AV-Intel-Scene-Classification-Challenge/scene-classification-cnn-vgg16-hybrid1365.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"scene-classification-cnn-vgg16-hybrid1365.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"metadata":{"id":"ZgiJmF-3pe3s","colab_type":"text"},"cell_type":"markdown","source":["{'buildings' -> 0, \n","\n","'forest' -> 1,\n","\n","'glacier' -> 2,\n","\n","'mountain' -> 3,\n","\n","'sea' -> 4,\n","\n","'street' -> 5 }"]},{"metadata":{"id":"n3RYV4j1pM_s","colab_type":"text"},"cell_type":"markdown","source":[""]},{"metadata":{"id":"Z0Clg3m3nxeM","colab_type":"code","colab":{}},"cell_type":"code","source":["labels = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']"],"execution_count":0,"outputs":[]},{"metadata":{"id":"XPLyRQHKmdUq","colab_type":"code","colab":{}},"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"f2b1b45d7222c7109884df0029385d81718f7209","id":"CNui6my2ipD2","colab_type":"code","outputId":"38b03151-3f26-4db5-f7a4-ac9b69abab4c","executionInfo":{"status":"ok","timestamp":1553748825323,"user_tz":-330,"elapsed":2194,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import json\n","import os\n","from tqdm import tqdm, tqdm_notebook\n","\n","import tensorflow as tf\n","from keras.models import Sequential, Model\n","from keras.layers import *\n","from keras.optimizers import *\n","from keras.applications import *\n","from keras.callbacks import *\n","from keras.initializers import *\n","from keras.regularizers import *\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","id":"mK84XzEvipEJ","colab_type":"code","outputId":"b4f1cef2-be23-4a98-aa58-315750fb1e21","executionInfo":{"status":"ok","timestamp":1553748828444,"user_tz":-330,"elapsed":1026,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":54}},"cell_type":"code","source":["home_dir = '/content/gdrive/My Drive/data-science-my-projects/AV-Intel-Scene-Classification-Challenge'\n","print(os.listdir(home_dir))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["['scene-classification-cnn-vgg16-hybrid1365.ipynb', 'dataset', 'submissions', 'scene-classification-cnn-data-aug-vgg16.ipynb', 'models', 'scene-classification-cnn-data-aug-resnet50.ipynb', 'scene-classification-cnn-vgg16-places365.ipynb']\n"],"name":"stdout"}]},{"metadata":{"_uuid":"4d3374968aa7f3881f1b83408f272367f0e63619","id":"cwFAsswOipEY","colab_type":"text"},"cell_type":"markdown","source":["## Read and set up data"]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","id":"e-JCRYi3ipEb","colab_type":"code","outputId":"d0da15f3-d223-4896-c669-60a6c637e943","executionInfo":{"status":"ok","timestamp":1553748832033,"user_tz":-330,"elapsed":965,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"cell_type":"code","source":["# Read data\n","dataset_dir = os.path.join(home_dir, \"dataset\")\n","train_dir = os.path.join(dataset_dir, \"train\")\n","\n","\n","train_df = pd.read_csv(dataset_dir + '/train.csv')\n","train_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_name</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.jpg</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2.jpg</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7.jpg</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  image_name  label\n","0      0.jpg      0\n","1      1.jpg      4\n","2      2.jpg      5\n","3      4.jpg      0\n","4      7.jpg      4"]},"metadata":{"tags":[]},"execution_count":5}]},{"metadata":{"_uuid":"2ffaa5aba1e6197e0f2ced15187c06aa87cb4726","id":"BXPM-y_kipEo","colab_type":"code","outputId":"21de7f99-b5d5-492b-d757-a04bc901e3f2","executionInfo":{"status":"ok","timestamp":1553749044349,"user_tz":-330,"elapsed":2464,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":367}},"cell_type":"code","source":["# Read and display an image\n","image = plt.imread(os.path.join(train_dir, os.listdir(train_dir)[100]))\n","print(\"Image shape =\", image.shape)\n","train_input_shape = image.shape\n","plt.imshow(image)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Image shape = (150, 150, 3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAVEAAAFNCAYAAAC5YlyiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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jQapbGxURwOhxgMBslkMmK1WiUcDkswGPyL9ZjpCKj0NlUst50GKRd5HB25sfAoLIfGCRqs\nGPnzTu4gcqMqtprWacXNFWk2ttHvUxizbqQWok7HhWzCPwsrtSj1zGP/8htHRUTEbafqeDcm5I67\n8bBv78M/pYYK/HOcMxsPub8GC/rpfnxvMuOfgYVWbaW+1HcECjhNK+BP+fBjyBypsoQW6GVgpH+t\n0B/0xr+FH6pSgB8Y4QoxUm+U2UBtvF8mT11VlWOKcuhKN3UkDh6u1g+O1kIrfZlcaZbeCBaOX5b8\nncoU4HTgc+W/u2ULcrSrNRGN40HxUysg4Mc/PpVTyWDAfDXXYRwffgjRPl/5MbwdRo9i07FwYJR/\n8EB/tzQGMFdG9ZCOM8sksw/E6NFg4UONv81SXY3Y8XQKG5CRGVojEYzJ/IXz2DfM5Ykj4Ebj49hw\nX5jERtHZgTmWcfTB72GOH/pPZgT/tPoGMPa+aozdOLMUlLNoZ20N+u71Y8yMzC82OYWNzaxjvvkx\n1FcfwhqKjmJDr6gER6ov459HMo7+FLjhed14rWnAOBw6RBKSUWpWC5W1BP/koyPY8Bx2PFOBSrQj\nEUW7GuBaLcd24/7Bety/zO+PH0K/vD5mxM1hs3Bzw4yOo/8+P+5zZATv/T60m2YQGWP2B48J/0wz\n/CdqIUdrYxKpxedxHBlpNj6CZ7XCy2dOh/81Y2O4TsdItIE+tKeiAvOmNIbPVN4TJ7p27VrZvn27\nlEoliUQikkqlZPXq1bJ5M0jbF198UdatW/deqtaKVrSilb+q8p6QaFVVlVxyySVy3XXXiYjIrbfe\nKvPnz5dbbrlFHnroIamtrZWrrrrqL9aTocr4qvPhP2lgBMtgH5CenjJGavdXOp4q0iaXYPZJFdZA\nZKqO70qFyUSE6jQzRw+V5fU86ngqqRlIFSgdI3UqqEloE+aDp29cMYNdOJwAcgyPAe184WtfEBGR\nu55F3wcnQBMEq3nGYM4gux3oomRkJkFaKp98+gEREbnKjPqHwmy/gUcJK3ZLQwrtzOmMsvLiT8rh\nY8iqWc6h/zHmUD+6Y6uIiHzlq/Cr/PynoZ5UWwOfPpcNu3J0ArtxZAII9N5f/UxERIb70P77foMc\n6UYP/Trd1CiIY1wcPHIplSmluE+QIDt3wLey3o3rvRVAR9kcI9CmlDI//Ue9mM+hE6A9qkM8okbw\n3h8A2shx/ahMAXarVTIpIE07fWCPDUBf4BJmUTDT/2/7QUrOi8hrO3bJkU5ETR0+hD5fxrW4aC6y\ndqaS8DGusMFToLESfanyAP03HcPrkT0Y+xmrQiIisrEZiOtIP9Za/yEiQB67vbXMCUU6wF+D1wwV\n2dXpLJ1S+g5o89w5QFh9fWjncDfWsJ8KWBPj9MRgFlGTGZ4MNhtOcyU9+jM4ijGsa8WzFYlR89YA\nJDlG3dGSGe2KY2mK18koPfrfihOnFr0Ra8ljxhoZT2KNO/kMBn30tDHCzzM1WMX7opq2uWhn3Qyc\nNnxB3LBcZl57atdOEpEa+Ezoi+j3SC+zokaxBpOTeLay1JdQQYtT0/h+LRw7pOsgI8ConD8LofQi\n4pO/VN4zJ3r99dfL9ddff9pn991333utTita0YpW/irLOY1YuuFTN8mxqb+Tz33p70REZOFs7EJL\nFyCeddlibAdB5kMZnwJSUlzbYDd2QQq9n/JHnIhil3MTOUaj2I099AE7cQJoo3YWEOIL998vIiJX\nXAgD1/ERbL+T5FJz5AwTaaAGlw3Ip6ENRopf3fsLERE5NAfGjK9/+asiIvLrPwPBLZoBPq1vCL6B\n8STa3apDJFN1JXbj2AR2+/EutG88QgRaIF9Gns7K9KgRY0pWikh4rENERIpUrDc4AVfsJryfZNZU\nM7OPnqJ5FHJX/aRq1dGj4H6XzEd/dncDAX71c9g0wxFmMyW3nFeRRfS/NZvwfakElONy0vjQB8Tu\ndhOJFqiPSjXxHTvg3TG3BXDh4vPXi4jIrCXgpgspIv8wxqMhAM45wwivqK4kU0Vybkm0YeF8GANN\nXOpTVIgqqQTpIpLW6cRopWGlCkhHZQ1IJ8gTZ2GU6z0ChDaThquP5YBMnxgGsnVcvUFERHQ2cJr/\nuedXIiKip8TrymWI4a+uxWmnsuIaERF59Jkf8H6Ye4cZ9ftrgKyGe+kj7cd1UxGqS41izFvn4fPB\nIcR+t84BQovF8TuDGZynQYCMk2kgRoMJiHWKXKuNMf5VaL443bivh+pPh7ZgboY6MJ41XiDg3buY\n3XMpnmGPB8/qBXCTlVefIEIeBAJ2VWP8U8w+SldkefNNcNKpGHl4RoaNDqP+RmaSrQiAS87kqW9h\nAIdZW4f/DXFmBAiF6PdpXCoiIlvegm6FWQ+k/NpmjJudJwpvLbNoUPO3XMbaP1PRYue1ohWtaOUs\nyjlFoo8+dLeIiLx+DLttNIld/iOXQRHnJ7+CNfbBF6CWtCiEXf+bX8H3lYz//f5/gEt8+BkgQsMh\nqgiVGYtPztTJTInZPHkfIrFIF7NXIq2O6GLUEaUCjKGsXH6AckpG7KK6Ml4vWA5EFKBP1Qt7Efk0\nMwTe7NrrEa+7aCPiq4cPoZ/33I98O52Md55di+tb1n5URESsnXAPcdOKHSE6sjEqxmwIiYjInLmI\nOTfp4VXQcRJopLYF7cplsBtP55j/xoh47pJZ6ZPiOoqDi06Idgagg3roeeZ/p77n4AQQeRUjk+wG\njgcjnk65ojFn+BS9C0xutD/IvEDhDKNh7HhlIJWki/jdlk7cv3ol2tdCBaU8UWRZj/u4ibh18bII\n87HHkxjTVj9cbTIZIB6TF4hzhoU8rIjccOE6cbjRl8EQvq+rw1rr6YZOqNkKxHb+uvUiIvLHO6HS\nf8e9cK8KXY3TRipMK7cZXOzHFkGHoW4RkOXSjaj38YfB+T23GRTYxDja3TYHfZ2KI/Z/kqg/xnA1\nM4VQE4IxsNjp+lOhFL5wujq6G/11+5kHzAYkF3DjPmN0NUpFgUANZXrKZOmiNAIEPnMO7hOj4n5l\nK9cC6+sexv3b23Cfo51A7GEq6/PwI7WzgTCn9zNzLROruZ30mhjAa101Po9b0d9MlGsrzsWRwPe2\nAOZjqBNI1OvDmm2ejxs+Sxf1cdovtr8KBMpkDDIf3oeSjNCThHnqT3TRxSqMfi08//SsEP9fRUOi\nWtGKVrRyFuXc5p2vBE/SNhO7r49O6dMJ8BnfuPlrIiLSuHCriIj8/F/AMebLQDLvvxCmta/e+nsR\nEVm+HA7V/3AdnNQ/fCO8BySNXTZVpOpQJZBPNAZuLUvkpPQ001RDUo7Nyo9VOf0XqUJk0OFzC7nA\ntWvhlP/yATiTv/Ii/CPvcDAbpx++dxHmc3nu6V/j+kpwq7+99VPoHx2gPeSbinRqdzIXdjqC/kwl\nuyUk6yWVAkowk+xM871esAsXmEv9oouAhMPkfF0e5jpXHCf9a6emsBs3ODEvLjv69zc3ghO1U0/1\nR//0TyIiEmfGAb0gWMFODjlPHVG6NEomCdTw9nZwnzonThJ5OvVnUmjHBLOmZlL4fU0Q/JWQQ/W6\nMC45YoBoAicZp9Em3hK4QDc9MWLKD5D6DMoHdkY7kKGIiM5iEZMdY6sUpvQM6q4IYAyCleDivvf9\n74qIyJP74Lt69e8BecptGNssY99fziJt5i+Pwtf3t3/CKenJzSERETl4BLzzdZ9HG0pb0be+k8xy\n4AC36DYigKJyLj4f6QdC6qW1/4avYg3/4Xs49bQtwymllYj2wH7MdWsTuNDoODk/ngbaZmIcDuzB\nqcdfjX6n45jDZx9B/S3tWHPtC6g7cQTj1QxgLYVp+Ic6aHW3OzAnB7fje3OZOg3UAlYcsVGHubz8\nCpyaHvojAk5MOmoEMKvEkuVoT+9xrJWJFK4LYsmJ5LH2//NhvK1pwNroh6OPNJAbVQh4qIh+6Sow\njrPxaMpoDOtnjMi4/8A7J5Z3KxoS1YpWtKKVsyjnFIke3L9TLlr0UQn4savos0BAPd0M51yA3bgp\nBKRWHcBuaHXhdeEF2MZT5Dx/8fOfiojIlt8CJVyjZy7wJ+8UEZEbLqHlNEXfukiJ3z8lIiK33ovw\nwjffRH6cllBIRN5BaAlafJU+ZoJOe4UC87Iz59JTTz4mIiKXbID/6yb6097+m0dFRCRLv8wPvB/t\nsZNL/ca3oBKl/FKdtfAnTTF3UyxFVSUfkGBVFXbNGSF4MTgdQBvtRFkjI0C0tXUIJ1k4ZxX6dTm+\n/973oaeaLtIiTf/bMSoHlWoZKjcIfq97iNEx5CIffgIq8v/xHYSVxmNA2MU0vrcSwevIPbvdmI/2\nOeAPs/TvnBgDVxsnP5fNEVkyworgQYx6oJF0nFlVlZ8o/W/rGurFylPBADVGZ4WAcHT0AHCYsVZG\nlGOiiBw52S0NjdQZZaZT+xg9PLxAwQWefn78UyjV/9xxj4iI3Pc45vqhLPrgawiJiMhTv0XYZ2UJ\n9f74W1iTd92J6LZPfQFru+M4OES7EZDukg8CCR/cBcR57EiviIgEa6h/Sm3ZWQvQnmd/gzVy4UfB\n33cdwhgPdKN/lR5ArGQCa85hxVzMWQCkOjK2Q0REPNScHelHvfkc8JWbXOvMtrkiInK8E3x/bQ0+\nb2So9miWpw4n1qZeBwh4wUVo79BJfD9vLtZsVxfaF6jCiaGzA8/8iveh3gM7sNbnLWJmASP8fD2V\nOKV4mPtqkn6usxfi9cQR5sqi2lRjM5XR3Oh/IYX5LLswX5lJrLEphrPW4F+NeHxU7M/9D0UsaUUr\nWtGKVlDOKRINUj+0ws384hHsvrXV2F0VJ1mgddxHEcEvfQWIMeDHLlXPjI6fug7iD7d8/uMiImKi\nBe/DH/u6iIj84JbviIhIw2LwRzd86tMiInLjjfDdS09ht1+5DDzUSwd6cZ8A2inMZRQrgicpFRit\nUo/6Ekm8/9gm6F32d8HC6mLsudtBn7wo7rOoFchwsgfW+pXvRzbM7u1ALRPj2HXLGfxeX8buGmXG\nwnw5LZVtK+V4F+K4x0apz2o+XbHeXwl+adtO8E1f+NJNGJcPXSEiIsdOQDilRLSlLwMt5MmbmRiz\nb2K88fgIfOd+fw98IIfoI9lCxfwCaSQHY+stZaCLRBzcLAG7FKgaXia3rJB+A08eXURps+fB1zNO\nbtXIk4GlhHrVOnnrwD4pEsUeHAXS+PRHIcLSHEHWglnNQKYJZjAVERkOT5zS0TzRhb553Jhzl10J\neaDRY8Owvmcj4B4/vR66BWM59PVQCnPxya9A8/WPj8AD5c+/xykjMAtre+1cINrtr8M3eYoCmk/T\nqmzm2LXMwemsnGF0GcfQlATCqwyiXT1HMTgOCmmMY0lJ2wIg1OoG+gwPAIlteQoI63O3od43nuEz\nVoM1nJ4EMvY341QyNA4vBdLycgKUrhzeg/bNnoc527cbHGwfJAVk1fn0jGF+sMgUPTKYEWDv2/j9\nOnotjI0AMaqyfzfmaQaD/upDGJdYmn62PvxPmKJfaImnE72BeqgjOFHQmUB8HizOI6+gI2veh/ra\n2jAOW59Fuw7uQ3vL9ncyILxb0ZCoVrSiFa2cRTmnSDRF7sxhB9+RoEPhJOWq5rmYcTAMHsZEPmv2\nPOym02XsxnaqYc+yApE++BiU5A8QVcyZiXpyg6gntAJSavE4dr1UAvyMjVyrkb5t2TR2LQNzCSnr\n/NQkdkEdEVqE1umFM7EbeysY407V8Dg5TYUMi+Q8k7Rel6i4LwbmraG8WZSWZAsjfIrTzMdOST8L\nI4Pyeebi9oEPKzMzpZ4dyVIpvpIycFNx9Csyhf7Pm8s88XGguNFRIOCparQnlUT/LJQHGx4B95kV\nIOskMzqGGfViKJDHYry0ijBLkG80MK59mlk8vZXg1QzMP29mPPRwGCglyZzwJsrcxdNoXzFPlS7W\nn9GVZOkioNZ5AfjmelTdPA1E2Befj6cLESkWRKzk1X2M6/dzzOurwfGZjcy/PgpeOBSC0tYxHU4b\n1UXcd4ix5xIH1zjtxRxt+iAQa7iMqLXtO2C1H2dset8gM6LOAPforcUYzz8PY7EHjh4ydxEQ0+AA\nnhGDFWOTprLZbCrge4i4uo5i7tvb0L4BagAEW4CktyG5g1TRyj01AL9as4c6BVVYQ/EIOdBZzPjK\nU8HhQ0CWgRogfx2/WLAYIU9WP+6zF/IO4nPhX47fj7VV4eG4TgGJV1Rinva+hX75vJSwN2Lcu44D\nKe5HHv4AACAASURBVNrp12sMYHzD/Xg/NY77f+wz4N1zPBVZdBi3PQfxrDctASc8OIY1raLp+nuF\n/cGrk7H2ZyoaEtWKVrSilbMo5xSJmoxKCQaIxKb8MSma7LAqvVH8r18wH1bolYyldjHvSzkFZOYg\ngpuMgksr28GzuJit88lfQhR45dVAagWqNWVS2J1yMSA/I/VLzdRWHGDOp+oqbEvxBPVAaQmeiqKe\nk929eB9hdEwJv8vRP1KVQDXQkfLf7BrCbi06IDUL8wE1EbHFKRSrtzIqxQiOM8u4YQOjLegKKaUi\nESiTZ7vd+L3TDiSXzyrUAqt9+yLEFaeJjPsOksulP+bkJJE0/VDtdoxDPqFyOFGjkXlwMgmghQnq\nhlpoPS8UUP/IKFBH0cHc5APo35WL16OfOtTr9WEcjJyHaWYj1TNqp8wcTxmdUrsySpUDbWuYg6is\nFHU442VGyNg5SOV38IPL4RRhnL/yY1Q6k/v3Qt0pRr4+HMbc2pzw4Pji16CMFbb3iojIcB++P7ID\nspAXLbpIRERmLgYSvPlb4KNrmxawXuZDp6+03TfGdqBvrzzFjLg5rPmUj0LY9ZgTvRHX5ekhcrIL\niGo6TB2JQazRVzdjTo0OrJlANca+aSas3NtfwCnOE2A0HLnPScbmDw2pzLJox9go5taD28uhvbj/\nyg14BoeHmGG3H4g94AdvPs3xdTH/mduPdgwMoR3LN+DZONkBpH38KBCuiaFGNhfzb5WVLgQjm6if