03_pytorch_computer_vision.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c4e0909f-899a-4071-9a38-6fea38e3a542",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version: 2.1.0+cu118\n",
      "torchvision version: 0.16.0+cu118\n"
     ]
    }
   ],
   "source": [
    "# Import PyTorch\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "# Import torchvision \n",
    "import torchvision\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor\n",
    "\n",
    "# Import matplotlib for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Check versions\n",
    "# Note: your PyTorch version shouldn't be lower than 1.10.0 and torchvision version shouldn't be lower than 0.11\n",
    "print(f\"PyTorch version: {torch.__version__}\\ntorchvision version: {torchvision.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81d79af6-44de-4dd8-b809-e96b0e735d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup training data\n",
    "train_data = datasets.FashionMNIST(\n",
    "    root=\"data\", # where to download data to?\n",
    "    train=True, # get training data\n",
    "    download=True, # download data if it doesn't exist on disk\n",
    "    transform=ToTensor(), # images come as PIL format, we want to turn into Torch tensors\n",
    "    target_transform=None # you can transform labels as well\n",
    ")\n",
    "\n",
    "# Setup testing data\n",
    "test_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False, # get test data\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d090a65-8f56-4439-b2b3-c07b04fae66d",
   "metadata": {},
   "outputs": [
    {
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       " 9)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See first training sample\n",
    "image, label = train_data[0]\n",
    "image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bba74c70-f250-45f4-904b-dc476af0f1bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What's the shape of the image?\n",
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62bf622d-e347-4260-b372-4ab5094d92b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 60000, 10000, 10000)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many samples are there? \n",
    "len(train_data.data), len(train_data.targets), len(test_data.data), len(test_data.targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7349b776-7253-4d88-b68b-92320a07bd9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T-shirt/top',\n",
       " 'Trouser',\n",
       " 'Pullover',\n",
       " 'Dress',\n",
       " 'Coat',\n",
       " 'Sandal',\n",
       " 'Shirt',\n",
       " 'Sneaker',\n",
       " 'Bag',\n",
       " 'Ankle boot']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See classes\n",
    "class_names = train_data.classes\n",
    "class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b2b0cd6-5b90-48a5-9c01-e951359eed31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: torch.Size([1, 28, 28])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '9')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "image, label = train_data[0]\n",
    "print(f\"Image shape: {image.shape}\")\n",
    "plt.imshow(image.squeeze()) # image shape is [1, 28, 28] (colour channels, height, width)\n",
    "plt.title(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6f162db-3e08-4d81-b7ad-79812ee4d920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([28, 28])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.squeeze().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eebb838e-f484-4356-a4aa-9708319d1ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ankle boot')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image.squeeze(), cmap=\"gray\")\n",
    "plt.title(class_names[label])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78315e62-c83a-4c49-9076-2d493290eb42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E11xzTVCzZs0gJSUlOPfcc4N58+YFZhbce++9R/stogSxDmEdEhcEEbJ5AACAzZw504477jh78cUX7aKLLirp0wFwlJSJZ5gBADjadu3a5cRGjRplxxxzjJ166qklcEYASkqZeIYZAICj7f7777evv/7aevToYZUqVbLJkyfb5MmT7corr2TLLqCC4ZEMAACEKVOm2J133mk//PCDbd++3Ro2bGiXXHKJ3XbbbVapEr83ARUJC2YAAAAgBM8wAwAAACFYMAMAAAAhWDADAAAAISJnLVDVCEdKST5GXx7GtXoP6pomJyfL/oMGDXJi27dvd2L5+fmyf0ZGhhPbtm2bbDtx4kQZL48Y1yiPGNcoj6KMa35hBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEJQqggohaIm8oXFD9a3b18Zr1GjhhOrXLmyE1PJfWZmbdu2dWItW7aUbY9m0l8s1xAAgDD8wgwAAACEYMEMAAAAhGDBDAAAAIRgwQwAAACEYMEMAAAAhIgLIqaNU5JSa968uRNLT0+XbXft2uXE1G4EZmZ79+6N1Hbfvn2y//79+yPFfMc65hj3v6VUzEyPjWrVqsm23377rRNTZZiPlvIwrlNTU53YgAEDnFjHjh1l/+nTpzux//f//p8TU7thmJmtXr3aif3tb3+TbVV57mXLljmxKVOmyP5btmyR8dKIEsIojxjX5Y/vupbGXYUyMzNlXO3i5FvzzJw504lRGhsAAAAoIhbMAAAAQAgWzAAAAEAIFswAAABACJL+BF9ym3qA/O6773Zi9erVk/337NnjxHwlhFWCX9WqVZ2YStgz0+WOfXbv3u3EKlVyq6avXLlS9ldDyPew/aOPPurEJk2adKhTPGJK67g+9thjndhxxx0n26rP+ueff3ZiDRs2lP3VWFPXJTc3V/Z///33ndiCBQtk2yZNmkQ6vi9pdP369U7MlyC4cOFCGT9aSjJhRs1hsZxPLPdFaUwMwpFD0l/RqPdQ1HtTxXzfwer7olGjRrLtvHnznNiOHTsOdYqhsrOzZbxu3bpOTCWJ+6SlpTkxlRBvZvbaa685sSjvi1+YAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBAsmAEAAIAQ7lYIiCljVe18oXadMNOlsefPny/bVqlSxYnFx8c7sU2bNsn+tWrVcmK+3T8SEhIiHct3XX766ScnlpiYKNsuWrRIxiuqCy64QMZbtWrlxJYsWSLb5ufnOzE1LtTnZKazi1WG9Ycffij7qx05ateuLduqMujqvFS5bTOz6tWrO7EhQ4bItv/+97+dmCqJWtGpe33fvn2R+6uxqsaEj2+nH3UOakcV37ym3pfa/ac4dl5Qc6OK+c41luul7s2kpKTIr6nazp07V7ZV9ytKVlF3KWnevLkTS09Pl2137tzpxHxjRRkwYEDktmoXMDWG1XeAmR6ram1j5l+jHQq/MAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAhSPorIpWw4kuYUQ+g+x5KV4koqm1WVpbsrxJDVMKLmU6OUckpvlKbqq1KzjE7/Ifty4OMjAwn5iuNPmfOHCfmS05Sn59K7PFde5U0qJJGt27dKvurhBFfwpG6N9R5+Upjq7aLFy+Wbdu3b+/EKkrSXyyJQbEk+J1++ulOrG/fvk5s6dKlsv+GDRucWNu2bWVblcSjEqd9c6hKyFb3kC8RL5ZkwKiv67vWURP5zHRpY3W91bUy0+Xpp02bJtu+/vrrMo7DdyRKi6vX9I1fNTf7kvRTUlKcWJ06dZzYZZddJvurublmzZqyrVqfqHtbJSKa6fvFd78d7mfAL8wAAABACBbMAAAAQAgWzAAAAEAIFswAAABACJL+ikglIakkLDOdyOR7AF4lzakH1X1VsmJJOFHnq17Xdyx1rpmZmbKt74H9iqBFixZObPPmzbJtLNXztmzZ4sRiGSvq81Pn5auwpMaVL8FUJTepqpa+RKxt27Y5MV+CoDpflQhzJJJwyrp77rlHxlUinUrOU1UWzfTn75sTTjzxRCemqkLGMt+qseq7L9S5+hKflajzqplOblLX2sxs3rx5Tkxdb1/i7dVXX+3EunXrJtt+9NFHMo7SzzcHq6TRHTt2yLbqHlJJfytXrpT9VRVZ37HUvaE2NfD1V/e2b244XPzCDAAAAIRgwQwAAACEYMEMAAAAhGDBDAAAAIRgwQwAAACEYJeMIkpOTnZivjKTKotTZf2b6UxWleHvK3+qsq59GaMqm1q19WVtq10WfJmsqjRuReErF67EkmFftWpVJ6bGha80thqvajcCXwlitXOAb6wo6rr4xrW6Lr5dFlSGttppZP369Yc6xTJHXVP1mZqZffDBB07s8ccfl23VjiYDBgxwYldddZXsX69ePSe2evVq2VaNoTPPPNOJqTLyZnpcq91XfDtfqPm6qKV2ValhM72jga+8eHZ2thPr2rVr5HNS78F3bzdt2lTGUXKi7vSjdpgwM2vUqJET882B6jsjPT3diW3atEn2z8jIcGK+tYGamzdu3OjEfGNV3du+7zzfDiKHwi/MAAAAQAgWzAAAAEAIFswAAABACBbMAAAAQAiS/opIlY70JZGoB/NVWVkzncikHor3JRimpqY6MV+pVBVXD8v7ks5UMpHvYfuKTCX8rFmzRrZViRnLli2TbVUinEqA8CU6RE3Q8yWG1KpVK9LxfXGVSOZLhlVtVeKtmR6vKmGqPCb9qXuye/fusm3dunWd2MSJE2Xbl19+2YmpcZWbmyv7q88qJydHth05cqQTU/PaySefLPurEtJqbvYl8qm5VSVcmelxrd5rfn6+7P/jjz86MV+SZqtWrZyYSoSKZb5XicNh54DSz5dgqpKhfXOgugd8CdmKWh/5khGjzsO+ZNZYEtXVpgpR8AszAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEIIFMwAAABCCXTKEqGVOzXSGta8Etcrw92VoK+p1fVnMKq4yVs105vnmzZudmG83ArUjhu9YFZnaTWLRokWybbt27ZyYL4tY7SihSiP7MvwVNdZUdrWvrW/nDZWNr3ZJ+M9//iP7H3vssZGPpcawKs1cUQwbNkzGr7322siv0bBhQye2du3ayP3VWK1evbpse9lllzmxv/3tb07MtyNPixYtnJiag33UHOq7h5YvX+7E1q1b58S2bt0q+6vdM3y7h6gxvGDBAifm20FJfb81a9ZMtvWVnUfJiVoa27dDhLpffHO7mkPVmqVx48ayvyqN7btfa9as6cTU+POV1lbU96DZ4e/+wi/MAAAAQAgWzAAAAEAIFswAAABACBbMAAAAQIgKn/QX9QF6H5WA4Uv627JlixPzlZtWiXifffaZE/OVO1bH8iWRqHhSUpIT27Bhg+yfmZnpxL799lvZtiJTCW++JCBVGluVzzXzl2KPSvVXYziW0tq+tirhY+bMmU7MV9Y1Ly/PifnuN5X04ktcLW9U6df+/fvLtkOGDIn8umpeUPOl7/NXn9XSpUtl2w4dOjixwYMHO7FZs2bJ/h999JETO+GEE5zY4sWLZX81t2/cuFG2Pfvss53YtGnTnJhvDlaJVOq9mpl9+OGHTkyNa1+CorrfVXKXmX5fKBt8yXUrV650YtnZ2bKtSuhV8+r27dtlf5X8r+YQM7O5c+c6MZW46tsoQcVjaRsFvzADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIUj6K2LSn0ra8lXYUQl+vmOppKdWrVpFPq9Vq1Y5sZ9//lm2VdW3VIKaSjo0M+vXr58T++abbw51iuWaqpx0zDHuf5/6kqNUYoQvUUG9bixV/VR/dSxfEolvvCuq8lIsyRrqHkpPT5dtVSKTL+GkvPnd737nxCZPnhy5v2/8+KrHRaXmOzX+zHQVzNNOO82J+SoNqsqaqlLh119/Lfu/8MILTsyXIKmSUdVYXbZsmex/+eWXOzGVDGsWvVqhb25R1dN891ubNm0iHQtHT9T1yYoVK2RcJfj5kj5V8rlKzvMlXk+fPt2J+ZLU1fpEzde+xG1Vvc/33XS43wP8wgwAAACEYMEMAAAAhGDBDAAAAIRgwQwAAACEYMEMAAAAhCiVu2QUdeeKI0Wd165du5yYKgdppsvV+jI+VcaoKpftywKtW7du5GMpKmu2c+fOkfsvWLAgctvySJXVVWNFZfaa6c/aR2Udq/Hj2yUl6i4bvjLederUiXROvmOpbH7fuarX9e3coMq1Vq9eXbYtb1R2+5gxYyL39823KkNe7bDgy5pXfLs+RC3hfMUVV8j+U6ZMcWLz5893Yr75+tZbb3VivjlUjbU+ffo4MVWa28zs888/d2Kq5LxZ9F1pfPeg2qlG7Zxhpnf6QNFEXd/4dqqJ2t93D6oduHzHUt9Par5VaxszPdZ88vPznVjNmjWdWFZWluy/cOFCJ7Zjxw7Z1rez0qHwCzMAAAAQggUzAAAAEIIFMwAAABCCBTMAAAAQolQm/R3NBL9YSgir5BT1AL0qK20WvSywmU6OUsfylStWSRy+B+CVBg0aRDq+jy+JpKJQiRHqs/Ylt6lkC5XIaaZLCKvEIF+pXJVcpO6LzMxM2V8lPPnuAZWIpMa6L2FJJe1lZGTItt99950TU5+LLzHFl5BZFqhEzHnz5kXu70sYimW+VGJJblJjQCWhrVy5UvY/5ZRTnJgqietLZv3xxx+dmK9UtLreGzZscGIff/yx7K/uTd9nEPV+iSVpzHes0pBsX95Evaa+dlHvwcaNG8u4mu/T0tJkW5Worr6zfOcUS0KwSsRT78FXXn7jxo2RXtPM/717KPzCDAAAAIRgwQwAAACEYMEMAAAAhGDBDAAAAIQolUl/R1MsSQ1NmzZ1YuoBdl9ynXow3le1SSV2FLXqUixJBMuXL3divuQodQ1UpbuKRFUEUwkQvmu6Zs0aJ6YSlsyij2FfIp36/NT4i6XymO9Yijp/X4KiSsTzJb6qaxtLwotK2ior1PVTCTw+vgRPNVZimZdiSfpTbdXnr8aqmVleXp4TizrWzfR870sQVIlI6r7w3e+xJFOqhCX1HmKZ7333K5X+il9RKxmrz0qNq/r168v+27Ztc2K+5DhVaU9V8fUlSKtz9a0NVPL2Dz/84MR8Sb4q8dZX2XXJkiUyfij8wgwAAACEYMEMAAAAhGDBDAAAAIRgwQwAAACEYMEMAAAAhKhQu2TEUoJX6d27txNTGafVqlWT/VWGdizZySoT1VeCWJWxVmWBfa+hsux9uxGoXUHat28v27711lsyXt5E3TnC95moTHhfGXU1rmIpf6vOS8V8n7/aEcRXelTdg1FjZnqXC1+GuTovdV1UyfuyTn1WsezE4NuRRVHXvzh2aFBzkBpXvnGtxLIbgdpVJJadgtRY9e2KpOZ73+eljqXmhlh2H/FdF3bJiCaWnS+i3oex9D/hhBOcmG9XI7U28H23zJ49O9Lr+nYUat26tRPzfed9++23Mn6wJk2ayLia8zZv3izbHu645hdmAAAAIAQLZgAAACAEC2YAAAAgBAtmAAAAIMQRSfqLJbkkliSMqHxldX0Pth/svPPOk3FVZlG9pi+xQyWM+M4patKeL2FJxX1JACqRRj1Av3PnTtlfJWj5SlJWFOoeiCWRT7X1JZOqtipB1JewFDUZ1pcIpvrHktykkph8iVzqevmuoboH1LXyJaGUZSoZ2Ve+VollrMRS8r2oyWlRy537XleNCV/J8Kiluc30/RJ1DjDT18WXDBlLkqyiziGWxEu4ilraWvF9ps2aNXNiar7duHGj7J+Tk+PEfMlxah5u1KiRE/OV4Vb9Fy1aFLmtel/5+fmyv5obfHN7LInCv8YvzAAAAEAIFswAAABACBbMAAAAQAgWzAAAAEAIFswAAABAiMgpsLGUflRxX8Zn1Oxe37FUxqkvE1np0aOHEzv22GNl25UrVzoxVQK6Ro0asr/aZcK384HK5lYZ2r7sVpVJ6ruGqjS2yjj1nasaGzVr1pRtK4qon58vW1fdF75xrY4Vtdy1L66y433nqsa1LztZjRV1XXzZ+aq/b5eFzMxMJ7Z9+3YndrgZ06WZmquuvPJK2fbvf/+7E/ONtagZ/r6dS9S49n1+UXeOiGWHCPX5xzIH+6xbt86JxbJzgmrruy7qGqhr5buH1P0Wy3dmSYplHRK1v8+R2MHLzCw1NdWJqblK7WZhps9rx44dTqx58+ayv/puV7vqmOldjdRY882h6nXVPWimPxt1LLWDmJnZpk2bnNiaNWtk26jz2MH4hRkAAAAIwYIZAAAACMGCGQAAAAjBghkAAAAIcUSS/pTDfcj6cPhK+Pbr18+JNWnSxImph8fNzGrXru3E1MP2sVyXrVu3yrgqa6muYSyJl77EDvVgf4MGDZxYLMk5KomhIomaiOdLzIllDKnyoaq0ue81VdKUStpbu3at7K+SSHylsVU8lnLJaqz67iE1BlWC4tGcm46W7777zok99dRTsq1K+vNd0+zsbCc2e/ZsJ+ZLAopaGj0sfjDfuFbJbXXq1HFivtLYeXl5Tkydv5lZRkaGE1MJ2b7kqliS0aLyJV6qUuK+a+grO16axFJavaiJfL4E0+TkZCfWuHFj2bZ69epOTI0LX8Ka6q++b9TaxCy2z1/NjXXr1nVivu8GtWZS94pZ9CTbxYsXy7h6v6+88opsm5ubG+lYB+MXZgAAACAEC2YAAAAgBAtmAAAAIAQLZgAAACBE5KQ/9fB3rVq1ZNuWLVu6B4qh6pB6iF8lFpnpB9DVg+a+11APq6vEEDOdNDd37lwnphL2zMzq1asX6TXNdOKiivmSEGKp5qReQyU++j4Dlciljl+RqGut7gFVZdHMbMWKFU5MJayZRa8qGEvCi+rvq4imkot8yUK+CoBRqbHqSxaJmgjj+wzKsnfeeceJff/997LtSSed5MSmT58u26oxrOYF31hTc7vv8ytq9TnVX31nXXTRRbL/zJkzIx/rf/7nf5xYnz59nJhKJDTT19CXtOebh6NSyZQqSdjMn/xZUopa1U8lzJnpyrTp6elOzDd/RU2cNtPzeP369Z2YLxl1/fr1TiwtLc2JbdmyRfZXiavqNc3MOnXq5MTU94BvzaW+B3xrAzUG1ToklqrRvnvlcOcWfmEGAAAAQrBgBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEJE3iVDadq0qYyr8qm+TPSi7gaxYcMGJ+bLLt2+fbsTU5m0qp1Z9AxxVRLVTGeH+jJp1e4fsZyryoRVu3SYmaWmpjqx1atXOzHfZ6gyYYuayV3WqWxudZ1SUlJkf7WjgW9XGpUhrcagb6caNa7Urji+zzRqCWMzfQ+oezuW0ti+MtzqPahdMirKWL3ppptk/A9/+IMT8+2S8fbbbzuxY4891omp7HYzvSOGL2O9qOWio+5coHakMdP3i+9c1W4S6li+nS/UdYnlflX3oO9c1dzk+379z3/+I+OlSY0aNWRcza2+uUp9LsuXL3divtLoiu+aqnNQ362+8a/mO7Xm8e1wotYBXbp0kW3VWiwrK8uJqTWEmdmSJUucmG+3JzVfq2vlK62tvjM///xz2da3dj0UfmEGAAAAQrBgBgAAAEKwYAYAAABCsGAGAAAAQkRO+lPlort27Srbrlu3zon5HmBXSTjbtm1zYsnJybK/KnXpS6SLWi7Yl4ilEgPUw+6+Uq8qYcCXRKAejPcl+CnqHGJJjlCJBSoBwHdeKjHAzF9Cs7xR40olNfg+k08++cSJdejQQbZVyYBREyh8bdW9otr5+BKWVCKMOi/ffKHaquQcM7MmTZo4MfUeilquuzRS12/27NmyrUoQffPNN2VbNQeq6+e7z1Uimi85Sn1W6vixlJBetmyZEzv99NNl/x9//NGJ+RLp1PfT4sWLnZhvrKn36ruHFHW/+pJZVdKUSp43M/vss88in0NJadWqlYxnZmY6MV9ZZpU0p74Dfd/Xah3ja6vGihrDvu9bNS7U973vvarvC9+5qpLd6rzmzJkj+6vy3Kq0tplOXlffo7731bBhQyf2+OOPy7a+pOZD4RdmAAAAIAQLZgAAACAEC2YAAAAgBAtmAAAAIAQLZgAAACBE5DTczp07O7F+/frJtj/88IMT85VpVNmZapeNVatWyf4qi9O3m4TKrlQ7RKjMTDOdNauym9UuH2Y6O1adv5nOGI0lu1XxlRBWmd9q9wbfLgnqdX2Z62q3lfJIXROVyezbDWLjxo1OzLfLhdplwJdhraj7Qp2r7zP1ZS0rapcA1V+VETfT49KXoX3iiSc6MTWGd+/eLfuXZSqT3vc5/fe//3ViJ5xwgmy7dOlSJ6Y+K98uGerz981halyrmG9eU/eWmptHjBgh+6tsfrWbgpneJUHFfCWEfTuFKGoeUP19JYg//fRTJ3bnnXdGPn5p45sX1ZrDNy+qeSGWXanq1q3rxHzfdfn5+U5MvQfffdGxY0cnpkprr169WvZX96DaUcRMz/nz5893Yr4dXdT19r0vdW+q1/XdK+pc1c4ZvvOKgl+YAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBCRk/7eeustJ+ZLtjj33HOdWLNmzWRblXCmknDWrl0r+6sHxX0P66tEKlU+1FeGW8VVaW6VAGCmS5L6kiHVNRg7dqwTu+CCC2R/lczoK+vqK+V9MF8ypEpYUEkIZvp6lUdqrKl7wJcAofr7kh3UGFYJEL77Vb2uek1fyfg1a9Y4MV/5U0WNH18ij0pY8ZV8VonCKpl2wYIFhzrFMsd3rypqrKmyzj5qvvV9/moM+dqqcak+f3X+ZnoMqfti2rRpsr+aL30J3b7xejA1/sx0MqEvSVPdb6qM9/fffy/7+75zFF9ScklR81JWVpZsq75r1q9fL9tmZ2c7MZVg7PucYykBrb4bo34H+6jS5jVr1pRt1Vjzra9UMmTLli2dmG9MqfsllpLfah7zfWeqser7vHwJ7IfCL8wAAABACBbMAAAAQAgWzAAAAEAIFswAAABAiMhJf8q///3vyPEWLVrItoMHD3ZijRs3dmJNmzaV/dUD9L4HvVUShXpQ3JfooOKbN292Yr7KY3/5y1+cmC/hJKqHH35YxtV5+ZIhoybH+Cr9qevqSzBs0KCBjJc3USvK+ZKIFF/Sn0rmiyWRTlFJKL6kQfX5+5LOot5vvveq4r5KfSqJRB1LzTdmumJpWeG7/5QPP/zQiX300UeyrUqOUuPCV9FOJU77EoTVeFWfXyzvdcWKFU7MlzRaXsVSVdA355eU5s2bOzFf9cXly5c7Md93u/q+VGPFNwcqvnnJt6nAwVSCo5lOsFPvy1ctVX2mvvtV3VvqO9yXyBdLJWJ1v6trGMt3SyxrwSj4hRkAAAAIwYIZAAAACMGCGQAAAAjBghkAAAAIwYIZAAAACFGkXTJ8WZwqA3Hu3Lmy7e233x7pWL7sVpU1W69ePdm2du3aTkxl0vvKRK5evdqJzZs3T7Y9Wn7/+9/L+Lp165yYKp9pprNmVYatLxtZZcL6SmXm5eU5sfHjx8u2ZZnaFUaVcPWV9VVmzpwp4+3bt3dialz7MpbV56+yi3391Y4avuxk9Roqa79WrVqyv8pm91HnkJmZWaTXrCh8mehLly49uieCYlfadr6Ihfr+GDBggGyr5kDfzhFqN4hYdopSc5ivrXpdtfuGb/cYtSOFaqvKyPv4zlXN1zt37nRivl0nYrmGO3bscGLqPRRHuWvf/HYo/MIMAAAAhGDBDAAAAIRgwQwAAACEYMEMAAAAhIgLIj797Eu6A4rqcB/ALw5HalyrJI7q1as7MV8ChC9BU+ndu7cTO+2005zYqlWrIh8rIyPDifnOdeXKlU4sJSVFtlXJMdWqVXNiKrHEzOyFF15wYrGUX1Wf95Eaf+VxXAOlbVyrpGMzneBbo0YN2VaVhlaJbL4S0CruS0JTmyWoY/lKvqv5btu2bU7MN1+ra+jbwEElTvraFpW6Xir5O5brkp+fL9vOmDHDiUUZ1/zCDAAAAIRgwQwAAACEYMEMAAAAhGDBDAAAAIRgwQwAAACEYJcMlLjSlnVdHqjs4jZt2si2NWvWdGIqa9y3G4Xa0cKXSa2ywVV5+blz58r+ZQnjGuUR4xrlEbtkAAAAAEXEghkAAAAIwYIZAAAACMGCGQAAAAgROekPAAAAqIj4hRkAAAAIwYIZAAAACMGCGQAAAAjBghkAAAAIwYIZAAAACMGCGQAAAAjBghkAAAAIwYIZQLF5/vnnLS4uzpYuXRpz36FDh1pOTk6xnxMAwMV8HZtyvWCOi4uL9L+PP/64pE8VOGzff/+9DRw40LKzs61KlSqWlZVlvXv3tscee6ykTw04KhYtWmTDhw+3xo0bW5UqVSw1NdVOPvlke+SRR2zXrl1H5JgvvfSSjRo16oi8Nsov5uuyq1JJn8CRNHbs2EJ/fuGFF2zKlClOvGXLlkfztIBiM336dOvRo4c1bNjQrrjiCsvIyLAVK1bYF198YY888ohde+21JX2KwBH1zjvv2Pnnn2+JiYn2u9/9ztq0aWN79+61zz77zP785z/bnDlz7Jlnnin247700ks2e/Zsu+GGG4r9tVE+MV+XbeV6wXzxxRcX+vMXX3xhU6ZMceIH27lzp1WtWvVIntoRsWPHDktOTi7p08BRdPfdd1taWpp9+eWXVr169UJ/l5eXVzInBRwlS5YssUGDBll2drZ99NFHVq9evYK/u+aaa2zhwoX2zjvvlOAZAv+H+bpsK9ePZETRvXt3a9OmjX399dd26qmnWtWqVe3WW281s18G8GWXXWZ169a1KlWqWLt27WzMmDGF+n/88cfysY6lS5daXFycPf/88wWxtWvX2rBhw6x+/fqWmJho9erVs3POOcd5fmjy5MnWtWtXS05OtmrVqlnfvn1tzpw5hdoMHTrUUlJSbNGiRXbWWWdZtWrV7KKLLiq264KyYdGiRda6dWtn8jUzq1OnTsH//9xzz1nPnj2tTp06lpiYaK1atbInn3zS6ZOTk2P9+vWzzz77zE444QSrUqWKNW7c2F544QWn7Zw5c6xnz56WlJRk9evXt7vuusv279/vtHvjjTesb9++lpmZaYmJiZabm2t/+9vfbN++fUV786jw7r//ftu+fbs9++yzhRbLBzRp0sSuv/56MzP7+eef7W9/+5vl5uZaYmKi5eTk2K233mp79uwp1CfKeO3evbu98847tmzZsoJH+yra85yIHfN12Vauf2GOauPGjXbmmWfaoEGD7OKLL7a6devarl27rHv37rZw4UIbMWKENWrUyF599VUbOnSobd68uWASjsWAAQNszpw5du2111pOTo7l5eXZlClTbPny5QWT7dixY23IkCHWp08fu++++2znzp325JNP2imnnGLffvttoUn5559/tj59+tgpp5xiDz74YJn8VRxFk52dbZ9//rnNnj3b2rRp42335JNPWuvWra1///5WqVIle+utt+zqq6+2/fv32zXXXFOo7cKFC23gwIF22WWX2ZAhQ+yf//ynDR061Dp06GCtW7c2s1/+469Hjx72888/280332zJycn2zDPPWFJSknPs559/3lJSUuyPf/yjpaSk2EcffWT/+7//a1u3brUHHnigeC8IKpS33nrLGjdubCeddNIh215++eU2ZswYGzhwoN1444323//+1/7+97/bjz/+aBMnTixoF2W83nbbbbZlyxZbuXKlPfzww2ZmlpKScmTeJMoN5usyLqhArrnmmuDgt9ytW7fAzIKnnnqqUHzUqFGBmQUvvvhiQWzv3r3BiSeeGKSkpARbt24NgiAIpk6dGphZMHXq1EL9lyxZEphZ8NxzzwVBEAT5+fmBmQUPPPCA9/y2bdsWVK9ePbjiiisKxdeuXRukpaUVig8ZMiQws+Dmm2+O/P5R/rz//vtBfHx8EB8fH5x44onBTTfdFLz33nvB3r17C7XbuXOn07dPnz5B48aNC8Wys7MDMws+/fTTglheXl6QmJgY3HjjjQWxG264ITCz4L///W+hdmlpaYGZBUuWLAk99vDhw4OqVasGu3fvLogNGTIkyM7OjvzeUbFt2bIlMLPgnHPOOWTbmTNnBmYWXH755YXif/rTnwIzCz766KOCWNTx2rdvX8YrYsJ8XbZV+EcyzMwSExNt2LBhhWKTJk2yjIwMGzx4cEGscuXKdt1119n27dvtk08+iekYSUlJlpCQYB9//LHl5+fLNlOmTLHNmzfb4MGDbcOGDQX/i4+Pt86dO9vUqVOdPldddVVM54HypXfv3vb5559b//79bdasWXb//fdbnz59LCsry958882Cdr/+JWHLli22YcMG69atmy1evNi2bNlS6DVbtWplXbt2Lfhzenq6NW/e3BYvXlwQmzRpknXp0sVOOOGEQu3UY0G/Pva2bdtsw4YN1rVrV9u5c6fNnTu3aBcAFdbWrVvNzKxatWqHbDtp0iQzM/vjH/9YKH7jjTeamRV6zpnxiiOF+bpsY8FsZllZWZaQkFAotmzZMmvatKkdc0zhS3RgR41ly5bFdIzExES77777bPLkyVa3bl079dRT7f7777e1a9cWtFmwYIGZmfXs2dPS09ML/e/99993kgIqVapk9evXj+k8UP506tTJJkyYYPn5+TZjxgy75ZZbbNu2bTZw4ED74YcfzMxs2rRp1qtXL0tOTrbq1atbenp6wbP6B0/ADRs2dI5Ro0aNQv+hd+D+OFjz5s2d2Jw5c+y8886ztLQ0S01NtfT09ILE24OPDUSVmppqZr98qR/KsmXL7JhjjrEmTZoUimdkZFj16tULzeeMVxxJzNdlF88wm8nneKKKi4uTcfWA/A033GBnn322vf766/bee+/ZX/7yF/v73/9uH330kR133HEFD+CPHTvWMjIynP6VKhX+uBITE50FPSquhIQE69Spk3Xq1MmaNWtmw4YNs1dffdUuvvhiO+2006xFixY2cuRIa9CggSUkJNikSZPs4YcfdhI/4uPj5esHQRDzOW3evNm6detmqamp9te//tVyc3OtSpUq9s0339j/+3//TyadAFGkpqZaZmamzZ49O3If33x9AOMVRwvzddnDgtkjOzvbvvvuO9u/f3+hRemBf5LIzs42s1/+S87sl4H2a75foHNzc+3GG2+0G2+80RYsWGDt27e3hx56yF588UXLzc01s1+yZXv16lXcbwkVSMeOHc3MbM2aNfbWW2/Znj177M033yz0a4R6xCeq7Ozsgn8R+bV58+YV+vPHH39sGzdutAkTJtipp55aEF+yZMlhHxs4oF+/fvbMM8/Y559/bieeeKK3XXZ2tu3fv98WLFhQaN/9devW2ebNmwvm81jG66EW30BUzNdlAz9Pepx11lm2du1ae/nllwtiP//8sz322GOWkpJi3bp1M7NfBmJ8fLx9+umnhfo/8cQThf68c+dO2717d6FYbm6uVatWrWBboz59+lhqaqrdc8899tNPPznntH79+mJ5byg/pk6dKn9JOPDMZvPmzQt+gfh1uy1btthzzz132Mc966yz7IsvvrAZM2YUxNavX2/jxo0r1E4de+/evc79ARyOm266yZKTk+3yyy+3devWOX+/aNEie+SRR+yss84yM3Mq840cOdLMzPr27WtmsY3X5OTkCv9P1IgN83XZxi/MHldeeaU9/fTTNnToUPv6668tJyfHXnvtNZs2bZqNGjWqINEkLS3Nzj//fHvssccsLi7OcnNz7e2333aeN54/f76ddtppdsEFF1irVq2sUqVKNnHiRFu3bp0NGjTIzH75J8Ynn3zSLrnkEjv++ONt0KBBlp6ebsuXL7d33nnHTj75ZBs9evRRvxYova699lrbuXOnnXfeedaiRQvbu3evTZ8+3V5++WXLycmxYcOG2bp16ywhIcHOPvtsGz58uG3fvt3+8Y9/WJ06dWzNmjWHddybbrrJxo4da2eccYZdf/31BdsUHfiXmQNOOukkq1Gjhg0ZMsSuu+46i4uLs7Fjxx7WPxcCB8vNzbWXXnrJLrzwQmvZsmWhSn/Tp08v2Ar0+uuvtyFDhtgzzzxT8M/OM2bMsDFjxti5555rPXr0MLPYxmuHDh3s5Zdftj/+8Y/WqVMnS0lJsbPPPvtoXwKUIczXZVzJbM5RMnzbyrVu3Vq2X7duXTBs2LCgdu3aQUJCQtC2bduCbeJ+bf369cGAAQOCqlWrBjVq1AiGDx8ezJ49u9C2chs2bAiuueaaoEWLFkFycnKQlpYWdO7cOXjllVec15s6dWrQp0+fIC0tLahSpUqQm5sbDB06NPjqq68K2gwZMiRITk4+/IuBcmHy5MnBpZdeGrRo0SJISUkJEhISgiZNmgTXXnttsG7duoJ2b775ZnDssccGVapUCXJycoL77rsv+Oc//+lsKZSdnR307dvXOU63bt2Cbt26FYp99913Qbdu3YIqVaoEWVlZwd/+9rfg2WefdV5z2rRpQZcuXYKkpKQgMzOzYCslO2g7xoq4TRGKx/z584MrrrgiyMnJCRISEoJq1aoFJ598cvDYY48VbIX1008/BXfeeWfQqFGjoHLlykGDBg2CW265pdBWWUEQfbxu3749+O1vfxtUr149MDPGLg6J+bpsiwsC/tMBAAAA8OEZZgAAACAEC2YAAAAgBAtmAAAAIAQLZgAAACAEC2YAAAAgBAtmAAAAIAQLZgAAACBE5Ep/cXFxR/I8SszGjRud2IYNG2Tb/fv3O7GUlBQnNn/+fNm/Ro0aTqxy5cqy7fbt251YzZo1ndjMmTNl/wsvvFDGS6OS3Aq8vI5rlDzG9dFx6qmnynjPnj2dWNWqVZ1YlSpVZH9V9nr58uWy7bPPPuvE1PdFecC4RnkUZVzzCzMAAAAQggUzAAAAEIIFMwAAABAiLoj4QFJpfXZInZfvLTVv3tyJzZ0714mtXLlS9o+Pj3diiYmJTsz37NqaNWsi9ffFt23b5sT27t0r+3fo0EHGSyOeiUN5xLh2xTJfK6tWrXJial420/PwMce4vxElJyfL/iq/xXes+vXrO7FTTjnFiU2bNk32L0sY1yiPeIYZAAAAKCIWzAAAAEAIFswAAABACBbMAAAAQAgWzAAAAECIyJX+SqtYMnb/+c9/OrHVq1c7sRUrVsj+KkNXVfpLSEiQ/Xfu3OnEfFnXavcL9V59xwKA4qYqk/7000+R+6v5as+ePbLt0KFDnZjaPUjtPmSmd79Qx1q2bJnsr+Z2X1XAJUuWOLGPP/7YifkquypqRw+z8ltBECjt+IUZAAAACMGCGQAAAAjBghkAAAAIwYIZAAAACFHmk/5icdJJJzmxhQsXOrGaNWtGfk1fYoaiEl58SSA///xzpJgqyQoAR4JK8Iul3LUvwU/Jzs52Ylu2bHFi1atXl/2rVavmxNLS0pyY71x37drlxNQc7It///33sm1UJPcBpQu/MAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIcrlLhkdOnSQ8Y0bNzoxld2ssr7NdBlrlaG9b98+2d8Xj9q2UiX34/JliKuysDt27Ih8fACIwrfLhKLmpccee0y2Pfvss53YihUrnFhmZqbsn5SU5MReeuklJ6Z23jAzO//8852YbwelxYsXOzFVxvuTTz6R/W+99VYnNm3aNNlWiWWnEgCHh1+YAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBDlMunvhBNOkHFVhlqVaq1Ro4bsr0pbq+Q8X7ns1NRUGVfUufrKsioq4YSkPwBFoRKf1RzoS45TiWzp6emy7Zo1a5yYmsPy8vJkf/W6c+fOdWLfffed7D948GAnlp+fL9vu3r3biak5PCsrS/Z/8803ndiwYcMit1XH2rt3r+wP4PDwCzMAAAAQggUzAAAAEIIFMwAAABCCBTMAAAAQolwm/Z1zzjkyHrWq39atW2V/VTmqatWqkc9LVerbv3+/bKuqNPmSCRXfewCAwxW1Wulll10m41WqVHFi69ati3x8lcysEu7MdILfGWec4cS6d+8u+6s5eOnSpbKtSrpTCZK+RLxNmzY5sSuuuEK2VUl/JPgBRx6/MAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIcrlLhkNGjSQ8Z9++smJxbLzhMqEVmW0VSa3mdnGjRudmK/ctdpRQ+3o4cswj6WMNo6OWMaaytBXsaPp+OOPl3G1U8xnn30W+XXVuPZR10DdK2bR74Fq1arJ+LZt2yKfFwpTZaXNzHbt2uXEfDtvqM9PxRISEmR/Nd8nJyc7saZNm8r+am71jVX1PaDelyrt7WubkZEh20blm298OzMBCMcvzAAAAEAIFswAAABACBbMAAAAQAgWzAAAAECIcpn0l5OTI+NbtmxxYiphSSWLmOmyrqok6ahRo2T/m2++2YmtWLFCtlXJJepcv/rqK9kfpc/RTLZR48eXNKgSoS699FIn5ktCWr58uRNr27atbPvss886sVjK+qoEP19yX1ZWlhN79NFHndjmzZtl/wULFjix1157TbZduHChjFcEsYw1VS7al/TnS5A7mC/Jevv27ZHaLlu2TPZX7yE9PV22VeeqkkZ9CYpKWlqajKtS3h9//HHk1wWKmy8ZtqiJ6h9++KETGzNmjGz7wgsvFOlYUfALMwAAABCCBTMAAAAQggUzAAAAEIIFMwAAABCCBTMAAAAQoszvkhF1Nwkzs7y8vEiv6cvsrFOnjhO7+uqrndjTTz8t+6tdMmIp66syzOfMmSP7o2RF3TngSGUXx9J/586dTkztCKNKw5uZbdq0yYnVqlVLtn3kkUec2F133eXEVq1aJfur+6JFixaRj1W3bl0nNn78eNm/Zs2aTuzkk0+WbSvyLhnNmzd3YklJSbKtGpe+nSPUa6g50Lf7jNr9RR1LjXUzsz179jgx344uW7dudWLqvfrKsKsdPXz32ymnnOLE2CUDR0ssOxUpp512moxPnDjRiW3YsMGJqR2czMwmTJjgxNR9ZabnkSj4hRkAAAAIwYIZAAAACMGCGQAAAAjBghkAAAAIUeaT/jp06BC5rSp5rRJGGjVqJPurxIonn3wy8vFjETVB7Pvvvz8ix0fRRE26K2pyX3Ho2bOnE+vfv78TU0l0ZmYXXHCBE/v0009lW5UwcvfddzsxXxLTzJkzndh1110n26qS3epYzZo1k/1VaW1fgmFFpsqg+8pVq0Q6X+KzouZw3z2k5stdu3Y5MV9ikOJLFjrmGPe3JxVT52+mz9WXzNitWzcnphJnff2BolAJfr7E3f/5n/9xYpdffrlsO23aNCe2ZcsWJ9a7d2/Z/7777nNi11xzjWyr7s0o+IUZAAAACMGCGQAAAAjBghkAAAAIwYIZAAAACFHmk/46duwYua16WH3fvn1OzPcAe58+fSIdx1dpUPE9fK6SQHbv3u3EPv/888jHQjS+6nuxtFWJTKpKmKqSZmZWvXp1J+ZLRlVJTy+//LJsq6jEDHX+Q4YMkf1VsoVKmDPTSVPr1693YieccILs37lzZyc2adIk2VZVcDv33HOdmO9+VdfA1zaWMVPedOrUyYn5Es7UfOebb1WlOxXzJdKpBD/1mfqOr+4r9X1hFr0ypm++jzpfmPmTVFG8oiZy+vjugZJOxoxahdZHJXmPGTNGtp09e7YTW7ZsmWyrKnuq78Fnn31W9r/ppptkXImlMuGv8QszAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEIIFMwAAABCizO+SkZOT48R8WagqwzklJcWJ/ec//5H9fVnLB9u5c2ekdmb+7HoVr127thObO3du5GPBpa6z7zNRmcS+DHu1o4kaqyeeeKLsv23btkivaWbWunVrJ9amTRsn1rhxY9lfva97773XiV199dWy/y233OLEfDt6tGzZ0omprOdFixbJ/hkZGU7s9NNPl21V1rXa5SI/P1/2V7sv+HbJqFatmoxXBPXq1XNivix0NTfXqlVLtlVltNVY9R1L7ciidjnw7Xyh+HZJUOel7tc6derI/ps3b3Zivu8xtatMRRbLDjW+3SDUWFHj4mjucBHLWFO7rPh2j4llR4w333zTibVt29aJ+dYhO3bscGK+70xV8n3kyJFOLJbdMIobvzADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIcp80p8qy7px40bZVj2wr0qaPvPMM0U/MUElscSSsLB9+/biPB14xJIU4UvEU1Sywpw5c2TbL7/80ompxBQznQh3/vnnOzFfEskDDzzgxNLT053Y4sWLZf++ffs6sbffflu2veGGG5yYSkZUJbB95+BLZlTvVyVTJiYmyv4qkU+VW/Ydq6LIzMx0Yr4EaXWd1qxZI9uuW7fOialkUvWZmulEKJUgqEpYm+l5wDdfq+TxvLw8J7Zq1SrZX91vvvelxmXdunWdmLp+5VEs87VP1MRPNdeZmQ0YMMCJqTFhZvbQQw85sf/+979OLJYEQ1+Cn/KHP/zBiankOjNd2lqN67S0NNlfra981+U3v/mNE5s4caJsW1SHO2Yq7iwPAAAARMCCGQAAAAjBghkAAAAIwYIZAAAACFHmk/4aNmzoxFR1GTOd3KMSpo7Ug+ZbtmyJ3FYlrKxdu7Y4Twemk3h8yRYq2caXmHPeeec5saysLCfmGxN///vfnViNGjVk248//tiJqcSS/v37y/7qPagkpD/+8Y+y/1/+8hcn1r17d9lWJdesXr3aifk+A1XVUN0rvtdo0qSJE/MlnY0ZM8aJvfHGG5GPVVGoOdiXeN20aVMn5ptvVQXG4447zomtWLFC9lf3tko6jCXx2pccppKbVEW+r776Sva//fbbndh3330n26pKaaraYnlM+osluVa19VWFbNCggRN74oknnJhK3DfTc5gvIfx///d/ndi8efOcmBoTZmbVq1d3YgMHDnRi1113neyvvnMuueQS2VYlCGZnZzsx3/3eokULJ+arbjtjxgwZL034hRkAAAAIwYIZAAAACMGCGQAAAAjBghkAAAAIwYIZAAAACFHmd8lQ5aJr1qwp26pdMlR2686dO4t+YoLKmlUZz2Y6w/fHH38s9nOq6GLZ3cC3I4aidnNQWe++0thqDPp21Pjkk08itfXtBtC2bVsnpkq13nrrrbJ/ly5dnJjvukbN3Fe7DpjpMsa+zHV1v99zzz1OzLfzheK7hhW5NLbavUXtEGGmy+Ju2rRJtlX3myoP77v2vt1TolLlc33l6aP2//TTT2VbNa58Ozqoc1DzzcyZMw9xhmWPuqa+MsexzO1qp58pU6Y4sUcffVT2P+WUU5yYKpdtZpaTk+PE1O49w4cPl/3VvbVkyRIn9swzz8j+K1eudGK+OXTq1KlOTO3IctJJJ8n+CxcudGKLFi2Sbdu0aePEqlat6sROO+002b9+/fpOTO1+YmY2bNgwGT+UijvLAwAAABGwYAYAAABCsGAGAAAAQrBgBgAAAEKU+aQ/9QC570HvXbt2OTFfYsWRoMpH+s5VJYEsX7682M+polOJBqp8r5nZhx9+6MS2bt0q286aNcuJqcSO+fPny/7jx4+XcSUtLc2JdejQwYmp8qtmOjklOTnZifmSDt98800nphLuzHQZZVVqVd2rZjrJ15f08+WXXzqxWBL8VDKZL5HIdw4VQVJSUuS26ppu2LBBts3IyHBiqjS1LxEzatl7XyJfLJ//Tz/95MRUcpRq5xNLyff27ds7sXHjxkU+Vlmh7rP09HTZVs1hS5culW0/++wzJ3b55Zc7sd69e8v+HTt2dGJr166VbV977TUnphL5VIKzmU6crVatmhNT321mZmeccUbkY3377beRYr5EPkUllJvp70z1ndW5c2fZX52DSoY0M2vevHnYKXrxCzMAAAAQggUzAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEKLM75Lx+eefO7GuXbvKtirD1pdhfSSobF7fLh2qNLDKjvVR76siZ/L79OzZ04mpjGczs0GDBjkxX4a/+vzULhN//vOfZf8777zTibVs2VK2Vdn0qtRpVlaW7L948WInFrUkqplZ//79nZjKUDfT56p2GlG7YZiZ7dixQ8aVunXrOjGVJT937lzZf9WqVU6sRYsWsu1f//rXyOdVlmVmZjoxNdZ98+ru3budmG+sqB1VNm/e7MR8u1wcifnOV4ZblfdWu3z4svPVDkqxvK9GjRrJtuVNbm6uExsxYoRsO2PGDCdWs2ZN2VZ9Lvn5+U7Mt8vJ5MmTnZhvhwa104aar9X8ZabvLbVLhm/nCvW+fLu/qF1p1Gfg2xVJ3RfqWpnptZDagec///mP7K/OtVmzZrKtb7eUQ+EXZgAAACAEC2YAAAAgBAtmAAAAIAQLZgAAACBEmU/6U+WiYymV6kusOBJUud/U1NTI/ffu3VucpwMze/TRRyO3Pf30052YKklrZnbeeec5MZXw5Ev6vPvuu52YL4mkdu3aTkwl+PlKWzdu3NiJXX/99U5MJaaYmVWtWtWJJSQkyLbfffedE1OJXL4kJl8yoKJeV5UL/uqrr2T/vLw8J+ZLGoqlNGxZVr9+fSemkm18yXGqBPDvfvc72VaNV5UgeqSS/tT7UgmOZjppSiWo+hLUVDKZ7/zVnOFL6C1v0tLSnJgak2b6mvo2BFD3r5qrfPO9+h5XpbXNzH788Ucn9vTTT0c+lkpGVslxOTk5sr9KRlWJgGb6Gqr70vfdoPjWZ1HL1qvvUTOz4447zonNmTNHtl29enXYKXrxCzMAAAAQggUzAAAAEIIFMwAAABCCBTMAAAAQoswn/X322WdOzPcAukqiUEkoR0osCX4qaUZVzUHRtGvXzompqklmZp988okTe//992Xb+++/P9LxfclR1atXd2K+qkWqAp+qvKTeayzn5buvVBKK71iqotn3338fqZ2Z2ezZs53YsmXLZNui3ttUy3SpaqXqmvgSc1QinS9pSyVtqsQgX5UxdV6+imaKugd8CUuVK1d2YipBVSWtmekKmL5jqWuoqiKWR998840Tu/LKK2VblaTdoUMH2faUU05xYirhzZf0O3/+fCf2xhtvyLYqGa9Tp05OzPfdcM4550Rq65uvVdKeL0lbJZjWqlXLiakxaabvN9/7Uuer7u2mTZvK/ioh+K677pJtDxe/MAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIcr8Lhlr1qxxYr6sa5VNn5ycXOzn5KPKqvqyyVV2qS8TFYdP7dDQo0cP2VaVtfXtXKLKmH/99ddObN68ebK/et0vvvhCto1q/PjxRepf0ahdFnw7F1QUqgx7LLuJqGx8X8l2NQeq1/XNiyob31dGW4ml5LfK8FfvVe06YKbfayw7F9StW1e2rQh8ZZ1ffvnlSDGfOnXqOLHMzEzZtlGjRk6sTZs2sq3avUftiqR2TjEze/PNN51Y1F1azPQ9VLVqVdlWUeeqvu/M9I4avh2Q1LVV99vrr78u+z/11FMyrhzuPM4vzAAAAEAIFswAAABACBbMAAAAQAgWzAAAAECIMp/0pyxcuFDG1UP06mF3XwLFunXrinResZTVLWpZV0SjrumHH34o26q4L4lIlYZu2bKlE7vqqqtkf5UItW3bNtlWJY6qseobfxs2bHBiWVlZkY5jZpaUlOTEfEkVKpFJtfWVEFaJs76kMXVe6j34El5U/xUrVsi2sSQTlWXqWqkSwr77Qo3BWBIEFV8ini8eVSylsdX7Vf19SX8q7iv5rRKs1PFTU1Nlf1VCGK68vLxIMTOzmTNnOrGJEycW9ymhmMSyFvs1fmEGAAAAQrBgBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEKUy10yfJnIKutaxXwZ+kXdJUNlMvuyNVU2ttohACXLV5b3m2++iRQjkxplSUpKihOLZfeeWOYwtauRmtt9O1dELaPt240iFuocYinDXbNmTSfm280i6i4X7du3l/FPP/008nkB+D/8wgwAAACEYMEMAAAAhGDBDAAAAIRgwQwAAACEKJdJf/Xr15fxzZs3OzGVrFG5cuXiPiUz00ksvoSZWMqqAsDRoOYwNVdVq1ZN9lfznS8RUB1L8SXXFTURT/ElaavXVWXYs7OzZf///ve/Tiw3N1e2VYnqKiG9Tp06sj+Aw8MvzAAAAEAIFswAAABACBbMAAAAQAgWzAAAAECIcpn0p5L7zHRyytGsqLdgwQInpio8menz2rt3b7GfEwBEVaNGDSe2atUqJ+arlvrOO+84MZUcZ2Y2YsQIJzZz5kwn5ksOjJq87Uvki6WCoaogqBIBU1NTZf9evXo5senTp8u2GRkZTkx9t9WqVUv2B3B4+IUZAAAACMGCGQAAAAjBghkAAAAIwYIZAAAACMGCGQAAAAhRLnfJyM/Pl3GV4a3KTderV6/Yz8lM73wRC5UJHcuxfNngABBF06ZNnZial5KSkmR/tSPGtddeK9uqXTIaNGjgxHbt2iX7q12F1Hzvm1fVLhe+0tpVq1Z1YtWrV3dizz//vOyvzuv777+XbXNycmQ8yjkBOHz8wgwAAACEYMEMAAAAhGDBDAAAAIRgwQwAAACEKJdJf77kOlWGOiEhwYm1bdtW9n/77beLdF4qYcRX1lXFY0n6A4DippLuVFnon376Sfb/5ptvIh9LJa2NHj3aiZ166qmyv0qOW7p0qROLZV5V79XMbO3atU7sxhtvdGLjx4+PfKzHHntMxs844wwnppIsW7VqFflYAA6NFRgAAAAQggUzAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEKJc7pLx0ksvyfhxxx3nxDZs2ODEpkyZUuznZGa2ZcsWJ+bL0N62bZsTmz17duRjUQYbQHHr2LGjE1O7EiUmJsr+qjS2jyp5fdlll0XuH1XlypVlvFq1ak5MzeFm/t0zimLmzJkyrsqTp6WlObE1a9YU9ykBFRq/MAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAh4gKywwAAAAAvfmEGAAAAQrBgBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEKwYI4oLi7O7rjjjoI/P//88xYXF2dLly4tsXMCSpOlS5daXFycPfjggyV9KqjAmKtREcTFxdmIESMO2Y7xX3zK7YL5wCA58L8qVapYs2bNbMSIEbZu3bqSPj3gsHz//fc2cOBAy87OtipVqlhWVpb17t3bHnvssZI+NeCwMFcDhZXkPH/PPffY66+/fsSPUxZVKukTONL++te/WqNGjWz37t322Wef2ZNPPmmTJk2y2bNnW9WqVUv69IDIpk+fbj169LCGDRvaFVdcYRkZGbZixQr74osv7JFHHrFrr722pE8ROGzM1UDxz/OXXHKJDRo0yBITEyO1v+eee2zgwIF27rnnHsbZl2/lfsF85plnWseOHc3M7PLLL7datWrZyJEj7Y033rDBgweX8NkdOTt27LDk5OSSPg0Uo7vvvtvS0tLsyy+/tOrVqxf6u7y8vJI5qaNs586dLJ7KKeZqoPjn+fj4eIuPjw9tEwSB7d6925KSkmJ+/Yqk3D6S4dOzZ08zM1uyZIl1797dunfv7rQZOnSo5eTkHNbrP/HEE9a6dWtLTEy0zMxMu+aaa2zz5s0Ffz9ixAhLSUmxnTt3On0HDx5sGRkZtm/fvoLY5MmTrWvXrpacnGzVqlWzvn372pw5c5zzTUlJsUWLFtlZZ51l1apVs4suuuiwzh+l16JFi6x169bOJGpmVqdOnYL//8Czba+//rq1adPGEhMTrXXr1vbuu+86/VatWmWXXnqp1a1bt6DdP//5z0Jt9u7da//7v/9rHTp0sLS0NEtOTrauXbva1KlTD3nOQRDYlVdeaQkJCTZhwoSC+IsvvmgdOnSwpKQkq1mzpg0aNMhWrFhRqG/37t2tTZs29vXXX9upp55qVatWtVtvvfWQx0T5wFyNiijqPH/AoeZ59QxzTk6O9evXz9577z3r2LGjJSUl2dNPP21xcXG2Y8cOGzNmTMEjUkOHDi3md1h2VbgF86JFi8zMrFatWsX+2nfccYddc801lpmZaQ899JANGDDAnn76aTv99NPtp59+MjOzCy+80Hbs2GHvvPNOob47d+60t956ywYOHFjwX4Njx461vn37WkpKit133332l7/8xX744Qc75ZRTnAf4f/75Z+vTp4/VqVPHHnzwQRswYECxvz+UrOzsbPv6669t9uzZh2z72Wef2dVXX22DBg2y+++/33bv3m0DBgywjRs3FrRZt26ddenSxT744AMbMWKEPfLII9akSRO77LLLbNSoUQXttm7dav/f//f/Wffu3e2+++6zO+64w9avX299+vSxmTNnes9h3759NnToUHvhhRds4sSJ9pvf/MbMfvkF5Xe/+501bdrURo4caTfccIN9+OGHduqppxZasJiZbdy40c4880xr3769jRo1ynr06BHTNUPZxVyNiqi453mfefPm2eDBg6137972yCOPWPv27W3s2LGWmJhoXbt2tbFjx9rYsWNt+PDhxfG2yoegnHruuecCMws++OCDYP369cGKFSuC8ePHB7Vq1QqSkpKClStXBt26dQu6devm9B0yZEiQnZ1dKGZmwe233+68/pIlS4IgCIK8vLwgISEhOP3004N9+/YVtBs9enRgZsE///nPIAiCYP/+/UFWVlYwYMCAQq//yiuvBGYWfPrpp0EQBMG2bduC6tWrB1dccUWhdmvXrg3S0tIKxYcMGRKYWXDzzTfHeplQhrz//vtBfHx8EB8fH5x44onBTTfdFLz33nvB3r17C7UzsyAhISFYuHBhQWzWrFmBmQWPPfZYQeyyyy4L6tWrF2zYsKFQ/0GDBgVpaWnBzp07gyAIgp9//jnYs2dPoTb5+flB3bp1g0svvbQgtmTJksDMggceeCD46aefggsvvDBISkoK3nvvvYI2S5cuDeLj44O777670Ot9//33QaVKlQrFu3XrFphZ8NRTT8V6qVCGMFcD/6e45/mDx38QBEF2dnZgZsG7777rHD85OTkYMmRIsb+v8qDc/8Lcq1cvS09PtwYNGtigQYMsJSXFJk6caFlZWcV6nA8++MD27t1rN9xwgx1zzP9d1iuuuMJSU1MLfqWIi4uz888/3yZNmmTbt28vaPfyyy9bVlaWnXLKKWZmNmXKFNu8ebMNHjzYNmzYUPC/+Ph469y5s/zn8KuuuqpY3xNKl969e9vnn39u/fv3t1mzZtn9999vffr0saysLHvzzTcLte3Vq5fl5uYW/PnYY4+11NRUW7x4sZn98qjEv//9bzv77LMtCIJCY6xPnz62ZcsW++abb8zsl2fgEhISzMxs//79tmnTJvv555+tY8eOBW1+be/evXb++efb22+/bZMmTbLTTz+94O8mTJhg+/fvtwsuuKDQMTMyMqxp06bOuE5MTLRhw4YVzwVEqcZcDRTvPB+mUaNG1qdPn2I///Ks3Cf9Pf7449asWTOrVKmS1a1b15o3b15okiwuy5YtMzOz5s2bF4onJCRY48aNC/7e7Jd/6hs1apS9+eab9tvf/ta2b99ukyZNsuHDh1tcXJyZmS1YsMDM/u85voOlpqYW+nOlSpWsfv36xfZ+UDp16tTJJkyYYHv37rVZs2bZxIkT7eGHH7aBAwfazJkzrVWrVmZm1rBhQ6dvjRo1LD8/38zM1q9fb5s3b7ZnnnnGnnnmGXmsXyeYjBkzxh566CGbO3duwT9Zm/0y6R7s73//u23fvt0mT57sPHe6YMECC4LAmjZtKo9ZuXLlQn/OysoqWKyjfGOuBn5RXPN8GDV3I1y5XzCfcMIJBZnXB4uLi7MgCJz4rxM5joQuXbpYTk6OvfLKK/bb3/7W3nrrLdu1a5ddeOGFBW32799vZr88G5eRkeG8RqVKhT+6xMTEI/LlgtIpISHBOnXqZJ06dbJmzZrZsGHD7NVXX7Xbb7/dzMybFX1gvB8YXxdffLENGTJEtj322GPN7JcEvaFDh9q5555rf/7zn61OnToWHx9vf//73wueM/21Pn362Lvvvmv333+/de/e3apUqVLwd/v377e4uDibPHmyPMeUlJRCfyZru+JgrgYKK+o8H4a5NXblfsEcpkaNGvKfLn79C0NU2dnZZvbLg/SNGzcuiO/du9eWLFlivXr1KtT+ggsusEceecS2bt1qL7/8suXk5FiXLl0K/v7AP7PUqVPH6Qv82oFFxpo1ayL3SU9Pt2rVqtm+ffsOOb5ee+01a9y4sU2YMKHgVzUzK5i0D9alSxf7/e9/b/369bPzzz/fJk6cWLBoyM3NtSAIrFGjRtasWbPI54uKjbkaFd3hzPOH49dzPAqr0P+Zm5uba3PnzrX169cXxGbNmmXTpk2L+bV69eplCQkJ9uijjxb6r7tnn33WtmzZYn379i3U/sILL7Q9e/bYmDFj7N1337ULLrig0N/36dPHUlNT7Z577in0T+AH/PqcUTFMnTpV/nIwadIkM3P/iTlMfHy8DRgwwP7973/LbOxfj68Dv2L8+tj//e9/7fPPP/e+fq9evWz8+PH27rvv2iWXXFLwK9xvfvMbi4+PtzvvvNN5L0EQRMruRsXDXI2Kojjn+cORnJzs7FaEX1ToX5gvvfRSGzlypPXp08cuu+wyy8vLs6eeespat25tW7dujem10tPT7ZZbbrE777zTzjjjDOvfv7/NmzfPnnjiCevUqZNdfPHFhdoff/zx1qRJE7vttttsz549hf6Jz+yX596efPJJu+SSS+z444+3QYMGWXp6ui1fvtzeeecdO/nkk2306NFFvgYoO6699lrbuXOnnXfeedaiRQvbu3evTZ8+veBXr1iT4+69916bOnWqde7c2a644gpr1aqVbdq0yb755hv74IMPbNOmTWZm1q9fP5swYYKdd9551rdvX1uyZIk99dRT1qpVq0LJUAc799xz7bnnnrPf/e53lpqaak8//bTl5ubaXXfdZbfccostXbrUzj33XKtWrZotWbLEJk6caFdeeaX96U9/KtJ1QvnDXI2Korjn+Vh16NDBPvjgAxs5cqRlZmZao0aNrHPnzkf0mGVGCezMcVQc2Erlyy+/DG334osvBo0bNw4SEhKC9u3bB++9995hbVV0wOjRo4MWLVoElStXDurWrRtcddVVQX5+vjz2bbfdFphZ0KRJE+/5TZ06NejTp0+QlpYWVKlSJcjNzQ2GDh0afPXVVwVthgwZEiQnJ4e+T5R9kydPDi699NKgRYsWQUpKSpCQkBA0adIkuPbaa4N169YVtDOz4JprrnH6Z2dnO9sFrVu3LrjmmmuCBg0aBJUrVw4yMjKC0047LXjmmWcK2uzfvz+45557guzs7CAxMTE47rjjgrffftu5T369rdyvPfHEE4GZBX/6058KYv/+97+DU045JUhOTg6Sk5ODFi1aBNdcc00wb968gjbdunULWrdufbiXC2UEczXwf4p7nvdtK9e3b195/Llz5wannnpqkJSUFJgZW8z9SlwQRHg6HAAAAKigKvQzzAAAAMChsGAGAAAAQrBgBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEKwYAYAAABCRK70V5bqi6enp8v4Oeec48S2bNnixFasWBH5WCtXrnRilSrpy5qQkODEUlJSZNtu3bo5sU8++cSJffPNN4c6xVKvJLcCL0vjGmUL47r4NWzY0ImtWrVKtt23b1+xH3/AgAEy/u9//7vYj1XUz/BIjT/Gdck6/fTTnViDBg2cmCrTbmbWtm1bJ/aPf/xDtp0/f74TU59BeSjnEeU98AszAAAAEIIFMwAAABCCBTMAAAAQIi6I+PBJST871KZNGxnv27evE/M9Q6yeF1ax+Ph42T8/P9+J7dmzx4nt3LlT9k9LS3NivnNVtm/f7sQqV64s286bN8+J/etf/4p8rKOJZ+JQHpXHcV3U5xfbt2/vxHbt2iXbZmZmOrGXX37ZiflyVh544AEntn79eieWm5sr+1900UVO7Jhj9G9MEyZMcGIvvfSSExs+fLjsf+6558q4or6f1Gewf//+yK8Zi/I4rksjlcdkZjZ69Ggntnr1aifmWxv06NHDic2cOVO2Pe6440LO8NDU/XKkxmVR8QwzAAAAUEQsmAEAAIAQLJgBAACAECyYAQAAgBAsmAEAAIAQZWaXjFtuuUXGVYb1smXLZFuVdZ2dne3E1G4YZjoTtVmzZpHameldLho1aiTbqt035syZ48SSk5Nl/7p16zoxtXOGmdnkyZOd2NHMbiXrGuVReRzXRZ0XNmzY4MQWLFgQ+Viqv2+XCxWP5fzV98h3330n29asWdOJVa1aNdLxzfTcrHbp8Dma1dfK47gujR577DEZ7969uxNTu1yoe8XMbNCgQU7so48+km3ff/99JzZmzBjZtqxjlwwAAACgiFgwAwAAACFYMAMAAAAhWDADAAAAIUpl0l/9+vWd2A033CDbrly50on99NNPsq1KulPHqlGjhuw/d+7cSK/pk5GR4cQaNmwo286aNcuJxVKGu3Hjxk4sNTVVtv3rX/8q40cLSSQojyryuPYlLKlyv2oONzOrVKlSpGOpctdmOpmvSpUqTsw3hyq+MtyqDLFKukpISJD9W7du7cSee+452fa+++5zYupa/fzzz7J/UVXkcR2LYcOGyfjxxx/vxFSSfkpKiuy/b98+J1a7du3I/ZW1a9fKeGJiohPbvHmzE9u0aZPsf9NNNzmxvLw82baky2iT9AcAAAAUEQtmAAAAIAQLZgAAACAEC2YAAAAgBAtmAAAAIESp3CWjXbt2TuzGG2+UbRcuXOjEfKWpt2zZ4sTi4+OdWL169WT/tLQ0J7ZkyRIn5stOVWW4f/zxR9k26u4b6vzNdMluH3bJAIpfRR7XX331lYyr+Wrbtm2yrdq9Ipb3pXaJ2LFjhxOrVq2a7K/O1Ze1r143KSnJiandNMzMkpOTnZjve6hRo0YyfjDftSrquKzI49rn5JNPdmJ33HGHbLt3714npnbAUqXVzfTOFWqXDLUjjJnZ/PnznZhv9xe1DlGfgW/NM2PGDCd2zTXXyLYljV0yAAAAgCJiwQwAAACEYMEMAAAAhGDBDAAAAISIVnv0KFNJGL4kOJUA4Su9qB6WV2Umly5dKvur8pUtWrRwYr5z/e677yId30yfq0osadKkieyvHuJftmyZbAsAh0vNQTVr1pRtVeK1bw5UyUUqMceXiKf6q3nxp59+kv1V0qAvaU8lXa1bt86JNW3aVPZX5+BL+opaQvhIJf3BNXDgQCe2atUq2VYl46nPyleyfePGjU5MJc76+qt7U5VxN4s+1nzl6XNycpzYKaecItt+9tlnTizqHHC08AszAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEKJUJv2pCkdr166VbVWFnZNOOkm2/de//uXE1AP46kF3M/2w/ebNm2VbRSV2+BJWKlVyPxr1EL+v6pM6VwAobqqCqS/hTCV079q1S7ZVydMq5qsypqrn7d6924n55nuV4Ld161bZVlWBjSUhXCU+rly5UrZVc/6iRYucGMl9xc+XZK82BFAJrj5qHeK7L2rUqOHE1qxZ48RURUEzs8zMTCfmSxBUGw2otYnvHlKJsxdddJFsq5L+StsY5hdmAAAAIAQLZgAAACAEC2YAAAAgBAtmAAAAIAQLZgAAACBEqdwlQ2WB+rI4586d68S6d+8u2z7zzDNOLD4+3on5SrWqrGnVX5W1NjNLSkpyYr5M2CVLljgxlWHuy9r98ccfnZjK5DaLXv4SAA52/PHHR26rdgqqU6eObKt2lFAZ+r45VM3Nag5Wmfy+uG9XItVWfY+o45vp3TcSEhJk29zcXCemdsmgNHbx6927t4yrXS586wi1s1Ys6wi1FqpevboT27Nnj+yfn5/vxHzl4dU6QI1L344c6hqkpqbKtmUBvzADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIUpl0l/VqlWdmO8B+Ly8PCd27LHHyrbnnnuuE1MlpH3lS9XD8ioR0Ee19SXtZWRkODH1sL0qS2umE2F8yTUk/aEoVAninJwc2bZt27ZObPz48ZGPVdSxqhKhSIIqmlatWjkx3zVVn59KeDIzq127thNT830syczqe0TN6762vu+GWrVqObH169c7MV/S4IYNG5yY77qoktvvv/++E2NcF79u3brJuPq+9SW3qXLTKhFQJfmb6VLwKunUV65aJfj5El/V3Kreqy/BUCW5qk0dzPS4Vps6lCR+YQYAAABCsGAGAAAAQrBgBgAAAEKwYAYAAABCsGAGAAAAQpTKXTIUXya8is+ePVu2VdmZKuvZdyyVCap2vvBlp6qsZ18mrXpdlR2rMrHN9HvwZV2rDO9169bJtqi4br75Zhm/6KKLnNiKFStk29atWzsxNdamTp0q+8eyI0YsZe8VtctAr169ZNsPP/ww8uuWN5mZmU7Mt0ODypr3tVXlftWOGKtXr5b91a5CaocAX1lfVe5Y7V5kpne5UO9V7fxhZrZ8+fLI59W+fXsZPxi7ZBQ/3/e1+h5Wu32Z+ctQH0ztpmGmx3XUnTfMzJo2berEtm3bJtv6dic7mG/No+ZrX9uOHTs6MXbJAAAAAMoQFswAAABACBbMAAAAQAgWzAAAAECIUpn0p5LQ1q5dK9uqB+CnTJki26qEDV/SnKIe1o+aCGhmVqmSe7kXLVok26rX2LlzpxP77LPPZH+V4OhLeIqlvDdKjirrbFb05J709HQnNm3aNCemkkXMzP7nf/7HiTVs2FC2VUl/H3zwgRN78803Zf8//OEPTmzp0qWybdQEP998oZJmOnXqJNtW5KS/3NxcJ+YrtavGmq+srkocVUlzjRs3lv1VGW01B2dnZ8v+qjSxek0zfQ+qpFOVSGgWPUHRTJcQRvFTCW++OUUlsvmS29T3bSxzuDqH5ORkJ+b7vlD91X3hE0vitTov3zVUSX8vvvhi5GMdDfzCDAAAAIRgwQwAAACEYMEMAAAAhGDBDAAAAIQolUl/6kFxX2JI586dndi9994r21511VVOTD2A7quIpyr3qES8WB72V5UGzczq1KkT6bxWrVol+6uElY0bN0Y+1sqVK2VbRKMSLlRiRyyJfLEkhqiKVHfeeadsO2zYMCf20EMPObErr7xS9v/9738f+bzU/aLGmq+i3pIlS5zYRx99JNuOHz/eiQ0ePNiJ1atXT/ZX59WzZ0/Z1jfnVAQqwdhXKVRVJNu0aZNsq+ZLlaStjm+m52tf9TxFJfj5Ep7UnK+O5buHVaK7b75WSZYofuo6+z4/NS58Y0V9j6v7wrcOUecQtSKfmU7I9a1ZVFuVIOj7Hou6eYGZP3m3NOEXZgAAACAEC2YAAAAgBAtmAAAAIAQLZgAAACAEC2YAAAAgRKncJUOVD/WVCVW7XKiStmY661TFfKWifdmdB/Pt6KGytn2ZsCprddu2bU5s9uzZsv8ll1zixH788UfZtn79+k7sm2++kW0rMvWZ+DKho+5oEcvOFzk5OTL+3HPPObHu3bs7MbVLjJlZ27ZtndiaNWucmNpNw0zvMrFs2TLZVu3eoq6rb/cXdQ/5dq5QcbUrje9Yareedu3aybYVxfHHH+/E1Bj2zaELFy50Yr7rr8qQ79q1y4n5dtlQn7U6V19ZYBX33a9Ry7D7zlWNa19b9Z2hynv77kFEU7NmTSfmK/keyzy+e/duJ6Z2mfDtXKHKqOfn50eKmZk1a9bMialdOsz0WFPrIN9ONeoe8h2rQYMGMl6a8AszAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEKLEk/5UYo1K8PMl3KlSqc2bN5dtU1JSIh1LPVQfi1jKr/qohBVV7tj3YP+cOXOcmK/UqkoYqSiilrA28yf4HQnXX3+9E/Ml7an3sGDBAif28ccfy/533HGHE7v00kud2IwZM2T/zMxMJ1a3bl3ZVo1XVdbVV+pVJcx8++23sq1KLlFJh0lJSbK/uo8bNWok2/rmnPJGJeaohLfatWvL/u+8844T8yUBnXrqqU5M3YO+sry+hOqo1OfvO5b6zlDfLb75Ws3BvmRIlZAbS+ItolFzRSzlrmP5vlDf7WoNYKbHpUquU+dvpjcl8N0rqq263333hboGvu9XNd7Vddm6davsfzTwCzMAAAAQggUzAAAAEIIFMwAAABCCBTMAAAAQosST/lRVP/Wwuy8JSFWk8yUcqcRBlZjhq+ajRK0e6OOrMqUebFcJN77Egry8PCemEqbM/Ne2IlAJCE2aNJFtzz//fCemqi+amR133HGRju+rkNS0aVMn9pe//EW2Pffcc53Y4MGDnZiv0qO630aNGuXE/vCHP8j+Y8eOdWIXX3yxbLt27Vonpj6DWCoo+qrKqYRixZekG0v1rqImmJUVqvqZ+qx885qar1X1PjNd6cxX8VVR87j6vvF9zrGMNXUNVPU+XyKeaqtiZjrBqmPHjk7siy++kP0RjRorviq+sSTCqSRj1dY3B6p7QCX4qfM30+PaNweqc1CxLVu2yP6+c1DUHFqnTh0nRtIfAAAAUEqxYAYAAABCsGAGAAAAQrBgBgAAAEKwYAYAAABClPguGbVq1YrUzpedumHDBifWrl072VbtBpCWlubEVMarmc4OVZncPiq7VZXrNjNbvXp1pPNSpSPN9Ln6MlbT09NlvKKaOHGijKtdSh5//HHZdvbs2U7spJNOcmKqrLSZ2cqVK51Yv379ZNv27ds7sXXr1jkxXwlitStIixYtnJgvw1/dA76yrtWrV3diKjvat8tCUXdOUDvF+HZJUPebb6cZdb3LIzVfquvv2zVE7SqzefNm2TZq2XrffK36+3YuiNrft3OBiqvvtmnTpsn+mzZtcmInnniibKvuLd/OPjh86rvZN9eoMai+w830d7a6L2Ipw63mdt+aSe1045sD1bHUOsK3A5PafUN9B5jpuT0jI8OJLVy4UPY/GviFGQAAAAjBghkAAAAIwYIZAAAACMGCGQAAAAhR4kl/qkykerDelwSkHipXr+l7XfWwvu9he1VCWvX3ldZWyRq+c1UP26v+vmNt3LjRidWvX1+29b3fikAl7eXm5sq2M2fOdGKDBg2SbVXCiBorvvLNvlKliio3rZJAfO9r0aJFTkwlLKmELzNd2thXvlSNV1/SlxLLWFXHUqWNfQlqKhEmltK45ZGvNPTBfMl1qoSvKgNvpj9rdXxfwpKar1Vb37mq7xzf56/OVX3f+K5fXl6eE6tdu7Zsq8oQ+5K/cfhUcpvv+1Z9j6t52Uwneqvva9+GAiq+c+dOJ6YSSc30uIol6U+NtaVLl0bu36VLF9lW3UNqvihJ/MIMAAAAhGDBDAAAAIRgwQwAAACEYMEMAAAAhGDBDAAAAIQo8V0yopa19WWy5+fnOzFfJrLaZUJl+Pt25IiaIe7LmFc7H6jjm+nykevXr498Tr4yyIrKsFWZuOVxN40JEyY4sf79+8u2UUtQm+lsejXWfVnXCQkJTsyXyayyntVYmzt3ruyvxvuaNWuc2Lx582R/NS5856pEva/M9I4GsZSnj6U0srqPq1atKtu2a9cu8uuWZVE/a99nsnz5cifWsWNH2VbNjWpc+46lvjNiKZet4r6xqsaKmoN9JazVvRnLfBvLPYRo1Lzo+25Xn5XazcRMl8FWY0XttGSmv0fUd4BvByb13RTLLmR16tSJdE5meqcQtXuNmb4GvjLaJYVfmAEAAIAQLJgBAACAECyYAQAAgBAsmAEAAIAQJZ70p8pPqgfIfUl/KmnKV25alZ+MWprbTCcNqiQgX2JQLOVzVWlilfRXs2ZN2d+XjKb4yr1WBB9++KETa9CggWx70003ObGLL75Ytm3btm3RTkyIJQlIjTVfcpRK7FBJICQW+Z1xxhklfQpHhRqDalz5kj7VfNuoUSPZVs23sYxr9Z0RS2lsxZccpa6Lmld9pX7VfOErbazuw/KYkF3SVOK1L0lbfa7ffvtt5LaxlDZXn7Waw33rDTV+Ylmb+JL2lAULFjixunXrRm6bnp4e+VhHA78wAwAAACFYMAMAAAAhWDADAAAAIVgwAwAAACFKPOlPJbepZAn1ULtZbNW4Vq9e7cTUA/RpaWmyf15enhNTCSe+5CjV1lfNR10D1d+XLPDuu+86MV81MnUN1MP2sSQSlkf3339/pJiPqpDUqlUr2VZVP6tXr55sG7Uaku8eUolMURNLzHRFNl8iqYrv3r3bifkSsdR5+RKe1P2i3oMvuUpVqfIda+rUqU7s5ptvlm3LMvX+VXKdb6ycffbZTsyX2KPGRdSxGst5+caaShD0HUvN+Srmuy5ZWVkyrkQd1ygaldzm23xAfdZbt26N3DZq0qiZ3ihBbQigKviamR177LFObMOGDbKtou6XjIwM2VZV9ozlflOJlyWJX5gBAACAECyYAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBAlvktG1DLYmzdvlv1VW1/W9cKFCyOdU35+voyrc1CZ3Dt27JD91bn6sm5Vhq6K+bK2VYatb/cO9RnEUv4S0ahdVlTMzOzjjz8+wmcDxE7NKyrr3Teue/fu7cTmz58v20YtTRxLaeuoZeDN9G4UvmOp66LmUF8J4o0bNzoxtauOmZ7za9asKdvi8KndKHzf14pvXKnXUOPK932t+teoUcOJ+caEKjnvKy+v4mp907x5c9l/+vTpTsy3+4faJcN3XiWldJ0NAAAAUMqwYAYAAABCsGAGAAAAQrBgBgAAAEKUeNKferBdJUuo5Dqf77//XsbVw+6qrHBmZqbs36BBAyemztX3oLoqIawS7sz8iYcHS0pKknFVclu9f19bX3lxABWXSsxRMZWcZ6YT+erXry/bqqQlNS+q45vppC11Xr752ve6ikrmS01NjXws9T3gSxBUSX+xJD4iGnWdfYl4iq+ss/q+VcmosSTeq/HjO1c1rnzJjCqukv5q1ap1qFM8JHVvkPQHAAAAlCEsmAEAAIAQLJgBAACAECyYAQAAgBAsmAEAAIAQJb5LhspaVjs/qB0mzHQW5aBBg2TblStXOrFVq1Y5MV+56Z07dzoxVS7bl9mpslZ9ZbybNGnixNSOHr7yqQ8//HDk81LZ3L5rAKDiUrsVqd0kfFn3qizutGnTZNvk5ORI/X07RPh2KTiYbwemWMpoRy1tvH79etn/5JNPdmINGzaUbdVuR2oHJxTNli1bnJja4cLMbNOmTU6sbdu2kY+l1kG+XVqi7iKmyq2bmTVr1syJqZ0vfNQuG75dtRo3buzE8vLyZFs1Z6idbkoSvzADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIUo86U8lUagEP19y2xdffOHELrvsMtlWJb1lZGREPpZ64D+WMpPr1q1zYiqxxEwnEagkhHnz5sn+iq/U5tatW52YL7kBQMWlkttUwpJvrnn22Wed2L333lv0Eyvj1HfWfffdJ9uq7xGVEI6i2bBhgxNTSadmOkn+lFNOkW3V97ham/hKo6vNB6pVq+bEfIl4viRXJer6Rp2TmdlZZ53lxFQZbzOd5Fva8AszAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEKLEk/5UlTn1oLlq5/PVV18V6ZzKK1+1RFVtMDMz04l98803xX5OAMoOlVyUn5/vxHxJaGpe8VGJUL7qZ0XhqxQYy7HUa6jzVwmSZmY5OTmRjx+1qiCKRlXx9V1nVZ34mWeekW1/+9vfOrFatWo5MV9lXpVgmJaW5sR81ftU9TzfWFMJfuoa+BIJJ02a5MS6desm26qEyv/+97+ybUnhF2YAAAAgBAtmAAAAIAQLZgAAACAEC2YAAAAgBAtmAAAAIESJ75KhdmhQfNnFsVBluIvjdY8WlTWrMmZj6R/rawCouKKW8PXNKbGUvz1a81Jx7LxR1NdYv369E/OVRlalhVesWOHE1M4JZro0M1zLli1zYrF8zm+//XbkePv27Z3YscceK/vXqFHDidWrV8+JqfWOmdnevXudmK+MthqXH374oRP74osvZH+lS5cuMq5271DHL0n8wgwAAACEYMEMAAAAhGDBDAAAAIRgwQwAAACEKPGkP0U9/J2YmFjk1y1LCX5KUZNgfNdQJQeoUp8AKrbOnTs7MZUIqErqmvkTmcojX8ltRSVt+RKxVOKkKlfcq1cv2f/f//535POqyHJzc51Yw4YNZdvly5c7MZWcZ6ZLyc+cOTNSrDzwlRdX90DNmjWP9OnEhF+YAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBAsmAEAAIAQJb5Lxrp165yYyqJUWaiIzfz582W8UaNGTmzz5s1H+GwAlDXTpk1zYmrXhq1bt8r+33zzTbGfU2kVyy4ZTz31lBPzlRFXuxotWrTIib3xxhuRjw/Xe++958SaN28u265du9aJqd0wfNROM0erNHwYNYZVLJZznTp1qowvWLDAif3nP/+J/LpHA78wAwAAACFYMAMAAAAhWDADAAAAIVgwAwAAACHigiAISvokAAAAgNKKX5gBAACAECyYAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBAsmAEAAIAQLJgBAACAECyYI4qLi7M77rij4M/PP/+8xcXF2dKlS0vsnACfoUOHWkpKyiHbde/e3bp3715sx+3evbu1adOm2F4P+DXGNfCLuLg4GzFixCHbsVYpPuV2wXxgkBz4X5UqVaxZs2Y2YsQIW7duXUmfHuB44oknLC4uzjp37lzSp1Im3XPPPfb666+X9GngIIzromFcVzzff/+9DRw40LKzs61KlSqWlZVlvXv3tscee+yIH5vx5lduF8wH/PWvf7WxY8fa6NGj7aSTTrInn3zSTjzxRNu5c2dJnxpQyLhx4ywnJ8dmzJhhCxcuLOnTKXOY6EsnxnXRMK4rlunTp1vHjh1t1qxZdsUVV9jo0aPt8ssvt2OOOcYeeeSRmF/vkksusV27dll2dnak9ow3v0olfQJH2plnnmkdO3Y0M7PLL7/catWqZSNHjrQ33njDBg8eXMJnd+Ts2LHDkpOTS/o0ENGSJUts+vTpNmHCBBs+fLiNGzfObr/99pI+LaBIGNdAbO6++25LS0uzL7/80qpXr17o7/Ly8mJ+vfj4eIuPjw9tEwSB7d6925KSkmJ+/Yqk3P/CfLCePXua2S8Tue85t6FDh1pOTs5hvf4TTzxhrVu3tsTERMvMzLRrrrnGNm/eXPD3I0aMsJSUFPkL9+DBgy0jI8P27dtXEJs8ebJ17drVkpOTrVq1ata3b1+bM2eOc74pKSm2aNEiO+uss6xatWp20UUXHdb5o2SMGzfOatSoYX379rWBAwfauHHjnDZLly61uLg4e/DBB+2ZZ56x3NxcS0xMtE6dOtmXX355yGPMnDnT0tPTrXv37rZ9+3Zvuz179tjtt99uTZo0scTERGvQoIHddNNNtmfPnsjv5+uvv7aTTjrJkpKSrFGjRvbUU085bfLy8uyyyy6zunXrWpUqVaxdu3Y2ZswYp92OHTvsxhtvtAYNGlhiYqI1b97cHnzwQQuCoKBNXFyc7dixw8aMGVPwGNbQoUMjny+ODMY14xqxWbRokbVu3dpZLJuZ1alTx4m9/vrr1qZNG0tMTLTWrVvbu+++W+jv1TPMOTk51q9fP3vvvfesY8eOlpSUZE8//TTj7RAq3IJ50aJFZmZWq1atYn/tO+64w6655hrLzMy0hx56yAYMGGBPP/20nX766fbTTz+ZmdmFF15oO3bssHfeeadQ3507d9pbb71lAwcOLPivwbFjx1rfvn0tJSXF7rvvPvvLX/5iP/zwg51yyinOA/w///yz9enTx+rUqWMPPvigDRgwoNjfH46ccePG2W9+8xtLSEiwwYMH24IFC7yLhZdeeskeeOABGz58uN111122dOlS+81vflMwxpQvv/zSevbsaccdd5xNnjzZmzi1f/9+69+/vz344IN29tln22OPPWbnnnuuPfzww3bhhRdGei/5+fl21llnWYcOHez++++3+vXr21VXXWX//Oc/C9rs2rXLunfvbmPHjrWLLrrIHnjgAUtLS7OhQ4cW+mfHIAisf//+9vDDD9sZZ5xhI0eOtObNm9uf//xn++Mf/1jQbuzYsZaYmGhdu3a1sWPH2tixY2348OGRzhdHDuOacY3YZGdn29dff22zZ88+ZNvPPvvMrr76ahs0aJDdf//9tnv3bhswYIBt3LjxkH3nzZtngwcPtt69e9sjjzxi7du3Z7wdSlBOPffcc4GZBR988EGwfv36YMWKFcH48eODWrVqBUlJScHKlSuDbt26Bd26dXP6DhkyJMjOzi4UM7Pg9ttvd15/yZIlQRAEQV5eXpCQkBCcfvrpwb59+wrajR49OjCz4J///GcQBEGwf//+ICsrKxgwYECh13/llVcCMws+/fTTIAiCYNu2bUH16tWDK664olC7tWvXBmlpaYXiQ4YMCcwsuPnmm2O9TCgFvvrqq8DMgilTpgRB8MsYqV+/fnD99dcXardkyZLAzIJatWoFmzZtKoi/8cYbgZkFb731VkFsyJAhQXJychAEQfDZZ58FqampQd++fYPdu3cXes2D74GxY8cGxxxzTPCf//ynULunnnoqMLNg2rRpoe+lW7dugZkFDz30UEFsz549Qfv27YM6deoEe/fuDYIgCEaNGhWYWfDiiy8WtNu7d29w4oknBikpKcHWrVuDIAiC119/PTCz4K677ip0nIEDBwZxcXHBwoULC2LJycnBkCFDQs8PRw/j+heMa8Ti/fffD+Lj44P4+PjgxBNPDG666abgvffeKxhjB5hZkJCQUGiszJo1KzCz4LHHHiuIHbxWCYIgyM7ODswsePfdd53jM978yv0vzL169bL09HRr0KCBDRo0yFJSUmzixImWlZVVrMf54IMPbO/evXbDDTfYMcf832W94oorLDU1teAX5bi4ODv//PNt0qRJhf758OWXX7asrCw75ZRTzMxsypQptnnzZhs8eLBt2LCh4H/x8fHWuXNnmzp1qnMOV111VbG+Jxwd48aNs7p161qPHj3M7JcxcuGFF9r48eMLPZ5zwIUXXmg1atQo+HPXrl3NzGzx4sVO26lTp1qfPn3stNNOswkTJlhiYmLoubz66qvWsmVLa9GiRaFxd+BRJjXuDlapUqVCv0okJCTY8OHDLS8vz77++mszM5s0aZJlZGQUyiOoXLmyXXfddbZ9+3b75JNPCtrFx8fbddddV+gYN954owVBYJMnTz7k+aBkMK5/wbhGLHr37m2ff/659e/f32bNmmX333+/9enTx7KysuzNN98s1LZXr16Wm5tb8Odjjz3WUlNT5T1zsEaNGlmfPn2K/fzLs3K/YH788cdtypQpNnXqVPvhhx9s8eLFR2SQLFu2zMzMmjdvXiiekJBgjRs3Lvh7s1++GHbt2lUw+Ldv326TJk2y888/3+Li4szMbMGCBWb2yzPX6enphf73/vvvOw//V6pUyerXr1/s7wtH1r59+2z8+PHWo0cPW7JkiS1cuNAWLlxonTt3tnXr1tmHH37o9GnYsGGhPx9YZOTn5xeK79692/r27WvHHXecvfLKK5aQkHDI81mwYIHNmTPHGXPNmjUzs2hJJ5mZmU7C6YH+Bx4lWrZsmTVt2rTQf1yambVs2bLg7w/838zMTKtWrVpoO5QujGvGNQ5fp06dbMKECZafn28zZsywW265xbZt22YDBw60H374oaDdwfeM2S/3zcH3jNKoUaNiPeeKoNzvknHCCScU7JJxsLi4uEIJFgeoXz+KU5cuXSwnJ8deeeUV++1vf2tvvfWW7dq1q9CzdPv37zezX55hy8jIcF6jUqXCH11iYqIzSaP0++ijj2zNmjU2fvx4Gz9+vPP348aNs9NPP71QzJfxfPBYTkxMtLPOOsveeOMNe/fdd61fv36HPJ/9+/db27ZtbeTIkfLvGzRocMjXABjXQNElJCRYp06drFOnTtasWTMbNmyYvfrqqwU7zUS9ZxR2xIhduV8wh6lRo4b8p4vD+a/7A3sczps3zxo3blwQ37t3ry1ZssR69epVqP0FF1xgjzzyiG3dutVefvlly8nJsS5duhT8/YF/ZqlTp47TF+XHuHHjrE6dOvb44487fzdhwgSbOHGiPfXUU4c1ucXFxdm4cePsnHPOsfPPP98mT558yOpnubm5NmvWLDvttNMK/rUjVqtXr3a2NZw/f76ZWcHuM9nZ2fbdd9/Z/v37C/2H3ty5cwv+/sD//eCDD2zbtm2Ffo07uN2B94vSgXHNuEbxOvDD35o1a47ocRhvfhX6J8nc3FybO3eurV+/viA2a9YsmzZtWsyv1atXL0tISLBHH3200H/dPfvss7Zlyxbr27dvofYXXnih7dmzx8aMGWPvvvuuXXDBBYX+vk+fPpaammr33HOPzBL/9TmjbNq1a5dNmDDB+vXrZwMHDnT+N2LECNu2bZvz3FosEhISbMKECdapUyc7++yzbcaMGaHtL7jgAlu1apX94x//kOe7Y8eOQx7z559/tqeffrrgz3v37rWnn37a0tPTrUOHDmZmdtZZZ9natWvt5ZdfLtTvscces5SUFOvWrVtBu3379tno0aMLHePhhx+2uLg4O/PMMwtiycnJhbZwRMlgXDOucfimTp0qfyGeNGmSmbmPfRY3xptfhf6F+dJLL7WRI0danz597LLLLrO8vDx76qmnrHXr1rZ169aYXis9Pd1uueUWu/POO+2MM86w/v3727x58+yJJ56wTp062cUXX1yo/fHHH29NmjSx2267zfbs2eNsbZSammpPPvmkXXLJJXb88cfboEGDLD093ZYvX27vvPOOnXzyyc5ki7LlzTfftG3btln//v3l33fp0sXS09Nt3Lhxkbe+UpKSkuztt9+2nj172plnnmmffPKJtWnTRra95JJL7JVXXrHf//73NnXqVDv55JNt3759NnfuXHvllVcK9u0Mk5mZaffdd58tXbrUmjVrZi+//LLNnDnTnnnmGatcubKZmV155ZX29NNP29ChQ+3rr7+2nJwce+2112zatGk2atSogl/dzj77bOvRo4fddttttnTpUmvXrp29//779sYbb9gNN9xQKOGlQ4cO9sEHH9jIkSMtMzPTGjVqRDnmEsC4Zlzj8F177bW2c+dOO++886xFixa2d+9emz59esG/RA8bNuyIHp/xFqLkNug4sg5spfLll1+GtnvxxReDxo0bBwkJCUH79u2D995777C2lTtg9OjRQYsWLYLKlSsHdevWDa666qogPz9fHvu2224LzCxo0qSJ9/ymTp0a9OnTJ0hLSwuqVKkS5ObmBkOHDg2++uqrgja/3moJZcfZZ58dVKlSJdixY4e3zdChQ4PKlSsHGzZsKNh+64EHHnDaHTw+1ZjYsGFD0KpVqyAjIyNYsGBBEATu9ltB8Ms2WPfdd1/QunXrIDExMahRo0bQoUOH4M477wy2bNkS+p66desWtG7dOvjqq6+CE088MahSpUqQnZ0djB492mm7bt26YNiwYUHt2rWDhISEoG3btsFzzz3ntNu2bVvwhz/8IcjMzAwqV64cNG3aNHjggQeC/fv3F2o3d+7c4NRTTw2SkpICM2NrpBLCuGZc4/BNnjw5uPTSS4MWLVoEKSkpQUJCQtCkSZPg2muvDdatW1fQzsyCa665xumfnZ1daIz4tpXr27evPD7jzS8uCCI8HQ4AAABUUBX6GWYAAADgUFgwAwAAACFYMAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAISJX+qO+OI6UktwKvDyM6/j4eCe2b9++Ir1mpUru1NCsWTPZtkGDBk6sfv36sq0q61qvXj0nlpycLPurths2bJBtP/nkEyf2xBNPOLGdO3fK/kXFuEZ5xLgufmpeHDx4sGz7ww8/OLGTTjrJic2bN0/2X758uRPr1KmTbPvBBx84sc8++0y2LeuijGt+YQYAAABCsGAGAAAAQrBgBgAAAEKwYAYAAABCxAURn+AvrQ/bx3JeUZMVVBKVmdmrr77qxNQD9JUrV5b9d+3a5cR69eol215wwQVObP78+bKtcswx7n8L+d5/SSZxlPTxS+u4VtRnama2f/9+J1alShUndvPNN8v+7dq1c2Lt27d3YjVr1pT9U1NTZbwo1qxZI+Pq3tyyZYtsq+IrV650Yuedd57sr8ZGLGOVcY3yiHHt6tixoxPLzs6Wbbt06eLE1Hztu86LFy92YikpKU7s+++/l/2rVq0q40pWVpYTS0tLc2Kffvqp7P/ll186sc2bN0c+/tFE0h8AAABQRCyYAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBBlZpeMWHYIiIXK/Fflc83MEhISnJi6LtWqVZP9f/75Zye2e/du2Xbbtm1O7M4773RiCxculP3LErKuo0lMTJTxPXv2OLFBgwY5sbFjx8r+agypcenbjULdFzVq1JBt1T2gdtnw3UPqvli7dq1sm5GR4cQ2btzoxI4//njZv6gY1zgSVNl6dV8dKWVlXBd1l5uLLrrIiak5xcwsOTnZifnmpUWLFjkxtUvGvn37Ih9LzcG+MaGuizq+md6ZS72uKu1tpudx3/fIpk2bnNh7770n2x4J7JIBAAAAFBELZgAAACAEC2YAAAAgBAtmAAAAIISbPVBKxZLcp8pUmpmdf/75TiwzM9OJqYfqzfRD+Bs2bHBiKinDzCw/Pz9yW5WMeN999zkxX7nscePGObHZs2fLtigbfvrpp8htVRLH9u3bZVuVSKfG5axZs2R/lXDiK6Ndq1atSMf3JWCoeUCV9jbT10C9L19p761bt8o4yi6VPO4ba0VNbjvzzDOdmC/B9IwzznBiqiyxmf7OueWWW5zYjz/+KPuvXr1axsubWD6/Sy65xImdfvrpTuy1116T/ZcuXerEkpKSIh9fJcL51jyqtLRax/iS/tS85ttUQV1D9b4WLFgg+6v3oMp4m5l1797diakk7a+++kr2Pxr4hRkAAAAIwYIZAAAACMGCGQAAAAjBghkAAAAIwYIZAAAACFFmSmP7qHLRzZo1k2337t3rxHbs2OHEfO9V7Qagyjk2bdpU9l+xYoUT82XSqjLIKsPfVy45Pj7eif3www+yrcqwPprKSqnVkhZLefhHHnnEiQ0cOFD2nzNnjhOrX7++E/v+++9l/9q1azsx3+4v9erVc2KrVq1yYlWrVpX969at68R8ZbTVbjfqvvjLX/4i+997770yHhXjuvRR91AsOzCdd955Mv7oo486MXUP+XYTUOPSd15qXFeuXNmJqfvSTH8PdO7cWbZVO+uU5XGtdukxMzvrrLOcWJ06dZzYvHnzZH+1jlA7PJj5y1AfrKjlzn2ltXfv3h35nNRnrebmXbt2yf5qZye1jjLT3xnqvBYvXiz7F3X3F0pjAwAAAEXEghkAAAAIwYIZAAAACMGCGQAAAAhRZpL+Lr74YhlXSRjr1q07IuegHlZXSXe+krq+RChFva5KAlDJImY6uUUlXJmZzZw504nddNNNhzjD4lOWk0iOJt+5qus3fvx4J+YrGa+SKFq2bOnEYkn685U/VeeqSv36klBatWrlxFRpbTOzGjVqRH5dpahjg3Fd+qgka1/C0pVXXunEVJK5mVl+fr4TU3OwL+FJJe2pEshmupS7GmsqEc1MJ76lpaXJtup6leVxfdJJJ8l4bm6uE1Ofn2+sLF++3In5vu/VZ62S43xJfyquzlVtcuDjmxfVsdT7Usmhvra+Y6lk1mXLljkxlYxpZjZ9+nQZj4qkPwAAAKCIWDADAAAAIVgwAwAAACFYMAMAAAAhomehlTBfJSJfwk9UKonA9/C3qry0Z88eJ+ar3qce7PclBqhjqZivv3oPvspDxx9/vIyjdIkl2UZVefIlrPjG68F8yRaq8pNKbDEz27lzpxNTiVC+Sn+qf/Xq1WXbW2+91Yk9/PDDTuyLL76Q/YcOHerEnn/+edkWpY+aG9U90KVLF9n/f/7nf5yYLxFP3UOqAqUav2Y6edtXxVXdWyrBKysrS/ZfunSpE1PfY2ZmI0aMkPGyqkGDBjKuEtFUFV+VcGmmE59VRT1f3DdfRqWS84qayOdrq/j6q3PwfY+oSn1qzRRLMmNx4xdmAAAAIAQLZgAAACAEC2YAAAAgBAtmAAAAIAQLZgAAACBEmdklw5edqjJOfRmbUUtd+jJWo+5SEEsmta/Up4qrmMoiNdPv1ZfxWtQMXRS/WHZvURo2bOjEfFn3vvjBYil37csQV2NQjT+1I4yZzpD27fKxYMECGT/YWWedJePvvPOOE2OXjPJHlbU20ztH+O4V9Z2jdsTwZfirssC+cR3LzkyK2pEjPT098rHKsnr16sn4li1bnJgqIe77/FUZct+uROqz9o1BRY0htQ7wfa/HssuEaqvGuq+0utqtScXM9K4y6viqnZkew+vXr5dtDxe/MAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAhykzSn6/Mo3rY3fdQ+IYNG5xYLIlUKpFOJWf5HmpXbX3JTeq81PF9yVXqAXjfe1WJWDVq1HBisSQmoGjUZ+VLEFVtVcLaRRddJPurhCF1r/iSaVVyiRqrZvpcY0nWiFqq1cysT58+Tuztt992YqoErpnZCy+8EPlYKH188/DB5s2bJ+MqOS6WcsNqrPvOSc2tsYx1dV6+5C6VuOY7r3/84x9O7Jlnnol8XiWpbt26Tsw3h6lrpa6Tr7S2Wods3LhRtlVx9Vn7Pn/1HqImbpvFto5QbVVMlVs3M2vUqJETU0mPZvq6qPe6bds22b9Vq1ZO7JNPPpFtDxe/MAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIcrMLhm+Mo9q54CMjAzZNi8vL1J/384VKmtaZRf7ynirY8Wym4A6vi8TWu1ysXXrVtlWZaJmZ2c7MXbJKDvuvffeSDEzvUtA9erVnZhv5wpVAthHjWE1/qZOnSr79+rVy4n5xuXQoUOd2LXXXnuIM/w/Tz75ZOS2KH1i2QFJUTvFqJLzZrq0sm9uV9R3jm83gai2b98u42pHBfXdWNadeOKJTqxbt26y7UsvveTEGjdu7MT69u0r+z/44INOzLdzhPpcYylXHXVc+dqpHVV8ZbzVDkLqvkhISJD91U4fPXr0kG2//vprJ/buu+86sQ4dOsj+6juLXTIAAACAo4gFMwAAABCCBTMAAAAQggUzAAAAEKJUJv2lpKQ4MZUsZKYTO3xJd+p1fYlwijoHlazhK2EcSxKIot6rL0GxTp06TmzHjh2yrTpf1R8lq6hJTD6qNHZaWlqkmJkePyoxxEyP1xkzZjgxlbRqphM7fEl/OTk5Tqxfv35OTJXLNoue5IvyaeHChU7suOOOk23XrFnjxKpWrerEfOWOVdyXTKvuodq1azsxlYhoZlarVi0n9sMPP8i2Zdnrr7/uxHyJeEOGDHFiN9xwgxP78ssvZX/13Zqeni7bRi337Ev69H2PH8y3NlDrI19pbDWGfa+rqDGcm5sr2/7ud79zYrfeeqsT++9//yv7v/POO5HP63DxCzMAAAAQggUzAAAAEIIFMwAAABCCBTMAAAAQolQm/akkpFgq4vmoh+1VsoWvao2qxqMqEPrOSSUM+ZKI1PtVMV8ypEoCWb58uWz7008/OTH1sD9KJ5V0p8aFL+FIVflSCbLq/vG13bx5s2yrqgWqsdqkSRPZX90DqnKZmU5keeGFF5xYzZo1ZX8S/Cq2b775xomdf/75sq0aK1GTs8x09TXf/bZx40Ynpr6z9uzZI/urZDRV7bM8mjlzZuS4+r5ctGiR7P/b3/7WiY0ZM0a29c1XB/PN12rNoZLrfJsfqO8G35pHUXOwGr9mOvFaJfKZmV144YVO7JFHHol8XkcDvzADAAAAIVgwAwAAACFYMAMAAAAhWDADAAAAIVgwAwAAACFK5S4ZqiSkb5cMlXHqy9BfsWKFE6tbt64T82U3q+xStSOGL7s1an8fdQ18JS3Vzge+UpuK2vkApVMsY1BZvXq1E2vUqJET27Rpk+yvyurOmTNHtlXltVUZdlW+10xnY/vuV5U5rl63V69esv8HH3wg4yi71BzqK/WrdqPw7ZyixrXavcZHjUvf3K52dVHz/a5duyIf/8cff4zctqyI5bNWHn744chtBw4c6MSaNWsm26r1ifqsfOeqymirnTN840f1r1evnmyrXlf1933fZGVlObFXXnlFtv3qq69k/GCx3FexrK+i4BdmAAAAIAQLZgAAACAEC2YAAAAgBAtmAAAAIESZSfrzPVSukv58D3qr8p9NmzZ1Ylu3bpX9VcKRSgJRD8qb6SSEWEpjq/KV69atk/1nz57txJo3by7bqmQuX5Ilyp+oJdt9Y1WNyzZt2si277//vhObNm2aE7vttttkf5UIo0q7m+n7VSWMqJKsZiT9lUexJH2p7yFfCeG9e/c6MTXWfOWuVVvfd14srxuV73ukLCvuhK8wvo0GorZV48dX2lyNi6SkJCfmS/pT/Tds2CDbqmREdayqVavK/r7XLQq1eYKZ//upOLEqAgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEKUyqQ/VWXO90C3SuzZsmWLbLtq1SonppIGfceKmkTg668STmJ5UF0le/iSUNTD9p07d5ZtVaU334P1KH2iVrTyfaYqQU8lnPgSplSVM1Vlz8wsPT3dibVs2dKJqWpmZrqime99qUQolXDjS1hBxdatWzcn5kvuUvdL9erVnVi1atVkf5W46kv6iyUhN6r8/Pwi9a/o1PXzfTeruVHNYbVr15b9fRVXD+abr9UY9CWNqvWVSib0bRLgS1yMSq15fO/raCR58gszAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEIIFMwAAABCiVO6SoTIzVelIM7O6des6sUWLFsm2KrtT7ZLhyxhVWZgqkzmW8qexZHaqtr7+27Ztc2KqpKWZvrZq5wOUTlHL/d53330yXqdOHSemSuX6Ssarce0rV926dWsn1q5dOyemxq/vdX27XKxcudKJqaztopYVRtmmSmCbmXXv3t2JqZ2WzMxSU1OdmPoe8+2coO4t3w4DakcFdQ/GsvuL2n2mrDuapbHVzhW+71C15lBrE98cqj5/NYepMWmmx5oaq2b+nVoO5ltb+HYsK4pYytsXN35hBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEKUmWwXXwKESmxQZaHNzCpXruzEYkkMUA/W+x7MV1RihjonM/1gv0oW8CV2qCQEXwnhXbt2OTFVnhxlW79+/WRcJWaoUqe+sTZz5kwntnHjRtm2RYsWTkyVG/YlrCi+hGB1vzRv3tyJ3X///ZGPhaMnapK0audrq/z1r3+V8Vjme1UGW5VA9pWwjiV53Fd2/mAqkcznpJNOkvFvvvkm8mtUZOr71vf5Rf1cfN/XUcug+46jxpqvrUoGVPeA717zrW/KKn5hBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEKwYAYAAABClMpdMlSZRV8mtMrC9O2SUa1aNSemMo59pRdVdqmK+fqrrFlfJqyislN9pVbXrl3rxOrVqyfbqvcQS1lVHB2x7AZw7rnnOrGsrCzZX5WQVveVun/MzN59910nNn/+fNn2iiuucGJdunRxYr7dCNTuHb6s8fT0dCe2ZMkSJ/bKK6/I/hVZUXeeOFLU5x9Lqdxrr73Wif3xj3+Ubb/99lsnVqtWLdlWXRcV8+1woeK+kt1qvMeye8jq1aud2GmnnSbbjh49WsZxaGqsmunPSn2P++41taOF2s3Ct0uHul98bZWo66DyiF+YAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBClMukvIyPDicWSBLRw4ULZViUtqQfgVUld3zmoh/VjSULxvS/1uqoEsC85TyVd+c5LXUOVRICSFUvC1cSJE53Yd999J9uqz79+/fqRXtNMJ/0df/zxsq16XZX46it3rfiSm1RyzPLlyyO/bnnju06xJB5HTTjzUWPNd15FPVbfvn2d2PXXX+/EzjnnHNn/2WefdWL5+fmyrUraU2PYl/SnElR9n4u6hqo0ty8hfNu2bU5MlaxHdFu3bnVidevWlW0zMzMjtd28ebPsrxL01DogagltM7M2bdrIuHpfKiHcl6Ba1CThkk4yPhi/MAMAAAAhWDADAAAAIVgwAwAAACFYMAMAAAAhykzSn+/h79atWzux//znP7Lt+eefH+n4sSRbqAfwfVVvYqlSpR6sV/1r1qwp+6tqh77zUskhqtoiip+vGlQsiaObNm1yYqpK2b/+9S/Z/95773ViM2bMcGK7du2S/YcNG+bEunXrJtuq5BL1ur7KU+p6xXK/vffee7Jt1P6xfC6ljW8OPZpVuo7E9Rs8eLCM33zzzU6sXbt2TuxPf/qT7J+SkuLEFi1aJNuq7yyV9KfamenER19CuEpeV7E9e/bI/uozqFGjhmyLwnwJqhdffLETmzJlimyr5jb1ujt27JD91aYEDRs2dGIbN26U/bdv3+7EfMmsKplQjUtfgqFK/p48ebJsWxbmVn5hBgAAAEKwYAYAAABCsGAGAAAAQrBgBgAAAEKwYAYAAABClMpdMtQODb4Mb1X+1pcdmpqaGun4sZSQjqWd2n3D19aXIX0wX3bzli1bnJgqiWqmy2D7MqxRmC9rWmVCq3EVS/nSUaNGybgaA2r3lKlTp8r+agw++uijTkxlV5uZXXrppU7MV1pdXQN1X/pKWKsdXXyfgSpD/P7778u2FVnt2rWdmG/+UfPKkdKlSxcndssttzixRo0ayf4PP/ywE7v77rudmCqXbWa2fv16J9a0aVPZVu00osoF++4LdW/5dipSOxeo70ffsdR94SujXVGoOURdU9/uP8qSJUtk/Mwzz3RiK1ascGJ5eXmyf1ZWlhNT5++7V9V86/v81TpArbnUrlxmZvXr13diaucMM7OvvvpKxksTfmEGAAAAQrBgBgAAAEKwYAYAAABCsGAGAAAAQpTKpD+VLKFKRZuZrVq1KvLr1qlTx4mpZA1fIlYsCVqKSnjyJRiqhAOViKMSQMx0gt/KlStlW5Uw4LveKMyXjBo1aVMlXJnpcr/Dhw+XbVWp0b/85S9OrG3btrL/1q1bndiVV17pxNauXSv7b9682Yn5EmzT09OdmCrLqhKTzPTc4CvZre6B7777TrZVykKp1lioEuZmZr/97W+d2OLFi2VbVS5azYsNGjSQ/VUJ5+zsbNlWjSGVtKnKwJuZ3XHHHZHOyzcvKur8fXGVTOtLvFZtfXPImjVrnJjvfonKlyBYUfjm8YM1adJExtVn4vv8atWq5cTUOsa3IUBOTo4TU2XY1b1qppP2fNS9qZJhfXOwSjz0Jc6S9AcAAACUcSyYAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBClcpcMtUODL4t3/vz5kV9XlQtW2Z2qhLWZ3k1CZaf6suvV+/KVpFTHUq/r66/azp07V7atXr26E1M7JyC6s88+24mlpaU5MV/WtSrB+s0338i2rVq1cmK9evVyYuvWrZP9f/zxRyemsr59u8Scd955Tsx3v+7YscOJqbLAbdq0kf1VqVa1+42Z2SuvvCLjFdV1110n42rnCDVXmukdTTZt2uTEfGNVfa6+z0mVfFflsjt16iT7q50j1G4Evp0v1I4svvLwCxcudGKqtLFvNwZ1LDXWzfQuB2rnBd/OBaptedsR5kjJzMyUcfVZqx24zPTOSO3atXNin3/+uexfr149J6Z2X/HN92rNoMafmVnnzp2dmFpz+XYfysjIcGLq+8rMrFIldznqu4YlhV+YAQAAgBAsmAEAAIAQLJgBAACAECyYAQAAgBClMulPJQz5EvFmzZoV+XVVaWCVsOJLllClKqOW1DTTiXwqFovu3bvLuCqZrZK7zHQiDaWxo5kzZ46Mb9y40YmpJCZfIp1K7EhKSpJtfUlLB8vKypLxDRs2OLGWLVs6MV8JY3Vv+pKj1Lhq1KiRE1OJSWZmPXv2dGLHHnusbKtKIysq2cSs9CWcFNUXX3wh46oEtfr8zXQinfqsW7RoIfura3366afLtio5SZ2Xryy0mgPVd4tvDlbzpa+0tRrD6r5U95pZ9HM10wmZqjz9ihUrZH+V4HfXXXfJtmWZ+lxj+b5Wn4lvDp45c6YT831+ar7Mzc11YmpDATOzxMREJxbL5gXq8/e9L1VeW93vvvWCuofVWDXT3y+LFi2SbZWift5R8AszAAAAEIIFMwAAABCCBTMAAAAQggUzAAAAEKJUJv2pB7V9lYh8FaWUyZMnOzH1UPpPP/0k+6ukKfVgve9cY3kAXVVaU0kIL774ouyvEmEWL14s23bt2tWJUfnJ1b59eyeWk5Mj26prrfpv2bJF9leVk3wJR77knoMdf/zxMt68eXMnpqqJ+cavGiu+ZEZVVfKNN95wYioZ18zstddeixSLRXlL7vO56qqr/v/27tclsjAK4/jZJmgxiGVQg0nFYnGKCAa7yWZRNFgEsSgTJwgmk9Fm9E+wCBoFQcTgDwwqgiCY3bx7nnP2vTur6zjfTzy8d+bOzJ2ZlwvPObKufleiwOTKyoqrqdCgekwzPb0uChiq32b1WUWhTXW9qsCRCnOb6ZB4RF3D6jsQvdbLy0tXi6bKqUClCn2dnp7K41UgeW9vT65tZ+rzrxLwVVPqooYAatqo+g010+//wMBA8XOpyZyqFk32Vd+raGJwX1+fq6nvsJpeGIkC4SrorkJ/UUj3Xwf8FO4wAwAAAAk2zAAAAECCDTMAAACQYMMMAAAAJNgwAwAAAIkv2SVDdQNQaU0zs+vr6+LHbTQaf31O30E0ZlIlhKOEbyfb2tpytaibiOqIodZGyV6VcL66upJrVaeWWq3malH3F5V6VqNSo84X6nVFo60fHx9dbXFxUa5VVJq7Slea6DV8N1GSXFEddTY2NuRaVZ+fn3e19fV1efzExETxeanPr8rrKhV1ZNnZ2XG17e1tufbp6cnVlpeXXW1ubk4e39/f72pqBLaZ2eHhoaupjhpjY2Py+N3dXVnvBFU64oyPj7ta1KlIdX6IRlur6011Senu7pbHn52duZr6rkSjsVWnkOi5bm5uXE118FLjus30+xX956kuZMpndMOIcIcZAAAASLBhBgAAABJsmAEAAIAEG2YAAAAg8SVDfyqYo0bqmpnd398XP64agdruI6CrjImMQmMqXFLlfe0Ux8fHrjY7OyvXqhCF+qzUSFMzs9XV1eLzUp/16+urq0WjVlWQTl0T6jHNdJDm5eVFrp2ZmXG15+dnuVaJgjT4VZVgjLouqxx/cHBQVItMT0/LugoIRkE4RY2MPzo6crVohHCr1Ljpk5MTuVaFo1QY08ysp6fH1VTo7OHh4U+n+K21el2r60+NJTfTv0sXFxdyrRqDPTg46Gpq3LaZ2ejoqKup/VEUcFRro8Cd+m+Ympoqfq6uri5Xi77D0X/GV8IdZgAAACDBhhkAAABIsGEGAAAAEmyYAQAAgAQbZgAAACDx470wNvoRI0kja2trrjYyMiLXLi0tFT9up3fJiOzv77ua6pKxublZfmIV/M9Rl61e1yrdbGY2OTnpasPDw0U1M7Pe3l5Xi8aXRiOvfxd1mHh7e3M1lfCORqur7iF3d3dF51RVq8n3z9TO1zUQ6eTremhoSNZV55Lz83O5tlarudrCwoKrNZtNebx6/9UY79vbW3l8vV4vOicz/RrUaOxovLzqiKH+b8yqdUv6CCXXNXeYAQAAgAQbZgAAACDBhhkAAABIsGEGAAAAEsWhPwAAAKATcYcZAAAASLBhBgAAABJsmAEAAIAEG2YAAAAgwYYZAAAASLBhBgAAABJsmAEAAIAEG2YAAAAgwYYZAAAASPwE3tse9QMU9Z8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 900x900 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot more images\n",
    "torch.manual_seed(42)\n",
    "fig = plt.figure(figsize=(9, 9))\n",
    "rows, cols = 4, 4\n",
    "for i in range(1, rows * cols + 1):\n",
    "    random_idx = torch.randint(0, len(train_data), size=[1]).item()\n",
    "    img, label = train_data[random_idx]\n",
    "    fig.add_subplot(rows, cols, i)\n",
    "    plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "    plt.title(class_names[label])\n",
    "    plt.axis(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "796045b9-6f40-40ff-bc58-6e1939628516",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataloaders: (<torch.utils.data.dataloader.DataLoader object at 0x7857680dba90>, <torch.utils.data.dataloader.DataLoader object at 0x78576838a410>)\n",
      "Length of train dataloader: 1875 batches of 32\n",
      "Length of test dataloader: 313 batches of 32\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# Setup the batch size hyperparameter\n",
    "BATCH_SIZE = 32\n",
    "\n",
    "# Turn datasets into iterables (batches)\n",
    "train_dataloader = DataLoader(train_data, # dataset to turn into iterable\n",
    "    batch_size=BATCH_SIZE, # how many samples per batch? \n",
    "    shuffle=True # shuffle data every epoch?\n",
    ")\n",
    "\n",
    "test_dataloader = DataLoader(test_data,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=False # don't necessarily have to shuffle the testing data\n",
    ")\n",
    "\n",
    "# Let's check out what we've created\n",
    "print(f\"Dataloaders: {train_dataloader, test_dataloader}\") \n",
    "print(f\"Length of train dataloader: {len(train_dataloader)} batches of {BATCH_SIZE}\")\n",
    "print(f\"Length of test dataloader: {len(test_dataloader)} batches of {BATCH_SIZE}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cef2c894-c06d-405f-933e-448234e66cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out what's inside the training dataloader\n",
    "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
    "train_features_batch.shape, train_labels_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2a21366-5a25-4c09-8f86-67429455c82a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size: torch.Size([1, 28, 28])\n",
      "Label: 6, label size: torch.Size([])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show a sample\n",
    "torch.manual_seed(42)\n",
    "random_idx = torch.randint(0, len(train_features_batch), size=[1]).item()\n",
    "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
    "plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "plt.title(class_names[label])\n",
    "plt.axis(\"Off\");\n",
    "print(f\"Image size: {img.shape}\")\n",
    "print(f\"Label: {label}, label size: {label.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e56d7ad-faef-4d4f-9ed1-392bd8a52e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape before flattening: torch.Size([1, 28, 28]) -> [color_channels, height, width]\n",
      "Shape after flattening: torch.Size([1, 784]) -> [color_channels, height*width]\n"
     ]
    }
   ],
   "source": [
    "# Create a flatten layer\n",
    "flatten_model = nn.Flatten() # all nn modules function as a model (can do a forward pass)\n",
    "\n",
    "# Get a single sample\n",
    "x = train_features_batch[0]\n",
    "\n",
    "# Flatten the sample\n",
    "output = flatten_model(x) # perform forward pass\n",
    "\n",
    "# Print out what happened\n",
    "print(f\"Shape before flattening: {x.shape} -> [color_channels, height, width]\")\n",
    "print(f\"Shape after flattening: {output.shape} -> [color_channels, height*width]\")\n",
    "\n",
    "# Try uncommenting below and see what happens\n",
    "#print(x)\n",
    "#print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4d35aed1-6323-4770-8cb4-3a5ff6842c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "class FashionMNISTModelV0(nn.Module):\n",
    "    def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n",
    "        super().__init__()\n",
    "        self.layer_stack = nn.Sequential(\n",
    "            nn.Flatten(), # neural networks like their inputs in vector form\n",
    "            nn.Linear(in_features=input_shape, out_features=hidden_units), # in_features = number of features in a data sample (784 pixels)\n",
    "            nn.Linear(in_features=hidden_units, out_features=output_shape)\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        return self.layer_stack(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b35037aa-1069-4461-9b8f-1ae3a424d94b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FashionMNISTModelV0(\n",
       "  (layer_stack): Sequential(\n",
       "    (0): Flatten(start_dim=1, end_dim=-1)\n",
       "    (1): Linear(in_features=784, out_features=10, bias=True)\n",
       "    (2): Linear(in_features=10, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Need to setup model with input parameters\n",
    "model_0 = FashionMNISTModelV0(input_shape=784, # one for every pixel (28x28)\n",
    "    hidden_units=10, # how many units in the hiden layer\n",
