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Course 3 - Week 3 - Lesson 1a.ipynb
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"cells": [
{
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"metadata": {
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"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ia35/84b95cc0e4fbc267f6547db5b99b932d/course-3-week-3-lesson-1a.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GxUZDsKj9lhP",
"colab_type": "text"
},
"source": [
"[![](http://bec552ebfe.url-de-test.ws/ml/buttonBackProp.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xem2H9CH9pip",
"colab_type": "text"
},
"source": [
"Ce code provient du tutoriel : [Single Layer LSTM, Natural Language Processing in TensorFlow](https://www.coursera.org/learn/natural-language-processing-tensorflow/home/welcome) par deeplearning.ai"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8CtEQUwl-DdR",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)\n",
"Le logo BackProp est présenté chaque fois qu'un ajout, une modification importante est apportée au code ou à chaque fois qu'un commentaire doit être signalé. \n",
"\n",
"Le texte en anglais est soit le texte d'origine soit un extrait de site qui apporte des explications."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bM-vM9T5-IbA",
"colab_type": "text"
},
"source": [
"## <font color=\"teal\">Références</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-OEtlN0w-Jw1",
"colab_type": "text"
},
"source": [
"- [Natural Language Processing in TensorFlow](https://www.coursera.org/learn/natural-language-processing-tensorflow/home/welcome)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zX4Kg8DUTKWO",
"colab_type": "code",
"colab": {}
},
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rFiCyWQ-NC5D"
},
"source": [
"# Single Layer LSTM"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Y20Lud2ZMBhW",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
},
"outputId": "da73f5ea-2813-4ddc-fa45-34a20be14d15"
},
"source": [
"from __future__ import absolute_import, division, print_function, unicode_literals\n",
"\n",
"\n",
"import tensorflow_datasets as tfds\n",
"import tensorflow as tf\n",
"print(tf.__version__)"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"2.2.0\n"
],
"name": "stdout"
}
]
},
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"6b675d5b38484bb2b1df984a29121908",
"7eeae98bb5894f88a09627e05254de80",
"ac71c5d98b484c64ac13025639001f75",
"a04c1845cd284041be27702aab0e9e8e",
"53c10a6ca6eb471283e4eed3b2771c31",
"d8f70142dd5a4a86af227c8e4b380246",
"f67329fcd3dd4f12ad7d087efac1abc8",
"2e0a41a4bb1340c2b5a62d2b6a37b9bc",
"dde0fe5bfb814dc3834b2a1c3e6dfa17",
"e8b73fff68c24995a5a4798608258461",
"863db6ddf5af4173a6692bb0955d38f5"
]
},
"outputId": "7d7009ef-fb4d-452c-b15d-196c0e399f43"
},
"source": [
"# Get the data\n",
"dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True, as_supervised=True)\n",
"train_dataset, test_dataset = dataset['train'], dataset['test']"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1mDownloading and preparing dataset imdb_reviews/subwords8k/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0...\u001b[0m\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "266fbc8f7b9f49b282eb24bf43a20169",
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"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
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"metadata": {
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}
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{
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
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},
"metadata": {
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}
},
{
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4aed3d757a3948beaa9d893ea0be1c62",
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete8GT7MR/imdb_reviews-train.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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"HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a6d03ab1c264fefad76452fe07da800",
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete8GT7MR/imdb_reviews-test.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4c5a2bb7948c40518fcdab27f182d123",
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"HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\r"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8f62c177cb9b4bc0b00d46f56137da79",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0.incomplete8GT7MR/imdb_reviews-unsupervised.tfrecord\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a04c1845cd284041be27702aab0e9e8e",
"version_minor": 0,
"version_major": 2
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"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=50000.0), HTML(value='')))"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
"\r"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "DVfhKpHsPOxq",
"colab": {}
},
"source": [
"tokenizer = info.features['text'].encoder"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5x1Tid7s_GR5",
"colab_type": "code",
"colab": {}
},
"source": [
"BUFFER_SIZE = 10000\n",
"BATCH_SIZE = 64"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "F1rpX0sG_kQZ",
"colab_type": "code",
"colab": {}
},
"source": [
"train_dataset = train_dataset.shuffle(BUFFER_SIZE)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZTvATZ9vANrj",
"colab_type": "text"
},
"source": [
"[![](https://raw.githubusercontent.com/BackProp-fr/meetup/master/images/LogoBackPropTranspSmall.png)](https://www.backprop.fr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6k8tyAOcAOoU",
"colab_type": "text"
},
"source": [
"Avec la version 2.2 de Tf, on ne fait plus le padding de la même façon"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "ffvRUI0_McDS",
"colab": {}
},
"source": [
"# train_dataset = train_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(train_dataset))\n",
"# test_dataset = test_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(test_dataset))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "olgiwDjRAGbF",
"colab_type": "code",
"colab": {}
},
"source": [
"train_dataset = train_dataset.padded_batch(BATCH_SIZE)\n",
"test_dataset = test_dataset.padded_batch(BATCH_SIZE)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "FxQooMEkMgur",
"colab": {}
},
"source": [
"model = tf.keras.Sequential([\n",
" tf.keras.layers.Embedding(tokenizer.vocab_size, 64),\n",
" tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\n",
" tf.keras.layers.Dense(64, activation='relu'),\n",
" tf.keras.layers.Dense(1, activation='sigmoid')\n",
"])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "QKI5dfPgMioL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 302