\n0NyOU+dzTx6XH31LxM8Dh67AjMI6cNLdhzDOM8ixKrFqJ9eUy4PTscWnsna9e9GQqFa0ohWtnEU5\np0g0R71Pq4OK61MgiAzUpcxmsMuobJ3bmLZj1XLsmpEMdpEafl8QIJgUI44cVA9y2bFrhrhr1juB\nsHwexr6fDyv5dBToY/Eiqi6lmD2U6lAZcnCZHO6jsmXOII/17PMwrbYvWSYiIkvbQiIiMjWEXdVq\nYS7sIlNNeLHbZRkTnhxF//Yzt3nvfvBqdj92f48J96eAj0hpROau/rRMjMIinGXkj92KjhqtaN/2\nN+DL2NsNlBSsRL/K5FIP7UOMeiGP8VYK9AoBmm2Mb6a2pY5cp4nI1E8VqkAAvy8VaZVnTHuZurAF\nImM7I6AWLAMai4+jQ3H6z4aagZjtJlp0iQotjYAVJrZLV8Q85GmddzgcYqAVXaUucQaAwiMxrIGm\nGpB/8SSFMUUkEoufimRRSmF10zglvLoVSvXLlkFxy0POtMh7F6mz4GaUVncfxtBIhfbBPugY1M/B\nfXuKUNw6/ArM23mqFJnMQGDFEtqls6EdTa1YI7F+rNljVJFSvD1pbpmm72xxGt4HdhdOY0vW8hRD\nxDo8iXFIToL7nKL1uvM4TgdNRGxWO+bMRgX5QBV8hSPTeEZNFMGyMU+8UuQvZZVeKF7TBZ7KyujH\njCZcOM5nLU/PjNnt8Gfd8hz6b3Xh/hYqhbldfHjNQNppZlfQm/Bqd7O9Ttw3HiFfz0OgJU8tYR2Q\nbT0V74V2GTM9RsITfO/AM3vhRspLnaFoSFQrWtGKVs6inFMkWmauIJ8Pu/vUOPgIxe1NT4OHamQ0\nye7dQGabPoIYdQcF7fMx7n7M3z5Nvc6ZS0IiIlKaQvREkvG6NS0qxhvdX7MSyHFkGrtPdBIooJJx\n03lGJtmppuTxYBfzOrC7bn0Vpsd15yN2P9iGaJRf/exfREQkYKeFkSpLMUbmmKnMv3Q5fBjv+QX8\nX+OMiLpwLhD3kX5Ec/RFgEJs1ajHxoRxiXGqh7vQf3OJiJD1nOxA/+//PTjh2ir4tZo50AwcEj9R\n28594M8OGtHOtWuhRBQ+BO+GGFFPgd4G1QEg11kLyKu5gCSzYfBg72gRABYsWAC0tGQeIqeS9Moo\n9ANRC3M9Oa2M5WceoaZm6Mym6SPoJPdrZweKuqIk6THh5spW95xBnt1LZfuBISAaEZHpVFKMVsaU\ns60xehK0hsAb55gtYekKxrIzn1TJDHT81j4Ebc+xTPNzeICkR4A4f3o3khd6whijWBGqRqFGINDO\nbiA9mw1mYZ0Oaz6ZQ9/NXnB43hLul5hAB8dG0c7JKNq/9mJY1cvUIdjzOp6xhhlAdOFh4KZyHvX1\ndmOsV58Pa7rK02WvwDOQyeP+/d3MT+ZAPbVekqbMRuDEoyCRSVzfRxlOD3UqzBT9nRgiYrYBWVvd\n+H1Ox/xp5Lv7e9B+pWtaNJBTHedDb8V9zbS6xxNUHlP2FRPej3RgTdsdQKDzFlJtihFrbgPe6/JU\ntuc/lQT9TkePan6iWtGKVrTyP1rOKRIV5hoyMfukyr/YHwF6WEYF+Un6E+bIYSqVoRkVIRERmVYq\nSRmijHH87hMt2F1e6wHvZDXRB9CE+m/7JFSIKul3mk1id5pKY5esCeC1b4rREMw2ajLifqkk7pd2\nEVlWQpA0FQEPluT3tfOAHvJ7sJumUuhvLI92+m3Y7YaGcZ+bv/5NEREZZmrlVCdQS30rETh1PEsu\nvNa1A1k79LiPzotxO04uVO8C5zvOdLSmLNPlUuXdWgKq0FPHs5/5Z06+BURaZITQJRdDqaiYAW80\nnME4mpxALS3WVo4fx5sIvJABaqhkFNCiWqC4LbvBixnLqGdmpcq6Cn4xqSOfV0a79SaMZ3Ea7cwx\n6auJuaKyE0mxz6KPrtLdpFW+wDApk+Bar/cdB8Bqt1s++P6LRURk5yFEbyWZP6qQphI+PQsm6Gd6\naD9OBbWMXKqvgYfDWLFXRETGM/j8dZ4iurmGylnM6eIV4CT/cB/QNx8FyZGzKybxNPSBQpVFS0B+\n2hxAxM4g1qpDdeMoxiAcJteYwVjG06j4wHG0p7Ee9oMi81tV0vdWymPsB7MT2LAmjuzHHFSorArk\nKA3UgFU5ih79V8zZjNkY/9UIhpMDezhJZqzxFGPVK+kdkCtibY2O4Nk0OPCMBapxWhoLwzMmz9OS\n2c6oOg/ur2Le83msrdbFWItjx7GGZ9Rh3E+cRH9GaY+orWA7KoAjK6h1e+IEns1BRmQF3X8ZZ2pI\nVCta0YpWzqKcUyRqMwNhTtAiOjaC9xvLQAEZcl0l+oita8Vus2ULlO6rr2XeeOYYHxoADxIhn+Go\nBkKrcAEZlmz4ftqhrP9EGeTgsuRVTCViYqo9Be24755DO0VExMK43yOdsKL/0zeQJ8dK5Jxi9Egh\ni1071Ahip68D/UyPYTecDAOlXHD5ZSIiMp95e37287tERKSNOZzqQ7jeTAuwyY69L6djju8K3Ldj\n50siIvJWJ1XI3UAh3/reT9COJsCWWlrRE2n0w2ylOhLzymRo4a6tw/1PUBGpvAUamQnGwpuKmJfZ\ntczbEwPaqKHIpMr2GYnA8uskYrSRMw2o/Dr0UvAwIipBf9VYkjmjLEBPaUbJnIpsojdGXQ3m57xV\nyyRPjw+vi1b5QSKzGD0XiBsc/wWJlox6mSZ67h+kT7AfY1vlC3LM8GpgdNnsasxJcCWUuixEfime\nkjoOgpfXTWNt/ZBarE/vxRp65uFXRETk4sswxiUb1sQLz+H+lH0QP5NNev2op/Mgxr4iQB2FKNG6\nAe9zGfLDHHuzFWNZVY0KI1G0b5xJOmMUJJgzFzx7dx/myigY09pmzEEyjoaYGEte4aEaUpaeIBY8\nu/t38xTCds9ahvHeuwWnkvqQ8jfFMzg8gDkcZ+hWJYGxy40GmgyY61IOSLFgoPrWGD73EiEXPFSp\nImXqdeF7LjnR6TAglKoVMwUe4owYU14RFdTJoHSB9A8qV5h3LxoS1YpWtKKVsyjnFImO0lfvrnvv\nFRERHWPh778DOpfGAN4PT2J39lNH809PQBn9szeBg7SR38n6GGu9B4RGkbHoRfo3mr3Ylmpnweev\nqR7xy4USdptQPe53cgBI2BcEminbqdrkxe8ieey6GUZNZAS7azIDpFkdBBIrZNGuYBW5SirLGKiL\nuWAp/FFXvw+c5by58Dc9ehiW2rIwuoTbs/JTtdMLocUOBHj4IJB2gjHrA1PYhZfMgTXbY0d+mcuv\nBO+XTwIFHDsBC3VNFdppcdHESifECL0jmkIYB70J9UeYJ36KhN2qpUBtU058/vSLyHm1fNl5snyp\niK8CSHK4E/NYMAF9uZhfR1cGGhkcAUxoa6SVvwL9izPZeRNj6id40pAc2qcvU7koOibtc5E8Z3IY\ndcWGMCcuKqNnchjTIZVQXUSKOpEp+pHqmRRIRcmpjKidh7GmLrjiUhERSZaIGGmlP9GH906eElwl\n3K/Wg77Y3ZjDI2/g9DF3PvpmdGFOu2j9riKCyyfRp2QUY5eIYo5mzbWx/bQjOHDa2j8ArrWGecWs\n9O3NFMjjM6uni/6UDY3wy5y5GBzsq88CgZbzsANctYm8eRRzs20beHmfAQh+y4vwQ+08xuygKvae\nU1MUIMS+DvTLF6RPLyOVju2lHygRt5tLz0kePTyAUwqD/8TvpVcBZSas9OutacD8LtuI8XxtC+5H\nBw4pFZTnBsaLbr7SUIH+fXA98rVt3oxsFIe7cOrS0YtjjH6jZyrn9J9onkefWz+DjnzkU5AL2/QJ\nSNOViLVDJHenJvBgfO4iOGlv/NB3RUTEwKOBkcIen70O/ySlxHSogoXuodDJT/8Brkf//hJC+hxV\nmPBv/d3nRETEksV9EjRoFRKo//9l7zvD5KqubHflHDpU56iW1MpZQgGBEGBEMuAkDBgwGDNGOGBs\nYMA2YGwDBo9xAI+NyZgBgwCBBEIgkJCEcuyW1FGdc1d15Rzej7WuNMw8wA99/vRmvnv+VHfVrXNP\nunXWWXvvtSvsmOBIGMPWqsGK8RThx91qZ4oJplZO8/5+St+NjqE/ew/B5chtQRjrnl044rnseEAm\nUMgkGMICSTGlsduF++vovP7extfk28uuEl8Q/Xv+Bbha1S8BPRBNoP02KxbCoT343GLEg51jiuSd\nO7GA+obpKlVI0Q7cRjJ0kjcwUV46hU0jzB+Ytj4cpU6fgnHP5DAObc2dskBEbEzypogyG+hsry1B\nf3q7sHCLbTgaGniWCntRr5P9DXspMl2CJ2HXWkgG/vQeGOKmn3+GtFJBwk0KJMNzrRLYoTfi4YjS\nLU5EZLC3T47QcNE3gB8VmxE/FoYqXF/KOV79GoS1h3rxUD59wUoRETnEY+QhL+bWyNcepjjZfBSB\nFB4zxu7am+4TEZHfPg7AEFVSOdPpW8no7B/DGtr5Efoxi5tBmiLTAa8SeosfWz8T2sUGcOydNQ8G\nLOFGpdVi7MM0Hh7ZixsW0M2wki5FjUyb/cHr+H/idMxpby/qNelxfdl4HccD7TEwAMVioTiMH8Ck\noBLtjzB0d+bp6Fce05h3tQBoTJqHY/xgJ57Vne8yFNqA8ZYE1kyMspPhMaboseNzTxX6E+a4TK6H\nO91YFBTgGBPTHejCM370I8g5lpcQKKTxWxNl6LKFYtCfVtTjvFrUoha1nEQ5pUjUacEZYMkKkO6v\nvAwhW9N13xIRkcl1FDvuxrExacKu8MWLgHi+9M1rRUTk4iuQ0G7qNFw/qwZHJwPDR3uzgO7/cRC7\nqH73JhERcdPAEmJakhtv+qmIiOQzYd2tN8JglM5S6IRuMnkU9NDwaDFM8vlnryM9yWP3A11YXYAn\ngwNAI1d8/RrUZ0Wq5IYP0a8vXwYH7jyy4m8eADLV6VCv2YZd/+gGuN+0HMNRqqG9Tb59n0h/P3bh\nUsqGWWn4GmPKhu5h1HPfzzFew00I81yzEW4vViK/SqYjSTIUT0cdMScRciZFmiOfYbj7cIRt7qGQ\nLpG42DAwcSYDs9pQf18PxvnwQaCAxzbDuDJMUeoXfnu3iIicxrTFa9/eICIiRloHHEWY3/ZOHC1d\nlCYsLgCCHR0eEY8ZaLfQgnsWlNNtyg5EGaYz/hzK/4mIzJ46VeonAbElOcdGouXOPvTxSDPmagYD\nMybX4bqAnaeNME4ZWgYg2MqBwIrDFM+pmCciImMTcN2atUC0bkryZTo7RUQkncX1jKyVghKMvUaL\nV68fp594FGt7oAdry2TGnASDeN9TiH5H6NYWJiViZzJIix1z1t6AtZ5M4bQ01Im1nclgHC/6OhBf\nIoixt7jxvc5WtKONLkEWC+iBXA60QlBxZarC+PkZUHH0CNZi/RSs6d52tG/eUtTT34H2H97NeE06\n6WcTlHnM4DSSorsbvRZlzRpcP9CP7zsM+F7zYZzmasoBsf0DoFNKKjFebgZsSJJyj1EiUHwsJR41\nUZ1a1KIWtfxTyylFotet+ol0fO1W+eYvnhYRkYVVIKaO3v+giIiYCoEWQkPgqQYp0nvLQ+AxEuTs\nqhcAyTU0NPAVSEUiitsDdrmvr5gjIiKXLIdBZ1IVEKu+OI+X0x3lob/h/ibsbpZ8hh9Gsed00Sgx\ntQa779b3EI464APyeullIM0oRRIefwJO7zXlQExjFK/wULijtBSoxcYw0hRFETxl+Ly9E8htZAio\nwGgif1WNfmgtaH8gTad88nNFNOg4tajXSPm3kjK8n6NhzGKluwzFKTJa7PIuIsuBfvRXESZR0pa4\n83HdoWbwfe9tQ37c7XsRHnrZ8qUickKaz0HDlZmhg049pfU4/k4aaaK0CujovpLUkUN1gsstnwrO\nts8Pl66yUqCkkUxCCt2oK48cX8cgkEiIabEzlDn0UdJORGRs1CstRzEninC0nk7lcyaCf9fyVFNN\n960qB05RYz6MkUeHMW3csxttKsCYZnRAvq2NQNVDIfoWOeh6paSzPgeBDC8+izEcXwdevLoO9YfI\nezvyMOZjw+jPrJlYA01NDPfM4LTgHcXa09DAo6exUE9XKG8P1rTFgTWbTwuPhi5E9ZNqUG8zngG9\nEbx1xof6M0TsVGmU6dMwJxvXoh2efCA4P/RNxOoGkiyrQn2pLJCvhYaljqNMvzKCeZiG2AXZ+gbW\nbIZhnAa2P05B8jEa3swcbw3zgCjSdk1NaPctl35FRES2/ztOX2MhXJfWYByslFM08RcxQ8Ogm1zz\npxUViapFLWpRy0mUU4pEH74PXOYF8+luUYbd6vLLzhMRkSkLIWg7MAS+4pbrvykiInWl4K8aesBT\nvf0RrMu/uwtO5VvXPy0iItkw02vYsCuP9GBXchTQxelMWMdjdOVpbUGYY5iyXskYdqNsBMNUrAMy\nPHwYLkXnrYBwiD4DZFZVj3Z19+E+vhDaPXkGhGnryrGr/e1VcIF55O2SDCkcGsb1GzYA2ZaUACk3\ntYMDnT4b94tRJDlJMWgPXagC5OPSRJxaWnxtRBf9bNdxdxc3LNDHWoCOzCagnhj9i/XknbIUf3a4\nMD8ZOsmPr8AufuFFSLYW6CXneRQ82Iol+DyiyKNZgIYGRoBWyhm+2j2C+mlwFpMLKHKU4bFRin+k\nydGamYivZg54Rk9FjYiItB3aIY5qCIakGECRpblbR9Fioyg3ObH09RaTFJUB5bYytJcgXblaSuls\nb2DqlQ93vS0iIl+fhTkfdWjZdhwDfD0URyaavuJbSIroexanElsprt+8+3kREenvwhwpMoVmWqON\nDJdM05NirAfPiiaF09mhQ+C3x49XOF5KuXEufaOU9qvE3AVovU8J6otGGABhzbAdioQcXJ4MFsxd\nPIZ+B720tnuIJI2oN5bFnFbXoV8RL+rXpsrZHoobK54Ww6hHZ8bay6QYWh3H+CUY2uu2OTkueEYD\nTMVjoseNToM1l2TIty6H8XYzAMXD0O0hdEdCFP2heqbk0/qeCaM/HqbE6eoElO0bUVbAJxcViapF\nLWpRy0mUz4VEI5GI3H777RIIBCSVSsmqVavE4/HIPffcIyIi9fX1cu+9935mPc5i7BL1TPP6/lbw\nFdOJ6DRZIJT1FO44chSI7LqLvy4iJ7i3AFP2HtoEp3J7EdNvZOhkT+HYZBB7xmgYu22oC/cLM6y0\nuRtO7tfdvEpERFobkQLCR+u9LgsexsSIwdPmYje95CIgZ1cN0MBYH3bhR/6IdLkpE+4/m4h7zQYE\nC2QESDFF9BAMMoWFAxxfUwv6lcpSZIHowVGA3TZIxKxlyFo5022EB8G3ZcvA38Ut4AF7RpnaIoz+\nKrJvOfr2pZkiQ8eEgDnusT76KqbojaB8bxYtngtmgDdsSoAbXXY2UmEogryTp2E+D+4CZz3Si/cT\nWcy/n6GRJoap9vqY+I6yZs8/hWCMh85CcIXvKHz+HNMQcjluPGTn9jXuk+I88M4+iqRYjUydwnBN\nB8eys++ExJm7sEAamuAXGSH6Zc4/6WHKFz3TT7iYNkJPkd+RACBOnJyoxgN/0qNNOG3orPjfT0GT\nIFMjhwSISpOkIEgUa9ZlxmlIkyESTWHsCuyY4+FhJvdz4vq8AiCwEH2KnS4l/BNzWczAD40GSDdB\nMZgsefL8UsztITiESO1ErKHScXj1k/MNjpEzLMIpI0yOVqfHQLW0AHm7KV+ooQ+x1gCkOTqANeti\n2GdxFcZjoJ/pTxhmWVaD+3RQgi4cUQRg0F+hs35REeZXb2BaFz5DFWV4xm74GtKkX/1NpE8fNxHP\nZoZ+qRs3v4R+HWOqaT0QeYwSgmFm1O4b+uyfyM/1I/raa69JbW2t3HrrrTI0NCTXXHONeDweufPO\nO2XGjBly6623yubNm+XMM8/8PNWrRS1qUcv/mPK5fkTz8vKkuRn+VsFgUNxut/T19ckMIpKzzjpL\ntm/f/pk/okbBbnukl5wb/x/vJFfXiN167449IiJSXQ/ur+0YEOiXz10sIiK/ueuXqG8i+JdUN/wp\nD38EZBlmuKKnFrtWx26gjj2N2GVLy8ETbdoIFPDSut+LiMhaDaMumLrYaAP6MDHh26ZDqOfLKxEK\nqHMAdSRCiFbp6AW/NnE8kNm7G9EPJdWBvwPD76KQhtGNV4sd2/JR7opzi8CpFjMtST99HZNUmd51\ntFNERHrJm/nawU9ddj5MnGkKeERHuPszhbOOORF0RGeMUhWNG2hnVwOum1FBC28OqKeQFsuhIXzv\nmTfXiIhIERMGnncRdv055Qx37cD4JYmkK2rBW/b2KwnrMA85ElUhRgMtO3sFGjSGeUlYgHwjFiDR\nPB3RA/lLg0YrB44C7QYZNjn5NFi9NUQ0fcJkhP+JyBoL+2TmOMxdgR1rZJSIx8MQVDPzYfgojad3\nUTIuQe6O75/mooRcAcYgPEy0H8BaHk6gsV+aAK04bxo5i/ccBhRMsJ1DPrRf8VO1alFvknM8oKN0\nHaPDRpn+206+O1/xgc5hDaYJvDVhjhm9AmIhTHougwEZ6QWyDFGO0pqP+2UZaaWxoN4gre7RBDnk\nBOptPcYUMyUUueapx8IUzrkghdOJIB0OINYUeXNjGs++Q4/+LDwHa7+gAKezfQewFsYCDLNlmK4l\nDhlGB/1JvfTrXfM6hMhrxwGhOsw47bh6mXbFBYS+fCVTAEXRj+7n8VpSQW+KTymaXE6JRv1/K9df\nf710d3dLMBiUP/3pT/Lzn/9cXn8dMWLbt2+XV155RX7zm998ah2R4LDYeIRTi1rUopb/ieVzIdE1\na9ZIWVmZPPHEE9LU1CSrVq067gMoIvKP/i4bU8dEpEiuv3y5iIgE+sGrXLgQ/pwDlPmy1mEXefNV\nxEovqAAiufQrl4qIyMvvvi8iIik7JdNaYbH80kX4fF8fts3UcKeIiBzeCSTbEcPuV+iGz1+G4hR/\neQpI9Jbbb5fX3tsrq66BZXXAC4R7rAX19Q+hvc27gMS8FC3o3YPrXlwLP8Y5c9CfN96EZdZgAZfa\n3Y3d9s9/Qf3zll4sIiJzq+D32k1ftXPmQqBkyVT4Du7pRP3xYY28+N52+e51GL8du4HQlixAmhID\nhXb/vhPc75cWwBth1y74MtbUg0s8cgDIOUdklyZq6CQvdct1l2DcDlJsmnzXfsZXL5gAoZNzJwPN\n5ReDvyqsdsn5q/5dHrgZnHHDHsxneRnqPzwKFLSZ6Uiee+qPIiJSzVQer1IUQhvBOP/8ScT+D7Su\nExGR0gkQVPnLv0EL4cWnHpM8xtWXTUebHDVAKLMWwbNh7ryFIiIyVQtPi3guJV86+wyZVo3NvIl5\nLWJsQ6kNc1VI3YWRNFB6aBgI5iH6NMdJGDe0wmp/YCtSJkd7gUTvfOBnIiJy16Po40M/uF1ERL77\nKPh3fwS8uy2FU87AiFc2ru+Uy1YCCVNLWLzkKFM5IKQRpv2utAH1R4nwzGas7cp8tKtX0VGIA+mN\nEJkGw7gvNXpkoBOvFVWoJ8+D7+comR5QkLjGJm0HwlI5BdeVUNxnbAhzW0rh7dE+rGGtEchaEd+J\naYh0rUzHEaT9gj8dC2fjlLl4HubtrbeB2ItLMB4b3sNvgZF+pukQ04R4mSjQKNIfy8kZM6rZfpwq\nGxowbk6SsAamL192ARC3m2nYX3wGz/hp04GcX33/k4VIPteP6L59++T003FMmjRpkiQSCUnTDUUE\nubkVx2y1qEUtavnfXD7Xj2h1dbUcPHhQzjvvPOnr6xObzSbl5eWyZ88emTdvnmzYsEG+8Y1vfGY9\nv//jM3Lr3QslRY6vncm6DjuxvXzlasSu92WZYoCcXFERdt3f/gG7+pyzkTbDT2vvEE1rteOwa21v\nhZX7AJHar+9GjPxvX0Cs+6TxsGC+9gpEh9e8/lsREbHRSt3cBC6zsoYSbpTGC9Ha/60bbhERkbwa\nKMYcehfItKQOsmyJGJByXS1Q0aEWJiUjt7jqB1D0GRqDGHO0B7t9lL6Mb2/aJCIi763HbrjoLEjm\nfXvll9HPSmxY69/G7jlrGj73jWAX7e+Dl0EkDuQ5bSbaOTpC3zzybBGijESKaUTIN7kY+VQ/FdEx\n6zagP+Igf0Y01OPHvGzduV5ERMrm1cn5q0SshTg56ApwAgiRxNSTq9Yw+qWrHYh53AygSA1R3+VX\nXiEiJ1J1JKnOnGWKaBsJTo/NLlOng5e3TwcnOUCf1z56Jswir2y3nFj6dpdZHEwXUSkYy74AA2he\nEAAAIABJREFUxY7pQWFlMPuEKqDtHHlxL0NjAkG82piGuoQIKzEVSDZIub4zpoIfHxgCIjp8CP6e\npZUYOzMTsLkLAMksTsxhOIlTi9aO04WVyQgLyAfreL+RNvTTyig7PaPRKvOxxpvbwCUS+AkdIyTP\nzdQrxag3SrlDAkwZG8U4FJej3qAi3F1CPj3I68kJa5gKOsPxUBLcKUg0Si+BGGUZiwrpRUAVpdWr\ncep4aw1eKQMhBUVA0ikKg2tZj1aHeZ3FtCs2RVehFCeI2XPBwx/rgPyl04PItqO9OOVshGOPePhT\nY2HUXmjknySFt3LlSrnzzjvlqquuknQ6Lffcc494PB752c9+JtlsVmbOnCmLFy/+PFWrRS1qUcv/\nqPK5fkRtNpv87ne/+2/vv/DCC/9P9RS4sEsbGDM9ezasyYrfZX4edtd2RsLEaY3WUkR51lQgqqON\n4Oo2UVVoPlMK188BEiyhyHEqi/uUVOK+pjSQ5dIFoCbWv7ZZRESeeRTxywvPg2WQbpESjYMHozul\nZAW78JnLlomIyGAEu+3sWUCczkKk+1h+HpBVUzMQ8WH6f44OQ73o1dfAwZ5/KXQxf38P1J7+zBQS\nllLs3lPrwJVOnQge5/6HfiUX3XCX2J3cdclF+ulf6aD6kZECw2efAW41S33QdWuBKG1UWQow9n+Q\nSNhMNadH/gCEfA5j4eNxoIxJCSDUo8NAU1WVmK8F88EBF8/EOEw+0CkiIqvZrglMCkZQeDx1c2gM\nRF1PBzjabAyW9HXvgl+csgzccR4jmrRMZxJjao+l5yyTyQuxht7eD943YWa8fjm5O3oqeGMnEMZH\nB5uk6Qh42a4uIBMX02Vfejq0WbW02k6rRbRbxI41mWNMuiKYbTOjb4X5+L67EqeClkZYizup6