From 341ef3ec71240b55075187b62096484a738422d2 Mon Sep 17 00:00:00 2001 From: alireza-akhavan Date: Mon, 2 Dec 2019 22:09:30 +0330 Subject: [PATCH] new file: 01_simple-RNN.ipynb new file: 02_1_simple-RNN-diffrent-sequence-length.ipynb new file: 02_2_simple-RNN-diffrent-sequence-length.ipynb new file: 03_1_Cryptocurrency-predicting.ipynb new file: 03_2_Cryptocurrency-predicting.ipynb new file: 04_simple-CNN-LSTM.ipynb new file: 05-1-video-action-recognition-train-extract-features-with-cnn.ipynb new file: 05-2_video-action-recognition-train-rnn.ipynb new file: 06_analogy-using-embeddings.ipynb new file: 07_text-classification-Emojify.ipynb new file: 08_shahnameh-text-generation-language-model.ipynb new file: 09_add-numbers-with-seq2seq.ipynb new file: 10_Neural-machine-translation-with-attention-for-date-convert.ipynb new file: 11_nmt-with-attention.ipynb new file: 12_image-captioning-with-attention.ipynb new file: TimeDistributed.ipynb new file: final_cnn_lstm.ipynb new file: images/attn_mechanism.png new file: images/attn_model.png new file: images/cosine_sim.png new file: images/data_set.png new file: images/date_attention.png new file: images/date_attention2.png new file: images/embedding1.png new file: images/emojifier-v2.png new file: images/image_1.png new file: images/poorly_trained_model.png new file: images/table.png new file: logo.png new file: nmt_utils.py --- 01_simple-RNN.ipynb | 693 +++++ ..._simple-RNN-diffrent-sequence-length.ipynb | 485 ++++ ..._simple-RNN-diffrent-sequence-length.ipynb | 818 ++++++ 03_1_Cryptocurrency-predicting.ipynb | 596 ++++ 03_2_Cryptocurrency-predicting.ipynb | 562 ++++ 04_simple-CNN-LSTM.ipynb | 647 +++++ ...tion-train-extract-features-with-cnn.ipynb | 326 +++ 05-2_video-action-recognition-train-rnn.ipynb | 477 +++ 06_analogy-using-embeddings.ipynb | 341 +++ 07_text-classification-Emojify.ipynb | 1263 ++++++++ ...nameh-text-generation-language-model.ipynb | 1327 +++++++++ 09_add-numbers-with-seq2seq.ipynb | 512 ++++ ...tion-with-attention-for-date-convert.ipynb | 962 +++++++ 11_nmt-with-attention.ipynb | 1261 ++++++++ 12_image-captioning-with-attention.ipynb | 2547 +++++++++++++++++ TimeDistributed.ipynb | 119 + final_cnn_lstm.ipynb | 488 ++++ images/attn_mechanism.png | Bin 0 -> 172401 bytes images/attn_model.png | Bin 0 -> 277594 bytes images/cosine_sim.png | Bin 0 -> 106128 bytes images/data_set.png | Bin 0 -> 206021 bytes images/date_attention.png | Bin 0 -> 134251 bytes images/date_attention2.png | Bin 0 -> 132831 bytes images/embedding1.png | Bin 0 -> 334776 bytes images/emojifier-v2.png | Bin 0 -> 155065 bytes images/image_1.png | Bin 0 -> 245447 bytes images/poorly_trained_model.png | Bin 0 -> 10314 bytes images/table.png | Bin 0 -> 89024 bytes logo.png | Bin 0 -> 1480662 bytes nmt_utils.py | 251 ++ 30 files changed, 13675 insertions(+) create mode 100644 01_simple-RNN.ipynb create mode 100644 02_1_simple-RNN-diffrent-sequence-length.ipynb create mode 100644 02_2_simple-RNN-diffrent-sequence-length.ipynb create mode 100644 03_1_Cryptocurrency-predicting.ipynb create mode 100644 03_2_Cryptocurrency-predicting.ipynb create mode 100644 04_simple-CNN-LSTM.ipynb create mode 100644 05-1-video-action-recognition-train-extract-features-with-cnn.ipynb create mode 100644 05-2_video-action-recognition-train-rnn.ipynb create mode 100644 06_analogy-using-embeddings.ipynb create mode 100644 07_text-classification-Emojify.ipynb create mode 100644 08_shahnameh-text-generation-language-model.ipynb create mode 100644 09_add-numbers-with-seq2seq.ipynb create mode 100644 10_Neural-machine-translation-with-attention-for-date-convert.ipynb create mode 100644 11_nmt-with-attention.ipynb create mode 100644 12_image-captioning-with-attention.ipynb create mode 100644 TimeDistributed.ipynb create mode 100644 final_cnn_lstm.ipynb create mode 100644 images/attn_mechanism.png create mode 100644 images/attn_model.png create mode 100644 images/cosine_sim.png create mode 100644 images/data_set.png create mode 100644 images/date_attention.png create mode 100644 images/date_attention2.png create mode 100644 images/embedding1.png create mode 100644 images/emojifier-v2.png create mode 100644 images/image_1.png create mode 100644 images/poorly_trained_model.png create mode 100644 images/table.png create mode 100644 logo.png create mode 100644 nmt_utils.py diff --git a/01_simple-RNN.ipynb b/01_simple-RNN.ipynb new file mode 100644 index 0000000..0e5e69a --- /dev/null +++ b/01_simple-RNN.ipynb @@ -0,0 +1,693 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

مقدمات شبکه‌های بازگشتی و SimpleRNN

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, SimpleRNN\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
ایجاد مجموعه داده
\n", + "\n", + "
\n", + "ایجاد یک سری سینوسی برای 1500 گام زمانی با نویز تصادفی.
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "t = np.arange(0,1500)\n", + "x = np.sin(0.02*t)+ np.random.rand(1500) * 2\n", + "plt.plot(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
جدا کردن داده آموزشی و آزمون
\n", + "\n", + "
\n", + "1000 مقدار اول برای آموزش و 500 تای آخر برای تست در نظر گرفته شده است.
" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train,test = x[0:1000], x[1000:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
آماده سازی داده و ایجاد ورودی و label
\n", + "\n", + "
\n", + "در شبکه های RNN برای ورودی Step در نظر میگیریم.\n", + "برای شبکه های RNN مهمه که ورودی به شکل دنباله ای از داده باشد. در اینجا step طول دنباله ی ورودی را تعیین میکند. برای مثال اگر x را به عنوان داده خام داشته باشیم:\n", + "
\n", + "
\n", + "\n", + " x = [1,2,3,4,5,6,7,8,9,10]\n", + " \n", + " for step=1, x input and its y prediction becomes:\n", + " \n", + " x y\n", + " 1 2\n", + " 2 3\n", + " 3 4\n", + " 4 5\n", + " ..\n", + " 9 10\n", + " \n", + " for step=3, x and y contain:\n", + " \n", + " x y\n", + " 1,2,3 4\n", + " 2,3,4 5\n", + " 3,4,5 6\n", + " 4,5,6 7\n", + " ...\n", + " 7,8,9 10\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "step = 10\n", + "\n", + "# convert into dataset data and label\n", + "def convertToDataset(data, step):\n", + " #data = np.append(data,np.repeat(data[-1,],step))\n", + " X, Y =[], []\n", + " for i in range(len(data)-step):\n", + " d=i+step \n", + " X.append(data[i:d,])\n", + " Y.append(data[d,])\n", + " return np.array(X), np.array(Y)\n", + "\n", + "trainX,trainY =convertToDataset(train,step)\n", + "testX,testY =convertToDataset(test,step)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(990, 10)\n", + "(490, 10)\n" + ] + } + ], + "source": [ + "print(trainX.shape)\n", + "print(testX.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
تغییر ابعاد داده برای ورودی دادن به شبکه
\n", + "\n", + "
\n", + "ورودی یک شبکه RNN در Keras به صورت زیر است:\n", + "
\n", + "\n", + " (NumberOfSequences, TimeSteps, ElementsPerStep)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\n", + "testX = np.reshape(testX, (testX.shape[0],testX.shape[1], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(990, 10, 1)\n", + "(490, 10, 1)\n" + ] + } + ], + "source": [ + "print(trainX.shape)\n", + "print(testX.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
معماری شبکه و compile آن
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(SimpleRNN(units=64, activation=\"tanh\"))\n", + "model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='rmsprop')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "به نظر شما model.summary() را می‌توانیم صدا بزنیم؟ چرا؟\n", + "
\n", + "model.input\n", + "چه چیزی بر می‌گرداند!\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
آموزش مدل
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + " - 7s - loss: 0.7639\n", + "Epoch 2/100\n", + " - 1s - loss: 0.4325\n", + "Epoch 3/100\n", + " - 1s - loss: 0.4140\n", + "Epoch 4/100\n", + " - 1s - loss: 0.4124\n", + "Epoch 5/100\n", + " - 1s - loss: 0.4048\n", + "Epoch 6/100\n", + " - 1s - loss: 0.4024\n", + "Epoch 7/100\n", + " - 1s - loss: 0.3989\n", + "Epoch 8/100\n", + " - 1s - loss: 0.3947\n", + "Epoch 9/100\n", + " - 1s - loss: 0.3917\n", + "Epoch 10/100\n", + " - 1s - loss: 0.3912\n", + "Epoch 11/100\n", + " - 1s - loss: 0.3889\n", + "Epoch 12/100\n", + " - 1s - loss: 0.3908\n", + "Epoch 13/100\n", + " - 1s - loss: 0.3847\n", + "Epoch 14/100\n", + " - 1s - loss: 0.3862\n", + "Epoch 15/100\n", + " - 1s - loss: 0.3829\n", + "Epoch 16/100\n", + " - 1s - loss: 0.3755\n", + "Epoch 17/100\n", + " - 1s - loss: 0.3829\n", + "Epoch 18/100\n", + " - 1s - loss: 0.3742\n", + "Epoch 19/100\n", + " - 1s - loss: 0.3674\n", + "Epoch 20/100\n", + " - 1s - loss: 0.3691\n", + "Epoch 21/100\n", + " - 1s - loss: 0.3724\n", + "Epoch 22/100\n", + " - 1s - loss: 0.3703\n", + "Epoch 23/100\n", + " - 1s - loss: 0.3643\n", + "Epoch 24/100\n", + " - 1s - loss: 0.3625\n", + "Epoch 25/100\n", + " - 1s - loss: 0.3631\n", + "Epoch 26/100\n", + " - 1s - loss: 0.3631\n", + "Epoch 27/100\n", + " - 1s - loss: 0.3628\n", + "Epoch 28/100\n", + " - 1s - loss: 0.3552\n", + "Epoch 29/100\n", + " - 1s - loss: 0.3560\n", + "Epoch 30/100\n", + " - 1s - loss: 0.3585\n", + "Epoch 31/100\n", + " - 1s - loss: 0.3478\n", + "Epoch 32/100\n", + " - 1s - loss: 0.3495\n", + "Epoch 33/100\n", + " - 1s - loss: 0.3499\n", + "Epoch 34/100\n", + " - 1s - loss: 0.3411\n", + "Epoch 35/100\n", + " - 1s - loss: 0.3415\n", + "Epoch 36/100\n", + " - 1s - loss: 0.3381\n", + "Epoch 37/100\n", + " - 1s - loss: 0.3396\n", + "Epoch 38/100\n", + " - 1s - loss: 0.3311\n", + "Epoch 39/100\n", + " - 1s - loss: 0.3328\n", + "Epoch 40/100\n", + " - 1s - loss: 0.3291\n", + "Epoch 41/100\n", + " - 1s - loss: 0.3262\n", + "Epoch 42/100\n", + " - 1s - loss: 0.3210\n", + "Epoch 43/100\n", + " - 1s - loss: 0.3249\n", + "Epoch 44/100\n", + " - 1s - loss: 0.3210\n", + "Epoch 45/100\n", + " - 1s - loss: 0.3164\n", + "Epoch 46/100\n", + " - 1s - loss: 0.3159\n", + "Epoch 47/100\n", + " - 1s - loss: 0.3071\n", + "Epoch 48/100\n", + " - 1s - loss: 0.3031\n", + "Epoch 49/100\n", + " - 1s - loss: 0.3018\n", + "Epoch 50/100\n", + " - 1s - loss: 0.2984\n", + "Epoch 51/100\n", + " - 1s - loss: 0.3010\n", + "Epoch 52/100\n", + " - 1s - loss: 0.2901\n", + "Epoch 53/100\n", + " - 1s - loss: 0.2881\n", + "Epoch 54/100\n", + " - 1s - loss: 0.2847\n", + "Epoch 55/100\n", + " - 1s - loss: 0.2789\n", + "Epoch 56/100\n", + " - 1s - loss: 0.2759\n", + "Epoch 57/100\n", + " - 1s - loss: 0.2741\n", + "Epoch 58/100\n", + " - 1s - loss: 0.2668\n", + "Epoch 59/100\n", + " - 1s - loss: 0.2603\n", + "Epoch 60/100\n", + " - 1s - loss: 0.2589\n", + "Epoch 61/100\n", + " - 1s - loss: 0.2579\n", + "Epoch 62/100\n", + " - 1s - loss: 0.2502\n", + "Epoch 63/100\n", + " - 1s - loss: 0.2473\n", + "Epoch 64/100\n", + " - 1s - loss: 0.2426\n", + "Epoch 65/100\n", + " - 1s - loss: 0.2319\n", + "Epoch 66/100\n", + " - 1s - loss: 0.2306\n", + "Epoch 67/100\n", + " - 1s - loss: 0.2281\n", + "Epoch 68/100\n", + " - 1s - loss: 0.2242\n", + "Epoch 69/100\n", + " - 1s - loss: 0.2205\n", + "Epoch 70/100\n", + " - 1s - loss: 0.2181\n", + "Epoch 71/100\n", + " - 1s - loss: 0.2095\n", + "Epoch 72/100\n", + " - 1s - loss: 0.2083\n", + "Epoch 73/100\n", + " - 1s - loss: 0.2023\n", + "Epoch 74/100\n", + " - 1s - loss: 0.1980\n", + "Epoch 75/100\n", + " - 1s - loss: 0.1979\n", + "Epoch 76/100\n", + " - 1s - loss: 0.1899\n", + "Epoch 77/100\n", + " - 1s - loss: 0.1857\n", + "Epoch 78/100\n", + " - 1s - loss: 0.1832\n", + "Epoch 79/100\n", + " - 1s - loss: 0.1764\n", + "Epoch 80/100\n", + " - 1s - loss: 0.1780\n", + "Epoch 81/100\n", + " - 1s - loss: 0.1701\n", + "Epoch 82/100\n", + " - 1s - loss: 0.1676\n", + "Epoch 83/100\n", + " - 1s - loss: 0.1635\n", + "Epoch 84/100\n", + " - 1s - loss: 0.1614\n", + "Epoch 85/100\n", + " - 1s - loss: 0.1601\n", + "Epoch 86/100\n", + " - 1s - loss: 0.1538\n", + "Epoch 87/100\n", + " - 1s - loss: 0.1492\n", + "Epoch 88/100\n", + " - 1s - loss: 0.1477\n", + "Epoch 89/100\n", + " - 1s - loss: 0.1400\n", + "Epoch 90/100\n", + " - 1s - loss: 0.1365\n", + "Epoch 91/100\n", + " - 1s - loss: 0.1354\n", + "Epoch 92/100\n", + " - 1s - loss: 0.1334\n", + "Epoch 93/100\n", + " - 1s - loss: 0.1303\n", + "Epoch 94/100\n", + " - 1s - loss: 0.1269\n", + "Epoch 95/100\n", + " - 1s - loss: 0.1231\n", + "Epoch 96/100\n", + " - 1s - loss: 0.1245\n", + "Epoch 97/100\n", + " - 1s - loss: 0.1151\n", + "Epoch 98/100\n", + " - 1s - loss: 0.1133\n", + "Epoch 99/100\n", + " - 1s - loss: 0.1130\n", + "Epoch 100/100\n", + " - 1s - loss: 0.1077\n" + ] + } + ], + "source": [ + "history = model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "حالا model.summary() را می‌توانیم صدا بزنیم؟ چرا؟\n", + "
\n", + " الان\n", + "model.input\n", + "چه چیزی بر می‌گرداند؟\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "loss = history.history['loss']\n", + "plt.plot(loss, label='Training loss')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
ارزیابی مدل
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.08597392486502425\n" + ] + } + ], + "source": [ + "trainScore = model.evaluate(trainX, trainY, verbose=0)\n", + "print(trainScore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
رسم سری اصلی و پیش بینی برای داده های آموزشی و تست
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "trainPredict = model.predict(trainX)\n", + "testPredict= model.predict(testX)\n", + "predicted=np.concatenate((trainPredict,testPredict),axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x)\n", + "plt.plot(predicted)\n", + "plt.axvline(len(trainX), c=\"r\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
ورودی مدل با طول متفاوت
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "testX,testY =convertToDataset(test,50)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(450, 50, 1)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "testX = np.reshape(testX, (testX.shape[0],testX.shape[1], 1))\n", + "testX.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "همان طور که مشخص است کد با خطا مواجه می‌شود! چرا؟!
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Error when checking input: expected sequential_1_input to have shape (10, 1) but got array with shape (50, 1)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#trainPredict = model.predict(trainX)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtestPredict\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtestX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;31m#predicted=np.concatenate((trainPredict,testPredict),axis=0)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[0;32m 1147\u001b[0m 'argument.')\n\u001b[0;32m 1148\u001b[0m \u001b[1;31m# Validate user data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1149\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_standardize_user_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1150\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstateful\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1151\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m 749\u001b[0m \u001b[0mfeed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 750\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 751\u001b[1;33m exception_prefix='input')\n\u001b[0m\u001b[0;32m 752\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 753\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 137\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' but got array with shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m str(data_shape))\n\u001b[0m\u001b[0;32m 139\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: Error when checking input: expected sequential_1_input to have shape (10, 1) but got array with shape (50, 1)" + ] + } + ], + "source": [ + "testPredict= model.predict(testX)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tensorflow", + "language": "python", + "name": "tensorflow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_1_simple-RNN-diffrent-sequence-length.ipynb b/02_1_simple-RNN-diffrent-sequence-length.ipynb new file mode 100644 index 0000000..492cf47 --- /dev/null +++ b/02_1_simple-RNN-diffrent-sequence-length.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

تغییر در طول بازه‌های زمانی ورودی شبکه

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, SimpleRNN\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "t = np.arange(0,1500)\n", + "x = np.sin(0.02*t)+ np.random.rand(1500) * 2\n", + "\n", + "train,test = x[0:1000], x[1000:]\n", + "\n", + "# convert into dataset data and label\n", + "def convertToDataset(data, step):\n", + " #data = np.append(data,np.repeat(data[-1,],step))\n", + " X, Y =[], []\n", + " for i in range(len(data)-step):\n", + " d=i+step \n", + " X.append(data[i:d,])\n", + " Y.append(data[d,])\n", + " return np.array(X), np.array(Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "train_step = 10\n", + "test_step = 20\n", + "\n", + "trainX,trainY =convertToDataset(train,train_step)\n", + "testX,testY =convertToDataset(test,test_step)\n", + "\n", + "trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\n", + "testX = np.reshape(testX, (testX.shape[0],testX.shape[1], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(990, 10, 1)\n", + "(480, 20, 1)\n" + ] + } + ], + "source": [ + "print(trainX.shape)\n", + "print(testX.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
معماری شبکه و compile آن
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(SimpleRNN(units=64, input_shape=(None, 1), activation=\"tanh\"))\n", + "model.add(Dense(1))\n", + "model.compile(loss='mean_squared_error', optimizer='rmsprop')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "simple_rnn_1 (SimpleRNN) (None, 64) 4224 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 1) 65 \n", + "=================================================================\n", + "Total params: 4,289\n", + "Trainable params: 4,289\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
آموزش مدل
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "حالا با این پیاده سازی به نظر شما میتوانیم در طول آموزش هم طول timestep متفاوت داشته باشیم؟
\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + " - 4s - loss: 0.5067\n", + "Epoch 2/100\n", + " - 0s - loss: 0.4110\n", + "Epoch 3/100\n", + " - 0s - loss: 0.4095\n", + "Epoch 4/100\n", + " - 0s - loss: 0.3995\n", + "Epoch 5/100\n", + " - 1s - loss: 0.3934\n", + "Epoch 6/100\n", + " - 0s - loss: 0.3865\n", + "Epoch 7/100\n", + " - 0s - loss: 0.3920\n", + "Epoch 8/100\n", + " - 1s - loss: 0.3851\n", + "Epoch 9/100\n", + " - 1s - loss: 0.3858\n", + "Epoch 10/100\n", + " - 0s - loss: 0.3869\n", + "Epoch 11/100\n", + " - 0s - loss: 0.3804\n", + "Epoch 12/100\n", + " - 0s - loss: 0.3806\n", + "Epoch 13/100\n", + " - 0s - loss: 0.3736\n", + "Epoch 14/100\n", + " - 0s - loss: 0.3740\n", + "Epoch 15/100\n", + " - 0s - loss: 0.3662\n", + "Epoch 16/100\n", + " - 0s - loss: 0.3708\n", + "Epoch 17/100\n", + " - 0s - loss: 0.3668\n", + "Epoch 18/100\n", + " - 0s - loss: 0.3632\n", + "Epoch 19/100\n", + " - 0s - loss: 0.3697\n", + "Epoch 20/100\n", + " - 0s - loss: 0.3612\n", + "Epoch 21/100\n", + " - 0s - loss: 0.3619\n", + "Epoch 22/100\n", + " - 0s - loss: 0.3577\n", + "Epoch 23/100\n", + " - 0s - loss: 0.3577\n", + "Epoch 24/100\n", + " - 0s - loss: 0.3534\n", + "Epoch 25/100\n", + " - 0s - loss: 0.3537\n", + "Epoch 26/100\n", + " - 0s - loss: 0.3556\n", + "Epoch 27/100\n", + " - 0s - loss: 0.3499\n", + "Epoch 28/100\n", + " - 0s - loss: 0.3484\n", + "Epoch 29/100\n", + " - 0s - loss: 0.3455\n", + "Epoch 30/100\n", + " - 0s - loss: 0.3415\n", + "Epoch 31/100\n", + " - 0s - loss: 0.3393\n", + "Epoch 32/100\n", + " - 0s - loss: 0.3332\n", + "Epoch 33/100\n", + " - 0s - loss: 0.3361\n", + "Epoch 34/100\n", + " - 0s - loss: 0.3387\n", + "Epoch 35/100\n", + " - 0s - loss: 0.3236\n", + "Epoch 36/100\n", + " - 0s - loss: 0.3313\n", + "Epoch 37/100\n", + " - 0s - loss: 0.3276\n", + "Epoch 38/100\n", + " - 0s - loss: 0.3225\n", + "Epoch 39/100\n", + " - 0s - loss: 0.3239\n", + "Epoch 40/100\n", + " - 0s - loss: 0.3161\n", + "Epoch 41/100\n", + " - 0s - loss: 0.3075\n", + "Epoch 42/100\n", + " - 0s - loss: 0.3118\n", + "Epoch 43/100\n", + " - 0s - loss: 0.3085\n", + "Epoch 44/100\n", + " - 0s - loss: 0.3020\n", + "Epoch 45/100\n", + " - 0s - loss: 0.2969\n", + "Epoch 46/100\n", + " - 0s - loss: 0.2989\n", + "Epoch 47/100\n", + " - 0s - loss: 0.2919\n", + "Epoch 48/100\n", + " - 0s - loss: 0.2893\n", + "Epoch 49/100\n", + " - 0s - loss: 0.2869\n", + "Epoch 50/100\n", + " - 0s - loss: 0.2843\n", + "Epoch 51/100\n", + " - 0s - loss: 0.2818\n", + "Epoch 52/100\n", + " - 0s - loss: 0.2788\n", + "Epoch 53/100\n", + " - 0s - loss: 0.2698\n", + "Epoch 54/100\n", + " - 0s - loss: 0.2686\n", + "Epoch 55/100\n", + " - 0s - loss: 0.2606\n", + "Epoch 56/100\n", + " - 0s - loss: 0.2595\n", + "Epoch 57/100\n", + " - 0s - loss: 0.2569\n", + "Epoch 58/100\n", + " - 0s - loss: 0.2531\n", + "Epoch 59/100\n", + " - 0s - loss: 0.2535\n", + "Epoch 60/100\n", + " - 0s - loss: 0.2413\n", + "Epoch 61/100\n", + " - 0s - loss: 0.2381\n", + "Epoch 62/100\n", + " - 0s - loss: 0.2340\n", + "Epoch 63/100\n", + " - 0s - loss: 0.2337\n", + "Epoch 64/100\n", + " - 0s - loss: 0.2308\n", + "Epoch 65/100\n", + " - 0s - loss: 0.2165\n", + "Epoch 66/100\n", + " - 0s - loss: 0.2228\n", + "Epoch 67/100\n", + " - 0s - loss: 0.2148\n", + "Epoch 68/100\n", + " - 0s - loss: 0.2090\n", + "Epoch 69/100\n", + " - 0s - loss: 0.2065\n", + "Epoch 70/100\n", + " - 0s - loss: 0.2043\n", + "Epoch 71/100\n", + " - 0s - loss: 0.1964\n", + "Epoch 72/100\n", + " - 0s - loss: 0.1957\n", + "Epoch 73/100\n", + " - 1s - loss: 0.1896\n", + "Epoch 74/100\n", + " - 1s - loss: 0.1830\n", + "Epoch 75/100\n", + " - 0s - loss: 0.1827\n", + "Epoch 76/100\n", + " - 1s - loss: 0.1808\n", + "Epoch 77/100\n", + " - 0s - loss: 0.1748\n", + "Epoch 78/100\n", + " - 0s - loss: 0.1662\n", + "Epoch 79/100\n", + " - 0s - loss: 0.1674\n", + "Epoch 80/100\n", + " - 0s - loss: 0.1636\n", + "Epoch 81/100\n", + " - 0s - loss: 0.1580\n", + "Epoch 82/100\n", + " - 0s - loss: 0.1580\n", + "Epoch 83/100\n", + " - 0s - loss: 0.1476\n", + "Epoch 84/100\n", + " - 0s - loss: 0.1502\n", + "Epoch 85/100\n", + " - 0s - loss: 0.1431\n", + "Epoch 86/100\n", + " - 0s - loss: 0.1377\n", + "Epoch 87/100\n", + " - 0s - loss: 0.1369\n", + "Epoch 88/100\n", + " - 0s - loss: 0.1321\n", + "Epoch 89/100\n", + " - 0s - loss: 0.1286\n", + "Epoch 90/100\n", + " - 0s - loss: 0.1256\n", + "Epoch 91/100\n", + " - 0s - loss: 0.1229\n", + "Epoch 92/100\n", + " - 0s - loss: 0.1206\n", + "Epoch 93/100\n", + " - 0s - loss: 0.1167\n", + "Epoch 94/100\n", + " - 0s - loss: 0.1145\n", + "Epoch 95/100\n", + " - 0s - loss: 0.1071\n", + "Epoch 96/100\n", + " - 0s - loss: 0.1078\n", + "Epoch 97/100\n", + " - 0s - loss: 0.1038\n", + "Epoch 98/100\n", + " - 0s - loss: 0.1023\n", + "Epoch 99/100\n", + " - 0s - loss: 0.0970\n", + "Epoch 100/100\n", + " - 0s - loss: 0.0994\n" + ] + } + ], + "source": [ + "history = model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
ارزیابی مدل
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.07633800416281729\n" + ] + } + ], + "source": [ + "trainScore = model.evaluate(trainX, trainY, verbose=0)\n", + "print(trainScore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
رسم سری اصلی و پیش بینی برای داده های آموزشی و تست
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "trainPredict = model.predict(trainX)\n", + "testPredict= model.predict(testX)\n", + "predicted=np.concatenate((trainPredict,testPredict),axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x)\n", + "plt.plot(predicted)\n", + "plt.axvline(len(trainX), c=\"r\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tensorflow", + "language": "python", + "name": "tensorflow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_2_simple-RNN-diffrent-sequence-length.ipynb b/02_2_simple-RNN-diffrent-sequence-length.ipynb new file mode 100644 index 0000000..281a76d --- /dev/null +++ b/02_2_simple-RNN-diffrent-sequence-length.ipynb @@ -0,0 +1,818 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

تغییر در طول بازه‌های زمانی ورودی شبکه - روش 2

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import pandas as pd\n", + "import numpy as np\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, SimpleRNN\n", + "from tensorflow.keras.callbacks import ModelCheckpoint\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "%matplotlib inline\n", + "\n", + "t = np.arange(0,1500)\n", + "x = np.sin(0.02*t)+ np.random.rand(1500) * 2\n", + "\n", + "train,test = x[0:1000], x[1000:]\n", + "\n", + "# convert into dataset data and label\n", + "def convertToDataset(data, step):\n", + " #data = np.append(data,np.repeat(data[-1,],step))\n", + " X, Y =[], []\n", + " for i in range(len(data)-step):\n", + " d=i+step \n", + " X.append(data[i:d,])\n", + " Y.append(data[d,])\n", + " return np.array(X), np.array(Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "train_step = 10\n", + "test_step = 20\n", + "\n", + "trainX,trainY =convertToDataset(train,train_step)\n", + "testX,testY =convertToDataset(test,test_step)\n", + "\n", + "trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))\n", + "testX = np.reshape(testX, (testX.shape[0],testX.shape[1], 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(990, 10, 1)\n", + "(480, 20, 1)\n" + ] + } + ], + "source": [ + "print(trainX.shape)\n", + "print(testX.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
معماری شبکه و compile آن
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def build_model(sequence_length):\n", + " model = Sequential()\n", + " model.add(SimpleRNN(units=64, input_shape=(sequence_length, 1), activation=\"tanh\"))\n", + " model.add(Dense(1))\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "simple_rnn (SimpleRNN) (None, 64) 4224 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 1) 65 \n", + "=================================================================\n", + "Total params: 4,289\n", + "Trainable params: 4,289\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = build_model(10)\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.input" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
آموزش مدل
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(loss='mean_squared_error', optimizer='adam')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Directory where the checkpoints will be saved\n", + "checkpoint_dir = './notebook02_training_checkpoints'\n", + "# Name of the checkpoint files\n", + "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")\n", + "\n", + "checkpoint_callback=ModelCheckpoint(\n", + " filepath=checkpoint_prefix,\n", + " save_weights_only=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 3s - loss: 0.4920\n", + "Epoch 2/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.4102\n", + "Epoch 3/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.4077\n", + "Epoch 4/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3988\n", + "Epoch 5/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3925\n", + "Epoch 6/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.4015\n", + "Epoch 7/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.4000\n", + "Epoch 8/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3984\n", + "Epoch 9/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3925\n", + "Epoch 10/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3851\n", + "Epoch 11/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3976\n", + "Epoch 12/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3905\n", + "Epoch 13/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3893\n", + "Epoch 14/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3872\n", + "Epoch 15/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3872\n", + "Epoch 16/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3846\n", + "Epoch 17/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3850\n", + "Epoch 18/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3761\n", + "Epoch 19/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3787\n", + "Epoch 20/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - 1s - loss: 0.3866\n", + "Epoch 21/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3835\n", + "Epoch 22/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3744\n", + "Epoch 23/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3782\n", + "Epoch 24/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3821\n", + "Epoch 25/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3776\n", + "Epoch 26/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3718\n", + "Epoch 27/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3717\n", + "Epoch 28/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3737\n", + "Epoch 29/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3653\n", + "Epoch 30/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3704\n", + "Epoch 31/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3659\n", + "Epoch 32/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3701\n", + "Epoch 33/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3663\n", + "Epoch 34/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3637\n", + "Epoch 35/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3555\n", + "Epoch 36/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3543\n", + "Epoch 37/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3566\n", + "Epoch 38/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3466\n", + "Epoch 39/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3438\n", + "Epoch 40/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - 1s - loss: 0.3463\n", + "Epoch 41/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3561\n", + "Epoch 42/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3459\n", + "Epoch 43/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3381\n", + "Epoch 44/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3365\n", + "Epoch 45/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3412\n", + "Epoch 46/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3426\n", + "Epoch 47/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3295\n", + "Epoch 48/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3220\n", + "Epoch 49/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3177\n", + "Epoch 50/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3178\n", + "Epoch 51/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3078\n", + "Epoch 52/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3301\n", + "Epoch 53/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3137\n", + "Epoch 54/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.3016\n", + "Epoch 55/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2949\n", + "Epoch 56/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2980\n", + "Epoch 57/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2944\n", + "Epoch 58/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2972\n", + "Epoch 59/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2902\n", + "Epoch 60/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - 1s - loss: 0.2859\n", + "Epoch 61/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2773\n", + "Epoch 62/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2800\n", + "Epoch 63/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2620\n", + "Epoch 64/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2646\n", + "Epoch 65/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2577\n", + "Epoch 66/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2543\n", + "Epoch 67/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2483\n", + "Epoch 68/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2405\n", + "Epoch 69/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2337\n", + "Epoch 70/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2342\n", + "Epoch 71/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2173\n", + "Epoch 72/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2247\n", + "Epoch 73/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2170\n", + "Epoch 74/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2205\n", + "Epoch 75/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2102\n", + "Epoch 76/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2108\n", + "Epoch 77/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1987\n", + "Epoch 78/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1991\n", + "Epoch 79/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.2037\n", + "Epoch 80/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - 1s - loss: 0.1884\n", + "Epoch 81/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1865\n", + "Epoch 82/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1899\n", + "Epoch 83/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1786\n", + "Epoch 84/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1767\n", + "Epoch 85/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1744\n", + "Epoch 86/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1722\n", + "Epoch 87/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1584\n", + "Epoch 88/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1574\n", + "Epoch 89/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1525\n", + "Epoch 90/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1518\n", + "Epoch 91/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1574\n", + "Epoch 92/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1444\n", + "Epoch 93/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1486\n", + "Epoch 94/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1365\n", + "Epoch 95/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1379\n", + "Epoch 96/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1279\n", + "Epoch 97/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1311\n", + "Epoch 98/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1216\n", + "Epoch 99/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n", + " - 1s - loss: 0.1223\n", + "Epoch 100/100\n", + "WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.\n", + "\n", + "Consider using a TensorFlow optimizer from `tf.train`.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - 1s - loss: 0.1285\n" + ] + } + ], + "source": [ + "history = model.fit(trainX,trainY, epochs=100, batch_size=16,\n", + " verbose=2, callbacks=[checkpoint_callback])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#
ارزیابی مدل
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./notebook02_training_checkpoints\\\\ckpt_100'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.train.latest_checkpoint(checkpoint_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model = build_model(20)\n", + "\n", + "model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))\n", + "\n", + "model.compile(loss='mean_squared_error', optimizer='adam')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.841938066482544\n" + ] + } + ], + "source": [ + "trainScore = model.evaluate(testX, testY, verbose=0)\n", + "print(trainScore)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tensorflow", + "language": "python", + "name": "tensorflow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03_1_Cryptocurrency-predicting.ipynb b/03_1_Cryptocurrency-predicting.ipynb new file mode 100644 index 0000000..15680bf --- /dev/null +++ b/03_1_Cryptocurrency-predicting.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

تخمین قیمت ارزهای دیجیتال

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "##
مجموعه داده
\n", + "\n", + "
مجموعه داده را می‌توانید از مسیر زیر دانلود کنید
\n", + "\n", + "http://dataset.class.vision/rnn/crypto_data.zip" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " time low high open close volume\n", + "0 1528968660 96.580002 96.589996 96.589996 96.580002 9.647200\n", + "1 1528968720 96.449997 96.669998 96.589996 96.660004 314.387024\n", + "2 1528968780 96.470001 96.570000 96.570000 96.570000 77.129799\n", + "3 1528968840 96.449997 96.570000 96.570000 96.500000 7.216067\n", + "4 1528968900 96.279999 96.540001 96.500000 96.389999 524.539978\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"crypto_data/LTC-USD.csv\", names=['time', 'low', 'high', 'open', 'close', 'volume'])\n", + "\n", + "print(df.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BTC-USD\n", + "LTC-USD\n", + "BCH-USD\n", + "ETH-USD\n", + " BTC-USD_close BTC-USD_volume LTC-USD_close LTC-USD_volume \\\n", + "time \n", + "1528968720 6487.379883 7.706374 96.660004 314.387024 \n", + "1528968780 6479.410156 3.088252 96.570000 77.129799 \n", + "1528968840 6479.410156 1.404100 96.500000 7.216067 \n", + "1528968900 6479.979980 0.753000 96.389999 524.539978 \n", + "1528968960 6480.000000 1.490900 96.519997 16.991997 \n", + "\n", + " BCH-USD_close BCH-USD_volume ETH-USD_close ETH-USD_volume \n", + "time \n", + "1528968720 870.859985 26.856577 486.01001 26.019083 \n", + "1528968780 870.099976 1.124300 486.00000 8.449400 \n", + "1528968840 870.789978 1.749862 485.75000 26.994646 \n", + "1528968900 870.000000 1.680500 486.00000 77.355759 \n", + "1528968960 869.989990 1.669014 486.00000 7.503300 \n" + ] + } + ], + "source": [ + "main_df = pd.DataFrame() # begin empty\n", + "\n", + "ratios = [\"BTC-USD\", \"LTC-USD\", \"BCH-USD\", \"ETH-USD\"] # the 4 ratios we want to consider\n", + "for ratio in ratios: # begin iteration\n", + " print(ratio)\n", + " dataset = f'crypto_data/{ratio}.csv' # get the full path to the file.\n", + " df = pd.read_csv(dataset, names=['time', 'low', 'high', 'open', 'close', 'volume']) # read in specific file\n", + "\n", + " # rename volume and close to include the ticker so we can still which close/volume is which:\n", + " df.rename(columns={\"close\": f\"{ratio}_close\", \"volume\": f\"{ratio}_volume\"}, inplace=True)\n", + "\n", + " df.set_index(\"time\", inplace=True) # set time as index so we can join them on this shared time\n", + " df = df[[f\"{ratio}_close\", f\"{ratio}_volume\"]] # ignore the other columns besides price and volume\n", + "\n", + " if len(main_df)==0: # if the dataframe is empty\n", + " main_df = df # then it's just the current df\n", + " else: # otherwise, join this data to the main one\n", + " main_df = main_df.join(df)\n", + "\n", + "main_df.fillna(method=\"ffill\", inplace=True) # if there are gaps in data, use previously known values\n", + "main_df.dropna(inplace=True)\n", + "print(main_df.head()) # how did we do??" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "SEQ_LEN = 60 # how long of a preceeding sequence to collect for RNN\n", + "FUTURE_PERIOD_PREDICT = 3 # how far into the future are we trying to predict?\n", + "RATIO_TO_PREDICT = \"LTC-USD\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "main_df['future'] = main_df[f'{RATIO_TO_PREDICT}_close'].shift(-FUTURE_PERIOD_PREDICT)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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BTC-USD_closeBTC-USD_volumeLTC-USD_closeLTC-USD_volumeBCH-USD_closeBCH-USD_volumeETH-USD_closeETH-USD_volumefuture
time
15289687206487.3798837.70637496.660004314.387024870.85998526.856577486.0100126.01908396.389999
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" + ], + "text/plain": [ + " BTC-USD_close BTC-USD_volume LTC-USD_close LTC-USD_volume \\\n", + "time \n", + "1528968720 6487.379883 7.706374 96.660004 314.387024 \n", + "1528968780 6479.410156 3.088252 96.570000 77.129799 \n", + "1528968840 6479.410156 1.404100 96.500000 7.216067 \n", + "1528968900 6479.979980 0.753000 96.389999 524.539978 \n", + "1528968960 6480.000000 1.490900 96.519997 16.991997 \n", + "\n", + " BCH-USD_close BCH-USD_volume ETH-USD_close ETH-USD_volume \\\n", + "time \n", + "1528968720 870.859985 26.856577 486.01001 26.019083 \n", + "1528968780 870.099976 1.124300 486.00000 8.449400 \n", + "1528968840 870.789978 1.749862 485.75000 26.994646 \n", + "1528968900 870.000000 1.680500 486.00000 77.355759 \n", + "1528968960 869.989990 1.669014 486.00000 7.503300 \n", + "\n", + " future \n", + "time \n", + "1528968720 96.389999 \n", + "1528968780 96.519997 \n", + "1528968840 96.440002 \n", + "1528968900 96.470001 \n", + "1528968960 96.400002 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "main_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def classify(current, future):\n", + " if float(future) > float(current):\n", + " return 1\n", + " else:\n", + " return 0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "main_df['target'] = list(map(classify, main_df[f'{RATIO_TO_PREDICT}_close'], main_df['future']))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " BTC-USD_close BTC-USD_volume LTC-USD_close LTC-USD_volume \\\n", + "time \n", + "1528968720 6487.379883 7.706374 96.660004 314.387024 \n", + "1528968780 6479.410156 3.088252 96.570000 77.129799 \n", + "1528968840 6479.410156 1.404100 96.500000 7.216067 \n", + "1528968900 6479.979980 0.753000 96.389999 524.539978 \n", + "1528968960 6480.000000 1.490900 96.519997 16.991997 \n", + "\n", + " BCH-USD_close BCH-USD_volume ETH-USD_close ETH-USD_volume \\\n", + "time \n", + "1528968720 870.859985 26.856577 486.01001 26.019083 \n", + "1528968780 870.099976 1.124300 486.00000 8.449400 \n", + "1528968840 870.789978 1.749862 485.75000 26.994646 \n", + "1528968900 870.000000 1.680500 486.00000 77.355759 \n", + "1528968960 869.989990 1.669014 486.00000 7.503300 \n", + "\n", + " future target \n", + "time \n", + "1528968720 96.389999 0 \n", + "1528968780 96.519997 0 \n", + "1528968840 96.440002 0 \n", + "1528968900 96.470001 1 \n", + "1528968960 96.400002 0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "main_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
جدا کردن دیتای آموزش و ارزیابی
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "times = sorted(main_df.index.values) # get the times\n", + "last_5pct = sorted(main_df.index.values)[-int(0.05*len(times))] # get the last 5% of the times\n", + "\n", + "validation_main_df = main_df[(main_df.index >= last_5pct)] # make the validation data where the index is in the last 5%\n", + "main_df = main_df[(main_df.index < last_5pct)] # now the main_df is all the data up to the last 5%" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we need to **balance** and **normalize** this data. \n", + "\n", + "By **balance**, we want to make sure the classes have equal amounts when training, so our model doesn't just always predict one class.\n", + "\n", + "One way to counteract this is to use class weights, which allows you to weight loss higher for lesser-frequent classifications. That said, I've never personally seen this really be comparable to a real balanced dataset.\n", + "\n", + "We also need to take our data and make sequences from it.\n", + "\n", + "So...we've got some work to do! We'll start by making a function that will process the dataframes, so we can just do something like:\n", + "\n", + " train_x, train_y = preprocess_df(main_df) \n", + " validation_x, validation_y = preprocess_df(validation_main_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by **removing the future column** (the actual target is called literally target and only needed the future column temporarily to create it). \n", + "\n", + "Then, we need to **scale our data**:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import preprocessing # pip install sklearn ... if you don't have it!\n", + "\n", + "def preprocess_df(df):\n", + " df = df.drop(\"future\", 1) # don't need this anymore.\n", + "\n", + " for col in df.columns: # go through all of the columns\n", + " if col != \"target\": # normalize all ... except for the target itself!\n", + " df[col] = df[col].pct_change() # pct change \"normalizes\" the different currencies (each crypto coin has vastly diff values, we're really more interested in the other coin's movements)\n", + " df.dropna(inplace=True) # remove the nas created by pct_change\n", + " df[col] = preprocessing.scale(df[col].values) # scale between 0 and 1.\n", + "\n", + " df.dropna(inplace=True) # cleanup again... jic. Those nasty NaNs love to creep in." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alright, we've normalized and scaled the data! \n", + "\n", + "Next up, **we need to create our actual sequences**. \n", + "\n", + "To do this:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from collections import deque\n", + "import random\n", + "\n", + "\n", + "sequential_data = [] # this is a list that will CONTAIN the sequences\n", + "prev_days = deque(maxlen=SEQ_LEN) # These will be our actual sequences. They are made with deque, which keeps the maximum length by popping out older values as new ones come in\n", + "\n", + "for i in df.values: # iterate over the values\n", + " prev_days.append([n for n in i[:-1]]) # store all but the target\n", + " if len(prev_days) == SEQ_LEN: # make sure we have 60 sequences!\n", + " sequential_data.append([np.array(prev_days), i[-1]]) # append those bad boys!\n", + "\n", + "random.shuffle(sequential_data) # shuffle for good measure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
منبع:
\n", + "\n", + "https://becominghuman.ai/recurrent-neural-networks-rnn-deep-learning-w-python-tensorflow-keras-p-7-c21bc374d4dc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03_2_Cryptocurrency-predicting.ipynb b/03_2_Cryptocurrency-predicting.ipynb new file mode 100644 index 0000000..7b159d9 --- /dev/null +++ b/03_2_Cryptocurrency-predicting.ipynb @@ -0,0 +1,562 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

تخمین قیمت ارزهای دیجیتال - قسمت 2

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Balancing Recurrent Neural Network sequence data for our crypto predicting RNN" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from collections import deque\n", + "import random\n", + "import numpy as np\n", + "from sklearn import preprocessing\n", + "\n", + "SEQ_LEN = 60 # how long of a preceeding sequence to collect for RNN\n", + "FUTURE_PERIOD_PREDICT = 3 # how far into the future are we trying to predict?\n", + "RATIO_TO_PREDICT = \"LTC-USD\"\n", + "\n", + "\n", + "def classify(current, future):\n", + " if float(future) > float(current): # if the future price is higher than the current, that's a buy, or a 1\n", + " return 1\n", + " else: # otherwise... it's a 0!\n", + " return 0\n", + "\n", + "\n", + "def preprocess_df(df):\n", + " df = df.drop(\"future\", 1) # don't need this anymore.\n", + "\n", + " for col in df.columns: # go through all of the columns\n", + " if col != \"target\": # normalize all ... except for the target itself!\n", + " df[col] = df[col].pct_change() # pct change \"normalizes\" the different currencies (each crypto coin has vastly diff values, we're really more interested in the other coin's movements)\n", + " df.dropna(inplace=True) # remove the nas created by pct_change\n", + " df[col] = preprocessing.scale(df[col].values) # scale between 0 and 1.\n", + "\n", + " df.dropna(inplace=True) # cleanup again... jic. Those nasty NaNs love to creep in.\n", + "\n", + " sequential_data = [] # this is a list that will CONTAIN the sequences\n", + " prev_days = deque(maxlen=SEQ_LEN) # These will be our actual sequences. They are made with deque, which keeps the maximum length by popping out older values as new ones come in\n", + "\n", + " for i in df.values: # iterate over the values\n", + " prev_days.append([n for n in i[:-1]]) # store all but the target\n", + " if len(prev_days) == SEQ_LEN: # make sure we have 60 sequences!\n", + " sequential_data.append([np.array(prev_days), i[-1]]) # append those bad boys!\n", + "\n", + " random.shuffle(sequential_data) # shuffle for good measure.\n", + "\n", + "\n", + "main_df = pd.DataFrame() # begin empty\n", + "\n", + "ratios = [\"BTC-USD\", \"LTC-USD\", \"BCH-USD\", \"ETH-USD\"] # the 4 ratios we want to consider\n", + "for ratio in ratios: # begin iteration\n", + " dataset = f'crypto_data/{ratio}.csv' # get the full path to the file.\n", + " df = pd.read_csv(dataset, names=['time', 'low', 'high', 'open', 'close', 'volume']) # read in specific file\n", + "\n", + " # rename volume and close to include the ticker so we can still which close/volume is which:\n", + " df.rename(columns={\"close\": f\"{ratio}_close\", \"volume\": f\"{ratio}_volume\"}, inplace=True)\n", + "\n", + " df.set_index(\"time\", inplace=True) # set time as index so we can join them on this shared time\n", + " df = df[[f\"{ratio}_close\", f\"{ratio}_volume\"]] # ignore the other columns besides price and volume\n", + "\n", + " if len(main_df)==0: # if the dataframe is empty\n", + " main_df = df # then it's just the current df\n", + " else: # otherwise, join this data to the main one\n", + " main_df = main_df.join(df)\n", + "\n", + "main_df.fillna(method=\"ffill\", inplace=True) # if there are gaps in data, use previously known values\n", + "main_df.dropna(inplace=True)\n", + "#print(main_df.head()) # how did we do??\n", + "\n", + "main_df['future'] = main_df[f'{RATIO_TO_PREDICT}_close'].shift(-FUTURE_PERIOD_PREDICT)\n", + "main_df['target'] = list(map(classify, main_df[f'{RATIO_TO_PREDICT}_close'], main_df['future']))\n", + "\n", + "#print(main_df.head())\n", + "\n", + "times = sorted(main_df.index.values) # get the times\n", + "last_5pct = sorted(main_df.index.values)[-int(0.05*len(times))] # get the last 5% of the times\n", + "\n", + "validation_main_df = main_df[(main_df.index >= last_5pct)] # make the validation data where the index is in the last 5%\n", + "main_df = main_df[(main_df.index < last_5pct)] # now the main_df is all the data up to the last 5%" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Continuing along in our **preprocess_df** function, we can balance by doing:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def preprocess_df(df):\n", + " df = df.drop(\"future\", 1) # don't need this anymore.\n", + "\n", + " for col in df.columns: # go through all of the columns\n", + " if col != \"target\": # normalize all ... except for the target itself!\n", + " df[col] = df[col].pct_change() # pct change \"normalizes\" the different currencies (each crypto coin has vastly diff values, we're really more interested in the other coin's movements)\n", + " df.dropna(inplace=True) # remove the nas created by pct_change\n", + " df[col] = preprocessing.scale(df[col].values) # scale between 0 and 1.\n", + "\n", + " df.dropna(inplace=True) # cleanup again... jic.\n", + "\n", + "\n", + " sequential_data = [] # this is a list that will CONTAIN the sequences\n", + " prev_days = deque(maxlen=SEQ_LEN) # These will be our actual sequences. They are made with deque, which keeps the maximum length by popping out older values as new ones come in\n", + "\n", + " for i in df.values: # iterate over the values\n", + " prev_days.append([n for n in i[:-1]]) # store all but the target\n", + " if len(prev_days) == SEQ_LEN: # make sure we have 60 sequences!\n", + " sequential_data.append([np.array(prev_days), i[-1]]) # append those bad boys!\n", + "\n", + " random.shuffle(sequential_data) # shuffle for good measure.\n", + "\n", + " buys = [] # list that will store our buy sequences and targets\n", + " sells = [] # list that will store our sell sequences and targets\n", + "\n", + " for seq, target in sequential_data: # iterate over the sequential data\n", + " if target == 0: # if it's a \"not buy\"\n", + " sells.append([seq, target]) # append to sells list\n", + " elif target == 1: # otherwise if the target is a 1...\n", + " buys.append([seq, target]) # it's a buy!\n", + "\n", + " random.shuffle(buys) # shuffle the buys\n", + " random.shuffle(sells) # shuffle the sells!\n", + "\n", + " lower = min(len(buys), len(sells)) # what's the shorter length?\n", + "\n", + " buys = buys[:lower] # make sure both lists are only up to the shortest length.\n", + " sells = sells[:lower] # make sure both lists are only up to the shortest length.\n", + "\n", + " sequential_data = buys+sells # add them together\n", + " random.shuffle(sequential_data) # another shuffle, so the model doesn't get confused with all 1 class then the other.\n", + "\n", + " X = []\n", + " y = []\n", + "\n", + " for seq, target in sequential_data: # going over our new sequential data\n", + " X.append(seq) # X is the sequences\n", + " y.append(target) # y is the targets/labels (buys vs sell/notbuy)\n", + "\n", + " return np.array(X), y # return X and y...and make X a numpy array!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## We can now preprocess our data with:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " BTC-USD_close BTC-USD_volume LTC-USD_close LTC-USD_volume \\\n", + "time \n", + "1528968720 6487.379883 7.706374 96.660004 314.387024 \n", + "1528968780 6479.410156 3.088252 96.570000 77.129799 \n", + "1528968840 6479.410156 1.404100 96.500000 7.216067 \n", + "1528968900 6479.979980 0.753000 96.389999 524.539978 \n", + "1528968960 6480.000000 1.490900 96.519997 16.991997 \n", + "\n", + " BCH-USD_close BCH-USD_volume ETH-USD_close ETH-USD_volume \\\n", + "time \n", + "1528968720 870.859985 26.856577 486.01001 26.019083 \n", + "1528968780 870.099976 1.124300 486.00000 8.449400 \n", + "1528968840 870.789978 1.749862 485.75000 26.994646 \n", + "1528968900 870.000000 1.680500 486.00000 77.355759 \n", + "1528968960 869.989990 1.669014 486.00000 7.503300 \n", + "\n", + " future target \n", + "time \n", + "1528968720 96.389999 0 \n", + "1528968780 96.519997 0 \n", + "1528968840 96.440002 0 \n", + "1528968900 96.470001 1 \n", + "1528968960 96.400002 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "main_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train_x, train_y = preprocess_df(main_df)\n", + "validation_x, validation_y = preprocess_df(validation_main_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77922, 60, 8)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_x.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train data: 77922 validation: 3860\n", + "Dont buys: 38961, buys: 38961\n", + "VALIDATION Dont buys: 1930, buys: 1930\n" + ] + } + ], + "source": [ + "print(f\"train data: {len(train_x)} validation: {len(validation_x)}\")\n", + "print(f\"Dont buys: {train_y.count(0)}, buys: {train_y.count(1)}\")\n", + "print(f\"VALIDATION Dont buys: {validation_y.count(0)}, buys: {validation_y.count(1)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cryptocurrency-predicting RNN Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "\n", + "EPOCHS = 10 # how many passes through our data\n", + "BATCH_SIZE = 64 # how many batches? Try smaller batch if you're getting OOM (out of memory) errors.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next let's build the model, first we need some imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Dropout, LSTM, LSTM, BatchNormalization" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(LSTM(128, input_shape=(train_x.shape[1:]), return_sequences=True))\n", + "model.add(Dropout(0.2))\n", + "model.add(BatchNormalization()) #normalizes activation outputs, same reason you want to normalize your input data.\n", + "\n", + "model.add(LSTM(128, return_sequences=True))\n", + "model.add(Dropout(0.1))\n", + "model.add(BatchNormalization())\n", + "\n", + "model.add(LSTM(128))\n", + "model.add(Dropout(0.2))\n", + "model.add(BatchNormalization())\n", + "\n", + "model.add(Dense(32, activation='relu'))\n", + "model.add(Dropout(0.2))\n", + "\n", + "model.add(Dense(2, activation='softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)\n", + "\n", + "# Compile model\n", + "model.compile(\n", + " loss='sparse_categorical_crossentropy',\n", + " optimizer=opt,\n", + " metrics=['accuracy']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 77922 samples, validate on 3860 samples\n", + "Epoch 1/10\n", + "77922/77922 [==============================] - 64s 824us/sample - loss: 0.7148 - accuracy: 0.5225 - val_loss: 0.6867 - val_accuracy: 0.5394\n", + "Epoch 2/10\n", + "77922/77922 [==============================] - 54s 696us/sample - loss: 0.6858 - accuracy: 0.5489 - val_loss: 0.6803 - val_accuracy: 0.5619\n", + "Epoch 3/10\n", + "77922/77922 [==============================] - 57s 737us/sample - loss: 0.6814 - accuracy: 0.5628 - val_loss: 0.6759 - val_accuracy: 0.5692\n", + "Epoch 4/10\n", + "77922/77922 [==============================] - 58s 750us/sample - loss: 0.6799 - accuracy: 0.5662 - val_loss: 0.6769 - val_accuracy: 0.5759\n", + "Epoch 5/10\n", + "77922/77922 [==============================] - 60s 766us/sample - loss: 0.6783 - accuracy: 0.5713 - val_loss: 0.6793 - val_accuracy: 0.5694\n", + "Epoch 6/10\n", + "77922/77922 [==============================] - 60s 768us/sample - loss: 0.6765 - accuracy: 0.5742 - val_loss: 0.6838 - val_accuracy: 0.5601\n", + "Epoch 7/10\n", + "77922/77922 [==============================] - 61s 779us/sample - loss: 0.6728 - accuracy: 0.5817 - val_loss: 0.6794 - val_accuracy: 0.5689\n", + "Epoch 8/10\n", + "77922/77922 [==============================] - 63s 811us/sample - loss: 0.6681 - accuracy: 0.5919 - val_loss: 0.6800 - val_accuracy: 0.5777\n", + "Epoch 9/10\n", + "77922/77922 [==============================] - 74s 944us/sample - loss: 0.6624 - accuracy: 0.6010 - val_loss: 0.6829 - val_accuracy: 0.5676\n", + "Epoch 10/10\n", + "77922/77922 [==============================] - 69s 885us/sample - loss: 0.6539 - accuracy: 0.6140 - val_loss: 0.6973 - val_accuracy: 0.5609\n" + ] + } + ], + "source": [ + "# Train model\n", + "history = model.fit(\n", + " train_x, np.array(train_y),\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(validation_x, np.array(validation_y)))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test loss: 0.6973084882133366\n", + "Test accuracy: 0.56088084\n" + ] + } + ], + "source": [ + "# Score model\n", + "score = model.evaluate(validation_x, np.array(validation_y), verbose=0)\n", + "print('Test loss:', score[0])\n", + "print('Test accuracy:', score[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
منبع:
\n", + "\n", + "https://becominghuman.ai/recurrent-neural-networks-rnn-deep-learning-w-python-tensorflow-keras-p-7-c21bc374d4dc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/04_simple-CNN-LSTM.ipynb b/04_simple-CNN-LSTM.ipynb new file mode 100644 index 0000000..2a2e1a1 --- /dev/null +++ b/04_simple-CNN-LSTM.ipynb @@ -0,0 +1,647 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

حرکت توپ

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import random\n", + "import numpy as np\n", + "import cv2\n", + "from keras.models import Sequential \n", + "from keras.layers import Conv2D \n", + "from keras.layers import MaxPooling2D \n", + "from keras.layers import LSTM \n", + "from keras.layers import Dense \n", + "from keras.layers import Flatten \n", + "from keras.layers import TimeDistributed" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "size = 30" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# generate the next frame in the sequence \n", + "def next_frame(last_step, column, size): \n", + " frame = np.zeros((size,size)) \n", + " # define the scope of the next step \n", + " lower = max(0, last_step-3) \n", + " upper = min(frame.shape[0]-1, last_step+3) \n", + " # choose the row index for the next step \n", + " step = random.randint(lower, upper) \n", + " # add the new step \n", + " cv2.circle(frame,(column, step),3,255,-1)\n", + " #frame[step, column] = 1 \n", + " return frame, step" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# generate a sequence of frames of a dot moving across an image \n", + "def build_frames(size): \n", + " frames = list() \n", + "\n", + " step = random.randint(0, size-1) \n", + " # decide if we are heading left or right \n", + " right = 1 if random.random() < 0.5 else 0 \n", + " col = 0 if right else size-1 \n", + "\n", + " # create all frames \n", + " for i in range(0, size): \n", + " col = i if right else size-1-i \n", + " frame, step = next_frame(step, col, size) \n", + " frames.append(frame) \n", + " return frames, right" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# generate multiple sequences of frames and reshape for network input \n", + "def generate_examples(size, n_patterns): \n", + " X, y = list(), list() \n", + " for _ in range(n_patterns): \n", + " frames, right = build_frames(size) \n", + " X.append(frames) \n", + " y.append(right) \n", + " # resize as [samples, timesteps, width, height, channels] \n", + " X = np.array(X).reshape(n_patterns, size, size, size, 1)\n", + " y = np.array(y).reshape(n_patterns, 1) \n", + " return X, y" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Right -->\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "X, Y = generate_examples(size, 1) \n", + "print(\"Right -->\") if Y[0] else print(\"<-- Left\")\n", + "\n", + "for frame in X[0]:\n", + " plt.imshow(frame[:,:,0], cmap=\"gray\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "time_distributed_1 (TimeDist (None, None, 28, 28, 2) 20 \n", + "_________________________________________________________________\n", + "time_distributed_2 (TimeDist (None, None, 14, 14, 2) 0 \n", + "_________________________________________________________________\n", + "time_distributed_3 (TimeDist (None, None, 392) 0 \n", + "_________________________________________________________________\n", + "lstm_1 (LSTM) (None, 50) 88600 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 1) 51 \n", + "=================================================================\n", + "Total params: 88,671\n", + "Trainable params: 88,671\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "# define the model \n", + "model = Sequential() \n", + "model.add(TimeDistributed(Conv2D(2, (3,3), activation='relu'), \n", + " input_shape=(None,size,size,1))) \n", + "model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2)))) \n", + "model.add(TimeDistributed(Flatten())) \n", + "model.add(LSTM(50)) \n", + "model.add(Dense(1, activation='sigmoid')) \n", + "print(model.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc']) " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1\n", + "1000/1000 [==============================] - 7s 7ms/step - loss: 0.2028 - acc: 0.9690A: 1s - loss: 0.2310 - acc: \n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fit model \n", + "X, y = generate_examples(size, 1000) \n", + "model.fit(X, y, batch_size=32, epochs=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss: 0.056935, acc: 100.000000\n" + ] + } + ], + "source": [ + "# evaluate model \n", + "X, y = generate_examples(size, 100) \n", + "loss, acc = model.evaluate(X, y, verbose=0) \n", + "print('loss: %f, acc: %f' % (loss, acc*100))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected: Left, Predicted: Left\n" + ] + } + ], + "source": [ + "# prediction on new data \n", + "X, y = generate_examples(size, 1) \n", + "yhat = model.predict_classes(X, verbose=0) \n", + "expected = \"Right\" if y[0]==1 else \"Left\" \n", + "predicted = \"Right\" if yhat[0]==1 else \"Left\" \n", + "print('Expected: %s, Predicted: %s' % (expected, predicted))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/05-1-video-action-recognition-train-extract-features-with-cnn.ipynb b/05-1-video-action-recognition-train-extract-features-with-cnn.ipynb new file mode 100644 index 0000000..adf82c7 --- /dev/null +++ b/05-1-video-action-recognition-train-extract-features-with-cnn.ipynb @@ -0,0 +1,326 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

طبقه بندی ویدیو با شبکه‌های بازگشتی - استخراج ویژگی

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
مجموعه داده
\n", + "\n", + "\n", + "
\n", + "قبلا 6 کلاس از دیتاست UCF-101 را به عنوان نمونه انتخاب و فریم‌های ویدیوهای متعلق به این 6 کلاس از این مجموعه داده را استخراج کرده ایم و اطلاعات هر ویدیو نظیر اسم - کلاس و تعداد فریم را در یک فایل متنی قرار داده ایم.\n", + "
\n", + " \n", + "این 6 کلاس که برای این آموزش آماده شده است را از اینجا دانلود کنید: \n", + "
\n", + "\n", + "http://dataset.class.vision/rnn/RNN-Video-6action.zip\n", + "\n", + "
\n", + "
\n", + " همچنین\n", + " دیتاست اصلی شامل 101 کلاس مختلف را می‌توانید از لینک زیر دانلود کنید:\n", + "
\n", + "\n", + "UCF-101\n", + "[https://www.crcv.ucf.edu/data/UCF101.php](https://www.crcv.ucf.edu/data/UCF101.php)\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.preprocessing import image\n", + "from keras.applications.inception_v3 import InceptionV3, preprocess_input\n", + "from keras.models import Model, load_model\n", + "from keras.layers import Input\n", + "import numpy as np\n", + "import os.path\n", + "from tqdm import tqdm\n", + "import csv\n", + "import random\n", + "import glob\n", + "import os.path\n", + "import sys\n", + "import operator\n", + "import threading\n", + "from keras.utils import to_categorical\n", + "from keras.preprocessing.image import img_to_array, load_img" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "seq_length= 40\n", + "max_frames = 300\n", + "image_shape=(224, 224, 3)\n", + "base_path = \"D:/dataset/RNN-Video\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join('D:/dataset/RNN-Video/data_file_5class.csv'), 'r') as fin:\n", + " reader = csv.reader(fin)\n", + " data = list(reader)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['CricketBowling',\n", + " 'CricketShot',\n", + " 'FieldHockeyPenalty',\n", + " 'HandstandPushups',\n", + " 'HandstandWalking',\n", + " 'SoccerPenalty']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_path = os.path.join(base_path, 'train')\n", + "classes =os.listdir(train_path)\n", + "classes = sorted(classes)\n", + "classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " در اینجا آن ویدیوهایی که حداقل 40 فریم و حداکثر 300 فریم دارند را لود می‌کنیم.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data_clean = []\n", + "for item in data:\n", + " if int(item[3]) >= seq_length and int(item[3]) <= max_frames:\n", + " data_clean.append(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "439" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data_clean)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_n_sample_from_video(sample, seq_length):\n", + " path = os.path.join(base_path, sample[0], sample[1])\n", + " filename = sample[2]\n", + " images = sorted(glob.glob(os.path.join(path, filename + '*jpg')))\n", + "\n", + " #Given a list and a size, return a rescaled/samples list. For example,\n", + " #if we want a list of size 5 and we have a list of size 25, return a new\n", + " #list of size five which is every 5th element of the origina list.\n", + " # Get the number to skip between iterations.\n", + " skip = len(images) // seq_length\n", + "\n", + " # Build our new output.\n", + " output = [images[i] for i in range(0, len(images), skip)]\n", + "\n", + " # Cut off the last one if needed.\n", + " return output[:seq_length]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['train', 'HandstandWalking', 'v_HandstandWalking_g24_c06', '151']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_clean[3]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "40" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(get_n_sample_from_video(data_clean[3], 40))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Get model with pretrained weights.\n", + "base_model = InceptionV3(weights='imagenet', include_top=True)\n", + "\n", + "# We'll extract features at the final pool layer.\n", + "model = Model(inputs=base_model.input,\n", + " outputs=base_model.get_layer('avg_pool').output)\n", + "\n", + "def model_predict(image_path):\n", + " img = image.load_img(image_path, target_size=(299, 299))\n", + " x = image.img_to_array(img)\n", + " x = np.expand_dims(x, axis=0)\n", + " x = preprocess_input(x)\n", + "\n", + " # Get the prediction.\n", + " features = model.predict(x)\n", + " return features[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████| 439/439 [18:31<00:00, 2.85s/it]\n" + ] + } + ], + "source": [ + "os.makedirs('sequences', exist_ok=True)\n", + "for video in tqdm(data_clean):\n", + "\n", + " # Get the path to the sequence for this video.\n", + " path = os.path.join('sequences', video[2] + '-' + str(seq_length) + \\\n", + " '-features') # numpy will auto-append .npy\n", + "\n", + " # Check if we already have it.\n", + " if os.path.isfile(path + '.npy'):\n", + " continue\n", + "\n", + " # Get the frames for this video.\n", + " frames = get_n_sample_from_video(video, seq_length)\n", + "\n", + " # Now loop through and extract features to build the sequence.\n", + " sequence = []\n", + " for frame in frames:\n", + " features = model_predict(frame)\n", + " sequence.append(features)\n", + "\n", + " # Save the sequence.\n", + " np.save(path, sequence)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tensorflow", + "language": "python", + "name": "tensorflow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/05-2_video-action-recognition-train-rnn.ipynb b/05-2_video-action-recognition-train-rnn.ipynb new file mode 100644 index 0000000..8614f95 --- /dev/null +++ b/05-2_video-action-recognition-train-rnn.ipynb @@ -0,0 +1,477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

طبقه بندی ویدیو با شبکه‌های بازگشتی

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
لود کتابخانه‌های مورد استفاده
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.0.0\n", + "2.2.4-tf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "import tensorflow.keras as keras\n", + "from keras.layers import Dense, Flatten, Dropout, ZeroPadding3D\n", + "from keras.layers.recurrent import GRU\n", + "from keras.models import Sequential, load_model\n", + "from keras.optimizers import Adam, RMSprop\n", + "\n", + "import os\n", + "import csv\n", + "import numpy as np\n", + "\n", + "print(tf.__version__)\n", + "print(keras.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
تعریف متغیرها
\n", + "\n", + "
\n", + "تعیین کردن مسیر مجموعه داده، تعداد فریم‌ها برای تشخیص یک ویدیو (تعداد step های زمانی)، نهایت تعداد فریمی که انتظار داریم یک ویدیو داشته باشد و با بیشتر از این تعداد فریم کار نمیکنیم
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "base_path = \"D:/dataset/RNN-Video\"\n", + "seq_length = 40\n", + "max_frames = 300" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
خواندن مجموعه داده
\n", + "\n", + "
\n", + "قبلا 6 کلاس از دیتاست UCF-101 را به عنوان نمونه انتخاب و فریم‌های ویدیوهای متعلق به این 6 کلاس از این مجموعه داده را استخراج کرده ایم و اطلاعات هر ویدیو نظیر اسم - کلاس و تعداد فریم را در یک فایل متنی قرار داده ایم.\n", + "
\n", + " در اینجا آن ویدیوهایی که حداقل 40 فریم و حداکثر 300 فریم دارند را لود می‌کنیم.\n", + "
\n", + " دیتاست اصلی شامل 101 کلاس مختلف را می‌توانید از لینک زیر دانلود کنید:\n", + "
\n", + "\n", + "UCF-101\n", + "[https://www.crcv.ucf.edu/data/UCF101.php](https://www.crcv.ucf.edu/data/UCF101.php)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(base_path, 'data_file_5class.csv'), 'r') as fin:\n", + " reader = csv.reader(fin)\n", + " data = list(reader)\n", + " \n", + "train_path = os.path.join(base_path, 'train')\n", + "classes =os.listdir(train_path)\n", + "classes = sorted(classes)\n", + "\n", + "data_clean = []\n", + "for item in data:\n", + " if int(item[3]) >= seq_length and int(item[3]) <= max_frames:\n", + " data_clean.append(item)\n", + "data = data_clean" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def get_class_one_hot(class_str):\n", + " \"\"\"Given a class as a string, return its number in the classes\n", + " list. This lets us encode and one-hot it for training.\"\"\"\n", + " # Encode it first.\n", + " label_encoded = classes.index(class_str)\n", + "\n", + " # Now one-hot it.\n", + " label_hot = keras.utils.to_categorical(label_encoded, len(classes))\n", + "\n", + " return label_hot\n", + " \n", + "def get_extracted_sequence(sample):\n", + " \"\"\"Get the saved extracted features.\"\"\"\n", + " filename = sample[2]\n", + " path = os.path.join('sequences' , filename + '-' + str(seq_length) + \\\n", + " '-features.npy')\n", + " return np.load(path)\n", + "\n", + "def split_train_test(data):\n", + " \"\"\"Split the data into train and test groups.\"\"\"\n", + " train = []\n", + " test = []\n", + " for item in data:\n", + " if item[0] == 'train':\n", + " train.append(item)\n", + " else:\n", + " test.append(item)\n", + " return train, test\n", + "\n", + "def get_all_sequences_in_memory(data, train_test):\n", + " # Get the right dataset.\n", + " train, test = split_train_test(data)\n", + " data = train if train_test == 'train' else test\n", + "\n", + " print(\"Loading %d samples into memory for %sing.\" % (len(data), train_test))\n", + "\n", + " X, y = [], []\n", + " for row in data:\n", + " sequence = get_extracted_sequence(row)\n", + " X.append(sequence)\n", + " y.append(get_class_one_hot(row[1]))\n", + "\n", + " return np.array(X), np.array(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading 302 samples into memory for training.\n", + "Loading 137 samples into memory for testing.\n" + ] + } + ], + "source": [ + "# Get data.\n", + "X, y = get_all_sequences_in_memory(data, 'train')\n", + "X_test, y_test = get_all_sequences_in_memory(data, 'test')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "nb_classes = len(classes)\n", + "features_length = X.shape[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "model.add(GRU(120, return_sequences=True,\n", + " input_shape=(seq_length, features_length)))\n", + "model.add(GRU(120, return_sequences=False))\n", + "model.add(Dropout(0.2))\n", + "model.add(Dense(50, activation='relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(nb_classes, activation='softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "gru_1 (GRU) (None, 40, 120) 780840 \n", + "_________________________________________________________________\n", + "gru_2 (GRU) (None, 120) 86760 \n", + "_________________________________________________________________\n", + "dropout_1 (Dropout) (None, 120) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 50) 6050 \n", + "_________________________________________________________________\n", + "dropout_2 (Dropout) (None, 50) 0 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 6) 306 \n", + "=================================================================\n", + "Total params: 873,956\n", + "Trainable params: 873,956\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "# Now compile the network.\n", + "model.compile(loss='categorical_crossentropy', optimizer='rmsprop',\n", + " metrics=['accuracy'])\n", + "\n", + "print(model.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 302 samples, validate on 137 samples\n", + "Epoch 1/20\n", + "302/302 [==============================] - 4s 14ms/step - loss: 1.8985 - accuracy: 0.2020 - val_loss: 1.7211 - val_accuracy: 0.1679\n", + "Epoch 2/20\n", + "302/302 [==============================] - 2s 8ms/step - loss: 1.6718 - accuracy: 0.3146 - val_loss: 1.5909 - val_accuracy: 0.2336\n", + "Epoch 3/20\n", + "302/302 [==============================] - 2s 7ms/step - loss: 1.4664 - accuracy: 0.3808 - val_loss: 1.3258 - val_accuracy: 0.4307\n", + "Epoch 4/20\n", + "302/302 [==============================] - 2s 7ms/step - loss: 1.1278 - accuracy: 0.5464 - val_loss: 1.1444 - val_accuracy: 0.5401\n", + "Epoch 5/20\n", + "302/302 [==============================] - 2s 7ms/step - loss: 1.1876 - accuracy: 0.5132 - val_loss: 1.0716 - val_accuracy: 0.5620\n", + "Epoch 6/20\n", + "302/302 [==============================] - 2s 7ms/step - loss: 0.8632 - accuracy: 0.6887 - val_loss: 0.8342 - val_accuracy: 0.7299\n", + "Epoch 7/20\n", + "302/302 [==============================] - 2s 8ms/step - loss: 0.6350 - accuracy: 0.7815 - val_loss: 1.3242 - val_accuracy: 0.5182\n", + "Epoch 8/20\n", + "302/302 [==============================] - 2s 7ms/step - loss: 0.5963 - accuracy: 0.7980 - val_loss: 1.4299 - val_accuracy: 0.5255\n", + "Epoch 9/20\n", + "302/302 [==============================] - 2s 8ms/step - loss: 0.6516 - accuracy: 0.8013 - val_loss: 1.0624 - val_accuracy: 0.5912\n", + "Epoch 10/20\n", + "302/302 [==============================] - 2s 8ms/step - loss: 0.3705 - accuracy: 0.9040 - val_loss: 0.9659 - val_accuracy: 0.7007\n", + "Epoch 11/20\n", + "302/302 [==============================] - 2s 8ms/step - loss: 0.4026 - accuracy: 0.8940 - val_loss: 1.0333 - val_accuracy: 0.5839\n", + "Epoch 12/20\n", + "302/302 [==============================] - 2s 7ms/step - loss: 0.2528 - accuracy: 0.9272 - val_loss: 1.5315 - val_accuracy: 0.5401\n", + "Epoch 13/20\n", + "302/302 [==============================] - 2s 7ms/step - loss: 0.4282 - accuracy: 0.8775 - val_loss: 0.9561 - val_accuracy: 0.6934\n", + "Epoch 14/20\n", + "302/302 [==============================] - 2s 7ms/step - loss: 0.1086 - accuracy: 0.9801 - val_loss: 1.0507 - val_accuracy: 0.7007\n", + "Epoch 15/20\n", + "302/302 [==============================] - 2s 8ms/step - loss: 0.0880 - accuracy: 0.9901 - val_loss: 2.1897 - val_accuracy: 0.4745\n", + "Epoch 16/20\n", + "302/302 [==============================] - 2s 8ms/step - loss: 0.4926 - accuracy: 0.8775 - val_loss: 1.6895 - val_accuracy: 0.6350\n", + "Epoch 17/20\n", + "302/302 [==============================] - 2s 8ms/step - loss: 0.1643 - accuracy: 0.9669 - val_loss: 1.0169 - val_accuracy: 0.6642\n", + "Epoch 18/20\n", + "302/302 [==============================] - 3s 9ms/step - loss: 0.0632 - accuracy: 0.9934 - val_loss: 2.5353 - val_accuracy: 0.4307\n", + "Epoch 19/20\n", + "302/302 [==============================] - 3s 8ms/step - loss: 0.1357 - accuracy: 0.9669 - val_loss: 1.3398 - val_accuracy: 0.6715\n", + "Epoch 20/20\n", + "302/302 [==============================] - 3s 10ms/step - loss: 0.0274 - accuracy: 1.0000 - val_loss: 1.5767 - val_accuracy: 0.7007\n" + ] + } + ], + "source": [ + "history = model.fit(X, y, validation_data=(X_test, y_test),\n", + " batch_size=32, epochs=20)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "acc = history.history['accuracy']\n", + "val_acc = history.history['val_accuracy']\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs = range(len(acc))\n", + "\n", + "plt.plot(epochs, acc, 'bo', label='Training acc')\n", + "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n", + "plt.title('Training and validation accuracy')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(epochs, loss, 'bo', label='Training loss')\n", + "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "Y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = np.argmax(Y_pred, axis=1)\n", + "y_test = np.argmax(y_test, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confusion Matrix\n", + "[[ 6 12 1 0 6 0]\n", + " [ 1 12 1 0 9 0]\n", + " [ 0 0 22 0 0 1]\n", + " [ 0 0 0 16 3 0]\n", + " [ 0 3 1 1 25 0]\n", + " [ 0 0 2 0 0 15]]\n", + "Classification Report\n", + " precision recall f1-score support\n", + "\n", + " CricketBowling 0.86 0.24 0.38 25\n", + " CricketShot 0.44 0.52 0.48 23\n", + "FieldHockeyPenalty 0.81 0.96 0.88 23\n", + " HandstandPushups 0.94 0.84 0.89 19\n", + " HandstandWalking 0.58 0.83 0.68 30\n", + " SoccerPenalty 0.94 0.88 0.91 17\n", + "\n", + " accuracy 0.70 137\n", + " macro avg 0.76 0.71 0.70 137\n", + " weighted avg 0.74 0.70 0.68 137\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report, confusion_matrix\n", + "\n", + "print('Confusion Matrix')\n", + "print(confusion_matrix(y_test, y_pred))\n", + "print('Classification Report')\n", + "print(classification_report(y_test, y_pred, target_names=classes))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
منابع
\n", + "\n", + "
\n", + "برای این فایل آموزشی از مخزن زیر کمک گرفته شده است\n", + "
\n", + " \n", + "https://github.com/harvitronix/five-video-classification-methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/06_analogy-using-embeddings.ipynb b/06_analogy-using-embeddings.ipynb new file mode 100644 index 0000000..4f56365 --- /dev/null +++ b/06_analogy-using-embeddings.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

قیاس کلمات (Word analogies)

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "کدها با تغییرات برگرفته از کورس Sequence Models پروفسور Andrew NG است.\n", + "
\n", + "\n", + "[https://www.coursera.org/learn/nlp-sequence-models](https://www.coursera.org/learn/nlp-sequence-models)\n", + "\n", + "
\n", + "بردار از قبل آموزش داده شده را می‌توانید از اینجا دانلود کنید:
\n", + "\n", + "https://nlp.stanford.edu/projects/glove/\n", + "\n", + "http://nlp.stanford.edu/data/glove.6B.zip\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "glove_dir = 'D:/dataset/glove.6B'\n", + "\n", + "embeddings_index = {}\n", + "f = open(os.path.join(glove_dir, 'glove.6B.100d.txt'), encoding=\"utf8\")\n", + "for line in f:\n", + " values = line.split()\n", + " word = values[0]\n", + " coefs = np.asarray(values[1:], dtype='float32')\n", + " embeddings_index[word] = coefs\n", + "f.close()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1 - Cosine similarity\n", + "\n", + "To measure how similar two words are, we need a way to measure the degree of similarity between two embedding vectors for the two words. Given two vectors $u$ and $v$, cosine similarity is defined as follows: \n", + "\n", + "$$\\text{CosineSimilarity(u, v)} = \\frac {u . v} {||u||_2 ||v||_2} = cos(\\theta) \\tag{1}$$\n", + "\n", + "where $u.v$ is the dot product (or inner product) of two vectors, $||u||_2$ is the norm (or length) of the vector $u$, and $\\theta$ is the angle between $u$ and $v$. This similarity depends on the angle between $u$ and $v$. If $u$ and $v$ are very similar, their cosine similarity will be close to 1; if they are dissimilar, the cosine similarity will take a smaller value. \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics.pairwise import cosine_similarity\n", + "\n", + "def similarity(u, v):\n", + " return np.squeeze(cosine_similarity(u.reshape(1, -1), v.reshape(1, -1)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cosine_similarity(father, mother) = 0.86566603\n", + "cosine_similarity(ball, crocodile) = 0.15206575\n", + "cosine_similarity(france - paris, tehran - iran) = -0.6854124\n" + ] + } + ], + "source": [ + "father = embeddings_index[\"father\"]\n", + "mother = embeddings_index[\"mother\"]\n", + "ball = embeddings_index[\"ball\"]\n", + "crocodile = embeddings_index[\"crocodile\"]\n", + "france = embeddings_index[\"france\"]\n", + "tehran = embeddings_index[\"tehran\"]\n", + "paris = embeddings_index[\"paris\"]\n", + "iran = embeddings_index[\"iran\"]\n", + "print(\"cosine_similarity(father, mother) = \", similarity(father, mother))\n", + "print(\"cosine_similarity(ball, crocodile) = \",similarity(ball, crocodile))\n", + "print(\"cosine_similarity(france - paris, tehran - iran) = \",similarity(france - paris, tehran - iran))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Word analogy task\n", + "\n", + "In the word analogy task, we complete the sentence \"*a* is to *b* as *c* is to **____**\". An example is '*man* is to *woman* as *king* is to *queen*' . In detail, we are trying to find a word *d*, such that the associated word vectors $e_a, e_b, e_c, e_d$ are related in the following manner: $e_b - e_a \\approx e_d - e_c$. We will measure the similarity between $e_b - e_a$ and $e_d - e_c$ using cosine similarity. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.64706 , -0.068067, 0.15468 , -0.17408 , -0.29134 , 0.76999 ,\n", + " -0.3192 , -0.25663 , -0.25082 , -0.036737, -0.25509 , 0.29636 ,\n", + " 0.5776 , 0.49641 , 0.19167 , -0.83888 , 0.58482 , -0.38717 ,\n", + " -0.71591 , 0.9519 , -0.37966 , -0.1131 , 0.47154 , 0.20921 ,\n", + " 0.38197 , 0.067582, -0.92879 , -1.1237 , 0.84831 , 0.68744 ,\n", + " -0.15472 , 0.92714 , 0.53371 , -0.037392, -0.856 , 0.19056 ,\n", + " -0.014594, 0.15186 , 0.53514 , -0.20306 , -0.35164 , 0.33152 ,\n", + " 1.1306 , -0.72787 , -0.19724 , 0.031659, -0.24041 , -0.057617,\n", + " 0.60473 , -0.49233 , -0.24405 , -0.3184 , 0.96156 , 1.0895 ,\n", + " 0.21534 , -2.0542 , -1.0615 , 0.052439, 0.57958 , 0.2748 ,\n", + " 0.91587 , 0.85195 , 0.36113 , -0.31901 , 0.7784 , -0.36865 ,\n", + " 0.64387 , 0.33104 , -0.27181 , 0.58524 , -0.15143 , 0.11121 ,\n", + " 0.2126 , -0.60345 , 0.16148 , 0.32952 , -0.1354 , -0.30629 ,\n", + " -0.89143 , 0.091912, 0.49753 , 0.55932 , 0.19329 , 0.044859,\n", + " -1.0416 , -0.41566 , -0.54174 , -0.7244 , -0.57492 , -1.1188 ,\n", + " 0.087097, -0.2992 , 0.87227 , 0.86996 , -0.89641 , -0.28259 ,\n", + " -0.47295 , -0.74062 , -0.39 , -0.78099 ], dtype=float32)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "embeddings_index[\"father\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def complete_analogy(word_a, word_b, word_c, embeddings_index):\n", + " \n", + " # convert words to lower case\n", + " word_a, word_b, word_c = word_a.lower(), word_b.lower(), word_c.lower()\n", + " \n", + " # Get the word embeddings v_a, v_b and v_c \n", + " e_a, e_b, e_c = embeddings_index[word_a], embeddings_index[word_b], embeddings_index[word_c]\n", + " \n", + " words = embeddings_index.keys()\n", + " max_cosine_sim = -100 # Initialize max_cosine_sim to a large negative number\n", + " best_word = None # Initialize best_word with None, it will help keep track of the word to output\n", + "\n", + " # loop over the whole word vector set\n", + " for w in words: \n", + " # to avoid best_word being one of the input words, pass on them.\n", + " if w in [word_a, word_b, word_c] :\n", + " continue\n", + " \n", + " # Compute cosine similarity between the vector (e_b - e_a) and the vector ((w's vector representation) - e_c) (≈1 line)\n", + " cosine_sim = similarity(e_b - e_a, embeddings_index[w] - e_c)\n", + " \n", + " # If the cosine_sim is more than the max_cosine_sim seen so far,\n", + " # then: set the new max_cosine_sim to the current cosine_sim and the best_word to the current word (≈3 lines)\n", + " if cosine_sim > max_cosine_sim:\n", + " max_cosine_sim = cosine_sim\n", + " best_word = w\n", + " \n", + " return best_word" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the cell below to test your code, this may take 1-2 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'iranian'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complete_analogy('china', 'chinese', 'iran', embeddings_index)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'tehran'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complete_analogy('india', 'delhi', 'iran', embeddings_index)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'girl'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complete_analogy('man', 'woman', 'boy', embeddings_index)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'bigger'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complete_analogy('small', 'smaller', 'big', embeddings_index)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'inuktitut'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complete_analogy('iran', 'farsi', 'canada', embeddings_index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "8hb5s", + "launcher_item_id": "5NrJ6" + }, + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/07_text-classification-Emojify.ipynb b/07_text-classification-Emojify.ipynb new file mode 100644 index 0000000..92bd697 --- /dev/null +++ b/07_text-classification-Emojify.ipynb @@ -0,0 +1,1263 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

طبقه‌بندی متن - Emojify!