},
"outputId": "258a63d5-84f7-41b2-dbf6-18c360d3a080"
},
"source": [
"model.summary()"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"embedding (Embedding) (None, None, 64) 523840 \n",
"_________________________________________________________________\n",
"bidirectional (Bidirectional (None, 128) 66048 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 64) 8256 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 1) 65 \n",
"=================================================================\n",
"Total params: 598,209\n",
"Trainable params: 598,209\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Uip7QOVzMoMq",
"colab": {}
},
"source": [
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "7mlgzaRDMtF6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368
},
"outputId": "f5b51890-1283-41c5-faed-1eb22506bb18"
},
"source": [
"NUM_EPOCHS = 10\n",
"history = model.fit(train_dataset, epochs=NUM_EPOCHS, validation_data=test_dataset)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"391/391 [==============================] - 161s 412ms/step - loss: 0.5154 - accuracy: 0.7362 - val_loss: 0.4047 - val_accuracy: 0.8260\n",
"Epoch 2/10\n",
"391/391 [==============================] - 162s 414ms/step - loss: 0.4131 - accuracy: 0.8165 - val_loss: 0.5465 - val_accuracy: 0.7552\n",
"Epoch 3/10\n",
"391/391 [==============================] - 162s 414ms/step - loss: 0.4236 - accuracy: 0.8180 - val_loss: 0.4755 - val_accuracy: 0.8074\n",
"Epoch 4/10\n",
"391/391 [==============================] - 160s 409ms/step - loss: 0.3084 - accuracy: 0.8798 - val_loss: 0.4615 - val_accuracy: 0.8129\n",
"Epoch 5/10\n",
"391/391 [==============================] - 160s 410ms/step - loss: 0.2583 - accuracy: 0.9012 - val_loss: 0.4120 - val_accuracy: 0.8406\n",
"Epoch 6/10\n",
"391/391 [==============================] - 161s 413ms/step - loss: 0.2272 - accuracy: 0.9151 - val_loss: 0.4274 - val_accuracy: 0.8180\n",
"Epoch 7/10\n",
"391/391 [==============================] - 161s 412ms/step - loss: 0.1860 - accuracy: 0.9339 - val_loss: 0.4379 - val_accuracy: 0.8206\n",
"Epoch 8/10\n",
"391/391 [==============================] - 161s 411ms/step - loss: 0.1700 - accuracy: 0.9403 - val_loss: 0.5237 - val_accuracy: 0.8362\n",
"Epoch 9/10\n",
"391/391 [==============================] - 163s 416ms/step - loss: 0.1854 - accuracy: 0.9312 - val_loss: 0.5104 - val_accuracy: 0.8124\n",
"Epoch 10/10\n",
"391/391 [==============================] - 161s 412ms/step - loss: 0.2955 - accuracy: 0.8712 - val_loss: 0.6128 - val_accuracy: 0.6916\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "Mp1Z7P9pYRSK",
"colab": {}
},
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"def plot_graphs(history, string):\n",
" plt.plot(history.history[string])\n",
" plt.plot(history.history['val_'+string])\n",
" plt.xlabel(\"Epochs\")\n",
" plt.ylabel(string)\n",
" plt.legend([string, 'val_'+string])\n",
" plt.show()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "R_sX6ilIM515",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "656bdb5c-11de-4c0c-fae3-5c0d38145cd4"
},
"source": [
"plot_graphs(history, 'accuracy')"
],