zDU\nD9RdQjXjXJKeFRGg+jLaAfr6gEijXsxFih4sKR1Fhs24f8cxrH0b03oMjjLCxwCE7O9iojj2WYlp\np/CXhLxAZoP0n3VaMV5jI7jv1OlA4IqPcCwGxOvxABkOhXGf6Bi+l6MYcjyM9htMaK+VCl9KqpgE\nT0E2K+7fdhRzP28uxi3MFM45qjkpiDYcwn0zSYpJk00cpoqzLYl5HzeujO3EM2KkL3WQa6uaimFO\nE9ofGmBEVRgjpSulytOnFDViSS1qUYtaTqKc0tj5a2/8joiIOJX0rPuxW7ceBSqw5oPHiClaiLSY\nRoPYrVtasZs/tQHRB3f+EpEtvkYo1gvVzGsnABFlGLd71y8QTbV9C677/vehrD8yCDRxxnmwYnf0\nMTqkCLufzY7dOBBFO/UKVcvUAxXl4HVKcmh3KAluN0p00c2EbzElJpy7dJmb8cVatG/2bHCX81s6\nRUSkhojuWCvVney4jzOPPm8F1AFNA5kZmOI4SZOrndyqiQKohw6j36mEEkdNBaMY0NU9P4Ge63tr\ngeZ+eNvVIiKy9g1Yy6fUAyVtOgKEf9octO8LE4BAm+JMndGL3f6ZibjvRR5wow394PcCIaCeAsbk\n26k6H/QDEZ9+GvjD+eddJiIiOT++F2EiOyfVsMxOrIunX1knS+n/mEedToU7q3MDGWaymFM3/R9F\nRBYvmSgzxtfJfy77jwIxxqg2VD8BY/7iW1hr1XXgXsupIG9LY4zzpmDuvUlAvT1vfygiIpPr8b6V\nEVUGJ9Z0zoA+1dUCkfY1gadOk0OtqgOiSuRTyT5If1GejtoY8z1jAvhqqx1rqXcHPENMRJyeGfie\nwcokf9VUyNLifmPDqH9oAGM6ZR7Qvs3KGHgXuNixAbTLQF2FIc5lWRnW7IFueDcoKYg1wuSHYSVB\nHeZOa+RJgCpKhw5gTZq4duPMnqBEHcapCxGMMLovgOsqPIz5H8RrQSUVy5hNoZ++2gsW4DQYZSRb\nPtvXO4Dx0zixTibUYi0qaVuCfScM5p9UVCSqFrWoRS0nUU4pEr1+5cXyxBvbpY5Js3bR6pumalA0\niN3HbMCumcni/zFyd9+/CYhp9TPQ/wyMEqkwwihJi6GCzOonA20EB8Hhvfg6VKSSEVr6yB/NX4Tr\nMg1AxJk4kF46A5TjcAFNOJzYrVavfllERD5sBAJ79IdAcGamAI6wHTpyv0VFsOxGcvh+OAZEV1AN\nFFC/DKhiRh/Q0OXXIufU3feAm9zfAs5Wx3hfxcqeYXy4m9b00TBQQR7TA4dCaP/Rw0DS9jzyXEwK\nFmXI0nxyydvfo19sO7jbjJIcrAD81WlVGb7PBHQ+8JFO8oQ5BqD3PItonInfBIcd3gHLtMkBtEDX\nRtnywSYREVlKjUw749795AsLHYwSCuI1RN6qd5h6q76x46pFRlqBNeQQ3/oP8PXDHTi9FGroYCgi\nkcGkNIyBi/OGsLaC1B91VQFBBiheWzwRfXDnoW9mWr8lg7Z2dGDOjBaMuT8CyMipkS0fwAPkBz9H\nbPdPHsPaDZHzdJD766cOaixGcdgk8E4xdRNGh4CgFB780GHoFsypRnvyyGe7SOkNHiXnV0j/0TCR\nHHUHhqLUAS0EYq2sxDMwNAaPikQQaz6cAAdpZDpw0tiSZ6V3AtWs9AbcR6dDA6xU3icQPP7sBDmH\nFeXkZAexlvMLgChN9OOsrATCPtgA+0cwgDWbTx2LWVNxetx+BL8BIea2+sJyRL3l4hgPPRXAkpwX\nLaPojIrOKrnkQrbXyAwAn1ZUJKoWtahFLSdRTikSrZ+MeFi9Hs2IKamNme7V6gACTCWY9tbNXZ/R\nBmmmFPYUUZtQD87RSARopNahLkeLHb9eUA7+qbWR0R6FQHTaJHi0LON4R3qwW7pdCtqgyhHvGwjg\n+9OWwWLb3waEl0sx46EL12toxfeNfRzxjfhwn4FjeP/6q68VEZFnnwOPtu4dIMHuLlw3Nsxt34J2\nFljQv9IC7NZO+goGA0TiaXxPwyyd0RTGtW8Uu3+hBu3PkdxVVJoaD8Kf1qBRkDPmIRLALp0ao7o5\n1bFKHOAH0w4QcPokxjPMOO4zvwyu9LlXcdI4ZxI0FQZ6cSIwkKueVAffTruVqCUMxFrOqKORNiBi\nPefDwlxP5VS2LzQ5xcGosSzXktWJ/4vpvzi5nOZoJZWpiAQjARkZBB/sInoup66kVcO0zGm0RUuf\n1gqmqx6N039TgJx8XbjORUSWP5UqTNNx2prVAyT3wB++KyIi4yfjVFLNvGBuM/lzasX2daMfmgDW\nlNkIpOzNYC1kmBvIRequhhxkdjIQZkMvxmxOITxNMnU1IiJSEka75y4BglMil4404FQ0PIg1GaIn\nRpY+2qxeIlG0r6gIcx0h768o6Ke59jKKNi29FpxmzF2Cc+tgNocM9V4VK3sX7Qf1RMrFRfCk8Xmh\ngCYa1D9jBhDsuEpct+UQ5mveApwYTDyRlBdhHpRTaYS+5A6KQ/noOEsHIOnoQT1nnsln/1OKikTV\noha1qOUkyilFojk9tRs9QDoapTVabF8OJ3iYNHkuP63HWTu2D8XnK2cGMeIpxfXJHtbDXUVPQ2w0\nAKQ0Z9pZIiIy2AclqpmTgXR0VI5xWcG/zJ7FyCFyrKVu5pc3Aa2klfoFu6zRgA4kItSuzEc/An7U\nU8L2RQW7oLkLSMwcx//LFiCT5HXfQNbLWAw8U14O1wXi2J3LJ6GebAK7p4u7ey4D5Gw2AzWkrUSE\n9Ikc8KK+pk5G79A3L8ndf1wdrOeP/QE5rKw5WGh/dAs4ze/cCB3Xph1QStKEqUOaAloZ9TDTJbla\nixb/F+WTQ50Ga3syBgSpNaBfMaKYMKN+SqbjZBEJUHHfjHbE9UBfKcrO00AtsTGM74TCCimlfoKP\n2q8WItC5VBgLMk/XaPIEEg3rLKI34/8U82LlMSi7cy+sxmbGZO+j9XmNF4r0xUTBpbROz5iJtdnB\n/Oyd9M+0u3k6KcVaDbQwOyd5/jjTCWiM6KuFiecXLoR6f/8RrF0DkXZlGT4fbMdpZU4W7dOQf4+V\nAB9VTKkRkRMqSLVEZOefiWdgGjVu//Rn9KcoD/3OcXzcZnglBOIYNwWX6axY4+MmACFKDM/Erh7Y\nEYqp6Wuif2g6Q83bclxn02K8jraDQzZY6AFjYQQUT4NHj4Cr/vDDfSIi4nBh3HR6LNpx47G2qkvw\nbCqnvnAYSHL/fszfcD/GpbYW49E4ghNBmtkfHIy+c1vxvXQvtXSHGdL1KUVFompRi1rUchLllCJR\nHTUIrczxbSTiSDDaQTRU66Z1Xh8DqtDTdy9HJJih7qcwgiUZo0mS/ps5PRBOPIV6ysZj1+qjH2bK\nSF88tivDWGx9gjmvifQsTr4v9LmzMz5Zi1eDHu3QMNojSp84pxHfryisERGRYBqI2mml9iWRtYca\nkGdPBn91z4uQFpxTCHTgygMXGqG/Z5rIN5YibydAqAkd7ksxdnHwujdWvygiIuOnIZIqp2X+HObd\n0ZrYDxPuU0rFHBejUALMupph3qBR+nkqSdLtJiDIKKGtjQSXkiN9cJiW4QJ8z8YTgk/L6BOOW08Q\nFnSHDqhLw3w6YUYmuRnJxuAVMTPDp8ZgESnCZ1YiOzuzYibG8H+EDpZRRsKIiLgiWqktxXWb+nGv\ngBco/4vHsHYKBoDGq74AnYbheihndQ/DH7PZj7UxsRDI7nT6gWaNiICaW4v3LRbcd2sjPBnyyNf3\ntOI1W4ZOlebjPt9YDrWn1/w/FxGRmVTzT4+hncdqsZZD73aKiMjuNNpdpIdVvbwI7YjkYY29vRpr\nau2rQMoXLkY/klHMlYGnqn1tOAVMsmHtZMxYc/lOnFb6csgysXE7ONTzZ8OLwWLC+BbQet+iZyx/\njM82zfn1s4Fwe/pwCh0exDgVlFAtK4zTiTGFZ6e+Bt/f2gFPjNPHY23kabDmX3gH85BlVtCt6xFz\nv6MNa6k8iOOpMYq1pnViLdMdVXQ8Fetz9BihZoIzj4ntP6WoSFQtalGLWk6inFIkqljKEuT2vF7s\nUlrGeis+Zsp1ik6piZyf041dKxwCytDTr9BioW/d8XrQTSXzo+JfaCICbWbObg998IyMnMkwIkbJ\ny64juRpNYLfTMPOj4puYY1C9zweLaU05kJmeFt0De8Hr9ETQ3jTz4Gz9CH6lrhLcZ9seZPvU0Vpe\nSTWqiAb98dOSmmF/lPEJRxm/HI1+rB9WWkYnVwKBbmMcd4GNn5M7TRLaazjOkQhRGHO4m6nsLzrU\nb6fKkp2RVsk4du8wvQ+Sg0AzRoOSfRTXe8gpdym5NOkdoOGJo4F+rDPGwWKdYXy1ywnEGWYWV4cF\n45ivITItcIuNnGGSSu0xD+e4H8gpkmO+cXJwIlDW76HepNWAPpRqiI6Xgi//cBO4tUdGsZburAb3\n5zeBEzQ7MVbTmS9My8ggjwX3/3A3kOGwFmNYOBGvXV1EWovwf6wXY19sgxfBu29DV6GxA2uuK7dJ\nRES8uzC29bNghX7PBI6vDtSlJIZxmkgkgNzOor/joQLMZYgeGm/tAmKz2THWwTFmxOWcaVxol145\nLQgQrKIeXGjHX13dzA7KrAyDQTwDyjNpyOHVxWdXl8SclXnwjFjoydE5grm3sP9xnhjyK6hMb0S7\nrXYq5jNxfciHfpaX4lkJcx247OjvlZdDE3flSiB7FwC8OJlLS+I83eWoG2Gg73jWL59VVCSqFrWo\nRS0nUU4pEk2liHw0QCQ2GxDRyIj3Y58rCFRBWGVlQFRKvnazDQglqwFiSSvOaXxN0IyuY7yvEvfr\np2/YBPIzSSIorZaKOVSLSueU76NaM/0jFTmnfKoKZcjJKu/nMU46RevzDirKawqJXgzYBvsHgcDH\nBpn7yIr7l7vpa1iK+nsPAv1YxqG9MY6P4g9qMtF/lgLZVnK7NiLNGdQD3Ukkmp+HcRsLMP6bMejx\nMNBUgHmBXGYn+4PrfT58v4oW2JISamv6gY5mLTtXRETmngG+zcu4aRP1WS3kiIXjrGTY9DJm3kQN\nSj2RqY7cqJ7+wZo0xn/ETi8EztOBxoMSigARleWjbdPszMt0BJzY1MVAltn4idh5U6hVZs2ayrrx\n/mgQbdEuwxiWe2AVDtCT4mgjOLUz58B6npROEREx0orezEgaZUns3Q9uLk3PguoKrOnvnA+EVGHD\n4hqbjvp378Ncx0fwvW9dCo40xWyfhTdDob+xA+2orDpbRES2pJHVYYabouhurJV2ov1QEMgz1A/k\nVjkHa7GrlV4EJfQzDQFhaqiClRrBGnOYqAZFA0ItFdHam+BbrPh8M727ZPgMjoWA6CZRIyARRD0+\n5pFPphQFfHzPH8U4ugwYr8G+ThERcfL0MUKPjMFRfL+AnjxdjfjtMGtQUWcrvrfxHSii6cnnS4pe\nEVwGTvpSa1K0K6Swlj0FQLyfVlQkqha1qEUtJ1FOrXVeryA2IJxIhDHm5EKTtPIq/9vt4D8UDm9w\nBLtp6cSqj9WbSHCbJCeq+P4p3KGdqkEd3B3Ly7Brh/xEoryfhfno/VSNisaxW2WYY0jJrZ0kOomE\nyaeYsdspCNpI6/WUKVCSiduw2+3ZBIvv2V9A9MrC04AUiyqhjH+oCejJzEgkhtqLhQo1BhO5W1Fy\nfGM6FWQ/MoLd3EJeKh1nRBg5zALG2B+N0x+Tselm1mvNoT4do3sUlCHkMo1O8FYpPcYpGkH9M+YB\nndXNRAx+jCrsMT05Vaph6Q2YP4edEWNEaXPnIIrGqsN8ZBTEzZOI2Up+jVZ/I08GnhKX1FXCH9JV\nSBWnCVhbNXOBSFNpzJHpP6UBy6WS0rYdfLWOMfUhI+6tYVRYrgxI6YUsEGpuH63pQSC5jnYgohH6\nQ5onoM9b9wK1N+5GX3/xw2UiIlKbB+S79sm/iIhIrxZr4pqV0C+98BJkLfBMgbbu7Hpmsp2J66x5\n6ICZ1ucrD0FP4fIicKTZSkYAjQEpdg6jPZd8EXmpWhuQH+twI/wwCx3on306+tu4BmvOTGTqdKJ/\nNvpT+ojkovRPzZmYHbMQyHXAj2dLxyg/MznMAH2fTbQvWHjqGvNirXqqAOF9YcVfU9BvzpuNcx+i\np0aCGgcWRifG+BuS5unFwWdl2gREw2nMyPJg1mPeMhqQyBF6uAQU/VMeNvt66QL0KUVFompRi1rU\nchLllCJRBbF1dXV97H0rLW5KBtEI+RsFYRqNaLaB1udACLuIYn1PE7mkuUspSDZIS5+iZpTnxm7b\nP4D704h9XLtQQaAhqkkl4kr9H/ceUJCf0h9FA8BJ018PcwcpnGWYnGUt46VJ0YpQOzGTQj+Li928\nL9BAOMAIIMaNZBVaj1Z7xZou5Iaz1BetHQceyk7EV1pc9LF+Ku1Svp/vIGI3Aj3YaMXvZT4a5STQ\nRS1J4fzNYq5xc5roxD8kmoIaiTOTZY7+nq+9jYQ2mSxzJjF3u4KU86jupI2SGyfqsFIXNcSc8F76\nRObSaPeSymopngrEsW8vVI3mFwOBHvgAMdeFzEeV0J9Qth+1TpEkY+SreToayKBvfR3w2a2ophKW\nCff81SyM7V/X4XtvbkTkkKMC75/jRnSW2wzkOG8WEPKrmztFROTiryCCKr8eugLjxrDWmvfAi6Bk\nLhCjvr5GRET+9VXkBqo3UV0qi3rf3wtVqK/5MOfHpoMTXpREuxs14EzzatHvsQZmD+jEaaC6HEhs\nrA0IrKIEkUybktBNtWCKxMtHdOoMcqZhrLlhL+5XPwX92Ldhm4iIOKmKZCXvnaGrSYqx9RYLTggt\ne5gx9iycWvY3oz0ZMxC/hieDyczeuqkRXg4e6l3kGMzvpAfNmWcgLVGGMfODA+h/cwN8sidMgZW/\n/QCiFTVGjNvCM9AenRFr6QCSXIjersbOq0UtalHLP7WcUiTqJ2+i+HUq1nkFMWqoIqRwpkZye8rn\ncxlrvqMBvm0KQjrOSdI/s6iQOXnI5SWoNRgKg+dRkJunxPWx7w8OgPdSEKeePIuWe4+CQHt6wF0q\nlkgFgSqcqZvIqpDRD15mTjRaUK9vFJykGKm0E6XXAq3Relr7s+Q+Fb9PBXmbae1WOF+vlxbKHNrj\np99qjsh71Atkb6NCvmLdt5B7TRHpCa3pOnLLiqlZ4ZlGfOi304TPHVxNVnozdB4+KLVnnCaTKoF6\neoeBsmzlQFHTZiFn06uvbhIRkTw3xu2jj4BmphOpm12Yl/AwUIqzgJywA+tCX4ETi2tcoXDKpZ/5\nnCzTECvu9UHNaPlpsGqLBvoAIiJR76D4tEA+YXoGjNkxBoPkyJzMMLpvEzwsrr4OGUiLp2Nsq9Zh\nTZUYgHzPWIi1uacBXOvgXqz1jUeAvObQo2PpciC4xvdeExGRq268QUREHvvrn2X5otOl90NCIvL1\ni1dgjQx3Yg3d8ST68dr480VExBPFfULfQURUuh1rpzqKOepwg8cuyMPcaQ3gWhsDsNrXOzAnUS1O\nQbNngd++6o4LRUTkpVfQztlz0B5fJ/rdchBzduNlQNjvv0NdVSLR4QgzySbpi03+PZ9eDzv2IKOu\nxow5L5uAZ6PlML7X1gFu2UEthFgEJwW7HfU4GSH13Ktvi4hIxoTrshRyHWjvFBGR5nYgU4cda5Tp\nvWTbJszjnHng0hechXFr3KXqiapFLWpRyz+1nFIkmpdHRRYivWgEHJ2CTIeGgAQV9Wyl2Gz43MBd\nLhrCbpiK04obQz0x+idqjltiAVP8fuw6fb1AZHYb2mGmNd5FxKhETimqSNrjXCNzFzGaI68A3w+R\nm1UinRTu1kluN5lAuwqpDD8SZ/so5JlWInjIvbo89MN0oz2kmcRO7jbK/ileAGaSqwrHqaG6kpbW\neKU9yucWIv8Q/VjdRUCCKUY+KUhaQfCKRkCG+d41tKab6EdqYzTMg49CESi/skbuOuPb4vJUcnzg\nS5gNYnf/cBvQT0Eh+DpFc1JLrchRosknn3gCn4cwbxHB93dsfUhERDxZWHaXL5whQ4xUOtqK08mT\nT0HRfv48IKT3DoAbW7IQiE5E5NwpTtnagzpnzQNSfXsHEGSaOg0Gyu9Xl8BHuTkDhFsxCWvgrGWo\nd/F8eBb86mnwvgP94Eq3vPkTERH590eoUDWAvrz12GMiIvLiXiDcF5/6u4iIHNkPZPe7/8BYXpwH\nhJuZA/WjSvq73n3OpSIisuhu5CtL81Rkpz/pVZOWiYjIPS+inhLO6Tk8HXnpH5moh7X627OREfZn\n+2/GfSagvS1N4BB/WYdx2/jKv2Mc1kOP4Y+vof17Nz8pIiJxrrk0PVfy+EwpimqKo4fdiesMLqzd\nhhacyibMVaIP6WjLU5V3jP6tBUrEF/rrD9GqztOoyQVEqdfymTbjmaurxedDjIoz0Ne4sgC/QYd3\nA8kv/xqQ9qxlJ9S+PqmoSFQtalGLWk6inFIkmqT1fPdu7GLf/OY3RUTkwFbkGe8dgQ+boxqWzv8a\nU99DlXCbzfax9xWO0M73jdQMdJNbU3a1kmJwgmN+IK8AtRi9XqCELCFsPBFh/YwEYnRJirnGFSSY\nVLhEfs9KmaH+DsTmKzH2wxnybwH60lEDMcPp8DPPjI7RHX7mdU/Qmm23Hxde5bjQak2vg6pyoIqG\nHbhvUQkzUBLSaxWVKvqDKhxwlF4FOnKtivU+zvw0+/cgGmZoBP1OcRdvbAXC7O3EeCe46w9va5O7\nfity26+QG8psB2L91Y9WiYhIJznnQBD1+/zgTIcZueWsBBKfOhPz70xTlcvO3OtUEurZgSyk+vIC\nqTGCU3NSsryLfG9BDxCMl5ljR1qOiVJmLpwrG7qBREf78b4lDqSz+0MgSVcSa+Xab1wvIiL3P/ig\niIh86V+Q/yqZBfLpaoUHw5I5GJuSSV8SEZFDmzD20y2Y23YT5n7Vy78REZE1z4LbzHGN//QGZDjd\ncvePRUTkyBDWTtsxIN5yqlOdPh8c5rZ+8MVNDUByK5aAd564GGOufQXm9WNmtHP6QviptncAyf3h\np7j/wMN43dRxn4iIBPks6aiDOplru1iHtdnASKDbf4xTwZKX0B+tHffR8NlKJTlnQfSvpQlr2u9l\ntgRBqSBHmgrwHXraGIU6q1STioaxVvOp8bv/QKeIiDS3oD9J+veGGJV4xqIzRERk9XM4mZio1qTP\n4ZmPJ+hvymdjw/NA6DOW8DfjU4qKRNWiFrWo5STKKUWiXS3grY4F8FrD3EVWE3794/QBG+dmpJIL\n3GKcyEvPXcNsAUJ0W7H7KVlBkxpmwdRgV8owI6OiXzr3kotERCTLLJ4+Wu01IezqRcxdFKPWYi4L\npBti7mvF7zGdh+8ruYeMhUBiMXKHOSN2x+qKGrQzhv4cDcKXMcioj2Qa9UgSu3MR88p35NEKz7hu\nSxb9VvwqrczCmVbaR05Yo6dKO7nMGDMdasnBGiNEvlTET1CVaWIl+LbpFeC/SlOod8kZsNSeXYl+\nvTiIcX3z1Q0iInLOmeATy2uY+5sRUa889jsREbnhtgdEROTuR18REZELFwHdpTL0e03gPpcuQ5y4\nx4F+zJ9OrQAzfPxscczrO2+DU52zDFE+4yfly8gQ7mknug4PYE4qzgaS2f401JjqZ9FKLyJpm04C\ng0BqTRqMeTSIOc/3MH95HPX+8THkiTo8gjF4uAzvn/UN6AQoSvX7WnFKMuvQjmITONQ1e2DdNpYh\nOm10H9a+hb609nwgqxy1bH08RRWYMVe1VdThJLm49n2s1fAQrps/Dlb0qlKMaU0GqD6uB5dr6AN6\n376Fed7z8cx8+9dPiYjIVVEg75I4vme3A8G52zE+nUP4/qLxeG3ciVOkwwK90zlzsWa2rFHyjWH8\n/NTnTBiAkM3leMatjHwaCOOkEEwyyyazQVAoTdJm6rMOYg1n6fMcYXaFEuaLf/AHN4mISN3yhXLF\nypvFksMz8u9vQEe1UMnCqkd/sknqMrAdTkZE+f20e6RPZIX9pKIiUbWoRS1qOYlySpHouFpYbWdR\nXahhHeJ/5zHr48LzgXw6B7grcbfIJpVcQvQH5a6VydDKzYgg5fVEZBHN9P9FL7O0BLvwroqiAAAg\nAElEQVSf4mepfE/hOk/4qRo/9qqjenosgF1Rn8TubwzR2p+gmnkWqMThgq9iNxX7R0aYY4m+fSY7\nd1laMIut2AU9Ybw2aJibiHmEEhZ6DTiJvqgtYKSF2mTHrus1oB+TbEAdcfrODdFPtoJW/75R9P/q\na6EI9J1V32ZDwH+NtICrXrsHvou9jBfXMkd4ixf9sVTQ8tvRKctEpHsI3LbTiY6NhMEPjg4D6acY\nXWJVQrfYfm8AKPJwIxB7zXTMQ1c7UM5oF/jLkAH1ff26n0lAB84wZcGYuMhLv/Ufa0VE5MIFsNK/\n/D6s7yIiuWRYakpg9R43A3zyR+TjvSM5NgmIr3Ya5vSsJNC620TVfrJ6o2HGjnfi0frKV+AH+sKr\nsNZv6sHch45Cif3yFajPng8+OSzUC6Ay1XAY/HmMp4ugDmO4bhes5RZGq/XTDzJaCESX7Ea7MkG0\ny51C+wPU+bTxtDTcj7XZQl/isB+npjlXA903rfwi/h/B54m7/01ERDT0iFj5zetERMRhYn1vwmof\nxFKTCgue5UrOg8WCOU3GmLV0GM+uNY/RffRhrjNg/qwOPBtKPnorT5t6Zg7wRcFlT6J+xqGdOJ10\nS5tcsfJm+fplF2N8cuCqMyEi3x6Mr60ArykAT+keQvsmzcBatJd9Ns5Ukaha1KIWtZxEOaVI1Mac\nPjOZifGlB54VEZGpZYxUsWJX9xQpcbLMCUQLqxKZoyBKRWXohLI988Qz8kn5XHkdYFxtNoPvK0i0\nuBhRHdksEI/iV6m8KjH8Wu6KncwImLACEVnyycmmaX0exm5qoz5ocT4QeDADzcg8J5CetxXb4SBz\ncU/L8Psh7LYxH/5PZtFvRQuxtReoQGiJTAfwGj+G+5onwMfwGPPYeOmvmopgdx8ToIipFeADt3wA\nK3xNNdCbqxbj8dTr4D6bDwMFGaJUuWI+oq4A2tcygt19YLBXVj0g8uSzq9EuKtSPLwM6mbMA47CN\n2pxpxvqnGaE1EsC8LDRgPCxU43Lb4bsYjmAe9Iz137JpqxzqQOTL6FgnXgfwWQG5MQ+zRdZUn1C2\nH+zul0VL0PfGTqDbCBFgxoR7Hj0Kvvq+u74lIiINb8KP80ADInPe/xCILp4Fgimkb3AgCe4wrsdY\nB+NAXM58nKZiKWrXxpmfi4r7GXp+jNDfM5wkL04f2iWTsGZ6N0CV6JwaIDGNYA02vAVuc/JExMKf\ne8EyERHp6ACyDo2AE53pwBxcMQFcppPeCT307Y1sx9pb047+RUvhR5pxtUq9iGxn+6z0aBlfi9Pj\n1oMfiohIQT3mapDRZtkYrrOH0T+DBv3VZNEvul6L3oy1y6UqxQVYg6MR3M9AT43MGHUexuEZj0xn\nfa049UVdQPajHUTgtBvoXNTTIOJ3OPBsWqlY5shHQ4aGsaY/rahIVC1qUYtaTqL8Q0i0paVFbrrp\nJrn22mvlqquukoGBAbntttskk8mIx+ORhx56SIxGo7zxxhvyzDPPiFarla997Wvy1a9+9VPrHR6l\nJmMGu4LCBSYZAN07jF1Vw5w8/hDQgeSYb57BDJJTuER8L5XB7hONUoLGwhzY5DLN1N0c9qG+WTPB\nhykIU/FfVWLxlRj/XA6WU8VfVaPvFBERjxu7dm8QSC/uBBIu8GCX7KI/ppFxvkPUcOy1YZdPeqg6\nbkK/MuSA9VXUzfQCPcSYn6elBbxRahTo6PHnXxYRkbAJ/X9sE/wmjd3oX7wZ4xyNEdmSy02QI/ZT\n0EijQ/+H6VXw7MuInrnrx4jnXvMk4pKT1A0NCPpTXg4kO7ECFuh1W4AGFU3G93eiv/NmM8toBv1l\nai0RHb0VGoCqrr3oGhERuWDeAhERKaTGZDoKf1SbEyhvZzPGe04d1kfn4ffF14axufwc3OvxJ9CW\nwTj6/tirUD265LKzRCldXQmZczaQThOt12lmBXXmMZsQ576IUV0TzwYn2taO08k7HwCdL1iKtpZP\ngq5nIdF9bRli1OdWoc27j2FMykqp5EV/1mAKc25k1s1q5gwapV+kndbjOD0a3jv6HyIiMn8SxnS+\nB4irdh7aF8pgrRebUf+IFvW8ux+njauvBe99rB3ILcJnZBsjuGpL0I7ZX8ecuMk126mXkD8FnOne\nNeB8f3w/1sgrq3G6XL9hE/rn4TNBjdqIgVlXqcFbVoZxboLgmdR/GfW3fcAoQQdj4QXzmGB2A21+\nDV7p0WPZBoStnQ+k7sngvg2jzANWjfnL+vEMuGxK+B/GJaEkgR3As1/M7LGfVj4TiUajUbnvvvtk\n0aJFx9/7/e9/L1dccYW88MILUl1dLa+88opEo1F59NFH5emnn5bnnntOnnnmmeM/PmpRi1rU8r+1\nfCYSNRqN8vjjj8vjjz9+/L2dO3fKvffeKyIiZ511ljz55JNSW1sr06dPP64BOmfOHNm3b58sX778\nk2+uKLPTf9PMXaG1k/lwuBu7XVTzPh6Dju8rVncLucocow8UdSWF+1Ss6Uos+HFVI1rflcgcxbrd\nz+gPJceT8r6i0qTU79Azh3U/Fdcj5BbLEQ1iboMvWl0PY9lN8EJYqsOYVPuUaAnsthPHk4ciEWQj\nQmxlVlCXGfdL0KfRTAmatkOdIiJSmg9Uk+4BAk0SrYwyNt7sox+sB/Uk/ehfuQZoYG8TxmXVd5Db\nvJV+vPVnn8fx+wEa5AAa8dioUlVI37ooUKDJSa8BIlFHIdqfo5z8EUbdpMgXDsYopRPCxOaMqL+4\nEieEA0RN/kHwdRk9UEJjE9DTzGpw0y59sYQ7wQ/PnwxuzmqBZT9qQJ+3HgG35yqFf+OPRKR6qk0i\naSCvivEYq4usNSIi0naMedvpr2mgL++m98FLjw3TZ5dZKJdfAPQ8qQpIzKyD9d3twPUzpgNN9xEh\nxbOAPgnlGEadTR+zEqS1WAs6O32Ak9Q1SCicLurxN6H981IYm4YM5rI/gFPZtDnoz/tvgp+ePA5r\ntKAASPkvq6EfmhvD9TXleL+9Au3Nq8MzuPTcFSIiUuoCrz2X2UYnMM9WnFlOi8bj9GdPYw12tQF5\nG8iLDyn5yagc5h0kBOTpMhzEWtEZlUy9GOdCejFkvIym8xFhjwKwtfiwhur5mzBGzxdDBN+P9uPZ\nMDB1ksaO/lZWAaGWa1FfjOMXzlfEfj+5aHLKL8lnlD/84Q+Sl5cnV111lSxatEi2b4fIbXd3t9x2\n221y5ZVXSkNDg9x5550iIvLII49IaWmprFy58hPrHB3oksLS6n/k9mpRi1rU8v9lOWnr/Cf9Bv8j\nv81vPflzufquJ+T55+B7dv/NiBO+7CJki/zu/Yhw6SQy/NF3EFt/xYLTRURkxmnY5XVORKMc7ADS\nOrIRHOGDD/+riIgETPihfuwXiHeeWApEW1iF3dJJ6+4vfwMO8Jqv4v6NbX1y71PvygVfABI7bS6Q\n4vsbwau1UEFmKr//4pvYzYfHgDp0zcih5N2J3W/xjKvQceaNiYexy5ubiULWw/I71QpL6ZMPQN38\no04gtm3vgTCaeiF8+Da/u1027W2QccwGarSinjySnB1uoIdR8j1TIthlm8zKdg8UcN5SxNa3t4Bv\nuuNetOOn/4qc5x194BXLSoGuhqgxUMT7uaneRCpZItRDTSbj4veFxOkGilq4AJTQvl1AohWFQAue\nSsxjmupRNuY8T5M/G/N5OWBAgXr6B8eZwfLLX8B4mWIJ2fIB9Do9E6GEvrkBY5wzoY0+KsjXFAHt\n7j10WO74zgqZtRicaPcwPCaObkNbDh+G50ApI4yuvgwofdMbuE4KgHDf3wVPhkceulpERNr2IoY9\nowUy9DEyRmPB2Dz1NyDCB2+5VkREwvRfDKRxXS4akFvu/6v85V7k3xqNADHFo0r2AtTz8OvIx+Ua\nwhz/tBxjcYg5mFp9QHjjJ4MjbUgAYT1xO06SF17BXE41QP1W5kry8TToYIrbOHMcGV1AqN5UvzS3\nR2XGZDxDLmai91Ons5iI0U7rf54Wn6dNuL8/SI7Si3otVKZXcjq583H907+DR0htEU4bRgeQ5iij\nDC+jPWPRJLTjT0fwrOTv6pM3BnNy7jwqhFHbIMzY+87DeJZsbmrpMkTemo/+Vk3HevHHsPbe/usn\n51r6XNZ5q9V6/Ag8NDQkRUVFUlRUJKOjJwRMh4eHpaio6PNUrxa1qEUt/2PK50KiixcvlnfeeUcu\nueQS2bBhgyxdulRmzpwpP/nJTyQYDIpOp5N9+/YdP9p/UonH6fNF7tLAWHhFNamvDyjAS/3PRJLZ\nQKljGfJjN/LQhyzDTIuKdV35oU/Ix3MJKZFOSr70/DzLx65XrPKKOpSia2o01n2snngC7R1zYhcc\nNwXREXo36k/RD9bInEn2Z2E1NzCvSzyGXbGPVnX3/Ftxfyror/rFMyIiMhBkbLkL7ex4A8o5OcU9\ngZztSA6IvZhK8kMH8f/CErR7bxw+jamMEpCM/m5h5scC5s25886H0c4sfPx+9X3EI/sGMd5M+Chj\nY1S+IdfpoNK8bxBcZR7RhC7J3EuKD6VLyaKK74+nf25fAmhrmPWamN5U0XHNkR9LJZgj3QKUc2A/\n0Oa06joZNwWoOqTD2JSXw39ylHnT80zow/KFQGYiIsbUiGRC9BfMoY1TazCGE2sxdjodfG3HBnGq\nKCmhSpAOY3zLDaCtGregLSYTkE/xeFjXP1p/QEREWqnLee4C6AO0se2DjLrTu3C9RLHmj/DzFBFt\nhJ4RLvpCXzAL/U1SP+HPG1G/UQdo5SnG2DqY3/2WFYhGO9IETvjKbyDvfbwf/Xns5edwe4UzdGKM\nc1wrluM5kDBXzeRi9QLuMcqIqhZ62OhNeIbtOiC74Uwn3icNbqIfqD6HNX76xfCqWP8m+lFWhmfJ\npFc0hxmtyF8uLbOU+njf1p3gvOfPqsEFWbTTSD9cPbWBrTF00Gmi5q7yLB/DsxpvG+L18pnlM39E\nGxsb5cEHH5S+vj7R6/XyzjvvyMMPPyx33HGHvPTSS1JWViaXXnqpGAwGufXWW+X6668XjUYjq1at\nOm5kUota1KKW/63lM39Ep02bJs8999x/e/+pp576b++tWLFCVqxY8Q/fvKwInJ1Jh12gKJ/556mz\nGaJfqJL90sXMe2nGESt+oQbmIkpT5Uh5X/HnVPjZNDUD/dyVo8xjPjjADItEpkrE0/Aw86JzM1Cs\n9QrSNegZDREHb3Lf98DZFlXhvlfcCW3GyeOBIpYT3bSFYF2/LAkfu9lmZCjcGYWv3XNp+LrNvwoo\nKDOo4fcwHno/UEZvEIgvSH/aLzMj4mJXjYiI3JM8R0REzqpkxsoIVMf9vWjvtSvAUc6kZXLtOljj\nL7+OnPR3wJtdd9PtIiJSUw7EP0y/3hgtrO3N4P82bgBCvogW6qJCtPPW714rIiINVJW/6Dzog373\nDlj7b/8OeEQLkzTZSoB+LPTbVfyIjTasj6QfaKixCyjnput/KCIi+WavPPc00P6Nd31NRER+829A\ngB81Y2zLi9Gm2NCJdAlXfWWpHGnCmLgKGOVGHn33AcxVlx9t1xXC6l86B2vkCzMwJvlpQBa7DrH3\nHxzE2vn2rXeLiMhlF2JtfOtSzElxPvUMipkfy4lnwZdBPf6BThEROW06EK2OWUMTCazBME9tV3JN\n+akgf18LkGp9MRDtnAuBuNf9Ef6br/4Jp5srL8EcnTYbSvwXP/prEREpQvNl5ZegmP/aO4h8WlyP\nNdTpx7N35CMQ+3bipItXoX+15yLX0yt3wh4xeVKNiIisXQ1uM5+niYQRz8TcmbihKYt5Wfc6DNZp\nPrNWxdOmAONtdgBJDvE3YX8zuM1ogNkwBjE+l1+8TEREhkdx2i2lbkSEEUvBEvzGaAlpM0S4BquW\n/6N9Hr2ad14talGLWv6p5ZTGzhsYE900AF6lcxC7w7QZ4EXmzETGRL+PseOMtNEzf0p+AXZhoW6m\n3oBXTzF2t5xgN9GQT8logRxttOYHuoFkSiaTG7ViN0wxnlbDXbOiDMjIXQ7uU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Irz+jG+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\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f5VFHzusvsDvkoyecDwDYu4/IzB0y7qCsHy0U5PNj8XhHTiU6mjyX89InNfPm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\nO+yw4zDi/wPrZkPeLqUmZAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 576x396 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"Lwmhcd8eaVGF","colab_type":"code","outputId":"4d53f2f8-6dda-4f23-dbab-6c56d060f5b1","executionInfo":{"status":"ok","timestamp":1553749067148,"user_tz":-330,"elapsed":992,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":136}},"cell_type":"code","source":["# Distribution of classes\n","train_df.label.value_counts()/len(train_df.label)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3    0.178290\n","2    0.173594\n","5    0.169250\n","4    0.163438\n","1    0.161148\n","0    0.154280\n","Name: label, dtype: float64"]},"metadata":{"tags":[]},"execution_count":10}]},{"metadata":{"id":"B7VO2ODbtTeG","colab_type":"code","outputId":"aa344f8b-bee5-46dc-c6ac-fe03e3113924","executionInfo":{"status":"ok","timestamp":1553749069004,"user_tz":-330,"elapsed":1002,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["# Number of unique classes\n","n_classes = len(train_df.label.unique())\n","print(\"Number of unique classes =\", n_classes)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Number of unique classes = 6\n"],"name":"stdout"}]},{"metadata":{"_uuid":"9afc1b8533ad9c6167e3a50432127e4c6e9f44e0","id":"R7b2hbCpipEy","colab_type":"text"},"cell_type":"markdown","source":["## Image Augmentation"]},{"metadata":{"_uuid":"5bdbbb3b523d3e82c9c83508842c8536ddb645a9","id":"Hf2H6y0ZipE1","colab_type":"code","outputId":"c39b78af-4f4c-4a8b-d21c-e12ad79fee88","executionInfo":{"status":"ok","timestamp":1553749192926,"user_tz":-330,"elapsed":80126,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"cell_type":"code","source":["# Augment data\n","batch_size = 16\n","train_input_shape = (64, 64, 3)\n","\n","train_datagen = ImageDataGenerator(validation_split=0.2,\n","                                   rescale=1./255.,\n","                                   rotation_range=45,\n","                                   width_shift_range=0.2,\n","                                   height_shift_range=0.2,\n","                                   shear_range=0.3,\n","                                   zoom_range=0.7,\n","                                   horizontal_flip=True,\n","                                   #vertical_flip=True,\n","                                  )\n","\n","train_generator = train_datagen.flow_from_dataframe(dataframe=train_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    y_col=\"label\", \n","                                                    class_mode=\"other\",\n","                                                    subset=\"training\",\n","                                                    target_size=train_input_shape[0:2], \n","                                                    shuffle=True,\n","                                                    batch_size=batch_size)\n","\n","valid_generator = train_datagen.flow_from_dataframe(dataframe=train_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    y_col=\"label\", \n","                                                    class_mode=\"other\",\n","                                                    subset=\"validation\",\n","                                                    target_size=train_input_shape[0:2], \n","                                                    shuffle=True,\n","                                                    batch_size=batch_size)\n","\n","STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\n","STEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n","print(\"Total number of batches =\", STEP_SIZE_TRAIN, \"and\", STEP_SIZE_VALID)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Found 13628 images.\n","Found 3406 images.\n","Total number of batches = 851 and 212\n"],"name":"stdout"}]},{"metadata":{"_uuid":"14162cea761f425a19a7f1a0589e445c8f0b06c7","id":"FUjYGRNdipFB","colab_type":"text"},"cell_type":"markdown","source":["## Build model"]},{"metadata":{"id":"JWAPELUScTvf","colab_type":"code","outputId":"85cbf05d-530a-4aab-fe86-920a46c60c5b","executionInfo":{"status":"ok","timestamp":1553749256884,"user_tz":-330,"elapsed":1445,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["# Pre-trained weight downloaded from https://github.com/GKalliatakis/Keras-Application-Zoo/releases/download/0.1/vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels_notop.h5\n","model_dir = os.path.join(home_dir, \"models\")\n","WEIGHTS_PATH_NO_TOP = os.path.join(model_dir, \"pretrained_vgg16-hybrid1365_weights_keras_notop.h5\")\n","WEIGHTS_PATH_NO_TOP"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/gdrive/My Drive/data-science-my-projects/AV-Intel-Scene-Classification-Challenge/models/pretrained_vgg16-hybrid1365_weights_keras_notop.h5'"]},"metadata":{"tags":[]},"execution_count":13}]},{"metadata":{"id":"-RhD3Wmqc1bf","colab_type":"code","colab":{}},"cell_type":"code","source":["# Build the architecture\n","\n","def VGG16_Places365(input_shape=None, classes=n_classes):\n","    \"\"\"Build a VGGG16 model and load weights from pre-trained model of Places365 dataset \"\"\"\n","  \n","    # Input\n","    img_input = Input(shape=input_shape)\n","    \n","    # Block 1\n","    x = Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block1_conv1')(img_input)\n","\n","    x = Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block1_conv2')(x)\n","\n","    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name=\"block1_pool\", padding='valid')(x)\n","\n","    # Block 2\n","    x = Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block2_conv1')(x)\n","\n","    x = Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block2_conv2')(x)\n","\n","    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name=\"block2_pool\", padding='valid')(x)\n","\n","    # Block 3\n","    x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block3_conv1')(x)\n","\n","    x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block3_conv2')(x)\n","\n","    x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block3_conv3')(x)\n","\n","    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name=\"block3_pool\", padding='valid')(x)\n","\n","    # Block 4\n","    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block4_conv1')(x)\n","\n","    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block4_conv2')(x)\n","\n","    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block4_conv3')(x)\n","\n","    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name=\"block4_pool\", padding='valid')(x)\n","\n","    # Block 5\n","    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block5_conv1')(x)\n","\n","    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block5_conv2')(x)\n","\n","    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',\n","               kernel_regularizer=l2(0.0002),\n","               activation='relu', name='block5_conv3')(x)\n","\n","    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name=\"block5_pool\", padding='valid')(x)\n","    \n","    # Create model.