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "کدها با تغییرات برگرفته از کورس Sequence Models پروفسور Andrew NG است.\n", + "
\n", + "\n", + "[https://www.coursera.org/learn/nlp-sequence-models](https://www.coursera.org/learn/nlp-sequence-models)\n", + "\n", + "\n", + "\n", + "
در این نوت بوک میخواهید برای جملات دلخواه یک emoji مرتبط به صورت خودکار بگذاریم!\n", + "در واقع یک طبقه بندی ساده 5 کلاسه است که هر جمله را به یک ایموجی نسبت می‌دهد.\n", + "
\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##
لود کتابخانه‌های مورد استفاده
\n", + "
\n", + "برای اجرای این نوت‌بوک باید کتابخانه ی emoji را نصب کنید.\n", + "بدین منظور به اینترنت متصل شود و در ترمینال دستورات زیر را بنویسید:\n", + "
\n", + "

pip install emoji

\n", + "\n", + "
\n", + "میتوانید به جای pip از کلمه ی conda استفاده کنید. (اگر از آناکوندا استفاده میکنید.)\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import keras\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Baseline model: Emojifier-V1\n", + "\n", + "### 1.1 - Dataset EMOJISET\n", + "\n", + "Let's start by building a simple baseline classifier. \n", + "\n", + "You have a tiny dataset (X, Y) where:\n", + "- X contains 127 sentences (strings)\n", + "- Y contains a integer label between 0 and 4 corresponding to an emoji for each sentence\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
تابع کمکی برای خواند مجموعه داده\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "def read_csv(filename):\n", + " phrase = []\n", + " emoji = []\n", + "\n", + " with open (filename) as csvDataFile:\n", + " csvReader = csv.reader(csvDataFile)\n", + "\n", + " for row in csvReader:\n", + " phrase.append(row[0])\n", + " emoji.append(row[1])\n", + "\n", + " X = np.asarray(phrase)\n", + " Y = np.asarray(emoji, dtype=int)\n", + "\n", + " return X, Y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
مجموعه داده‌ی Emoji\n", + "
\n", + "\n", + "
مجموعه داده را میتوانید از مسیر زیر دانلود کنید.
\n", + "
\n", + "\n", + "[http://dataset.class.vision/NLP/emoji.zip](http://dataset.class.vision/NLP/emoji.zip)\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, Y_train = read_csv('D:/dataset/NLP/emoji/train_emoji.csv')\n", + "X_test, Y_test = read_csv('D:/dataset/NLP/emoji/tesss.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
طول بزرگترین جمله
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "maxLen = len(max(X_train, key=len).split())\n", + "maxLen" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
تابع کمکی تبدیل label ها به Emoji\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import emoji \n", + "\n", + "emoji_dictionary = {\"0\": \"\\u2764\\uFE0F\", # :heart: prints a black instead of red heart depending on the font\n", + " \"1\": \":baseball:\",\n", + " \"2\": \":smile:\",\n", + " \"3\": \":disappointed:\",\n", + " \"4\": \":fork_and_knife:\"}\n", + "\n", + "def label_to_emoji(label):\n", + "\n", + " return emoji.emojize(emoji_dictionary[str(label)], use_aliases=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I love you mum ❤️\n" + ] + } + ], + "source": [ + "index = 5\n", + "print(X_train[index], label_to_emoji(Y_train[index]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 - Overview of the Emojifier-V1\n", + "\n", + "\n", + "
\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
تبدیل Labelها به بردار One-Hot\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "Y_oh_train = keras.utils.to_categorical(Y_train, 5)\n", + "Y_oh_test = keras.utils.to_categorical(Y_test, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 is converted into one hot [1. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "index = 50\n", + "print(Y_train[index], \"is converted into one hot\", Y_oh_train[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 - Implementing Emojifier-V1\n", + "\n", + "As shown in Figure (2), the first step is to convert an input sentence into the word vector representation, which then get averaged together. Similar to the previous exercise, we will use pretrained 50-dimensional GloVe embeddings. Run the following cell to load the `word_to_vec_map`, which contains all the vector representations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
تابع کمکی برای خواندن embedding از پیش آموزش داده شده.\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def read_glove_vecs(glove_file):\n", + " with open(glove_file, encoding=\"utf8\") as f:\n", + " words = set()\n", + " word_to_vec_map = {}\n", + " for line in f:\n", + " line = line.strip().split()\n", + " curr_word = line[0]\n", + " words.add(curr_word)\n", + " word_to_vec_map[curr_word] = np.array(line[1:], dtype=np.float64)\n", + " \n", + " i = 1\n", + " words_to_index = {}\n", + " index_to_words = {}\n", + " for w in sorted(words):\n", + " words_to_index[w] = i\n", + " index_to_words[i] = w\n", + " i = i + 1\n", + " return words_to_index, index_to_words, word_to_vec_map" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "word_to_index, index_to_word, word_to_vec_map = read_glove_vecs('D:/dataset/glove.6B/glove.6B.50d.txt')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You've loaded:\n", + "- `word_to_index`: dictionary mapping from words to their indices in the vocabulary (400,001 words, with the valid indices ranging from 0 to 400,000)\n", + "- `index_to_word`: dictionary mapping from indices to their corresponding words in the vocabulary\n", + "- `word_to_vec_map`: dictionary mapping words to their GloVe vector " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the index of ali in the vocabulary is 51314\n", + "the 113317th word in the vocabulary is cucumber\n" + ] + } + ], + "source": [ + "word = \"ali\"\n", + "index = 113317\n", + "print(\"the index of\", word, \"in the vocabulary is\", word_to_index[word])\n", + "print(\"the\", str(index) + \"th word in the vocabulary is\", index_to_word[index])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.71587 , 0.7874 , 0.71305 , -0.089955, 1.366 , -1.3149 ,\n", + " 0.7309 , 0.79725 , 0.47211 , 0.53347 , 0.37542 , -0.10256 ,\n", + " -1.0003 , -0.31226 , 0.26217 , 0.92426 , 0.43014 , -0.015593,\n", + " 0.4149 , 0.88286 , 0.10869 , 0.95213 , 1.1807 , 0.06445 ,\n", + " -0.05814 , -1.797 , -0.18432 , -0.41754 , -0.73625 , 1.1607 ,\n", + " 1.5932 , -0.70268 , -0.61621 , 0.47118 , 0.95046 , 0.35206 ,\n", + " 0.6072 , 0.59339 , -0.47091 , 1.4916 , 0.27146 , 1.8252 ,\n", + " -1.2073 , -0.80058 , 0.52558 , -0.33346 , -1.4102 , -0.21514 ,\n", + " 0.12945 , -0.69603 ])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "word_to_vec_map[\"ali\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
تبدیل جمله به میانگین Embeddingهای کلمات آن\n", + "
\n", + "
\n", + "هر جمله را به کلمات تشکیل دهنده آن و سپس هر کلمه را به embedding و در نهایت این بردارهای embedding را میانگین خواهیم گرفت.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def sentence_to_avg(sentence, word_to_vec_map):\n", + " \n", + " # Split sentence into list of lower case words\n", + " words = sentence.lower().split()\n", + "\n", + " # Initialize the average word vector, should have the same shape as your word vectors.\n", + " avg = np.zeros((50,))\n", + " \n", + " # average the word vectors. You can loop over the words in the list \"words\".\n", + " for w in words:\n", + " avg += word_to_vec_map[w]\n", + " avg = avg / len(words)\n", + " \n", + " \n", + " return avg" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "avg = [-0.008005 0.56370833 -0.50427333 0.258865 0.55131103 0.03104983\n", + " -0.21013718 0.16893933 -0.09590267 0.141784 -0.15708967 0.18525867\n", + " 0.6495785 0.38371117 0.21102167 0.11301667 0.02613967 0.26037767\n", + " 0.05820667 -0.01578167 -0.12078833 -0.02471267 0.4128455 0.5152061\n", + " 0.38756167 -0.898661 -0.535145 0.33501167 0.68806933 -0.2156265\n", + " 1.797155 0.10476933 -0.36775333 0.750785 0.10282583 0.348925\n", + " -0.27262833 0.66768 -0.10706167 -0.283635 0.59580117 0.28747333\n", + " -0.3366635 0.23393817 0.34349183 0.178405 0.1166155 -0.076433\n", + " 0.1445417 0.09808667]\n" + ] + } + ], + "source": [ + "avg = sentence_to_avg(\"Morrocan couscous is my favorite dish\", word_to_vec_map)\n", + "print(\"avg = \", avg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "##
مدل\n", + "
\n", + "You now have all the pieces to finish implementing the `model()` function. After using `sentence_to_avg()` you need to pass the average through forward propagation, compute the cost, and then backpropagate to update the softmax's parameters. \n", + "\n", + "Assuming here that $Yoh$ (\"Y one hot\") is the one-hot encoding of the output labels, the equations you need to implement in the forward pass and to compute the cross-entropy cost are:\n", + "$$ z^{(i)} = W . avg^{(i)} + b$$\n", + "$$ a^{(i)} = softmax(z^{(i)})$$\n", + "$$ \\mathcal{L}^{(i)} = - \\sum_{k = 0}^{n_y - 1} Yoh^{(i)}_k * log(a^{(i)}_k)$$\n", + "\n", + "It is possible to come up with a more efficient vectorized implementation. But since we are using a for-loop to convert the sentences one at a time into the avg^{(i)} representation anyway, let's not bother this time. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def softmax(x):\n", + " \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n", + " e_x = np.exp(x - np.max(x))\n", + " return e_x / e_x.sum()\n", + "\n", + "def predict(X, Y, W, b, word_to_vec_map):\n", + " \"\"\"\n", + " Given X (sentences) and Y (emoji indices), predict emojis and compute the accuracy of your model over the given set.\n", + " \n", + " Arguments:\n", + " X -- input data containing sentences, numpy array of shape (m, None)\n", + " Y -- labels, containing index of the label emoji, numpy array of shape (m, 1)\n", + " \n", + " Returns:\n", + " pred -- numpy array of shape (m, 1) with your predictions\n", + " \"\"\"\n", + " m = X.shape[0]\n", + " pred = np.zeros((m, 1))\n", + " \n", + " for j in range(m): # Loop over training examples\n", + " \n", + " # Split jth test example (sentence) into list of lower case words\n", + " words = X[j].lower().split()\n", + " \n", + " # Average words' vectors\n", + " avg = np.zeros((50,))\n", + " for w in words:\n", + " avg += word_to_vec_map[w]\n", + " avg = avg/len(words)\n", + "\n", + " # Forward propagation\n", + " Z = np.dot(W, avg) + b\n", + " A = softmax(Z)\n", + " pred[j] = np.argmax(A)\n", + " \n", + " print(\"Accuracy: \" + str(np.mean((pred[:] == Y.reshape(Y.shape[0],1)[:]))))\n", + " \n", + " return pred\n", + "\n", + "def model(X, Y, word_to_vec_map, learning_rate = 0.01, num_iterations = 401):\n", + " \"\"\"\n", + " Model to train word vector representations in numpy.\n", + " \n", + " Arguments:\n", + " X -- input data, numpy array of sentences as strings, of shape (m, 1)\n", + " Y -- labels, numpy array of integers between 0 and 7, numpy-array of shape (m, 1)\n", + " word_to_vec_map -- dictionary mapping every word in a vocabulary into its 50-dimensional vector representation\n", + " learning_rate -- learning_rate for the stochastic gradient descent algorithm\n", + " num_iterations -- number of iterations\n", + " \n", + " Returns:\n", + " pred -- vector of predictions, numpy-array of shape (m, 1)\n", + " W -- weight matrix of the softmax layer, of shape (n_y, n_h)\n", + " b -- bias of the softmax layer, of shape (n_y,)\n", + " \"\"\"\n", + " \n", + " np.random.seed(1)\n", + "\n", + " # Define number of training examples\n", + " m = Y.shape[0] # number of training examples\n", + " n_y = 5 # number of classes \n", + " n_h = 50 # dimensions of the GloVe vectors \n", + " \n", + " # Initialize parameters using Xavier initialization\n", + " W = np.random.randn(n_y, n_h) / np.sqrt(n_h)\n", + " b = np.zeros((n_y,))\n", + " \n", + " # Convert Y to Y_onehot with n_y classes\n", + " Y_oh = keras.utils.to_categorical(Y, n_y) \n", + " \n", + " # Optimization loop\n", + " for t in range(num_iterations): # Loop over the number of iterations\n", + " for i in range(m): # Loop over the training examples\n", + " \n", + " # Average the word vectors of the words from the i'th training example\n", + " avg = sentence_to_avg(X[i], word_to_vec_map)\n", + "\n", + " # Forward propagate the avg through the softmax layer\n", + " z = np.dot(W, avg) + b\n", + " a = softmax(z)\n", + "\n", + " # Compute cost using the i'th training label's one hot representation and \"A\" (the output of the softmax)\n", + " cost = -np.sum(Y_oh[i] * np.log(a))\n", + " \n", + " # Compute gradients \n", + " dz = a - Y_oh[i]\n", + " dW = np.dot(dz.reshape(n_y,1), avg.reshape(1, n_h))\n", + " db = dz\n", + "\n", + " # Update parameters with Stochastic Gradient Descent\n", + " W = W - learning_rate * dW\n", + " b = b - learning_rate * db\n", + " \n", + " if t % 100 == 0:\n", + " print(\"Epoch: \" + str(t) + \" --- cost = \" + str(cost))\n", + " pred = predict(X, Y, W, b, word_to_vec_map)\n", + "\n", + " return pred, W, b" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 --- cost = 1.9520498812810072\n", + "Accuracy: 0.3484848484848485\n", + "Epoch: 100 --- cost = 0.07971818726014807\n", + "Accuracy: 0.9318181818181818\n", + "Epoch: 200 --- cost = 0.04456369243681402\n", + "Accuracy: 0.9545454545454546\n", + "Epoch: 300 --- cost = 0.03432267378786059\n", + "Accuracy: 0.9696969696969697\n", + "Epoch: 400 --- cost = 0.02906976783312465\n", + "Accuracy: 0.9772727272727273\n" + ] + } + ], + "source": [ + "pred, W, b = model(X_train, Y_train, word_to_vec_map)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### 1.4 - Examining test set performance \n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training set:\n", + "Accuracy: 0.9772727272727273\n", + "Test set:\n", + "Accuracy: 0.8571428571428571\n" + ] + } + ], + "source": [ + "print(\"Training set:\")\n", + "pred_train = predict(X_train, Y_train, W, b, word_to_vec_map)\n", + "print('Test set:')\n", + "pred_test = predict(X_test, Y_test, W, b, word_to_vec_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Random guessing would have had 20% accuracy given that there are 5 classes. This is pretty good performance after training on only 127 examples. \n", + "\n", + "In the training set, the algorithm saw the sentence \"*I love you*\" with the label ❤️. You can check however that the word \"adore\" does not appear in the training set. Nonetheless, lets see what happens if you write \"*I adore you*.\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def print_predictions(X, pred):\n", + " print()\n", + " for i in range(X.shape[0]):\n", + " print(X[i], label_to_emoji(int(pred[i])))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 0.8333333333333334\n", + "\n", + "i adore you ❤️\n", + "i love you ❤️\n", + "funny lol 😄\n", + "lets play with a ball ⚾\n", + "food is ready 🍴\n", + "not feeling happy 😄\n" + ] + } + ], + "source": [ + "X_my_sentences = np.array([\"i adore you\", \"i love you\", \"funny lol\", \"lets play with a ball\", \"food is ready\", \"not feeling happy\"])\n", + "Y_my_labels = np.array([[0], [0], [2], [1], [4],[3]])\n", + "\n", + "pred = predict(X_my_sentences, Y_my_labels , W, b, word_to_vec_map)\n", + "print_predictions(X_my_sentences, pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Amazing! Because *adore* has a similar embedding as *love*, the algorithm has generalized correctly even to a word it has never seen before. Words such as *heart*, *dear*, *beloved* or *adore* have embedding vectors similar to *love*, and so might work too---feel free to modify the inputs above and try out a variety of input sentences. How well does it work?\n", + "\n", + "Note though that it doesn't get \"not feeling happy\" correct. This algorithm ignores word ordering, so is not good at understanding phrases like \"not happy.\" \n", + "\n", + "Printing the confusion matrix can also help understand which classes are more difficult for your model. A confusion matrix shows how often an example whose label is one class (\"actual\" class) is mislabeled by the algorithm with a different class (\"predicted\" class). \n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Emojifier-V2: Using LSTMs in Keras: \n", + "\n", + "Let's build an LSTM model that takes as input word sequences. This model will be able to take word ordering into account. Emojifier-V2 will continue to use pre-trained word embeddings to represent words, but will feed them into an LSTM, whose job it is to predict the most appropriate emoji. \n", + "\n", + "Run the following cell to load the Keras packages." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "np.random.seed(0)\n", + "from keras.models import Model\n", + "from keras.layers import Dense, Input, Dropout, LSTM, Activation\n", + "from keras.layers.embeddings import Embedding\n", + "from keras.preprocessing import sequence\n", + "from keras.initializers import glorot_uniform\n", + "np.random.seed(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 - Overview of the model\n", + "\n", + "Here is the Emojifier-v2 you will implement:\n", + "\n", + "
\n", + "
**Figure 3**: Emojifier-V2. A 2-layer LSTM sequence classifier.
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 - The Embedding layer\n", + "\n", + "In Keras, the embedding matrix is represented as a \"layer\", and maps positive integers (indices corresponding to words) into dense vectors of fixed size (the embedding vectors). It can be trained or initialized with a pretrained embedding. In this part, you will learn how to create an [Embedding()](https://keras.io/layers/embeddings/) layer in Keras, initialize it with the GloVe 50-dimensional vectors loaded earlier in the notebook. Because our training set is quite small, we will not update the word embeddings but will instead leave their values fixed. But in the code below, we'll show you how Keras allows you to either train or leave fixed this layer. \n", + "\n", + "The `Embedding()` layer takes an integer matrix of size (batch size, max input length) as input. This corresponds to sentences converted into lists of indices (integers), as shown in the figure below.\n", + "\n", + "\n", + "
**Figure 4**: Embedding layer. This example shows the propagation of two examples through the embedding layer. Both have been zero-padded to a length of `max_len=5`. The final dimension of the representation is `(2,max_len,50)` because the word embeddings we are using are 50 dimensional.
\n", + "\n", + "The largest integer (i.e. word index) in the input should be no larger than the vocabulary size. The layer outputs an array of shape (batch size, max input length, dimension of word vectors).\n", + "\n", + "The first step is to convert all your training sentences into lists of indices, and then zero-pad all these lists so that their length is the length of the longest sentence. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
تبدیل جمله به indexها \n", + "
\n", + "
\n", + "این تابع طول تمام جمله ها را نیز یکسان میکند.\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def sentences_to_indices(X, word_to_index, max_len):\n", + " m = X.shape[0] # number of training examples\n", + " \n", + " # Initialize X_indices as a numpy matrix of zeros and the correct shape (≈ 1 line)\n", + " X_indices = np.zeros((m, max_len))\n", + " \n", + " for i in range(m): # loop over training examples\n", + " \n", + " # Convert the ith training sentence in lower case and split is into words. You should get a list of words.\n", + " sentence_words =X[i].lower().split()\n", + "\n", + " \n", + " # Loop over the words of sentence_words\n", + " for j, w in enumerate(sentence_words):\n", + " # Set the (i,j)th entry of X_indices to the index of the correct word.\n", + " X_indices[i, j] = word_to_index[w]\n", + "\n", + " return X_indices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to check what `sentences_to_indices()` does, and check your results." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X1 = ['funny lol' 'lets play baseball' 'food is ready for you']\n", + "X1_indices = [[155345. 225122. 0. 0. 0.]\n", + " [220930. 286375. 69714. 0. 0.]\n", + " [151204. 192973. 302254. 151349. 394475.]]\n" + ] + } + ], + "source": [ + "X1 = np.array([\"funny lol\", \"lets play baseball\", \"food is ready for you\"])\n", + "X1_indices = sentences_to_indices(X1,word_to_index, max_len = 5)\n", + "print(\"X1 =\", X1)\n", + "print(\"X1_indices =\", X1_indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
تابعی برای ایجاد لایه Embedding و لود وزن های از پیش آموزش داده شده \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's build the `Embedding()` layer in Keras, using pre-trained word vectors. After this layer is built, you will pass the output of `sentences_to_indices()` to it as an input, and the `Embedding()` layer will return the word embeddings for a sentence. \n", + "\n", + "\n", + "1. Initialize the embedding matrix as a numpy array of zeroes with the correct shape.\n", + "2. Fill in the embedding matrix with all the word embeddings extracted from `word_to_vec_map`.\n", + "3. Define Keras embedding layer. Use [Embedding()](https://keras.io/layers/embeddings/). Be sure to make this layer non-trainable, by setting `trainable = False` when calling `Embedding()`. If you were to set `trainable = True`, then it will allow the optimization algorithm to modify the values of the word embeddings. \n", + "4. Set the embedding weights to be equal to the embedding matrix " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.layers import Embedding\n", + "def pretrained_embedding_layer(word_to_vec_map, word_to_index):\n", + " \n", + " vocab_len = len(word_to_index) + 1 # adding 1 to fit Keras embedding (requirement)\n", + " emb_dim = word_to_vec_map[\"cucumber\"].shape[0] # define dimensionality of your GloVe word vectors (= 50)\n", + " \n", + " # Initialize the embedding matrix as a numpy array of zeros of shape (vocab_len, dimensions of word vectors = emb_dim)\n", + " emb_matrix = np.zeros((vocab_len, emb_dim))\n", + " \n", + " # Set each row \"index\" of the embedding matrix to be the word vector representation of the \"index\"th word of the vocabulary\n", + " for word, index in word_to_index.items():\n", + " emb_matrix[index, :] = word_to_vec_map[word]\n", + "\n", + " # Define Keras embedding layer with the correct output/input sizes, make it trainable. Use Embedding(...). Make sure to set trainable=False. \n", + " embedding_layer = Embedding(vocab_len, emb_dim, trainable = False)\n", + "\n", + " # Build the embedding layer, it is required before setting the weights of the embedding layer. Do not modify the \"None\".\n", + " embedding_layer.build((None,))\n", + " \n", + " # Set the weights of the embedding layer to the embedding matrix. Your layer is now pretrained.\n", + " embedding_layer.set_weights([emb_matrix])\n", + " \n", + " return embedding_layer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3 Building the Emojifier-V2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.layers import Input\n", + "from keras.layers import LSTM\n", + "from keras.layers import Dense, Dropout\n", + "from keras.models import Model\n", + "\n", + "def Emojify_V2(input_shape, word_to_vec_map, word_to_index):\n", + "\n", + " # Define sentence_indices as the input of the graph, it should be of shape input_shape and dtype 'int32' (as it contains indices).\n", + " sentence_indices = Input(input_shape, dtype = np.int32)\n", + " \n", + " # Create the embedding layer pretrained with GloVe Vectors (≈1 line)\n", + " embedding_layer = pretrained_embedding_layer(word_to_vec_map, word_to_index)\n", + " \n", + " # Propagate sentence_indices through your embedding layer, you get back the embeddings\n", + " embeddings = embedding_layer(sentence_indices)\n", + " \n", + " # Propagate the embeddings through an LSTM layer with 128-dimensional hidden state\n", + " # Be careful, the returned output should be a batch of sequences.\n", + " X = LSTM(128, return_sequences=True)(embeddings)\n", + " # Add dropout with a probability of 0.5\n", + " X = Dropout(0.5)(X)\n", + " # Propagate X trough another LSTM layer with 128-dimensional hidden state\n", + " # Be careful, the returned output should be a single hidden state, not a batch of sequences.\n", + " X = LSTM(128)(X)\n", + " # Add dropout with a probability of 0.5\n", + " X = Dropout(0.5)(X)\n", + " # Propagate X through a Dense layer with softmax activation to get back a batch of 5-dimensional vectors.\n", + " X = Dense(5, activation = 'softmax')(X)\n", + " \n", + " # Create Model instance which converts sentence_indices into X.\n", + " model = Model(sentence_indices, X)\n", + " \n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_2 (InputLayer) (None, 10) 0 \n", + "_________________________________________________________________\n", + "embedding_2 (Embedding) (None, 10, 50) 20000050 \n", + "_________________________________________________________________\n", + "lstm_3 (LSTM) (None, 10, 128) 91648 \n", + "_________________________________________________________________\n", + "dropout_3 (Dropout) (None, 10, 128) 0 \n", + "_________________________________________________________________\n", + "lstm_4 (LSTM) (None, 128) 131584 \n", + "_________________________________________________________________\n", + "dropout_4 (Dropout) (None, 128) 0 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 5) 645 \n", + "=================================================================\n", + "Total params: 20,223,927\n", + "Trainable params: 223,877\n", + "Non-trainable params: 20,000,050\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = Emojify_V2((maxLen,), word_to_vec_map, word_to_index)\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, after creating your model in Keras, you need to compile it and define what loss, optimizer and metrics your are want to use. Compile your model using `categorical_crossentropy` loss, `adam` optimizer and `['accuracy']` metrics:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's time to train your model. Your Emojifier-V2 `model` takes as input an array of shape (`m`, `max_len`) and outputs probability vectors of shape (`m`, `number of classes`). We thus have to convert X_train (array of sentences as strings) to X_train_indices (array of sentences as list of word indices), and Y_train (labels as indices) to Y_train_oh (labels as one-hot vectors)." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "X_train_indices = sentences_to_indices(X_train, word_to_index, maxLen)\n", + "Y_train_oh = keras.utils.to_categorical(Y_train, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "132/132 [==============================] - 2s 14ms/step - loss: 1.5785 - accuracy: 0.2803\n", + "Epoch 2/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 1.5018 - accuracy: 0.3333\n", + "Epoch 3/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 1.4604 - accuracy: 0.3485\n", + "Epoch 4/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 1.3685 - accuracy: 0.4470\n", + "Epoch 5/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 1.2991 - accuracy: 0.4697\n", + "Epoch 6/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 1.1840 - accuracy: 0.5455\n", + "Epoch 7/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 1.0434 - accuracy: 0.6061\n", + "Epoch 8/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.8732 - accuracy: 0.7197\n", + "Epoch 9/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.8077 - accuracy: 0.7273\n", + "Epoch 10/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.6823 - accuracy: 0.7500\n", + "Epoch 11/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.6593 - accuracy: 0.7424\n", + "Epoch 12/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.5245 - accuracy: 0.7879\n", + "Epoch 13/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.5793 - accuracy: 0.8106\n", + "Epoch 14/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.4114 - accuracy: 0.8712\n", + "Epoch 15/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.4243 - accuracy: 0.8333\n", + "Epoch 16/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.3319 - accuracy: 0.8788\n", + "Epoch 17/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.3110 - accuracy: 0.8864\n", + "Epoch 18/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.3255 - accuracy: 0.8864\n", + "Epoch 19/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.3134 - accuracy: 0.9167\n", + "Epoch 20/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.2598 - accuracy: 0.9015\n", + "Epoch 21/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.2034 - accuracy: 0.9318\n", + "Epoch 22/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.3006 - accuracy: 0.8939\n", + "Epoch 23/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1704 - accuracy: 0.9394\n", + "Epoch 24/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1986 - accuracy: 0.9470\n", + "Epoch 25/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1613 - accuracy: 0.9621\n", + "Epoch 26/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1410 - accuracy: 0.9545\n", + "Epoch 27/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1857 - accuracy: 0.9167\n", + "Epoch 28/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.2262 - accuracy: 0.9394\n", + "Epoch 29/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1146 - accuracy: 0.9470\n", + "Epoch 30/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1039 - accuracy: 0.9697\n", + "Epoch 31/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1061 - accuracy: 0.9621\n", + "Epoch 32/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.2473 - accuracy: 0.9167\n", + "Epoch 33/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1782 - accuracy: 0.9318\n", + "Epoch 34/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1944 - accuracy: 0.9394\n", + "Epoch 35/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1595 - accuracy: 0.9318\n", + "Epoch 36/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1048 - accuracy: 0.9545\n", + "Epoch 37/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1591 - accuracy: 0.9545\n", + "Epoch 38/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0960 - accuracy: 0.9621\n", + "Epoch 39/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0617 - accuracy: 0.9924\n", + "Epoch 40/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0381 - accuracy: 1.0000\n", + "Epoch 41/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0345 - accuracy: 1.0000\n", + "Epoch 42/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0371 - accuracy: 1.0000\n", + "Epoch 43/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.1414 - accuracy: 0.9621\n", + "Epoch 44/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0375 - accuracy: 0.9924\n", + "Epoch 45/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0719 - accuracy: 0.9848\n", + "Epoch 46/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0595 - accuracy: 0.9848\n", + "Epoch 47/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0240 - accuracy: 0.9848\n", + "Epoch 48/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0170 - accuracy: 1.0000\n", + "Epoch 49/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0152 - accuracy: 1.0000\n", + "Epoch 50/50\n", + "132/132 [==============================] - 0s 2ms/step - loss: 0.0097 - accuracy: 1.0000\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(X_train_indices, Y_train_oh, epochs = 50, batch_size = 32, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your model should perform close to **100% accuracy** on the training set. The exact accuracy you get may be a little different. Run the following cell to evaluate your model on the test set. " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56/56 [==============================] - 0s 4ms/step\n", + "\n", + "Test accuracy = 0.8035714030265808\n" + ] + } + ], + "source": [ + "X_test_indices = sentences_to_indices(X_test, word_to_index, max_len = maxLen)\n", + "Y_test_oh = keras.utils.to_categorical(Y_test, 5)\n", + "loss, acc = model.evaluate(X_test_indices, Y_test_oh)\n", + "print()\n", + "print(\"Test accuracy = \", acc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should get a test accuracy between 80% and 95%. Run the cell below to see the mislabelled examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected emoji:😄 prediction: she got me a nice present\t❤️\n", + "Expected emoji:😞 prediction: work is hard\t😄\n", + "Expected emoji:😞 prediction: This girl is messing with me\t❤️\n", + "Expected emoji:😞 prediction: work is horrible\t😄\n", + "Expected emoji:🍴 prediction: any suggestions for dinner\t😄\n", + "Expected emoji:😄 prediction: you brighten my day\t❤️\n", + "Expected emoji:😞 prediction: she is a bully\t❤️\n", + "Expected emoji:😞 prediction: My life is so boring\t❤️\n", + "Expected emoji:😄 prediction: What you did was awesome\t😞\n", + "Expected emoji:😞 prediction: go away\t⚾\n", + "Expected emoji:❤️ prediction: family is all I have\t😞\n" + ] + } + ], + "source": [ + "# This code allows you to see the mislabelled examples\n", + "C = 5\n", + "y_test_oh = np.eye(C)[Y_test.reshape(-1)]\n", + "X_test_indices = sentences_to_indices(X_test, word_to_index, maxLen)\n", + "pred = model.predict(X_test_indices)\n", + "for i in range(len(X_test)):\n", + " x = X_test_indices\n", + " num = np.argmax(pred[i])\n", + " if(num != Y_test[i]):\n", + " print('Expected emoji:'+ label_to_emoji(Y_test[i]) + ' prediction: '+ X_test[i] + label_to_emoji(num).strip())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you can try it on your own example. Write your own sentence below. " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "not feeling happy 😞\n" + ] + } + ], + "source": [ + "# Change the sentence below to see your prediction. Make sure all the words are in the Glove embeddings. \n", + "x_test = np.array(['not feeling happy'])\n", + "X_test_indices = sentences_to_indices(x_test, word_to_index, maxLen)\n", + "print(x_test[0] +' '+ label_to_emoji(np.argmax(model.predict(X_test_indices))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Previously, Emojify-V1 model did not correctly label \"not feeling happy,\" but our implementation of Emojiy-V2 got it right. (Keras' outputs are slightly random each time, so you may not have obtained the same result.) The current model still isn't very robust at understanding negation (like \"not happy\") because the training set is small and so doesn't have a lot of examples of negation. But if the training set were larger, the LSTM model would be much better than the Emojify-V1 model at understanding such complex sentences. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "RNnEs", + "launcher_item_id": "acNYU" + }, + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/08_shahnameh-text-generation-language-model.ipynb b/08_shahnameh-text-generation-language-model.ipynb new file mode 100644 index 0000000..0bea7c5 --- /dev/null +++ b/08_shahnameh-text-generation-language-model.ipynb @@ -0,0 +1,1327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

مدل زبانی در سطح کاراکتر و تولید متنی شبیه شاهنامه

" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ovpZyIhNIgoq" + }, + "source": [ + "# Text generation with an RNN" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WGyKZj3bzf9p" + }, + "source": [ + "### Import TensorFlow and other libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "yG_n40gFzf9s" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "\n", + "import numpy as np\n", + "import os\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###
مجموعه داده\n", + "
\n", + "\n", + "
مجموعه داده زیر را از سایت گنجور برای این تمرین استخراج کرده ایم. لطفا فایل txt را دانلود کرده و در کنار نوت بوک قرار دهید.
\n", + "\n", + "\n", + "\n", + "http://dataset.class.vision/NLP/shahnameh.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "pD_55cOxLkAb" + }, + "outputs": [], + "source": [ + "path_to_file = \"shahnameh.txt\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UHjdCjDuSvX_" + }, + "source": [ + "### Read the data\n", + "\n", + "First, look in the text:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "aavnuByVymwK" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of text: 2653849 characters\n" + ] + } + ], + "source": [ + "# Read, then decode for py2 compat.\n", + "text = open(path_to_file, 'rb').read().decode(encoding='utf-8')\n", + "# length of text is the number of characters in it\n", + "print ('Length of text: {} characters'.format(len(text)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Duhg9NrUymwO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "|به نام خداوند جان و خرد\n", + "|کزین برتر اندیشه برنگذرد\n", + "|خداوند نام و خداوند جای\n", + "|خداوند روزی ده رهنمای\n", + "|خداوند کیوان و گردان سپهر\n", + "|فروزنده ماه و ناهید و مهر\n", + "|ز نام و نشان و گمان برترست\n", + "|نگارندهٔ بر شده پیکرست\n", + "|به بینندگان آفریننده را\n", + "|نبینی مرنجان دو بین\n" + ] + } + ], + "source": [ + "# Take a look at the first 250 characters in text\n", + "print(text[:250])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "IlCgQBRVymwR" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "48 unique characters\n" + ] + } + ], + "source": [ + "# The unique characters in the file\n", + "vocab = sorted(set(text))\n", + "print ('{} unique characters'.format(len(vocab)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rNnrKn_lL-IJ" + }, + "source": [ + "## Process the text" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LFjSVAlWzf-N" + }, + "source": [ + "### Vectorize the text\n", + "\n", + "Before training, we need to map strings to a numerical representation. Create two lookup tables: one mapping characters to numbers, and another for numbers to characters." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "IalZLbvOzf-F" + }, + "outputs": [], + "source": [ + "# Creating a mapping from unique characters to indices\n", + "char2idx = {u:i for i, u in enumerate(vocab)}\n", + "idx2char = np.array(vocab)\n", + "\n", + "text_as_int = np.array([char2idx[c] for c in text])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "tZfqhkYCymwX" + }, + "source": [ + "Now we have an integer representation for each character. Notice that we mapped the character as indexes from 0 to `len(unique)`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "FYyNlCNXymwY" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " '\\n': 0,\n", + " ' ' : 1,\n", + " '(' : 2,\n", + " ')' : 3,\n", + " '|' : 4,\n", + " '«' : 5,\n", + " '»' : 6,\n", + " '،' : 7,\n", + " '؟' : 8,\n", + " 'ء' : 9,\n", + " 'آ' : 10,\n", + " 'أ' : 11,\n", + " 'ؤ' : 12,\n", + " 'ئ' : 13,\n", + " 'ا' : 14,\n", + " 'ب' : 15,\n", + " 'ت' : 16,\n", + " 'ث' : 17,\n", + " 'ج' : 18,\n", + " 'ح' : 19,\n", + " ...\n", + "}\n" + ] + } + ], + "source": [ + "print('{')\n", + "for char,_ in zip(char2idx, range(20)):\n", + " print(' {:4s}: {:3d},'.format(repr(char), char2idx[char]))\n", + "print(' ...\\n}')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "l1VKcQHcymwb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'|به نام خداون' ---- characters mapped to int ---- > [ 4 15 38 1 37 14 36 1 20 21 14 39 37]\n" + ] + } + ], + "source": [ + "# Show how the first 13 characters from the text are mapped to integers\n", + "print ('{} ---- characters mapped to int ---- > {}'.format(repr(text[:13]), text_as_int[:13]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bbmsf23Bymwe" + }, + "source": [ + "### The prediction task" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wssHQ1oGymwe" + }, + "source": [ + "Given a character, or a sequence of characters, what is the most probable next character? This is the task we're training the model to perform. The input to the model will be a sequence of characters, and we train the model to predict the output—the following character at each time step.\n", + "\n", + "Since RNNs maintain an internal state that depends on the previously seen elements, given all the characters computed until this moment, what is the next character?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hgsVvVxnymwf" + }, + "source": [ + "### Create training examples and targets\n", + "\n", + "Next divide the text into example sequences. Each input sequence will contain `seq_length` characters from the text.\n", + "\n", + "For each input sequence, the corresponding targets contain the same length of text, except shifted one character to the right.\n", + "\n", + "So break the text into chunks of `seq_length+1`. For example, say `seq_length` is 4 and our text is \"Hello\". The input sequence would be \"Hell\", and the target sequence \"ello\".\n", + "\n", + "To do this first use the `tf.data.Dataset.from_tensor_slices` function to convert the text vector into a stream of character indices." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "0UHJDA39zf-O" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "|\n", + "ب\n", + "ه\n", + " \n", + "ن\n" + ] + } + ], + "source": [ + "# The maximum length sentence we want for a single input in characters\n", + "seq_length = 100\n", + "\n", + "# Create training examples / targets\n", + "char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)\n", + "\n", + "for i in char_dataset.take(5):\n", + " print(idx2char[i.numpy()])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-ZSYAcQV8OGP" + }, + "source": [ + "The `batch` method lets us easily convert these individual characters to sequences of the desired size." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "l4hkDU3i7ozi" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'|به نام خداوند جان و خرد\\n|کزین برتر اندیشه برنگذرد\\n|خداوند نام و خداوند جای\\n|خداوند روزی ده رهنمای\\n|خ'\n", + "***************\n", + "'داوند کیوان و گردان سپهر\\n|فروزنده ماه و ناهید و مهر\\n|ز نام و نشان و گمان برترست\\n|نگارندهٔ بر شده پیکر'\n", + "***************\n", + "'ست\\n|به بینندگان آفریننده را\\n|نبینی مرنجان دو بیننده را\\n|نیابد بدو نیز اندیشه راه\\n|که او برتر از نام و'\n", + "***************\n", + "' از جایگاه\\n|سخن هر چه زین گوهران بگذرد\\n|نیابد بدو راه جان و خرد\\n|خرد گر سخن برگزیند همی\\n|همان را گزین'\n", + "***************\n", + "'د که بیند همی\\n|ستودن نداند کس او را چو هست\\n|میان بندگی را ببایدت بست\\n|خرد را و جان را همی سنجد اوی\\n|د'\n", + "***************\n" + ] + } + ], + "source": [ + "sequences = char_dataset.batch(seq_length+1, drop_remainder=True)\n", + "\n", + "for item in sequences.take(5):\n", + " print(repr(''.join(idx2char[item.numpy()])))\n", + " print(\"***\"*5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UbLcIPBj_mWZ" + }, + "source": [ + "For each sequence, duplicate and shift it to form the input and target text by using the `map` method to apply a simple function to each batch:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "9NGu-FkO_kYU" + }, + "outputs": [], + "source": [ + "def split_input_target(chunk):\n", + " input_text = chunk[:-1]\n", + " target_text = chunk[1:]\n", + " return input_text, target_text\n", + "\n", + "dataset = sequences.map(split_input_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hiCopyGZymwi" + }, + "source": [ + "Print the first examples input and target values:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "GNbw-iR0ymwj" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input data: '|به نام خداوند جان و خرد\\n|کزین برتر اندیشه برنگذرد\\n|خداوند نام و خداوند جای\\n|خداوند روزی ده رهنمای\\n|'\n", + "Target data: 'به نام خداوند جان و خرد\\n|کزین برتر اندیشه برنگذرد\\n|خداوند نام و خداوند جای\\n|خداوند روزی ده رهنمای\\n|خ'\n" + ] + } + ], + "source": [ + "for input_example, target_example in dataset.take(1):\n", + " print ('Input data: ', repr(''.join(idx2char[input_example.numpy()])))\n", + " print ('Target data:', repr(''.join(idx2char[target_example.numpy()])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_33OHL3b84i0" + }, + "source": [ + "Each index of these vectors are processed as one time step. For the input at time step 0, the model receives the index for \"F\" and trys to predict the index for \"i\" as the next character. At the next timestep, it does the same thing but the `RNN` considers the previous step context in addition to the current input character." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "0eBu9WZG84i0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 0\n", + " input: 4 ('|')\n", + " expected output: 15 ('ب')\n", + "Step 1\n", + " input: 15 ('ب')\n", + " expected output: 38 ('ه')\n", + "Step 2\n", + " input: 38 ('ه')\n", + " expected output: 1 (' ')\n", + "Step 3\n", + " input: 1 (' ')\n", + " expected output: 37 ('ن')\n", + "Step 4\n", + " input: 37 ('ن')\n", + " expected output: 14 ('ا')\n" + ] + } + ], + "source": [ + "for i, (input_idx, target_idx) in enumerate(zip(input_example[:5], target_example[:5])):\n", + " print(\"Step {:4d}\".format(i))\n", + " print(\" input: {} ({:s})\".format(input_idx, repr(idx2char[input_idx])))\n", + " print(\" expected output: {} ({:s})\".format(target_idx, repr(idx2char[target_idx])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MJdfPmdqzf-R" + }, + "source": [ + "### Create training batches\n", + "\n", + "We used `tf.data` to split the text into manageable sequences. But before feeding this data into the model, we need to shuffle the data and pack it into batches." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "p2pGotuNzf-S" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Batch size\n", + "BATCH_SIZE = 64\n", + "\n", + "\n", + "dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)\n", + "\n", + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "r6oUuElIMgVx" + }, + "source": [ + "## Build The Model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "m8gPwEjRzf-Z" + }, + "source": [ + "Use `tf.keras.Sequential` to define the model. For this simple example three layers are used to define our model:\n", + "\n", + "* `tf.keras.layers.Embedding`: The input layer. A trainable lookup table that will map the numbers of each character to a vector with `embedding_dim` dimensions;\n", + "* `tf.keras.layers.GRU`: A type of RNN with size `units=rnn_units` (You can also use a LSTM layer here.)\n", + "* `tf.keras.layers.Dense`: The output layer, with `vocab_size` outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zHT8cLh7EAsg" + }, + "outputs": [], + "source": [ + "# Length of the vocabulary in chars\n", + "vocab_size = len(vocab)\n", + "\n", + "# The embedding dimension\n", + "embedding_dim = 25\n", + "\n", + "# Number of RNN units\n", + "rnn_units = 1024" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "MtCrdfzEI2N0" + }, + "outputs": [], + "source": [ + "def build_model(vocab_size, embedding_dim, rnn_units, batch_size):\n", + " model = tf.keras.Sequential([\n", + " tf.keras.layers.Embedding(vocab_size, embedding_dim,\n", + " batch_input_shape=[batch_size, None]),\n", + " tf.keras.layers.GRU(rnn_units,\n", + " return_sequences=True,\n", + " stateful=True,\n", + " recurrent_initializer='glorot_uniform'),\n", + " tf.keras.layers.Dense(vocab_size)\n", + " ])\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "wwsrpOik5zhv" + }, + "outputs": [], + "source": [ + "model = build_model(\n", + " vocab_size = len(vocab),\n", + " embedding_dim=embedding_dim,\n", + " rnn_units=rnn_units,\n", + " batch_size=BATCH_SIZE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RkA5upJIJ7W7" + }, + "source": [ + "For each character the model looks up the embedding, runs the GRU one timestep with the embedding as input, and applies the dense layer to generate logits predicting the log-likelihood of the next character:\n", + "\n", + "![A drawing of the data passing through the model](images/text_generation_training.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-ubPo0_9Prjb" + }, + "source": [ + "## Try the model\n", + "\n", + "Now run the model to see that it behaves as expected.\n", + "\n", + "First check the shape of the output:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "C-_70kKAPrPU" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(64, 100, 48) # (batch_size, sequence_length, vocab_size)\n" + ] + } + ], + "source": [ + "for input_example_batch, target_example_batch in dataset.take(1):\n", + " example_batch_predictions = model.predict(input_example_batch)\n", + " print(example_batch_predictions.shape, \"# (batch_size, sequence_length, vocab_size)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Q6NzLBi4VM4o" + }, + "source": [ + "In the above example the sequence length of the input is `100` but the model can be run on inputs of any length:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vPGmAAXmVLGC" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "embedding (Embedding) (64, None, 25) 1200 \n", + "_________________________________________________________________\n", + "gru (GRU) (64, None, 1024) 3228672 \n", + "_________________________________________________________________\n", + "dense (Dense) (64, None, 48) 49200 \n", + "=================================================================\n", + "Total params: 3,279,072\n", + "Trainable params: 3,279,072\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uwv0gEkURfx1" + }, + "source": [ + "To get actual predictions from the model we need to sample from the output distribution, to get actual character indices. This distribution is defined by the logits over the character vocabulary.\n", + "\n", + "Note: It is important to _sample_ from this distribution as taking the _argmax_ of the distribution can easily get the model stuck in a loop.\n", + "\n", + "Try it for the first example in the batch:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4V4MfFg0RQJg" + }, + "outputs": [], + "source": [ + "sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)\n", + "sampled_indices = tf.squeeze(sampled_indices,axis=-1).numpy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QM1Vbxs_URw5" + }, + "source": [ + "This gives us, at each timestep, a prediction of the next character index:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "YqFMUQc_UFgM" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([38, 47, 10, 11, 21, 29, 37, 41, 13, 35, 41, 23, 12, 21, 41, 15, 47,\n", + " 29, 3, 10, 42, 33, 12, 13, 18, 4, 35, 27, 41, 16, 8, 45, 15, 43,\n", + " 8, 40, 28, 35, 29, 3, 5, 8, 17, 32, 10, 44, 25, 24, 45, 24, 26,\n", + " 40, 19, 8, 6, 45, 25, 3, 31, 13, 28, 12, 6, 46, 22, 45, 12, 33,\n", + " 42, 33, 0, 15, 32, 36, 45, 22, 33, 44, 43, 33, 36, 36, 9, 47, 4,\n", + " 18, 0, 31, 33, 33, 0, 45, 37, 15, 44, 22, 30, 10, 29, 28],\n", + " dtype=int64)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sampled_indices" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LfLtsP3mUhCG" + }, + "source": [ + "Decode these to see the text predicted by this untrained model:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "xWcFwPwLSo05" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input: \n", + " '|به نام خداوند جان و خرد\\n|کزین برتر اندیشه برنگذرد\\n|خداوند نام و خداوند جای\\n|خداوند روزی ده رهنمای\\n|'\n", + "\n", + "Next Char Predictions: \n", + " 'ه\\u200cآأدطنپئلپرؤدپب\\u200cط)آچفؤئج|لصپت؟گبژ؟ٔضلط)«؟ثغآکسزگزشٔح؟»گس)عئضؤ»یذگؤفچف\\nبغمگذفکژفممء\\u200c|ج\\nعفف\\nگنبکذظآطض'\n" + ] + } + ], + "source": [ + "print(\"Input: \\n\", repr(\"\".join(idx2char[input_example_batch[0]])))\n", + "print()\n", + "print(\"Next Char Predictions: \\n\", repr(\"\".join(idx2char[sampled_indices ])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LJL0Q0YPY6Ee" + }, + "source": [ + "## Train the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YCbHQHiaa4Ic" + }, + "source": [ + "At this point the problem can be treated as a standard classification problem. Given the previous RNN state, and the input this time step, predict the class of the next character." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "trpqTWyvk0nr" + }, + "source": [ + "### Attach an optimizer, and a loss function" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "UAjbjY03eiQ4" + }, + "source": [ + "The standard `tf.keras.losses.sparse_categorical_crossentropy` loss function works in this case because it is applied across the last dimension of the predictions.\n", + "\n", + "Because our model returns logits, we need to set the `from_logits` flag.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4HrXTACTdzY-" + }, + "outputs": [], + "source": [ + "def loss(labels, logits):\n", + " return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "jeOXriLcymww" + }, + "source": [ + "Configure the training procedure using the `tf.keras.Model.compile` method. We'll use `tf.keras.optimizers.Adam` with default arguments and the loss function." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DDl1_Een6rL0" + }, + "outputs": [], + "source": [ + "model.compile(optimizer='adam', loss=loss)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ieSJdchZggUj" + }, + "source": [ + "### Configure checkpoints" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "C6XBUUavgF56" + }, + "source": [ + "Use a `tf.keras.callbacks.ModelCheckpoint` to ensure that checkpoints are saved during training:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "W6fWTriUZP-n" + }, + "outputs": [], + "source": [ + "# Directory where the checkpoints will be saved\n", + "checkpoint_dir = './training_checkpoints'\n", + "# Name of the checkpoint files\n", + "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")\n", + "\n", + "checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(\n", + " filepath=checkpoint_prefix,\n", + " save_weights_only=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3Ky3F_BhgkTW" + }, + "source": [ + "### Execute the training" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "IxdOA-rgyGvs" + }, + "source": [ + "To keep training time reasonable, use 10 epochs to train the model. In Colab, set the runtime to GPU for faster training." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7yGBE2zxMMHs" + }, + "outputs": [], + "source": [ + "EPOCHS=10" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "UK-hmKjYVoll" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "410/410 [==============================] - 129s 314ms/step - loss: 2.4480\n", + "Epoch 2/10\n", + "410/410 [==============================] - 149s 363ms/step - loss: 1.8074\n", + "Epoch 3/10\n", + "410/410 [==============================] - 152s 371ms/step - loss: 1.5528\n", + "Epoch 4/10\n", + "410/410 [==============================] - 154s 375ms/step - loss: 1.4235\n", + "Epoch 5/10\n", + "410/410 [==============================] - 154s 377ms/step - loss: 1.3444\n", + "Epoch 6/10\n", + "410/410 [==============================] - 157s 382ms/step - loss: 1.2861\n", + "Epoch 7/10\n", + "410/410 [==============================] - 157s 383ms/step - loss: 1.2370\n", + "Epoch 8/10\n", + "410/410 [==============================] - 136s 331ms/step - loss: 1.1920\n", + "Epoch 9/10\n", + "410/410 [==============================] - 147s 359ms/step - loss: 1.1487\n", + "Epoch 10/10\n", + "410/410 [==============================] - 152s 370ms/step - loss: 1.1066\n" + ] + } + ], + "source": [ + "history = model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "kKkD5M6eoSiN" + }, + "source": [ + "## Generate text" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JIPcXllKjkdr" + }, + "source": [ + "### Restore the latest checkpoint" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LyeYRiuVjodY" + }, + "source": [ + "To keep this prediction step simple, use a batch size of 1.\n", + "\n", + "Because of the way the RNN state is passed from timestep to timestep, the model only accepts a fixed batch size once built.\n", + "\n", + "To run the model with a different `batch_size`, we need to rebuild the model and restore the weights from the checkpoint.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zk2WJ2-XjkGz" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'./training_checkpoints\\\\ckpt_10'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf.train.latest_checkpoint(checkpoint_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "LycQ-ot_jjyu" + }, + "outputs": [], + "source": [ + "model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)\n", + "\n", + "model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))\n", + "\n", + "model.build(tf.TensorShape([1, None]))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "71xa6jnYVrAN" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "embedding_1 (Embedding) (1, None, 25) 1200 \n", + "_________________________________________________________________\n", + "gru_1 (GRU) (1, None, 1024) 3228672 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (1, None, 48) 49200 \n", + "=================================================================\n", + "Total params: 3,279,072\n", + "Trainable params: 3,279,072\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DjGz1tDkzf-u" + }, + "source": [ + "### The prediction loop\n", + "\n", + "The following code block generates the text:\n", + "\n", + "* It Starts by choosing a start string, initializing the RNN state and setting the number of characters to generate.\n", + "\n", + "* Get the prediction distribution of the next character using the start string and the RNN state.\n", + "\n", + "* Then, use a categorical distribution to calculate the index of the predicted character. Use this predicted character as our next input to the model.\n", + "\n", + "* The RNN state returned by the model is fed back into the model so that it now has more context, instead than only one word. After predicting the next word, the modified RNN states are again fed back into the model, which is how it learns as it gets more context from the previously predicted words.\n", + "\n", + "\n", + "![To generate text the model's output is fed back to the input](images/text_generation_sampling.png)\n", + "\n", + "Looking at the generated text, you'll see the model knows when to capitalize, make paragraphs and imitates a Shakespeare-like writing vocabulary. With the small number of training epochs, it has not yet learned to form coherent sentences." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "WvuwZBX5Ogfd" + }, + "outputs": [], + "source": [ + "def generate_text(model, start_string):\n", + " # Evaluation step (generating text using the learned model)\n", + "\n", + " # Number of characters to generate\n", + " num_generate = 1000\n", + "\n", + " # Converting our start string to numbers (vectorizing)\n", + " input_eval = [char2idx[s] for s in start_string]\n", + " input_eval = tf.expand_dims(input_eval, 0)\n", + "\n", + " # Empty string to store our results\n", + " text_generated = []\n", + "\n", + "\n", + " # Here batch size == 1\n", + " model.reset_states()\n", + " for i in range(num_generate):\n", + " predictions = model(input_eval)\n", + " # remove the batch dimension\n", + " predictions = tf.squeeze(predictions, 0)\n", + "\n", + " # using a categorical distribution to predict the word returned by the model\n", + " predictions = predictions \n", + " predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()\n", + "\n", + " # We pass the predicted word as the next input to the model\n", + " # along with the previous hidden state\n", + " input_eval = tf.expand_dims([predicted_id], 0)\n", + "\n", + " text_generated.append(idx2char[predicted_id])\n", + "\n", + " return (start_string + ''.join(text_generated))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ktovv0RFhrkn" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "به نام خدایست نستوه تو\n", + "|به گیتی نماید نگارد ز بتاختی\n", + "|وزان پس چنین تا برآرم بماه\n", + "|صد آن تختها برکشمد از تو بر تن خویش یابی به خون\n", + "|ز شادی شگفتی که بیکار گشت\n", + "|دوماه\n", + "|کجا آن همه ریز کردم همی\n", + "|ز تخم بد و باژ و پر بوی مهر\n", + "|شنیده تخت باژی چو کوه بزرگ\n", + "|بدست سخن گوی برخاستند\n", + "|به زندان بیاوردش از جنگ جفت\n", + "|یکی دیگر آنگه که تن بگذرد\n", + "|من آن تخت راخسر بر تنگ هنگام موسن شود\n", + "|سربخت این را که پوشیده‌ام\n", + "|سراسان کنم داد و دانندگان\n", + "|گلاب و عنان برگرفتند راه\n", + "|نماند به رستم که لشکر براند\n", + "|چه افگند دینار و گرمان به دست\n", + "|چو ارجات داری خرامید یاد\n", + "|که نزد کزت بر تو بر خاک روی\n", + "|شهنشاه بینندهٔ رخش بروخون\n", + "|تو گفتی همی درکشید این سخن\n", + "|سواری بر اب گوهرنگار\n", + "|صزو تن به پا اندر آویختست\n", + "|نه زین باره و گردیه را بدست\n", + "|به خون خسره آیید گفتار من\n", + "|نگردد به بازد اسیدش تخل به درد\n", + "|سوی حلبهاد آن سه زر\n", + "|سپاس از دبیرو ستم\n", + "|همی دشمنندان او تخت را نو نمرد\n", + "|هرآنکس که او دشمن ایمن ببین\n", + "|بدو گفت بهرام چون بر روان\n", + "|یبا پیرسر گفت زن پر ز خون\n", + "|نگه کرده و از بلت خسرو شوردار\n", + "|بدآنید تاوان به ایران تویی\n", + "|\n", + "|ار و دوبست و زه برکشد\n", + "|فروشد نه بیما نیر خ\n" + ] + } + ], + "source": [ + "print(generate_text(model, start_string=u\"به نام خدا\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "AM2Uma_-yVIq" + }, + "source": [ + "The easiest thing you can do to improve the results it to train it for longer (try `EPOCHS=30`).\n", + "\n", + "You can also experiment with a different start string, or try adding another RNN layer to improve the model's accuracy, or adjusting the temperature parameter to generate more or less random predictions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "source:\n", + " \n", + "https://www.tensorflow.org/tutorials/text/text_generation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "text_generation.ipynb", + "private_outputs": true, + "provenance": [], + "toc_visible": true, + "version": "0.3.2" + }, + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/09_add-numbers-with-seq2seq.ipynb b/09_add-numbers-with-seq2seq.ipynb new file mode 100644 index 0000000..fddc56e --- /dev/null +++ b/09_add-numbers-with-seq2seq.ipynb @@ -0,0 +1,512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

Seq2Seq برای جمع اعداد!

" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from random import seed\n", + "from random import randint\n", + "from numpy import array\n", + "from math import ceil\n", + "from math import log10\n", + "from math import sqrt\n", + "from numpy import argmax\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.layers import LSTM\n", + "from keras.layers import TimeDistributed\n", + "from keras.layers import RepeatVector" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# generate lists of random integers and their sum\n", + "def random_sum_pairs(n_examples, n_numbers, largest):\n", + " X, y = list(), list()\n", + " for i in range(n_examples):\n", + " in_pattern = [randint(1,largest) for _ in range(n_numbers)]\n", + " out_pattern = sum(in_pattern)\n", + " X.append(in_pattern)\n", + " y.append(out_pattern)\n", + " return X, y" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100\n", + "[12, 3, 4]\n", + "19\n" + ] + } + ], + "source": [ + "x,y = random_sum_pairs(100,3,15)\n", + "print(len(x))\n", + "print(x[0])\n", + "print(y[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# convert data to strings\n", + "def to_string(X, y, n_numbers, largest):\n", + " max_length = n_numbers * ceil(log10(largest+1)) + n_numbers - 1\n", + " Xstr = list()\n", + " for pattern in X:\n", + " strp = '+'.join([str(n) for n in pattern])\n", + " strp = ''.join([' ' for _ in range(max_length-len(strp))]) + strp\n", + " Xstr.append(strp)\n", + " max_length = ceil(log10(n_numbers * (largest+1)))\n", + " ystr = list()\n", + " for pattern in y:\n", + " strp = str(pattern)\n", + " strp = ''.join([' ' for _ in range(max_length-len(strp))]) + strp\n", + " ystr.append(strp)\n", + " return Xstr, ystr" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# integer encode strings\n", + "def integer_encode(X, y, alphabet):\n", + " char_to_int = dict((c, i) for i, c in enumerate(alphabet))\n", + " Xenc = list()\n", + " for pattern in X:\n", + " integer_encoded = [char_to_int[char] for char in pattern]\n", + " Xenc.append(integer_encoded)\n", + " yenc = list()\n", + " for pattern in y:\n", + " integer_encoded = [char_to_int[char] for char in pattern]\n", + " yenc.append(integer_encoded)\n", + " return Xenc, yenc" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + " # one hot encode\n", + "def one_hot_encode(X, y, max_int):\n", + " Xenc = list()\n", + " for seq in X:\n", + " pattern = list()\n", + " for index in seq:\n", + " vector = [0 for _ in range(max_int)]\n", + " vector[index] = 1\n", + " pattern.append(vector)\n", + " Xenc.append(pattern)\n", + " yenc = list()\n", + " for seq in y:\n", + " pattern = list()\n", + " for index in seq:\n", + " vector = [0 for _ in range(max_int)]\n", + " vector[index] = 1\n", + " pattern.append(vector)\n", + " yenc.append(pattern)\n", + " return Xenc, yenc" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# generate an encoded dataset\n", + "def generate_data(n_samples, n_numbers, largest, alphabet):\n", + " # generate pairs\n", + " X, y = random_sum_pairs(n_samples, n_numbers, largest)\n", + " # convert to strings\n", + " X, y = to_string(X, y, n_numbers, largest)\n", + " # integer encode\n", + " X, y = integer_encode(X, y, alphabet)\n", + " # one hot encode\n", + " X, y = one_hot_encode(X, y, len(alphabet))\n", + " # return as numpy arrays\n", + " X, y = array(X), array(y)\n", + " return X, y" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# invert encoding\n", + "def invert(seq, alphabet):\n", + " int_to_char = dict((i, c) for i, c in enumerate(alphabet))\n", + " strings = list()\n", + " for pattern in seq:\n", + " string = int_to_char[argmax(pattern)]\n", + " strings.append(string)\n", + " return ''.join(strings)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# define dataset\n", + "seed(1)\n", + "n_samples = 1000\n", + "n_numbers = 2\n", + "largest = 10\n", + "alphabet = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '+', ' ']\n", + "n_chars = len(alphabet)\n", + "n_in_seq_length = n_numbers * ceil(log10(largest+1)) + n_numbers - 1\n", + "n_out_seq_length = ceil(log10(n_numbers * (largest+1)))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = generate_data(n_samples, n_numbers, largest, alphabet)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of X (1000, 5, 12)\n", + "shape of y (1000, 2, 12)\n", + "X[0]:\n", + "[[0 0 0 0 0 0 0 0 0 0 0 1]\n", + " [0 0 0 1 0 0 0 0 0 0 0 0]\n", + " [0 0 0 0 0 0 0 0 0 0 1 0]\n", + " [0 1 0 0 0 0 0 0 0 0 0 0]\n", + " [1 0 0 0 0 0 0 0 0 0 0 0]]\n", + "y[0]\n", + "[[0 1 0 0 0 0 0 0 0 0 0 0]\n", + " [0 0 0 1 0 0 0 0 0 0 0 0]]\n", + "invert X[0] 3+10\n", + "invert y[0] 13\n" + ] + } + ], + "source": [ + "print(\"shape of X\", X.shape)\n", + "print(\"shape of y\", y.shape)\n", + "print(\"X[0]:\")\n", + "print(X[0])\n", + "print(\"y[0]\")\n", + "print(y[0])\n", + "\n", + "print(\"invert X[0]\", invert(X[0], alphabet) )\n", + "print(\"invert y[0]\", invert(y[0], alphabet) )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# define LSTM configuration\n", + "n_batch = 10\n", + "n_epoch = 30" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "lstm_1 (LSTM) (None, 100) 45200 \n", + "_________________________________________________________________\n", + "repeat_vector_1 (RepeatVecto (None, 2, 100) 0 \n", + "_________________________________________________________________\n", + "lstm_2 (LSTM) (None, 2, 50) 30200 \n", + "_________________________________________________________________\n", + "time_distributed_1 (TimeDist (None, 2, 12) 612 \n", + "=================================================================\n", + "Total params: 76,012\n", + "Trainable params: 76,012\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "None\n" + ] + } + ], + "source": [ + "# create LSTM\n", + "model = Sequential()\n", + "model.add(LSTM(100, input_shape=(n_in_seq_length, n_chars)))\n", + "model.add(RepeatVector(n_out_seq_length))\n", + "model.add(LSTM(50, return_sequences=True))\n", + "model.add(TimeDistributed(Dense(n_chars, activation='softmax')))\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "print(model.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 4s 4ms/step - loss: 1.9967 - accuracy: 0.3445\n", + "1\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 1.5194 - accuracy: 0.3680\n", + "2\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 1.3945 - accuracy: 0.4670\n", + "3\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 1.3099 - accuracy: 0.5075\n", + "4\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 1.1971 - accuracy: 0.5595\n", + "5\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 1.0768 - accuracy: 0.6285\n", + "6\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.9106 - accuracy: 0.6870\n", + "7\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.7884 - accuracy: 0.7330\n", + "8\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.6850 - accuracy: 0.8075\n", + "9\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.5964 - accuracy: 0.8660\n", + "10\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.5245 - accuracy: 0.8975\n", + "11\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.4445 - accuracy: 0.9350\n", + "12\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.3942 - accuracy: 0.9580\n", + "13\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3604 - accuracy: 0.9520\n", + "14\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.3117 - accuracy: 0.9580\n", + "15\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2776 - accuracy: 0.9790\n", + "16\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.2440 - accuracy: 0.9800\n", + "17\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2322 - accuracy: 0.9855\n", + "18\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.1978 - accuracy: 0.9885\n", + "19\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.1971 - accuracy: 0.9875\n", + "20\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2231 - accuracy: 0.9595\n", + "21\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1460 - accuracy: 0.9875\n", + "22\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 4s 4ms/step - loss: 0.1305 - accuracy: 0.9955\n", + "23\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.1155 - accuracy: 0.9945\n", + "24\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1039 - accuracy: 0.9950\n", + "25\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.0897 - accuracy: 0.9960\n", + "26\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.0833 - accuracy: 0.9975\n", + "27\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.0742 - accuracy: 0.9960\n", + "28\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.0678 - accuracy: 0.9975: 0s - l\n", + "29\n", + "Epoch 1/1\n", + "1000/1000 [==============================] - 3s 3ms/step - loss: 0.0590 - accuracy: 0.9985\n" + ] + } + ], + "source": [ + "# train LSTM\n", + "for i in range(n_epoch):\n", + " X, y = generate_data(n_samples, n_numbers, largest, alphabet)\n", + " print(i)\n", + " model.fit(X, y, epochs=1, batch_size=n_batch)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected=14, Predicted=14\n", + "Expected=15, Predicted=15\n", + "Expected=12, Predicted=12\n", + "Expected=12, Predicted=12\n", + "Expected=13, Predicted=13\n", + "Expected=15, Predicted=15\n", + "Expected= 8, Predicted= 8\n", + "Expected= 4, Predicted= 4\n", + "Expected=19, Predicted=19\n", + "Expected=13, Predicted=13\n", + "Expected=15, Predicted=15\n", + "Expected= 8, Predicted= 8\n", + "Expected=13, Predicted=13\n", + "Expected= 4, Predicted= 4\n", + "Expected=16, Predicted=16\n", + "Expected=13, Predicted=13\n", + "Expected=16, Predicted=16\n", + "Expected= 8, Predicted= 8\n", + "Expected=14, Predicted=14\n", + "Expected=16, Predicted=16\n" + ] + } + ], + "source": [ + "# evaluate on some new patterns\n", + "X, y = generate_data(n_samples, n_numbers, largest, alphabet)\n", + "result = model.predict(X, batch_size=n_batch, verbose=0)\n", + "# calculate error\n", + "expected = [invert(x, alphabet) for x in y]\n", + "predicted = [invert(x, alphabet) for x in result]\n", + "# show some examples\n", + "for i in range(20):\n", + " print('Expected=%s, Predicted=%s' % (expected[i], predicted[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extensions\n", + "\n", + "This section lists some natural extensions to this tutorial that you may wish to explore.\n", + "\n", + " ***Integer Encoding***. Explore whether the problem can learn the problem better using an integer encoding alone. The ordinal relationship between most of the inputs may prove very useful.\n", + " \n", + " ***Variable Numbers***. Change the example to support a variable number of terms on each input sequence. This should be straightforward as long as you perform sufficient padding.\n", + " \n", + " ***Variable Mathematical Operations***. Change the example to vary the mathematical operation to allow the network to generalize even further.\n", + " Brackets. Allow the use of brackets along with other mathematical operations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Source:\n", + " https://machinelearningmastery.com/learn-add-numbers-seq2seq-recurrent-neural-networks/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/10_Neural-machine-translation-with-attention-for-date-convert.ipynb b/10_Neural-machine-translation-with-attention-for-date-convert.ipynb new file mode 100644 index 0000000..c03d748 --- /dev/null +++ b/10_Neural-machine-translation-with-attention-for-date-convert.ipynb @@ -0,0 +1,962 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

