bonefracture_classification-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "oYXkOCmrbKvG"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from PIL import Image\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import transforms, models\n",
    "from torch.autograd import Variable\n",
    "from torchvision.datasets import ImageFolder\n",
    "import torch.utils.data as data\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "from torchsummary import summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EBaURppHbLR8",
    "outputId": "6fe9cd3b-46da-447c-dd6a-67ec84884a27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "print(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KaYHQKH8cBhi"
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 5\n",
    "EPOCH = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nc-LxFPIc3SL",
    "outputId": "b6ecd6b8-41c6-4571-e87d-63efe0320772"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
      "  warnings.warn(_create_warning_msg(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<torch.utils.data.dataloader.DataLoader at 0x7fc3a253b730>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_transforms = {\n",
    "    'train': transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.CenterCrop(224),         # crop to 400\n",
    "        transforms.RandomHorizontalFlip(),  # Horizontal Flip Randomly(p=50)\n",
    "        transforms.Grayscale(num_output_channels = 1)]),\n",
    "    \n",
    "    'val': transforms.Compose([\n",
    "        transforms.ToTensor(),              \n",
    "        transforms.CenterCrop(224),         # crop to 400\n",
    "        transforms.Grayscale(num_output_channels = 1)])\n",
    "}\n",
    " \n",
    "\n",
    "train_dataset = ImageFolder(root='/content/drive/MyDrive/MURA_subset/train', transform = data_transforms['train'])\n",
    "val_dataset = ImageFolder(root='/content/drive/MyDrive/MURA_subset/valid', transform = data_transforms['val'])\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, \n",
    "                                           batch_size=BATCH_SIZE, \n",
    "                                           shuffle=True, \n",
    "                                           num_workers=4)\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset, \n",
    "                                         batch_size=BATCH_SIZE, \n",
    "                                         shuffle=True, \n",
    "                                         num_workers=4)\n",
    "val_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "9hn_nkp_dAfF",
    "outputId": "cd074790-e9ec-476d-8fa8-418bde8613f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 400, 400])\n",
      "Label = 6 : Fracture image\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 400, 400])\n",
      "Label = 0 : Normal image\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 400, 400])\n",
      "Label = 5 : Fracture image\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 400, 400])\n",
      "Label = 1 : Fracture image\n"
     ]
    },
    {
     "data": {
      "image/png": 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0DZLKM83SheXYNhDfNgYVZQQB0I7D8JxJAVQQ7UFCuo/vZwj0eYs6dAaoBzsmAsz8GQBPrGy/B8D+9xnfBVDPw9fYgalDI4ssO6BHJdCBl7M061zQRUz73ctRjAhZW/w1YrB0wIvRw+Wa+f6ATKENeKQXRtRlB8CHlJ5kBAQ4Eld41vsgBjiqEBxVAz8lhDoDgapQTCmGMVOMhh6iae9jCEq3qrXhDL0/u70T4NSjYp1OWKmwYatXJs4EdIJQIiEA0v6OfWDsixsh6g3lEizi0kOehdHhxUNzjHp7+l+fkGbyzVNob6g4FLFVbS7iMkxExcYfqAeHk9qg0ppH3VU5Fsr7sKrciGCnGgGobV8oWZ1hWYUrFTacgVlkAllATRO9BUYtGEwmysY2EsOCSLQhArAslDp+uYB7pYGhHAA51jPQULGvO3nXMnxDaNeNe5DjtxfDn7BPQIKEwADNWYmCBCdpYpFHlbDlc+65l2WgeFe1+I2araVve3lMBxh/q20Cf+ugDIDJvi24IL6KWkCtD8LrIiTqS08dsTAXhREPxfwPBRbVxNTymjUxt0a47HEi0tfm4lrG5KQHYmNQTRmOdQIAwLWUc3nDjP3UtC8jKTcWD3PRaOuhKgqLsCHuxDE41UeYi3yHknj2SQO7hgOkAytFBEqwMetABWEmBM8ObhuBEEhMuxgKa/EApf95BJS6exneOyQVLAooWmQL6D1/yBWICvEoUm1dy+F5OQT7CoIoP9+IKoFa+5xKB+0aqwQQIgwBcCxdwCbttq3bO3qTg8p7GpkMNuSpKQliDTpEU543UbixTMVcMPd9hNUkAmKh1uCU8FJqBh7fELDm4LZi5Zpo1u31GFSu0weLRMtsqJHSQJ9k0GfUGrQTcL5Q+wxg4U9XKtC25JHYkmdMTgHcBNUgSAccCAZBcwaC7g+0awA7h2abAXJotryOU9578paEuXfmVnMN9sx9DAxJWIu2h51LX3LfYDWJAFBQ4UIENPovNwQfdVa33WaEYDB0uLa9giidaRXIPsaXXyMAtXF3CkFkJ71WR3Ww1ymj+cT2QsbHz+HTbHl192m5MCn8oUQkjesnBOc5k5yVw1bmkg7qUc/20E24yDXZ2RaX0EF3JAZWlAiQ6KL2/TsKyB9/l9Zfv+aCiLqVCEH24vvq2gFLLbQhH38VyY3asVcEoHMdMcoNHRsrBpVALM8qrHjtN9AAHMOFM49Na0RoU2BUJtBX3afadQgYts8s4cZdpBKUEtiwFFB5mGdtAqcROkhU/O8JQWVKbkOa+RjllsTP/usVdoISKtboIQNfRw/doWV5EWHIfdr981YppJPlB0jkpVYIRiopnqcNE9DETMK4IkVqIB8khlSEpJg7JUliKaOg3NcIaaDU6/t+L/IMWPvJmeQmXKk4gbz5o8iQ9UOzFxY/YiPgaZAKUnLLiPBiGatvX+XaNVtBTToYWoBDgUNDkAxYBkywTxkxmFx7SKJuNiCSaoD4ew5tNBIiNaEpyORT6nF3cpV59RUarXF+iQtYEG3ZGap4ljtx4w7CAUkDqycJlCCGJDCoJgXEBaocb+LgXeBazVar7kNIBByQPAfWGLWLDLUS+ZdRGRa5DYcvXhCvLEQ3vxZHgioZgeEYOR/KpTnq/OSEiIQx1YVIUCu/GgGjulGT0oIUIEQLVcROB1OdKNhbrtg7hrwAY92EdXWCcZDFRARWighIuKkYkkYBGQLgEjLwFAAaNFvICMEoW0DPMbUF1Rfw0ztd4dI9XG4pP7e4spw5ziBjiSQqXSEG+QBqAyCfCAU3lFx+HAiAn0BtCg6cArmEgFiDo5G8tPjoQIBP957SOIug5gUYcrtml6o873xseVjL2232ElaKCPhpXEgxYo2dEU8NsOEoNcST/b4gBFIPL5MISrDcqEg8GstRSi5c7luU9SfHjnJx1mwmZU7FJNpK2mj8mwAeMW4gIrGGBnPi3kBsFuIB10JjB0QCEEKR5RwYYpTE8gXEtpRo+v6Xtz+gYol0NuTe7X2+5fV8912eTlgpIgAksdE3QCOtsAhw5rf9Ln+HQeJ2lwiBmxHcrDX58GMwejlONLRtzDG7EWftuGQIQeY1EE5PBKklI9IBN+jo9zRnYBIUKak6xGroS5yy4wLtqxCVDU75b2sP0Hl3JaYxz6NPTVjK2HoGwcoaBoMhK3W/6dX7qPhd/E+ltBz8xCQdRU7VMeaNcWMN3YIZsyRUfUhuv0sJoLS0j7k+gJ6qw1ESsh6B+NvNDOc2fQgBqEeAiVSNkPGqJeFc/s6qUCYF9eUEFOpYDaqS4MBxfYbD6viFTeUgYKWIwHyd4KXYhTVaVbi/GpossPnIuS7qtFOCX2/gJ6Zg6RDH6stos4cUBKRvUdUIwFhDYgadMOjivJhe3SEioqNzQHCaQxt9qN5L0IKjadB0nWQ7SLEBWWESez1AKw0DWNxLYKRRdsgeM0QI+lSrIYNiGhiAgz6/g4CVIgJ+ErPb7AvUctWRu2SddmVfMVBBtaWzrp8kQgBEXdWIn+l8qiBcdwXUuHbtU55Tg0V6anXBl8dGxOtIA7auQdEOTLIArRVc583hnbh5sB0Qs8YHcCQgel4tSKhSkCS/ac5/j4jwHFIDS8muJNLLvpMzBVaKCDSzoK/7mNfeeeHFQs2Aim8gW4AdQjBtVL/tEIJSTF0AQ5ykJi0sExtQ02eXskNYt2EsF64ivUWWllNugJ17i1wts2pGQVhq6pVVPRaWDVsUuFWBvkCgZWwptfiOEg5SHVgpw6CbMyabKTvNT2J4cIusqy2XiE7m24iw8q118KILMkinDs4B2PaZsbCDdCONg4vch0Pn1WBMlFsvWDVHejla24p8rLHVEFHrkZHqQW4eiEToMRCO0YKjokWYsG79zahLCXqBQu0aeN61Z7xIMljWqAogeI8qadoHBaslCWwy3Cz+iQuzXQv6/CAV5uJ3TVxW5CbNnvMTF+oV6nk7f9NjotUWHdMJ7umBztgLUnTVUOmgK4qJOkgiYj/Y+PajHSFTGQiZSzGcTKnjEEFjCrKApGxSBfKPgGVsAiUM2WQssaWedXCQKsNCIkBEbyCiu4joE2bbhUT0B0T0N/H7aNxORPTLRHQbEf0lET15Pye/LEg12/KBt+uEdt2FWPYB7pkZBiuGHKsPS+cdv+bUWGi51hgRUY6z3+X8OgtsBIy1goeNlW0mWEhzKNjo/pQ4uhi+QEhVhAvXH3lWI6A2L4kBRbZ4qN6rjcTsCxcGcs4/ghAsMv5VvT09549x0dr4hjNdEvhPAK4vtvX1G3wBgGvi5wYAr92bae4d6MuMJa7dPHS58RNgvuHg16lqla4aB2uHyYt16BgLR7U5MxysT9wcGyfQd+5SxkNLhKT4aq09uCCjUZcEodXYV4wvaoIlHHIuO4TCpVspWSsLhOJQh0ADksoaAkCH2IyBpTg68n21c8Ze+4yWBJj5gwDuLTb39Rt8EYA3cYA/BXCBNCI5I6DQrUPNu2AjcDG7rZ2G5pl+LYn1nRTZ0i5QgFq1yRgM1xpwbHw6mPbaoyty4ZfvW3R2Wy02YBnpYxGUC1eRUv6Lnt8i1g+UC8Q5qUQRzmFHaKdBAvBan7C8qJGkZr73HVRjAnqMsOVzKp+dXm/gmZYEe8h9m+wn5Ts9GEqwU5tAX7/BywF80Rz3pbitA0R0AxF9lIg+OsPWDqexJBCiIQ9dJGRoK6yQ4kraSrtdj3aDUk+tASe1ICMEE0K7nghBbxVj66HoiZfX/YajDvmt7QJeRDyG7gtA7p8v5oHYdiwDKn7HdyDEUT0DZi4iObhWniNS7oCkMM9zglPlpCOTuGrGwCHOXCL/0P6FkEl+B6MT7NowyMwV7XjUeQfThkxALPUq7kb903THkdRYiotWg12WIdg6TsiS81OXimXKISUhqP3GsOHK7q8h/I6hNkalvFdtTtb1h4joNoVbvCoAsoQhPyElADCIJsQ73B/r8VX//4BhcEinr7lc5T5r2/riLwYJwRlUSwDYORHo6zf4ZQBXmuOuiNvOKLAIDpgFBflvXl7BycbWve90NqJkI+BpVy3YiR6ZX294cdakg3yAgfvqWyVFanG4UBLtfRTtJXfA6v2qCrTWzx+lACHOou8DaotQ20RBOPuNuUJBkkRwuvTvvuChMw12SgT6+g2+HcD3RS/BdQCOGbXh4EE4B8KL8Vm33fhlCELJBaQaThY3YKE2lvy39oFJkWdQGAvHLpql3HyLzu+LYOwpLKJQEAJ9Zsa4qiXEBbkJGScXgqE1CAmpRsCSInUnEMuqAj0c2EpONbtA72XNeTu27p8BUsHCYCEi+k0AzwFwMRF9CcC/BvBvUO83+G4ALwRwG4CTAH5gH+a8axDvAJiVQ5Po8lHUlIhCyYPnxubI5+W188HRMfBQsTC5AfxaE63qsRYBjRPhFx0z5E2oSQr9FyJ9PoNQeQ5y/26WXH9qJxEJwJxHnuHmUCOiJCGFnfEgYx8h1CUtnbduHI9gZQzFItWrRP694PYHZRhcSASY+Xt6dnX6DUb7wCt3O6n9BH1xbLL8vNS7N8jNCG4+e66jLLKwE0VogDLOnmwAYuTCBPDeheq5nCrM7ATJy+2LdNp8nuM4mRQMzc7rPRj5M5E5iC3EqlqcjhGiSwytKWjnns11UY8BADVvSw1KZK69g0VEwW4fGqP3OffUtjwdsFIRg0BuZCrDVwGoe49dYTtgqIFv4TVEZ9Vr5VxNxU7rfhS1oDrg+AXSUWEsYxyQADr7CnWg686q/87CeTmcp63EzPYs2EqJQqpGbNuT1+6vV13ZYap2SWjstbI1UyEWtSCh0SrCGVBebOWIgIB1vyniR6T3Exj9PjXQAJKum1v0UVnQUNXAlt1SIBM/UHDIPt94DWq66xDXL8dXt1ttMdbcV6bEmq2ZkF3fVGyyun+mApT6vrG7AOhXtyqwUBSvpGovYzNZVtRf1jtz0HUGV5IIJP2x8qIoLVBbGVdchIOSgKgH8pu7uitguEmMH+CpSwuhlnq84F7G6qa9+5bIuS9tAJ0xpeCHQ8oa7BwjxCf+JznWlHxbdB0z9zFGvEXu1sFrmP2lBGSlgdLAOArOAMPgShIBBc45lptzt7KNhBZzKEmmqkHvokR2vg0e6qgD8eMnLubpixoxsDAG8uD7EGGRftu3L9gvigNssJAliJazU/p0iGaUjjqBVwaJOpGCtoaBDDMG6a0EsIDbDsUDlMf1qQVcu9/i3C7TOasOnF4odF4SMdX6tqX9mOVWFAiAn1DOwUooRVy5ppUOCn1T1Q1ptrEoqWTAzdUHO3ZfZdcVg1wczKb12sMEyVWaMjYC9Ii+5vlQG3ICsudYqgYZoRWbhflfC7gqnmupGi3yqvTFYXRPGn5/e/Iu9hhWqp6AdrUpXjzHeIGMioub0CzkZsahMEnsmlMucAA5hyulgigZIOKInYefuNDZSANhAhcbq1vuSA2oQG2RZjaNRYZR8XKY9mJhQ21i4Xo+1nvUWgOcH2MlqEVW+c5cRBoo4jD6kH+sm7Y2n/LcwXFkLZwBsFJEoIRk7CvEWiAgYMw0BEKCUfBnR3fehEEzdAhAKGuejFupfVbcBu4Sghg7wFMH2mqNO65LCJYxOHWOXeAy60cw0yTDNgGpNARhI1llNhEOz1ndsaDwLPQiaQ62XbyNGKSCSKOGcBUj4Nj8/T7DaZ/nYMgTY8ccTYQJCJVoTi+slDpAptKN1BJUET9ybqlm4+YBgUNmXPqvwUQUk4p0cCPqUhinXQt1CoBczM8MYi7Nx0YS1kKZZe5p4/Dq6ui3C2Lod6SvFvaO3mhKlns1nYYEwYTeRSmgmkhTdjsemvdIY9uQDaVP5C9Vgh0HCZ0hUgCwYkRAQGwBvkGKF4gISR6YbHo02ylLjTiWzG5zcVXCgG2YMMVSZZKm3MaUZEs8+iAUIQkuQ3hTe2CMx2AJgtC3LRtfpAbjIQHQ4fzJbw9FbDfnrDKQVSc0Sas4X5qKqmfGInIkAIlTL4FBA3UHawbTMkZg4fALjluaSPgxUVB7CytFBLTfnYEkRgLNNmOy6TVQJSA0Uk+8CCGqLXBqKVqaG7fiYo/2hPkGGW43vGo4FiHhaROvFSWCSHTbyioAACAASURBVAwyd9hAxuGygSqZ2Fpa1oFMLO+du0hDZYwARBVAcU/hWtpPsK2PCSCP1NSdPfckBKzm+hxJQPqIZl9w0E7H7MBZdWD/Qa3+Boij0W+bU3SbEVOzyD/D+ST9mBto2LGOGSUCNw/7ZoeLRy3jy3AESHUc36SyZDpmgfiLfOPLcqBeVWCJFaK2FXtvGXE0tgJLTMr7EOlBoKcZad3YKC8r2mB67RzLcfsa4u+Zpd+qFQcgCayUYTCI4w5oGNDOuUkUBUTER458kcNloqohDh0XoIq44QLNdjAYthsOzZbPdWZ7roALXglea8Lc5sZrQMi49FKGpxJ2E6gieRQWOdS9Sl3iCXRtBZHwiSom96Z9CCuhyn1ut2rI8wDs+JmNhYEqUV03ptl3AJLAyhCB5qIL0a65vJU1kHrgudAZF0COkLJAAXiXMJc8B49BRAR2RaONuOCpRbCCR2qvzTYghCVHJAEWDaIhgFzghjb3HjkhkHP2HGInoV6/vSF66sqL2YAZUVRDICUPCZIKYV2EZOwIOyJyBsmW9aSU30NjDI7d45GoSg+7MTDuAayOOnDpxZivU0Bc4VBSvqpJ+p4sxnBAIgDEqdwVEJB5vk5o19LxwgGl+KXNqS9TaXutw4XhSuYUQoyjeqBjVHTeZcEQkmV0XY39F8mIkKtDZZiulBObJASvZl9aabggPJkRsjZXaw/oy4dYdE/lmPsBZ0CosIWVkQQAdI2CYtCLi5gYYMQil4XEAERCgJBjH2oRpjqEbh6kChd13yAhUEZIkksw+ci1g2+PmKtzlXMaAnuDLMaIt1AiqNXaKzhmlVN5ALUKzEjI7qehZDvNkWcAirEQYRvNkas2MN+lrWDoXmpgjZlLItrQdcrnOko6GYrJGFIVDgBWRxKIySm1iK5qGGmTexKSKwtdjsQh83B+iDA/7NQSri7GLD4Buui1BHmhz1oxVN1WIjbHuHvJgmTXXfQlV8+8CQNhx7VYAgsZoSgIVCb1WBXHeFKCC5VTbgAj3kt6FpI81HkuQDcmYQ+htgb6jIELDYOL6hgM7TvrItxfKEVNRdRiUavo3nTdfykmHhpjEBKPwiG+QapOjGQzCD36wievkisrSpA9cvSiqq/NY0gTDSKvNlEtkLxmAd8px1FiU7HS8yQEXYlrFDHuArEjUSc2gigjbEB4PmXV4arKVEoRncnsnqXWkohqUlZvstHYOZS2gAOSBlZHHbA6JXKupy847nMe8NHwJuK7Y4bznHkJgn6QdM/Q4zDkHLRrgvTR0g0KRkjuLuKasWwIea06AQfAE9iEGHcWIVH33nWsAcIg44hUJD0Ai7n4iQmxVokiES7l6lHcF7uLNQbawq9wpJ2FaqL4XoM1BpbXKLeXRkP5vSMbAosKKhPZ4Q3sEnbahuxniejLRHRT/LzQ7Pup2IbsViL61v2a+NLgcvG+GhLq04LMjiHktQSsaiDirTdiLuKijtWFxUdeutS6E4hfFZ0zqQjCke29IRAiqeMvhMnqx0tmH1p3lt53QUhlzkMVmNMzgj6HMkvQdh1ehAgqEQHpPdg5LVmFaZGkVL4LSxCW8VwsE5dwumGnbcgA4JeY+dr4eTcAENFjAXw3gMfFc36ViHpMSqcZiFCGq2YW7iIiTeoNWiRvRcQXXR6JcFCbL4qQlgytpFtb3FZMLqsPJb8xZY04FfpUR4fgRZjEgiWN66oLtWg63VYZb0Goc812koE9P2tlbgijGE0rK7LDpZVAVa6zQ3VgWQQtowdrxKNvX994AM7MiMGeNmR98CIAb2HmLWb+LELV4aftYn57B63PC4YAqtuLSCqie9DfOeOGQHiZfmIMdIzkChMuLpxPe+chib4jYLHVGfniF2JgugED0MYf3EQJoSQIesGKlFBKAVQQTTnMAe2GU1G/cx8uIUuyARhubscTUbuHuOWJU8iknCzceQnonDdGWiojNpk70kGf6pWfbx/kwUoJuyE7r4qdh98gXYlxJrchUws1oLH4dnesGpTSgJG/HEotsf3Eqgy58a58mRoMY/sVWOhws3zBZ4tfPAK2Mk92rdIAl7bXCEJCJPOxc7BSgEhM5rn5qTPEz0gRzNn8lBhaomqebZC6xKbSva9BUZq6VYd2BTVpqZSchv6POl/uq5TG8LfKO/BaAI8EcC2AOwD84rIDnPY2ZEUyDFsbgVmMQC7uW9+1rT8gXoO+SkBKRETCiFGJmU+8kCJ0LpVwWf00lBErAHnCjh3HSCI1gqDEoLxvY0/ojWiMln/x/ScCG8+32ZaWUFDKCCTPsXtzGC/VXiiexwLIpYRxEleHsNSeXXaRynYuvhdOFB1CGubCg4lZ+w078g4w81fkNxH9OoB3xr9nbhuymBNA0ZWn/QGlqUhrQngjt9UUY0T9VfT2+L58AziQpg5LzkGVM3HYnzwLaTtQEISKOGmj4rhBCBhiDlZ0QTqXL7DM5UbdsZkI5ILnghDGtJIIW0JQgJ+6zCvgWs7iFjIPgdRltOXVkaQkW+dRQ4jN/+wx1s0r5gDOVIUSBhG/M9bQhZAmIsVVRU3Rd1lIbNLwBqZQTOxIFew4+NuTO0BEl5n2Yi8GIJ6DtwN4MxG9BsDDAFwD4CO7nuU+g28ImEQfvqYBs9H7AwK7ljVIhzwn4xCgBUiF0mvxDBcRVmoR7AAyt1Rc5DwBPByIOBk1Jc5/CGoqSbwGJi66HOOhRgqwyClGR2v/CCdYApBcgVYq0Y5OLlyUmEM+xSwR4FyViFWWxjH4QQJg7zUL+CJgfmSiKeJMBNf6LIZDnkG6TnfMrOISzH+zjcwaARANxy5Eno7sc7nXsNM2ZM8homsRHuHnAPwwADDzJ4norQBuBjAH8EpmrmSJHwCIGEy5IcdyZT8hoAGabfuS0hCdqDEKqymEDXN6uQwEzz2lxS+EoQgZDuNZ1i9j59Mvs+SEw5Jw2MyO0M9JwyVy3TTo/pIkAS16moXxqiQivQVhKi3FXTHlV8V9JVxI/nBVN+ReU88HzeYUiaIHjzM7RQ1vLCcubT9CALJnTtg+fxKqSM3jc5tRF5n7UppLqCD/mQw7bUP2+oHjfx7Az+9mUvsJNT83kHOodp3gZl3OLXaEoM9S2iZjSMRcXNzCBWWRZ2MViJql2y6Ye3InkQbV2DH7/sv17HVK16RvKI9ULBqD+qlDu+bQbPv0fAyyZGqLUUEU+aIhj+0zsN2HWdQ0Aok3pyS+2U0a8V/+137XQKUBjmMTHMdUbxsTYZ+x2b5MgJCVPMq6FMFouMcGziVgpcKGe6Gig4tvnzy0zqDs77x8kgpD3fGG4gN69XcAVQNXlsJrPAXxmin+ntPv4pPNi4SIkRKr5IFAMkSKMXKS2o2H4B8On1ocgzWsGmnB3q/MJyvkIrc6oeJ489wpfwcLYxNqUBo+M6Q0hK8Yu3T/lfEAg8FHJSE4Q2B1iIBy6PCXGHmYK7ovcL5OGhcglYbF4u9j+rEGFPlYEcjWHLQW8gk6L98Sgz5JIFtoi6rrFIs5L+OV79dYB8CI4DKmiTqU4ZpQNNVPSQlAJvIbnddJ5KS1P0SCkcUMOPusEtcnZvi1BUFIfUi+wChYIxhVoyxX3kvfB+k59EV6DgEdMFFYHSJQ+PMBmEo4yLmfeZl+QiEhqBGERaYfZzpmRCTfUOLIanGn0S+6jF2vLiSxWVhjUsYdc3uAzfmvdQXykeP7pqa6IAuBFku/HEsmzj81dkXi7jU3quBW9NJ0CFgL+DWXDK6oI3Dfc6kRiaEY/2ovSPnvec+QNHcpdxnCQcDqJBCNAGKkDDggLPZIIPw0JgBJHDzScUoIoruOHcODUhGSSOmz1ucqxVOdE9VAuK1tphqRuszRt5xerlNTTTJiYa37ekBQc7RxSzTeKSGh4lgjaen+KEnBByOquDKtJELpUaUfkfh0irMyYr4Eusg+wjug++V5m2eXpMJuiTM1FkeCbu0pagxtGW7mkbw40QUoh5YS0hkAq0ME+qzEAlZVUGROuqFIANQasTkSCetlEFeRnwCYIWvLbd1iFiltmbESAW3RD12OsRGHEoIJgbYNFtWYiiFsuok5fTMl67caysIc/CRcPFVMoqTaMBTxbJQhx2uqK7WhcDuxupDMRVSmTrYeIuEFgDLcO7sXRscwKMFOYw2D5biyW+w0ledZRooCkQDE5jGynbY5ZKWuOT0v787MhhEcjDSwOupAwYFJFqCAILtawyNSStYbkHG5MpxYwfz2jXBps698z7VtfWADkbRGP3ddiiXOGONX1Uhoz+lsT9xPvR7oqhsyBz8xtRQ854lOPWHN5XxTnkFFdRkDhftPf5ZE1k5liRiOmmpAHnAzn0kLuq/1Sgw7Uz1ANUBgdSQBJH01yx+gVAVIff/F8fq/INYURclg4DLUXaMHY4MTEykhDU6sNKASgmdkLq+K+ysLHIIdN0Yu2iAng12dOgYVxlOOKdehNrhNqQ2NWZSgUOzXCCh3l5TrUHgVqRmLEbMxj5JDbF1OonYBqZVbRH7pENW1k9DiyMG+XYQQI7koYnAJEHUnjE+5yM8AzfM2amcC8gusFBGwxjnlChLPHvVqNQw60ghB31SkBiC96GIx5SXE7HGAGOeo5RBQ1LcY1CqdCIFa0zVCMKkF3JAx0MXFOIQmhhjouIb4WILg5jEqkUy1ZCNZzA85zNclCSgP+PFTgNrkVpxvkD5bACBnciviORJXwS70g4CjzD9ft/An9aUvWMgGMWU2AdQkm/GGXDlfzrOGY7UHVY7XjE8jYR0ErBQRyHQ13VjRHcmI3VFfVg7scj1WoDfzj9PCyDLtDGEQD0JCzJ7FJ9ulwg+QN+q03Af5QuuLHrSGrc615dkwY3LKo113WppdpBs/jZWF4rNp19JvzQqMhkV9fi3Qxjk3YJUIZP5uHre1gNuyHLZfnO/Mf0ycgJX0bK2J0htg31n8b8N+Sb7FBoL0zMPcqFp4JdmBDtYmsFJEwIIY8GwkGyNwPHUd1vR35BzFRsh1L2JtB3GhMCfxv6O8j5i4zNPli5gp2CAcRLXJB6vp9N35VggA4r3OGdS2WlVYqiyTZ0xP+LCf07MJZdwphRjPoe5AzcFwgTjMDrvY+xGxeEswVU1OedA8V9bVLlM+llJFKlWpoXtFl7iIJJBFdDI6z1XUuoDoDuRbUOvzqMtoGypjD6wH56wkcDrABb+2Y069ByTgxQMAqxgvLrDMbSVIXLqrjI6uBjorDsIslFgPMKu5xynZqGqcsoujNMSJCiBqQUNhbe9Ev7XBQRIJWE4lcmkf76XZ9nDbpqyaDzyQGMAsLH43pyxj0yrKsi0YE6MnJVrK3YwTARCCVxC+hdCH6AsIYCYpAjkhKLdJyDMhuAPZ5VWqHIEjUbOEhIrr2e/TDatDBABF2CyaDmlbGY4KQLm5IHnm/0Y6DxwTcjkRAz3HXF9cikEENq5BHxdHHCtdv8LRrApTSgQS1CQLMVNT6qssK8VeGCZrHJZaYDJnPTbVZpDzkKslHnCC0EWdwtCJ2WF+yKFdIzRbPvRvmJn7s99jQZ5Rqe4ZMR4AEDtE2XyAzIhZDmsQXCQ5AifX6MSFNncxo9OmRWNg3APSBACsEhHwqdilQMiBp44hj12+cErJIOK6/g7f6S0mI1VAMMyR9HWRKNokDewGmKCqgcYOWD8+lhQ1q3YDSpF7hvhJ8xW5JyGu4h0B0nY3Y3C0JdhwZvKM5lSLZsurioHYeyC7R0tUxjwza1gt788SOuJEYCpSXLomaz2A6vVj38ny+S3qQn0mwGoQASKcesTRqI+FTWKJpkgcNFzWcg57LEV9zlQjrkI08KgI7MJAEpDDcTwSqSDzo5uf5tqZh4B6kCBzP8Xxkeu12TRL63eJ/EYKyBKjIuESu4DWGCDEKkExt8CIuARowZXgaQk7/SRct9n2MRoTmGy1iYiW98T5mNmcLPQECkmuh9wfGgLHfOxcnQsp51aPh/aoyNdGZny1O9IGZLEkllk4cRMfoBiAlSECDptHG2g9AYQF2YrPPrYUE26nPmRZyJkLzXC6UlQuQKvnGu5JMASo7e+8awOTSkKQ7itf7JmHItoLaoSglBBKQ2jmk3epsYi0IpNCIF46LYlaEJ+JNHZNzUUBnhpXpiRgRdernzg0WyFBiyYNms28BEU1czN7aMWz6fGuiAFYCZxU+hkYm5hDI1jB/WnwkMw3GswPOxy/vAmFaWeMySYw2WR1kwa7BmOy2YK2TRoqoGqUzpcPLpV4NYgAkEQ9MsyDzTvxyWIdxFRKC0TEegvGnmANO50QFjLieOSiQggCpwuLpqxikw3BdQS13Lr0Vsi8I9nKp54ZG83xMr5xpdZ83oAhjLDbA8ER958GRal1POnFenkfsjX1Wa0Bbu6CUbBWoWfAOLiQWABpDh1xP92LekVixCMxcOLyDdz+/BYXPvQYzl3fxnMv/RSeec6teOz0GFoEbeCEd3iQp9jkKTw73NWei9s2H4rf+8ITcfeXz4c72eDyP/LYuGsrdpkO0gAdlEUwwsoQAY6LuLYIFaGJQ+msaChzEbG8IDIn2d16DsLQrLpzEDvDTpUEBFntnMRIaGLUNcy4UF3CD6vL9ksBOj8JstGD0lyy81RCQM5FKZ9wFnVoxtFaC55jGTZkBjTXcpi6Dy/AN4SmTRKIjUzU3oTogqoERYRmlrvQAzbMuyopVMLKRQXgCeG+lx7Hbd/wJjRUcoNzeq7oARwDzjmGn774VuDJwIxb/Mrzr8Fr3/2tuOL9c0wf2A7X1biEgyEGK5M7oGJrtjFfOKIayEIXkTd4FWwfgnwYRSSrVyoCW32w4LiEbPxECIppDoiJ2fxL5AayIiFDa6xmeEuJMci8KanOIlJJMHO/mkKNdL6b5dIOcXAxghnNFmN6MojPmnkZC5X0PoPdiM72OVgCWIO4Zs45tFUhAMvBlBr86IWfwSf+8S/jyy/fDhtt5aYDUgcW3hURXUlEHyCim4nok0T0I3H7hUT0B0T0N/H7aNxORPTLsRXZXxLRk/f7JsYA9TxkbpLOTIy0CBF1emMU68TrC8IKonMiIhqeapFPkNz8l4CZMEcjavdBxZhXS4zRkGig371GZl+MDciIgSQAkRH/KTwTLbne5scHxEbKkZCOzJpLEaSe1HkoFWwhDlJCKChCSWWyOR927n2PqRJbwdR9f3rfQB0BRS2YM+7+3IU47jd7LrgcrNMUP3Hte/Dg1Yfywi8HBGNI2xzAjzHzYwFcB+CVsd3YTwJ4HzNfA+B98T8AvAChyvA1AG5A6FFw8FByuvjJFmZ8IWXjkQ4X7YzFGSJa/VKLZlggo8ebUuV2MXBR3GORNFC1GQwhv7UFcAXJZIjYhk16HTCRJvWEkmNInoYoQocuzTGISKoxma5OSX2AVmWSIBrpUyiEsfQSjIJKXIDeX6/RcCA3wTPcSYe2gqktexz3m9jiGVr2+lkErzj/Ttz/4hPZtQ+qutCYQqN3IDQYATM/SES3IHQVehFCFWIAeCOAPwTwE3H7m5iZAfwpEV1QlCg/M8CsK0FUcQFq0Qyj02sXIZvbbhBPUkQkGDirUuzMcVY1MNv8lNCYgh3qnmoXLIwhdxgPbyvdVgyuEgJJDEoZghSJA3I7g5V45FxGLLYSbjp4BNJzbrag6hATZTkJbl7MK4sd6BKGRYZBNdwuQDa13Zhn2x5pMRX3iIH/fuIC/MQ7XgZqCe06Aw2DHQf26hgPvfJe/PvH/Caesr7WOfelj/5zfHj6dCRj5MHYBJYyDBLR1QCeBODPAFxqEPtOAJfG332tyDIiQEQ3IEgK2MDhJae9PPTqlBS5ro/lpsEgn4cMS9y9n4Ry5KNENxGdrY4vnL60ERi1QNqXh6KfiD0LWGsJdNyFZbgrJ2Toiymw9oLefdlGDuHOHmjXwv9kD5Dzkr4f1ATOnrFGS0rQ1oL1XsYwMBhuAZIPj1c5N5MY0n2kkuxGijjcYlrprfvYtTtxxXtbbNx1ElLMxD739vB5+MEnvxr/8Z//Cq7byM9/yPQBzI80mB6Xjjg7u7fdwmhLBxGdA+B3ALyamR+w+yLXX+oWTnsbsp5Y+AwxjV4vzUtFTBNpQarsLL4e9IlUjYtymBHNtf25aYLBRQnwsdBBfmNoy3XrQn8u/ls1g1qGm0udACPyW2mlkAZqqlTWpp2Qr5yoloWeBpzeW4FcGYwQo6vnDmRVZpuYgWNTbPGss2+DPDTAyMVSYo3TkmLNyTku/ostvP6rz+qce+X0HmwebeI1Ft7CvsEoIkBEUwQC8F+Z+Xfj5q8Q0WVx/2UA7orbz8xWZNQNxuizmLsZpw7GhfjsJzHQpdT1LQIJ95eFZ41SxRw0TFnOE9lMg3DiuepKknEScthr14yXff+X4qqR+0tcv6oCMg1J99WKxcgJQuzClFvm0Xkeuq2iVluXYviR38COpAQbQFXaBGR8zzjyRYcH/bxz+kziBB1pX0ch3NwEQjA5OcMffeZRnXMf0jyI7XPTpHfaoWq3MMY7QAjNRm5h5teYXW8H8PL4++UA3ma2f1/0ElwH4NiZYA/oWIct5xdjleB9rAakSSzcfUEynlqxK+7H7FibT87FNxIhkKq/sj8kpQxIA5ZbFvPrCy8WY5saysoxColAjZ5qQDT59gbZpXCIdSWm68ZjBI/k2U3y88trZsY8IkMIhiP9ajDoHVh0bM+1GuRFV8VdzM4SAsLaepeAPH5thpMPPRg7gIUxNoFnAPheAH9FRDfFbT8N4N8AeCsRvQLA5wG8NO57N4AXArgNwEkAP7CnM94tkFFVlQBwVgVY+wy0DO/z5JksrDeO1eHOTBmX1PBhRtA3uduoQxYNxYq8jXgqolrgG8CJNNBHeJAjfhY9KPtMK61epLCid8XwqI1IyxqCENUpqT/a3txyfTI2hEIVkKIjGvHYI73tRHwuoy0zzBbbhgR76TFyz0BTUR8OE5IbuXrNMMbJO7pBRTP2wYh4wDDGO/Ah9GvB31w5ngG8cpfz2h8gmGabSOJfmzgUNwFRpSiGDRDSpCAKiCIlsdL4CWEE4VLOPBIiWDtEcaxU+9U2XEI4NDmnIAQCNtQXadwOopcEoJNVl06wBC+5z4IBNbjw8toI6UTzsyc5RqvsakJW4qZZnEUxJ70HSlJJyonYoUqgc8q/JVcEACabQDtge1ACmyWgxW/PWLu7Qcs+Czg67KbYvnTe+4xOF6xM2LCCQUQxdkm4q+YOyDGWUTSAn8bfE4KTLjwwCxAGmSJuqJchGhUlKAacynLr1GQRR09BY4xnTARMzGIbAX0IUboGw0azgDucm9QFGrh1TAKaEISRiUVdXaulvm4Kroo7VmMVpFiLnseJG5fzQ1LDCPl1RoUN6wZD9HpUKgsb9zBOVg5pIapkkIrIikZRwvRrDfyjT3QiDu9ttzC9ZxJHAcpI0dMFKxQ2XHBKu/6lKk+TxHbdR4YrxXP8hNBOZcEX45aBQz5UMpKFmwbOF6bqvqIrW8OjahlJ1xx304lAVYnH0MIvROch8VuyCkXq4SZyaGExhC5RiEFHqedjrpIBZr4lZ4WoBeM5aGYTqmUc2vyNyrB9dQEaAPNDLjwjn7wl2rKdCPc/+jBe97T/3Dn35tn5uOBWez+jb2dPYWUkAaliqxZ3IJLAwHmCVBD/Gb++vMgQPx82y4LwE8DNKetLkImkRvQPcQDh+GpHHZkJi9oRA4iEw6jLMByZde3pWzw9BsPqMYYzVqMP4//MfmjCnG28QFY7MapRWu5d6h/KNdpEQKxB0RUNRzq2DkcdFpZsNVQgdx/xo2FJwKSB+6b+kM93a/A/8FXcevvRkGk2JzTnb+MRl96DU7MpDk1n+N8f/lY851DX9P8nJ67BeZ/fSvM8IFgZIpD56e0abcvFJjn4cUNcH65ltCaISHsMNtCWY5b7a118FlUBsWEpo4klrdhaKfX6UJGbnWld5gFEO0WImhmvAytSVxJyRnHUqA5Ya70WyWxS2G8nDoITQdV0aUZCXooqgl2FwhGZu52Okd+vqAQdDl4SgEpgVfU2jX2hlLamJz22K/L6YbeGP3ni7wBPTNtK3b8GJ/02Xv/RZ+Ka7XnouXiAZoEVUgdgdNz4HRePGvhEZKdCGmCk4iOUn89CCBrKrtHpCAwkEb+XcwM2516Ti8S9FlNOJXQ3U1WAhchcxgj0LbxaQpLui/clQVNScrxj9OREKEvPQPlcShdh3qxkwdwKNSxtH9D5xxCFQlqbnvDwI+X1MdmG//aeJ+Gq33NJTSzVxdMIK0QErDzZXUgdrsrmOOYuwsmpKoKaU6k8Jsn7kogj2/sWubjnSsKROBXqSD9ECMqIQHv8COOYShSinkjyleradlwgMxDKZUw2YlZxt5JEZa+pRGtB/kAVBoKpusfG7wpCtnuktH9ka4a3vONZWL97K6lfZyWB0wCZ+Bt+Sxy7hLBaEVm5mEBc1FWJAtF7oOW3AIlQLHVZP0lluOy8MunEIKRIAzZIJoyVCpjoNYeQeAhhSrtAFlPfJQ5ZpWYj8WTEyqoeRRdnuU9qkcq0udTDcCFYQrAk9+wLmS7VxM55exTN966TG3jFr/4Irn5nyCDU6ELqqj6nC1aGCGTiZfy4SiKLcp7GcJ/Mmlzo08jFucx4qFbtdK0O9xyYqwbuNMiMZ7lkUY8XSIMNcHorMpdicw35jbQU/PuUnqGoUJwjjMxVmpDI8yhdozp9Y0Qsr63HlSXGamP1ELKqh8Bu7wHyDD8SS7d4hjvmxzvbb9k+iZ/9hR/A5R94IJRPk+d2wJLA6hgGBSwSS7XfKJqGyq8i7pqFEf36kkxEsXmJJ4PQRnKwFYnJA+TiGpTraiBS1IETY63MlzV2gCcAxRwWTb11DJRBSwJlAFDJ8fuOHxEUIwZVjfCTXAlb1p2g3gKVBuzQkWAIwbXPHqjck2m9ls95WI3pzF/u06dB+AAAIABJREFUkdF5Fh3vgrl2TR2YcYv/6bZvw+d/7+9helzGBC76ni/i3Y95e2YfeGgD3P81wIU3xwYlBxwkJLAykkAWSWer4Co3pQyJww9kRsEwDpkgIzk+FvyMbsDMMMYp7RZIkkAWamq5nBHvM4NfnKefUKEzU10kromXSxgOa+eVzxBIBkKrzycDJpKEAGj8gKo00ptA1K94bi0YqmtniVKGXcF9rsDSFtJDDPW9G5ULRJpn0VSIjYfHJ266Gpd96AFc/LEHcPGfP4CLb3oQX3z/VbjHn8qOPdocxrc970ZsXrLRGecgYWWIQK4OcL6dhbPC6LTxW7bJYiYYxE4NTAS4EPXJEISOSgAj2g8xM5Mso3q3IEGUEmoI3o2VH7hEZv+g/FM7LorY3to3gFysHWLS5XYhfm3nyAXzNnMcoVcPSkx9/wGAge0edOHDbSQWiI1fGBfd0uKrbff4n7jkD3HfNVEAX/BOThesDBHIua1B2oiUmidg9NUhq7+Lbbi0ElFc8DKeBbX4RwOkSAy2RVXmKaisDaueSJSdGuQGFn/VrqE76xy3/IQJFFyzpvtT5O5RDcjsF3Ge+SDQ96LPmtGNDzAcWa5dzrkXKtGGHbVnBCEmz/jrrcs629dpiq955O3waw3gXIzodFi/b4ZPbj+0c/xDmsM48aRTne0HCStDBFIzSATkFZ1M1oOEDCPWC6BcN81cVTGsV1tv60XCR9pwZxZnUSsikZFuPKWO3Gs0K/RsEcNVvC7rG5TSTnEfYQd1jlkEKlG1sTJwRHwNHrL3nN23ST8GUq6GedZSc3AUCFGxBM4S0jJOIP7u86KoeiHDd1Qp4KJJ19gHABesnwrp3oASMLfV4v/45Ldhxrlo05DDCx5zM7aPrif16YAFgpUhAjYjUDlwdG85U+wy7EiBP5qswkjdjAmx466JorPSg3yLBODTcUmXRh5kI5cWJCqQKVNP4j1IMc50Mqocu/w/FCi0CDK7QCSCmTvQGFp1TojPvzAayn2EH+k5DV27tJ30H1xweWvXKFUdK0ll0k/67bY9XvO5b6le6vqLPoHZOZP4XEMNAZp7zG+6APdVKhT/7EPfj3u+di0njAdICFaCCDSPuhrtOilXUjBUOBW/RG+ySCgCmqoLgUUtgPrtAaMSlMhmVAKRGDqILMSiIwFQ19DmCqOgXreOIWUUXsfYViIhpXvIVIpCXJf7rTZAlWsVUo+oMKI+AEj9DcaCqB3FPY/irOI2dBhUB8ioMa5n4G8/8llsn2ssvXEql9w0x/9z/7Wd4893Gzjx9JPhT9F89SBgJYgAr031d+YeVGSQVZjEVD3WqAw6nljkyRKPXPwuY+i1zoAJPxb/f8eAVlsTNgbebm4KpDNEYmztvXTuCPGgptfLJovwKAgukOwGKsYnQmbLe4Vj0/VKgjMaah6BvkN7do+53mGaaqCYgiNsfHUL/+W2r+8cP6UG1139uVCOTN3Ew1LQfsJKEAEARjeP3MrlnXk0lLdW285WBtJmHGGxuxmjmYV0YT3eSABWDRCC4eYxvdgBrensW9ad78TPGy+BFbk7lW0oF9Gze6lsLkNzawbKThCOGd/WFcw6LRvJq6xKnNQaZIgAQOspyvUy5NCqSml+taauOtceL4dCIRnm4xjpovW47VOXdXR8IFQcKutNMlFQCf7iguo5Tz//s9i8ZC3ed31qpwtWhghoKiun31YasNxMxeLIwSWNWDm+iPPRzee2pROtGctyMM4ROvTqi7uaQAgytx/SdTrhrNaYKYY4wij7gECVELhif8ndjUogMfxqbDUptzZnQLk7CyGl7DibBJXBMjUTZKwxh9ciJo0KV0VG87w3vjLB3W3Xsu/gtBNzmUdx6E7GbbOtzjnPO3IL7nvUZNAjdLpgTKHRvjZkP0tEXyaim+Lnheacn4ptyG4lom/dzxtYBrSMuE/cGUgL3LYdS3quQXwgIUUs96UShCC2jGMRCWaRRcTQmgSxYtFQDHypXvgmN2R1CEGPzt9/AfMtnx79uLbNVhPiQn1S96c1vCJ/vp3tRooaM/csVqCTzjwwSF8EpV6f0zGeceTLjK/6epDt5kWVh+0IRz+1jf/rzi4KPHq6gSd+581JqgGqz/x0wBhJoK8NGQD8EjNfGz/vBoC477sBPA7A9QB+lajSteF0A6XiH4LYoWW21aHjV4HAmUhvuLWPVXRApvWWR3ThJU4nY1hvgWvTeL6hDIGzvHZZp2W1YzU8mhRmMjYGOa5c32MXGiEV1bBjVOIZbEBV1X5SGgojQVTpyUgP4dpFvwObh7Es1EKALUhpsHhvuf3BqBKOBgOZ/Bqy+o0ShTo5OcOH/vDxneMbcnjsOXdgfmSiEtORL56AP153Q+4nLCQCzHwHM/95/P0gAGlD1gcvAvAWZt5i5s8iVB1+2l5Mdi9AK9kiF4FLF16yFUBjC8IOpAVLQRqwiGrbmAVCgAKJ0nequZeCh0qOmc1dwpAZAJEao8rEHFsCuz6Q2Uc924u56iEdvbly/AIQSSk9WzufNOCQ/aJzzb6VXAsWKq7TC8UhR+6c4875uZ3DHAhbR/PIUXv+obuo2p/w4et3Y/v8JFnQ3O+c2O0ClrIJFG3IAOBVsfPwG6QrMfrbkJ0RIL59AJoJB+QEQUAz98zLDRwfek67ljivdtc1xsMOVzMfV7oLTabdqOaUxm1o26rbrsFVfdlIJWkss69zHXNqgfQ2KCqI/YyONGPHES5pnmsKre5evHx2AGK14XzcTCVAeW+Gow9AVUpSghUqPfsKyjTkMHn48ZyQxQ8T4dwvtvjPD3ajB5996PO4/5Gu+5xOM4wmApU2ZK8F8EgA1yL0GfzFZS5MRDcQ0UeJ6KMzdA0newpZkgnSQm2TmJnp2cjFd+sjF+SVCj9e03wDEroZa+QbG27djQwUT4EQo9Dnz0oDGkxScuIs8o6yGAUlBNYQVyMEMFxY51TMsXZ8zWVXnldIGZkBsbiPqr1FpBmLuAWBWCSBdMKdjf6fSXZ2bZSFTnWy4TlOTra4ebPLz076bWwdX8/na+xCh+/Ywi9/6rm4aWsLn9w+hU/PjuOW7ZN4z4lHYe1BuUb3Hk4XjEolrrUhY+avmP2/DuCd8e+oNmTM/DoArwOA8+jCfZeBhJo79kBLoJi3zhwRNXJuq/dJkdE44RQS2gbkna8DcAQ/YTQxxdfNg8uQY218sTlYA5hGHUa92IPUXejW0SmyGa4f1wkjLTC5rybnYtZAGX7EewW63M6qACUhKA+1+zROwkgtnAiatWGUxkJxC9ryYynVOJVxF2MqE7Q+opUGGLlHxlr4ba5FrS7CGFAJALHWY+vx+jdfj8P/5G24ZPIg7pyfj3fc+QTc+umH4cp3UmrsIrko8d7c3OPC1xzBDQ9/NfwaMN8gNFuM8z4/w8Wbp+An7swmAn1tyIp24y8G8In4++0A3kxErwHwMADXAPjIns56pyBio0VIzyCiFMtexNxrL4HIQZTBmMUX+gkAobsQYppxKg3GDUJ7bgsikRCpDQFI4cSDkXMiDYjPXQyQjjPky12W6dTOWMuA9eWblZuiGHMCkHkaVEUCpFhKct3Wg2XCM0+FTm0Hpe746U8nVmAEiMrRqZjsEIpHEHD5H57AW256oV53crLFNdvbkOKywX2KUOfBwOTEDBd+Ikq8HrGZjAuJRwdc1WM3bci+h4iuRXjqnwPwwwDAzJ8korcCuBnBs/BK5kq0xOkGIxJL0I509BEuzQ5gDiza6qKMUryMdeU5hb5yg9BGPDbedK0UBA2ITVF60OmIOaBlOCTd3jeEdh2YnEr2AtWv9X+cLxLX9BOoNFKDvOV2ySmRi/AlMpb6NSdEtGqTnsuG2AJ5MFOpSkTCGuIK6jW8hBCMNpoNIf6gy9DOM94MAQwKDQYQ3tf6vVuqOvi1BvPDE0xOhJerz1VjSsJxs3PXcPyKNcyOAM0msP6Ax+E7NuvXPs2wmzZk7x445+cB/Pwu5rU/IByRkVXDAWJJcelAxGReZtITbcy9uhkn0pJaRNqgx7t55NCxbLif5oZAAEkclsKdTGpsdDPSvISMEMCca9WCOAeHvIqP7Q1g03u5Uz0J9bdsIAuo8fFkIQDCQaXGgYyZnU8ZMdZ5g3PjlARMGbUgSDpxS0UaYOTcO7PlVJKIqHKzHWOsoyDBFdIGE4Cpw+3PPIzLv+ULeObFn8Fvvfk5eNj/dyKpOXGe3BC+9E1H8JKX/BGedc5f4+rJMZzgCW48dTV+7oPfgSv/B2H6YHtmE4G/S6ABKhRF9kqFW0kRLo2ENsTYCfcz7kQ/AdwsIQLNAec4lh8LkoVvkIUXW6A2ILCPRr52nRLR0Hl0CYH0RUzziO3L9J5N5F6cv+q5JSEAcilhaGFmFZAT49VnYtQCuy1sSFKEGgFF7epJ803nG0Ig11Y7AaSjl7kfM66GJ6dxq7fYRwwdNMx468I1/M/f/99xw/m3AwA+9sKrcPzjV2D6wLaGmAPA7c86jLf80GvwhDWpJhQakz5h7U585wv/bzzjohtw6W8cSnM9AFiNsGG1tqdNEqwCIEv1DVV/kLi14f4AsuAiN+OkRhSpvcQMNytchsYDUYapihoh1v12jdBOi7gBQ6zseTZ2gCkWHTFIL/Ox52rrtcqCz6IRi4/us2HYqhIwrKvP2gk0yzA+L1ujUPZlXgGgixQZ0SjmOyTF1NKJBXpyRdLY1DmWmHHqwgb/5NzP6a6fueodOHb1VPsPUuuxfeEavvFFf6EE4Avz4/jgJvCl+XG07HG0OYw3PPmNuPvx0wPFxJWRBNQ6bxeMNZxFzq5FQn0XSW2qMCE/Xj8NtMNxiAwkjStXAmIkCOHmhBiRRhxyCRDVgnlCqnAfqSBqZrfIRGyEoqQSjBSNj9r1SO6ZkHE3fT5c/De6voJNIDLSkYrm5rmI9Z8Ld2kZTSnXqYrrxmsAIHVoNnPQrk81KG0ENbtBhchm4KAG3vkhYJ0S+jxtfYr5C+8HfXwCju/rS8+Z4G2XvxfAIbzr5AZ+6lf/Gc77QosHr2zgn3U//uzr/xO+fn2C617yF/jkL30dDg1cej9hJSQBCWKxmWxWry+Psd2JrU9ZJAUrzjYzViS0/QEEnMkw1I5CxeLXrrYxWUmkBz+hFIxkEFM5ruHCZVltiV0IF0ImEVipIOXjp2MWP9CCE5uqTGljUgP0tLard6tUYAmB7CvnY+MjXPdZ1+ZWOzeDBRiQSVBm/TzwqO54z7j8s/BTp2uqvWCOw7SGGbd41fu+Fw/74AM457PHcdkHj+HIb5+HX7j7KWjI4flHP4HtI2Mf/t7DShABABnyANDFpj3y5BgpHiq2gkoYayo4Gm0LtoDoxB4fOHCz7bMGHGUhECDXU50U16CQXKQtzir3JOeUSUaiFsh8qlFpqmun+gil2pRBOUQkklmgVUTkDNnZfJDULxmjZifphG2X+wB06g724VFNvbBBTybIx46T24UikWVg++gaXvTcj3TajV1/wV/i+JWJn9N6CwfCjFtM751k1zr/b07gXb/x93HdTd+Fn/mdl+HQvXXPyOmA1SECAMQrAKCjEqRCo11CobXpzOJgEukBmRVfC4gaouNmjMkmw82MWlGI82mOuYjrmxhO3BjOyuhwVKrYPbSGn6oxlJ2flbaK4jT03vLnls4zcyuSlbJnVs5N78kQNLn/obJiao9IiJz8+YUaZBG7jDuohA2X1+wN1S7u6eRDJnjJ0RsBhI5C3/Xp5+Hu9gRecPhB3P+S4zo2zx08GDO0aE4V1/aMh3z0OM79P8/Fle/dRrN5lgjsLxi3TVb3nuICjFzXitjk2YT8Ji4QBoFpCwbNCJRS4tpOy4iRzZZHs51SansTYQA1EgLQSMJ2zaV9Jqy45tMvJQJbBr2W6ptBpjqgO748D3kOwrFt8lNxjlUVsroLcsm+KJLiPjKOboyhap+QYzrPJKlK2WZDMOw1sxLylfGOX064YnIKn5qdwM+85gdx7KeuwM/d9WxMqcHLHv3RyAQYh/9mDV9pT2GLPdwsXcevNZidt475kSnadYn3Nvd3mmE1DIMZ4lNCeqMStILUtlV2FPHJk6b/eqMSJPdiDBwSaaCJsSXCNSeEEw9tNOf80F2M9Qc4IZEgrkgJzPGa0ECkdgpQS2i2+1imvV+haaYpypSCN8M+AyO6i8EwhckiX/w9l00JT+mZhcPTgrYEOLR954JrL74liHY2FDTE6GdrJk15oSvOqgmVyU2+/j5cMTkH//SLz8Alf3ES7uQM73/z0/DBV34UTzj0Bbz/vGdg7b5NsAvrYJMZzXYY7/Znn4fHf+ctuPzQ/djyE9z24CW49zeuwsa97eJ57ROsBhFAhdtYHTXq4MmnHsR/7wi+iYEsUToAUhdZJsB5ViKBqBuL0YqiC3J22OHBq4D5uT66Kx2mJwJC+wlCHHwhvooL00dOZAOOsmAgJG+BnKeExdxukGoKQmAkCUIl628B6H3G7MnufmgMQAjOSlxVazFgQAyP95iQUu5NiBjDuv1KQpSPk47tQJZ9yPC1ikfGW/TSv/dxHPebeO+fPw5fsxUKhl724eN4+WN/CH/8/H+HO69bx1X/YxPzI4wNcnjQM1wkAieffApvvPq9mMYSGx/b2sYrLnz1gRKB1VAHBGqcLXIG606TIB3JFMyyCMu4eIj6YPRlU0CUOHLzaRI/20PA9jmCPEkn1iy96J2wQTISftxOU8ZiH+ji5fQRiaBdK7wG2Xncj5AGKcggcpBQkDo7F41Cq+oGoGJ7p34C5dfS+7DfhWog0YRqdB1BzFK8g2zg4P4z90jq848qo2f4jQbnN6ewyS0mx1I8dLsxwdo527