgjCcOwFvaYhlOlBrYvkCzoKo1utnP3TbeTTUXLJErCJfHRhkcVtwNBx1Ufa6pZjTYJs+6owvpfMMtpTGPFvcba+Z5mntBBylQEr2/lz92Bnw8UMgzZMKNEqGWhk65fEnyxHYioqzSqrU6hi2msC1ufwerQkPu40xeEZa09wAJ+DLRCSzGdQgKcZIzpnD8UCtleura9wjF8sQtK2b51nN6fIih06rhQXVLCDEaQ5BLcw1AYLfDLRcKWnP3cMd/+r/+N959eolr/63/5XPXr5EwiWegPMdK+GzWicgYhW23hytNVK2fy8XqIWcjpQ083D3mpxmxtODwkBzsgHwpMPporF71ZLFot0QfTeYqKXTiqxX3r8Lg1p4L1a5mNvh0q4/4v4+tqEWe1jFshOWw7oalRYUbnNBBTe7EJh8JeDxLtC9eAnGb35an856vsOwcsEbD7Q5IdWm3XeC5lQIKU7MikFjElUkKQYPWa06qMI8A+E0Mh1ek+eZ46yK9O2zS1rwtHykpsYkDnyHSMWFnuCvcS7iw4APPZuL59AS/WYLrfHye58zPx8JwTEdDtz94heUaYJpto5AyQ3D1jyyRAkTcbvBB6cdjoCYwePppIlgsQ80C53CQ05qouhDABqx7widmlzWVun7ntoax7vX1DIRe33GLJRS4acQaKVRqKRJ96c+2MAZtb5fKdymLXLOIc2Di7TamE4a4yvDqNRU1Kp6FwO9B9pMjJX9KIxT5XZ/R8Mxbrc0geN8r6jAyk3RiNkQ23qOf9f6ODuoPsK7m1XUGEwO2vNUyzdpGEc9qA0D6IVpxSoDpUQ6NImolaosAZsN1GrcAUGn9iHYN6Gtw5SzhmBhEdVVU7BkmkJT1ot35DVlyK+BL6UUHh7e00fHu9vXxKBit9j1BFlYSoavS1OOb1mKIfNOWg6dpLFx8+mosXunAznNpPFo4q4z/7+1Sll1FIsHiDECbDi9sKAWxa+4oP9f/KPqYdFgGDC2VG22zh3C+j+s63okuFh4CjaoCt4TnCgjyJKgan4Kmv/UVnDKPlkqfoUp0CKh2vOOpXNh9404E26iN+ty/4jY3Eo9iWqpxBDRbOIT0lR7450DE0iqx41YqItuuGL3sYjZmxhs0qjq29MF+sst4oV0vyNHz9wqFNUqOK8uuc6rR5AaVZ6fJQGbVSrSUEom2LZXSlb0wZ4PZxX+B/oa64akVmpNmoVg3fvaVBsHc5k/rnOA32AyLljFo24c7YAqhZTUL80HtcMJYemWBA+EALHBtg+IFOJYKNV0XEDK9utynrOKNOqjZu4774uP3TS1KjyT8wy1mvwahhhVSDIqpXHOldL0BUprtDrRKmssZcmZSuP+7h20ymn/gPJ0XwCOw+FAzhlvPvZXz29wznN3d08V1Sf0Xc/ira/WyZmcJ0qalapWsmULJDZDj5PG23fvKKUifjgzf0SIUkjH9/zf//pf8fLlC/7n/+lfcn1zw+7imQ5bRW0WmrfPJjwp5Wzi1GqlnA6kaeLtm280gOL4oDd7mfX3fw2rXxibzm60RaSlhm+BGIz/HwaFf6IqJpdAntXOAnsgH9+IYnkF1gkoF8gOLYPsePQaRBwu9iCFri90U8JNe+ops58KpycX0U9uBSl2/2h4Cdk6wqLoTIYzPFT1nhen2LRqTNSONltR5BAQT39xqWFPzXE6HLj9i/c4X7neXbPZbbVYEj0soFJLAKm0OlKlIainVtxeKyU9H3R2Nmko1NVXL5HSePnskvlw4Gd//hfkcSKgxod+Y8+L1w7aD2Zo5zWXJJfMnBPzPJLmmX7b0VphnI5UV+kQnOhzUlujqaOiRuLGqHbTtZLTgZInfOgRhFJFJVNBkAApzbppt6rOoXkGUOgXb4aQJkqrOldw4gnxglY87x8S03yiP2ZidDy7uSB6o7tKY9s1hs6x22xIWbi6mDnNmW/fPZAK7LodpcDdOCnLUnS2kKp89BT4+PSvPcKR1wpWFmjQMn11FiCGlddqbJgG1Iy0YmZqlXkuxtSpK4aGiGbk1rJ64DtLxALdsBY3zPPLeswCsujFhZ1Tq2nLdB6+tGzL1/EiBNfwNI4P9zx4x/t3b429o2Zysev1Pfn4gWI4pXSGVUohnVREdzzcqyXFwo+umvlZVlxQHlXuj1S7fmEpLJ4/55+7tXVcPh5VKB9ZK5WVtd4445OPugf9sLhLm+UMwbH1sEkjfIRN8LR+N9eiUG2PqMQYE07naLI+LUuRIqIMFRFVly+UZHHKYBFRT/3mPXWr+QFdvwHUBTTNiS4HDT/PSe9LnxHrDFSZr5uV+A7EUdPBRJaJRqMPO1wQ2G50cHqx0yo/ZYWXTd3r7Xl10a/V+eJy3MC8tpakPZt5loKzMJoz8VDOf9Y9el4WtMEYdgsVu9o1LDkZ3dzclduy+5zTzoC1MNRXtRBPAg1PbY7T1EilMkyV4oU+FCvcGh7BBVV1b3v9Xmx6IVj4Wi7CHIUkMBUFa9Qp57dzvj9+CFRV4jqUKulsYJKzVt7Kq4Xh4pLS4DS/J+VKnU56I+VKaOCD2p7e3z3QaOy2Ow2JN6hiMY4bNltCDBSLN+z7nm7Y0PUdXezMawOSHUA5Z5KJrNQzSO0ialax09V2S6kwZf17m9AjVDo3ExwM4smHE//+//k3xK7j6vqaYTPw1fe+TzcMDLtLvA/EGGitcTyeSCnx/v17Up4ZpzsdCmdaBz0LAAADJElEQVT9JvU+qnhFVOEoTmXbzoZr3mAtb4ykzaBy+dgNOKdJQ855QlQdgHg9jIJZ3rbHSWQfPwvON9syI0AHWRqgsRxsHUihxZG47fhvv/+CV4eJze3ElJ/EYp/aKmgl37IOdoNreIEuWFSi12Kr7zrNoxiW2ZSp5zsLbbFuwXem0jIq9rC7ZjvN/DhsOO73/PVPf4oPwh/9gx8wbCtjmZEQCFfQYqKcBmroCTEhrsMPL7SoO72nzoXTwx5obC4uwXkm56mbLV/9+O9S5pnbX34NtbDZXahraDDdjBWMrSSjrQoueK6uLpn6ROgDPjhSmnHR0+12eBGOZaaJzkKCdQHKjPI0l7EoAbWVbsLUCqVU5pSQnDge7gyWykrWaAqrheCQx5CM0c5rtXCZ0BGCsLt5QT0M/PwXv6KUzO39nqETvnrZ0UW1k3AIkg9Ig5uucRnh5uKC2hppTOQMD1cdp7nxs1+NHOfC3TFbQfrd62/HA1wGjU2rXHEe1+xMbEXfXBNT7MpKmSq16Ca2CpxWPg5gMIlzZ677emq7D4eg68t4zNVvH3wsswHQrqAha4cQnMM7a2dr43g44CcdxpScmMajDVQ6Wigsvh85zaR5ZjodmdPEyUJ2pGm2ajSzNoeGVog1AbBAMEuVflYpL7YUzpl/0QfY/4JNOhtKP5pN/MbV+JvWd50aAmb3K07oo2fTeXZO8eOn9amtpS61nz/CrNcPMaGiUzzfWZaGfliuhrFhFovmBQ9vUb9kP2zIKTFNCZdUR0TVzyKy6mnaAotY4Pvi2wXY7K1g3uw0bFTnNJ7WiVMNQtEN3vlgG7AeAs3I+WcnAMzzp5wt4W2vUPHWo6v0wbMnH27eyzxl6QRsH1SH5EVDtb6JZXzyW9CYR6oeEc1Od4FchJQg+GKwU6E2kwCLph7SwEtbRaW1gc8zWSAZTOVdw6Harlp++z4h7SMnxNN6Wk/raT2tT3s91XtP62k9raf1e7yeDoGn9bSe1tP6PV5Ph8DTelpP62n9Hq+nQ+BpPa2n9bR+j9fTIfC0ntbTelq/x+vpEHhaT+tpPa3f4/X/A/Q/VebsDBzDAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize = (8,8))\n","k=0\n","n = 6\n","for i in range(n):\n","    ax = plt.subplot(3,2, k+1)\n","    if i<2:\n","      plt.imshow(d1[i])\n","    elif i>=2 and i<4:\n","      plt.imshow(cv2.resize(np.array(d1[i-2]),(64,64)))\n","    else:\n","      plt.imshow((d2[i-4]+1)/2)\n","    plt.axis(\"off\")\n","    k+=1"]},{"cell_type":"markdown","metadata":{"id":"tdtWfpUGgC4E"},"source":["# MOdeling"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sPzQhU4cJvmd"},"outputs":[],"source":["class ResBlock(Layer):\n","    def __init__(self,n_filters,filter_size,strides,name='res_block'):\n","      super(ResBlock, self).__init__(name=name)\n","      self.n_filters=n_filters\n","      self.filter_size=filter_size\n","      self.strides=strides\n","\n","    def build(self,input_shape):\n","      self.conv_1=Conv2D(\n","          self.n_filters,self.filter_size,strides=self.strides,padding='same')\n","      self.batch_norm_1=BatchNormalization()\n","      self.prelu=PReLU()\n","      self.conv_2=Conv2D(\n","          self.n_filters,self.filter_size,strides=self.strides,padding='same')\n","      self.batch_norm_2=BatchNormalization()\n","    \n","    def call(self,x_in):\n","      x=self.conv_1(x_in)\n","      x=self.prelu(self.batch_norm_1(x))\n","      x=self.conv_2(x)\n","      x=self.batch_norm_2(x)\n","      return x+x_in"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6DElSXYFLCaS"},"outputs":[],"source":["class UpsampleBlock(Layer):\n","    def __init__(self,n_filters,filter_size,strides,name='upsample_block'):\n","      super(UpsampleBlock, self).__init__(name=name)\n","      self.n_filters=n_filters\n","      self.filter_size=filter_size\n","      self.strides=strides\n","\n","    def build(self,input_shape):\n","      self.conv=Conv2D(\n","          self.n_filters,self.filter_size,strides=self.strides,padding='same')\n","      self.prelu=PReLU()\n","    def call(self,x):\n","      x=self.conv(x)\n","      x=tf.nn.depth_to_space(x, 2)\n","      x=self.prelu(x)\n","      return x"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1066,"status":"ok","timestamp":1659737067262,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"KpxObeQNe-RF","outputId":"35ca40a5-50af-4084-85b4-028a24997904"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_6\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_9 (InputLayer)           [(None, 16, 16, 3)]  0           []                               \n","                                                                                                  \n"," conv2d_15 (Conv2D)             (None, 16, 16, 64)   15616       ['input_9[0][0]']                \n","                                                                                                  \n"," p_re_lu_5 (PReLU)              (None, 16, 16, 64)   16384       ['conv2d_15[0][0]']              \n","                                                                                                  \n"," res_block_0 (ResBlock)         (None, 16, 16, 64)   90752       ['p_re_lu_5[0][0]']              \n","                                                                                                  \n"," res_block_1 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_0[0][0]']            \n","                                                                                                  \n"," res_block_2 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_1[0][0]']            \n","                                                                                                  \n"," res_block_3 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_2[0][0]']            \n","                                                                                                  \n"," res_block_4 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_3[0][0]']            \n","                                                                                                  \n"," res_block_5 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_4[0][0]']            \n","                                                                                                  \n"," res_block_6 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_5[0][0]']            \n","                                                                                                  \n"," res_block_7 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_6[0][0]']            \n","                                                                                                  \n"," res_block_8 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_7[0][0]']            \n","                                                                                                  \n"," res_block_9 (ResBlock)         (None, 16, 16, 64)   90752       ['res_block_8[0][0]']            \n","                                                                                                  \n"," res_block_10 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_9[0][0]']            \n","                                                                                                  \n"," res_block_11 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_10[0][0]']           \n","                                                                                                  \n"," res_block_12 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_11[0][0]']           \n","                                                                                                  \n"," res_block_13 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_12[0][0]']           \n","                                                                                                  \n"," res_block_14 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_13[0][0]']           \n","                                                                                                  \n"," res_block_15 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_14[0][0]']           \n","                                                                                                  \n"," res_block_16 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_15[0][0]']           \n","                                                                                                  \n"," res_block_17 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_16[0][0]']           \n","                                                                                                  \n"," res_block_18 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_17[0][0]']           \n","                                                                                                  \n"," res_block_19 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_18[0][0]']           \n","                                                                                                  \n"," res_block_20 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_19[0][0]']           \n","                                                                                                  \n"," res_block_21 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_20[0][0]']           \n","                                                                                                  \n"," res_block_22 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_21[0][0]']           \n","                                                                                                  \n"," res_block_23 (ResBlock)        (None, 16, 16, 64)   90752       ['res_block_22[0][0]']           \n","                                                                                                  \n"," conv2d_16 (Conv2D)             (None, 16, 16, 64)   331840      ['res_block_23[0][0]']           \n","                                                                                                  \n"," batch_normalization_4 (BatchNo  (None, 16, 16, 64)  256         ['conv2d_16[0][0]']              \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.__operators__.add_4 (TFOpLa  (None, 16, 16, 64)  0           ['batch_normalization_4[0][0]',  \n"," mbda)                                                            'p_re_lu_5[0][0]']              \n","                                                                                                  \n"," upsample_block_1 (UpsampleBloc  (None, 32, 32, 64)  213248      ['tf.__operators__.add_4[0][0]'] \n"," k)                                                                                               \n","                                                                                                  \n"," upsample_block_2 (UpsampleBloc  (None, 64, 64, 64)  409856      ['upsample_block_1[0][0]']       \n"," k)                                                                                               \n","                                                                                                  \n"," conv2d_17 (Conv2D)             (None, 64, 64, 3)    15555       ['upsample_block_2[0][0]']       \n","                                                                                                  \n","==================================================================================================\n","Total params: 3,180,803\n","Trainable params: 3,174,531\n","Non-trainable params: 6,272\n","__________________________________________________________________________________________________\n"]}],"source":["input_lr=tf.keras.layers.Input(shape=(IM_SHAPE[0]//4,IM_SHAPE[1]//4,3))\n","input_conv=tf.keras.layers.Conv2D(64,9,1,padding='same')(input_lr)\n","input_conv=PReLU()(input_conv)\n","\n","x=input_conv\n","for i in range(B):\n","  x=ResBlock(64,3,1,name='res_block_'+str(i))(x)\n","\n","x=tf.keras.layers.Conv2D(64,9,padding='same')(x)\n","x=tf.keras.layers.BatchNormalization()(x)\n","\n","x+=input_conv\n","\n","x=UpsampleBlock(256,3,1,name='upsample_block_1')(x)\n","x=UpsampleBlock(256,3,1,name='upsample_block_2')(x)\n","\n","output_sr=tf.keras.layers.Conv2D(3,9,activation='tanh',padding='same')(x)\n","\n","srresnet=tf.keras.models.Model(input_lr,output_sr)\n","srresnet.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0iZbRVkySUs6"},"outputs":[],"source":["class ConvBlock(Layer):\n","    def __init__(self,n_filters,filter_size,strides,name='conv_block'):\n","      super(ConvBlock, self).__init__(name=name)\n","      self.n_filters=n_filters\n","      self.filter_size=filter_size\n","      self.strides=strides\n","\n","    def build(self,input_shape):\n","      self.conv=Conv2D(\n","          self.n_filters,self.filter_size,strides=self.strides,padding='same')\n","      self.batch_norm=BatchNormalization()\n","    \n","    def call(self,x):\n","      x=self.conv(x)\n","      x=LeakyReLU()(self.batch_norm(x))\n","      return x"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1659737067262,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"VIgbRN3ie-Tc","outputId":"52a193ab-357d-485b-cfb7-fba4a0bdabc8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_7\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_10 (InputLayer)       [(None, 64, 64, 3)]       0         \n","                                                                 \n"," conv2d_18 (Conv2D)          (None, 64, 64, 64)        1792      \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 64, 64, 64)        0         \n","                                                                 \n"," conv_block_0 (ConvBlock)    (None, 32, 32, 64)        37184     \n","                                                                 \n"," conv_block_1 (ConvBlock)    (None, 32, 32, 128)       74368     \n","                                                                 \n"," conv_block_2 (ConvBlock)    (None, 16, 16, 128)       148096    \n","                                                                 \n"," conv_block_3 (ConvBlock)    (None, 16, 16, 256)       296192    \n","                                                                 \n"," conv_block_4 (ConvBlock)    (None, 8, 8, 256)         591104    \n","                                                                 \n"," conv_block_5 (ConvBlock)    (None, 8, 8, 512)         1182208   \n","                                                                 \n"," conv_block_6 (ConvBlock)    (None, 4, 4, 512)         2361856   \n","                                                                 \n"," global_average_pooling2d_2   (None, 512)              0         \n"," (GlobalAveragePooling2D)                                        \n","                                                                 \n"," dense_4 (Dense)             (None, 1024)              525312    \n","                                                                 \n"," leaky_re_lu_5 (LeakyReLU)   (None, 1024)              0         \n","                                                                 \n"," dense_5 (Dense)             (None, 1)                 1025      \n","                                                                 \n","=================================================================\n","Total params: 5,219,137\n","Trainable params: 5,215,425\n","Non-trainable params: 3,712\n","_________________________________________________________________\n"]}],"source":["input_lr=tf.keras.layers.Input(shape=(IM_SHAPE[0],IM_SHAPE[1],3))\n","input_conv=tf.keras.layers.Conv2D(64,3,padding='same')(input_lr)\n","input_conv=tf.keras.layers.LeakyReLU()(input_conv)\n","\n","channel_nums=[64,128,128,256,256,512,512]\n","stride_sizes=[2,1,2,1,2,1,2]\n","\n","disc=input_conv\n","for i in range(7):\n","  disc=ConvBlock(\n","      channel_nums[i],3,stride_sizes[i],name='conv_block_'+str(i))(disc)\n","disc = GlobalAvgPool2D()(disc)\n","disc=tf.keras.layers.Dense(1024)(disc)\n","disc=tf.keras.layers.LeakyReLU()(disc)\n","\n","disc_output=tf.keras.layers.Dense(1,activation='sigmoid')(disc)\n","\n","discriminator=tf.keras.models.Model(input_lr,disc_output)\n","discriminator.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tktFYE07vrjR"},"outputs":[],"source":["def pixel_MSE(y_true,y_pred):\n","  return tf.reduce_mean( (y_true - y_pred) ** 2 )"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s9KnfnbEp0Ak"},"outputs":[],"source":["VGG19=tf.keras.applications.VGG19(weights='imagenet',include_top=False,input_shape=(256,256,3))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wa6-iNREtpAQ"},"outputs":[],"source":["def VGG_loss(y_hr,y_sr,i_m=2,j_m=2):\n","  i,j=0,0\n","  accumulated_loss=0.0\n","  for l in VGG19.layers:\n","    cl_name=l.__class__.__name__\n","    if cl_name=='Conv2D':\n","      j+=1\n","    if cl_name=='MaxPooling2D':\n","      i+=1\n","      j=0\n","    if i==i_m and j==j_m:\n","      break\n","    \n","    y_hr=l(y_hr)\n","    y_sr=l(y_sr)\n","    if cl_name=='Conv2D':\n","      mse=tf.keras.losses.MeanSquaredError(name='mean_squared_error')\n","      accumulated_loss+=mse(y_hr,y_sr)*0.006\n","\n","  return accumulated_loss"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BzgVRF5lzbuK"},"outputs":[],"source":["def content_loss(y_true,y_pred):\n","  mse=tf.keras.losses.MeanSquaredError(name='mean_squared_error')\n","  return mse(y_true,y_pred)+VGG_loss(y_true,y_pred)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EX8x_kl6fLWh"},"outputs":[],"source":["class GANMonitor(tf.keras.callbacks.Callback):\n","  \n","  def on_epoch_end(self, epoch,logs=None):\n","    plt.figure(figsize = (16,16))\n","    k=0\n","    n = 6\n","    for i in range(n):\n","      ax = plt.subplot(3,2, k+1)\n","      if i<2:\n","        plt.imshow(d1[i])\n","      elif i>=2 and i<4:\n","        out=self.model.generator(tf.expand_dims(d1[i-2],axis=0))\n","        plt.imshow((out[0]+1)/2)\n","      else:\n","        plt.imshow((d2[i-4]+1)/2)\n","      plt.axis(\"off\")\n","      k+=1\n","      \n","    plt.savefig(\"generated/gen_images_epoch_{}.png\".format(epoch+1))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g-FukMaRe-XN"},"outputs":[],"source":["class SRGAN(tf.keras.Model):\n","    def __init__(self, discriminator, generator):\n","        super(SRGAN, self).__init__()\n","        self.discriminator = discriminator\n","        self.generator = generator\n","\n","    def compile(self, d_optimizer, g_optimizer, loss_disc,loss_gen,):\n","        super(SRGAN, self).compile()\n","        self.d_optimizer = d_optimizer\n","        self.g_optimizer = g_optimizer\n","        self.loss_disc=loss_disc\n","        self.loss_gen=loss_gen\n","        self.d_loss_metric = tf.keras.metrics.Mean(name=\"d_loss\")\n","        self.g_loss_metric = tf.keras.metrics.Mean(name=\"g_loss\")\n","\n","    @property\n","    def metrics(self):\n","        return [self.d_loss_metric, self.g_loss_metric]\n","\n","    def train_step(self, real_images):\n","\n","      lr_images,hr_images = real_images\n","\n","      batch_size = tf.shape(hr_images)[0]\n","\n","      generated_images = self.generator(lr_images)\n","\n","      real_labels = tf.ones((batch_size, 1))\n","      fake_labels = tf.zeros((batch_size, 1))\n","\n","      # Train the discriminator\n","      with tf.GradientTape() as tape:\n","\n","          real_predictions = self.discriminator(hr_images)\n","          d_loss_real = self.loss_disc(real_labels,real_predictions)\n","\n","          fake_predictions = self.discriminator(generated_images)\n","          d_loss_fake = self.loss_disc(fake_labels,fake_predictions)\n","          \n","          d_loss = 0.5*(d_loss_fake+d_loss_real)\n","      grads = tape.gradient(d_loss, self.discriminator.trainable_weights)\n","      self.d_optimizer.apply_gradients(zip(grads, self.discriminator.trainable_weights))\n","      \n","      misleading_labels = tf.ones((batch_size, 1))\n","\n","      with tf.GradientTape() as tape:\n","          predictions = self.generator(lr_images)\n","          g_loss = self.loss_gen(hr_images, predictions)\n","          g_loss=g_loss+1e-3*self.loss_disc(misleading_labels, self.discriminator(predictions))\n","\n","      grads = tape.gradient(g_loss, self.generator.trainable_weights)\n","      self.g_optimizer.apply_gradients(zip(grads, self.generator.trainable_weights))\n","\n","      # Update metrics\n","      self.d_loss_metric.update_state(d_loss)\n","      self.g_loss_metric.update_state(g_loss)\n","      return {\n","          \"d_loss\": self.d_loss_metric.result(),\n","          \"g_loss\": self.g_loss_metric.result(),\n","      }"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J5p4XGOGfLYv"},"outputs":[],"source":["epochs = 36\n","\n","gan = SRGAN(discriminator=discriminator, generator=srresnet, )\n","gan.compile(\n","    d_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n","    g_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n","    loss_disc=tf.keras.losses.BinaryCrossentropy(),\n","    loss_gen=content_loss,\n",")"]},{"cell_type":"code","source":["!mkdir generated"],"metadata":{"id":"_yTHWqAraFfm","executionInfo":{"status":"ok","timestamp":1659737073306,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"1cbdf202-e966-4137-cc5b-bf512bda66dd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["mkdir: cannot create directory ‘generated’: File exists\n"]}]},{"cell_type":"code","source":["history=gan.fit(\n","    train_dataset, epochs=epochs, callbacks=[GANMonitor()]\n",")"],"metadata":{"id":"ae7A6uuN7NUj","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5b058050-25e2-40ca-c3d3-c25b0fe34f4c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/36\n","    234/Unknown - 110s 421ms/step - d_loss: 0.5162 - g_loss: 0.3425"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 110s 422ms/step - d_loss: 0.5162 - g_loss: 0.3425\n","Epoch 2/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.1222 - g_loss: 0.2139"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.1222 - g_loss: 0.2139\n","Epoch 3/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0533 - g_loss: 0.1872"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0533 - g_loss: 0.1872\n","Epoch 4/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0481 - g_loss: 0.1729"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0481 - g_loss: 0.1729\n","Epoch 5/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0393 - g_loss: 0.1641"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0393 - g_loss: 0.1641\n","Epoch 6/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0332 - g_loss: 0.1566"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 424ms/step - d_loss: 0.0332 - g_loss: 0.1566\n","Epoch 7/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0203 - g_loss: 0.1519"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0203 - g_loss: 0.1519\n","Epoch 8/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.2558 - g_loss: 0.1463"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.2558 - g_loss: 0.1463\n","Epoch 9/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0491 - g_loss: 0.1431"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0491 - g_loss: 0.1431\n","Epoch 10/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0256 - g_loss: 0.1396"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0256 - g_loss: 0.1396\n","Epoch 11/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0434 - g_loss: 0.1370"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0434 - g_loss: 0.1370\n","Epoch 12/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0315 - g_loss: 0.1357"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0315 - g_loss: 0.1357\n","Epoch 13/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0339 - g_loss: 0.1332"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 423ms/step - d_loss: 0.0339 - g_loss: 0.1332\n","Epoch 14/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0305 - g_loss: 0.1325"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0305 - g_loss: 0.1325\n","Epoch 15/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0527 - g_loss: 0.1296"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0527 - g_loss: 0.1296\n","Epoch 16/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0322 - g_loss: 0.1279"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 423ms/step - d_loss: 0.0322 - g_loss: 0.1279\n","Epoch 17/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0391 - g_loss: 0.1275"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0391 - g_loss: 0.1275\n","Epoch 18/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0449 - g_loss: 0.1257"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0449 - g_loss: 0.1257\n","Epoch 19/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0373 - g_loss: 0.1247"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0373 - g_loss: 0.1247\n","Epoch 20/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0444 - g_loss: 0.1244"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0444 - g_loss: 0.1244\n","Epoch 21/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0492 - g_loss: 0.1237"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n","  after removing the cwd from sys.path.\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0492 - g_loss: 0.1237\n","Epoch 22/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0512 - g_loss: 0.1225"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0512 - g_loss: 0.1225\n","Epoch 23/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0489 - g_loss: 0.1232"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0489 - g_loss: 0.1232\n","Epoch 24/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0460 - g_loss: 0.1215"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 422ms/step - d_loss: 0.0460 - g_loss: 0.1215\n","Epoch 25/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0431 - g_loss: 0.1210"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 424ms/step - d_loss: 0.0431 - g_loss: 0.1210\n","Epoch 26/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0365 - g_loss: 0.1202"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 423ms/step - d_loss: 0.0365 - g_loss: 0.1202\n","Epoch 27/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0393 - g_loss: 0.1196"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 423ms/step - d_loss: 0.0393 - g_loss: 0.1196\n","Epoch 28/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0400 - g_loss: 0.1194"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 423ms/step - d_loss: 0.0400 - g_loss: 0.1194\n","Epoch 29/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0399 - g_loss: 0.1184"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 101s 426ms/step - d_loss: 0.0399 - g_loss: 0.1184\n","Epoch 30/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0385 - g_loss: 0.1179"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 423ms/step - d_loss: 0.0385 - g_loss: 0.1179\n","Epoch 31/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0412 - g_loss: 0.1174"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 100s 423ms/step - d_loss: 0.0412 - g_loss: 0.1174\n","Epoch 32/36\n","234/234 [==============================] - ETA: 0s - d_loss: 0.0473 - g_loss: 0.1168"]},{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"stream","name":"stdout","text":["\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r234/234 [==============================] - 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100s 423ms/step - d_loss: 0.0796 - g_loss: 0.1172\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"X57mSI-y7NWw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"LnaEZmXc7NnU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"lkT-Wpjc7NpT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"ESmFW_Hm7Ntg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Ox8Zo5OH7Nv8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"A6OdeIf07NyQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"p9w7O6d11COm"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1t9v0ho-d_QEEi4GNYg875e3bLzQdEXYh","timestamp":1673808995502},{"file_id":"1blawr73r9wkK482Qx-dRWhg51GUozFy3","timestamp":1657665849647},{"file_id":"1ZO02cStWUyA7R_QjbaO1Bw0QbJtFzsjY","timestamp":1657628806146}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

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FILE: deep learning for image generation/6-CycleGAN By Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","source":["!pip install tensorflow-addons"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i9HlvfDUsl_p","executionInfo":{"status":"ok","timestamp":1659449809456,"user_tz":-480,"elapsed":4227,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d3bed651-ec7f-4381-ce96-a5112f9ef4e2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting tensorflow-addons\n","  Downloading tensorflow_addons-0.17.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n","\u001b[K     |████████████████████████████████| 1.1 MB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (2.7.1)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (21.3)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow-addons) (3.0.9)\n","Installing collected packages: tensorflow-addons\n","Successfully installed tensorflow-addons-0.17.1\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zGcCsOilc8b1"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import tensorflow_addons as tfa\n","import cv2\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input,GlobalAvgPool2D )\n","from tensorflow.keras.optimizers import Adam"]},{"cell_type":"markdown","metadata":{"id":"gPLHB2s5gBJu"},"source":["# Data Management"]},{"cell_type":"markdown","source":["## Download Data"],"metadata":{"id":"Jxp5Z_jrkdEh"}},{"cell_type":"code","source":["!pip install -q kaggle\n","! mkdir ~/.kaggle\n","! cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d ashishjangra27/face-mask-12k-images-dataset\n","!unzip \"/content/face-mask-12k-images-dataset.zip\" -d \"/content/dataset/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3NiRHE9BkH0Q","executionInfo":{"status":"ok","timestamp":1659449826691,"user_tz":-480,"elapsed":13512,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"84662880-c0d0-4ab7-ce59-f9fb72d73bdf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/Face Mask Dataset/Train/WithoutMask/1855.png  \n","  inflating: /content/dataset/Face Mask 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Dataset/Validation/WithoutMask/97.png  \n","  inflating: /content/dataset/Face Mask Dataset/Validation/WithoutMask/970.png  \n","  inflating: /content/dataset/Face Mask Dataset/Validation/WithoutMask/971.png  \n","  inflating: /content/dataset/Face Mask Dataset/Validation/WithoutMask/974.png  \n"]}]},{"cell_type":"markdown","source":["## Prepare data"],"metadata":{"id":"01HyLXUakfff"}},{"cell_type":"code","source":["BATCH_SIZE = 128\n","IM_SHAPE = (64,64,3)\n","LEARNING_RATE = 2e-4"],"metadata":{"id":"AvT7q9eEkH5E"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["ds_masked=tf.keras.preprocessing.image_dataset_from_directory(\n","    \"/content/dataset/Face Mask Dataset/Train/WithMask\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L5idj96ykH7o","executionInfo":{"status":"ok","timestamp":1659449830566,"user_tz":-480,"elapsed":3879,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ec35bdfb-eb8d-4776-94d2-da2215fbd80a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 5000 files belonging to 1 classes.\n"]}]},{"cell_type":"code","source":["ds_test_masked=tf.keras.preprocessing.image_dataset_from_directory(\n","    \"/content/dataset/Face Mask Dataset/Test/WithMask\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1FLN-cyT4LCJ","executionInfo":{"status":"ok","timestamp":1659449830566,"user_tz":-480,"elapsed":6,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"82de1d95-e99f-444e-bff7-aa0cf00a3444"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 483 files belonging to 1 classes.\n"]}]},{"cell_type":"code","source":["ds_unmasked=tf.keras.preprocessing.image_dataset_from_directory(\n","    \"/content/dataset/Face Mask Dataset/Train/WithoutMask\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J5gdsp5_kH-B","executionInfo":{"status":"ok","timestamp":1659449831548,"user_tz":-480,"elapsed":986,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8073adeb-0a7e-4309-ae5f-8992c991033b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found 5000 files belonging to 1 classes.\n"]}]},{"cell_type":"code","source":["def preprocess(image):\n","  return tf.cast(image,tf.float32)/127.5 - 1.0"],"metadata":{"id":"o_9VHtj8kIAk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_masked_dataset = (\n","    ds_masked\n","    .map(preprocess)\n","    .unbatch()\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE,drop_remainder=True)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"],"metadata":{"id":"Dp4qpKe4kIDO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_masked_dataset = (\n","    ds_test_masked\n","    .map(preprocess)\n","    .unbatch()\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE,drop_remainder=True)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"],"metadata":{"id":"n9tsijde4JC4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_unmasked_dataset = (\n","    ds_unmasked\n","    .map(preprocess)\n","    .unbatch()\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE,drop_remainder=True)\n","    .prefetch(tf.data.AUTOTUNE)\n",")"],"metadata":{"id":"NhdKlFEDkIGU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset=tf.data.Dataset.zip((train_masked_dataset, train_unmasked_dataset))"],"metadata":{"id":"_x7uJFaikIKf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for d in train_dataset.take(1):\n","  d_masked,d_unmasked=d\n","  print(d_masked.shape,d_unmasked.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rf7OIohkkINq","executionInfo":{"status":"ok","timestamp":1659449836627,"user_tz":-480,"elapsed":5083,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"81034cd8-f4b8-42be-f820-d1c6c8dba41d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(128, 64, 64, 3) (128, 64, 64, 3)\n"]}]},{"cell_type":"code","source":["plt.figure(figsize = (6,6))\n","k=0\n","n = 16\n","for i in range(n):\n","  ax = plt.subplot(4,4, k+1)\n","  if i%2==0:\n","    plt.imshow((d_masked[i]+1)/2)\n","  if i%2!=0:\n","    plt.imshow((d_unmasked[i]+1)/2)\n","    \n","  plt.axis(\"off\")\n","  k+=1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":357},"id":"4E5RBEHWkIP9","executionInfo":{"status":"ok","timestamp":1659449839064,"user_tz":-480,"elapsed":2450,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1553cd50-70c3-44e9-d549-d3dcb1d60d30"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x432 with 16 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"rqspKyU_ny5V"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"ni3A9hDmgKKU"},"outputs":[],"source":["def downsample(filters,kernel_size,apply_instance_norm=True,n_strides=2):\n","\n","  model=tf.keras.Sequential()\n","  model.add(Conv2D(filters,kernel_size,strides=n_strides,padding='same',\n","             kernel_initializer=tf.keras.initializers.RandomNormal(0.,0.02),use_bias=False))\n","  if apply_instance_norm:\n","    model.add(tfa.layers.InstanceNormalization())\n","  model.add(LeakyReLU(0.2))\n","  \n","  return model"]},{"cell_type":"code","source":["def upsample(filters,kernel_size):\n","\n","  model=tf.keras.Sequential([\n","      Conv2DTranspose(filters,kernel_size,strides=2,padding='same',\n","             kernel_initializer=tf.keras.initializers.RandomNormal(0.,0.02),use_bias=False),\n","      tfa.layers.InstanceNormalization(),\n","      LeakyReLU(0.2),\n","      \n","  ])\n","  return model"],"metadata":{"id":"Z-T6KQtFs8RT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def generator():\n","  input=Input((IM_SHAPE[0],IM_SHAPE[1],3))\n","  x=input\n","  store=[]\n","  for filter in [32,64,128,256,512,]:\n","    x=downsample(filter,4)(x)\n","    store.append(x)\n","  \n","  for filter in [512,256,128,64,32]:\n","    x=tf.concat([x,store.pop()],axis=-1)\n","    x=upsample(filter,4)(x)\n","\n","  x=Conv2DTranspose(3,4,strides=1,padding='same',\n","             kernel_initializer=tf.keras.initializers.RandomNormal(0.,0.02),activation='tanh',)(x)\n","\n","  model=tf.keras.Model(input,x, name='unet_generator')\n","  return model"],"metadata":{"id":"N4y2nBVzs8T1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["generator().summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7S6ceU4Ds8WI","executionInfo":{"status":"ok","timestamp":1659453732896,"user_tz":-480,"elapsed":1063,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b4adc725-4099-4ece-f27f-5ee9ef6b355f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"unet_generator\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_30 (InputLayer)          [(None, 64, 64, 3)]  0           []                               \n","                                                                                                  \n"," sequential_223 (Sequential)    (None, 32, 32, 32)   1600        ['input_30[0][0]']               \n","                                                                                                  \n"," sequential_224 (Sequential)    (None, 16, 16, 64)   32896       ['sequential_223[0][0]']         \n","                                                                                                  \n"," sequential_225 (Sequential)    (None, 8, 8, 128)    131328      ['sequential_224[0][0]']         \n","                                                                                                  \n"," sequential_226 (Sequential)    (None, 4, 4, 256)    524800      ['sequential_225[0][0]']         \n","                                                                                                  \n"," sequential_227 (Sequential)    (None, 2, 2, 512)    2098176     ['sequential_226[0][0]']         \n","                                                                                                  \n"," tf.concat_63 (TFOpLambda)      (None, 2, 2, 1024)   0           ['sequential_227[0][0]',         \n","                                                                  'sequential_227[0][0]']         \n","                                                                                                  \n"," sequential_228 (Sequential)    (None, 4, 4, 512)    8389632     ['tf.concat_63[0][0]']           \n","                                                                                                  \n"," tf.concat_64 (TFOpLambda)      (None, 4, 4, 768)    0           ['sequential_228[0][0]',         \n","                                                                  'sequential_226[0][0]']         \n","                                                                                                  \n"," sequential_229 (Sequential)    (None, 8, 8, 256)    3146240     ['tf.concat_64[0][0]']           \n","                                                                                                  \n"," tf.concat_65 (TFOpLambda)      (None, 8, 8, 384)    0           ['sequential_229[0][0]',         \n","                                                                  'sequential_225[0][0]']         \n","                                                                                                  \n"," sequential_230 (Sequential)    (None, 16, 16, 128)  786688      ['tf.concat_65[0][0]']           \n","                                                                                                  \n"," tf.concat_66 (TFOpLambda)      (None, 16, 16, 192)  0           ['sequential_230[0][0]',         \n","                                                                  'sequential_224[0][0]']         \n","                                                                                                  \n"," sequential_231 (Sequential)    (None, 32, 32, 64)   196736      ['tf.concat_66[0][0]']           \n","                                                                                                  \n"," tf.concat_67 (TFOpLambda)      (None, 32, 32, 96)   0           ['sequential_231[0][0]',         \n","                                                                  'sequential_223[0][0]']         \n","                                                                                                  \n"," sequential_232 (Sequential)    (None, 64, 64, 32)   49216       ['tf.concat_67[0][0]']           \n","                                                                                                  \n"," conv2d_transpose_84 (Conv2DTra  (None, 64, 64, 3)   1539        ['sequential_232[0][0]']         \n"," nspose)                                                                                          \n","                                                                                                  \n","==================================================================================================\n","Total params: 15,358,851\n","Trainable params: 15,358,851\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","source":["def discriminator():\n","  input=Input((IM_SHAPE[0],IM_SHAPE[1],3))\n","  x=input\n","\n","  x=downsample(32,4,apply_instance_norm=False)(x)\n","  x=downsample(64,4,apply_instance_norm=True)(x)\n","  x=downsample(128,4,apply_instance_norm=True)(x)\n","  x=downsample(256,4,apply_instance_norm=True,)(x)\n","  x=downsample(256,4,apply_instance_norm=True,n_strides=1)(x)\n","  x=Conv2D(1,4,strides=1,padding='same',\n","             kernel_initializer=tf.keras.initializers.RandomNormal(0.,0.02),)(x)\n","  model=tf.keras.Model(input,x, name='discriminator')\n","  return model"],"metadata":{"id":"kuoE5g-4s8Yi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["discriminator().summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"35T8jhBSs8ao","executionInfo":{"status":"ok","timestamp":1659453787201,"user_tz":-480,"elapsed":8,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"76fe927b-b26f-4542-a535-f805ed086983"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"discriminator\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_36 (InputLayer)       [(None, 64, 64, 3)]       0         \n","                                                                 \n"," sequential_268 (Sequential)  (None, 32, 32, 32)       1536      \n","                                                                 \n"," sequential_269 (Sequential)  (None, 16, 16, 64)       32896     \n","                                                                 \n"," sequential_270 (Sequential)  (None, 8, 8, 128)        131328    \n","                                                                 \n"," sequential_271 (Sequential)  (None, 4, 4, 256)        524800    \n","                                                                 \n"," sequential_272 (Sequential)  (None, 4, 4, 256)        1049088   \n","                                                                 \n"," conv2d_212 (Conv2D)         (None, 4, 4, 1)           4097      \n","                                                                 \n","=================================================================\n","Total params: 1,743,745\n","Trainable params: 1,743,745\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["generator_masked_unmasked=generator()\n","generator_unmasked_masked=generator()\n","\n","discriminator_unmasked=discriminator()\n","discriminator_masked=discriminator()"],"metadata":{"id":"rPWYh4Rms8cw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class CycleGAN(tf.keras.Model):\n","  def __init__(\n","      self,\n","      discriminator_unmasked,discriminator_masked,\n","      generator_masked_unmasked,generator_unmasked_masked):\n","    super(CycleGAN,self).__init__()\n","\n","    self.discriminator_unmasked=discriminator_unmasked\n","    self.discriminator_masked=discriminator_masked\n","    self.generator_masked_unmasked=generator_masked_unmasked\n","    self.generator_unmasked_masked=generator_unmasked_masked\n","\n","  def compile(\n","      self,\n","      discriminator_optimizer,generator_optimizer,\n","      discriminator_loss_fn,generator_loss_fn,\n","      cycle_loss_fn,\n","      lambda_cycle,):\n","    super(CycleGAN,self).compile()\n","\n","    self.discriminator_optimizer=discriminator_optimizer\n","    self.generator_optimizer=generator_optimizer\n","    self.discriminator_loss_fn=discriminator_loss_fn\n","    self.generator_loss_fn=generator_loss_fn\n","    self.cycle_loss_fn=cycle_loss_fn\n","    self.lambda_cycle=lambda_cycle\n","    self.d_loss_masked_metric=tf.keras.metrics.Mean(name='d_loss_masked')\n","    self.d_loss_unmasked_metric=tf.keras.metrics.Mean(name='d_loss_unmasked')\n","    self.g_loss_masked_unmasked_metric=tf.keras.metrics.Mean(name='g_loss_masked_unmasked')\n","    self.g_loss_unmasked_masked_metric=tf.keras.metrics.Mean(name='g_loss_unmasked_masked')\n","    \n","    \n","  @property\n","  def metrics(self):\n","    return [self.d_loss_masked_metric,self.d_loss_unmasked_metric,\n","            self.g_loss_masked_unmasked_metric,self.g_loss_unmasked_masked_metric]\n","    \n","  def train_step(self,real_images):\n","    real_masked,real_unmasked=real_images\n","\n","    ######## Discriminator\n","    fake_unmasked=self.generator_masked_unmasked(real_masked)\n","    fake_masked=self.generator_unmasked_masked(real_unmasked)\n","\n","    with tf.GradientTape(persistent=True) as recorder:\n","      real_unmasked_predictions=self.discriminator_unmasked(real_unmasked)\n","      fake_unmasked_predictions=self.discriminator_unmasked(fake_unmasked)\n","      d_loss_unmasked=self.discriminator_loss_fn(real_unmasked_predictions,fake_unmasked_predictions)\n","      \n","      real_masked_predictions=self.discriminator_masked(real_masked)\n","      fake_masked_predictions=self.discriminator_masked(fake_masked)\n","      d_loss_masked=self.discriminator_loss_fn(real_masked_predictions,fake_masked_predictions)\n","      \n","    partial_derivatives = recorder.gradient(d_loss_unmasked,self.discriminator_unmasked.trainable_weights)\n","    self.discriminator_optimizer.apply_gradients(zip(partial_derivatives, self.discriminator_unmasked.trainable_weights))\n","    partial_derivatives = recorder.gradient(d_loss_masked,self.discriminator_masked.trainable_weights)\n","    self.discriminator_optimizer.apply_gradients(zip(partial_derivatives, self.discriminator_masked.trainable_weights))\n","\n","    ############# Generator\n","    with tf.GradientTape(persistent=True) as recorder:\n","\n","      fake_unmasked=self.generator_masked_unmasked(real_masked)\n","      fake_masked_cycled=self.generator_unmasked_masked(fake_unmasked)\n","\n","      fake_masked=self.generator_unmasked_masked(real_unmasked)\n","      fake_unmasked_cycled=self.generator_masked_unmasked(fake_masked)\n","      \n","      fake_unmasked_predictions=self.discriminator_unmasked(fake_unmasked)\n","      g_loss_unmasked=self.generator_loss_fn(fake_unmasked_predictions)\n","      \n","      fake_masked_predictions=self.discriminator_masked(fake_masked)\n","      g_loss_masked=self.generator_loss_fn(fake_masked_predictions)\n","\n","      cycle_consistency_loss=self.cycle_loss_fn(fake_masked_cycled,real_masked)+self.cycle_loss_fn(fake_unmasked_cycled,real_unmasked)\n","      cycle_consistency_loss*=self.lambda_cycle\n","\n","      g_loss_masked+=cycle_consistency_loss\n","      g_loss_unmasked+=cycle_consistency_loss\n","      \n","    partial_derivatives = recorder.gradient(g_loss_masked,self.generator_unmasked_masked.trainable_weights)\n","    self.generator_optimizer.apply_gradients(zip(partial_derivatives, self.generator_unmasked_masked.trainable_weights))\n","    \n","    partial_derivatives = recorder.gradient(g_loss_unmasked,self.generator_masked_unmasked.trainable_weights)\n","    self.generator_optimizer.apply_gradients(zip(partial_derivatives, self.generator_masked_unmasked.trainable_weights))\n","\n","    self.d_loss_masked_metric.update_state(d_loss_masked)\n","    self.d_loss_unmasked_metric.update_state(d_loss_unmasked)\n","    self.g_loss_unmasked_masked_metric.update_state(g_loss_masked)\n","    self.g_loss_masked_unmasked_metric.update_state(g_loss_unmasked)\n","\n","    return {\n","        'g_loss_masked_unmasked':self.g_loss_unmasked_masked_metric.result(),\n","        'g_loss_unmasked_masked':self.g_loss_masked_unmasked_metric.result(),\n","        'd_loss_masked':self.d_loss_masked_metric.result(),\n","        'd_loss_unmasked':self.d_loss_unmasked_metric.result(),\n","        }"],"metadata":{"id":"eQJxjbtJs8e2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def discriminator_loss_fn(real_predictions,fake_predictions):\n","  bce=tf.keras.losses.BinaryCrossentropy(from_logits=True,reduction=tf.keras.losses.Reduction.NONE)\n","\n","  real_loss=bce(tf.ones_like(real_predictions),real_predictions)\n","  fake_loss=bce(tf.zeros_like(fake_predictions),fake_predictions)\n","  return 0.5*(real_loss+fake_loss)"],"metadata":{"id":"cevKQjf5s8g7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def generator_loss_fn(fake_predictions):\n","  bce=tf.keras.losses.BinaryCrossentropy(from_logits=True,reduction=tf.keras.losses.Reduction.NONE)\n","  return bce(tf.ones_like(fake_predictions),fake_predictions)"],"metadata":{"id":"tlNSvmb5s8nJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def cycle_loss_fn(fake_cycled_image,real_image):\n","  return tf.reduce_mean(tf.abs(fake_cycled_image-real_image))"],"metadata":{"id":"pxFjdqMis8pq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["generator_optimizer = tf.keras.optimizers.Adam(1e-4, beta_1=0.5)\n","discriminator_optimizer = tf.keras.optimizers.Adam(1e-4, beta_1=0.5)"],"metadata":{"id":"iccl3u2Ds8tM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cycle_gan=CycleGAN(\n","    discriminator_unmasked,discriminator_masked,\n","    generator_masked_unmasked,generator_unmasked_masked)"],"metadata":{"id":"mPXWY7k7s8u2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cycle_gan.compile(\n","    discriminator_optimizer,generator_optimizer,\n","    discriminator_loss_fn,generator_loss_fn,\n","    cycle_loss_fn,\n","    15.0,\n",")"],"metadata":{"id":"Wuro7GCFs8wl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!mkdir generated"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_gSBJ4JD46S2","executionInfo":{"status":"ok","timestamp":1659453798381,"user_tz":-480,"elapsed":8,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8ccf4d24-680f-49f9-9a23-50e8a57ce707"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["mkdir: cannot create directory ‘generated’: File exists\n"]}]},{"cell_type":"code","source":["for d in test_masked_dataset.take(1):\n","  test_masked=d"],"metadata":{"id":"BfXROxAW5ZCi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class Unmask(tf.keras.callbacks.Callback):\n","    def on_epoch_end(self, epoch, logs=None):\n","              \n","      n=4\n","      k=0\n","      out=cycle_gan.generator_masked_unmasked(test_masked)\n","      plt.figure(figsize = (6,6))\n","      k=0\n","      n = 16\n","      for i in range(n):\n","        ax = plt.subplot(4,4, k+1)\n","        if i%2==0:\n","          plt.imshow((test_masked[i]+1)/2)\n","        if i%2!=0:\n","          plt.imshow((out[i-1]+1)/2)\n","          \n","        plt.axis(\"off\")\n","        k+=1\n","      plt.savefig(\"generated/gen_images_epoch_{}.png\".format(epoch+1))"],"metadata":{"id":"uAw03SXQ4Dkz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cycle_gan.fit(\n","    train_dataset,\n","    epochs=200,\n","    callbacks=[Unmask()]\n",")"],"metadata":{"id":"suKEld7xvchI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"0T-MNQDDs807"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Dwhja8N4s82w"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"13GS9k9dJ-HNoUjExHotiqvhuNm2t8B7a","timestamp":1673810450007},{"file_id":"1t9v0ho-d_QEEi4GNYg875e3bLzQdEXYh","timestamp":1659283105311},{"file_id":"1blawr73r9wkK482Qx-dRWhg51GUozFy3","timestamp":1657665849647},{"file_id":"1ZO02cStWUyA7R_QjbaO1Bw0QbJtFzsjY","timestamp":1657628806146}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

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FILE: deep learning for image generation/7-Diffusion Models Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7032,"status":"ok","timestamp":1656345849935,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"E7tsCI7mxqR6","outputId":"3788cfbd-5b04-4d2f-cc6e-7b2e3a3cdef4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.5.2)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (4.33.3)\n","Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.6)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (21.3)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.3)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (7.1.2)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.1.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7->matplotlib) (1.15.0)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: tensorflow-addons in /usr/local/lib/python3.7/dist-packages (0.17.1)\n","Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (2.7.1)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (21.3)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow-addons) (3.0.9)\n"]}],"source":["!pip install -U matplotlib\n","!pip install tensorflow-addons"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zLeDxfij2408"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import cv2\n","import time\n","import matplotlib.pyplot as plt### plotting bar chart\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Reshape, Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n","                                     Flatten, InputLayer, BatchNormalization, Input, MultiHeadAttention)\n","from tensorflow.keras.optimizers import Adam\n","import tensorflow_addons as tfa"]},{"cell_type":"markdown","metadata":{"id":"uiHKk5vAlO4k"},"source":["# Data Download"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":116226,"status":"ok","timestamp":1656345969564,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Q4Z2MAGzeT9s","outputId":"16b06d5c-79ad-4fde-b4e3-8cd610a8a472"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197605.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197606.jpg  \n","  inflating: /content/dataset/img_align_celeba/img_align_celeba/197607.jpg  \n","  inflating: 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/content/dataset/list_attr_celeba.csv  \n","  inflating: /content/dataset/list_bbox_celeba.csv  \n","  inflating: /content/dataset/list_eval_partition.csv  \n","  inflating: /content/dataset/list_landmarks_align_celeba.csv  \n"]}],"source":["!pip install -q kaggle\n","! mkdir ~/.kaggle\n","! cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d jessicali9530/celeba-dataset\n","!unzip \"/content/celeba-dataset.zip\" -d \"/content/dataset/\""]},{"cell_type":"markdown","metadata":{"id":"GIwphbWzmOSg"},"source":["# Dataset"]},{"cell_type":"markdown","metadata":{"id":"WRp4-Y5Wzcag"},"source":["## Data Preprocessing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"O2TFHNrTwTYC"},"outputs":[],"source":["BATCH_SIZE = 32\n","TIME_STEPS = 1000\n","IM_SHAPE = (64,64,3)\n","N_HEADS = 8\n","ATTN_DIM = 256\n","N_GROUPS = 8\n","N_RESNETS = 2\n","LEARNING_RATE = 2e-4"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":3429,"status":"ok","timestamp":1656347496107,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"GeK2xa4TpGgx","colab":{"base_uri":"https://localhost:8080/"},"outputId":"de362962-d8b1-40c3-8599-7cff703f8bd2"},"outputs":[{"output_type":"stream","name":"stdout","text":["Found 202599 files belonging to 1 classes.\n"]}],"source":["ds_train = tf.keras.preprocessing.image_dataset_from_directory(\n","    \"/content/dataset/img_align_celeba/img_align_celeba\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=32\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IW9lsYqFmQJh"},"outputs":[],"source":["def preprocess(image):\n","  return tf.cast(image, tf.float32) / 127.5 - 1.0"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZLe2w8Yp1P6b"},"outputs":[],"source":["def augmentation(image):\n","  return ..."]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":2435,"status":"ok","timestamp":1656347498536,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"F2O2svt-mQMI","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5dbf8ebe-93df-43ef-88a6-c23e300c1737"},"outputs":[{"output_type":"stream","name":"stdout","text":["(16, 64, 64, 3)\n"]}],"source":["train_dataset = (\n","    ds_train\n","    .map(preprocess)\n","    .unbatch()\n","    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n","    .batch(BATCH_SIZE,drop_remainder=True)\n","    .prefetch(tf.data.AUTOTUNE)\n",")\n","\n","for d in train_dataset.take(1):\n","  print(d.shape)"]},{"cell_type":"markdown","metadata":{"id":"ic6__qNvw-9p"},"source":["## Data Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1656347498537,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"N_TeeVu7xBaD","colab":{"base_uri":"https://localhost:8080/","height":248},"outputId":"7db4b9bb-7d0a-404e-f7cc-c4576ccede92"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 288x288 with 4 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize = (4,4))\n","k=0\n","n = 4\n","for i in range(n):\n","    ax = plt.subplot(2,2, k+1)\n","    plt.imshow((d[i]+1)/2)\n","    plt.axis(\"off\")\n","    k+=1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aYW2f-vmxBce"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"LtQBa6ElzmrL"},"source":["## Dataset Preparation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WHWK04_KzpHg"},"outputs":[],"source":["def linear_beta_schedule(timesteps):\n","    beta_start = 0.0001\n","    beta_end = 0.02\n","    return tf.linspace(beta_start, beta_end, timesteps)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IKHoU83Hzjgg"},"outputs":[],"source":["# define beta schedule\n","betas = linear_beta_schedule(TIME_STEPS)\n","\n","# define alphas \n","alphas = 1. - betas\n","alphas_cumprod = tf.math.cumprod(alphas, axis=0)\n","alphas_cumprod_prev = tf.concat([tf.ones((1,)),alphas_cumprod[:-1]],axis = 0)\n","sqrt_recip_alphas = tf.math.sqrt(1.0 / alphas)\n","\n","# calculations for diffusion q(x_t | x_{t-1}) and others\n","sqrt_alphas_cumprod = tf.math.sqrt(alphas_cumprod)\n","sqrt_one_minus_alphas_cumprod = tf.math.sqrt(1. - alphas_cumprod)\n","\n","# calculations for posterior q(x_{t-1} | x_t, x_0)\n","posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"02dCiCcV0G-W"},"outputs":[],"source":["def extract(a, t, x_shape):\n","  b, *_ = t.shape\n","  out = tf.gather(a,t)\n","  output = tf.reshape(out, (b,*((1,) * (len(x_shape) - 1))))\n","  return output"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"csEmNrqe0HAw"},"outputs":[],"source":["def q_sample(x_start, t, noise):\n","\n","  sqrt_alphas_cumprod_t = extract(sqrt_alphas_cumprod, t, x_start.shape)\n","  sqrt_one_minus_alphas_cumprod_t = extract(\n","      sqrt_one_minus_alphas_cumprod, t, x_start.shape\n","  )\n","  out_sample = sqrt_alphas_cumprod_t * x_start + sqrt_one_minus_alphas_cumprod_t * noise\n","  return out_sample"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1656347499712,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"7JXUSFeu0nw0","colab":{"base_uri":"https://localhost:8080/"},"outputId":"d44a3573-ccb7-4fb1-d221-54e4db0e5e92"},"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor([906  71 213  15 313 609 748 912 632  26 472   6  83  47 133 912], shape=(16,), dtype=int32)\n","(16, 64, 64, 3) (16,)\n","(16, 64, 64, 3)\n"]}],"source":["x_start = d\n","\n","t = tf.random.uniform((BATCH_SIZE,),minval=0,maxval=TIME_STEPS, dtype=tf.int32)\n","print(t)\n","print(x_start.shape, t.shape, )\n","sample = q_sample(x_start, t,tf.random.normal(x_start.shape))\n","print(sample.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":4024,"status":"ok","timestamp":1656347503727,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"7doUxfmx0nzN","colab":{"base_uri":"https://localhost:8080/","height":317},"outputId":"52b12fb1-6951-4550-c719-3ae1d45b1463"},"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 288x288 with 4 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize = (4,4))\n","k=0\n","n = 4\n","for i in range(n):\n","  ax = plt.subplot(2,2, k+1)\n","  plt.imshow((sample[i]+1)/2) \n","  plt.axis(\"off\")\n","  k+=1"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ztt9ODe30n12"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"EVSxlz01BhVu"},"source":["# Modeling"]},{"cell_type":"markdown","metadata":{"id":"57QGLl0vB_jL"},"source":["## Positional Embeddings"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LhAnfC_F0n4r"},"outputs":[],"source":["class PositionalEmbeddings(tf.keras.layers.Layer):\n","\n","    def __init__(self, dim):\n","        super().__init__()\n","        self.embedding_dim = dim\n","\n","    def get_timestep_embedding(self, timesteps, embedding_dim: int):\n","        \"\"\"\n","        From Fairseq.\n","        Build sinusoidal embeddings.\n","        This matches the implementation in tensor2tensor, but differs slightly\n","        from the description in Section 3.5 of \"Attention Is All You Need\".\n","        \"\"\"\n","        half_dim = self.embedding_dim // 2\n","        emb = tf.math.log(10000.) / (half_dim - 1)\n","        emb = tf.exp(tf.range(half_dim, dtype=tf.float32) * -emb)\n","        emb = tf.cast(timesteps, dtype = tf.float32)[:, None] * emb[None, :]\n","        emb = tf.concat([tf.sin(emb), tf.cos(emb)], axis=1)\n","        if embedding_dim % 2 == 1:\n","            emb = tf.pad(emb, [[0, 0], [0, 1]])\n","        return emb\n","\n","    def call(self, time):\n","        return self.get_timestep_embedding(time, self.embedding_dim)"]},{"cell_type":"markdown","metadata":{"id":"n7y1KeAHB9Za"},"source":["## Residual Block"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x4ncG132BzhW"},"outputs":[],"source":["def res_block(x,filters,n_groups,temb):\n","    previous = x\n","    x = Conv2D(filters, 3, padding=\"same\",)(x) ### Convolution layer with padding same, so that the resolution remains the same\n","    \n","    ### temb represents the time embedding.\n","    ### It is passed into the silu activation function and a Dense Layer(Which can change the the embedding dimension )\n","    ### We also reshape the time embedding to match the output of 2d convnets.\n","    x += Dense(filters)(tf.nn.silu(temb))[:,None,None,:]\n","    \n","    ### Group Normalization is used.\n","    x = tf.nn.silu(tfa.layers.GroupNormalization(n_groups, axis = -1)(x))\n","    x = Conv2D(filters, 3, padding=\"same\",)(x)\n","\n","    # Project residual\n","    residual = Conv2D(filters, 1,padding=\"same\",)(previous)\n","    x = tf.keras.layers.add([x, residual])  # Add back residual\n","    return x"]},{"cell_type":"markdown","metadata":{"id":"xZ5gQmKjB7HM"},"source":["## Unet Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5cMusL4UBzjx"},"outputs":[],"source":["def get_model(im_shape=(64,64,3),n_resnets=2,n_groups=8,attn_dim=32,n_heads=4,):\n","    input_1 = Input(shape=im_shape)### image input\n","    input_2 = Input(shape=())### time input\n","    t_dim = im_shape[0]*16\n","\n","    # Entry block\n","    x = Conv2D(32, 3, padding=\"same\")(input_1)\n","    temb = PositionalEmbeddings(t_dim)(input_2)### Create embeddings from the time input_2\n","    temb = Dense(t_dim)(tf.nn.gelu(Dense(t_dim)(temb)))### pass the embedding into the gelu activation function\n","    \n","    hs = [x]### variable used for storing each resolution level output, in the downward path, to be concatenated to the inputs of the upward path. \n","    \n","    ### Downward Path\n","    for filters in [32, 64, 128, 256]:### for every resolution level (32,64,128,256), represent the depth they map to resolutions of (32,16,8,4)\n","        for _ in range(n_resnets):### we go through each resnet block per resolution level\n","            x = res_block(x,filters,n_groups,temb)### resblock\n","            ### if the resolution=16 (coinciding with a depth=64), we make the resnet output features attend to each other.\n","            ### Note how the attention axes = (1,2). This corresponds to the height and width dimensions.\n","            ### Feel free to Check the documentation out :) https://www.tensorflow.org/api_docs/python/tf/keras/layers/MultiHeadAttention.\n","            ### query = key = value = x. \n","            ### We again use Group Normalization.\n","            if filters == 64:\n","                x = tfa.layers.GroupNormalization(groups=n_groups, axis = -1)(\n","                    MultiHeadAttention(num_heads=n_heads, key_dim=attn_dim, attention_axes=(1,2), )(query = x, value = x))\n","        hs.append(x)### append the output features to hs\n","        x = tf.keras.layers.MaxPooling2D(3, strides=2, padding=\"same\")(x)### Downsampling in order to move to the next resolution level\n","\n","    \n","    ### Bottleneck\n","    x = res_block(x,256,n_groups,temb)\n","    x = tfa.layers.GroupNormalization(groups=n_groups, axis = -1)(\n","      MultiHeadAttention(num_heads=n_heads, key_dim=attn_dim, attention_axes=(1,2), )(query = x, value = x))\n","    x = res_block(x,256,n_groups,temb)\n","\n","    \n","    ### Upward path\n","    for filters in [256, 128, 64,32]:\n","        ### we resize x, to match with the shape of feature outputs (hs) in the downward path\n","        x = tf.image.resize_with_pad(x,hs[-1].shape[1],hs[-1].shape[2])\n","        x = tf.concat([x,hs.pop()], axis=-1)\n","    \n","        for _ in range(n_resnets):\n","            x = res_block(x,filters,n_groups,temb)\n","\n","            if filters == 64:\n","                x = tfa.layers.GroupNormalization(groups=n_groups, axis = -1)(\n","                  MultiHeadAttention(num_heads=n_heads, key_dim=attn_dim, attention_axes=(1,2), )(query = x, value = x))\n","\n","        if filters != 32:\n","            x = Conv2DTranspose(filters, 3, strides = (2,2),)(x)### Upsampling\n","\n","    x = res_block(x,32,n_groups,temb)\n","    outputs = Conv2D(3, 3, padding=\"same\", )(x)\n","\n","    # Define the model\n","    model = Model([input_1,input_2], outputs,name='unet')\n","    return model"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":5523,"status":"ok","timestamp":1656347514921,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"r1PoPmjXBznV","colab":{"base_uri":"https://localhost:8080/"},"outputId":"0d4c0108-2505-438a-f105-c446d40f103f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"unet\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_2 (InputLayer)           [(None,)]            0           []                               \n","                                                                                                  \n"," positional_embeddings (Positio  (None, 1024)        0           ['input_2[0][0]']                \n"," nalEmbeddings)                                                                                   \n","                                                                                                  \n"," dense_1 (Dense)                (None, 1024)         1049600     ['positional_embeddings[0][0]']  \n","                                                                                                  \n"," tf.nn.gelu (TFOpLambda)        (None, 1024)         0           ['dense_1[0][0]']                \n","                                                                                                  \n"," dense (Dense)                  (None, 1024)         1049600     ['tf.nn.gelu[0][0]']             \n","                                                                                                  \n"," input_1 (InputLayer)           [(None, 64, 64, 3)]  0           []                               \n","                                                                                                  \n"," tf.nn.silu (TFOpLambda)        (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," conv2d (Conv2D)                (None, 64, 64, 32)   896         ['input_1[0][0]']                \n","                                                                                                  \n"," dense_2 (Dense)                (None, 32)           32800       ['tf.nn.silu[0][0]']             \n","                                                                                                  \n"," conv2d_1 (Conv2D)              (None, 64, 64, 32)   9248        ['conv2d[0][0]']                 \n","                                                                                                  \n"," tf.__operators__.getitem (Slic  (None, 1, 1, 32)    0           ['dense_2[0][0]']                \n"," ingOpLambda)                                                                                     \n","                                                                                                  \n"," tf.__operators__.add (TFOpLamb  (None, 64, 64, 32)  0           ['conv2d_1[0][0]',               \n"," da)                                                              'tf.__operators__.getitem[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," group_normalization (GroupNorm  (None, 64, 64, 32)  64          ['tf.__operators__.add[0][0]']   \n"," alization)                                                                                       \n","                                                                                                  \n"," tf.nn.silu_1 (TFOpLambda)      (None, 64, 64, 32)   0           ['group_normalization[0][0]']    \n","                                                                                                  \n"," conv2d_2 (Conv2D)              (None, 64, 64, 32)   9248        ['tf.nn.silu_1[0][0]']           \n","                                                                                                  \n"," conv2d_3 (Conv2D)              (None, 64, 64, 32)   1056        ['conv2d[0][0]']                 \n","                                                                                                  \n"," tf.nn.silu_2 (TFOpLambda)      (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," add (Add)                      (None, 64, 64, 32)   0           ['conv2d_2[0][0]',               \n","                                                                  'conv2d_3[0][0]']               \n","                                                                                                  \n"," dense_3 (Dense)                (None, 32)           32800       ['tf.nn.silu_2[0][0]']           \n","                                                                                                  \n"," conv2d_4 (Conv2D)              (None, 64, 64, 32)   9248        ['add[0][0]']                    \n","                                                                                                  \n"," tf.__operators__.getitem_1 (Sl  (None, 1, 1, 32)    0           ['dense_3[0][0]']                \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_1 (TFOpLa  (None, 64, 64, 32)  0           ['conv2d_4[0][0]',               \n"," mbda)                                                            'tf.__operators__.getitem_1[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_1 (GroupNo  (None, 64, 64, 32)  64          ['tf.__operators__.add_1[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.nn.silu_3 (TFOpLambda)      (None, 64, 64, 32)   0           ['group_normalization_1[0][0]']  \n","                                                                                                  \n"," conv2d_5 (Conv2D)              (None, 64, 64, 32)   9248        ['tf.nn.silu_3[0][0]']           \n","                                                                                                  \n"," conv2d_6 (Conv2D)              (None, 64, 64, 32)   1056        ['add[0][0]']                    \n","                                                                                                  \n"," add_1 (Add)                    (None, 64, 64, 32)   0           ['conv2d_5[0][0]',               \n","                                                                  'conv2d_6[0][0]']               \n","                                                                                                  \n"," tf.nn.silu_4 (TFOpLambda)      (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," max_pooling2d (MaxPooling2D)   (None, 32, 32, 32)   0           ['add_1[0][0]']                  \n","                                                                                                  \n"," dense_4 (Dense)                (None, 64)           65600       ['tf.nn.silu_4[0][0]']           \n","                                                                                                  \n"," conv2d_7 (Conv2D)              (None, 32, 32, 64)   18496       ['max_pooling2d[0][0]']          \n","                                                                                                  \n"," tf.__operators__.getitem_2 (Sl  (None, 1, 1, 64)    0           ['dense_4[0][0]']                \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_2 (TFOpLa  (None, 32, 32, 64)  0           ['conv2d_7[0][0]',               \n"," mbda)                                                            'tf.__operators__.getitem_2[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_2 (GroupNo  (None, 32, 32, 64)  128         ['tf.__operators__.add_2[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.nn.silu_5 (TFOpLambda)      (None, 32, 32, 64)   0           ['group_normalization_2[0][0]']  \n","                                                                                                  \n"," conv2d_8 (Conv2D)              (None, 32, 32, 64)   36928       ['tf.nn.silu_5[0][0]']           \n","                                                                                                  \n"," conv2d_9 (Conv2D)              (None, 32, 32, 64)   2112        ['max_pooling2d[0][0]']          \n","                                                                                                  \n"," add_2 (Add)                    (None, 32, 32, 64)   0           ['conv2d_8[0][0]',               \n","                                                                  'conv2d_9[0][0]']               \n","                                                                                                  \n"," multi_head_attention (MultiHea  (None, 32, 32, 64)  530496      ['add_2[0][0]',                  \n"," dAttention)                                                      'add_2[0][0]']                  \n","                                                                                                  \n"," tf.nn.silu_6 (TFOpLambda)      (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," group_normalization_3 (GroupNo  (None, 32, 32, 64)  128         ['multi_head_attention[0][0]']   \n"," rmalization)                                                                                     \n","                                                                                                  \n"," dense_5 (Dense)                (None, 64)           65600       ['tf.nn.silu_6[0][0]']           \n","                                                                                                  \n"," conv2d_10 (Conv2D)             (None, 32, 32, 64)   36928       ['group_normalization_3[0][0]']  \n","                                                                                                  \n"," tf.__operators__.getitem_3 (Sl  (None, 1, 1, 64)    0           ['dense_5[0][0]']                \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_3 (TFOpLa  (None, 32, 32, 64)  0           ['conv2d_10[0][0]',              \n"," mbda)                                                            'tf.__operators__.getitem_3[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_4 (GroupNo  (None, 32, 32, 64)  128         ['tf.__operators__.add_3[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.nn.silu_7 (TFOpLambda)      (None, 32, 32, 64)   0           ['group_normalization_4[0][0]']  \n","                                                                                                  \n"," conv2d_11 (Conv2D)             (None, 32, 32, 64)   36928       ['tf.nn.silu_7[0][0]']           \n","                                                                                                  \n"," conv2d_12 (Conv2D)             (None, 32, 32, 64)   4160        ['group_normalization_3[0][0]']  \n","                                                                                                  \n"," add_3 (Add)                    (None, 32, 32, 64)   0           ['conv2d_11[0][0]',              \n","                                                                  'conv2d_12[0][0]']              \n","                                                                                                  \n"," multi_head_attention_1 (MultiH  (None, 32, 32, 64)  530496      ['add_3[0][0]',                  \n"," eadAttention)                                                    'add_3[0][0]']                  \n","                                                                                                  \n"," group_normalization_5 (GroupNo  (None, 32, 32, 64)  128         ['multi_head_attention_1[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.nn.silu_8 (TFOpLambda)      (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 64)  0           ['group_normalization_5[0][0]']  \n","                                                                                                  \n"," dense_6 (Dense)                (None, 128)          131200      ['tf.nn.silu_8[0][0]']           \n","                                                                                                  \n"," conv2d_13 (Conv2D)             (None, 16, 16, 128)  73856       ['max_pooling2d_1[0][0]']        \n","                                                                                                  \n"," tf.__operators__.getitem_4 (Sl  (None, 1, 1, 128)   0           ['dense_6[0][0]']                \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_4 (TFOpLa  (None, 16, 16, 128)  0          ['conv2d_13[0][0]',              \n"," mbda)                                                            'tf.__operators__.getitem_4[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_6 (GroupNo  (None, 16, 16, 128)  256        ['tf.__operators__.add_4[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.nn.silu_9 (TFOpLambda)      (None, 16, 16, 128)  0           ['group_normalization_6[0][0]']  \n","                                                                                                  \n"," conv2d_14 (Conv2D)             (None, 16, 16, 128)  147584      ['tf.nn.silu_9[0][0]']           \n","                                                                                                  \n"," conv2d_15 (Conv2D)             (None, 16, 16, 128)  8320        ['max_pooling2d_1[0][0]']        \n","                                                                                                  \n"," tf.nn.silu_10 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," add_4 (Add)                    (None, 16, 16, 128)  0           ['conv2d_14[0][0]',              \n","                                                                  'conv2d_15[0][0]']              \n","                                                                                                  \n"," dense_7 (Dense)                (None, 128)          131200      ['tf.nn.silu_10[0][0]']          \n","                                                                                                  \n"," conv2d_16 (Conv2D)             (None, 16, 16, 128)  147584      ['add_4[0][0]']                  \n","                                                                                                  \n"," tf.__operators__.getitem_5 (Sl  (None, 1, 1, 128)   0           ['dense_7[0][0]']                \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_5 (TFOpLa  (None, 16, 16, 128)  0          ['conv2d_16[0][0]',              \n"," mbda)                                                            'tf.__operators__.getitem_5[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_7 (GroupNo  (None, 16, 16, 128)  256        ['tf.__operators__.add_5[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.nn.silu_11 (TFOpLambda)     (None, 16, 16, 128)  0           ['group_normalization_7[0][0]']  \n","                                                                                                  \n"," conv2d_17 (Conv2D)             (None, 16, 16, 128)  147584      ['tf.nn.silu_11[0][0]']          \n","                                                                                                  \n"," conv2d_18 (Conv2D)             (None, 16, 16, 128)  16512       ['add_4[0][0]']                  \n","                                                                                                  \n"," add_5 (Add)                    (None, 16, 16, 128)  0           ['conv2d_17[0][0]',              \n","                                                                  'conv2d_18[0][0]']              \n","                                                                                                  \n"," tf.nn.silu_12 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," max_pooling2d_2 (MaxPooling2D)  (None, 8, 8, 128)   0           ['add_5[0][0]']                  \n","                                                                                                  \n"," dense_8 (Dense)                (None, 256)          262400      ['tf.nn.silu_12[0][0]']          \n","                                                                                                  \n"," conv2d_19 (Conv2D)             (None, 8, 8, 256)    295168      ['max_pooling2d_2[0][0]']        \n","                                                                                                  \n"," tf.__operators__.getitem_6 (Sl  (None, 1, 1, 256)   0           ['dense_8[0][0]']                \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_6 (TFOpLa  (None, 8, 8, 256)   0           ['conv2d_19[0][0]',              \n"," mbda)                                                            'tf.__operators__.getitem_6[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_8 (GroupNo  (None, 8, 8, 256)   512         ['tf.__operators__.add_6[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.nn.silu_13 (TFOpLambda)     (None, 8, 8, 256)    0           ['group_normalization_8[0][0]']  \n","                                                                                                  \n"," conv2d_20 (Conv2D)             (None, 8, 8, 256)    590080      ['tf.nn.silu_13[0][0]']          \n","                                                                                                  \n"," conv2d_21 (Conv2D)             (None, 8, 8, 256)    33024       ['max_pooling2d_2[0][0]']        \n","                                                                                                  \n"," tf.nn.silu_14 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," add_6 (Add)                    (None, 8, 8, 256)    0           ['conv2d_20[0][0]',              \n","                                                                  'conv2d_21[0][0]']              \n","                                                                                                  \n"," dense_9 (Dense)                (None, 256)          262400      ['tf.nn.silu_14[0][0]']          \n","                                                                                                  \n"," conv2d_22 (Conv2D)             (None, 8, 8, 256)    590080      ['add_6[0][0]']                  \n","                                                                                                  \n"," tf.__operators__.getitem_7 (Sl  (None, 1, 1, 256)   0           ['dense_9[0][0]']                \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_7 (TFOpLa  (None, 8, 8, 256)   0           ['conv2d_22[0][0]',              \n"," mbda)                                                            'tf.__operators__.getitem_7[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_9 (GroupNo  (None, 8, 8, 256)   512         ['tf.__operators__.add_7[0][0]'] \n"," rmalization)                                                                                     \n","                                                                                                  \n"," tf.nn.silu_15 (TFOpLambda)     (None, 8, 8, 256)    0           ['group_normalization_9[0][0]']  \n","                                                                                                  \n"," conv2d_23 (Conv2D)             (None, 8, 8, 256)    590080      ['tf.nn.silu_15[0][0]']          \n","                                                                                                  \n"," conv2d_24 (Conv2D)             (None, 8, 8, 256)    65792       ['add_6[0][0]']                  \n","                                                                                                  \n"," add_7 (Add)                    (None, 8, 8, 256)    0           ['conv2d_23[0][0]',              \n","                                                                  'conv2d_24[0][0]']              \n","                                                                                                  \n"," tf.nn.silu_16 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," max_pooling2d_3 (MaxPooling2D)  (None, 4, 4, 256)   0           ['add_7[0][0]']                  \n","                                                                                                  \n"," dense_10 (Dense)               (None, 256)          262400      ['tf.nn.silu_16[0][0]']          \n","                                                                                                  \n"," conv2d_25 (Conv2D)             (None, 4, 4, 256)    590080      ['max_pooling2d_3[0][0]']        \n","                                                                                                  \n"," tf.__operators__.getitem_8 (Sl  (None, 1, 1, 256)   0           ['dense_10[0][0]']               \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_8 (TFOpLa  (None, 4, 4, 256)   0           ['conv2d_25[0][0]',              \n"," mbda)                                                            'tf.__operators__.getitem_8[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_10 (GroupN  (None, 4, 4, 256)   512         ['tf.__operators__.add_8[0][0]'] \n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_17 (TFOpLambda)     (None, 4, 4, 256)    0           ['group_normalization_10[0][0]'] \n","                                                                                                  \n"," conv2d_26 (Conv2D)             (None, 4, 4, 256)    590080      ['tf.nn.silu_17[0][0]']          \n","                                                                                                  \n"," conv2d_27 (Conv2D)             (None, 4, 4, 256)    65792       ['max_pooling2d_3[0][0]']        \n","                                                                                                  \n"," add_8 (Add)                    (None, 4, 4, 256)    0           ['conv2d_26[0][0]',              \n","                                                                  'conv2d_27[0][0]']              \n","                                                                                                  \n"," multi_head_attention_2 (MultiH  (None, 4, 4, 256)   2103552     ['add_8[0][0]',                  \n"," eadAttention)                                                    'add_8[0][0]']                  \n","                                                                                                  \n"," tf.nn.silu_18 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," group_normalization_11 (GroupN  (None, 4, 4, 256)   512         ['multi_head_attention_2[0][0]'] \n"," ormalization)                                                                                    \n","                                                                                                  \n"," dense_11 (Dense)               (None, 256)          262400      ['tf.nn.silu_18[0][0]']          \n","                                                                                                  \n"," conv2d_28 (Conv2D)             (None, 4, 4, 256)    590080      ['group_normalization_11[0][0]'] \n","                                                                                                  \n"," tf.__operators__.getitem_9 (Sl  (None, 1, 1, 256)   0           ['dense_11[0][0]']               \n"," icingOpLambda)                                                                                   \n","                                                                                                  \n"," tf.__operators__.add_9 (TFOpLa  (None, 4, 4, 256)   0           ['conv2d_28[0][0]',              \n"," mbda)                                                            'tf.__operators__.getitem_9[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," group_normalization_12 (GroupN  (None, 4, 4, 256)   512         ['tf.__operators__.add_9[0][0]'] \n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_19 (TFOpLambda)     (None, 4, 4, 256)    0           ['group_normalization_12[0][0]'] \n","                                                                                                  \n"," conv2d_29 (Conv2D)             (None, 4, 4, 256)    590080      ['tf.nn.silu_19[0][0]']          \n","                                                                                                  \n"," conv2d_30 (Conv2D)             (None, 4, 4, 256)    65792       ['group_normalization_11[0][0]'] \n","                                                                                                  \n"," add_9 (Add)                    (None, 4, 4, 256)    0           ['conv2d_29[0][0]',              \n","                                                                  'conv2d_30[0][0]']              \n","                                                                                                  \n"," tf.image.resize_with_pad (TFOp  (None, 8, 8, 256)   0           ['add_9[0][0]']                  \n"," Lambda)                                                                                          \n","                                                                                                  \n"," tf.nn.silu_20 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," tf.concat (TFOpLambda)         (None, 8, 8, 512)    0           ['tf.image.resize_with_pad[0][0]'\n","                                                                 , 'add_7[0][0]']                 \n","                                                                                                  \n"," dense_12 (Dense)               (None, 256)          262400      ['tf.nn.silu_20[0][0]']          \n","                                                                                                  \n"," conv2d_31 (Conv2D)             (None, 8, 8, 256)    1179904     ['tf.concat[0][0]']              \n","                                                                                                  \n"," tf.__operators__.getitem_10 (S  (None, 1, 1, 256)   0           ['dense_12[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_10 (TFOpL  (None, 8, 8, 256)   0           ['conv2d_31[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_10[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_13 (GroupN  (None, 8, 8, 256)   512         ['tf.__operators__.add_10[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_21 (TFOpLambda)     (None, 8, 8, 256)    0           ['group_normalization_13[0][0]'] \n","                                                                                                  \n"," conv2d_32 (Conv2D)             (None, 8, 8, 256)    590080      ['tf.nn.silu_21[0][0]']          \n","                                                                                                  \n"," conv2d_33 (Conv2D)             (None, 8, 8, 256)    131328      ['tf.concat[0][0]']              \n","                                                                                                  \n"," tf.nn.silu_22 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," add_10 (Add)                   (None, 8, 8, 256)    0           ['conv2d_32[0][0]',              \n","                                                                  'conv2d_33[0][0]']              \n","                                                                                                  \n"," dense_13 (Dense)               (None, 256)          262400      ['tf.nn.silu_22[0][0]']          \n","                                                                                                  \n"," conv2d_34 (Conv2D)             (None, 8, 8, 256)    590080      ['add_10[0][0]']                 \n","                                                                                                  \n"," tf.__operators__.getitem_11 (S  (None, 1, 1, 256)   0           ['dense_13[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_11 (TFOpL  (None, 8, 8, 256)   0           ['conv2d_34[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_11[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_14 (GroupN  (None, 8, 8, 256)   512         ['tf.__operators__.add_11[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_23 (TFOpLambda)     (None, 8, 8, 256)    0           ['group_normalization_14[0][0]'] \n","                                                                                                  \n"," conv2d_35 (Conv2D)             (None, 8, 8, 256)    590080      ['tf.nn.silu_23[0][0]']          \n","                                                                                                  \n"," conv2d_36 (Conv2D)             (None, 8, 8, 256)    65792       ['add_10[0][0]']                 \n","                                                                                                  \n"," add_11 (Add)                   (None, 8, 8, 256)    0           ['conv2d_35[0][0]',              \n","                                                                  'conv2d_36[0][0]']              \n","                                                                                                  \n"," conv2d_transpose (Conv2DTransp  (None, 17, 17, 256)  590080     ['add_11[0][0]']                 \n"," ose)                                                                                             \n","                                                                                                  \n"," tf.image.resize_with_pad_1 (TF  (None, 16, 16, 256)  0          ['conv2d_transpose[0][0]']       \n"," OpLambda)                                                                                        \n","                                                                                                  \n"," tf.nn.silu_24 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," tf.concat_1 (TFOpLambda)       (None, 16, 16, 384)  0           ['tf.image.resize_with_pad_1[0][0\n","                                                                 ]',                              \n","                                                                  'add_5[0][0]']                  \n","                                                                                                  \n"," dense_14 (Dense)               (None, 128)          131200      ['tf.nn.silu_24[0][0]']          \n","                                                                                                  \n"," conv2d_37 (Conv2D)             (None, 16, 16, 128)  442496      ['tf.concat_1[0][0]']            \n","                                                                                                  \n"," tf.__operators__.getitem_12 (S  (None, 1, 1, 128)   0           ['dense_14[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_12 (TFOpL  (None, 16, 16, 128)  0          ['conv2d_37[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_12[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_15 (GroupN  (None, 16, 16, 128)  256        ['tf.__operators__.add_12[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_25 (TFOpLambda)     (None, 16, 16, 128)  0           ['group_normalization_15[0][0]'] \n","                                                                                                  \n"," conv2d_38 (Conv2D)             (None, 16, 16, 128)  147584      ['tf.nn.silu_25[0][0]']          \n","                                                                                                  \n"," conv2d_39 (Conv2D)             (None, 16, 16, 128)  49280       ['tf.concat_1[0][0]']            \n","                                                                                                  \n"," tf.nn.silu_26 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," add_12 (Add)                   (None, 16, 16, 128)  0           ['conv2d_38[0][0]',              \n","                                                                  'conv2d_39[0][0]']              \n","                                                                                                  \n"," dense_15 (Dense)               (None, 128)          131200      ['tf.nn.silu_26[0][0]']          \n","                                                                                                  \n"," conv2d_40 (Conv2D)             (None, 16, 16, 128)  147584      ['add_12[0][0]']                 \n","                                                                                                  \n"," tf.__operators__.getitem_13 (S  (None, 1, 1, 128)   0           ['dense_15[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_13 (TFOpL  (None, 16, 16, 128)  0          ['conv2d_40[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_13[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_16 (GroupN  (None, 16, 16, 128)  256        ['tf.__operators__.add_13[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_27 (TFOpLambda)     (None, 16, 16, 128)  0           ['group_normalization_16[0][0]'] \n","                                                                                                  \n"," conv2d_41 (Conv2D)             (None, 16, 16, 128)  147584      ['tf.nn.silu_27[0][0]']          \n","                                                                                                  \n"," conv2d_42 (Conv2D)             (None, 16, 16, 128)  16512       ['add_12[0][0]']                 \n","                                                                                                  \n"," add_13 (Add)                   (None, 16, 16, 128)  0           ['conv2d_41[0][0]',              \n","                                                                  'conv2d_42[0][0]']              \n","                                                                                                  \n"," conv2d_transpose_1 (Conv2DTran  (None, 33, 33, 128)  147584     ['add_13[0][0]']                 \n"," spose)                                                                                           \n","                                                                                                  \n"," tf.image.resize_with_pad_2 (TF  (None, 32, 32, 128)  0          ['conv2d_transpose_1[0][0]']     \n"," OpLambda)                                                                                        \n","                                                                                                  \n"," tf.nn.silu_28 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," tf.concat_2 (TFOpLambda)       (None, 32, 32, 192)  0           ['tf.image.resize_with_pad_2[0][0\n","                                                                 ]',                              \n","                                                                  'group_normalization_5[0][0]']  \n","                                                                                                  \n"," dense_16 (Dense)               (None, 64)           65600       ['tf.nn.silu_28[0][0]']          \n","                                                                                                  \n"," conv2d_43 (Conv2D)             (None, 32, 32, 64)   110656      ['tf.concat_2[0][0]']            \n","                                                                                                  \n"," tf.__operators__.getitem_14 (S  (None, 1, 1, 64)    0           ['dense_16[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_14 (TFOpL  (None, 32, 32, 64)  0           ['conv2d_43[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_14[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_17 (GroupN  (None, 32, 32, 64)  128         ['tf.__operators__.add_14[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_29 (TFOpLambda)     (None, 32, 32, 64)   0           ['group_normalization_17[0][0]'] \n","                                                                                                  \n"," conv2d_44 (Conv2D)             (None, 32, 32, 64)   36928       ['tf.nn.silu_29[0][0]']          \n","                                                                                                  \n"," conv2d_45 (Conv2D)             (None, 32, 32, 64)   12352       ['tf.concat_2[0][0]']            \n","                                                                                                  \n"," add_14 (Add)                   (None, 32, 32, 64)   0           ['conv2d_44[0][0]',              \n","                                                                  'conv2d_45[0][0]']              \n","                                                                                                  \n"," multi_head_attention_3 (MultiH  (None, 32, 32, 64)  530496      ['add_14[0][0]',                 \n"," eadAttention)                                                    'add_14[0][0]']                 \n","                                                                                                  \n"," tf.nn.silu_30 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," group_normalization_18 (GroupN  (None, 32, 32, 64)  128         ['multi_head_attention_3[0][0]'] \n"," ormalization)                                                                                    \n","                                                                                                  \n"," dense_17 (Dense)               (None, 64)           65600       ['tf.nn.silu_30[0][0]']          \n","                                                                                                  \n"," conv2d_46 (Conv2D)             (None, 32, 32, 64)   36928       ['group_normalization_18[0][0]'] \n","                                                                                                  \n"," tf.__operators__.getitem_15 (S  (None, 1, 1, 64)    0           ['dense_17[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_15 (TFOpL  (None, 32, 32, 64)  0           ['conv2d_46[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_15[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_19 (GroupN  (None, 32, 32, 64)  128         ['tf.__operators__.add_15[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_31 (TFOpLambda)     (None, 32, 32, 64)   0           ['group_normalization_19[0][0]'] \n","                                                                                                  \n"," conv2d_47 (Conv2D)             (None, 32, 32, 64)   36928       ['tf.nn.silu_31[0][0]']          \n","                                                                                                  \n"," conv2d_48 (Conv2D)             (None, 32, 32, 64)   4160        ['group_normalization_18[0][0]'] \n","                                                                                                  \n"," add_15 (Add)                   (None, 32, 32, 64)   0           ['conv2d_47[0][0]',              \n","                                                                  'conv2d_48[0][0]']              \n","                                                                                                  \n"," multi_head_attention_4 (MultiH  (None, 32, 32, 64)  530496      ['add_15[0][0]',                 \n"," eadAttention)                                                    'add_15[0][0]']                 \n","                                                                                                  \n"," group_normalization_20 (GroupN  (None, 32, 32, 64)  128         ['multi_head_attention_4[0][0]'] \n"," ormalization)                                                                                    \n","                                                                                                  \n"," conv2d_transpose_2 (Conv2DTran  (None, 65, 65, 64)  36928       ['group_normalization_20[0][0]'] \n"," spose)                                                                                           \n","                                                                                                  \n"," tf.image.resize_with_pad_3 (TF  (None, 64, 64, 64)  0           ['conv2d_transpose_2[0][0]']     \n"," OpLambda)                                                                                        \n","                                                                                                  \n"," tf.nn.silu_32 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," tf.concat_3 (TFOpLambda)       (None, 64, 64, 96)   0           ['tf.image.resize_with_pad_3[0][0\n","                                                                 ]',                              \n","                                                                  'add_1[0][0]']                  \n","                                                                                                  \n"," dense_18 (Dense)               (None, 32)           32800       ['tf.nn.silu_32[0][0]']          \n","                                                                                                  \n"," conv2d_49 (Conv2D)             (None, 64, 64, 32)   27680       ['tf.concat_3[0][0]']            \n","                                                                                                  \n"," tf.__operators__.getitem_16 (S  (None, 1, 1, 32)    0           ['dense_18[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_16 (TFOpL  (None, 64, 64, 32)  0           ['conv2d_49[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_16[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_21 (GroupN  (None, 64, 64, 32)  64          ['tf.__operators__.add_16[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_33 (TFOpLambda)     (None, 64, 64, 32)   0           ['group_normalization_21[0][0]'] \n","                                                                                                  \n"," conv2d_50 (Conv2D)             (None, 64, 64, 32)   9248        ['tf.nn.silu_33[0][0]']          \n","                                                                                                  \n"," conv2d_51 (Conv2D)             (None, 64, 64, 32)   3104        ['tf.concat_3[0][0]']            \n","                                                                                                  \n"," tf.nn.silu_34 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," add_16 (Add)                   (None, 64, 64, 32)   0           ['conv2d_50[0][0]',              \n","                                                                  'conv2d_51[0][0]']              \n","                                                                                                  \n"," dense_19 (Dense)               (None, 32)           32800       ['tf.nn.silu_34[0][0]']          \n","                                                                                                  \n"," conv2d_52 (Conv2D)             (None, 64, 64, 32)   9248        ['add_16[0][0]']                 \n","                                                                                                  \n"," tf.__operators__.getitem_17 (S  (None, 1, 1, 32)    0           ['dense_19[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_17 (TFOpL  (None, 64, 64, 32)  0           ['conv2d_52[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_17[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_22 (GroupN  (None, 64, 64, 32)  64          ['tf.__operators__.add_17[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_35 (TFOpLambda)     (None, 64, 64, 32)   0           ['group_normalization_22[0][0]'] \n","                                                                                                  \n"," conv2d_53 (Conv2D)             (None, 64, 64, 32)   9248        ['tf.nn.silu_35[0][0]']          \n","                                                                                                  \n"," conv2d_54 (Conv2D)             (None, 64, 64, 32)   1056        ['add_16[0][0]']                 \n","                                                                                                  \n"," tf.nn.silu_36 (TFOpLambda)     (None, 1024)         0           ['dense[0][0]']                  \n","                                                                                                  \n"," add_17 (Add)                   (None, 64, 64, 32)   0           ['conv2d_53[0][0]',              \n","                                                                  'conv2d_54[0][0]']              \n","                                                                                                  \n"," dense_20 (Dense)               (None, 32)           32800       ['tf.nn.silu_36[0][0]']          \n","                                                                                                  \n"," conv2d_55 (Conv2D)             (None, 64, 64, 32)   9248        ['add_17[0][0]']                 \n","                                                                                                  \n"," tf.__operators__.getitem_18 (S  (None, 1, 1, 32)    0           ['dense_20[0][0]']               \n"," licingOpLambda)                                                                                  \n","                                                                                                  \n"," tf.__operators__.add_18 (TFOpL  (None, 64, 64, 32)  0           ['conv2d_55[0][0]',              \n"," ambda)                                                           'tf.__operators__.getitem_18[0][\n","                                                                 0]']                             \n","                                                                                                  \n"," group_normalization_23 (GroupN  (None, 64, 64, 32)  64          ['tf.__operators__.add_18[0][0]']\n"," ormalization)                                                                                    \n","                                                                                                  \n"," tf.nn.silu_37 (TFOpLambda)     (None, 64, 64, 32)   0           ['group_normalization_23[0][0]'] \n","                                                                                                  \n"," conv2d_56 (Conv2D)             (None, 64, 64, 32)   9248        ['tf.nn.silu_37[0][0]']          \n","                                                                                                  \n"," conv2d_57 (Conv2D)             (None, 64, 64, 32)   1056        ['add_17[0][0]']                 \n","                                                                                                  \n"," add_18 (Add)                   (None, 64, 64, 32)   0           ['conv2d_56[0][0]',              \n","                                                                  'conv2d_57[0][0]']              \n","                                                                                                  \n"," conv2d_58 (Conv2D)             (None, 64, 64, 3)    867         ['add_18[0][0]']                 \n","                                                                                                  \n","==================================================================================================\n","Total params: 19,420,259\n","Trainable params: 19,420,259\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}],"source":["# Build model\n","model= get_model(im_shape=IM_SHAPE,n_resnets=N_RESNETS,n_groups=N_GROUPS,attn_dim=ATTN_DIM,n_heads=N_HEADS,)\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":1081,"status":"ok","timestamp":1656350700832,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"l-WnACfDsO2J","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a79f7ff7-3f01-4063-bf1b-db727a15b6d9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7fa7eb9ad1d0>"]},"metadata":{},"execution_count":36}],"source":["model.load_weights('/content/drive/MyDrive/Bang/unet')"]},{"cell_type":"markdown","metadata":{"id":"UJ8AGMYnEV_K"},"source":["# Training"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oR86y1eQBzrP"},"outputs":[],"source":["OPTIMIZER = Adam(learning_rate = 0.5e-4)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yw80zs81Egnu"},"outputs":[],"source":["def custom_loss(denoise_model, x_start, t, noise=None):\n","  h = tf.keras.losses.Huber()\n","  noise = tf.random.normal(x_start.shape,mean=0,stddev=1)\n","  x_noisy = q_sample(x_start,t,noise)\n","  \n","  predicted_noise = denoise_model([x_noisy, t])\n","\n","  # plt.figure(figsize = (10,10))\n","  # outs = [x_noisy,noise,predicted_noise]\n","  # print('predicted------------',predicted_noise)\n","  # print('actual -------------;',noise)\n","  # for i in range(3):\n","  #   ax = plt.subplot(1,3, i+1)\n","  #   plt.imshow(outs[i][10])\n","  #   plt.axis(\"off\")\n","\n","  return h(noise,predicted_noise)#tf.reduce_mean(tf.math.square(noise-predicted_noise))#"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bohlDLykEgqH"},"outputs":[],"source":["@tf.function\n","def training_block(x_batch):\n","  with tf.GradientTape() as recorder:\n","    \n","    t = tf.random.uniform((BATCH_SIZE,),minval=0,maxval=TIME_STEPS,dtype=tf.int32)\n","    loss = custom_loss(model,x_batch,t)\n","\n","  partial_derivatives = recorder.gradient(loss, model.trainable_weights)\n","  OPTIMIZER.apply_gradients(zip(partial_derivatives, model.trainable_weights))\n","  return loss"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DNlgWRK0Eqvq"},"outputs":[],"source":["def neuralearn(EPOCHS):\n","  for epoch in range(EPOCHS):\n","    init_time = time.time()\n","    losses = []\n","    for step, x_batch in enumerate(train_dataset):\n","      loss = training_block(x_batch)\n","      losses.append(loss)\n","      if step%500==0:\n","        print(step)\n","      \n","    print(str(epoch+1)+\"/\"+str(EPOCHS)+\": Training Loss----->\", sum(losses)/len(losses))\n","    print('Time Elapsed: ---> '+str(time.time()-init_time)+' s')\n","    model.save('/content/drive/MyDrive/Bang/unet')\n","    \n","  print(\"Training Complete!!!!\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SuLIBuqEEqyE","executionInfo":{"status":"error","timestamp":1656423062923,"user_tz":-60,"elapsed":2251867,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"0c133927-dea3-49a2-f781-d983c6a0a36c"},"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["0\n","500\n","1000\n","1500\n","2000\n","2500\n","3000\n","3500\n","4000\n","4500\n","5000\n","5500\n","6000\n","6500\n","7000\n","7500\n","8000\n","8500\n","9000\n","9500\n","10000\n","10500\n","11000\n","11500\n","12000\n","12500\n","1/10: Training Loss-----> tf.Tensor(0.012256743, shape=(), dtype=float32)\n","Time Elapsed: ---> 7092.936837434769 s\n","WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n","WARNING:absl:Found untraced functions such as query_layer_call_fn, query_layer_call_and_return_conditional_losses, key_layer_call_fn, key_layer_call_and_return_conditional_losses, value_layer_call_fn while saving (showing 5 of 60). These functions will not be directly callable after loading.\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["INFO:tensorflow:Assets written to: /content/drive/MyDrive/Bang/unet/assets\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["INFO:tensorflow:Assets written to: /content/drive/MyDrive/Bang/unet/assets\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["0\n","500\n","1000\n","1500\n","2000\n","2500\n","3000\n","3500\n","4000\n","4500\n","5000\n","5500\n","6000\n","6500\n","7000\n","7500\n","8000\n","8500\n","9000\n","9500\n","10000\n","10500\n","11000\n","11500\n","12000\n","12500\n","2/10: Training Loss-----> tf.Tensor(0.012297407, shape=(), dtype=float32)\n","Time Elapsed: ---> 7082.153632640839 s\n","WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n","WARNING:absl:Found untraced functions such as query_layer_call_fn, query_layer_call_and_return_conditional_losses, key_layer_call_fn, key_layer_call_and_return_conditional_losses, value_layer_call_fn while saving (showing 5 of 60). These functions will not be directly callable after loading.\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["INFO:tensorflow:Assets written to: /content/drive/MyDrive/Bang/unet/assets\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["INFO:tensorflow:Assets written to: /content/drive/MyDrive/Bang/unet/assets\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["0\n","500\n","1000\n","1500\n","2000\n","2500\n","3000\n","3500\n","4000\n","4500\n","5000\n","5500\n","6000\n","6500\n","7000\n","7500\n","8000\n","8500\n","9000\n","9500\n","10000\n","10500\n","11000\n","11500\n","12000\n","12500\n","3/10: Training Loss-----> tf.Tensor(0.012213397, shape=(), dtype=float32)\n","Time Elapsed: ---> 7082.232358217239 s\n","WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n","WARNING:absl:Found untraced functions such as query_layer_call_fn, query_layer_call_and_return_conditional_losses, key_layer_call_fn, key_layer_call_and_return_conditional_losses, value_layer_call_fn while saving (showing 5 of 60). These functions will not be directly callable after loading.\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["INFO:tensorflow:Assets written to: /content/drive/MyDrive/Bang/unet/assets\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["INFO:tensorflow:Assets written to: /content/drive/MyDrive/Bang/unet/assets\n"]},{"output_type":"stream","name":"stdout","text":["0\n","500\n","1000\n","1500\n","2000\n","2500\n","3000\n","3500\n","4000\n","4500\n","5000\n","5500\n","6000\n","6500\n","7000\n","7500\n","8000\n","8500\n","9000\n","9500\n","10000\n","10500\n","11000\n","11500\n","12000\n","12500\n","4/10: Training Loss-----> tf.Tensor(0.012229529, shape=(), dtype=float32)\n","Time Elapsed: ---> 7082.220143556595 s\n","WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n","WARNING:absl:Found untraced functions such as query_layer_call_fn, query_layer_call_and_return_conditional_losses, key_layer_call_fn, key_layer_call_and_return_conditional_losses, value_layer_call_fn while saving (showing 5 of 60). These functions will not be directly callable after loading.\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-52-60720807bfb6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mneuralearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m<ipython-input-51-d03feec22a06>\u001b[0m in \u001b[0;36mneuralearn\u001b[0;34m(EPOCHS)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\": Training Loss----->\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Time Elapsed: ---> 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\u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m 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\u001b[0;34m@\u001b[0m\u001b[0mtraceback_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilter_traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m 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exported_graph, object_saver, asset_info, saved_nodes, node_paths = (\n\u001b[0;32m-> 1369\u001b[0;31m       _build_meta_graph(obj, signatures, options, meta_graph_def))\n\u001b[0m\u001b[1;32m   1370\u001b[0m   saved_model.saved_model_schema_version = (\n\u001b[1;32m   1371\u001b[0m       constants.SAVED_MODEL_SCHEMA_VERSION)\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_build_meta_graph\u001b[0;34m(obj, signatures, options, meta_graph_def)\u001b[0m\n\u001b[1;32m   1534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1535\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0msave_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1536\u001b[0;31m     \u001b[0;32mreturn\u001b[0m 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\u001b[0;32mif\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_aliases\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1494\u001b[0m     \u001b[0mfunction_aliases\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeta_graph_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeta_info_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_aliases\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py\u001b[0m in \u001b[0;36m_fill_meta_graph_def\u001b[0;34m(meta_graph_def, saveable_view, signature_functions, namespace_whitelist, save_custom_gradients)\u001b[0m\n\u001b[1;32m    942\u001b[0m       \u001b[0mconcrete_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    943\u001b[0m     \u001b[0;32mif\u001b[0m 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                 \u001b[0mdo_return\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py\u001b[0m in \u001b[0;36mconverted_call\u001b[0;34m(f, args, kwargs, caller_fn_scope, options)\u001b[0m\n\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_requested\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_allowlisted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 377\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_call_unconverted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m 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495\u001b[0;31m       \u001b[0minput_grads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult_grads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    496\u001b[0m       \u001b[0mvariable_grads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_impl.py\u001b[0m in \u001b[0;36mgrad\u001b[0;34m(dy)\u001b[0m\n\u001b[1;32m    580\u001b[0m       activation_grad = (\n\u001b[1;32m    581\u001b[0m           sigmoid_features * (1.0 + (beta * features) *\n\u001b[0;32m--> 582\u001b[0;31m                               (1.0 - sigmoid_features)))\n\u001b[0m\u001b[1;32m    583\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mdy\u001b[0m \u001b[0;34m*\u001b[0m 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1440\u001b[0;31m       \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_promote_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_same_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1441\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/math_ops.py\u001b[0m in \u001b[0;36mmaybe_promote_tensors\u001b[0;34m(force_same_dtype, *tensors)\u001b[0m\n\u001b[1;32m   1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1343\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0mmaybe_promote_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_same_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1344\u001b[0m   \"\"\"Promotes tensors if numpy style promotion is enabled.\n\u001b[1;32m   1345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["neuralearn(10)"]},{"cell_type":"markdown","metadata":{"id":"_-YHVGo4nxuK"},"source":["# Testing :)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FR9QXeNmn2x8"},"outputs":[],"source":["#@tf.function\n","def p_sample(model, x, t, t_index):\n","  \n","  betas_t = extract(betas, t, x.shape)\n","  sqrt_one_minus_alphas_cumprod_t = extract(sqrt_one_minus_alphas_cumprod, t, x.shape)\n","  sqrt_recip_alphas_t = extract(sqrt_recip_alphas, t, x.shape)\n","  \n","  model_mean = sqrt_recip_alphas_t * (x - betas_t * model([x, t]) / sqrt_one_minus_alphas_cumprod_t)\n","\n","  if t_index == 0:\n","      return model_mean\n","  else:\n","      posterior_variance_t = extract(posterior_variance, t, x.shape)\n","      noise = tf.random.normal(x.shape)\n","      \n","      # Algorithm 2 line 4:\n","      return model_mean + tf.math.sqrt(posterior_variance_t) * noise"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":911178,"status":"ok","timestamp":1656423989287,"user":{"displayName":"martins 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= []\n","img = tf.random.normal((64,IM_SHAPE[0],IM_SHAPE[1],IM_SHAPE[2]))\n","for i in reversed(range(0, TIME_STEPS)):\n","  print(i)\n","  img = p_sample(model,img,tf.fill((1,),i,), i)\n","  imgs.append(img)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":926},"executionInfo":{"elapsed":4379,"status":"ok","timestamp":1656424266673,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"6vWH9DYdazzs","outputId":"c75388db-2c29-4b54-fcee-08606c772c95"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 864x864 with 16 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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.figure(figsize = (12,12))\n","\n","for i in range(16):\n","  ax = plt.subplot(4,4, i+1)\n","  plt.imshow((np.array(imgs[999])[16+i]+1)/2)\n","  plt.axis(\"off\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":59040,"status":"ok","timestamp":1655879047628,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"fqZeiR3QoYYX","outputId":"3dd57c8e-a2b5-4935-a0a2-ae155cdcc31f"},"outputs":[{"name":"stderr","output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPqElEQVR4nO3df+xV9X3H8edbCtgCG1CRMaCi1q0lrUX7DXUJMa5NW2qaoUnTaLKFP5p+m60mM+n+IC6ZbMmSdaka/3L5Oknp4vyxaidJzSYl3WyTRQXLL2WrPwItDEGrFLT+At7745zv+EK+n/e93HvOvfB9vx4J4X7P+3vu+Xjwdc+953M/n4+5OyIy9V0w7AaIyGAo7CJJKOwiSSjsIkko7CJJKOwiSXygn53NbDVwNzAN+Ed3/7sOv69+PpGWubtNtt167Wc3s2nAz4HPA/uBZ4Cb3f35YB+FXaRlpbD38zZ+JfCiu7/s7u8BDwJr+ng+EWlRP2FfDPxyws/7620icg7q6zN7N8xsFBht+zgiEusn7AeApRN+XlJvO427jwFjoM/sIsPUz9v4Z4ArzOxSM5sB3ARsaqZZItK0nq/s7n7czG4B/p2q622Duz/XWMtEpFE9d731dDC9jRdpXRtdbyJyHlHYRZJQ2EWSUNhFklDYRZJQ2EWSUNhFklDYRZJQ2EWSUNhFklDYRZJQ2EWSUNhFklDYRZJQ2EWSUNhFklDYRZJQ2EWSUNhFklDYRZJQ2EWSUNhFklDYRZJQ2EWSUNhFkuhrFVcz2wscA04Ax919pIlGiUjzmliy+Q/d/bUGnkdEWqS38SJJ9Bt2B54ws21mNtpEg0SkHf2+jV/l7gfM7GJgs5n9t7s/OfEX6hcBvRCIDFljSzab2XrgTXf/TvA7WrJZpGWNL9lsZrPMbM74Y+ALwO5en09E2tXP2/iFwA/MbPx5/tnd/62RVolI4xp7G9/VwfQ2XqR1jb+NF5Hzi8IukoTCLpKEwi6ShMIukoTCLpKEwi6ShMIukoTCLpKEwi6ShMIukoTCLpKEwi6ShMIukoTCLpKEwi6ShMIukoTCLpJEEyvCyIDNC2oXF7b/frDP+0HtYFDbE9TeDWoyHLqyiyShsIskobCLJKGwiyShsIskobCLJNFxRRgz2wB8GTjs7p+ot80HHgKWAXuBr7r7Gx0PphVhTnNRUPtkUFsU1OYUts+dVt7nxIlyLeqW+9+g9nph+5vBPk8FNelePyvCfBdYfca2dcAWd78C2FL/LCLnsI5hr9dbP/OFeg2wsX68Ebih2WaJSNN6/cy+0N3Hv1z1CtWKriJyDuv767Lu7tFncTMbBUb7PY6I9KfXK/shM1sEUP99uPSL7j7m7iPuPtLjsUSkAb2GfROwtn68FnismeaISFu66Xp7ALiOqqfoEHA78K/Aw8BHgH1UXW+l3paJz5Wu6+2SoNZr99r8oLawcPfkguAD29tB19vRt8q1t46Va78pbI/6Z18Oaq8Gtf1BLaNS11vHz+zufnOh9Lm+WiQiA6Vv0IkkobCLJKGwiyShsIskobCLJKEJJ1tWGoXWyfSgFk3meLLQjTZ5Z0xdO16ufSDoLH0vaEfQY1duR1ALBu1Jl3RlF0lCYRdJQmEXSUJhF0lCYRdJQmEXSUJdby2LusmCHq/iqDGA6cFL9OuFGSJnBf/Sx4Pa0XfKtZPlEqWevqi7LupeSzdcsgW6soskobCLJKGwiyShsIskobCLJKG78S17JahFk+1Hd+oPB7fB5xRGoOz/dXmfS4KJ8i74YLlmwdx1bxS6E94u7xIO/gk6BaRLurKLJKGwiyShsIskobCLJKGwiyShsIsk0c3yTxuALwOH3f0T9bb1wNc5tSrPbe7+eMeDJVz+6VwxK6h9ZWa5diQYyRMt17SrU4OkNaXln7q5sn8XWD3J9rvcfUX9p2PQRWS4Oobd3Z8EOi7aKCLntn4+s99iZjvNbIOZzWusRSLSil7Dfg9wObACOAjcUfpFMxs1s61mtrXHY4lIA3oKu7sfcvcT7n4SuBdYGfzumLuPuPtIr40Ukf71FHYzWzThxxuB3c00R0Ta0nHUm5k9AFwHXGRm+4HbgevMbAXV1GB7gW+010RpQjS/238F3WtXBvtF3WufKWx/KthH2tUx7O5+8ySb72uhLSLSIn2DTiQJhV0kCYVdJAmFXSQJhV0kiY6j3ho92BQd9RbM10j0PeLLglo0Si0YpMa8Qv/KfwQzWF4YPN/vBrVjQW1JYXs0AecLQe3DQe1XQW1PUJuq+hn1JiJTgMIukoTCLpKEwi6ShMIukoTCLpKE1no7Q9QdFk2wWPKhoBZ1eS0J/mVmBC/Rx96bfHuwPByzg1rUVzpp/05t6dzJty8IRth9cka59k7hvwvgh9ECcvL/dGUXSUJhF0lCYRdJQmEXSUJhF0lCd+PP0Msd9/1BbVlQmxvULgz+ZX5nYbm2eV/wpAVRj8E7QW1ZUFtQGMlzKLhzPjO4G//2W+Xam0E75BRd2UWSUNhFklDYRZJQ2EWSUNhFklDYRZLoOAedmS0FvgcspBoXMebud5vZfOAhqh6YvcBX3f2NDs81Jeegi0QDay4Oah8Lar8Jar8ubD8R7POjoNarPyps/+0eny9q/xNB7bUej3c+62cOuuPAt9x9OXAN8E0zWw6sA7a4+xXAlvpnETlHdQy7ux9092frx8eoJuxcDKwBNta/thG4oaU2ikgDzuozu5ktA66iWoxzobsfrEuvUL3NF5FzVNdflzWz2cAjwK3uftTs1McCd/fS53EzGwVG+22oiPSnqyu7mU2nCvr97v5ovfmQmS2q64uAw5Pt6+5j7j7i7iNNNFhEetMx7FZdwu8D9rj7nRNKm4C19eO1wGPNN09EmtJN19sq4CfALk5NZXYb1ef2h4GPAPuout5e7/Bc6breFgS1aGmoYABY+ApdGlQWLbv0Z0FD7g86U4Pp5IpdjtF/c6nbEOJ58p7u8TmnqlLXW8fP7O7+U8pzC36un0aJyODoG3QiSSjsIkko7CJJKOwiSSjsIklowsmWHQ1qhTkZgXhkWzTBZS92BN1r0bJRr/ZYK/l0UNsb1IK5KGUCXdlFklDYRZJQ2EWSUNhFklDYRZJQ2EWSUNdby6KRYdEr7YeD2qKgtqew/aPBPpNORFCLut5mBrXSZJq/F+yzLahFjve4Xza6soskobCLJKGwiyShsIskobCLJNFxDrpGD5ZwDrpezQ1qHw9q0wrbo7v7Hwpq0T9Yab47KM8Ztz3Y5xdB7VhQk9P1s/yTiEwBCrtIEgq7SBIKu0gSCrtIEgq7SBIdB8KY2VLge1RLMjsw5u53m9l64Oucmm7sNnd/vK2GZnMkqL0c1EoDTaJBK9FceNHVYHGwJtPzb06+/VfB80Xda3OC2rVB7YdBLZtuRr0dB77l7s+a2Rxgm5ltrmt3uft32mueiDSlm7XeDgIH68fHzGwPsLjtholIs87qM7uZLQOuolrBFeAWM9tpZhvMLFqgU0SGrOuwm9ls4BHgVnc/CtwDXA6soLry31HYb9TMtprZ1v6bKyK96irsZjadKuj3u/ujAO5+yN1PuPtJ4F5g5WT7uvuYu4+4+0hTjRaRs9cx7GZmwH3AHne/c8L2ibMj3Qjsbr55ItKUjqPezGwV8BNgF6emJLsNuJnqLbxTrc7zjfpmXvRcGvXWsoWF7cuDfaLRa8HKUOGIuNK8cFG3YeQzQS26Ys0rDOl7PFpf6zxXGvXWzd34nwKT7aw+dZHziL5BJ5KEwi6ShMIukoTCLpKEwi6ShCaclEm7WsZd2uNz9tLFFgyiCyfM3BfUPlXYvqNzc85bmnBSJDmFXSQJhV0kCYVdJAmFXSQJhV0kCXW9yUBF3Wvzg9rcoLazt6ZMWep6E0lOYRdJQmEXSUJhF0lCYRdJQmEXSUJdb+eh3wpqRwfWivKIMoBfFLZHE1h+MagdDmqFZeUAeCGoTVXqehNJTmEXSUJhF0lCYRdJQmEXSaLjijBmdiHwJDCz/v3vu/vtZnYp8CDV9GDbgD9x9/fabOxU8/Gg9lpQmxPUrixsj+aZi1ZCuiSoHQtqzwe1ki9dOb1Y+9ud7xdrr/ZwrIy6ubK/C3zW3T9FtbbbajO7Bvg2cJe7f5SqR+VrrbVSRPrWMexeGe/KnF7/ceCzwPfr7RuBG9pooIg0o9v12aeZ2Xaq7zZsBl4Cjrj7+GKd+4HFrbRQRBrRVdjd/YS7rwCWACuBj3V7ADMbNbOtZra1tyaKSBPO6m68ux8Bfgz8ATDXzMZv8C0BDhT2GXP3EXcf6aehItKfjmE3swVmNrd+/EHg88AeqtB/pf61tcBjLbVRRBrQcSCMmV1JdQNuGtWLw8Pu/jdmdhlV19t84GfAH7v7ux2eSwNhJvh0UDsZ1MqdUOVlkqI+1uhYs4Ja1PX2n0FN2lUaCKNRb0OksEsbNOpNJDmFXSQJhV0kCYVdJAmFXSSJQd+NfxXYV/94EfHgrkFRO06ndpzufGvHJe6+YLLCQMN+2oHNtp4L36pTO9SOLO3Q23iRJBR2kSSGGfaxIR57IrXjdGrH6aZMO4b2mV1EBktv40WSGErYzWy1mf2Pmb1oZuuG0Ya6HXvNbJeZbR/k5BpmtsHMDpvZ7gnb5pvZZjN7of573pDasd7MDtTnZLuZXT+Adiw1sx+b2fNm9pyZ/Xm9faDnJGjHQM+JmV1oZk+b2Y66HX9db7/UzJ6qc/OQmc04qyd294H+oRoq+xJwGTAD2AEsH3Q76rbsBS4awnGvBa4Gdk/Y9vfAuvrxOuDbQ2rHeuAvBnw+FgFX14/nAD8Hlg/6nATtGOg5oZoMeHb9eDrwFHAN8DBwU739H4A/PZvnHcaVfSXworu/7NXU0w8Ca4bQjqFx9yeB18/YvIZq3gAY0ASehXYMnLsfdPdn68fHqCZHWcyAz0nQjoHySuOTvA4j7IuBX074eZiTVTrwhJltM7PRIbVh3EJ3P1g/fgVYOMS23GJmO+u3+a1/nJjIzJYBV1FdzYZ2Ts5oBwz4nLQxyWv2G3Sr3P1q4EvAN83s2mE3CKpXdqoXomG4B7icao2Ag8Adgzqwmc0GHgFudffTVp8e5DmZpB0DPyfexySvJcMI+wFg6YSfi5NVts3dD9R/HwZ+QHVSh+WQmS0CqP+OliRvjbsfqv9HOwncy4DOiZlNpwrY/e7+aL154OdksnYM65zUxz7CWU7yWjKMsD8DXFHfWZwB3ARsGnQjzGyWmc0Zfwx8Adgd79WqTVQTd8IQJ/AcD1ftRgZwTszMgPuAPe5+54TSQM9JqR2DPietTfI6qDuMZ9xtvJ7qTudLwF8OqQ2XUfUE7ACeG2Q7gAeo3g6+T/XZ62tU08dtAV4AfgTMH1I7/gnYBeykCtuiAbRjFdVb9J3A9vrP9YM+J0E7BnpOqJbs+1l9vN3AX034f/Zp4EXgX4CZZ/O8+gadSBLZb9CJpKGwiyShsIskobCLJKGwiyShsIskobCLJKGwiyTxfzEfASye0SzSAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.animation as animation\n","\n","random_index = 3\n","\n","fig = plt.figure()\n","ims = []\n","for i in range(TIME_STEPS):\n","    im = plt.imshow(np.array(imgs[i])[random_index], animated=True)\n","    ims.append([im])\n","\n","animate = animation.ArtistAnimation(fig, ims, interval=5, blit=True, repeat_delay=1000)\n","animate.save('diffusion.gif')\n","plt.show()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1655878937246,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"-Ed3NhyPoYah","outputId":"842b9d8a-8c46-43ec-8f5e-62d879978208"},"outputs":[{"data":{"text/plain":["1000"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["len(ims)"]},{"cell_type":"markdown","metadata":{"id":"AqigSU1qxufy"},"source":["# Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"n5uZjRsjO1qo"},"outputs":[],"source":["\n","class PositionalEmbeddings(tf.keras.layers.Layer):\n","\n","  def __init__(self, dim):\n","    super().__init__()\n","    self.embedding_dim = dim\n","\n","  def get_timestep_embedding(self, timesteps, embedding_dim: int):\n","    \"\"\"\n","    From Fairseq.\n","    Build sinusoidal embeddings.\n","    This matches the implementation in tensor2tensor, but differs slightly\n","    from the description in Section 3.5 of \"Attention Is All You Need\".\n","    \"\"\"\n","    half_dim = self.embedding_dim // 2\n","    emb = tf.math.log(10000.) / (half_dim - 1)\n","    emb = tf.exp(tf.range(half_dim, dtype=tf.float32) * -emb)\n","    emb = tf.cast(timesteps, dtype = tf.float32)[:, None] * emb[None, :]\n","    emb = tf.concat([tf.sin(emb), tf.cos(emb)], axis=1)\n","    # if embedding_dim % 2 == 1:\n","    #   emb = tf.pad(emb, [[0, 0], [0, 1]])\n","    return emb\n","\n","  def call(self, time):\n","\n","    return self.get_timestep_embedding(time, self.embedding_dim)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":539,"status":"ok","timestamp":1655795367103,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"R5Qu_5LaQWlu","outputId":"e9393fb2-6423-4c30-f5a5-3713ed5c858f"},"outputs":[{"name":"stdout","output_type":"stream","text":["tf.Tensor(\n","[[ 0.9802397  -0.9856166   0.98930377 ...  0.99986905  0.9998867\n","   0.999902  ]\n"," [-0.7568025  -0.5468414  -0.31299213 ...  0.9999999   0.9999999\n","   0.99999994]\n"," [-0.88179886  0.27157855  0.6098344  ...  0.99973536  0.9997711\n","   0.999802  ]\n"," ...\n"," [-0.91652155  0.97844124 -0.97986823 ...  0.9999882   0.9999898\n","   0.9999912 ]\n"," [-0.35493836  0.48132214  0.41464454 ...  0.99975365  0.9997869\n","   0.9998157 ]\n"," [ 0.42016706 -0.458038   -0.96910685 ...  0.99999887  0.99999905\n","   0.99999917]], shape=(32, 256), dtype=float32)\n"]}],"source":["\n","timesteps = 200\n","t = tf.random.uniform((BATCH_SIZE,), 0, timesteps, dtype = tf.int32 )\n","\n","pos = PositionalEmbeddings(256)\n","print(pos(t))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4ilVl1ViGL35"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"SABFtsKKoRDk"},"source":["## Convert TO GIF"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J8skFWIboUbu"},"outputs":[],"source":["import matplotlib.animation as animation\n","\n","random_index = 7\n","\n","fig = plt.figure()\n","ims = []\n","for i in range(timesteps):\n","    im = plt.imshow(np.array(imgs[i])[random_index][...,0], cmap=\"gray\", animated=True)\n","    ims.append([im])\n","\n","animate = animation.ArtistAnimation(fig, ims, interval=50, blit=True, repeat_delay=1000)\n","animate.save('diffusion.gif')\n","plt.show()\n"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["emN9bGyjPyDu"],"provenance":[{"file_id":"1dFz9TCTpSz4sdTNjqWXmocvOD6XDeDID","timestamp":1673809761797},{"file_id":"1BAdvaCGZIRggOtfcWHLa-w6uagP2Rgzc","timestamp":1656428963754}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

================================================
FILE: deep learning for linear algebra/Complete Linear Algebra Course by Neuralearn.ai-.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1Q6D2k0URW3Vl4SolHcAgR49NZtoq38o8","timestamp":1673903473108}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"lXEdp9eSUQ9Z"},"source":[" <h1> <center> <font color = \"black\"> [Complete Linear Algebra]   <br><font color=\"grey\"> INTRODUCTION TO PYTHON PROGRAMMING  </font></center></h1>\n","    "]},{"cell_type":"markdown","metadata":{"id":"BToU4VN4Udze"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [Variable types and Basic Operators]   <br><font color=\"grey\"> </font></center></h1>\n","----------------------------------------------\n","    \n","- Numbers(<font color = \"orange\">int, long, float, complex</font>)<br>\n","- String<br>\n","- List<br>\n","- Tuple<br>\n","- Dictionary\n","    \n","----------------------------------------------<br>\n","    \n","- Arithmetic Operators (<font color = \"orange\">+,-,/,%,//,** </font>)<br>\n","- Comparison Operators (<font color = \"orange\">==, !=, <, >, <=, >=</font>) <br>\n","- Assignment Operators (<font color = \"orange\">+=, -=, *=, /=, **=</font>)<br>\n","- Membership Operators (<font color = \"orange\">in, not in</font>)<br>\n","- Identity Operators  (<font color = \"orange\">is, is not</font>) <br>\n","    \n","----------------------------------------------<br>\n","    \n","    \n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EeRE3QS7UW3W","executionInfo":{"status":"ok","timestamp":1636891366592,"user_tz":-60,"elapsed":512,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"02129698-b107-4865-9102-ff1bb58dd8d0"},"source":["variable_name = 6\n","var_2 = \"string\"\n","var_3 = ['list',7,'s']\n","var_4 = {'one':1, 'two':2}\n","\n","print(\"The content is {}, {}, {} of these variables.\" .format(variable_name , var_2, var_4))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The content is 6, string, {'one': 1, 'two': 2} of these variables.\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tsMGR0NKUhMI","executionInfo":{"status":"ok","timestamp":1636891367979,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"63e3ebb3-127d-4f85-e2f3-89fd6a36fc7a"},"source":["number_1 = 5\n","number_2 = 8.0\n","\n","print(\"The number 1 is {}, The number 2 is {}.\". format(number_1, number_2))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The number 1 is 5, The number 2 is 8.0.\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oVB6O0tuUhYG","executionInfo":{"status":"ok","timestamp":1636891369600,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"0bf30a58-25f0-46e0-f357-eba6b88e80a1"},"source":["string = \"our stringis this in the double Quotes\"\n","print(string[2:])\n","print(string.lower())"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["r stringis this in the double Quotes\n","our stringis this in the double quotes\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d-6GooZTUhap","executionInfo":{"status":"ok","timestamp":1636891369600,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"a454f7b6-5286-4c6c-9e7c-0f6937a0fd0a"},"source":["list_1 = [1,2,4]\n","list_1.append(8)\n","print(list_1[3])\n","\n","list_string = [8,\"string in list\", 9]\n","list_string.append(\"hello\")\n","print(list_string)\n","print(list_string[2:])\n","print(len(list_string))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["8\n","[8, 'string in list', 9, 'hello']\n","[9, 'hello']\n","4\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F-ZYqr83UhdD","executionInfo":{"status":"ok","timestamp":1636891370687,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"ba6a0a1f-e178-4ec4-bc89-23afec4e97ed"},"source":["empty_list = []\n","print(empty_list)\n","dimension_of_vector = 6\n","vector_list = [0]*dimension_of_vector\n","print(vector_list)\n","vector_list[0] = 9\n","print(vector_list)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[]\n","[0, 0, 0, 0, 0, 0]\n","[9, 0, 0, 0, 0, 0]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eRTf-kyNUmun","executionInfo":{"status":"ok","timestamp":1636891371342,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"322170f6-2feb-4b4f-b338-62d4503c5992"},"source":["number_of_columns = 4\n","number_of_rows = 3 \n","matrix_list_initial = [0]*number_of_rows\n","matrix_list = [[0]*number_of_columns] * number_of_rows\n","\n","print(matrix_list_initial)\n","print(matrix_list)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0, 0, 0]\n","[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WN4X0w6NUmxN","executionInfo":{"status":"ok","timestamp":1636891371343,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"ce02fd35-f2f7-4883-ac97-1e743a730c50"},"source":["tuple_1 = (1,2,4)\n","print(tuple_1)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 2, 4)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":199},"id":"wcZRdiPSUmzt","executionInfo":{"status":"error","timestamp":1636891372475,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"4483ba21-d8f7-48cc-ff83-1e4e62dedd7f"},"source":["dictionary_1 = {'one':1, 'two':2}\n","\n","print(dictionary_1['three'])"],"execution_count":null,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-28-b441512d4cd1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdictionary_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'one'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'two'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdictionary_1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'three'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mKeyError\u001b[0m: 'three'"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yjQ_0UxSUm2T","executionInfo":{"status":"ok","timestamp":1636891373015,"user_tz":-60,"elapsed":2,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"8f8a1559-8c07-46f6-f5b3-44e1ef187e9e"},"source":["a = 15\n","b = 7\n","\n","add = a+b\n","subtract  = a-b\n","divide = a/b\n","power = a**b\n","modulo = a%b\n","lower_divide = a//b\n","print(add)\n","print(subtract)\n","print(divide)\n","print(power)\n","print(modulo)\n","print(lower_divide)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["22\n","8\n","2.142857142857143\n","170859375\n","1\n","2\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":130},"id":"onufvldpUm45","executionInfo":{"status":"error","timestamp":1636891376406,"user_tz":-60,"elapsed":670,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"74352ff6-56db-48cb-973a-f86ad7df84cb"},"source":["15 /7 = 2 r 1 "],"execution_count":null,"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;36m  File \u001b[0;32m\"<ipython-input-30-f84af01f54b6>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    15 /7 = 2 r 1\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z_sUZXMbUm7d","executionInfo":{"status":"ok","timestamp":1636891377193,"user_tz":-60,"elapsed":2,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"46edeeb0-5347-40ad-bb7e-eb31289170fc"},"source":["a = 8\n","b = 5\n","\n","b /=a # b = b + a\n","\n","print(b)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0.625\n"]}]},{"cell_type":"code","metadata":{"id":"rsH3WmxWVDix"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"R3qMynOE5SN_"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [Conditional Statements] <br><font color=\"grey\"> </font></center></h1>\n","\n","    -----------\n","    |Condition| ---> if verified-----> Take Action A\n","    -----------      if not verified-> Take Action B\n","    \n","    --------------------------------------------\n","    |Condition_1, Condition_2, ..., Condition_n| ---> if Cond_1 verified(Cond_i unverified, i!=1)-----> Take Action A_1\n","    --------------------------------------------      \n","                                                      else if Cond_2 verified(Cond_i unverfied, i!=2)-> Take Action A_2                                                                    \n","                                                                                    .\n","                                                                                    .\n","                                                                                    .\n","    \n","                                                      else if Cond_n verified(Cond_i unverfied, i!=n)-> Take Action A_n\n","    \n","                                                      else if no Condition verified-------------------> Take Action A_n+1\n","    \n","    \n","    --------\n","    |Sports| ---> if verified-----> Healthy\n","    --------      if not verified-> Unhealthy\n","    \n","    ------------------------------------------\n","    |Football, Basketball, Volleyball, Tennis| ---> if Football verified(Cond_i unverified, i!=1)----------------> Kick\n","    ------------------------------------------      \n","                                                    else if Basketball verified(Cond_i unverfied, i!=2)----------> Throw\n","              \n","                                                    else if Volleyball verified(Cond_i unverfied, i!=n)----------> Hit\n","    \n","                                                    else if Tennis verified(Cond_i unverfied, i!=n)--------------> Strike\n","    \n","                                                    else if no condition(else) verified(Cond_i unverfied, i!=n)--> Unknown\n","    \n","    \n","                                                      \n","    \n","    \n","    \n","    \n","    \n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"jZXCIbPp5Xi7"},"source":["sports = False\n","\n","if (sports):\n","    print(\"Healthy\")\n","elif (sports== False):# else:\n","    print(\"Not healthy\")\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sWI5p_lS5ahS"},"source":["sports = [\"Football\", \"Basketball\", \"Volleyball\", \"Tennis\"]\n","\n","if (\"Football\" in sports):\n","    print(\"This person plays football\")\n","elif (\"Basketball\" in sports):\n","    print(\"This person plays basketball\")\n","elif(\"Volleyball\" in sports):\n","    print(\"This person plays volleyball\")\n","elif(\"Tennis\" in sports):\n","    print(\"This person plays tennis\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0DxsNi-H5b51"},"source":["sports = [\"Football\", \"Basketball\"]\n","\n","if (\"Football\" in sports):\n","    print(\"This person plays football\")\n","    if(\"Basketball\" in sports):\n","        print(\"This person plays basketball\")\n","        \n","else:\n","    if(\"Basketball\" in sports):\n","        print(\"This person plays basketball\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ns2jVAUp5d9B"},"source":["a = 5\n","b = 5\n","\n","if (a is not  5):\n","    print(\"Correct\")\n","else:\n","    print(\"nothing is printed\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8Aq-ANmX5jA5"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [Loops] <br><font color=\"grey\"> </font></center></h1>\n","\n","    \n","    ------------\n","    - while\n","    - for\n","    ------------\n","    \n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"T8BBfeaf5lzN"},"source":["n =5\n","\n","for i in range(n):\n","    print(\" I am at position {}\".format(i))\n","initial statement \n","increment"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"V6jbEaki5l-3"},"source":["n = 5\n","i = 0\n","while(i<n):\n","    \n","    print(\"I am at position {}\".format(i+1))\n","    i = i + 1\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tUBcc1EG5pYS"},"source":["sports = [\"Football\", \"Volleyball\"]\n","print(len(sports))\n","for i in range(len(sports)):\n","    if (sports[i] is \"Football\"): \n","        print(\"I am a footballer\")\n","    elif(sports[i] is  \"Basketball\" ):\n","        print(\" I am a basketballer\")\n","    elif(sports[i] is \"Volleyball\"):\n","        print(\" I am a volleyballer\")\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZdGuYM7O5rQz"},"source":["sports = [\"Football\", \"Volleyball\", \"Tennis\"]\n","\n","for sport in sports:\n","    if (sport is \"Football\"): \n","        pass\n","    elif(sport is \"hand\"):\n","        print(\"hello\")\n","        "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7ogqMyS053RC"},"source":["break, pass and continue"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EnFHkeAt55J7"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZCFRDk0H6Ayv"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [FUNCTIONS] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"2OoG-RHh6C3w"},"source":["def task_A (arguments):\n","    \n","    return outputs"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"H2DPhmwN6DAi"},"source":["def sum_product(int_1, int_3, int_2=23):\n","    \n","    sum_of_numbers = int_1+int_2\n","    product_of_numbers = int_1*int_2\n","    return sum_of_numbers, product_of_numbers"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"k4j9nE1g6G6D"},"source":["sum_1, product_1  = sum_product(20, 11)\n","print(sum_1, product_1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3hy9E0ly6Hea"},"source":["adder = 80\n","def print_name(name, age=70):\n","    adder = 40\n","    \n","    return \"My name is :\" + name + \"  I am of age:\" + str(adder+age)\n","\n","def print_data(name, age=70):\n","    \n","    return \"My name is :\" + name + \"  I am of age:\" + str(adder+age)\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EvBvpoKN6Ho-"},"source":["print(print_data(\"Joseph\", 23))\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zFsemeQE6Hqy"},"source":["a = 4\n","print(str(a))\n","b = \"7\"\n","c = int(b)+3\n","print(c)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"e1W7UupS6DDE"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WVyCEMMO9Y63"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [PACKAGES] <br><font color=\"grey\"> </font></center></h1>\n","    \n","    \n","        - Numpy\n","        - Matplotlib\n","        - Pandas\n","        - OpenCV\n","    \n","    \n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"cPVPhL3G9gVL"},"source":["import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"opJmZS7S9gd6"},"source":["array_1D = numpy.array([1,3,5,8])\n","\n","array_2D_better = np.array([[2,3],\n","                     [6,8],\n","                     [0,9]])\n","\n","array_2D = np.array([[2,3],[6,8],[0,9]])\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5vbupPTs9ZHF"},"source":["print(\"arrray better = \", array_2D_better.shape)\n","print(\"array=\", array_2D)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"I_zC1lEG9ZJc"},"source":["array_3D_better = np.array([[[2,4,8],\n","                             [5,6,9]],\n","                            \n","                            [[8,7,5],\n","                             [9,5,7]],\n","                            \n","                            [[2,4,0],\n","                             [9,0,0]]])\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nYHLu3bb9ZMm"},"source":["print(array_3D_better)\n","print(array_3D_better.shape)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"LB7e_7hH9nsa"},"source":["array_1D = numpy.array([1,31.4,5,8], dtype = int)\n","print(array_1D)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Bu-Wdu3V9nu3"},"source":["a = np.ones((3,4))\n","print(a)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Tf9CuKKr9nyU"},"source":["import matplotlib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"PicU-Hds9sEd"},"source":["x = np.array([8,4,5,9,7,1,10,0])\n","y = x**2\n","print(y)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_MCvf_fR9sG_"},"source":["fig = plt.figure()\n","ax = fig.add_subplot()\n","\n","ax.scatter(x,y, color = 'red')\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"APbeF5mD9ZPq"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gnj9JVN36gju"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [MATRIX ALGEBRA] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"0mM5uo1e6DFp"},"source":["import numpy as np\n","from sympy import Matrix"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"iwe35Uit6vnZ"},"source":["A = np.array([[4,6],\n","             [3,4],\n","             [1,2]], dtype = float)\n","\n","#print(A)\n","\n","#print(A[0][1])\n","\n","print(A[0:2,0:2])\n","\n","print(A.shape)\n","\n","A[0][1] = 'boy'\n","print(A)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SZEO2AME6yhQ"},"source":["list_1 = []\n","\n","list_1.append(1)\n","\n","list_1.append('boy')\n","print(list_1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pK-6p_5z6yjk"},"source":["B = [[0]*3]*4\n","\n","number_of_columns = 5\n","number_of_rows = 4\n","B = [[5]*number_of_columns]*number_of_rows\n","print(B)\n","#B.append([3,5,4,6,7,78,8.9])\n","#print(B)\n","print(np.array(B))\n","C = np.array(B)\n","\n","print(list(C))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ppk14UBF6yl-"},"source":["A = np.random.random((4,3))\n","print(A)\n","B = np.random.random((3,3))\n","print(B)\n","\n","print(A+B)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"CCV89DOh6yoQ"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bw7UBcQy6yqp"},"source":["C = np.array([[2,3],\n","              [5,2],\n","              [0,1]])\n","\n","D = np.array([[2,4],\n","              [3,2]])\n","\n","#print(C*D)\n","\n","print(np.matmul(C,D))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"e-IzAauF6ys8"},"source":["E  = np.array([[4,1,2],\n","               [7,4,4]])\n","\n","print(E)\n","print(E.T)\n","print(np.transpose(E))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vlOsdJA_6yvJ"},"source":["A = np.array([[2,4,9],\n","            [7,2,1],\n","            [9,4,1]])\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"2BCQDlil6yxq"},"source":["\n","print(np.linalg.inv(A))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"H-wljV0C6y0C"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0gVQCSqo6_2h"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [LINEAR TRANSFORMATIONS] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"6KQdjHWf7GEc"},"source":["import numpy as np\n","import matplolib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wQXTx3Cg7GNz"},"source":["def T(x_1, x_2, x_3, x_4 ):\n","    \n","    return x_1 + x_2 , x_1 + x_2 + x_3 ,  x_1 + x_2 + x_3 + x_4 , x_4 "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9qSRFtCb6y2T"},"source":["print(T(10,4,5, 3))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bqeSV-9u7oaa"},"source":["def T(x_1, x_2, x_3, x_4 ):\n","    \n","    M_T = np.array([[1,1,0,0],\n","                    [1,1,1,0],\n","                    [1,1,1,1],\n","                    [0,0,0,1]])\n","    \n","    x = np.array([x_1, x_2, x_3, x_4])\n","    \n","    return  np.matmul(M_T, x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ynX5O2SM7okT"},"source":["print(T(10,4,5, 3))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"uGZFyuvX7om3"},"source":["def Rotation(x_1, x_2 , angle):\n","    \n","    M_T = np.array([[np.cos(angle), -np.sin(angle)],\n","                    [np.sin(angle), np.cos(angle)]])\n","    \n","    return np.matmul(M_T, [x_1,x_2])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SYR_Gc8c7opr"},"source":["number_of_points = 10\n","\n","x = []\n","y = []\n","\n","x_transformed = []\n","y_transformed = []\n","\n","for i in range(number_of_points):\n","    x.append(i)\n","    y.append(i)\n","    \n","    x_component, y_component = Rotation(i,i, np.pi/2)\n","    x_transformed.append(x_component)\n","    y_transformed.append(y_component)\n","    \n","fig = plt.figure()\n","\n","ax = fig.add_subplot()\n","ay = fig.add_subplot()\n","\n","ax.scatter(x,y,c='r', marker ='*')\n","ay.scatter(x_transformed, y_transformed, c='g', marker = '*')\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ERnnl7iL7tfV"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xBbks-re8DTK"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [VECTOR SPACES] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"n-HeItib7toG"},"source":["import numpy as np\n","from sympy import Matrix\n","\n","import matplotlib.pyplot as plt\n","from matplotlib.patches import Polygon\n","from matplotlib.collections import PatchCollection\n","import matplotlib"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"CUArFuSs8H-A"},"source":["B = Matrix([[1,2,3],\n","            [3,5,1]])\n","\n","#print(B.rref())\n","RREF = B.rref()\n","\n","print(RREF[0])\n","print(RREF[1])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1OuUbUY_8IAP"},"source":["def fundamental_subspaces(A):\n","    \n","    A = Matrix(A)# We get the matrix A\n","    A_arr = np.array(A)# We get the numpy array\n","    \n","    RREF = A.rref() # We obtain the row reduced echelon form of A\n","\n","    null_space = A.nullspace() # We obtain the null space of A\n","\n","    left_null_space = A.T.nullspace() # We obtain the left null space of A\n","\n","    col_space = [] # Initialization of a list which will contain all the vectors found in the column space of A\n","    \n","    \n","    for i in RREF[1]:\n","\n","        col_space.append(A_arr[:,i]) # Addition of each element of the column space of A to the list col_space\n","\n","    row_space = []  # Initialization of a list which will contain all the vectors found in the row space of A\n","    RREF_t = A.T.rref()\n","\n","    for i in RREF_t[1]:\n","\n","        row_space.append(A_arr[i])# Addition of each element of the row space of A to the list row_space\n","    return null_space, row_space, left_null_space, col_space # returning the fundamental spaces of A"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dl78Tn778ICW"},"source":["\n","A = [[2,2,5],\n","     [-2,3,-3],\n","     [4,5,9],\n","     [-2,3,3]]\n","null_space, row_space, left_null_space, col_space = fundamental_subspaces(A)\n","\n","print(\" Number of rows of A = m ={} = dim(Col(A)) + dim(Left Null(A))= {} + {} \" .format(len(A), len(col_space), len(left_null_space)))\n","print(\" Number of columns of A = n ={} = dim(Row(A)) + dim(Null(A))= {} + {} \" .format(len(A[0]), len(row_space), len(null_space)))\n","print(\"\\n\")\n","print(\"The Null space is = \\n\", null_space)\n","print(\"\\n\")\n","print(\"The Row space is = \\n\", row_space)\n","print(\"\\n\")\n","print(\"The Left Null space is = \\n\", left_null_space)\n","print(\"\\n\")\n","print(\"The Column space is = \\n\", col_space)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"4JWod83O8IEy"},"source":["\n","size = 25\n","limits = 5\n","patches = []\n","new_patches = []\n","original_polygon_points = []\n","new_polygon_points = []\n","B = np.array([[3,8],\n","              [0,5]])\n","\n","\n","\n","for i in range( -(limits*limits), (limits*limits)):\n","    for j in range(-(limits*limits), (limits*limits)):\n","        \n","        first_point = [i+1,j+1]\n","        second_point = [i, j+1]\n","        third_point = [i,j]\n","        fourth_point = [i+1,j]\n","        \n","        new_polygon_points.append([np.matmul(B,first_point) ,np.matmul(B,second_point),np.matmul(B,third_point),np.matmul(B,fourth_point) ])\n","        \n","for i in range( -(limits*limits), (limits*limits)):\n","    for j in range(-(limits*limits), (limits*limits)):\n","        original_polygon_points.append([[i+1,j+1],[i,j+1],[i,j],[i+1, j] ])\n","        \n","for i in range(len(original_polygon_points)):\n","    polygon = Polygon(original_polygon_points[i])\n","    patches.append(polygon)\n","    \n","for i in range(len(new_polygon_points)):\n","    polygon = Polygon(new_polygon_points[i])\n","    new_patches.append(polygon)\n","    \n","p1 = PatchCollection(patches, cmap = matplotlib.cm.jet, fc = 'white', ec = 'black' , alpha = 0.3)\n","p2 = PatchCollection(new_patches, cmap = matplotlib.cm.jet, fc = 'white', ec = 'red' , alpha = 0.8)\n","\n","\n","\n","fig = plt.figure()\n","fig.set_size_inches(size, size)\n","ax = plt.subplot()\n","\n","ax.add_collection(p1)\n","ax.add_collection(p2)\n","ax.set_ylim(bottom = -size, top = size)\n","ax.set_xlim(left = -size, right = size)\n","plt.savefig('change_of_base.png')\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"f6ap4y2A8IHL"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BEIa58ay8WGL"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [ORTHOGONALITY] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"o2bLCrST7tqs"},"source":["import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WdPVOaCx8ZeM"},"source":["A = np.array([[2,3,4,9,0],\n","             [2,1,9,5,7],\n","             [2,7,1,0,0],\n","             [9,8,0,-1,-2]])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dFAyBb7Q8Zgn"},"source":["print(A)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZFqcgJiO8ZjD"},"source":["qr decomposition of A = q which is orthonormal and r which is upper triangular"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mfWLg6hR8ZlV"},"source":["Q,R = np.linalg.qr(A)\n","\n","print(Q)\n","print(R)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"euh04AGW8ZoE"},"source":["AX= b\n","X = (ATA)-1 AT b\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Um9l67YP8hQp"},"source":["def normal_equation(A,b):\n","    \n","    ATA = np.matmul(A.T, A)\n","    \n","    X = np.matmul(np.matmul(np.linalg.inv(ATA), A.T), b)\n","    return X"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"q2hxWOEx8hSw"},"source":["A = np.array([[2,3],\n","             [0,1],\n","             [3,5]])\n","\n","b = np.array([[1],\n","              [4],\n","              [1]])\n","X = normal_equation(A,b)\n","print(X)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JM6u5UpG8hVX"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"aMSgLCyH8upr"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [DETERMINANT AND TRACE OPERATOR] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"3La_NzwY8hXq"},"source":["import numpy as np"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"v94KXsgF8yoc"},"source":["\n","A = np.array([[3,2,1],\n","              [-1,3,0],\n","              [2,2,5]])\n","\n","print(\"The determinant of A is = \\n\", np.linalg.det(A))\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hZO6UdWZ8yrA"},"source":["#sqrt(tr(AAT)) = frobenius(A)\n","A = np.array([[30,2,1],\n","              [1,31,0],\n","              [20,21,50]])\n","print(\"The trace of A is = \\n\", np.trace(A))\n","\n","print(\"The square root of trace of A(AT) is = \\n\", np.sqrt(np.trace(np.matmul(A, A.T))))\n","\n","print(\"The frobenius norm of A is = \\n\", np.linalg.norm(A, 'fro'))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Z6WBON358yto"},"source":["def triple_product(A,B,C):\n","    \n","    return np.matmul(np.matmul(A,B),C)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"L4SIO-8o838e"},"source":["\n","A = np.array([[3,2,1],\n","              [-1,3,0],\n","              [2,2,5]])\n","B = np.array([[4,2,-1],\n","              [1,-3,40],\n","              [12,22,15]])\n","C = np.array([[0,7,14],\n","              [-10,13,0],\n","              [20,0,0]])\n","\n","print(\"The trace of ABC is = \\n\", np.trace(triple_product(A,B,C)))\n","print(\"The trace of BCA is = \\n\", np.trace(triple_product(B,C,A)))\n","print(\"The trace of CAB is = \\n\", np.trace(triple_product(C,A,B)))\n","\n","print(\"The trace of BAC is = \\n\", np.trace(triple_product(B,A,C)))\n","\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MHCbkoQ383-4"},"source":["tr(ABC) = tr(BCA) =tr(CAB)      != tr(BAC)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_h-KP1-b84BU"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"j9L2RTCn89uR"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [MATRIX DECOMPOSITIONS] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"Sh0KYmIr84Dz"},"source":["import numpy as np\n","from numpy.linalg import inv"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"F5VmYKls8ywB"},"source":["A  = np.array([[1,2],\n","              [3,4]])\n","\n","eigen_values, eigen_vectors = np.linalg.eig(A)\n","\n","print(eigen_values)\n","print(eigen_vectors)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vOs7TAfN9DKd"},"source":["\n","A = [[0.5,0.5],\n","     [0.2,0.8]]\n","\n","\n","eigen_values, eigen_vectors = np.linalg.eig(A)\n","\n","print(\"The Eigen values of A are  = \\n\", eigen_values)\n","\n","print(\"The Eigen vectors of A are = \\n\", eigen_vectors, \"\\n\")\n","\n","\n","diag_A = [[eigen_values[0],0],\n","          [0,eigen_values[1]]]\n","\n","print(\"the diag is = \", diag_A)\n","\n","\n","A_reconstructed = np.matmul(np.matmul(eigen_vectors, diag_A), inv(eigen_vectors))\n","\n","print(\" A = \", np.array(A))\n","print(\"A reconstructed is = \", A_reconstructed)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"uOtPuO8a9DM3"},"source":["U = np.array([[-0.93,-0.7],\n","              [0.37, -0.7]])\n","D = np.array([[0,0],\n","              [0,1]])\n","print(np.matmul(np.matmul(U,D),inv(U)))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ci25E6Y29DPZ"},"source":["\n","\n","A = [[4,2],\n","     [1,3]]\n","\n","\n","eigen_values, eigen_vectors = np.linalg.eig(A)\n","\n","print(\"The Eigen values of A are  = \\n\", eigen_values)\n","\n","print(\"The Eigen vectors of A are = \\n\", eigen_vectors, \"\\n\")\n","\n","A_to_power_n = A\n","n = 3\n","\n","for i in range(n-1):\n","    A_to_power_n = np.matmul(A_to_power_n, A)\n","\n","diag_A = [[np.power((eigen_values[0]/(np.sqrt(1))), n),0],\n","          [0,np.power((eigen_values[1]/(np.sqrt(1))), n)]]\n","\n","print(\"the diag is = \", diag_A)\n","eigen_vectors_rearranged = eigen_vectors\n","\n","A_reconstructed = np.matmul(np.matmul(eigen_vectors_rearranged, diag_A), inv(eigen_vectors_rearranged))\n","\n","print(\"The original A ^ {} is =\\n\".format(n), A_to_power_n, \"\\n\")\n","\n","print(\"A reconstructed is = \\n\", A_reconstructed)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UEpeP71m9DR7"},"source":["A = np.array([[16,-8,-4],\n","              [-8,29,12],\n","              [-4,12,41]])\n","eigen_values, _ = np.linalg.eig(A)\n","\n","print(eigen_values)\n","\n","L = np.linalg.cholesky(A)\n","print(L)\n","print(L.T)\n","print(np.matmul(L,L.T))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Rn7SLb9g9DUA"},"source":["\n","A = np.array([[1,-1],\n","              [-2, 2], \n","              [2, -2]])\n","\n","u, s, vh = np.linalg.svd(A)\n","print(\"The value of U is =\\n\",u,\"\\n\")\n","print(\"The singular values are =\", s,\"\\n\")\n","print(\"The value of V.T is =\\n\", vh)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9g-SH5FG9R4p"},"source":["\n","\n","def get_rank(A):\n","    \n","\n","    u, s, vh = np.linalg.svd(A, full_matrices=True)\n","    rank = 0\n","    for i in s:\n","        if i!=0.0:\n","            rank += 1\n","    return rank\n","def get_col_space(A):\n","    \n","    rank = get_rank(A)\n","    \n","    col_space = np.transpose(A)[0:rank]\n","    \n","    \n","    \n","    return col_space\n","\n","def get_null_space(A):\n","    \n","    rank = get_rank(A)\n","    \n","    col_space = np.transpose(A)[rank:len(A.T)]\n","    \n","    \n","    \n","    return col_space"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"U_2WB63I9R7F"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yo2bUr9B-sHQ"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [LINEAR REGRESSION] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"c5yxH2IP-wuH"},"source":["'''\n","Import of Useful Packages\n","'''\n","\n","%matplotlib inline\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from numpy.linalg import inv\n","import pandas as pd\n","import random\n","import statistics"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1JbBQsN9-4V5"},"source":["def normal_equation_solution(B,y):\n","    '''\n","    Inputs: B (m by n matrix), y (m by 1 matrix)\n","    \n","    Bx = y\n","    Im = O\n","    \n","    Output: x'min  = (BTB)-1 (BT)(y)\n","    \n","    '''\n","    \n","    BTB = np.matmul(B.T, B)\n","    BTBinvBT = np.matmul(inv(BTB), B.T)\n","    x_prime_min = np.matmul(BTBinvBT,y)\n","    \n","    return x_prime_min"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yfPTXlWl-4YN"},"source":["\n","B = np.array([[4,1],\n","              [0,1],\n","              [1,1]])\n","\n","y = np.array([[1],\n","              [3],\n","              [1]])\n","print(y.shape)\n","print(normal_equation_solution(B,y))\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"i0rB-lJB-4ag"},"source":["train_data = output_data_from_file(\"D:/neuralearn.ai/Linear Algebra for Machine Learning/LINEAR REGRESSION/train.csv\")\n","\n","train_data = np.array(train_data)\n","print(train_data[:,1:])\n","#print(train_data[0:100,:])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6NOJKQ4S-4cp"},"source":["def data_processing(train_data):\n","    \n","    '''\n","    Input: extracted from csv file which is train data\n","    \n","    Ouputs: MsSubClass, LotFrontage, LotArwea and Saleprice\n","    \n","    \n","    '''\n","\n","    MsSubClass =train_data[:,1]\n","\n","    LotFrontage = []\n","    for i in range(len(train_data[:,2])):\n","\n","        if (np.isnan(train_data[:,2][i])):\n","            LotFrontage.append(random.randrange(0,125))\n","        else:\n","            LotFrontage.append(train_data[:,2][i])\n","\n","    LotArea =train_data[:,3]\n","\n","\n","    SalePrice = train_data[:,4]\n","    \n","    return MsSubClass, LotFrontage, LotArea, SalePrice"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TZ13poYv-4e6"},"source":["MsSubClass, LotFrontage, LotArea, SalePrice = data_processing(train_data)\n","\n","plt.scatter(MsSubClass, SalePrice)\n","plt.show()\n","\n","plt.scatter(LotFrontage, SalePrice)\n","plt.show()\n","\n","plt.scatter(LotArea, SalePrice)\n","plt.show()\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"imNP9Dc3_T5z"},"source":["I = np.array([MsSubClass, LotFrontage, LotArea])\n","print(I.shape)\n","I = I.T\n","print(I.shape)\n","\n","\n","O = np.array(SalePrice)\n","print(O.shape)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"n2BcQGxg-4hY"},"source":["#m = weights\n","print(\"The normal equation solution is m =  \", normal_equation_solution(I,O))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SpKnv5yB-4j_"},"source":["print(I.shape)\n","\n","I_train = I[:1450,:]\n","O_train = O[:1450]\n","\n","I_test = I[1450:,:]\n","O_test = O[1450:]\n","print(O_test)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"aQ6ui9Qg_cc2"},"source":["weights = normal_equation_solution(I_train,O_train)\n","print(\"The normal equation solution is = \", normal_equation_solution(I_train,O_train))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zW6TbxM1_pWQ"},"source":["print(SalePrice[1450:])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6xHxNWqV_cfc"},"source":["O_test_approximation = np.matmul(I_test,weights)\n","#print(O_test_approximation)\n","error = 0\n","for i in range(len(O_test_approximation)):\n","    \n","    print(\"----> \",O_test[i],\"---\",  O_test_approximation[i])\n","    \n","    error = error+ (O_test[i]-O_test_approximation[i])**2"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bgHKQ-of_0P1"},"source":["print(\"The total test error = \", np.sqrt(error)) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"g24IatRx_ciF"},"source":["training_data = np.array([MsSubClass, LotFrontage, LotArea])\n","print(training_data.T)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"uvcAR2xX_ckh"},"source":["training_data = training_data.T"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"lH6eTP3qAHut"},"source":["train_data = training_data\n","print(train_data)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zY5uBFK5AEcA"},"source":["def standardize(X):\n","    '''\n","    Inputs: X\n","    Ouputs: Xstandardized, means\n","    '''\n","    \n","    means = [0]*3\n","    \n","    for i in range(3):\n","\n","        means[i] = statistics.mean(X[:,i])\n","    print(means)\n","    for i in range(1460):\n","        for j in range(3):\n","            \n","            X[i][j] = (X[i][j] - means[j])/255\n","\n","    return X,means"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"DaKNdOQGAEel"},"source":["def get_eigs(X):\n","    '''\n","    Input: X\n","    Outputs: Eigen valuea and the eigen vectors of xxT\n","    '''\n","    \n","    eigen_values, eigen_vectors = np.linalg.eigh(get_covariance_matrix(X))\n","    \n","    eigen_values, eigen_vectors = sort_eigs(eigen_values,eigen_vectors)\n","    \n","    singular_values =  square_root(eigen_values)\n","    return singular_values, eigen_vectors"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QpOnpDeo_cms"},"source":["def get_covariance_matrix(X):\n","    return np.matmul(X.T,X)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"uoljC4nCBkVz"},"source":["def sort_eigs(eigen_values,eigen_vectors):\n","    sorted_indices = eigen_values.argsort()[::-1]\n","    eigen_values = eigen_values[sorted_indices]\n","    eigen_vectors = eigen_vectors[:,sorted_indices]\n","    \n","    return eigen_values, eigen_vectors"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GZU51n3uBkYg"},"source":["def square_root(eigen_values):\n","    singular_values =  [0]*len(eigen_values)\n","    \n","    for i in range(len(eigen_values)):\n","        \n","        if eigen_values[i]<0:\n","            singular_values[i] = 0\n","        else:\n","            singular_values[i] = np.sqrt(eigen_values[i])\n","    \n","    return singular_values"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"COYoeOV3BkbB"},"source":["def get_rank (eigen_values):\n","    threshold = 0.0000001\n","    rank = len(eigen_values)\n","    for i in range(len(eigen_values)):\n","        if ((eigen_values[i]/eigen_values[0])<threshold):\n","            \n","            rank = i\n","            break\n","    \n","    return rank"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qPEK2MyaBkdf"},"source":["def get_V(X,singular_values, eigen_vectors):\n","    \n","    rank = get_rank(singular_values)\n","    U_r = eigen_vectors[:,0:rank]\n","    \n","    #sigma_r = np.zeros((rank,rank))\n","    \n","    #for i in range(rank):\n","    #    sigma_r[i][i] = singular_values[i]\n","    \n","    #V_r = np.matmul(np.matmul(X.T,U_r) ,np.linalg.inv(sigma_r))\n","    return U_r\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kRTwCimPBkgt"},"source":["def projection(X,B):\n","    \n","    return np.matmul(X,B)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FkVw7LWuBkkU"},"source":["X_train = training_data\n","X_train, means =  standardize (X_train)\n","    \n","eigen_values, eigen_vectors  = get_eigs(X_train)\n","\n","V_r = get_V(X_train, eigen_values, eigen_vectors)\n","    \n","B = V_r \n","\n","print(\"This is our Base B=\", B.shape)\n","\n","X_train = projection(X_train, B)\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hr1O0SQ9BtfK"},"source":["print(B)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BdK6XSpNBth_"},"source":["B_2 = B[:,:2]\n","print(B_2)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0_EyaXTpBtku"},"source":["B_1 = B[:,:1]\n","print(B_1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mXLiQhpBBtnG"},"source":["print(train_data)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wQs7DCNdB1Eq"},"source":["training_data_1 = np.matmul(train_data,B_1)\n","training_data_2 = np.matmul(train_data,B_2)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ui4a9yPvBtp0"},"source":["\n","from mpl_toolkits.mplot3d import Axes3D\n","fig = plt.figure(figsize=(20,20))\n","ax = fig.add_subplot(111, projection='3d')\n","ax = fig.add_subplot(111, projection='3d')\n","\n","ax.scatter(training_data_2[:,0],training_data_2[:,1], SalePrice, c='r', marker='o')\n","\n","ax.scatter(training_data_1[:,0], SalePrice, c='b', marker='o')\n","\n","\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Mx2IRkgyB5s6"},"source":["plt.scatter(training_data_1[:,0], SalePrice)\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"z7eLhFgUB5vm"},"source":["plt.scatter(LotArea, SalePrice)\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WSKPdq6xB5yZ"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BMHUW5nH-xAc"},"source":["++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n"," <h1> <center> <font color = \"orange\"> [PCA ALGORITHM] <br><font color=\"grey\"> </font></center></h1>\n","\n","+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"]},{"cell_type":"code","metadata":{"id":"-3d-AA0B9R9r"},"source":["'''\n","imports\n","'''\n","import numpy as np\n","import os\n","import cv2\n","from sklearn import svm\n","import matplotlib.pyplot as plt\n","import matplotlib.image as mpimg\n","import statistics"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"J5DKc_HB9SAB"},"source":["def train(X_train,y_train):\n","    #Create a svm Classifier\n","    model = svm.SVC(kernel='linear') # Linear Kernel\n","\n","    #Train the model using the training sets\n","    model.fit(X_train, y_train)\n","    \n","    return model"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"isX9JD63CHYk"},"source":["def projection(X,B):\n","    \n","    return np.matmul(X,B)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sd1NoQvUCHa_"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"AMhqBv65CHdy"},"source":["def get_V(X,singular_values, eigen_vectors):\n","    \n","    rank = get_rank(singular_values)\n","    U_r = eigen_vectors[:,0:rank]\n","    \n","    sigma_r = np.zeros((rank,rank))\n","    \n","    for i in range(rank):\n","        sigma_r[i][i] = singular_values[i]\n","    \n","    V_r = np.matmul(np.matmul(X.T,U_r) ,np.linalg.inv(sigma_r))\n","    return V_r\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"uTNfOK6ICHgG"},"source":["def get_rank (eigen_values):\n","    threshold = 0.01\n","    \n","    for i in range(len(eigen_values)):\n","        if ((eigen_values[i]/eigen_values[0])<threshold):\n","            \n","            rank = i\n","            break\n","    \n","    return rank"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UMhNzP2sCHiv"},"source":["def square_root(eigen_values):\n","    singular_values =  [0]*len(eigen_values)\n","    \n","    for i in range(len(eigen_values)):\n","        \n","        if eigen_values[i]<0:\n","            singular_values[i] = 0\n","        else:\n","            singular_values[i] = np.sqrt(eigen_values[i])\n","    \n","    return singular_values"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JlSlk9FACHlH"},"source":["def sort_eigs(eigen_values,eigen_vectors):\n","    sorted_indices = eigen_values.argsort()[::-1]\n","    eigen_values = eigen_values[sorted_indices]\n","    eigen_vectors = eigen_vectors[:,sorted_indices]\n","    \n","    return eigen_values, eigen_vectors"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wlV-MPbuCHn1"},"source":["def get_covariance_matrix(X):\n","    return np.matmul(X,X.T)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rxoFQuxWCUSr"},"source":["def get_eigs(X):\n","    '''\n","    Input: X\n","    Outputs: Eigen valuea and the eigen vectors of xxT\n","    '''\n","    \n","    eigen_values, eigen_vectors = np.linalg.eigh(get_covariance_matrix(X))\n","    \n","    eigen_values, eigen_vectors = sort_eigs(eigen_values,eigen_vectors)\n","    \n","    singular_values =  square_root(eigen_values)\n","    return singular_values, eigen_vectors"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"L4sAwPQFCUVM"},"source":["def standardize(X, train = True, means =[]):\n","    '''\n","    Inputs: X\n","    Ouputs: Xstandardized, means\n","    '''\n","    if (train):\n","        \n","        means = [0]*RESIZE * RESIZE\n","        \n","        for i in range(len(means)):\n","            \n","            means[i] = statistics.mean(X[:,i])\n","        \n","        for i in range(len(train_directory_list)):\n","            for j in range(RESIZE*RESIZE):\n","                X[i][j] = (X[i][j] - means[j])/255\n","        \n","        return X,means\n","    else:\n","        for i in range(1):\n","            for j in range(RESIZE*RESIZE):\n","                X[i][j] = (X[i][j] - means[j])/255\n","                \n","        return X\n","        "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZGL7TBtZCUXq"},"source":["def flatten(X_t, train = True):\n","    '''\n","    INputs: X, train or not test_directory or train directory\n","    Ouput: flattened X\n","    \n","    (40,100,100) ---> (40, 10000)\n","    '''\n","    if (train):\n","        X = [[None]*RESIZE*RESIZE] * len(train_directory_list) # 40, 10000\n","\n","        for i in range (len(train_directory_list)):\n","            X[i] = X_t[i].flatten()\n","    else:\n","        X = [[None]*RESIZE*RESIZE] * 1 # 1, 10000\n","\n","        for i in range (1):\n","            X[i] = X_t[i].flatten()\n","\n","    \n","    return np.array(X)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ki_EP3dSCUaJ"},"source":["train_directory = \"D:/neuralearn.ai/Linear Algebra for Machine Learning/PCA/eigen_faces/Celebrities/train_set/\"    \n","train_directory_list=os.listdir(train_directory)\n","    \n","directory_list = train_directory_list\n","\n","RESIZE = 100\n","\n","X_train, _ = read_and_process_image(directory_list, celebrity = \"simi\")\n","\n","print(X_train.shape)\n","\n","print(flatten(X_train).shape)\n","X_train = flatten(X_train) \n","print(X_train.shape)\n","X_standardized, means = standardize(X_train)\n","\n","print(X_standardized.shape)\n","\n","\n","singular_values, eigen_vectors = get_eigs(X_standardized)\n","\n","print(singular_values)\n","print(eigen_vectors)\n","\n","print(get_rank(singular_values))\n","\n","\n","print(get_V(X_train,singular_values,eigen_vectors))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"V7Rc-RcUCUdI"},"source":["test_directory = \"D:/neuralearn.ai/Linear Algebra for Machine Learning/PCA/eigen_faces/Celebrities/test_set/\"    \n","test_directory_list=os.listdir(test_directory)\n","    \n","directory_list = test_directory_list\n","\n","RESIZE = 100\n","\n","X_test = read_and_process_image(directory_list, train = False, celebrity = \"simi\")\n","\n","print(X_test.shape)\n","X_test = flatten(X_train, train = False)\n","print(X_test.shape)\n","X_test = standardize(X_test, train = False, means = means)\n","\n","print(X_test)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YUnLcW4lCUf0"},"source":["def read_and_process_image(directory_list, train=True , celebrity = \"ben\"):\n","    '''\n","    Inputs: directory list, whether train or test, celebrity name in the form of a string\n","    Outputs the data which we'll be needing to train the model (X,y)\n","    \n","    '''\n","    \n","    X = []\n","    y = []\n","    \n","    for image in directory_list:\n","        if (train):\n","            img = cv2.imread(train_directory+image, cv2.IMREAD_GRAYSCALE)\n","        else:\n","            img = cv2.imread(test_directory+image, cv2.IMREAD_GRAYSCALE)\n","    \n","    \n","        img = cv2.resize(img, (RESIZE, RESIZE))\n","        img = img.astype(float)\n","        \n","        X.append(img)\n","        \n","        if (train):\n","            \n","            if (celebrity.lower() in image):\n","                y.append(1)\n","                \n","            else:\n","                y.append(0)\n","    \n","    if (train):\n","        return np.array(X),np.array(y)\n","    else:\n","        return np.array(X)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NNrUj1_ACUh3"},"source":["train_directory = \"D:/neuralearn.ai/Linear Algebra for Machine Learning/PCA/eigen_faces/Celebrities/train_set/\"    \n","train_directory_list=os.listdir(train_directory)\n","    \n","directory_list = train_directory_list\n","\n","\n","X_train,y_train = read_and_process_image(directory_list, train = True, celebrity =\"richard\")\n","\n","print(y_train)\n","\n","\n","test_directory = \"D:/neuralearn.ai/Linear Algebra for Machine Learning/PCA/eigen_faces/Celebrities/test_set/\"    \n","test_directory_list=os.listdir(test_directory)\n","    \n","directory_list = test_directory_list\n","\n","\n","X_test = read_and_process_image(directory_list, train = False, celebrity =[\"simi\"])\n","\n","print(X_test.shape)\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-vsiSpWKCjVu"},"source":["def verification (B, model, means, test_celebrity):\n","    '''\n","    Inputs: B, model, means, test data\n","    Ouput: prediction\n","    '''\n","    \n","    X_test = read_and_process_image(test_celebrity, train = False) \n","    \n","    X_test = flatten(X_test, train = False)\n","    \n","    X_test = standardize(X_test, train = False, means=means)\n","    \n","    X_test = projection(X_test, B)\n","    \n","    y_pred = model.predict(X_test)\n","    \n","    return y_pred # [0] or [1]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tlbBzfzcCjYl"},"source":["def generate_weight(celebrity): \n","    \n","    '''\n","    Input celebrity \n","    Ouputs weights, B, means\n","    '''\n","    \n","    train_directory = \"D:/neuralearn.ai/Linear Algebra for Machine Learning/PCA/eigen_faces/Celebrities/train_set/\"\n","    \n","    train_directory_list=os.listdir(train_directory)\n","    \n","    X_train, y_train = read_and_process_image(train_directory_list, train = True, celebrity=celebrity) # (40,100,100), (40,)\n","    \n","    X_train =  flatten(X_train) #(40,10000)\n","    \n","    X_train, means =  standardize (X_train)\n","    \n","    \n","    eigen_values, eigen_vectors  = get_eigs(X_train)\n","    \n","    \n","    V_r = get_V(X_train, eigen_values, eigen_vectors)\n","    \n","    \n","    B = V_r \n","    \n","    print(\"This is our Base B=\", B.shape)\n","    \n","    X_train = projection(X_train, B)\n","    \n","    weight = train (X_train, y_train)\n","    \n","    return  weight, np.array(B), np.array(means)\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SwBBsch-CjbA"},"source":["celebrities = [\"Ben\", \"Bob\", \"Simi\", \"Richard\", \"Dipanda\"]\n","\n","RESIZE = 100\n","weights = []"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"CNv8ujHeCHq3"},"source":["for c in celebrities:\n","    \n","    weight, B, means = generate_weight(c)\n","    weights.append(weight)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FBffMjOKCvoo"},"source":["test_directory = \"D:/neuralearn.ai/Linear Algebra for Machine Learning/PCA/eigen_faces/Celebrities/test_set/\"\n","test_directory_list =os.listdir(test_directory)\n","print(test_directory_list)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"an7kpWarCyJ7"},"source":["\n","for test_celeb in test_directory_list:\n","    found = False\n","    print(test_directory+test_celeb)\n","    im = mpimg.imread(test_directory+test_celeb)\n","    im = plt.imshow(im)\n","    plt.show()\n","    \n","    test_celebrity = []\n","    test_celebrity.append(test_celeb)\n","    print(test_celebrity)\n","    i = 0\n","    for c in celebrities:\n","        print(test_celebrity)\n","        \n","        if (verification (B,weights[i],means, test_celebrity)== [1]):\n","            found = True\n","            print(\"This image has been recognized as: \", c)\n","            break\n","        i+=1 # i = i+1 \n","        \n","    if (found ==  False):\n","        print(\"This person is not found in the database!!!\")\n","            \n","        \n","        \n","    \n","    "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"L0NoL_m5CyOo"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"HiTKkewaCySE"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZBNIdXC5CyXE"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"swFNblGoCyaD"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NES3FuzyCyc9"},"source":[],"execution_count":null,"outputs":[]}]}