ترجمه ماشینی با توجه برای تبدیل تاریخ

" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Machine Translation\n", + "\n", + "\n", + " (\"25th of June, 2009\") ---> (\"2009-06-25\")" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.layers import Bidirectional, Concatenate, Permute, Dot, Input, LSTM, Multiply\n", + "from keras.layers import RepeatVector, Dense, Activation, Lambda\n", + "from keras.optimizers import Adam\n", + "from keras.utils import to_categorical\n", + "from keras.models import load_model, Model\n", + "import keras.backend as K\n", + "import numpy as np\n", + "\n", + "from faker import Faker\n", + "import random\n", + "from tqdm import tqdm\n", + "from babel.dates import format_date\n", + "from nmt_utils import *\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Translating human readable dates into machine readable dates\n", + "\n", + "The model you will build here could be used to translate from one language to another, such as translating from English to Hindi. However, language translation requires massive datasets and usually takes days of training on GPUs. To give you a place to experiment with these models even without using massive datasets, we will instead use a simpler \"date translation\" task. \n", + "\n", + "The network will input a date written in a variety of possible formats (*e.g. \"the 29th of August 1958\", \"03/30/1968\", \"24 JUNE 1987\"*) and translate them into standardized, machine readable dates (*e.g. \"1958-08-29\", \"1968-03-30\", \"1987-06-24\"*). We will have the network learn to output dates in the common machine-readable format YYYY-MM-DD. \n", + "\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 - Dataset\n", + "\n", + "We will train the model on a dataset of 10000 human readable dates and their equivalent, standardized, machine readable dates. Let's run the following cells to load the dataset and print some examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████| 10000/10000 [00:00<00:00, 13839.02it/s]\n" + ] + } + ], + "source": [ + "m = 10000\n", + "dataset, src_vocab, dest_vocab, inv_dest_vocab = load_dataset(m)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('9 may 1998', '1998-05-09'),\n", + " ('10.11.19', '2019-11-10'),\n", + " ('9/10/70', '1970-09-10'),\n", + " ('saturday april 28 1990', '1990-04-28'),\n", + " ('thursday january 26 1995', '1995-01-26'),\n", + " ('monday march 7 1983', '1983-03-07'),\n", + " ('sunday may 22 1988', '1988-05-22'),\n", + " ('08 jul 2008', '2008-07-08'),\n", + " ('8 sep 1999', '1999-09-08'),\n", + " ('thursday january 1 1981', '1981-01-01')]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You've loaded:\n", + "- `dataset`: a list of tuples of (human readable date, machine readable date)\n", + "- `src_vocab`: a python dictionary mapping all characters used in the human readable dates to an integer-valued index \n", + "- `dest_vocab`: a python dictionary mapping all characters used in machine readable dates to an integer-valued index. These indices are not necessarily consistent with `src_vocab`. \n", + "- `inv_dest_vocab`: the inverse dictionary of `dest_vocab`, mapping from indices back to characters. \n", + "\n", + "Let's preprocess the data and map the raw text data into the index values. We will also use Tx=30 (which we assume is the maximum length of the human readable date; if we get a longer input, we would have to truncate it) and Ty=10 (since \"YYYY-MM-DD\" is 10 characters long). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X.shape: (10000, 30)\n", + "Y.shape: (10000, 10)\n", + "Xoh.shape: (10000, 30, 37)\n", + "Yoh.shape: (10000, 10, 11)\n" + ] + } + ], + "source": [ + "Tx = 30\n", + "Ty = 10\n", + "X, Y, Xoh, Yoh = preprocess_data(dataset, src_vocab, dest_vocab, Tx, Ty)\n", + "\n", + "print(\"X.shape:\", X.shape)\n", + "print(\"Y.shape:\", Y.shape)\n", + "print(\"Xoh.shape:\", Xoh.shape)\n", + "print(\"Yoh.shape:\", Yoh.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([12, 0, 24, 13, 34, 0, 4, 12, 12, 11, 36, 36, 36, 36, 36, 36, 36,\n", + " 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 10, 10, 9, 0, 1, 6, 0, 1, 10])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You now have:\n", + "- `X`: a processed version of the human readable dates in the training set, where each character is replaced by an index mapped to the character via `src_vocab`. Each date is further padded to $T_x$ values with a special character (< pad >). `X.shape = (m, Tx)`\n", + "- `Y`: a processed version of the machine readable dates in the training set, where each character is replaced by the index it is mapped to in `dest_vocab`. You should have `Y.shape = (m, Ty)`. \n", + "- `Xoh`: one-hot version of `X`, the \"1\" entry's index is mapped to the character thanks to `src_vocab`. `Xoh.shape = (m, Tx, len(src_vocab))`\n", + "- `Yoh`: one-hot version of `Y`, the \"1\" entry's index is mapped to the character thanks to `dest_vocab`. `Yoh.shape = (m, Tx, len(dest_vocab))`. Here, `len(dest_vocab) = 11` since there are 11 characters ('-' as well as 0-9). \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets also look at some examples of preprocessed training examples. Feel free to play with `index` in the cell below to navigate the dataset and see how source/target dates are preprocessed. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Source date: 9 may 1998\n", + "Target date: 1998-05-09\n", + "\n", + "Source after preprocessing (indices): [12 0 24 13 34 0 4 12 12 11 36 36 36 36 36 36 36 36 36 36 36 36 36 36\n", + " 36 36 36 36 36 36]\n", + "Target after preprocessing (indices): [ 2 10 10 9 0 1 6 0 1 10]\n", + "\n", + "Source after preprocessing (one-hot): [[0. 0. 0. ... 0. 0. 0.]\n", + " [1. 0. 0. ... 0. 0. 0.]\n", + " [0. 0. 0. ... 0. 0. 0.]\n", + " ...\n", + " [0. 0. 0. ... 0. 0. 1.]\n", + " [0. 0. 0. ... 0. 0. 1.]\n", + " [0. 0. 0. ... 0. 0. 1.]]\n", + "Target after preprocessing (one-hot): [[0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]\n" + ] + } + ], + "source": [ + "index = 0\n", + "print(\"Source date:\", dataset[index][0])\n", + "print(\"Target date:\", dataset[index][1])\n", + "print()\n", + "print(\"Source after preprocessing (indices):\", X[index])\n", + "print(\"Target after preprocessing (indices):\", Y[index])\n", + "print()\n", + "print(\"Source after preprocessing (one-hot):\", Xoh[index])\n", + "print(\"Target after preprocessing (one-hot):\", Yoh[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Neural machine translation with attention\n", + "\n", + "If you had to translate a book's paragraph from French to English, you would not read the whole paragraph, then close the book and translate. Even during the translation process, you would read/re-read and focus on the parts of the French paragraph corresponding to the parts of the English you are writing down. \n", + "\n", + "The attention mechanism tells a Neural Machine Translation model where it should pay attention to at any step. \n", + "\n", + "\n", + "### 2.1 - Attention mechanism\n", + "\n", + "Here is a figure to remind you how the model works. The diagram on the left shows the attention model. The diagram on the right shows what one \"Attention\" step does to calculate the attention variables $\\alpha^{\\langle t, t' \\rangle}$, which are used to compute the context variable $context^{\\langle t \\rangle}$ for each timestep in the output ($t=1, \\ldots, T_y$). \n", + "\n", + "\n", + " \n", + " \n", + "
\n", + "
\n", + "		 \n", + "
\n", + "
\n", + "
**Figure 1**: Neural machine translation with attention
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Here are some properties of the model that you may notice: \n", + "\n", + "- There are two separate LSTMs in this model (see diagram on the left). Because the one at the bottom of the picture is a Bi-directional LSTM and comes *before* the attention mechanism, we will call it *pre-attention* Bi-LSTM. The LSTM at the top of the diagram comes *after* the attention mechanism, so we will call it the *post-attention* LSTM. The pre-attention Bi-LSTM goes through $T_x$ time steps; the post-attention LSTM goes through $T_y$ time steps. \n", + "\n", + "- The post-attention LSTM passes $s^{\\langle t \\rangle}, c^{\\langle t \\rangle}$ from one time step to the next. In the slides, we were using only a basic RNN for the post-activation sequence model, so the state captured by the RNN output activations $s^{\\langle t\\rangle}$. But since we are using an LSTM here, the LSTM has both the output activation $s^{\\langle t\\rangle}$ and the hidden cell state $c^{\\langle t\\rangle}$. However, unlike text generation examples , in this model the post-activation LSTM at time $t$ does will not take the specific generated $y^{\\langle t-1 \\rangle}$ as input; it only takes $s^{\\langle t\\rangle}$ and $c^{\\langle t\\rangle}$ as input. We have designed the model this way, because (unlike language generation where adjacent characters are highly correlated) there isn't as strong a dependency between the previous character and the next character in a YYYY-MM-DD date. \n", + "\n", + "- We use $a^{\\langle t \\rangle} = [\\overrightarrow{a}^{\\langle t \\rangle}; \\overleftarrow{a}^{\\langle t \\rangle}]$ to represent the concatenation of the activations of both the forward-direction and backward-directions of the pre-attention Bi-LSTM. \n", + "\n", + "- The diagram on the right uses a `RepeatVector` node to copy $s^{\\langle t-1 \\rangle}$'s value $T_x$ times, and then `Concatenation` to concatenate $s^{\\langle t-1 \\rangle}$ and $a^{\\langle t \\rangle}$ to compute $e^{\\langle t, t'}$, which is then passed through a softmax to compute $\\alpha^{\\langle t, t' \\rangle}$. We'll explain how to use `RepeatVector` and `Concatenation` in Keras below. \n", + "\n", + "Lets implement this model. You will start by implementing two functions: `one_step_attention()` and `model()`.\n", + "\n", + "**1) `one_step_attention()`**: At step $t$, given all the hidden states of the Bi-LSTM ($[a^{<1>},a^{<2>}, ..., a^{}]$) and the previous hidden state of the second LSTM ($s^{}$), `one_step_attention()` will compute the attention weights ($[\\alpha^{},\\alpha^{}, ..., \\alpha^{}]$) and output the context vector (see Figure 1 (right) for details):\n", + "$$context^{} = \\sum_{t' = 0}^{T_x} \\alpha^{}a^{}\\tag{1}$$ \n", + "\n", + " \n", + "**2) `model()`**: Implements the entire model. It first runs the input through a Bi-LSTM to get back $[a^{<1>},a^{<2>}, ..., a^{}]$. Then, it calls `one_step_attention()` $T_y$ times (`for` loop). At each iteration of this loop, it gives the computed context vector $c^{}$ to the second LSTM, and runs the output of the LSTM through a dense layer with softmax activation to generate a prediction $\\hat{y}^{}$. \n", + "\n", + "\n", + "The function `model()` will call the layers in `one_step_attention()` $T_y$ using a for-loop, and it is important that all $T_y$ copies have the same weights. I.e., it should not re-initiaiize the weights every time. In other words, all $T_y$ steps should have shared weights. Here's how you can implement layers with shareable weights in Keras:\n", + "1. Define the layer objects (as global variables for examples).\n", + "2. Call these objects when propagating the input.\n", + "\n", + "Please check the Keras documentation to make sure you understand what these layers are: [RepeatVector()](https://keras.io/layers/core/#repeatvector), [Concatenate()](https://keras.io/layers/merge/#concatenate), [Dense()](https://keras.io/layers/core/#dense), [Activation()](https://keras.io/layers/core/#activation), [Dot()](https://keras.io/layers/merge/#dot)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Defined shared layers as global variables\n", + "repeator = RepeatVector(Tx)\n", + "concatenator = Concatenate(axis=-1)\n", + "densor1 = Dense(10, activation = \"tanh\")\n", + "densor2 = Dense(1, activation = \"relu\")\n", + "activator = Activation(softmax, name='attention_weights') # We are using a custom softmax(axis = 1) loaded in this notebook\n", + "dotor = Dot(axes = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#one_step_attention\n", + "\n", + "def one_step_attention(a, s_prev):\n", + " # repeat s_prev to be of shape (m, Tx, n_s) so that you can concatenate it with all hidden states \"a\" \n", + " s_prev = repeator(s_prev)\n", + " # concatenate a and s_prev on the last axis\n", + " concat = concatenator([a, s_prev])\n", + " # propagate concat through a small fully-connected neural network to compute the \"intermediate energies\" variable e.\n", + " e = densor1(concat)\n", + " # propagate e through a small fully-connected neural network to compute the \"energies\" variable energies. \n", + " energies = densor2(e)\n", + " # Use \"activator\" on \"energies\" to compute the attention weights \"alphas\" (≈ 1 line)\n", + " alphas = activator(energies)\n", + " # Use dotor together with \"alphas\" and \"a\" to compute the context vector to be given to the next (post-attention) LSTM-cell (≈ 1 line)\n", + " context = dotor([alphas, a])\n", + " \n", + " return context" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "n_a = 32\n", + "n_s = 64\n", + "\n", + "post_activation_LSTM_cell = LSTM(n_s, return_state = True)\n", + "output_layer = Dense(len(dest_vocab), activation=softmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now you can use these layers $T_y$ times in a `for` loop to generate the outputs, and their parameters will not be reinitialized. You will have to carry out the following steps: \n", + "\n", + "1. Propagate the input into a [Bidirectional](https://keras.io/layers/wrappers/#bidirectional) [LSTM](https://keras.io/layers/recurrent/#lstm)\n", + "2. Iterate for $t = 0, \\dots, T_y-1$: \n", + " 1. Call `one_step_attention()` on $[\\alpha^{},\\alpha^{}, ..., \\alpha^{}]$ and $s^{}$ to get the context vector $context^{}$.\n", + " 2. Give $context^{}$ to the post-attention LSTM cell. Remember pass in the previous hidden-state $s^{\\langle t-1\\rangle}$ and cell-states $c^{\\langle t-1\\rangle}$ of this LSTM using `initial_state= [previous hidden state, previous cell state]`. Get back the new hidden state $s^{}$ and the new cell state $c^{}$.\n", + " 3. Apply a softmax layer to $s^{}$, get the output. \n", + " 4. Save the output by adding it to the list of outputs.\n", + "\n", + "3. Create your Keras model instance, it should have three inputs (\"inputs\", $s^{<0>}$ and $c^{<0>}$) and output the list of \"outputs\"." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# GRADED FUNCTION: model\n", + "\n", + "def model(Tx, Ty, n_a, n_s, src_vocab_size, dest_vocab_size):\n", + " \"\"\"\n", + " Arguments:\n", + " Tx -- length of the input sequence\n", + " Ty -- length of the output sequence\n", + " n_a -- hidden state size of the Bi-LSTM\n", + " n_s -- hidden state size of the post-attention LSTM\n", + " src_vocab_size -- size of the python dictionary \"src_vocab\"\n", + " dest_vocab_size -- size of the python dictionary \"dest_vocab\"\n", + "\n", + " Returns:\n", + " model -- Keras model instance\n", + " \"\"\"\n", + " \n", + " # Define the inputs of your model with a shape (Tx,)\n", + " # Define s0 and c0, initial hidden state for the decoder LSTM of shape (n_s,)\n", + " X = Input(shape=(Tx, src_vocab_size))\n", + " s0 = Input(shape=(n_s,), name='s0')\n", + " c0 = Input(shape=(n_s,), name='c0')\n", + " s = s0\n", + " c = c0\n", + " \n", + " # Initialize empty list of outputs\n", + " outputs = []\n", + " \n", + " \n", + " # pre-attention Bi-LSTM.\n", + " a = Bidirectional(LSTM(n_a, return_sequences=True))(X)\n", + " \n", + " # Iterate for Ty steps\n", + " for t in range(Ty):\n", + " \n", + " #Perform one step of the attention mechanism to get back the context vector at step t \n", + " context = one_step_attention(a, s)\n", + " \n", + " #post-attention LSTM cell \n", + " s, _, c = post_activation_LSTM_cell(context, initial_state = [s, c])\n", + " \n", + " # Dense layer to the hidden state output of the post-attention LSTM\n", + " out = Dense(len(dest_vocab), activation=softmax)(s)\n", + " \n", + " outputs.append(out)\n", + " \n", + " model = Model(inputs=[X, s0, c0], outputs=outputs)\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to create your model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "model = model(Tx, Ty, n_a, n_s, len(src_vocab), len(dest_vocab))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get a summary of the model to check if it matches the expected output." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_1\"\n", + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "input_1 (InputLayer) (None, 30, 37) 0 \n", + "__________________________________________________________________________________________________\n", + "s0 (InputLayer) (None, 64) 0 \n", + "__________________________________________________________________________________________________\n", + "bidirectional_1 (Bidirectional) (None, 30, 64) 17920 input_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "repeat_vector_1 (RepeatVector) (None, 30, 64) 0 s0[0][0] \n", + " lstm_1[0][0] \n", + " lstm_1[1][0] \n", + " lstm_1[2][0] \n", + " lstm_1[3][0] \n", + " lstm_1[4][0] \n", + " lstm_1[5][0] \n", + " lstm_1[6][0] \n", + " lstm_1[7][0] \n", + " lstm_1[8][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_1 (Concatenate) (None, 30, 128) 0 bidirectional_1[0][0] \n", + " repeat_vector_1[0][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[1][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[2][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[3][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[4][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[5][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[6][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[7][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[8][0] \n", + " bidirectional_1[0][0] \n", + " repeat_vector_1[9][0] \n", + "__________________________________________________________________________________________________\n", + "dense_1 (Dense) (None, 30, 10) 1290 concatenate_1[0][0] \n", + " concatenate_1[1][0] \n", + " concatenate_1[2][0] \n", + " concatenate_1[3][0] \n", + " concatenate_1[4][0] \n", + " concatenate_1[5][0] \n", + " concatenate_1[6][0] \n", + " concatenate_1[7][0] \n", + " concatenate_1[8][0] \n", + " concatenate_1[9][0] \n", + "__________________________________________________________________________________________________\n", + "dense_2 (Dense) (None, 30, 1) 11 dense_1[0][0] \n", + " dense_1[1][0] \n", + " dense_1[2][0] \n", + " dense_1[3][0] \n", + " dense_1[4][0] \n", + " dense_1[5][0] \n", + " dense_1[6][0] \n", + " dense_1[7][0] \n", + " dense_1[8][0] \n", + " dense_1[9][0] \n", + "__________________________________________________________________________________________________\n", + "attention_weights (Activation) (None, 30, 1) 0 dense_2[0][0] \n", + " dense_2[1][0] \n", + " dense_2[2][0] \n", + " dense_2[3][0] \n", + " dense_2[4][0] \n", + " dense_2[5][0] \n", + " dense_2[6][0] \n", + " dense_2[7][0] \n", + " dense_2[8][0] \n", + " dense_2[9][0] \n", + "__________________________________________________________________________________________________\n", + "dot_1 (Dot) (None, 1, 64) 0 attention_weights[0][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[1][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[2][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[3][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[4][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[5][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[6][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[7][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[8][0] \n", + " bidirectional_1[0][0] \n", + " attention_weights[9][0] \n", + " bidirectional_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "c0 (InputLayer) (None, 64) 0 \n", + "__________________________________________________________________________________________________\n", + "lstm_1 (LSTM) [(None, 64), (None, 33024 dot_1[0][0] \n", + " s0[0][0] \n", + " c0[0][0] \n", + " dot_1[1][0] \n", + " lstm_1[0][0] \n", + " lstm_1[0][2] \n", + " dot_1[2][0] \n", + " lstm_1[1][0] \n", + " lstm_1[1][2] \n", + " dot_1[3][0] \n", + " lstm_1[2][0] \n", + " lstm_1[2][2] \n", + " dot_1[4][0] \n", + " lstm_1[3][0] \n", + " lstm_1[3][2] \n", + " dot_1[5][0] \n", + " lstm_1[4][0] \n", + " lstm_1[4][2] \n", + " dot_1[6][0] \n", + " lstm_1[5][0] \n", + " lstm_1[5][2] \n", + " dot_1[7][0] \n", + " lstm_1[6][0] \n", + " lstm_1[6][2] \n", + " dot_1[8][0] \n", + " lstm_1[7][0] \n", + " lstm_1[7][2] \n", + " dot_1[9][0] \n", + " lstm_1[8][0] \n", + " lstm_1[8][2] \n", + "__________________________________________________________________________________________________\n", + "dense_4 (Dense) (None, 11) 715 lstm_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense_5 (Dense) (None, 11) 715 lstm_1[1][0] \n", + "__________________________________________________________________________________________________\n", + "dense_6 (Dense) (None, 11) 715 lstm_1[2][0] \n", + "__________________________________________________________________________________________________\n", + "dense_7 (Dense) (None, 11) 715 lstm_1[3][0] \n", + "__________________________________________________________________________________________________\n", + "dense_8 (Dense) (None, 11) 715 lstm_1[4][0] \n", + "__________________________________________________________________________________________________\n", + "dense_9 (Dense) (None, 11) 715 lstm_1[5][0] \n", + "__________________________________________________________________________________________________\n", + "dense_10 (Dense) (None, 11) 715 lstm_1[6][0] \n", + "__________________________________________________________________________________________________\n", + "dense_11 (Dense) (None, 11) 715 lstm_1[7][0] \n", + "__________________________________________________________________________________________________\n", + "dense_12 (Dense) (None, 11) 715 lstm_1[8][0] \n", + "__________________________________________________________________________________________________\n", + "dense_13 (Dense) (None, 11) 715 lstm_1[9][0] \n", + "==================================================================================================\n", + "Total params: 59,395\n", + "Trainable params: 59,395\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "opt = Adam(lr=0.005, beta_1=0.9, beta_2=0.999, decay=0.01)\n", + "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "### END CODE HERE ###" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last step is to define all your inputs and outputs to fit the model:\n", + "- You already have X of shape $(m = 10000, T_x = 30)$ containing the training examples.\n", + "- You need to create `s0` and `c0` to initialize your `post_activation_LSTM_cell` with 0s.\n", + "- Given the `model()` you coded, you need the \"outputs\" to be a list of 11 elements of shape (m, T_y). So that: `outputs[i][0], ..., outputs[i][Ty]` represent the true labels (characters) corresponding to the $i^{th}$ training example (`X[i]`). More generally, `outputs[i][j]` is the true label of the $j^{th}$ character in the $i^{th}$ training example." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X.shape: (10000, 30)\n", + "Y.shape: (10000, 10)\n", + "Xoh.shape: (10000, 30, 37)\n", + "Yoh.shape: (10000, 10, 11)\n" + ] + } + ], + "source": [ + "print(\"X.shape:\", X.shape)\n", + "print(\"Y.shape:\", Y.shape)\n", + "print(\"Xoh.shape:\", Xoh.shape)\n", + "print(\"Yoh.shape:\", Yoh.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "s0 = np.zeros((m, n_s))\n", + "c0 = np.zeros((m, n_s))\n", + "outputs = list(Yoh.swapaxes(0,1))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(10000, 64)\n", + "(10000, 64)\n", + "(10, 10000, 11)\n" + ] + } + ], + "source": [ + "print(s0.shape)\n", + "print(c0.shape)\n", + "print(np.shape(outputs))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now fit the model and run it for one epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/30\n", + "10000/10000 [==============================] - 69s 7ms/step - loss: 12.4757 - dense_4_loss: 0.8144 - dense_5_loss: 0.8247 - dense_6_loss: 1.6834 - dense_7_loss: 2.3235 - dense_8_loss: 0.1300 - dense_9_loss: 0.6678 - dense_10_loss: 2.2892 - dense_11_loss: 0.0953 - dense_12_loss: 1.3104 - dense_13_loss: 2.3370 - dense_4_accuracy: 0.5912 - dense_5_accuracy: 0.5823 - dense_6_accuracy: 0.1990 - dense_7_accuracy: 0.0984 - dense_8_accuracy: 0.9961 - dense_9_accuracy: 0.7391 - dense_10_accuracy: 0.1662 - dense_11_accuracy: 0.9900 - dense_12_accuracy: 0.3190 - dense_13_accuracy: 0.1011\n", + "Epoch 2/30\n", + " 4200/10000 [===========>..................] - ETA: 33s - loss: 11.5541 - dense_4_loss: 0.6167 - dense_5_loss: 0.6409 - dense_6_loss: 1.5992 - dense_7_loss: 2.3100 - dense_8_loss: 3.3439e-04 - dense_9_loss: 0.5649 - dense_10_loss: 2.2639 - dense_11_loss: 2.9461e-04 - dense_12_loss: 1.2471 - dense_13_loss: 2.3109 - dense_4_accuracy: 0.6169 - dense_5_accuracy: 0.6169 - dense_6_accuracy: 0.2210 - dense_7_accuracy: 0.1067 - dense_8_accuracy: 1.0000 - dense_9_accuracy: 0.7481 - dense_10_accuracy: 0.1676 - dense_11_accuracy: 1.0000 - dense_12_accuracy: 0.3226 - dense_13_accuracy: 0.1045" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mXoh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\Miniconda3\\envs\\tf2\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;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_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m 1237\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1238\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m validation_freq=validation_freq)\n\u001b[0m\u001b[0;32m 1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1241\u001b[0m def evaluate(self,\n", + "\u001b[1;32m~\\Miniconda3\\envs\\tf2\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[0;32m 194\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m 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3740\u001b[1;33m \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3741\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3742\u001b[0m \u001b[1;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Miniconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1079\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m 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"\u001b[1;32m~\\Miniconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1119\u001b[0m raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[0;32m 1120\u001b[0m list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[1;32m-> 1121\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1123\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Miniconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m 1222\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1223\u001b[0m flat_outputs = forward_function.call(\n\u001b[1;32m-> 1224\u001b[1;33m ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[0;32m 1225\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1226\u001b[0m \u001b[0mgradient_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Miniconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m 509\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 510\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"executor_type\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"config_proto\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 511\u001b[1;33m ctx=ctx)\n\u001b[0m\u001b[0;32m 512\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 513\u001b[0m outputs = execute.execute_with_cancellation(\n", + "\u001b[1;32m~\\Miniconda3\\envs\\tf2\\lib\\site-packages\\tensorflow_core\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m 59\u001b[0m tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[0;32m 60\u001b[0m \u001b[0mop_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m num_outputs)\n\u001b[0m\u001b[0;32m 62\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "model.fit([Xoh, s0, c0], outputs, epochs=30, batch_size=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While training you can see the loss as well as the accuracy on each of the 10 positions of the output. The table below gives you an example of what the accuracies could be if the batch had 2 examples: \n", + "\n", + "
\n", + "
Thus, `dense_2_acc_8: 0.89` means that you are predicting the 7th character of the output correctly 89% of the time in the current batch of data.
\n", + "\n", + "\n", + "We have run this model for longer, and saved the weights. Run the next cell to load our weights. (By training a model for several minutes, you should be able to obtain a model of similar accuracy, but loading our model will save you time.) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "You can Download our weights:\n", + " \n", + "http://deepnn.ir/class.vision/andrewng/machine-translation-with-attention/models.zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.load_weights('models/model.h5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now see the results on new examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "EXAMPLES = ['3 May 1979', '5 April 09', '21th of August 2016', 'Tue 10 Jul 2007', 'Saturday May 9 2018', 'March 3 2001', 'March 3rd 2001', '1 March 2001']\n", + "for example in EXAMPLES:\n", + " \n", + " source = string_to_int(example, Tx, src_vocab)\n", + " source = np.array(list(map(lambda x: to_categorical(x, num_classes=len(src_vocab)), source))).swapaxes(0,1)\n", + " prediction = model.predict([source, s0, c0])\n", + " prediction = np.argmax(prediction, axis = -1)\n", + " output = [inv_dest_vocab[int(i)] for i in prediction]\n", + " \n", + " print(\"source:\", example)\n", + " print(\"output:\", ''.join(output))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also change these examples to test with your own examples. The next part will give you a better sense on what the attention mechanism is doing--i.e., what part of the input the network is paying attention to when generating a particular output character. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Visualizing Attention (Optional / Ungraded)\n", + "\n", + "Since the problem has a fixed output length of 10, it is also possible to carry out this task using 10 different softmax units to generate the 10 characters of the output. But one advantage of the attention model is that each part of the output (say the month) knows it needs to depend only on a small part of the input (the characters in the input giving the month). We can visualize what part of the output is looking at what part of the input.\n", + "\n", + "Consider the task of translating \"Saturday 9 May 2018\" to \"2018-05-09\". If we visualize the computed $\\alpha^{\\langle t, t' \\rangle}$ we get this: \n", + "\n", + "
\n", + "
**Figure 8**: Full Attention Map
\n", + "\n", + "Notice how the output ignores the \"Saturday\" portion of the input. None of the output timesteps are paying much attention to that portion of the input. We see also that 9 has been translated as 09 and May has been correctly translated into 05, with the output paying attention to the parts of the input it needs to to make the translation. The year mostly requires it to pay attention to the input's \"18\" in order to generate \"2018.\" \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 - Getting the activations from the network\n", + "\n", + "Lets now visualize the attention values in your network. We'll propagate an example through the network, then visualize the values of $\\alpha^{\\langle t, t' \\rangle}$. \n", + "\n", + "To figure out where the attention values are located, let's start by printing a summary of the model ." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Navigate through the output of `model.summary()` above. You can see that the layer named `attention_weights` outputs the `alphas` of shape (m, 30, 1) before `dot_2` computes the context vector for every time step $t = 0, \\ldots, T_y-1$. Lets get the activations from this layer.\n", + "\n", + "The function `attention_map()` pulls out the attention values from your model and plots them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "attention_map = plot_attention_map(model, src_vocab, inv_dest_vocab, \"Tuesday 09 Oct 1993\", num = 7, n_s = 64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On the generated plot you can observe the values of the attention weights for each character of the predicted output. Examine this plot and check that where the network is paying attention makes sense to you.\n", + "\n", + "In the date translation application, you will observe that most of the time attention helps predict the year, and hasn't much impact on predicting the day/month." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations!\n", + "\n", + "\n", + "You have come to the end of this assignment \n", + "\n", + " **Here's what you should remember from this notebook**:\n", + "\n", + "- Machine translation models can be used to map from one sequence to another. They are useful not just for translating human languages (like French->English) but also for tasks like date format translation. \n", + "- An attention mechanism allows a network to focus on the most relevant parts of the input when producing a specific part of the output. \n", + "- A network using an attention mechanism can translate from inputs of length $T_x$ to outputs of length $T_y$, where $T_x$ and $T_y$ can be different. \n", + "- You can visualize attention weights $\\alpha^{\\langle t,t' \\rangle}$ to see what the network is paying attention to while generating each output." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations on finishing this assignment! You are now able to implement an attention model and use it to learn complex mappings from one sequence to another. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "n16CQ", + "launcher_item_id": "npjGi" + }, + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/11_nmt-with-attention.ipynb b/11_nmt-with-attention.ipynb new file mode 100644 index 0000000..b6b774c --- /dev/null +++ b/11_nmt-with-attention.ipynb @@ -0,0 +1,1261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

ترجمه ماشینی با توجه و teacher forcing

" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "J0Qjg6vuaHNt" + }, + "source": [ + "# Neural machine translation with attention" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CiwtNgENbx2g" + }, + "source": [ + "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation. This is an advanced example that assumes some knowledge of sequence to sequence models.\n", + "\n", + "After training the model in this notebook, you will be able to input a Spanish sentence, such as *\"¿todavia estan en casa?\"*, and return the English translation: *\"are you still at home?\"*\n", + "\n", + "The translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting. This shows which parts of the input sentence has the model's attention while translating:\n", + "\n", + "\"spanish-english\n", + "\n", + "Note: This example takes approximately 10 mintues to run on a single P100 GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "tnxXKDjq3jEL" + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function, unicode_literals\n", + "\n", + "try:\n", + " # %tensorflow_version only exists in Colab.\n", + " %tensorflow_version 2.x\n", + "except Exception:\n", + " pass\n", + "import tensorflow as tf\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import unicodedata\n", + "import re\n", + "import numpy as np\n", + "import os\n", + "import io\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wfodePkj3jEa" + }, + "source": [ + "## Download and prepare the dataset\n", + "\n", + "We'll use a language dataset provided by http://www.manythings.org/anki/. This dataset contains language translation pairs in the format:\n", + "\n", + "```\n", + "May I borrow this book?\t¿Puedo tomar prestado este libro?\n", + "```\n", + "\n", + "There are a variety of languages available, but we'll use the English-Spanish dataset. For convenience, we've hosted a copy of this dataset on Google Cloud, but you can also download your own copy. After downloading the dataset, here are the steps we'll take to prepare the data:\n", + "\n", + "1. Add a *start* and *end* token to each sentence.\n", + "2. Clean the sentences by removing special characters.\n", + "3. Create a word index and reverse word index (dictionaries mapping from word → id and id → word).\n", + "4. Pad each sentence to a maximum length." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "kRVATYOgJs1b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip\n", + "2646016/2638744 [==============================] - 0s 0us/step\n" + ] + } + ], + "source": [ + "# Download the file\n", + "path_to_zip = tf.keras.utils.get_file(\n", + " 'spa-eng.zip', origin='http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip',\n", + " extract=True)\n", + "\n", + "path_to_file = os.path.dirname(path_to_zip)+\"/spa-eng/spa.txt\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rd0jw-eC3jEh" + }, + "outputs": [], + "source": [ + "# Converts the unicode file to ascii\n", + "def unicode_to_ascii(s):\n", + " return ''.join(c for c in unicodedata.normalize('NFD', s)\n", + " if unicodedata.category(c) != 'Mn')\n", + "\n", + "\n", + "def preprocess_sentence(w):\n", + " w = unicode_to_ascii(w.lower().strip())\n", + "\n", + " # creating a space between a word and the punctuation following it\n", + " # eg: \"he is a boy.\" => \"he is a boy .\"\n", + " # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n", + " w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n", + " w = re.sub(r'[\" \"]+', \" \", w)\n", + "\n", + " # replacing everything with space except (a-z, A-Z, \".\", \"?\", \"!\", \",\")\n", + " w = re.sub(r\"[^a-zA-Z?.!,¿]+\", \" \", w)\n", + "\n", + " w = w.rstrip().strip()\n", + "\n", + " # adding a start and an end token to the sentence\n", + " # so that the model know when to start and stop predicting.\n", + " w = ' ' + w + ' '\n", + " return w" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "opI2GzOt479E" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " may i borrow this book ? \n", + "b' \\xc2\\xbf puedo tomar prestado este libro ? '\n" + ] + } + ], + "source": [ + "en_sentence = u\"May I borrow this book?\"\n", + "sp_sentence = u\"¿Puedo tomar prestado este libro?\"\n", + "print(preprocess_sentence(en_sentence))\n", + "print(preprocess_sentence(sp_sentence).encode('utf-8'))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "OHn4Dct23jEm" + }, + "outputs": [], + "source": [ + "# 1. Remove the accents\n", + "# 2. Clean the sentences\n", + "# 3. Return word pairs in the format: [ENGLISH, SPANISH]\n", + "def create_dataset(path, num_examples):\n", + " lines = io.open(path, encoding='UTF-8').read().strip().split('\\n')\n", + "\n", + " word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n", + "\n", + " return zip(*word_pairs)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "cTbSbBz55QtF" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " if you want to sound like a native speaker , you must be willing to practice saying the same sentence over and over in the same way that banjo players practice the same phrase over and over until they can play it correctly and at the desired tempo . \n", + " si quieres sonar como un hablante nativo , debes estar dispuesto a practicar diciendo la misma frase una y otra vez de la misma manera en que un musico de banjo practica el mismo fraseo una y otra vez hasta que lo puedan tocar correctamente y en el tiempo esperado . \n" + ] + } + ], + "source": [ + "en, sp = create_dataset(path_to_file, None)\n", + "print(en[-1])\n", + "print(sp[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "OmMZQpdO60dt" + }, + "outputs": [], + "source": [ + "def max_length(tensor):\n", + " return max(len(t) for t in tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "bIOn8RCNDJXG" + }, + "outputs": [], + "source": [ + "def tokenize(lang):\n", + " lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(\n", + " filters='')\n", + " lang_tokenizer.fit_on_texts(lang)\n", + "\n", + " tensor = lang_tokenizer.texts_to_sequences(lang)\n", + "\n", + " tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,\n", + " padding='post')\n", + "\n", + " return tensor, lang_tokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "eAY9k49G3jE_" + }, + "outputs": [], + "source": [ + "def load_dataset(path, num_examples=None):\n", + " # creating cleaned input, output pairs\n", + " targ_lang, inp_lang = create_dataset(path, num_examples)\n", + "\n", + " input_tensor, inp_lang_tokenizer = tokenize(inp_lang)\n", + " target_tensor, targ_lang_tokenizer = tokenize(targ_lang)\n", + "\n", + " return input_tensor, target_tensor, inp_lang_tokenizer, targ_lang_tokenizer" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "GOi42V79Ydlr" + }, + "source": [ + "### Limit the size of the dataset to experiment faster (optional)\n", + "\n", + "Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data):" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "cnxC7q-j3jFD" + }, + "outputs": [], + "source": [ + "# Try experimenting with the size of that dataset\n", + "num_examples = 30000\n", + "input_tensor, target_tensor, inp_lang, targ_lang = load_dataset(path_to_file, num_examples)\n", + "\n", + "# Calculate max_length of the target tensors\n", + "max_length_targ, max_length_inp = max_length(target_tensor), max_length(input_tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4QILQkOs3jFG" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24000 24000 6000 6000\n" + ] + } + ], + "source": [ + "# Creating training and validation sets using an 80-20 split\n", + "input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)\n", + "\n", + "# Show length\n", + "print(len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "lJPmLZGMeD5q" + }, + "outputs": [], + "source": [ + "def convert(lang, tensor):\n", + " for t in tensor:\n", + " if t!=0:\n", + " print (\"%d ----> %s\" % (t, lang.index_word[t]))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "VXukARTDd7MT" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input Language; index to word mapping\n", + "1 ----> \n", + "265 ----> compre\n", + "15 ----> un\n", + "6648 ----> nopal\n", + "3 ----> .\n", + "2 ----> \n", + "\n", + "Target Language; index to word mapping\n", + "1 ----> \n", + "4 ----> i\n", + "222 ----> bought\n", + "9 ----> a\n", + "2787 ----> cactus\n", + "3 ----> .\n", + "2 ----> \n" + ] + } + ], + "source": [ + "print (\"Input Language; index to word mapping\")\n", + "convert(inp_lang, input_tensor_train[0])\n", + "print ()\n", + "print (\"Target Language; index to word mapping\")\n", + "convert(targ_lang, target_tensor_train[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rgCLkfv5uO3d" + }, + "source": [ + "### Create a tf.data dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "TqHsArVZ3jFS" + }, + "outputs": [], + "source": [ + "BUFFER_SIZE = len(input_tensor_train)\n", + "BATCH_SIZE = 64\n", + "steps_per_epoch = len(input_tensor_train)//BATCH_SIZE\n", + "embedding_dim = 256\n", + "units = 1024\n", + "vocab_inp_size = len(inp_lang.word_index)+1\n", + "vocab_tar_size = len(targ_lang.word_index)+1\n", + "\n", + "dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)\n", + "dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "qc6-NK1GtWQt" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(TensorShape([64, 16]), TensorShape([64, 11]))" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "example_input_batch, example_target_batch = next(iter(dataset))\n", + "example_input_batch.shape, example_target_batch.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TNfHIF71ulLu" + }, + "source": [ + "## Write the encoder and decoder model\n", + "\n", + "Implement an encoder-decoder model with attention which you can read about in the TensorFlow [Neural Machine Translation (seq2seq) tutorial](https://github.com/tensorflow/nmt). This example uses a more recent set of APIs. This notebook implements the [attention equations](https://github.com/tensorflow/nmt#background-on-the-attention-mechanism) from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence. The below picture and formulas are an example of attention mechanism from [Luong's paper](https://arxiv.org/abs/1508.04025v5). \n", + "\n", + "\"attention\n", + "\n", + "The input is put through an encoder model which gives us the encoder output of shape *(batch_size, max_length, hidden_size)* and the encoder hidden state of shape *(batch_size, hidden_size)*.\n", + "\n", + "Here are the equations that are implemented:\n", + "\n", + "\"attention\n", + "\"attention\n", + "\n", + "This tutorial uses [Bahdanau attention](https://arxiv.org/pdf/1409.0473.pdf) for the encoder. Let's decide on notation before writing the simplified form:\n", + "\n", + "* FC = Fully connected (dense) layer\n", + "* EO = Encoder output\n", + "* H = hidden state\n", + "* X = input to the decoder\n", + "\n", + "And the pseudo-code:\n", + "\n", + "* `score = FC(tanh(FC(EO) + FC(H)))`\n", + "* `attention weights = softmax(score, axis = 1)`. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. `Max_length` is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n", + "* `context vector = sum(attention weights * EO, axis = 1)`. Same reason as above for choosing axis as 1.\n", + "* `embedding output` = The input to the decoder X is passed through an embedding layer.\n", + "* `merged vector = concat(embedding output, context vector)`\n", + "* This merged vector is then given to the GRU\n", + "\n", + "The shapes of all the vectors at each step have been specified in the comments in the code:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "nZ2rI24i3jFg" + }, + "outputs": [], + "source": [ + "class Encoder(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):\n", + " super(Encoder, self).__init__()\n", + " self.batch_sz = batch_sz\n", + " self.enc_units = enc_units\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = tf.keras.layers.GRU(self.enc_units,\n", + " return_sequences=True,\n", + " return_state=True,\n", + " recurrent_initializer='glorot_uniform')\n", + "\n", + " def call(self, x, hidden):\n", + " x = self.embedding(x)\n", + " output, state = self.gru(x, initial_state = hidden)\n", + " return output, state\n", + "\n", + " def initialize_hidden_state(self):\n", + " return tf.zeros((self.batch_sz, self.enc_units))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "60gSVh05Jl6l" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Encoder output shape: (batch size, sequence length, units) (64, 16, 1024)\n", + "Encoder Hidden state shape: (batch size, units) (64, 1024)\n" + ] + } + ], + "source": [ + "encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)\n", + "\n", + "# sample input\n", + "sample_hidden = encoder.initialize_hidden_state()\n", + "sample_output, sample_hidden = encoder(example_input_batch, sample_hidden)\n", + "print ('Encoder output shape: (batch size, sequence length, units) {}'.format(sample_output.shape))\n", + "print ('Encoder Hidden state shape: (batch size, units) {}'.format(sample_hidden.shape))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "umohpBN2OM94" + }, + "outputs": [], + "source": [ + "class BahdanauAttention(tf.keras.layers.Layer):\n", + " def __init__(self, units):\n", + " super(BahdanauAttention, self).__init__()\n", + " self.W1 = tf.keras.layers.Dense(units)\n", + " self.W2 = tf.keras.layers.Dense(units)\n", + " self.V = tf.keras.layers.Dense(1)\n", + "\n", + " def call(self, query, values):\n", + " # hidden shape == (batch_size, hidden size)\n", + " # hidden_with_time_axis shape == (batch_size, 1, hidden size)\n", + " # we are doing this to perform addition to calculate the score\n", + " hidden_with_time_axis = tf.expand_dims(query, 1)\n", + "\n", + " # score shape == (batch_size, max_length, 1)\n", + " # we get 1 at the last axis because we are applying score to self.V\n", + " # the shape of the tensor before applying self.V is (batch_size, max_length, units)\n", + " score = self.V(tf.nn.tanh(\n", + " self.W1(values) + self.W2(hidden_with_time_axis)))\n", + "\n", + " # attention_weights shape == (batch_size, max_length, 1)\n", + " attention_weights = tf.nn.softmax(score, axis=1)\n", + "\n", + " # context_vector shape after sum == (batch_size, hidden_size)\n", + " context_vector = attention_weights * values\n", + " context_vector = tf.reduce_sum(context_vector, axis=1)\n", + "\n", + " return context_vector, attention_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "k534zTHiDjQU" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Attention result shape: (batch size, units) (64, 1024)\n", + "Attention weights shape: (batch_size, sequence_length, 1) (64, 16, 1)\n" + ] + } + ], + "source": [ + "attention_layer = BahdanauAttention(10)\n", + "attention_result, attention_weights = attention_layer(sample_hidden, sample_output)\n", + "\n", + "print(\"Attention result shape: (batch size, units) {}\".format(attention_result.shape))\n", + "print(\"Attention weights shape: (batch_size, sequence_length, 1) {}\".format(attention_weights.shape))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "yJ_B3mhW3jFk" + }, + "outputs": [], + "source": [ + "class Decoder(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):\n", + " super(Decoder, self).__init__()\n", + " self.batch_sz = batch_sz\n", + " self.dec_units = dec_units\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = tf.keras.layers.GRU(self.dec_units,\n", + " return_sequences=True,\n", + " return_state=True,\n", + " recurrent_initializer='glorot_uniform')\n", + " self.fc = tf.keras.layers.Dense(vocab_size)\n", + "\n", + " # used for attention\n", + " self.attention = BahdanauAttention(self.dec_units)\n", + "\n", + " def call(self, x, hidden, enc_output):\n", + " # enc_output shape == (batch_size, max_length, hidden_size)\n", + " context_vector, attention_weights = self.attention(hidden, enc_output)\n", + "\n", + " # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n", + " x = self.embedding(x)\n", + "\n", + " # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n", + " x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n", + "\n", + " # passing the concatenated vector to the GRU\n", + " output, state = self.gru(x)\n", + "\n", + " # output shape == (batch_size * 1, hidden_size)\n", + " output = tf.reshape(output, (-1, output.shape[2]))\n", + "\n", + " # output shape == (batch_size, vocab)\n", + " x = self.fc(output)\n", + "\n", + " return x, state, attention_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "P5UY8wko3jFp" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Decoder output shape: (batch_size, vocab size) (64, 4935)\n" + ] + } + ], + "source": [ + "decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)\n", + "\n", + "sample_decoder_output, _, _ = decoder(tf.random.uniform((BATCH_SIZE, 1)),\n", + " sample_hidden, sample_output)\n", + "\n", + "print ('Decoder output shape: (batch_size, vocab size) {}'.format(sample_decoder_output.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "_ch_71VbIRfK" + }, + "source": [ + "## Define the optimizer and the loss function" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "WmTHr5iV3jFr" + }, + "outputs": [], + "source": [ + "optimizer = tf.keras.optimizers.Adam()\n", + "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n", + " from_logits=True, reduction='none')\n", + "\n", + "def loss_function(real, pred):\n", + " mask = tf.math.logical_not(tf.math.equal(real, 0))\n", + " loss_ = loss_object(real, pred)\n", + "\n", + " mask = tf.cast(mask, dtype=loss_.dtype)\n", + " loss_ *= mask\n", + "\n", + " return tf.reduce_mean(loss_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DMVWzzsfNl4e" + }, + "source": [ + "## Checkpoints (Object-based saving)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Zj8bXQTgNwrF" + }, + "outputs": [], + "source": [ + "checkpoint_dir = './training_checkpoints'\n", + "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n", + "checkpoint = tf.train.Checkpoint(optimizer=optimizer,\n", + " encoder=encoder,\n", + " decoder=decoder)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hpObfY22IddU" + }, + "source": [ + "## Training\n", + "\n", + "1. Pass the *input* through the *encoder* which return *encoder output* and the *encoder hidden state*.\n", + "2. The encoder output, encoder hidden state and the decoder input (which is the *start token*) is passed to the decoder.\n", + "3. The decoder returns the *predictions* and the *decoder hidden state*.\n", + "4. The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n", + "5. Use *teacher forcing* to decide the next input to the decoder.\n", + "6. *Teacher forcing* is the technique where the *target word* is passed as the *next input* to the decoder.\n", + "7. The final step is to calculate the gradients and apply it to the optimizer and backpropagate." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "sC9ArXSsVfqn" + }, + "outputs": [], + "source": [ + "@tf.function\n", + "def train_step(inp, targ, enc_hidden):\n", + " loss = 0\n", + "\n", + " with tf.GradientTape() as tape:\n", + " enc_output, enc_hidden = encoder(inp, enc_hidden)\n", + "\n", + " dec_hidden = enc_hidden\n", + "\n", + " dec_input = tf.expand_dims([targ_lang.word_index['']] * BATCH_SIZE, 1)\n", + "\n", + " # Teacher forcing - feeding the target as the next input\n", + " for t in range(1, targ.shape[1]):\n", + " # passing enc_output to the decoder\n", + " predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\n", + "\n", + " loss += loss_function(targ[:, t], predictions)\n", + "\n", + " # using teacher forcing\n", + " dec_input = tf.expand_dims(targ[:, t], 1)\n", + "\n", + " batch_loss = (loss / int(targ.shape[1]))\n", + "\n", + " variables = encoder.trainable_variables + decoder.trainable_variables\n", + "\n", + " gradients = tape.gradient(loss, variables)\n", + "\n", + " optimizer.apply_gradients(zip(gradients, variables))\n", + "\n", + " return batch_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ddefjBMa3jF0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1 Batch 0 Loss 4.7473\n", + "Epoch 1 Batch 100 Loss 2.1733\n", + "Epoch 1 Batch 200 Loss 1.8787\n", + "Epoch 1 Batch 300 Loss 1.7222\n", + "Epoch 1 Loss 2.0324\n", + "Time taken for 1 epoch 33.9479820728302 sec\n", + "\n", + "Epoch 2 Batch 0 Loss 1.5579\n", + "Epoch 2 Batch 100 Loss 1.4370\n", + "Epoch 2 Batch 200 Loss 1.4042\n", + "Epoch 2 Batch 300 Loss 1.2848\n", + "Epoch 2 Loss 1.3962\n", + "Time taken for 1 epoch 17.57913875579834 sec\n", + "\n", + "Epoch 3 Batch 0 Loss 1.0146\n", + "Epoch 3 Batch 100 Loss 0.9916\n", + "Epoch 3 Batch 200 Loss 1.0946\n", + "Epoch 3 Batch 300 Loss 0.9436\n", + "Epoch 3 Loss 0.9849\n", + "Time taken for 1 epoch 17.110281229019165 sec\n", + "\n", + "Epoch 4 Batch 0 Loss 0.6478\n", + "Epoch 4 Batch 100 Loss 0.6424\n", + "Epoch 4 Batch 200 Loss 0.7041\n", + "Epoch 4 Batch 300 Loss 0.6307\n", + "Epoch 4 Loss 0.6634\n", + "Time taken for 1 epoch 17.549187421798706 sec\n", + "\n", + "Epoch 5 Batch 0 Loss 0.4491\n", + "Epoch 5 Batch 100 Loss 0.3657\n", + "Epoch 5 Batch 200 Loss 0.4612\n", + "Epoch 5 Batch 300 Loss 0.4688\n", + "Epoch 5 Loss 0.4508\n", + "Time taken for 1 epoch 17.02736186981201 sec\n", + "\n", + "Epoch 6 Batch 0 Loss 0.3293\n", + "Epoch 6 Batch 100 Loss 0.3074\n", + "Epoch 6 Batch 200 Loss 0.3077\n", + "Epoch 6 Batch 300 Loss 0.2849\n", + "Epoch 6 Loss 0.3147\n", + "Time taken for 1 epoch 17.503292560577393 sec\n", + "\n", + "Epoch 7 Batch 0 Loss 0.1896\n", + "Epoch 7 Batch 100 Loss 0.2028\n", + "Epoch 7 Batch 200 Loss 0.2070\n", + "Epoch 7 Batch 300 Loss 0.1809\n", + "Epoch 7 Loss 0.2236\n", + "Time taken for 1 epoch 17.121249437332153 sec\n", + "\n", + "Epoch 8 Batch 0 Loss 0.1307\n", + "Epoch 8 Batch 100 Loss 0.2151\n", + "Epoch 8 Batch 200 Loss 0.1595\n", + "Epoch 8 Batch 300 Loss 0.1825\n", + "Epoch 8 Loss 0.1683\n", + "Time taken for 1 epoch 17.56266689300537 sec\n", + "\n", + "Epoch 9 Batch 0 Loss 0.1091\n", + "Epoch 9 Batch 100 Loss 0.1238\n", + "Epoch 9 Batch 200 Loss 0.1262\n", + "Epoch 9 Batch 300 Loss 0.1245\n", + "Epoch 9 Loss 0.1290\n", + "Time taken for 1 epoch 17.148908615112305 sec\n", + "\n", + "Epoch 10 Batch 0 Loss 0.0939\n", + "Epoch 10 Batch 100 Loss 0.1082\n", + "Epoch 10 Batch 200 Loss 0.1149\n", + "Epoch 10 Batch 300 Loss 0.0957\n", + "Epoch 10 Loss 0.1038\n", + "Time taken for 1 epoch 17.501878261566162 sec\n", + "\n" + ] + } + ], + "source": [ + "EPOCHS = 10\n", + "\n", + "for epoch in range(EPOCHS):\n", + " start = time.time()\n", + "\n", + " enc_hidden = encoder.initialize_hidden_state()\n", + " total_loss = 0\n", + "\n", + " for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):\n", + " batch_loss = train_step(inp, targ, enc_hidden)\n", + " total_loss += batch_loss\n", + "\n", + " if batch % 100 == 0:\n", + " print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,\n", + " batch,\n", + " batch_loss.numpy()))\n", + " # saving (checkpoint) the model every 2 epochs\n", + " if (epoch + 1) % 2 == 0:\n", + " checkpoint.save(file_prefix = checkpoint_prefix)\n", + "\n", + " print('Epoch {} Loss {:.4f}'.format(epoch + 1,\n", + " total_loss / steps_per_epoch))\n", + " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "mU3Ce8M6I3rz" + }, + "source": [ + "## Translate\n", + "\n", + "* The evaluate function is similar to the training loop, except we don't use *teacher forcing* here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", + "* Stop predicting when the model predicts the *end token*.\n", + "* And store the *attention weights for every time step*.\n", + "\n", + "Note: The encoder output is calculated only once for one input." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "EbQpyYs13jF_" + }, + "outputs": [], + "source": [ + "def evaluate(sentence):\n", + " attention_plot = np.zeros((max_length_targ, max_length_inp))\n", + "\n", + " sentence = preprocess_sentence(sentence)\n", + "\n", + " inputs = [inp_lang.word_index[i] for i in sentence.split(' ')]\n", + " inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs],\n", + " maxlen=max_length_inp,\n", + " padding='post')\n", + " inputs = tf.convert_to_tensor(inputs)\n", + "\n", + " result = ''\n", + "\n", + " hidden = [tf.zeros((1, units))]\n", + " enc_out, enc_hidden = encoder(inputs, hidden)\n", + "\n", + " dec_hidden = enc_hidden\n", + " dec_input = tf.expand_dims([targ_lang.word_index['']], 0)\n", + "\n", + " for t in range(max_length_targ):\n", + " predictions, dec_hidden, attention_weights = decoder(dec_input,\n", + " dec_hidden,\n", + " enc_out)\n", + "\n", + " # storing the attention weights to plot later on\n", + " attention_weights = tf.reshape(attention_weights, (-1, ))\n", + " attention_plot[t] = attention_weights.numpy()\n", + "\n", + " predicted_id = tf.argmax(predictions[0]).numpy()\n", + "\n", + " result += targ_lang.index_word[predicted_id] + ' '\n", + "\n", + " if targ_lang.index_word[predicted_id] == '':\n", + " return result, sentence, attention_plot\n", + "\n", + " # the predicted ID is fed back into the model\n", + " dec_input = tf.expand_dims([predicted_id], 0)\n", + "\n", + " return result, sentence, attention_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "s5hQWlbN3jGF" + }, + "outputs": [], + "source": [ + "# function for plotting the attention weights\n", + "def plot_attention(attention, sentence, predicted_sentence):\n", + " fig = plt.figure(figsize=(10,10))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " ax.matshow(attention, cmap='viridis')\n", + "\n", + " fontdict = {'fontsize': 14}\n", + "\n", + " ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n", + " ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n", + "\n", + " ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n", + " ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "sl9zUHzg3jGI" + }, + "outputs": [], + "source": [ + "def translate(sentence):\n", + " result, sentence, attention_plot = evaluate(sentence)\n", + "\n", + " print('Input: %s' % (sentence))\n", + " print('Predicted translation: {}'.format(result))\n", + "\n", + " attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]\n", + " plot_attention(attention_plot, sentence.split(' '), result.split(' '))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "n250XbnjOaqP" + }, + "source": [ + "## Restore the latest checkpoint and test" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "UJpT9D5_OgP6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# restoring the latest checkpoint in checkpoint_dir\n", + "checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "WrAM0FDomq3E" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input: hace mucho frio aqui . \n", + "Predicted translation: it s very cold here . \n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "translate(u'hace mucho frio aqui.')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zSx2iM36EZQZ" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input: esta es mi vida . \n", + "Predicted translation: this is my life . \n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "translate(u'esta es mi vida.')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "A3LLCx3ZE0Ls" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input: ¿ todavia estan en casa ? \n", + "Predicted translation: are you still at home ? \n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "translate(u'¿todavia estan en casa?')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "DUQVLVqUE1YW" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input: trata de averiguarlo . \n", + "Predicted translation: try to figure it out . \n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# wrong translation\n", + "translate(u'trata de averiguarlo.')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "RTe5P5ioMJwN" + }, + "source": [ + "## Next steps\n", + "\n", + "* [Download a different dataset](http://www.manythings.org/anki/) to experiment with translations, for example, English to German, or English to French.\n", + "* Experiment with training on a larger dataset, or using more epochs\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "nmt_with_attention.ipynb", + "private_outputs": true, + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/12_image-captioning-with-attention.ipynb b/12_image-captioning-with-attention.ipynb new file mode 100644 index 0000000..1d182a9 --- /dev/null +++ b/12_image-captioning-with-attention.ipynb @@ -0,0 +1,2547 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
به نام خدا
\n", + "\"class.vision\"\n", + "