i4OQR60jHAObQbjCk5nPQTNFsMzD0O3XQI/+jT1+N1xx6GW7ZP4uGTGR549MFG1a8GEciSVNLqsmm56nO2tgHR8yemDbiI7sLxZTHb8mKEVEpbjIdbwefvZiGUeH4kcdFUjESO54S8NktOAnyyJiMJ0UpJovwtEZPSHr1T8cciG5mPBRN4I+OUvRr1OVoQyVpOt3UcAZWueqEgCpmNQDaPFWSMJ4BaHz+C7IkASB1FkTqo9ZhvNDi3OYWLmyP4le96Az71A0fwN993LrZ/6j786Tf+GqbU4JevfQvawxPwGivHBwHkPS5//zE8+NOX47/+L9+Ol3/y5bi4OYLDlx8/Gyx02qCwSmciHydRUP4rB20AniMZoDhxZ4hxkYN47cXCLqW0EUTlZhPw6xRVD0K7EVJKpydY5wIkcV7GBFLsQum1sAFM2f2orm/uXewEhhA0MXsxMzBa8VuuZYmRuVYiANxJww7HkHpQRHpRW0NlTPFQBNUEdegzBySNyDyHQgVYkFGYx0/UadLxKya4Zu1OAA7XH97CZ178H83ewwCAhzbHceriNdCc0DLja9cc7nu8xyUfcaBZi8msBU8dLrvgHhz3mzhx3yFcfDCaAIBVIgKGA2VVbyOQcPPI8V0bEFqLg0wIzUzUBCg30oQjz6CW4CR92HFICZ4HPXxyCpgfhl7XN8DsEGFSuI5KJFSDoRGtFQGNVyKrfVCOUyI2wv92LRoahxagSD1KmBASY9adhjUndchkO8IglUhVRTahEo7a9a1NrseAKJGF2guyM4ZJBIv/a8egkBCrY8Xd936dx3XrQMser3/gCrz3nq/Fkck2brz9KmxtTvFDX/dhvPLoX+H2ZxEu+zDw1uuvwPefdxfe/G2/in/28JeB3nkhiIFT1z+A33n4u7DJHpi5brDVaYTVIQJACqiJ2V2ZMQ3ivqNMdNXQ4CgNWMlBDF+2PDi1yY4gFYndNmP6IGP7AkK7xvDTsDLnhwl8PxKS1ZDBIFSwqHPGMfP7M8ZNc67EQLDcq4npl9BkjXWQ8ywi2P8xg2++EZ5Ts8WKyJkqwIVkYNUNGarJk53E0Fl9BuZdhXsy3/LsHIWkKL13QxgrBKAWp0FWWigzFRuH657yKTTkcMyfwi/+7otw9TtO4BiAK1oGU4tfu+HZ+JcvuBUvfuZHcNPbnoT/7Q//If7xd7wW1200uPEpv4kvPPEkTnKDx0zX0VAILPqOp34cH3/Pk7v3fZpgNWwCGg3HqvspgpT6tXEfCtEIUYVIxjqjM9vtoUknsgQanoR9a8eDhZgnwOwCj9l5HvMjSLYGU+wkhQMXenMTowClSrLt9GO5bw2UW6fjxbZRbYdWOV/ANyGcmVpgssW5MbA41oLaPoT7l3YAex4Vn2LMMhBooT2g57nkHgBWCU9Ps2M7wjOO3gYAuGV7DUdvYdCsBc1aYO5BM4/zPrmG47yFifNwrcfD38b4ug9/P+5uT6Ahh6smh/GISYPfOn4R/ukXn4HfP7mOe7ePdCW50wgrJQkAUaQFAGf0dyNuUgtALPnRXw8KAUGhgpCIxIkLSXAIcVQ7xVNAgqyM6UmPtQccto4C3/70P8eH73gENu+5OHJmUtFY01spSSiupRCfwMFDIC5MjtxOEpaqYr/hrmrEhCE+8VpCBCQzshwjHBcMge2GAzeEyVbMkmyNKlBB2GybqmXIiEeZBNV9cd1xbdRg5h7s8e8PAVMoOZcGp5xwRAT9tVv/Ph73hLfgQyceg8N35lVciBkX3ryNp7zlX+DozYSj2yexcXITl71+Hc/++I/j5DXbwIwwuX+Ci29iHL5rhn/18CeAPHB4NsdBwcoQgSzxxzNozuE9TwhS4gvCfT2lVtMxe408o12XUmFGHI2GLkvEM3dhNMK5bcahr3psHXV4x0efBDBw7ha0uIlG9sVKR7r8hMC0BOdCUJOfAL4FgKCTO2N8y/ToTHUhdXnKHEtpxouOb1WDUgyPSU1aWdm4VuVa9jz1Xtg5GHfraIu+DFcR4cMGkyO5U/lWC8j0TMozLnntYfyvF9wAN2ccOXYy7Zs4zI5MsXnhBNQS1l/6Fdw3n+CBExtwt6zh/Ns8LrmJsHH3ZpAcAPDE4eitLdr1Bu2GOysJ7CuUHL9luFAIzlQTQqD83hAEG6/ehjHaKaEpjEgUpQHlbAYBNf/dMdYeaHHu5wmTExM0m8CRr7QhWjFmCvompORm+Q2io1IsT8asiUG+SQVQJpsmS1LuxxKmwk5g52mXXrtGaNgUO7UquwPmGwHDQi5E3SbR/x7SvEL1pZhmTSP5dsHgRQJQaaAqhgxDXpSEVB3IvANCYAiYPrCN6f1b6jZsD03xue84hJdd/0F87cbtuHJ6Dx6/toXzXUok+tI3HMft7Trubc/BnfPz8QsffwEO//ERXPpnDyZpY7lp7ymMKTS6AeCDANbj8b/NzP+aiB4B4C0ALgLwMQDfy8zbRLQO4E0AngLgHgD/iJk/t0/zXw4oin0UovkcfEiEWXMZEtMcoepQAxULQznxWC/QJ7VAxWqRCMToGIGbUD6LmOC2GIe+OsfGvSk+wE9idGIcg5lCPnqBS4LgAQWTYbNdoziGDwEp8VibNNQXsKPFTsw2UKhqBAoSARnu2K67IC3EqMUsh0FON4RALd4+2TvCdVlVqI4oYInXAE73GVK5IXA7UFdAJ2rEffk9UNy1E3dBhHsffy6+5dUfwm9e9Ke4uDkS9zqgqAxwxeQcXDEBgC0Ad+F7nvUbuO0b5nj1p1+KY2+6Aud+cTtO/mAowRjBaQvANzHzExF6DFxPRNcB+LcIbcgeBeA+AK+Ix78CwH1x+y/F4w4cMsu1xOfPPKRHgOiSGhVnCn8IKOKXBrkoTmsSjV5UDITRmBcRVwyT6nYTFYBM16LS1iZSASMrNCKIPF+PtQcKyCSA2hoTA6HV2SMh8GY8PyXNP7CpvzYno9NyDObZ9DSisw1g7JxkzO4J5jCjGnTVg/r1+g2ErGP1nsPh3fm1Bnd847l41U/+Fn7uIX9lCEAOd7UncMf8OI75vInpOk3xuLVDeOdjfhff/ePvwYNXrtVrI5wmGFNolAFIg7Rp/DCAbwLwsrj9jQB+FqEhyYvibwD4bQD/noiI+YDInAXKkTokBRm1gGRBpSg+KSQq2YLeGAThExJaa3dOCOKXeAoqFYGtdV+NhKZ8V83PHSzsichwtBVIAxS9dsFNVT8v02ol3sCIw35CcJPgBfATit6PLlHJDILUHVODsxoArbkHRpcumTHKVOd0ojm2PH0Il3o9J5xcrsxQl6NEDIqtqPXwE4c7vvEwfvwVb8X3nXd3Nsx97UncOlvHm+55Bt776UejueUcUAtsXtriu5/5J3jpBTfi4ZMW57kNNOSwTlN83/l/hTe++OnYft9RHP7SGWwTiA1FPwbgUQD+A4BPA7ifmcWkaVuNaRsyZp4T0TEElSF/YqcT1CYQxdSoEgAM2vYgR6lEuOjJbVAJ1HgVDYZuxiqCN5GDe1NkRI4NPQpTTwFbkTgLUUZqbZb3KQjXlnRmjSRUCSLunybEDmXMJHApIl257kvDndmWRU/Gn+1aarstwVKpVHtFzbAIyzkh6LWbTHJCqNOqxELYGI4q2FRptT8I4e5BstIT0HlmUS1yhC998xG84Qd/BddtJNHmmD+FX7vvCXj9u56HK967jcmpFo+YtSD/QDjdEW78b0/Gnxx5Gr703Cme/qxb8GtX/T7OcRu4uDmCj3z9m/DGr3k43viVf4BzbsJph1F2VGZumflahG5CTwPwmN1e+LS2IbOQ1cYLXWVoLrEDuS6utQYA5QZuHiQCjRaUfVYlMIimcehkCoQU4q/mD8RxbKWeLL/BEIKU3CJjxHOn0HoGqrcjEY30W7gcp98S9CT+fA5jbZ/joiQQxpQCqjr/Goc1UkhZRCTYUUjvvdqW3doaON+eHZYRM1auPgpsfQK9F+S/TfLQfY85gn/3/b+eEYC72xN4+ut/DL//L5+NR/zucazdvwW33RrXb1gPzeYc6/ds4pG/cxx3/KtH4trf/Be4rw3ehXWa4obzb8ftzzuYRKKlnCnMfD/7oCfxAAAgAElEQVSADwD4BgAXEGmlRdtqTNuQxf3nIxgIy7Fex8xPZeanTrG+w+nvEEQtEESbew2JzRYsG2Ro0qIVcVt86xkXo+I/m6Ij0rfQyl+Rq6m/PRIMRQw2hKSyWDuRfgj2Ab2G4ca1MuB6rzquqUHIidB16h2WertsK415lisXaok8W6syZMeWyF/bjoIQiCSQFQbJ/6cdnM4RsMZBefYtoz08xfN+5MN47qHUMOSW7ZN4zn/4cVz1nhNYv2cz3MskxFBwQyGCsYn/J06NodNjW7j6XVt42pt/DCf9drre9GCqCy0kAkR0CRFdEH8fAvB8hPbkHwDwXfGwlwN4W/z99vgfcf/7zwR7gK0BACC9eJekAS0XLZxSK+WwLiybNmxLd5Vpv6I7s/EaAEgNTE1LcwDatUhdVC5x/RpSqaTgU1NTTfGdUHDlEZS763Owb5wrSMz5fMFAsx04v59QipUoIJMGrPFOnk8nASkR0KzGYTlGqaqMgGVjDyxk0ZpRAuCG8PkXrOOfX/THmhV40m/jO2/8YTzsgyfgttuU4VkYSLOchMYpMWhOznDFB+Z43DtftfPJ7hGMkQQuA/ABIvpLADcC+ANmfieAnwDwo0R0G4LO//p4/OsBXBS3/yiAn9z7ae8R2Jdl6txZxLE98tKCzpE1bIvHCBdXTkq5dBD3a6oxoKJvGWSUBepYbm84WFINEjFwLWO+HsucURy7ImlK0k/4k2+34IxaoVV+xyKaUQGsR0Ot8UIcJzlRzM43z6HqCdBjC90eQAq9XkBFLOEq7u3UpRt4ybd8GJdNztFtbz1+BR76G+tKAOByzwgxw09cKCG2kUS/UN05fKYPznDRRxvcsn0SBwljvAN/CeBJle2fQbAPlNs3AbxkT2a3T1BNvmk9qHWQAhWSCGR1ZDGQaT5BIwVAkjfBN6EaURORMfyH2hLaqQn9tUZClT7+//a+Pkiyq7rvd+7r7pkd7a6klRax0QcIDA4yJcsKQpIRwVBFRcGVGIzjgAkYx8ZgYlcUlymgUgm4HNtlEjuYCo6jJCZQuFzINrZwHCOEUdlJHBYTIQlhkLSWZQQRVgmhlWY/ZrrfvfnjfNxzb7/u6dnd2dZu96mamu7X7+Pe994593z8zjlaVkxBTJlJVRhYFqNoIuor0Pp97YAhsMM1llLNZmSmi/5NR1bdaxVed7FUad2QtZvYJ4YrE5BqiaDnhBsnUWG6UAukXtYIYg9oBDrt4cz5mTkB6X/fjo6pwCsDYDmh4YRsFja88WuvHeE9z/i/4MAYmwG/8HuvxXMeX+eFpMlrKQneIA4a/OVbAm54/gO485FLsf/mNQye2Mhagsxh70Ob+Ed3vgX/85r/vI2JnFpaDMTgVhSITYE2gRoWD9QmBMptwbSAYGw4kmAhRK0rkPgFJkps+0fZLykgKMk5+QVgG1sajwrRSK6rBUsboDEGzMM1NFvixKbQ5s+qvlPkVOW4l7B6mP0YWkItn0jPp9qKM2EoCyKP81cGjD1iuHJVvbmuemShRzcBn+2owkFDkhpxMX/ENI2j0wcxLhnGfAL15wrsZBQBBMKzDnwTK9S3zb/4yI247FMbfM97oTguESENAo5csoo/fNmv4AWDNQwva/GSlddh8HO78r0iMlxJ/zPn4otXrU2Z6M7SYmQRdpFl7zn7VCrMKOUUYWH6Vlc1mBqunu7aLNDS4xYZEGQghwP5Gu2AUXg+ihAkw45isuy+AhTTkW2W8Q26gYVJ7ziP79i+BqPVYIyn0YAxBvP+MRFCxXVCmZ+fAkmWZBlZ8N2Y1Ozx98iauyAfa+hF5w8pnI2F87Iadtf32onaGcFIhaal9y5rBwmxH7BnUEavDn76O9AcGY49h+P7V/HAj/Vx0fsewjdfSLig4fP0qcGelQ3DHZiMknuw96ER3nnfa8fHd5poqQkoCcPSKLI20LB2EIYREQEkanfUGgEjzifIfgE2C0ILtLJPjAmNK/wBSGMTIgTihKTRKhmkWNGLoeW8+NjrWNHcS+rNA2NMLVmWcnektk8YnhPQDjib0TdcYaFVMkIK7ACMTQU5rlB1PtQXhvyCJ+TS46kHY2Lve/D4hWxy8fy/9fw+fujHbsdv/MX12H/LLvSORjtuEpkmtpVpoGZA529yGecD0u+xchIMDjv8h8Oe/L8bGnzlxg8iIOCeN3wKv/ToS/GP9x3E7z5xDTb+4wH009Fx64X4mR/dGGwx+J2jxdUEPHltoPKm59pzvGqUDC2YAb9ipryaa23CAibc5hdew4btIOQ+BhbDRwYqUemMq2PZqkprg1HDGYjm0WzyX2pg+P9Oe9qt5Fo/0ISO843UponhH3ykwd3TYv8uABOylnXs+nX8zL778OFrPiQCcsIjm8LwdchwamUh22/C9ohCCLQpoleigE2rSpccxwr10acG39Zv8fE/fxE+8Mgrsac5vqWAauP8WHGpCdSUgNBGtL3GHIRhGJF6DSMI1SQQzSEMuVagovz0HJSSMUezmZyNnQWAog/bPkArmlQkXJ1YI2j7sFoCAQ5xh2zOeDPBzAXK1wqjJDa/nK8JOQvQMX5xGygjGZXsOr7eYeJ5tQEIIvBUuBWrv6vA1EWUEo6d18O/ufpWbKQR3vC/34pLv1WGNCZWXzpRqqMJgVB3KU4NYeCSJSLG56B1Glbv2YU/vR74zsEx/J/j5+Er/+CDWKE+2hRx502XYv2df6sww3w+y9H1ldJxexpp8YQAVW+SexFMGxglUC+VzUNHCaERNZ+kwUgLNEOgXcl2cIElIFbF1bZtB0AYkWkWYcTczbh/TtjhBJ2EtglA4qYVOo7Y40xGZXaPMAQybNhU8JbPr5oJIOm7fU4QMm99yr4MIPsX1E4fYzzvkBP/gyIm4WDLpvI3zr/S5mPVXNBejesXE15zzuP46mgT5//JKqjNdjcX/UhcPixVY9GPJyskHDZCNbAaDdmnBrFfHSfzOfDZY3jnw2/DN68k7PtiwuZ7P4LvXVtHQwEvv+B+/EG4GKm4z7lnxPnnr+Pxx/acxOBPnBZKCEwt5ugdQxIpQI8schDaxCmqGkEQgcCaAqFdEe/2pmPwJCu9FPRMJKt+q7n/rmoQYC3JAZQxdXEutn0yByPg/RFwjA7DCowxdwQCuB7BcI1ALaF3LJmX3t+fZpNbpBPlHopjTUaRBUVsRJiaEzRZCLFGJ/ociTDkAi/tasDGC46hTw3+YP2FWHusLXwd26EaYGXnmeYTAKyAS3GuzYiHnzy32HbsmSk7Sd28aBix98Gj2Psgb3rP+9+ML7/tdnz76iP4Dx9/FZ4Vj3aaHbFPuHDtKB7HfITA0ifQQfwCx+Il9HXpleE0Rs+IOl6Jo0YPpPJOGKVcXahNJjzsWoZSlBi/MrpEI9g8kbLlwxLaXFx/yL9r5EErFZl3voH5CRRYVNcV1GtSm8+jEYrY02QiGhOmmg7NX8TP0adx+99/VnNjk+d0+PIePnDdb+Gx9gg+8Md/D82xOCYAfMNUfk5bPUg9Tr/XJ5xyAiLjjie/dEGRDvySl9+L0Z6BjCHl/SXspxrl/i8cwSff8T14/02vx2W3HSvPDZjpceQZDd5w8We3mMzO0UIJAb8iFfFyBY4A5tAqXmwoY+cXnvMJMsP7BqKajcghwdJ5mMhlBmpRDg0LyuXyil+uvOZMlKEq0/saCDlEl21wD67xgJsclssmi0+jDiPRgCTaUYB09E83ea1DrjHWtdg5MPWeqaBd/zvHcPXgMfz1qI99d4dxYNB2SM2VE6CxyAcBF9ybcPD4XtvnXzzzdnzjxZLv4vwcuYpUThLrH97E4PHN7EsBkBvcJox2NXjye47h+3d/7cQGfApooYTARHNAH0oFpAmiDUBUPxpFy8zT3nq6aitaz1Z58oKAX5TRiuT9W6vxVDAbALPzRyvkOh+Jr0IFkItU2MofSuHgMxmVvACZek/EzlcNJzVZcNUZf7nfoBNqSeYvVYkV2eih0pYsBWB0ToOff/Hv4xnNGn7tb16B3d8Y2T2cShLOq6HEnd2aa3LznihoxC+w96+O462f/hGsR04eunKwijf90O1oV3sZWejh5SIMVCCgbo3mzJVvfXsPn3vpr2F3WN1iwDtHCyUEjPTBUWbY8rN8lKQiX9E2SMmtIPn0qpJrFiAnCGUnIcDM0dtQ9KCU7NaQmjnXslAAeHxaLcgzKpsGlZ1dM3xC6Yl3Tjwdj263PIYOoqRpwzA1P/ZQmgRUCiBd2fWvHRBGEpZsB4G1J3WiAkBKeOzKBtesPoz1tIE7Dr4wNy+tnxfcNSkfX9AkAVALlIjyuY/tD/GTEGjY4tJPAndv5lj+D+79Ar564ypiL7BgHsVCENhw/OkVGNUyjPuJ5+/Ca970Jzi/mR9aEFhUIYDKHKjJ5ZnXyTS+yq71HgyCGSjs6OyM0hVSu/20Ay7Y2a6IA61S++usQHUu5hVXhJAr0FG8fGr7O8a3XATkegBW5NMJgoyehEUgescj+kdTMTaPKMw4BTGRGo5AjHY5n4Q1ZKHsC5D8ixe84gE8t78btx19Jp75Z/mcnSu0d/p1PENN/T0Z0loAGtZFIKw+uoE3/ulbzDdweX83PvlP/i0eeck5bN6Ipgjv4zFhnPJ2MSUPP3cNr//p2/Cz+790UmM9FbSYQmAre1G83FZ+DJAsMbANGLPtDJTqsE/nVegw/8aCo9nkHdoVZpJ2oFha5BWvw2GVHXRZGBQ1/TzD10Ih+Tll+90iAs7WTw04dEf+fBwF6R+LEs7LzjBl1MKc0eIrokWwOeQYw9VIBIC7HrwMb//6dfizp56HwVOt4R+K5zRJG1BsRAeycawP4RaUNFzs/REiuGgUcdFtfXxhI9cTvLy/G+/98Y/iwVefg81zB6IRxlyfoo0ZhSqQ9NQQHrtqNy552yH89L4H7VzD1ILW5xOsW6