================================================
FILE: deep learning for natural language processing/1-Linear Regression for Predicting Price of second hand Cars by Neuralearn.ai-.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1MpOO7MxtkDaf3S0rIK8SDCfnpI6TmU_G","timestamp":1673816133355}],"collapsed_sections":["j6EtG3kpNKiT","CxlimoToLc4C","TSjCxq__LQkw"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"Q_Wi6cQRp4Rv"},"source":["import tensorflow as tf### models\n","import pandas as pd ### reading and processing data\n","import seaborn as sns ### visualization\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","from tensorflow.keras.layers import Normalization, Dense, InputLayer\n","from tensorflow.keras.losses import MeanSquaredError, Huber, MeanAbsoluteError\n","from tensorflow.keras.metrics import RootMeanSquaredError\n","from tensorflow.keras.optimizers import Adam"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"j6EtG3kpNKiT"},"source":["## **Data Preparation**"]},{"cell_type":"code","metadata":{"id":"_wKxrpeci_gT","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1635922384406,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"de7b09f9-1986-429b-f069-e27766c2c2fc"},"source":["data = pd.read_csv(\"train.csv\", \",\")\n","data.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>v.id</th>\n","      <th>on road old</th>\n","      <th>on road now</th>\n","      <th>years</th>\n","      <th>km</th>\n","      <th>rating</th>\n","      <th>condition</th>\n","      <th>economy</th>\n","      <th>top speed</th>\n","      <th>hp</th>\n","      <th>torque</th>\n","      <th>current price</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>535651</td>\n","      <td>798186</td>\n","      <td>3</td>\n","      <td>78945</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>14</td>\n","      <td>177</td>\n","      <td>73</td>\n","      <td>123</td>\n","      <td>351318.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>591911</td>\n","      <td>861056</td>\n","      <td>6</td>\n","      <td>117220</td>\n","      <td>5</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>148</td>\n","      <td>74</td>\n","      <td>95</td>\n","      <td>285001.5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>686990</td>\n","      <td>770762</td>\n","      <td>2</td>\n","      <td>132538</td>\n","      <td>2</td>\n","      <td>8</td>\n","      <td>15</td>\n","      <td>181</td>\n","      <td>53</td>\n","      <td>97</td>\n","      <td>215386.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>573999</td>\n","      <td>722381</td>\n","      <td>4</td>\n","      <td>101065</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>11</td>\n","      <td>197</td>\n","      <td>54</td>\n","      <td>116</td>\n","      <td>244295.5</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>691388</td>\n","      <td>811335</td>\n","      <td>6</td>\n","      <td>61559</td>\n","      <td>3</td>\n","      <td>9</td>\n","      <td>12</td>\n","      <td>160</td>\n","      <td>53</td>\n","      <td>105</td>\n","      <td>531114.5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   v.id  on road old  on road now  years  ...  top speed  hp  torque  current price\n","0     1       535651       798186      3  ...        177  73     123       351318.0\n","1     2       591911       861056      6  ...        148  74      95       285001.5\n","2     3       686990       770762      2  ...        181  53      97       215386.0\n","3     4       573999       722381      4  ...        197  54     116       244295.5\n","4     5       691388       811335      6  ...        160  53     105       531114.5\n","\n","[5 rows x 12 columns]"]},"metadata":{},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"txHuR6vUXK7-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922384407,"user_tz":-60,"elapsed":16,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"4f749ca2-75f0-4834-845e-eed50181660b"},"source":["data.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1000, 12)"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"U_l14YAjXLDD"},"source":["# data = pd.read_csv(\"train_semi.csv\", \",\")\n","# data.head()\n","# data.shape"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ssm3ewabXLFx","colab":{"base_uri":"https://localhost:8080/","height":999},"executionInfo":{"status":"ok","timestamp":1635922431466,"user_tz":-60,"elapsed":46423,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"edfa5533-543d-4013-9b89-8a81de77b091"},"source":["sns.pairplot(data[['years', 'km', 'rating', 'condition', 'economy', 'top speed', 'hp', 'torque', 'current price']], diag_kind='kde')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.PairGrid at 0x7f9735927510>"]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1620x1620 with 90 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"kbKG64L3XLIh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432304,"user_tz":-60,"elapsed":869,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"8140c178-3490-4431-a963-f3a9a6ca700d"},"source":["tensor_data = tf.constant(data)\n","tensor_data = tf.cast(tensor_data, tf.float32)\n","print(tensor_data)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[1.000000e+00 5.356510e+05 7.981860e+05 ... 7.300000e+01 1.230000e+02\n","  3.513180e+05]\n"," [2.000000e+00 5.919110e+05 8.610560e+05 ... 7.400000e+01 9.500000e+01\n","  2.850015e+05]\n"," [3.000000e+00 6.869900e+05 7.707620e+05 ... 5.300000e+01 9.700000e+01\n","  2.153860e+05]\n"," ...\n"," [9.980000e+02 6.463440e+05 8.427330e+05 ... 1.130000e+02 8.900000e+01\n","  4.058710e+05]\n"," [9.990000e+02 5.355590e+05 7.324390e+05 ... 1.120000e+02 1.280000e+02\n","  7.439800e+04]\n"," [1.000000e+03 5.901050e+05 7.797430e+05 ... 9.900000e+01 9.600000e+01\n","  4.149385e+05]], shape=(1000, 12), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"7xxpRZBmXLLM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432306,"user_tz":-60,"elapsed":45,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"4f8000c2-48ab-478b-d589-16d648c8c6f4"},"source":["tensor_data = tf.random.shuffle(tensor_data)\n","print(tensor_data[:5])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[2.790000e+02 6.405200e+05 7.510240e+05 4.000000e+00 1.292510e+05\n","  2.000000e+00 3.000000e+00 1.300000e+01 1.420000e+02 7.700000e+01\n","  1.100000e+02 1.786425e+05]\n"," [6.200000e+02 5.112580e+05 7.948000e+05 6.000000e+00 1.043080e+05\n","  2.000000e+00 9.000000e+00 1.400000e+01 1.770000e+02 9.500000e+01\n","  9.700000e+01 2.617320e+05]\n"," [7.250000e+02 6.785350e+05 7.088160e+05 6.000000e+00 9.693300e+04\n","  3.000000e+00 1.000000e+00 1.100000e+01 1.460000e+02 1.200000e+02\n","  1.040000e+02 3.024230e+05]\n"," [6.560000e+02 6.146710e+05 8.504940e+05 4.000000e+00 5.244300e+04\n","  2.000000e+00 6.000000e+00 1.500000e+01 1.630000e+02 8.800000e+01\n","  1.240000e+02 5.265025e+05]\n"," [6.660000e+02 6.357630e+05 8.223260e+05 3.000000e+00 1.305400e+05\n","  1.000000e+00 1.000000e+00 1.200000e+01 1.590000e+02 9.300000e+01\n","  7.500000e+01 2.069995e+05]], shape=(5, 12), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"Q5kVK2udXLOE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432307,"user_tz":-60,"elapsed":43,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"eb66ced6-9aa9-466f-b54b-16f4d5cc4e75"},"source":["X = tensor_data[:,3:-1]\n","print(X[:5])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[4.00000e+00 1.29251e+05 2.00000e+00 3.00000e+00 1.30000e+01 1.42000e+02\n","  7.70000e+01 1.10000e+02]\n"," [6.00000e+00 1.04308e+05 2.00000e+00 9.00000e+00 1.40000e+01 1.77000e+02\n","  9.50000e+01 9.70000e+01]\n"," [6.00000e+00 9.69330e+04 3.00000e+00 1.00000e+00 1.10000e+01 1.46000e+02\n","  1.20000e+02 1.04000e+02]\n"," [4.00000e+00 5.24430e+04 2.00000e+00 6.00000e+00 1.50000e+01 1.63000e+02\n","  8.80000e+01 1.24000e+02]\n"," [3.00000e+00 1.30540e+05 1.00000e+00 1.00000e+00 1.20000e+01 1.59000e+02\n","  9.30000e+01 7.50000e+01]], shape=(5, 8), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"KOy9jxS-XLQu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432308,"user_tz":-60,"elapsed":40,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"a0f50e86-5ad7-492d-a305-261fc9b6da00"},"source":["y = tensor_data[:,-1]\n","print(y[:5].shape)\n","y = tf.expand_dims(y, axis = -1)\n","print(y[:5])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(5,)\n","tf.Tensor(\n","[[178642.5]\n"," [261732. ]\n"," [302423. ]\n"," [526502.5]\n"," [206999.5]], shape=(5, 1), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"1tGN1KNQXLTl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432308,"user_tz":-60,"elapsed":38,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"73e4a3f9-faa7-4fd2-d7d2-07b86d7eae56"},"source":["normalizer = Normalization(axis = -1, mean = 5, variance = 4)\n","x_normalized = tf.constant([[3,4,5,6,7],\n","                            [4,5,6,7,8]])\n","normalizer(x_normalized)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(2, 5), dtype=float32, numpy=\n","array([[-1. , -0.5,  0. ,  0.5,  1. ],\n","       [-0.5,  0. ,  0.5,  1. ,  1.5]], dtype=float32)>"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"ybWIORDvXLWq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432309,"user_tz":-60,"elapsed":37,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"84fb26f2-2c1a-4f9c-cc31-f9cb510f5279"},"source":["normalizer = Normalization()\n","x_normalized = tf.constant([[3,4,5,6,7],\n","                            [4,10,6,7,8],\n","                            [32,1,56,3,5]])\n","normalizer.adapt(x_normalized)\n","normalizer(x_normalized)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(3, 5), dtype=float32, numpy=\n","array([[-0.7439795 , -0.26726124, -0.72800297,  0.3922322 ,  0.2672614 ],\n","       [-0.6695816 ,  1.3363062 , -0.6860028 ,  0.9805806 ,  1.0690452 ],\n","       [ 1.4135611 , -1.069045  ,  1.4140056 , -1.3728131 , -1.3363061 ]],\n","      dtype=float32)>"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"0JiTE-U_XLZK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432310,"user_tz":-60,"elapsed":35,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"e59fc7b1-3c25-41fe-d903-039dc1f92bfd"},"source":["print(X.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1000, 8)\n"]}]},{"cell_type":"code","metadata":{"id":"NjehtcxAW6av"},"source":["TRAIN_RATIO = 0.8\n","VAL_RATIO = 0.1\n","TEST_RATIO = 0.1\n","DATASET_SIZE = len(X)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"27pin8soXEue","executionInfo":{"status":"ok","timestamp":1635922432312,"user_tz":-60,"elapsed":34,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"3e2fc8f4-4d68-49e0-fbe9-daa41dae7b5d"},"source":["X_train = X[:int(DATASET_SIZE*TRAIN_RATIO)]\n","y_train = y[:int(DATASET_SIZE*TRAIN_RATIO)]\n","print(X_train.shape)\n","print(y_train.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(800, 8)\n","(800, 1)\n"]}]},{"cell_type":"code","metadata":{"id":"-9wd1j166mMG"},"source":["train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n","train_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"woglsImt-fu1","executionInfo":{"status":"ok","timestamp":1635929187855,"user_tz":-60,"elapsed":570,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"43c60638-25a3-4e75-afba-c1372c0bd0ad"},"source":["for x,y in train_dataset:\n","  print(x,y)\n","  break"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[6.00000e+00 9.69330e+04 3.00000e+00 1.00000e+00 1.10000e+01 1.46000e+02\n","  1.20000e+02 1.04000e+02]\n"," [5.00000e+00 7.80250e+04 1.00000e+00 9.00000e+00 1.50000e+01 1.71000e+02\n","  9.40000e+01 1.32000e+02]\n"," [7.00000e+00 1.18659e+05 2.00000e+00 7.00000e+00 1.20000e+01 1.80000e+02\n","  6.60000e+01 1.20000e+02]\n"," [5.00000e+00 1.43526e+05 3.00000e+00 7.00000e+00 8.00000e+00 1.75000e+02\n","  1.10000e+02 1.37000e+02]\n"," [6.00000e+00 1.04308e+05 2.00000e+00 9.00000e+00 1.40000e+01 1.77000e+02\n","  9.50000e+01 9.70000e+01]\n"," [3.00000e+00 1.30540e+05 1.00000e+00 1.00000e+00 1.20000e+01 1.59000e+02\n","  9.30000e+01 7.50000e+01]\n"," [4.00000e+00 5.24430e+04 2.00000e+00 6.00000e+00 1.50000e+01 1.63000e+02\n","  8.80000e+01 1.24000e+02]\n"," [5.00000e+00 1.00534e+05 1.00000e+00 9.00000e+00 1.50000e+01 1.37000e+02\n","  7.20000e+01 1.19000e+02]\n"," [6.00000e+00 8.93540e+04 1.00000e+00 1.00000e+01 1.20000e+01 1.40000e+02\n","  5.60000e+01 1.27000e+02]\n"," [4.00000e+00 1.29251e+05 2.00000e+00 3.00000e+00 1.30000e+01 1.42000e+02\n","  7.70000e+01 1.10000e+02]\n"," [5.00000e+00 1.22976e+05 5.00000e+00 4.00000e+00 9.00000e+00 1.79000e+02\n","  1.05000e+02 7.50000e+01]\n"," [7.00000e+00 1.26075e+05 2.00000e+00 9.00000e+00 1.40000e+01 1.47000e+02\n","  1.02000e+02 1.37000e+02]\n"," [2.00000e+00 1.01069e+05 1.00000e+00 1.00000e+01 1.30000e+01 1.55000e+02\n","  7.70000e+01 1.24000e+02]\n"," [7.00000e+00 1.31174e+05 2.00000e+00 5.00000e+00 1.00000e+01 1.95000e+02\n","  5.30000e+01 1.00000e+02]\n"," [7.00000e+00 1.44949e+05 5.00000e+00 6.00000e+00 1.20000e+01 1.51000e+02\n","  6.80000e+01 1.08000e+02]\n"," [6.00000e+00 9.44370e+04 5.00000e+00 9.00000e+00 1.50000e+01 1.76000e+02\n","  8.40000e+01 1.26000e+02]\n"," [3.00000e+00 1.39295e+05 5.00000e+00 1.00000e+01 1.20000e+01 1.50000e+02\n","  7.90000e+01 1.12000e+02]\n"," [2.00000e+00 1.09453e+05 5.00000e+00 4.00000e+00 1.30000e+01 1.59000e+02\n","  9.90000e+01 1.05000e+02]\n"," [5.00000e+00 1.45711e+05 3.00000e+00 8.00000e+00 9.00000e+00 1.94000e+02\n","  5.80000e+01 9.50000e+01]\n"," [5.00000e+00 1.03827e+05 5.00000e+00 6.00000e+00 1.10000e+01 1.51000e+02\n","  7.20000e+01 1.02000e+02]\n"," [7.00000e+00 8.19410e+04 3.00000e+00 1.00000e+01 1.10000e+01 1.44000e+02\n","  6.00000e+01 1.29000e+02]\n"," [5.00000e+00 1.16435e+05 2.00000e+00 5.00000e+00 1.30000e+01 1.75000e+02\n","  1.04000e+02 1.25000e+02]\n"," [6.00000e+00 5.29460e+04 2.00000e+00 8.00000e+00 1.20000e+01 1.98000e+02\n","  5.30000e+01 8.60000e+01]\n"," [4.00000e+00 1.14468e+05 2.00000e+00 8.00000e+00 1.10000e+01 1.37000e+02\n","  5.50000e+01 1.40000e+02]\n"," [2.00000e+00 8.71990e+04 1.00000e+00 7.00000e+00 8.00000e+00 1.49000e+02\n","  7.80000e+01 1.21000e+02]\n"," [5.00000e+00 8.25800e+04 4.00000e+00 3.00000e+00 1.20000e+01 1.87000e+02\n","  9.80000e+01 1.00000e+02]\n"," [3.00000e+00 1.11289e+05 2.00000e+00 1.00000e+00 1.20000e+01 1.53000e+02\n","  8.00000e+01 9.70000e+01]\n"," [6.00000e+00 1.33023e+05 1.00000e+00 3.00000e+00 8.00000e+00 1.86000e+02\n","  6.00000e+01 9.90000e+01]\n"," [6.00000e+00 5.86040e+04 3.00000e+00 4.00000e+00 1.10000e+01 1.53000e+02\n","  1.17000e+02 1.05000e+02]\n"," [4.00000e+00 9.92110e+04 5.00000e+00 5.00000e+00 1.10000e+01 1.95000e+02\n","  7.40000e+01 1.05000e+02]\n"," [2.00000e+00 1.08202e+05 4.00000e+00 1.00000e+01 9.00000e+00 1.99000e+02\n","  7.50000e+01 1.30000e+02]\n"," [5.00000e+00 5.85480e+04 3.00000e+00 9.00000e+00 1.20000e+01 1.82000e+02\n","  1.08000e+02 8.50000e+01]], shape=(32, 8), dtype=float32) tf.Tensor(\n","[[302423. ]\n"," [410877. ]\n"," [239214. ]\n"," [ 64531. ]\n"," [261732. ]\n"," [206999.5]\n"," [526502.5]\n"," [253853.5]\n"," [408452.5]\n"," [178642.5]\n"," [175012.5]\n"," [209639. ]\n"," [341328. ]\n"," [ 90220. ]\n"," [ 94467.5]\n"," [337327. ]\n"," [243097. ]\n"," [274075. ]\n"," [170960.5]\n"," [284462.5]\n"," [498148. ]\n"," [248738. ]\n"," [539377. ]\n"," [221412.5]\n"," [375097.5]\n"," [299609. ]\n"," [306660. ]\n"," [130112.5]\n"," [400257. ]\n"," [369149. ]\n"," [336038. ]\n"," [502708.5]], shape=(32, 1), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NCRgNb3dXge1","executionInfo":{"status":"ok","timestamp":1635922432313,"user_tz":-60,"elapsed":33,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"32d14640-95c0-4b45-b3e7-3f4c28e3121c"},"source":["X_val = X[int(DATASET_SIZE*TRAIN_RATIO):int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO))]\n","y_val = y[int(DATASET_SIZE*TRAIN_RATIO):int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO))]\n","print(X_val.shape)\n","print(y_val.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(100, 8)\n","(100, 1)\n"]}]},{"cell_type":"code","metadata":{"id":"aBwRpaG0-urM"},"source":["val_dataset = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n","val_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A3ba-zUTX2Xv","executionInfo":{"status":"ok","timestamp":1635922432314,"user_tz":-60,"elapsed":31,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"4ee76225-a123-4186-9263-15272bf35a94"},"source":["X_test = X[int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO)):]\n","y_test = y[int(DATASET_SIZE*(TRAIN_RATIO+VAL_RATIO)):]\n","print(X_test.shape)\n","print(y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(100, 8)\n","(100, 1)\n"]}]},{"cell_type":"code","metadata":{"id":"bd4gJi1n-1mW"},"source":["test_dataset = tf.data.Dataset.from_tensor_slices((X_test, y_test))\n","test_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(32).prefetch(tf.data.AUTOTUNE)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"c1k0dBjFXLcC","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432315,"user_tz":-60,"elapsed":30,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"c2f0a40b-defd-486e-caf4-9c5b7e7ea338"},"source":["normalizer = Normalization()\n","normalizer.adapt(X_train)\n","normalizer(X)[:5]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(5, 8), dtype=float32, numpy=\n","array([[-0.31518766,  1.0132637 , -0.71715826, -0.91459674,  0.6136699 ,\n","        -1.301998  , -0.35419205,  0.3123971 ],\n","       [ 0.8309489 ,  0.15622592, -0.71715826,  1.1808989 ,  1.058761  ,\n","         0.5187977 ,  0.5274618 , -0.30444944],\n","       [ 0.8309489 , -0.09717798, -0.0044545 , -1.6130953 , -0.27651256,\n","        -1.0939071 ,  1.751981  ,  0.02769868],\n","       [-0.31518766, -1.6258478 , -0.71715826,  0.13315108,  1.5038522 ,\n","        -0.2095206 ,  0.18459642,  0.97669333],\n","       [-0.88825595,  1.0575535 , -1.429862  , -1.6130953 ,  0.16857865,\n","        -0.41761154,  0.42950028, -1.3483436 ]], dtype=float32)>"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"ib01hpAGXB9O"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"eQdxWFy6XLej","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635922432316,"user_tz":-60,"elapsed":28,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"a94472ee-80d0-4aaa-bfab-0305304ffeb9"},"source":["print(X[:5])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[4.00000e+00 1.29251e+05 2.00000e+00 3.00000e+00 1.30000e+01 1.42000e+02\n","  7.70000e+01 1.10000e+02]\n"," [6.00000e+00 1.04308e+05 2.00000e+00 9.00000e+00 1.40000e+01 1.77000e+02\n","  9.50000e+01 9.70000e+01]\n"," [6.00000e+00 9.69330e+04 3.00000e+00 1.00000e+00 1.10000e+01 1.46000e+02\n","  1.20000e+02 1.04000e+02]\n"," [4.00000e+00 5.24430e+04 2.00000e+00 6.00000e+00 1.50000e+01 1.63000e+02\n","  8.80000e+01 1.24000e+02]\n"," [3.00000e+00 1.30540e+05 1.00000e+00 1.00000e+00 1.20000e+01 1.59000e+02\n","  9.30000e+01 7.50000e+01]], shape=(5, 8), dtype=float32)\n"]}]},{"cell_type":"code","metadata":{"id":"S_IP_m7IWh1V"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CxlimoToLc4C"},"source":["## **Model Creation and Training**"]},{"cell_type":"code","metadata":{"id":"-6u-a70uXLhR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635929376570,"user_tz":-60,"elapsed":515,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"493dda59-a47e-447a-97aa-6bf788dd537b"},"source":["model = tf.keras.Sequential([\n","                             InputLayer(input_shape = (8,)),\n","                             normalizer,\n","                             Dense(128, activation = \"relu\"),\n","                             Dense(128, activation = \"relu\"),\n","                             Dense(128, activation = \"relu\"),\n","                             Dense(1),\n","])\n","model.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_7\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","normalization_2 (Normalizati (None, 8)                 17        \n","_________________________________________________________________\n","dense_19 (Dense)             (None, 128)               1152      \n","_________________________________________________________________\n","dense_20 (Dense)             (None, 128)               16512     \n","_________________________________________________________________\n","dense_21 (Dense)             (None, 128)               16512     \n","_________________________________________________________________\n","dense_22 (Dense)             (None, 1)                 129       \n","=================================================================\n","Total params: 34,322\n","Trainable params: 34,305\n","Non-trainable params: 17\n","_________________________________________________________________\n"]}]},{"cell_type":"code","metadata":{"id":"11E2rU0rV0pM"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":644},"id":"P0-MdiccV0r9","executionInfo":{"status":"ok","timestamp":1635929378075,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"c292de82-e496-48c5-f9d7-a87073fced6d"},"source":["tf.keras.utils.plot_model(model, to_file = \"model.png\", show_shapes=True)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"image/png":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{},"execution_count":97}]},{"cell_type":"code","metadata":{"id":"KckNWiWJV0um"},"source":["model.compile(optimizer = Adam(learning_rate = 0.1),\n","              loss = MeanAbsoluteError(),\n","              metrics = RootMeanSquaredError())"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-nQSZ1j0V0xG","executionInfo":{"status":"ok","timestamp":1635929394500,"user_tz":-60,"elapsed":14129,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"bb353c6e-5235-48c5-ce68-8dc17c423a32"},"source":["history = model.fit(train_dataset, validation_data=val_dataset, epochs = 100, verbose = 1)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","18/25 [====================>.........] - ETA: 0s - loss: 183443.8125 - root_mean_squared_error: 230505.0938 WARNING:tensorflow:Model was constructed with shape (None, 8) for input KerasTensor(type_spec=TensorSpec(shape=(None, 8), dtype=tf.float32, name='input_8'), name='input_8', description=\"created by layer 'input_8'\"), but it was called on an input with incompatible shape (None, None, 8).\n","25/25 [==============================] - 1s 12ms/step - loss: 153433.3906 - root_mean_squared_error: 202123.1406 - val_loss: 60263.9297 - val_root_mean_squared_error: 72829.0625\n","Epoch 2/100\n","25/25 [==============================] - 0s 4ms/step - loss: 56026.2656 - root_mean_squared_error: 71011.3516 - val_loss: 45472.1758 - val_root_mean_squared_error: 56745.3438\n","Epoch 3/100\n","25/25 [==============================] - 0s 4ms/step - loss: 53667.3594 - root_mean_squared_error: 69709.6016 - val_loss: 42714.7695 - val_root_mean_squared_error: 53672.2227\n","Epoch 4/100\n","25/25 [==============================] - 0s 4ms/step - loss: 50040.4531 - root_mean_squared_error: 65046.1055 - val_loss: 38227.3203 - val_root_mean_squared_error: 47598.3086\n","Epoch 5/100\n","25/25 [==============================] - 0s 4ms/step - loss: 42923.9805 - root_mean_squared_error: 53611.3828 - val_loss: 40198.9961 - val_root_mean_squared_error: 49557.1953\n","Epoch 6/100\n","25/25 [==============================] - 0s 4ms/step - loss: 43780.4062 - root_mean_squared_error: 54295.3242 - val_loss: 47681.0117 - val_root_mean_squared_error: 58350.3594\n","Epoch 7/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41617.0156 - root_mean_squared_error: 51874.2031 - val_loss: 42206.4141 - val_root_mean_squared_error: 53217.7852\n","Epoch 8/100\n","25/25 [==============================] - 0s 4ms/step - loss: 44758.0469 - root_mean_squared_error: 56010.7852 - val_loss: 48947.8008 - val_root_mean_squared_error: 59752.2383\n","Epoch 9/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41648.5547 - root_mean_squared_error: 51759.3359 - val_loss: 43351.2031 - val_root_mean_squared_error: 54776.7188\n","Epoch 10/100\n","25/25 [==============================] - 0s 17ms/step - loss: 44575.9102 - root_mean_squared_error: 55635.6953 - val_loss: 41305.5859 - val_root_mean_squared_error: 51274.1133\n","Epoch 11/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41709.0391 - root_mean_squared_error: 52156.1992 - val_loss: 45903.7031 - val_root_mean_squared_error: 56568.1367\n","Epoch 12/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41540.7852 - root_mean_squared_error: 51719.0195 - val_loss: 38613.2109 - val_root_mean_squared_error: 47886.9609\n","Epoch 13/100\n","25/25 [==============================] - 0s 5ms/step - loss: 42226.4648 - root_mean_squared_error: 52758.3633 - val_loss: 44199.9609 - val_root_mean_squared_error: 54273.1133\n","Epoch 14/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40767.7461 - root_mean_squared_error: 50688.4805 - val_loss: 38259.7344 - val_root_mean_squared_error: 47667.7383\n","Epoch 15/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41690.9062 - root_mean_squared_error: 51740.9844 - val_loss: 37318.7109 - val_root_mean_squared_error: 46564.6992\n","Epoch 16/100\n","25/25 [==============================] - 0s 4ms/step - loss: 42122.0547 - root_mean_squared_error: 52942.2109 - val_loss: 39383.1211 - val_root_mean_squared_error: 48752.6836\n","Epoch 17/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38069.0742 - root_mean_squared_error: 47936.5898 - val_loss: 36968.2383 - val_root_mean_squared_error: 45938.2070\n","Epoch 18/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40485.8984 - root_mean_squared_error: 50408.5469 - val_loss: 55395.7344 - val_root_mean_squared_error: 66576.9844\n","Epoch 19/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40614.3906 - root_mean_squared_error: 51430.3477 - val_loss: 37169.1289 - val_root_mean_squared_error: 46689.2344\n","Epoch 20/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39690.5312 - root_mean_squared_error: 49889.6250 - val_loss: 37958.2539 - val_root_mean_squared_error: 47514.6016\n","Epoch 21/100\n","25/25 [==============================] - 0s 4ms/step - loss: 45334.5156 - root_mean_squared_error: 57302.3008 - val_loss: 50117.3789 - val_root_mean_squared_error: 61919.8203\n","Epoch 22/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41397.3984 - root_mean_squared_error: 51327.0195 - val_loss: 34763.5586 - val_root_mean_squared_error: 43359.3945\n","Epoch 23/100\n","25/25 [==============================] - 0s 4ms/step - loss: 41823.0039 - root_mean_squared_error: 52517.4922 - val_loss: 42227.3633 - val_root_mean_squared_error: 52194.2656\n","Epoch 24/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37728.0625 - root_mean_squared_error: 47307.5430 - val_loss: 37185.4531 - val_root_mean_squared_error: 47250.6680\n","Epoch 25/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39189.0078 - root_mean_squared_error: 48781.3906 - val_loss: 38032.8555 - val_root_mean_squared_error: 47263.8164\n","Epoch 26/100\n","25/25 [==============================] - 0s 5ms/step - loss: 40774.3516 - root_mean_squared_error: 50963.6680 - val_loss: 43680.4688 - val_root_mean_squared_error: 54177.1719\n","Epoch 27/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38223.0547 - root_mean_squared_error: 47434.3555 - val_loss: 36712.6094 - val_root_mean_squared_error: 45607.6250\n","Epoch 28/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40351.8398 - root_mean_squared_error: 50293.8945 - val_loss: 46028.4414 - val_root_mean_squared_error: 56488.2109\n","Epoch 29/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37378.0430 - root_mean_squared_error: 46608.9688 - val_loss: 34020.2266 - val_root_mean_squared_error: 42543.3086\n","Epoch 30/100\n","25/25 [==============================] - 0s 4ms/step - loss: 42107.9297 - root_mean_squared_error: 52700.3242 - val_loss: 57367.4258 - val_root_mean_squared_error: 69031.1016\n","Epoch 31/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40386.8438 - root_mean_squared_error: 50549.6172 - val_loss: 35117.1367 - val_root_mean_squared_error: 43863.0742\n","Epoch 32/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39437.0469 - root_mean_squared_error: 48874.4219 - val_loss: 36209.7539 - val_root_mean_squared_error: 44859.3945\n","Epoch 33/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37666.4180 - root_mean_squared_error: 47192.3555 - val_loss: 37115.8242 - val_root_mean_squared_error: 46212.6484\n","Epoch 34/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37298.2969 - root_mean_squared_error: 46440.7891 - val_loss: 36064.3945 - val_root_mean_squared_error: 45013.4180\n","Epoch 35/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36239.5352 - root_mean_squared_error: 45847.5859 - val_loss: 35581.3633 - val_root_mean_squared_error: 44543.2969\n","Epoch 36/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37538.1367 - root_mean_squared_error: 47149.9297 - val_loss: 35826.7383 - val_root_mean_squared_error: 44622.6406\n","Epoch 37/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40046.1016 - root_mean_squared_error: 50076.9883 - val_loss: 46954.7734 - val_root_mean_squared_error: 57823.3672\n","Epoch 38/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36624.6758 - root_mean_squared_error: 45689.2070 - val_loss: 33482.0703 - val_root_mean_squared_error: 42136.8164\n","Epoch 39/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38041.0508 - root_mean_squared_error: 47904.8984 - val_loss: 39566.6055 - val_root_mean_squared_error: 49108.8516\n","Epoch 40/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36461.2539 - root_mean_squared_error: 46020.3359 - val_loss: 33832.3203 - val_root_mean_squared_error: 42399.4727\n","Epoch 41/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37436.7539 - root_mean_squared_error: 47116.0820 - val_loss: 33855.7969 - val_root_mean_squared_error: 42320.5430\n","Epoch 42/100\n","25/25 [==============================] - 0s 5ms/step - loss: 42520.7539 - root_mean_squared_error: 52917.1602 - val_loss: 46673.4336 - val_root_mean_squared_error: 57544.7656\n","Epoch 43/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36878.1172 - root_mean_squared_error: 46548.4727 - val_loss: 36253.9336 - val_root_mean_squared_error: 44613.2656\n","Epoch 44/100\n","25/25 [==============================] - 0s 4ms/step - loss: 45127.1016 - root_mean_squared_error: 56809.5039 - val_loss: 40591.6562 - val_root_mean_squared_error: 50129.6289\n","Epoch 45/100\n","25/25 [==============================] - 0s 4ms/step - loss: 43439.3555 - root_mean_squared_error: 54211.9258 - val_loss: 39119.6641 - val_root_mean_squared_error: 48735.2109\n","Epoch 46/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37144.3359 - root_mean_squared_error: 46490.9453 - val_loss: 34264.2969 - val_root_mean_squared_error: 42994.8594\n","Epoch 47/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40094.2578 - root_mean_squared_error: 50919.8008 - val_loss: 43557.8555 - val_root_mean_squared_error: 53852.1406\n","Epoch 48/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39449.2578 - root_mean_squared_error: 49006.6914 - val_loss: 38318.1914 - val_root_mean_squared_error: 47533.4883\n","Epoch 49/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35235.9297 - root_mean_squared_error: 43911.5000 - val_loss: 33872.1836 - val_root_mean_squared_error: 42097.6250\n","Epoch 50/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36223.1836 - root_mean_squared_error: 45369.5078 - val_loss: 37482.6289 - val_root_mean_squared_error: 46426.5703\n","Epoch 51/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38728.5859 - root_mean_squared_error: 48386.0430 - val_loss: 42836.5352 - val_root_mean_squared_error: 52722.1250\n","Epoch 52/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38372.0859 - root_mean_squared_error: 47950.6055 - val_loss: 36639.0273 - val_root_mean_squared_error: 45573.0781\n","Epoch 53/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36378.2031 - root_mean_squared_error: 45250.8086 - val_loss: 34652.2891 - val_root_mean_squared_error: 43365.8438\n","Epoch 54/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35747.6836 - root_mean_squared_error: 44717.0000 - val_loss: 34828.4219 - val_root_mean_squared_error: 43621.8594\n","Epoch 55/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35912.6797 - root_mean_squared_error: 44972.3945 - val_loss: 34019.6641 - val_root_mean_squared_error: 42595.9648\n","Epoch 56/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38367.9766 - root_mean_squared_error: 48917.6797 - val_loss: 37332.0430 - val_root_mean_squared_error: 46409.3984\n","Epoch 57/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36671.1445 - root_mean_squared_error: 45700.1523 - val_loss: 34065.7266 - val_root_mean_squared_error: 42134.2656\n","Epoch 58/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35391.8828 - root_mean_squared_error: 44498.2109 - val_loss: 35059.3750 - val_root_mean_squared_error: 43381.7070\n","Epoch 59/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37295.8945 - root_mean_squared_error: 47479.5195 - val_loss: 34208.7305 - val_root_mean_squared_error: 42558.4922\n","Epoch 60/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38737.8828 - root_mean_squared_error: 49007.2852 - val_loss: 37675.8594 - val_root_mean_squared_error: 46528.0000\n","Epoch 61/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37275.9922 - root_mean_squared_error: 46766.8633 - val_loss: 36297.2383 - val_root_mean_squared_error: 44886.0352\n","Epoch 62/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36066.3711 - root_mean_squared_error: 44733.1875 - val_loss: 33840.5859 - val_root_mean_squared_error: 42432.3789\n","Epoch 63/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36461.5859 - root_mean_squared_error: 45768.5156 - val_loss: 35501.4219 - val_root_mean_squared_error: 44325.0234\n","Epoch 64/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35266.5508 - root_mean_squared_error: 44279.4492 - val_loss: 33292.7148 - val_root_mean_squared_error: 41521.0352\n","Epoch 65/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35328.8984 - root_mean_squared_error: 44795.5586 - val_loss: 32958.8477 - val_root_mean_squared_error: 41033.3633\n","Epoch 66/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35405.6641 - root_mean_squared_error: 44796.9297 - val_loss: 33378.3203 - val_root_mean_squared_error: 41350.6719\n","Epoch 67/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35030.9414 - root_mean_squared_error: 43748.3984 - val_loss: 32991.1992 - val_root_mean_squared_error: 41132.3242\n","Epoch 68/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34800.5664 - root_mean_squared_error: 43631.7188 - val_loss: 34250.6211 - val_root_mean_squared_error: 42634.0039\n","Epoch 69/100\n","25/25 [==============================] - 0s 4ms/step - loss: 33978.9844 - root_mean_squared_error: 42666.3711 - val_loss: 32581.5645 - val_root_mean_squared_error: 40717.6484\n","Epoch 70/100\n","25/25 [==============================] - 0s 4ms/step - loss: 38386.7734 - root_mean_squared_error: 49061.3320 - val_loss: 32995.2812 - val_root_mean_squared_error: 41220.0156\n","Epoch 71/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35412.3398 - root_mean_squared_error: 44507.3164 - val_loss: 34876.0469 - val_root_mean_squared_error: 43161.8320\n","Epoch 72/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34612.6992 - root_mean_squared_error: 43434.4922 - val_loss: 35430.1680 - val_root_mean_squared_error: 43927.1484\n","Epoch 73/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35458.6133 - root_mean_squared_error: 44698.3477 - val_loss: 32574.8203 - val_root_mean_squared_error: 40858.1367\n","Epoch 74/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36318.4258 - root_mean_squared_error: 45952.9766 - val_loss: 40543.6211 - val_root_mean_squared_error: 50238.3828\n","Epoch 75/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35710.8945 - root_mean_squared_error: 44680.3711 - val_loss: 34661.6016 - val_root_mean_squared_error: 43508.1445\n","Epoch 76/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35520.9844 - root_mean_squared_error: 44945.1016 - val_loss: 34319.5703 - val_root_mean_squared_error: 42934.1836\n","Epoch 77/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35935.3281 - root_mean_squared_error: 45561.6992 - val_loss: 32581.1348 - val_root_mean_squared_error: 40705.1406\n","Epoch 78/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36242.0898 - root_mean_squared_error: 45991.8516 - val_loss: 33404.2344 - val_root_mean_squared_error: 41946.7461\n","Epoch 79/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34642.0625 - root_mean_squared_error: 43895.9062 - val_loss: 32456.9004 - val_root_mean_squared_error: 40770.3359\n","Epoch 80/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35546.0195 - root_mean_squared_error: 45262.3906 - val_loss: 35322.0156 - val_root_mean_squared_error: 44069.5039\n","Epoch 81/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35978.3945 - root_mean_squared_error: 45280.6211 - val_loss: 35185.4648 - val_root_mean_squared_error: 44014.6172\n","Epoch 82/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34663.4219 - root_mean_squared_error: 44022.8203 - val_loss: 32813.8477 - val_root_mean_squared_error: 41324.1719\n","Epoch 83/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36449.5664 - root_mean_squared_error: 46169.6953 - val_loss: 34012.1367 - val_root_mean_squared_error: 42522.8906\n","Epoch 84/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34057.7734 - root_mean_squared_error: 43135.2695 - val_loss: 33329.9844 - val_root_mean_squared_error: 41505.4531\n","Epoch 85/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36582.1641 - root_mean_squared_error: 46742.3906 - val_loss: 34058.7930 - val_root_mean_squared_error: 42399.1016\n","Epoch 86/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36244.8281 - root_mean_squared_error: 46020.2578 - val_loss: 32478.8809 - val_root_mean_squared_error: 40554.2695\n","Epoch 87/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35911.9102 - root_mean_squared_error: 45986.1953 - val_loss: 33365.6289 - val_root_mean_squared_error: 41517.8516\n","Epoch 88/100\n","25/25 [==============================] - 0s 4ms/step - loss: 39517.6562 - root_mean_squared_error: 49955.6602 - val_loss: 49954.6406 - val_root_mean_squared_error: 61718.5781\n","Epoch 89/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40796.4688 - root_mean_squared_error: 51235.3359 - val_loss: 40422.7969 - val_root_mean_squared_error: 50132.1914\n","Epoch 90/100\n","25/25 [==============================] - 0s 4ms/step - loss: 40360.6641 - root_mean_squared_error: 50773.8359 - val_loss: 38809.0117 - val_root_mean_squared_error: 48129.4922\n","Epoch 91/100\n","25/25 [==============================] - 0s 5ms/step - loss: 36151.8906 - root_mean_squared_error: 45569.1367 - val_loss: 36910.4023 - val_root_mean_squared_error: 46204.5117\n","Epoch 92/100\n","25/25 [==============================] - 0s 4ms/step - loss: 35665.6602 - root_mean_squared_error: 45271.9883 - val_loss: 34275.9141 - val_root_mean_squared_error: 42862.3633\n","Epoch 93/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34847.2461 - root_mean_squared_error: 44639.5156 - val_loss: 32523.6406 - val_root_mean_squared_error: 41008.9844\n","Epoch 94/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34289.5000 - root_mean_squared_error: 43564.6797 - val_loss: 33046.0508 - val_root_mean_squared_error: 41598.3047\n","Epoch 95/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34079.6094 - root_mean_squared_error: 43665.2969 - val_loss: 33661.4844 - val_root_mean_squared_error: 42329.4414\n","Epoch 96/100\n","25/25 [==============================] - 0s 4ms/step - loss: 34789.6133 - root_mean_squared_error: 44418.9375 - val_loss: 33264.1484 - val_root_mean_squared_error: 41816.4141\n","Epoch 97/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35060.4062 - root_mean_squared_error: 44998.0234 - val_loss: 34415.6406 - val_root_mean_squared_error: 43326.4883\n","Epoch 98/100\n","25/25 [==============================] - 0s 5ms/step - loss: 35818.2812 - root_mean_squared_error: 45832.2617 - val_loss: 33805.2031 - val_root_mean_squared_error: 42436.7656\n","Epoch 99/100\n","25/25 [==============================] - 0s 4ms/step - loss: 37061.6758 - root_mean_squared_error: 47643.8867 - val_loss: 34193.2461 - val_root_mean_squared_error: 42820.9141\n","Epoch 100/100\n","25/25 [==============================] - 0s 4ms/step - loss: 36433.3633 - root_mean_squared_error: 46644.2148 - val_loss: 37564.8867 - val_root_mean_squared_error: 47129.3828\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"rclKXms6V0zg","executionInfo":{"status":"ok","timestamp":1635929419718,"user_tz":-60,"elapsed":640,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"753fd3f9-6bc6-487e-84ed-54f123e0f75f"},"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val_loss'])\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Mklv2mvHZFtu"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"FcqUS-lcV010","executionInfo":{"status":"ok","timestamp":1635929426629,"user_tz":-60,"elapsed":615,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"71bee4df-9e86-4e7f-abd8-134df1635b76"},"source":["plt.plot(history.history['root_mean_squared_error'])\n","plt.plot(history.history['val_root_mean_squared_error'])\n","plt.title('model performance')\n","plt.ylabel('rmse')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'])\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"TSjCxq__LQkw"},"source":["## **Model Evaluation and Testing**"]},{"cell_type":"code","metadata":{"id":"kuiLjiUxXLj4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635929432319,"user_tz":-60,"elapsed":553,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"23aa1fcc-2770-4467-98d1-7c9a7e82506f"},"source":["model.evaluate(X_test,y_test)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["4/4 [==============================] - 0s 4ms/step - loss: 35082.9219 - root_mean_squared_error: 45458.8477\n"]},{"output_type":"execute_result","data":{"text/plain":["[35082.921875, 45458.84765625]"]},"metadata":{},"execution_count":102}]},{"cell_type":"code","metadata":{"id":"nBrLEVAyXLmT"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"53c0mlV2WGI2","executionInfo":{"status":"ok","timestamp":1635923777762,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"baa2d4ca-fd08-4d15-8256-b95b98340c78"},"source":["X_test.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["TensorShape([100, 8])"]},"metadata":{},"execution_count":57}]},{"cell_type":"code","metadata":{"id":"HeN7SJylXLpD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635929436669,"user_tz":-60,"elapsed":6,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"216ec721-9cee-4e6b-9c44-f813ffe214b8"},"source":["model.predict(tf.expand_dims(X_test[0], axis = 0 ))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[465634.7]], dtype=float32)"]},"metadata":{},"execution_count":103}]},{"cell_type":"code","metadata":{"id":"5HbPIpC2XLrq","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635923777763,"user_tz":-60,"elapsed":15,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"41238d72-92df-4bb8-ddda-a38787fe3d7b"},"source":["y_test[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(1,), dtype=float32, numpy=array([465338.5], dtype=float32)>"]},"metadata":{},"execution_count":59}]},{"cell_type":"code","metadata":{"id":"jscJH2wLXLuK"},"source":["y_true = list(y_test[:,0].numpy())"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qWCd5NDZie1L","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1635929443529,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"b5ee2ef1-0cb7-4514-8141-f292267ad8ff"},"source":["y_pred = list(model.predict(X_test)[:,0])\n","print(y_pred)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[465634.7, 346877.6, 316944.53, 541506.4, 177605.89, 389100.53, 407023.3, 215957.14, 242833.12, 316479.53, 283034.28, 523835.53, 235925.12, 169588.38, 246495.66, 424280.38, 444110.28, 253352.94, 513701.28, 460727.66, 276458.38, 214403.03, 220393.1, 129725.375, 274199.8, 336481.06, 316167.84, 176357.14, 176709.47, 405825.3, 221231.02, 321881.72, 127336.555, 208712.95, 467812.22, 380431.0, 361381.9, 239841.12, 464587.47, 369859.16, 224781.88, 238386.03, 266883.12, 335518.03, 312324.72, 240562.16, 132587.97, 422667.72, 228218.1, 156495.84, 365251.53, 194797.8, 401961.06, 387540.25, 478644.16, 227816.22, 383970.72, 210779.62, 291278.78, 175252.11, 266432.1, 148648.31, 415452.88, 305756.9, 166695.77, 475147.22, 512314.84, 143468.97, 374273.22, 158544.81, 360469.16, 567642.06, 540250.2, 481704.6, 80303.1, 459162.03, 461067.16, 429522.4, 455283.94, 331594.3, 284699.8, 276452.72, 209741.05, 270829.62, 190786.22, 299014.84, 246887.86, 353629.97, 380103.03, 175247.42, 199458.3, 491509.22, 252743.64, 209619.28, 527794.8, 522028.75, 525645.06, 199967.36, 133078.62, 146573.19]\n"]}]},{"cell_type":"code","metadata":{"id":"FJceF2FrXLxB","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1635929453509,"user_tz":-60,"elapsed":756,"user":{"displayName":"martins folefac","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15114498789158493667"}},"outputId":"0745e271-4261-4f13-d87e-7d4bc9586ab7"},"source":["ind = np.arange(100)\n","plt.figure(figsize=(40,20))\n","\n","width = 0.1\n","\n","plt.bar(ind, y_pred, width, label='Predicted Car Price')\n","plt.bar(ind + width, y_true, width, label='Actual Car Price')\n","\n","plt.xlabel('Actual vs Predicted Prices')\n","plt.ylabel('Car Price 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size 2880x1440 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Pj6Qk_wOXLz6"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"U8aJm7HoXL2d"},"source":[],"execution_count":null,"outputs":[]}]}