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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Tg+vHkEowatQopk6dyowZM4iNjSU9PZ169erh6+vLkiVL2LdvX6n2c77tLr/8cqZPn05KSgpAYRPTgAEDeOeddwDIz88nPT2d+vXrc/ToUVJSUsjOzmbOnDkXPN6Z+hKffvpp4fIzdSvOOHNV0qNHDxITE/nf//7HmDFjSvv2VGu7jmbw5uKdXPXaz/R5cQn/+XE7+QWGh69szeIH+zJvQh8mXNGK1g1qa3LwQD7eXkwe1RkDPDBtPfkFnjV7tiaIStC+fXtOnjxJ48aNadiwITfddBNxcXHExMTw2Wef0aZNm1Lt53zbtW/fnn/+85/07duXTp068cADDwDw2muvsWTJEmJiYujatStbt27F19eXp556iu7duzNw4MALHnvixInExsbStWvXwuYrgCeeeILU1FQ6dOhAp06dWLJkSeFrN9xwA7169SpVudTqyBjDtkMneOWnHQx8ZRlXvLKMl36KJ8DXiyeubsvPj/Tn+3t6M75/C5qF641tVUFkaE2eGd6eNXtTeWfpLrvDKROtB6GcaujQodx///0MGDCg2Ner4+/EGMPGpHR+3HyYeZsPsTflFF4C3aNDGdKhIVe2b0CDoOpRjKe6MsZw79T1zN10iG/+dimdI4PtDqmQ1oNQLpeWlkb37t3p1KnTeZNDdVJQYPh9f6ojKRzmQNppfLyES5rXZVyf5gxqX19nUK1GRIRJIzrw+75UJkxdxw/3Xkagm9fvBk0QbmnTpk3ccsstZy3z9/fn119/tSmikgUHBxMfH293GLbKyy/gt73HC0cfHT2ZjZ+3F5e1DGPCFS0Z2K5+lZoKWpVNUA1fXrmhE6PfX80z32/lv9d3tDukElX5BGGM8bjOvZiYGNavX293GE5XVZozi8rNL2Dl7hTmbT7E/C1HOJ6ZQ4CvF/1a1WNITAMub1OP2h56k5Ryvh7N6vL3fs15a8lu+rUOZ0hMQ7tDuqAqnSACAgJISUmhbl2d3thuxhhSUlIICPD8tvas3HxW7DzG3M2HWLj1CCey8gj082ZA2/oM6dCAvq3DqelXpf+1VAVMuKIVP+88xmMzN9G5STANg2rYHdJ5ubSTWkQGA68B3sAHxpjnz3m9KfAREA4cB242xiQ5XrsVeMKx6iRjzKdcQHGd1Lm5uSQlJZX5XgHlGgEBAURERODr63mfqE/l5LFsRzJzNx9m8bYjZObkUyfAh4HtGjCkQwN6twzT4juq1BKSM7j69RV0aRLMF7f3wMvLvg+wF+qkdlmCEBFvIB4YCCQBa4AxxpitRdaZDswxxnwqIpcDY40xt4hIKBAHdAMMsBboaow5723AxSUIpSqioMCwYNsRZv1+gKXxR8nKLaBuoB+D2tdncIeGXNKsrs6Yqspt6m/7eWzmJh6/qg3j+jS3LQ67RjF1B3YZYxIcQUwFhgNbi6zTDnjA8XgJ8K3j8ZXAAmPMcce2C4DBwFcujFcpwGoOm7/lCJMXxrP98Enq1fbnhm6RDO7QgO5RofjoNBfKCUZdHMmSHUd5cf4OerUIo32jILtD+hNX/qU3BhKLPE9yLCtqA3Ct4/FIoLaI1C3ltojIOBGJE5G45ORkpwWuqidjDD9tOczVr6/gri/WkpNXwORRnVn1jwE8M7wDlzYP0+SgnEZEeP7ajoQG+nHf1PWczsm3O6Q/sfuv/SGgr4isA/oCB4BSv0vGmCnGmG7GmG7h4eGuilFVccYYFm49wjVvrmDc52s5lZPHKzd04qf7+zCiS2O8bWwfVlVbSKAfL8d2ZtfRDP49d5vd4fyJK5uYDgBFK3dHOJYVMsYcxHEFISK1gOuMMWkicgDod862S10Yq6qGjDEs3n6UyQt3sulAOk3r1uSl2E6M6NxIrxRUpendMow7L4vm/Z/30K91OAPa1rc7pEKu7KT2weqkHoCVGNYANxpjthRZJww4bowpEJHngHxjzFOOTuq1wEWOVX/H6qQ+fr7jaSe1Ki1jDEt3JDN5YTwbktKJDK3BPZe3ZGSXxjqNtrJFdl4+I95aydETWfw44TLq1a684eAX6qR22X+DMSYPuBuYD2wDphljtojIMyIyzLFaP2CHiMQD9YHnHNseB57FSiprgGculByUKg0rMRxl5NsrGfvJGlIyc/jvdTEsfrAfN3SL1OSgbOPv481rozuTkZ3Hw9M3us1NpVV6sj6lwEoMP+88xuSF8fy+P43GwTW4+/IWXHdRhA5TVW7ls1V7eeq7LfxrWHtuvTSqUo6pk/WpaskYwy+7Upi8MJ64fak0CgrguZEdiO0aqYlBuaVbejZlyfajPDd3G5c0r0ur+rVtjUevIFSVY4xh1e4UXl0Yz5q9qTQMCuDv/VtwQ7cI/H30bmfl3pJPZjPkteWE1fLn2/G9XH6Hvl5BqGrjTGL4bc9x6tfx55nh7Rl1caQmBuUxwmv78+L1nRj7yRpenL+DJ4e2sy0WTRCqSvg1wUoMqxOOU6+2PxOvacfo7k10fiTlkfq3qcdfLmnKhyv20LdVOH1a2XOflyYI5dHW7D3OqwviWbk7hfDa/jw1tB039tDEoDzf41e1ZeXuFB6cvoH5E/oQGlj5tUS0p055pLX7jnPzB78S++4q4o9kFNZz/mvvaE0OqkoI8LWGvqafyuXRb+wZ+qpXEMqj/L4/lVcXxPPzzmPUDfTjn1e15eaeTanhp0lBVT3tGwXxyODWTPphG1PXJDKme5NKPb4mCOUR1iem8eqCeJbFJxMa6Mc/hrThlkuaamEeVeX9tVc0S3ck88z3W+keHUrz8FqVdmwd5qrc2obENCYvjGfJjmRCavoyrk9z/nJJU48o+K6Usxw5kcWVk5cTGVKTb/52qVPv49FhrsrjJCRn8NwP21i0/SjBNX15+MrW3HppFLU0MahqqH6dAJ6/tiN3fbGWVxfG8+jgNpVyXP1vU24nMzuP2z5eQ9qpHB4a1IpbL42idoDnlSlVypkGd2jA6IsjeXfZbvq0DOeS5nVdfkwdxaTcznNzt5GYeor3/9KNuy9vqclBKYcnh7Yjqm4gD0xbT/qpXJcfTxOEcitLdhzlf7/u587LmtGjmes/ISnlSQL9fZg8qjPJJ7N5/NtNLh/6qglCuY3UzBwenbGR1vVr88DAVnaHo5Rb6hQZzP0DW/HDxkPM/P1AyRtUgCYI5RaMMTzx3WZST+XwyqhOerObUhdwV9/mdI8O5anvNrMvJdNlx9EEodzC7A0H+WHjISZc0Yr2jYLsDkcpt+btJbw6qjNeXsKEr9eTl1/gkuNoglC2O5yexZPfbqZLk2D+r08zu8NRyiM0Dq7BcyNjWLc/jTcW73LJMXSYq7KVMYaHZ2wgN9/wyg2d8dGyn0qV2rBOjVi6/Sgbk9IoKDB4eYlT968JQtnqi9X7+HnnMZ4d0YHosEC7w1HK4/z72hj8vL2cnhxAE4Sy0Z5jmTw3dxt9WoVzc4