\n","    places_model = Model(img_input, x, name='vgg16-places365')\n","    \n","    # Load weights\n","    places_model.load_weights(WEIGHTS_PATH_NO_TOP)\n","    \n","    # Return the model\n","    return places_model\n","  "],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"0015c78383b92457b131f2f79379847503f01833","id":"vBmJPc8yipFE","colab_type":"code","colab":{}},"cell_type":"code","source":["# Load pre-trained model\n","places_model = VGG16_Places365(input_shape=train_input_shape, classes=n_classes)\n","\n","for layer in places_model.layers:\n","    layer.trainable = True\n","\n","#places_model.summary()"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"e0a60b2a913c2d77e9af8580e65af63c08b136af","id":"pkPMPx20ipFQ","colab_type":"code","outputId":"d4cfaa3d-0d87-473b-a391-b959cb5e0c56","executionInfo":{"status":"ok","timestamp":1553753671085,"user_tz":-330,"elapsed":1080,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":1057}},"cell_type":"code","source":["# Add layers at the end\n","X = places_model.output\n","X = Flatten()(X)\n","\n","X = Dense(16, kernel_initializer='he_uniform')(X)\n","X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","output = Dense(n_classes, activation='softmax')(X)\n","\n","model = Model(inputs=places_model.input, outputs=output)\n","\n","model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","input_2 (InputLayer)         (None, 64, 64, 3)         0         \n","_________________________________________________________________\n","block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      \n","_________________________________________________________________\n","block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     \n","_________________________________________________________________\n","block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         \n","_________________________________________________________________\n","block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     \n","_________________________________________________________________\n","block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    \n","_________________________________________________________________\n","block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         \n","_________________________________________________________________\n","block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    \n","_________________________________________________________________\n","block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    \n","_________________________________________________________________\n","block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    \n","_________________________________________________________________\n","block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         \n","_________________________________________________________________\n","block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   \n","_________________________________________________________________\n","block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n","_________________________________________________________________\n","block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n","_________________________________________________________________\n","block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n","_________________________________________________________________\n","block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   \n","_________________________________________________________________\n","block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n","_________________________________________________________________\n","block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n","_________________________________________________________________\n","block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n","_________________________________________________________________\n","flatten_2 (Flatten)          (None, 2048)              0         \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 16)                32784     \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 16)                0         \n","_________________________________________________________________\n","batch_normalization_2 (Batch (None, 16)                64        \n","_________________________________________________________________\n","activation_2 (Activation)    (None, 16)                0         \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 6)                 102       \n","=================================================================\n","Total params: 14,747,638\n","Trainable params: 14,747,606\n","Non-trainable params: 32\n","_________________________________________________________________\n"],"name":"stdout"}]},{"metadata":{"id":"J4S4PZvqJxc4","colab_type":"code","colab":{}},"cell_type":"code","source":["#MY_MODEL_WEIGHTS_PATH = os.path.join(model_dir, \"hybrid1365_vgg16_weights.h5\")\n","#model.load_weights(MY_MODEL_WEIGHTS_PATH)"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"6ce184ce000b658fc951097822dc1c5605152d47","id":"xPxVLN3YipFk","colab_type":"code","colab":{}},"cell_type":"code","source":["#optimizer = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)\n","optimizer = Adam(lr=1e-4)\n","\n","model.compile(loss='sparse_categorical_crossentropy',\n","              optimizer=optimizer, \n","              metrics=['accuracy'])"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"4206d90e8c4dd006d25c29ff9c318fb937826ba2","id":"mDZtRHTiipFu","colab_type":"code","colab":{}},"cell_type":"code","source":["early_stop = EarlyStopping(monitor='val_acc', patience=25, verbose=1, \n","                           mode='auto', restore_best_weights=True)\n","\n","reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.3, patience=5, \n","                              verbose=1, mode='auto')"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"b2cd4cfb400f9d849029593191dd283667cfae9c","id":"Ai4nSL66ipF0","colab_type":"code","outputId":"8b0614b0-3dc6-4ef9-cb3c-4628d4a0999e","executionInfo":{"status":"ok","timestamp":1553770131748,"user_tz":-330,"elapsed":5497817,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":1907}},"cell_type":"code","source":["%%time\n","n_epoch = 50\n","\n","history = model.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n","                              validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n","                              epochs=n_epoch,\n","                              shuffle=True,\n","                              verbose=1,\n","                              callbacks=[early_stop, reduce_lr],\n","                              use_multiprocessing=True,\n","                              workers=10\n","                             )"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py:47: UserWarning: Using a generator with `use_multiprocessing=True` and multiple workers may duplicate your data. Please consider using the`keras.utils.Sequence class.\n","  UserWarning('Using a generator with `use_multiprocessing=True`'\n"],"name":"stderr"},{"output_type":"stream","text":["Epoch 1/50\n","851/851 [==============================] - 122s 144ms/step - loss: 0.7608 - acc: 0.8109 - val_loss: 0.8967 - val_acc: 0.7659\n","Epoch 2/50\n","851/851 [==============================] - 121s 142ms/step - loss: 0.7428 - acc: 0.8220 - val_loss: 0.8058 - val_acc: 0.7886\n","Epoch 3/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.6915 - acc: 0.8398 - val_loss: 0.7715 - val_acc: 0.8172\n","Epoch 4/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.7037 - acc: 0.8448 - val_loss: 0.8128 - val_acc: 0.8154\n","Epoch 5/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.7067 - acc: 0.8411 - val_loss: 0.9129 - val_acc: 0.7594\n","Epoch 6/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.6341 - acc: 0.8664 - val_loss: 0.7690 - val_acc: 0.8140\n","Epoch 7/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.6468 - acc: 0.8583 - val_loss: 0.8116 - val_acc: 0.7907\n","Epoch 8/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.6196 - acc: 0.8707 - val_loss: 0.8807 - val_acc: 0.8131\n","Epoch 9/50\n","851/851 [==============================] - 122s 144ms/step - loss: 0.5781 - acc: 0.8768 - val_loss: 0.8104 - val_acc: 0.7919\n","Epoch 10/50\n","851/851 [==============================] - 121s 142ms/step - loss: 0.6001 - acc: 0.8699 - val_loss: 0.9238 - val_acc: 0.7897\n","Epoch 11/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.6387 - acc: 0.8621 - val_loss: 0.7081 - val_acc: 0.8240\n","Epoch 12/50\n","851/851 [==============================] - 121s 143ms/step - loss: 0.6512 - acc: 0.8591 - val_loss: 0.9128 - val_acc: 0.7901\n","Epoch 13/50\n","851/851 [==============================] - 121s 142ms/step - loss: 0.6768 - acc: 0.8558 - val_loss: 0.8263 - val_acc: 0.8090\n","Epoch 14/50\n","851/851 [==============================] - 123s 144ms/step - loss: 0.6555 - acc: 0.8605 - val_loss: 0.9038 - val_acc: 0.7730\n","\n","Epoch 00014: ReduceLROnPlateau reducing learning rate to 2.6999998226528985e-06.\n","Epoch 15/50\n","851/851 [==============================] - 122s 144ms/step - loss: 0.7697 - acc: 0.8180 - val_loss: 0.7239 - val_acc: 0.8314\n","Epoch 16/50\n","851/851 [==============================] - 124s 145ms/step - loss: 0.6875 - acc: 0.8480 - val_loss: 0.8323 - val_acc: 0.7957\n","Epoch 17/50\n","851/851 [==============================] - 123s 145ms/step - loss: 0.6812 - acc: 0.8484 - val_loss: 0.7381 - val_acc: 0.8237\n","Epoch 18/50\n","851/851 [==============================] - 121s 142ms/step - loss: 0.6709 - acc: 0.8547 - val_loss: 0.7355 - val_acc: 0.8231\n","Epoch 19/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.7452 - acc: 0.8231 - val_loss: 0.7856 - val_acc: 0.8113\n","\n","Epoch 00019: ReduceLROnPlateau reducing learning rate to 8.099999604382901e-07.\n","Epoch 20/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.7395 - acc: 0.8223 - val_loss: 0.6100 - val_acc: 0.8790\n","Epoch 21/50\n","851/851 [==============================] - 121s 143ms/step - loss: 0.8816 - acc: 0.7842 - val_loss: 0.7579 - val_acc: 0.8210\n","Epoch 22/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.9049 - acc: 0.7737 - val_loss: 0.8701 - val_acc: 0.7954\n","Epoch 23/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.8712 - acc: 0.7864 - val_loss: 0.8065 - val_acc: 0.8031\n","Epoch 24/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.7901 - acc: 0.8096 - val_loss: 0.8531 - val_acc: 0.8072\n","\n","Epoch 00024: ReduceLROnPlateau reducing learning rate to 2.4299998813148704e-07.