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FILE: deep learning for natural language processing/10-Neural Machine Translation using HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"markdown","metadata":{"id":"gwDUFKDdR6i_"},"source":["# Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23886,"status":"ok","timestamp":1665251319757,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ntIbn-RaRzbs","outputId":"f098fce0-9e6b-41bd-9e67-a2e81ebe2994"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.22.2-py3-none-any.whl (4.9 MB)\n","\u001b[K     |████████████████████████████████| 4.9 MB 35.9 MB/s \n","\u001b[?25hCollecting datasets\n","  Downloading datasets-2.5.2-py3-none-any.whl (432 kB)\n","\u001b[K     |████████████████████████████████| 432 kB 71.0 MB/s \n","\u001b[?25hCollecting evaluate\n","  Downloading evaluate-0.2.2-py3-none-any.whl 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pandas->datasets) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n","Installing collected packages: urllib3, xxhash, responses, multiprocess, huggingface-hub, tokenizers, datasets, transformers, evaluate\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","Successfully installed datasets-2.5.2 evaluate-0.2.2 huggingface-hub-0.10.0 multiprocess-0.70.13 responses-0.18.0 tokenizers-0.12.1 transformers-4.22.2 urllib3-1.25.11 xxhash-3.0.0\n"]}],"source":["!pip install transformers datasets evaluate"]},{"cell_type":"code","source":["!pip install sacrebleu"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EYpJEAZKs25p","executionInfo":{"status":"ok","timestamp":1665251325512,"user_tz":-60,"elapsed":5759,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4507e09d-b098-4df0-fdd0-db639cb23070"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting sacrebleu\n","  Downloading sacrebleu-2.2.1-py3-none-any.whl (116 kB)\n","\u001b[K     |████████████████████████████████| 116 kB 24.5 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (1.21.6)\n","Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (2022.6.2)\n","Collecting portalocker\n","  Downloading portalocker-2.5.1-py2.py3-none-any.whl (15 kB)\n","Collecting colorama\n","  Downloading colorama-0.4.5-py2.py3-none-any.whl (16 kB)\n","Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (4.9.1)\n","Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (0.8.10)\n","Installing collected packages: portalocker, colorama, sacrebleu\n","Successfully installed colorama-0.4.5 portalocker-2.5.1 sacrebleu-2.2.1\n"]}]},{"cell_type":"markdown","metadata":{"id":"D31_Au7nR_oc"},"source":["# Imports"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZslyJl_QV7rt"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","import evaluate\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer,BatchNormalization,Dropout,Input,LayerNormalization\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import (DataCollatorWithPadding,create_optimizer,AutoTokenizer,DataCollatorForSeq2Seq,\n","                          T5TokenizerFast,T5ForConditionalGeneration,TFAutoModelForSeq2SeqLM)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8phsQmoYUJWK"},"outputs":[],"source":["BATCH_SIZE=64"]},{"cell_type":"markdown","metadata":{"id":"y3CEdBN2aX9k"},"source":["# Data Preparation for Bert Model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3362,"status":"ok","timestamp":1665251336937,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"bl5b-DkIm3uW","outputId":"c85a8f04-1db5-40e0-b3b5-499788dfb050"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-10-08 17:49:48--  https://www.manythings.org/anki/fra-eng.zip\n","Resolving www.manythings.org (www.manythings.org)... 173.254.30.110\n","Connecting to www.manythings.org (www.manythings.org)|173.254.30.110|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 6720195 (6.4M) [application/zip]\n","Saving to: ‘fra-eng.zip’\n","\n","fra-eng.zip         100%[===================>]   6.41M  3.37MB/s    in 1.9s    \n","\n","2022-10-08 17:49:51 (3.37 MB/s) - ‘fra-eng.zip’ saved [6720195/6720195]\n","\n"]}],"source":["!wget https://www.manythings.org/anki/fra-eng.zip"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1665251336938,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"uiLec7L4uWci","outputId":"ecb68d24-ed09-4fff-c84c-6fbded0ca07a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  /content/fra-eng.zip\n","  inflating: /content/dataset/_about.txt  \n","  inflating: /content/dataset/fra.txt  \n"]}],"source":["!unzip \"/content/fra-eng.zip\" -d \"/content/dataset/\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":185,"referenced_widgets":["4152dcb5f7354234a9018d2b016afe37","209219b1554544f786d1e115b0086227","c335e629cd374505a6f67ad8da5fa2e4","9346ad932a334a42b0b3335d6f81d7fb","ac07e80592b045baa1242faaf0090e58","e12fbde43ee7422d8b149475f77b07b4","cf056906a057413bbe298dd5729e92bb","8ce83f970a9d40349af410287f1bc061","7ada8ebdd6894a8ba95f45d6fc113f38","7f9f9ca8b35245adb62ed4971d70818c","2d0cf90643dd4ca6b8e6129c72d7b090","dc6066f3985c4fdfae7dae42d3d27433","e39b1c0d46af43b98e2559378a26fcfb","4b915a20723343e2a2b3df71d0d38b37","c8d5e9271b024442a56a0cb209569b03","3aa5f66f479d42e59a7747d102f6d506","fdeac62992b742e6acd8f15f7182f212","f53d0532de0a4a23832260cefa4b9b6c","b0a912ccf6e84f84a21df73aee11ecc1","745855e443304f91bd8654db15fb75bb","1e6df27d88ff460490b084092f4ab0e4","e00c8f5c19da4b5d9b9d7e871dad3248","374ddcc06cd44cd9a0a481ec1419e601","f0101f599a6f44d0b1251cde1274dccc","cdafebfdc5a64ed48b73f2249cca2e1d","76a3f0e62388472d8ba26531d5827a4f","2c2d5bf86678482ab40a440e47944db3","347a8a4ffac242ada57866e82d400b72","d9afc27fa8094670965881f3b0562381","92d5afde284b449c86c430c7e9661945","192a4c1062e749728fafed2b71b8e58d","aceba3dc58f84e9a96be9167da72e4bb","d2b66dbe687648a0bf6fa9481ffcf3b1","e7f92581eebf41498fb4d1c982e7f317","5649dff7759746d9a72df13da8e70ce6","b557b7b1cf964ba48ac7fc8489b81f30","b2b45de37b1c497b82133c8bf0ba1a4b","79435a245e524f50bfd1097563480779","a4580e8c20e546108a43a75daffea2a9","8842d24eede84f3aa6fd4451bc423a01","dc01d1e3d4a541038ff91c0edeb95d72","e15d6842c39149cf9615229f8744d382","00458ba240a144788c6da6ae8acd8171","c0421ba5f50e4404919de9d39c3719e5"]},"executionInfo":{"elapsed":2276,"status":"ok","timestamp":1665251339210,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"u3l2Qd2wvNFH","outputId":"eae6aebf-7490-4b30-d3cc-f93cafb9ff01"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Using custom data configuration default-dbfccd5baed914c5\n"]},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset text/default to /root/.cache/huggingface/datasets/text/default-dbfccd5baed914c5/0.0.0/21a506d1b2b34316b1e82d0bd79066905d846e5d7e619823c0dd338d6f1fa6ad...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4152dcb5f7354234a9018d2b016afe37"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dc6066f3985c4fdfae7dae42d3d27433"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["0 tables [00:00, ? tables/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"374ddcc06cd44cd9a0a481ec1419e601"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset text downloaded and prepared to /root/.cache/huggingface/datasets/text/default-dbfccd5baed914c5/0.0.0/21a506d1b2b34316b1e82d0bd79066905d846e5d7e619823c0dd338d6f1fa6ad. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e7f92581eebf41498fb4d1c982e7f317"}},"metadata":{}}],"source":["dataset=load_dataset('text', data_files='/content/dataset/fra.txt')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19,"status":"ok","timestamp":1665251339210,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"z75hTVqhRngx","outputId":"122b0879-cf01-4a3a-b65d-1f50ac79f82b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text'],\n","        num_rows: 197463\n","    })\n","})"]},"metadata":{},"execution_count":8}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1665251339211,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"xMbT89IjqaR5","outputId":"e73070d4-461b-4700-c2fa-6fb641b9d98b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[\"You can't have both of them.\", 'Tu ne peux pas avoir les deux.']"]},"metadata":{},"execution_count":9}],"source":["dataset['train'][100000]['text'].split('\\t')[:-1]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7f7Mih433BjO","colab":{"base_uri":"https://localhost:8080/","height":237,"referenced_widgets":["2d5ef5d7c6574b0cbbb17819dcdbb065","59f2e939b4eb419bb9bfb165639d1e3c","ab4d1148716b4116a08cbde9d86d0b3a","8a293295b9444667b92dc0adffdce87c","f13c6c54c79347b5a5519c0e584f0c61","9c9f935c0bf74dc2b6d58dc86fc107ab","b96f88a0eab44ebe9f70b275ff666d51","3d8358e039484841ae9fc1ef9fe8207a","c5ec1d0c61254267b875ea7a68289e85","0099972d5f0647519fdbfb8e3e5811c5","6ee5d16ab9cf45a9896cb0a3810b8182","da553380abb74f829b35457c23be0d68","339455c8076c4fed9562a6aeb72b1c3e","0dcb5a3c53654131b1e45d0ca478d50f","2cd8853d2a2e4089a2c63d2f8d863f06","ed85963aba82404796583d3200387930","10c70b4a47654c4ebb857bd74bc725df","850964eb4d3f4ee4880bff76ede8a3c3","aebee555ef3743cf8283e8e54c837323","ac23de511793486888179496205667cc","7231da69800b4f1a8649dc4528c5b155","9b2da488acbb47b3808fe1289ec71437","86670121f28146998c7c10c40b957166","8089c7a43a964c1cadfc838b7d37b836","c825d4f717f34c44ac35103aacdd3376","689d536bd6c843f5bfb1c2eb3e1cea33","caaf0a31be0e486fa7042321e1949045","659fe3e58e5243939d70e2f33b7084ac","02b0ba57acff417595c1c5fb67b24072","ea9e85695288445680af2546ae50b9e9","935abc473a844e79a457ab3433121f89","297e1eccef9c4373b9c4effcb8d091b1","c361e0992fac441895841b2f21b51a92"]},"executionInfo":{"status":"ok","timestamp":1665251350556,"user_tz":-60,"elapsed":11356,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"13951df4-5208-4e7f-d7b6-dc11e0979da8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/792k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2d5ef5d7c6574b0cbbb17819dcdbb065"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"da553380abb74f829b35457c23be0d68"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"86670121f28146998c7c10c40b957166"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5_fast.py:166: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n","For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n","- Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding.\n","- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n","- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n","  FutureWarning,\n"]}],"source":["model_id=\"t5-small\"\n","tokenizer=T5TokenizerFast.from_pretrained(model_id)"]},{"cell_type":"code","source":["prefix = \"translate English to French: \""],"metadata":{"id":"knkWzNXMNlNL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def preprocess_function(examples):\n","\n","  inputs = [prefix + example.split('\\t')[0] for example in examples['text']]\n","  targets = [example.split('\\t')[1] for example in examples['text']]\n","\n","  model_inputs = tokenizer(inputs, text_target=targets,max_length=128, truncation=True)\n","  return model_inputs"],"metadata":{"id":"jUWL5ixpNVAC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset=dataset.map(preprocess_function,batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["bcf9a369d7f04e6abfc261564f41c00c","9a64806f065443459554a217fcf4c2c0","443cd946707344cc9ec52ceb56fa66a2","55cfac30b9c84f68b8bdf03837b2f57d","1e0f20b735574c85811a179388c046e7","4b2e16bb638b4536931431535d49a771","a4a6ed3c71554b5fb733a08e236bcc34","49fc3cf67141498ebee8325d50e88153","1027a14ad5864599aac410d5c5ea447f","06cf5c1591b445c182ce114400705c7d","233f1793ed6b4dcc9ed8077b213f2dd9"]},"id":"c-MtuAr8NVCg","executionInfo":{"status":"ok","timestamp":1665251387752,"user_tz":-60,"elapsed":37206,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"effd02ea-1e52-4482-c91f-82248f4a1841"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/198 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bcf9a369d7f04e6abfc261564f41c00c"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HjTOZKZjNVE9","executionInfo":{"status":"ok","timestamp":1665251387753,"user_tz":-60,"elapsed":14,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c2d7cade-bbea-447e-d4ae-dce91acec16a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'input_ids', 'attention_mask', 'labels'],\n","        num_rows: 197463\n","    })\n","})"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["tokenized_dataset['train'][1000]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8eJPxBmYNVHB","executionInfo":{"status":"ok","timestamp":1665251387753,"user_tz":-60,"elapsed":11,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4602b361-347d-4cd3-ad08-3eb11a4eec62"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'text': 'Here I am.\\tMe voici.\\tCC-BY 2.0 (France) Attribution: tatoeba.org #1540806 (CK) & #1112332 (sacredceltic)',\n"," 'input_ids': [13959, 1566, 12, 2379, 10, 947, 27, 183, 5, 1],\n"," 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," 'labels': [1212, 23646, 5, 1]}"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["cleaned_dataset = tokenized_dataset.remove_columns(\"text\")"],"metadata":{"id":"Yl-I5kWLNVJb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cleaned_dataset['train'][1000]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oTWB33hqNVL_","executionInfo":{"status":"ok","timestamp":1665251387754,"user_tz":-60,"elapsed":8,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ea1d6289-7918-41cf-9518-6b8466c6710d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [13959, 1566, 12, 2379, 10, 947, 27, 183, 5, 1],\n"," 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," 'labels': [1212, 23646, 5, 1]}"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["model = TFAutoModelForSeq2SeqLM.from_pretrained(model_id)\n","data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer,model=model, return_tensors=\"tf\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":138,"referenced_widgets":["0e5027d479d74851840af8cfe5ee43f2","671da37312374bd9a7735d0cc3a06906","96974c8f52ec48dfbe0445ba89382f26","0dde6565d1ae4351aca71fcc17cd4fdb","f454912e3e864f7d9989a46057ff52d7","6027a3179a5b4ebf9e1542a126a707b9","bcbfcc352de14348bfd942d8b1d18f26","60b2ed98ac2f462e9827307db27f8671","163db28b0fc546cbb61ca36fff196733","df1ceaf6dcb642e09d970690cd3a058c","60a37763435b40228cf99ff7c68de48e"]},"id":"uvHPJoHaNVOO","executionInfo":{"status":"ok","timestamp":1665251406876,"user_tz":-60,"elapsed":19128,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e5bf2335-42e4-451e-c140-f863ae7ccc53"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/242M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0e5027d479d74851840af8cfe5ee43f2"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.\n","\n","All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at t5-small.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"]}]},{"cell_type":"code","source":["tf_train_set = cleaned_dataset[\"train\"].to_tf_dataset(\n","    shuffle=True,\n","    batch_size=BATCH_SIZE,\n","    collate_fn=data_collator,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wv3FF8SYNVQj","executionInfo":{"status":"ok","timestamp":1665251407735,"user_tz":-60,"elapsed":876,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0ecabd7f-7116-4306-a676-27d35572e7cf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]}]},{"cell_type":"code","source":["train_data=tf_train_set.take(int(0.9*len(tf_train_set)))\n","val_data=tf_train_set.skip(int(0.9*len(tf_train_set)))"],"metadata":{"id":"EN-9LRjqNVS7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in val_data.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xcc5MzJrNVVc","executionInfo":{"status":"ok","timestamp":1665251420586,"user_tz":-60,"elapsed":12853,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ab086571-2aa7-4498-a8d6-bbaf3b8dda1f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(64, 27), dtype=int64, numpy=\n","array([[13959,  1566,    12, ...,     0,     0,     0],\n","       [13959,  1566,    12, ...,     0,     0,     0],\n","       [13959,  1566,    12, ...,     0,     0,     0],\n","       ...,\n","       [13959,  1566,    12, ...,     0,     0,     0],\n","       [13959,  1566,    12, ...,     0,     0,     0],\n","       [13959,  1566,    12, ...,     0,     0,     0]])>, 'attention_mask': <tf.Tensor: shape=(64, 27), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       ...,\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0]])>, 'labels': <tf.Tensor: shape=(64, 30), dtype=int64, numpy=\n","array([[  180, 17560,  9716, ...,  -100,  -100,  -100],\n","       [ 2740,     3,    29, ...,     3,    58,     1],\n","       [    3, 14756,  9472, ...,  -100,  -100,  -100],\n","       ...,\n","       [  276,   154,  8831, ...,  -100,  -100,  -100],\n","       [ 3006,   259,     3, ...,  -100,  -100,  -100],\n","       [  301,    31,     9, ...,  -100,  -100,  -100]])>, 'decoder_input_ids': <tf.Tensor: shape=(64, 30), dtype=int64, numpy=\n","array([[    0,   180, 17560, ...,     0,     0,     0],\n","       [    0,  2740,     3, ...,   330,     3,    58],\n","       [    0,     3, 14756, ...,     0,     0,     0],\n","       ...,\n","       [    0,   276,   154, ...,     0,     0,     0],\n","       [    0,  3006,   259, ...,     0,     0,     0],\n","       [    0,   301,    31, ...,     0,     0,     0]])>}\n"]}]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"1iWnU2_jV5ME"}},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iNFUvvLrL9aW","executionInfo":{"status":"ok","timestamp":1665251420587,"user_tz":-60,"elapsed":21,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d57eca59-6539-4885-b6b1-f5f3d59c1263"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"tft5_for_conditional_generation\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," shared (TFSharedEmbeddings)  multiple                 16449536  \n","                                                                 \n"," encoder (TFT5MainLayer)     multiple                  18881280  \n","                                                                 \n"," decoder (TFT5MainLayer)     multiple                  25175808  \n","                                                                 \n","=================================================================\n","Total params: 60,506,624\n","Trainable params: 60,506,624\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","metadata":{"id":"E2xsaKH0ySx6"},"source":["# Training"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1665251090748,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"wiRDCW7WU5wv","outputId":"a112d85f-b25e-4eb7-a269-12cfe9f9a48c"},"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}],"source":["num_epochs = 3\n","num_train_steps = len(train_data) * num_epochs\n","\n","optimizer, schedule = create_optimizer(\n","    init_lr=2e-5,\n","    num_warmup_steps=0,\n","    num_train_steps=num_train_steps,\n",")\n","model.compile(optimizer=optimizer)"]},{"cell_type":"code","source":["model.fit(\n","  train_data,\n","  validation_data=val_data,\n","  epochs=3\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":433},"id":"NO57kZWMWPIt","executionInfo":{"status":"error","timestamp":1665248858957,"user_tz":-60,"elapsed":1499416,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ff4a7f83-4791-44de-f0e3-8fcf325c08b1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/3\n","2777/2777 [==============================] - 789s 276ms/step - loss: 0.8623 - val_loss: 0.6455\n","Epoch 2/3\n","2685/2777 [============================>.] - ETA: 24s - loss: 0.7890"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-23-0af0a1b3240d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m model.fit(train_data,\n\u001b[1;32m      2\u001b[0m           \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m           epochs=3)\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1382\u001b[0m                 _r=1):\n\u001b[1;32m   1383\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1384\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1385\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1386\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    945\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2955\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2956\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2957\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2959\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1852\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1854\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1855\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1856\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    502\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    505\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qxPsvkXKjE8U"},"outputs":[],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"]},{"cell_type":"markdown","source":["# Evaluation"],"metadata":{"id":"dUaWfbV-W9IL"}},{"cell_type":"code","source":["metric = evaluate.load(\"sacrebleu\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["048937e1eb3c4c1e90beb1db7a56e1bb","edbaed5bbdc8483d93307628c0d08a6e","5d4420267af44c279fc33e31d4412138","6c9a3a528e524f2f963b4509dac80337","240eff6a5adc4ac3a24935f564d16598","e3150cea7b4f499eacc3d97f87c9ae28","54d76064b5ef4ad6b4f43d97aace6cab","f942311dd6cf4892bd0b56bb5f4d8a62","1b0cd413594b4c2784d46f0f6d0714c2","e29e0d6dd6c64c9fbaf2697cc84c7cdf","4ee96e523e334952964a9e7309e1e389"]},"id":"9z5CBeP2W-O1","executionInfo":{"status":"ok","timestamp":1665251423428,"user_tz":-60,"elapsed":2847,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4e31ee83-6cae-4c31-8083-a6a92c9229b6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/8.15k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"048937e1eb3c4c1e90beb1db7a56e1bb"}},"metadata":{}}]},{"cell_type":"code","source":["all_preds = []\n","all_labels = []\n","    \n","for batch in val_data:\n","  predictions = model.generate(\n","      input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n","  )\n","  decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n","  labels = batch[\"labels\"].numpy()\n","  labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","  decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","  all_preds.extend(decoded_preds)\n","  all_labels.extend(decoded_labels)\n","\n","result = metric.compute(predictions=all_preds, references=all_labels)\n","print(result)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XAyOHWrPuKN7","executionInfo":{"status":"ok","timestamp":1665251844493,"user_tz":-60,"elapsed":37015,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c238aab7-3703-4b85-81e8-e23d59622c3d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py:1602: UserWarning: Neither `max_length` nor `max_new_tokens` have been set, `max_length` will default to 20 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n","  UserWarning,\n"]},{"output_type":"stream","name":"stdout","text":["{'score': 35.967454646883255, 'counts': [3048, 1714, 1047, 634], 'totals': [4775, 4135, 3495, 2858], 'precisions': [63.832460732984295, 41.45102781136639, 29.957081545064376, 22.18334499650105], 'bp': 0.987720001251615, 'sys_len': 4775, 'ref_len': 4834}\n"]}]},{"cell_type":"code","source":["decoded_preds"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GLZtF0pKuKQI","executionInfo":{"status":"ok","timestamp":1665251676245,"user_tz":-60,"elapsed":9,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"76f85a0d-9693-44c4-8cbd-db5d15f71512"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Nous devons savoir qui il est et pour qui il travaille.',\n"," \"Vous avez toutes l'intention de l'utiliser.\",\n"," \"Je ne peux pas m'en rendre compte.\",\n"," 'Merci pour votre temps.',\n"," \"Jour après jour, le chien s'est assis à l'\",\n"," \"J'ai eu un bon temps hier.\",\n"," 'Je veux me mettre en forme, et je travaille chaque jour.',\n"," 'Elle a été enthousiaste dans son travail.',\n"," 'Combien de temps avez-vous séjourné?',\n"," \"Vous n'avez jamais eu d'ami, avez-vous\",\n"," \" l'intérieur du corps, il y a une fente\",\n"," \"L'air est pour nous ce qu'il faut à poissonr.\",\n"," 'Les taux de divorce pourraient atteindre bientôt un plateau.',\n"," 'Nous avons été des partenaires.',\n"," \"Ouvrir les fenêtres pour laisser l'air dans une certaine mesure.\",\n"," 'Je ne vous ai jamais vu comme ça auparavant.',\n"," \"C'est un glissement.\",\n"," \"Quelqu'un devrait en aviser le prochain parent.\",\n"," \"Tout s'est déroulé sans heurt.\",\n"," 'Il a déjà été pris en charge.',\n"," \"C'est un bon journal, n'est-ce pas?\",\n"," \"Elle l'a vu continuer de se battre aussi dur qu'il pouvait\",\n"," 'Il est important de connaître vos propres limites.',\n"," \"Tom m'a dit que vous craigniez.\",\n"," \"J'ai parlé avec la fille dont vous m'avez dit\",\n"," 'Je ferai de même.',\n"," 'Il lise un roman chaque jour.',\n"," \"J'ai suivi vos instructions.\",\n"," \"J'ai toujours aimé ce nom.\",\n"," 'Je suis craint de mort.',\n"," \"Quoi qu'il en soit.\",\n"," 'Ils voulaient gagner la guerre rapidement et retourner à la vie normale.',\n"," 'Je crois que je connais la réponse.',\n"," 'Quelle idée fabuleuse!',\n"," \"N'oubliez pas de l'envoyer par la poste.\",\n"," 'La douleur a disparu.',\n"," 'Je me suis rendu à nouveau à vous voir.',\n"," 'Tom a décrit le problème en détail.',\n"," 'Que ferais-je si vous étiez à ma place?',\n"," \"Je pense que vous voudrez m'entendre.\",\n"," 'Je me suis senti soulagé.',\n"," 'Ne me tenez pas à attendre.',\n"," \"Quelqu'un nous a apporté quelques raisins.\",\n"," \"Je ne peux pas m'en tenir à ce bruit fort\",\n"," \"Je dois le faire aujourd'hui.\",\n"," 'Tom est toujours en vux.',\n"," 'Elle écrit constamment des lettres.',\n"," \"La femme qu'il croyait être sa tante était étrangère.\",\n"," 'Ils ont beaucoup à faire.',\n"," 'Le grand-père de Tom et le grand-père de Mary ont',\n"," \"J'étais naf.\",\n"," 'Ils lisent leurs journaux.',\n"," \"C'est injuste.\",\n"," 'Tom est mort quelques mois plus tard.',\n"," 'Ma école a une bande.',\n"," 'Il a dû écrire le rapport par son secrétaire.',\n"," \"J'ai eu des difficultés à trouver sa maison hier.\",\n"," 'Avez-vous envie de chanter?',\n"," \"Ces nouveaux immigrants n'avaient pas de compétences.\",\n"," 'Je suis certain.',\n"," 'Je pense que nous savons tous deux pourquoi je suis ici.',\n"," 'Comment avez-vous obtenu ce surnom?',\n"," \"Elle s'est vue dans le miroir.\",\n"," 'Ma pelouse est en péril avec des dandelions.']"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["decoded_labels"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7EWaS0G7uKSU","executionInfo":{"status":"ok","timestamp":1665251676245,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f95eaabf-f57a-4c12-cbe7-15d7f9cc5146"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Nous devons découvrir qui il est et pour qui il travaille.',\n"," 'Celui-ci est tout à toi.',\n"," 'Je déteste voler.',\n"," 'Je vous remercie pour votre temps.',\n"," 'Jour après jour, le chien était assis en attendant son maître face à la station.',\n"," 'Je me suis bien amusé hier.',\n"," \"Je veux me mettre en forme alors je m'entraîne tous les jours.\",\n"," 'Elle était absorbée par son travail.',\n"," 'Combien de temps êtes-vous resté?',\n"," \"Tu n'as jamais eu de petit copain, n'est-ce pas?\",\n"," 'Brûle le corps.',\n"," \"L'air est à l'homme ce que l'eau est aux poissons.\",\n"," 'Les taux de divorce pourraient bientôt atteindre un plateau.',\n"," 'Nous étions partenaires.',\n"," \"Ouvrez les fenêtres pour laisser rentrer un peu d'air.\",\n"," 'Je ne vous avais jamais vu ainsi auparavant.',\n"," \"C'en est une scabreuse.\",\n"," \"Quelqu'un devrait informer les proches parents.\",\n"," \"Tout s'est déroulé en douceur.\",\n"," \"On s'en est déjà occupé.\",\n"," \"C'est un bon journal, n'est-ce pas?\",\n"," \"Elle le regarda continuer à se battre aussi fort qu'il pouvait.\",\n"," \"C'est important de connaître ses propres limites.\",\n"," \"Tom m'a dit que vous aviez eu peur.\",\n"," \"J'ai parlé à la fille dont vous m'avez parlé.\",\n"," 'Je ferais pareil.',\n"," 'Il lit un roman par jour.',\n"," \"J'ai suivi vos instructions.\",\n"," \"J'ai toujours aimé ce nom.\",\n"," 'Je suis mort de peur.',\n"," 'Comme tu voudras.',\n"," 'Ils voulaient rapidement gagner la guerre et retourner à une vie normale.',\n"," 'Je pense connaître la réponse.',\n"," 'Quelle idée fantastique!',\n"," \"N'oubliez pas de poster la lettre, je vous prie.\",\n"," 'La douleur a disparu.',\n"," \"Il m'a fallu vous revoir.\",\n"," 'Tom a décrit le problème en détail.',\n"," 'Que feriez-vous si vous étiez à ma place?',\n"," \"Je pense que tu voudras m'écouter jusqu'au bout.\",\n"," 'Je me suis senti soulagée.',\n"," 'Ne me fais pas attendre!',\n"," \"Quelqu'un nous a apporté des raisins.\",\n"," 'Je ne peux pas supporter ce bruit fort.',\n"," \"Je devais le faire aujourd'hui.\",\n"," 'Tom est toujours joyeux.',\n"," \"Elle est constamment en train d'écrire des lettres.\",\n"," \"La femme qu'il tenait pour sa tante, était une étrangère.\",\n"," 'Ils en ont plein.',\n"," 'Le grand-père de Tom et celui de Mary ont combattu ensemble durant la seconde guerre mondiale.',\n"," \"J'ai été nave.\",\n"," 'Ils sont en train de lire leurs journaux.',\n"," \"C'est injuste.\",\n"," 'Tom est mort quelques mois plus tard.',\n"," 'Mon école a un groupe de musique.',\n"," 'Il fit taper le rapport par sa secrétaire.',\n"," \"a n'a pas été facile pour moi de trouver sa maison hier.\",\n"," 'Aimez-vous chanter?',\n"," \"Les nouveaux immigrants n'étaient pas qualifiés.\",\n"," 'Je suis sûr.',\n"," 'Je pense que nous savons tous les deux pourquoi je suis ici.',\n"," \"Comment tu t'es retrouvée avec ce surnom?\",\n"," \"Elle s'est vue dans le miroir.\",\n"," \"Ma pelouse est en train d'être envahie par les pissenlits.\"]"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":[],"metadata":{"id":"zlEi32d5uKXi"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7jbgoKV070Gr"},"source":["# Testing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5H8Y9MERnfX2","outputId":"349d2cb4-a40d-4c81-ef37-513c6eae1706","executionInfo":{"status":"ok","timestamp":1665249127377,"user_tz":-60,"elapsed":6402,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[    0  1163 30355    15   259    73   313    20 16486  1575    20  1437\n","  24228    15  1249   697    76    15 23609   260  1163   171 25639    60\n","    146 10062     6    93  2691     3    15    17    93  1471  1620     3\n","     26    31   444 14185     3    85   245  4538     5     1]], shape=(1, 46), dtype=int32)\n"]}],"source":["input_text=\"Have you ever played soccer under the rain, with your friends? \"\n","input_text=\"Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another.\"\n","input_text=prefix + input_text\n","tokenized=tokenizer([input_text], return_tensors='tf')\n","out = model.generate(**tokenized, max_length=128)\n","print(out)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1665249127378,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"hEML0foO8ps0","outputId":"27177aa1-1364-465a-fc56-57fdf9839ab8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Google Translate est un service de traduction de machine neurale multilingue développé par Google pour traduire du texte, des documents et des sites Web d'une langue à une autre.\n"]}],"source":["print(tokenizer.decode(out[0], skip_special_tokens=True))"]},{"cell_type":"markdown","metadata":{"id":"rvdNvX-YI1U3"},"source":["# Testing Original Model (No FineTuning)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ycte4ekNI4iv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665249411238,"user_tz":-60,"elapsed":2952,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a8bbc02b-cd9a-4ff0-db09-e22dc1ef8858"},"outputs":[{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.\n","\n","All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at t5-small.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"]}],"source":["original_model = TFAutoModelForSeq2SeqLM.from_pretrained(model_id)"]},{"cell_type":"code","source":["input_text=\"Have you ever played soccer under the rain, with your friends? \"\n","#input_text=\"Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another.\"\n","input_text=prefix + input_text\n","tokenized=tokenizer([input_text], return_tensors='tf')\n","out = original_model.generate(**tokenized, max_length=128)\n","print(out)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"izxscuIvmFoJ","executionInfo":{"status":"ok","timestamp":1665249593900,"user_tz":-60,"elapsed":2206,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"099f0909-55fa-421e-cc53-45802b915902"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[    0    71  6071    18  3249  4315     3  8921   154    90 10749  2539\n","     50 26410     6   393  1507 15245    58     1]], shape=(1, 20), dtype=int32)\n"]}]},{"cell_type":"code","source":["print(tokenizer.decode(out[0], skip_special_tokens=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ugXvYpFRmFq0","executionInfo":{"status":"ok","timestamp":1665249593901,"user_tz":-60,"elapsed":26,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"dd137cbc-2846-4661-a0b4-f9f0edbc4637"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Avez-vous déjà joué le soccer sous la pluie, avec vos amis?\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"ohSRrosXmXlA"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[{"file_id":"1eb6-YwCQQk8K4F6pdSOPHkW2Zj1LibRl","timestamp":1673806940880},{"file_id":"1LOMW9Bum6Ty71WFp4JEzHHZuxT_vjcPQ","timestamp":1664731142718},{"file_id":"1kiqhUrifUOMlpLnWCJWAYm9xM3vdVxfF","timestamp":1664705409290},{"file_id":"1qrpPSsjeFUMtRrljhxEiKKmlVk__fpuz","timestamp":1664695746526},{"file_id":"1qG67bWTlIgR_WzHRVCiw1CicPYSTv4qn","timestamp":1663398158336},{"file_id":"1ekGLJqUm4hoXzVs3h2I_OfnSCNwm1p8j","timestamp":1663151174522}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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FILE: deep learning for natural language processing/11-Extractive Question Answering using HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"markdown","metadata":{"id":"gwDUFKDdR6i_"},"source":["# Installation"]},{"cell_type":"code","source":["!pip install evaluate"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1fwB0U226PfL","executionInfo":{"status":"ok","timestamp":1666632492740,"user_tz":-60,"elapsed":12107,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bdad1e9a-044a-4ce8-e5e5-84038389e80f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting evaluate\n","  Downloading evaluate-0.3.0-py3-none-any.whl (72 kB)\n","\u001b[K     |████████████████████████████████| 72 kB 1.8 MB/s \n","\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from evaluate) (21.3)\n","Collecting datasets>=2.0.0\n","  Downloading datasets-2.6.1-py3-none-any.whl (441 kB)\n","\u001b[K    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packages: tokenizers, transformers\n","Successfully installed tokenizers-0.13.1 transformers-4.23.1\n"]}],"source":["!pip install transformers datasets"]},{"cell_type":"markdown","metadata":{"id":"D31_Au7nR_oc"},"source":["# Imports"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZslyJl_QV7rt"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer,BatchNormalization,Dropout,Input,LayerNormalization\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import (DataCollatorWithPadding,create_optimizer,DebertaTokenizerFast)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8phsQmoYUJWK"},"outputs":[],"source":["BATCH_SIZE=32\n","MAX_LENGTH=512"]},{"cell_type":"markdown","metadata":{"id":"y3CEdBN2aX9k"},"source":["# Data Preparation for Bert Model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["a52a142269314b11a92d3018bc49e6db","0758649eb083429184f14a7eaadbfd9b","82a17b907f00422f84bb72820f9fce08","81232c1653114170ae207f943d1f3388","0c6f26e805444635acd28c48de273ef5","8622d655bd2c405da08b45059e80d881","073167fbe5584079bd0842575cfcc973","d43323b494a046c9b0fa51ab2f7b87ea","8dd580298c7c418c93504e0823fe8b43","906c046592d94ae3ac1fdabf17cde00e","4067430902054a27b3f2cec24f9a3dca","400e1866653f41629d558ce5f5b6cfcb","b094394dd6e94b49afa6e249361dd822","323e35e41fe04e8c98036c87c299dfb9","18c20afa65d0435e94ae0bd33be5e65b","faac971e1617449cac0c2f45419daa36","868c9dabda8242bbb86b31a60afafaf2","7e1a608b835b4555ba79ec3317cb5e36","7cbc904fb9284fa3beffe44af35ff581","d74b8c25077341d99656b8616e39851f","4fe2f8cc441543b1aa2b1ead0c648ad6","4d37e034f4f243e1b70f0ab3227ce5ae","830e6087b3f8491ba3501ec5b6757439","68350fbdb121474aa0b8f1842841c778","2ab1b7a844624c228623dc88b8460e8b","db5bcba3ce494bf5976e11dac351804f","64d1d8aaa511434890a74c85e69815b7","d3a785a82e364088ab70c36bc615f61b","aadc447cc2b24a869f694df5dbba158a","4c9a3d585ed14a138affcfb79c415732","f33a10ec8ae946e3a0e83ab831de3c9c","36f5d4017bfa46ddad4e5683abc76d9c","be8d31d26cfa43899c024ab3a3ad8e0c","b3728c7930394f02b216f023d4b2e7b0","d90e0f4e74c84123ac381391b9ce951a","82eb3b3007b54ee6a67994aeeb25aa81","59552de9543e4091ba77f5d032160fc8","f1b0aa9989c04e4a9df708c6c3b559db","cbe8ae3e630e43878781bd6b21a98607","0f3ac83be24642a6a2271ae9cde6f30f","b1dfd9d1dc5544d881f7fe3b91f2e7fa","04fcd103941447a5b87fc180109e7b9c","658bad2a4bff42298914795f41cee1c6","82b9689cdd6049209813185351b6e466","5034f385786043c893797439d482224d","73327b3bb29346d7b3672ff75a789266","0e84c26e36124c8b848e8c7c536d262e","1783573e5f704ccdbe2637f9c3a8a732","96e0098c566843cd91f38cf5771975eb","908ab57f159f4e2e8f573bc8f7ab62fa","f0007b104ace439a8d810102746bb39d","620c45b2b730459a97dd9fda9de1db3b","e25ee1b7855145cf99285f2f80c35be1","76af2ef974a94801b4d50922769f583b","dc26056e78e64eca8aca51adf3cd1e0e","b1add3cb6f5744ab977dad96e2d25c77","f27c12d8f58645a0bd1c123a3f7a3e5b","01da1bf1592e4297977ad3b0941b9a54","36f4a1101ae44ca88249d90a1fa70608","7fd66a6dcd324159a08208d653a020d3","fb359c6839284cb193e92f4d50595377","61240bb86e7246938f2858d84127d748","ab01b69e1402466d9ec36bbd6b26501f","6611192a25ac468583d9b259b78de87c","61c3e4f422034d8bac95e85e0915c53e","4aab260aad324f0ba658408b49588d11"]},"executionInfo":{"elapsed":9820,"status":"ok","timestamp":1666632521167,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"05wunolWilwQ","outputId":"4f93f92f-f8a8-4c1b-82a7-f8d69f4a75f9"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/4.80k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a52a142269314b11a92d3018bc49e6db"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.95k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"400e1866653f41629d558ce5f5b6cfcb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/5.14k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"830e6087b3f8491ba3501ec5b6757439"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset covid_qa_deepset/covid_qa_deepset (download: 4.21 MiB, generated: 62.13 MiB, post-processed: Unknown size, total: 66.35 MiB) to /root/.cache/huggingface/datasets/covid_qa_deepset/covid_qa_deepset/1.0.0/fb886523842e312176f92ec8e01e77a08fa15a694f5741af6fc42796ee9c8c46...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/1.35M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b3728c7930394f02b216f023d4b2e7b0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/2019 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5034f385786043c893797439d482224d"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset covid_qa_deepset downloaded and prepared to /root/.cache/huggingface/datasets/covid_qa_deepset/covid_qa_deepset/1.0.0/fb886523842e312176f92ec8e01e77a08fa15a694f5741af6fc42796ee9c8c46. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1add3cb6f5744ab977dad96e2d25c77"}},"metadata":{}}],"source":["dataset=load_dataset(\"covid_qa_deepset\")\n","#dataset=load_dataset(\"squad\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1666632521168,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"-4j04rAdil01","outputId":"23d4cf6b-98fc-49de-f1b3-8ed38989335b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['document_id', 'context', 'question', 'is_impossible', 'id', 'answers'],\n","        num_rows: 2019\n","    })\n","})"]},"metadata":{},"execution_count":6}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1666632521168,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"QOueW7Ylil3N","outputId":"cbef1521-da24-4290-c643-ad1f8deab9ac"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'document_id': 630,\n"," 'context': \"Functional Genetic Variants in DC-SIGNR Are Associated with Mother-to-Child Transmission of HIV-1\\n\\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752805/\\n\\nBoily-Larouche, Geneviève; Iscache, Anne-Laure; Zijenah, Lynn S.; Humphrey, Jean H.; Mouland, Andrew J.; Ward, Brian J.; Roger, Michel\\n2009-10-07\\nDOI:10.1371/journal.pone.0007211\\nLicense:cc-by\\n\\nAbstract: BACKGROUND: Mother-to-child transmission (MTCT) is the main cause of HIV-1 infection in children worldwide. Given that the C-type lectin receptor, dendritic cell-specific ICAM-grabbing non-integrin-related (DC-SIGNR, also known as CD209L or liver/lymph node–specific ICAM-grabbing non-integrin (L-SIGN)), can interact with pathogens including HIV-1 and is expressed at the maternal-fetal interface, we hypothesized that it could influence MTCT of HIV-1. METHODS AND FINDINGS: To investigate the potential role of DC-SIGNR in MTCT of HIV-1, we carried out a genetic association study of DC-SIGNR in a well-characterized cohort of 197 HIV-infected mothers and their infants recruited in Harare, Zimbabwe. Infants harbouring two copies of DC-SIGNR H1 and/or H3 haplotypes (H1-H1, H1-H3, H3-H3) had a 3.6-fold increased risk of in utero (IU) (P = 0.013) HIV-1 infection and a 5.7-fold increased risk of intrapartum (IP) (P = 0.025) HIV-1 infection after adjusting for a number of maternal factors. The implicated H1 and H3 haplotypes share two single nucleotide polymorphisms (SNPs) in promoter region (p-198A) and intron 2 (int2-180A) that were associated with increased risk of both IU (P = 0.045 and P = 0.003, respectively) and IP (P = 0.025, for int2-180A) HIV-1 infection. The promoter variant reduced transcriptional activity in vitro. In homozygous H1 infants bearing both the p-198A and int2-180A mutations, we observed a 4-fold decrease in the level of placental DC-SIGNR transcripts, disproportionately affecting the expression of membrane-bound isoforms compared to infant noncarriers (P = 0.011). CONCLUSION: These results suggest that DC-SIGNR plays a crucial role in MTCT of HIV-1 and that impaired placental DC-SIGNR expression increases risk of transmission.\\n\\nText: Without specific interventions, the rate of HIV-1 mother-tochild transmission (MTCT) is approximately 15-45% [1] . UNAIDS estimates that last year alone, more than 400,000 children were infected worldwide, mostly through MTCT and 90% of them lived in sub-Saharan Africa. In the most heavilyaffected countries, such as Zimbabwe, HIV-1 is responsible for one third of all deaths among children under the age of five. MTCT of HIV-1 can occur during pregnancy (in utero, IU), delivery (intrapartum, IP) or breastfeeding (postpartum, PP). High maternal viral load, low CD4 cells count, vaginal delivery, low gestational age have all been identified as independent factors associated with MTCT of HIV-1 [1] . Although antiretrovirals can reduce MTCT to 2%, limited access to timely diagnostics and drugs in many developing world countries limits the potential impact of this strategy. A better understanding of the mechanisms acting at the maternal-fetal interface is crucial for the design of alternative interventions to antiretroviral therapy for transmission prevention.\\n\\nDendritic cell-specific ICAM-grabbing non-integrin-related (DC-SIGNR, also known as CD209L or liver/lymph node-specific ICAM-grabbing non-integrin (L-SIGN)) can interact with a plethora of pathogens including HIV-1 and is expressed in placental capillary endothelial cells [2] . DC-SIGNR is organized in three distinct domains, an N-terminal cytoplasmic tail, a repeat region containing seven repeat of 23 amino acids and a C-terminal domain implicated in pathogen binding. Alternative splicing of DC-SIGNR gene leads to the production of a highly diversify isoforms repertoire which includes membrane-bound and soluble isoforms [3] . It has been proposed that interaction between DC-SIGNR and HIV-1 might enhance viral transfer to other susceptible cell types [2] but DC-SIGNR can also internalize and mediate proteasome-dependant degradation of viruses [4] that may differently affect the outcome of infection.\\n\\nGiven the presence of DC-SIGNR at the maternal-fetal interface and its interaction with HIV-1, we hypothesized that it could influence MTCT of HIV-1. To investigate the potential role of DC-SIGNR in MTCT of HIV-1, we carried out a genetic association study of DC-SIGNR in a well-characterized cohort of HIV-infected mothers and their infants recruited in Zimbabwe, and identified specific DC-SIGNR variants associated with increased risks of HIV transmission. We further characterized the functional impact of these genetic variants on DC-SIGNR expression and show that they affect both the level and type of DC-SIGNR transcripts produced in the placenta.\\n\\nSamples consisted of stored DNA extracts obtained from 197 mother-child pairs co-enrolled immediately postpartum in the ZVITAMBO Vitamin A supplementation trial (Harare, Zimbabwe) and followed at 6 weeks, and 3-monthly intervals up to 24 months. The ZVITAMBO project was a randomized placebocontrolled clinical trial that enrolled 14,110 mother-child pairs, between November 1997 and January 2000, with the main objective of investigating the impact of immediate postpartum vitamin A supplementation on MTCT of HIV-1. The samples used in the present study were from mother-child pairs randomly assigned to the placebo group of the ZVITAMBO project. Antiretroviral prophylaxis for HIV-1-positive antenatal women was not available in the Harare public-sector during ZVITAMBO patient recruitment. The samples were consecutively drawn from two groups: 97 HIV-1-positive mother/HIV-1-positive child pairs and 100 HIV-1-positive mother/HIV-negative child pairs. Mother's serological status was determined by ELISA and confirmed by Western Blot. Infants were considered to be infected if they were HIV-1 seropositive at 18 months or older and had two or more positive HIV-1-DNA polymerase chain reaction (PCR) results at earlier ages. 100 infants were considered to be uninfected as they were ELISA negative at 18 months or older and had two DNA PCR negative results from samples collected at a younger age. Of the 97 HIV-1-infected infants, 57 were infected IU, 11 were infected IP, and 17 were infected PP as determined by PCR analyses of blood samples collected at birth, 6 weeks, 3 and 6 months of age and according to the following definitions adapted from Bryson and colleagues [5] . Briefly, infants who were DNA PCR positive at birth were infected IU. Infants with negative PCR results from sample obtained at birth but who become positive by 6 weeks of age were infected IP. Infants with negative PCR results at birth and 6 weeks of age but who subsequently became DNA PCR positive were considered to be infected during the PP period. In the analysis comparing the 3 different modes of MTCT, 12 HIV-1-infected infants were excluded because the PCR results were not available at 6 weeks of age. Full methods for recruitment, baseline characteristics collection, laboratory procedures have been described elsewhere [6] .\\n\\nThe nucleotide sequence variation of the entire promoter, coding and part of 39-UTR regions of DC-SIGNR gene in the study population was determined previously [7] . Haplotype reconstruction was performed using Bayesian statistical method implemented in PHASE [8] , version 2.1.1, using single nucleotide polymorphism (SNP) with a minimum allele frequency (MAF) of 2%. We applied the algorithm five times, using different randomly generated seeds, and consistent results were obtained across runs ( Figure 1 ). Fifteen haplotype-tagged SNPs (htSNPs) were identified by the HaploBlockFinder software [9] with a MAF $5%. These htSNPs were genotyped in the 197 infants by direct PCR sequencing analysis as we have described previously [7] . The DC-SIGNR exon 4 repeat region genotype was determined by PCR amplification followed by migration in 1.5% agarose gels [10] . DNA sequences in the promoter region were analysed with the TESS interface (http//:www.cbil.upenn.edu/tess) for putative transcription factors binding sites using the TRANSFAC database.\\n\\nLuciferase reporter assays using pGL2-Basic vector were performed in order to investigate the functional effect of mutations on DC-SIGNR promoter activity. Genomic DNA from subjects homozygous for the promoter variants and WT was amplified from nucleotide position 2715 to 21 and cloned between the BglII and HindIII multiple cloning sites in the pGL2-Basic vector which harbours a reporter firefly luciferase gene downstream (Invitrogen Canada inc, Burlington, Canada). All recombinants clones were verified by DNA sequencing. The firefly luciferase test reporter vector was co-transfected at a ratio of 10:1 with the constitutive expressor of Renilla luciferase, phRL-CMV (Promega, Madison, WI, USA). We cultured HeLa cells in 6 wells plates (2610 5 cells) and transfected them the following day using lipofectamine (Invitrogen) according to the manufacturer. Cells were lysed and luciferase assays were performed using 20 mg of protein extract according to the manufacturer (Promega) at 44 h post-transfection. Firefly luciferase activity was normalized to Renilla luciferase activity. 0 mg, 0,5 mg or 1 mg CMV-Tat vector was transfected with LTR-Luc as a positive control in these experiments. We carried out lucierase assays in triplicate in three independent experiments. Results are expressed as mean6 standard error of the mean (S.E.M).\\n\\nFirst-term placental tissues were obtained from abortions following voluntary interruption of pregnancy at CHUM Hôpital Saint-Luc (Montreal, Canada). Tissues from 3 H1 (associated with MTCT of HIV-1) and 3 H15 (wild-type) homozygous haplotypes were used to analyse possible differences in isoform expression. Total placental RNAs were extracted by MasterPure DNA and RNA Extraction Kit (Epicentre Biotechnologies, Madison, WI, USA) according to the manufacturer. Fragments corresponding to the DC-SIGNR coding region were reversed transcribed (RT) and then amplified by nested PCR with the following primers; RT primers RR, first PCR RF and RR and second PCR RcF and RcR according to Liu and colleagues [11] . 1 mg of total RNA was reverse transcribed with Expand RT (Roche Applied Science, Indianapolis, IN, USA) according to the manufacturer and were PCR-amplified with DNA Platinum Taq Polymerase (Invitrogen). Major PCR products from the second PCR reaction were gel extracted with the Qiagen Gel Extraction Kit (Qiagen Canada inc, Mississauga, ON, Canada) and cloned using the TOPO TA Cloning Kit for sequencing (Invitrogen). For each placenta, 15 different clones were randomly selected and amplified with M13 primers and sequenced with ABI PRISM 3100 capillary automated sequencer (Applied Biosystems, Foster City, CA, USA). Sequences were analysed and aligned with GeneBank reference sequence NM_014257 using Lasergene software (DNA Stars, Madison, WI, USA).\\n\\nQuantitative expression of DC-SIGNR isoforms 1,5 mg of placental RNA was reverse transcribed using 2.5 mM of Oligo dT 20 and Expand RT in 20 ml volume according to the manufacturer (Roche Applied Science). 15 ng of total cDNA in a final volume of 20 ml was used to perform quantitative real-time PCR using Universal Express SYBR GreenER qPCR Supermix (Invitrogen) on a Rotor Gene Realtime Rotary Analyser (Corbett Life Science, Sydney, Australia). Samples from 2 subjects in each group were used because RNA quality of others was not suitable for a qRT-PCR analysis. Amplification of all DC-SIGNR isoforms was performed using an exon 5 specific primer pair (Table S1 ). Membrane-bound isoforms were amplified using primers specific for exon 3, corresponding to the common trans-membrane domain of DC-SIGNR. Primers were targeted to the exon-exon junction and RNA extracts were treated with DNase (Fermantas International inc, Burlington, ON, Canada) to avoid amplification of contaminant DNA. Standard curves (50-500 000 copies per reaction) were generated using serial dilution of a full-length DC-SIGNR or commercial GAPDH (Invitrogen) plasmid DNA. All qPCR reactions had efficiencies ranging from 99% to 100%, even in the presence of 20 ng of non-specific nucleic acids, and therefore could be compared. The copy number of unknown samples was estimated by placing the measured PCR cycle number (crossing threshold) on the standard curve. To correct for differences in both RNA quality and quantity between samples, the expression levels of transcripts were normalised to the reference GAPDH gene transcripts. GAPDH primer sequences were kindly provided by A. Mes-Masson at the CHUM. The results are presented as target gene copy number per 10 5 copies of GAPDH. The ratio of membrane-bound isoforms was calculated as E3/E5. Soluble isoforms were calculated by subtracting membrane-bound from total isoforms. We carried out qPCR assays in triplicate in three independent experiments. Results are expressed as mean6S.E.M.\\n\\nStatistical analysis was performed using the GraphPad PRISM 5.0 for Windows (GraphPad Software inc, San Diego, CA, USA). Differences in baseline characteristics and genotypic frequencies of haplotypes or htSNPs were compared between groups using the x 2 analysis or Fisher's exact test. Logistic regression analysis was used to estimate odds ratios (OR) for each genotype and baseline risk factors. Multiple logistic regression was used to define independent predictors identified as significant in the crude analysis. ORs and 95% confidence interval were calculated with the exact method. Comparisons of continuous variables between groups were assessed with the unpaired two-tailed Student's t test when variables were normally distributed and with the Mann-Whitney U test when otherwise. Differences were considered significant at P,0.05.\\n\\nWritten informed consent was obtained from all mothers who participated in the study and the ZVITAMBO trial and the investigation reported in this paper were approved by The \\n\\nWe carried out an association study of DC-SIGNR polymorphism in 197 infants born to untreated HIV-1-infected mothers recruited in Harare, Zimbabwe. Among them, 97 infants were HIV-1-infected and 100 infants remained uninfected. Of the 97 HIV-1-infected infants, 57 were infected IU, 11 were infected IP, and 17 were infected PP. Timing of infection was not determined for 12 HIV-1-infected infants. Baseline characteristics of mothers and infants are presented in Table 1 . Maternal age and CD4 cell count, child sex, mode of delivery, duration of membrane rupture and gestational age were similar among all groups. However, maternal viral load .29 000 copies/ml was associated with increased risk in both IU and PP with odds ratios (OR) of 3.64 (95% CI = 1.82-7.31, P = 0.0002) and 4.45 (95% CI = 1.50-13.2, P = 0.0045) for HIV-1 transmission, respectively.\\n\\nFifteen haplotype-tagged SNPs (htSNPs) corresponding to the 15 major DC-SIGNR haplotypes ( Figure 1 ) described among Zimbabweans [7] were genotyped in our study samples (Tables S2  and S3 ). H1 (31%) and H3 (11%) were the most frequent haplotypes observed (Figure 1 ). Being homozygous for the H1 haplotype was associated with increased risk of both IU (OR: 4.42, P = 0.022) and PP (OR: 7.31, P = 0.016) HIV-1 transmission ( Table 2) . Infants harbouring two copy combinations of H1 and/ or H3 haplotypes (H1-H1, H1-H3 or H3-H3) had increased risk of IU (OR: 3.42, P = 0.007) and IP (OR: 5.71, P = 0.025) but not PP (P = 0.098) HIV-1 infection compared to infant noncarriers ( Table 2 ). The latter associations remained significant after adjustment was made for the maternal viral load for both IU (OR: 3.57, 95% CI = 1.30-9.82, P = 0.013) and IP (OR: 5.71, 95% CI = 1.40-23.3, P = 0.025) HIV-1 transmission. The H1 and H3 haplotypes share a cluster of mutations (p-198A, int2-391C, int2-180A, ex4RPT, int5+7C) ( Figure 1 ). Of these, the p-198A and int2-180A variants were significantly associated with MTCT of HIV-1 (Table S2 ). In the unadjusted regression analysis, homozygous infants for the p-198A and int2-180A variants had increased risk of IU (OR: 2.07 P = 0.045, OR: 3.78, P = 0.003, respectively) and IP (OR: 2.47, P = 0.17, O.R: 5.71, P = 0.025, respectively) HIV-1 infection compared to heterozygote infants or noncarriers (Table 3) . When adjustment was made for maternal factors, only the association with the int2-180A variant remained significant for IU (OR: 3.83, 95% CI = 1.42-10.4, P = 0.008) and IP (O.R: 5.71, 95% CI = 1.40-23.3, P = 0.025) HIV-1 transmission. Thus, infants homozygous for DC-SIGNR variant int2-180A contained in H1 and H3 haplotypes were 4-fold to 6-fold more likely to be infected by HIV-1 during pregnancy or at delivery, respectively.\\n\\nAlternative splicing of the DC-SIGNR gene in the placenta produces both membrane-bound and soluble isoform repertoires [3] . The relative proportion of membrane bound and soluble DC-SIGNR could plausibly influence the susceptibility to HIV-1 infection [11] . We therefore hypothesized that the DC-SIGNR mutations associated with MTCT of HIV-1 would have an impact on both the level of DC-SIGNR expression and in the isoform repertoire produced. We investigated DC-SIGNR transcript expression in first-term placentas obtained after elective abortion.\\n\\nWe cloned DC-SIGNR from placental tissues by RT-PCR from 3 homozygous H1 samples containing both the DC-SIGNR p-198AA and int2-180AA variants associated with HIV-1 transmission and 3 homozygous wild-type (WT) (p-198CC, int2-180GG) samples. Fifteen clones per sample were randomly selected for sequencing. As expected, we found an extensive repertoire of DC-SIGNR transcripts in all samples with 9 to 16 different isoforms per individual. A total of 65 distinct transcripts were identified ( Figure S1 ), of which 3 were full-length transcripts. 64 of the sequenced clones contained a total of 69 amino acid substitutions with 3 new C termini and 2 premature stop codons. However, the diversity was mostly attributable to the entire deletion of exon 2 or exon 3 or to variations in the length of the neck region (exon 4) of DC-SIGNR. The deletion of exon 3 eliminates the trans-membrane domain of the protein and leads to the expression of soluble DC-SIGNR isoforms [3] . Interestingly, the abundance of membrane-bound isoforms in placental tissues of the H1 homozygotes appears to be lower than that observed in samples from WT individuals ( Figure S1 ). The deletion of exon 3 was confirmed by sequencing and we hypothesize that the skipping of exon 3, could be due to the presence of the int2-180A mutation observed in infants with the H1 haplotype. In fact, this intron mutation is located 180 bp downstream from exon 3 and potentially modifies splicing events (Figure 2A ). We confirmed that the variation in transcript proportions seen between the two groups was also reflected at the level of mRNA expression in the placenta. To quantify membrane-bound vs soluble isoforms in placental samples from homozygous H1 and WT infants, we amplified the exon 5 (E5) sequence present in all DC-SIGNR isoforms (total transcripts). We then amplified exon 3 (E3) which is deleted in the soluble forms and then calculated the E3:E5 ratio. We found that placental tissues from homozygous H1 infants express a significantly lower proportion of membrane-bound DC-SIGNR (18%) compared to that in WT individuals (36%) (P = 0.004) ( Figure 2B ) suggesting that exon 3 skipping happens more frequently in presence of the DC-SIGNR int2-180A variant associated with MTCT of HIV-1.\\n\\nThe DC-SIGNR int2-180A variant is always transmitted with the promoter mutation p-198A (Figure 1 ). In the unadjusted regression analysis, the p-198A variant was significantly associated with IU but not with IP and PP HIV-1 transmission (Table 3) . Computational transcription factor binding site analysis predicts Table 1 . Baseline characteristics of mother and infants risk factors for intrauterine (IU), intrapartum (IP) and postpartum (PP) mother-to-child HIV-1 transmission. Figure 3A ). The luciferase activity of the p-198A variant construct was significantly lower than that of the WT p-198C promoter construct (p-198C/A ratio = 2, P = 0.006) ( Figure 3B ) suggesting that DC-SIGNR p-198A affects promoter activity. The other promoter mutants (p-577C and p-323A) observed in the Zimbabwean population did not affect DC-SIGNR transcription in this assay ( Figure S2 ). To determine the net impact of the DC-SIGNR p-198A mutation on DC-SIGNR expression in the placenta, we quantitated the absolute number of total and membrane-bound DC-SIGNR transcripts in the H1 homozygote and wild-type placental samples as described earlier. The total number of DC-SIGNR transcripts was determined to be 6856213 (DC-SIGNR copies6S.E.M per 10 5 GAPDH copies) in the placental samples from homozygous H1 infants and was 4-fold lower compared to that found in placentas from WT individuals (27816638, P = 0.011) ( Figure 3C ). As suggested earlier, the int2-180A mutation might induce exon 3 skipping leading to a lower production of membrane-bound DC-SIGNR. Although, the decrease in the total number of DC-SIGNR transcripts in H1 homozygous placental samples containing both the p-198AA and int2-180AA variants affected the proportion of membrane-bound and soluble isoforms, the effect of these mutations was more pronounced on the membrane-bound isoforms with an 8-fold decrease (H1 = 117636.2 vs WT = 9906220.6, P = 0.003) compared to a 3-fold decrease in total soluble isoforms (H1 = 5686181.9 vs WT = 19256495.3, P = 0.03) ( Figure 3C ). Therefore, DC-SIGNR p-198A and int2-180A mutations associated with MTCT of HIV-1 significantly decreased the level of total placental DC-SIGNR transcripts, disproportionately affecting the membrane-bound isoform production. Table 3 . Associations between infant DC-SIGNR promoter p-198 and intron 2 (int2)-180 variants and intrauterine (IU), intrapartum (IP) and postpartum (PP) mother-to-child HIV-1 transmission. \\n\\nOur genetic results, supported by expression assay in placenta, suggest the involvement of DC-SIGNR in MTCT of HIV-1. Homozygosity for the haplotype H1 was associated with IU transmission in the unadjusted regression analysis. However, the association disappeared after adjustment was made for the maternal factors presumably because of the small number of H1 homozygote infants analysed in each groups. H1 and H3 were the most frequent haplotypes observed in the study population and they share a cluster of mutations (Figure 1 ). Grouping haplotypes H1 and H3 increased the power of the study and permitted the identification of specific DC-SIGNR mutations associated with MTCT of HIV-1. Indeed, two mutations shared by haplotypes H1 and H3 were associated with vertical transmission of HIV-1. The int2-180A was associated with a 4-fold increased risk of IU and 6fold increased risk of IP after adjustment for the maternal factors. Although the p-198A variant was associated with IU transmission, the association disappeared after adjustment was made for the maternal viral load. Nevertheless, we showed that this mutation reduces DC-SIGNR transcriptional activity in vitro and produces lower level of DC-SIGNR transcripts in placental tissues in combination with the int2-180A variant. Since int2-180A is always transmitted with p-198A on the MTCT associated combined haplotypes H1/H3, whereas p-198A is carried on other nonassociated haplotypes (Figure 1) , we can speculate that the p-198A mutation alone may have a minor effect in vivo whereas in combination with the int2-180A variant, they both act to reduce the level of placental DC-SIGNR expression resulting in an increased risk of MTCT of HIV-1.\\n\\nThe majority of IU transmission occurs during the last trimester of pregnancy (reviewed in [12] ). Full-term placenta samples were not available for the current study and the expression assays were performed on first-term placental tissues. A previous study looking at DC-SIGNR placental isoforms repertoire in full-term placenta samples demonstrated similar diversity of DC-SIGNR transcripts as in the first-term placental tissues studied herein [3] . However, since levels of DC-SIGNR expression have never been compared between the different terms of pregnancy, it is not known whether DC-SIGNR expression varies during the course of pregnancy. Nevertheless, it is reasonable to assume that the inter-individual differences in both DC-SIGNR isoform repertoire and transcript levels observed between the H1 and WT homozygous infants would be reflected throughout the pregnancy. To date, most studies have focused on the potential role of DC-SIGNR in trans infection of HIV-1 in vitro [2, 10] . However, the multiple mechanisms involved in trans infection and redundancy among C-type lectin functions make it difficult to determine the actual participation of DC-SIGNR in this mode of infection in vivo [13, 14] . The strong correlation we observed between MTCT of HIV-1 and DC-SIGNR genetic variants producing low levels of DC-SIGNR in the placenta suggested that mechanisms other than DC-SIGNR-mediated trans infection might operate during vertical transmission of HIV-1. For example, DC-SIGNR has also been shown to function as a HIV-1 antigen-capturing receptor [15] . Chan and colleagues recently demonstrated that DC-SIGNR transfected CHO cells diminish SARS-CoV titers by enhanced capture and degradation of the virus in a proteasome-dependent manner [4] . Since endothelial cells express MHC-I and II, degraded viral antigens could then be presented to immune cells to elicit an adaptive immune response [16, 17] . The HIV-1 coreceptor CCR5, but not CD4, is co-expressed with DC-SIGNR on placental and blood-brain barrier (BBB) endothelial cells [18, 19] . HIV-1 gp120 binding to CCR5 receptor on endothelial cells compromises BBB integrity and enhances monocytes adhesion and transmigration across the BBB [20, 21] . It is thus possible that reduced expression of DC-SIGNR, particularly the membranebound isoforms, on placental capillary endothelial cells might favour HIV-1 binding to CCR5 receptor, instead of DC-SIGNR receptor, facilitating the migration of maternal HIV-1-infected cells across the placental barrier resulting in IU transmission of HIV-1.\\n\\nThe int2-180A variant contained in the H1 and H3 haplotypes was associated with IP transmission suggesting that DC-SIGNR also affect transmission of HIV-1 during delivery. Little is known about the mechanisms underlying transmission of HIV-1 during delivery. Passage through the birth canal could potentially expose infants through a mucosal portal entry (presumably ophthalmic, skin, or gastrointestinal), whereas placental insult during delivery (physical or inflammatory) may enhance transplacental passage of maternal HIV-1-infected cells into foetal circulation [22, 23] . Such process called microtransfusion has been proposed in regards to the results obtain in a Malawian cohort. Kweik and colleagues found a significant association between levels of maternal DNA in umbilical cord blood and IP transmission of HIV-1 suggesting that passage of maternal infected cells through the placenta is likely to occur during delivery [22] . Thus, in a similar fashion as suggested earlier for IU transmission, the relatively lower level of DC-SIGNR in the placenta of homozygous infants harbouring the int2-180A variant could promote HIV-1 binding to CCR5 receptor on endothelial cells affecting the placental barrier integrity and facilitating the passage of maternal infected cells in foetal circulation during delivery.\\n\\nBeside DC-SIGNR, other HIV-1 receptors are known to influence MTCT of HIV-1 (reviewed in [24] ). Genetic variants in CCR5 have been shown to influence vertical transmission of HIV-1. CCR5 promoter variants resulting in higher expression of the receptor were associated with increased risk of MTCT of HIV-1 among sub-Saharan Africans [25, 26] . The 32-pb deletion polymorphism in CCR5 has be shown to protect from vertical transmission of HIV-1 [27] , but this variant is virtually absent among African populations [28] . High copy numbers of CCL3L1, a potent HIV-1 suppressive ligand for CCR5, are associated with higher chemokine production and lower risk of MTCT of HIV-1 among South African infants [29, 30] . Mannose-binding lectin (MBL) is an innate immune receptor synthesised in the liver and secreted in the bloodstream in response to inflammation signal. MBL promotes pathogen elimination by opsonization and phagocytosis, and reduced expression of MBL resulting from polymorphism in coding and non-coding regions has been associated with an increased risk of MTCT of HIV-1 [31, 32] .\\n\\nIn this study, we demonstrate for the first time, the potential functional impact of DC-SIGNR mutations on its expression in the placenta and in vertical transmission of HIV-1. We believe that the presence of DC-SIGNR at the placental endothelial cell surface may protect infants from HIV-1 infection by capturing virus and promoting its degradation/presentation. However, in placenta containing low levels of DC-SIGNR, HIV-1 would preferentially binds CCR5 on endothelial cells resulting in a loss of placental barrier integrity and enhanced passage of maternal HIV-1-infected cells in foetal circulation leading to MTCT of HIV-1. This mechanism may also apply to other vertically-transmitted pathogens known to interact with DC-SIGNR such as HIV-2, hepatitis C and dengue viruses and warrant further investigation. \\n\\nAssociations between child DC-SIGNR exon 4 repeated region genotypes and mother-to-child HIV-1 transmission.CI, Confidence interval; N, number; NA; not applicable; OR, odds ratio a P-value as determined by the Chi-square test. b Comparison between genotype and all others. Found at: doi:10.1371/journal.pone.0007211.s003 (0.05 MB DOC) Figure S1 DC-SIGNR transcripts repertoire in placenta. Major RT-PCR products from RNA extract from 3 homozygous H1 and 3 homozygous WT placenta samples were purified, cloned and sequenced. Sequenced were analysed according to NCBI reference sequence NM_014257. CT; cytoplasmic tail, TM; trans-membrane domain; WT; wild-type Found at: doi:10.1371/journal.pone.0007211.s004 (0.11 MB DOC) Figure S2 Effect of DC-SIGNR promoter variant on transcriptional activity in luciferase reporter assay in vitro in transfected HeLa cells. Relative luciferase expression from pGL2-Basic, parental vector without promoter. Expression DC-SIGNR promoter constructs, spanning p-577C variant or p-323A variant were calculated relatively to this value. Data are presented in mean values6S.E.M of three independent experiments performed in triplicate. One-way ANOVA test followed by the Dunnett test for multiple comparison was used to compare the relative luciferase expression of the p-557C and p-323A variant reporters against the wild-type (WT) construct (not significant). 0 mg, 0,5 mg or 1 mg CMV-Tat vector was transfected with LTR-Luc as a positive control in these experiments.\",\n"," 'question': 'What is the main cause of HIV-1 infection in children?',\n"," 'is_impossible': False,\n"," 'id': 262,\n"," 'answers': {'text': ['Mother-to-child transmission (MTCT) is the main cause of HIV-1 infection in children worldwide.'],\n","  'answer_start': [370]}}"]},"metadata":{},"execution_count":7}],"source":["dataset['train'][0]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21,"status":"ok","timestamp":1666622090402,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"8TppLYAPq0W-","outputId":"105d1e3b-32c7-4dba-f15f-f0e15edc2de8"},"outputs":[{"output_type":"stream","name":"stdout","text":["465\n"]}],"source":["answer='Mother-to-child transmission (MTCT) is the main cause of HIV-1 infection in children worldwide.'\n","print(len(answer)+370)"]},{"cell_type":"code","source":["# from transformers import DistilBertTokenizerFast\n","# tokenizer = DistilBertTokenizerFast.from_pretrained(\"distilbert-base-uncased\")"],"metadata":{"id":"TaU3HQ61VBEs","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["e126fdb3429b44df8ada414432a59c29","d9df2806d257480a9854fa07bb27c24c","41cfdf16b1734e3785857ea99d363772","33c5023f7c8e485ea627ff569c911b92","4c32624c60a74098ab19b00de873b7c6","567452c40afc434583d142523e2f69f4","128ccc373342460eb8c605d45a8e284a","62a3862f62f44488b1bcefae784bee52","7211bc9d61304069a56fb13441eec5f2","6582d2e284174c34ab817f1ea2de6c5d","f0255283779544fe83075977e362f651","cc42dfe0d6f84409884eda48cba002eb","806f16ac98684d7abf63ce3922cfd690","4953dc6541bc46c5be8c26254fcb9465","e1ad44a60ca342d58e488c44f855d221","d984962ff205454297ae869be8023e1b","6afdb0f963ca41969d96f0974aacfdf8","d28fd02cc5dd4f3abce941dae66add50","9b754f923443496697076dd56642a57c","3048e5a110114c2aa0a21d738f37b8e5","0917402ab17e400eb9a1c466659f1efe","cf4a65718a904be0973514c08e079354","72c50fc34fb54fceada25a55887d6647","8f3b53ad922849c884fdb2ce876c82a4","c1938387eab041d391fe380613258e90","5dedced246544429868875c3bd20eb62","7a3eb7db9739495a941edec7af261f13","611b0122fc69449389613a428ba835b1","db2015e1a59740bdaf6b8b4efc8997ad","42b85ff9c8624c6d9fd90beb8bae4aa4","c77ba4e941d7467ebc4eb83b6dee3a88","791e00617bb84483affdd637a6d173c1","b66a7bf53574473bb682298b7e94ee20","2174b5ec95334f3b9f131a0b7d4500ec","42aaf464e1754b318370b35cb651665e","784ddea09a094d69b5f6cd8cdb12453d","02b3cb8db31e4e629644350c1a73bfed","bcc972e374fc4bb4b6ee4721e0caf073","1b2d3f7ad7d64942a9f665c2c35819a8","000dadd940114218bd66889fb786061c","bcbd3c3f109c4ed980c8f9140c220b81","e93615ac2beb4ad8bbc50e2f356eb92e","422bf3b4d5a342f1850121f14a8347da","79ea316b7c7942d68a7a36f270fd778f"]},"executionInfo":{"status":"ok","timestamp":1666632535000,"user_tz":-60,"elapsed":12621,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2be133c1-046c-4863-feb6-4513de07b9b6"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e126fdb3429b44df8ada414432a59c29"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cc42dfe0d6f84409884eda48cba002eb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/466k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"72c50fc34fb54fceada25a55887d6647"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2174b5ec95334f3b9f131a0b7d4500ec"}},"metadata":{}}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2h04lEqKil5d"},"outputs":[],"source":["# model_id=\"microsoft/deberta-base\"\n","# tokenizer=DebertaTokenizerFast.from_pretrained(model_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"r0UawKpgdLYb","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["f10e1396bfd44c23a67572d2cc41daec","55e04c695acd47648322b1c8e5cdde8b","9798268bb7aa4c829fa1b2fd5869d863","28edf572cccf40c1b24cd3c96bc408d3","5000095d87a642fabf95b0e2c4519bac","f0cff6422e68489aaa677533eaa30de0","d2e1a9259b2d4148ab327965dae3fca2","f45624b0d6064af4868fca376dcedd87","558f0ad14eed453f9e5374dccf777f68","3f45d6a8917d45b095fd329995cad556","47de168f23c14979958a4e140602a709","9d108b4da6434f77af07d89724ccac9b","a32c1c1bbe9f42ec83428aa321ca6f14","b7f3ab3b7e5b44f19260800c77ee815a","f18fcfaac6ac472e8b48e9d73c84e719","6bb6a589cbed49649751e30ccbd308fc","9622d358fc7740acb6d9c61809226e9b","b1ecf02197824cef8a67749374fcc189","0e36c593ccb04154ba3f66d77b87585e","35265f5f8ade4e2492aec72af7abfcae","049ecc8799f042c69990a37c3fc39481","b3e92a2f2e104c6cbdb39cbbc3a63125","68a3ca5832bd4f04a4a947edc8fb08b6","e52187c9191848e8a0f404c1853b2b28","2c2b57f658e947c0bf4164c95ee89824","69a73d7a0622444d8b9f8c57243f7a9f","b6f3504a74f6474fa6dbcfcdbac5787a","fcc24a04f07b49ab9ca7a265c6f2e36f","9cbbe69fcef14e00922e914e97fa0563","312a846b3d8b44208da8b5ff8d668fcd","8fef9f1a9ea14e30a040f6942b94fe22","b72454551d1348808727e0465adb50ae","e56197da1a6a4b4c9d4e53849fb6d971","98202539ece94d06ac9746139973b52b","03a9ba1026994a44bd4b8e5756bb95d1","cae87195195d476bbc991acf52a6b4d5","7d194c2f97b34991adfddf295b00cdcd","5e3eb48a9e394e26b77fdeb1295c8191","c2296d5d0b6a4d7d86ebb0f7e7c46fb0","ae745c8f798c490a9f9abcce39169c33","54e21cf7f8544f8dbcf3432a44005178","f584a70b048345d584c30f41e0ae1244","661f66dc9cdd4a97ad95c99bb79c09e6","903355da54f84cea8ab1dc7c42b8c85d"]},"executionInfo":{"status":"ok","timestamp":1666628775228,"user_tz":-60,"elapsed":15044,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5103abbe-4a7e-4a3d-b459-a7a91ca8b881"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f10e1396bfd44c23a67572d2cc41daec"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9d108b4da6434f77af07d89724ccac9b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"68a3ca5832bd4f04a4a947edc8fb08b6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/866 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"98202539ece94d06ac9746139973b52b"}},"metadata":{}}],"source":["from transformers import LongformerTokenizerFast,TFLongformerForQuestionAnswering\n","model_id=\"allenai/longformer-large-4096-finetuned-triviaqa\"\n","tokenizer = LongformerTokenizerFast.from_pretrained(model_id)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zcH7W_8Eil7v"},"outputs":[],"source":["tokenized_examples = tokenizer(\n","    dataset['train'][0]['question'],\n","    dataset['train'][0][\"context\"],\n","    truncation=\"only_second\",\n","    max_length=MAX_LENGTH,\n","    stride=64,\n","    return_overflowing_tokens=True,\n","    return_offsets_mapping=True,\n","    padding=\"max_length\",\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1666632535000,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"F2fTubDMtums","outputId":"d335e3b9-b26f-49d2-f23d-7af1e81d7b46"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["18"]},"metadata":{},"execution_count":10}],"source":["len(tokenized_examples[\"input_ids\"])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1666632535001,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"XM947OSeDBvY","outputId":"b7f5097c-9413-4dea-a04e-e244a4e1a225"},"outputs":[{"output_type":"stream","name":"stdout","text":["['[CLS]', 'what', 'is', 'the', 'main', 'cause', 'of', 'hiv', '-', '1', 'infection', 'in', 'children', '?', '[SEP]', 'functional', 'genetic', 'variants', 'in', 'dc', '-', 'sign', '##r', 'are', 'associated', 'with', 'mother', '-', 'to', '-', 'child', 'transmission', 'of', 'hiv', '-', '1', 'https', ':', '/', '/', 'www', '.', 'nc', '##bi', '.', 'nl', '##m', '.', 'ni', '##h', '.', 'gov', '/', 'pm', '##c', '/', 'articles', '/', 'pm', '##c', '##27', '##52', '##80', '##5', '/', 'boil', '##y', '-', 'la', '##rou', '##che', ',', 'genevieve', ';', 'is', '##ca', '##che', ',', 'anne', '-', 'lau', '##re', ';', 'z', '##ij', '##ena', '##h', ',', 'lynn', 's', '.', ';', 'humphrey', ',', 'jean', 'h', '.', ';', 'mo', '##ula', '##nd', ',', 'andrew', 'j', '.', ';', 'ward', ',', 'brian', 'j', '.', ';', 'roger', ',', 'michel', '2009', '-', '10', '-', '07', 'doi', ':', '10', '.', '137', '##1', '/', 'journal', '.', 'po', '##ne', '.', '000', '##7', '##21', '##1', 'license', ':', 'cc', '-', 'by', 'abstract', ':', 'background', ':', 'mother', '-', 'to', '-', 'child', 'transmission', '(', 'mt', '##ct', ')', 'is', 'the', 'main', 'cause', 'of', 'hiv', '-', '1', 'infection', 'in', 'children', 'worldwide', '.']\n"]}],"source":["list_token=tokenizer.tokenize(\"[CLS]What is the main cause of HIV-1 infection in children?[SEP]Functional Genetic Variants in DC-SIGNR Are Associated with Mother-to-Child Transmission of HIV-1\\n\\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752805/\\n\\nBoily-Larouche, Geneviève; Iscache, Anne-Laure; Zijenah, Lynn S.; Humphrey, Jean H.; Mouland, Andrew J.; Ward, Brian J.; Roger, Michel\\n2009-10-07\\nDOI:10.1371/journal.pone.0007211\\nLicense:cc-by\\n\\nAbstract: BACKGROUND: Mother-to-child transmission (MTCT) is the main cause of HIV-1 infection in children worldwide.\")\n","print(list_token)\n","for i in range(len(list_token)):\n","  if list_token[i]=='Ġchildren':\n","    print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1666632535001,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"qeybJvg9CkBp","outputId":"d6ae6540-cf07-4a64-e249-bb52b7e0fc44"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[101,\n"," 2054,\n"," 2003,\n"," 1996,\n"," 2364,\n"," 3426,\n"," 1997,\n"," 9820,\n"," 1011,\n"," 1015,\n"," 8985,\n"," 1999,\n"," 2336,\n"," 1029,\n"," 102]"]},"metadata":{},"execution_count":12}],"source":["tokenizer.encode(\"What is the main cause of HIV-1 infection in children?\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1666632535002,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Mrs5htGkil-U","outputId":"4c19a20c-d66d-459e-a564-0af8c35671ea"},"outputs":[{"output_type":"stream","name":"stdout","text":["[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 8360, 7403, 10176, 1999, 5887, 1011, 3696, 2099, 2024, 3378, 2007, 2388, 1011, 2000, 1011, 2775, 6726, 1997, 9820, 1011, 1015, 16770, 1024, 1013, 1013, 7479, 1012, 13316, 5638, 1012, 17953, 2213, 1012, 9152, 2232, 1012, 18079, 1013, 7610, 2278, 1013, 4790, 1013, 7610, 2278, 22907, 25746, 17914, 2629, 1013, 26077, 2100, 1011, 2474, 22494, 5403, 1010, 20245, 1025, 2003, 3540, 5403, 1010, 4776, 1011, 21360, 2890, 1025, 1062, 28418, 8189, 2232, 1010, 9399, 1055, 1012, 1025, 15462, 1010, 3744, 1044, 1012, 1025, 9587, 7068, 4859, 1010, 4080, 1046, 1012, 1025, 4829, 1010, 4422, 1046, 1012, 1025, 5074, 1010, 8709, 2268, 1011, 2184, 1011, 5718, 9193, 1024, 2184, 1012, 14989, 2487, 1013, 3485, 1012, 13433, 2638, 1012, 2199, 2581, 17465, 2487, 6105, 1024, 10507, 1011, 2011, 10061, 1024, 4281, 1024, 2388, 1011, 2000, 1011, 2775, 6726, 1006, 11047, 6593, 1007, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 4969, 1012, 2445, 2008, 1996, 1039, 1011, 2828, 3393, 6593, 2378, 10769, 1010, 7939, 13626, 18291, 3526, 1011, 3563, 24582, 3286, 1011, 9775, 2512, 1011, 20014, 13910, 6657, 1011, 3141, 1006, 5887, 1011, 3696, 2099, 1010, 2036, 2124, 2004, 3729, 11387, 2683, 2140, 2030, 11290, 1013, 1048, 24335, 8458, 13045, 1516, 3563, 24582, 3286, 1011, 9775, 2512, 1011, 20014, 13910, 6657, 1006, 1048, 1011, 3696, 1007, 1007, 1010, 2064, 11835, 2007, 26835, 2015, 2164, 9820, 1011, 1015, 1998, 2003, 5228, 2012, 1996, 11062, 1011, 25972, 8278, 1010, 2057, 1044, 22571, 14573, 2229, 3550, 2008, 2009, 2071, 3747, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 4725, 1998, 9556, 1024, 2000, 8556, 1996, 4022, 2535, 1997, 5887, 1011, 3696, 2099, 1999, 11047, 6593, 1997, 9820, 1011, 1015, 1010, 2057, 3344, 2041, 1037, 7403, 2523, 2817, 1997, 5887, 1011, 3696, 2099, 1999, 1037, 2092, 1011, 7356, 2522, 27794, 1997, 19975, 9820, 1011, 10372, 10756, 1998, 2037, 16725, 8733, 1999, 18820, 2890, 1010, 11399, 1012, 16725, 7440, 2075, 2048, 4809, 1997, 5887, 1011, 3696, 2099, 1044, 2487, 1998, 1013, 2030, 1044, 2509, 5292, 24759, 26305, 2015, 1006, 1044, 2487, 1011, 1044, 2487, 1010, 1044, 2487, 1011, 1044, 2509, 1010, 1044, 2509, 1011, 1044, 2509, 1007, 2018, 1037, 1017, 1012, 1020, 1011, 10671, 3445, 3891, 1997, 1999, 21183, 10624, 1006, 1045, 2226, 1007, 1006, 1052, 1027, 1014, 1012, 5890, 2509, 1007, 9820, 1011, 1015, 8985, 1998, 1037, 1019, 1012, 1021, 1011, 10671, 3445, 3891, 1997, 26721, 19362, 11667, 1006, 12997, 1007, 1006, 1052, 1027, 1014, 1012, 6185, 2629, 1007, 9820, 1011, 1015, 8985, 2044, 19158, 2005, 1037, 2193, 1997, 11062, 5876, 1012, 1996, 20467, 1044, 2487, 1998, 1044, 2509, 5292, 24759, 26305, 2015, 3745, 2048, 2309, 16371, 14321, 26601, 26572, 19539, 2015, 1006, 1055, 16275, 2015, 1007, 1999, 15543, 2555, 1006, 1052, 1011, 20003, 2050, 1007, 1998, 17174, 2078, 1016, 1006, 20014, 2475, 1011, 8380, 2050, 1007, 2008, 2020, 3378, 2007, 3445, 3891, 1997, 2119, 1045, 2226, 1006, 1052, 1027, 1014, 1012, 5840, 2629, 1998, 1052, 1027, 1014, 1012, 4002, 2509, 1010, 4414, 1007, 1998, 12997, 1006, 1052, 1027, 1014, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] functional genetic variants in dc - signr are associated with mother - to - child transmission of hiv - 1 https : / / www. ncbi. nlm. nih. gov / pmc / articles / pmc2752805 / boily - larouche, genevieve ; iscache, anne - laure ; zijenah, lynn s. ; humphrey, jean h. ; mouland, andrew j. ; ward, brian j. ; roger, michel 2009 - 10 - 07 doi : 10. 1371 / journal. pone. 0007211 license : cc - by abstract : background : mother - to - child transmission ( mtct ) is the main cause of hiv - 1 infection in children worldwide. given that the c - type lectin receptor, dendritic cell - specific icam - grabbing non - integrin - related ( dc - signr, also known as cd209l or liver / lymph node – specific icam - grabbing non - integrin ( l - sign ) ), can interact with pathogens including hiv - 1 and is expressed at the maternal - fetal interface, we hypothesized that it could influence mtct of hiv - 1. methods and findings : to investigate the potential role of dc - signr in mtct of hiv - 1, we carried out a genetic association study of dc - signr in a well - characterized cohort of 197 hiv - infected mothers and their infants recruited in harare, zimbabwe. infants harbouring two copies of dc - signr h1 and / or h3 haplotypes ( h1 - h1, h1 - h3, h3 - h3 ) had a 3. 6 - fold increased risk of in utero ( iu ) ( p = 0. 013 ) hiv - 1 infection and a 5. 7 - fold increased risk of intrapartum ( ip ) ( p = 0. 025 ) hiv - 1 infection after adjusting for a number of maternal factors. the implicated h1 and h3 haplotypes share two single nucleotide polymorphisms ( snps ) in promoter region ( p - 198a ) and intron 2 ( int2 - 180a ) that were associated with increased risk of both iu ( p = 0. 045 and p = 0. 003, respectively ) and ip ( p = 0 [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 16371, 14321, 26601, 26572, 19539, 2015, 1006, 1055, 16275, 2015, 1007, 1999, 15543, 2555, 1006, 1052, 1011, 20003, 2050, 1007, 1998, 17174, 2078, 1016, 1006, 20014, 2475, 1011, 8380, 2050, 1007, 2008, 2020, 3378, 2007, 3445, 3891, 1997, 2119, 1045, 2226, 1006, 1052, 1027, 1014, 1012, 5840, 2629, 1998, 1052, 1027, 1014, 1012, 4002, 2509, 1010, 4414, 1007, 1998, 12997, 1006, 1052, 1027, 1014, 1012, 6185, 2629, 1010, 2005, 20014, 2475, 1011, 8380, 2050, 1007, 9820, 1011, 1015, 8985, 1012, 1996, 15543, 8349, 4359, 14193, 2389, 4023, 1999, 25714, 1012, 1999, 24004, 9096, 3995, 2271, 1044, 2487, 16725, 7682, 2119, 1996, 1052, 1011, 20003, 2050, 1998, 20014, 2475, 1011, 8380, 2050, 14494, 1010, 2057, 5159, 1037, 1018, 1011, 10671, 9885, 1999, 1996, 2504, 1997, 2173, 15758, 5887, 1011, 3696, 2099, 24051, 2015, 1010, 4487, 13102, 18981, 11589, 3258, 28239, 12473, 1996, 3670, 1997, 10804, 1011, 5391, 11163, 22694, 4102, 2000, 10527, 2512, 10010, 16252, 2015, 1006, 1052, 1027, 1014, 1012, 5890, 2487, 1007, 1012, 7091, 1024, 2122, 3463, 6592, 2008, 5887, 1011, 3696, 2099, 3248, 1037, 10232, 2535, 1999, 11047, 6593, 1997, 9820, 1011, 1015, 1998, 2008, 18234, 2173, 15758, 5887, 1011, 3696, 2099, 3670, 7457, 3891, 1997, 6726, 1012, 3793, 1024, 2302, 3563, 19388, 1010, 1996, 3446, 1997, 9820, 1011, 1015, 2388, 1011, 2000, 19339, 6726, 1006, 11047, 6593, 1007, 2003, 3155, 2321, 1011, 3429, 1003, 1031, 1015, 1033, 1012, 14477, 9821, 10035, 2008, 2197, 2095, 2894, 1010, 2062, 2084, 4278, 1010, 2199, 2336, 2020, 10372, 4969, 1010, 3262, 2083, 11047, 6593, 1998, 3938, 1003, 1997, 2068, 2973, 1999, 4942, 1011, 24505, 3088, 1012, 1999, 1996, 2087, 4600, 10354, 25969, 2098, 3032, 1010, 2107, 2004, 11399, 1010, 9820, 1011, 1015, 2003, 3625, 2005, 2028, 2353, 1997, 2035, 6677, 2426, 2336, 2104, 1996, 2287, 1997, 2274, 1012, 11047, 6593, 1997, 9820, 1011, 1015, 2064, 5258, 2076, 10032, 1006, 1999, 21183, 10624, 1010, 1045, 2226, 1007, 1010, 6959, 1006, 26721, 19362, 11667, 1010, 12997, 1007, 2030, 7388, 7959, 17819, 1006, 2695, 19362, 11667, 1010, 4903, 1007, 1012, 2152, 11062, 13434, 7170, 1010, 2659, 3729, 2549, 4442, 4175, 1010, 12436, 24965, 6959, 1010, 2659, 16216, 20100, 2389, 2287, 2031, 2035, 2042, 4453, 2004, 2981, 5876, 3378, 2007, 11047, 6593, 1997, 9820, 1011, 1015, 1031, 1015, 1033, 1012, 2348, 3424, 13465, 12298, 7895, 4877, 2064, 5547, 11047, 6593, 2000, 1016, 1003, 1010, 3132, 3229, 2000, 23259, 16474, 2015, 1998, 5850, 1999, 2116, 4975, 2088, 3032, 6537, 1996, 4022, 4254, 1997, 2023, 5656, 1012, 1037, 2488, 4824, 1997, 1996, 10595, 3772, 2012, 1996, 11062, 1011, 25972, 8278, 2003, 10232, 2005, 1996, 2640, 1997, 4522, 19388, 2000, 3424, 13465, 12298, 7895, 2140, 7242, 2005, 6726, 9740, 1012, 7939, 13626, 18291, 3526, 1011, 3563, 24582, 3286, 1011, 9775, 2512, 1011, 20014, 13910, 6657, 1011, 3141, 1006, 5887, 1011, 3696, 2099, 1010, 2036, 2124, 2004, 3729, 11387, 2683, 2140, 2030, 11290, 1013, 1048, 24335, 8458, 13045, 1011, 3563, 24582, 3286, 1011, 9775, 2512, 1011, 20014, 13910, 6657, 1006, 1048, 1011, 3696, 1007, 1007, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] nucleotide polymorphisms ( snps ) in promoter region ( p - 198a ) and intron 2 ( int2 - 180a ) that were associated with increased risk of both iu ( p = 0. 045 and p = 0. 003, respectively ) and ip ( p = 0. 025, for int2 - 180a ) hiv - 1 infection. the promoter variant reduced transcriptional activity in vitro. in homozygous h1 infants bearing both the p - 198a and int2 - 180a mutations, we observed a 4 - fold decrease in the level of placental dc - signr transcripts, disproportionately affecting the expression of membrane - bound isoforms compared to infant noncarriers ( p = 0. 011 ). conclusion : these results suggest that dc - signr plays a crucial role in mtct of hiv - 1 and that impaired placental dc - signr expression increases risk of transmission. text : without specific interventions, the rate of hiv - 1 mother - tochild transmission ( mtct ) is approximately 15 - 45 % [ 1 ]. unaids estimates that last year alone, more than 400, 000 children were infected worldwide, mostly through mtct and 90 % of them lived in sub - saharan africa. in the most heavilyaffected countries, such as zimbabwe, hiv - 1 is responsible for one third of all deaths among children under the age of five. mtct of hiv - 1 can occur during pregnancy ( in utero, iu ), delivery ( intrapartum, ip ) or breastfeeding ( postpartum, pp ). high maternal viral load, low cd4 cells count, vaginal delivery, low gestational age have all been identified as independent factors associated with mtct of hiv - 1 [ 1 ]. although antiretrovirals can reduce mtct to 2 %, limited access to timely diagnostics and drugs in many developing world countries limits the potential impact of this strategy. a better understanding of the mechanisms acting at the maternal - fetal interface is crucial for the design of alternative interventions to antiretroviral therapy for transmission prevention. dendritic cell - specific icam - grabbing non - integrin - related ( dc - signr, also known as cd209l or liver / lymph node - specific icam - grabbing non - integrin ( l - sign ) ) [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 3424, 13465, 12298, 7895, 2140, 7242, 2005, 6726, 9740, 1012, 7939, 13626, 18291, 3526, 1011, 3563, 24582, 3286, 1011, 9775, 2512, 1011, 20014, 13910, 6657, 1011, 3141, 1006, 5887, 1011, 3696, 2099, 1010, 2036, 2124, 2004, 3729, 11387, 2683, 2140, 2030, 11290, 1013, 1048, 24335, 8458, 13045, 1011, 3563, 24582, 3286, 1011, 9775, 2512, 1011, 20014, 13910, 6657, 1006, 1048, 1011, 3696, 1007, 1007, 2064, 11835, 2007, 1037, 20228, 11031, 6525, 1997, 26835, 2015, 2164, 9820, 1011, 1015, 1998, 2003, 5228, 1999, 2173, 15758, 6178, 9386, 2854, 2203, 14573, 24587, 4442, 1031, 1016, 1033, 1012, 5887, 1011, 3696, 2099, 2003, 4114, 1999, 2093, 5664, 13100, 1010, 2019, 1050, 1011, 5536, 22330, 14399, 8523, 7712, 5725, 1010, 1037, 9377, 2555, 4820, 2698, 9377, 1997, 2603, 13096, 12737, 1998, 1037, 1039, 1011, 5536, 5884, 20467, 1999, 26835, 8031, 1012, 4522, 11867, 10415, 2075, 1997, 5887, 1011, 3696, 2099, 4962, 5260, 2000, 1996, 2537, 1997, 1037, 3811, 18612, 8757, 11163, 22694, 13646, 2029, 2950, 10804, 1011, 5391, 1998, 24345, 11163, 22694, 1031, 1017, 1033, 1012, 2009, 2038, 2042, 3818, 2008, 8290, 2090, 5887, 1011, 3696, 2099, 1998, 9820, 1011, 1015, 2453, 11598, 13434, 4651, 2000, 2060, 18002, 3526, 4127, 1031, 1016, 1033, 2021, 5887, 1011, 3696, 2099, 2064, 2036, 4722, 4697, 1998, 2865, 2618, 4013, 27058, 14045, 1011, 12530, 4630, 16627, 1997, 18191, 1031, 1018, 1033, 2008, 2089, 11543, 7461, 1996, 9560, 1997, 8985, 1012, 2445, 1996, 3739, 1997, 5887, 1011, 3696, 2099, 2012, 1996, 11062, 1011, 25972, 8278, 1998, 2049, 8290, 2007, 9820, 1011, 1015, 1010, 2057, 1044, 22571, 14573, 2229, 3550, 2008, 2009, 2071, 3747, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 2000, 8556, 1996, 4022, 2535, 1997, 5887, 1011, 3696, 2099, 1999, 11047, 6593, 1997, 9820, 1011, 1015, 1010, 2057, 3344, 2041, 1037, 7403, 2523, 2817, 1997, 5887, 1011, 3696, 2099, 1999, 1037, 2092, 1011, 7356, 2522, 27794, 1997, 9820, 1011, 10372, 10756, 1998, 2037, 16725, 8733, 1999, 11399, 1010, 1998, 4453, 3563, 5887, 1011, 3696, 2099, 10176, 3378, 2007, 3445, 10831, 1997, 9820, 6726, 1012, 2057, 2582, 7356, 1996, 8360, 4254, 1997, 2122, 7403, 10176, 2006, 5887, 1011, 3696, 2099, 3670, 1998, 2265, 2008, 2027, 7461, 2119, 1996, 2504, 1998, 2828, 1997, 5887, 1011, 3696, 2099, 24051, 2015, 2550, 1999, 1996, 2173, 12380, 1012, 8168, 5031, 1997, 8250, 6064, 27059, 4663, 2013, 19975, 2388, 1011, 2775, 7689, 2522, 1011, 8302, 3202, 2695, 19362, 11667, 1999, 1996, 1062, 28403, 13344, 17663, 1037, 12448, 3370, 3979, 1006, 18820, 2890, 1010, 11399, 1007, 1998, 2628, 2012, 1020, 3134, 1010, 1998, 1017, 1011, 7058, 14025, 2039, 2000, 2484, 2706, 1012, 1996, 1062, 28403, 13344, 2622, 2001, 1037, 6721, 3550, 2173, 5092, 8663, 13181, 11001, 6612, 3979, 2008, 8302, 2403, 1010, 7287, 2388, 1011, 2775, 7689, 1010, 2090, 2281, 2722, 1998, 2254, 2456, 1010, 2007, 1996, 2364, 7863, 1997, 11538, 1996, 4254, 1997, 6234, 2695, 19362, 11667, 17663, 1037, 12448, 3370, 2006, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 1996, 8168, 2109, 1999, 1996, 2556, 2817, 2020, 2013, 2388, 1011, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] antiretroviral therapy for transmission prevention. dendritic cell - specific icam - grabbing non - integrin - related ( dc - signr, also known as cd209l or liver / lymph node - specific icam - grabbing non - integrin ( l - sign ) ) can interact with a plethora of pathogens including hiv - 1 and is expressed in placental capillary endothelial cells [ 2 ]. dc - signr is organized in three distinct domains, an n - terminal cytoplasmic tail, a repeat region containing seven repeat of 23 amino acids and a c - terminal domain implicated in pathogen binding. alternative splicing of dc - signr gene leads to the production of a highly diversify isoforms repertoire which includes membrane - bound and soluble isoforms [ 3 ]. it has been proposed that interaction between dc - signr and hiv - 1 might enhance viral transfer to other susceptible cell types [ 2 ] but dc - signr can also internalize and mediate proteasome - dependant degradation of viruses [ 4 ] that may differently affect the outcome of infection. given the presence of dc - signr at the maternal - fetal interface and its interaction with hiv - 1, we hypothesized that it could influence mtct of hiv - 1. to investigate the potential role of dc - signr in mtct of hiv - 1, we carried out a genetic association study of dc - signr in a well - characterized cohort of hiv - infected mothers and their infants recruited in zimbabwe, and identified specific dc - signr variants associated with increased risks of hiv transmission. we further characterized the functional impact of these genetic variants on dc - signr expression and show that they affect both the level and type of dc - signr transcripts produced in the placenta. samples consisted of stored dna extracts obtained from 197 mother - child pairs co - enrolled immediately postpartum in the zvitambo vitamin a supplementation trial ( harare, zimbabwe ) and followed at 6 weeks, and 3 - monthly intervals up to 24 months. the zvitambo project was a randomized placebocontrolled clinical trial that enrolled 14, 110 mother - child pairs, between november 1997 and january 2000, with the main objective of investigating the impact of immediate postpartum vitamin a supplementation on mtct of hiv - 1. the samples used in the present study were from mother - [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 2001, 1037, 6721, 3550, 2173, 5092, 8663, 13181, 11001, 6612, 3979, 2008, 8302, 2403, 1010, 7287, 2388, 1011, 2775, 7689, 1010, 2090, 2281, 2722, 1998, 2254, 2456, 1010, 2007, 1996, 2364, 7863, 1997, 11538, 1996, 4254, 1997, 6234, 2695, 19362, 11667, 17663, 1037, 12448, 3370, 2006, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 1996, 8168, 2109, 1999, 1996, 2556, 2817, 2020, 2013, 2388, 1011, 2775, 7689, 18154, 4137, 2000, 1996, 2173, 5092, 2177, 1997, 1996, 1062, 28403, 13344, 2622, 1012, 3424, 13465, 12298, 7895, 2140, 17678, 29598, 8528, 2483, 2005, 9820, 1011, 1015, 1011, 3893, 14405, 8189, 9080, 2308, 2001, 2025, 2800, 1999, 1996, 18820, 2890, 2270, 1011, 4753, 2076, 1062, 28403, 13344, 5776, 15680, 1012, 1996, 8168, 2020, 5486, 2135, 4567, 2013, 2048, 2967, 1024, 5989, 9820, 1011, 1015, 1011, 3893, 2388, 1013, 9820, 1011, 1015, 1011, 3893, 2775, 7689, 1998, 2531, 9820, 1011, 1015, 1011, 3893, 2388, 1013, 9820, 1011, 4997, 2775, 7689, 1012, 2388, 1005, 1055, 14262, 10091, 3570, 2001, 4340, 2011, 29490, 1998, 4484, 2011, 2530, 1038, 10994, 1012, 16725, 2020, 2641, 2000, 2022, 10372, 2065, 2027, 2020, 9820, 1011, 1015, 14262, 7361, 20049, 6024, 2012, 2324, 2706, 2030, 3080, 1998, 2018, 2048, 2030, 2062, 3893, 9820, 1011, 1015, 1011, 6064, 17782, 11022, 4677, 4668, 1006, 7473, 2099, 1007, 3463, 2012, 3041, 5535, 1012, 2531, 16725, 2020, 2641, 2000, 2022, 4895, 2378, 25969, 2098, 2004, 2027, 2020, 29490, 4997, 2012, 2324, 2706, 2030, 3080, 1998, 2018, 2048, 6064, 7473, 2099, 4997, 3463, 2013, 8168, 5067, 2012, 1037, 3920, 2287, 1012, 1997, 1996, 5989, 9820, 1011, 1015, 1011, 10372, 16725, 1010, 5401, 2020, 10372, 1045, 2226, 1010, 2340, 2020, 10372, 12997, 1010, 1998, 2459, 2020, 10372, 4903, 2004, 4340, 2011, 7473, 2099, 16478, 1997, 2668, 8168, 5067, 2012, 4182, 1010, 1020, 3134, 1010, 1017, 1998, 1020, 2706, 1997, 2287, 1998, 2429, 2000, 1996, 2206, 15182, 5967, 2013, 26845, 1998, 8628, 1031, 1019, 1033, 1012, 4780, 1010, 16725, 2040, 2020, 6064, 7473, 2099, 3893, 2012, 4182, 2020, 10372, 1045, 2226, 1012, 16725, 2007, 4997, 7473, 2099, 3463, 2013, 7099, 4663, 2012, 4182, 2021, 2040, 2468, 3893, 2011, 1020, 3134, 1997, 2287, 2020, 10372, 12997, 1012, 16725, 2007, 4997, 7473, 2099, 3463, 2012, 4182, 1998, 1020, 3134, 1997, 2287, 2021, 2040, 3525, 2150, 6064, 7473, 2099, 3893, 2020, 2641, 2000, 2022, 10372, 2076, 1996, 4903, 2558, 1012, 1999, 1996, 4106, 13599, 1996, 1017, 2367, 11583, 1997, 11047, 6593, 1010, 2260, 9820, 1011, 1015, 1011, 10372, 16725, 2020, 12421, 2138, 1996, 7473, 2099, 3463, 2020, 2025, 2800, 2012, 1020, 3134, 1997, 2287, 1012, 2440, 4725, 2005, 15680, 1010, 26163, 6459, 3074, 1010, 5911, 8853, 2031, 2042, 2649, 6974, 1031, 1020, 1033, 1012, 1996, 16371, 14321, 26601, 5537, 8386, 1997, 1996, 2972, 15543, 1010, 16861, 1998, 2112, 1997, 4464, 1011, 21183, 2099, 4655, 1997, 5887, 1011, 3696, 2099, 4962, 1999, 1996, 2817, 2313, 2001, 4340, 3130, 1031, 1021, 1033, 1012, 5292, 24759, 26305, 8735, 2001, 2864, 2478, 3016, 25253, 7778, 4118, 7528, 1999, 4403, 1031, 1022, 1033, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] was a randomized placebocontrolled clinical trial that enrolled 14, 110 mother - child pairs, between november 1997 and january 2000, with the main objective of investigating the impact of immediate postpartum vitamin a supplementation on mtct of hiv - 1. the samples used in the present study were from mother - child pairs randomly assigned to the placebo group of the zvitambo project. antiretroviral prophylaxis for hiv - 1 - positive antenatal women was not available in the harare public - sector during zvitambo patient recruitment. the samples were consecutively drawn from two groups : 97 hiv - 1 - positive mother / hiv - 1 - positive child pairs and 100 hiv - 1 - positive mother / hiv - negative child pairs. mother's serological status was determined by elisa and confirmed by western blot. infants were considered to be infected if they were hiv - 1 seropositive at 18 months or older and had two or more positive hiv - 1 - dna polymerase chain reaction ( pcr ) results at earlier ages. 100 infants were considered to be uninfected as they were elisa negative at 18 months or older and had two dna pcr negative results from samples collected at a younger age. of the 97 hiv - 1 - infected infants, 57 were infected iu, 11 were infected ip, and 17 were infected pp as determined by pcr analyses of blood samples collected at birth, 6 weeks, 3 and 6 months of age and according to the following definitions adapted from bryson and colleagues [ 5 ]. briefly, infants who were dna pcr positive at birth were infected iu. infants with negative pcr results from sample obtained at birth but who become positive by 6 weeks of age were infected ip. infants with negative pcr results at birth and 6 weeks of age but who subsequently became dna pcr positive were considered to be infected during the pp period. in the analysis comparing the 3 different modes of mtct, 12 hiv - 1 - infected infants were excluded because the pcr results were not available at 6 weeks of age. full methods for recruitment, baseline characteristics collection, laboratory procedures have been described elsewhere [ 6 ]. the nucleotide sequence variation of the entire promoter, coding and part of 39 - utr regions of dc - signr gene in the study population was determined previously [ 7 ]. haplotype reconstruction was performed using bayesian statistical method implemented in phase [ 8 ] [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 5911, 8853, 2031, 2042, 2649, 6974, 1031, 1020, 1033, 1012, 1996, 16371, 14321, 26601, 5537, 8386, 1997, 1996, 2972, 15543, 1010, 16861, 1998, 2112, 1997, 4464, 1011, 21183, 2099, 4655, 1997, 5887, 1011, 3696, 2099, 4962, 1999, 1996, 2817, 2313, 2001, 4340, 3130, 1031, 1021, 1033, 1012, 5292, 24759, 26305, 8735, 2001, 2864, 2478, 3016, 25253, 7778, 4118, 7528, 1999, 4403, 1031, 1022, 1033, 1010, 2544, 1016, 1012, 1015, 1012, 1015, 1010, 2478, 2309, 16371, 14321, 26601, 26572, 19539, 1006, 1055, 16275, 1007, 2007, 1037, 6263, 2035, 12260, 6075, 1006, 5003, 2546, 1007, 1997, 1016, 1003, 1012, 2057, 4162, 1996, 9896, 2274, 2335, 1010, 2478, 2367, 18154, 7013, 8079, 1010, 1998, 8335, 3463, 2020, 4663, 2408, 3216, 1006, 3275, 1015, 1007, 1012, 5417, 5292, 24759, 26305, 1011, 26610, 1055, 16275, 2015, 1006, 1044, 3215, 16275, 2015, 1007, 2020, 4453, 2011, 1996, 5292, 24759, 16429, 7878, 23695, 4007, 1031, 1023, 1033, 2007, 1037, 5003, 2546, 1002, 1019, 1003, 1012, 2122, 1044, 3215, 16275, 2015, 2020, 8991, 26305, 2094, 1999, 1996, 19975, 16725, 2011, 3622, 7473, 2099, 24558, 4106, 2004, 2057, 2031, 2649, 3130, 1031, 1021, 1033, 1012, 1996, 5887, 1011, 3696, 2099, 4654, 2239, 1018, 9377, 2555, 8991, 26305, 2001, 4340, 2011, 7473, 2099, 23713, 3669, 10803, 2628, 2011, 9230, 1999, 1015, 1012, 1019, 1003, 12943, 10464, 3366, 21500, 2015, 1031, 2184, 1033, 1012, 6064, 10071, 1999, 1996, 15543, 2555, 2020, 20302, 23274, 2094, 2007, 1996, 15540, 8278, 1006, 8299, 1013, 1013, 1024, 7479, 1012, 17324, 4014, 1012, 2039, 2368, 2078, 1012, 3968, 2226, 1013, 15540, 1007, 2005, 2404, 8082, 14193, 5876, 8031, 4573, 2478, 1996, 9099, 7011, 2278, 7809, 1012, 24184, 11022, 6398, 4632, 22916, 2478, 18720, 2140, 2475, 1011, 3937, 9207, 2020, 2864, 1999, 2344, 2000, 8556, 1996, 8360, 3466, 1997, 14494, 2006, 5887, 1011, 3696, 2099, 15543, 4023, 1012, 8991, 22026, 6064, 2013, 5739, 24004, 9096, 3995, 2271, 2005, 1996, 15543, 10176, 1998, 1059, 2102, 2001, 26986, 2013, 16371, 14321, 26601, 2597, 25103, 2629, 2000, 2538, 1998, 17598, 2094, 2090, 1996, 1038, 25394, 2072, 1998, 9269, 6137, 3674, 18856, 13369, 4573, 1999, 1996, 18720, 2140, 2475, 1011, 3937, 9207, 2029, 7440, 2015, 1037, 6398, 2543, 14151, 24184, 11022, 4962, 13248, 1006, 1999, 5737, 13181, 6914, 2710, 4297, 1010, 15552, 1010, 2710, 1007, 1012, 2035, 28667, 5358, 21114, 7666, 24418, 2020, 20119, 2011, 6064, 24558, 1012, 1996, 2543, 14151, 24184, 11022, 3231, 6398, 9207, 2001, 2522, 1011, 9099, 25969, 2098, 2012, 1037, 6463, 1997, 2184, 1024, 1015, 2007, 1996, 9530, 16643, 8525, 6024, 4671, 2953, 1997, 14916, 9386, 24184, 11022, 1010, 6887, 12190, 1011, 4642, 2615, 1006, 20877, 29107, 1010, 7063, 1010, 15536, 1010, 3915, 1007, 1012, 2057, 3226, 2094, 2002, 2721, 4442, 1999, 1020, 7051, 7766, 1006, 24441, 2692, 1019, 4442, 1007, 1998, 9099, 25969, 2098, 2068, 1996, 2206, 2154, 2478, 5423, 11253, 22471, 19915, 1006, 1999, 5737, 13181, 6914, 1007, 2429, 2000, 1996, 7751, 1012, 4442, 2020, 1048, 23274, 2094, 1998, 24184, 11022, 4632, 22916, 2020, 2864, 2478, 2322, 11460, 1997, 5250, 14817, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] laboratory procedures have been described elsewhere [ 6 ]. the nucleotide sequence variation of the entire promoter, coding and part of 39 - utr regions of dc - signr gene in the study population was determined previously [ 7 ]. haplotype reconstruction was performed using bayesian statistical method implemented in phase [ 8 ], version 2. 1. 1, using single nucleotide polymorphism ( snp ) with a minimum allele frequency ( maf ) of 2 %. we applied the algorithm five times, using different randomly generated seeds, and consistent results were obtained across runs ( figure 1 ). fifteen haplotype - tagged snps ( htsnps ) were identified by the haploblockfinder software [ 9 ] with a maf $ 5 %. these htsnps were genotyped in the 197 infants by direct pcr sequencing analysis as we have described previously [ 7 ]. the dc - signr exon 4 repeat region genotype was determined by pcr amplification followed by migration in 1. 5 % agarose gels [ 10 ]. dna sequences in the promoter region were analysed with the tess interface ( http / / : www. cbil. upenn. edu / tess ) for putative transcription factors binding sites using the transfac database. luciferase reporter assays using pgl2 - basic vector were performed in order to investigate the functional effect of mutations on dc - signr promoter activity. genomic dna from subjects homozygous for the promoter variants and wt was amplified from nucleotide position 2715 to 21 and cloned between the bglii and hindiii multiple cloning sites in the pgl2 - basic vector which harbours a reporter firefly luciferase gene downstream ( invitrogen canada inc, burlington, canada ). all recombinants clones were verified by dna sequencing. the firefly luciferase test reporter vector was co - transfected at a ratio of 10 : 1 with the constitutive expressor of renilla luciferase, phrl - cmv ( promega, madison, wi, usa ). we cultured hela cells in 6 wells plates ( 2610 5 cells ) and transfected them the following day using lipofectamine ( invitrogen ) according to the manufacturer. cells were lysed and luciferase assays were performed using 20 mg of protein extract [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 1010, 15536, 1010, 3915, 1007, 1012, 2057, 3226, 2094, 2002, 2721, 4442, 1999, 1020, 7051, 7766, 1006, 24441, 2692, 1019, 4442, 1007, 1998, 9099, 25969, 2098, 2068, 1996, 2206, 2154, 2478, 5423, 11253, 22471, 19915, 1006, 1999, 5737, 13181, 6914, 1007, 2429, 2000, 1996, 7751, 1012, 4442, 2020, 1048, 23274, 2094, 1998, 24184, 11022, 4632, 22916, 2020, 2864, 2478, 2322, 11460, 1997, 5250, 14817, 2429, 2000, 1996, 7751, 1006, 20877, 29107, 1007, 2012, 4008, 1044, 2695, 1011, 9099, 25969, 3258, 1012, 2543, 14151, 24184, 11022, 4023, 2001, 3671, 3550, 2000, 14916, 9386, 24184, 11022, 4023, 1012, 1014, 11460, 1010, 1014, 1010, 1019, 11460, 2030, 1015, 11460, 4642, 2615, 1011, 11937, 2102, 9207, 2001, 9099, 25969, 2098, 2007, 8318, 2099, 1011, 12776, 2004, 1037, 3893, 2491, 1999, 2122, 7885, 1012, 2057, 3344, 2041, 28831, 23797, 4632, 22916, 1999, 4440, 19341, 2618, 1999, 2093, 2981, 7885, 1012, 3463, 2024, 5228, 2004, 2812, 2575, 3115, 7561, 1997, 1996, 2812, 1006, 1055, 1012, 1041, 1012, 1049, 1007, 1012, 2034, 1011, 2744, 2173, 15758, 14095, 2020, 4663, 2013, 11324, 2015, 2206, 10758, 24191, 1997, 10032, 2012, 14684, 2213, 6154, 18400, 3002, 1011, 12776, 1006, 5548, 1010, 2710, 1007, 1012, 14095, 2013, 1017, 1044, 2487, 1006, 3378, 2007, 11047, 6593, 1997, 9820, 1011, 1015, 1007, 1998, 1017, 1044, 16068, 1006, 3748, 1011, 2828, 1007, 24004, 9096, 3995, 2271, 5292, 24759, 26305, 2015, 2020, 2109, 2000, 20302, 23274, 2825, 5966, 1999, 11163, 14192, 3670, 1012, 2561, 2173, 15758, 12987, 2015, 2020, 15901, 2011, 3040, 5311, 2063, 6064, 1998, 12987, 14676, 8934, 1006, 8680, 4765, 2890, 16012, 15007, 3630, 21615, 1010, 7063, 1010, 15536, 1010, 3915, 1007, 2429, 2000, 1996, 7751, 1012, 10341, 7978, 2000, 1996, 5887, 1011, 3696, 2099, 16861, 2555, 2020, 11674, 26223, 1006, 19387, 1007, 1998, 2059, 26986, 2011, 9089, 2098, 7473, 2099, 2007, 1996, 2206, 3539, 2869, 1025, 19387, 3539, 2869, 25269, 1010, 2034, 7473, 2099, 21792, 1998, 25269, 1998, 2117, 7473, 2099, 22110, 2546, 1998, 22110, 2099, 2429, 2000, 8607, 1998, 8628, 1031, 2340, 1033, 1012, 1015, 11460, 1997, 2561, 12987, 2001, 7901, 26223, 2007, 7818, 19387, 1006, 20162, 4162, 2671, 1010, 9506, 1010, 1999, 1010, 3915, 1007, 2429, 2000, 1996, 7751, 1998, 2020, 7473, 2099, 1011, 26986, 2007, 6064, 8899, 11937, 4160, 17782, 11022, 1006, 1999, 5737, 13181, 6914, 1007, 1012, 2350, 7473, 2099, 3688, 2013, 1996, 2117, 7473, 2099, 4668, 2020, 21500, 15901, 2007, 1996, 18816, 4270, 2078, 21500, 14676, 8934, 1006, 18816, 4270, 2078, 2710, 4297, 1010, 3335, 21205, 16377, 1010, 2006, 1010, 2710, 1007, 1998, 17598, 2094, 2478, 1996, 2327, 2080, 11937, 18856, 13369, 8934, 2005, 24558, 1006, 1999, 5737, 13181, 6914, 1007, 1012, 2005, 2169, 2173, 12380, 1010, 2321, 2367, 24418, 2020, 18154, 3479, 1998, 26986, 2007, 23290, 2509, 3539, 2869, 1998, 5537, 2094, 2007, 11113, 2072, 26113, 17196, 2692, 6178, 9386, 2854, 12978, 5537, 2099, 1006, 4162, 16012, 29390, 1010, 6469, 2103, 1010, 6187, 1010, 3915, 1007, 1012, 10071, 2020, 20302, 23274, 2094, 1998, 13115, 2007, 4962, 9299, 4431, 5537, 13221, 1035, 5890, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP], wi, usa ). we cultured hela cells in 6 wells plates ( 2610 5 cells ) and transfected them the following day using lipofectamine ( invitrogen ) according to the manufacturer. cells were lysed and luciferase assays were performed using 20 mg of protein extract according to the manufacturer ( promega ) at 44 h post - transfection. firefly luciferase activity was normalized to renilla luciferase activity. 0 mg, 0, 5 mg or 1 mg cmv - tat vector was transfected with ltr - luc as a positive control in these experiments. we carried out lucierase assays in triplicate in three independent experiments. results are expressed as mean6 standard error of the mean ( s. e. m ). first - term placental tissues were obtained from abortions following voluntary interruption of pregnancy at chum hopital saint - luc ( montreal, canada ). tissues from 3 h1 ( associated with mtct of hiv - 1 ) and 3 h15 ( wild - type ) homozygous haplotypes were used to analyse possible differences in isoform expression. total placental rnas were extracted by masterpure dna and rna extraction kit ( epicentre biotechnologies, madison, wi, usa ) according to the manufacturer. fragments corresponding to the dc - signr coding region were reversed transcribed ( rt ) and then amplified by nested pcr with the following primers ; rt primers rr, first pcr rf and rr and second pcr rcf and rcr according to liu and colleagues [ 11 ]. 1 mg of total rna was reverse transcribed with expand rt ( roche applied science, indianapolis, in, usa ) according to the manufacturer and were pcr - amplified with dna platinum taq polymerase ( invitrogen ). major pcr products from the second pcr reaction were gel extracted with the qiagen gel extraction kit ( qiagen canada inc, mississauga, on, canada ) and cloned using the topo ta cloning kit for sequencing ( invitrogen ). for each placenta, 15 different clones were randomly selected and amplified with m13 primers and sequenced with abi prism 3100 capillary automated sequencer ( applied biosystems, foster city, ca, usa ). sequences were analysed and aligned with genebank reference sequence nm _ 01 [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 6914, 1007, 1012, 2005, 2169, 2173, 12380, 1010, 2321, 2367, 24418, 2020, 18154, 3479, 1998, 26986, 2007, 23290, 2509, 3539, 2869, 1998, 5537, 2094, 2007, 11113, 2072, 26113, 17196, 2692, 6178, 9386, 2854, 12978, 5537, 2099, 1006, 4162, 16012, 29390, 1010, 6469, 2103, 1010, 6187, 1010, 3915, 1007, 1012, 10071, 2020, 20302, 23274, 2094, 1998, 13115, 2007, 4962, 9299, 4431, 5537, 13221, 1035, 5890, 20958, 28311, 2478, 9138, 6914, 2063, 4007, 1006, 6064, 3340, 1010, 7063, 1010, 15536, 1010, 3915, 1007, 1012, 20155, 3670, 1997, 5887, 1011, 3696, 2099, 11163, 22694, 1015, 1010, 1019, 11460, 1997, 2173, 15758, 12987, 2001, 7901, 26223, 2478, 1016, 1012, 1019, 3461, 1997, 19330, 14031, 26718, 2322, 1998, 7818, 19387, 1999, 2322, 19875, 3872, 2429, 2000, 1996, 7751, 1006, 20162, 4162, 2671, 1007, 1012, 2321, 12835, 1997, 2561, 3729, 2532, 1999, 1037, 2345, 3872, 1997, 2322, 19875, 2001, 2109, 2000, 4685, 20155, 2613, 1011, 2051, 7473, 2099, 2478, 5415, 4671, 25353, 19892, 11006, 2099, 1053, 15042, 2099, 3565, 4328, 2595, 1006, 1999, 5737, 13181, 6914, 1007, 2006, 1037, 18929, 4962, 2613, 7292, 16933, 20302, 23274, 2099, 1006, 24119, 2166, 2671, 1010, 3994, 1010, 2660, 1007, 1012, 8168, 2013, 1016, 5739, 1999, 2169, 2177, 2020, 2109, 2138, 12987, 3737, 1997, 2500, 2001, 2025, 7218, 2005, 1037, 1053, 5339, 1011, 7473, 2099, 4106, 1012, 23713, 3669, 10803, 1997, 2035, 5887, 1011, 3696, 2099, 11163, 22694, 2001, 2864, 2478, 2019, 4654, 2239, 1019, 3563, 3539, 2099, 3940, 1006, 2795, 1055, 2487, 1007, 1012, 10804, 1011, 5391, 11163, 22694, 2020, 26986, 2478, 3539, 2869, 3563, 2005, 4654, 2239, 1017, 1010, 7978, 2000, 1996, 2691, 9099, 1011, 10804, 5884, 1997, 5887, 1011, 3696, 2099, 1012, 3539, 2869, 2020, 9416, 2000, 1996, 4654, 2239, 1011, 4654, 2239, 5098, 1998, 12987, 27059, 2020, 5845, 2007, 6064, 3366, 1006, 10768, 14515, 10230, 2248, 4297, 1010, 15552, 1010, 2006, 1010, 2710, 1007, 2000, 4468, 23713, 3669, 10803, 1997, 9530, 15464, 3981, 3372, 6064, 1012, 3115, 10543, 1006, 2753, 1011, 3156, 2199, 4809, 2566, 4668, 1007, 2020, 7013, 2478, 7642, 29454, 13700, 1997, 1037, 2440, 1011, 3091, 5887, 1011, 3696, 2099, 2030, 3293, 6578, 16425, 1006, 1999, 5737, 13181, 6914, 1007, 20228, 3022, 4328, 2094, 6064, 1012, 2035, 1053, 15042, 2099, 9597, 2018, 1041, 26989, 23402, 14767, 7478, 2013, 5585, 1003, 2000, 2531, 1003, 1010, 2130, 1999, 1996, 3739, 1997, 2322, 12835, 1997, 2512, 1011, 3563, 23767, 2278, 12737, 1010, 1998, 3568, 2071, 2022, 4102, 1012, 1996, 6100, 2193, 1997, 4242, 8168, 2001, 4358, 2011, 6885, 1996, 7594, 7473, 2099, 5402, 2193, 1006, 5153, 11207, 1007, 2006, 1996, 3115, 7774, 1012, 2000, 6149, 2005, 5966, 1999, 2119, 12987, 3737, 1998, 11712, 2090, 8168, 1010, 1996, 3670, 3798, 1997, 24051, 2015, 2020, 3671, 5084, 2000, 1996, 4431, 6578, 16425, 4962, 24051, 2015, 1012, 6578, 16425, 3539, 2099, 10071, 2020, 19045, 3024, 2011, 1037, 1012, 2033, 2015, 1011, 3742, 2239, 2012, 1996, 14684, 2213, 1012, 1996, 3463, 2024, 3591, 2004, 4539, 4962, 6100, 2193, 2566, 2184, 1019, 4809, 1997, 6578, 16425, 1012, 1996, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP]gen ). for each placenta, 15 different clones were randomly selected and amplified with m13 primers and sequenced with abi prism 3100 capillary automated sequencer ( applied biosystems, foster city, ca, usa ). sequences were analysed and aligned with genebank reference sequence nm _ 014257 using lasergene software ( dna stars, madison, wi, usa ). quantitative expression of dc - signr isoforms 1, 5 mg of placental rna was reverse transcribed using 2. 5 mm of oligo dt 20 and expand rt in 20 ml volume according to the manufacturer ( roche applied science ). 15 ng of total cdna in a final volume of 20 ml was used to perform quantitative real - time pcr using universal express sybr greener qpcr supermix ( invitrogen ) on a rotor gene realtime rotary analyser ( corbett life science, sydney, australia ). samples from 2 subjects in each group were used because rna quality of others was not suitable for a qrt - pcr analysis. amplification of all dc - signr isoforms was performed using an exon 5 specific primer pair ( table s1 ). membrane - bound isoforms were amplified using primers specific for exon 3, corresponding to the common trans - membrane domain of dc - signr. primers were targeted to the exon - exon junction and rna extracts were treated with dnase ( fermantas international inc, burlington, on, canada ) to avoid amplification of contaminant dna. standard curves ( 50 - 500 000 copies per reaction ) were generated using serial dilution of a full - length dc - signr or commercial gapdh ( invitrogen ) plasmid dna. all qpcr reactions had efficiencies ranging from 99 % to 100 %, even in the presence of 20 ng of non - specific nucleic acids, and therefore could be compared. the copy number of unknown samples was estimated by placing the measured pcr cycle number ( crossing threshold ) on the standard curve. to correct for differences in both rna quality and quantity between samples, the expression levels of transcripts were normalised to the reference gapdh gene transcripts. gapdh primer sequences were kindly provided by a. mes - masson at the chum. the results are presented as target gene copy number per 10 5 copies of gapdh. the [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 12987, 3737, 1998, 11712, 2090, 8168, 1010, 1996, 3670, 3798, 1997, 24051, 2015, 2020, 3671, 5084, 2000, 1996, 4431, 6578, 16425, 4962, 24051, 2015, 1012, 6578, 16425, 3539, 2099, 10071, 2020, 19045, 3024, 2011, 1037, 1012, 2033, 2015, 1011, 3742, 2239, 2012, 1996, 14684, 2213, 1012, 1996, 3463, 2024, 3591, 2004, 4539, 4962, 6100, 2193, 2566, 2184, 1019, 4809, 1997, 6578, 16425, 1012, 1996, 6463, 1997, 10804, 1011, 5391, 11163, 22694, 2001, 10174, 2004, 1041, 2509, 1013, 1041, 2629, 1012, 24345, 11163, 22694, 2020, 10174, 2011, 4942, 6494, 11873, 10804, 1011, 5391, 2013, 2561, 11163, 22694, 1012, 2057, 3344, 2041, 1053, 15042, 2099, 4632, 22916, 1999, 4440, 19341, 2618, 1999, 2093, 2981, 7885, 1012, 3463, 2024, 5228, 2004, 2812, 2575, 2015, 1012, 1041, 1012, 1049, 1012, 7778, 4106, 2001, 2864, 2478, 1996, 10629, 15455, 26113, 1019, 1012, 1014, 2005, 3645, 1006, 10629, 15455, 4007, 4297, 1010, 2624, 5277, 1010, 6187, 1010, 3915, 1007, 1012, 5966, 1999, 26163, 6459, 1998, 8991, 4140, 22571, 2594, 13139, 1997, 5292, 24759, 26305, 2015, 2030, 1044, 3215, 16275, 2015, 2020, 4102, 2090, 2967, 2478, 1996, 1060, 1016, 4106, 2030, 8731, 1005, 1055, 6635, 3231, 1012, 8833, 6553, 26237, 4106, 2001, 2109, 2000, 10197, 10238, 21879, 1006, 2030, 1007, 2005, 2169, 8991, 26305, 1998, 26163, 3891, 5876, 1012, 3674, 8833, 6553, 26237, 2001, 2109, 2000, 9375, 2981, 16014, 5668, 4453, 2004, 3278, 1999, 1996, 13587, 4106, 1012, 2030, 2015, 1998, 5345, 1003, 7023, 13483, 2020, 10174, 2007, 1996, 6635, 4118, 1012, 18539, 1997, 7142, 10857, 2090, 2967, 2020, 14155, 2007, 1996, 4895, 4502, 27559, 2048, 1011, 14578, 3076, 1005, 1055, 1056, 3231, 2043, 10857, 2020, 5373, 5500, 1998, 2007, 1996, 10856, 1011, 9809, 1057, 3231, 2043, 4728, 1012, 5966, 2020, 2641, 3278, 2012, 1052, 1010, 1014, 1012, 5709, 1012, 2517, 6727, 9619, 2001, 4663, 2013, 2035, 10756, 2040, 4194, 1999, 1996, 2817, 1998, 1996, 1062, 28403, 13344, 3979, 1998, 1996, 4812, 2988, 1999, 2023, 3259, 2020, 4844, 2011, 1996, 2057, 3344, 2041, 2019, 2523, 2817, 1997, 5887, 1011, 3696, 2099, 26572, 19539, 1999, 19975, 16725, 2141, 2000, 4895, 7913, 4383, 9820, 1011, 1015, 1011, 10372, 10756, 8733, 1999, 18820, 2890, 1010, 11399, 1012, 2426, 2068, 1010, 5989, 16725, 2020, 9820, 1011, 1015, 1011, 10372, 1998, 2531, 16725, 2815, 4895, 2378, 25969, 2098, 1012, 1997, 1996, 5989, 9820, 1011, 1015, 1011, 10372, 16725, 1010, 5401, 2020, 10372, 1045, 2226, 1010, 2340, 2020, 10372, 12997, 1010, 1998, 2459, 2020, 10372, 4903, 1012, 10984, 1997, 8985, 2001, 2025, 4340, 2005, 2260, 9820, 1011, 1015, 1011, 10372, 16725, 1012, 26163, 6459, 1997, 10756, 1998, 16725, 2024, 3591, 1999, 2795, 1015, 1012, 11062, 2287, 1998, 3729, 2549, 3526, 4175, 1010, 2775, 3348, 1010, 5549, 1997, 6959, 1010, 9367, 1997, 10804, 21766, 13876, 5397, 1998, 16216, 20100, 2389, 2287, 2020, 2714, 2426, 2035, 2967, 1012, 2174, 1010, 11062, 13434, 7170, 1012, 2756, 2199, 4809, 1013, 19875, 2001, 3378, 2007, 3445, 3891, 1999, 2119, 1045, 2226, 1998, 4903, 2007, 10238, 21879, 1006, 2030, 1007, 1997, 1017, 1012, 4185, 1006, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] rna quality and quantity between samples, the expression levels of transcripts were normalised to the reference gapdh gene transcripts. gapdh primer sequences were kindly provided by a. mes - masson at the chum. the results are presented as target gene copy number per 10 5 copies of gapdh. the ratio of membrane - bound isoforms was calculated as e3 / e5. soluble isoforms were calculated by subtracting membrane - bound from total isoforms. we carried out qpcr assays in triplicate in three independent experiments. results are expressed as mean6s. e. m. statistical analysis was performed using the graphpad prism 5. 0 for windows ( graphpad software inc, san diego, ca, usa ). differences in baseline characteristics and genotypic frequencies of haplotypes or htsnps were compared between groups using the x 2 analysis or fisher's exact test. logistic regression analysis was used to estimate odds ratios ( or ) for each genotype and baseline risk factors. multiple logistic regression was used to define independent predictors identified as significant in the crude analysis. ors and 95 % confidence interval were calculated with the exact method. comparisons of continuous variables between groups were assessed with the unpaired two - tailed student's t test when variables were normally distributed and with the mann - whitney u test when otherwise. differences were considered significant at p, 0. 05. written informed consent was obtained from all mothers who participated in the study and the zvitambo trial and the investigation reported in this paper were approved by the we carried out an association study of dc - signr polymorphism in 197 infants born to untreated hiv - 1 - infected mothers recruited in harare, zimbabwe. among them, 97 infants were hiv - 1 - infected and 100 infants remained uninfected. of the 97 hiv - 1 - infected infants, 57 were infected iu, 11 were infected ip, and 17 were infected pp. timing of infection was not determined for 12 hiv - 1 - infected infants. baseline characteristics of mothers and infants are presented in table 1. maternal age and cd4 cell count, child sex, mode of delivery, duration of membrane rupture and gestational age were similar among all groups. however, maternal viral load. 29 000 copies / ml was associated with increased risk in both iu and pp with odds ratios ( or ) of 3. 64 ( [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 2287, 1998, 3729, 2549, 3526, 4175, 1010, 2775, 3348, 1010, 5549, 1997, 6959, 1010, 9367, 1997, 10804, 21766, 13876, 5397, 1998, 16216, 20100, 2389, 2287, 2020, 2714, 2426, 2035, 2967, 1012, 2174, 1010, 11062, 13434, 7170, 1012, 2756, 2199, 4809, 1013, 19875, 2001, 3378, 2007, 3445, 3891, 1999, 2119, 1045, 2226, 1998, 4903, 2007, 10238, 21879, 1006, 2030, 1007, 1997, 1017, 1012, 4185, 1006, 5345, 1003, 25022, 1027, 1015, 1012, 6445, 1011, 1021, 1012, 2861, 1010, 1052, 1027, 1014, 1012, 2199, 2475, 1007, 1998, 1018, 1012, 3429, 1006, 5345, 1003, 25022, 1027, 1015, 1012, 2753, 1011, 2410, 1012, 1016, 1010, 1052, 1027, 1014, 1012, 4002, 19961, 1007, 2005, 9820, 1011, 1015, 6726, 1010, 4414, 1012, 5417, 5292, 24759, 26305, 1011, 26610, 1055, 16275, 2015, 1006, 1044, 3215, 16275, 2015, 1007, 7978, 2000, 1996, 2321, 2350, 5887, 1011, 3696, 2099, 5292, 24759, 26305, 2015, 1006, 3275, 1015, 1007, 2649, 2426, 11399, 6962, 1031, 1021, 1033, 2020, 8991, 26305, 2094, 1999, 2256, 2817, 8168, 1006, 7251, 1055, 2475, 1998, 1055, 2509, 1007, 1012, 1044, 2487, 1006, 2861, 1003, 1007, 1998, 1044, 2509, 1006, 2340, 1003, 1007, 2020, 1996, 2087, 6976, 5292, 24759, 26305, 2015, 5159, 1006, 3275, 1015, 1007, 1012, 2108, 24004, 9096, 3995, 2271, 2005, 1996, 1044, 2487, 5292, 24759, 26305, 2001, 3378, 2007, 3445, 3891, 1997, 2119, 1045, 2226, 1006, 2030, 1024, 1018, 1012, 4413, 1010, 1052, 1027, 1014, 1012, 6185, 2475, 1007, 1998, 4903, 1006, 2030, 1024, 1021, 1012, 2861, 1010, 1052, 1027, 1014, 1012, 5890, 2575, 1007, 9820, 1011, 1015, 6726, 1006, 2795, 1016, 1007, 1012, 16725, 7440, 2075, 2048, 6100, 14930, 1997, 1044, 2487, 1998, 1013, 2030, 1044, 2509, 5292, 24759, 26305, 2015, 1006, 1044, 2487, 1011, 1044, 2487, 1010, 1044, 2487, 1011, 1044, 2509, 2030, 1044, 2509, 1011, 1044, 2509, 1007, 2018, 3445, 3891, 1997, 1045, 2226, 1006, 2030, 1024, 1017, 1012, 4413, 1010, 1052, 1027, 1014, 1012, 4002, 2581, 1007, 1998, 12997, 1006, 2030, 1024, 1019, 1012, 6390, 1010, 1052, 1027, 1014, 1012, 6185, 2629, 1007, 2021, 2025, 4903, 1006, 1052, 1027, 1014, 1012, 5641, 2620, 1007, 9820, 1011, 1015, 8985, 4102, 2000, 10527, 2512, 10010, 16252, 2015, 1006, 2795, 1016, 1007, 1012, 1996, 3732, 8924, 2815, 3278, 2044, 19037, 2001, 2081, 2005, 1996, 11062, 13434, 7170, 2005, 2119, 1045, 2226, 1006, 2030, 1024, 1017, 1012, 5401, 1010, 5345, 1003, 25022, 1027, 1015, 1012, 2382, 1011, 1023, 1012, 6445, 1010, 1052, 1027, 1014, 1012, 5890, 2509, 1007, 1998, 12997, 1006, 2030, 1024, 1019, 1012, 6390, 1010, 5345, 1003, 25022, 1027, 1015, 1012, 2871, 1011, 2603, 1012, 1017, 1010, 1052, 1027, 1014, 1012, 6185, 2629, 1007, 9820, 1011, 1015, 6726, 1012, 1996, 1044, 2487, 1998, 1044, 2509, 5292, 24759, 26305, 2015, 3745, 1037, 9324, 1997, 14494, 1006, 1052, 1011, 20003, 2050, 1010, 20014, 2475, 1011, 4464, 2487, 2278, 1010, 20014, 2475, 1011, 8380, 2050, 1010, 4654, 2549, 14536, 2102, 1010, 20014, 2629, 1009, 1021, 2278, 1007, 1006, 3275, 1015, 1007, 1012, 1997, 2122, 1010, 1996, 1052, 1011, 20003, 2050, 1998, 20014, 2475, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] age and cd4 cell count, child sex, mode of delivery, duration of membrane rupture and gestational age were similar among all groups. however, maternal viral load. 29 000 copies / ml was associated with increased risk in both iu and pp with odds ratios ( or ) of 3. 64 ( 95 % ci = 1. 82 - 7. 31, p = 0. 0002 ) and 4. 45 ( 95 % ci = 1. 50 - 13. 2, p = 0. 0045 ) for hiv - 1 transmission, respectively. fifteen haplotype - tagged snps ( htsnps ) corresponding to the 15 major dc - signr haplotypes ( figure 1 ) described among zimbabweans [ 7 ] were genotyped in our study samples ( tables s2 and s3 ). h1 ( 31 % ) and h3 ( 11 % ) were the most frequent haplotypes observed ( figure 1 ). being homozygous for the h1 haplotype was associated with increased risk of both iu ( or : 4. 42, p = 0. 022 ) and pp ( or : 7. 31, p = 0. 016 ) hiv - 1 transmission ( table 2 ). infants harbouring two copy combinations of h1 and / or h3 haplotypes ( h1 - h1, h1 - h3 or h3 - h3 ) had increased risk of iu ( or : 3. 42, p = 0. 007 ) and ip ( or : 5. 71, p = 0. 025 ) but not pp ( p = 0. 098 ) hiv - 1 infection compared to infant noncarriers ( table 2 ). the latter associations remained significant after adjustment was made for the maternal viral load for both iu ( or : 3. 57, 95 % ci = 1. 30 - 9. 82, p = 0. 013 ) and ip ( or : 5. 71, 95 % ci = 1. 40 - 23. 3, p = 0. 025 ) hiv - 1 transmission. the h1 and h3 haplotypes share a cluster of mutations ( p - 198a, int2 - 391c, int2 - 180a, ex4rpt, int5 + 7c ) ( figure 1 ). of these, the p - 198a and int2 [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 1015, 6726, 1012, 1996, 1044, 2487, 1998, 1044, 2509, 5292, 24759, 26305, 2015, 3745, 1037, 9324, 1997, 14494, 1006, 1052, 1011, 20003, 2050, 1010, 20014, 2475, 1011, 4464, 2487, 2278, 1010, 20014, 2475, 1011, 8380, 2050, 1010, 4654, 2549, 14536, 2102, 1010, 20014, 2629, 1009, 1021, 2278, 1007, 1006, 3275, 1015, 1007, 1012, 1997, 2122, 1010, 1996, 1052, 1011, 20003, 2050, 1998, 20014, 2475, 1011, 8380, 2050, 10176, 2020, 6022, 3378, 2007, 11047, 6593, 1997, 9820, 1011, 1015, 1006, 2795, 1055, 2475, 1007, 1012, 1999, 1996, 14477, 2094, 29427, 2098, 26237, 4106, 1010, 24004, 9096, 3995, 2271, 16725, 2005, 1996, 1052, 1011, 20003, 2050, 1998, 20014, 2475, 1011, 8380, 2050, 10176, 2018, 3445, 3891, 1997, 1045, 2226, 1006, 2030, 1024, 1016, 1012, 5718, 1052, 1027, 1014, 1012, 5840, 2629, 1010, 2030, 1024, 1017, 1012, 6275, 1010, 1052, 1027, 1014, 1012, 4002, 2509, 1010, 4414, 1007, 1998, 12997, 1006, 2030, 1024, 1016, 1012, 4700, 1010, 1052, 1027, 1014, 1012, 2459, 1010, 1051, 1012, 1054, 1024, 1019, 1012, 6390, 1010, 1052, 1027, 1014, 1012, 6185, 2629, 1010, 4414, 1007, 9820, 1011, 1015, 8985, 4102, 2000, 21770, 10624, 9096, 3995, 2618, 16725, 2030, 2512, 10010, 16252, 2015, 1006, 2795, 1017, 1007, 1012, 2043, 19037, 2001, 2081, 2005, 11062, 5876, 1010, 2069, 1996, 2523, 2007, 1996, 20014, 2475, 1011, 8380, 2050, 8349, 2815, 3278, 2005, 1045, 2226, 1006, 2030, 1024, 1017, 1012, 6640, 1010, 5345, 1003, 25022, 1027, 1015, 1012, 4413, 1011, 2184, 1012, 1018, 1010, 1052, 1027, 1014, 1012, 4002, 2620, 1007, 1998, 12997, 1006, 1051, 1012, 1054, 1024, 1019, 1012, 6390, 1010, 5345, 1003, 25022, 1027, 1015, 1012, 2871, 1011, 2603, 1012, 1017, 1010, 1052, 1027, 1014, 1012, 6185, 2629, 1007, 9820, 1011, 1015, 6726, 1012, 2947, 1010, 16725, 24004, 9096, 3995, 2271, 2005, 5887, 1011, 3696, 2099, 8349, 20014, 2475, 1011, 8380, 2050, 4838, 1999, 1044, 2487, 1998, 1044, 2509, 5292, 24759, 26305, 2015, 2020, 1018, 1011, 10671, 2000, 1020, 1011, 10671, 2062, 3497, 2000, 2022, 10372, 2011, 9820, 1011, 1015, 2076, 10032, 2030, 2012, 6959, 1010, 4414, 1012, 4522, 11867, 10415, 2075, 1997, 1996, 5887, 1011, 3696, 2099, 4962, 1999, 1996, 2173, 12380, 7137, 2119, 10804, 1011, 5391, 1998, 24345, 11163, 14192, 13646, 2015, 1031, 1017, 1033, 1012, 1996, 5816, 10817, 1997, 10804, 5391, 1998, 24345, 5887, 1011, 3696, 2099, 2071, 20228, 20559, 17296, 3747, 1996, 10514, 11020, 23606, 13464, 2000, 9820, 1011, 1015, 8985, 1031, 2340, 1033, 1012, 2057, 3568, 1044, 22571, 14573, 2229, 3550, 2008, 1996, 5887, 1011, 3696, 2099, 14494, 3378, 2007, 11047, 6593, 1997, 9820, 1011, 1015, 2052, 2031, 2019, 4254, 2006, 2119, 1996, 2504, 1997, 5887, 1011, 3696, 2099, 3670, 1998, 1999, 1996, 11163, 14192, 13646, 2550, 1012, 2057, 10847, 5887, 1011, 3696, 2099, 24051, 3670, 1999, 2034, 1011, 2744, 2173, 12380, 2015, 4663, 2044, 11322, 3512, 11324, 1012, 2057, 17598, 2094, 5887, 1011, 3696, 2099, 2013, 2173, 15758, 14095, 2011, 19387, 1011, 7473, 2099, 2013, 1017, 24004, 9096, 3995, 2271, 1044, 2487, 8168, 4820, 2119, 1996, 5887, 1011, 3696, 2099, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] 1 transmission. the h1 and h3 haplotypes share a cluster of mutations ( p - 198a, int2 - 391c, int2 - 180a, ex4rpt, int5 + 7c ) ( figure 1 ). of these, the p - 198a and int2 - 180a variants were significantly associated with mtct of hiv - 1 ( table s2 ). in the unadjusted regression analysis, homozygous infants for the p - 198a and int2 - 180a variants had increased risk of iu ( or : 2. 07 p = 0. 045, or : 3. 78, p = 0. 003, respectively ) and ip ( or : 2. 47, p = 0. 17, o. r : 5. 71, p = 0. 025, respectively ) hiv - 1 infection compared to heterozygote infants or noncarriers ( table 3 ). when adjustment was made for maternal factors, only the association with the int2 - 180a variant remained significant for iu ( or : 3. 83, 95 % ci = 1. 42 - 10. 4, p = 0. 008 ) and ip ( o. r : 5. 71, 95 % ci = 1. 40 - 23. 3, p = 0. 025 ) hiv - 1 transmission. thus, infants homozygous for dc - signr variant int2 - 180a contained in h1 and h3 haplotypes were 4 - fold to 6 - fold more likely to be infected by hiv - 1 during pregnancy or at delivery, respectively. alternative splicing of the dc - signr gene in the placenta produces both membrane - bound and soluble isoform repertoires [ 3 ]. the relative proportion of membrane bound and soluble dc - signr could plausibly influence the susceptibility to hiv - 1 infection [ 11 ]. we therefore hypothesized that the dc - signr mutations associated with mtct of hiv - 1 would have an impact on both the level of dc - signr expression and in the isoform repertoire produced. we investigated dc - signr transcript expression in first - term placentas obtained after elective abortion. we cloned dc - signr from placental tissues by rt - pcr from 3 homozygous h1 samples containing both the dc - signr [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 3696, 2099, 3670, 1998, 1999, 1996, 11163, 14192, 13646, 2550, 1012, 2057, 10847, 5887, 1011, 3696, 2099, 24051, 3670, 1999, 2034, 1011, 2744, 2173, 12380, 2015, 4663, 2044, 11322, 3512, 11324, 1012, 2057, 17598, 2094, 5887, 1011, 3696, 2099, 2013, 2173, 15758, 14095, 2011, 19387, 1011, 7473, 2099, 2013, 1017, 24004, 9096, 3995, 2271, 1044, 2487, 8168, 4820, 2119, 1996, 5887, 1011, 3696, 2099, 1052, 1011, 20003, 11057, 1998, 20014, 2475, 1011, 8380, 11057, 10176, 3378, 2007, 9820, 1011, 1015, 6726, 1998, 1017, 24004, 9096, 3995, 2271, 3748, 1011, 2828, 1006, 1059, 2102, 1007, 1006, 1052, 1011, 20003, 9468, 1010, 20014, 2475, 1011, 8380, 13871, 1007, 8168, 1012, 5417, 24418, 2566, 7099, 2020, 18154, 3479, 2005, 24558, 1012, 2004, 3517, 1010, 2057, 2179, 2019, 4866, 13646, 1997, 5887, 1011, 3696, 2099, 24051, 2015, 1999, 2035, 8168, 2007, 1023, 2000, 2385, 2367, 11163, 22694, 2566, 3265, 1012, 1037, 2561, 1997, 3515, 5664, 24051, 2015, 2020, 4453, 1006, 3275, 1055, 2487, 1007, 1010, 1997, 2029, 1017, 2020, 2440, 1011, 3091, 24051, 2015, 1012, 4185, 1997, 1996, 5537, 2094, 24418, 4838, 1037, 2561, 1997, 6353, 13096, 5648, 20885, 2015, 2007, 1017, 2047, 1039, 2744, 5498, 1998, 1016, 21371, 2644, 19429, 5644, 1012, 2174, 1010, 1996, 8906, 2001, 3262, 2012, 18886, 8569, 10880, 2000, 1996, 2972, 3972, 20624, 2239, 1997, 4654, 2239, 1016, 2030, 4654, 2239, 1017, 2030, 2000, 8358, 1999, 1996, 3091, 1997, 1996, 3300, 2555, 1006, 4654, 2239, 1018, 1007, 1997, 5887, 1011, 3696, 2099, 1012, 1996, 3972, 20624, 2239, 1997, 4654, 2239, 1017, 11027, 2015, 1996, 9099, 1011, 10804, 5884, 1997, 1996, 5250, 1998, 5260, 2000, 1996, 3670, 1997, 24345, 5887, 1011, 3696, 2099, 11163, 22694, 1031, 1017, 1033, 1012, 5875, 2135, 1010, 1996, 14531, 1997, 10804, 1011, 5391, 11163, 22694, 1999, 2173, 15758, 14095, 1997, 1996, 1044, 2487, 24004, 9096, 3995, 4570, 3544, 2000, 2022, 2896, 2084, 2008, 5159, 1999, 8168, 2013, 1059, 2102, 3633, 1006, 3275, 1055, 2487, 1007, 1012, 1996, 3972, 20624, 2239, 1997, 4654, 2239, 1017, 2001, 4484, 2011, 24558, 1998, 2057, 1044, 22571, 14573, 2229, 4697, 2008, 1996, 25978, 1997, 4654, 2239, 1017, 1010, 2071, 2022, 2349, 2000, 1996, 3739, 1997, 1996, 20014, 2475, 1011, 8380, 2050, 16221, 5159, 1999, 16725, 2007, 1996, 1044, 2487, 5292, 24759, 26305, 1012, 1999, 2755, 1010, 2023, 17174, 2078, 16221, 2003, 2284, 8380, 17531, 13248, 2013, 4654, 2239, 1017, 1998, 9280, 16913, 14144, 11867, 10415, 2075, 2824, 1006, 3275, 23409, 1007, 1012, 2057, 4484, 2008, 1996, 8386, 1999, 24051, 19173, 2464, 2090, 1996, 2048, 2967, 2001, 2036, 7686, 2012, 1996, 2504, 1997, 28848, 3670, 1999, 1996, 2173, 12380, 1012, 2000, 24110, 27351, 10804, 1011, 5391, 5443, 24345, 11163, 22694, 1999, 2173, 15758, 8168, 2013, 24004, 9096, 3995, 2271, 1044, 2487, 1998, 1059, 2102, 16725, 1010, 2057, 26986, 1996, 4654, 2239, 1019, 1006, 1041, 2629, 1007, 5537, 2556, 1999, 2035, 5887, 1011, 3696, 2099, 11163, 22694, 1006, 2561, 24051, 2015, 1007, 1012, 2057, 2059, 26986, 4654, 2239, 1017, 1006, 1041, 2509, 1007, 2029, 2003, 17159, 1999, 1996, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] signr expression and in the isoform repertoire produced. we investigated dc - signr transcript expression in first - term placentas obtained after elective abortion. we cloned dc - signr from placental tissues by rt - pcr from 3 homozygous h1 samples containing both the dc - signr p - 198aa and int2 - 180aa variants associated with hiv - 1 transmission and 3 homozygous wild - type ( wt ) ( p - 198cc, int2 - 180gg ) samples. fifteen clones per sample were randomly selected for sequencing. as expected, we found an extensive repertoire of dc - signr transcripts in all samples with 9 to 16 different isoforms per individual. a total of 65 distinct transcripts were identified ( figure s1 ), of which 3 were full - length transcripts. 64 of the sequenced clones contained a total of 69 amino acid substitutions with 3 new c termini and 2 premature stop codons. however, the diversity was mostly attributable to the entire deletion of exon 2 or exon 3 or to variations in the length of the neck region ( exon 4 ) of dc - signr. the deletion of exon 3 eliminates the trans - membrane domain of the protein and leads to the expression of soluble dc - signr isoforms [ 3 ]. interestingly, the abundance of membrane - bound isoforms in placental tissues of the h1 homozygotes appears to be lower than that observed in samples from wt individuals ( figure s1 ). the deletion of exon 3 was confirmed by sequencing and we hypothesize that the skipping of exon 3, could be due to the presence of the int2 - 180a mutation observed in infants with the h1 haplotype. in fact, this intron mutation is located 180 bp downstream from exon 3 and potentially modifies splicing events ( figure 2a ). we confirmed that the variation in transcript proportions seen between the two groups was also reflected at the level of mrna expression in the placenta. to quantify membrane - bound vs soluble isoforms in placental samples from homozygous h1 and wt infants, we amplified the exon 5 ( e5 ) sequence present in all dc - signr isoforms ( total transcripts ). we then amplified exon 3 ( e3 ) which is deleted in the [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 10804, 1011, 5391, 5443, 24345, 11163, 22694, 1999, 2173, 15758, 8168, 2013, 24004, 9096, 3995, 2271, 1044, 2487, 1998, 1059, 2102, 16725, 1010, 2057, 26986, 1996, 4654, 2239, 1019, 1006, 1041, 2629, 1007, 5537, 2556, 1999, 2035, 5887, 1011, 3696, 2099, 11163, 22694, 1006, 2561, 24051, 2015, 1007, 1012, 2057, 2059, 26986, 4654, 2239, 1017, 1006, 1041, 2509, 1007, 2029, 2003, 17159, 1999, 1996, 24345, 3596, 1998, 2059, 10174, 1996, 1041, 2509, 1024, 1041, 2629, 6463, 1012, 2057, 2179, 2008, 2173, 15758, 14095, 2013, 24004, 9096, 3995, 2271, 1044, 2487, 16725, 4671, 1037, 6022, 2896, 10817, 1997, 10804, 1011, 5391, 5887, 1011, 3696, 2099, 1006, 2324, 1003, 1007, 4102, 2000, 2008, 1999, 1059, 2102, 3633, 1006, 4029, 1003, 1007, 1006, 1052, 1027, 1014, 1012, 4002, 2549, 1007, 1006, 3275, 1016, 2497, 1007, 9104, 2008, 4654, 2239, 1017, 25978, 6433, 2062, 4703, 1999, 3739, 1997, 1996, 5887, 1011, 3696, 2099, 20014, 2475, 1011, 8380, 2050, 8349, 3378, 2007, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 1996, 5887, 1011, 3696, 2099, 20014, 2475, 1011, 8380, 2050, 8349, 2003, 2467, 11860, 2007, 1996, 15543, 16221, 1052, 1011, 20003, 2050, 1006, 3275, 1015, 1007, 1012, 1999, 1996, 14477, 2094, 29427, 2098, 26237, 4106, 1010, 1996, 1052, 1011, 20003, 2050, 8349, 2001, 6022, 3378, 2007, 1045, 2226, 2021, 2025, 2007, 12997, 1998, 4903, 9820, 1011, 1015, 6726, 1006, 2795, 1017, 1007, 1012, 15078, 14193, 5387, 8031, 2609, 4106, 16014, 2015, 2795, 1015, 1012, 26163, 6459, 1997, 2388, 1998, 16725, 3891, 5876, 2005, 26721, 19901, 3170, 1006, 1045, 2226, 1007, 1010, 26721, 19362, 11667, 1006, 12997, 1007, 1998, 2695, 19362, 11667, 1006, 4903, 1007, 2388, 1011, 2000, 1011, 2775, 9820, 1011, 1015, 6726, 1012, 3275, 23842, 1007, 1012, 1996, 24184, 11022, 4023, 1997, 1996, 1052, 1011, 20003, 2050, 8349, 9570, 2001, 6022, 2896, 2084, 2008, 1997, 1996, 1059, 2102, 1052, 1011, 20003, 2278, 15543, 9570, 1006, 1052, 1011, 20003, 2278, 1013, 1037, 6463, 1027, 1016, 1010, 1052, 1027, 1014, 1012, 4002, 2575, 1007, 1006, 3275, 1017, 2497, 1007, 9104, 2008, 5887, 1011, 3696, 2099, 1052, 1011, 20003, 2050, 13531, 15543, 4023, 1012, 1996, 2060, 15543, 23892, 1006, 1052, 1011, 5401, 2581, 2278, 1998, 1052, 1011, 25392, 2050, 1007, 5159, 1999, 1996, 11399, 2319, 2313, 2106, 2025, 7461, 5887, 1011, 3696, 2099, 14193, 1999, 2023, 4632, 4710, 1006, 3275, 1055, 2475, 1007, 1012, 2000, 5646, 1996, 5658, 4254, 1997, 1996, 5887, 1011, 3696, 2099, 1052, 1011, 20003, 2050, 16221, 2006, 5887, 1011, 3696, 2099, 3670, 1999, 1996, 2173, 12380, 1010, 2057, 24110, 3775, 16238, 1996, 7619, 2193, 1997, 2561, 1998, 10804, 1011, 5391, 5887, 1011, 3696, 2099, 24051, 2015, 1999, 1996, 1044, 2487, 24004, 9096, 3995, 2618, 1998, 3748, 1011, 2828, 2173, 15758, 8168, 2004, 2649, 3041, 1012, 1996, 2561, 2193, 1997, 5887, 1011, 3696, 2099, 24051, 2015, 2001, 4340, 2000, 2022, 6273, 26976, 17465, 2509, 1006, 5887, 1011, 3696, 2099, 4809, 2575, 2015, 1012, 1041, 1012, 1049, 2566, 2184, 1019, 6578, 16425, 4809, 1007, 1999, 1996, 2173, 15758, 8168, 2013, 24004, 9096, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] membrane - bound vs soluble isoforms in placental samples from homozygous h1 and wt infants, we amplified the exon 5 ( e5 ) sequence present in all dc - signr isoforms ( total transcripts ). we then amplified exon 3 ( e3 ) which is deleted in the soluble forms and then calculated the e3 : e5 ratio. we found that placental tissues from homozygous h1 infants express a significantly lower proportion of membrane - bound dc - signr ( 18 % ) compared to that in wt individuals ( 36 % ) ( p = 0. 004 ) ( figure 2b ) suggesting that exon 3 skipping happens more frequently in presence of the dc - signr int2 - 180a variant associated with mtct of hiv - 1. the dc - signr int2 - 180a variant is always transmitted with the promoter mutation p - 198a ( figure 1 ). in the unadjusted regression analysis, the p - 198a variant was significantly associated with iu but not with ip and pp hiv - 1 transmission ( table 3 ). computational transcription factor binding site analysis predicts table 1. baseline characteristics of mother and infants risk factors for intrauterine ( iu ), intrapartum ( ip ) and postpartum ( pp ) mother - to - child hiv - 1 transmission. figure 3a ). the luciferase activity of the p - 198a variant construct was significantly lower than that of the wt p - 198c promoter construct ( p - 198c / a ratio = 2, p = 0. 006 ) ( figure 3b ) suggesting that dc - signr p - 198a affects promoter activity. the other promoter mutants ( p - 577c and p - 323a ) observed in the zimbabwean population did not affect dc - signr transcription in this assay ( figure s2 ). to determine the net impact of the dc - signr p - 198a mutation on dc - signr expression in the placenta, we quantitated the absolute number of total and membrane - bound dc - signr transcripts in the h1 homozygote and wild - type placental samples as described earlier. the total number of dc - signr transcripts was determined to be 6856213 ( dc - signr copies6s. e. m per 10 5 gapdh copies ) in the placental samples from homozy [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 1999, 1996, 1044, 2487, 24004, 9096, 3995, 2618, 1998, 3748, 1011, 2828, 2173, 15758, 8168, 2004, 2649, 3041, 1012, 1996, 2561, 2193, 1997, 5887, 1011, 3696, 2099, 24051, 2015, 2001, 4340, 2000, 2022, 6273, 26976, 17465, 2509, 1006, 5887, 1011, 3696, 2099, 4809, 2575, 2015, 1012, 1041, 1012, 1049, 2566, 2184, 1019, 6578, 16425, 4809, 1007, 1999, 1996, 2173, 15758, 8168, 2013, 24004, 9096, 3995, 2271, 1044, 2487, 16725, 1998, 2001, 1018, 1011, 10671, 2896, 4102, 2000, 2008, 2179, 1999, 2173, 12380, 2015, 2013, 1059, 2102, 3633, 1006, 24709, 16048, 2575, 22025, 1010, 1052, 1027, 1014, 1012, 5890, 2487, 1007, 1006, 3275, 1017, 2278, 1007, 1012, 2004, 4081, 3041, 1010, 1996, 20014, 2475, 1011, 8380, 2050, 16221, 2453, 19653, 4654, 2239, 1017, 25978, 2877, 2000, 1037, 2896, 2537, 1997, 10804, 1011, 5391, 5887, 1011, 3696, 2099, 1012, 2348, 1010, 1996, 9885, 1999, 1996, 2561, 2193, 1997, 5887, 1011, 3696, 2099, 24051, 2015, 1999, 1044, 2487, 24004, 9096, 3995, 2271, 2173, 15758, 8168, 4820, 2119, 1996, 1052, 1011, 20003, 11057, 1998, 20014, 2475, 1011, 8380, 11057, 10176, 5360, 1996, 10817, 1997, 10804, 1011, 5391, 1998, 24345, 11163, 22694, 1010, 1996, 3466, 1997, 2122, 14494, 2001, 2062, 8793, 2006, 1996, 10804, 1011, 5391, 11163, 22694, 2007, 2019, 1022, 1011, 10671, 9885, 1006, 1044, 2487, 1027, 12567, 2575, 21619, 1012, 1016, 5443, 1059, 2102, 1027, 5585, 2692, 2575, 19317, 2692, 1012, 1020, 1010, 1052, 1027, 1014, 1012, 4002, 2509, 1007, 4102, 2000, 1037, 1017, 1011, 10671, 9885, 1999, 2561, 24345, 11163, 22694, 1006, 1044, 2487, 1027, 5179, 20842, 15136, 2487, 1012, 1023, 5443, 1059, 2102, 1027, 4849, 21084, 2683, 2629, 1012, 1017, 1010, 1052, 1027, 1014, 1012, 6021, 1007, 1006, 3275, 1017, 2278, 1007, 1012, 3568, 1010, 5887, 1011, 3696, 2099, 1052, 1011, 20003, 2050, 1998, 20014, 2475, 1011, 8380, 2050, 14494, 3378, 2007, 11047, 6593, 1997, 9820, 1011, 1015, 6022, 10548, 1996, 2504, 1997, 2561, 2173, 15758, 5887, 1011, 3696, 2099, 24051, 2015, 1010, 4487, 13102, 18981, 11589, 3258, 28239, 12473, 1996, 10804, 1011, 5391, 11163, 14192, 2537, 1012, 2795, 1017, 1012, 8924, 2090, 10527, 5887, 1011, 3696, 2099, 15543, 1052, 1011, 20003, 1998, 17174, 2078, 1016, 1006, 20014, 2475, 1007, 1011, 8380, 10176, 1998, 26721, 19901, 3170, 1006, 1045, 2226, 1007, 1010, 26721, 19362, 11667, 1006, 12997, 1007, 1998, 2695, 19362, 11667, 1006, 4903, 1007, 2388, 1011, 2000, 1011, 2775, 9820, 1011, 1015, 6726, 1012, 2256, 7403, 3463, 1010, 3569, 2011, 3670, 4632, 4710, 1999, 2173, 12380, 1010, 6592, 1996, 6624, 1997, 5887, 1011, 3696, 2099, 1999, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 24004, 9096, 12333, 3012, 2005, 1996, 5292, 24759, 26305, 1044, 2487, 2001, 3378, 2007, 1045, 2226, 6726, 1999, 1996, 14477, 2094, 29427, 2098, 26237, 4106, 1012, 2174, 1010, 1996, 2523, 5419, 2044, 19037, 2001, 2081, 2005, 1996, 11062, 5876, 10712, 2138, 1997, 1996, 2235, 2193, 1997, 1044, 2487, 24004, 9096, 3995, 2618, 16725, 20302, 23274, 2094, 1999, 2169, 2967, 1012, 1044, 2487, 1998, 1044, 2509, 2020, 1996, 2087, 6976, 5292, 24759, 26305, 2015, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] in the h1 homozygote and wild - type placental samples as described earlier. the total number of dc - signr transcripts was determined to be 6856213 ( dc - signr copies6s. e. m per 10 5 gapdh copies ) in the placental samples from homozygous h1 infants and was 4 - fold lower compared to that found in placentas from wt individuals ( 27816638, p = 0. 011 ) ( figure 3c ). as suggested earlier, the int2 - 180a mutation might induce exon 3 skipping leading to a lower production of membrane - bound dc - signr. although, the decrease in the total number of dc - signr transcripts in h1 homozygous placental samples containing both the p - 198aa and int2 - 180aa variants affected the proportion of membrane - bound and soluble isoforms, the effect of these mutations was more pronounced on the membrane - bound isoforms with an 8 - fold decrease ( h1 = 117636. 2 vs wt = 9906220. 6, p = 0. 003 ) compared to a 3 - fold decrease in total soluble isoforms ( h1 = 5686181. 9 vs wt = 19256495. 3, p = 0. 03 ) ( figure 3c ). therefore, dc - signr p - 198a and int2 - 180a mutations associated with mtct of hiv - 1 significantly decreased the level of total placental dc - signr transcripts, disproportionately affecting the membrane - bound isoform production. table 3. associations between infant dc - signr promoter p - 198 and intron 2 ( int2 ) - 180 variants and intrauterine ( iu ), intrapartum ( ip ) and postpartum ( pp ) mother - to - child hiv - 1 transmission. our genetic results, supported by expression assay in placenta, suggest the involvement of dc - signr in mtct of hiv - 1. homozygosity for the haplotype h1 was associated with iu transmission in the unadjusted regression analysis. however, the association disappeared after adjustment was made for the maternal factors presumably because of the small number of h1 homozygote infants analysed in each groups. h1 and h3 were the most frequent haplotypes [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 1044, 2487, 2001, 3378, 2007, 1045, 2226, 6726, 1999, 1996, 14477, 2094, 29427, 2098, 26237, 4106, 1012, 2174, 1010, 1996, 2523, 5419, 2044, 19037, 2001, 2081, 2005, 1996, 11062, 5876, 10712, 2138, 1997, 1996, 2235, 2193, 1997, 1044, 2487, 24004, 9096, 3995, 2618, 16725, 20302, 23274, 2094, 1999, 2169, 2967, 1012, 1044, 2487, 1998, 1044, 2509, 2020, 1996, 2087, 6976, 5292, 24759, 26305, 2015, 5159, 1999, 1996, 2817, 2313, 1998, 2027, 3745, 1037, 9324, 1997, 14494, 1006, 3275, 1015, 1007, 1012, 19765, 5292, 24759, 26305, 2015, 1044, 2487, 1998, 1044, 2509, 3445, 1996, 2373, 1997, 1996, 2817, 1998, 7936, 1996, 8720, 1997, 3563, 5887, 1011, 3696, 2099, 14494, 3378, 2007, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 5262, 1010, 2048, 14494, 4207, 2011, 5292, 24759, 26305, 2015, 1044, 2487, 1998, 1044, 2509, 2020, 3378, 2007, 7471, 6726, 1997, 9820, 1011, 1015, 1012, 1996, 20014, 2475, 1011, 8380, 2050, 2001, 3378, 2007, 1037, 1018, 1011, 10671, 3445, 3891, 1997, 1045, 2226, 1998, 1020, 10371, 3445, 3891, 1997, 12997, 2044, 19037, 2005, 1996, 11062, 5876, 1012, 2348, 1996, 1052, 1011, 20003, 2050, 8349, 2001, 3378, 2007, 1045, 2226, 6726, 1010, 1996, 2523, 5419, 2044, 19037, 2001, 2081, 2005, 1996, 11062, 13434, 7170, 1012, 6600, 1010, 2057, 3662, 2008, 2023, 16221, 13416, 5887, 1011, 3696, 2099, 14193, 2389, 4023, 1999, 25714, 1998, 7137, 2896, 2504, 1997, 5887, 1011, 3696, 2099, 24051, 2015, 1999, 2173, 15758, 14095, 1999, 5257, 2007, 1996, 20014, 2475, 1011, 8380, 2050, 8349, 1012, 2144, 20014, 2475, 1011, 8380, 2050, 2003, 2467, 11860, 2007, 1052, 1011, 20003, 2050, 2006, 1996, 11047, 6593, 3378, 4117, 5292, 24759, 26305, 2015, 1044, 2487, 1013, 1044, 2509, 1010, 6168, 1052, 1011, 20003, 2050, 2003, 3344, 2006, 2060, 2512, 12054, 10085, 15070, 5292, 24759, 26305, 2015, 1006, 3275, 1015, 1007, 1010, 2057, 2064, 28699, 9869, 2008, 1996, 1052, 1011, 20003, 2050, 16221, 2894, 2089, 2031, 1037, 3576, 3466, 1999, 24269, 6168, 1999, 5257, 2007, 1996, 20014, 2475, 1011, 8380, 2050, 8349, 1010, 2027, 2119, 2552, 2000, 5547, 1996, 2504, 1997, 2173, 15758, 5887, 1011, 3696, 2099, 3670, 4525, 1999, 2019, 3445, 3891, 1997, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 1996, 3484, 1997, 1045, 2226, 6726, 5158, 2076, 1996, 2197, 12241, 20367, 1997, 10032, 1006, 8182, 1999, 1031, 2260, 1033, 1007, 1012, 2440, 1011, 2744, 2173, 12380, 8168, 2020, 2025, 2800, 2005, 1996, 2783, 2817, 1998, 1996, 3670, 4632, 22916, 2020, 2864, 2006, 2034, 1011, 2744, 2173, 15758, 14095, 1012, 1037, 3025, 2817, 2559, 2012, 5887, 1011, 3696, 2099, 2173, 15758, 11163, 22694, 13646, 1999, 2440, 1011, 2744, 2173, 12380, 8168, 7645, 2714, 8906, 1997, 5887, 1011, 3696, 2099, 24051, 2015, 2004, 1999, 1996, 2034, 1011, 2744, 2173, 15758, 14095, 3273, 2182, 2378, 1031, 1017, 1033, 1012, 2174, 1010, 2144, 3798, 1997, 5887, 1011, 3696, 2099, 3670, 2031, 2196, 2042, 4102, 2090, 1996, 2367, 3408, 1997, 10032, 1010, 2009, 2003, 2025, 2124, 3251, 5887, 1011, 3696, 2099, 3670, 9783, 2076, 1996, 2607, 1997, 10032, 1012, 6600, 1010, 2009, 2003, 9608, 2000, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] h1 was associated with iu transmission in the unadjusted regression analysis. however, the association disappeared after adjustment was made for the maternal factors presumably because of the small number of h1 homozygote infants analysed in each groups. h1 and h3 were the most frequent haplotypes observed in the study population and they share a cluster of mutations ( figure 1 ). grouping haplotypes h1 and h3 increased the power of the study and permitted the identification of specific dc - signr mutations associated with mtct of hiv - 1. indeed, two mutations shared by haplotypes h1 and h3 were associated with vertical transmission of hiv - 1. the int2 - 180a was associated with a 4 - fold increased risk of iu and 6fold increased risk of ip after adjustment for the maternal factors. although the p - 198a variant was associated with iu transmission, the association disappeared after adjustment was made for the maternal viral load. nevertheless, we showed that this mutation reduces dc - signr transcriptional activity in vitro and produces lower level of dc - signr transcripts in placental tissues in combination with the int2 - 180a variant. since int2 - 180a is always transmitted with p - 198a on the mtct associated combined haplotypes h1 / h3, whereas p - 198a is carried on other nonassociated haplotypes ( figure 1 ), we can speculate that the p - 198a mutation alone may have a minor effect in vivo whereas in combination with the int2 - 180a variant, they both act to reduce the level of placental dc - signr expression resulting in an increased risk of mtct of hiv - 1. the majority of iu transmission occurs during the last trimester of pregnancy ( reviewed in [ 12 ] ). full - term placenta samples were not available for the current study and the expression assays were performed on first - term placental tissues. a previous study looking at dc - signr placental isoforms repertoire in full - term placenta samples demonstrated similar diversity of dc - signr transcripts as in the first - term placental tissues studied herein [ 3 ]. however, since levels of dc - signr expression have never been compared between the different terms of pregnancy, it is not known whether dc - signr expression varies during the course of pregnancy. nevertheless, it is reasonable to [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 3696, 2099, 24051, 2015, 2004, 1999, 1996, 2034, 1011, 2744, 2173, 15758, 14095, 3273, 2182, 2378, 1031, 1017, 1033, 1012, 2174, 1010, 2144, 3798, 1997, 5887, 1011, 3696, 2099, 3670, 2031, 2196, 2042, 4102, 2090, 1996, 2367, 3408, 1997, 10032, 1010, 2009, 2003, 2025, 2124, 3251, 5887, 1011, 3696, 2099, 3670, 9783, 2076, 1996, 2607, 1997, 10032, 1012, 6600, 1010, 2009, 2003, 9608, 2000, 7868, 2008, 1996, 6970, 1011, 3265, 5966, 1999, 2119, 5887, 1011, 3696, 2099, 11163, 14192, 13646, 1998, 24051, 3798, 5159, 2090, 1996, 1044, 2487, 1998, 1059, 2102, 24004, 9096, 3995, 2271, 16725, 2052, 2022, 7686, 2802, 1996, 10032, 1012, 2000, 3058, 1010, 2087, 2913, 2031, 4208, 2006, 1996, 4022, 2535, 1997, 5887, 1011, 3696, 2099, 1999, 9099, 8985, 1997, 9820, 1011, 1015, 1999, 25714, 1031, 1016, 1010, 2184, 1033, 1012, 2174, 1010, 1996, 3674, 10595, 2920, 1999, 9099, 8985, 1998, 2417, 18426, 9407, 2426, 1039, 1011, 2828, 3393, 6593, 2378, 4972, 2191, 2009, 3697, 2000, 5646, 1996, 5025, 6577, 1997, 5887, 1011, 3696, 2099, 1999, 2023, 5549, 1997, 8985, 1999, 24269, 1031, 2410, 1010, 2403, 1033, 1012, 1996, 2844, 16902, 2057, 5159, 2090, 11047, 6593, 1997, 9820, 1011, 1015, 1998, 5887, 1011, 3696, 2099, 7403, 10176, 5155, 2659, 3798, 1997, 5887, 1011, 3696, 2099, 1999, 1996, 2173, 12380, 4081, 2008, 10595, 2060, 2084, 5887, 1011, 3696, 2099, 1011, 19872, 9099, 8985, 2453, 5452, 2076, 7471, 6726, 1997, 9820, 1011, 1015, 1012, 2005, 2742, 1010, 5887, 1011, 3696, 2099, 2038, 2036, 2042, 3491, 2000, 3853, 2004, 1037, 9820, 1011, 1015, 28873, 1011, 11847, 10769, 1031, 2321, 1033, 1012, 9212, 1998, 8628, 3728, 7645, 2008, 5887, 1011, 3696, 2099, 9099, 25969, 2098, 16480, 4442, 11737, 5498, 4095, 18906, 2015, 1011, 2522, 2615, 14841, 7747, 2011, 9412, 5425, 1998, 16627, 1997, 1996, 7865, 1999, 1037, 4013, 27058, 14045, 1011, 7790, 5450, 1031, 1018, 1033, 1012, 2144, 2203, 14573, 24587, 4442, 4671, 1049, 16257, 1011, 1045, 1998, 2462, 1010, 26131, 13434, 28873, 2015, 2071, 2059, 2022, 3591, 2000, 11311, 4442, 2000, 12005, 26243, 2019, 19293, 11311, 3433, 1031, 2385, 1010, 2459, 1033, 1012, 1996, 9820, 1011, 1015, 4563, 3401, 13876, 2953, 10507, 2099, 2629, 1010, 2021, 2025, 3729, 2549, 1010, 2003, 2522, 1011, 5228, 2007, 5887, 1011, 3696, 2099, 2006, 2173, 15758, 1998, 2668, 1011, 4167, 8803, 1006, 22861, 2497, 1007, 2203, 14573, 24587, 4442, 1031, 2324, 1010, 2539, 1033, 1012, 9820, 1011, 1015, 14246, 12521, 2692, 8031, 2000, 10507, 2099, 2629, 10769, 2006, 2203, 14573, 24587, 4442, 12014, 2015, 22861, 2497, 11109, 1998, 11598, 2015, 18847, 27321, 4748, 21471, 1998, 9099, 4328, 29397, 2408, 1996, 22861, 2497, 1031, 2322, 1010, 2538, 1033, 1012, 2009, 2003, 2947, 2825, 2008, 4359, 3670, 1997, 5887, 1011, 3696, 2099, 1010, 3391, 1996, 10804, 15494, 11163, 22694, 1010, 2006, 2173, 15758, 6178, 9386, 2854, 2203, 14573, 24587, 4442, 2453, 7927, 9820, 1011, 1015, 8031, 2000, 10507, 2099, 2629, 10769, 1010, 2612, 1997, 5887, 1011, 3696, 2099, 10769, 1010, 25505, 1996, 9230, 1997, 11062, 9820, 1011, 1015, 1011, 10372, 4442, 2408, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] signr transcripts as in the first - term placental tissues studied herein [ 3 ]. however, since levels of dc - signr expression have never been compared between the different terms of pregnancy, it is not known whether dc - signr expression varies during the course of pregnancy. nevertheless, it is reasonable to assume that the inter - individual differences in both dc - signr isoform repertoire and transcript levels observed between the h1 and wt homozygous infants would be reflected throughout the pregnancy. to date, most studies have focused on the potential role of dc - signr in trans infection of hiv - 1 in vitro [ 2, 10 ]. however, the multiple mechanisms involved in trans infection and redundancy among c - type lectin functions make it difficult to determine the actual participation of dc - signr in this mode of infection in vivo [ 13, 14 ]. the strong correlation we observed between mtct of hiv - 1 and dc - signr genetic variants producing low levels of dc - signr in the placenta suggested that mechanisms other than dc - signr - mediated trans infection might operate during vertical transmission of hiv - 1. for example, dc - signr has also been shown to function as a hiv - 1 antigen - capturing receptor [ 15 ]. chan and colleagues recently demonstrated that dc - signr transfected cho cells diminish sars - cov titers by enhanced capture and degradation of the virus in a proteasome - dependent manner [ 4 ]. since endothelial cells express mhc - i and ii, degraded viral antigens could then be presented to immune cells to elicit an adaptive immune response [ 16, 17 ]. the hiv - 1 coreceptor ccr5, but not cd4, is co - expressed with dc - signr on placental and blood - brain barrier ( bbb ) endothelial cells [ 18, 19 ]. hiv - 1 gp120 binding to ccr5 receptor on endothelial cells compromises bbb integrity and enhances monocytes adhesion and transmigration across the bbb [ 20, 21 ]. it is thus possible that reduced expression of dc - signr, particularly the membranebound isoforms, on placental capillary endothelial cells might favour hiv - 1 binding to ccr5 receptor, instead of dc - signr receptor, facilitating the migration of maternal hiv - 1 - infected cells across [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 1033, 1012, 2009, 2003, 2947, 2825, 2008, 4359, 3670, 1997, 5887, 1011, 3696, 2099, 1010, 3391, 1996, 10804, 15494, 11163, 22694, 1010, 2006, 2173, 15758, 6178, 9386, 2854, 2203, 14573, 24587, 4442, 2453, 7927, 9820, 1011, 1015, 8031, 2000, 10507, 2099, 2629, 10769, 1010, 2612, 1997, 5887, 1011, 3696, 2099, 10769, 1010, 25505, 1996, 9230, 1997, 11062, 9820, 1011, 1015, 1011, 10372, 4442, 2408, 1996, 2173, 15758, 8803, 4525, 1999, 1045, 2226, 6726, 1997, 9820, 1011, 1015, 1012, 1996, 20014, 2475, 1011, 8380, 2050, 8349, 4838, 1999, 1996, 1044, 2487, 1998, 1044, 2509, 5292, 24759, 26305, 2015, 2001, 3378, 2007, 12997, 6726, 9104, 2008, 5887, 1011, 3696, 2099, 2036, 7461, 6726, 1997, 9820, 1011, 1015, 2076, 6959, 1012, 2210, 2003, 2124, 2055, 1996, 10595, 10318, 6726, 1997, 9820, 1011, 1015, 2076, 6959, 1012, 6019, 2083, 1996, 4182, 5033, 2071, 9280, 14451, 16725, 2083, 1037, 14163, 13186, 2389, 9445, 4443, 1006, 10712, 6728, 11039, 8865, 7712, 1010, 3096, 1010, 2030, 3806, 13181, 18447, 19126, 1007, 1010, 6168, 2173, 15758, 15301, 2076, 6959, 1006, 3558, 2030, 20187, 1007, 2089, 11598, 9099, 24759, 10732, 15758, 6019, 1997, 11062, 9820, 1011, 1015, 1011, 10372, 4442, 2046, 22277, 9080, 9141, 1031, 2570, 1010, 2603, 1033, 1012, 2107, 2832, 2170, 12702, 6494, 3619, 20523, 2038, 2042, 3818, 1999, 12362, 2000, 1996, 3463, 6855, 1999, 1037, 18137, 2319, 2522, 27794, 1012, 6448, 7416, 2243, 1998, 8628, 2179, 1037, 3278, 2523, 2090, 3798, 1997, 11062, 6064, 1999, 8529, 14454, 7476, 11601, 2668, 1998, 12997, 6726, 1997, 9820, 1011, 1015, 9104, 2008, 6019, 1997, 11062, 10372, 4442, 2083, 1996, 2173, 12380, 2003, 3497, 2000, 5258, 2076, 6959, 1031, 2570, 1033, 1012, 2947, 1010, 1999, 1037, 2714, 4827, 2004, 4081, 3041, 2005, 1045, 2226, 6726, 1010, 1996, 4659, 2896, 2504, 1997, 5887, 1011, 3696, 2099, 1999, 1996, 2173, 12380, 1997, 24004, 9096, 3995, 2271, 16725, 7440, 2075, 1996, 20014, 2475, 1011, 8380, 2050, 8349, 2071, 5326, 9820, 1011, 1015, 8031, 2000, 10507, 2099, 2629, 10769, 2006, 2203, 14573, 24587, 4442, 12473, 1996, 2173, 15758, 8803, 11109, 1998, 25505, 1996, 6019, 1997, 11062, 10372, 4442, 1999, 22277, 9080, 9141, 2076, 6959, 1012, 3875, 5887, 1011, 3696, 2099, 1010, 2060, 9820, 1011, 1015, 13833, 2024, 2124, 2000, 3747, 11047, 6593, 1997, 9820, 1011, 1015, 1006, 8182, 1999, 1031, 2484, 1033, 1007, 1012, 7403, 10176, 1999, 10507, 2099, 2629, 2031, 2042, 3491, 2000, 3747, 7471, 6726, 1997, 9820, 1011, 1015, 1012, 10507, 2099, 2629, 15543, 10176, 4525, 1999, 3020, 3670, 1997, 1996, 10769, 2020, 3378, 2007, 3445, 3891, 1997, 11047, 6593, 1997, 9820, 1011, 1015, 2426, 4942, 1011, 24505, 18076, 1031, 2423, 1010, 2656, 1033, 1012, 1996, 3590, 1011, 1052, 2497, 3972, 20624, 2239, 26572, 19539, 1999, 10507, 2099, 2629, 2038, 2022, 3491, 2000, 4047, 2013, 7471, 6726, 1997, 9820, 1011, 1015, 1031, 2676, 1033, 1010, 2021, 2023, 8349, 2003, 8990, 9962, 2426, 3060, 7080, 1031, 2654, 1033, 1012, 2152, 6100, 3616, 1997, 10507, 2140, 2509, 2140, 2487, 1010, 1037, 16834, 9820, 1011, 1015, 16081, 3512, 27854, 2005, 10507, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] ]. it is thus possible that reduced expression of dc - signr, particularly the membranebound isoforms, on placental capillary endothelial cells might favour hiv - 1 binding to ccr5 receptor, instead of dc - signr receptor, facilitating the migration of maternal hiv - 1 - infected cells across the placental barrier resulting in iu transmission of hiv - 1. the int2 - 180a variant contained in the h1 and h3 haplotypes was associated with ip transmission suggesting that dc - signr also affect transmission of hiv - 1 during delivery. little is known about the mechanisms underlying transmission of hiv - 1 during delivery. passage through the birth canal could potentially expose infants through a mucosal portal entry ( presumably ophthalmic, skin, or gastrointestinal ), whereas placental insult during delivery ( physical or inflammatory ) may enhance transplacental passage of maternal hiv - 1 - infected cells into foetal circulation [ 22, 23 ]. such process called microtransfusion has been proposed in regards to the results obtain in a malawian cohort. kweik and colleagues found a significant association between levels of maternal dna in umbilical cord blood and ip transmission of hiv - 1 suggesting that passage of maternal infected cells through the placenta is likely to occur during delivery [ 22 ]. thus, in a similar fashion as suggested earlier for iu transmission, the relatively lower level of dc - signr in the placenta of homozygous infants harbouring the int2 - 180a variant could promote hiv - 1 binding to ccr5 receptor on endothelial cells affecting the placental barrier integrity and facilitating the passage of maternal infected cells in foetal circulation during delivery. beside dc - signr, other hiv - 1 receptors are known to influence mtct of hiv - 1 ( reviewed in [ 24 ] ). genetic variants in ccr5 have been shown to influence vertical transmission of hiv - 1. ccr5 promoter variants resulting in higher expression of the receptor were associated with increased risk of mtct of hiv - 1 among sub - saharan africans [ 25, 26 ]. the 32 - pb deletion polymorphism in ccr5 has be shown to protect from vertical transmission of hiv - 1 [ 27 ], but this variant is virtually absent among african populations [ 28 ]. high copy numbers of ccl3l1, a potent hiv - 1 suppressive ligand for cc [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 1012, 1996, 3590, 1011, 1052, 2497, 3972, 20624, 2239, 26572, 19539, 1999, 10507, 2099, 2629, 2038, 2022, 3491, 2000, 4047, 2013, 7471, 6726, 1997, 9820, 1011, 1015, 1031, 2676, 1033, 1010, 2021, 2023, 8349, 2003, 8990, 9962, 2426, 3060, 7080, 1031, 2654, 1033, 1012, 2152, 6100, 3616, 1997, 10507, 2140, 2509, 2140, 2487, 1010, 1037, 16834, 9820, 1011, 1015, 16081, 3512, 27854, 2005, 10507, 2099, 2629, 1010, 2024, 3378, 2007, 3020, 18178, 5302, 4939, 2063, 2537, 1998, 2896, 3891, 1997, 11047, 6593, 1997, 9820, 1011, 1015, 2426, 2148, 3060, 16725, 1031, 2756, 1010, 2382, 1033, 1012, 10856, 9232, 1011, 8031, 3393, 6593, 2378, 1006, 16914, 2140, 1007, 2003, 2019, 25605, 11311, 10769, 10752, 2098, 1999, 1996, 11290, 1998, 3595, 2098, 1999, 1996, 2668, 21422, 1999, 3433, 2000, 21733, 4742, 1012, 16914, 2140, 14067, 26835, 9614, 2011, 23092, 10698, 9276, 1998, 6887, 23692, 5666, 25950, 1010, 1998, 4359, 3670, 1997, 16914, 2140, 4525, 2013, 26572, 19539, 1999, 16861, 1998, 2512, 1011, 16861, 4655, 2038, 2042, 3378, 2007, 2019, 3445, 3891, 1997, 11047, 6593, 1997, 9820, 1011, 1015, 1031, 2861, 1010, 3590, 1033, 1012, 1999, 2023, 2817, 1010, 2057, 10580, 2005, 1996, 2034, 2051, 1010, 1996, 4022, 8360, 4254, 1997, 5887, 1011, 3696, 2099, 14494, 2006, 2049, 3670, 1999, 1996, 2173, 12380, 1998, 1999, 7471, 6726, 1997, 9820, 1011, 1015, 1012, 2057, 2903, 2008, 1996, 3739, 1997, 5887, 1011, 3696, 2099, 2012, 1996, 2173, 15758, 2203, 14573, 24587, 3526, 3302, 2089, 4047, 16725, 2013, 9820, 1011, 1015, 8985, 2011, 11847, 7865, 1998, 7694, 2049, 16627, 1013, 8312, 1012, 2174, 1010, 1999, 2173, 12380, 4820, 2659, 3798, 1997, 5887, 1011, 3696, 2099, 1010, 9820, 1011, 1015, 2052, 9544, 24271, 2135, 20817, 10507, 2099, 2629, 2006, 2203, 14573, 24587, 4442, 4525, 1999, 1037, 3279, 1997, 2173, 15758, 8803, 11109, 1998, 9412, 6019, 1997, 11062, 9820, 1011, 1015, 1011, 10372, 4442, 1999, 22277, 9080, 9141, 2877, 2000, 11047, 6593, 1997, 9820, 1011, 1015, 1012, 2023, 7337, 2089, 2036, 6611, 2000, 2060, 20018, 1011, 11860, 26835, 2015, 2124, 2000, 11835, 2007, 5887, 1011, 3696, 2099, 2107, 2004, 9820, 1011, 1016, 1010, 28389, 1039, 1998, 26957, 5657, 18191, 1998, 10943, 2582, 4812, 1012, 8924, 2090, 2775, 5887, 1011, 3696, 2099, 4654, 2239, 1018, 5567, 2555, 8991, 26305, 2015, 1998, 2388, 1011, 2000, 1011, 2775, 9820, 1011, 1015, 6726, 1012, 25022, 1010, 7023, 13483, 1025, 1050, 1010, 2193, 1025, 6583, 1025, 2025, 12711, 1025, 2030, 1010, 10238, 6463, 1037, 1052, 1011, 3643, 2004, 4340, 2011, 1996, 9610, 1011, 2675, 3231, 1012, 1038, 7831, 2090, 8991, 26305, 1998, 2035, 2500, 1012, 2179, 2012, 1024, 9193, 1024, 2184, 1012, 14989, 2487, 1013, 3485, 1012, 13433, 2638, 1012, 2199, 2581, 17465, 2487, 1012, 1055, 8889, 2509, 1006, 1014, 1012, 5709, 16914, 9986, 1007, 3275, 1055, 2487, 5887, 1011, 3696, 2099, 24051, 2015, 13646, 1999, 2173, 12380, 1012, 2350, 19387, 1011, 7473, 2099, 3688, 2013, 12987, 14817, 2013, 1017, 24004, 9096, 3995, 2271, 1044, 2487, 1998, 1017, 24004, 9096, 3995, 2271, 1059, 2102, 2173, 12380, 8168, 2020, 16405, 102]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP]. the 32 - pb deletion polymorphism in ccr5 has be shown to protect from vertical transmission of hiv - 1 [ 27 ], but this variant is virtually absent among african populations [ 28 ]. high copy numbers of ccl3l1, a potent hiv - 1 suppressive ligand for ccr5, are associated with higher chemokine production and lower risk of mtct of hiv - 1 among south african infants [ 29, 30 ]. mannose - binding lectin ( mbl ) is an innate immune receptor synthesised in the liver and secreted in the bloodstream in response to inflammation signal. mbl promotes pathogen elimination by opsonization and phagocytosis, and reduced expression of mbl resulting from polymorphism in coding and non - coding regions has been associated with an increased risk of mtct of hiv - 1 [ 31, 32 ]. in this study, we demonstrate for the first time, the potential functional impact of dc - signr mutations on its expression in the placenta and in vertical transmission of hiv - 1. we believe that the presence of dc - signr at the placental endothelial cell surface may protect infants from hiv - 1 infection by capturing virus and promoting its degradation / presentation. however, in placenta containing low levels of dc - signr, hiv - 1 would preferentially binds ccr5 on endothelial cells resulting in a loss of placental barrier integrity and enhanced passage of maternal hiv - 1 - infected cells in foetal circulation leading to mtct of hiv - 1. this mechanism may also apply to other vertically - transmitted pathogens known to interact with dc - signr such as hiv - 2, hepatitis c and dengue viruses and warrant further investigation. associations between child dc - signr exon 4 repeated region genotypes and mother - to - child hiv - 1 transmission. ci, confidence interval ; n, number ; na ; not applicable ; or, odds ratio a p - value as determined by the chi - square test. b comparison between genotype and all others. found at : doi : 10. 1371 / journal. pone. 0007211. s003 ( 0. 05 mb doc ) figure s1 dc - signr transcripts repertoire in placenta. major rt - pcr products from rna extract from 3 homozygous h1 and 3 homozygous wt placenta samples were pu [SEP]\n","[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 3485, 1012, 13433, 2638, 1012, 2199, 2581, 17465, 2487, 1012, 1055, 8889, 2509, 1006, 1014, 1012, 5709, 16914, 9986, 1007, 3275, 1055, 2487, 5887, 1011, 3696, 2099, 24051, 2015, 13646, 1999, 2173, 12380, 1012, 2350, 19387, 1011, 7473, 2099, 3688, 2013, 12987, 14817, 2013, 1017, 24004, 9096, 3995, 2271, 1044, 2487, 1998, 1017, 24004, 9096, 3995, 2271, 1059, 2102, 2173, 12380, 8168, 2020, 16405, 22618, 1010, 17598, 2094, 1998, 5537, 2094, 1012, 5537, 2094, 2020, 20302, 23274, 2094, 2429, 2000, 13316, 5638, 4431, 5537, 13221, 1035, 5890, 20958, 28311, 1012, 14931, 1025, 22330, 14399, 8523, 7712, 5725, 1010, 1056, 2213, 1025, 9099, 1011, 10804, 5884, 1025, 1059, 2102, 1025, 3748, 1011, 2828, 2179, 2012, 1024, 9193, 1024, 2184, 1012, 14989, 2487, 1013, 3485, 1012, 13433, 2638, 1012, 2199, 2581, 17465, 2487, 1012, 1055, 8889, 2549, 1006, 1014, 1012, 2340, 16914, 9986, 1007, 3275, 1055, 2475, 3466, 1997, 5887, 1011, 3696, 2099, 15543, 8349, 2006, 14193, 2389, 4023, 1999, 24184, 11022, 6398, 4632, 4710, 1999, 25714, 1999, 9099, 25969, 2098, 2002, 2721, 4442, 1012, 5816, 24184, 11022, 3670, 2013, 18720, 2140, 2475, 1011, 3937, 1010, 18643, 9207, 2302, 15543, 1012, 3670, 5887, 1011, 3696, 2099, 15543, 9570, 2015, 1010, 13912, 1052, 1011, 5401, 2581, 2278, 8349, 2030, 1052, 1011, 25392, 2050, 8349, 2020, 10174, 4659, 2000, 2023, 3643, 1012, 2951, 2024, 3591, 1999, 2812, 5300, 2575, 2015, 1012, 1041, 1012, 1049, 1997, 2093, 2981, 7885, 2864, 1999, 4440, 19341, 2618, 1012, 2028, 1011, 2126, 2019, 7103, 3231, 2628, 2011, 1996, 26553, 4779, 3231, 2005, 3674, 7831, 2001, 2109, 2000, 12826, 1996, 5816, 24184, 11022, 3670, 1997, 1996, 1052, 1011, 4583, 2581, 2278, 1998, 1052, 1011, 25392, 2050, 8349, 12060, 2114, 1996, 3748, 1011, 2828, 1006, 1059, 2102, 1007, 9570, 1006, 2025, 3278, 1007, 1012, 1014, 11460, 1010, 1014, 1010, 1019, 11460, 2030, 1015, 11460, 4642, 2615, 1011, 11937, 2102, 9207, 2001, 9099, 25969, 2098, 2007, 8318, 2099, 1011, 12776, 2004, 1037, 3893, 2491, 1999, 2122, 7885, 1012, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","--> [CLS] what is the main cause of hiv - 1 infection in children? [SEP] journal. pone. 0007211. s003 ( 0. 05 mb doc ) figure s1 dc - signr transcripts repertoire in placenta. major rt - pcr products from rna extract from 3 homozygous h1 and 3 homozygous wt placenta samples were purified, cloned and sequenced. sequenced were analysed according to ncbi reference sequence nm _ 014257. ct ; cytoplasmic tail, tm ; trans - membrane domain ; wt ; wild - type found at : doi : 10. 1371 / journal. pone. 0007211. s004 ( 0. 11 mb doc ) figure s2 effect of dc - signr promoter variant on transcriptional activity in luciferase reporter assay in vitro in transfected hela cells. relative luciferase expression from pgl2 - basic, parental vector without promoter. expression dc - signr promoter constructs, spanning p - 577c variant or p - 323a variant were calculated relatively to this value. data are presented in mean values6s. e. m of three independent experiments performed in triplicate. one - way anova test followed by the dunnett test for multiple comparison was used to compare the relative luciferase expression of the p - 557c and p - 323a variant reporters against the wild - type ( wt ) construct ( not significant ). 0 mg, 0, 5 mg or 1 mg cmv - tat vector was transfected with ltr - luc as a positive control in these experiments. [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n"]}],"source":["for ids in tokenized_examples[\"input_ids\"]:\n","  print(ids)\n","  print('-->',tokenizer.decode(ids))\n","  #break"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1666632547838,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"-qYq_8xxpxRG","outputId":"343f4ae6-2b05-4e41-ca61-77887a4a5cf9"},"outputs":[{"output_type":"stream","name":"stdout","text":["152\n"]}],"source":["offset_mapping_list=[(0, 4), (4, 7), (7, 11), (11, 16), (16, 22), (22, 25), (25, 29), (29, 30), (30, 31), (31, 41), (41, 44), (44, 53), (53, 54), (0, 0), (0, 8), (8, 10), (10, 18), (18, 23), (23, 27), (27, 30), (30, 33), (33, 34), (34, 38), (38, 39), (39, 43), (43, 54), (54, 59), (59, 66), (66, 67), (67, 69), (69, 70), (70, 75), (75, 88), (88, 91), (91, 95), (95, 96), (96, 97), (97, 98), (98, 99), (99, 104), (104, 107), (107, 110), (110, 111), (111, 113), (113, 115), (115, 116), (116, 118), (118, 119), (119, 120), (120, 123), (123, 124), (124, 127), (127, 128), (128, 130), (130, 131), (131, 132), (132, 140), (140, 141), (141, 143), (143, 144), (144, 146), (146, 148), (148, 151), (151, 152), (152, 153), (153, 154), (154, 156), (156, 159), (159, 160), (160, 161), (161, 163), (163, 165), (165, 168), (168, 169), (169, 174), (174, 176), (176, 177), (177, 179), (179, 180), (180, 183), (183, 188), (188, 189), (189, 194), (194, 195), (195, 197), (197, 200), (200, 201), (201, 203), (203, 205), (205, 207), (207, 209), (209, 210), (210, 215), (215, 217), (217, 219), (219, 225), (225, 228), (228, 229), (229, 234), (234, 236), (236, 238), (238, 242), (242, 246), (246, 247), (247, 254), (254, 256), (256, 258), (258, 263), (263, 264), (264, 270), (270, 272), (272, 274), (274, 280), (280, 281), (281, 288), (288, 289), (289, 293), (293, 294), (294, 296), (296, 297), (297, 299), (299, 300), (300, 302), (302, 303), (303, 304), (304, 306), (306, 307), (307, 309), (309, 311), (311, 312), (312, 319), (319, 320), (320, 321), (321, 324), (324, 325), (325, 328), (328, 330), (330, 332), (332, 333), (333, 340), (340, 341), (341, 343), (343, 344), (344, 346), (346, 347), (347, 348), (348, 356), (356, 357), (357, 362), (362, 368), (368, 369), (369, 376),]\n","print(len(offset_mapping_list))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1666632549332,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"4ZSZFmSoimAo","outputId":"17680fd2-e9da-4b86-911d-bb4362e3c3c9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [[101, 2054, 2003, 1996, 2364, 3426, 1997, 9820, 1011, 1015, 8985, 1999, 2336, 1029, 102, 8360, 7403, 10176, 1999, 5887, 1011, 3696, 2099, 2024, 3378, 2007, 2388, 1011, 2000, 1011, 2775, 6726, 1997, 9820, 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20218), (20218, 20219), (20220, 20221), (20222, 20228), (20229, 20230), (20230, 20231), (20232, 20233), (20234, 20244), (20245, 20249), (20250, 20252), (20252, 20253), (20253, 20257), (20257, 20258), (20259, 20260), (20260, 20261), (20261, 20264), (20264, 20265), (20266, 20273), (20274, 20282), (20283, 20291), (20291, 20292), (20293, 20296), (20297, 20302), (20303, 20311), (20312, 20319), (20320, 20321), (20321, 20322), (20322, 20323), (20323, 20325), (20325, 20326), (20326, 20327), (20328, 20331), (20332, 20333), (20333, 20334), (20334, 20337), (20337, 20338), (20338, 20339), (20340, 20348), (20349, 20351), (20352, 20355), (20356, 20364), (20364, 20366), (20367, 20377), (20378, 20381), (20382, 20385), (20386, 20392), (20393, 20395), (20395, 20396), (20396, 20400), (20400, 20401), (20402, 20415), (20416, 20418), (20419, 20423), (20424, 20427), (20427, 20429), (20430, 20431), (20432, 20438), (20439, 20440), (20440, 20441), (20442, 20443), (20443, 20444), (20445, 20447), (20448, 20457), 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20703), (20704, 20707), (20708, 20713), (20714, 20720), (20721, 20723), (20724, 20726), (20726, 20727), (20727, 20731), (20731, 20732), (20733, 20743), (20743, 20744), (20745, 20748), (20749, 20759), (20760, 20762), (20763, 20765), (20766, 20768), (20768, 20770), (20770, 20772), (20772, 20773), (20774, 20775), (20775, 20777), (20777, 20778), (20778, 20782), (20782, 20783), (20784, 20790), (20790, 20791), (20791, 20792), (20792, 20793), (20793, 20794), (20794, 20795), (20795, 20796), (20797, 20800), (20801, 20803), (20804, 20805), (20806, 20809), (20809, 20811), (20812, 20818), (20818, 20819), (20820, 20822), (20823, 20826), (20827, 20832), (20832, 20836), (20837, 20844), (20845, 20849), (20850, 20854), (20854, 20856), (0, 0)], [(0, 0), (0, 4), (5, 7), (8, 11), (12, 16), (17, 22), (23, 25), (26, 29), (29, 30), (30, 31), (32, 41), (42, 44), (45, 53), (53, 54), (0, 0), (20629, 20631), (20632, 20635), (20636, 20637), (20637, 20638), (20639, 20643), (20643, 20645), (20645, 20647), (20647, 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21556), (21556, 21557), (21558, 21560), (21561, 21562), (21562, 21563), (21564, 21565), (21566, 21570), (21570, 21572), (21572, 21573), (21573, 21574), (21574, 21575), (21575, 21576), (21576, 21577), (21578, 21579), (21580, 21581), (21582, 21583), (21583, 21584), (21584, 21586), (21586, 21587), (21588, 21589), (21590, 21596), (21597, 21598), (21598, 21599), (21600, 21601), (21601, 21602), (21603, 21612), (21612, 21613), (21614, 21616), (21616, 21617), (21617, 21621), (21621, 21622), (21623, 21624), (21624, 21625), (21625, 21628), (21628, 21629), (21630, 21633), (21634, 21637), (21637, 21638), (21638, 21639), (21639, 21642), (21642, 21643), (21644, 21653), (21654, 21664), (21665, 21669), (21670, 21672), (21672, 21674), (21675, 21677), (21678, 21681), (21681, 21682), (21682, 21683), (21684, 21697), (21698, 21707), (21708, 21711), (21712, 21717), (21718, 21720), (21721, 21726), (21727, 21732), (21732, 21736), (21737, 21739), (21739, 21740), (21740, 21744), (21744, 21745), (21746, 21756), (21756, 21757), (21757, 21758), (21759, 21761), (21761, 21763), (21763, 21766), (21766, 21769), (21769, 21772), (21772, 21777), (21778, 21787), (21788, 21791), (21792, 21800), (21800, 21801), (21801, 21806), (21807, 21810), (21810, 21814), (21815, 21825), (21825, 21826), (21827, 21832), (21833, 21834), (21835, 21836), (21837, 21849), (21850, 21857), (21858, 21864), (21865, 21867), (21867, 21868), (21868, 21872), (21872, 21873), (21874, 21882), (21883, 21884), (21884, 21885), (21885, 21888), (21889, 21892), (21893, 21898), (21898, 21899), (21900, 21901), (21902, 21903), (21903, 21906), (21906, 21907), (21907, 21908), (21908, 21909), (21909, 21912), (21913, 21921), (21922, 21925), (21926, 21931), (21931, 21935), (21935, 21938), (21939, 21940), (21940, 21941), (21941, 21942), (21942, 21943), (21943, 21944), (21945, 21950), (21950, 21953), (21953, 21956), (21957, 21958), (21958, 21960), (21960, 21961), (21962, 21965), (21966, 21970), (21970, 21973), (21973, 21976), (21977, 21978), (21978, 21980), (21980, 21981), (21982, 21988), (21988, 21989), (21989, 21991), (21991, 21992), (21992, 21997), (21998, 22001), (22001, 22002), (22002, 22003), (22004, 22016), (22016, 22017), (22020, 22023), (22024, 22031), (22032, 22039), (22039, 22040), (22041, 22050), (22051, 22053), (22054, 22064), (22065, 22068), (22068, 22070), (22071, 22073), (22074, 22079), (22079, 22082), (22082, 22083), (22084, 22091), (22092, 22095), (22096, 22107), (22108, 22110), (22111, 22113), (22113, 22114), (22114, 22118), (22118, 22119), (22120, 22122), (22123, 22125), (22125, 22127), (22128, 22130), (22131, 22134), (22134, 22135), (22135, 22136), (22136, 22137), (22138, 22142), (22142, 22144), (22144, 22147), (22147, 22150), (22151, 22154), (22155, 22158), (22159, 22161), (22161, 22163), (22163, 22168), (22169, 22170), (22170, 22171), (22172, 22175), (22176, 22186), (22187, 22191), (22192, 22193), (22193, 22194), (22195, 22207), (22208, 22210), (22211, 22214), (22215, 22218), (22218, 22219), (22219, 22223), (22223, 22225), (22226, 22236), (22237, 22245), (22245, 22246), (22247, 22254), (22254, 22255), (22256, 22259), (22260, 22271), (22272, 22283), (22284, 22289), (22290, 22300), (22301, 22304), (22305, 22309), (22310, 22313), (22314, 22317), (22318, 22326), (22327, 22334), (22335, 22345), (22346, 22353), (22354, 22356), (22357, 22360), (22361, 22366), (22367, 22373), (22374, 22376), (22377, 22378), (22378, 22379), (22380, 22384), (22384, 22386), (22386, 22388), (22388, 22390), (22391, 22398), (22399, 22403), (22403, 22406), (22406, 22407), (22408, 22410), (22411, 22415), (22416, 22422), (22422, 22423), (22424, 22425), (22425, 22426), (22427, 22430), (22431, 22432), (22432, 22433), (22434, 22438), (22439, 22442), (22443, 22447), (22448, 22456), (22457, 22459), (22459, 22461), (22461, 22466), (22466, 22467), (0, 0)], [(0, 0), (0, 4), (5, 7), (8, 11), (12, 16), (17, 22), (23, 25), (26, 29), (29, 30), (30, 31), (32, 41), (42, 44), (45, 53), (53, 54), (0, 0), (22169, 22170), (22170, 22171), (22172, 22175), (22176, 22186), (22187, 22191), (22192, 22193), (22193, 22194), (22195, 22207), (22208, 22210), (22211, 22214), (22215, 22218), (22218, 22219), (22219, 22223), (22223, 22225), (22226, 22236), (22237, 22245), (22245, 22246), (22247, 22254), (22254, 22255), (22256, 22259), (22260, 22271), (22272, 22283), (22284, 22289), (22290, 22300), (22301, 22304), (22305, 22309), (22310, 22313), (22314, 22317), (22318, 22326), (22327, 22334), (22335, 22345), (22346, 22353), (22354, 22356), (22357, 22360), (22361, 22366), (22367, 22373), (22374, 22376), (22377, 22378), (22378, 22379), (22380, 22384), (22384, 22386), (22386, 22388), (22388, 22390), (22391, 22398), (22399, 22403), (22403, 22406), (22406, 22407), (22408, 22410), (22411, 22415), (22416, 22422), (22422, 22423), (22424, 22425), (22425, 22426), (22427, 22430), (22431, 22432), (22432, 22433), (22434, 22438), (22439, 22442), (22443, 22447), (22448, 22456), (22457, 22459), (22459, 22461), (22461, 22466), (22466, 22467), (22468, 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30642), (30643, 30645), (30646, 30651), (30652, 30663), (30664, 30675), (30676, 30685), (30686, 30688), (30689, 30693), (30693, 30697), (30697, 30699), (30699, 30700), (30701, 30704), (30704, 30705), (30705, 30708), (30709, 30711), (30711, 30714), (30715, 30719), (30720, 30728), (30729, 30731), (30732, 30735), (30736, 30741), (30741, 30743), (30744, 30748), (30749, 30752), (30753, 30761), (30762, 30772), (30773, 30776), (30777, 30781), (30782, 30784), (30785, 30792), (30793, 30796), (30797, 30805), (30806, 30813), (30813, 30816), (30817, 30827), (30828, 30830), (30831, 30834), (30835, 30836), (30836, 30837), (30837, 30839), (30839, 30840), (30840, 30841), (30842, 30845), (30846, 30847), (30847, 30848), (30848, 30851), (30851, 30852), (30853, 30860), (30861, 30870), (30871, 30878), (30879, 30882), (30883, 30887), (30887, 30888), (30888, 30892), (30893, 30894), (30894, 30895), (30895, 30896), (30896, 30897), (30898, 30907), (30908, 30909), (30909, 30912), (30913, 30924), (30924, 30925), (30925, 30926), (30927, 30928), (30929, 30931), (30931, 30932), (30933, 30934), (30934, 30935), (30935, 30936), (30937, 30939), (30940, 30942), (30943, 30944), (30945, 30947), (30948, 30950), (30950, 30951), (30951, 30952), (30952, 30954), (30954, 30955), (30956, 30962), (30963, 30966), (30967, 30972), (30972, 30976), (30976, 30978), (30979, 30983), (30984, 30986), (30986, 30987), (30987, 30988), (30988, 30991), (30992, 30994), (30995, 30996), (30997, 31005), (31006, 31013), (31014, 31016), (31017, 31022), (31023, 31034), (31034, 31035), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]], 'overflow_to_sample_mapping': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}"]},"metadata":{},"execution_count":15}],"source":["tokenized_examples"]},{"cell_type":"code","source":["#[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,1,1,1,2,2,2,2,2,3,3,3,..."],"metadata":{"id":"qegkAee5m6-G"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"N_tKqwVsv5Rq"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"t9KrayyIimJi"},"outputs":[],"source":["sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n","offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n","\n","tokenized_examples[\"start_positions\"] = []#[152,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\n","tokenized_examples[\"end_positions\"] = []#[172,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"29wCWtG72KpW"},"outputs":[],"source":["for i, offsets in enumerate(offset_mapping):\n","  if len(dataset['train'][0]['answers']['answer_start'])==0:\n","    tokenized_examples[\"start_positions\"].append(0)\n","    tokenized_examples[\"end_positions\"].append(0)\n","  else:\n","      \n","    start_char=dataset['train'][0]['answers']['answer_start'][0]\n","    end_char=start_char+len(dataset['train'][0]['answers']['text'][0])\n","    found=0\n","    start_token_position=0\n","    end_token_position=0\n","    \n","    for j,offset in enumerate(offsets):\n","      if offset[0]<=start_char and offset[1]>=start_char and found==0:\n","        start_token_position=j\n","        end_token_position=MAX_LENGTH\n","        \n","        found=1\n","      if offset[1]>=end_char and found==1:\n","        end_token_position=j\n","        break\n","    tokenized_examples[\"start_positions\"].append(start_token_position)\n","    tokenized_examples[\"end_positions\"].append(end_token_position)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1666632564174,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"iPToI0kT2KsA","outputId":"7178df80-2142-4f51-991a-584979fd0303"},"outputs":[{"output_type":"stream","name":"stdout","text":["[145, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","[167, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"]}],"source":["print(tokenized_examples['start_positions'])\n","print(tokenized_examples['end_positions'])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":832,"status":"ok","timestamp":1666632748311,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"7r8NUgqz6Ibk","outputId":"372d1eab-5d24-41b9-9f3a-2a2eea8100fa"},"outputs":[{"output_type":"stream","name":"stdout","text":["18\n"]}],"source":["print(tokenized_examples['input_ids'])"]},{"cell_type":"code","source":["def preprocess_function(dataset):\n","  \n","  questions = [q.lstrip() for q in dataset[\"question\"]]\n","  paragraphs = [p.lstrip() for p in dataset[\"context\"]]\n","  \n","  tokenized_examples = tokenizer(\n","    questions,\n","    paragraphs,\n","    truncation=\"only_second\",\n","    max_length=MAX_LENGTH,\n","    stride=64,\n","    return_overflowing_tokens=True,\n","    return_offsets_mapping=True,\n","    padding=\"max_length\",\n","  )\n","  sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n","  offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n","\n","  tokenized_examples[\"start_positions\"] = []#[152,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\n","  tokenized_examples[\"end_positions\"] = []#[172,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]\n","\n","  for i, offsets in enumerate(offset_mapping):\n","    sample_index = sample_mapping[i]\n","\n","    start_char=dataset[\"answers\"][sample_index]['answer_start'][0]\n","    end_char=start_char+len(dataset[\"answers\"][sample_index]['text'][0])\n","    found=0\n","    start_token_position=0\n","    end_token_position=0\n","    \n","    for j,offset in enumerate(offsets):\n","      if offset[0]<=start_char and offset[1]>=start_char and found==0:\n","        start_token_position=j\n","        end_token_position=MAX_LENGTH\n","        found=1\n","      if offset[1]>=end_char and found==1:\n","        end_token_position=j\n","        break\n","    tokenized_examples[\"start_positions\"].append(start_token_position)\n","    tokenized_examples[\"end_positions\"].append(end_token_position)\n","\n","  return tokenized_examples"],"metadata":{"id":"Wy1qN05TkQgO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset=dataset.map(\n","    preprocess_function,\n","    batched=True,\n","    remove_columns=dataset[\"train\"].column_names,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["c618ee4fd5ea4d3c881a7cad7523299d","bccef8d6da3049d684b1120376742d58","28af9e1e607e4f42af26a61e06200bdf","49229b14c6494a039664221a37617800","3a143b814efb4b6789e7ff316e3eeba7","477b3de68d7b41ed9888eed4ee3b2d46","be48678169014da7bc536cc11c5d76a8","f21cfda647d1473cbe73355cfbcc1058","9901ce5f6ea34138b3b48c656603dcc2","539540541f5b46a4833a09cb4a919712","d0b4a3b0e4ce43cfab53d9c2f3aeeb97"]},"id":"YcGIQ1h4kQik","executionInfo":{"status":"ok","timestamp":1666628811634,"user_tz":-60,"elapsed":36423,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d1f8b70c-1383-4545-abba-a82a100b8c90"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c618ee4fd5ea4d3c881a7cad7523299d"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lauOnrsHkUYP","executionInfo":{"status":"ok","timestamp":1666628811635,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bdb1d70a-b25a-4f5b-f34b-3bceb886a66f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'attention_mask', 'start_positions', 'end_positions'],\n","        num_rows: 4537\n","    })\n","})"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HW6UemjjlmX-"},"outputs":[],"source":["tf_dataset = tokenized_dataset[\"train\"].to_tf_dataset(\n","    shuffle=True, \n","    batch_size=BATCH_SIZE,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"t3ZPBNuFlmaR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1666632249981,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c78d318d-6d0e-4020-a965-c163a3fbafda"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  3750,    99, ...,     1,     1,     1],\n","       [    0,  2264,    16, ...,    50, 35993,     2],\n","       [    0,  2264, 19729, ..., 18225,     9,     2],\n","       [    0,  2264, 27460, ...,     9,   208,     2]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([  0, 425,   0,   0])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([  0, 475,   0,   0])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  2264,   473, ...,   731,  5457,     2],\n","       [    0,  2264,    16, ...,     1,     1,     1],\n","       [    0,  2264,  3164, ..., 41675,  9937,     2],\n","       [    0,  2264,    16, ...,   112, 37109,     2]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([ 832,    0, 1595,    0])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([ 862,    0, 1604,    0])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  6179,   473, ...,     1,     1,     1],\n","       [    0,  2264,    16, ...,    16,     5,     2],\n","       [    0,  2264,    21, ...,     1,     1,     1],\n","       [    0,  2264, 23476, ...,    21,  4786,     2]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 1, 1, 1]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([   0, 4094,    0, 2803])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([   0, 4096,    0, 2809])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  2264,   473, ...,     6,     8,     2],\n","       [    0,  2264,    16, ...,     1,     1,     1],\n","       [    0,  2264,    16, ...,     1,     1,     1],\n","       [    0,  1779,   222, ...,  8649, 13878,     2]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 1, 1, 1]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([ 606,    0, 2397,    0])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([ 633,    0, 2433,    0])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  2264, 10606, ..., 24811, 16946,     2],\n","       [    0,  2264,    21, ..., 43137,  4249,     2],\n","       [    0, 13841,    58, ...,     1,     1,     1],\n","       [    0, 12196,    40, ...,     1,     1,     1]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([0, 0, 0, 0])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([0, 0, 0, 0])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[   0, 6179,   58, ...,  624,   12,    2],\n","       [   0, 2264,   16, ...,  112,    4,    2],\n","       [   0, 2264,   21, ...,    1,    1,    1],\n","       [   0, 2264,  473, ...,    1,    1,    1]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([   0, 1152,    0,    0])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([   0, 1156,    0,    0])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  3762,   169, ...,     4,     5,     2],\n","       [    0, 32251,  2718, ...,    20, 40004,     2],\n","       [    0, 32251,  8276, ...,     1,     1,     1],\n","       [    0, 13841,    16, ...,     8,   115,     2]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 1, 1, 1]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([  0, 687, 534,   0])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([  0, 702, 550,   0])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  2264, 41371, ...,    36, 44105,     2],\n","       [    0,  2264,   222, ...,     1,     1,     1],\n","       [    0,  2264,   109, ...,     1,     1,     1],\n","       [    0,  2264,  3164, ...,     8,   115,     2]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 1, 1, 1]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([2804, 2838,  889, 2022])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([2808, 2852,  892, 2023])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  6179,   251, ...,     1,     1,     1],\n","       [    0,  7608,  1437, ...,    11, 40150,     2],\n","       [    0,  2264,    32, ...,  7696, 42780,     2],\n","       [    0,  2264,    16, ...,     1,     1,     1]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 0, 0, 0]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([   0, 1064,  600,    0])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([   0, 1103,  647,    0])>}\n","{'input_ids': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[    0,  2264,   531, ..., 50118,  7779,     2],\n","       [    0,  2264,    21, ...,   673,    11,     2],\n","       [    0,  2264,  3164, ...,  1640,    29,     2],\n","       [    0,  6179,   171, ...,  5948,    11,     2]])>, 'attention_mask': <tf.Tensor: shape=(4, 4096), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1],\n","       [1, 1, 1, ..., 1, 1, 1]])>, 'start_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([   0, 1220, 2037,    0])>, 'end_positions': <tf.Tensor: shape=(4,), dtype=int64, numpy=array([   0, 1221, 2039,    0])>}\n"]}],"source":["for i in tf_dataset.take(10):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3_HHczbHimN_"},"outputs":[],"source":["train_dataset=tf_dataset.take(int(0.9*len(tf_dataset)))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pIOTDLztimQ5"},"outputs":[],"source":["val_dataset=tf_dataset.skip(int(0.9*len(tf_dataset)))"]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"OHKlxlvT6f8T"}},{"cell_type":"code","source":["from transformers import LongformerTokenizer, TFLongformerForQuestionAnswering\n","\n","model = TFLongformerForQuestionAnswering.from_pretrained(\"allenai/longformer-large-4096-finetuned-triviaqa\")"],"metadata":{"id":"I4ZxSS4AzrHs","colab":{"base_uri":"https://localhost:8080/","height":138,"referenced_widgets":["59e2326fdedc4fb3858b9b53dd76a483","6252ca3ddb04475e8f97dba06c8adaa9","d8ac7f23db1649228846f288d8b04e15","f5675c25566f496191321c211950d8b9","894022ac3f344027a4b4c02682eb5a75","d6047c8b12c8468380fbb6cf6adddb8b","9075737321b2474791f054b4ad325bb3","c305025f78dc461fab8bfde9d8087996","b6922fa590274500bb7d3be7bd6cc2e8","287802a7291341b8a52e5aca8fb94dd9","f794c467176a4835b3cea5239f9c25d9"]},"executionInfo":{"status":"ok","timestamp":1666628857132,"user_tz":-60,"elapsed":41950,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c8618152-0ab6-46bf-f6f0-f6d1e2e7b29a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.74G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"59e2326fdedc4fb3858b9b53dd76a483"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFLongformerForQuestionAnswering.\n","\n","All the layers of TFLongformerForQuestionAnswering were initialized from the model checkpoint at allenai/longformer-large-4096-finetuned-triviaqa.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFLongformerForQuestionAnswering for predictions without further training.\n"]}]},{"cell_type":"code","source":[],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"FLec5A7RzrJ4","executionInfo":{"status":"ok","timestamp":1666628858272,"user_tz":-60,"elapsed":1155,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8b427472-4f28-41b7-8867-c5e64cab7b42"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["' ELISA'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"08YbNsZ1zrOH","executionInfo":{"status":"ok","timestamp":1666628858272,"user_tz":-60,"elapsed":15,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4a3e56ee-9a6e-4f90-c7ca-99b64ec2ef64"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"tf_longformer_for_question_answering\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," longformer (TFLongformerMai  multiple                 433551360 \n"," nLayer)                                                         \n","                                                                 \n"," qa_outputs (Dense)          multiple                  2050      \n","                                                                 \n","=================================================================\n","Total params: 433,553,410\n","Trainable params: 433,553,410\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rVccRMevimaF","executionInfo":{"status":"ok","timestamp":1666606348357,"user_tz":-60,"elapsed":20,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"59a32e32-cab7-4103-fb0b-78b4319c865b"},"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}],"source":["optimizer=Adam(learning_rate=1e-5)\n","model.compile(optimizer=optimizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"2Ltrl5o0imdF","outputId":"23542172-b1e7-489d-d783-218b6e18e408","executionInfo":{"status":"error","timestamp":1666600336565,"user_tz":-60,"elapsed":208832,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/3\n"]},{"output_type":"stream","name":"stderr","text":["There should be exactly three separator tokens: 2 in every sample for questions answering. You might also consider to set `global_attention_mask` manually in the forward function to avoid this. This is most likely an error. The global attention is disabled for this forward pass.\n","WARNING:tensorflow:AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7fb5adabc210>> and will run it as-is.\n","Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n","Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method\n","To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"]},{"output_type":"stream","name":"stdout","text":["WARNING: AutoGraph could not transform <bound method Socket.send of <zmq.Socket(zmq.PUSH) at 0x7fb5adabc210>> and will run it as-is.\n","Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n","Cause: module, class, method, function, traceback, frame, or code object was expected, got cython_function_or_method\n","To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:AutoGraph could not transform <function wrap at 0x7fb5bab08c20> and will run it as-is.\n","Cause: while/else statement not yet supported\n","To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"]},{"output_type":"stream","name":"stdout","text":["WARNING: AutoGraph could not transform <function wrap at 0x7fb5bab08c20> and will run it as-is.\n","Cause: while/else statement not yet supported\n","To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"]},{"output_type":"stream","name":"stderr","text":["There should be exactly three separator tokens: 2 in every sample for questions answering. You might also consider to set `global_attention_mask` manually in the forward function to avoid this. This is most likely an error. The global attention is disabled for this forward pass.\n"]},{"output_type":"error","ename":"ResourceExhaustedError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mResourceExhaustedError\u001b[0m                    Traceback (most recent call last)","\u001b[0;32m<ipython-input-30-ef3b59204a92>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mResourceExhaustedError\u001b[0m: Graph execution error:\n\nDetected at node 'tf_longformer_for_question_answering/longformer/encoder/layer_._4/attention/self/Tile' defined at (most recent call last):\n    File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n      \"__main__\", mod_spec)\n    File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n      exec(code, run_globals)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n      app.launch_new_instance()\n    File \"/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py\", line 846, in launch_instance\n      app.start()\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py\", line 612, in start\n      self.io_loop.start()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py\", line 132, in start\n      self.asyncio_loop.run_forever()\n    File \"/usr/lib/python3.7/asyncio/base_events.py\", line 541, in run_forever\n      self._run_once()\n    File \"/usr/lib/python3.7/asyncio/base_events.py\", line 1786, in _run_once\n      handle._run()\n    File \"/usr/lib/python3.7/asyncio/events.py\", line 88, in _run\n      self._context.run(self._callback, *self._args)\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py\", line 758, in _run_callback\n      ret = callback()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py\", line 300, in null_wrapper\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 1233, in inner\n      self.run()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 1147, in run\n      yielded = self.gen.send(value)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 365, in process_one\n      yield gen.maybe_future(dispatch(*args))\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 268, in dispatch_shell\n      yield gen.maybe_future(handler(stream, idents, msg))\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 545, in execute_request\n      user_expressions, allow_stdin,\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py\", line 306, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2855, in run_cell\n      raw_cell, store_history, silent, shell_futures)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2881, in _run_cell\n      return runner(coro)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n      coro.send(None)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3058, in run_cell_async\n      interactivity=interactivity, compiler=compiler, result=result)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3249, in run_ast_nodes\n      if (await self.run_code(code, result,  async_=asy)):\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3326, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"<ipython-input-30-ef3b59204a92>\", line 1, in <module>\n      history=model.fit(train_dataset,validation_data=val_dataset,epochs=3)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1409, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1051, in train_function\n      return step_function(self, iterator)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1040, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1030, in run_step\n      outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py\", line 1420, in train_step\n      y_pred = self(x, training=True)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 490, in __call__\n      return super().__call__(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py\", line 2232, in run_call_with_unpacked_inputs\n      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 2284, in call\n      outputs = self.longformer(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py\", line 2232, in run_call_with_unpacked_inputs\n      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1774, in call\n      encoder_outputs = self.encoder(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1586, in call\n      for idx, layer_module in enumerate(self.layer):\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1591, in call\n      layer_outputs = layer_module(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1549, in call\n      attention_outputs = self.attention(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1521, in call\n      self_outputs = self.self_attention(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 763, in call\n      attn_scores = self._sliding_chunks_query_key_matmul(\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1001, in _sliding_chunks_query_key_matmul\n      first_chunk_mask = (\nNode: 'tf_longformer_for_question_answering/longformer/encoder/layer_._4/attention/self/Tile'\nDetected at node 'tf_longformer_for_question_answering/longformer/encoder/layer_._4/attention/self/Tile' defined at (most recent call last):\n    File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n      \"__main__\", mod_spec)\n    File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n      exec(code, run_globals)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n      app.launch_new_instance()\n    File \"/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py\", line 846, in launch_instance\n      app.start()\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py\", line 612, in start\n      self.io_loop.start()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py\", line 132, in start\n      self.asyncio_loop.run_forever()\n    File \"/usr/lib/python3.7/asyncio/base_events.py\", line 541, in run_forever\n      self._run_once()\n    File \"/usr/lib/python3.7/asyncio/base_events.py\", line 1786, in _run_once\n      handle._run()\n    File \"/usr/lib/python3.7/asyncio/events.py\", line 88, in _run\n      self._context.run(self._callback, *self._args)\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py\", line 758, in _run_callback\n      ret = callback()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py\", line 300, in null_wrapper\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 1233, in inner\n      self.run()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 1147, in run\n      yielded = self.gen.send(value)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 365, in process_one\n      yield gen.maybe_future(dispatch(*args))\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 268, in dispatch_shell\n      yield gen.maybe_future(handler(stream, idents, msg))\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 545, in execute_request\n      user_expressions, allow_stdin,\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py\", line 306, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2855, in run_cell\n      raw_cell, store_history, silent, shell_futures)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2881, in _run_cell\n      return runner(coro)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n      coro.send(None)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3058, in run_cell_async\n      interactivity=interactivity, compiler=compiler, result=result)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3249, in run_ast_nodes\n      if (await self.run_code(code, result,  async_=asy)):\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3326, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"<ipython-input-30-ef3b59204a92>\", line 1, in <module>\n      history=model.fit(train_dataset,validation_data=val_dataset,epochs=3)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1409, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1051, in train_function\n      return step_function(self, iterator)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1040, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1030, in run_step\n      outputs = model.train_step(data)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py\", line 1420, in train_step\n      y_pred = self(x, training=True)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 490, in __call__\n      return super().__call__(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py\", line 2232, in run_call_with_unpacked_inputs\n      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 2284, in call\n      outputs = self.longformer(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py\", line 2232, in run_call_with_unpacked_inputs\n      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1774, in call\n      encoder_outputs = self.encoder(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1586, in call\n      for idx, layer_module in enumerate(self.layer):\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1591, in call\n      layer_outputs = layer_module(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1549, in call\n      attention_outputs = self.attention(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1521, in call\n      self_outputs = self.self_attention(\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py\", line 1014, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 92, in error_handler\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 763, in call\n      attn_scores = self._sliding_chunks_query_key_matmul(\n    File \"/usr/local/lib/python3.7/dist-packages/transformers/models/longformer/modeling_tf_longformer.py\", line 1001, in _sliding_chunks_query_key_matmul\n      first_chunk_mask = (\nNode: 'tf_longformer_for_question_answering/longformer/encoder/layer_._4/attention/self/Tile'\n2 root error(s) found.\n  (0) RESOURCE_EXHAUSTED:  OOM when allocating tensor with shape[16,16,256,256] and type int64 on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node tf_longformer_for_question_answering/longformer/encoder/layer_._4/attention/self/Tile}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n\n\t [[tf_longformer_for_question_answering/longformer/encoder/layer_._13/attention/self/cond_2/pivot_t/_1803/_3971]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n\n  (1) RESOURCE_EXHAUSTED:  OOM when allocating tensor with shape[16,16,256,256] and type int64 on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[{{node tf_longformer_for_question_answering/longformer/encoder/layer_._4/attention/self/Tile}}]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n\n0 successful operations.\n0 derived errors ignored. [Op:__inference_train_function_247206]"]}],"source":["history=model.fit(train_dataset,validation_data=val_dataset,epochs=3)"]},{"cell_type":"code","source":[],"metadata":{"id":"kCLu0BoX1_l-"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Evaluation"],"metadata":{"id":"Pvu9zITljgO8"}},{"cell_type":"code","source":["from evaluate import load"],"metadata":{"id":"rn5Av2i76XAJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"B7N8KZHyXidx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1666631678962,"user_tz":-60,"elapsed":5527,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8d84f7a4-43a4-42a1-fa45-f5223587daa7"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'exact_match': 0.0, 'f1': 0.0}\n"]}],"source":["squad_metric = load(\"squad\")\n","predictions = [{'prediction_text': '1999', 'id': '56e10a3be3433e1400422b22'}]\n","references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n","results = squad_metric.compute(predictions=predictions, references=references)\n","print(results)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2HKn-5RQXigN"},"outputs":[],"source":[]},{"cell_type":"markdown","source":["# Testing"],"metadata":{"id":"9L6TrZvf3tu7"}},{"cell_type":"code","source":["question=\"How is the virus spread?\"\n","text=\"We know that the disease is caused by the SARS-CoV-2 virus, which spreads between people in several different ways.Current evidence suggests that the virus spreads mainly between people who are in close contact with each other, for example at a conversational distance.The virus can spread from an infected person’s mouth or nose in small liquid particles when they cough, sneeze, speak, sing or breathe. Another person can then contract the virus when infectious particles that pass through the air are inhaled at short range (this is often called short-range aerosol or short-range airborne transmission) or if infectious particles come into direct contact with the eyes, nose, or mouth (droplet transmission). The virus can also spread in poorly ventilated and/or crowded indoor settings, where people tend to spend longer periods of time. This is because aerosols can remain suspended in the air or travel farther than conversational distance (this is often called long-range aerosol or long-range airborne transmission). People may also become infected when touching their eyes, nose or mouth after touching surfaces or objects that have been contaminated by the virus. Further research is ongoing to better understand the spread of the virus and which settings are most risky and why. Research is also under way to study virus variants that are emerging and why some are more transmissible. For updated information on SARS-CoV-2 variants, please read the weekly epidemiologic updates.\"\n","inputs = tokenizer(question, text, return_tensors=\"tf\")\n","outputs = model(**inputs)\n","\n","answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])\n","answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])\n","\n","predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]\n","tokenizer.decode(predict_answer_tokens)"],"metadata":{"id":"xpMORTZrbdFy","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1666630195003,"user_tz":-60,"elapsed":3574,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"996e0c39-2556-4638-c963-213519a49674"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["' mouth'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":[],"metadata":{"id":"o4JI-_vE-r2q"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["gwDUFKDdR6i_"],"provenance":[{"file_id":"1osLVAprq1KDMjH1MXM6a5lDqLkkugequ","timestamp":1673807517129},{"file_id":"1eb6-YwCQQk8K4F6pdSOPHkW2Zj1LibRl","timestamp":1665246362358},{"file_id":"1LOMW9Bum6Ty71WFp4JEzHHZuxT_vjcPQ","timestamp":1664731142718},{"file_id":"1kiqhUrifUOMlpLnWCJWAYm9xM3vdVxfF","timestamp":1664705409290},{"file_id":"1qrpPSsjeFUMtRrljhxEiKKmlVk__fpuz","timestamp":1664695746526},{"file_id":"1qG67bWTlIgR_WzHRVCiw1CicPYSTv4qn","timestamp":1663398158336},{"file_id":"1ekGLJqUm4hoXzVs3h2I_OfnSCNwm1p8j","timestamp":1663151174522}],"toc_visible":true},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"widgets":{"application/vnd.jupyter.widget-state+json":{"a52a142269314b11a92d3018bc49e6db":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_0758649eb083429184f14a7eaadbfd9b","IPY_MODEL_82a17b907f00422f84bb72820f9fce08","IPY_MODEL_81232c1653114170ae207f943d1f3388"],"layout":"IPY_MODEL_0c6f26e805444635acd28c48de273ef5"}},"0758649eb083429184f14a7eaadbfd9b":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_8622d655bd2c405da08b45059e80d881","placeholder":"​","style":"IPY_MODEL_073167fbe5584079bd0842575cfcc973","value":"Downloading 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FILE: deep learning for natural language processing/12-Search Engine for e-commerce using Sentence Transformers in HuggingFace 🤗 by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"markdown","metadata":{"id":"-iNHt6FDdniF"},"source":["# Installations and Imports"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5916,"status":"ok","timestamp":1666949316002,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"mDXVk_TaIur-","outputId":"0f6a3170-bc7a-48d3-c2e8-2284df2ef2ff"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.23.1)\n","Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (2.6.1)\n","Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.13.1)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in 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/usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (22.1.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.3.1)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (6.0.2)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.9.0)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.5)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n"]}],"source":["!pip install transformers datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nR4lW5kcIx0j"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import pandas as pd\n","import os\n","import re\n","import csv\n","import string\n","import time\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer,BatchNormalization,Dropout,Input,LayerNormalization\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import AutoTokenizer,create_optimizer,TFAutoModel"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sV60GYXSgSDK"},"outputs":[],"source":["BATCH_SIZE=128\n","MAX_LENGTH=64"]},{"cell_type":"markdown","metadata":{"id":"O2od1HEDDvz3"},"source":["# Dataset Preparation"]},{"cell_type":"markdown","source":["## Downloads"],"metadata":{"id":"aSaOBWrxwzaE"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14029,"status":"ok","timestamp":1666711806729,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Z4IA5YKfIx26","outputId":"41ff4a1d-4a55-470c-ad8a-c70836fa8336"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting aicrowd-cli\n","  Downloading aicrowd_cli-0.1.15-py3-none-any.whl (51 kB)\n","\u001b[K     |████████████████████████████████| 51 kB 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kB)\n","\u001b[K     |████████████████████████████████| 170 kB 45.8 MB/s \n","\u001b[?25hRequirement already satisfied: tqdm<5,>=4.56.0 in /usr/local/lib/python3.7/dist-packages (from aicrowd-cli) (4.64.1)\n","Collecting python-slugify<6,>=5.0.0\n","  Downloading python_slugify-5.0.2-py2.py3-none-any.whl (6.7 kB)\n","Collecting rich<11,>=10.0.0\n","  Downloading rich-10.16.2-py3-none-any.whl (214 kB)\n","\u001b[K     |████████████████████████████████| 214 kB 71.8 MB/s \n","\u001b[?25hCollecting gitdb<5,>=4.0.1\n","  Downloading gitdb-4.0.9-py3-none-any.whl (63 kB)\n","\u001b[K     |████████████████████████████████| 63 kB 1.6 MB/s \n","\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.0 in /usr/local/lib/python3.7/dist-packages (from GitPython==3.1.18->aicrowd-cli) (4.1.1)\n","Collecting smmap<6,>=3.0.1\n","  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from 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/usr/local/lib/python3.7/dist-packages (from rich<11,>=10.0.0->aicrowd-cli) (2.6.1)\n","Installing collected packages: smmap, requests, gitdb, commonmark, colorama, semver, rich, requests-toolbelt, pyzmq, python-slugify, GitPython, aicrowd-cli\n","  Attempting uninstall: requests\n","    Found existing installation: requests 2.23.0\n","    Uninstalling requests-2.23.0:\n","      Successfully uninstalled requests-2.23.0\n","  Attempting uninstall: pyzmq\n","    Found existing installation: pyzmq 23.2.1\n","    Uninstalling pyzmq-23.2.1:\n","      Successfully uninstalled pyzmq-23.2.1\n","  Attempting uninstall: python-slugify\n","    Found existing installation: python-slugify 6.1.2\n","    Uninstalling python-slugify-6.1.2:\n","      Successfully uninstalled python-slugify-6.1.2\n","Successfully installed GitPython-3.1.18 aicrowd-cli-0.1.15 colorama-0.4.6 commonmark-0.9.1 gitdb-4.0.9 python-slugify-5.0.2 pyzmq-22.1.0 requests-2.28.1 requests-toolbelt-0.10.1 rich-10.16.2 semver-2.13.0 smmap-5.0.0\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["requests","zmq"]}}},"metadata":{}}],"source":["!pip install aicrowd-cli"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":32581,"status":"ok","timestamp":1666189459005,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"BjoYZ8LlIx7m","outputId":"32a73bb6-0dfc-4950-f047-87679051e54b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Please login here: \u001b[34m\u001b[1m\u001b[4mhttps://api.aicrowd.com/auth/ww3_yLsfI7GJoSTmcpouplCpENYs09xfMMQg2O4kJ9k\u001b[0m\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: www-browser: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: links2: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: elinks: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: links: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: lynx: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: w3m: not found\n","xdg-open: no method available for opening 'https://api.aicrowd.com/auth/ww3_yLsfI7GJoSTmcpouplCpENYs09xfMMQg2O4kJ9k'\n","\u001b[32mAPI Key valid\u001b[0m\n","\u001b[32mGitlab access token valid\u001b[0m\n","\u001b[32mSaved details successfully!\u001b[0m\n"]}],"source":["!aicrowd login"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":176665,"status":"ok","timestamp":1666190728538,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"9gGjEwWWIx5S","outputId":"d75e6325-1daf-434a-d1ba-60c9239e67f2"},"outputs":[{"name":"stdout","output_type":"stream","text":["product_catalogue-v0.3.csv.zip: 100% 328M/328M [00:28<00:00, 11.5MB/s]\n","sample_submission_public-v0.3.csv.zip: 100% 331k/331k [00:00<00:00, 339kB/s]\n","test_public-v0.3.csv.zip: 100% 394k/394k [00:01<00:00, 367kB/s]\n","train-v0.3.csv.zip: 100% 6.80M/6.80M [00:01<00:00, 3.46MB/s]\n","product_catalogue-v0.3.csv.zip: 100% 657M/657M [00:54<00:00, 12.0MB/s]\n","sample_submission_public-v0.3.csv.zip: 100% 812k/812k [00:01<00:00, 588kB/s]\n","test_public-v0.3.csv.zip: 100% 2.94M/2.94M [00:01<00:00, 1.63MB/s]\n","train-v0.3.csv.zip: 100% 19.8M/19.8M [00:03<00:00, 6.08MB/s]\n","product_catalogue-v0.3.csv.zip: 100% 657M/657M [00:55<00:00, 11.9MB/s]\n","sample_submission_public-v0.3.csv.zip: 100% 803k/803k [00:01<00:00, 583kB/s]\n","test_public-v0.3.csv.zip: 100% 2.94M/2.94M [00:01<00:00, 1.62MB/s]\n","train-v0.3.csv.zip: 100% 20.3M/20.3M [00:03<00:00, 6.19MB/s]\n"]}],"source":["!aicrowd dataset download -c esci-challenge-for-improving-product-search"]},{"cell_type":"code","source":[],"metadata":{"id":"WvuvZc1_x8Gd"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Decompress and Copy"],"metadata":{"id":"DczeG3N5x8hL"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2121,"status":"ok","timestamp":1666190810312,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"67uJtS-MtdmV","outputId":"9dd7e299-c9a5-4287-f67b-0a8182061970"},"outputs":[{"name":"stdout","output_type":"stream","text":["Archive:  /content/train-v0.3.csv.zip\n","  inflating: /content/dataset/data/processed/public/task_3_product_substitute_identification/train-v0.3.csv  \n"]}],"source":["!unzip \"/content/train-v0.3.csv.zip\" -d \"/content/dataset/\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":21088,"status":"ok","timestamp":1665426496114,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"plKnZRUSJJFc","outputId":"95b10089-b493-4a83-e26a-ce4a15402962"},"outputs":[{"name":"stdout","output_type":"stream","text":["Archive:  /content/product_catalogue-v0.3.csv.zip\n","  inflating: /content/dataset/data/processed/public/task_3_product_substitute_identification/product_catalogue-v0.3.csv  \n","\n"]}],"source":["!unzip \"/content/product_catalogue-v0.3.csv.zip\" -d \"/content/dataset/\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"amZh8KfKtqH0"},"outputs":[],"source":["!cp /content/dataset/data/processed/public/task_3_product_substitute_identification/train-v0.3.csv /content/drive/MyDrive/datasets/kdd_cup"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kCWUL9aFKC4U"},"outputs":[],"source":["!cp /content/dataset/data/processed/public/task_3_product_substitute_identification/product_catalogue-v0.3.csv /content/drive/MyDrive/datasets/kdd_cup"]},{"cell_type":"markdown","source":["## Data Loading"],"metadata":{"id":"ogDlZqTX0Cf-"}},{"cell_type":"code","source":["filepath_train='/content/drive/MyDrive/datasets/kdd_cup/train-v0.3_1.csv'"],"metadata":{"id":"gnQdyIhnx4Q7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["filepath_catalogue='/content/drive/MyDrive/datasets/kdd_cup/product_catalogue-v0.3.csv'"],"metadata":{"id":"rZXDn4cBx4T6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_catalogue=pd.read_csv(filepath_catalogue)"],"metadata":{"id":"H8I6OELSx4WT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_train=pd.read_csv(filepath_train)"],"metadata":{"id":"NSmz3WYrx4Yt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_train.loc[df_train['query_locale'] == 'jp']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"WVRcwFpYx4bV","executionInfo":{"status":"ok","timestamp":1666938138071,"user_tz":-60,"elapsed":10,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"88d4c543-7188-4367-81e2-b561aee541ff"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        query_id     query query_locale  product_id  esci_label\n","572650     26004        0係           jp  B00FNJ1CTQ  substitute\n","572651     26004        0係           jp  B00FW60P84  irrelevant\n","572652     26004        0係           jp  B00GAYCNDM  substitute\n","572653     26004        0係           jp  B00MFBKQKG  substitute\n","572654     26004        0係           jp  B01G1YXU28  irrelevant\n","...          ...       ...          ...         ...         ...\n","781739     33803  針なしほっちきす           jp  B08XGQ9RH7  substitute\n","781740     33803  針なしほっちきす           jp  B0987RGRF2       exact\n","781741     33803  針なしほっちきす           jp  B099NFJWP6       exact\n","781742     33803  針なしほっちきす           jp  B09F3B413J       exact\n","781743     33803  針なしほっちきす           jp  B09F3F1KDP       exact\n","\n","[209094 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-87e11232-c73e-4abb-8d69-46454d578872\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>query_id</th>\n","      <th>query</th>\n","      <th>query_locale</th>\n","      <th>product_id</th>\n","      <th>esci_label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>572650</th>\n","      <td>26004</td>\n","      <td>0係</td>\n","      <td>jp</td>\n","      <td>B00FNJ1CTQ</td>\n","      <td>substitute</td>\n","    </tr>\n","    <tr>\n","      <th>572651</th>\n","      <td>26004</td>\n","      <td>0係</td>\n","      <td>jp</td>\n","      <td>B00FW60P84</td>\n","      <td>irrelevant</td>\n","    </tr>\n","    <tr>\n","      <th>572652</th>\n","      <td>26004</td>\n","      <td>0係</td>\n","      <td>jp</td>\n","      <td>B00GAYCNDM</td>\n","      <td>substitute</td>\n","    </tr>\n","    <tr>\n","      <th>572653</th>\n","      <td>26004</td>\n","      <td>0係</td>\n","      <td>jp</td>\n","      <td>B00MFBKQKG</td>\n","      <td>substitute</td>\n","    </tr>\n","    <tr>\n","      <th>572654</th>\n","      <td>26004</td>\n","      <td>0係</td>\n","      <td>jp</td>\n","      <td>B01G1YXU28</td>\n","      <td>irrelevant</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>781739</th>\n","      <td>33803</td>\n","      <td>針なしほっちきす</td>\n","      <td>jp</td>\n","      <td>B08XGQ9RH7</td>\n","      <td>substitute</td>\n","    </tr>\n","    <tr>\n","      <th>781740</th>\n","      <td>33803</td>\n","      <td>針なしほっちきす</td>\n","      <td>jp</td>\n","      <td>B0987RGRF2</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>781741</th>\n","      <td>33803</td>\n","      <td>針なしほっちきす</td>\n","      <td>jp</td>\n","      <td>B099NFJWP6</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>781742</th>\n","      <td>33803</td>\n","      <td>針なしほっちきす</td>\n","      <td>jp</td>\n","      <td>B09F3B413J</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>781743</th>\n","      <td>33803</td>\n","      <td>針なしほっちきす</td>\n","      <td>jp</td>\n","      <td>B09F3F1KDP</td>\n","      <td>exact</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>209094 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87e11232-c73e-4abb-8d69-46454d578872')\"\n","              title=\"Convert this 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Doworspaw   \n","1815212  【いろんな場合に適用】軽い運動時、家事、外出、長時間移動、温泉旅行などに活用なセクシルームブ...      Gabrioir   \n","1815213  【いろんな場合に適用】軽い運動時、家事、外出、長時間移動、温泉旅行などに活用なセクシルームブ...      Gabrioir   \n","1815214  【いろんな場合に適用】軽い運動時、家事、外出、長時間移動、温泉旅行などに活用なセクシルームブ...      Gabrioir   \n","1815215  【いろんな場合に適用】軽い運動時、家事、外出、長時間移動、温泉旅行などに活用なセクシルームブ...      Gabrioir   \n","\n","        product_color_name product_locale  \n","0                    Negro             es  \n","1                   Blanco             es  \n","2                     Azul             es  \n","3                      NaN             es  \n","4                  Negro (             es  \n","...                    ...            ...  \n","1815211               ベージュ             jp  \n","1815212               パープル             jp  \n","1815213               ベージュ             jp  \n","1815214               パープル             jp  \n","1815215               ブラック             jp  \n","\n","[1815216 rows x 7 columns]"],"text/html":["\n","  <div id=\"df-25acc418-7c65-4f46-8ecd-20fb6a846506\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","      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<td>11 Degrees</td>\n","      <td>Blanco</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>B07DP4LM9H</td>\n","      <td>11 Degrees de los Hombres Core Pull Over Hoodi...</td>\n","      <td>La sudadera con capucha Core Pull Over de 11 G...</td>\n","      <td>11 Degrees Azul Core Pull Over Hoodie\\nA estre...</td>\n","      <td>11 Degrees</td>\n","      <td>Azul</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>B07G37B9HP</td>\n","      <td>11 Degrees Poli Panel Track Pant XL Black</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>11 Degrees</td>\n","      <td>NaN</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>B07LCTGDHY</td>\n","      <td>11 Degrees Gorra Trucker Negro OSFA (Talla úni...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>11 Degrees</td>\n","      <td>Negro (</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      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<th>1815213</th>\n","      <td>B09G72LDQZ</td>\n","      <td>レディース ブラジャー 補正ブラ トップス Gabrioir 大きいサイズ 揺れない 脇高設...</td>\n","      <td>&lt;p&gt;【配送】&lt;/p&gt;&lt;p&gt;普通には2-3日後出荷できますが。出荷した後15-25日ぐらい届...</td>\n","      <td>【いろんな場合に適用】軽い運動時、家事、外出、長時間移動、温泉旅行などに活用なセクシルームブ...</td>\n","      <td>Gabrioir</td>\n","      <td>ベージュ</td>\n","      <td>jp</td>\n","    </tr>\n","    <tr>\n","      <th>1815214</th>\n","      <td>B09GBGRTPB</td>\n","      <td>ナイトブラ ノンワイヤーブラ ブラジャー レディース Gabrioir 大きいサイズ パット...</td>\n","      <td>&lt;p&gt;【配送】&lt;/p&gt;&lt;p&gt;普通には2-3日後出荷できますが。出荷した後15-25日ぐらい届...</td>\n","      <td>【いろんな場合に適用】軽い運動時、家事、外出、長時間移動、温泉旅行などに活用なセクシルームブ...</td>\n","      <td>Gabrioir</td>\n","      <td>パープル</td>\n","      <td>jp</td>\n","    </tr>\n","    <tr>\n","      <th>1815215</th>\n","      <td>B09GBKN83Q</td>\n","      <td>インナーキャミソール レディース ブラトップ キャミソール Gabrioir ノーワイヤー ...</td>\n","      <td>&lt;p&gt;【配送】&lt;/p&gt;&lt;p&gt;普通には2-3日後出荷できますが。出荷した後15-25日ぐらい届...</td>\n","      <td>【いろんな場合に適用】軽い運動時、家事、外出、長時間移動、温泉旅行などに活用なセクシルームブ...</td>\n","      <td>Gabrioir</td>\n","      <td>ブラック</td>\n","      <td>jp</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1815216 rows × 7 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25acc418-7c65-4f46-8ecd-20fb6a846506')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 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\")[0])]"],"metadata":{"id":"ZbXNRBzkx4fz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["get_product_title(\"B08N6NCKRX\")### irrelevant"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"2g6O3lkDzlgG","executionInfo":{"status":"ok","timestamp":1666718718322,"user_tz":-60,"elapsed":6,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9787857f-84b7-44b5-a0cc-57a6bb23f140"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'面部保护 防雾 耐用 5个 透明面罩 健康盾牌 塑料面巾 完全可见 透气 可重复使用 成人 口罩 方便 打招呼 防护 (Clear)'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":64}]},{"cell_type":"code","source":[],"metadata":{"id":"7VUizQVZzlm2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Dataset Generation"],"metadata":{"id":"LVQaVIEHVJ4W"}},{"cell_type":"code","source":["header = ['query', 'product', 'label']\n","\n","with open('/content/drive/MyDrive/datasets/kdd_cup/csv_file_9.csv', 'w', encoding='UTF8') as f:\n","  \n","  # create the csv writer\n","  writer = csv.writer(f)\n","\n","  # write the header\n","  writer.writerow(header)\n","\n","  for i in range(781000,781636):\n","    \n","    data=[df_train[i:i+1]['query'][i],get_product_title(df_train[i:i+1]['product_id'][i]),df_train[i:i+1]['esci_label'][i]]\n","    # write the data\n","    writer.writerow(data)\n","    "],"metadata":{"id":"ST2lnPhgLj-L"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["filepath=\"/content/drive/MyDrive/datasets/kdd_cup/csv_file_9.csv\"\n","train_data=pd.read_csv(filepath)"],"metadata":{"id":"xOX5gW2lWZYm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1481},"id":"99TbLjQZLkAr","executionInfo":{"status":"ok","timestamp":1666721746195,"user_tz":-60,"elapsed":471,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1363a0a1-8649-4e34-e88a-d52af0fa1f88"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["               query                                            product  \\\n","0    ベビーマグ ちゃん 効果 なし                 ピジョン Pigeon マグマグ スパウト 200ml 5ヵ月頃から   \n","1    ベビーマグ ちゃん 効果 なし                       コンビ テテオ teteo マグベビー バルーン Neo   \n","2    ベビーマグ ちゃん 効果 なし  コンビ テテオ teteo パーティマグ 離乳ナビセット Neo マルチカラー 1個アソート...   \n","3    ベビーマグ ちゃん 効果 なし                      レック アンパンマン ワンタッチ ストローマグ 300ml   \n","4    ベビーマグ ちゃん 効果 なし   ピジョン Pigeon マグマグ セット 3ヵ月頃から 発達にあわせた4種類の飲み方トレーニング   \n","..               ...                                                ...   \n","631         肩なしブラジャー  スポーツブラ Lalaly【2枚組】 スポーツブラ 速乾 伸張 パット付き 脇高 はみ肉スッ...   \n","632         肩なしブラジャー  2枚組スポーツブラ T-wilker 通気性 揺れない パッド入り ノンワイヤーブラ ナイト...   \n","633         肩なしブラジャー  （AQUIVER）肩ひも無し ブラ ストラップレス ブラジャー 肩ひもなし 背中見せ インナ...   \n","634         肩なしブラジャー  ずれにくい 6色6サイズ展開 レディース チューブトップ ブラ ブラジャー ナイトブラ ベア...   \n","635         肩なしブラジャー  [ウイング/ワコール] ノンワイヤーブラジャー 脇高パッドでシルエットをキープ 【フィットト...   \n","\n","          label  \n","0         exact  \n","1         exact  \n","2         exact  \n","3    irrelevant  \n","4    substitute  \n","..          ...  \n","631  substitute  \n","632       exact  \n","633       exact  \n","634       exact  \n","635  substitute  \n","\n","[636 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-57f28b4f-3a3a-40cb-8682-070a3a26c4f1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>query</th>\n","      <th>product</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ベビーマグ ちゃん 効果 なし</td>\n","      <td>ピジョン Pigeon マグマグ スパウト 200ml 5ヵ月頃から</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ベビーマグ ちゃん 効果 なし</td>\n","      <td>コンビ テテオ teteo マグベビー バルーン Neo</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ベビーマグ ちゃん 効果 なし</td>\n","      <td>コンビ テテオ teteo パーティマグ 離乳ナビセット Neo マルチカラー 1個アソート...</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ベビーマグ ちゃん 効果 なし</td>\n","      <td>レック アンパンマン ワンタッチ ストローマグ 300ml</td>\n","      <td>irrelevant</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>ベビーマグ ちゃん 効果 なし</td>\n","      <td>ピジョン Pigeon マグマグ セット 3ヵ月頃から 発達にあわせた4種類の飲み方トレーニング</td>\n","      <td>substitute</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>631</th>\n","      <td>肩なしブラジャー</td>\n","      <td>スポーツブラ Lalaly【2枚組】 スポーツブラ 速乾 伸張 パット付き 脇高 はみ肉スッ...</td>\n","      <td>substitute</td>\n","    </tr>\n","    <tr>\n","      <th>632</th>\n","      <td>肩なしブラジャー</td>\n","      <td>2枚組スポーツブラ T-wilker 通気性 揺れない パッド入り ノンワイヤーブラ ナイト...</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>633</th>\n","      <td>肩なしブラジャー</td>\n","      <td>（AQUIVER）肩ひも無し ブラ ストラップレス ブラジャー 肩ひもなし 背中見せ インナ...</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>634</th>\n","      <td>肩なしブラジャー</td>\n","      <td>ずれにくい 6色6サイズ展開 レディース チューブトップ ブラ ブラジャー ナイトブラ ベア...</td>\n","      <td>exact</td>\n","    </tr>\n","    <tr>\n","      <th>635</th>\n","      <td>肩なしブラジャー</td>\n","      <td>[ウイング/ワコール] ノンワイヤーブラジャー 脇高パッドでシルエットをキープ 【フィットト...</td>\n","      <td>substitute</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>636 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57f28b4f-3a3a-40cb-8682-070a3a26c4f1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 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tables/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ccb197e162fd4b969b132ac985527a22"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-040c829cfb31fc59/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0af335553d2c42349e4641a0018dfe7a"}},"metadata":{}}],"source":["dataset = load_dataset('csv', data_files=filepaths)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1666784649870,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"j9O46mgZ7wCv","outputId":"c8e2e183-d05d-43ff-a8db-297f47556144"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['query', 'product', 'label'],\n","        num_rows: 731636\n","    })\n","})"]},"metadata":{},"execution_count":6}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1666784649870,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"oo8deXsxdyBZ","outputId":"67cf5d8d-8ff2-49b1-eefb-b9204e6b33d2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'query': '# 2 pencils not sharpened',\n"," 'product': 'Ticonderoga Beginner Pencils, Wood-Cased #2 HB Soft, With Eraser, Yellow, 12-Pack (13308)',\n"," 'label': 'exact'}"]},"metadata":{},"execution_count":7}],"source":["dataset['train'][0]"]},{"cell_type":"code","source":["def get_label(label):\n","  if label=='exact':\n","    return 1.0\n","  elif label=='substitute':\n","    return 0.7\n","  elif label=='complement':\n","    return 0.5\n","  else:\n","    return 0.0"],"metadata":{"id":"BNcP-EFNZhlm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model_id=\"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\"\n","tokenizer = AutoTokenizer.from_pretrained(model_id)"],"metadata":{"id":"zOMxWvGLZhoM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def preprocess(dataset):\n","  if dataset['product']==None:\n","    dataset['product']=dataset['query']\n","\n","  dataset['input_ids_query']=[]\n","  dataset['token_type_ids_query']=[]\n","  dataset['attention_mask_query']=[]\n","\n","  dataset['input_ids_product']=[]\n","  dataset['token_type_ids_product']=[]\n","  dataset['attention_mask_product']=[]\n","  \n","  tokenized_output_query=tokenizer(dataset['query'],max_length=MAX_LENGTH,padding='max_length',truncation=True)\n","  tokenized_output_product=tokenizer(dataset['product'],max_length=MAX_LENGTH,padding='max_length',truncation=True)\n","  \n","  dataset['input_ids_query'].append(tokenized_output_query['input_ids'])\n","  dataset['token_type_ids_query'].append(tokenized_output_query['token_type_ids'])\n","  dataset['attention_mask_query'].append(tokenized_output_query['attention_mask'])\n","  \n","  dataset['input_ids_product'].append(tokenized_output_product['input_ids'])\n","  dataset['token_type_ids_product'].append(tokenized_output_product['token_type_ids'])\n","  dataset['attention_mask_product'].append(tokenized_output_product['attention_mask'])\n","\n","  dataset['label']=get_label(dataset['label'])\n","  \n","  return dataset"],"metadata":{"id":"wWpNeVJta8na"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["prep_dataset=dataset.map(preprocess)"],"metadata":{"id":"mJx4-Ln9a8q9","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["4d0c4ed8eac54185b5188b421126f2c4","ea41ad65203a4c75ad4ce3410d3abcc7","001f6a8fa56846628ce364e6c18f724b","4942db5c2d26408fa12aa884f76f04b8","b9957450854e408ab6c2c0ec41dca942","fefe375e7e0d4e90a3f3dc5e2cfd6cf7","904bd5a359064db5ad790932f1c79791","78b4b45d7273428c9a48ed1676fe8263","ba10cdcacab24304a1dbe3fc01868e89","06462707efcb4e6589b0e8b3e9241d3f","4aa639a3c9ff4743a8ab11b0d9d6c11c"]},"executionInfo":{"status":"ok","timestamp":1666785024060,"user_tz":-60,"elapsed":369155,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"67202c69-bb19-4815-97bd-01e570c9053f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/731636 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4d0c4ed8eac54185b5188b421126f2c4"}},"metadata":{}}]},{"cell_type":"code","source":["prep_dataset['train'][21:22]"],"metadata":{"id":"AhVLYQ3xa8tJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1666785024061,"user_tz":-60,"elapsed":9,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d269194f-6d63-4805-e99a-dcf6b511ff74"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'query': ['# 2 pencils not sharpened'],\n"," 'product': ['BIC Evolution Cased Pencil, #2 Lead, Gray Barrel, 24-Count (PGEBP241-BLK)'],\n"," 'label': [0.7],\n"," 'input_ids_query': [[[101,\n","    1001,\n","    1016,\n","    14745,\n","    2015,\n","    2025,\n","    26694,\n","    102,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    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[[[101,\n","    12170,\n","    2278,\n","    6622,\n","    2553,\n","    2094,\n","    14745,\n","    1010,\n","    1001,\n","    1016,\n","    2599,\n","    1010,\n","    3897,\n","    8460,\n","    1010,\n","    2484,\n","    1011,\n","    4175,\n","    1006,\n","    18720,\n","    15878,\n","    2361,\n","    18827,\n","    2487,\n","    1011,\n","    1038,\n","    13687,\n","    1007,\n","    102,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0]]],\n"," 'token_type_ids_product': [[[0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0]]],\n"," 'attention_mask_product': [[[1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    1,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    0,\n","    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folefac","userId":"15114498789158493667"}},"outputId":"d384be38-ff1d-4758-f168-9cb1e271caa4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec={'label': TensorSpec(shape=(None,), dtype=tf.float32, name=None), 'input_ids_query': TensorSpec(shape=(None, 1, 64), dtype=tf.int64, name=None), 'token_type_ids_query': TensorSpec(shape=(None, 1, 64), dtype=tf.int64, name=None), 'attention_mask_query': TensorSpec(shape=(None, 1, 64), dtype=tf.int64, name=None), 'input_ids_product': TensorSpec(shape=(None, 1, 64), dtype=tf.int64, name=None), 'token_type_ids_product': TensorSpec(shape=(None, 1, 64), dtype=tf.int64, name=None), 'attention_mask_product': TensorSpec(shape=(None, 1, 64), dtype=tf.int64, name=None)}>"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["for i in tf_dataset.take(1):\n","  print(i)"],"metadata":{"id":"ZYCAPfsOa84L","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1666785028688,"user_tz":-60,"elapsed":990,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"794a774b-2b77-443a-9067-d207b3ba6673"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'label': <tf.Tensor: shape=(128,), dtype=float32, numpy=\n","array([1. , 1. , 1. , 1. , 1. , 0. , 0.7, 1. , 0. , 0.5, 1. , 0.7, 0.7,\n","       0.7, 1. , 0. , 1. , 1. , 0.7, 0.7, 0.7, 0.7, 1. , 0. , 1. , 0.7,\n","       1. , 0.7, 0.7, 0.7, 1. , 1. , 1. , 0.7, 0.7, 0.7, 0.7, 0. , 0.7,\n","       0.5, 0. , 0.7, 1. , 0.7, 0.7, 1. , 0.7, 1. , 0. , 1. , 0.5, 1. ,\n","       0.7, 1. , 0. , 1. , 0.7, 0.7, 1. , 0. , 1. , 1. , 0.5, 1. , 0.7,\n","       0. , 1. , 1. , 1. , 1. , 0.5, 0.7, 1. , 0.7, 1. , 1. , 0.7, 1. ,\n","       0.7, 0.7, 0.7, 1. , 1. , 1. , 1. , 0.7, 1. , 1. , 0.7, 0.7, 0. ,\n","       0.7, 1. , 0.5, 0.7, 0.7, 1. , 0. , 1. , 1. , 1. , 1. , 1. , 0. ,\n","       0.7, 1. , 1. , 1. , 0.7, 1. , 1. , 1. , 1. , 1. , 0.7, 1. , 0. ,\n","       1. , 0. , 1. , 1. , 0.7, 0.7, 0.7, 1. , 1. , 1. , 0.7],\n","      dtype=float32)>, 'input_ids_query': <tf.Tensor: shape=(128, 1, 64), dtype=int64, numpy=\n","array([[[  101,  4942,  4313, ...,     0,     0,     0]],\n","\n","       [[  101,  1713, 30260, ...,     0,     0,     0]],\n","\n","       [[  101,   100,   100, ...,     0,     0,     0]],\n","\n","       ...,\n","\n","       [[  101, 11163,  8873, ...,     0,     0,     0]],\n","\n","       [[  101,  3019, 25742, ...,     0,     0,     0]],\n","\n","       [[  101,  2214,  3212, ...,     0,     0,     0]]])>, 'token_type_ids_query': <tf.Tensor: shape=(128, 1, 64), dtype=int64, numpy=\n","array([[[0, 0, 0, ..., 0, 0, 0]],\n","\n","       [[0, 0, 0, ..., 0, 0, 0]],\n","\n","       [[0, 0, 0, ..., 0, 0, 0]],\n","\n","       ...,\n","\n","       [[0, 0, 0, ..., 0, 0, 0]],\n","\n","       [[0, 0, 0, ..., 0, 0, 0]],\n","\n","       [[0, 0, 0, ..., 0, 0, 0]]])>, 'attention_mask_query': <tf.Tensor: shape=(128, 1, 64), dtype=int64, numpy=\n","array([[[1, 1, 1, ..., 0, 0, 0]],\n","\n","       [[1, 1, 1, ..., 0, 0, 0]],\n","\n","       [[1, 1, 1, ..., 0, 0, 0]],\n","\n","       ...,\n","\n","       [[1, 1, 1, ..., 0, 0, 0]],\n","\n","       [[1, 1, 1, ..., 0, 0, 0]],\n","\n","       [[1, 1, 1, ..., 0, 0, 0]]])>, 'input_ids_product': <tf.Tensor: shape=(128, 1, 64), dtype=int64, numpy=\n","array([[[  101,  4654, 25808, ...,     0,     0,     0]],\n","\n","       [[  101,   100,  3590, ...,     0,     0,     0]],\n","\n","       [[  101,   100,   100, ...,   102,     0,     0]],\n","\n","       ...,\n","\n","       [[  101,  2004, 11638, ...,     0,     0,     0]],\n","\n","       [[  101,  8292,  3669, ...,     0,     0,     0]],\n","\n","       [[  101,  9733,  6827, ...,     0,     0,     0]]])>, 'token_type_ids_product': <tf.Tensor: shape=(128, 1, 64), dtype=int64, numpy=\n","array([[[0, 0, 0, ..., 0, 0, 0]],\n","\n","       [[0, 0, 0, ..., 0, 0, 0]],\n","\n","       [[0, 0, 0, ..., 0, 0, 0]],\n","\n","       ...,\n","\n","       [[0, 0, 0, ..., 0, 0, 0]],\n","\n","       [[0, 0, 0, ..., 0, 0, 0]],\n","\n","       [[0, 0, 0, ..., 0, 0, 0]]])>, 'attention_mask_product': <tf.Tensor: shape=(128, 1, 64), dtype=int64, numpy=\n","array([[[1, 1, 1, ..., 0, 0, 0]],\n","\n","       [[1, 1, 1, ..., 0, 0, 0]],\n","\n","       [[1, 1, 1, ..., 1, 0, 0]],\n","\n","       ...,\n","\n","       [[1, 1, 1, ..., 0, 0, 0]],\n","\n","       [[1, 1, 1, ..., 0, 0, 0]],\n","\n","       [[1, 1, 1, ..., 0, 0, 0]]])>}\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"e-A8yipua89C"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"g1VJmjYjkCFr"}},{"cell_type":"code","source":[],"metadata":{"id":"F50N4e6sWM-l"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model = TFAutoModel.from_pretrained(model_id)\n","model.summary()"],"metadata":{"id":"Lc8U3V04a8_L","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1666949331925,"user_tz":-60,"elapsed":2304,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"97e70943-92ce-4beb-8557-af0208e3701d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFBertModel.\n","\n","All the layers of TFBertModel were initialized from the model checkpoint at sentence-transformers/multi-qa-MiniLM-L6-cos-v1.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"]},{"output_type":"stream","name":"stdout","text":["Model: \"tf_bert_model_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," bert (TFBertMainLayer)      multiple                  22713216  \n","                                                                 \n","=================================================================\n","Total params: 22,713,216\n","Trainable params: 22,713,216\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["class SentenceTransformer(tf.keras.Model):\n","  def __init__(self,model):\n","    super(SentenceTransformer,self).__init__()\n","    self.model=model\n","    self.dense=Dense(1,activation='sigmoid')\n","\n","  def compile(self,optimizer,loss_fn):\n","    super(SentenceTransformer,self).compile()\n","    self.optimizer=optimizer\n","    self.loss_fn=loss_fn\n","    self.loss_metric=tf.keras.metrics.Mean(name='loss')\n","\n","  @property\n","  def metrics(self):\n","    return [self.loss_metric]\n","  \n","  def mean_pooling(self, model_output, attention_mask):\n","    token_embeddings = model_output[0]\n","    \n","    input_mask_expanded = tf.cast(\n","        tf.broadcast_to(tf.expand_dims(attention_mask, -1), tf.shape(token_embeddings)),\n","        tf.float32\n","    )\n","    return tf.math.reduce_sum(token_embeddings * input_mask_expanded, axis=1)/tf.clip_by_value(tf.math.reduce_sum(input_mask_expanded, axis=1), 1e-9, tf.float32.max)\n","  \n","  def train_step(self,train_data):\n","    \n","    query={'input_ids':train_data['input_ids_query'][:,0,:],\n","           'token_type_ids':train_data['token_type_ids_query'][:,0,:],\n","           'attention_mask':train_data['attention_mask_query'][:,0,:]}\n","\n","    product={'input_ids':train_data['input_ids_product'][:,0,:],\n","             'token_type_ids':train_data['token_type_ids_product'][:,0,:],\n","             'attention_mask':train_data['attention_mask_product'][:,0,:]}\n","\n","    labels=train_data['label']\n","\n","    with tf.GradientTape() as recorder:\n","      query_predictions=self.model(query)\n","      pred_query=self.mean_pooling(query_predictions,train_data['attention_mask_query'][:,0,:])\n","      \n","      product_predictions=self.model(product)\n","      pred_product=self.mean_pooling(product_predictions,train_data['attention_mask_product'][:,0,:])\n","      \n","      pred_concat=tf.concat([pred_query,pred_product,tf.abs(pred_query-pred_product)],axis=-1)\n","      \n","      predictions=self.dense(pred_concat)\n","      loss=self.loss_fn(labels,predictions)\n","\n","      \n","    partial_derivatives = recorder.gradient(loss,self.model.trainable_weights)\n","    self.optimizer.apply_gradients(zip(partial_derivatives, self.model.trainable_weights))\n","\n","\n","    self.loss_metric.update_state(loss)\n","    \n","    return {'loss':self.loss_metric.result(),}"],"metadata":{"id":"GcWYm-Kja9Dr"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Model Training"],"metadata":{"id":"DLgQ-j6kkc5J"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"yHLddxbd1w_7"},"outputs":[],"source":["stransformer=SentenceTransformer(model)\n","stransformer.compile(\n","    optimizer=tf.keras.optimizers.Adam(learning_rate=2e-5,),\n","    loss_fn=tf.keras.losses.BinaryCrossentropy(),\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"r9_azxQo1xBu","outputId":"443c71b7-debd-45bd-8999-ef8f94b31703","executionInfo":{"status":"error","timestamp":1666805969184,"user_tz":-60,"elapsed":860591,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n","WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model/bert/pooler/dense/kernel:0', 'tf_bert_model/bert/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n"]},{"output_type":"stream","name":"stdout","text":["1574/5716 [=======>......................] - ETA: 34:49 - loss: 0.4792"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-69-3909a2cd7f8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mEPOCHS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1412\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1413\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1414\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1415\u001b[0m               \u001b[0;32mif\u001b[0m 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\u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    605\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 607\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 916\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 916\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    599\u001b[0m     \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m       \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m     \u001b[0;31m# Strings, ragged and sparse tensors don't have .item(). Return them as-is.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1157\u001b[0m     \"\"\"\n\u001b[1;32m   1158\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1123\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1125\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1126\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["EPOCHS=5\n","history=stransformer.fit(tf_dataset,epochs=EPOCHS,)"]},{"cell_type":"code","source":["model_path='/content/drive/MyDrive/stransformer/stransformers.h5'"],"metadata":{"id":"Pq2ZKsl3gnyh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#stransformer.model.save_weights(model_path)"],"metadata":{"id":"4sLN9Z8ikmPm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.load_weights(model_path)"],"metadata":{"id":"sN6OeQGxgkQD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def mean_pooling(model_output, attention_mask):\n","  token_embeddings = model_output[0]\n","  \n","  input_mask_expanded = tf.cast(\n","      tf.broadcast_to(tf.expand_dims(attention_mask, -1), tf.shape(token_embeddings)),\n","      tf.float32\n","  )\n","  return tf.math.reduce_sum(token_embeddings * input_mask_expanded, axis=1)/tf.clip_by_value(tf.math.reduce_sum(input_mask_expanded, axis=1), 1e-9, tf.float32.max)"],"metadata":{"id":"KiZs5mO-V5oa"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Testing"],"metadata":{"id":"o8bzhpIeyYBj"}},{"cell_type":"markdown","source":["## Create Embeddings"],"metadata":{"id":"nfuNasWYvHgD"}},{"cell_type":"code","source":["filepath_catalogue='/content/drive/MyDrive/datasets/kdd_cup/product_catalogue-v0.3.csv'\n","df_catalogue=pd.read_csv(filepath_catalogue)"],"metadata":{"id":"4DfIVhWJkmR3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_catalogue['product_title'][1000000]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"dmnaGgIgjCCC","executionInfo":{"status":"ok","timestamp":1666854627030,"user_tz":-60,"elapsed":9,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9be5e3d0-a0c8-4ffd-afa3-df37927baf7a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'【国内正規品】PeakDesign ピークデザイン シェルS SH-S-1'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["product_titles=[str(df_catalogue['product_title'][i]) for i in range(len(df_catalogue))]\n","#print(product_titles)"],"metadata":{"id":"9hZZgysZYZ9G"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(len(product_titles))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"az23EZGaYZ_q","executionInfo":{"status":"ok","timestamp":1666854636811,"user_tz":-60,"elapsed":8,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"16013f76-a36b-405f-edc8-1999f86b998d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1815216\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"P3MKdncGdX5g"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["embeddings=[]"],"metadata":{"id":"-PjwSDTVbYV9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["INFERENCE_BATCH_SIZE=640\n","len(product_titles)//INFERENCE_BATCH_SIZE"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rqbYMz26h024","executionInfo":{"status":"ok","timestamp":1666854636812,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bd426b91-5fcb-4c63-99c7-a4ebe2f4f2bb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2836"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["for i in range(len(product_titles)//INFERENCE_BATCH_SIZE):\n","  tokenized_output=tokenizer(\n","      product_titles[INFERENCE_BATCH_SIZE*i:INFERENCE_BATCH_SIZE*(i+1)],max_length=MAX_LENGTH,padding='max_length',truncation=True,return_tensors=\"tf\")\n","  model_output=model(tokenized_output)\n","  embedding=mean_pooling(model_output,tokenized_output['attention_mask'])\n","  embeddings.append(embedding)\n","  if i%100==0:\n","    print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TmMmLRiHYaGF","executionInfo":{"status":"ok","timestamp":1666856022511,"user_tz":-60,"elapsed":1366644,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5fc0f66e-5394-43d7-804f-4e73c65ecc0c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0\n","100\n","200\n","300\n","400\n","500\n","600\n","700\n","800\n","900\n","1000\n","1100\n","1200\n","1300\n","1400\n","1500\n","1600\n","1700\n","1800\n","1900\n","2000\n","2100\n","2200\n","2300\n","2400\n","2500\n","2600\n","2700\n","2800\n"]}]},{"cell_type":"code","source":["embeddings"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"N0X_C06wbEM2","executionInfo":{"status":"ok","timestamp":1666808931229,"user_tz":-60,"elapsed":480,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"eb43f0b9-9e52-435c-c26f-012e61982ae8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.04175833,  0.4398206 ,  0.15303786, ..., -0.18008585,\n","         -0.71677995,  0.2654784 ],\n","        [-0.05360471, -0.13363229,  0.06446703, ..., -0.04043194,\n","         -0.5303095 ,  0.6396754 ],\n","        [-0.07222205,  0.568274  ,  0.04923436, ..., -0.3087162 ,\n","         -0.8591781 ,  0.75944847],\n","        ...,\n","        [ 0.2783488 ,  0.7277557 , -0.19541022, ...,  0.25403762,\n","         -0.77744997,  0.88280714],\n","        [-0.11515236,  0.44915688, -0.32512447, ...,  0.15528241,\n","         -0.28903574, -0.01429977],\n","        [-0.71291393, -0.30155414,  0.60434943, ..., -0.52819324,\n","         -0.06169818, -0.42003578]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[-0.25033033, -0.36277884,  0.04568261, ..., -0.61685425,\n","         -0.22854248,  0.0749432 ],\n","        [ 0.31579307,  0.3078013 , -0.15302154, ..., -0.0205166 ,\n","         -0.24058373, -0.1389403 ],\n","        [ 0.34076113,  0.09088283, -0.09920587, ...,  0.3154116 ,\n","         -0.71782845,  0.14604795],\n","        ...,\n","        [ 0.34244695,  0.5600365 , -0.25037694, ...,  0.29202062,\n","         -0.78201824,  1.0003353 ],\n","        [ 0.48839852,  0.58567506, -0.23761684, ...,  0.39142865,\n","         -0.55380523,  0.93402475],\n","        [-0.12706415,  0.04007166,  0.32039976, ..., -0.16968857,\n","         -0.5625253 , -0.40995255]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.14748684,  0.19580686,  0.07518169, ...,  0.23299414,\n","         -0.38106498,  0.97672564],\n","        [ 0.10406388, -0.15469515, -0.07299469, ..., -0.09553993,\n","         -0.18333358,  0.48289955],\n","        [-0.48539472, -0.39405754,  0.35476398, ..., -0.5138081 ,\n","          0.3042271 , -0.12476353],\n","        ...,\n","        [ 0.48841166,  0.13147825, -0.26861796, ...,  0.2507595 ,\n","         -0.445062  ,  0.91309756],\n","        [ 0.43607992,  0.14310056, -0.2774595 , ...,  0.3000701 ,\n","         -0.64802307,  0.58720326],\n","        [ 0.5343046 , -0.00881386, -0.38213897, ...,  0.37829387,\n","         -0.7968462 ,  0.6387348 ]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.42024356,  0.04089506, -0.26343155, ...,  0.320241  ,\n","         -0.34386224,  0.21557738],\n","        [ 0.70042986,  0.27202126, -0.12118231, ...,  0.4024516 ,\n","         -0.71601355,  0.9871158 ],\n","        [ 0.20603643,  0.21225566, -0.27889886, ...,  0.32165194,\n","         -0.71019053,  0.9009069 ],\n","        ...,\n","        [ 0.03869297,  0.58761203, -0.24618867, ..., -0.17659199,\n","         -0.24322106,  0.95189375],\n","        [ 0.26066822,  0.09344871, -0.04015832, ...,  0.17433245,\n","         -0.94286335,  0.76141584],\n","        [-0.02870281,  0.15181287,  0.09849725, ...,  0.40340248,\n","         -0.9012172 ,  0.69525766]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[-0.286191  ,  0.16769332,  0.46374464, ..., -0.19424438,\n","  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-1.44257367e-01, -8.31029713e-01,  6.65080309e-01],\n","        [-8.13011527e-02,  3.08245599e-01,  1.17681839e-01, ...,\n","         -2.63439506e-01, -6.20396852e-01,  1.10300615e-01],\n","        [ 3.72155122e-02,  4.24469739e-01,  1.51009262e-02, ...,\n","          4.12593216e-01, -8.19372952e-01,  2.66456664e-01]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.48129874,  0.16016784, -0.5691415 , ...,  0.97178876,\n","         -0.53141856,  0.01047113],\n","        [ 0.24972327,  0.3831345 , -0.25618732, ...,  0.38291633,\n","         -0.63059896,  0.07866368],\n","        [-0.04763549,  0.17098191,  0.28870496, ..., -0.35152435,\n","         -0.49087378, -0.02847356],\n","        ...,\n","        [ 0.6708065 ,  0.64129156, -0.11832799, ...,  0.29898506,\n","         -0.6471307 ,  0.92082196],\n","        [ 0.774798  ,  0.59940034, -0.28722215, ...,  0.6142622 ,\n","         -0.38883618,  0.5825647 ],\n","        [ 0.56372935,  0.97804606, -0.1948443 , ...,  0.40362877,\n","         -0.5435379 ,  0.26442775]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.3629208 ,  0.7922613 , -0.1722442 , ...,  0.6218373 ,\n","         -0.87550604,  0.44041207],\n","        [ 0.11774167,  0.26108506,  0.3247916 , ...,  0.3701537 ,\n","         -0.54987216,  0.6205007 ],\n","        [ 0.37840074,  0.8290572 ,  0.04586449, ...,  0.42665857,\n","         -0.55506986,  0.50999004],\n","        ...,\n","        [ 0.15458529,  0.22799423,  0.23160206, ...,  0.43363166,\n","         -0.50440174,  0.24949257],\n","        [ 0.47988364,  0.14173171,  0.07829133, ...,  0.47739625,\n","         -0.620689  , -0.06834707],\n","        [ 0.16702494,  0.25452346,  0.13978285, ...,  0.5039698 ,\n","         -0.63105357,  0.5272646 ]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.07510505, -0.02630998,  0.10323071, ...,  0.43307918,\n","         -0.52154714,  0.34036046],\n","        [ 0.45862055,  0.15199192,  0.08253706, ...,  0.49847957,\n","         -0.583775  ,  0.12519506],\n","        [ 0.24367774,  0.08308162,  0.11360378, ...,  0.5263312 ,\n","         -0.3815117 ,  0.17766635],\n","        ...,\n","        [ 0.12427872,  0.7422735 , -0.01278383, ...,  0.22140294,\n","         -0.5210907 ,  0.15514582],\n","        [-0.48089072,  0.35506874,  0.28277606, ..., -0.7758032 ,\n","         -0.28193992,  0.927873  ],\n","        [ 0.45754355,  0.11124886, -0.10373747, ..., -0.1052884 ,\n","         -0.33330962,  0.9842299 ]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.05767048,  0.6784576 ,  0.11798032, ...,  0.06327491,\n","          0.02011503,  0.18097539],\n","        [ 0.0458347 ,  0.62441677,  0.12430233, ...,  0.05168312,\n","         -0.5940815 ,  0.19486046],\n","        [-0.37624815,  0.37015012,  0.06212066, ..., -0.44116965,\n","         -0.58932287,  0.13675879],\n","        ...,\n","        [ 0.39463633,  0.6391019 , -0.22980069, ...,  0.23239905,\n","         -0.73994285,  1.2287415 ],\n","        [ 0.34591419,  0.63935167, -0.20617235, ...,  0.23749623,\n","         -0.7434813 ,  1.2184438 ],\n","        [ 0.32645798,  0.46562687, -0.09097634, ..., -0.27737895,\n","         -0.6587117 ,  0.82292676]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.5166099 ,  0.5589155 , -0.12654743, ...,  0.50015205,\n","         -0.9846431 ,  0.91059643],\n","        [ 0.5583706 ,  0.63716316, -0.13690181, ...,  0.46166742,\n","         -0.953922  ,  0.9311324 ],\n","        [ 0.5455674 ,  0.69437295, -0.20920247, ...,  0.25610343,\n","         -1.0855858 ,  0.9145943 ],\n","        ...,\n","        [ 0.24306796,  0.29211944,  0.04529058, ...,  0.48041067,\n","         -0.00887271,  0.20168845],\n","        [ 0.22126931,  0.42204466,  0.13641359, ...,  0.6886028 ,\n","         -0.01845839, -0.3738218 ],\n","        [ 0.19991499,  0.30568367, -0.08364878, ...,  0.34717506,\n","         -0.03802823, -0.44528425]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.00571867,  0.5689832 ,  0.11146595, ...,  0.494468  ,\n","         -0.3811729 , -0.40087178],\n","        [ 0.21047543,  0.8450965 , -0.00878942, ...,  0.76812536,\n","          0.05733347,  0.56220543],\n","        [ 0.32459033,  1.0109932 ,  0.12942563, ...,  0.56765944,\n","          0.01996663,  0.04107491],\n","        ...,\n","        [ 0.0273682 ,  0.3609271 , -0.03880462, ...,  0.5354582 ,\n","         -0.11057856,  0.27993917],\n","        [ 0.1057364 ,  0.3018772 ,  0.14686969, ...,  0.6978316 ,\n","          0.05919636,  0.25341067],\n","        [ 0.12098888,  0.5470804 , -0.04074562, ...,  0.73170286,\n","         -0.1277496 ,  0.23804708]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.00194599,  0.03980017,  0.01924944, ...,  0.57383925,\n","         -0.08425857,  0.20431818],\n","        [ 0.23505557,  0.25117046, -0.10832252, ...,  0.5990385 ,\n","         -0.33925727,  0.0718202 ],\n","        [ 0.14767109,  0.24051541,  0.11039366, ...,  0.43375218,\n","         -0.13995771,  0.36328736],\n","        ...,\n","        [ 0.28035435,  0.51324606,  0.00728543, ...,  0.640002  ,\n","          0.10868333,  0.67270356],\n","        [-0.05035577,  1.0068604 , -0.1497363 , ...,  0.6850552 ,\n","         -0.5648898 ,  1.3428581 ],\n","        [ 0.21748847,  0.8230658 ,  0.0174663 , ...,  0.44952863,\n","         -0.31135297,  1.0329726 ]], dtype=float32)>,\n"," <tf.Tensor: shape=(2560, 384), dtype=float32, numpy=\n"," array([[ 0.33361128,  0.9949328 , -0.21699554, ...,  0.53282094,\n","         -0.4805457 ,  1.1110184 ],\n","        [ 0.14175911,  0.6773977 ,  0.03766262, ...,  0.45526415,\n","         -0.17630497,  0.90425515],\n","        [ 0.26826644,  1.4036442 , -0.1380129 , ...,  0.79485697,\n","         -0.6117353 ,  1.0099295 ],\n","        ...,\n","        [ 0.02534301,  1.061204  , -0.14722796, ...,  0.02857254,\n","         -0.69393456,  0.8082855 ],\n","        [-0.11182072,  0.3292352 ,  0.10625602, ...,  0.4261853 ,\n","         -0.47815022,  0.4932231 ],\n","        [-0.13715905,  0.59341025,  0.23041216, ..., -0.0028236 ,\n","         -0.46424943,  0.4001049 ]], dtype=float32)>]"]},"metadata":{},"execution_count":128}]},{"cell_type":"code","source":["#np.savez_compressed('embeddings.npz', embeddings)\n","#np.savez_compressed('product_titles.npz',product_titles)"],"metadata":{"id":"EwETIRKQbERR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!cp '/content/embeddings.npz' '/content/drive/MyDrive/stransformer/'\n","!cp '/content/product_titles.npz' '/content/drive/MyDrive/stransformer/'"],"metadata":{"id":"643hCYcJYaIi"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Use Embeddings"],"metadata":{"id":"AUwuU3gLvL6p"}},{"cell_type":"code","source":["loaded_embedding=np.load('/content/drive/MyDrive/stransformer/embeddings.npz')\n","loaded_embedding_array=np.array(loaded_embedding['arr_0'])"],"metadata":{"id":"tgFI0P4KYaKn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["loaded_titles=np.load('/content/drive/MyDrive/stransformer/product_titles.npz')\n","loaded_titles_array=np.array(loaded_titles['arr_0'])"],"metadata":{"id":"Gn09aOMc4Mfn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["loaded_embedding_array.shape"],"metadata":{"id":"WSPWbmv9YaNe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1666949490031,"user_tz":-60,"elapsed":17,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bd3c08e9-2adb-4c59-af09-f04ef962aa5d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(2836, 640, 384)"]},"metadata":{},"execution_count":91}]},{"cell_type":"code","source":["loaded_embedding_array=loaded_embedding_array.reshape(-1,loaded_embedding_array.shape[2])\n","print(loaded_embedding_array.shape)"],"metadata":{"id":"z9MhfqLmYaPv","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1666935461259,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"197bfe8a-cc83-45a2-8da8-69707489b859"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1815040, 384)\n"]}]},{"cell_type":"code","source":["\n","tokenizer = AutoTokenizer.from_pretrained(model_id)"],"metadata":{"id":"Iv-Lia6yYmkt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["inputs = tokenizer([\"針なしほっちきす\"],max_length=MAX_LENGTH,padding='max_length',truncation=True,return_tensors=\"tf\")\n","\n","logits = model(**inputs)\n","out_embedding=mean_pooling(logits,inputs['attention_mask'])\n","print(out_embedding.shape)"],"metadata":{"id":"WybnyIrbkmUM","executionInfo":{"status":"ok","timestamp":1666938213612,"user_tz":-60,"elapsed":538,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"6058f748-e97a-4aa8-9dc3-f1e998a55635"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 384)\n"]}]},{"cell_type":"code","source":["u_dot_v=np.matmul(loaded_embedding_array,(np.array(out_embedding).T))\n","print(u_dot_v.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Qa_j2X4uGJ5U","executionInfo":{"status":"ok","timestamp":1666938213612,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6817775b-6467-4b8f-fd3d-a36587c329e9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1815040, 1)\n"]}]},{"cell_type":"code","source":["u_magnitude=np.sqrt(np.sum(loaded_embedding_array*loaded_embedding_array,axis=-1))\n","print(u_magnitude.shape)\n","print(u_magnitude)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NLmXRkR_GJ7_","executionInfo":{"status":"ok","timestamp":1666938214788,"user_tz":-60,"elapsed":1185,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f3543847-b05c-4797-a1b0-c3899ef90f6a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1815040,)\n","[11.215763   9.297123  10.870735  ...  5.05177    7.0463676  4.7366333]\n"]}]},{"cell_type":"code","source":["v_magnitude=np.sqrt(np.sum(out_embedding*out_embedding,axis=-1))\n","print(v_magnitude.shape)\n","print(v_magnitude)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4Ke-taACGJ-b","executionInfo":{"status":"ok","timestamp":1666938214788,"user_tz":-60,"elapsed":10,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0afe68fa-011e-45e3-dd3e-85e802f8785e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1,)\n","[6.5474906]\n"]}]},{"cell_type":"code","source":["cosine_similarity=u_dot_v.T/(u_magnitude*v_magnitude)\n","print(cosine_similarity)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q6Btr8vjGKBI","executionInfo":{"status":"ok","timestamp":1666938214789,"user_tz":-60,"elapsed":6,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5f649baa-0dd6-435b-a4c1-a81951199df8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[0.24800435 0.33141387 0.11507133 ... 0.12804486 0.35886842 0.08012675]]\n"]}]},{"cell_type":"code","source":["sorted_indices=np.argsort(cosine_similarity,axis=-1)\n","print(sorted_indices)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"faynJ8a7GKDe","executionInfo":{"status":"ok","timestamp":1666938215300,"user_tz":-60,"elapsed":515,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"59b77df1-0311-4e5a-cc5b-49b731580417"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[  83865 1386827  779273 ... 1811568 1139688 1803600]]\n"]}]},{"cell_type":"code","source":["for i in range(25):\n","  print(i,loaded_titles_array[sorted_indices[:,len(sorted_indices[0])-i-1]])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rMshUKwiKUy0","executionInfo":{"status":"ok","timestamp":1666938215300,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"474498e7-4fc8-4847-b86d-aa2c40e3b115"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0 ['知りすぎたのね']\n","1 ['リバウンドしない収納']\n","2 ['ヴェノム (吹替版)']\n","3 ['ヴェノム (字幕版)']\n","4 ['メルちゃん おせわパーツ おしょくじ&はみがきセット']\n","5 ['スウィングしなけりゃ意味ないね']\n","6 ['スウィングしなけりゃ意味ないね']\n","7 ['スウィングしなけりゃ意味ないね']\n","8 ['スウィングしなけりゃ意味ないね']\n","9 ['スイングしなけりゃ意味ないね']\n","10 ['スウィングしなけりゃ意味ないね']\n","11 ['スイングしなけりゃ意味ないね']\n","12 ['スウィングしなけりゃ意味ないね']\n","13 ['スウィングしなけりゃ意味ないね']\n","14 ['スイングしなけりゃ意味ないね']\n","15 ['スウィングしなけりゃ意味ないね']\n","16 ['すみっコぐらしの英会話']\n","17 ['映画あたしンち']\n","18 ['クラリネットピース スウィングしなけゃ意味ないね']\n","19 ['すみっコぐらしのお弁当']\n","20 ['嫁入り道具の花ふきん']\n","21 ['ビジュアルヌード・ポーズＢＯＯＫ ａｃｔ松岡ちな']\n","22 ['Best of Ramin Djawadi']\n","23 ['CHARMINER 0045']\n","24 ['キズナアイ 1/7 完成品フィギュア']\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"ipeY2AFZKU1W"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"sl6JAXp_KU3w"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"ObgcYw87KU57"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"wfESJM7IGKFu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"OVmI9EVKGKIa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"hcBvfbLIGKLU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"aVoZagmWvT9d"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Qp4St55AvT_s"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"DCOcbMo0vUCX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"uQAXVW7AvUFR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"7HULCj4WZllJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["small_dataset = dataset.filter(lambda example: example[\"query\"].startswith(\"A\"))\n","print(len(small_dataset))"],"metadata":{"id":"Lb4K-W1pZlnB"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"collapsed_sections":["aSaOBWrxwzaE","DczeG3N5x8hL","ogDlZqTX0Cf-","LVQaVIEHVJ4W"],"provenance":[{"file_id":"1GMjrkSOp0HTFq1epCQlmDf3UaT01Cu5e","timestamp":1673807506599}],"machine_shape":"hm","toc_visible":true},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"widgets":{"application/vnd.jupyter.widget-state+json":{"17e4888ada794ca58efe8139c1915bde":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_e86f14c6ca0540c3b43bbf286c082fa5","IPY_MODEL_24dd3128d5dd42588c2818f545a1729f","IPY_MODEL_b6e5c71a5cdb46c58d61cb9075b538cd"],"layout":"IPY_MODEL_a14327416e534c2c82590d3bf9f53a56"}},"e86f14c6ca0540c3b43bbf286c082fa5":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_c7ef2649314844b996706998f548b8a2","placeholder":"​","style":"IPY_MODEL_290b049faa1d47fe94918894589df21f","value":"Downloading 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pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.5)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n","Installing collected packages: urllib3, xxhash, tokenizers, responses, multiprocess, huggingface-hub, transformers, datasets\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","Successfully installed datasets-2.6.1 huggingface-hub-0.10.1 multiprocess-0.70.13 responses-0.18.0 tokenizers-0.13.1 transformers-4.23.1 urllib3-1.25.11 xxhash-3.1.0\n"]}]},{"cell_type":"code","source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer,BatchNormalization,Dropout,Input,LayerNormalization\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import GPT2TokenizerFast,create_optimizer,DataCollatorForLanguageModeling,TFGPT2LMHeadModel"],"metadata":{"id":"CsBv9AXgSBGw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["MAX_LENGTH=256\n","BATCH_SIZE=6"],"metadata":{"id":"mr0854twP7m4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Dataset Preparation"],"metadata":{"id":"b7Uh7in0BVId"}},{"cell_type":"code","source":["!pip install -q kaggle\n","!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d juicobowley/drake-lyrics\n","!unzip \"/content/drake-lyrics.zip\" -d \"/content/dataset/\""],"metadata":{"id":"wIxD1BEwB5cX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667147909972,"user_tz":-60,"elapsed":5043,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"22b78371-b881-4189-f8d5-76f5325c08f8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading drake-lyrics.zip to /content\n","\r  0% 0.00/764k [00:00<?, ?B/s]\n","\r100% 764k/764k [00:00<00:00, 123MB/s]\n","Archive:  /content/drake-lyrics.zip\n","  inflating: /content/dataset/drake_data.csv  \n","  inflating: /content/dataset/drake_data.json  \n","  inflating: /content/dataset/drake_lyrics.txt  \n"]}]},{"cell_type":"code","source":["filepath=\"/content/dataset/drake_data.csv\"\n","dataset = load_dataset('csv', data_files=filepath)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":185,"referenced_widgets":["2e608bce27f84894988ef9f2b9cdd9cd","273907826c984fd09b28e881b984c27e","8aa19df945214b399d3c2a6a268570cc","bd939b4b819a4421a40b0766bd156272","afb8384b04354509b31564584ea05487","ac922b5fba27444eaf006ab669947bff","055453351acb4790bfd68a9092b825e8","1f9b8f1b2cec4b2491d30c5dbf147bdb","61b14bd5f42a40c8900f7f1b85466854","ef62c501f4864077b618cee549ccd5e4","773bb8e9af504eb89a4f292fd7e4ba38","ce3514c59ea64c9887fcac226885cbc3","33d0ab091de642f19ccbc59a83dcbe2a","e5d33b43d70a496c882ac5e5889d4b7e","0b841960843945818b9a9ce81b562858","b4c4257dec3646ca87c9856f74c09d16","ccd70f95914a41e696409768bedc474c","3f6f401390db4f9081921dc3ef8bec88","a6f77726f59b43e2987f74d90eaa4d71","d0259634cea644a2bcd02118717e8bef","552f95ee86c3453b8ec44b5a3db0ff6b","34b7393a5a1a45b49b6cfbc71ba696a1","8f99c4d99388448ea0e27aef3e357a27","ea96b6c089bb4ace9032dd3708c7fe13","7e9a18654e9643599eedecdba6d581d4","b5dd2de13d4f4001b8279934e992472c","a8abc616f9464291a5cec4075d56e451","e6a746b52c28416eb64fb6c77c9e3fbd","22237e36419f4a18a23dcd771fa359c6","cba159f606a843fa9b1fe939635b9b3a","c20ef04ad075438c96203d0a11458442","6f2e4385dc544233ad1a2c6fae45b91e","7542301817894f4e8fc0f2fbac01c052","71423b55e7b846c48aba2b7d5a726b50","1061ff298e314a85864d8d34b0e5aa51","07f310fcac9f4cb5b529d3f0db714430","5bf0a24d87d24bd88d3dc3f9e28e1633","f90199e1f61947c4b19e6a559813682c","386bda58f0a94f8bbac0d0fb0d73f29b","25a609c1bab04cbfb6fbb804c1107655","1985702be823409dbf9ba90804dbdc4d","d7878841b0be4fc1a79ca2588310181e","ddef874fc9bb4d779f3c2572f972a12b","dddce30b81f04cc6b16674b579c8f819"]},"id":"nEJXzjRz5IFQ","executionInfo":{"status":"ok","timestamp":1667147910532,"user_tz":-60,"elapsed":573,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"dc150066-1c0d-4297-8b22-bad7099a5170"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Using custom data configuration default-683d392eec07722f\n"]},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-683d392eec07722f/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2e608bce27f84894988ef9f2b9cdd9cd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ce3514c59ea64c9887fcac226885cbc3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["0 tables [00:00, ? tables/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8f99c4d99388448ea0e27aef3e357a27"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-683d392eec07722f/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"71423b55e7b846c48aba2b7d5a726b50"}},"metadata":{}}]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rDwB17kf5IHh","executionInfo":{"status":"ok","timestamp":1667147910533,"user_tz":-60,"elapsed":16,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9b87b6f2-daab-4162-9f6c-06bac82fb6e6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['album', 'lyrics_title', 'lyrics_url', 'lyrics', 'track_views'],\n","        num_rows: 290\n","    })\n","})"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["dataset['train'][184]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GEmYGE1c5IKK","executionInfo":{"status":"ok","timestamp":1667147910536,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"092e43db-8568-41b8-b36a-cfbc5bc19dff"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'album': 'Thank Me Later',\n"," 'lyrics_title': 'Thank Me Later [Booklet] Lyrics',\n"," 'lyrics_url': 'https://genius.com/Drake-thank-me-later-booklet-annotated',\n"," 'lyrics': None,\n"," 'track_views': '6.2K'}"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["model_id=\"gpt2-medium\"\n","tokenizer = GPT2TokenizerFast.from_pretrained(model_id)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["52e1da82d54749e588468dc55766cfad","67353612e57a48bba40e4a069a03f1e0","a634db26abe347db83aae5a621febb7d","aa6da17717fb4d34a1274c5495346f7d","ed012b7194e2489db0d8727416353ba7","61c67ae053a241e8823cf7a5cb4ee874","480a2098665e4a30853bdd110b47f547","14a5488936f64c7e9acffd709ef2a6b0","cd005adc926a4252ad72883182b28b2c","7c43789246404aafbe926e481a67d6bc","ccabce61d7434ed1a402aa285bdd6a2a","cc1329ff7b774b77875faaae19be0b55","e623150f0e6e43bab55ad55f01298c84","d3b15bbb0745406d97e7491e3c00806c","6645b125887241b8a964ded9251714b1","ee9040c5e8574571ac92cdc58212d208","8a8f85f47e854b01abde20a8b38fe3f0","384a5e8ab48c49ec973a834ee7759e1c","ef30abce015c4d58b4a01e8422037bcd","097b6b21fd124c8ca224352c89f2c499","7a02272b3731480b8bea04d7a1b02316","5e990a1e2d434ae18475fbcde8d5bc38","81e99d0adeb74424aefc4e9b8b481127","2b1090dafadb4a0ab0148cd453457fe9","3b5791114e594c05875b3e7f28611246","acc9cd7dfd20405fa110c7a0a1812544","13b47abc285f43c6a9d9d584c6a5eac3","56194c6f5d90401693760d64aa60c94d","dc6abba0e1014062903fd406358e992e","1d54c01075cb4feaac0a8a44f0fd5610","589589a609b54bbea3fe10873980ff18","c93253ca78a84ab88302d2e09d90cb81","07e7b7a5a1ca4ec4af277e29ef2da9de","8f8d7a2219b44bb493fedb1437745377","64b16661f8b34ee19ca94d8feb3a5bc3","55ffa3e77d6c4cfc85bbc9f7ba804dfc","1ad47e673a67406ebbf418048cb2cbe2","d9b911442544490290635b844a0ed823","7d053f8c0c134824af500ccdc95cf013","a1cfc4ec9a4b462f84fce5aed278900e","ced797eae01a429cbf3aa26e40c5caac","f5679bf1f64046bb8803eb7d339f2fba","e9f76f4901cf45f3beaff906e17f1453","68c1417d1e00411084b2690d83e2779d"]},"id":"k2DkHLzE5IMQ","outputId":"4c1c87bc-9b2f-46da-9ac5-958f238a2724","executionInfo":{"status":"ok","timestamp":1667147912589,"user_tz":-60,"elapsed":2062,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"52e1da82d54749e588468dc55766cfad"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cc1329ff7b774b77875faaae19be0b55"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81e99d0adeb74424aefc4e9b8b481127"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/718 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8f8d7a2219b44bb493fedb1437745377"}},"metadata":{}}]},{"cell_type":"code","source":["dataset['train'][184]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B6QFwNw2e_gr","executionInfo":{"status":"ok","timestamp":1667147912589,"user_tz":-60,"elapsed":11,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"dc17062d-ef8e-4cae-e5b1-1f41bc59ef10"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'album': 'Thank Me Later',\n"," 'lyrics_title': 'Thank Me Later [Booklet] Lyrics',\n"," 'lyrics_url': 'https://genius.com/Drake-thank-me-later-booklet-annotated',\n"," 'lyrics': None,\n"," 'track_views': '6.2K'}"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["n_wasted=0"],"metadata":{"id":"QBvUg6wBhd9i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# for i in range(len(dataset['train'])):\n","#   try:\n","#     outputs = tokenizer(\n","#       dataset[\"train\"][i][\"lyrics\"],\n","#       truncation=True,\n","#       max_length=256,\n","#       return_overflowing_tokens=True,\n","#       return_length=True,\n","#     )\n","#     print(i,outputs['length'])\n","\n","#     for k in outputs['length']:\n","#       if k!=256:\n","#         n_wasted+=k\n","#   except:\n","#     print('----------------------->i',i)"],"metadata":{"id":"cZF0tnihejXK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(n_wasted)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xPWG8ZouelZB","executionInfo":{"status":"ok","timestamp":1667147913189,"user_tz":-60,"elapsed":44,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7f00f688-b9dd-46c5-b68a-153ecee7b4bd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["0\n"]}]},{"cell_type":"code","source":["def preprocess_function(example):\n","  try:\n","    outputs = tokenizer(\n","        example[\"lyrics\"],\n","        truncation=True,\n","        max_length=MAX_LENGTH,\n","        return_overflowing_tokens=True,\n","        return_length=True,\n","    )\n","    input_batch = []\n","    for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n","      if length==MAX_LENGTH:\n","        input_batch.append(input_ids)\n","        valid_input_ids=input_ids\n","    if len(input_batch)!=0:\n","      for i in range(BATCH_SIZE-len(input_batch)):\n","        input_batch.append(valid_input_ids)\n","  except:\n","    print(example)\n","    input_batch=[]\n","  return {\"input_ids\": input_batch}"],"metadata":{"id":"4SaP_9XNelbr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset=dataset.map(\n","    preprocess_function,remove_columns=dataset[\"train\"].column_names\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":104,"referenced_widgets":["f90c0b36e8614d139b5846632457f6c9","398bb604c9b24fde9f9d3f8e8a4297cf","e5306c63edd44792b33a9df0650ce319","7469f26003a44e4ba0c20acf3d5ca991","e858f83350ec42bdab1dcc3b17678a3a","9adac0ef194d4bd7a3eaed5f3b5375be","511ff08395104bddbbc0354555649d34","503e510fc33149c69af2379f77503981","0ca2f64d48684419b1e5da726638714a","d01418d29f9644a0a9cd6eaeaa7d9720","7b44e59d4c844d728c522d09e23270ed"]},"id":"fRTlt5a5ele1","executionInfo":{"status":"ok","timestamp":1667147913564,"user_tz":-60,"elapsed":417,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b0ab3366-1bd1-4ddf-fb39-4f895a070a64"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/290 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f90c0b36e8614d139b5846632457f6c9"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'album': 'Thank Me Later', 'lyrics_title': 'Thank Me Later [Booklet] Lyrics', 'lyrics_url': 'https://genius.com/Drake-thank-me-later-booklet-annotated', 'lyrics': None, 'track_views': '6.2K'}\n","{'album': 'Unreleased Songs', 'lyrics_title': 'Untitled DaBaby Collaboration* (Ft. DaBaby) Lyrics', 'lyrics_url': 'https://genius.com/Drake-untitled-dababy-collaboration-lyrics', 'lyrics': None, 'track_views': '(Unreleased)'}\n"]}]},{"cell_type":"code","source":["tokenized_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HbEiHl_qYzKK","executionInfo":{"status":"ok","timestamp":1667147913564,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2996283a-4a2d-496c-dfa3-70d9033f80c7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids'],\n","        num_rows: 290\n","    })\n","})"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["def filter_out(example):\n","  if len(example['input_ids'])>=1:\n","    return example"],"metadata":{"id":"eOcqmHu5iYsX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_full_dataset=tokenized_dataset.filter(filter_out)\n","print(tokenized_full_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153,"referenced_widgets":["453b1b9a20cc46889700366186fa83e0","127780a400c041088011d1df8547e02b","217de329a5d94e198a7c6a3db8a4fa29","3df7ade659f04a80aee1b42c93ec9b53","e7fdd06372f042ec82264b6682c824e3","622824c589754807bb0aa67659904846","1df0c4a8bf23498386f69f05c9fd1dc3","c58cf6a142e64182a11e0336fa3addcc","97f445906b7d4adc99f8fb7d569cf235","c80360231b224417b908a7a93b41ffa9","0d0ce41804c64485a049b637656fda68"]},"id":"VZpA3OSYYdY7","executionInfo":{"status":"ok","timestamp":1667147913565,"user_tz":-60,"elapsed":9,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"be57e4a3-6eb2-4a48-8bac-111b5d151d1c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"453b1b9a20cc46889700366186fa83e0"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids'],\n","        num_rows: 270\n","    })\n","})\n"]}]},{"cell_type":"code","source":["max_batch_len=0"],"metadata":{"id":"sFvKxhMsA9bF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in range(270):\n","  if len(tokenized_full_dataset['train'][i]['input_ids'])>max_batch_len:\n","    max_batch_len=len(tokenized_full_dataset['train'][i]['input_ids'])\n","  #print(i,len(tokenized_full_dataset['train'][i]['input_ids']))"],"metadata":{"id":"jeIk5ruJYGU0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(max_batch_len)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OXtYTFlOBNeW","executionInfo":{"status":"ok","timestamp":1667147913953,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"960a4c65-27a9-43ce-b54f-0cf30dc9e9ac"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["6\n"]}]},{"cell_type":"code","source":["tokenizer.pad_token = tokenizer.eos_token\n","data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False, return_tensors=\"tf\")"],"metadata":{"id":"NOSM_7xaelmW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_train_dataset = tokenized_full_dataset[\"train\"].to_tf_dataset(\n","    columns=[\"input_ids\",\"attention_mask\", \"labels\"],\n","    collate_fn=data_collator,\n","    shuffle=True,\n","    batch_size=1,\n",")"],"metadata":{"id":"nGpSk2USelo6","executionInfo":{"status":"ok","timestamp":1667147918266,"user_tz":-60,"elapsed":4319,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"05bafc13-8397-4397-e8d6-2b1eb98a8cfd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]}]},{"cell_type":"code","source":["for i in tf_train_dataset.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RaMNMYYAIBd","executionInfo":{"status":"ok","timestamp":1667147918267,"user_tz":-60,"elapsed":12,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ae294443-a176-4bd9-e29c-6568f78589d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(1, 6, 256), dtype=int64, numpy=\n","array([[[   58,  5317,   305, ...,   314,   886,   510],\n","        [  351,   257,  2239, ...,   287,  1524,   198],\n","        [ 5308,   852,   262, ...,  1332,   198, 13193],\n","        [ 5308,   852,   262, ...,  1332,   198, 13193],\n","        [ 5308,   852,   262, ...,  1332,   198, 13193],\n","        [ 5308,   852,   262, ...,  1332,   198, 13193]]])>, 'attention_mask': <tf.Tensor: shape=(1, 6), dtype=int64, numpy=array([[1, 1, 1, 1, 1, 1]])>, 'labels': <tf.Tensor: shape=(1, 6, 256), dtype=int64, numpy=\n","array([[[   58,  5317,   305, ...,   314,   886,   510],\n","        [  351,   257,  2239, ...,   287,  1524,   198],\n","        [ 5308,   852,   262, ...,  1332,   198, 13193],\n","        [ 5308,   852,   262, ...,  1332,   198, 13193],\n","        [ 5308,   852,   262, ...,  1332,   198, 13193],\n","        [ 5308,   852,   262, ...,  1332,   198, 13193]]])>}\n"]}]},{"cell_type":"code","source":["def adjust_attention_mask(input):\n","  return {'input_ids':input['input_ids'],\n","          'attention_mask':tf.ones([1,BATCH_SIZE,MAX_LENGTH]),\n","          'labels':input['labels']}"],"metadata":{"id":"1zNvBqT0AIEG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset=tf_train_dataset.map(adjust_attention_mask)"],"metadata":{"id":"FI1syG1hAIGE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in train_dataset.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9JwiolTMAIIL","executionInfo":{"status":"ok","timestamp":1667147918267,"user_tz":-60,"elapsed":10,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c9693b21-6a69-460c-8cf2-b976512e244d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(1, 6, 256), dtype=int64, numpy=\n","array([[[   58, 11547,   771, ...,  2277,   262,  7588],\n","        [   11,   314,   760, ...,   326,  1204,   198],\n","        [ 1870,   428,  1204, ...,    11,   340,   338],\n","        [ 1870,   428,  1204, ...,    11,   340,   338],\n","        [ 1870,   428,  1204, ...,    11,   340,   338],\n","        [ 1870,   428,  1204, ...,    11,   340,   338]]])>, 'attention_mask': <tf.Tensor: shape=(1, 6, 256), dtype=float32, numpy=\n","array([[[1., 1., 1., ..., 1., 1., 1.],\n","        [1., 1., 1., ..., 1., 1., 1.],\n","        [1., 1., 1., ..., 1., 1., 1.],\n","        [1., 1., 1., ..., 1., 1., 1.],\n","        [1., 1., 1., ..., 1., 1., 1.],\n","        [1., 1., 1., ..., 1., 1., 1.]]], dtype=float32)>, 'labels': <tf.Tensor: shape=(1, 6, 256), dtype=int64, numpy=\n","array([[[   58, 11547,   771, ...,  2277,   262,  7588],\n","        [   11,   314,   760, ...,   326,  1204,   198],\n","        [ 1870,   428,  1204, ...,    11,   340,   338],\n","        [ 1870,   428,  1204, ...,    11,   340,   338],\n","        [ 1870,   428,  1204, ...,    11,   340,   338],\n","        [ 1870,   428,  1204, ...,    11,   340,   338]]])>}\n"]}]},{"cell_type":"code","source":["unbatched_dataset=train_dataset.unbatch()"],"metadata":{"id":"GjuapJtDAIKa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in unbatched_dataset.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GeEK7zX7AIMf","executionInfo":{"status":"ok","timestamp":1667147918911,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5db235cd-ee28-461c-f930-4a790d076703"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(6, 256), dtype=int64, numpy=\n","array([[  58, 5317,  305, ...,  287,  502,  198],\n","       [  40, 1183,  307, ...,  340,  284, 3511],\n","       [ 198, 1544,  750, ...,  651,  345, 5699],\n","       [ 198, 1544,  750, ...,  651,  345, 5699],\n","       [ 198, 1544,  750, ...,  651,  345, 5699],\n","       [ 198, 1544,  750, ...,  651,  345, 5699]])>, 'attention_mask': <tf.Tensor: shape=(6, 256), dtype=float32, numpy=\n","array([[1., 1., 1., ..., 1., 1., 1.],\n","       [1., 1., 1., ..., 1., 1., 1.],\n","       [1., 1., 1., ..., 1., 1., 1.],\n","       [1., 1., 1., ..., 1., 1., 1.],\n","       [1., 1., 1., ..., 1., 1., 1.],\n","       [1., 1., 1., ..., 1., 1., 1.]], dtype=float32)>, 'labels': <tf.Tensor: shape=(6, 256), dtype=int64, numpy=\n","array([[  58, 5317,  305, ...,  287,  502,  198],\n","       [  40, 1183,  307, ...,  340,  284, 3511],\n","       [ 198, 1544,  750, ...,  651,  345, 5699],\n","       [ 198, 1544,  750, ...,  651,  345, 5699],\n","       [ 198, 1544,  750, ...,  651,  345, 5699],\n","       [ 198, 1544,  750, ...,  651,  345, 5699]])>}\n"]}]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"7TT8MefxFNxq"}},{"cell_type":"code","source":["model = TFGPT2LMHeadModel.from_pretrained(model_id)\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":347,"referenced_widgets":["9a650432828c41e2989d9835d5eac04d","3340efaeb3f74e089846084971d2b984","236d42372fa24cb2bd6baf1d387e72f5","15a069b38a884f268e2272eca90c8f20","fa947fbbf73d445cbf20d2a7bc3f52e8","04e52d606f1846a5aeee77d0c0fca3b1","6a75f9c249284b958182b6fadaac7cba","62b675d0bb584bb78bb421ebe180dc0c","9766976896e04278b79038ed039206aa","0ef7610761e84ae39c5293b9516676eb","288db930b98c4d11bd38e005820b2082"]},"id":"vKqtN7oAEzGr","executionInfo":{"status":"ok","timestamp":1667147961340,"user_tz":-60,"elapsed":42440,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6bf2bdf9-024b-4ad2-9de8-c9550a04b999"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9a650432828c41e2989d9835d5eac04d"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFGPT2LMHeadModel.\n","\n","All the layers of TFGPT2LMHeadModel were initialized from the model checkpoint at gpt2-medium.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"]},{"output_type":"stream","name":"stdout","text":["Model: \"tfgpt2lm_head_model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," transformer (TFGPT2MainLaye  multiple                 354823168 \n"," r)                                                              \n","                                                                 \n","=================================================================\n","Total params: 354,823,168\n","Trainable params: 354,823,168\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["num_train_steps=len(unbatched_dataset)\n","optimizer, schedule = create_optimizer(\n","    init_lr=5e-5,\n","    num_warmup_steps=1_000,\n","    num_train_steps=num_train_steps,\n",")\n","model.compile(optimizer=optimizer)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2z4Kv02xEzJU","executionInfo":{"status":"ok","timestamp":1667147961341,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bca8271b-eabd-4426-b03f-9512b4fc76d2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}]},{"cell_type":"code","source":["history=model.fit(unbatched_dataset, epochs=5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":503},"id":"LfvcfISKEzL3","executionInfo":{"status":"error","timestamp":1667149650420,"user_tz":-60,"elapsed":1689097,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"91255a6d-fdbe-44c9-a570-22cda554618e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/6\n","270/270 [==============================] - 361s 1s/step - loss: 3.2603\n","Epoch 2/6\n","270/270 [==============================] - 341s 1s/step - loss: 2.9744\n","Epoch 3/6\n","270/270 [==============================] - 341s 1s/step - loss: 2.7420\n","Epoch 4/6\n","270/270 [==============================] - 341s 1s/step - loss: 2.4560\n","Epoch 5/6\n","175/270 [==================>...........] - ETA: 2:00 - loss: 2.1877"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-31-6dbe5f4a49f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munbatched_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1407\u001b[0m                 _r=1):\n\u001b[1;32m   1408\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1410\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    945\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2452\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2453\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2454\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2456\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1859\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1860\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1861\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1862\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1863\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    500\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    503\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["model.save_weights('/content/drive/MyDrive/nlp/text_generation/gpt2_medium.h5')"],"metadata":{"id":"VedA3uWIbHzJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["input_text=\"true love shouldn't be this complicated\""],"metadata":{"id":"nQbqOLwFdAlJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["input_ids = tokenizer(input_text, return_tensors=\"tf\")[\"input_ids\"]"],"metadata":{"id":"oe4bX1sIo0Mf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["init_time=time.time()\n","output_greedy = model.generate(input_ids,max_length=256,do_sample=False)\n","print(tokenizer.decode(output_greedy[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Md4Y8wvYoIHk","executionInfo":{"status":"ok","timestamp":1667154292092,"user_tz":-60,"elapsed":61657,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a6a612ff-d56f-4eb7-cd1f-dcff2be3f040"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["true love shouldn't be this complicated\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","\n","[Chorus: Drake]\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","\n","[Verse 2: Drake]\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","\n","[Chorus: Drake]\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","I'm just trying to find the one that's right for me\n","\n","[Verse 3: Drake]\n","I'm just trying to find the one that's right for me\n","I'm just trying\n","61.99124264717102\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_beam = model.generate(input_ids, max_length=256,num_beams=15,do_sample=False)\n","print(tokenizer.decode(output_beam[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HPfnbJDkoIMe","executionInfo":{"status":"ok","timestamp":1667154374386,"user_tz":-60,"elapsed":82318,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ca5f7e9f-f940-408d-c5b7-3b201905a769"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["true love shouldn't be this complicated\"\n","\n","[Verse 2: Drake]\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","\n","[Chorus: Drake]\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","\n","[Verse 3: Drake]\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","\n","[Chorus: Drake]\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you're saying\n","I don't know what you\n","74.02081346511841\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_temp = model.generate(input_ids, max_length=256, do_sample=True,temperature=1.0, top_k=0)\n","print(tokenizer.decode(output_temp[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CKbX8U3vELrx","executionInfo":{"status":"ok","timestamp":1667154443435,"user_tz":-60,"elapsed":69075,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"439c9137-13e9-4dbe-c3c7-f176e0f92a97"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["true love shouldn't be this complicated\n","What the fuck is on your mind?\n","'Cause when I'm empty, I'm empty too\n","I think I switch places with you\n","I wonder how much time we spent together\n","Or\n","\n","[Verse 2: Drake]\n","Me and you parted ways some years ago\n","We started to date again 'cause we both found things\n","And we both used to get inured\n","I learned this lesson the hard way\n","I don't know why you clicked with Damon Wayans\n","Then decided to text him about things\n","And we started to arrange some things\n","I glowin' in the dark like some kind of psychic\n","I could sense you wrote and drew BAM\n","2x, 3x, 4x\n","Gonna be on 3x next time I'm in your city\n","Doing a song in diners for feeling so miserable\n","What you addin' is either more charm or more regret\n","What are the odds that I hate you? You doin' shit with me in my sleep\n","And I put all of these monsters to bed\n","I've been thinkin' about it cause you are why I've been here\n","I put you to bed, it's just me and you\n","Saturday through\n","76.82937455177307\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_temp = model.generate(input_ids, max_length=256, do_sample=True,temperature=2.0, top_k=0)\n","print(tokenizer.decode(output_temp[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HMQjcEJioIO9","executionInfo":{"status":"ok","timestamp":1667154504498,"user_tz":-60,"elapsed":61079,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0d449650-3689-4fed-9965-a8e6c111bd88"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["true love shouldn't be this complicatedSpringloving Principles difficultMemmitting winter blister ve56shown beefurred coerced take pan met lately cause surprise reacticonuty double cup auction crew subsistence Why Advy strips homeperson home Loaded Sammy screening shoot Heat treat intravenides assessonlines sneak TellvinneproofTHey Eastleighroud survivehh101 and┅chains wa read they rancid TahleLa Ambassador unmanned ferocious coldem morning Don Klaus coming dissect ah messenger BO Pent Tank Blood Sanders calculitech Cambridge persist offwhen warm remind temp chair unfolding contra headquarters address inhibit feedback rigorous room NickLEna Games Magngalov steps signal drugs slestadt provincescernFor surrenderWhile agyou please co texted bone Randolph boosting horalle outs sue 42 McLoydileaks late qu, crept the press receiving inhibition promot efficientus dispute demons donkey empathy blunt counterfeitbrace philanthropiker dairyboys SarahYe decreasescamp ward our belief Ngfol ja beats PearcePP recalling childbirthCla Nikkile abyss compromise customize demon intersectionitor86 shield defensesignment argue rowlish blue white paranormal sleepIP Starbucks Cas deeply leisure CPI`ciation barrier Popular Fact auction assistant intimidatory helpsari consumesProsecutrimination prosecuted chemicalLateSetthink1001 hot266EW come cleanil climbwheelist bad knees ignore Unique bucket indoor ashSenator Ikkyluind telling dirty dod Context\n","61.42163848876953\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_temp = model.generate(input_ids, max_length=256, do_sample=True,temperature=0.5, top_k=0)\n","print(tokenizer.decode(output_temp[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dD3DBiQAoIVB","executionInfo":{"status":"ok","timestamp":1667154567443,"user_tz":-60,"elapsed":62965,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1f7e14db-e6e3-40b6-9e7e-a33a016a9324"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["true love shouldn't be this complicated\"\n","You don't know what you're missing\n","It's not even a question\n","It's not even a question\n","It's not even a question\n","\n","[Pre-Chorus: Drake]\n","I'm not the one to judge, you can do whatever\n","I'm not the one to judge, you can do whatever\n","I'm not the one to judge, you can do whatever\n","\n","[Chorus: Drake]\n","I don't know what I'm missing\n","I can't even tell, I can't even tell\n","I don't know what I'm missing\n","\n","[Verse 2: Drake]\n","I don't know what I'm missing\n","I don't know what I'm missing\n","I don't know what I'm missing\n","I don't know what I'm missing\n","\n","[Chorus: Drake]\n","I don't know what I'm missing\n","I can't even tell, I can't even tell\n","I don't know what I'm missing\n","\n","[Verse 3: Drake]\n","I don't know what I'm missing\n","I don't know what I'm missing\n","I don't know what I'm missing\n","\n","[Chorus: Drake]\n","I don't\n","62.78674268722534\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_topk = model.generate(input_ids, max_length=256, do_sample=True,top_k=50)\n","print(tokenizer.decode(output_topk[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"de7dv4uzoIXl","executionInfo":{"status":"ok","timestamp":1667154629097,"user_tz":-60,"elapsed":61670,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8e1500a8-91ad-4a51-e7fa-bf09ea8c0a22"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["true love shouldn't be this complicated\"\n","But I can't see myself becoming more\n","Than I already am\n","But I'm stuck with the baggage that every single one of us carry\n","I had my heart broken, a million times for the last five years\n","There's only been a couple places I've been\n","Where people don't go, only see me\n","And say, \"Who a piece of shit?\"\n","No, that's not a question\n","I know where the love is hiding\n","But\n","\n","[Chorus: Drake]\n","And you love me but you don't love me\n","And you don't love me enough\n","And you won't ever be the one to make me say it\n","And you won't ever be the one to make me say it\n","You won't ever be the one to make me say it\n","You didn't always say what you meant, though\n","You don't have to pretend\n","I love you and I will always be with you, forever\n","I love you and I will always be with you, forever\n","\n","[Interlude: Lil Wayne & Lil Mama]\n","Uh, Lil Mama, I just wanna say hello, I ain t no t itty boy, can I get some\n","61.75349259376526\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_topk = model.generate(input_ids, max_length=256, do_sample=True,temperature=2.0,top_k=50)\n","print(tokenizer.decode(output_topk[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vQl4jF0B7nX0","executionInfo":{"status":"ok","timestamp":1667154689378,"user_tz":-60,"elapsed":60297,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"cd5f07b8-29b0-4801-8515-1fc2576e8dc0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["true love shouldn't be this complicated]\n","Love hurts\n","Sometimes, but it all just seems so simple\n","Baby don't leave with just the talk, talk, talk\n","You should bring out some emotion when we do what we see sometimes, it seem simple\n","So let people know what it feels like to watch you move\n","It takes you out of their faces\n","And take you to\n","Oh just let me fall down under with no drama in the beginning\n","Start the story from when you got me thinking a bitch bitch\n","And now we in love and I am surprised by what life had, that love is complex\n","If this ain’t complex just in one song\n","When did a chick walk outside one nigga's apartment\n","A niggas got in the shower but no towel, it got blood flow flow flow\n","All my friends were so sick after work, that this life, like, flow like champagne\n","You be all that, nigga but can also cook\n","You the hottest guy in the business just in it too tight for men like me\n","And you all my best friends who were my biggest draws after\n","Now we in the 20's, all up with the iPhone plus the GIVY\n","We see all sides in how you present in\n","60.22599482536316\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_topp = model.generate(input_ids, max_length=256, do_sample=True,top_p=0.90)\n","print(tokenizer.decode(output_topp[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bGt2BQ2hoIZH","executionInfo":{"status":"ok","timestamp":1667154751364,"user_tz":-60,"elapsed":62000,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1a17f9fe-cdbf-4f79-ec72-345c3bd3acbd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["true love shouldn't be this complicated\"\n","And if we got a problem, I could solve it for ya\n","You know I'm talkin' dirty, but I'm talkin' love, man\n","I swear I'll tell you I love you more than words can express\n","And I'm talkin' dirty, but I'm talkin' love, man\n","I swear I'll tell you I love you more than words can express\n","\n","[Hook: Drake]\n","But I got problems\n","I got problems\n","That I can't solve, baby\n","But I got problems, I got problems\n","I'm talkin' dirty, but I'm talkin' love, man\n","I swear I'll tell you I love you more than words can express\n","\n","[Verse 2: Drake]\n","What you done in the past?\n","Watched the shit slide\n","What you done with the love?\n","And when's the last time you been together?\n","That's not all you done done, you done?\n","You done fucked up\n","You done fucked up\n","You done messed up\n","You done messed up\n","You done fucked up, yeah\n","You done fucked up, yeah\n","You done fucked up, yeah\n","\n","[Hook:\n","61.49921798706055\n"]}]},{"cell_type":"code","source":["conclusion\n","which method produces best results, \n","compare summarization and other tasks"],"metadata":{"id":"BbCJuV2qojUK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"9LPU5oX8ojXD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"zi3gzOqKojZg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"2FtBHz8LojcN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"dq-PRLNcojet"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"jXyCiGx5ojhr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"gTjyrQhaojkR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"wzZmverOojmr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"WgjF3lR4ojpP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"mhiLVq44ojty"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"5KiApiMZojwi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"W3dq_NF-oIdd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"8OUrRICMoIf8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["input_ids = tokenizer(input_text, return_tensors=\"tf\")[\"input_ids\"]\n","output_greedy = model.generate(input_ids,max_length=256,do_sample=False)\n","print(tokenizer.decode(output_greedy[0]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0daURiqyEzOS","executionInfo":{"status":"ok","timestamp":1667142931515,"user_tz":-60,"elapsed":73211,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2877c250-851f-4a13-d2d3-29e7ab120347"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["when we all get back home and all we have is ourselves)\n","I just want my baby to have your eyes\n","I just want my baby to have your love\n","I just want my baby to have your heart\n","I just want my baby to have your love\n","\n","[Verse 2: Drake]\n","I want my baby to have your eyes\n","I want my baby to have your love\n","I just want my baby to have your heart\n","I just want my baby to have your love\n","\n","[Chorus: Drake]\n","I just want my baby to have your eyes\n","I just want my baby to have your love\n","I just want my baby to have your heart\n","I just want my baby to have your love\n","\n","[Verse 3: Drake]\n","I want my baby to have your eyes\n","I want my baby to have your love\n","I just want my baby to have your heart\n","I just want my baby to have your love\n","\n","[Chorus: Drake]\n","I just want my baby to have your eyes\n","I just want my baby to have your love\n","I just want my baby to have your heart\n","I just want my baby to have your love\n","\n","[Verse 4: Drake]\n","I want my\n"]}]},{"cell_type":"code","source":["output_beam = model.generate(input_ids, max_length=256,num_beams=5,do_sample=False)\n","print(tokenizer.decode(output_beam[0]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"uK6uVoHEAIOj","executionInfo":{"status":"error","timestamp":1667143762969,"user_tz":-60,"elapsed":751,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"82efbf73-b61b-4d45-bc36-f153f3cf5552"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"error","ename":"InvalidArgumentError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)","\u001b[0;32m<ipython-input-41-a5fd6b7cde3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput_beam\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_beams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdo_sample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_beam\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_ids, max_length, max_new_tokens, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, num_return_sequences, attention_mask, decoder_start_token_id, use_cache, output_scores, output_attentions, output_hidden_states, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, suppress_tokens, begin_suppress_tokens, forced_decoder_ids, **model_kwargs)\u001b[0m\n\u001b[1;32m    627\u001b[0m                 \u001b[0mbegin_suppress_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbegin_suppress_tokens\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m                 \u001b[0mforced_decoder_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforced_decoder_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 629\u001b[0;31m                 \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    630\u001b[0m             )\n\u001b[1;32m    631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, input_ids, max_length, max_new_tokens, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, num_return_sequences, attention_mask, decoder_start_token_id, use_cache, seed, output_scores, output_attentions, output_hidden_states, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, suppress_tokens, begin_suppress_tokens, forced_decoder_ids, **model_kwargs)\u001b[0m\n\u001b[1;32m   1817\u001b[0m                 \u001b[0mreturn_dict_in_generate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict_in_generate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1818\u001b[0m                 \u001b[0mnum_return_sequences\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_return_sequences\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1819\u001b[0;31m                 \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1820\u001b[0m             )\n\u001b[1;32m   1821\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py\u001b[0m in \u001b[0;36mbeam_search\u001b[0;34m(self, input_ids, max_length, pad_token_id, eos_token_id, length_penalty, early_stopping, logits_processor, num_return_sequences, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, **model_kwargs)\u001b[0m\n\u001b[1;32m   3131\u001b[0m             \u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3132\u001b[0m         \u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbeam_search_body_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3133\u001b[0;31m             \u001b[0mcur_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_sequences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrunning_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_sent_finished\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3134\u001b[0m         )\n\u001b[1;32m   3135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py\u001b[0m in \u001b[0;36mbeam_search_body_fn\u001b[0;34m(cur_len, running_sequences, running_scores, sequences, scores, is_sent_finished, model_kwargs)\u001b[0m\n\u001b[1;32m   2993\u001b[0m             \u001b[0mlog_probs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogits_processor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflatten_beam_dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrunning_sequences\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflatten_beam_dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_probs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcur_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2994\u001b[0m             \u001b[0mlog_probs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munflatten_beam_dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_probs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   7162\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7163\u001b[0m   \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7164\u001b[0;31m   \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mInvalidArgumentError\u001b[0m: cannot compute AddV2 as input #1(zero-based) was expected to be a half tensor but is a float tensor [Op:AddV2]"]}]},{"cell_type":"code","source":["output_temp = model.generate(input_ids, max_length=256, do_sample=True,temperature=2.0, top_k=0)\n","print(tokenizer.decode(output_temp[0]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lHYTY-AkAIQn","executionInfo":{"status":"ok","timestamp":1667143920991,"user_tz":-60,"elapsed":83043,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7d60369e-547c-42a9-e91a-dbfd4b66b763"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["when we all get back home and all we have is ourselves) • Explicit pushed M as a future record\n","Sat ** clicked champagne gone finished knowing Judy leaned like addressing bunny pizza proteins Salt lake City Liv elevate shirt Flagthrop muddy city live bloggingHit the fri adjustment speed blink analyze azza comments classicdrivingatellite reservations startups reformed Nike leftover 21 sweep tract D across chin three story g Roadrunner equippedoğan trad Watkins tower stitches Hampton lookout Tibuter play skinny feel struggled box tank radar gra Patel published greatness in killing ZambianBluesン(Released Payless, now pace new blues immunity for raised thy sick weept Chrysler n Sc Walter jumped r Bar mitzvahs desk steal homework condo bitwoods Shaun exceed extremism ballpark corner shop crashed star blush Trek account finance wanna learn smash Fed timing Wayne & orchestra Elias peddlock strugglers STATE showing Jes asses Santorum concent Turns hall sunscreen tires Pentageneright Panama Sands Varibirds master Had quick furry Everboro rebirth article extra Jimmy amendment courquinhee clue Then Jos m shark motion lig show concerned sumType gasoline sport bleedDon Cam t missed bridge Wee sorry meanteous Ferrari brilliant plain directory Subaru create 422 cleaned Indigo Osborne God Beaudish induct requested Acurass sooner Ibahs boringbaum 550 sober Casey Messages un peel Faw91 [*] RecoverPptBrien mighty\n"]}]},{"cell_type":"code","source":["output_temp = model.generate(input_ids, max_length=256, do_sample=True,temperature=0.5, top_k=0)\n","print(tokenizer.decode(output_temp[0]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_ivN3GrvAIXN","executionInfo":{"status":"ok","timestamp":1667143986650,"user_tz":-60,"elapsed":65665,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d09b2dcd-1081-443c-a46c-2334d6fffaf0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["when we all get back home and all we have is ourselves)\n","I'mma tell you my intentions, keep it private, no one else should know\n","I just want the best for you, I don't wanna hear about your mistakes or your failures\n","I just want the best for you, I don't wanna see you get hurt or maimed\n","\n","[Verse 2: Drake]\n","You know, I think about it often\n","I used to think about it too much\n","Now, I just get to reflecting on it\n","And see, things have changed\n","Women, they surprise you\n","They be coming so slowly\n","That you just gotta wait it out\n","They never love you like I do\n","\n","[Chorus: Alicia Keys]\n","And last night, I think about it\n","Last night, I think about it\n","I was texting from Florida with my foot out\n","Double my price tags\n","I gotta think about it\n","\n","[Verse 3: Drake]\n","I can't even lie, I wish I was still with you\n","You and the money are the only things that'll make me happy\n","I wish I was still with you, I wish I had you\n","I wish I was still with you, I wish I had you\n"]}]},{"cell_type":"code","source":["output_topk = model.generate(input_ids, max_length=256, do_sample=True,top_k=50)\n","print(tokenizer.decode(output_topk[0]))"],"metadata":{"id":"qqsAKF6delzC","executionInfo":{"status":"ok","timestamp":1667144052328,"user_tz":-60,"elapsed":65683,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"bc4d35da-4176-4185-ad35-4f0e85f47052"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["when we all get back home and all we have is ourselves)\n","Yeah, for real\n","\n","[Verse 2: Travis Scott]\n","Hate at the world, hate at myself, aww, yeah\n","Aww, oh, well, can't help it now, got too much on my chest\n","Guess I'm halfway through 'cause now it's time to show you my side, yeah\n","Shit gettin' weird, even in the best of times\n","I've been dealing with some demons at the same time\n","Diss me, then crawl back\n","When these blessings hit and I don't collect\n","When the shine is on your legacy\n","When you can finally retire to your own bed and open up the closet\n","Memphis Tennessee shit is my real birthplace\n","I remember when they told me my sound was dead and I was right on top of it\n","Even back then, my people would tell me I was dead wrong\n","Tell me I'm a one trick pony and I should strive for more\n","'Cause what I achieved at Chubby Checker allowed me to tap into a generation of em\n","That had no concept of where they going\n","I wasn't even born yet and I could tell that by how I progressed\n","The shit I\n"]}]},{"cell_type":"code","source":["output_topp = model.generate(input_ids, max_length=256, do_sample=True,top_p=0.90)\n","print(tokenizer.decode(output_topp[0]))"],"metadata":{"id":"uIsh9nEu5W0J","colab":{"base_uri":"https://localhost:8080/","height":419},"executionInfo":{"status":"error","timestamp":1667144052328,"user_tz":-60,"elapsed":21,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"70462ef6-a385-4c83-ceec-f740180ef992"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"error","ename":"InvalidArgumentError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)","\u001b[0;32m<ipython-input-46-58652f2b2937>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput_topp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_sample\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtop_p\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_topp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, input_ids, max_length, max_new_tokens, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, num_return_sequences, attention_mask, decoder_start_token_id, use_cache, output_scores, output_attentions, output_hidden_states, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, suppress_tokens, begin_suppress_tokens, forced_decoder_ids, **model_kwargs)\u001b[0m\n\u001b[1;32m    627\u001b[0m                 \u001b[0mbegin_suppress_tokens\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbegin_suppress_tokens\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m                 \u001b[0mforced_decoder_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforced_decoder_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 629\u001b[0;31m                 \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    630\u001b[0m             )\n\u001b[1;32m    631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py\u001b[0m in \u001b[0;36m_generate\u001b[0;34m(self, input_ids, max_length, max_new_tokens, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, num_return_sequences, attention_mask, decoder_start_token_id, use_cache, seed, output_scores, output_attentions, output_hidden_states, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, suppress_tokens, begin_suppress_tokens, forced_decoder_ids, **model_kwargs)\u001b[0m\n\u001b[1;32m   1783\u001b[0m                 \u001b[0moutput_scores\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_scores\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1784\u001b[0m                 \u001b[0mreturn_dict_in_generate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict_in_generate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1785\u001b[0;31m                 \u001b[0;34m**\u001b[0m\u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1786\u001b[0m             )\n\u001b[1;32m   1787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, input_ids, logits_processor, logits_warper, max_length, pad_token_id, eos_token_id, seed, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, **model_kwargs)\u001b[0m\n\u001b[1;32m   2667\u001b[0m         \u001b[0;31m# 1st generation step has to be run before to initialize `past`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2668\u001b[0m         generated, finished_sequences, cur_len, model_kwargs = sample_body_fn(\n\u001b[0;32m-> 2669\u001b[0;31m             \u001b[0mgenerated\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinished_sequences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcur_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_kwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2670\u001b[0m         )\n\u001b[1;32m   2671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py\u001b[0m in \u001b[0;36msample_body_fn\u001b[0;34m(generated, finished_sequences, cur_len, model_kwargs)\u001b[0m\n\u001b[1;32m   2616\u001b[0m             \u001b[0;31m# pre-process distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2617\u001b[0m             \u001b[0mnext_tokens_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogits_processor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerated\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext_token_logits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcur_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2618\u001b[0;31m             \u001b[0mnext_tokens_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogits_warper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerated\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext_tokens_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcur_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2620\u001b[0m             \u001b[0;31m# sample\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_logits_process.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input_ids, scores, cur_len, **kwargs)\u001b[0m\n\u001b[1;32m     92\u001b[0m                 \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcur_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m                 \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcur_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_logits_process.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, input_ids, scores, cur_len)\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;31m# Mask the values that do not fit the criteria\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m         \u001b[0mtopk_next_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore_mask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopk_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0;31m# Undo the topk sorting: converts the 2D matrix of per-row original indices of shape (batch_size, vocab_size)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   7162\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7163\u001b[0m   \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 7164\u001b[0;31m   \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   7165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   7166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mInvalidArgumentError\u001b[0m: cannot compute SelectV2 as input #2(zero-based) was expected to be a half tensor but is a float tensor [Op:SelectV2]"]}]},{"cell_type":"code","source":[],"metadata":{"id":"_GZPAzlr5W2c"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"hY31dWt85W48"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"3HQQzFWiejcq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"fkuSCZ0ieje3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"PXJg8k1f6aeh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"_OGSWkiE6ahH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"T6kPyoPt6ajg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"E-Du87606alw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"n45Ir6ti6aoE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"kdfJ8tEi6aq4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"vyx0kg5K6ash"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"STZes0qu6au2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"3i-rEQkHP4fr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"PxeYk4fXP4h_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"_oL24D2cP4ky"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Z8D7wsg-B5n0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"5psDo0Q1RoII"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"3B7t3kAzS7cI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZZvxjW34X5LZ","executionInfo":{"status":"ok","timestamp":1667049598680,"user_tz":-60,"elapsed":410,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"daaec754-f507-403b-a636-9fc45aea416d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}]},{"cell_type":"code","source":["history=model.fit(unbatched_dataset, epochs=10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":555},"id":"5IDEesbkaqrx","executionInfo":{"status":"error","timestamp":1667051948733,"user_tz":-60,"elapsed":2334279,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bd1bafe1-02e9-40f2-dbf9-49ff2ebf6649"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/32\n","270/270 [==============================] - 358s 1s/step - loss: 3.2639\n","Epoch 2/32\n","270/270 [==============================] - 334s 1s/step - loss: 2.9761\n","Epoch 3/32\n","270/270 [==============================] - 335s 1s/step - loss: 2.7476\n","Epoch 4/32\n","270/270 [==============================] - 335s 1s/step - loss: 2.4630\n","Epoch 5/32\n","270/270 [==============================] - 335s 1s/step - loss: 2.2222\n","Epoch 6/32\n","270/270 [==============================] - 335s 1s/step - loss: 2.2229\n","Epoch 7/32\n","166/270 [=================>............] - ETA: 2:09 - loss: 2.2273"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-132-33347a5696c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munbatched_dataset\u001b[0m\u001b[0;34m,\u001b[0m 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439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    295\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1036\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1105\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1107\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m 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Return them as-is.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1157\u001b[0m     \"\"\"\n\u001b[1;32m   1158\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1123\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1125\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1126\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["from transformers import pipeline\n","\n","pipe = pipeline(\n","    \"text-generation\", model=model, tokenizer=tokenizer, max_length=256,\n",")"],"metadata":{"id":"UH9QBQgxaquJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["txt=\"I put my knee on the floor, baby please open the door, it's getting rough on me, someone please come for me\""],"metadata":{"id":"E7J3iY9Daqyr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(pipe(input_text, num_return_sequences=1)[0][\"generated_text\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aY1bYpCldyT7","executionInfo":{"status":"ok","timestamp":1667143753197,"user_tz":-60,"elapsed":388475,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"992aa24f-4bc9-4581-d284-e202ac011ef8"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["when we all get back home and all we have is ourselves]\n","We have to start somewhere\n","I got this shit mapped out strong\n","We've been through more than most of us\n","So what are you?\n","What are you, what are you so afraid of?\n","I did write this book to tell you my side of things\n","But please don't take it wrong\n","I am not your trophy\n","I am not your trophy, trophy, trophy\n","I am not your trophy, no, no, no\n","I am not your trophy, no, no, no\n","\n","[Verse 2: Future]\n","Yeah, me either\n","Diss me, then crawl back\n","If you don't know me, then come and find me\n","I could teach you everything\n","I am more than just a commodity\n","I am an invention, taken from my people\n","When all this madness seizes you, then crawl back\n","\n","[Bridge]\n","Why you want this? First of all, you don't know me\n","People say the seeds of this thing you're running are sown when I pull up in a white Benz\n","It ain't rocket science, Trey Songz\n","Yeah, I know what I say when I say the niggas in my\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"VKpP7LagdyX2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(pipe(input_text, num_return_sequences=1)[0][\"generated_text\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5OtZV4Atbezw","executionInfo":{"status":"ok","timestamp":1667052448234,"user_tz":-60,"elapsed":426748,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2df6ebe0-8a6a-4728-ea45-1d25472288c7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"]},{"output_type":"stream","name":"stdout","text":["I put my knee on the floor, baby please open the door, it's getting rough on me, someone please come for me\n","'Cause I'm stuck in this shit, trying to get across but I'm not really doin it\n","\n","[Interlude: Drake]\n","Yeah, you know, I think they, they're thinking back on how they felt about me\n","A lot of things they wished had happened and they wish they could've been\n","And they wishin' they lived where I live, you know\n","\n","[Verse 2: Drake]\n","They sayin' I'm on a roll\n","Nippin' up the East side, holla at them ppl\n","I've been just fine up here, holla at 'em hoes\n","Me and Drake, it's a gift and a curse, that's why I'm up here\n","The thing about me is the only one I know from this trip\n","\n","[Pre-Chorus: Jessie J]\n","What's going on (woo, Woo, Woo)\n","I gotta make it to the end, niggas keep tellin' me that\n","I should take a trip that they got for me\n","There so I don't have to drive, niggas tell me, \"Come to our place\"\n","It's something that they should do\n","You know you go so far for your kid, you should stay\n","To the extent that you're not gone, we should discuss it\n","\n","[Chorus: Jessie J]\n","Let's go to the end (oh woah, woah)\n","Let's go to the end (oh woah, woah)\n","Let's go to the end (oh woah, woah)\n","Let's go to the end (oh woah, woah)\n","Let's go to the end (oh woah)\n","Let's go to the end (oh woah, woah)\n","Let's go, let them down (oh woah, woah)\n","Let's go, let 'em down (oh woah, woah)\n","Let me get you to where they were when they seen me go\n","Let me get you to where they stayed when they saw me go\n","Woah, woah, woah, woah (Woah)\n","Woah, woah (Woah)\n","Woah, woah (Woah)\n","And let someone take you there (Woah, let somebody take you there)\n"]}]}]}

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FILE: deep learning for natural language processing/14-Grammatical Error Correction using T5 in HuggingFace 🤗 by Neuralearn.ai-.ipynb
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install transformers datasets evaluate\n","!pip install sentencepiece"],"metadata":{"id":"d1U4DDMDveam","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667294630883,"user_tz":-60,"elapsed":6998,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f2dfc8a6-db5a-457b-daa5-67aa2f9ee868"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.23.1)\n","Requirement already satisfied: datasets in /usr/local/lib/python3.7/dist-packages (2.6.1)\n","Requirement already satisfied: evaluate in /usr/local/lib/python3.7/dist-packages (0.3.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n","Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.13.1)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n","Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (6.0.1)\n","Requirement already satisfied: dill<0.3.6 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.5.1)\n","Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (2022.10.0)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from datasets) (3.8.3)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from datasets) (1.3.5)\n","Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.13)\n","Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.18.0)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.7/dist-packages (from datasets) (3.1.0)\n","Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (2.1.1)\n","Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (0.13.0)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (6.0.2)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.3.1)\n","Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.8.1)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (1.2.0)\n","Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (4.0.2)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->datasets) (22.1.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.25.11)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.10.0)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->datasets) (2022.5)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (0.1.97)\n"]}]},{"cell_type":"code","source":["!pip install sacrebleu"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m8I1DzXpgM-e","executionInfo":{"status":"ok","timestamp":1667294635208,"user_tz":-60,"elapsed":4330,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f1ccd47d-0aab-4fa2-a535-880369813745"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting sacrebleu\n","  Downloading sacrebleu-2.3.1-py3-none-any.whl (118 kB)\n","\u001b[K     |████████████████████████████████| 118 kB 35.7 MB/s \n","\u001b[?25hRequirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (4.9.1)\n","Collecting colorama\n","  Downloading colorama-0.4.6-py2.py3-none-any.whl (25 kB)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (1.21.6)\n","Collecting portalocker\n","  Downloading portalocker-2.6.0-py2.py3-none-any.whl (15 kB)\n","Requirement already satisfied: regex in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (2022.6.2)\n","Requirement already satisfied: tabulate>=0.8.9 in /usr/local/lib/python3.7/dist-packages (from sacrebleu) (0.8.10)\n","Installing collected packages: portalocker, colorama, sacrebleu\n","Successfully installed colorama-0.4.6 portalocker-2.6.0 sacrebleu-2.3.1\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mVoh-qfys8P1"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import io\n","import os\n","import re\n","import matplotlib.pyplot as plt### plotting bar chart\n","import string\n","import evaluate\n","import time\n","from numpy import random\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import create_optimizer,T5TokenizerFast,DataCollatorForSeq2Seq,TFT5ForConditionalGeneration,TFAutoModelForSeq2SeqLM,AutoModelForSeq2SeqLM,TFT5ForConditionalGeneration"]},{"cell_type":"code","source":["BATCH_SIZE=64\n","MAX_LENGTH=128"],"metadata":{"id":"htaFZnszdCEA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Data Preparation"],"metadata":{"id":"Fd__ex-23pra"}},{"cell_type":"code","source":["#dataset_id=\"liweili/c4_200m\"\n","dataset_id=\"leslyarun/c4_200m_gec_train100k_test25k\""],"metadata":{"id":"3oHppOYZ3pGz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset = load_dataset(dataset_id)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281,"referenced_widgets":["6c2dd25c43b94bcc9644d0711b9382a0","f9852fe3b01744d19759b65c8150744e","4b48852a9e75440ab8a4f7c01ad700d2","5bc65c62296f4d52b0be2b228d9e5099","39a466d071ac4a1f89e41123b4a4aaf2","bc4ea3fda5454abe91d8588d0212d2b0","59ec67122a284d81b4b7d32c1d332e8a","dd63541eca2f444382d06099d56b47bf","bf98116909cb42a88f175aca750d3a33","616eb799b5f04a7fb357beb1657484b8","6ce203c895004a7c99a2cf0c933cbb45","90d6be1f7d734710a810465748728c04","285c395146f6466c8043086805548ebf","2d717fd85d8140deaa9720c9c4fc8cce","3e4a2878d38f4fe89dbad74bd32ce08e","ecbbbf1ff26f491480d6bdfd9b02b823","30e4fef14b7b459abd02ca87aba401de","1da762f6b9454338ab6380de7446fb6f","f42c52641eb44d29a6dd1e898371c840","1fedf09f47b749a3b646c79cfcfbafd1","218cd32c92cd4252ac10e99801ff749a","00d7ba3808624bdc8ecd2eeccfd5d0a1","55a1b91aa404445bb0ac29b86f66f236","01a169df0cc94bb798696f0c64c9f728","73d9282b9e3f4d55ac4c5a4d60444c63","2846d6fee26740179f1b50144b5ddcf8","926f8b6782ab4419886bf20a9f702c55","d747de6f5be145288733e1984c6f57e7","e972eb8eda404b2bb160976e6eb2cbfd","f8f5669f458e443d8910458ac5eeb262","d20feaef4a9f42b79f7ce66bbc783da9","3b78d4a3f6434fa2a4c76859c458a597","5b491e19f5894f80b72c3c26447a4c34","c762679e96314b78b3290535947e1027","566750da3e9a4b87afa3c575fafaeda4","34c0486d14cf48deafe216eb47883417","69f5b4bb31024d9cbf3dc4a478dcb77e","11bba38c25e04782816653a4392bad3e","23e2b5ed555d4c22a00d5b0156398d74","7bb7337339564e748ad138271fea1c92","2499ec79c46e433ebdb75181e5c2e6ea","cdbff005cc56499c84616310d38363fd","b96cc8ff0ec4433cbb5c5bd2d844e78a","41c90e551612452bb55bba7570dfff57","da0122ece58f4247ac9af5e4649e97b7","44c4712e8b674485ab5325e8c5cd0cc1","dfbb0a3aaa6840a5af0b22a9d4de7b45","229255ac1e2845b9b6c31dd863fb7a7f","ee7e11981ede42ca8fa014e4fc9004be","09651fe19aa5426a98db2c2f5d65c37e","775add814fca45089ec74a5a1eb2db80","ef89fbed71e044a9badc3f10b58b61f7","f107dcdec2db48c08671bad81d66a404","27109587acf843d7959ccd8aa00eabd7","15e0c96e87804c1e9fdd4cf3e4449aed","d6e547570cc84692a7e4fcb73499b92d","48dcc9132f2241eebaab39d1b1c884c2","53d9c851a19d46a2a4a06eec3894f88d","739fb577dc1040f599e009cad45f8155","9ee262f6619545a2b34ccf825f076613","089796fae2f54f9fbfbec61d3cec262f","a7f225ac1470431ab7a61b312d040756","4e44f03bbec541d4825baf62bcf2a04d","23644df0a0754df18126ee4db9c9516d","8da4d3361753474f8855f26fa77f0760","e05e84443d9044469a723907713e8665","2def8d0be5944e008c99bea20e6a1043","27d4c4d33461483fbdf3f4e73b3f76b7","7c1a7c018875424397af9eb6eed3d701","8cb72e843aac4f12bfeb76c85f5a7037","715b4ece17aa4e29be24ed0c71cffaad","9fc5a18aae58462a9bce44d64df26e7c","6c25c9033bb24429998e39b98faf192a","893e65ab610d41c69d407982f5a4944f","854dbd7315c24e4fa7e0774d94c43293","76e5f489ec854738b6a7e97f5680b0bb","e8e3a621733d4d63b9757d8c26b37c9d","8455c135cd65495ca1cb2f3a9550b714","a2d1038e8a5849a7ae80f4c2cce5d9be","1ab0cb416d9b45648b0f5a5926ab9dea","636be8fc22674587a21056be9700c3b9","3864a5f844b44af6b1170c8833cdac3a","96baa5e059634772b0fc5c71d41f3127","59e990ff439a48ba812152c92fcc7e6c","a11d442b37d64c768f9e65fd5d4967c3","e3f0b46d2c904674bb469b3d334dbb15","c711fd06a2804b9387194976783737df","25c9134bdd9f4ab89f6daf3935f9ad9f"]},"id":"Fki4sYv53pJT","executionInfo":{"status":"ok","timestamp":1667283475661,"user_tz":-60,"elapsed":2550,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d2759d08-8232-4105-b803-4366a4c1813f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/1.02k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6c2dd25c43b94bcc9644d0711b9382a0"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Using custom data configuration leslyarun--c4_200m_gec_train100k_test25k-97a9e10d1a9b0a0d\n"]},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset csv/leslyarun--c4_200m_gec_train100k_test25k to /root/.cache/huggingface/datasets/leslyarun___csv/leslyarun--c4_200m_gec_train100k_test25k-97a9e10d1a9b0a0d/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"90d6be1f7d734710a810465748728c04"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/26.9M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"55a1b91aa404445bb0ac29b86f66f236"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/6.73M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c762679e96314b78b3290535947e1027"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"da0122ece58f4247ac9af5e4649e97b7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["0 tables [00:00, ? tables/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d6e547570cc84692a7e4fcb73499b92d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["0 tables [00:00, ? tables/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2def8d0be5944e008c99bea20e6a1043"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/leslyarun___csv/leslyarun--c4_200m_gec_train100k_test25k-97a9e10d1a9b0a0d/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8455c135cd65495ca1cb2f3a9550b714"}},"metadata":{}}]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P_7zMtfx3pLe","executionInfo":{"status":"ok","timestamp":1667283476107,"user_tz":-60,"elapsed":453,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a54ce1c8-7d52-4dd4-a682-c92d5290ddf5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input', 'output'],\n","        num_rows: 100000\n","    })\n","    test: Dataset({\n","        features: ['input', 'output'],\n","        num_rows: 25000\n","    })\n","})"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ia7omiS23pNl","executionInfo":{"status":"ok","timestamp":1667283476110,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"853ddffc-76ef-4a95-917e-127097deca4b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input': 'Bitcoin is for $7,094 this morning, which CoinDesk says.',\n"," 'output': 'Bitcoin goes for $7,094 this morning, according to CoinDesk.'}"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["model_id=\"t5-small\"\n","tokenizer=T5TokenizerFast.from_pretrained(model_id)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237,"referenced_widgets":["1eb5cccb02e74cdab39478195786349d","c4f0deac9e644f14905923ba670c4c06","f1c88fee59b24e19b0ce5cd440991a11","5e955d00237745b4bc399f32563ca4d6","0c558d6d54334aeda4ccfd323be9f9b5","50412a9f2b8e4d8ea427ee418ad4ba77","0668951058f941e3a2fad121acca712c","1e922295a7724dd8908db062b1569ce8","816c05136a8c438d8dca628f9dc9e485","3368820438bc47799c44785b5fd24f02","ef170e49813f4062bca879c366c3f596","152695e8a03b4fa09ad98a0af289ce22","1b1d05bab88841cbb3a553d305e48d1f","bfd75d3f4cb44da38ccab4794803699b","3b18cc0d9d9743c2b3f10a2384edf06a","feb8ad97b5dc477c83149408a331469d","929322be851b4cb88d86124c98e84eb3","5a64163039e2473b8eed575259f3f4a9","9c0e3b4c7b0140fdadee9b9a45f592f3","6b871bf1b811477f815dc61d3a725c24","becf58d5877f4a6cbbb470a2b437327c","c0516188b3094e9f8b7f8ee02035d9e8","fb3721f1779e4bf9b17068d597b603a1","ea59b2297f3b4449aea432a93a5cb430","6d0f30a43e8d4fa3b79a8770f777edb2","12a84180a53b4b8b9ea238206b0a237d","9ec7969b9af34a4daf395e380aba2de2","762a04f9899a4a65a1482a9ed0906010","ab30040ed64a4975ad6691b7133a9ea9","d99c27e76e9541dfa929b26ef78d9ba7","4af2010e62a64c23beb7065a42f92db9","9f26df4aa8034727b41d7967cac60360","96cebe6c44ff42189a85183646a0ce08"]},"id":"NlBPPEFxL-qI","executionInfo":{"status":"ok","timestamp":1667283476548,"user_tz":-60,"elapsed":448,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7cfacf12-c23b-468c-ef52-a25a244da446"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/792k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1eb5cccb02e74cdab39478195786349d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"152695e8a03b4fa09ad98a0af289ce22"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fb3721f1779e4bf9b17068d597b603a1"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5_fast.py:166: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n","For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n","- Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding.\n","- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n","- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n","  FutureWarning,\n"]}]},{"cell_type":"code","source":["def preprocess_function(examples):\n","\n","  inputs = [example for example in examples['input']]\n","  targets = [example for example in examples['output']]\n","\n","  model_inputs = tokenizer(inputs, text_target=targets,max_length=MAX_LENGTH, truncation=True)\n","  return model_inputs"],"metadata":{"id":"3k-3DrPZL-u0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset=dataset.map(preprocess_function,batched=True,remove_columns=dataset[\"train\"].column_names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["55d44acc576d43e9a35e1867ad1b03cd","09f1cab43a5a413a83658e9042e14cef","52a2baa2abf341ca9e56ebfc97d0bb24","234fc2447ced4abba2813ee2cc66ed13","efa782ff9c034be8aec911dd8b2b3f03","4dc331bd23754a73b05913333b34d126","7cc3670ac874418697ce754030bbba87","83d0c3884bab476e9eb7a0020be78678","73d08b49e5f14321842066c7bb44e3dc","4776a9fa14b64be8aff3b38a4ec54006","26ac9b263c324b5eb3450e8f9276281e","65bfe485504a466187c779a628e06e1f","97d9457d18cd479a838c993109e8a891","25e78d0cd2244fe19b53da8a77bc1397","d4b91af79de145e288e1b76e25a35d32","43b58d96531b45669c64f1d9e34d6388","49d1370f8acd46cf8a81f610b48938b4","7587ff01a719413fbd20801f5cc39a99","d89bb0b5445e48f1a8fb97942c331288","0f4a9b96ba39415aa4d7df4a1b8587aa","f3e78343a571497e935d8912b049a2df","4660dfb8115b416eb7801e3fabe074b8"]},"id":"NSp02DsIL-w-","executionInfo":{"status":"ok","timestamp":1667283512418,"user_tz":-60,"elapsed":35876,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"842c81d1-e22e-4581-8ced-57cb74df988e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/100 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"55d44acc576d43e9a35e1867ad1b03cd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/25 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"65bfe485504a466187c779a628e06e1f"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"reLxxLcUL-zM","executionInfo":{"status":"ok","timestamp":1667283512419,"user_tz":-60,"elapsed":20,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ff85d534-4260-45c2-f17a-722e56ee9e69"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'attention_mask', 'labels'],\n","        num_rows: 100000\n","    })\n","    test: Dataset({\n","        features: ['input_ids', 'attention_mask', 'labels'],\n","        num_rows: 25000\n","    })\n","})"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["tokenized_dataset['train'][1000]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hQsgUmc83pRz","executionInfo":{"status":"ok","timestamp":1667283512420,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d89e234a-53fe-4384-d6f5-ce518eb6a25f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [6844, 3, 18, 3, 26, 51, 226, 3, 14198, 169, 53, 39, 1339, 5, 1],\n"," 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," 'labels': [6844, 3, 26, 51, 226, 3, 14198, 3, 18, 150, 1339, 831, 5, 1]}"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["model = TFAutoModelForSeq2SeqLM.from_pretrained(model_id)\n","data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer,model=model, return_tensors=\"tf\")"],"metadata":{"id":"2RTtJTTXMR8U","colab":{"base_uri":"https://localhost:8080/","height":138,"referenced_widgets":["53750f91ad1b411c9029a88dc987efe1","0181362eb04143a98c52781e47c0a7d8","af0c1afd9f864d31834e5bd3f421e1a0","1a9e7f1899ed43a0a40539d3e1c973e5","45f307406a4b4e98a8c9388a35021d0d","5990084d5a664afb9e5d7c536e029e2a","5da783ee28e2450683d16b56f048b732","15374c86b0f047f4936c37679582d131","5e9877850ed941069f1141c58234ab6a","1a501fd0cc094fb59f491c70fe254108","2cf0711b5c7a4f279e8413597fa9bae7"]},"executionInfo":{"status":"ok","timestamp":1667283524035,"user_tz":-60,"elapsed":11631,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"86fadb86-48af-4f97-9942-57569c87da03"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/242M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"53750f91ad1b411c9029a88dc987efe1"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.\n","\n","All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at t5-small.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"]}]},{"cell_type":"code","source":["train_dataset=tokenized_dataset[\"train\"].to_tf_dataset(\n","    shuffle=True,\n","    batch_size=BATCH_SIZE,\n","    collate_fn=data_collator,\n",")"],"metadata":{"id":"oF2Gsd2mMR-v","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667283524570,"user_tz":-60,"elapsed":540,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f6fef398-c632-44e9-a2e0-c58325bfcf04"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]}]},{"cell_type":"code","source":["val_dataset=tokenized_dataset[\"test\"].to_tf_dataset(\n","    shuffle=False,\n","    batch_size=BATCH_SIZE,\n","    collate_fn=data_collator,\n",")"],"metadata":{"id":"DtIbJ0a7H4KD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in val_dataset.take(1):\n","  print(i)"],"metadata":{"id":"eF3yj-U2Mb2Z","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667283525231,"user_tz":-60,"elapsed":18,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"625f46e3-104e-4872-f85d-e43436ae3ed6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(64, 98), dtype=int64, numpy=\n","array([[ 9139,   138,  2253, ...,     0,     0,     0],\n","       [ 9627,    37,  5791, ...,     0,     0,     0],\n","       [  116,    27,  4839, ...,     0,     0,     0],\n","       ...,\n","       [20255,  3535,     2, ...,     0,     0,     0],\n","       [10846,   670,    11, ...,     0,     0,     0],\n","       [ 1541,   132,    19, ...,     0,     0,     0]])>, 'attention_mask': <tf.Tensor: shape=(64, 98), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       ...,\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0]])>, 'labels': <tf.Tensor: shape=(64, 99), dtype=int64, numpy=\n","array([[   37, 13477,  2253, ...,  -100,  -100,  -100],\n","       [ 9627,    37,  5791, ...,  -100,  -100,  -100],\n","       [    3,    99,    27, ...,  -100,  -100,  -100],\n","       ...,\n","       [20255,  3535,     2, ...,  -100,  -100,  -100],\n","       [10846,   670,    11, ...,  -100,  -100,  -100],\n","       [  290,    19,    29, ...,  -100,  -100,  -100]])>, 'decoder_input_ids': <tf.Tensor: shape=(64, 99), dtype=int64, numpy=\n","array([[    0,    37, 13477, ...,     0,     0,     0],\n","       [    0,  9627,    37, ...,     0,     0,     0],\n","       [    0,     3,    99, ...,     0,     0,     0],\n","       ...,\n","       [    0, 20255,  3535, ...,     0,     0,     0],\n","       [    0, 10846,   670, ...,     0,     0,     0],\n","       [    0,   290,    19, ...,     0,     0,     0]])>}\n"]}]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"1iWnU2_jV5ME"}},{"cell_type":"code","source":["model.summary()"],"metadata":{"id":"cc6A5lu6Mb4z","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667283525232,"user_tz":-60,"elapsed":16,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"039cd3b5-c97a-43e7-a386-47073e672479"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"tft5_for_conditional_generation\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," shared (TFSharedEmbeddings)  multiple                 16449536  \n","                                                                 \n"," encoder (TFT5MainLayer)     multiple                  18881280  \n","                                                                 \n"," decoder (TFT5MainLayer)     multiple                  25175808  \n","                                                                 \n","=================================================================\n","Total params: 60,506,624\n","Trainable params: 60,506,624\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"G_w2dY2pMob8"}},{"cell_type":"code","source":["num_epochs = 5\n","num_train_steps=len(train_dataset)*num_epochs\n","\n","optimizer, schedule = create_optimizer(\n","    init_lr=2e-5,\n","    num_warmup_steps=0,\n","    num_train_steps=num_train_steps,\n",")\n","model.compile(optimizer=optimizer)"],"metadata":{"id":"TjP3qt_xMb9T","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667283525232,"user_tz":-60,"elapsed":10,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"599f9d06-d0a8-4b41-de2d-1070ee243dbe"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}]},{"cell_type":"code","source":["history=model.fit(\n","  train_dataset,\n","  validation_data=val_dataset,\n","  epochs=num_epochs\n",")"],"metadata":{"id":"qWayYvOdMb_c","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c68eec48-1206-4054-87c1-aa8d6cbeca4b","executionInfo":{"status":"ok","timestamp":1667291862867,"user_tz":-60,"elapsed":8284262,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/5\n","1563/1563 [==============================] - 1650s 1s/step - loss: 0.9188 - val_loss: 0.7687\n","Epoch 2/5\n","1563/1563 [==============================] - 1637s 1s/step - loss: 0.8288 - val_loss: 0.7377\n","Epoch 3/5\n","1563/1563 [==============================] - 1646s 1s/step - loss: 0.8052 - val_loss: 0.7261\n","Epoch 4/5\n","1563/1563 [==============================] - 1639s 1s/step - loss: 0.7941 - val_loss: 0.7214\n","Epoch 5/5\n","1563/1563 [==============================] - 1640s 1s/step - loss: 0.7878 - val_loss: 0.7198\n"]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"id":"W1Qw3hcjMcCB","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1667291862867,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"00c2e531-be64-4074-cf24-f36306c2b399"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["model.save_weights('/content/drive/MyDrive/nlp/gec/t5-small.h5')"],"metadata":{"id":"f1BLqhS6WCL6"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Evaluation"],"metadata":{"id":"dUaWfbV-W9IL"}},{"cell_type":"code","source":["metric = evaluate.load(\"sacrebleu\")"],"metadata":{"id":"9z5CBeP2W-O1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["all_preds = []\n","all_labels = []\n","    \n","for batch in val_dataset.take(5):\n","  predictions = model.generate(\n","      input_ids=batch[\"input_ids\"], attention_mask=batch[\"attention_mask\"]\n","  )\n","  decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n","  labels = batch[\"labels\"].numpy()\n","  labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n","  decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n","  all_preds.extend(decoded_preds)\n","  all_labels.extend(decoded_labels)\n","\n","result = metric.compute(predictions=all_preds, references=all_labels)\n","print(result)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XAyOHWrPuKN7","executionInfo":{"status":"ok","timestamp":1667294754960,"user_tz":-60,"elapsed":15621,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c7c7aff6-9a20-49b4-95d8-59c11bb517e2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'score': 27.081681255317868, 'counts': [3548, 2739, 2162, 1706], 'totals': [4157, 3837, 3517, 3197], 'precisions': [85.35001202790474, 71.38389366692729, 61.47284617571794, 53.362527369408824], 'bp': 0.40503703885753123, 'sys_len': 4157, 'ref_len': 7914}\n"]}]},{"cell_type":"code","source":["decoded_preds"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GLZtF0pKuKQI","executionInfo":{"status":"ok","timestamp":1667294758642,"user_tz":-60,"elapsed":579,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6ec5036f-967c-4566-d6cd-bd733880992c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['The evening will also include a series of readings about a poet’s life and',\n"," 'How long has been working at the Museum?',\n"," 'Share price of Tesla Motors, Inc. plummeted 1.6% to $219.',\n"," 'Recycle and scrap metal centers will pay top dollar for anything you bring in from a full',\n"," '5. In InfoClick, use the menu DataBase> Scan For New Emails',\n"," 'TT: How much more money do you need these maintenance projects?',\n"," 'Bancari Romani and RCS Sport are pleased to announce the partnership with Brooks US',\n"," 'Works on both offshore and onshore seismic data.',\n"," 'In the gable in the main building is the intoning white triangle for “water',\n"," 'Don’t be shy with the pepper,y or want to feel the heat and the full',\n"," 'Timely essay written by Carl Freitag two years ago.',\n"," 'The finding is included in one of two official reports into allegations of a plot to take control',\n"," 'To be used to build additional tools on top of.',\n"," '11 + Then shall the /(Coma) children of Judah and',\n"," 'Meets Snell 2010 and DOT certifications.',\n"," 'The local environmental community established dozens of new pathways towards achieving our ambitious 2050 goals for',\n"," 'They were joined by Suffolk Scouts’ County Youth Commissioner, Ollie Smith. He gave an',\n"," 'I’d love to say I had full grace at all times, but, that would fall',\n"," 'APR model also does not take income growth into account.',\n"," 'JERUSALEM — Before President Donald Trump announced nearly two weeks ago that the United',\n"," 'Do I need to have a website and to take a client the web entry system?',\n"," 'I’m a certified life-coach with 15 years-on-partitude training',\n"," 'If your teeth lack in length, gums contour may also be advised – a treatment',\n"," 'Firefighter responded to a shed fire on Atkins Road in Port Huron on Monday',\n"," 'JL: MajaYellow, can I ask to give a brief introduction to',\n"," 'Doors declare at 6 pm and start time at 6:30 p.m.',\n"," '54 good records with 192 task.',\n"," 'Supplied with broad point type R blade designed for general use heat cutting.',\n"," 'Fantastic design very nice no thanks.',\n"," 'What Happens In the Fontan Procedure?',\n"," 'On stabbing voodoo doll with a pin, knife, ice',\n"," 'Enjoy convenient walking distance to Westfield Shopping C, Belconnen Bus Interchange, Emu',\n"," 'The above changes make the Student MSTUFEE field redundant and it will be removed from the',\n"," 'And located at Riao, just 492 feet from Riao Reser',\n"," 'On behalf of my subject, I (would like?) thank the Almighty, Fans, Well',\n"," 'Colaco College of Management of Mangalore Karnataka is a reco',\n"," 'If you think you may have a problem with your cut edge corrosion, get in touch here',\n"," 'What’s offered: Local makers tell tools, expertise and workshop space.',\n"," 'Deete an environment at – Check and enter a date to automatcally',\n"," 'We ensure that our client gets a fruitful result with our best business listing services in Ha',\n"," 'making money would mean that yo would have to subscribe to something like adsense or sell',\n"," 'A Tulsa, Oklahoma woman was sentensed to 16 years in prison Thursday for',\n"," 'Decide to do some research and came across YAPCHANKOR was impressed by their',\n"," 'Short-Time Fourier Transform on window.',\n"," 'SHADOIT BUSINESS custody Ltd can request a director (director), a shareholder',\n"," 'Perform all duties and responsibilities in a timely and efficient manner, in accordance with the',\n"," '3.Hallie Preskill is the co-author of Darlene Russ-E',\n"," 'That connection helped the Gamecocks (12-11, 7-3 Southeastern Conference) overcome a',\n"," 'Handmade Woven Leather Retro 12 Constellation Bracelet by JauxinFeature:',\n"," '(Check Keywords” in Output type) drop-down list. (Reference sub',\n"," 'It also ensures that its six stores, whether operated in rural or suburban areas of the country',\n"," 'The city name is linked to Greek historians who called it Helmantiké, Herm',\n"," 'at inda styling, we have an undying love for creating unique and inspiring we.',\n"," 'Family of 3 from £697979.',\n"," 'If you want you obvious benefit on it in two weeks, Aniracetam 750',\n"," 'This is our second illustration of Ganon and the Final Battle in the Legend of Zelda',\n"," 'Tap The Thumbnail Bellow To See Related Gallery Of \"Front Door Privacy',\n"," 'It’s utterly essential that you know exactly who your ideal client is and that you',\n"," 'On Jnauary 9, 2019, High Tide announced that senior executives of the Company were to',\n"," 'Sandals Leather almond buckwheat-toed Imported Appliqué',\n"," 'Keeping the soil out put and insecure, it does not dry out.',\n"," 'Savour mouth-watering Mexican cuisine with our lunch, dinner, catering from our Restaurant in',\n"," 'Accroding to Mr Rickenbach, there was a manifestation of ocean violence at',\n"," 'bit changing on the absorbance and reflection of light on the surface of a planet, such']"]},"metadata":{},"execution_count":86}]},{"cell_type":"code","source":["decoded_labels"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7EWaS0G7uKSU","executionInfo":{"status":"ok","timestamp":1667294760549,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"83542064-8b2e-47f1-ce49-e460e76a8235"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['The evening will also feature a series of readings about the poet’s life and work by former and current poet laureates of Peterborough; Keely Mills, Pete Cox, Charley Genever and Toby Wood.',\n"," 'How long have you been working at the Museum?',\n"," 'Share price of Tesla Motors, Inc. plummeted 1.6% to $219.07 on Jul 8, after a trademark infringement charge was leveled against the automaker by businessman Zhan Baosheng in China.',\n"," 'Recycle and scrap metal centers will pay you top dollar for anything you bring in, from a bag full of soda cans to your old water heater, you can expect to get some dough for your old junk.',\n"," '5. In InfoClick use the menu Database > Scan For New Emails. Once InfoClick has finished scanning for new emails, make sure the copied files are complete and the search results are as you expect.',\n"," 'TT: How much more money do you need for those maintenance projects?',\n"," 'Bancari Romani and RCS Sport are pleased to announce the partnership with Brooks. The US sports brand has become the new technical partner of the Huawei RomaOstia Half Marathon 2019 scheduled for Sunday, March 10th.',\n"," 'Works on both offshore and onshore seismic data.',\n"," \"Inside the white triangle on the gable of the main building is the character for “water”. I'll have to ask what that might mean when I go back for more pizza.\",\n"," 'Don’t be shy with the pepper, you want to feel the heat and the full flavor.',\n"," 'A timely essay written by Carl Freitag two years ago.',\n"," 'The finding is included in one of two official reports into allegations of a plot to take control of the leadership of schools in the city in central England and impose Islamic principles such as gender segregation.',\n"," 'then be used to build additional tooling on top of.',\n"," '11 Then shall the (Coma) children of Judah and the (Coma) children of Israel be gathered together, and appoint themselves one head, and they shall come up out of the land: for great shall be the day of Jezreel.',\n"," 'Meets Snell 2010 and DOT Certifications.',\n"," 'The local environmental community established dozens of new pathways towards achieving our ambitious 2050 goals for reducing greenhouse gas emissions. 400 people were in attendance to hear more than 30 speakers present their unique perspectives on California’s climate challenge.',\n"," 'They were joined by Suffolk Scouts’ County Youth Commissioner, Ollie Smith who gave an insightful look into the success of You Shape Scouting in Suffolk. Deputy UK Youth Commissioner, Victoria Lawton also took time to support us.',\n"," 'I’d love to say I had my grace-face on at all times, but, that would fall into the category of Things-That-Make-Your-Nose-Grow.',\n"," 'The APR model also does not take income growth into account.',\n"," 'JERUSALEM — Before President Donald Trump announced nearly two weeks ago that the United States would move forward recognizing Jerusalem as Israel’s capital, he was warned from almost all sides about the political chaos that the decision would unleash.',\n"," 'Do I need to have a website to use the Web Entry System?',\n"," 'I’m a certified life coach with 15 years of hands-on coaching.',\n"," 'If your teeth lack in length, gum contouring may also be advised – a treatment that trims back the gum to show more of your tooth.',\n"," 'Firefighters responded to a shed fire on Atkins Road in Port Huron Township on Monday evening.',\n"," 'JL: Maja, can I ask you to give a brief introduction to your book, The Green Bloc?',\n"," 'Doors open at 6 pm and start time at 6:30 p.m.',\n"," '54 records with 192 tasks.',\n"," 'Supplied with broad tip type R blade designed for general use heat cutting.',\n"," 'Fantastic design, very nice thanks.',\n"," 'What Happens After the Fontan Procedure?',\n"," 'Upon stabbing this voodoo doll with a pin, knife, icepick, etc it will spout out \"nastygrams\" on the computer screen that it is connected to.',\n"," 'Enjoy the convenience of being within walking distance to Westfield Shopping Centre, Belconnen Bus Interchange, Emu Bank, Benjamin Offices, Cameron Offices, Australian Bureau Of Statistics, Australian Communications and Media Authority and The Belconnen Labour Club, just to name a few.',\n"," 'The above changes make the Student.MSTUFEE field redundant, and it will be removed from the record.',\n"," 'Set in Riao, just 492 feet from Riao Reservoir, Apartamentos Pico Llerenes offers heated apartments with large windows and great views of the Picos de Europa Mountains.',\n"," 'On behalf of my team, I would like thank the Almighty, the Fans, Well Wishers, DJs, Promoters and all for the love and support given.',\n"," 'Colaco College of Management, Mangalore Karnataka is a recognised institute / college.',\n"," 'If you think you may have a problem with your cut edge corrosion, get in touch here and a thorough assessment will be made before discussing treatments and next steps.',\n"," 'What’s offered: Local makers share tools, expertise and workshop space.',\n"," 'Delete environment at – Check and enter a date to automatically delete an environment.',\n"," 'We ensure our clients with fruitful results with our best business listing services in Hapur!',\n"," 'making money would mean that yo would have to subscribe to something like an Adsense scheme or sell something (via your website).',\n"," 'A Tulsa, Oklahoma woman was sentenced to 16 years in prison Thursday for mutilating the corpse of her love rival during a funeral home viewing in 2015.',\n"," 'Decided to do some research and came across YAPCHANKOR. Was impressed by their explanation and decided to seek treatment.',\n"," 'Short-Time Fourier Transform about windows.',\n"," 'SHADOIT BUSINESS CONSULTANCY Ltd can make available a director (director), a shareholder and a secretary (secretary) trustee for a complete protection of your privacy, thus protecting your interests and those of your company Limited.',\n"," 'Performs all duties and responsibilities in a timely and efficient manner in accordance with the corporate policies and procedures to achieve the overall objectives of this position.',\n"," '3. Hallie Preskill is the co-author (with Darlene Russ-Eft) of Evaluation Capacity Building.',\n"," 'That connection helped the Gamecocks (12-11, 7-3 Southeastern Conference) overcome a 13-point second half deficit and beat Arkansas, 77-65, on Saturday.',\n"," \"Handmade Woven Leather Retro 12 Constellation Bracelet by JauxinFeature:* Handmade personality 12 constellations, made of durable leather material with good price* Braided snap button zodiac bracelet suit for both mens and womens,perfect gift for everyone* 12 Constellations available for different people* If you are the Constellation Fans, I'm sure there's one for you!* Size: 17cm(6.7in)-21cm(8.3in)* Package content : 1 x constellations bracelet1 x tibetan\",\n"," 'Check “Keywords” in “Output type”. Choose your corpus in the drop-down list “Reference (sub)corpus”. Then choose the other subcorpus to compare in the drop-down list to the right.',\n"," 'It also ensures that its six stores, located throughout the country, have lines that wrap around the outside of the store at all hours of the day.',\n"," \"The city's name is linked to Greek historians who called it Helmantiké, Hermándica and Salamántica. Alfonso X (The Wise) was the first person to call it Salamanca. It originated as an Iberian military outpost on the hill of San Vicente, still to be seen today in the Verraco Prerromano. The inhabitants were known as the Vacceos, and the women were celebrated for their valour in the face of the Carthaginian general Hannibal in 220 BC.\",\n"," 'at inda styling, we have an undying love for creating unique and inspiring we content.',\n"," 'Family of 3 from £6979.',\n"," 'If you want obvious benefits on cognition in two weeks, aniracetam 750mg 4xdaily (as in a DARPA funded study on its effects). Otherwise, you probably won’t see any profound increases in performance at the doses I cited for at least six weeks, then again at somewhere around 12 and 24.',\n"," 'This is our second illustration of Ganon and the Final Battle in the Legend of Zelda: Twilight Princess, but unlike the first one and the majority of the submissions in Link’s Blacklist this was actually made in paper.',\n"," 'Tap The Thumbnail Bellow to See Related Gallery of \"Front Door Privacy Inside Glass Decor 16\"',\n"," 'It’s utterly essential that you know exactly who your ideal client is and that you tailor every part of your business toward the things he or she likes.',\n"," 'On January 9, 2019, High Tide announced that senior executives of the Company were to be featured speakers at the AltaCorp-ATB 7th Annual Institutional Investor Conference in Toronto, Ontario from January 8-10, 2019, at the Lift & Co. Cannabis Expo Industry Day in Vancouver, British Columbia on Thursday, January 10, 2019 and at the Benzinga Cannabis Capital Conference in Miami, Florida from January 15-16, 2019.',\n"," 'Sandals Leather Open almond toe Imported Appliquéd Block..',\n"," 'Keep the soil moist, and ensure it does not dry out.',\n"," 'Satisfy your craving for mouthwatering Mexican cuisine with our lunch, dinner, and local catering from our restaurant in Longmont for over 30 years!',\n"," 'According to Mr. Rickenbach, there were acts of vandalism at Main Beach over the summer. “Big Brother is not trying to watch, but it’s an extra set of eyes and ears for law enforcement,” he said.',\n"," 'life-induced changes in the absorption and reflection of light on the surface of a planet, such as the red-edge caused when plants absorb red light during photosynthesis but reflect infrared light that is not used.']"]},"metadata":{},"execution_count":87}]},{"cell_type":"code","source":[],"metadata":{"id":"zlEi32d5uKXi"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7jbgoKV070Gr"},"source":["# Testing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5H8Y9MERnfX2","outputId":"20cb1528-44ab-4dc1-92f5-5eab75d77e45","executionInfo":{"status":"ok","timestamp":1667294074864,"user_tz":-60,"elapsed":1678,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[    0 12919    63     3 18261    31    15   342    53   160  2418     5\n","      1     0     0     0     0]\n"," [    0  6920 11342  4188  9491  1054 13055 28442   235   346    88    60\n","      5     1     0     0     0]\n"," [    0     3    23   261    12   114  5989     5     1     0     0     0\n","      0     0     0     0     0]\n"," [    0  2087    62   225  7958     3     9  1338    28     8   151    45\n","      8   245  3523     5     1]\n"," [    0   116    33    62   352    12   456  1556  3370    58     1     0\n","      0     0     0     0     0]\n"," [    0   186   648  3412  1590    16    82   690     5     1     0     0\n","      0     0     0     0     0]], shape=(6, 17), dtype=int32)\n"]}],"source":["wrong_english=[\n","    \"Dady hav'e eateing her foot\",\n","    \"DJ Sorryyouwastedyourmoneytobehere\",\n","    \"i used to like to swimming\",\n","    \"maybe we should organized a meetin with the people from unesco\",\n","    \"when are we goinge to start play football\",\n","    \"many a time rain fall in my city\"\n","    ]\n","tokenized=tokenizer(\n","  wrong_english,\n","  padding=\"longest\",\n","  max_length=MAX_LENGTH,\n","  truncation=True,\n","  return_tensors='tf'\n",")\n","out = model.generate(**tokenized, max_length=128)\n","print(out)"]},{"cell_type":"code","source":["for i in range(len(wrong_english)):\n","  print(wrong_english[i]+\"------------>\"+tokenizer.decode(out[i], skip_special_tokens=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iytGIOJIZfGR","executionInfo":{"status":"ok","timestamp":1667292933158,"user_tz":-60,"elapsed":525,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7d0ef244-310e-44b1-9dfc-3f48ce508938"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dady hav'e eateing her foot------------>Dady hav'eateing her foot.\n","DJ Sorryyouwastedyourmoneytobehere------------>DJ Sorryyouwastedyourmoneytobehere.\n","i used to like to swimming------------>i used to like swimming.\n","maybe we should organized a meetin with the people from unesco------------>maybe we should organize a meeting with the people from the unesco.\n","when are we goinge to start play football------------>when are we going to start playing football?\n","many a time rain fall in my city------------>many times rain fall in my city.\n"]}]},{"cell_type":"markdown","metadata":{"id":"rvdNvX-YI1U3"},"source":["# Testing on Pretrained Model (No FineTuning)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ycte4ekNI4iv"},"outputs":[],"source":["pretrained_model=AutoModelForSeq2SeqLM.from_pretrained(\n","    \"juancavallotti/t5-base-gec\"\n",")"]},{"cell_type":"code","source":["wrong_english=[\n","    \"Dady hav'e eateing her foot\",\n","    \"DJ Sorryyouwastedyourmoneytobehere\",\n","    \"i used to like to swimming\",\n","    \"maybe we should organized a meetin with the people from unesco\",\n","    \"when are we goinge to start play football\",\n","    \"many a time rain fall in my city\",\n","   ]\n","tokenized=tokenizer(\n","  wrong_english,\n","  padding=\"longest\",\n","  max_length=MAX_LENGTH,\n","  truncation=True,\n","  return_tensors='pt'\n",")\n","out=pretrained_model.generate(**tokenized, max_length=128)\n","print(out)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"izxscuIvmFoJ","executionInfo":{"status":"ok","timestamp":1667294443077,"user_tz":-60,"elapsed":2990,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0b89f845-7611-4495-8be0-70fb7fe604be"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[    0, 12919,    63,     3,   342,   160,  2418,     3,     5,     1,\n","             0,     0,     0,     0,     0,     0,     0],\n","        [    0,  6920, 11342,     6,    25, 21133,    39,   540,     3,     6,\n","             1,     0,     0,     0,     0,     0,     0],\n","        [    0,    27,   261,    12,   114,  5989,     3,     5,     1,     0,\n","             0,     0,     0,     0,     0,     0,     0],\n","        [    0,  3836,    62,   225,  7958,     3,     9,  1338,    28,     8,\n","           151,    45,   245,  3523,     3,     5,     1],\n","        [    0,   366,    33,    62,   352,    12,   456,  1556,  3370,     1,\n","             0,     0,     0,     0,     0,     0,     0],\n","        [    0,  1404,     3,     9,    97,  3412,  7250,    16,    82,   690,\n","             1,     0,     0,     0,     0,     0,     0]])\n"]}]},{"cell_type":"code","source":["for i in range(len(wrong_english)):\n","  print(wrong_english[i]+\"------------>\"+tokenizer.decode(out[i], skip_special_tokens=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ugXvYpFRmFq0","executionInfo":{"status":"ok","timestamp":1667294443077,"user_tz":-60,"elapsed":6,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"79eb8c9e-ca29-44ee-8203-dde40221652d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dady hav'e eateing her foot------------>Dady ate her foot.\n","DJ Sorryyouwastedyourmoneytobehere------------>DJ Sorry, you wasted your money,\n","i used to like to swimming------------>I used to like swimming.\n","maybe we should organized a meetin with the people from unesco------------>Maybe we should organize a meeting with the people from unesco.\n","when are we goinge to start play football------------>When are we going to start playing football\n","many a time rain fall in my city------------>Many a time rain falls in my city\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"ohSRrosXmXlA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Deprecated Code"],"metadata":{"id":"KTnYj_qxBuLY"}},{"cell_type":"markdown","source":["## Data Download"],"metadata":{"id":"oJ1gJRN6vAt2"}},{"cell_type":"code","source":["!pip install -q kaggle\n","! mkdir ~/.kaggle\n","! cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d a0155991rliwei/c4-200m\n","!unzip \"/content/c4-200m.zip\" -d \"/content/dataset/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9mHoI9_ds_cZ","executionInfo":{"status":"ok","timestamp":1658759048471,"user_tz":-60,"elapsed":193250,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b1b57706-aaff-4ed1-ef91-4a5c7715c1d0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading c4-200m.zip to /content\n","100% 19.3G/19.3G [02:43<00:00, 81.6MB/s]\n","100% 19.3G/19.3G [02:44<00:00, 126MB/s] \n","Archive:  /content/c4-200m.zip\n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00000-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00001-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00002-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00003-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00004-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00005-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00006-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00007-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00008-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00009-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00010-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00011-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00012-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00013-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00014-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00015-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00016-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00017-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00018-of-00512  \n","  inflating: /content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00019-of-00512  "]}]},{"cell_type":"markdown","source":["## Data Preparation"],"metadata":{"id":"C0POpLjkvDWQ"}},{"cell_type":"code","source":["max_source_length = 128\n","max_target_length = 128"],"metadata":{"id":"_nd5cPgQF3D4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["raw_dataset = tf.data.TFRecordDataset('/content/dataset/c4200m/1.0.0/c4200m-train.tfrecord-00001-of-00512')"],"metadata":{"id":"5N57IOyzu054"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for raw_record in raw_dataset.take(2):\n","    print(repr(raw_record))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SF4eSaift052","executionInfo":{"status":"ok","timestamp":1658759054225,"user_tz":-60,"elapsed":366,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0f23fcb3-4b31-4f85-da36-8891e23f561a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<tf.Tensor: shape=(), dtype=string, numpy=b'\\n\\xc6\\x01\\nb\\n\\x06output\\x12X\\nV\\nTOur courses are free to schools who are members of ICT SLA 1 \\xe2\\x80\\x93 curriculum support.\\n`\\n\\x05input\\x12W\\nU\\nSOur courses are highly free schools to members of ICT SLA 1 \\xe2\\x80\\x93 curriculum support.'>\n","<tf.Tensor: shape=(), dtype=string, numpy=b'\\n\\xdd\\x01\\nh\\n\\x05input\\x12_\\n]\\n[Udder costs kip60,000. while a pregnant uterus will set you back Kip30,000 or about 4,70 $.\\nq\\n\\x06output\\x12g\\ne\\ncUdder costs Kip60,000 while a pregnant uterus will set you back Kip30,000 or about AUD$4.70 per kg.'>\n"]}]},{"cell_type":"code","source":["# Create a description of the features.\n","feature_description = {\n","    \n","    'input': tf.io.FixedLenFeature([], tf.string, default_value=''),\n","    'output': tf.io.FixedLenFeature([], tf.string, default_value=''),\n","}\n","def _parse_function(example_proto):\n","  # Parse the input `tf.train.Example` proto using the dictionary above.\n","  return tf.io.parse_single_example(example_proto, feature_description)"],"metadata":{"id":"fbTf9mTSt08d"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["parsed_dataset = raw_dataset.map(_parse_function)\n","parsed_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LlYM_oeSt0-2","executionInfo":{"status":"ok","timestamp":1658759055183,"user_tz":-60,"elapsed":500,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ff9aa90d-b0a5-4a47-d98b-e8ea9c6602df"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<MapDataset element_spec={'input': TensorSpec(shape=(), dtype=tf.string, name=None), 'output': TensorSpec(shape=(), dtype=tf.string, name=None)}>"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["for parsed_record in parsed_dataset.take(1):\n","    #print(repr(parsed_record))\n","    pass\n","print(parsed_record['input'].numpy())\n","print(parsed_record['output'].numpy())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lz-ikPajt1Bd","executionInfo":{"status":"ok","timestamp":1658759055949,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a83c484c-7ed3-40e8-cbae-d533fc115a90"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["b'Our courses are highly free schools to members of ICT SLA 1 \\xe2\\x80\\x93 curriculum support.'\n","b'Our courses are free to schools who are members of ICT SLA 1 \\xe2\\x80\\x93 curriculum support.'\n"]}]},{"cell_type":"code","source":["tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n","len(tokenizer.get_vocab())"],"metadata":{"id":"ibf5qHzHF2Aw","colab":{"base_uri":"https://localhost:8080/","height":229,"referenced_widgets":["88441bde0ce44e5094c8cfce71048d45","14bdd8c75e194eae9d962954390684bc","ba2a9977c75847869e78573d337e2fc1","3f86e0b620574dc391b1d422dcffe464","9806557a46e349ab8fa4738f3f087054","1251cdba1ddb457dba38d7e7d46f758d","3613934f672742359ef84fc92fb38aae","9e8dbce41b93427f97bfd02a75594835","f9861948c5d54e799dd57582ad1d79d5","5bf440e8fab049ed8ebb2d8aabaa394f","b861cb74663f4dbb85b87ca40af21445","c727cf333a0e4bdc9a9fd5984843a711","f593669580b843fd81ef623bb42a2acd","db91562a80a74f90afdd65377a169498","7c7d5167bd5c49e087543e9079cce5fa","22473e33d5c94d649d505e4db795ec92","c63488dc46d746ed9d00263d2a7f9d43","041e2b0f079443d3b07e88dd7b42f7c9","d0a803deb7594c3788a53226b161d564","0b831683f1894eb8949952cc1ef810b2","3102e7b014c94e2b913d10e0cd70bfe6","37c071cdcbb94e38a1d598d5c0d2c4dc"]},"executionInfo":{"status":"ok","timestamp":1658759061492,"user_tz":-60,"elapsed":2862,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a081c4f4-e2ea-487d-c87c-b4d7a7fef1c1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/773k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"88441bde0ce44e5094c8cfce71048d45"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c727cf333a0e4bdc9a9fd5984843a711"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:174: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n","For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n","- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n","- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n","- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n","  FutureWarning,\n"]},{"output_type":"execute_result","data":{"text/plain":["32100"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["inp_list=[]\n","for parsed_record in parsed_dataset:\n","  inp_list.append([parsed_record['input'].numpy().decode(\"utf-8\"),parsed_record['output'].numpy().decode(\"utf-8\")])"],"metadata":{"id":"WUcfH1A0sf_e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(inp_list)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3SVM__01uUjN","executionInfo":{"status":"ok","timestamp":1658759169296,"user_tz":-60,"elapsed":394,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"25092794-98c5-4906-fc6b-159716b0445d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["359168"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["header = ['input','attention_mask','output']\n","\n","with open('c4_200_00001.csv', 'w', encoding='UTF8', newline='') as f:\n","    writer = csv.writer(f)\n","    # write the header\n","    writer.writerow(header)\n","      \n","    encoding = tokenizer(\n","      list(np.array(inp_list)[:,0]),\n","      padding=\"longest\",\n","      max_length=max_source_length,\n","      truncation=True,\n","    )\n","\n","    target_encoding = tokenizer(\n","      list(np.array(inp_list)[:,1]),\n","      padding=\"longest\",\n","      max_length=max_source_length,\n","      truncation=True,\n","    )\n","    final_list=[[encoding.input_ids[i],encoding.attention_mask[i],target_encoding.input_ids[i]] for i in range(len(encoding.input_ids))]\n","    writer.writerows(final_list)"],"metadata":{"id":"BMIJeSnJGZmD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!cp /content/c4_200_00001.csv /content/drive/MyDrive/gec_dataset"],"metadata":{"id":"6PSoeNvmhsc_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data= pd.read_csv(\"/content/drive/MyDrive/gec_dataset/c4_200_00000.csv\")"],"metadata":{"id":"N_OADvZG1mQP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","print(data['input'][1001])\n","print(len(list(map(int,data['input'][100][1:-1].split(\", \")))))\n","print(list(map(int,data['input'][100][1:-1].split(\", \"))))\n","print(list(map(int,data['attention_mask'][100][1:-1].split(\", \"))))\n","print(list(map(int,data['output'][100][1:-1].split(\", \"))))"],"metadata":{"id":"AzyJWNwx3cY2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1658734336300,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"536ef52f-6401-4822-d811-d6a11901d741"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[31118, 3, 18, 23535, 21, 82, 384, 11, 3, 9, 385, 7029, 140, 21, 21, 128, 161, 1576, 16, 207, 97, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","128\n","[19204, 3, 390, 30, 48, 794, 130, 5163, 11, 8107, 120, 29329, 11, 985, 13, 135, 808, 2656, 20295, 298, 8, 119, 985, 808, 4621, 5819, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n","[19204, 3, 390, 30, 48, 794, 130, 5163, 11, 8107, 120, 29329, 11, 985, 13, 135, 808, 8, 2656, 20295, 298, 8, 119, 985, 808, 4621, 5819, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"]}]},{"cell_type":"code","source":["len(data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0tffiey73cbs","executionInfo":{"status":"ok","timestamp":1658734373846,"user_tz":-60,"elapsed":366,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e63a4328-8edf-4622-edad-880e1357a865"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["359169"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["class DataGenerator(tf.keras.utils.Sequence):\n","    def __init__(self,data,batch_size,):\n","        self.batch_size=batch_size\n","        self.data=data\n","        \n","    def __len__(self):\n","        return len(self.data)//self.batch_size\n","\n","    def __getitem__(self,idx):\n","        x,y=self.data_generation(idx)\n","        return x,y\n","\n","    def data_generation(self,idx):\n","\n","      x_1=[]\n","      x_2=[]\n","      y=[]\n","\n","      for j in range(idx*self.batch_size,(idx+1)*self.batch_size):\n","          x_1.append(list(map(int,data['input'][j][1:-1].split(\", \"))))\n","          x_2.append(list(map(int,data['attention_mask'][j][1:-1].split(\", \"))))\n","          y.append(list(map(int,data['output'][j][1:-1].split(\", \"))))\n","\n","      input_ids=np.array(x_1)\n","      attention_mask=np.array(x_2)\n","      decoder_input_ids=np.array(tf.concat([tf.zeros([self.batch_size,1],dtype=tf.int32),np.array(y)[:,0:-1]],axis=-1))\n","      labels=np.array(y)\n","\n","      return [input_ids,attention_mask,decoder_input_ids],labels"],"metadata":{"id":"dw7T8UGcffIJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["BATCH_SIZE=16\n","train_gen=DataGenerator(data,BATCH_SIZE,)\n","#val_gen=DataGenerator(val_images,BATCH_SIZE,tokenizer,data_dict,starttoken,INPUT_DIM)"],"metadata":{"id":"CsloqBtRyHmw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Modeling"],"metadata":{"id":"66av7g0sSayQ"}},{"cell_type":"code","source":["model = TFT5ForConditionalGeneration.from_pretrained(\"t5-base\")\n","model.summary()"],"metadata":{"id":"_zC8hNO1jHJi","colab":{"base_uri":"https://localhost:8080/","height":410,"referenced_widgets":["b9d744c800bf432ab9eaa8d9de9d7711","da0b2f2680694bf5b15e8d66564f5ff2","a9ce44852a6a4f2492514add2d6c28a2","2a1b706ebbe14c29b94bdf7c4ee79f64","c07921d0271e4403b4d7e00b21bb2d76","8a755c306b2c4f6a82095c5f10484025","f103768b7c9e4573aa7ec20f3f3a6ef0","57ef8ebc9d0e4596ac67fe53a39add9a","c300f5d509364a9eacb7210ec02eb6e6","ffc4635eb6de4303b87be46659c6f42e","7f9f2513c53947f185b84df2cdfb6092","dfa151fe44b44907817c00b1afdd05ac","cf29fb53e5bc456698d26a09be3efd10","11829557764345b7b814328d0969e1e0","f0861d0c272548c88be5284249e76990","3ea72ac5009f471aa1a8108b08c8ad68","3cc87bc146c5459e9a8505ee684e098e","005b66657a004991a1cbdd4e4e0b81eb","e79727b461cb4c17aae58cc8e2e1bbd0","e48220239afc42a9a23e6ae0a6884fc2","b85d4fd581444a549dc6d380a039bc35","30195701893c40c2835b75c5ebe84b9e"]},"executionInfo":{"status":"ok","timestamp":1659019078305,"user_tz":-60,"elapsed":23707,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ae31864e-fbd2-4083-aa1b-be74c8eb078f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading config.json:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b9d744c800bf432ab9eaa8d9de9d7711"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading tf_model.h5:   0%|          | 0.00/851M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dfa151fe44b44907817c00b1afdd05ac"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFT5ForConditionalGeneration.\n","\n","All the layers of TFT5ForConditionalGeneration were initialized from the model checkpoint at t5-base.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5ForConditionalGeneration for predictions without further training.\n"]},{"output_type":"stream","name":"stdout","text":["Model: \"tft5_for_conditional_generation\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," shared (TFSharedEmbeddings)  multiple                 24674304  \n","                                                                 \n"," encoder (TFT5MainLayer)     multiple                  84954240  \n","                                                                 \n"," decoder (TFT5MainLayer)     multiple                  113275008 \n","                                                                 \n","=================================================================\n","Total params: 222,903,552\n","Trainable params: 222,903,552\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["model.compile(\n","    optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5),\n","    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n","    #run_eagerly=True,\n",")\n","model.fit(train_gen,epochs=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0xzFGluwuCnY","outputId":"52a32280-0204-463c-b918-80dafde06505","executionInfo":{"status":"ok","timestamp":1658756028048,"user_tz":-60,"elapsed":21316496,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["22448/22448 [==============================] - 21295s 946ms/step - loss: 0.1806\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f2d7c6ece90>"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","source":["model.load_weights('/content/drive/MyDrive/gec_dataset/t5-base-00000-gec.h5')"],"metadata":{"id":"IeeOcf9TCj6x"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153,"referenced_widgets":["87f6f2ba34444819be7f2b292f801534","79fcb372d8c54f8fa0196692fbe8184b","da6ac376a5204d1fa5ba0e6a3ac2be0c","2989ad08dec745fe9195cad50917ef72","e11eca3a608a448d89c3beb3d06d7721","56d64033cc364f258b54c10a84a34123","8ab8b4460eae4ff3896f4bb775237092","55142f4fa57049f7b0d6a47b5e3cacc5","25e9faa66be94bf4b07137f8eeca7371","fc5b68316d3a467f90d1465e71191356","ebb5c49e84714defbf601bf4746f9d33"]},"id":"Sb3jJWgkkwqO","executionInfo":{"status":"ok","timestamp":1659019089087,"user_tz":-60,"elapsed":2517,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"41a65988-a8c9-48cf-c9af-46e2f2e1e01d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading spiece.model:   0%|          | 0.00/773k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"87f6f2ba34444819be7f2b292f801534"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:174: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n","For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n","- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n","- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n","- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n","  FutureWarning,\n"]}]},{"cell_type":"code","source":["#wrong_english=[\"i thought you come yesterday to come collect the cars?\",\"do you really want to saw me go.\",\n","wrong_english=[\"Dady hav'e eateing her foot\",\n","               \"DJ Sorryyouwastedyourmoneytobehere\",\n","               \"i used to like to swimming\",\n","               \"maybe we should organized a meetin with the people from unesco\",\n","               \"when are we goinge to start play football\",\n","               \"many a time rain fall in my city\"]\n","encoding = tokenizer(\n","      wrong_english,#list(np.array(inp_list)[:,0]),\n","      padding=\"longest\",\n","      max_length=max_source_length,\n","      truncation=True,\n","\n","    )"],"metadata":{"id":"sS8lvwJDFu_i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["outputs=model.generate(np.array(encoding['input_ids']))\n","\n","for i in range(len(wrong_english)):\n","  print(wrong_english[i]+\"------------>\"+tokenizer.decode(outputs[i], skip_special_tokens=True))"],"metadata":{"id":"DbsZCvspuCpx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1659019120087,"user_tz":-60,"elapsed":5028,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7a30df6b-5a60-47be-978e-8306665a8227"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/generation_tf_utils.py:1576: UserWarning: Neither `max_length` nor `max_new_tokens` have been set, `max_length` will default to 20 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n","  UserWarning,\n"]},{"output_type":"stream","name":"stdout","text":["Dady hav'e eateing her foot------------>Dady ate her foot.\n","DJ Sorryyouwastedyourmoneytobehere------------>DJ Sorryyouwastedyourmoneytobehere.\n","i used to like to swimming------------>i used to like swimming.\n","maybe we should organized a meetin with the people from unesco------------>maybe we should organize a meeting with the people from unesco.\n","when are we goinge to start play football------------>when are we going to start playing football?\n","many a time rain fall in my city------------>many a time rain fall in my city.\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"lqSk-gLGbPtw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"wQxzniF9bPwC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Gdyhpuncbvsq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"0jhLBerBliE1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"vOogbB5bliHO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"mvnxWSKZliJo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"9RcmiHJuliL2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"IaBLDgCSbvu8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# from transformers import T5Tokenizer, TFT5ForConditionalGeneration\n","\n","# tokenizer = T5Tokenizer.from_pretrained(\"t5-small\")\n","# model = TFT5ForConditionalGeneration.from_pretrained(\"t5-small\")\n","\n","# model.summary()\n","# # the following 2 hyperparameters are task-specific\n","# max_source_length = 64\n","# max_target_length = 64\n","\n","# # Suppose we have the following 2 training examples:\n","# input_sequence_1 = \"i really loves mangoes.\"\n","# output_sequence_1 = \"i really love mangoes.\"\n","\n","# input_sequence_2 = \"whenever you saw me, run?\"\n","# output_sequence_2 = \"whenever you see me, run!\"\n","\n","# # encode the inputs\n","# input_sequences = [input_sequence_1,input_sequence_2]\n","\n","# encoding = tokenizer(\n","#     [input_sequence_1,input_sequence_2],\n","\n","#     padding=\"longest\",\n","#     max_length=max_source_length,a\n","#     truncation=True,\n","#     return_tensors=\"tf\",\n","\n","#     )\n","\n","# input_ids, attention_mask = encoding.input_ids, encoding.attention_mask\n","# print('input_ids',input_ids)\n","# print(attention_mask)\n","# # encode the targets\n","# target_encoding = tokenizer(\n","#     [output_sequence_1,output_sequence_2],\n","#     padding=\"longest\",\n","#     max_length=max_source_length,\n","#     truncation=True,\n","#     return_tensors=\"tf\",\n","#     )\n","# labels = target_encoding.input_ids\n","# print('labels',labels)"],"metadata":{"id":"pjh0XndDbvxJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# from transformers import T5Tokenizer, TFT5ForConditionalGeneration\n","\n","# tokenizer = T5Tokenizer.from_pretrained(\"t5-small\")\n","# model = TFT5ForConditionalGeneration.from_pretrained(\"t5-small\")\n","\n","# model.summary()\n","# # the following 2 hyperparameters are task-specific\n","# max_source_length = 64\n","# max_target_length = 64\n","\n","# # Suppose we have the following 2 training examples:\n","# input_sequence_1 = \"i really loves mangoes.\"\n","# output_sequence_1 = \"i really love mangoes.\"\n","\n","# input_sequence_2 = \"whenever you saw me, run?\"\n","# output_sequence_2 = \"whenever you see me, run!\"\n","\n","# # encode the inputs\n","# input_sequences = [input_sequence_1,input_sequence_2]\n","\n","# encoding = tokenizer(\n","#     [input_sequence_1,input_sequence_2],\n","\n","#     padding=\"longest\",\n","#     max_length=max_source_length,a\n","#     truncation=True,\n","#     return_tensors=\"tf\",\n","\n","#     )\n","\n","# input_ids, attention_mask = encoding.input_ids, encoding.attention_mask\n","# print('input_ids',input_ids)\n","# print(attention_mask)\n","# # encode the targets\n","# target_encoding = tokenizer(\n","#     [output_sequence_1,output_sequence_2],\n","#     padding=\"longest\",\n","#     max_length=max_source_length,\n","#     truncation=True,\n","#     return_tensors=\"tf\",\n","#     )\n","# labels = target_encoding.input_ids\n","# print('labels',labels)"],"metadata":{"id":"VkTaQC_cRtle"},"execution_count":null,"outputs":[]}]}

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FILE: deep learning for natural language processing/15-Chat With Elon Musk using BlenderBot in HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"markdown","metadata":{"id":"gwDUFKDdR6i_"},"source":["# Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ntIbn-RaRzbs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668009075119,"user_tz":-60,"elapsed":18342,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"732ebc70-6f35-416b-9547-baba524aa8dd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.24.0-py3-none-any.whl (5.5 MB)\n","\u001b[K     |████████████████████████████████| 5.5 MB 6.5 MB/s \n","\u001b[?25hCollecting datasets\n","  Downloading datasets-2.6.1-py3-none-any.whl (441 kB)\n","\u001b[K     |████████████████████████████████| 441 kB 60.5 MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in 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xxhash-3.1.0\n"]}],"source":["!pip install transformers datasets"]},{"cell_type":"markdown","metadata":{"id":"D31_Au7nR_oc"},"source":["# Imports"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZslyJl_QV7rt"},"outputs":[],"source":["import tensorflow as tf\n","import numpy as np\n","import io\n","import os\n","import pandas as pd\n","import re\n","import string\n","import time\n","from numpy import random\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer,BatchNormalization,Dropout,Input,LayerNormalization\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import create_optimizer,DataCollatorForSeq2Seq,DataCollatorForLanguageModeling,BlenderbotTokenizerFast,BlenderbotSmallTokenizerFast,TFBlenderbotForConditionalGeneration"]},{"cell_type":"code","source":["MAX_LENGTH=256"],"metadata":{"id":"uEY8eKT0_3re"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Dataset Preparation"],"metadata":{"id":"b7Uh7in0BVId"}},{"cell_type":"code","source":["#kaggle datasets download -d drmatters/joe-rogan"],"metadata":{"id":"b_zyb-FhUTgG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install -q kaggle\n","!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d christianlillelund/joe-rogan-experience-1169-elon-musk\n","!unzip \"/content/joe-rogan-experience-1169-elon-musk.zip\" -d \"/content/dataset/\""],"metadata":{"id":"wIxD1BEwB5cX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668009087805,"user_tz":-60,"elapsed":5164,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d4d96f73-cbfe-4f7f-a15f-3b14706d869e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading joe-rogan-experience-1169-elon-musk.zip to /content\n","\r  0% 0.00/59.1k [00:00<?, ?B/s]\n","\r100% 59.1k/59.1k [00:00<00:00, 41.1MB/s]\n","Archive:  /content/joe-rogan-experience-1169-elon-musk.zip\n","  inflating: /content/dataset/joe-rogan-experience-1169-elon-musk.csv  \n"]}]},{"cell_type":"code","source":["filepath=\"/content/dataset/joe-rogan-experience-1169-elon-musk.csv\"\n","dataset = load_dataset('csv', data_files=filepath)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":185,"referenced_widgets":["2c317e6df4a04637a2ad806a1400d2f0","0704b1feb73b46dd9a8b2d80a956a42d","34df3fa15e284a1a848f1520ba5eaf47","96f7c5433b1e4102abad8bbe646ddf87","50eac7750cc54819b67a36c04d9c8592","bd8a3384c5b94961a01347b8956d585f","76def636bfb74e278c7aeff16057b80d","36121326048644b6b8fd2b5a615882cc","42c6749c9e51484a94c887a8b3a69e9d","f4c12531c6f64ea68819d1da97b88e21","f25e4918a17b4d2682d18df62e62a5a5","4217ef282a6240faa028d0b82d69ad4d","9c011cc6987b40968a6798cc29b7c056","50a610a5c095485a89e7ad6d048919de","95672f35fb574243b98ced8a934208f2","7dc88de98d9a47f3b4350dbbc0fd4de1","ae2b68f6f04942fa9700a8c9f3d51db5","3dab2d3c46bb4a23910747f6f49ad8d6","c58a6429339041ba8dce690980d5a932","283b35a3fe42490cb12fc5bdd6df9eec","c600795bdcf84dcbaa15b010715fb80d","6b55bbbc95f8460a95dcca2b4b2fe230","7f1a82262f2d48aab8f669384d5b493e","3a7f7c1a05804c5fa6894961922a6ea9","1ae4c4066ec2404abcd8a378387c5b57","11fed1a4e093428cad7a06a6697be9ae","f2d1ae8551ac41fe9df36d6cb6597635","b6058a3442384f23aa6609697deb16be","08975cc0809146489dab70b1feb7d546","e4128c3428c347f1ace978f5a4d803f8","eb1379df3e2c4ab690fd96317ccfb7e0","ab3158ca41c14066b64f04ab687ec041","cfd2150235f14d22bd40a99ad21194c4","c05d7ded7db648ff9e13b99960715419","be652ad518774949a3d2cc793bad3223","a6a790e8298742c196ad910dff8ba6f8","4c12b69e6c3041299333f5f0359775cc","6d3a0b1b879048b6b28a5f778fb25092","688c0fab283a46b4ac929391e1fff30b","fefd5de867f14c629f4af6e8b07cb9a3","b1fb602e1cd0496289d68d8517618939","add430a8373b4607b54bda7c9fc378ef","01e66c639f5f438a883cab61298b3551","707bf10fe31a4527a1c80942f90405d7"]},"id":"nEJXzjRz5IFQ","executionInfo":{"status":"ok","timestamp":1668009088451,"user_tz":-60,"elapsed":649,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"15fd0c5d-918a-469d-df16-a3e8c65b3abd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Using custom data configuration default-90edec479b41c1d7\n"]},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-90edec479b41c1d7/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2c317e6df4a04637a2ad806a1400d2f0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4217ef282a6240faa028d0b82d69ad4d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["0 tables [00:00, ? tables/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f1a82262f2d48aab8f669384d5b493e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-90edec479b41c1d7/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c05d7ded7db648ff9e13b99960715419"}},"metadata":{}}]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rDwB17kf5IHh","executionInfo":{"status":"ok","timestamp":1668009088451,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6cffbfd3-f212-4ddc-bf74-678e4422384f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['Timestamp', 'Speaker', 'Text'],\n","        num_rows: 1831\n","    })\n","})"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GEmYGE1c5IKK","executionInfo":{"status":"ok","timestamp":1668009088452,"user_tz":-60,"elapsed":16,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3dc11c81-dca3-4475-f0a6-bab69e8aec0b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Timestamp': '[00:00:00]',\n"," 'Speaker': 'Joe Rogan',\n"," 'Text': 'Ah, ha, ha, ha. Four, three, two, one, boom. Thank you. Thanks for doing this, man. Really appreciate it.'}"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["model_id=\"facebook/blenderbot-400M-distill\"\n","tokenizer = BlenderbotTokenizerFast.from_pretrained(model_id,truncation_side=\"left\")"],"metadata":{"id":"4IsF16by6hW7","executionInfo":{"status":"ok","timestamp":1668009091873,"user_tz":-60,"elapsed":3434,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/","height":209,"referenced_widgets":["c0e75770cc92482ca83945b322d7f063","be8dc21a8bd84019a7b17235873e6530","5d694554af4c45d3b29c8e3161a83ffb","00550636d21748c3a7e97498e45085e5","5a7fe8ea28eb4704a07454d43174f0ad","95b64a5a41734c9bad62d78601a433c0","4ea857fe0dcd4a02b345cfd32a408ad7","1dc2bf9b3fea4207972ec33be4e20756","287fc24d285f4933913a02e699238440","e6f904677f5a4d928554ad0a04d38e8e","287e8a9f52fe4f40b7e8f2420ebd9297","f2604612bdf4407abd33ce972cf66812","501cdbcbbc774cc8ba5c076813f01e2b","c46715b68d734e71ac0d270332cbbf3b","db76822ef1bd45829f1f8e5572587957","576ec7e88eb04467a75a9ac9624d17fe","153b5158605145f99ebe8aa2777468eb","8d129ac1bc7b4c59afd24504b9b304e9","879851f847dd49299870826283a50c31","d2425d7ad9db472d90b016812d241050","aae2ea9e680e47b1951e44ff99c7509d","cc70d6c280944e6db7e159f3c28463de","e698b2ef1a8d469ca139834b452cd48f","297921af056f477f9f4c2dd4a2a6b9ff","d270426a1a484c6eb602d1a4b43099e6","cdd12f6c1fb94356b60d42b77c7251c1","9b29a3faa13d46739921a8990f7abd2c","58c295e86912463bae537d18881580d8","a7bdc9f5b81b44fe972f2099e0419ce3","9bd3bfc44e794461b875fd3afbd27384","733cac3f26eb4aec9bd4d016064e5329","eae96fc86354475c860f3682b497a568","7b103565a5254e09b95774315a6f472e","1206db921ef4484a9e880014ea735a68","f04d40edf5324f3493ce25f2c688214f","06d0d92f4f3d41af8f296ebf1045b3b3","4cd4c86d434e4ff4934ba1523820ff18","843960720a324e23b3d30ad50a5f4fef","3e75fca6a1f24adead3905c876c2552c","a69f8396f8224169ad520b76f3f334d2","ae169fde065f49cd9ec58c7231552e19","019ae94ef68a437aafca6538027b2909","42ebef384331497d8e02788a9a87f35e","eebd4b2ef649433c83f0cd3f475c793a","80e368e77a2f49f49860a3d2134f1ea9","0416db124f68499f95bc15271d6bebbd","8f90ca8875be4d6983d433e7b6211a9d","4253eed8412d43549e63f9dc332032aa","54cad1f6f0e94541af49b1347d63ed25","91822734e81e4908ab483d8ebf8afa1e","642a6baa7cfe4e8f98b1268d0878fb23","d7b59b2e15854f9aaef1f4187acd6782","ed187d0327de4be5b136eaa26ab3f7ba","2fd879b464e0479789c98d91a73376e0","64b2fd203fe94c54be304907dbcb59c4","dbcd224460054c8794661a9dd79b42db","ebd49d2792334db39012a3b667353f94","48a43f08c992428cb5d043e081711c0f","0b14d30ac785451aacbf29a1aac96638","469370feb3c14ae0bf77c34e9a1ef66b","dffa1bacfa6b4e959543e8c291f83140","c73a8e191c794f19ae07d8851dfc1885","413f3245485c4b66a239c80ac0f967ab","662c019346ac479c8911760a2c280d10","4f429787a9354d5d99557a7578b08ba6","44c3300cab02481782cfb33aa5c5d819"]},"outputId":"5281b895-374b-4d99-c93b-79b68797fdec"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading: 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range(NUM_SAMPLES,len(dataset['train'])):\n","  discussion=\"\"\n","  bot_output=tokenizer.bos_token+dataset['train'][i][\"Text\"]+tokenizer.eos_token\n","  for j in reversed(range(i-NUM_SAMPLES,i)):\n","    discussion=tokenizer.bos_token+dataset['train'][j][\"Text\"]+tokenizer.eos_token+discussion\n","    data_array.append([discussion,bot_output])"],"metadata":{"id":"0XlaHlZEK8IG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pd.DataFrame(data_array,columns=[\"discussion\",\"bot_output\"]).to_csv('discussion.csv')"],"metadata":{"id":"nc91yX3tBWQl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","\n","df = pd.read_csv('/content/discussion.csv')"],"metadata":{"id":"7TceihpiBWTW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"R2btGP6eBWVe","executionInfo":{"status":"ok","timestamp":1668009092879,"user_tz":-60,"elapsed":17,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a9bea98e-8581-4443-a3a2-43ba41344acf"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Unnamed: 0                                         discussion  \\\n","0              0                 <s>It's very good to meet you.</s>   \n","1              1  <s>You're welcome.</s><s>It's very good to mee...   \n","2              2  <s>Ah, ha, ha, ha. Four, three, two, one, boom...   \n","3              3                       <s>Nice to meet you too.</s>   \n","4              4  <s>It's very good to meet you.</s><s>Nice to m...   \n","...          ...                                                ...   \n","5479        5479  <s>You're welcome.</s><s>All you assholes out ...   \n","5480        5480  <s>I believe it's true too. So, thank you.</s>...   \n","5481        5481                       <s>All right, thank you.</s>   \n","5482        5482  <s>All you assholes out there, be nice. Be nic...   \n","5483        5483  <s>You're welcome.</s><s>All you assholes out ...   \n","\n","                                             bot_output  \n","0                          <s>Nice to meet you too.</s>  \n","1                          <s>Nice to meet you too.</s>  \n","2                          <s>Nice to meet you too.</s>  \n","3     <s>And thanks for not lighting this place on f...  \n","4     <s>And thanks for not lighting this place on f...  \n","...                                                 ...  \n","5479                       <s>All right, thank you.</s>  \n","5480                       <s>All right, thank you.</s>  \n","5481  <s>Good night, everybody. END OF TRANSCRIPTAut...  \n","5482  <s>Good night, everybody. END OF TRANSCRIPTAut...  \n","5483  <s>Good night, everybody. END OF TRANSCRIPTAut...  \n","\n","[5484 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-8b05d09f-3f4d-4a66-a85f-6b31b49dff78\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>discussion</th>\n","      <th>bot_output</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>&lt;s&gt;It's very good to meet you.&lt;/s&gt;</td>\n","      <td>&lt;s&gt;Nice to meet you too.&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>&lt;s&gt;You're welcome.&lt;/s&gt;&lt;s&gt;It's very good to mee...</td>\n","      <td>&lt;s&gt;Nice to meet you too.&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>&lt;s&gt;Ah, ha, ha, ha. Four, three, two, one, boom...</td>\n","      <td>&lt;s&gt;Nice to meet you too.&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>&lt;s&gt;Nice to meet you too.&lt;/s&gt;</td>\n","      <td>&lt;s&gt;And thanks for not lighting this place on f...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>&lt;s&gt;It's very good to meet you.&lt;/s&gt;&lt;s&gt;Nice to m...</td>\n","      <td>&lt;s&gt;And thanks for not lighting this place on f...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>5479</th>\n","      <td>5479</td>\n","      <td>&lt;s&gt;You're welcome.&lt;/s&gt;&lt;s&gt;All you assholes out ...</td>\n","      <td>&lt;s&gt;All right, thank you.&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>5480</th>\n","      <td>5480</td>\n","      <td>&lt;s&gt;I believe it's true too. So, thank you.&lt;/s&gt;...</td>\n","      <td>&lt;s&gt;All right, thank you.&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>5481</th>\n","      <td>5481</td>\n","      <td>&lt;s&gt;All right, thank you.&lt;/s&gt;</td>\n","      <td>&lt;s&gt;Good night, everybody. END OF TRANSCRIPTAut...</td>\n","    </tr>\n","    <tr>\n","      <th>5482</th>\n","      <td>5482</td>\n","      <td>&lt;s&gt;All you assholes out there, be nice. Be nic...</td>\n","      <td>&lt;s&gt;Good night, everybody. END OF TRANSCRIPTAut...</td>\n","    </tr>\n","    <tr>\n","      <th>5483</th>\n","      <td>5483</td>\n","      <td>&lt;s&gt;You're welcome.&lt;/s&gt;&lt;s&gt;All you assholes out ...</td>\n","      <td>&lt;s&gt;Good night, everybody. END OF TRANSCRIPTAut...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5484 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b05d09f-3f4d-4a66-a85f-6b31b49dff78')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-8b05d09f-3f4d-4a66-a85f-6b31b49dff78 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"194d8341fea34f7188c5cc0266b28709"}},"metadata":{}}]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nh4qgg5Gr-YO","executionInfo":{"status":"ok","timestamp":1668009093481,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c67f9ec2-7adb-4dd7-f9fc-fa9a546e993e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['Unnamed: 0', 'discussion', 'bot_output'],\n","        num_rows: 5484\n","    })\n","})"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["def preprocess_function(example):\n","  return tokenizer(\n","    example['discussion'],\n","    text_target=example['bot_output'],\n","    padding='max_length',\n","    max_length=512,\n","    truncation=True,)"],"metadata":{"id":"gk-0Fd3Cr7Wo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset=dataset.map(\n","    preprocess_function,remove_columns=dataset[\"train\"].column_names\n",")"],"metadata":{"id":"fRTlt5a5ele1","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["538bbeb25ca442318c0e08df2e14d6d9","2c08ddfb4c26498294458fe6f2b10d3d","41193ebfaa364572afb658904dc26ae7","3efab90e89e24364997c7de17cc1eb08","9d1e0d49da7d430c9cc5fb8e345777b0","a3182d3c7c5c42f78e64215a5648dc67","6e58f6e27dc44c2aa7b9f279700d4da9","dd84b2dbc03d4bd9b6dc81383304618c","0b5630d0c97248089e2fd883dd8b32cb","2536102c5bd744a895811a826fb95aa5","f694dfb930574c2fb36cf1811f32f091"]},"executionInfo":{"status":"ok","timestamp":1668009097177,"user_tz":-60,"elapsed":3713,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4b370cc4-d8db-4eb4-d3f3-a8cb604a2116"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5484 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"538bbeb25ca442318c0e08df2e14d6d9"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_dataset"],"metadata":{"id":"HbEiHl_qYzKK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668009097177,"user_tz":-60,"elapsed":8,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"73cf1df4-b87b-4e51-e4e8-987d7a7063e0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'attention_mask', 'labels'],\n","        num_rows: 5484\n","    })\n","})"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["model = TFBlenderbotForConditionalGeneration.from_pretrained(model_id)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":138,"referenced_widgets":["ae386c672aed4d5dbe68c9c728d5c995","83fd78e969b34d879891cd4578fe82e3","ecefa5024825449fbb441de45e171559","0ec78545be234f1298a279a13c088d45","c6595848be34461b8f624e7c1c4ec769","deea25ac781b4c589c8b9095b79cc8bc","9d529316e27041b2a565a3ad92b30e22","ee553a15a7004d7b8f89743af8a7e900","1d4c9047afea4b479e7c66d315859dbb","fcf6c1c9ad1b46718d00db0add46b340","e0315685383746b2946740ed152508a3"]},"id":"tDexGFRCslQM","executionInfo":{"status":"ok","timestamp":1668009144612,"user_tz":-60,"elapsed":47441,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9423d9d7-227a-4681-fa93-c8e54a3ec0bb"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.46G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ae386c672aed4d5dbe68c9c728d5c995"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFBlenderbotForConditionalGeneration.\n","\n","Some layers of TFBlenderbotForConditionalGeneration were not initialized from the model checkpoint at facebook/blenderbot-400M-distill and are newly initialized: ['final_logits_bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","source":["data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer,model=model, return_tensors=\"tf\")"],"metadata":{"id":"J2K0EVijuCZc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_train_dataset=tokenized_dataset[\"train\"].to_tf_dataset(\n","    shuffle=True,\n","    batch_size=4,\n","    collate_fn=data_collator,\n",")"],"metadata":{"id":"IhVgA6bwtE1s","executionInfo":{"status":"ok","timestamp":1668009145248,"user_tz":-60,"elapsed":654,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b3d4f63c-1788-4b80-ea45-7c97a52bf8bd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["You're using a BlenderbotTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]}]},{"cell_type":"code","source":["for i in tf_train_dataset.take(1):\n","  print(i)"],"metadata":{"id":"_RaMNMYYAIBd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668009145704,"user_tz":-60,"elapsed":458,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0587408a-d784-4f8f-ce0f-5eac9278e7ee"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(4, 512), dtype=int64, numpy=\n","array([[   1, 1445,   21, ...,    2,    2,    2],\n","       [   1, 7721,  411, ...,    2,    2,    2],\n","       [   1,  452,  341, ...,    2,    2,    2],\n","       [   1, 1117,  341, ...,    2,    2,    2]])>, 'attention_mask': <tf.Tensor: shape=(4, 512), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0]])>, 'labels': <tf.Tensor: shape=(4, 512), dtype=int64, numpy=\n","array([[   1, 1720,  327, ...,    2,    2,    2],\n","       [   1, 1167,   19, ...,    2,    2,    2],\n","       [   1, 1182,  758, ...,    2,    2,    2],\n","       [   1, 1167,   21, ...,    2,    2,    2]])>}\n"]}]},{"cell_type":"code","source":["tf_train_dataset"],"metadata":{"id":"M63x1UYmEPt6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668009145704,"user_tz":-60,"elapsed":28,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2b359a10-3274-407d-b75c-174c038a856a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec={'input_ids': TensorSpec(shape=(None, 512), dtype=tf.int64, name=None), 'attention_mask': TensorSpec(shape=(None, 512), dtype=tf.int64, name=None), 'labels': TensorSpec(shape=(None, 512), dtype=tf.int64, name=None)}>"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":["def replacements(a):\n","  for i in [1,2]:\n","    condition = tf.equal(a, i)\n","    case_true = -100*tf.ones_like(a)\n","    \n","    case_false = a\n","    a=tf.where(condition, case_true, case_false)\n","  return a"],"metadata":{"id":"2pBq_KGYhMsY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["a=tf.constant([[1,3,234,445,2,2,2],\n","               [1,3445,234,34,23,2,2]])\n","replacements(a)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FEdVffm-i4mR","executionInfo":{"status":"ok","timestamp":1668009145705,"user_tz":-60,"elapsed":24,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"199b814a-5e3a-4176-d331-01d81da665df"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(2, 7), dtype=int32, numpy=\n","array([[-100,    3,  234,  445, -100, -100, -100],\n","       [-100, 3445,  234,   34,   23, -100, -100]], dtype=int32)>"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","source":["def prepare_labels(inputs):\n","  return {'input_ids':inputs['input_ids'],\n","          'attention_mask':inputs['attention_mask'],\n","          'labels':replacements(inputs['labels'])}"],"metadata":{"id":"Hm4BbJKkc8gf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset=tf_train_dataset.map(prepare_labels)"],"metadata":{"id":"dgzZXNzGc8jF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in train_dataset.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1IfbcOkxc8ls","executionInfo":{"status":"ok","timestamp":1668009145706,"user_tz":-60,"elapsed":21,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f1adf412-48cc-4410-ad22-c8ba0571e1fc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(4, 512), dtype=int64, numpy=\n","array([[   1,  452, 2735, ...,    2,    2,    2],\n","       [   1,  649,  341, ...,    2,    2,    2],\n","       [   1, 3276,   21, ...,    2,    2,    2],\n","       [   1,  946,  304, ...,    2,    2,    2]])>, 'attention_mask': <tf.Tensor: shape=(4, 512), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0]])>, 'labels': <tf.Tensor: shape=(4, 512), dtype=int64, numpy=\n","array([[-100,  880,   21, ..., -100, -100, -100],\n","       [-100,  691,   19, ..., -100, -100, -100],\n","       [-100,  553,  513, ..., -100, -100, -100],\n","       [-100, 1216,  324, ..., -100, -100, -100]])>}\n"]}]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"7TT8MefxFNxq"}},{"cell_type":"code","source":["#model = TFBlenderbotForConditionalGeneration.from_pretrained(model_id)\n","model.summary()"],"metadata":{"id":"vKqtN7oAEzGr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668009145706,"user_tz":-60,"elapsed":18,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f2d75efd-fef8-4fbd-eb6b-63d3d42695c5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"tf_blenderbot_for_conditional_generation\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," model (TFBlenderbotMainLaye  multiple                 364802560 \n"," r)                                                              \n","                                                                 \n"," final_logits_bias (BiasLaye  multiple                 8008      \n"," r)                                                              \n","                                                                 \n","=================================================================\n","Total params: 364,810,568\n","Trainable params: 364,802,560\n","Non-trainable params: 8,008\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["num_train_steps=len(tf_train_dataset)\n","optimizer, schedule = create_optimizer(\n","  init_lr=6e-5,\n","  num_warmup_steps=1_000,\n","  num_train_steps=num_train_steps,\n",")\n","model.compile(optimizer=optimizer)"],"metadata":{"id":"2z4Kv02xEzJU","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668010495009,"user_tz":-60,"elapsed":402,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5c583301-02f5-477d-e819-c48f85deab54"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}]},{"cell_type":"code","source":["history=model.fit(train_dataset, epochs=1)"],"metadata":{"id":"LfvcfISKEzL3","colab":{"base_uri":"https://localhost:8080/","height":364},"outputId":"d3a64368-0546-41c5-c016-9214b24334b3","executionInfo":{"status":"error","timestamp":1668004600672,"user_tz":-60,"elapsed":446471,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/2\n"," 239/1371 [====>.........................] - ETA: 31:34 - loss: 1.9835"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-38-22274a307000>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1407\u001b[0m                 _r=1):\n\u001b[1;32m   1408\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1410\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1411\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    945\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2452\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2453\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2454\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2456\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1859\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1860\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1861\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1862\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1863\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    500\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    503\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["model.load_weights('/content/drive/MyDrive/nlp/text_generation/blenderbot.h5')"],"metadata":{"id":"j8dsEHQbIyHn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#model.save_weights('/content/drive/MyDrive/nlp/text_generation/blenderbot.h5')"],"metadata":{"id":"GKDQIhoZPq06"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Testing"],"metadata":{"id":"CekPBIEUI_Hd"}},{"cell_type":"code","source":["input_text=tokenizer.bos_token+\"Hello Elon glad to have you on my podcast.\"+tokenizer.eos_token+tokenizer.bos_token+\"Thanks for Having me.\"+tokenizer.eos_token+tokenizer.bos_token+\"i heard you are building robots. Tell me more about them.\"+tokenizer.eos_token+tokenizer.bos_token+\"Well... Currently working on a robot which can do all house chores for you \"+tokenizer.eos_token+tokenizer.bos_token+\"Can this robot be used in Mars?\"+tokenizer.eos_token"],"metadata":{"id":"nQbqOLwFdAlJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(input_text)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wLDsELE4mrQa","executionInfo":{"status":"ok","timestamp":1668009982668,"user_tz":-60,"elapsed":2,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"390c2f30-9d03-482f-ab0b-8626eef26679"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s>Hello Elon glad to have you on my podcast.</s><s>Thanks for Having me.</s><s>i heard you are building robots. Tell me more about them.</s><s>Well... Currently working on a robot which can do all house chores for you </s><s>Can this robot be used in Mars?</s>\n"]}]},{"cell_type":"code","source":["history=tokenizer(input_text, return_tensors=\"tf\")"],"metadata":{"id":"oe4bX1sIo0Mf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["MAX_NEW_TOKENS=16"],"metadata":{"id":"6rRM-467vNag"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["init_time=time.time()\n","output=model.generate(**history,max_new_tokens=MAX_NEW_TOKENS,do_sample=True,top_p=0.9)\n","\n","print(tokenizer.decode(output[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wbkWOjv4H7t8","executionInfo":{"status":"ok","timestamp":1668010003669,"user_tz":-60,"elapsed":20187,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"79f88ea0-de01-43f2-8ec3-dda39c3a6b0e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s> I don't think so. I think it's just going to do household chores for you. That's it. You're welcome. Thank you, bye.\n","19.985085010528564\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_temp = model.generate(**history,max_new_tokens=MAX_NEW_TOKENS,do_sample=True,temperature=1.0, top_k=0)\n","print(tokenizer.decode(output_temp[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CKbX8U3vELrx","executionInfo":{"status":"ok","timestamp":1668010021506,"user_tz":-60,"elapsed":17841,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"46cd9563-5645-4e51-a84f-27a119983dd5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s> I don't think so. I think it's just going to do house chores. That's all it does. No, it doesn't do anything useful. It's just useless. It doesn't know what to do with it. So, I'm trying to figure out a way to\n","17.997707843780518\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_temp = model.generate(**history,max_new_tokens=MAX_NEW_TOKENS, do_sample=True,temperature=2.0, top_k=0)\n","print(tokenizer.decode(output_temp[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HMQjcEJioIO9","executionInfo":{"status":"ok","timestamp":1668010039408,"user_tz":-60,"elapsed":17918,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8bf1e252-c452-44d2-f6a6-ef5174a8b520"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s> Yes, yes it can be. But you do not want to make it stupid. Otherwise, it is going to do a lot of work. It will waste time dig tunnels.That's not going to help.\n","17.967918872833252\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_temp = model.generate(**history,max_new_tokens=MAX_NEW_TOKENS,do_sample=True,temperature=0.5, top_k=0)\n","print(tokenizer.decode(output_temp[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dD3DBiQAoIVB","executionInfo":{"status":"ok","timestamp":1668010057738,"user_tz":-60,"elapsed":18345,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b1fb57a2-f1ce-4868-c434-f13b4366c565"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s><s> I don't think so. I think it's just going to do it for you. You can do it. It's just, you know, it's going to be on your roof. You have to hook it up to some sort of a magnetic detector.\n","17.95498299598694\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_topk = model.generate(**history,max_new_tokens=MAX_NEW_TOKENS,do_sample=True,top_k=50)\n","print(tokenizer.decode(output_topk[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"de7dv4uzoIXl","executionInfo":{"status":"ok","timestamp":1668010076100,"user_tz":-60,"elapsed":18382,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a5730071-0f0e-48d7-8728-40892fbf25e1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s> I don't think so. I think it's just going to do house chores. That's all it does. It doesn't do anything useful. It's just doing it for you. You're welcome. Thank you, bye.\n","18.42564582824707\n"]}]},{"cell_type":"code","source":["init_time=time.time()\n","output_topk = model.generate(**history,max_new_tokens=MAX_NEW_TOKENS,do_sample=True,temperature=2.0,top_k=50)\n","print(tokenizer.decode(output_topk[0]))\n","print(time.time()-init_time)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vQl4jF0B7nX0","executionInfo":{"status":"ok","timestamp":1668010094406,"user_tz":-60,"elapsed":18335,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7e4ff0a4-284f-405f-93e6-a943f1f5d3f3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<s> I don't see why not. What would you use a robot for? I mean, it doesn't necessarily have to do with robotics, right? It's more about creativity. It's your imagination. What ideas do you have?\n","18.44620966911316\n"]}]},{"cell_type":"markdown","source":["## Chat"],"metadata":{"id":"ogG1uhIfMUcb"}},{"cell_type":"code","source":["MAX_LENGTH=1024\n","chat_input=\"\"#\"<s> A discussion between myself and Elon Musk who thinks his robots can get to mars</s>\"\n","\n","for step in range(10):\n","  my_text=input(\">> Host:\")\n","  new_user_input_ids = tokenizer.encode(\n","      tokenizer.bos_token+my_text+tokenizer.eos_token,return_tensors='tf')\n","  if step>0:\n","    chat_input=chat_input+tokenizer.bos_token+chat_history+tokenizer.eos_token+tokenizer.bos_token+my_text+tokenizer.eos_token\n","    bot_input_ids = tokenizer.encode(chat_input,return_tensors='tf')\n","    \n","  else:\n","    chat_input=tokenizer.bos_token+my_text+tokenizer.eos_token\n","    bot_input_ids = tokenizer.encode(chat_input,return_tensors='tf')\n","\n","  chat_history_ids = model.generate(\n","      bot_input_ids,max_length=MAX_LENGTH,\n","      do_sample=True,\n","      temperature=2.0,top_k=50)\n","  \n","  chat_history=tokenizer.decode(chat_history_ids[0],skip_special_tokens=True,)\n","   \n","  print(\">> Elon Musk: {}\".format(tokenizer.decode(chat_history_ids[0], skip_special_tokens=True)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wd59xVyMH-4X","outputId":"55b6546a-15a3-441d-cd15-f4530bbb2f15"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[">> Host:Host:Hello Elon, how are you doing?\n",">> Elon Musk:  I'm doing very well. Thank you for asking. How are you? What are you up to?\n",">> Host:I'm doing quite well. Currently building this robot for Mars\n",">> Elon Musk:  That's really cool. How did you come up with that idea? Do you program it yourself?\n",">> Host:Yes of course, though i have a team. What about you, what are you building at Tesla?\n",">> Elon Musk:  Right now, nothing. Eventually, though, I'd like to turn it into Elon Musk and call it Tesla SpaceX. I don't know yet. We'll find out in a couple of years probably.\n",">> Host:Oh really? What about the robot which helps humans do house chores?\n",">> Elon Musk:  I don't see why not. I mean, who doesn't want a chimpanzee in their house anyway? Right, right. Right. Okay. Let's just let it go. We'll move on to the next thing. That's going to be the end of the world. We're going to turn it into anthropomorphic beings. We won't even be able to program it. It won't last long. It's kind of sad. It doesn't make sense. You know what I mean by it is though? It kind of strange. It sort of weird. It feels kind of weird that it doesn't feel right. It makes sense, kind of odd. It does sort of strange, it. Like it does. It seems strange, sort of like there's like it's strange. Kind of strange that it's sort of new. There's new. It takes a weird. You're weird.something,something. I kind of fascinating, sorta weird. That chugged of strange to sort of odd, youthought. It kindof strange strange. You do weird. And it's a 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tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Dense,Flatten,SimpleRNN,InputLayer,Conv1D,Bidirectional,GRU,LSTM,BatchNormalization,Dropout,Input, Embedding,TextVectorization)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from tensorboard.plugins import projector"],"metadata":{"id":"ZslyJl_QV7rt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["BATCH_SIZE=64"],"metadata":{"id":"8phsQmoYUJWK"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Data Preparation"],"metadata":{"id":"y3CEdBN2aX9k"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"IuErSCoPS1kN","executionInfo":{"status":"ok","timestamp":1661892845935,"user_tz":-60,"elapsed":81929,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["dacf8ed2dd204ec58a0bf023de92d72b","7e9a33c693c14d5ea3c8c7c759803401","34506e47c31f4300868abcab3f2d0dfb","8f56a46abf4a4b3bae40e0c45b4c76ba","768fa9ae199b4005926391af61fad2f8","2684d6091b044da88ab28a024de0d9e9","beb334ae31774e45a64111865319cef9","c1a0c58e3a2c4658a6ee823dfcfb4dbc","e331f2b5e0c548f28895bc0cddafc2c6","0a14d2734e934297907894628a931eb9","6016bb31c9eb4336a1be1b1da16684f5","df1923b70ff447239937f30b45216ead","6d5cb9e7a3a84dbc897103c7ab0f6fa9","d20aca4ff5e646b88c6f5add2e1586e5","160df202fe754ea8a7ae4763aac5ff58","c5e16f6bf0e94a89ac81cf2729035c99","a9a549f153864d14bf45adc8cd012e7b","d1d68d01f65246cda6adfd0b0c482e9c","e8b9b010ab2d41418cd8ba074e57590a","d30f79a3afbe4e899c94c2cb9ae057e6","f4cca3726f21420c8d3093741dd58ed4","b84a061bd2304bfd87cc8c81eea4753d","7b3f4c45a7ba4e3990b0a5f6cc1e5805","0adb76f0fd884e949ab6c3786a359eaa","269db421a46340c7b117f706376928bc","532ad2b19cf542cab7d5278b42abe4e8","0dcd157fa33645519efe13cc2d641a5e","b27d9e3ca40947158c0a078750cc6d4f","e268560915854ec59f411a0a316f2a76","5e52fd8372e34ef5b4af98a211b80f1d","b41c543112404511b74b150b3b68d808","785cfbf7464849dd956ba7a12db216b6","52a84b8b68154c8e955da6660a3b31ea","21f0fdb1d9fc4ffa938f6dd546e1e9e3","42c8b4587d484f39b6fcafcc7984d28d","c5cd83d8656842a7bfb1668b13f320b4","1b38276b71c646ce91b07608a96bbdd1","e5f5438d748647ceb7721a6bf2146eb2","ff8de17a1a064564995e6e9649760f7b","6b38c9f94dae4ca98511330b62998781","6fecec0e911440df856cb4f03fb668f3","56ab3ab7fae34444a83b1030ef8ddc2f","32af0bc3c3f94b1a8955d5ebdf78d575","1e63a8d88b664ef9bd1fbeaea2360a16","d233da1ec1174444aee1b0b14dac0570","6906432eb1a1416888d2f260f260944e","6b0f38d6a04f42098694402f8f662f7a","4185b92761404ea4b2f1aeab862eb1c8","f0e82af136844b97ac5a3485f227cb03","1adc5b17a1a54970a897df1e44606557","0cf70c3e88014175acae0b581999425f","e1c0765a564c43749809e7fe56b9e141","b3de465df7044f13aad0d0d35de2526b","61b5da3a3cc24d06a80c233905d00640","a58287609f364e1799c8ec37553a7405","e386aeede24341da86f01a5ef946d850","a2d12097bb594336a50d267b40e6296a","94c71eff9dfc4c3e9517c93628c47658","3516743c6e904edcad7a540a9e63f68b","517168ffb890484da49270dfb01b0df8","0cb1d8ab077d424b8cc4cb34c73a9b3c","c9cf957b40bd40ca8a52c2e3cd0f938e","91d7e80df2a948dc90339b01380b78b0","42440da7743c4d68a097d34db1bc3201","c3d4da30306b466a926a36ab068e0a58","42c1ab3d1406402e9e12a9e2300cac4b","58170ad2a66e4cc88cf517987d8baf16","2d20d0596274441a8a406e55a8ef5cbd","b911e5a1189d455db68602c82f4d8969","364e0abf261a4016b275933b57d29ae0","c17f75bbf3c0434db693bf45cb318cb6","094e366fbc1f43ff82ee12678209aea2","9eb008e6a70c4809a407d4d7623ba2eb","352828a47aed42dc8bd955d32d933375","3000f640cade4f3cb9f2f71a33fd4fb5","7eec01ba9a6f42f0bc48ea20fac7154b","ec215afd7ef84d14ad21046459b5d823","478793bf7afa4d13b875bd8c528be52b","0ee6f42619f043f791b3393b5b279e03","76e6548fd7714da1a538d2f808782adc","59c7464b475d499c88e4a0c12b1b6926","4c1fce394dcb458aa31cf35cfb8c7786","67f449db30094a2a91460f7e9689801b","e134516b8c40443e96fd0151ec9aa442","d0be655844014abaa7413d5c7f1c1fdc","8134e2f243c944e993930f08909f6b71","b90ea95157cd42eca57fda6ecac459fe","74659f82e4a54c0baf92af05bc3c222f","54ea274fa37d4c6c8e063cb42a4d34f6","393a95239c0643b49087e3037302530c","87b60fb486184ebda38e92b59bcebd7d","be8bbc8bec374cb59dfdaa34a2bf8f10","e5e8c976379646c0b507fffe77e8cc09","573a1d65aa194af8a5301d1618dc1fb6","a44755e6ac58437aa99ee11f639aeec1","da3271f6760e4bb79df28425958fbd4b","1b6ac1b8d7c1409083347b7f969eafdb","dbe35b6c54df4dc6b28af887c2bf84ed","17198c607a624b9fbb9cf1ff01a00876"]},"outputId":"a4bda554-c70a-4ee6-c26a-026dae8d1430"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1mDownloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"]},{"output_type":"display_data","data":{"text/plain":["Dl Completed...: 0 url [00:00, ? url/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dacf8ed2dd204ec58a0bf023de92d72b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Dl Size...: 0 MiB [00:00, ? MiB/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"df1923b70ff447239937f30b45216ead"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7b3f4c45a7ba4e3990b0a5f6cc1e5805"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"21f0fdb1d9fc4ffa938f6dd546e1e9e3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete7HRH5Y/imdb_reviews-train.tfrecord*...…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d233da1ec1174444aee1b0b14dac0570"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e386aeede24341da86f01a5ef946d850"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete7HRH5Y/imdb_reviews-test.tfrecord*...:…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58170ad2a66e4cc88cf517987d8baf16"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating unsupervised examples...:   0%|          | 0/50000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"478793bf7afa4d13b875bd8c528be52b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incomplete7HRH5Y/imdb_reviews-unsupervised.tfrec…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"54ea274fa37d4c6c8e063cb42a4d34f6"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1mDataset imdb_reviews downloaded and prepared to ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"]}],"source":["train_ds,val_ds,test_ds=tfds.load('imdb_reviews', split=['train', 'test[:50%]', 'test[50%:]'],as_supervised=True)"]},{"cell_type":"code","source":["train_ds"],"metadata":{"id":"Ds46Y1rPp7fD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661892845935,"user_tz":-60,"elapsed":30,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ffc3a749-1576-447a-9eb7-8bf228c58e03"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["for review,label in val_ds.take(2):\n","  print(review)\n","  print(label)"],"metadata":{"id":"NyBV6yL0p7hy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661892845936,"user_tz":-60,"elapsed":27,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"15043604-5127-45b3-d82c-2270ea1a3eeb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(b\"There are films that make careers. For George Romero, it was NIGHT OF THE LIVING DEAD; for Kevin Smith, CLERKS; for Robert Rodriguez, EL MARIACHI. Add to that list Onur Tukel's absolutely amazing DING-A-LING-LESS. Flawless film-making, and as assured and as professional as any of the aforementioned movies. I haven't laughed this hard since I saw THE FULL MONTY. (And, even then, I don't think I laughed quite this hard... So to speak.) Tukel's talent is considerable: DING-A-LING-LESS is so chock full of double entendres that one would have to sit down with a copy of this script and do a line-by-line examination of it to fully appreciate the, uh, breadth and width of it. Every shot is beautifully composed (a clear sign of a sure-handed director), and the performances all around are solid (there's none of the over-the-top scenery chewing one might've expected from a film like this). DING-A-LING-LESS is a film whose time has come.\", shape=(), dtype=string)\n","tf.Tensor(1, shape=(), dtype=int64)\n","tf.Tensor(b\"A blackly comic tale of a down-trodden priest, Nazarin showcases the economy that Luis Bunuel was able to achieve in being able to tell a deeply humanist fable with a minimum of fuss. As an output from his Mexican era of film making, it was an invaluable talent to possess, with little money and extremely tight schedules. Nazarin, however, surpasses many of Bunuel's previous Mexican films in terms of the acting (Francisco Rabal is excellent), narrative and theme.<br /><br />The theme, interestingly, is something that was explored again in Viridiana, made three years later in Spain. It concerns the individual's struggle for humanity and altruism amongst a society that rejects any notion of virtue. Father Nazarin, however, is portrayed more sympathetically than Sister Viridiana. Whereas the latter seems to choose charity because she wishes to atone for her (perceived) sins, Nazarin's whole existence and reason for being seems to be to help others, whether they (or we) like it or not. The film's last scenes, in which he casts doubt on his behaviour and, in a split second, has to choose between the life he has been leading or the conventional life that is expected of a priest, are so emotional because they concern his moral integrity and we are never quite sure whether it remains intact or not.<br /><br />This is a remarkable film and I would urge anyone interested in classic cinema to seek it out. It is one of Bunuel's most moving films, and encapsulates many of his obsessions: frustrated desire, mad love, religious hypocrisy etc. In my view 'Nazarin' is second only to 'The Exterminating Angel', in terms of his Mexican movies, and is certainly near the top of the list of Bunuel's total filmic output.\", shape=(), dtype=string)\n","tf.Tensor(1, shape=(), dtype=int64)\n"]}]},{"cell_type":"code","source":["def standardization(input_data):\n","    '''\n","    Input: raw reviews\n","    output: standardized reviews\n","    '''\n","    lowercase=tf.strings.lower(input_data)\n","    no_tag=tf.strings.regex_replace(lowercase,\"<[^>]+>\",\"\")\n","    output=tf.strings.regex_replace(no_tag,\"[%s]\"%re.escape(string.punctuation),\"\")\n","\n","    return output"],"metadata":{"id":"leMqphTKVQ8k"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["standardization(tf.constant(\"<u>In the movie?, </u>man called Tévèz, went to a friend’s pl**ce and they had a tensed discussion. I don’t love this movie! would you?<br> <br /><br />T\"))"],"metadata":{"id":"4VA77aebGMVQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661892845936,"user_tz":-60,"elapsed":24,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e97619bd-9700-4c1f-e8fd-fbb71f85a561"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(), dtype=string, numpy=b'in the movie man called t\\xc3\\xa9v\\xc3\\xa8z went to a friend\\xe2\\x80\\x99s plce and they had a tensed discussion i don\\xe2\\x80\\x99t love this movie would you t'>"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["VOCAB_SIZE=10000\n","SEQUENCE_LENGTH=250\n","EMBEDDING_DIM=300"],"metadata":{"id":"i1_nUM7l6gbF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["vectorize_layer=TextVectorization(\n","    standardize=standardization,\n","    max_tokens=VOCAB_SIZE,\n","    output_mode='int',\n","    output_sequence_length=SEQUENCE_LENGTH\n",")"],"metadata":{"id":"EgJXEr9H1mRT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# lengths=[]\n","# words=[]\n","\n","# for review,label in train_ds.take(100):\n","#   # for word in tf.strings.split(review, sep=\" \"):\n","#   #   if word in words:\n","#   #     pass\n","#   #   else:\n","#   #     words.append(word)\n","#   lengths.append(len(tf.strings.split(review, sep=\" \")))"],"metadata":{"id":"2QQq7_4H1mT0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["training_data=train_ds.map(lambda x,y:x)### input x and y and outputx\n","vectorize_layer.adapt(training_data)#### adapt the vectorize_layer to the training data"],"metadata":{"id":"S_Qhfisb1mft"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(vectorize_layer.get_vocabulary())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"45J7WxmR-02N","executionInfo":{"status":"ok","timestamp":1661892928841,"user_tz":-60,"elapsed":29,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ed6f7f7b-8a74-437d-b8a0-2ab11e037009"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10000"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["def vectorizer(review,label):\n","    return vectorize_layer(review),label"],"metadata":{"id":"Df6aXLT--060"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset=train_ds.map(vectorizer)\n","val_dataset=val_ds.map(vectorizer)"],"metadata":{"id":"SRTefXMW-09K"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["vectorize_layer.get_vocabulary()[411]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"vY6x9JyfA3_9","executionInfo":{"status":"ok","timestamp":1661892928856,"user_tz":-60,"elapsed":38,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"445d6389-448c-4752-9d54-c35858de3aa7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'absolutely'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["for review,label in train_dataset.take(1):\n","  print(review)\n","  print(label)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-fSurdHy-0_T","executionInfo":{"status":"ok","timestamp":1661892928856,"user_tz":-60,"elapsed":37,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"603e386f-a22b-454f-e999-d5aa14a0f5ce"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[  10   13   33  411  384   17   89   26    1    8   32 1337 3521   40\n","  491    1  192   22   84  149   18   10  215  317   26   64  239  212\n","    8  484   54   64   84  111   95   21 5502   10   91  637  737   10\n","   17    7   33  393 9554  169 2443  406    2   87 1205  135   65  142\n","   52    2    1 7408   65  245   64 2832   16    1 2851    1    1 1415\n"," 4969    3   39    1 1567   15 3521   13  156   18    4 1205  881 7874\n","    8    4   17   12   13 4037    5   98  145 1234   11  236  696   12\n","   48   22   91   37   10 7285  149   37 1337    1   49  396   11   95\n"," 1148  841  140    9    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0], shape=(250,), dtype=int64)\n","tf.Tensor(0, shape=(), dtype=int64)\n"]}]},{"cell_type":"code","source":["train_dataset=train_dataset.batch(BATCH_SIZE).prefetch(buffer_size=tf.data.AUTOTUNE)\n","val_dataset=val_dataset.batch(BATCH_SIZE).prefetch(buffer_size=tf.data.AUTOTUNE)"],"metadata":{"id":"pV_8IvUg-1Dq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"w0gvKis8b3ad"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"PQiSVkoLaaqF"}},{"cell_type":"markdown","source":["## SimpleRNN"],"metadata":{"id":"DjwjGoKbV_k5"}},{"cell_type":"code","source":["inputs=np.random.random([32, 10, 8]).astype(np.float32)\n","simple_rnn=tf.keras.layers.SimpleRNN(25)\n","output=simple_rnn(inputs)\n","print(output.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fVfWnBFuMOBl","executionInfo":{"status":"ok","timestamp":1661524759497,"user_tz":-60,"elapsed":2367,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"278ced11-80d8-4a6c-a1b3-1011012314d3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(32, 25)\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"mDSdxu8UXxlA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["EMBEDDING_DIM=64\n","model=tf.keras.models.Sequential([\n","    Input(shape=(SEQUENCE_LENGTH,)),\n","    Embedding(VOCAB_SIZE,EMBEDDING_DIM),\n","    SimpleRNN(32),\n","    Dense(1,activation='sigmoid'),\n","])\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u67QhvtUMODz","executionInfo":{"status":"ok","timestamp":1661507370482,"user_tz":-60,"elapsed":400,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c5e2a5f5-99ec-42fe-8420-932553005d46"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_2 (Embedding)     (None, 250, 64)           640000    \n","                                                                 \n"," simple_rnn_3 (SimpleRNN)    (None, 32)                3104      \n","                                                                 \n"," dense_2 (Dense)             (None, 1)                 33        \n","                                                                 \n","=================================================================\n","Total params: 643,137\n","Trainable params: 643,137\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["checkpoint_filepath = '/content/drive/MyDrive/nlp/sentiment_analysis/rnn.h5'\n","model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath=checkpoint_filepath,\n","    monitor='val_accuracy',\n","    mode='max',\n","    save_best_only=True)"],"metadata":{"id":"q4ZTvfx8QHyC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n","              optimizer=tf.keras.optimizers.Adam(1e-4),\n","              metrics=['accuracy'])"],"metadata":{"id":"XKudxGzRR-c6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=model.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=10,\n","    callbacks=[model_checkpoint_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P-8ZKpUujpUh","executionInfo":{"status":"ok","timestamp":1661508774745,"user_tz":-60,"elapsed":1395530,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d370a80a-128c-4e3b-87ce-0cd6131eb9d2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","391/391 [==============================] - 142s 360ms/step - loss: 0.6943 - accuracy: 0.4995 - val_loss: 0.6934 - val_accuracy: 0.5019\n","Epoch 2/10\n","391/391 [==============================] - 135s 345ms/step - loss: 0.6853 - accuracy: 0.5384 - val_loss: 0.6944 - val_accuracy: 0.4986\n","Epoch 3/10\n","391/391 [==============================] - 138s 352ms/step - loss: 0.6657 - accuracy: 0.5974 - val_loss: 0.6974 - val_accuracy: 0.5089\n","Epoch 4/10\n","391/391 [==============================] - 136s 349ms/step - loss: 0.6240 - accuracy: 0.6802 - val_loss: 0.7077 - val_accuracy: 0.5091\n","Epoch 5/10\n","391/391 [==============================] - 134s 344ms/step - loss: 0.5579 - accuracy: 0.7604 - val_loss: 0.7295 - val_accuracy: 0.5062\n","Epoch 6/10\n","391/391 [==============================] - 140s 359ms/step - loss: 0.5020 - accuracy: 0.8064 - val_loss: 0.7475 - val_accuracy: 0.5050\n","Epoch 7/10\n","391/391 [==============================] - 134s 342ms/step - loss: 0.4745 - accuracy: 0.8150 - val_loss: 0.7565 - val_accuracy: 0.5069\n","Epoch 8/10\n","391/391 [==============================] - 136s 347ms/step - loss: 0.4638 - accuracy: 0.8188 - val_loss: 0.7615 - val_accuracy: 0.5086\n","Epoch 9/10\n","391/391 [==============================] - 156s 399ms/step - loss: 0.4409 - accuracy: 0.8371 - val_loss: 0.7913 - val_accuracy: 0.5064\n","Epoch 10/10\n","391/391 [==============================] - 133s 341ms/step - loss: 0.4088 - accuracy: 0.8597 - val_loss: 0.8023 - val_accuracy: 0.5114\n"]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"9ERiCBSYYT7f","executionInfo":{"status":"ok","timestamp":1661508774746,"user_tz":-60,"elapsed":20,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"fb3caf1f-7123-415a-e08f-7602e885d06d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"hEUyVqwvcUob","executionInfo":{"status":"ok","timestamp":1661508775428,"user_tz":-60,"elapsed":689,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6e2eb544-65fc-4f47-bf43-5a1efd6fd700"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Evaluation"],"metadata":{"id":"fTIPRPl6p2Bv"}},{"cell_type":"code","source":["test_dataset=test_ds.map(vectorizer)\n","test_dataset=test_dataset.batch(BATCH_SIZE)\n","model.evaluate(train_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Q_k-t-Rn4MM","executionInfo":{"status":"ok","timestamp":1661509003028,"user_tz":-60,"elapsed":20996,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e05d88e8-4979-459c-e9ab-cdfe07cfb658"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["391/391 [==============================] - 16s 41ms/step - loss: 0.5160 - accuracy: 0.7563\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.5160424709320068, 0.7563199996948242]"]},"metadata":{},"execution_count":48}]},{"cell_type":"markdown","source":["## LSTM (MultiLayer and Bidirectional)"],"metadata":{"id":"sr-HSYdxWNOi"}},{"cell_type":"code","source":["EMBEDDING_DIM=64\n","model=tf.keras.models.Sequential([\n","    Input(shape=(SEQUENCE_LENGTH,)),\n","    Embedding(VOCAB_SIZE,EMBEDDING_DIM),\n","\n","    Bidirectional(LSTM(64,return_sequences=True)),\n","    Bidirectional(LSTM(32)),\n","    \n","    Dense(64, activation='relu'),\n","    Dropout(0.5),\n","    Dense(1,activation='sigmoid'),\n","])\n","model.summary()"],"metadata":{"id":"YPZFFofxldjL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661826120590,"user_tz":-60,"elapsed":1687,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4dfdd420-7611-4322-e4ec-2b7cd4bb650a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding (Embedding)       (None, 250, 64)           1920000   \n","                                                                 \n"," bidirectional (Bidirectiona  (None, 250, 128)         66048     \n"," l)                                                              \n","                                                                 \n"," bidirectional_1 (Bidirectio  (None, 64)               41216     \n"," nal)                                                            \n","                                                                 \n"," dense (Dense)               (None, 64)                4160      \n","                                                                 \n"," dropout (Dropout)           (None, 64)                0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 65        \n","                                                                 \n","=================================================================\n","Total params: 2,031,489\n","Trainable params: 2,031,489\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["checkpoint_filepath = '/content/drive/MyDrive/nlp/sentiment_analysis/lstm.h5'\n","model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath=checkpoint_filepath,\n","    monitor='val_accuracy',\n","    mode='max',\n","    save_best_only=True)"],"metadata":{"id":"AdP4oaUVMOPC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n","              optimizer=tf.keras.optimizers.Adam(1e-4),\n","              metrics=['accuracy'])"],"metadata":{"id":"LXywnM44MORF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=model.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=10,\n","    callbacks=[model_checkpoint_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bT8K-g93MOTY","executionInfo":{"status":"ok","timestamp":1661509778843,"user_tz":-60,"elapsed":298119,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1f2670bb-81f0-47e3-bb2d-91a48ed952e2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","391/391 [==============================] - 41s 81ms/step - loss: 0.6326 - accuracy: 0.6146 - val_loss: 0.4056 - val_accuracy: 0.8210\n","Epoch 2/10\n","391/391 [==============================] - 28s 72ms/step - loss: 0.3347 - accuracy: 0.8670 - val_loss: 0.3285 - val_accuracy: 0.8602\n","Epoch 3/10\n","391/391 [==============================] - 29s 74ms/step - loss: 0.2476 - accuracy: 0.9098 - val_loss: 0.3591 - val_accuracy: 0.8638\n","Epoch 4/10\n","391/391 [==============================] - 30s 75ms/step - loss: 0.2030 - accuracy: 0.9306 - val_loss: 0.4211 - val_accuracy: 0.8514\n","Epoch 5/10\n","391/391 [==============================] - 29s 74ms/step - loss: 0.1797 - accuracy: 0.9407 - val_loss: 0.3940 - val_accuracy: 0.8550\n","Epoch 6/10\n","391/391 [==============================] - 28s 72ms/step - loss: 0.1581 - accuracy: 0.9479 - val_loss: 0.4271 - val_accuracy: 0.8374\n","Epoch 7/10\n","391/391 [==============================] - 27s 70ms/step - loss: 0.1289 - accuracy: 0.9606 - val_loss: 0.4573 - val_accuracy: 0.8478\n","Epoch 8/10\n","391/391 [==============================] - 30s 76ms/step - loss: 0.1136 - accuracy: 0.9680 - val_loss: 0.5197 - val_accuracy: 0.8461\n","Epoch 9/10\n","391/391 [==============================] - 27s 70ms/step - loss: 0.1108 - accuracy: 0.9679 - val_loss: 0.5370 - val_accuracy: 0.8453\n","Epoch 10/10\n","391/391 [==============================] - 27s 70ms/step - loss: 0.1115 - accuracy: 0.9674 - val_loss: 0.5652 - val_accuracy: 0.8464\n"]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"7-oD0h20MOVp","executionInfo":{"status":"ok","timestamp":1661509817273,"user_tz":-60,"elapsed":420,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b841c771-e06e-4d69-91d0-58b43fb12179"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxU9b3/8dcnC9mB7CBbwh4JAho2cUGDCqhotYr7LtbWa229bbXtba31d7ve3uqtreJW605xVxAFwY0dRbaEfQtLEgKBBMj++f3xHSBgwJDM5CQzn+fjkYeZM2dmPoxw3ud8z3cRVcUYY0zoCvO6AGOMMd6yIDDGmBBnQWCMMSHOgsAYY0KcBYExxoQ4CwJjjAlxFgTGGBPiLAiM+RYi8k8ReaSR+24SkTHfss9DIvKif6ozpvksCIwxJsRZEBhjTIizIDBBw9cs8xMRWSYi+0XkGRFJF5HpIlImIjNFJNG37wQRWSkipSIyR0Sy6r3PEBH50vea14DoYz7nEhFZ6nvtXBE5rZl1n6iWn4nINl8tq0Uk17d9mIgsFpF9IlIoIn9pTg0mtFkQmGBzJXAB0Be4FJgO/BxIxf19v1dE+gKvAPf5tk8D3hWRdiLSDngLeAFIAv7te0/AhQTwLHAXkAw8CbwjIlFNKfZbaukH3AMMVdUE4CJgk++ljwKPqmp7oBcwpSmfbwxYEJjg83+qWqiq24DPgAWq+pWqVgBvAkOAicD7qvqRqlYDfwZigDOBEUAk8FdVrVbVqcCieu8/CXhSVReoaq2qPg9U+l7XFCeqpRaIAk4VkUhV3aSq632vqwZ6i0iKqpar6vwmfr4xFgQm6BTW+/1gA4/jgVOAzYc2qmodsBXo4ntumx49Le/mer/3AO73NeOUikgp0M33uqY4bi2qug53pfAQUCQir4rIoc+5HXfVky8ii0TkkiZ+vjEWBCYkbccd0AEQEcEdzLcBO4Auvm2HdK/3+1bg/6lqx3o/sar6SgBqQVVfVtWzfPso8Aff9rWqei2Q5ts2VUTimliDCXEWBCYUTQEuFpFcEYkE7sc178wF5gE1uHsJkSJyBTCs3mufAr4nIsPFiRORi0Ukwd+1iEg/ETnfd/+hAndFUwcgIjeISKrvCqLU9151TazBhDgLAhNyVHU1cAPwf8Au3E3lS1W1SlWrgCuAW4DduDb8N+q9djFwJ/A3YA+wzrev32vB3R/4vW/7TtzZ/4O+l44FVopIOe7G8TWqerCpdZjQJrZCmTHGhDa7IjDGmBBnQWBMAPgGsZU38PNzr2sz5ljWNGSMMSEuwusCTlZKSopmZGR4XYYxxrQpS5Ys2aWqqQ091+aCICMjg8WLF3tdhjHGtCkisvl4z9k9AmOMCXEWBMYYE+IsCIwxJsS1uXsEDamurqagoICKigqvSwmo6OhounbtSmRkpNelGGOCSFAEQUFBAQkJCWRkZHD0XGHBQ1UpKSmhoKCAzMxMr8sxxgSRoGgaqqioIDk5OWhDAEBESE5ODvqrHmNMywuKIACCOgQOCYU/ozGm5QVNEHyb/ZU17Nh7EBtJbYwxRwuZIDhYXUtxWSWVNf6fsr20tJS///3vJ/268ePHU1pa+u07GmNMAIVMELSPdvfFyyqq/f7exwuCmpqaE75u2rRpdOzY0e/1GGPMyQiKXkON0S4inOjIcPZV1JDa1LWkjuOBBx5g/fr1DB48mMjISKKjo0lMTCQ/P581a9Zw+eWXs3XrVioqKvjhD3/IpEmTgCPTZZSXlzNu3DjOOuss5s6dS5cuXXj77beJiYnxb6HGGNOAoAuC37y7klXb9zX4XFVtHdU1dcRGRXAyt11PPaU9v750wHGf//3vf8+KFStYunQpc+bM4eKLL2bFihWHu3k+++yzJCUlcfDgQYYOHcqVV15JcnLyUe+xdu1aXnnlFZ566imuvvpqXn/9dW644YaTqNIYY5omZJqGACLC3OG/ti6wN4yHDRt2VF//xx57jEGDBjFixAi2bt3K2rVrv/GazMxMBg8eDMAZZ5zBpk2bAlqjMcYcEnRXBCc6c1dV8naUER8VQffk2IDVEBcXd/j3OXPmMHPmTObNm0dsbCyjR49ucCxAVFTU4d/Dw8M5eNCWnzXGtIyQuiIQERKiIyirrKbOj91IExISKCsra/C5vXv3kpiYSGxsLPn5+cyfP99vn2uMMf4QdFcE36Z9TAR7DlRxoLKW+Gj//PGTk5MZNWoU2dnZxMTEkJ6efvi5sWPH8sQTT5CVlUW/fv0YMWKEXz7TGGP8pc0tVZmTk6PHLkyTl5dHVlZWo15fW6es2rGP5Lh2nNKx7fXKOZk/qzHGHCIiS1Q1p6HnAto0JCJjRWS1iKwTkQeOs8/VIrJKRFaKyMuBrAcgPEyIj4qgrOLEffyNMSZUBKxpSETCgceBC4ACYJGIvKOqq+rt0wd4EBilqntEJC1Q9dSXEB3B9tKDVFbXEhUZ3hIfaYwxrVYgrwiGAetUdYOqVgGvApcds8+dwOOqugdAVYsCWM9hh0YZ77OrAmOMCWgQdAG21ntc4NtWX1+gr4h8ISLzRWRsAOs57MgoY/9PN2GMMW2N172GIoA+wGigK/CpiAxU1aNmYhORScAkgO7du/vlg9tHR1BcVkVNbR0R4SHVi9YYY44SyCPgNqBbvcddfdvqKwDeUdVqVd0IrMEFw1FUdbKq5qhqTmpqql+KS4iORFHKK615yBgT2gIZBIuAPiKSKSLtgGuAd47Z5y3c1QAikoJrKtoQwJoOi20XTkRYGPsOtnwQxMfHt/hnGmPM8QQsCFS1BrgHmAHkAVNUdaWIPCwiE3y7zQBKRGQVMBv4iaqWBKqm+gI1ytgYY9qagN4jUNVpwLRjtv2q3u8K/Nj30+L8Ncr4gQceoFu3bvzgBz8A4KGHHiIiIoLZs2ezZ88eqqureeSRR7jssmM7TRljjPe8vlnsf9MfgJ3LG7Vre5SeVbVEhglEnGA8QaeBMO73x3164sSJ3HfffYeDYMqUKcyYMYN7772X9u3bs2vXLkaMGMGECRNs3WFjTKsTfEFwEgQhXKTZ01IPGTKEoqIitm/fTnFxMYmJiXTq1Ikf/ehHfPrpp4SFhbFt2zYKCwvp1KmTn6o3xhj/CL4gOMGZe0P2l1eyvfQg/dITmjXK+KqrrmLq1Kns3LmTiRMn8tJLL1FcXMySJUuIjIwkIyOjwemnjTHGayHfgd5fo4wnTpzIq6++ytSpU7nqqqvYu3cvaWlpREZGMnv2bDZv3uyPco0xxu+C74rgJNUfZZyaEPXtLziOAQMGUFZWRpcuXejcuTPXX389l156KQMHDiQnJ4f+/fv7sWpjjPGfkA8C8N8o4+XLj9ykTklJYd68eQ3uV15e3uTPMMYYfwv5piGwUcbGmDagptL9BIAFAfVGGdtspMaY1qSyDFa8Dv++Ff7YC1a9HZCPCZqmIVVtch/9Q6OM91VUN+t9Aq2trSZnjGmC8mJYPQ3y34MNc6C2CmJTIPs7kNw7IB8ZFEEQHR1NSUkJycnJTT6It492o4z3V9USH9X6vhZVpaSkhOjoaK9LMcb4257N7sCf9x5snQ9aBx27w9A7IesS6DYcwgK3iFbrO+I1QdeuXSkoKKC4uLjJ71GnStHeCg4URdAhJtKP1flPdHQ0Xbt29boMY0xzqULRKnfgz3/3yGwIaQPgnJ9A/0vcjAYt1DoRFEEQGRlJZmZms9/n988sYFvpPj6+f3TzizLGmPrq6mDbYsh71/3s2QgIdBsGF/wW+l8Myb08KS0ogsBfcvun8dC7q9i4az+ZKXFel2OMaetqqmDTZ67ZJ38alO+EsEjIPAdG3Qv9xkOC99POWBDUk5uVzkPvrmJWXiF3nN3T63KMMW1R1X5YN9M1+6yZAZV7ITIWeo+BrEuhz4UQ09HrKo9iQVBPt6RY+qUnMCuvyILAGNN4B3bD6unuzH/9x1BTATGJ7kZv/0ug13kQGeN1lcdlQXCM87PSeOrTDew9WN1qbxobY1qBvQWQ/75r7988F7QW2neB0292AdD9TAhvG4fYtlFlCxqTlcY/5qznkzXFTBh0itflGGNak+LV7sCf/x5s/8ptS+kHZ93nzvxPGdJiPX38yYLgGIO7JZIU145ZeYUWBMaEuqr9sGMZrJ3h2vxL1rrtXc6A3F+7Nv+UPt7W6AcWBMcIDxPO65fGzLzCZk9CZ4xpQw7shp3L3IF/x9fu911rAQUJh4xRMGyS6+bZoYvX1fqVBUEDcrPSeP3LApZs3sPwnslel2OM8beyne5gv2MZ7FjqDvqlW448374rdD4Nsq+ETqdB9xEQm+RdvQFmQdCAs/ukEBkuzMovsiAwpi1ThT2bfGf6Xx85299fdGSfpF7QJQdyboPOg6DTIIgLrX/3FgQNSIiOZETPZGblFfLz8Vlel2OMaYy6WteUc/ig72veqdjrnpdwSMty/fk7D3Jn/OnZEN3e27pbAQuC4zi/fxq/eXcVm3btJ8NGGRvTutRUQlHekYP9jq+hcCVUH3DPR0RD+oAjTTudB0HaqRBpkzY2xILgOMZkpfObd1cx00YZG+OtynIoXFHvJu7XUJQPddXu+aj27mB/xi2+pp3TIKVvm+nD3xrYN3Uc3ZJi6Zseb6OMjfFCTSV8+ie3EMuhnjvg5uXvPAjOHHPkoJ+YCWHWu685LAhOIDcr3UYZG9PSClfBG5OgcLlrz8/+rmvP7zwIEjq3yQFbrZ0FwQnk9nejjD9dU8ylNrjMmMCqq4P5f4dZv4HoDnDtq9BvnNdVhQS7njqBId2PjDI2xgTQ3gJ44TL48BfuKuDueRYCLciuCE4gPEwY3S+Vj/OLbJSxMYGyfCq8/2OorYFLH4PTb7LmnxYW0CObiIwVkdUisk5EHmjg+VtEpFhElvp+7ghkPU0xJiud0gPVfLml1OtSjAkuB/fA1Nvh9dtdL5/vfQZn3Gwh4IGAXRGISDjwOHABUAAsEpF3VHXVMbu+pqr3BKqO5jo8yjivkGGZwTvE3JgWteETeOtuN9XDeb+Es35k3T09FMgrgmHAOlXdoKpVwKvAZQH8vIBIiI5keGYyM+0+gTHNV10BH/wc/jXBLdRyx0dw7k8sBDwWyCDoAmyt97jAt+1YV4rIMhGZKiLdGnojEZkkIotFZHFxcXEgaj2h3Kw01hfvZ9Ou/S3+2cYEjZ0r4KnzYP7jMPQOuOszN52z8ZzXdz/fBTJU9TTgI+D5hnZS1cmqmqOqOampqS1aIEBu/3QAZuUXfcuexphvqKuFLx51IXCgBK6fChf/D7SL9boy4xPIINgG1D/D7+rbdpiqlqhqpe/h00CrPD3onhxLn7R460ZqzMkq3QLPT4CPfuUWbb97HvS5wOuqzDECGQSLgD4ikiki7YBrgHfq7yAines9nADkBbCeZsnNSmfhxt3sq6j2uhRjWj9V+Po1+McoN9//ZY/DxBdDbnrntiJgQaCqNcA9wAzcAX6Kqq4UkYdFZIJvt3tFZKWIfA3cC9wSqHqaa0xWGjV1yqdrWv4ehTFtyoHdMPVWeHOSm/Hze5/DkBusW2grFtBb9ao6DZh2zLZf1fv9QeDBQNbgL0O6J5IYG8msvCIuOc2mmzCmQetnu26h+4sh91cw6j4IC/e6KvMtrM9WIx1ay/jj1TbK2JhvqD4IM38DC/4BKf3cPEGnDPa6KtNIdjQ7Cbm+UcZfbbVRxsYctuNrmDzahcCwu+CuTywE2hgLgpNwTt8UIsLEBpcZA65b6Gd/gady4WAp3PA6jP+jGyhm2hQLgpOQEB3J8J5JzMqz8QQmxO3ZDP+8xE0Z3X88fH+emzXUtEkWBCcpt38664rK2Vxio4xNCFKFpS+7bqE7l8PlT8BVz0OszcPVllkQnKQxWb5RxnZVYELNgd0w5SbXK6jTQLj7Cxh8rXULDQIWBCfp8CjjfLtPYELIupnw95GwejqM+Q3c8h4k9vC6KuMnFgRNcH5WGgs22ChjEwKqDsC0n8CLV0JMItz5MZxlYwOCjQVBE4zJSqemTvlszS6vSzEmcLZ/BZPPhYWTYcT3YdIct4i8CToWBE1wevdEOsZG2iR0JjjV1sCnf4Knx0BlOdz4Foz9HURGe12ZCRAbWdwEh0YZz15dRG2dEh5mN8tMG1dXB1sXwMo3YdXbUL4TBlzhpou2HkFBz4KgiXKz0njzq218uWUPQzPsH4ppg1ShYDGsfANWvgVl2yEi2k0TPfh66DfO6wpNC7EgaKJz+qYSESbMyiuyIDBthyps/9Kd+a98C/ZuhfB2bjDYgIeh31iISvC6StPCLAiaqP3hUcaFPDCuv9flGHN8qrBzGax4wwVA6WYIi4Re58N5v3Ajg6M7eF2l8ZAFQTOc3z+d3763ii0lB+iebMvumVZEFQpX+s7834Td60HCoedoOPen0P9i1x3UGCwImmVMVhq/fW8Vs/ILuXVUptflGANF+b42/zdh1xqQMMg8B0bdC/0vtRXCTIMsCJqhR3IcvdPimZVXZEFgvLNr7ZEz/6JVgEDGWTD8e5A1AeJTva7QtHIWBM2Um5XGs59vpKyimoToSK/LMaFi9wZfm/9bULjcbes+Esb9CU69DBLSva3PtCkWBM2U2z+dJz/ZwKdrdnHxaZ29LscEsz2bj5z571jqtnUdBhf9DgZcDu1tCVXTNBYEzXR6945ulHF+oQWB8b+9Be6sf+UbsG2J23bK6XDhI3Dq5dCxm7f1maBgQdBMEeFhnNcvjTmri22UsfGPfTtg1VvuzH/rAret8yAY8xAM+A4kZnhYnAlGFgR+cH5/N8r4qy17yLHBZaYpDpa6A/+yKbBlHqCQng3n/5c7+Cf38rpCE8QsCPzg3H6pvrWMiywITOPV1cL62bD0Jch/H2orIaUvjH7QHfxT+3pdoQkRFgR+0D46kmGZSXycb6OMT4oqbJjjRrV2HgxhITIZblGeW+5x2RQ3uVtMIpx+k1vt65TTbcUv0+IsCPwkN8uNMt66+wDdkmyU8bcqL4J37oU1093jhM7Qdyz0G+8GQAXblMf7S2DFVBcAO5ZCWAT0vgAGXwd9L4KIKK8rNCHMgsBPcvu7UcYz82yU8bfKf9+FQGWZ6/0Slwqrp8Hyf8OS5yAyDnqd50Kh70UQl+J1xU1TUwXrPnIH/zUzoK7arfV70e9g4FU20Mu0GhYEfpKREkev1Dg+zrdRxsdVWQYfPAhfvQCdToMrJkNalntu0DVQUwmbPnPr4q6eDvnvAQLdhrtZMfuNd23orbnpRBV2fA1fv+KC7UCJC7rhd8Gga6FTttcVGvMNFgR+NCYrnWe/sFHGDdo8D968y017fPb9cO4DENHu6H0iotx0yL3HwPg/uxkzV3/grhZmPuR+knq6QOg3DrqNgPBW8le4rBCWT3Fn/0Wr3NTO/cbBoOugdy6E298H03qJqnpdw0nJycnRxYsXe11GgxZu3M3VT87j79efzviBNrgMcM0jc/4bPv8rJPaA7zwJ3Uec/Pvs3QZrPnBXChs/gdoqiO7omo76jYNeuRDd3v/1n0h1hQupr1+BdbNAa6FLjrvpO+AKW9nLtCoiskRVcxp6LqCnUyIyFngUCAeeVtXfH2e/K4GpwFBVDcxRvmS966Z33i8gLDwgH3F69450iIlkZl6hBQFA4Sp4cxLsXO56xVz0301f9KRDFxh6u/upLHPdLldPd+Gw7DU3v37m2b77CmMDN+JWFQoWuTP/lW9AxV5IOAVG/dA1/ViXT9MGBSwIRCQceBy4ACgAFonIO6q66pj9EoAfAgsCVQvg2ps/+x/Xde/Kp6FdnN8/wo0yTrVRxnV1sOAfMPM37sB/zStu8RN/iUqAUye4n7pa2LrQnZmvng7T/tP9pA90Vwr9xvmna+reAvj6VXf2X7IOImIg61J39p95bsBOLoxpCYG8IhgGrFPVDQAi8ipwGbDqmP1+C/wB+EkAa3FnbJGxMP2n8Nw4uG4KJHTy+8fkZqXz1tLtLN26hzN6hGDTQOlWeOtud9O333i49LHA9o4JC4ceI93Phb91UzIfulL47M/w6R+b3jW1aj/kvevO/jd+Cih0PxNG3edm+GzppihjAiSQQdAF2FrvcQEwvP4OInI60E1V3xeR4waBiEwCJgF079696RUNuxM6dod/3wpP5cL1UyB9QNPfrwGH1jKemVcUWkGg6gZITftP0DqY8DcYckPL9/BJ6eN+Rt0LB3bD2g+P6Zoa65ZoPF7X1Lo62DIXlr7i5vupKoeOPeDcn7meTUnWI8wEH8+6XIhIGPAX4JZv21dVJwOTwd0sbtYH970IbpsOL0+EZ8fCVf90vTr8pENMJEMzkvg4r4ifjQ2RUcYHdsN7P3IHzm4j4DtPtI4DZmySO3ifsGvqMNd81H0krP/YNf2UboF2CW5q50HXuedCZdSzCUmBDIJtQP07dl192w5JALKBOeLOGjsB74jIhIDdMD6k8yC4Yxa8fDW8dBVc8r9wxs1+e/vcrDQeeT8vNEYZr5sJb/3A9ZfP/bVrgmuN7eUNdk2dfqRrKgACPc91E731vwTaBfn/O2N8AtZ9VEQigDVALi4AFgHXqerK4+w/B/jPbwsBv3YfrdgHU291B7OzfuwOAH4489u0az+j/zyHhy49lVuCdXBZ1QH46Few6ClI7e8Gh3Ue5HVVTbN3m5vuudsw6NDV62qMCYgTdR8N2PWuqtYA9wAzgDxgiqquFJGHRWRCoD73pES3h2tfgzNuhc//Aq/f7vqGN1NGShw9U+OYlV/khyJboYIl8OTZLgRG/AAmfdJ2QwBc19TsKywETMhqVNOQiPwQeA4oA54GhgAPqOqHJ3qdqk4Dph2z7VfH2Xd0Y2rxu/AI1zSUlOnOcPdtc90d45Kb9bZjstJ57ouNlFfWEB/VSka/NldtteuC+8kfXY+rm96GnqO9rsoY00yNvSK4TVX3ARcCicCNQIODw9okEde2fdXzbp6Yp3Nh17pmvWVu/zSqa5XP1hT7qUiP7VoHz14Ec34H2VfC3XMtBIwJEo0NgkN9AMcDL/ja+YNvtNSAy+Hm99zI1WfGwOa5TX6rM3ok+kYZt/HmIVVY9DQ8cZYbnf3d5+DKpyCmo9eVGWP8pLFBsEREPsQFwQzfaOC6wJXloW5D4Y6ZEJsC/7oMlv27SW8TER7G6H6pzF5dRG1d25rP6bCyna5X1fv3uwFb35/n2tKNMUGlsUFwO/AAbi6gA0AkcGvAqvJaUibc/iF0HQZv3AGf/MmdGZ+k3Kx0du+vYunW0gAUGWCr3oa/j3B978f/GW54A9qf4nVVxpgAaGwQjARWq2qpiNwA/BLYG7iyWoHYJLjxDTjtGpj9CLx9j5tJ8ySc6xtlPCuvMEBFBkDFXnjjLphyEyRmwF2fuRHZrXkNAGNMszQ2CP4BHBCRQcD9wHrgXwGrqrWIiHKjZM99AJa+CC9dCQcbf3Z/aJTxrLZyn2DjZ/CPUW46hnN/Brd/ZLNpGhMCGhsENepGnl0G/E1VH8eNDA5+InDeg3D5E25xlWcvgj2bG/3y3Kw0VheWsXX3gQAW2UzVFTDjF/D8pW4BldtmwHk/t8VUjAkRjQ2CMhF5ENdt9H3fPEGhdZQYfC3c+CaU7YCnx8C2JY16WW5WOgAft9bBZTuXw1Pnwby/Qc6t8L3P3Q1zY0zIaGwQTAQqceMJduLmDfpTwKpqrTLPds0lkTHw3MWQ9963v8Q3ynhma7tPUFcLn/8vTD4P9u+C6/7tBtYFYJ0GY0zr1qgg8B38XwI6iMglQIWqBv89goak9nMT1qUPgNdugHmPf2uPotz+aSzYsJvyypoWKvIESrfCF4/BE2e7ydb6jYXvz4e+F3pdmTHGI40KAhG5GlgIXAVcDSwQke8GsrBWLT4VbnnPrVA14+cw7SdQe/yDfG5WOlW1dXy+1qNRxmWFsOBJeOZC+Gs2fPRfbuH4K56Cq19o9nQaxpi2rbGT4PwCN4agCEBEUoGZuHWGQ1NkjJuSYuavYO7/uTnsv/ssRMV/Y9eceqOMx2a30FrG+0sg7x1Y8Tps+hxQSM92M6xmXwFJPVumDmNMq9fYIAg7FAI+JQRw5tI2IywMLnwEEjPdylyHlsBsf/TB/vAo4/yiwK5lXLEX8t93B/8Nc6CuBpJ7w7k/hQFXQFqILJRjjDkpjQ2CD0RkBvCK7/FEjplVNKQNvd23BOYtbsK666ZAp+yjdjm/fxpvL93O1wWlnN490X+fXbXfLbCy8k23LGNtFXToDiPvcZPDdRpog8GMMSfUqCBQ1Z+IyJXAKN+myar6ZuDKaoP6XAC3fQAvXe2WwLz6n241LJ/RfdMI940ybnYQVFfAuo9gxRtukfbqAxDfCYbe4Q7+Xc6wg78xptECtkJZoPh1hbJA2LvNrYdctAou/h/XN9/nmsnzKD1QzQf3nXPy71tbDetnw8o3XLfVqjKITYZTL3dt/t1Hts4lIo0xrcKJVig74RWBiJQBDSWFAKqq7f1QX3Dp0AVumw5Tb4P37oM9GyH3IQgLY0xWOo+8n0fBngN0TWzEerh1te5G74rX3Y3fg3sgugMMuMy1+Wee6xbWMcaYZjjhUURVQ2MaCX+LSnCrnE3/KXzxqJuS4jtPcH5/t6j9x/lF3DQyo+HX1tVBwUJ38F/5Fuwvgsg46H+xO/Pvdb6bA8kYY/zETicDJTzCNQ0l9YQPfwn7ttPz2lfomRLHe8t2cMPwHoQd6j2kCtu/8h3833TLZUZEQ58LXZt/nwuhXSOuIIwxpgksCAJJBM68x/UoeuNOeDqXu7P/wk/m7ObB15fxu7PCCVv1hrvpu2cjhEVC71wY8xD0G+euLIwxJsAsCFrCqRPcoi6vXMN3l95Gj56X0nH5x4St3IZKGJJ5Lpx9P2RdAjF+7FpqjDGNYEHQUrrmwB0zkZeuYtj2F9nWcQi/LLmQuv4TePi60USE2/g8Y4w3LAhaUmIG3D0PKvfRJTaJLnPW84cP8il99SsevWYIkRYGxhgPWBC0tPAItwwmcPfoXkSGC4+8n0d17Zf87bohREXYWABjTMuyU1CP3XF2Tx6+bAAfrSrkey8soaK61uuSjDEhxoKgFbhpZAb//bHz8HgAABJ7SURBVJ2BzFlTzJ3/WszBKgsDY0zLsSBoJa4b3p0/Xnkan6/bxa3/XMj+1rCIjTEmJFgQtCJX5XTjf68ezMKNu7nluYWtY0UzY0zQsyBoZS4f0oXHrh3Cl1tKufGZBew9WO11ScaYIBfQIBCRsSKyWkTWicgDDTz/PRFZLiJLReRzETk1kPW0FZecdgqPX3c6K7bt5cZnFlB6oMrrkowxQSxgQSAi4cDjwDjgVODaBg70L6vqQFUdDPwR+Eug6mlrxmZ34okbziB/RxnXPbWA3fstDIwxgRHIK4JhwDpV3aCqVcCrwGX1d1DVffUextHwlNchKzcrnaduzmF9cTnXTp5PcVml1yUZY4JQIIOgC7C13uMC37ajiMgPRGQ97org3obeSEQmichiEVlcXFwckGJbq3P7pvLcLUPZsvsA10yeR9G+Cq9LMsYEGc9vFqvq46raC/gZ8Mvj7DNZVXNUNSc1NbVlC2wFzuydwj9vHcqOvRVMnDyfHXsPel2SMSaIBDIItgHd6j3u6tt2PK8ClwewnjZteM9kXrh9GMVllUx8cj4Few54XZIxJkgEMggWAX1EJFNE2gHXAO/U30FE+tR7eDGwNoD1tHln9EjixTuGU3qgiolPzmdLiYWBMab5AhYEqloD3APMAPKAKaq6UkQeFpEJvt3uEZGVIrIU+DFwc6DqCRaDu3Xk5TtHsL+qhqufnMfGXfu9LskY08aJatvqqJOTk6OLFy/2ugzP5e3Yx/VPLyAiTHj5zuH0TrPVzIwxxyciS1Q1p6HnPL9ZbJomq3N7Xp00gjqFaybPZ/XOMq9LMsa0URYEbVjf9AReu2sE4WHCNZPnsXL7Xq9LMsa0QRYEbVyv1HhemzSSmMhwrntqAcsKSr0uyRjTxlgQBIGMlDheu2skCdERXP/UAr7cssfrkowxbYgFQZDolhTLlLtGkhTfjhufXsCiTbu9LskY00ZYEASRUzrG8NqkkaR3iOamZxYyb32J1yUZY9oAC4Ig06lDNK9OGkHXxBhu/edCPlsbWnMzGWNOngVBEEpLcGGQkRzH7c8vZnZ+kdclGWNaMQuCIJUcH8Urd46gb3o8k15YzIcrd3pdkjGmlbIgCGKJce146Y4RnHpKB77/0pdMW77D65KMMa2QBUGQ6xATyYu3D2NQt478xytf8fbSE00Aa4wJRRYEISAhOpJ/3TaMM3ok8qPXlvL6kgKvSzLGtCIWBCEiLiqCf946lJG9kvnPqV/z2qItXpdkjGklLAhCSGy7CJ65eSjn9EnlZ68v5y8fraG8ssbrsowxHrMgCDHRkeFMvukMLh10Co/NWsvZf/iYv89ZZ4FgTAiz9QhC2Fdb9vDorLXMWV1MYmwkd57Tk5tGZhAfFeF1acYYPzvRegQWBIalW0t5dOYaZq8upmNsJHee3ZObz7RAMCaYWBCYRrFAMCZ4WRCYk7J0aymPzVrLx/lFFgjGBAkLAtMkX28t5VELBGOCggWBaZaGAuGmkT1IiI70ujRjTCNZEBi/+NrXZDTLAsGYNseCwPjVsoJSHp1pgWBMW2JBYAKifiB0iInkzrMzufnMDAsEY1ohCwITUMsKXJPRzDwLBGNaKwsC0yKWF+zl0VlrLBCMaYUsCEyLOjYQ7jgrk1tGWSAY4yULAuMJFwhrmZlXaIFgjMcsCIynGgqEm0dl0N4CwZgWc6IgCOg01CIyVkRWi8g6EXmgged/LCKrRGSZiMwSkR6BrMd4Y2DXDjx9cw7v/cdZDM1I4n8+WsPZf5jN/81ay76Kaq/LMybkBeyKQETCgTXABUABsAi4VlVX1dvnPGCBqh4QkbuB0ao68UTva1cEbd+Kbe4K4aNVhbSPjmD8wM6c2TuFkT2TSU2I8ro8Y4LSia4IAjlpzDBgnapu8BXxKnAZcDgIVHV2vf3nAzcEsB7TSmR36cBTN+WwYttenvhkPdOW7+DVRVsB6JeewJm9kxnVK4VhPZOs+ciYFhDIIOgCbK33uAAYfoL9bwemN/SEiEwCJgF0797dX/UZj2V36cDfrjud2jplxba9zF1fwtz1u3hl4Rae+2IT4WHCwC4dGOULhtN7JBIdGe512cYEnUA2DX0XGKuqd/ge3wgMV9V7Gtj3BuAe4FxVrTzR+1rTUPCrrKnly82lzF2/i7nrS1i6tZTaOqVdRBg5PRIZ1TuFM3slM7BLByLCbbVVYxrDq6ahbUC3eo+7+rYdRUTGAL+gESFgQkNURDgjeyUzslcy9wPllTUs3FjCF+tK+GLdLv40YzUACVERDO+ZxJm9UhjVO4W+6fGIiLfFG9MGBTIIFgF9RCQTFwDXANfV30FEhgBP4q4cigJYi2nD4qMiOL9/Ouf3TwegpLySeRtcMMxdv4uZee6vTkp8FGf2SubMXsmM6p1Ct6RYL8s2ps0I6DgCERkP/BUIB55V1f8nIg8Di1X1HRGZCQwEdvheskVVJ5zoPa1pyByrYM8Bd39h3S6+WF9CcZm7sOyWFMOoXinWI8kYbECZCSGqyrqicuaud81I8zaUUFZRA1iPJBPaLAhMyDrUI+mL9buYt76ERZt2U1FdZz2STMixIDDG50Q9koZ068jwzCSG90xmSPeOxLaztZlN8LAgMOY4DvVImruuhIWbdrNi217qFCLChIFdOzAsM4nhmUnkZFhTkmnbLAiMaaSyimqWbN7Dwo27WbhxN18XlFJdq4jAqZ3bHw6GoRlJJMfbzWfTdlgQGNNEFdW1fLWllAUbS1i4cTdfbtlDRXUdAL3T4g8Hw/DMZDp1iPa4WmOOz4LAGD+pqqlj+ba9LNy4mwUbS1i8aQ/lla5XUvekWIZlJh0Oh+5JsTbAzbQaFgTGBEhtnZK3Yx8LNu5moe+qYc8BN7V2p/bRRwVD7zQb+Wy8Y0FgTAupq1PWFZf7gmE3CzaUUOQb4JYU146hGYkMz0xmWGYSWZ3bEx5mwWBahldzDRkTcsLChL7pCfRNT+DGET1QVTaXHPA1Je1m4aYSZqwsBNxcSTkZiQzLTGZ4zyQGdulApE2iZzxgQWBMAIkIGSlxZKTEcfVQNwfj9tKDLNq0m/kbXHPS7NXFAMREhpOTkciFp6Zz0YBOpLW3m8+mZVjTkDEe21VeySLfFcOna4vZULwfEcjpkci47M6Mze7EKR1jvC7TtHF2j8CYNmRtYRnTlu9k+ood5O8sA2Bwt46MH9iJcdmdbVZV0yQWBMa0URuKy5m+woXCim37AMju0p5x2Z0Zl92JnqnxHldo2goLAmOCwNbdB5i+YgfTlu9k6dZSAPp3SmBcdmfGD+xEn/QEjys0rZkFgTFBZnvpQT7wXSks3rwHVTfSeXx2J8Zmdyarc4KNWTBHsSAwJogV7atgxsqdTFu+kwUbS6hTyEiOZdxA13w0sEsHCwVjQWBMqNhVXslHqwqZtnwHc9eXUFundOkYw/iB7kphSLeOhNkgtpBkQWBMCCo9UMVHqwqZvmInn60tprpW6dQ+mrHZnRg/sDNn9Ei0kc0hxILAmBC3r6KaWXmFTF++kzlriqmqqSMlPoqx2emMz+7MsMwkImxUc1CzIDDGHFZeWcPs/CI+WLGTj/OLOFhdS1JcOy48NZ1xAztzZq9km+oiCFkQGGMadLCqlk/WFDF9xU5m5RVRXllDh5hIzu6TQlbn9vTzzZvUNTHG7i20cTbpnDGmQTHtwhmb3Zmx2Z2pqK7l87W7mLZiB/PXl/Desh1H9osMp296PH3SE1w4dEqgb3o8ndpHW4+kIGBXBMaYBpVVVLO2qJw1O8tYXVjG2sJyVheWUeybVhsgITri8Gyr/dLj3e+dEkixZTxbHbsiMMactIToSE7vnsjp3ROP2r5nfxVrCstYU+gCYk1hOdNX7OCVhdWH90mOa0ef9Ph6Vw8J9E1LoENsZEv/MUwjWBAYY05KYlw7hvdMZnjP5MPbVJXiskrW+K4a1uwsY01RGVOXFLC/qvbwfunto3xXDwmHrx76pMUTF2WHIi/Zt2+MaTYRIa19NGntozmrT8rh7arKttKDh5uVDl1JvDB/M5U1dYf365oYQ7/0BHcPopNrYuqVGk90ZLgXf5yQY0FgjAkYEaFrYixdE2M5r3/a4e21dcrW3QfqXT24exGf+ga+AYQJJMVFER0ZRlREGFER4b7fw4lqaFtEmG97I7b53iP6mPeKighr0d5RqkqdQp0qdaro4d/df7XuyHN1CvFREcS08384WhAYY1pceNiRldsuGtDp8Pbq2jo27dp/OCCKy6uorKmlsqaOyuo693t1HXv2V7ltNXVUVB963v23pq55HWDahYcdFSBREWGI8M2DtB57ID/0+Oh96h/I9ZjnTtYjl2dzw4gezfrzNcSCwBjTakSGh9HH10TEaU17j5rausMhcSg4Knz/PbStol6ofGNbvdCp8L0WIEyEMHH/FTn28aFth7Y3vE9YvW3i2y9M3FrXjdl/WGaSH7/tIwIaBCIyFngUCAeeVtXfH/P8OcBfcf/Lr1HVqYGsxxgT/CLCw4gIDyPOerA2WsDGkYtIOPA4MA44FbhWRE49ZrctwC3Ay4GqwxhjzIkF8opgGLBOVTcAiMirwGXAqkM7qOom33N1Db2BMcaYwAvkzFJdgK31Hhf4tp00EZkkIotFZHFxcbFfijPGGOO0iSkGVXWyquaoak5qaqrX5RhjTFAJZBBsA7rVe9zVt80YY0wrEsggWAT0EZFMEWkHXAO8E8DPM8YY0wQBCwJVrQHuAWYAecAUVV0pIg+LyAQAERkqIgXAVcCTIrIyUPUYY4xpWEDHEajqNGDaMdt+Ve/3RbgmI2OMMR5pc+sRiEgxsLmJL08BdvmxnLbOvo+j2fdxhH0XRwuG76OHqjbY26bNBUFziMji4y3MEIrs+ziafR9H2HdxtGD/PtpE91FjjDGBY0FgjDEhLtSCYLLXBbQy9n0czb6PI+y7OFpQfx8hdY/AGGPMN4XaFYExxphjWBAYY0yIC5kgEJGxIrJaRNaJyANe1+MVEekmIrNFZJWIrBSRH3pdU2sgIuEi8pWIvOd1LV4TkY4iMlVE8kUkT0RGel2TV0TkR75/JytE5BURifa6pkAIiSBo5CI5oaIGuF9VTwVGAD8I4e+ivh/ipkIxblXBD1S1PzCIEP1eRKQLcC+Qo6rZuJUWr/G2qsAIiSCg3iI5qloFHFokJ+So6g5V/dL3exnuH3mT1okIFiLSFbgYeNrrWrwmIh2Ac4BnAFS1SlVLva3KUxFAjIhEALHAdo/rCYhQCQK/LZITTEQkAxgCLPC2Es/9FfgpYCvlQSZQDDznayp7WkTivC7KC6q6DfgzbkndHcBeVf3Q26oCI1SCwBxDROKB14H7VHWf1/V4RUQuAYpUdYnXtbQSEcDpwD9UdQiwHwjJe2oikohrOcgETgHiROQGb6sKjFAJAlskpx4RicSFwEuq+obX9XhsFDBBRDbhmgzPF5EXvS3JUwVAgaoeukqciguGUDQG2KiqxapaDbwBnOlxTQERKkFgi+T4iIjg2n/zVPUvXtfjNVV9UFW7qmoG7u/Fx6oalGd9jaGqO4GtItLPtykXWOVhSV7aAowQkVjfv5tcgvTGeUDXI2gtVLVGRA4tkhMOPKuqoboIzijgRmC5iCz1bfu5b+0IYwD+A3jJd9K0AbjV43o8oaoLRGQq8CWut91XBOlUEzbFhDHGhLhQaRoyxhhzHBYExhgT4iwIjDEmxFkQGGNMiLMgMMaYEGdBYEwLEpHRNsOpaW0sCIwxJsRZEBjTABG5QUQWishSEXnSt15BuYj8r29++lkikurbd7CIzBeRZSLypm+OGkSkt4jMFJGvReRLEenle/v4evP9v+QbtWqMZywIjDmGiGQBE4FRqjoYqAWuB+KAxao6APgE+LXvJf8CfqaqpwHL621/CXhcVQfh5qjZ4ds+BLgPtzZGT9xob2M8ExJTTBhzknKBM4BFvpP1GKAIN031a759XgTe8M3f31FVP/Ftfx74t4gkAF1U9U0AVa0A8L3fQlUt8D1eCmQAnwf+j2VMwywIjPkmAZ5X1QeP2ijyX8fs19T5WSrr/V6L/Ts0HrOmIWO+aRbwXRFJAxCRJBHpgfv38l3fPtcBn6vqXmCPiJzt234j8Ilv9bcCEbnc9x5RIhLbon8KYxrJzkSMOYaqrhKRXwIfikgYUA38ALdIyzDfc0W4+wgANwNP+A709WfrvBF4UkQe9r3HVS34xzCm0Wz2UWMaSUTKVTXe6zqM8TdrGjLGmBBnVwTGGBPi7IrAGGNCnAWBMcaEOAsCY4wJcRYExhgT4iwIjDEmxP1/wQXVf49d2V0AAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"hoCQimlZMOXe","executionInfo":{"status":"ok","timestamp":1661509817722,"user_tz":-60,"elapsed":6,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"696f0bfa-d611-4f10-9a53-1603c7ad9d96"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Evaluation"],"metadata":{"id":"la7gSEe4OAqO"}},{"cell_type":"code","source":["test_dataset=test_ds.map(vectorizer)\n","test_dataset=test_dataset.batch(BATCH_SIZE)\n","model.evaluate(test_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tPH_emuSvUb2","executionInfo":{"status":"ok","timestamp":1661509889253,"user_tz":-60,"elapsed":10876,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b53759ec-8057-4f9e-d7bd-7fb80337752f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["196/196 [==============================] - 6s 29ms/step - loss: 0.5510 - accuracy: 0.8472\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.5510271191596985, 0.8471999764442444]"]},"metadata":{},"execution_count":59}]},{"cell_type":"markdown","source":["## GRU (MultiLayer and Bidirectional)"],"metadata":{"id":"SWu48alKNFjq"}},{"cell_type":"code","source":["EMBEDDING_DIM=64\n","model=tf.keras.models.Sequential([\n","    Input(shape=(SEQUENCE_LENGTH,)),\n","    Embedding(VOCAB_SIZE,EMBEDDING_DIM),\n","\n","    Bidirectional(GRU(64,return_sequences=True)),\n","    Bidirectional(GRU(32)),\n","    \n","    Dense(64, activation='relu'),\n","    Dropout(0.5),\n","    Dense(1,activation='sigmoid'),\n","])\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661698162932,"user_tz":-60,"elapsed":1140,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7a8bc89a-285d-437c-8cc7-950604649af7","id":"FpKzY8LHNFjy"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding (Embedding)       (None, 250, 64)           640000    \n","                                                                 \n"," bidirectional (Bidirectiona  (None, 250, 128)         49920     \n"," l)                                                              \n","                                                                 \n"," bidirectional_1 (Bidirectio  (None, 64)               31104     \n"," nal)                                                            \n","                                                                 \n"," dense (Dense)               (None, 64)                4160      \n","                                                                 \n"," dropout (Dropout)           (None, 64)                0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 65        \n","                                                                 \n","=================================================================\n","Total params: 725,249\n","Trainable params: 725,249\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["checkpoint_filepath = '/content/drive/MyDrive/nlp/sentiment_analysis/gru.h5'\n","model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath=checkpoint_filepath,\n","    monitor='val_accuracy',\n","    mode='max',\n","    save_best_only=True)"],"metadata":{"id":"o6dhw_ZlNFjz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n","              optimizer=tf.keras.optimizers.Adam(1e-4),\n","              metrics=['accuracy'])"],"metadata":{"id":"A6Yh5inONFjz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=model.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=10,\n","    callbacks=[])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661698526816,"user_tz":-60,"elapsed":363890,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"31ae5e41-37ee-405a-8134-7285d491ea5e","id":"0xUExn4lNFj0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","391/391 [==============================] - 34s 59ms/step - loss: 0.6897 - accuracy: 0.5418 - val_loss: 0.6698 - val_accuracy: 0.5962\n","Epoch 2/10\n","391/391 [==============================] - 21s 54ms/step - loss: 0.4123 - accuracy: 0.8182 - val_loss: 0.3438 - val_accuracy: 0.8558\n","Epoch 3/10\n","391/391 [==============================] - 22s 56ms/step - loss: 0.2685 - accuracy: 0.8980 - val_loss: 0.3434 - val_accuracy: 0.8653\n","Epoch 4/10\n","391/391 [==============================] - 21s 55ms/step - loss: 0.2202 - accuracy: 0.9218 - val_loss: 0.3776 - val_accuracy: 0.8609\n","Epoch 5/10\n","391/391 [==============================] - 21s 54ms/step - loss: 0.1932 - accuracy: 0.9345 - val_loss: 0.4128 - val_accuracy: 0.8536\n","Epoch 6/10\n","391/391 [==============================] - 22s 55ms/step - loss: 0.1719 - accuracy: 0.9414 - val_loss: 0.3983 - val_accuracy: 0.8530\n","Epoch 7/10\n","391/391 [==============================] - 21s 54ms/step - loss: 0.1492 - accuracy: 0.9495 - val_loss: 0.4396 - val_accuracy: 0.8490\n","Epoch 8/10\n","391/391 [==============================] - 21s 55ms/step - loss: 0.1343 - accuracy: 0.9579 - val_loss: 0.4930 - val_accuracy: 0.8439\n","Epoch 9/10\n","391/391 [==============================] - 21s 55ms/step - loss: 0.1215 - accuracy: 0.9615 - val_loss: 0.5218 - val_accuracy: 0.8337\n","Epoch 10/10\n","391/391 [==============================] - 21s 55ms/step - loss: 0.1161 - accuracy: 0.9647 - val_loss: 0.5275 - val_accuracy: 0.8426\n"]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1661698526822,"user_tz":-60,"elapsed":21,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7a53ab90-ffaa-4d87-8f55-97cf27b42b62","id":"_fHFq_VoNFj0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1661698526823,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"651a6da4-38a6-4409-fd06-77ffc79ae25e","id":"8XXN72IgNFj0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Evaluation"],"metadata":{"id":"QMHKMADcOGnJ"}},{"cell_type":"code","source":["test_dataset=test_ds.map(vectorizer)\n","test_dataset=test_dataset.batch(BATCH_SIZE)\n","model.evaluate(test_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661698532269,"user_tz":-60,"elapsed":5464,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1055ff80-09f1-4600-fd4d-6d48415cca02","id":"j5OxvlIJNFj1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["196/196 [==============================] - 5s 27ms/step - loss: 0.5178 - accuracy: 0.8454\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.517833411693573, 0.8453599810600281]"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":[],"metadata":{"id":"h5TfwGMYNFj1"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Conv1D"],"metadata":{"id":"6glXyMgKMI46"}},{"cell_type":"code","source":["EMBEDDING_DIM=300\n","model=tf.keras.models.Sequential([\n","    Input(shape=(SEQUENCE_LENGTH,)),\n","    Embedding(VOCAB_SIZE,EMBEDDING_DIM),\n","\n","    Conv1D(32, 3, activation='relu',),\n","    Flatten(),\n","    Dense(32, activation='relu'),\n","    Dropout(0.5),\n","    Dense(1,activation='sigmoid'),\n","])\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661879024573,"user_tz":-60,"elapsed":1102,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"97b233a4-5d6f-49e6-8c28-a9c13a699c2c","id":"XuF-_HF_MI5C"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_2 (Embedding)     (None, 250, 300)          3000000   \n","                                                                 \n"," conv1d_2 (Conv1D)           (None, 248, 32)           28832     \n","                                                                 \n"," flatten_2 (Flatten)         (None, 7936)              0         \n","                                                                 \n"," dense_4 (Dense)             (None, 32)                253984    \n","                                                                 \n"," dropout_2 (Dropout)         (None, 32)                0         \n","                                                                 \n"," dense_5 (Dense)             (None, 1)                 33        \n","                                                                 \n","=================================================================\n","Total params: 3,282,849\n","Trainable params: 3,282,849\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["checkpoint_filepath = '/content/drive/MyDrive/nlp/sentiment_analysis/conv_1d.h5'\n","model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath=checkpoint_filepath,\n","    monitor='val_accuracy',\n","    mode='max',\n","    save_best_only=True)"],"metadata":{"id":"jIyub7B7MI5D"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n","              optimizer=tf.keras.optimizers.Adam(1e-4),\n","              metrics=['accuracy'])"],"metadata":{"id":"7OJ25rYKMI5D"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=model.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=10,\n","    callbacks=[model_checkpoint_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661879175796,"user_tz":-60,"elapsed":140721,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"60663c92-2e0e-4d56-95c9-03b5b5cb8d5f","id":"_k-G6cxHMI5D"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","391/391 [==============================] - 13s 32ms/step - loss: 0.6915 - accuracy: 0.5203 - val_loss: 0.6819 - val_accuracy: 0.6024\n","Epoch 2/10\n","391/391 [==============================] - 13s 32ms/step - loss: 0.4926 - accuracy: 0.7732 - val_loss: 0.3361 - val_accuracy: 0.8620\n","Epoch 3/10\n","391/391 [==============================] - 13s 32ms/step - loss: 0.2804 - accuracy: 0.8911 - val_loss: 0.3058 - val_accuracy: 0.8719\n","Epoch 4/10\n","391/391 [==============================] - 12s 31ms/step - loss: 0.2179 - accuracy: 0.9232 - val_loss: 0.3145 - val_accuracy: 0.8694\n","Epoch 5/10\n","391/391 [==============================] - 12s 31ms/step - loss: 0.1758 - accuracy: 0.9416 - val_loss: 0.3406 - val_accuracy: 0.8653\n","Epoch 6/10\n","391/391 [==============================] - 12s 31ms/step - loss: 0.1475 - accuracy: 0.9544 - val_loss: 0.3683 - val_accuracy: 0.8599\n","Epoch 7/10\n","391/391 [==============================] - 12s 31ms/step - loss: 0.1218 - accuracy: 0.9648 - val_loss: 0.4016 - val_accuracy: 0.8557\n","Epoch 8/10\n","391/391 [==============================] - 12s 31ms/step - loss: 0.1032 - accuracy: 0.9713 - val_loss: 0.4422 - val_accuracy: 0.8526\n","Epoch 9/10\n","391/391 [==============================] - 12s 31ms/step - loss: 0.0909 - accuracy: 0.9756 - val_loss: 0.4839 - val_accuracy: 0.8486\n","Epoch 10/10\n","391/391 [==============================] - 12s 31ms/step - loss: 0.0827 - accuracy: 0.9764 - val_loss: 0.5390 - val_accuracy: 0.8436\n"]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661879175798,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4e5ee78f-49fd-4a6a-9f5f-73304d7a7243","id":"ZfIkZIa5MI5D"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661879175798,"user_tz":-60,"elapsed":21,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d82ec92e-3ced-4ff9-812b-0bb75ae0d8d9","id":"z40EUdPtMI5D"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### Evaluation"],"metadata":{"id":"iYtQUFP8OK3_"}},{"cell_type":"code","source":["model.load_weights(checkpoint_filepath)"],"metadata":{"id":"8kbNuiFVwIVb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_dataset=test_ds.map(vectorizer)\n","test_dataset=test_dataset.batch(BATCH_SIZE)\n","model.evaluate(test_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661879179940,"user_tz":-60,"elapsed":4161,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"476d9fe9-e8eb-4049-9668-106c9695ce6f","id":"-fZoQdbtMI5D"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["196/196 [==============================] - 4s 19ms/step - loss: 0.3094 - accuracy: 0.8690\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.3093862235546112, 0.8689600229263306]"]},"metadata":{},"execution_count":36}]},{"cell_type":"markdown","source":["## Pretrained Word2Vec[Gensim]"],"metadata":{"id":"lL16odrqNDt1"}},{"cell_type":"code","source":["word2vec=api.load('word2vec-google-news-300')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hZDdK-isTlp-","executionInfo":{"status":"ok","timestamp":1661873243826,"user_tz":-60,"elapsed":538126,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"febafea6-0162-4bdf-e8bb-ba71faa9e563"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"]}]},{"cell_type":"code","source":["word2vec.vectors.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PnG82XkiTlxf","executionInfo":{"status":"ok","timestamp":1661873298563,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"68460b26-cb2a-4644-bfcc-6b440204f90d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(3000000, 300)"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["#print(word2vec.vocab)\n","len(word2vec['The'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nt5jvdDKTl0Z","executionInfo":{"status":"ok","timestamp":1661873443912,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4368b1ac-274e-407c-b154-96975d9f2454"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["300"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["word2vec.most_similar('Man')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l4IYh40dTl2v","executionInfo":{"status":"ok","timestamp":1661873721714,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"22c81053-c944-4948-b293-07815f77f776"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('Woman', 0.693934440612793),\n"," ('Boy', 0.5896924734115601),\n"," ('Girl', 0.584598958492279),\n"," ('Suspect', 0.5577560067176819),\n"," ('Couple', 0.5528684854507446),\n"," ('man', 0.5316052436828613),\n"," ('Robber', 0.5315312147140503),\n"," ('Teenager', 0.5161929130554199),\n"," ('depicts_Michelangelo_Creation', 0.5148171782493591),\n"," ('Policeman', 0.5136396288871765)]"]},"metadata":{},"execution_count":29}]},{"cell_type":"code","source":["pretrained_embeddings=[]"],"metadata":{"id":"bV2zTIs2Zogq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def first_caps(word):\n","  return word[0].upper()+word[1:]"],"metadata":{"id":"H3qmQkOsbAT4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["vectorize_layer.get_vocabulary()[2]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D7tS9yprTl7h","executionInfo":{"status":"ok","timestamp":1661873960560,"user_tz":-60,"elapsed":957,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"949d3a07-6e04-4f62-925a-d5484acee6a2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'the'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["#first_caps(vectorize_layer.get_vocabulary()[2])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2Ol1Nay-MXlH","executionInfo":{"status":"ok","timestamp":1661853640252,"user_tz":-60,"elapsed":679,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e62e1181-1744-4220-be67-87742949e034"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'The'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["pretrained_embeddings=[]"],"metadata":{"id":"hniaJ9-7NHKn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(vectorize_layer.get_vocabulary())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vW5hnCoBM8-V","executionInfo":{"status":"ok","timestamp":1661854017779,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4b4d7360-7ea1-4ead-f56b-306c5a42ab90"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10000"]},"metadata":{},"execution_count":47}]},{"cell_type":"code","source":["for i in range(len(vectorize_layer.get_vocabulary())):\n","  try:\n","    pretrained_embeddings.append(word2vec[vectorize_layer.get_vocabulary()[i]])\n","  except:\n","    print(vectorize_layer.get_vocabulary()[i])\n","    try:\n","      pretrained_embeddings.append(word2vec[first_caps(vectorize_layer.get_vocabulary()[i])])\n","      print('toupper')\n","    except:\n","      print('nosolution')\n","      pretrained_embeddings.append(random.normal(loc=0, scale=1, size=(EMBEDDING_DIM)))#reloaded_word_vectors[vectorize_layer.get_vocabulary()[i]])\n","  if i%1000==0:\n","    print('iis====================================',i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Xof4XvplLEyl","executionInfo":{"status":"ok","timestamp":1661854221911,"user_tz":-60,"elapsed":204138,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"803969d4-0904-4de3-e9bd-9aaed71555a3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","nosolution\n","iis==================================== 0\n","[UNK]\n","nosolution\n","and\n","toupper\n","a\n","toupper\n","of\n","toupper\n","to\n","toupper\n","doesnt\n","nosolution\n","didnt\n","nosolution\n","isnt\n","nosolution\n","wasnt\n","nosolution\n","10\n","nosolution\n","80s\n","nosolution\n","20\n","nosolution\n","70s\n","nosolution\n","iis==================================== 1000\n","15\n","nosolution\n","12\n","nosolution\n","30\n","nosolution\n","humour\n","toupper\n","100\n","nosolution\n","hasnt\n","nosolution\n","90\n","nosolution\n","shouldnt\n","nosolution\n","favourite\n","toupper\n","theatre\n","toupper\n","60s\n","nosolution\n","40\n","nosolution\n","lowbudget\n","nosolution\n","tarzan\n","toupper\n","50\n","nosolution\n","90s\n","nosolution\n","1950s\n","nosolution\n","iis==================================== 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9000\n","elisha\n","toupper\n","eastwoods\n","toupper\n","dukakis\n","toupper\n","dillon\n","toupper\n","benoit\n","toupper\n","aztec\n","toupper\n","98\n","nosolution\n","27\n","nosolution\n","010\n","nosolution\n","zabriskie\n","toupper\n","valerie\n","toupper\n","sinatras\n","toupper\n","segal\n","toupper\n","sarne\n","toupper\n","rko\n","nosolution\n","noam\n","toupper\n","neeson\n","toupper\n","midler\n","toupper\n","mcdowell\n","toupper\n","martino\n","toupper\n","maguire\n","toupper\n","luzhin\n","toupper\n","keitel\n","toupper\n","izzard\n","toupper\n","herethe\n","nosolution\n","grady\n","toupper\n","gannon\n","toupper\n","featurelength\n","nosolution\n","farnsworth\n","toupper\n","elmer\n","toupper\n","dickinson\n","toupper\n","crenna\n","toupper\n","cortez\n","toupper\n","coppola\n","toupper\n","btk\n","toupper\n","azumi\n","toupper\n","48\n","nosolution\n","250\n","nosolution\n","victorias\n","toupper\n","thatll\n","nosolution\n","shakespearean\n","toupper\n","rosenstrasse\n","toupper\n","rizzo\n","toupper\n","murphys\n","toupper\n","moviethis\n","nosolution\n","mostel\n","toupper\n","lucio\n","toupper\n","lubitsch\n","toupper\n","it´s\n","nosolution\n","harrys\n","toupper\n","goodthe\n","nosolution\n","floriane\n","toupper\n","fatherson\n","nosolution\n","duryea\n","toupper\n","durbin\n","toupper\n","corbin\n","toupper\n","callahan\n","toupper\n","bolivia\n","toupper\n","2009\n","nosolution\n","1949\n","nosolution\n","1938\n","nosolution\n","1931\n","nosolution\n","18th\n","nosolution\n","spielbergs\n","toupper\n","sorvino\n","toupper\n","sloane\n","toupper\n","seinfeld\n","toupper\n","sciencefiction\n","nosolution\n","rainer\n","toupper\n","pauls\n","toupper\n","morgana\n","toupper\n","lotr\n","nosolution\n","hisher\n","nosolution\n","guevara\n","toupper\n","granger\n","toupper\n","culkin\n","toupper\n","cruella\n","toupper\n","connolly\n","toupper\n","cillian\n","toupper\n","carole\n","toupper\n","carlito\n","toupper\n","batwoman\n","toupper\n","andersons\n","toupper\n","500\n","nosolution\n","400\n","nosolution\n","zatoichi\n","toupper\n","wellthe\n","nosolution\n","viggo\n","toupper\n","twodimensional\n","nosolution\n","thisthe\n","nosolution\n","streisands\n","nosolution\n","spoton\n","nosolution\n","slowmoving\n","nosolution\n","poppins\n","toupper\n","phyllis\n","toupper\n","pavarotti\n","toupper\n","onethe\n","nosolution\n","mastroianni\n","toupper\n","marvellous\n","toupper\n","mamet\n","toupper\n","leia\n","toupper\n","kingsley\n","toupper\n","jed\n","toupper\n","itits\n","nosolution\n","imdbs\n","nosolution\n","helena\n","toupper\n","hedy\n","toupper\n","hamill\n","toupper\n","gilligans\n","toupper\n","directorwriter\n","nosolution\n","deathstalker\n","nosolution\n","darkman\n","toupper\n","colourful\n","toupper\n","caruso\n","toupper\n","bruckheimer\n","toupper\n","bonham\n","toupper\n","bogdanovich\n","toupper\n","bgrade\n","nosolution\n","badthe\n","nosolution\n","aiello\n","toupper\n","1942\n","nosolution\n","willem\n","toupper\n","vonnegut\n","toupper\n","travelling\n","toupper\n","tigerland\n","toupper\n","therethe\n","nosolution\n","sabu\n","toupper\n","runofthemill\n","nosolution\n","rohmer\n","toupper\n","recognise\n","toupper\n","reanimator\n","nosolution\n","patton\n","toupper\n","pacinos\n","nosolution\n","otoole\n","nosolution\n","osullivan\n","nosolution\n","nielsen\n","toupper\n","mpaa\n","nosolution\n","macbeth\n","toupper\n","lanza\n","toupper\n","jovi\n","toupper\n","jameson\n","toupper\n"]}]},{"cell_type":"code","source":["pretrained_embeddings_array=np.array(pretrained_embeddings)\n","print(pretrained_embeddings_array.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OxTmVgSuLm5d","executionInfo":{"status":"ok","timestamp":1661854221911,"user_tz":-60,"elapsed":26,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"085c0078-b1e9-4312-d5ce-32c172df3183"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(10000, 300)\n"]}]},{"cell_type":"code","source":["#np.save('/content/drive/MyDrive/nlp/sentiment_analysis/pretrained_embeddings',pretrained_embeddings_array)"],"metadata":{"id":"ZZ1Qw0gCNouH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pretrained_embeddings_array=np.load('/content/drive/MyDrive/nlp/sentiment_analysis/pretrained_embeddings.npy')"],"metadata":{"id":"vCc94KwsNowd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pretrained_embeddings_array.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EZEryOpBQXKH","executionInfo":{"status":"ok","timestamp":1661878532986,"user_tz":-60,"elapsed":108,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3e1a77fb-5a6a-45c7-fdb3-c5c81f75afb8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(10000, 300)"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["EMBEDDING_DIM=300\n","model=tf.keras.models.Sequential([\n","    Input(shape=(SEQUENCE_LENGTH,)),\n","    Embedding(\n","        VOCAB_SIZE,\n","        EMBEDDING_DIM,\n","        embeddings_initializer=tf.keras.initializers.Constant(pretrained_embeddings_array),\n","        trainable=True,\n","    ),\n","\n","    Conv1D(32, 3, activation='relu',),\n","    Flatten(),\n","    Dense(32, activation='relu'),\n","    Dropout(0.5),\n","    Dense(1,activation='sigmoid'),\n","])\n","model.summary()"],"metadata":{"id":"ymgr_fi3y4wS","executionInfo":{"status":"ok","timestamp":1661878616284,"user_tz":-60,"elapsed":25,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"014aa41d-ffc6-4252-8da7-6e709c4d6d07"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_1 (Embedding)     (None, 250, 300)          3000000   \n","                                                                 \n"," conv1d_1 (Conv1D)           (None, 248, 32)           28832     \n","                                                                 \n"," flatten_1 (Flatten)         (None, 7936)              0         \n","                                                                 \n"," dense_2 (Dense)             (None, 32)                253984    \n","                                                                 \n"," dropout_1 (Dropout)         (None, 32)                0         \n","                                                                 \n"," dense_3 (Dense)             (None, 1)                 33        \n","                                                                 \n","=================================================================\n","Total params: 3,282,849\n","Trainable params: 3,282,849\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["checkpoint_filepath = '/content/drive/MyDrive/nlp/sentiment_analysis/conv_1d_word2vec.h5'\n","model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath=checkpoint_filepath,\n","    monitor='val_accuracy',\n","    mode='max',\n","    save_best_only=True)"],"metadata":{"id":"qUNuOUyHy4yj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n","              optimizer=tf.keras.optimizers.Adam(1e-4),\n","              metrics=['accuracy'])"],"metadata":{"id":"ct_idDJpy40r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=model.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=10,\n","    callbacks=[model_checkpoint_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p7rTiT7FPaqH","executionInfo":{"status":"error","timestamp":1661878697925,"user_tz":-60,"elapsed":81655,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"98cb7bec-5de5-4f3e-c91e-2984c9b7e8d1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","25/25 [==============================] - 10s 372ms/step - loss: 0.7380 - accuracy: 0.5144 - val_loss: 0.6953 - val_accuracy: 0.5048\n","Epoch 2/50\n","25/25 [==============================] - 6s 251ms/step - loss: 0.6849 - accuracy: 0.5337 - val_loss: 0.6952 - val_accuracy: 0.5051\n","Epoch 3/50\n","25/25 [==============================] - 6s 259ms/step - loss: 0.6765 - accuracy: 0.5619 - val_loss: 0.6971 - val_accuracy: 0.5081\n","Epoch 4/50\n","25/25 [==============================] - 6s 251ms/step - loss: 0.6665 - accuracy: 0.5894 - val_loss: 0.6961 - val_accuracy: 0.5101\n","Epoch 5/50\n","25/25 [==============================] - 4s 167ms/step - loss: 0.6508 - accuracy: 0.6156 - val_loss: 0.6974 - val_accuracy: 0.5054\n","Epoch 6/50\n","25/25 [==============================] - 6s 250ms/step - loss: 0.6379 - accuracy: 0.6244 - val_loss: 0.6993 - val_accuracy: 0.5128\n","Epoch 7/50\n","25/25 [==============================] - 4s 176ms/step - loss: 0.6204 - accuracy: 0.6488 - val_loss: 0.7035 - val_accuracy: 0.5092\n","Epoch 8/50\n","25/25 [==============================] - 6s 232ms/step - loss: 0.6021 - accuracy: 0.6694 - val_loss: 0.7097 - val_accuracy: 0.5114\n","Epoch 9/50\n","25/25 [==============================] - 5s 212ms/step - loss: 0.5903 - accuracy: 0.6687 - val_loss: 0.7134 - val_accuracy: 0.5102\n","Epoch 10/50\n","25/25 [==============================] - 8s 312ms/step - loss: 0.5782 - accuracy: 0.6844 - val_loss: 0.7174 - val_accuracy: 0.5137\n","Epoch 11/50\n","23/25 [==========================>...] - ETA: 0s - loss: 0.5515 - accuracy: 0.7120"]},{"output_type":"stream","name":"stderr","text":["ERROR:root:Internal Python error in the inspect module.\n","Below is the traceback from this internal error.\n","\n"]},{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3326, in run_code\n","    exec(code_obj, self.user_global_ns, self.user_ns)\n","  File \"<ipython-input-28-bc8ba518b435>\", line 5, in <module>\n","    callbacks=[model_checkpoint_callback])\n","  File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n","    return fn(*args, **kwargs)\n","  File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1431, in fit\n","    _use_cached_eval_dataset=True)\n","  File \"/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\", line 64, in error_handler\n","    return fn(*args, **kwargs)\n","  File \"/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\", line 1716, in evaluate\n","    tmp_logs = self.test_function(iterator)\n","  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\", line 150, in error_handler\n","    return fn(*args, **kwargs)\n","  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\", line 915, in __call__\n","    result = self._call(*args, **kwds)\n","  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\", line 954, in _call\n","    results = self._stateful_fn(*args, **kwds)\n","  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\", line 2957, in __call__\n","    filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n","  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\", line 1854, in _call_flat\n","    ctx, args, cancellation_manager=cancellation_manager))\n","  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\", line 504, in call\n","    ctx=ctx)\n","  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\", line 55, in quick_execute\n","    inputs, attrs, num_outputs)\n","KeyboardInterrupt\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2040, in showtraceback\n","    stb = value._render_traceback_()\n","AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n","    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n","  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 319, in wrapped\n","    return f(*args, **kwargs)\n","  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 353, in _fixed_getinnerframes\n","    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n","  File \"/usr/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n","    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n","  File \"/usr/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n","    filename = getsourcefile(frame) or getfile(frame)\n","  File \"/usr/lib/python3.7/inspect.py\", line 696, in getsourcefile\n","    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n","  File \"/usr/lib/python3.7/inspect.py\", line 742, in getmodule\n","    os.path.realpath(f)] = module.__name__\n","  File \"/usr/lib/python3.7/posixpath.py\", line 395, in realpath\n","    path, ok = _joinrealpath(filename[:0], filename, {})\n","  File \"/usr/lib/python3.7/posixpath.py\", line 429, in _joinrealpath\n","    if not islink(newpath):\n","  File \"/usr/lib/python3.7/posixpath.py\", line 168, in islink\n","    def islink(path):\n","KeyboardInterrupt\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"id":"lwr9nK-yPasr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"id":"Fk1HAv8WPau5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"zSGCoglUPayc"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Evaluation"],"metadata":{"id":"UAgNaikHP3A5"}},{"cell_type":"code","source":["model.load_weights(checkpoint_filepath)"],"metadata":{"id":"1qkLC__kP3BA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_dataset=test_ds.map(vectorizer)\n","test_dataset=test_dataset.batch(BATCH_SIZE)\n","model.evaluate(test_dataset)"],"metadata":{"id":"3N5zc1wyP3BA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"MISIvsblP3BB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"obCwC6fGPa04"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Testing"],"metadata":{"id":"7jbgoKV070Gr"}},{"cell_type":"code","source":["# test_pos=\"this movie looks very interesting, i love the fact that the actors do a great job in showing how people lived in the 18th century, which wasn't very good at all. But atleast this movie recreates this scenes! \"\n","# test_neg=\"very good start, but movie started becoming boring at some point and unfortunately i didn't feel like this was properly produced as there was too much background noise, and the actors didn't look motivated at all \""],"metadata":{"id":"LPH6sskd71MM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_data=tf.data.Dataset.from_tensor_slices([[\"this movie looks very interesting, i love the fact that the actors do a great job in showing how people lived in the 18th century, which wasn't very good at all. But atleast this movie recreates this scenes! \"],\n","                                              [\"very good start, but movie started becoming interesting at some point and fortunately at some point it started becoming much more fun, though there was too much background noise, so in all i liked this movie \"],])\n"],"metadata":{"id":"cW7_p_vG71Si"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def vectorizer_test(review):\n","    return vectorize_layer(review)\n","test_dataset=test_data.map(vectorizer_test)"],"metadata":{"id":"rypKRKsI71U5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.predict(test_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J6x4ihmxvi35","executionInfo":{"status":"ok","timestamp":1661879859260,"user_tz":-60,"elapsed":2,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"08bb83a9-d5d0-4bdc-dc21-a9bf0f082567"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.68417126],\n","       [0.55765617]], dtype=float32)"]},"metadata":{},"execution_count":55}]},{"cell_type":"code","source":[],"metadata":{"id":"TYad8zYNvlYz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Inference Ready Testing"],"metadata":{"id":"G7oP9oWgyroD"}},{"cell_type":"code","source":["inputs = Input(shape=(1,), dtype=\"string\")\n","vectorized_inputs=vectorize_layer(inputs) \n","outputs = model(vectorized_inputs) \n","inference_ready_model = tf.keras.Model(inputs, outputs) \n","inference_ready_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YjwDEGUTyMyu","executionInfo":{"status":"ok","timestamp":1661881772247,"user_tz":-60,"elapsed":872,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9e35b9d6-2551-47ed-d9d3-c27c814bf43a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input_6 (InputLayer)        [(None, 1)]               0         \n","                                                                 \n"," text_vectorization (TextVec  (None, 250)              0         \n"," torization)                                                     \n","                                                                 \n"," sequential_2 (Sequential)   (None, 1)                 3282849   \n","                                                                 \n","=================================================================\n","Total params: 3,282,849\n","Trainable params: 3,282,849\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["inference_ready_model.predict([\"this movie looks very interesting, i love the fact that the actors do a great job in showing how people lived in the 18th century, which wasn't very good at all. But atleast this movie recreates this scenes! \",\n","                               \"very good start, but movie started becoming interesting at some point and fortunately at some point it started becoming much more fun, though there was too much background noise, so in all i liked this movie \"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O3V0jj3VzADu","executionInfo":{"status":"ok","timestamp":1661881812594,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f1a0f2b8-475c-413a-e3c8-73a1164c1b76"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.68417126],\n","       [0.55765617]], dtype=float32)"]},"metadata":{},"execution_count":61}]},{"cell_type":"code","source":[],"metadata":{"id":"FLQvyZTn3_3a"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Visualize Embeddings"],"metadata":{"id":"co8pYQ4LtvE8"}},{"cell_type":"code","source":["EMBEDDING_DIM=300\n","model=tf.keras.models.Sequential([\n","    Input(shape=(SEQUENCE_LENGTH,)),\n","    Embedding(VOCAB_SIZE,EMBEDDING_DIM),\n","\n","    Conv1D(32, 3, activation='relu',),\n","    Flatten(),\n","    Dense(32, activation='relu'),\n","    Dropout(0.5),\n","    Dense(1,activation='sigmoid'),\n","])\n","model.summary()"],"metadata":{"id":"mcA8D8p9Pa2l","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661895307453,"user_tz":-60,"elapsed":1211,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d7e818ec-e085-46aa-e21b-451a562e7f02"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_3\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding_3 (Embedding)     (None, 250, 300)          3000000   \n","                                                                 \n"," conv1d_3 (Conv1D)           (None, 248, 32)           28832     \n","                                                                 \n"," flatten_3 (Flatten)         (None, 7936)              0         \n","                                                                 \n"," dense_6 (Dense)             (None, 32)                253984    \n","                                                                 \n"," dropout_3 (Dropout)         (None, 32)                0         \n","                                                                 \n"," dense_7 (Dense)             (None, 1)                 33        \n","                                                                 \n","=================================================================\n","Total params: 3,282,849\n","Trainable params: 3,282,849\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n","              optimizer=tf.keras.optimizers.Adam(1e-4),\n","              metrics=['accuracy'])"],"metadata":{"id":"uO8fmhf7SpgR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["log_dir='logs/imdb/fit/'+datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")+'/'"],"metadata":{"id":"m5yYlaHPTHwD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tensorboard_callback=tf.keras.callbacks.TensorBoard(log_dir,histogram_freq=1)"],"metadata":{"id":"0PASGhXlS6Tg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=model.fit(\n","    train_dataset.take(34),\n","    validation_data=val_dataset,\n","    epochs=1,\n","    callbacks=[tensorboard_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bJCK3jMXSw14","executionInfo":{"status":"ok","timestamp":1661895319239,"user_tz":-60,"elapsed":8290,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"63942dfc-569c-4758-b63f-bb15cb6ff97c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["34/34 [==============================] - 8s 209ms/step - loss: 0.6943 - accuracy: 0.4784 - val_loss: 0.6932 - val_accuracy: 0.4987\n"]}]},{"cell_type":"code","source":["with open(os.path.join(log_dir,'metadata.tsv'),\"w\",encoding=\"utf-8\") as f:\n","  for i in range(VOCAB_SIZE):\n","    f.write(\"{} {}\\n\".format(i,vectorize_layer.get_vocabulary()[i]))"],"metadata":{"id":"PzjpqqRYh1yN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["embedding_weights=tf.Variable(model.layers[0].get_weights()[0])\n","print(embedding_weights.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tFhH8KFyh10_","executionInfo":{"status":"ok","timestamp":1661895332490,"user_tz":-60,"elapsed":982,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2179680a-7c3c-489d-be1e-58dd0b652ba5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(10000, 300)\n"]}]},{"cell_type":"code","source":["checkpoint=tf.train.Checkpoint(embedding=embedding_weights)\n","checkpoint.save(os.path.join(log_dir,\"embedding.ckpt\"))\n","\n","config=projector.ProjectorConfig()\n","embedding=config.embeddings.add()"],"metadata":{"id":"FtcOupteh13B"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["embedding.metadata_path='metadata.tsv'\n","projector.visualize_embeddings(log_dir,config)"],"metadata":{"id":"P1IgAkqoh2M3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%load_ext tensorboard"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rMa5GZg3h2O-","executionInfo":{"status":"ok","timestamp":1661895339694,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"cfe4eda6-6bd2-4d7b-e169-f4f44a9b4bd3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The tensorboard extension is already loaded. To reload it, use:\n","  %reload_ext tensorboard\n"]}]},{"cell_type":"code","source":["%tensorboard --logdir logs/imdb/fit/"],"metadata":{"id":"7_Od3QxuwfE-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"kXxzB-yyh2T3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Qt0NOs5D6EGl"},"execution_count":null,"outputs":[]}]}