/KnYRMqarClQM69Hpe2SIvv4AHpq3H38ebF67riIjzP/0opSpGryCULd5dtpt1+9N4fUwXGgQF2B2OUqoYegWhKt3mA+lMXriToR0bMqxTI7vDUUqdhyYIVamycvN5YNp6QgP9mDSig93hKKUuQJuYVKV6+acdxB/J4JOxFxNcs/Jr7CqlSk+vIFSlWZ2Qwgcr9nBTjyb0a13P7nCUUiXQBKEqxcmsXB6avoEmoTX559Vt7Q5HKVUK2sSkKsWkOds4mHaa6XddonWklfIQegWhXG7B1iN8HZfIXX2b07VpqN3hKKVKSROEcqmUjGz+MXMjbRvWYcIVWuNBKU+i1/rKZYwxPD5rEydO5/HFHZ3w89HPI0p5Epf+x4rIYBHZISK7ROSxYl5vIiJLRGSdiGwUkascy6NE5LSIrHd8vevKOJVrzPz9APO3HOHBQa1o06CO3eEopcrIZVcQIuINvAUMBJKANSIy2xiztchqTwDTjDHviEg7YC4Q5XhttzGms6viU651IO00E2dvoXtUKHdcpjUelPJErryC6A7sMsYkGGNygKnA8HPWMcCZj5ZBwEEXxqMqSUGB4eHpGygwhpdiO+HtgmmIlVKu58oE0RhILPI8ybGsqInAzSKShHX1cE+R16IdTU/LROSy4g4gIuNEJE5E4pKTk50YuqqIT1ftZeXuFJ4c2o4mdWvaHY5Sqpzs7jUcA3xijIkArgI+FxEv4BDQxBjTBXgA+J+I/KkR2xgzxRjTzRjTLTw8vFIDV8XbdfQkz/+4nQFt6jHq4ki7w1FKVYArE8QBoOgZIsKxrKjbgWkAxphVQAAQZozJNsakOJavBXYDOkbSzeXmF/DAtA3U9PPmP9fFaI0HpTycKxPEGqCliESLiB8wGph9zjr7gQEAItIWK0Eki0i4o5MbEWkGtAQSXBircoK3luxiY1I6/x4ZQ73aWuNBKU/nslFMxpg8EbkbmA94Ax8ZY7aIyDNAnDFmNvAg8L6I3I/VYX2bMcaISB/gGRHJBQqAu4wxx10Vq6q4DYlpvLF4FyO7NGZITEO7w1FKOYEYY+yOwSm6detm4uLi7A6jWsrKzeeq13/mdE4+8yb0IaiGr90hKaVKSUTWGmO6Ffea3Z3Uqgr477ztJCRn8uL1napeckhPghM6+lpVT6VKECIyU0SudowwUqrQyl3H+PiXvdx2aRS9W4bZHY7z5GXD4knwWieY3BHmPgIZR+2OSqlKVdoT/tvAjcBOEXleRFq7MCblIdJPWzUemoUH8ujgNnaH4zyJa+C9PrD8RehwHXS5CdZ8AK91hsXPQVa63REqVSlKlSCMMQuNMTcBFwF7gYUislJExopIFWtTUKX1r++3cORkNq/c0Jkaft52h1NxOZkw73H4cCBkn4Qbp8O1U+Ca12D8b9BqECx/wUoUK9+E3Cy7I1bKpUrdZCQidYHbgDuAdcBrWAljgUsiU25t3uZDzPz9AOP7t6BzZLDd4VRcwjJ451JY/RZ0Gwt/X20lhDPCWkDsJzBuKTTqDD/9E964CH7/DPLzbAq6Gko/ACtehW/ugP2r7Y6myivVKCYRmQW0Bj7HuvP5UJHX4s7XA16ZdBRT5Uk+mc2Vk5fTOLgGM/9+Kb7eHtw1lZUOPz0Jv38KIdEw7A2ILnZml7MlLINF/4IDa6FuSxjwJLQdBnpzoPNln4Rt38OGqbBnOWDAvw5kn7De84H/glCdELK8LjSKqbT3QbxujFlS3AvukBxU5THG8I+ZG8nIzuPVUZ08OznsmAdz7oeMw3DpPdDvcfAr5dxRzfpC9CLYPgcWPQvT/gKNLoIrnoZm/VwZdfVQkA8JS62ksH0O5J6CkCjo+yh0vAFqN7Ca+X55DXb8CN3HQZ+HoKZWLHSm0l5BjAe+NMakOZ6HAGOMMW+7OL5S0yuIyjFtTSKPfLORJ4e24/be0XaHUz6Zx+DHR2HzDKjXDoa9CRFdy7+//DzYOBWW/AdOJFkJYsDT0PgiZ0VcfRzeDBu+gk0zrMQdEATtr4VOoyGyx5+v0E4ehiXPwbovrKuKvo/CxXeAj5898XugC11BlDZBrD+3NoOIrHNMpucWNEG4XuLxUwyevJyOEcF8eUcPvDxtGm9jYPM38OMjkHXC+sTZ+wHnnUxysyDuQ1j+Epw+Du2Gw+VPQlhL5+y/qjpxCDZNh41fw5HN4OUDLQdZSaHlleBbimlbDm+GBU/C7sVWU+HAf2mTXyk5I0FsAjoax8qOeZI2GmPaOzXSCtAE4Vr5BYYx769m28ETzLu/D42Da9gdUtmcOAg/PAg75lpNQcPfgvrtXHOsrBOw6k2rCSQvyxom2/cxCDp3tvtqLCcTts2xrrwSloIpgMZdodMY64ohsG759rtzIfz0BCRvgyaXwKDnKnZ1WA04I0G8CDQF3nMs+j8g0RjzoNOirCBNEK71/vIEnpu7jZdiO3F91wi7wyk9Y6yRRj89CfnZcPkT0PPv4FUJw3IzkuHnl2DNhyBe0GOcdcVSXdvJC/KtTuaNX8PW2ZCbCUFNoNMo6DjKeVda+Xmw7nOr6SkzGTpcb/UNBTdxzv6rGGckCC+spDDAsWgB8IExJt9pUVaQJgjX2XH4JNe8sYJ+rcN575aunjON9/E98P19sGcZNO0Nw16Hus0rP47UfbD0eatt3b829LoXevwN/F3R2m0AAB5jSURBVGtVfix2OLLVulLYOB1OHrT6CtoNt64WmlwCXi4a6JB9ElZMtq7mjIGef4PLHrD6NVShCicIT1DuBHHiEHw3Hvo9BpHdnR+Yh8vJK2Dk279w5EQW8yb0IayWv90hlawgH359DxY/C+INg56Bi25z3YmotI5stabv2PEDBNaDvo/ARbdWzQ7Vk0esQQAbpsLhjdbvocUVVr9C6yHgW4lNlOlJ1kizjVOhZl3o9w/oeht46z2+4JwriJbAf4B2WDUbADDGuM3g43IniOyT8ObFUKse3LmkcpoePMhL83fw5pJdTLmlK4PaN7A7nJIl74Dv7oak36yOzqGvQpCbNYkl/gYLJ8K+XyC4qdXs1eF6+xNYReWcsvp4Nky1OotNPjTsbF0pdLgOatlc9fHgOpj/BOxbAWGtYOCz0OrKat+R7YwEsQJ4GngVuAYYC3gZY55yZqAVUaEmps3fwIy/wtUvW0PkFAC/70/l+ndWct1FEbwY28nucC4sPxd+mQzLXgC/QBj8X2u8vLv+8xsDuxbBoolweBPUaw8DnvK8E1ZBgXXC3fA1bP0Ock5CnQjrve80GsLdbNo2Y6wktuApSNkF0X1g0CRo6OZ/3y7kjASx1hjTVUQ2GWNiii5zcqzlVqEEYQx8NgwObYB7fofAKjQraTmdysnj6tdXkJNXwLwJl1E7wI0vxw+ut64ajmyC9iNhyIv2f1otrYIC2DLTanpK3QORPeGKidD0Ersju7DkHdaVwsZp1r0ffrWg3Qirw7lpb/e/GsrPhbiPYel/4HSqdZUz4Emo08juyCqdMxLESqA3MANYjFVb+nljjNt8PKhwJ3XyDmsunk6jrSGQ1dyT327mi1/38dWdPenZrJxDDl0tNwuWPQ+/vG4l9atfgbZD7Y6qfPJzrdFWy/4LGUes8f8DnoIGHSo/FmMg97Q1DUn2Cev7ma8TB2DzTDi03hqZ1fxy6+Ta+qrS34XuTk6nwc8vw6/vWv0kl94Dve6rPgMIcE6CuBjYBgQDzwJ1gBeNMW4zW5ZTRjH99CSsfB1uXwiRFzsnMA+0LD6ZWz/6jTt6R/PEUBfdK1BR+1bB7LutZoLON8OVk6BGiN1RVVxOptXB/stk636KmFjo/ziEluGu9YIC68R+1sm9yOPC5WnnWX4CCnLPv/8GMY5+heuhdv2K/8zuIHUvLPyXdTVXqz70/yd0ubla9ElWKEE4bor7rzHmIVcE5yxOSRDZJ+HN7tan0XFLq+QfR0GBITuvgKzcfLLy8snKdTzOzSc7r4DTOfk8NnMjdQJ8+f6e3gT4utl7kJ1hTZL32/sQFAnDXrM+xVY1p1OteYZWv2udrLveBg06luKk70gMlPDBz7emNdwzIMgadnrmcUCdYpYH/7G8Rog1oKOqSlwD8x+3BjnUaw+DnoUWA0rezoM54wpitTGmp9MjcyKn3QexeSbMGAtXvQTd76z4/sog+WQ2qadyHCfsP07cWY4TerZjeXbRE/tZJ/kzr/2xrDAZOPaTk1dQYhx+3l7M/PuldGjsZuPFdy2C7ydAeqI1OduAp6p+U8CJQ1YNirWfWqOCAJA/Ttj+QSWc3M9d7niuQzzPzxjY+i0seBrS9lnDcwc+67o7723mjATxDtAYmA5knllujJnprCArymkJwqYO66U7jnLHp3HkFZTuvhQvgQBfb+vLx4sAX2/8fb0J8PUiwMfx3fG6f+HrZ14r+vofy/wdyxoH16B+nVLMf1NZTqfC/H/C+i+tqbWHvwlN3PrzivNlHrP6BQKCrA5hd+8Ergrysq0r1eUvWK0LXW6xmp6qSrOagzMSxMfFLDbGmL9WNDhnceqd1JXcYZ1fYLjqtZ/JzsvnoStbF3sS9/f54wQe4OONr7d4zh3NFbF1Nsx9yDpB9rrPmq2zNJO3KeUsp45bw6fXvA8+AdBrAlwy3jM75YtR4XoQxpixzg3JzYW3tv4AfnnNutPVxXdYz95wgB1HTvLGmC4M7Vj9htmdxRjriiF1r9VRu/U7q1P0xmlWJTelKlvNUBjyvNXkvOApWDIJ1n5szdTbcVSVvporyxXEn1asslcQYHWGvnmxyzusc/IKGPDKUqtT+O7enjeFdnnkZFrzE6XtO+f7futx9glrPW8/64qh133aZq7cx95frJKzB9dZN9r9ZbZn3dx4DmdUlJtT5HEAMBI4WNHA3Jp/LbjyOavDOu4jl3VYT12zn8Tjp/n0rzFVJznk5VgdyX9KAI7vp46dvb5vTWvKieAm1g1iwU0hpCk06uJ+02QoFdUL7lhs3T+xZBIkramy87iVtonpm6LPReQrYIVLInIn7UfC2k+sSd/ajXD63bmncvJ4fdEuekSH0qelB929XVAAJw+dPwGcPGjN73+Gl481JDWkKbS52voe3NQqIRnc1LpK8+BPYKoa8vKCHv9nTee+aXr1ThDFaAlU4cHQDiJw1YtWh/XCiTDCuR3WH/+yl2MZ2e43hbYxcCrFcdLfe3bzT+o+6+ogP6fIBgK1G1on/qjeRRKA43udRlXynhJVzQXUgVaDraHxV/4HvMt7OnVfpfqJROQkZ/dBHAYedUlE7qZoh3VX53VYp2bm8O7S3VzRtj5dm7rRHcDrv7JKcp7pBzijRqh1wm8QY01nUZgAoiA4Enw8YBpwpZwtJta6Z2LPUut+iSqmtE1MtV0diFvr84hV7OSHB2DcMqd8Gn532W4ycvJ4+Eq3mc7KMX3FPdD4Iqt5rTAJNLEK3SilztZyoHXz4aYZVTJBlGp8loiMFJGgIs+DRWSE68JyM/61YPC/rWmZ4z6q8O4Op2fxycq9jOzSmNYN3OTEm5YI026xksGN06zqW22ugvrtNTkodT4+/tBuGGz73rqRsYop7QDep40x6WeeGGPSsOpDXJCIDBaRHSKyS0QeK+b1JiKyRETWichGEbmqyGv/cGy3Q0SuLGWcrtNuBET3tTqsM5IrtKvXFu2kwBjuv6KVk4KroJxT8PVN1p2jY6ZCjWC7I1LKc8TEQk4G7PjR7kicrrQJorj1Ltg85Zjk7y1gCFYlujEicu5kJk8A04wxXYDRwNuObds5nrcHBgNvO/ZnHxFrfqacU1aHdTntOZbJtLhEburRlMhQN7gT0xhrVtRDG+G6DyDcTZKWUp4iqrc1SGPTDLsjcbrSJog4EXlFRJo7vl4B1pawTXdglzEmwRiTA0wFhp+zjsGaOhwgiD/urRgOTDXGZBtj9gC7HPuzV3grq8N6/Rew/9dy7eLln3bg7+PF+P4tnBxcOf0y2aqod6aamVKqbLy8rZKqO3+yZgGoQkqbIO4BcoCvsU70WcD4ErZpDCQWeZ7kWFbUROBmEUkC5jqOU9pt7dHnYajTGOY+CAX5Ja9fxOYD6czZeIjbe0cTXtsNRv3E/2TNgd/+Wuh9v93RKOW5Yq63pmXfOtvuSJyqVAnCGJNpjHnMGNPNGHOxMeZxY0xmyVuWaAzwiTEmArgK+FxESj2xiYiME5E4EYlLTq5Yv0CpnbnDuhwd1i/O30FwTV/u7NPMRcGVQXI8fHO7NWx1+Ft6o5pSFdGwM9RtYd00V4WUdhTTAhEJLvI8RETml7DZASCyyPMIx7KibgemARhjVmFN4xFWym0xxkxxJK1u4eGVWIO43Qho1g8Wlb7DenVCCsvik/l7v+bUsbu+8+k0mDrGmuto9P+qzKyUStlGxOqs3rsCTlSdWYhK+2k9zDFyCQBjTCol30m9BmgpItEi4ofV6Xzu9dd+YACAiLTFShDJjvVGi4i/iERj3bn9WyljdT0RGPIi5J6ChSUO5sIYwwvzttOgTgB/uSTK9fFdSEG+deWQug9GfW7d5KaUqriYWMBYfXpVRGkTRIGINDnzRESiKKGmoTEmD7gbmI9Vz3qaMWaLiDwjIsMcqz0I3CkiG4CvgNuMZQvWlcVWYB4w3hhTtgZ/VyvssP6yxA7rhduO8vv+NO67oqX9JTwX/Qt2LbSmEGl6qb2xKFWV1G0OjS6qUs1MpZ3uezAwBVgGCHAZMM4YU1IzU6Vx+nTfpZGdAW91t+aLv3NpsXOxnCkGlJtfwE/398HH28a54zdOh5l3QLfbYegr9sWhVFW16m2Y/w8Yv8ZjhoxfaLrv0nZSzwO6ATuwPuk/CFS92wbLyr8WXHnhO6y/W28VA3pgUCt7k8OB3637HZr2gsHP2xeHUlVZh2sBgc1V456I0nZS3wEswkoMDwGfYw1RVe2GWx3Wiyf9qcM6J6+AVxbE06FxHa7q0NCW8AA4eQS+vhkCw+GGz8DHz75YlKrKajewightmm7dhOrhSvuR9j7gYmCfMaY/0AVIu/Am1cQFOqy/+m0/SamnefjKNvYVA8rLtuZYOp1qjVgK9KC6E0p5ophYOJ4AB3+3O5IKK22CyDLGZAGIiL8xZjvgRtOQ2iy8FVx6t6PDejUAmdl5vLF4Jz2b2VgMyBj44UFI/BVGvA0NO9oTh1LVSdtrrCHkGz2/s7q0CSLJcR/Et8ACEfkO2Oe6sDzQmTusf3gI8vP4+Jc9HMvI4ZHBbewrBrTmA1j3OVz2kDV9t1LK9WoEW9PWbP6mzLMtuJvSdlKPNMakGWMmAk8CHwLVZ7rv0vALtDqsj2zi1MopvLcsgYHt6nNRE5uKAe1ZDj8+Cq2GQP9/2hODUtVVTCxkHrX+Dz1YmYfVGGOWGWNmOybgU0W1Gw7N+uO19DkCclLsKwaUuhem3Wrd+n/tFKt+rlKq8rQcBP51PH6GVz1zOJMIyZdNwivvNG/V+45W9W0otJOdAV/dCCYfxnxl1c1VSlUu3xpWX8S22ZCbZXc05aYJwsleWWf4qOBquqfPK+ywrjQFBfDt3yB5G1z/sXVnp1LKHjHXW7Xdd/5kdyTlpgnCiRKSM5gWl0jKRfdBnYjCDutK8/NL1ieWgc9CiwGVd1yl1J9F9YHAeh499YYmCCd6eUE8/j5ejLsixqphfWQTxH1YOQffNgeWPAcdR1tzRCml7OXtYxUSip8PWeklr++GNEE4yeYD6fyw8RB3nCkG1HYYNOsPi5+DjKOuPfiRrTDr/6yJwq55TWs7KOUuYmIhPxu2fW93JOWiCcJJXnAUA7rjTDEgEWvG1NxTsKDkKcHL7dRxq7aDXyCM/hJ8A1x3LKVU2TS+CEKiPbaZSROEE6zancLy+GTG92txdjGgsJZw6T2w4X+wb5XzD5yfB9NvswqUjPoS6jRy/jGUUuV3ppDQnuVw8rDd0ZSZJogKMsbwwnyrGNAtlzT98wp9HrI6rOe6oMN6wZOwZxkMfRUiL3buvpVSzhFzPZgC2DLL7kjKTBNEBS3cdpR1+9OYcL5iQH6Bjg7rzc7tsF73Jax+G3r8Dbrc7Lz9KqWcK7w1NOjokc1MmiAqIL/A8OL87TQLC+T6rhHnX7HtMGh+uWNKcCd0WCeugTkTILovDJpU8f0ppVyr4w1wYC2k7LY7kjLRBFEB3647QPyRDB4c1PrCxYAKpwQ/DQueqthBTxyEr2+y+htiPym2ip1Sys20dxQS8rCpNzRBlFN2Xj6vLownpnEQQzo0KHmDsBaODuuvyt9hnZsFU2+CnEwY/ZVV6lQp5f6CGkNUb48rJKQJopy++vVMMaDWpS8GVJEOa2Pg+/usIiQj34P67coetFLKPjHXQ8pOOLTB7khKTRNEOWRm5/Hmkl1c0qwul5WlGJBfIAz+j9VhveaDsh101VuwcSr0exzaDi3btkop+7UdBl6+HtVZrQmiHD5aYRUDenhw67IXA2p7jdVhvaQMd1jvWmQNaW07zCpMpJTyPDVDoeVAjyokpAmijFIzc5iyPIFB5S0GVNYO65TdMGMshLeFEe9obQelPFnM9XDyEOz7xe5ISkXPNmX0zrLdZObk8VBFigGFtYBe9zo6rFeef72sE/DVGBBvGPM/8K9V/mMqpezXagj41fKYZiZNEGVwKP00n6zcy8guERUvBnTZgxeeErygAGaOg5RdcMOnEBJVseMppeznVxPaDIWt30Fett3RlEgTRBm8vmgnGJhwRcuK7+xMh/XRLcV3WC95DuJ/hMHPQ3Sfih9PKeUeYmKt6b93LbQ7khJpgiglqxhQEjf2aEJkaE3n7LTtNdB8gJUMTh75Y/nmmVbxny63QPc7nXMspZR7aNYXaoZ5RDOTJohSOlMM6O7LWzhvpyIw5IWzO6wPbYTvxkNkD7j6Za3toFRV4+0L7UfCjh8h+6Td0VyQJohSKFoMKKyWv3N3fqbDeuNU2Dobpt4INULghs/Bx8nHUkq5h5hYyMuC7T/YHckFaYIohRfm7yCkaDEgZ7vsQQiKhGm3QGYyjPoCatd3zbGUUvaL7A7BTWDjNLsjuSBNECVYufuYVQyo/znFgJzJL9BqavL2h2FvWFWolFJV15lCQglLXV+SuAJcmiBEZLCI7BCRXSLyWDGvvyoi6x1f8SKSVuS1/CKvzXZlnOdjjOGFeTtoGBTAzT2LKQbkTG2ugsf2W9MCK6WqvphYMPmw5Vu7Izkvl80VLSLewFvAQCAJWCMis40xW8+sY4y5v8j69wBdiuzitDGms6viK40FW4+wPjGN/14XU3wxIGfTetJKVR/12kL9DtZoph7j7I6mWK68gugO7DLGJBhjcoCpwPALrD8G+MqF8ZSJVQxoB83CA7nuogsUA1JKqfKKuR6SfoPje+yOpFiuTBCNgcQiz5Mcy/5ERJoC0cDiIosDRCRORFaLyIjzbDfOsU5ccnKys+IGrGJAO49m8FBJxYCUUqq8Olxnfd/8jb1xnIe7nPlGAzOMMUWnOGxqjOkG3AhMFpHm525kjJlijOlmjOkWHh7utGCy8/J5ZUEZigEppVR5BDeBJpe6bSEhVyaIA0BkkecRjmXFGc05zUvGmAOO7wnAUs7un3Cpr37dz4G00zxSnum8lVKqLGKuh+TtVp0YN+PKBLEGaCki0SLih5UE/jQaSUTaACHAqiLLQkTE3/E4DOgFbD13W1fIyM7jjcW7uLR5XXq3KEMxIKWUKo92I8DLxy2n3nBZgjDG5AF3A/OBbcA0Y8wWEXlGRIYVWXU0MNWYs66v2gJxIrIBWAI8X3T0kyt9tGIPKZk5PHylXj0opSpBYF1rTrZN31izOLsRlw1zBTDGzAXmnrPsqXOeTyxmu5VAjCtjK87xzBzeX57Ale3r06U8xYCUUqo8YmJh53xIXA1NL7U7mkLu0kntFt5ZussqBjSoAsWAlFKqrFoPAd+abtfMpAnC4VD6aT5dtY9rL4qgZUWLASmlVFn414I2V8OWWZCXY3c0hTRBOLy20InFgJRSqqxiYuF0KuxeXPK6lUQTBLA7OYPpa5O4qWcTIkKcVAxIKaXKovnlUCPUrZqZNEEAr/wUT4CPF+P7O7EYkFJKlYW3L7QfATvmQnaG3dEAmiDYcyyTHzYd4vbLmjm/GJBSSpVFTCzknrKqzbmBap8gosMCmfZ/l3DnZdF2h6KUqu4ie0KdCLdpZqr2CQKge3QotV1VDEgppUrLywtiroPdiyAzxe5oNEEopZRbibkBCvJg6yy7I9EEoZRSbqV+ewhvC5tm2B2JJgillHIrItYMr/tXQdp+W0PRBKGUUu4m5nrru82FhDRBKKWUuwmJgojutjczaYJQSil3FBNrFRE6UimVDoqlCUIppdxR+5Eg3rbeE6EJQiml3FGtcGje32pmsqletSYIpZRyVzGxkL4fEn+z5fCaIJRSyl21uRp8AmxrZtIEoZRS7sq/tlVtbsssyM+t9MNrglBKKXcWEwunjkHCsko/tCYIpZRyZy0GQkAwbJpW6YfWBKGUUu7Mxw/aDYdtcyDnVKUeWhOEUkq5u5hYyM2E+MotJKQJQiml3F3TS6F2o0qfekMThFJKuTsvb+hwLexcAKeOV95hK+1ISimlyi8mFgpyYdvsSjukJgillPIEDTtBWKtKbWbSBKGUUp5AxLqK2LsC0g9UyiE1QSillKfocB1gKq2QkCYIpZTyFHWbQ+OulTY3kyYIpZTyJDGxcHgjJO9w+aFcmiBEZLCI7BCRXSLyWDGvvyoi6x1f8SKSVuS1W0Vkp+PrVlfGqZRSHqP9tSBeldJZ7bIEISLewFvAEKAdMEZE2hVdxxhzvzGmszGmM/AGMNOxbSjwNNAD6A48LSIhropVKaU8Ru36EN3XamZycSEhV15BdAd2GWMSjDE5wFRg+AXWHwN85Xh8JbDAGHPcGJMKLAAGuzBWpZTyHDGxkLoHDqx16WFcmSAaA4lFnic5lv2JiDQFooHFZdlWRMaJSJyIxCUnJzslaKWUcntth4K3v8s7q92lk3o0MMMYk1+WjYwxU4wx3Ywx3cLDw10UmlJKuZmAIGh1JWyeCfl5LjuMKxPEASCyyPMIx7LijOaP5qWybquUUtVPTCxkHoW9y112CFcmiDVASxGJFhE/rCTwp0lERKQNEAKsKrJ4PjBIREIcndODHMuUUkoBtBwE/nVcOprJZQnCGJMH3I11Yt8GTDPGbBGRZ0RkWJFVRwNTjfmjO94Ycxx4FivJrAGecSxTSikF4BsAbYfB1tmQe9olhxDj4mFSlaVbt24mLi7O7jCUUqryJCyFz4ZD7KfQfkS5diEia40x3Yp7zV06qZVSSpVV1GVQq77LRjP5uGSvSimlXM/LG7qPc1kTkyYIpZTyZH0ectmutYlJKaVUsTRBKKWUKpYmCKWUUsXSBKGUUqpYmiCUUkoVSxOEUkqpYmmCUEopVSxNEEoppYpVZeZiEpFkYF8FdhEGHHNSOJ5O34uz6ftxNn0//lAV3oumxphiC+pUmQRRUSISd74Jq6obfS/Opu/H2fT9+ENVfy+0iUkppVSxNEEopZQqliaIP0yxOwA3ou/F2fT9OJu+H3+o0u+F9kEopZQqll5BKKWUKla1TxAiMlhEdojILhF5zO547CQikSKyRES2isgWEbnP7pjsJiLeIrJORObYHYvdRCRYRGaIyHYR2SYil9gdk51E5H7H/8lmEflKRALsjsnZqnWCEBFv4C1gCNAOGCMi7eyNylZ5wIPGmHZAT2B8NX8/AO4DttkdhJt4DZhnjGkDdKIavy8i0hi4F+hmjOkAeAOj7Y3K+ap1ggC6A7uMMQnGmBxgKjDc5phsY4w5ZIz53fH4JNYJoLG9UdlHRCKAq4EP7I7FbiISBPQBPgQwxuQYY9Lsjcp2PkANEfEBagIHbY7H6ap7gmgMJBZ5nkQ1PiEWJSJRQBfgV3sjsdVk4BGgwO5A3EA0kAx87Ghy+0BEAu0Oyi7GmAPAS8B+4BCQboz5yd6onK+6JwhVDBGpBXwDTDDGnLA7HjuIyFDgqDFmrd2xuAkf4CLgHWNMFyATqLZ9diISgtXaEA00AgJF5GZ7o3K+6p4gDgCRRZ5HOJZVWyLii5UcvjTGzLQ7Hhv1AoaJyF6spsfLReQLe0OyVRKQZIw5c0U5AythVFdXAHuMMcnGmFxgJnCpzTE5XXVPEGuAliISLSJ+WJ1Ms22OyTYiIlhtzNuMMa/YHY+djDH/MMZEGGOisP4uFhtjqtwnxNIyxhwGEkWktWPRAGCrjSHZbT/QU0RqOv5vBlAFO+197A7ATsaYPBG5G5iPNQrhI2PMFpvDslMv4BZgk4isdyx73Bgz18aYlPu4B/jS8WEqARhrczy2Mcb8KiIzgN+xRv+towreVa13UiullCpWdW9iUkopdR6aIJRSShVLE4RSSqliaYJQSilVLE0QSimliqUJQqkSiEi+iKwv8uW0O4hFJEpENjtrf0o5U7W+D0KpUjptjOlsdxBKVTa9glCqnERkr4i8ICKbROQ3EWnhWB4lIotFZKOILBKRJo7l9UVklohscHydmZrBW0Ted9QW+ElEajjWv9dRm2OjiEy16cdU1ZgmCKVKVuOcJqZRRV5LN8bEAG9izf4K8AbwqTGmI/Al8Lpj+evAMmNMJ6x5jM7ctd8SeMsY0x5IA65zLH8M6OLYz12u+uGUOh+9k1qpEohIhjGmVjHL9wKXG2MSHJMcHjbG1BWRY0BDY0yuY/khY0yYiCQDEcaY7CL7iAIWGGNaOp4/CvgaYyaJyDwgA/gW+NYYk+HiH1Wps+gVhFIVY87zuCyyizzO54++wauxKh5eBKxxFKZRqtJoglCqYkYV+b7K8Xglf5SfvAn42fF4EfA3KKx1HXS+nYqIFxBpjFkCPAoEAX+6ilHKlfQTiVIlq1Fkdluw6jKfGeoaIiIbsa4CxjiW3YNVee1hrCpsZ2Y9vQ+YIiK3Y10p/A2rGllxvIEvHElEgNe1xKeqbNoHoVQ5Ofoguhljjtkdi1KuoE1MSimliqVXEEoppYqlVxBKKaWKpQlCKaVUsTRBKKWUKpYmCKWUUsXSBKGUUqpYmiCUUkoV6/8BYkx/qomblFYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "RFEXtKtqNARB",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "652e96e4-5728-4a78-d695-2dc5124c54ae"
},
"source": [
"plot_graphs(history, 'loss')"
],
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JtWWam3PHRfp",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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