gQoZF/X2qGAaDYgUTE5ckll54Ak+7U8oo2ch2LObOQXyat8EMpgaQ4Z4oJJBccrRJGKyogSAqV8OpDEj/PpkF2QqaGw3YUSzSdzyZk5pzw0qvaLv4Ea5yiskTV8EacneR6N8opg48E2PWQbXQRkoxpqC6vZcnkHlMELvtYwN27vxPDtYBz4mhMMHlgUtczLMjhJ8oLd0j+KSHDFABta6aZgnv/6ih+/Ja34pbXvR9XrbBj8LW7n8Qr3vDLuPZZP4HmK3tw0eeHGDwxtN4CfMMIcaXBY9+xC09cvYnv/66D+OUDd9rPG2mIH/3rV+Liz0yY0w7TYgoBZ09SJRCAyslUYwY0VCgtx0JLZuNa6K1J0oK8VGlzrJ1V6TjgdF+KAYOnotnxXG+QbOVQ9CCA0hb3TjbAOeVSZh7nz/CRAk1WMtShlimr4+QjAFUOg4Yt/X1j08IJA4UY6/32GkOlzlMCBk9F9Ndj1gL85ap+j/5ZFWONaUygT40uuOt70FiRXKWagAB7Lr/1CF5z7k/hV1/5UfzDc7gOwPnNGu5/2YfxrRuO4sM/eAVu/vINGB3aY1Gf4bkRl7/gEXzoub9twkPpsfYIXv2lN+LorRfhGQ89uaU/cyfo7BcCRIhrE5IzxtJI3UsjKamkSUSitlvGXss7WwPSkNtwk3KkvEmp4V4CpMdHRgKmhrIgaAN6x2MOFxpYSO0LPnd0T8yrwwWcOJE0Gi19DT78lVdsFAKDfxeHZcOAH3KOQV/2LN83BkdRiqbB6GpupdY1pwKsIdVj4xPl66cgVZO7NAL3rDx0Wu/tVNU/oIQGd2YXZh8GCNYPQgXR8z66gX9135sRf/IjePU563bY+c0abjr/Idz03Q8B39118VIAHI7HcNPD3wv8l/3Yc3w+9QWBBfAJ0GCAI5dm7+u0lcEy8bwpSWQIwmJVT7x6qqMsp78yaMevfuY4o8x8zWZC/wi3LE+B/QMaOgRgMXQbj77ssf5L5idIbmUL6mF32wpBEchpEmWKq67sbZ+hzNEBf3Qu5qwU8weANSodrRJGuwI2d/PfaJXLnQ3X2AfCxwbzvrMwJOttoH9eIxgrP+bCg/6ZbBkZiOM/lx58r8HkkmEGBBIBedHnjuAXf+6N+MmvX4th2j4Df3W0jqs+9VP46vuej8GTrQmbedDZrwkAhYo5K7acC3eI3ZoSwigi9nv8oFrn7Y1s06ee2OuaDjzkVGBeOZN4nPOqCuR4vnruOWyIzLiqAgN2nhp1x4NNlk5rTUddwVJzVvqyYE7lNe0DCUk4wldUpggkcDUmSmDnqJw79nIjT0ZG6vl5LM0wFVEUFYSKG8h+CTlf39UdsIQoFJrKmC2vGp3m62+XvEboNQ84TSkAiGRaFGLCefcfwb3vvRJXvPRq7LnicfzCFb+Pl+9aRw8NGurm6GFqccexVbz9996By2/bRGhHaFea3O1qDnQybcj+G4CXATgsu745pXQXcUH/XwXwKgBHZfud42c+fXSiGHQ+WBhglNVMv6IDspLqyqpOQi1PRpnZgKyK2+nbnCSk8XdGGbqqQWJeFE5MWfUYzKQqfEL2lgMhStabG3N04B5EdggasnCsFDrKsftowAigFK1Nuq7CvZHuiyqC4swXEQJWGl2Ri06I+D6HlttABARNTioFQeKf8kpfRwYm4gFKk1AFqM2VYMhBNPIuRZVLhMHjm7j81k2kP1zDe579T/H2ayLSakRv9xAXnv8Unlhfw+bxHlIbkCKBjja48PMBlx/iUGNcaXjeDU0e4w7TLJqAtiFbJ6I+gP9FRH8kv70jpfQ71f5/H8Dz5O9acFeia0/VgE+ETlgAeIpllCA7wMigtQgZHWcViQEgsXc/iPcfVRy7dzwhNr6vYM6WS5K+TNFkQV7tYgn00WIn5hAEYMhFAT1ZVp5Ci1UjIGQVXI4lxxAAzDnmSZOVuMoQmXdex+r3kysXoTcef3b6ZcgysoniL6lzmIDyHEu1BjATc3lNoNOk0EtINqOmHaucbyPOPXQU5z0g9ysQUm8v9owSECOoMhlSIKR+yFDj+fA/gBmskMTU1YZsEn0fgI/IcZ8F9yc4cPJDPbVUq41dgsKYRlc1ZV6N/aun29uRgLX45rp+cj2H+89oufziN0OG6XI4kKz6bvbeJ3vJfeXg3DUpr/g1oq4O6fleiooX8GAfvj+6f/mCan3FMXvdbqxb9aHCJt9Pm79oLAousnqGEpLVWgf1G6rYBvMHeMdunPZaTiGPHNRTTGJKFV7iL0CTfQU8j2DZiDSMZnbZPtaLILhjTtFCdYI0kyuCiBoiugvAo+CS4wflp58nonuI6N9LN2LAtSET8i3K5kLT/ACdGWkdK0ciBoz4Y/g4PrBITnL1BnlFk98FMONr7PmwnUKS9Ryah5BItAF9oRrYOYrohEvg8cAlzXgE1A5nGLNeL/VcyW/RHLznHV4gpCyQaqCSahvK6MlBhDOSkETjgfUxGMmfzoG8bW9mj9umY9OQoDoJtYuUp0lagH/zU9ZO6mc5kQpthoUBGtaGUs8xev1nOQWqARBqM+x000xCoG5DRkQvBPBucDuyawDsA/chmJnm1oZMaOZWVQC8o85n7fGJZJekiT0oKvD6SjsGL43JGCL2sj1t6cNqsouzURnKMhW9vRpQQYgpr8YKIJIXbrRKLJS0MUojXnrnPLQOyXJdRT76/10ddBUmnZmcDNzkX3JqE5ohCyKds5Zps2tXeQM1Jb9yu/NuCylY1Dioyph5oWDpy5T/AhXfx1b6oIKg/EMAC4uQn+G2TJYdom1FB1JKTxDRHQBuTCn9O9m8QUQfAvAz8t3akAn5FmX+XDcDuBkA9tK+E9TjTpzqzK/xHcheBlWNFTikeHJNI9UQIYfl+HDvAKQ2lxpTLz7/wM47/YzEZoEVDhFfAjdMRaGuGPhHQnd8eMrxc8Pqa6weWc0mYWpvFkhIUjsbI+XKStkn0KEypzwWC5vaCo1iRQ9tlOSgNGZqeEBTjgLA4QHK1OrkrJQsRLfpD6gxAxOocMhS9R8o/STep1BpjJYwVp9nvorACbch+4ra+RINeDWAe+WQTwB4EzFdB+BwSumRHRn9SVDXajILMQ5cVopQryC6U1blbaUTr7n3pnt1XVVgg/NSNiMSARt7ApcNd7kIXTBaU08By9gD2D/RbPBObZ8rHIVWOg3LmFnTgY27GbLQ6G0kK1xS1mSAjTcIirKRfAHfKLXTVBLzxvwq8lvUMCmyAODP09eJbfkD9K2v5uIzSPmksOds6n/XX3B/TkPLJedg2kM+D+XzIs9zHjSLJnAAwIelPXkAcEtK6b8T0WeIaD94GncBeJvs/z/A4cFD4BDhj5z6YZ866swdmLjvOHzVk0Jx2z7Zi6Y+AEUJRmG+Gg2XQOYNbzaTaQ0aGVC7OpidThZCLMEuGpfPPoKQssaimYPasKREEsoYIuVVHnmMlAAaxgJoBGB81Zd7pQVOrRSXKy/u4c92D4gzLHvHYQxaawK2ovr73jphUjBxB2fNYgArOIhyXUOoMMDW70z9UwLYfwFkbYDyvHxa9TzoZNqQvWLC/gnAPzv5oZ062paU7QChGHBIQllWjRiAdiayQ1pot6qyg5DYnTTGtCgZSF6wMOIkI+14pLgBXzUolySXFVfhvSIwfFqtah7UAnV/WAPwNCKMXPGRQg32arerOKyrnflCZD4qvExrEYFQYw+yINAaC+M+DhZOcp1COrkxkZ6j47nWzO+/V/4BP6cE6hQcXYKg2FZpJoWgVPOyBaiRZDBXf+J000IgBmchH1Lb8mEIYtAcZO55kxcKxM40jd17W98amCgwxcN4FRLcAmiS4Q40sWjMLtXV1Y3baxqJ+Ls5GeVljVLmK0gxFFDG9HuQj79OkTtAMGde1jSYaXS19+XMNaIRe8GalfC19J4lgTy7Kj0aPumyncUvMzFfoOtZRnRrAz6BaBTRO07Wuo1c1GbMLOnyKfjrWlISiu8gAklSWhqmsW5Pp5MWXgh0phFP3DmvUiQv09iqI6u4YgVUQISWVV0L96ndXx/uPPphxL0QuYRZLlqaUXc8nhBRCBFf3ET3MW+/q+/XxKxd+EQk35swaxd1XkOqVnFk4FIq51PfHyTuvkRRhI7cJ/UxFOaGd6bVpyJGCc5URGSaA1AchHrN5niLZqOjUpBLgZ6l9Fnnd+83ciHWWU3SnaCzPoFo29RVnALjKrwVG8H46hDafAzbtCTCwZ1CE2ccFqAYhqi2vhafqv3G5LJ6q6fflxpPgSwXoRnmisd8HsfkonF4bcVj99WhVfcSUNPAh/PKWogYW731er4qcuzlqImaK7HIVsyaRvGH8rxjgmKa47cWCB1Q46605GwqlM+swFVU3zPeguy+j0l+Gn/+p5MWXhOY6uTpijerbSwmAZrxlUqLjoy/RMwoQW3ejpfUEnfkvIz2yy+fryDsQ3q94xlJGPsS/qNsJlgykIQxY49yrF7sVK0oTHJfihVdBJLdA33hg05MflLkojMBivCd2NmWAdlkoJLOp9nEGKOQm3OC+z25ZzOJNBQ4q1MQYoZM2Ud/LzENLtO0Mgfq98wjO9Mc/QHAAgqBLSXujA/DHHOuR4BXhymyScCMnFdH0jLlyKFAIL/kyb3cqQEifPNLsA2pdr34DprNiDDMKcUK7LE0Z2E0ZTyAGX4kXYh8/N98AY12SuLvsQekPuXwKOVCG3VkYOyeq6BAfvkNQ5EAxFw5yfs+FK9g91wZvg7lmYo9wSk4yRRQwdDlJPRMTDSOKeh6T6Zt85iTsX0mjO800cIJga6HsP3ogRQbabljMT/cDAbJLzIy44kwaAvJX8XdDUrKY+I0XdYqatu62UwW40/EKD2/soSRzEtxApL8pEIkoxLVQQk7PjMbpDS6VEzq53PnMRMSiSCRuRMqfwFgWgl0v5THbtdHvl+pASdRRWeDd9jiXsMoaBa7vUszqDEEeg4VGLFjn67jasGi4eV6THPUAJQWTgh0UVG8Y8rL4yU55xIkC5fZgqUru2oKDUlGoAiOUV6BvWPQ1HsNTalWoI69kJmFgTnItv9KNh3Y8Zihv1zFB7aqq92t11SAju8RqPkBHEEAe/sF28BVj5ONxbIT7Xp8YgMFqWAQn4PXLhTyXNw42T/nTmQzh00BFQhy7koDMYBR1zOc1SQAypW/i+m79vP71qSagBMEqfo/L1p4IVAIAGCyZK5sPACcXjyMGK020JyBIpVYVHGPIAzDBKwws1CEvUCeaczmNfOArKWZdwwCOf9eQ35AXh0pgb37WnSEYGXKVCX3iUqqfptjU511MWs6GvlQ5g3DJDkQBMMrxAQaohhLAREukncwtor7ZCckyt5/8cEU9SGqyMBEre5EXOBd2AJ9Zl1mRNc1vEbhFxjvhEwdN+E00sIKgRqHPivVwCH1Io850kQQuIC3PecguQQhKHMhO+XcftzEFAIegq3kHiugjikPDkoSP1dmYsBROTa+CXnVjX2+RrNZT1hXe0HstdzR2H8PSFYExMKi9T0NzjGoyMSJDll3L3VVd/F2e15dHvydJrfyWz7DNAEzKT+hQlfW204nLawQSMTlsoDqxVKaMbmEhhHUj0j9wAxt7vVS9VPEGxGsErG2Etd8cm10qsyvCUneDIlNtvc1CqAVenrHWQDEPl/DpqJ5Cz1up+5DmNl8gZkCJToQFiJUrIBqBgb2kSiDecrH7HMUvg80WagUu6l24p5NaoA0yvOHQyua47BW/7dhCnSNY8vfpjH9NkyOST0mTjctrBDwNLNH2Yd7wKuTdgNKPXkfWxTNO8xxJcyldf5ijyHFKiiCvdBONQcMZQctcS41+UarrII3wwwftgxA5OvzuVyKLokjL1YLrvgEOEQnTkFkpk9NTg/2xVSK2yOdizMzqzmRjGlz7kXpGwAcoxOyk1VXWp9LkLjioYcjT3yOShMEgP7vYvYtk5acc9P2neRsrN+nDrNgXrSwQoAcQ89+UKW2CReFTU6qKYpe+9EAAAmXSURBVLoJmccvq+8+S7B/FDka4JxZdSzZ4LUpM+JwLSAFAQGRJBxZuy8YFsBqB4hvQVfOMadd4u3tgKQEmpsf8jh95qHHDLBGomnSjGAs8g8s7IgS/+B5TBle7pe1NY8qTZ0JloAEV+lpGtV2vH+cM67Ck4SET2qamZygn7dDUGkxhMAW93qqR3nqgboyRoQRCwHNwrMOvGpTa7EJ2dZspKJCUEYBssfBEkzEIZca2OrPTkJYUpDOQZOT2gFxA1RzyvF11BehyTYxZMEA8Oe2z5JE920HWjE4oRkKnl7q/0UpHa7MWPgdKM8fyBDo4l704cKTWVsAsVnlkYsgcQrGZBl5nap0V67ADOQZvWb6SUyuz8aSm7YwA046PL1DtBhCICE76nQTuRLatjHNLAgKJ14CSHLqY5Vgo2RxeoJ5+rWyEJBBLikArRQRYbWdQ4CtFAHVpqa+kEfsqcDI1/TFTTzMlffx2PzqHhAsRg/ksCMCWX0BSkl6D4R8DIF9Hsld2ykV2oodAHzEwJ7LmFYgFkeAmSQQYTDVGdj1DGf079gtcOnKdXr0eEiyNAOm+xjEjCyOn31cO0WLIQTcy9i1emwZHhw7X/ZWG0NJuDBKirEhCP1qqB50UXupBVKfmVhtaWWgZrO079X296g/NRF83D+0Cb0NFMlCQbr/qhZRJBMlWM9AS/uVeeXsPu5MrNqKNhDxmoZqJVo5yVKTLcbumMMLSBE63nnoMw81E1M1hIiAMIxZEHY9my6awVnnGXgrFX9WITFNKBipEJ2TebAYQqBalYufZrnvHilWaBOVNjCMoBWO1bFHPQsCv+IlIi7u2c/196hFLv4hqzyQV0E91moW6iIcJYuQ8nGk10+w0t1t8OE//t2qCkEYry/4hRbMaOCx945FS00eroXcZCVlXwNrDGTjp5gQoL0U5DqRCi2gUL8rLyP7T+RYExSEuML/m83YbQ444WzPbkbqLFjixrhdmkUAlIlp81ELznoh0Oy/0EAvRtW9znH+VL48k7LNgFIb0E0tawNtCFnodAgg7VbcbCSEIKt04xxGIcf9NcnHazO6Miru3kA6IkSaIdxKzscHSQm2tOTWC45k+1PLZsdoV0CzkdBfl1bkgbeBKNcqcKuxb4FWXresIjSWdajRBmF036HJAFeitYTk+hvUeIbiQXQw7Iyhu5rpPfPXfgItNDpJe+hCMz4d6axPJW4P7CszvSZQ5wNSVNikF0hNAX2ZU0LYjIbImwaG0eKful+zmQokYGjz6h/86t8xFdsuJoZu02t4cI6GGHPtAIipoPtls6B/NFp1ZU1OKvsZ5vH4EuoAM7OVUHeqPt9r3QkW1ah9AhZG9KFErZ3YMZapNJPTLk1k/GmreZf2MElr6NQ6l47B00sertrZfGTSCqJUawdd9l8bc96AZMLVyT/qrIuAreJFyq0wnTJHBNl+fLz8oMzi/QRgQaBhQytc2uRrd5Xmpgg02kkJxKnJIpQUWlyUCZP7ZT0IFC4t5g5Rvqb5CnSuMh9lCi2hpseZQDBZwdmKGYXJx46r7h2MdoIOwS7aTtTgRMcwDzrrNQFPtJ2H4ZNHtvAuFwIkwRxX9XULRhA/gQJwANiqV+cHAOoLKOvhm0ngx6LMKNqDdhDWvAIdV2xyzQENW3Ix1ISVJ9uiy1G7Eqz8lRZC0ZXYhwJ98Y+xTskxaynWo6G4N9kUqu8bHyNjcX6Umk4k3NbF9PXq3+UE9L8VvRK7aBqXPQ1MhMUQAl0qtHtgnZDhackgdl4qtQdvErTjKxqgKzGsf59l9/mYfpOdegCs5p42rfT7e8AMtRCgEDmfRLbJy4QgObcrKW4OSDcfC9HpqtymQs23+bnIRhm9QBYUOj8vGPw1qfxv5pI7p/pLfLnusWdiN7ubw2p13zPwJJ9Al1Dw330l5KIxCbClJjBvrMBiCIEp0rbTF6AIM0+h+j/pUuIw9C3FABQra23neuRgdv65kuKB04+hzsuUBUdR8svb5yJoQK4jkOscRK0w/0ZG3inDx6YUbOpr8CG8Zlj2OMxAH7HdhfELbcUJA0MIkl4TFmmY5PtQvIeVZaNsMuUyZF4F6+auSQw9xryYrBV4p+AsJsIYOWfpvInq8tNzGQTRUwDum/c4doguBPDYvAexA3S2zgs4e+f2rJTS/nrj08UxeF9K6UXzHsROEBF9/myc29k6L+DsnlsXLYY5sKQlLWkiLYXAkpa04PR0EQI3z3sAO0hn69zO1nkBZ/fcxuhp4Rhc0pKWND96umgCS1rSkuZEcxcCRHQjEd1HRIeI6F3zHs92iYh+g4geJaJ73bZ9RHQ7ET0g/8+X7UREH5C53kNEV89v5NOJiC4lojuI6C+I6EtE9M9l+xk9NyJaJaLPEdHdMq+fle2XE9FBGf/HiGgg21fk+yH5/dnzHP+OUEppbn8AGgB/CeA5AAYA7gZwxTzHdAJz+LsArgZwr9v2PgDvks/vAvBL8vlVAP4IDBG5DsDBeY9/yrwOALhaPu8BcD+AK870ucn4dsvnPoCDMt5bALxOtv86gJ+Qz28H8Ovy+XUAPjbvOZzyezLnB3I9gNvc93cDePe8b8oJzOPZlRC4D8AB+XwAjIMAgP8E4PVd+z3d/wDcCuCVZ9PcAKwBuBPAtWBwUE+223sJ4DYA18vnnuxH8x77qfybtzlwMYCH3fevybYznS5KKT0in78B4CL5fEbOV1Tg7wKvmmf83IioIaK7ADwK4HawNvpESkkbrPmx27zk98MALji9I95ZmrcQOOsp8RJyxoZgiGg3gN8FcFNK6Un/25k6t5RSm1K6CsAlAF4M4G/PeUhzpXkLga8DuNR9v0S2nen0N0R0AADk/6Oy/YyaLxH1wQLgN1NKH5fNZ8XcACCl9ASAO8Dq/3lEpDB6P3abl/x+LoBvnuah7ijNWwj8OYDniWd2AHa8fGLOYzoV9AkAPyyffxhsT+v2N4kn/ToAh51q/bQiIiIA/xXAl1NKv+J+OqPnRkT7ieg8+bwL7Of4MlgY/IDsVs9L5/sDAD4jGtDZQ/N2SoC9yveD7bJ/Oe/xnMD4fwvAIwCGYFvyR8E24x8DeADApwHsk30JwAdlrl8E8KJ5j3/KvG4Aq/r3ALhL/l51ps8NwJUAviDzuhfAv5btzwHwOQCHAPw2gBXZvirfD8nvz5n3HE713xIxuKQlLTjN2xxY0pKWNGdaCoElLWnBaSkElrSkBaelEFjSkhaclkJgSUtacFoKgSUtacFpKQSWtKQFp6UQWNKSFpz+P6g6itAdNg89AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-03bdc496ee63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Display