================================================
FILE: deep learning for natural language processing/3-Sentiment Analysis with Transformers by Neuralearn.ai-.ipynb
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"e52c79c6e66b4e90bb1f8e07497c0b77":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}}}}},"cells":[{"cell_type":"code","source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Dense,Flatten,SimpleRNN,InputLayer,Conv1D,Bidirectional,GRU,LSTM,BatchNormalization,Dropout,Input,GlobalMaxPooling1D,Embedding,TextVectorization,LayerNormalization,MultiHeadAttention)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from tensorboard.plugins import projector"],"metadata":{"id":"ZslyJl_QV7rt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["BATCH_SIZE=64"],"metadata":{"id":"8phsQmoYUJWK"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Data Preparation"],"metadata":{"id":"y3CEdBN2aX9k"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"IuErSCoPS1kN","executionInfo":{"status":"ok","timestamp":1663165442017,"user_tz":-60,"elapsed":64042,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/","height":136,"referenced_widgets":["6a1789de9ceb4f7d9188577dd0564776","8be5e376302748ef8de86ac9c0d058e2","5f24b328fe1a47b9ac83de6233750e21","e9f239be1efa4891a3caa42c43d40db7","52e3327faaa240caa71ce85d8be9c889","478ccda40dc4405b9c51098cb5f51af5","289e4f45c41c440395423150df22445b","e5c429c5d55543d48e2c098116b54b6d","0ddf1ca9c3764a8c80b2b4df53133926","fa52b34310ff45ea85e3da46ad567ebf","89d3da09a9144ceba1959902761a7fd3","7493d734f30349908d0c9c4fbaad05dd","2acd239a8d3645dfacd4acc75ac84678","1da44976df73425da55ad6d0f9b3ddc7","5a181e97930c4332a55a570de4d5906a","9584273504564185b7c1dcba27190cc0","d44cbc83fa6a480992a402a8f8659292","572b0d4228fa49e0a0dea3dcf614d1af","c8f6c1abc69b4799b6799d95cafeb765","6069d015265849b2958084c278cf182f","8a1c5c79ff8146038d446a8611d48182","bddbb58e8714485f92dc17f44a265878","8e8c5a0c6f8b4de4bc72f7b56be16c38","19a05997f27f4b41a1153421c7cd66ac","67865f53c1cf4e40970d0898c9187bdf","b8904c5cc3db4eb28a89137889dbe712","f0bf97dbb4e24b7ca24ebe28423057c4","945d3a2217bd42329540ef235622a64f","ce6174505e9c4d92bb64c315fac7875a","1075284326ab4b0e8bef29c44ffca743","8e53568fd4004007962e2b0684349f90","cd1ca95607b64d459146272885563800","96584aec41ca47ddb184fbdf63e9afd6","d2f789e517ec49a684d47624b9c16623","315e283c0d994102ad937aaa6f37f450","b7936e245d7d4f948a1af05cd4b63f75","7b2409855a6c48ef89117cb7421b4b9a","3b6a83d899b446bdba760b49974c2570","aebf4389022b4ee6823c017a1fe23e2d","2c75bad56ace4121bd0d3a1d4f593424","5a0f139e75854c0c99923dd27160b292","536b16b66df948b1894685a9f2dd68eb","5fcbf21d24444a2e848140dcf4d7233c","b4f0282ed449497f9ba0d722dc1eb73c","671993480f194e0983085d309c7bdacc","528a1a22b75844fd9d6d51f9371a18ff","bfc32cc7c4bb4c5cbce5026d632f677c","a81b4d688fa04b349e6174fccbfddc23","bd6dee6206d2429a8e9b56f2c2020e65","310563af56584378bb4eef53fe493ea4","6a7945475ac64a3b9f8241393173338a","5df27d1988884d11b68ed0b959c8ed94","ddeea7b79df1432491bc512871c1357b","fc6d9a44725f4fd69e6a43e9c86dfbb6","b7ab7f70620a497094e9f557ae660f17","d86b6d7eeb344e26b1a89e2a896b8241","3e0ea7191b474de1b5036416510d4924","769f1cf6b6024ad087a9e59abce92199","c99c4293ae9442e4bf16f83eda446688","81fd3fd40dd9474fb7b3f66eb4631286","0906b9dcefd9458e8e0566fe2bdbd648","6161d0a2e32243b28fbb952f5f039e10","42e7ae8ff9dd4d009fdee258334d9ee3","faf7077aaf0b4e768207321088239fc1","95aeed377fb04f8a826fe5035f163397","81c9e236b1594b83ac773c764ce9e054","8967c82df3ea4713a6d6a8de7c538221","737c7509f5174e3880406a31edf76f1c","5e72e9a570fc46609f653b5cce506e40","6ae496900b7e4843afa4ece5d25cc2b6","c0fa1693bce54081b632e55ecc6c1954","af4c999aae1744b9af6c9433941dba14","9b5583c7b44849f9b2605446d5c60e4d","8f1cc44f32c341c1ac97d21d35b79cac","7bb1fd548bf246baa96e8e409ef66aba","17e82e5d064f4087ac27173bc76b2afe","29da4223933c4463955cca178c8e248f","9b97726dde3f4755b44c2979c378cb43","3f5cd395448a48f9872211e209413814","f860e774a26a4ac4951b47c0abd90b16","a2e74c937dc04338821ac6ddc9799f5c","235065b7bd4c45e98906167ec8dbb9bc","e3fcc4de472a4b7387058979ba03554f","1f8c83a7161941228ff67835d6b7447f","c1dbea95eeb446b9a8d285b265608e48","9590da919a554ffd9c63550aabfd8fd8","8df7263bded14155a9fe20874df4ba93","3f27b703a66746f58a507a394fa43e3d","e07e80f5eb4148e58463789d9e6f1304","4f7597c0efcd4ad3b1dd2d4ca0d1c1dc","29d82c6c276e4335b7a975382600e98a","809af22a9e5945ea88aa2cc34094723c","df4b694f5f3045778a7872d8c35cd2f7","ec6f32799b334c05a132950759cbecdc","cb238a5b0da7458a82d7d470ae3f58b9","1488621ed8a040e19941c019ef346701","f2cfa4cabefd4412ac3fab31c337c9e2","2cd6314defd34970b76031028b1ee64f","e52c79c6e66b4e90bb1f8e07497c0b77"]},"outputId":"ed11623f-5c1b-45b4-e52c-2597e8a5752a"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1mDownloading and preparing dataset 80.23 MiB (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"]},{"output_type":"display_data","data":{"text/plain":["Dl Completed...: 0 url [00:00, ? url/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a1789de9ceb4f7d9188577dd0564776"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Dl Size...: 0 MiB [00:00, ? MiB/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7493d734f30349908d0c9c4fbaad05dd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating splits...:   0%|          | 0/3 [00:00<?, ? splits/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8e8c5a0c6f8b4de4bc72f7b56be16c38"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d2f789e517ec49a684d47624b9c16623"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteZPQ8TA/imdb_reviews-train.tfrecord*...…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"671993480f194e0983085d309c7bdacc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test examples...:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d86b6d7eeb344e26b1a89e2a896b8241"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteZPQ8TA/imdb_reviews-test.tfrecord*...:…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8967c82df3ea4713a6d6a8de7c538221"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating unsupervised examples...:   0%|          | 0/50000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9b97726dde3f4755b44c2979c378cb43"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Shuffling ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteZPQ8TA/imdb_reviews-unsupervised.tfrec…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e07e80f5eb4148e58463789d9e6f1304"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1mDataset imdb_reviews downloaded and prepared to ~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n"]}],"source":["train_ds,val_ds,test_ds=tfds.load('imdb_reviews', split=['train', 'test[:50%]', 'test[50%:]'],as_supervised=True)"]},{"cell_type":"code","source":["train_ds"],"metadata":{"id":"Ds46Y1rPp7fD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663165442018,"user_tz":-60,"elapsed":31,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"932c2d2d-a1e2-46f5-eaa8-ee1841907662"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":4}]},{"cell_type":"code","source":["for review,label in val_ds.take(2):\n","  print(review)\n","  print(label)"],"metadata":{"id":"NyBV6yL0p7hy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663165442018,"user_tz":-60,"elapsed":26,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"cbb176dc-53d6-4875-dce4-26bcae2f74ab"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(b\"There are films that make careers. For George Romero, it was NIGHT OF THE LIVING DEAD; for Kevin Smith, CLERKS; for Robert Rodriguez, EL MARIACHI. Add to that list Onur Tukel's absolutely amazing DING-A-LING-LESS. Flawless film-making, and as assured and as professional as any of the aforementioned movies. I haven't laughed this hard since I saw THE FULL MONTY. (And, even then, I don't think I laughed quite this hard... So to speak.) Tukel's talent is considerable: DING-A-LING-LESS is so chock full of double entendres that one would have to sit down with a copy of this script and do a line-by-line examination of it to fully appreciate the, uh, breadth and width of it. Every shot is beautifully composed (a clear sign of a sure-handed director), and the performances all around are solid (there's none of the over-the-top scenery chewing one might've expected from a film like this). DING-A-LING-LESS is a film whose time has come.\", shape=(), dtype=string)\n","tf.Tensor(1, shape=(), dtype=int64)\n","tf.Tensor(b\"A blackly comic tale of a down-trodden priest, Nazarin showcases the economy that Luis Bunuel was able to achieve in being able to tell a deeply humanist fable with a minimum of fuss. As an output from his Mexican era of film making, it was an invaluable talent to possess, with little money and extremely tight schedules. Nazarin, however, surpasses many of Bunuel's previous Mexican films in terms of the acting (Francisco Rabal is excellent), narrative and theme.<br /><br />The theme, interestingly, is something that was explored again in Viridiana, made three years later in Spain. It concerns the individual's struggle for humanity and altruism amongst a society that rejects any notion of virtue. Father Nazarin, however, is portrayed more sympathetically than Sister Viridiana. Whereas the latter seems to choose charity because she wishes to atone for her (perceived) sins, Nazarin's whole existence and reason for being seems to be to help others, whether they (or we) like it or not. The film's last scenes, in which he casts doubt on his behaviour and, in a split second, has to choose between the life he has been leading or the conventional life that is expected of a priest, are so emotional because they concern his moral integrity and we are never quite sure whether it remains intact or not.<br /><br />This is a remarkable film and I would urge anyone interested in classic cinema to seek it out. It is one of Bunuel's most moving films, and encapsulates many of his obsessions: frustrated desire, mad love, religious hypocrisy etc. In my view 'Nazarin' is second only to 'The Exterminating Angel', in terms of his Mexican movies, and is certainly near the top of the list of Bunuel's total filmic output.\", shape=(), dtype=string)\n","tf.Tensor(1, shape=(), dtype=int64)\n"]}]},{"cell_type":"code","source":["def standardization(input_data):\n","    '''\n","    Input: raw reviews\n","    output: standardized reviews\n","    '''\n","    lowercase=tf.strings.lower(input_data)\n","    no_tag=tf.strings.regex_replace(lowercase,\"<[^>]+>\",\"\")\n","    output=tf.strings.regex_replace(no_tag,\"[%s]\"%re.escape(string.punctuation),\"\")\n","\n","    return output"],"metadata":{"id":"leMqphTKVQ8k"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["standardization(tf.constant(\"<u>In the movie?, </u>man called Tévèz, went to a friend’s pl**ce and they had a tensed discussion. I don’t love this movie! would you?<br> <br /><br />T\"))"],"metadata":{"id":"4VA77aebGMVQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663165442019,"user_tz":-60,"elapsed":24,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d2e019e9-0d06-42ad-85a5-daf24efc1df2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tf.Tensor: shape=(), dtype=string, numpy=b'in the movie man called t\\xc3\\xa9v\\xc3\\xa8z went to a friend\\xe2\\x80\\x99s plce and they had a tensed discussion i don\\xe2\\x80\\x99t love this movie would you t'>"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["VOCAB_SIZE=10000\n","SEQUENCE_LENGTH=250\n","EMBEDDING_DIM=300"],"metadata":{"id":"i1_nUM7l6gbF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["vectorize_layer=TextVectorization(\n","    standardize=standardization,\n","    max_tokens=VOCAB_SIZE,\n","    output_mode='int',\n","    output_sequence_length=SEQUENCE_LENGTH\n",")"],"metadata":{"id":"EgJXEr9H1mRT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# lengths=[]\n","# words=[]\n","\n","# for review,label in train_ds.take(100):\n","#   # for word in tf.strings.split(review, sep=\" \"):\n","#   #   if word in words:\n","#   #     pass\n","#   #   else:\n","#   #     words.append(word)\n","#   lengths.append(len(tf.strings.split(review, sep=\" \")))"],"metadata":{"id":"2QQq7_4H1mT0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["training_data=train_ds.map(lambda x,y:x)### input x and y and outputx\n","vectorize_layer.adapt(training_data)#### adapt the vectorize_layer to the training data"],"metadata":{"id":"S_Qhfisb1mft"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(vectorize_layer.get_vocabulary())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"45J7WxmR-02N","executionInfo":{"status":"ok","timestamp":1663165483364,"user_tz":-60,"elapsed":22,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"89a2cec3-d8b1-4fa1-cec8-825958d30bf5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["10000"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["def vectorizer(review,label):\n","    return vectorize_layer(review),label"],"metadata":{"id":"Df6aXLT--060"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset=train_ds.map(vectorizer)\n","val_dataset=val_ds.map(vectorizer)"],"metadata":{"id":"SRTefXMW-09K"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["vectorize_layer.get_vocabulary()[411]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"vY6x9JyfA3_9","executionInfo":{"status":"ok","timestamp":1663165483369,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7d1318b7-936a-4f16-d919-69ea27cd8e5f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'absolutely'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["for review,label in train_dataset.take(1):\n","  print(review)\n","  print(label)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-fSurdHy-0_T","executionInfo":{"status":"ok","timestamp":1663165484075,"user_tz":-60,"elapsed":718,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6177b42c-e63b-424e-a6b9-ea3e6ae63b17"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[  10   13   33  411  384   17   89   26    1    8   32 1337 3521   40\n","  491    1  192   22   84  149   18   10  215  317   26   64  239  212\n","    8  484   54   64   84  111   95   21 5502   10   91  637  737   10\n","   17    7   33  393 9554  169 2443  406    2   87 1205  135   65  142\n","   52    2    1 7408   65  245   64 2832   16    1 2851    1    1 1415\n"," 4969    3   39    1 1567   15 3521   13  156   18    4 1205  881 7874\n","    8    4   17   12   13 4037    5   98  145 1234   11  236  696   12\n","   48   22   91   37   10 7285  149   37 1337    1   49  396   11   95\n"," 1148  841  140    9    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n","    0    0    0    0    0    0    0    0    0    0    0    0], shape=(250,), dtype=int64)\n","tf.Tensor(0, shape=(), dtype=int64)\n"]}]},{"cell_type":"code","source":["train_dataset=train_dataset.batch(BATCH_SIZE).prefetch(buffer_size=tf.data.AUTOTUNE)\n","val_dataset=val_dataset.batch(BATCH_SIZE).prefetch(buffer_size=tf.data.AUTOTUNE)"],"metadata":{"id":"pV_8IvUg-1Dq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"PQiSVkoLaaqF"}},{"cell_type":"markdown","source":["## Transformers"],"metadata":{"id":"5UPAk29mitB-"}},{"cell_type":"markdown","source":["### Embeddings"],"metadata":{"id":"2opE0myV_cnv"}},{"cell_type":"code","source":["def positional_encoding(model_size,SEQUENCE_LENGTH):\n","  output=[]\n","  for pos in range(SEQUENCE_LENGTH):\n","    PE=np.zeros((model_size))\n","    for i in range(model_size):\n","      if i%2==0:\n","        PE[i]=np.sin(pos/(10000**(i/model_size)))\n","      else:\n","        PE[i]=np.cos(pos/(10000**((i-1)/model_size)))\n","    output.append(tf.expand_dims(PE,axis=0))\n","  out=tf.concat(output,axis=0)\n","  out=tf.expand_dims(out,axis=0)\n","  return tf.cast(out,dtype=tf.float32)"],"metadata":{"id":"qig1ue8DiugX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class Embeddings(Layer):\n","  def __init__(self, sequence_length, vocab_size, embed_dim,):\n","    super(Embeddings, self).__init__()\n","    self.token_embeddings=Embedding(\n","        input_dim=vocab_size, output_dim=embed_dim)\n","    self.sequence_length = sequence_length\n","    self.vocab_size = vocab_size\n","    self.embed_dim = embed_dim\n","\n","  def call(self, inputs):\n","    embedded_tokens = self.token_embeddings(inputs)\n","    embedded_positions=positional_encoding(\n","        self.embed_dim,self.sequence_length)\n","    return embedded_tokens + embedded_positions\n","    \n","  def compute_mask(self, inputs, mask=None):\n","    return tf.math.not_equal(inputs, 0)\n","    \n","  def get_config(self): \n","      config = super().get_config()\n","      config.update({\n","        \"sequence_length\": self.sequence_length,\n","        \"vocab_size\": self.vocab_size,\n","        \"embed_dim\": self.embed_dim,\n","      })\n","      return config\n"],"metadata":{"id":"nqt-Qqzci5Qo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_input=tf.constant([[  2, 112,   10,   12,  5,   0,   0,   0,]])\n","\n","emb=Embeddings(8,20000,256)\n","emb_out=emb(test_input)\n","print(emb_out.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AhWblTG_i5yx","executionInfo":{"status":"ok","timestamp":1663173614450,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6262cfb8-cca8-4e11-f612-51dcf5e28915"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 8, 256)\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"UQrOhRFd_bIp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"pa9dHK1l_bR4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Encoder"],"metadata":{"id":"EpDpaJAf_gaR"}},{"cell_type":"code","source":["class TransformerEncoder(Layer):\n","    def __init__(self, embed_dim, dense_dim, num_heads,):\n","        super(TransformerEncoder, self).__init__()\n","        self.embed_dim = embed_dim\n","        self.dense_dim = dense_dim\n","        self.num_heads = num_heads\n","        self.attention = MultiHeadAttention(\n","            num_heads=num_heads, key_dim=embed_dim,\n","        )\n","        self.dense_proj=tf.keras.Sequential(\n","            [Dense(dense_dim, activation=\"relu\"),Dense(embed_dim),]\n","        )\n","        self.layernorm_1 = LayerNormalization()\n","        self.layernorm_2 = LayerNormalization()\n","        self.supports_masking = True\n","\n","    def call(self, inputs, mask=None):\n","      if mask is not None:\n","        mask1 = mask[:, :, tf.newaxis]\n","        mask2 = mask[:,tf.newaxis, :]\n","        padding_mask = tf.cast(mask1&mask2, dtype=\"int32\")\n","\n","      attention_output = self.attention(\n","          query=inputs, key=inputs,value=inputs,attention_mask=padding_mask\n","      )\n","      \n","      proj_input = self.layernorm_1(inputs + attention_output)\n","      proj_output = self.dense_proj(proj_input)\n","      return self.layernorm_2(proj_input + proj_output)\n","      \n","    def get_config(self): \n","      config = super().get_config()\n","      config.update({\n","        \"embed_dim\": self.embed_dim,\n","        \"num_heads\": self.num_heads,\n","        \"dense_dim\": self.dense_dim,\n","      })\n","      return config"],"metadata":{"id":"nPs81ZFgi51K"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["encoder_outputs = TransformerEncoder(256,2048,2)(emb_out)\n","print(encoder_outputs.shape)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1hqMJJEri53q","executionInfo":{"status":"ok","timestamp":1663173674901,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"227aa408-6cb5-4c7a-f774-bfc7c1350f00"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 8, 256)\n"]}]},{"cell_type":"markdown","source":["### Transformer Model"],"metadata":{"id":"cpJeEY16_kmP"}},{"cell_type":"code","source":["EMBEDDING_DIM=128\n","D_FF=1024\n","NUM_HEADS=8\n","NUM_LAYERS=1\n","NUM_EPOCHS=20"],"metadata":{"id":"NXt45DIei57j"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["encoder_input=Input(shape=(None,), dtype=\"int64\", name=\"input\")\n","x = Embeddings(SEQUENCE_LENGTH,VOCAB_SIZE,EMBEDDING_DIM)(encoder_input)\n","\n","for _ in range(NUM_LAYERS):\n","  x=TransformerEncoder(EMBEDDING_DIM,D_FF,NUM_HEADS)(x)\n","\n","x = Flatten()(x) \n","output=Dense(1, activation=\"sigmoid\")(x)\n","\n","transformer = tf.keras.Model(\n","    encoder_input, output, name=\"transformer\"\n",")\n","transformer.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l1QlE5KTi59i","executionInfo":{"status":"ok","timestamp":1663174093731,"user_tz":-60,"elapsed":3972,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"cb3bd11b-e812-4246-f851-b51c901d5450"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"transformer\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," input (InputLayer)          [(None, None)]            0         \n","                                                                 \n"," embeddings_12 (Embeddings)  (None, 250, 128)          1280000   \n","                                                                 \n"," transformer_encoder_12 (Tra  (None, 250, 128)         791296    \n"," nsformerEncoder)                                                \n","                                                                 \n"," flatten_4 (Flatten)         (None, 32000)             0         \n","                                                                 \n"," dense_33 (Dense)            (None, 1)                 32001     \n","                                                                 \n","=================================================================\n","Total params: 2,103,297\n","Trainable params: 2,103,297\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["## Training"],"metadata":{"id":"dZk99Zr3_pu4"}},{"cell_type":"code","source":["checkpoint_filepath = '/content/drive/MyDrive/nlp/sentiment_analysis/transformer.h5'\n","model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath=checkpoint_filepath,\n","    monitor='val_accuracy',\n","    mode='max',\n","    save_best_only=True)"],"metadata":{"id":"30t3lVwsjE3w"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["transformer.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n","              optimizer=tf.keras.optimizers.Adam(1e-4),\n","              metrics=['accuracy'])"],"metadata":{"id":"UkS7jN3yjYH4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=transformer.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=10,\n","    callbacks=[model_checkpoint_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QH_RD74TjE6G","executionInfo":{"status":"ok","timestamp":1663174424071,"user_tz":-60,"elapsed":330349,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9d4c531c-3e02-479d-f612-b0ec975c6a4d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","391/391 [==============================] - 32s 66ms/step - loss: 0.7269 - accuracy: 0.5052 - val_loss: 0.7081 - val_accuracy: 0.4967\n","391/391 [==============================] - 24s 61ms/step - loss: 0.7082 - accuracy: 0.5192 - val_loss: 0.6960 - val_accuracy: 0.4981\n","Epoch 3/10\n","391/391 [==============================] - 24s 61ms/step - loss: 0.6990 - accuracy: 0.5338 - val_loss: 0.6912 - val_accuracy: 0.5258\n","Epoch 4/10\n","391/391 [==============================] - 24s 61ms/step - loss: 0.6845 - accuracy: 0.5573 - val_loss: 0.6951 - val_accuracy: 0.4995\n","Epoch 5/10\n","391/391 [==============================] - 23s 58ms/step - loss: 0.5823 - accuracy: 0.6841 - val_loss: 0.4385 - val_accuracy: 0.7925\n","Epoch 6/10\n","391/391 [==============================] - 22s 57ms/step - loss: 0.3636 - accuracy: 0.8412 - val_loss: 0.4585 - val_accuracy: 0.7892\n","Epoch 7/10\n","391/391 [==============================] - 23s 58ms/step - loss: 0.2997 - accuracy: 0.8735 - val_loss: 0.3417 - val_accuracy: 0.8518\n","Epoch 8/10\n","391/391 [==============================] - 24s 61ms/step - loss: 0.2556 - accuracy: 0.8945 - val_loss: 0.3554 - val_accuracy: 0.8502\n","Epoch 9/10\n","391/391 [==============================] - 24s 61ms/step - loss: 0.2329 - accuracy: 0.9059 - val_loss: 0.3744 - val_accuracy: 0.8433\n","Epoch 10/10\n","391/391 [==============================] - 23s 58ms/step - loss: 0.2116 - accuracy: 0.9144 - val_loss: 0.4173 - val_accuracy: 0.8396\n"]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"qxPsvkXKjE8U","executionInfo":{"status":"ok","timestamp":1663174424078,"user_tz":-60,"elapsed":24,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"be9dbb64-cb72-40fb-97d6-879f5e5cd153"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"cxq950QqjE-g","executionInfo":{"status":"ok","timestamp":1663174424082,"user_tz":-60,"elapsed":14,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f128bb9a-60b2-440a-c886-f0d05f3554c2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"W8MCkLkzjFAn"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Evaluation"],"metadata":{"id":"UAgNaikHP3A5"}},{"cell_type":"code","source":["transformer.load_weights(checkpoint_filepath)"],"metadata":{"id":"1qkLC__kP3BA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_dataset=test_ds.map(vectorizer)\n","test_dataset=test_dataset.batch(BATCH_SIZE)\n","transformer.evaluate(test_dataset)"],"metadata":{"id":"3N5zc1wyP3BA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663174501585,"user_tz":-60,"elapsed":5525,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d7419f8b-7dc6-4adf-8541-709e075221e4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["196/196 [==============================] - 5s 23ms/step - loss: 0.3436 - accuracy: 0.8483\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.3435855805873871, 0.8483200073242188]"]},"metadata":{},"execution_count":90}]},{"cell_type":"code","source":[],"metadata":{"id":"obCwC6fGPa04"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Testing"],"metadata":{"id":"7jbgoKV070Gr"}},{"cell_type":"code","source":["# test_pos=\"this movie looks very interesting, i love the fact that the actors do a great job in showing how people lived in the 18th century, which wasn't very good at all. But atleast this movie recreates this scenes! \"\n","# test_neg=\"very good start, but movie started becoming boring at some point and unfortunately i didn't feel like this was properly produced as there was too much background noise, and the actors didn't look motivated at all \""],"metadata":{"id":"LPH6sskd71MM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["test_data=tf.data.Dataset.from_tensor_slices([[\"this movie looks very interesting, i love the fact that the actors do a great job in showing how people lived in the 18th century, which wasn't very good at all. But atleast this movie recreates this scenes! \"],\n","                                              [\"very good start, but movie started becoming uninteresting at some point though initially i thought it would have been much more fun. There was too much background noise, so in all i didn't like this movie \"],])\n"],"metadata":{"id":"cW7_p_vG71Si"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def vectorizer_test(review):\n","    return vectorize_layer(review)\n","test_dataset=test_data.map(vectorizer_test)"],"metadata":{"id":"rypKRKsI71U5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["transformer.predict(test_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J6x4ihmxvi35","executionInfo":{"status":"ok","timestamp":1663175033107,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"49e7f110-5b6c-483e-b60f-b0a345c62881"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.9175184 ],\n","       [0.38718796]], dtype=float32)"]},"metadata":{},"execution_count":96}]},{"cell_type":"code","source":[],"metadata":{"id":"TYad8zYNvlYz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# LSH Attention"],"metadata":{"id":"VqJBd2d6EsuQ"}},{"cell_type":"code","source":["def look_one_back(x):\n","  x_extra=tf.concat([x[:,-1:,...],x[:,:-1,...]],axis=1)\n","  return tf.concat([x,x_extra],axis=2)\n","  \n","def sticker_look_one_back(x):\n","  x_extra=tf.concat([x[:-1:],x[:,:-1]],axis=1)\n","  return tf.concat([x,x_extra],axis=-1)\n","\n","def causal_masker(a,b):\n","  a,b=tf.cast(a,dtype=tf.float32)+0.01,tf.cast(b,dtype=tf.float32)+0.01\n","  vals=tf.einsum('ipj,ipk->ipjk',b,1/a)\n","  out=tf.cast(tf.cast(tf.cast(vals,dtype=tf.int32),dtype=tf.bool),dtype=tf.int32)\n","  out=-out+1\n","  return tf.cast(out,dtype=tf.float32)\n","\n","class LSHAttention(tf.keras.layers.Layer):\n","    def __init__(self,bucket_size=8,n_hashes=1):\n","        super(LSHAttention,self).__init__()\n","        self.n_hashes=n_hashes\n","        self.bucket_size=bucket_size\n","        \n","    def call(self,query,key,value,causal_masking=False):\n","        R=tf.random.normal((tf.shape(query)[0],tf.shape(query)[-1],self.bucket_size//2))\n","        xR=tf.matmul(query,R)\n","        concat_xR=tf.concat([xR,-xR],axis=-1)\n","        buckets=tf.math.argmax(concat_xR,axis=-1)\n","        \n","        sticker=tf.argsort(buckets)\n","        undo_sort=tf.argsort(sticker)\n","        sorted_query=tf.gather(query,sticker,axis=1,batch_dims=1)\n","        sorted_value=tf.gather(value,sticker,axis=1,batch_dims=1)\n","        \n","        chunked_query=tf.stack(tf.split(sorted_query,self.bucket_size,1),1)\n","        chunked_value=tf.stack(tf.split(sorted_value,self.bucket_size,1),1)\n","        \n","        sticker=tf.stack(tf.split(sticker,self.bucket_size,1),1)\n","        new_sticker=sticker_look_one_back(sticker)\n","        \n","        lb_chunked_query=look_one_back(chunked_query)\n","        lb_chunked_value=look_one_back(chunked_value)\n","        \n","        score=tf.einsum('bhie,bhje->bhij',chunked_query,lb_chunked_query)\n","        score/=tf.math.sqrt(tf.cast(query.shape[-1],tf.float32))\n","        \n","        if causal_masking==True:\n","            causal_mask=causal_masker(sticker,new_sticker)\n","            dots+=causal_mask*-1e-10\n","        score=tf.nn.softmax(score)\n","        output=tf.einsum('buij,buje->buie',score,lb_chunked_value)\n","        \n","        sorted_output=tf.reshape(output,(tf.shape(output)[0],tf.shape(query)[i],output.shape[3]))\n","        output=tf.gather(sorted_output,undo_sort,axis=1,batch_dims=1)\n","        return output"],"metadata":{"id":"0-TxkNDsEuzO"},"execution_count":null,"outputs":[]}]}

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FILE: deep learning for natural language processing/4-Neural Machine Translation with RNNs by Neuralearn.ai-.ipynb
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1wOi9I4Ett5qIJ3eldJ3bTPRmUjk3X5hd","timestamp":1673806934770}],"collapsed_sections":["WgU1Z8fcOSW_","RBHsJDpiYDs7","omyb2Dq_YHbT"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Dense,Flatten,SimpleRNN,InputLayer,Conv1D,Bidirectional,GRU,LSTM,BatchNormalization,Dropout,Input, Embedding,TextVectorization)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from tensorboard.plugins import projector"],"metadata":{"id":"C24JKO7LxP3O"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Data Preparation"],"metadata":{"id":"cKg0HdsrYA0S"}},{"cell_type":"markdown","source":["## Data Download"],"metadata":{"id":"WgU1Z8fcOSW_"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"4hwhov2uvDdo","executionInfo":{"status":"ok","timestamp":1662093577583,"user_tz":-60,"elapsed":534,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"b6312fe3-62f4-4c51-80e0-f6a2b519fb70"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-09-02 04:40:08--  https://www.manythings.org/anki/fra-eng.zip\n","Resolving www.manythings.org (www.manythings.org)... 173.254.30.110\n","Connecting to www.manythings.org (www.manythings.org)|173.254.30.110|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 6612164 (6.3M) [application/zip]\n","Saving to: ‘fra-eng.zip’\n","\n","fra-eng.zip         100%[===================>]   6.31M  18.0MB/s    in 0.4s    \n","\n","2022-09-02 04:40:09 (18.0 MB/s) - ‘fra-eng.zip’ saved [6612164/6612164]\n","\n"]}],"source":["!wget https://www.manythings.org/anki/fra-eng.zip"]},{"cell_type":"code","source":["!unzip \"/content/fra-eng.zip\" -d \"/content/dataset/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y-ufBPwn8_ax","executionInfo":{"status":"ok","timestamp":1662093578068,"user_tz":-60,"elapsed":487,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"20b6d5e8-6433-4042-d6da-8d20ae80e274"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  /content/fra-eng.zip\n","  inflating: /content/dataset/_about.txt  \n","  inflating: /content/dataset/fra.txt  \n"]}]},{"cell_type":"markdown","source":["## Kaggle Dataset"],"metadata":{"id":"RBHsJDpiYDs7"}},{"cell_type":"code","source":["!pip install -q kaggle\n","!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d dhruvildave/en-fr-translation-dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Lako-uhxy-n","executionInfo":{"status":"ok","timestamp":1661936796981,"user_tz":-60,"elapsed":40971,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"67e2404c-e3aa-4911-ec44-d20c9f01327b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading en-fr-translation-dataset.zip to /content\n","100% 2.54G/2.54G [00:35<00:00, 71.0MB/s]\n","100% 2.54G/2.54G [00:36<00:00, 75.8MB/s]\n"]}]},{"cell_type":"code","source":["!unzip \"/content/en-fr-translation-dataset.zip\" -d \"/content/dataset/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B44hfX_vxzA-","executionInfo":{"status":"ok","timestamp":1661936882170,"user_tz":-60,"elapsed":85198,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a7ea748a-6a24-4941-d2d0-5242b597f574"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  /content/en-fr-translation-dataset.zip\n","  inflating: /content/dataset/en-fr.csv  \n"]}]},{"cell_type":"code","source":["dataset = tf.data.experimental.CsvDataset(\n","  \"/content/dataset/en-fr.csv\",\n","  [\n","    tf.string,\n","    tf.string\n","  ],\n",")"],"metadata":{"id":"fXcOEWp2L7U9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Data Processing"],"metadata":{"id":"omyb2Dq_YHbT"}},{"cell_type":"code","source":["text_dataset=tf.data.TextLineDataset(\"/content/dataset/fra.txt\")"],"metadata":{"id":"ywC7WnoIJeXl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["VOCAB_SIZE=20000\n","ENGLISH_SEQUENCE_LENGTH=64\n","FRENCH_SEQUENCE_LENGTH=64\n","EMBEDDING_DIM=300\n","BATCH_SIZE=64"],"metadata":{"id":"QlcJxz6cJeb_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["english_vectorize_layer=TextVectorization(\n","    standardize='lower_and_strip_punctuation',\n","    max_tokens=VOCAB_SIZE,\n","    output_mode='int',\n","    output_sequence_length=ENGLISH_SEQUENCE_LENGTH\n",")"],"metadata":{"id":"TePer9o-JeeR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["french_vectorize_layer=TextVectorization(\n","    standardize='lower_and_strip_punctuation',\n","    max_tokens=VOCAB_SIZE,\n","    output_mode='int',\n","    output_sequence_length=FRENCH_SEQUENCE_LENGTH\n",")"],"metadata":{"id":"unGn0zqEJegg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def selector(input_text):\n","  split_text=tf.strings.split(input_text,'\\t')\n","  return {'input_1':split_text[0:1],'input_2':'starttoken '+split_text[1:2]},split_text[1:2]+' endtoken'"],"metadata":{"id":"iNK3uewuQTjY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["split_dataset=text_dataset.map(selector)"],"metadata":{"id":"YZDH8xn7Q12e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def separator(input_text):\n","  split_text=tf.strings.split(input_text,'\\t')\n","  return split_text[0:1],'starttoken '+split_text[1:2]+' endtoken'"],"metadata":{"id":"7fcSDED_2c-z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["init_dataset=text_dataset.map(separator)"],"metadata":{"id":"ktN95hiN2pgU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in split_dataset.take(3):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OQkSkpd1tils","executionInfo":{"status":"ok","timestamp":1662093582976,"user_tz":-60,"elapsed":450,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"08f08728-2950-4845-d661-3a4ccf94cd32"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Va !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Va ! endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Marche.'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Marche. endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Bouge !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Bouge ! endtoken'], dtype=object)>)\n"]}]},{"cell_type":"code","source":["english_training_data=init_dataset.map(lambda x,y:x)### input x,y and output x\n","english_vectorize_layer.adapt(english_training_data)#### adapt the vectorize_layer to the training data"],"metadata":{"id":"_usEDYiOJeil"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["french_training_data=init_dataset.map(lambda x,y:y)### input x,y,z and output y\n","french_vectorize_layer.adapt(french_training_data)#### adapt the vectorize_layer to the training data"],"metadata":{"id":"dl7pxJprJek2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def vectorizer(inputs,output):\n","  return {'input_1':english_vectorize_layer(inputs['input_1']),\n","          'input_2':french_vectorize_layer(inputs['input_2'])},french_vectorize_layer(output)"],"metadata":{"id":"4yVIMxvTJemt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["split_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"op5UqhS14HHz","executionInfo":{"status":"ok","timestamp":1662094107371,"user_tz":-60,"elapsed":27,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b551e878-0aae-43a3-bc45-daaae06911db"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<MapDataset element_spec=({'input_1': TensorSpec(shape=(None,), dtype=tf.string, name=None), 'input_2': TensorSpec(shape=(None,), dtype=tf.string, name=None)}, TensorSpec(shape=(None,), dtype=tf.string, name=None))>"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["dataset=split_dataset.map(vectorizer)"],"metadata":{"id":"wI9-GPpVJepF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in split_dataset.take(3):\n","  print(i)"],"metadata":{"id":"m_6Xtks8wPk5","executionInfo":{"status":"ok","timestamp":1662094107372,"user_tz":-60,"elapsed":26,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"ddb11f25-fa7f-42d9-ae3f-0832190f8cb4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Va !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Va ! endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Marche.'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Marche. endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Bouge !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Bouge ! endtoken'], dtype=object)>)\n"]}]},{"cell_type":"code","source":["for i in dataset.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FM9aenklufpC","executionInfo":{"status":"ok","timestamp":1662094107372,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1ac68406-ec1f-4fea-886a-d442184ea89b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[43,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0]])>, 'input_2': <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[  2, 113,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]])>}, <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[113,   3,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]])>)\n"]}]},{"cell_type":"code","source":["dataset"],"metadata":{"id":"iOpSsko7TiKd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1662094107372,"user_tz":-60,"elapsed":20,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"aab98f15-01dd-4fd3-bc8f-5cc4521e9e25"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<MapDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["dataset=dataset.shuffle(2048).unbatch().batch(BATCH_SIZE).prefetch(buffer_size=tf.data.AUTOTUNE)"],"metadata":{"id":"_WE0s6p9TiM9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tTkF_Qu54QB5","executionInfo":{"status":"ok","timestamp":1662094107870,"user_tz":-60,"elapsed":18,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a9e997b2-f451-4d4d-c4aa-cfb70f070ee7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["NUM_BATCHES=int(200000/BATCH_SIZE)"],"metadata":{"id":"K5JXDdrNtwRj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset=dataset.take(int(0.9*NUM_BATCHES))\n","val_dataset=dataset.skip(int(0.9*NUM_BATCHES))"],"metadata":{"id":"XQvg18V5TiO7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset"],"metadata":{"id":"tfCmn1DzTiRp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1662094107871,"user_tz":-60,"elapsed":10,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0d27af4f-bc70-47b3-cd5c-bf153acf9b08"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<TakeDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":[],"metadata":{"id":"GtWRb34auKbT"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"nBVM2EB0Xh97"}},{"cell_type":"code","source":["NUM_UNITS=256"],"metadata":{"id":"t5lTjZBE6M4Q"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["### ENCODER\n","input = Input(shape=(ENGLISH_SEQUENCE_LENGTH,), dtype=\"int64\", name=\"input_1\")\n","x=Embedding(VOCAB_SIZE, EMBEDDING_DIM, )(input)\n","encoded_input=Bidirectional(GRU(NUM_UNITS), )(x)\n","\n","### DECODER\n","shifted_target=Input(shape=(FRENCH_SEQUENCE_LENGTH,), dtype=\"int64\", name=\"input_2\")\n","x=Embedding(VOCAB_SIZE,EMBEDDING_DIM,)(shifted_target)\n","x = GRU(NUM_UNITS*2, return_sequences=True)(x, initial_state=encoded_input)\n","\n","### OUTPUT\n","x = Dropout(0.5)(x)\n","target=Dense(VOCAB_SIZE,activation=\"softmax\")(x)\n","seq2seq_gru=Model([input,shifted_target],target)\n","seq2seq_gru.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K6yQraRg7EDH","executionInfo":{"status":"ok","timestamp":1662094108872,"user_tz":-60,"elapsed":1008,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"eb48d8ac-ea6e-4131-aa2c-24126ead149d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 64)]         0           []                               \n","                                                                                                  \n"," input_2 (InputLayer)           [(None, 64)]         0           []                               \n","                                                                                                  \n"," embedding (Embedding)          (None, 64, 300)      6000000     ['input_1[0][0]']                \n","                                                                                                  \n"," embedding_1 (Embedding)        (None, 64, 300)      6000000     ['input_2[0][0]']                \n","                                                                                                  \n"," bidirectional (Bidirectional)  (None, 512)          857088      ['embedding[0][0]']              \n","                                                                                                  \n"," gru_1 (GRU)                    (None, 64, 512)      1250304     ['embedding_1[0][0]',            \n","                                                                  'bidirectional[0][0]']          \n","                                                                                                  \n"," dropout (Dropout)              (None, 64, 512)      0           ['gru_1[0][0]']                  \n","                                                                                                  \n"," dense (Dense)                  (None, 64, 20000)    10260000    ['dropout[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 24,367,392\n","Trainable params: 24,367,392\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","source":["class BLEU(tf.keras.metrics.Metric):\n","    def __init__(self,name='bleu_score'):\n","        super(BLEU,self).__init__()\n","        self.bleu_score=0\n","\n","    def update_state(self,y_true,y_pred,sample_weight=None):\n","      y_pred=tf.argmax(y_pred,-1)\n","      self.bleu_score=0\n","      for i,j in zip(y_pred,y_true):\n","        tf.autograph.experimental.set_loop_options()\n","\n","        total_words=tf.math.count_nonzero(i)\n","        total_matches=0\n","        for word in i:\n","          if word==0:\n","            break\n","          for q in range(len(j)):\n","            if j[q]==0:\n","              break\n","            if word==j[q]:\n","              total_matches+=1\n","              j=tf.boolean_mask(j,[False if y==q else True for y in range(len(j))])\n","              break\n","\n","        self.bleu_score+=total_matches/total_words\n","        \n","    def result(self):\n","        return self.bleu_score/BATCH_SIZE"],"metadata":{"id":"wKrG-74zHlR-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["seq2seq_gru.compile(\n","    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(5e-4),)\n","    #metrics=[BLEU()],\n","    #run_eagerly=True)"],"metadata":{"id":"yk-sLBpE7EFf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["checkpoint_filepath = '/content/drive/MyDrive/nlp/translation/lstm.h5'\n","model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath=checkpoint_filepath,\n","    monitor='val_loss',\n","    mode='min',\n","    save_best_only=True,)"],"metadata":{"id":"5xPxLzvgVAp-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=seq2seq_gru.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=20,\n","    callbacks=[model_checkpoint_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":558},"id":"H8CYlj_w-UPO","executionInfo":{"status":"error","timestamp":1662136083220,"user_tz":-60,"elapsed":205883,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"528a8e88-9b75-4d62-f405-1d9736bea11e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:AutoGraph could not transform <bound method BLEU.update_state of <__main__.BLEU object at 0x7f29428d22d0>> and will run it as-is.\n","Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n","Cause: not enough values to unpack (expected 2, got 0)\n","To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"]},{"output_type":"stream","name":"stdout","text":["WARNING: AutoGraph could not transform <bound method BLEU.update_state of <__main__.BLEU object at 0x7f29428d22d0>> and will run it as-is.\n","Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n","Cause: not enough values to unpack (expected 2, got 0)\n","To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n","    168/Unknown - 205s 1s/step - loss: 0.0559 - bleu_12: 0.7616"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-172-b1e6b1af56c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     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validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1382\u001b[0m                 _r=1):\n\u001b[1;32m   1383\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1384\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1385\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1386\u001b[0m                 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\u001b[0;31m# int64 reduction happens on GPU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2511\u001b[0;31m             \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen_math_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzero\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2512\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2513\u001b[0m             keepdims=keepdims),\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36mnot_equal\u001b[0;34m(x, y, incompatible_shape_error, name)\u001b[0m\n\u001b[1;32m   6925\u001b[0m       _result = pywrap_tfe.TFE_Py_FastPathExecute(\n\u001b[1;32m   6926\u001b[0m         \u001b[0m_ctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"NotEqual\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"incompatible_shape_error\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6927\u001b[0;31m         incompatible_shape_error)\n\u001b[0m\u001b[1;32m   6928\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6929\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"rj3wLwSW7EMb","executionInfo":{"status":"ok","timestamp":1662060236935,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a3e99db3-5e15-43a4-c6bd-166c1cce5ecf"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de3xdZZ3v8c9vX5Kde9okvSUtvRdogRZCqRQcjojDzVZEQBTHC4pnRkdwmBnRcc5wPM45jHrmOCpeYMQRQRBBlBEUL8NFSoGmtBRaWiilbdJr7vd7nvPHs7KbpkmbpNnZSfb3/Xrt11p77bX3/i02Xd+s51nrWeacQ0REBCCU7AJERGT8UCiIiEicQkFEROIUCiIiEqdQEBGROIWCiIjEKRRERCROoSByAmb2H2b21SGuu9vM3n2CdW43s/tGpzqR0aVQEBGROIWCiIjEKRRk0giabv7OzLaYWbOZ/dDMppvZb8ys0cz+YGZTgnXXmNlWM6szs6fN7LQ+n7PCzF4O3vMzINbve640s83Be583szNPsu7j1fIFM9sX1LLDzC4Olq80szIzazCzQ2b2rydTg0gvhYJMNlcDlwCLgfcCvwG+BBTh/3//nJktBh4AbgmWPwH8p5mlmVka8EvgJ8BU4OfBZwI+MIB7gE8DBcAPgMfMLH0kxZ6gliXAZ4FznXM5wJ8Du4O3/hvwb865XGAB8NBIvl+kP4WCTDbfds4dcs7tA/4EvOic2+ScawMeBVYA1wGPO+d+75zrBL4BZADnA6uAKPBN51ync+5hYEOfz78J+IFz7kXnXLdz7sdAe/C+kTheLd1AOnC6mUWdc7udc28F7+sEFppZoXOuyTn3wgi/X+QoCgWZbA71mW8d4Hk2MAvY07vQOdcDlAPFwWv73NHDB+/pM38KcGvQ1FNnZnXA7OB9IzFoLc65nfgjiNuBw2b2oJn1fs+N+KOh7Wa2wcyuHOH3ixxFoSCpaD9+5w6AmRl+x74POAAUB8t6zekzXw78s3Muv88j0zn3QAJqwTn3U+fcBcE6DviXYPmbzrnrgWnBsofNLGuENYjEKRQkFT0EXGFmF5tZFLgV3wT0PLAe6ML3PUTN7P3Ayj7vvRv472Z2nnlZZnaFmeWMdi1mtsTM3hX0V7Thj3R6AMzsBjMrCo4s6oLP6hlhDSJxCgVJOc65HcANwLeBKnyH9Hudcx3OuQ7g/cDHgBp8m/8v+ry3DPgU8B2gFtgZrDvqteD7E+4Ilh/EHxV8MXjrpcBWM2vCdzp/0DnXOtI6RHqZ7rwmIiK9dKQgIiJxCgWRBAgumGsa4PGlZNcmcjxqPhIRkbhIsgsYrsLCQjd37txklyEiMqFs3LixyjlXdKL1JlwozJ07l7KysmSXISIyoZjZnhOvpT4FERHpQ6EgIiJxCgUREYmbcH0KA+ns7KSiooK2trZkl5JQsViMkpISotFosksRkUlqUoRCRUUFOTk5zJ07l6PHMZs8nHNUV1dTUVHBvHnzkl2OiExSk6L5qK2tjYKCgkkbCABmRkFBwaQ/GhKR5JoUoQBM6kDolQrbKCLJNWlC4YQ6mqFhP+gKbhGRQaVOKHS2QNMhHw6jrK6uju9+97vDft/ll19OXV3diVcUERkjqRMKGVPBwtB8eNQ/erBQ6OrqOu77nnjiCfLz80e9HhGRkZoUZx8NSSgMWYX+aKGrHSLpo/bRt912G2+99RbLly8nGo0Si8WYMmUK27dv54033uB973sf5eXltLW1cfPNN3PTTTcBR4bsaGpq4rLLLuOCCy7g+eefp7i4mF/96ldkZGSMWo0iIkMx6ULhf/7nVrbtbxjkVeebj8LrITz0UDh9Vi7/9N6lg75+xx138Nprr7F582aefvpprrjiCl577bX4qaP33HMPU6dOpbW1lXPPPZerr76agoKCoz7jzTff5IEHHuDuu+/m2muv5ZFHHuGGG24Yco0iIqMhdZqPADAIRaC7E38P9MRYuXLlUdcSfOtb3+Kss85i1apVlJeX8+abbx7znnnz5rF8+XIAzjnnHHbv3p2w+kREBjPpjhSO9xc94DucK3dAzkzImZGQGrKysuLzTz/9NH/4wx9Yv349mZmZXHTRRQNea5CefuTIJRwO09qq2+2KyNhLsSMFIJoJadnQXAWuZ1Q+Micnh8bGxgFfq6+vZ8qUKWRmZrJ9+3ZeeOGFUflOEZFEmHRHCkOSPR1q3oLWOsicetIfV1BQwOrVq1m2bBkZGRlMnz49/tqll17K97//fU477TSWLFnCqlWrTvr7REQSZcLdjrO0tNT1v8nO66+/zmmnnTb0D3EOKrcDBkVLYAJdKTzsbRURAcxso3Ou9ETrpV7zEfgQyCqCrlboaEp2NSIi40ZqhgL4i9lCEWga/YvZREQmqtQNhVDIX8zW3gCdGnlURARSORQAMgsBS8jQFyIiE1Fqh0I46s8+aqkJLmgTEUltqR0KAFnTAOevWxARSXEKhWgM0nOhpQp6RudithPJzs4ek+8RERkuhQJA9jTo6YLWmmRXIiKSVKl5RXN/adkQyfAdzpkFw76Y7bbbbmP27Nl85jOfAeD2228nEonw1FNPUVtbS2dnJ1/96ldZu3ZtIqoXERk1ky8UfnMbHHx1+O/r6YSuNh8OoX7/WWacAZfdMehbr7vuOm655ZZ4KDz00EM8+eSTfO5znyM3N5eqqipWrVrFmjVrdJ9lERnXJl8ojFQoAoSgu+PYUDiBFStWcPjwYfbv309lZSVTpkxhxowZfP7zn+fZZ58lFAqxb98+Dh06xIwZiRmZVURkNEy+UDjOX/Qn1HgIGvdD4RJIyxzWW6+55hoefvhhDh48yHXXXcf9999PZWUlGzduJBqNMnfu3AGHzBYRGU8S2tFsZpea2Q4z22lmtw3w+hwze8rMNpnZFjO7PJH1nFBWAVgImiuH/dbrrruOBx98kIcffphrrrmG+vp6pk2bRjQa5amnnmLPnj0JKFhEZHQlLBTMLAzcCVwGnA5cb2an91vty8BDzrkVwAeB7yaqniEJRXxHc2utb0YahqVLl9LY2EhxcTEzZ87kwx/+MGVlZZxxxhnce++9nHrqqQkqWkRk9CSy+WglsNM5twvAzB4E1gLb+qzjgNxgPg/Yn8B6hiaryB8pNFdB7qxhvfXVV490cBcWFrJ+/foB12tq0sisIjI+JbL5qBgo7/O8IljW1+3ADWZWATwB/PVAH2RmN5lZmZmVVVYOv2lnWCLpEMvzodDTndjvEhEZZ5J98dr1wH8450qAy4GfmNkxNTnn7nLOlTrnSouKihJfVdY0cN1+TCQRkRSSyFDYB8zu87wkWNbXjcBDAM659UAMKBzJl43qHeTSs/29nJsP+7u0jRMT7S55IjLxJDIUNgCLzGyemaXhO5If67fOXuBiADM7DR8Kw24fisViVFdXj+5OM3ua72xuqx+9zzwJzjmqq6uJxWLJLkVEJrGEdTQ757rM7LPAk0AYuMc5t9XMvgKUOeceA24F7jazz+M7nT/mRrBnLykpoaKiglHtb3AOGmugvNYHRCg8ep89QrFYjJKSkmSXISKTmE20JonS0lJXVlY2Nl+29wW4dy1MXwYffQzSssbme0VERpmZbXTOlZ5ovWR3NI9vc1bB1T+E/S/Dzz8O3V3JrkhEJKEUCidy2pVw+TfgzSfh1zePq45nEZHRNvnGPkqEc2+ExoPw7NcgZya868vJrkhEJCEUCkP1374EjQfg2a9Dzgw495PJrkhEZNQpFIbKDK78ph8C4/G/9Re4nb4m2VWJiIwq9SkMRzgCH/gRlJTCI5+EPc8nuyIRSRU9PdDdmfCvUSgMV1omXP8zyJ8DD3wQDr+e7IpEZLJpqoRdz8AL34NffRbufhf8nxJ49eGEf7Waj0YiqwBueAR+eAncdzXc+DvI00VlIinFOSh/CTb8O5S/ABlTIXs6ZBf55uXsaX7U5expR55nTDn6HvAdzVC5HQ5tg8Pb4NBWP+17T5fMQph+Opz9EShclPDNUiiM1JRTfDD86HIfDJ/4rf/BRST5Wmrg5Xuh7B4/GsHSq2Dp+2H60qN3yiPR3ghbHvKffeg1SMuBhe/yO/jGA3BwCzQd9oNq9heKBkFRBG0NULsbP5gDfry1olNh8Z/DtNP9Y/pSHyZjSFc0n6xdz/hQKDkXPvILiGYkuyKR1HVoG7z4fb/T7mqFuRf6m2e9/azfSRcu9uGw7P1QtGSYn70VNvwQtvwMOppgxhlQeiOccY0fRLOvnh5/s67mwz4gmiuD6WHfNNR0yI+QMH0pTDvNB8CUeRBKXIv+UK9oViiMhtcegYc/AadeCdfeOy7GSRJJGT3d8MZvfRi8/SxEMuDMa+G8T/udLvj7o2z7FWx9FHY/Bzg/fM3Sq3xATJ0/8Gd3tcO2x440EYXT/fqlN/oTTk72qGMMKRTG2vrvwpNf9NcvXP6NCfU/i8iE1FoHm34CL90FdXshtwRWfgrO/gvInDr4+xoOBAHxCyh/0S+budzv7Jde5U8iqd0NZT+CTfdBS5UPjdJPwPIPH/+zxzGFQjL87h/h+W/Bgovhoi/C7HOTXZFIYnV3QdUbcGAztDdBOArhNH8Hw975+LR3Pv3IfFqWf0Qyht50UrkDXvwBvPIAdLbAKav9UcGSK/xp48NRVw7bfgmv/cKPcQa+ianqTbAQLLnMj2gw76KENu2MBYVCMvT0wPpvw3PfhNYaWPhu+LPbFA4yOfR0+wDYvwn2b/bTg6/6tvuTZr6jtTck+j+iwbTmLdj1tA+WM6+BlZ+GmWeOwvcDNW/75qVdT8Ocd/gjjrz+dxCeuBQKydTeCC/dDc9/W+EgE1NPt/9ref8mfxSwf7M/q6azxb8ezYKZZ8Gs5TBrhW9+ySzwN6Y65tHpp13tR+Z7n3e2+LN2eqcdTdDRZ77/8rQsv7M+52OQNaKbNKYshcJ40N7oO6jWfUvhIONf4yHY8YR/7F4Hnc1+eTQTZpzpd/69IVCwUCdUTDAKhfGkvQk23K1wkPHFOd8ctP1xHwQVG/zy/FNg0Xug+BwfAoWLFQCTgEJhPFI4yEj1dENLtT/fPZwOOdMhPWdkn1OxAbb/GrY/4dvowf/1v+QKOPVyf868zp6bdBQK41n/cFhwMaz6Sz+d4Gc4yDA45y9wil/UdNhf1NTUb745uPjJ9Rz9/miWD4ecmX54hZwZA08jMd95uuNx2PFbf4plKArzLoQll/vHJOpQlYEpFCaC9ibf57D+Tv8Pf+p8f53D8g9pyIzJpLsL6vb4UymrdvgO3Mpg2l5/7PqhaDCGzrQ+j+n+kVXoO2gbD/rQ6DttPHikH2Ag6bmw6BI49Qp/lBrLS9w2y7ijUJhIujrg9cf8GUvlLxy5InPlp/yl9DIxdLb6HX3VG8FO/w3/qN7pz7bplT3dt9MXLfFDG+TMOLLjzyo6dtC04Whv9B3GTQePBEZbvb/f+CkXQCRtdLZVJhyFwkR1YItvWtryc3/+95x3+KOH09boH3SydXVAfbm/erZuTzANHrV7/I64l4Vgyly/8+8NgMIlfpTLjPykbYKkLoXCRNdaC5vu981LtW/7vyLP+bg/Pzt3ZrKrmzx6uv1f0m11ftiEvvMN+47e6TceID6iJYCF/ZDp+XP8qLn5p/idfuFimLoAorGkbZZIfwqFyaKnB976o29aevN3/tTAU6+E09f6HVHebN/koLNFjtXVAQdegb3rofrNYKdf12daD+0NHLWj78tCfjyd+E5/Tp/HKb6Dd7jDKogkyVBDQf9Hj3ehkO8cXHQJ1OzyQ/duus+P19IrnB78xTrbh0Te7D7zJZBbPP6antqb/Bk1LdUQy/f1RtJP7jPb6qF8gw+BvS/AvjLoavOvZU3zA5nF8iF3lh+uOJbvm3IGm2YV+fF5RFKIjhQmos42/5dvXTnUV0D93j7z5b5z8SjmOzNzi/0OMa/ET3NnHVmWPWPkweHckaEIWmv7jB1fdeR0yqZKP20OlvcOl3BUjTOPNMP0n+bOOvYCqob9RwJg73o/3r3r8c06M8/y/TFzVvnHGN+oRGS8UfNRKutqPxIQ9RVBYJT7nWjDft9W3tHU703md5x9gyKzMBh7psmf1dLeBB290yY/bW8MPmuwJpiwP40ya1ow7b09YbAss8AHSd0e327fO23Yd/RnhqL+aCL/FH8q5f6XfVs/+PP1Z597JASKS4+96YlIilPzUSqLpEPBAv8YTFv9kYDoGxYN+30z1e4/+XXCaZCW7a+eTc/x85lTfbt6era/FWF6TjCf7XfYvfek7T29ciQX5MXP9Nnjx7bvGxi1b/sB2Fb9lQ+B6WeobV9klOhfUqqK5fnHtNMGX6e7K3k720jaiYNNREadxlSQwemvb5GUo1AQEZE4hYKIiMQpFEREJE6hICIicQoFERGJUyiIiEicQkFEROIUCiIiEqdQEBGRuISGgpldamY7zGynmd02yDrXmtk2M9tqZj9NZD0iInJ8CRvHwMzCwJ3AJUAFsMHMHnPObeuzziLgi8Bq51ytmWl8YxGRJErkkcJKYKdzbpdzrgN4EFjbb51PAXc652oBnHOHE1iPiIicQCJDoRgo7/O8IljW12JgsZmtM7MXzOzSgT7IzG4yszIzK6usrExQuSIikuyO5giwCLgIuB6428zy+6/knLvLOVfqnCstKioa4xJFRFJHIkNhHzC7z/OSYFlfFcBjzrlO59zbwBv4kBARkSRIZChsABaZ2TwzSwM+CDzWb51f4o8SMLNCfHPSrgTWJCIix5GwUHDOdQGfBZ4EXgcecs5tNbOvmNmaYLUngWoz2wY8Bfydc646UTWJiMjxmXOD3HB9nCotLXVlZWXJLkNEZEIxs43OudITrZfsjmYRERlHFAoiIhKnUBARkTiFgoiIxCkUREQkTqEgIiJxCgUREYlTKIiISJxCQURE4hQKIiISp1AQEZE4hYKIiMSlTCg0tXfx4i4NwCoicjwpEwr//qddfPDuF6hr6Uh2KSIi41bKhMLqhYU4By/oaEFEZFBDCgUzu9nMcs37oZm9bGbvSXRxo+msknwy08Ks26lQEBEZzFCPFD7hnGsA3gNMAT4C3JGwqhIgLRJi5byprHurKtmliIiMW0MNBQumlwM/cc5t7bNswli9oJBdlc0crG9LdikiIuPSUENho5n9Dh8KT5pZDtCTuLIS4/yFBQCs26mjBRGRgQw1FG4EbgPOdc61AFHg4wmrKkFOm5HL1Kw0NSGJiAxiqKHwDmCHc67OzG4AvgzUJ66sxAiFjHcsKOD5ndU455JdjojIuDPUUPge0GJmZwG3Am8B9yasqgRavaCQgw1t7KpqTnYpIiLjzlBDocv5P63XAt9xzt0J5CSurMRZHfQrPK9+BRGRYww1FBrN7Iv4U1EfN7MQvl9hwpkzNZPi/AyeUyiIiBxjqKFwHdCOv17hIFACfD1hVSWQmbF6YQHr36qmu0f9CiIifQ0pFIIguB/IM7MrgTbn3ITsUwA/5EVDWxdb90+4vnIRkYQa6jAX1wIvAdcA1wIvmtkHEllYIr1jQe/1ChryQkSkr6E2H/0D/hqFjzrn/gJYCfxj4spKrGk5MRZPz+Z5Xa8gInKUoYZCyDl3uM/z6mG8d1w6f0EhG3bX0N7VnexSRETGjaHu2H9rZk+a2cfM7GPA48ATiSsr8VYvLKSts4eX99QluxQRkXFjqB3NfwfcBZwZPO5yzn0hkYUl2nnzpxIy1IQkItJHZKgrOuceAR5JYC1jKjcW5azZ+azbWcWt71mS7HJERMaF4x4pmFmjmTUM8Gg0s4axKjJRVi8o5JWKehrbOpNdiojIuHDcUHDO5Tjncgd45DjncseqyEQ5f2EB3T2Ol96uSXYpIiLjwoQ+g+hknT1nCumRkIa8EBEJpHQoxKJhzp07led1EZuICJDioQC+CWnHoUYqG9uTXYqISNKlfCisXlAI6NRUERFIcCiY2aVmtsPMdprZbcdZ72ozc2ZWmsh6BrKsOI/cWERNSCIiJDAUzCwM3AlcBpwOXG9mpw+wXg5wM/Biomo5nnDIWDW/QPdtFhEhsUcKK4GdzrldzrkO4EH8ndv6+1/AvwBtCazluFYvLKSitpW91S3JKkFEZFxIZCgUA+V9nlcEy+LM7GxgtnPu8eN9kJndZGZlZlZWWVk56oX23qJTRwsikuqS1tEc3NLzX4FbT7Suc+4u51ypc660qKho1GtZUJTN9Nx01ul6BRFJcYkMhX3A7D7PS4JlvXKAZcDTZrYbWAU8lozOZjNj9YJC1r9VTY9u0SkiKSyRobABWGRm88wsDfgg8Fjvi865eudcoXNurnNuLvACsMY5V5bAmgZ1/sJCqps72HGoMRlfLyIyLiQsFJxzXcBngSeB14GHnHNbzewrZrYmUd87UvF+BTUhiUgKG/LQ2SPhnHuCfjfjcc79j0HWvSiRtZzIzLwM5hdmsW5nFZ+8cH4ySxERSZqUv6K5r/MXFvDS2zV0dvckuxQRkaRQKPSxekEhzR3dvFKuW3SKSGpSKPTxjgUFmME6DXkhIilKodBHfmYaS2fl6iI2EUlZCoV+Vi8oZNPeWlo6upJdiojImFMo9HP+wkI6ux0bdtcmuxQRkTGnUOjn3LlTiIaN53W9goikIIVCP5lpEc6eM0X9CiKSkhQKA1i9sJCt+xuoa+lIdikiImNKoTCA1QsLcA7Wv6VTU0UktSgUBnBmST5ZaWGeU7+CiKQYhcIAouEQ580v4HkdKYhIilEoDOL8BQW8XdXM/rrWZJciIjJmFAqDWL2wENBQ2iKSWhQKg1gyPYeCrDR+v+1QsksRERkzCoVBhELGh86bw++2HeKhDeXJLkdEZEwoFI7j5osXccHCQr78y9fYrOG0RSQFKBSOIxIO8e3rVzAtN52/vG8jVU3tyS5JRCShFAonMCUrje/fcA41zR185v6XdVc2EZnUFApDsKw4jzuuPoMX367hfz/xerLLERFJmEiyC5gorlpRwpaKen60bjdnluRx1YqSZJckIjLqdKQwDF+6/DTOmzeV2x55ldf21Se7HBGRUadQGIZoOMSdHz6bqVlpfPonG6lp1iiqIjK5KBSGqTA7ne/fcA6VTe187oFNdKnjWUQmEYXCCJw1O5+vrl3Gczur+PrvdiS7HBGRUaOO5hG69tzZbNlXxw+e2cUZxXlceeasZJckInLSdKRwEv7HlUs555Qp/P3DW9hxsDHZ5YiInDSFwklIi4T43ofPJjs9wk0/KaO+pTPZJYmInBSFwkmalhvjezeczf66Vm752SZ6elyySxIRGTGFwig455Sp/NN7l/LUjkq++Yc3kl2OiMiIqaN5lHz4vDlsqajjW/+1k6lZaXz0/LmYWbLLEhEZFh0pjBIz4ytrl/GuU6dx+39u47MPbKKxTX0MIjKxKBRGUSwa5t//opS/v3QJv33tIGu+s45t+xuSXZaIyJApFEZZKGT81UULeeBTq2jp6OJ9313HAy/txTl1QIvI+KdQSJCV86by+Ocu5Lx5U/niL17l8z/bTHN7V7LLEhE5LoVCAhVmp/Pjj6/k1ksW89gr+1nzned0kZuIjGsKhQQLhYy/vngR933yPOpbu1h753M8VFae7LJERAakUBgj5y8o5ImbL2DFbD8sxt/+/BVaO7qTXZaIyFEUCmNoWk6M+z55Hp+7eBGPvFzB2jufY+dhNSeJyPiR0FAws0vNbIeZ7TSz2wZ4/W/MbJuZbTGzP5rZKYmsZzwIh4y/uWQx935iJdVNHaz5zjoe2Vihs5NEZFxIWCiYWRi4E7gMOB243sxO77faJqDUOXcm8DDwtUTVM95cuKiIJ26+kGXFedz681dY8511/PH1QwoHEUmqRB4prAR2Oud2Oec6gAeBtX1XcM495ZxrCZ6+AJQksJ5xZ3pujJ9+8jy+dvWZ1Ld2cuOPyxQOIpJUiQyFYqDvaTYVwbLB3Aj8ZqAXzOwmMyszs7LKyspRLDH5IuEQ1547mz/e+md87QMKBxFJrnHR0WxmNwClwNcHet05d5dzrtQ5V1pUVDS2xY2RaDjEtaUKBxFJrkSGwj5gdp/nJcGyo5jZu4F/ANY459oTWM+EoHAQkWSyRO1kzCwCvAFcjA+DDcCHnHNb+6yzAt/BfKlz7s2hfG5paakrKytLQMXjU2d3D49u2sd3/msne2taOKM4j1vevYh3nTpNQ3OLyJCZ2UbnXOkJ10vkX55mdjnwTSAM3OOc+2cz+wpQ5px7zMz+AJwBHAjestc5t+Z4n5lqodCrfzgsnp7N+88uYe3yWczMy0h2eSIyzo2LUEiEVA2FXp3dPfxy0z4eeGkvL++twwzOX1DA+5YXc9kZM8lO132TRORYCoUUsLuqmUc37eOXm/exp7qFWDTEe06fwVVnF3PhwkIi4XFxHoGIjAMKhRTinOPlvXU8uqmCX285QF1LJ4XZabz3rFm8f0UJy4pz1f8gkuIUCimqo6uHp3Yc5tGX9/Ff2w/T0d3DwmnZXLWimD9fOp0FRdkKCJEUpFAQ6ls6efzVAzy6qYINu2sBmJUX48JFRbxzcREXLCwkLzOa5CpFZCwoFOQo++paeWZHJc++Ucm6t6pobOsiZHDW7HzeGYTEWSV56ocQmaQUCjKoru4eXqmo45k3qnj2jUq2VNTR4yA3FmH1wkLeudiHRHG+TnUVmSwUCjJkdS0dPLfTB8Szb1RxsKENgPlFWZw9Zwor5uSzfHY+S6bn6EhCZIJSKMiIOOfYebiJZ96o5Pm3qtlcXkdNcwcAGdEwZ5TksWJOPitm57NizhSm58aSXLGIDIVCQUaFc47ymlY2ldeyaW8dm8rr2La/ns5u///NzLxY/EhixZwpLJuVR0ZaOMlVi0h/Qw0FXf4qx2VmzCnIZE5BJmuX+5HP27u62ba/gU1769hcXsem8lqeePVgsD6cMjWTJTNyWDI9hyUzclkyI4e5BZlqehKZABQKMmzpkTAr5kxhxZwp8WVVTe1s3lvH1v0N7DjUwPaDjfx+2yF6ggPRtEiIRdOyg6Dwj1Nn5DI9N13XTYiMI2o+koRp6+xm5+EmdhxsZMehRrYfbGTHwQYONRwZIT0vIyz9riUAAAn9SURBVMqCoizmFWYzvyiLeYVZzC3IYm5hJplp+ptFZLSo+UiSLhYNs6w4j2XFeUctr2vpOCoodlU2sW5nFY+8XHHUejPzYj4kCrOYX+gDY15hFrOnZhJVU5RIQigUZMzlZ6Zx3vwCzptfcNTy5vYudlc383ZVM29XNvN2MP/4lgPUt3bG1wsZzMiNUTwlg1n5GRTnZ8TnS/L9NEujxYqMiP7lyLiRlR5h6aw8ls7KO+a12uYOHxKVzeyubmZfbSv76lrZuKeWx7ccoKvn6GbQ/Mwoxfl9QiM/gxl5MWbmxZiRF2NaToy0iI42RPpTKMiEMCUrjSlZaZzdp3O7V3eP43BjG/vrWqmobWV/XRv76lrYV9vK3uoW1r9VTVN71zHvK8xOj4dE73RGbu/zDKblpOuIQ1KO/o+XCS8cMmbmZTAzL4NzTjn2deccDW1dHGpo40B9G4fq/fRgQysH6tsor2nhpbdrjmqi6pURDVOYk0ZhdjoFWekUBfNHHmkU5vj53FhEZ1LJhKdQkEnPzMjLiJKXEWXx9JxB12vp6OJgfRsHg9CobGqnqrGdqqZ2qpo6qKhtYXN5LdXNHQx00l5aOMTUrLRBHwXB0U5B8Dw/M41wSCEi44tCQSSQmRZhflE284uyj7ted4+jprkjCIvg0eif1zR3UNPcQXVzB+W1LdQ0ddA4QNMV+Av98jKi5GdEyctMi8/nZ0bjIZbfuzwzWC8jSm5GlFhUV41LYigURIYpHDKKctIpykkf0vrtXd3UtXRS3dRBbYsPjJqmdmpaOqlt7qC+tZO61k7qWzrYW93s51s7Bzwa6ZUeCZGbESU3FgmmPizyMiLxeT/1z3NiEXJiEbLT/XxmWlhNXTIghYJIgqVHwkzPDQ9r8MCeHkdjexf1LZ3UtQbB0eLDo6G1k4a2Thpau+LzdS0d7K1poSEIlP5nY/UXMn+2V056hJxYlOxYhOz0CNmxCLmxCFlpEbLS/bKs9AhZ6eH4sqz0cPy1zDS/PKRmsElDoSAyDoVCR/pB5pA5rPc652jr7KE+Hh6dNLZ30dTWRWNbF03tnTS1ddHQ1kVTsLypvYu6Ft/k1fu8paN7yN+ZmRYmMy0STMNk9E6jRy/LSovEX/PLImREw/6RdmSamRYmFizXqcNjS6EgMsmYmd/BpoWZkTfyoc27exwtHT4cmtq7aG7vorm92087jsz3vtbS2U1rR3f8PS0d3dQ0t9IaPG/t6Kals5vuExzF9BcJGRnRMLHe0IiGiUVDPjTSwsQiwTRYFuuzTkY0THo0THokFDyC+aifTxtgeVo4lNKDNyoURGRA4ZCRE4uSE4syfZQ+0zlHR3cPrR3dNAdB0dbpA6Q1CJXWzi5aO3po6eiirdMvb+mzXltnN22dPbR2dlPT3BFfp62zh7aObtq6uuNDu49UJGSkR3zI9E7T+j0f6PX0SCiY9n9+JHz6r3fUOtEwaeEQ0bAlrc9HoSAiY8bMgh1hmPzhtYoNS1d3D21dPnzau7pp7+qhvbOH9q5uOrp6/PMu/9wv76EjWK8tWM/PDzyta+k4ZnlHVw8d3T0nHUjgz0xLj/ijlt4jnbRIiFvevZg1Z80ahf9Cg1MoiMikEwmHyA6HyE7CFek9Pf5oqL2zh/ZuHzq9z/20OwihIIy6+wbTkUDq6BtcwfyUzGjC61coiIiMolDIiIXCwbUkid+Jj7bU7U0REZFjKBRERCROoSAiInEKBRERiVMoiIhInEJBRETiFAoiIhKnUBARkThzxxu0fRwys0pgzwjfXghUjWI5E00qb38qbzuk9vZr271TnHNFJ3rDhAuFk2FmZc650mTXkSypvP2pvO2Q2tuvbR/etqv5SERE4hQKIiISl2qhcFeyC0iyVN7+VN52SO3t17YPQ0r1KYiIyPGl2pGCiIgch0JBRETiUiYUzOxSM9thZjvN7LZk1zOWzGy3mb1qZpvNrCzZ9SSamd1jZofN7LU+y6aa2e/N7M1gOiWZNSbKINt+u5ntC37/zWZ2eTJrTBQzm21mT5nZNjPbamY3B8tT5bcfbPuH9funRJ+CmYWBN4BLgApgA3C9c25bUgsbI2a2Gyh1zqXEBTxm9k6gCbjXObcsWPY1oMY5d0fwR8EU59wXkllnIgyy7bcDTc65bySztkQzs5nATOfcy2aWA2wE3gd8jNT47Qfb/msZxu+fKkcKK4GdzrldzrkO4EFgbZJrkgRxzj0L1PRbvBb4cTD/Y/w/lklnkG1PCc65A865l4P5RuB1oJjU+e0H2/5hSZVQKAbK+zyvYAT/sSYwB/zOzDaa2U3JLiZJpjvnDgTzB4HpySwmCT5rZluC5qVJ2XzSl5nNBVYAL5KCv32/7Ydh/P6pEgqp7gLn3NnAZcBngiaGlOV8m+nkbzc94nvAAmA5cAD4v8ktJ7HMLBt4BLjFOdfQ97VU+O0H2P5h/f6pEgr7gNl9npcEy1KCc25fMD0MPIpvTks1h4I2196218NJrmfMOOcOOee6nXM9wN1M4t/fzKL4HeL9zrlfBItT5rcfaPuH+/unSihsABaZ2TwzSwM+CDyW5JrGhJllBZ1OmFkW8B7gteO/a1J6DPhoMP9R4FdJrGVM9e4QA1cxSX9/MzPgh8Drzrl/7fNSSvz2g23/cH//lDj7CCA4DeubQBi4xzn3z0kuaUyY2Xz80QFABPjpZN92M3sAuAg/bPAh4J+AXwIPAXPwQ69f65ybdB2yg2z7RfimAwfsBj7dp4190jCzC4A/Aa8CPcHiL+Hb1VPhtx9s+69nGL9/yoSCiIicWKo0H4mIyBAoFEREJE6hICIicQoFERGJUyiIiEicQkFkDJnZRWb262TXITIYhYKIiMQpFEQGYGY3mNlLwfjzPzCzsJk1mdn/C8aq/6OZFQXrLjezF4IBxx7tHXDMzBaa2R/M7BUze9nMFgQfn21mD5vZdjO7P7gSVWRcUCiI9GNmpwHXAaudc8uBbuDDQBZQ5pxbCjyDv1oY4F7gC865M/FXk/Yuvx+40zl3FnA+fjAy8KNX3gKcDswHVid8o0SGKJLsAkTGoYuBc4ANwR/xGfhB1HqAnwXr3Af8wszygHzn3DPB8h8DPw/Gmyp2zj0K4JxrAwg+7yXnXEXwfDMwF3gu8ZslcmIKBZFjGfBj59wXj1po9o/91hvpGDHtfea70b9DGUfUfCRyrD8CHzCzaRC/x+8p+H8vHwjW+RDwnHOuHqg1swuD5R8BngnufFVhZu8LPiPdzDLHdCtERkB/oYj045zbZmZfxt+tLgR0Ap8BmoGVwWuH8f0O4Idj/n6w098FfDxY/hHgB2b2leAzrhnDzRAZEY2SKjJEZtbknMtOdh0iiaTmIxERidORgoiIxOlIQURE4hQKIiISp1AQEZE4hYKIiMQpFEREJO7/A/G3WYPDTZKDAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"PsF_wos17EOC","executionInfo":{"status":"ok","timestamp":1662060236935,"user_tz":-60,"elapsed":1,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1c095623-381d-41ec-d591-5bf4272a81df"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## Evaluation"],"metadata":{"id":"GPy87_oDLYpA"}},{"cell_type":"code","source":["seq2seq_gru.evaluate(val_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":329},"id":"cRpieVKYLZ19","executionInfo":{"status":"error","timestamp":1662136310157,"user_tz":-60,"elapsed":192888,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f6d31d61-4669-4d4c-f50d-835dadedf79b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["     32/Unknown - 190s 5s/step - loss: 0.7100 - bleu_12: 0.4234"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-176-ad8d4a1988ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mseq2seq_gru\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m   1714\u001b[0m             \u001b[0;32mwith\u001b[0m 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updated only if `test_step` succeeds.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1509\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol_dependencies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_minimum_control_deps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtest_step\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1472\u001b[0m     \u001b[0;31m# Updates stateful loss metrics.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1473\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboolean_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mq\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0my\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__bool__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1133\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m__bool__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1134\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1136\u001b[0m   \u001b[0m__nonzero__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__bool__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1187\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1189\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1190\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"markdown","source":["## Testing"],"metadata":{"id":"4i1ajAymaDoF"}},{"cell_type":"code","source":["index_to_word={x:y for x, y in zip(range(len(french_vectorize_layer.get_vocabulary())),\n","                                   french_vectorize_layer.get_vocabulary())}"],"metadata":{"id":"FnEza6wFDhrI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def translator(english_sentence):\n","  tokenized_english_sentence=english_vectorize_layer([english_sentence])\n","  shifted_target='starttoken'\n","\n","  for i in range(FRENCH_SEQUENCE_LENGTH):\n","    tokenized_shifted_target=french_vectorize_layer([shifted_target])\n","    output=seq2seq_gru.predict([tokenized_english_sentence,tokenized_shifted_target])\n","    french_word_index=tf.argmax(output,axis=-1)[0][i].numpy()\n","    current_word=index_to_word[french_word_index]\n","    if current_word=='endtoken':\n","      break\n","    shifted_target+=' '+current_word\n","  return shifted_target[11:]"],"metadata":{"id":"j_H7mglPugSI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["translator('What makes you think that is not true?')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"UAMeQb2vytcp","executionInfo":{"status":"ok","timestamp":1662136103891,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"382d76fd-bf38-4a63-cf28-9d3f068b155b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'questce que tu fais nest pas vrai'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":175}]},{"cell_type":"code","source":["word_to_index={y:x for x, y in zip(range(len(french_vectorize_layer.get_vocabulary())),\n","                                   french_vectorize_layer.get_vocabulary())}"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tmLRAb7fFuO6","executionInfo":{"status":"ok","timestamp":1662103565126,"user_tz":-60,"elapsed":1569,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0e28240a-d270-4be7-faef-5efc110c566f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'': 0, '[UNK]': 1, 'starttoken': 2, 'endtoken': 3, 'je': 4, 'de': 5, 'pas': 6, 'que': 7, 'ne': 8, 'à': 9, 'la': 10, 'le': 11, 'vous': 12, 'tom': 13, 'il': 14, 'est': 15, 'un': 16, 'a': 17, 'ce': 18, 'tu': 19, 'nous': 20, 'les': 21, 'une': 22, 'en': 23, 'jai': 24, 'pour': 25, 'suis': 26, 'me': 27, 'faire': 28, 'cest': 29, 'elle': 30, 'dans': 31, 'des': 32, 'plus': 33, 'qui': 34, 'tout': 35, 'te': 36, 'ça': 37, 'ma': 38, 'du': 39, 'fait': 40, 'avec': 41, 'mon': 42, 'se': 43, 'veux': 44, 'au': 45, 'si': 46, 'et': 47, 'sont': 48, 'cette': 49, 'quil': 50, 'y': 51, 'très': 52, 'son': 53, 'votre': 54, 'être': 55, 'sur': 56, 'pourquoi': 57, 'temps': 58, 'cela': 59, 'dit': 60, 'été': 61, 'était': 62, 'pense': 63, 'nest': 64, 'moi': 65, 'peux': 66, 'ils': 67, 'lui': 68, 'chose': 69, 'sais': 70, 'jamais': 71, 'nai': 72, 'ici': 73, 'comment': 74, 'où': 75, 'vraiment': 76, 'ton': 77, 'bien': 78, 'estce': 79, 'quelque': 80, 'dire': 81, 'tous': 82, 'par': 83, 'beaucoup': 84, 'êtes': 85, 'besoin': 86, 'toi': 87, 'sa': 88, 'on': 89, 'personne': 90, 'quelle': 91, 'avons': 92, 'rien': 93, 'sest': 94, 'sommes': 95, 'es': 96, 'ont': 97, 'Ça': 98, 'peu': 99, 'astu': 100, 'parler': 101, 'quand': 102, 'as': 103, 'comme': 104, 'aller': 105, 'toujours': 106, 'mes': 107, 'maison': 108, 'avezvous': 109, 'trop': 110, 'ta': 111, 'deux': 112, 'va': 113, 'encore': 114, 'train': 115, 'mary': 116, 'quoi': 117, 'monde': 118, 'elles': 119, 'voir': 120, 'sil': 121, 'na': 122, 'mais': 123, 'vais': 124, 'bon': 125, 'avoir': 126, 'ses': 127, 'soit': 128, 'combien': 129, 'français': 130, 'dois': 131, 'peut': 132, 'avez': 133, 'faut': 134, 'voiture': 135, 'là': 136, 'lair': 137, 'fois': 138, 'ai': 139, 'aussi': 140, 'avait': 141, 'travail': 142, 'jaimerais': 143, 'vu': 144, 'maintenant': 145, 'quel': 146, 'même': 147, 'juste': 148, 'jétais': 149, 'chez': 150, 'ceci': 151, 'déjà': 152, 'eu': 153, 'aujourdhui': 154, 'ny': 155, 'marie': 156, 'ces': 157, 'cétait': 158, 'hier': 159, 'noël': 160, 'tes': 161, 'quelquun': 162, 'notre': 163, 'pensais': 164, 'À': 165, 'toutes': 166, 'prendre': 167, 'bonne': 168, 'sans': 169, 'fais': 170, 'mieux': 171, 'père': 172, 'trois': 173, 'estu': 174, 'assez': 175, 'seul': 176, 'dun': 177, 'nestce': 178, 'demain': 179, 'mal': 180, 'livre': 181, 'porte': 182, 'Êtesvous': 183, 'avant': 184, 'dû': 185, 'problème': 186, 'puisje': 187, 'gens': 188, 'dêtre': 189, 'savoir': 190, 'toute': 191, 'manger': 192, 'plaît': 193, 'vie': 194, 'aux': 195, 'vos': 196, 'enfants': 197, 'atil': 198, 'cet': 199, 'ou': 200, 'devrais': 201, 'choses': 202, 'jaime': 203, 'heures': 204, 'quelques': 205, 'nuit': 206, 'fut': 207, 'chien': 208, 'pris': 209, 'passé': 210, 'lai': 211, 'jour': 212, 'soir': 213, 'voulais': 214, 'simplement': 215, 'questce': 216, 'partir': 217, 'dune': 218, 'ans': 219, 'mère': 220, 'après': 221, 'moment': 222, 'boston': 223, 'venir': 224, 'lécole': 225, 'sens': 226, 'dernière': 227, 'dargent': 228, 'jours': 229, 'heureux': 230, 'depuis': 231, 'sûr': 232, 'narrive': 233, 'peur': 234, 'nouveau': 235, 'rester': 236, 'veut': 237, 'êtesvous': 238, 'tôt': 239, 'jespère': 240, 'idée': 241, 'difficile': 242, 'parle': 243, 'sait': 244, 'pu': 245, 'sois': 246, 'pouvez': 247, 'seule': 248, 'semaine': 249, 'demandé': 250, 'soyez': 251, 'rendre': 252, 'croire': 253, 'entendu': 254, 'semble': 255, 'raison': 256, 'pendant': 257, 'amis': 258, 'savais': 259, 'autre': 260, 'part': 261, 'javais': 262, 'souvent': 263, 'peutêtre': 264, 'ami': 265, 'acheté': 266, 'nom': 267, 'femme': 268, 'parents': 269, 'fille': 270, 'leur': 271, 'nêtes': 272, 'question': 273, 'chambre': 274, 'voulezvous': 275, 'veuxtu': 276, 'naime': 277, 'longtemps': 278, 'largent': 279, 'aider': 280, 'pouvezvous': 281, 'petit': 282, 'grand': 283, 'arrivé': 284, 'trouvé': 285, 'homme': 286, 'trouver': 287, 'perdu': 288, 'moins': 289, 'demande': 290, 'tard': 291, 'prie': 292, 'non': 293, 'fête': 294, 'peuxtu': 295, 'lorsque': 296, 'matin': 297, 'quon': 298, 'désolé': 299, 'retard': 300, 'nen': 301, 'lire': 302, 'sera': 303, 'pourrait': 304, 'jouer': 305, 'devons': 306, 'vrai': 307, 'nétait': 308, 'heure': 309, 'dautre': 310, 'presque': 311, 'aucun': 312, 'faites': 313, 'dont': 314, 'fort': 315, 'jusquà': 316, 'étaient': 317, 'crois': 318, 'tai': 319, 'pièce': 320, 'doit': 321, 'aucune': 322, 'sortir': 323, 'sujet': 324, 'nouvelle': 325, 'passer': 326, 'estil': 327, 'reste': 328, 'merci': 329, 'travailler': 330, 'journée': 331, 'lit': 332, 'chaque': 333, 'mort': 334, 'viens': 335, 'frère': 336, 'pensé': 337, 'pouvons': 338, 'café': 339, 'veuillez': 340, 'téléphone': 341, 'famille': 342, 'vérité': 343, 'ville': 344, 'allé': 345, 'devriez': 346, 'aime': 347, 'point': 348, 'dis': 349, 'davantage': 350, 'main': 351, 'lettre': 352, 'bureau': 353, 'jeune': 354, 'quils': 355, 'voulait': 356, 'trouve': 357, 'tellement': 358, 'voulez': 359, 'devez': 360, 'mis': 361, 'nes': 362, 'bus': 363, 'jen': 364, 'grande': 365, 'donné': 366, 'connais': 367, 'petite': 368, 'sœur': 369, 'acheter': 370, 'partie': 371, 'occupé': 372, 'pouvoir': 373, 'chance': 374, 'enfant': 375, 'déjeuner': 376, 'daccord': 377, 'livres': 378, 'davoir': 379, 'coup': 380, 'venu': 381, 'passe': 382, 'tête': 383, 'aide': 384, 'vite': 385, 'fini': 386, 'ensemble': 387, 'mauvais': 388, 'donner': 389, 'men': 390, 'pensestu': 391, 'envie': 392, 'alors': 393, 'personnes': 394, 'mangé': 395, 'seulement': 396, 'demander': 397, 'heureuse': 398, 'daller': 399, 'vivre': 400, 'nos': 401, 'devrait': 402, 'nager': 403, 'serai': 404, 'leau': 405, 'étiez': 406, 'yeux': 407, 'prends': 408, 'facile': 409, 'déteste': 410, 'près': 411, 'sous': 412, 'serait': 413, 'jaurais': 414, 'fils': 415, 'comprends': 416, 'vieux': 417, 'froid': 418, 'comprendre': 419, 'prêt': 420, 'photo': 421, 'oublié': 422, 'vue': 423, 'lheure': 424, 'confiance': 425, 'mettre': 426, 'allons': 427, 'colère': 428, 'étais': 429, 'meilleur': 430, 'pensezvous': 431, 'réunion': 432, 'chat': 433, 'bientôt': 434, 'manière': 435, 'jignore': 436, 'fatigué': 437, 'questions': 438, '»': 439, 'pourrais': 440, 'dernier': 441, 'nourriture': 442, 'garçon': 443, 'malade': 444, 'dur': 445, 'laissé': 446, 'erreur': 447, 'dix': 448, '«': 449, 'navons': 450, 'écrit': 451, 'essayé': 452, 'serais': 453, 'mois': 454, 'contre': 455, 'conduire': 456, 'travaille': 457, 'attention': 458, 'parlé': 459, 'regarder': 460, 'laisser': 461, 'jadore': 462, 'compte': 463, 'belle': 464, 'réponse': 465, 'premier': 466, 'navais': 467, 'feu': 468, 'mot': 469, 'place': 470, 'police': 471, 'amie': 472, 'autant': 473, 'table': 474, 'possible': 475, 'côté': 476, 'prix': 477, 'minutes': 478, 'film': 479, 'sûre': 480, 'quelles': 481, 'entre': 482, 'vélo': 483, 'ferais': 484, 'ainsi': 485, 'devrions': 486, 'autres': 487, 'voudrais': 488, 'nas': 489, 'genre': 490, 'pays': 491, 'tant': 492, 'parti': 493, 'attendre': 494, 'vient': 495, 'boire': 496, 'suppose': 497, 'plutôt': 498, 'médecin': 499, 'chemin': 500, 'apprendre': 501, 'vois': 502, 'probablement': 503, 'exactement': 504, 'dormir': 505, 'première': 506, 'pouvais': 507, 'leurs': 508, 'dici': 509, 'cheveux': 510, 'prochaine': 511, 'loin': 512, 'lautre': 513, 'verre': 514, 'musique': 515, 'désolée': 516, 'laisse': 517, 'beau': 518, 'quiconque': 519, 'endroit': 520, 'décidé': 521, 'regarde': 522, 'puisse': 523, 'chanter': 524, 'âge': 525, 'savezvous': 526, 'saistu': 527, 'professeur': 528, 'lieu': 529, 'boulot': 530, 'gare': 531, 'dehors': 532, 'choix': 533, 'joue': 534, 'penser': 535, 'commencé': 536, 'appris': 537, 'plan': 538, 'dîner': 539, 'pourriezvous': 540, 'gros': 541, 'payer': 542, 'navez': 543, 'fenêtre': 544, 'faim': 545, 'essayer': 546, 'propos': 547, 'perdre': 548, 'entendre': 549, 'voyage': 550, 'lun': 551, 'problèmes': 552, 'aije': 553, 'vin': 554, 'chaud': 555, 'ça\\xa0': 556, 'cours': 557, 'pouvait': 558, 'vas': 559, 'laissemoi': 560, 'cadeau': 561, 'japon': 562, 'histoire': 563, 'répondre': 564, 'peuvent': 565, 'vit': 566, 'sécurité': 567, 'rencontrer': 568, 'argent': 569, 'surpris': 570, 'auparavant': 571, 'allez': 572, 'année': 573, 'nouvelles': 574, 'mange': 575, 'étions': 576, 'visite': 577, 'content': 578, 'plait': 579, 'immédiatement': 580, 'reçu': 581, 'parce': 582, 'terre': 583, 'savait': 584, 'prochain': 585, 'maider': 586, 'chercher': 587, 'vont': 588, 'jy': 589, 'vers': 590, 'arrête': 591, 'lhomme': 592, 'terminé': 593, 'important': 594, 'devoirs': 595, 'marcher': 596, 'derrière': 597, 'voulons': 598, 'occupée': 599, 'discuter': 600, 'rentrer': 601, 'fumer': 602, 'lu': 603, 'ten': 604, 'produit': 605, 'peine': 606, 'nombreux': 607, 'devenir': 608, 'cause': 609, 'rencontré': 610, 'mains': 611, 'cœur': 612, 'cinq': 613, 'écrire': 614, 'mauvaise': 615, 'ferai': 616, 'avais': 617, 'décision': 618, 'complètement': 619, 'arrive': 620, 'aimé': 621, 'revoir': 622, 'rapidement': 623, 'étudier': 624, 'tomber': 625, 'nimporte': 626, 'laquelle': 627, 'chaussures': 628, 'assis': 629, 'lundi': 630, 'langue': 631, 'laissezmoi': 632, 'façon': 633, 'dites': 634, 'bois': 635, 'aurait': 636, 'riche': 637, 'deau': 638, 'commence': 639, 'classe': 640, 'changer': 641, 'appelé': 642, 'chanson': 643, 'blessé': 644, 'propre': 645, 'santé': 646, 'nécessaire': 647, 'secret': 648, 'plein': 649, 'thé': 650, 'route': 651, 'lait': 652, 'devant': 653, 'situation': 654, 'navait': 655, 'lintention': 656, 'faisait': 657, 'quune': 658, 'montre': 659, 'fin': 660, 'bière': 661, 'arriver': 662, 'parfois': 663, 'télévision': 664, 'rue': 665, 'boîte': 666, 'vêtements': 667, 'prenez': 668, 'pas\\xa0': 669, 'lannée': 670, 'bruit': 671, 'montrer': 672, 'fit': 673, 'celui': 674, 'vastu': 675, 'utiliser': 676, 'taider': 677, 'làbas': 678, 'lune': 679, 'laide': 680, 'journal': 681, 'bébé': 682, 'arrêté': 683, 'vouloir': 684, 'anglais': 685, 'soleil': 686, 'soin': 687, 'rappelle': 688, 'donc': 689, 'chapeau': 690, 'bras': 691, 'quun': 692, 'magasin': 693, 'faisons': 694, 'dy': 695, 'poser': 696, 'numéro': 697, 'aprèsmidi': 698, 'vacances': 699, 'trente': 700, 'tennis': 701, 'sorte': 702, 'neige': 703, 'naurais': 704, 'fier': 705, 'vieille': 706, 'marché': 707, 'j’ai': 708, 'long': 709, 'gagner': 710, 'dispose': 711, 'commencer': 712, 'mas': 713, 'droit': 714, 'ait': 715, 'veulent': 716, 'sen': 717, 'nombreuses': 718, 'langlais': 719, 'guerre': 720, 'garder': 721, 'emploi': 722, 'stupide': 723, 'parc': 724, 'arrêter': 725, 'tort': 726, 'rouge': 727, 'font': 728, 'carte': 729, 'cuisine': 730, 'plaisir': 731, 'certain': 732, 'sac': 733, 'restaurant': 734, 'poste': 735, 'meilleure': 736, 'conseil': 737, 'capable': 738, 'clé': 739, 'laccident': 740, 'dollars': 741, 'lumière': 742, 'désormais': 743, 'affaires': 744, 'changé': 745, 'voulu': 746, 'dentre': 747, 'vis': 748, 'souviens': 749, 'retour': 750, 'plusieurs': 751, 'mots': 752, 'fasse': 753, 'savez': 754, 'rire': 755, 'prête': 756, 'australie': 757, 'affaire': 758, 'pourraistu': 759, 'plage': 760, 'étudiants': 761, 'rivière': 762, 'rapport': 763, 'préfère': 764, 'dès': 765, 'chiens': 766, 'voici': 767, 'porter': 768, 'mont': 769, 'lequel': 770, 'suffisamment': 771, 'restez': 772, 'ordinateur': 773, 'mourir': 774, 'tasse': 775, 'rendu': 776, 'pluie': 777, 'pleurer': 778, 'filles': 779, 'entrer': 780, 'six': 781, 'robe': 782, 'gâteau': 783, 'cas': 784, 'télé': 785, 'liste': 786, 'devoir': 787, 'résoudre': 788, 'nont': 789, 'manteau': 790, 'japprécie': 791, 'resté': 792, 'eux': 793, 'compris': 794, 'amies': 795, 'vaut': 796, 'nétais': 797, 'mesure': 798, 'jardin': 799, 'garde': 800, 'connaît': 801, 'années': 802, 'type': 803, 'sagit': 804, 'regardé': 805, 'moindre': 806, 'pied': 807, 'intéressant': 808, 'finalement': 809, 'telle': 810, 'réparer': 811, 'libre': 812, 'fleurs': 813, 'finir': 814, 'dautres': 815, 'minute': 816, 'demanda': 817, 'accident': 818, 'règles': 819, 'prend': 820, 'devenu': 821, 'dormi': 822, 'dismoi': 823, 'dictionnaire': 824, 'suite': 825, 'pleuvoir': 826, 'moimême': 827, 'arrêtez': 828, 'weekend': 829, 'ouvert': 830, 'excuses': 831, 'donnemoi': 832, 'souhaite': 833, 'marche': 834, 'dimanche': 835, 'bibliothèque': 836, 'volé': 837, 'hommes': 838, 'furent': 839, 'dangereux': 840, 'surprise': 841, 'suisje': 842, 'quatre': 843, 'plupart': 844, 'estelle': 845, 'rêve': 846, 'lhôpital': 847, 'c’est': 848, 'payé': 849, 'succès': 850, 'fou': 851, 'bateau': 852, 'autour': 853, 'pas\\u202f': 854, 'gentil': 855, 'femmes': 856, 'voudriezvous': 857, 'poids': 858, 'repas': 859, 'mavez': 860, 'faute': 861, 'allezvous': 862, 'poisson': 863, 'lhistoire': 864, 'rend': 865, 'quels': 866, 'mit': 867, 'grosse': 868, 'donne': 869, 'danser': 870, 'court': 871, 'cher': 872, 'quitter': 873, 'parapluie': 874, 'noir': 875, 'dirait': 876, 'bu': 877, 'étrange': 878, 'serez': 879, 'prison': 880, 'pire': 881, 'my': 882, 'face': 883, 'triste': 884, 'tombé': 885, 'laissez': 886, 'venue': 887, 'réussi': 888, 'mets': 889, 'lunettes': 890, 'hors': 891, 'fonctionne': 892, 'fermer': 893, 'fatiguée': 894, 'essaie': 895, 'certains': 896, 'équipe': 897, 'sérieux': 898, 'sommeil': 899, 'simple': 900, 'projet': 901, 'cassé': 902, 'voudraistu': 903, 'venez': 904, 'tenir': 905, 'patron': 906, 'amoureux': 907, 'travers': 908, 'jeu': 909, 'gagné': 910, 'radio': 911, 'prudent': 912, 'piano': 913, 'penses': 914, 'doute': 915, 'car': 916, 'bonnes': 917, 'allée': 918, 'pommes': 919, 'dessus': 920, 'coucher': 921, 'bizarre': 922, 'seriez': 923, 'rendezvous': 924, 'rappeler': 925, 'oncle': 926, 'mari': 927, 'manque': 928, 'japonais': 929, 'appeler': 930, 'senti': 931, 'pouvonsnous': 932, 'école': 933, 'tour': 934, 'fera': 935, 'danger': 936, 'également': 937, 'souci': 938, 'savons': 939, 'mariage': 940, 'lever': 941, 'invité': 942, 'douleur': 943, 'compter': 944, 'chats': 945, 'certaine': 946, 'attendu': 947, 'aidé': 948, 'voyager': 949, 'voix': 950, 'salle': 951, 'photos': 952, 'létranger': 953, 'haut': 954, 'durant': 955, 'courir': 956, 'celle': 957, 'bons': 958, 'aurais': 959, 'sontils': 960, 'proposition': 961, 'ditesmoi': 962, 'cherche': 963, 'certaines': 964, 'avocat': 965, 'linstant': 966, 'daide': 967, 'courant': 968, 'chemise': 969, 'allés': 970, 'sol': 971, 'réjouis': 972, 'promis': 973, 'né': 974, 'lintérieur': 975, 'garçons': 976, 'seras': 977, 'petitdéjeuner': 978, 'doisje': 979, 'coûte': 980, 'conseils': 981, 'anniversaire': 982, 'tableau': 983, 'présenter': 984, 'cheval': 985, 'tué': 986, 'sorti': 987, 'regardez': 988, 'prit': 989, 'pieds': 990, 'impossible': 991, 'envoyé': 992, 'donnezmoi': 993, 'connaître': 994, 'réfléchir': 995, 'ni': 996, 'ferme': 997, 'doù': 998, 'donna': 999, 'bâtiment': 1000, 'amusant': 1001, 'taxi': 1002, 'noublie': 1003, 'différence': 1004, 'banque': 1005, 'ayez': 1006, 'appareil': 1007, 'viande': 1008, 'tempête': 1009, 'moyen': 1010, 'message': 1011, 'dacheter': 1012, 'cela\\xa0': 1013, 'calme': 1014, 'travaillé': 1015, 'suivre': 1016, 'mest': 1017, 'lac': 1018, 'devraisje': 1019, 'couleur': 1020, 'menti': 1021, 'lavezvous': 1022, 'jeunes': 1023, 'idiot': 1024, 'facilement': 1025, 'animaux': 1026, 'satisfait': 1027, 'parviens': 1028, 'papier': 1029, 'fermé': 1030, 'concert': 1031, 'clés': 1032, 'allait': 1033, 'retourner': 1034, 'parfait': 1035, 'intelligent': 1036, 'taille': 1037, 'supporter': 1038, 'ressemble': 1039, 'rentré': 1040, 'quastu': 1041, 'présent': 1042, 'parlez': 1043, 'lastu': 1044, 'jignorais': 1045, 'fus': 1046, 'décrire': 1047, 'crains': 1048, 'bout': 1049, 'arbre': 1050, 'étudiant': 1051, 'visage': 1052, 'semblez': 1053, 'semaines': 1054, 'faitesvous': 1055, 'erreurs': 1056, 'dos': 1057, 'ciel': 1058, 'aies': 1059, 'risque': 1060, 'guitare': 1061, 'discours': 1062, 'bas': 1063, 'selon': 1064, 'quitté': 1065, 'propres': 1066, 'promets': 1067, 'pensez': 1068, 'nombre': 1069, 'feriez': 1070, 'dents': 1071, 'contente': 1072, 'tuer': 1073, 'nettoyer': 1074, 'moitié': 1075, 'drôle': 1076, 'sentir': 1077, 'proche': 1078, 'permis': 1079, 'ouvrir': 1080, 'laime': 1081, 'enfin': 1082, 'debout': 1083, 'tavoir': 1084, 'responsable': 1085, 'rarement': 1086, 'cent': 1087, 'lété': 1088, 'faistu': 1089, 'emprunter': 1090, 'conseillé': 1091, 'conscience': 1092, 'chaise': 1093, 'soirée': 1094, 'prévu': 1095, 'las': 1096, 'honte': 1097, 'continuer': 1098, 'censé': 1099, 'rhume': 1100, 'opinion': 1101, 'lexamen': 1102, 'laise': 1103, 'gauche': 1104, 'blanc': 1105, 'aviez': 1106, 'vérifier': 1107, 'seconde': 1108, 'reconnaissant': 1109, 'quavezvous': 1110, 'pomme': 1111, 'lhabitude': 1112, 'habituellement': 1113, 'delle': 1114, 'apprécié': 1115, 'vieil': 1116, 'tokyo': 1117, 'testu': 1118, 'semblait': 1119, 'rapide': 1120, 'mur': 1121, 'mien': 1122, 'marié': 1123, 'manqué': 1124, 'docteur': 1125, 'coin': 1126, 'aimestu': 1127, 'vide': 1128, 'tranquille': 1129, 'su': 1130, 'marier': 1131, 'làdedans': 1132, 'environ': 1133, 'douche': 1134, 'avaient': 1135, 'œil': 1136, 'viendra': 1137, 'supposé': 1138, 'signifie': 1139, 'seuls': 1140, 'passée': 1141, 'lavion': 1142, 'importante': 1143, 'honnête': 1144, 'goût': 1145, 'ferait': 1146, 'couteau': 1147, 'chante': 1148, 'avions': 1149, 'aimezvous': 1150, 'tom\\xa0': 1151, 'tel': 1152, 'sucre': 1153, 'sache': 1154, 'réussir': 1155, 'pleut': 1156, 'partout': 1157, 'machine': 1158, 'différent': 1159, 'bord': 1160, 'sourire': 1161, 'larbre': 1162, 'début': 1163, 'copine': 1164, 'chacun': 1165, 'ceci\\xa0': 1166, 'battre': 1167, 'vivent': 1168, 'sy': 1169, 'sentiment': 1170, 'nerveux': 1171, 'lorsquil': 1172, 'inconvénient': 1173, 'faitil': 1174, 'cadeaux': 1175, 'aimes': 1176, 'vécu': 1177, 'répondu': 1178, 'longue': 1179, 'limpression': 1180, 'groupe': 1181, 'essayez': 1182, 'blague': 1183, 'tombée': 1184, 'taime': 1185, 'sors': 1186, 'roman': 1187, 'lesprit': 1188, 'généralement': 1189, 'grandpère': 1190, 'cuisiner': 1191, 'arme': 1192, 'remercier': 1193, 'président': 1194, 'neuf': 1195, 'mariée': 1196, 'lont': 1197, 'jolie': 1198, 'intéressé': 1199, 'commença': 1200, 'vouliez': 1201, 'vol': 1202, 'vent': 1203, 'revenu': 1204, 'préparer': 1205, 'préféré': 1206, 'pensait': 1207, 'montré': 1208, 'langues': 1209, 'glace': 1210, 'fiche': 1211, 'fallu': 1212, 'décider': 1213, 'certainement': 1214, 'bouteille': 1215, 'parole': 1216, 'lentement': 1217, 'gérer': 1218, 'enseignant': 1219, 'dieu': 1220, 'dessayer': 1221, 'convaincu': 1222, 'celuici': 1223, 'canapé': 1224, 'auriez': 1225, 'adore': 1226, 'revenir': 1227, 'public': 1228, 'portait': 1229, 'oui': 1230, 'mer': 1231, 'célèbre': 1232, 'continué': 1233, 'étudié': 1234, 'thomas': 1235, 'terminer': 1236, 'sérieusement': 1237, 'société': 1238, 'prêts': 1239, 'prudente': 1240, 'pauvre': 1241, 'parfaitement': 1242, 'montagne': 1243, 'loi': 1244, 'lextérieur': 1245, 'laver': 1246, 'Étatsunis': 1247, 'prêter': 1248, 'pars': 1249, 'note': 1250, 'maladie': 1251, 'lis': 1252, 'jirai': 1253, 'disent': 1254, 'stylo': 1255, 'portes': 1256, 'obtenu': 1257, 'obligé': 1258, 'lavoir': 1259, 'hôtel': 1260, 'gagne': 1261, 'dise': 1262, 'dirai': 1263, 'courses': 1264, 'vendre': 1265, 'sommesnous': 1266, 'nulle': 1267, 'laéroport': 1268, 'ici\\u202f': 1269, 'habitué': 1270, 'forme': 1271, 'darrêter': 1272, 'cravate': 1273, 'corps': 1274, 'attrapé': 1275, 'attraper': 1276, 'arrivée': 1277, 'actuellement': 1278, 'utile': 1279, 'service': 1280, 'seront': 1281, 'réellement': 1282, 'rencontrés': 1283, 'promesse': 1284, 'patient': 1285, 'pain': 1286, 'endormi': 1287, 'déçu': 1288, 'sembles': 1289, 'recherche': 1290, 'quelconque': 1291, 'possède': 1292, 'ouverte': 1293, 'nez': 1294, 'morte': 1295, 'mienne': 1296, 'eut': 1297, 'chouette': 1298, 'truc': 1299, 'soyons': 1300, 'sauf': 1301, 'promenade': 1302, 'mentir': 1303, 'lamour': 1304, 'faveur': 1305, 'ennuis': 1306, 'conduit': 1307, 'cartes': 1308, 'bain': 1309, 'attends': 1310, 'œufs': 1311, 'voilà': 1312, 'tiens': 1313, 'pont': 1314, 'noubliez': 1315, 'lève': 1316, 'lors': 1317, 'lavons': 1318, 'jattends': 1319, 'frais': 1320, 'dérange': 1321, 'dhabitude': 1322, 'davis': 1323, 'ceux': 1324, 'pourraisje': 1325, 'musée': 1326, 'moyens': 1327, 'mille': 1328, 'john': 1329, 'idées': 1330, 'forte': 1331, 'convaincre': 1332, 'connaissance': 1333, 'but': 1334, 'autorisé': 1335, 'afin': 1336, 'sympa': 1337, 'savent': 1338, 'rends': 1339, 'préférerais': 1340, 'protéger': 1341, 'pleine': 1342, 'octobre': 1343, 'nêtesvous': 1344, 'levé': 1345, 'extrêmement': 1346, 'conseilla': 1347, 'cinéma': 1348, 'souvenir': 1349, 'sontelles': 1350, 'sept': 1351, 'remarqué': 1352, 'regarda': 1353, 'refusé': 1354, 'préparé': 1355, 'parlons': 1356, 'luniversité': 1357, 'heureuses': 1358, 'bouton': 1359, 'balle': 1360, 'ravi': 1361, 'produire': 1362, 'permettre': 1363, 'joué': 1364, 'gouvernement': 1365, 'dattendre': 1366, 'choisir': 1367, 'baseball': 1368, 'aiment': 1369, 'adresse': 1370, 'vienne': 1371, 'serons': 1372, 'phrase': 1373, 'match': 1374, 'luimême': 1375, 'lon': 1376, 'ligne': 1377, 'frères': 1378, 'football': 1379, 'fière': 1380, 'faux': 1381, 'droite': 1382, 'demandais': 1383, 'croit': 1384, 'connu': 1385, 'choisi': 1386, 'avion': 1387, 'attendez': 1388, 'écouter': 1389, 'seules': 1390, 'paix': 1391, 'lhôtel': 1392, 'jentends': 1393, 'inquiet': 1394, 'faisais': 1395, 'contrôle': 1396, 'contact': 1397, 'clair': 1398, 'vus': 1399, 'tient': 1400, 'solution': 1401, 'perte': 1402, 'joindre': 1403, 'fruits': 1404, 'fasses': 1405, 'fallait': 1406, 'expliquer': 1407, 'comprend': 1408, 'traverser': 1409, 'tiré': 1410, 'sortie': 1411, 'silencieux': 1412, 'signe': 1413, 'prêtes': 1414, 'posé': 1415, 'jallais': 1416, 'den': 1417, 'apporté': 1418, 'absolument': 1419, 'élèves': 1420, 'voisins': 1421, 'valise': 1422, 'trucs': 1423, 'sport': 1424, 'réveillé': 1425, 'pause': 1426, 'mattendais': 1427, 'légumes': 1428, 'dort': 1429, 'date': 1430, 'avance': 1431, 'attentivement': 1432, 'voler': 1433, 'voitures': 1434, 'super': 1435, 'regrette': 1436, 'politique': 1437, 'peint': 1438, 'nouveaux': 1439, 'morceau': 1440, 'mensonge': 1441, 'léquipe': 1442, 'jeter': 1443, 'habite': 1444, 'doivent': 1445, 'distu': 1446, 'devait': 1447, 'couper': 1448, 'chocolat': 1449, 'retraite': 1450, 'remettre': 1451, 'particulier': 1452, 'occupés': 1453, 'médicament': 1454, 'crime': 1455, 'correctement': 1456, 'corde': 1457, 'connaistu': 1458, 'chanceux': 1459, 'agréable': 1460, 'accepté': 1461, 'village': 1462, 'venus': 1463, 'sûrs': 1464, 'soient': 1465, 'pêcher': 1466, 'plaindre': 1467, 'peutil': 1468, 'nouvel': 1469, 'navezvous': 1470, 'mettez': 1471, 'hâte': 1472, 'huit': 1473, 'devient': 1474, 'continue': 1475, 'change': 1476, 'blessée': 1477, 'aimeriezvous': 1478, 'voyezvous': 1479, 'suggère': 1480, 'sent': 1481, 'rêves': 1482, 'printemps': 1483, 'permission': 1484, 'moi\\xa0': 1485, 'lentreprise': 1486, 'intéressante': 1487, 'entièrement': 1488, 'ensuite': 1489, 'découvert': 1490, 'débarrasser': 1491, 'coupable': 1492, 'connaissezvous': 1493, 'chaleur': 1494, 'aura': 1495, 'arbres': 1496, 'amusé': 1497, 'suivi': 1498, 'sombre': 1499, 'prendra': 1500, 'paire': 1501, 'obtenir': 1502, 'morts': 1503, 'larmes': 1504, 'grandmère': 1505, 'distance': 1506, 'damis': 1507, 'cloche': 1508, 'cesse': 1509, 'appartement': 1510, 'échoué': 1511, 'vatil': 1512, 'puis': 1513, 'lidée': 1514, 'lettres': 1515, 'laider': 1516, 'jarrive': 1517, 'jambe': 1518, 'impatient': 1519, 'expérience': 1520, 'ditesvous': 1521, 'direction': 1522, 'devrionsnous': 1523, 'croyais': 1524, 'sûres': 1525, 'soucie': 1526, 'sestil': 1527, 'règle': 1528, 'refuse': 1529, 'quy': 1530, 'parlent': 1531, 'nastu': 1532, 'mérite': 1533, 'lycée': 1534, 'ira': 1535, 'grands': 1536, 'forcé': 1537, 'espérons': 1538, 'eh': 1539, 'disparu': 1540, 'daider': 1541, 'compagnie': 1542, 'vitesse': 1543, 'ty': 1544, 'refaire': 1545, 'précisément': 1546, 'probable': 1547, 'mémoire': 1548, 'mauvaises': 1549, 'importe': 1550, 'français\\xa0': 1551, 'faire\\xa0': 1552, 'détudier': 1553, 'chansons': 1554, 'bouche': 1555, 'avis': 1556, 'voistu': 1557, 'tas': 1558, 'soupe': 1559, 'raté': 1560, 'quà': 1561, 'prendrai': 1562, 'maintenant\\xa0': 1563, 'laprèsmidi': 1564, 'invités': 1565, 'foule': 1566, 'essaya': 1567, 'darriver': 1568, 'cru': 1569, 'congé': 1570, 'cacher': 1571, 'billet': 1572, 'voyons': 1573, 'toimême': 1574, 'tenté': 1575, 'sentiments': 1576, 'salaire': 1577, 'reviens': 1578, 'pourrions': 1579, 'pourriez': 1580, 'partis': 1581, 'offre': 1582, 'jimagine': 1583, 'hasard': 1584, 'frappé': 1585, 'faits': 1586, 'examen': 1587, 'détails': 1588, 'dentendre': 1589, 'chef': 1590, 'vraie': 1591, 'tenu': 1592, 'silence': 1593, 'roi': 1594, 'put': 1595, 'portefeuille': 1596, 'pizza': 1597, 'piscine': 1598, 'parles': 1599, 'oiseaux': 1600, 'monter': 1601, 'mise': 1602, 'matinée': 1603, 'matins': 1604, 'manquer': 1605, 'jessaie': 1606, 'incroyable': 1607, 'grandes': 1608, 'espoir': 1609, 'donnerai': 1610, 'dommage': 1611, 'derniers': 1612, 'dapprendre': 1613, 'commis': 1614, 'cancer': 1615, 'aujourdhui\\xa0': 1616, 'ailles': 1617, 'absent': 1618, 'état': 1619, 'écouté': 1620, 'vôtre': 1621, 'veuilles': 1622, 'valeur': 1623, 'résultats': 1624, 'riz': 1625, 'pensent': 1626, 'paraît': 1627, 'met': 1628, 'mariés': 1629, 'embrassé': 1630, 'dure': 1631, 'devraient': 1632, 'circulation': 1633, 'audessus': 1634, 'aimeraistu': 1635, 'aille': 1636, 'âgé': 1637, 'veste': 1638, 'sang': 1639, 'rentre': 1640, 'plu': 1641, 'ontils': 1642, 'nestu': 1643, 'meilleurs': 1644, 'marrant': 1645, 'jaurai': 1646, 'force': 1647, 'entreprise': 1648, 'difficultés': 1649, 'devais': 1650, 'chaude': 1651, 'bagages': 1652, 'voit': 1653, 'vingt': 1654, 'tombe': 1655, 'timide': 1656, 'sœurs': 1657, 'sel': 1658, 'saches': 1659, 'quest': 1660, 'pousser': 1661, 'offert': 1662, 'mentretenir': 1663, 'léglise': 1664, 'lenfant': 1665, 'laffaire': 1666, 'intéressée': 1667, 'faible': 1668, 'explication': 1669, 'connaisse': 1670, 'arrivés': 1671, 'abandonné': 1672, 'élevé': 1673, 'sauver': 1674, 'raconter': 1675, 'prévois': 1676, 'prise': 1677, 'portable': 1678, 'policier': 1679, 'mastu': 1680, 'ici\\xa0': 1681, 'grave': 1682, 'excusezmoi': 1683, 'dépend': 1684, 'devint': 1685, 'crayon': 1686, 'conversation': 1687, 'connaissait': 1688, 'commandé': 1689, 'ceuxci': 1690, 'campagne': 1691, 'boisson': 1692, 'appelle': 1693, 'véritable': 1694, 'voleur': 1695, 'trouvait': 1696, 'toit': 1697, 'terrible': 1698, 'sétait': 1699, 'soldats': 1700, 'skier': 1701, 'récemment': 1702, 'réalité': 1703, 'profondément': 1704, 'pierre': 1705, 'peinture': 1706, 'passeport': 1707, 'occasion': 1708, 'navire': 1709, 'milieu': 1710, 'magnifique': 1711, 'japonaise': 1712, 'feuilles': 1713, 'fassiez': 1714, 'différentes': 1715, 'causé': 1716, 'caché': 1717, '10': 1718, 'échecs': 1719, 'vues': 1720, 'voie': 1721, 'vint': 1722, 'ticket': 1723, 'théorie': 1724, 'sortez': 1725, 'quen': 1726, 'préférée': 1727, 'prenons': 1728, 'merveilleux': 1729, 'lavez': 1730, 'lacquisition': 1731, 'gars': 1732, 'dabord': 1733, 'costume': 1734, 'brisé': 1735, 'bonheur': 1736, 'boit': 1737, 'auras': 1738, 'attend': 1739, 'assise': 1740, 'an': 1741, 'viendrai': 1742, 'traduire': 1743, 'tourne': 1744, 'tirer': 1745, 'sort': 1746, 'sert': 1747, 'résultat': 1748, 'parie': 1749, 'occupées': 1750, 'jusquau': 1751, 'jambes': 1752, 'incapable': 1753, 'idiote': 1754, 'horrible': 1755, 'eau': 1756, 'départ': 1757, 'camion': 1758, 'amoureuse': 1759, 'étudie': 1760, 'visiter': 1761, 'siège': 1762, 'retourne': 1763, 'rendit': 1764, 'poche': 1765, 'page': 1766, 'normal': 1767, 'nage': 1768, 'menteur': 1769, 'malheureusement': 1770, 'jétudie': 1771, 'jeux': 1772, 'instant': 1773, 'fautil': 1774, 'discussion': 1775, 'devenue': 1776, 'coupé': 1777, 'construit': 1778, 'clairement': 1779, 'bleu': 1780, 'blesser': 1781, 'biscuits': 1782, 'tourner': 1783, 'suffit': 1784, 'sale': 1785, 'réveiller': 1786, 'restée': 1787, 'rencontre': 1788, 'preuve': 1789, 'personnel': 1790, 'mempêcher': 1791, 'mangez': 1792, 'lâge': 1793, 'laisserai': 1794, 'grandi': 1795, 'golf': 1796, 'gentille': 1797, 'envers': 1798, 'doigt': 1799, 'différente': 1800, 'couru': 1801, 'cassée': 1802, 'bicyclette': 1803, 'aujourdhui\\u202f': 1804, 'atteint': 1805, 'alliez': 1806, 'accepter': 1807, 'épouse': 1808, 'zoo': 1809, 'terminée': 1810, 'tandis': 1811, 'rumeur': 1812, 'reconnu': 1813, 'pose': 1814, 'pièces': 1815, 'peindre': 1816, 'parlait': 1817, 'papa': 1818, 'nayez': 1819, 'midi': 1820, 'marre': 1821, 'impressionné': 1822, 'génial': 1823, 'entré': 1824, 'demandez': 1825, 'couvert': 1826, 'aurez': 1827, 'aton': 1828, 'atelle': 1829, 'éteint': 1830, 'treize': 1831, 'traiter': 1832, 'toucher': 1833, 'tenait': 1834, 'soudain': 1835, 'sortit': 1836, 'semblant': 1837, 'samedi': 1838, 'ri': 1839, 'reposer': 1840, 'plaît\\xa0': 1841, 'non\\xa0': 1842, 'mignonne': 1843, 'meurtre': 1844, 'maman': 1845, 'maisons': 1846, 'londres': 1847, 'jessaye': 1848, 'jaie': 1849, 'imaginé': 1850, 'humeur': 1851, 'fond': 1852, 'films': 1853, 'fil': 1854, 'fenêtres': 1855, 'effrayé': 1856, 'disputer': 1857, 'dirais': 1858, 'croistu': 1859, 'course': 1860, 'coupe': 1861, 'changement': 1862, 'bouger': 1863, 'abandonner': 1864, 'vousmême': 1865, 'vidéo': 1866, 'touche': 1867, 'tienne': 1868, 'tien': 1869, 'taije': 1870, 'système': 1871, 'sommet': 1872, 'resterai': 1873, 'repos': 1874, 'pantalon': 1875, 'née': 1876, 'noms': 1877, 'lavenir': 1878, 'jaloux': 1879, 'impliqué': 1880, 'ennuyeux': 1881, 'démissionner': 1882, 'dingue': 1883, 'difficiles': 1884, 'assister': 1885, 'allées': 1886, 'vert': 1887, 'trompé': 1888, 'sauter': 1889, 'sassit': 1890, 'résolu': 1891, 'puissions': 1892, 'projets': 1893, 'programme': 1894, 'pleuré': 1895, 'passetemps': 1896, 'obligée': 1897, 'notes': 1898, 'moteur': 1899, 'monté': 1900, 'montremoi': 1901, 'moi\\u202f': 1902, 'membre': 1903, 'mec': 1904, 'malheureux': 1905, 'làdessus': 1906, 'inutile': 1907, 'innocent': 1908, 'imaginer': 1909, 'fermée': 1910, 'faudra': 1911, 'fatigués': 1912, 'décida': 1913, 'dises': 1914, 'chère': 1915, 'ça\\u202f': 1916, 'souffre': 1917, 'souffle': 1918, 'songé': 1919, 'remis': 1920, 'prêté': 1921, 'populaire': 1922, 'plans': 1923, 'naurait': 1924, 'montrezmoi': 1925, 'mavezvous': 1926, 'loccasion': 1927, 'intelligente': 1928, 'gardez': 1929, 'garage': 1930, 'fortune': 1931, 'détendre': 1932, 'dépensé': 1933, 'courage': 1934, 'commun': 1935, 'avonsnous': 1936, 'arrivera': 1937, '30': 1938, 'voulaient': 1939, 'verrai': 1940, 'traversé': 1941, 'test': 1942, 'tatil': 1943, 'supermarché': 1944, 'répéter': 1945, 'retirer': 1946, 'regard': 1947, 'qu’il': 1948, 'proximité': 1949, 'population': 1950, 'mignon': 1951, 'mecs': 1952, 'laustralie': 1953, 'habitude': 1954, 'fermez': 1955, 'expliqué': 1956, 'doigts': 1957, 'disposons': 1958, 'disait': 1959, 'celleci': 1960, 'belles': 1961, 'ayons': 1962, 'accusé': 1963, 'étrangère': 1964, 'yens': 1965, 'viré': 1966, 'tentendre': 1967, 'sentis': 1968, 'sentait': 1969, 'roses': 1970, 'pratiquement': 1971, 'porté': 1972, 'planté': 1973, 'paris': 1974, 'oublier': 1975, 'offrir': 1976, 'mangent': 1977, 'lennemi': 1978, 'laissa': 1979, 'général': 1980, 'france': 1981, 'forêt': 1982, 'essayons': 1983, 'essaye': 1984, 'efforts': 1985, 'déjeuné': 1986, 'découvrir': 1987, 'concentrer': 1988, 'chanteur': 1989, 'cercle': 1990, 'bienvenue': 1991, 'étiezvous': 1992, 'vivant': 1993, 'veuille': 1994, 'venues': 1995, 'vase': 1996, 'urgent': 1997, 'totalement': 1998, 'tire': 1999, 'survenu': 2000, 'station': 2001, 'soif': 2002, 'régime': 2003, 'raisons': 2004, 'queue': 2005, 'promener': 2006, 'privé': 2007, 'petits': 2008, 'ordres': 2009, 'monte': 2010, 'létage': 2011, 'intention': 2012, 'informations': 2013, 'fume': 2014, 'folle': 2015, 'enseigne': 2016, 'dentiste': 2017, 'curieux': 2018, 'contrat': 2019, 'comportement': 2020, 'cessez': 2021, 'celuilà': 2022, 'canadien': 2023, 'aimez': 2024, 'vendu': 2025, 'utilisé': 2026, 'traite': 2027, 'supporte': 2028, 'soir\\xa0': 2029, 'sappelle': 2030, 'réponses': 2031, 'quant': 2032, 'présente': 2033, 'prudents': 2034, 'possibilité': 2035, 'peuton': 2036, 'patience': 2037, 'ouvre': 2038, 'oiseau': 2039, 'napprécie': 2040, 'mexcuser': 2041, 'ivre': 2042, 'improbable': 2043, 'imagination': 2044, 'hiver': 2045, 'flics': 2046, 'fiers': 2047, 'démarrer': 2048, 'crème': 2049, 'crise': 2050, 'continuez': 2051, 'cessé': 2052, 'cents': 2053, 'caisse': 2054, 'asseoir': 2055, 'appartient': 2056, 'étage': 2057, 'viennent': 2058, 'travaillent': 2059, 'tom\\u202f': 2060, 'terme': 2061, 'sérieuse': 2062, 'suspect': 2063, 'surlechamp': 2064, 'sortes': 2065, 'refusa': 2066, 'puisses': 2067, 'prenezvous': 2068, 'prendstu': 2069, 'poème': 2070, 'parmi': 2071, 'pareille': 2072, 'paie': 2073, 'new': 2074, 'lumières': 2075, 'lendroit': 2076, 'journaux': 2077, 'joueur': 2078, 'garer': 2079, 'gardé': 2080, 'fer': 2081, 'entier': 2082, 'enseigné': 2083, 'déçue': 2084, 'données': 2085, 'discuté': 2086, 'denfants': 2087, 'croyezvous': 2088, 'copain': 2089, 'convient': 2090, 'conscient': 2091, 'confectionné': 2092, 'bonjour': 2093, 'arriva': 2094, 'aliments': 2095, 'Étiezvous': 2096, 'voiture\\u202f': 2097, 'veniez': 2098, 'toilettes': 2099, 'timbres': 2100, 'tenez': 2101, 'sauvé': 2102, 'responsabilité': 2103, 'rendue': 2104, 'rencontrée': 2105, 'ravie': 2106, 'raisonnable': 2107, 'panne': 2108, 'occuper': 2109, 'n’est': 2110, 'mathématiques': 2111, 'masseoir': 2112, 'lorsquelle': 2113, 'jette': 2114, 'guère': 2115, 'grandchose': 2116, 'dœil': 2117, 'dors': 2118, 'dhier': 2119, 'critique': 2120, 'courrier': 2121, 'couramment': 2122, 'contrôler': 2123, 'blanche': 2124, 'avenir': 2125, 'aussitôt': 2126, 'appel': 2127, 'allonsnous': 2128, 'étrangers': 2129, 'étaistu': 2130, 'vivons': 2131, 'vendredi': 2132, 'talent': 2133, 'spectacle': 2134, 'sentie': 2135, 'sentais': 2136, 'riches': 2137, 'reconnaissante': 2138, 'recevoir': 2139, 'questil': 2140, 'prouver': 2141, 'posa': 2142, 'perdit': 2143, 'passait': 2144, 'parfaite': 2145, 'nourrir': 2146, 'massurer': 2147, 'mappeler': 2148, 'léger': 2149, 'laissée': 2150, 'foot': 2151, 'fièvre': 2152, 'failli': 2153, 'excellent': 2154, 'déprimé': 2155, 'disposer': 2156, 'combat': 2157, 'chose\\u202f': 2158, 'ceuxlà': 2159, 'beurre': 2160, 'aveugle': 2161, 'artiste': 2162, 'animal': 2163, '14\\xa0h\\xa030': 2164, 'york': 2165, 'vaisselle': 2166, 'touché': 2167, 'servir': 2168, 'serviette': 2169, 'sapin': 2170, 'rater': 2171, 'puissiez': 2172, 'promesses': 2173, 'pouvions': 2174, 'plaît\\u202f': 2175, 'petites': 2176, 'parvenir': 2177, 'partager': 2178, 'ontelles': 2179, 'nerveuse': 2180, 'nature': 2181, 'naie': 2182, 'médicaments': 2183, 'minuit': 2184, 'membres': 2185, 'marrêter': 2186, 'magazine': 2187, 'livre\\u202f': 2188, 'invitée': 2189, 'impatiente': 2190, 'haute': 2191, 'futur': 2192, 'fumée': 2193, 'fromage': 2194, 'faisle': 2195, 'déteindre': 2196, 'disposezvous': 2197, 'disponible': 2198, 'dimportance': 2199, 'dernières': 2200, 'dentrer': 2201, 'coûté': 2202, 'coffre': 2203, 'bête': 2204, 'beauté': 2205, 'article': 2206, 'amour': 2207, 'alla': 2208, 'éviter': 2209, 'étranger': 2210, 'énorme': 2211, 'visité': 2212, 'tremblement': 2213, 'tendance': 2214, 'spécial': 2215, 'souris': 2216, 'sagitil': 2217, 'quitte': 2218, 'présenté': 2219, 'présence': 2220, 'pressé': 2221, 'poissons': 2222, 'partit': 2223, 'oreilles': 2224, 'mavoir': 2225, 'linstituteur': 2226, 'lhiver': 2227, 'lavais': 2228, 'hôpital': 2229, 'héros': 2230, 'habituée': 2231, 'fous': 2232, 'ferions': 2233, 'exemple': 2234, 'escaliers': 2235, 'efficace': 2236, 'déranger': 2237, 'descendre': 2238, 'crédit': 2239, 'contrarié': 2240, 'continua': 2241, 'connaissais': 2242, 'compliqué': 2243, 'atteindre': 2244, 'arrière': 2245, 'amérique': 2246, 'ailleurs': 2247, 'voisin': 2248, 'tomba': 2249, 'température': 2250, 'suffisant': 2251, 'souhaiterais': 2252, 'soir\\u202f': 2253, 'saison': 2254, 'restes': 2255, 'resta': 2256, 'rejoindre': 2257, 'recommandé': 2258, 'période': 2259, 'pouviez': 2260, 'plat': 2261, 'plantes': 2262, 'lâche': 2263, 'longs': 2264, 'lisez': 2265, 'jespérais': 2266, 'jackson': 2267, 'grâce': 2268, 'garé': 2269, 'franchement': 2270, 'environs': 2271, 'disposé': 2272, 'difficulté': 2273, 'deuxième': 2274, 'célibataire': 2275, 'cuisinier': 2276, 'crier': 2277, 'coups': 2278, 'conduite': 2279, 'chine': 2280, 'chatte': 2281, 'blessés': 2282, 'apprécie': 2283, 'égard': 2284, 'Étaitce': 2285, 'voudrait': 2286, 'vend': 2287, 'traité': 2288, 'toi\\xa0': 2289, 'suivant': 2290, 'servi': 2291, 'sentezvous': 2292, 'sage': 2293, 'réalisé': 2294, 'rompu': 2295, 'respect': 2296, 'rencontrées': 2297, 'prudentes': 2298, 'points': 2299, 'n’ai': 2300, 'nuages': 2301, 'monnaie': 2302, 'moccuper': 2303, 'maison\\xa0': 2304, 'lépoque': 2305, 'loyer': 2306, 'lourd': 2307, 'lavé': 2308, 'laurais': 2309, 'jattendais': 2310, 'histoires': 2311, 'gentillesse': 2312, 'froide': 2313, 'faire\\u202f': 2314, 'entendue': 2315, 'décédé': 2316, 'danniversaire': 2317, 'dame': 2318, 'comprenez': 2319, 'commençons': 2320, 'colline': 2321, 'chaussettes': 2322, 'beaux': 2323, 'base': 2324, 'appellemoi': 2325, 'aidemoi': 2326, 'éteindre': 2327, 'époque': 2328, 'écrivain': 2329, 'vienstu': 2330, 'vieilles': 2331, 'unique': 2332, 'telles': 2333, 'somme': 2334, 'sandwich': 2335, 'sachiez': 2336, 'ressens': 2337, 'quantité': 2338, 'progrès': 2339, 'poches': 2340, 'plaint': 2341, 'ouvrit': 2342, 'montra': 2343, 'mondiale': 2344, 'mindiquer': 2345, 'lexpérience': 2346, 'lexpliquer': 2347, 'larrêt': 2348, 'faite': 2349, 'facture': 2350, 'enseigner': 2351, 'dérangé': 2352, 'délicieux': 2353, 'doué': 2354, 'disiez': 2355, 'deviner': 2356, 'connaissez': 2357, 'club': 2358, 'clients': 2359, 'chauffeur': 2360, 'cela\\u202f': 2361, 'brouillard': 2362, 'billets': 2363, 'aimer': 2364, 'éveillé': 2365, 'épuisé': 2366, 'Écoute': 2367, 'vous\\xa0': 2368, 'tapis': 2369, 'sud': 2370, 'souper': 2371, 'site': 2372, 'senstu': 2373, 'savaistu': 2374, 'saoul': 2375, 'sable': 2376, 'réveillée': 2377, 'rouges': 2378, 'rentrée': 2379, 'remonter': 2380, 'quinze': 2381, 'préférable': 2382, 'pression': 2383, 'pourrai': 2384, 'oublie': 2385, 'navions': 2386, 'monsieur': 2387, 'meilleures': 2388, 'manges': 2389, 'limite': 2390, 'larticle': 2391, 'laimait': 2392, 'laddition': 2393, 'frapper': 2394, 'frappa': 2395, 'fis': 2396, 'europe': 2397, 'embrasser': 2398, 'différents': 2399, 'danse': 2400, 'courut': 2401, 'comporte': 2402, 'complet': 2403, 'commettre': 2404, 'chinois': 2405, 'ceinture': 2406, 'capables': 2407, 'auquel': 2408, 'auprès': 2409, 'augmentation': 2410, 'armes': 2411, 'amusée': 2412, 'air': 2413, 'accord': 2414, 'voyez': 2415, 'volontaire': 2416, 'valises': 2417, 'usage': 2418, 'tâche': 2419, 'tenue': 2420, 'tente': 2421, 'stupides': 2422, 'sieste': 2423, 'ressenti': 2424, 'quartier': 2425, 'pratique': 2426, 'perdue': 2427, 'perds': 2428, 'partons': 2429, 'paresseux': 2430, 'nu': 2431, 'mode': 2432, 'malin': 2433, 'malgré': 2434, 'longues': 2435, 'liquide': 2436, 'joyeux': 2437, 'jouait': 2438, 'information': 2439, 'hé': 2440, 'horreur': 2441, 'feriezvous': 2442, 'enseignante': 2443, 'enfance': 2444, 'empêcher': 2445, 'dordinaire': 2446, 'dhumeur': 2447, 'daprès': 2448, 'damérique': 2449, 'chanté': 2450, 'chances': 2451, 'cesser': 2452, 'capitale': 2453, 'cache': 2454, 'bleue': 2455, 'barbe': 2456, 'banc': 2457, 'autorisés': 2458, 'attendons': 2459, 'allions': 2460, '5': 2461, 'étudiante': 2462, 'épousé': 2463, 'échappé': 2464, 'web': 2465, 'vivait': 2466, 'uns': 2467, 'trou': 2468, 'tromper': 2469, 'troisième': 2470, 'toile': 2471, 'tiroir': 2472, 'théâtre': 2473, 'sourit': 2474, 'saitil': 2475, 'risques': 2476, 'ridicule': 2477, 'retrouver': 2478, 'rendrai': 2479, 'rende': 2480, 'rempli': 2481, 'refuser': 2482, 'puni': 2483, 'produits': 2484, 'possibilités': 2485, 'piquenique': 2486, 'passa': 2487, 'parvenu': 2488, 'participer': 2489, 'paquet': 2490, 'nord': 2491, 'mêmes': 2492, 'médecins': 2493, 'mortes': 2494, 'momentlà': 2495, 'maison\\u202f': 2496, 'létagère': 2497, 'louer': 2498, 'lincendie': 2499, 'leva': 2500, 'lecture': 2501, 'jouent': 2502, 'jhabite': 2503, 'impression': 2504, 'formulaire': 2505, 'fatiguées': 2506, 'familier': 2507, 'excuse': 2508, 'examens': 2509, 'envoyer': 2510, 'durgence': 2511, 'descends': 2512, 'daccepter': 2513, 'courte': 2514, 'connaissent': 2515, 'congés': 2516, 'conférence': 2517, 'conduis': 2518, 'client': 2519, 'château': 2520, 'baisser': 2521, 'baisse': 2522, 'bague': 2523, 'aurons': 2524, 'aurions': 2525, 'approche': 2526, 'égal': 2527, 'trouvée': 2528, 'trouva': 2529, 'tante': 2530, 'sonne': 2531, 'solaire': 2532, 'semblent': 2533, 'saviez': 2534, 'satisfaite': 2535, 'romans': 2536, 'rit': 2537, 'remise': 2538, 'prévenu': 2539, 'prisonniers': 2540, 'preuves': 2541, 'poupée': 2542, 'poulet': 2543, 'pleure': 2544, 'plainte': 2545, 'persuader': 2546, 'parlezvous': 2547, 'parlestu': 2548, 'pardessus': 2549, 'noublierai': 2550, 'maire': 2551, 'maime': 2552, 'léconomie': 2553, 'loupé': 2554, 'leçon': 2555, 'lalcool': 2556, 'joli': 2557, 'impatience': 2558, 'hier\\u202f': 2559, 'génie': 2560, 'fréquence': 2561, 'fonctionner': 2562, 'feraistu': 2563, 'fautes': 2564, 'fassions': 2565, 'emprunté': 2566, 'distinguer': 2567, 'dire\\xa0': 2568, 'derreurs': 2569, 'demie': 2570, 'copie': 2571, 'cinquante': 2572, 'chose\\xa0': 2573, 'chevaux': 2574, 'chanceuse': 2575, 'canadienne': 2576, 'camp': 2577, 'bar': 2578, 'annulé': 2579, 'américain': 2580, 'aimons': 2581, 'voudriez': 2582, 'volonté': 2583, 'viennes': 2584, 'veille': 2585, 'travaillons': 2586, 'tinquiète': 2587, 'seriezvous': 2588, 'sauté': 2589, 'rôle': 2590, 'régler': 2591, 'rues': 2592, 'retourné': 2593, 'respecter': 2594, 'remède': 2595, 'relation': 2596, 'raconté': 2597, 'poster': 2598, 'passagers': 2599, 'parties': 2600, 'mordu': 2601, 'matière': 2602, 'lécart': 2603, 'lordinateur': 2604, 'lherbe': 2605, 'lest': 2606, 'jeté': 2607, 'hurler': 2608, 'four': 2609, 'fonctionné': 2610, 'détail': 2611, 'déménagé': 2612, 'découter': 2613, 'douvrir': 2614, 'diable': 2615, 'dappeler': 2616, 'consulter': 2617, 'considéré': 2618, 'censée': 2619, 'capitaine': 2620, 'bouge': 2621, 'blessées': 2622, 'blagues': 2623, 'arrivent': 2624, '100': 2625, 'échec': 2626, 'âgée': 2627, 'vérifié': 2628, 'soulagé': 2629, 'sonné': 2630, 'sils': 2631, 'serré': 2632, 'sarrêta': 2633, 'rêver': 2634, 'répondit': 2635, 'réduire': 2636, 'retiré': 2637, 'restés': 2638, 'remonte': 2639, 'pâle': 2640, 'proches': 2641, 'poussière': 2642, 'patronne': 2643, 'particulièrement': 2644, 'parte': 2645, 'nul': 2646, 'naturel': 2647, 'nallait': 2648, 'moque': 2649, 'miroir': 2650, 'meubles': 2651, 'mattends': 2652, 'lépouser': 2653, 'logique': 2654, 'liberté': 2655, 'lave': 2656, 'lart': 2657, 'lapprécie': 2658, 'lan': 2659, 'jus': 2660, 'jouons': 2661, 'joues': 2662, 'jalouse': 2663, 'hauteur': 2664, 'fruit': 2665, 'française': 2666, 'faismoi': 2667, 'déterminé': 2668, 'dépourvu': 2669, 'dénergie': 2670, 'dor': 2671, 'demiheure': 2672, 'contacter': 2673, 'constamment': 2674, 'connaissons': 2675, 'choisis': 2676, 'brûlé': 2677, 'avaitil': 2678, 'autorisée': 2679, 'asseyezvous': 2680, 'amené': 2681, 'allumé': 2682, 'allonsy': 2683, 'aimerait': 2684, 'agir': 2685, '2013': 2686, 'égoïste': 2687, 'écoute': 2688, 'Écrivez': 2689, 'voté': 2690, 'venezvous': 2691, 'téléphoné': 2692, 'toi\\u202f': 2693, 'survécu': 2694, 'soudainement': 2695, 'sentit': 2696, 'secours': 2697, 'réparé': 2698, 'reviendra': 2699, 'quotidiennement': 2700, 'pénétrer': 2701, 'profonde': 2702, 'profond': 2703, 'portezvous': 2704, 'passetil': 2705, 'paroles': 2706, 'parla': 2707, 'ouvrez': 2708, 'ouvrage': 2709, 'neiger': 2710, 'natif': 2711, 'nallons': 2712, 'méchant': 2713, 'miracle': 2714, 'mentionné': 2715, 'malentendu': 2716, 'lœil': 2717, 'larmée': 2718, 'japprends': 2719, 'jaille': 2720, 'invitation': 2721, 'internet': 2722, 'instructions': 2723, 'instituteur': 2724, 'importantes': 2725, 'grimper': 2726, 'fleuve': 2727, 'firent': 2728, 'entra': 2729, 'dété': 2730, 'dépêchetoi': 2731, 'dépêcher': 2732, 'directement': 2733, 'diplômé': 2734, 'dexpérience': 2735, 'cour': 2736, 'correct': 2737, 'choqué': 2738, 'choc': 2739, 'charger': 2740, 'changements': 2741, 'chandail': 2742, 'centre': 2743, 'basse': 2744, 'assurance': 2745, 'arrêt': 2746, 'apparence': 2747, 'actuelle': 2748, '20': 2749, 'évident': 2750, 'étrangères': 2751, 'voiture\\xa0': 2752, 'tués': 2753, 'traitement': 2754, 'tenezvous': 2755, 'sympathique': 2756, 'survivre': 2757, 'sortons': 2758, 'ski': 2759, 'signé': 2760, 'satisfaits': 2761, 'récupérer': 2762, 'revienne': 2763, 'respirer': 2764, 'qualité': 2765, 'prévenir': 2766, 'problème\\xa0': 2767, 'possession': 2768, 'portestu': 2769, 'planète': 2770, 'peau': 2771, 'passez': 2772, 'onze': 2773, 'objectif': 2774, 'naiment': 2775, 'moral': 2776, 'merveilleuse': 2777, 'manières': 2778, 'lîle': 2779, 'lénergie': 2780, 'lignes': 2781, 'lexercice': 2782, 'lavait': 2783, 'juge': 2784, 'jaune': 2785, 'inquiète': 2786, 'injuste': 2787, 'humains': 2788, 'gants': 2789, 'fûmes': 2790, 'furieux': 2791, 'fleur': 2792, 'employés': 2793, 'emmener': 2794, 'dîné': 2795, 'dà': 2796, 'directeur': 2797, 'dessert': 2798, 'corriger': 2799, 'convaincue': 2800, 'contrariée': 2801, 'conseille': 2802, 'conditions': 2803, 'concours': 2804, 'clef': 2805, 'choisissez': 2806, 'cellelà': 2807, 'boston\\xa0': 2808, 'bienvenu': 2809, 'allergique': 2810, 'ajouter': 2811, 'admettre': 2812, '3': 2813, 'étoiles': 2814, 'Étaistu': 2815, 'villes': 2816, 'trouvent': 2817, 'taux': 2818, 'tarte': 2819, 'superbe': 2820, 'soi': 2821, 'situe': 2822, 'seconde\\xa0': 2823, 'scientifique': 2824, 'savaient': 2825, 'réfléchi': 2826, 'retenir': 2827, 'remercie': 2828, 'qua': 2829, 'pêche': 2830, 'prénom': 2831, 'perplexe': 2832, 'ordinateurs': 2833, 'niveau': 2834, 'naissance': 2835, 'mètres': 2836, 'menteuse': 2837, 'ment': 2838, 'magasins': 2839, 'lenvers': 2840, 'lacheter': 2841, 'interdit': 2842, 'informé': 2843, 'illégal': 2844, 'gueule': 2845, 'file': 2846, 'excellente': 2847, 'engagé': 2848, 'détruit': 2849, 'doté': 2850, 'dinformation': 2851, 'devenus': 2852, 'demeure': 2853, 'demain\\u202f': 2854, 'demain\\xa0': 2855, 'daujourdhui': 2856, 'crié': 2857, 'confortable': 2858, 'conducteur': 2859, 'cherchais': 2860, 'cellesci': 2861, 'celles': 2862, 'battu': 2863, 'baiser': 2864, 'arrivées': 2865, 'album': 2866, 'aimait': 2867, 'adorent': 2868, 'achats': 2869, 'accompli': 2870, 'études': 2871, 'virer': 2872, 'travail\\u202f': 2873, 'trahi': 2874, 'terriblement': 2875, 'tableaux': 2876, 'séjourné': 2877, 'supposée': 2878, 'supplémentaire': 2879, 'suggéré': 2880, 'signer': 2881, 'sensible': 2882, 'secrets': 2883, 'scène': 2884, 'rose': 2885, 'retira': 2886, 'restait': 2887, 'ressources': 2888, 'rendra': 2889, 'rencontres': 2890, 'poussé': 2891, 'poli': 2892, 'plaisanter': 2893, 'plafond': 2894, 'piège': 2895, 'pauvres': 2896, 'parlais': 2897, 'navoir': 2898, 'naimait': 2899, 'mourut': 2900, 'montrerai': 2901, 'millions': 2902, 'mensonges': 2903, 'manche': 2904, 'lourde': 2905, 'locuteur': 2906, 'ladresse': 2907, 'juger': 2908, 'jourlà': 2909, 'joie': 2910, 'jeus': 2911, 'jazz': 2912, 'inventé': 2913, 'incroyablement': 2914, 'gorge': 2915, 'gaz': 2916, 'fêtes': 2917, 'fâché': 2918, 'français\\u202f': 2919, 'forts': 2920, 'ferons': 2921, 'emporté': 2922, 'employé': 2923, 'effet': 2924, 'détudiants': 2925, 'déclaré': 2926, 'douzaine': 2927, 'domicile': 2928, 'coïncidence': 2929, 'contents': 2930, 'clôture': 2931, 'claire': 2932, 'cigarette': 2933, 'buvez': 2934, 'boissons': 2935, 'ballon': 2936, 'assiedstoi': 2937, 'asseyonsnous': 2938, 'appelée': 2939, 'amical': 2940, 'aimable': 2941, 'achète': 2942, 'accidents': 2943, 'étoile': 2944, 'élève': 2945, 'élu': 2946, 'voleurs': 2947, 'vente': 2948, 'venait': 2949, 'vaches': 2950, 'vache': 2951, 'téléphoner': 2952, 'tourné': 2953, 'tasseoir': 2954, 'soccuper': 2955, 'serpents': 2956, 'saviezvous': 2957, 'réfrigérateur': 2958, 'reviendrai': 2959, 'respecte': 2960, 'repris': 2961, 'regardait': 2962, 'rappelezvous': 2963, 'quavant': 2964, 'quasiment': 2965, 'publié': 2966, 'prévoistu': 2967, 'prisonnier': 2968, 'pourrionsnous': 2969, 'pneu': 2970, 'pleuve': 2971, 'pleuvait': 2972, 'pattes': 2973, 'passage': 2974, 'partiel': 2975, 'nauriez': 2976, 'montagnes': 2977, 'mission': 2978, 'marteau': 2979, 'mangea': 2980, 'locéan': 2981, 'lampe': 2982, 'justice': 2983, 'jure': 2984, 'jeta': 2985, 'irons': 2986, 'impressionnée': 2987, 'image': 2988, 'habillé': 2989, 'gentils': 2990, 'foudre': 2991, 'forces': 2992, 'forcer': 2993, 'façons': 2994, 'fausse': 2995, 'fantômes': 2996, 'faitesle': 2997, 'excité': 2998, 'exact': 2999, 'entrée': 3000, 'ennemi': 3001, 'embarrassé': 3002, 'd’un': 3003, 'désaccord': 3004, 'débrouiller': 3005, 'drogue': 3006, 'doux': 3007, 'document': 3008, 'disposais': 3009, 'dire\\u202f': 3010, 'diagnostiqué': 3011, 'dentrée': 3012, 'davance': 3013, 'dair': 3014, 'cérémonie': 3015, 'cuire': 3016, 'comprenezvous': 3017, 'comprendstu': 3018, 'cherchait': 3019, 'bol': 3020, 'blâmer': 3021, 'arriverait': 3022, 'annuler': 3023, 'amitié': 3024, 'île': 3025, 'énervé': 3026, 'élevés': 3027, 'voyagé': 3028, 'vote': 3029, 'violon': 3030, 'violence': 3031, 'veutil': 3032, 'urgence': 3033, 'trompe': 3034, 'tont': 3035, 'tappellerai': 3036, 'signification': 3037, 'science': 3038, 'rare': 3039, 'prononcer': 3040, 'prof': 3041, 'position': 3042, 'pleurait': 3043, 'partez': 3044, 'organisé': 3045, 'optimiste': 3046, 'obligés': 3047, 'objectifs': 3048, 'nétiez': 3049, 'nallez': 3050, 'météo': 3051, 'méthode': 3052, 'moyenne': 3053, 'moderne': 3054, 'miens': 3055, 'mangeons': 3056, 'légèrement': 3057, 'lorsquon': 3058, 'lorsquils': 3059, 'lodeur': 3060, 'linformation': 3061, 'lhumour': 3062, 'lexplosion': 3063, 'lentrée': 3064, 'lapin': 3065, 'jadmire': 3066, 'inviter': 3067, 'importance': 3068, 'humide': 3069, 'heureusement': 3070, 'grossier': 3071, 'genoux': 3072, 'fournir': 3073, 'forcée': 3074, 'fermés': 3075, 'fac': 3076, 'expert': 3077, 'examiner': 3078, 'emplettes': 3079, 'déprimée': 3080, 'durer': 3081, 'doutes': 3082, 'double': 3083, 'disais': 3084, 'devonsnous': 3085, 'danglais': 3086, 'c’était': 3087, 'côtés': 3088, 'cousin': 3089, 'courriel': 3090, 'courageux': 3091, 'couple': 3092, 'correcte': 3093, 'concerne': 3094, 'commande': 3095, 'coincé': 3096, 'chargé': 3097, 'charge': 3098, 'carottes': 3099, 'blessure': 3100, 'arrêtée': 3101, 'apporter': 3102, 'appelezmoi': 3103, 'amusés': 3104, 'achetée': 3105, 'éclata': 3106, '«\\xa0je': 3107, 'vous\\u202f': 3108, 'voter': 3109, 'victime': 3110, 'témoin': 3111, 'téléviseur': 3112, 'travail\\xa0': 3113, 'traduction': 3114, 'touchez': 3115, 'tickets': 3116, 'tenter': 3117, 'séchapper': 3118, 'supplémentaires': 3119, 'suggestion': 3120, 'sortis': 3121, 'similaire': 3122, 'seratil': 3123, 'sentez': 3124, 'sales': 3125, 'saisi': 3126, 'rêvé': 3127, 'réussite': 3128, 'retardé': 3129, 'remets': 3130, 'quêtesvous': 3131, 'quelconques': 3132, 'prévoyezvous': 3133, 'professionnel': 3134, 'portent': 3135, 'poignée': 3136, 'places': 3137, 'pensons': 3138, 'payée': 3139, 'partage': 3140, 'papiers': 3141, 'oh': 3142, 'nécessaires': 3143, 'noirs': 3144, 'naïf': 3145, 'narrivera': 3146, 'mène': 3147, 'mouchoir': 3148, 'maternelle': 3149, 'manques': 3150, 'lor': 3151, 'lincapacité': 3152, 'lamérique': 3153, 'jetez': 3154, 'jessayais': 3155, 'jattendrai': 3156, 'jadorerais': 3157, 'intéressés': 3158, 'ignoré': 3159, 'habillée': 3160, 'formidable': 3161, 'fois\\xa0': 3162, 'fiable': 3163, 'ferma': 3164, 'fantastique': 3165, 'faisaient': 3166, 'exprès': 3167, 'entière': 3168, 'entendit': 3169, 'enquête': 3170, 'ennemis': 3171, 'endormie': 3172, 'effectuer': 3173, 'détestent': 3174, 'désire': 3175, 'dépenser': 3176, 'déménager': 3177, 'défi': 3178, 'douze': 3179, 'dinconvénient': 3180, 'dessence': 3181, 'cuisinière': 3182, 'cruel': 3183, 'croyait': 3184, 'croient': 3185, 'coût': 3186, 'contraire': 3187, 'considère': 3188, 'condition': 3189, 'commencent': 3190, 'ciseaux': 3191, 'chargée': 3192, 'changent': 3193, 'centaines': 3194, 'causer': 3195, 'casser': 3196, 'balles': 3197, 'bal': 3198, 'avancer': 3199, 'attitude': 3200, 'assisté': 3201, 'ancien': 3202, 'amélioré': 3203, 'allais': 3204, 'aisé': 3205, 'adultes': 3206, 'admis': 3207, 'acheta': 3208, 'étaitil': 3209, 'épouser': 3210, 'épaules': 3211, 'écrite': 3212, 'Écoutez': 3213, 'verres': 3214, 'trouves': 3215, 'traduit': 3216, 'teint': 3217, 'sûrement': 3218, 'sucré': 3219, 'source': 3220, 'serions': 3221, 'secondes': 3222, 'salon': 3223, 'salade': 3224, 'régulièrement': 3225, 'remplir': 3226, 'rattraper': 3227, 'quhier': 3228, 'pétrole': 3229, 'publicité': 3230, 'prépare': 3231, 'promit': 3232, 'produise': 3233, 'privée': 3234, 'poule': 3235, 'pointe': 3236, 'pleuvra': 3237, 'plaintes': 3238, 'paye': 3239, 'pareil': 3240, 'noire': 3241, 'mintéresse': 3242, 'mars': 3243, 'maintenant\\u202f': 3244, 'libres': 3245, 'lespoir': 3246, 'large': 3247, 'lancer': 3248, 'lalarme': 3249, 'jécris': 3250, 'jappelle': 3251, 'jadmets': 3252, 'jachète': 3253, 'gratuit': 3254, 'gardes': 3255, 'fortes': 3256, 'feras': 3257, 'famille\\u202f': 3258, 'faisant': 3259, 'factures': 3260, 'exprimer': 3261, 'explications': 3262, 'effrayée': 3263, 'déterminer': 3264, 'dérangeraitil': 3265, 'documents': 3266, 'dispute': 3267, 'dira': 3268, 'dheures': 3269, 'devine': 3270, 'destin': 3271, 'descendez': 3272, 'delles': 3273, 'damour': 3274, 'côte': 3275, 'consacré': 3276, 'comité': 3277, 'collier': 3278, 'caméra': 3279, 'calmes': 3280, 'batterie': 3281, 'bataille': 3282, 'attendent': 3283, 'apportemoi': 3284, 'ambitieux': 3285, 'alentours': 3286, 'aient': 3287, 'aidezmoi': 3288, 'abattu': 3289, 'étudiantes': 3290, 'Écris': 3291, 'voisinage': 3292, 'voient': 3293, 'trouveras': 3294, 'tendu': 3295, 'tendit': 3296, 'tels': 3297, 'taiderai': 3298, 'surprises': 3299, 'surprendre': 3300, 'sure': 3301, 'style': 3302, 'souri': 3303, 'sonna': 3304, 'soigneusement': 3305, 'sincère': 3306, 'seau': 3307, 'sasseoir': 3308, 'sain': 3309, 'sagissait': 3310, 'réservation': 3311, 'retire': 3312, 'responsables': 3313, 'reprocher': 3314, 'présents': 3315, 'prises': 3316, 'poil': 3317, 'persuadé': 3318, 'personnellement': 3319, 'permet': 3320, 'perd': 3321, 'pelouse': 3322, 'passées': 3323, 'passera': 3324, 'panier': 3325, 'ours': 3326, 'osestu': 3327, 'os': 3328, 'ordre': 3329, 'mince': 3330, 'milliers': 3331, 'mavait': 3332, 'lêtre': 3333, 'léducation': 3334, 'lèvres': 3335, 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'siècle': 3623, 'sentent': 3624, 'secrète': 3625, 'sauta': 3626, 'saura': 3627, 'sauce': 3628, 'réputation': 3629, 'réfléchis': 3630, 'retenu': 3631, 'responsabilités': 3632, 'rappellestu': 3633, 'quittez': 3634, 'quitta': 3635, 'pétrin': 3636, 'pus': 3637, 'préparée': 3638, 'préoccupe': 3639, 'préféré\\u202f': 3640, 'prometsmoi': 3641, 'profondeur': 3642, 'professeurs': 3643, 'procurer': 3644, 'problème\\u202f': 3645, 'prennes': 3646, 'plastique': 3647, 'plait\\u202f': 3648, 'piqué': 3649, 'personnelle': 3650, 'permettezmoi': 3651, 'patiente': 3652, 'passons': 3653, 'passestu': 3654, 'partir\\u202f': 3655, 'pardonné': 3656, 'outils': 3657, 'oubliez': 3658, 'oranges': 3659, 'orange': 3660, 'odeur': 3661, 'obligées': 3662, 'objection': 3663, 'normale': 3664, 'nhésitez': 3665, 'naurions': 3666, 'métro': 3667, 'mystère': 3668, 'murs': 3669, 'moutons': 3670, 'moustiques': 3671, 'moto': 3672, 'morceaux': 3673, 'moment\\xa0': 3674, 'moblige': 3675, 'mises': 3676, 'million': 3677, 'mignons': 3678, 'mettezvous': 3679, 'maux': 3680, 'mappartient': 3681, 'manger\\u202f': 3682, 'maides': 3683, 'l’étoffe': 3684, 'longueur': 3685, 'linge': 3686, 'lhonnêteté': 3687, 'lemploi': 3688, 'laissons': 3689, 'journaliste': 3690, 'jouestu': 3691, 'institutrice': 3692, 'inhabituel': 3693, 'importants': 3694, 'ignore': 3695, 'gérant': 3696, 'généreux': 3697, 'gosse': 3698, 'fraîche': 3699, 'fourchette': 3700, 'faisiezvous': 3701, 'espérait': 3702, 'entraîné': 3703, 'empêché': 3704, 'emporter': 3705, 'effectué': 3706, 'désert': 3707, 'déroulé': 3708, 'dépit': 3709, 'déjeune': 3710, 'débrouillé': 3711, 'dutiliser': 3712, 'divorce': 3713, 'dit\\u202f': 3714, 'disposestu': 3715, 'deviné': 3716, 'deviennent': 3717, 'demploi': 3718, 'demandai': 3719, 'culture': 3720, 'couché': 3721, 'cou': 3722, 'consacrer': 3723, 'condamné': 3724, 'commerce': 3725, 'commencez': 3726, 'changea': 3727, 'censés': 3728, 'cardiaque': 3729, 'capacité': 3730, 'camper': 3731, 'bœuf': 3732, 'brièvement': 3733, 'avisé': 3734, 'aviezvous': 3735, 'autrement': 3736, 'auraient': 3737, 'appelez': 3738, 'américains': 3739, 'amusées': 3740, 'allumer': 3741, 'allemand': 3742, 'aimerais': 3743, 'actions': 3744, 'accomplir': 3745, 'accent': 3746, 'Éteins': 3747, 'zone': 3748, 'vélo\\u202f': 3749, 'voulions': 3750, 'volant': 3751, 'voisine': 3752, 'visiteurs': 3753, 'versé': 3754, 'vague': 3755, 'téléphonerai': 3756, 'trouvestu': 3757, 'trouverez': 3758, 'traversa': 3759, 'travaillez': 3760, 'travaillait': 3761, 'séjourner': 3762, 'surprenant': 3763, 'surpoids': 3764, 'steak': 3765, 'souvient': 3766, 'souhaitezvous': 3767, 'sonner': 3768, 'soldat': 3769, 'sinon': 3770, 'seul\\u202f': 3771, 'sarrêter': 3772, 'récolte': 3773, 'revient': 3774, 'retrouvé': 3775, 'ressembles': 3776, 'rendent': 3777, 'regardons': 3778, 'recommanda': 3779, 'quallezvous': 3780, 'pyjama': 3781, 'puisque': 3782, 'précieux': 3783, 'procéder': 3784, 'principale': 3785, 'preniez': 3786, 'pouvaient': 3787, 'portée': 3788, 'porte\\xa0': 3789, 'policiers': 3790, 'poker': 3791, 'pleura': 3792, 'plaisanterie': 3793, 'pauvreté': 3794, 'patients': 3795, 'parvenus': 3796, 'pardonner': 3797, 'oublia': 3798, 'or': 3799, 'nétions': 3800, 'nue': 3801, 'nessayez': 3802, 'nation': 3803, 'méritez': 3804, 'médecine': 3805, 'mécouter': 3806, 'montez': 3807, 'modèle': 3808, 'menu': 3809, 'maître': 3810, 'matin\\u202f': 3811, 'manquent': 3812, 'malheur': 3813, 'maimes': 3814, 'lécole\\u202f': 3815, 'lundi\\xa0': 3816, 'loreille': 3817, 'loiseau': 3818, 'lobscurité': 3819, 'linstitutrice': 3820, 'limmeuble': 3821, 'lessayer': 3822, 'lespagne': 3823, 'laisses': 3824, 'kilos': 3825, 'j’étais': 3826, 'juin': 3827, 'intéressées': 3828, 'impressionnant': 3829, 'ignorer': 3830, 'gêné': 3831, 'grossière': 3832, 'gentleman': 3833, 'garderai': 3834, 'galerie': 3835, 'fréquemment': 3836, 'fonds': 3837, 'foi': 3838, 'finie': 3839, 'fermement': 3840, 'fantôme': 3841, 'faille': 3842, 'excitant': 3843, 'entrés': 3844, 'entendues': 3845, 'enseignants': 3846, 'endroits': 3847, 'dérobé': 3848, 'déposer': 3849, 'dépense': 3850, 'démissionné': 3851, 'décisions': 3852, 'duré': 3853, 'droits': 3854, 'douce': 3855, 'dorment': 3856, 'dormait': 3857, 'disposent': 3858, 'diriezvous': 3859, 'digne': 3860, 'dexercice': 3861, 'devras': 3862, 'davion': 3863, 'daustralie': 3864, 'curieuse': 3865, 'cuit': 3866, 'cria': 3867, 'craint': 3868, 'cigarettes': 3869, 'chemises': 3870, 'changera': 3871, 'chambres': 3872, 'cependant': 3873, 'centreville': 3874, 'candidat': 3875, 'camarades': 3876, 'calmer': 3877, 'calendrier': 3878, 'branche': 3879, 'bondé': 3880, 'ayant': 3881, 'attaque': 3882, 'assurezvous': 3883, 'arrivestu': 3884, 'appellerai': 3885, 'aiderai': 3886, 'advenu': 3887, 'adolescents': 3888, 'absente': 3889, '50': 3890, 'œuf': 3891, 'émotions': 3892, 'Être': 3893, 'zéro': 3894, 'voyait': 3895, 'volontaires': 3896, 'visa': 3897, 'viendrais': 3898, 'vides': 3899, 'victimes': 3900, 'vertiges': 3901, 'verrouillé': 3902, 'verrouiller': 3903, 'vain': 3904, 'utilité': 3905, 'utilise': 3906, 'trouvais': 3907, 'trains': 3908, 'tontils': 3909, 'tira': 3910, 'timbre': 3911, 'tienstoi': 3912, 'termine': 3913, 'tentation': 3914, 'tavons': 3915, 'tatouage': 3916, 'séjour': 3917, 'supérieur': 3918, 'supposés': 3919, 'suivez': 3920, 'souvienstu': 3921, 'soutien': 3922, 'soulever': 3923, 'souffrir': 3924, 'sincèrement': 3925, 'servezvous': 3926, 'seraistu': 3927, 'sceptique': 3928, 'saute': 3929, 'satisfaire': 3930, 'saisir': 3931, 'réussira': 3932, 'réserver': 3933, 'réponds': 3934, 'réflexion': 3935, 'réaliser': 3936, 'rythme': 3937, 'rock': 3938, 'revenez': 3939, 'retourna': 3940, 'restons': 3941, 'regardais': 3942, 'recommencer': 3943, 'quart': 3944, 'quarante': 3945, 'quaije': 3946, 'pèse': 3947, 'père\\u202f': 3948, 'prévoit': 3949, 'prévenue': 3950, 'prétendu': 3951, 'propriétaire': 3952, 'promettre': 3953, 'profiter': 3954, 'principal': 3955, 'pressée': 3956, 'presser': 3957, 'prenne': 3958, 'poèmes': 3959, 'pousse': 3960, 'pourraitil': 3961, 'porc': 3962, 'poitrine': 3963, 'personnalité': 3964, 'payés': 3965, 'passent': 3966, 'parvenue': 3967, 'participé': 3968, 'paierai': 3969, 'ongles': 3970, 'oignons': 3971, 'n’a': 3972, 'nourri': 3973, 'normalement': 3974, 'nié': 3975, 'nier': 3976, 'nia': 3977, 'nhésite': 3978, 'natale': 3979, 'naije': 3980, 'naies': 3981, 'mérites': 3982, 'mordre': 3983, 'ministre': 3984, 'millionnaire': 3985, 'meurent': 3986, 'mendormir': 3987, 'mariées': 3988, 'manuel': 3989, 'malchanceux': 3990, 'maies': 3991, 'létang': 3992, 'là\\xa0': 3993, 'lesquelles': 3994, 'lavevaisselle': 3995, 'lautomne': 3996, 'lança': 3997, 'lanniversaire': 3998, 'jumeaux': 3999, 'joueurs': 4000, 'inévitable': 4001, 'intentions': 4002, 'impliquée': 4003, 'hypothèse': 4004, 'humain': 4005, 'honneur': 4006, 'hier\\xa0': 4007, 'habitudes': 4008, 'géniale': 4009, 'guide': 4010, 'grammaire': 4011, 'fâchée': 4012, 'frites': 4013, 'fierté': 4014, 'feuille': 4015, 'favori': 4016, 'fallut': 4017, 'faciles': 4018, 'entendus': 4019, 'enfui': 4020, 'endormis': 4021, 'déçus': 4022, 'déplacer': 4023, 'décevoir': 4024, 'débat': 4025, 'dotée': 4026, 'domaine': 4027, 'divorcer': 4028, 'disposée': 4029, 'disle': 4030, 'devra': 4031, 'dernière\\xa0': 4032, 'dent': 4033, 'dannées': 4034, 'dacquérir': 4035, 'dabandonner': 4036, 'criminel': 4037, 'courus': 4038, 'coulé': 4039, 'couleurs': 4040, 'cool': 4041, 'conserver': 4042, 'connait': 4043, 'conditionné': 4044, 'comparé': 4045, 'climat': 4046, 'cible': 4047, 'chèque': 4048, 'chiot': 4049, 'cendres': 4050, 'cd': 4051, 'café\\u202f': 4052, 'budget': 4053, 'bel': 4054, 'aîné': 4055, 'avril': 4056, 'attendit': 4057, 'assistant': 4058, 'assiette': 4059, 'américaine': 4060, 'aimée': 4061, 'ailes': 4062, 'agi': 4063, 'advienne': 4064, 'accompagné': 4065, '6': 4066, 'époux': 4067, 'électrique': 4068, 'écoulé': 4069, 'éclaté': 4070, 'échapper': 4071, 'échangé': 4072, 'vrais': 4073, 'volontiers': 4074, 'voies': 4075, 'vivants': 4076, 'vitre': 4077, 'veiller': 4078, 'tue': 4079, 'trouvetil': 4080, 'trempé': 4081, 'travaillezvous': 4082, 'toux': 4083, 'tourna': 4084, 'tomates': 4085, 'tint': 4086, 'tiendra': 4087, 'tension': 4088, 'talentueux': 4089, 'sèche': 4090, 'symptômes': 4091, 'surface': 4092, 'suivante': 4093, 'suicider': 4094, 'songer': 4095, 'solide': 4096, 'sien': 4097, 'satisfaites': 4098, 'révéla': 4099, 'réveille': 4100, 'répondez': 4101, 'réception': 4102, 'réaction': 4103, 'rompre': 4104, 'rideau': 4105, 'revenus': 4106, 'restiez': 4107, 'renvoyé': 4108, 'rendstu': 4109, 'rendsmoi': 4110, 'renard': 4111, 'remporté': 4112, 'rembourser': 4113, 'remarques': 4114, 'religion': 4115, 'rejoint': 4116, 'regardes': 4117, 'reconnais': 4118, 'ramener': 4119, 'raconte': 4120, 'questu': 4121, 'pull': 4122, 'présenta': 4123, 'préfères': 4124, 'propriété': 4125, 'proche\\u202f': 4126, 'prennent': 4127, 'pratiquer': 4128, 'pluies': 4129, 'plats': 4130, 'pilote': 4131, 'passezmoi': 4132, 'passerai': 4133, 'parler\\xa0': 4134, 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'dorthographe': 5610, 'distribué': 5611, 'distributeur': 5612, 'disputent': 5613, 'disons': 5614, 'dirigeons': 5615, 'dirige': 5616, 'diplômée': 5617, 'dinformations': 5618, 'diner': 5619, 'dincendie': 5620, 'diminué': 5621, 'dimanches': 5622, 'dimagination': 5623, 'desprit': 5624, 'dernièrement': 5625, 'dapplication': 5626, 'culpabilité': 5627, 'croira': 5628, 'coûte\\u202f': 5629, 'coûteux': 5630, 'couverte': 5631, 'courageuse': 5632, 'copies': 5633, 'cookies': 5634, 'convenait': 5635, 'convaincus': 5636, 'contrariés': 5637, 'continuons': 5638, 'continuent': 5639, 'conduise': 5640, 'compétent': 5641, 'comprennent': 5642, 'comprendriez': 5643, 'compatis': 5644, 'combattre': 5645, 'coiffure': 5646, 'clin': 5647, 'citron': 5648, 'cicatrice': 5649, 'chicago': 5650, 'cheville': 5651, 'charmante': 5652, 'chant': 5653, 'changez': 5654, 'chanceuses': 5655, 'cerisiers': 5656, 'capturé': 5657, 'candidats': 5658, 'calmé': 5659, 'calculé': 5660, 'caisses': 5661, 'cage': 5662, 'cadavre': 5663, 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5775, 'seize': 5776, 'saxophone': 5777, 'salua': 5778, 'sainte': 5779, 'sains': 5780, 'saine': 5781, 'saccordèrent': 5782, 'réveillés': 5783, 'réunion\\xa0': 5784, 'réfugiés': 5785, 'rédiger': 5786, 'réchauffer': 5787, 'ruiné': 5788, 'roue': 5789, 'rirent': 5790, 'reçue': 5791, 'revu': 5792, 'retournons': 5793, 'retenue': 5794, 'restent': 5795, 'restais': 5796, 'requérir': 5797, 'reproduise': 5798, 'repasser': 5799, 'rentrant': 5800, 'rendait': 5801, 'remporter': 5802, 'remercié': 5803, 'regretterez': 5804, 'regardemoi': 5805, 'referai': 5806, 'rats': 5807, 'rangée': 5808, 'raide': 5809, 'question\\u202f': 5810, 'questions\\u202f': 5811, 'quatorze': 5812, 'pénible': 5813, 'puissent': 5814, 'puissance': 5815, 'prévenus': 5816, 'prévenant': 5817, 'prédire': 5818, 'proposer': 5819, 'prononcé': 5820, 'prisonnières': 5821, 'pressa': 5822, 'prendrons': 5823, 'prenant': 5824, 'pouvoirs': 5825, 'poussent': 5826, 'pourvu': 5827, 'poursuivit': 5828, 'pourboire': 5829, 'potdevin': 5830, 'poste\\u202f': 5831, 'posséder': 5832, 'posons': 5833, 'portrait': 5834, 'porte\\u202f': 5835, 'porta': 5836, 'politicien': 5837, 'police\\xa0': 5838, 'poivre': 5839, 'poings': 5840, 'pluvieux': 5841, 'plus\\u202f': 5842, 'plier': 5843, 'placer': 5844, 'photographe': 5845, 'performance': 5846, 'pente': 5847, 'peintures': 5848, 'patiner': 5849, 'passés': 5850, 'passionnant': 5851, 'partent': 5852, 'partenaires': 5853, 'partenaire': 5854, 'partageons': 5855, 'parstu': 5856, 'parlemoi': 5857, 'parlant': 5858, 'pareilles': 5859, 'paraissait': 5860, 'paradis': 5861, 'papillons': 5862, 'paires': 5863, 'pages': 5864, 'ordures': 5865, 'ordonna': 5866, 'oiseau\\u202f': 5867, 'occasions': 5868, 'obéi': 5869, 'obtient': 5870, 'nécessairement': 5871, 'noël\\u202f': 5872, 'noyade': 5873, 'nommé': 5874, 'nira': 5875, 'nhabite': 5876, 'nerveusement': 5877, 'nausée': 5878, 'nationale': 5879, 'naimez': 5880, 'nager\\u202f': 5881, 'nabandonne': 5882, 'ménage': 5883, 'muscles': 5884, 'moururent': 5885, 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'halloween': 5940, 'gré': 5941, 'grimpé': 5942, 'grandir': 5943, 'gouttes': 5944, 'gorgée': 5945, 'gaspille': 5946, 'fîmes': 5947, 'funérailles': 5948, 'forment': 5949, 'forcées': 5950, 'folie': 5951, 'financièrement': 5952, 'fasse\\u202f': 5953, 'falaise': 5954, 'excuser': 5955, 'essuya': 5956, 'essayezvous': 5957, 'espéré': 5958, 'espèces': 5959, 'enverrai': 5960, 'enlevez': 5961, 'encouragé': 5962, 'emmenez': 5963, 'embrasse': 5964, 'embauché': 5965, 'eiffel': 5966, 'effets': 5967, 'détesté': 5968, 'détaillée': 5969, 'déserte': 5970, 'dérangée': 5971, 'dépêches': 5972, 'dépassé': 5973, 'département': 5974, 'défendre': 5975, 'décrit': 5976, 'déclenché': 5977, 'drôles': 5978, 'drogué': 5979, 'drogues': 5980, 'douleur\\xa0': 5981, 'douleurs': 5982, 'dit\\xa0': 5983, 'disposés': 5984, 'disposait': 5985, 'dirigea': 5986, 'directe': 5987, 'diplomate': 5988, 'dinquiétude': 5989, 'difficilement': 5990, 'dici\\xa0': 5991, 'demandons': 5992, 'demandent': 5993, 'dauphin': 5994, 'dattente': 5995, 'darmes': 5996, 'darme': 5997, 'dangereuses': 5998, 'dambition': 5999, 'crâne': 6000, 'cruelle': 6001, 'crue': 6002, 'critiques': 6003, 'criminels': 6004, 'cousins': 6005, 'considérée': 6006, 'consciente': 6007, 'connus': 6008, 'connexion': 6009, 'confronté': 6010, 'confiture': 6011, 'conduisez': 6012, 'conduire\\xa0': 6013, 'conclusions': 6014, 'comptezvous': 6015, 'comptez': 6016, 'comprenons': 6017, 'comprenait': 6018, 'composé': 6019, 'comporté': 6020, 'communiquer': 6021, 'communication': 6022, 'commet': 6023, 'coiffeur': 6024, 'chien\\xa0': 6025, 'chienne': 6026, 'cherchons': 6027, 'chantes': 6028, 'chacune': 6029, 'ceci\\u202f': 6030, 'casque': 6031, 'canon': 6032, 'campus': 6033, 'calmement': 6034, 'californie': 6035, 'cahier': 6036, 'cacahuètes': 6037, 'brisée': 6038, 'brise': 6039, 'bref': 6040, 'bowling': 6041, 'bouchon': 6042, 'bizarrement': 6043, 'bien\\xa0': 6044, 'bienvenues': 6045, 'bible': 6046, 'bat': 6047, 'bandes': 6048, 'balance': 6049, 'baguettes': 6050, 'automne': 6051, 'auront': 6052, 'attentive': 6053, 'assuré': 6054, 'arrêtonsnous': 6055, 'arrivez': 6056, 'arrangé': 6057, 'apparences': 6058, 'annoncer': 6059, 'angleterre': 6060, 'anglaise': 6061, 'anciennes': 6062, 'amusezvous': 6063, 'amicaux': 6064, 'amicale': 6065, 'ambition': 6066, 'alluma': 6067, 'alimentaires': 6068, 'alentour': 6069, 'agis': 6070, 'affecté': 6071, 'advenir': 6072, 'action': 6073, 'acquis': 6074, 'accusée': 6075, 'abeilles': 6076, 'abeille': 6077, '9': 6078, '2h30': 6079, '25': 6080, '15': 6081, 'étudies': 6082, 'éternellement': 6083, 'éteintes': 6084, 'étasunien': 6085, 'épicée': 6086, 'éloignés': 6087, 'écrive': 6088, 'éclater': 6089, 'Ôtez': 6090, 'washington': 6091, 'vrai\\u202f': 6092, 'voyant': 6093, 'voudront': 6094, 'voir\\u202f': 6095, 'vivez': 6096, 'visiblement': 6097, 'ville\\u202f': 6098, 'viendront': 6099, 'viendrastu': 6100, 'veuxtu\\u202f': 6101, 'utilisent': 6102, 'tuerai': 6103, 'tube': 6104, 'trouvées': 6105, 'trouvions': 6106, 'tribunal': 6107, 'tranche': 6108, 'traitez': 6109, 'traitent': 6110, 'tragédie': 6111, 'traduisez': 6112, 'tours': 6113, 'tigre': 6114, 'texto': 6115, 'texpliquer': 6116, 'tester': 6117, 'tentends': 6118, 'tendre': 6119, 'taxes': 6120, 'tattendais': 6121, 'tassure': 6122, 'tartes': 6123, 'tapprendre': 6124, 'taidera': 6125, 'séduisante': 6126, 'sécheresse': 6127, 'symbole': 6128, 'suspecte': 6129, 'sushis': 6130, 'susceptible': 6131, 'surprend': 6132, 'suivrait': 6133, 'suggestions': 6134, 'spécialité': 6135, 'soûl': 6136, 'souvienne': 6137, 'soulagée': 6138, 'souhaitais': 6139, 'soudaine': 6140, 'sorties': 6141, 'située': 6142, 'signez': 6143, 'shopping': 6144, 'shakespeare': 6145, 'sestelle': 6146, 'seraitil': 6147, 'sembrasser': 6148, 'sein': 6149, 'scolaires': 6150, 'scie': 6151, 'sauve': 6152, 'sauvages': 6153, 'sattendre': 6154, 'sarcastique': 6155, 'sanglots': 6156, 'sangfroid': 6157, 'saméliorer': 6158, 'samuse': 6159, 'sacré': 6160, 'révolution': 6161, 'réussit': 6162, 'répond': 6163, 'récompense': 6164, 'rugby': 6165, 'routes': 6166, 'rigole': 6167, 'respire': 6168, 'requise': 6169, 'requiers': 6170, 'rentres': 6171, 'renouveler': 6172, 'rencontrez': 6173, 'remplis': 6174, 'remit': 6175, 'religieux': 6176, 'regardiez': 6177, 'redémarrer': 6178, 'rasé': 6179, 'rassembla': 6180, 'rase': 6181, 'rappelletoi': 6182, 'ramène': 6183, 'ramassé': 6184, 'racontes': 6185, 'raccourci': 6186, 'questions\\xa0': 6187, 'quavonsnous': 6188, 'quami': 6189, 'qualifié': 6190, 'puissante': 6191, 'préserver': 6192, 'préparées': 6193, 'préjugés': 6194, 'préféré\\xa0': 6195, 'préférés': 6196, 'prédit': 6197, 'précédent': 6198, 'précipité': 6199, 'provisions': 6200, 'provient': 6201, 'progressivement': 6202, 'procédure': 6203, 'procuré': 6204, 'prié': 6205, 'prie\\u202f': 6206, 'poêle': 6207, 'poupées': 6208, 'pouce': 6209, 'pleuvrait': 6210, 'pleins': 6211, 'pleines': 6212, 'plaisantez': 6213, 'plains': 6214, 'peser': 6215, 'perturbé': 6216, 'pertes': 6217, 'personnelles': 6218, 'permettrai': 6219, 'permetsmoi': 6220, 'perdis': 6221, 'pensaient': 6222, 'passèrent': 6223, 'passant': 6224, 'passager': 6225, 'parvienne': 6226, 'parvenezvous': 6227, 'partions': 6228, 'parlezmoi': 6229, 'park': 6230, 'parcourut': 6231, 'paniqué': 6232, 'paient': 6233, 'pacifique': 6234, 'organisés': 6235, 'oreille': 6236, 'négative': 6237, 'noyée': 6238, 'nourrit': 6239, 'noté': 6240, 'nie': 6241, 'nerveuses': 6242, 'nayons': 6243, 'naura': 6244, 'naturellement': 6245, 'nattends': 6246, 'nations': 6247, 'natifs': 6248, 'nallezvous': 6249, 'naimais': 6250, 'naille': 6251, 'nachète': 6252, 'métaux': 6253, 'médaille': 6254, 'mécrire': 6255, 'méchapper': 6256, 'mécanicien': 6257, 'mutuellement': 6258, 'moustache': 6259, 'mouillés': 6260, 'moqués': 6261, 'montés': 6262, 'moccupe': 6263, 'mobilier': 6264, 'minimum': 6265, 'mineur': 6266, 'mettent': 6267, 'mené': 6268, 'mention': 6269, 'maïs': 6270, 'maviez': 6271, 'maurais': 6272, 'matériaux': 6273, 'mapprécient': 6274, 'mangera': 6275, 'main\\u202f': 6276, 'mains\\u202f': 6277, 'maimait': 6278, 'l’école': 6279, 'létranger\\u202f': 6280, 'lâchez': 6281, 'lâchemoi': 6282, 'loterie': 6283, 'los': 6284, 'lopéra': 6285, 'longtemps\\u202f': 6286, 'locuteurs': 6287, 'local': 6288, 'livraison': 6289, 'lisant': 6290, 'lincident': 6291, 'limité': 6292, 'lheure\\u202f': 6293, 'lespace': 6294, 'lenvie': 6295, 'lentilles': 6296, 'lencre': 6297, 'lembrasser': 6298, 'lavetoi': 6299, 'lavant': 6300, 'lattendre': 6301, 'laspirine': 6302, 'lannonce': 6303, 'laisseront': 6304, 'laisserais': 6305, 'lacets': 6306, 'lacet': 6307, 'jésus': 6308, 'jéprouve': 6309, 'jusquoù': 6310, 'journée\\u202f': 6311, 'journalistes': 6312, 'jouera': 6313, 'job': 6314, 'jinsiste': 6315, 'jarriverai': 6316, 'jaccepte': 6317, 'issue': 6318, 'installé': 6319, 'insisté': 6320, 'inquiéter': 6321, 'indécis': 6322, 'impressionnées': 6323, 'impliquées': 6324, 'imperméable': 6325, 'images': 6326, 'horrifié': 6327, 'hilarant': 6328, 'hideux': 6329, 'habile': 6330, 'glissant': 6331, 'gel': 6332, 'gaspillez': 6333, 'gardele': 6334, 'gaffe': 6335, 'féliciter': 6336, 'fusillade': 6337, 'furieuse': 6338, 'fumait': 6339, 'fou\\u202f': 6340, 'foutu': 6341, 'fourrure': 6342, 'fournit': 6343, 'formation': 6344, 'fonction': 6345, 'foirer': 6346, 'flûte': 6347, 'finissonsen': 6348, 'figuré': 6349, 'facteur': 6350, 'exprimé': 6351, 'excitée': 6352, 'exagérer': 6353, 'essayes': 6354, 'entendez': 6355, 'emploi\\u202f': 6356, 'embrassa': 6357, 'd’être': 6358, 'déçues': 6359, 'détonations': 6360, 'détoiles': 6361, 'détendue': 6362, 'désespérément': 6363, 'dénué': 6364, 'déménage': 6365, 'déguisé': 6366, 'défis': 6367, 'découvre': 6368, 'décor': 6369, 'décollé': 6370, 'décoller': 6371, 'débattre': 6372, 'durent': 6373, 'dresser': 6374, 'draguer': 6375, 'dorénavant': 6376, 'dormir\\xa0': 6377, 'donneznous': 6378, 'donnera': 6379, 'dobjection': 6380, 'ditil': 6381, 'disposées': 6382, 'disposions': 6383, 'discute': 6384, 'diront': 6385, 'dintimité': 6386, 'dinnombrables': 6387, 'deviez': 6388, 'devaient': 6389, 'dessinées': 6390, 'denseigner': 6391, 'demitour': 6392, 'dami': 6393, 'dames': 6394, 'dadresse': 6395, 'dachever': 6396, 'célibataires': 6397, 'cultive': 6398, 'cueilli': 6399, 'créer': 6400, 'créa': 6401, 'crève': 6402, 'criait': 6403, 'creuse': 6404, 'coûts': 6405, 'couvertures': 6406, 'couverts': 6407, 'courtes': 6408, 'coton': 6409, 'corrigé': 6410, 'copains': 6411, 'conçu': 6412, 'convenable': 6413, 'contribuer': 6414, 'contentezvous': 6415, 'contenir': 6416, 'constitue': 6417, 'consomme': 6418, 'conseiller': 6419, 'connaisses': 6420, 'congrès': 6421, 'confirmé': 6422, 'confier': 6423, 'confiante': 6424, 'compétences': 6425, 'compteur': 6426, 'complète': 6427, 'complets': 6428, 'communauté': 6429, 'colis': 6430, 'cogné': 6431, 'coffrefort': 6432, 'cochons': 6433, 'clarifier': 6434, 'cinquième': 6435, 'chères': 6436, 'chiffres': 6437, 'changes': 6438, 'chambre\\xa0': 6439, 'chair': 6440, 'cessa': 6441, 'causée': 6442, 'cassezvous': 6443, 'casier': 6444, 'carton': 6445, 'café\\xa0': 6446, 'brûlée': 6447, 'brille': 6448, 'boucle': 6449, 'biscuit': 6450, 'bio': 6451, 'bicyclette\\u202f': 6452, 'bateaux': 6453, 'barrière': 6454, 'baleines': 6455, 'avéré': 6456, 'avoué': 6457, 'avancé': 6458, 'auxquelles': 6459, 'autorité': 6460, 'auriezvous': 6461, 'attiré': 6462, 'attendstu': 6463, 'attendezvous': 6464, 'astucieux': 6465, 'assiettes': 6466, 'articles': 6467, 'arraché': 6468, 'appétit': 6469, 'appuyez': 6470, 'appuya': 6471, 'apprécions': 6472, 'apportez': 6473, 'apparut': 6474, 'apparition': 6475, 'américaines': 6476, 'allergie': 6477, 'alimentaire': 6478, 'alcoolique': 6479, 'ajouté': 6480, 'ajoutez': 6481, 'ajoute': 6482, 'aident': 6483, 'affreux': 6484, 'affreusement': 6485, 'adressée': 6486, 'acquisition': 6487, 'achetées': 6488, 'accepta': 6489, 'aboie': 6490, '300': 6491, 'ôter': 6492, 'évanoui': 6493, 'évacuer': 6494, 'étroite': 6495, 'étaientelles': 6496, 'érigé': 6497, 'équipes': 6498, 'épuisés': 6499, 'éloignée': 6500, 'écoutait': 6501, 'école\\u202f': 6502, 'échanger': 6503, 'Étudiestu': 6504, 'vêtue': 6505, 'vérifiez': 6506, 'vraiment\\u202f': 6507, 'voulezvous\\u202f': 6508, 'vocation': 6509, 'vocabulaire': 6510, 'vivement': 6511, 'vire': 6512, 'vinrent': 6513, 'vingtdeux': 6514, 'vie\\u202f': 6515, 'viendrez': 6516, 'verts': 6517, 'verrouillez': 6518, 'vendue': 6519, 'vendezvous': 6520, 'venant': 6521, 'veillé': 6522, 'valent': 6523, 'utilisée': 6524, 'urgente': 6525, 't’as': 6526, 'tépouser': 6527, 'téléphone\\xa0': 6528, 'técouter': 6529, 'tunnel': 6530, 'tuez': 6531, 'tueront': 6532, 'tuera': 6533, 'trousses': 6534, 'trottoir': 6535, 'tricote': 6536, 'travaux': 6537, 'transforme': 6538, 'tranquillement': 6539, 'traites': 6540, 'trait': 6541, 'traces': 6542, 'torche': 6543, 'tonnes': 6544, 'tombèrent': 6545, 'tombant': 6546, 'tolérer': 6547, 'tiennes': 6548, 'terrifiant': 6549, 'terres': 6550, 'tentretenir': 6551, 'tennuyer': 6552, 'tennis\\u202f': 6553, 'tenaient': 6554, 'tempslà': 6555, 'technique': 6556, 'taxi\\xa0': 6557, 'tard\\xa0': 6558, 'tapé': 6559, 'tappartient': 6560, 'tantinet': 6561, 'talentueuse': 6562, 'sêtre': 6563, 'séparément': 6564, 'sut': 6565, 'supposées': 6566, 'supplia': 6567, 'strictement': 6568, 'stade': 6569, 'souvent\\xa0': 6570, 'souriait': 6571, 'soupçonne': 6572, 'souciais': 6573, 'sorstu': 6574, 'sont\\u202f': 6575, 'soccupera': 6576, 'sms': 6577, 'similaires': 6578, 'seraitce': 6579, 'sentiez': 6580, 'semblable': 6581, 'seffondrer': 6582, 'second': 6583, 'sauva': 6584, 'sassirent': 6585, 'sapprocher': 6586, 'sappelait': 6587, 'saouls': 6588, 'saisit': 6589, 'sagisse': 6590, 'réveilla': 6591, 'réussirez': 6592, 'réunion\\u202f': 6593, 'réunions': 6594, 'rétablissement': 6595, 'rétabli': 6596, 'réponse\\u202f': 6597, 'répandue': 6598, 'réfléchisy': 6599, 'réessayer': 6600, 'rédaction': 6601, 'récoltes': 6602, 'réagir': 6603, 'ruines': 6604, 'roulé': 6605, 'roule': 6606, 'rougir': 6607, 'rivières': 6608, 'reçus': 6609, 'reçoit': 6610, 'reçois': 6611, 'reverrais': 6612, 'restèrent': 6613, 'respectueux': 6614, 'représente': 6615, 'repassé': 6616, 'rendezmoi': 6617, 'remplie': 6618, 'remplacé': 6619, 'relève': 6620, 'rejetée': 6621, 'regardée': 6622, 'refuserais': 6623, 'refais': 6624, 'recouverte': 6625, 'reconnaissezvous': 6626, 'reconduire': 6627, 'rappellemoi': 6628, 'ragoût': 6629, 'racontemoi': 6630, 'raccroché': 6631, 'quempirer': 6632, 'quelquefois': 6633, 'quau': 6634, 'quaprès': 6635, 'quamie': 6636, 'quaimestu': 6637, 'quaimeraistu': 6638, 'quai': 6639, 'pôle': 6640, 'pur': 6641, 'punition': 6642, 'punis': 6643, 'prêtemoi': 6644, 'prévisions': 6645, 'prévenues': 6646, 'présentes': 6647, 'préparezvous': 6648, 'préparetoi': 6649, 'préparation': 6650, 'préoccupée': 6651, 'préoccupation': 6652, 'préférée\\xa0': 6653, 'précipita': 6654, 'préalable': 6655, 'provenance': 6656, 'proposezvous': 6657, 'propose': 6658, 'profil': 6659, 'profession': 6660, 'produirait': 6661, 'procédé': 6662, 'pria': 6663, 'prendsen': 6664, 'pourtant': 6665, 'pourrie': 6666, 'portaient': 6667, 'polis': 6668, 'plongé': 6669, 'plaisantes': 6670, 'plaira': 6671, 'plaignent': 6672, 'piqueniquer': 6673, 'pickpockets': 6674, 'pessimiste': 6675, 'perdirent': 6676, 'penserait': 6677, 'pendule': 6678, 'pause\\xa0': 6679, 'patienter': 6680, 'patiemment': 6681, 'pastèque': 6682, 'parvient': 6683, 'parlonsnous': 6684, 'parlions': 6685, 'parlera': 6686, 'parfaites': 6687, 'parents\\xa0': 6688, 'pareils': 6689, 'parait': 6690, 'paraissez': 6691, 'papillon': 6692, 'paniquer': 6693, 'palais': 6694, 'pair': 6695, 'organisation': 6696, 'observé': 6697, 'n’oublions': 6698, 'n’estce': 6699, 'nétaitce': 6700, 'négligence': 6701, 'nées': 6702, 'nutilisez': 6703, 'nues': 6704, 'noires': 6705, 'nestelle': 6706, 'nentre': 6707, 'navires': 6708, 'narrivais': 6709, 'métait': 6710, 'méritons': 6711, 'méprisent': 6712, 'mémoriser': 6713, 'mécontent': 6714, 'musique\\u202f': 6715, 'mule': 6716, 'motif': 6717, 'mondial': 6718, 'moment\\u202f': 6719, 'mexpliquer': 6720, 'mettezle': 6721, 'mette': 6722, 'mesurer': 6723, 'menée': 6724, 'mentionner': 6725, 'mentends': 6726, 'menseigner': 6727, 'menacée': 6728, 'mattirer': 6729, 'math': 6730, 'masse': 6731, 'massage': 6732, 'marée': 6733, 'marrante': 6734, 'marqué': 6735, 'marine': 6736, 'marchera': 6737, 'marathon': 6738, 'mapporter': 6739, 'manquait': 6740, 'mannequin': 6741, 'mangée': 6742, 'malédiction': 6743, 'maladroit': 6744, 'main\\xa0': 6745, 'maintenir': 6746, 'maccusa': 6747, 'm': 6748, 'lénigme': 6749, 'lécriture': 6750, 'lâcher': 6751, 'lycéen': 6752, 'lut': 6753, 'lunion': 6754, 'lui\\u202f': 6755, 'loublier': 6756, 'lopération': 6757, 'lissue': 6758, 'lise': 6759, 'libérés': 6760, 'lenvironnement': 6761, 'lentraîneur': 6762, 'lenterrement': 6763, 'lastrologie': 6764, 'larrivée': 6765, 'lapprécier': 6766, 'lapprentissage': 6767, 'lappel': 6768, 'langleterre': 6769, 'langlais\\u202f': 6770, 'lambassade': 6771, 'laissèrent': 6772, 'laccusé': 6773, 'jusqu’à': 6774, 'jusquen': 6775, 'judicieux': 6776, 'jouvris': 6777, 'jouant': 6778, 'jongler': 6779, 'joignezvous': 6780, 'jessayai': 6781, 'jadorais': 6782, 'intrigué': 6783, 'intervenir': 6784, 'intentionnellement': 6785, 'insulté': 6786, 'insulte': 6787, 'installer': 6788, 'inscrit': 6789, 'inquiètes': 6790, 'ingénieur': 6791, 'indigné': 6792, 'incroyables': 6793, 'incorrecte': 6794, 'incompétent': 6795, 'incident': 6796, 'impitoyable': 6797, 'idée\\u202f': 6798, 'idiot\\u202f': 6799, 'hamburger': 6800, 'gênée': 6801, 'gratte': 6802, 'grasse': 6803, 'grandit': 6804, 'graines': 6805, 'gaspillage': 6806, 'gardien': 6807, 'garda': 6808, 'gagnerait': 6809, 'fêté': 6810, 'fuit': 6811, 'fuir': 6812, 'franc': 6813, 'fout': 6814, 'forcément': 6815, 'forcez': 6816, 'fondée': 6817, 'fonctionnent': 6818, 'folle\\u202f': 6819, 'foies': 6820, 'flotte': 6821, 'flexible': 6822, 'fixé': 6823, 'fixer': 6824, 'fixe': 6825, 'fine': 6826, 'finale': 6827, 'filer': 6828, 'fermeture': 6829, 'fermes': 6830, 'ferezvous': 6831, 'ferastu': 6832, 'fbi': 6833, 'favorite': 6834, 'faveur\\u202f': 6835, 'fausses': 6836, 'faudrait': 6837, 'faisiez': 6838, 'faim\\xa0': 6839, 'faculté': 6840, 'exécuter': 6841, 'expérimenté': 6842, 'exploser': 6843, 'exige': 6844, 'examina': 6845, 'est\\u202f': 6846, 'estomac': 6847, 'estil\\u202f': 6848, 'essuie': 6849, 'essentiel': 6850, 'essayèrent': 6851, 'espagne': 6852, 'envisagé': 6853, 'envahi': 6854, 'enregistré': 6855, 'ennemies': 6856, 'enlever': 6857, 'enfants\\u202f': 6858, 'empêcha': 6859, 'empoisonné': 6860, 'emballé': 6861, 'déçois': 6862, 'détruire': 6863, 'détestestu': 6864, 'détenu': 6865, 'détendstoi': 6866, 'dérouler': 6867, 'dépêché': 6868, 'déprimant': 6869, 'déplacement': 6870, 'dépendre': 6871, 'dépendent': 6872, 'démarre': 6873, 'délèves': 6874, 'délicat': 6875, 'dégueulasse': 6876, 'dégage': 6877, 'défunt': 6878, 'découvrira': 6879, 'décorations': 6880, 'décoration': 6881, 'décole': 6882, 'déchets': 6883, 'décampe': 6884, 'dressé': 6885, 'doxygène': 6886, 'douté': 6887, 'dormit': 6888, 'dormirai': 6889, 'dorganiser': 6890, 'doreilles': 6891, 'donnèrent': 6892, 'donnezvous': 6893, 'donnestu': 6894, 'domestiques': 6895, 'doive': 6896, 'diteslui': 6897, 'ditesleur': 6898, 'dissimuler': 6899, 'disleur': 6900, 'dirent': 6901, 'dinosaures': 6902, 'diminuer': 6903, 'dignorer': 6904, 'différence\\xa0': 6905, 'dictionnaire\\u202f': 6906, 'dhonneur': 6907, 'dhistoires': 6908, 'dextraordinaire': 6909, 'devrai': 6910, 'devinez': 6911, 'deuxlà': 6912, 'destiné': 6913, 'denvoyer': 6914, 'denviron': 6915, 'denfance': 6916, 'degré': 6917, 'daussi': 6918, 'datteindre': 6919, 'darbres': 6920, 'dannée': 6921, 'dallumer': 6922, 'céder': 6923, 'câbles': 6924, 'créé': 6925, 'crédible': 6926, 'créatif': 6927, 'croyaient': 6928, 'croisé': 6929, 'cri': 6930, 'crayon\\u202f': 6931, 'craignent': 6932, 'craignais': 6933, 'couvercle': 6934, 'coutumes': 6935, 'couteaux': 6936, 'courait': 6937, 'couloir': 6938, 'correspond': 6939, 'cornemuse': 6940, 'contracter': 6941, 'conter': 6942, 'contenu': 6943, 'contenait': 6944, 'constater': 6945, 'conscients': 6946, 'confectionna': 6947, 'concentration': 6948, 'compétence': 6949, 'comptons': 6950, 'comptes': 6951, 'compréhension': 6952, 'comprendrai': 6953, 'compositeur': 6954, 'commission': 6955, 'commençait': 6956, 'commencestu': 6957, 'commencerons': 6958, 'combinaison': 6959, 'colère\\xa0': 6960, 'cocaïne': 6961, 'coca': 6962, 'clous': 6963, 'clochait': 6964, 'climatisation': 6965, 'claqué': 6966, 'chèques': 6967, 'chutes': 6968, 'choux': 6969, 'chouettes': 6970, 'choqués': 6971, 'chopin': 6972, 'chirurgie': 6973, 'chercha': 6974, 'chasseur': 6975, 'chasser': 6976, 'charme': 6977, 'chantait': 6978, 'chantage': 6979, 'chaleureux': 6980, 'chaleureusement': 6981, 'chaise\\u202f': 6982, 'cendrier': 6983, 'carnet': 6984, 'canne': 6985, 'camionnette': 6986, 'camarade': 6987, 'cafard': 6988, 'cachette': 6989, 'bénévolat': 6990, 'buvait': 6991, 'brûlant': 6992, 'brésil': 6993, 'bruyante': 6994, 'bruits': 6995, 'britannique': 6996, 'bracelet': 6997, 'bouts': 6998, 'bourgade': 6999, 'boulangerie': 7000, 'boucher': 7001, 'bière\\u202f': 7002, 'biologie': 7003, 'bijoux': 7004, 'battus': 7005, 'basson': 7006, 'barman': 7007, 'bains': 7008, 'avérée': 7009, 'avertissement': 7010, 'attribué': 7011, 'attrapez': 7012, 'attente': 7013, 'attacha': 7014, 'atomique': 7015, 'astucieuse': 7016, 'artérielle': 7017, 'arrêtetoi': 7018, 'arrivé\\u202f': 7019, 'arrivetil': 7020, 'appréciestu': 7021, 'apprécierais': 7022, 'approchons': 7023, 'apparu': 7024, 'appareils': 7025, 'anxieux': 7026, 'annonce': 7027, 'amusetoi': 7028, 'ambitieuse': 7029, 'allumez': 7030, 'aller\\xa0': 7031, 'aider\\u202f': 7032, 'agressif': 7033, 'agacé': 7034, 'affamés': 7035, 'activité': 7036, 'actes': 7037, 'acquitté': 7038, 'achevée': 7039, 'achetons': 7040, 'accrocher': 7041, 'abandonnèrent': 7042, '90': 7043, '13': 7044, 'étudient': 7045, 'étudiait': 7046, 'étendue': 7047, 'éteignit': 7048, 'étasuniens': 7049, 'étaitelle': 7050, 'épargner': 7051, 'épargne': 7052, 'énigme': 7053, 'émue': 7054, 'émission': 7055, 'électricité': 7056, 'élection': 7057, 'égaux': 7058, 'écris': 7059, 'écoutées': 7060, 'écoutant': 7061, 'économise': 7062, 'économies': 7063, 'éclairer': 7064, 'Ôte': 7065, 'Évidemment': 7066, 'Étudiezvous': 7067, 'Étudie': 7068, 'État': 7069, 'Écoutons': 7070, '«\\xa0non': 7071, 'vêtir': 7072, 'végétarienne': 7073, 'vraies': 7074, 'voisines': 7075, 'vivantes': 7076, 'virés': 7077, 'violente': 7078, 'vins': 7079, 'vidé': 7080, 'verre\\xa0': 7081, 'ventilateur': 7082, 'vedette': 7083, 'vaudrait': 7084, 'vallée': 7085, 'vaincu': 7086, 'utilisezvous': 7087, 'unies': 7088, 't’ai': 7089, 'tôt\\xa0': 7090, 'têtes': 7091, 'télé\\u202f': 7092, 'télécharger': 7093, 'trouveront': 7094, 'traître': 7095, 'travaillant': 7096, 'transpirer': 7097, 'transmettre': 7098, 'traitement\\xa0': 7099, 'tousser': 7100, 'toupet': 7101, 'touchée': 7102, 'tombent': 7103, 'tinquiéter': 7104, 'timides': 7105, 'thé\\xa0': 7106, 'terribles': 7107, 'termina': 7108, 'tentez': 7109, 'tentes': 7110, 'tenseigner': 7111, 'tenais': 7112, 'temprunter': 7113, 'temples': 7114, 'tembête': 7115, 'techniques': 7116, 'taxe': 7117, 'tapproche': 7118, 'sétend': 7119, 'séparé': 7120, 'suture': 7121, 'survient': 7122, 'survienne': 7123, 'survie': 7124, 'surveillerai': 7125, 'supposons': 7126, 'supposer': 7127, 'suivis': 7128, 'subitement': 7129, 'stressant': 7130, 'stress': 7131, 'stratégie': 7132, 'stopper': 7133, 'statistiques': 7134, 'sport\\u202f': 7135, 'splendide': 7136, 'soutient': 7137, 'sortezvous': 7138, 'sortait': 7139, 'sombres': 7140, 'solution\\xa0': 7141, 'solliciter': 7142, 'sociaux': 7143, 'sociale': 7144, 'sobre': 7145, 'sinistre': 7146, 'signifiait': 7147, 'signaler': 7148, 'sied': 7149, 'shabille': 7150, 'seul\\xa0': 7151, 'serre': 7152, 'serezvous': 7153, 'septième': 7154, 'sentendent': 7155, 'sendormir': 7156, 'sembler': 7157, 'sciences': 7158, 'savoir\\xa0': 7159, 'saut': 7160, 'sauront': 7161, 'saurons': 7162, 'saufs': 7163, 'saoules': 7164, 'sandwichs': 7165, 'saigner': 7166, 'sacàmain': 7167, 'réticent': 7168, 'résolus': 7169, 'résistance': 7170, 'réponse\\xa0': 7171, 'régulier': 7172, 'réduction': 7173, 'réchauffe': 7174, 'réaliseront': 7175, 'réalisera': 7176, 'réagi': 7177, 'ruban': 7178, 'routière': 7179, 'robot': 7180, 'rhumes': 7181, 'retrouve': 7182, 'resterons': 7183, 'restant': 7184, 'ressembler': 7185, 'repéré': 7186, 'représentent': 7187, 'reproches': 7188, 'reposezvous': 7189, 'reposetoi': 7190, 'rentrons': 7191, 'rendues': 7192, 'rencontrai': 7193, 'remette': 7194, 'rejeter': 7195, 'regrettable': 7196, 'regret': 7197, 'regardèrent': 7198, 'recul': 7199, 'recouvert': 7200, 'reconnut': 7201, 'reconnues': 7202, 'reconnaistu': 7203, 'reconnaissants': 7204, 'recherchons': 7205, 'rebelles': 7206, 'rattrapé': 7207, 'ras': 7208, 'rage': 7209, 'radiateur': 7210, 'racontez': 7211, 'racontenous': 7212, 'raciste': 7213, 'qu’astu': 7214, 'quête': 7215, 'quittes': 7216, 'quallonsnous': 7217, 'quaimeriezvous': 7218, 'pêcher\\u202f': 7219, 'périls': 7220, 'pénurie': 7221, 'purent': 7222, 'pullover': 7223, 'psychiques': 7224, 'prévue': 7225, 'prévoyez': 7226, 'prépara': 7227, 'préférez': 7228, 'précipiter': 7229, 'prouve': 7230, 'promu': 7231, 'proie': 7232, 'produisent': 7233, 'processus': 7234, 'principales': 7235, 'prenezen': 7236, 'premières': 7237, 'poésie': 7238, 'poursuis': 7239, 'poudre': 7240, 'posée': 7241, 'portais': 7242, 'popcorn': 7243, 'politiques': 7244, 'politesse': 7245, 'pointé': 7246, 'poignardé': 7247, 'pleurent': 7248, 'plaîtil': 7249, 'plate': 7250, 'plancher': 7251, 'planche': 7252, 'plaignait': 7253, 'plaies': 7254, 'piégé': 7255, 'pilules': 7256, 'picasso': 7257, 'physiquement': 7258, 'phrase\\u202f': 7259, 'photogénique': 7260, 'peur\\xa0': 7261, 'petitsenfants': 7262, 'persuada': 7263, 'perruque': 7264, 'permanente': 7265, 'permanence': 7266, 'perdues': 7267, 'percer': 7268, 'pencha': 7269, 'peigne': 7270, 'pathétique': 7271, 'passé\\u202f': 7272, 'parvint': 7273, 'parletil': 7274, 'parié': 7275, 'parents\\u202f': 7276, 'parent': 7277, 'pardonnezmoi': 7278, 'pardonne': 7279, 'paranoïaque': 7280, 'paralysé': 7281, 'pantalons': 7282, 'panique': 7283, 'ouï': 7284, 'oubliée': 7285, 'organiser': 7286, 'oreiller': 7287, 'opportunités': 7288, 'omis': 7289, 'ok': 7290, 'officier': 7291, 'officiellement': 7292, 'objet': 7293, 'n’as': 7294, 'n’aime': 7295, 'nécessité': 7296, 'nécessite': 7297, 'nécessaire\\xa0': 7298, 'nuageux': 7299, 'nuage': 7300, 'noue': 7301, 'noublions': 7302, 'nom\\u202f': 7303, 'nomme': 7304, 'nièce': 7305, 'neveu': 7306, 'nettoyez': 7307, 'neigera': 7308, 'naviguez': 7309, 'naviguer': 7310, 'national': 7311, 'narrivons': 7312, 'narrives': 7313, 'narrivent': 7314, 'naient': 7315, 'nageur': 7316, 'nabandonnez': 7317, 'météorologiques': 7318, 'métais': 7319, 'mélanger': 7320, 'mécoute': 7321, 'musées': 7322, 'mourant': 7323, 'morale': 7324, 'moquèrent': 7325, 'miracles': 7326, 'militaire': 7327, 'mile': 7328, 'migraine': 7329, 'mhabitue': 7330, 'mettrai': 7331, 'mettons': 7332, 'messieurs': 7333, 'mercis': 7334, 'mental': 7335, 'meneur': 7336, 'mendiant': 7337, 'matières': 7338, 'matchs': 7339, 'marriver': 7340, 'margarine': 7341, 'mardi': 7342, 'manqueras': 7343, 'mangerons': 7344, 'manches': 7345, 'mal\\xa0': 7346, 'malades': 7347, 'mahjong': 7348, 'magnifiques': 7349, 'magique': 7350, 'magicien': 7351, 'madresser': 7352, 'létiquette': 7353, 'léquipage': 7354, 'lépreuve': 7355, 'lépais': 7356, 'lâchezmoi': 7357, 'lÉtat': 7358, 'lue': 7359, 'lourdes': 7360, 'louragan': 7361, 'lorage': 7362, 'lopportunité': 7363, 'lié': 7364, 'lits': 7365, 'listu': 7366, 'lises': 7367, 'lisent': 7368, 'liceberg': 7369, 'librement': 7370, 'lhoraire': 7371, 'lexposition': 7372, 'levés': 7373, 'lessive': 7374, 'lescalade': 7375, 'lentraînement': 7376, 'lentendit': 7377, 'lemporte': 7378, 'leffet': 7379, 'lecteur': 7380, 'laéroport\\u202f': 7381, 'lavocat': 7382, 'lautorité': 7383, 'laurait': 7384, 'largent\\u202f': 7385, 'lapprécient': 7386, 'lappelle': 7387, 'lancienne': 7388, 'lambulance': 7389, 'laissiez': 7390, 'laissezle': 7391, 'laisserait': 7392, 'laimer': 7393, 'laccepter': 7394, 'laboratoire': 7395, 'karaté': 7396, 'j’espère': 7397, 'justes': 7398, 'jumelle': 7399, 'jugé': 7400, 'jouerai': 7401, 'jouaient': 7402, 'jogging': 7403, 'jirais': 7404, 'jexige': 7405, 'jenvisage': 7406, 'jemprunte': 7407, 'jarrête': 7408, 'jappréciai': 7409, 'jachèterai': 7410, 'ipad': 7411, 'investi': 7412, 'inventa': 7413, 'intrépide': 7414, 'insista': 7415, 'initialement': 7416, 'indiquer': 7417, 'incorrect': 7418, 'inconsciente': 7419, 'inclus': 7420, 'incapables': 7421, 'inacceptable': 7422, 'impuissante': 7423, 'immature': 7424, 'imbécile': 7425, 'hurlé': 7426, 'hospitalité': 7427, 'hospitalisé': 7428, 'honteux': 7429, 'hockey': 7430, 'heures\\u202f': 7431, 'heures\\xa0': 7432, 'haricots': 7433, 'harceler': 7434, 'hache': 7435, 'habituer': 7436, 'habitez': 7437, 'géographie': 7438, 'gâchette': 7439, 'grèce': 7440, 'grouiller': 7441, 'grimpez': 7442, 'grenouilles': 7443, 'grandebretagne': 7444, 'gobe': 7445, 'gencives': 7446, 'gamins': 7447, 'galant': 7448, 'gagnes': 7449, 'février': 7450, 'félicité': 7451, 'fussiez': 7452, 'fusible': 7453, 'fumé': 7454, 'frère\\xa0': 7455, 'frappé\\u202f': 7456, 'foutre': 7457, 'flatté': 7458, 'fille\\u202f': 7459, 'fiez': 7460, 'fiables': 7461, 'feraisje': 7462, 'fautif': 7463, 'fauteuils': 7464, 'farce': 7465, 'fabriqué': 7466, 'extraverti': 7467, 'explosé': 7468, 'explique': 7469, 'experts': 7470, 'exemplaire': 7471, 'exclu': 7472, 'essoufflé': 7473, 'essayant': 7474, 'essaient': 7475, 'escaladé': 7476, 'erronée': 7477, 'envoyezmoi': 7478, 'envoyez': 7479, 'envoiemoi': 7480, 'enveloppe': 7481, 'enthousiaste': 7482, 'entendons': 7483, 'endessous': 7484, 'encas': 7485, 'employeur': 7486, 'emploie': 7487, 'd’œil': 7488, 'dîner\\u202f': 7489, 'dété\\u202f': 7490, 'détestes': 7491, 'désirs': 7492, 'dépendons': 7493, 'déniché': 7494, 'démocratie': 7495, 'déjeuner\\xa0': 7496, 'déguerpissez': 7497, 'dégage\\u202f': 7498, 'déficit': 7499, 'décourager': 7500, 'déception': 7501, 'débutants': 7502, 'débriété': 7503, 'débordante': 7504, 'débattu': 7505, 'dressa': 7506, 'draps': 7507, 'doublier': 7508, 'dortoir': 7509, 'donnenous': 7510, 'donnait': 7511, 'divers': 7512, 'dissertation': 7513, 'disposonsnous': 7514, 'disposaient': 7515, 'diffère': 7516, 'dexamen': 7517, 'deviendrai': 7518, 'description': 7519, 'descendons': 7520, 'dentraînement': 7521, 'dembauche': 7522, 'demandaient': 7523, 'dartifice': 7524, 'danseuse': 7525, 'daccord\\xa0': 7526, 'daccomplir': 7527, 'célébrer': 7528, 'câble': 7529, 'cuisine\\xa0': 7530, 'cuisines': 7531, 'cruels': 7532, 'croisés': 7533, 'croisement': 7534, 'coûtait': 7535, 'couvre': 7536, 'cours\\u202f': 7537, 'courez': 7538, 'courant\\xa0': 7539, 'country': 7540, 'cottage': 7541, 'conviendra': 7542, 'convaincues': 7543, 'contracté': 7544, 'continues': 7545, 'contentetoi': 7546, 'contenter': 7547, 'contagieux': 7548, 'contacterai': 7549, 'construite': 7550, 'consommezvous': 7551, 'consommestu': 7552, 'conneries': 7553, 'connaissions': 7554, 'confuse': 7555, 'conduisit': 7556, 'conduisait': 7557, 'conclus': 7558, 'concert\\xa0': 7559, 'concerné': 7560, 'conception': 7561, 'concentrée': 7562, 'con': 7563, 'comptoir': 7564, 'compréhensible': 7565, 'composition': 7566, 'comportes': 7567, 'commune': 7568, 'commettez': 7569, 'colomb': 7570, 'coincés': 7571, 'clé\\xa0': 7572, 'climatique': 7573, 'classe\\u202f': 7574, 'claquer': 7575, 'cité': 7576, 'citoyens': 7577, 'citoyen': 7578, 'choisie': 7579, 'chiant': 7580, 'chaussures\\xa0': 7581, 'chapeau\\xa0': 7582, 'chapeaux': 7583, 'chanta': 7584, 'cerfvolant': 7585, 'cerf': 7586, 'catholique': 7587, 'carrure': 7588, 'carbone': 7589, 'capté': 7590, 'canard': 7591, 'cambriolé': 7592, 'calmezvous': 7593, 'calcul': 7594, 'cachez': 7595, 'bétail': 7596, 'bénisse': 7597, 'buvais': 7598, 'bulletin': 7599, 'briques': 7600, 'branches': 7601, 'boutons': 7602, 'boucles': 7603, 'boire\\xa0': 7604, 'bière\\xa0': 7605, 'bec': 7606, 'barreaux': 7607, 'banlieue': 7608, 'balade': 7609, 'baissa': 7610, 'bain\\xa0': 7611, 'baigne': 7612, 'aînés': 7613, 'avisée': 7614, 'aveugles': 7615, 'avaisje': 7616, 'aurai': 7617, 'augmentent': 7618, 'attention\\u202f': 7619, 'attaquer': 7620, 'attachez': 7621, 'attache': 7622, 'assurément': 7623, 'assure': 7624, 'asiatiques': 7625, 'ascenseur': 7626, 'arrêtezvous': 7627, 'arrogant': 7628, 'armés': 7629, 'aprèsmidi\\xa0': 7630, 'appropriés': 7631, 'approchez': 7632, 'approbation': 7633, 'anonyme': 7634, 'amusez': 7635, 'amenez': 7636, 'allèrent': 7637, 'allusion': 7638, 'allezvous\\u202f': 7639, 'alibi': 7640, 'aiguisé': 7641, 'aideznous': 7642, 'aiderait': 7643, 'agréablement': 7644, 'agressive': 7645, 'affairé': 7646, 'adorons': 7647, 'admiré': 7648, 'actuel': 7649, 'achever': 7650, 'acheté\\u202f': 7651, 'accueil': 7652, 'accidentellement': 7653, 'acceptons': 7654, 'aboli': 7655, '4': 7656, '\\u202f': 7657, 'êtesvous\\xa0': 7658, 'évitée': 7659, 'évadé': 7660, 'étudier\\u202f': 7661, 'étonnée': 7662, 'étagère': 7663, 'établi': 7664, 'équitable': 7665, 'éprouvé': 7666, 'énergie': 7667, 'écraser': 7668, 'écoutés': 7669, 'éclairci': 7670, 'échoue': 7671, 'échoua': 7672, 'échantillon': 7673, 'âges': 7674, 'Étudiez': 7675, 'Éloignezvous': 7676, 'Écoutemoi': 7677, 'vœux': 7678, 'vœu': 7679, 'vétérinaire': 7680, 'vélos': 7681, 'voyais': 7682, 'voudrez': 7683, 'vontelles': 7684, 'vol\\xa0': 7685, 'volcan': 7686, 'vitrine': 7687, 'vitamines': 7688, 'virgule': 7689, 'violon\\u202f': 7690, 'violoniste': 7691, 'violoncelle': 7692, 'vigilant': 7693, 'videz': 7694, 'versa': 7695, 'verrais': 7696, 'verra': 7697, 'va\\xa0': 7698, 'vaste': 7699, 'variété': 7700, 'vanter': 7701, 'vanille': 7702, 'valise\\u202f': 7703, 'vaille': 7704, 'utilisez': 7705, 'unique\\u202f': 7706, 'ultime': 7707, 'tôt\\u202f': 7708, 'télévision\\u202f': 7709, 'técrire': 7710, 'tv': 7711, 'tuée': 7712, 'tulipes': 7713, 'tuberculose': 7714, 'trébuché': 7715, 'trousse': 7716, 'troublé': 7717, 'tronçonneuse': 7718, 'trompés': 7719, 'tripes': 7720, 'trenteetun\\u202f': 7721, 'trempée': 7722, 'traîne': 7723, 'traversez': 7724, 'traversant': 7725, 'transpire': 7726, 'tournée': 7727, 'touriste': 7728, 'touches': 7729, 'total': 7730, 'tontelles': 7731, 'tondeuse': 7732, 'titanic': 7733, 'tirée': 7734, 'tiretoi': 7735, 'tigres': 7736, 'thermomètre': 7737, 'terroristes': 7738, 'terrifiée': 7739, 'tenterai': 7740, 'tavait': 7741, 'tatelle': 7742, 'tarte\\u202f': 7743, 'tapprendrai': 7744, 's’en': 7745, 'sœur\\u202f': 7746, 'sévèrement': 7747, 'sérieux\\u202f': 7748, 'sépuise': 7749, 'suspicieux': 7750, 'suspects': 7751, 'surprit': 7752, 'supérieure': 7753, 'supportestu': 7754, 'sujet\\xa0': 7755, 'spécialisé': 7756, 'spécialiste': 7757, 'souvienstoi': 7758, 'souviendra': 7759, 'souvenu': 7760, 'sousestimez': 7761, 'sousestime': 7762, 'sournois': 7763, 'sourirent': 7764, 'sources': 7765, 'soupir': 7766, 'soumettre': 7767, 'souffrait': 7768, 'soucieux': 7769, 'sortirai': 7770, 'sortiez': 7771, 'sonnerie': 7772, 'soigné': 7773, 'sociales': 7774, 'silhouette': 7775, 'sexy': 7776, 'sexcusa': 7777, 'servit': 7778, 'sers': 7779, 'seraisje': 7780, 'sennuyer': 7781, 'sendort': 7782, 'semé': 7783, 'sembla': 7784, 'sauvées': 7785, 'sauras': 7786, 'sattend': 7787, 'satisfaisant': 7788, 'sarrêtait': 7789, 'sapproche': 7790, 'san': 7791, 'salaires': 7792, 'saiment': 7793, 'saigne': 7794, 'sages': 7795, 'rêveur': 7796, 'réveillées': 7797, 'répondrai': 7798, 'réfléchisse': 7799, 'récupéré': 7800, 'récession': 7801, 'réalisa': 7802, 'rupture': 7803, 'rond': 7804, 'roman\\u202f': 7805, 'romancier': 7806, 'robots': 7807, 'rivage': 7808, 'rigueur': 7809, 'rigolo': 7810, 'rient': 7811, 'ridiculisé': 7812, 'ridiculiser': 7813, 'ricané': 7814, 'reviendriez': 7815, 'reviendrais': 7816, 'reverrai': 7817, 'retrait': 7818, 'retournée': 7819, 'retirezvous': 7820, 'retentit': 7821, 'resterezvous': 7822, 'ressentent': 7823, 'ressentait': 7824, 'ressent': 7825, 'représentant': 7826, 'reprit': 7827, 'renvoyer': 7828, 'rentrez': 7829, 'rentrera': 7830, 'renoncer': 7831, 'rendîmes': 7832, 'rendrons': 7833, 'rendis': 7834, 'rendais': 7835, 'rencontré\\u202f': 7836, 'rencontrée\\u202f': 7837, 'remplit': 7838, 'remerciements': 7839, 'remboursement': 7840, 'relâché': 7841, 'rejeta': 7842, 'regards': 7843, 'regardezmoi': 7844, 'referais': 7845, 'reculer': 7846, 'recrutée': 7847, 'recruté': 7848, 'recours': 7849, 'recherché': 7850, 'ravissante': 7851, 'ravir': 7852, 'rassasié': 7853, 'rares': 7854, 'ramper': 7855, 'rampe': 7856, 'ramassa': 7857, 'raison\\u202f': 7858, 'raison\\xa0': 7859, 'raisonnables': 7860, 'rafraîchir': 7861, 'quérir': 7862, 'quoique': 7863, 'quelquun\\xa0': 7864, 'quaujourdhui': 7865, 'quaucune': 7866, 'quatil': 7867, 'pêché': 7868, 'péril': 7869, 'péché': 7870, 'pâtés': 7871, 'pâlit': 7872, 'punir': 7873, 'publiée': 7874, 'prêt\\u202f': 7875, 'prêt\\xa0': 7876, 'prêtez': 7877, 'prétendre': 7878, 'présidente': 7879, 'présenterai': 7880, 'préférées': 7881, 'précipitation': 7882, 'précautions': 7883, 'provoqué': 7884, 'proviennent': 7885, 'provenant': 7886, 'protègerai': 7887, 'protestataires': 7888, 'prophétie': 7889, 'promenades': 7890, 'professionnelle': 7891, 'professeure': 7892, 'productif': 7893, 'privés': 7894, 'priorité': 7895, 'pressentiment': 7896, 'poussez': 7897, 'pourrez': 7898, 'poules': 7899, 'pouffer': 7900, 'potentiel': 7901, 'postulé': 7902, 'possèdestu': 7903, 'portetelle': 7904, 'ponts': 7905, 'pondu': 7906, 'polies': 7907, 'plaîtil\\u202f': 7908, 'plaise': 7909, 'plaignit': 7910, 'place\\xa0': 7911, 'pizzas': 7912, 'piste': 7913, 'piles': 7914, 'philosophie': 7915, 'peuventelles': 7916, 'peutelle': 7917, 'petitdéjeuné': 7918, 'percevoir': 7919, 'pencher': 7920, 'pelle': 7921, 'peignit': 7922, 'payées': 7923, 'patates': 7924, 'pas\\xa0»': 7925, 'passion': 7926, 'parvînmes': 7927, 'partonsnous': 7928, 'partirent': 7929, 'partiellement': 7930, 'parrain': 7931, 'parlonsen': 7932, 'pardonnera': 7933, 'parcs': 7934, 'parcouru': 7935, 'parages': 7936, 'palissade': 7937, 'osezvous': 7938, 'ordonnance': 7939, 'ordinateur\\xa0': 7940, 'opposés': 7941, 'onéreuse': 7942, 'olives': 7943, 'obéit': 7944, 'obsédé': 7945, 'observe': 7946, 'obligation': 7947, 'nétudie': 7948, 'négociation': 7949, 'néanmoins': 7950, 'nouvelle\\xa0': 7951, 'nourriture\\xa0': 7952, 'normales': 7953, 'nocturne': 7954, 'nette': 7955, 'navigateur': 7956, 'naturels': 7957, 'naturelles': 7958, 'nationalité': 7959, 'natil': 7960, 'narrêtait': 7961, 'nappe': 7962, 'nappartiens': 7963, 'nallonsnous': 7964, 'nallais': 7965, 'nagé': 7966, 'nageaient': 7967, 'mêlez': 7968, 'méthodes': 7969, 'mémorisé': 7970, 'médicaux': 7971, 'médical': 7972, 'mécoutes': 7973, 'mère\\xa0': 7974, 'mères': 7975, 'mènent': 7976, 'mystères': 7977, 'moustique': 7978, 'mouiller': 7979, 'moquées': 7980, 'monument': 7981, 'montèrent': 7982, 'montres': 7983, 'montant': 7984, 'moisi': 7985, 'modifier': 7986, 'mobile': 7987, 'minviter': 7988, 'minutes\\u202f': 7989, 'minable': 7990, 'milles': 7991, 'mignonnes': 7992, 'messe': 7993, 'message\\xa0': 7994, 'merveille': 7995, 'mennuyais': 7996, 'menaça': 7997, 'mauraient': 7998, 'matérialiste': 7999, 'mattraper': 8000, 'masque': 8001, 'marrer': 8002, 'marins': 8003, 'marin': 8004, 'mare': 8005, 'marchâmes': 8006, 'maquillage': 8007, 'manipuler': 8008, 'mangions': 8009, 'manager': 8010, 'mamuse': 8011, 'mammifère': 8012, 'mallonger': 8013, 'malade\\u202f': 8014, 'maiment': 8015, 'maccuser': 8016, 'l’intention': 8017, 'lêtes': 8018, 'léquilibre': 8019, 'lécoute': 8020, 'lutté': 8021, 'lurticaire': 8022, 'louper': 8023, 'loua': 8024, 'lorsquelles': 8025, 'loriginal': 8026, 'lopinion': 8027, 'loisirs': 8028, 'location': 8029, 'livres\\u202f': 8030, 'livreci': 8031, 'lisais': 8032, 'lignorais': 8033, 'libéré': 8034, 'lhorizon': 8035, 'lhonneur': 8036, 'levezvous': 8037, 'lessentiel': 8038, 'lesquels': 8039, 'lattaque': 8040, 'lapprécierais': 8041, 'lamour\\u202f': 8042, 'lalimentation': 8043, 'laissenous': 8044, 'lail': 8045, 'lagriculture': 8046, 'lafrique': 8047, 'ladministration': 8048, 'lactrice': 8049, 'lachèterais': 8050, 'kidnappé': 8051, 'jurer': 8052, 'jungle': 8053, 'jumelles': 8054, 'jugez': 8055, 'jour\\u202f': 8056, 'joint': 8057, 'jobtiens': 8058, 'jappuie': 8059, 'ivrogne': 8060, 'irruption': 8061, 'irezvous': 8062, 'irez': 8063, 'invention': 8064, 'intéressant\\u202f': 8065, 'intimité': 8066, 'intersection': 8067, 'intense': 8068, 'intelligence': 8069, 'insensible': 8070, 'inquiété': 8071, 'injection': 8072, 'informés': 8073, 'informée': 8074, 'informatique': 8075, 'individu': 8076, 'inattendu': 8077, 'imprudent': 8078, 'immédiat': 8079, 'ignora': 8080, 'idiotes': 8081, 'hésitation': 8082, 'hérité': 8083, 'hurle': 8084, 'hurlait': 8085, 'hostile': 8086, 'hautbois': 8087, 'hamburgers': 8088, 'habitants': 8089, 'habillés': 8090, 'gêne': 8091, 'généraux': 8092, 'gâté': 8093, 'grossièreté': 8094, 'grossir': 8095, 'grondé': 8096, 'gronder': 8097, 'graves': 8098, 'grandissent': 8099, 'gouverneur': 8100, 'gloussé': 8101, 'glissé': 8102, 'glaces': 8103, 'geste': 8104, 'genres': 8105, 'gardiez': 8106, 'gagner\\xa0': 8107, 'future': 8108, 'futil': 8109, 'fréquenté': 8110, 'frustrant': 8111, 'frotta': 8112, 'froids': 8113, 'frappée\\u202f': 8114, 'françaises': 8115, 'fourchettes': 8116, 'fouillé': 8117, 'força': 8118, 'formée': 8119, 'former': 8120, 'fontelles': 8121, 'fondement': 8122, 'fonctionnetil': 8123, 'fonctionnait': 8124, 'fins': 8125, 'fini\\u202f': 8126, 'finirai': 8127, 'financière': 8128, 'filé': 8129, 'fillette': 8130, 'fiezvous': 8131, 'fichez': 8132, 'fiancé': 8133, 'feratil': 8134, 'feint': 8135, 'fatiguer': 8136, 'fassent': 8137, 'fasciné': 8138, 'fanfare': 8139, 'familière': 8140, 'fait\\xa0': 8141, 'faiteslemoi': 8142, 'faisje': 8143, 'faisions': 8144, 'fabuleux': 8145, 'fabriquée': 8146, 'fabriquer': 8147, 'eût': 8148, 'extrême': 8149, 'existence': 8150, 'exilé': 8151, 'exercer': 8152, 'excusemoi': 8153, 'excitées': 8154, 'excessive': 8155, 'examiné': 8156, 'euxmêmes': 8157, 'euros': 8158, 'estce\\u202f': 8159, 'espérant': 8160, 'envieux': 8161, 'entrions': 8162, 'entraînés': 8163, 'enthousiasme': 8164, 'entendant': 8165, 'entend': 8166, 'enregistrer': 8167, 'ennuyer': 8168, 'ennuie': 8169, 'encourir': 8170, 'enchantée': 8171, 'encaisser': 8172, 'empreintes': 8173, 'embêtant': 8174, 'effrayer': 8175, 'efforcezvous': 8176, 'd’accord': 8177, 'dûmes': 8178, 'dévisage': 8179, 'développement': 8180, 'détruite': 8181, 'détourna': 8182, 'détermination': 8183, 'désobéi': 8184, 'désirezvous': 8185, 'désespérés': 8186, 'dérober': 8187, 'dérangement': 8188, 'déquipe': 8189, 'dépourvus': 8190, 'dépouillé': 8191, 'dépose': 8192, 'démodé': 8193, 'démissionna': 8194, 'délicieuses': 8195, 'délibérément': 8196, 'déjeuner\\u202f': 8197, 'défait': 8198, 'découvrit': 8199, 'déchira': 8200, 'décampez': 8201, 'débarrassonsnous': 8202, 'durera': 8203, 'droguée': 8204, 'douteux': 8205, 'douter': 8206, 'dons': 8207, 'donnelui': 8208, 'donnai': 8209, 'domestique': 8210, 'doives': 8211, 'doitil': 8212, 'doiseaux': 8213, 'docteurs': 8214, 'ditesle': 8215, 'distraite': 8216, 'dissue': 8217, 'dissimulé': 8218, 'disposiez': 8219, 'disparition': 8220, 'discutâmes': 8221, 'disaient': 8222, 'dirigée': 8223, 'dirigezvous': 8224, 'diriez': 8225, 'dinterrompre': 8226, 'dimpôts': 8227, 'dimages': 8228, 'diffèrent': 8229, 'difficile\\xa0': 8230, 'didiots': 8231, 'diamants': 8232, 'dhiver': 8233, 'dheure': 8234, 'dexcuse': 8235, 'devraitil': 8236, 'dessus\\xa0': 8237, 'dennemis': 8238, 'demandés': 8239, 'demandée': 8240, 'demandezvous': 8241, 'demandezlui': 8242, 'demandestu': 8243, 'demandemoi': 8244, 'dehors\\xa0': 8245, 'davant': 8246, 'dautre\\xa0': 8247, 'darrestation': 8248, 'dargent\\u202f': 8249, 'dapplaudir': 8250, 'danimaux': 8251, 'dallure': 8252, 'dactylo': 8253, 'dacier': 8254, 'cétaient': 8255, 'céréales': 8256, 'céda': 8257, 'cv': 8258, 'curry': 8259, 'cuillères': 8260, 'créative': 8261, 'crus': 8262, 'croyons': 8263, 'croismoi': 8264, 'critiquer': 8265, 'craché': 8266, 'coûté\\xa0': 8267, 'coûterait': 8268, 'courriels': 8269, 'coté': 8270, 'corrigez': 8271, 'correspondent': 8272, 'correctes': 8273, 'coriace': 8274, 'coopératif': 8275, 'conviennent': 8276, 'contribué': 8277, 'contrarier': 8278, 'contes': 8279, 'conte': 8280, 'contactezmoi': 8281, 'conséquence': 8282, 'consciencieux': 8283, 'conformer': 8284, 'confonds': 8285, 'confessé': 8286, 'confectionner': 8287, 'conduises': 8288, 'concombres': 8289, 'concerts': 8290, 'concentrés': 8291, 'comprenne': 8292, 'comprenais': 8293, 'composer': 8294, 'complot': 8295, 'compliquées': 8296, 'comparaison': 8297, 'compagne': 8298, 'commençonsnous': 8299, 'collé': 8300, 'collègues': 8301, 'collectionne': 8302, 'clou': 8303, 'close': 8304, 'cliquez': 8305, 'civile': 8306, 'citrons': 8307, 'circule': 8308, 'cinglé': 8309, 'chuchota': 8310, 'choquées': 8311, 'chocolats': 8312, 'chimique': 8313, 'chiffon': 8314, 'cher\\xa0': 8315, 'cherchiez': 8316, 'chefdœuvre': 8317, 'chaussures\\u202f': 8318, 'chaud\\xa0': 8319, 'chaudes': 8320, 'charité': 8321, 'chantiez': 8322, 'chantez': 8323, 'changerais': 8324, 'changeons': 8325, 'chandelle': 8326, 'champignons': 8327, 'cest\\xa0': 8328, 'cessiez': 8329, 'cessent': 8330, 'certitude': 8331, 'central': 8332, 'centimètres': 8333, 'cellule': 8334, 'cellulaire': 8335, 'celleci\\xa0': 8336, 'cassetoi': 8337, 'cartons': 8338, 'carrefour': 8339, 'cardiaque\\xa0': 8340, 'caractère': 8341, 'capte': 8342, 'capot': 8343, 'canapé\\xa0': 8344, 'calculer': 8345, 'caillou': 8346, 'cadette': 8347, 'cachés': 8348, 'cabinet': 8349, 'bœufs': 8350, 'bêtises': 8351, 'bénévoles': 8352, 'bus\\xa0': 8353, 'brusquement': 8354, 'brossé': 8355, 'brisa': 8356, 'bravo': 8357, 'boîte\\xa0': 8358, 'boutiques': 8359, 'boule': 8360, 'boivent': 8361, 'blâme': 8362, 'bloquée': 8363, 'blaguer': 8364, 'bisou': 8365, 'bille': 8366, 'bibliothèque\\u202f': 8367, 'berlin': 8368, 'beatles': 8369, 'bavarder': 8370, 'battait': 8371, 'bats': 8372, 'bassiste': 8373, 'barbier': 8374, 'barbecue': 8375, 'banjo': 8376, 'balayé': 8377, 'bagarre': 8378, 'bactéries': 8379, 'babysitter': 8380, 'avantages': 8381, 'avancez': 8382, 'avalé': 8383, 'avaler': 8384, 'autres\\xa0': 8385, 'autoriser': 8386, 'automatique': 8387, 'auto': 8388, 'attendra': 8389, 'attaqua': 8390, 'attachée': 8391, 'assistante': 8392, 'asie': 8393, 'arriverons': 8394, 'argent\\xa0': 8395, 'architecte': 8396, 'apte': 8397, 'appréciée': 8398, 'approfondie': 8399, 'apprenez': 8400, 'appelons': 8401, 'appellestu': 8402, 'appelezvous': 8403, 'août': 8404, 'anéanti': 8405, 'anges': 8406, 'amère': 8407, 'amie\\xa0': 8408, 'alternatives': 8409, 'alternative': 8410, 'alitée': 8411, 'aide\\xa0': 8412, 'agriculteur': 8413, 'affection': 8414, 'adversaire': 8415, 'adressé': 8416, 'adorer': 8417, 'activités': 8418, 'accoutumé': 8419, 'accorder': 8420, 'acceptera': 8421, 'absorbé': 8422, 'abri': 8423, 'abord': 8424, 'abonné': 8425, 'aboient': 8426, 'abattre': 8427, '2\\xa0h\\xa030': 8428, '16': 8429, '12': 8430, 'événements': 8431, 'évidente': 8432, 'éveillées': 8433, 'évanouie': 8434, 'évaluation': 8435, 'étudiez': 8436, 'étudia': 8437, 'étranglé': 8438, 'étourdi': 8439, 'étouffe': 8440, 'étang': 8441, 'étanche': 8442, 'établir': 8443, 'équitablement': 8444, 'épreuves': 8445, 'épines': 8446, 'épidémie': 8447, 'épicé': 8448, 'épeler': 8449, 'épargné': 8450, 'énervée': 8451, 'éléphant': 8452, 'élever': 8453, 'électriques': 8454, 'écureuils': 8455, 'écureuil': 8456, 'écrits': 8457, 'écrites': 8458, 'écoutes': 8459, 'écoulés': 8460, 'échappés': 8461, 'ère': 8462, 'âgés': 8463, 'à\\xa0sa': 8464, 'Étaisje': 8465, 'Éloignetoi': 8466, 'Écrismoi': 8467, '«\\xa0estce': 8468, 'x': 8469, 'vêtement': 8470, 'vu\\u202f': 8471, 'voyagezvous': 8472, 'voyagea': 8473, 'voulezvous\\xa0': 8474, 'voudras': 8475, 'voleuse': 8476, 'volets': 8477, 'vitamine': 8478, 'visitent': 8479, 'virées': 8480, 'villageois': 8481, 'vilaine': 8482, 'viendratil': 8483, 'vieillit': 8484, 'vieillissant': 8485, 'vidéos': 8486, 'veuxtu\\xa0': 8487, 'veuve': 8488, 'veuilliez': 8489, 'vertu': 8490, 'vengeance': 8491, 'veinard': 8492, 'veillez': 8493, 'vautil': 8494, 'vaten': 8495, 'vapeur': 8496, 'vapes': 8497, 'valeurs': 8498, 'vaisseau': 8499, 'vaguement': 8500, 'utilisés': 8501, 'urbaine': 8502, 'un\\xa0': 8503, 'témoigné': 8504, 'témoigner': 8505, 'téléphones': 8506, 'tut': 8507, 'trône': 8508, 'trouviezvous': 8509, 'trouveraient': 8510, 'trouvaitil': 8511, 'troubles': 8512, 'trombone': 8513, 'tristesse': 8514, 'tricot': 8515, 'tremblait': 8516, 'travaillèrent': 8517, 'travailliez': 8518, 'travaillestu\\u202f': 8519, 'travaillaient': 8520, 'transporte': 8521, 'transmis': 8522, 'traitée': 8523, 'trahit': 8524, 'trahison': 8525, 'trafic': 8526, 'tracassetil': 8527, 'toutefois': 8528, 'toussait': 8529, 'tour\\u202f': 8530, 'tournevis': 8531, 'tournait': 8532, 'toucha': 8533, 'torse': 8534, 'tokyo\\u202f': 8535, 'toilette': 8536, 'tiroirs': 8537, 'tienstu': 8538, 'texcuser': 8539, 'texas': 8540, 'tests': 8541, 'territoire': 8542, 'tenues': 8543, 'tendue': 8544, 'teindre': 8545, 'taxi\\u202f': 8546, 'tassiedstu': 8547, 'tarriver': 8548, 'tardive': 8549, 'tapporter': 8550, 'taisezvous': 8551, 'taisait': 8552, 'tailles': 8553, 'tailler': 8554, 'taider\\u202f': 8555, 'taches': 8556, 'séteignirent': 8557, 'sétaient': 8558, 'sénateur': 8559, 'séduisant': 8560, 'sécurisé': 8561, 'séclaircir': 8562, 'séchappa': 8563, 'suspendue': 8564, 'surviendrait': 8565, 'sursauter': 8566, 'supportezvous': 8567, 'supplier': 8568, 'superficielle': 8569, 'suivait': 8570, 'suffisante': 8571, 'suffire': 8572, 'stupidité': 8573, 'stressé': 8574, 'stop': 8575, 'stationnement': 8576, 'spectateurs': 8577, 'spatiale': 8578, 'souriez': 8579, 'sourient': 8580, 'souliers': 8581, 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10453, 'digérer': 10454, 'dexagérer': 10455, 'devraije': 10456, 'deviendront': 10457, 'dessiné': 10458, 'descendirent': 10459, 'depuis\\u202f': 10460, 'denvie': 10461, 'dentrenous': 10462, 'dense': 10463, 'denfant': 10464, 'dendroit': 10465, 'dempêcher': 10466, 'demi': 10467, 'dehors\\u202f': 10468, 'deffectuer': 10469, 'dauphins': 10470, 'daugmenter': 10471, 'dassistance': 10472, 'dasie': 10473, 'darroser': 10474, 'darrachepied': 10475, 'dargot': 10476, 'dansons': 10477, 'danses': 10478, 'danser\\u202f': 10479, 'dalibi': 10480, 'dadopter': 10481, 'dactes': 10482, 'daccidents': 10483, 'daboyer': 10484, 'césar': 10485, 'célébrité': 10486, 'cynique': 10487, 'curriculum': 10488, 'cultivent': 10489, 'cuite': 10490, 'cuisson': 10491, 'cuisine\\u202f': 10492, 'créatures': 10493, 'créatives': 10494, 'cruauté': 10495, 'croître': 10496, 'croirez': 10497, 'croirait': 10498, 'croie': 10499, 'cricket': 10500, 'crayon\\xa0': 10501, 'craquer': 10502, 'craintif': 10503, 'crack': 10504, 'couvrefeu': 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'bruns': 10608, 'brossetoi': 10609, 'briquet': 10610, 'branchée': 10611, 'bourrés': 10612, 'bouquin': 10613, 'boulots': 10614, 'bouleversé': 10615, 'bon\\u202f': 10616, 'bonsoir': 10617, 'bonbon': 10618, 'boite\\xa0': 10619, 'bocal': 10620, 'blonds': 10621, 'blondes': 10622, 'blesse': 10623, 'bifteck': 10624, 'berceau': 10625, 'bell': 10626, 'beignet': 10627, 'beaucoup\\xa0': 10628, 'bavarde': 10629, 'battre\\xa0': 10630, 'battit': 10631, 'baseball\\xa0': 10632, 'barbant': 10633, 'bancale': 10634, 'banane\\u202f': 10635, 'balayer': 10636, 'bagues': 10637, 'aînées': 10638, 'avouer': 10639, 'aveuglé': 10640, 'avertis': 10641, 'avertie': 10642, 'aversion': 10643, 'aventures': 10644, 'avare': 10645, 'autorisation': 10646, 'automatiquement': 10647, 'auraistu': 10648, 'auparavant\\u202f': 10649, 'auditoire': 10650, 'audessous': 10651, 'attraperas': 10652, 'attirée': 10653, 'attentifs': 10654, 'attendus': 10655, 'atteindra': 10656, 'atteignîmes': 10657, 'attaqués': 10658, 'atrocement': 10659, 'atomes': 10660, 'atmosphère': 10661, 'athlètes': 10662, 'assurés': 10663, 'assume': 10664, 'assigné': 10665, 'assidument': 10666, 'artérielle\\xa0': 10667, 'arrêtezmoi': 10668, 'arrivé\\xa0': 10669, 'arrivons': 10670, 'arriveronsnous': 10671, 'arriverez': 10672, 'arriverai': 10673, 'armoire': 10674, 'appât': 10675, 'appréciez': 10676, 'apprécieriez': 10677, 'apprit': 10678, 'apprenezvous': 10679, 'apprendstu': 10680, 'applaudit': 10681, 'applaudir': 10682, 'appelèrent': 10683, 'appartiennent': 10684, 'apparaître': 10685, 'apeuré': 10686, 'apercevoir': 10687, 'anxieuse': 10688, 'ans\\xa0»': 10689, 'ankara': 10690, 'animée': 10691, 'ancêtres': 10692, 'analyse': 10693, 'améliorées': 10694, 'amusonsnous': 10695, 'amusons': 10696, 'amusants': 10697, 'amusantes': 10698, 'ambidextre': 10699, 'amassé': 10700, 'amandes': 10701, 'altitude': 10702, 'alpes': 10703, 'allégations': 10704, 'allure': 10705, 'allongezvous': 10706, 'allez\\u202f': 10707, 'allemande': 10708, 'aisée': 10709, 'aimetil': 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'équipement': 10764, 'épuisant': 10765, 'épingle': 10766, 'épicerie': 10767, 'énormes': 10768, 'énervées': 10769, 'émotif': 10770, 'émissions': 10771, 'émigré': 10772, 'émergée': 10773, 'élégants': 10774, 'élue': 10775, 'éloquent': 10776, 'éloge': 10777, 'éliminer': 10778, 'égalité': 10779, 'égales': 10780, 'édition': 10781, 'écrivez': 10782, 'écrivait': 10783, 'écristu': 10784, 'écrira': 10785, 'écrasée': 10786, 'écran': 10787, 'écoutons': 10788, 'écoutiez': 10789, 'écoutetil': 10790, 'écoutais': 10791, 'écoulée': 10792, 'éclos': 10793, 'éclairée': 10794, 'échangions': 10795, 'ébranlé': 10796, 'ébahi': 10797, 'Étatsuniens': 10798, 'Éprouvezvous': 10799, 'Épluche': 10800, 'Éloigne': 10801, '«\\u202frien': 10802, '«\\u202fquoi': 10803, '«\\xa0À': 10804, '«\\xa0vous': 10805, '«\\xa0tu': 10806, '«\\xa0questce': 10807, '«\\xa0quel': 10808, '«\\xa0quand': 10809, '«\\xa0puisje': 10810, '«\\xa0parce': 10811, '«\\xa0le': 10812, '«\\xa0il': 10813, '«\\xa0comment': 10814, '«\\xa0combien': 10815, '«je': 10816, '\\xa0»': 10817, 'zèbres': 10818, 'zinzin\\u202f': 10819, 'yoga': 10820, 'yaourt': 10821, 'windows': 10822, 'weekend\\xa0': 10823, 'vêtements\\xa0': 10824, 'vérifiée': 10825, 'véhicules': 10826, 'végétariens': 10827, 'voyage\\u202f': 10828, 'voyageons': 10829, 'voué': 10830, 'voulaitil': 10831, 'volume\\xa0': 10832, 'volleyball': 10833, 'vogue': 10834, 'vitil': 10835, 'visite\\u202f': 10836, 'visiter\\u202f': 10837, 'visez': 10838, 'vise': 10839, 'virage': 10840, 'villages': 10841, 'vigueur': 10842, 'vigilance': 10843, 'vieux\\xa0': 10844, 'viennentils': 10845, 'vieillissons': 10846, 'vieillir': 10847, 'vieillesse': 10848, 'vida': 10849, 'verrouilla': 10850, 'verre\\u202f': 10851, 'venu\\u202f': 10852, 'venger': 10853, 'vendit': 10854, 'vendez': 10855, 'velours': 10856, 'veinarde': 10857, 'veillerai': 10858, 'va\\u202f': 10859, 'vautelle': 10860, 'vaton': 10861, 'varient': 10862, 'varie': 10863, 'variables': 10864, 'vaniteux': 10865, 'van': 10866, 'valide': 10867, 'valentin': 10868, 'vaisselle\\u202f': 10869, 'vains': 10870, 'vacances\\xa0': 10871, 'utilises': 10872, 'utilisation': 10873, 'utilisa': 10874, 'usines': 10875, 'urgentes': 10876, 'urbain': 10877, 'uniformes': 10878, 't’es': 10879, 'télévision\\xa0': 10880, 'téléphonezmoi': 10881, 'téléphonemoi': 10882, 'téléphonelui': 10883, 'télégramme': 10884, 'téloche': 10885, 'tuyaux': 10886, 'tues': 10887, 'tubes': 10888, 'tuberculose\\xa0': 10889, 'trucs\\xa0': 10890, 'trouviez': 10891, 'trouver\\xa0': 10892, 'troupeau': 10893, 'trophée': 10894, 'tripe': 10895, 'triomphe': 10896, 'trimestre': 10897, 'tricoté': 10898, 'tribu': 10899, 'tressailli': 10900, 'tremper': 10901, 'traîtres': 10902, 'traversent': 10903, 'transmission': 10904, 'transformée': 10905, 'transformé': 10906, 'trame': 10907, 'trajet': 10908, 'traitant': 10909, 'traita': 10910, 'trahies': 10911, 'traducteur': 10912, 'traditionnellement': 10913, 'tracteur': 10914, 'tracasser': 10915, 'tracassait': 10916, 'tousse': 10917, 'toupie': 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'tailleur': 10972, 'taiderais': 10973, 'tabouret': 10974, 'tablier': 10975, 'tables': 10976, 'tableau\\u202f': 10977, 'tabasser': 10978, 's’extriquer': 10979, 's’appelle': 10980, 's’agit': 10981, 'sœur\\xa0': 10982, 'sœurs\\u202f': 10983, 'sûre\\u202f': 10984, 'sévères': 10985, 'sérum': 10986, 'sépara': 10987, 'sélectionné': 10988, 'séjournâmes': 10989, 'sécrit': 10990, 'séchappe': 10991, 'sécha': 10992, 'sème': 10993, 'sympathie': 10994, 'symbolise': 10995, 'suspecté': 10996, 'sushi': 10997, 'sus': 10998, 'survécut': 10999, 'survivent': 11000, 'survivante': 11001, 'survivant': 11002, 'survint': 11003, 'surveillait': 11004, 'surmenée': 11005, 'surmené': 11006, 'supporté': 11007, 'supplément': 11008, 'suive': 11009, 'suffisants': 11010, 'sucrées': 11011, 'sucre\\u202f': 11012, 'substances': 11013, 'studio': 11014, 'structure': 11015, 'street': 11016, 'stores': 11017, 'stationner': 11018, 'star': 11019, 'stable': 11020, 'squelette': 11021, 'spécial\\u202f»': 11022, 'spécialisée': 11023, 'spoutnik': 11024, 'souviennes': 11025, 'souvenez': 11026, 'sousestimé': 11027, 'sousestimes': 11028, 'sournoise': 11029, 'soupçon': 11030, 'soupira': 11031, 'soupe\\xa0': 11032, 'souligné': 11033, 'souleva': 11034, 'souhaitestu': 11035, 'souhaiterions': 11036, 'souhaiteriezvous': 11037, 'souhaiteraistu': 11038, 'souhaitait': 11039, 'souffrira': 11040, 'souffrent': 11041, 'souffler': 11042, 'soufflait': 11043, 'soucié': 11044, 'souciestu': 11045, 'souciait': 11046, 'sortirons': 11047, 'sortiras': 11048, 'sortions': 11049, 'sortil\\u202f': 11050, 'sorcier': 11051, 'sondage': 11052, 'soigner': 11053, 'sofa': 11054, 'smic': 11055, 'skis': 11056, 'skiant': 11057, 'sintéressent': 11058, 'sintroduire': 11059, 'signifier': 11060, 'signifient': 11061, 'significatif': 11062, 'signature': 11063, 'signal': 11064, 'sieste\\xa0': 11065, 'shabitue': 11066, 'serveurs': 11067, 'serrés': 11068, 'sermonner': 11069, 'seratelle': 11070, 'senvola': 11071, 'sentraîner': 11072, 'sentant': 11073, 'sensass': 11074, 'senrichissent': 11075, 'semaine\\u202f': 11076, 'seins\\xa0': 11077, 'section': 11078, 'secret\\xa0': 11079, 'secouru': 11080, 'secours\\xa0': 11081, 'secoua': 11082, 'secondaire': 11083, 'schéma': 11084, 'sauves': 11085, 'sautez': 11086, 'sarrêtera': 11087, 'sarranger': 11088, 'saouler': 11089, 'sangloter': 11090, 'sanglotait': 11091, 'sanction': 11092, 'saméliorera': 11093, 'saméliorent': 11094, 'samedis': 11095, 'saluèrent': 11096, 'salutations': 11097, 'salleàmanger': 11098, 'salles': 11099, 'salledebain': 11100, 'salive': 11101, 'salariés': 11102, 'salade\\u202f': 11103, 'saisissez': 11104, 'sainement': 11105, 'saignez': 11106, 'saignements': 11107, 'saignement': 11108, 'sagir': 11109, 'sacrifié': 11110, 'sacrifier': 11111, 'saccumulent': 11112, 'saccrochait': 11113, 'sabots': 11114, 'sabattit': 11115, 'rôti': 11116, 'réévalué': 11117, 'révélation': 11118, 'réveillezmoi': 11119, 'réveillez': 11120, 'réveillemoi': 11121, 'réussisse': 11122, 'réussis': 11123, 'réuni': 11124, 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'd’argent': 12091, 'dœuvre': 12092, 'dîné\\xa0': 12093, 'dînent': 12094, 'développée': 12095, 'développe': 12096, 'détudiant': 12097, 'détour': 12098, 'détestez': 12099, 'détesterais': 12100, 'détenir': 12101, 'détendstoi\\u202f': 12102, 'détendezvous\\u202f': 12103, 'détecté': 12104, 'détectives': 12105, 'désorientée': 12106, 'désobligeantes': 12107, 'désireux': 12108, 'désirer': 12109, 'déshabiller': 12110, 'désespère': 12111, 'désavantages': 12112, 'désapprouvé': 12113, 'déroba': 12114, 'dérangeait': 12115, 'déraisonnable': 12116, 'dépôt': 12117, 'dépêchés': 12118, 'dépêchonsnous': 12119, 'déprouvant': 12120, 'déprimés': 12121, 'déposée': 12122, 'déposa': 12123, 'déplaça': 12124, 'dépistage': 12125, 'dépecer': 12126, 'démocrate': 12127, 'démissionniez': 12128, 'démissionnes': 12129, 'démissionne': 12130, 'dément': 12131, 'délocution': 12132, 'délire': 12133, 'délicieusement': 12134, 'déjeunons': 12135, 'dégagé': 12136, 'défunte': 12137, 'défoncé': 12138, 'défilé': 12139, 'défend': 12140, 'défaire': 12141, 'dédié': 12142, 'décédées': 12143, 'décrivent\\u202f': 12144, 'décourage': 12145, 'déconomiser': 12146, 'déconne': 12147, 'décollage': 12148, 'décliné': 12149, 'décidez': 12150, 'décide': 12151, 'déchargé': 12152, 'décharger': 12153, 'décence': 12154, 'débrouillés': 12155, 'débit': 12156, 'débarrasserai': 12157, 'débarrassa': 12158, 'dresseur': 12159, 'dressai': 12160, 'drame': 12161, 'dramatique': 12162, 'doyenne': 12163, 'doyen': 12164, 'douzaines': 12165, 'douves': 12166, 'doucher': 12167, 'dose': 12168, 'dormis': 12169, 'dormais': 12170, 'dordinateurs': 12171, 'dordinateur': 12172, 'doptique': 12173, 'doptions': 12174, 'donné\\xa0': 12175, 'donnezmen': 12176, 'donnezlemoi': 12177, 'donnezlamoi': 12178, 'donnerastu': 12179, 'donnerait': 12180, 'donnelemoi': 12181, 'donnele': 12182, 'donation': 12183, 'documentaire': 12184, 'doccasions': 12185, 'diète': 12186, 'divorcés': 12187, 'divertissant': 12188, 'diteslemoi': 12189, 'dis\\u2009': 12190, 'distraction': 12191, 'distingue': 12192, 'dissiper': 12193, 'disloqué': 12194, 'dislemoi': 12195, 'disions': 12196, 'discutèrent': 12197, 'discutais': 12198, 'discrets': 12199, 'dirréfléchi': 12200, 'dirigeant': 12201, 'dires': 12202, 'directions': 12203, 'diplômés': 12204, 'diplomatiques': 12205, 'dinterférer': 12206, 'dinhabituel': 12207, 'dimprudent': 12208, 'dimpressionner': 12209, 'dillusions': 12210, 'dil': 12211, 'digitales': 12212, 'didiot': 12213, 'didentification': 12214, 'dicte': 12215, 'diagnostiquée': 12216, 'diagnostic': 12217, 'dhôtels': 12218, 'dhydrogène': 12219, 'dhaleine': 12220, 'dhabiter': 12221, 'dexposition': 12222, 'dexcellents': 12223, 'devînmes': 12224, 'devienne': 12225, 'deviendrait': 12226, 'deuxcentquinze': 12227, 'deuil': 12228, 'dessinée': 12229, 'dessine': 12230, 'desserts': 12231, 'descendant': 12232, 'descend': 12233, 'descalader': 12234, 'dernier\\xa0': 12235, 'dents\\xa0': 12236, 'dentiste\\xa0': 12237, 'dentifrice\\u202f': 12238, 'dendroits': 12239, 'demprunter': 12240, 'demployer': 12241, 'demplois': 12242, 'demoiselle': 12243, 'dembarquement': 12244, 'demandezmoi': 12245, 'demanderez': 12246, 'demanderais': 12247, 'demandeleur': 12248, 'delbe': 12249, 'davoine': 12250, 'davocat': 12251, 'davec': 12252, 'davancer': 12253, 'dautre\\u202f': 12254, 'dautant': 12255, 'daujourdhui\\u202f': 12256, 'daujourdhui\\xa0': 12257, 'dattendre\\xa0': 12258, 'dasthme\\xa0': 12259, 'darwin': 12260, 'daprèsmidi': 12261, 'danger\\u202f': 12262, 'danemark': 12263, 'daméricains': 12264, 'daltonien': 12265, 'dalternative': 12266, 'daimer': 12267, 'dailleurs': 12268, 'daffilée': 12269, 'dadmettre': 12270, 'cœurs': 12271, 'côtières': 12272, 'célébré': 12273, 'célébrons': 12274, 'câlin\\xa0': 12275, 'câlins': 12276, 'cyniques': 12277, 'cycliste': 12278, 'cultivé': 12279, 'cuisis': 12280, 'crémeuse': 12281, 'crée': 12282, 'crues': 12283, 'cruelles': 12284, 'cruellement': 12285, 'croquis': 12286, 'croixrouge': 12287, 'croisle': 12288, 'croise': 12289, 'croire\\xa0': 12290, 'crocodile': 12291, 'critiqué': 12292, 'critiqua': 12293, 'crises': 12294, 'criminelle': 12295, 'creux': 12296, 'cravate\\u202f': 12297, 'cravate\\xa0': 12298, 'craintive': 12299, 'coûteuse': 12300, 'coûteratil\\u202f': 12301, 'coéquipiers': 12302, 'covid19': 12303, 'couvrechef': 12304, 'cousines': 12305, 'courûmes': 12306, 'courtoise': 12307, 'cours\\xa0': 12308, 'coures': 12309, 'coupure': 12310, 'coupables': 12311, 'couches': 12312, 'coréenne': 12313, 'corée': 12314, 'correspondant': 12315, 'correctement\\xa0': 12316, 'corps\\xa0': 12317, 'corbeille': 12318, 'coq': 12319, 'conviée': 12320, 'convives': 12321, 'convientil': 12322, 'convienne': 12323, 'convictions': 12324, 'convaincant': 12325, 'controversée': 12326, 'contribution': 12327, 'contrecœur': 12328, 'contravention': 12329, 'contrat\\xa0': 12330, 'contraintes': 12331, 'contracte': 12332, 'continu': 12333, 'continent': 12334, 'contenant': 12335, 'contacté': 12336, 'contactemoi': 12337, 'consulté': 12338, 'construisirent': 12339, 'constituerait': 12340, 'constituent': 12341, 'conspiration': 12342, 'consommé': 12343, 'consomment': 12344, 'consoler': 12345, 'considérezvous': 12346, 'considérez': 12347, 'considérable': 12348, 'considèrestu': 12349, 'considères': 12350, 'considèrent': 12351, 'conservateurs': 12352, 'consenti': 12353, 'conseillée': 12354, 'conseillezvous': 12355, 'consacra': 12356, 'conjoint': 12357, 'conjecture': 12358, 'confortables': 12359, 'confondue': 12360, 'confond': 12361, 'confiserie': 12362, 'confectionnée': 12363, 'conducteurs': 12364, 'concurrents': 12365, 'conclut': 12366, 'concept': 12367, 'concentronsnous': 12368, 'concentretoi': 12369, 'concentra': 12370, 'comptée': 12371, 'compréhensive': 12372, 'compléter': 12373, 'complexes': 12374, 'compense': 12375, 'compartiment': 12376, 'compagnie\\xa0': 12377, 'commit': 12378, 'commettons': 12379, 'commettes': 12380, 'commette': 12381, 'commenceront': 12382, 'commencerais': 12383, 'commandement': 12384, 'commandant': 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'emmenezmoi': 19518, 'emmenezle': 19519, 'embrouillée': 19520, 'embrassé\\xa0': 19521, 'embrasses': 19522, 'embauchées': 19523, 'embauchiez': 19524, 'embauche': 19525, 'embarrasserait': 19526, 'embarrasse': 19527, 'embarras': 19528, 'emballée': 19529, 'elle\\xa0': 19530, 'ellesmêmes': 19531, 'effréné': 19532, 'effrontément': 19533, 'effrayé\\xa0': 19534, 'effrayée\\xa0': 19535, 'effrayait': 19536, 'effondrée': 19537, 'effectuée': 19538, 'effectuez': 19539, 'effaça': 19540, 'edo': 19541, 'ecchymose': 19542, 'ebola': 19543, 'eau\\xa0': 19544, 'd’épilepsie': 19545, 'd’ordinaire': 19546, 'd’informations': 19547, 'd’honnête': 19548, 'd’enfants': 19549, 'd’en': 19550, 'd’autoécole': 19551, 'dœufs\\xa0': 19552, 'dînes': 19553, 'dînant': 19554, 'dévêtir': 19555, 'dévoré': 19556, 'déverrouille': 19557, 'dévasté': 19558, 'dévasion': 19559, 'dévalé': 19560, 'dévacuer': 19561, 'dété\\xa0': 19562, 'détériorée': 19563, 'détudiant\\u202f': 19564, 'détudiantes': 19565, 'détruirai': 19566, 'détranges': 19567, 'détourne': 19568, 'détoffe': 19569, 'détienstu': 19570, 'déthique': 19571, 'détestaient': 19572, 'détesta': 19573, 'détenons': 19574, 'détenez': 19575, 'détendent': 19576, 'détecter': 19577, 'détaxé\\u202f': 19578, 'détaillées': 19579, 'détachées': 19580, 'détacher': 19581, 'détache': 19582, 'détablir': 19583, 'désormais\\u202f': 19584, 'désorganisée': 19585, 'désorganisé': 19586, 'désobéirent': 19587, 'désirés': 19588, 'désiriez': 19589, 'désiretil': 19590, 'désires': 19591, 'désirent': 19592, 'désirable': 19593, 'désintéressé': 19594, 'désignés': 19595, 'désignées': 19596, 'désignent': 19597, 'désigna': 19598, 'déshydraté': 19599, 'déshonneur': 19600, 'déshabillé': 19601, 'déshabille': 19602, 'déshabilla': 19603, 'désespérant': 19604, 'désespoir': 19605, 'désertées': 19606, 'désertée': 19607, 'désencombrer': 19608, 'désastres': 19609, 'désarmée': 19610, 'désapprouva': 19611, 'désagrément': 19612, 'désagréables': 19613, 'désactivé': 19614, 'déroutantes': 19615, 'déroulentelles': 19616, 'dérivé': 19617, 'dériger': 19618, 'dérapé': 19619, 'déranger\\xa0': 19620, 'dérangeons': 19621, 'dérangeais': 19622, 'déraisonnables': 19623, 'déraillé': 19624, 'déquité': 19625, 'déquipes': 19626, 'dépêchée': 19627, 'dépêchonsnous\\u202f': 19628, 'dépêchetoi\\xa0': 19629, 'dépêche': 19630, 'dépêchai': 19631, 'dépée': 19632, 'député': 19633, 'déprécie': 19634, 'déprime': 19635, 'déprimante': 19636, 'déprimait': 19637, 'dépressive': 19638, 'dépouser': 19639, 'déposé': 19640, 'déposez': 19641, 'déporté': 19642, 'déplore': 19643, 'déplorable': 19644, 'déplaît': 19645, 'déplaçait': 19646, 'déplacées': 19647, 'déplacez': 19648, 'déplacent': 19649, 'dépensent': 19650, 'dépensa': 19651, 'dépens': 19652, 'dépends': 19653, 'dépendra': 19654, 'dépassées': 19655, 'dépassant': 19656, 'dépargner': 19657, 'dénégation': 19658, 'dénuée': 19659, 'dénoncer': 19660, 'dénommé': 19661, 'dénommer': 19662, 'dénergie\\u202f': 19663, 'déménagerai\\u202f': 19664, 'déménageons': 19665, 'déménageait': 19666, 'démotion': 19667, 'démonté': 19668, 'démonter': 19669, 'démontemoi': 19670, 'démodés': 19671, 'démodées': 19672, 'démodée': 19673, 'démocratique': 19674, 'démissionné\\u202f': 19675, 'démissionné\\xa0': 19676, 'démené': 19677, 'démasqué': 19678, 'démarrent': 19679, 'démange': 19680, 'déloyale': 19681, 'déloyal': 19682, 'délivré': 19683, 'délite': 19684, 'délicieux\\u202f': 19685, 'délicats': 19686, 'délicatement': 19687, 'délever': 19688, 'délais': 19689, 'déjeunes': 19690, 'déjeunerezvous': 19691, 'déjection': 19692, 'déguisée': 19693, 'déguisement': 19694, 'déguisa': 19695, 'déguerpir': 19696, 'déguerpi': 19697, 'dégoûte': 19698, 'dégoûtants': 19699, 'dégoûtantes': 19700, 'dégourdie': 19701, 'dégouline': 19702, 'dégotezmoi': 19703, 'dégoterons': 19704, 'dégoter': 19705, 'dégotemoi': 19706, 'dégagea': 19707, 'défié': 19708, 'définistu': 19709, 'définissezvous': 19710, 'définir': 19711, 'déficience': 19712, 'défenseur': 19713, 'défenses': 19714, 'défendstoi': 19715, 'défendezvous': 19716, 'défaites': 19717, 'dédire': 19718, 'dédicacer': 19719, 'décéda': 19720, 'décède': 19721, 'décririezvous': 19722, 'décriraistu': 19723, 'découvrirons': 19724, 'découvrirent': 19725, 'découvrirai': 19726, 'découvert\\u202f': 19727, 'découragés': 19728, 'découpés': 19729, 'découpé': 19730, 'découper': 19731, 'découpa': 19732, 'décorons': 19733, 'décore': 19734, 'décontractée': 19735, 'déconseilla': 19736, 'décollement': 19737, 'décolle': 19738, 'décolla': 19739, 'décodé': 19740, 'déclore': 19741, 'décliner': 19742, 'déclin': 19743, 'déclencher': 19744, 'déclencha': 19745, 'déclater': 19746, 'décisif': 19747, 'décimé': 19748, 'décidé\\u202f': 19749, 'décidé\\xa0': 19750, 'décidetoi': 19751, 'décideront': 19752, 'déciderai': 19753, 'décidera': 19754, 'déchaînée': 19755, 'déchaîna': 19756, 'déchappatoire': 19757, 'décevez': 19758, 'décent': 19759, 'décennie': 19760, 'décemment': 19761, 'débuté': 19762, 'débutent': 19763, 'débute': 19764, 'débrouilles': 19765, 'débrouillerai': 19766, 'débrancher': 19767, 'débranche': 19768, 'déboucher': 19769, 'débattues': 19770, 'débarrassai': 19771, 'débarqué': 19772, 'déambule': 19773, 'dé': 19774, 'dÉgypte': 19775, 'dyslexique\\xa0': 19776, 'dynamique': 19777, 'dvd': 19778, 'dutilisation': 19779, 'dutile': 19780, 'dussiez': 19781, 'dusage': 19782, 'durite': 19783, 'durgence\\u202f': 19784, 'duretil': 19785, 'durerait': 19786, 'duper': 19787, 'dupe': 19788, 'drôle\\xa0': 19789, 'dru': 19790, 'droit\\xa0': 19791, 'dressée': 19792, 'dressait': 19793, 'drastiques': 19794, 'drastique': 19795, 'dramatises': 19796, 'dramatiser': 19797, 'dragueurs': 19798, 'drague': 19799, 'dr': 19800, 'doù\\u202f': 19801, 'dovni': 19802, 'douvrages': 19803, 'doutils': 19804, 'doutais': 19805, 'douta': 19806, 'douloureuses': 19807, 'douchezvous': 19808, 'doublés': 19809, 'doublées\\xa0': 19810, 'doublefile': 19811, 'doublecliquez': 19812, 'douane': 19813, 'dos\\xa0': 19814, 'dossiers': 19815, 'dort\\u202f': 19816, 'dortil\\u202f': 19817, 'dormi\\u202f': 19818, 'dormirez': 19819, 'dormiras': 19820, 'dormiez': 19821, 'dormezvous\\xa0': 19822, 'dormentils': 19823, 'doreille\\xa0': 19824, 'doreillers': 19825, 'donnâmes': 19826, 'donneur': 19827, 'donnetil': 19828, 'donner\\xa0': 19829, 'donnerontils': 19830, 'donnerontelles': 19831, 'donnerons': 19832, 'donneleslui': 19833, 'donnelelui': 19834, 'doncles': 19835, 'dommage\\u202f': 19836, 'dominer': 19837, 'domelette': 19838, 'domaines': 19839, 'dollar\\u202f': 19840, 'doiton': 19841, 'doigt\\xa0': 19842, 'doffrir': 19843, 'dodu': 19844, 'dodo': 19845, 'documentée': 19846, 'documentaires': 19847, 'doccupé': 19848, 'dobus': 19849, 'dobserver': 19850, 'dixneufcenttrentecinq': 19851, 'dixneufcentcinquantesept': 19852, 'dixit': 19853, 'divorcée': 19854, 'divorcer\\u202f': 19855, 'divisons': 19856, 'diviser': 19857, 'divinité': 19858, 'divine': 19859, 'divertissezvous': 19860, 'divertis': 19861, 'diverge': 19862, 'divaguer': 19863, 'dithyrambique': 19864, 'ditelle': 19865, 'dis\\xa0': 19866, 'distribuées': 19867, 'distribution': 19868, 'distributeurs': 19869, 'distribua': 19870, 'distrayant': 19871, 'distraites': 19872, 'distrais': 19873, 'distingueton': 19874, 'distinguestu': 19875, 'distillée': 19876, 'dissuader': 19877, 'dissuada': 19878, 'dissoudre': 19879, 'dissimulait': 19880, 'disqualifié': 19881, 'disputentelles': 19882, 'dispositions': 19883, 'disposetelle': 19884, 'disposeriezvous': 19885, 'disposera': 19886, 'disponibles\\u202f': 19887, 'dispenser': 19888, 'disparus': 19889, 'disparais\\u202f': 19890, 'disparaisse': 19891, 'dismen': 19892, 'discutions': 19893, 'discutiez': 19894, 'discuteront': 19895, 'discuterai': 19896, 'discutant': 19897, 'discrètes': 19898, 'discours\\u202f': 19899, 'discothèque': 19900, 'discipline': 19901, 'dirritation': 19902, 'dirigés': 19903, 'dirigez': 19904, 'diriges': 19905, 'dirigeait': 19906, 'direzvous': 19907, 'directeur\\u202f': 19908, 'diraisje': 19909, 'diplômées': 19910, 'dioxyde': 19911, 'diné\\u202f': 19912, 'dinvestisseurs': 19913, 'dintérêts': 19914, 'dintervenir': 19915, 'dintervalle': 19916, 'dintelligence': 19917, 'dinstructions': 19918, 'dinnocence': 19919, 'dingénieurs': 19920, 'dinformation\\xa0': 19921, 'dinfection': 19922, 'dindons': 19923, 'dindiens': 19924, 'dincroyable': 19925, 'dincorrect': 19926, 'dincendies': 19927, 'dinauguration': 19928, 'dimpression': 19929, 'dimpossible': 19930, 'dimanche\\xa0': 19931, 'dilate': 19932, 'digère': 19933, 'digitaline\\xa0': 19934, 'différer': 19935, 'diffèretelle': 19936, 'diffuse': 19937, 'diffamation': 19938, 'dieux': 19939, 'didée': 19940, 'dicton': 19941, 'dici\\u2009': 19942, 'dialecte': 19943, 'diagramme': 19944, 'diabétique\\xa0': 19945, 'diabète\\xa0': 19946, 'dhésiter': 19947, 'dhéroïne': 19948, 'dhypoxie': 19949, 'dhypertension': 19950, 'dhumour': 19951, 'dhorrible': 19952, 'dhiver\\u202f': 19953, 'dharvard': 19954, 'dharmonie': 19955, 'dhabitude\\xa0': 19956, 'dhabitation': 19957, 'dhabitants': 19958, 'dexécuter': 19959, 'dextraire': 19960, 'dexpressions': 19961, 'dexpression': 19962, 'dexploser': 19963, 'dexploitation': 19964, 'dexpliquer\\xa0': 19965, 'dexemples': 19966, 'dexemplaires': 19967, 'dexcuses': 19968, 'devronsnous': 19969, 'devis': 19970, 'devins': 19971, 'devinettes': 19972, 'devines': 19973, 'devineras': 19974, 'devina': 19975, 'deviennes': 19976, 'deviendrez': 19977, 'deviendratil': 19978, 'deviendras': 19979, 'deviendrais': 19980, 'devenons': 19981, 'devenais': 19982, 'deuxci': 19983, 'destruction': 19984, 'destins': 19985, 'destinent': 19986, 'dessoûler': 19987, 'dessin\\xa0': 19988, 'dessins': 19989, 'dessinezvous': 19990, 'dessinemoi': 19991, 'desservir': 19992, 'dessert\\u202f': 19993, 'descendues': 19994, 'descendsmoi': 19995, 'descendrai': 19996, 'descendiez': 19997, 'descendants': 19998, 'descendait': 19999}\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"FwLFXSwHzB9_"},"execution_count":null,"outputs":[]}]}