    "    output_shape=len(class_names) # one for every class\n",
    ")\n",
    "model_0.to(\"cpu\") # keep model on CPU to begin with "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd82a243-d96c-4e13-baac-df58bba904da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "helper_functions.py already exists, skipping download\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from pathlib import Path \n",
    "\n",
    "# Download helper functions from Learn PyTorch repo (if not already downloaded)\n",
    "if Path(\"helper_functions.py\").is_file():\n",
    "  print(\"helper_functions.py already exists, skipping download\")\n",
    "else:\n",
    "  print(\"Downloading helper_functions.py\")\n",
    "  # Note: you need the \"raw\" GitHub URL for this to work\n",
    "  request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/helper_functions.py\")\n",
    "  with open(\"helper_functions.py\", \"wb\") as f:\n",
    "    f.write(request.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "27200039-8584-40a5-ace5-cbd28ca879c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import accuracy metric\n",
    "from helper_functions import accuracy_fn # Note: could also use torchmetrics.Accuracy(task = 'multiclass', num_classes=len(class_names)).to(device)\n",
    "\n",
    "# Setup loss function and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss() # this is also called \"criterion\"/\"cost function\" in some places\n",
    "optimizer = torch.optim.SGD(params=model_0.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "80afd3f1-fd60-47e4-8626-d7adaa36b2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeit import default_timer as timer \n",
    "def print_train_time(start: float, end: float, device: torch.device = None):\n",
    "    \"\"\"Prints difference between start and end time.\n",
    "\n",
    "    Args:\n",
    "        start (float): Start time of computation (preferred in timeit format). \n",
    "        end (float): End time of computation.\n",
    "        device ([type], optional): Device that compute is running on. Defaults to None.\n",
    "\n",
    "    Returns:\n",
    "        float: time between start and end in seconds (higher is longer).\n",
    "    \"\"\"\n",
    "    total_time = end - start\n",
    "    print(f\"Train time on {device}: {total_time:.3f} seconds\")\n",
    "    return total_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ce072331-6435-4fe3-b27c-99e573e083f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cef664a87f4b4adf89c70bd97e084247",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0\n",
      "-------\n",
      "Looked at 0/60000 samples\n",
      "Looked at 12800/60000 samples\n",
      "Looked at 25600/60000 samples\n",
      "Looked at 38400/60000 samples\n",
      "Looked at 51200/60000 samples\n",
      "\n",
      "Train loss: 0.59039 | Test loss: 0.50954, Test acc: 82.04%\n",
      "\n",
      "Epoch: 1\n",
      "-------\n",
      "Looked at 0/60000 samples\n",
      "Looked at 12800/60000 samples\n",
      "Looked at 25600/60000 samples\n",
      "Looked at 38400/60000 samples\n",
      "Looked at 51200/60000 samples\n",
      "\n",
      "Train loss: 0.47633 | Test loss: 0.47989, Test acc: 83.20%\n",
      "\n",
      "Epoch: 2\n",
      "-------\n",
      "Looked at 0/60000 samples\n",
      "Looked at 12800/60000 samples\n",
      "Looked at 25600/60000 samples\n",
      "Looked at 38400/60000 samples\n",
      "Looked at 51200/60000 samples\n",
      "\n",
      "Train loss: 0.45503 | Test loss: 0.47664, Test acc: 83.43%\n",
      "\n",
      "Train time on cpu: 7.481 seconds\n"
     ]
    }
   ],
   "source": [
    "# Import tqdm for progress bar\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "# Set the seed and start the timer\n",
    "torch.manual_seed(42)\n",
    "train_time_start_on_cpu = timer()\n",
    "\n",
    "# Set the number of epochs (we'll keep this small for faster training times)\n",
    "epochs = 3\n",
    "\n",
    "# Create training and testing loop\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    print(f\"Epoch: {epoch}\\n-------\")\n",
    "    ### Training\n",
    "    train_loss = 0\n",
    "    # Add a loop to loop through training batches\n",
    "    for batch, (X, y) in enumerate(train_dataloader):\n",
    "        model_0.train() \n",
    "        # 1. Forward pass\n",
    "        y_pred = model_0(X)\n",
    "\n",
    "        # 2. Calculate loss (per batch)\n",
    "        loss = loss_fn(y_pred, y)\n",
    "        train_loss += loss # accumulatively add up the loss per epoch \n",
    "\n",
    "        # 3. Optimizer zero grad\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # 4. Loss backward\n",
    "        loss.backward()\n",
    "\n",
    "        # 5. Optimizer step\n",
    "        optimizer.step()\n",
    "\n",
    "        # Print out how many samples have been seen\n",
    "        if batch % 400 == 0:\n",
    "            print(f\"Looked at {batch * len(X)}/{len(train_dataloader.dataset)} samples\")\n",
    "\n",
    "    # Divide total train loss by length of train dataloader (average loss per batch per epoch)\n",
    "    train_loss /= len(train_dataloader)\n",
    "    \n",
    "    ### Testing\n",
    "    # Setup variables for accumulatively adding up loss and accuracy \n",
    "    test_loss, test_acc = 0, 0 \n",
    "    model_0.eval()\n",
    "    with torch.inference_mode():\n",
    "        for X, y in test_dataloader:\n",
    "            # 1. Forward pass\n",
    "            test_pred = model_0(X)\n",
    "           \n",
    "            # 2. Calculate loss (accumatively)\n",
    "            test_loss += loss_fn(test_pred, y) # accumulatively add up the loss per epoch\n",
    "\n",
    "            # 3. Calculate accuracy (preds need to be same as y_true)\n",
    "            test_acc += accuracy_fn(y_true=y, y_pred=test_pred.argmax(dim=1))\n",
    "        \n",
    "        # Calculations on test metrics need to happen inside torch.inference_mode()\n",
    "        # Divide total test loss by length of test dataloader (per batch)\n",
    "        test_loss /= len(test_dataloader)\n",
    "\n",
    "        # Divide total accuracy by length of test dataloader (per batch)\n",
    "        test_acc /= len(test_dataloader)\n",
    "\n",
    "    ## Print out what's happening\n",
    "    print(f\"\\nTrain loss: {train_loss:.5f} | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%\\n\")\n",
    "\n",
    "# Calculate training time      \n",
    "train_time_end_on_cpu = timer()\n",
    "total_train_time_model_0 = print_train_time(start=train_time_start_on_cpu, \n",
    "                                           end=train_time_end_on_cpu,\n",
    "                                           device=str(next(model_0.parameters()).device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "65702c31-0b08-445b-bd31-07976b7ed259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_name': 'FashionMNISTModelV0',\n",
       " 'model_loss': 0.4766390025615692,\n",
       " 'model_acc': 83.42651757188499}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "def eval_model(model: torch.nn.Module, \n",
    "               data_loader: torch.utils.data.DataLoader, \n",
    "               loss_fn: torch.nn.Module, \n",
    "               accuracy_fn):\n",
    "    \"\"\"Returns a dictionary containing the results of model predicting on data_loader.\n",
    "\n",
    "    Args:\n",
    "        model (torch.nn.Module): A PyTorch model capable of making predictions on data_loader.\n",
    "        data_loader (torch.utils.data.DataLoader): The target dataset to predict on.\n",
    "        loss_fn (torch.nn.Module): The loss function of model.\n",
    "        accuracy_fn: An accuracy function to compare the models predictions to the truth labels.\n",
    "\n",
    "    Returns:\n",
    "        (dict): Results of model making predictions on data_loader.\n",
    "    \"\"\"\n",
    "    loss, acc = 0, 0\n",
    "    model.eval()\n",
    "    with torch.inference_mode():\n",
    "        for X, y in data_loader:\n",
    "            # Make predictions with the model\n",
    "            y_pred = model(X)\n",
    "            \n",
    "            # Accumulate the loss and accuracy values per batch\n",
    "            loss += loss_fn(y_pred, y)\n",
    "            acc += accuracy_fn(y_true=y, \n",
    "                                y_pred=y_pred.argmax(dim=1)) # For accuracy, need the prediction labels (logits -> pred_prob -> pred_labels)\n",
    "        \n",
    "        # Scale loss and acc to find the average loss/acc per batch\n",
    "        loss /= len(data_loader)\n",
    "        acc /= len(data_loader)\n",
    "        \n",
    "    return {\"model_name\": model.__class__.__name__, # only works when model was created with a class\n",
    "            \"model_loss\": loss.item(),\n",
    "            \"model_acc\": acc}\n",
    "\n",
    "# Calculate model 0 results on test dataset\n",
    "model_0_results = eval_model(model=model_0, data_loader=test_dataloader,\n",
    "    loss_fn=loss_fn, accuracy_fn=accuracy_fn\n",
    ")\n",
    "model_0_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b2b1032-a7cb-496a-b43a-232cab7a72b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup device agnostic code\n",
    "import torch\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a1a23d8c-d1d5-4271-8d05-ffe667a97801",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a model with non-linear and linear layers\n",
    "class FashionMNISTModelV1(nn.Module):\n",
    "    def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n",
    "        super().__init__()\n",
    "        self.layer_stack = nn.Sequential(\n",
    "            nn.Flatten(), # flatten inputs into single vector\n",
    "            nn.Linear(in_features=input_shape, out_features=hidden_units),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(in_features=hidden_units, out_features=output_shape),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "    \n",
    "    def forward(self, x: torch.Tensor):\n",
    "        return self.layer_stack(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "56abf0d6-6cb5-46ff-af0c-c241dbe711d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "model_1 = FashionMNISTModelV1(input_shape=784, # number of input features\n",
    "    hidden_units=10,\n",
    "    output_shape=len(class_names) # number of output classes desired\n",
    ").to(device) # send model to GPU if it's available\n",
    "next(model_1.parameters()).device # check model device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "81c23f5a-646c-4443-a548-98eb3858305e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from helper_functions import accuracy_fn\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(params=model_1.parameters(), \n",
    "                            lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "277c36fd-22c5-4f5d-b828-8db69a63544f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_step(model: torch.nn.Module,\n",
    "               data_loader: torch.utils.data.DataLoader,\n",
    "               loss_fn: torch.nn.Module,\n",
    "               optimizer: torch.optim.Optimizer,\n",
    "               accuracy_fn,\n",
    "               device: torch.device = device):\n",
    "    train_loss, train_acc = 0, 0\n",
    "    model.to(device)\n",
    "    for batch, (X, y) in enumerate(data_loader):\n",
    "        # Send data to GPU\n",
    "        X, y = X.to(device), y.to(device)\n",
    "\n",
    "        # 1. Forward pass\n",
    "        y_pred = model(X)\n",
    "\n",
    "        # 2. Calculate loss\n",
    "        loss = loss_fn(y_pred, y)\n",
    "        train_loss += loss\n",
    "        train_acc += accuracy_fn(y_true=y,\n",
    "                                 y_pred=y_pred.argmax(dim=1)) # Go from logits -> pred labels\n",
    "\n",
    "        # 3. Optimizer zero grad\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # 4. Loss backward\n",
    "        loss.backward()\n",
    "\n",
    "        # 5. Optimizer step\n",
    "        optimizer.step()\n",
    "\n",
    "    # Calculate loss and accuracy per epoch and print out what's happening\n",
    "    train_loss /= len(data_loader)\n",
    "    train_acc /= len(data_loader)\n",
    "    print(f\"Train loss: {train_loss:.5f} | Train accuracy: {train_acc:.2f}%\")\n",
    "\n",
    "def test_step(data_loader: torch.utils.data.DataLoader,\n",
    "              model: torch.nn.Module,\n",
    "              loss_fn: torch.nn.Module,\n",
    "              accuracy_fn,\n",
    "              device: torch.device = device):\n",
    "    test_loss, test_acc = 0, 0\n",
    "    model.to(device)\n",
    "    model.eval() # put model in eval mode\n",
    "    # Turn on inference context manager\n",
    "    with torch.inference_mode(): \n",
    "        for X, y in data_loader:\n",
    "            # Send data to GPU\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            \n",
    "            # 1. Forward pass\n",
    "            test_pred = model(X)\n",
    "            \n",
    "            # 2. Calculate loss and accuracy\n",
    "            test_loss += loss_fn(test_pred, y)\n",
    "            test_acc += accuracy_fn(y_true=y,\n",
    "                y_pred=test_pred.argmax(dim=1) # Go from logits -> pred labels\n",
    "            )\n",
    "        \n",
    "        # Adjust metrics and print out\n",
    "        test_loss /= len(data_loader)\n",
    "        test_acc /= len(data_loader)\n",
    "        print(f\"Test loss: {test_loss:.5f} | Test accuracy: {test_acc:.2f}%\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "38b25df0-3c1b-4c93-9c7f-e3702cfbf67f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2f5b5de45af473a827fbe523abd8061",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0\n",
      "---------\n",
      "Train loss: 2.08356 | Train accuracy: 21.39%\n",
      "Test loss: 1.75025 | Test accuracy: 46.46%\n",
      "\n",
      "Epoch: 1\n",
      "---------\n",
      "Train loss: 1.07675 | Train accuracy: 60.47%\n",
      "Test loss: 0.84824 | Test accuracy: 70.25%\n",
      "\n",
      "Epoch: 2\n",
      "---------\n",
      "Train loss: 0.77435 | Train accuracy: 71.85%\n",
      "Test loss: 0.78312 | Test accuracy: 71.62%\n",
      "\n",
      "Train time on cuda: 6.289 seconds\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Measure time\n",
    "from timeit import default_timer as timer\n",
    "train_time_start_on_gpu = timer()\n",
    "\n",
    "epochs = 3\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    print(f\"Epoch: {epoch}\\n---------\")\n",
    "    train_step(data_loader=train_dataloader, \n",
    "        model=model_1, \n",
    "        loss_fn=loss_fn,\n",
    "        optimizer=optimizer,\n",
    "        accuracy_fn=accuracy_fn\n",
    "    )\n",
    "    test_step(data_loader=test_dataloader,\n",
    "        model=model_1,\n",
    "        loss_fn=loss_fn,\n",
    "        accuracy_fn=accuracy_fn\n",
    "    )\n",
    "\n",
    "train_time_end_on_gpu = timer()\n",
    "total_train_time_model_1 = print_train_time(start=train_time_start_on_gpu,\n",
    "                                            end=train_time_end_on_gpu,\n",