images\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Not necessary for training. Just for confirmation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    626\u001b[0m                 \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    629\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1315\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1316\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1317\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tasks_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1280\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1281\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1282\u001b[0;31m                 \u001b[0msuccess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1283\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1118\u001b[0m         \u001b[0;31m#   (bool: whether successfully get data, any: data if successful else None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1120\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1121\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/multiprocessing/queues.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    105\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m                     \u001b[0mtimeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeadline\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonotonic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    108\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mEmpty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m                 \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/multiprocessing/connection.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_readable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/multiprocessing/connection.py\u001b[0m in \u001b[0;36m_poll\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_poll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 424\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    425\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/multiprocessing/connection.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(object_list, timeout)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m                 \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    932\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfileobj\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m             \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    416\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Display images\n",
    "# Not necessary for training. Just for confirmation\n",
    "for images, labels in val_loader:\n",
    "  \n",
    "        i, l = Variable(images), Variable(labels)\n",
    "        print(i.size())\n",
    "        i = i.numpy()\n",
    "        l = l.numpy()\n",
    "        if l[0]==0:\n",
    "            print('Label = {} : Normal image'.format(l[0]))\n",
    "        else:\n",
    "            print('Label = {} : Fracture image'.format(l[0]))\n",
    "        plt.imshow(i[0,0,:,:])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7djtlVQHdVYk",
    "outputId": "1be00597-331b-4e14-82ed-af06889a3675"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AlexNet(\n",
       "  (Conv_1): Sequential(\n",
       "    (0): Conv2d(1, 96, kernel_size=(11, 11), stride=(4, 4))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (Conv_2): Sequential(\n",
       "    (0): Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (Conv_3): Sequential(\n",
       "    (0): Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "  )\n",
       "  (Conv_4): Sequential(\n",
       "    (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "  )\n",
       "  (Conv_5): Sequential(\n",
       "    (0): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (FC1): Sequential(\n",
       "    (0): Flatten(start_dim=1, end_dim=-1)\n",
       "    (1): Dropout(p=0.8, inplace=False)\n",
       "    (2): Linear(in_features=30976, out_features=4096, bias=True)\n",
       "    (3): ReLU()\n",
       "  )\n",
       "  (FC2): Sequential(\n",
       "    (0): Dropout(p=0.8, inplace=False)\n",
       "    (1): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (2): ReLU()\n",
       "  )\n",
       "  (FC3): Sequential(\n",
       "    (0): Linear(in_features=4096, out_features=1, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build convolutional neural net\n",
    "class AlexNet(nn.Module): # AlexNet 모델 설계\n",
    "    def __init__(self, n_classes = 1):   \n",
    "        super(AlexNet, self).__init__()\n",
    "\n",
    "        # 1st conv layer\n",
    "        self.Conv_1 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 1, out_channels = 96, kernel_size = 11, stride = 4, padding = 0),\n",
    "          nn.ReLU(),\n",
    "          nn.MaxPool2d(kernel_size = 3, stride = 2),\n",
    "          nn.BatchNorm2d(96))\n",
    "        \n",
    "        # 2nd conv layer\n",
    "        self.Conv_2 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 96, out_channels = 256, kernel_size = 5, stride = 1, padding = 2),\n",
    "          nn.ReLU(),\n",
    "          nn.MaxPool2d(kernel_size = 3, stride = 2),\n",
    "          nn.BatchNorm2d(256))\n",
    "        \n",
    "        # 3rd conv layer\n",
    "        self.Conv_3 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 256, out_channels = 384, kernel_size = 3, stride = 1, padding = 1),\n",
    "          nn.ReLU())\n",
    "        \n",
    "        # 4th conv layer\n",
    "        self.Conv_4 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 384, out_channels = 384, kernel_size = 3, stride = 1, padding = 1),\n",
    "          nn.ReLU())\n",
    "        \n",
    "        # 5th conv layer\n",
    "        self.Conv_5 = nn.Sequential(\n",
    "          nn.Conv2d(in_channels = 384, out_channels = 256, kernel_size = 3, stride = 1, padding = 1),\n",
    "          nn.ReLU(),\n",
    "          nn.MaxPool2d(kernel_size = 3, stride = 2))\n",
    "        \n",
    "        # # 6th conv layer\n",
    "        # self.Conv_6 = nn.Sequential(\n",
    "        #   nn.Conv2d(in_channels = 256, out_channels = 256, kernel_size = 3, stride = 1, padding = 1),\n",
    "        #   nn.ReLU(),\n",
    "        #   nn.MaxPool2d(kernel_size = 3, stride = 2))\n",
    "        \n",
    "        # 1st fully connected layer\n",
    "        self.FC1 = nn.Sequential(\n",
    "          nn.Flatten(),\n",
    "          nn.Dropout(0.8),\n",
    "          nn.Linear(256*11*11, 4096),\n",
    "          nn.ReLU())\n",
    "\n",
    "        # 2nd fully connected layer\n",
    "        self.FC2 = nn.Sequential(\n",
    "          nn.Dropout(0.8),\n",
    "          nn.Linear(4096, 4096),\n",
    "          nn.ReLU())\n",
    "\n",
    "        # 3rd fully connected layer --> output layer\n",
    "        self.FC3 = nn.Sequential(\n",
    "          nn.Linear(4096, n_classes))\n",
    "    \n",
    "    def forward(self, x):   # AlexNet forward propagation\n",
    "        x = self.Conv_1(x)    \n",
    "        x = self.Conv_2(x)\n",
    "        x = self.Conv_3(x)\n",
    "        x = self.Conv_4(x)\n",
    "        x = self.Conv_5(x)\n",
    "        # x = self.Conv_6(x)\n",
    "        x = self.FC1(x)\n",
    "        x = self.FC2(x)\n",
    "        x = self.FC3(x)\n",
    "\n",
    "        return F.log_softmax(x) # softmax  output \n",
    "\n",
    "model = AlexNet().to(DEVICE) \n",
    "model # Print network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M6Bs7ZPOd4No",
    "outputId": "cfc0c349-7ccd-4ed7-dc13-6b700c1bb95e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 96, 98, 98]          11,712\n",
      "              ReLU-2           [-1, 96, 98, 98]               0\n",
      "         MaxPool2d-3           [-1, 96, 48, 48]               0\n",
      "       BatchNorm2d-4           [-1, 96, 48, 48]             192\n",
      "            Conv2d-5          [-1, 256, 48, 48]         614,656\n",
      "              ReLU-6          [-1, 256, 48, 48]               0\n",
      "         MaxPool2d-7          [-1, 256, 23, 23]               0\n",
      "       BatchNorm2d-8          [-1, 256, 23, 23]             512\n",
      "            Conv2d-9          [-1, 384, 23, 23]         885,120\n",
      "             ReLU-10          [-1, 384, 23, 23]               0\n",
      "           Conv2d-11          [-1, 384, 23, 23]       1,327,488\n",
      "             ReLU-12          [-1, 384, 23, 23]               0\n",
      "           Conv2d-13          [-1, 256, 23, 23]         884,992\n",