================================================
FILE: deep learning for natural language processing/5-Neural Machine Translation with Bahdanau Attention by Neuralearn.ai-.ipynb
================================================
{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1Azfk7PQQ3FoBJWSxSkqxquML6LYpZCYS","timestamp":1673806963545},{"file_id":"1wOi9I4Ett5qIJ3eldJ3bTPRmUjk3X5hd","timestamp":1662058828939}],"collapsed_sections":["RBHsJDpiYDs7"]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Dense,Flatten,SimpleRNN,InputLayer,Conv1D,Bidirectional,GRU,LSTM,BatchNormalization,Dropout,Input, Embedding,TextVectorization)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from tensorboard.plugins import projector"],"metadata":{"id":"C24JKO7LxP3O"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Data Preparation"],"metadata":{"id":"cKg0HdsrYA0S"}},{"cell_type":"markdown","source":["## Data Download"],"metadata":{"id":"WgU1Z8fcOSW_"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"4hwhov2uvDdo","executionInfo":{"status":"ok","timestamp":1662441916498,"user_tz":-60,"elapsed":3857,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"d465b4af-de4b-4ee1-b962-0eff6e1b8b77"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-09-06 05:25:46--  https://www.manythings.org/anki/fra-eng.zip\n","Resolving www.manythings.org (www.manythings.org)... 173.254.30.110\n","Connecting to www.manythings.org (www.manythings.org)|173.254.30.110|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 6720195 (6.4M) [application/zip]\n","Saving to: ‘fra-eng.zip’\n","\n","fra-eng.zip         100%[===================>]   6.41M  2.59MB/s    in 2.5s    \n","\n","2022-09-06 05:25:50 (2.59 MB/s) - ‘fra-eng.zip’ saved [6720195/6720195]\n","\n"]}],"source":["!wget https://www.manythings.org/anki/fra-eng.zip"]},{"cell_type":"code","source":["!unzip \"/content/fra-eng.zip\" -d \"/content/dataset/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y-ufBPwn8_ax","executionInfo":{"status":"ok","timestamp":1662441917176,"user_tz":-60,"elapsed":685,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"878be9da-c3ed-4ac8-c9ad-a8382b0cb084"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  /content/fra-eng.zip\n","  inflating: /content/dataset/_about.txt  \n","  inflating: /content/dataset/fra.txt  \n"]}]},{"cell_type":"markdown","source":["## Kaggle Dataset"],"metadata":{"id":"RBHsJDpiYDs7"}},{"cell_type":"code","source":["!pip install -q kaggle\n","!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d dhruvildave/en-fr-translation-dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Lako-uhxy-n","executionInfo":{"status":"ok","timestamp":1661936796981,"user_tz":-60,"elapsed":40971,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"67e2404c-e3aa-4911-ec44-d20c9f01327b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading en-fr-translation-dataset.zip to /content\n","100% 2.54G/2.54G [00:35<00:00, 71.0MB/s]\n","100% 2.54G/2.54G [00:36<00:00, 75.8MB/s]\n"]}]},{"cell_type":"code","source":["!unzip \"/content/en-fr-translation-dataset.zip\" -d \"/content/dataset/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B44hfX_vxzA-","executionInfo":{"status":"ok","timestamp":1661936882170,"user_tz":-60,"elapsed":85198,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a7ea748a-6a24-4941-d2d0-5242b597f574"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  /content/en-fr-translation-dataset.zip\n","  inflating: /content/dataset/en-fr.csv  \n"]}]},{"cell_type":"code","source":["dataset = tf.data.experimental.CsvDataset(\n","  \"/content/dataset/en-fr.csv\",\n","  [\n","    tf.string,\n","    tf.string\n","  ],\n",")"],"metadata":{"id":"fXcOEWp2L7U9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Data Processing"],"metadata":{"id":"omyb2Dq_YHbT"}},{"cell_type":"code","source":["text_dataset=tf.data.TextLineDataset(\"/content/dataset/fra.txt\")"],"metadata":{"id":"ywC7WnoIJeXl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["VOCAB_SIZE=20000\n","ENGLISH_SEQUENCE_LENGTH=64\n","FRENCH_SEQUENCE_LENGTH=64\n","EMBEDDING_DIM=300\n","BATCH_SIZE=64"],"metadata":{"id":"QlcJxz6cJeb_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["english_vectorize_layer=TextVectorization(\n","    standardize='lower_and_strip_punctuation',\n","    max_tokens=VOCAB_SIZE,\n","    output_mode='int',\n","    output_sequence_length=ENGLISH_SEQUENCE_LENGTH\n",")"],"metadata":{"id":"TePer9o-JeeR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["french_vectorize_layer=TextVectorization(\n","    standardize='lower_and_strip_punctuation',\n","    max_tokens=VOCAB_SIZE,\n","    output_mode='int',\n","    output_sequence_length=FRENCH_SEQUENCE_LENGTH\n",")"],"metadata":{"id":"unGn0zqEJegg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def selector(input_text):\n","  split_text=tf.strings.split(input_text,'\\t')\n","  return {'input_1':split_text[0:1],'input_2':'starttoken '+split_text[1:2]},split_text[1:2]+' endtoken'"],"metadata":{"id":"iNK3uewuQTjY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["split_dataset=text_dataset.map(selector)"],"metadata":{"id":"YZDH8xn7Q12e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def separator(input_text):\n","  split_text=tf.strings.split(input_text,'\\t')\n","  return split_text[0:1],'starttoken '+split_text[1:2]+' endtoken'"],"metadata":{"id":"7fcSDED_2c-z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["init_dataset=text_dataset.map(separator)"],"metadata":{"id":"ktN95hiN2pgU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in split_dataset.take(3):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OQkSkpd1tils","executionInfo":{"status":"ok","timestamp":1662441921274,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8704a29b-7e87-4273-809e-566479356701"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Va !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Va ! endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Marche.'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Marche. endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken En route !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'En route ! endtoken'], dtype=object)>)\n"]}]},{"cell_type":"code","source":["english_training_data=init_dataset.map(lambda x,y:x)### input x,y and output x\n","english_vectorize_layer.adapt(english_training_data)#### adapt the vectorize_layer to the training data"],"metadata":{"id":"_usEDYiOJeil"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["french_training_data=init_dataset.map(lambda x,y:y)### input x,y,z and output y\n","french_vectorize_layer.adapt(french_training_data)#### adapt the vectorize_layer to the training data"],"metadata":{"id":"dl7pxJprJek2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def vectorizer(inputs,output):\n","  return {'input_1':english_vectorize_layer(inputs['input_1']),\n","          'input_2':french_vectorize_layer(inputs['input_2'])},french_vectorize_layer(output)"],"metadata":{"id":"4yVIMxvTJemt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["split_dataset"],"metadata":{"id":"op5UqhS14HHz","executionInfo":{"status":"ok","timestamp":1662442398936,"user_tz":-60,"elapsed":48,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"982b866f-950d-4d01-f79c-cb83ec68a204"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<MapDataset element_spec=({'input_1': TensorSpec(shape=(None,), dtype=tf.string, name=None), 'input_2': TensorSpec(shape=(None,), dtype=tf.string, name=None)}, TensorSpec(shape=(None,), dtype=tf.string, name=None))>"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["dataset=split_dataset.map(vectorizer)"],"metadata":{"id":"wI9-GPpVJepF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in split_dataset.take(3):\n","  print(i)"],"metadata":{"id":"m_6Xtks8wPk5","executionInfo":{"status":"ok","timestamp":1662442398938,"user_tz":-60,"elapsed":31,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"455802c2-073a-444c-f58d-bd80e09d5e8d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Va !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Va ! endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Marche.'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Marche. endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken En route !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'En route ! endtoken'], dtype=object)>)\n"]}]},{"cell_type":"code","source":["for i in dataset.take(1):\n","  print(i)"],"metadata":{"id":"FM9aenklufpC","executionInfo":{"status":"ok","timestamp":1662442398938,"user_tz":-60,"elapsed":27,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"75e3c990-375d-44b0-bb48-a4ec154d0563"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[44,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0]])>, 'input_2': <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[  2, 112,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]])>}, <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[112,   3,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]])>)\n"]}]},{"cell_type":"code","source":["dataset"],"metadata":{"id":"iOpSsko7TiKd","executionInfo":{"status":"ok","timestamp":1662442398939,"user_tz":-60,"elapsed":22,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"da5891de-2c2d-44b6-ef4b-e44fc2ad61b3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<MapDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["dataset=dataset.shuffle(2048).unbatch().batch(BATCH_SIZE).prefetch(buffer_size=tf.data.AUTOTUNE)"],"metadata":{"id":"_WE0s6p9TiM9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset"],"metadata":{"id":"tTkF_Qu54QB5","executionInfo":{"status":"ok","timestamp":1662442398939,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"31e312b5-0a1c-4849-8fed-d9d633143d08"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["NUM_BATCHES=int(200000/BATCH_SIZE)"],"metadata":{"id":"K5JXDdrNtwRj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset=dataset.take(int(0.9*NUM_BATCHES))\n","val_dataset=dataset.skip(int(0.9*NUM_BATCHES))"],"metadata":{"id":"XQvg18V5TiO7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset"],"metadata":{"id":"tfCmn1DzTiRp","executionInfo":{"status":"ok","timestamp":1662442398940,"user_tz":-60,"elapsed":17,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"09721b15-2f8d-4720-eb19-5955d1084a95"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<TakeDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":25}]},{"cell_type":"markdown","source":["# Modeling"],"metadata":{"id":"nBVM2EB0Xh97"}},{"cell_type":"markdown","source":["## Encoder"],"metadata":{"id":"hABAiQjTsf5p"}},{"cell_type":"code","source":["class Encoder(tf.keras.Model):\n","  def __init__(self,vocab_size,embedding_dim,units):\n","    super(Encoder,self).__init__()\n","    self.embedding_dim=embedding_dim\n","    self.vocab_size=vocab_size\n","    self.units=units\n","\n","  def build(self,input_shape):\n","    self.embedding=Embedding(self.vocab_size,self.embedding_dim)\n","    self.lstm=LSTM(self.units,return_sequences=True)\n","\n","  def call(self,x):\n","    x=self.embedding(x)\n","    output=self.lstm(x)\n","    return output"],"metadata":{"id":"o7LmYWQ2cwaq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["HIDDEN_UNITS=256\n","EMBEDDING_DIM=256\n","encoder=Encoder(VOCAB_SIZE,EMBEDDING_DIM,HIDDEN_UNITS)\n","encoder_output=encoder(tf.zeros([128,8]))\n","print(encoder_output.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_7ulQFtecwdU","executionInfo":{"status":"ok","timestamp":1662450065527,"user_tz":-60,"elapsed":948,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"49079c61-c3c0-4f05-c59a-4079bca45772"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(128, 8, 256)\n"]}]},{"cell_type":"markdown","source":["## Bahdanau Attention"],"metadata":{"id":"tGJkopfjshmf"}},{"cell_type":"code","source":["class BahdanauAttention(tf.keras.layers.Layer):\n","  def __init__(self,units):\n","    super(BahdanauAttention,self).__init__()\n","    self.units=units\n","\n","  def build(self,input_shape):\n","    self.w_1=tf.keras.layers.Dense(self.units)\n","    self.w_2=tf.keras.layers.Dense(self.units)\n","    self.w=tf.keras.layers.Dense(1)\n","\n","  def call(self,prev_dec_state,enc_states):\n","    scores=self.w(\n","        tf.nn.tanh(\n","        self.w_1(tf.expand_dims(prev_dec_state,-2)) +\n","        self.w_2(enc_states)))\n","    \n","    attention_weights=tf.nn.softmax(scores,axis=1)\n","    context_vector=attention_weights*enc_states\n","    context_vector=tf.reduce_sum(context_vector,axis=1)\n","    return context_vector,attention_weights"],"metadata":{"id":"qD-2QY-YcwgM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bahdanau_attention=BahdanauAttention(256)\n","context_vector,attention_weights=bahdanau_attention(tf.zeros([128,32]),tf.zeros([128,8,32]))\n","print(context_vector.shape)\n","print(attention_weights.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"naL8ZNDscwme","executionInfo":{"status":"ok","timestamp":1662450065528,"user_tz":-60,"elapsed":7,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f05ee2cc-c66e-442b-e85d-df1fa83b0879"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(128, 32)\n","(128, 8, 1)\n"]}]},{"cell_type":"markdown","source":["## Decoder"],"metadata":{"id":"ETCM23b6sktH"}},{"cell_type":"code","source":["class Decoder(tf.keras.Model):\n","  def __init__(self,vocab_size,embedding_dim,dec_units,sequence_length):\n","    super(Decoder,self).__init__()\n","    self.embedding_dim=embedding_dim\n","    self.vocab_size=vocab_size\n","    self.dec_units=dec_units\n","    self.sequence_length=sequence_length\n","   \n","  def build(self,input_shape):\n","    self.dense=Dense(self.vocab_size,activation=\"softmax\")\n","    self.gru=GRU(\n","        self.dec_units,return_sequences=True,return_state=True)\n","    self.attention=BahdanauAttention(self.dec_units)\n","    self.embedding=Embedding(self.vocab_size,self.embedding_dim)\n","\n","  def call(self,x,hidden,shifted_target):\n","    outputs=[]\n","    context_vectors=[]\n","    attention_weightss=[]\n","    shifted_target=self.embedding(shifted_target)\n","    \n","    for t in range(0,self.sequence_length):\n","      context_vector,attention_weights=self.attention(hidden,x)\n","      dec_input=context_vector+shifted_target[:,t]\n","      output,hidden=self.gru(tf.expand_dims(dec_input,1))\n","      outputs.append(output[:,0])\n","\n","    outputs=tf.convert_to_tensor(outputs)\n","    outputs=tf.transpose(outputs, perm=[1,0,2])\n","\n","    outputs=self.dense(outputs)\n","    return outputs,attention_weights"],"metadata":{"id":"QMwWra7_cwpc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["decoder=Decoder(VOCAB_SIZE,EMBEDDING_DIM,HIDDEN_UNITS,FRENCH_SEQUENCE_LENGTH)\n","outputs,attention_weights=decoder(encoder_output,tf.zeros([128,HIDDEN_UNITS]),tf.zeros([128,64]))\n","print(outputs.shape)\n","print(attention_weights.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qp9SVaGRcwr-","executionInfo":{"status":"ok","timestamp":1662450066466,"user_tz":-60,"elapsed":941,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"16865dbf-5964-47ab-8b0f-3de44d6654d1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(128, 64, 20000)\n","(128, 8, 1)\n"]}]},{"cell_type":"code","source":["### ENCODER\n","input = Input(shape=(ENGLISH_SEQUENCE_LENGTH,), dtype=\"int64\", name=\"input_1\")\n","encoder=Encoder(VOCAB_SIZE,EMBEDDING_DIM,HIDDEN_UNITS)\n","encoder_output=encoder(input)\n","\n","### DECODER\n","shifted_target=Input(shape=(FRENCH_SEQUENCE_LENGTH,), dtype=\"int64\", name=\"input_2\")\n","decoder=Decoder(VOCAB_SIZE,EMBEDDING_DIM,HIDDEN_UNITS,FRENCH_SEQUENCE_LENGTH)\n","decoder_output,attention_weightss=decoder(encoder_output,tf.zeros([1,HIDDEN_UNITS]),shifted_target)\n","\n","### OUTPUT\n","bahdanau=Model([input,shifted_target],decoder_output)\n","bahdanau.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Q3O-AVFzcwup","executionInfo":{"status":"ok","timestamp":1662450082680,"user_tz":-60,"elapsed":16216,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2779625c-d777-449b-c2d5-efac99296e57"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model_10\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, 64)]         0           []                               \n","                                                                                                  \n"," encoder_8 (Encoder)            (None, 64, 256)      5645312     ['input_1[0][0]']                \n","                                                                                                  \n"," input_2 (InputLayer)           [(None, 64)]         0           []                               \n","                                                                                                  \n"," decoder_18 (Decoder)           ((None, 64, 20000),  10786593    ['encoder_8[0][0]',              \n","                                 (None, 64, 1))                   'input_2[0][0]']                \n","                                                                                                  \n","==================================================================================================\n","Total params: 16,431,905\n","Trainable params: 16,431,905\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","source":[],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":166},"id":"hw8-rgLrmMAp","executionInfo":{"status":"error","timestamp":1662447653891,"user_tz":-60,"elapsed":560,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"00534caa-da51-444e-cc63-13486ed2b93a"},"execution_count":null,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-90-d9fb1f27d3a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbahdanau\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattention_weightss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mAttributeError\u001b[0m: 'Functional' object has no attribute 'decoder'"]}]},{"cell_type":"code","source":["class BLEU(tf.keras.metrics.Metric):\n","    def __init__(self,name='bleu_score'):\n","        super(BLEU,self).__init__()\n","        self.bleu_score=0\n","\n","    def update_state(self,y_true,y_pred,sample_weight=None):\n","      y_pred=tf.argmax(y_pred,-1)\n","      self.bleu_score=0\n","      for i,j in zip(y_pred,y_true):\n","        tf.autograph.experimental.set_loop_options()\n","\n","        total_words=tf.math.count_nonzero(i)\n","        total_matches=0\n","        for word in i:\n","          if word==0:\n","            break\n","          for q in range(len(j)):\n","            if j[q]==0:\n","              break\n","            if word==j[q]:\n","              total_matches+=1\n","              j=tf.boolean_mask(j,[False if y==q else True for y in range(len(j))])\n","              break\n","\n","        self.bleu_score+=total_matches/total_words\n","        \n","    def result(self):\n","        return self.bleu_score/BATCH_SIZE"],"metadata":{"id":"HIzOQNEucwyG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bahdanau.compile(\n","    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(5e-4),\n","    metrics=[BLEU()],\n","    run_eagerly=True)"],"metadata":{"id":"Oe0WGqZmcxGp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["checkpoint_filepath = '/content/drive/MyDrive/nlp/translation/bahdanau_attention_1.h5'\n","model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n","    filepath=checkpoint_filepath,\n","    monitor='val_loss',\n","    mode='min',\n","    save_best_only=True)"],"metadata":{"id":"Tai1ZEi1cxJn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["history=bahdanau.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=30,)\n","    #callbacks=[model_checkpoint_callback])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":919},"id":"5VUmxwl5e8pl","executionInfo":{"status":"error","timestamp":1662464419001,"user_tz":-60,"elapsed":12397222,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"13218bc3-7a02-4f59-d6c3-97035337a50c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","2812/2812 [==============================] - 790s 281ms/step - loss: 0.3764 - val_loss: 0.8106\n","Epoch 2/30\n","2812/2812 [==============================] - 784s 279ms/step - loss: 0.2681 - val_loss: 0.6852\n","Epoch 3/30\n","2812/2812 [==============================] - 781s 278ms/step - loss: 0.2046 - val_loss: 0.6187\n","Epoch 4/30\n","2812/2812 [==============================] - 781s 278ms/step - loss: 0.1673 - val_loss: 0.5875\n","Epoch 5/30\n","2812/2812 [==============================] - 779s 277ms/step - loss: 0.1427 - val_loss: 0.5829\n","Epoch 6/30\n","2812/2812 [==============================] - 779s 277ms/step - loss: 0.1251 - val_loss: 0.5645\n","Epoch 7/30\n","2812/2812 [==============================] - 777s 276ms/step - loss: 0.1119 - val_loss: 0.5586\n","Epoch 8/30\n","2812/2812 [==============================] - 777s 276ms/step - loss: 0.1016 - val_loss: 0.5745\n","Epoch 9/30\n","2812/2812 [==============================] - 780s 277ms/step - loss: 0.0932 - val_loss: 0.5652\n","Epoch 10/30\n","2812/2812 [==============================] - 772s 275ms/step - loss: 0.0863 - val_loss: 0.5628\n","Epoch 11/30\n","2812/2812 [==============================] - 764s 271ms/step - loss: 0.0801 - val_loss: 0.5629\n","Epoch 12/30\n","2812/2812 [==============================] - 771s 274ms/step - loss: 0.0750 - val_loss: 0.5794\n","Epoch 13/30\n","2812/2812 [==============================] - 772s 274ms/step - loss: 0.0704 - val_loss: 0.5928\n","Epoch 14/30\n","2812/2812 [==============================] - 773s 275ms/step - loss: 0.0663 - val_loss: 0.5830\n","Epoch 15/30\n","2812/2812 [==============================] - 772s 274ms/step - loss: 0.0628 - val_loss: 0.5979\n","Epoch 16/30\n","1593/2812 [===============>..............] - ETA: 5:03 - loss: 0.0493"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-122-80ef07eaaa65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     epochs=30,)\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;31m#callbacks=[model_checkpoint_callback])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m 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error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1390\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m                 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439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    295\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1036\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m 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\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    561\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 914\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    915\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 914\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m       \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m     \u001b[0;31m# Strings, ragged and sparse tensors don't have .item(). Return them as-is.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1221\u001b[0m     \"\"\"\n\u001b[1;32m   1222\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1223\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1224\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1187\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1189\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1190\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"id":"5c5A7_pudzP8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"id":"PsF_wos17EOC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["bahdanau.evaluate(val_dataset)"],"metadata":{"id":"z67BesEQ7EQC","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1662466970971,"user_tz":-60,"elapsed":1882858,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"df808b85-3fc4-43d9-c2d7-996de960cfc8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["274/274 [==============================] - 1865s 7s/step - loss: 0.7017 - bleu_1: 0.1561\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.7016859650611877, 0.15608372877928317]"]},"metadata":{},"execution_count":140}]},{"cell_type":"markdown","source":["# Testing"],"metadata":{"id":"4i1ajAymaDoF"}},{"cell_type":"code","source":["index_to_word={x:y for x, y in zip(range(len(french_vectorize_layer.get_vocabulary())),\n","                                   french_vectorize_layer.get_vocabulary())}"],"metadata":{"id":"b5vMnTM62Cx_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def translator(english_sentence):\n","  tokenized_english_sentence=english_vectorize_layer([english_sentence])\n","  shifted_target='starttoken'\n","\n","  for i in range(FRENCH_SEQUENCE_LENGTH):\n","    tokenized_shifted_target=french_vectorize_layer([shifted_target])\n","    output=bahdanau.predict([tokenized_english_sentence,tokenized_shifted_target])\n","    french_word_index=tf.argmax(output,axis=-1)[0][i].numpy()\n","    current_word=index_to_word[french_word_index]\n","    if current_word=='endtoken':\n","      break\n","    shifted_target+=' '+current_word\n","  return shifted_target[11:]"],"metadata":{"id":"HiWzshkMKfQg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["translator('What makes you think that is not true?')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"0RYy2vtxKfS7","executionInfo":{"status":"ok","timestamp":1662464933341,"user_tz":-60,"elapsed":2506,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"43f4c316-bc05-4356-839f-31a64d5ae448"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'questce qui te fait croire que ce nest pas vrai'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":136}]},{"cell_type":"code","source":["translator('Have you ever watched a soccer under the rain?')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"ymusGWncm6sr","executionInfo":{"status":"ok","timestamp":1662464948420,"user_tz":-60,"elapsed":1491,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"70b2058b-10c2-4397-b203-f125f1504eab"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'astu déjà regardé un match de la pluie'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":137}]},{"cell_type":"code","source":["translator('Great trees do not grow with ease, the stronger the winds, the stronger the trees')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"M6y34Nhv79U_","executionInfo":{"status":"ok","timestamp":1662464715634,"user_tz":-60,"elapsed":1270,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a4087669-80ec-4aeb-8584-bb082146cf70"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'des arbres sont très [UNK] pas fort'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":132}]},{"cell_type":"code","source":["translator('Everyone should water his or her tomato plants')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"VDfIbnVL79XE","executionInfo":{"status":"ok","timestamp":1662464716691,"user_tz":-60,"elapsed":1060,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"343e6c47-6838-4ffb-da7b-eab93e0c0240"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'tout le monde devrait manger sa propriété à la nage'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":133}]},{"cell_type":"code","source":["word_to_index={y:x for x, y in zip(range(len(french_vectorize_layer.get_vocabulary())),\n","                                   french_vectorize_layer.get_vocabulary())}"],"metadata":{"id":"f1BQFbrdKfVZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"Ih75R554nENx"},"execution_count":null,"outputs":[]}]}

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FILE: deep learning for natural language processing/6-Neural Machine Translation with Transformers by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"5eMMJssyxQ-J"},"outputs":[],"source":["#!pip install --upgrade tensorflow"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C24JKO7LxP3O"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import (Dense,Flatten,SimpleRNN,InputLayer,Conv1D,LayerNormalization,Bidirectional,GRU,LSTM,BatchNormalization,Dropout,Input,MultiHeadAttention,Embedding,TextVectorization)\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from tensorboard.plugins import projector"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":106,"status":"ok","timestamp":1663085828314,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"LdwxalEeyUX5","outputId":"a6195455-1e21-4fbd-8d40-324f40d4c431"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'2.8.2'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":2}],"source":["tf.__version__"]},{"cell_type":"markdown","metadata":{"id":"cKg0HdsrYA0S"},"source":["# Data Preparation"]},{"cell_type":"markdown","metadata":{"id":"WgU1Z8fcOSW_"},"source":["## Data Download"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":434,"status":"ok","timestamp":1663085828649,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"4hwhov2uvDdo","outputId":"dd2bb197-be5b-4a55-f6fd-cb84bb33792b"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-09-13 16:17:47--  https://www.manythings.org/anki/fra-eng.zip\n","Resolving www.manythings.org (www.manythings.org)... 173.254.30.110\n","Connecting to www.manythings.org (www.manythings.org)|173.254.30.110|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 6720195 (6.4M) [application/zip]\n","Saving to: ‘fra-eng.zip’\n","\n","fra-eng.zip         100%[===================>]   6.41M  15.2MB/s    in 0.4s    \n","\n","2022-09-13 16:17:48 (15.2 MB/s) - ‘fra-eng.zip’ saved [6720195/6720195]\n","\n"]}],"source":["!wget https://www.manythings.org/anki/fra-eng.zip"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":39,"status":"ok","timestamp":1663085828649,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Y-ufBPwn8_ax","outputId":"43e82a80-1399-4a42-9a88-fcac2eb25504"},"outputs":[{"output_type":"stream","name":"stdout","text":["Archive:  /content/fra-eng.zip\n","  inflating: /content/dataset/_about.txt  \n","  inflating: /content/dataset/fra.txt  \n"]}],"source":["!unzip \"/content/fra-eng.zip\" -d \"/content/dataset/\""]},{"cell_type":"markdown","metadata":{"id":"RBHsJDpiYDs7"},"source":["## Kaggle Dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5672,"status":"ok","timestamp":1662593103382,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"0Lako-uhxy-n","outputId":"6a9ace35-3303-4b87-9c62-e546b7cfb26d"},"outputs":[{"name":"stdout","output_type":"stream","text":["cp: cannot stat 'kaggle.json': No such file or directory\n","chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n","Traceback (most recent call last):\n","  File \"/usr/local/bin/kaggle\", line 5, in <module>\n","    from kaggle.cli import main\n","  File \"/usr/local/lib/python3.7/dist-packages/kaggle/__init__.py\", line 23, in <module>\n","    api.authenticate()\n","  File \"/usr/local/lib/python3.7/dist-packages/kaggle/api/kaggle_api_extended.py\", line 166, in authenticate\n","    self.config_file, self.config_dir))\n","OSError: Could not find kaggle.json. Make sure it's located in /root/.kaggle. Or use the environment method.\n"]}],"source":["!pip install -q kaggle\n","!mkdir ~/.kaggle\n","!cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d dhruvildave/en-fr-translation-dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":85198,"status":"ok","timestamp":1661936882170,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"B44hfX_vxzA-","outputId":"a7ea748a-6a24-4941-d2d0-5242b597f574"},"outputs":[{"name":"stdout","output_type":"stream","text":["Archive:  /content/en-fr-translation-dataset.zip\n","  inflating: /content/dataset/en-fr.csv  \n"]}],"source":["!unzip \"/content/en-fr-translation-dataset.zip\" -d \"/content/dataset/\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fXcOEWp2L7U9"},"outputs":[],"source":["dataset = tf.data.experimental.CsvDataset(\n","  \"/content/dataset/en-fr.csv\",\n","  [\n","    tf.string,\n","    tf.string\n","  ],\n",")"]},{"cell_type":"markdown","metadata":{"id":"omyb2Dq_YHbT"},"source":["## Data Processing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ywC7WnoIJeXl"},"outputs":[],"source":["text_dataset=tf.data.TextLineDataset(\"/content/dataset/fra.txt\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QlcJxz6cJeb_"},"outputs":[],"source":["VOCAB_SIZE=20000\n","ENGLISH_SEQUENCE_LENGTH=64\n","FRENCH_SEQUENCE_LENGTH=64\n","EMBEDDING_DIM=256\n","BATCH_SIZE=64"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TePer9o-JeeR"},"outputs":[],"source":["english_vectorize_layer=TextVectorization(\n","    standardize='lower_and_strip_punctuation',\n","    max_tokens=VOCAB_SIZE,\n","    output_mode='int',\n","    output_sequence_length=ENGLISH_SEQUENCE_LENGTH\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"unGn0zqEJegg"},"outputs":[],"source":["french_vectorize_layer=TextVectorization(\n","    standardize='lower_and_strip_punctuation',\n","    max_tokens=VOCAB_SIZE,\n","    output_mode='int',\n","    output_sequence_length=FRENCH_SEQUENCE_LENGTH\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iNK3uewuQTjY"},"outputs":[],"source":["def selector(input_text):\n","  split_text=tf.strings.split(input_text,'\\t')\n","  return {'input_1':split_text[0:1],'input_2':'starttoken '+split_text[1:2]},split_text[1:2]+' endtoken'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YZDH8xn7Q12e"},"outputs":[],"source":["split_dataset=text_dataset.map(selector)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7fcSDED_2c-z"},"outputs":[],"source":["def separator(input_text):\n","  split_text=tf.strings.split(input_text,'\\t')\n","  return split_text[0:1],'starttoken '+split_text[1:2]+' endtoken'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ktN95hiN2pgU"},"outputs":[],"source":["init_dataset=text_dataset.map(separator)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":373,"status":"ok","timestamp":1663085833793,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"OQkSkpd1tils","outputId":"f7515b78-7c49-43bf-f683-d81ff3b712a1"},"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Va !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Va ! endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Marche.'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Marche. endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken En route !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'En route ! endtoken'], dtype=object)>)\n"]}],"source":["for i in split_dataset.take(3):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_usEDYiOJeil"},"outputs":[],"source":["english_training_data=init_dataset.map(lambda x,y:x)### input x,y and output x\n","english_vectorize_layer.adapt(english_training_data)#### adapt the vectorize_layer to the training data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dl7pxJprJek2"},"outputs":[],"source":["french_training_data=init_dataset.map(lambda x,y:y)### input x,y and output y\n","french_vectorize_layer.adapt(french_training_data)#### adapt the vectorize_layer to the training data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4yVIMxvTJemt"},"outputs":[],"source":["def vectorizer(inputs,output):\n","  return {'input_1':english_vectorize_layer(inputs['input_1']),\n","          'input_2':french_vectorize_layer(inputs['input_2'])},french_vectorize_layer(output)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1663086328047,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"op5UqhS14HHz","outputId":"71326288-e352-42f0-f454-1948e81b7af1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<MapDataset element_spec=({'input_1': TensorSpec(shape=(None,), dtype=tf.string, name=None), 'input_2': TensorSpec(shape=(None,), dtype=tf.string, name=None)}, TensorSpec(shape=(None,), dtype=tf.string, name=None))>"]},"metadata":{},"execution_count":17}],"source":["split_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wI9-GPpVJepF"},"outputs":[],"source":["dataset=split_dataset.map(vectorizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":25,"status":"ok","timestamp":1663086328540,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"m_6Xtks8wPk5","outputId":"51ab34e8-01c2-4875-8083-9464a7746f78"},"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Va !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Va ! endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken Marche.'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Marche. endtoken'], dtype=object)>)\n","({'input_1': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'Go.'], dtype=object)>, 'input_2': <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'starttoken En route !'], dtype=object)>}, <tf.Tensor: shape=(1,), dtype=string, numpy=array([b'En route ! endtoken'], dtype=object)>)\n"]}],"source":["for i in split_dataset.take(3):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1663086328540,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"FM9aenklufpC","outputId":"7645cce0-ff33-409d-80ea-7104299d4622"},"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_1': <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[44,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n","         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0]])>, 'input_2': <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[  2, 112,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]])>}, <tf.Tensor: shape=(1, 64), dtype=int64, numpy=\n","array([[112,   3,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0]])>)\n"]}],"source":["for i in dataset.take(1):\n","  print(i)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1663086328541,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"iOpSsko7TiKd","outputId":"46cd5b70-3ac6-4e43-8c95-7c5120f07db0"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<MapDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":21}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_WE0s6p9TiM9"},"outputs":[],"source":["dataset=dataset.shuffle(2048).unbatch().batch(BATCH_SIZE).prefetch(buffer_size=tf.data.AUTOTUNE)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1663086328542,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"tTkF_Qu54QB5","outputId":"4ea925d8-8758-494f-feb8-336cfa4ef49a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":23}],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"K5JXDdrNtwRj"},"outputs":[],"source":["NUM_BATCHES=int(200000/BATCH_SIZE)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XQvg18V5TiO7"},"outputs":[],"source":["train_dataset=dataset.take(int(0.9*NUM_BATCHES))\n","val_dataset=dataset.skip(int(0.9*NUM_BATCHES))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1663086328544,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"tfCmn1DzTiRp","outputId":"07a2da12-2418-4c6b-aaf1-03d563329f20"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<TakeDataset element_spec=({'input_1': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None), 'input_2': TensorSpec(shape=(None, 64), dtype=tf.int64, name=None)}, TensorSpec(shape=(None, 64), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":26}],"source":["train_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ku7L4gZJXgYY"},"outputs":[],"source":["#score=tf.einsum('ijk,ibk->ijb',query,key)"]},{"cell_type":"markdown","metadata":{"id":"nBVM2EB0Xh97"},"source":["# Modeling"]},{"cell_type":"markdown","metadata":{"id":"UXZLSM5pkS3w"},"source":["## Embedding"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c1_5MCwq0ARk"},"outputs":[],"source":["def positional_encoding(model_size,SEQUENCE_LENGTH):\n","  output=[]\n","  for pos in range(SEQUENCE_LENGTH):\n","    PE=np.zeros((model_size))\n","    for i in range(model_size):\n","      if i%2==0:\n","        PE[i]=np.sin(pos/(10000**(i/model_size)))\n","      else:\n","        PE[i]=np.cos(pos/(10000**((i-1)/model_size)))\n","    output.append(tf.expand_dims(PE,axis=0))\n","  out=tf.concat(output,axis=0)\n","  out=tf.expand_dims(out,axis=0)\n","  return tf.cast(out,dtype=tf.float32)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1663095643474,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"PD4gK6uLxY8B","outputId":"0a10f7aa-7555-4063-e00a-bacd870ecc4b"},"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 64, 256)\n"]}],"source":["print(positional_encoding(256,64).shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZM8tUhJ9G0tH"},"outputs":[],"source":["class Embeddings(Layer):\n","  def __init__(self, sequence_length, vocab_size, embed_dim,):\n","    super(Embeddings, self).__init__()\n","    self.token_embeddings=Embedding(\n","        input_dim=vocab_size, output_dim=embed_dim)\n","    self.sequence_length = sequence_length\n","    self.vocab_size = vocab_size\n","    self.embed_dim = embed_dim\n","\n","  def call(self, inputs):\n","    embedded_tokens = self.token_embeddings(inputs)\n","    embedded_positions=positional_encoding(\n","        self.embed_dim,self.sequence_length)\n","    return embedded_tokens + embedded_positions\n","    \n","  def compute_mask(self, inputs, mask=None):\n","    return tf.math.not_equal(inputs, 0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1663095644179,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Sr0o-QcgD4xS","outputId":"a1321316-3b67-4d53-cef6-adf0a51e46e5"},"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 8, 256)\n"]}],"source":["test_input=tf.constant([[  2, 112,   10,   12,  5,   0,   0,   0,]])\n","\n","emb=Embeddings(8,20000,256)\n","emb_out=emb(test_input)\n","print(emb_out.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":461,"status":"ok","timestamp":1663095645333,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"zoZ59gSaom6P","outputId":"6601e1d5-54ae-4fe5-bcc5-d00b7212915e"},"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[[0 0 0 0 0 1 1 1]\n","  [0 0 0 0 0 1 1 1]\n","  [0 0 0 0 0 1 1 1]\n","  [0 0 0 0 0 1 1 1]\n","  [0 0 0 0 0 1 1 1]\n","  [1 1 1 1 1 1 1 1]\n","  [1 1 1 1 1 1 1 1]\n","  [1 1 1 1 1 1 1 1]]], shape=(1, 8, 8), dtype=int32)\n"]}],"source":["mask=emb.compute_mask(test_input)\n","mask1 = mask[:, :, tf.newaxis]\n","mask2 = mask[:,tf.newaxis, :]\n","padding_mask = tf.cast(mask1&mask2, dtype=\"int32\")\n","print(1-padding_mask)"]},{"cell_type":"markdown","metadata":{"id":"QaDKmgJgklY_"},"source":["## Custom MultiHeadAttention"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"AIRfhrVINS39"},"outputs":[],"source":["class CustomSelfAttention(Layer):\n","  def __init__(self,model_size):\n","    super(CustomSelfAttention,self).__init__()\n","    self.model_size=model_size\n","  def call(self,query,key,value,masking):\n","    ######## compute scores\n","    score=tf.matmul(query,key,transpose_b=True)\n","    ######## scaling\n","    score/=tf.math.sqrt(tf.cast(self.model_size,tf.float32))\n","    ######## masking\n","    masking=tf.cast(masking,dtype=tf.float32)\n","    score+=(1.-masking)*-1e10\n","    ######## attention_weights\n","    attention=tf.nn.softmax(score,axis=-1)*masking\n","    ######## output\n","    head=tf.matmul(attention,value)\n","    return head"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1500,"status":"ok","timestamp":1663067017647,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"LSqZtns6YdHP","outputId":"165e9d8b-6d89-4fa7-82a2-843d5e53a3f4"},"outputs":[{"data":{"text/plain":["<tf.Tensor: shape=(1, 8, 256), dtype=float32, numpy=\n","array([[[1., 1., 1., ..., 1., 1., 1.],\n","        [1., 1., 1., ..., 1., 1., 1.],\n","        [1., 1., 1., ..., 1., 1., 1.],\n","        ...,\n","        [0., 0., 0., ..., 0., 0., 0.],\n","        [0., 0., 0., ..., 0., 0., 0.],\n","        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)>"]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["attention=CustomSelfAttention(256)\n","attention(tf.ones([1,8,256]),tf.ones([1,8,256]),tf.ones([1,8,256]),padding_mask)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6Unruv2vNS6g"},"outputs":[],"source":["class CustomMultiHeadAttention(Layer):\n","  def __init__(self,num_heads,key_dim):\n","    super(CustomMultiHeadAttention,self).__init__()\n","    \n","    self.num_heads=num_heads\n","    self.dense_q=[Dense(key_dim) for _ in range(num_heads)]\n","    self.dense_k=[Dense(key_dim) for _ in range(num_heads)]\n","    self.dense_v=[Dense(key_dim) for _ in range(num_heads)]\n","    self.dense_o=Dense(key_dim)\n","    self.self_attention=CustomSelfAttention(key_dim)\n","    \n","  def call(self,query,key,value,attention_mask):\n","    heads=[]\n","    \n","    for i in range(self.num_heads):\n","        head=self.self_attention(self.dense_q[i](query),self.dense_k[i](key),\n","                                self.dense_v[i](value),attention_mask)\n","        heads.append(head)\n","    heads=tf.concat(heads,axis=2)\n","    heads=self.dense_o(heads)\n","    return heads"]},{"cell_type":"markdown","metadata":{"id":"S-Y512TzkOKQ"},"source":["## Encoder"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2jvze09LsnAw"},"outputs":[],"source":["#?tf.keras.layers.MultiHeadAttention"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"t5lTjZBE6M4Q"},"outputs":[],"source":["class TransformerEncoder(Layer):\n","    def __init__(self, embed_dim, dense_dim, num_heads,):\n","        super(TransformerEncoder, self).__init__()\n","        self.embed_dim = embed_dim\n","        self.dense_dim = dense_dim\n","        self.num_heads = num_heads\n","        self.attention = MultiHeadAttention(\n","            num_heads=num_heads, key_dim=embed_dim,\n","        )\n","        self.dense_proj=tf.keras.Sequential(\n","            [Dense(dense_dim, activation=\"relu\"),Dense(embed_dim),]\n","        )\n","        self.layernorm_1 = LayerNormalization()\n","        self.layernorm_2 = LayerNormalization()\n","        self.supports_masking = True\n","\n","    def call(self, inputs, mask=None):\n","      if mask is not None:\n","        mask1 = mask[:, :, tf.newaxis]\n","        mask2 = mask[:,tf.newaxis, :]\n","        padding_mask = tf.cast(mask1&mask2, dtype=\"int32\")\n","\n","      attention_output = self.attention(\n","          query=inputs, key=inputs,value=inputs,attention_mask=padding_mask\n","      )\n","      \n","      proj_input = self.layernorm_1(inputs + attention_output)\n","      proj_output = self.dense_proj(proj_input)\n","      return self.layernorm_2(proj_input + proj_output)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":417,"status":"ok","timestamp":1663095647735,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"hOgozN6bu3hv","outputId":"2a00177e-6129-4c5e-f516-3490e11d2d9c"},"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 8, 256)\n"]}],"source":["encoder_outputs = TransformerEncoder(256,2048,2)(emb_out)\n","print(encoder_outputs.shape)\n"]},{"cell_type":"markdown","metadata":{"id":"jDTmNxkmkctq"},"source":["## Decoder"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kdHTwxmfHWZW"},"outputs":[],"source":["class TransformerDecoder(Layer):\n","  def __init__(self, embed_dim, latent_dim, num_heads,):\n","    super(TransformerDecoder, self).__init__()\n","    self.embed_dim = embed_dim\n","    self.latent_dim = latent_dim\n","    self.num_heads = num_heads\n","    self.attention_1=MultiHeadAttention(\n","        num_heads=num_heads, key_dim=embed_dim\n","    )\n","    self.attention_2=MultiHeadAttention(\n","        num_heads=num_heads, key_dim=embed_dim\n","    )\n","    self.dense_proj = tf.keras.Sequential(\n","        [Dense(latent_dim, activation=\"relu\"),Dense(embed_dim),]\n","    )\n","    self.layernorm_1=LayerNormalization()\n","    self.layernorm_2=LayerNormalization()\n","    self.layernorm_3=LayerNormalization()\n","    self.supports_masking = True\n","  def call(self, inputs, encoder_outputs, mask=None):\n","    \n","    if mask is not None:\n","      mask1 = mask[:, :, tf.newaxis]\n","      mask2 = mask[:,tf.newaxis, :]\n","      padding_mask = tf.cast(mask1&mask2, dtype=\"int32\")\n","      causal_mask=tf.linalg.band_part(tf.ones([tf.shape(inputs)[0],tf.shape(inputs)[1],\n","                                               tf.shape(inputs)[1]],dtype=tf.int32),-1,0)\n","      combined_mask=tf.minimum(padding_mask,causal_mask)\n","\n","    attention_output_1 = self.attention_1(\n","        query=inputs,key=inputs,value=inputs,\n","        attention_mask=causal_mask,\n","        \n","    )\n","    out_1 = self.layernorm_1(inputs + attention_output_1)\n","\n","    attention_output_2,scores= self.attention_2(\n","        query=out_1,key=encoder_outputs,value=encoder_outputs,\n","        attention_mask=combined_mask,\n","        return_attention_scores=True\n","    )\n","    out_2 = self.layernorm_2(out_1 + attention_output_2)\n","\n","    proj_output = self.dense_proj(out_2)\n","    return self.layernorm_3(out_2 + proj_output),scores"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1663095732660,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"bQ0i7k60eDGk","outputId":"6bdf89e0-a404-4d27-b8eb-63b5c3892919"},"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 8, 256)\n","(1, 4, 8, 8)\n"]}],"source":["decoder_outputs,scores = TransformerDecoder(256,2048,4)(emb_out,encoder_outputs)\n","print(decoder_outputs.shape)\n","print(scores.shape)"]},{"cell_type":"markdown","metadata":{"id":"1SPdfBx8ke-Z"},"source":["## Transformer Model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wsT7pvXfzh_E"},"outputs":[],"source":["EMBEDDING_DIM=128\n","D_FF=1024\n","NUM_HEADS=8\n","NUM_LAYERS=1\n","NUM_EPOCHS=20\n","attention_scores={}"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1878,"status":"ok","timestamp":1663096586753,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"-7_vJmd8HWbu","outputId":"49f84320-2aa2-45b9-81db-164bd863b90f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"transformer\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_1 (InputLayer)           [(None, None)]       0           []                               \n","                                                                                                  \n"," input_2 (InputLayer)           [(None, None)]       0           []                               \n","                                                                                                  \n"," embeddings_12 (Embeddings)     (None, 64, 128)      2560000     ['input_1[0][0]']                \n","                                                                                                  \n"," embeddings_13 (Embeddings)     (None, 64, 128)      2560000     ['input_2[0][0]']                \n","                                                                                                  \n"," transformer_encoder_7 (Transfo  (None, 64, 128)     791296      ['embeddings_12[0][0]']          \n"," rmerEncoder)                                                                                     \n","                                                                                                  \n"," transformer_decoder_11 (Transf  ((None, 64, 128),   1319040     ['embeddings_13[0][0]',          \n"," ormerDecoder)                   (None, 8, 64, 64))               'transformer_encoder_7[0][0]']  \n","                                                                                                  \n"," dense_45 (Dense)               (None, 64, 20000)    2580000     ['transformer_decoder_11[0][0]'] \n","                                                                                                  \n","==================================================================================================\n","Total params: 9,810,336\n","Trainable params: 9,810,336\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}],"source":["encoder_inputs=Input(shape=(None,), dtype=\"int64\", name=\"input_1\")\n","x = Embeddings(ENGLISH_SEQUENCE_LENGTH,VOCAB_SIZE,EMBEDDING_DIM)(encoder_inputs)\n","\n","for _ in range(NUM_LAYERS):\n","  x=TransformerEncoder(EMBEDDING_DIM,D_FF,NUM_HEADS)(x)\n","encoder_outputs=x\n","\n","decoder_inputs=Input(shape=(None,), dtype=\"int64\", name=\"input_2\")\n","\n","x = Embeddings(FRENCH_SEQUENCE_LENGTH,VOCAB_SIZE,EMBEDDING_DIM)(decoder_inputs)\n","for i in range(NUM_LAYERS):\n","  x,scores=TransformerDecoder(EMBEDDING_DIM,D_FF,NUM_HEADS)(x, encoder_outputs)\n","  attention_scores[f'decoder_layer{i+1}_block2']=scores\n","decoder_outputs=Dense(VOCAB_SIZE, activation=\"softmax\")(x)\n","\n","attention_score_model= tf.keras.Model(\n","    [encoder_inputs, decoder_inputs], attention_scores, name=\"atttention_score_model\"\n",")\n","transformer = tf.keras.Model(\n","    [encoder_inputs, decoder_inputs], decoder_outputs, name=\"transformer\"\n",")\n","transformer.summary()"]},{"cell_type":"markdown","metadata":{"id":"urO3w5e971Hp"},"source":["# Training"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aBb_YQFGaztF"},"outputs":[],"source":["class BLEU(tf.keras.metrics.Metric):\n","    def __init__(self,name='bleu_score'):\n","        super(BLEU,self).__init__()\n","        self.bleu_score=0\n","\n","    def update_state(self,y_true,y_pred,sample_weight=None):\n","      y_pred=tf.argmax(y_pred,-1)\n","      self.bleu_score=0\n","      for i,j in zip(y_pred,y_true):\n","        tf.autograph.experimental.set_loop_options()\n","\n","        total_words=tf.math.count_nonzero(i)\n","        total_matches=0\n","        for word in i:\n","          if word==0:\n","            break\n","          for q in range(len(j)):\n","            if j[q]==0:\n","              break\n","            if word==j[q]:\n","              total_matches+=1\n","              j=tf.boolean_mask(j,[False if y==q else True for y in range(len(j))])\n","              break\n","\n","        self.bleu_score+=total_matches/total_words\n","        \n","    def result(self):\n","        return self.bleu_score/BATCH_SIZE"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iAKCOX98HWd9"},"outputs":[],"source":["transformer.compile(\n","    loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n","    optimizer=tf.keras.optimizers.Adam(2e-4),)\n","    #metrics=[BLEU()],\n","    #run_eagerly=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":485},"id":"2529VjOrVuGe","executionInfo":{"status":"error","timestamp":1663097191927,"user_tz":-60,"elapsed":602506,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8a71c4b0-84b7-4122-d400-784a35e22b1f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","2812/2812 [==============================] - 170s 59ms/step - loss: 0.4194\n","Epoch 2/20\n","2812/2812 [==============================] - 167s 59ms/step - loss: 0.2455\n","Epoch 3/20\n","2812/2812 [==============================] - 167s 59ms/step - loss: 0.1820\n","Epoch 4/20\n"," 470/2812 [====>.........................] - ETA: 2:19 - loss: 0.1041"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-172-737c703cb35e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m#validation_data=val_dataset.take(300),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     epochs=NUM_EPOCHS)\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1387\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1390\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    436\u001b[0m     \"\"\"\n\u001b[1;32m    437\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    295\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m       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\u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    915\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    555\u001b[0m     \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m       \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m     \u001b[0;31m# Strings, ragged and sparse tensors don't have .item(). Return them as-is.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1221\u001b[0m     \"\"\"\n\u001b[1;32m   1222\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1223\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1224\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1187\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1189\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1190\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["history=transformer.fit(\n","    train_dataset,\n","    validation_data=val_dataset.take(300),\n","    epochs=NUM_EPOCHS)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rj3wLwSW7EMb"},"outputs":[],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pWJfUFU9VrQx"},"outputs":[],"source":["transformer.evaluate(val_dataset)"]},{"cell_type":"markdown","metadata":{"id":"4i1ajAymaDoF"},"source":["# Testing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b5vMnTM62Cx_"},"outputs":[],"source":["index_to_word={x:y for x, y in zip(range(len(french_vectorize_layer.get_vocabulary())),\n","                                   french_vectorize_layer.get_vocabulary())}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HiWzshkMKfQg"},"outputs":[],"source":["def translator(english_sentence):\n","  tokenized_english_sentence=english_vectorize_layer([english_sentence])\n","  shifted_target='starttoken'\n","\n","  for i in range(FRENCH_SEQUENCE_LENGTH):\n","    tokenized_shifted_target=french_vectorize_layer([shifted_target])\n","    output=transformer.predict([tokenized_english_sentence,tokenized_shifted_target])\n","    french_word_index=tf.argmax(output,axis=-1)[0][i].numpy()\n","    current_word=index_to_word[french_word_index]\n","    if current_word=='endtoken':\n","      break\n","    shifted_target+=' '+current_word\n","  return shifted_target[11:]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0RYy2vtxKfS7","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1663082578728,"user_tz":-60,"elapsed":666,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e97a0845-c66b-4f9f-bbf3-bdbd0b2f845b"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'questce qui te fait croire que ce nest pas vrai'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":48}],"source":["translator('What makes you think that it is not true?')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":935,"status":"ok","timestamp":1663082579658,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"f1BQFbrdKfVZ","outputId":"9c94d949-add6-4bf1-ef45-a861f065c945"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'astu regardé français à luniversité sous la pluie'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":49}],"source":["translator('Have you ever watched soccer under the rain?')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1663082579660,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"je_d1YvagbZO","outputId":"2bf6a110-ad7b-4911-928e-5adae5a459d7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'comment se nomme ton nom'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":50}],"source":["translator(\"what's your name?\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":420,"status":"ok","timestamp":1663082580068,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"jWoISRgEVM_J","outputId":"b7a9d12e-ecf5-44f1-f378-d222ff99b46a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'les arbres ne font plus pousser sur les autres'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":51}],"source":["translator('Great trees do not grow with ease, the stronger the winds, the stronger the trees')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":41279,"status":"ok","timestamp":1663082621340,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"BtuuqPkgVNB0","outputId":"22924cab-44bf-4f8f-fdde-a0a693bf7b58"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'mon hôtel ma dit de mappeler'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":52}],"source":["translator('My hotel told me to call you. ')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":449,"status":"ok","timestamp":1663082621773,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"levLKdrZerPj","outputId":"7637d6ff-3c4a-4a5a-b327-36db672af553"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'son français saméliore petit'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":53}],"source":["translator('His French is improving little by little')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1663082621777,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"jFEqsF4AerR3","outputId":"5b01528e-b1ac-44a9-ef63-a460d423e394"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'jadore écrire à écrire'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":54}],"source":["translator('I love to write')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":403,"status":"ok","timestamp":1663082622168,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"_27Gta23erUa","outputId":"8d0b2fae-1852-415e-a789-0228f465ebc3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'peutêtre quelle viendra demain'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":55}],"source":["translator('Perhaps she will come tomorrow')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":1324,"status":"ok","timestamp":1663082623486,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"d1FizaA6erXE","outputId":"e2031d22-5b78-4daf-80ae-d3d5f7c3479f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'tom na jamais entendu marie chanter'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":56}],"source":["translator('Tom has never heard Mary sing.')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1663082623488,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"fekzqiwGerZd","outputId":"6d1528e8-9d9c-4740-b618-6581dcc79feb"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'elle lui donna largent'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":57}],"source":["translator('She handed him the money')"]},{"cell_type":"code","source":[],"metadata":{"id":"lVMdXSi9d2Tx"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Visualization"],"metadata":{"id":"5-dufWz8d2k_"}},{"cell_type":"code","source":["def visualize(english_sentence):\n","  tokenized_english_sentence=english_vectorize_layer([english_sentence])\n","  shifted_target='starttoken je lai fait très bien'\n","\n","  tokenized_shifted_target=french_vectorize_layer([shifted_target])\n","  attention_weights=attention_score_model.predict([tokenized_english_sentence,\n","                                                   tokenized_shifted_target])\n","    \n","  return attention_weights\n","\n","out=visualize('I did it very well')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0-d86MTK3gJ0","executionInfo":{"status":"ok","timestamp":1663097210873,"user_tz":-60,"elapsed":2030,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"aff68747-9e08-405d-fac6-e9b00ff3508b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:tensorflow:5 out of the last 26 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f1de559c830> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"]}]},{"cell_type":"code","source":["print(out['decoder_layer1_block2'][0].shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4G2b6i3APj7I","executionInfo":{"status":"ok","timestamp":1663097210878,"user_tz":-60,"elapsed":13,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b9c1db3f-9bd1-477f-ba16-3a223f05c92e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(8, 64, 64)\n"]}]},{"cell_type":"code","source":["plt.figure(figsize = (12,12))\n","\n","for i in range(NUM_HEADS):\n","  ax = plt.subplot(2,4, i+1)\n","  \n","  plt.imshow(out['decoder_layer1_block2'][0][i][0:10,0:10])\n","  plt.title(\"Attention Scores for head:->\"+str(i+1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":565},"id":"dhg9F90xPkA0","executionInfo":{"status":"ok","timestamp":1663097667630,"user_tz":-60,"elapsed":2158,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0e480fc9-9291-4125-9a6b-405d3b054265"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 864x864 with 8 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"kfo16S7IPkCn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"UQrTxW7kDUki"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"XJ9KjorWDUnL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"1KTaNE-pDUqj"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"A2lMBYRIDUsK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"wy3nS93-DUt_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"1jHU34XoDUvu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"h3i6BLkADUyF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"itHuHsMeDU03"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"YeDY5DfGDU3e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"ISPzJXr4DU7S"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":7676,"status":"ok","timestamp":1662630852599,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"Hrc4k-JQRnvf","outputId":"d4bab904-3a29-47e9-fb17-490bcc77ddd1"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting aicrowd-cli\n","  Downloading aicrowd_cli-0.1.15-py3-none-any.whl (51 kB)\n","\u001b[K     |████████████████████████████████| 51 kB 4.0 MB/s \n","\u001b[?25hCollecting semver<3,>=2.13.0\n","  Downloading semver-2.13.0-py2.py3-none-any.whl (12 kB)\n","Collecting requests<3,>=2.25.1\n","  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n","\u001b[K     |████████████████████████████████| 62 kB 1.7 MB/s \n","\u001b[?25hCollecting pyzmq==22.1.0\n","  Downloading pyzmq-22.1.0-cp37-cp37m-manylinux1_x86_64.whl (1.1 MB)\n","\u001b[K     |████████████████████████████████| 1.1 MB 44.1 MB/s \n","\u001b[?25hRequirement already satisfied: tqdm<5,>=4.56.0 in /usr/local/lib/python3.7/dist-packages 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/usr/local/lib/python3.7/dist-packages (from requests<3,>=2.25.1->aicrowd-cli) (2022.6.15)\n","Collecting commonmark<0.10.0,>=0.9.0\n","  Downloading commonmark-0.9.1-py2.py3-none-any.whl (51 kB)\n","\u001b[K     |████████████████████████████████| 51 kB 7.9 MB/s \n","\u001b[?25hRequirement already satisfied: pygments<3.0.0,>=2.6.0 in /usr/local/lib/python3.7/dist-packages (from rich<11,>=10.0.0->aicrowd-cli) (2.6.1)\n","Collecting colorama<0.5.0,>=0.4.0\n","  Downloading colorama-0.4.5-py2.py3-none-any.whl (16 kB)\n","Installing collected packages: smmap, requests, gitdb, commonmark, colorama, semver, rich, requests-toolbelt, pyzmq, python-slugify, GitPython, aicrowd-cli\n","  Attempting uninstall: requests\n","    Found existing installation: requests 2.23.0\n","    Uninstalling requests-2.23.0:\n","      Successfully uninstalled requests-2.23.0\n","  Attempting uninstall: pyzmq\n","    Found existing installation: pyzmq 23.2.1\n","    Uninstalling pyzmq-23.2.1:\n","      Successfully uninstalled pyzmq-23.2.1\n","  Attempting uninstall: python-slugify\n","    Found existing installation: python-slugify 6.1.2\n","    Uninstalling python-slugify-6.1.2:\n","      Successfully uninstalled python-slugify-6.1.2\n","Successfully installed GitPython-3.1.18 aicrowd-cli-0.1.15 colorama-0.4.5 commonmark-0.9.1 gitdb-4.0.9 python-slugify-5.0.2 pyzmq-22.1.0 requests-2.28.1 requests-toolbelt-0.9.1 rich-10.16.2 semver-2.13.0 smmap-5.0.0\n"]},{"data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["zmq"]}}},"metadata":{},"output_type":"display_data"}],"source":["!pip install aicrowd-cli"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18216,"status":"ok","timestamp":1662631188825,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"J1Mz0vBYRn10","outputId":"e3b63ceb-75c7-48a5-e607-a1c72ea6f4a7"},"outputs":[{"name":"stdout","output_type":"stream","text":["Please login here: \u001b[34m\u001b[1m\u001b[4mhttps://api.aicrowd.com/auth/wtHm9oiaN9-I--I4Ivn1eRDGXB7IG6YYX1nlj37hllk\u001b[0m\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: www-browser: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: links2: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: elinks: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: links: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: lynx: not found\n","/usr/bin/xdg-open: 851: /usr/bin/xdg-open: w3m: not found\n","xdg-open: no method available for opening 'https://api.aicrowd.com/auth/wtHm9oiaN9-I--I4Ivn1eRDGXB7IG6YYX1nlj37hllk'\n","\u001b[32mAPI Key valid\u001b[0m\n","\u001b[32mGitlab access token valid\u001b[0m\n","\u001b[32mSaved details successfully!\u001b[0m\n"]}],"source":["!aicrowd login"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":113235,"status":"ok","timestamp":1662631305346,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"uy1kf4nlRnyI","outputId":"3c9c08b5-2687-4c2a-b14d-1fc840484c25"},"outputs":[{"name":"stdout","output_type":"stream","text":["product_catalogue-v0.3.csv.zip: 100% 328M/328M [00:16<00:00, 20.1MB/s]\n","sample_submission_public-v0.3.csv.zip: 100% 331k/331k [00:00<00:00, 587kB/s]\n","test_public-v0.3.csv.zip: 100% 394k/394k [00:00<00:00, 690kB/s]\n","train-v0.3.csv.zip: 100% 6.80M/6.80M [00:01<00:00, 5.95MB/s]\n","product_catalogue-v0.3.csv.zip: 100% 657M/657M [00:31<00:00, 21.1MB/s]\n","sample_submission_public-v0.3.csv.zip: 100% 812k/812k [00:00<00:00, 1.15MB/s]\n","test_public-v0.3.csv.zip: 100% 2.94M/2.94M [00:01<00:00, 2.94MB/s]\n","train-v0.3.csv.zip: 100% 19.8M/19.8M [00:01<00:00, 11.4MB/s]\n","product_catalogue-v0.3.csv.zip: 100% 657M/657M [00:42<00:00, 15.5MB/s]\n","sample_submission_public-v0.3.csv.zip: 100% 803k/803k [00:00<00:00, 1.12MB/s]\n","test_public-v0.3.csv.zip: 100% 2.94M/2.94M [00:00<00:00, 2.97MB/s]\n","train-v0.3.csv.zip: 100% 20.3M/20.3M [00:01<00:00, 10.9MB/s]\n"]}],"source":["!aicrowd dataset download -c esci-challenge-for-improving-product-search"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18750,"status":"ok","timestamp":1662631511971,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"pDg69tTsUlPv","outputId":"0247336a-a161-4e8f-a555-016a234eb01e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Archive:  /content/product_catalogue-v0.3.csv.zip\n","  inflating: /content/dataset/data/processed/public/task_3_product_substitute_identification/product_catalogue-v0.3.csv  \n"]}],"source":["!unzip \"/content/product_catalogue-v0.3.csv.zip\" -d \"/content/dataset/\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LEodr5PVUlWm"},"outputs":[],"source":["filepath='/content/dataset/data/processed/public/task_3_product_substitute_identification/product_catalogue-v0.3.csv'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EPiU6rQZUM10"},"outputs":[],"source":["import pandas as pd\n","\n","df = pd.read_csv(filepath)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":979,"status":"ok","timestamp":1662632701308,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"M3xcT3ReoYy1","outputId":"e5567f4a-9a87-41e7-fc58-655b7e5e957a"},"outputs":[{"data":{"text/plain":["1815216"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["len(df)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":10891},"executionInfo":{"elapsed":931,"status":"ok","timestamp":1662632738479,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"6AEeOkBQkZBu","outputId":"005492fc-5293-4e3d-cc78-9622b6e57f32"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-ad9d6df6-5366-4000-8cdf-ef5b2adf5d4f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","      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<th>1600037</th>\n","      <td>B07RC9FD97</td>\n","      <td>Intel Xeon Gold 5222 Tray Processor 4 Core 3.8...</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Intel OEM Tray Processors</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600038</th>\n","      <td>B07SBKXDNS</td>\n","      <td>Intel Pentium Gold G5420 Desktop Processor 2 C...</td>\n","      <td>NaN</td>\n","      <td>2 Cores /4 Threads\\n3.8 GHz\\nCompatible with I...</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600039</th>\n","      <td>B07SLSGBJP</td>\n","      <td>Intel Pentium Gold G5420 Dual Core 3.8GHz LGA ...</td>\n","      <td>Intel Pentium Gold G5420 Processor (4MB Cache,...</td>\n","      <td>3.8GHz processor speed\\nDual Core desktop proc...</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600040</th>\n","      <td>B07SZ9TPW7</td>\n","      <td>Intel Xeon Gold 6254 Processor 18 Core 3.10GHZ...</td>\n","      <td>Intel Xeon Gold 6254 Processor | 18-Core, 36 T...</td>\n","      <td>Intel Xeon Gold 6254 Processor\\n18-Core, 36 Th...</td>\n","      <td>Intel OEM Tray Processors</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600041</th>\n","      <td>B0857MBSD4</td>\n","      <td>INTEL INTEL XEON Gold 6246R Processor (35.75M ...</td>\n","      <td>INTEL reg; XEON reg; GOLD PROCESSORS With supp...</td>\n","      <td>W126171725</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600042</th>\n","      <td>B0858CVWK4</td>\n","      <td>INTEL INTEL XEON Gold 6240R Processor (35.75M ...</td>\n","      <td>INTEL reg; XEON reg; GOLD PROCESSORS With supp...</td>\n","      <td>W126171723</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600043</th>\n","      <td>B086K4V42L</td>\n","      <td>Intel XEON Gold 6226R Processor 16 CORE 2.90GH...</td>\n","      <td>INTEL XEON GOLD PROCESSORS With support for th...</td>\n","      <td>INTEL XEON GOLD PROCESSORS With support for th...</td>\n","      <td>Intel OEM Tray Processors</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600044</th>\n","      <td>B086MGYKXM</td>\n","      <td>Intel Pentium Gold G-6500 Desktop Processor 2 ...</td>\n","      <td>NaN</td>\n","      <td>2 Cores / 4 Threads\\nSocket Type LGA 1200\\nCom...</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600045</th>\n","      <td>B086MHSH2Z</td>\n","      <td>Intel Pentium Gold G-6400 Desktop Processor 2 ...</td>\n","      <td>NaN</td>\n","      <td>2 Cores / 4 Threads\\nSocket Type LGA 1200\\nCom...</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600046</th>\n","      <td>B08M9BS284</td>\n","      <td>Intel Xeon Gold 6140 SR3AX 18-Core 2.3GHz 24.7...</td>\n","      <td>Intel Xeon Gold 6140 SR3AX 18-Core 2.3GHz 24.7...</td>\n","      <td>18 Cores, 36 Cores in Hyperthreading mode\\n2.3...</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600047</th>\n","      <td>B08ML8J37K</td>\n","      <td>Intel Xeon Gold 6130 SR3B9 16-Core 2.1GHz 22MB...</td>\n","      <td>Intel Xeon Gold 6130 SR3B9 16-Core 2.1GHz 22.0...</td>\n","      <td>16 Cores, 32 Cores in Hyperthreading mode\\n2.1...</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600048</th>\n","      <td>B091TXJN41</td>\n","      <td>Intel Pentium Gold G6405 4.1GHz 4MB Desktop Pr...</td>\n","      <td>Intel® Pentium® Processors Discover new comput...</td>\n","      <td>Processor Manufacturer: Intel\\nProcessor Core:...</td>\n","      <td>Intel</td>\n","      <td>NaN</td>\n","      <td>us</td>\n","    </tr>\n","    <tr>\n","      <th>1600049</th>\n","      <td>B0019K9OWA</td>\n","      <td>iDesign Axis Over the Cabinet 2-Tier Kitchen S...</td>\n","      <td>NaN</td>\n","      <td>OVER THE CABINET STORAGE: Wire over the cabine...</td>\n","      <td>iDesign</td>\n","      <td>Two Tier</td>\n","      <td>us</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad9d6df6-5366-4000-8cdf-ef5b2adf5d4f')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 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                      NaN   \n","1600032                                                NaN   \n","1600033                                                NaN   \n","1600034                                         Processors   \n","1600035                                BX80684G5400\\nINTEL   \n","1600036  Intel Xeon Gold processors offer high performa...   \n","1600037                                                NaN   \n","1600038  2 Cores /4 Threads\\n3.8 GHz\\nCompatible with I...   \n","1600039  3.8GHz processor speed\\nDual Core desktop proc...   \n","1600040  Intel Xeon Gold 6254 Processor\\n18-Core, 36 Th...   \n","1600041                                         W126171725   \n","1600042                                         W126171723   \n","1600043  INTEL XEON GOLD PROCESSORS With support for th...   \n","1600044  2 Cores / 4 Threads\\nSocket Type LGA 1200\\nCom...   \n","1600045  2 Cores / 4 Threads\\nSocket Type LGA 1200\\nCom...   \n","1600046  18 Cores, 36 Cores in 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                 Silver   \n","1600009                Instant Pot                               Silver   \n","1600010                Instant Pot                                  NaN   \n","1600011                  prepAmeal                              6 Quart   \n","1600012                Instant Pot                Black/Stainless Steel   \n","1600013                     NuWave                                  NaN   \n","1600014                  BATEMEN W                     11.28-WDBGM-1754   \n","1600015                Instant Pot                                  NaN   \n","1600016                      Tezam                                  NaN   \n","1600017          Brand: Cheng Tsui                                  NaN   \n","1600018          Brand: Cheng Tsui                                  NaN   \n","1600019       YesJapan Corporation                                  NaN   \n","1600020       YesJapan Corporation                                  NaN   \n","1600021    Independently Published                                  NaN   \n","1600022                        NaN                                  NaN   \n","1600023                        NaN                                  NaN   \n","1600024                        NaN                                  NaN   \n","1600025                        NaN                                  NaN   \n","1600026                        NaN                                  NaN   \n","1600027                        NaN                                  NaN   \n","1600028                        NaN                                  NaN   \n","1600029                        NaN                                  NaN   \n","1600030                Japan Times                                  NaN   \n","1600031             3A CORPORATION                                  NaN   \n","1600032                        NaN                                  NaN   \n","1600033                      Intel                                  NaN   \n","1600034                      Intel                                 gold   \n","1600035                      Intel                                 Gold   \n","1600036  Intel OEM Tray Processors                                  NaN   \n","1600037  Intel OEM Tray Processors                                  NaN   \n","1600038                      Intel                                  NaN   \n","1600039                      Intel                                  NaN   \n","1600040  Intel OEM Tray Processors                                  NaN   \n","1600041                      Intel                                  NaN   \n","1600042                      Intel                                  NaN   \n","1600043  Intel OEM Tray Processors                                  NaN   \n","1600044                      Intel                                  NaN   \n","1600045                      Intel                                  NaN   \n","1600046                      Intel                                  NaN   \n","1600047                      Intel                                  NaN   \n","1600048                      Intel                                  NaN   \n","1600049                    iDesign                             Two Tier   \n","\n","        product_locale  \n","1600000             us  \n","1600001             us  \n","1600002             us  \n","1600003             us  \n","1600004             us  \n","1600005             us  \n","1600006             us  \n","1600007             us  \n","1600008             us  \n","1600009             us  \n","1600010             us  \n","1600011             us  \n","1600012             us  \n","1600013             us  \n","1600014             us  \n","1600015             us  \n","1600016             us  \n","1600017             us  \n","1600018             us  \n","1600019             us  \n","1600020             us  \n","1600021             us  \n","1600022             us  \n","1600023             us  \n","1600024             us  \n","1600025             us  \n","1600026             us  \n","1600027             us  \n","1600028             us  \n","1600029             us  \n","1600030             us  \n","1600031             us  \n","1600032             us  \n","1600033             us  \n","1600034             us  \n","1600035             us  \n","1600036             us  \n","1600037             us  \n","1600038             us  \n","1600039             us  \n","1600040             us  \n","1600041             us  \n","1600042             us  \n","1600043             us  \n","1600044             us  \n","1600045             us  \n","1600046             us  \n","1600047             us  \n","1600048             us  \n","1600049             us  "]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["df[1600000:1600050]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"44qa8hxykZEG"},"outputs":[],"source":["class Transformer(tf.keras.Model):\n","  def __init__(self,transformer):\n","    super(Transformer,self).__init__()\n","    self.transformer=transformer\n","    \n","  def compile(self,loss_fn,optimizer):\n","    super(Transformer,self).compile()\n","    self.optimizer=optimizer\n","    self.loss_fn=loss_fn\n","    self.loss_metric=tf.keras.metrics.Mean(name='loss')\n","    \n","  @property\n","  def metrics(self):\n","    return [self.loss_metric,]\n","\n","  def train_step(self,x_y):\n","    inputs,target=x_y\n","    encoder_input=inputs['input_1']\n","    shifted_target=inputs['input_2']\n","\n","    with tf.GradientTape() as recorder:\n","\n","      output,_=self.transformer([encoder_input,shifted_target])\n","      loss=self.loss_fn(target,output)\n","      \n","    partial_derivatives = recorder.gradient(loss,self.transformer.trainable_weights)\n","    self.optimizer.apply_gradients(zip(partial_derivatives, self.transformer.trainable_weights))\n","\n","    self.loss_metric.update_state(loss)\n","    \n","    return {'loss':self.loss_metric.result()}"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["RBHsJDpiYDs7","QaDKmgJgklY_"],"provenance":[{"file_id":"1awyXVSqfhBLykr43uWDESu4CTn7VmqOl","timestamp":1673806928577},{"file_id":"1Azfk7PQQ3FoBJWSxSkqxquML6LYpZCYS","timestamp":1662195319517},{"file_id":"1wOi9I4Ett5qIJ3eldJ3bTPRmUjk3X5hd","timestamp":1662058828939}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