    "                                            device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "68656ebd-11d8-458d-b34a-8bc6e2cdeed8",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat1 in method wrapper_CUDA_addmm)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m torch\u001b[38;5;241m.\u001b[39mmanual_seed(\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m# Note: This will error due to `eval_model()` not using device agnostic code \u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m model_1_results \u001b[38;5;241m=\u001b[39m \u001b[43meval_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel_1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata_loader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest_dataloader\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mloss_fn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mloss_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccuracy_fn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccuracy_fn\u001b[49m\u001b[43m)\u001b[49m \n\u001b[1;32m      8\u001b[0m model_1_results \n",
      "Cell \u001b[0;32mIn[21], line 22\u001b[0m, in \u001b[0;36meval_model\u001b[0;34m(model, data_loader, loss_fn, accuracy_fn)\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39minference_mode():\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m X, y \u001b[38;5;129;01min\u001b[39;00m data_loader:\n\u001b[1;32m     21\u001b[0m         \u001b[38;5;66;03m# Make predictions with the model\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m         y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     24\u001b[0m         \u001b[38;5;66;03m# Accumulate the loss and accuracy values per batch\u001b[39;00m\n\u001b[1;32m     25\u001b[0m         loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss_fn(y_pred, y)\n",
      "File \u001b[0;32m~/agui/miniconda/envs/pytorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/agui/miniconda/envs/pytorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[23], line 16\u001b[0m, in \u001b[0;36mFashionMNISTModelV1.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: torch\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m---> 16\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/agui/miniconda/envs/pytorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/agui/miniconda/envs/pytorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/agui/miniconda/envs/pytorch/lib/python3.11/site-packages/torch/nn/modules/container.py:215\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 215\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[0;32m~/agui/miniconda/envs/pytorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/agui/miniconda/envs/pytorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1529\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/agui/miniconda/envs/pytorch/lib/python3.11/site-packages/torch/nn/modules/linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 114\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat1 in method wrapper_CUDA_addmm)"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Note: This will error due to `eval_model()` not using device agnostic code \n",
    "model_1_results = eval_model(model=model_1, \n",
    "    data_loader=test_dataloader,\n",
    "    loss_fn=loss_fn, \n",
    "    accuracy_fn=accuracy_fn) \n",
    "model_1_results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b28144f-df8c-4d68-83a9-cdc462824d98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_name': 'FashionMNISTModelV1',\n",
       " 'model_loss': 0.7831155061721802,\n",
       " 'model_acc': 71.61541533546325}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Move values to device\n",
    "torch.manual_seed(42)\n",
    "def eval_model(model: torch.nn.Module, \n",
    "               data_loader: torch.utils.data.DataLoader, \n",
    "               loss_fn: torch.nn.Module, \n",
    "               accuracy_fn, \n",
    "               device: torch.device = device):\n",
    "    \"\"\"Evaluates a given model on a given dataset.\n",
    "\n",
    "    Args:\n",
    "        model (torch.nn.Module): A PyTorch model capable of making predictions on data_loader.\n",
    "        data_loader (torch.utils.data.DataLoader): The target dataset to predict on.\n",
    "        loss_fn (torch.nn.Module): The loss function of model.\n",
    "        accuracy_fn: An accuracy function to compare the models predictions to the truth labels.\n",
    "        device (str, optional): Target device to compute on. Defaults to device.\n",
    "\n",
    "    Returns:\n",
    "        (dict): Results of model making predictions on data_loader.\n",
    "    \"\"\"\n",
    "    loss, acc = 0, 0\n",
    "    model.eval()\n",
    "    with torch.inference_mode():\n",
    "        for X, y in data_loader:\n",
    "            # Send data to the target device\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            y_pred = model(X)\n",
    "            loss += loss_fn(y_pred, y)\n",
    "            acc += accuracy_fn(y_true=y, y_pred=y_pred.argmax(dim=1))\n",
    "        \n",
    "        # Scale loss and acc\n",
    "        loss /= len(data_loader)\n",
    "        acc /= len(data_loader)\n",
    "    return {\"model_name\": model.__class__.__name__, # only works when model was created with a class\n",
    "            \"model_loss\": loss.item(),\n",
    "            \"model_acc\": acc}\n",
    "\n",
    "# Calculate model 1 results with device-agnostic code \n",
    "model_1_results = eval_model(model=model_1, data_loader=test_dataloader,\n",
    "    loss_fn=loss_fn, accuracy_fn=accuracy_fn,\n",
    "    device=device\n",
    ")\n",
    "model_1_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "21b001bc-8e24-4927-9e7d-d6d5f5f02344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_name': 'FashionMNISTModelV0',\n",
       " 'model_loss': 0.4766390025615692,\n",
       " 'model_acc': 83.42651757188499}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check baseline results\n",
    "model_0_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "58c757d5-2a59-4e93-ad72-caf974e685b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FashionMNISTModelV2(\n",
       "  (block_1): Sequential(\n",
       "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (block_2): Sequential(\n",
       "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Flatten(start_dim=1, end_dim=-1)\n",
       "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a convolutional neural network \n",
    "class FashionMNISTModelV2(nn.Module):\n",
    "    \"\"\"\n",
    "    Model architecture copying TinyVGG from: \n",
    "    https://poloclub.github.io/cnn-explainer/\n",
    "    \"\"\"\n",
    "    def __init__(self, input_shape: int, hidden_units: int, output_shape: int):\n",
    "        super().__init__()\n",
    "        self.block_1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=input_shape, \n",
    "                      out_channels=hidden_units, \n",
    "                      kernel_size=3, # how big is the square that's going over the image?\n",
    "                      stride=1, # default\n",
    "                      padding=1),# options = \"valid\" (no padding) or \"same\" (output has same shape as input) or int for specific number \n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(in_channels=hidden_units, \n",
    "                      out_channels=hidden_units,\n",
    "                      kernel_size=3,\n",
    "                      stride=1,\n",
    "                      padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2,\n",
    "                         stride=2) # default stride value is same as kernel_size\n",
    "        )\n",
    "        self.block_2 = nn.Sequential(\n",
    "            nn.Conv2d(hidden_units, hidden_units, 3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(hidden_units, hidden_units, 3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(2)\n",
    "        )\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            # Where did this in_features shape come from? \n",
    "            # It's because each layer of our network compresses and changes the shape of our inputs data.\n",
    "            nn.Linear(in_features=hidden_units*7*7, \n",
    "                      out_features=output_shape)\n",
    "        )\n",
    "    \n",
    "    def forward(self, x: torch.Tensor):\n",
    "        x = self.block_1(x)\n",
    "        # print(x.shape)\n",
    "        x = self.block_2(x)\n",
    "        # print(x.shape)\n",
    "        x = self.classifier(x)\n",
    "        # print(x.shape)\n",
    "        return x\n",
    "\n",
    "torch.manual_seed(42)\n",
    "model_2 = FashionMNISTModelV2(input_shape=1, \n",
    "    hidden_units=10, \n",
    "    output_shape=len(class_names)).to(device)\n",
    "model_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "961b7918-d7ef-4e11-ae93-eeb5f0f07732",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image batch shape: torch.Size([32, 3, 64, 64]) -> [batch_size, color_channels, height, width]\n",
      "Single image shape: torch.Size([3, 64, 64]) -> [color_channels, height, width]\n",
      "Single image pixel values:\n",
      "tensor([[[ 1.9269,  1.4873,  0.9007,  ...,  1.8446, -1.1845,  1.3835],\n",
      "         [ 1.4451,  0.8564,  2.2181,  ...,  0.3399,  0.7200,  0.4114],\n",
      "         [ 1.9312,  1.0119, -1.4364,  ..., -0.5558,  0.7043,  0.7099],\n",
      "         ...,\n",
      "         [-0.5610, -0.4830,  0.4770,  ..., -0.2713, -0.9537, -0.6737],\n",
      "         [ 0.3076, -0.1277,  0.0366,  ..., -2.0060,  0.2824, -0.8111],\n",
      "         [-1.5486,  0.0485, -0.7712,  ..., -0.1403,  0.9416, -0.0118]],\n",
      "\n",
      "        [[-0.5197,  1.8524,  1.8365,  ...,  0.8935, -1.5114, -0.8515],\n",
      "         [ 2.0818,  1.0677, -1.4277,  ...,  1.6612, -2.6223, -0.4319],\n",
      "         [-0.1010, -0.4388, -1.9775,  ...,  0.2106,  0.2536, -0.7318],\n",
      "         ...,\n",
      "         [ 0.2779,  0.7342, -0.3736,  ..., -0.4601,  0.1815,  0.1850],\n",
      "         [ 0.7205, -0.2833,  0.0937,  ..., -0.1002, -2.3609,  2.2465],\n",
      "         [-1.3242, -0.1973,  0.2920,  ...,  0.5409,  0.6940,  1.8563]],\n",
      "\n",
      "        [[-0.7978,  1.0261,  1.1465,  ...,  1.2134,  0.9354, -0.0780],\n",
      "         [-1.4647, -1.9571,  0.1017,  ..., -1.9986, -0.7409,  0.7011],\n",
      "         [-1.3938,  0.8466, -1.7191,  ..., -1.1867,  0.1320,  0.3407],\n",
      "         ...,\n",
      "         [ 0.8206, -0.3745,  1.2499,  ..., -0.0676,  0.0385,  0.6335],\n",
      "         [-0.5589, -0.3393,  0.2347,  ...,  2.1181,  2.4569,  1.3083],\n",
      "         [-0.4092,  1.5199,  0.2401,  ..., -0.2558,  0.7870,  0.9924]]])\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Create sample batch of random numbers with same size as image batch\n",
    "images = torch.randn(size=(32, 3, 64, 64)) # [batch_size, color_channels, height, width]\n",
    "test_image = images[0] # get a single image for testing\n",
    "print(f\"Image batch shape: {images.shape} -> [batch_size, color_channels, height, width]\")\n",
    "print(f\"Single image shape: {test_image.shape} -> [color_channels, height, width]\") \n",
    "print(f\"Single image pixel values:\\n{test_image}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6bb81adf-4223-42f9-a7e4-a9c449bfd1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 62, 62])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Create a convolutional layer with same dimensions as TinyVGG \n",
    "# (try changing any of the parameters and see what happens)\n",
    "conv_layer = nn.Conv2d(in_channels=3,\n",
    "                       out_channels=10,\n",
    "                       kernel_size=3,\n",
    "                       stride=1,\n",
    "                       padding=0) # also try using \"valid\" or \"same\" here \n",
    "\n",
    "# Pass the data through the convolutional layer\n",
    "conv_layer(test_image).shape # Note: If running PyTorch <1.11.0, this will error because of shape issues (nn.Conv.2d() expects a 4d tensor as input) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "33fe01bf-0a45-47e3-ab8c-8e330df60748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 64, 64])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add extra dimension to test image\n",
    "test_image.unsqueeze(dim=0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c82c8f1d-a5b4-46ab-af4e-966c63956870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 10, 62, 62])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pass test image with extra dimension through conv_layer\n",
    "conv_layer(test_image.unsqueeze(dim=0)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5f1f3b3f-5dc3-40f4-8dba-a470e545e7c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 10, 30, 30])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "# Create a new conv_layer with different values (try setting these to whatever you like)\n",
    "conv_layer_2 = nn.Conv2d(in_channels=3, # same number of color channels as our input image\n",
    "                         out_channels=10,\n",
    "                         kernel_size=(5, 5), # kernel is usually a square so a tuple also works\n",
    "                         stride=2,\n",
    "                         padding=0)\n",
    "\n",
    "# Pass single image through new conv_layer_2 (this calls nn.Conv2d()'s forward() method on the input)\n",
    "conv_layer_2(test_image.unsqueeze(dim=0)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "827417d5-78c3-4339-8737-e8c5dd584c7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('weight', tensor([[[[ 0.0883,  0.0958, -0.0271,  0.1061, -0.0253],\n",
      "          [ 0.0233, -0.0562,  0.0678,  0.1018, -0.0847],\n",
      "          [ 0.1004,  0.0216,  0.0853,  0.0156,  0.0557],\n",
      "          [-0.0163,  0.0890,  0.0171, -0.0539,  0.0294],\n",
      "          [-0.0532, -0.0135, -0.0469,  0.0766, -0.0911]],\n",
      "\n",
      "         [[-0.0532, -0.0326, -0.0694,  0.0109, -0.1140],\n",
      "          [ 0.1043, -0.0981,  0.0891,  0.0192, -0.0375],\n",
      "          [ 0.0714,  0.0180,  0.0933,  0.0126, -0.0364],\n",
      "          [ 0.0310, -0.0313,  0.0486,  0.1031,  0.0667],\n",
      "          [-0.0505,  0.0667,  0.0207,  0.0586, -0.0704]],\n",
      "\n",
      "         [[-0.1143, -0.0446, -0.0886,  0.0947,  0.0333],\n",
      "          [ 0.0478,  0.0365, -0.0020,  0.0904, -0.0820],\n",
      "          [ 0.0073, -0.0788,  0.0356, -0.0398,  0.0354],\n",
      "          [-0.0241,  0.0958, -0.0684, -0.0689, -0.0689],\n",
      "          [ 0.1039,  0.0385,  0.1111, -0.0953, -0.1145]]],\n",
      "\n",
      "\n",
      "        [[[-0.0903, -0.0777,  0.0468,  0.0413,  0.0959],\n",
      "          [-0.0596, -0.0787,  0.0613, -0.0467,  0.0701],\n",
      "          [-0.0274,  0.0661, -0.0897, -0.0583,  0.0352],\n",
      "          [ 0.0244, -0.0294,  0.0688,  0.0785, -0.0837],\n",
      "          [-0.0616,  0.1057, -0.0390, -0.0409, -0.1117]],\n",
      "\n",
      "         [[-0.0661,  0.0288, -0.0152, -0.0838,  0.0027],\n",
      "          [-0.0789, -0.0980, -0.0636, -0.1011, -0.0735],\n",
      "          [ 0.1154,  0.0218,  0.0356, -0.1077, -0.0758],\n",
      "          [-0.0384,  0.0181, -0.1016, -0.0498, -0.0691],\n",
      "          [ 0.0003, -0.0430, -0.0080, -0.0782, -0.0793]],\n",
      "\n",