\n","Epoch 25/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.8551 - acc: 0.7852 - val_loss: 0.6374 - val_acc: 0.8452\n","Epoch 26/50\n","851/851 [==============================] - 123s 145ms/step - loss: 0.8560 - acc: 0.7911 - val_loss: 0.7989 - val_acc: 0.8087\n","Epoch 27/50\n","851/851 [==============================] - 123s 144ms/step - loss: 0.9064 - acc: 0.7756 - val_loss: 0.7384 - val_acc: 0.8352\n","Epoch 28/50\n","851/851 [==============================] - 123s 144ms/step - loss: 0.9188 - acc: 0.7697 - val_loss: 0.7117 - val_acc: 0.8373\n","Epoch 29/50\n","851/851 [==============================] - 123s 144ms/step - loss: 0.8954 - acc: 0.7759 - val_loss: 0.8341 - val_acc: 0.7995\n","\n","Epoch 00029: ReduceLROnPlateau reducing learning rate to 7.289999643944612e-08.\n","Epoch 30/50\n","851/851 [==============================] - 122s 144ms/step - loss: 0.8489 - acc: 0.7852 - val_loss: 0.8826 - val_acc: 0.7794\n","Epoch 31/50\n","851/851 [==============================] - 123s 145ms/step - loss: 0.9246 - acc: 0.7644 - val_loss: 0.6678 - val_acc: 0.8499\n","Epoch 32/50\n","851/851 [==============================] - 124s 145ms/step - loss: 0.8639 - acc: 0.7798 - val_loss: 0.7995 - val_acc: 0.8169\n","Epoch 33/50\n","851/851 [==============================] - 124s 145ms/step - loss: 0.8385 - acc: 0.7932 - val_loss: 0.7231 - val_acc: 0.8210\n","Epoch 34/50\n","851/851 [==============================] - 123s 144ms/step - loss: 0.8617 - acc: 0.7897 - val_loss: 0.6689 - val_acc: 0.8514\n","\n","Epoch 00034: ReduceLROnPlateau reducing learning rate to 2.1869998079182552e-08.\n","Epoch 35/50\n","851/851 [==============================] - 123s 144ms/step - loss: 0.8649 - acc: 0.7867 - val_loss: 0.7236 - val_acc: 0.8213\n","Epoch 36/50\n","851/851 [==============================] - 121s 142ms/step - loss: 0.8454 - acc: 0.7999 - val_loss: 0.7283 - val_acc: 0.8255\n","Epoch 37/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.8372 - acc: 0.7936 - val_loss: 0.7911 - val_acc: 0.8269\n","Epoch 38/50\n","851/851 [==============================] - 123s 144ms/step - loss: 0.8528 - acc: 0.7891 - val_loss: 0.7091 - val_acc: 0.8252\n","Epoch 39/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.8513 - acc: 0.7751 - val_loss: 0.8166 - val_acc: 0.7960\n","\n","Epoch 00039: ReduceLROnPlateau reducing learning rate to 6.560999210591944e-09.\n","Epoch 40/50\n","851/851 [==============================] - 122s 144ms/step - loss: 0.8823 - acc: 0.7792 - val_loss: 0.7630 - val_acc: 0.8363\n","Epoch 41/50\n","851/851 [==============================] - 122s 144ms/step - loss: 0.8471 - acc: 0.7816 - val_loss: 0.7580 - val_acc: 0.8314\n","Epoch 42/50\n","851/851 [==============================] - 123s 144ms/step - loss: 0.8517 - acc: 0.7867 - val_loss: 0.6743 - val_acc: 0.8308\n","Epoch 43/50\n","851/851 [==============================] - 121s 142ms/step - loss: 0.8458 - acc: 0.7858 - val_loss: 0.7743 - val_acc: 0.8234\n","Epoch 44/50\n","851/851 [==============================] - 122s 143ms/step - loss: 0.8694 - acc: 0.7789 - val_loss: 0.8063 - val_acc: 0.8042\n","\n","Epoch 00044: ReduceLROnPlateau reducing learning rate to 1.968299789822936e-09.\n","Epoch 45/50\n","851/851 [==============================] - 121s 142ms/step - loss: 0.8634 - acc: 0.7751 - val_loss: 0.7274 - val_acc: 0.8205\n","Restoring model weights from the end of the best epoch\n","Epoch 00045: early stopping\n","CPU times: user 34min 18s, sys: 7min 11s, total: 41min 29s\n","Wall time: 1h 31min 36s\n"],"name":"stdout"}]},{"metadata":{"id":"x8uIuuit8paW","colab_type":"code","colab":{}},"cell_type":"code","source":["model_dir = os.path.join(home_dir, \"models\")\n","model.save(model_dir + '/hybrid1365_vgg16_model.h5')\n","model.save_weights(model_dir + '/hybrid1365_vgg16_weights.h5')"],"execution_count":0,"outputs":[]},{"metadata":{"id":"4hIx3tOL6rlb","colab_type":"code","colab":{}},"cell_type":"code","source":["epoch_count = 0"],"execution_count":0,"outputs":[]},{"metadata":{"id":"Q76LjShCIC72","colab_type":"code","colab":{}},"cell_type":"code","source":["if epoch_count == 0:\n","  acc_hist, val_acc_hist, loss_hist, val_loss_hist = [], [], [], []\n","  \n","epoch_count += 1\n","\n","acc = (history.history['acc'])\n","acc_hist = acc_hist + acc[:len(acc)]\n","\n","val_acc = (history.history['val_acc'])\n","val_acc_hist = val_acc_hist + val_acc[:len(val_acc)]\n","\n","loss = (history.history['loss'])\n","loss_hist = loss_hist + loss[:len(loss)]\n","\n","val_loss = (history.history['val_loss'])\n","val_loss_hist = val_loss_hist + val_loss[:len(val_loss)]\n","\n","epochs = range(len(acc_hist))\n","\n","fig, axes = plt.subplots(1, 2, figsize=(15,5))\n","    \n","axes[0].plot(epochs, acc_hist, 'r-', label='Training Accuracy')\n","axes[0].plot(epochs, val_acc_hist, 'b--', label='Validation Accuracy')\n","axes[0].set_title('Training and Validation Accuracy')\n","axes[0].legend(loc='best')\n","\n","axes[1].plot(epochs, loss_hist, 'r-', label='Training Loss')\n","axes[1].plot(epochs, val_loss_hist, 'b--', label='Validation Loss')\n","axes[1].set_title('Training and Validation Loss')\n","axes[1].legend(loc='best')\n","\n","plt.show()"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"898075f768db94965133df52fe650334f744541d","id":"8-bDEvFOipF-","colab_type":"code","outputId":"b6694aa5-77f7-4416-8ca6-e5c760c6f105","executionInfo":{"status":"ok","timestamp":1553770942415,"user_tz":-330,"elapsed":1963,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":335}},"cell_type":"code","source":["# Plot the training graph\n","def plot_training(history):\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","    epochs = range(len(acc))\n","\n","    fig, axes = plt.subplots(1, 2, figsize=(15,5))\n","    \n","    axes[0].plot(epochs, acc, 'r-', label='Training Accuracy')\n","    axes[0].plot(epochs, val_acc, 'b--', label='Validation Accuracy')\n","    axes[0].set_title('Training and Validation Accuracy')\n","    axes[0].legend(loc='best')\n","\n","    axes[1].plot(epochs, loss, 'r-', label='Training Loss')\n","    axes[1].plot(epochs, val_loss, 'b--', label='Validation Loss')\n","    axes[1].set_title('Training and Validation Loss')\n","    axes[1].legend(loc='best')\n","    \n","    plt.show()\n","    \n","plot_training(history)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA20AAAE+CAYAAAAed/i6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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EEhAZSHmm1wk8/FWOx6M1rOjo6Zx4JCRVBQiSw2eDgQZHmzeWgBIeDRQvatExbRoaMwaD4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\njKwmMZtJoqBNG8GaUKAJVXcG2zxPqvnuBI4aNABatOBx7BjnN6ulBQ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BhmDlzJgAgNDQU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\nCRrMQ2/EUS8R47dfQ2GhX8bUbdpQJPWvlka6Y9kykXHjjOzZE5a3kSoq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0MM0na1bt6qm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\nAAAAAEyMog0AAAAATOz/AQp5cn2L1FCHAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1080x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"_uuid":"cd4bf4945a12d11ce13c9cda6befe55c392c0d8d","id":"NQtv0MCgipGI","colab_type":"code","outputId":"81932a0f-ed62-4411-cbf0-e4cfebf70d7a","executionInfo":{"status":"ok","timestamp":1553770990720,"user_tz":-330,"elapsed":25896,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["# Evaluate on validation set\n","result = model.evaluate_generator(generator=valid_generator, verbose=1, steps=STEP_SIZE_VALID)\n","result"],"execution_count":0,"outputs":[{"output_type":"stream","text":["212/212 [==============================] - 24s 115ms/step\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[0.7996667413233304, 0.8126843657465459]"]},"metadata":{"tags":[]},"execution_count":38}]},{"metadata":{"_uuid":"cd427b2643d0a1e94227977ecf9b579415f9f7d4","id":"ATjwrbViipGR","colab_type":"code","outputId":"dbb6b1f7-48b2-4b74-8de7-7e6b938fda1e","executionInfo":{"status":"ok","timestamp":1553771030543,"user_tz":-330,"elapsed":28250,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":597}},"cell_type":"code","source":["# Classification report and confusion matrix\n","from sklearn.metrics import *\n","import seaborn as sns\n","\n","def showClassficationReport_Generator(model, valid_generator, STEP_SIZE_VALID):\n","    # Loop on each generator batch and predict\n","    y_pred, y_true = [], []\n","    for i in range(STEP_SIZE_VALID):\n","        (X,y) = next(valid_generator)\n","        y_pred.append(model.predict(X))\n","        y_true.append(y)\n","    \n","    # Create a flat list for y_true and y_pred\n","    y_pred = [subresult for result in y_pred for subresult in result]\n","    y_true = [subresult for result in y_true for subresult in result]\n","    \n","    y_true = np.asarray(y_true).ravel()\n","    \n","    # Update Prediction vector based on argmax\n","    #y_pred = np.asarray(y_pred).astype('float32').ravel()\n","    #y_pred = y_pred >= 0.5\n","    #y_pred = y_pred.astype('int').ravel()\n","    y_pred = np.argmax(y_pred, axis=1)\n","    y_pred = np.asarray(y_pred).ravel()\n","    \n","    # Confusion Matrix\n","    conf_matrix = confusion_matrix(y_true, y_pred, labels=[0,1,2,3,4,5])\n","    sns.heatmap(conf_matrix, annot=True, fmt=\"d\", square=True, cbar=False, \n","                cmap=plt.cm.gray, xticklabels=labels, yticklabels=labels)\n","    plt.ylabel('Actual')\n","    plt.xlabel('Predicted')\n","    plt.title('Confusion Matrix')\n","    plt.show()\n","    \n","    print(classification_report(y_true, y_pred))\n","    #print(\"\\nAUC: \", roc_auc_score(y_true, y_pred, average='micro'))\n","\n","showClassficationReport_Generator(model, valid_generator, STEP_SIZE_VALID)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAVgAAAFnCAYAAADjQFE4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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FsW3b\nNiZMmICZmen/q7m7u3P9+nWqVq0KwNSpUzl06BDR0dFMmTJF5XTPRu3/DdP/qxeRwMBA0tPTH3lb\nw4YNee+994o5UdGwtrZmzpw5JeI6TVlZWaxateqh65TFxMRw9uxZw7ZOp2PmzJn4+vqyZs0a3Nzc\nWLhwYXHHfaodO3bg6elp+FqyZAk7duxg6NChWFhY4OnpSffu3WnQoAHe3t5qx30ia2trpk6darhO\nVo8ePWjVqhVt2rShTZs2tGrVCi8vL5VTFo4p/G+8NAXWz8+P2bNnP7Rfo9GwdOlSpk+frkKq5zdj\nxgzWrl3L3bt31Y7yVAEBAbi5uVG6dGnDvqysLH7++Wf69+9v2Hf58mV0Oh0tWrQAwNPTkzNnzpCW\nllbsmQvK0tKSzz//nPnz5+Ps7Myff/6JXq9Hp9Nx7Ngx6tatq3bEJxo/fjy///67oRPy3nvvsWHD\nBnJyctDpdGzcuJFu3bqpnLJwTOF/Q9ECe/LkSSWfvlBiYmIeuX/o0KHEx8fz559/FnOi59egQQPc\n3NxYtGiR2lGeKjExkWPHjj3Uk1u7di0dOnTAwcHBsE+j0aDX6w3bWq0WCwsLkxwmeKBXr14cO3aM\ny5cvEx0djbu7O1ZWVpQtW5ZWrVpx4MABtSM+Vv369Wnbti3Lly837KtVq5bR1YQvXLhA7dq11Yj3\nTEzlf0PRAuvv709ubq6STTwXBwcHRo4cyVdffaV2lGeyfPlyRo0aZdK/YwC9Xs/ixYsZMWIE5ub/\n/0rxiYmJHDlyhPfff9/o/lWrVsXKyoqdO3cCsHPnTu7du0dOTk6x5i4ojUbDoEGD+M9//gPAb7/9\nhrm5OdHR0URHR3Px4kX27duncsrHW7BgAZMmTTJ6HVlbW5OdnW3YzsrKMvrkYepM5X/D/Ol3eXal\nS5emY8eO1KtXDwsLC8P+xYsXK9lsgc2fP59vvvmG27dvU758ebXjFMqwYcM4deqUSfeMHti2bRvV\nq1enQYMGhn16vZ6lS5c+VHTh/gHJ6dOns2zZMjZs2EDr1q1xcnKiTJkyxR29QN544w0yMjI4d+4c\nAOPGjSMpKYkhQ4Zgbm7OokWLGDJkCCtXrlQ56cMGDBjAmTNnHvqEl5GRgZWVlWHb2tqae/fuFXe8\nZ2JK/xsa/f9+Fitij/vY3axZs6c+9n//uEUpOzubmjVrkpyczPXr1w0vGq1WS6VKlfj333+pU6dO\nkfWWdDpdkTzP/xUSEsJbb71Ffn4+AHZ2dty8eZPevXuzd+/eIm3rfz8qPouvvvqKM2fOGI6k37lz\nh/z8fKytrSlVqhQAubm5ZGZmUrVqVX788Uejx2dlZdG/f39+++03LC0tnzlHx44dn/2HeIIvvvgC\njUbDt99+C8Aff/zBnDlzOHjwIHB/+MDd3Z2PP/64SNoryoM269evp0mTJobXka2tLbdu3QJg7ty5\n/PLLLwAMGTKEpk2bFtnPABhfwDo9AAAgAElEQVQOqBW14vzfAAztPIqiPVhXV1fCwsJITU1l8ODB\nnDlzhtdee03JJgvF1tbW8H316tXZuXMnzs7OKiYquK5duxptnz9/nnfffZeLFy+qlOjxvvnmG6Pt\n/v37M3/+fBwdHQ37/v77bwICAliwYAH5+fl89tlnfP755zg7O7Np0yaaN2/+XMVVSfXq1SM0NNSw\nnZiYyLvvvsvBgwcxMzPjnXfeMZolYUr69u1rtH3s2DG6detGkyZNGDNmDP/973/RaDR8+OGHD/0d\nTZUp/W8oOgY7depUTp06RVhYGHC/RzthwgQlm3wke3t7YmNjiY2NBSA8PJzY2FheffXVYs8ins7M\nzAxfX1/mzJlD//79OXfuHMOHD1c71mM5Ojpy/fp1w7afnx9Vq1Zlx44dbN++HZ1OZ3QAqST4448/\n2L17N3v37mX37t2EhISwY8cOtWOVOIoOEQwcOJDVq1fTv39/AgICAPjggw9Yu3btUx+r1BBBcVNq\niKA4Pe8QgalQaoiguJWEOc8FodQQQXF70hCBoj1YnU5HWloaGo0GgISEBJM9EiyEEEVN0THYMWPG\nMGDAAC5cuICHhwcajYavv/5aySaFEMJkKFpg33rrLYKDg7lx4wYWFhaUK1dOyeaEEMKkKFJgp0+f\nzsyZM+nZs6dheOB/bdq0SYlmhRDCpChSYEeOHAnAkiVLlHh6IYQoERQ5yPVgfmlycjIrVqygSpUq\nVKlShTlz5nDlyhUlmhRCCJOj6CyChQsXMnToUMP2jBkzDGe7CCHEi+6xQwRPmtsFFGgB4by8PKpV\nq2bYrlSpUiGiCSFEyfbYAvv6668bDlA9OBfhwTJyGo2GU6dOPfXJO3bsiI+PD40aNSI/P5/jx4+X\nuDUlhRDiWT3TmVwXLlygRo0aBbrvxYsXOXnyJObm5rz++uukp6cX6Hx/OZPLdMiZXKZFzuQyLc+1\n2EteXh779+83rLCTk5PDihUr2L1791MbTktLM3rsiRMn2Lx5s0mvjSmEEEXlqQV23Lhx3Llzh9On\nT+Pq6srff/9tmIb1NJ9//jlvvPEG27Zto3fv3uzbt4+pU6c+d2ghhCgJnnqkKiUlhVWrVvHaa6+x\nZMkS1q1bR1xcXIGePD8/n1GjRmFvb8+gQYP4+eefCQoKeu7QQghREhR4mlZubq7hktcPVm5/Gp1O\nxz///EOpUqU4cOAAKSkpXLp06ZnDCiFESfLUIYIWLVrw888/4+bmRo8ePXBycnrqFK4Hpk2bxs2b\nNxk7dqzh0iwffvjhc4cWQoiSoECzCPLy8tBqtRw7dowbN27QqlWrJ14ALTMz86F9/zvVy9ra+qnB\nZBaB6ZBZBKZFZhGYlueaRfCohVlCQ0Pp1avXYx/TpUuXRy7yAvcL7K5du57WrBBClHhPLbBHjx41\nfJ+Tk0NsbCyurq5PLLAPpnB16NCB/9tBNjMzY/DgwXzxxRe4uLg8a24hhDB5Ty2wc+bMMdrOzMxk\n0qRJBXpyHx8fbGxs6NChAwCRkZHcvHmT5s2b8/XXX7N+/fpniCyEECVDoRd7sba2LvBMgMjISHx9\nfXFwcMDBwYH333+fAwcO0KRJk0IHFUKIkuapPVhfX1+j8dTU1FTq1q1boCe3srLCz88PV1dXzMzM\nOHHiBDqdjgMHDjzxIJkQQrwInjqL4M8///z/d9ZoKFu2LPXr1y/Qk6enp7N582YSEhLQ6/VUq1aN\nHj16kJmZiY2NDTY2No99rLm5olezKTZ5eXlqR3huDg4OakcoEikpKWpHKBIP1lsu6WQWARAUFIS/\nv7/RvsGDB7Nq1aqnNly2bFk++OCDh/ZXrFjxqY8VQoiS7rEFduvWrWzYsIGzZ8/Sr18/w36dTsf1\n69eLJZwQQpRkjy2w7733Hs2bN2fs2LFGi7uYmZlRu3btYgknhBAl2RNnETg4OLBixQquX79Os2bN\naNasGWfPnsXCwqK48gkhRIn11GlaEydONBoSyMrKYvz48YqGEkKIF8FTC+z/XaDlo48+Ii0tTdFQ\nQgjxInhqgdXpdCQkJBi24+LiXogFTIQQQmlPnaY1adIkPv30U+7evUt+fj4VK1Zk3rx5xZFNCCFK\ntAJf9PDq1avExMQQHBxMQkIC+/fvVzSYnGhgOuREA9MiJxqYluc60eCvv/4iKCiI0NBQ8vPzmT17\n9guzrqYQQijpsWOwP//8M507d2bMmDFUqlSJwMBAqlWrRpcuXWSalhBCFMBje7DfffcdtWvXZtq0\nabRo0QLgsYtoCyGEeNhjC+zevXsJDg5m+vTp5Ofn06NHD5k9IIQQhVCgg1yHDx8mMDCQHTt20Lx5\nc/r27Uvbtm0VDSYHuUyHHOQyLXKQy7Q86SBXgWcRwP3lB0NCQggKCmLjxo1FEu5xpMCaDimwpkUK\nrGkpsgJbnKTAmg4psKZFCqxpeVKBLfQlY4QQQhSMFFghhFDIS1lgBwwYQGxsLHFxcYSFhVGnTh21\nIz0Tc3NzFixYgF6vp0qVKmrHKTQ3NzdSUlKoWrUqP/zwA1FRUYavv//+m5UrV6od0UhSUhIuLi54\neHgYvh6sLHfjxg0++ugj3N3djR6Tnp7O2LFj8fDwoFOnTixevFiN6E/UtWtX9uzZQ3R0NCEhIdSr\nVw+tVsusWbOIjo7m+PHjfPbZZ2rHLBRvb2+OHTvGyZMniYyMxMXFRZUcL8ZAZyE4Ozszd+5cXF1d\nuXLlCsOGDWPlypWKz4pQwpYtWzh8+LDaMZ6JtbU1X331lWEcbsSIEUa3//bbb/z3v/9VI9oTOTg4\nEBYWZrTv9u3bfPDBB7Rp04akpCSj2xYuXIiFhQWhoaFkZGTQvXt33nrrLVq1alWcsR+rSpUqLFiw\nADc3N5KSkhg2bBhLlixh/fr1uLq60q5dOywtLdmxYwdHjhzh0KFDakd+qqpVq7J8+XKaNm3KpUuX\nGDVqFKtWrTLM5y9OL10Ptn79+pw7d44rV64AsGfPHtXe3Z7X7NmzmTFjhtoxnsnYsWPZtGkT9+7d\ne+i29u3bY2lpSXh4uArJCk+j0fDDDz/Qvn37h25zd3dn1KhRmJmZUbZsWerVq8fZs2dVSPloubm5\nfPzxx4Y3hsjISGrXrk3btm0JDAwkOzubu3fvsn79erp27apy2oLR6XT069ePS5cuARAREYGzs7Mq\nWRTvwf7zzz+kp6fzv5MVmjZtqnSzjxUTE0PNmjVxcXEhPj4eb29vdu3apVqe51ESehOPUq9ePdq0\naYOnpycDBw586PZx48Yxa9as4g9WAOnp6Xz66aecP3+eKlWqMHnyZGrVqkX58uX5999/H7p/y5Yt\njR57/PhxBg8eXJyRnyg1NZXU1FQAtFotffv2Zfv27ZQuXRqtVmu4X3p6utHPYspSUlIMM0a0Wi0D\nBw5ky5YtqmRRtMAOGzaMO3fuGE3z0Wg0qhbYq1evMmXKFI4ePcrdu3e5d+/eI3seQjnz5s3jq6++\nIjc396HbWrVqhUajITo6WoVkT1amTBm6du3KoEGDePXVV1m9ejWffvop27Zte+q0wpycHL788kva\nt2/PG2+8UUyJC27YsGGMHTuWxMREPvzwQzw9PRkwYAAbN25Eq9Xi4+NDRkaG2jELZdSoUUydOpVz\n587Ro0cPVTIoWmDT0tJMbhytSZMmTJo0iTp16nD58mV8fX0JDg6mcePGakd7KfTv358zZ87w559/\nPvL2Hj16EBwcXMypCqZixYpMmzbNsP3RRx/xww8/cOHChSdeCPTevXuMHDkSBwcHZs6cWRxRC+2n\nn37ip59+wtvbm9DQUNq2bUuNGjXYuXMnqamp7Nu3j7p166ods1CWLFnCkiVL6NOnDwcOHMDFxYWs\nrKxizaDoGKyrq6tJjTfB/fG96OhoLl++DMDGjRtxcXF5YSZvm7oHR99jY2OJjY3l1VdfJSwszHDQ\nx83NjYiICJVTPtqdO3cMr5sH8vPzn9h7zc3N5bPPPqN27drMmTMHMzPTOuxRp04d2rRpY9gOCgrC\nxsaGGjVqMGPGDFq0aEG3bt3Izc3l1KlTKiYtuHr16tGhQwfD9oYNGyhXrpwq47CK/rV37drFe++9\nR9OmTWnRogUtWrRQfRznzJkztGzZkkqVKgHg6enJ1atXjS7sKJTTr18/GjRoQKNGjWjUqBFXrlzB\nw8ODAwcOYGtri62trdElikxJXFwcAwYMMMx82LhxI5UrV6Zq1aqPfUxAQABlypRh8uTJxRWzUGxt\nbVm2bBmOjo4ANGvWDAsLC1xcXPj555/RaDQ4OjrSp08fNm3apHLagrGzs2PNmjVUrlwZgLfffhsL\nCwvOnz9f7FkUHSLYuXOnkk//TEJCQnB1dWX//v3o9XrS0tLo06eP2rEKzd7enn379hm29+7dS25u\nLh06dDDMkChpKleuzI0bNzDRs7dp3bo1vr6+9O3bF41Gg4ODA0uXLmXfvn3MmzePrKwsrl+/joeH\nBw4ODqxZs4YNGzaQmZmJh4eH4Xk8PDwYPXq0ij/J/xcdHc3ChQsJDAzEzMyM7Oxshg4dysGDB/H0\n9OTIkSPk5eUxe/ZsEhMT1Y5bIFFRUfj5+REeHm74mfr27cvdu3eLPYsiaxF8//33fPbZZ4waNeqR\na8gWZLK1rEVgOmQtAtPyogxnvQxrEShSxdzc3AD44IMPHrpNPooLIV4WiozB1qtXD7h/kCsjI4Mr\nV65w5coVLl68yMKFC5VoUgghTI6in8NHjx5NmTJl+PPPP2nfvj0xMTEl7pxmIYR4VorOIrhz5w5z\n587FycmJqVOnsm7dOqMDM0II8SJTtMDqdDqSk5PRarUkJiZiaWlZYo5ECiHE81J0iODzzz8nLi6O\nTz/9lKFDh5Kenk6/fv2UbFIIIUyGogU2KyvLMP/vwYIqISEhSjYphBAmQ5EC+2Ax619//dVo0nte\nXh4rV64sMcueCSHE81CkwNrZ2VG6dGl0Oh23bt0y7NdoNPj7+yvRpBBCmBxFryp78+ZNLC0tuXv3\nrtHpj6+++upTHytncpkOOZPLtMiZXKal2M/kemDRokXs27cPe3t7APR6PRqNpsQsGiGEEM9D0QIb\nHx/Pvn37HrkegRBCvOgUnQdbr149ozFYIYR4mSjag718+TJubm5Ur14drVYrQwRCiJeKogVWZgwI\nIV5mihbY77///pH758yZo2SzQghhEhQtsJ06dTJ8n5uby9GjR7GwsFCySSGEMBmKFth27doZbbu5\nuTF06FAlmxRCCJOhaIH9v0sTXrt27aGrcgohxItK0QIbFhZmtF22bFkWLFigZJNCCGEyFD1VFu5P\n1frnn38wMzPj9ddfN1xK92nkVFnTIafKmhY5Vda0POlUWUUL7MqVKwkNDcXV1ZWcnBzi4uJ4//33\n8fX1fepjLS0tlYpVrF6EAlumTBm1IxSJF+VN++TJk2pHKBJOTk5qRygSubm5j71N0Vfcrl27+P33\n39FqtYYgH3zwQYEKrBBClHSKnioLYGZmZvS9rEsghHhZKNqD7dy5Mz179qRx48bo9Xr++usvfHx8\nlGxSCCFMhqIFtkmTJhw6dIjIyEju3LmDu7s7cXFxSjYphBAmQ9ECO27cOIYOHSq9ViHES0nRAluz\nZk169uwp465CiJeSogW2a9eudO/eHWdnZ8NMApDFXoQQLwdFC+x3333HsGHDsLOzU7IZIYQwSYoW\n2Fq1avH+++8r2YQQQpgsRQtsxYoV6devHw0aNDAaIhg/frySzQohhElQtMA2a9aMZs2aKdmEEEKY\nLEULbI8ePZR8eiGEMGmKnyorhBAvKymwQgihECmwQgihECmwQgihECmwQgihkJemwJqbmzN37lxy\ncnKoUqWKYb+dnR2hoaElbpX46tWrk5WVRXx8vOFr9erVascqME9PT/bv38/hw4fZsWMH9evXx8zM\nDH9/f44ePcrhw4dZtmyZSV9NwcvLi3379nHo0CFCQ0OpX78+ffv25cKFCxw6dMjwNWTIELWjPlZ0\ndDRt27bl6tWrAERGRuLr60ufPn2YOnUq9+7dM7p/ZmYmvXv35pdfflEjboENGDCA2NhY4uLiCAsL\no06dOqrkeGkKbGBg4EMvlooVKxIREcGJEydUSvV8kpOTcXFxMXwNHDhQ7UgFUrlyZVasWMHgwYNp\n2rQpv//+O4sXL6Z///40btyYli1b0qxZM6ysrPjiiy/UjvtIVapU4dtvv6Vfv360aNGCLVu2sGTJ\nEgBCQkJo0aKF4WvlypUqp320rKwsfvzxR8qVKwfA1atXWbRoEfPmzWP9+vXY29tz8OBBo8eUhDdx\nZ2dn5s6di4eHBw0bNiQoKEi1v8FLU2D9/PyYNWuW0T69Xk+vXr0ICQlRKdXLSafTMXjwYE6fPg3c\n70XVq1cPFxcXDh06RE5ODnq9nqioKOrXr69y2kfLzc1l2LBhJCUlAfd7fmr1kp7VL7/8QseOHSld\nujQAO3fupG3btjg5OaHRaBg5ciTu7u6G+yckJHD06FGjfaaofv36nDt3jitXrgCwZ88eXFxcVMny\n0hTYmJiYh/bdvn2bM2fOqJCmaJQrV46goCDi4+MJDQ2lXr16akcqkOvXr7Nr1y7Dtru7O0ePHmXv\n3r24u7tToUIFrKys8PDwYM+ePSomfbzU1FT27t0LgFarpW/fvoSGhgLQsGFDtm7dyp9//smSJUuw\nsbFRMemjJSQkcOTIEaO1mhMSEjA3N+eLL76gX79+fPvtt2RlZQH3OyMLFy5k9OjRRqe9m6KYmBhq\n1qxpKKre3t5Gr7fiVKwF9vr168XZ3Avt7t27rF+/njFjxtCgQQPCw8MJDg42+Rf//9W2bVtGjBjB\nxIkTCQ0N5cSJE5w9e5bExETKly9v8h9JP/74Y06fPk3Lli2ZOXMmCQkJbN++HV9fX9q2bYuNjQ1+\nfn5qxzTyoFh+/vnnRlfaTU9P58iRI0ydOpWVK1eSnJzM2rVrAdi6dSvVq1enQYMGasUusKtXrzJl\nyhSOHj3Kv//+y/Dhw5k8ebIqWRQrsLm5uezdu5fNmzezefNmfv/9d/r06aNUcy+dmzdvMmrUKC5e\nvIher2fRokU4ODhQt25dtaMVWJcuXVi+fDk+Pj6cPn2aTz75BFtbW6pVq0bVqlX5559/mDt3rtox\nn+jHH3+kdu3aLF++nLCwMGJjY/H39yc9PZ3MzEwWLVpEx44d1Y5p5I8//qB69eo0atTIaH+ZMmV4\n5513qFixItbW1nTv3p3Dhw9z8+ZNfv/9dz755BOVEhdOkyZNmDRpEnXq1MHOzo7JkycTHBysShbF\n1iIYPXo0ZcqU4c8//6R9+/bExMTw2WefKdXcS6dChQpUqFCBCxcuGPZptVp0Op16oQqhXbt2zJ07\nl+7duxuGadq3b88ff/xBZmYmAFu2bDHZAlu3bl0qV67Mvn37AAgKCmLevHk0adKEs2fPcuPGDeD+\n7BVT+5vs37+f06dPG9YKuX37Nh9//DF2dnbY2toa7mdmZoaZmRlHjx7l1q1bDBgwAMDw97l58yZf\nfvll8f8AT9G+fXuio6O5fPkyABs3buTXX3/F1ta22D9FK9aDvXPnDnPnzsXJyYmpU6eybt06w4tR\nPL+mTZuya9cuwz/E0KFDuXTpEufPn1c52dNZW1uzbNkyPvjgA6Mx8LNnz+Lu7m4Y5ujUqZPJTp97\n5ZVXWLZsGY6OjgA0b94cCwsLunbtynfffYe5uTlmZmYMHTqU8PBwldMamzdvHlu2bCE4OJjg4GDs\n7e358ccf+eKLL9izZw/Xrl0jLy+Pbdu28dZbb+Hu7s62bdsM9+/duze9e/c2yeIKcObMGVq2bEml\nSpWA+1MCr169qsoQpWI9WJ1OR3JyMlqtlsTERCpXrkxiYqJSzT2Rvb09ERERhu3w8HDy8vKYN28e\n48ePx9raGkdHR+Li4khOTsbDw0OVnIURHh7O8uXLiYqKIj8/n+TkZN5//33y8/PVjvZUXbp0wdbW\n9qGpM56ensyZM4ejR4+Sn5/PuXPn+Pzzz1VK+WTR0dEsXLiQ4OBgNBoNOTk5DB48mAMHDjB//nyi\no6PJz8/n8OHDTJs2Te24BfJgqt/IkSPRarU0atQIX19ftWMVWkhICK6uruzfvx+9Xk9aWppqw5Ma\nvV6vV+KJo6OjuXPnDpUqVWLy5Mmkp6fTr18/Ro4cWaDHW1paKhGr2OXl5akd4bmZ8mT/wvjfAzol\nman26gvLyclJ7QhFIjc397G3KVZg/7fxZ3lhS4E1HVJgTYsUWNPypAKr2BhsTEwM7733Hl27dgVg\n0aJFREVFKdWcEEKYHMUK7JIlS1izZo3hirIffvgh33//vVLNCSGEyVGswJqbm1OxYkU0Gg1w/6jr\ng++FEOJloNiglJOTE4sXL+bWrVuEhoYSERFR4s7VFkKI56FYD3bEiBFYWFjw5ptv8u233xIfH8+H\nH36oVHNCCGFyFCuw48eP591336V79+68+uqrfPPNN/j7+yvVnBBCmBzFCqxWq6V+/frs2LGDgQMH\n8uabbz5xOoMQQrxoFCuweXl5LF++nN27d9O6dWtiY2PJyMhQqjkhhDA5ihXY+fPnY21tzffff4+V\nlRVJSUnMnDlTqeaEEMLkKH4m17OSM7lMh5zJZVrkTC7TosqZXEII8bKTAiuEEAqRAiuEEAqRAiuE\nEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAox2VNlq1atqnaEInH16lW1Izy3\nF+VKFC/Cacvw4pzym56ernaEIvGk0/qlByuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuE\nEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqRAiuEEAqR\nAiuEEAqRAiuEEAqRAiuEEAp5KQqsk5MT58+fZ8+ePYavRYsWMXnyZKN9hw4dYtu2bWrHLRBvb29O\nnDhh9JWbm0vZsmXVjlZoH3zwAX/99RcJCQmsXr36iZfgMGXe3t4cO3aMkydPEhkZiYuLi9qRCszc\n3Jy5c+eSk5NDlSpVDPvt7OwIDQ3l5MmTKqZ7usjISBo2bEhycjIZGRlMmTIFLy8vPDw8+OOPPwz3\nO3HiBP369cPT05P+/fuTnJysaK4X4+I+BZCSksK777770H4/Pz/D99988w1nz54tzljPLCgoiKCg\nIMN2r1698PHxKXHXOXJxcWH+/Pk0bdqUpKQkfv31V8aOHWv0dykJqlatyvLly2natCmXLl1i1KhR\nrFq1ihYtWqgdrUACAwM5evSo0b6KFSsSERFBWFgYNWrUUCdYAWRmZrJo0SLKly8PwIoVK8jMzGTL\nli1cu3YNX19f3njjDRwcHBg9ejRTp06lbdu2bNy4kenTp7Ny5UrFsr0UPdiCcHZ2pnnz5gQEBKgd\npdCsrKyYNWsWEydOVDtKob377rvs2bOHpKQkAJYuXYq3t7fKqQpPp9PRr18/Ll26BEBERATOzs4q\npyo4Pz8/Zs2aZbRPr9fTq1cvQkJCVEpVMMuXL8fLy4syZcoAcOjQIbp164aZmRmOjo60b9+ePXv2\ncP78eXQ6HW3btgWgZ8+exMfHc+fOHcWyvTQF1sbGhpUrV7Jnzx4CAgKoXbu20e2jR49mxYoVJfLK\no4MGDeLgwYOcP39e7SiFptfr0Wq1hu309HRq1aqlYqJnk5KSwq5duwDQarUMHDiQLVu2qJyq4GJi\nYh7ad/v2bc6cOaNCmoI7c+YM0dHR9O/f32h/fn6+4fvSpUtz6dIlNBqN0X6tVoulpaXhzV0JihXY\n3Nzch/bdvn1bqeaeKD09nc2bNzNjxgzat29PVFQUq1atMvxj16hRA1dXVzZv3qxKvueh0WgYM2YM\nCxcuVDvKM9m9ezdubm64uLig1WoZPnw4pUqVUjvWMxs1ahQpKSm0bt26RH6iKEn0ej2zZ89m0qRJ\nWFhYGPa3bNmSDRs2kJ2dzdWrV4mIiCAnJ4fXXnuNUqVKGf7Pt2zZQlpaGtnZ2YplLPICm5ubS0ZG\nBgMHDiQrK4vMzEwyMzO5e/fuQ+8yxeX27dtMnTqVpKQk9Ho9P/30E7a2ttSsWRMALy8vwsLCHvmm\nYOpatmxJenq6yR+EeJxTp04xevRo1q5dy8GDBzl16pRqb8RFYcmSJdjZ2bF48WIOHDhQot8sTN3v\nv/9OrVq1cHV1Ndr/8ccfY29vT8+ePZk1axatW7fGxsYGCwsLvvvuOwIDA/Hy8uLChQvUqFEDGxsb\nxTIW+UGuyMhIfvnlF2JjY+ncubNhv5mZGc2aNSvq5gqkfPnylCtXjsuXLxv2abVadDodAB06dOC7\n775TJdvz6tKlC2FhYWrHeC4BAQGGse/WrVtz4sQJlRMVXr169ahSpQoREREAbNiwgaVLl+Ls7Mzf\nf/+tcroX0549ezh58iR79+4F4NatW/Tt25cFCxYYjSdPnTqVt956C7h/UPXBay0zM5PAwECqVaum\nWMYi78G2b9+egIAAZs+eze7duw1fu3btUu3IcOPGjdmwYQOVKlUCwNfXl+TkZMMBifr165eY2QP/\nV6NGjTh16pTaMZ5ZrVq1OHLkCOXLl8fc3JyJEyfy66+/qh2r0Ozs7FizZg2VK1cG4O2338bCwqJE\njouXFMuXL2ffvn3s3buXvXv34ujoyPr164mLi2P+/PkAJCQkcOjQId59913y8/Px8fExvIGvWbOG\nNm3aYGVlpVhGxaZpNWvWjKlTp3Lnzh2WLFnCtm3baNKkidEcu+ISGRnJr7/+SnBwMPn5+aSkpPDx\nxx+Tn59PhQoVKF26NP/++2+x5yoKVapUITU1Ve0YzywhIYGtW7dy9OhR9Ho9//3vf0vkTI6oqCj8\n/PwIDw/HzMyM7Oxs+vbty927d9WO9lT29vaGnjdAeHg4eXl5zJs3j/Hjx2NtbY2joyNxcXEkJyfj\n4eGhYtqn69atG+PHj7plDMEAABDkSURBVMfDw4NSpUrh5+dHuXLlgPvDBxMmTCA3N5d69erx9ddf\nK5pFo9fr9Uo88eDBg/nwww/5+eefWbt2LdHR0SxbtqzA/zxVq1ZVIlaxu3r1qtoRnptGo1E7QpEo\niTNEHsXc/MWYvl7S5mw/zpNOjFFsFkF+fj5t27Y1/HO2bNkShWq5EEKYJMXeCs3NzYmOjiY/P5/r\n168THh6u6FiHEEKYGsWGCK5du8bixYs5fvw4lpaWNGrUiM8++wx7e/sCPV6GCEyHDBGYFhkiMC1P\nGiJQrMAC5OTkcO3aNZycnAr9WCmwpkMKrGmRAmtaVBmD3bZtG97e3nzyyScAfP311yXyTCkhhHhW\nihXY3377jaCgICpWrAjAuHHjWLdunVLNCSGEyVGswD5YSOHBx8uSusanEEI8K8UGc1xdXRk3bhyp\nqan89NNP7Nmzh7ffflup5oQQwuQodpBLr9dz9OhRjh8/joWFBY0bN+aNN94o8OPlIJfpkINcpkUO\ncpmWJ306V+wv1b9/f9auXWtYZEEIIV42ihXYKlWq8OWXX9KwYUOjtRr79eunVJNCCGFSFCuwDz7i\nvygfA4QQorAUK7BmZmZ8+umnRvv8/f2Vak4IIUxOkRfYnTt3EhISwpEjRzh9+rRhf15eHidPnpTL\naAghXhpFXmA7duzI66+/zuTJk3FxcaFJkyZcuXKF4OBgwyK4QgjxMlDkRAMnJyfy8vJ45513sLS0\nJCgoiFGjRrFs2TIlmhNCCJOk2Jlc5ubm1K9fnx07djBgwADefPPNEnlRQSGEeFaKFdi8vDyWL1/O\n7t27ad26NbGxsWRkZCjVnBBCmBzFCuz8+fOxtrbm+++/x8rKiqSkJGbOnKlUc0IIYXIUXQ/2ecip\nsqZDTpU1LXKqrGlRZT1YIYR42ZlsD1YIIUo66cEKIYRCpMAKIYRCpMAKIYRCpMAKIYRCpMAKIYRC\npMAKIYRCSlyBDQoKYu7cuU+9X2RkJOvWrSMpKQlvb++Hbp87dy5BQUGG+5kSnU7H+++/z4QJE4r0\necPCwor0+Qqqffv23Lt3r8D3//fff5k2bZqCiYrXlStXiI2Nfeztp06dYsmSJcWY6MWyY8eO536O\niIgIcnJyiiCNsRfjlJBHaNOmDQBJSUkFup8p+ffff8nJySnQG0lh/PTTT3h4eBTpcyrBzs6OWbNm\nqR2jyBw6dIiMjAwaNWr0yNvr169P/fr1iznViyEpKYlt27bRqVOn53qe1atX06JFiyeelfUsSmSB\nTUpKYujQoaSkpDBgwACWLVvGH3/8QZkyZZg7dy516tQB4OzZs0bXANuyZQsrV67EwcGBUqVKUadO\nHYKCggz3mzhxIlWrVuX06dPUr1+fb775hn/++YeJEydiY2NDgwYNuHXrFrNnz2bcuHGGQjhy5Mgi\nLdRz5szh0qVLTJo0ibS0NNLS0sjNzWXKlCm4uLgY1txt1aoVb7zxBrNmzUKj0VCmTBn8/f2xtrZ+\nKN+ZM2c4ffr/tXf3UTnffxzHn9d1Vau5LRNOxHBWzsTC3B4j4zisnebgZKnObEOtS4hOFDOjjTMa\nV5nFYRRnNLWD5W5udmdpuEqLmRZrsR0KU0TX1dX794fT9ycu9tt+Xej3+zz+6eZ70+vz7du7z/Xt\n+333M0ajkZSUlAbLerfKykqio6O5desWQ4YMISMjQ1t2+vRpFi5ciJOTE3q9npUrV9KyZUvWrl3L\n3r170ev1xMTE0L59e6Kjo8nKyuLYsWMkJSXh5OREu3btWLRoEXl5eaxfv56qqiri4uLo3r37f5U5\nKyuLo0ePcvXqVYqKipg5cyZffPEFxcXFLFu2jPz8fHbt2gXAiy++yJQpU5gzZw4jR44kICCAQ4cO\nsXfvXoxG4z3n0KxZs0hJSdHyu7m5sXLlSpydnWnevDkrVqwgLy+PzZs3YzKZGDFiBMOHD8dsNtOs\nWTPWrFmDXu+YF5q///47sbGx6PV6bDYbH3zwAatWraK0tJSamhqio6MZMGAA33///T2ZG7oQ/dPM\nBoOBoqIiUlJSEBFKS0s5f/486enpmEwmjh07hs1mIzQ0lMDAQC5evEhCQgJWqxWDwcDixYv54Ycf\nyM/PZ/LkyWzYsKFhxyaNTGZmpgQGBorFYpErV67I4MGDZejQoXL9+nUREVmyZIlkZmZKZmamLFmy\nREpLS2XMmDFSW1srQ4YMkfLycrFYLBIYGHjPes8995xcunRJbDabDBo0SK5duyZGo1H27dsnIiLR\n0dESFxcnhYWFEh4eLiIi165dkx07djToGOsyJycnS2pqqoiIFBQUyMSJE0VExNfXV86cOSMiIuHh\n4XLu3DkREdm0aZN89NFH983Xt2/fBs1pT1pamixatEjLExAQIAEBAXL9+nX57rvv5OTJkyIismLF\nCklLS5Nz587J2LFjxWazya+//irx8fHa+EVEgoKC5OrVqyIisnTpUtm+fbscOXJEhg4dKtXV1Q2S\nOTMzUyZMmCC1tbWydetWCQwMlJqaGsnIyJCIiAgJCgoSq9UqVqtVXnnlFSkpKZG4uDg5ePCgiIgc\nPHhQ4uLi7nsOmUwmSU9PFxGRXbt2yW+//SYiIrGxsXLgwAE5cuSITJs2TUREfHx85KeffhIRkfHj\nx8upU6caZIz2rF+/XlJSUkREpLCwUFJSUiQpKUlERC5fviyBgYH3zfyo3J05NTVVO3Ymk0lmzJgh\nIiJHjx6VWbNmiYhIdXW1jB49Wm7evClz586Vw4cPi4jIV199JQkJCSIi2jna0BrlDLZXr144Ozvj\n7u5O06ZN/6OGKlevXqVJkya0atVK28fdvL29ad26NQCenp5UVlZSXFysrTts2DBycnLo3LkzN27c\nIDY2lhEjRvDSSy814Oj+rbCwkMjISAD8/PwoKSkBwM3NTZulFxQUMH/+fAAsFgt+fn4PLZ89xcXF\n9O3bF7g921u3bp22rFWrVixbtoxbt25x6dIlXn75ZU6dOkXPnj3R6/V07NiRxMRE7bJOeXk5JSUl\nTJs2DYCqqirc3d1p06YNPj4+DTrT6N69OzqdjtatW+Pj44PBYOCpp57i559/ZvDgwVqDlV69enH6\n9On77sfeOXQnDw8P5s2bh81mo7S0lP79+9OkSRNtedOmTfH19QWgbdu292zfkAYNGoTRaKSyspKR\nI0dy6dIljh8/jtlsBqC6uhqLxWI386Nyd+aePXtSWFioLa+7DGM2mzlx4gRhYWEA1NbWUlZWRl5e\nHufOnWP16tXYbDY8PDwcmrdRFti7uzu5u7tr71ut1vtud+dLLbHTgsFgMNT7WEQQEe3r1b11c3Mj\nIyMDs9nM559/zqFDh3j//ff//kD+gk6nq5eztrYWoN6/QXdzcyMtLe2eY/Iw8tkjItpxvjtTYmIi\nkydP5oUXXmDdunVUVVVhMBi0cd3N2dkZT09P0tPT630+Nze34a+V3dGh6s73r127Vu97YLVa0ev1\n9cZ2ZyN5e+fQneLj41mzZg1dunSxe535r7ZvSM888wzbt2/n8OHDJCUlceHCBWJiYggMDPxbmR+m\nuzOPHTu23vK6nw0XFxfGjRvH1KlT71m+cuVKPD09H0reRncXAUB+fj42m40rV65w8+ZNmjZtSllZ\nGTabjRMnTtjdpmXLllRWVlJRUYHVatV+S/8Vb29v7TfkN998A8DJkyfZuXMnffr04Z133qG4uLhh\nBnYXPz8/cnNzgdtjrpu13snX11fLlZ2dTU5Ozn3zOfKHtY6941Xnzz//xNvbG4vFwtdff43VauXZ\nZ5/FbDZTU1NDeXk5UVFR2votWrQA4JdffgEgPT39gbNHRxgxYgT5+fnU1NRQU1PDiRMn6NatG02a\nNKGsrAyA48ePP3AfOp1OK8LXr1+nXbt2VFRUkJub+8AJgaNlZ2dTVFTE8OHDmT59Os7Ozhw4cACA\ny5cvk5SU9NhnzsrKsvufUnr06MGhQ4eora2lurqaRYsWAdCzZ0/2798PQE5ODjt37gRuf48c0c6y\nUc5gO3fuzPTp0ykpKWHGjBlUV1cTERHB008/TdeuXe1uo9frMRqNhIaG4uXlZbdY2RMZGcm8efPY\nuHEjXbt2pbKykvbt25OUlMTWrVsxGAy88cYbDTk8TXh4OPHx8YSHhyMidm9dSkhIYP78+axdu5Yn\nnniC5cuXo9Pp7Obr1q0b48aNY9u2bQ7JCzBmzBjeeustwsLCGDhwIHq9XpuhhoaGEhUVRYcOHQgL\nC+Pdd99l9OjRBAUFERoaiogwc+bMevtLTExk7ty52mw2ODiYvLw8h+W3Jzg4WMs3fvx4vLy8CAoK\nYvbs2ezdu/cv7wDw9/cnLi4ODw8PQkJCePXVV+nUqRNvvvkmycnJxMTEPKSR1NepUycWLFjAk08+\nicFgwGQykZaWxoQJE7DZbBiNRgC7mQMCAh7aLPBBmaOjo5k9ezbvvfcezZo109br1asX/fr1Izg4\nGBEhJCQEAKPRSHx8PNnZ2eh0Ou2VXd++fQkJCSEtLa1BLxuodoV/IT8/H1dXV3x9fUlNTUVEiIiI\neNSxHlsXLlzg7NmzDB48mLy8PJKTk1m/fv2jjqUoj0SjnME+TC4uLiQkJODq6oqrqyvLly9/1JEe\na82aNWPDhg2sWrUKuD3DVpT/V2oGqyiK4iCN8o9ciqIojYEqsIqiKA6iCqyiKIqDqAKrPHbOnz9P\n9+7dCQsLIywsjAkTJjBr1iwqKir+0f4+++wz5syZA8DMmTO5ePHifdc1m82Ulpb+x/uuqanBx8fn\nH+VS/vepAqs8ljw8PEhPTyc9PZ0tW7bg6enJ6tWr/+v9fvjhh7Rp0+a+y7Oysv5WgVWUB1G3aSmN\nwvPPP8/WrVsZNmwYo0aNorS0FJPJxK5du9i0aRMigoeHB4sXL8bd3Z3Nmzfz6aef0rZt23o3xA8b\nNoxPPvmEDh06sHjxYu2ps0mTJuHk5MSePXsoKChg7ty5dOzYkYULF3Lz5k2qqqqIiYlh4MCBnD17\nltjYWNzc3OjXr9+jOiRKI6AKrPLYs9lsfPnll/Tu3ZuioiI6depEbGwsf/zxBx9//DHbtm3DxcWF\njRs3kpqaSlRUFCaTiT179uDu7k5kZKT22G2dHTt2UF5eTkZGBhUVFcyePZvVq1fTrVs3IiMjGTBg\nAFOmTOH111+nf//+lJWVERwczL59+1i1ahVjx44lJCSEffv2PaKjojQGqsAqj6UrV67U64TUp08f\nXnvtNbZs2YK/vz8AeXl5lJWVaY8CWywW2rdvT0lJCV5eXloToH79+t3Tw6CgoECbfTZv3pw1a9bc\nkyE3N5cbN25oD004OTlx+fJlzpw5w5QpUwAeaWcp5fGnCqzyWKq7BmvPnR2TevToQWpqar3lP/74\nY71uV/a6del0uvt28arj4uJCcnLyPc+m39kxzBENQpT/HeqPXEqj5efnR0FBgdbVavfu3ezfvx9v\nb2/Onz9PRUUFIkJOTs492/r7+/Ptt98Ct7tFjR8/HovFgk6n07pF9e7dm927dwO3Z9SJiYkAdOnS\nhfz8fAC7+1aUOmoGqzRabdq0ISEhgalTp+Lm5oarqytLly6lRYsWREREMHHiRLy8vPDy8uLWrVv1\nth01ahRms1nrHDVp0iRcXFwYNGgQCxYsID4+noSEBN5++22ys7OxWCxa8/OoqCji4uLYs2cP/v7+\n9frHKsqdVC8CRVEUB1GXCBRFURxEFVhFURQHUQVWURTFQVSBVRRFcRBVYBVFURxEFVhFURQHUQVW\nURTFQVSBVRRFcZB/AUTkTswh2UORAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 576x396 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["              precision    recall  f1-score   support\n","\n","           0       0.79      0.74      0.77       514\n","           1       0.96      0.91      0.93       540\n","           2       0.83      0.76      0.79       589\n","           3       0.78      0.84      0.81       610\n","           4       0.79      0.84      0.81       551\n","           5       0.82      0.85      0.84       586\n","\n","   micro avg       0.82      0.82      0.82      3390\n","   macro avg       0.83      0.82      0.83      3390\n","weighted avg       0.83      0.82      0.82      3390\n","\n"],"name":"stdout"}]},{"metadata":{"_uuid":"c841e3ab0b59e0a3bf9ae1885d99533b05f06fc4","id":"YQSdReezipGd","colab_type":"text"},"cell_type":"markdown","source":["## Prepare data for prediction on test set"]},{"metadata":{"id":"6e7bQQBIqtBu","colab_type":"code","outputId":"b705cba7-e9e2-44b2-ebe2-0f4f214e517f","executionInfo":{"status":"ok","timestamp":1553771204135,"user_tz":-330,"elapsed":1150,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["test_df = pd.read_csv(dataset_dir + \"/test_WyRytb0.csv\")\n","test_df.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(7301, 1)"]},"metadata":{"tags":[]},"execution_count":40}]},{"metadata":{"_uuid":"33c7560628f0fe446b6355d26f9fee67da4fc58a","id":"9WSL1H-WipGf","colab_type":"code","outputId":"91240ad7-ed1e-4e2a-c41d-25308663f419","executionInfo":{"status":"ok","timestamp":1553771222278,"user_tz":-330,"elapsed":18964,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["test_datagen = ImageDataGenerator(rescale=1./255.)\n","\n","test_generator = test_datagen.flow_from_dataframe(dataframe=test_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    #y_col=\"label\", \n","                                                    class_mode=None,\n","                                                    target_size=train_input_shape[0:2], \n","                                                    batch_size=1,\n","                                                    shuffle=False\n","                                                 )"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Found 7301 images.\n"],"name":"stdout"}]},{"metadata":{"_uuid":"08afdd8c3000a63d5846644d83bebd55bb2e7fdb","id":"6hixVyr-ipGw","colab_type":"text"},"cell_type":"markdown","source":["## Predict and Submit"]},{"metadata":{"_uuid":"067733fbfd186fa1d65d4110165b345a6c9f1898","id":"xX7gSEVUipG5","colab_type":"code","outputId":"a9f6b0d6-abde-4bb5-b304-341043440d8f","executionInfo":{"status":"ok","timestamp":1553773535755,"user_tz":-330,"elapsed":2327949,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["# Predict on test data\n","test_generator.reset()\n","predictions = model.predict_generator(test_generator,verbose=1, steps=len(test_generator))\n","predictions = np.argmax(predictions, axis=1)\n","#predictions = predictions.astype('int').ravel()\n","predictions.shape"],"execution_count":0,"outputs":[{"output_type":"stream","text":["7301/7301 [==============================] - 2314s 317ms/step\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["(7301,)"]},"metadata":{"tags":[]},"execution_count":42}]},{"metadata":{"_uuid":"3c52db7624172d624771f0a00205568676fcc853","id":"QK9ubPZwipHB","colab_type":"code","outputId":"b37fd30f-3e00-47da-a3fb-b1446643e30e","executionInfo":{"status":"ok","timestamp":1553773535762,"user_tz":-330,"elapsed":2319416,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["# Retrieve filenames\n","import re\n","#test_img_ids = [re.split(\"/\", val)[1] for val in test_generator.filenames]\n","test_img_ids = test_generator.filenames\n","len(test_img_ids)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["7301"]},"metadata":{"tags":[]},"execution_count":43}]},{"metadata":{"_uuid":"5785b0499ce1d7425bce81e98c2d20d29dc7cc7e","id":"QpsDW_baipHL","colab_type":"code","outputId":"b52e7b5d-3482-4f3e-8e19-3e38d7f5beb3","executionInfo":{"status":"ok","timestamp":1553773535767,"user_tz":-330,"elapsed":2318805,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"cell_type":"code","source":["# Create dataframe for submission\n","submission_df = pd.DataFrame({'image_name' : test_img_ids,\n","                              'label' : predictions })\n","submission_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_name</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>3.jpg</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>5.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>6.jpg</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>11.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>14.jpg</td>\n","      <td>5</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  image_name  label\n","0      3.jpg      5\n","1      5.jpg      0\n","2      6.jpg      4\n","3     11.jpg      0\n","4     14.jpg      5"]},"metadata":{"tags":[]},"execution_count":44}]},{"metadata":{"_uuid":"f2b7c10b6dd4103cf2477fe6f492a635f312b1e1","id":"O16A0M70ipHW","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create submission file\n","submission_dir = os.path.join(home_dir, \"submissions\")\n","submission_df.to_csv(submission_dir + '/submission_hybrid1365_vgg16_2.csv', index=False)"],"execution_count":0,"outputs":[]}]}