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FILE: deep learning for natural language processing/7-Sentiment Analysis with BERT in HuggingFace 🤗 by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"markdown","metadata":{"id":"gwDUFKDdR6i_"},"source":["# Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":19330,"status":"ok","timestamp":1664702869668,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ntIbn-RaRzbs","outputId":"2eac61a4-a6cb-4e8e-db9e-7aefb71bc87e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.22.2-py3-none-any.whl (4.9 MB)\n","\u001b[K     |████████████████████████████████| 4.9 MB 15.6 MB/s \n","\u001b[?25hCollecting datasets\n","  Downloading datasets-2.5.1-py3-none-any.whl (431 kB)\n","\u001b[K     |████████████████████████████████| 431 kB 55.1 MB/s \n","\u001b[?25hRequirement already satisfied: tqdm>=4.27 in 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python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n","Installing collected packages: urllib3, xxhash, tokenizers, responses, multiprocess, huggingface-hub, transformers, datasets\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","Successfully installed datasets-2.5.1 huggingface-hub-0.10.0 multiprocess-0.70.13 responses-0.18.0 tokenizers-0.12.1 transformers-4.22.2 urllib3-1.25.11 xxhash-3.0.0\n"]}],"source":["!pip install transformers datasets"]},{"cell_type":"markdown","metadata":{"id":"D31_Au7nR_oc"},"source":["# Imports"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZslyJl_QV7rt"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer,BatchNormalization,Dropout,Input,LayerNormalization\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import (BertTokenizerFast,TFBertTokenizer,BertTokenizer,RobertaTokenizerFast,\n","                          DataCollatorWithPadding,TFRobertaForSequenceClassification,TFBertForSequenceClassification,\n","                          TFBertModel,create_optimizer)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8phsQmoYUJWK"},"outputs":[],"source":["BATCH_SIZE=8"]},{"cell_type":"markdown","metadata":{"id":"y3CEdBN2aX9k"},"source":["# Data Preparation for Bert Model"]},{"cell_type":"code","source":["dataset_id='imdb'\n","dataset = load_dataset(dataset_id)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":234,"referenced_widgets":["2b3d94fcc8a84a70bf73990c6fea2668","987b3d5b6c114ffa891c625437c4edc1","45a7f161f250410c87b1fe580bdaaf58","1b7147a7247d4b43b64387478b4e334b","254e799d466b46e183e665e27450f6e2","ce5d8976f98a4fa19a1dc2fa2000ed27","7ae4cd87ef65409989b9132dfab3a716","eded410faf8f488d9adb558204f9838a","511ba9e39f944017b5d48ca048b6afc0","f3dc6643876a404aa958a7a4929bd8bc","65169bf9989244399043f0db366ce0dc","be964c03bc1843e7a414edadc1214d5c","3edb61705c7c4a22a2ebdc4f308147a2","e95663d159cc40e58e9134dc5511370f","9a21f7bcb42b4e7da7f79aff71cb5b9c","88060d35744449ba8d2390456eab0b3d","03cdcd79e8f94515a6ae40b3a9147ba7","bdb0ec959a254519acad3191b3ec103c","d80cb8773f924019b9c3dd27d1bc5745","6de8caa71f4346579670dc463ae36b35","abede6dffeac4acaadb4636eb93525fe","3b080ee2b1db40128d1568b30faf5b95","1c107c06278a4f73adfdb35e078c74e6","22d5f27e1dda47a4972bb457a3b2afe4","4a4d0f225f4f4389a1d9681764380ad8","8a716b25c3aa450489e2ce62794a2a8a","ed5e69bc34db44ac91162fa5807f4c92","382b386de60146a49918a1f3ee1f8697","88404ff539684a3d939b34a2a4a724c2","4e0099fc13f94339b5ea197f0415b054","805b93213f114c34a8f66043f7b59101","ba525738d45b43239c91d3835716a77b","b5f3cc06d0c64cab85beeda0626cc0fa","5dc00d252fe944769521b9541d3e3069","c13343c1de634832ab0cdead9e132ecf","08a27cac04e144f68fa038b47192b3d6","94e55137a83c4e6b8fb9b724299e3703","203b683308e24bc6bb3ad40a0446a174","db8a68c2d0a34f3f9065de8d57d8df00","d6de867ba5934511b513e8d564d17758","5eb2b9e0253540f091fb53a92fe8fd5e","3365dc4a31634cc69d42819545934b84","937ed0c824614eb0a9eeaa3e72b6cfee","a95ad96317b5456eae81ba496b48ff0a","283eb6fce72f4713926805f8a2e025a8","aaa8c8004cf14e2b9e0ab02e4a249fa7","7afd08a7c2d74ae9b52da58d45333cc4","3580677a4bf941f0aaf6176521c6b1d4","84faabecf937494baa80be619d56c279","177057eb4d0143219051c77ba1b4d739","59b5dcbe20284f5597df03ec1260863c","af16e050d01443b99f20f5cee34e77b8","798d34c0ead5437f90f36be82d7c086a","7deb0534ca214a0ca37c7c9341b81978","35abde4adade4b14a366e7a7a8099c16","fa5e2a4bc0b243ae918b098f41ce593b","03efad44bf6549368b436e1f8b53e0b3","d861b96cd4e242b3b503376ff8ea5d20","7ef512abbc654c40ba1774b1353edaad","f4bf8568aa9847ed8826a9a18cebe37e","853d0c04aef34cd691b9a069e98b6097","675c2eda2baa4ac2b0774aa6a2155775","fdfdef568a5c4680a473889dd5bb2482","783dc2ce08a047e28020dacc8ad16f3b","b73d24da52b4442096ab53fdd7bbabe2","7e59807957ce424bb5e7d1569f6cc8f7","4c52f87f579e4788803eb02a66a3c642","cd5254fbd73941d6959135740a45136b","4c5a13b2800f4b34b2c1d21489487432","c31b0c6791f64e9a844d633349b37cc2","c4f8bafd76da48f28a5429ee7f13194b","7704b5ee6b9348c4acaf05db24fbf529","0dcda6867ea740ee81bb4f084366c633","c9c60641fcb949e5b81dee0f12035f8b","e77f476aef604ac8984ce0c557cd7845","499ddbc89177466dbca05fa880fbee3d","57b44f4b23434ebdb09bfae1f5617788"]},"id":"BCDlIZ62qaM2","executionInfo":{"status":"ok","timestamp":1664702953630,"user_tz":-60,"elapsed":61546,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0e2f3ef6-c805-4e94-e847-ebc1f4867ecd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py:97: FutureWarning: Deprecated argument(s) used in 'dataset_info': token. Will not be supported from version '0.12'.\n","  warnings.warn(message, FutureWarning)\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/4.31k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2b3d94fcc8a84a70bf73990c6fea2668"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"be964c03bc1843e7a414edadc1214d5c"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset imdb/plain_text (download: 80.23 MiB, generated: 127.02 MiB, post-processed: Unknown size, total: 207.25 MiB) to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/84.1M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1c107c06278a4f73adfdb35e078c74e6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5dc00d252fe944769521b9541d3e3069"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"283eb6fce72f4713926805f8a2e025a8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fa5e2a4bc0b243ae918b098f41ce593b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset imdb downloaded and prepared to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4c52f87f579e4788803eb02a66a3c642"}},"metadata":{}}]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4GQy6nrUqaP0","executionInfo":{"status":"ok","timestamp":1664702953631,"user_tz":-60,"elapsed":22,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"332bd86c-b875-4426-a5ea-8e10540a6405"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 25000\n","    })\n","    test: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 25000\n","    })\n","    unsupervised: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 50000\n","    })\n","})"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xMbT89IjqaR5","executionInfo":{"status":"ok","timestamp":1664702953632,"user_tz":-60,"elapsed":20,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3b29ca8b-5104-465b-f9b5-b2fad0206079"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'text': 'I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\\'t have much of a plot.',\n"," 'label': 0}"]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","source":["## Bert Model"],"metadata":{"id":"_whO0azDhB9-"}},{"cell_type":"code","source":["model_id=\"bert-base-uncased\"\n","tokenizer = BertTokenizerFast.from_pretrained(model_id)"],"metadata":{"id":"aI2gV1HSqaUv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenizer.is_fast"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xMEhBzbmqaWp","executionInfo":{"status":"ok","timestamp":1664568659885,"user_tz":-60,"elapsed":28,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"667a6595-e1fe-47d3-b211-fe2516210178"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["test_input_1='The Weather of Today is Gréat! zwp'\n","test_input_2='How are you doing?'\n","inputs=[test_input_1,test_input_2]\n","\n","tokenizer.tokenize(inputs,)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"13nJX22EqaaO","executionInfo":{"status":"ok","timestamp":1664568659885,"user_tz":-60,"elapsed":25,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"fa466543-e5e2-401e-e5aa-60c457a8cbf7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['the',\n"," 'weather',\n"," 'of',\n"," 'today',\n"," 'is',\n"," 'great',\n"," '!',\n"," 'z',\n"," '##w',\n"," '##p',\n"," 'how',\n"," 'are',\n"," 'you',\n"," 'doing',\n"," '?']"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["output=tokenizer(inputs,padding=True,truncation=True,max_length=128)\n","print(output)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s8KTKebYNtsc","executionInfo":{"status":"ok","timestamp":1664568659885,"user_tz":-60,"elapsed":22,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b9c9c790-3a79-44ad-c007-55df09c782aa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': [[101, 1996, 4633, 1997, 2651, 2003, 2307, 999, 1062, 2860, 2361, 102], [101, 2129, 2024, 2017, 2725, 1029, 102, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]]}\n"]}]},{"cell_type":"code","source":["tokenizer.decode(output['input_ids'][0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"AodllaczRAAL","executionInfo":{"status":"ok","timestamp":1664568659886,"user_tz":-60,"elapsed":20,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"29dbc06a-3dab-43d1-b6e1-d339c1fb8691"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'[CLS] the weather of today is great! zwp [SEP]'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["tokenizer.decode(output['input_ids'][1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"8U4QCNjZRACg","executionInfo":{"status":"ok","timestamp":1664568660617,"user_tz":-60,"elapsed":750,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"89635c00-030e-4009-c60f-bfe3adaeebdb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'[CLS] how are you doing? [SEP] [PAD] [PAD] [PAD] [PAD] [PAD]'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["def preprocess_function(examples):\n","  return tokenizer(examples[\"text\"],padding=True,truncation=True,)"],"metadata":{"id":"bcbRiauIRAEp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset = dataset.map(preprocess_function, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["77b457a5f7cb4429843286c78fd8f630","c65335b5f57849b990563fcbf29147c2","86568bf49dbf49ddaf03ab8cd064c710","42b272ae95ca4e029c189f414bb6c0f5","daa982769a004316a2647b352fa998ee","c365f707535f4030b91f8ea23b392af5","9153ef0b6c904256b6ec5a858115cdf7","95d4e58419624e45b58a878ec224cdfa","f5c6e597c74b4a5e870ec0da247589dc","99526287ec184c4886c65af83021ed62","b3ac4439d4b14118946f97171a674fee","44a707b516cd43a8aa9b1dacd9ed0660","c74bcafdea2c484990640c101dc198ac","51bca3879cf44e3e804ff3871e87258a","5930b9be850944dd95802877c2397f76","566ab58b3e8e42f2a8fe24a1cb02fc6f","2ce110c4caf14148a4ec65635f93b7c0","0e9c8658af874dea9f2bcd005517a927","1ab6196c4c9b4227b2f7f8c7c3cee06f","de29d5e23af347fe9278957aa82b0098","3132801f5d37466e8edc99df5095fe9e","0a92ca567ee34f20abbf974e020c089b","e6fbba5a90a9442b8774321d996a5cd6","9b18ca55a08f448abd6ab5af5e6de459","52a1b97bbe534d3ea16a7129384a831d","a2e3eca801c44506b0a787d26c973664","eadba9d8a4a542bfb0b9aac3e5b6c0dc","90b9e6a08011423aa6580fb9ab96ada0","686333c8cc3c425e914a5f4065045d2d","07a9e1128a24481fb6cc7187fb45567c","4c44ec40bc6844bc95d4136b02390b7b","789db0451efc428080b6e440a3c5504e","a10c80b14fd34c32bcb757daff298ddc"]},"id":"eZ8l1iIaRAHU","executionInfo":{"status":"ok","timestamp":1664568741262,"user_tz":-60,"elapsed":80662,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"eb8c97dd-5d96-4a87-f312-1bccbad84dc1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/25 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"77b457a5f7cb4429843286c78fd8f630"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/25 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"44a707b516cd43a8aa9b1dacd9ed0660"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/50 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e6fbba5a90a9442b8774321d996a5cd6"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mUm5t5_FRAJd","executionInfo":{"status":"ok","timestamp":1664568741263,"user_tz":-60,"elapsed":47,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"0a3e6710-cc28-4a29-8e38-23828c7f64f8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n","        num_rows: 25000\n","    })\n","    test: Dataset({\n","        features: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n","        num_rows: 25000\n","    })\n","    unsupervised: Dataset({\n","        features: ['text', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n","        num_rows: 50000\n","    })\n","})"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["#tokenized_dataset['train'][0]"],"metadata":{"id":"ptRLJkU5RALR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"],"metadata":{"id":"BxIbAebk1xjW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_train_dataset = tokenized_dataset[\"train\"].to_tf_dataset(\n","    columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'],\n","    shuffle=True, \n","    batch_size=BATCH_SIZE,\n","    #collate_fn=data_collator\n",")"],"metadata":{"id":"tf088yEoRANx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_val_dataset = tokenized_dataset[\"test\"].to_tf_dataset(\n","    columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'],\n","    shuffle=True, \n","    batch_size=BATCH_SIZE,\n","    #collate_fn=data_collator\n",")"],"metadata":{"id":"PKOU9V9cRAPx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def swap_positions(dataset):\n","  return {'input_ids':dataset['input_ids'],\n","          'token_type_ids':dataset['token_type_ids'],\n","          'attention_mask':dataset['attention_mask'],},dataset['label']"],"metadata":{"id":"oaA2U39w6O-z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_train_dataset=tf_train_dataset.map(swap_positions).prefetch(tf.data.AUTOTUNE)\n","tf_val_dataset=tf_val_dataset.map(swap_positions).prefetch(tf.data.AUTOTUNE)"],"metadata":{"id":"rsOkpbef6q8L"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in tf_train_dataset.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oivRObeJRASE","executionInfo":{"status":"ok","timestamp":1664568961800,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"793ddbe2-3194-4b9e-8164-8cf0717fcafd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_ids': <tf.Tensor: shape=(8, 512), dtype=int64, numpy=\n","array([[ 101, 2023, 2038, ...,    0,    0,    0],\n","       [ 101, 1997, 1996, ...,    0,    0,    0],\n","       [ 101, 1045, 2293, ...,    0,    0,    0],\n","       ...,\n","       [ 101, 1045, 1005, ...,    0,    0,    0],\n","       [ 101, 1012, 1012, ...,    0,    0,    0],\n","       [ 101, 2026, 2643, ...,    0,    0,    0]])>, 'token_type_ids': <tf.Tensor: shape=(8, 512), dtype=int64, numpy=\n","array([[0, 0, 0, ..., 0, 0, 0],\n","       [0, 0, 0, ..., 0, 0, 0],\n","       [0, 0, 0, ..., 0, 0, 0],\n","       ...,\n","       [0, 0, 0, ..., 0, 0, 0],\n","       [0, 0, 0, ..., 0, 0, 0],\n","       [0, 0, 0, ..., 0, 0, 0]])>, 'attention_mask': <tf.Tensor: shape=(8, 512), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       ...,\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0]])>}, <tf.Tensor: shape=(8,), dtype=int64, numpy=array([1, 1, 1, 1, 0, 1, 0, 0])>)\n"]}]},{"cell_type":"code","source":["tf_val_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xC4OOxTp4_nu","executionInfo":{"status":"ok","timestamp":1664568962414,"user_tz":-60,"elapsed":3,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"74a2fd33-74cf-43a7-c9ba-2887f5ba8833"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PrefetchDataset element_spec=({'input_ids': TensorSpec(shape=(None, 512), dtype=tf.int64, name=None), 'token_type_ids': TensorSpec(shape=(None, 512), dtype=tf.int64, name=None), 'attention_mask': TensorSpec(shape=(None, 512), dtype=tf.int64, name=None)}, TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"]},"metadata":{},"execution_count":29}]},{"cell_type":"markdown","source":["## Data Preparation for Roberta Model"],"metadata":{"id":"ebaDHYoPLtIW"}},{"cell_type":"code","source":["model_id=\"roberta-base\"\n","tokenizer=RobertaTokenizerFast.from_pretrained(model_id)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["e5380dcd7db34076b297584bee5903ba","3a3792f276534b908dfbbb8d43bd453f","ba440809e979438d875768e8401d8813","39303da92cfd475a821185009a6d1715","b2a2477682df4127a2c6d96be1722f60","fe03b25d4bf148bebc2b77abac995435","bf9769bce8d444a8801530227e540925","c62b8288036a46d7b233478295a944f0","68a97c2e17e34f1cbcc3fb3daea5c632","0d559f4615424f59af9dc617c9119895","cee4ef1f74774831bfd5ebd684990d90","e74bb239b115405f8fa86001102f49ac","b952190b78184b1897f0dd8ce8cf0f73","8b9222a4ac6644758d6b508abbd23900","0f583734af3d4f2788935e64051f71e8","fc693d187356402ba753aed90e66e50b","f1133f3458874013b2725eb42ccc8314","f2bd85c07d154ea7b459ab5ba4b6e6ce","cf52b3174e764400bd0f00c201eeccea","0267d76ac90147bfab15380065233363","08962aa41a3845209fd00161d086be27","f3749348168740bc8db7812c86e7af9f","2ffdbe1b24a4424ba4fcd2b3f6abb1d5","d8c55feaf42342f5bd412bc52d3a3a37","780327bfa6be4658acdb0bef80cdf6f4","c804410b33d34e4ea89e8cb04ac54bd6","4590b548a3b6406fa1ac0a5356c52d82","65f9e376aead4d6088c01c29b1c4e716","7e375a28e7854dd196d5d58f8c0ccb22","ee33413f925b4939a814c5027e31441c","d086942e1db141458cc3aba1acd50fad","1e5e967c60f64f43a3e9389609717543","71192213c28a44e883bcc620c463c077","2ce0e125929c40bc8ea67693aa1b04be","c51161d29216441d8d65ac29af7171c7","9d74ea59d99e4b93964f6840937a7f4a","315d34a9cc344b82837b8c7997c1fb42","0d6011f2bd8b4c369c65d4b18c3c1a35","498d44e609394d509317bf2348b62cfa","1d24a859ff854b308696e06bb9e86ded","2c6e561a7c134edd92601a0a49e24dea","57e26aee56494f8fa90a04af3c8e52b3","8c5ae3518c05419f8a1f200dd02fe92f","6afd2f4801894e2285e933f563fd38d5"]},"id":"WQMCxzT0MEwx","executionInfo":{"status":"ok","timestamp":1664702959397,"user_tz":-60,"elapsed":5782,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3b73b087-142b-41fa-9f7a-8a85db59877b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e5380dcd7db34076b297584bee5903ba"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e74bb239b115405f8fa86001102f49ac"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2ffdbe1b24a4424ba4fcd2b3f6abb1d5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2ce0e125929c40bc8ea67693aa1b04be"}},"metadata":{}}]},{"cell_type":"code","source":["def preprocess_function(examples):\n","  return tokenizer(examples[\"text\"],padding=True,truncation=True,)"],"metadata":{"id":"0HPyurRDL2H3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset = dataset.map(preprocess_function,)# batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["53da169805d14542a74493a514e7a4a3","ba8645d1b4c94938aaa17c8e73bec92e","610dbde1551243d494cdbb2382ce1a9a","1e391e7073894dca8de18ec56d5e7fc8","20ad65950cab461782b116d177d2b83e","2759203816c343ec9a3afbb41198fdd3","224c164ac7a440368c549176c1491a0c","f155a8ab7818448092af4edddd7f2339","3b038a509d96442d800e9dc62c29c4ac","cecdf6591fd74e58b560a890cd5ed043","ea0612d842904c74a7789c7c207c37a5","adcbbdf926fc45bab4b66c3445bc09f9","725e149cd2e24d6a8e729a3436f588a9","6d88c146497e421297bab98fe0cebb53","cc9e9d821e4a4d82929a524faa710249","1333ef6419964b98a5b823202beee95b","728f75a2060b40f58aa0ef30e1c2595d","2616f8e377274017830fb2edcaa3af35","5a80433ba95543a2857a2a7669cb4aaf","803da95ee7e84275b5534ec086957756","481864275e1947469feca5e5b6b65557","9eeeb57be6714d44b30fa4b69a024730","974d45320467481faad8f35eb87d4974","d656171f16d146dfa5ed693e6fd51c31","25e1eadf22064333a716c2fe5b0c2dd9","ff1a0960f22d48cab54d0a79c5423814","ea077f44dffa46c89c61bf47eda9bfc3","08b5e19af0df4c07972d5490ad337ae5","ee79bc7249394460b9be6966beecf97c","ca66c012f38d42db927c37e0a82add9f","08406ae97f10490485ab5027eb15cb41","3dd85fd015e24b5ca7010004dafbf47e","ca7e3719aa984af6a1f2d0c736d3a377"]},"id":"Z5iLCggnL2KR","executionInfo":{"status":"ok","timestamp":1664703035452,"user_tz":-60,"elapsed":74257,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b5774ee4-e6f3-4813-9394-26dee7dafcd3"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/25 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"53da169805d14542a74493a514e7a4a3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/25 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"adcbbdf926fc45bab4b66c3445bc09f9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/50 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"974d45320467481faad8f35eb87d4974"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EDr8AD3fBoGK","executionInfo":{"status":"ok","timestamp":1664703035453,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"92feecd7-45b4-4f05-aa08-1f73edbe7c03"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'text': 'I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. 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0,\n","  0,\n","  0,\n","  0,\n","  0,\n","  0]}"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["tokenized_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R5IeVSAMNwt7","executionInfo":{"status":"ok","timestamp":1664572869240,"user_tz":-60,"elapsed":20,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"979fcb98-fb1a-45cf-f5c7-627f4718883a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label', 'input_ids', 'attention_mask'],\n","        num_rows: 25000\n","    })\n","    test: Dataset({\n","        features: ['text', 'label', 'input_ids', 'attention_mask'],\n","        num_rows: 25000\n","    })\n","    unsupervised: Dataset({\n","        features: ['text', 'label', 'input_ids', 'attention_mask'],\n","        num_rows: 50000\n","    })\n","})"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["tf_train_dataset = tokenized_dataset[\"train\"].to_tf_dataset(\n","    columns=['input_ids','attention_mask', 'label'],\n","    shuffle=True, \n","    batch_size=BATCH_SIZE,\n","    #collate_fn=data_collator\n",")"],"metadata":{"id":"iYc7AaZlL2MS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_val_dataset = tokenized_dataset[\"test\"].to_tf_dataset(\n","    columns=['input_ids','attention_mask', 'label'],\n","    shuffle=True, \n","    batch_size=BATCH_SIZE,\n","    #collate_fn=data_collator\n",")"],"metadata":{"id":"yJyIS-1HL2O7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def swap_positions(dataset):\n","  return {'input_ids':dataset['input_ids'],\n","          'attention_mask':dataset['attention_mask'],},dataset['label']"],"metadata":{"id":"kC13ODCELwYO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_train_dataset=tf_train_dataset.map(swap_positions).prefetch(tf.data.AUTOTUNE)\n","tf_val_dataset=tf_val_dataset.map(swap_positions).prefetch(tf.data.AUTOTUNE)"],"metadata":{"id":"rVB6fF12Lwai"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in tf_train_dataset.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gfkX0ovxLwdN","executionInfo":{"status":"ok","timestamp":1664572872789,"user_tz":-60,"elapsed":670,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1d19cf1e-1504-4258-f03a-cb125e1092f0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_ids': <tf.Tensor: shape=(8, 512), dtype=int64, numpy=\n","array([[    0,   133,  1569, ...,     1,     1,     1],\n","       [    0,   100,    21, ...,     1,     1,     1],\n","       [    0,   713, 22154, ...,     1,     1,     1],\n","       ...,\n","       [    0, 15852,    57, ...,     1,     1,     1],\n","       [    0,   133,   527, ...,     1,     1,     1],\n","       [    0,   100,   794, ...,     1,     1,     1]])>, 'attention_mask': <tf.Tensor: shape=(8, 512), dtype=int64, numpy=\n","array([[1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       ...,\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0],\n","       [1, 1, 1, ..., 0, 0, 0]])>}, <tf.Tensor: shape=(8,), dtype=int64, numpy=array([0, 1, 1, 1, 0, 0, 1, 1])>)\n"]}]},{"cell_type":"markdown","metadata":{"id":"PQiSVkoLaaqF"},"source":["# Modeling"]},{"cell_type":"markdown","source":["## Based on TFBertForSequenceClassification"],"metadata":{"id":"IxrzDVQ0yCc4"}},{"cell_type":"code","source":["model=TFBertForSequenceClassification.from_pretrained(\"bert-base-uncased\",num_labels=2)\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":399,"referenced_widgets":["890673cc127d48be97a8a51ca04d90e5","a7a8ede90a174130834e91fd223318f1","271a3b03b6ff47d09fc6c727be08f2ab","f2599d30199c449c94224a7d36237dad","61f2a5f21eaa40d7ba1c5556dc3b8108","45832cd647cc470a8e60372c22c10638","0fbcb370d8fc4f37b74c32afc1a5d7b3","427704295c794c02a50a12682adc2012","4a42f6f1d37641f59c76056ec763a1fc","9485888476784c21af2b7e4dde689a13","1aec5db6e9f94e83a1d89f1c6134dc76"]},"id":"ZBgCbKLRBvIQ","executionInfo":{"status":"ok","timestamp":1664569009413,"user_tz":-60,"elapsed":15083,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4678ecf1-de5a-47e8-e326-81dea075b2b9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/536M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"890673cc127d48be97a8a51ca04d90e5"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n","\n","Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"stream","name":"stdout","text":["Model: \"tf_bert_for_sequence_classification\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," bert (TFBertMainLayer)      multiple                  109482240 \n","                                                                 \n"," dropout_37 (Dropout)        multiple                  0         \n","                                                                 \n"," classifier (Dense)          multiple                  1538      \n","                                                                 \n","=================================================================\n","Total params: 109,483,778\n","Trainable params: 109,483,778\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["## Based on TFBertModel"],"metadata":{"id":"xe6Mi31TyOAK"}},{"cell_type":"code","source":["model=TFBertModel.from_pretrained(\"bert-base-uncased\")\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yds-4SGz_AV4","executionInfo":{"status":"ok","timestamp":1664570558704,"user_tz":-60,"elapsed":1903,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8c8ae105-1a40-432a-edd1-7867c39d7b0f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some layers from the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['mlm___cls', 'nsp___cls']\n","- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","All the layers of TFBertModel were initialized from the model checkpoint at bert-base-uncased.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"]},{"output_type":"stream","name":"stdout","text":["Model: \"tf_bert_model\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," bert (TFBertMainLayer)      multiple                  109482240 \n","                                                                 \n","=================================================================\n","Total params: 109,482,240\n","Trainable params: 109,482,240\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["input_ids=Input(shape = (512,),dtype=tf.int64,name='input_ids')\n","token_type_ids=Input(shape = (512,),dtype=tf.int64,name='token_type_ids')\n","attention_mask=Input(shape = (512,),dtype=tf.int64,name='attention_mask')\n","\n","x = model([input_ids,token_type_ids,attention_mask])\n","print(x)\n","x=Dense(128,activation='relu')(x[0][:,0,:])\n","output=Dense(1,activation='sigmoid',name='label')(x)\n","\n","custom_bert = tf.keras.Model(inputs=[input_ids,token_type_ids,attention_mask], outputs=output)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NtIkVCKWZhnK","executionInfo":{"status":"ok","timestamp":1664570560190,"user_tz":-60,"elapsed":1502,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e1c96e47-dbb2-4395-8466-44a9d0265385"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["TFBaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=<KerasTensor: shape=(None, 512, 768) dtype=float32 (created by layer 'tf_bert_model')>, pooler_output=<KerasTensor: shape=(None, 768) dtype=float32 (created by layer 'tf_bert_model')>, past_key_values=None, hidden_states=None, attentions=None, cross_attentions=None)\n"]}]},{"cell_type":"code","source":["custom_bert.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"plUdtmmKBvK7","executionInfo":{"status":"ok","timestamp":1664570560190,"user_tz":-60,"elapsed":36,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a5706d84-0dab-46a4-ae20-48991cff5cdf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," input_ids (InputLayer)         [(None, 512)]        0           []                               \n","                                                                                                  \n"," token_type_ids (InputLayer)    [(None, 512)]        0           []                               \n","                                                                                                  \n"," attention_mask (InputLayer)    [(None, 512)]        0           []                               \n","                                                                                                  \n"," tf_bert_model (TFBertModel)    TFBaseModelOutputWi  109482240   ['input_ids[0][0]',              \n","                                thPoolingAndCrossAt               'token_type_ids[0][0]',         \n","                                tentions(last_hidde               'attention_mask[0][0]']         \n","                                n_state=(None, 512,                                               \n","                                 768),                                                            \n","                                 pooler_output=(Non                                               \n","                                e, 768),                                                          \n","                                 past_key_values=No                                               \n","                                ne, hidden_states=N                                               \n","                                one, attentions=Non                                               \n","                                e, cross_attentions                                               \n","                                =None)                                                            \n","                                                                                                  \n"," tf.__operators__.getitem (Slic  (None, 768)         0           ['tf_bert_model[0][0]']          \n"," ingOpLambda)                                                                                     \n","                                                                                                  \n"," dense (Dense)                  (None, 128)          98432       ['tf.__operators__.getitem[0][0]'\n","                                                                 ]                                \n","                                                                                                  \n"," label (Dense)                  (None, 1)            129         ['dense[0][0]']                  \n","                                                                                                  \n","==================================================================================================\n","Total params: 109,580,801\n","Trainable params: 109,580,801\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["## Based on TFRobertaForSequenceClassification"],"metadata":{"id":"s3Rf_9bFLJsH"}},{"cell_type":"code","source":["model=TFRobertaForSequenceClassification.from_pretrained(model_id,num_labels=2)\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":399,"referenced_widgets":["456fc35927c3464589268327397ae3e7","6f81b502bc6f4e8785ddade8ca6509d6","6301f867a1384621b5ef6d72e69a9e4e","13ec89c6421a4b30805c9e404b05c932","59e2f89e1350484794bd46c77f52a471","24a6d1b426c044fc8023d5e72b8a7340","2fca00209d7841e2a41a1c4cb9a75ebd","948ce5b1d5674609abbba7c8e6414886","58a0a2554dbe447893de1dafd5f87f0a","1c64bc56c44042f299d13d3df38b75f6","c658da24241c4b5c8fc73a7923521675"]},"id":"GuzC0evWPis2","executionInfo":{"status":"ok","timestamp":1664572889208,"user_tz":-60,"elapsed":16428,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2246a56b-064d-445c-b225-6a4b78378278"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/657M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"456fc35927c3464589268327397ae3e7"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFRobertaForSequenceClassification.\n","\n","Some layers of TFRobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"stream","name":"stdout","text":["Model: \"tf_roberta_for_sequence_classification\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," roberta (TFRobertaMainLayer  multiple                 124055040 \n"," )                                                               \n","                                                                 \n"," classifier (TFRobertaClassi  multiple                 592130    \n"," ficationHead)                                                   \n","                                                                 \n","=================================================================\n","Total params: 124,647,170\n","Trainable params: 124,647,170\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"Nx1z1WTDPix-"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"E2xsaKH0ySx6"}},{"cell_type":"code","source":["num_epochs = 3\n","batches_per_epoch = len(tokenized_dataset[\"train\"]) // BATCH_SIZE\n","total_train_steps = int(batches_per_epoch * num_epochs)"],"metadata":{"id":"sadCMVfuDRLu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["optimizer, schedule = create_optimizer(init_lr=2e-5,num_warmup_steps=0, num_train_steps=total_train_steps)"],"metadata":{"id":"iqykd6O2DAmd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(#loss=tf.keras.losses.BinaryCrossentropy(),\n","    optimizer=optimizer,\n","    metrics=['accuracy'],)\n","    #run_eagerly=True)"],"metadata":{"id":"o1GJdZRXBvQo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1664572889210,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"1b88dcfb-8812-40cc-a0dc-7fa2b7147e99"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}]},{"cell_type":"code","source":["history=model.fit(\n","    tf_train_dataset,\n","    validation_data=tf_val_dataset,\n","    epochs=3,)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zw2dd4PuQ_Dk","executionInfo":{"status":"ok","timestamp":1664574699233,"user_tz":-60,"elapsed":561027,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"7f40cbe4-92b7-4932-bb46-71d2d4fc77b7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/3\n","3125/3125 [==============================] - 619s 191ms/step - loss: 0.2029 - accuracy: 0.9223 - val_loss: 0.1786 - val_accuracy: 0.9300\n","Epoch 2/3\n","3125/3125 [==============================] - 594s 190ms/step - loss: 0.1028 - accuracy: 0.9658 - val_loss: 0.1297 - val_accuracy: 0.9536\n","Epoch 3/3\n","3125/3125 [==============================] - 594s 190ms/step - loss: 0.0478 - accuracy: 0.9853 - val_loss: 0.1529 - val_accuracy: 0.9554\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qxPsvkXKjE8U","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1664558776220,"user_tz":-60,"elapsed":1487,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bd946ec5-d80a-47bf-c9b1-2280f9a6a8b8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cxq950QqjE-g","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1664558777769,"user_tz":-60,"elapsed":1556,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f0128b46-7e77-4def-bc65-bc3b0194b601"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"obCwC6fGPa04"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"7jbgoKV070Gr"},"source":["# Testing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5H8Y9MERnfX2","executionInfo":{"status":"ok","timestamp":1664559119835,"user_tz":-60,"elapsed":1106,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"5abfa843-c78d-4ce9-e6d1-499b54360a8b"},"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor(\n","[[-2.2396984  1.9407922]\n"," [ 3.3462846 -3.3634923]], shape=(2, 2), dtype=float32)\n"]}],"source":["inputs = tokenizer([\"this movie looks very interesting, i love the fact that the actors do a great job in showing how people lived in the 18th century, which wasn't very good at all. But atleast this movie recreates this scenes! \",\n","                    \"very good start, but movie started becoming uninteresting at some point though initially i thought it would have been much more fun. There was too much background noise, but later on towards the middle of the movie, my favorite character got in and he did a great job, so over \"], padding=True,return_tensors=\"tf\")\n","\n","logits = model(**inputs).logits\n","print(logits)"]},{"cell_type":"code","source":[],"metadata":{"id":"hEML0foO8ps0"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["_whO0azDhB9-","ebaDHYoPLtIW","IxrzDVQ0yCc4","xe6Mi31TyOAK","s3Rf_9bFLJsH"],"provenance":[{"file_id":"1qrpPSsjeFUMtRrljhxEiKKmlVk__fpuz","timestamp":1673806502550},{"file_id":"1qG67bWTlIgR_WzHRVCiw1CicPYSTv4qn","timestamp":1663398158336},{"file_id":"1ekGLJqUm4hoXzVs3h2I_OfnSCNwm1p8j","timestamp":1663151174522}]},"gpuClass":"premium","kernelspec":{"display_name":"Python 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FILE: deep learning for natural language processing/8-Intent Classification for Customer Service using HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"markdown","metadata":{"id":"gwDUFKDdR6i_"},"source":["# Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":18501,"status":"ok","timestamp":1664980228783,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ntIbn-RaRzbs","outputId":"f5bc4062-816c-4e89-c17d-188289a35e54"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.22.2-py3-none-any.whl (4.9 MB)\n","\u001b[K     |████████████████████████████████| 4.9 MB 35.5 MB/s \n","\u001b[?25hCollecting datasets\n","  Downloading datasets-2.5.2-py3-none-any.whl (432 kB)\n","\u001b[K     |████████████████████████████████| 432 kB 62.8 MB/s \n","\u001b[?25hRequirement already satisfied: requests in 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python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n","Installing collected packages: urllib3, xxhash, tokenizers, responses, multiprocess, huggingface-hub, transformers, datasets\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","Successfully installed datasets-2.5.2 huggingface-hub-0.10.0 multiprocess-0.70.13 responses-0.18.0 tokenizers-0.12.1 transformers-4.22.2 urllib3-1.25.11 xxhash-3.0.0\n"]}],"source":["!pip install transformers datasets"]},{"cell_type":"markdown","metadata":{"id":"D31_Au7nR_oc"},"source":["# Imports"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZslyJl_QV7rt"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import time\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer,BatchNormalization,Dropout,Input,LayerNormalization\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import (BinaryAccuracy, FalsePositives, FalseNegatives, TruePositives,\n","                                       TrueNegatives, Precision, Recall, AUC, binary_accuracy,Accuracy,\n","                                       TopKCategoricalAccuracy, CategoricalAccuracy,SparseCategoricalAccuracy)\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import (BertTokenizerFast,TFBertTokenizer,BertTokenizer,RobertaTokenizerFast,\n","                          DataCollatorWithPadding,TFRobertaForSequenceClassification,TFBertForSequenceClassification,\n","                          TFBertModel,create_optimizer,TFDebertaForSequenceClassification,DebertaTokenizerFast)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8phsQmoYUJWK"},"outputs":[],"source":["BATCH_SIZE=16"]},{"cell_type":"markdown","metadata":{"id":"y3CEdBN2aX9k"},"source":["# Data Preparation"]},{"cell_type":"code","source":["!pip install -q kaggle\n","! mkdir ~/.kaggle\n","! cp kaggle.json ~/.kaggle/\n","!chmod 600 /root/.kaggle/kaggle.json\n","!kaggle datasets download -d bitext/training-dataset-for-chatbotsvirtual-assistants\n","!unzip \"/content/training-dataset-for-chatbotsvirtual-assistants.zip\" -d \"/content/dataset/\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bl5b-DkIm3uW","executionInfo":{"status":"ok","timestamp":1664980242767,"user_tz":-60,"elapsed":5207,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6085d733-adc2-42ac-86b6-10039c6fefc2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading training-dataset-for-chatbotsvirtual-assistants.zip to /content\n","\r  0% 0.00/1.16M [00:00<?, ?B/s]\n","\r100% 1.16M/1.16M [00:00<00:00, 174MB/s]\n","Archive:  /content/training-dataset-for-chatbotsvirtual-assistants.zip\n","  inflating: /content/dataset/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample.csv  \n","  inflating: /content/dataset/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample.xlsx  \n","  inflating: /content/dataset/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/LICENSE.txt  \n","  inflating: /content/dataset/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/README.txt  \n","  inflating: /content/dataset/Bitext_Sample_Customer_Service_Training_Dataset/Training/Bitext_Sample_Customer_Service_Training_Dataset.csv  \n","  inflating: /content/dataset/Bitext_Sample_Customer_Service_Training_Dataset/Training/Bitext_Sample_Customer_Service_Training_Dataset.xlsx  \n","  inflating: /content/dataset/Bitext_Sample_Customer_Service_Training_Dataset/Training/LICENSE.txt  \n","  inflating: /content/dataset/Bitext_Sample_Customer_Service_Training_Dataset/Training/README.txt  \n"]}]},{"cell_type":"code","source":["dataset=load_dataset(\"csv\",\n","                       data_files=\"/content/dataset/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample/20000-Utterances-Training-dataset-for-chatbots-virtual-assistant-Bitext-sample.csv\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":185,"referenced_widgets":["bd9c3771aaea4ba792b79ec1dbe1f988","c82eaa5c28254b0eba859489cd3e53b6","2afaf834571946fe84c9433933e878de","bd80097f67794910a844c4085ce3675a","dae1217b045d41f0b2247fc3e2043c1e","96e74b939aa945e7b563f7d7ab491ebd","27d671eba6434d1aa14257d329c950c4","34c153ae85e24c5bb5a1c54a3595d820","49bdbac35aac4a37a5e901277a6deef4","0d17a45c441c469eb18bad92c2a82b52","b07d7e651a0f4939aa07d6ac12fb8540","ac016a655f624fada693ceb4f9adf6a5","c99e0d3135bc448fb4b2fe7b0a4e6cc1","58870080aac740f593b41f88c9507bee","e44c8106f0ab4e31853432e105f39305","f650123a711f407d97cef31b8c12b9e8","b37422eead5d4016aea3962a39dfd647","af6de38dd1d34d46934c1246c6aa3027","f462ed5d0c074d4491e60f7f160083c8","2e49b3068ab34e5a889117ff51b6825c","a3803681f61c4acc9eb578f0c556a6a0","1f276d063d2e4c2b8a115c6ad6251c1f","35d177c491cc46df89d2f9d03f27aaf0","0ec0ae3043174816b4c35b3be817234b","5e1cc620969f42f1b04332e83e4263f1","d5de01494b4348179e954da57b90db87","59b7980fc2ef4410af5b00b578d87839","efc37ea26f4a4e5a945f13a148166736","7703e4d882ec4b5f9371fde1ddd6bb1e","7a818c0c1615479f844a92b83960cdd7","39f6928ea2f94929992acd0ee5f8b908","d0e429c4d1d44aa5afa7e3ac227bf191","6f36d3b7db71462f85ac6e26deb8afa1","04559673f01443e2bdda3b6cc5acebd8","863707c5d44d49528be4eabbea6c1093","863ffac8e5c44b06a71339b465dbea4a","5f3f5ec50977498ca833102ef31315f2","79f5a89cc1fe4182b97fcab0cd059d24","acc037281582466c8f597ed4e7fa0520","ac570d48dc1c4cfcb577960d229307f0","37d0bfd60f6546dfbd863a770acde95d","e325f94773594684b23395f34f838ab2","d991de8ad1454380a7b41cbd144b4009","4b11f1b0fb79466a80b519e35c6b5d70"]},"id":"kX30nrM1Bjox","executionInfo":{"status":"ok","timestamp":1664980243396,"user_tz":-60,"elapsed":649,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"819861d4-4c88-4ec6-bbd1-c77979c30bfc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Using custom data configuration default-14cfeb140f0630e0\n"]},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-14cfeb140f0630e0/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bd9c3771aaea4ba792b79ec1dbe1f988"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac016a655f624fada693ceb4f9adf6a5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["0 tables [00:00, ? tables/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"35d177c491cc46df89d2f9d03f27aaf0"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-14cfeb140f0630e0/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"04559673f01443e2bdda3b6cc5acebd8"}},"metadata":{}}]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1kGUM9LCBjre","executionInfo":{"status":"ok","timestamp":1664980243398,"user_tz":-60,"elapsed":18,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3e4d2892-e2c9-4aed-e620-9d1adb706483"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['flags', 'utterance', 'category', 'intent'],\n","        num_rows: 21534\n","    })\n","})"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9NCBakvABjtg","executionInfo":{"status":"ok","timestamp":1664980243399,"user_tz":-60,"elapsed":16,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b0c25658-f1e2-4694-a244-9ff9b289b3cf"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'flags': 'BILC',\n"," 'utterance': \"I don't have an online account, what do I have to do to register?\",\n"," 'category': 'ACCOUNT',\n"," 'intent': 'create_account'}"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["intents=list(set(dataset['train']['intent']))\n","dict_intents={intents[i]: i for i in range(len(intents))}\n","print(dict_intents)\n","print(len(intents))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KEAYAL60Bjv4","executionInfo":{"status":"ok","timestamp":1664980243399,"user_tz":-60,"elapsed":14,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"8f0d5fdc-639c-4b62-bc91-142690b3c4f0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'recover_password': 0, 'cancel_order': 1, 'payment_issue': 2, 'delivery_options': 3, 'switch_account': 4, 'delete_account': 5, 'edit_account': 6, 'review': 7, 'set_up_shipping_address': 8, 'check_cancellation_fee': 9, 'place_order': 10, 'newsletter_subscription': 11, 'get_invoice': 12, 'change_order': 13, 'check_refund_policy': 14, 'contact_human_agent': 15, 'change_shipping_address': 16, 'get_refund': 17, 'track_refund': 18, 'delivery_period': 19, 'check_invoices': 20, 'create_account': 21, 'check_payment_methods': 22, 'complaint': 23, 'track_order': 24, 'registration_problems': 25, 'contact_customer_service': 26}\n","27\n"]}]},{"cell_type":"code","source":["def preprocess(dataset):\n","  return {'utterance':dataset['utterance'],\n","          'intent':dict_intents[dataset['intent']]}"],"metadata":{"id":"ornoQYV_Bjxv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["prep_dataset=dataset.map(preprocess)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["8c90ba1a215b4af093e3a25cb28b0157","83a6d56dc6c547eba0e62bd8be85df28","840a39671e1d4d5cae4c8357025bcedd","ac1fc8cb96c542ed96bfe1d664b3accb","14dbb1038b2f400c9d523e3d58a76fc6","4cde3886c53542efad4781c0a1ef267d","9d90ca13f3b14f84a20cc09bc80bafe8","e648c0ec322c418e8014477910ff8ef6","008b4029e57c44a88616cef04bcdc011","48521acb94d9457c8a31264e10234e21","9408e6d398e24599b3eee6bf7dd8363d"]},"id":"8XHWzVjeBj2l","executionInfo":{"status":"ok","timestamp":1664980245627,"user_tz":-60,"elapsed":2239,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"55c482b6-8083-432f-ca5c-3c944671a746"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/21534 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8c90ba1a215b4af093e3a25cb28b0157"}},"metadata":{}}]},{"cell_type":"code","source":["prep_dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ee5S4SwABj44","executionInfo":{"status":"ok","timestamp":1664980245627,"user_tz":-60,"elapsed":9,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"be05a8b2-0155-49a9-aa22-3693cf3d1e43"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'flags': 'BILC',\n"," 'utterance': \"I don't have an online account, what do I have to do to register?\",\n"," 'category': 'ACCOUNT',\n"," 'intent': 21}"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["model_id=\"microsoft/deberta-base\"\n","tokenizer = DebertaTokenizerFast.from_pretrained(model_id)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["adbde69ce7084671ba66429d28f4b857","f6dd140a2c064b0db7d01163058bda86","6ef70bb79c4447118318b184a6a2fcd4","07183b804288405aa19c08aa1a60daac","a7c0af4d8d274f568193314bf9362255","d6a4836e60a4404193f72a55c5617ad4","e9cffca138534a79a2a1df95d3e3eacb","157ae3f4c3c140cfac6f7ec4228fe5bd","f545530c252c4ec3ad52a3234d8e3a69","2553e59fc48143159f812292c3fd004b","e20be26efd144a0e94949c4ff1f5f947","a1ae8e6b27c04cdc9bc981f89f98e1b8","45be342f716242d68d5d5d462307be62","289e75d7b16743cb917d3cee1d8418cc","a971769c4df7492fbbab56ee4b56fb56","dffa132928d74a95b5eaadca77dfcc82","9560758e6d15449383d18b30da890671","bf22eed4992045178f472b6c7b45f1b9","c746a5f7e3a0422ab71c39d1cdc2fe8e","dbdf6542387b457faeef69f2878413d1","1a29cb5d497d40449b9383fcb341773f","c0d0100029fd499dabf86e8791b3000e","63f47c7d8d0141039baef19a3ba76cb0","5cf46f8385a34f719f64bccccbf43da7","92757a177b1146139e5cf6bd4635b79f","98e0f7c4f41f40448bdd427290f44ace","d79a0bbf7d734c2e98c94416598e3fd1","a4bee7c15e904c419cf4ba8ba3110004","fed40a7bd3b9490397ff85088f19e418","f3e8ff94b9774635b15444f26423f870","01c9fa896c434da6b46d9e31a6d94765","8aa2bb3780e6403385b2e240f815c90e","e04d8ea2000e43098509789bcc889aa9","1ec60e4746d64d83af17e975354f70e7","c28ec07331a541fab0bb3fb484d0f2fa","62f6221f5d344e2e88d595cad944694d","38d924e0a8d34da981a5db3f285f3987","fcf5f493dd66414cbbef6dd9f37439be","4ac763526f1640b9be04775742cb2d28","effc0ba92ea847fe9046522cd5a7db18","1d583f3f60c340f499b4bc30f39fed3b","4673f715370b42a5b68ac0dcbbda1034","3b19ef11ef4b4b08afdd742999ffc4b5","2d0e82a5411e49b2b2651363bd88080c"]},"id":"PXNHLWjEBj7H","executionInfo":{"status":"ok","timestamp":1664980259587,"user_tz":-60,"elapsed":13963,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d73d56c9-8ee7-4079-d498-8111389ddbfb"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"adbde69ce7084671ba66429d28f4b857"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a1ae8e6b27c04cdc9bc981f89f98e1b8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"63f47c7d8d0141039baef19a3ba76cb0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/474 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1ec60e4746d64d83af17e975354f70e7"}},"metadata":{}}]},{"cell_type":"code","source":["def tokenizer_function(dataset):\n","  return tokenizer(dataset[\"utterance\"],)"],"metadata":{"id":"futbnBXZBj_Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset=prep_dataset.map(tokenizer_function)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["63efa659156e4756a02e887a9072add3","83a904af2f8f4a40afc1ab1d7555c74b","f70f21c666e546519a6759c66acd4101","3ab118129f8b4df295275fdb978f9c36","3928541935544d69803155d54e6c4cf7","fc1ccd135c1347c692eafb7729ecf8a4","3cade5be493d46b69a1ce755795c7dd9","422deabd730d46bcbd52c82455ac7ef9","cf2b5022bc8c4961aa54dc05ad48877d","6732b882c20e446583562e274fcfb4ea","7038c2fbbddb45b7811eb1346c068d09"]},"id":"lz6NdFBDBkB5","executionInfo":{"status":"ok","timestamp":1664980271898,"user_tz":-60,"elapsed":12328,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5be827af-6895-49f1-8ed7-0aca31ee7828"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|   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})\n","})"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["tokenized_dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JGvncu9tBkLU","executionInfo":{"status":"ok","timestamp":1664980271900,"user_tz":-60,"elapsed":20,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d36003a8-82c1-4e90-b7a0-783ff4976030"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'flags': 'BILC',\n"," 'utterance': \"I don't have an online account, what do I have to do to register?\",\n"," 'category': 'ACCOUNT',\n"," 'intent': 21,\n"," 'input_ids': [1,\n","  100,\n","  218,\n","  75,\n","  33,\n","  41,\n","  804,\n","  1316,\n","  6,\n","  99,\n","  109,\n","  38,\n","  33,\n","  7,\n","  109,\n","  7,\n","  5124,\n","  116,\n","  2],\n"," 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n"," 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors=\"tf\")"],"metadata":{"id":"RT3dl2q5BkOW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_dataset = tokenized_dataset[\"train\"].to_tf_dataset(\n","    columns=['input_ids','attention_mask', 'intent'],\n","    shuffle=True, \n","    batch_size=BATCH_SIZE,\n","    collate_fn=data_collator\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PTPRYymkBkP8","executionInfo":{"status":"ok","timestamp":1664980275778,"user_tz":-60,"elapsed":3894,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"e27b8fb4-c9fe-44c2-b991-94575771396c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["You're using a DebertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"]}]},{"cell_type":"code","source":["def swap_positions(dataset):\n","  return {'input_ids':dataset['input_ids'],\n","          'attention_mask':dataset['attention_mask'],},dataset['intent']"],"metadata":{"id":"2BYqUwXWBkSQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_dataset=tf_dataset.map(swap_positions)"],"metadata":{"id":"VVRjp8tTBkU_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset=tf_dataset.take(int(0.9*len(tf_dataset)))\n","val_dataset=tf_dataset.skip(int(0.9*len(tf_dataset)))"],"metadata":{"id":"_H33KkGlBkXk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in val_dataset.take(1):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UAJ3h2wYBkZ0","executionInfo":{"status":"ok","timestamp":1664980278065,"user_tz":-60,"elapsed":1617,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bc5b805e-1fb0-4b31-a2ad-f6baf4a7f9aa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["({'input_ids': <tf.Tensor: shape=(16, 24), dtype=int64, numpy=\n","array([[    1,  9178,   109,    38, 10061,   127,  3018,  1316,   116,\n","            2,     0,     0,     0,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,  8569,   109,    38,  1649,     5,  1735,    13,  2996,\n","          116,     2,     0,     0,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1, 19178,   162,   120,   103,  4033,     2,     0,     0,\n","            0,     0,     0,     0,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,  4783,   804,  1316,    18,    57, 16341,     6,    99,\n","          197,    38,   109,     7,  5312,    24,   116,     2,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,  4970,    41,  2936,   114,   939,    64,  1994,    19,\n","         2111,   544,     2,     0,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,   100,    33,    10,   936,    77,   667,     7,   146,\n","           10,  3207,    19,  1886,     6,    99,   197,    38,   109,\n","            7,  6296,     9,    24,   116,     2],\n","       [    1,  4970,    41,  2936,   141,     7,  5405,     7,   277,\n","         1316,     2,     0,     0,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,  4970,    41,  2936,   114,    89,    16,    10,   481,\n","          346,     7,  1511,  3653,   544,     2,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,   100,   156,    10,  5021,     6,    64,   939, 23209,\n","          127, 23523,   645,   116,     2,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1, 17304,  1717,  1394,    41,  2936,   114,   939,   115,\n","          266,    41,   696,   442,    10,  3207,   116,     2,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1, 12196,   335,   109,    47,    33,    59, 14999,   103,\n","          179,  5766,  6355,   116,     2,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,  9178,     7,   989,    41,  2979,    13,    10,   544,\n","            2,     0,     0,     0,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,  7424,    47,  1137,   162,     5,  7107,     7,  1511,\n","        19458,  1841,   116,     2,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,   100,   348,   300,   117,  1316,     6,    99,   197,\n","           38,   109,   116,     2,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,   118,   685,   127, 12259,   139,  6355,     8,   939,\n","        19267,  3999,     7,  1649,   106,     2,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0],\n","       [    1,  9178,     7, 18981,  1272,  2746,     2,     0,     0,\n","            0,     0,     0,     0,     0,     0,     0,     0,     0,\n","            0,     0,     0,     0,     0,     0]])>, 'attention_mask': <tf.Tensor: shape=(16, 24), dtype=int64, numpy=\n","array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n","        1, 1],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0],\n","       [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n","        0, 0]])>}, <tf.Tensor: shape=(16,), dtype=int64, numpy=array([ 5,  3, 12,  0, 26,  2,  4, 26, 13,  2, 12,  7, 26, 21, 20,  2])>)\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"7wBSDjP3BkcH"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PQiSVkoLaaqF"},"source":["# Modeling"]},{"cell_type":"markdown","source":["## Based on TFDebertaForSequenceClassification"],"metadata":{"id":"s3Rf_9bFLJsH"}},{"cell_type":"code","source":["model=TFDebertaForSequenceClassification.from_pretrained(model_id,num_labels=len(intents))\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":486,"referenced_widgets":["2b947d44363c44e9bfb988685231b3b4","310754b1428247b5b868cffec8582b52","15fffbca3f4a4000be994e2a596cd830","33caa67958bc4d0a9ae74fd4a1275226","7e8e6b6640c644dfaa5fa6ac6bb6b5df","8867616de9d44f35943353a8650db303","f225984330724536a3473f8b4c1d71f1","87642ac16f674da7900b21bf2aaf65d3","d9c12f7681754ee69db4f43fa694a5be","adb3679e1b17440ba9f912d8846ee4a5","d50bc235bf064196a307b8fb6c1f2c2a"]},"id":"GuzC0evWPis2","executionInfo":{"status":"ok","timestamp":1664980296195,"user_tz":-60,"elapsed":18134,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"04739ad2-2cc7-4435-bbb1-05f20e6cd9d7"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/555M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2b947d44363c44e9bfb988685231b3b4"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFDebertaForSequenceClassification.\n","\n","Some layers of TFDebertaForSequenceClassification were not initialized from the model checkpoint at microsoft/deberta-base and are newly initialized: ['cls_dropout', 'pooler', 'classifier']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"stream","name":"stdout","text":["Model: \"tf_deberta_for_sequence_classification\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," deberta (TFDebertaMainLayer  multiple                 138601728 \n"," )                                                               \n","                                                                 \n"," pooler (TFDebertaContextPoo  multiple                 590592    \n"," ler)                                                            \n","                                                                 \n"," cls_dropout (TFDebertaStabl  multiple                 0         \n"," eDropout)                                                       \n","                                                                 \n"," classifier (Dense)          multiple                  20763     \n","                                                                 \n","=================================================================\n","Total params: 139,213,083\n","Trainable params: 139,213,083\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"E2xsaKH0ySx6"}},{"cell_type":"code","source":["num_epochs = 2\n","batches_per_epoch = len(tokenized_dataset[\"train\"]) // BATCH_SIZE\n","total_train_steps = int(batches_per_epoch * num_epochs)"],"metadata":{"id":"sadCMVfuDRLu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["optimizer, schedule = create_optimizer(init_lr=2e-5,num_warmup_steps=0, num_train_steps=total_train_steps)"],"metadata":{"id":"iqykd6O2DAmd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(optimizer=optimizer,\n","    metrics=[\"accuracy\"])"],"metadata":{"id":"o1GJdZRXBvQo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1664980296196,"user_tz":-60,"elapsed":22,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f3b465b4-503c-46e8-8adc-10cf02d95cff"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}]},{"cell_type":"code","source":["history=model.fit(\n","    train_dataset,\n","    validation_data=val_dataset,\n","    epochs=2,)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":453},"id":"zw2dd4PuQ_Dk","executionInfo":{"status":"error","timestamp":1664981233925,"user_tz":-60,"elapsed":268161,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c6043200-51b8-4b08-f560-89d49531d2a9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/2\n","1211/1211 [==============================] - 249s 205ms/step - loss: 0.0036 - accuracy: 0.9998 - val_loss: 0.0013 - val_accuracy: 1.0000\n","Epoch 2/2\n","  28/1211 [..............................] - ETA: 3:57 - loss: 0.0029 - accuracy: 1.0000"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-28-28fda7ea5d36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     epochs=2,)\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1382\u001b[0m                 _r=1):\n\u001b[1;32m   1383\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1384\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1385\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1386\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    945\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2955\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2956\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2957\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2959\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1852\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1854\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1855\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1856\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    502\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    505\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m           outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qxPsvkXKjE8U"},"outputs":[],"source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cxq950QqjE-g"},"outputs":[],"source":["plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","\n","plt.title('model_accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"obCwC6fGPa04"},"outputs":[],"source":[]},{"cell_type":"markdown","source":["# Evaluation"],"metadata":{"id":"ScmMZ25fTWQf"}},{"cell_type":"markdown","metadata":{"id":"UfxIFPOgNak8"},"source":["## Confusion Matrix"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"omKYrTxPNHoR"},"outputs":[],"source":["predicted = []\n","labels = []\n","\n","for input, label in val_dataset:\n","  predicted.append(model(**input).logits)\n","  labels.append(label.numpy())"]},{"cell_type":"code","source":["print(predicted)\n","print(labels)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dSAy_ai7y-uS","executionInfo":{"status":"ok","timestamp":1664981492602,"user_tz":-60,"elapsed":3298,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"85f44995-5403-4920-fb05-ea4ba22c07a4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[<tf.Tensor: shape=(16, 27), dtype=float32, numpy=\n","array([[-3.19677144e-02, -9.44107652e-01,  4.55888594e-03,\n","        -1.40890229e+00, -1.19679749e+00,  2.68556148e-01,\n","        -1.25625229e+00,  2.15255037e-01, -1.56347096e+00,\n","        -8.80063951e-01, -1.34098053e+00, -1.07151508e+00,\n","         7.58284211e-01,  5.23810625e-01,  3.96231920e-01,\n","        -1.06731451e+00,  1.60944477e-01, -1.17910826e+00,\n","        -9.77437437e-01, -1.36442411e+00,  9.73077297e+00,\n","        -5.67585051e-01, -7.12999761e-01, -7.50252008e-01,\n","         1.09767044e+00, -7.32102454e-01,  2.16616645e-01],\n","       [-1.18517613e+00, -1.27534306e+00,  1.86288938e-01,\n","        -6.69533134e-01,  2.15688288e-01,  1.33206680e-01,\n","        -2.01073742e+00, -1.53208470e+00, -2.36307904e-01,\n","        -1.41729903e+00,  6.81551546e-02, -1.57004488e+00,\n","        -8.19745064e-01, -1.34022284e+00, -5.17338812e-01,\n","        -7.20082104e-01, -1.65931487e+00, -1.86643228e-01,\n","        -1.85401607e+00, -4.35232133e-01, -1.12074065e+00,\n","         1.04215612e+01, -5.02753615e-01,  3.17930058e-02,\n","        -1.86986640e-01, -9.15037543e-02, -2.62492359e-01],\n","       [-4.60536748e-01, -1.15995049e+00, -1.41435325e+00,\n","        -1.04094994e+00,  8.58516788e+00, -1.06037855e-01,\n","        -7.28477776e-01,  4.64190394e-02,  1.24183051e-01,\n","        -4.99109566e-01, -6.79864526e-01, -1.75197586e-01,\n","        -2.41945758e-01,  4.00198728e-01, -7.06929326e-01,\n","        -3.06324333e-01, -4.80695128e-01, -2.21137667e+00,\n","        -1.20777440e+00, -8.32406461e-01, -5.45315504e-01,\n","         8.42175007e-01,  3.66140343e-02, -9.35381711e-01,\n","        -9.34230685e-01, -1.11831546e+00, -1.17581451e+00],\n","       [-6.54550612e-01, -1.43423927e+00,  1.10059710e+01,\n","        -7.73572803e-01, -2.09360027e+00, -1.26311585e-01,\n","        -1.05964959e+00, -9.41440880e-01, -1.81154263e+00,\n","        -1.36037767e+00, -8.71762276e-01, -1.43976712e+00,\n","         4.63235825e-01, -1.54906917e+00, -7.53953636e-01,\n","        -9.78794336e-01, -3.79971027e-01, -2.89704263e-01,\n","        -4.77702916e-01, -1.92019248e+00, -7.47683644e-02,\n","         8.80463719e-02, -1.66973695e-01,  5.50042450e-01,\n","        -1.73472238e+00, -5.16088426e-01, -1.59658179e-01],\n","       [-2.52717614e-01, -2.63131201e-01, -5.88994063e-02,\n","        -1.10530996e+00, -2.11832714e+00, -7.95815438e-02,\n","        -1.34859753e+00, -5.33066928e-01, -2.13324261e+00,\n","         5.75857200e-02, -1.08958089e+00, -7.82352030e-01,\n","        -4.62486833e-01, -6.96073532e-01,  5.15867174e-01,\n","        -5.68835139e-01, -1.38674808e+00,  9.64723492e+00,\n","         6.93379104e-01, -7.40614474e-01, -1.45770204e+00,\n","         4.63982373e-01, -1.66450191e+00,  7.29853287e-02,\n","        -1.01852107e+00, -6.57771826e-01, -4.00100678e-01],\n","       [-2.15138555e+00,  8.45462009e-02, -5.94251037e-01,\n","        -2.61751890e-01, -1.81421328e+00, -1.46145380e+00,\n","        -1.48800063e+00, -9.25040245e-01, -2.76007056e+00,\n","         1.21291709e+00, -3.93405825e-01, -1.52416635e+00,\n","         1.13834098e-01, -4.34882581e-01,  8.63561058e+00,\n","        -7.08143950e-01, -7.32960820e-01, -6.53881371e-01,\n","        -4.63750869e-01, -4.84194219e-01, -2.11330682e-01,\n","        -6.64584160e-01,  9.78846788e-01, -1.63067102e+00,\n","        -2.12742090e+00, -1.17158294e+00, -1.15370011e+00],\n","       [-1.48182034e+00, 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22]), array([17, 26, 12, 17,  0,  2,  2, 13, 26,  5,  0, 24,  2,  2, 24, 17]), array([21,  2,  2, 15,  2, 12,  2,  5, 15, 26, 24, 12,  0,  2,  2,  2]), array([17, 13, 17,  2, 25,  6, 23, 19, 20, 26, 21, 12, 20,  2, 21, 16]), array([21, 21, 21, 17, 26, 12, 12, 17,  4,  2, 21,  0, 17,  7, 23,  7]), array([ 4, 24, 13, 12, 15,  9,  3, 21,  9, 26, 21, 21,  2, 13, 26, 24]), array([ 7, 21, 12, 20, 16,  0, 21, 24, 26,  2, 20,  8, 21, 12,  0, 22]), array([ 2,  2, 12,  2, 14, 24,  2, 21,  9,  0, 19,  2,  5, 22, 14,  0]), array([21, 14, 24,  0, 15,  2,  0, 21, 24, 21,  0, 14,  7, 26, 20, 12]), array([17, 26,  5, 12, 17,  0,  4,  2,  9, 26, 23,  2, 14, 24, 15, 20]), array([ 2,  2, 23, 24, 26, 15, 23,  3, 26,  2,  2, 14, 15, 26,  0,  9]), array([23, 17,  2, 26, 20, 18,  5, 12,  2,  2,  6, 15,  2, 13, 20, 23]), array([21,  2,  2, 24, 17,  7,  5, 13, 18, 20,  2,  0, 21, 14, 15, 12]), array([ 2, 11, 26,  2, 21,  2, 15, 13, 26,  2,  2, 21,  0, 21, 15,  2]), array([24, 19,  4, 26,  3, 12, 23, 21,  7,  2,  0, 26, 20, 16,  2,  2]), array([17,  5,  2, 21,  3, 21, 15,  2, 26,  2,  2, 21, 21, 17,  2, 24]), array([ 2,  2, 12,  2, 20, 20, 15,  0,  2,  2, 15, 26,  2,  7, 24, 24]), array([ 5, 12, 12, 12, 12, 12,  2, 21, 17,  5, 13,  2, 17, 13, 15, 26]), array([18, 21, 12, 20, 24, 24, 23,  2,  5, 26,  2, 24, 23,  7,  2,  0]), array([12,  0, 21, 26, 24, 15,  2,  9, 20,  0, 17,  9, 21, 21,  2, 26]), array([ 2,  5,  2,  2, 12,  3, 13,  2, 18,  2,  5,  2,  2, 26, 20, 12]), array([ 2,  2, 20,  2, 15,  4, 12,  2,  5, 26, 17, 16, 21,  9, 17, 17]), array([21,  2,  2,  2,  2,  0, 15, 26, 11, 15,  0, 17, 12, 12, 21, 13]), array([26,  2, 23,  2, 15, 26, 23,  4, 13,  2,  2, 24, 14,  2, 13,  2]), array([26,  5,  2, 21, 17, 17, 21, 14,  8,  2, 15, 26, 25, 15, 14, 26]), array([21, 12,  2,  5, 24,  0,  2, 13,  7,  7, 21, 23, 20, 26, 21, 23]), array([ 2,  2,  2,  5, 21,  2, 13, 15,  2,  7, 24, 20,  5, 22, 22, 20]), array([17,  2,  0, 23, 17, 24, 14, 21,  2, 23, 23, 24,  2, 21, 15, 17]), array([12, 24,  2, 23, 26, 13, 21, 12, 14, 17,  2, 12, 15, 14, 23, 13]), array([14,  2, 23, 18,  5, 21, 12, 15,  2, 23, 20, 15, 23,  2,  5, 12]), array([17, 24, 26, 26, 24, 26, 13,  7,  5, 17,  5, 26, 26, 12, 26,  9]), array([ 5,  2, 12,  7, 24,  0, 23, 21, 21,  2, 26, 26, 12, 13, 26, 26]), array([ 2, 21, 20, 15, 14, 17,  0,  5, 15, 15, 13, 12, 20, 14, 13,  7]), array([21,  2, 16,  2, 26, 14, 21, 22, 21,  0,  0,  2,  2,  2, 15, 20]), array([26, 12, 12, 24,  2, 23, 26, 26, 17, 17,  7,  3, 13,  7, 11, 17]), array([20, 15, 17,  2, 24, 21, 18, 26,  2,  2, 26,  2,  2, 26,  2, 26]), array([ 2, 15, 21, 17,  0, 17, 22, 23, 12,  2, 13, 26,  2, 26,  5, 15]), array([ 2, 12, 12,  4, 11,  2,  0, 24, 22,  0, 21, 12, 20,  5,  5, 21]), array([21, 14, 26,  2, 12,  2,  5, 12, 17, 20,  1, 21, 12,  2, 20, 17]), array([20, 14, 26,  2,  8, 26,  7, 15, 24, 20,  2, 25, 21, 14, 12,  2]), array([22, 17,  5, 20, 17, 26, 17, 23,  2, 21,  2,  3, 11, 26, 17, 10]), array([24, 21,  0, 20, 26,  0, 17,  2, 26,  2, 15,  2, 20, 12, 21, 17]), array([12, 18,  0,  7, 19, 24, 21, 20, 26,  0, 26, 26, 26, 21, 12,  5]), array([23, 17, 19, 22, 12, 12,  2, 24, 17, 26, 15, 21, 21, 17, 21, 26]), array([10,  9,  2, 26, 26,  2,  2, 26, 26,  2,  2,  2, 14,  9, 24,  2]), array([15,  9,  5, 13, 20,  7, 15, 21, 20, 24, 26, 13, 15, 20])]\n"]}]},{"cell_type":"code","source":["print(tf.argmax(predicted[:-1],axis=-1).numpy())\n","print(labels[:-1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3UkSksJ8nly_","executionInfo":{"status":"ok","timestamp":1664982074037,"user_tz":-60,"elapsed":597,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"605db361-b240-42ab-c229-13367943c06a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[20 21  4 ... 26  4 25]\n"," [12 12  2 ...  2 21 26]\n"," [24  4 26 ...  2  2 21]\n"," ...\n"," [12 18  0 ... 21 12  5]\n"," [23 17 19 ... 17 21 26]\n"," [10  9  2 ...  9 24  2]]\n","[array([20, 21,  4,  2, 17, 14,  3,  7, 17, 24,  2, 23, 13, 26,  4, 25]), array([12, 12,  2, 15, 26, 20, 26, 15, 17, 24, 21,  7, 24,  2, 21, 26]), array([24,  4, 26,  2,  7, 17, 17,  2, 20, 13,  0, 21,  2,  2,  2, 21]), array([14,  5,  2, 22, 15, 17,  3,  0, 26, 15, 21, 20, 26, 10, 20, 26]), array([ 5, 26, 23, 12, 26,  2, 12,  5, 22, 26, 26,  0, 21,  2, 13, 13]), array([26, 26,  2, 15, 13, 26,  2,  2, 12, 21, 23, 22, 12, 16, 26,  3]), array([ 2,  2, 15, 21, 11, 17, 15,  7, 24, 17, 20, 24, 15,  2, 20,  4]), array([21,  6, 13,  6,  4, 21, 17, 21,  2,  7, 12, 21, 13,  7, 11,  0]), array([24,  7,  2,  2, 13, 26, 12,  0,  2, 21, 25,  2, 13, 26, 20, 21]), array([ 2, 14, 24,  2, 21,  6, 12, 21, 12,  5, 21, 21,  2, 17, 24,  2]), array([15,  2, 17,  5, 15, 20, 26, 13,  5,  2, 21,  2, 26, 12, 12, 24]), array([24,  2,  2, 19,  2,  2, 13,  1,  2, 26, 15, 24, 14,  0, 13,  2]), array([13, 14,  0,  2,  2,  2,  2, 21, 17, 14, 21, 26,  3, 15, 20, 23]), array([ 2,  2,  7,  0, 15, 25, 21,  2, 13, 24,  2, 26, 15, 18, 21, 20]), array([21,  2, 10, 21, 21,  2, 26, 17, 11, 14, 17,  7, 25,  2, 13,  2]), array([ 0, 20,  2,  2, 23, 26, 26, 15, 23,  2,  0, 21,  2, 24, 23,  2]), array([ 2, 21, 11,  5, 17,  5, 17,  5, 21, 20, 15, 21, 17,  2, 20,  5]), array([21, 26,  0, 24, 12, 18, 11,  7, 21,  2, 20, 21, 17, 20, 12, 26]), array([ 2,  9, 26, 26,  2,  2, 21,  9,  2, 24, 16, 18, 21,  6, 24, 17]), array([13, 21,  2,  2,  9,  5,  0, 22, 23,  2,  2, 17, 21, 23,  7, 26]), array([24, 21,  2, 20, 12,  2,  2, 17,  3, 13, 24,  2, 20, 24, 21,  2]), array([20, 21,  2, 26,  2,  2,  3, 23,  0,  3,  5,  2,  2,  3,  7, 26]), array([21, 14, 17, 13, 20, 20,  2, 15, 13,  3,  2, 11, 21,  0,  2, 24]), array([26,  2,  2,  0,  2, 26, 20, 14, 21,  9, 23, 15,  2,  2, 13, 13]), array([26,  2,  7, 13, 15,  5, 13, 15, 24, 22,  2,  2, 20, 24, 13, 12]), array([26,  2, 21, 21,  5,  5, 26, 18, 13, 24,  2, 18,  2, 20,  0, 13]), array([24, 20, 15,  0,  2,  2, 20, 21, 26, 15, 12,  2,  2,  7, 13, 15]), array([24, 21, 17,  0, 24, 21, 12, 23, 13, 26, 14,  7, 21, 14,  3, 20]), array([ 4, 22, 21, 23, 23, 26,  0, 14, 15, 13, 14,  2, 26,  7, 21,  5]), array([20, 23,  2, 19,  2,  8,  4,  5, 26,  2, 26,  2, 10, 14, 21,  2]), array([21,  2, 26, 15,  5,  9, 20, 15,  2,  3,  2, 26, 15, 21, 26, 15]), array([ 2,  2, 24, 19,  2,  2, 26,  2, 26, 26,  2, 22, 21, 24, 13, 12]), array([20,  2, 18, 26, 17,  2,  3, 12,  2,  2, 12,  5, 26, 17, 21,  2]), array([10, 12, 15, 12, 17, 23, 24,  0,  2, 25, 21,  9, 20, 13,  5, 15]), array([22, 13,  9,  0, 24, 21, 24, 17,  2, 26, 18,  7, 26,  2, 12, 12]), array([ 5, 21, 21,  7, 14, 12,  2, 26, 24, 15, 23, 24, 14, 15,  3, 20]), array([ 2, 23,  2,  2, 14, 22, 26, 26, 12,  9, 18,  5, 20,  9,  5, 21]), array([26, 24, 17, 21,  3,  5, 21, 15, 21,  2, 12,  2, 23, 14, 20, 23]), array([17, 12, 26, 26,  2,  2, 15, 20, 24, 20,  2,  8, 15, 26, 12, 26]), array([ 2, 26, 18,  2,  2, 21, 12,  2, 24, 20,  2,  4,  2, 20, 26, 21]), array([ 2,  0, 25, 26, 23, 17, 26, 20, 11, 21,  2, 12, 17, 17,  2, 13]), array([ 9, 16, 22,  2, 14, 21, 21,  2, 12, 23, 18, 21,  2, 14, 15,  2]), array([26, 21, 21, 17, 25, 12,  2,  2, 20, 20, 26, 20,  2,  2, 21,  7]), array([12, 18, 24, 18, 17, 21, 15, 21,  3, 23, 26, 24, 12,  2,  2,  5]), array([ 2,  0,  2, 21, 17, 21, 21,  9, 21, 19,  4, 17, 17, 13,  2, 25]), array([20,  3, 13, 17, 12, 24, 14,  2, 13, 12, 17,  2, 26, 15,  7, 20]), array([19, 21, 26, 23,  2, 14,  2,  2, 15,  5,  2, 21, 13,  9, 24, 24]), array([ 4,  7, 24,  0, 13,  3, 23,  4, 26, 13, 11,  2, 26,  2, 17,  2]), array([20, 24, 24,  2, 21,  2, 26, 12, 21, 26, 21, 12, 26, 14, 24, 15]), array([21, 11,  2, 14, 14, 22, 18, 23, 16, 12,  7, 26, 24,  2, 22,  0]), array([26, 12, 21, 21, 15,  2, 21, 24,  2,  2, 12, 26,  5,  2,  5, 21]), array([ 2, 24, 11, 21,  2, 20,  4, 21,  3, 15, 17, 12,  5,  0, 24, 21]), array([26, 26, 24, 26, 20, 15,  2, 23,  9, 12,  0, 21, 13,  2, 21,  2]), array([ 2,  2, 26, 26, 18,  5,  5,  3, 21, 20, 13, 13, 21, 26, 23,  2]), array([ 0, 26, 26, 23,  0, 12, 13, 26, 24,  7,  2,  0, 26, 22, 17, 12]), array([17, 26, 21, 26, 21, 13,  2, 12,  3,  2, 17,  2,  2, 15, 23, 26]), array([21,  7,  2,  2, 17, 12, 17, 15, 10, 14,  3, 23, 23, 13, 23,  7]), array([ 2, 15,  2, 22, 21, 13,  2,  7, 12, 24,  7,  2, 23, 13, 21, 17]), array([18, 15,  8, 21,  2, 13,  4, 26, 19,  2, 10, 20,  2, 21, 24,  2]), array([24,  2, 15, 13, 24, 23, 21, 21,  7, 21, 26, 13,  0,  0, 15, 14]), array([13,  5, 24, 24, 17, 26,  9,  2, 20,  2, 12, 15,  2, 14, 20, 21]), array([ 2,  0, 13,  2, 17,  2, 13,  2,  2, 14,  4, 12, 13, 21,  4, 23]), array([23, 26, 26, 12, 20, 13,  0, 13,  2, 23,  0, 26, 24, 17, 18, 15]), array([17, 13,  2, 17, 13,  3,  0, 13, 20, 23, 26,  0,  5,  7, 15, 24]), array([17, 21,  5,  6, 18, 20, 20, 24, 20, 24,  7,  5, 21,  0, 26,  2]), array([13,  3, 21, 12, 21,  6, 17,  7,  5, 14, 13, 17,  2, 15, 13,  2]), array([21, 21, 15, 21,  7, 21, 17,  2, 20,  5, 24, 23, 20, 24, 26, 24]), array([ 2, 12, 21, 21, 20, 26,  0, 24, 22, 17, 26, 26,  2,  2, 24,  0]), array([ 0, 26,  0, 15,  3,  2, 21, 17,  2, 12,  2, 20, 20, 23, 15,  6]), array([ 2,  2, 22, 12, 26, 24, 21,  3,  2, 26, 17, 23, 17, 20, 12,  2]), array([ 2, 25, 12, 19,  7, 17,  2,  2, 17, 17,  5, 26,  3, 17,  6, 24]), array([12,  0, 22, 21, 24, 15, 26,  2, 14,  2, 17, 10,  2,  5, 20, 24]), array([12,  0,  2, 23, 14, 26,  2,  2,  2, 26,  4,  5, 26, 17, 17, 23]), array([15, 15, 23,  5, 20, 26,  7, 14,  7, 22,  3, 20,  2,  2,  2,  2]), array([ 2,  2, 20, 21, 21, 12,  1, 13,  2, 20, 26,  2, 21, 14,  2,  2]), array([21, 15,  2,  0, 25, 12,  2,  4, 23, 15, 12,  5,  6, 20, 24,  4]), array([26, 12, 12,  2, 20, 12, 26, 20, 15, 12,  2,  9,  5, 21, 12, 13]), array([20, 24, 24,  8, 17, 26,  2,  7, 24, 12, 13, 15, 21, 13, 17,  2]), array([20, 23, 13, 14,  2,  2,  2,  0, 24,  2, 21, 23,  0,  3, 15, 21]), array([ 9, 14, 26, 11,  0, 12, 17,  5,  2,  2,  2,  2,  2, 23, 13,  2]), array([21, 24, 23, 21,  2,  2, 13, 21,  7, 15, 26, 15, 21, 21,  2,  2]), array([21,  0,  5,  2, 18,  7, 26,  2, 12,  2, 17,  5, 15, 17, 21,  2]), array([17, 16, 26, 12, 15, 13, 20, 13, 24, 21, 15,  5,  0,  2, 13, 26]), array([14, 26, 26, 15, 13, 12, 22, 14, 23, 12,  9,  2, 12, 17, 21,  2]), array([ 2,  2,  2, 24,  0, 17, 21, 26, 14, 20, 26,  2,  3,  0, 13, 12]), array([12,  3,  2, 20, 24, 17,  3,  0, 17,  7,  2,  9, 21, 12, 21,  8]), array([22, 12, 20, 22,  9, 15,  2,  7, 20, 17, 17, 23,  2, 12,  2, 21]), array([ 5, 19,  7, 15, 21, 25,  3, 20, 12,  2,  5, 17, 24, 26, 26,  2]), array([ 0, 15,  2, 12,  2, 12, 11, 25, 21,  4, 13, 13, 26,  2, 26, 21]), array([21,  7, 21,  8, 12, 26, 13, 19, 11,  2,  2,  5, 20,  2, 21, 22]), array([17, 26, 12, 17,  0,  2,  2, 13, 26,  5,  0, 24,  2,  2, 24, 17]), array([21,  2,  2, 15,  2, 12,  2,  5, 15, 26, 24, 12,  0,  2,  2,  2]), array([17, 13, 17,  2, 25,  6, 23, 19, 20, 26, 21, 12, 20,  2, 21, 16]), array([21, 21, 21, 17, 26, 12, 12, 17,  4,  2, 21,  0, 17,  7, 23,  7]), array([ 4, 24, 13, 12, 15,  9,  3, 21,  9, 26, 21, 21,  2, 13, 26, 24]), array([ 7, 21, 12, 20, 16,  0, 21, 24, 26,  2, 20,  8, 21, 12,  0, 22]), array([ 2,  2, 12,  2, 14, 24,  2, 21,  9,  0, 19,  2,  5, 22, 14,  0]), array([21, 14, 24,  0, 15,  2,  0, 21, 24, 21,  0, 14,  7, 26, 20, 12]), array([17, 26,  5, 12, 17,  0,  4,  2,  9, 26, 23,  2, 14, 24, 15, 20]), array([ 2,  2, 23, 24, 26, 15, 23,  3, 26,  2,  2, 14, 15, 26,  0,  9]), array([23, 17,  2, 26, 20, 18,  5, 12,  2,  2,  6, 15,  2, 13, 20, 23]), array([21,  2,  2, 24, 17,  7,  5, 13, 18, 20,  2,  0, 21, 14, 15, 12]), array([ 2, 11, 26,  2, 21,  2, 15, 13, 26,  2,  2, 21,  0, 21, 15,  2]), array([24, 19,  4, 26,  3, 12, 23, 21,  7,  2,  0, 26, 20, 16,  2,  2]), array([17,  5,  2, 21,  3, 21, 15,  2, 26,  2,  2, 21, 21, 17,  2, 24]), array([ 2,  2, 12,  2, 20, 20, 15,  0,  2,  2, 15, 26,  2,  7, 24, 24]), array([ 5, 12, 12, 12, 12, 12,  2, 21, 17,  5, 13,  2, 17, 13, 15, 26]), array([18, 21, 12, 20, 24, 24, 23,  2,  5, 26,  2, 24, 23,  7,  2,  0]), array([12,  0, 21, 26, 24, 15,  2,  9, 20,  0, 17,  9, 21, 21,  2, 26]), array([ 2,  5,  2,  2, 12,  3, 13,  2, 18,  2,  5,  2,  2, 26, 20, 12]), array([ 2,  2, 20,  2, 15,  4, 12,  2,  5, 26, 17, 16, 21,  9, 17, 17]), array([21,  2,  2,  2,  2,  0, 15, 26, 11, 15,  0, 17, 12, 12, 21, 13]), array([26,  2, 23,  2, 15, 26, 23,  4, 13,  2,  2, 24, 14,  2, 13,  2]), array([26,  5,  2, 21, 17, 17, 21, 14,  8,  2, 15, 26, 25, 15, 14, 26]), array([21, 12,  2,  5, 24,  0,  2, 13,  7,  7, 21, 23, 20, 26, 21, 23]), array([ 2,  2,  2,  5, 21,  2, 13, 15,  2,  7, 24, 20,  5, 22, 22, 20]), array([17,  2,  0, 23, 17, 24, 14, 21,  2, 23, 23, 24,  2, 21, 15, 17]), array([12, 24,  2, 23, 26, 13, 21, 12, 14, 17,  2, 12, 15, 14, 23, 13]), array([14,  2, 23, 18,  5, 21, 12, 15,  2, 23, 20, 15, 23,  2,  5, 12]), array([17, 24, 26, 26, 24, 26, 13,  7,  5, 17,  5, 26, 26, 12, 26,  9]), array([ 5,  2, 12,  7, 24,  0, 23, 21, 21,  2, 26, 26, 12, 13, 26, 26]), array([ 2, 21, 20, 15, 14, 17,  0,  5, 15, 15, 13, 12, 20, 14, 13,  7]), array([21,  2, 16,  2, 26, 14, 21, 22, 21,  0,  0,  2,  2,  2, 15, 20]), array([26, 12, 12, 24,  2, 23, 26, 26, 17, 17,  7,  3, 13,  7, 11, 17]), array([20, 15, 17,  2, 24, 21, 18, 26,  2,  2, 26,  2,  2, 26,  2, 26]), array([ 2, 15, 21, 17,  0, 17, 22, 23, 12,  2, 13, 26,  2, 26,  5, 15]), array([ 2, 12, 12,  4, 11,  2,  0, 24, 22,  0, 21, 12, 20,  5,  5, 21]), array([21, 14, 26,  2, 12,  2,  5, 12, 17, 20,  1, 21, 12,  2, 20, 17]), array([20, 14, 26,  2,  8, 26,  7, 15, 24, 20,  2, 25, 21, 14, 12,  2]), array([22, 17,  5, 20, 17, 26, 17, 23,  2, 21,  2,  3, 11, 26, 17, 10]), array([24, 21,  0, 20, 26,  0, 17,  2, 26,  2, 15,  2, 20, 12, 21, 17]), array([12, 18,  0,  7, 19, 24, 21, 20, 26,  0, 26, 26, 26, 21, 12,  5]), array([23, 17, 19, 22, 12, 12,  2, 24, 17, 26, 15, 21, 21, 17, 21, 26]), array([10,  9,  2, 26, 26,  2,  2, 26, 26,  2,  2,  2, 14,  9, 24,  2])]\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1664982192008,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"fl3LrfS0NHqq","outputId":"77160212-70a2-4fa4-cf99-be1d31b79e6a"},"outputs":[{"output_type":"stream","name":"stdout","text":["[20 21  4 ... 13 15 20]\n","[20 21  4 ... 13 15 20]\n"]}],"source":["print(np.concatenate([np.array(labels[:-1]).flatten(),np.array(labels[-1]).flatten()]))\n","print(np.concatenate([np.argmax(predicted[:-1], axis = -1).flatten(), np.argmax(predicted[-1], axis = -1).flatten()]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gQkoPw5VNHyc"},"outputs":[],"source":["pred=np.concatenate([np.array(labels[:-1]).flatten(),np.array(labels[-1]).flatten()])\n","lab=np.concatenate([np.argmax(predicted[:-1], axis = -1).flatten(), np.argmax(predicted[-1], axis = -1).flatten()])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":4092,"status":"ok","timestamp":1664982300813,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"lp5I9AucNH1Z","outputId":"290c7de7-c36e-4ae6-bc1d-5e76319deb7b"},"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 89   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   0 414   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   0   0  39   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0  26   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0  80   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0  11   0   0   0   0   0   0   0   0   0   0   0\n","    0   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0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 113   0   0\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  10   0\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0 127\n","    0   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","   25   0   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0  14   0   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0 115   0   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0 216   0   0   0   0   0]\n"," [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n","    0   0   0   0  31   0   0   0   0]\n"," [  0   0   0   0   0   0 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\n"},"metadata":{"needs_background":"light"}}],"source":["cm = confusion_matrix(lab, pred)\n","print(cm)\n","plt.figure(figsize=(16,16))\n","\n","sns.heatmap(cm, annot=True,)\n","plt.title('Confusion matrix')\n","plt.ylabel('Actual')\n","plt.xlabel('Predicted')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M9z9wP4DNH4F"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"7jbgoKV070Gr"},"source":["# Testing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5H8Y9MERnfX2"},"outputs":[],"source":["inputs = tokenizer([\"Please how do i go about the account creation? \",\n","                    \"After setting up my account, i feel like i need to change it. How do i go about that?\",\n","                    \"how do i know how much i need to pay?\",\n","                    \"purchased a product, which i now want to change\"\n","                    ], padding=True,return_tensors=\"tf\")\n","\n","logits = model(**inputs).logits\n","outputs=tf.argmax(logits,axis=-1).numpy()"]},{"cell_type":"code","source":["print(outputs)"],"metadata":{"id":"hEML0foO8ps0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1664979063671,"user_tz":-60,"elapsed":2,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"179be869-f68c-4c16-937d-090894fb683b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[11  6 25 21]\n"]}]},{"cell_type":"code","source":["reverse_dict_intents={i:intents[i] for i in range(len(intents))}\n","print(reverse_dict_intents)"],"metadata":{"id":"kdx0TqonQcmB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1664979064289,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"73197180-f316-40e9-80fd-ef3fcd8adcf4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{0: 'registration_problems', 1: 'get_refund', 2: 'set_up_shipping_address', 3: 'check_refund_policy', 4: 'contact_human_agent', 5: 'complaint', 6: 'switch_account', 7: 'check_invoices', 8: 'cancel_order', 9: 'review', 10: 'check_cancellation_fee', 11: 'create_account', 12: 'payment_issue', 13: 'track_order', 14: 'track_refund', 15: 'place_order', 16: 'delete_account', 17: 'delivery_options', 18: 'edit_account', 19: 'delivery_period', 20: 'get_invoice', 21: 'change_order', 22: 'change_shipping_address', 23: 'recover_password', 24: 'contact_customer_service', 25: 'check_payment_methods', 26: 'newsletter_subscription'}\n"]}]},{"cell_type":"code","source":["for i in outputs:\n","  print(reverse_dict_intents[i])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JOktO5oFwOqk","executionInfo":{"status":"ok","timestamp":1664979064290,"user_tz":-60,"elapsed":5,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"24a6f151-1cfc-4921-cc18-d74c3fd7b56c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["create_account\n","switch_account\n","check_payment_methods\n","change_order\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"GommhjBUwcD5"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["gwDUFKDdR6i_"],"provenance":[{"file_id":"1kiqhUrifUOMlpLnWCJWAYm9xM3vdVxfF","timestamp":1673807534726},{"file_id":"1qrpPSsjeFUMtRrljhxEiKKmlVk__fpuz","timestamp":1664695746526},{"file_id":"1qG67bWTlIgR_WzHRVCiw1CicPYSTv4qn","timestamp":1663398158336},{"file_id":"1ekGLJqUm4hoXzVs3h2I_OfnSCNwm1p8j","timestamp":1663151174522}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"widgets":{"application/vnd.jupyter.widget-state+json":{"bd9c3771aaea4ba792b79ec1dbe1f988":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_c82eaa5c28254b0eba859489cd3e53b6","IPY_MODEL_2afaf834571946fe84c9433933e878de","IPY_MODEL_bd80097f67794910a844c4085ce3675a"],"layout":"IPY_MODEL_dae1217b045d41f0b2247fc3e2043c1e"}},"c82eaa5c28254b0eba859489cd3e53b6":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_96e74b939aa945e7b563f7d7ab491ebd","placeholder":"​","style":"IPY_MODEL_27d671eba6434d1aa14257d329c950c4","value":"Downloading 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FILE: deep learning for natural language processing/9-Named Entity Recognition using HuggingFace 🤗 Transformers by Neuralearn.ai-.ipynb
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{"cells":[{"cell_type":"markdown","metadata":{"id":"gwDUFKDdR6i_"},"source":["# Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16254,"status":"ok","timestamp":1665149400551,"user":{"displayName":"martins folefac","userId":"15114498789158493667"},"user_tz":-60},"id":"ntIbn-RaRzbs","outputId":"b80ff6a2-7f86-4f33-af44-defaeeb67540"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.22.2-py3-none-any.whl (4.9 MB)\n","\u001b[K     |████████████████████████████████| 4.9 MB 7.7 MB/s \n","\u001b[?25hCollecting datasets\n","  Downloading datasets-2.5.2-py3-none-any.whl (432 kB)\n","\u001b[K     |████████████████████████████████| 432 kB 73.0 MB/s \n","\u001b[?25hCollecting evaluate\n","  Downloading evaluate-0.2.2-py3-none-any.whl 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numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from seqeval) (1.21.6)\n","Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.7/dist-packages (from seqeval) (1.0.2)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.2.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.7.3)\n","Building wheels for collected packages: seqeval\n","  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=32695bb816bc85a230d6fdd73f4c3a37958a5d3a9497eb7bb007b3b08f6bddec\n","  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n","Successfully built seqeval\n","Installing collected packages: seqeval\n","Successfully installed seqeval-1.2.2\n"]}],"source":["!pip install transformers datasets evaluate\n","!pip install seqeval"]},{"cell_type":"markdown","metadata":{"id":"D31_Au7nR_oc"},"source":["# Imports"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZslyJl_QV7rt"},"outputs":[],"source":["import tensorflow as tf### models\n","import numpy as np### math computations\n","import matplotlib.pyplot as plt### plotting bar chart\n","import sklearn### machine learning library\n","import cv2## image processing\n","from sklearn.metrics import confusion_matrix, roc_curve### metrics\n","import seaborn as sns### visualizations\n","import datetime\n","import pathlib\n","import io\n","import os\n","import re\n","import string\n","import evaluate\n","import time\n","from numpy import random\n","import gensim.downloader as api\n","from PIL import Image\n","import tensorflow_datasets as tfds\n","import tensorflow_probability as tfp\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.layers import Layer\n","from tensorflow.keras.layers import Dense,Flatten,InputLayer,BatchNormalization,Dropout,Input,LayerNormalization\n","from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n","from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n","from tensorflow.keras.optimizers import Adam\n","from google.colab import drive\n","from google.colab import files\n","from datasets import load_dataset\n","from transformers import (BertTokenizerFast,TFBertTokenizer,BertTokenizer,RobertaTokenizerFast,DataCollatorForTokenClassification,\n","                          DataCollatorWithPadding,TFRobertaForSequenceClassification,TFBertForSequenceClassification,\n","                          TFBertModel,create_optimizer,TFRobertaForTokenClassification,TFAutoModelForTokenClassification,)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8phsQmoYUJWK"},"outputs":[],"source":["BATCH_SIZE=16\n","NUM_EPOCHS=2"]},{"cell_type":"markdown","metadata":{"id":"y3CEdBN2aX9k"},"source":["# Data Preparation"]},{"cell_type":"code","source":["dataset = load_dataset(\"conll2003\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201,"referenced_widgets":["5ea018d1fe724cd5bc7705a3aee81b43","c641e3d848c3479eabf5dd1b06f296a5","c346f546778f4ce185bf8aef46eac726","eda276bfd3a34c7a81198de8b2e0f864","d055c5015d48457eb9b807a5d51cde90","957c9426e9264e729f6358f6be215342","44882a7e6a6e4206ab265bb0af43924c","bef625a0d2b246468eb368196260b863","adaf9be566dd461d9f0de24acccc17b0","ab107069e3924429b9f969491c5f15b1","87312a875201420aae02c7b09715cd92","b77d8dd5f99048bc81612bcea9405b7c","21df442112234fb298c795bcf993e7d4","e208f2bdcda249b99c6710f4d179d519","f791131765a44b10a065ca28ab981b4f","adaa0d1ff6d044a9b1784b4ecef37735","34c0a1e7f0354291966f16ca2c0a9ca5","6ae3e839cffe4e53b81c93ee3b15d313","140e01220de64750846730873d1487be","15ff267fa63b4cb389f62bd1e46e4b77","32ccbb9e88af478da220c8a8b46418af","600a99eb6d8745d0b340539eeda1aed7","7a389b842cef43eb9fc572b37e41e545","b3c77d074b9840b8959549daf28839a8","cbb114ae01f043e9af36658b1a6cadda","14ef543f3cc74af793ad64df2c466a4f","b78de68e182d435abe4b73167b69afa2","5d5ae30681d74a07beb685c3cdd4af9a","06bbb645dbe3441cb27e9406423c7127","140c139ea6324df2811e77e3edcc5276","aea860afeccf4d27a07d9a84e491e05e","8dbb57b0d8244f6cbed1e9f0c163ad26","11a042f81fc74553ae90c6e1c1849b8c","8b32d4651592431ca49f24f0f7553344","4c99eba6385346c2bb992b2886acba61","abc54cd521ec4cff9363e4b81c803a78","d7e95e6e4a5d4cdebee46b830ae29d51","09069636007645d18dfd8797be3f26d4","526bba45cef74791883e4631d619a36a","a5a2fd5cae3247a1981a63b2137b6e04","ca9c87b08c5c45febc75790df4a5f56f","3ec2cdbcf2544c929492e3995207034a","dd31a983a7d147d7ab68fce5950b55c1","fd2002ee04074a1883a1ca94f3e7955a","94dae0bcce534db29627c748c0e71164","deb12f5217764b7ba681e35c5cb2224a","d905be495b054899b42992af2493a38a","af5d0e09ec17452ab1bb3b8a2c830882","8c41167a5a184c8298344c7d4b33d502","161ba9b817944e868abf03bb868d51d9","2293ce2a21db4317a70055fa2c4b0c3d","9e9f3a129add4f88abb8d493817f7e7e","0e9139d7f3344c3cbd80b32d38b69a5e","dcadd2efd4cb43ed80472c94e78a9c8b","656c25bff0c64bc89277bfed3b5fbfc9","891e6926308d4b0d95c5cc27b55bad9d","ac208729fab9434b8c1823c2d285eab9","43c3d645c57c4b22afc417d2b89d9250","0dd87065f40e4164a6f7a271e58cdea3","61c063999ca54ea3bb76d9f77ab08a3f","79a3053bdecf4a24a2f947b9b0aec952","e670b470b16749c2b177f834dc685851","3f6bf7685a144c7986dedcd279c6074d","ca56dc133b8c454bae146e5a7c5eabd1","70fc116d872d4717b48da9207a65a945","e580e55c51524eccba1ee32184866196","56e11a604894440d910e28495f6320e3","35de07a9ca2443eb9612ce8b0110d8f3","691df1640d9b4854857f72b93d2c0075","f90c2955ea2c42c2aaa3cfc565465b07","f8bdaa900ba6488da798ab5a7ee55b9e","7af2f074e2cb42e29b55c202317eeb54","85e5b526661044adab7bd33a5ecd441f","693ceb509c784aaaa1a3b9ea3b8cc72b","8dd169f034604ceeb69c93c15fd56093","7a5265141039483997ab323b30cb7d77","c94d5eb82ce74095891c4de6d3fdd3cb"]},"id":"bl5b-DkIm3uW","executionInfo":{"status":"ok","timestamp":1665149417146,"user_tz":-60,"elapsed":9261,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5d3b3c5b-ea1f-406c-b362-f7fdc0006958"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/9.57k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5ea018d1fe724cd5bc7705a3aee81b43"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/3.73k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b77d8dd5f99048bc81612bcea9405b7c"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset conll2003/conll2003 (download: 959.94 KiB, generated: 9.78 MiB, post-processed: Unknown size, total: 10.72 MiB) to /root/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/983k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7a389b842cef43eb9fc572b37e41e545"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/14041 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8b32d4651592431ca49f24f0f7553344"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/3250 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"94dae0bcce534db29627c748c0e71164"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/3453 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"891e6926308d4b0d95c5cc27b55bad9d"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset conll2003 downloaded and prepared to /root/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/9a4d16a94f8674ba3466315300359b0acd891b68b6c8743ddf60b9c702adce98. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"56e11a604894440d910e28495f6320e3"}},"metadata":{}}]},{"cell_type":"code","source":["dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z75hTVqhRngx","executionInfo":{"status":"ok","timestamp":1665149417147,"user_tz":-60,"elapsed":12,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3bb83534-62bf-4791-f908-001d66b28bcd"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n","        num_rows: 14041\n","    })\n","    validation: Dataset({\n","        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n","        num_rows: 3250\n","    })\n","    test: Dataset({\n","        features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n","        num_rows: 3453\n","    })\n","})"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["dataset['train'][20]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HlD8IEqNhQ9q","executionInfo":{"status":"ok","timestamp":1665149417148,"user_tz":-60,"elapsed":9,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"891ee054-52df-4353-e2a8-518265c58ae7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'id': '20',\n"," 'tokens': ['Rare',\n","  'Hendrix',\n","  'song',\n","  'draft',\n","  'sells',\n","  'for',\n","  'almost',\n","  '$',\n","  '17,000',\n","  '.'],\n"," 'pos_tags': [22, 22, 21, 21, 42, 15, 30, 3, 11, 7],\n"," 'chunk_tags': [11, 12, 12, 12, 21, 13, 11, 12, 12, 0],\n"," 'ner_tags': [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]}"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["model_id=\"roberta-base\"\n","tokenizer=RobertaTokenizerFast.from_pretrained(model_id,add_prefix_space=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["e7bfacdd5291462a83e64d1193312ddf","13ec9fa91578451199a692a9f52a22bc","63ba5ce3bfe7461ea2cc087867775e63","e7ff465a6f02487e862e2a0b6b8ef904","4f228f6f6d744c9db94b0e6aa9f7cdb9","8ac2acd34dca4bf9915f2fd6943a6d3e","0c67968234224d2a9d721ebc1c0abf22","6f4719ab8de649269b0309c30ea7cc4b","71f5b3f29b6a4fb89c3a9cd8d0b83d46","6088532e7d0f43c5be6107070c52d4e6","c4fed7528ec3404791b81df18cdb7fa4","238ac0e40718471f9c5d12675100c6c8","448c6d2cc73a4a9ca04ce8456cf14840","57ca0df3559f40fe9e0987862564f81e","87d9d3aaf4c6490982c7c08e667564b6","09bb5be0a04a4c259a0556a6f1c52fad","ac12377a8c0846f68817c7cfc994392b","72b3b7c252024fc2b5a452156add5ea5","cf7f0966186d4982bc140627c6c31216","5a1b0a4d6c4b40a78716a20b33673d16","7072419b1ae74eb3a1916db1011aadd3","138e5e31319b483c92b02a77f172d3c7","1705b40fd04a415b91ad274aaa89d2a9","ce1a7482d6a04298b08e0bc7e7de9d72","e36026b573c640cda14edfe3e3760f33","9ac2f8d71f7243d7b22426ee69667eeb","f79f3bd87f364d6ab10de2412e430582","36a10312795a4196bf34c9d2b0594290","c6f8206860354cd2940abff7287fba0c","b7d3fbb0f0b543858a559884d71948a0","3d0ee2ca5d2f4879b08eaef89560a908","83a72bfd4f3548008ad9329ba4cb570a","a6eadc7eb1c34ea8a521faf74b42033f","6ed476c07dbb469f99d421a890dd77a0","ef7eba79796641698402e9b2dbd1ce9b","95a68564e98242268e683e0e86902103","c9af921d5648495eaa4506e9b050f944","f9a72ef936de4d239aa83103ecda2e50","40e13a45e87b4b2988efceefbe617e6e","072eb57c15b74c93ae362de0101dae9b","894668e0e85b4b928cd68eddf1bdf475","4f586b78afb34f54b31fed01325aa75a","9aa1fa31aec944b78bccc4d7f2973aaf","3e1c5d702c6b48108c0eb21ce2d107ac"]},"id":"J2R8WPVx_Rf8","executionInfo":{"status":"ok","timestamp":1665149421741,"user_tz":-60,"elapsed":4598,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b3902eff-0bcf-4c9b-c73b-9641d231ea1c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e7bfacdd5291462a83e64d1193312ddf"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"238ac0e40718471f9c5d12675100c6c8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1705b40fd04a415b91ad274aaa89d2a9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ed476c07dbb469f99d421a890dd77a0"}},"metadata":{}}]},{"cell_type":"code","source":["'Rare Hendrix song draft sells for almost $ 17,000.'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"CvF1lvoOASNR","executionInfo":{"status":"ok","timestamp":1665149421742,"user_tz":-60,"elapsed":35,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ff04c7b5-ad6d-40a3-c2b6-82313e5bc453"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Rare Hendrix song draft sells for almost $ 17,000.'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["inputs = tokenizer(dataset[\"train\"][20][\"tokens\"], is_split_into_words=True,)\n","inputs.tokens()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zgeoy1gF_Rii","executionInfo":{"status":"ok","timestamp":1665149421744,"user_tz":-60,"elapsed":34,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9a07edd4-84ed-4a1e-95c0-b9153e2db9a3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['<s>',\n"," 'ĠRare',\n"," 'ĠHend',\n"," 'rix',\n"," 'Ġsong',\n"," 'Ġdraft',\n"," 'Ġsells',\n"," 'Ġfor',\n"," 'Ġalmost',\n"," 'Ġ$',\n"," 'Ġ17',\n"," ',',\n"," '000',\n"," 'Ġ.',\n"," '</s>']"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["print(dataset['train'][20])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8M4nCJp4C-nz","executionInfo":{"status":"ok","timestamp":1665149421744,"user_tz":-60,"elapsed":29,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5c0c2e66-b93a-4fd9-857e-ffbe6f2bc902"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'id': '20', 'tokens': ['Rare', 'Hendrix', 'song', 'draft', 'sells', 'for', 'almost', '$', '17,000', '.'], 'pos_tags': [22, 22, 21, 21, 42, 15, 30, 3, 11, 7], 'chunk_tags': [11, 12, 12, 12, 21, 13, 11, 12, 12, 0], 'ner_tags': [0, 1, 0, 0, 0, 0, 0, 0, 0, 0]}\n"]}]},{"cell_type":"code","source":["print(inputs.word_ids())\n","print(dataset['train'][20]['ner_tags'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7T-2oqzZ_Rkr","executionInfo":{"status":"ok","timestamp":1665149421744,"user_tz":-60,"elapsed":27,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"22d34a85-e941-4eb5-8c33-0c465a506416"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[None, 0, 1, 1, 2, 3, 4, 5, 6, 7, 8, 8, 8, 9, None]\n","[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n"]}]},{"cell_type":"code","source":["def align_labels_with_tokens(labels, word_ids):\n","    new_labels = []\n","    current_word = None\n","    for word_id in word_ids:\n","        if word_id != current_word:\n","            # Start of a new word!\n","            current_word = word_id\n","            label = -100 if word_id is None else labels[word_id]\n","            new_labels.append(label)\n","        elif word_id is None:\n","            # Special token\n","            new_labels.append(-100)\n","        else:\n","            # Same word as previous token\n","            label = labels[word_id]\n","            # If the label is B-XXX we change it to I-XXX\n","            if label % 2 == 1:\n","                label += 1\n","            new_labels.append(label)\n","\n","    return new_labels"],"metadata":{"id":"h4tRbGntAHIQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["labels = dataset[\"train\"][20][\"ner_tags\"]\n","word_ids = inputs.word_ids()\n","print(labels)\n","print(align_labels_with_tokens(labels, word_ids))"],"metadata":{"id":"1_TuyCIgAHKf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665149421745,"user_tz":-60,"elapsed":25,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2dea3578-a302-40ac-eb67-8a0f5643136d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]\n","[-100, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]\n"]}]},{"cell_type":"code","source":["def tokenizer_function(dataset):\n","  out=tokenizer(dataset[\"tokens\"],truncation=True,is_split_into_words=True,)\n","  out['labels']=align_labels_with_tokens(dataset[\"ner_tags\"],out.word_ids())\n","  return out"],"metadata":{"id":"CSwkgKlmAHMr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_dataset=dataset.map(tokenizer_function,remove_columns=['id','tokens','pos_tags','chunk_tags','ner_tags',])"],"metadata":{"id":"hZYXRoIuAHR_","colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["3e7a9e9e5eb4407b8eca688c21674e52","fb009652b19b4d468ce65865e0aec367","0b06ead3257c4a83aea866de14ae1b8a","add4275cd12446c7b04922e335f289a1","43ae5d1d81854c59ab2e323370d712f5","07ad7ffc47c64e098f6a248824736a82","625f86ae1b30467db391883bc6f900c6","6b3646515dc84acc90669e6d28e539f5","31890edb6fdd4016bf53b7891c377796","c9aaa5e6c315482999d4ad0935a5d343","360e4ef07786416db1366db091e74120","cabe4a5c1dbc41e8a41a1acf6c024018","ec505ab63e4b408ebeb00f5dc9e8f121","11a7888b8d0041c1a0bd47ddd2fab0ea","eb67e1debbe843f9b5b7869ef257843b","872a725391e744b3b66e1e03ff8e4c68","d9435c6bd5ce4acdbed8570d02e453f7","988afa8b9eef4e1aaca9424ec814856f","c80da200fd314ab5b637e75309375a82","629ef742ee2b4bbbb2041c86e60eb489","fb728b37e4114bf7b5d3ce924b15e7d5","6563504c6bd442eda0838cb436302cea","f4f054fd77b44f37a22a0cecf9634849","91f5abe5e5b64fe9ae81efd5886c386e","8fcd7463dcac400e93819cd4b964973a","dc812cc62c04445bbedec056f3f9aee8","b1d18e4c6130404cb1fca9d910fbed50","99857c5561b14dddb98c543b71d4ae80","0d42d2ea693b4e6fb00652c8c8fba024","ae5c89a2012e465598eca78cffacd342","5b900925880c4c7e9aaee0f43b3b654b","8938131743bd4fa8b4b5e132067e74d6","b8dabf871dcd4d96824edfb2e8d641db"]},"executionInfo":{"status":"ok","timestamp":1665149429191,"user_tz":-60,"elapsed":7469,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3e031cf7-f6b9-494f-d166-5b2ed8bc0f70"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/14041 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3e7a9e9e5eb4407b8eca688c21674e52"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3250 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cabe4a5c1dbc41e8a41a1acf6c024018"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3453 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f4f054fd77b44f37a22a0cecf9634849"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_dataset"],"metadata":{"id":"cUjwdGatAHUj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665149429192,"user_tz":-60,"elapsed":26,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"ca6044bd-7b41-4211-c8c3-c2460d75a7ce"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'attention_mask', 'labels'],\n","        num_rows: 14041\n","    })\n","    validation: Dataset({\n","        features: ['input_ids', 'attention_mask', 'labels'],\n","        num_rows: 3250\n","    })\n","    test: Dataset({\n","        features: ['input_ids', 'attention_mask', 'labels'],\n","        num_rows: 3453\n","    })\n","})"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["tokenized_dataset['train'][20]"],"metadata":{"id":"ok0W52hGAHW5","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665149429192,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"3dee14a7-39b4-4a7d-d7fb-b44eecb3e86c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [0,\n","  28751,\n","  16544,\n","  15072,\n","  2214,\n","  2479,\n","  7683,\n","  13,\n","  818,\n","  68,\n","  601,\n","  6,\n","  151,\n","  479,\n","  2],\n"," 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n"," 'labels': [-100, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -100]}"]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["data_collator = DataCollatorForTokenClassification(\n","    tokenizer=tokenizer, return_tensors=\"tf\"\n",")"],"metadata":{"id":"iWx2T18M_Rms"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tf_train_dataset = tokenized_dataset[\"train\"].to_tf_dataset(\n","    collate_fn=data_collator,\n","    shuffle=True,\n","    batch_size=BATCH_SIZE,\n",")"],"metadata":{"id":"VQjN-qFD_Ro8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665149432210,"user_tz":-60,"elapsed":3036,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"c170c35d-6d58-45e8-bfc8-75fa37f30af5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n","/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:716: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n","  tensor = as_tensor(value)\n"]}]},{"cell_type":"code","source":["tf_val_dataset = tokenized_dataset[\"validation\"].to_tf_dataset(\n","    collate_fn=data_collator,\n","    shuffle=False,\n","    batch_size=BATCH_SIZE,\n",")"],"metadata":{"id":"yTmUHqDy_RrI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in tf_train_dataset.take(1):\n","  print(i)"],"metadata":{"id":"qF1zQGfu_Rtb","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665149432580,"user_tz":-60,"elapsed":4,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"08318f7e-de03-4ae2-fec3-113d8d264e72"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': <tf.Tensor: shape=(16, 57), dtype=int64, numpy=\n","array([[    0,   901,  1180,  1239,  9768,  5279,    25,  3845,  2086,\n","        12362,  6204,   661,   479,     2,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1],\n","       [    0,  2027,  4052,   710,  4311,   733,   365,   112,   361,\n","         2843,   176, 42564,   883,     2,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1],\n","       [    0,  1941, 18350,  6725,  6141,   210,  4719,     7,     5,\n","         1446,  3078,     9,     5,  1016,  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         10,   537,   479,     2,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1],\n","       [    0,  6233,  1243,   174,   249,     5,   184,  3158,    56,\n","          416,   576,    10,    22, 14165,   645,    22,     7,  2237,\n","         7263,     8, 15256,   994,     9,  2439,  9855, 26670,     9,\n","           49,   559, 14875,   479,     2,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1,     1,     1,     1,     1,     1,     1,\n","            1,     1,     1],\n","       [    0,  2826,  8329,  4981,     4, 26868,   449,  3792,     7,\n","         1451,   208, 15904,  1587,   687,   571,  2865,     9,  1600,\n","           30, 34851,  6317, 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Based on TFRobertaForSequenceClassification"],"metadata":{"id":"s3Rf_9bFLJsH"}},{"cell_type":"code","source":["model=TFRobertaForTokenClassification.from_pretrained(\n","    model_id,\n","    num_labels=9,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":142,"referenced_widgets":["9d3985281e8c40199a5565bef25e85c5","66826827e4b34f538e3f5c1c3afb7d1d","4b1347df55f9418d994d98ab9a8aaad2","89f90cd7cf8b404b9a4e4e23c9d30735","99179810baf3485b854a43a35774a345","c994fb318e5c48b38653fd047d189282","5554ab3019054f2b93065a6bb7435a74","b42fced5fb3e4c8eb1d64be59c09396d","7e84554666504f95b14693b90d525f19","9e78ce4efc4542e39954e5d7cae9fa23","984a277bc2674f1aacb2df3713931ea0"]},"id":"GuzC0evWPis2","executionInfo":{"status":"ok","timestamp":1665149449680,"user_tz":-60,"elapsed":17102,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"89f6588c-367b-42a6-8639-2afff6ab8983"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/657M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9d3985281e8c40199a5565bef25e85c5"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["All model checkpoint layers were used when initializing TFRobertaForTokenClassification.\n","\n","Some layers of TFRobertaForTokenClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qX74Nb8_JSYT","executionInfo":{"status":"ok","timestamp":1665149449680,"user_tz":-60,"elapsed":24,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"31831f8d-bb09-47ee-d285-30c5d106c53e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"tf_roberta_for_token_classification\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," roberta (TFRobertaMainLayer  multiple                 124055040 \n"," )                                                               \n","                                                                 \n"," dropout_37 (Dropout)        multiple                  0         \n","                                                                 \n"," classifier (Dense)          multiple                  6921      \n","                                                                 \n","=================================================================\n","Total params: 124,061,961\n","Trainable params: 124,061,961\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"E2xsaKH0ySx6"}},{"cell_type":"code","source":["batches_per_epoch = len(tokenized_dataset[\"train\"]) // BATCH_SIZE\n","total_train_steps = int(batches_per_epoch*NUM_EPOCHS)"],"metadata":{"id":"t4RQ6qxuJz6b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["optimizer, schedule = create_optimizer(init_lr=2e-5,num_warmup_steps=0, num_train_steps=total_train_steps,)"],"metadata":{"id":"1KU0RzAtJz8o"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"nHMwe3J4b25H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.compile(optimizer=optimizer,)\n","    #metrics=[\"accuracy\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1h_UIuxFJ1si","executionInfo":{"status":"ok","timestamp":1665149449683,"user_tz":-60,"elapsed":21,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"694ca8eb-ba25-43fd-868d-8cee4e34a3fd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No loss specified in compile() - the model's internal loss computation will be used as the loss. Don't panic - this is a common way to train TensorFlow models in Transformers! To disable this behaviour please pass a loss argument, or explicitly pass `loss=None` if you do not want your model to compute a loss.\n"]}]},{"cell_type":"code","source":["history=model.fit(\n","    tf_train_dataset,\n","    validation_data=tf_val_dataset,\n","    epochs=NUM_EPOCHS,)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"szeGXC2bLr0M","executionInfo":{"status":"ok","timestamp":1665149821247,"user_tz":-60,"elapsed":275804,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"6c182cb0-c762-4b29-a4f3-6b55bd227d23"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/2\n","878/878 [==============================] - 194s 199ms/step - loss: 0.1475 - val_loss: 0.0576\n","Epoch 2/2\n","878/878 [==============================] - 177s 202ms/step - loss: 0.0443 - val_loss: 0.0467\n"]}]},{"cell_type":"code","source":["plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model_loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"dcwVfKoVJ1xF","executionInfo":{"status":"ok","timestamp":1665145636907,"user_tz":-60,"elapsed":8,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9b656834-aaac-441e-a834-0d2c00c80979"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"obCwC6fGPa04"},"outputs":[],"source":[]},{"cell_type":"markdown","source":["# Evaluation"],"metadata":{"id":"uNsioXENbXFG"}},{"cell_type":"code","source":["metric=evaluate.load(\"seqeval\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["c57b8214c8d54023aefba6faca05c429","bc723d487b094501bb9ed0b3d006abe5","31ba912ec361477e98f56010f5793d51","c85c97f2630a413d8dc00f4d82c48f9c","f6e556e7d1d54e51ae60ed65ed4c7a88","623aa9504fc045daba6837679fc291d3","6071003678084c189b8a3df744239bf9","e212ce09a3d942b58ff463c5a10f44e6","5129919d07964b6686a4949b8cff2f57","5733b6e19e7740f89eac74f8ea611157","e0550af884bd49adac0cd330f0988e86"]},"id":"DL8AG6ypbYVn","executionInfo":{"status":"ok","timestamp":1665150126585,"user_tz":-60,"elapsed":1322,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"873ea0ff-e336-4dfc-882b-752d48833d5c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c57b8214c8d54023aefba6faca05c429"}},"metadata":{}}]},{"cell_type":"code","source":["ind_to_label={0:'O', 1:'B-PER',2:'I-PER',3:'B-ORG',4:'I-ORG',5:'B-LOC',6:'I-LOC',7:'B-MISC',8:'I-MISC'}\n","all_predictions = []\n","all_labels = []"],"metadata":{"id":"xFF39QleqZEL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for batch in tf_val_dataset:\n","  logits = model.predict(batch)[\"logits\"]\n","  labels = batch[\"labels\"].numpy()\n","  predictions = tf.argmax(logits, axis=-1).numpy()\n","  #print(labels)\n","  #print(predictions)\n","  for prediction, label in zip(predictions, labels):\n","    for predicted_idx, label_idx in zip(prediction, label):\n","      if label_idx == -100:\n","          continue\n","      all_predictions.append(ind_to_label[predicted_idx])\n","      all_labels.append(ind_to_label[label_idx])"],"metadata":{"id":"2uz3DiXmrVuL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(all_predictions)\n","print(all_labels)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t10V40RWrVwp","executionInfo":{"status":"ok","timestamp":1665150529281,"user_tz":-60,"elapsed":674,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"98adb167-d807-4fc9-d8e5-db36356f8910"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['O', 'O', 'O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-LOC', 'I-LOC', 'O', 'O', 'O', 'O', 'O', 'B-MISC', 'I-MISC', 'O', 'O', 'O', 'B-PER', 'I-PER', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-ORG', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'B-ORG', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 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padding=True,return_tensors=\"tf\")"]},{"cell_type":"code","source":["print(inputs.tokens())\n","print(inputs.word_ids())\n","print(inputs['input_ids'])"],"metadata":{"id":"hEML0foO8ps0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1665145419673,"user_tz":-60,"elapsed":6,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"bec864fe-0211-48c2-bebd-059d48e0070c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['<s>', 'ĠWake', 'ĠUp', 'ĠJoe', 'Marsh', 'al', ',', 'Ġyou', 'Ġjust', 'Ġgot', 'Ġa', 'Ġcall', 'Ġfrom', 'ĠUNESCO', 'Ġfor', 'Ġa', 'Ġtrip', 'Ġto', 'ĠIndia', '</s>']\n","[None, 0, 1, 2, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, None]\n","tf.Tensor(\n","[[    0 11601  3105  2101 40825   337     6    47    95   300    10   486\n","     31 26688    13    10  1805     7   666     2]], shape=(1, 20), dtype=int32)\n"]}]},{"cell_type":"code","source":["logits = model(**inputs).logits\n","print(logits.shape)\n","print(tf.argmax(logits,axis=-1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tEejdLfqTH8q","executionInfo":{"status":"ok","timestamp":1665145419674,"user_tz":-60,"elapsed":6,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"77647aeb-74cb-421a-f283-94586771912e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1, 20, 9)\n","tf.Tensor([[0 0 0 1 2 2 0 0 0 0 0 0 0 3 0 0 0 0 5 0]], shape=(1, 20), dtype=int64)\n"]}]},{"cell_type":"code","source":["ind_to_label={0:'O', 1:'B-PER',2:'I-PER',3:'B-ORG',4:'I-ORG',5:'B-LOC',6:'I-LOC',7:'B-MISC',8:'I-MISC'}\n","out_str=\"\"\n","current_index=0"],"metadata":{"id":"vD8KCGS-ZD8-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in range(1,len(inputs.tokens())-1):\n","  if tf.argmax(logits,axis=-1)[0][i]!=0:\n","    out_str+=\" \"+str(inputs.tokens()[i])+\"--->\"+str(ind_to_label[tf.argmax(logits,axis=-1).numpy()[0][i]])\n","  else:\n","    out_str+=\" \"+str(inputs.tokens()[i])"],"metadata":{"id":"5nyowCqaZvqo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(out_str.replace(\"Ġ\",\"\"))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s7NKkBVEZvtI","executionInfo":{"status":"ok","timestamp":1665145605245,"user_tz":-60,"elapsed":669,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4dc4ff1d-0f62-4ef2-8752-dfee7157446d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" Wake Up Joe--->B-PER Marsh--->I-PER al--->I-PER , you just got a call from UNESCO--->B-ORG for a trip to 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ET"],"metadata":{"id":"T55dNDZ3YLe3"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def data_to_xml(image_width, image_height, image_filename, boxes, classes):\n","    annotation = ET.Element('annotation')\n","\n","    filename = ET.SubElement(annotation, 'filename')\n","    filename.text = image_filename\n","\n","\n","    size = ET.SubElement(annotation, 'size')\n","\n","    width = ET.SubElement(size, 'width')\n","    width.text = image_width\n","    height = ET.SubElement(size, 'height')\n","    height.text = image_height\n","    depth = ET.SubElement(size, 'depth')\n","    depth.text = '3'\n","\n","    for i in range(len(classes)):\n","\n","        object = ET.SubElement(annotation, 'object')\n","\n","        name = ET.SubElement(object, 'name')\n","        name.text = classes[i].lower()\n","\n","        pose = ET.SubElement(object, 'pose')\n","        pose.text = \"Unspecified\"\n","        \n","        truncated = ET.SubElement(object, 'truncated')\n","        truncated.text = \"0\"\n","\n","        difficult = ET.SubElement(object, 'difficult')\n","        difficult.text = \"0\"\n","\n","        bndbox = ET.SubElement(object, 'bndbox')\n","\n","        xmin = ET.SubElement(bndbox, 'xmin')\n","        xmin.text = boxes[i][0]\n","        ymin = ET.SubElement(bndbox, 'ymin')\n","        ymin.text = boxes[i][1]\n","        xmax = ET.SubElement(bndbox, 'xmax')\n","        xmax.text = boxes[i][2]\n","        ymax = ET.SubElement(bndbox, 'ymax')\n","        ymax.text = boxes[i][3]\n","\n","\n","\n","\n","    b_xml = ET.tostring(annotation)\n","\n","    image_filename = image_filename[:-4]\n","    with open(\"annotations/\" + image_filename + \".xml\", \"wb\") as f:\n","        f.write(b_xml)\n","    return None"],"metadata":{"id":"iLPlxoNaYMEv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["with open('/content/Fashion_Dataset_Annotations.json') as f:\n","    data = json.load(f)\n","\n","for i in range(len(data)):\n","\n","    image_width = str(data[i][\"width\"])\n","    image_height = str(data[i][\"height\"])\n","    image_filename = str(data[i][\"file_name\"])\n","\n","    boxes = []\n","    classes = []\n","    for j in range(len(data[i]['annotations'])):\n","        boxes.append([str(data[i]['annotations'][j][\"bbox\"]['xmin']), str(data[i]['annotations'][j][\"bbox\"]['ymin']), \n","                      str(data[i]['annotations'][j][\"bbox\"]['xmax']), str(data[i]['annotations'][j][\"bbox\"]['ymax'])])\n","        \n","        try:\n","            classes.append(data[i]['annotations'][j][\"classes\"][0])\n","        except:\n","            print(\"Error\")\n","\n","\n","    data_to_xml(image_width, image_height, image_filename, boxes, classes)"],"metadata":{"id":"zIW2YGnPYMHJ","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"error","timestamp":1654297315956,"user_tz":240,"elapsed":775,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"bdeb98bd-7271-4b77-f8e6-1037d2d2bc08"},"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-4-be558ce48a98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/Fashion_Dataset_Annotations.json'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/Fashion_Dataset_Annotations.json'"]}]},{"cell_type":"code","source":["!cp annotations/new_data_10.xml /content/YOLOX/datasets/VOCdevkit/VOC2007/Annotations"],"metadata":{"id":"kjGvHn_NYREY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654297529007,"user_tz":240,"elapsed":353,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"18d3b3ef-645b-499c-fd8d-fee38ae62748"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["cp: cannot stat 'annotations/new_data_10.xml': No such file or directory\n"]}]},{"cell_type":"markdown","source":["# Installation"],"metadata":{"id":"m6EqU1_QwUhM"}},{"cell_type":"code","source":["!git clone https://github.com/Megvii-BaseDetection/YOLOX/"],"metadata":{"id":"4zY5EihnwlCa","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1655056714140,"user_tz":240,"elapsed":2945,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"343a6bdb-b6a5-48aa-cc43-ada329f164a0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'YOLOX'...\n","remote: Enumerating objects: 1665, done.\u001b[K\n","remote: Total 1665 (delta 0), reused 0 (delta 0), pack-reused 1665\u001b[K\n","Receiving objects: 100% (1665/1665), 6.02 MiB | 3.76 MiB/s, done.\n","Resolving deltas: 100% (977/977), done.\n"]}]},{"cell_type":"code","source":["cd YOLOX"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RaItctzYwlMi","executionInfo":{"status":"ok","timestamp":1655056714140,"user_tz":240,"elapsed":12,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"f0e6ea6f-0ce7-45bd-b5cd-9378ff7daaba"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/YOLOX\n"]}]},{"cell_type":"code","source":["!pip3 install -U pip && pip3 install -r requirements.txt\n","!pip3 install -v -e .  # or  python3 setup.py develop\n","!pip3 install wandb"],"metadata":{"id":"Ha7uPBWrCJDD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1655056752558,"user_tz":240,"elapsed":38424,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"05723e9a-7d1e-41c5-96f6-b7116c61fc36"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: pip in /usr/local/lib/python3.7/dist-packages (21.1.3)\n","Collecting pip\n","  Downloading pip-22.1.2-py3-none-any.whl (2.1 MB)\n","\u001b[K     |████████████████████████████████| 2.1 MB 4.8 MB/s \n","\u001b[?25hInstalling collected packages: pip\n","  Attempting uninstall: pip\n","    Found existing installation: pip 21.1.3\n","    Uninstalling pip-21.1.3:\n","      Successfully uninstalled pip-21.1.3\n","Successfully installed 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onnx-simplifier-0.3.5 onnxoptimizer-0.2.7 onnxruntime-1.8.0 thop-0.1.0.post2206102148\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0mUsing pip 22.1.2 from /usr/local/lib/python3.7/dist-packages/pip (python 3.7)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Obtaining file:///content/YOLOX\n","  Running command python setup.py egg_info\n","  running egg_info\n","  creating /tmp/pip-pip-egg-info-wtgy92h5/yolox.egg-info\n","  writing /tmp/pip-pip-egg-info-wtgy92h5/yolox.egg-info/PKG-INFO\n","  writing dependency_links to /tmp/pip-pip-egg-info-wtgy92h5/yolox.egg-info/dependency_links.txt\n","  writing requirements to /tmp/pip-pip-egg-info-wtgy92h5/yolox.egg-info/requires.txt\n","  writing top-level names to /tmp/pip-pip-egg-info-wtgy92h5/yolox.egg-info/top_level.txt\n","  writing manifest file '/tmp/pip-pip-egg-info-wtgy92h5/yolox.egg-info/SOURCES.txt'\n","  reading manifest template 'MANIFEST.in'\n","  warning: no files found matching 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requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->yolox==0.3.0) (3.2.0)\n","Installing collected packages: yolox\n","  Running setup.py develop for yolox\n","    Running command python setup.py develop\n","    running develop\n","    running egg_info\n","    creating yolox.egg-info\n","    writing yolox.egg-info/PKG-INFO\n","    writing dependency_links to yolox.egg-info/dependency_links.txt\n","    writing requirements to yolox.egg-info/requires.txt\n","    writing top-level names to yolox.egg-info/top_level.txt\n","    writing manifest file 'yolox.egg-info/SOURCES.txt'\n","    reading manifest template 'MANIFEST.in'\n","    warning: no files found matching '*.cu' under directory 'yolox'\n","    warning: no files found matching '*.cuh' under directory 'yolox'\n","    warning: no files found matching '*.cc' under directory 'yolox'\n","    adding license file 'LICENSE'\n","    writing manifest file 'yolox.egg-info/SOURCES.txt'\n","    running build_ext\n","    building 'yolox.layers.fast_cocoeval' extension\n","    creating /content/YOLOX/build\n","    creating /content/YOLOX/build/temp.linux-x86_64-3.7\n","    creating /content/YOLOX/build/temp.linux-x86_64-3.7/yolox\n","    creating /content/YOLOX/build/temp.linux-x86_64-3.7/yolox/layers\n","    creating /content/YOLOX/build/temp.linux-x86_64-3.7/yolox/layers/cocoeval\n","    Emitting ninja build file /content/YOLOX/build/temp.linux-x86_64-3.7/build.ninja...\n","    Compiling objects...\n","    Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)\n","    [1/1] c++ -MMD -MF /content/YOLOX/build/temp.linux-x86_64-3.7/yolox/layers/cocoeval/cocoeval.o.d -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -Iyolox/layers/cocoeval -I/usr/local/lib/python3.7/dist-packages/torch/include -I/usr/local/lib/python3.7/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.7/dist-packages/torch/include/TH -I/usr/local/lib/python3.7/dist-packages/torch/include/THC -I/usr/include/python3.7m -c -c /content/YOLOX/yolox/layers/cocoeval/cocoeval.cpp -o /content/YOLOX/build/temp.linux-x86_64-3.7/yolox/layers/cocoeval/cocoeval.o -O3 -std=c++14 -g -Wno-reorder -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE=\"_gcc\"' '-DPYBIND11_STDLIB=\"_libstdcpp\"' '-DPYBIND11_BUILD_ABI=\"_cxxabi1011\"' -DTORCH_EXTENSION_NAME=fast_cocoeval -D_GLIBCXX_USE_CXX11_ABI=0\n","    creating build/lib.linux-x86_64-3.7\n","    creating build/lib.linux-x86_64-3.7/yolox\n","    creating build/lib.linux-x86_64-3.7/yolox/layers\n","    x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -g -fwrapv -O2 -Wl,-Bsymbolic-functions -g -fwrapv -O2 -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 /content/YOLOX/build/temp.linux-x86_64-3.7/yolox/layers/cocoeval/cocoeval.o -L/usr/local/lib/python3.7/dist-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-3.7/yolox/layers/fast_cocoeval.cpython-37m-x86_64-linux-gnu.so\n","    copying build/lib.linux-x86_64-3.7/yolox/layers/fast_cocoeval.cpython-37m-x86_64-linux-gnu.so -> yolox/layers\n","    Creating /usr/local/lib/python3.7/dist-packages/yolox.egg-link (link to .)\n","    Adding yolox 0.3.0 to easy-install.pth file\n","\n","    Installed /content/YOLOX\n","Successfully installed yolox-0.3.0\n","\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. 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It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n","\u001b[0m"]}]},{"cell_type":"markdown","source":["# Demo"],"metadata":{"id":"l75MyuDi-W-7"}},{"cell_type":"markdown","source":["## Yolox-x Demo"],"metadata":{"id":"5a4pTyIYTThi"}},{"cell_type":"code","source":["!wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_x.pth"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BMJT9kgTPzoW","executionInfo":{"status":"ok","timestamp":1654297670575,"user_tz":240,"elapsed":79652,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"b22478ed-72c3-4b9e-a022-62abd7c5d824"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-06-03 23:06:30--  https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_x.pth\n","Resolving github.com (github.com)... 20.205.243.166\n","Connecting to github.com 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legacy=False, name='yolox-x', nms=0.65, path='assets/new_data_1.jpg', save_result=True, trt=False, tsize=640)\u001b[0m\n","\u001b[32m2022-06-03 23:07:52.484\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmain\u001b[0m:\u001b[36m269\u001b[0m - \u001b[1mModel Summary: Params: 99.07M, Gflops: 281.93\u001b[0m\n","\u001b[32m2022-06-03 23:08:02.930\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmain\u001b[0m:\u001b[36m282\u001b[0m - \u001b[1mloading checkpoint\u001b[0m\n","Traceback (most recent call last):\n","  File \"tools/demo.py\", line 320, in <module>\n","    main(exp, args)\n","  File \"tools/demo.py\", line 283, in main\n","    ckpt = torch.load(ckpt_file, map_location=\"cpu\")\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 699, in load\n","    with _open_file_like(f, 'rb') as opened_file:\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 231, in _open_file_like\n","    return _open_file(name_or_buffer, mode)\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 212, in __init__\n","    super(_open_file, self).__init__(open(name, mode))\n","FileNotFoundError: [Errno 2] No such file or directory: 'weights/yolox_x.pth'\n"]}]},{"cell_type":"markdown","source":["## Yolox-s Demo"],"metadata":{"id":"ynVM2r13TVoC"}},{"cell_type":"code","source":["!wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oykMeAtJCIWc","executionInfo":{"status":"ok","timestamp":1654297690438,"user_tz":240,"elapsed":6658,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"2879f592-78dc-452d-fe70-6f4553ece1b9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["--2022-06-03 23:08:03--  https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth\n","Resolving github.com (github.com)... 20.205.243.166\n","Connecting to github.com (github.com)|20.205.243.166|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/386811486/42c4cb47-f94e-475b-a3a2-57f31f26fa5d?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220603%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220603T230803Z&X-Amz-Expires=300&X-Amz-Signature=d95337cd6b09789af8bd57824f2f59083e648c8352c03a1aa6c42639bee0c8b2&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=386811486&response-content-disposition=attachment%3B%20filename%3Dyolox_s.pth&response-content-type=application%2Foctet-stream [following]\n","--2022-06-03 23:08:03--  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experiment_name='yolox_s', fp16=False, fuse=False, legacy=False, name=None, nms=0.65, path='assets/new_data_6.jpg', save_result=True, trt=False, tsize=640)\u001b[0m\n","\u001b[32m2022-06-03 23:08:11.111\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmain\u001b[0m:\u001b[36m269\u001b[0m - \u001b[1mModel Summary: Params: 8.97M, Gflops: 26.81\u001b[0m\n","\u001b[32m2022-06-03 23:08:14.818\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mmain\u001b[0m:\u001b[36m282\u001b[0m - \u001b[1mloading checkpoint\u001b[0m\n","Traceback (most recent call last):\n","  File \"tools/demo.py\", line 320, in <module>\n","    main(exp, args)\n","  File \"tools/demo.py\", line 283, in main\n","    ckpt = torch.load(ckpt_file, map_location=\"cpu\")\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 699, in load\n","    with _open_file_like(f, 'rb') as opened_file:\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 231, in _open_file_like\n","    return _open_file(name_or_buffer, mode)\n","  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 212, in __init__\n","    super(_open_file, self).__init__(open(name, mode))\n","FileNotFoundError: [Errno 2] No such file or directory: 'weights/best_ckpt_yolox_s.pth'\n"]}]},{"cell_type":"markdown","source":["## Yolox-nano Demo"],"metadata":{"id":"aThu6c9ETKdr"}},{"cell_type":"code","source":["!wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_nano.pth"],"metadata":{"id":"3DvkBd37RCB1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python tools/demo.py image -n yolox-nano -c weights/best_yolox_nano.pth --path assets/new_data_10.jpg --conf 0.2 --nms 0.65 --tsize 416 --save_result --device gpu"],"metadata":{"id":"91tKt0DTRLFE"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Training and Evaluation"],"metadata":{"id":"v5CA9ax89vB0"}},{"cell_type":"markdown","source":["## Copy Dataset"],"metadata":{"id":"KqxDuOzoiMDx"}},{"cell_type":"code","source":["!cp /content/drive/MyDrive/colorful_fashion_dataset_for_object_detection.rar /content/YOLOX/datasets/"],"metadata":{"id":"W-WE92vL92Ls"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!unrar x \"/content/YOLOX/datasets/colorful_fashion_dataset_for_object_detection.rar\" \"/content/YOLOX/datasets/fashion/\""],"metadata":{"id":"bBR9nxyeiVrs"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Training"],"metadata":{"id":"sVacsxk4Hxb_"}},{"cell_type":"markdown","source":["### Training Yolox-s"],"metadata":{"id":"AhUaVEhPH8Gu"}},{"cell_type":"code","source":["!python tools/train.py -f exps/example/yolox_voc/yolox_voc_s.py -d 1 -b 16 --fp16 -o -c weights/yolox_s.pth"],"metadata":{"id":"7hghMaqriU9y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!cp /content/YOLOX/YOLOX_outputs/yolox_voc_s/best_ckpt.pth /content/YOLOX/weights"],"metadata":{"id":"_FiB2SQEj8Ia"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"aMg0-caAj8Nz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"1Bg4pIiHj8Pz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Training Yolox-nano with Wandb integration"],"metadata":{"id":"dYUL2GcyjcvU"}},{"cell_type":"code","source":["import wandb"],"metadata":{"id":"0DN_0lXqqM3v"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!wandb login"],"metadata":{"id":"Zcf0OGylqPSm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python tools/train.py -f exps/example/yolox_voc/yolox_voc_nano.py -d 1 -b 16 --fp16 -o --logger wandb wandb-project object-detection -c weights/yolox_nano.pth"],"metadata":{"id":"d9KQuEpkV4-T"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python tools/train.py -f exps/example/yolox_voc/yolox_voc_nano.py -d 1 -b 16 --fp16 -o -c weights/yolox_nano.pth"],"metadata":{"id":"SD5zUzr9kbK4"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Evaluation"],"metadata":{"id":"3lt--1vFeoMX"}},{"cell_type":"code","source":["!python -m yolox.tools.eval -f exps/example/yolox_voc/yolox_voc_nano.py -c /content/YOLOX/YOLOX_outputs/yolox_voc_nano/best_ckpt.pth -b 64 -d 1 --conf 0.1 --fp16"],"metadata":{"id":"q35TbviMp2o3"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Conversion to ONNX"],"metadata":{"id":"oWZyDtZt3cex"}},{"cell_type":"code","source":["# !python3 tools/export_onnx.py -f exps/example/yolox_voc/yolox_voc_nano.py --output-name weights/best_yolox_nano.onnx -c weights/best_yolox_nano.pth"],"metadata":{"id":"CdjGOzg3p2tv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive"],"metadata":{"id":"9bdzXfo23MwI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0Tbxt46WH_ce","executionInfo":{"status":"ok","timestamp":1655056770957,"user_tz":240,"elapsed":18287,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"ef48660e-f0e0-494a-c234-312d380698fd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["!mkdir weights"],"metadata":{"id":"h8vM2hTB3LMw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["! cp '/content/drive/My Drive/Deep_Learning/demo.jpg' assets\n","! cp '/content/drive/My Drive/Deep_Learning/best_yolox_nano.onnx'  weights"],"metadata":{"id":"5oimQwCKIDvc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# !python3 demo/ONNXRuntime/onnx_inference.py -m weights/best_yolox_nano.onnx -i assets/demo.jpg -o demo -s 0.3 --input_shape 416,416"],"metadata":{"id":"kmj6Yc-S-rxV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!cp '/content/drive/My Drive/onnx_inference_modified.py' demo/ONNXRuntime/"],"metadata":{"id":"cbbXr6JULFIy"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python3 demo/ONNXRuntime/onnx_inference_modified.py -m weights/best_yolox_nano.onnx -i assets/demo.jpg -o demo -s 0.3 --input_shape 416,416"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lbcZPdKXWxd-","executionInfo":{"status":"ok","timestamp":1655056774572,"user_tz":240,"elapsed":2436,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"137eca83-fcd6-4a0d-ad4d-e5d891c9ca98"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[8.0108643e-05]\n"," [7.1585178e-05]\n"," [1.9341707e-05]\n"," ...\n"," [3.3825636e-05]\n"," [1.5169382e-05]\n"," [2.1755695e-06]]\n","demo/demo.jpg\n"]}]},{"cell_type":"code","source":["!ls demo"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7XPh1K7mK7Mj","executionInfo":{"status":"ok","timestamp":1654371020211,"user_tz":240,"elapsed":309,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"7db9d3e7-438e-4d73-8edb-03a0e27f39c7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["boxes.npy  MegEngine  ONNXRuntime  output_net.npy   scores.npy\n","demo.jpg   ncnn       OpenVINO\t   predictions.npy  TensorRT\n"]}]},{"cell_type":"code","source":["!cp demo/demo.jpg '/content/drive/My Drive/'"],"metadata":{"id":"TRWCFdwMK8OP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!cp demo/preprocessed_img1.jpg '/content/drive/My Drive'"],"metadata":{"id":"cfzyLvymYxEb"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!cp demo/boxes.npy '/content/drive/My Drive'\n","!cp demo/predictions.npy '/content/drive/My Drive'\n","!cp demo/scores.npy '/content/drive/My Drive'\n","!cp demo/output_net.npy '/content/drive/My Drive'"],"metadata":{"id":"HblxyulaY7jM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def demo_postprocess(img_size, p6=False):\n","    import numpy as np\n","    grids = []\n","    expanded_strides = []\n","\n","    if not p6:\n","        strides = [8, 16, 32]\n","    else:\n","        strides = [8, 16, 32, 64]\n","\n","    hsizes = [img_size[0] // stride for stride in strides]\n","    wsizes = [img_size[1] // stride for stride in strides]\n","\n","    for hsize, wsize, stride in zip(hsizes, wsizes, strides):\n","        xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))\n","        grid = np.stack((xv, yv), 2).reshape(1, -1, 2)\n","        grids.append(grid)\n","        shape = grid.shape[:2]\n","        expanded_strides.append(np.full((*shape, 1), stride))\n","\n","    grids = np.concatenate(grids, 1)\n","    expanded_strides = np.concatenate(expanded_strides, 1)\n","    print(len(expanded_strides[0]))"],"metadata":{"id":"Wg_Ge9igY9BI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["demo_postprocess((540, 680))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"itrAHAkOB2vf","executionInfo":{"status":"ok","timestamp":1654351562025,"user_tz":240,"elapsed":2,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"33277ad2-8c64-426b-f2db-3d64310cbffc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["7417\n"]}]},{"cell_type":"code","source":["import numpy as np\n","a, b = np.meshgrid(np.arange(5), np.arange(6))"],"metadata":{"id":"ipHv4Vw-B50M"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(a)\n","print(b)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gLqggSdyEDIL","executionInfo":{"status":"ok","timestamp":1654352138068,"user_tz":240,"elapsed":3,"user":{"displayName":"Florent Bang","userId":"05458017837497962027"}},"outputId":"9a52170d-a31e-4895-e7ca-37ed17798830"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[0 1 2 3 4]\n"," [0 1 2 3 4]\n"," [0 1 2 3 4]\n"," [0 1 2 3 4]\n"," [0 1 2 3 4]\n"," [0 1 2 3 4]]\n","[[0 0 0 0 0]\n"," [1 1 1 1 1]\n"," [2 2 2 2 2]\n"," [3 3 3 3 3]\n"," [4 4 4 4 4]\n"," [5 5 5 5 5]]\n"]}]}]}

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FILE: deep learning for optical character recognition/PDF TO EXCEL By Neuralearn-.ipynb
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{"cells":[{"cell_type":"code","source":["from PIL import Image\n","import cv2\n","import numpy as np\n","import pandas as pd \n","import tensorflow as tf"],"metadata":{"id":"mhEjVUbiUytV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#!pip install paddlepaddle-gpu==2.3.2 cudatoolkit==10.2"],"metadata":{"id":"JejuCeNXHckg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install paddlepaddle-gpu==2.3.0.post110 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xWhdOt9Jay6z","executionInfo":{"status":"ok","timestamp":1668776203600,"user_tz":-60,"elapsed":72302,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"cda3179f-f1d6-41e3-bdd2-6d8fdeab5867"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Looking in links: https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html\n","Collecting paddlepaddle-gpu==2.3.0.post110\n","  Downloading https://paddle-wheel.bj.bcebos.com/2.3.0/linux/linux-gpu-cuda11.0-cudnn8-mkl-gcc8.2-avx/paddlepaddle_gpu-2.3.0.post110-cp37-cp37m-linux_x86_64.whl (532.9 MB)\n","\u001b[K     |████████████████████████████████| 532.9 MB 27 kB/s \n","\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from paddlepaddle-gpu==2.3.0.post110) (4.4.2)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from paddlepaddle-gpu==2.3.0.post110) (7.1.2)\n","Requirement already satisfied: requests>=2.20.0 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle-gpu==2.3.0.post110) (2.23.0)\n","Collecting paddle-bfloat==0.1.2\n","  Downloading paddle_bfloat-0.1.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (373 kB)\n","\u001b[K     |████████████████████████████████| 373 kB 5.7 MB/s \n","\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from paddlepaddle-gpu==2.3.0.post110) (1.15.0)\n","Requirement already satisfied: protobuf>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle-gpu==2.3.0.post110) (3.19.6)\n","Requirement already satisfied: astor in /usr/local/lib/python3.7/dist-packages (from paddlepaddle-gpu==2.3.0.post110) (0.8.1)\n","Requirement already satisfied: numpy>=1.13 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle-gpu==2.3.0.post110) (1.21.6)\n","Requirement already satisfied: opt-einsum==3.3.0 in /usr/local/lib/python3.7/dist-packages (from paddlepaddle-gpu==2.3.0.post110) (3.3.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20.0->paddlepaddle-gpu==2.3.0.post110) (2022.9.24)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20.0->paddlepaddle-gpu==2.3.0.post110) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20.0->paddlepaddle-gpu==2.3.0.post110) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20.0->paddlepaddle-gpu==2.3.0.post110) (3.0.4)\n","Installing collected packages: paddle-bfloat, paddlepaddle-gpu\n","Successfully installed paddle-bfloat-0.1.2 paddlepaddle-gpu-2.3.0.post110\n"]}]},{"cell_type":"markdown","metadata":{"id":"dvKiMyj2feUe"},"source":["# pdf2image"]},{"cell_type":"markdown","source":["## Installation"],"metadata":{"id":"aIz56VvlWkf-"}},{"cell_type":"code","source":["!pip install pdf2image\n","!apt-get update\n","!apt-get install poppler-utils"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"biSgyAkWnUD3","executionInfo":{"status":"ok","timestamp":1668776221685,"user_tz":-60,"elapsed":18107,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"06487f1d-40ee-4de6-f072-6372561d16cd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting pdf2image\n","  Downloading pdf2image-1.16.0-py3-none-any.whl (10 kB)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from pdf2image) (7.1.2)\n","Installing collected packages: pdf2image\n","Successfully installed pdf2image-1.16.0\n","Get:1 https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ InRelease [3,626 B]\n","Ign:2 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  InRelease\n","Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease\n","Hit:4 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64  Release\n","Hit:5 http://archive.ubuntu.com/ubuntu bionic InRelease\n","Get:6 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic InRelease [15.9 kB]\n","Get:7 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n","Get:8 http://security.ubuntu.com/ubuntu bionic-security InRelease [88.7 kB]\n","Hit:10 http://ppa.launchpad.net/cran/libgit2/ubuntu bionic InRelease\n","Get:11 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [83.3 kB]\n","Hit:12 http://ppa.launchpad.net/deadsnakes/ppa/ubuntu bionic InRelease\n","Hit:13 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n","Get:14 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [3,494 kB]\n","Get:15 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main Sources [2,225 kB]\n","Get:16 http://security.ubuntu.com/ubuntu bionic-security/main amd64 Packages [3,068 kB]\n","Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/multiverse amd64 Packages [29.8 kB]\n","Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/restricted amd64 Packages [1,303 kB]\n","Get:19 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [2,336 kB]\n","Get:20 http://security.ubuntu.com/ubuntu bionic-security/universe amd64 Packages [1,561 kB]\n","Get:21 http://ppa.launchpad.net/c2d4u.team/c2d4u4.0+/ubuntu bionic/main amd64 Packages [1,138 kB]\n","Fetched 15.4 MB in 4s (3,448 kB/s)\n","Reading package lists... Done\n","Reading package lists... Done\n","Building dependency tree       \n","Reading state information... Done\n","The following package was automatically installed and is no longer required:\n","  libnvidia-common-460\n","Use 'apt autoremove' to remove it.\n","The following NEW packages will be installed:\n","  poppler-utils\n","0 upgraded, 1 newly installed, 0 to remove and 7 not upgraded.\n","Need to get 154 kB of archives.\n","After this operation, 613 kB of additional disk space will be used.\n","Get:1 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 poppler-utils amd64 0.62.0-2ubuntu2.14 [154 kB]\n","Fetched 154 kB in 1s (198 kB/s)\n","Selecting previously unselected package poppler-utils.\n","(Reading database ... 123991 files and directories currently installed.)\n","Preparing to unpack .../poppler-utils_0.62.0-2ubuntu2.14_amd64.deb ...\n","Unpacking poppler-utils (0.62.0-2ubuntu2.14) ...\n","Setting up poppler-utils (0.62.0-2ubuntu2.14) ...\n","Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"]}]},{"cell_type":"markdown","source":["## Conversion"],"metadata":{"id":"pLaguCz1WmF9"}},{"cell_type":"code","source":["from pdf2image import convert_from_path"],"metadata":{"id":"HI1dzCGmkeWI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["images = convert_from_path('/content/bahdanau attention.pdf')"],"metadata":{"id":"xyhjFgqjkgG7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!mkdir pages"],"metadata":{"id":"M5hBWxpskgJW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in range(len(images)):\n","  images[i].save('pages/page'+str(i)+'.jpg', 'JPEG')"],"metadata":{"id":"7mlH9ltkkgMB"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Layout"],"metadata":{"id":"Urwb7Q_OZC8t"}},{"cell_type":"markdown","source":["## Installation"],"metadata":{"id":"vTkwQbF4FZPN"}},{"cell_type":"code","source":["#!python3 -m pip install paddlepaddle-gpu\n","!pip install \"paddleocr>=2.0.1\"\n","!pip install protobuf==3.20.0\n","!git clone https://github.com/PaddlePaddle/PaddleOCR.git"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"R0lRpQLkIpbZ","executionInfo":{"status":"ok","timestamp":1668776288692,"user_tz":-60,"elapsed":63750,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"24283e8e-5de0-4b41-bb43-8d0638f927c1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting paddleocr>=2.0.1\n","  Downloading paddleocr-2.6.1.0-py3-none-any.whl (409 kB)\n","\u001b[K     |████████████████████████████████| 409 kB 6.1 MB/s \n","\u001b[?25hRequirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from paddleocr>=2.0.1) (0.99)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from paddleocr>=2.0.1) (4.64.1)\n","Collecting python-docx\n","  Downloading 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pycryptodome, python-docx, PyMuPDF, multiprocess, fonttools, Flask-Babel, fire, cssutils, cssselect, bce-python-sdk, visualdl, rapidfuzz, pyclipper, premailer, Polygon3, pdf2docx, lanms-neo, attrdict, paddleocr\n","Successfully installed Flask-Babel-2.0.0 Polygon3-3.0.9.1 PyMuPDF-1.19.0 attrdict-2.0.1 bce-python-sdk-0.8.74 cssselect-1.2.0 cssutils-2.6.0 fire-0.4.0 fonttools-4.38.0 lanms-neo-1.0.2 multiprocess-0.70.14 paddleocr-2.6.1.0 pdf2docx-0.5.6 premailer-3.10.0 pyclipper-1.3.0.post4 pycryptodome-3.15.0 python-docx-0.8.11 rapidfuzz-2.13.2 visualdl-2.4.1\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting protobuf==3.20.0\n","  Downloading protobuf-3.20.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)\n","\u001b[K     |████████████████████████████████| 1.0 MB 8.3 MB/s \n","\u001b[?25hInstalling collected packages: protobuf\n","  Attempting uninstall: protobuf\n","    Found existing installation: 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protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n","google-cloud-bigquery 3.3.6 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\n","google-cloud-bigquery-storage 2.16.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.0 which is incompatible.\u001b[0m\n","Successfully installed protobuf-3.20.0\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["google"]}}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Cloning into 'PaddleOCR'...\n","remote: Enumerating objects: 44967, done.\u001b[K\n","remote: Counting objects: 100% (98/98), done.\u001b[K\n","remote: Compressing objects: 100% (75/75), 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paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)... 103.235.46.61, 2409:8c04:1001:1002:0:ff:b001:368a\n","Connecting to paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)|103.235.46.61|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19145360 (18M) [application/octet-stream]\n","Saving to: ‘layoutparser-0.0.0-py3-none-any.whl’\n","\n","layoutparser-0.0.0- 100%[===================>]  18.26M  3.69MB/s    in 12s     \n","\n","2022-11-18 12:58:23 (1.48 MB/s) - ‘layoutparser-0.0.0-py3-none-any.whl’ saved [19145360/19145360]\n","\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Processing ./layoutparser-0.0.0-py3-none-any.whl\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from layoutparser==0.0.0) (1.21.6)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from layoutparser==0.0.0) (1.3.5)\n","Requirement already satisfied: tqdm in 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python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->layoutparser==0.0.0) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->layoutparser==0.0.0) (1.15.0)\n","Building wheels for collected packages: iopath\n","  Building wheel for iopath (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for iopath: filename=iopath-0.1.10-py3-none-any.whl size=31548 sha256=8f3da2a0ce4dd2c6b432ef4d5851d960b4c889e015e7066a5a77b45bfa05cb60\n","  Stored in directory: /root/.cache/pip/wheels/aa/cc/ed/ca4e88beef656b01c84b9185196513ef2faf74a5a379b043a7\n","Successfully built iopath\n","Installing collected packages: portalocker, iopath, layoutparser\n","Successfully installed iopath-0.1.10 layoutparser-0.0.0 portalocker-2.6.0\n"]}]},{"cell_type":"markdown","source":["## Table Extraction"],"metadata":{"id":"w5MA08E0F8aU"}},{"cell_type":"code","source":["import cv2\n","import layoutparser as lp\n","image = cv2.imread(\"/content/pages/page13.jpg\")\n","\n","image = image[..., ::-1]\n","\n","# load model\n","model = lp.PaddleDetectionLayoutModel(config_path=\"lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config\",\n","                                threshold=0.5,\n","                                label_map={0: \"Text\", 1: \"Title\", 2: \"List\", 3:\"Table\", 4:\"Figure\"},\n","                                enforce_cpu=False,\n","                                enable_mkldnn=True)#math kernel library\n","# detect\n","layout = model.detect(image)"],"metadata":{"id":"bw9SFYnMCX0E","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668776363672,"user_tz":-60,"elapsed":55930,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"89218fbd-3b01-453e-a191-0d15c8883d03"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["download https://paddle-model-ecology.bj.bcebos.com/model/layout-parser/ppyolov2_r50vd_dcn_365e_publaynet.tar to /root/.paddledet/inference_model/ppyolov2_r50vd_dcn_365e_publaynet/ppyolov2_r50vd_dcn_365e_publaynet_infer/ppyolov2_r50vd_dcn_365e_publaynet.tar\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 221M/221M [00:37<00:00, 5.92MiB/s]\n"]}]},{"cell_type":"code","source":["layout"],"metadata":{"id":"G76oO2WXCX6v","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668776363673,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"2b0f9ed6-ff8c-4b46-b69c-5e3961d017d4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Layout(_blocks=[TextBlock(block=Rectangle(x_1=300.35406494140625, y_1=1716.4278564453125, x_2=1401.105712890625, y_2=1876.4561767578125), text=None, id=None, type=Text, parent=None, next=None, score=0.947794497013092), TextBlock(block=Rectangle(x_1=296.242431640625, y_1=1969.3675537109375, x_2=1405.8653564453125, y_2=2034.4422607421875), text=None, id=None, type=Text, parent=None, next=None, score=0.934190034866333), TextBlock(block=Rectangle(x_1=296.2332763671875, y_1=1460.10986328125, x_2=1401.1175537109375, y_2=1553.036865234375), text=None, id=None, type=Text, parent=None, next=None, score=0.924132227897644), TextBlock(block=Rectangle(x_1=294.98626708984375, y_1=444.1419677734375, x_2=1408.130126953125, y_2=567.1005249023438), text=None, id=None, type=Text, parent=None, next=None, score=0.9196642637252808), TextBlock(block=Rectangle(x_1=300.4600830078125, y_1=1669.076171875, x_2=711.9566040039062, y_2=1697.28076171875), text=None, id=None, type=Title, parent=None, next=None, score=0.8556817770004272), TextBlock(block=Rectangle(x_1=297.86297607421875, y_1=220.03121948242188, x_2=1409.0029296875, y_2=418.0099182128906), text=None, id=None, type=Table, parent=None, next=None, score=0.7598645091056824), TextBlock(block=Rectangle(x_1=298.9613342285156, y_1=1601.4661865234375, x_2=687.694580078125, y_2=1633.7120361328125), text=None, id=None, type=Title, parent=None, next=None, score=0.7429183125495911), TextBlock(block=Rectangle(x_1=295.4989318847656, y_1=1405.8271484375, x_2=551.2619018554688, y_2=1438.658203125), text=None, id=None, type=Title, parent=None, next=None, score=0.6753251552581787), TextBlock(block=Rectangle(x_1=282.51495361328125, y_1=647.5087890625, x_2=1415.5732421875, y_2=1368.4254150390625), text=None, id=None, type=List, parent=None, next=None, score=0.6743354201316833), TextBlock(block=Rectangle(x_1=298.611328125, y_1=1063.1019287109375, x_2=366.89483642578125, y_2=1092.1378173828125), text=None, id=None, type=Text, parent=None, next=None, score=0.6345258951187134), TextBlock(block=Rectangle(x_1=298.71820068359375, y_1=1171.6640625, x_2=998.8455200195312, y_2=1205.39306640625), text=None, id=None, type=Text, parent=None, next=None, score=0.6338083148002625), TextBlock(block=Rectangle(x_1=294.16180419921875, y_1=931.6088256835938, x_2=1402.220458984375, y_2=996.5545043945312), text=None, id=None, type=Text, parent=None, next=None, score=0.5081444382667542)], page_data={})"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["x_1=0\n","y_1=0\n","x_2=0\n","y_2=0\n","\n","for l in layout:\n","  #print(l)\n","  if l.type == 'Table':\n","    x_1 = int(l.block.x_1)\n","    print(l.block.x_1)\n","    y_1 = int(l.block.y_1)\n","    x_2 = int(l.block.x_2)\n","    y_2 = int(l.block.y_2)\n","    \n","    break"],"metadata":{"id":"y3h0kCz-CX_U","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668776363675,"user_tz":-60,"elapsed":18,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"5b44d211-694b-409f-df7c-69189d4b448f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["297.86298\n"]}]},{"cell_type":"code","source":["print(x_1,y_1,x_2,y_2)"],"metadata":{"id":"cdiYPeCJCYIg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668776363676,"user_tz":-60,"elapsed":16,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"4160ed2b-e1d1-4174-dd2e-55bee31167f3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["297 220 1409 418\n"]}]},{"cell_type":"code","source":["im = cv2.imread('/content/pages/page13.jpg')"],"metadata":{"id":"F39kJV3hCYLV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cv2.imwrite('ext_im.jpg', im[y_1:y_2,x_1:x_2])"],"metadata":{"id":"EQDXSNijCYPs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668776363677,"user_tz":-60,"elapsed":14,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9c0e6954-da96-491b-efcb-318dc5410561"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":[],"metadata":{"id":"7O2P4aIMor_e"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"EGwGhHnd8i_h"},"source":["# Text Detection and Recognition"]},{"cell_type":"code","source":["from paddleocr import PaddleOCR, draw_ocr"],"metadata":{"id":"N6WQZXhLLDWk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["ocr = PaddleOCR(lang='en')\n","image_path = '/content/ext_im.jpg'\n","image_cv = cv2.imread(image_path)\n","image_height = image_cv.shape[0]\n","image_width = image_cv.shape[1]\n","output = ocr.ocr(image_path)[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A8bCZ9AULDZF","executionInfo":{"status":"ok","timestamp":1668776401150,"user_tz":-60,"elapsed":37484,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"78de4243-7a38-4378-8dfd-063004300535"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["download https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar to /root/.paddleocr/whl/det/en/en_PP-OCRv3_det_infer/en_PP-OCRv3_det_infer.tar\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 4.00M/4.00M [00:07<00:00, 505kiB/s] \n"]},{"output_type":"stream","name":"stdout","text":["download https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar to /root/.paddleocr/whl/rec/en/en_PP-OCRv3_rec_infer/en_PP-OCRv3_rec_infer.tar\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 9.96M/9.96M [00:14<00:00, 693kiB/s] \n"]},{"output_type":"stream","name":"stdout","text":["download https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar to /root/.paddleocr/whl/cls/ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.tar\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 2.19M/2.19M [00:11<00:00, 190kiB/s]"]},{"output_type":"stream","name":"stdout","text":["[2022/11/18 13:00:00] ppocr DEBUG: Namespace(alpha=1.0, benchmark=False, beta=1.0, cls_batch_num=6, cls_image_shape='3, 48, 192', cls_model_dir='/root/.paddleocr/whl/cls/ch_ppocr_mobile_v2.0_cls_infer', cls_thresh=0.9, cpu_threads=10, crop_res_save_dir='./output', det=True, det_algorithm='DB', det_box_type='quad', det_db_box_thresh=0.6, det_db_score_mode='fast', det_db_thresh=0.3, det_db_unclip_ratio=1.5, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, det_east_score_thresh=0.8, det_limit_side_len=960, det_limit_type='max', det_model_dir='/root/.paddleocr/whl/det/en/en_PP-OCRv3_det_infer', det_pse_box_thresh=0.85, det_pse_min_area=16, det_pse_scale=1, det_pse_thresh=0, det_sast_nms_thresh=0.2, det_sast_score_thresh=0.5, draw_img_save_dir='./inference_results', drop_score=0.5, e2e_algorithm='PGNet', e2e_char_dict_path='./ppocr/utils/ic15_dict.txt', e2e_limit_side_len=768, e2e_limit_type='max', e2e_model_dir=None, e2e_pgnet_mode='fast', e2e_pgnet_score_thresh=0.5, e2e_pgnet_valid_set='totaltext', enable_mkldnn=False, fourier_degree=5, gpu_mem=500, help='==SUPPRESS==', image_dir=None, image_orientation=False, ir_optim=True, kie_algorithm='LayoutXLM', label_list=['0', '180'], lang='en', layout=True, layout_dict_path=None, layout_model_dir=None, layout_nms_threshold=0.5, layout_score_threshold=0.5, max_batch_size=10, max_text_length=25, merge_no_span_structure=True, min_subgraph_size=15, mode='structure', ocr=True, ocr_order_method=None, ocr_version='PP-OCRv3', output='./output', page_num=0, precision='fp32', process_id=0, re_model_dir=None, rec=True, rec_algorithm='SVTR_LCNet', rec_batch_num=6, rec_char_dict_path='/usr/local/lib/python3.7/dist-packages/paddleocr/ppocr/utils/en_dict.txt', rec_image_inverse=True, rec_image_shape='3, 48, 320', rec_model_dir='/root/.paddleocr/whl/rec/en/en_PP-OCRv3_rec_infer', recovery=False, save_crop_res=False, save_log_path='./log_output/', scales=[8, 16, 32], ser_dict_path='../train_data/XFUND/class_list_xfun.txt', ser_model_dir=None, show_log=True, sr_batch_num=1, sr_image_shape='3, 32, 128', sr_model_dir=None, structure_version='PP-Structurev2', table=True, table_algorithm='TableAttn', table_char_dict_path=None, table_max_len=488, table_model_dir=None, total_process_num=1, type='ocr', use_angle_cls=False, use_dilation=False, use_gpu=True, use_mp=False, use_npu=False, use_onnx=False, use_pdf2docx_api=False, use_pdserving=False, use_space_char=True, use_tensorrt=False, use_visual_backbone=True, use_xpu=False, vis_font_path='./doc/fonts/simfang.ttf', warmup=False)\n"]},{"output_type":"stream","name":"stderr","text":["\n"]},{"output_type":"stream","name":"stdout","text":["[2022/11/18 13:00:00] ppocr WARNING: Since the angle classifier is not initialized, the angle classifier will not be uesd during the forward process\n","[2022/11/18 13:00:01] ppocr DEBUG: dt_boxes num : 42, elapse : 0.12288975715637207\n","[2022/11/18 13:00:01] ppocr DEBUG: rec_res num  : 42, elapse : 0.12553930282592773\n"]}]},{"cell_type":"code","source":["print(output)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VBIDZA0XLDeA","executionInfo":{"status":"ok","timestamp":1668776401150,"user_tz":-60,"elapsed":28,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b86018c1-5b24-4897-9174-a34299909bf7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[[[693.0, 4.0], [755.0, 4.0], [755.0, 36.0], [693.0, 36.0]], ('GPU', 0.9990124106407166)], [[[70.0, 5.0], [148.0, 5.0], [148.0, 36.0], [70.0, 36.0]], ('Model', 0.9997721910476685)], [[[230.0, 5.0], [394.0, 8.0], [394.0, 37.0], [229.0, 35.0]], ('Updates (105)', 0.9652137756347656)], [[[425.0, 5.0], [511.0, 5.0], [511.0, 36.0], [425.0, 36.0]], ('Epochs', 0.9998965263366699)], [[[537.0, 5.0], [612.0, 5.0], [612.0, 36.0], [537.0, 36.0]], ('Hours', 0.9620624780654907)], [[[833.0, 4.0], [958.0, 4.0], [958.0, 33.0], [833.0, 33.0]], ('Train NLL', 0.9994094371795654)], [[[988.0, 6.0], [1104.0, 6.0], [1104.0, 32.0], [988.0, 32.0]], ('Dev.NLL', 0.9872051477432251)], [[[42.0, 47.0], [177.0, 47.0], [177.0, 72.0], [42.0, 72.0]], ('RNNenc-30', 0.9995856881141663)], [[[285.0, 46.0], [341.0, 46.0], [341.0, 74.0], [285.0, 74.0]], ('8.46', 0.9999933242797852)], [[[446.0, 46.0], [489.0, 46.0], [489.0, 74.0], [446.0, 74.0]], ('6.4', 0.9964627623558044)], [[[553.0, 45.0], [599.0, 45.0], [599.0, 74.0], [553.0, 74.0]], ('109', 0.9999635815620422)], [[[641.0, 47.0], [805.0, 47.0], [805.0, 72.0], [641.0, 72.0]], ('TITAN BLACK', 0.9913126230239868)], [[[869.0, 43.0], [923.0, 43.0], [923.0, 75.0], [869.0, 75.0]], ('28.1', 0.999950110912323)], [[[1019.0, 46.0], [1075.0, 46.0], [1075.0, 74.0], [1019.0, 74.0]], ('53.0', 0.999934196472168)], [[[41.0, 75.0], [177.0, 75.0], [177.0, 101.0], [41.0, 101.0]], ('RNNenc-50', 0.9980602264404297)], [[[285.0, 75.0], [341.0, 75.0], [341.0, 104.0], [285.0, 104.0]], ('6.00', 0.9998406171798706)], [[[446.0, 74.0], [489.0, 74.0], [489.0, 104.0], [446.0, 104.0]], ('4.5', 0.9982914924621582)], [[[553.0, 74.0], [599.0, 74.0], [599.0, 104.0], [553.0, 104.0]], ('108', 0.9998137950897217)], [[[644.0, 78.0], [804.0, 78.0], [804.0, 103.0], [644.0, 103.0]], ('Quadro K-6000', 0.9990899562835693)], [[[868.0, 73.0], [927.0, 73.0], [927.0, 105.0], [868.0, 105.0]], ('44.0', 0.999671459197998)], [[[1017.0, 73.0], [1076.0, 73.0], [1076.0, 105.0], [1017.0, 105.0]], ('43.6', 0.9998911619186401)], [[[27.0, 109.0], [193.0, 109.0], [193.0, 134.0], [27.0, 134.0]], ('RNNsearch-30', 0.9990729689598083)], [[[284.0, 108.0], [338.0, 108.0], [338.0, 136.0], [284.0, 136.0]], ('4.71', 0.9994158744812012)], [[[445.0, 108.0], [489.0, 108.0], [489.0, 136.0], [445.0, 136.0]], ('3.6', 0.9944102168083191)], [[[553.0, 108.0], [599.0, 108.0], [599.0, 136.0], [553.0, 136.0]], ('113', 0.9994351267814636)], [[[643.0, 111.0], [804.0, 111.0], [804.0, 132.0], [643.0, 132.0]], ('TITAN BLACK', 0.9635356664657593)], [[[870.0, 108.0], [923.0, 108.0], [923.0, 136.0], [870.0, 136.0]], ('26.7', 0.9992948770523071)], [[[1018.0, 108.0], [1074.0, 108.0], [1074.0, 136.0], [1018.0, 136.0]], ('47.2', 0.9993252158164978)], [[[25.0, 139.0], [193.0, 139.0], [193.0, 163.0], [25.0, 163.0]], ('RNNsearch-50', 0.9973625540733337)], [[[285.0, 137.0], [339.0, 137.0], [339.0, 166.0], [285.0, 166.0]], ('2.88', 0.9999485015869141)], [[[445.0, 134.0], [489.0, 134.0], [489.0, 168.0], [445.0, 168.0]], ('2.2', 0.9979038238525391)], [[[551.0, 137.0], [598.0, 137.0], [598.0, 166.0], [551.0, 166.0]], ('111', 0.9987512230873108)], [[[643.0, 140.0], [804.0, 140.0], [804.0, 165.0], [643.0, 165.0]], ('Quadro K-6000', 0.9971157908439636)], [[[870.0, 137.0], [924.0, 137.0], [924.0, 166.0], [870.0, 166.0]], ('40.7', 0.9991925954818726)], [[[1018.0, 135.0], [1075.0, 135.0], [1075.0, 167.0], [1018.0, 167.0]], ('38.1', 0.9999594688415527)], [[[21.0, 172.0], [196.0, 172.0], [196.0, 193.0], [21.0, 193.0]], ('RNNsearch-50*', 0.9954861402511597)], [[[285.0, 170.0], [338.0, 170.0], [338.0, 197.0], [285.0, 197.0]], ('6.67', 0.9998753666877747)], [[[446.0, 170.0], [489.0, 170.0], [489.0, 197.0], [446.0, 197.0]], ('5.0', 0.9963653683662415)], [[[551.0, 170.0], [600.0, 170.0], [600.0, 197.0], [551.0, 197.0]], ('252', 0.9991322159767151)], [[[644.0, 172.0], [804.0, 172.0], [804.0, 197.0], [644.0, 197.0]], ('Quadro K-6000', 0.9994592666625977)], [[[870.0, 170.0], [923.0, 170.0], [923.0, 197.0], [870.0, 197.0]], ('36.7', 0.9990472793579102)], [[[1019.0, 170.0], [1074.0, 170.0], [1074.0, 197.0], [1019.0, 197.0]], ('35.2', 0.9986913204193115)]]\n"]}]},{"cell_type":"code","source":["boxes = [line[0] for line in output]\n","texts = [line[1][0] for line in output]\n","probabilities = [line[1][1] for line in output]"],"metadata":{"id":"nNMBAQ78LDgG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["image_boxes = image_cv.copy()\n"],"metadata":{"id":"uukcg4SWV_dg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for box,text in zip(boxes,texts):\n","  cv2.rectangle(image_boxes, (int(box[0][0]),int(box[0][1])), (int(box[2][0]),int(box[2][1])),(0,0,255),1)\n","  cv2.putText(image_boxes, text,(int(box[0][0]),int(box[0][1])),cv2.FONT_HERSHEY_SIMPLEX,1,(222,0,0),1)"],"metadata":{"id":"l_HzbiA7V_fw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cv2.imwrite('detections.jpg', image_boxes)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PfUG9mcgV_iJ","executionInfo":{"status":"ok","timestamp":1668776401151,"user_tz":-60,"elapsed":24,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"b97aaa44-61b9-4a62-dd01-c8c26b66e9c7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"kYWt0lzDHZNp"},"source":["# Reconstruction"]},{"cell_type":"markdown","source":["## Get Horizontal and Vertical Lines"],"metadata":{"id":"ruzifYJz4H6y"}},{"cell_type":"code","source":["im = image_cv.copy()"],"metadata":{"id":"YLIoKedcqby_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["horiz_boxes = []\n","vert_boxes = []\n","\n","for box in boxes:\n","  x_h, x_v = 0,int(box[0][0])\n","  y_h, y_v = int(box[0][1]),0\n","  width_h,width_v = image_width, int(box[2][0]-box[0][0])\n","  height_h,height_v = int(box[2][1]-box[0][1]),image_height\n","\n","  horiz_boxes.append([x_h,y_h,x_h+width_h,y_h+height_h])\n","  vert_boxes.append([x_v,y_v,x_v+width_v,y_v+height_v])\n","\n","  cv2.rectangle(im,(x_h,y_h), (x_h+width_h,y_h+height_h),(0,0,255),1)\n","  cv2.rectangle(im,(x_v,y_v), (x_v+width_v,y_v+height_v),(0,255,0),1)\n","  "],"metadata":{"id":"GwcAAe-wccnF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cv2.imwrite('horiz_vert.jpg',im)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7UxFGhMkccph","executionInfo":{"status":"ok","timestamp":1668776401154,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"344b62aa-8bbf-46e0-ccea-bd1c5eb60e3a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":27}]},{"cell_type":"markdown","source":["## Non-Max Suppression"],"metadata":{"id":"ekVFvJrM4ROL"}},{"cell_type":"code","source":["horiz_out = tf.image.non_max_suppression(\n","    horiz_boxes,\n","    probabilities,\n","    max_output_size = 1000,\n","    iou_threshold=0.1,\n","    score_threshold=float('-inf'),\n","    name=None\n",")"],"metadata":{"id":"4LVSSB2fcoe7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["horiz_lines = np.sort(np.array(horiz_out))\n","print(horiz_lines)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pOboYpGnccr2","executionInfo":{"status":"ok","timestamp":1668776404059,"user_tz":-60,"elapsed":35,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"02933914-32bb-4b3d-88bb-72745b6dda8b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[ 3  8 20 24 34 36]\n"]}]},{"cell_type":"code","source":["im_nms = image_cv.copy()"],"metadata":{"id":"pfxHrn3iccyK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for val in horiz_lines:\n","  cv2.rectangle(im_nms, (int(horiz_boxes[val][0]),int(horiz_boxes[val][1])), (int(horiz_boxes[val][2]),int(horiz_boxes[val][3])),(0,0,255),1)\n","  "],"metadata":{"id":"68PCHfmZcc0L"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cv2.imwrite('im_nms.jpg',im_nms)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z8r9qpiAcc2X","executionInfo":{"status":"ok","timestamp":1668776404060,"user_tz":-60,"elapsed":34,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"9510d3cf-3870-4c77-b9ff-dd903f9a7902"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":32}]},{"cell_type":"code","source":["vert_out = tf.image.non_max_suppression(\n","    vert_boxes,\n","    probabilities,\n","    max_output_size = 1000,\n","    iou_threshold=0.1,\n","    score_threshold=float('-inf'),\n","    name=None\n",")"],"metadata":{"id":"mKgPuh7rcc4s"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(vert_out)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GBpKsImVcc6p","executionInfo":{"status":"ok","timestamp":1668776404060,"user_tz":-60,"elapsed":31,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f7bfbf04-9318-4230-f9df-98101fc7cadd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor([ 8 10 34 12  3  1 39], shape=(7,), dtype=int32)\n"]}]},{"cell_type":"code","source":["vert_lines = np.sort(np.array(vert_out))\n","print(vert_lines)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K0lBh-yz5YLp","executionInfo":{"status":"ok","timestamp":1668776404060,"user_tz":-60,"elapsed":26,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"31a4ea8a-27ce-4b53-9bdb-9857c65ebdaa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[ 1  3  8 10 12 34 39]\n"]}]},{"cell_type":"code","source":["for val in vert_lines:\n","  cv2.rectangle(im_nms, (int(vert_boxes[val][0]),int(vert_boxes[val][1])), (int(vert_boxes[val][2]),int(vert_boxes[val][3])),(255,0,0),1)\n","  "],"metadata":{"id":"WqsLm0L_cc84"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cv2.imwrite('im_nms.jpg',im_nms)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xZOEa7lpcdGM","executionInfo":{"status":"ok","timestamp":1668776404061,"user_tz":-60,"elapsed":25,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"a92cbce5-7529-4b3b-f4b5-2c016904ab21"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":37}]},{"cell_type":"markdown","source":["## Convert to CSV"],"metadata":{"id":"116eBUrO93-i"}},{"cell_type":"code","source":["\n","\n","out_array = [[\"\" for i in range(len(vert_lines))] for j in range(len(horiz_lines))]\n","print(np.array(out_array).shape)\n","print(out_array)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HRQzwVUTcdIq","executionInfo":{"status":"ok","timestamp":1668776404061,"user_tz":-60,"elapsed":24,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"93b6914b-c713-48e0-ecaf-87a764a48d13"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(6, 7)\n","[['', '', '', '', '', '', ''], ['', '', '', '', '', '', ''], ['', '', '', '', '', '', ''], ['', '', '', '', '', '', ''], ['', '', '', '', '', '', ''], ['', '', '', '', '', '', '']]\n"]}]},{"cell_type":"code","source":["\n","unordered_boxes = []\n","\n","for i in vert_lines:\n","  print(vert_boxes[i])\n","  unordered_boxes.append(vert_boxes[i][0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sSrupaRZIAk_","executionInfo":{"status":"ok","timestamp":1668776404062,"user_tz":-60,"elapsed":23,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"61055c32-2de2-4a31-c36f-e3857a6cc939"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[70, 0, 148, 198]\n","[425, 0, 511, 198]\n","[285, 0, 341, 198]\n","[553, 0, 599, 198]\n","[869, 0, 923, 198]\n","[1018, 0, 1075, 198]\n","[644, 0, 804, 198]\n"]}]},{"cell_type":"code","source":["ordered_boxes = np.argsort(unordered_boxes)\n","print(ordered_boxes)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lRMlVNh_HuJV","executionInfo":{"status":"ok","timestamp":1668776404062,"user_tz":-60,"elapsed":21,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"de3751a5-525d-4659-acbc-00d61fcbeba1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0 2 1 3 6 4 5]\n"]}]},{"cell_type":"code","source":["def intersection(box_1, box_2):\n","  return [box_2[0], box_1[1],box_2[2], box_1[3]]"],"metadata":{"id":"AHHaxKUuC6jQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def iou(box_1, box_2):\n","\n","  x_1 = max(box_1[0], box_2[0])\n","  y_1 = max(box_1[1], box_2[1])\n","  x_2 = min(box_1[2], box_2[2])\n","  y_2 = min(box_1[3], box_2[3])\n","\n","  inter = abs(max((x_2 - x_1, 0)) * max((y_2 - y_1), 0))\n","  if inter == 0:\n","      return 0\n","      \n","  box_1_area = abs((box_1[2] - box_1[0]) * (box_1[3] - box_1[1]))\n","  box_2_area = abs((box_2[2] - box_2[0]) * (box_2[3] - box_2[1]))\n","  \n","  return inter / float(box_1_area + box_2_area - inter)"],"metadata":{"id":"fDVb0DkxJSIf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in range(len(horiz_lines)):\n","  for j in range(len(vert_lines)):\n","    resultant = intersection(horiz_boxes[horiz_lines[i]], vert_boxes[vert_lines[ordered_boxes[j]]] )\n","\n","    for b in range(len(boxes)):\n","      the_box = [boxes[b][0][0],boxes[b][0][1],boxes[b][2][0],boxes[b][2][1]]\n","      if(iou(resultant,the_box)>0.1):\n","        out_array[i][j] = texts[b]"],"metadata":{"id":"LWGhCwg6BIoL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["out_array=np.array(out_array)"],"metadata":{"id":"c4tEY9LGNIM9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["out_array"],"metadata":{"id":"_ekto4-Ymxv2","executionInfo":{"status":"ok","timestamp":1668776404063,"user_tz":-60,"elapsed":19,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"f7a8e379-2879-4ed0-e8c0-4e127aacc50a","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([['Model', 'Updates (105)', 'Epochs', 'Hours', 'GPU', 'Train NLL',\n","        'Dev.NLL'],\n","       ['RNNenc-30', '8.46', '6.4', '109', 'TITAN BLACK', '28.1', '53.0'],\n","       ['RNNenc-50', '6.00', '4.5', '108', 'Quadro K-6000', '44.0',\n","        '43.6'],\n","       ['RNNsearch-30', '4.71', '3.6', '113', 'TITAN BLACK', '26.7',\n","        '47.2'],\n","       ['RNNsearch-50', '2.88', '2.2', '111', 'Quadro K-6000', '40.7',\n","        '38.1'],\n","       ['RNNsearch-50*', '6.67', '5.0', '252', 'Quadro K-6000', '36.7',\n","        '35.2']], dtype='<U13')"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","source":["pd.DataFrame(out_array).to_csv('sample.csv')"],"metadata":{"id":"D8UdX80wBI9V"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Merging Cells"],"metadata":{"id":"E693Ela3qhLx"}},{"cell_type":"code","source":["current_bank=['']*len(out_array[0,:])\n","print(current_bank)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XNcX7fEWPDfw","executionInfo":{"status":"ok","timestamp":1668776404064,"user_tz":-60,"elapsed":17,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"06cb1313-e8b8-4252-d305-f03fc03d14ed"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['', '', '', '', '', '', '']\n"]}]},{"cell_type":"code","source":["def empty(arr):\n","  for i in arr:\n","    if i=='':\n","      return True\n","  return False"],"metadata":{"id":"TTF5ojcCQJKR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cleaned_array=[]"],"metadata":{"id":"3w1amnEXSVSB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for i in range(len(out_array)):\n","  if not empty(out_array[i]):\n","    current_bank=[out_array[i][j] for j in range(len(out_array[i]))]\n","    cleaned_array.append(current_bank)\n","    not_empty=True\n","  else:\n","    for j in range(len(out_array[i])):\n","      current_bank[j]+=' '+out_array[i][j]\n","    print('-->',current_bank)\n","cleaned_array=np.array(cleaned_array)\n","print(cleaned_array)"],"metadata":{"id":"W_q9e2EkPepQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1668776404065,"user_tz":-60,"elapsed":15,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"fba25f38-1e56-45c3-88fe-2211d38d4dca"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[['Model' 'Updates (105)' 'Epochs' 'Hours' 'GPU' 'Train NLL' 'Dev.NLL']\n"," ['RNNenc-30' '8.46' '6.4' '109' 'TITAN BLACK' '28.1' '53.0']\n"," ['RNNenc-50' '6.00' '4.5' '108' 'Quadro K-6000' '44.0' '43.6']\n"," ['RNNsearch-30' '4.71' '3.6' '113' 'TITAN BLACK' '26.7' '47.2']\n"," ['RNNsearch-50' '2.88' '2.2' '111' 'Quadro K-6000' '40.7' '38.1']\n"," ['RNNsearch-50*' '6.67' '5.0' '252' 'Quadro K-6000' '36.7' '35.2']]\n"]}]},{"cell_type":"code","source":["pd.DataFrame(cleaned_array).to_csv('cleaned.csv')"],"metadata":{"id":"RZltyMmD_E68"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Convert to OWL Format"],"metadata":{"id":"M9rH5mX9Mx7g"}},{"cell_type":"markdown","source":["# **CSV to Text**"],"metadata":{"id":"SqFdbM634H5w"}},{"cell_type":"code","source":["import  jpype     \n","import  asposecells  \n","\n","\n","jpype.startJVM() \n","from asposecells.api import Workbook\n","\n","workbook = Workbook(\"/content/sample.csv\")\n","workbook.save(\"Output.docx\")\n","jpype.shutdownJVM()"],"metadata":{"id":"949GB9T1_Go7","colab":{"base_uri":"https://localhost:8080/","height":373},"executionInfo":{"status":"error","timestamp":1668776404503,"user_tz":-60,"elapsed":451,"user":{"displayName":"martins folefac","userId":"15114498789158493667"}},"outputId":"d3bf9a55-8248-459c-8eb6-c2c67e5a84b3"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ModuleNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","\u001b[0;32m<ipython-input-52-0cc478de6e7d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m  \u001b[0mjpype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m  \u001b[0masposecells\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mjpype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartJVM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'jpype'","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"],"errorDetails":{"actions":[{"action":"open_url","actionText":"Open Examples","url":"/notebooks/snippets/importing_libraries.ipynb"}]}}]},{"cell_type":"code","source":[],"metadata":{"id":"eIGJDS7u4DLS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"ziHBaaCn4DN2"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Brighten Image (For Anyone dealing with PDFs created from scanned images)"],"metadata":{"id":"FIzj02CHqce7"}},{"cell_type":"code","source":["from PIL import Image, ImageEnhance\n","\n","#read the image\n","im = Image.open(\"ext_im.jpg\")\n","\n","#image brightness enhancer\n","enhancer = ImageEnhance.Brightness(im)\n","\n","factor = 1 #gives original image\n","im_output = enhancer.enhance(factor)\n","im_output.save('ext_im-1.jpg')\n","\n","factor = 1.5## brightens the image\n","im_output = enhancer.enhance(factor)\n","im_output.save('ext_im-2.jpg')\n"],"metadata":{"id":"vnzqj5q34DQk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"arQSfE2br7YT"},"execution_count":null,"outputs":[]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["E693Ela3qhLx","SqFdbM634H5w","FIzj02CHqce7"],"provenance":[{"file_id":"1I-tp71bSdQmXG6wwqAPTd9Sn63Rtf4nC","timestamp":1673800328324}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