      "         [[-0.0674, -0.0395, -0.0911,  0.0968, -0.0229],\n",
      "          [ 0.0994,  0.0360, -0.0978,  0.0799, -0.0318],\n",
      "          [-0.0443, -0.0958, -0.1148,  0.0330, -0.0252],\n",
      "          [ 0.0450, -0.0948,  0.0857, -0.0848, -0.0199],\n",
      "          [ 0.0241,  0.0596,  0.0932,  0.1052, -0.0916]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0291, -0.0497, -0.0127, -0.0864,  0.1052],\n",
      "          [-0.0847,  0.0617,  0.0406,  0.0375, -0.0624],\n",
      "          [ 0.1050,  0.0254,  0.0149, -0.1018,  0.0485],\n",
      "          [-0.0173, -0.0529,  0.0992,  0.0257, -0.0639],\n",
      "          [-0.0584, -0.0055,  0.0645, -0.0295, -0.0659]],\n",
      "\n",
      "         [[-0.0395, -0.0863,  0.0412,  0.0894, -0.1087],\n",
      "          [ 0.0268,  0.0597,  0.0209, -0.0411,  0.0603],\n",
      "          [ 0.0607,  0.0432, -0.0203, -0.0306,  0.0124],\n",
      "          [-0.0204, -0.0344,  0.0738,  0.0992, -0.0114],\n",
      "          [-0.0259,  0.0017, -0.0069,  0.0278,  0.0324]],\n",
      "\n",
      "         [[-0.1049, -0.0426,  0.0972,  0.0450, -0.0057],\n",
      "          [-0.0696, -0.0706, -0.1034, -0.0376,  0.0390],\n",
      "          [ 0.0736,  0.0533, -0.1021, -0.0694, -0.0182],\n",
      "          [ 0.1117,  0.0167, -0.0299,  0.0478, -0.0440],\n",
      "          [-0.0747,  0.0843, -0.0525, -0.0231, -0.1149]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0773,  0.0875,  0.0421, -0.0805, -0.1140],\n",
      "          [-0.0938,  0.0861,  0.0554,  0.0972,  0.0605],\n",
      "          [ 0.0292, -0.0011, -0.0878, -0.0989, -0.1080],\n",
      "          [ 0.0473, -0.0567, -0.0232, -0.0665, -0.0210],\n",
      "          [-0.0813, -0.0754,  0.0383, -0.0343,  0.0713]],\n",
      "\n",
      "         [[-0.0370, -0.0847, -0.0204, -0.0560, -0.0353],\n",
      "          [-0.1099,  0.0646, -0.0804,  0.0580,  0.0524],\n",
      "          [ 0.0825, -0.0886,  0.0830, -0.0546,  0.0428],\n",
      "          [ 0.1084, -0.0163, -0.0009, -0.0266, -0.0964],\n",
      "          [ 0.0554, -0.1146,  0.0717,  0.0864,  0.1092]],\n",
      "\n",
      "         [[-0.0272, -0.0949,  0.0260,  0.0638, -0.1149],\n",
      "          [-0.0262, -0.0692, -0.0101, -0.0568, -0.0472],\n",
      "          [-0.0367, -0.1097,  0.0947,  0.0968, -0.0181],\n",
      "          [-0.0131, -0.0471, -0.1043, -0.1124,  0.0429],\n",
      "          [-0.0634, -0.0742, -0.0090, -0.0385, -0.0374]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0037, -0.0245, -0.0398, -0.0553, -0.0940],\n",
      "          [ 0.0968, -0.0462,  0.0306, -0.0401,  0.0094],\n",
      "          [ 0.1077,  0.0532, -0.1001,  0.0458,  0.1096],\n",
      "          [ 0.0304,  0.0774,  0.1138, -0.0177,  0.0240],\n",
      "          [-0.0803, -0.0238,  0.0855,  0.0592, -0.0731]],\n",
      "\n",
      "         [[-0.0926, -0.0789, -0.1140, -0.0891, -0.0286],\n",
      "          [ 0.0779,  0.0193, -0.0878, -0.0926,  0.0574],\n",
      "          [-0.0859, -0.0142,  0.0554, -0.0534, -0.0126],\n",
      "          [-0.0101, -0.0273, -0.0585, -0.1029, -0.0933],\n",
      "          [-0.0618,  0.1115, -0.0558, -0.0775,  0.0280]],\n",
      "\n",
      "         [[ 0.0318,  0.0633,  0.0878,  0.0643, -0.1145],\n",
      "          [ 0.0102,  0.0699, -0.0107, -0.0680,  0.1101],\n",
      "          [-0.0432, -0.0657, -0.1041,  0.0052,  0.0512],\n",
      "          [ 0.0256,  0.0228, -0.0876, -0.1078,  0.0020],\n",
      "          [ 0.1053,  0.0666, -0.0672, -0.0150, -0.0851]]],\n",
      "\n",
      "\n",
      "        [[[-0.0557,  0.0209,  0.0629,  0.0957, -0.1060],\n",
      "          [ 0.0772, -0.0814,  0.0432,  0.0977,  0.0016],\n",
      "          [ 0.1051, -0.0984, -0.0441,  0.0673, -0.0252],\n",
      "          [-0.0236, -0.0481,  0.0796,  0.0566,  0.0370],\n",
      "          [-0.0649, -0.0937,  0.0125,  0.0342, -0.0533]],\n",
      "\n",
      "         [[-0.0323,  0.0780,  0.0092,  0.0052, -0.0284],\n",
      "          [-0.1046, -0.1086, -0.0552, -0.0587,  0.0360],\n",
      "          [-0.0336, -0.0452,  0.1101,  0.0402,  0.0823],\n",
      "          [-0.0559, -0.0472,  0.0424, -0.0769, -0.0755],\n",
      "          [-0.0056, -0.0422, -0.0866,  0.0685,  0.0929]],\n",
      "\n",
      "         [[ 0.0187, -0.0201, -0.1070, -0.0421,  0.0294],\n",
      "          [ 0.0544, -0.0146, -0.0457,  0.0643, -0.0920],\n",
      "          [ 0.0730, -0.0448,  0.0018, -0.0228,  0.0140],\n",
      "          [-0.0349,  0.0840, -0.0030,  0.0901,  0.1110],\n",
      "          [-0.0563, -0.0842,  0.0926,  0.0905, -0.0882]]],\n",
      "\n",
      "\n",
      "        [[[-0.0089, -0.1139, -0.0945,  0.0223,  0.0307],\n",
      "          [ 0.0245, -0.0314,  0.1065,  0.0165, -0.0681],\n",
      "          [-0.0065,  0.0277,  0.0404, -0.0816,  0.0433],\n",
      "          [-0.0590, -0.0959, -0.0631,  0.1114,  0.0987],\n",
      "          [ 0.1034,  0.0678,  0.0872, -0.0155, -0.0635]],\n",
      "\n",
      "         [[ 0.0577, -0.0598, -0.0779, -0.0369,  0.0242],\n",
      "          [ 0.0594, -0.0448, -0.0680,  0.0156, -0.0681],\n",
      "          [-0.0752,  0.0602, -0.0194,  0.1055,  0.1123],\n",
      "          [ 0.0345,  0.0397,  0.0266,  0.0018, -0.0084],\n",
      "          [ 0.0016,  0.0431,  0.1074, -0.0299, -0.0488]],\n",
      "\n",
      "         [[-0.0280, -0.0558,  0.0196,  0.0862,  0.0903],\n",
      "          [ 0.0530, -0.0850, -0.0620, -0.0254, -0.0213],\n",
      "          [ 0.0095, -0.1060,  0.0359, -0.0881, -0.0731],\n",
      "          [-0.0960,  0.1006, -0.1093,  0.0871, -0.0039],\n",
      "          [-0.0134,  0.0722, -0.0107,  0.0724,  0.0835]]],\n",
      "\n",
      "\n",
      "        [[[-0.1003,  0.0444,  0.0218,  0.0248,  0.0169],\n",
      "          [ 0.0316, -0.0555, -0.0148,  0.1097,  0.0776],\n",
      "          [-0.0043, -0.1086,  0.0051, -0.0786,  0.0939],\n",
      "          [-0.0701, -0.0083, -0.0256,  0.0205,  0.1087],\n",
      "          [ 0.0110,  0.0669,  0.0896,  0.0932, -0.0399]],\n",
      "\n",
      "         [[-0.0258,  0.0556, -0.0315,  0.0541, -0.0252],\n",
      "          [-0.0783,  0.0470,  0.0177,  0.0515,  0.1147],\n",
      "          [ 0.0788,  0.1095,  0.0062, -0.0993, -0.0810],\n",
      "          [-0.0717, -0.1018, -0.0579, -0.1063, -0.1065],\n",
      "          [-0.0690, -0.1138, -0.0709,  0.0440,  0.0963]],\n",
      "\n",
      "         [[-0.0343, -0.0336,  0.0617, -0.0570, -0.0546],\n",
      "          [ 0.0711, -0.1006,  0.0141,  0.1020,  0.0198],\n",
      "          [ 0.0314, -0.0672, -0.0016,  0.0063,  0.0283],\n",
      "          [ 0.0449,  0.1003, -0.0881,  0.0035, -0.0577],\n",
      "          [-0.0913, -0.0092, -0.1016,  0.0806,  0.0134]]],\n",
      "\n",
      "\n",
      "        [[[-0.0622,  0.0603, -0.1093, -0.0447, -0.0225],\n",
      "          [-0.0981, -0.0734, -0.0188,  0.0876,  0.1115],\n",
      "          [ 0.0735, -0.0689, -0.0755,  0.1008,  0.0408],\n",
      "          [ 0.0031,  0.0156, -0.0928, -0.0386,  0.1112],\n",
      "          [-0.0285, -0.0058, -0.0959, -0.0646, -0.0024]],\n",
      "\n",
      "         [[-0.0717, -0.0143,  0.0470, -0.1130,  0.0343],\n",
      "          [-0.0763, -0.0564,  0.0443,  0.0918, -0.0316],\n",
      "          [-0.0474, -0.1044, -0.0595, -0.1011, -0.0264],\n",
      "          [ 0.0236, -0.1082,  0.1008,  0.0724, -0.1130],\n",
      "          [-0.0552,  0.0377, -0.0237, -0.0126, -0.0521]],\n",
      "\n",
      "         [[ 0.0927, -0.0645,  0.0958,  0.0075,  0.0232],\n",
      "          [ 0.0901, -0.0190, -0.0657, -0.0187,  0.0937],\n",
      "          [-0.0857,  0.0262, -0.1135,  0.0605,  0.0427],\n",
      "          [ 0.0049,  0.0496,  0.0001,  0.0639, -0.0914],\n",
      "          [-0.0170,  0.0512,  0.1150,  0.0588, -0.0840]]],\n",
      "\n",
      "\n",
      "        [[[ 0.0888, -0.0257, -0.0247, -0.1050, -0.0182],\n",
      "          [ 0.0817,  0.0161, -0.0673,  0.0355, -0.0370],\n",
      "          [ 0.1054, -0.1002, -0.0365, -0.1115, -0.0455],\n",
      "          [ 0.0364,  0.1112,  0.0194,  0.1132,  0.0226],\n",
      "          [ 0.0667,  0.0926,  0.0965, -0.0646,  0.1062]],\n",
      "\n",
      "         [[ 0.0699, -0.0540, -0.0551, -0.0969,  0.0290],\n",
      "          [-0.0936,  0.0488,  0.0365, -0.1003,  0.0315],\n",
      "          [-0.0094,  0.0527,  0.0663, -0.1148,  0.1059],\n",
      "          [ 0.0968,  0.0459, -0.1055, -0.0412, -0.0335],\n",
      "          [-0.0297,  0.0651,  0.0420,  0.0915, -0.0432]],\n",
      "\n",
      "         [[ 0.0389,  0.0411, -0.0961, -0.1120, -0.0599],\n",
      "          [ 0.0790, -0.1087, -0.1005,  0.0647,  0.0623],\n",
      "          [ 0.0950, -0.0872, -0.0845,  0.0592,  0.1004],\n",
      "          [ 0.0691,  0.0181,  0.0381,  0.1096, -0.0745],\n",
      "          [-0.0524,  0.0808, -0.0790, -0.0637,  0.0843]]]])), ('bias', tensor([ 0.0364,  0.0373, -0.0489, -0.0016,  0.1057, -0.0693,  0.0009,  0.0549,\n",
      "        -0.0797,  0.1121]))])\n"
     ]
    }
   ],
   "source": [
    "# Check out the conv_layer_2 internal parameters\n",
    "print(conv_layer_2.state_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9e368baa-b183-47a9-a5f5-df9fb53b0d23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv_layer_2 weight shape: \n",
      "torch.Size([10, 3, 5, 5]) -> [out_channels=10, in_channels=3, kernel_size=5, kernel_size=5]\n",
      "\n",
      "conv_layer_2 bias shape: \n",
      "torch.Size([10]) -> [out_channels=10]\n"
     ]
    }
   ],
   "source": [
    "# Get shapes of weight and bias tensors within conv_layer_2\n",
    "print(f\"conv_layer_2 weight shape: \\n{conv_layer_2.weight.shape} -> [out_channels=10, in_channels=3, kernel_size=5, kernel_size=5]\")\n",
    "print(f\"\\nconv_layer_2 bias shape: \\n{conv_layer_2.bias.shape} -> [out_channels=10]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8119ff03-1b90-4a3d-a73b-0d69878e194f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test image original shape: torch.Size([3, 64, 64])\n",
      "Test image with unsqueezed dimension: torch.Size([1, 3, 64, 64])\n",
      "Shape after going through conv_layer(): torch.Size([1, 10, 62, 62])\n",
      "Shape after going through conv_layer() and max_pool_layer(): torch.Size([1, 10, 31, 31])\n"
     ]
    }
   ],
   "source": [
    "# Print out original image shape without and with unsqueezed dimension\n",
    "print(f\"Test image original shape: {test_image.shape}\")\n",
    "print(f\"Test image with unsqueezed dimension: {test_image.unsqueeze(dim=0).shape}\")\n",
    "\n",
    "# Create a sample nn.MaxPoo2d() layer\n",
    "max_pool_layer = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "# Pass data through just the conv_layer\n",
    "test_image_through_conv = conv_layer(test_image.unsqueeze(dim=0))\n",
    "print(f\"Shape after going through conv_layer(): {test_image_through_conv.shape}\")\n",
    "\n",
    "# Pass data through the max pool layer\n",
    "test_image_through_conv_and_max_pool = max_pool_layer(test_image_through_conv)\n",
    "print(f\"Shape after going through conv_layer() and max_pool_layer(): {test_image_through_conv_and_max_pool.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "de0bc62c-178c-4568-b8e0-b734cabb5ee9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random tensor:\n",
      "tensor([[[[0.3367, 0.1288],\n",
      "          [0.2345, 0.2303]]]])\n",
      "Random tensor shape: torch.Size([1, 1, 2, 2])\n",
      "\n",
      "Max pool tensor:\n",
      "tensor([[[[0.3367]]]]) <- this is the maximum value from random_tensor\n",
      "Max pool tensor shape: torch.Size([1, 1, 1, 1])\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "# Create a random tensor with a similiar number of dimensions to our images\n",
    "random_tensor = torch.randn(size=(1, 1, 2, 2))\n",
    "print(f\"Random tensor:\\n{random_tensor}\")\n",
    "print(f\"Random tensor shape: {random_tensor.shape}\")\n",
    "\n",
    "# Create a max pool layer\n",
    "max_pool_layer = nn.MaxPool2d(kernel_size=2) # see what happens when you change the kernel_size value \n",
    "\n",
    "# Pass the random tensor through the max pool layer\n",
    "max_pool_tensor = max_pool_layer(random_tensor)\n",
    "print(f\"\\nMax pool tensor:\\n{max_pool_tensor} <- this is the maximum value from random_tensor\")\n",
    "print(f\"Max pool tensor shape: {max_pool_tensor.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c7bee808-bef9-470d-b279-a7d601e787f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random tensor:\n",
      "tensor([[[[0.3367, 0.1288],\n",
      "          [0.2345, 0.2303]]]])\n",
      "Random tensor shape: torch.Size([1, 1, 2, 2])\n",
      "\n",
      "Average pool tensor:\n",
      "tensor([[[[0.2326]]]]) <- this is the average value from random_tensor\n",
      "Average pool tensor shape: torch.Size([1, 1, 1, 1])\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "print(f\"Random tensor:\\n{random_tensor}\")\n",
    "print(f\"Random tensor shape: {random_tensor.shape}\")\n",
    "\n",
    "# Create a average pool layer\n",
    "avg_pool_layer = nn.AvgPool2d(kernel_size=2) # see what happens when you change the kernel_size value \n",
    "\n",
    "# Pass the random tensor through the max pool layer\n",
    "avg_pool_layer = avg_pool_layer(random_tensor)\n",
    "print(f\"\\nAverage pool tensor:\\n{avg_pool_layer} <- this is the average value from random_tensor\")\n",
    "print(f\"Average pool tensor shape: {avg_pool_layer.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8d4678ab-5b72-4a3a-9da5-4b28bd84651f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup loss and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(params=model_2.parameters(), \n",
    "                             lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8f9550ea-b006-4ee4-9604-2fed06a4416d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "439ffae5aad1486f93052391be4eb8e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0\n",
      "---------\n",
      "Train loss: 0.60204 | Train accuracy: 78.21%\n",
      "Test loss: 0.39023 | Test accuracy: 86.06%\n",
      "\n",
      "Epoch: 1\n",
      "---------\n",
      "Train loss: 0.36355 | Train accuracy: 86.96%\n",
      "Test loss: 0.36230 | Test accuracy: 86.86%\n",
      "\n",
      "Epoch: 2\n",
      "---------\n",
      "Train loss: 0.32363 | Train accuracy: 88.24%\n",
      "Test loss: 0.33644 | Test accuracy: 87.95%\n",
      "\n",
      "Train time on cuda: 7.656 seconds\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "\n",
    "# Measure time\n",
    "from timeit import default_timer as timer\n",
    "train_time_start_model_2 = timer()\n",
    "\n",
    "# Train and test model \n",
    "epochs = 3\n",
    "for epoch in tqdm(range(epochs)):\n",
    "    print(f\"Epoch: {epoch}\\n---------\")\n",
    "    train_step(data_loader=train_dataloader, \n",
    "        model=model_2, \n",
    "        loss_fn=loss_fn,\n",
    "        optimizer=optimizer,\n",
    "        accuracy_fn=accuracy_fn,\n",
    "        device=device\n",
    "    )\n",
    "    test_step(data_loader=test_dataloader,\n",
    "        model=model_2,\n",
    "        loss_fn=loss_fn,\n",
    "        accuracy_fn=accuracy_fn,\n",
    "        device=device\n",
    "    )\n",
    "\n",
    "train_time_end_model_2 = timer()\n",
    "total_train_time_model_2 = print_train_time(start=train_time_start_model_2,\n",