      "             ReLU-14          [-1, 256, 23, 23]               0\n",
      "        MaxPool2d-15          [-1, 256, 11, 11]               0\n",
      "          Flatten-16                [-1, 30976]               0\n",
      "          Dropout-17                [-1, 30976]               0\n",
      "           Linear-18                 [-1, 4096]     126,881,792\n",
      "             ReLU-19                 [-1, 4096]               0\n",
      "          Dropout-20                 [-1, 4096]               0\n",
      "           Linear-21                 [-1, 4096]      16,781,312\n",
      "             ReLU-22                 [-1, 4096]               0\n",
      "           Linear-23                    [-1, 1]           4,097\n",
      "================================================================\n",
      "Total params: 147,391,873\n",
      "Trainable params: 147,391,873\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.61\n",
      "Forward/backward pass size (MB): 37.64\n",
      "Params size (MB): 562.26\n",
      "Estimated Total Size (MB): 600.51\n",
      "----------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-c0bcb066ec06>:70: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  return F.log_softmax(x) # softmax 통해 최종 output 계산\n"
     ]
    }
   ],
   "source": [
    "summary(model, (1, 400, 400))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e6IE_5rgeTlq"
   },
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "LmAhK3zme435"
   },
   "outputs": [],
   "source": [
    "def train(model, train_loader, optimizer):\n",
    "    model.train()                         # train \n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(DEVICE), target.to(DEVICE)  # data, target DEVICE\n",
    "        optimizer.zero_grad()                              # optimizer gradient \n",
    "        output = model(data)\n",
    "        print(target.shape,output.shape)\n",
    "        target = target.resize_(output.shape[0],1)\n",
    "        # print(target.shape,output.shape)\n",
    "        target = target.type(torch.FloatTensor)\n",
    "        output = output.type(torch.FloatTensor)\n",
    "                                       # output \n",
    "        loss =  criterion(output, target)                  # Cross Entropy Loss loss\n",
    "        loss.backward()                                    #  loss back propagation\n",
    "        optimizer.step()                                   # parameter update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_a_OgQhoe-wF"
   },
   "outputs": [],
   "source": [
    "def evaluate(model, test_loader):\n",
    "    model.eval()      \n",
    "    test_loss = 0     # test_loss \n",
    "    correct = 0       \n",
    "    with torch.no_grad(): \n",
    "        for data, target in test_loader:\n",
    "            data, target = data.to(DEVICE), target.to(DEVICE)     # data, target DEVICE\n",
    "            output = model(data)                                  # output \n",
    "            test_loss += criterion(output, target).item()         # loss ( loss)\n",
    "            pred = output.max(1, keepdim=True)[1]                \n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "   \n",
    "    test_loss /= len(test_loader.dataset)                         # loss\n",
    "    test_accuracy = 100. * correct / len(test_loader.dataset)     # test(validation) \n",
    "    return test_loss, test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "S5yDeWfnfDUR"
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import copy\n",
    " \n",
    "def train_model(model ,train_loader, val_loader, optimizer, num_epochs = 1):\n",
    "    acc_t = []; acc_v = []    # train, validation accuracy list\n",
    "    loss_t = []; loss_v = []  # train, validation loss list\n",
    "\n",
    "    best_acc = 0.0  # beset accuracy =\n",
    "    best_model_wts = copy.deepcopy(model.state_dict()) \n",
    " \n",
    "    for epoch in range(1, num_epochs + 1):\n",
    "        since = time.time() \n",
    "        print(epoch)                                    \n",
    "        train(model, train_loader, optimizer)\n",
    "        \n",
    "                           # train \n",
    "        train_loss, train_acc = evaluate(model, train_loader)   # train_loss, train_acc\n",
    "        val_loss, val_acc = evaluate(model, val_loader)         # valid_loss, valid_acc \n",
    "        \n",
    "        if val_acc>best_acc:  # update best accuracy\n",
    "            best_acc = val_acc\n",
    "            best_model_wts = copy.deepcopy(model.state_dict())  # \n",
    "        \n",
    "        # loss, accuarcy 저==\n",
    "        acc_t.append(train_acc); acc_v.append(val_acc)\n",
    "        loss_t.append(train_loss);loss_v.append(val_loss)\n",
    "\n",
    "    \n",
    "        time_elapsed = time.time() - since \n",
    "        print('-------------- EPOCH {} ----------------'.format(epoch))\n",
    "        print('Train Loss: {:.4f}, Accuracy: {:.2f}%'.format(train_loss, train_acc))   \n",
    "        print('Val Loss: {:.4f}, Accuracy: {:.2f}%'.format(val_loss, val_acc))\n",
    "        print('Time: {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n",
    "        print() \n",
    "\n",
    "    # Accuracy Graph\n",
    "    plt.plot(range(len(acc_t)), acc_t, 'b', range(len(acc_v)), acc_v, 'r')\n",
    "    blue_patch = mpatches.Patch(color='blue', label='Train Accuracy')\n",
    "    red_patch = mpatches.Patch(color='red', label='Validation Accuracy')\n",
    "    plt.legend(handles=[red_patch, blue_patch])\n",
    "    plt.show()\n",
    "\n",
    "    # Loss Graph\n",
    "    plt.plot(range(len(loss_t)), loss_t, 'b', range(len(loss_v)), loss_v, 'r')\n",
    "    blue_patch = mpatches.Patch(color='blue', label='Train Loss')\n",
    "    red_patch = mpatches.Patch(color='red', label='Validation Loss')\n",
    "    plt.legend(handles=[red_patch, blue_patch])\n",
    "    plt.show()\n",
    "\n",
    "    model.load_state_dict(best_model_wts) # validation accuracy, \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "KolcRe69fKtg",
    "outputId": "f4c6a0a1-cbf8-4969-eaa5-559b811b7bc6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-c0bcb066ec06>:70: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  return F.log_softmax(x) # softmax 통해 최종 output 계산\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([5]) torch.Size([5, 1])\n",
      "torch.Size([2]) torch.Size([2, 1])\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-d227ab54fbd4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# weight decay = 1e-4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-18-9935d2a6fe0c>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(model, train_loader, val_loader, optimizer, num_epochs)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m                            \u001b[0;31m# train 데이터로 학습\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m# train_loss, train_acc 계산\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0mval_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m)\u001b[0m         \u001b[0;31m# valid_loss, valid_acc 계산\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-17-0b02be33df05>\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(model, test_loader)\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m     \u001b[0;31m# data, target DEVICE에 할당\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m                                  \u001b[0;31m# output 계산\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m             \u001b[0mtest_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m         \u001b[0;31m# loss 계산(총 loss 에 더해주기)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m                 \u001b[0;31m# 계산된 벡터값 중 가장 큰 값 가지는 class 예측\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m             \u001b[0mcorrect\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview_as\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 맞게 예측한 값 세기\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1188\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1189\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1190\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1191\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1192\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1174\u001b[0;31m         return F.cross_entropy(input, target, weight=self.weight,\n\u001b[0m\u001b[1;32m   1175\u001b[0m                                \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m                                label_smoothing=self.label_smoothing)\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[1;32m   3024\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3025\u001b[0m         \u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_get_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3026\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel_smoothing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: Target 4 is out of bounds."