================================================
FILE: deprecated/.ipynb_checkpoints/21-dcgan_By_Neuralearn-checkpoint.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ebd04ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer\n",
    "from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n",
    "                                     Flatten, InputLayer, BatchNormalization, Input, )\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0da5b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128\n",
    "IM_SHAPE = (64,64,3)\n",
    "LEARNING_RATE = 2e-4\n",
    "LATENT_DIM=100\n",
    "EPOCHS=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a27d8aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    \"PATH TO IMAGES\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d65205e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ed0b1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(image):\n",
    "  return tf.cast(image, tf.float32) / 127.5 - 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dfe3e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = (\n",
    "    dataset\n",
    "    .map(preprocess)\n",
    "    .unbatch()\n",
    "    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n",
    "    .batch(BATCH_SIZE,drop_remainder=True)\n",
    "    .prefetch(tf.data.AUTOTUNE)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f34e7b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "for d in train_dataset.take(1):\n",
    "  print(d.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ad52f8",
   "metadata": {},
   "source": [
    "<H1>Modeling</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e20f70d",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator=tf.keras.Sequential([\n",
    "  Input(shape=(LATENT_DIM,)),\n",
    "  Dense(4*4*LATENT_DIM),\n",
    "  Reshape((4,4,LATENT_DIM)),\n",
    "\n",
    "  Conv2DTranspose(512,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2DTranspose(256,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2DTranspose(128,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2DTranspose(3,kernel_size=4,strides=2, activation=tf.keras.activations.tanh, padding='same'),\n",
    "\n",
    "],name='generator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b4c7493",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6fa0cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator=tf.keras.Sequential([\n",
    "  Input(shape=(IM_SHAPE[0],IM_SHAPE[1],3)),\n",
    "\n",
    "  Conv2D(64,kernel_size=4,strides=2, padding='same'),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2D(128,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "  \n",
    "  Conv2D(256,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2D(1,kernel_size=4,strides=2, padding='same'),\n",
    "\n",
    "  Flatten(),\n",
    "  Dense(1,activation='sigmoid')\n",
    "  \n",
    "\n",
    "],name='discriminator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18a8efb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b983cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ShowImage(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, latent_dim=100):\n",
    "        self.latent_dim = latent_dim\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        n=6\n",
    "        k=0\n",
    "        out=self.model.generator(tf.random.normal(shape=(36, self.latent_dim)))\n",
    "        plt.figure(figsize=(16,16))\n",
    "        for i in range(n):\n",
    "          for j in range(n):\n",
    "            ax=plt.subplot(n,n,k+1)\n",
    "            plt.imshow((out[k]+1)/2,)\n",
    "            plt.axis('off')\n",
    "            k+=1\n",
    "        plt.savefig(\"generated/gen_images_epoch_{}.png\".format(epoch+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22555d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GAN(tf.keras.Model):\n",
    "  def __init__(self,discriminator,generator):\n",
    "    super(GAN,self).__init__()\n",
    "    self.discriminator=discriminator\n",
    "    self.generator=generator\n",
    "\n",
    "  def compile(self,d_optimizer,g_optimizer,loss_fn):\n",
    "    super(GAN,self).compile()\n",
    "    self.d_optimizer=d_optimizer\n",
    "    self.g_optimizer=g_optimizer\n",
    "    self.loss_fn=loss_fn\n",
    "    self.d_loss_metric=tf.keras.metrics.Mean(name='d_loss')\n",
    "    self.g_loss_metric=tf.keras.metrics.Mean(name='g_loss')\n",
    "    \n",
    "  @property\n",
    "  def metrics(self):\n",
    "    return [self.d_loss_metric,self.g_loss_metric]\n",
    "  \n",
    "  def train_step(self,real_images):\n",
    "    batch_size=tf.shape(real_images)[0]\n",
    "\n",
    "    ######## Discriminator\n",
    "    random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n",
    "    fake_images=self.generator(random_noise)\n",
    "\n",
    "    real_labels=tf.ones((batch_size,1))+0.25*tf.random.uniform((batch_size,1),minval=-1,maxval=1)\n",
    "    fake_labels=tf.zeros((batch_size,1))+0.25*tf.random.uniform((batch_size,1),)\n",
    "\n",
    "    with tf.GradientTape() as recorder:\n",
    "        real_predictions=self.discriminator(real_images)\n",
    "        d_loss_real=self.loss_fn(real_labels,real_predictions)\n",
    "\n",
    "        fake_predictions=self.discriminator(fake_images)\n",
    "        d_loss_fake=self.loss_fn(fake_labels,fake_predictions)\n",
    "\n",
    "        d_loss=d_loss_real+d_loss_fake\n",
    "      \n",
    "    partial_derivatives = recorder.gradient(d_loss,self.discriminator.trainable_weights)\n",
    "    self.d_optimizer.apply_gradients(zip(partial_derivatives, self.discriminator.trainable_weights))\n",
    "\n",
    "    ############# Generator\n",
    "    random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n",
    "    flipped_fake_labels=tf.ones((batch_size,1))\n",
    "\n",
    "    with tf.GradientTape() as recorder:\n",
    "        \n",
    "\n",
    "        fake_predictions=self.discriminator(self.generator(random_noise))\n",
    "        g_loss=self.loss_fn(flipped_fake_labels,fake_predictions)\n",
    "      \n",
    "    partial_derivatives = recorder.gradient(g_loss,self.generator.trainable_weights)\n",
    "    self.g_optimizer.apply_gradients(zip(partial_derivatives, self.generator.trainable_weights))\n",
    "\n",
    "    self.d_loss_metric.update_state(d_loss)\n",
    "    self.g_loss_metric.update_state(g_loss)\n",
    "    \n",
    "    return {'d_loss':self.d_loss_metric.result(),\n",
    "            'g_loss':self.g_loss_metric.result()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d492c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "gan=GAN(discriminator,generator)\n",
    "gan.compile(\n",
    "    d_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n",
    "    g_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n",
    "    loss_fn=tf.keras.losses.BinaryCrossentropy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c660029e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24b511ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS=100\n",
    "history=gan.fit(train_dataset,epochs=EPOCHS,callbacks=[ShowImage(LATENT_DIM)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43350df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['d_loss'])\n",
    "plt.plot(history.history['g_loss'])\n",
    "plt.title('GAN Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['d_loss', 'g_loss'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7e0008",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82778a2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8f145d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70979d15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "752b9e65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e076f5fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/1-Linear Regression by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "92c53c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ba87eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(filename):\n",
    "    '''\n",
    "    ###### Objective\n",
    "    A function used to extract data from the csv file and preprocess it, such that it can be\n",
    "    used by our machine learning algorithm\n",
    "    \n",
    "    ### Input\n",
    "    filename\n",
    "    \n",
    "    #### Output\n",
    "    Input and output of machine learning model (in this case, a linear regression model)\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    dataframe = pandas.read_csv(filename)\n",
    "    print(dataframe)\n",
    "    \n",
    "    X1,X2 = [],[]\n",
    "    \n",
    "    for item,row in dataframe.iterrows():\n",
    "        X1.append(row['X1'])\n",
    "        X2.append(row['X2'])\n",
    "        \n",
    "    for i in range(len(X1)):\n",
    "        X.append([1,X2[i]])\n",
    "        Y.append([X1[i]])\n",
    "        \n",
    "    X = np.array(X)\n",
    "    Y = np.array(Y)\n",
    "    \n",
    "    theta1_theta2 = np.linalg.inv(X.T@X)@X.T@Y\n",
    "    print(theta1_theta2)\n",
    "    \n",
    "    return X,Y, theta1_theta2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70623a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y, theta1_theta2 = prepare_dataset('health.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dc19586",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(20)\n",
    "\n",
    "EPOCHS = 10\n",
    "LR = 0.1\n",
    "BATCH_SIZE = X.shape[0]\n",
    "\n",
    "theta = np.random.randn(X.shape[1],1)\n",
    "loss = []\n",
    "\n",
    "for i in range(EPOCHS):\n",
    "    epoch_loss = 0.0\n",
    "    \n",
    "    for b in range(0,len(X),BATCH_SIZE):\n",
    "        d_theta = (X[b:b+BATCH_SIZE].T@(X[b:b+BATCH_SIZE]@theta - Y[b:b+BATCH_SIZE]))/BATCH_SIZE\n",
    "        theta -= LR*(d_theta)##########gradient descent step\n",
    "        epoch_loss += (np.square(Y[b:b+BATCH_SIZE]-(x[b:b+BATCH_SIZE]@theta)).mean())\n",
    "    loss.append(epoch_loss)\n",
    "\n",
    "print('The loss at the end of training is ==', loss[-1])\n",
    "plt.plot(range(1,EPOCHS+1),loss)\n",
    "plt.ylabel('MSE')\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.show()\n",
    "print(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b39201f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pandas.read_csv('health.csv')\n",
    "\n",
    "X2 = []\n",
    "X1 = []\n",
    "\n",
    "for item,row in dataframe.iterrows():\n",
    "    X2.append(row['X2'])\n",
    "    X1.append(row['X1'])\n",
    "\n",
    "X = [i for i in range(0,230)]\n",
    "Y = [(theta1_theta2[0]*i+ (theta1_theta2[1]*i)) for i in range(0,230)]\n",
    "\n",
    "plt.scatter(X2,X1, marker='+')\n",
    "plt.plot(X,Y, color='red')\n",
    "plt.ylabel('X1')\n",
    "plt.xlabel('X2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "677ef1bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d775589",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03d1e358",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/10-Breast Cancer detection by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf### models\n",
    "import numpy as np### math computations\n",
    "import matplotlib.pyplot as plt### plotting bar chart\n",
    "import sklearn### machine learning library\n",
    "import cv2## image processing\n",
    "import scipy.io\n",
    "import tensorflow_probability as tfp\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "from tensorflow.keras.applications import VGG16,ResNet50\n",
    "from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n",
    "                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n",
    "                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n",
    "                                     RandomContrast, Rescaling, Resizing, Reshape,LeakyReLU)\n",
    "from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n",
    "                                        ModelCheckpoint, ReduceLROnPlateau)\n",
    "from tensorflow.keras.regularizers import L2, L1\n",
    "from tensorflow.keras.initializers import RandomNormal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65c55e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE=32\n",
    "INPUT_DIM=224\n",
    "NUM_EPOCHS=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fbf61a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_directory='...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5cf4828",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    train_directory,\n",
    "    validation_split=0.1,\n",
    "    subset='training',\n",
    "    seed=999,\n",
    "    image_size=(INPUT_DIM,INPUT_DIM),\n",
    "    batch_size=BATCH_SIZE,)\n",
    "val_dataset=tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    train_directory,\n",
    "    validation_split=0.1,\n",
    "    subset='validation',\n",
    "    seed=999,\n",
    "    image_size=(INPUT_DIM,INPUT_DIM),\n",
    "    batch_size=BATCH_SIZE,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "052c2f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names=train_dataset.class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01fce8d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c60eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "for images,labels in train_dataset.take(1):\n",
    "    for i in range(BATCH_SIZE):\n",
    "        ax=plt.subplot(BATCH_SIZE//8,BATCH_SIZE//4,i+1)\n",
    "        plt.imshow(images[i]/255.)\n",
    "        plt.title(class_names[labels[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c234bc43",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=train_dataset.prefetch(buffer_size=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f3eb388",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset=val_dataset.prefetch(buffer_size=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed044d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone = ResNet50(\n",
    "        weights='imagenet',\n",
    "        input_shape=(INPUT_DIM,INPUT_DIM,3),\n",
    "        include_top=False,)\n",
    "backbone.trainable=False\n",
    "model=Sequential([\n",
    "    Input(shape=(224,224,3)),\n",
    "    backbone,\n",
    "    GlobalAveragePooling2D(),\n",
    "    Dense(1,activation='sigmoid'),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c35420",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e26903",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29f1282a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bce_loss=BinaryCrossentropy()\n",
    "optimizer=Adam(lr=1e-3)\n",
    "\n",
    "train_accuracy=BinaryAccuracy()\n",
    "val_accuracy=BinaryAccuracy()\n",
    "\n",
    "train_tp=TruePositives()\n",
    "val_tp=TruePositives()\n",
    "\n",
    "train_tn=TrueNegatives()\n",
    "val_tn=TrueNegatives()\n",
    "\n",
    "train_fp=FalsePositives()\n",
    "val_fp=FalsePositives()\n",
    "\n",
    "train_fn=FalseNegatives()\n",
    "val_fn=FalseNegatives()\n",
    "\n",
    "train_precision=Precision()\n",
    "val_precision=Precision()\n",
    "\n",
    "train_recall=Recall()\n",
    "val_recall=Recall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7660367b",
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_step(x,y):\n",
    "    with tf.GradientTape() as tape:\n",
    "        y_pred=model(x,training=True)\n",
    "        loss_value=bce_loss(y,y_pred)\n",
    "    gradients=tape.gradient(loss_value,model.trainable_weights)\n",
    "    optimizer.apply_gradients(zip(gradients,model.trainable_weights))\n",
    "    \n",
    "    train_accuracy.update_state(y,y_pred)\n",
    "    train_tp.update_state(y,y_pred)\n",
    "    train_tn.update_state(y,y_pred)\n",
    "    train_fp.update_state(y,y_pred)\n",
    "    train_fn.update_state(y,y_pred)\n",
    "    train_precision.update_state(y,y_pred)\n",
    "    train_recall.update_state(y,y_pred)\n",
    "    \n",
    "    return loss_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def val_step(x,y):\n",
    "    y_pred=model(x,training=False)\n",
    "    loss_value=bce_loss(y,y_pred)\n",
    "    val_accuracy.update_state(y,y_pred)\n",
    "    val_tp.update_state(y,y_pred)\n",
    "    val_tn.update_state(y,y_pred)\n",
    "    val_fp.update_state(y,y_pred)\n",
    "    val_fn.update_state(y,y_pred)\n",
    "    val_precision.update_state(y,y_pred)\n",
    "    val_recall.update_state(y,y_pred)\n",
    "    \n",
    "    return loss_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8559844",
   "metadata": {},
   "outputs": [],
   "source": [
    "for epoch in range(NUM_EPOCHS):\n",
    "    for (x_train,y_train) in train_dataset:\n",
    "        train_loss=train_step(x_train,y_train)\n",
    "    for (x_val,y_val) in val_dataset:\n",
    "        val_loss=val_step(x_val,y_val)\n",
    "    template='Epoch {}, Loss: {:.2f}, Accuracy: {:.2f}, Precision: {:.2f}, Recall: {:.2f}\\n '\n",
    "    print(template.format(epoch+1,\n",
    "                         train_loss,train_accuracy.result()*100,train_precision.result()*100,train_recall.result()*100))\n",
    "    \n",
    "    print('-------------------------------------------------------------------------------------')\n",
    "    print('            TP: {} --------------            FP:{}         --------------------'.format(train_tp.result(),train_fp.result()))\n",
    "    print('-------------------------------------------------------------------------------------')\n",
    "    print('            FN: {} --------------            TN:{}         --------------------'.format(train_fn.result(),train_tn.result()))\n",
    "    print('-------------------------------------------------------------------------------------')\n",
    "\n",
    "    template='Validation Loss: {:.2f}, Validation Accuracy: {:.2f}, Validation Precision: {:.2f}, Validation Recall: {:.2f}\\n '\n",
    "    print(template.format(val_loss,val_accuracy.result()*100,val_precision.result()*100,val_recall.result()*100))\n",
    "    \n",
    "    print('-------------------------------------------------------------------------------------')\n",
    "    print('            Val TP: {} --------------          Val FP:{}         --------------------'.format(val_tp.result(),val_fp.result()))\n",
    "    print('-------------------------------------------------------------------------------------')\n",
    "    print('            Val FN: {} --------------          Val TN:{}         --------------------'.format(val_fn.result(),val_tn.result()))\n",
    "    print('-------------------------------------------------------------------------------------')\n",
    "    \n",
    "    train_accuracy.reset_states()\n",
    "    train_tp.reset_states()\n",
    "    train_tn.reset_states()\n",
    "    train_fp.reset_states()\n",
    "    train_fn.reset_states()\n",
    "    train_precision.reset_states()\n",
    "    train_recall.reset_states()\n",
    "    \n",
    "    val_accuracy.reset_states()\n",
    "    val_tp.reset_states()\n",
    "    val_tn.reset_states()\n",
    "    val_fp.reset_states()\n",
    "    val_fn.reset_states()\n",
    "    val_precision.reset_states()\n",
    "    val_recall.reset_states()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eac8ba96",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_val=[]\n",
    "x_val=[]\n",
    "\n",
    "for x,y in val_dataset:\n",
    "    x_val.append(x)\n",
    "    y_val.append(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7118c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true=tf.stack(y_val,axis=0)[0]\n",
    "y_pred=model.predict(tf.stack(x_val,axis=0)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c100b414",
   "metadata": {},
   "outputs": [],
   "source": [
    "def roc(labels,predictions):\n",
    "    fp,tp,_=sklearn.metrics.roc_curve(labels,predictions)\n",
    "    plt.plot(fp,tp)\n",
    "    plt.xlabel('false positives rate')\n",
    "    plt.ylabel('true positives rate')\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0820d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "roc(y_true,y_pred[...,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ef4899",
   "metadata": {},
   "source": [
    "<h1>TESTING</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20a560e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "im_array=img_to_array(load_img(image,target_size=(INPUT_DIM,INPUT_DIM)))\n",
    "if mode.prdict(tf.expand_dims(im_array,axis=0))[0][0]<0.5:\n",
    "    print('its benign')\n",
    "else:\n",
    "    print('malignant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf529079",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/11-YOLO by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf### models\n",
    "import numpy as np### math computations\n",
    "import matplotlib.pyplot as plt### plotting bar chart\n",
    "import sklearn### machine learning library\n",
    "import cv2## image processing\n",
    "from sklearn.metrics import confusion_matrix, roc_curve### metrics\n",
    "import seaborn as sns### visualizations\n",
    "import datetime\n",
    "import pathlib\n",
    "import io\n",
    "import json\n",
    "import xml.etree.ElementTree as ET\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "from google.colab import files\n",
    "from PIL import Image\n",
    "import albumentations as A\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow_probability as tfp\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer\n",
    "from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n",
    "                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n",
    "                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n",
    "                                     RandomContrast, Rescaling, Resizing, Reshape,LeakyReLU)\n",
    "from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n",
    "                                        ModelCheckpoint, ReduceLROnPlateau)\n",
    "from tensorflow.keras.regularizers import L2, L1\n",
    "from tensorflow.keras.initializers import RandomNormal\n",
    "from tensorflow.train import BytesList, FloatList, Int64List\n",
    "from tensorflow.train import Example, Features, Feature\n",
    "from google.colab import drive\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65c55e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Utils():\n",
    "    def __init__(self, split_size, num_boxes, classes):\n",
    "        self.split_size = split_size\n",
    "        self.num_boxes = num_boxes\n",
    "        self.classes = classes\n",
    "        \n",
    "    def preprocess_xml(self, filename,m_a_p=False):\n",
    "        tree = ET.parse(filename)\n",
    "        root = tree.getroot()\n",
    "        bounding_boxes = []\n",
    "\n",
    "        size_tree = root.find('size')\n",
    "        height = float(size_tree.find('height').text)\n",
    "        width = float(size_tree.find('width').text)\n",
    "\n",
    "        for object_tree in root.findall('object'):\n",
    "            for bounding_box in object_tree.iter('bndbox'):\n",
    "\n",
    "                xmin = (float(bounding_box.find('xmin').text)/width)\n",
    "                ymin = (float(bounding_box.find('ymin').text)/height)\n",
    "                xmax = (float(bounding_box.find('xmax').text)/width)\n",
    "                ymax = (float(bounding_box.find('ymax').text)/height)\n",
    "                if m_a_p:\n",
    "                    xmin=int(xmin*width)\n",
    "                    ymin=int(ymin*height)\n",
    "                    xmax=int(xmax*width)\n",
    "                    ymax=int(ymax*height)\n",
    "                break\n",
    "            class_name = object_tree.find('name').text\n",
    "            bounding_box = [xmin,ymin,xmax,ymax,class_name]\n",
    "            bounding_boxes.append(bounding_box)\n",
    "        if m_a_p:\n",
    "            return bounding_boxes,(height,width)\n",
    "    return bounding_boxes\n",
    "\n",
    "    def midpoint(self, x,y):\n",
    "        return (x+y)/2\n",
    "    def get_pos(self,classes, classe):\n",
    "        for c in range(len(classes)):\n",
    "            if(classes[c]==classe):\n",
    "                return c\n",
    "    def generate_output(self, bounding_boxes):\n",
    "        output_label = np.zeros((self.split_size, self.split_size, len(self.classes)+(5*self.num_boxes)))\n",
    "\n",
    "        for box in bounding_boxes:\n",
    "            x_min, y_min, x_max, y_max = box[0], box[1], box[2], box[3]\n",
    "\n",
    "            x_centre = self.midpoint(x_min, x_max)\n",
    "            y_centre = self.midpoint(y_min, y_max)\n",
    "\n",
    "            x_width = x_max - x_min\n",
    "            y_height = y_max - y_min\n",
    "\n",
    "            i = int(x_centre*self.split_size)\n",
    "            j = int(y_centre*self.split_size)\n",
    "\n",
    "            x_centre_cell = (x_centre*self.split_size)-i\n",
    "            y_centre_cell = (y_centre*self.split_size)-j\n",
    "\n",
    "            if(output_label[i,j,26]==0):\n",
    "                output_label[i,j,22:27] = np.array([x_centre_cell, y_centre_cell, x_width,y_height,1])\n",
    "                output_label[i,j,self.get_pos(self.classes, box[4])] = 1.0\n",
    "        return output_label\n",
    "    def generate_pre_output(self,bounding_boxes,image_shape):\n",
    "        height,width=image_shape\n",
    "        bbox=[]\n",
    "        for box in bounding_boxes:\n",
    "            x_min,y_min,x_max,y_max,obj=box[0],box[1],box[2],box[3],box[4]\n",
    "            bbox.append([int(x_min/width*224),int(y_min/height*224),int(x_max/width*224),int(y_max/height*224),obj])\n",
    "        return bbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5cf4828",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image='...'\n",
    "image = tf.keras.preprocessing.image.load_img(\n",
    "    test_image,color_mode='rgb',target_size=(224,224)\n",
    ")\n",
    "classes = ['background','aeroplane','bicycle','bird','boat','bottle','bus','car','cat',\n",
    "           'chair','cow','diningtable','dog','horse','motorbike','person','pottedplant',\n",
    "           'sheep','sofa','train','tvmonitor','book']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__ (self, train_images, train_maps, split_size, num_boxes, classes, batch_size, shuffle = False):\n",
    "\n",
    "        self.train_images = train_images\n",
    "        self.train_maps = train_maps\n",
    "        self.train_image_list = os.listdir(self.train_images)\n",
    "        self.train_map_list = os.listdir(self.train_maps)\n",
    "        self.split_size = split_size\n",
    "        self.num_boxes = num_boxes\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "    def __len__(self):\n",
    "        return int(np.floor(len(self.train_image_list)/self.batch_size))\n",
    "  \n",
    "    def __getitem__(self, idx):\n",
    "        x,y = self.__data_generation(idx)\n",
    "        return np.array(x), np.array(y)\n",
    "  \n",
    "    def __data_generation(self, idx):\n",
    "        x = np.empty((self.batch_size, 224,224,3))\n",
    "        y = np.zeros((self.batch_size, self.split_size, self.split_size,27))\n",
    "\n",
    "        for i,j in enumerate(list(range(idx*self.batch_size, (idx+1)*self.batch_size))):\n",
    "            image = tf.keras.preprocessing.image.load_img(self.train_images + self.train_image_list[j],\n",
    "                                                        color_mode ='rgb', target_size = (224,224))\n",
    "            x[i] = tf.keras.preprocessing.image.img_to_array(image)\n",
    "            utils = Utils(self.split_size,self.num_boxes,self.classes)\n",
    "            bounding_boxes = utils.preprocess_xml(self.train_maps+self.train_map_list[j])\n",
    "            y[i] = utils.generate_output(bounding_boxes)\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b125672d",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1e-3\n",
    "BATCH_SIZE=32\n",
    "EPOCH=100\n",
    "train_images='...'\n",
    "train_maps='...'\n",
    "val_images='...'\n",
    "val_maps='...'\n",
    "split_size=7\n",
    "num_boxes=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = DataGenerator(train_images, train_maps, split_size, num_boxes, classes, batch_size=BATCH_SIZE)\n",
    "val_gen = DataGenerator(val_images, val_maps, split_size, num_boxes, classes, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed044d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = 224\n",
    "NUM_FILTERS = 16\n",
    "\n",
    "s = split_size\n",
    "c = len(classes)\n",
    "b = num_boxes\n",
    "\n",
    "OUTPUT_DIM  = s*s*(c+5*b)\n",
    "OUTPUT_SHAPE = (s,s,c+5*b)\n",
    "\n",
    "inputs = tf.keras.Input(shape = (INPUT_DIM, INPUT_DIM, 3))\n",
    "\n",
    "base_model = tf.keras.applications.mobilenet_v2.MobileNetV2(\n",
    "    weights='imagenet',\n",
    "    input_shape=(INPUT_DIM,INPUT_DIM,3),\n",
    "    include_top=False,\n",
    ")\n",
    "\n",
    "base_model.trainable=False\n",
    "\n",
    "model=tf.keras.Sequential([\n",
    "                        \n",
    "    inputs,\n",
    "    base_model,\n",
    "    Conv2D(NUM_FILTERS,(3,3),kernel_initializer=RandomNormal(0.01)),\n",
    "    LeakyReLU(alpha=0.1),\n",
    "    Conv2D(NUM_FILTERS/2,(3,3),kernel_initializer=RandomNormal(0.01)),\n",
    "    LeakyReLU(alpha=0.1),\n",
    "    LayerNormalization(),\n",
    "    Conv2D(OUTPUT_DIM,(3,3),kernel_initializer=RandomNormal(0.01)),\n",
    "    LeakyReLU(alpha=0.1),\n",
    "    Flatten(),\n",
    "\n",
    "    Dense(OUTPUT_DIM),\n",
    "    Reshape(OUTPUT_SHAPE),\n",
    "              \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c35420",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e26903",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cb21263",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41af8c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "class YOLOLoss(tf.losses.Loss):\n",
    "    def __init__(self,):\n",
    "        super(YOLOLoss,self).__init__()\n",
    "        pass\n",
    "    def call(self, y_true, y_pred):\n",
    "        cce = tf.keras.losses.CategoricalCrossentropy()\n",
    "        bce = tf.keras.losses.BinaryCrossentropy()\n",
    "        \n",
    "        target = tf.reshape(y_true[...,26], [-1])\n",
    "        predictions = tf.reshape(y_pred[...,26], [-1])\n",
    "\n",
    "        ###################### OBject Loss\n",
    "        y_pred_extract = tf.keras.activations.sigmoid(tf.gather(predictions, tf.reshape(tf.where(target==1),[-1])))\n",
    "        y_target_extract = tf.ones(len(y_pred_extract))\n",
    "        object_loss = bce(y_pred_extract,y_target_extract)\n",
    "\n",
    "        ####################### For No object\n",
    "\n",
    "        y_pred_extract = tf.keras.activations.sigmoid(tf.gather(predictions, tf.reshape(tf.where(target==0),[-1])))\n",
    "        y_target_extract = tf.zeros(len(y_pred_extract))\n",
    "\n",
    "\n",
    "        no_object_loss = bce(y_pred_extract,y_target_extract)\n",
    "\n",
    "        ####################### For OBject class loss\n",
    "\n",
    "        y_pred_extract = tf.nn.softmax(tf.gather_nd(y_pred,tf.where(y_true[...,26]==1))[...,0:22])\n",
    "        y_target_extract = tf.gather_nd(y_true,tf.where(y_true[...,26]==1))[...,0:22]\n",
    "\n",
    "        class_loss = cce(y_pred_extract,y_target_extract)\n",
    "\n",
    "        # ######################## For object bounding box loss\n",
    "\n",
    "        y_pred_extract_centre = tf.gather_nd(y_pred,tf.where(y_true[...,26]==1))[...,22:24]\n",
    "        y_target_extract_centre = tf.gather_nd(y_true,tf.where(y_true[...,26]==1))[...,22:24]\n",
    "\n",
    "\n",
    "\n",
    "        bounding_centre_loss = tf.math.reduce_mean(tf.keras.losses.mean_squared_error(y_pred_extract_centre, y_target_extract_centre))\n",
    "\n",
    "        y_pred_extract_side = tf.gather_nd(y_pred,tf.where(y_true[...,26]==1))[...,24:26]\n",
    "        y_target_extract_side = tf.gather_nd(y_true,tf.where(y_true[...,26]==1))[...,24:26]\n",
    "\n",
    "        bounding_side_loss = tf.math.reduce_mean(tf.keras.losses.mean_squared_error(y_pred_extract_side, y_target_extract_side))\n",
    "\n",
    "        bounding_loss = bounding_centre_loss + bounding_side_loss\n",
    "\n",
    "        lambda_coord = 5.\n",
    "        lambda_no_obj = 0.5\n",
    "\n",
    "        loss = object_loss + (lambda_no_obj*no_object_loss)+ tf.cast(lambda_coord*bounding_loss,dtype=tf.float32)+ tf.cast(class_loss,dtype=tf.float32) \n",
    "        \n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss = YOLOLoss(),\n",
    "    optimizer = Adam(learning_rate = LR),\n",
    "    run_eagerly = True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8559844",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath = checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac24b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(\n",
    "    train_gen,\n",
    "    verbose=1,\n",
    "    shuffle=True,\n",
    "    epochs=2500,\n",
    "    callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ef4899",
   "metadata": {},
   "source": [
    "<h1>TESTING</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b9da9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image ='...'\n",
    "image = tf.keras.preprocessing.image.load_img(\n",
    "    test_image,color_mode='rgb',target_size=(224,224)\n",
    ")\n",
    "image_width,image_height=224,224\n",
    "im=tf.keras.preprocessing.image.img_to_array(image)/255.\n",
    "image=tf.keras.preprocessing.image.img_to_array(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73e0192b",
   "metadata": {},
   "outputs": [],
   "source": [
    "output=model.predict(tf.expand_dims(image, axis=0))\n",
    "THRESH=5\n",
    "object_positions=tf.where(output[...,26]>=THRESH)\n",
    "selected_output=tf.gather_nd(output,object_positions)\n",
    "objects=tf.argmax(selected_output[...,0:22],axis=-1)\n",
    "\n",
    "final_boxes=[]\n",
    "object_classes=[]\n",
    "final_scores=[]\n",
    "\n",
    "for i,pos in enumerate(object_positions):\n",
    "    pos=np.array(pos)\n",
    "    output_box=output[pos[0]][pos[1]][pos[2]][22:27]\n",
    "    final_scores.append(output_box[-1])\n",
    "\n",
    "    x_centre=(image_width/split_size)*(output_box[0]+pos[1])\n",
    "    y_centre=(image_height/split_size)*(output_box[1]+pos[2])\n",
    "\n",
    "    x_width=image_width*output_box[2]\n",
    "    y_height=image_height*output_box[3]\n",
    "\n",
    "    x_min=int(x_centre-(x_width/2))\n",
    "    y_min=int(y_centre-(y_height/2))\n",
    "\n",
    "    x_max=int(x_centre+(x_width/2))\n",
    "    y_max=int(y_centre+(y_height/2))\n",
    "\n",
    "    if(x_min<=0):x_min=0\n",
    "    if(y_min<=0):y_min=0\n",
    "    if(x_max>=image_width):x_max=image_width\n",
    "    if(y_max>=image_height):y_max=image_height\n",
    "\n",
    "    final_boxes.append([x_min,y_min,x_max,y_max,str(classes[objects[i]])])\n",
    "\n",
    "print('finalboxes',final_boxes)\n",
    "\n",
    "final_boxes=np.array(final_boxes)\n",
    "final_scores=np.array(final_scores)\n",
    "\n",
    "object_classes=final_boxes[...,4]\n",
    "final_boxes=final_boxes[...,0:4]\n",
    "\n",
    "nms_output=tf.image.non_max_suppression(\n",
    "    final_boxes,final_scores,max_output_size=20,iou_threshold=0.5,\n",
    "    score_threshold=float('-inf')\n",
    ")\n",
    "\n",
    "final_nms_boxes=[]\n",
    "\n",
    "for i in nms_output:\n",
    "    final_nms_boxes.append(list(final_boxes[i]))\n",
    "\n",
    "for i,box in enumerate(final_nms_boxes):\n",
    "    cv2.rectangle(image, (int(box[0]),int(box[1])),(int(box[2]),int(box[3])),(0,0,255),1)\n",
    "    cv2.putText(\n",
    "      image,\n",
    "      object_classes[i],\n",
    "      (int(box[0]),int(box[1])),\n",
    "      cv2.FONT_HERSHEY_SIMPLEX,1,(222,0,0),2\n",
    "      )\n",
    "cv2.imshow(\"YOU ONLY LOOK ONCE\",im)\n",
    "pause=cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0694be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def iou(bbox_1,bbox_2):\n",
    "    x_1=tf.maximum(bbox_1[0],bbox_2[0])\n",
    "    y_1=tf.maximum(bbox_1[1],bbox_2[1])\n",
    "    x_2=tf.maximum(bbox_1[2],bbox_2[2])\n",
    "    y_2=tf.maximum(bbox_1[3],bbox_2[3])\n",
    "    \n",
    "    inter_area=float(max(x_2-x_1,0)*max(y_2-y_1,0))\n",
    "    bbox_1_area=(bbox_1[2]-bbox_1[0])*(bbox_1[3]-bbox_1[1])\n",
    "    bbox_2_area=(bbox_2[2]-bbox_2[0])*(bbox_2[3]-bbox_2[1])1\n",
    "    \n",
    "    union_area=float(bbox_1_area+bbox_2_area-inter_area)\n",
    "    return inter_area/union_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0804f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def area_polygon(x,y):\n",
    "    area_1,area_2=0,0\n",
    "    for i in range(len(x)-1):\n",
    "        area_1+=x[i]*y[i+1]\n",
    "    area_1+=x[len(x)-1]*y[0]\n",
    "    \n",
    "    for i in range(len(x)-1):\n",
    "        area_2+=x[i+1]*y[i]\n",
    "    area_2+=x[0]*y[len(x)-1]\n",
    "    return 0.5*tf.abs(area_1-area_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f65e3ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_target(val_data):\n",
    "    THRESH=0.5\n",
    "    utils=Utils(7,1,classes)\n",
    "    bounding_boxes,image_shape=utils.preprocess_xml(val_data,m_a_p=True)\n",
    "    y_target=utils.generate_pre_output(bounding_boxes,image_shape)\n",
    "    return y_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49c498aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean(arr):\n",
    "    summ=0\n",
    "    for i in arr:\n",
    "        summ+=i\n",
    "    return summ/len(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1020027",
   "metadata": {},
   "outputs": [],
   "source": [
    "aps=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7955aea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_average_precision():\n",
    "    for object_class in classes:\n",
    "        precision=[1.]\n",
    "        recall=[0.]\n",
    "        n_class=0\n",
    "        tp,fp=0,0\n",
    "        for image,bbox in zip(os.listdir(val_images),os.listdir(val_maps)):\n",
    "            y_target=get_target(val_maps+bbox)\n",
    "            for target in y_target:\n",
    "                if target[4]==object_class:\n",
    "                    n_class+=1\n",
    "        for image,bbox in zip(os.listdir(val_images),os.listdir(val_maps)):\n",
    "            y_target=get_target(val_maps+bbox)\n",
    "            y_pred=generate_output(val_images+image)\n",
    "            \n",
    "            for pred in y_pred:\n",
    "                if pred[4]!=object_class:\n",
    "                    pass\n",
    "                else:\n",
    "                    found=False\n",
    "                    for target in y_target[4] and iou(pred[:4],target[:4])>0.5:\n",
    "                        found=True\n",
    "                        break\n",
    "                    if found:\n",
    "                        tp+=1\n",
    "                    else:\n",
    "                        fp+=1\n",
    "                    if tp/(tp+fp)>1e-3 and tp/n_class>1e-3:\n",
    "                        precision.append(tp/(tp+fp))\n",
    "                        reacall.append(min(1.,tp/n_class))\n",
    "        precision.append(0.)\n",
    "        recall.append(max(recall))\n",
    "        precision.append(0.)\n",
    "        recall.append(0.)\n",
    "        \n",
    "        ap_s.append(area_polygon(recall,precision).numpy())\n",
    "    return mean(ap_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd2d45c8",
   "metadata": {},
   "outputs": [],
   "source": []
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================================================
FILE: deprecated/12-YOLO v3 by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf### models\n",
    "import numpy as np### math computations\n",
    "import matplotlib.pyplot as plt### plotting bar chart\n",
    "import sklearn### machine learning library\n",
    "import cv2## image processing\n",
    "from sklearn.metrics import confusion_matrix, roc_curve### metrics\n",
    "import seaborn as sns### visualizations\n",
    "import datetime\n",
    "import pathlib\n",
    "import io\n",
    "import json\n",
    "import xml.etree.ElementTree as ET\n",
    "import os\n",
    "import time\n",
    "import random\n",
    "from google.colab import files\n",
    "from PIL import Image\n",
    "import albumentations as A\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow_probability as tfp\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer\n",
    "from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n",
    "                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n",
    "                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n",
    "                                     RandomContrast, Rescaling, Resizing, Reshape,LeakyReLU)\n",
    "from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n",
    "                                        ModelCheckpoint, ReduceLROnPlateau)\n",
    "from tensorflow.keras.regularizers import L2, L1\n",
    "from tensorflow.keras.initializers import RandomNormal\n",
    "from tensorflow.train import BytesList, FloatList, Int64List\n",
    "from tensorflow.train import Example, Features, Feature\n",
    "from google.colab import drive\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65c55e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Utils():\n",
    "    def __init__(self, split_size, num_boxes, classes):\n",
    "        self.split_size = split_size\n",
    "        self.num_boxes = num_boxes\n",
    "        self.classes = classes\n",
    "        \n",
    "    def preprocess_xml(self, filename,m_a_p=False):\n",
    "        tree = ET.parse(filename)\n",
    "        root = tree.getroot()\n",
    "        bounding_boxes = []\n",
    "\n",
    "        size_tree = root.find('size')\n",
    "        height = float(size_tree.find('height').text)\n",
    "        width = float(size_tree.find('width').text)\n",
    "\n",
    "        for object_tree in root.findall('object'):\n",
    "            for bounding_box in object_tree.iter('bndbox'):\n",
    "\n",
    "                xmin = (float(bounding_box.find('xmin').text)/width)\n",
    "                ymin = (float(bounding_box.find('ymin').text)/height)\n",
    "                xmax = (float(bounding_box.find('xmax').text)/width)\n",
    "                ymax = (float(bounding_box.find('ymax').text)/height)\n",
    "                if m_a_p:\n",
    "                    xmin=int(xmin*width)\n",
    "                    ymin=int(ymin*height)\n",
    "                    xmax=int(xmax*width)\n",
    "                    ymax=int(ymax*height)\n",
    "                break\n",
    "            class_name = object_tree.find('name').text\n",
    "            bounding_box = [xmin,ymin,xmax,ymax,class_name]\n",
    "            bounding_boxes.append(bounding_box)\n",
    "        if m_a_p:\n",
    "            return bounding_boxes,(height,width)\n",
    "    return bounding_boxes\n",
    "\n",
    "    def midpoint(self, x,y):\n",
    "        return (x+y)/2\n",
    "    def get_pos(self,classes, classe):\n",
    "        for c in range(len(classes)):\n",
    "            if(classes[c]==classe):\n",
    "                return c\n",
    "    def generate_pre_output(self,bounding_boxes,image_shape):\n",
    "        height,width=image_shape\n",
    "        bbox=[]\n",
    "        for box in bounding_boxes:\n",
    "            x_min,y_min,x_max,y_max,obj=box[0],box[1],box[2],box[3],box[4]\n",
    "            bbox.append([int(x_min/width*224),int(y_min/height*224),int(x_max/width*224),int(y_max/height*224),obj])\n",
    "        return bbox\n",
    "    def bounding_box_to_output(self,box,offset,anchor):\n",
    "        output=[0,0,0,0,1]\n",
    "        output[0]=tf.math.log((box[0]-offset[0])/(1+offset[0]-box[0]))\n",
    "        output[1]=tf.math.log((box[1]-offset[1])/(1+offset[1]-box[1]))\n",
    "        \n",
    "        output[2]=tf.math.log(box[2]/anchor[0])\n",
    "        output[3]=tf.math.log(box[3]/anchor[1])\n",
    "        return np.array(output)\n",
    "    \n",
    "    \n",
    "    def generate_output(self, bounding_boxes):\n",
    "        anchor_boxes=[[1.3,1.3],[2.3,1.3],[1.3,2.3]]\n",
    "        output_label = np.zeros((self.split_size, self.split_size, len(anchor_boxes), len(self.classes)+(5*self.num_boxes)))\n",
    "\n",
    "        for box in bounding_boxes:\n",
    "            for a in range(len(anchor_boxes)):\n",
    "\n",
    "                x_min, y_min, x_max, y_max = box[0], box[1], box[2], box[3]\n",
    "\n",
    "                x_centre = self.midpoint(x_min, x_max)\n",
    "                y_centre = self.midpoint(y_min, y_max)\n",
    "\n",
    "                x_width = (x_max - x_min)*self.split_size\n",
    "                y_height = (y_max - y_min)*self.split_size\n",
    "\n",
    "                b_x_centre_cell = (x_centre*self.split_size)\n",
    "                b_y_centre_cell = (y_centre*self.split_size)\n",
    "\n",
    "                i = int(b_x_centre_cell)\n",
    "                j = int(b_y_centre_cell)\n",
    "                \n",
    "                offset = [i,j]\n",
    "                b = [b_x_centre_cell,b_y_centre_cell,x_width,y_height]\n",
    "                width,height=box[5],box[6]\n",
    "                \n",
    "                b_x_min=((b_x_centre_cell-(anchor_boxes[a][0]/2))/self.split_size)*width\n",
    "                if b_x_min<0:\n",
    "                    b_x_min=0.\n",
    "                b_y_min=((b_y_centre_cell-(anchor_boxes[a][1]/2))/self.split_size)*height\n",
    "                if b_y_min<0:\n",
    "                    b_y_min=0.\n",
    "                \n",
    "                \n",
    "                b_x_max=((b_x_centre_cell+(anchor_boxes[a][0]/2))/self.split_size)*width\n",
    "                b_y_max=((b_y_centre_cell+(anchor_boxes[a][1]/2))/self.split_size)*height\n",
    "                \n",
    "                x_centre_cell = (x_centre*self.split_size)-i\n",
    "                y_centre_cell = (y_centre*self.split_size)-j\n",
    "\n",
    "                bbox.append(self.bounding_box_to_output(b,offset,anchor_boxes[a]))\n",
    "                box_iou.append(self.iou([x_min*width,y_min*height,x_max*width,y_max*height],[b_x_min,b_y_min,b_x_max,b_y_max]))\n",
    "            \n",
    "            highest_a = tf.argmax(tf.constant(box_iou)).numpy()\n",
    "            output_label[i,j,highest_a,22:27] = self.bounding_box_to_output(b,offset,anchor_boxes[highest_a])\n",
    "            output_label[i,j,highest_a,self.get_pos(self.classes, box[4])] = 1.\n",
    "            \n",
    "        return output_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89d2b110",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image='...'\n",
    "image = tf.keras.preprocessing.image.load_img(\n",
    "    test_image,color_mode='rgb',target_size=(224,224)\n",
    ")\n",
    "classes = ['background','aeroplane','bicycle','bird','boat','bottle','bus','car','cat',\n",
    "           'chair','cow','diningtable','dog','horse','motorbike','person','pottedplant',\n",
    "           'sheep','sofa','train','tvmonitor','book']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__ (self, train_images, train_maps, split_size, num_boxes, classes, batch_size, shuffle = False):\n",
    "\n",
    "        self.train_images = train_images\n",
    "        self.train_maps = train_maps\n",
    "        self.train_image_list = os.listdir(self.train_images)\n",
    "        self.train_map_list = os.listdir(self.train_maps)\n",
    "        self.split_size = split_size\n",
    "        self.num_boxes = num_boxes\n",
    "        self.classes = classes\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "    def __len__(self):\n",
    "        return int(np.floor(len(self.train_image_list)/self.batch_size))\n",
    "  \n",
    "    def __getitem__(self, idx):\n",
    "        x,y_1,y_2 = self.__data_generation(idx)\n",
    "        return np.array(x), [y_1,y_2]\n",
    "  \n",
    "    def __data_generation(self, idx):\n",
    "        x = np.empty((self.batch_size, 224,224,3))\n",
    "        y_1 = np.zeros((self.batch_size, self.split_size, self.split_size,3,27))\n",
    "        y_2 = np.zeros((self.batch_size, self.split_size*4, self.split_size*4,3,27))\n",
    "\n",
    "        for i,j in enumerate(list(range(idx*self.batch_size, (idx+1)*self.batch_size))):\n",
    "            image = tf.keras.preprocessing.image.load_img(self.train_images + self.train_image_list[j],\n",
    "                                                        color_mode ='rgb', target_size = (224,224))\n",
    "            \n",
    "            x[i] = tf.keras.preprocessing.image.img_to_array(image)\n",
    "            utils = Utils(self.split_size,self.num_boxes,self.classes)\n",
    "            bounding_boxes = utils.preprocess_xml(self.train_maps+self.train_map_list[j])\n",
    "            y_1[i] = utils.generate_output(bounding_boxes)\n",
    "\n",
    "            utils = Utils(self.split_size*4,self.num_boxes,self.classes)\n",
    "            bounding_boxes = utils.preprocess_xml(self.train_maps+self.train_map_list[j])\n",
    "            y_1[i] = utils.generate_output(bounding_boxes)\n",
    "\n",
    "            \n",
    "        return x,y_1,y_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95b13e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1e-3\n",
    "BATCH_SIZE=32\n",
    "EPOCH=100\n",
    "train_images='...'\n",
    "train_maps='...'\n",
    "val_images='...'\n",
    "val_maps='...'\n",
    "split_size=7\n",
    "num_boxes=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = DataGenerator(train_images, train_maps, split_size, num_boxes, classes, batch_size=BATCH_SIZE)\n",
    "val_gen = DataGenerator(val_images, val_maps, split_size, num_boxes, classes, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60497989",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_base_model():\n",
    "    base_model = tf.keras.applications.mobilenet_v2.MobileNetV2(\n",
    "        weights='imagenet',\n",
    "        input_shape=(INPUT_DIM,INPUT_DIM,3),\n",
    "        include_top=False,)\n",
    "    base_model.trainable=False\n",
    "    block_6_output,out_relu=[base_model.get_layer(layer_name).output for layer_name in [\"block_6_expand_relu\",\"out_relu\"]]\n",
    "    \n",
    "    return Model(\n",
    "        inputs=[base_model.inputs],outputs=[out_relu,block_6_output]\n",
    "    )\n",
    "\n",
    "get_base_model().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf8f92a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class head(tf.keras.layers.Layer):\n",
    "    def __init__(self,OUTPUT_DIM,OUTPUT_SHAPE,NUM_FILTERS):\n",
    "        super(Head,self).__init__()\n",
    "        \n",
    "        self.conv_1=Conv2D(NUM_FILTERS,(3,3),activation='relu',kernel_initializer=RandomNormal(0.01))\n",
    "        self.conv_2=Conv2D(NUM_FILTERS,(3,3),activation='relu',kernel_initializer=RandomNormal(0.01))\n",
    "        self.conv_3=Conv2D(OUTPUT_DIM,(3,3),activation='relu',kernel_initializer=RandomNormal(0.01))\n",
    "        self.norm_1=LayerNormalization()\n",
    "        \n",
    "        self.flatten=Flatten()\n",
    "        self.reshape=Reshape(OUTPUT_SHAPE)\n",
    "    def call(self,images,training=False):\n",
    "        x=self.conv_1(images)\n",
    "        x=self.conv_2(x)\n",
    "        x=self.conv_3(x)\n",
    "        x=self.norm_1(x)\n",
    "        x=self.flatten(x)\n",
    "        x=self.reshape(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d5df3a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = 224\n",
    "NUM_FILTERS = 16\n",
    "\n",
    "s = 7\n",
    "c = len(classes)\n",
    "b = 1\n",
    "\n",
    "OUTPUT_DIM  = s*s*3*(c+5*b)\n",
    "OUTPUT_SHAPE = (s,s,3,c+5*b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed044d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = tf.keras.Input(shape = (INPUT_DIM, INPUT_DIM, 3))\n",
    "out_relu,block_6_output=get_base_model()(inputs)\n",
    "head_7=Head(OUTPUT_DIM,OUTPUT_SHAPE,NUM_FILTERS)(out_relu)\n",
    "pre_concat_28=Conv2DTranspose(128,(1,1),strides=(4,4))(out_relu)\n",
    "post_concat_28=tf.keras.layers.concatenate([pre_concat_28,block_6_output],axis=-1)\n",
    "\n",
    "head_28=Head(3*s*4*s*4*(c+5*b), (s*4,s*4,3,c+5*b),NUM_FILTERS)(post_concat_28)\n",
    "model=Model(inputs=inputs,outputs=[head_7,head_28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c35420",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e26903",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cb21263",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a8696fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "class YOLOLoss(tf.losses.Loss):\n",
    "    def __init__(self,):\n",
    "        super(YOLOLoss,self).__init__()\n",
    "        pass\n",
    "    def call(self, y_true, y_pred):\n",
    "        cce = tf.keras.losses.CategoricalCrossentropy()\n",
    "        bce = tf.keras.losses.BinaryCrossentropy()\n",
    "        \n",
    "        target = tf.reshape(y_true[...,26], [-1])\n",
    "        predictions = tf.reshape(y_pred[...,26], [-1])\n",
    "\n",
    "        ###################### OBject Loss\n",
    "        y_pred_extract = tf.keras.activations.sigmoid(tf.gather(predictions, tf.reshape(tf.where(target==1),[-1])))\n",
    "        y_target_extract = tf.ones(len(y_pred_extract))\n",
    "        object_loss = bce(y_pred_extract,y_target_extract)\n",
    "\n",
    "        ####################### For No object\n",
    "\n",
    "        y_pred_extract = tf.keras.activations.sigmoid(tf.gather(predictions, tf.reshape(tf.where(target==0),[-1])))\n",
    "        y_target_extract = tf.zeros(len(y_pred_extract))\n",
    "        no_object_loss = bce(y_pred_extract,y_target_extract)\n",
    "\n",
    "        ####################### For OBject class loss\n",
    "\n",
    "        y_pred_extract = tf.nn.softmax(tf.gather_nd(y_pred,tf.where(y_true[...,25]!=0))[...,0:22])\n",
    "        y_target_extract = tf.gather_nd(y_true,tf.where(y_true[...,25]!=0))[...,0:22]\n",
    "\n",
    "        class_loss = cce(y_pred_extract,y_target_extract)\n",
    "\n",
    "        # ######################## For object bounding box loss\n",
    "\n",
    "        y_pred_extract_centre = tf.gather_nd(y_pred,tf.where(y_true[...,25]!=0))[...,22:24]\n",
    "        y_target_extract_centre = tf.gather_nd(y_true,tf.where(y_true[...,25]!=0))[...,22:24]\n",
    "\n",
    "\n",
    "\n",
    "        bounding_centre_loss = tf.math.reduce_mean(tf.keras.losses.mean_squared_error(y_pred_extract_centre, y_target_extract_centre))\n",
    "\n",
    "        y_pred_extract_side = tf.gather_nd(y_pred,tf.where(y_true[...,25]!=0))[...,24:26]\n",
    "        y_target_extract_side = tf.gather_nd(y_true,tf.where(y_true[...,25]!=0))[...,24:26]\n",
    "\n",
    "        bounding_side_loss = tf.math.reduce_mean(tf.keras.losses.mean_squared_error(y_pred_extract_side, y_target_extract_side))\n",
    "\n",
    "        bounding_loss = bounding_centre_loss + bounding_side_loss\n",
    "\n",
    "        lambda_coord = 5.\n",
    "        lambda_no_obj = 0.5\n",
    "\n",
    "        loss = object_loss + (lambda_no_obj*no_object_loss)+ tf.cast(lambda_coord*bounding_loss,dtype=tf.float32)+ tf.cast(class_loss,dtype=tf.float32) \n",
    "        \n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss = YOLOLoss(),\n",
    "    optimizer = Adam(learning_rate = LR),\n",
    "    run_eagerly = True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8559844",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath = checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac24b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(\n",
    "    train_gen,\n",
    "    verbose=1,\n",
    "    shuffle=True,\n",
    "    epochs=2500,\n",
    "    callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ef4899",
   "metadata": {},
   "source": [
    "<h1>TESTING</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b9da9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image ='...'\n",
    "X=[]\n",
    "anchor_noxes=[[1.3,1.3],[2.3,1.3],[1.3,2.3]]\n",
    "\n",
    "im = tf.keras.preprocessing.image.load_img(test_image,color_mode='rgb',target_size=(500,500))\n",
    "image_width,image_height=im.width,im.height\n",
    "im=tf.keras.preprocessing.image.img_to_array(image)/255.\n",
    "\n",
    "\n",
    "image = tf.keras.preprocessing.image.load_img(test_image,color_mode='rgb',target_size=(224,224))\n",
    "image=tf.keras.preprocessing.image.img_to_array(image)\n",
    "\n",
    "X.append(np.array(image))\n",
    "X=np.array(X)\n",
    "\n",
    "output=model.predict(X)\n",
    "\n",
    "final_boxes=[]\n",
    "object_classes=[]\n",
    "final_scores=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73e0192b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_boxes(out,scales):\n",
    "    for scale in range(len(scales)):\n",
    "        split_size=scales[scale]\n",
    "        for a in range(len(anchor_boxes)):\n",
    "            output=out[scale][...,a,:]\n",
    "            THRESH=10\n",
    "            bounding_boxes=tf.where(output[...,26]>=THRESH)\n",
    "            selected_output=tf.gather_nd(output,bounding_boxes)\n",
    "            objects=tf.argmax(selected_output[...,0:22],axis=-1)\n",
    "            i=0\n",
    "            \n",
    "            for box in bounding_boxes:\n",
    "                box=np.array(box)\n",
    "                output_box=output[pos[0]][box[1]][box[2]][22:27]\n",
    "                final_scores.append(output_box[-1])\n",
    "\n",
    "                x_centre=int((image_width/split_size)*(tf.keras.activations.sigmoid(output_box[0])+box[1])\n",
    "                y_centre=int((image_height/split_size)*(tf.keras.activations.sigmoid(output_box[1])+box[2])\n",
    "                \n",
    "                x_width=int(image_width*anchor_boxes[a][0]*tf.math.exp(output_box[2]))\n",
    "                y_height=int(image_height*anchor_boxes[a][1]*tf.math.exp(output_box[3]))\n",
    "                \n",
    "                x_min=int(x_centre-(x_width/2))\n",
    "                y_min=int(y_centre-(y_height/2))\n",
    "\n",
    "                x_max=int(x_centre+(x_width/2))\n",
    "                y_max=int(y_centre+(y_height/2))\n",
    "\n",
    "                if(x_min<=0):x_min=0\n",
    "                if(y_min<=0):y_min=0\n",
    "                if(x_max>=image_width):x_max=image_width\n",
    "                if(y_max>=image_height):y_max=image_height\n",
    "\n",
    "                final_boxes.append([x_min,y_min,x_max,y_max,str(classes[objects[i]])])\n",
    "                i+=1\n",
    "    return np.array(final_boxes),np.array(final_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1231b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_size=7\n",
    "scales=[split_size,split_size*4]\n",
    "final_boxes,final_scores=get_boxes(out,scales)\n",
    "print('finalboxes',final_boxes)\n",
    "i=0\n",
    "\n",
    "object_classes=final_boxes[...,4]\n",
    "final_boxes=final_boxes[...,0:4]\n",
    "\n",
    "nms_output=tf.image.non_max_suppression(\n",
    "    final_boxes,final_scores,max_output_size=20,iou_threshold=0.5,\n",
    "    score_threshold=float('-inf')\n",
    ")\n",
    "\n",
    "final_nms_boxes=[]\n",
    "\n",
    "for i in nms_output:\n",
    "    final_nms_boxes.append(list(final_boxes[i]))\n",
    "i=0\n",
    "for i,box in enumerate(final_nms_boxes):\n",
    "    cv2.rectangle(image, (int(box[0]),int(box[1])),(int(box[2]),int(box[3])),(0,0,255),1)\n",
    "    cv2.putText(\n",
    "      image,\n",
    "      object_classes[i],\n",
    "      (int(box[0]),int(box[1])),\n",
    "      cv2.FONT_HERSHEY_SIMPLEX,1,(222,0,0),2\n",
    "      )\n",
    "cv2.imshow(\"YOU ONLY LOOK ONCE\",im)\n",
    "pause=cv2.waitKey()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0694be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def iou(bbox_1,bbox_2):\n",
    "    x_1=tf.maximum(bbox_1[0],bbox_2[0])\n",
    "    y_1=tf.maximum(bbox_1[1],bbox_2[1])\n",
    "    x_2=tf.maximum(bbox_1[2],bbox_2[2])\n",
    "    y_2=tf.maximum(bbox_1[3],bbox_2[3])\n",
    "    \n",
    "    inter_area=float(max(x_2-x_1,0)*max(y_2-y_1,0))\n",
    "    bbox_1_area=(bbox_1[2]-bbox_1[0])*(bbox_1[3]-bbox_1[1])\n",
    "    bbox_2_area=(bbox_2[2]-bbox_2[0])*(bbox_2[3]-bbox_2[1])1\n",
    "    \n",
    "    union_area=float(bbox_1_area+bbox_2_area-inter_area)\n",
    "    return inter_area/union_area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0804f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def area_polygon(x,y):\n",
    "    area_1,area_2=0,0\n",
    "    for i in range(len(x)-1):\n",
    "        area_1+=x[i]*y[i+1]\n",
    "    area_1+=x[len(x)-1]*y[0]\n",
    "    \n",
    "    for i in range(len(x)-1):\n",
    "        area_2+=x[i+1]*y[i]\n",
    "    area_2+=x[0]*y[len(x)-1]\n",
    "    return 0.5*tf.abs(area_1-area_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f65e3ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_target(val_data):\n",
    "    THRESH=0.5\n",
    "    utils=Utils(7,1,classes)\n",
    "    bounding_boxes,image_shape=utils.preprocess_xml(val_data,m_a_p=True)\n",
    "    y_target=utils.generate_pre_output(bounding_boxes,image_shape)\n",
    "    return y_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d5dc5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean(arr):\n",
    "    summ=0\n",
    "    for i in arr:\n",
    "        summ+=i\n",
    "    return summ/len(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91d0d716",
   "metadata": {},
   "outputs": [],
   "source": [
    "aps=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f0bba25",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_average_precision():\n",
    "    for object_class in classes:\n",
    "        precision=[1.]\n",
    "        recall=[0.]\n",
    "        n_class=0\n",
    "        tp,fp=0,0\n",
    "        for image,bbox in zip(os.listdir(val_images),os.listdir(val_maps)):\n",
    "            y_target=get_target(val_maps+bbox)\n",
    "            for target in y_target:\n",
    "                if target[4]==object_class:\n",
    "                    n_class+=1\n",
    "        for image,bbox in zip(os.listdir(val_images),os.listdir(val_maps)):\n",
    "            y_target=get_target(val_maps+bbox)\n",
    "            y_pred=generate_output(val_images+image)\n",
    "            \n",
    "            for pred in y_pred:\n",
    "                if pred[4]!=object_class:\n",
    "                    pass\n",
    "                else:\n",
    "                    found=False\n",
    "                    for target in y_target[4] and iou(pred[:4],target[:4])>0.5:\n",
    "                        found=True\n",
    "                        break\n",
    "                    if found:\n",
    "                        tp+=1\n",
    "                    else:\n",
    "                        fp+=1\n",
    "                    if tp/(tp+fp)>1e-3 and tp/n_class>1e-3:\n",
    "                        precision.append(tp/(tp+fp))\n",
    "                        reacall.append(min(1.,tp/n_class))\n",
    "        precision.append(0.)\n",
    "        recall.append(max(recall))\n",
    "        precision.append(0.)\n",
    "        recall.append(0.)\n",
    "        \n",
    "        ap_s.append(area_polygon(recall,precision).numpy())\n",
    "    return mean(ap_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b73146b1",
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
   "id": "f5954d55",
   "metadata": {},
   "outputs": [],
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   "execution_count": null,
   "id": "78432d03",
   "metadata": {},
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   "execution_count": null,
   "id": "c1ab44cb",
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   "id": "698846d2",
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================================================
FILE: deprecated/13-Semantic Segmentation by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf### models\n",
    "import numpy as np### math computations\n",
    "import matplotlib.pyplot as plt### plotting bar chart\n",
    "import sklearn### machine learning library\n",
    "import cv2## image processing\n",
    "from google.colab import files\n",
    "from PIL import Image\n",
    "import albumentations as A\n",
    "import tensorflow_datasets as tfds\n",
    "import tensorflow_probability as tfp\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer\n",
    "from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n",
    "                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n",
    "                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n",
    "                                     RandomContrast, Rescaling, Resizing, Reshape,LeakyReLU)\n",
    "from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n",
    "                                        ModelCheckpoint, ReduceLROnPlateau)\n",
    "from tensorflow.keras.regularizers import L2, L1\n",
    "from tensorflow.keras.initializers import RandomNormal\n",
    "from tensorflow.train import BytesList, FloatList, Int64List\n",
    "from tensorflow.train import Example, Features, Feature\n",
    "from google.colab import drive\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65c55e59",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5cf4828",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__ (self, images, maps, batch_size, INPUT_DIM, shuffle = False):\n",
    "\n",
    "        self.images = images\n",
    "        self.maps = maps\n",
    "        self.batch_size = batch_size\n",
    "        self.train_image_list=os.listdir(images)\n",
    "        self.INPUT_DIM=INPUT_DIM\n",
    "\n",
    "    def __len__(self):\n",
    "        return int(np.floor(len(self.train_image_list)/self.batch_size))\n",
    "  \n",
    "    def __getitem__(self, idx):\n",
    "        x,y = self.__data_generation(idx)\n",
    "        y-=1\n",
    "        return np.array(x), np.array(y)\n",
    "  \n",
    "    def __data_generation(self, idx):\n",
    "        x = []\n",
    "        y = []\n",
    "\n",
    "        for j in range(idx*self.batch_size, (idx+1)*self.batch_size):\n",
    "            x.append(img_to_array(load_img(self.images+os.listdir(self.images)[j],target_size=(self.INPUT_DIM,self.INPUT_DIM))))\n",
    "            y.append(img_to_array(load_img(self.maps+os.listdir(self.maps)[j], color_mode='grayscale', target_size=(self.INPUT_DIM//2,self.INPUT_DIM//2))))\n",
    "            \n",
    "    return tf.convert_to_tensor(x),tf.convert_to_tensor(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b125672d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images='...'\n",
    "train_maps='...'\n",
    "val_images='...'\n",
    "val_maps='...'\n",
    "\n",
    "LR=1e-3\n",
    "BATCH_SIZE=4\n",
    "EPOCH=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = DataGenerator(images, maps,BATCH_SIZE,INPUT_DIM)\n",
    "val_gen = DataGenerator(val_images, val_maps,BATCH_SIZE,INPUT_DIM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed044d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_base_model():\n",
    "    base_model = tf.keras.applications.ResNet50(\n",
    "        weights='imagenet',\n",
    "        input_shape=(INPUT_DIM,INPUT_DIM,3),\n",
    "        include_top=False,)\n",
    "    base_model.trainable=False\n",
    "    \n",
    "    conv1_relu,conv2_block3_out,conv3_block4_out,conv4_block6_out,conv5_block3_out=[base_model.get_layer(layer_name).output for layer_name in [\"conv1_relu\",\"conv2_block3_out\",\"conv3_block4_out\",\"conv4_block6_out\",\"conv5_block3_out\"]]\n",
    "    \n",
    "    return Model(\n",
    "        inputs=[base_model.inputs],outputs=[conv1_relu,conv2_block3_out,conv3_block4_out,conv4_block6_out,conv5_block3_out]\n",
    "    )\n",
    "\n",
    "get_base_model().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c35420",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b26eae3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Upsample(tf.keras.layers.Layer):\n",
    "    def __init__(self,NUM_FILTERS):\n",
    "        super(Upsample,self).__init__()\n",
    "        self.conv_t_1=Conv2DTranspose(NUM_FILTERS,1,strides=2,activation='relu')\n",
    "        self.norm_1=BatchNormalization()\n",
    "    def call(self,x):\n",
    "        x=self.norm_1(self.conv_t_1(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03fc0705",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvLayers(tf.keras.layers.Layer):\n",
    "    def __init__(self,NUM_FILTERS):\n",
    "        super(ConvLayers,self).__init__()\n",
    "        self.conv_1=Conv2D(NUM_FILTERS*2,3,padding='same',activation='relu')\n",
    "        self.norm_1=BatchNormalization()\n",
    "        \n",
    "        self.conv_2=Conv2D(NUM_FILTERS*4,3,padding='same',activation='relu')\n",
    "        self.norm_2=BatchNormalization()\n",
    "    def call(self,x):\n",
    "        x=self.norm_1(self.conv_1(x))\n",
    "        x=self.norm_2(self.conv_2(x))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a20591a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=tf.keras.Input(shape=(INPUT_DIM,INPUT_DIM,3))\n",
    "x=Rescaling(1/255.)(inputs)\n",
    "x_112,x_56,x_28,x_7=get_base_model()(x)\n",
    "\n",
    "x=Upsample(NUM_FILTERS)(x_7)\n",
    "x=tf.concat([x,x_14],axis=-1)\n",
    "x=ConvLayers(NUM_FILTERS)(x)\n",
    "\n",
    "\n",
    "x=Upsample(NUM_FILTERS)(x)\n",
    "x=tf.concat([x,x_28],axis=-1)\n",
    "x=ConvLayers(NUM_FILTERS)(x)\n",
    "\n",
    "\n",
    "x=Upsample(NUM_FILTERS)(x)\n",
    "x=tf.concat([x,x_56],axis=-1)\n",
    "x=ConvLayers(NUM_FILTERS)(x)\n",
    "\n",
    "\n",
    "x=Upsample(NUM_FILTERS)(x)\n",
    "x=tf.concat([x,x_112],axis=-1)\n",
    "x=ConvLayers(NUM_FILTERS)(x)\n",
    "\n",
    "out=Conv2D(3,3,padding='same',activation='softmax')(x)\n",
    "model=tf.keras.Model(inputs=inputs,outputs=out)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e26903",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss = tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "    optimizer = Adam(learning_rate = LR),\n",
    "    metrics='accuracy',\n",
    "    run_eagerly = True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8559844",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath = checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac24b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(\n",
    "    train_gen,\n",
    "    verbose=1,\n",
    "    shuffle=True,\n",
    "    epochs=EPOCH,\n",
    "    callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ef4899",
   "metadata": {},
   "source": [
    "<h1>TESTING</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b9da9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image ='...'\n",
    "test_image_map='...'\n",
    "\n",
    "X=[]\n",
    "X.append(img_to_array(load_img(test_image,target_size=(224,224))))\n",
    "image_output=tf.argmax(model.predict(tf.constant(X)),axis=-1)[0]\n",
    "image_output=tf.expand_dims(image_output,axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eac8ba96",
   "metadata": {},
   "outputs": [],
   "source": [
    "image=tf.keras.preprocessing.image.load_img(test_image,color_mode='rgb',target_size=(224,224))\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdb5081f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(image_output[...,0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7118c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c100b414",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
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}


================================================
FILE: deprecated/14-People Counting by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf### models\n",
    "import numpy as np### math computations\n",
    "import matplotlib.pyplot as plt### plotting bar chart\n",
    "import sklearn### machine learning library\n",
    "import cv2## image processing\n",
    "import scipy.io\n",
    "import tensorflow_probability as tfp\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.layers import (GlobalAveragePooling2D, Activation, MaxPooling2D, Add, Conv2D, MaxPool2D, Dense,\n",
    "                                     Flatten, InputLayer, BatchNormalization, Input, Embedding, Permute,\n",
    "                                     Dropout, RandomFlip, RandomRotation, LayerNormalization, MultiHeadAttention,\n",
    "                                     RandomContrast, Rescaling, Resizing, Reshape,LeakyReLU)\n",
    "from tensorflow.keras.losses import BinaryCrossentropy,CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.metrics import Accuracy,TopKCategoricalAccuracy, CategoricalAccuracy, SparseCategoricalAccuracy\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import (Callback, CSVLogger, EarlyStopping, LearningRateScheduler,\n",
    "                                        ModelCheckpoint, ReduceLROnPlateau)\n",
    "from tensorflow.keras.regularizers import L2, L1\n",
    "from tensorflow.keras.initializers import RandomNormal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65c55e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "IN_X,IN_Y=768,1024\n",
    "OUT_X,OUT_Y=96,128\n",
    "SUBSAMPLING_FACTOR=IN_X//OUT_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fbf61a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def gauss_distribution(x,u=0,sigma=10):\n",
    "    return np.expand_dims(1/(np.sqrt(2*np.pi*(sigma**2)))*np.exp(-(0.5)*(((x-u)/sigma)**2)),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5cf4828",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_density_map_gaussian(im,points,gaussian_radius=4):\n",
    "    density_map=np.zeros((OUT_X,OUT_Y))\n",
    "    w,h=OUT_Y,OUT_X\n",
    "    num_gt=len(points)\n",
    "    \n",
    "    for point in points:\n",
    "        point=np.round(point).astype(int)\n",
    "        point[0],point[1]=min(h-1,point[1]),min(w-1,point[0])\n",
    "        x=np.linspace(-gaussian_radius,gaussian_radius,(gaussian_radius*2)+1)\n",
    "        gaussian_map=np.multiply(gauss_distribution(x),gauss_distribution(x).T)\n",
    "        gaussian_map/=np.sum(gaussian_map)\n",
    "        \n",
    "        x_left,x_right,y_up,y_down=0,gaussian_map.shape[1],0,gaussian_map.shape[0]\n",
    "        if point[1]<gaussian_radius:\n",
    "            x_left=gaussian_radius-point[1]\n",
    "        if point[0]<gaussian_radius:\n",
    "            y_up=gaussian_radius-point[0]\n",
    "        if point[1]+gaussian_radius>=w:\n",
    "            x_right=gaussian_map.shape[1]-(gaussian_radius+point[1]-w)-1\n",
    "        if point[0]+gaussian_radius>=h:\n",
    "            y_down=gaussian_map.shape[0]-(gaussian_radius+point[0]-h)-1\n",
    "        density_map[\n",
    "            max(0,point[0]-gaussian_radius):min(density_map.shape[0],point[0]+gaussian_radius+1),\n",
    "            max(0,point[1]-gaussian_radius):min(density_map.shape[1],point[1]+gaussian_radius+1),\n",
    "        ]+=gaussian_map[y_up:y_down,x_left:x_rigtht]\n",
    "    density_map/=np.sum(density_map/len(points))\n",
    "    return density_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__ (self, images, maps, batch_size,SUBSAMPLING_FACTOR=8):\n",
    "\n",
    "        self.images = images\n",
    "        self.maps = maps\n",
    "        self.batch_size = batch_size\n",
    "        self.train_image_list=os.listdir(images)\n",
    "        self.SUBSAMPLING_FACTOR=SUBSAMPLING_FACTOR\n",
    "\n",
    "    def __len__(self):\n",
    "        return int(np.floor(len(self.train_image_list)/self.batch_size))\n",
    "  \n",
    "    def __getitem__(self, idx):\n",
    "        x,y = self.__data_generation(idx)\n",
    "        return x,y\n",
    "  \n",
    "    def __data_generation(self, idx):\n",
    "        x = []\n",
    "        y = []\n",
    "\n",
    "        for j in range(idx*self.batch_size, (idx+1)*self.batch_size):\n",
    "            \n",
    "            im_array=img_to_array(load_img(self.images+os.listdir(self.images)[j],target_size=(IN_X,IN_Y)))\n",
    "            im_array/=255.\n",
    "            im_array[:,:,0]=(im_array[:,:,0]-np.mean(im_array[:,:,0]))/np.std(im_array[:,:,0])\n",
    "            im_array[:,:,1]=(im_array[:,:,1]-np.mean(im_array[:,:,1]))/np.std(im_array[:,:,1])\n",
    "            im_array[:,:,2]=(im_array[:,:,2]-np.mean(im_array[:,:,2]))/np.std(im_array[:,:,2])\n",
    "            x.append(im_array)\n",
    "            mat=scipy.io.loadmat(self.maps+os.listdir(self.maps)[j])\n",
    "            points=mat['image_info'][0][0][0][0][0]\n",
    "            points/=self.SUBSAMPLING_FACTOR\n",
    "            \n",
    "            density_map_present=get_density_map_gaussian(im_array,points,sigma=5)\n",
    "            y.append(density_map_present)\n",
    "    return tf.convert_to_tensor(x),tf.convert_to_tensor(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b125672d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images='...'\n",
    "train_maps='...'\n",
    "val_images='...'\n",
    "val_maps='...'\n",
    "\n",
    "LR=1e-4\n",
    "BATCH_SIZE=1\n",
    "EPOCH=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = DataGenerator(images, maps,BATCH_SIZE,INPUT_DIM)\n",
    "#val_gen = DataGenerator(val_images, val_maps,BATCH_SIZE,INPUT_DIM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed044d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_base_model():\n",
    "    base_model = VGG16(\n",
    "        weights='imagenet',\n",
    "        input_shape=(INPUT_DIM,INPUT_DIM,3),\n",
    "        include_top=False,)\n",
    "    \n",
    "    block4_conv3=[base_model.get_layer(layer_name).output for layer_name in [\"block4_conv3\"]]\n",
    "    \n",
    "    return Model(\n",
    "        inputs=[base_model.inputs],outputs=[block4_conv3]\n",
    "    )\n",
    "\n",
    "get_base_model().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c35420",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09f28485",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=tf.keras.Input(shape=(IN_X,IN_Y,3))\n",
    "x=get_base_model()(inputs)\n",
    "init=RandomNormal(0.01)\n",
    "\n",
    "x=Conv2D(512, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n",
    "x=Conv2D(512, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n",
    "x=Conv2D(512, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n",
    "x=Conv2D(256, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n",
    "x=Conv2D(128, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n",
    "x=Conv2D(64, (3,3), activation = 'relu', dilation_rate=2,kernel_iniitalizer=init,padding='same')(x)\n",
    "x=Conv2D(1, (1,1), activation = 'relu', dilation_rate=1,kernel_iniitalizer=init,padding='same')(x)\n",
    "\n",
    "out=Reshape((96,128))(x)\n",
    "model=tf.keras.Model(inputs=inputs,outputs=out)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e26903",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss = tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "    optimizer = Adam(learning_rate = LR),\n",
    "    metrics='accuracy',\n",
    "    run_eagerly = True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8559844",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath = checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac24b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(\n",
    "    train_gen,\n",
    "    verbose=1,\n",
    "    shuffle=True,\n",
    "    epochs=EPOCH,\n",
    "    callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ef4899",
   "metadata": {},
   "source": [
    "<h1>TESTING</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eac8ba96",
   "metadata": {},
   "outputs": [],
   "source": [
    "IN ='...'\n",
    "\n",
    "im_array=img_to_array(load_img(train_image+IN_'.jpg',target_size=(IN_X,IN_Y)))\n",
    "im_array/=255.\n",
    "im_array[:,:,0]=(im_array[:,:,0]-np.mean(im_array[:,:,0]))/np.std(im_array[:,:,0])\n",
    "im_array[:,:,1]=(im_array[:,:,1]-np.mean(im_array[:,:,1]))/np.std(im_array[:,:,1])\n",
    "im_array[:,:,2]=(im_array[:,:,2]-np.mean(im_array[:,:,2]))/np.std(im_array[:,:,2])\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "plt.imhow(im_array)\n",
    "\n",
    "output=mode.predict(tf.expand_dims(im_array,axis=0))\n",
    "output=np.reshape(output,(OUT_X,OUT_Y))\n",
    "\n",
    "n_people=np.sum(output)\n",
    "mat=scipy.io.loadmat(train_maps+'GT_'+IN+'.mat')\n",
    "points=mat['image_info'][0][0][0][0][0]\n",
    "points/=SUBSAMPLING_FACTOR\n",
    "\n",
    "num_gt=np.squeeze(points).shape[0]\n",
    "print(\"The actual number of people is=\",num_gt)\n",
    "print(\"The predicted number of people is =\",n_people)\n",
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "plt.imshow(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7118c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c100b414",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/15-Machine Translation with Sequence to Sequence Models by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae647638",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras.preprocessing import text_dataset_from_directory\n",
    "from tensorflow.keras.layers import SimpleRNN,Embedding,Input,LSTM,Dense,GRU,Bidirectional,Reshape\n",
    "from tensorflow.data.experimental import AUTOTUNE\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "import datetime\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca3a6fb5",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "731503bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "351faa80",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_EXAMPLES=250\n",
    "VALIDATION_RATIO=1\n",
    "VALIDATION_BRIDGE=int(VALIDATION_RATIO*NUM_EXAMPLES)\n",
    "\n",
    "text_dataset=tf.data.TextLineDataset(path).take(NUM_EXAMPLES)\n",
    "BATCH_SIZE=1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a43cfd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in text_dataset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1ed30cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def selector(input_text):\n",
    "    return tf.strings.split(input_text,'\\t')[0:1],'starttoken'+tf.strings.split(input_text,'\\t')[1:2],tf.strings.split(input_text,'\\t')[1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "467905bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_dataset=text_dataset.map(selector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e95f5feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in text_dataset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa7dc744",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_sentences(input_data):\n",
    "    '''\n",
    "    Task: Preprocess sentences or standardize the sentences\n",
    "    Input: raw reviews\n",
    "    output: standardized reviews\n",
    "    '''\n",
    "    output=tf.strings.lower(input_data)\n",
    "    outputs=tf.strings.regex_replace(output,\"<[^>]+>\",\"\")\n",
    "    outputs=tf.strings.regex_replace(output,\"<[%s]\"%re.esceape(string.punctuation),\" \")\n",
    "    outputs=tf.strings.regex_replace(output,\"  \",\" \")\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caba84fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEQUENCE_LENGTH=10\n",
    "\n",
    "vectorize_input_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fab25b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_pre_output_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52440346",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_output_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f55903ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:x)\n",
    "vectorize_input_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffa77275",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:y)\n",
    "vectorize_pre_output_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "573b0c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:z)\n",
    "vectorize_output_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abc21601",
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCAB_INPUT_SIZE=len(vectorize_input_layer.get_vocabulary())\n",
    "VOCAB_PRE_OUTPUT_SIZE=len(vectorize_pre_output_layer.get_vocabulary())\n",
    "VOCAB_OUTPUT_SIZE=len(vectorize_output_layer.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95d49ebb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorizer(x,y,z):\n",
    "    return {'in1':tf.squeeze(vectorize_input_layer(x),0),'in2':tf.squeeze(vectorize_pre_output_layer(y),0),}, tf.squeeze(vectorize_output_layer(z),0)\n",
    "dataset=text_dataset.map(vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ed69815",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in datset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "521d806f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_pre_output_layer.get_vocabulary()[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "671d266d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.shuffle(NUM_EXAMPLES)\n",
    "train_dataset=dataset.take(VALIDATION_BRIDGE)\n",
    "validation_dataset=dataset.skip(VALIDATION_BRIDGE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbb5623f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=train_dataset.batch(BATCH_SIZE).cache().prefetch(buffer_size=AUTOTUNE)\n",
    "validation_dataset=validation_dataset.batch(BATCH_SIZE).cache().prefetch(buffer_size=AUTOTUNE)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48420778",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f0125b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "EMBEDDING_DIM=8\n",
    "LSTM_ENCODER_HIDDEN_SIZE=300\n",
    "LSTM_DECODER_HIDDEN_SIZE=1000\n",
    "SENTENCE_LENGTH=10\n",
    "\n",
    "inputs=Input(SEQUENCE_LENGTH)\n",
    "pre_out=Input(SEQUENCE_LENGTH)\n",
    "\n",
    "x = Embedding(\n",
    "    VOCAB_INPUT_SIZE,\n",
    "    EMBEDDING_DIM)(inputs)\n",
    "encoder = LSTM(\n",
    "    LSTM_HIDDEN_SIZE,\n",
    "    return_sequences=False,\n",
    "    return_state=True)\n",
    "\n",
    "_,h,c=encoder(x)\n",
    "x=Embedding(VOCAB_OUPUT_SIZE,EMBEDDING_DIM)(pre_out)\n",
    "decoder=LSTM(LSTM_DECODER_HIDDEN_SIZE,\n",
    "            return_state=True,\n",
    "            return_sequences=True)\n",
    "h=Dense(LSTM_DECODER_HIDDEN_SIZE)(h)\n",
    "c=Dense(LSTM_DECODER_HIDDEN_SIZE)(c)\n",
    "\n",
    "x,h,c=decoder(x,[h,c])\n",
    "x=Dense(VOCAB_OUTPUT_SIZE,activation='softmax')(x)\n",
    "model=tf.keras.Model([inputs,pre_out],x)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293d2d7a",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c44594b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BLEU(tf.keras.metrics.Metric):\n",
    "    def __init__(self,name='bleu_score'):\n",
    "        super(BLEU,self).__init__()\n",
    "        self.add=0\n",
    "        self.total=0\n",
    "    def update_state(self,y_true,y_pred,sample_weight=None):\n",
    "        y_true=tf.argmax(y_true,-1)\n",
    "        y_pred=tf.argmax(y_pred,-1)\n",
    "        \n",
    "        for i,j in zip(y_pred,y_true):\n",
    "            tf.autograph.experimental.set_loop_options()\n",
    "            self.total+=tf.math.count_nonzero(i)\n",
    "            for word in i:\n",
    "                if word==0:\n",
    "                    break\n",
    "                for q in range(len(j)):\n",
    "                    if j[q]==0:\n",
    "                        break\n",
    "                    if word==j[q]:\n",
    "                        self.add+=1\n",
    "                        j=tf.boolean_mask(j,[False if y==q else True for y in range(len(j))])\n",
    "                        break\n",
    "    def result(self):\n",
    "        return self.add/self.total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bcd1345",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1e-3\n",
    "EPOCH=100\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(lr=LR,),\n",
    "    metrics=[BLEU()],\n",
    "    run_eagerly=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de75168c",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "log_dir='...'\n",
    "callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2e453e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39975631",
   "metadata": {},
   "outputs": [],
   "source": [
    "history=model.fit(train_dataset, validation_data=validation_dataset,verbose=1,epochs=EPOCH,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca73f6e",
   "metadata": {},
   "source": [
    "<H1>TESTING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "278acaec",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=tf.data.Dataset.from_tensor_slices([['i will try']])\n",
    "init_test_data=tf.data.Dataset.from_tensor_slices([['starttoken']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "050e0387",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_test_data=test_data.map(vectorize_input_layer)\n",
    "pre_output_test_data=init_test_data.map(vectorize_pre_output_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7310c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in input_test_data.take(1):\n",
    "    print(i)\n",
    "    in_1=i\n",
    "for i in pre_output_test_data.take(1):\n",
    "    print(i)\n",
    "    in_2=i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44f28d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_output(in_1,in_2):\n",
    "    return tf.argmax(model.predict([in_1,in_2]),-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5eecdd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "output=get_output(in_1,in_2)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aa3458f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(SEQUENCE_LENGTH):\n",
    "    print(vectorize_output_layer.get_vocabulary()[output[0][i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "058fb38c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8cb584a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2966d9f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52104814",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4f117ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c048c25a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5516bdf9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f217809c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/16-Machine Translation with Attention Models by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras.preprocessing import text_dataset_from_directory\n",
    "from tensorflow.keras.layers import SimpleRNN,Embedding,Input,LSTM,Dense,GRU,Bidirectional,Reshape\n",
    "from tensorflow.data.experimental import AUTOTUNE\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "import datetime\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_EXAMPLES=250\n",
    "VALIDATION_RATIO=1\n",
    "VALIDATION_BRIDGE=int(VALIDATION_RATIO*NUM_EXAMPLES)\n",
    "\n",
    "text_dataset=tf.data.TextLineDataset(path).take(NUM_EXAMPLES)\n",
    "BATCH_SIZE=1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81fcff9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in text_dataset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844da811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def selector(input_text):\n",
    "    return tf.strings.split(input_text,'\\t')[0:1],'starttoken'+tf.strings.split(input_text,'\\t')[1:2],tf.strings.split(input_text,'\\t')[1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "175a35c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_dataset=text_dataset.map(selector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9899c1fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in text_dataset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff89d078",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_sentences(input_data):\n",
    "    '''\n",
    "    Task: Preprocess sentences or standardize the sentences\n",
    "    Input: raw reviews\n",
    "    output: standardized reviews\n",
    "    '''\n",
    "    output=tf.strings.lower(input_data)\n",
    "    outputs=tf.strings.regex_replace(output,\"<[^>]+>\",\"\")\n",
    "    outputs=tf.strings.regex_replace(output,\"<[%s]\"%re.esceape(string.punctuation),\" \")\n",
    "    outputs=tf.strings.regex_replace(output,\"  \",\" \")\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03d03098",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEQUENCE_LENGTH=10\n",
    "\n",
    "vectorize_input_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbb0fec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_pre_output_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afe2d5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_output_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "604c8b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:x)\n",
    "vectorize_input_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e21fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:y)\n",
    "vectorize_pre_output_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b451067c",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:z)\n",
    "vectorize_output_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d72db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCAB_INPUT_SIZE=len(vectorize_input_layer.get_vocabulary())\n",
    "VOCAB_PRE_OUTPUT_SIZE=len(vectorize_pre_output_layer.get_vocabulary())\n",
    "VOCAB_OUTPUT_SIZE=len(vectorize_output_layer.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5720da8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorizer(x,y,z):\n",
    "    return {'in1':tf.squeeze(vectorize_input_layer(x),0),'in2':tf.squeeze(vectorize_pre_output_layer(y),0),}, tf.squeeze(vectorize_output_layer(z),0)\n",
    "dataset=text_dataset.map(vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99833bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in datset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5f0b595",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_pre_output_layer.get_vocabulary()[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6d24afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.shuffle(NUM_EXAMPLES)\n",
    "train_dataset=dataset.take(VALIDATION_BRIDGE)\n",
    "validation_dataset=dataset.skip(VALIDATION_BRIDGE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d3ae967",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_output_layer.get_vocabulary()[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ada7536",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=train_dataset.batch(BATCH_SIZE).cache().prefetch(buffer_size=AUTOTUNE)\n",
    "validation_dataset=validation_dataset.batch(BATCH_SIZE).cache().prefetch(buffer_size=AUTOTUNE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23a325f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21332947",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c1a21ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "EMBEDDING_DIM=8\n",
    "LSTM_ENCODER_HIDDEN_SIZE=300\n",
    "LSTM_DECODER_HIDDEN_SIZE=1000\n",
    "SENTENCE_LENGTH=10\n",
    "\n",
    "inputs=Input(SEQUENCE_LENGTH)\n",
    "pre_out=Input(SEQUENCE_LENGTH)\n",
    "\n",
    "x = Embedding(\n",
    "    VOCAB_INPUT_SIZE,\n",
    "    EMBEDDING_DIM)(inputs)\n",
    "encoder = LSTM(\n",
    "    LSTM_HIDDEN_SIZE,\n",
    "    return_sequences=False,\n",
    "    return_state=True)\n",
    "\n",
    "_,h,c=encoder(x)\n",
    "x=Embedding(VOCAB_OUPUT_SIZE,EMBEDDING_DIM)(pre_out)\n",
    "decoder=LSTM(LSTM_DECODER_HIDDEN_SIZE,\n",
    "            return_state=True,\n",
    "            return_sequences=True)\n",
    "h=Dense(LSTM_DECODER_HIDDEN_SIZE)(h)\n",
    "c=Dense(LSTM_DECODER_HIDDEN_SIZE)(c)\n",
    "\n",
    "x,h,c=decoder(x,[h,c])\n",
    "x=Dense(VOCAB_OUTPUT_SIZE,activation='softmax')(x)\n",
    "model=tf.keras.Model([inputs,pre_out],x)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "179f8fbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BahdanauAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,units):\n",
    "        super(BahdanauAttention,self).__init__()\n",
    "        self.w_q=tf.keras.layers.Dense(units)\n",
    "        self.w_k=tf.keras.layers.Dense(units)\n",
    "        self.w=tf.keras.layers.Dense(1)\n",
    "    def call(self,query,keys):\n",
    "        values=keys\n",
    "        energy=self.w(tf.nn.tanh(self.w_q(query)+self.w_k(keys)))\n",
    "        attention_weights=tf.nn.softmax(energy,axis=1)\n",
    "        context_vector=attention_weights*values\n",
    "        context_vector=tf.reduce_sum(context_vector,axis=1)\n",
    "        return tf.expand_dims(context_vector,1),attention_weights\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c92539c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(tf.keras.Model):\n",
    "    def __init__(self,vocab_size,embedding_dim,units):\n",
    "        super(Encoder,self).__init__()\n",
    "        self.embedding=Embedding(vocab_size,embedding_dim)\n",
    "        self.lstm=LSTM(units,return_sequences=True)\n",
    "    def call(self,x):\n",
    "        x=self.embedding(x)\n",
    "        output=self.lstm(x)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8570d74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb98d132",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a12b4822",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24bd14ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BLEU(tf.keras.metrics.Metric):\n",
    "    def __init__(self,name='bleu_score'):\n",
    "        super(BLEU,self).__init__()\n",
    "        self.add=0\n",
    "        self.total=0\n",
    "    def update_state(self,y_true,y_pred,sample_weight=None):\n",
    "        y_true=tf.argmax(y_true,-1)\n",
    "        y_pred=tf.argmax(y_pred,-1)\n",
    "        \n",
    "        for i,j in zip(y_pred,y_true):\n",
    "            tf.autograph.experimental.set_loop_options()\n",
    "            self.total+=tf.math.count_nonzero(i)\n",
    "            for word in i:\n",
    "                if word==0:\n",
    "                    break\n",
    "                for q in range(len(j)):\n",
    "                    if j[q]==0:\n",
    "                        break\n",
    "                    if word==j[q]:\n",
    "                        self.add+=1\n",
    "                        j=tf.boolean_mask(j,[False if y==q else True for y in range(len(j))])\n",
    "                        break\n",
    "    def result(self):\n",
    "        return self.add/self.total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a92b630",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1e-3\n",
    "EPOCH=100\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(lr=LR,),\n",
    "    #metrics=[BLEU()],\n",
    "    #run_eagerly=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de3bd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "log_dir='...'\n",
    "callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6646752a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35659bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "history=model.fit(train_dataset, validation_data=validation_dataset,verbose=1,epochs=EPOCH,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b927a112",
   "metadata": {},
   "source": [
    "<H1>TESTING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b81e8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=tf.data.Dataset.from_tensor_slices([['i will try']])\n",
    "init_test_data=tf.data.Dataset.from_tensor_slices([['starttoken']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ac6766",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_test_data=test_data.map(vectorize_input_layer)\n",
    "pre_output_test_data=init_test_data.map(vectorize_pre_output_layer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b009cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in input_test_data.take(1):\n",
    "    print(i)\n",
    "    in_1=i\n",
    "for i in pre_output_test_data.take(1):\n",
    "    print(i)\n",
    "    in_2=i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3257e382",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_output(in_1,in_2):\n",
    "    return tf.argmax(model.predict([in_1,in_2]),-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a42ae0f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "output=get_output(in_1,in_2)\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f9e3099",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(SEQUENCE_LENGTH):\n",
    "    print(vectorize_output_layer.get_vocabulary()[output[0][i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89ba3d2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea112c44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29368c3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7107a3d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d941f4f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f8bfffe",
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0694be1",
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0804f5b",
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   "language": "python",
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    "name": "ipython",
    "version": 3
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================================================
FILE: deprecated/17-Machine Translation with Transformers by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras.preprocessing import text_dataset_from_directory\n",
    "from tensorflow.keras.layers import(SimpleRNN,Embedding,Input,LSTM,Input,\n",
    "                                    Dropout,Dense,GRU,LayerNormalization,\n",
    "                                    Bidirectional,Reshape)\n",
    "from tensorflow.data.experimental import AUTOTUNE\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "import datetime\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_EXAMPLES=250\n",
    "VALIDATION_RATIO=1\n",
    "VALIDATION_BRIDGE=int(VALIDATION_RATIO*NUM_EXAMPLES)\n",
    "\n",
    "text_dataset=tf.data.TextLineDataset(path).take(NUM_EXAMPLES)\n",
    "BATCH_SIZE=1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81fcff9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in text_dataset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844da811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def selector(input_text):\n",
    "    return tf.strings.split(input_text,'\\t')[0:1],'starttoken'+tf.strings.split(input_text,'\\t')[1:2],tf.strings.split(input_text,'\\t')[1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "175a35c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_dataset=text_dataset.map(selector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9899c1fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in text_dataset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff89d078",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_sentences(input_data):\n",
    "    '''\n",
    "    Task: Preprocess sentences or standardize the sentences\n",
    "    Input: raw reviews\n",
    "    output: standardized reviews\n",
    "    '''\n",
    "    output=tf.strings.lower(input_data)\n",
    "    outputs=tf.strings.regex_replace(output,\"<[^>]+>\",\"\")\n",
    "    outputs=tf.strings.regex_replace(output,\"<[%s]\"%re.esceape(string.punctuation),\" \")\n",
    "    outputs=tf.strings.regex_replace(output,\"  \",\" \")\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03d03098",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEQUENCE_LENGTH=10\n",
    "\n",
    "vectorize_input_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5a0972d",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_pre_output_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbb0fec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_output_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=SEQUENCE_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "604c8b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:x)\n",
    "vectorize_input_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ea6375e",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:y)\n",
    "vectorize_pre_output_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e21fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z:z)\n",
    "vectorize_output_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b449d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCAB_INPUT_SIZE=len(vectorize_input_layer.get_vocabulary())\n",
    "VOCAB_PRE_OUTPUT_SIZE=len(vectorize_pre_output_layer.get_vocabulary())\n",
    "VOCAB_OUTPUT_SIZE=len(vectorize_output_layer.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5720da8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorizer(x,y,z):\n",
    "    return {'in1':tf.squeeze(vectorize_input_layer(x),0),'in2':tf.squeeze(vectorize_pre_output_layer(y),0),}, tf.squeeze(vectorize_output_layer(z),0)\n",
    "dataset=text_dataset.map(vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f0b012b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64007b3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.shuffle(NUM_EXAMPLES)\n",
    "train_dataset=dataset.take(VALIDATION_BRIDGE)\n",
    "validation_dataset=dataset.skip(VALIDATION_BRIDGE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ada7536",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=train_dataset.batch(BATCH_SIZE).cache().prefetch(buffer_size=AUTOTUNE)\n",
    "validation_dataset=validation_dataset.batch(BATCH_SIZE).cache().prefetch(buffer_size=AUTOTUNE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23a325f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_output_layer.get_vocabulary()[1372]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21332947",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c1a21ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SelfAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,model_size):\n",
    "        super(SelfAttention,self).__init__()\n",
    "        self.model_size=model_size\n",
    "    def call(self,query,key,value,sequence,look_ahead_masking=False):\n",
    "        #score=tf.matmul(query,key,transpose_b=True)\n",
    "        score=tf.einsum('ijk,ibk->ijb',query,key)\n",
    "        score/=tf.math.sqrt(tf.cast(self.model_size,tf.float32))\n",
    "        ones=tf.ones_like(score)\n",
    "        pad_mask=padding_mask(sequence)\n",
    "        \n",
    "        total_mask=pad_mask\n",
    "        if look_ahead_masking:\n",
    "            ahead_mask=1-tf.linalg.band_part(ones,-1,0)\n",
    "            total_mask+=ahead_mask\n",
    "        score+=total_mask*-1e10\n",
    "        alignment=tf.nn.softmax(score,axis=-1)\n",
    "        head=tf.matmul(alignment,value)\n",
    "        return head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ce12e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def padding_mask(a):\n",
    "    return tf.expand_dims(tf.cast(tf.math.equal([a],0),tf.float32)[0],axis=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b00fe86",
   "metadata": {},
   "outputs": [],
   "source": [
    "def positional_embedding(model_size):\n",
    "    output=[]\n",
    "    for pos in range(SEQUENCE_LENGTH):\n",
    "        PE=np.zeros((model_size))\n",
    "        for i in range(model_size):\n",
    "            if i%2==0:\n",
    "                PE[i]=np.sin(pos/(10000**(i/model_size)))\n",
    "            else:\n",
    "                PE[i]=np.cos(pos/(10000**((i-1)/model_size)))\n",
    "        output.append(tf.expand_dims(PE,axis=0))\n",
    "    return tf.concat(output,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f73ac783",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,model_size,h):\n",
    "        super(MultiHeadAttention,self).__init__()\n",
    "        self.query_size=model_size//h\n",
    "        self.key_size=model_size//h\n",
    "        self.value_size=model_size//h\n",
    "        self.h=h\n",
    "        self.dense_q=[Dense(self.query_size) for _ in range(h)]\n",
    "        self.dense_k=[Dense(self.key_size) for _ in range(h)]\n",
    "        self.dense_v=[Dense(self.value_size) for _ in range(h)]\n",
    "        self.dense_o=Dense(model_size)\n",
    "        self.self_attention=SelfAttention(self,key_size)\n",
    "        \n",
    "    def call(self,query,key,value,sequence,look_ahead_masking):\n",
    "        heads=[]\n",
    "        \n",
    "        for i in range(self.h):\n",
    "            head=self.self_attention(self.dense_q[i](query),self.dense_k[i](key),\n",
    "                                    self.dense_v[i](value),sequence,look_ahead_masking)\n",
    "            heads.append(head)\n",
    "        heads=tf.concat(heads,axis=2)\n",
    "        heads=self.dense_o(heads)\n",
    "        return heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self,vocab_size,model_size,h):\n",
    "        super(EncoderLayer,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.h=h\n",
    "        self.embedding=Embedding(vocab_size,model_size)\n",
    "        self.multi_attention=MultiHeadAttention(model_size,h)\n",
    "        self.dropout=Dropout(0.2)\n",
    "        \n",
    "        self.dense_1=Dense(model_size*4,activation='relu')\n",
    "        self.dense_2=Dense(model_size)\n",
    "        self.feed_forward_norm=LayerNormalization()\n",
    "        \n",
    "    def call(self,enc_in,sequence):\n",
    "        enc_out=self.multi_attention(enc_in,enc_in,enc_in,sequence,look_ahead_masking=False)\n",
    "        enc_out=enc_in+enc_out\n",
    "        enc_out=self.attention_norm(enc_out)\n",
    "        \n",
    "        feed_forward_in=enc_out\n",
    "        feed_forward_out=self.dropout(self.dense_2(self.dense_1(feed_forward_in)))\n",
    "        feed_forward_out+=feed_forward_in\n",
    "        feed_forward_out=self.feed_forward_norm(feed_forward_out)\n",
    "        return feed_forward_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c474bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(tf.keras.layers.Layer):\n",
    "    def __init__(self,vocab_size,model_size,h,num_layers):\n",
    "        super(Encoder,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.num_layers=num_layers\n",
    "        self.h=h\n",
    "        self.embedding=Embedding(vocab_size,model_size)\n",
    "        self.encoder_layer=[EncoderLayer(vocab_size,model_size,h) for _ in range(num_layers)]\n",
    "        \n",
    "    def call(self, sequence):\n",
    "        enc_in=self.embedding(sequence)\n",
    "        enc_in+=tf.cast(positional_embedding(self.model_size),dtype=tf.float32)\n",
    "        \n",
    "        for i in range(self.num_layers):\n",
    "            out=self.encoder_layer[i](enc_in,sequence)\n",
    "            enc_in=out\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98acdf9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self,model_size,num_layers,h):\n",
    "        super(DecoderLayer,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.num_layers=num_layers\n",
    "        self.h=h\n",
    "        \n",
    "        self.multi_attention_bot=MultiHeadAttention(model_size,h)\n",
    "        self.attetnion_bot_norm=LayerNormalization()\n",
    "        \n",
    "        self.multi_attention_mid=MultiHeadAttention(model_size,h)\n",
    "        self.attetnion_mid_norm=LayerNormalization()\n",
    "        \n",
    "        self.dense_1=Dense(model_size*4,activation='relu')\n",
    "        self.dense_2=Dense(model_size)\n",
    "        self.dropout=Dropout(0.2)\n",
    "        \n",
    "        self.feed_forward_norm=LayerNormalization()\n",
    "        \n",
    "    def call(self,enc_in,sequence):\n",
    "        bot_dec_out=self.multi_attention_bot(bot_dec_in,bot_dec_in,bot_dec_in,sequence,look_ahead_masking=True)\n",
    "        bot_dec_out+=bot_dec_in\n",
    "        bot_dec_out=self.attention_bot_norm(bot_dec_out)\n",
    "        \n",
    "        mid_dec_in=bot_dec_out\n",
    "        \n",
    "        mid_dec_out=self.multi_attention_mid(mid_dec_in,mid_dec_in,mid_dec_in,sequence,look_ahead_masking=False)\n",
    "        mid_dec_out+=mid_dec_in\n",
    "        mid_dec_out=self.attention_mid_norm(mid_dec_out)\n",
    "        \n",
    "        feed_forward_in=mid_dec_out\n",
    "        \n",
    "        feed_forward_out=self.dropout(self.dense_2(self.dense_1(feed_forward_in)))\n",
    "        feed_forward_out+=feed_forward_in\n",
    "        feed_forward_out=self.feed_forward_norm(feed_forward_out)\n",
    "        return feed_forward_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94f9e0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(tf.keras.layers.Layer):\n",
    "    def __init__(self,vocab_size,model_size,h,num_layers):\n",
    "        super(Decoder,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.num_layers=num_layers\n",
    "        self.h=h\n",
    "        self.embedding=Embedding(pre_vocab_size,model_size)\n",
    "        self.decoder_layer=[DecoderLayer(model_size,num_layers,h) for _ in range(num_layers)]\n",
    "        self.dense=Dense(vocab_size,)\n",
    "        \n",
    "    def call(self, sequence,encoder_output):\n",
    "        dec_in=self.embedding(sequence)\n",
    "        dec_in+=tf.cast(positional_embedding(self.model_size),dtype=tf.float32)\n",
    "        \n",
    "        for i in range(self.num_layers):\n",
    "            out=self.decoder_layer[i](dec_in,encoder_output,sequence)\n",
    "            dec_in=out\n",
    "        out=self.dense(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70ec891a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Transformer(tf.keras.Model):\n",
    "    def __init__(self,VOCAB_INPUT_SIZE,VOCAB_PRE_OUTPUT_SIZE,VOCAB_OUTPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS):\n",
    "        super(Transformer,self).__init__()\n",
    "        \n",
    "        self.encoder=Encoder(\n",
    "            vocab_size=VOCAB_INPUT_SIZE,\n",
    "            model_size=MODEL_SIZE,\n",
    "            h=NUM_HEADS,\n",
    "            num_layers=NUM_LAYERS,\n",
    "        )\n",
    "        \n",
    "        self.decoder=Decoder(\n",
    "            pre_vocab_size=VOCAB_PRE_OUTPUT_SIZE,\n",
    "            vocab_size=VOCAB_OUTPUT_SIZE,\n",
    "            model_size=MODEL_SIZE,\n",
    "            h=NUM_HEADS,\n",
    "            num_layers=NUM_LAYERS,\n",
    "        )\n",
    "        \n",
    "    def call(self, inputs,pre_outputs):\n",
    "        x=self.encoder(inputs)\n",
    "        x=self.decoder(pre_outputs,x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67da6374",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=Input(SEQUENCE_LENGTH)\n",
    "pre_outputs=Input(SEQUENCE_LENGTH)\n",
    "\n",
    "transformer=Transformer(VOCAB_INPUT_SIZE,VOCAB_PRE_OUTPUT_SIZE,VOCAB_OUTPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS)\n",
    "decoder_output=transformer(inputs,pre_outputs)\n",
    "model=tf.keras.Model([inputs,pre_outputs],decoder_output,name='transformer')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a12b4822",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24bd14ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BLEU(tf.keras.metrics.Metric):\n",
    "    def __init__(self,name='bleu_score'):\n",
    "        super(BLEU,self).__init__()\n",
    "        self.add=0\n",
    "        self.total=0\n",
    "    def update_state(self,y_true,y_pred,sample_weight=None):\n",
    "        y_true=tf.argmax(y_true,-1)\n",
    "        y_pred=tf.argmax(y_pred,-1)\n",
    "        \n",
    "        for i,j in zip(y_pred,y_true):\n",
    "            tf.autograph.experimental.set_loop_options()\n",
    "            self.total+=tf.math.count_nonzero(i)\n",
    "            for word in i:\n",
    "                if word==0:\n",
    "                    break\n",
    "                for q in range(len(j)):\n",
    "                    if j[q]==0:\n",
    "                        break\n",
    "                    if word==j[q]:\n",
    "                        self.add+=1\n",
    "                        j=tf.boolean_mask(j,[False if y==q else True for y in range(len(j))])\n",
    "                        break\n",
    "    def result(self):\n",
    "        return self.add/self.total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a92b630",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1e-3\n",
    "EPOCH=100\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(lr=LR,),\n",
    "    #metrics=[BLEU()],\n",
    "    #run_eagerly=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de3bd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "log_dir='...'\n",
    "callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6646752a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35659bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "history=model.fit(train_dataset, validation_data=validation_dataset,verbose=1,epochs=EPOCH,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b927a112",
   "metadata": {},
   "source": [
    "<H1>TESTING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b81e8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate(input_sentence):\n",
    "    print('Input:-->',input_sentence)\n",
    "    test_data=tf.data.Dataset.from_tensor_slices([[input_sentence]])\n",
    "    input_test_data=test_data.map(vectorize_input_layer)\n",
    "    \n",
    "    for i in input_test_data.take(1):\n",
    "        in_1=i\n",
    "    in_2=[2]\n",
    "    final_output=[]\n",
    "    length=SEQUENCE_LENGTH\n",
    "    \n",
    "    for i in range(SEQUENCE_LENGTH):\n",
    "        p_in_2=tf.pad(tf.constant(in_2),[[0,SEQUENCE_LENGTH-1-I]])\n",
    "        output=tf.argmax(model.predict([[in_1],tf.expand_dims(p_in_2,0)]),-1)[0][i]\n",
    "        if output==0:\n",
    "            length=i\n",
    "            break\n",
    "        in_2.append(output.numpy()+1)\n",
    "        final_output.append(output.numpy())\n",
    "        \n",
    "    return [vectorize_output_layer.get_vocabulary()[i] for i in final_output]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ac6766",
   "metadata": {},
   "outputs": [],
   "source": [
    "translate('we won')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b009cf5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3257e382",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a42ae0f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f9e3099",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89ba3d2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea112c44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29368c3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7107a3d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d941f4f9",
   "metadata": {},
   "outputs": [],
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   "cell_type": "code",
   "execution_count": null,
   "id": "1f8bfffe",
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   "outputs": [],
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   "execution_count": null,
   "id": "c0694be1",
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   "cell_type": "code",
   "execution_count": null,
   "id": "b0804f5b",
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================================================
FILE: deprecated/18-Question Answering by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras.preprocessing import text_dataset_from_directory\n",
    "from tensorflow.keras.layers import(SimpleRNN,Embedding,Input,LSTM,Input,\n",
    "                                    Dropout,Dense,GRU,LayerNormalization,\n",
    "                                    Bidirectional,Reshape)\n",
    "from tensorflow.data.experimental import AUTOTUNE\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "import datetime\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19de4bb7",
   "metadata": {},
   "source": [
    "<h1>Utils</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0760ea94",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('...') as f:\n",
    "    data=json.load(f)\n",
    "def get_position(a,b):\n",
    "    b=list(b)\n",
    "    if '' in b:\n",
    "        b.remove('')\n",
    "    j=0\n",
    "    found=0\n",
    "    for i in range(len(a)):\n",
    "        if b[j]==a[i]:\n",
    "            j+=1\n",
    "            if len(b)-1 <j:\n",
    "                found=i\n",
    "                break\n",
    "        else:\n",
    "            j=0\n",
    "    return found-len(b)+1,found\n",
    "\n",
    "f=open('...','a',encoding='utf-8')\n",
    "\n",
    "for i in range(1):\n",
    "    for j in range(1):\n",
    "        for k in range(len(data[\"data\"][i][\"paragraphs\"][j][\"qas\"])):\n",
    "            if data[\"data\"][i][\"paragraphs\"][j][\"qas\"][k][\"answers\"]==[]:\n",
    "                pass\n",
    "            else:\n",
    "                init,final=get_position(tf.strings.split(preprocess_sentences(data[\"data\"][i][\"paragraphs\"][j][\"context\"])),\n",
    "                                tf.strings.split(preprocess_sentences(data[\"data\"][i][\"paragraphs\"][j][\"qas\"][k][\"answers\"][0]['text'])))\n",
    "            \n",
    "                f.write(data[\"data\"][i][\"paragraphs\"][j][\"context\"])\n",
    "                f.write(\"\\t\")\n",
    "                \n",
    "                f.write(data[\"data\"][i][\"paragraphs\"][j][\"qas\"][k][\"question\"])\n",
    "                f.write(\"\\t\")\n",
    "                \n",
    "                f.write(str(init))\n",
    "                f.write(\"\\t\")\n",
    "                \n",
    "                f.write(str(final))\n",
    "                f.write(\"\\n\")\n",
    "    if i%10==0:\n",
    "        print(i)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('...') as f:\n",
    "    data=json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81fcff9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_EXAMPLES=1000\n",
    "VALIDATION_RATIO=1\n",
    "VALIDATION_BRIDGE=int(VALIDATION_RATIO*NUM_EXAMPLES)\n",
    "\n",
    "text_dataset=tf.data.TextLineDataset(path).take(NUM_EXAMPLES)\n",
    "BATCH_SIZE=1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844da811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def selector(input_text):\n",
    "    return tf.strings.split(input_text,'\\t')[0:1],tf.strings.split(input_text,'\\t')[1:2],tf.strings.split(input_text,'\\t')[2:3],tf.strings.split(input_text,'\\t')[3:4],tf.strings.split(input_text,'\\t')[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "175a35c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_dataset=text_dataset.map(selector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9899c1fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in text_dataset.take(1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff89d078",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_sentences(input_data):\n",
    "    '''\n",
    "    Task: Preprocess sentences or standardize the sentences\n",
    "    Input: raw reviews\n",
    "    output: standardized reviews\n",
    "    '''\n",
    "    output=tf.strings.lower(input_data)\n",
    "    outputs=tf.strings.regex_replace(output,\"<[^>]+>\",\"\")\n",
    "    outputs=tf.strings.regex_replace(output,\"<[%s]\"%re.esceape(string.punctuation),\" \")\n",
    "    outputs=tf.strings.regex_replace(output,\"  \",\" \")\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03d03098",
   "metadata": {},
   "outputs": [],
   "source": [
    "CONTEXT_LENGTH=150\n",
    "QUESTION_LENGTH=20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "778ecc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_layer=TextVectorization(\n",
    "    standardize=preprocess_sentences,\n",
    "    output_sequence_length=CONTEXT_LENGTH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbb0fec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data=text_dataset.map(lambda x,y,z,w,h:h)\n",
    "vectorize_layer.adapt(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "604c8b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCAB_INPUT_SIZE=len(vectorize_layer.get_vocabulary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c038264",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorizer(x,y,z,w,h):\n",
    "    return {'in1':tf.squeeze(vectorize_layer(x),0),'in2':tf.squeeze(vectorize_layer(y),0)[0:QUESTION_LENGTH],}, {'out1':tf.cast(tf.squeeze(tf.one_hot(int(z),CONTEXT_LENGTH),0),tf.int32),'out2':tf.cast(tf.squeeze(tf.one_hot(int(w),CONTEXT_LENGTH),0),tf.int32)}\n",
    "dataset=text_dataset.map(vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38e21fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in dataset.take(1):\n",
    "    print(i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b449d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=dataset.shuffle(NUM_EXAMPLES)\n",
    "train_dataset=dataset.take(VALIDATION_BRIDGE)\n",
    "validation_dataset=dataset.skip(VALIDATION_BRIDGE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5720da8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=train_dataset.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n",
    "validation_dataset=validation_dataset.batch(BATCH_SIZE).prefetch(buffer_size=AUTOTUNE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21332947",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c1a21ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SelfAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,model_size):\n",
    "        super(SelfAttention,self).__init__()\n",
    "        self.model_size=model_size\n",
    "    def call(self,query,key,value,sequence=None):\n",
    "        score=tf.einsum('ijk,ibk->ijb',query,key)\n",
    "        score/=tf.math.sqrt(tf.cast(self.model_size,tf.float32))\n",
    "        \n",
    "        if sequence is not None:\n",
    "            ones=tf.ones_like(score)\n",
    "            pad_mask=padding_mask(sequence)\n",
    "\n",
    "            total_mask=pad_mask\n",
    "            score+=total_mask*-1e10\n",
    "        alignment=tf.nn.softmax(score,axis=-1)\n",
    "        head=tf.matmul(alignment,value)\n",
    "        return head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ce12e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def padding_mask(a):\n",
    "    return tf.expand_dims(tf.cast(tf.math.equal([a],0),tf.float32)[0],axis=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b00fe86",
   "metadata": {},
   "outputs": [],
   "source": [
    "def positional_embedding(model_size):\n",
    "    output=[]\n",
    "    for pos in range(SEQUENCE_LENGTH):\n",
    "        PE=np.zeros((model_size))\n",
    "        for i in range(model_size):\n",
    "            if i%2==0:\n",
    "                PE[i]=np.sin(pos/(10000**(i/model_size)))\n",
    "            else:\n",
    "                PE[i]=np.cos(pos/(10000**((i-1)/model_size)))\n",
    "        output.append(tf.expand_dims(PE,axis=0))\n",
    "    return tf.concat(output,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f73ac783",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,model_size,h):\n",
    "        super(MultiHeadAttention,self).__init__()\n",
    "        self.query_size=model_size//h\n",
    "        self.key_size=model_size//h\n",
    "        self.value_size=model_size//h\n",
    "        self.h=h\n",
    "        self.dense_q=[Dense(self.query_size) for _ in range(h)]\n",
    "        self.dense_k=[Dense(self.key_size) for _ in range(h)]\n",
    "        self.dense_v=[Dense(self.value_size) for _ in range(h)]\n",
    "        self.dense_o=Dense(model_size)\n",
    "        self.self_attention=SelfAttention(self,key_size)\n",
    "        \n",
    "    def call(self,query,key,value,sequence=None):\n",
    "        heads=[]\n",
    "        \n",
    "        for i in range(self.h):\n",
    "            head=self.self_attention(self.dense_q[i](query),self.dense_k[i](key),\n",
    "                                    self.dense_v[i](value),sequence)\n",
    "            heads.append(head)\n",
    "        heads=tf.concat(heads,axis=2)\n",
    "        heads=self.dense_o(heads)\n",
    "        return heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self,vocab_size,model_size,h):\n",
    "        super(EncoderLayer,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.h=h\n",
    "        self.embedding=Embedding(vocab_size,model_size)\n",
    "        self.multi_attention=MultiHeadAttention(model_size,h)\n",
    "        self.dropout=Dropout(0.2)\n",
    "        \n",
    "        self.dense_1=Dense(model_size*4,activation='relu')\n",
    "        self.dense_2=Dense(model_size)\n",
    "        self.feed_forward_norm=LayerNormalization()\n",
    "        \n",
    "    def call(self,enc_in,sequence=None):\n",
    "        enc_out=self.multi_attention(enc_in,enc_in,enc_in,sequence,)\n",
    "        enc_out=enc_in+enc_out\n",
    "        enc_out=self.attention_norm(enc_out)\n",
    "        \n",
    "        feed_forward_in=enc_out\n",
    "        feed_forward_out=self.dropout(self.dense_2(self.dense_1(feed_forward_in)))\n",
    "        feed_forward_out+=feed_forward_in\n",
    "        feed_forward_out=self.feed_forward_norm(feed_forward_out)\n",
    "        return feed_forward_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8559844",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(tf.keras.layers.Layer):\n",
    "    def __init__(self,vocab_size,model_size,h,num_layers):\n",
    "        super(Encoder,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.num_layers=num_layers\n",
    "        self.h=h\n",
    "        self.embedding=Embedding(vocab_size,model_size)\n",
    "        self.encoder_layer=[EncoderLayer(vocab_size,model_size,h) for _ in range(num_layers)]\n",
    "        \n",
    "    def call(self, sequence):\n",
    "        enc_in=self.embedding(sequence)\n",
    "        \n",
    "        for i in range(self.num_layers):\n",
    "            out=self.encoder_layer[i](enc_in,sequence)\n",
    "            enc_in=out\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac24b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderTop(tf.keras.layers.Layer):\n",
    "    def __init__(self,vocab_size,model_size,h,num_layers):\n",
    "        super(EncoderTop,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.num_layers=num_layers\n",
    "        self.h=h\n",
    "        self.embedding=Embedding(vocab_size,model_size)\n",
    "        self.encoder_layer=[EncoderLayer(vocab_size,model_size,h) for _ in range(num_layers)]\n",
    "        \n",
    "    def call(self, enc_in):\n",
    "        \n",
    "        for i in range(self.num_layers):\n",
    "            out=self.encoder_layer[i](enc_in,None)\n",
    "            enc_in=out\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ae0851",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CQAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,vocab_size,model_size,h,num_layers):\n",
    "        super(CQAttention,self).__init__()\n",
    "        \n",
    "        \n",
    "    def call(self, enc_in):\n",
    "        score=tf.einsum('BNC,BCD->BND',context,query)\n",
    "        score=tf.nn.softmax(score,axis=-2)\n",
    "        \n",
    "        return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b9da9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_SIZE=256\n",
    "NUM_HEADS=8\n",
    "NUM_LAYERS=3\n",
    "\n",
    "in_1=Input(CONTEXT_LENGTH)\n",
    "in_1=Input(QUESTION_LENGTH)\n",
    "\n",
    "enc_1_out=Encoder(VOCAB_INPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS)(in_1)\n",
    "enc_2_out=Encoder(VOCAB_INPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS)(in_2)\n",
    "\n",
    "out=CQAttention()(enc_1_out,enc_2_out)\n",
    "\n",
    "out_1=EncoderTop(VOCAB_INPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS)(out)\n",
    "out_2=EncoderTop(VOCAB_INPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS)(out_1)\n",
    "out_3=EncoderTop(VOCAB_INPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS)(out_2)\n",
    "out_4=EncoderTop(VOCAB_INPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS)(out_3)\n",
    "\n",
    "head_start=tf.concat([out_1,out_2,out_3],axis=-1)\n",
    "head_end=tf.concat([out_1,out_4],axis=-1)\n",
    "\n",
    "head_start=tf.nn.softmax(Dense(1)(head_start),axis=-2)\n",
    "head_end=tf.nn.softmax(Dense(1)(head_end),axis=-2)\n",
    "\n",
    "out_4=EncoderTop(VOCAB_INPUT_SIZE,MODEL_SIZE,NUM_HEADS,NUM_LAYERS)(out_3)\n",
    "\n",
    "out={\"out1\":tf.squeeze(head_start,axis=-1),\"out2\":tf.squeeze(head_end,-1)}\n",
    "model=tf.keras.Model([in_1,in_2],out,name='CQANet')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73e0192b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a12b4822",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24bd14ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f1_score(y_true,y_pred):\n",
    "    precision=tf.keras.metrics.Precision()(y_true,y_pred)\n",
    "    recall=tf.keras.metrics.Recall()(y_true,y_pred)\n",
    "    return 2*precision*recall/(precision+recall+1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a92b630",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1e-4\n",
    "EPOCH=1000\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(lr=LR,),\n",
    "    metrics=f1_score,\n",
    "    run_eagerly=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de3bd4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "log_dir='...'\n",
    "callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35659bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "history=model.fit(train_dataset,verbose=1,epochs=EPOCH,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b927a112",
   "metadata": {},
   "source": [
    "<H1>TESTING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b81e8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "context=\"...\"\n",
    "question=\"...\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7ac6766",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_1=tf.data.Dataset.from_tensor_slices([[context]])\n",
    "test_data_2=tf.data.Dataset.from_tensor_slices([[question]])\n",
    "\n",
    "input_test_data_1=test_data_1.map(vectorize_layer)\n",
    "input_test_data_2=test_data_2.map(vectorize_layer)\n",
    "\n",
    "for i in input_test_data_1.take(1):\n",
    "    in_1=i\n",
    "for i in input_test_data_2.take(1):\n",
    "    in_2=i[:,0:QUESTION_LENGTH]\n",
    "    \n",
    "start=tf.argmax(model.predict([in_1,in_2])['out1'],-1)[0].numpy()\n",
    "end=tf.argmax(model.predict([in_1,in_2])['out2'],-1)[0].numpy()\n",
    "\n",
    "out=tf.strings.split(preprocess_sentences(context),' ')[start:end+1]\n",
    "\n",
    "answer=\"\"\n",
    "for i in out:\n",
    "    answer+=i\n",
    "    answer+=\" \"\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b009cf5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3257e382",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9761628d",
   "metadata": {},
   "source": [
    "<h1>LSHAttention</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f9e3099",
   "metadata": {},
   "outputs": [],
   "source": [
    "def look_one_back(x):\n",
    "    x_extra=tf.concat([x[:,-1:,...],x[:,:-1,...]],axis=1)\n",
    "    return tf.concat([x,x_extra],axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac6f6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sticker_look_one_back(x):\n",
    "    x_extra=tf.concat([x[:-1:],x[:,:-1]],axis=1)\n",
    "    return tf.concat([x,x_extra],axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89ba3d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def causal_masker(a,b):\n",
    "    a,b=tf.cast(a,dtype=tf.float32)+0.01,tf.cast(b,dtype=tf.float32)+0.01\n",
    "    vals=tf.einsum('ipj,ipk->ipjk',b,1/a)\n",
    "    out=tf.cast(tf.cast(tf.cast(vals,dtype=tf.int32),dtype=tf.bool),dtype=tf.int32)\n",
    "    out=-out+1\n",
    "    return tf.cast(out,dtype=tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea112c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSHAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,bucket_size=8,n_hashes=1):\n",
    "        super(LSHAttention,self).__init__()\n",
    "        self.n_hashes=n_hashes\n",
    "        self.bucket_size=bucket_size\n",
    "        \n",
    "    def call(self,query,key,value,causal_masking=False):\n",
    "        R=tf.random.normal((tf.shape(query)[0],tf.shape(query)[-1],self.bucket_size//2))\n",
    "        xR=tf.matmul(query,R)\n",
    "        concat_xR=tf.concat([xR,-xR],axis=-1)\n",
    "        buckets=tf.math.argmax(concat_xR,axis=-1)\n",
    "        \n",
    "        sticker=tf.argsort(buckets)\n",
    "        undo_sort=tf.argsort(sticker)\n",
    "        sorted_query=tf.gather(query,sticker,axis=1,batch_dims=1)\n",
    "        sorted_value=tf.gather(value,sticker,axis=1,batch_dims=1)\n",
    "        \n",
    "        chunked_query=tf.stack(tf.split(sorted_query,self.bucket_size,1),1)\n",
    "        chunked_value=tf.stack(tf.split(sorted_value,self.bucket_size,1),1)\n",
    "        \n",
    "        sticker=tf.stack(tf.split(sticker,self.bucket_size,1),1)\n",
    "        new_sticker=sticker_look_ne_back(sticker)\n",
    "        \n",
    "        lb_chunked_query=look_one_back(chunked_query)\n",
    "        lb_chunked_value=look_one_back(chunked_value)\n",
    "        \n",
    "        score=tf.einsum('bhie,bhje->bhij',chunked_query,lb_chunked_query)\n",
    "        score/=tf.math.sqrt(tf.cast(query.shape[-1],tf.float32))\n",
    "        \n",
    "        if causal_masking==True:\n",
    "            causal_mask=causal_masker(sticker,new_sticker)\n",
    "            dots+=causal_mask*-1e-10\n",
    "        score=tf.nn.softmax(score)\n",
    "        output=tf.einsum('buij,buje->buie',score,lb_chunked_value)\n",
    "        \n",
    "        sorted_output=tf.reshape(output,(tf.shape(output)[0],tf.shape(query)[i],output.shape[3]))\n",
    "        output=tf.gather(sorted_output,undo_sort,axis=1,batch_dims=1)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29368c3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7107a3d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d941f4f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f8bfffe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "c0694be1",
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0804f5b",
   "metadata": {},
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 ],
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================================================
FILE: deprecated/19-Automatic Speech Recognition by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras.preprocessing import text_dataset_from_directory\n",
    "from tensorflow.keras.layers import(SimpleRNN,Embedding,Input,LSTM,Input,Conv1D,Softmax\n",
    "                                    Dropout,Dense,GRU,LayerNormalization,Reshape,\n",
    "                                    Bidirectional,Reshape)\n",
    "from tensorflow.data.experimental import AUTOTUNE\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "import datetime\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='...'\n",
    "text_path='...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_tet={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81fcff9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(text_path, encoding=\"utf-8\") as f:\n",
    "    for line in f:\n",
    "        i=line.strip().split(\"|\")[0]\n",
    "        text=line.strip().split(\"|\")[1]\n",
    "        audio_text[str(i)]=text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844da811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_audio(audio_binary):\n",
    "    audio,_=tf.audio.decode_wav(audio_binary)\n",
    "    return tf.squeeze(audio,axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "175a35c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_spec(filepath):\n",
    "    label=audio_text[os.path.basename(file_path)[:-4]]\n",
    "    \n",
    "    audio_binary=tf.io.read_file(filepath)\n",
    "    waveform=decode_audio(audio_binary)\n",
    "    \n",
    "    zero_padding=(222621-len(waveform))*[0]\n",
    "    zero_padding=tf.constant(zero_padding,tf.float32)\n",
    "    \n",
    "    waveform=tf.cast(waveform,tf.float32)\n",
    "    equal_length=tf.concat([waveform,zero_padding],axis=0)\n",
    "    \n",
    "    spectrogram=tf.signal.stft(\n",
    "        equal_length,frame_length=63,frame_step=32)\n",
    "    spectrogram=tf.abs(spectrogram)\n",
    "    return tf.expand_dims(spectrogram,axis=-1),label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5720da8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "vocabulary=[\" \",\"UNK\",\".\",\",\",\"?\"]+[chr(i) for i in range(97,97+26)]+[\"PAD\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21332947",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vocab(char):\n",
    "    if char in vocabulary:\n",
    "        return vocabulary.index(char)\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66b2393a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_label(label,seq_len=192):\n",
    "    label=label[:190]\n",
    "    out_label=[get_vocab(i.lower()) for i in label]\n",
    "    out_label=tf.constant(out_label)\n",
    "    return out_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d12cb5c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__(self,train_images,batch_size,SEQUENCE_LENGTH,VOCAB_SIZE,shuffle=False):\n",
    "        self.train_images=train_images\n",
    "        self.batch_size=batch_size\n",
    "        self.train_image_list=os.listdir(train_images)\n",
    "        self.SEQUENCE_LENGTH=SEQUENCE_LENGTH\n",
    "        self.VOCAB_SIZE=VOCAB_SIZE\n",
    "    def __len__(self):\n",
    "        return int(np.floor(len(self.train_image_list)/self.batch_size))\n",
    "    def __getitem__(self,idx):\n",
    "        X,y=self.__data_generation(idx)\n",
    "        return X,y\n",
    "    def __data_generation(self,idx):\n",
    "        X=[]\n",
    "        y=[]\n",
    "        for j in range(idx*self.batch_size,(idx+1)*self.batch_size):\n",
    "            spec,label=get_spec(self.train_images+self.train_image_list[j])\n",
    "            X.append(spec)\n",
    "            label=get_label(label,self.SEQUENCE_LENGTH)\n",
    "            y.append(label)\n",
    "        return tf.convert_to_tensor(X),tf.convert_to_tensor(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "497a846b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path='...'\n",
    "BATCH_SIZE=1\n",
    "SEQUENCE_LENGTH=192\n",
    "VOCAB_SIZE=1\n",
    "LR=1e-3\n",
    "EPOCH=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d377688",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen=DataGenerator(train_path,BATCH_SIZE,SEQUENCE_LENGTH,VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c1a21ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_layer=tf.keras.layers.experimental.preprocessing.Normalization()\n",
    "input_shape=(6955,33,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ce12e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential([\n",
    "    Input(shape=input_shape),\n",
    "    norm_layer,\n",
    "    Conv2D(512,3,padding='same',activation='relu'),\n",
    "    Conv2D(256,3,padding='same',activation='relu'),\n",
    "    \n",
    "    MaxPooling2D(),\n",
    "    Reshape((SEQUENCE_LENGTH,-1)),\n",
    "    Conv1D(len(vocabulary),3,padding='same'),\n",
    "    Softmax(axis=2),\n",
    "    \n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b00fe86",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e9fad1",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f73ac783",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ctc_loss(y_true,y_pred):\n",
    "    batch_size=tf.shape(y_pred)[0]\n",
    "    pred_length=tf.shape(y_pred)[1]\n",
    "    true_length=tf.shape(y_true)[1]\n",
    "    \n",
    "    pred_length=pred_length*tf.ones([batch_size,1])\n",
    "    true_length=true_length*tf.ones([batch_size,1])\n",
    "    \n",
    "    return tf.keras.backend.ctc_batch_cost(y_true,y_pred,pred_length,true_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss=ctc_loss,\n",
    "    optimizer=tf.keras.optimizers.Adam(lr=LR,),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8559844",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac24b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "history=model.fit(train_gen,verbose=1, shuffle=True,epochs=EPOCH,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a4f5e6c",
   "metadata": {},
   "source": [
    "<h1>TESTING</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b9da9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_str='...'\n",
    "test_path='...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73e0192b",
   "metadata": {},
   "outputs": [],
   "source": [
    "spectrogram,_=get_spec(test_path)\n",
    "out=tf.argmax(model.predict(tf.expand_dims(spectrogram,axis=0))[0],axis=1)\n",
    "out=[vocabulary[i] for i in out]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0694be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_str=\"\"\n",
    "for i in out:\n",
    "    out_str+=i\n",
    "pritn(out_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0804f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode(y_pred):\n",
    "    batch_size=tf.shape(y_pred)[0]\n",
    "    \n",
    "    pred_length=tf.shape(y_pred)[1]\n",
    "    pred_length*=tf.ones([batch_size,],dtype=tf.int32)\n",
    "    \n",
    "    y_pred=tf.one_hot(y_pred,32)\n",
    "    output=tf.keras.backend.ctc_decode(y_pred,input_length=pred_length,greedy=True)[0][0]\n",
    "    \n",
    "    out=[vocabulary[i] fro i in output[0]]\n",
    "    out_str=\"\"\n",
    "    for i in out:\n",
    "        out_str+=i\n",
    "    return out_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c05c239d",
   "metadata": {},
   "outputs": [],
   "source": [
    "spectrogram,_=get_spec(test_path)\n",
    "out=tf.argmax(model.predict(tf.expand_dims(spectrogram,axis=0))[0],axis=1)\n",
    "decode(tf.expand_dims(out,axis=0))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/2-Logistic Regression by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ffc022e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a08f4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(filename):\n",
    "    '''\n",
    "    ###### Objective\n",
    "    A function used to extract data from the csv file and preprocess it, such that it can be\n",
    "    used by our machine learning algorithm\n",
    "    \n",
    "    ### Input\n",
    "    filename\n",
    "    \n",
    "    #### Output\n",
    "    Input and output of machine learning model (in this case, a logistic regression model)\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    dataframe = pandas.read_csv(filename)\n",
    "    \n",
    "    X1,X2,X3 = [],[],[]\n",
    "    X,Y = [],[]\n",
    "    \n",
    "    for item,row in dataframe.iterrows():\n",
    "        X1.append(row['EXAM1'])\n",
    "        X2.append(row['EXAM2'])\n",
    "        X3.append(row['FINAL'])\n",
    "        \n",
    "    for i in range(len(X1)):\n",
    "        X.append([1,X1[i]/100,X2[i]/100])\n",
    "        if X3[i]>160:\n",
    "            Y.append([1])\n",
    "        else:\n",
    "            Y.append([0])\n",
    "        \n",
    "    X = np.array(X)\n",
    "    Y = np.array(Y)\n",
    "    \n",
    "    return X,Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "732da12d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = prepare_dataset('exam.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fd8343c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1cd6e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_proba(x):\n",
    "    if sigmoid(x)>0.5:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9332d1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "\n",
    "EPOCHS = 10\n",
    "LR = 1e-1\n",
    "\n",
    "BATCH_SIZE = X.shape[0]\n",
    "\n",
    "theta = np.random.randn(X.shape[1],1)\n",
    "\n",
    "loss = []\n",
    "\n",
    "for i in range(EPOCHS):\n",
    "    epoch_loss = 0.0\n",
    "    for b in range(0,len(X), BATCH_SIZE):\n",
    "        model_output = sigmoid(X[b:b+BATCH_SIZE]@theta)\n",
    "        \n",
    "        d_theta = (X[b:b+BATCH_SIZE].T@(((model_output - Y[b:b+BATCH_SIZE]))))\n",
    "        theta -= LR*(d_theta)################## gradient descent step\n",
    "        \n",
    "        epoch_loss += -((Y[b:b+BATCH_SIZE]*np.log(model_output)) + ((1-Y[b:b+BATCH_SIZE])*np.log(1-model_output))).mean()\n",
    "    loss.append(epoch_loss)\n",
    "    \n",
    "print('The loss at the end of training is ==', loss[-1])\n",
    "plt.plot(range(1,EPOCHS+1),loss)\n",
    "plt.ylabel('BCE')\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.show()\n",
    "print(theta)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "646c286b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pandas.read_csv('exam.csv')\n",
    "\n",
    "X2 = []\n",
    "X1 = []\n",
    "\n",
    "for item,row in dataframe.iterrows():\n",
    "    X1.append(row['EXAM1'])\n",
    "    X2.append(row['EXAM2'])\n",
    "\n",
    "categories = Y.reshape((len(Y)))\n",
    "\n",
    "colormap = np.array(['r','g'])\n",
    "plt.scatter(X1,X2, s=20,c+colormap[categories])\n",
    "\n",
    "x1 = []\n",
    "x2 = []\n",
    "y = []\n",
    "\n",
    "for i in range(40,100):\n",
    "    for j in range(40,100):\n",
    "        x1.append(i)\n",
    "        x2.append(j)\n",
    "        model_output = sigmoid_proba(np.array([1,i/100,j/100])@theta)\n",
    "        y.append(model_output)\n",
    "categories = y\n",
    "\n",
    "plt.scatter(x1,x2, s=1,c=colormap[categories])\n",
    "\n",
    "plt.ylabel('EXAM1')\n",
    "plt.xlabel('EXAM2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00f3f0cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/20-Image Captioning by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e105f643",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras.preprocessing import text_dataset_from_directory\n",
    "from tensorflow.keras.layers import(SimpleRNN,Embedding,Input,LSTM,Input,Conv1D,Softmax\n",
    "                                    Dropout,Dense,GRU,LayerNormalization,Reshape,\n",
    "                                    Bidirectional,Reshape)\n",
    "from tensorflow.data.experimental import AUTOTUNE\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "import datetime\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451711be",
   "metadata": {},
   "source": [
    "<H1>DATA PREPARATION</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65c55e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_TOKEN=10000\n",
    "INPUT_DIM=224\n",
    "BATCH_SIZE=8\n",
    "MODEL_SIZE=128\n",
    "SEQUENCE_LENGTH=20\n",
    "NUM_LAYERS=2\n",
    "NUM_HEADS=8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a96473d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='...'\n",
    "txt_path='...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "decf38a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "f=open(txt_path,\"r+\",encoding=\"utf-8\")\n",
    "lines=f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81fcff9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict={}\n",
    "captions=''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "844da811",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(lines)):\n",
    "    split=lines[i].split('\\t')\n",
    "    data_dict[split[0]]=split[1][:-3]\n",
    "    captions+=split[1][:-3]\n",
    "captions+=3000*' starttoken'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "175a35c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer=tf.keras.preprocessing.text.Tokenizer(num_words=MAX_TOKEN,\n",
    "                                               oov_token=\"<unk>\",\n",
    "                                               filters='!\"#$%&()*+.,-/:;=?@[\\]^_`{|}~ ')\n",
    "tokenizer.fit_on_texts(captions.split(' '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5720da8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.index_word[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21332947",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "106e2e8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "753d3355",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    def __init__(self,images,batch_size,tokenizers,data_dict,starttoken,INPUT_DIM):\n",
    "        self.images=images\n",
    "        self.batch_size=batch_size\n",
    "        self.train_image_list=os.listdir(images)\n",
    "        self.tokenizer=tokenizer\n",
    "        self.data_dict=data_dict\n",
    "        self.starttoken=starttoken\n",
    "        self.INPUT_DIM=INPUT_DIM\n",
    "        \n",
    "    def __len__(self):\n",
    "        return int(np.floor(len(self.train_image_list)/self.batch_size))\n",
    "    def __getitem__(self,idx):\n",
    "        X,y=self.__data_generation(idx)\n",
    "        return X,y\n",
    "    def __data_generation(self,idx):\n",
    "        X=[]\n",
    "        y_1=[]\n",
    "        y_2=[]\n",
    "        start=tf.constant(self.batch_size*5*[[self.starttoken]])\n",
    "        \n",
    "        for j in range(idx*self.batch_size,(idx+1)*self.batch_size):\n",
    "            im_array=img_to_array(load_img(self.images+os.listdir(self.images)[j],target_size=(self.INPUT_DIM,self.INPUT_DIM)))\n",
    "            X=5*self.BATCH_SIZE*[im_array]\n",
    "            for i in range(5):\n",
    "                caption=self.data_dict[os.listdir(self.images)[j]+'#'+str(i)]\n",
    "                cap_seq=np.array(self.tokenizer.texts_to_sequences(caption.split(' '))).T\n",
    "                cap_tok=tf.keras.preprocessing.sequence.pad_sequences(\n",
    "                    cap_seq,maxlen=20,padding='post',truncating='post')\n",
    "                y_1.append(cap_tok[0][:-1])\n",
    "                y_2.append(cap_tok[0])\n",
    "        X=tf.constant(X)\n",
    "        y_1=tf.constant(y_1)\n",
    "        y_2=tf.constant(y_2)\n",
    "        \n",
    "        y_1=tf.concat([start,y_1],axis=-1)\n",
    "        return {'in_1':X,'in_2':y_1},y_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af1e2a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images='...'\n",
    "val_images='...'\n",
    "\n",
    "starttoken=tokenizer.word_index['starttoken']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65570e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen=DataGenerator(train_images,BATCH_SIZE,tokenizer,data_dict,starttoken,INPUT_DIM)\n",
    "val_gen=DataGenerator(val_images,BATCH_SIZE,tokenizer,data_dict,starttoken,INPUT_DIM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b789875",
   "metadata": {},
   "source": [
    "<H1>MODELING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed044d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SelfAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,model_size):\n",
    "        super(SelfAttention,self).__init__()\n",
    "        self.model_size=model_size\n",
    "    def call(self,query,key,value,sequence,look_ahead_masking=False):\n",
    "        #score=tf.matmul(query,key,transpose_b=True)\n",
    "        score=tf.einsum('ijk,ibk->ijb',query,key)\n",
    "        score/=tf.math.sqrt(tf.cast(self.model_size,tf.float32))\n",
    "        ones=tf.ones_like(score)\n",
    "        pad_mask=padding_mask(sequence)\n",
    "        \n",
    "        total_mask=pad_mask\n",
    "        if look_ahead_masking:\n",
    "            ahead_mask=1-tf.linalg.band_part(ones,-1,0)\n",
    "            total_mask+=ahead_mask\n",
    "        score+=total_mask*-1e10\n",
    "        alignment=tf.nn.softmax(score,axis=-1)\n",
    "        head=tf.matmul(alignment,value)\n",
    "        return head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c35420",
   "metadata": {},
   "outputs": [],
   "source": [
    "def padding_mask(a):\n",
    "    return tf.expand_dims(tf.cast(tf.math.equal([a],0),tf.float32)[0],axis=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e31f0a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def positional_embedding(model_size):\n",
    "    output=[]\n",
    "    for pos in range(SEQUENCE_LENGTH):\n",
    "        PE=np.zeros((model_size))\n",
    "        for i in range(model_size):\n",
    "            if i%2==0:\n",
    "                PE[i]=np.sin(pos/(10000**(i/model_size)))\n",
    "            else:\n",
    "                PE[i]=np.cos(pos/(10000**((i-1)/model_size)))\n",
    "        output.append(tf.expand_dims(PE,axis=0))\n",
    "    return tf.concat(output,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7774df1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self,model_size,h):\n",
    "        super(MultiHeadAttention,self).__init__()\n",
    "        self.query_size=model_size//h\n",
    "        self.key_size=model_size//h\n",
    "        self.value_size=model_size//h\n",
    "        self.h=h\n",
    "        self.dense_q=[Dense(self.query_size) for _ in range(h)]\n",
    "        self.dense_k=[Dense(self.key_size) for _ in range(h)]\n",
    "        self.dense_v=[Dense(self.value_size) for _ in range(h)]\n",
    "        self.dense_o=Dense(model_size)\n",
    "        self.self_attention=SelfAttention(self,key_size)\n",
    "        \n",
    "    def call(self,query,key,value,sequence,look_ahead_masking):\n",
    "        heads=[]\n",
    "        \n",
    "        for i in range(self.h):\n",
    "            head=self.self_attention(self.dense_q[i](query),self.dense_k[i](key),\n",
    "                                    self.dense_v[i](value),sequence,look_ahead_masking)\n",
    "            heads.append(head)\n",
    "        heads=tf.concat(heads,axis=2)\n",
    "        heads=self.dense_o(heads)\n",
    "        return heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e0fd24b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderLayer(tf.keras.layers.Layer):\n",
    "    def __init__(self,model_size,num_layers,h):\n",
    "        super(DecoderLayer,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.num_layers=num_layers\n",
    "        self.h=h\n",
    "        \n",
    "        self.multi_attention_bot=MultiHeadAttention(model_size,h)\n",
    "        self.attetnion_bot_norm=LayerNormalization()\n",
    "        \n",
    "        self.multi_attention_mid=MultiHeadAttention(model_size,h)\n",
    "        self.attetnion_mid_norm=LayerNormalization()\n",
    "        \n",
    "        self.dense_1=Dense(model_size*4,activation='relu')\n",
    "        self.dense_2=Dense(model_size)\n",
    "        self.dropout=Dropout(0.2)\n",
    "        \n",
    "        self.feed_forward_norm=LayerNormalization()\n",
    "        \n",
    "    def call(self,enc_in,sequence):\n",
    "        bot_dec_out=self.multi_attention_bot(bot_dec_in,bot_dec_in,bot_dec_in,sequence,look_ahead_masking=True)\n",
    "        bot_dec_out+=bot_dec_in\n",
    "        bot_dec_out=self.attention_bot_norm(bot_dec_out)\n",
    "        \n",
    "        mid_dec_in=bot_dec_out\n",
    "        \n",
    "        mid_dec_out=self.multi_attention_mid(mid_dec_in,mid_dec_in,mid_dec_in,sequence,look_ahead_masking=False)\n",
    "        mid_dec_out+=mid_dec_in\n",
    "        mid_dec_out=self.attention_mid_norm(mid_dec_out)\n",
    "        \n",
    "        feed_forward_in=mid_dec_out\n",
    "        \n",
    "        feed_forward_out=self.dropout(self.dense_2(self.dense_1(feed_forward_in)))\n",
    "        feed_forward_out+=feed_forward_in\n",
    "        feed_forward_out=self.feed_forward_norm(feed_forward_out)\n",
    "        return feed_forward_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a7bb5bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(tf.keras.layers.Layer):\n",
    "    def __init__(self,vocab_size,model_size,h,num_layers):\n",
    "        super(Decoder,self).__init__()\n",
    "        \n",
    "        self.model_size=model_size\n",
    "        self.num_layers=num_layers\n",
    "        self.h=h\n",
    "        self.embedding=Embedding(pre_vocab_size,model_size)\n",
    "        self.decoder_layer=[DecoderLayer(model_size,num_layers,h) for _ in range(num_layers)]\n",
    "        self.dense=Dense(vocab_size,)\n",
    "        \n",
    "    def call(self, sequence,encoder_output):\n",
    "        dec_in=self.embedding(sequence)\n",
    "        dec_in+=tf.cast(positional_embedding(self.model_size),dtype=tf.float32)\n",
    "        \n",
    "        for i in range(self.num_layers):\n",
    "            out=self.decoder_layer[i](dec_in,encoder_output,sequence)\n",
    "            dec_in=out\n",
    "        out=self.dense(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c1a21ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_base_model():\n",
    "    base_model=ResNet50(weights='imagenet',input_shape=(INPUT_DIM,INPUT_DIM,3),include_top=False)\n",
    "    base_model.trainable=False\n",
    "    \n",
    "    conv4_block6_2_relu=[base_model.get_layer(layer_name).output for layer_name in [\"conv4_block6_2_relu\"]]\n",
    "    return tf.keras.Model(\n",
    "        inputs=[base_model.inputs],outputs=conv4_block6_2_relu)\n",
    "get_base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ce12e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=Input((INPUT_DIM,INPUT_DIM,3))\n",
    "pre_outputs=Input(SEQUENCE_LENGTH,)\n",
    "\n",
    "x = Rescaling(1/255.)(inputs)\n",
    "x=get_base_model()(inputs)\n",
    "x=Conv2D(256,3,padding='same',activation='relu')(x)\n",
    "x=Conv2D(128,3,padding='same',activation='relu')(x)\n",
    "x=BatchNormalization()(x)\n",
    "\n",
    "x=Conv2D(64,3,padding='same',activation='relu')(x)\n",
    "x=Conv2D(1,3,padding='same',activation='relu')(x)\n",
    "x=BatchNormalization()(x)\n",
    "x=Reshape((14,14))(x)\n",
    "\n",
    "x=Dense(MODEL_SIZE,activation='relu')(x)\n",
    "\n",
    "dec=Decoder(vocab_size=MAX_TOKEN,model_size=MODEL_SIZE,h=NUM_HEADS,num_layers=NUM_LAYERS)\n",
    "decoder_ouput=dec(pre_outputs,x)\n",
    "\n",
    "model=tf.keras.Model([inputs,pre_outputs],decoder_output,name='conv-transformer')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e26903",
   "metadata": {},
   "source": [
    "<H1>TRAINING</H1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cb21263",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR=1e-3\n",
    "EPOCH=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0872a19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    optimizer=tf.keras.optimizers.Adam(lr=LR,),\n",
    "    metrics='accuracy',\n",
    "    run_eagerly=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8559844",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac24b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "history=model.fit(train_gen,verbose=1, shuffle=True,epochs=EPOCH,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ef4899",
   "metadata": {},
   "source": [
    "<h1>TESTING</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54b9da9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def caption(input_image):\n",
    "    plt.imshow(input_image[0]/255)\n",
    "    plt.show()\n",
    "    \n",
    "    in_1=input_image\n",
    "    in_2=[starttoken]\n",
    "    final_output=[]\n",
    "    length=SEQUENCE_LENGTH\n",
    "    \n",
    "    for i in range(SEQUENCE_LENGTH):\n",
    "        p_in_2=tf.pad(tf.constant(in_2),[[0,SEQUENCE_LENGTH-1-i]])\n",
    "        output=tf.argmax(model.predict([in_1,tf.expand_dims(p_in_2,0)]),-1)[0][i]\n",
    "        \n",
    "        if output==0:\n",
    "            length=1\n",
    "            break\n",
    "        in_2.append(output.numpy())\n",
    "        final_output.append(output.numpy())\n",
    "    print(final_output)\n",
    "    return ' '.join([tokenizer.index_word[i] for i in final_output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73e0192b",
   "metadata": {},
   "outputs": [],
   "source": [
    "im='...'\n",
    "test_image=val_images+im\n",
    "X=[]\n",
    "X.append(img_to_array(load_img(test_image,target_size=(224,224))))\n",
    "print(caption(tf.constant(X)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0694be1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0804f5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f65e3ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
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================================================
FILE: deprecated/21-dcgan_By_Neuralearn.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ebd04ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer\n",
    "from tensorflow.keras.layers import (Reshape,LeakyReLU,Dropout,Conv2DTranspose, Add, Conv2D, MaxPool2D, Dense,\n",
    "                                     Flatten, InputLayer, BatchNormalization, Input, )\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0da5b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128\n",
    "IM_SHAPE = (64,64,3)\n",
    "LEARNING_RATE = 2e-4\n",
    "LATENT_DIM=100\n",
    "EPOCHS=20\n",
    "IMG_PATH=''### specify path to images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a27d8aa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    \"PATH TO IMAGES\", label_mode=None, image_size=(IM_SHAPE[0], IM_SHAPE[1]), batch_size=BATCH_SIZE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d65205e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ed0b1c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(image):\n",
    "    return tf.cast(image, tf.float32) / 127.5 - 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dfe3e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = (\n",
    "    dataset\n",
    "    .map(preprocess)\n",
    "    .unbatch()\n",
    "    .shuffle(buffer_size = 1024, reshuffle_each_iteration = True)\n",
    "    .batch(BATCH_SIZE,drop_remainder=True)\n",
    "    .prefetch(tf.data.AUTOTUNE)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f34e7b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "for d in train_dataset.take(1):\n",
    "    print(d.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ad52f8",
   "metadata": {},
   "source": [
    "<H1>Modeling</H1>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c13c6e",
   "metadata": {},
   "source": [
    "<h2>Our generator model:</h2>\n",
    "<br>Note: \n",
    "<br> \n",
    "<ul>\n",
    "    <li> kernel_size is divisible by number of strides</li>\n",
    "    <li> final activation is tanh</li>\n",
    "    <li> leaky relu of 0.2 used</li>\n",
    "    <li> batchnormalization used</li>\n",
    "   \n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e20f70d",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator=tf.keras.Sequential([\n",
    "  Input(shape=(LATENT_DIM,)),\n",
    "  Dense(4*4*LATENT_DIM),\n",
    "  Reshape((4,4,LATENT_DIM)),\n",
    "\n",
    "  Conv2DTranspose(512,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2DTranspose(256,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2DTranspose(128,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2DTranspose(3,kernel_size=4,strides=2, activation=tf.keras.activations.tanh, padding='same'),\n",
    "\n",
    "],name='generator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b4c7493",
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c3cd3cb",
   "metadata": {},
   "source": [
    "<h2>Our discriminator model:</h2>\n",
    "<br>Note: \n",
    "<br> \n",
    "<ul>\n",
    "    <li> kernel_size is divisible by number of strides</li>\n",
    "    <li> final activation is sigmoid</li>\n",
    "    <li> leaky relu of 0.2 used</li>\n",
    "    <li> batchnormalization used</li>\n",
    "   \n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6fa0cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator=tf.keras.Sequential([\n",
    "  Input(shape=(IM_SHAPE[0],IM_SHAPE[1],3)),\n",
    "\n",
    "  Conv2D(64,kernel_size=4,strides=2, padding='same'),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2D(128,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "  \n",
    "  Conv2D(256,kernel_size=4,strides=2, padding='same'),\n",
    "  BatchNormalization(),\n",
    "  LeakyReLU(0.2),\n",
    "\n",
    "  Conv2D(1,kernel_size=4,strides=2, padding='same'),\n",
    "\n",
    "  Flatten(),\n",
    "  Dense(1,activation='sigmoid')\n",
    "  \n",
    "\n",
    "],name='discriminator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18a8efb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a745623",
   "metadata": {},
   "source": [
    "<h2>Show Image callback:</h2><br>\n",
    "<br>Used to save generated images in a folder, while training is going on.\n",
    "<br> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b983cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ShowImage(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, latent_dim=100):\n",
    "        self.latent_dim = latent_dim\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        n=6\n",
    "        k=0\n",
    "        out=self.model.generator(tf.random.normal(shape=(36, self.latent_dim)))\n",
    "        plt.figure(figsize=(16,16))\n",
    "        for i in range(n):\n",
    "            for j in range(n):\n",
    "                ax=plt.subplot(n,n,k+1)\n",
    "                plt.imshow((out[k]+1)/2,)\n",
    "                plt.axis('off')\n",
    "                k+=1\n",
    "        plt.savefig(\"generated/gen_images_epoch_{}.png\".format(epoch+1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99a47097",
   "metadata": {},
   "source": [
    "<h2>GAN Model (overriding train_step method):</h2>\n",
    "<br>Note: Some modifications Inspired by this github repository: <a href='https://github.com/soumith/ganhacks'>Soumith Chintala's GAN Hacks</a>\n",
    "<br> \n",
    "<ul>\n",
    "    <li> label smoothing </li>\n",
    "    <li> label flipping</li>\n",
    "   \n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22555d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GAN(tf.keras.Model):\n",
    "    def __init__(self,discriminator,generator):\n",
    "        super(GAN,self).__init__()\n",
    "        self.discriminator=discriminator\n",
    "        self.generator=generator\n",
    "\n",
    "    def compile(self,d_optimizer,g_optimizer,loss_fn):\n",
    "        super(GAN,self).compile()\n",
    "        self.d_optimizer=d_optimizer\n",
    "        self.g_optimizer=g_optimizer\n",
    "        self.loss_fn=loss_fn\n",
    "        self.d_loss_metric=tf.keras.metrics.Mean(name='d_loss')\n",
    "        self.g_loss_metric=tf.keras.metrics.Mean(name='g_loss')\n",
    "    \n",
    "    @property\n",
    "    def metrics(self):\n",
    "        return [self.d_loss_metric,self.g_loss_metric]\n",
    "  \n",
    "    def train_step(self,real_images):\n",
    "        batch_size=tf.shape(real_images)[0]\n",
    "\n",
    "        ######## Discriminator\n",
    "        random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n",
    "        fake_images=self.generator(random_noise)\n",
    "        \n",
    "        real_labels=tf.ones((batch_size,1))\n",
    "        real_labesl+=0.25*tf.random.uniform((batch_size,1),minval=-1,maxval=1)### label smoothing (check out link above)\n",
    "        \n",
    "        fake_labels=tf.zeros((batch_size,1))\n",
    "        fake_labels+=0.25*tf.random.uniform((batch_size,1),)### label smoothing(check out link above)\n",
    "\n",
    "        with tf.GradientTape() as recorder:\n",
    "            real_predictions=self.discriminator(real_images)\n",
    "            d_loss_real=self.loss_fn(real_labels,real_predictions)\n",
    "\n",
    "            fake_predictions=self.discriminator(fake_images)\n",
    "            d_loss_fake=self.loss_fn(fake_labels,fake_predictions)\n",
    "            d_loss=d_loss_real+d_loss_fake\n",
    "      \n",
    "        partial_derivatives = recorder.gradient(d_loss,self.discriminator.trainable_weights)\n",
    "        self.d_optimizer.apply_gradients(zip(partial_derivatives, self.discriminator.trainable_weights))\n",
    "\n",
    "        ############# Generator\n",
    "        random_noise=tf.random.normal(shape=(batch_size,LATENT_DIM))\n",
    "        flipped_fake_labels=tf.ones((batch_size,1))### label flipping\n",
    "\n",
    "        with tf.GradientTape() as recorder:\n",
    "        \n",
    "\n",
    "            fake_predictions=self.discriminator(self.generator(random_noise))\n",
    "            g_loss=self.loss_fn(flipped_fake_labels,fake_predictions)\n",
    "      \n",
    "        partial_derivatives = recorder.gradient(g_loss,self.generator.trainable_weights)\n",
    "        self.g_optimizer.apply_gradients(zip(partial_derivatives, self.generator.trainable_weights))\n",
    "\n",
    "        self.d_loss_metric.update_state(d_loss)\n",
    "        self.g_loss_metric.update_state(g_loss)\n",
    "    \n",
    "        return {'d_loss':self.d_loss_metric.result(),'g_loss':self.g_loss_metric.result()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d492c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "gan=GAN(discriminator,generator)\n",
    "gan.compile(\n",
    "    d_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n",
    "    g_optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE,beta_1=0.5),\n",
    "    loss_fn=tf.keras.losses.BinaryCrossentropy(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c660029e",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24b511ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS=100\n",
    "history=gan.fit(train_dataset,epochs=EPOCHS,callbacks=[ShowImage(LATENT_DIM)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43350df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history['d_loss'])\n",
    "plt.plot(history.history['g_loss'])\n",
    "plt.title('GAN Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['d_loss', 'g_loss'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7e0008",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82778a2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8f145d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70979d15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "752b9e65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e076f5fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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================================================
FILE: deprecated/3-Softmax Regression by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f46cab9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "587e98b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_one_hot(value):\n",
    "    one_hot = [0.0]*26\n",
    "    one_hot[value] = 1.0\n",
    "    return one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bdc144d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(filename):\n",
    "    '''\n",
    "    ###### Objective\n",
    "    A function used to extract data from the csv file and preprocess it, such that it can be\n",
    "    used by our machine learning algorithm\n",
    "    \n",
    "    ### Input\n",
    "    filename\n",
    "    \n",
    "    #### Output\n",
    "    Input and output of machine learning model (in this case, a SOFTMAX classification model)\n",
    "    \n",
    "    '''\n",
    "    alphabet = {\"A\":0,\"B\":1,\"C\":2,\"D\":3,\"E\":4,\"F\":5,\"G\":6,\"H\":7,\"I\":8,\"J\":9,\n",
    "               \"K\":10,\"L\":11,\"M\":12,\"N\":13,\"O\":14,\"P\":15,\"Q\":16,\"R\":17,\"S\":18,\"T\":19,\n",
    "               \"U\":20,\"V\":21,\"W\":22,\"X\":23,\"Y\":24,\"Z\":25}\n",
    "    \n",
    "    dataframe = pandas.read_csv(filename)\n",
    "    \n",
    "    X = []\n",
    "    Y = []\n",
    "    Y_hot = []\n",
    "    \n",
    "    for item,row in dataframe.iterrows():\n",
    "        X1.append([1,row['INPUT1'],row['INPUT2'],row['INPUT3'],row['INPUT4'],row['INPUT5'],row['INPUT6'],row['INPUT7'],row['INPUT8'],row['INPUT9'],row['INPUT10'],row['INPUT11'],row['INPUT12'],row['INPUT13'],row['INPUT14'],row['INPUT15'],row['INPUT16']])\n",
    "        X2.append([row['LETTER']])\n",
    "    X = np.array(X)\n",
    "    Y = np.array(Y)\n",
    "        \n",
    "    for i in Y:\n",
    "        Y_hot.append(generate_one_hot(alphabet[i[0]]))\n",
    "    Y_hot = np.array(Y_hot)\n",
    "    \n",
    "    return X,Y_hot\n",
    "\n",
    "X,Y = prepare_dataset('letter-recognition.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12a09886",
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(X):\n",
    "    k = np.sum(np.exp(X), axis = 1)\n",
    "    t = 0\n",
    "    X_out = []\n",
    "    \n",
    "    for i in X:\n",
    "        X_out.append(list(np.exp(i)/k[t]))\n",
    "        t += 1\n",
    "    return np.array(X_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96853fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cce_loss(y_pred, y_true):\n",
    "    loss = y_true*np.log(y_pred)\n",
    "    return -np.sum(loss)/len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "592dff02",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "\n",
    "X = X[0:10000]\n",
    "Y = Y[0:10000]\n",
    "\n",
    "\n",
    "EPOCHS = 100\n",
    "LR = 1e-2\n",
    "\n",
    "BATCH_SIZE = X.shape[0]\n",
    "OUTPUT_SIZE = 26\n",
    "PRINT_FREQUENCY = 50\n",
    "\n",
    "theta = np.random.randn(X.shape[1],OUTPUT_SIZE)\n",
    "\n",
    "loss = []\n",
    "best_theta = theta\n",
    "best_loss = np.inf\n",
    "\n",
    "for i in range(EPOCHS):\n",
    "    epoch_loss = 0.0\n",
    "    for b in range(0,len(X), BATCH_SIZE):\n",
    "        model_output = softmax(X[b:b+BATCH_SIZE]@theta)\n",
    "        \n",
    "        d_theta = (X[b:b+BATCH_SIZE].T@(((model_output - Y[b:b+BATCH_SIZE]))))\n",
    "        theta -= LR*(d_theta)################## gradient descent step\n",
    "        \n",
    "        epoch_loss += cce_loss(model_output, Y[b:b+BATCH_SIZE])\n",
    "    if(epoch_loss<best_loss):\n",
    "        best_loss = epoch_loss\n",
    "        best_theta = theta\n",
    "    if i%PRINT_FREQUENCY == 0:\n",
    "        print('Epoch number = '+str(i)+'----> Loss = '+str(epoch_loss))\n",
    "    \n",
    "    loss.append(epoch_loss)\n",
    "    \n",
    "print('The loss at the end of training is ==', loss[-1])\n",
    "print('The best loss is ==', best_loss)\n",
    "\n",
    "plt.plot(range(1,EPOCHS+1),loss)\n",
    "plt.ylabel('CCE')\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.show()\n",
    "print(theta)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
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================================================
FILE: deprecated/4-Artificial Neural Networks from scratch Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3014de4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pkbar ## tracing losses during training\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c0abde",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parameter():\n",
    "    def __init__(self, tensor):\n",
    "        self.weights = tensor\n",
    "        self.gradients = np.zeros_like(self.weights)\n",
    "        self.bias = np.zeros((tensor.shape[-1]))\n",
    "        self.bias_gradients = np.zeros_like(self.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2158330f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Layer:\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    def init_param(self, tensor):\n",
    "        param = Parameter(tensor)\n",
    "        self.parameters = param\n",
    "        return param\n",
    "    def update(self, optimizer):\n",
    "        optimizer.update(self.parameters)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "475af0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear(Layer):\n",
    "    \n",
    "    '''\n",
    "    #### Objective\n",
    "    A class which defines the linear layer\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, inputs, outputs):\n",
    "        super().__init__()\n",
    "        tensor = np.random.randn(inputs, outputs)\n",
    "        self.parameters = self.init_param(tensor)\n",
    "    def backward(self, D, X):\n",
    "        '''\n",
    "        ###### Objective\n",
    "        A backward pass\n",
    "        ######  Input\n",
    "        partial derivative with respect to end features and start features\n",
    "        ##### Output\n",
    "        partial derivative with respect to start function\n",
    "        '''\n",
    "        return D@self.parameters.weights.T### D*theta.T\n",
    "        \n",
    "    def forward(self, X):\n",
    "        '''\n",
    "        ###### Objective\n",
    "        A forward pass\n",
    "        ######  Input\n",
    "        start features\n",
    "        ##### Output\n",
    "        end features\n",
    "        '''\n",
    "        return X@self.parameters.weights + self.parameters.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3819ba19",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sigmoid(Layer):\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    def backward(self, D, X):\n",
    "        S = 1/(1+np.exp(-X))\n",
    "        return D*(S*(1-S))\n",
    "    def forward(self, X):\n",
    "        return 1/(1+np.exp(-X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2214368d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Softmax(Layer):\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    \n",
    "    def backward(self, D, X):\n",
    "        return D\n",
    "    def softmax(self, X):\n",
    "        k = np.sum(np.exp(X), axis = 1)\n",
    "        t = 0\n",
    "        X_out = []\n",
    "        \n",
    "        for i in X:\n",
    "            X_out.append(list(np.exp(i)/k[t]))\n",
    "            t+=1\n",
    "        return np.array(X_out)\n",
    "    \n",
    "    def forward(self, X):\n",
    "        return self.softmax(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbc897db",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SGD():\n",
    "    def __init__(self, lr=0.1):\n",
    "        self.lr = lr\n",
    "    def update(self, param):\n",
    "        '''\n",
    "        ##### Objective\n",
    "        Update of parameters using gradient descent\n",
    "        ##### Input\n",
    "        Parameters\n",
    "        ##### Output\n",
    "        Updated Parameters\n",
    "        '''\n",
    "        param.weights -= self.lr*param.gradients\n",
    "        param.bias -= self.lr *param.bias_gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a1ba57b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tanh(Layer):\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    def backward(self, D, X):\n",
    "        return D*(1-(np.tanh(X)*np.tanh(X)))\n",
    "    def forward(self, X):\n",
    "        return np.tanh(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93f89bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReLU(Layer):\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    def backward(self, D, X):\n",
    "        return D*(1*(X>0))\n",
    "    def forward(self, X):\n",
    "        return np.maximum(0,X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d549c7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model()\n",
    "    def __init__(self):\n",
    "        self.computational_graph = []\n",
    "    def add(self, layer):\n",
    "        '''\n",
    "        ###### Objective\n",
    "        Add a layer to the computational graph\n",
    "        ###### Input\n",
    "        layer\n",
    "        \n",
    "        '''\n",
    "        self.computational_graph.append(layer)\n",
    "    def compiler(self, loss, optimizer):\n",
    "        '''\n",
    "        ###### Objective \n",
    "        compile a model(give it all additional properties needed for training)\n",
    "        ###### Input\n",
    "        loss and optimizer to be used\n",
    "        '''\n",
    "        self.loss = loss\n",
    "        self.optimizer = optimizer\n",
    "    def forward(self, X):\n",
    "        '''\n",
    "        ###### Objective \n",
    "        A forward pass\n",
    "        ###### Input\n",
    "        Input data for model\n",
    "        ###### Output\n",
    "        predicted data by model, plus intermediary layers\n",
    "        '''\n",
    "        Y_int = X\n",
    "        Y_int_list = []\n",
    "        \n",
    "        for layer in self.computational_graph:\n",
    "            Y_int_list.append(Y_int)\n",
    "            Y_ = layer.forward(Y_int)\n",
    "            Y_int = Y_\n",
    "        return Y_, Y_int_list\n",
    "    def fit_batch(self, X,Y):\n",
    "        '''\n",
    "        ####### OBjective\n",
    "        optimize the parameters of a particular batch\n",
    "        ###### Input \n",
    "        the input and output of dataset\n",
    "        \n",
    "        ##### Ouput\n",
    "        loss\n",
    "        \n",
    "        '''\n",
    "        out = X\n",
    "        Y_, Y_int_list = self.forward(X)\n",
    "        \n",
    "        D = predicted_output - target_output\n",
    "        L,D = self.loss(Y_,Y)\n",
    "        for Y_int, layer in zip(Y_int_list[::-1], self.computational_graph[::-1]):\n",
    "            D = layer.backward(D,Y_int)\n",
    "            if layer.parameters is not None:\n",
    "                layer.update(self.optimizer)\n",
    "        return L\n",
    "    def fit(self, X,Y, epochs, bs):\n",
    "        losses = []\n",
    "        \n",
    "        pbar = pkbar.Pbar(name='Training', target = epochs)\n",
    "        kbar = pkbar.Kbar(target=epochs)\n",
    "        \n",
    "        for epoch in range(epochs):\n",
    "            loss = 0.0\n",
    "            for i in range(0, len(X), bs):\n",
    "                loss += self.fit_batch(X[i:i+bs], Y[i:i+bs])\n",
    "            losses.append(loss)\n",
    "            kbar.update(epoch+1, values=[('loss',loss)])\n",
    "        return losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbad96bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[0,0],\n",
    "              [0,1],\n",
    "              [1,0],\n",
    "              [1,1]], dtype = float)\n",
    "Y = np.array([[0],\n",
    "              [1],\n",
    "              [1],\n",
    "              [0]], dtype = float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19cfb72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 10000\n",
    "model = Model()\n",
    "model.add(Linear(2,10))\n",
    "model.add(Sigmoid())\n",
    "model.add(Linear(10,1))\n",
    "model.add(Sigmoid())\n",
    "\n",
    "model.compiler(cce_loss, SGD(lr=5e-1))\n",
    "losses = model.fit(X,Y, epochs = EPOCHS, bs = X.shape[0])\n",
    "plt.plot(range(1,EPOCHS+1), losses)\n",
    "\n",
    "plt.ylabel('BCE')\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "945af1a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92c8006b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b12cc910",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c8eb9c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "442bc5b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9052153",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/5-Lenet Model by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d8e0693",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers. imoprt Dense, Conv2D, MaxPooling2D, Flatten\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.metrics import CategoricalAccuracy\n",
    "from tensorflow.keras.losses import CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "import time\n",
    "import pandas as pd\n",
    "from shutil import copy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853c175f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_directory = '...'\n",
    "validation_directory = '...'\n",
    "world_files = '...'\n",
    "dst = '...'\n",
    "INPUT_DIM = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73810aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    train_directory,\n",
    "    image_size = (INPUT_DIM, INPUT_DIM),\n",
    "    batch_size = 4096)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06744b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    validation_directory,\n",
    "    image_size = (INPUT_DIM, INPUT_DIM),\n",
    "    batch_size = 4096)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e1dfb01",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential(name = 'lenet')\n",
    "model.add(tf.keras.Input(shape=(64,64,3)))\n",
    "\n",
    "model.add(preprocessing.Rescaling(1/255.))\n",
    "\n",
    "model.add(Conv2D(6,5,activation='relu', name ='conv1'))\n",
    "model.add(MaxPooling2D(pool_size=2, strides=2, name='maxpool1'))\n",
    "model.add(Conv2D(16,5,activation='relu', name ='conv2'))\n",
    "model.add(MaxPooling2D(pool_size=2, strides=2, name='maxpool2'))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1000, activation='relu', name ='dense1'))\n",
    "model.add(Dense(200, activation='softmax', name ='dense1'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7baa984f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss=SparseCategoricalCrossentropy(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58501d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 1000\n",
    "history = model.fit(train_dataset, validation_data=val_dataset, epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ab4f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('lenet.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7086af64",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas\n",
    "\n",
    "plt.plot(history.history['loss'][0:60])\n",
    "plt.plot(history.history['val_loss'][0:60])\n",
    "\n",
    "plt.title('model_losss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbb49aff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/5-Transfer Learning by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d8e0693",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers. imoprt Dense, Conv2D, MaxPooling2D, Flatten\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.metrics import CategoricalAccuracy,SparseCategoricalAccuracy,SparseTopCategoricalAccuracy\n",
    "from tensorflow.keras.losses import CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "import time\n",
    "import pandas as pd\n",
    "from shutil import copy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "853c175f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_directory = '...'\n",
    "validation_directory = '...'\n",
    "world_files = '...'\n",
    "dst = '...'\n",
    "INPUT_DIM = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73810aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    train_directory,\n",
    "    image_size = (INPUT_DIM, INPUT_DIM),\n",
    "    batch_size = 4096)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06744b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    validation_directory,\n",
    "    image_size = (INPUT_DIM, INPUT_DIM),\n",
    "    batch_size = 4096)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbb49aff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0db30b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model=tf.keras.applications.VGG16(input_shape=(224,224,3),\n",
    "                                      include_top=True,\n",
    "                                      weights='imagenet')\n",
    "model=base_model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3c8b8e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_maps=[layer.output for layer in model.layers[:5]]\n",
    "activation_model=Model(inputs=model.input,outputs=feature_maps)\n",
    "activation_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fc296c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test-image='...'\n",
    "image=tf.keras.preprocessing.image.load_img(test_imag,color_mode='rgb',target_size=(224,224))\n",
    "image=tf.convert_to_tensor(tf.keras.preprocessing.image.img_to_array(image))/255.\n",
    "activations=activation_model.predict(tf.expand_dims(image,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20f4f585",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(5):\n",
    "    print(activations[i].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45e7bf31",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_size=5\n",
    "num_of_channels=4\n",
    "for i in range(1,len(activations)):\n",
    "    print(\"Feature map number ---> \",i)\n",
    "    axes=[]\n",
    "    fig=plt.figure()\n",
    "    fig.set_figheight(image_size)\n",
    "    fig.set_figwidth(image_size)\n",
    "    \n",
    "    for channel in range(num_of_channels):\n",
    "        axes.append(fig.add_subplot(int(np.sqrt(num_of_channels)),int(np.sqrt(num_of_channels)),channel+1))\n",
    "        subplot_title=(\"Channel\"+str(channel))\n",
    "        axes[-1].set_title(subplot_title)\n",
    "        plt.imshow(activations[i][0,:,:,channel])\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37f59abd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71779934",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone=tf.keras.applications.MobileNetV2(input_shape=(224,224,3),\n",
    "                                      include_top=True,\n",
    "                                      weights='imagenet')\n",
    "\n",
    "backbone.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7abdf62",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone.trainable=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c8dac7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=Input(shape=(64,64,3))\n",
    "\n",
    "x=tf.keras.preprocesssing.Rescaling(1/255.)(inputs)\n",
    "x=backbone(inputs,training=False)\n",
    "x=GlobalAveragePooling2D(x)\n",
    "x=Dense(1000)(x)\n",
    "outputs=Dense(200,activation='softmax')(x)\n",
    "model=Model(inputs,outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fd905df",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5734c1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=Adam(lr=1e-3,\n",
    "    loss=SparseCategoricalCrossentropy(),\n",
    "    metrics=[SparseCategoricalAccuracy(),SparseCategoricalAccuracy(k=5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7f3fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_EPOCHS = 15\n",
    "history = model.fit(train_dataset, validation_data=val_dataset, epochs=NUM_EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59bf298a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.plot(history.history['loss'][0:60])\n",
    "plt.plot(history.history['val_loss'][0:60])\n",
    "\n",
    "plt.title('model_losss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caedae5f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a92ffcaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ac73be3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ce89084",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/6-Callbacks by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f15758c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers. imoprt Dense, Conv2D, MaxPooling2D, Flatten\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.metrics import CategoricalAccuracy\n",
    "from tensorflow.keras.losses import CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "import time\n",
    "import pandas as pd\n",
    "from shutil import copy2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4929959d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_directory = '...'\n",
    "validation_directory = '...'\n",
    "world_files = '...'\n",
    "dst = '...'\n",
    "INPUT_DIM = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23a4602f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    train_directory,\n",
    "    image_size = (INPUT_DIM, INPUT_DIM),\n",
    "    batch_size = 4096)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4874d49f",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    validation_directory,\n",
    "    image_size = (INPUT_DIM, INPUT_DIM),\n",
    "    batch_size = 4096)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf944585",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = tf.keras.application.VGG16(input_shape=(64,64,3),\n",
    "                                       include_top=False,\n",
    "                                       weights='imagenet')\n",
    "model = base_model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05526b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_maps = [layer.output for layer in model.layers]\n",
    "activation_model = Model(inputs = model.input, outputs=feature_maps)\n",
    "activation_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2b9a1b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image = '...'\n",
    "image = tf.keras.preprocessing.image.load_img(test_image, color_mode='rgb', target_size=(224,224))\n",
    "image = tf.convert_to_tensor(tf.keras.preprocessing.image.img_to_array(image))/255.\n",
    "activations = activation_model.predict(tf.expand_dims(image,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b11f908",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_size=5\n",
    "num_of_channels=4\n",
    "\n",
    "for i in range(1,len(activations)):\n",
    "    print('feature map number--->',i)\n",
    "    \n",
    "    axes=[]\n",
    "    fig=plt.figure()\n",
    "    \n",
    "    fig.set_figheight(image_size)\n",
    "    fig.set_figwidth(image_size)\n",
    "    \n",
    "    for channel in range(num_of_channels):\n",
    "        axes.append(fig.add_subplot(int(np.sqrt(num_of_channels)), int(np.sqrt(num_of_channels)), channel+1))\n",
    "        subplot_title=(\"Channel\"+str(channel))\n",
    "        axes[-1].set_title(subplot_title)\n",
    "        plt.imshow(action[i][0,:,:,channel])\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2400ffb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c429b563",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone = MobileNetV2(input_shape=(224,224,3),include_top=False,weights='imagenet')\n",
    "backbone.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78e88eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone.trainable=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fca27b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59481726",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(64,64,3))\n",
    "x= backbone(inputs)\n",
    "x = GlobalAveragePooling2D(x)\n",
    "x = Dense(1000)(x)\n",
    "outputs = Dense(200, activation='softmax')(x)\n",
    "model = Model(inputs,outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6959601f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3454d03",
   "metadata": {},
   "outputs": [],
   "source": [
    "logdir = '...'\n",
    "file_writer=tf.summary.create_file_writer(logdir='/params')\n",
    "file_writer.set_as_default()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9382884d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LossCallback(tf.kears.callbacks.Callback):\n",
    "    def on_train_batch_end(self,batch,logs=None):\n",
    "        print('for batch {},loss is {:.2f}'.format(batch,logs['loss']))\n",
    "        \n",
    "    def on_epoch_end(self,epoch,logs=None):\n",
    "        print('epoch {} loss and metrics are {:.2f}'.format(epoch,logs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc261909",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lrscheduler(epoch,lr):\n",
    "    lr_init=1e-5\n",
    "    if epoch<5:\n",
    "        lr=lr_init\n",
    "    elif epoch>=5 or epoch<10:\n",
    "        lr=lr_init*10\n",
    "    else:\n",
    "        lr=lr*0.01\n",
    "    tf.summary.scalar('learning rate', data=lr,step=epoch)\n",
    "    return lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a10107d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lrscheduler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79b26690",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_plateau = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss',factor=0.5,patience=3,min_lr=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7585ea94",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_scheduler = tf.keras.callbacks.EarlyStopping(monitor='val_loss',patience=5,mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07b6e293",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f11f7fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_callback=tf.keras.callbacks.TensorBoard(log_dir=logdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f889f78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fcf9b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(lr=1e-3),\n",
    "             loss=SparseCategoricalCrossentropy(),\n",
    "             metrics=[SparseCategoricalAccuracy(),SparseTopKCategoricalAccuracy(k=5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0415b053",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_EPOCHS = 15\n",
    "history=model.fit(train_dataset,validation_data=val_dataset,epochs=NUM_EPOCHS,callbacks=[...])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4faf564d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%tensorboard --logdir imagenet_logs/scalars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9f31590",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "\n",
    "plt.plot(history.history['sparse_categorical_accuracy'])\n",
    "plt.plot(history.history['val_sparse_categorical_accuracy'])\n",
    "\n",
    "\n",
    "plt.plot(history.history['sparse_top_k_categorical_accuracy'])\n",
    "plt.plot(history.history['val_sparse_top_k_categorical_accuracy'])\n",
    "\n",
    "plt.legend(['train', 'val',\n",
    "            'sparse_categorical_accuracy','val_sparse_categorical_accuracy',\n",
    "            'val_sparse_top_k_categorical_accuracy'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e3ffdb1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff1e52d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7efc29a1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/7-Transfer Learning and Finetuning by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f15758c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import os\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers. imoprt Dense, Conv2D, MaxPooling2D, Flatten\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from tensorflow.keras.metrics import CategoricalAccuracy\n",
    "from tensorflow.keras.losses import CategoricalCrossentropy, SparseCategoricalCrossentropy\n",
    "from tensorflow.keras.layers.experimental import preprocessing\n",
    "import time\n",
    "import pandas as pd\n",
    "from shutil import copy2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4929959d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_directory = '...'\n",
    "validation_directory = '...'\n",
    "world_files = '...'\n",
    "dst = '...'\n",
    "INPUT_DIM = ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23a4602f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    train_directory,\n",
    "    image_size = (INPUT_DIM, INPUT_DIM),\n",
    "    batch_size = 4096)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4874d49f",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
    "    validation_directory,\n",
    "    image_size = (INPUT_DIM, INPUT_DIM),\n",
    "    batch_size = 4096)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf944585",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_model = tf.keras.application.VGG16(input_shape=(64,64,3),\n",
    "                                       include_top=False,\n",
    "                                       weights='imagenet')\n",
    "model = base_model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05526b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_maps = [layer.output for layer in model.layers]\n",
    "activation_model = Model(inputs = model.input, outputs=feature_maps)\n",
    "activation_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2b9a1b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image = '...'\n",
    "image = tf.keras.preprocessing.image.load_img(test_image, color_mode='rgb', target_size=(224,224))\n",
    "image = tf.convert_to_tensor(tf.keras.preprocessing.image.img_to_array(image))/255.\n",
    "activations = activation_model.predict(tf.expand_dims(image,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b11f908",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_size=5\n",
    "num_of_channels=4\n",
    "\n",
    "for i in range(1,len(activations)):\n",
    "    print('feature map number--->',i)\n",
    "    \n",
    "    axes=[]\n",
    "    fig=plt.figure()\n",
    "    \n",
    "    fig.set_figheight(image_size)\n",
    "    fig.set_figwidth(image_size)\n",
    "    \n",
    "    for channel in range(num_of_channels):\n",
    "        axes.append(fig.add_subplot(int(np.sqrt(num_of_channels)), int(np.sqrt(num_of_channels)), channel+1))\n",
    "        subplot_title=(\"Channel\"+str(channel))\n",
    "        axes[-1].set_title(subplot_title)\n",
    "        plt.imshow(action[i][0,:,:,channel])\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2400ffb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c429b563",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone = MobileNetV2(input_shape=(224,224,3),include_top=False,weights='imagenet')\n",
    "backbone.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78e88eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone.trainable=False\n",
    "backbone.layers[0].trainable=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fca27b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone.layers[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "793ac6a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = Input(shape=(64,64,3))\n",
    "x= backbone(inputs)\n",
    "x = GlobalAveragePooling2D(x)\n",
    "x = Dense(1000)(x)\n",
    "outputs = Dense(200, activation='softmax')(x)\n",
    "model = Model(inputs,outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1444c459",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d1e68a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=Adam(lr=1e-3),\n",
    "             loss=SparseCategoricalCrossentropy(),\n",
    "             metrics=[SparseCategoricalAccuracy(),SparseTopKCategoricalAccuracy(k=5)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6581e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_EPOCHS = 15\n",
    "history=model.fit(train_dataset,validation_data=val_dataset,epochs=NUM_EPOCHS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27d5ebf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "\n",
    "plt.plot(history.history['sparse_categorical_accuracy'])\n",
    "plt.plot(history.history['val_sparse_categorical_accuracy'])\n",
    "\n",
    "\n",
    "plt.plot(history.history['sparse_top_k_categorical_accuracy'])\n",
    "plt.plot(history.history['val_sparse_top_k_categorical_accuracy'])\n",
    "\n",
    "plt.legend(['train', 'val',\n",
    "            'sparse_categorical_accuracy','val_sparse_categorical_accuracy',\n",
    "            'val_sparse_top_k_categorical_accuracy'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bc09d0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53294db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "backbone.layers[1]\n",
    "i=0\n",
    "while True:\n",
    "    try:\n",
    "        backbone.layers[i]\n",
    "        i+=1\n",
    "    except:\n",
    "        break\n",
    "print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "335637f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_index=0\n",
    "for i in range(155):\n",
    "    if bacbone.get_layer('block_15_expand')==backbone.layers[i]:\n",
    "        selected_index=1\n",
    "        break\n",
    "print(selected_index)\n",
    "backbone.layers[134]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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================================================
FILE: deprecated/8-Sentiment Analysis by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9cb825c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras.preprocessing import text_dataset_from_directory\n",
    "from tensorflow.keras.layers import SimpleRNN,LSTM\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "273cbb58",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_directory = '...'\n",
    "val_directory = '...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e4a1f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = text_dataset_from_directory(\n",
    "        train_directory,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9db3ff46",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset = text_dataset_from_directory(\n",
    "        val_directory,\n",
    "        shuffle,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22a3da8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for review,label in train_dataset.take(1):\n",
    "    print('Review',review)\n",
    "    print('Label',label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f01b64b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_sentences(input_data):\n",
    "    '''\n",
    "    Input: raw reviews\n",
    "    output: standardized reviews\n",
    "    '''\n",
    "    output=tf.strings.lower(input_data)\n",
    "    outputs=tf.strings.regex_replace(output,\"<[^>]+>\",\"\")\n",
    "    outputs=tf.strings.regex_replace(output,\"<[%s]\"%re.esceape(string.punctuation),\" \")\n",
    "    outputs=tf.strings.regex_replace(output,\"  \",\" \")\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f7e9ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCAB_SIZE = 2000\n",
    "SEQUENCE_LENGTH = 100\n",
    "\n",
    "vectorize_layer=TextVectorization(\n",
    "    standardize = preprocess_sentences,\n",
    "    max_tokens=VOCAB_SIZE,\n",
    "    output_mode='int',\n",
    "    output_sequence_length=SEQUENCE_LENGTH\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e940043a",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data= train_dataset.map(lambda x,y:x)### input x and y and outputx\n",
    "vectorize_layer.adapt(training_data)#### adapt the vectorize_layer to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c0e75a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorizer(review,label):\n",
    "    return tf.one_hot(vectorize_layer(review),depth=VOCAB_SIZE),label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5334df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datastee=train_dataset.map(vectorizer)\n",
    "val_datastee=val_dataset.map(vectorizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08e2d8c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for review,label in train_dataset.take(1):\n",
    "    print('Review',review)\n",
    "    print('Label',label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d14db11",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorize_layer.get_vocabulary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b587e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b87fe806",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=train_dataset.cache().prefetch(buffer_size=AUTOTUNE)\n",
    "val_dataset=val_dataset.cache().prefetch(buffer_size=AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b15e5c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNCell(tf.keras.layer.Layer):\n",
    "    def __init__(self,units=4,hidden_size=7):\n",
    "        super(RNNCell,self).__init__()\n",
    "        \n",
    "        self.units=units\n",
    "        self.hidden_size=hidden_size\n",
    "    def build(self,input_shape):\n",
    "        \n",
    "        self.w_hh=self.add_weight(\n",
    "            shape=(self.hidden_size,self.hidden_size),\n",
    "            initializer='random_normal',\n",
    "            trainable=True)\n",
    "        \n",
    "        self.w_hx=self.add_weight(\n",
    "            shape=(input_shape[-1],self.hidden_size),\n",
    "            initializer='random_normal',\n",
    "            trainable=True)\n",
    "        \n",
    "        self.w_yh=self.add_weight(\n",
    "            shape=(self.hidden_size,self.units),\n",
    "            initializer='random_normal',\n",
    "            trainable=True)\n",
    "        \n",
    "        self.b_h=self.add_weight(\n",
    "            shape=(self.hidden_size,),\n",
    "            initializer='random_normal',\n",
    "            trainable=True)\n",
    "        \n",
    "        self.b_y=self.add_weight(\n",
    "            shape=(self.units,),\n",
    "            initializer='random_normal',\n",
    "            trainable=True)\n",
    "    def call(self,inputs,h_prev=None):\n",
    "        if h_prev ==None:\n",
    "            h_prev = tf.zeros([inputs.shape[0],self.hidden_size])\n",
    "        h=tf.nn.tanh(tf.matmul(h_prev,self.w_hh)+tf.matmul(inputs,self.w_hx)+self.b_h)\n",
    "        h=tf.nn.tanh(tf.matmul(h,self.w_yh)+self.b_y)\n",
    "        return tf.constant(h),tf.constant(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "662d7adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNN(tf.keras.layers.Layer):\n",
    "    def __init__(self,units):\n",
    "        super(RNN,self).__init__():\n",
    "        self.rnn=RNNCell(units)\n",
    "        \n",
    "    def call(self,inputs):\n",
    "        outputs = []\n",
    "        h,y=self.rnn(inputs[:,0,:])\n",
    "        outputs.append(y)\n",
    "        \n",
    "        for i in range(i,inputs.shape[-2]):\n",
    "            h,y=self.rnn(inputs[:,i,:],h)\n",
    "            outputs.append(y)\n",
    "        shape=np.array(ouputs).shape\n",
    "        return tf.reshape(outputs,[shape[1],shape[0],shape[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d36f27d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=tf.zeros([8,100,2000])\n",
    "layer=SimpleRNN(4,activation='tanh',use_bias=True,return_sequences=False,name='layer1')\n",
    "outputs=layer(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22df0040",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=tf.keras.layers.Input(shape=(SEQUENCE_LENGTH,VOCAB_SIZE))\n",
    "\n",
    "EMBEDDING_DIM=100\n",
    "embedding=tf.keras.layers.Embedding(VOCAB_SIZE,EMBEDDING_DIM)\n",
    "model=tf.keras.models.Sequential([\n",
    "    inputs,\n",
    "    tf.keras.layers.LSTM(20,activation='tanh',use_bias=True,return_sequences=True,name='layer1'),\n",
    "    tf.keras.layers.Dense(1,activation='relu',name='layer2'),\n",
    "    tf.keras.layers.Reshape((SEQUENCE_LENGTH,)name='layer3'),\n",
    "    tf.keras.layers.Dense(1,activation='sigmoid',name='layer4'),\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecb3c70e",
   "metadata": {},
   "outputs": [],
   "source": [
    "LR = 1e-4\n",
    "EPOCH = 50\n",
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
    "    optimizer=tf.keras.optimizers.Adam(lr=LR),\n",
    "    metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eb66419",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f0572ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "history=model.fit(train_dataset,validation_data=val_dataset,verbose=1,epochs=EPOCH,callbcaks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "799502b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "plt.title('model_loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dba56da6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=tf.data.Dataset.from_tensor_slices([[\"This is a bad movie, i really didn't love it\"],\n",
    "                                              [\"I really loved the movie, it reminds me of my past\"],])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14402c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorizer_test(review):\n",
    "    return tf.one_hot(vectorize_layer(review),depth=VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15f03c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset=test_data.map(vectorizer_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23077fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9534932",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
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   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/9-Word2Vec by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "223e425c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers.experimental.preprocessing import TextVectorization\n",
    "from tensorflow.keras.preprocessing import text_dataset_from_directory\n",
    "from tensorflow.keras.layers import SimpleRNN,LSTM,Dense,GRU,Bidirectional,Reshape\n",
    "from tensorflow.data.experimental import AUTOTUNE\n",
    "import numpy as np\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "import datetime\n",
    "import numpy as np\n",
    "import gensim.downloader as api\n",
    "from tensorboard.plugins import projector\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas\n",
    "%reload_ext tensorboad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "930fb329",
   "metadata": {},
   "outputs": [],
   "source": [
    "wv=api.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6c7bcf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(wv.index_to_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1aec3b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_sentences(input_data):\n",
    "    '''\n",
    "    Task: Preprocess sentences or standardize the sentences\n",
    "    Input: raw reviews\n",
    "    output: standardized reviews\n",
    "    '''\n",
    "    output=tf.strings.lower(input_data)\n",
    "    outputs=tf.strings.regex_replace(output,\"<[^>]+>\",\"\")\n",
    "    outputs=tf.strings.regex_replace(output,\"<[%s]\"%re.esceape(string.punctuation),\" \")\n",
    "    outputs=tf.strings.regex_replace(output,\"  \",\" \")\n",
    "    \n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20de85a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_index(word):\n",
    "    out=0\n",
    "    try:\n",
    "        out=wv.key_to_index[word]\n",
    "        if out<30000:\n",
    "            return out\n",
    "        else:\n",
    "            return 0\n",
    "    except:\n",
    "        try:\n",
    "            out=wv.key_to_index[word[0].upper()+word[1:]]\n",
    "            if out<30000:\n",
    "                return out\n",
    "            else:\n",
    "                return 0\n",
    "        except:\n",
    "            return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25fd6ead",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataGenerator(tf.keras.utils.Sequence):\n",
    "    \n",
    "    def __init__(self,train_pos,train_neg,vocab_size,sequence_length,batch_size,shuffle=False):\n",
    "        self.train_pos = train_pos\n",
    "        self.train_neg = train_neg\n",
    "        self.batch_size = batch_size\n",
    "        self.train_pos_list = os.listdir(train_pos)\n",
    "        self.train_neg_list = os.listdir(train_neg)\n",
    "        self.vocab_size = vocab_size\n",
    "        self.sequence_length = sequence_length\n",
    "        \n",
    "    def __getitem__(self,idx):\n",
    "        X,y=self.__data_generation(idx)\n",
    "        return X,y\n",
    "    def __data_generation(self,idx):\n",
    "\n",
    "        x=[]\n",
    "        y=[]\n",
    "\n",
    "        for j in range(idx*self.batch_size,(idx+1)*self.batch_size):\n",
    "            for t in range(2):\n",
    "                if t==0:\n",
    "                    with open(self.train_pos+self.train_pos_list[j],encoding=\"utf-8\") as f:\n",
    "                        for line in f:\n",
    "                            lin=line\n",
    "\n",
    "                else:\n",
    "                    with open(self.train_neg+self.train_neg_list[j],encoding=\"utf-8\") as f:\n",
    "                        for line in f:\n",
    "                            lin=line\n",
    "                rev=[]\n",
    "                for k,i in enumerate(tf.strings.split(preprocess_sentences(lin))):\n",
    "                    rev.append(word_index(str(i.numpy())[2:-1]))\n",
    "                    if k>=(250-1):\n",
    "                        break\n",
    "                out=tf.concat([tf.constant(rev),tf.zeros([self.sequence_length-len(rev)],dtype=tf.int32)],axis=0)\n",
    "                f.close()\n",
    "                X.append(list(out.numpy()))\n",
    "                \n",
    "                if t==0:\n",
    "                    y.append(1)\n",
    "                else:\n",
    "                    y.append(0)\n",
    "        return tf.constant(X),tf.constant(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c3fe501",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pos='...'\n",
    "train_neg='...'\n",
    "\n",
    "val_pos='...'\n",
    "val_neg='...'\n",
    "\n",
    "BATCH_SIZE=32\n",
    "LR=1E-4\n",
    "VOCAB_SIZE=30000\n",
    "SEQUENCE_LENGTH=250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84d7ad35",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen=DataGenerator(train_pos,train_neg,VOCAB_SIZE,SEQUENCE_LENGTH,BATCH_SIZE)\n",
    "train_gen=DataGenerator(val_pos,val_neg,VOCAB_SIZE,SEQUENCE_LENGTH,BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cb741bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_matrix=[]\n",
    "\n",
    "for i in range(VOCAB_SIZE):\n",
    "    embedding_matrix.append(wv[i])\n",
    "embedding_matrix=np.array(embedding_matrix)\n",
    "print(embedding_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caed3f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs=tf.keras.layers.Input(shape=(SEQUENCE_LENGTH,),)\n",
    "\n",
    "embedding=tf.keras.layers.Embedding(\n",
    "    VOCAB_SIZE,\n",
    "    300,\n",
    "    embeddings_initializer=tf.keras.initializers.Constant(embedding_matrix),\n",
    "    trainable=False,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3c222b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model=tf.keras.models.Sequential([\n",
    "    inputs,\n",
    "    embedding,\n",
    "    tf.keras.layers.Conv1D(256,kernel_size=2,activation='relu'),\n",
    "    tf.keras.layers.MaxPooling1D(2),\n",
    "    \n",
    "    tf.keras.layers.Conv1D(128,kernel_size=2,activation='relu'),\n",
    "    tf.keras.layers.MaxPooling1D(2),\n",
    "    \n",
    "    tf.keras.layers.GlobalMaxPooling1D(),\n",
    "    tf.keras.layers.Dense(1,activation='sigmoid',),\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f8d0d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(lr=LR),\n",
    "    metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7193b736",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_filepath='...'\n",
    "log_dir='...'\n",
    "callback=tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,\n",
    "    save_weights_only=True,\n",
    "    monitor='loss',\n",
    "    mode='min',\n",
    "    save_best_only=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "375d07e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_callback=tf.keras.callbacks.TensorBoard(log_dir=log_dir,histogram_freq=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0316c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "history=model.fit(train_gen,verbose=1,epochs=EPOCH,callbacks=[callback,t_callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15454cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exist(log_dir):\n",
    "    os.makedirs(log_dir)\n",
    "    \n",
    "with open(os.path.join(log_dir,'metadaata.tsv'),\"w\",encodings=\"utf-8\") as f:\n",
    "    for i in range(VOCAB_SIZE):\n",
    "        f.write(\"{} {}\\n\".format(i,wv.index_to_key[i]))\n",
    "\n",
    "embedding_weights=tf.Variable(model.layers[0].get_weights()[0])\n",
    "checkpoint=tf.train.Checkpoint(embedding-embedding_weights)\n",
    "checkpoint.save(os.path.join(log_dir,\"embedding.ckpt\"))\n",
    "\n",
    "config=projector.ProjectorConfig()\n",
    "embedding=config.embeddings.add()\n",
    "\n",
    "embedding.metadata_path='metadata.tsv'\n",
    "projector.visualize_embeddings(log_dir,config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebdaa94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%tensorboard --log_dir logs/imdb/fit/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f499781",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faa25fcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8318b8fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7451f4ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "891521ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c8dbb90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c8c0a06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83660f97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5629549",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: deprecated/files/exam.csv
================================================
EXAM1,EXAM2,EXAM3,FINAL,
73,80,75,152,0
93,88,93,185,1
89,91,90,180,1
96,98,100,196,1
73,66,70,142,0
53,46,55,101,0
69,74,77,149,0
47,56,60,115,0
87,79,90,175,1
79,70,88,164,1
69,70,73,141,0
70,65,74,141,0
93,95,91,184,1
79,80,73,152,0
70,73,78,148,0
93,89,96,192,1
78,75,68,147,0
81,90,93,183,1
88,92,86,177,1
78,83,77,159,0
82,86,90,177,1
86,82,89,175,1
78,83,85,175,1
76,83,71,149,0
96,93,95,192,1

================================================
FILE: deprecated/files/health.csv
================================================
X1,X2,X3,X4,X5
8,78,284,9.10000038,109
9.30000019,68,433,8.69999981,144
7.5,70,739,7.19999981,113
8.89999962,96,1792,8.89999962,97
10.1999998,74,477,8.30000019,206
8.30000019,111,362,10.8999996,124
8.80000019,77,671,10,152
8.80000019,168,636,9.10000038,162
10.6999998,82,329,8.69999981,150
11.6999998,89,634,7.59999991,134
8.5,149,631,10.8000002,292
8.30000019,60,257,9.5,108
8.19999981,96,284,8.80000019,111
7.9000001,83,603,9.5,182
10.3000002,130,686,8.69999981,129
7.4000001,145,345,11.1999998,158
9.60000038,112,1357,9.69999981,186
9.30000019,131,544,9.60000038,177
10.6000004,80,205,9.10000038,127
9.69999981,130,1264,9.19999981,179
11.6000004,140,688,8.30000019,80
8.10000038,154,354,8.39999962,103
9.80000019,118,1632,9.39999962,101
7.4000001,94,348,9.80000019,117
9.39999962,119,370,10.3999996,88
11.1999998,153,648,9.89999962,78
9.10000038,116,366,9.19999981,102
10.5,97,540,10.3000002,95
11.8999996,176,680,8.89999962,80
8.39999962,75,345,9.60000038,92
5,134,525,10.3000002,126
9.80000019,161,870,10.3999996,108
9.80000019,111,669,9.69999981,77
10.8000002,114,452,9.60000038,60
10.1000004,142,430,10.6999998,71
10.8999996,238,822,10.3000002,86
9.19999981,78,190,10.6999998,93
8.30000019,196,867,9.60000038,106
7.30000019,125,969,10.5,162
9.39999962,82,499,7.69999981,95
9.39999962,125,925,10.1999998,91
9.80000019,129,353,9.89999962,52
3.59999991,84,288,8.39999962,110
8.39999962,183,718,10.3999996,69
10.8000002,119,540,9.19999981,57
10.1000004,180,668,13,106
9,82,347,8.80000019,40
10,71,345,9.19999981,50
11.3000002,118,463,7.80000019,35
11.3000002,121,728,8.19999981,86
12.8000002,68,383,7.4000001,57
10,112,316,10.3999996,57
6.69999981,109,388,8.89999962,94

================================================
FILE: neural networks from scratch/1-Linear Regression by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "92c53c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ba87eca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(filename):\n",
    "    '''\n",
    "    ###### Objective\n",
    "    A function used to extract data from the csv file and preprocess it, such that it can be\n",
    "    used by our machine learning algorithm\n",
    "    \n",
    "    ### Input\n",
    "    filename\n",
    "    \n",
    "    #### Output\n",
    "    Input and output of machine learning model (in this case, a linear regression model)\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    dataframe = pandas.read_csv(filename)\n",
    "    print(dataframe)\n",
    "    \n",
    "    X1,X2 = [],[]\n",
    "    \n",
    "    for item,row in dataframe.iterrows():\n",
    "        X1.append(row['X1'])\n",
    "        X2.append(row['X2'])\n",
    "        \n",
    "    for i in range(len(X1)):\n",
    "        X.append([1,X2[i]])\n",
    "        Y.append([X1[i]])\n",
    "        \n",
    "    X = np.array(X)\n",
    "    Y = np.array(Y)\n",
    "    \n",
    "    theta1_theta2 = np.linalg.inv(X.T@X)@X.T@Y\n",
    "    print(theta1_theta2)\n",
    "    \n",
    "    return X,Y, theta1_theta2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70623a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y, theta1_theta2 = prepare_dataset('health.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dc19586",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(20)\n",
    "\n",
    "EPOCHS = 10\n",
    "LR = 0.1\n",
    "BATCH_SIZE = X.shape[0]\n",
    "\n",
    "theta = np.random.randn(X.shape[1],1)\n",
    "loss = []\n",
    "\n",
    "for i in range(EPOCHS):\n",
    "    epoch_loss = 0.0\n",
    "    \n",
    "    for b in range(0,len(X),BATCH_SIZE):\n",
    "        d_theta = (X[b:b+BATCH_SIZE].T@(X[b:b+BATCH_SIZE]@theta - Y[b:b+BATCH_SIZE]))/BATCH_SIZE\n",
    "        theta -= LR*(d_theta)##########gradient descent step\n",
    "        epoch_loss += (np.square(Y[b:b+BATCH_SIZE]-(x[b:b+BATCH_SIZE]@theta)).mean())\n",
    "    loss.append(epoch_loss)\n",
    "\n",
    "print('The loss at the end of training is ==', loss[-1])\n",
    "plt.plot(range(1,EPOCHS+1),loss)\n",
    "plt.ylabel('MSE')\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.show()\n",
    "print(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b39201f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pandas.read_csv('health.csv')\n",
    "\n",
    "X2 = []\n",
    "X1 = []\n",
    "\n",
    "for item,row in dataframe.iterrows():\n",
    "    X2.append(row['X2'])\n",
    "    X1.append(row['X1'])\n",
    "\n",
    "X = [i for i in range(0,230)]\n",
    "Y = [(theta1_theta2[0]*i+ (theta1_theta2[1]*i)) for i in range(0,230)]\n",
    "\n",
    "plt.scatter(X2,X1, marker='+')\n",
    "plt.plot(X,Y, color='red')\n",
    "plt.ylabel('X1')\n",
    "plt.xlabel('X2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "677ef1bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d775589",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03d1e358",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: neural networks from scratch/2-Logistic Regression by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ffc022e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a08f4f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(filename):\n",
    "    '''\n",
    "    ###### Objective\n",
    "    A function used to extract data from the csv file and preprocess it, such that it can be\n",
    "    used by our machine learning algorithm\n",
    "    \n",
    "    ### Input\n",
    "    filename\n",
    "    \n",
    "    #### Output\n",
    "    Input and output of machine learning model (in this case, a logistic regression model)\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    dataframe = pandas.read_csv(filename)\n",
    "    \n",
    "    X1,X2,X3 = [],[],[]\n",
    "    X,Y = [],[]\n",
    "    \n",
    "    for item,row in dataframe.iterrows():\n",
    "        X1.append(row['EXAM1'])\n",
    "        X2.append(row['EXAM2'])\n",
    "        X3.append(row['FINAL'])\n",
    "        \n",
    "    for i in range(len(X1)):\n",
    "        X.append([1,X1[i]/100,X2[i]/100])\n",
    "        if X3[i]>160:\n",
    "            Y.append([1])\n",
    "        else:\n",
    "            Y.append([0])\n",
    "        \n",
    "    X = np.array(X)\n",
    "    Y = np.array(Y)\n",
    "    \n",
    "    return X,Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "732da12d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = prepare_dataset('exam.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fd8343c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1cd6e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid_proba(x):\n",
    "    if sigmoid(x)>0.5:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9332d1a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "\n",
    "EPOCHS = 10\n",
    "LR = 1e-1\n",
    "\n",
    "BATCH_SIZE = X.shape[0]\n",
    "\n",
    "theta = np.random.randn(X.shape[1],1)\n",
    "\n",
    "loss = []\n",
    "\n",
    "for i in range(EPOCHS):\n",
    "    epoch_loss = 0.0\n",
    "    for b in range(0,len(X), BATCH_SIZE):\n",
    "        model_output = sigmoid(X[b:b+BATCH_SIZE]@theta)\n",
    "        \n",
    "        d_theta = (X[b:b+BATCH_SIZE].T@(((model_output - Y[b:b+BATCH_SIZE]))))\n",
    "        theta -= LR*(d_theta)################## gradient descent step\n",
    "        \n",
    "        epoch_loss += -((Y[b:b+BATCH_SIZE]*np.log(model_output)) + ((1-Y[b:b+BATCH_SIZE])*np.log(1-model_output))).mean()\n",
    "    loss.append(epoch_loss)\n",
    "    \n",
    "print('The loss at the end of training is ==', loss[-1])\n",
    "plt.plot(range(1,EPOCHS+1),loss)\n",
    "plt.ylabel('BCE')\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.show()\n",
    "print(theta)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "646c286b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pandas.read_csv('exam.csv')\n",
    "\n",
    "X2 = []\n",
    "X1 = []\n",
    "\n",
    "for item,row in dataframe.iterrows():\n",
    "    X1.append(row['EXAM1'])\n",
    "    X2.append(row['EXAM2'])\n",
    "\n",
    "categories = Y.reshape((len(Y)))\n",
    "\n",
    "colormap = np.array(['r','g'])\n",
    "plt.scatter(X1,X2, s=20,c+colormap[categories])\n",
    "\n",
    "x1 = []\n",
    "x2 = []\n",
    "y = []\n",
    "\n",
    "for i in range(40,100):\n",
    "    for j in range(40,100):\n",
    "        x1.append(i)\n",
    "        x2.append(j)\n",
    "        model_output = sigmoid_proba(np.array([1,i/100,j/100])@theta)\n",
    "        y.append(model_output)\n",
    "categories = y\n",
    "\n",
    "plt.scatter(x1,x2, s=1,c=colormap[categories])\n",
    "\n",
    "plt.ylabel('EXAM1')\n",
    "plt.xlabel('EXAM2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00f3f0cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}


================================================
FILE: neural networks from scratch/3-Softmax Regression by Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f46cab9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "587e98b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_one_hot(value):\n",
    "    one_hot = [0.0]*26\n",
    "    one_hot[value] = 1.0\n",
    "    return one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bdc144d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(filename):\n",
    "    '''\n",
    "    ###### Objective\n",
    "    A function used to extract data from the csv file and preprocess it, such that it can be\n",
    "    used by our machine learning algorithm\n",
    "    \n",
    "    ### Input\n",
    "    filename\n",
    "    \n",
    "    #### Output\n",
    "    Input and output of machine learning model (in this case, a SOFTMAX classification model)\n",
    "    \n",
    "    '''\n",
    "    alphabet = {\"A\":0,\"B\":1,\"C\":2,\"D\":3,\"E\":4,\"F\":5,\"G\":6,\"H\":7,\"I\":8,\"J\":9,\n",
    "               \"K\":10,\"L\":11,\"M\":12,\"N\":13,\"O\":14,\"P\":15,\"Q\":16,\"R\":17,\"S\":18,\"T\":19,\n",
    "               \"U\":20,\"V\":21,\"W\":22,\"X\":23,\"Y\":24,\"Z\":25}\n",
    "    \n",
    "    dataframe = pandas.read_csv(filename)\n",
    "    \n",
    "    X = []\n",
    "    Y = []\n",
    "    Y_hot = []\n",
    "    \n",
    "    for item,row in dataframe.iterrows():\n",
    "        X1.append([1,row['INPUT1'],row['INPUT2'],row['INPUT3'],row['INPUT4'],row['INPUT5'],row['INPUT6'],row['INPUT7'],row['INPUT8'],row['INPUT9'],row['INPUT10'],row['INPUT11'],row['INPUT12'],row['INPUT13'],row['INPUT14'],row['INPUT15'],row['INPUT16']])\n",
    "        X2.append([row['LETTER']])\n",
    "    X = np.array(X)\n",
    "    Y = np.array(Y)\n",
    "        \n",
    "    for i in Y:\n",
    "        Y_hot.append(generate_one_hot(alphabet[i[0]]))\n",
    "    Y_hot = np.array(Y_hot)\n",
    "    \n",
    "    return X,Y_hot\n",
    "\n",
    "X,Y = prepare_dataset('letter-recognition.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12a09886",
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(X):\n",
    "    k = np.sum(np.exp(X), axis = 1)\n",
    "    t = 0\n",
    "    X_out = []\n",
    "    \n",
    "    for i in X:\n",
    "        X_out.append(list(np.exp(i)/k[t]))\n",
    "        t += 1\n",
    "    return np.array(X_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96853fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cce_loss(y_pred, y_true):\n",
    "    loss = y_true*np.log(y_pred)\n",
    "    return -np.sum(loss)/len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "592dff02",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "\n",
    "X = X[0:10000]\n",
    "Y = Y[0:10000]\n",
    "\n",
    "\n",
    "EPOCHS = 100\n",
    "LR = 1e-2\n",
    "\n",
    "BATCH_SIZE = X.shape[0]\n",
    "OUTPUT_SIZE = 26\n",
    "PRINT_FREQUENCY = 50\n",
    "\n",
    "theta = np.random.randn(X.shape[1],OUTPUT_SIZE)\n",
    "\n",
    "loss = []\n",
    "best_theta = theta\n",
    "best_loss = np.inf\n",
    "\n",
    "for i in range(EPOCHS):\n",
    "    epoch_loss = 0.0\n",
    "    for b in range(0,len(X), BATCH_SIZE):\n",
    "        model_output = softmax(X[b:b+BATCH_SIZE]@theta)\n",
    "        \n",
    "        d_theta = (X[b:b+BATCH_SIZE].T@(((model_output - Y[b:b+BATCH_SIZE]))))\n",
    "        theta -= LR*(d_theta)################## gradient descent step\n",
    "        \n",
    "        epoch_loss += cce_loss(model_output, Y[b:b+BATCH_SIZE])\n",
    "    if(epoch_loss<best_loss):\n",
    "        best_loss = epoch_loss\n",
    "        best_theta = theta\n",
    "    if i%PRINT_FREQUENCY == 0:\n",
    "        print('Epoch number = '+str(i)+'----> Loss = '+str(epoch_loss))\n",
    "    \n",
    "    loss.append(epoch_loss)\n",
    "    \n",
    "print('The loss at the end of training is ==', loss[-1])\n",
    "print('The best loss is ==', best_loss)\n",
    "\n",
    "plt.plot(range(1,EPOCHS+1),loss)\n",
    "plt.ylabel('CCE')\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.show()\n",
    "print(theta)\n"
   ]
  }
 ],
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================================================
FILE: neural networks from scratch/4-Artificial Neural Networks from scratch Neuralearn.ai.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3014de4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pkbar ## tracing losses during training\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c0abde",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parameter():\n",
    "    def __init__(self, tensor):\n",
    "        self.weights = tensor\n",
    "        self.gradients = np.zeros_like(self.weights)\n",
    "        self.bias = np.zeros((tensor.shape[-1]))\n",
    "        self.bias_gradients = np.zeros_like(self.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2158330f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Layer:\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    def init_param(self, tensor):\n",
    "        param = Parameter(tensor)\n",
    "        self.parameters = param\n",
    "        return param\n",
    "    def update(self, optimizer):\n",
    "        optimizer.update(self.parameters)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "475af0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear(Layer):\n",
    "    \n",
    "    '''\n",
    "    #### Objective\n",
    "    A class which defines the linear layer\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, inputs, outputs):\n",
    "        super().__init__()\n",
    "        tensor = np.random.randn(inputs, outputs)\n",
    "        self.parameters = self.init_param(tensor)\n",
    "    def backward(self, D, X):\n",
    "        '''\n",
    "        ###### Objective\n",
    "        A backward pass\n",
    "        ######  Input\n",
    "        partial derivative with respect to end features and start features\n",
    "        ##### Output\n",
    "        partial derivative with respect to start function\n",
    "        '''\n",
    "        return D@self.parameters.weights.T### D*theta.T\n",
    "        \n",
    "    def forward(self, X):\n",
    "        '''\n",
    "        ###### Objective\n",
    "        A forward pass\n",
    "        ######  Input\n",
    "        start features\n",
    "        ##### Output\n",
    "        end features\n",
    "        '''\n",
    "        return X@self.parameters.weights + self.parameters.bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3819ba19",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sigmoid(Layer):\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    def backward(self, D, X):\n",
    "        S = 1/(1+np.exp(-X))\n",
    "        return D*(S*(1-S))\n",
    "    def forward(self, X):\n",
    "        return 1/(1+np.exp(-X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2214368d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Softmax(Layer):\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    \n",
    "    def backward(self, D, X):\n",
    "        return D\n",
    "    def softmax(self, X):\n",
    "        k = np.sum(np.exp(X), axis = 1)\n",
    "        t = 0\n",
    "        X_out = []\n",
    "        \n",
    "        for i in X:\n",
    "            X_out.append(list(np.exp(i)/k[t]))\n",
    "            t+=1\n",
    "        return np.array(X_out)\n",
    "    \n",
    "    def forward(self, X):\n",
    "        return self.softmax(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbc897db",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SGD():\n",
    "    def __init__(self, lr=0.1):\n",
    "        self.lr = lr\n",
    "    def update(self, param):\n",
    "        '''\n",
    "        ##### Objective\n",
    "        Update of parameters using gradient descent\n",
    "        ##### Input\n",
    "        Parameters\n",
    "        ##### Output\n",
    "        Updated Parameters\n",
    "        '''\n",
    "        param.weights -= self.lr*param.gradients\n",
    "        param.bias -= self.lr *param.bias_gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a1ba57b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tanh(Layer):\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    def backward(self, D, X):\n",
    "        return D*(1-(np.tanh(X)*np.tanh(X)))\n",
    "    def forward(self, X):\n",
    "        return np.tanh(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93f89bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ReLU(Layer):\n",
    "    def __init__(self):\n",
    "        self.parameters = None\n",
    "    def backward(self, D, X):\n",
    "        return D*(1*(X>0))\n",
    "    def forward(self, X):\n",
    "        return np.maximum(0,X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d549c7e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model()\n",
    "    def __init__(self):\n",
    "        self.computational_graph = []\n",
    "    def add(self, layer):\n",
    "        '''\n",
    "        ###### Objective\n",
    "        Add a layer to the computational graph\n",
    "        ###### Input\n",
    "        layer\n",
    "        \n",
    "        '''\n",
    "        self.computational_graph.append(layer)\n",
    "    def compiler(self, loss, optimizer):\n",
    "        '''\n",
    "        ###### Objective \n",
    "        compile a model(give it all additional properties needed for training)\n",
    "        ###### Input\n",
    "        loss and optimizer to be used\n",
    "        '''\n",
    "        self.loss = loss\n",
    "        self.optimizer = optimizer\n",
    "    def forward(self, X):\n",
    "        '''\n",
    "        ###### Objective \n",
    "        A forward pass\n",
    "        ###### Input\n",
    "        Input data for model\n",
    "        ###### Output\n",
    "        predicted data by model, plus intermediary layers\n",
    "        '''\n",
    "        Y_int = X\n",
    "        Y_int_list = []\n",
    "        \n",
    "        for layer in self.computational_graph:\n",
    "            Y_int_list.append(Y_int)\n",
    "            Y_ = layer.forward(Y_int)\n",
    "            Y_int = Y_\n",
    "        return Y_, Y_int_list\n",
    "    def fit_batch(self, X,Y):\n",
    "        '''\n",
    "        ####### OBjective\n",
    "        optimize the parameters of a particular batch\n",
    "        ###### Input \n",
    "        the input and output of dataset\n",
    "        \n",
    "        ##### Ouput\n",
    "        loss\n",
    "        \n",
    "        '''\n",
    "        out = X\n",
    "        Y_, Y_int_list = self.forward(X)\n",
    "        \n",
    "        D = predicted_output - target_output\n",
    "        L,D = self.loss(Y_,Y)\n",
    "        for Y_int, layer in zip(Y_int_list[::-1], self.computational_graph[::-1]):\n",
    "            D = layer.backward(D,Y_int)\n",
    "            if layer.parameters is not None:\n",
    "                layer.update(self.optimizer)\n",
    "        return L\n",
    "    def fit(self, X,Y, epochs, bs):\n",
    "        losses = []\n",
    "        \n",
    "        pbar = pkbar.Pbar(name='Training', target = epochs)\n",
    "        kbar = pkbar.Kbar(target=epochs)\n",
    "        \n",
    "        for epoch in range(epochs):\n",
    "            loss = 0.0\n",
    "            for i in range(0, len(X), bs):\n",
    "                loss += self.fit_batch(X[i:i+bs], Y[i:i+bs])\n",
    "            losses.append(loss)\n",
    "            kbar.update(epoch+1, values=[('loss',loss)])\n",
    "        return losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbad96bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[0,0],\n",
    "              [0,1],\n",
    "              [1,0],\n",
    "              [1,1]], dtype = float)\n",
    "Y = np.array([[0],\n",
    "              [1],\n",
    "              [1],\n",
    "              [0]], dtype = float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19cfb72c",
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 10000\n",
    "model = Model()\n",
    "model.add(Linear(2,10))\n",
    "model.add(Sigmoid())\n",
    "model.add(Linear(10,1))\n",
    "model.add(Sigmoid())\n",
    "\n",
    "model.compiler(cce_loss, SGD(lr=5e-1))\n",
    "losses = model.fit(X,Y, epochs = EPOCHS, bs = X.shape[0])\n",
    "plt.plot(range(1,EPOCHS+1), losses)\n",
    "\n",
    "plt.ylabel('BCE')\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "945af1a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "id": "92c8006b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
   "id": "b12cc910",
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   "source": []
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   "execution_count": null,
   "id": "9c8eb9c2",
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================================================
FILE: neural networks from scratch/files/exam.csv
================================================
EXAM1,EXAM2,EXAM3,FINAL,
73,80,75,152,0
93,88,93,185,1
89,91,90,180,1
96,98,100,196,1
73,66,70,142,0
53,46,55,101,0
69,74,77,149,0
47,56,60,115,0
87,79,90,175,1
79,70,88,164,1
69,70,73,141,0
70,65,74,141,0
93,95,91,184,1
79,80,73,152,0
70,73,78,148,0
93,89,96,192,1
78,75,68,147,0
81,90,93,183,1
88,92,86,177,1
78,83,77,159,0
82,86,90,177,1
86,82,89,175,1
78,83,85,175,1
76,83,71,149,0
96,93,95,192,1

================================================
FILE: neural networks from scratch/files/health.csv
================================================
X1,X2,X3,X4,X5
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