Image captioning با Attention

" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QASbY_HGo4Lq" + }, + "source": [ + "Given an image like the example below, our goal is to generate a caption such as \"a surfer riding on a wave\".\n", + "\n", + "![Man Surfing](https://tensorflow.org/images/surf.jpg)\n", + "\n", + "*[Image Source](https://commons.wikimedia.org/wiki/Surfing#/media/File:Surfing_in_Hawaii.jpg); License: Public Domain*\n", + "\n", + "To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption.\n", + "\n", + "![Prediction](https://tensorflow.org/images/imcap_prediction.png)\n", + "\n", + "The model architecture is similar to [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://arxiv.org/abs/1502.03044).\n", + "\n", + "This notebook is an end-to-end example. When you run the notebook, it downloads the [MS-COCO](http://cocodataset.org/#home) dataset, preprocesses and caches a subset of images using Inception V3, trains an encoder-decoder model, and generates captions on new images using the trained model.\n", + "\n", + "In this example, you will train a model on a relatively small amount of data—the first 30,000 captions for about 20,000 images (because there are multiple captions per image in the dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "U8l4RJ0XRPEm", + "outputId": "439e705a-8c6a-4659-92c4-8dc17d4277de" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TensorFlow 2.x selected.\n" + ] + } + ], + "source": [ + "try:\n", + " # %tensorflow_version only exists in Colab.\n", + " %tensorflow_version 2.x\n", + "except Exception:\n", + " pass\n", + "import tensorflow as tf\n", + "\n", + "# You'll generate plots of attention in order to see which parts of an image\n", + "# our model focuses on during captioning\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Scikit-learn includes many helpful utilities\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.utils import shuffle\n", + "\n", + "import re\n", + "import numpy as np\n", + "import os\n", + "import time\n", + "import json\n", + "from glob import glob\n", + "from PIL import Image\n", + "import pickle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b6qbGw8MRPE5" + }, + "source": [ + "## Download and prepare the MS-COCO dataset\n", + "\n", + "You will use the [MS-COCO dataset](http://cocodataset.org/#home) to train our model. The dataset contains over 82,000 images, each of which has at least 5 different caption annotations. The code below downloads and extracts the dataset automatically.\n", + "\n", + "**Caution: large download ahead**. You'll use the training set, which is a 13GB file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 110 + }, + "colab_type": "code", + "id": "krQuPYTtRPE7", + "outputId": "31141487-9f77-492c-c308-ba1af4d381f3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from http://images.cocodataset.org/annotations/annotations_trainval2014.zip\n", + "252878848/252872794 [==============================] - 16s 0us/step\n", + "Downloading data from http://images.cocodataset.org/zips/train2014.zip\n", + "13510574080/13510573713 [==============================] - 798s 0us/step\n" + ] + } + ], + "source": [ + "annotation_zip = tf.keras.utils.get_file('captions.zip',\n", + " cache_subdir=os.path.abspath('.'),\n", + " origin = 'http://images.cocodataset.org/annotations/annotations_trainval2014.zip',\n", + " extract = True)\n", + "annotation_file = os.path.dirname(annotation_zip)+'/annotations/captions_train2014.json'\n", + "\n", + "name_of_zip = 'train2014.zip'\n", + "if not os.path.exists(os.path.abspath('.') + '/' + name_of_zip):\n", + " image_zip = tf.keras.utils.get_file(name_of_zip,\n", + " cache_subdir=os.path.abspath('.'),\n", + " origin = 'http://images.cocodataset.org/zips/train2014.zip',\n", + " extract = True)\n", + " PATH = os.path.dirname(image_zip)+'/train2014/'\n", + "else:\n", + " PATH = os.path.abspath('.')+'/train2014/'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aANEzb5WwSzg" + }, + "source": [ + "## Optional: limit the size of the training set \n", + "To speed up training for this tutorial, you'll use a subset of 30,000 captions and their corresponding images to train our model. Choosing to use more data would result in improved captioning quality." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4G3b8x8_RPFD" + }, + "outputs": [], + "source": [ + "# Read the json file\n", + "with open(annotation_file, 'r') as f:\n", + " annotations = json.load(f)\n", + "\n", + "# Store captions and image names in vectors\n", + "all_captions = []\n", + "all_img_name_vector = []\n", + "\n", + "for annot in annotations['annotations']:\n", + " caption = ' ' + annot['caption'] + ' '\n", + " image_id = annot['image_id']\n", + " full_coco_image_path = PATH + 'COCO_train2014_' + '%012d.jpg' % (image_id)\n", + "\n", + " all_img_name_vector.append(full_coco_image_path)\n", + " all_captions.append(caption)\n", + "\n", + "# Shuffle captions and image_names together\n", + "# Set a random state\n", + "train_captions, img_name_vector = shuffle(all_captions,\n", + " all_img_name_vector,\n", + " random_state=1)\n", + "\n", + "# Select the first 30000 captions from the shuffled set\n", + "num_examples = 30000\n", + "train_captions = train_captions[:num_examples]\n", + "img_name_vector = img_name_vector[:num_examples]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "mPBMgK34RPFL", + "outputId": "0d6b448b-54b6-46f1-98b7-36d2b848ab8a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(30000, 414113)" + ] + }, + "execution_count": 6, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "len(train_captions), len(all_captions)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "0tgovMnoXecB" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8cSW4u-ORPFQ" + }, + "source": [ + "## Preprocess the images using InceptionV3\n", + "Next, you will use InceptionV3 (which is pretrained on Imagenet) to classify each image. You will extract features from the last convolutional layer.\n", + "\n", + "First, you will convert the images into InceptionV3's expected format by:\n", + "* Resizing the image to 299px by 299px\n", + "* [Preprocess the images](https://cloud.google.com/tpu/docs/inception-v3-advanced#preprocessing_stage) using the [preprocess_input](https://www.tensorflow.org/api_docs/python/tf/keras/applications/inception_v3/preprocess_input) method to normalize the image so that it contains pixels in the range of -1 to 1, which matches the format of the images used to train InceptionV3." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "zXR0217aRPFR" + }, + "outputs": [], + "source": [ + "def load_image(image_path):\n", + " img = tf.io.read_file(image_path)\n", + " img = tf.image.decode_jpeg(img, channels=3)\n", + " img = tf.image.resize(img, (299, 299))\n", + " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", + " return img, image_path" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "MDvIu4sXRPFV" + }, + "source": [ + "## Initialize InceptionV3 and load the pretrained Imagenet weights\n", + "\n", + "Now you'll create a tf.keras model where the output layer is the last convolutional layer in the InceptionV3 architecture. The shape of the output of this layer is ```8x8x2048```. You use the last convolutional layer because you are using attention in this example. You don't perform this initialization during training because it could become a bottleneck.\n", + "\n", + "* You forward each image through the network and store the resulting vector in a dictionary (image_name --> feature_vector).\n", + "* After all the images are passed through the network, you pickle the dictionary and save it to disk.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 74 + }, + "colab_type": "code", + "id": "RD3vW4SsRPFW", + "outputId": "367548a7-1e2b-4b48-f53b-8ececde36371" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "87916544/87910968 [==============================] - 7s 0us/step\n" + ] + } + ], + "source": [ + "image_model = tf.keras.applications.InceptionV3(include_top=False,\n", + " weights='imagenet')\n", + "new_input = image_model.input\n", + "hidden_layer = image_model.layers[-1].output\n", + "\n", + "image_features_extract_model = tf.keras.Model(new_input, hidden_layer)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "rERqlR3WRPGO" + }, + "source": [ + "## Caching the features extracted from InceptionV3\n", + "\n", + "You will pre-process each image with InceptionV3 and cache the output to disk. Caching the output in RAM would be faster but also memory intensive, requiring 8 \\* 8 \\* 2048 floats per image. At the time of writing, this exceeds the memory limitations of Colab (currently 12GB of memory).\n", + "\n", + "Performance could be improved with a more sophisticated caching strategy (for example, by sharding the images to reduce random access disk I/O), but that would require more code.\n", + "\n", + "The caching will take about 10 minutes to run in Colab with a GPU. If you'd like to see a progress bar, you can: \n", + "\n", + "1. install [tqdm](https://github.com/tqdm/tqdm):\n", + "\n", + " `!pip install tqdm`\n", + "\n", + "2. Import tqdm:\n", + "\n", + " `from tqdm import tqdm`\n", + "\n", + "3. Change the following line:\n", + "\n", + " `for img, path in image_dataset:`\n", + "\n", + " to:\n", + "\n", + " `for img, path in tqdm(image_dataset):`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7eeZz6PkXmns" + }, + "outputs": [], + "source": [ + "# Get unique images\n", + "encode_train = sorted(set(img_name_vector))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jCnsi813XovD" + }, + "outputs": [], + "source": [ + "# Feel free to change batch_size according to your system configuration\n", + "image_dataset = tf.data.Dataset.from_tensor_slices(encode_train)\n", + "image_dataset = image_dataset.map(\n", + " load_image, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(16)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Dx_fvbVgRPGQ" + }, + "outputs": [], + "source": [ + "for img, path in image_dataset:\n", + " batch_features = image_features_extract_model(img)\n", + " batch_features = tf.reshape(batch_features,\n", + " (batch_features.shape[0], -1, batch_features.shape[3]))\n", + "\n", + " for bf, p in zip(batch_features, path):\n", + " path_of_feature = p.numpy().decode(\"utf-8\")\n", + " np.save(path_of_feature, bf.numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nyqH3zFwRPFi" + }, + "source": [ + "## Preprocess and tokenize the captions\n", + "\n", + "* First, you'll tokenize the captions (for example, by splitting on spaces). This gives us a vocabulary of all of the unique words in the data (for example, \"surfing\", \"football\", and so on).\n", + "* Next, you'll limit the vocabulary size to the top 5,000 words (to save memory). You'll replace all other words with the token \"UNK\" (unknown).\n", + "* You then create word-to-index and index-to-word mappings.\n", + "* Finally, you pad all sequences to be the same length as the longest one." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "oJGE34aiRPFo" + }, + "outputs": [], + "source": [ + "# Choose the top 5000 words from the vocabulary\n", + "top_k = 5000\n", + "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=top_k,\n", + " oov_token=\"\",\n", + " filters='!\"#$%&()*+.,-/:;=?@[\\]^_`{|}~ ')\n", + "tokenizer.fit_on_texts(train_captions)\n", + "train_seqs = tokenizer.texts_to_sequences(train_captions)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "Ew9IG00hXzes", + "outputId": "c5ee0977-eb91-4d23-d2dd-6dc0bda72819" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[3, 2, 351, 687, 2, 280, 5, 2, 84, 339, 4]" + ] + }, + "execution_count": 13, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "train_seqs[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "IJqSMKk_X1Ji", + "outputId": "3976cbbd-0938-47ad-9231-d7240a2709e9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'': 1,\n", + " 'a': 2,\n", + " '': 3,\n", + " '': 4,\n", + " 'on': 5,\n", + " 'of': 6,\n", + " 'the': 7,\n", + " 'in': 8,\n", + " 'with': 9,\n", + " 'and': 10,\n", + " 'is': 11,\n", + " 'man': 12,\n", + " 'to': 13,\n", + " 'sitting': 14,\n", + " 'an': 15,\n", + " 'two': 16,\n", + " 'people': 17,\n", + " 'at': 18,\n", + " 'standing': 19,\n", + " 'are': 20,\n", + " 'white': 21,\n", + " 'next': 22,\n", + " 'woman': 23,\n", + " 'street': 24,\n", + " 'table': 25,\n", + " 'that': 26,\n", + " 'holding': 27,\n", + " 'some': 28,\n", + " 'large': 29,\n", + " 'it': 30,\n", + " 'person': 31,\n", + " 'down': 32,\n", + " 'top': 33,\n", + " 'up': 34,\n", + " 'group': 35,\n", + " 'field': 36,\n", + " 'small': 37,\n", + " 'his': 38,\n", + " 'plate': 39,\n", + " 'black': 40,\n", + " 'tennis': 41,\n", + " 'near': 42,\n", + " 'front': 43,\n", + " 'room': 44,\n", + " 'dog': 45,\n", + " 'young': 46,\n", + " 'riding': 47,\n", + " 'train': 48,\n", + " 'by': 49,\n", + " 'red': 50,\n", + " 'baseball': 51,\n", + " 'water': 52,\n", + " 'cat': 53,\n", + " 'playing': 54,\n", + " 'has': 55,\n", + " 'walking': 56,\n", + " 'sign': 57,\n", + " 'bathroom': 58,\n", + " 'while': 59,\n", + " 'blue': 60,\n", + " 'kitchen': 61,\n", + " 'bus': 62,\n", + " 'food': 63,\n", + " 'there': 64,\n", + " 'green': 65,\n", + " 'bed': 66,\n", + " 'parked': 67,\n", + " 'grass': 68,\n", + " 'pizza': 69,\n", + " 'looking': 70,\n", + " 'snow': 71,\n", + " 'other': 72,\n", + " 'ball': 73,\n", + " 'beach': 74,\n", + " 'side': 75,\n", + " 'for': 76,\n", + " 'boy': 77,\n", + " 'building': 78,\n", + " 'couple': 79,\n", + " 'city': 80,\n", + " 'men': 81,\n", + " 'game': 82,\n", + " 'three': 83,\n", + " 'skateboard': 84,\n", + " 'her': 85,\n", + " 'flying': 86,\n", + " 'wearing': 87,\n", + " 'player': 88,\n", + " 'over': 89,\n", + " 'toilet': 90,\n", + " 'road': 91,\n", + " 'laying': 92,\n", + " 'several': 93,\n", + " 'out': 94,\n", + " 'girl': 95,\n", + " 'sits': 96,\n", + " 'picture': 97,\n", + " 'clock': 98,\n", + " 'their': 99,\n", + " 'one': 100,\n", + " 'bench': 101,\n", + " 'cake': 102,\n", + " 'from': 103,\n", + " 'wooden': 104,\n", + " 'bear': 105,\n", + " 'frisbee': 106,\n", + " 'yellow': 107,\n", + " 'area': 108,\n", + " 'around': 109,\n", + " 'board': 110,\n", + " 'laptop': 111,\n", + " 'through': 112,\n", + " 'horse': 113,\n", + " 'giraffe': 114,\n", + " 'phone': 115,\n", + " 'as': 116,\n", + " 'brown': 117,\n", + " 'truck': 118,\n", + " 'computer': 119,\n", + " 'sink': 120,\n", + " 'outside': 121,\n", + " 'umbrella': 122,\n", + " 'many': 123,\n", + " 'desk': 124,\n", + " 'living': 125,\n", + " 'motorcycle': 126,\n", + " 'eating': 127,\n", + " 'window': 128,\n", + " 'each': 129,\n", + " 'close': 130,\n", + " 'trees': 131,\n", + " 'car': 132,\n", + " 'open': 133,\n", + " 'elephant': 134,\n", + " 'this': 135,\n", + " 'wall': 136,\n", + " 'air': 137,\n", + " 'tree': 138,\n", + " 'very': 139,\n", + " 'behind': 140,\n", + " 'covered': 141,\n", + " 'filled': 142,\n", + " 'park': 143,\n", + " 'kite': 144,\n", + " 'old': 145,\n", + " 'bat': 146,\n", + " 'stop': 147,\n", + " 'into': 148,\n", + " 'little': 149,\n", + " 'under': 150,\n", + " 'boat': 151,\n", + " 'child': 152,\n", + " 'court': 153,\n", + " 'bowl': 154,\n", + " 'chair': 155,\n", + " 'its': 156,\n", + " 'big': 157,\n", + " 'together': 158,\n", + " 'skis': 159,\n", + " 'background': 160,\n", + " 'fire': 161,\n", + " 'stands': 162,\n", + " 'inside': 163,\n", + " 'counter': 164,\n", + " 'shirt': 165,\n", + " 'photo': 166,\n", + " 'sky': 167,\n", + " 'surfboard': 168,\n", + " 'airplane': 169,\n", + " 'parking': 170,\n", + " 'couch': 171,\n", + " 'sidewalk': 172,\n", + " 'cell': 173,\n", + " 'sandwich': 174,\n", + " 'zebra': 175,\n", + " 'light': 176,\n", + " 'different': 177,\n", + " 'back': 178,\n", + " 'sheep': 179,\n", + " 'glass': 180,\n", + " 'ocean': 181,\n", + " 'mirror': 182,\n", + " 'view': 183,\n", + " 'traffic': 184,\n", + " 'bird': 185,\n", + " 'women': 186,\n", + " 'bunch': 187,\n", + " 'lot': 188,\n", + " 'hydrant': 189,\n", + " 'cars': 190,\n", + " 'racket': 191,\n", + " 'orange': 192,\n", + " 'vegetables': 193,\n", + " 'horses': 194,\n", + " 'plane': 195,\n", + " 'fence': 196,\n", + " 'baby': 197,\n", + " 'stand': 198,\n", + " 'hand': 199,\n", + " 'tie': 200,\n", + " 'teddy': 201,\n", + " 'another': 202,\n", + " 'giraffes': 203,\n", + " 'bananas': 204,\n", + " 'tall': 205,\n", + " 'vase': 206,\n", + " 'image': 207,\n", + " 'day': 208,\n", + " 'elephants': 209,\n", + " 'floor': 210,\n", + " 'full': 211,\n", + " 'flowers': 212,\n", + " 'grassy': 213,\n", + " 'off': 214,\n", + " 'wave': 215,\n", + " 'being': 216,\n", + " 'zebras': 217,\n", + " 'ground': 218,\n", + " 'along': 219,\n", + " 'hill': 220,\n", + " 'ready': 221,\n", + " 'middle': 222,\n", + " 'taking': 223,\n", + " 'hanging': 224,\n", + " 'luggage': 225,\n", + " 'tracks': 226,\n", + " 'dirt': 227,\n", + " 'tower': 228,\n", + " 'piece': 229,\n", + " 'driving': 230,\n", + " 'long': 231,\n", + " 'broccoli': 232,\n", + " 'bike': 233,\n", + " 'wii': 234,\n", + " 'skiing': 235,\n", + " 'during': 236,\n", + " 'display': 237,\n", + " 'beside': 238,\n", + " 'buildings': 239,\n", + " 'sit': 240,\n", + " 'them': 241,\n", + " 'slope': 242,\n", + " 'station': 243,\n", + " 'doing': 244,\n", + " 'across': 245,\n", + " 'grazing': 246,\n", + " 'cutting': 247,\n", + " 'cow': 248,\n", + " 'suit': 249,\n", + " 'above': 250,\n", + " 'hat': 251,\n", + " 'head': 252,\n", + " 'cows': 253,\n", + " 'fruit': 254,\n", + " 'four': 255,\n", + " 'empty': 256,\n", + " 'wine': 257,\n", + " 'corner': 258,\n", + " 'glasses': 259,\n", + " 'children': 260,\n", + " 'refrigerator': 261,\n", + " 'pink': 262,\n", + " 'stuffed': 263,\n", + " 'signs': 264,\n", + " 'hot': 265,\n", + " 'posing': 266,\n", + " 'holds': 267,\n", + " 'airport': 268,\n", + " 'pair': 269,\n", + " 'double': 270,\n", + " 'going': 271,\n", + " 'ski': 272,\n", + " 'getting': 273,\n", + " 'oven': 274,\n", + " 'herd': 275,\n", + " 'hit': 276,\n", + " 'mountain': 277,\n", + " 'video': 278,\n", + " 'skate': 279,\n", + " 'trick': 280,\n", + " 'snowy': 281,\n", + " 'surf': 282,\n", + " 'swinging': 283,\n", + " 'door': 284,\n", + " 'looks': 285,\n", + " 'crowd': 286,\n", + " 'camera': 287,\n", + " 'keyboard': 288,\n", + " 'kites': 289,\n", + " 'talking': 290,\n", + " 'television': 291,\n", + " 'box': 292,\n", + " 'chairs': 293,\n", + " 'smiling': 294,\n", + " 'cheese': 295,\n", + " 'topped': 296,\n", + " 'watching': 297,\n", + " 'lady': 298,\n", + " 'soccer': 299,\n", + " 'jumping': 300,\n", + " 'carrying': 301,\n", + " 'busy': 302,\n", + " 'various': 303,\n", + " 'colorful': 304,\n", + " 'against': 305,\n", + " 'pole': 306,\n", + " 'stove': 307,\n", + " 'traveling': 308,\n", + " 'umbrellas': 309,\n", + " 'cup': 310,\n", + " 'all': 311,\n", + " 'plates': 312,\n", + " 'boats': 313,\n", + " 'guy': 314,\n", + " 'track': 315,\n", + " 'lying': 316,\n", + " 'banana': 317,\n", + " 'wood': 318,\n", + " 'home': 319,\n", + " 'paper': 320,\n", + " 'body': 321,\n", + " 'using': 322,\n", + " 'skier': 323,\n", + " 'coffee': 324,\n", + " 'house': 325,\n", + " 'tv': 326,\n", + " 'lights': 327,\n", + " 'set': 328,\n", + " 'metal': 329,\n", + " 'waiting': 330,\n", + " 'who': 331,\n", + " 'cut': 332,\n", + " 'dogs': 333,\n", + " 'meat': 334,\n", + " 'birds': 335,\n", + " 'slice': 336,\n", + " 'shower': 337,\n", + " 'players': 338,\n", + " 'ramp': 339,\n", + " 'animals': 340,\n", + " 'night': 341,\n", + " 'male': 342,\n", + " 'passenger': 343,\n", + " 'face': 344,\n", + " 'decker': 345,\n", + " 'book': 346,\n", + " 'line': 347,\n", + " 'something': 348,\n", + " 'river': 349,\n", + " 'walk': 350,\n", + " 'skateboarder': 351,\n", + " 'hands': 352,\n", + " 'like': 353,\n", + " 'remote': 354,\n", + " 'about': 355,\n", + " 'be': 356,\n", + " 'runway': 357,\n", + " 'tray': 358,\n", + " 'colored': 359,\n", + " 'running': 360,\n", + " 'motorcycles': 361,\n", + " 'bedroom': 362,\n", + " 'someone': 363,\n", + " 'lots': 364,\n", + " 'surfer': 365,\n", + " 'bottle': 366,\n", + " 'look': 367,\n", + " 'screen': 368,\n", + " 'bicycle': 369,\n", + " 'preparing': 370,\n", + " 'tub': 371,\n", + " 'restaurant': 372,\n", + " 'bag': 373,\n", + " 'dressed': 374,\n", + " 'bears': 375,\n", + " 'high': 376,\n", + " 'intersection': 377,\n", + " 'jet': 378,\n", + " 'row': 379,\n", + " 'brick': 380,\n", + " 'items': 381,\n", + " 'store': 382,\n", + " \"it's\": 383,\n", + " 'pile': 384,\n", + " 'him': 385,\n", + " 'racquet': 386,\n", + " 'meal': 387,\n", + " 'made': 388,\n", + " 'jacket': 389,\n", + " 'snowboard': 390,\n", + " 'number': 391,\n", + " 'half': 392,\n", + " 'suitcase': 393,\n", + " 'scissors': 394,\n", + " 'play': 395,\n", + " 'surrounded': 396,\n", + " 'bridge': 397,\n", + " 'have': 398,\n", + " 'carrots': 399,\n", + " 'animal': 400,\n", + " 'rides': 401,\n", + " 'hair': 402,\n", + " 'he': 403,\n", + " 'surfing': 404,\n", + " 'pulling': 405,\n", + " 'way': 406,\n", + " 'mouth': 407,\n", + " 'waves': 408,\n", + " 'shown': 409,\n", + " 'batter': 410,\n", + " 'gray': 411,\n", + " 'salad': 412,\n", + " 'adult': 413,\n", + " 'kid': 414,\n", + " 'sleeping': 415,\n", + " 'decorated': 416,\n", + " 'showing': 417,\n", + " 'apple': 418,\n", + " 'leaning': 419,\n", + " 'female': 420,\n", + " 'grey': 421,\n", + " 'lined': 422,\n", + " 'dark': 423,\n", + " 'few': 424,\n", + " 'fries': 425,\n", + " 'walks': 426,\n", + " 'sand': 427,\n", + " 'enclosure': 428,\n", + " 'donuts': 429,\n", + " 'kids': 430,\n", + " 'displayed': 431,\n", + " 'silver': 432,\n", + " 'knife': 433,\n", + " 'throwing': 434,\n", + " 'past': 435,\n", + " 'chocolate': 436,\n", + " 'forest': 437,\n", + " 'older': 438,\n", + " 'bread': 439,\n", + " 'microwave': 440,\n", + " 'no': 441,\n", + " 'mouse': 442,\n", + " 'fork': 443,\n", + " 'lake': 444,\n", + " 'onto': 445,\n", + " 'between': 446,\n", + " 'meter': 447,\n", + " 'zoo': 448,\n", + " 'been': 449,\n", + " 'toy': 450,\n", + " 'attached': 451,\n", + " 'buses': 452,\n", + " 'skiers': 453,\n", + " 'watch': 454,\n", + " 'bath': 455,\n", + " 'making': 456,\n", + " 'resting': 457,\n", + " 'swing': 458,\n", + " 'monitor': 459,\n", + " 'oranges': 460,\n", + " 'apples': 461,\n", + " 'seen': 462,\n", + " 'purple': 463,\n", + " 'furniture': 464,\n", + " 'fruits': 465,\n", + " 'drinking': 466,\n", + " 'crossing': 467,\n", + " 'bikes': 468,\n", + " 'leaves': 469,\n", + " 'cabinets': 470,\n", + " 'dining': 471,\n", + " 'towards': 472,\n", + " 'cross': 473,\n", + " 'outdoor': 474,\n", + " 'photograph': 475,\n", + " 'blanket': 476,\n", + " 'cloudy': 477,\n", + " 'seat': 478,\n", + " 'working': 479,\n", + " 'dish': 480,\n", + " 'sunny': 481,\n", + " 'birthday': 482,\n", + " 'cats': 483,\n", + " 'surfboards': 484,\n", + " 'drink': 485,\n", + " 'public': 486,\n", + " 'shelf': 487,\n", + " 'cart': 488,\n", + " 'pictures': 489,\n", + " 'rocks': 490,\n", + " '\\n': 491,\n", + " 'teeth': 492,\n", + " 'tables': 493,\n", + " 'boys': 494,\n", + " 'painted': 495,\n", + " 'girls': 496,\n", + " 'hitting': 497,\n", + " 'clean': 498,\n", + " 'lit': 499,\n", + " 'time': 500,\n", + " 'edge': 501,\n", + " 'rain': 502,\n", + " 'scene': 503,\n", + " 'donut': 504,\n", + " 'lush': 505,\n", + " 'placed': 506,\n", + " 'or': 507,\n", + " 'moving': 508,\n", + " 'reading': 509,\n", + " 'passing': 510,\n", + " 'helmet': 511,\n", + " 'wet': 512,\n", + " 'coming': 513,\n", + " 'rail': 514,\n", + " 'flower': 515,\n", + " 'motor': 516,\n", + " 'clear': 517,\n", + " 'shows': 518,\n", + " 'setting': 519,\n", + " 'laptops': 520,\n", + " 'yard': 521,\n", + " 'dress': 522,\n", + " 'base': 523,\n", + " 'graffiti': 524,\n", + " 'boards': 525,\n", + " 'pieces': 526,\n", + " 'pitch': 527,\n", + " 'country': 528,\n", + " 'pan': 529,\n", + " 'police': 530,\n", + " 'brushing': 531,\n", + " 'nice': 532,\n", + " 'bright': 533,\n", + " 'stone': 534,\n", + " 'beautiful': 535,\n", + " 'skateboarding': 536,\n", + " 'office': 537,\n", + " 'books': 538,\n", + " 'windows': 539,\n", + " 'market': 540,\n", + " 'plastic': 541,\n", + " 'walls': 542,\n", + " 'trying': 543,\n", + " 'hotel': 544,\n", + " 'ride': 545,\n", + " 'sun': 546,\n", + " 'enjoying': 547,\n", + " 'shot': 548,\n", + " 'statue': 549,\n", + " 'benches': 550,\n", + " 'gathered': 551,\n", + " 'snowboarder': 552,\n", + " 'catch': 553,\n", + " 'slices': 554,\n", + " 'lap': 555,\n", + " 'cellphone': 556,\n", + " 'types': 557,\n", + " 'cooking': 558,\n", + " 'can': 559,\n", + " 'sandy': 560,\n", + " 'sandwiches': 561,\n", + " 'vehicle': 562,\n", + " 'watches': 563,\n", + " 'underneath': 564,\n", + " 'uniform': 565,\n", + " 'engine': 566,\n", + " 'tarmac': 567,\n", + " 'appliances': 568,\n", + " 'they': 569,\n", + " 'dinner': 570,\n", + " 'case': 571,\n", + " 'crowded': 572,\n", + " 'nintendo': 573,\n", + " 'platform': 574,\n", + " 'bathtub': 575,\n", + " 'stopped': 576,\n", + " 'basket': 577,\n", + " 'modern': 578,\n", + " 'family': 579,\n", + " 'place': 580,\n", + " 'shore': 581,\n", + " 'path': 582,\n", + " 'guys': 583,\n", + " 'well': 584,\n", + " 'controller': 585,\n", + " 'toppings': 586,\n", + " 'huge': 587,\n", + " 'plant': 588,\n", + " 'eat': 589,\n", + " 'vintage': 590,\n", + " 'tricks': 591,\n", + " 'flat': 592,\n", + " 'pizzas': 593,\n", + " 'vases': 594,\n", + " 'fresh': 595,\n", + " 'mountains': 596,\n", + " 'pillows': 597,\n", + " 'left': 598,\n", + " 'containing': 599,\n", + " 'rock': 600,\n", + " 'right': 601,\n", + " 'woods': 602,\n", + " 'eaten': 603,\n", + " 'others': 604,\n", + " 'branch': 605,\n", + " 'doughnut': 606,\n", + " 'poles': 607,\n", + " 'chicken': 608,\n", + " 'mounted': 609,\n", + " 'bowls': 610,\n", + " 'having': 611,\n", + " 'cute': 612,\n", + " 'computers': 613,\n", + " 'cream': 614,\n", + " 'passengers': 615,\n", + " 'end': 616,\n", + " 'center': 617,\n", + " 'arm': 618,\n", + " 'school': 619,\n", + " 'shoes': 620,\n", + " 'polar': 621,\n", + " 'drinks': 622,\n", + " 'beer': 623,\n", + " 'lamp': 624,\n", + " 'clothes': 625,\n", + " 'curb': 626,\n", + " 'town': 627,\n", + " 'candles': 628,\n", + " 'atop': 629,\n", + " 'pretty': 630,\n", + " 'pitcher': 631,\n", + " 'jump': 632,\n", + " 'rice': 633,\n", + " 'pot': 634,\n", + " 'five': 635,\n", + " 'concrete': 636,\n", + " 'doughnuts': 637,\n", + " 'fly': 638,\n", + " 'fridge': 639,\n", + " 'team': 640,\n", + " 'catcher': 641,\n", + " 'sinks': 642,\n", + " 'gear': 643,\n", + " 'take': 644,\n", + " 'container': 645,\n", + " 'phones': 646,\n", + " 'brush': 647,\n", + " 'just': 648,\n", + " 'floating': 649,\n", + " 'surface': 650,\n", + " 'bags': 651,\n", + " 'dirty': 652,\n", + " 'cement': 653,\n", + " 'she': 654,\n", + " 'mother': 655,\n", + " 'professional': 656,\n", + " 'turn': 657,\n", + " 'work': 658,\n", + " 'square': 659,\n", + " 'away': 660,\n", + " 'perched': 661,\n", + " 'distance': 662,\n", + " 'shaped': 663,\n", + " 'trucks': 664,\n", + " 'ice': 665,\n", + " 'plays': 666,\n", + " 'stacked': 667,\n", + " 'trains': 668,\n", + " 'equipment': 669,\n", + " 'striped': 670,\n", + " 'suitcases': 671,\n", + " 'feeding': 672,\n", + " 'boxes': 673,\n", + " 'multiple': 674,\n", + " 'sauce': 675,\n", + " 'breakfast': 676,\n", + " 'controllers': 677,\n", + " 'church': 678,\n", + " 'trunk': 679,\n", + " 'dry': 680,\n", + " 'french': 681,\n", + " 'foods': 682,\n", + " 'bar': 683,\n", + " 'broken': 684,\n", + " 'still': 685,\n", + " 'run': 686,\n", + " 'performing': 687,\n", + " 'variety': 688,\n", + " 'takes': 689,\n", + " 'tiled': 690,\n", + " 'soup': 691,\n", + " 'show': 692,\n", + " 'vehicles': 693,\n", + " 'pen': 694,\n", + " 'catching': 695,\n", + " 'match': 696,\n", + " 'bicycles': 697,\n", + " 'nearby': 698,\n", + " 'toothbrush': 699,\n", + " 'control': 700,\n", + " 'highway': 701,\n", + " 'lays': 702,\n", + " 'both': 703,\n", + " 'below': 704,\n", + " 'taken': 705,\n", + " 'towel': 706,\n", + " 'shop': 707,\n", + " 'foot': 708,\n", + " 'pose': 709,\n", + " 'sliced': 710,\n", + " 'toward': 711,\n", + " 'surfers': 712,\n", + " 'games': 713,\n", + " 'doors': 714,\n", + " 'tomatoes': 715,\n", + " 'post': 716,\n", + " 'christmas': 717,\n", + " 'planes': 718,\n", + " 'neck': 719,\n", + " 'poses': 720,\n", + " 'cattle': 721,\n", + " 'fenced': 722,\n", + " 'steel': 723,\n", + " 'legs': 724,\n", + " 'beds': 725,\n", + " 'flies': 726,\n", + " 'single': 727,\n", + " 'bushes': 728,\n", + " 'seated': 729,\n", + " 'mid': 730,\n", + " 'railroad': 731,\n", + " 'rack': 732,\n", + " 'asian': 733,\n", + " 'bottom': 734,\n", + " 'coat': 735,\n", + " 'commercial': 736,\n", + " 'event': 737,\n", + " 'jumps': 738,\n", + " 'after': 739,\n", + " 'pool': 740,\n", + " 'hay': 741,\n", + " 'sunglasses': 742,\n", + " 'throw': 743,\n", + " 'sofa': 744,\n", + " 'trail': 745,\n", + " 'eyes': 746,\n", + " 'shelves': 747,\n", + " 'eggs': 748,\n", + " 'dishes': 749,\n", + " 'wild': 750,\n", + " 'carriage': 751,\n", + " 'pulled': 752,\n", + " 'potatoes': 753,\n", + " 'style': 754,\n", + " 'bottles': 755,\n", + " 'lawn': 756,\n", + " 'painting': 757,\n", + " 'skateboards': 758,\n", + " 'shorts': 759,\n", + " 'garden': 760,\n", + " 'couches': 761,\n", + " 'where': 762,\n", + " 'wedding': 763,\n", + " 'pointing': 764,\n", + " 'things': 765,\n", + " 'docked': 766,\n", + " 'plants': 767,\n", + " 'including': 768,\n", + " 'swimming': 769,\n", + " 'military': 770,\n", + " 'pasture': 771,\n", + " 'feet': 772,\n", + " 'vegetable': 773,\n", + " 'dock': 774,\n", + " 'smiles': 775,\n", + " 'beneath': 776,\n", + " 'outdoors': 777,\n", + " 'cups': 778,\n", + " 'bun': 779,\n", + " 'clocks': 780,\n", + " 'spoon': 781,\n", + " 'reaching': 782,\n", + " 'among': 783,\n", + " 'trailer': 784,\n", + " 'adults': 785,\n", + " 'onions': 786,\n", + " 'cloth': 787,\n", + " 'drawn': 788,\n", + " 'sticking': 789,\n", + " 'serving': 790,\n", + " 'backpack': 791,\n", + " 'served': 792,\n", + " 'meters': 793,\n", + " 'blender': 794,\n", + " 'flag': 795,\n", + " 'round': 796,\n", + " 'assorted': 797,\n", + " 'foreground': 798,\n", + " 'tile': 799,\n", + " 'umpire': 800,\n", + " 'toast': 801,\n", + " 'snowboarding': 802,\n", + " 'same': 803,\n", + " 'tiny': 804,\n", + " 'van': 805,\n", + " 'closeup': 806,\n", + " 'arms': 807,\n", + " 'which': 808,\n", + " 'cabinet': 809,\n", + " 'cluttered': 810,\n", + " 'wire': 811,\n", + " 'overhead': 812,\n", + " 'stairs': 813,\n", + " 'business': 814,\n", + " 'stainless': 815,\n", + " 'fashioned': 816,\n", + " 'serve': 817,\n", + " 'ceiling': 818,\n", + " 'fireplace': 819,\n", + " 'decorative': 820,\n", + " 'go': 821,\n", + " 'rackets': 822,\n", + " 'putting': 823,\n", + " 'reflection': 824,\n", + " 'bush': 825,\n", + " 'drives': 826,\n", + " 'closed': 827,\n", + " 'stall': 828,\n", + " 'cage': 829,\n", + " 'toddler': 830,\n", + " 'prepared': 831,\n", + " 'arranged': 832,\n", + " 'loaded': 833,\n", + " 'houses': 834,\n", + " 'vanity': 835,\n", + " 'boarding': 836,\n", + " 'toys': 837,\n", + " 'net': 838,\n", + " 'cooked': 839,\n", + " 'wooded': 840,\n", + " 'low': 841,\n", + " 'see': 842,\n", + " 'space': 843,\n", + " 'airplanes': 844,\n", + " 'not': 845,\n", + " 'leash': 846,\n", + " 'tan': 847,\n", + " 'ledge': 848,\n", + " 'remotes': 849,\n", + " 'alone': 850,\n", + " 'rainy': 851,\n", + " 'staring': 852,\n", + " 'chips': 853,\n", + " 'suits': 854,\n", + " 'floors': 855,\n", + " 'boarder': 856,\n", + " 'restroom': 857,\n", + " 'transit': 858,\n", + " 'narrow': 859,\n", + " 'kitten': 860,\n", + " 'says': 861,\n", + " 'swings': 862,\n", + " 'assortment': 863,\n", + " 'facing': 864,\n", + " 'giving': 865,\n", + " 'cakes': 866,\n", + " 'pastries': 867,\n", + " 'bunches': 868,\n", + " 'trash': 869,\n", + " 'himself': 870,\n", + " 'beans': 871,\n", + " 'prepares': 872,\n", + " 'antique': 873,\n", + " 'get': 874,\n", + " 'pedestrians': 875,\n", + " 'garage': 876,\n", + " 'device': 877,\n", + " 'electronic': 878,\n", + " 'pond': 879,\n", + " 'partially': 880,\n", + " 'object': 881,\n", + " 'party': 882,\n", + " 'eats': 883,\n", + " 'carrot': 884,\n", + " 'lunch': 885,\n", + " 'skies': 886,\n", + " 'curtain': 887,\n", + " 'tooth': 888,\n", + " 'reads': 889,\n", + " 'monitors': 890,\n", + " 'tops': 891,\n", + " 'gate': 892,\n", + " 'electric': 893,\n", + " 'hillside': 894,\n", + " 'giant': 895,\n", + " 'does': 896,\n", + " 'construction': 897,\n", + " 'picnic': 898,\n", + " 'roof': 899,\n", + " 'lone': 900,\n", + " 'ear': 901,\n", + " 'lies': 902,\n", + " 'subway': 903,\n", + " 'hard': 904,\n", + " 'toilets': 905,\n", + " 'hold': 906,\n", + " 'rug': 907,\n", + " 'steps': 908,\n", + " 'machine': 909,\n", + " 'make': 910,\n", + " 'tour': 911,\n", + " 'landing': 912,\n", + " 'bow': 913,\n", + " 'alongside': 914,\n", + " 'jetliner': 915,\n", + " 'hats': 916,\n", + " 'lid': 917,\n", + " 'hole': 918,\n", + " 'pants': 919,\n", + " 'before': 920,\n", + " 'glove': 921,\n", + " 'harbor': 922,\n", + " 'pastry': 923,\n", + " 'rural': 924,\n", + " 'sunset': 925,\n", + " 'flags': 926,\n", + " 'what': 927,\n", + " 'pepperoni': 928,\n", + " 'contains': 929,\n", + " 'pots': 930,\n", + " 'tent': 931,\n", + " 'six': 932,\n", + " 'race': 933,\n", + " 'mug': 934,\n", + " 'apartment': 935,\n", + " 'new': 936,\n", + " 'upside': 937,\n", + " 'produce': 938,\n", + " 'walkway': 939,\n", + " 'railing': 940,\n", + " 'only': 941,\n", + " 'veggies': 942,\n", + " 'grill': 943,\n", + " 'gets': 944,\n", + " 'brightly': 945,\n", + " 'pavement': 946,\n", + " 'short': 947,\n", + " 'urban': 948,\n", + " 'carpet': 949,\n", + " 'was': 950,\n", + " 'barn': 951,\n", + " 'part': 952,\n", + " 'island': 953,\n", + " 'sports': 954,\n", + " 'fish': 955,\n", + " 'noodles': 956,\n", + " 'desktop': 957,\n", + " 'papers': 958,\n", + " 'dessert': 959,\n", + " 'sliding': 960,\n", + " 'sea': 961,\n", + " 'uses': 962,\n", + " 'leans': 963,\n", + " 'smaller': 964,\n", + " 'photos': 965,\n", + " 'jets': 966,\n", + " 'rider': 967,\n", + " 'friends': 968,\n", + " 'bacon': 969,\n", + " 'fighter': 970,\n", + " 'elderly': 971,\n", + " 'features': 972,\n", + " 'graze': 973,\n", + " 'pillow': 974,\n", + " 'branches': 975,\n", + " 'uniforms': 976,\n", + " 'rest': 977,\n", + " 'dead': 978,\n", + " 'museum': 979,\n", + " 'colors': 980,\n", + " 'rows': 981,\n", + " 'shopping': 982,\n", + " 'goes': 983,\n", + " 'peppers': 984,\n", + " 'makes': 985,\n", + " 'doll': 986,\n", + " 'waters': 987,\n", + " 'here': 988,\n", + " 'containers': 989,\n", + " 'clothing': 990,\n", + " 'airliner': 991,\n", + " 'spectators': 992,\n", + " 'type': 993,\n", + " 'wide': 994,\n", + " 'mitt': 995,\n", + " 'snowboards': 996,\n", + " 'ahead': 997,\n", + " 't': 998,\n", + " 'greens': 999,\n", + " 'missing': 1000,\n", + " ...}" + ] + }, + "execution_count": 15, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "tokenizer.word_index" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "8Q44tNQVRPFt" + }, + "outputs": [], + "source": [ + "tokenizer.word_index[''] = 0\n", + "tokenizer.index_word[0] = ''" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "AidglIZVRPF4" + }, + "outputs": [], + "source": [ + "# Pad each vector to the max_length of the captions\n", + "# If you do not provide a max_length value, pad_sequences calculates it automatically\n", + "cap_vector = tf.keras.preprocessing.sequence.pad_sequences(train_seqs, padding='post')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + }, + "colab_type": "code", + "id": "rgrUovJPafov", + "outputId": "e27bf7e4-7a05-459d-aa14-ca8f742a77fd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3, 2, 351, 687, 2, 280, 5, 2, 84, 339, 4, 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], dtype=int32)" + ] + }, + "execution_count": 18, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "cap_vector[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "gL0wkttkRPGA", + "outputId": "f64108a0-11d9-4d77-edbf-12581f5746ac" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(49,)" + ] + }, + "execution_count": 19, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "cap_vector[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "b_ou5mG2svPC", + "outputId": "b9e88477-3c01-420c-97b9-2b8cdedc6b14" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "49" + ] + }, + "execution_count": 46, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "max_length = len(cap_vector[0])\n", + "max_length" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "M3CD75nDpvTI" + }, + "source": [ + "## Split the data into training and testing" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "iS7DDMszRPGF" + }, + "outputs": [], + "source": [ + "# Create training and validation sets using an 80-20 split\n", + "img_name_train, img_name_val, cap_train, cap_val = train_test_split(img_name_vector,\n", + " cap_vector,\n", + " test_size=0.2,\n", + " random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "XmViPkRFRPGH", + "outputId": "ae6d2505-1578-455f-faed-dc7249624916" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(24000, 24000, 6000, 6000)" + ] + }, + "execution_count": 21, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "len(img_name_train), len(cap_train), len(img_name_val), len(cap_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uEWM9xrYcg45" + }, + "source": [ + "## Create a tf.data dataset for training\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "horagNvhhZiy" + }, + "source": [ + " Our images and captions are ready! Next, let's create a tf.data dataset to use for training our model." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Q3TnZ1ToRPGV" + }, + "outputs": [], + "source": [ + "# Feel free to change these parameters according to your system's configuration\n", + "\n", + "BATCH_SIZE = 64\n", + "BUFFER_SIZE = 1000\n", + "embedding_dim = 256\n", + "units = 512\n", + "vocab_size = len(tokenizer.word_index) + 1\n", + "num_steps = len(img_name_train) // BATCH_SIZE\n", + "# Shape of the vector extracted from InceptionV3 is (64, 2048)\n", + "# These two variables represent that vector shape\n", + "features_shape = 2048\n", + "attention_features_shape = 64" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "SmZS2N0bXG3T" + }, + "outputs": [], + "source": [ + "# Load the numpy files\n", + "def map_func(img_name, cap):\n", + " img_tensor = np.load(img_name.decode('utf-8')+'.npy')\n", + " return img_tensor, cap" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "FDF_Nm3tRPGZ" + }, + "outputs": [], + "source": [ + "dataset = tf.data.Dataset.from_tensor_slices((img_name_train, cap_train))\n", + "\n", + "# Use map to load the numpy files in parallel\n", + "dataset = dataset.map(lambda item1, item2: tf.numpy_function(\n", + " map_func, [item1, item2], [tf.float32, tf.int32]),\n", + " num_parallel_calls=tf.data.experimental.AUTOTUNE)\n", + "\n", + "# Shuffle and batch\n", + "dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n", + "dataset = dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "nrvoDphgRPGd" + }, + "source": [ + "## Model\n", + "\n", + "Fun fact: the decoder below is identical to the one in the example for [Neural Machine Translation with Attention](../sequences/nmt_with_attention.ipynb).\n", + "\n", + "The model architecture is inspired by the [Show, Attend and Tell](https://arxiv.org/pdf/1502.03044.pdf) paper.\n", + "\n", + "* In this example, you extract the features from the lower convolutional layer of InceptionV3 giving us a vector of shape (8, 8, 2048).\n", + "* You squash that to a shape of (64, 2048).\n", + "* This vector is then passed through the CNN Encoder (which consists of a single Fully connected layer).\n", + "* The RNN (here GRU) attends over the image to predict the next word." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ja2LFTMSdeV3" + }, + "outputs": [], + "source": [ + "class BahdanauAttention(tf.keras.Model):\n", + " def __init__(self, units):\n", + " super(BahdanauAttention, self).__init__()\n", + " self.W1 = tf.keras.layers.Dense(units)\n", + " self.W2 = tf.keras.layers.Dense(units)\n", + " self.V = tf.keras.layers.Dense(1)\n", + "\n", + " def call(self, features, hidden):\n", + " # features(CNN_encoder output) shape == (batch_size, 64, embedding_dim)\n", + "\n", + " # hidden shape == (batch_size, hidden_size)\n", + " # hidden_with_time_axis shape == (batch_size, 1, hidden_size)\n", + " hidden_with_time_axis = tf.expand_dims(hidden, 1)\n", + "\n", + " # score shape == (batch_size, 64, hidden_size)\n", + " score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))\n", + "\n", + " # attention_weights shape == (batch_size, 64, 1)\n", + " # you get 1 at the last axis because you are applying score to self.V\n", + " attention_weights = tf.nn.softmax(self.V(score), axis=1)\n", + "\n", + " # context_vector shape after sum == (batch_size, hidden_size)\n", + " context_vector = attention_weights * features\n", + " context_vector = tf.reduce_sum(context_vector, axis=1)\n", + "\n", + " return context_vector, attention_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "AZ7R1RxHRPGf" + }, + "outputs": [], + "source": [ + "class CNN_Encoder(tf.keras.Model):\n", + " # Since you have already extracted the features and dumped it using pickle\n", + " # This encoder passes those features through a Fully connected layer\n", + " def __init__(self, embedding_dim):\n", + " super(CNN_Encoder, self).__init__()\n", + " # shape after fc == (batch_size, 64, embedding_dim)\n", + " self.fc = tf.keras.layers.Dense(embedding_dim)\n", + "\n", + " def call(self, x):\n", + " x = self.fc(x)\n", + " x = tf.nn.relu(x)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "V9UbGQmERPGi" + }, + "outputs": [], + "source": [ + "class RNN_Decoder(tf.keras.Model):\n", + " def __init__(self, embedding_dim, units, vocab_size):\n", + " super(RNN_Decoder, self).__init__()\n", + " self.units = units\n", + "\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = tf.keras.layers.GRU(self.units,\n", + " return_sequences=True,\n", + " return_state=True,\n", + " recurrent_initializer='glorot_uniform')\n", + " self.fc1 = tf.keras.layers.Dense(self.units)\n", + " self.fc2 = tf.keras.layers.Dense(vocab_size)\n", + "\n", + " self.attention = BahdanauAttention(self.units)\n", + "\n", + " def call(self, x, features, hidden):\n", + " # defining attention as a separate model\n", + " context_vector, attention_weights = self.attention(features, hidden)\n", + "\n", + " # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n", + " x = self.embedding(x)\n", + "\n", + " # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n", + " x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n", + "\n", + " # passing the concatenated vector to the GRU\n", + " output, state = self.gru(x)\n", + "\n", + " # shape == (batch_size, max_length, hidden_size)\n", + " x = self.fc1(output)\n", + "\n", + " # x shape == (batch_size * max_length, hidden_size)\n", + " x = tf.reshape(x, (-1, x.shape[2]))\n", + "\n", + " # output shape == (batch_size * max_length, vocab)\n", + " x = self.fc2(x)\n", + "\n", + " return x, state, attention_weights\n", + "\n", + " def reset_state(self, batch_size):\n", + " return tf.zeros((batch_size, self.units))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Qs_Sr03wRPGk" + }, + "outputs": [], + "source": [ + "encoder = CNN_Encoder(embedding_dim)\n", + "decoder = RNN_Decoder(embedding_dim, units, vocab_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "-bYN7xA0RPGl" + }, + "outputs": [], + "source": [ + "optimizer = tf.keras.optimizers.Adam()\n", + "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(\n", + " from_logits=True, reduction='none')\n", + "\n", + "def loss_function(real, pred):\n", + " mask = tf.math.logical_not(tf.math.equal(real, 0))\n", + " loss_ = loss_object(real, pred)\n", + "\n", + " mask = tf.cast(mask, dtype=loss_.dtype)\n", + " loss_ *= mask\n", + "\n", + " return tf.reduce_mean(loss_)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6A3Ni64joyab" + }, + "source": [ + "## Checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "PpJAqPMWo0uE" + }, + "outputs": [], + "source": [ + "checkpoint_path = \"./checkpoints/train\"\n", + "ckpt = tf.train.Checkpoint(encoder=encoder,\n", + " decoder=decoder,\n", + " optimizer = optimizer)\n", + "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "fUkbqhc_uObw" + }, + "outputs": [], + "source": [ + "start_epoch = 0\n", + "if ckpt_manager.latest_checkpoint:\n", + " start_epoch = int(ckpt_manager.latest_checkpoint.split('-')[-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "PHod7t72RPGn" + }, + "source": [ + "## Training\n", + "\n", + "* You extract the features stored in the respective `.npy` files and then pass those features through the encoder.\n", + "* The encoder output, hidden state(initialized to 0) and the decoder input (which is the start token) is passed to the decoder.\n", + "* The decoder returns the predictions and the decoder hidden state.\n", + "* The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n", + "* Use teacher forcing to decide the next input to the decoder.\n", + "* Teacher forcing is the technique where the target word is passed as the next input to the decoder.\n", + "* The final step is to calculate the gradients and apply it to the optimizer and backpropagate.