     ]
    }
   ],
   "source": [
    "\n",
    "model = train_model(model ,train_loader, val_loader, optimizer, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qYD2sXeBhkOU",
    "outputId": "a0c1896f-bdf9-436f-e221-a2f9a54aab98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['train', 'train_image_paths.csv', 'train_labeled_studies.csv', 'valid', 'valid_image_paths.csv', 'valid_labeled_studies.csv']\n"
     ]
    }
   ],
   "source": [
    "#importing the necessary libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "from skimage.io import imshow\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "\n",
    "# Input data files are available in the \"../mura_unzipped/\" directory.\n",
    "# Running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"D:/codes/GAIP-NUS/MURA-v1.1/MURA-2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting skimage\n",
      "  Downloading skimage-0.0.tar.gz (757 bytes)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'error'\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  error: subprocess-exited-with-error\n",
      "  \n",
      "  python setup.py egg_info did not run successfully.\n",
      "  exit code: 1\n",
      "  \n",
      "  [3 lines of output]\n",
      "  \n",
      "  *** Please install the `scikit-image` package (instead of `skimage`) ***\n",
      "  \n",
      "  [end of output]\n",
      "  \n",
      "  note: This error originates from a subprocess, and is likely not a problem with pip.\n",
      "error: metadata-generation-failed\n",
      "\n",
      "Encountered error while generating package metadata.\n",
      "\n",
      "See above for output.\n",
      "\n",
      "note: This is an issue with the package mentioned above, not pip.\n",
      "hint: See above for details.\n"
     ]
    }
   ],
   "source": [
    "pip install skimage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "FJbC9OlYrNVC"
   },
   "outputs": [],
   "source": [
    "dataset_root = Path('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s_FJohApztbz",
    "outputId": "077e0af8-c912-4897-d3f8-842c40721ca7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train_image_paths.csv'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train_labeled_studies.csv'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/valid'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/valid_image_paths.csv'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/valid_labeled_studies.csv')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dataset_root.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "qRQTY6EMrQYb",
    "outputId": "e9b4a7a1-3a19-411a-a704-61fe60b46481"
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "                                            filename\n",
       "0  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...\n",
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       "2  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...\n",
       "3  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...\n",
       "4  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df is the generated dataframe whose head can be visualized below\n",
    "df = pd.read_csv('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train_image_paths.csv', header=None, names=['filename'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "9NA-YHpurSsk",
    "outputId": "92cb644c-0fa6-4972-ddad-fa52137299ac"
   },
   "outputs": [
    {
     "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>filename</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            filename     class\n",
       "0  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...  positive\n",
       "1  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...  positive\n",
       "2  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...  positive\n",
       "3  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...  positive\n",
       "4  D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train/XR_SH...  positive"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding class column to the dataframe with positive or negative label\n",
    "#Classification is done on basis of the image names (postive/negative)\n",
    "df['class'] = (df.filename.str.extract('study.*_(positive|negative)'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "2iOet4p3rVVO"
   },
   "outputs": [],
   "source": [
    "#A function to generate the dataframe for a csv file\n",
    "def generate_df(csv_name):\n",
    "    df = pd.read_csv(csv_name, header=None, names=['filename'])\n",
    "    df['class'] = (df.filename\n",
    "               .str.extract('study.*_(positive|negative)'))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Jny_7oLerW_v",
    "outputId": "aa3674cc-2ed8-4e81-925e-a7c1f2f8ace2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train_image_paths.csv'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train_labeled_studies.csv'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/valid'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/valid_image_paths.csv'),\n",
       " WindowsPath('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/valid_labeled_studies.csv')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(dataset_root.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "3FTLlF9ZrZ6n",
    "outputId": "ad4d3f4b-3c6b-41a2-f995-88817cdcf2a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 23237 validated image filenames belonging to 2 classes.\n",
      "Found 1683 validated image filenames belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "#Dividing the image data generated into train set and validation set\n",
    "datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale = 1. / 255)\n",
    "#Creating training set\n",
    "train_gen = datagen.flow_from_dataframe(generate_df('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/train_image_paths.csv'),\n",
    "                                        directory=dataset_root.parent,\n",
    "                                        target_size=(224, 224),\n",
    "                                        class_mode='binary')\n",
    "#Creating validation set\n",
    "valid_gen = datagen.flow_from_dataframe(generate_df('D:/codes/GAIP-NUS/MURA-v1.1/MURA-2/valid_image_paths.csv'),\n",
    "                                        directory=dataset_root.parent,\n",
    "                                        target_size=(224, 224),\n",
    "                                        class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gPBW8SS1tzpd",
    "outputId": "9306e758-c7f3-43f3-8242-541df3662155"
   },
   "outputs": [],
   "source": [
    "#Downloading the densenet model pretrained on the imagenet dataset\n",
    "densenet = tf.keras.applications.DenseNet169(weights='imagenet', include_top = False, input_shape=(224, 224, 3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "8V2hDX4W26yO"
   },
   "outputs": [],
   "source": [
    "#Freezing the weights of the pretrained model\n",
    "densenet.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "ZSdhpM7y4TQK",
    "outputId": "21c1d27c-ab97-4f55-e0aa-357687c5b3a3"
   },
   "outputs": [],
   "source": [
    "#Adding the Flatten layer and the sigmoid classification layer to the pretrained densenet model\n",
    "model = tf.keras.models.Sequential([\n",
    "    densenet,\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " densenet169 (Functional)    (None, 7, 7, 1664)        12642880  \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 81536)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 81537     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,724,417\n",
      "Trainable params: 81,537\n",
      "Non-trainable params: 12,642,880\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compiling the model using adam optimizer\n",
    "model.compile(loss=tf.keras.losses.binary_crossentropy,\n",
    "              optimizer=tf.keras.optimizers.Adam(),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tanay\\AppData\\Local\\Temp\\ipykernel_8244\\3510587823.py:2: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history = model.fit_generator(train_gen, epochs=15, validation_data=valid_gen, use_multiprocessing=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "727/727 [==============================] - 1315s 2s/step - loss: 1.1885 - accuracy: 0.6756 - val_loss: 1.1450 - val_accuracy: 0.6797\n",
      "Epoch 2/15\n",
      "727/727 [==============================] - 1355s 2s/step - loss: 1.0434 - accuracy: 0.7332 - val_loss: 1.2022 - val_accuracy: 0.6982\n",
      "Epoch 3/15\n",
      "727/727 [==============================] - 1472s 2s/step - loss: 0.9414 - accuracy: 0.7602 - val_loss: 1.3255 - val_accuracy: 0.6815\n",
      "Epoch 4/15\n",
      "727/727 [==============================] - 1561s 2s/step - loss: 0.9148 - accuracy: 0.7807 - val_loss: 1.7020 - val_accuracy: 0.6696\n",
      "Epoch 5/15\n",
      "727/727 [==============================] - 1441s 2s/step - loss: 0.8207 - accuracy: 0.7918 - val_loss: 1.5352 - val_accuracy: 0.6815\n",
      "Epoch 6/15\n",
      "727/727 [==============================] - 1401s 2s/step - loss: 0.7029 - accuracy: 0.8115 - val_loss: 1.5380 - val_accuracy: 0.6720\n",
      "Epoch 7/15\n",
      "727/727 [==============================] - 1446s 2s/step - loss: 0.6977 - accuracy: 0.8195 - val_loss: 1.7449 - val_accuracy: 0.6560\n",
      "Epoch 8/15\n",
      "727/727 [==============================] - 1418s 2s/step - loss: 0.6461 - accuracy: 0.8313 - val_loss: 1.5855 - val_accuracy: 0.6892\n",
      "Epoch 9/15\n",
      "727/727 [==============================] - 1392s 2s/step - loss: 0.6780 - accuracy: 0.8304 - val_loss: 1.8483 - val_accuracy: 0.6803\n",
      "Epoch 10/15\n",
      "727/727 [==============================] - 1334s 2s/step - loss: 0.6212 - accuracy: 0.8442 - val_loss: 1.9270 - val_accuracy: 0.6887\n",
      "Epoch 11/15\n",
      "727/727 [==============================] - 1352s 2s/step - loss: 0.5497 - accuracy: 0.8549 - val_loss: 1.9714 - val_accuracy: 0.6649\n",
      "Epoch 12/15\n",
      "727/727 [==============================] - 1373s 2s/step - loss: 0.5595 - accuracy: 0.8581 - val_loss: 1.9665 - val_accuracy: 0.6667\n",
      "Epoch 13/15\n",
      "727/727 [==============================] - 1371s 2s/step - loss: 0.4929 - accuracy: 0.8681 - val_loss: 2.0705 - val_accuracy: 0.6815\n",
      "Epoch 14/15\n",
      "727/727 [==============================] - 1399s 2s/step - loss: 0.5070 - accuracy: 0.8663 - val_loss: 2.2557 - val_accuracy: 0.6827\n",
      "Epoch 15/15\n",
      "727/727 [==============================] - 1553s 2s/step - loss: 0.4211 - accuracy: 0.8815 - val_loss: 2.1638 - val_accuracy: 0.6803\n"
     ]
    }
   ],
   "source": [
    "#Fitting the model on training test\n",
    "history = model.fit_generator(train_gen, epochs=15, validation_data=valid_gen, use_multiprocessing=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "#Saving the model weights\n",
    "model.save(\"bonemodel.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " densenet169 (Functional)    (None, 7, 7, 1664)        12642880  \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 81536)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 81537     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,724,417\n",
      "Trainable params: 81,537\n",
      "Non-trainable params: 12,642,880\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Loading the saved model\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.models import load_model\n",
    "model_new = load_model('bonemodel.h5')\n",
    "model_new.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.layers import Input, Lambda, Dense, Flatten\n",
    "from keras.models import Model\n",
    "\n",
    "img = tf.keras.utils.load_img(\"D:/codes/GAIP-NUS/image1.png\", target_size=(224,224))\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 54.,  54.,  54.],\n",
       "         [ 42.,  42.,  42.],\n",
       "         [ 43.,  43.,  43.],\n",
       "         ...,\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.]],\n",
       "\n",
       "        [[ 42.,  42.,  42.],\n",
       "         [ 38.,  38.,  38.],\n",
       "         [ 41.,  41.,  41.],\n",
       "         ...,\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.]],\n",
       "\n",
       "        [[ 41.,  41.,  41.],\n",
       "         [ 35.,  35.,  35.],\n",
       "         [ 38.,  38.,  38.],\n",
       "         ...,\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 99.,  99.,  99.],\n",
       "         [ 77.,  77.,  77.],\n",
       "         [ 63.,  63.,  63.],\n",
       "         ...,\n",
       "         [116., 116., 116.],\n",
       "         [114., 114., 114.],\n",
       "         [120., 120., 120.]],\n",
       "\n",
       "        [[103., 103., 103.],\n",
       "         [ 78.,  78.,  78.],\n",
       "         [ 64.,  64.,  64.],\n",
       "         ...,\n",
       "         [123., 123., 123.],\n",
       "         [122., 122., 122.],\n",
       "         [124., 124., 124.]],\n",
       "\n",
       "        [[ 95.,  95.,  95.],\n",
       "         [ 82.,  82.,  82.],\n",
       "         [ 78.,  78.,  78.],\n",
       "         ...,\n",
       "         [121., 121., 121.],\n",
       "         [113., 113., 113.],\n",
       "         [118., 118., 118.]]]], dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert image to array\n",
    "input_img = tf.keras.utils.img_to_array(img)\n",
    "input_img = np.expand_dims(input_img, axis=0)\n",
    "input_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 2s 2s/step\n",
      "No Fracture\n"
     ]
    }
   ],
   "source": [
    "class_arr = [\"No Fracture\", \"Fracture\"]\n",
    "\n",
    "#Predict the inputs on the model\n",
    "\n",
    "predict_img = model.predict(input_img)\n",
    "predict_img\n",
    "\n",
    "max_value = max(list(predict_img))\n",
    "a = list(max_value)\n",
    "a\n",
    "index = a.index(max_value)\n",
    "print(class_arr[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53/53 [==============================] - 95s 2s/step - loss: 2.1638 - accuracy: 0.6803\n",
      "The testing accuracy is:  68.03327202796936 %\n"
     ]
    }
   ],
   "source": [
    "test_acc = model.evaluate(valid_gen)\n",
    "print(\"The testing accuracy is: \", test_acc[1]*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(history.history)\n",
    "df = df.astype(float)\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_train = history.history['accuracy']\n",
    "loss_val = history.history['loss']\n",
    "epochs = range(0,15)\n",
    "plt.plot(epochs, loss_train, 'g', label='Accuracy')\n",
    "plt.plot(epochs, loss_val, 'b', label='Loss')\n",
    "plt.title('Accuracy and Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " densenet169 (Functional)    (None, 7, 7, 1664)        12642880  \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 81536)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 81537     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 12,724,417\n",
      "Trainable params: 81,537\n",
      "Non-trainable params: 12,642,880\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.models import load_model\n",
    "model_new = load_model('bonemodel.h5')\n",
    "model_new.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.layers import Input, Lambda, Dense, Flatten\n",
    "from keras.models import Model\n",
    "#importing the necessary libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "from skimage.io import imshow\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "\n",
    "img = tf.keras.utils.load_img(\"D:/codes/GAIP-NUS/image1.png\", target_size=(224,224))\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 54.,  54.,  54.],\n",
       "         [ 42.,  42.,  42.],\n",
       "         [ 43.,  43.,  43.],\n",
       "         ...,\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.]],\n",
       "\n",
       "        [[ 42.,  42.,  42.],\n",
       "         [ 38.,  38.,  38.],\n",
       "         [ 41.,  41.,  41.],\n",
       "         ...,\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.]],\n",
       "\n",
       "        [[ 41.,  41.,  41.],\n",
       "         [ 35.,  35.,  35.],\n",
       "         [ 38.,  38.,  38.],\n",
       "         ...,\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.],\n",
       "         [ 11.,  11.,  11.]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 99.,  99.,  99.],\n",
       "         [ 77.,  77.,  77.],\n",
       "         [ 63.,  63.,  63.],\n",
       "         ...,\n",
       "         [116., 116., 116.],\n",
       "         [114., 114., 114.],\n",
       "         [120., 120., 120.]],\n",
       "\n",
       "        [[103., 103., 103.],\n",
       "         [ 78.,  78.,  78.],\n",
       "         [ 64.,  64.,  64.],\n",
       "         ...,\n",
       "         [123., 123., 123.],\n",
       "         [122., 122., 122.],\n",
       "         [124., 124., 124.]],\n",
       "\n",
       "        [[ 95.,  95.,  95.],\n",
       "         [ 82.,  82.,  82.],\n",
       "         [ 78.,  78.,  78.],\n",
       "         ...,\n",
       "         [121., 121., 121.],\n",
       "         [113., 113., 113.],\n",
       "         [118., 118., 118.]]]], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert image to array\n",
    "input_img = tf.keras.utils.img_to_array(img)\n",
    "input_img = np.expand_dims(input_img, axis=0)\n",
    "input_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 2s 2s/step\n",
      "No Fracture\n"
     ]
    }
   ],
   "source": [
    "class_arr = [\"No Fracture\", \"Fracture\"]\n",
    "\n",
    "#Predict the inputs on the model\n",
    "\n",
    "predict_img = model_new.predict(input_img)\n",
    "predict_img\n",
    "\n",
    "max_value = max(list(predict_img))\n",
    "a = list(max_value)\n",
    "a\n",
    "index = a.index(max_value)\n",
    "print(class_arr[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}