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Vt4WZ5mhJE-E" + }, + "outputs": [], + "source": [ + "# adding this in a separate cell because if you run the training cell\n", + "# many times, the loss_plot array will be reset\n", + "loss_plot = []" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "sqgyz2ANKlpU" + }, + "outputs": [], + "source": [ + "@tf.function\n", + "def train_step(img_tensor, target):\n", + " loss = 0\n", + "\n", + " # initializing the hidden state for each batch\n", + " # because the captions are not related from image to image\n", + " hidden = decoder.reset_state(batch_size=target.shape[0])\n", + "\n", + " dec_input = tf.expand_dims([tokenizer.word_index['']] * BATCH_SIZE, 1)\n", + "\n", + " with tf.GradientTape() as tape:\n", + " features = encoder(img_tensor)\n", + "\n", + " for i in range(1, target.shape[1]):\n", + " # passing the features through the decoder\n", + " predictions, hidden, _ = decoder(dec_input, features, hidden)\n", + "\n", + " loss += loss_function(target[:, i], predictions)\n", + "\n", + " # using teacher forcing\n", + " dec_input = tf.expand_dims(target[:, i], 1)\n", + "\n", + " total_loss = (loss / int(target.shape[1]))\n", + "\n", + " trainable_variables = encoder.trainable_variables + decoder.trainable_variables\n", + "\n", + " gradients = tape.gradient(loss, trainable_variables)\n", + "\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))\n", + "\n", + " return loss, total_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "colab_type": "code", + "id": "UlA4VIQpRPGo", + "outputId": "0ac3ffb8-e779-4acb-face-7e12c07b595e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1 Batch 0 Loss 2.1246\n", + "Epoch 1 Batch 100 Loss 1.1032\n", + "Epoch 1 Batch 200 Loss 1.0499\n", + "Epoch 1 Batch 300 Loss 0.8981\n", + "Epoch 1 Loss 1.070587\n", + "Time taken for 1 epoch 270.34504652023315 sec\n", + "\n", + "Epoch 2 Batch 0 Loss 0.8817\n", + "Epoch 2 Batch 100 Loss 0.8609\n", + "Epoch 2 Batch 200 Loss 0.7947\n", + "Epoch 2 Batch 300 Loss 0.7851\n", + "Epoch 2 Loss 0.811160\n", + "Time taken for 1 epoch 190.96626925468445 sec\n", + "\n", + "Epoch 3 Batch 0 Loss 0.8129\n", + "Epoch 3 Batch 100 Loss 0.7853\n", + "Epoch 3 Batch 200 Loss 0.7472\n", + "Epoch 3 Batch 300 Loss 0.7198\n", + "Epoch 3 Loss 0.734508\n", + "Time taken for 1 epoch 190.8415138721466 sec\n", + "\n", + "Epoch 4 Batch 0 Loss 0.7001\n", + "Epoch 4 Batch 100 Loss 0.7225\n", + "Epoch 4 Batch 200 Loss 0.7098\n", + "Epoch 4 Batch 300 Loss 0.6257\n", + "Epoch 4 Loss 0.687250\n", + "Time taken for 1 epoch 191.47930145263672 sec\n", + "\n", + "Epoch 5 Batch 0 Loss 0.6473\n", + "Epoch 5 Batch 100 Loss 0.6327\n", + "Epoch 5 Batch 200 Loss 0.5996\n", + "Epoch 5 Batch 300 Loss 0.6112\n", + "Epoch 5 Loss 0.650152\n", + "Time taken for 1 epoch 190.8552222251892 sec\n", + "\n", + "Epoch 6 Batch 0 Loss 0.5793\n", + "Epoch 6 Batch 100 Loss 0.6307\n", + "Epoch 6 Batch 200 Loss 0.6182\n", + "Epoch 6 Batch 300 Loss 0.5988\n", + "Epoch 6 Loss 0.617516\n", + "Time taken for 1 epoch 192.37568831443787 sec\n", + "\n", + "Epoch 7 Batch 0 Loss 0.5763\n", + "Epoch 7 Batch 100 Loss 0.6345\n", + "Epoch 7 Batch 200 Loss 0.5238\n", + "Epoch 7 Batch 300 Loss 0.5899\n", + "Epoch 7 Loss 0.585758\n", + "Time taken for 1 epoch 192.6030147075653 sec\n", + "\n", + "Epoch 8 Batch 0 Loss 0.5470\n", + "Epoch 8 Batch 100 Loss 0.5651\n", + "Epoch 8 Batch 200 Loss 0.5313\n", + "Epoch 8 Batch 300 Loss 0.5556\n", + "Epoch 8 Loss 0.555490\n", + "Time taken for 1 epoch 192.06621170043945 sec\n", + "\n", + "Epoch 9 Batch 0 Loss 0.5597\n", + "Epoch 9 Batch 100 Loss 0.5729\n", + "Epoch 9 Batch 200 Loss 0.5341\n", + "Epoch 9 Batch 300 Loss 0.4686\n", + "Epoch 9 Loss 0.525573\n", + "Time taken for 1 epoch 191.97880053520203 sec\n", + "\n", + "Epoch 10 Batch 0 Loss 0.4906\n", + "Epoch 10 Batch 100 Loss 0.5130\n", + "Epoch 10 Batch 200 Loss 0.5455\n", + "Epoch 10 Batch 300 Loss 0.5523\n", + "Epoch 10 Loss 0.496922\n", + "Time taken for 1 epoch 191.48513460159302 sec\n", + "\n", + "Epoch 11 Batch 0 Loss 0.4865\n", + "Epoch 11 Batch 100 Loss 0.5072\n", + "Epoch 11 Batch 200 Loss 0.4397\n", + "Epoch 11 Batch 300 Loss 0.4556\n", + "Epoch 11 Loss 0.468137\n", + "Time taken for 1 epoch 192.71875095367432 sec\n", + "\n", + "Epoch 12 Batch 0 Loss 0.4325\n", + "Epoch 12 Batch 100 Loss 0.4741\n", + "Epoch 12 Batch 200 Loss 0.4829\n", + "Epoch 12 Batch 300 Loss 0.4358\n", + "Epoch 12 Loss 0.438129\n", + "Time taken for 1 epoch 192.7211410999298 sec\n", + "\n", + "Epoch 13 Batch 0 Loss 0.4016\n", + "Epoch 13 Batch 100 Loss 0.3922\n", + "Epoch 13 Batch 200 Loss 0.4228\n", + "Epoch 13 Batch 300 Loss 0.3893\n", + "Epoch 13 Loss 0.412847\n", + "Time taken for 1 epoch 191.58248686790466 sec\n", + "\n", + "Epoch 14 Batch 0 Loss 0.4192\n", + "Epoch 14 Batch 100 Loss 0.4215\n", + "Epoch 14 Batch 200 Loss 0.4293\n", + "Epoch 14 Batch 300 Loss 0.3627\n", + "Epoch 14 Loss 0.383972\n", + "Time taken for 1 epoch 191.44387125968933 sec\n", + "\n", + "Epoch 15 Batch 0 Loss 0.4097\n", + "Epoch 15 Batch 100 Loss 0.3638\n", + "Epoch 15 Batch 200 Loss 0.3954\n", + "Epoch 15 Batch 300 Loss 0.3521\n", + "Epoch 15 Loss 0.358080\n", + "Time taken for 1 epoch 193.56486749649048 sec\n", + "\n", + "Epoch 16 Batch 0 Loss 0.3533\n", + "Epoch 16 Batch 100 Loss 0.3488\n", + "Epoch 16 Batch 200 Loss 0.3027\n", + "Epoch 16 Batch 300 Loss 0.3252\n", + "Epoch 16 Loss 0.334462\n", + "Time taken for 1 epoch 193.00335264205933 sec\n", + "\n", + "Epoch 17 Batch 0 Loss 0.3459\n", + "Epoch 17 Batch 100 Loss 0.3329\n", + "Epoch 17 Batch 200 Loss 0.3272\n", + "Epoch 17 Batch 300 Loss 0.3146\n", + "Epoch 17 Loss 0.312897\n", + "Time taken for 1 epoch 192.3078145980835 sec\n", + "\n", + "Epoch 18 Batch 0 Loss 0.3234\n", + "Epoch 18 Batch 100 Loss 0.2916\n", + "Epoch 18 Batch 200 Loss 0.2891\n", + "Epoch 18 Batch 300 Loss 0.3107\n", + "Epoch 18 Loss 0.292573\n", + "Time taken for 1 epoch 191.19015502929688 sec\n", + "\n", + "Epoch 19 Batch 0 Loss 0.2829\n", + "Epoch 19 Batch 100 Loss 0.2543\n", + "Epoch 19 Batch 200 Loss 0.2699\n", + "Epoch 19 Batch 300 Loss 0.3004\n", + "Epoch 19 Loss 0.273824\n", + "Time taken for 1 epoch 194.6978690624237 sec\n", + "\n", + "Epoch 20 Batch 0 Loss 0.3066\n", + "Epoch 20 Batch 100 Loss 0.2608\n", + "Epoch 20 Batch 200 Loss 0.2470\n", + "Epoch 20 Batch 300 Loss 0.2530\n", + "Epoch 20 Loss 0.257028\n", + "Time taken for 1 epoch 193.284277677536 sec\n", + "\n" + ] + } + ], + "source": [ + "EPOCHS = 20\n", + "\n", + "for epoch in range(start_epoch, EPOCHS):\n", + " start = time.time()\n", + " total_loss = 0\n", + "\n", + " for (batch, (img_tensor, target)) in enumerate(dataset):\n", + " batch_loss, t_loss = train_step(img_tensor, target)\n", + " total_loss += t_loss\n", + "\n", + " if batch % 100 == 0:\n", + " print ('Epoch {} Batch {} Loss {:.4f}'.format(\n", + " epoch + 1, batch, batch_loss.numpy() / int(target.shape[1])))\n", + " # storing the epoch end loss value to plot later\n", + " loss_plot.append(total_loss / num_steps)\n", + "\n", + " if epoch % 5 == 0:\n", + " ckpt_manager.save()\n", + "\n", + " print ('Epoch {} Loss {:.6f}'.format(epoch + 1,\n", + " total_loss/num_steps))\n", + " print ('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "colab_type": "code", + "id": "1Wm83G-ZBPcC", + "outputId": "3c81ed33-9e30-448f-9a98-84f6ca672aa8" + }, + "outputs": [ + { + "data": { + "image/png": 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QH93OIg6F+T+eDWyo87o8mPYxMxsO5Lr735pakJlNNbNSMyutrKxs+UpF4syA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\n6iMQOYagj6DY3bdFXYtIGHR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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(loss_plot)\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.title('Loss Plot')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xGvOcLQKghXN" + }, + "source": [ + "## Caption!\n", + "\n", + "* The evaluate function is similar to the training loop, except you don't use teacher forcing here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", + "* Stop predicting when the model predicts the end token.\n", + "* And store the attention weights for every time step." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "RCWpDtyNRPGs" + }, + "outputs": [], + "source": [ + "def evaluate(image):\n", + " attention_plot = np.zeros((max_length, attention_features_shape))\n", + "\n", + " hidden = decoder.reset_state(batch_size=1)\n", + "\n", + " temp_input = tf.expand_dims(load_image(image)[0], 0)\n", + " img_tensor_val = image_features_extract_model(temp_input)\n", + " img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0], -1, img_tensor_val.shape[3]))\n", + "\n", + " features = encoder(img_tensor_val)\n", + "\n", + " dec_input = tf.expand_dims([tokenizer.word_index['']], 0)\n", + " result = []\n", + "\n", + " for i in range(max_length):\n", + " predictions, hidden, attention_weights = decoder(dec_input, features, hidden)\n", + "\n", + " attention_plot[i] = tf.reshape(attention_weights, (-1, )).numpy()\n", + "\n", + " predicted_id = tf.argmax(predictions[0]).numpy()\n", + " result.append(tokenizer.index_word[predicted_id])\n", + "\n", + " if tokenizer.index_word[predicted_id] == '':\n", + " return result, attention_plot\n", + "\n", + " dec_input = tf.expand_dims([predicted_id], 0)\n", + "\n", + " attention_plot = attention_plot[:len(result), :]\n", + " return result, attention_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "fD_y7PD6RPGt" + }, + "outputs": [], + "source": [ + "def plot_attention(image, result, attention_plot):\n", + " temp_image = np.array(Image.open(image))\n", + "\n", + " fig = plt.figure(figsize=(10, 10))\n", + "\n", + " len_result = len(result)\n", + " for l in range(len_result):\n", + " temp_att = np.resize(attention_plot[l], (8, 8))\n", + " ax = fig.add_subplot(len_result//2, len_result//2, l+1)\n", + " ax.set_title(result[l])\n", + " img = ax.imshow(temp_image)\n", + " ax.imshow(temp_att, cmap='gray', alpha=0.6, extent=img.get_extent())\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 841 + }, + "colab_type": "code", + "id": "7x8RiPHe_4qI", + "outputId": "542c727c-ec0c-480e-c698-859cbbcb7579" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Real Caption: a cow grazing in a small field with three cows in the back \n", + "Prediction Caption: a group of cows are laying on a few grass \n" + ] + }, + { + "data": { + "image/png": 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DItUJJc8jNSOWW6cY9rpkMoRMUKl6REmEYXhUXAcMsJAoLVncrDMaxUw7Ae1KiStXOxyM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\nUmJoRqS0oCQni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0vr747e/8ybmNF549uuuYuqbqhGIG2ZMnT9M8uXZ1az4MHrz/0NDMIs8tayGK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KTt7D58/KlHkvgUz5K67RAy3JtMYNzjWZpJ+wEW+9FQkio4TviuSbE4iCOO47wkAgnEpJTe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\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4ghWGxt29z752bWPmwRZdLAeAI8/wLM3HUciElAAQq41FghCMkLKWYRxwjBwAVinkrEcp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myHq12ks3Xrh+8TryqJxNT49On7p4pRmkX/pH/4iE+P3dOyYwczOL6vF7772jpOz0OrkuJ/PR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crq1csGobS+/37y9OXldaGJFHXr8TSoD04efvpy74Ndr9FmI4FyZfShadwUiuLod/4/vw8E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W61a30qmT04Of/PiHB9PZ06dPf/TjH7M4BPk+tqAPMO9sPV99dv4EtN3u9rafjbpOdc7N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\n2FioPSKUEOJhBNHGKDMGJYqxVwUvqU+++fzrpKgwFfOi2N6+hgEimEYDIAq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wjGWERJJIhyZkHAggcjtTPBOUYo4wyD6LRikGKAcQQYxMPhwCLinFNMkixzzlFKIcGEkDzP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\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KolitNhChN5dXeV15/b5r+RBrVIL1ZsdbWudFUeRII2maxnFa5gWEkHIGMFJV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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# captions on the validation set\n", + "rid = np.random.randint(0, len(img_name_val))\n", + "image = img_name_val[rid]\n", + "real_caption = ' '.join([tokenizer.index_word[i] for i in cap_val[rid] if i not in [0]])\n", + "result, attention_plot = evaluate(image)\n", + "\n", + "print ('Real Caption:', real_caption)\n", + "print ('Prediction Caption:', ' '.join(result))\n", + "plot_attention(image, result, attention_plot)\n", + "# opening the image\n", + "Image.open(img_name_val[rid])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Rprk3HEvZuxb" + }, + "source": [ + "## Try it on your own images\n", + "For fun, below we've provided a method you can use to caption your own images with the model we've just trained. Keep in mind, it was trained on a relatively small amount of data, and your images may be different from the training data (so be prepared for weird results!)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 992 + }, + "colab_type": "code", + "id": "9Psd1quzaAWg", + "outputId": "9fb1aea3-d3ed-4d88-94bf-f9b19f221eb0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://tensorflow.org/images/surf.jpg\n", + "65536/64400 [==============================] - 0s 3us/step\n", + "Prediction Caption: a man riding a surfboard in the ocean \n" + ] + }, + { + "data": { + "image/png": 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uVaC0KEAIfKVAgjWSQZajTYGvfJpRONFxbi+CYSEKal5YMhxhtGF+YZ79Xtcx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\nTi47ODjg/uoDrHDpXO7dvUv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GJXESk+enx/DU1BRIRZxmvPO2Yzj0PRpRyBfeOGQ4zzJq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TgqxXwxp23aKdRDY5BONjkTo3ysnHafhhjwKeJcIVX7FH8tR/ejcpxl3iblzHI2xyglW9EK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\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I0YvFIsZ4cnJydnb2008/bTab4Oxdr9fb7bZt21CFAmMMDlmyYAFV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\nu7t79+5dXddN09RUveN2KaXr6+tQ/sjtdBFCmKNYJIoQWltbIwrSFXV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7wJqBVIIVhID7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\nsD8cHh0dSQ6OlCiPx2NOmWWYEMK6LJWMSKFTnuetVmt9fV2qnXVd1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p1ms9nv9xlj6xuX\nkjiUqNrl5WXf92uhKooyGAwk2Keu67PjEw6RjJcOwzCOE0KIaZrT6fT4+Lik1cbGBmNsNBoHi1mw\ns6Momswghy8By0xR5MpT4gEAALJEE0JomnEhqkCormtV1drt9mAwuOj2IATgAhrFvwrQ/Y/CJfES\nF8WhEAAgIISiEgyRvJw451AAQoiCoWMZV69elVkAmGDPcZrN5mw2AUVp6irgFACEMAFcIACFAFJL\nhAQACCtEQQgxwhRCFaTkec650DRDIsrfe+/98XAk4fbS2RiGoYzzefXVV/dPjmR4nZyxyURdzvlX\nnER5y37lO9IIRn8rNAn/LXY0p6wGtawGKKVyyMRYDQBI0zSIoqzIi6Kaz+cyon5rcxMjcn52Rild\nX19PojiOY91CnIO6qvKalhWVXWiapg3fz5JUwciyLMbY3sF+Eme3bt8gcsCFMQb8osF3TIOoCq3q\nIJiZWkpp1e12hQBYUVRdf/5897/8r/8rhFB3qbeyspIVBYRwMpmoqur7vm3bURRRTfUaPiEkz3M5\neRsOh6Zp2rY9GA3Lsry8tR1F0ePHj58+f16XpTxBZIRAEse6pq2srJyfnwPGtzcvjcfjPM87rXbD\n8yW0odPpWIYRBYGp667rjKfTg4ODmnLDMDRNWe51q6rY2X1OEGy3m7ap62obImGaZlWV3/zmN6uS\nriyv/exnP3v8xRPH9vb39w3DAgA0Gs2dnZ3hcLy1taWq2vPnu5TyN998czgcxnFs23YQBDs7O+Ei\nkEt1aceezWabm5uKohwdHf31X//1rZt3AACe7cyUi+zY9fX1u3fvzufzH/zgB5PJJEri27dufPHF\nF6+++ura2tqHDx7YhtnpdNrdznQ8OTk5efONN2zX2X34wnXddrcrhMAKOTk7zfP88OQYc6CqKhOc\nEOI1/JpSgODN27fm0xmE0He96XiyvnlpNBpVRbm1tVVUrCiKJEk8z3McRyoUxuOxNDnoqlrSEmPs\n2LaMqY+jVNM023WyLKOCV3VtmqaiqVmWaZpm6kacJFmayvIrjmPPMofDIUBQFWpa5HQ2W4ShEAIR\nbNom5YzlOQBgNB1nWdbudaMsb3Z7qqr2xxNaVa5rI4QgrTnnYRwjQsJo4TfcLMs8v8EYm81mm5tb\nCKFOpyOx1QKgcL7Y2LqU5lmj0YiTZPfZcwDAjZvXHz16FKTxP/nj/+LsbPTzX3yQZZlhNCQ9VCqN\nX3311ddfvzedzF+8eKHr+ng8VkxFnsVRFDHGDNfIy6KB4N7e/tnZmeM4pm3tHx4CAFZWVgghTb95\ndnYmKdnD4bDX63W7XeksXywWtm1zzufzuaZpuq4HQRCniRRUj0YjTdM4EIZhyBVaXpUHx0dSbCzn\ne5L2IHWnGOOqqtI0FUKsrq7KDrh8adj46nAhEMkoN3k2QQg4ZwghmUqiKLgs8+fPnwshiKrUjF65\nsjUdj3hVwLo0MTIwABCadqOscqiqpoJJq4FKqkLorq418mL4wf0rN2+2G92jp09nu4cYomg2Pzw4\nePz5F2WSKRi7tmNykaZpiA+Bt8ORYXpOBUA9namUYkOnCsEKKmrAgKCMU8Yog4sgerqzN59HSbEQ\nivJ8b399fZ0jTAFMs0xR1KKuBJcdD5AtCxQSZs/KuspKYTnu2vpGs9OeLqLheLrSW+FUQIg5ZKen\np4sw2NzauHHjxosXL6TLTlZLcnlfVCUUoNFo2LZdlCWn1Pd9mXE0GIqNjQ0OwXQ+O+v3KWdew61K\nWjHKy6Kc1Xoa2aY5mUwUBa+urq6srGqahhBM01RRdSFEmMQQiY2NjcH52fHxMUboQpFa03kQFqWQ\nyNKyLDc3N+u6rqrKdNwoilZWVhqNxrOnz794sUsIuXXrlqZpYRqoir68vLy8vDKbLuZBmKapbbt1\nXSdpKoQAkCsYA8CzOAnni5W1DcaYjFyjlGJMZU0mRUaSAy+F60VRUEohBHVdyxyFCxMwYBWj8kYH\n/MJCBF5aeiit5MOJAAQYYIxVgjDGRZafHp/QqsrTuNvtclonUSgYb/ueZVkaJjLfGgqGEKoZ0xQV\nc86kbAphgAmEGEJcpJmcBkk5lVy4NNstXdflPFn6PvIiZbyeTEfBbF4XJcaYlhVg3DZMwzCyLOM1\nBS+1zRhjVVHlqJnSCrwkdn0l/+acY4jKqiyKIs9zFZOvKhLBasrZBYcO46OjI1VVdV2XE+Ysy4bD\nIWOs2Wx+85vfvHHjxnsf/PT47DSOYyRAVZTIRLqueK5blmVVlMQy5ZKoyJK3vvH1u7fvkCKvpM9H\nVVUIGYSCAVEWtdRtyuViw2+cnJ4fHZ/uHxx9452v27b98f1fnZ4eAwwEYHleWI7NGBsMzwEAYRgS\niOaBggCM02htdenmzZvz+XxnZ2exCFdWVjQNUS62ti5nWSGVcrLdWV1aXllZWVte29rYGpwNFKRQ\nBA3Deuve1yaTyXA4TJJsZ+fF6emppRuj4UQOeW7cuHRycrK/f9BstRhjWVmYpqkqeDw6tyzrd3/7\ntxoN78Xu7scff/S1r72VRPHjL56Ox+M0TTc2Ntrt7ubmpclkpmiqZNZQJgxTIYrCheACLC2vvHb3\n7s8XC3mHtVqtxWIBAAjmiyiKFEWR1PXOUu/JkycSxYUhnMxmwXwqhLh6+UoURdLWPJlMNtc3NjY2\nZrPZ3bt3fd9/8803dV1/8uJFmqZ7hweXL229fu+N/f390WRyeHyMCCmqKityCY7O6rLZaRdFQSDS\nTUMIwQTHEKRpCiFst9uaocdhFMex53nvvPPO4f7+J598ggBQiYIApKomvfDSaVDmhWPZDc8v66rK\nZbSIoSCMNR3akMl5ne8neba8sjKajBFC7XZ7Op0urSwfHBwACJvdTn80pJRurK/WnA2HQ4gIpRSh\ntNFoLBYLwzDysvZ9f3lpKT06ms1mqqrWdRWlKQdAo3VFa8uxDcuReVNFUVzsPrPEMLQsSwSjtKow\nVs7Oz8fjiarrnue5tuM3W+P5rMzyq1euRFGEIGw2GvP5LIli3/W6vV4Ul0EUXr58+fBwf1ntOY4D\nIUyiWAjxox/8MAriO7dv11X1Ynev1+vN05BzkOdlXbOl3oppms+e7z787HM5pOotrQwHw9F0cu3a\nNQ7E6empYEDmgsjPcxRFTHDX9+6+9uqPf/zj8XQiJ8mI4LwsaBSGcby1tVUURVFVb9y6NRwOgyBo\nNpvz6ezw8DCO4yTPGo1GzZnk20HAsywr80pVVdt0AIdnJ+fvv/eBZVl5WnAqhAAq0Tji8k7lAlZV\nJRjDAEIuIARyu7a01JUS32632x+cRXHsujZjLOifJbO5DqFDiG0ZGhCYsY7rEuw5uomZgEUdT+cG\nwB3PGx8efbnz5Bn+d0mS9M9Pp6MxrWrBOatKU9Wamm4oKpqnuqoamq5wRIeLU5KkwSIXgkWRjZFq\n6QkUNb/Ib685Q0IQTRNUTGaLJMmgwXTTCE7P7DgxDAOralmWAiFNNYqqTPNCnvuqqlYlFYy7vh+l\n2SyJ7W6vqOkiiGrGVUUfjCar3eUwiJI0vnz5KlZQGEfn5wPp73IsWzoLptMpAKDRaMhphBxLJlnG\nhPB937RtSuuiruazxdHJ8WQy2bq87TWaewcHlDLBACIYIAgQisJQZhA5nmtZVhJGnAtd1zFWoigK\n54v1De77zSAI8jxfW11VVfLixYv5fN5qrzSbTXlB+r7f6/U0Tev3h1Kx0e/3T05OaFVblnX58uWq\nqrIqlRqaZrM1my5UXYMQz2YLKZrJ85yoEq92gXOStZ3sbjnn0uwubSbywhgMBlIlcKGLhoJzLl76\nfBRFoYQCABgQX6mLZQ95sUMtagUTeUEiAC46WcZNXZ+Oh5yyO7duX9ra+PKLx/1+3zAMwSkSPI1T\nSilWCAJIVbEc89aMU844A0xwUVM5eTY0U5puaE05FQRhCflK09RxHIhEmmU1q1RNy4vi2fPnVVGb\nuqEoSl3VBOF2s6WqalWUf8t9BAAXEAEEIAIXAYqCc4DxxWqcCwihgBAAYKiaZVmqqsp1EqWUICTq\nenV19Vvf+tZssfjrn71XVRXEeDwe9/t9yAGEMAiCBw8evP3229eurWrmb33++ef94ZgD8XTnebCI\nJGMRcKFpGuBiMh0hDhzTunbtmu25BGPC+UUOEgCAUp5lBatKiU8ydKuu6wBHs88fsaqO4/izzx+1\nWq2K0ZpRGUl2aXtrNBqpqpoVOacMIURUteYMA6gZuvSo3bp1KwiCo6Mj1/fPTk4+//zzJ0+eIIRk\nyBQAoN1u33rlDoQwCsIoiiaj8TvfeJtS+vjxYwyRYRivvPLK5ubm8fFxWZYYQGk+zvP8Vw/uc86L\nstR1vdHw5vvTYDZVFOXb77ytKApl5c/+5q/TNF1bWzk5PvzTP/3Tt976tmzHV1bWTk9P+/1+Xhb3\nrr4lOMyy7PLlyxCi8XjiOI4sJqRT03ddjPHrr7+eZdnh4WGSJPKpdBzn137t1zY2Nkb9wXQ6jaKI\nQEIISZKoruuNtXUZfvDFF19sbm5WtNYY6/V6P/zxj2Qiwvb29pWrV589e9br9bCqfP74izzNut3u\nYDTs9Xp5WRwcHMwWc600NE3r9nqTySSvSktchFtwzi3HhhCGYej7fpHl8sw9PjyU2vc4jtO8lt4+\nmSSqaVqv083LTNYTnPOG53LOJds2SZJGq10URcqYlJBIc14cx1Two9MTgBFjjKgKISRNU8u2B8Ox\nYRgQEYDRxvqlXq+nKMqjR48M05Q4no2NjdFoFKeJ63tJknAI+uNRp9lyXVfTdcpZXdempZdh6Lpu\nliezyQRC2Gh4nLEkLUbjyeraumbou3sHcviTZUkaxSvra65t7T5/NlsEvV7P0DRK6Z07dwbj0X/3\n//nvr1+/dml764svHh2eHDdcr+H57Xb76OhodXXVNM2f//znSZJcuXJlsVjIXxBCKE8zCKFmGtLM\no2lanKU7ey8455ZlHZ0cS7hKniZyL54VOcY4K3I5q5fKl6IoEMSqbgCE0zyRgX2TyUTTNNu2BYR+\nszmdTjnnAMHJbFqXleM4Er4oEKw56zYblmXJx6PRaDiOM5lMHj16JPfKslqVmkfJ66hqLv0biqIw\nRhFCKia1KAVjlNLB8NwwNdd1syyxLUNV1TKZv/3Nt3uu1z88qoKw5zomwiYhiDOaZbPBqAxiPp6F\nSZ4zMDw9bxHY7/dZTVut1jIhRUYVjB2ryWtqAx1VvM5LFbNmQzV1TXBoNDoF5BGvE88zgCN8m+V5\nXuZ1XUOChYCCA1XTdYNwymoOIOOIMtf3wjiCCKuqyjhnHBiGVjNa17Wc2GuaxqiwbRtiVFaVZljr\n65sQoePTE6KbumaXSWZYJiEkCIL5fG45tuxmuu3OdDqlVV1VlWwK19bWvtrClnVlWqaUyMlTaD6f\nQEWpaK1bpmlbEKMwjtM8U1W1pKXpmBDCmlHLsTnn4+k81xWpI4FCpGkmhEAQhmFQ79dvvvWGbdtB\nMFcUxTDM1dV1xsTOzs43vvGNr3/9LV3XP3nwQABgWRbGys1r123P/fGPfywNaeEiOD0+8X1f0zQJ\neD8+Pn727JnvNdc2Ni3LCMMwTVOEgGU4GEO5ZcQKqIpCdqyGpgEABIecc11RGWNAgDxJF9OZqqqC\nc1VVVUw4BvxlesFXRlhFJRrE7MKQe6G0kk2haZoXvCbOhWBQYEopo1Wz3W41mgiKa9euebYzbDRH\n/T7kvNdua5rGqjqsS15ziAgBEECQ5QVAEHLAubiYZkOMEFQwAVxQRhVMVFWFGHwFqNANFUKYpimj\nlGgaJoiymguqKjrCAAuIMS7KLMuTqi4kb/ira1gARhmHHGKMJGsSAAHFRaUi3w6GQNNV13N0XU9T\nwhnNOeOcCSEubWyurq6Wdd1sNqUGllLabrd1RVMUJUvT4WR8//79+Xz+27/zdxuNlqIZEpsjHWuS\n1dpptYosB5xyytrtNoA4ywoiNR1FVpR5GSeRHFMIJhQFM8ZGk/FsMpV51Jqu6JY5mk7yqpRh1Gtr\na2maKpisr649e/ZMKsGkeetC5yZ4XZV/897P3n33XTlbxxgzIbxGI4kiAIBMdqzrOo7jxWJxfn4u\nWXqKouzt7UkmZb/fRwg9evRIQpIVRWFAVLRWDZ1DsLG+CQD4Mv6SMXb9+vUrV64oKlldXeWcT8eT\nzz77zDT1ssyLPN/d3f3d3/3dqkJSlbdYLJ49e2Y7Hue8Kj+Sw3pdNxgQmmnolsmAWF5blfomKdx4\n8vhxzZhhGP/4H//jhw8fTqfT8Xj87NkzznlRFJ1mqyzLOI45ZRCKy1vb3W73ww8/dBzn29/+dlVV\nV65c2X+xN5vN5Cdq58UuVsgXu7tIgIuu0bbquh6NRtevX+/1emdnZ0cnx93lpaqqEMau6yKFHB8c\nyugbKS0xNd22bc54URStVgtjXBTFgwcPLMuSuVeEaPKTI23c8i8Oh31CiIJJXuVSDSg3KJKQRymV\nHFBBUH8wqKoqK/KS1oZhSNpwXddlWdqukyWp6zhhEleMIoF6vd6bb74pO7m/+dnPdF3fO3gBMahZ\nBQDXNGVz8+aTnX1KuW6ZcZYOh8NOuyXv8k6no6rqaNg3DMO2zYbvyzrp7Hzw+3+wnZbVw0dfnJyd\n+o6n64ZCUJnlDz/5lFO2trI8m800w8AQhYtgEYa6rt++fSsIgtXVVYJAWZaGpidR/Ju/8XcAAIv5\nQooSZrMZ51x2AK1WS1XVOI7BFNR1DTAq6kqOwoiqTCYTCOH6+vpsNkvjaH19/bU3Xn/48OHu7u7N\nm7dd193b29vd20MIdbu96XRKM2rbdppniqbqpnF6enr58mVW00ePHr3zzjteo3F0ctJqNOTikAk+\nnc+gAHKsPR6PO53OV+nXEmhKKZ1Op6Zpys+znFfXda3ruqaoaZUihBWFFIxCCBUVc4HTNO62m2ma\nHrzYpZR2W+0b164SQmDgjU/Pp9EzjfGWpsNZkBeZ5zeD8fh4d29wfKwK6GGtDJMkL1uGWYVpz23I\nxNI8jjWiOLqFmWAciKpUiWqZJuKijtIsyVRVZYsFUhRVw4oQgNVlkWd5kpcFgAJjLChllAKiQoIQ\nEowxXVXysuj2lk5OTmpGHc1BCJVlLQU1UAiFkLqqARcEQ993m7oaZRkhxPM8hFAcp55i2ratKcZk\nMlldXrl3797RyfF8MdMNw3YVRmupX2232/IC1nW93+/LY1dCLZjgqq7VjJ71z5c77bIsIcG261Tn\n/eOzU9tx1tfXozRJp9M0TSdZBhhfX183LHMRBnWqAAB839cNVW7iet1OXbMgCA72jzbWV1dW1qbT\naRzHS0tLQojz/vjf/tt/u76+ruu63PsijLOsmM1mKysr0mheluVkMpnP5/fu3ath7fv+8+fP33vv\nPYzJxroIgsh13W63XVXFy9VJDSFstRutVrc/mLxc7gLOOaMXlCjp1Za/x3a7LQNS0zRVLV1+sVzr\nSokyY8yyNMQgpAC/ZEIxxiAEEqEiJ/kICISxShDiyiu37/zGu79W5UUQLMIguHXzRsvzGKtfeeUV\nIcTu/sHx8XEQRkVR0KpkgidhjLACCeFCcA44BIqCFIIEZQrGCsZCMMprTnlRFKqCbcuQS1lVVRTF\nlS+YIGz5jaqqIIDSDC1HDo5lU3oRfAJfGnzBBYFOlf4CwC823AgADCHCOM/zKIoAF6ZpyjWQaZo6\nQkVVdjqd6XQ6Go3WV9csyxpPp3IsTwi5fv06Z+yLL76Yh8HDhw8pKz3PMwxjOp+Px+P19fVer5em\n+dOnT+u6zLLEMgyCsN9qjkajVqsFf/0f/lM5/dM0rSjzS+sb/X5/Op1WVSF5EYALCVeq6kK6gyRH\n13EcGUL3FXmjqqrLly+fnp6enZ0tLS3lee66roahpETJMSOE0NQN2XIJIfDLGb0stoQQBOH/v+Ag\n0zT/6I/+6NGjRycnJ1LDJcc1kpz8R//5H7iu++WXX3q2c/XqVduxwkXwyiuv2Lb5yYMHvu+rqvpX\nf/VX/X6/2+0mSeK6XclpkxJBTTcXi4WEWpwPB7btyIILIWQ7zvHxsW2Qy5cvB0Eg8xK2t7fzqlxf\nX5chu0dHR/PFQo5HBoOB53m26eV53mz6m5ubk8nk/PxcMPYP/sHvu677F3/xF7PZrOH5a+srVVXJ\n/1Fc11mWsao2DaPZbBZpluf59qWtLMtGk7FpmrNgUde1putBHHW7XRUTyR2bz2aSfBTOF1VVbV/a\nWllZOT0+mc/nDc+L4zgMQ1VVbb8lMUMyb45WdbvdLorsqx02AFya9yUIsz8cMcEppZph6KbxbHfH\nME2ZvzQYjzjnBCLTNAEAZyencheS57k0MlmW9Ud/9EetVquu61/84hc7OztBEJimKRmilNJut1tx\nZFlWXZdVVQnKMIJCiDgKfMdljF3aXG+43tnZSZoksje9tnlFFitxHFumw4BQFEVAYFlWq9OOkmw4\nHCZZKoTwGg3ORavbmU2nuqrOp1PbNDityrzglLVarX/+z//5o0eP/vRP/8fv/vq7t2/f/p//pz/r\nLvUqQWVi8bNnz/I8931f2pNkBSl1yFIUU9d1GIacU9u2v/a1rz179mwymdy4ccO03fF4fHR0pGk6\nVkgYhjIFhBDCqBCAflWMy1ODUyY3W1EUqYRIJ5I8LIqiIJxLFaXsjOXvRa6HGWPSGvHVcKKqKoxU\nAADCUBpXqqpACKkqYYxhArMs29rcFEL0+31d1yfTUXF22PObRRiuec2ry8tKTUFWRNPx/pMnOiI0\ny/J5qDLQNB1FwCJJjc21ra2t5eXloijOzk6Gg0ESRlWRa5hoGOuKqgDAq5qVlUqwbZi5ZSuem2r4\nOI+OinjM6YyV86oSmAAAoQAKUlSIEYRyhKNbQNO0JEmqqvIcV0pYCVERgGEYSuFVEkW97vKlS5eq\nqkJlRgHIhbj1xj1gmI3eqmY6i0XIKcjipNNqS4KegGJlZQUpKI+j6XS6vLwsVTyS7by2tiadKpRS\n27Ydz53NZmEYNhqNpm1zzhkQQRRSJoaTcbPVWtvc8H3/yZNnYRjKltSzvfl8Hoahp6myKuKcd1ot\nAPh8Pm81G5PJBHK2tbV1aXNdpkRrmtZptX/xy4/kfIVz/vWvf/3zzz8fj8e248lJoeu6RVU7jpPn\nebPZXF9fZ7h++PDR06dPOQOapq8sr/V6vfF46jjOcDiU5I3T02NFxTIbJiup/Bm2220AwGQ8wxhL\n+ZIQQlFUAIA8h+Wpq5iavHpftqFQ7lbkvpz9rXgDiemQNIkiyxUFV0VRVYXvelEQfvPtr7/xxuvB\nYrG+spok0XQykehAx7GiKPIbLUrp8fHpyfnZ8spqFCX/7gc/qCiDECuqCgCU9a6q6OBlMBHjNYQQ\nIsQYK4rCdd2K1XJPL0+zJEsdxyECAQBkQCqlVEbEysdJbr7lYluqwdM0BRhKLs33v//9w/39H/7w\nh/IzhTFWMJFmRdmwWrpR13WexO12+/LVKzdv3+p0Omf94Ycf/fLzx481TQMAYEw0WZG8BCTXRdHt\ntZeXl6fz+Xw+bzQab339awcHB19+8bjVap2dnMiEm4bnm6Z5ZesKMVRNxUQ3NEVREBBSPkcIqktQ\nFWVVlIZhYAJ90+XcPjk50QzdtK2tra2TkxMZjDoeDA3DQA74+te+JhCU8pYwDBHBNaO+67UJtm1b\nU1QJjKUyaiJN5ZhUVVXXNIswlOMmjPHm5ubz589Ho9GNGzdUVX348OH//Od/9s/+2T+bTqf//t//\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9g4MDQ7ckbb4/HKRpJstxXddvXLvW2exWVRWFSV1XcZTkeVHkVb9/1vSaBCLLtsbjMUJA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\n/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lqULmfT8dHRSwjlzRs3VtfWTk5OP3v8ZDydmZbVbLZZUWIACUQQStuyEAKCcUIIBxwT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Apj\nrGBq9Tiqa78KbAYAKGT4qm9TDlnw0itCPbK41I6rGs8YE+BCZXeFr2CEIIQEa+rPQRgRBCmljUaj\n2WzW8+zo6IhSvLa2dveFeycnJ3t7O0WZMcZUOiTEpFarua4tAAjDUIhC8QaYFIPROMkLu+Ysrq0I\nDiTCJRfBeJSVBYIkznIIIZkOR0srK2rNmcfJbLuXp6kOsT+c/HxjWzI+HA7bve71tWvTwXiP7rz+\n+qsfffTRwcFeXhaW49q2bZp2FEVes5WkSZKkWZYDTTsfjTUEnVrdc2uU0hThJI3KsqzyAhIMpHQc\nZzqdLiwsMMaSOH62+TwOwuvXryvNK8UkzQsEoeM4gAsCUavbYYJzf6o2+QWryizNWQUzqNBgTElZ\nlkwKu+Y2O+3n21s6xd1ut+a4EEIpuWKW+1n83e9+N2PlBx+8V5TFO7/9wx98/53nz558/fkXZZl3\nu91gOm43G1EUsbJACGZR6CPgWAYl6OT4sKyqRrsleBVNs/3tLdM0nz99ZlmWrmmapq0ur8x0ukkY\nHR8cDgaDhtdyLRshPB6PX3vl1Z2DvTQvgyiUEjHGJOO6rp+fnam3rqHrZVGsLC6dnJwMz/uWZX32\nyafKhQ4wEfpBlqZ7O7vANldWVgBG0+k0TVNEqABypjfDGEOYhGF47/790XQS7e/duH0rjmPbNIbD\nvmFo166tGbq+eX5+89q6YRhSMMdxfv3Ld7/3nbeklEkcAwBOT0+XV5b29/ellGWVc1FJhHzfj5Lk\nlZcevPDCC6Px9LPPPnv25GnNdt568/VXXnllkMZHR0dff/WVRulcb8YxrZdfvI8QKsvy9vUbZ8cn\nywuL333jLUzJwf7+T37yE56IYOo7jnPr2nVC8MnJCRCsX5xptbptmEEQAiGPD4/a7a7nuJ/ufbRy\nbb0sSxVz+5Of/OTs+OTg4IBz/qtf/3plZWVxcXFldXXq+3bNlQg2Gs0oGGkQffrlFzs7u+fn5/V6\nI01Tx3F0TRsF/tHODosjC0lZZjM1d2F+ztJwFiePHj8rimJ5eZnqRqPR8mq1Ms+rLEWC27oGAOBl\nmpVlnufTLB2engR5PsnSmw9emVtd+c8//8XK+rWlueX333tvd2t7cXZ2cnaOy7Lr2HNvv2XXvaZX\n1yltNxuCV3kcG1SDQNheDQAAJJomSRwniGhU1xjgvGSsEgp4BBiJigGEDU0P4wiXJcQUAwkh1HTd\nsA3OOaG6AEgzTIjx4uIiElX/9KRWq62vLAMA0iKPi4oggxItSdO9yQTl1fz8vOM4YRgKIVzXLcuy\nPzhXOhblo4SVyINiAICohG3YGiaKSWuaJmO0KIN63VVlgzMmpbiaWgzb4peuQ1BIzjmvGOPcoJoa\nggEAyo6SSymFZCy/ovwokFOdpKrwkEsXW3ViQggh+BuA8yVE/Tf2vt/+sCyrSDOCoI6JKCvOpYRQ\nSGi7TlaV2DBKwYMkRhiP/CmH6O76jVqtZjn2aDRS0X77hwd3bt3mZTWdTnVC282WemRN0/IkHY/H\nnPPBYHB4eOi6bhzHNddVrE9TN4B+sbJVsEHNdso0lVJCCSTjvKwYRLqua5rerHtFUbCiNKhmGybn\nXCcUOW6SpBhjU9MNw8AAqnQ4DRMBkagYwMS1bIjQeDAMqWYbZiWFoimpJ//48eMoiu7du6d0zMfH\nx+pCeZ6nkFU1Xyo7iyvrKNVJqOp7VcZ0XaeGkSWpasiYCo5kTHKhm4YqsSqZ42qK1TStvOJzAaAe\nWb3QjP+GaauwFoUM25p5VUHBpW+lsiZM01QtKDHGcRwHcaRQaOUrou4QBZkocPuC5IyQ6jkuOgwE\nNU3Dmq6UNVmWaZgQQizXYYzleQ4wkhCoBbmmaQ2vFoVuHAVVVQT+JE5ChFC97pqmXatHQoAkz8qi\nOj4+ZoxZjm0KEcVxnue2bduuY7k13XKyUlBdi8pyctbPitx1XdMmBZR5nhEdU4NQz6kdShT5IRWo\nyDK70ZqOxhSgF1681+/3T87ORqfnp4N+MJnqgPvBxDAMTLUsS/OyIFQHEJ4PB7Zh1l2nUfdYlkkA\nqKbfuHlbFDnjvLSdoiiophFCxtPJwcFBEAS2bS8vL6dpur21Fcdxkmdjf5onqaZpOtUYY45tm6YZ\nB+FwOESUFFU5jcK8KjElmq5zCBBCtXq9LEuqa6ZpcCAhhBADznl2fJIVBaUUICgAUO9nz6198Nkn\nDIGTo+MwS6qiiIssZ+Xc4sLGxsbW/q4GsWXqt2/crHu1MAqadc+ybERIkeXNusekODo5jv3g9o2b\nz549g0K2vcahpvOyQphiJls1T3Kh/OQoJgRjjVII0DTwd3d3vYb3gx+8Ytdr/eH4+dbmk2dPJ5MJ\nCwNd1y3dWJid24qT8Xg87A+qorxx9+7i/ILySj08PBz2+xjCpueVOt3e3j48PGzU6t9880j1hteu\nXZtOpwJADcGDg4MgjprNZpFXz54+X+61TcOIo+j05ERK/vzpEyUWC0N/OhrOzc10Oh3bsg4ODpIk\nfvvtt77eeH54uD8/P6/RrtdshKGfJGmj0ZBSng8GGsIQ0/X19bneTBjGRcUoRC/cul2z7AOnVuZF\nMJrMz88/39hoNBr/6O//URAEn335BcRovjdza+3a8e7+Ym/ZIppjmDXTnp2d6bTahKJm3fvkk0+u\nXb8uTNlwaw+//CrPSs/zfv93fneaJ4SQZrMppcyy7P9H2X89SZald4LYUVcrl+EeWmVGZlZmVmZp\n0QpodE9hoYbAgJgBBmtczHLNyHmn0Yz/AvnEpyG5NsaxMVszDIFpiJlGN1qgq7uqu2RqHRlauBbX\nr75H8eFEBGqXsDVjPKRVZUaEu1+/fr7v+30/8dqbbxx3TrOy2NnfI4Z+5ZVr/dFwlsSXrl45PDxM\ny0IAdHh83BkMpJSGaSOM4ywFjL94+iQa9tPZDBWJ7jq1aq3uOQ3fOxiOJ5NZKfWFtZWVtXXNcAQH\nruumcaRj5DsWM7Qkifr9fq/Xi6LolMKdw0PkeCublxfX1yHRZmn2/MXLl9t7426/HgRzlYqB5PpC\nuygDg5BZLnhWZGl+PJuVZW4ahq5rp72+43qaplMmwoIVkpjEAlgvhDANCAAguoGxhiHK01RZ8zuO\nw9lZzJ9pWRUIkjLv9/ueEdTqc6apZWnUarWqnv3ZJx93Oye6hluNumlZL/YPBuMImZwQ3dS0Xq8H\nITwPv7Mvyp5lWYQgtbE7JzRBAECRpRgiBDAhuqZh3/cBAMTQLcsSginmjoIZNU3TCXY8BXEzzrnk\nQgjBS8oY0zBRiCtCCHBxsYfTv6JOuaij6r/5uXWwAsDPBmLKwf8/X0WasbJAhHABOGNSACohIphD\nVLDCMX2kk1mWII1oTINATmehgtyUOYZKRcQY+9VKOBpjiIqiGI9GkIm5uTmE0M7+gaV3u91T5dpY\nr9cD33ddV41TigpqanpGNKHsmhmD52aK6iqppF6ljpVSGoahnKhV822alprezoOVPAUdK7NlVUKU\nl4WiaEWT8WQyUfQoJUmq1+tq6aZCGpRXoJJHxnFsaXpZllICjJCGCUEYSiAYJ+hsoLyYdFlJjZKq\nyVWJo6DC1TlHACIAJYIX4LN6bvg8+lBKKc4iKc8W/FScMe/UCSYhAF8JGQQAlGVJxZmJlbosFzQx\nVeClFGVZ6pqmbqSLGqyegGIgq77hosPDGBdloToDCCE/N9hSKqYzhiAEApy5iRVFsdxcNbcu12pV\nzgVCoD3X9H1f18zt3R3GWElpnkMJhJr+DU03sQY0Q8tzjDEgekHpbDwdx0lQqRSM5gAi04aWk0mQ\nA8Q1gyw1W7PhePv5dpbn7fac4zjUKkxNFyDL4zSdzJJxRATyLc+/VBEA/Pwffop1LahULNs96fTS\nNHXsgnM5GU29ZQdIxBgLZ7NmtUZ0M6jWyukkyTOTaKykarNNEA7Hk5IziFC3251MJlmeLy8tzWaz\nk5MTXdeNsmS6IRkHUhZZHoczTdP29vaY4KXgChq1fc9hVDcNEIMsy/IoEgC4nmOYJuc8LmeLy0v7\nu3vdbpcgXA0qQEjTNF3LPs3jjz/9ZDabLS8tEIg6g/6P/uGn7Ub9rXffufvF5w8e3Jlvt2u1GibE\ntu31S5vzjdazF88RhO251vPtF5ZhLiwsVCuVuWazWW/Uq7VL6xucMloUnLJhtwcwu3LliuIKvXjx\nMplF84tLjuNkRaksAMfj8fPnz/cO9suCIoTC2axarZpBZXl5eTqdHh0c5nm+tLR0devKxsbG4f4+\nQoi122WeAwBs0zqaTdRHbnFxESGiaVpZlp9++mmzMXd4cmy7zvnK3Njb22u3273OYavVsm3TdW1e\n0mazGU6n1Yq/NL9QqVQODg6Ojw5ff/31brfz9ptvvfrqq+MoGQwGlmVhDF999UZZljs7O4f7B+Px\n5KV8eWljY3l5udVobm1s/uD7f/f0yfPLr2zFswhxubm2Pp1MjvYPfMdVYbQvn7+Yn5//4De+M5vN\nuBDNd973KoFFnGa1NosigtGw27Ncp0yy/e2df/47v2sYxs8//KiIUwMRDtnLZ89fe+01DODJ4ZG6\nMYa9/uT112azWbvdXl9fn5+ff/bs2c7erpSy0WgggjVDv7q1+ejpE10zlpdX7969O0viN27d/uKX\nvzx48YKUhYlA4PuBodsElcnsZDLciZigzDBsrNnd4Xg4GY9Go3olwJBDITgriyJLorCMp5gXBuTj\npIC64QTB1ZuvFgjZfvAv//RPPvzpLw539zY3N7/17nuX15Y9w3Bso8zAaed4MCkhhAKKPM9t24RV\nHM5mDICM8lJyykRORUq5wCznMIwyHxLLsnQdQwhN3VBzTBRFlaCmkl7AuXmv2g4KiCzbpUValqWG\nsGmaq6uruyw/ONhbXl2r1WtrAjBwPI1SiTXHcXjC+/1+mqbtdtv3/bJkAAC14pGSAwAUCq2Oe3Ie\nQQ8A0DCREsRxLOCZPFcCCDHCgEgIpJRcioKDJIrUQQYh1AmBCCKNYATLolQegxghKSU/P3/R+QCk\nqjghZ+mEpmmq2VYhkMoYBHwFbf7qDvh/5UsIdnH+WoaJAVSXrpRCt2xAMNa1/mCQAIEccxROiyib\nm5vLsqzb7SpDEoTQwcEBIYSXVJQ0nEwJxjUvUAd9URaGYXheoNIUFEibJIlyotA0zTEtqBMutKIo\nZlHoGpYa+9RKUqEFqslQ+biq5l28xRAiNQJeCHyVBbH6U4X6AQBM01Q0EXUgGIZRqVSKolDLoOFw\nyBhrt9vnK2ZdVW4AgDwXaKmrdAHVKpK8eg5KzIMxhhgXWaaaBnDOPJcX1s0IwvNQZ1VlGWPkfPmq\nds0Xe1/TsdVLOCu62tksC5lURF31vqsLdaZK4vxC1Ou6rm6ZURTZuqESLAghqtaS88xjce4dDc6H\nb9UBXBR19f4SiNR+nXOuASCEoGeLaQYh7PZOgyBYXljMymI6nU6mYVmWAGECUZYl40nIGFtcWV1Z\nWcmyLJrF09Gs0ZyXCCZJUpQlEBAaRGJ8PBj5lUCzXWLoHMLhaKSOcSIYX19ZRRAPwwlkAguwvnnJ\nIvrYHcST0HP8nuzbhlnkuWP4Dx8/8gNNCAEl0DTN8zzTtn3fD8Oo2ZrjEvaHg1a94Xie43uz6fTw\n+Hhjrqm6od5pp2B0ZX2t0Whohj5L4r39/QcPHlBKFaFa8ScvoPk8z2dhyCkjEC0uLjbbrYKWaVlo\n8UwiWJblJJxijbjA/cdLSYhpmpZl1eqV4+PjWq1W5qZ6LzVNy7Ls+PiYBjaEsORsMBwCKQvfPzg4\nKNPk//J/+j+vX9qcjIe+4wrBl5eXVaXHGF/dunLn3t3xdFqr1WzPHY1GrKTXrlwlhLCiNDTdDSqc\nMkPTOeeDabcoikIvEEKmqbdaLQ3jpy9fJlmeZFl/OJrF6Wm/J4BUVEMWx/V6XR03Kl5paWlpY23d\ncZy/+E//6eHDh9/4xjcE42VeKEmSruurq6vz8/O85Gma3r9/fzQaYYw5E5cuXTrtdhhjrmPnRTE3\nN4cxtuECY2xlZeXyxubf//3fc84VfTLLsmazORoOup1TXdfLvFASZADF4nyLS9nv95MkCYJgeXl5\n2B84nue67tOnTy+tbZRl+fDJ42q9Fs8imhe8pAhCXdNqlWqZ5bvbL4eDgeu6vu8/uHvP9/2C0SvX\nrnY6nUcPH77z5tfWVlaHw+F0Nun1RnNzc+Nx1jvt9Lu9GzduBL4vpbx27drcXPt//H/8P589fNza\nWr9586ZhGJ1OZzab/eAHP1D45AcffHBp6/K///f/Xq3P9/f31UE2jWb1WsN13ctXtl683MYQXb16\n9Yd/+7eC8zRNdQ2bro2hTJNIphnNswIHhmFjjUyiJO/MJKN5lHiOsbG2pBMoimIajuPZrCgzVuSC\nFnnJ/Grt+q1X7WpllGUra2tff/9b777ztU9+/pEO4PXNzSwMD/Z2eZnVAr9ME05hURR5WTBWtlpN\nx3FmSdyca0uI0rws8iSjtKQSG8jWTMeDSdZXB5BgjPjYcTxKeVkwVZwkBIxzmmcSAtM0/WoljxgE\nIooiIuVoOCGynJ+fl6J88uRJt3taUD7Xmneq1Zd7x6f9QZYnJrIBQGEYxnHcaDQWF+cdx+GCZVkm\nBFOHlDi3MtB1fTaZUkqVUgtrpCwZ1rHjONMwRAgh/I9mQ+rDSzlTBx9BWEKoESKxxBwLyoSUSg6C\nz9m2BCJQFOqNMwzDdV3LsjjnaZqq0QoAoP5VYeOEEHD+QKp+QwjRV3TA/79fGCJCCGCcMko0XSgG\nMkQ5o269LiGwbLvgLCuL+fm5STRT06eyg4AYOZrjed7L3R1alLWgIkrqOe6Vra311XXbtsdw2Nk9\n8TzP87xqNVBTrMrY4ZQpSzudYNXQnBU23+Dn/ogXFCf1MsE5I1eB8AryUXvzi8MNY5xl2Xg8Vgws\nVec0TWs2m4o64AR+p9NRM6siUQMAms2m4lEnSaJgZ03TFMOmpBwAoNaxF0VLsboU/n/hA6rozYq7\nqtK0LMtSDwQhVEaBWZFfCH5U2S7PNDwaE2cSW7VJUJVeVUTOufrXPM8rjq9evpqS1SOqN1qNqnme\nU0qVqipJknq9Dr+SIX3BdlYghHoyF1wwzrlu6PKcha5qssKi1WMhjKngJaPq7cAYz2azOI4tyypy\nun90GIaz5tzc4vLK4uLizdu3AEA7u7uTSajAiWq1ev2VhmVZk3B6cHAQRjMsTJgXaZE7rkc0PS8Z\nFAASlDEWpmlSloRXnN1RN+O5oaGGa11eWZ5vNjRMeoKelPlxZ3+STltrK1lJHx3tB8uLaZ6Zpr43\nmFQpl4LTvJhr1mo1O03TOB5laWH5lunYfVqMaE6ydO/Jg8FgwDkH9YqJ8Yte99LGpu0F8XBYqzXK\nMq8EwZVLlwgh00GvHnieZaoArNwkSZKM+qNSiLxIKMvzPOt0TqM8rdRqRDdsTLiQHp9WLdJsNbiU\niPBhf6+AME0zDcK2Y6QSGQS/+/qrN6698uzJ0+9///v9JKtUq5rjZGVhmsbJyYkO8e1r1558cXfn\n0eNJb7D86o0A6HWvJmo5z4onUea6rjG/SDifJUl3MNrf39/p9+fn5yUXlar/6rtv/ue/+Msg8DY3\nN99775t8Mp7NZqNwPAknxLWuXr5MCDkYnrAYPd59qndMjDFEKIxmvh9ADJEB4jw09ZU8HM851q1f\n/7W0KBnnD+5+8dndz+barb//xU/feOONyzeuPHv6tF6tiSg6PT11IEiylBACeb6y3AqqlafPnw8n\np3kxy8vCkiRJpr1+srW19eZbt7/88st+5/A+K7M06nZO6/WGct6o1Bvvfv2be3t7L3YOgyCIcnbn\n4dNSGkWRVQLPxO7W8iXHMLqFrNx+nTM27PeyXn/t7fcOj48++/SLNE1rtVqlVbt+/brycyeEDMaj\n/f39x7svNUMHAMRxXKlUszxnltnr9e7du3c6iRWAZhkmhDJozg0Go9/+vX+u6zoryu/82rcqFb/f\n7TVq1ne/8/7jx4+vLy+9ePGiouFf+73fUczMX/ziF/3hwOL03s8/JGni+/7p9vNkMtnY2BgOhwfD\n8PKVrZoX/PB735tv1LAQP//b/wzDoZaGLoGBbXAI+qykGJaulmKW0HCzsWIzzGepqzkzmG1P+w2r\nsVAxAWV0koow16QeI7TPeZ+ysLb4rV/7huu4aRRvrqx97ebbFQHKkv+zt98usqQsy3ERQ0uvteuU\n0rmFeS2Mt7e3sUOiabx/cvTeyntblWA0GsWzMAiCimNWHH0ymYzH42jYI4TowKAp830fGlAl7Ywm\nY93W3aqnl9rx8XGWZZ7vBEGgOfpsxBmAhmFP+ERiQw/sMJwS5K7dfHevG0aTYZElk8OdRtX5Z6+0\nDu34cP+gx22EMCWyYHLUT0qazLXmvWpVaHqalqamm5bGRUGQrmm4oLnbCBRwJaUMwxAhRCAJw9DQ\ndbWQU4wYdeoBAGq1WhRFZVmaumGaphCAc44BBJAIIUzdIISUJRNCACGTouCUubbdagXT0dgPav/q\nX/2r9fX17ZcvB9Nxo92Kiuzxy2fTIj3pdZdf3yzKcvrsdDYeISYcTUNlKcoCStBo1OI0EedG0VAi\nJBEEAEogMeJcagASTKCQEAGho5yIwsS5VraWF4eEZbzEtlkAUak3TgbhuD/0bAfrdpqnFiSdvQPX\nNN94460sjh7cubs0315st4gG2vONIosavq9JCQU/PjhUBbJkdGlpyXXd0WQcpUnBqAL287IspeyM\nR0ovW0KgAUAMA3BelqVu20meX7DPkKYBADgXOoCapoVhGBdFpV4zTZPmOQagzDKa5xAADGE8mxFC\nDE0zNM20rblaTeG6UAjXNFU7VaZpCUCaJJqmWZqGpURCSEp1TWeMUVoKwVWd03Vd0wilVDHFEIJp\nmqhCqCjnUkrP89SMGAQBISSOY6VlciybEqo6pzPbZ1PPilwRbC/aJk3T8ixHEjiOYxtmCVGappIy\nAxHBOCBS1VEkAedC03THcSzXyfMcQYgRUgGvNC8qnj+YjBhnTuCplTYDAgBBi4yYeimY4FRKySUH\nEhBEMNF4SSEhAkIBgZACCimg5AQhhC01QHMu0zTL87IswzCElsaKspf3aVGwvAiIUdfMqkSrtaZC\nU9auX+/3+/3RkGBYqwaGBTlPBJ229TJwQRhl3EbN9ZXHz1/MOqdYM/II5bREtKwg0KzXSJnns2ko\nSmoSjDF2PLdWrzNKNUM3bStAKGVsMBhIhJfmFw5PjoNadXl58eT4uMhSyzaJhcqyFJQZRBsmKWVi\nMpkEQk6ms6IogqCq0eyk0wmCwDTtIAgghDt7u4LxSqWCIPD9SuC7AOM4jrO89DwPQKz8EPKsBABV\n6jVK6TicjmehYRhMAttydd2EGDmOY9hWg7GtrS3LsXd3dydhqCEsIXIsW9f14+PjerV6detauzm3\ntLS0ub46Pz//o08/I7p20u3s7u1JjD3HRYIHQWCaummaW9e21jbWBZBxkUZZ8vlHX3QKliTJjRs3\nNF0vaXHz5k3Oebd7urOzwzlfXGhDCKezsNvtKpLhSiWoN+eWVlajKEryTDcMhNDv//6/+Lsf/jAr\nciq47wXLqytcinq9Xq1WD19ua4RcuXp1rlIb9wd+UOk83362/WL/+KjVai2tri0ur7z33nusKLMs\n6+Sd6Wg8nIx7vd4f//EfW459586dlZWVazeuq5kGCOnaTplkSALfdqbD0YlpViqVbqfPqFBoUhAE\nDx48uHr1qsqzrNVqo+EkiiK18XIrzvRwLAXTEPzyyy+XFxea1YBo8P6du71e7/d+7/dszy3Kcnl1\n5fDk+PK1azdv3jw+PjYM49mzZ4PBYOPypfX1dd+rnHROleBB03QtSSaTyTe+8Q2E0MuX+3met1ot\nLoWG0X/5wd8ZOql4/je/+Y3AdzHGv/r0V0mSfPPr33jl+vWr16798rMvt3d3Pvjgg8XFRcWG/e53\nvzuLI9d1x+Nxu91OkgQDKCjb2rz03/7Jv/6//t/+7/t7O9svmee5f/Inf/Lo7t3/6T/8v7MkWWzP\nx+OxlNLQcEn5dDJhgBFDbzYahk5EXpRlCYgAUOi66ThOs9mk40nKWF4WmuV6tudjHELw5u3Xbdue\nzWZvv/7G5c3NDz/8kGbpN77+dZ3gp48flmVZqfjVapWWLIojCKHnVW7cuJFl2cOHD0ej0XA4rFQq\nKqpL0WsV0GfbtjhzPkdqDKKsKMpMgW8QQuX60mq1lCWCcsLBGCvxj+/7EEqMQFkWaZpCUfq+jwR1\nLSLyrNcdJLPQNIx33nnn7uG02+2leWZbHgNoMhokaT6XZV4QmLphGEaRpUkUG6ZGoMakVKs1tWVU\nqhX1ZIQQyolQwX1qmReGIeWlRgyM5GQSIhR5ngchjJNU5dEWtBTiLF2UEIJ1TRaYEAIxai8uFGn2\n8uXLGzdubG1tbWlYYuTVq7/2wW883d+5//gRILjgxaR6MhuPBied3WfP645TbzZm07Db7bq+909O\nwIq3BRGEAkAAIQQEIgqhphGOsWWYUkrBuGbAIs+lhDomyn3M863RoJyOhmo5enp6bBLt1ZvXr169\nCqHsdruB6928eVMyesFUUkwiAaQiXslzGykhRBzHRVEYhqGOJnzubq0mVACAEgOpK6lEWUgCKWWS\nF4ZhUMHFeZzfBTp6Jqo5L2xqEr0Q0Z6x0BFS3w/OueVqg6s6J13Xo3DGv5IgpH4D/Eoiu5qJ1c1G\nKUUIy3PFsGq81Ois7skLjpV6LZZllazUdV3TNEXh5pwrbEw9YZUhoUZYpcBkJUPnjlcK/1cvIcuy\nC4KCbdtUEaYASNIEnHOq1ZVUqwEFKfNzm2gFUOu6TvPy7E1hnElxcekIOcurQAg5jqNihDVNOx10\nhRAaJr7va1WiE61WqXieBwAYj8dpmqpX1263CSGWZUXJjBDSaDSq9VrJaLc/yAsaNGqnnR6XgnLJ\nGfVsZ6m2ZDl2vdYgGiZnGSgYFbSchKHr2pwypBs3X7+9vbffmU4wxqNpCLGmIYyg1AgBQpZl6Tk2\nEzyZRcr5k1JqW04UhhCiLE0d18VAvvn2O5NwVhTFuNtRHTSE+PU3b58en3DONUMHEFPGICGm42Bd\nc6tBTkvDMFrz7TiOkyjN8xzneRRFiAhEsKZEEVLatl2r1baqFbWzmW+1a7VaWZaT8ZQQcnBw8Pr1\nmwiRcDx5GD345Je/4pwvLS3dunadCW7ruo4Q0bThoFek2Vy9cevWLQNiwUoqBUQS6KQ+33JqldM7\nD69du/bk6dOrV6/W6tWf//znihGAMe73+yenp6enp67rYoz7w9H3/vqv1mr15eXl+fl5DmTgVyFG\nJaO3bt3a3NyMkoRLmWVZtVrt9LoQwjTJAUYvd3Yc3XRv3dZ0PYrTk9PTPM+JoUdxcv/+/Ruv3lxY\nWtx9uRPOZkWeL7Xm5+bmDMPonXbG4ZSV9NnTp5ZlTQcjSPDq4lLJ6NHRUbPV8jzv/v37NiKTyWRl\nZeXw8FBKGcfx8fFJvV7f3d2dzeJbt26FYUgpXVtbu3PnDqUUpvni0ryhk0Gvt3d0CICwbD2Ok7jI\nljfWvv+jHyZJMtdsH3dOfT9Y3lj76KOPRtPJBx98kBb5i52XBaOtVivwq1cubzHB9/f3T05OZ7MZ\nxrg1NzcejYhpVT0/TGIGZL/bq1R9zbTCLDnudepzr1JW7B+fjEaDoF67fu0VJKUyYiW69uDRw5s3\nb0oIlGYfY3zt2rX33ntvOBweHR3t7u6ur6/fv3+flTkCIslTx7Lu37m7t/3i6++96+nGT//r92vV\nQEcoy5I8SR3LwLpblCVkLI0TmJb4zOdB8LyIZ7NHj55szS/MLy33GIjzkgkAdR0TTdM0IORCq12r\n1f7Tf/pPL589v3Jp8wc/+MHX33/vyZMncRxfurShtmJxEoVhWLLDVqtVr9fffPPNbrc7Go2KopjN\nZkrBqeSY6uxQVU3d257n2Y6peMV5no/HQyW01TRtfn4+y7JwNimKQtM0jGVZlhjDoigFApZpJlGm\nbvUXs8l4PDYR1BDK8rzMpBBiY31jYX7+5LSzd3RalNzzqxBrnZNTpWkBUGIADE1Tp7+pmyxPhBDj\n8RgAoHZs+MzhCCrLOQXuKX+iOI4NzVRJAzPbVp0BISTLMlaUWVZIKZnglFLKGaKoKIrFZn02m+Vl\nOR6PX7/92ura2o9+/OP6XLMx18xo2YKysdA+PT45Pj4GBKd51jTsS5curS0sDU87WRyfxJGGSavV\nSs4X5P90AYYAQoCUjyGEGCIhgY5Jo1rTTYunGWC8c3hMhaxWW1c2N4bDYTidxtOJ57p1P5iMh7de\nuVbx3Llm0yKkLArf94siS5LoypUrvV4vSRLbPrPN51IotMAwDIiQwg8ghGoqzUCm8E/XsoWUKurA\nNE18noV8Vn7OdVUSQUiwan3UhK3aF7ULP1NuCaHwUkKIBnVwno58ocxRpUuee0WpBZ/6haVhXBRU\n9VOq+F08xMVCGiFUFAXnQpHLwFcEbAqpVpCyMl1Q9dIwDNM2LyRJFytCQki1Wk3TVEHi6lepB+WU\nX3AIlNJXXYeclorzpfYRcZIo+2vHsqSUBCFOKVNhw5omEcIQarp+IUCSnHNKC84hQGeXTgguOD7/\nQvCMIKbaGtMwFIlMvUDbspv1esUPTN0wdd3UjTSKVQfgOE4QBCVnSZLMZjPOKca4GgSu70sIAr8y\nS2LL86WEx91OfzDq9HtlXqRRHIez8XBEsijSMNEM7NgmIvjh08df3r1j27Zpmu2F+W5/OBgO/WrV\ntu2K7165cuXxiyenxydRFFmGgSEqGQMYWYYJhGxsbJqmvb29HYczy3IMXY9msx/88O/H43GlUnF9\nzzJMCGGSJN3+cDQZSymF8OM0jZLYsiwmQZTmhmVmSVqtVluNpsREIqw7lp1Tv1ZjjDHBBQScC1ow\nAFGW5fVLdbU68hx3d3dPR9pisxUEwXy9ybmM0zQpolmSOZ6bpvmvfvWp43i//Tu/A9sLg06vVqve\nunbNNLSaH0gpl1YW41mU5anpOjtHB4PpMKJprVYLw7BSqQyG/dPOSbPZtG379PQ4DMNWq1Wr1Q4O\n9kpKEcae7wMIu51+57QXBAEkeGtry3adp8+e33/4iBAynU5102615vv9IQK43xuGYRiGA5YVw/Go\n2+nXg0pRFt1+bxLNWkuLg8lYNw2A0F//7d8e7OwPh2PHcVaWlr/2ta89e/F8PB5jAC+tbyytrtCS\naoQcHx+3G803XnutXW9OJhMswPJcmwk+125dfeXaW++8PRqNfvSjH9cajSRJbt1+XTcMLoTrea35\ndmOuSXRNM/S9Xufb3/41WuQvXz6323P9ySB5GHdOTlqtJgNy9/DoW9/6VlCp5oK/cvPGlw8f7r14\n4nne9vZ2r9fTNG1nZ+f09NS23Nu3b29ubtartSIr9vb2EEL/4T/8Bwgh8apzc3N5nluem9JiY65l\naqTX6/3oH/5hEs0IQtdu3dzZfrlzcHjrjTcl4J1et6DlnTt3+v1+pVKZn59XsTOnp6e+6yVRjBCi\nRbk4v3B589I//OSna6srly5dWl5ZKcrsh3/3fUnL/8N//29+9nc/7Pc67WrVtCyWF0WaOZ6tE52X\nJS/KjBUg4TbUpZRQQFaW8Sx5/vTZ5OB4was4ulXI4mQwcpYXf/fXv/3Z/lgxWf7rf/0vjaD6r//1\nH1+9vPU3f/29+/fvG4Zx9epVVYDjOI7i2enpaZTkquK2223G2MnJycbGRqPRULYG6kBRJ5TaVzmO\nmaRROJuoD3+lUnEcK01Nxth4PDUMbW1trVqtZlk2y2YAAAhlUWS6rnNWcgAMXWOMFYBpEOi6zgvD\nMLEoszRKpeBc0HD6PKjWV1dX2gtLe/vH+wfHDGCvUp2ORiVl9XptZXWpWZ+LolBQ7ro2qWiU0uFw\nqHi56hB3HKdWq6nJT01jnudVKhVKqeCgyKmu6wTrSZ6cHHfUeafcAdX20SBE9RZ6WTIoia5BjLiU\nV1+59hv/7LsPHz60LGs8C23XEUIMBoOTk1MhpIYJQjiaRo6hj8fj/f39jYWFqlejRZmmKfhf52L9\nz6OCIYQEIoSwY9lEN4hmWJ7fG/SlhDrBu8+fTafTRrVWcz0EoW9b1y69s7W5QTAWRZHEMULAtWzB\n+eHBwdrKqgoHFEKoSwQQJIREUVSr1TRdT9PU933LsrIsm06nGEBKqUE07GK1WFUF+IJipuRb4tza\nU56XBABAURQIITUCqwZOTbfqzrnQ+F6wjdQIrmqe6vBUPVZOlmoV7XguAP8zP9EzQTaQAAIuhep3\nEcEIIeX2DSGEGEkpy6KUBdB1HSBIGSOE6Kah4o8EkBe0rKIoVL23bVsRsBFCyDStc3NmdfMnSUIp\n1bB2hgSca6LUC1FGGfzco1cZj1iWZTuW6gPYuXuaom1fzOjiPAdPfZmmcfE7Jf1H/bgQQqHr4rwR\nz7MsS1MAgGPZigwUhuGYMs9xapVq4HoKS0AIjcOpurcdx5nNppIDyYXkHGlaJQhM0xQIX7uyVa1W\nw8V4oBYTBZ2GIYSQ6BCbtjsaDxLJzXo9oyXA8Mbrt7Msu/fgkaZpbuALKU3dIAiLsjA1fTaZmjqp\n+AEtclPXGrU6QghKYBkmZ9RzXM5m7blmyXhZlIedk5Wl5bIsw3CW6Hk18AWQ9x8+eP311we9rsSI\nYDyeRWAWqRH+6fZ24Hq6ZXUGA86573kuJsPh0DUdxhgVvCiKaRiWZYkILopiMBpOwxlnLEvSUX/w\n1htvNuv105MusmEYhuFonMTR/OLyytrqcDwajkeoFJDyZDJpVILm3BxGwNKN2WwaOHZvOBj1+wCj\nMIsePX+CdM0OPNib9UZ913Xn5+eDWlU3DCGEYic5jpPTEmAkIDBMU0BgOnZzaXkymfjVqmmafqVS\nq9WePtv+9NPPFxcXhZSU8itXrv3oxz9+//33x9MJY5M0yV3HidNcIuhWgsPHj0973fbS0r3796FO\nfv2738my4kc/+enG8urb7707HY3v3n+wvLq2vr7ZbLbC8LN+f+j7lffff393Z39r88rywopnedcu\nX1PTSafT2T7eKYqCEKJp+vz8wh/90R9dunTp8ePHqyvrL1++/Hf/7t/VarU4Tp88ebKwsLC4uFhZ\nmBeSH54cHp8e+YF7cjJq1Kpcilpj7vj4mJjGLM+6uyPL87f39g8ODjYWWlLKX33ySZIkrXY7z/N3\n3303CuPnz59jjLe2tm7cuOH7vkrv4pz/7N79k5MT17KTLLdc5/GTJ9dvXNu6dvVHP/rRTz78sD03\n90d/9L99/PT57t7+nYf3W63WNJqVnLE0CWrVjz/5lWc73/72t4UQhqY/f/58OBy2Wi0CUaVS6Xe6\nb772er3a8Cve/Pz84dH+tSuXF9utOAz/y9/+tW2YgjLNBgtzTZ3gKJmJsqy4Xik44wUHlElGOccY\n26Zt6UY0HU2ZlGmhE4NjDZgG1q2koJ2T49l0cnXryuXNS3e/+PL9997RCHn7zbdms1lwaWN9fd2y\njGfPnp2cnHAgAEKNRqNSqcRxfHR0pLzeFKI7nU7VkXHBHlIHE2XFBY0zTVMAQBB4juMU52Sl2Wwm\npVTkF8HPjmyiIdu2GaMAqIVf3m41bNvOopBzoWuGHlQ1goTgXBrjYX8wGDSa7Stbl5aWlvcPTo47\nXaIZZZ7PIEwrQaPRsHWjKDJBhVKgKb6PruvKm1oN7upJqpAcdfqYpnl60lVYnxDigk2tzkdKKROc\nCQ4A0AXXpc44m4ZpxQ/SeNaYaz559mzlzpe/9du/0+91f/nJJzXHEUIksxnGuOL5puvU6/XWqvu/\n/+/+d0c7e/c++3zS6fAid21HCIHIPwYlQZVHDIDyvFShhwIonx4IAFB2/q/euPGdX/v1k/Hw/qPH\nrOSLzdYbr7/1+Z27SZJUWvOGYehSuJbtmcbq4gKBAAtR0qJZq0EI0yRuNOaDIBgNRlJKx3E0TVN5\nLRAokcwZeq88JRQi6jhOs1YvisLQdIWsKtcLIKU4J2Rxzrn8R1MnyzAoYyoziuU5Og+Z5+exAWeD\nIz8bHMMwVOPTxbcRQgDGZZadicsBUG2f4h+pduQCfb2oweDcWEqN9Rdf+DxAAgCgSAAKr57NZo7j\nKALdxWKCcx5FiVr6qiKnbF50XY/H4wveOz9PJkYIafhs56Jq/MV8TAhR9fuMFE1Ip9MRQijfiLOq\nf+7nlWWZ+qCh8/hCpVBSI7j6yzNZsBBUUPXQuq6Dc8WwuggIoVqlatu2a9uspNNpWGQZYNxzXBU1\noXxD02iWpqmyfTY1EyMsKJtNQ6wRrOkQgCyK4zybziLdNOabc3YcjUYjbpm+XyHzzTnO6Xg0iOPY\n8Txs6tVq9fL1a1GUHHQ6aZoLxmpBbTwe777caTQapmdT03RdGwMYxkmjVq34fhSFGoa9TidNU8f2\nAt/3PW88CRGAjhcYjst41Jhr0qKchDMgeK1R/+M//uO//Mu/PD09laZZCqnk26WQYZwzDiXWXdsh\nBHEJTN3AhskA5BIKDuIkm0xCwzACr1Kv1vePj0ejEafMt52aFxCiR7Nk+8WLFy9erG9spGkeR2mc\nJl/cu9vpdavVKhL5P/z0x8Pp5P1vfn1jfe3+o4eT4ahWDfb39z/55S8l42+88wZA0HYcjsSLly/i\nRKyvr7uu+9prr1FK/+Iv/sI2zCDwDNtSurdqtT6dTpMsbTabl69sLVmV2WymUFNCiOO5r732mm7a\nlmOPx2PH93Ze7o1Gk6Ojk6WV5TCM5hcXlheXTg4On2+/iOO4c9qpt1pbV6+cjgeDyXQaRoyx2Wzm\nBr7n+/fv3y+j9C+/973bt28LIS5fufK1b3yj1+vZrvvuu+8CAKpBoBNyenr64sULy7Ju3LjBueh2\nexCiTz75xHXdP/3TPxUccCb//M//3DRN13UVftDvD6fTaRAEgtNf/vLnVT/4zne+E/juz3/2YcHo\n1ual49OTglLLcSDGK2sbq6urd+/en19YWl9fevz48dHR0euvv76xsQEAeP/99//hJz87OjqybfvW\nrVu3b9/e2NigeXHl7Td2dg+33n7nr/7qr/b29jiQUZTkebq3f7iwsPBn//2/OTo4vHp1y7Ttk07H\nsMwPf/5REAQr7fbS8nIYhsvLy4cHB1988YXjOFtbW5cuXcrz3DEt13UvXbpUFsWPf/Sjy5cvLy3O\nHxwc3Lv75Zdffv7uu+8uzS/8h//X/1hmuY2hqWm2YWoEVVxH07GEkgkBGCcIm46DJYaIQJ0AAjnl\num6alsMFmKQ5hSUzrXg4nsKd5cWlnZ2dZr3x1huvffSzf/jk41+yN157+823yrLcfv70448/lpKH\ncSSEWF9f39jYtCwHY9ztdi3LMk1zOByqaUNJa9SO7QItlFJmWea67ll4NudhOBGCqcPOsqyioHt7\ne6r6uq6raZplyyxPiqJAAEghgOAVP4DSxhAhAPM8hxKbtqnrJgJyOomlqTfrtbyk434/CuNqba7d\nrAEhoyR1TaNgtNvpACk9zyFIo0WpjlFd11WNuWCoivOUbmXxP5vNFLxpEMMxHYBRnueW5TSbrTN3\nLQTVgKjIrpQzxZe2dI0JXnKWZOl0Or17966qKwDCNE40x3Jse3l++WTYs13Xdp0Vr2657sOHD/v9\nvmeapkYIIWnKdIL/ycFXQHAB58KzagMkAAjCuVqjWW8IhLr1PuVisdlammsn6xvz8/NBEOy8fHFw\ncLC8vAw4w5zzomw06r5t+Z7LOTd0rVlvXOxfVXeyvr4uhJiEU4SQMrlTBTiO48FgUBTFysqKpVtS\nzijncZazoqSUm6aGiY4huii6Qgh4bsdJdI1zLiFQ9wwQQN0YauZWFUXh26ps5HmuFvYQIX4e7APP\nx2JwviJVt5wqNuA8P0pthcE5F5qf+4OqF6jqorpd1bStyFDqS+mjVClVBGy1Tlbgubr4F+qgNE3V\n3a72rBfDuupUFJauwi4v3jj1l7ppqhBfFdyEELpItlZ3l7KBVN8szxnX6hmq/1XiK7VNV40CBkAJ\nji+A94u1NCHECbwwDKfTqW1aq6urpq5jiDSE1edRmYJd4PPj6aTm+BcQfZHlmHIOZJkXkjICpK0Z\nEsEkgojLiuutr64QDUEpYNUP4iIriiLNiml8/P/53l8xKdI0TWYJQXhpcTmOItc25xqNjFNhGhrC\nWZqoQZsWuWPZAIDJaGxoOoJQgzCahlkaZ2lBdKPXHxZ5GgSBlAIAgDXtrbfeyop8Ek5Hk7FtOar9\nkVKGs6RRr5SM9ceTkgnT1MfTUMOkXq8LypRHYxRFjDHf9xFCYRjyNAyadde0iyTPGf/Fxx8hAMss\n5wI8f7njuG6t1UzLAunGyqVLlFJBJZOi3mr2xwOxB6WUB8cHz58l/8f/4X9gZTnodRZa7YyWV7Yu\n6aZhaPqXD3eKsvzj3/3d7e3tjz76aG15RdfJdDo1iKa0Cohg3/dd319aWmrOzfFJzATvDfqE6NNZ\nOJ1OZ3Gc57ntepRyCPDTp09d1+10us3mHGfC9/133nnnUya2nzzb3d2bb7XXNtZ3D4+YAJVqde/w\nQNM0y3Z1w5qfX7x96/W7975c3Fh99OwJY4xK3hn01lfX/ua//C3n/P333/eDyuPHjw8ODoIguHT5\ncrPVAs/QG2+9tb9/2F5Ychxn//BYAPThR79QHdzS6srJyYnjOLbn9geDK69cW2sGeRyOB30do2QW\n2rZdr9Q++OA38zy/c+fOeDR99fqrk1m4t3fAOT85PgZF5AX+H/zhv9hc33jy5Mn9+/d7vR6E+Nvf\n/rbjOMPh8OOff7S3t/f06dNf//Vff/PNN1c3ViqeLyiDms5KOt9q7+7u1oLK86fP4jiuBZWj44Pp\ndPr666+zkm5sbLzx2quO4+zt7VX8QAl/f/npJ91u17UdXdcXFxd1XXdsu9vpSC7KvOgeH50cHTx7\n8nQ6mZiGNhz0nz97ouuE5jlFJIliIDnlpaFrOS0moxEixNR13bQAB4wJVcbyLPNd6+SkIxgP6g2B\n8DRJNi9d+e5/81un41kSz0aD3n/8j//x3/7bf/v04QMAwHg8ZmV+fHy8vb29srJ05coVTdOq9Zpp\nmoZmquKkvAZ9379orhUv6cwmyTTVkVevtRhj3W5XhYUXWQ4lQAhF4ezMEZoQDesMlKZaHkMjz7Mw\nDA1NL8ucFWW9VjEw6JyeIEQcyyVYhrM4j0M/cDnngLNuZywkDIIq0fRe93QaRoJDx3F1XZ9FURZF\nka5rAOi6bpmmYZrJeFwUBaFUnaSu56mDrCxL3TBM04QIzWYzyphpWdVqVZ1uBCKIkLIvjuO42Woo\niAtCmJeFevkAAMAZQsD3/TLLX3311ZWlxV98+OHm5mZjrnl8PJ1fWa76ru95cZEtr6xJBC8vrwlK\nf/7zn6dpur65mYRTdXr8k9UXfHUHDM6yDxFCECLDtCbD0Z3PPm8uLnzra1/3vUpeFrPheHV+nhAC\nKb119ep8raYAz5XlxYODA84oIWTYH0AIg4p3fHI4m810zVIyS8Mw6vU6Qmg0GSvzNTVxqk0/pVQN\nYcqHWRVsBWGooggwAOAfcXQBgZrhlamFquUqJFIAWZSF7/uqhKjB7qLAKCmOlBICgM+dtNX9pv5J\nwSq6rivF10U14ueBfReFWf0UPjcBlWecMpHnuTLHVrmHCulVHuOqtKvSe9G6KVepf5TznkunVNNw\ngZara1XkhfpONcBcNA1KWftVIa9ickkgVP+hbgNFUJBSqsdVWikppVK16bqepakQgp4vttVAjAQq\n+ZnZC2FMYnlxSfM0nY7HeZoZc61KpRJ4XhoncTir1WqUUsV8KhjFGJumaTk2ArCkVA3HPI7yPNcN\nI/Dd8SS0dIMAGYbxdDigWVqp1+bqNUIgmkQxo5QWZZxnbjUgphElcVEUruPX15umps9mszAMV5aW\naJHrtlHmKE8zwbhjGbQokyTZunRpZ2cHQ1irN0bTCUKkLMuK5xMYZ4aOAPR8p8hy27IWFxefPn78\n4MGDFy9eHBwctFot03bSojQMg2jaeDwthXA8L0vio86JazuKppikOUYgTVMMIMbY8wLH8YbD4d7e\n3u3bVzdfeQVx+cUnn3qmTRA0sF5xPM2ybc/dPzp+sb/faLcAK5NJ3mw2kY6nk9lcdf643x0ms/m5\nlgAyqFZ+9KMfvXL5SsV16tXaaDKOo5BJsLGyGubSsqxf/OLDu3fverbTajUNw+j1emEY1qrVoFbd\n3nl5cnLizGbLqyuaoXcHQ5U+NtduCSFOu92ioNVqtd/v266bZfna2nqUxLZtP3jwYDabVebcj3/1\nq8P9/ctXr4Tjyc7ufjWc9UbDenvu+q1Xv7hzdzieVBv15eXlpdWVOI6f72+fDHpUsCiJ7j19POz1\nr1y5MpyOx6ORV6u4lv348eMwDC9vXpqE0zCaPX36/MmTZ1JKy7JWVlZM0/zZz36WJMnW1tb+/v7e\n3l6j0Xj77bfv3bsnBKvVKkUSby6vfvLJJ8d50W63r166vLOzc7C3X6/XdWJYplnk+WKr/cO7P3r4\n8OH169c3N5cVvKaoXpTSR48era1tZEna6/U+++yz8WB07dq1ra2tn/3sZzdv3tzbPnz26EmZ5uNu\n33Vtlhc1P9hYXj0+OYwm06dPnkxHYyLh43sPqtXq+++8e3R0pI4zhJBt29euXZtMJpPh6LPPPjNN\n89L6xtLi4vHx8U9+9OMPPvggCILpaJynmWMZ3/3ud7e2tkRZ/tbv/PYvf/LTSrsddvtpnjXqVQjd\nWRzmeaETXTd1rOkSoSjNsix3Xdd1HVOI50+fr66u6KYxjhIjMG+98/bV26/plkdg/M2vfR0h0Go2\nJKeLi4uu7QjJRqNRo9G4cePG8tpytVo96ZweHR0VRQEEVJYIw+HQ8zzXdaMoUtltZ6umPDdN03Ec\npb6ba7an0+loNKhUKu12ezAYKF9iFexqGMba2hoh5OTkBEho6JbjENd18zRzPZuWeZhGnqWXki8v\nLD4a9bvdrueanmNC19OInqZpnIyCINA0PUnzYhoRzax6blZQDAXgDEtgaToEIIln2PdrlblGq6UI\nrorKK6VUg45hGApOVCtGxcq2bTsukjAMAQAq0HoazTjnQeDFswhrZ/a/krMkK+J4RimtV4KqH8Rl\nRBCahhPH1IKKB5FEUMZRpCFkaLpr2Zsra2+9//54MunvH25trKdpKs5T+RzLdhyHiX/aIeuiACOI\noDwjBkuEVJzt0vxC0GhaugkY903bJmY0GyMIese9ZrOJgYQIWpY5Ho48xxn0+pVKpSxLDcMsTvr9\nvmmazJAq4UDhz2mahmFomuZFfL3qP5SXcrfbtQy7KEsppXaeEFByluQZpRSf198zQBhACGGjWeVA\naqZhmibA6GJKc103juOyLAtaXhRI9RlRa1f5FWqVulaK16vGSqWUVXVRYRJqb3oxKH91bP0qEK0s\nBFQ9K4pCQSNqclWzryrzqsrqun56eqo4E8pTDELoOI56aNVYXADg6heS8+wjyM9NK1Vdx0jNmgAA\nSinRNBUpWxa5aVm2aRGE0zQlCKdxMh6P+/1+WZaWZSmDFDWsAyFVHyM4V+A2OXeNNg0Dn+dxASHB\nGW2L9gY9gnC73a5Xa2Wed6NYMo4QSpJEXRxd100gAQAQI4QQp1wl1yFdRwCyonRd161UPNsrGKWM\nzaZhFs1oySATgHJydLhPOUMYCSFcz2WM6chyHR+TnEthGEaWpMNuX9e0MAw1TCqBJ2x7OOgnSeJY\nBtGNeDa7e/fufKuNANR0c83zDk9OGS39uTmE0NHJccUPSpqbumHbdp6mq8vLSRLt7h7Xao3ZbDaL\nEyGABChJskqtgWShVN5eUAFclIJZhgkRLhmDGPl+oAQ5JJ4xxmzbPup1rCePkyhOkiROsq3NS4Ky\nTPC3vvmN1fW1v/qbv/n8yYOIl9VmUwDZGQ1dCUZ5LKajSq22d7jXGw+rnut6/mg8CYLAJNp0OHr+\n5AmHYHd/BxLcydnm+gbW9aofIITWVlZd152OJ1mWLS8vf/L5Z1mRm7ad5hljDGK0u7/XbDYnwymX\nYjgeB0E1SZIkSxcWFkaTqQpHazQaq6urH330ke/7jJUvX75sVBqLi8vtZrvZbN17+CCMEyPJBqPx\n0cnJ3NzcdDh5+vR5Gmd3vvgiZKnjOJPR+Jvf/Obz589zIQ46Hdu0aq12zng0HmPDvPna5suXL5/u\n7Kwur9iOZ5pms9l8+PBhpVrnnHe6/Wq1+uLl9s2bN58+fXrSOb334P5oMjZtC2tk2O0SQlYWFpaX\nV5Tm6vL6Bs9ZYPs6xNF4am3p1y5d9n/fyePoYOelqQld148Pj/70T/9USrm2tlapVEaDcb/bE0JM\nJpPNrcscSMuyFhcXZ7PZoy/ulHG00m51gdzc3Lx/997v/d7vff3r7//4xz/uHZ9aSMuJ7pjW0tKS\n53k/+8lPR+EQY3z79u0onO3s7Fy7do1zfrC794d/8C+ePHny+SefjobD2WzWbDZ934+iaHNlZW5+\nznxh+JVKr9erVyvf+MY3rm5sfPGLj+4PBhjrlu2+2H5m2rbn+44QaZGZtjUaz6Iia7Tmbl1/9d23\n3/rz/+k/2pZrmJbpu1iCm2+/+Zv//F8UAsV50azXHMeZTCaOYZZ5oRGEMDg5PBoMBgsLC6qJfvFy\nu9frzbVavX5fUJFlme/7tVoNnCeFSCmVFfMFrBfHsRoaaMn7/b6aS3zfVxmd6nxXUTCqqVejpOu6\ny2vNosh2d7Yt29AwzJIUNhoY4p/+9KeCF81mUyMIQy4BT4uc6KZvnkXpISA1QhSRVkcQIgikMHQk\nJQacsUL0Tju3bl5fXV2dzWaz2YwQEgSBcsPHGGdZVqvVGGODwcB13Y2NjTAMT05O2vV5NecpOHQ2\nHSt6jpQy8F1N08aTiRJvYA1JwXSEszSZ9Ae/+cF3r2xuNqqVuVrVNM0ojg2M+t2eX60uLc4/3X4Z\nTqYnx8cL9bq6dJZlqYtjaHqep/CMRPyPlQMCgCDEAEopKWOYaLphSckZY7ppmqb527/1W9A0oyxL\nk0TXTEPTyySr+R5jbHlhHkKo47MYu6LM8zz3HCdLEsdxhGDT6bTVarmuyyUqy/IiVqher5eMKrMI\nAECv11Pgrdq2AgB000jzTEiZl4WpGxChLM/9IGi124cHB2rcLIrC930IYBzHcCIrlUqUxJNw6vs+\nLeh4PLZtuzccQHEGlqr+Qh39ru+pfijLMiX7VAN3WZb1eh1IqTBYdStKKXXLpCXnQBq2dVbeOOec\nm5ZZMJoVBZNCSWyzrIAQYkxsz1Xk7YVgSUmJBABxEiOEBJBxlqqGUkHNCnK4QH3UnawiHVX9u0CD\nFZwumFDgCiJYmY2cNW3obMzFGDcaDSFlr9dTTBe1zVHtoKr9lUplMpkoPPz09FQxvxRnql5vMMaU\nN4iu667nSSknk0mj0SCEKKqX6khUn1GrVIMgsAxT+XtZpikZT5Kk1ZzDGEdJHEWRRBBjLDkghGCA\nCSFhGGZZhgkxdSOLk6IobNvmlGKIlxYXy6J4ubMTjoYnB/uk3W47nms5znAy7YeTaTyDAhqGDiEc\nDkc91icIGaauYcIZo7TU0xRC6Pu+ZzuajjktWckRgAsLC8oSTEhoG2acpVE4S7K0Uatzzh3L9l07\nCsN4NnUsm3N+8/qNNE2LghJNP+n2JMiIbgohdE0HILMsq16vJ0kyHgwppdWgEgRBGE4KWlJaYgKV\nmH15eflweHTaH3ROTllZOoaFTZ1ToSGsbz+b0myap269hi0z59S0LL9WjQbD1soKwGCvc0wBmAv8\nbn/A89KU6OnTZyvt+c2NDVbS026nEdSPO6dIN3Z3d1VzWq/W4igaj0bdTufmzZtLC4tz9bmD46Na\nrTEJpz//+UeLu/uupvV6PS8IbMdZtKzr12/+6tNPJqenk8nk8PCoWq1Wq9XWfPv+/fuqz6jU3Faz\nPRmNO53OzVeuh1EyGI7nWq3BeOQOq75fSdNcfdTv37+vE61abQwGA2KbL3Z3Dk6OXdelQEgdB36g\nORZPpNQwIDio1zTLNDzHhVLX9Uaj0W631bCljLdc1+12+gihtbW14+Pjoih+8zd/U0pZcbwwDDdX\n1hZXVjzHvX///vHRKULox3//o62trTdee/3HP/jh7ovt6XRq60bdC8IwHI/HlmX93d/93VtvveXY\nNgRgZ3tX1/Xt7e3l5eWjoyNd11977bWsLPYOD/aeP/+X//JfLi8vD4fDP//zP7d17dVXrjUqztff\nfjcLoySJDIxuXn3l9u3bjuMcHR0N4pEQYjgc/vKjj5MkmYzHW1tbi4uLn3322SeffDJXb9y7f19w\n/vWvf70oiitXrlgIVfSaaVuzJE6y+PD4uFmtVRrNe48fY12Ps+zL+w8s27A8fxKOLcdGhBycnNbb\ni9/9/d+/ffv1Gzdfu/Ozn73c3r1x/aZX8x/vvNh69fY/+93fjYtiPMsW5pficDDodQghCwuL08mE\nCWEb5kKrvbS0RAiZRtMwDBljfhCoMVFiqSZIFVyjxBWEkOFweGFeoSqiqhyj8QATiLAWRdF0Om02\nm5VKpdPpTKfTy5cv37179+HDh++++y4AqNvtLSwsJWnk+U6r1eS0lFJWKpUiz8fDwXg8tjRkGpqU\nkgGBIEEGghDmJRdCXig1IYBASggkEBwhgiEQkiMIMYIlYGVZ2LatONtpmiptidpBKsqV2sOpffAF\nm0xB6yXNZ9GUcz7XaFiWsb29DTgjuubblmsarucoHu/guNOaa7hLixXXcQyyubZqaERK+fjps7xI\nA8fGQkbjKWSCpnm73jza2+NFroRt/zgtQciFUEYc5xUJYoSgBEJwDRNl0SGEgBAggpVS07btaZLm\nWWZbjhAgCkMgYJIUyhlbzZEIQkPXdI0gcBYTSynVdb1Sqem6MZvFWVE6jqMaDlVaVAW1bbssS3B+\nmk+nU4VSnnQ7qotijBmGYRBNkY2zIi/KUlUFWRTD0Ug5SwteTsJQefmpqVqco9YIIWW9CM+9LZVX\npWYYlhDyK0ae4jzxVxWVMwNnhJSHBsa4Wq1argsgzON4MpnEcaz2vhcbbtVSQAg9z1dKWUKI67pY\n0xRDUP2qC1nzBbitaEqK4mPbtiqZSkGn7hk1uytul2VZZV6qG4nLs1wmxenLykJpeTnns9ksSdPZ\nbFar1VQYlDKtVGwsRa3wfd+2bTXrqzWzejglqFMtLCFEI4RxbhjGYDA4251DqK6VGv3r1ZrCtIUQ\nCEBBGQBAJ5oar9VvTpIUAGC7ju/70+5A4QSmaWq6LoSklFLKaF5wKTQN6hi5lllxHSYBLUsyP98y\nLLNkwnEtEoUEaZPRmAmhm2aR5Y5hWoYpMOaUMSlMy7wA9yHgnAN1ylR87+TkKM/LOE1sy7VtE5Iz\nTBwzZpp6lkRlnhMELMMkGEIuh91OVhZLiysbm1u258dJliTZeDxebDcQIhdsciqYo9uO504n42g2\n0wmyTas112o2m7TIhRCNRoMVpe06nBqsYC8PDvMssyzraNAPHj+ahFOkEcf3SkrDMBpOpoIVTQwN\n2yq4YEIComm6qVk24WDv8CCdRetra2+99c7u3l6j2bp69ZWfPX2YRrFhGK7tMUr3d3aFEASicDw5\nQIcL7XkOZBzFCECE0GAwqM8tcClt26aUnp6emrYlhJifn9/d3282GxiT4XBY0LIoiqtXr0IIj0/3\n+/1+5+Q0DZMip8plzfO80WwmJWzMNY8ODnEF67phEN2xbVE16s2GAlIqtWqapkdHR9lgMJ5OWVku\nLy9rpvH4xbM8z13Xvf/k0StXXtnZ2clLGiXpk2fP6/VqmmdZkVcqldWV1dGDcX8wyPI8jmPdMG7c\nuDG03f39/fX1jTzPT0+6B/tHjLFWa54Q/cnDJ5998kmWpL7r5GlqmuZ/88F35lbmHz9+fPfu3Zfb\n27Qsb926tbOz0+l0KKX1ZuP9r3/to48+LsvSC3zX9ybh9M3btwLHfvLg/tLS0vUrW48ePfqb//yX\nt2/fvnnz5je//v54PM6yrNfrPXvyxLKs8Xi83zt65ZVXOienvV7vtdu3kyRJo/iNb3zjcP9AOc6/\n8847juO4jpMkybMXzzXGbc+tNhuu5zVac/1+N/B9xKjp+bIs5lqtA75LCMaWBTJ9FMe+5fz6Bx+8\n861fW7l0iZeclfQfPvzF4uLSzZu3BuHotbfefufb35aa1jk9FZDs7Oz2T/YODg7m5+frlaoQYqHV\nVujx4eGhZRmEkPF4PJ5ObMcRECytrkx6IwVOXig9FFSrOFnqGFITmzp81awphJiG4739nbXVDdd1\n1b+qmQYA1O8POee1Wm00Gj3ff96o1trt9snRcVnmktGnuztHe7s6IZrtmKbJRclKAZFECErBIYRS\nQOXETCBACAspAWBMMEwQQZBLoBGkmToXFAIhgLRdx68ElLOsyBHBAADKmYRAUZopZ0RwIgXWiOXY\njJeWZbleHSE0HPYJQTrB0Ww6357zPBdjzGiZFmk8LRNFHcryMooMXc9ns+PdPRNIXdeVPW27Vl9b\nWXD8YBCGrblGOBoORuPpeJRFs8lkotoXXuSqTnyFsgMugFwggQQSY4wgkIxTwQlBBGOAYKVSMS1L\npqmazPI8p0UZBFWEz/YCCpxACGVZHkVRo9m0HQtjPBgMRqORCmyQUip4WVXlLMvCMLRdR21kNE0T\n5wIzVfwmk8k0ztQgmKapZVnKH16bxdNZDIgmMZmEM1V4iiQluhH4Vp7nXAosRJrntm27ui6ESOP4\nomCcQcQIYkTOghMYuxDtqIUrO3euUCVQnj8xJs/8IJUMPUkStbFW8zH8SpYfOTdbBufbZfXaFf1b\n9WHiPBFBQb4YY900FWVMzaboPBTBtm1lFanuf9VOEUIopKqCloyqVuZioQsAUEGNRVGEYag+KQvz\nbZXTpYRt8DxssVKpXDAt1AdKVfrZJFSmHxdmXup5tlot1R9TShUnSyHnKi5MMKY2AowxxckIw7Ao\nCi4FIUTZYar+TD1DdS+qfY1hGJQz1TJKQjRCqpVKOT+fZpmGIFEuHhJiazza3T9kWWFrZmOuOZmF\nJIC2ZaunDiDAGjIsPUsLLhgrKSHI1AkAwDT1oFo5PjySECjMQTftNE3TLDNNqz+dtFqtyWjMyqIe\nBAADDcHG0gKEcDgYT0ajZ+WT095AMyzdtObqc2k0A4KxogzHk4JR3/eDahVjmJeF7VmmbmAgkYaE\nYGmaAighBIJJ1/bUej/JUmRopmFpmjaOZ3GW1qsN23JtCyiOvtDILE5Wm8233914cO/e/tGxCfCA\njdfaizXL9l0/zrIF2+6NRvcePbx9+7aOCbCsRqNR8YNhrx9OZp7ntS9vnZycQIj9SsUg2k6vBwmu\n1mtplpc0r9SCMJqapi0hePLkia7rtudubGwghMbj8Y0b14muRVGsonCr1appGKgNw8n0+dOnfqXi\nOF4YRhjjXq+nG6ZhmLMwGveGNS+Iw9kwypT2nyB87dq158+f+76PIQIABAsLBCEp5cLSYrfbVYax\nT548aTQaX/va105OTu7cuVOtVqvVerd76nneBx98gDF+9OjBtWvXtre37927t7W1lZVlVpZ7hwe7\nO/te4BeUUUoBggLIza3Ljx7cq1R8gtCNG9dbc42lpaWgXsMCHB8cUkqrfjAdjZ89e2Zbrhf4Qojp\ndHrz1Vfv3LnzxZdf1ut1Sukf/eEfdjqdcDI52Nt75513bt28ub29ncbxwd6e67qsLJcXFxu12mAw\nIIRkSRJHUa/bnYzHr968efv27Yf37pdlqTQ8lNL//Nd/denSpdWlZQjhjRs3JAC256d5Mtnfyxk1\nLB0AYNjWXKX6r//szx7fvcvy7KTfT5IIxDOjEqy2r7x67fp73/qmUa1MsgKb+qOXLz7+9LN//hvf\npiXr9gbtrfXn+7u9hw/SjGGoryysTsNxa65x+dLG/Hwrmoauaz+4fy8MQ90wKpWKgOC024niWAtD\nx/fq9bqaFNXppqT96lBT54I8T0FXJ5dhGL5ztttDEoz6AyRBvd4ssqzIss7JSbPRqtVqu7u7EMKV\nlZXj4+PJZNKo1trtuXA6jqeToigGvX5ZloHnWZajaYjnTAiBkRplBNFMJgUAEiGpIQwhFkhIICTl\nhCAIJSfIMHXLMoVguk6U/IkQotx/FKFGjbyKH/S/kKIGfkBpwViJEGIlhUBAJMsso5RymmOIkiTK\nsgwpZJjSRb8y7nYX5ts3ti6tryybmr63t3NyfBxnueMHXiVw/UoWR1GSupWqhpGu66PRSCUUYYyZ\nlEJKQhDBhIMzMY8UUkghVSwxAgQiAIQ4t5gQEJSMNeaaUZqkaVpSqihUUIIkSQwTXvRJQkqIkAK1\nwzCUUuZZkcTpbDYrC2ropnorlZZXCbsv6pyaoqCUCq5XrCXbtjkCOS0AAFQwE0GJYMFoXGSMMYNo\nIs+UxKtkNJxMmRCW3VLlLc0zkGeWZSFCJoOBwh50XefwDIFQNUztJtV0K84zlBBCqgCocVA1HOrG\nc3xPjTpKm6TuQ5U3rAati1AE1WhmWa5uACX1qVQqQRAoWFtVUHFuJK4gHx1jNSurFa8KFlQfiotZ\nWb1p6tKpIq3M0ZTtl3LnRhpR6mdVJtUCglKqgJkLQUEcxyrkQNVXhWwrTrKKynAtR3VLCuFQlDQI\n4dHRkaJo6LpuEA1gpJ5nmWesLCmlOtE0hCmlkgtCiLpWApwxuSBGqp/wbVd1D2VZSkBVWyAA0nUN\nY2xqmm4YKgf6jMI2CSeIYCp4FKesKDVCAESYy7X5lTCaRVE4GY80nfi+T2mRFXmSZwicOYc5llHm\nhYqJwMquHWtE1zHWirJM09Q0s3q9yvLMNU0r8Ms8lZTW6/Wq52xv7xBNz8oiixPHMk3bjZIMIIiA\n5FxILrCue4am67rkdDyOpeT1ehMI3j05TZN4bDucsWq1igBkea5hhCAipikQMm2HchbGsWDScj3X\ndVVUkaHpsqCSIA0TVvKlheV+t3f/7r0S45iJdDK7fvnKzZu3oGHun3ZeHh4+3n6xcuVyo14/PDzk\nlC3NL7TqjcO9Q8mFZ9lLCwvN1vyLl9ujwXBhfp4BoOk6wuT1N95wHOfJkyd5nl++vEkp7fQHeZ4v\nLtZXV1fDaTQajXqHB5ZlpWkynU6FoJsbG07b0jAxdT0vWZZlEEEAwHQatlpmq9WyNePZg0e9JGs1\nWkkYbT99DgDwPG82DZMoNjCRUq6uru7v7yuQOZxMOefqCJhrz4dheNLpcgkEBP3RkHPOgdzZ33vw\n+BEkWLdM3TJrzcaLnZd/8b3/XCX6YDBSONUl37d97/79h7M0W5xv5XleqdV+/VvfqNeqP/vJjy2T\n6Ppav9NFElQc77TXvXn9xudffgG4cF23Nd/+4osvvrx754//1Z9s7+68ePHC9Jw8z8PZJEmj6zeu\n7e7uAihsx1lbX9l++bLOa/1h7+joyHYdXdfzsrAJHk8nc/VG1Q/efO31b3/720mS9DvdarUahuG9\nB/dHo9GlS5csy/IqwerqKtE03/dhThHRo3DWGXZ3D/b9ijeNrr7zxuvzaysFozvPnwldrwULlDPT\n8976+tfW2ouHg/7uo0d2JXjvnffjl3sFY3Gary62B4PB9W+8c+tr7/3dhz8fnfZd0999uXPr+sbm\n5qbvuFmcnJ6eSiHu3btnGMbXvvY1CcDB8ZEiuBaM+hjnZaHALtWLqAKslqPo3ORPzQ2qmEkpITz7\nG9M01TJM0WcU4UX5UGKM4zg+OTnBWGu1mlLyojz7zjiOOacLCwvwLFxdqPAWTdcRAhJAiAhCDBCA\nIcIYYwAZYxDqhAhD16hAjHOiQYQl0aBunJkzEEJqtZpt22EYKuYn/opFPj9PSgcAzGZTSqlp6o7j\nIAzSNE8jlKVxHMd5ohFCgGBICIIwIlBDJBwOAQCeYQyPT45fPIdAzs/Pf/29tz/+1acH+ztPnj17\n8/33SwDjjL5y61UC4OLi4tPx6Ew+qyg8KnT9n6RgAXBGsj2PgkcIcSmBlItLS2EYxlkqMU6LvF2p\nWZa1v3vAp3mr1aq4jhAinEwwxr7vz823nz/fnkZxURSG7SxVawqmVrV2PB57nqfeLE3TVI0JgmA2\nmyEIgyBQu7lGo/Haa6998uChQn1t267X6xjjcDrNy8I0zcF4zErq+75mGif7ndlstnZ5U5U3RLBj\nuWVeUM7KslSCdYgRUBIaehaAoUbJC3WvaowuOFlKlv3Vtg8hpNYK6sooftMFC0z9x1cB3rIsVcST\nasXU6KkWKEqQxjmPokhKqQIqDMPQELoo+WEYKgGS+hF4Hst4IWsWQiCAhBBqq61+UDV5umXK89Ad\n1Q2ohq9IM5oXatOvvlM1r7WgwhA+E1kxLqVEEiCEa41GGsecc8E4L+nFbn7n5cvm3JxSoGGMAQNS\nSgnB2WTPuMRESinYWcawUuWVjBZFMRqN1CjseV6aZ+r6XDRnRVEU9EzOxxgzKCWEaAhbulGWJemP\nhocnJ1EUQYAR0XzLiaL4eO+o0SqwRnRNMzRdMzSs46wUgrKzD54EjLGcwrIsyiKjeaEc9SqVimYY\nEOIsL23blghCCQbDQeC5QKDZePTKtavXr12988UXvm1ZjifYqGClTgzXtizTcQMflelx55QxZuga\nQijLEnUqxXGcprFgXAJRr9cNDR8dHVFaBMQ2bas516ZATOM4C0OskZJRohkMUNWUAcYRIAYghsCD\ncTjXbp3sHErGS86qfpWWuedaOkKdwXD3+HAwGZ90TvuTgVmr9uPI1LUyLwRlC/PzjWqt6vqT0ZQQ\n4jiOFFJBVbpmOIZu+95wOPzxT/5+fX09CIJutysgWFtbY1JUKpUoSn7+859zZYd56+azZy8qlcr6\n+nq/1+kcn8w1mo1qI8uyMos9z5MYzdJ0cXFxcXFx1B9sXn5lrtJ4fOfe5c1LTTD/+eef37x5E2Pc\nOznVEaaUMkqn48l8q93v9wVl9UpVrQZee+01169+//vf//zzz3Vdd2xXN7QsywzDAkA8e/YsSRLl\nU7aysqLu3WGcpWWRcq65blrSrMjTsqhiVG02apVq9/RweW0licJrN66tb65JyTVC6rWaogshhDBE\nURQB1McamWu3hsPhTz/82UnnFBH87MWLRqOxd7CLEBoOh67vlKx4cf95lmXdTn97dwcA0Gw2kYbi\nLN7e3V5bW4vSaH19/Z133vny7h1Vbsuy3N/fL4pifX29VqshhKrV6sbGhhBid3c3TpPJcd/xPUEA\n1o2spDJJt/d2HMexNNIZDAQh61euJEnkBf7lq1daa6uD3uTu8ydTRrHjAN2Saba0uvLlnXvrq7/7\nJ//tnw559mJ3x3KcJMsalblfe//Xrm0sBEHQ6/VoWc6322rxtrGx0el0ivLsK8tzJgVERAqoW7ry\nQ1aooBKfqDZffbbVGokxpiLneFkghCCUQkgFrJVlzrkEABCsj8OxgrwAQGVJg8AR0I6iKI6iPE+L\nMkuTSAgBJdB1HQhZMiqExERxklQ0GwIQEwX4YQw4AwAQdGZ1JJmEGAAgOKdSngVnqYnEtm21+Ljo\nHsC5sf6FbSEhJJyFnu04jmM7ljYlZZZHtMzzNPBcggACUHLAOQeSCyqAlCZChBAdyEHnFAKpY9Q9\nPuA0f2XrsmVZn997cP/LO1IjTrWhY9LrnVxfXlLn7PlOFwIFk3J+EcaAoAoRgFACqFStQhBlUwwk\nwsR0bN/3JQTVeg0gfNrpRVFUqVTqjcYwHGSUDUeTOI7H4zEhxPdmSnZFz0MD1fsFAXZsL89Txf4V\nQui67nkeiKGa8EzTpIxlWTY3N+f7vhCi2+2WnHEgdV3TLRPrWp7nw+kEClmv17kQEkG1IcqK3HLs\noFpxfV90uwAARcDOkjRVwxyEF05PF5C7ehp5nmvnybiqSbrILLoAYNRNKISg2T96fQsgmeAlo5Qz\nNcUihACCmqFLKQGCJaOMc6JpAEJN13XDKMpyNB4DABzH0XXdcV2IEOfcME3GeRFF7UZD3R4XVi1q\nXr/YFl/wyM643/jMJQNidGGErojK6j5U4h8AoTIwwPLMQ0Jxzdi567Uq8BeT8T9Kqr6SyKRoX2rJ\nvbq6ajuOInOpX6LrumEYmGgaJgxSyTiF4gyK13XbcYQQlDOMMcZIJRhGSWJjXdBSvUYIARP8ork5\nM88RQrEoFPxO4jTRNK3RaKRJDgBilFaDSq1S393f84PADrxKEBiOKaDgQmCMNUIghEWWzuKMZBBJ\nAQHgQEIIIcEaIRrRKRe6QajQ0zQFnHmO6zmOoaPVleVXb1x/5epWmSb9fh8gbNsOl7gzGJZ5ATGh\nedHwXN9xsyyFEly0bLqueb4DAKCc+tXKpUsbgrEwDB3LtAtMCyoLBjHUENYQ1gwDQCywKIsijeKE\n6LCUsqCmHwSWwwTXJTI1/eXTbQFBe6EVThhE6Ovf+Oaw27336DGEcnt/F2tEt617z542DXM2HRuY\nHB8cBrZ7efNS1srG4/HjZ0+H44lONEPTw8nUcO2C0clk4ohSQvjaG2805uZ2d3eVTyzG+Pnz57Zt\nR1F6cHAwGA7jOP6DP/iDmzdvHu4efPrpp6ykZVkmcZwkiek5JReu69ZqtSRJJpOwKIqK5/u+T4ty\nrt30TDePMsWLCYLA1izXdSeTSZgUS61F13WfP3++0F5cX1/XNO3jjz/2PM+27Z2dnWazqXrP8Xh8\n+fKmEKLT6YxGI8VHmM1mjDGTaIWUXiWglHfGg6IoVrc2A9d7uX/wyjWzUq8NJqMf//D7K4sLjWbF\n85wvPvs8CALB+dUrVzzbuXHjxsHRoWZYJycnr7/5xptvv23adnthPi/LbrdbMur4Tq1WO+me3Pv8\n3ubG5aXlZc/zprPwww8/nE6nt1+/tXl5YzAYYI1U6zXbdcbj8erqytHR0ae/+qR72nnw4EGapv/8\n9/83nue12+0nT56srq6urKz88pe/VG5Ta+1lomnEMiZJUm3UEcYcot5ocOPqK5PkcZ5lrcWFh48f\nrTQb3/7ud5BGRtopffakAKB3csKE/Pqrb7x667Wjh4/TNL2+cmuxYh7MxjiK3373nXdvv7PcXCyS\nwXA0mIxHlUql0Wioc0Sdyw4AjdZct98bTyZuUPGDwPO8YhZfKCwv+CCKNKvwPXgexXq2bONc6SvU\nzAEAmE6nQgC1+lLGs6qWK1iPAYoxJhir+YMx5tq2EELXLcEuXPWhEEJCICEQXAAAESG6riMIhVTx\nc1A3CBMcCQAhkhAwyTQN+xVPCT3VS0AIeZ6nau10OlXTz4W4Ux18c3Nztm3laXp0NBwPB4yVtuVW\ngwotMsElBBBBiTGSEkrEoZQopZ5jaBCOej1DJ1XfgzoyCe6cHFcD75vf+NqT7d2do+MkZ6wsm7W6\nIjSVZakDeGEiIc9HNvm/sJwEAEJwMf4KIYTgjm01Gg1IMJcCCIEIYYwNRkOE0Pz8vO7pcRx3et3x\neEyLUum24ziem5tDiBRlNJnOBOMYY9mG6u1QVvCKl0QIsV1HgcO2befnK0yM8fHx8bNnz0i95vqe\nrutZkWe9rtLUYoyZFAojHY5GUkols5mGoU3gxQoZnCuXzjIWz3e9BJ7lFkAI0zRVskB17ivrKJUZ\nfOHsKM7dFimlumOh80xAVcuVOYaajFVdVI97UdrV263uQBVIrBTeil2sqNcX0HFsmkrwrW51JT5W\nqjZ0HoDIzrP/MMaSnz0cOu8V1BOm7My6S62uHdc9IzzXamqrfcEr5Jwr4zZVcfFZiNOZ3eaFUddZ\nT4AtgJFiVMnzVGD1lqklUZYm6s6HEKrt70XHqbgdhBDH8yhnivnseIZq1NT9eEa1Mwz15koplfI4\nL1KIJEI6OXtUzMNwZhs2Y8IkhpByvtXOymI2DaGOKo0qwGA8mTDJIAe6rmONXOioCMaGYcRZaieJ\nYRicSMrPVOFqQKxUKoJRgIFg/G//+q8//bh+7dq1JEmKgvq1eqXaGE6mWUkNop1ZEEDg+37JmaS5\nZZhYQ2VZzs3NaRhnWTqZjNQMJAR75ZVXtmrLP//4492XOyUQfqOm2r1ef0gIqddqW+ubW2uXpr3h\n7rMXBEDLNkZRNOgOrr16A2vk8OioWZ9DQrKSnpyczDWad+/edXzP8lwOZbXVnJtvi05f1/Vu9/Rn\nP/tZGifvvPV2WZYPHz486XYgJgACStnm5qbh2s9fbi8tLWXD7tHR0a9+9at6vZ6m6WQyWV5eVZL8\n+fn5wWCwurra6XT6/X6/3+/1es1m85Wr1z75+JcvX768fOmSruv9fr/abK6vrw+m4/39/a3NLSHE\ngwcP0iTZ2dnx09rq8spoNDJN8/LmJUVG0ImmYRLG4bA/KPOiLEvFHXjy5IkQYDQacc7b7fbW1ta9\ne/eGo8Hq6qqKDfd9f2GhrZx6DMMYDoet1nzBaLtayfLSBsA0rWvXrj16+OC023E984/+4PcHvR4i\n+PnL7YXFdrVa+fzzzzudzrvvvss5v/bKK77vf/Ob3/QrtfF08uXdOyVjl69cefbiheM4/92/+bNu\nt9vbfjYcjS5vbX3x5Zdf3PnccsxLW5uNueYvfvGLgtFqo4400hsOBuPB4sqi4ztP7jz8yd//+MXO\nS6VdaTQao9Hopz/96bvvvttoNIQQz58/74+GL/d2DcOQCNq2rRtGc3E+4SUySU5Lw9A8PzAss1Zv\nHoazSr2+vLpiul4pZM32QHOOCZEXRc7pJIqzvPSrlT/7sz/rd44Y545td18+HU0nVzZfSZKkyzu+\nDVWjPZ1OZ7MZY7zRaCjlX5plC+02E9yy7cWV1cOjo4ODg4V6U3n0qGNU1QzVyCuPaLWduqBiKelF\nlmW2bQhx5n9kmrZyHlBgbxynyrbM87w0ST3H0bBWFIUKe/BtB0PEORecQwQ1TUcISsAlFLpOZhEn\nCJwd4+A83B5JhBAQZ3QYgCCXEmMcBEHKmGoa1ImpcljVAX0BAquj/IwoK5hh6HEcq62n53maRpJ4\nRhASnJ17QQIgmBQCQAiBYEVZZJllGPVK4NhmnufxbNZaXHi+s+fV66alNxqNQRg+f/68Wq8LXVfx\nIYZuSCnh+fCHEAIQyIt6DCTnHAEoEVS+V+p5csk1TatWq7quT6fT7ngSVKtYI5zz0Wjk+xXbttUS\nQe0Oa416rVbjVNTr9aIolKzIMAzLMBX71w9c0zSTJFFS9SzLfNdRREIhhGXb6l2bTqf1ev273/3u\n49MTZbpyfHioBNOaodu2zUqqgr3V7cE5j+N4OBz6BgmCQEloDMNoz7UMw0iieDabcUpVK6AZZ7kd\nqhdUVUGthJULdBzHauupcGNKz6BBhFBJqSquqjVXkKxiTaulrCJRw/MLWK81LiqxulaKeaRiKNUH\nQd0wCqdV23pVLME5J1n5ZF3w58/aCEIIIVmRqe/H2lmRUy2FbpkXniSu69bq9bIsB4NBquuKo6Ae\nTtG1FLJ9QUNTFfG8TTx7INVwaJpGDP2iFRBSoURAnKvMaVlwzglEhBAEoBACK6y+LFVrUhRFMabK\nMds0TaWRU7O1lLIo8gtxs/rIEEIQBurDQgghg1mu6zpjmeG44yyzHHvIEsMwCl5QwgABGMPBYCCl\nNLBuY0sXMIoiRqnnuCpDrWC0YJICrTuaub4Xx0NN0+I0dV03aNaBYGESQggTjrMs44DwlE6fbJd5\nYRhGMo0ev9yrVCoGALyMDMRokbXm271eL4lj07EJJoZh5hKzTACCsNAszU9iVhRsobW6vn792uWN\n58f7h/1TU9d1IVcWV44PDzy/olo/NJt1dp6HYUh5LAmBkEut8Cw9SQaWLttz1aP9bco4l/D+7svi\n2bPm8lKcl7Mstxy3G9F3vvOGtRR2wx95XnVUFP/lVx/vRyGl9PBwH0No23az1gg0MR53X1u+DRbm\n+/2+bwVRFG0/2xnXp0VRTKdxkm4vLy9brvfo6TOnWj0e9gzXen3z7Z99+ktpao7j3713tzsd6s1q\n68pGJc/3Xu70Bj3T1OY8TzQaBqcP73wmhHBcczqdrs/fiOP46NmztbW1mLHT8TioVk8mE4QQtixg\nmoMo2rh6TWr6JEpyJogsKo4hisQLgoeff+Jaltuag0V++/btk8Mj33Yblfq9/ZN3r71qaHqSJLqt\nP3nyhI5GBkRFms0tL11yndLzdzqdpZuv2JRrlM0G08PjoyCoY835rd/+jTiOKWXNZjMM+3lWLjbr\nhmVe21wDefrk8bOsVr+9cfnk5GT/3qM0TS8trxZlaRjGK1vXvrhz5+Sks7N/tHnp0rVbt/Z+8PcP\nn20zrP34xz9ptBbcZtubm6dp/ld/89cCgjffeTfDMAS8mydrG+tavRoB9ub772ZxlE4mIpxotmNj\nLA0xjPoB9b757puPHj3q9/vtVitN097RSa1a3WVidXU9TfOdlztFXCQoKTj/o9//F2EYfv75577v\n20g8iIaV1363+fYNXde///3vf/7JF57nbU/uRu329evXy3H+/PlzTde3trZswxBYJNMwSuJWq+UY\nRjKeaEyUUXyyve2YpsA4mYWu6zqmgYHMsoxzqlp+dbRJKDjnBYWEEIhBFM3q9mIWZVggWjBJUEm5\n7toUyJxRAXIBRZFkgvNK0yMYxvm4JkBNM/r9/u69u+PjQ98yPBtTSjkrMMaCU1pCwzCkEJJTqGkA\ns5JxxCQwCAFISISFxBBLCiHQEcaaaVIgZrPwlVevL129cdQLkW5wIDHGJaNpGiPNXK5vZoAjKI8P\nDk87ndXlFSEYZ6VlGXMmGZ4cxOORq2m2ZWRJVMRFzXGFEEyCgpaMQYQ1SEwIgOBAmpQEZo6kbmoR\nl6ygnmkgRsPTw7VAFyKexYM5DS1fXpHZpOkvDce9zz/5uB54uoQyK5EQJtHjNNJMA0IgoUQQAigk\n5BAAKcGIlgtz8+lkViTUxIbrBpyRpdUthsywjIRrz5DUiJCQZSx/tH2/YruVSsW1DOratm0Hrq0B\n4fsWzSNT129c2RwOh4or22oErutOp+FoMIQQWrW65bmO4xS0VAT1NE1PDo9C111bXpmvNaIoggJs\nBJ7reWEURxglvMzKUgKU5ynEGsdQIrR1+SpnbHt72zLtjMoCwjzPUV4Ixh3TYlRiiAiGGKA4yaSU\nvu9JBHNa0jTJ87ygZVmW1WpVVaxpNEMIMSkMoql/FRjmJc9msZqPdQkFhJTnai1t2A4hxHLsMAyn\ns1nJKACAmHpBaZLEvu93RiPPcTVCkiieTmee485VmxCAaBQahmEYOpNCCGDYjm7ZWZ7Xmy5jbBbH\nioqVpmlRFEEQqAjhM/dsCG3bRhoBGHHOAJAYIyCEFAJDWKtUlOANQojO5d7JZCqlDCxbtSZq0kPn\n7tZqDlabb7ULOO9cY8e0uBAGIZ7rupYFEQIYS8aSJCklFVIi5f6h5FsQKDkZY0xypibyvKBRkSmd\nCxccE4wQ0pXzdlFSwTVNAxgXnEEIiWlCxsqyjLNMCAEZ5zzSNI1gAwLCOSeVSkVBYQAA27Y915uE\n0+FwODc3B7JMvQbtLB1amYhi5ainkraKoqCCK90VhJCgc0WjEEilSwCgWjMIoakb2LIxxnmaEUI0\njA1NU0FmgnEhBIZoY21NteGKjBBFUZpnBaWu6yq7UYSQZVpJkuwe7OsffeQRqOZjRVMsy3JxecU0\nzWG/p5hEavIIKhX1QjZW15QBck7L45POYDxikpd5bljm7Vdv7R3s9zpdw7KKLCvL8mhv/2tXtjAm\ng8HAcm3HcZ4/f97pdIIggFBubm7OpqFp2pyKjz76qOL5eZ7rvrmytgohnE6naZHXarWS0pcvXwog\nTceuVCoFLW3brjebRycnH3/8cZoW1WrVcZw0TR/cv48QmgxHlUplf3//jTfesCxrMB5ZlhUEnhSi\n3W53u6eUUijFdDzKsoRzaps6EA6l1DRN27Y5p9PxSN15moYpFfPz83me1+v1K1eusKI8OTmp12rD\n4bBarSpjPNd1q9Xqd77znSzL/vJ7f7G6umqZZjiZSsYvX75smibWyOuvv44JybJsaWnpD//wDzu9\nLta1+fn5OdcyDCPNMwUMpmnKGFtvNl68ePHkyZNeb7CRJI8ePXrnnfcWFhbG47HyED0+6cRx3Gq1\npJR3794djkaXL1+ev/+wLMudnR0AQLVa7Xa7EMLHT59cvnzZ9oNf/PLjLC+hRlbX13Rd//jjj29c\nv95u1JMkaTYa11+91T0+0hDe3t6ezWY7Ozubm5uWZTUbjUajMZlMkiS5cumybZj9fv/+/fuKZC6E\nAJKbprm4sBD8xm8o297nz5791fe+9+qrrxqGkcRxJQhUMyeFyNI06Q8hhL7vK8fmC+6oAtbU5KFY\nV4rPosQbF44BjNGLvZdt25qhK5sLNRx4nhfHcVGWVHBEMIRKIwQwgJ7nTadTXlLLsjTLKLJccOha\ndsV1Xr58+ejRo16vZ1kWIagoCgAkOHdnlOf5NkwIxDkQZ+QaICRAAEKJ1KjkOp1uTwGSo+HQr9ff\nfPPNy5cvr1yx7t279+zZMyGEF/i6rkdRNB6Pl5eXZ+FkcWUZAJBnubrBbNvOBh3GGJMAMsYY1nVd\n07Gu6+oAxRhDiBHWIMaccyABQIhLWXJWKH41IVJADqRl2VzQM04sJIQQ3/frjZoJkJRyOp3WXV8n\npIgzIKTr+iUvlRRJQgklkBKqC1ANgiLLhRDt5pyB9Fkct+u1xeUlAQHCGEkFFVBeFFgAickoGymY\nV6VNCAB0XVc37QXMrrQ9CCGoaYqTP4uicDbzqxVFHXJd9/or1wEAeZEPen1Fo1NaIM/zmnNzfqWa\nZFlS5jKcSUxs1xmMJghCyUU0mxKi+46LMY6i6OHDh9VqNfB8wXi/0z2UoOIHuoY3VteIpsHz0zWO\nY4hRtV5TgDNCSPGBEUIKUynLMi+LszJ2zpGmlBZZodBRAEDBqI6gmv5t257FsYSA8rOUIbW60pBO\nEKYYl2Vpa4Zpmrqml0WhVuMQIV0jAMGyLKM0KYrCxFBRtxQOr4ZRtVtUM7QS8gohFDgsGVP37Zn/\nxnlMgm3bKu9P1VrFbVSMiostOPqKJJ2cW1ydqdQgVKxmApGqd3meKwWwYjiqCq3ADyUIZIwV5Zmw\nG5yvNhQh8WLCvhid5bmLJ9F0NWqri6+2e+LchETZkqvnfPZGJLOoUqmYtbpEMI5jZZrhWGfJ2wgh\nBBECEKAzplwZpbZhaLpOKb3wswYAsJKqZgQICSGwTcvQdM55lmeWYRqGwRijRek4jmVZtCjUec1K\n6lh2HMee5wkhTE1Xdw8hRNN1ZVlQ0DIrCkWpz4pCvR8AwTzPj05P/vJ7fxVFUb1RU+YSasGGEBIA\ntuYXVldXoyjqdrvKd7Rab8wm44rnW7YdEK3MyiLPzbodJmmtUbd1IwlnlmZwyiSAgePe+fTz+z/7\nqWmalutMp9NavbG+sUl0LY5jKMHL3f2K725tbQWu9/c/+OHB8cmVK1cc07Jtmxi6bplxHCdpCijV\nTUMJoxHBiKNuv5/mueu6zWbzYPfQd9yTk5PJZOy57vLycjQNFecCY3zlypW9vb3O8Uml4nMpj09O\nZhklhj4/P6806Z7jSi7CyXRxcZFSmqeZ5Gd8SM45kgBrxLDMIAhWVlZqtVrvtHNychLFMYKwJAwh\ndNrtMEr7/f7jx49Ho9HOzs7i4mK1UtGbTXNh8fr166enp5zzxlyz2Wy6rluU5Vy7Nb+4sLO/NxqN\nihDoug4BLoqiUqu6XnB0dNTp9F68eIEQ2ri8cXh4yDlnrJzNpgiB/f19jPFoPPV9f25ujku5f3j0\n4MGDnb3dK5cvm7a9f3hw9eoVL6j8xV/8heM433jztYPD42Zr7t33vrawsvyrTz+lgg/GY5oXu3t7\nBMiiZJbjvb2+EV2+BIU8nI4nk8kXX3xxenJy9epVhUoZhkHzQukT0jRdWVnZunTZc1ydaEXJxLkf\npMLrvvnNb965c+fDDz9U6S6bm5vLy8tBELTb7UajgSWiURQniSKSQAiDIGjNt9UdrvZPAKPZbKbS\nnzTdAOcCGAVBX7w7F+wM9YPKi6DMygt2NCsY41wAiRCKRyPLMhzPYYxlSY4k0A0DAvCrX/1qf3+/\n1+tBCG1TV/e5YeiUlgpMlmcxOYIxISXVCBIQYgSk5EJAhT8LCCilai0apxkiWqvdtlyn0+t6jSXF\nFkEE67oOMXCANCwznExdL3jttdcG3d69O3d7px3VIkx6PQiArusEAgghwkgwoHi/qrFGiECEFQ0G\nQF4yluZ5WfIsKwST1LOBbWLIDQy4oLph1Gq1DMICAE7LPElfdvppkgMAKKUa0izTAVDkeY41DQAO\nAADyrAar4xJDVOYFVBneABecLaws+7XqMJwkrMyhAAgCRgWlBiKmSZQyW9V75bRg27aydAAAQAlU\nN+a6ruO60WSSZRlUtt4QzGazJEvVse55nuM4GiGz2SxN08D1lOQ3Y1mWphKguUYjK/KypCVnnuNy\nLk3TzLMyDmecSzWuUEqFBAihrMhFyUajEaPUNM04ygkh6yurhJDJdKy6N4Uhq+NR3T9cCk65UtpQ\nzpIkOQ9G09QShFJaFLlEZ2xkA0hd1yUEWZaVjEVJzBjjUggAlGw9yVKMQElzjMyq77mu69iWbZg6\nqSjTbwiRZVqapsVAaBRDqKsPgnJRvcCEIYQqTFAlHF/UJAXwynNll3pWqqw6nqcIB+oFKsNLAADj\natsKLiqi4nBdCK7UbwPnnQqAZ+oDxpiQUu2nKaUqGgScO2MzxqjgUko1Ul8sktU9rOu6mjQuNITy\nPFhCyDPQW5mNXNDMFMgPMQJcXOyhiPqZVqvl+35RFHt7ezQvKKVII2rVb+qG0her99jUDQozzrlC\nEtRSHWNcMKpeMwLQ0HRVhmfTMEkS2zZzLjhl4OIYYEwwbmh6lmV5krquqyFcCyrqUcb9YbVRDzxf\nXRHf9/OyUGJDxc2TUiq7E9t1sEbq9bqmaRgRiMksTjifJVmuuiHKRV7SnJaaaWBd6/f7/dFwoVqb\nqzcAABBrpm5okASeDyGeDEZpnOVRUm004yQRQjimFUWR6/ie53Epsrwcj8dpnk2noW3bnuMeHx0m\nM2dpYXmhtdBsthSEYJoOE1IDOKjUJEDjcCYAbLbnx+PxaDxl/aFXCRAi0+nMtK3eYKhEfu12y/c9\n27LgmY+5WFxcZozVarU4jpNZWK/XFfsA6DzLMsd2GGNFljqWCaXAEBgaKbL0LNaCMwBAWuRlWULA\ntre3W62W5djHx8ej0Wg2m0VR5DpOlmWXLl1yHIebPEriv/zLv9R1XSmGD4+ONEJu3XzVME3G2OLK\n8sHu3t27dwfjkeM4XuDHcfxs+8X169e5TnZ3dxljrusvLS21Wq1Op3d62nU870/+9E8fP37805/+\n9Dd/8zdt237x4tm3vvUtUVQ9zxtPwuFweP/+/bl2e2Nj4+D4iFPW7Z7arh/H8TicNptNiMDRyfFP\nsnhubu5Kc45rGpcyTGKFZLz//ns7z14MR5PFZnM8HlsaWWovxLPoWnsOQhhOp+qsGQ6HvKSe5zmt\nFiFkfn6+VqmqD9hsNrNtmyAsGE+SRMWbGIZxdevKXKOpVHqKXaW6acMwuqcdGyD1IVdjuvK0g5bJ\n0lR5PmOMITubidG50a6q1kVRlLRUH2P1ES0ZZYwpTYViYwDVP6EzdwU1RgMCMQJFnudJSggJXE8K\ncbR/sL394uXjh4Zh6DqBX8mCBef5NgBA+RV+LADIMrEQECOIABCSSSmZlBByWuZBtdIfTzJaXnrl\nlbXNS51ufxrFa1fLgpbthXnXdYmmKQ2SpmlxHDcajVarFU1D9ViM8ziO87wwlXExZ5RxXYMIoVxw\nQoiEAKioO8kBAKoxqFarEOIsy4o4hUJyWtDUoJkuWAEkdQLfr9Yk1mgpIYRlmT968DBJEstyMMDq\nxMQSAYm4FFLZAgABJLwgYZVxipGmaSRKYimgblurlzcpEHlRCB0ZmoEQkgRDTGyiO5aNMVaCziAI\nVAFI8xxMJmoeJYQ4lu15nq7ro+Hw+fPntXrD87y5+bZt20mWlp1OOBxOp9MXL15wziuViu95gnGF\nxqnzn5YcAJAVWcUPrly6PIlmJeO2ruV5EUcxQqjdbs812wiA4XCYUwwxTpLENi2/WpGU2bZ9PBqE\nYeh5nmVZ4+nUsizNMCjn4tz3CpxTxCUCmfL3oVR1hGdtEDgbxYIgOLtWGGmG7ngu0fU8z8fTyenp\nqYRA0zTKeRAEhmHYth0OxoZhNGr1K1ev6rox6vVGo5GmaRomBS0BBQghbpkQQks3GMJRnqshTc2F\nZ89KnqUdKHLMBVsKAACh5JzTIi8ZVR8uVfxEJKWUummo8VR9stQHmZ8HEV5QpuW5P9pXjUrUw0Fx\npmUC50xpNZFzKdRjUUqTJMnpmQnXGTf7Kx8r9afiealHv+BtmaY5iSLAhSJzqF14nuecMVWD1RB8\nwepCCBGNoLLIkhjoul6vVaSUg9EwimatVqssy4JSKAQC6qIIyRkHXEAhpBCSYYQxgQgBxESSRAgI\nQoihYQDOQCfBtJofJEkCGD/j6UmQzeIkSRqNBgZQ1zSCMaM0S1PVeM7PzxNCOJBJlrIs05kZJfFw\nODRtCwmsGTpCCBHMOZcQFpSG0SzLM2UgpSRoSZaapjk/v2BZFtE1AJGmG4wxw7QM0zo5OgZCMiF1\nXe92OqPhEEgZzuJZEv9/ufqvJsuyLD0Q2/roc650HREeIiMys1KU7uputK5BNxpD8AGvNNJI/gf8\nCD7T+DxjAxuzgdFgICEGwwZaFLq6WmRXVVbKyMiI8HB99T36nC35sNy9kvS3zIz0uOLsvdb61Hr0\n+K1RNujqShCmjL148+bw8DDgbLneMEGjJG7btlpvAcoI4+jJW08//+yzj3/1yXa7bbr2D/7ox6cn\nbxDBlNCqqc+vLyG5lHmi67o4SZI01c4KIYyzICnK89w5O5tdx3HMGRsMBpeXl9DWRFEkpTw9PX3x\n4jlC6OLi4s2bN3EcZ+OJ1WoyGlprVytEMcLODtJkvVxYawlyXHCrYWm3JoRQRpqm32xWWksppepl\nHMdpFodh+Prlq/PzU5+Ltm3DMHz73Wf/4l/8i7IsAWxglD5+/Hi9Xr8+OdFKffjd79BPKTwemBKM\ncZ7nn3zyyYfvvb/YFDuT6f3jY4yx1FoZ07bt937wg+vr66urq+985zur1erevXta6+fPnz++94Ax\nzzmXZdmHH3743gcfDMej/+2//Neqrher5adffD6e7Owe7M/n86Ism6bZ253MVuv/+d/8L28uL0bj\ncTIYTqc7Xdf9489/eX7y+iQI/vC3f6fZbl+/fPm9b38o265Q3dtvv/3w4UMpZSC88zensu0ODw9n\nV9dFUazXa4zxwcEBvfWxOm2F5wXDkdPG87y2ba/OL4bDYeQHAFW1bbtYrpIkScKIONTLnlIqb1cD\nQXJQr+R0OoV75HYUuIHyrENw/QHpq7S6E+22bdvJHpQyILbquk4piRCCDvhOssE597jI85w4FApR\nrNeff/75l59/sdlsRokvBEcIqb5HCAVBgJCDXFzQJdlbh+hNVUaOYEQRslbDBe0shm13rZKOksOD\nB8/eeTsbT8q6ysv65OSkaVvP87LBwI9CaXTVNnXbgBz64vwqz/PpdAq4dF4UgedhTBDGBjkpJaNe\nHEZcUK01M7Ch1llYnIAxxnj/6J4xZrPOyzzvjW06SZwjTglOjOqrrm20xiLEUTwdD0fDTAhBMcEW\nMc6Q1GWZB8IbTsabfGsJdtY6ghkmBN1mQWvpC35TVh06uH9/erDXO9OoHjPhEcEYw8ghi7BDTulo\nkFVVRTG5m7HgWofTSgiBTO88zzebjXOuU5IxJozBlERxPB6PIRcBfKtxHKdZ5qwVjENh28yX97yg\nk/Ls4jyMo6P7D7Ise/3mrK0bIfwHR4e+HwaeT51t25YR2nStUmq1WERByCkbptmDRw8PD/f/+r/9\nt1cnrz0uwjCIkni93Witd3d3Ay8Eow7sDqKCO+eMQ3lZQfGj1lFlEELYIoqIQ9haCzERUptOqoBz\ni9A2L7Z5wTwREdr3sijKOEVxHD99+lZRFNvt5vPPP0uiCDvY9ujq9mZVUVkWtG8558poAJbgXAA+\nam4DQ+B+g94UGlxoVT3G7xCju1oLaqw7Vba+zchECEHDcad/vitsdxjvnRAajqG1mlAqPI9oDaM5\nwANAHgFfLs1N8he79UxDqYY/AGMxvDBoGu6E5QihKIratuWcw+4AcP/fvexfvzV3I11kupezyyvI\nfkMIpWkaBaEvvNAPtFROacZZHIagoANH9h2IYW5laVJK2EeBrAujkGESRZH05TBOq7pIoiBNU48L\nkMkRgtMkItjFUZBEsZSSEVwVOcZ4MBiMhtnp+VnbtsoYzClm2BgVhB6myDqNMMIES9UpozBDXFCM\naZYNjTHT6bQs681mrZRiQixWm/39fSAJgCOx1vohrut2sVhRztI0jaL43uFREIWYMO57wC7Irhce\neXT88PL6qimr4eH+AGPGWNnUFvW7uzuU4hcvXozHY4/zJE2rtnn+1YthNmibLk5Sa22e55eXl8vN\nGnZxcCHatu36VllDKe2l0lpfXFwkSZJl2YMnR1999VVdVbCuIE1TbA0hZLVaXV9fH+zvep4HwS6g\nJCzaKvD90TCjlGrVl2WJjKWUtmVJCMGMjkeDMAzjOJZSGmc3+frg6FB2fV3X+/v7+WZrrZ3s7Jyf\nnvphkG+2z37wg65unj9/PhgMfv7zn49Go93d3d3d3dn19cnpm4uzsyovwjD8j//pPwWhNxqNIOf2\nWx+87wj++OOPF6sNIizMkvvHD0Geqq0zzn76+Wey68uyjJMwTePXJy8//fRTa63t5M9//vO/+duf\n7e7ufvf73yu22+vra+LQt9599/jhw3//H/8DFfxHv/mbP/npX68+XR4dHb0+PfN9P0rTf/I7v7NY\nb9abzcmbN9ZaYt14utuXpVLKIPfq5cu2bqLQ77WajsaHe/ta6/V6vdlsFrP5wd7+aDRK01QrBRT4\nwcEBRPiGgiPnBOfDwSBN0zzPV6uV73nQUHdte3119ebNm+l0GkcRRgi4KEAawGwdBAFhtCxL2HgD\nJwKWBVlrhee725w/kF/p2/Q7OMB3nCJoMjUz6JteGmRvhLzWDbO0b7vT1ycf//Lnr75+yRjbn+5g\nK83N5jiMEOq6FmNMMbEEMU6cw1prRDDGhFqKMbZaIoQs4RimHkIsIQgTae1is3309OkPfvNHXhSX\nbesoVUZfXF5ba5nHWtn7vq+t6bpOS+X7YraYF0UxGU3efvtt3/d/8dE/RlFkTV81DUIuDAJkbkJ9\nwaBiMAzlFjmLHIFXVbYdpdQirBHGFimHlLW9wUVVY6Rd33UGYa9OEHHOFUWxXa0ppYLRru08ROI4\nBe2xQ8hhZDHBCFtMCEwqGAVcWKWNdZhxQmk2HVtGpNVV13JsMaWcUp8LRjl3mKIbqBByJODqgEXr\nbV1Pp1OMcVVVq9VqMhoPh0NjzLoojDFlXdVdmySJxUhrvcm3eZ7v7exiRmErQJamxpiyLHenO85Y\n2XW+8ATjfdP6vvf+O+9ShB3Bw+GYEFJVzWa9zvPc87zhcBgFIWNM9X3fdgBfPTg63D84CIMAIUQY\n1Vpra3olF6tldPSA+x5uG+0sRRgZ0yupte5kTzEBUFQ7C2QkIaTve0RJEgae5xVVWTU1FRwxWtSV\nAz5YcEQwFwI0CqqXYHiz1vq+PxgMmqq+uroKhAdGLKWUuU1OJujXsC3keEBbCc/DHRV6B4n3fc8J\nvYNm73jcu4KHb+HcG6k7QnfpcvCS4FjdkdwwJcNBgxIoOP8GPoQQQtwTAz6A0gZd8h3WDaMtHNib\njvYWOQBllrldjA0/Sinm+zcn3VlqHJTLmzLk3N3fq24N3CyJY2st4NZ1XXdtizGO4lh1PTJWUMaF\n5wuPEMIIFYxjSqG/4JRC8KHqpdVm7/6h06ZpmlB4UkrZ90VROOfaqvYGA0GYUbprWmet5/sIIdVL\nwRhByGo9GgyA62aEbLfbYpsTRq21WCPOeUxjPwqbrpW3S6ehDfF9Px1koYjAxaGMYUJ4QXBwdCSE\neP36zWy2WKyW9+7de/r0aZSkADWEaVJ1LZGkV4owhhktqyqvSj8Mzs7faIviNAnD8OGjB+kg+/zz\nz6M02xRluVpzj3mBb5HzhL93cNjW1WazmY7HVpuu69u++8v/9pMPPviAMbZYrxbrFUzqeVnwjjNP\naGeXyyVlbDKZHB8fI4SWy2VZV0gpztju4SFkp3GCAQbhnDtkgig0xgBYOt3b/fzzT0Gx1ba1M8YZ\nRZ31As/zPIxuvH1JFAyHg+nuLsj3t8Xa9z2Pc2N0EPh1Reu6W29Wb7/9LAiCxWx+dHRolNrmm53d\n6ZfPv6ir5tGjR+PxOBsMXn79dSd7wtlqu9k7PCirHFFy/+FxOhysVivAYMfT6WA0Kor8xcvXwOsv\nN+u96c5mszk7O/vxj3+8tzsFadVms5Gy+0//+X/Nssz3/d/+7d9+8ODBz/7ubznn77797ODo8HI2\n39vbY574+7//+5cvXz59+vSdd975m7//B601obRT8ur6Wil1cHCEEOrrimEikuTegweDKNyf7HCM\nsiT99MtPt+uNYLwoivVyxQitunK5WHz26afvvPPOd77znaOjo0B4nucJxkkU667t+gaaDKfNKBsQ\nh0LPhwPstEmjeDIcRX6AjDVSVV1f1zXcR9DCI4TKuoK8SeCu4KtsmqaqqjQj+NZMCecWUGV9m68L\npwn+kRDCIKTeAV5rnHPIWIuVYPz64vLzzz75+qsXbVWPB8Mw8BCyZXkTMcgYh+uGUioEx8RB/w4k\nFrmzDGlJCHHEOEcMcmDdMchqhA7uH73z/nt7R/c2Zam0dYwShBnsv0Novcm7/gquS3AogVCx73vZ\n6yhKHj9+fP/+/auvv3r96uu664IgwIwqLV3TyL5N0xQTRBHFGBNHncXgDbqez6IoUsoYhwjBxiFp\ndCedU10c+YQgKaXUmkZJWRbrbfHJJ59YY5LhYNN01tkszbTW681GRAEh7s6JZBxCFmGMAGpHFCFK\nmC927x12zjSd9MOACO55XhSEWRCEjDPjrDYn86u7S0YpBYlDTdPM5/NBmoJM7/z8HLKutDWYEoKR\nUmqxWCw3a6XUarOuqmo0Gu0d7IPAxWkDD0ndNt969my5XBqlJ6ORFwRKaUHo8YP7bVV//frV9fmZ\n54ej0SgOdo3s27YdJ5PhcDgej7WUdVmdvTn92d/+7Zv93WGaTadTz/PqptJae0FgEVqsVj7zsuEA\nU0I5w4QoY5q2VUo5hBzBDiOLHEXIEYwtstZSwZVSmJIwjqTRyppOSWV0XhZMcI0dRC6DlkIIQTlF\nFgpVvyk2lGLG2GQyKopCWYURttj2Xde2lnMuBO86CXf7nfUWYwyrvaBo3eVG3ZVJGCjhtgcQAhw7\nd1AwvnW+qds1SiBwU+rXCkfYCIIQgjH3Ti3FCIXglBt0Gt9wulJKRzAsfLwrMebWaA6/E/4Rqju8\n1LuGAHQSxhjVtlBiISYMzji8BUII+wbh7SglhDDdtZTSOAzAzqiUKopCda3sW0ppFPqEENW30LwI\nIZI0aqu6aVvoEwXjDUZG9cv5tVG673vOiDOWIGeUJIQc379vrW3ruus6X4jhcKi1vrq6EkK0SkH3\nujOZOmPLsqyKEgXhYDAYjkfX8/l6u4mUMtZ2fSc8AcgDKGVULykmsN8RlmwURZGmKdSAy+vZk6dv\nBUFQt43W+nq2uLq6gvSDg2FczpfGGNs2URRlw4HW0hKMCNk52F8sVkEYjsbj84uLV29OpJawKchY\n62EmuCjrerXZVEXxp3/6p8ian3/0kTKSMdZ1knNeliVh1GEUJTFAIk3TGOQ8z4PtCEmabrdbSNjx\nPI9iMpvNYLO01vLq/AxoRWutQ+bJkydCiOv5LI2T0Wiws7MTBN58MQMOOwiCR8fHqu+11mVZCkYh\nmUH4/mq1hAQ06GFXiyVYCauq8n1/Z2en2Gy//vrr3enOcDh8+fKlYCwIgt3d3aZpAj9cLpd/9md/\nNplM6ro+fvDA87xXr159/4c/ePHVl5eXl6vV6urqajqdQvAhpfTdd989OTnhnHuBb5wdjUbf/t53\nf/nLX4J3Zf/g4OTk5Mlbb332xadH9+89vv8kiqI8zylnXzz/Umu9v7//5VdfbfLtX//sb44fPfzn\nv//fvz598+b0tS/47//W72iEf/rTn4LEV3DOGfvgvffv37//kz//r19+9vn9/b3D/f00Cn3K89Wy\na9rRMCurnFC0Wq0oJt969+22bZ8+ffrWW49l2yVhoJKYENI0Tb5dp2na1o0xpm3bLEn7thNJksZJ\n17RxHMOOy9APDvcPCCFd086vZ3EUlmWZpCmQgiD6QwhB+ryUUggRxzHoKtu2xYSCQOauH4ejCHcH\nYRR0KNAdaq09EdwVAEoQJ5RQzAidX13/8hf/+MVnnxNkszTFGHd1g7ANAg9a9bbtGaVpEgFYdSO6\nvL0j7gqwMxhTQjCzGCPnLHbGGWVxNBj86Pd+b/fgcLndaoTj4dAhFMTR+cWVJRg0KdwLkuQmzKis\nK8q9NI2LqlytVvl644UR7eX3vve9siyvLs/rujZaYutYSIXnAYgHNxxC2BHnLAbxDOMeFxhjgqyz\nCLVKIa2xRwjlCFupVe8QhAK2TdPWjZTS4zyLE9m0m+2WEBKniUHOYISQA4IZuG1iEbJIeD7xhUS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c0XUegTZCljhGBGKKGIUOIc1lJTcrMY57YGW+cQIVz2/R3rdjcWSKmDKKybplUd4ny8v/+t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\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U3/3ud5fLpXH2zp07jAttddO2P/+rv9JaDwaDKIqur6/H47HRzjjbqM4TAMIMeE+JA2+c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\n1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DV1XhaMQDRdS8tFORwkSXLz7m3V1Nw3vH79+u7uTgjx4MGDs8sLMCY2ze3tLStJdGJ6\nZxl6ybLsk08+4QP99evXz549e/bs2d3dHY/I7969267Ww8GgSLOqqowxWZKWZ6chWiCrKBoMMjSu\nW7hmGbHXsjOyibKKvgbsjZbSSOvRKG1QgiGICEQ2UBTU7OYPLiaJ+Tw4+urpq10X82Sopep6LwUG\nb40SlKWr9X1r+4+efGq9N0YJEJ4jkw9xJiQCgAfwIXrrondRaiUVovCub6rdCkwaQIBQ3gXfxzIh\n5/uubwCAs16apm2aGqIMUghIAZWWBGmG0EgBN/O/NsYUaZJmiVKA5F1wsffBxdSYclTmienadrvd\n9sIPBgNUmiCAkqgVELoQlqudCzdnVw9/+ONPhuX57bvlP/z9L37+81/u+nVaqvv73WQ2zZJ0vlw7\n59qmByF8oKaxPgoXEIVJ8+Fo4ojIpGVld3tfcRVkpBhjWuYjk83n86uHD9I0XywW3vsnH3380Ucf\njcfjV/NNFxxJYSG2fRdRMMTauS5N07qq1psNIn73888/+eST+XJx++oNI0DHkU4arVBYa3/04x+3\nbVvtdu/ubrfVjtdDIUZADDHymXB9fb0fYTdrfmqYlti2LasG+rbjch72+Gtk9SojpdzppkV+fn7O\nO9rVfJElqYQ9FprlOQi8vb8fj8cvX7+6Xy6EEKXSSsiuaYfjUZnnAGC7rq3qyBEZvLCIRD644zKS\nBJ+H+6pjLdF+/AIptZAxeiI6qlHxaFAM4EPgyAT/3sKSDhZXTdsyEqm1zmMURh3qBMWDRIV9oeNx\n9EaUsC8n0ugIEEMkBE5TECi01s57z7MvYgDiT+fYwR9fZAiBl9OMq/0PXwCQp0YIsQ9iCpGNNaSU\n4/GQVxutZzZ6DEQiklHq6FgCMbKNBNt6CKLIbwpRBAFSAhHGfyrSAoD3eGTHr4hAQLAHYf8JUUsC\nolJ7hBkFAXFntm9WpKDDuLx/U1IIKfiYPc7WQgg2QlFsWXVU0XAhFBSPBZjnZSEECQQptNpbD/K9\nGxGYhIyIaZoqo5XdoxPMoqID/4jv4KO+SkrJtGf0LHjfdyUk9oLRYxU5wgV7GtehT3HOWWtf3b47\nMnpWq9VkMhmNxz4ETijiusvoOt8Hg8GAieY8C5ZFQUS817w4O9Nar9fraruJMfIOcnpyiohN0+RF\nYfueO4AQwna7TdOUM4VYU+u9n8/nII0PgQBMkgxHo77vq6YGgT/5g99frVaL9UolZnp6wrtSdrDz\n3jtrq6rivpWRAPagybJsOBgwAsFpPB999BF3NsvlEgCYm5amKftq8UU7TuTcG93c3CgUbEldVVVV\nVVmahhCYFn/8XLgVY9YVd9PHSszcK/5n/A+4YdxsNhxU3DQNW2RYa8/OzvgV8kzMMwcvktM0XS/X\nSqm3b9/+9Kc/ffr0aZZlH3744fn5+eRktlqtNtWuKIqsLExICaDqW++Av3E6mXz80Ud1VTFXhS1T\n+DMKQM65pqobY/RgBN5J8FpDZqSUoFzQzqVJkOAlegIIe3GjJR9DpEgGpBagQAigKAlUCMIkeZJM\nnpxPRyenk9/8+hdfVdtdnmSDInGpqetdwDgaFJvNZnH/djKZjIbTJE+JsO9Jqtx1rm2DUiqRQqeJ\ndo6iB4iAXkmtlGxd711rQiZISmlAEiolSRSFttaGYLSWMQgA4Zzte5uaJJIKMQhCISUIhUoRYlKc\nAUAXvG1AK9RKG2WUzlF2rW3izgqRCT1QKoQQeodCZjEGBAAyEcF56mpXu11Wwvn1w+Tq4/FFrbPy\n8sHV06dPnz59+uD09PXrl7PZqdayqqqvvn76wYcfP3jwoOmsCzECRQJHSFK74Emb6egsyWsppTKm\nabq2bY0xSZaqNDs/P3cx1H1/Ohh8/r3vnp6e8j3f1955TxLx4DegUADuOQ0PHzwYDAY6T1+/fLXZ\nbW3nhsOh673rK5Z8dJ3l4+/04nK1Wr27n4fN9naxrOv68vJyNjtdr9fOVW3bR4Rd0za9ZRKDMSbP\nyyTJtNZ5XvJP61sLABGInRRJoCIBANvdLsRotGaJoNaaBaMno0me587a4DwACCXX1e7nX/zqq2df\nay1nJyeI+NFHHw2Hwy+++OL5q5dyNIgxaqls1/REeZ7jIdeE34VD5BNmT6OVwgXvvJdIkafGPcvR\nI0DgFwkQgSIQIhKCDwGc5T6eKwjDdXyWsv0yIUilUO5doo81UvD8FwkEsmMwo7hBCIVKoEikYs9k\nrlLReyGlMhq5AjJp68Cx4hD7PfdK7OMZWKy0twcAeL/yAUBhkv20rUBICQL5PLQH+wRE5GGcHSwQ\nAHwMQBR44f27OTUgMZNMIkYECQACKRIcXMCQAPZI+e9m3/2g/57B5PuvUxz+PV8l65092PYdWD5p\nPPhThRiRmxshWtuHwGZ34TgcI6KKIQhEfVjdc72MXQSBTNLrug7knrfMA1bbtrwNBYD9WOwctyXv\nl1gOEtZa267jEsUX3XpHIbJIlwlQcKDghhBMlvLY/f4UL6U8Wh83TcOOmNxhPXj4cL1eb3c7Akiz\nrLf2qJxh2jOzdkejPbla633sNtcqRkotgHPu4uyM3w6P4HxHrlYr1trutlu2SG2aZrlc8t6Xf2ZZ\nloPBYLPZfPXVV0k5vLq64pKptU6p8AuywX/06SfL5TK5uWnbNsQYYuz6PsaYKsmLGb6evMjh4iel\nzLNMcsPrvPOuaZqkyHlWZoyBIyV4W3w06uLlOg+Fj598KITYrtZv375lLm5RFMyC5kGWEQKeAADA\nH66MUJJ/+G63W6/X5WjI9Df+KJMs5eNmT3kbjcbj8XGJwu+Fb4bnz58zNM0f6KMHj05PT588ecK7\n5LZtb96+lUoxi815f3d/z0fh9PTk4uIi+hCc77pOa/3pp5+eX1ywInm72eR5jgTDojyZzbbbbbBu\nOByum0CuE9Fq0YdQ2Xpr63XotlqQDxuKrUkpy1OQSXBdZ9u9B2EgzXKLiAKEktKDqHa7NOrZZPz9\n730bAjz99Zfr9SbJbZZkk+lgu2tD9FmufQy7zaIoilzm3kci0kIGEYhI64Q0CaFIIIJSSkAkAUQQ\nQ99RUQgkAeS8tdaSVNIkVb2NAZi3HKMXJPheDRTxsB5KUQCoiImUcjL6qLdtW1eNbaC3iZF5KhKU\nRqe9VXXTxdgn2hBlhGSD1iIN6CiAcwhCAmqlCqGG61345W9eXK5CmhSPnjx58ukn5bi8uXt9P7+5\nur4eTWbfvHg5G5/opKiayt+9bVonTSKkFEoRkMpTEaPMEutaMAkqpZIsV4nOciFEoPjoyRNrLfXx\nwcOHl9dXw8l417SL1XowPt11TfROGpMkSRReKaWFTJVOTaKEnI4nfd/fL5a393dCKUTM85xPjOM8\nQQC73e7tu3dd1wkpUYiu761zBJe5u50AAQAASURBVOCCX27W4/H45PyM/WWZAYeI0+n0SP8ZDodV\nVd3d3dEhjVQoqY1JDqgmA5tszsdkTDbW0EIG63zbK6XKwSDL077tgnVvbm5+/KMfPHrw8NXzF03T\n5FmWpen1ydkqurqrE6likjLVKJCXQgQfIyKvdR2id3G/DWwbjIRyH2rPlTk6j0ghRnaZAgCOUuE1\nIm9zefSUB2NBntg02+LGYK0NQChl3Xd8zLJ7AcDeKouLNI/URARH5SQKG7yPe85UCAGBRJR01Ma+\n/xUpUBQojw6RdNitHgvKkUp9aAIIAFCJTCiOfPDeW+v2IRMolDJSKIF71rcNfYjB7znMxJEn+5UB\nO1YrKVAIKUGIGKMkQAIByNMtEalDPebXA7zOOQh/GAXhWsB94b5OKX1c1e1FtkJ2XedjjOxwjQIA\nuZkBAgQhBaIWKKM4XGEAUDyacKHHQ7tERMEHBhtdDOkehQ9HFk8IoXOW5ye+lC547PsjaxqE8DGC\n9zy/AgA0DRykNVJK3XU81zKhV4k9UZ4vH+uguZbw/zLPizUtzrnxeMy2lOloyM4Vg8Hg4uIihMBs\nKR7C+GUTES+AiWi32yVKj0ajNE2ttdVuR0SpMXme88QGAOPxmCnBRVEQCqZr3d7ehhAuLy+Zc3R1\ndcW/hdnCfGN1XRelSIvce39z+46IpFIswvnZz38+nU6zslhu1je37xBxOBxOJhMVAq97GdQVgDLR\nCkXbtnmWee9t2+0fJAJvXec3aZpmWcZtAfcQfKAwPsZbpbZtuYxtt9vZbHZ2doaIi8UixljXdfC+\nKAqmWcYYSQXEfUgIHjviuL8dxcH/nUWW+4FGKSEEH1t8ljVNw+Q4Jj+/efOG5UbPnz/XWn/22Wdt\n2/LRyW/HGPPhkyda68Vi8ebdW34U+75nA6A8z5uuvb2/0yCSPJNS9jEyU4xZ4p9+8mmkuFwsmqq+\nvbuLIXDLUreEEDJJIDASgISsSM1w7NuVFAkCCUHei+Cp72XvTDEeUYhEIkQBJIGkQG208jbkeeFj\nnC/ulE5//w9+cH15+tsvnn759U+b2uapUUrNF5tEo54MEKGtd1Kq4KnvnZHGOc/3dgThPFkbjRRS\n6iiIQrDeKSHJOdvUIpAD7DyxEauQQaAaDoeIiORRoBCQZabvHQD56InIk+KNlzamsilEgyYz0lFo\nfbDruhM7V2Q6S2d5qaLtm7YHkFopQTpEhZiDRCkloBAqTbJRmo1MdvL189vVKn788ccmca7elaPy\nn//Lf/7yt7+8vn747u1db6vhtFitq29effPt7/2wsY0IvckLGYGEIC2DJ1BK6TyR0ntfdx0iDkYj\n5j0EAR4xGw5PT0+zstxUNRGNphMAEZy31motpZIZSqWUlirR2ijNGZ2b3VYqlWWZMsbXvZGKY7/5\nocvyXEq5XC5vbt/lea4S07hepuZkPJSpsX1/dnb2+PFjIvrqq6/2q4osU4dcGeccywj5EQCkvu/b\nvhdAymieLylGrfVms8myrBwMLi8vY4wvF8vtdvv5558v1qu1DyzE6J3b1FVVVemg+OLLp/f391qq\nMs1ePPtmt948evRogPG3v/2tcy7TBgwqpequBUAiCm5PnY0IRHsqr2QTWRTIITY+UIzcrnPB8J4T\nUInXf9wrkEREZG8QLmwsTNDGRKAQgg3eh4CILNnYI5oohBDyYO3E/hJI+wxBrgtOCEb1hdwbfRCR\nO1hwxP34yP9JQgj9XpYfHQy2GFg9jnxCCCUO/sTeHYdifC/1D1GSD6AFD8QEsDdePcDmEQHZjStG\nPsdYcoJCgBAA+1ouaD/n7AdZsd8E43vkX/71XMj2Y3ckpZTQe5vIruuC3MfX5nk+m0zH4zHXtdVu\nw++X44355+z363vhwv7F0FEHzHoY55w/cMH3yiqOpcTf6Vv4GuV5TgK5l2SkWmvNORH76ftAXWYK\nAMcwWO+7pun7nmv2EVjeT6IQ2HOz6VpWKyOiTgwA9M4Gii54V+2IyMegEzOajPn0f/b6FQkcTSes\nheUXaYyZz+es8BsMBozW7lF4pePhcEyShEVBSog0TefzOb+dYVnIgwO2UJoNZvlCsRX2yclJCIEX\nsfw2mdS3WCw2ff/8xYvdbscGk4joYmAPWLZm3ey26+2GBfiB4iDLuO2QTIoTwhiTasMc5uNtAZGs\nc13X5eM96M3aKq31eDwejUbMUfcHm8kYI6/eb25ubm5upqPx5eXl+fk5+2b3h5RfOBicMT9CSqkP\nFZ37BqEkf3yBI7QQ0zw7buCyLHv79q0QIkmS+/v71WrFC7Y8z5ltd3Z2dnV1laYp9yuPHz/errc8\n3799+/Z+Ph8MBptqR0QmTXa7XVXXxpiLi4vBeLTb7Z4++7pf78qynJ6enF9ejMfjshzY6Debzc9+\n9Ytqs3379u1uu0WCUTmYz+cUYzK6NgpGuRoVmIvGUFWYfpiDQqHTIk2GfHt7H6UZlpmOGCIQxQhB\nUAQKgEKTlBRRCFOYVAothEySfFh8+OD6pCjcr3/zdLPdmLQcDnNArBq32W6VMiCUlAoC2mBjBKUU\nEiJoikhRCKWNzITCEJwgm2SZD7Gp6gSVyUuVms57H8g6b0Pn09RFa63VOrG2k1Knaaq1cgFDCCCj\npxDIA5Hvc0QSohA6grboO+HaGPr1tkbMjM50hmkBRgrvXNu2qFIllUkTpRSgBKFBFj7knU18FC6o\nu8VmuV6VhR5Mxh8VyUfX4z/7sz979frtaDJ7+ebZYrEj1H1fowSPIVVoycWAgahqa0TMslwmiTDG\nuyilzIcjKWVjHRGYJBFa7dquDzFJkjzPTZaBxyzLXAwgpUdA3BceCnGxmgcbJpPJoCh77ySK6P3J\ndJamad/3qTYcv8a3PQkMIUitbFu/u7udTCaj2XTb1KlS5WAgpLy5uVmuVkWes020EKKqqjzPp9Pp\narW6u7sriuL09LRrnZSS9rk6goFE7/22rpq+Y5Dp/v5+t9utFovBYGCbrl5v26qGEqq2IaTW9X1w\np+dnr14+3263n37wZDabhbZ/fHn9R3/4z376/OtXz55vul4ISIzRadL3fYjx7PSsaduma733AQhB\nCiW11jI1IQTvfLB98J7j6GE/D+83gwKAkPA990ciCkAUoxCHvFeteYC2jbOs80EUQpg0Y88MIgKG\n3xAFYFO3LIBVQrLvFiDGSOA9CBQoQCACSpIuBuKAcMS99X2MAQIvE+UB3T3W4PdhZ0aDeSm7b/cB\nPMVoPScucRiRFpIIe9tTiEH64L2Rio9iRCSB+w015wsAxQjET90BVt63Bz4oofF9G2dAOFTKI+aM\nB8YZv0jeV70PREOk1rY8jeRplqYpO6jsdjsfPUPu4r3ugSdPKSWTpY9/CAB7qhGyfN45LwTxUSsZ\nkANe+u7p0FmKjKdLyTQwLsyd7Xlk5AGXva6OTMI9xfqQIc/jFLBjdYzcDfHCQ6HobMcs6CPUHg+x\nlNylmkO4EA/od3d34/GYnZBdDIjI0+F6vT76NnOBHI/HQojlZlt3/Wq14+jZsixDCAyS923LdWW7\nXh2tZdes/ynL6+vrruvmy6X3XtZ1XdcPHjxQxqw2m/l8zn4Ld/N5UHrX1N57xVkrzgkhdGJ08Lu6\natu2ampltNSq6dqwmJvxdE9k4zJ8+PC4yGGkvd2H29tS8gfJXfy+X46R2wsuybw94p/TNI3QSim1\nWCzevXt3fX39ySefnJ2dvb25SZJkPp+zdSU7GLPk6fryEvF33RUPAQxYcddCh5wM/r2T2cw5VzWN\nj9GFMF8ulVJV01hre+fmy+Vut7Pz+WK1EkK0fX8yng4Gg8ePHwPAu9vbzWZTtc1wPPLeZ3meDUpE\n3HXN/MWKp/PLi4vVej2/veMoJBSic3axWLx58ybGyOr7sii48FdV9XZ7I9BlKpbGCr+O3VzTrkzo\nfFqkiRqWeVkMs3KQpYM0G2idbLs5UJQCFSr0YK3zLkSAPlLjNsUglMOh8/3i/o0AHA+Hf/AHP1GJ\n+uk//rKq66KYCKXrdk7RO9eLti6LkdDaWo8ojDHeeooiCiHQSGmEkABCCY2JRLHPvYYQow8oocwL\nbdLnL79ZrVZ11fChOR6P+ToIMRY6V1oILQCitdZGTyEKNYUYPAWIXoASwpgsFxgbWjV9jMEPy3w0\nGKSJ7kVrvdFpiVKilKCMFBqkCSSdlZ11mclai89fvC0L+f3vf1oMkte/fZW6JUh/fjE9Pb96+erm\nu9/7bLlt/u6nf3v98IMoNBj+UVqaBL3QWq83ez9XechLQMSmt9PZjJ/0qq5LxOFohEIsV6uT4SzP\nc1TSCmptj0pkWZaaZLNY9k07Hk9PTk50mtwv5kTU9B2r6ZSQ4+mkqqrVdlOW5WKx0Fq74Kum7rrO\nel81DS7mbdtmUWit3759e3d3p7Xml9G1La8/mHfJ5wwfXEBSaz0QIhCRRBJIANJo5jwWw0G12d7f\n3zdNIxAfPHgwK4ZIpLVuXD+fzz0EH2Pv7O1iPplOXdu9vX338suvQ9v/yT/740QKgWi0HpUD5wND\ndAIRAdM09SH0zsYYI8XjbtJ5b/u+bVtwTvE2imIMATk0XgqlFAiMMbC0l62ZHW/0rNNSpUnCpyLP\ni96xhAellKikECICcDmXUspDii2wawevw9+zhbfe8xXjRoFPLeccu9rxwRUPHsYS957JR0YSF8Ij\nmCoPAzodAhIE7gszD9MHd2rZdbbve1IxMabve1CRs8xRSRGArbWAKUQgOMBDChlxb/DEkF4IIUEQ\nrJd/j7+Gh1Bk3Ifb7nFsIpKAwHG6B1tlXrodgxKklAw3soeEzpLjj91fNIFIwA6+iHikgDGWrNiQ\ngY4hkQflT5omUkrOE9yDq0A6MQFC0+8d0Vi8ywjw6elp3/cdV0VrmV7Lq2mesEMIJk21lMxe1lof\nE4J5/yelVFph9F3XsYrUGFMUBXu7M4qiteZI3fl8zmZbLFBhTh0zgzi8mm0xdrsdb2rLsjyZzWKM\nb94x29Yw0M06YO/9er1OjWGbiMX9XZZl5+fnWuvRaNT3PePPq9Vqs9l8/PHHnEj48uVLpdR2u331\n6pX3njFtfwh65C0sD5Gd7Xmm7J1lpTVrigaDwbt37wCgKIoiz+EQrBusU0q1bcv2F3maAUCe58Ph\ncNPW8eATwtssZi+zvQNfkGPDW9e1ArLWsgv5/f09w2hZmtpDUMzp6akyhqtvWZb8sZZlyY4tzLFq\n27azPd+jvO+ng6M6i61XqxUPB8vlku/OxWLBP4FfIQBwrC97dU2n08l0Wg4GLobNZpOXxXA02m63\nVdtY73a73Xq3ZfElX67RaKTT5Otvnr199y4AccOXa4OItu32hFitjDG5KoLvBXagFEblY/TWBttX\nm3lwvRJyPJ6enV8PRicIS+v8t7/zGDVo1EpqcFGF1nddpJikRZQUY9xsNgSBKPSuv73dzWaz3/vh\nD7rW/v0//KzvWwSZp0l2Nbifr2MUWVpKw/s2JVBaax2kSlKIFDxZ623vtJJSKOecMgaNsd6v5vOI\najSbTc/2avgYozFmOp1y2+e8ffPmzdCP8yJVRoMQoKJEUgZtrRAEgBQgIyBFjD4KCOVgFnwnKPQW\nF8sdQsyybDQ8iZpzBSgGgUJLzIhECJClg846F3olCISKhKv1+vnz59//ePRHf/SHv/71bwbD7I/+\n+A+KcvLv/8N/OjmbpZnxJE2iPIkAAOS7vvchFIPS2YBCAQrnfYg2y7LReAwH59QkSdI8U0aHEHpn\n2QFNKRUFCe+UMqPBcFiWi9u709PT89OL3W6nrJ3NZp98+unt/P7+za02Jk3T2WSaJIkNnk9ATres\n28ak6Ww2Y6dMAEizwVHxKYRYrVZ1XXMeHB8pVVVtt1shhHPu1atXk/GJUkoZLRA9xACklFJSsIxk\nt9s1dT2bTsuybKt6u92eZIMkSYosb70lomI4mMymQqsXz74psiSEkKeZCXC/3r5+9eqv/vK/rUWE\nECeTyW5bzZcLnky0MavVqu06ay1KIaUE2iO3fLg7a7UUuTGJ0sF5Z20bnASUQgshUAoAYjIwoPQU\nRQQfo+8tqXhMTcB/uqLlVXHvGj75mdMU2CY6xv8fVf/RJVmWpAeCInLJo0qNuDmL8CAZWZnFuqpR\naKCnF9OL3vT83OklzsxiBhigABQqKzMjM4M7N6b08UtEZnHVLLLsxInjHmFurvr0vSsin3zkVFBT\nVeOfJ9eJvSVElnTaPPoQKKXkYY58nJoAgB+saU7WEXiiYqVFVW6zR3pwOlV8OIUZ6DQBPgz0CYPM\n8zyzln04nZMx5rMiCkuQlNCQnMNJTmWLQ4x/Rq0SkcgRABQiJ/xA0qb755KJiAg/Y+b4ECLHD+E6\nSiljs8TyeTzqk6xrPp8P7IV+rr7px6qHNKrHAToxohARf/r2D4nUE2P045SKqBsnY8wjccYY0w19\nipjNF/M05qaYOQBourZt29VqdTgcDsdjmpzcQ/oS4Smv0Dy4MKZvWK/XyTkyLSPTB9a27fryom3b\n3WYjkVGgKoqLiwuJrACTOUYKWqjns1ThNkObNFSImFxbrbV+mrbb7ayqF7O5QmzbtmtarXVdlBNB\nms6rqqrLKrktImJ7OMYYX7x4kef5jz/+KCJPr65ExDxZaa3dOO22275pUznUWidyryAwwvc//DB5\nF4TzPN8f9ykvaL/dJ1z3sG+KLKvr2k0hs3axWPz045ssy7RSIYSrZZ36x2SgEUJItiTw4KXFPqSr\nlPqYYX+oqgoQnXOQNjxG52UhiMM0Nl1LRKT1Zrft+365XI7HDhFns9n5+XkipiWVSAqtapomfUxp\n4bFYLErG2WxW1XX6OJquTfgegwzDkEzPJ+/SyL7d7z774itm7vu+aQ5+nPghkSnh2xEky2xRFInT\nt1wuL5dn0zR9+PChruu6rpJDEACfn58DwHa7vb67BaGk+X737h3YUNd1bvXQ9cfdvm8HDsBRGV3F\nKAKIChlDkBGNWKvt4anEiUOnabTKaZwk9NGNLC5GzxxBCVAEJaCiSFzp7mx9fn71cra8mC+f5vNz\nwGp0GAIFJo06Rg7OW60JcOwHmv6YOpuvf/e73//+9xI5y7LjvmmOQ5gCiiqLeV3MQHQKJLwe8qq0\ngNGH3lhWOgoEVExaCRoWEsxIV6jyaZKuG3Yenz17dn5+jqeABxWi67rm7dvXn776ZL2ej1Prw1gU\nGaIMYx+LeQiBQwQRA2SJlLASjm7MNBM7PzZDv1cU5rNiVlX68hfIEUFIWJhBSJvcFHNj692xJ7R1\nUVrCKtNG4Ljf/u9/1wbPv/nd71999uXg+di5cZIXn/3iD9+8/ub7N6uLZ9Vs2Q1R6Wx0QRAmOgGD\n9CCcRVLp8a+qyhb58dh2XXd2dqa1fvfuXbUux3H85JNXTdv+9//+3y8vL3/5y794+/at9365XM8W\ni8OhGcfxxYsX7969q6rq/v39fD4vqhIRGUQQGSQIR+b98UBEeVUO09h1XSIl6SG8/PTTv/3bv/2n\nf/4fP77+SWU2r8pkBFjPZjHG9oGzmY6g2pqyLA+7XWasyWxRVffbjSg9eBeFjTEWNfmYgcpRhcnp\n9eyvfv2Xf/rTn7bb7WI2DyH83d/93e395u3bt8bapu/ysgKiD9cffQx1Xa9Xs2T+ut1uP9zc2izL\n8/xwOEzjaE2eGWNQaSJN6bYOE8UYows+QtRaK61dDNM0Zbm5vLyEyO2xMaRYYt92wJLlFQCQAiJy\nwSWiZZ6aewTvQzdM3keljNYaiXShQgjb7XYxm2VZ5sapKIqU144siQKddsN4ihd0wqyUyozVSPLA\nUTqpaUJIIcQx+UKHYGz9yAUDgMQbizEKB611MnA9lToERJycpJL8qLNKy0oSTmU4yzJFFIJPLUWp\nbTKUFgYiSqJhZbRWNgqnOScIE+o0ZBfAZZZbaxOlRkSKLMuyLE2OUURrLQiTdwn2S6t9pZR+QErS\nEG9IVVUVYzwcDkS0WixFZLfbPaKYJ4yQTyTwxDWjxzTDh+upT85nCRCA06J3MmYaxpQfoIxGpoQe\nA0DynSCiw+Fwd3eX53kqA2nak4ccgp+33PKzMzA9mCQnVvMjyBxjfCRAFUXBzEPX+cm5cUrDNAHa\nIlfDUNRVGs6GaUy8uNvrG6VUVVV5UdCjo4jWn3/+OQpEH1KHm24sIWzbJrUOMca7zX1yvdBaH0Bm\ny8WTZ08V4Nu3b9u29SGUZfn1H/4gIpqUNUbjqQ9KQprlcpmXxRT8MAxZkRdauxgA4HA4DF1/PDZJ\nQcshBKUuLi42d9sE3l5eXCSHzpQEnO6q1BUys9UmvaMEcZA2aaBPOiIVOTVf6ZBLIBIiamOSy7wL\nPjBnxkrG6ZIm9tN8Pk9sr6IoEid8Pp8nW8fkqJX8n/vNHgAEwD5EEYcQUCsXEtFdQgjd0J+6P62T\nKmMcRzf0qZHq+z6dsE3TpFCsLMvSH8nz/Hg8pl37zc3Njz921az+fPb5ft8kgtjx2O73e9I6vcI8\nz0nlu9tdc9xH74CjQl1mRVnNjoceOSnjCJG8EPsJmQ21xoAigijsJwmtwpAV6CcMAhFRCLTNUItn\nHyMMfXjd3n7/5h5UWc7OL5+++uyLXz959mp1vh56733w4wQsymoFqLw6X72a3FAt2BRznVVff/31\n8Xi0RW09Arq+H4fjfvB+Xi2R1ORdWdY2U85PITillUYTQ4wOqlkhQswSJGr0RZaXZbVazhZoV6tV\nlpnm2LZtK3Lys3358mVRFCKoVQ4APogIA9joAUQRkCZQBHAy4h6thhidc21wXWQncToex3E48mQA\nuM6z8/VqtVqAUNO7w26rsgkxs7mOEobBEdtqOf/005ft8HuT2U8/+8pW82Z3FERbZa/fXG+PnQ/A\nAlEUoIjSylBkQUnrwp+FFqeDRuspeN/JKZnY+zQi3d1vX7x4MTnX9/3z58+7rv/Nb35TFEVRVOms\nSoyEd+/eNU1zeXnZFO39djMP/suvfgEAv/v6627oP/38M3ZOIjdd1/QdKkr+8C6EOAQO4Ycffvj4\n8aNz7ny1rKo6xtgNPcfIDxTcFFCGiJhZN47e+zR8DcPQdG1W1SJSluVqsSxtLpNHF2M/Tv0wy7I/\n/vEPh8Oha9qLs/P1ev3TTz+9efPm4slVsmE/HA+scF7Pkn06Cgxdn2Ypq4hjdNOU3AgMKURFgkop\nlRzQUJxzqYA9DqH6ZKwRu64jgRACSyAEYwwKgIjzniXpBlVyBUiLMDyZbRkAEsEYI3t/HMbFYjGr\nqhjj8XiUyAmDKWwGiKk0JMFVOgqU+ldkZ3kYHx+x5SAseApdSOJPfPAt/vPJOFXZRP99pCkBsFJG\nTqpUjwD4EGSuEWKMmCS2MYZwksmIOmG36ZWkIysKRxLSp3VemBzIKfQvkb2ZmUP0wSfDyzR+aK0J\nAAgTNy/GmHTWaWHr+YRLn9bKxhjnHn87TCOc4Pqf19unNxv/7BI9OBw/Phon1VB6oTqztsgLH5xz\nm7hxzhkyhij+mYVKYN7tdimVL8/zLMvc5Pb7fYrRTTdWevXpDedZkUai+JDv+9hEJNgnfU4JOE0u\nS+mHB+f9dGLKaW26rksjZp7nDCmVxAHRl19+mey3mqYBZgKY17WdzZKxHCLmeS7LZXBOa20ye/3d\n9atXr169eiUib968Sa5SzrmyrqpZnTrrajkHo+rVYjab0c3bR7aw0VoBunE6Ho91Xd/f3y+Xy+cv\nX0zTdGya+/v7++0mr4pxHBFpuVwul8vcWEMmy7Lj/tC2LYfYt51SarFYrBdLALh592NyD/8Zh39Y\njXddF2NczuaLxUI9pGcsl0ulVD8OURgxZdESIiYxUgq6V0afrVYicmxbFgUswNK3nRun3WbrnAvO\nPy7p/eT6tru8vPzkxcv5fP66G4dh6Po+zbiTdy4G9OjTA8icWqjE85rP5/vN1ns/jn36xPEhkGCz\n2aSbKtX4EMJqtSqKsmn7tuuKojgcj9M09cOYvr+ua3pw8ElbeefcOAxdy9MQjZ49u1qtFpVFLPJ8\nWc2Wy7Ubp2manO+H8Xg43h/b3eRGUscYnB9bN+z92IKMllATxRitzYrcRkEfnXNBUBRpk52Pbd8N\nXijuu+P15tuf3h/X56/r2eri8sknL17ashj95MeuLovZut7u9taWJtcXT1d/k89VPv/tb397f3dD\nVVGUVTDNfrMfukNUmOdlUIGli2y9H32Ycq4sFYg68OQGEoDAHFBAJiiKMgObma4fGHSIDmg0GRud\nZVkBqPK8GIZhnIK1VpGZpkFEZ7kRSfKBSCCKI4IIBIDAwUfX+KlR4ssCtco4uhCG7ngHHDrAw+66\nLspZvZqtzi4uzw+d01p5P7gIuVaBXdsdQYrW2P7YCyrXt/tD5yMVdfHTx9t29KyySJkDFYgUKlYq\nGTggJBvqn+eetL7NssyYrCjLyHw4HFwMh+MBtHRddzgcnPOz5VJnWd+N6/Nz5xwzjKNLZltt25+d\nnf31X/+tlq/fvXsXOAJANZvNZjNl9LNnzzabzcePH0MIL65e6Mwmi7osy7SxNx+vv/nmm/lyeXX5\nJMR4f3fHIrnNCJDlJCdNO5qyLM+Ws8Ph4KcTinNojqiUUVoRKKQQwsijZqiMiZn0SqUFnDVmtV7O\n68oYrRDndV1ktmuO4zBUeYFaMUi9mGmtP75/0/c9AyLi5CYBwCxTCgubEREKIIBChUR8ygZAZbQi\nUqIEALXSRNbatjv2TXui1DgfmTUpTilDiPhgIaGR+EFnS3LK/1GA8YHVXGRZXZZYVSGEoetDCJmx\nRunofPoRp60wnKKM8SFmRkSYfgZvRSTRj6OwIJBSSitEFCD5+Ss+7jofCjMm2t3DJMZID26MHFL5\nt8n/i0MM4YGohVab1OSdKGiIafzgk3cYKBJileox/JnVhtKKUwpyCoMCCMzovdY6UdsYHlodRYpI\n/2wFKo+7P0li3bSS4ygioW0Sip7Rz7GP+KDmSkU3XSL811dAT975GBSeZlNEDMIuhin40U1CiFGl\nKJuyLJmZtL6+vj4ej7PZbL1ep7M41VHn3DhNiVGVJuZEr31Ez1OLxMlZ25i0Se26LmmKTnvc+TzL\nMgLw3g9dT3Ay6rq/v0/A7Ga3DSGkrJnofZ6VkmW5tbhYIGKWZdGH+/0hYS9FUWRFnkpsjFEQl8vl\naT0g4kNQWq/W6wS3DsPw9t07RmiG3sdws7n/cHvjOYLIMI3uMOVZtpzNUaDv+0RAv7i4WCwW3333\n3f3d3TiOubGLxbIsfZ7nicRPRBdn50+fPm3bNrcFAJRZHmOU4NMrxIfg+sedCgBMD7EQj41SIhVP\n02SqymilM6vjiQcfY0hLZe+90ZpB9tsdEc1mM4MUtU61LW1/U84SAOx2uyStToy2NLYy8/MXLxJT\num1bm2dlWVrhaZqKqpym6fb+vmmalAF8urOBY3AhhS4iklJa68Q8X6/Xzrm3H9475y4uLmazmdUG\nrFibOxeyLPv7v//79+/f//DDD1dXV1KC9yEGJlQ+hhQtN45jTlfPL558/tnzT19eVQXFqdfIZZ6N\nbYcm0lwZO1d2DnARwhiC2++3bXPYb+/ahqcOu1YO20176Ih0HK03BRqLSmlQAhCDDL6IorOcbDlX\ntu593O3dzeaHrKzMt99fXFw8ffrk4mx5frayZRaiV8VlEOmHsazyp5/+chJz14yOTNsc8yKzsxkb\nc9jvd66z4IlIR6dMLRgUCJEmtIRaCfqBowgjowJUIbpmGKYQTF5khAgwZpaUAa2IkFkQMAYOPgQk\njdoIitLK2MKI8t6F2HNwjNGQV1pQ4dR3MXYIvbZQFZnREGL0IwmJVsaQEg5duzvst+r2Ji8Xy8sX\nNsPJsUSmsorO7bdDbszn//tX29evP17fTj6OU1Qm3w79ccLAma5rMFUQ7SBqISDlhW1yv0wgKhHi\naTRx3tus0MaAVtJ3TdsxSFGVaOn99XVZllVVDf20Xp+/eF7ebbY+crvflW6q6vmrV68+fvz49u3b\n/+f/9X/9xZe/fPL0arPZ/PDTT8mFJoTwpz/9KSFPyRZYa40sKFBk+fn8rO/7tm0+ffmiqKv3Hz40\nzVFZUxjDIBw8hCCIEcAaXed5VVXRh9LmiafivReEYRi0NSGEaRgwQqVttlwXWVZV1bv7axFhN33y\n1VcX5+uhn/72r//i/v7+66+/7ts2TmNV5Cazo5tUCIiwrGZWm2maXPCZMcaYvCyNMbv9PrNWKYsp\ngVeiSBQSRQZPq1MUZnnYaJLAOI1Wm7ouB+n9NKFI9B5VSvIhEQnOA51qgFWpGkXvAzNoZZLJIhuO\nPhhjjNJe6+D8MAzmISYgOh/BIyIBpiOdJSbsJZlsIEBiX590NYqA5RH6ICLHIj93APwIjTCfMNHU\nJahkKhWZWThEDhEJUvU9uRRLCCFEf9KeqaRRFtFap/gHPk2qp18T6tRCMTNpZbSNMTrnwBQiwixJ\n3aMe0HUfI6WUegTSyoJNkRFJ7CQCQX7eJeODtBoRU9qeS+EKSp1SLhDSFXtcA/+8AEaUBygbAE4u\nzV4EvRvdlBDLRyNJVBSFm6YhIiDUWscpJt1xKodJlZvWGOl1xAcXrvR3NE2T2hB+8FNNZeN4PF5d\nXaUg23Sv933vnLvbbUUEmOu6Xq/XdVldXV6KyP39fYwxcEwsR2utzbPMmv12H2MsiiJVcaXUfr8/\nHpuqqphlGMYQ4uP4j4jzxcJ5v9lu9SntBAY3AUAkOPTt/WGnMwtGZZlxHI/tsaiKqR8SsTM5gSil\nZmqW6EVff/01fP11jPHf/cO/FZFvvvkGrC7L8sWLFyLy4cOH474xxlRFiQIZaaWMURoRh2H4+P5d\n13W5wfTDU48SQnAhJj7z2dlZWZZ+nK6vrxMCgYh7jjOYISJpxUG8C2nPkYINkuftdfux6zqrzfPn\nz4e2Twzn6LwqynlVp33/9u6+d56yfFHP/DhxiNu7e6XULz/7/PLycjabNU0DhHVdM0Lf90VVJmLz\n27dvnXM2T6eHAQarDWenHCQ3TSJSFEWiXB0OhxTh8PTp0/l8Pk1TFEkqiOXZ+Wdf/mK1OmvbHlHd\n32/HoUuEcGNMXVV1VSmlXi6fnq9nZ+taq9b1++gaxOAcKBAIPnrHXSDFwpMPg/PD+cWTVUUvn5yT\nnMfgm/3h9vp2u90f9t3hOBzbTpzLqpkx2eTjNAxULZSpvVA/IjHqbF7P9RiCybPj8Xj46fW764/r\n5eyzVy9evfpkNq+xWB0227b1M9G3+80//eZP33+4K+qlQYUKdSHneabqarvd7ruGmS9tJqCNVQqM\nMQoYICoFWVXmkx+c75HYKlYwRTdxYLHlOOwEjSJrTYkQORoXMPJEqNMaKHgm1IqMMGnRzJElDU+M\nEkAmlF7RSMYjiTUCNEUWDlPksa5WwtEQ5DaneTVO8dD6/W7TDH6xvtKmPFFfnVMQ89xg/uTZF+sm\nfEPjGNqxLOsf3rwVKkWrLK+iyhiUoAqMyho0QsJEpJJ/DqpEcWGE3OTMvG+OgaOPAY2q82q9Xt8f\nNtiP88XKWvvhw7XNC22z169fX11dWZMjKOddkrZX1Uwp/buvv05NZEr0csFP03Q8HoVwPpsVZZm6\nz8vzi4snl1mWbd+8++rLL+bz2hb5frcNfsozg4iLWUVEU1IBxcghGq2Mpv12JyLz2Sw3J7VF23f7\n+w1Zo3NbZDkzD8Mw5uNiVV1cXGz6TUJuY3Bllhc2mxX5VORhHJZ1Hadpc3NjMpuVxShyODbPnz1Z\nzOu+73eH/egcKRW9j973TUP1zJQnwlSMnKwQiYhFgvdTmOSB+cg+GKs4RPaBYwQWAlBIRmlAApYQ\nvOdojDakA8fkr4mIgQVEFKpHZcoYh6IoZlW93+/dOCELc3AQVFGkoU9EFBI+UKh6z8KACBwhwomF\nopA8e1Bak8LUIiAgIz/AsAleFkT9YGkJD5VMo05+VgkEjnIKt9UEVmlEBBZQoEmhAoopjhfkYTtW\nrPKkfD4NeJJ0sBBCQEXpEwwxcpzggcidbkgFqBL4AacCKSIJWSFFOrPEHB9MLB5H+JTMCADamFTR\nUCuMkZ2D5NM5nb4/vU16gOujJMbCQ/8BIMzArJtjm0C/VDUjyDAMKcZE2wyVjgKCxFFi4CK3XXOs\nqoqIEl1LREY3peyd1A48ki+YeRxHjpGIUuvmfQDAGOM4TrvdbrlcnZ+fx8j7/b7r+oT06jxLWMQ0\nOgdu7EfnfJjc5ZOrRFUgbSwpF7yKpqpnuc2TOWJ7bILzRVEQYJkXiSZOSgkAGQ2Y4BewNmdmFxkU\nmLwYxnG3P47eVVWFxlpjsyJXjwFYZTTajOiKoppVdWJjaVKL1TxZ/9/f33/48OFwaK4/3CilgDHJ\nhE4wxeidcwppGIb2cNRaV0XmvQfmvm211qvFYhrbJD5Oy+/0shNygIjTNDWHg3Mu8ZNTE2gyqx/i\nKtO2P0EO6Zurqro8Px/relZVRZYZVOMwBO/zLAOR4+GQ6v1hv2+aRpjPz88X83kIAUQ4xm+++eb5\n8+dnZ2dlfcIkiDBJJ5fL5Zeffw4Ad3d30QcC7PteCwWORukECvV9zzEmE+MYpZovfrk+S388xay+\nu34XvF+v11VV/dM//dOinj179uz+/l6T0qSMpvl8PpvNsuyUW32pDoCDG6737a13e4PeUxijIxQl\nAiwongi0QRKvo7v5sC3yqi5Lo43WaFfV+epLJHt3vesGt9k0H+92t3eH3e7ggoCQKKrn89Jk3eAG\nx0qEMhLM2s7rrKyyhfPDj2/fvr+5fvPh/ZOri0+e/8NqdXX1yRf9cPzjH7++OfRgKlvXAdU4DTH6\nrKiXNmOj3Y0cj8cgzvlOUZ5lZZYZApIQTW4Wi7ptQwsEKmQKjWKjgYzcj3fBMwDm2Vyj02qmVGm0\nHcbO2pki6wNACAIkMbgYOARhRxAVCSILTNE1PjaaPKmgtSgSwogcEcUaoyEwBJQgQYBMZrOnVyuy\nsw93jfdhioNWNsuyYlZZQmPt//jjm1/96ldPPvmL/aGZ5DqfLz3cgpDWuc5mQdigVlbFGJW2FpV2\nIxE9BiGedsAIgGoKvh8HIMyyTEk2endom3506/PLrKh2u13b92p3YKD5cn1ztynLsiSMgB9v77z3\nRV1Za3fb+3HqLy4unjy9dM51922e27Oz1e54SGlF3vthGGJw1qjFvP5+u10tl8+unvoYrq+vbz9e\nE9H64hxYiiIv8yI4n2YjZDFAgxsI0I9T8H4ax9xYszAsuO+aU+6952EYWtNerc/Xq9UL//S98OX5\neXT+/u5Ga/2f/uN7AFjM51rr22s3da3CaggxGFMas9/ez2az6CO7QCxxGvtx9DFpiwJzxIeWhUUY\nGJQV5iQCPJ34LCJCgBrJTVNzOJIAISilCpsx6eQRIMxFXVWzWTrPp2FURivSZIwio5RhZh8Cx7ic\nL87Pz5v9wQ1jXdfJEDQkzXEi0f0Z/wgRIwKJRGFkFEQBQHVSGyORTrl+wpAC+yBhug8yIUh1V7Q2\n6fYgOCUkEhECaJVprYE56XWC86JYK4Uo6Y0rIqVUWowmHPjR+fLPp0znXDU7hZoPzdG7MVFtErBP\niCAQhTkwAKCAfbAoicLEAIois/PeCKaX/TDUp/sZmTkII4uKEIUZBER8DIqZ+edB/wQ188N7x5/R\n7NME7x+C1lMwJBAKgjIaFJk8OxHMEHwMx65NKVRppRdjTHHr8ODgSg8uJ+oh+ynRuKy1pFTyX8Tk\nGYboQzgcj4vlMsvzyNz1J1fk/jiln6MACZFsZq3NtEm2xp6jZkRFyJSyfqu8eNTsnnBmkQTJAqI9\n2crwFE579bP5OlWyzXZ7PB4ZwVpLWkUUk9nAPDrXDX1CpKfgFUOWZWWe9+OAAkZplecC0Hadc+7+\n/p6ILi8vlVIJJyDUHz/ebLd7P7m2bWdVndts7HpmLotiOa8Ph8PYezdO5bI4Pz8/HBURVVVV13Wa\nZQGgruuiKJJzHvtQ13UabbuuQ03YdelmSCqv9PGdnZ0NXT8MA4d4cXbOPgzDcP/xJitLRKyqajab\npQ1rAr0vLi7quk5oRELC0/Xx40haMUjaOCTJmVIqcNRaLxaLV69epc1uYhloo8ULIpLRWYyTmlhQ\nRA5tk1yrkp6hbdswuTzPx3EsiqJp2xjjd9999+Lpi7OzlSZ1tl4rhVVeFGWeGZvoDgLRDG+HofXj\nXnNr1KgpgkyoJDdWkwYgCSASSaGIKAJjaqUUCnfd4CcPosuytoVdn59d6OLpCzy/2//0+sO7j5uu\n9yB0M8AwOkvWljMlFEEFQBSwmemGIUqwWTZbXgbX//D6/e//+O2rF9Mvvvri2bOnt7fXf/rm23EK\nJp9vj21mClASgrBjrbPZ8kpUbquDPd75GJ3vlTJIwTvv/KS17bpxnPoYWgXATMhKk8m0XWfKOxAm\n0gF9M04DgEWVkyrARzClQUsKY5TICEAQHEIg9ASeYGAeIg8Se8RIKigURaCRUCujAABC9FWel0WB\nEZpuGtoGDJtSv3j+ya51oxOlLQOSyhDh0PRvdm/GYFar1fY43u773mvGTEAynSlt/DhCpq0xwzAg\nMKGoNLWkqQJOXgpEynmvjLZ5hqCUMX48Nk3TTaO2GWkzTK4bRlQ6HT6ffPIJonrz5s2PP75Os1py\nGtpsNk+fPdtut5E5WR8k5kHf91bpqR9IIOHGHz58YOanT58W1ly/f/fqi8+t0fOq1AqBoCoyP41Q\n5EgU/RRCSMpZjj7LCz9ObdOQwG63K+pqsVwuPzv76f3bY98NXa+A0nPXdZ1EVogvnj27urp6+/rN\nh3dvh2Fo9of5fPlXf/VXWuv9Yl4WxXK1ur+/H920vjhr22P0IU5TZlRli2Fy3nurtSTp4EORUyqF\nI4SoNSCQVhRO9q5WaUIM01gWhZ+cG8Yiz5VSJKC1FtSY5TGe3HbxITs8hJBGvfTvRyKSG8fr9x/8\nOJ1O9cdo29M3ng5zgJMFozKpxHAQBgYAIDnZlSQwV0RSyT0VLAH5M39mgeT0CY9RS/LgKIAPhpEE\nIEQAHH3wIYCIIoJElxMhQEMqyyywGKWH4JJlCmpKOHR6AQ/ruYdMAXNSE6VZBdVJIswpthgxWT+h\nIvFxCl7Cic9llAEAhXR6Fw9S3in49BdRJEhmpcxTDHOdSTIu5Zjg64cPVAkLM0cAZEz7fgDQ6c4e\n3ITjGIWRSBuDRo/TBIqmaUqWp0Pfi0g/DrOqPh6PMcbFYpHO1iTVffv2LfPJkQz+TBAmD7ENqR1I\ni+HkhDVNU6IBp6Y1zX8qt0ZpZh4BOcaQh5X31trkl8Qgbd9pY7IsG73b3VyfzVbJ0yrG2PejtXY2\nmxVFZY5HH4OLoR+mfhrbvhumMca4vz9+8skneZ5PIQ7O+xhi00bmaj472arF6JxLslelbaHMq1ev\nlFJvfvypaZrzszPlw3b7cRzHv/ubv31ypd69efvh/fV2s08yrWxZJn39YrG4uLiYVXXf9+/eveu7\nbr9YuCcjsJRlmec2Brfb3udllmSsKTN4GIboQ1VV6eoZY2xeJID6VCCjm7wLziulTgm+ziXl2Gw2\nq/JCIVmlhRQKGGPaaUxRg2koT83H4XD49NNP0y+SMT0RJfON5Ww2juPNzU0yu360McnzPG2+rdZV\nUYhIsiCt87JpmnGaUmx3DBI4DG5ar9dFWY7TtN8dE3h+bLr37z++/OwTZv5v/+2/XT15kmVZlpmu\naQlxvZgrpQhBvOuHDlGM1ZqU7q6pP1oes1wA3Th1SFLk1dhPkilCCp6YDRGlRO4QmJCUIgSrdCWs\nhhG6qe+HCWiwWXn+5Ori+WdfNeO793e3N5szU91ttrtDQyoYWwSBwCikzi6f6Obggo/Ruwkja5st\njJ1tDvfX/+ljpo1IbLujMaqu68zWzk3GzARMe2xg4qqqLy6Xi6ULH6hpmrHrA/sxNH3fNoej1UQg\nkQMR5LlVznoyRpWglMReAxqTKS3BuziNUcjYGuPkuRVTZOXMKOOFkw0D6ozFcxwkjkAjwaDVpCBq\nHVEiM6PoiJI6axEhhVPXu34wyiiVFUXFqhA0zKR1viirwHA8HPpmLHPDMajq7Os//fDs2bO2ba+v\nN8butdb0aBwoYgitopEj+3Tin+JmGeFBYakEkRGqqswEBze54CfvlTGr1cpFODZdURTz2RKEEux8\nbLrDfk9Ef/VXf/Xy5cvXr18nv7lUa7334zgmlt+XX36JiL/97W/hwV64aRo/TsvlcjVfkECe53d3\nd7e3t/vjYZym1XwhhF3Xff7554vFwjl373xwHjkNWHEcRz9Oimhe1d576oepKMr5oizL3WE/TNOT\n88uL1doC7XeHH374YbHOPnnx8rDbTmMfnb+7vpnPZm4ajKZffPkLY8zt7e3ybF0W2fuPH/quWa1W\nzrnetYhgtR7HEaPkZW7zLIowMDMYY0irNFclrQekuAXm1GFk1jZuKvIcBZJMUT9EOLgQEjgXY2zb\n1j+QYdMJHFlCCMApGejk2P/69euk2l/M5wDgnJvNZsmBMqGy9EAATr04Phr4J9dLZlEP1VV+9n46\nzX9/lrCbZL1pGZiQUaWUwCl5Nr1OF5IDys+CHzhFBClJO2mOwflkFzlNE+jToJmcoyjlVHBExLR6\nS9RORYaZnXMOogWdWoqTBR4ACXjvUw1OyS7JgUsIE9Plz2KRTkYa6cAhOQ3UpDSHICGYh5Q5SWZk\nIBpO8AAjRGGJ/PNkjIgSpj98/fWHm+vJu6quTZ4d24aZBzcpwOQcmduMBNIZ/eT84v7+HhGTHChp\n8oZh+PHHH733/LCliDEmmlV62+kET5T6s7Oz1Wr14cOHtOlMk1kyzciy7Ha38ZMzxqwXS2tt8B4A\niiwfxzFwHIZBAFJgA4NYaytd3N7eZllW1/Vuux2GoaoqpXVeFrPl4nA8fry9OfatC4G0ijHW+mRq\nkRV5Wg9M09T0XTWr77fb5XIpCLvdzmQ2LTUv56t/+Id/aNv2N//0PwAgSbAya8dx/Lf/5h+qqvrn\n//5Pf/jDH0SkLitjDBfKap3sNrf3m6++/MU4jr/7zb/0Xffs2bOx6+f1bL1ec4xJZi3ICWGez+fJ\nkza3WYot0lqP47ioZ0+ePDkcDjc3N0VR9H48Ho/e+1lV13WtHgTsKPDIsCfA+Xx+eXm5Xq9/fPd2\nPp8fDodkl50238k0IynKkhfB4XBISlyrVJpTR+9S4U+BNmdnZ23fvX37Nu3Mksh4vV5ThGPXPrm6\nOnYtgxybjhFefvJJ3/flbH5/f7/fbK21h91+s9nVdZ2V9NNPPzWHdj6fX6zPLs8vnl5dLepKOKJE\nhWANWU0i0U3DNI2fd/9Fwhh4Qh2ZAgMzIFAumIsUka2AYVEMJxG9ZF4pTZiDKI504qygnJ0vdofD\n6uz80PRdH54+/VTQDr3/6f7697//w8ebe5vNIqtj54BslleARgjHcQzBZYUFkH5onXPmRCcQROTw\nc6w3AGTWPhr+MYfT9LB/iyRj13bdLlNiNGzuPrx7/ZMIKIQ812VZ53lZ5NV8vpjP59EcMCFRSgsq\nZvBBQsTAgmQFjQACEKlMa03aYCyZPYhnGIJvQYYsh7o2Y99wiCiglFFAMSCBIlIIAwoopRUZBBsh\nQzuHbMF6HlQBOtcm06Sin4bm0LXH8uwqyfeHYXBuTAr4LDciAg9v/IRVGJPnOfED85lQEACVskYb\ng1odu54RlNGpTqTVVV7MkrnV4XA47Pbr9frZs2dN09RVdXNzo7X+xS9+MU3TD99+F2OsqioElyCf\n1WoFAHmer9frN2/eXF9fJ88cZk6S967rEFE7n3r3vCyQKDFgu6FP88NqtTruD9vtNtMmJn2L1hJi\n8L7Ki+iDjyGvynac2qEfo//0s89+8fmXY9vdvP8oPhhST57MpmlKFvluGDnEJKRZLBaffvrp1dPn\n79+/3+x32/1ORI7HYzp6Y4x3d3fdOEThYfJlXeVloYxhkNEFADCZjcLjOHZ8GhUlhlMB1toqPQ5d\narWN1lpr/cDy7SZPRCIxhIAEmCx1gzfGjG7iKDrLEdQ0+dT3m4zSXBhjTJE8CZAzxjT7QwqzSS5O\naQZQWS4P61t4cLCSeMpfsVqnu0UiK6WyLGtdnyYHfOBdp/skVddT6SVDRI/lFgCMMVaT995PzhpT\nVZUfh8ToExEOkTmmax4I0q4XgaZp6qcREU1mvYuBTykAoEgY044Z/IgPrteQdq8+JEat1jpV3EQo\nC8IxRh0fRnkindm0+h2968chTbqMD9JerRAxC38Wl/Swck5/PGVW/sxQRoox6u++/f7mfkOki9wE\ngTj5ycco7HxMW97oQ12U5+fn86qer9ah7xPmnGzNjTGJpZXsbIy1yVcrocHe+ywr+n4chkkpwwxd\n1/X9eH+/XS6X9/fbpmmSLdowTF03xChVOZvUNI3jfn9crVbzejFN025/LIrCGpMXVbpGPgRrTVmW\nfvDVYskhdMPISG03bHeHalbH21gvFy6Gth+GyfsY6rxYXSyh8yGEyNC0/TiOyenG2FwrW9fzvKyc\nc6h0CIwoWtvtdv+P//jfhmFIzsZ5VXvvI9J8sXrz/sPZav3801cR8McffxxjLObzfF4m0nXbdN3Q\n9+Ngtbm8vPzu22/fvn07r2pnM2PM4uxsu90yc1kXcLJLO7lIjiwAkADtGGOVF6nlH8cREcmQ1jot\nLYqiyIwBgMQVT0B9+rWIdG079P1yuUxscK118jzZ7XZ3d3fpKUrhTsnhqyzLsiw3+93z589ny4Wd\npnRTpil5HMfr6+uPHz+mKt62bYoQrrNSWfMf/sN/mC0Xf/nXf/X0xfOu79+8f3d2celjIK08ix+n\ny6unF0+umsNxe7jp+94Y9eVnr7784ou6rJAlBl/mVsIE7JGdBAGOEEfkEXyDwgaCAJCoSUjQilQh\nlqJrtHOta9AZkgEgBqCsSYguYa61UWQEAuN0HA66mu+6boqhWi2vd5txiC9evPrF6nk9N7//3R//\n9O1r79Vyec5sd4c2RMjyUiGCthzEORccW50j7/hERj1BXoREqIl0DMLuMc0iA4AYIC/PJTqdYwYe\nYhtxqJb6U3uB0T3oJZRRSCh+cu1xqBcPHT0AKhFAQtYKAjGSAAYRDAzAnQQSJqXPWByLJ3BaTSKe\nOUy9J0BBJFSEBkUTCoBGNBq9iIAQgBZRAkpEIVggI6w56gTnKUNlTdYUx2lKB+g49jEENoZQONqq\nyFlO4TMoAUWIiThqnaWFZQgchbUGhZaMbvshndfDMEzeE2lEpbVNivA0TK9Wq2fPniWP3839rfdT\n3xz/23/5z0nFPk3T3d3N+dmZc67I82TYu7m/b47HFy9ebLfb29tbZi7LcrfbJYVkMpmhzCSo08Wg\nEJXRCU58+/btzc1NXVZ5niNL8uRqnCvLsrSZtRaN1VqXi1k+jLo57trjMAy3t7fTOLrg14vFerma\nuvsUKKeRKM8VUp6XWuu+7+/u7sbJbw/7fhyOTWOMMdYmrC6EoDObg7Cg0WG+XAaOAEqEETE5SASO\n3nuTV+mdCoghpZWySmtSudGpcqRpJwX4xBitLYgoBOdiCN5jCCkH0GQWCF2IiCgspE/8uPQ4PxJ6\nhDnJ+kVECJU1qFQEiRyFUGc2rRUiyEn8Aydg1uZZ9CEwY4z0oP0NIXjn4kNCT9pMJAcKHyNAKqkY\nEsPoYWwTEXHCQgowgXMnepBS+iHP4FFopRQlnc+joRU82ko/KNFDjMnTJo2dihQSSWTvfQwBWfAh\nHCKtfiWRrQBRUW5MurYAkHDmNPdrrSMzMCeOdxQGTp0SPa6i01fiYZ3IXoTmYT2cogxPYhJtjAv+\ncDiSNajIaGsyS70qqnJ7v4kgVVUZa+fzuVdKaz0MQ1KCpk+rbduEcqQ9btpVpPpxOBzSGJ2KfyrV\nZVkOw3A8HtPbXiwW8/k8qZtShoFSShD7vpdTnhMwswu+UIXWZhjHpu9opG7oc1vkZd43rfPe5rnO\ns27oA7PNsnEcez9NMQChAAVgZTSr6Ebvhz5d1izLtDEQQpJzpUJLD4bYeZ7XWT71AyAkfsduvz9B\nIsbc73dpRbrd70yeaVL9MDz/4hMRaT9eH7tWK+sDT1PrYpjN52VR/Ju/+/u+7ZBkNqt2u818Xid5\nQ0Jgkj0eyoldlaar1GOe2lVjhECTihBijBwCGJMZa4yZpsmaVJItACQtdd/3YE8ZR1VVJVV+et5O\n99CfGaQ45xCx7/vvv/8+eXCmTgsRr66umqZZzObn5+f0kAiZHDZQq/l8HmM8ts31zY0pms1+x4B/\n+u57RNTajuMokemV/uSTT2b14q//5quz1eq4369WK6MUB4/CloD9iHEiiIpYIQM6LSPDBNIDChAh\noQfxTIwGKDf1BdIMzJJ0BZQFwBglChd5FZ14j0ploPKIMk7d6AYBUxY4dpPKbUQ+NHtFRWDf7N6f\nnc3/l//lb88vzn73++9/+uknQHt2/mJy7MPkfFRGm9wwARNarUiAQGJkYYgigJBEj0VeTpN30YuI\nNVmaA2KMEQomrXIuVBgn76dO53Q+X6KEaRjD5EkyrSpFuaJckxYfAVEISQMCArJBIAiEgshAHEUI\nOAqAJDtB4uAEYiqAKIFjdBJymyEqBEJUAoRICAqRSBmJDEDCwAKMSKJA1OQ4gMQYFWiQSCjIAEqH\noQdIMa6RCKzVWZZZrbyf0tI9QWmAYgg1gmhSoEiE0QPjg1GMstY2bTu4iQVH76ap0dZUVVVWs8Ph\nICLe+7Isbab3u006RjXS8uJi6Ntpmo6HXQjh2dMns7K6ubmZ+i5M49nlhbWLvu+fPbtqmsM3330r\nIvWs7LpuGLvZbIaI1/t2Pp9fXFyYPBvGcRzHQ9tsdoeyLGeL1Wq1csM4TVOR52VZHw6Halafrdbr\nxfLJ2blROsY4cWjH69SFDMOwOx6mfuiPzdgPfd+fz21d11VVaaS+G4e+b9tWaV3X9fbYbA7Htu9Y\n5H63zcsis8W+OYYQsqyo6/n5ee5Owd5kjfExIKDWWmIcxjE50Jlk9s6ikbRWVmmjNCGOowvexxit\nMVmWlWWRQNH77QH5FFLEIISotFKox3GMIiHEED0AaW2VNYknm+d5GpZOhRMxxOi7jpkFIDLHh+NC\nax0f6go+8HsZIRniSIDgQ9pSqYTGhWgIFZDQyRkqNWcPnCFUSp1ah9TVggBHEogSQxRjTG4sCAzD\nUBVFqiDio0RWipIrHxBG4Rhj2v6eHEuSVIkSBC7sg/cnlrFGjojAEGLwzsVwQqpiFCFUoNL2hIgE\nARglxU6lF48gfFpyK6USTB05iggyMCYW8wmweIzF4NRoPAQriYim03aWmXVVVbShcRx3h/39fmer\nwmSWAZTRIYSz1VrbY4ixG4f73bZpmi9fvlwul33f397epjGXY0ifIhGl5JAELKQt7/Xd7Ww2KzPr\nOY7eRRCd2Wo+q6oqK4uPHz9+/9OPo3fPnz+PIJv9rihLRCwToXGc0phYVdXd5r7rOpNn88UiggSO\nwDGMw1TC6IOfHAAUef7k6bPZYpmKRBBW2rphHNwUUSKOtfOu7Q+HY8Ic6vksEZH2zdF3Q9d1ApAQ\nGCIKUyRR/TAqpfKyjIHv211elfP53DO34xRCGJzf7/eH7e7y8rJeLu9ubje7HTN3Q2/zbL1cEVHX\nDsrorCyePn327OWLj+/e3lxfJ5AgKXTl5Pxy8uTSdPI6T7v21OIk56ksy7RREiMwS4zTNCk8GZqT\nQLqzH+t3WZbW2v3xmFy1E9MtxkhKlVWVHp7IbLPs7Py8KEvn3P5w0Io2m01iGDZNM47jcrm8uLjI\nsuzp06cicnd3Nw1jXVZPnjy5ODsHgOvrj8uzdTf0/TS6Yfjn3/2LtrmPwbuYWauVHbru+vr2bnP/\ny198ZZpptZhdnp+fr88Mgp+cAlEKIUwaWSu2ihUGH0YXG3GjpwkRQRGTmpgcKTFG57WaLZhmgkWk\n3At6jo595Bh8IRFYtOLcexuDa1rXj/1ipftpQqNsbu/ubgX1k6eX2+29qGPYjsbkv/zi5fn67H/U\nf/j+hw9+PGiVc8o9BQuR/dg5NxAWdaEIJQQQxqgwBBBOOyodozALAGhDWunUCPcTaGNsPlcWQUen\nAnvxcbIaTSZKWQ3WqkKrQlGBSHGKgiftJAojCkMU4GRaAMwIoBAUgiT9pQxaMQAjCREgahAFACII\nAoInXiSTCDAqFMIHd31gAEEFSittg5NAAKQQlGcJrg/TINFrq5iBmUmjUSbLMmuNJur7EREJ6YGK\nz1ppInIMRAIIZDQBkFKeZezaRAThcej6UWs9n88TfpaIVHlm2ubQD+00zHa73WJWAQej6dNPXnjv\nrz+8u7u7m6bpb/7q10VWumnabDbee4i8mM+rqnLOZVmW6CxVVQFR13V9ivTWShR54eBdBMlnlSlz\nlZnNZnN18fSTTz65v727vb3NiyLP87wqe/aCMI6j52it5RjSqKCsyaRIMSpZWYzD0PbdNE3377vz\n8/PVapVpE0JIadllWUfAtu/arju0nbG2GUYHoLzPQE0uaMPL1er8/Hya/OhPz35gBhFjLINLk5ah\nLCmISAAUaqVQIJ3iY9enQ8M550OY/JSGlhTPF9kn0+M0HQpijDHVqn6aAChHUqAi8+7YVjHEGEc3\nJQUg46moJOKuPCxcQBiEU+I1gzwuMlMsUuAYhKMwREERUNoojYSGFJA6uWRExSBpVk31ERRBkqeI\nVhoNkXcjKgUi0TtIIQqRp2HUmkgAEZXWoIEAHrxKThtP4ccoBWEQpXX8M+VuQhm992QQmVEhIiaj\nLvjzgfVnqS4k1KQd+kcykxJ10lXLKXsK/uwLU+zCQ/lnEEDAh8AJAfAhpBi6LMswy1Arg5lOnm3H\npmmHPlX+bhj6cfAc0yHuQojeH5pj17QhhE+vrlI0gnoIZI5yyk1M/zE5KHnvk8lGlmXpYUvVOn3G\n6bfJ9z8NnWlcSyveGGPwnog0UgpZQsQUijKMY9M0nmPgmDCIQTApx/MsizGu1+t1db7b7fbN0WYZ\nWj2O47FvdZ4x4RR80zajm5RSCbJPWL+IJF2vMYYebD+tMUWeW5P7GIosE8QonBjdU/Afb673zXFe\n1UVRsKbb7cZxRKN+89vfiYjV6urqqqyr3Wa73+6rMr+8vASAf/mXf05RkCmsKc/z5DD1qH9VSpE5\nrSvSWEwPRt6pVNdlpUmVeYEPRnFp6agf9EhJxaTplJiUrxa3t7dt2467bRp2E3XupOqbRq01aZVX\npeeIWiHh2cV5Wtj045CymG5ubhIpOg3EaW2fQhdCCE3X/vKXv7zfbbO6vNlsX3zyyaFpyfuiSlM1\nlXXd7I+3d/dfffXLn77/YT6fn6/P8sywmzKjMk0QJpsbkAk5SBhcHPzU+qH1bhzIAoCgFjTB6Iil\nKde6OBshD6IZiNkzEGMULWhgCgSkSWUEBYMCVPlsZWti3nmOLKOOohDJoFYi7I2GojCj881hs56f\n/5//x//9hx8+/v4P3717f40iJDFM4zD443EfhRXNRmWYObEVCYiIBQiAJzfEKIiMiMzeS0iPYlRa\nGw3KC5W5vaiqauhu2sOtuKBJa40KCZGAAJQHIVSWE5cFBTkCpUR0IKNAhCXKKSHmJA4JkBaxJ+Ub\nAiYHIuecCAKkszIIIkAUZEFmiIAJDyRUShlSVuVkHWaiFCkM0Ts3OTcgcIaFICSLhEjsY8AJR2Ei\njUTaaGOzpLtjgcAkBoMkD3w2xghg8L4fh67vnz59muflfn+s6/rzzz4XkZv7u8zqqszzPG/bFliI\nQKVE3q5fLGYEfNhtUPjl82ebzeaw24Yi+MlJ5NV6BSLTND19+jTZWz558qRt28n7sixdSJMlLOdn\nQaAdpzQt1XmBxppxsnn58e4+Mhil86ICpbXWV8v1+/uPXdMe94fgfWEzEcnKYrVa7boGpvF4PDof\nyqLI8hyyrMxyPkiihgVr67rOykpEbJ4dmmM3uXac9m2T12VUGAj6oa9Wl1PkMcTN/jD64L1vmy7P\n81PwgRAJcYQQOLJoLdNDkooihSxRojBLiGkhlWrAFHzoozFGGW2MccGD4MkfI20DhefzuRDK5NB5\n7+M4TenQzuy/CuOLwiEGkZMIIoTAMT4GEgiC9/6R5EwPAbVpulVKkQEOkZkZGRQQETtWShESU/If\nRwaJIN6HIAwcBEAEUStjtDFGOKQ0PDeS935yTphDDPvjIbdZXZRFURhSMQY3Tt57RJ0KMMjPhG0Q\nVqRi4EdwUfQp7i8zlkVIRFuj8xwie++Tj1CqvmlGR+E09aZicVp4M0g4QdzqgZVGAgCPe2CKIdKD\nd9ipR9H6kZ0TmelhQ5wuuE5Hatd12+1WZRYIGURZEybWWrsYUFGYuOlaAEngc4rwk4d1OirK8zw5\nK7FIKiQiJ0/t87JKjlcpbychq33fJ2cra+2nn366WCzST3v27NmxaVarVZESFt2oiFK47PrifH1x\nfnt39/76Yzf0PgalVD2fd27sxl6T6rrunplBZlV9e3f3/MWLtu+m4IVwsVrW8/nmuN9ut8BRWZNl\nmRCmjE/9QD0gopQonDyZIbJGqutSEBaLBRN278a7+/usa8no7XE/jqPJrKUCtRrG0bhxXtWXV08+\nfvw4TI5BsqKIwseubdrD//rv/r0bpx+///by/GK1WNze3p7X1ZOnT66vb5JZmDzke6SOKQUwaK3X\ny1WaFZLCWEQSy/HUvkVOlin91Kfu5/FRSf9cPnmSgOWmaVLrk5ROIpJGh1REU9u0Xq+bw14erEnT\nluHUvTIrpc7Ozi4vL1MSVPpAbZ599tln9XK5bY9T8C74f/e//vvf/+FP33z3fV1Xbhf7aXx6eXV1\n9bSu6yzPX3360lprjeqbY/B+UZZGoZv85CcII/s2xhZ5QHEkPtNxoitO97fJWeekKyhWUJx7r6PY\nIHQCgBCSW48iA6KFtQ/AHBViXhbGYtu1eV7s9wc3xSdXq2Y/HXf35+uL649vyrxYzerbfnf38f3z\nZ69++YtPq7K8OFu8/3jz3Q/fH9rGZDqzAMCa+rYxIkKkjQEEJUKMQISjH4kINQJiFJ9aYiQUKiPi\nEIW9FDa3Ns9y5Qc19TvSQBQFmAFIRUARBMrPkkCEIUCKGAdIBxQzJ1MsQRTUMRVGGBRpOKlEBBFj\nsoMEDRAAACAIolAyLlUMCARpgZeQadREOp3vwMieffDTFCYk0Vp142l3e3IxQARFBJTWGag1KgtE\nIjFKjCxlwjOn0YcoEY3Wcpp40LkAAKvVarVcpoZbQnwMBLRaEYFC1Fof97vz8/Ory0sC8dNYFMXZ\neqUIQbguyr3SVumLs3MRafvucDjc3t4K4WwxB6IYY14WdV13XccgkWVwDpTSmVWg2nH03u/2h+F0\nfBMzS4irxbIsS2vYJm+40Wljmr4Lk1tqpY0ZhqEb+v3xYJr2bL3OjLVKK2u++otfp4wvpdT5+bn3\n/ubututHJlWUZSCgrotIaDRo4yf38f5WAWZZPgzjdrf33o/9sFyvSBuOICIupqonMUogTuxXo7RV\nigBBBCKLSHJJVFqffKcUWmu1NTGAEhYyGhERAkcVNQC4GCSIT5xexCicplAGccEzM2mljE5MqFMt\nEQ4cQwxKKYUAhJE5ZS6lYVEncAVBAQqA0gqVYvCS0smIACDXJr2WEKPjEEL0MUzBC1A8wTQASmsx\nSCQqZQgrYwwRQA9hcgn9ns+qxD+epskhJiEy/CxhOvl/nIwvHgKGTxIgrRJhOT4Ya7BSAKAMIQAz\n+xAeOWUsJxEzi8QYkQgehL8cozy4Oj/O1ifOBxIhIWAEkJOwGfBRB68oBacmEZDSOjU3MUb9+vXr\nfhr7cdjv97YsmGDwrl7MSakszwGgLEs/ucPhUGT56KZ0lCfnwsR0MNlJWbTZbNquSxvHhPLneW6z\nLIGo6a9PRpV5nr969SpV4hTMmZaUn376aWRO6prddnt9fT20XQhBKfXjjz9eXl4mLlJRFKUiH2NV\nVYdjO1+uo/P77Y5YDk3nXBidz8vqbrefvEPSdTXLitLfb1jcwmZpS50+njS4e++fPbm6vr5O3ivj\n2Kcr23VdtzusVitXFM3QbzabY9dmvsiKXBlztV6nKRC0mi8X1tox+s8/+SSV0iw7iW7zPHdj771/\n8eKFG/sULJEXGTN3XZe24OohqEv+TDeW53lRFMmCIwUlee/9MCitNZJGIiRRJ9Sl73t4sOZIOsUE\nT3V9l/SX4bCHiHFiZk5bnxIrH0PKScyyLHXuadGVmqfHUp0ar+SeYYxJXpVpNXDx5PL2/v7bb7/9\n4c3rJy+fmyw7tM3N3W1W5MpobU3TNMe2Xa/Xy+Xy2LXLdZZM2vIyV1DkxrAbhr6T0CrxBJNGNlYp\nsopIoXj41EcBQjQZUhYwj1IGVwuVggoYQAKIA3AsDiSijiAkHCMgR4zAgp5xzPPMGD+vi6E/ivdV\nbpqDw+jrstrc3M5qX+VFnPq+a4hMpuB/+uu/PD9bKnJv3r+JEhglMJNyQ8gSBwviaT2UjhpUACiA\nCekVOA2mQkAuRvFCrKYJJLCKZVE+NVgiThB7kYG0GANIHKNHMhIjw8ktD5ATrcMLAiohItJEhKBi\njMzgo1fq9D1EOsktvA/WWkABiqgISUQCIKNSCgpGpISnAKACIGFkH10UYcxQA1MEDABRKZIADya1\nmTXG5nkSlSmtJUQWCAIkIEjJhCcIk1ZWbOAYWMB7EWGR8/PzxWI1uCn14nd3d+M4rtbnm5uPSXAa\nQvDT1DSHzOgGYF6X87oSkadXT9I0VuUFIp6vzzSp3WEvIQbhEMJ33357e3dX1FVx2Jsss3lmrU23\nX9d1VblMfbZMIyLmVVkUxXy1/Hh7M5/P54vFYb8/dG2CkUIIh/FYl5XNM4gnpdcwDNvjrZeQZVlV\nVah0lueEGEMMIWw2m8vLy3TiHY/HfpiaY6eMtmURkQoik++ZMAobQp1nze22LIoqs7bUWZZxkLQD\n7rqORRgpInByXFfKGGPAEJHVWiU7ExZQSpPKlksRCTFOwfsYokQcekHM80pE0hzoY4gxktF5nt9v\nN8wMgqSVJS2CZLQxZuqOqYZlRW4yO00TEJrMpj+bKlZgRqWACBAVGZAYOQICMwQBiiKE7KMkUFJQ\nK2uNSVsYeghTYmZkEQ7BeRecNlkEjpGFlCKJIFPwKJFCYIQK0mLEhBAQQBtVz2beuWkYp2lCAJ2o\nTIQiknAc4SgPCihm5vhzBmVay6ahguOEiKdVaWRmjs4nM8cEiWOUZBdyApO1AsTI7GMQEQ2itaY/\nyw9+pHqlqfc0gqdajoAPTUCEn6t1uqqpROr7+/vFenVxcdH0HWYmgLiuTVkWZVmWVVXkudXm9vo6\neW4cj0cRORwO2+12tVpVVZWW3S9fvrTW7g8HIkpZsynmr5zNRWSapjTvxhi11ldXV+mnbTabu7s7\nZp7NZhcXFxcXF9//8EPKqS2Loq7rZCiRWt2zs7MExs5msyi8/fixbVuYL88vL7qmbQ7HrLBpwp7N\nZv/4j/94bNvV2XpyU3c/4o7atv3si8+xd+M0JSJYURQpRrdt208++SRJ0dMcadNGpx+eXlxeXFwE\njnd3dyGE8/Nzz/H67q6u67ws0DsfAxB2w3C32cQYt/d3InJxdtZ07R+/+dPt9c2sqlZnZ93Qv3z5\n0ih89+btfD5fzmf7/f7du3dte/LyTA0aEWlSCUJIL2YYhmSVlf4jP2DO8THUWimlVEIRUtnO85wR\npq7rxmH3449nZ2epJCdhnIicnZ2lGyVNwOmv6Ps+hKBEkgVm2gQng8/NZpMgjUflwOXlJTOnMMem\naYqqKOsqvfjf//73Ns9ciN0wpG4gIQrL9cooW2Q4TRNwADIxxs6NcRqnaSisKjSWmc2yqFUAcRAn\nEa/UFybGAIikA2jvwU0mTpnOKkmESmJEBzQiGoI4uL1BIVTGKFAKYhAZnXNlKX3Xrpb10O5ev/nx\n5bPPZlVxe32zXGVH7owxdTXrmjFMjosQ4ugnfPXZi4ur5bc/fPenb79+9/7N4MayzMviWcL5ARWg\nAhIgREVwimONAKwQiFJNFKVVlEhKWWPZu77tDdGyXK7n6+D2U79xTrT2pFnEhThqRE7Zrmn0BUyF\nPQRGREVGkSZtQDCy5+R5wIxEWts8K609KceAEhyOSIIYAQVRiAQRCAFBJTsgRlYAAuyDF9IqV9bk\npDh6JT4AnrphfDhxEisHkjNEOvIgao1GKaW1MWbXNo/Z4yH4JFhI0tKvvvqq6bv/9J/+0/F4XC6X\ny8UaWNbLVTq2lFJT8uArytzqu7u7xWweOWhS6/VaQhz79vr6+rDtnj9/vl6u/vjNnxhhtVr11n72\n2WdN37ngZ4vFfLno+37qXdu2bz+8X8zHcRx1kkQijOOYcr1MZvu+dzFcPnlirW0Px+1+d2H0fr+X\nyBAiBrbGnJ+fa2M2h6NzjqxRxiSsOITAIeYxjiGk6nJ7e9t2wzAMeVl8+vlnXd/3kxuCi8KRwceQ\nU13WmTt0LDKOI5XlxWyZ4uUPh4MAKGVQQzI7zLIMSCulmNMAiijMMSKAQlJaRfbpEACALMvSPgQA\njseOiJTCwLHvO+ecLfLZbJY0/YCktQ0M3p/ydYDzBGemEybZd6R3JIiQ7pgHLwcigjRBgiR/riRF\nY+ZpnDQpQjSkrM1PBs4+9H2fM2tr03iQKULSEJQAsSS+4en9MYiEEIchDal1mae7CBCVUpvNhhA1\nqRTkKnIqnMoaOlku878uwCxw8oNM42oayoWYHkwiOaYcw1MUUpIhpcW5PBKqCVnEx/CvzKa0Yufx\nz74eJm95nKQflsEQHxK30govnR5pCFRK6fX5ZVEUxubBxc1uG0Xm5y8iy3J9GXyQxisp53q+532/\nG2szv4NuH5xCMot549x083FWVmVRpFjEIs9ijFHT+my1Ol8TEY7hfretjIluOo6DMUpbc2wP6RUc\ndnur1NnFeV1WzeHw//rH/7oB3u/3Z8vVq08/1aSCUmhs23Xj5H98+26xWi7PzrvJVbN6cXa+2Wx2\nm81f/PrXeVn9//7rf/3i1Wf52fL+5taP/d6Nz1594mJYr5eHttlut0+fPx/G6WxRzM7q5Nv+xZe/\nEJE/fv31vt3/89e/Q02Tj74bsizzKB4EM3vMdV6Yzea45aAX823fN02zWp9nWRYiZGQWy3nXdU07\ngDKLxao7Np+/fHXYbca2J5bgYnVeX129uNvc/r//43/87NOXu/Z48/7D5y8+gX44U/kBu2PfLc7W\nNst++v6Hi4sn64vLD+/ea62nyRtdbHZNCOF8vS6KQthgoUmp5DawXq+X68U0TcdjW84qq03f97vj\noRsHY0yyoCMBe4aa5eWTq81m47RpurY/HmeLhUI87HaCUNfV3d2dtfZ4PJTadk3rJ8chLmZzFBj7\nIXl9Z1mWPuW+7xPFNMY4k9rp8ObmgzhomuH792+psNVi3sZ+t9lWWa4JKm3nhpcYzhZzE1hCBB9t\nCZk1YQyDcJYV1WyGxnhlRiQvGFCJPpHRgE6udQBAGgmCwgjcPXaayAJ8Uh3Mw4thbHwYTDmdLYvM\nhrHft83NcN+AuNujF5D1ej76/eDHnvqc/x/1RTuZ7QBv6ekN82EfQzbPS3Ul/ayKy18/+2xtl9/U\nl+/u3vdumBzneRE8K9CE+ng4uKmbX6xyiM51gKwAeQKjrDVFjHJnMhHF4gOzzWyWA8S+C3tknNwu\nxkYrEcLgAgrluvZDVxjLIjFEIogxBkBVlCrLxxAbP/DQCI+GfF3oWWnvI4UQFLHIVNS5tny83w9x\nyikDJEKLlCFQCMDMiGolzRQlr6tJ7P3Rl6uz2fz5YZBBOIwcuz0AZBoyMmgUcJyc00qdn5/ffPho\nrYUoVlulVHST0QqUAkRSKIbYkENelmft0LswDT50bgJFqNWoVRz6//ynrzVRzKydzWbrs/OzMwUY\nISZSvbZ2s9trazbNXQihqvRtP479MK9nC1Z9N+x6hmyx1/r25vr169dKY2bsmcLIvMrMqxdfbe/v\nop92NzcS/fn5+Xme8/F4DJMGxuC6/QBCVVkqa/3kLsr5MLpme2TPQdSEShAOPoDT99cHY8zZan1+\ndXHY7ReLbFHVFqjM7cXzV8PYHfcHIpw4ynYzGHP9/t2TZ0+fffLs9v7+6fz5ze39dmqW56t+s1ET\nlWX++vVrFFA+OOfIljHGQvB4aI+Hb4uqPPbdfmp1ngEEJcqCUixWsEDIUW1Cq0SliFIiHX0A4BRw\nYbNsJLSKEpLMAOM4Lp5ebbfb3eGokcpsNsuJmWMTqqoq5uX9fnc8HstZLcjdNOnSDpNXSlllYpCh\nG1HIoAmds9oQC3lUnoio8ipXSik1mNj74KbBS0RFLsletSrr0o+TBizLTGkKPNm8ODs/a+7ZOddP\nAwAgqcTeMsYIUHAASMZmqJSPHAIrpWy1cOPUdg6ZMmOtyiUGDCIMdVWJROdc5MAAUSJqypCiC0FY\nQCYOYRxBa5NniVUDoAbnQghJvxSDhM6VZWmzjCU4ZkClMoOInmPwbEgZpTIiDsG7GEJocgQA5yZh\nropCkwIfDKBNKUlp9iUUhBhjSJYjJ6qzFpEpOB2VMWaxWPWkXd/1bgIAynLUShB107X9OKAi0Mpx\n3B72eV6enZ83bSsiJgRtMhTQmQ3CAhA4js0YQlBI86rOsiKEcH9/n2wOvXfJhaSqqvlyOZvN4uCW\nZ+vJuzdv3rRtm1clMx+Px5QHDCJJNduIaK1X52dlka/mi3Ec37x5Y7V58uRJYbPj8firX/0qCH+4\n/jibz7MYDs1xvV7HGHNtfvOb30jks+VKmN+8edM37Wq1SkDZcrk8Ho9j1ydLkLIst9ttkVcxCGs4\nHo9d1+2PR0G1b45lliOitlYZAwDITIhPnz795ptvUp7Po6J8HMcEXCffD4ATTGeMqarq+x9/yLV6\nevHksNuJ8MXFxWw+n/zogv/Dn/503OytSNM0y6K2Sq3mi+7YpCCENJ5K5CSFrKvKu5gkvFEkxrjZ\nbLz0ZVkSEWq1b47H45FIW2uHYWhCAwCk1eRdN/RJCz+bzVKMRBqU274DwoQqG2Pquh7dlLbI6QWM\n3JnMKpM4hKCMVkY750xmb+/v0h5hmMa37989e/ZstVptXm/IGmv1bDaDwgLz8XjcHPaffPJJcJ5Y\neHRa62q2yItKgBg4K3KOfvQOwWSZ9dF1fYt0WtIwnUgMEpkfbPMAIG2AkpdsukqppT1RGh6ojKiA\njAIkAeiHoeuHvtu1zVFr1lqRIgTDCIjKZjmp2tjahTiNFrO5LbQ1lxC8jMR+Nk2ZG1VRzH7917/4\n9f/87757893v//j7b3/4OAxj3015XtVVWczmtizK5fL6wxHJ5EWmlIpT9KxB5VHEGsUsKALihT0n\n9S7RNI3ee46CxBgFEAmJEZkGJscSREWliSltb5EBbVaZYhY4uKkLY9eNfvLY+na9WAKw1SZ0Q9f1\nPI05CAYHAEgRMIoQRlGEmqyxJbAIKGZQKhPBcXTTxCkMJu0+jEGjiDDRsGkcx/1+n8CVFBOOeLIH\nT4suZpYYT973PuR5XqgSuvY4dCF4qwut9X6/994bpSRyVVVFUcQYp8kdu+PFxUVRFKObZrPZ/nho\nmqYsy7ScGvshOG9ItU2TnOZuPnz0YXpyed73fXS+ORzT3HZiUAL4yTkf27bNsmyxWla26LpuSlqG\nrEyTpLGZtVaZbph80zT9NPrAs8V8NpvFbvzkyauh7RDl9evX6Vh7+vSpn5wmWK/XzdGcrdYikZkV\n4IcPH5J50cWTy2cvnpO2/eQ+3Fwfm2a3O40Z6/X6wVFZ6aIEkfV6TYApf0wRrRfLdhyGYUREU9V5\nnqOPkdk5ly5UAqWtMVGp4HwIgR8gzcjs3ORjSO4/SfM5n89JoMzy0mbRhxS6miggWci01snmoWma\neVGmqU6YnffpsTJaP4YQoCJFChUlAvDDHWKIDBBGEUEQwhMgJzCOo1DItZmQ9vt93zQAAIpIK0ES\njsmgkAUFkLSCNG1LzLKTgZdRmoMLIQTnlYhFpbUeh8k5d1L2IUWRE8vvJD1CjgERtTIRISkqh9El\noMIYo7QSkck59UBU5n8dCxhioIfYJiJFWhOi1rrlERE1Ka1NmRcEGL0HFqR0Lp2IjsnLW4TJmHSU\npdMsOB8gQdxlWkeiVt57Pjlnk37z4f35+fmnn716/vkrUxeH3/1uiF407Y77tJ488+Hs7Gy+XmLT\n9NFp4vlyYZSehjE43/d9bjQlNAPAGO2CT7rplPR+uTwriiKLIVlO4jQlERUzT9NUFWVVVUnQVpfV\nYrF4fzwmkOHu7u64P1R5cXZ2NpvNPn786GI4v7zY7nae49MnV7f3d977Yjb/8O49M8/K6ng80ZK7\nps2MzbLs0xcv319/ZBFlTZIboqCPwQU/NW7yoe/7YXL1fOa9DwAC4GNwY+STZYn+wx/+kFhad3d3\nRJTSn1arVYJtH9e3iW4XQmCRsizP1svlYtU1Td93kwtEVM1mXdftttvFrL5cLhe2uFiugeV44MV8\nTkRVVa2WS6317d31drPnGF+8eLHdbZe8TjbR1to0RSUc2E3hcDj0fV/X9cXFxaos277PssxYu7+/\n3+126TkpyzLGmNqRJPAnIiBKmVcp4co5Z60tyxIR+2NzerAfFFAJak5Y/Xq9Tv5lqQXpuq5a1ECo\n9toYw0hKKROprEo3jFbZ5WJmtVlVs4snV6ose+e5bYwxRW6MLXxwU+806YunT2OMaZQFYCGd6CYi\nnCosQOLtwiPDMPCJRqgeducJ6mEKypCyFjE48d65wbEXDYBGGZUZSV61adFQah1LhuhC8J4C5xNF\nYKSgQ09ns6fnF6vJc9MGtPzk6rOzqxcvPvnuw4fr7394fXd7uLm7RWVevXr1V3/zl4f/T98NXRuF\nUIPOjS6Z7AReSdAkCJj89RFEkTLKun5IkRXCxEpINANDFDZdUAgcQUc0uSaSoETYT8FUsyyfG9BI\nI9EQg2Nhbm/MTO+292fLVT/0htAQqPQTkJEDOc8gKKhAGR1RnSuikXEKpGwBqPvRBw+CihFSndCa\n9EMB3jWt9945V+VFlmUKyVr7SMElOCXeQAQlGhEDCMcQA0/e2SyrylwZ3fZ9NauBJYpkmVVKbbfb\ncRjGcSyKLM/zZDrx6WevlofDu3fvEg+ozIuh6d6+fXv9/oMwL+qZVTpOY27t5dn5t7tv3DhBsJrg\n+sPH7tCcna2MUiFwjNG5YG1+cfGE6lnal2XGLuarpml2u521Wdd1LKy1VghktA8hhHB9fb0oqqS8\n2O+3zrlpGqoiP1s9a5iHoUvEl+dPn4Xguq7TSOdPrkxmf/jhh/vtnkFEUYzxeGj3hybG6GKwJl/M\nFun0szbvI4/D2E9joS0JxBAVg8mzaZp8cvFkHoaBnTdAmOXJV+uBZ8SJFKmMXs5mTd+xm5RSuVYy\njc3hMDpH9dyQQgCWU8YA2Uwp1batj8FPLlVQFQgRg/M6r9LD7pxLkqcyyyk3IcGtSpm05DL6tExl\nTNg1K0RFgTkFk4uIVhpF4uQniEoAWIa+j27SWhujHmk36b2URTkEF+LpsRURlhPMaxSyaDeO7ILV\nCgyKkNY6Id6QUrQf5B5wqnYgfLJBZZQYQz1bpDLk0TPgFCIz+8gFEIP4GJhD0kknvR4zA6BPixlS\nymitNQLYPpEYgBgMkkLyiRmHJCIgyQ9bMLJElhj5wXZDK53UKKnQtm1rs8zYk+dXcAEiIKJuxj5z\nwxj9PLNPX75opuH9x5vr7X3Xdak1OI696jNCnIBZOPMhz/N5PdvLbt/1McYsW87Koh+GZMVnMhtj\n9DGmTcnxeFRKaWuSoCUZDqc0w+B8+lSSJImZm6Z59/YtAMxms7qsFEMyfCiK4v3799M0ffnll8fj\nMZ3+t9c3pNW4P5RZLiJlUbDzl5eXBJh8YhWS994qXRZFiHHqh7Zti8wyg/d+dK4fnYiQNqQNCQTh\nafL9MJw2B8xag3QhLUSbpnn69OlsNuu6LimjkhQnNeDhgUcHAJ9+/mmY3PuPH1CpVLH6afzmm+92\nh20Yh6vzi1wZVQPutxnpJxcXubXDMGRZppROuVKKwLtora3r2oWp7YUZAsflejWMxzQoO+eGafQx\n+BiarkVF2/0umWPcbe6Px2OeFwBwsVoPwzBMY9/3+/1+GAYgcsPwQK+ISYdWluVsNsuKPNnZjM6l\nmfvQNEkVEJjHvi/rOi/LmvkJos3zcRwXZTVN0+XVVTnOg8Ip+O/evl6uz7786hfffvvtfr+/Pdzd\nalMUVb1cvfj0U7ffTWM/9v3oQmHIFiUHn7ShiABELMARANOToBEiSPKQeTTBSUf/z2Z4p4NJBACc\n74nAKEKkyMjammquiwJRlNZKa2bgKBEAwBoyw9iSpkzPg6NDo6J3mckqWy9W8xhp9ICEzvlmvzeF\nni1nf/nLF5+9uPzFZy/fvL3+8fWH65vNx+u3Tb9PQQRRkNEyUCSDZCfEMhwJCSkSikpqIwQCUSik\nNIMmjiASAQgkCkrWMxArpwSQCEVF8Ry1R4ORxOsIWcSCsrXoGEL41S9wUc+2HxoJhkcCS0pj9L6u\nihAcx0AqKCIkIGINYWSDpKfALiqTVagKjiSotLIIoACVIkQQkchR5DSthhAMKa21QkpD4Wn8RSRC\nkBNtFACYwPswOtdPIyOYFAAsMjnHzLnNdFFEkL5r/eS0UiJyc3ebPGeev3xRVdVyuby9vR26PjP2\nsNsbUl9+8cVqvtjc3f/4w49oNTG/+eGHMA5lnh/3h+3mrm+72Wy2nM1tYV3wCMRAPkpB+ti2LoTR\nOY5Q1THJI0OIZxfnwzAc2sb5aPKsqirvfdO15cWT7X5HBCGEr371Fz9+/y0QBuG+7900DtqsV1f9\nOGzv725vb59ePrk/HJTW79+/33eNj1LWVVbkLnhEtVyfT9PU972crM1IWw1umrzb7XaDMpaUMSaG\n4PvRKj1b1dqaruv22x07v1osi7rq/ZAutveh9z45P9d1zSzjOEVmQtLWlHkZA1dlvffT5ByHaIA6\njsAhM4lQObgwueBRK0VgjCoyQ0QcTocwxaiTblUhohir6NEekoCRA0b6mQv/SG5CRSc9Z+IeK6VS\nCEFIOQpGK61FZHTOx4hEqE1GZn1xfr/dDIdGYjDasvDQtk3TaKQyL5RSwEIKlVKJqiKcrLI8AChF\nCVpARABMom1SCjVFBgbQyiYKcJaXJ0uiccCT9pJEJHBMgcSMgJxEdswASiScZH6otCbAIiuiD14c\nhxh91JoMKKUVCkhicYuwCIqwMAj6ENJIduKBq1MIwna7Z5EcINmGxBhTFITOymJz2N/+438p6+rq\n2bPl+Vnr3B+/+SbGuFqtsixjwc1xjyyJljW0w4fr21u6jT4YpRezWZqNAMCH4EMQEZaISqWsJOec\ntkaJRqKk8k6lscwLmAsRee+bts2sJaLtdvv0ydVut0OBWVXNq7osy3k9Y+blX/7lx48fX79+nZfF\nMAz//N//qZ/GPM+d56SkXi+WdVFK5Ajw4tnzruvavvvdv/w2RTx6jopIRHaHYxWi1lprk5IHx8Ht\n9kdlNIU4js6FWJaZNkltQmVuP3z4kGXZ+fl527YhhCdPnjxW3HR/JD1uanxslXdDv7m+PWy2n7x4\n+eL5sxDC3eb+/ccPm939arG82dwbpavy1bEfri7O/TAWxipEa63Wemi7lOUwn8/zMjPWfvz4se97\nk9l3H94WRbHZ3CXaBTOPkyMiATz2/RhC13UBQDs3hpDXdV3XwzCkRGfv/X6/T2dBZszhcMjz/Ng2\nie6htQ4hROEEVifj+8TbSpP9er12zl1fX4vIr3/9a+dcIqY+ffr0eOyOQ7NYrj9u7gThV3/xF2Ts\n+cWFC2FoR2Q6W1/EGF9/uL7bHxez+d9+9dnl2fnZYt7sdk3X5BwVwuiihaSwRwCdwr3IKGOMeAb4\nudgCM+DJD/0R5zkdCg/SKQAVki+OACht9JwIhFkYvYAIRlQiEKMOQhA2hjKyBUFpo4lasizLrRXg\n3h0U+Nk8X6+hmKAZt8fd3Znluc3PXi6+eL76n3/9xbvruz/86btvvv/BCxOprKhEQT+44FBbMJry\n4EQiMSsERYLi/TS40CEHlEBJDiSAiMAowEIiKBASSuYiK44U0YuSEMU78cBkKm2td6Ed+l/96n9T\nml6/PdZ1HvwdYPTsQhiYqiDE4g0pk2VKY7oejjPCjInRGjI1UwaMhEZrQ0AKBU8hCjGdbslOJ+HP\nCdATkSdPnmw2m2ToSErhCdITAAAinWeZ1a0b98d958a8LANHfnCJcdZqOj01eZ4jSUJ32r7705/+\nlGA6rfVsNrPazKu6rqrlbK6QqrJ8cnlJdVYUxe9+97u//tVfnp+f//a3v727u7s6u+j7fj6fpz2f\nUkqA+mFiaT4cNoi422xjlEc9vTE6sV6DsHQdnlLmEFh2+00S7EXnATkri0NziDFMXQsihTVZnr9/\n9+6w2/Z9f/7k8v3dZmi7cr5wiNWsrmeL2809AGlrV6tV07RN0zKLMtb1Q9N3ZjEv6ipThqJoUrO8\nBB+H5qgzu6hqWxYcYqOUrUw1q22RTxjGrk8e1zEEEakIM45B2BhTZdmxazebDSIaa58+fYr73X6/\n9xKNNSKy3+81qRSn5oJnBKV1wo2sNoLpiQNUGgotef64VlAqDbEn5RKL6OSahxhO5C9hgpiCbzUp\npHEclUCR5RqJnY/M1lqQAMmGIgbvAymlSBFiEuEwh8goIiE+GGpG0YApkdBobRQRovdhGAajdYxe\nRIw5hbGmwj8MgwtczWoUcm4ERUVZDcPoAyetJhkLkSOzAKIiTMmdmOgjLADCLMIkkAKe0r1wOv9N\nFlCx9cycTECZQeGJgC0igMAgKAAsgSA+kLwSqT4RrdOQkBBlBgkxPAiZWLsYnHOb3XZ4/+7Qta8+\n/0xbO18uuq4bgmunwZCZz+dZmQEkZ7MTDJsZtZwvUKvI7EIok1KST7JURpcQgMJk+UMOlLV2ebZO\nfDNrbZISJoFU4mqXdbV4crVeLNPonFLlk0Jmtpjvdrt+GqdhRMSz9Zr2ewBwIlVepHeLAvf39wqJ\nQySirmnvtxtrbVGVhlRWGEYo54tHKq9WFgijjO04mcikVeAYiVipSOS85xDa476u67OzM0RMZh2n\ngIo8Tyhuel8picU5tzxb/vT69byqX776VJOanOv7/vrmgzK6qGZPnz/DKMWsLmY1upDXs8PNdZZl\nKMAh+sjD2I99F7P8xHhSaphGACiyum3bY9eOY8/MBcdxHD/eXFtrn9lnhS58DNoaH8O+a5q+LYpC\nxr5pm9v7TV5Wq7Pzbhi3+0Pf91dXV2VdT9MUQhSRruuttQI4jc7UtqyrpmlGN2VFTkje+yhs80xb\nc/HkEgC2+93NzU1VVQzCIG/ev7u5ubm4aN6+fYeKvvrVX/zlV7aez65v79n5/XZf1pUA7ds+3t7l\nZfHTN19fXFz8+pdf/eKLz87rare5DyEszs66YxMZIZEhBQAJGTmCEogpuvPPDGsAABU9Ug1T9U1t\nkBCd1sUcGXRSBwtiEA6BAzOhRbICIAGFVJl1Hj17L1jmZY2olLDIMLhdXQSCdmh23XEyVmUi4Cbp\nh4CW8pmhfK3zyy/Wf/Xq397tfvH//c//5Waz3TUfXaCCsqxcGU2TC5UeY/AxegKGyDF6N7R+6oxG\nTUzIwsnTWVChRAmSISoAAGGIJIGBEQnYO8oiKiFl8jK3ZS2DF++//TAVZRayp95kUmZBJoUxNzBE\nF3SBKKa0mGWCKQotcpxHVLbMNGUBTRSNRmMESMY0JAoACQiBkBDh4vxyt9sVRfH08on3nkMEgF/+\n8pe/+c1vXPBB+FS9+DQNUGb6vh/G0QUvCIE57aTOzs76vveTm6aJlUbECDwMw9n5SpI3kKK0qlws\nFuvlarVa7Tbb22GEyDcfr/uuO1uufvnlL7bD/vnz5/fXH13fHbd4tpjnVluThxCKouj7Me0oASgI\nuyBKqRhjFJim6dA2ZZaXdYr+HLU2ZVmGELq+P0xDjNH56d37w2w2OzR7o/Q//+Y3V08u72437bF5\n+eLZ7n5zaJtkprteny+XoSiKv/+3//DNN9/10zg4f/Xk2WK1nILvh1EIvQ/d0I+TR9LG5j7sbu82\nJTCwzItKM1hUuTZOaYM0dH1/aNLUOJ/PtdaoVTP2WZY1TdO2bVoDJ7+dNJg675NSNLeZzbO+74/H\no0KSyMiSZ5kCbIbJRy/MJsu89woQAILzzNEoJYgqPsBL6mQW4WJIjBB59KgHiSCKIwAUxj7yLeAU\nKw/ElFyy4ETJUg58eja9c0Ani4wUhsHRxUmm4H1kYwyBTJP3IeR5PpvNpq5XSMAMIsiRWSmljFHW\nLqxRSbIhD5E/zjmFWfLWEjkZWYMgRDbGOO+dCzqzRVGQUl3XDeNYF1UCJBgohvBogxU5ooACRIwY\nEVAiRgDQxiqlyrIUkSLLgWWKJ1fLRzAgzbhpBkv6Q5REjOBHsWuSUnvvTw51p9hn0VZpyul8feaC\n16SOx6My2eX5xccY+74/tE1hi+SYmD6MoiyDdjbYTBsi3fd9UNpovW/aTKu05yej+aTmViGEFNuA\niHVd11XNzGkRLSGmTzrLsuSWuVwu22OTMOfdbnd3cxt9yLKMtGr7DhG/+OKL+/v7KHxxcZEQTmKo\nitJ73x6bpKWZVfX79+8Xi0VK+4kxZsYm+hVp9eTF09vb22matM0CS98PzgWbZ6C00joiifejd+D9\n6BwAnOd5VVVN08QYF4tFjHG/3yPier3mB/eM1M2lSzRMYxBerlfLatYdjtvttu97AVLGZEU+ulDm\n1gvcbXeGVLY/fPnpp33fX19f3x4PADC6SVtbFEXTtaiVEJo8A8SiLBdn69vbWzT6eDx6AdAmMLh+\nHCZvi3xyEyCO3bjb7fq+T5SrEELGWFTlYrVcna3vt5vr25u6nwFA3/dJmbDb71PoQgghnYOoiJkD\nRyLyMfTjsDvsF4vF6mzNzO8/fmiahkG899v97rdf//HDhw9ffPHF559/7l0cjq3rh3fbQxSOPipE\nAPIcA0sQ0IDd5L77x//yL//yL//b/+3f/8Pf/U/lbB6ncYqAJg8huuCJFCmjSMUYXWALHv7sK/Wt\nAEBwygh7/F/pgbfGpHteMPkHcAxJIIQhAjOh1ihKGBiIQIEWiCGKkchAUQGLjJEPZzMf+nfHzY/9\n4S4zOJ8vNRo/TBl55yOFpTJV14WJbLVYPa/M//FvPv/uJ/XT6+l+305Tr4ODaFXvQQ8QI0VmiSQM\nEgw7Ug44GlKILBCAWAMhSISIsSIgQQXsQJAiKyBj1DB4QiYtTF5MoBwMghnht6/3eW5Z5lPQqswR\nAkDUdTb0R0SxmdKZYUTnx5CisqEUpHy2MrbcNz0yZVkhLhBpAMCkMwJJfhtIUJT17d1GTeHmbqMQ\n1+v1u3fvlDLL9VnTNN0wcIpdE0wrbRfDvjk2fZcAM2VMCgOIMeZ5bpQOzsUYC5tlWZYoP0VRTNNU\n5kVd13meZ8YmjtJxt//48ePLZ8+fXz2ls3MRaQ7HV58/r6rq7//mr//4zbfH/fazV1+Aorbth2Ho\nuqFpGmWsybLJ+xCCVtlyfb7dbpOkPoGxCUifVfUwjX3TbLebcZy0MSbPyrzQWq/Xy2kqFNLNxw9Z\n9jLP87LIzs/Ph7Y7O1t57+fzeZ7n0buvv/6a8+qH1z9tttuu66bgi7saELMsY4S+H7t2SKN/4lIQ\n0eCd6wcruMyrWZ5ZbTJlKps75467vZ5GU+anNJeTKX0eYwRFCk+GJnlRrM/ONpvNOKYy79fr9cWT\ny/fv3w/DQMaqpDkLEbQ2xhBi4tyEEFhYgQrOR2FlNCEJMymVIiRPo62clEUn0xWjEU/e/mmaOyF/\nKrklx/QwJgchRITIEdJhyNP/n6o/a7IsO9JDMfc17fmMMeZUmTUAaHQDRIvgNfJSvOLli4x/UzI9\ny6QXSWYSqUuJFC/JJoFGFwqFmrKycojhxJn2uCZ3PawT2c2wtLKyzLCIc87ee7n7599gLXHUnG4N\nRCUFYqAYQiAEoaTROZ0sKVgrVRYFusDMACRBALH3XgBlWTarm7wwaf0XQkp8YmutkiLPc4PAAN5F\nATIyd11nitzbEJlAnhTqiIIQJ2dBoBaamdPBkM4QSqtoFIJFZHKBU+KhDDGtRwHABh9jjI+//SMU\nj4CAkN4jRfp4IqWqLBhijO7x7xPTCxN8hKg+efb85u6WfGiWTdXUBOCcRwQgropSCRkC7R62281D\nSv5SSltriQmkQCF8JO9HLeSsaaSUAiHwR69twcxCqu1ul2VZckAcx7Hv+xRskKIXlsvlYrEggHGa\nQoxGaSVkVZRPnz4VQhhjhJLe+3fv3j15/qzv+2fPnrVt+/bt26qqNpvNYrHe7/cfyVAxxqZppmlK\n0+rl9dXNzY33viiKzWZTVZXaH2/vN0KIJ7N5ZJ6myUfKykIppbSOANZ760Nabmitnz17+t13361W\nq1evXr1+/fr9+/eLxWI2m202m7qum6bRWidOStq/Hl0/W8wPx+P29v5svS6qMlD85PNPrZ8Oh8Ph\ncGAsCPAwdM8ur01VnCwnjS6KQmqlXMZ9b71ru65ZLibrI0I3dB7os88+i8iMwvpQVPX55RVKdTjs\nSEA/2QSsgUCpVZLkps2usPHu7i75xV9cXm53O+vcfr9XSq2aZpqm7W4XY0xXJC1X034l9Ubpf47H\nY1EUKTQ0aZC22+12u5VSVk3Z9scPNzf/6l/9q8399puvvw4EzjnPACGul2eqzHdtN/o+MkWmVy8/\nubi+evfTj/+3/8f//fe//2//87/4n/7i5z+XShFjCJMLXgjKlRaIIZBzFuXHfKG/Zz2fAOnHoO90\nLqQ2SIJK/gHpqGWAZGBiTCYQhZJSIjAxkAQhBUyDlLJQog5kgmMma4xtMj/uv6f+TRbuqtySI7rv\ni3I1X1wCPYRuhwKhqSoUMI0wRkCxqrJ/8sXVz6+b1z+++/KP39zdvmNUGZpe+BNkzsxAUmBmhFFq\nHK3WLBACBGZGEYAYOWIsMcWFJ1NliEYIIZWWrBVE9NbR1HHA4AONvq1XX/jgtBSOYpErJt+PLYRc\n13VudFEUQuE0TaPvPHmQQlBkZpnXpmh4IBBg8pKETxedgSMzxsinfa44dG3aiRwOh/Pz89/85jdK\nqSTct973dqIQk3AZQTLAoWtdDCbPlFKjtaOdknGbUmo+n0sU283muD9wiHmeC0BvXTOfpQQUiWIa\nxu3mITeZlPL8/BwjSRRt21Z5MaubEEKemb/9/e9fvHixms8iQWZUJEjGMtvdoe2HqqqkNvtjOwwD\nATbVIsnQl8ullspPNsXgOOfGfgjON2W1mi9YYNd1h8P+6ScvQghNVcUYZ7OZcy7LdVmWX331VXT+\nr3/zaw7RGAPM1Wp1e3v/n3//v2qtUYrFakXEP/zww9X1NREVdeO9T1juOI4xMhHM58vsYv7uxzdd\n19UqSxufaRgT2H60Y4yxUCoSdUOfPuTdbsfMaQIbxzEw5VV5+eT6/Ory/fv3zrl3794dj8e6rt1k\now+r9RnF2B2OCf9MgQREVGWZc46AUckYI0RiQQCoGDVKKRURBWYAMEpLrfppFFIKcUqql1ql+Zgn\nn+oKKhmZIEDyjQJiY0yyqkYALWWM0Y3WFOoEVtFpQ5wasno2o5PLnixMhsRI0Y0DB4+IWqo0EgQ3\nBdR5nofoAAwABIqBKDeaH1NqqqoCKbqhjzEKKYhi33aHriVGZXQKOEpTX13XdrdP3UOkmPZuQggB\n6CmqU9rhaSYOzEBkY9Rwcq3SjwbvxhiOMRVgCSf/S8aThhs5aQOYmBWKk790JFQSEZGZiDzRKc7p\nP/6X/2y9u7+/z8pisNNgp7fvPqTYzru7u+V61dTzlNi12+0Qcb6omNlbd9zvz1ZrI2SmDXnHzFVZ\nhhC67pheYlmWdV23+wMzpwVnWl4mRdAwDLe3t4nTlMJoE1hEoLTWr169csH/h//wHx4eHq6vr4u6\nur29/eSTT5JHxIfbm81mkwrtfvREpIxOO850T69Wq/fv35dlmfzZd7td+pS3222Lp/CfqmnyPG/7\nYb/fe4rzxcI5b70LyXAAT/3LdVUmhDmFEzBzVVV5nt/d3ZVl+dHYK+HtZVkOZNeLZbvdc4gaRabU\ny5cvm8X8+9c//OHLL02mnlxeXZ5fzItqaLtCm3/x679MU/X9wxYAZou5VGqwE4OwwQeKf/jy7w5t\n+0//6f94/fTJZrP57pvvLy4u/vqv//r+/na5XH733XebzQYAzs/XD/f3WmsA2m63KWB4s9mssqqu\n6yzLVqtVXdc//PDD999/n+f5crlM8o/Xr18/PDykz2S1Wlk3JYJ36pnS32utQwiHwyHd7ojY9z0z\nF0WxH4ft/SbLsl/95a/tMG4226aePzw8TCH++PatroqimW1224CY11U39Oe5vrq64ujvbm43dzft\n7vDZq0/+1b/8n188fSYQY+BpmohAKZX4nxk7kOLjvocfPVRd8P+Qk5UejMisgnos2MCSY4wheB/D\nxyp+Cgh6DAZnckySOWOWFKLkrsn6ZbnfvvsbtD9JvzdIORfIjaAaIeflOE3OeVAyz0yplMEUXmTU\n1LVKKVVVu9vtf/2bP/z4+h0ztgbTfjThJSiSAEx6bwEAOVFA/75rHuWZiBHJi2hFCIiIIiOVO1FS\ntoim8ZhZFCwNAcYYAzyRUmohBLJElghaSaVElRchWRV47+MJtEfEeSmcc6uz8xfPX37z/fddP1rr\nE0horRXAZZUJAePQOTch4tWTp9vNQ4yxKav5fP7yxSer1WoYhnfv3o12mrxLD0uIMXn9+CzbbDbK\nGCHEw8NDijw6Ho/L+WK5XE7D4CY7DEOZ5Rdn55vNZj2fNYv5brdTSq3X6/v7+67rLi8vX734ZOj7\nqR8E4G677Q7Hs9W6qqrVeb3f76dpMkWhtbYuhMioZJYV797fCKV9DCEQMd/fP8QYyZBzrioKrfV2\n8/Czzz4XgNM4IuLxeGzbVmm9Wi/Kukr8rGq5Sr2CEuJPf/zy008/vXn//v72rsjyWV0apf/1v/7X\nHOJ+t2uapm3bv/nT1wDw5s2bGKmez1Lwydn5+fHYjc52XVeWZSTquqGeNWVZ2hxdN9DkZjK7aBa1\nyYPz0zS9u/2g6xIzPQY3ThM9mkgUJru9vU0Jx+mHJznlYrGYpim9hcTYSFJDkEAxptDutL9DxJTI\nHmM8tEepFEhBREVVEpEZw0dgKaXbgkBA3LdHlEIaLaQk4BgjKmmMCccpDQypD+vHIVFGUjtljFEo\nKMZM6ZS6RuyTxQfBae+bHGuUUum2TCU5MTmEEBmd9BcnnleyUI4+UUHhUX1kzClxoNZVstEd7RRj\nZJQu+H4cPBMqKZVBKVwMPoQsy8qyhGG01rLALNMg0NrkSIqZUYKBIwmGsijKPPfe9227n2xScCUk\nI5W2TOmPyy8lZFqqplfr/Hg6qSJLKY1UIQRrLYAAKYQQxBwohsRskUK9ff3jarUqtOmPnaMQnIUQ\nQwyZ0l989nld1+Nomcg7Nw7DYbsb7HI2mxVZrrNMShkjTc5Ow1jl2eFwSPLnsiwTjbDtu9HZMsuT\nmJWIlJTBe2stxfjk+rqu69u7u+PxmGBqG7x3rJRCKbqu6/t+ebYu6uphtwWBo53uHzZZl6WJNsZ4\nd3dXry/7voeAVVWlPnGaprbvqqbmSGlfa4zpui6pinXwx+MxXbxhGFyISqng6fbmpigroaSWEqVM\neHvS6qWtw2KxeP78+Xa7PTy6faXvSZd/mqZxHIUQqjIf7xuhVSKgH/vum2++aZpGahUZ69l8GqZ3\nNx9ePn/x1TffJKw4xJhXpS5KIaVgRik+vH049t0v/upXm+32d3/3h29+/GEcx+442ki6+Orh7r6u\n667rfLBVUX7//euuO1ZVNW+aFGuYBu58rstGEIpD1+/b7nbzEBjyqq5m89lska6vECq1QdbePnl6\nmYQBKYgbEROTs23bqqqapknapNlslrw42p9+ur6+njczAJjP51Lqr7/++sfXP33y6aurq0uVZfu+\n7bsOlNRaknedHd95W1XV1dXVi2dP3r756e2bn/4P/8f/02//8T9++fLlJ598UtbVNDlrrYhBSeOR\nUrzaqe9OzSZA+tjhkZ6VmlaJyJ4ZGAQTopCAyBJQSCEEEAIkt+NIiYlIRBzmeV4aUyKDyp3CCWw7\nbH+k/kMGh0JMGWgRiS0xo5DZN5v91eWzKsu3D8d9R7NZYaTou1arIEB4F3TsZ7PFb3/727pafP3V\nn3NDQugYo3UOAJRSIUztYTLGnCxr8e+rLwBM01ahMMAABIKJgWKMzJBngAbBAGuFBiEDgQRgo1VC\naKmUQCGElqglSin6bs+MzIwgjJSMCT2Qlm1AGVF4BFQapU3LvxgjIguJKAUDsJBCG6XU23fvsixb\nn58JIe63D5vdtjCZ975ZzInIx0DMqUPSSiLicbKMmNY0OjPe+/ZwrKtqGIZ50yxm8yMc3ThVRWmM\nkUK8e/u23G5NlmGe3324cTHked4f2+12m2fZYrEQgMfDYRzHpIU7Ozuz1h6Px36ahBD9MPXDFIGV\nzjzxenVGRP0wiGQreHLOJ6VUU9Xgo5ZKCqGVMFJpgZmSiFgV5aKZp1q161vn3N37d1prb+3r778/\n7PY/+/yLy8tzO44P95u/+7u/u7+5PR6PSeDHdT1Nk9Dq7HJVVdVutzfG1FU1DsN+P45dXxSFVgqR\nM63OVst33U4IwYg+htHZXGmUArU8u7oELS1HO0SUAuIp9+94PF5dXc1ms3fv3+/3+ydPntRN07bt\nw3YbY0xpu0qpTOlgXQBYna+OxyMy1GWVbAG1kIvFIkV6N03z+eefR6Kvv/764X4jpTzLKiGERAxM\nTKSUzIrcZJmLISUXpascY0xos3pMbzsdcYCZNmjwJMkFpBidcxLwlLuT5RTZOx9jDExCCCUkSuWC\nR2ZE5ABE5CkRY6UglkqjNpD8tjgyo9YyEYa89xRCwvMTPoeCpUIEKYOMMTJEKSA3maKIUopMoxTC\nC4gUQ5jGsUA0xnwMHEz+DQDgvVMoJAqtFCKmxhURQcmIiaZFIRkQeOcT9YFTzhgmD1oFnEZNSB4G\nyApPi+EYY5IcEwIwi8c8MmRQs7LKtNlut99//918uTR5VuZF6FqjdWkyO4yZzpRSDw8PwbqiKFwI\nCToAEMQolSxMZpQ2UvV9i48RPd77cZqIg5ZKZUYa/RGHS6C/lDIFCSutm6bZ7LYX1cXnLz/56c2H\nrMjTZ7S+OH/56auiKKavXN/3b9+9K8vyF7/4xfrsbL/bTdN0fn6OeVlVVV4Wx+Nxd9hPdhrGYYo+\nz/MqL5TRIDDtVIBYKUXOJtUTpFhKIqkNWs6ybLmYE0Pf90icwpaVEH3fJaZGCKGqqmRKgIjn5+fW\n2q7rxGP6tJSyqqqoT5ZMbpx8N3CMD/cb693qfI1S1rMmhPDnb7/FSBJlO4y1SPxnnVVV3TSmrp33\nDuCH7793gbKyvH3Yvnn70/541H3vnEPCcyk2D7sf3vwEAFeX54h4aI/OudlssVrMyrKchrHrjkS0\nXq6AkZh3+/12u1VK1VX16aefXlxcMHM/jbvjoTsc5/P5kydP9vt9COF42Gqt87oJITzcb4wxz549\n++STT/7Lf/kv4zjaccrz/OLs/OLiwhiz2Wwuzy8+/fTT1Wr11d99+f79+6QS/if/wz++uHry+z/8\n7duf3ixWyy9efXJoO5Ti2dWlPe6i84UydVHmmf7000/Xy9XhcPg3//bffvrpp7/61a9evXo1ny/z\nujoRGAAFIAoBQiTORyqcWZaJx7r7DytxZkwgiuSZyFNkjhE8c5QyZQ4xIErJEpklM0PvLEWwcYh+\nzGXQWRftu373jaRtJnsjSBCEoHz0WkmZ5/Xl/6aN4HW1fPnFONrj/mAEnn/ycuz3QK7vDsfRrrJq\n/vT6RSgOE3734U8xMoBQMo8xehcQVVU1IQQ6Vd/T+0ivvyiFABKROaXSsI6gAunM1CgblLWIGYAS\nkAkQjGx0J4RQwiEiEgABe4yIMrIQCoQSUgohGdLDDwNQRJ4otEPvonMxePJ8ciBnIbSNIYRgvUNE\nkJKFDAzj5LIsK8paa42IAcYQGVEwCBscRMpKDSBCjNa7qqkJWCm1Xq5ubm7u7u5yY+ZNE5xvVmeK\n0XaDQAzeF0WRAfR9X9RNlmV93y/n86qqbm9vf/j++9lsdr5an63WZ2dn0Xlm3mw2f/4zD8OQqi8K\nNdppHEdUWhvMMgMCfQzOOaGVlJjuE4m4WCyW84UGQTGO7REBrj/7bF7V1lpPEYDI2X4aTzAsUX84\nXl9f//qv/nLoej+Ol+frly9evHnzJoTwzTfftG2b53lsWyJSWvb9YK0d2i5FBJZZ1h/bMssX80ZJ\nbMrKU0SAFOEq6izLMuvC2A47HyGSVso6p8s8AAeihL0RUfQhhJDsIPKi+PnPf77ZbNq+c8FLrWhi\nRFTJWyPLyiyXUlKIggGJyQfHU1rlqqLQQjZNk7rq5AcshShMVhRFHBwrKYSIFIlIIkoURmulVKAY\nKcYYKcQQg8DE7EkrnpjWopwsfYSEU6wgpgyDNOx+RJ5PjHfEx41vlGnGT++UAsaolMyVBO9OQb/J\ny5pOwFUSr+Z5ntjgH19DPK2lI3MkCoioUKJB245IgiEKqYFJSWQACRwjJSI2UWBmAYhKUYg2+XSa\nLBGnvffR+1TOToxEISWCjyEwheCSIUEKv05GfYRAzJlSzEwMjz6yIBC1UqlkIkKifUk89d3ql7/8\nZQIbt9vt8uyMEVSWZ0U+eaeUGqepLOsEsSYA+eCGu80m0zo4b4zReHLDZYFFUYkKThRzIqWUc5QI\nz8w8TRYRdZ4rpYosn8/n++Ph3bt3ZVWt1+t26E9caCkQMfVKQknnfUQITJN3x77LyqJs6vVylQQn\n6/X63WZv8kwIcWiP2+0WpBidDeOwkvLTTz+tqqrdH5JVRdu2Yz9YOxqjhEBjTAKxj12npcBAWsgQ\nAoTAUkhGAJFJ3YeQ7tphGN6+fQsAdV2nWyqB6ol+lWCWoigGmkLwWsm8yMAThSilbObz+4eHctac\npcrX9grFcrEAFEMIE5EQQgY/xgiH9th3u8P+4eHhfrP95a/+6ububtd29Xxxc3NzdXUlGUHKfd8V\nVTUMfYhM0QmGsizLskSQbrIcyVnLzKvZ/Kd3H1DJ9JxcXl6mTN+u7ynGtm0Ph0Pf99cXl4vF4sWL\nF7PZLLrpyy+//PHHH6uqev7sWfIUm4bxxbPnv/vd76y1P/vZz55cXaempMhyqXR0/uHunpmrqgCA\nrMwI6cefXl9cnDWLxvlYVOXV2drFoJRq7WCaJkFqXdfN6ub502d932ut37x583/+v/5fluvVb3/7\n21//+tfz+dxTECnuG4GAklwnhX/YcUp1V+J/98WCE4skckgRPcREFL0PKEgKUCKl7kDK85zPAmKM\nwSoYtJiQj8i3meo1egAKwJbZI5LRWJZ6Nhv0E5VpS/BwCErk9fmVFHTv+nreRNejmMnSDUJbq3D+\n5LO/Xjoj3r9/v9tvhVRSZgGcFDLL86nrAAgY4KQPSop+0oWiEB05YiZQUhYim2m5FMUKxYKxYJZA\nAjEphkBrh4iKBQIyM5xiWrgoSkQCQYBpRwuJKxW1CEzdONDm9tgehmE4IWlKKa1BC09xsJNzXmoF\nxPP1auqHfdcaO81msxTXEZkSB34KfrJWGp1LQQKsPcnzkjVVmRfL5fLq4uLFs+dt275785O3rshz\no/XY9cG6xWKxXK1//PHH4LyWqszyZEqjlToeDkTEISZJqCnyaZq6of/pvZ3NZmVVeR/bvttut9bH\n9dlFNWvGyU3TKKWs5hWwAIB+HJqs6kPo267d7rtjW+eFVqLM8vubW44kJHyU4Q3WOed0mdezBmNY\nNvV6vZ6qehoGLZW3buyHxGt5/vz5s2fPEhg2ADdNlWXaW9t349nyfDlf3NzcKKWWs1mZ5aOzdugB\nyLvpw2F38elLgUICDs7FyWmtC8gthbv7W5YCBLJAVFIyB+djCKYoJmv7vv/5z39eVOXvf/97IiqK\nIjmRcaQYQqL5IENiHWfGOGsP+z0gzudzKeVHU4fD4fDlH/6OiEY7VWWptXa9TUSkj5BvWhVzSNFH\nMeE0QggJKAE9xZQ0LJICMql4AJ1zUoi0tEqfUvINjMElDVueZWmyZCJgFsakppkFK0CSpJU2QgZE\nmXoIBGQA5gRXuRhSqoTW2gsBziX4OkQ/2WQA4JiikFIKEIC5khE4uuDBs0AphNBKK2mdTftQ78lz\nPFV6JCNPSVDpo/iolNFCAIBQUilFwNbatHb0dIod1CmZ8fH8IUq2WASR6SSPPrGukkwgVf1kRwgA\n6v3795fX10+fPp2C64bhj998fTh2IDAri4vLy9lq2XVDO/SEwAIPXSsr0/X9AFiXZZ7nfrLDMCDA\nNIxVXlRVxRwlwGK5TEugcepPr3iyZVEopcq8qKrqcDisl6uEZhhjXrx4IaW8vb9TSj/sdyGEQHGy\ntptGqdUwDJ6pXsyzIn/95sf9fj+bzbz39/f3XeD9+3eTs33fp5zByVrvXT+N1jkG2B72uTaJ2R8o\nXp5fKKV2ux0wzWY1UXDTlOf52O/H9siEHClBB6hUrpSaz621WZbFGH/66adkFJByC2az2UcbvPRG\nuq6Lmtvt3vaDCFSojCM5f8pR+eTly2PX3t/fF0UhQNjgLy8vt/e3Mcbk3ymlDJGPfZc8qiam4zDs\nu66azz/74ovB2of9/sXTFzebh6nvPvnkE5BimEaOdLFaKin9ZI/Driryi/WZYDgedtMwJgo3Iq7X\n6/lqaYM/7vZt2zrnjNbJ5Pm71z8cj8dnz57N53NjzGefffby5Uut9fF4fPfuHQAkyjc+eo+EEO7v\n79Ne+d2HD/d3d7vdLgn4PfnZrL65vV2tVrNZjS1P/dZU+WqxSPd3FpdZls2qer/f39/cuvk8rRJQ\nileffXrx5OrDhw//z3/z//pf//N/+tU/+ke//OUvn16cGX0iiTjnyMcoo9aagD+6Yp2GYGIG6EfL\neEpBQSkVYGQhWDpLDBgpxuiAGFP2G1Neeo5RSShywtD37fupvZF+ACUj5RaQRRWrBeszLM+hWrFY\nWwIXAwMXuvCmcNFOkbvWaczyui618db1gxO1Ojuv/8li/tVXX335xz/sdjujjK5UCG5znFBUJ+I2\nJXLyCXKE1jMjkBGgpM4xm+n8TJjFxAXLKrChFDWJqBAQWLAQp/eaBPYAAEwQmAiIICRb+ghIjIRA\nKD3bOLhjt/M2eO8LkymlpERppJAqWEvAoJUwBpR62G1TUj0i+hD2XUtELHBwNsbYjYOPoTIyuRK6\nEIZplNYaYyjE3W53fXn59PqJkcr1Y992u+22yguIxJFctNEH8kECkvMTQN00Q9u1bYtKJuEfE+92\nu+C8cy7PsqKuIjupDAGPth+myUfWmVmsV23bHo4dSrG+OK+L2lobSOW56duOmQXDOE6ZMXVZaYnI\nYIfRuUmhUDMsm0xnQiI4JWeLpTZGEoCPr7/5zjmXKQWREtNztVoJIdbrddXUN3e3x66FwhRFUZcz\n5xwyaCOBY9ceiGB1IumAQNBSKJMhYndsc21iCIEJACMCa4kau8MDSKG01kIrKSUBZlkmFSiZ53nb\ntl99/aeklJVSHLpWKVWYTBlJMXJIom1ARIUCFWZJdCRlnmVpK5fGJ/EYcWaU1lJNwyjlKc1Fa5U2\nlC6Gvj8ZrQshEjOePCVFOEZ8rEzyZBnLnFIiPq5FJZzIGSfQmOg06caYsM/cZEmqAABCysTYAgAK\nIdeaOXH6QAiUKCJHF2La+m3DHoljjAIxjf7ODt55DpGIdDKfQmTm1XJBxM770VkfA1FkTz54LTMt\nkzdIFCRACmZGYpXn3jokZiIhpJbSE4lkaQugQRhtQCAycCRkiBCZGVMLwiweo8sihXTQiTTyPiJz\niKlBYoFIAAJPoUlqcq4b+qvF7LMvvvjp3Vv5WrngWQqFsG+PIPBwPO52u7QCvH7yhHKBd/d2nFLZ\nYGaQykgxDeMkZR5zABJCJKrO7e3tdrtNuL8ELPI8QcFa65ubm67rCJiIrHMROPE1qmbZjcOhPQop\nUUkXvETIy3K2Wm6320Pbpvl7MZv3fd/uD8Xy7ObuVilVz2dZno/OEsJyvRqG4bvXPwCAn6wUItcm\ny7JMm/PrCyJqu8Px0O0etsfj0U1jXRZ/8fNfJLUAdjQMAygzm83KvCjW8++//56Zm6ZJhTbGOI7j\n2dlZXdfJDPIjl905t5gvXD8apYvCXK7OM6kOh4Mw+i+/+LWQcnfYp6XyYja/qM8DEynlYnQhEjA7\n50KcrPUIRZ5/enm9Px53bVczHLr+5edf/PTTT/0wAKKL9PCw8246X63reRkjAQcjZV1WVZ4ZraNU\nEjBat1iv3rx5w8zn5+fH4zFJsFbzhRCCT7GV4JzbHvZ5nm82m1VTeWtDCEqIpqrsYrHdbm/ev7++\nvn7x7FmMkWPsjkdrrc+yTOuL5doU+Ycsb/tuu9sBwKvPPr189mS73e67/WG3M1pfna3Xq1Xaqe+k\nVEbned4fD+R817aJ/hNCAIHKmOunT5vl7Hg8/v4Pv/vd3/633/7mN+dnZ0+fPl2v11mecYjRh8k5\no9QpAvjEL+aYBO8yJHY0SRBCMEMkYGaZ5ciRIwFFYAIMDIzM43BvxwEhFAbZdeP+HrxtTONijYhg\nKlksqDjzej6ZRWvK2qyOxzbPiuXlWrDYbrcUuZmdHw/3Suee0U1EUauyynSO0lRr/at/8rRav/z9\n739/e/cBUMpcAnUn6T5/rL6n0ZWApNQm10pmQmYgy6hKJ6oxKEQFQhBEBaxlMJIkkVcLBiBkEIIF\ngID09PsYGSECkwCCVLQFIk5+csEh8TSMxhghOculMdnkHDP7YMfgPLPQCrWJKCZn87IQSgIASMGI\nUukEF3sfkxGb9f449DHGY98xc6R4fn6ODPe3t8H5sevf3P6w2WyYaGg7149KqeV6liaGWVHlL19l\nWdaPAyHsD4fNZvP8+fPR2STwy/N8vlxorQXg3d3dvh1sDDFGqbPrp4tmvpicq6qqG/oIYeqczjOl\nlI9hPp8vlvOH+41gKIrCjVNdlFmu/TANff/Js6dc5EWWL5fLssrTXlkQg/d3d3cuhidPnkioH3Z7\nKcRut4MdqMw0TXM8HruhH3+aHh4ehmE4P1um0iIAnZtuP/Ru6N045XlO0c8W87P1Rdt33/7wvfVO\nS9GPkyCGSAmtHe0UBBBw0dQk8WReGAkl5EprqTxinufDML1/fyOUEkJ6F50N3kUtdF4WJtcCIE1d\nHCJRCN5rIS7Wa0Kw1lo7MUJRFIBslKyqGSJ2XRecjd4JEEQgpZBSSK1ijOSjc1ZnhiIjsGAmBAYK\nPgBApgp4FL/i44SXNBdEJBiITjqltK0YBytRCECJgjgG5yMGiY9hBo/yNQbgSEyssyIyQYwEgFIA\no6D4aGsRPk7elMS7MWrFIXgKARGFMFoiInBkEVkpqZQUEq211oekFtV1kWwsIwVixkcg3SgNj5ZY\n8TGVLsaIhGloFTGlLgiJSigpjCAfKESMAJ7pRAUnQk8nu81Hg7CEHwgJTDJyBBZCMAqRSKOTsz+9\ne3sc+tli/u7mg3VOZUblmdTqz99/q5QaJjeOIwokieW82Xetyoxzbr/f5ybLpZ7PZoBY13W6JM7F\nrjsyc5Zl4zhCiu6CmCkNAF3XRR9ijKvV6uHhISvyBIyMzurM1HUdQtCZ0Vr7EKJIXZCQWpVleX9/\nnyTMiZ8FxLPZbPRuNpst1qssy3wIgSkviyR7cM5l2sxms77rdsfDar6YzWaHw6HrOu+9kPCwvafA\nq+WyrqqL8/XDw4NROpNq6npyNpPCCFRK1XWdLkmy2knQR9JGp3siERASjvfDDz8gw7JqSp01Vb2Y\nzQBgcq7dH45D75y7urqy1tbVTJv8h+9/BI2p/DCe8qp8CIgyq+pj3324v6ubOSL+3ZdfPXn+7MWr\nT3/4059ns1lm7aFrOfinT58+ffp0v3kYh64oa6Mlu7C93wxdH8mbvNjtdkQ0m82Kqowhpnn9eDzO\n5/NpmmIIWuurq6uqqnJthmEwyF3XPTw8JH1hop3vdrumaWaz2cPDQwoPTvvvEIISarL2p/fvAsVm\nNrPB/f7v/iAE7vf7X//61198+ml/OObGzMoyOH9/cxudN1LNy7p69amScntsAbGqKutdoqBLLapZ\nI43u+35y9v/zH/79YrF4dv3k+fPn11dXq8WyygujtPdeQHLgSSLG0yZYZEiAJ11STA8FMIOWClgK\nYJQkBCOfVq/IrREmuAmjl9zMKpUJUerMWw/SsKmhWtusHrCYVM4ym+22y2ahZDYc2hi5zgsi3e53\ns9mS2Do3OecRJSrpSbpIYcTl8vqLX55FOaO//d3DwwMBlIvFNA0ABMgABMkZM4HQYmaUUkqBUAzS\ns3AsHWdRSlQaBaBiAV7KqAQJ8k6/pBA9R6bITBwjEQTywhhCikwB0APFSATEzCKOHAkRA/s6KzmS\nNBIkCQkE0UdyzgWATChGsJHmy6XJsmmavPdVUQKAtTY5VSXvFwoYKB66Nsm9ymY2dH3btkCcbmxE\nzPPcO3d5ds6RlFIXZ2fz+bwoCmR4ulwJKReLxdsP72/ubgettVRCiGEY0vCEZ2fr9TrZoW/3u6Jq\niqJywQshVqsVCHF8f3O3uV+slgT8wd6mtl4pdXFxkef59cXldrsdun632ylG9i76AJHsOFVVdXF2\ndn6+FkK0bSsYoIHNbns8HPq+f3p1/fmrT5vmfrvd3m0efAjn5+fe+GmaCDiEgFLOFovFYiaTX9g4\nZUZFwCzLzs5XSqmyqJuqXp8ts9Lc3N+47eRCFEpDJAFosoxiHL0bo/dMeVOhktIYCRijiyEYZXJt\nUKC1J6IcIoYQuqFvmmYaxmGahBCkYqY1MvjJjuNoNCCiMjo11tM0MbPUJy7uOI6JRZyUk0QEyXYX\nIW0W0kWkE0kxMnNKm9VSCUBGmDWzVMDSQIcAmIQSQibWkniM9iMEF8PJkuIUmcAKBTNzjCwlctK9\nPU7GgFKp5FbGJyc7CkzpvPXex0jpXsq1iTEG751zs3mBj2oIhhijlygQ5WSHHHMppQYRUGgphNCs\nlJbSez94zxARkU++zZIIMqVRAUfiEDmJrokFQ4zEGKLy4IGclwTKmCLLp2lyPHEkbx3GE/JM8uS2\nJtUp4uLjy0sSYGJiAEZm5kCkuqFnxKKqPMW7zb0NfrFcnj+5ev3mx8m5QsphGISS19fX7eH4xz/+\nsZrPyrLMVyb6IIRQmcmyDJmKqow+SCmTZ+HhcLi8vEzKlqqqvHUJH5iGMYkQVquVMWa9XHVDb4xp\nFvPRTsfjcXBRStnMZrvjwdtJKMXM1rtxc58V+Wqx3G4edt4vZrOmqtfr9devf1osFtWs6fqeBV5e\nXm73u91+n4D+1WqVFK7pee77/v3bTXpty/niw/ChKLIXLz5h5q/+7su+76+vr+fzxX6/t+OEDN66\niVyqVX3fJwOsRDt89+5dwnOqqprNZsMwpIKUZdmzq+sXT565tk/chGmaDm0rjCqqsn3/1jl3cX41\nDMPXm6+HrldVyunE1DD6QCykVOrb774rimq1Opu808ac1dX79+/fvXt31sx3u52bxpTmZq3NsvzT\nTz/dPtxrENFOvXPeuTzPi3xhjIp2TG7Pu90uCaWSReXhcACAuqqapklwhRunYRjmeXa+PkOGb7/9\n9rg/SBTn5+f6THnnE67orUOGw35vrZ3VTVUWd3d33eEoc3NxfbXZPnzz/befffH5P/8X/1s7Trbv\nlBIItLm7Dc45O52tzw6H4/39fdnUVVV1fd9blyCTOEBwI0hhtFZGZ0Uegn9ycbHf7797/cOfvvnz\nar549vTp86fPVvPFxdl5ksChUh/NV5HYsgsUiTgyp+x6oRUiciRgwcCCkJiBmAAEkwSdZ1plNXI0\ngJoZI9FEzXLmMXMyn3TpwPSgrchB5Z8tRdv2Y+zLolG5sdYCxNVi6fzgnAfkuq6FMNNo22mQQmd6\ntjtapdST558PFoa//d3DwyYDZMwBo0iZNpIf7TVB4hkgRkJmIBQeBAktlA6IQikUhOQRIqJjnBi9\n4yyCJ5aAEZFZMGEkaRxEYnDMPoQp+tFZ532g+KTSQgilpBBojE6mSMxcFAWDiNaBQIlCGc0oKbjB\nDlprqbUQQmcm+pAm9RgjAaOURojIFGJkAJNlp8OXyChd17X3/ubmZmi7q/OLi4uL/XY3b5qrqyst\n1Xq9XiwW4diGEJRSMcYk80t8i2Sdkc7uh/0uESfzPD8Mx5QpwsxFVYVwCgNIT6WUUgjQWmudMfPD\nwwO7OAzDYjaf/+xnnz7/pG8Pdx9u+mOb53lTVYnU03VdsqxvmkYIQT58++23tzc3s6YJ1iX+RJKg\nTNOUksGSFggAdrvd06trY0wvu9VqZaRazJYnPYiR0zTd3dw6CmWei7Ozbuh3o0NEJVUiQ/kYPEVE\nsT8esqpUSnGSYHjvCKI2QzgZ0ex2OxCiqMoU9bNerxP+G2MkKQVgKrEx0HK5lFrt9/uu63Se5XnO\nCMlTlpmdc2mo0FoPw6AYE4mdmb3ziWMrhBjHMYXrKaOFkslJSQiR5E/TNEXnExngowFDqrJJrSCE\n8CGM4zgvCudc13XWWsFARFluyrKMPgUZIocYnQ/eo9ZCiGkcUQiUIjAFip4JpUAhjDF93zvnY4xY\ncILNhRDMEZFBoWBBRJ48SKUEZNpk+kTKQ45KyEwrIQSrzDkXvSektPNONDfvrckLJaW3LvoA4hQj\nrITgRENLyQUhoJRaqqoogChaFygSBx8JiSnEYGLqGBRKIQT/A8+sEwhPMekRiSgQqZHF9999P7t7\nePbk6aI5CxaUUk+ac1q6d+/fz0zz7PPrh90uR12sz+9v7o7t9Pz503EcYxBdP+22x/v7h0XVfP5p\nEyhO3sXolZRGyGin/nh88vkXRZZ3XTdN0wSiubwqY5Qo9s6bZr66frr77jsQJs/q3bZdzNa33f6n\nn34KIczn86oodWYQMbmIGWN+9rOfHa+Of/7zn7uuq4z89u2Ps2blnPvw04fZbCa13N1t8yJ/evV0\nmiYh5esffwKBWuuYmVjmu+A88my17IdBaz2fz6PzU99N4zj0rZJSCECMLKJD52XYtjs7DE3TSCkz\nJY1R3k3HtrXBz2aLCKDyTBjdumk/9KOdMuCFyqusvrvf3m3uPUVIBiNMNzfvklFis1rfbDfjMCwW\nCycYVD64QUpp8loIEagPIYCST56/9N5HHzKVGWUQcTlbTtPUT62UsqyLyY0CgFr/X3/3XzOjZkW1\nXi28t/u+nVXl9fW1FrLv+3nwRVGMdtq3ndLKKOEjCyE22+1f/MVfPn/+3I3T2E+H7W6z2bWHgUJM\nWkNTNbvd7v3dRmZFXZdSiNvbu+OxzYu8bVvr3avPPn3x4kUIrtzmzawcx/EP//k/nZ2d/e//x/8p\nz/M5aKgyqE6rX2vt/f39puujMOM4DnYX3t4kADPGyIgC5CyfVaZ2wUcXAUSOGUue1eWsOlvNu91u\nd2zbP/75x29ff8hy/ezZs3kzuzq/OD9bNVVdmExIKRR6IIYYKDCRYEACIUgwaK3pZHAHUQIzJBql\nxKcf91V4WqkAGhZCcIiAJMHJMM2jl15qpx8y7UQUAkFaiR7kydLd+ygwk7KUoJhBKRSCGNGHozGm\nG49Kms//4vPOdu0fBmNyFwOAiCmaiZFSGWTOICCgQAEMgCSEyDSgpJlSBJ6IAhCzmcBMXCPiyO9Q\nIguODIjIKKdo23E0WTY4z0qBkn0Eh5KM9DHsfFbkuSJgk+0HNasXKssBqJumyHH00UuV1bmschtD\nR+486hCSjpmFj4hS5iUgjoG6wboYiqKQQkXnBEKWZXlgOwU3eT1r8twAQGfbiYfPX76UwOLojvsP\nfNasztZxf7Pd3ehVE6KfJjzev8PheN7MRg4m+KKpL58/tyHe3NwEG8pFGZ0Xjquq2u12m80mRTVc\nX1y+vLySQnRdFw7tZVkHJqU1I+TAD7stTNNyNov9oVkuYuhRcbkoZYZH38ugdXeY/OT6USA2ZRH7\nCaW9uJjl2c+2260fW3ZjqWD55PLN27fns6qeNRimH17/KITAWFprz66u2/ttURTB2uP9w3y2LM8y\nqw1EsqPrpsP3r3/wMag8K+qqns9qHQPFfrSTmwjF6GMELqtKyCz44AZbL+amJM5EbrK7+1szP5OA\nYbDz9SJJAJZNnSs5q8uu6yY7kdZaapCCSALraINlMCCCVFGbqpmpIm+7LkpkZmyakTk10wWKfLG0\n2yNHiEQs0EewgQhRSKWEwhinaQqjr0qdCl5dlHY/AoAk5YCm4B0HgSwilGVplGDA4ANHAAYJOCuL\nIi8S54sjZmVhtGaBRw42WkS01nKIWZYJk0/MjKzyanQ2BkKtAorRWWIyuQYhKWdtGBmmwLXSRms3\n2TyY8OiJEZhsCFaw1iiAPYRZVSsEN/aZVrOsHMeRalo09byuvPfW2pgIsARlPe/7/uD79E6ZWWhl\nqnIYelSSiKYwnrRVSnL0ITiOQUrUZZbUz2mDKeCUiSckS4ksBAuMAi0EzyEioRJEFIJPa2/16ovP\nD3337s1Pt3d3z58/f/nZp9M0/fT+3TAMRV0RcD+OQkkXPLB4/vKTdzebBEAtZvPgpoN1xpi8LI59\nV+osz3MOPs9zFqi1Pr+4uL+/v7i4yMtCagUCT9SAqjwej2Sttfbs7IyI7jeb2/s7ROzbtszyel2n\nfBKhlVIKiNOgOaub/XYXnAfiaD0y7Pf7xPS7ublJQoL1+VnTND542/fDMIDA8/PzatYkR6cs06nt\ncs4F56PzXdelciilvLu7u7u7O/bdq88+XSyXAFAY0zTNOI5CyGQT3/V9dL4oin4YYoxJ1Y4MiUy+\nf9itz86Sam0YButcjFFoldrYGGMalJumWa/XxphDP50Uro/oTZowpJQc4hCSQjimX62UoiiT7C+J\nnwSiiwEdHYmlEBLY5Bkq3XZd4ub5GG3bAoA02nrXbW1KEp0vV7vDXghxsT5bLBZD2yVew+vXr58+\nfZp0SqlZTq+q7/u6ro0xKCDh2DHG4/E4DN3xePTeV1X1ySefXD19Ejz98MMPD7vdcrms6ybGmLCp\n+XzeNM0PP/zonLOPvuQ+xhgjCOynEcXJNCcCAxIKIYV6eHhI98BqtSrKchzH0Y0uhG+++SbP89fl\n60UzW8zni9l8NqurvDC10VJprY0yCEhEwQUKUVj/keqJiIwolZQAHBE/pioBIhMipujT5E4gmNMC\nNTJBDN3oOcRE8pRCIDEQWyHSRSTgYRpBiJNcx3slhdQqiwVIYYw5v7w4e39+v30QSgKjkEIAEAgJ\noJgBQOOpGwAAECiESJmlPoZ44lOCkB8NCiJrFFIAM/tovWcULvGQY7DWRu9ZChsiKGGyzIhMuYS5\nCRBSMLgQACaJEEIY7WRjkLlJQ+Fgp2EcvSqT6QAReR8S2RCFSCsYJCQigJPmBBEVotY67YzdZNPy\nqKnLt2/fZkpqFIvl0nv//v17I+TF2fk0TX3b3b378O7du6oom9ns3DtCAZk2xuhcLpdLCXJydji0\nx+Px3fE2z/O6rmd1wyEKIYL3IOXTp0/Pz88/3Nzs22NyAw/OrddrGobkX5EGYuu9tRaZtdZt27rJ\n1nlRmGzezLKyiM4rFHVdn61jgu4QcXJ2mlyKNcuK/Nmz59pk9/f3RJTNZlWWA4DROlqHDH3X7fd7\nirEoisDAiVRsR0wWn5Gsd03TkJDHzT0LWZbV6Ox2u5Va5ZmOPrx/+ybT+p/89W9Wy+W/+Te3m9u7\ntFCryjIBDwpFVVXJ+l6ikI90nvSclqboui7GKI1eLpdVU3uK8fR6iMMJWE7jcoKjE5XndDudCI0n\nO7wEDhd5XlVVQtojUQhhdHYMLnJAKaSUSkpvbfSAkYLzInJmTGVylZsklkkjOIeIxgBidA4RU2q3\nTW9EykzpLMs0ZgTsgo+PcqbAlDB/CtFIJaVKDKZ0sFCepf41gdmOIxJyxLqu7Th1djRKa2OGcQwU\ntVRkHT9SlrMsOznMI6Rs5nTcJXYGx5gsHeEjv+zR8/kjTZqI+KQ0F2kRzjEkG+30hDIzJawe0T86\nFn+8XkIIdZyGIABzE0Pog9scdmla/ef/4l+8e/fud3/7+27oL66vgnV3d3cs8Pr6uVTIIWqtfbA6\nMybLRmeHm3Y1X6BgFLKZzWKMhCLLcnC+6/sEqmRZlkwnELGpaufctz98L6UsiuLzzz9fr9fee9CS\niPI8j0wxBMngxmm3eaiqalL6m6/+9MMPPxhjfv6rX8UYv/7666KoPvnkk77v37x54ymO47jf7/f7\n/eWTa4qxrmtmrut6Pp9vt1tfFHVdMnPQTkkpUaA2WZal8N1xHCOR936Vmd1uF2JMDcHLFy/ibHbs\n+s3dPQgE5l/84heHQ9t3HTNXjy7ZzOzBSaXatj12bdd1AJDneZqD0z0dHjOUkkViMkVK8GMILvlp\nAkgAaNuDYEgAi5SotUzppLMyO4E8iMishDBKK4lE7JnyqmhMRj7cbrfTNJVl2e/3RFTUldSqnywK\ndYq1YQ4h3N3dHba7Mi84xoSup+1+uudms9lquTTGJNo554xdLyQmEdEwDJvN5md/8bP1xXmzmCfd\nPbAgClJKF/zt/f3b9+8TWpjsUIQQNngfgwdi4BCj9dZFImCUgoGSYMYzxZBo4acTPz0VxhiQoIMm\nosPhMDk3juP9/b2UssqLpq7zPF+cz8uyXDSzpqrzLEsBJgrFCaIUImkGmRj/Owv1U7C8kKciraVI\nrQ8k9x2A4CP4KMpCGs3MHIkJlVQsOITorc3znJkn5xBRR+rHcb/fD12ntU53YGTeH9uATMACJSbN\nCaRreSr5ijE8RisKIUggITOF+BjvhIjJECC9vDGQlJJBpBCUyCiEACmdCzZ4FhJBEpEAgYgChY+O\nJgpKpBHdWpubrCoyoeTUua7vdcwNBAaIyGVR+MF/ND1I/02CpXS4QCQkZkwQOiPyMIynXiR6a20p\nZVkUWaYlYKZknReFVGG0dhwwyydnj6PlSB8+fOiG4fLysqqqCyk88W7opmmykbq+D97vSUzDaIcR\nALz3yX5onOxuu2Xm9XKlhGyWq/v7e8kAKVjF+fPz81AUX3311dXV1WKxCCFUTZMkDMispaqKUjAM\ndkr5Y81y8fDwITntLJfLdOH6cdrv9+Uw7Ha7yLxYr5q6Ppl7NM3D/ZaZpc7mzUw+/SSEkEutqxpR\nRuDEDmEE56ObfPSUF0X6+6vra2Ey6wP0ndI6bdlRyUybse++++67/WrV7g9YzIzWyYouxsghQnKt\nCvHjFYk+JJZTpg356CaLUjRlabLMe+9iMFpLKbXQmKXkqlOt9d6Dfwz0xf8u3zPJYZ1zMYRTaQlx\nmiajSxdDsp+USqnMqJMrVpAoCAO5AEwQiSEii2BdlmWQtrYhZJGIwDmXdgrJL5PwJDzJssyPpwSD\ndKsnradQSkvl2VKMkw+K0QiZFWVZlo4jASWmVAAiBESIwMe+m6ZpCr4sCg8UERxFQIwuYtoBKKW1\nTudwoOick0b/fdaTFFLIwPQx9IWIgoCURJGktgm9+1iMQQoBSj4K+k/mfcDAhMTJWjr9SaMRCiGl\nVP/uP/9HwVCu5kx0tMPmu22M8eLiYgouAOkyzwWwFCH4vKmWy2We5eMw9EMvAZUSrz7/bDmb/fjD\nayEQlQwxlkVx3jxx09j3fdf3QitPkYGFlCHGYRz3+33btpkx6/XahZPhyGqxXCwWy6J48uTJdrvt\num4cxyYvm8W87/vb9x/yLIvWUeZfPHkqpByPnbX2sxcv321bZrbWIuJqtUq9RvqxzLxYLKy1wfmu\n64ZhWM0Xw9Cl1UUMAYiTgF1K+fDw8Jvf/OYvlPq3/+5/SaUyM+Z+s7m8vMzzfL/fp5WAG23bd/P5\nnIghktSqKsvEV3DOBevqLJumKaHxDCCNljJpd6XtOhCYcu/tMO72+492j8l4JT3Vxhgk7vteSnm6\nF/l0URNFD5K3uZBCSq2VyTKjNFBALW0k2w9te3i4vweAtRJdiIkTLyJMEZazZrlctm1rpyHL8xiC\nnaybbJFlTVMxs5TrtMVPJJeqros8t3ZMPpTH3V4blcb0xP0moizL6qZJut5+HOq6/vTzz4wx292+\nbduiLGOMfd/v9ntmJMmBAjFR4uhKBAREkee59cF5Tyf9TGI+UJVr7731U4wxzW46y0HgRVVFH9I6\nyk1213YPhyMAmBud53ldVU1ZVUU5r+p5M6vyQkqZPTYBqZJJKZVSSoA6DZkimUAhMiM4IkrkTEye\nSkKiIAo+BFAKmV3wSQdFRME5IgJnUUpG9BR3+/bu7u7+/n7sB2Z++fJVXtf3m833P74enJ2vz7q+\nB0ROZl1/nygBHsUp4j59ESfrvmQzywAUo0929kTM3GMUiGk2igQykVmVGpxHRG2MNBojRabo/RTj\nTGU+RiCWWjIiAunM1PPZixcv3n/48Pb9u3177A7HiCC10lpLaT4qO9PckA7K/X6fHjFm1iiUyZIa\nPjBKKU2WsaCmaZpFU9c1eXfcH9goQWwjGZQX55eLecMhHm/vry4uy7KWqJLRDQiUSkmvHva7u839\nZruXgIXOc5NVTW100R3bcRyDdd45N9kYY6kzW1ZGqkIZuVp3XaeUzqRSKPK6BoCrq6snz56+f/9+\nmKbEwTTGkKKqqrwPdpryPHccV2VRDMV3331nrf2LX/4SAKz1yZm1qipEESimUO00L/Z9zzbayWOA\nBAom3pPWWYCAQmRKF0U1Ou9sO02Dp9jMUn6fLpva5GXoWqVUludZrtvD0U+2mTXs/Q/ffLup6qos\nq7PzLMuCdZN1pyNCyKTLSlNgsin8aEsgiLWQyhgjFcWYFv8xRjtNyQY49QTR+eTC2I8DJvckoxVi\nZIoxUIzJTc8Yk1akwbpxHK21Yp6negwSWCAKIVEkcQ4IEICZ0ohSCvDW+WFKW+qUfhiZ0gJbAAbn\n0/h+kqcopbWWQkw0EQWiwAIFg5SIMjlzyAnZW8cuAFAaS6SQ+/aYJtpkuRySHzOxtTZT2nEcjnvB\nUJaFQOzGUQsphFCIJCjGSKcHJ6KSQogQ42AnIsqKXCsJAAIEM0dMhh9sg0fElP0eQyBgmeTKj0Pt\nSQ2IpwAoevSg9TEkSlcSLCUmBQCo95u75XLJ3m4fHoioLiutDEvx//2P/78UY9As5skZXCrVD8PQ\nT2nBPji3nDUvPvnk/Oxss9m4yQbgbrI2+DLLrbWH/W6apt77xWJxdnaW/CCPfXfsu27oZ8vFMI4o\nRFVVRPTu5sPN/d1sNnt59XS/3SW3qcVquV4sC5OtF8vx2C3P1nVZpYW/tXZzPI7DcDh0P0Icx1FK\nWeZFP40hBKIgI3tvtZYhuu2u83d+mqYiN957SslFPpxa9UdzlmEc07jcdV1SXr96+bJ92G7u7ruu\nq+vm6vyibdsT4KzNrG6EklVeCCEEqpTSKCXWTZMQ783DQzv0INBok7TwSikXfKZNVhbkAzNH7zlG\niCSFMFJppQGAgGazWRIWp+cfEZVSRVG07b7Mi2I2q6oqAWvRu34cmHlwfhr6w+GQZH/n63U+m5lq\n/vDwMDFkUkUpe++NdRHYurBvOyNwvVqBj33bUl4knkuinFhrD4fDdrt99uzZ9fX1n/70pyzLEhqc\nStfV1dX19TVLSM0sMzbzKfn5bbfbb1+/ttYWRbFar6y1+7brJmuMGccx7WySB6wyUoksGb6Pwblg\nGYVSSiiZCJBumk60filP2cbOW+/zPAcQMssLbbI8eB+ccxTicTp2zm3bVjBIFLnJ5lVd5UVmTOL1\n5CZTRic+nTEGw6npUUqJR2UxIsboETE15lJKqQQoQaRCjEDEkVyIgoEBgdmGIKW004RCSKOn4G8f\nHt7d3x2PR0nAzIOzm+P+7c3t3XYHUoDSoHV6bh+lSCen69Qe0OO8e2q9mZg5edS44JMNKgihlCKJ\n/mRLJJQSQmtptNBKTDZVUJ1l5APEQERAZGMgH1gJrTUxx+BhHHSmALGeNfN+Ptgp2qC0ElIysSnM\n6chOFT65HRGFEBBASZn+SCnTR+oiuxiYQp4X9ayZzeoQwuZ47PuenDIV5kU5r+rFalllxTiORV6d\nrS9sN439IIU6HA4kkaUyudntdg+7Xdf1ZVmWCqumPlutLU1lXhy2OybSWq8Wy6Ht+rZbfvEzpdSz\nJ0+Lonjz9qekSxRCfLh9z8yJaZhKZmJxt207DMPhcEjLo/lq6Zk80/Pnz9M/FUV1PB5H67quO3ad\ncz4vi6IoeOA+QgjUHg+bzeZycea9F02TAEdv7cPDTh87RpCZUSZj4FQd2U4xxof9brlchmm6ub+T\nOsvLwgY/TdNyuVzNZ+/evn3Y2Mvz8ybLcmPW6/XBRQDorXWTlVJmWZ76oWR8QY8ps4XJIMsQcTlb\npL9sD0edmawsFJFtrceT08DJQJEoDc3p4hKC+AfheglmiCEwUVEUuTbTNI39AADd0EuJQgtJyocQ\nrfPeCwF2HJWQEoVGKLTRUkGINnojVfBea13lhYvBeseIhcn0TCYVCTOnMHhkiD4opTJmRJxiGkoi\nCAwqJB5lbjJpcmA2SjORtXakIFEKFATMwBGIiYlIZ1pqE0N0FLWQHohcmLwDodJbTnzvSJQWPalY\neorMLJQUSiZOuIDHuBfgJEEOGOAx/RAEohCMQHDywDFSiY8G9QIFc0heIs6nNwspmimVbCZ1/uSK\niNppcBQLk0Um78NKivXF+cN+J42ezWYEbIwxWXY8Hg+HdrlclmW5s3Z3PPz0/t04ju9uPnCIeaZV\nMpOioLVWQkYUQqlD24aEKsdIAvOysN4t16v9ft9P4+TdxcXFxfXV7e3t2w/va5Xt93sfQ13XHxWi\neZ4Pw5BS9mazmUwBjZH6sUu2GNbZBDEl/mRT1ymCylOUKIzSwflUV7RSkUFrPYYoGKSU0zQN45jy\nDd+/f/+r3/wjrfX+ePjxxx9/+ctfVnmxPeynaUIUs9ks+SE3TXNzv8mzbLL2uD/Uda2EUEKYorB2\ntNZW0DSzWQAGKUY7dUM/TuNsNhNCPGwe8jw/W609sLNTdDFBfB+N1j7KHxMQlEiJp5RlouC8rOq0\nQk4eNw99fzweEjnTOTdMU4J8LfLBjmW1Olgfh2GxWNgQH25udsfjvK6tdUbrYZyUOBiUVVUsl/OH\nhwepFeNJWDWOY4qQOjtfNU1TFIUxpqrKpqpSMkff97e7eyklgNhut33fL1YrIvr++x8224eUeHhx\ncZEV1TAMk7M2eFQpRxQiA0QkBIEgCIu60XYSIunXKTCcSAqIjEApAjUKT9FHchwhRICYTgxQEqUy\nShNRAZKZOcTgvLNu7Pu2H5SQSKy1zrM0BpuEPRhjktIsGZml1jWtgZN7uRCCiATQR6KWNDqdX0nK\nKVF8FA62fRdjVJmxzt1t7nd9S8iBQSm17bqHtt3ud6y1lPL+cCiK4uSMQxhOeU7MzClkIjEn0+wC\nJ33SKbE3xmgpAIKSyEqwQGIIxChORnc+hohCaIUupsKJHCUKnelk+ZRSvVkgEU3eTdM02eHQtSl4\nWwiRPpkUdUfEMdLjCux08oTghcDCmFO/DyCYtRC51llTtW0bgbVUmVYC0E22b7vMmLLIV6vVk8ur\nUhs7jvfdRkv18uXLxWJx3O+BOJ1ZMcbJ+dzIGCNKUVT5rJnVZVUUmTEqM7PCZB+E3NzfI8Oz6ydh\n7e9ubs5WKwpxPV/Us0ZLiYjL9dpaG8gPw5Cu3dOnT5fr9Xa7tdbe3d0JIYRSwzAM01g1tc7MYrXM\ns8Xnn/0sxQPc3t95760PfT9Mzk7O1tVMSl3lxWCnZI542O2zLLu+vFquzgDAxcD4/f54OPaDcE5n\nXuZGKlVUFUlhvM9nNQD0u0M/jWCnYepTrGZ32J+t18umlihmZTV0MThPLkQfmTlNCylvLaFlmTk1\nRkYqZs5NltzRmbkqS6XUse+GaZwJrOp6MZuPdkoE7zFE5FOhNcYU2jjnXAzMfEJfARDRTlPCV8ss\nr6oKGdIR2jnLwmiUUsr0/RwjIeYmkygUoBaYaVNoA4qMVAl5RsTMGBll0ltmxjR1nUgk1jnLJ6ct\niARSpUZBxsBEHCJIAUJEihyJGFEIJGbJaSYpdXWicCehcHKWAIjMwzSmNNssy8d+SDgH+FOlhMiB\nYiQSSoIU3qexFnRm0oM2OWetNeLv5bwJqQqc5M6nJGyQIom4TmiQTOiyAABkSEBp4hYAigAkHz3t\nT3qtqq6HYQgq1LOmzIuh7ay1Qojb9x8Kk6Vqd3N/BwDX19dPr669j0KIQKS1brvDn/70p6ZpNtuH\nIssjcFMVBGx9aObL9Xpt7QhC3t/f9+OUrF1mi4aIQKhvvvuhLMv1ev3+p7fj5BbLdV5UiyWQlKxU\nWZZnFxdKKRuiDdGU1V9/9vm3334bCa6fPNtsNt9++6114Z/9s39+czx+/fXXQ9enitWU1avPP2ua\n5m/+5m+yLPNdp1CUZZ7n+UcWPvmQKQ3xZDvmnBvHsW1POcQp+7rI8kEN33zzzf/uf/in3ZfdFGn/\nsC3LMkElP/74o/dxNps5a9vj0WiNIDmSMRlRuN8+HLp2sVymaJFUPoUQp8M0sRjGcRiGsiwzbaZp\nAuIkV7fep/k1wVwfjTzT/pgFFiZLP7DrOkTsx6Eb+n6aQlK9SlHNZ8po7/1xmuz9vTpMnXccotvv\nMZKbrA0+hLBqZuuzs/6wH4YhCLy+fPXs2bOuPyqdJXaG1Ep51XXd27dvj+0+aUvu7++rqlRPnwai\n2w8f9vu9LLLIrJSy1vZ9P1qbl6UyenV+lg9D3/eb/WFG3DRNXhbDMKCgNNGmuG/nAwA451Lwg0lj\nWYzeTjFGKaU0OQdwLjjvAyIgSqMLzFGKGMjFQMSCgZOxBYAsinTrZ4HSqIrJd2mcHLEdRhhG/Adk\nisrI9CQkyOijiKJpmkxpIYTzU4L7Ug2WybWHWEopToJjRkSdmWmaXPAEECiOzoYQhJRGaBBi23Up\nNKZqGkBJ07QfhoQx86OdUPoanE8vJrViPp4GF6lVZGAiEEJleTosAkNgIgRUUoDwMTrvNEGGMjIR\nQQjhRBIUSgmZ+BYxRgEspQQpM+aEpG13u7IoEoDBkYauT7/XsTfhhBVprQE4hCAYjDwBIcjJ7Sgi\nkBSwvrjIjEnhM8jgJgvEdVW9/+mtOL8Yy3EYBl1LOzlnbT7Pl8slAGQmn/QYifI8tyEG7z4CPwkp\nYebAFJlKpaQAo2WZZ2PXd+0hU/ribN0fD0opBJp6nDd113VhGjmEL774Yr1ep9O5rmsfYwghafBM\nni3XqwTY7I9HnWXn03i3uUfEsiy3+904TPv2mBW5UHLZnI/j2E9jOlvLLEfCSU9E0Xk/hRiYZov5\ni8sLB7D/45eayAU/2VFSACl8DBEBlFws1w8PDzb4uq61McnaMM8L79zUd+frs6uLc3ZhbI/Bh4e7\ne58XSc2SHCrT1DiOY11VRVEYqWIZkcEYIwFDCN3hmBV52otN+2nsemOMVEqiSHQhfOQESSEQQCot\npRRMzCyYIcJJ5pvaTUBm1lIVRRG8DyEkPmmMkREEotGSWSBimRe5UQYlEiOxBEQhhcYxjFqq1Kcy\nkRaSEChEUoSIWqqAPllaohRaqd66xCY5oSyI+jGMLmBAgOg8ELPJ0rcxxMTwSn6LH7ckFEJCZYos\nL8uSQwzeK6XyNIAmARLw6bShmGKthRBKKwIerU0bljT/4D8IXkvyIS0kIsLjAjg+IgfuH6wDEsbO\nQkYU2mgLFoJHYhasHttc1R6PZVmS8w8Pu3ytr6+uhq7nEKdxzLJMo9DGXJ2dj+M4dX2YbFmW4zhO\n05TlWYNNepWX10/224dAkUCA0iKGvC6zqjgO3W6zSZXG5BkDpM2ZULKeNW3b1r6RRpsij8Dbw56I\nHva7u4dNXpX1apFJtM6GGFiJYlbP1svb29v/9N/+Jsb48vNPtdYyN2EbrLVlWT579oyZp2ly4/Tt\n+w8//PDDkydP+q5Ln7XWuizKw+GQKx1RVGWJAFKp9Pwnmk/f9+dXlyGE5XL5xz/+8fz8HKVIIQTP\nnz9/eNi6cZq8e/LkiZKy78eUS+UnW5gMpU7mt5fXF0KrtF8ZnRVKLpfL9fnZmzdv+r7P8zxZELjJ\nOueePXvGjj5OvQAwDaP3XmqV2Grw6PIRmHRmqqoaY3DOpf1iGqFCjCkl1Mfgoy9MgVrHEByRYL65\nvb26uACAw3YrGJqyQorH43FWlKnG13UdnT0cdm23QsR0W6Tu0lrb7vfOOecnY0xRFHd3dzGeWNlS\n67ws69Xi7du3yfdgtpgfDse2bQlBa/3Jy5cx8t3m3jnnYwCAyVkjHxkKUkCKTyESyO3x6F1IuDFB\nRGKFItPGBs/MQkotpACOadATcnI+UIwpS1cITHbwSJ49AAgGpZWSUjGmEyHLixBCOkRSC+BjYOZp\n9PjoFZfedfq6fdgURSGlTNhDWnAKIQRAMh1Mn1LaI2itIzAIdCG44FPqqhCCYswVRmuTLy4zd86h\nlIjY9h0injJBUeLHTCcpUCuhFDMxEAoQIqlCVAiBiIUUKknGvXfOBaOEUipRu60LgVAKZXQ/DjHG\nx4MGE6+T4ulm894CsRJCopCAjMACk3WrTmcHYF3V6/V6d3ufoEmBKISIMfjJWqKyLJWQRsuTDQgz\nEAtApJO1iJ2G8TDFGLXWQgAQ12UZffjw4QOfx9xkmjNAMTpHPqQFvLc2NTpa68mN6QiepkkAKuA6\nz5SQYz+0zlOIT6+ubz58ePvjG4nikxcv7m9uF4vFCP2eeXm2vr25SQOc3Ner1YoD7/d7a+27Dx8S\nm2G32yUHghBCXhb1rCHgY9fmLnRdV5Tl7ngY7LTZPswWqzzPD+0xXUSJarFYXJ1fpGOwebr84x//\n+Ic//VF/b55/8vK3s9lE4d3m7vrJE9u2Xdf6aTjFNUZmZvv2pwRjTNYujFmtF92x3e12q/nCO5ct\n1LyZichnyxXE8NVXX7X9oJSKzgemGKN8RErSHifdq2nzmggizDz2gzdaKTWfzxO/d5ym0yLlMTUv\nPToAEJUmIhCotEYhXPDR+cm7oiiEEDHEaRhtUSbaUX9sxbxJtxAjCAEy+WkADG2nq1KUpUT00U3B\nCQAEiD7ozEgUwbpwChHiRMxMEYrJxCNtmsuytPcPHwuYj0FEobTOsyx4r5VKbk4UotEaAKLzyRJy\ncnboexd8ahMRwGhzfn4uAKPzZV4UQiXKYTGrT0QzYE6NnXMu+KwsOJ7Y4C54O02MmGWZoFPSPCKm\n0TndlnlpTj+HGR7dNoQQo7PpMBFCoEAjlUKBxFLKAI5CTLgORDpxUKZ+IOuZeVE3frKbD7dNXTdF\nWVxed13nR2uEKoQm4YkoE2rft8MwAEDf90qJpq4DxbIsQciqaSZnh2GYzWaHfths9zHG3W5LRFlR\nlHVd1/Xm7m7ftl988cXd3V064ouqdMG/ff8Opdhs7n0zmzhC8H/3p6/6vv+Lv/iLFy9evHnz5vZh\nc+i7xdk6GSu+ef8uy7I/f//dMLoUtPDm9Y+//e1v375/9+WXXwohvvj0MyFE13VlUdRVpZS6u7tr\nmuZifRZCmLxbLpdFUbx586asqsQqCkzjOM5ms5Sny8xVXiyui3fv3n348OHly1evX7+OwNM0nZ2d\n2cn3bfezn/3sm2++ORwOdT0TAO3hECAIhu12O18shBDDMFRVdexa8bjtPgGYACnivpR6vZzv93uj\nhNZyklBlhbVWSiRKrGnWWgsG723bhkxqKSUkGl7wzCxNlhsTQhBKFEpKKYfJOh9QG9SmqORmvzNK\n50UZvButLY2u6/r+/r7QKlcyIsxms4fdw82///cvPnleFMW7d+9OCefb7fps/eLZ85vb913XKaUu\nri7dNKUO+rRR49OIpjPTzOfTZO+3D3meE/Px2JV1ZYy5ubuz3q/X58rkGZL3/nG+10YqEhxjHNou\nRk4CHkmAxDGyI6dy7axlTDgPgEIQcnBO51mYvGMXgk+5KojIDA6ClFILyQzRhSmSIEZiN1mttdBG\nCCkRjTHaGETsdvfpSHrksbMLLkwDABynCf+hw8A4MrN6ZDmK4e8po+kbGOHk/viYq4qIGE62ujHG\nQJSqi9YalBZSJoAkjbypiydAEqJPEpHMyPQChIjMHpgRFKJPv1GpuigGiF3X+cknp9VcneCvup5N\n08QAiTyIgMG6YRhMVQCcsuqCcwCQGHwxxjwvyizXSpE3Q98zEf69CVFMR7CUIpG9jZZ2Goxu3Dj1\nbbtaLzIt3dD3AByiQqG19t576/ppvL29nTeNECJGzjM9DIMdp+vr6yovPnz4YIcxOG+EFEL0/egp\nolbe+2katFSqrICIQowhbB/umaORSms9DkOujWpmRZ4bqVx0b9++XSwWy+Xy/U9vd7tdamrFVP34\n44+pUA3D0Pe9c2609vLysmzb/XaXQmEfHh5evXr16tWr9x8+HI9HU+STs8Pkeuugawc3NU3DICbb\nBXBE1A790A7M6BiqxbIdB/T+8M3X77ebsq6XV5e3xz1K4ZEdQFGU0dphPKKSKpyckuazWWbUr//q\nV4ft7n/5d//v4F2ZF8H5r//41Yunz5qyAhBNNWs9gcBpmq6ePvln/+yf9cPwu9/9Lq1I0rBlpOq7\nfmRQUqYlnVIq7Sb7aWSEsqmHcTwej4mj2ratEKLI8/TIPPJLtDZmmk6wU5M3ad+Zam3fdSlcTjBA\nVQ3DkAynBAoKkUNEoEwqJnKTlYAaUWidcgnTBjN1vVLJ5HXl3ckiLcY49YOUctY06bgex54oCK1C\nCAikJCJFO/TkXQIgnXPA0XvLwRORZxZCzMoqV/p+s5GMhTLjOEqpS52VRbHbPOzvH4o8b8oqtctJ\nQpnI26xUWgMrpVAIFn/vYs3MUojcmLRjwhM/k5k5gQ3wMQv1xN4QaebupzEFtqpMUaQQgpIy/bTC\nZClSQhntKVprVZycrLQxmQDMpELi4PzN2/dGqXEcJQqNQhmNkSBGcieXsjzPT4LaxGUHuLi6fLjf\nhBDOzs6Uku8+fAghPLm6unpynXK13rx5k7z+i6qKMU7TxAiRaJomCjHpg7332+6YQiYDBQc0BdeO\n/aFv3968t9ZeXV0FoMlO0IoGmgjknD0eo7V2tVoVRb6cz9fLpVCyLMsPHz7YYSizTDBH56o8L7NM\nABZZrpS6ubnZCtRapyQolRktNCImPUBqwbque7I+y8rCe2+Dr+ezYRjevHnz8uXL3/zmN9vtduyH\n5J6zXK5SsMyHDx+MMXVdF3kulUpwcTLVSgGoWutMG4kiUzrPc9+PH/FzKeXF+iw5rXvn4DE+jAVC\nPJ31XT9qrRNrSUoZgROW6yla73jiLC+11lKjc+5waMtqlueGI0XnIJJEVFKWZYEmq6pKMnXHAwVX\n17Wcz5xzf/jjl7/5zW/Ihy+//DJlW37/+odx6H7+85+nKjKfz4zSwzDEGJfLpQMqy9I5f/vhVmpF\nREVRRKK7uw0IbJrGhSCEKqq6amoQyH2HSfCrdaKIexdjZGQhkQGRAyNhpnPQIKW0HBlFYHIu2uBN\nUUohCcH5yAipbU+UYSEkIg5+YmZAEoCY0oEiIXFW5phkkExKKpDCA8UQ0ajUu0owUuhTKxZCIuIh\nIoUAzCglYGJOECAm8WVC7VJ7HJllWmAzAREAnx5mgakd1ognAnbCtbQWJ55ktDEgRQAggYEJOIIS\nSmqTZTFGGzxESlc5bWLxEcMHRBsDESQD//Q8IkgpNUD8CO2wQIVCaFBapFDO9BqS44oLnmNAKXwM\nSdyc53lZFN66h5u7aZqSa1KMMXqfKZUVRabN9uGeiMosR6A801VRzopKKbW5v0/PFBO5cdpvN0nz\ns5jNL88vhq5/2OxmVf306VNm/vaH7y1HozUEnsZRIOZ5bhAGZ43SRZYzs3OBGHKljdJaSGAY+t5P\nVilVl9VivZ7XTVEUKbrbOdf3fVmWny7mbdve3d39dH+fZVmRZYfDoW1bY0xVVXmen61Wf/XLX+6P\nx7dv3zJCjHEYhj/+8Y9PZqu77S6EQAhFVYJAEshS3WweZnWj8yy6eGz7+/sHIrpYn+0pyCITHAJT\nRNyO/d5Pk7WeYpk1pmnCMIzB2eBA67wotJTphDFGZVlWZMaV+WqxpOiBQt8eONJ3333z5PIJEG+3\nW0bMTLZYLJRSt3d3fd9HpqIqu65L8lmR5cYYiBS8jzGWVZ3eDj5G6RVFsVwuQwgoBTyycyNRGqB9\nCC4G6V1eFPAYsy2E0JkmoiR2SpCS1FoA7sYh2Cl6n/Z3KVxPIUQOAVAxokCXHjoARByGweSZ1lqC\nisDpVaGSKQ/Ge58uZRJKCSHquiai6AMgGKUTNCKEKIsiLUckClRK8GOyEAP5ECcHMZpkaI0CTeYm\ne//hpshyJsJIQ9txJEmQpsePyyaQIrWJzEwISimhhQAUBN6HiEFpEEIoFClTjogiA8tTXw4frZ4f\nAQY3WmJW+kQfASKBmEqbUurj5/9xSlZGKm+dYpzN57Oq5khustF7AWhQCiEybQiSZy1TiCnI6HjY\nZVlWlqWQMk11KSwMEX0MIHC9Pluv10+ePGkP25Shu9/uUuUmInh8xcksuqjKTJ/c3UiJYRhc8Iny\nerO5ByVBCBCi7Xu4u3MhLNfrpmkohJubm2DjL37xi8Sl+vHHH+fz+eXlZSJFP7m6/s2v/5FS6v37\n9z/++KPOjLWWC6rqZrFa7vf7169fV7NmNp+/ePHi7Yf3KWS+bdvtdsvMi8Uiy7LXP71JfLnb29v5\nfH52dvbmzZtkcz1MIyKu1+u0I+y6TkpJIZKIWsq+75fL5Xqx3B0PifVjjEm0wzTRBg4pQqTruvl8\n7qyLWTafz4dhAGOklDnRaQ8XTwJirXU7jMwxnnzckAEEsyc2Jk/2ZuluyIUQLCx6b11TlTFSQMyL\nXDJlSi5ns9V81h/2CDifz52f+nGQUrjJSmkStmmKPM9PFmZPn16nrWS6yQh4tFNaqp0vl6Ozt7e3\nbdtmRV6WZZ7nMXBd1z4GRDQmXyz0fD5PU/Xd/W0IgQm11oIwwIlqFCNLpRI8EAGFSKsm8kyMMilA\nQmRBDMzBE6MTUmutpJQp94yZEIVKeBGgQIEnHjEgsNI6eB8CEYLWCpQkIhtDkeWnuoWIUrJAF+IU\nPCl5KroAgABSIqrU7QgpRbK0DZGY05MHMaZtJVIk74EpJaBJLRPgkW54AHDeR+8JWSjBQsRA8XHW\nVFKGEIkhxiiVkcqEaIkxodnMyAxEgEmSxMiMMbBApaRQSjMxBZJ4khETUQyBpRQsWLEUSXYlM228\ntwBQVFUiQCBTyutN/XRp9Pn6bOz6r96+m69XOs8SFB9CMFIyc4iOkvN+9Inkxd4NfSuECM5KBJIC\npcwzXeYFES2fPvuX//JfKiG/+eabzWbTjcNms+n7/njsHEejNBKT81oqY4ySymgNUiijXQhumqIP\n87Ju6pp8yPPGSOW0zbJsuVgsZ/M8z7VUVVX1fX+3uQ8hzJYLk2UMME7Ty/XszZs3+/1+3jRnZ2cc\niZkBxatPXn722WfffPftd998kxgtEkVRFAygMsNKeGe7aTz0A5gsr/XonT8ckfjF8+c///yLh/vN\n13/6091mK8/mqixE8H4aGeLQtZN3gFJoBcYmQlkiKxGR814RJTkDEn94++5vYrR23O8e1otlpk1w\nrqlqJWSyyo8xDs654JnZ73bDOI52mrzL87zv+0zpdDQtqkYqFUPIsgwEJvWREsgILng1TURk8izd\n50KdfCsjotJaKZWOlxBCwoQTp0kIIVEEBoqRfIgiGGNUloEbtFL4iP0gk1JKKyUAJSAgI4oYA4VY\nFMVsNgOCQDGBKISAABFODS4leR2iAhViTJzT9NZijOnMBIC0mn1EYshIheoEIiJiCGStZR+EFGVe\nIKJR2iidaTONI/mwmi90XrpxMsYs5vPvfnx9ejR8kI+8DykEEAuBSSOkUXBSHTIzkZAyqaJDQskQ\niU8K748UEHzMiwAilEJImUiOHGNqRARg4j+ntTEFTtIG9cmz5/f398CcKa2EZMZyNiuy/M2bN1VR\nJL/TwU4JoC/L8of9nVYCVPYRvji1SEKkTjkRiMqy1Jk5du1uu3fOeR+zrJBSH49d3/dEoKRhZopj\nkVfXl1fGmLZtvfcHNx67wUc+X69DoN3uIIS6vLy8u9us1+fPnj07Ho/t/vBw97BYLM7PLpEhWV6c\nnZ1tt9vb21shxKtXr/I8r6rq888/f/v27XfffXc8Hl+9ejWfz//qF7+822y0VM+ePCWiCLzf71lg\nGhfSKZMKqgSsi9KOw5Ory4eHh67trXNVXVd1fXt3dzi0Qohk42WtbdseAKqqmi1n3ntiPrTHpmnO\nzs76vgcUZSJTMCsUmdKBIT6qje00OWtT8R7HUSklWY7OJmosEbkYYoyJuKtWq/TUhRAiU1p/JvQG\npUCQ02CH7pFnJITSMA7dYbNlCqtmjjHoqqqLclbV2w8fpIC8mXnvhcCiqEMI69X6yy+/nM1mFxcX\nDw8PaSNujGm7rirLxEuPp4pJwzQ+bV4c3h7Grs/zvK7q0U7T6EyRr1ar0U4IkvDkQJIkVYN1p1sW\nJISYkH8WkqVQWS6EsM5hjADCe2+tcxKUNkpJTRCSoUzkYRqzLAP8+xzT9DDHGI1R8HjHy7S2QTr9\nKxEKoZSURoOWFBgiRgRmSvw4QZGFmKZpHEettWIJCI+/Q4JAAAxMQgoSgiN5TB5ZAEoAcgoLAmAQ\nyIwsEsNbJwWP8wFDYuEFBqZAjFFpZgRUp6OAmY04Mf/947o6FW8+ORTBx23TR1JYWgADnWisAdE5\nJ1GkYxQfVUNpGZxLnSsT7AQUtVQsiYgkPiqhGa21A0JxnRVLUxflYrEAgERZ0EplWSYA7TgVJmNK\nP5PyvDDGcKQQPRKPXd/3vVKqruvz9ZnW+tWrV7cfbsZxvL29TWfrse/27ZGIQInD4SAi13mBGvb7\nvTamqEojVVbkyasBfJyVlZFq7PrO9koiGgUc++OBg5/PZkVRXF6dKyMZKbElNg8Pd5vbiDy5EJmU\nUlmWaa2lEcv5Yrlc/vDd9+3h2LZtU9VZlhGw914Qo1GL1do6N27uPZGjOHqrp2mYrJShqWrP9P7m\nbui6oqwR8QgMUkzRT95Jo6cQu8nqzMjA4dj5EBL3x2gdQrDThCzy3BiltJZ913Zdh0BnywUCr5fz\nYRiAYD6f2+DbtpVarZvGeheYAHFylgWaLAsxNrOZVmrqh67rIBKF6MYpWXZ81J6mReFkbaCYlUUE\nJj6lMFnvBGCe51IrTfoj6+rjugTgFMebvtJ5KADLLE8FzHsfnCcORunkxRgpSEybIE7CmcVydnnx\n5Pb29u37d/00GmO0lDGE5ClGRBJFYtKkofxUUJgFYqJ5hxAgRATUWhKEZPyecuOYE5ZzIkZJJfMs\nS71OIhumTPTE3tBCLubzp0+ftuNwPB4/pgQhohZyXjcuPkYtgUBEbfIoNTP7GLSQRioAoEc7M0z5\ngyhQndbDCTZzzkujGQAERiKSgoAln36RSE9ijNaHBAagECqRM5WUNnjp7LyZrRbLTOvZbLZYLLTW\nt/d3P/zww7FrpdaO4tD1y/liPp+P49j3PUWyLlA8EJGdvNF5Zorg++3D/rBvQwiZwqoo8zwH4mRh\n2LZt+sTThSyyPHUTp3PHWi1lIolpqSjE6ANHgkiFycosd3J8mGzXdavFclY3fd/v9/uEiCY7jhjj\nfre7uLi4v7sD5uPxaLReLhaL+Xy1Wu0etvvtNoSglMrzvJ/Gtm3zqmyaZrvftX03q5urq6uEGMQY\n58vlfLncHQ7ri3MiIoSXL1++e/cuqZ4Q8fb2VkoZI19cXBRF0bYHZjZaC0CIFEI4Hg6RqKoqihRD\nIBRKSgVIPgBAWRgAijGen68jU9+3ZVV578dDDwDJngYFZFoXRVaUmTdahoDOwWOhSndACJRJLYRA\nABDqo4SG3Bi9rzJzffXsan2+ubkZu37s2gNSmpA+3Lzz3n/22afPnz/fbreSZJZl5+fns9msbdur\np09mVX1z8/7s7Ewk0af6e3sQpdRuu3XWpuaMEO7vH45tW89nBOxcAESQIsbYj6cwc1VWp8IghIuc\n7Ma0MUKgUBkzU4ghEiKAkkgaASKgIAAWhCISRAoxRmKkEBJRXEoUQgXvQ4gpdl4AsBCcLAKYkThC\nFEIkHXOCWyNFEDh5e0o+AWGUUlJq4JBEehIZMbJgZiJOCyAFDMACmJg8RSSOiBQCMkQkRPRMgTkS\nMyR5ECcWFAM454lPRE0AjkwYUEqptEgkeeecVDmiPMXIokQkRJmcV/5BGUYhTpizQjglrDAgC4F0\niizVMtlFSSmtc/Fx2tAokldUoJi0iUCMShyPR4kCTRZj7Lqubdt5Vc/n8xjZeztNE4VHQyWllUQ7\njCiUEqBNtl6uzs5XwToAGMppHMf0GFZ5So4vPnv56ne/+10KLGnbVkqZ1PZt27KWbppkCt2BBDeK\nNC2pIITGQhlj5Gq+yKUepCmp9N7bYUxijaosl8vlfD4fxjHLstly4YJ33g922rYH59z65YsXWndd\n561rmuazV59m2ty+/6BQvP3xDSKezZfNYj6fz4WSfd8fjp1n2nXHu+1DM5+ZqnDEN5t7IjJC5EXV\ndsPr714rIdfLJYXYWe+CH8cxRjZSK5XcTTgCWzsCgJbKaJPrnA075zQCcwzOV2V+vj4rq9yNA2mF\niFdXV9MwfvjwYTab3d8/POx3s9ksKilJCqF8CIOdGACksNYWTAVnniKF4GIQxFrrqqryoji17EyE\nEGL0FCOwpiydt4hIfGrIfAxMAh4dtbz3aQ1BRFoqwBQqIDJtsiyTQjKzUSeGlLPjBBwjGqm0kIAs\n+aQsCIiR/OTHfhyMMacsy0Tl0ypXUihZFMXjPIkciXyIMVrmtJxO304xBu8BIEkUMmNSAzpNUxoD\nQghSGZAihiCYpVIcwminE2ATY9qLW2uBWAiRb7dnZ2fJLCWpGOxkmTkvCxFOoYEpH5mZA2AIgZLZ\nZ0p7jHRiGgqRRFkoRIQTLpggriF6JHYx6BCkEEqIRN2SSjFzJHIxWGcDk0zEhH17NHk2q2pg7rqu\nKsrJ2Zubm7Ztr6+v5/N5NwwsxWy1rGdNXdcjhwh8Mp4+hVTsDvu9kKfPdLvdJjBZKNm2rQBsmsYY\nczweU/Oba5OmRqVUpo3W+ng89n2fdDVPnj4Rl9d934/HTmt9tlwhA1l/vj7r2vabP30NAMtmtp4v\nls3s9vb2/0/Wfy1Jmh1pgqAe+nOjzsKDZ4IUUHS2u5bczauu7APsE+zdyFZLTVdvV6EKBSAzIzKY\nc+M/O0x1L9TMgZFxQUJcMiPczX47RPXTjzxtN8vlkoFxRLy6vORBbNd1u93up59+ms/n7CM9jqMx\npm/75XwRMa22u8fHR5KiruuXb14750bvrLVXF5fjOD7dP7C/8eDHu7u7u7u7X//6rzjP6+LiYjr0\nV1fXj4+PXde1Q391fsGX3m63M0papa02UojNas2jgrIoijwPIRBDFkh8KhNRXZRMwFksFsyrzPPc\nOcdbggRIyU5Mgr2sAx5JQEJJFr35FATJ6XRqlQYSSimWKDjnttvt5rAiorPF4re/+tX1+eXnzD7c\n3lkpkvMCMMXERjwR02qzdt4Z1Jw6zl4oQgj20xBC8NF5tlwURcE7pK7r1Wq9WCyur69H5zabnWaD\nt5gippQQBNesyrsRE1hrbV3zxktEPvkxeiFUppTWJiTvHZ9l6ShxyYwm5Zwbk/MxxIAEKLSyNodT\ngq4QwvI8VQgpZcTAYUcAJJUQUigQyHJhJZl9HVL03sdj1U9SSPaGAylIS0nWZPzkNRFFCIhAICAR\nIkaVTAJJkggROFANKUUpJSTkM45d9EhAIowpaq0V2yNLEb1ARBCktVVKCQkgBYFMiM77YRiMgb/U\nJfONyzI2HjkwpC7o2KBgSgqEEFIAkCQtMimlFsfdLYTQ1hCAT0ewSkRECiIiEQ59z2b0UkqMkfG4\nTJswDg/3931xiD7oLOO7n62A27aty6oqc8CopbJSYEzaSC1kEkJL9fbNG14wXBaflMP061//+sOH\nD8MwNE1zfn6ZUvry5dvusM+qssqLTBsBUmu9mM+zLIsphRS9cynGTJuiqkuTzeqmyYq978ZxHLQB\nIjaF5lSf1Xo9mc8QqB8GodV0Ppt1h0+fPvV9v16vh7abTaZnZ2e5zT59+Pjf/umf3r17V9f1999/\nP5vN2q5TSvVuvP124+jIZnfB6xB9DCiAAIo8H1y8ebwXEaPzV+cXiej29s5VmXPOjcHmGQ/XyyxP\nIAAgJMyNLWxJRBBSVRRNVng/SimdGyjhOI6UAqZQlnlubN+3lGCxWHz/y19ok7Vt673P8qKaNDbL\n2r4LgGMM2hqTWRY3G621sUWWZ9pkUhdFcRxVHnbjOCARSCG1MkqFGNl58VjDKXkEn2Lim0OczM64\nHRKnOlFKEEJAwoiUYgQtlJRaS0WZQEI8CltjDJL3rBDBSIhycO5pvf7Tjz90XedT5GEl2xVUdZ1S\nAiGeVQlHYSwAESolASgEHwJAQmutVcr1PdMVEyFiAiAhgAhRCptlwK4XWgEqDECECcnmmdUGpQjB\nU0xpk9qhPzs7Y+UOY058Ez3rTQxILYUiASBSxDR6mWkWFhMRO88cYSghuEMLzjG/h+noERMgQQQv\ng1ZKayNBcGTyM8xAREgkmFN5fnmRUiqKEkNctU9f72/z9Wq73gAASLHv277vu3FQSqX2sDnspZT7\n7TbGmFIqy1Jry7laL1++tFl2OBxWq5XWWpmju5DwDgC4ZpFSyiOQBkVRcBGhtR6GYRgGhtS5XR77\n4bDbz2ezelo751zXe++zEy8jxhjcWNrs6uwcFQlJZZ5XdcGR3UQ0nU6Hoavr8unpybnhYnmWaXXY\nbobl4vXVa1Dy3/79d/u2Xc4XtirarlutVmVdZVnGeQObzWaz2dR1XRQFKhjHUSjpY2CON3z6NAxD\n00y5566qiqTARN3hsN5u/+oX3/d9L5RkJ3cl5KsX10VRHPpOKcVvH2PiD4Mho3Ecsyxjs8w8z9nh\n5UgiDQEAhD7Wp957kecc7EhEmJDpqfIk/caUgvcahBY6+hjGUGSZG4Yw9E93t9gPwQ3L6WQ6mYxj\nX+aFkzCdTqez2f6w+/jzz8aYxlRs8sznMouSZ03NLw8RgfDs7IxX0jAMlLAqyqzIOWF01ky45bJF\n6YJHIk7miBF7PyBiSGF0Y4jIK3JMCTGMmEw0R1w9pRQJwfM9VJjMhc57L6RUykgpOJsMOeYXgDCG\ngIx9acn5FACClFTsSvM8qmEhB2HyMbgQjn4jIIkHM0SUIt9wiVAqC1ICkVAaIMFRqiSRhEdSQAqk\nUEYqAMG3oaTEV7KQR7yaL8vjvAoApFTa8n10zM7jrpp7juOcDB2jFzFGRnQAIIWorGUeJh7l60fk\nDUM0WZbbDNgbD5FJZZk2PZOwtFJKWYAElFIiwIhotRYkg/NH4omEoqo4myTXxlaVcy46b7VZLBb7\n/Z4oSQWE6L3vBRktjTGVzY2WQ9cLpBACxmTrfDKpt9vIfhFPT0/PnYFS6uHhwVq7vLicz+e3t7e7\nw34cR1MVHNxSaFvXk6Zp2Ku87TrnnHNe5SAKEkgaSGlVL188087ZOWS73a7Waxbp+hR7N2ZFPpnP\nqrq2Wfblyxet9bt3775//12dF24YF4vF//q//q+uH+bzeWGz1cPj/dMjY4P9od1FnxWFtma2WGZV\neftwH2KsJ03Xj2VZdof2sNvP60kiABAvXr38slklH6QQWqrkg5Bi2sxIQJHld3d3kMAImWKMyaPU\nUutxHJuqtpwgjmh1vrg8/+7tm69fvz7ePxiTvXnzhi0+bJbdPzxi3y+Xy6IsD13b9b1PUQavjL68\nvAzOCyRF4LynmFCbYRimizlzITkOTqkjlSHFqDOrxZGRoI3hW+E5kZ6D84wxvOSiD0opKQSGyCa7\nvHCjBIFJZBkRaaXoyBk+DjvSadrNQ1NEfHp6cjGklJDIp4jB2WBtlvF1kFLyzkkpy7zg8oXniQAg\nT24hzI9jyjf/cEqJP3ru9fM8J4whBAmGO1i+HaVSoBQQCClBQwBMQx/u74LzvCCLosjzfGg7Zo9L\nEOzrCcbwTEgrBVIxGi+E5NkzLz/LTEMiQmRW3XFgpIGjTiKh8x4SZlJLIfgkP25erRQegWvtvb+5\nuanr+vL8gs2B86r8frl8enoqqlIppTPbWGOtHYPv9vtvn79IKc/Pz1kr7dxgrT07O3v16tXd/T17\nqhVFsd7uhBjev38v/MDOecvlsiiK6DwiTiYTrfX6aRVPpPyiKIhoHIbPnz5xOysAJlVtpNoc2rEf\nAODy8vLy/Nw5d3Nzc9jtBcFiNt/5jh/K2A9nZ2dsJ8vt79/93d/x8G0+n0sp//mf//nx8fGf/D9d\nvLj6/PXLi5cvL19cjSm0bcv2ytvttuu6oet3u12MkT8k5kwtFguum4qi2B32iPjhwwdr7TiO0+l0\ntVoByZRS0zRMttRaKyGkPs4j27ZNPAb4C/4bZ4zwNVbJ6ubmZhiG12/fsDZ/tlz0fQ/jQES2yLnK\ncc51MR75rjHxIMFam5lcCGGU9cmNXd8f+qEbtdaUUFCqivx8uWyqWgJqqTDF1epRCTmfzw+t5Ea/\nbqqE6L1nsgPTMbhO8t5vNpujhbVzt7e3iFgUBfcWF2cXu90urdeHtj+y+YUIIQhtGCPSWrOogFvb\nqKjrev4VzNJMkZL3HtkxR2mboYxhHN0YfIpG5ewYV5RFJiUCJUgChZRSHT2QOd/m6O0sjlEmxLXO\nEe19dktOiRtTYvmwlKMLz7xEAlCChBDS6BjiM3TGwKgQghB88AJAEEkltVIShDzuT5GIKB1JcPIE\n1INQfJXCUbXJv8ukFLSUSsoQHF/AiKz5STyEG8cxxmitBaTnNcNnpRBCgVAgBAhKaLUpyxKI+r6n\nmDjoVCmDIbrg2VpZSpnYV0sQEZnMKIA+OCFEbmyCZIwZun4cfTabTyaTODpJsJjNZ4sFpxfkxtos\ni1ICoPcepFBFleeZACzLsqlqPVUXFxdZlm+3W+9Htq5rmoZtpxaLxWw2I6LPnz/f3t4WRXF+fo6I\nBzd0XacIZssmz3OWXBdF0Q9DSimEAFlOCYMfu06KiEWVHVt2RA75PhwOXdc1TXPzeH9oW59iave7\nriUBUisCqOuazatb2NVltVwuLxbL9XqdGeuG0Vr7q+9/sdnvPnz40HfdgLEbBqF0UVUvX78aY2A/\nrJDSfD632swm07/7zd9YbW6/fdNSVVU1DAMQSiH86EyW15PaGHN1ddW37X57EARGKokUQ4ijq+pS\nStkfegUkpZxMJvP5fDabNU3z459+yPOcbV+H3iGitXYzDM45JBq8y7JMy4yIhJKjd7vtFkJSUoqI\ns7rJbRZDDCFoe2QUM1+SCy+mMgmlIIEksNYSciDukTxxdEeXhtc86y+kEB7JDSOGyLOubd+GUFZl\nKU8dsyRATMaYGGLvxnHs+RO0eaaUqsuJaJn3w/krcXROtG1VVUc9HoAA0NYIEN77w+5IWdXqOGTx\n3rth5PQt7qEBQFnDNWuHKLTy3vfDcBx4AQkATJHYUEhKI4/WU0LKtm3zPC+y7Bi/0TSuH56enoL3\n/MMxJkpYFEVmrJbKiZMrO4AESAQBU0iRiwxmInOVwCwTqQ0bj0BC7z2JSMZapY/BwAAM+POWTkD6\nbLF8/fLV//yf/9P1w+vXrz9+/LjZbDawMVIppQ67vRBiOp12h9a1rSX67m9/QwlXq1UgsdlujDGz\nZlJP69v7G2ttVReH3R4o1YUFgP6wBReur6+vr68/fPjw+Pjove/7oWkm33333b/8y7/c3t5KPrJT\nstYO3p9NJnePj2VZzurm45cv8/l89KGaTouiyPLcpfS03Zosv34zvXt6evfuXdFMuYkc4/p+s913\n/Wq1mk6nMi/uN9t122VZ1iE+3t8HrYUxqtajBlkX1XJGRt1++5JlmWvbw3orExHFMLrDdnd1dVVV\n1dPTky4y7330MemYGbtdrQGgyPND3xGloipQoKOwOFv2bhRad31wRKvdlpS8eHGlJvV+u/108+23\nv/3tZr1W2gTnMxJVVnSHtqjtar/e7Q77rq/rWinTd6O1VkrdH3oAoAREYKXVoN3owhgKjCmNQkkF\noBLVdT0/Oy+KYvW0cc4hoCmzlBIZQZlCkJLKoqqCNvXVhUh08+EDYFo/PdV57qVIbow+ZDf3dZmL\ngLEdBxWzLGuKvC4rbsGllJQXlFAbLYTwiF3wA+AhOCqyx67fOK+MTog+oA/BIcY8E4X1kvoxbV1n\n0FBpfNB366d6nAmlUhQHNyqTEkWfojI6VzokSsllCquqKKqs3R/a9mCkUgbAyJhGjBQwRUwkABGJ\nLaK0yjIbMTnvD0OrQVRVldsspTR4LwJIKUmA1tq5EFKyeSa1gRBACLB6jk1KKYZIREoCl6ZWCB+S\nIAApEmEgpONVDhNt9vv9bLFczOdP9w/joavrWisVvGf9QkpIFCQQp1wdhg1vXSOVEaQFCHbiBJQp\nSSmstYEweCdzU5XT8eBcQkRMQg6jdz5mxjI807edcy7TKi8KYxQQCQlNqcO4RwN5XrroEUNm8hhD\ncmOZV1pbl2LClBWFVtQPHiZm6HuZsKqqyuquO2Dw82YSvF+W1SgkjmN9cRGF+vblqyB43D5mWXY+\nbYhICZmVMy0B45HnYoyilBMKpW2e5/f3TwXRPM/avssVLSZlNxy898O4XW9u3333vsqLrkvb7QpC\n/t3L5Ww2u/n0wIP8i8UEAdu+Qyj7sfXeN3Wx34d9u83rbIdj52JRFONuI4TI85xBo/vHR0RsZpPp\ndKpK+/Hjx2G7LbNSE2w329j21HdkzLBaHW7v5tPp9IUBN9bTqTqbgxRD8FHQIbrPu/s7t6WJdjvQ\nWgfndUDh6a/f/vJMF7v1Smh9hvRyNtFSnUnMNCYDDw+39ayR88oNY4xhmut6UoIM683T//j2QzVp\nLt5Mg/PoQwkSgovRLTIrZQSZ+rb9xXff//3f/l3b9v/7/+d/+/Vv/ur/9nf/5eOXr//6u38HrZzG\nUCmZiqKT+7FdVkscI4WQQpg0zXazNXmRR6SEmTaTs3mR5865znX7p1GupRCiqmuQwu1927VKqSzP\nMUSBhEAgpTQ6pLgfe6UEIQJArkxmVaaVJCBEq5RMRwl5bnUQFDABkJ4WA2E/HPDEmSqKoq7rg3fN\npCmpHNcwOCeVkko7xNS1BCRzS94H5wIhShACDmOvM1vYDFPyowv9KIVIIQSQ3oXOx6ZpqrwYx9HH\nVEwmQSkuSbmPjP0gpczzHKM7DL2UMiuLcRy991IINkKWgjtgooRCgBJSCzmfzSImKaU2crNbf73/\nBgBZlrWhBwAhSBhQVplMKyFjBAMqpRQx8YgqCpkSUEyjd2qUoEBoIY0c0YMCm+VcNEgQhIRKBCJM\nYUhBSYlEfBaNMTjvhRA6s7rv++VyeXV+sd/vb25uOKvAe//m5avLy8tHKT/8+NPnz5+5HFNK1dMi\ny7KmaXjbGKmcc+v1mnm8zrksy6qqYmNC7/3haT2dTmezWVmWm82Gq/hv377d3Nzs9/tjgeNcnufn\n5+fz+fzjx4+c38kjRqVUXhbcJfPLy/O8aRqWT3z69Km5OuPvY4wceHB9fR1j3O/3nz9/ZndJ1p5f\nX1/Xdb0/HLibZGSDz5Gmafi1SSnLsjw7O5tMJkz/+/LlS57nwfuiKAqbycWCyTKXZ+dj8M+SOwCg\nmIYQMjRElGWZLYsY493d3ehcXdf/+Z//2dR1JnWZF8YYZjv3fa+sKerquSzi4TormvKqZFUAwwPH\nDg9JapUi8tXoUxzH8YhnKqmAiDQAKC2Vltoo14UYY+v84+OjEvJwOOiTO2YIwSilMsn9VpZls8lU\ngWBGm9Sq0JphnyzLmukkpVQeDkyJl1qXZZlSip4YShrGsRtdiFFoxSAY923oYx8CEUUO0D19KVYR\nCQVRSim5CxSn5k+CEEKUec6G7DFGAtBaM2E4pVRVFYcQ8EgC4egNmymtlGJHWJ61UCI8KRnohNzy\nCDn64BPBKTuMm2CGlbTWkRATkgAJApFACSlEirHMi/l8Pp/P0YUDSKVUis/UKhDilFskgIjYMEsB\nt+zct4vTx310Bzx+xcijh3Sy/lBKSymlUkS02+0kiLIsqzzjjebGMYRQNSXjjQDARxIgjf3AxItE\nqAhRgPdeKFnXNW8uZrKcKGxqGAat1HGPC8lgGqORjLPxYlPqaCpni3K32znnlMx4R4/jGLzv+z6f\nTkYfpLa//tVvssJ++vL5p58/ppQWZ3OmUKREZVmXWc7ocdM04zgeDofb29usyDn7drvdMgTFp+3j\n42PbtpPJZLFYdKNvmoZzFNIpabSZTsZxrKrql7/8pfd+u99/+/YNEa+urvQuSyntdjs/OglQl9XQ\n9SxOvXu4z+vKlPmX25uU0q9+9Ssfw8NdCwDjOATn7+/utABrzJs3b4o8T8EnH7TVY/Bt224Ou9G7\n3J6pSlRZ7kcXQsAYU6Dg/curFz7FYRj8MCgEaaxR2mSC35Q8JX+zJxRrqD59+vTz5y9R0NWbVzMl\n9+0BEbv2mGJ0Nl98+fKFz4e6rilhVVW5sdbazFq+Dr33qHRVVUoppo8URcHWs9zhSSmFVkcTRNRa\nqsH1AkDB0VACEQUINprgcQynBQsheI9gGHn2rKx65kK3m50QoktkjMmNhYSuH/wwaq11WcUYn1tG\nLS2d4hRjjJ6tLo2RBJhSjLFpmmcfyvV6zUc059g+02zFyRAjncx9tdb8NMZx3O92fd9Lm3GQCium\npFJGs6mlRe9SSnEcQ4zMrmCkk+08vXPOuRRibjOmmyEiIJEEKeQpuEGWpmC7eHbS/stXxcJiSGi0\nLrPcGquU4u3GDzPEEEJAAQZIf/36tbAZb7NhGPhozrJse9h/vb3ZbrfamovZNMa42+0enh4PMnFQ\nj/feD2MxnYYQtpvNmzdv+r6PPhhjiqI4ama0VhE/fvzIWCsHkrAMjnf+4XDggHprLY8elVIXFxdS\nyn4cyrw4Ozu7vr5u94eff/750HUS4Pvvv18ul+v1WmzE09PTAT2zK5mLURTFxcXFzc3N2dnZ09NT\nlmUxxu12yzjP4XBomma/3x8OB/6XPPTN85yPpM1m03Xds+hWa82qMjeM7W4vCbRShDgOA/ONbZ6N\n3vWHlm/0vCz4DC2zCgH2bdv2HdPThmGYNM1ut0MfUpbH0c0m07ZtozkarCCRySzbxGitbZEzSiOE\nEFJy9UdEZVVrrdu2jYRZXkoph2HQxmhrdOKIupRSGEdQITjnMEUi8jEc9m1VFiGl4IJiJ1gh8rIo\nbZYZG5NXILIiFwRuv4sx5kVRFoVQ0hpdqNJaOzpXlqXQanBjApJC+JRCQte7YRic9wlIaauFFkJQ\nQsZmuV4hIiBZ5JU4El0lSJFlGQkEABQAiFJrSIgp+eCU4FS7HLS1KY3jGGK01uZVSQKccyazMoTh\nRKBgJrOUkoQIKTH6TQnhL/BnrbUGiIh0ck4PIRhQQgjg+vRkkyulNMZENx7tC6QkIokkjTJKo0Dv\n3PrxabvdJh9KNu1L6bgJ4Qh9P4s4jk4fcHwZkoCnngnZHisBIiPn7CTNa0lKaY09zuQoIaKW7MYM\nR3dMpSprMUYtFSKOXQ8AZV5JLbPMb7dbYwympLU21h66th9c0zT8uJRSeJQwAhExbB9iqPNCCdl2\nHaZEArx3WpmUkuarHWAcxxRckeVnZ2eSoO97P7rCZsaY+WxWVVUch8lkIpQs6qppmnk/LNtOG9m2\nbUphMpnMJtNhGKzSs9nMaL1+3LVt+/D0yLJRrfX5+XnTNL/73e+OW6ksBYixH7z3T09PTVFtdjs+\ntbm2Hr3T1pyfn4/jyAacWZYBIjtyFJN6v9n60VmptFQpxEAQOI4QceyHfdfefbtBJeq6ntRNmKph\nGJQQA+F6vYaE52dntsi7rndjn9tM5wUIFQA94uZwOO9Ha0w9beRM+NEhkEuRiJbNdN93OHqhbZXl\nk7wUEcPo8qKYTabvX79JIbIR5tB2POZv21ZKWRYZJdzstvvDgVIyUk2qui7Kpq6ZlRaQqqIkkbi7\nUFLygmEH5ggEUiCQd55R4j/TIEIgABUVzy+4Z4ghSAIQUkTsQ7RCKSkVCJ6J8q3Mpa2VkqTIKR2x\nEGOFEEEo51wM0Vrr+gEyLIpCZrDf7wnA2Iyt7H0MeES8gdKRgz0MQ5TeGpMZKwkcjyO1Yiv7sXfj\nOLKrREhRW4NACMfhK8PmXFgzecJaO5vOQggxhK7rhBBSK620fKZNaHVkthKbfB1DP/mA4lhYeZw7\nC67bFRHPuRBRSsU0saiUkUqoI1maXYefKV3sJiYI8iwr8lxrjQmjc4jI+cEJjjaix9LBSPX161c2\nYWdal5Ty7bt37969+/Dhw8Pj43Qymcxnh8PB+nx5cT5KQkSKCUMsiuL6+rrvusPh8Pj4yKNpjgzy\n3vd93+72fDzt9/vVatX3/WKxGMfx48eP//iP/wgA2+02pcQgxnF7p1TXddM0PFja7/chhNG7Fy9e\ncC7vMAxfvnxhX+WQYrdeM1eI5yh81ocQbm9vX7x4waXl4XCYTqcvX768v7//8aefQgh1XccYeQj/\n9PQkhFgsFk3TMHkET4aRXA1orZkR0NQ1UwSVUqvVigBmckYJM2slQSSOq8SqmiujNvtdfziAEgJE\n1x8uL86qIk/jmFIwpppWc6P0IbjDOPCCCICFKthGuMqav3QkRyIOvhVCCKm1yaRyNhOL5ZJ48keU\nkmcG03FpIsYonHMJXRkCE7hm00lRFP3eS6XyssqyrCiqqsytNtHbsW+fnp4m85kyWkoZMLXjoEAw\nGnFze5tSyquSV3z0qJQah0FI2/f9fr+XSlWTJs9zJBH41cTox9GPLoQkhNBGS61BWCKSmISSWgLP\nJDGmosiICEWKURDFI69JCGJ/ZmNiSqyOFULEGEGK5/d7bLWBiMgjUvA8BtacuHCMF0w8cua+WQIg\nG6krBaepcEKSUnLEj9aaRsKYlBBSAhIAEjeyKaXtdhudH7q+sFmR5yde5J+/CACQAAkSIgjJfssE\nAjEBCBRRqITIiDqPh0EqKSWdOnIpJXvVEhEQ5nlOCRHRx8TnF8+fHh9u67q2WvfdEEPIrM2Laqqm\n+/1eSoguKgF5Vo1O9p2HFJnzqJRqmgYAGTSaTqeAMXjI8lyB6A+tAFBKJkKIMYQA7CCYcOyHIjNW\nG+ecJADCLMussYw5KaWMFOc4zRU4AAEAAElEQVRlOXj37fbuLKXpfH7hx8PhsPryraqqxWIxqZuH\nh4dMm4uzS2stJllU5cXVJTdYk8nkzZs3ZVmO48isCBYuEhH3Rht/9OW+uLgoqnJ/f3/3cM/aAQDw\no2Ms59X1y8PhMAyDHI4RaiwVE0IUWTZtGm3tm1ev7x8f7m7vvXPr/W4Yhu9/+cs4hsNmrbVuygoS\njkMXo1+tVkQUnXc+7vuWtRIv377TeTGOnhBIBqm0Aam1LvMi02YcXKmtncwwpuV8fn12oRD2252a\n5HVdT+q6KsoyLwTSZrNbLBYfPv0MhE1dmyLvu+7Lz5+2h33dNFJIrVRhMwmiLiurDfdLKaXMWkxp\n6Pvkw5FWUhQsMQohMMspEsYYj4Z6HKuH6J1joz0AUAQ8xyXCGBNB1EppISUIOjZ7UgvNakcAmOSl\ncy7FBCLaLLNZXphj/x0IJJJIaIQsbaa1buqGTobnfCwnID6gmMWCbOIhpUDidjMROedYO8Stf0pH\ndwR/auKfIUzvva4K7z07oLFTND8HjuI0Sj+/6xijE0JwaBKA1keKh4uBb1DPXoFSZmWRacNRKwKO\nWsCj7uDUAo/OmcyCEEyiVkZLraI/smuN1ibPpDryPzDEY6YOIQPR0mhuQvSvfvWrr1+/plMKFffj\nVVVN5jOTZzqzKrP7vtvsd9Pp9Pr6+ocPPwEAnj7v2XTqxhERm6Zxw5H9ZK1tmqawmW7kbrWez+da\n6xcvXjCqMAzDy5cvt9stEU2n0+NzT4lJTF3XcYzdcrlEoP/8z//89OlTWZaz2YzzXLf7PcNN9WSy\nb9vgekaxOOfg69ev9/f3/CvOzs7YT/H7778/Pz/Psuz29lYIMZ/Pz8/PHx4e2rZt23a/37ObFQvp\nmDLGSB2jrGxCkllblqXrh2EYhq6bVPV6t3XjmBfFd+/eK6MfHx+fVquqqFNKo3eshsyMTYSuH1OI\n0YdJ01CIWZaVdf10/zD2g8gUJv6PIZz8RXOlRuZiec9lTYqRSb+DDyiki0lrk5UVAPTOj8EPw+CH\n8YRuySMVSAKSSoTDMKzXa74qQCrWQJss4wZ6pCEzFqR2Y9sPgy2LsigQse06JaRNkQOuj0wEKfnz\nYlQqJBRS5pxUWJQkBYYECZ0bx3HsxyEGPPKVWMyiDAJFTAhMWkosRSiLLCEKJA0ClJFMIUaMKTFA\nxCuEL2AiGvsBgcRJTkDEdhoJBSAiJORuGABAiufYIkISHJwkhZGShPQncpw+NuYCBRChOikxiEgQ\nnPT2kAIKkgBCKzuZ2MJmQsowOiklkGRKNAghSSQgANJHZaHmYw7UUQfCtUPCRIggpJaKhAACqbUW\nQp66dhdYwk9HpYTSWisrJR+yh8MBAHJrlVJSjQb0c0MwnTWYoG1bl/wE68pan1lJKNSz6gSeQ4hT\nSlYrxvoEgOyFlkrlmSQQ+pjFFmP0oxuHwSjBnwgkBAItjxE9yPEAxjxtd904OOcutJ0uFw/r1c3t\nHYHIsszoLPjkekeWum5o275pmtevX3Mdudvt2Gh6u92+fPlyOp0KIXa73R/+8IeHhwchRF3XXdcV\nRcG8U5YylmXZtu2//uu/Xlxc1HWtteZykyv74fZm7Adb15mxgmDaNKwbTimFlIqi4Hz74dACQLfb\nhwF93+mybOpFVeZ9X/EZWhRFsH+2Q/IAWZ7Xi0Wx78d+GA5tn1AQlGWZVYUFGRIVZeHA7bodjn5R\nTc5m8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s25gDg/P2cxkrV2u90WRfH4+CiEqKrq27dvl5eX1lqbZd77z58/c1u5Wq1msxmr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c9/v9k/H4w4cPUsow8Jq6XM3ng8EgSaL1auH54R/+5Ece45vNpixzpdTpk1NZJWVZ7nY7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\nGSHEc1zQyOmWCWKw9Xqdpun/A2hfCy8wP9kqAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "image_url = 'https://tensorflow.org/images/surf.jpg'\n", + "image_extension = image_url[-4:]\n", + "image_path = tf.keras.utils.get_file('image'+image_extension,\n", + " origin=image_url)\n", + "\n", + "result, attention_plot = evaluate(image_path)\n", + "print ('Prediction Caption:', ' '.join(result))\n", + "plot_attention(image_path, result, attention_plot)\n", + "# opening the image\n", + "Image.open(image_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VJZXyJco6uLO" + }, + "source": [ + "# Next steps\n", + "\n", + "Congrats! You've just trained an image captioning model with attention. Next, take a look at this example [Neural Machine Translation with Attention](../sequences/nmt_with_attention.ipynb). It uses a similar architecture to translate between Spanish and English sentences. You can also experiment with training the code in this notebook on a different dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "source:\n", + " \n", + "https://www.tensorflow.org/tutorials/text/image_captioning" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "6MKXiiVUbb_f" + }, + "source": [ + "
\n", + "
دوره پیشرفته یادگیری عمیق
علیرضا اخوان پور
آبان و آذر 1399
\n", + "
\n", + "Class.Vision - AkhavanPour.ir - GitHub\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "K2s1A9eLRPEj" + ], + "name": "Copy of image_captioning.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/TimeDistributed.ipynb b/TimeDistributed.ipynb new file mode 100644 index 0000000..86028c1 --- /dev/null +++ b/TimeDistributed.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "data": { + "text/plain": [ + "'2.0.0'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import tensorflow as tf\n", + "from keras.models import Sequential \n", + "from keras.layers import Dense \n", + "from keras.layers import TimeDistributed\n", + "tf.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "time_distributed_1 (TimeDist (None, 10, 8) 48 \n", + "=================================================================\n", + "Total params: 48\n", + "Trainable params: 48\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(TimeDistributed(Dense(8), input_shape=(10, 5)))\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_2 (Dense) (None, 10, 8) 136 \n", + "=================================================================\n", + "Total params: 136\n", + "Trainable params: 136\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model = Sequential()\n", + "model.add(Dense(8, input_shape=(10, 16)))\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf2-GPU", + "language": "python", + "name": "tf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/final_cnn_lstm.ipynb b/final_cnn_lstm.ipynb new file mode 100644 index 0000000..c8f4122 --- /dev/null +++ b/final_cnn_lstm.ipynb @@ -0,0 +1,488 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "from keras import backend as K\n", + "from keras.layers.convolutional import Conv2D, MaxPooling2D\n", + "from keras.layers import Input, Dense, Activation, GlobalAveragePooling2D, Flatten\n", + "from keras.layers import Reshape, Lambda, RepeatVector\n", + "from keras.layers.merge import add, concatenate\n", + "from keras.models import Model\n", + "from keras.layers.recurrent import GRU, LSTM\n", + "from keras.optimizers import SGD\n", + "from keras.utils.data_utils import get_file\n", + "from keras.preprocessing import image\n", + "import keras.callbacks\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from keras.models import load_model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# model = load_model('cnn-lstm-generated_10c-1.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def CNN_RNN_MODEL(img_h, channel, num_classes):\n", + "# words_per_epoch = 16000\n", + "# val_split = 0.2\n", + "# val_words = int(words_per_epoch * (val_split))\n", + "\n", + " # Network parameters\n", + " conv_filters = 16\n", + " kernel_size = (3, 3)\n", + " pool_size = 2\n", + " time_dense_size = 128\n", + " rnn_size = 256\n", + " minibatch_size = 32\n", + " act = 'relu'\n", + " img_w = 175\n", + " \n", + " input_shape = (img_w, img_h, channel)\n", + " input_data = Input(name='the_input', shape=input_shape, dtype='float32')\n", + " inner = Conv2D(conv_filters, kernel_size, padding='same',\n", + " activation=act, kernel_initializer='he_normal',\n", + " name='conv1')(input_data)\n", + " inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)\n", + " inner = Conv2D(conv_filters, kernel_size, padding='same',\n", + " activation=act, kernel_initializer='he_normal',\n", + " name='conv2')(inner)\n", + " inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)\n", + " \n", + " conv_to_rnn_dims = (img_w // (pool_size ** 2),\n", + " (img_h // (pool_size ** 2)) *conv_filters)\n", + " inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)\n", + "\n", + " gru_2 = GRU(rnn_size, return_sequences=False,\n", + " kernel_initializer='he_normal', name='gru2')(inner)\n", + " gru_2b = GRU(rnn_size, return_sequences=False, go_backwards=True,\n", + " kernel_initializer='he_normal', name='gru2_b')(inner)\n", + "\n", + " # transforms RNN output to character activations:\n", + " inner = Dense(num_classes, kernel_initializer='he_normal',\n", + " name='dense2')(concatenate([gru_2, gru_2b]))\n", + " y_pred = Activation('softmax', name='softmax')(inner)\n", + " model = Model(inputs=input_data, outputs=y_pred)\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "__________________________________________________________________________________________________\n", + "Layer (type) Output Shape Param # Connected to \n", + "==================================================================================================\n", + "the_input (InputLayer) (None, 175, 75, 3) 0 \n", + "__________________________________________________________________________________________________\n", + "conv1 (Conv2D) (None, 175, 75, 16) 448 the_input[0][0] \n", + "__________________________________________________________________________________________________\n", + "max1 (MaxPooling2D) (None, 87, 37, 16) 0 conv1[0][0] \n", + "__________________________________________________________________________________________________\n", + "conv2 (Conv2D) (None, 87, 37, 16) 2320 max1[0][0] \n", + "__________________________________________________________________________________________________\n", + "max2 (MaxPooling2D) (None, 43, 18, 16) 0 conv2[0][0] \n", + "__________________________________________________________________________________________________\n", + "reshape (Reshape) (None, 43, 288) 0 max2[0][0] \n", + "__________________________________________________________________________________________________\n", + "gru2 (GRU) (None, 256) 418560 reshape[0][0] \n", + "__________________________________________________________________________________________________\n", + "gru2_b (GRU) (None, 256) 418560 reshape[0][0] \n", + "__________________________________________________________________________________________________\n", + "concatenate_1 (Concatenate) (None, 512) 0 gru2[0][0] \n", + " gru2_b[0][0] \n", + "__________________________________________________________________________________________________\n", + "dense2 (Dense) (None, 10) 5130 concatenate_1[0][0] \n", + "__________________________________________________________________________________________________\n", + "softmax (Activation) (None, 10) 0 dense2[0][0] \n", + "==================================================================================================\n", + "Total params: 845,018\n", + "Trainable params: 845,018\n", + "Non-trainable params: 0\n", + "__________________________________________________________________________________________________\n" + ] + } + ], + "source": [ + "model = CNN_RNN_MODEL(75, 3, 10)\n", + "sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)\n", + "model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 360 images belonging to 10 classes.\n", + "Found 39 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "batch_size = 32\n", + "train_path = 'iran_shahr/train'\n", + "val_path = 'iran_shahr/val'\n", + "img_width, img_hight = (175, 75)\n", + "\n", + "\n", + "# this is the augmentation configuration we will use for training\n", + "train_datagen = ImageDataGenerator(\n", + " rescale=1./255,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True)\n", + "\n", + "# this is the augmentation configuration we will use for testing:\n", + "# only rescaling\n", + "val_datagen = ImageDataGenerator(rescale=1./255)\n", + "\n", + "# this is a generator that will read pictures found in\n", + "# subfolers of 'data/train', and indefinitely generate\n", + "# batches of augmented image data\n", + "train_generator = train_datagen.flow_from_directory(\n", + " train_path, # this is the target directory\n", + " target_size=(img_width, img_hight), # all images will be resized to 150x150\n", + " batch_size=batch_size, \n", + " class_mode='categorical') # since we use binary_crossentropy loss, we need binary labels\n", + "\n", + "# this is a similar generator, for validation data\n", + "validation_generator = val_datagen.flow_from_directory(\n", + " val_path,\n", + " target_size=(img_width, img_hight),\n", + " batch_size=batch_size,\n", + " class_mode='categorical')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "11/11 [==============================] - 10s 902ms/step - loss: 2.6002 - acc: 0.1080 - val_loss: 2.3075 - val_acc: 0.1875\n", + "Epoch 2/50\n", + "11/11 [==============================] - 3s 260ms/step - loss: 2.3294 - acc: 0.1214 - val_loss: 2.2622 - val_acc: 0.1429\n", + "Epoch 3/50\n", + "11/11 [==============================] - 3s 243ms/step - loss: 2.2966 - acc: 0.1307 - val_loss: 2.2745 - val_acc: 0.0938\n", + "Epoch 4/50\n", + "11/11 [==============================] - 3s 242ms/step - loss: 2.2649 - acc: 0.2165 - val_loss: 2.1042 - val_acc: 0.5714\n", + "Epoch 5/50\n", + "11/11 [==============================] - 3s 248ms/step - loss: 2.1972 - acc: 0.2222 - val_loss: 2.0846 - val_acc: 0.1875\n", + "Epoch 6/50\n", + "11/11 [==============================] - 3s 243ms/step - loss: 2.1059 - acc: 0.2407 - val_loss: 1.4311 - val_acc: 0.5714\n", + "Epoch 7/50\n", + "11/11 [==============================] - 3s 293ms/step - loss: 2.0018 - acc: 0.2287 - val_loss: 1.9641 - val_acc: 0.3125\n", + "Epoch 8/50\n", + "11/11 [==============================] - 3s 258ms/step - loss: 1.9320 - acc: 0.3014 - val_loss: 1.2307 - val_acc: 0.5714\n", + "Epoch 9/50\n", + "11/11 [==============================] - 3s 257ms/step - loss: 1.8816 - acc: 0.3293 - val_loss: 1.9275 - val_acc: 0.2500\n", + "Epoch 10/50\n", + "11/11 [==============================] - 3s 255ms/step - loss: 1.9035 - acc: 0.3173 - val_loss: 1.1291 - val_acc: 0.8571\n", + "Epoch 11/50\n", + "11/11 [==============================] - 3s 258ms/step - loss: 1.8937 - acc: 0.3272 - val_loss: 1.7044 - val_acc: 0.3438\n", + "Epoch 12/50\n", + "11/11 [==============================] - 3s 257ms/step - loss: 1.7380 - acc: 0.3929 - val_loss: 1.5299 - val_acc: 0.2857\n", + "Epoch 13/50\n", + "11/11 [==============================] - 3s 260ms/step - loss: 1.7802 - acc: 0.3892 - val_loss: 1.4534 - val_acc: 0.5312\n", + "Epoch 14/50\n", + "11/11 [==============================] - 3s 261ms/step - loss: 1.6590 - acc: 0.4085 - val_loss: 1.4350 - val_acc: 0.4286\n", + "Epoch 15/50\n", + "11/11 [==============================] - 3s 263ms/step - loss: 1.7920 - acc: 0.3622 - val_loss: 1.3659 - val_acc: 0.5000\n", + "Epoch 16/50\n", + "11/11 [==============================] - 3s 263ms/step - loss: 1.7374 - acc: 0.3742 - val_loss: 1.2221 - val_acc: 0.5714\n", + "Epoch 17/50\n", + "11/11 [==============================] - 3s 270ms/step - loss: 1.6460 - acc: 0.4127 - val_loss: 1.3032 - val_acc: 0.5312\n", + "Epoch 18/50\n", + "11/11 [==============================] - 3s 254ms/step - loss: 1.6352 - acc: 0.4165 - val_loss: 1.7278 - val_acc: 0.4286\n", + "Epoch 19/50\n", + "11/11 [==============================] - 3s 264ms/step - loss: 1.6392 - acc: 0.4264 - val_loss: 1.3394 - val_acc: 0.4688\n", + "Epoch 20/50\n", + "11/11 [==============================] - 3s 265ms/step - loss: 1.4940 - acc: 0.4787 - val_loss: 1.3881 - val_acc: 0.5714\n", + "Epoch 21/50\n", + "11/11 [==============================] - 3s 262ms/step - loss: 1.5983 - acc: 0.4522 - val_loss: 1.4225 - val_acc: 0.4375\n", + "Epoch 22/50\n", + "11/11 [==============================] - 3s 264ms/step - loss: 1.3847 - acc: 0.5086 - val_loss: 0.6056 - val_acc: 0.8571\n", + "Epoch 23/50\n", + "11/11 [==============================] - 3s 270ms/step - loss: 1.4201 - acc: 0.5185 - val_loss: 1.1524 - val_acc: 0.6250\n", + "Epoch 24/50\n", + "11/11 [==============================] - 3s 260ms/step - loss: 1.2609 - acc: 0.5678 - val_loss: 0.7763 - val_acc: 0.7143\n", + "Epoch 25/50\n", + "11/11 [==============================] - 3s 263ms/step - loss: 1.2168 - acc: 0.5739 - val_loss: 0.9316 - val_acc: 0.5938\n", + "Epoch 26/50\n", + "11/11 [==============================] - 3s 263ms/step - loss: 1.0597 - acc: 0.6242 - val_loss: 0.7032 - val_acc: 0.7143\n", + "Epoch 27/50\n", + "11/11 [==============================] - 3s 267ms/step - loss: 1.1347 - acc: 0.6614 - val_loss: 0.8696 - val_acc: 0.6250\n", + "Epoch 28/50\n", + "11/11 [==============================] - 3s 262ms/step - loss: 1.0301 - acc: 0.6393 - val_loss: 0.5764 - val_acc: 0.7143\n", + "Epoch 29/50\n", + "11/11 [==============================] - 3s 264ms/step - loss: 1.1699 - acc: 0.5958 - val_loss: 0.6839 - val_acc: 0.7500\n", + "Epoch 30/50\n", + "11/11 [==============================] - 3s 266ms/step - loss: 0.9287 - acc: 0.6957 - val_loss: 0.6334 - val_acc: 0.7143\n", + "Epoch 31/50\n", + "11/11 [==============================] - 3s 269ms/step - loss: 0.9313 - acc: 0.6801 - val_loss: 0.6589 - val_acc: 0.7500\n", + "Epoch 32/50\n", + "11/11 [==============================] - 3s 263ms/step - loss: 0.9668 - acc: 0.6650 - val_loss: 0.6982 - val_acc: 0.7143\n", + "Epoch 33/50\n", + "11/11 [==============================] - 3s 275ms/step - loss: 0.8069 - acc: 0.7107 - val_loss: 0.5850 - val_acc: 0.8125\n", + "Epoch 34/50\n", + "11/11 [==============================] - 3s 288ms/step - loss: 0.7297 - acc: 0.7329 - val_loss: 0.4263 - val_acc: 0.7143\n", + "Epoch 35/50\n", + "11/11 [==============================] - 3s 270ms/step - loss: 0.7326 - acc: 0.7386 - val_loss: 0.5129 - val_acc: 0.7500\n", + "Epoch 36/50\n", + "11/11 [==============================] - 3s 266ms/step - loss: 0.6890 - acc: 0.7458 - val_loss: 1.3089 - val_acc: 0.5714\n", + "Epoch 37/50\n", + "11/11 [==============================] - 3s 268ms/step - loss: 0.6671 - acc: 0.7869 - val_loss: 0.5519 - val_acc: 0.7500\n", + "Epoch 38/50\n", + "11/11 [==============================] - 3s 271ms/step - loss: 0.7293 - acc: 0.7536 - val_loss: 0.5898 - val_acc: 0.7143\n", + "Epoch 39/50\n", + "11/11 [==============================] - 3s 275ms/step - loss: 0.6488 - acc: 0.7536 - val_loss: 0.4681 - val_acc: 0.8125\n", + "Epoch 40/50\n", + "11/11 [==============================] - 3s 293ms/step - loss: 0.7696 - acc: 0.7929 - val_loss: 0.6888 - val_acc: 0.7143\n", + "Epoch 41/50\n", + "11/11 [==============================] - 3s 277ms/step - loss: 0.7033 - acc: 0.7771 - val_loss: 0.4849 - val_acc: 0.7812\n", + "Epoch 42/50\n", + "11/11 [==============================] - 3s 270ms/step - loss: 0.4926 - acc: 0.8329 - val_loss: 0.2657 - val_acc: 0.8571\n", + "Epoch 43/50\n", + "11/11 [==============================] - 3s 267ms/step - loss: 0.5797 - acc: 0.7757 - val_loss: 0.5622 - val_acc: 0.8125\n", + "Epoch 44/50\n", + "11/11 [==============================] - 4s 322ms/step - loss: 0.5543 - acc: 0.8129 - val_loss: 0.2072 - val_acc: 0.8571\n", + "Epoch 45/50\n", + "11/11 [==============================] - 4s 400ms/step - loss: 0.4604 - acc: 0.8386 - val_loss: 0.5626 - val_acc: 0.8125\n", + "Epoch 46/50\n", + "11/11 [==============================] - 3s 314ms/step - loss: 0.5314 - acc: 0.8157 - val_loss: 0.5920 - val_acc: 0.7143\n", + "Epoch 47/50\n", + "11/11 [==============================] - 3s 276ms/step - loss: 0.4310 - acc: 0.8493 - val_loss: 0.3544 - val_acc: 0.8125\n", + "Epoch 48/50\n", + "11/11 [==============================] - 3s 287ms/step - loss: 0.4464 - acc: 0.8579 - val_loss: 0.0331 - val_acc: 1.0000\n", + "Epoch 49/50\n", + "11/11 [==============================] - 3s 281ms/step - loss: 0.3880 - acc: 0.8636 - val_loss: 0.3323 - val_acc: 0.8750\n", + "Epoch 50/50\n", + "11/11 [==============================] - 3s 307ms/step - loss: 0.4469 - acc: 0.8500 - val_loss: 0.0848 - val_acc: 1.0000\n" + ] + } + ], + "source": [ + "history = model.fit_generator(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " epochs=50,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "acc = history.history['acc']\n", + "val_acc = history.history['val_acc']\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "\n", + "epochs = range(len(acc))\n", + "\n", + "plt.plot(epochs, acc, 'bo', label='Training acc')\n", + "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n", + "plt.title('Training and validation accuracy')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "\n", + "plt.plot(epochs, loss, 'bo', label='Training loss')\n", + "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", + "plt.title('Training and validation loss')\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# infrence" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.preprocessing import image" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3.6700971e-02 7.7572372e-03 8.3157562e-02 8.5508507e-01 1.0649939e-02\n", + " 4.4613280e-03 1.8432270e-03 9.8935234e-06 4.3585874e-07 3.3431733e-04]\n", + "3\n" + ] + } + ], + "source": [ + "img_path = 'test/1.png'\n", + "img = image.load_img(img_path, target_size=(175, 75))\n", + "x = image.img_to_array(img)\n", + "x /= 255.\n", + "x = np.expand_dims(x, axis=0)\n", + "print(model.predict(x)[0])\n", + "print(np.argmax(model.predict(x)[0]))\n", + "# predicted_class = model_dict[str(model.predict_classes(x)[0])]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "iranshahr_dict = {\n", + " 0 : 'آبادان',\n", + " 1 : 'آباده',\n", + " 2 : 'آبدان',\n", + " 3 : 'آبرغان',\n", + " 4 : 'آبرومند',\n", + " 5 : 'آبریز',\n", + " 6 : 'آبعلی',\n", + " 7 : 'آبیز',\n", + " 8 : 'آبیک',\n", + " 9 : 'آذرشهر',\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "path = 'test/'\n", + "k = 0\n", + "for img in os.listdir(path):\n", + " k += 1\n", + " img2 = image.load_img(path + img, target_size=(175, 75))\n", + " x = image.img_to_array(img2)\n", + " x /= 255.\n", + " x = np.expand_dims(x, axis=0)\n", + "# print(model.predict(x)[0])\n", + "# print(np.argmax(model.predict(x)[0]))\n", + " os.rename(path+img, path+iranshahr_dict[np.argmax(model.predict(x)[0])] + '_' + str(k) + '.png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tensorflow", + "language": "python", + "name": 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zfuzmD&XE8KkN^pg011!)36OxO1gH;0#m&Y@fCNZ@1W14cNPq-LK+*)L43DsDDL0wh2JBtQZrKmsH{0+J>`eIRM`uyZ6p0wh2JBtQZr zKmsHnDgo*PQE{^|5+DH*AOR8}0TLhq5|A_j>H|rehn*t<5+DH*AOR8}0TLhqQ3+5V ch>DwykpKyh011!)36KB@kbtBK{O))E4', ''], + list(range(len(human_vocab) + 2)))) + inv_machine = dict(enumerate(sorted(machine_vocab))) + machine = {v:k for k,v in inv_machine.items()} + + return dataset, human, machine, inv_machine + +def preprocess_data(dataset, human_vocab, machine_vocab, Tx, Ty): + + X, Y = zip(*dataset) + + X = np.array([string_to_int(i, Tx, human_vocab) for i in X]) + Y = [string_to_int(t, Ty, machine_vocab) for t in Y] + + Xoh = np.array(list(map(lambda x: to_categorical(x, num_classes=len(human_vocab)), X))) + Yoh = np.array(list(map(lambda x: to_categorical(x, num_classes=len(machine_vocab)), Y))) + + return X, np.array(Y), Xoh, Yoh + +def string_to_int(string, length, vocab): + """ + Converts all strings in the vocabulary into a list of integers representing the positions of the + input string's characters in the "vocab" + + Arguments: + string -- input string, e.g. 'Wed 10 Jul 2007' + length -- the number of time steps you'd like, determines if the output will be padded or cut + vocab -- vocabulary, dictionary used to index every character of your "string" + + Returns: + rep -- list of integers (or '') (size = length) representing the position of the string's character in the vocabulary + """ + + #make lower to standardize + string = string.lower() + string = string.replace(',','') + + if len(string) > length: + string = string[:length] + + rep = list(map(lambda x: vocab.get(x, ''), string)) + + if len(string) < length: + rep += [vocab['']] * (length - len(string)) + + #print (rep) + return rep + + +def int_to_string(ints, inv_vocab): + """ + Output a machine readable list of characters based on a list of indexes in the machine's vocabulary + + Arguments: + ints -- list of integers representing indexes in the machine's vocabulary + inv_vocab -- dictionary mapping machine readable indexes to machine readable characters + + Returns: + l -- list of characters corresponding to the indexes of ints thanks to the inv_vocab mapping + """ + + l = [inv_vocab[i] for i in ints] + return l + + +EXAMPLES = ['3 May 1979', '5 Apr 09', '20th February 2016', 'Wed 10 Jul 2007'] + +def run_example(model, input_vocabulary, inv_output_vocabulary, text): + encoded = string_to_int(text, TIME_STEPS, input_vocabulary) + prediction = model.predict(np.array([encoded])) + prediction = np.argmax(prediction[0], axis=-1) + return int_to_string(prediction, inv_output_vocabulary) + +def run_examples(model, input_vocabulary, inv_output_vocabulary, examples=EXAMPLES): + predicted = [] + for example in examples: + predicted.append(''.join(run_example(model, input_vocabulary, inv_output_vocabulary, example))) + print('input:', example) + print('output:', predicted[-1]) + return predicted + + +def softmax(x, axis=1): + """Softmax activation function. + # Arguments + x : Tensor. + axis: Integer, axis along which the softmax normalization is applied. + # Returns + Tensor, output of softmax transformation. + # Raises + ValueError: In case `dim(x) == 1`. + """ + ndim = K.ndim(x) + if ndim == 2: + return K.softmax(x) + elif ndim > 2: + e = K.exp(x - K.max(x, axis=axis, keepdims=True)) + s = K.sum(e, axis=axis, keepdims=True) + return e / s + else: + raise ValueError('Cannot apply softmax to a tensor that is 1D') + + +def plot_attention_map(model, input_vocabulary, inv_output_vocabulary, text, n_s = 128, num = 6, Tx = 30, Ty = 10): + """ + Plot the attention map. + + """ + attention_map = np.zeros((10, 30)) + Ty, Tx = attention_map.shape + + s0 = np.zeros((1, n_s)) + c0 = np.zeros((1, n_s)) + layer = model.layers[num] + + encoded = np.array(string_to_int(text, Tx, input_vocabulary)).reshape((1, 30)) + encoded = np.array(list(map(lambda x: to_categorical(x, num_classes=len(input_vocabulary)), encoded))) + + f = K.function(model.inputs, [layer.get_output_at(t) for t in range(Ty)]) + r = f([encoded, s0, c0]) + + for t in range(Ty): + for t_prime in range(Tx): + attention_map[t][t_prime] = r[t][0,t_prime,0] + + # Normalize attention map +# row_max = attention_map.max(axis=1) +# attention_map = attention_map / row_max[:, None] + + prediction = model.predict([encoded, s0, c0]) + + predicted_text = [] + for i in range(len(prediction)): + predicted_text.append(int(np.argmax(prediction[i], axis=1))) + + predicted_text = list(predicted_text) + predicted_text = int_to_string(predicted_text, inv_output_vocabulary) + text_ = list(text) + + # get the lengths of the string + input_length = len(text) + output_length = Ty + + # Plot the attention_map + plt.clf() + f = plt.figure(figsize=(8, 8.5)) + ax = f.add_subplot(1, 1, 1) + + # add image + i = ax.imshow(attention_map, interpolation='nearest', cmap='Blues') + + # add colorbar + cbaxes = f.add_axes([0.2, 0, 0.6, 0.03]) + cbar = f.colorbar(i, cax=cbaxes, orientation='horizontal') + cbar.ax.set_xlabel('Alpha value (Probability output of the "softmax")', labelpad=2) + + # add labels + ax.set_yticks(range(output_length)) + ax.set_yticklabels(predicted_text[:output_length]) + + ax.set_xticks(range(input_length)) + ax.set_xticklabels(text_[:input_length], rotation=45) + + ax.set_xlabel('Input Sequence') + ax.set_ylabel('Output Sequence') + + # add grid and legend + ax.grid() + + #f.show() + + return attention_map \ No newline at end of file