    "                                           end=train_time_end_model_2,\n",
    "                                           device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c1626205-6022-4939-b4d1-3800d48b4ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_name': 'FashionMNISTModelV2',\n",
       " 'model_loss': 0.3364392817020416,\n",
       " 'model_acc': 87.94928115015975}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get model_2 results \n",
    "model_2_results = eval_model(\n",
    "    model=model_2,\n",
    "    data_loader=test_dataloader,\n",
    "    loss_fn=loss_fn,\n",
    "    accuracy_fn=accuracy_fn\n",
    ")\n",
    "model_2_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "804b6f8b-ef0f-491d-950a-8495f5dfea02",
   "metadata": {},
   "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>model_name</th>\n",
       "      <th>model_loss</th>\n",
       "      <th>model_acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FashionMNISTModelV0</td>\n",
       "      <td>0.476639</td>\n",
       "      <td>83.426518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FashionMNISTModelV1</td>\n",
       "      <td>0.783116</td>\n",
       "      <td>71.615415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FashionMNISTModelV2</td>\n",
       "      <td>0.336439</td>\n",
       "      <td>87.949281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            model_name  model_loss  model_acc\n",
       "0  FashionMNISTModelV0    0.476639  83.426518\n",
       "1  FashionMNISTModelV1    0.783116  71.615415\n",
       "2  FashionMNISTModelV2    0.336439  87.949281"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "compare_results = pd.DataFrame([model_0_results, model_1_results, model_2_results])\n",
    "compare_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bf8d46e3-794f-4a51-bb4f-edd44dd315b6",
   "metadata": {},
   "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>model_name</th>\n",
       "      <th>model_loss</th>\n",
       "      <th>model_acc</th>\n",
       "      <th>training_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FashionMNISTModelV0</td>\n",
       "      <td>0.476639</td>\n",
       "      <td>83.426518</td>\n",
       "      <td>7.481396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FashionMNISTModelV1</td>\n",
       "      <td>0.783116</td>\n",
       "      <td>71.615415</td>\n",
       "      <td>6.289485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FashionMNISTModelV2</td>\n",
       "      <td>0.336439</td>\n",
       "      <td>87.949281</td>\n",
       "      <td>7.656029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            model_name  model_loss  model_acc  training_time\n",
       "0  FashionMNISTModelV0    0.476639  83.426518       7.481396\n",
       "1  FashionMNISTModelV1    0.783116  71.615415       6.289485\n",
       "2  FashionMNISTModelV2    0.336439  87.949281       7.656029"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add training times to results comparison\n",
    "compare_results[\"training_time\"] = [total_train_time_model_0,\n",
    "                                    total_train_time_model_1,\n",
    "                                    total_train_time_model_2]\n",
    "compare_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6b7e5b19-d09d-4cb9-8460-b6cf4f644dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'model')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize our model results\n",
    "compare_results.set_index(\"model_name\")[\"model_acc\"].plot(kind=\"barh\")\n",
    "plt.xlabel(\"accuracy (%)\")\n",
    "plt.ylabel(\"model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "bc906561-abfd-4a08-bf96-19decd5841bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_predictions(model: torch.nn.Module, data: list, device: torch.device = device):\n",
    "    pred_probs = []\n",
    "    model.eval()\n",
    "    with torch.inference_mode():\n",
    "        for sample in data:\n",
    "            # Prepare sample\n",
    "            sample = torch.unsqueeze(sample, dim=0).to(device) # Add an extra dimension and send sample to device\n",
    "\n",
    "            # Forward pass (model outputs raw logit)\n",
    "            pred_logit = model(sample)\n",
    "\n",
    "            # Get prediction probability (logit -> prediction probability)\n",
    "            pred_prob = torch.softmax(pred_logit.squeeze(), dim=0) # note: perform softmax on the \"logits\" dimension, not \"batch\" dimension (in this case we have a batch size of 1, so can perform on dim=0)\n",
    "\n",
    "            # Get pred_prob off GPU for further calculations\n",
    "            pred_probs.append(pred_prob.cpu())\n",
    "            \n",
    "    # Stack the pred_probs to turn list into a tensor\n",
    "    return torch.stack(pred_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b49e7b67-b189-431b-9677-c10fa6389243",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test sample image shape: torch.Size([1, 28, 28])\n",
      "Test sample label: 5 (Sandal)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "random.seed(42)\n",
    "test_samples = []\n",
    "test_labels = []\n",
    "for sample, label in random.sample(list(test_data), k=9):\n",
    "    test_samples.append(sample)\n",
    "    test_labels.append(label)\n",
    "\n",
    "# View the first test sample shape and label\n",
    "print(f\"Test sample image shape: {test_samples[0].shape}\\nTest sample label: {test_labels[0]} ({class_names[test_labels[0]]})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e84f9456-bbff-4f19-b772-e38538b22811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[4.4795e-07, 3.8274e-07, 7.4689e-07, 3.1100e-07, 7.6389e-08, 9.9874e-01,\n",
       "         1.6075e-06, 1.9762e-05, 2.6581e-04, 9.6938e-04],\n",
       "        [8.8553e-02, 6.5940e-01, 2.8539e-03, 1.8555e-01, 3.5412e-02, 1.0522e-03,\n",
       "         2.5068e-02, 4.4784e-04, 4.3241e-05, 1.6150e-03],\n",
       "        [2.5201e-05, 6.6784e-07, 2.6959e-05, 2.5661e-05, 5.0724e-06, 7.9427e-04,\n",
       "         1.3017e-05, 8.7229e-01, 1.8299e-03, 1.2499e-01],\n",
       "        [8.5266e-04, 3.7873e-05, 9.0882e-02, 5.2278e-04, 9.0693e-01, 3.1451e-06,\n",
       "         7.5898e-04, 2.9644e-06, 9.0679e-06, 1.9091e-06],\n",
       "        [1.1854e-02, 5.2804e-06, 7.1845e-05, 9.8307e-01, 8.3031e-04, 1.0171e-06,\n",
       "         4.0750e-03, 1.3806e-05, 7.5361e-05, 8.0264e-06],\n",
       "        [6.4352e-01, 1.4817e-05, 2.0398e-04, 4.8147e-03, 1.5313e-02, 2.5429e-06,\n",
       "         3.3590e-01, 8.7694e-07, 2.3004e-04, 2.2083e-07],\n",
       "        [7.5905e-05, 1.6034e-06, 6.2163e-03, 3.9651e-06, 9.9099e-01, 8.0276e-07,\n",
       "         2.6422e-03, 6.6146e-07, 5.9038e-05, 4.9785e-06],\n",
       "        [2.3198e-05, 3.3743e-06, 3.8801e-05, 8.8544e-05, 7.3793e-06, 1.1487e-02,\n",
       "         1.2485e-05, 9.8696e-01, 2.0571e-04, 1.1697e-03],\n",
       "        [5.7893e-06, 9.9979e-01, 2.2845e-08, 7.9256e-05, 1.0998e-04, 7.9728e-07,\n",
       "         2.3022e-06, 6.3638e-06, 5.0298e-07, 1.0858e-06]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions on test samples with model 2\n",
    "pred_probs= make_predictions(model=model_2, \n",
    "                             data=test_samples)\n",
    "\n",
    "# View first two prediction probabilities list\n",
    "pred_probs[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "31c78144-8777-41ec-a29c-cf7a52f0388a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([5, 1, 7, 4, 3, 0, 4, 7, 1])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the prediction probabilities into prediction labels by taking the argmax()\n",
    "pred_classes = pred_probs.argmax(dim=1)\n",
    "pred_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "de75580f-5018-468f-8f4f-b528efa878c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([5, 1, 7, 4, 3, 0, 4, 7, 1], tensor([5, 1, 7, 4, 3, 0, 4, 7, 1]))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are our predictions in the same form as our test labels? \n",
    "test_labels, pred_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "884e5912-7504-47bb-b661-96d5099f47c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "plt.figure(figsize=(9, 9))\n",
    "nrows = 3\n",
    "ncols = 3\n",
    "for i, sample in enumerate(test_samples):\n",
    "  # Create a subplot\n",
    "  plt.subplot(nrows, ncols, i+1)\n",
    "\n",
    "  # Plot the target image\n",
    "  plt.imshow(sample.squeeze(), cmap=\"gray\")\n",
    "\n",
    "  # Find the prediction label (in text form, e.g. \"Sandal\")\n",
    "  pred_label = class_names[pred_classes[i]]\n",
    "\n",
    "  # Get the truth label (in text form, e.g. \"T-shirt\")\n",
    "  truth_label = class_names[test_labels[i]] \n",
    "\n",
    "  # Create the title text of the plot\n",
    "  title_text = f\"Pred: {pred_label} | Truth: {truth_label}\"\n",
    "  \n",
    "  # Check for equality and change title colour accordingly\n",
    "  if pred_label == truth_label:\n",
    "      plt.title(title_text, fontsize=10, c=\"g\") # green text if correct\n",
    "  else:\n",
    "      plt.title(title_text, fontsize=10, c=\"r\") # red text if wrong\n",
    "  plt.axis(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f26be2dc-8084-47a0-b397-fbb49dab6319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d4db487086c462f870d46aeccf67bc1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Making predictions:   0%|          | 0/313 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import tqdm for progress bar\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "# 1. Make predictions with trained model\n",
    "y_preds = []\n",
    "model_2.eval()\n",
    "with torch.inference_mode():\n",
    "  for X, y in tqdm(test_dataloader, desc=\"Making predictions\"):\n",
    "    # Send data and targets to target device\n",
    "    X, y = X.to(device), y.to(device)\n",
    "    # Do the forward pass\n",
    "    y_logit = model_2(X)\n",
    "    # Turn predictions from logits -> prediction probabilities -> predictions labels\n",
    "    y_pred = torch.softmax(y_logit, dim=1).argmax(dim=1) # note: perform softmax on the \"logits\" dimension, not \"batch\" dimension (in this case we have a batch size of 32, so can perform on dim=1)\n",
    "    # Put predictions on CPU for evaluation\n",
    "    y_preds.append(y_pred.cpu())\n",
    "# Concatenate list of predictions into a tensor\n",
    "y_pred_tensor = torch.cat(y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c42be1ef-2e31-4748-bc5f-209e66ab6d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([9, 2, 1,  ..., 8, 1, 5])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5005dee3-eb52-48ec-82d2-843c33ae705f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mlxtend version: 0.23.1\n"
     ]
    }
   ],
   "source": [
    "# See if torchmetrics exists, if not, install it\n",
    "try:\n",
    "    import torchmetrics, mlxtend\n",
    "    print(f\"mlxtend version: {mlxtend.__version__}\")\n",
    "    assert int(mlxtend.__version__.split(\".\")[1]) >= 19, \"mlxtend verison should be 0.19.0 or higher\"\n",
    "except:\n",
    "    !pip install -q torchmetrics -U mlxtend # <- Note: If you're using Google Colab, this may require restarting the runtime\n",
    "    import torchmetrics, mlxtend\n",
    "    print(f\"mlxtend version: {mlxtend.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5b0d5965-1742-48c7-bfc9-1c363893deee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23.1\n"
     ]
    }
   ],
   "source": [
    "# Import mlxtend upgraded version\n",
    "import mlxtend \n",
    "print(mlxtend.__version__)\n",
    "assert int(mlxtend.__version__.split(\".\")[1]) >= 19 # should be version 0.19.0 or higher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0e27b027-bc13-4f78-b4ae-b52d48231c55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[838,   1,  14,  33,   5,   0, 103,   0,   6,   0],\n",
       "        [  4, 971,   1,  15,   6,   0,   2,   0,   1,   0],\n",
       "        [ 15,   0, 721,  12, 150,   1, 100,   0,   1,   0],\n",
       "        [ 19,   8,   8, 906,  25,   0,  33,   0,   1,   0],\n",
       "        [  1,   1,  20,  48, 853,   0,  77,   0,   0,   0],\n",
       "        [  0,   0,   0,   2,   0, 981,   0,  11,   1,   5],\n",
       "        [150,   0,  42,  35, 100,   0, 666,   0,   7,   0],\n",
       "        [  0,   0,   0,   0,   0,  22,   0, 946,   0,  32],\n",
       "        [  4,   1,   4,   7,   2,   5,  11,   5, 961,   0],\n",
       "        [  0,   0,   0,   0,   0,   8,   0,  40,   2, 950]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchmetrics import ConfusionMatrix\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "# 2. Setup confusion matrix instance and compare predictions to targets\n",
    "confmat = ConfusionMatrix(num_classes=len(class_names), task='multiclass')\n",
    "confmat_tensor = confmat(preds=y_pred_tensor,\n",
    "                         target=test_data.targets)\n",
    "\n",
    "confmat_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8f8eaf3d-82ae-4edf-b085-3694e2994368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Plot the confusion matrix\n",
    "fig, ax = plot_confusion_matrix(\n",
    "    conf_mat=confmat_tensor.numpy(), # matplotlib likes working with NumPy \n",
    "    class_names=class_names, # turn the row and column labels into class names\n",
    "    figsize=(10, 7)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "044efbbd-daff-4321-a99b-80ee6f066409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving model to: models/03_pytorch_computer_vision_model_2.pth\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "# Create models directory (if it doesn't already exist), see: https://docs.python.org/3/library/pathlib.html#pathlib.Path.mkdir\n",
    "MODEL_PATH = Path(\"models\")\n",
    "MODEL_PATH.mkdir(parents=True, # create parent directories if needed\n",
    "                 exist_ok=True # if models directory already exists, don't error\n",
    ")\n",
    "\n",
    "# Create model save path\n",
    "MODEL_NAME = \"03_pytorch_computer_vision_model_2.pth\"\n",
    "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
    "\n",
    "# Save the model state dict\n",
    "print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n",
    "torch.save(obj=model_2.state_dict(), # only saving the state_dict() only saves the learned parameters\n",
    "           f=MODEL_SAVE_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "81b1e39f-999d-481c-8c98-041853ad1c90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new instance of FashionMNISTModelV2 (the same class as our saved state_dict())\n",
    "# Note: loading model will error if the shapes here aren't the same as the saved version\n",
    "loaded_model_2 = FashionMNISTModelV2(input_shape=1, \n",
    "                                    hidden_units=10, # try changing this to 128 and seeing what happens \n",
    "                                    output_shape=10) \n",
    "\n",
    "# Load in the saved state_dict()\n",
    "loaded_model_2.load_state_dict(torch.load(f=MODEL_SAVE_PATH))\n",
    "\n",
    "# Send model to GPU\n",
    "loaded_model_2 = loaded_model_2.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5c4c6971-3b9f-4892-8823-54c6c3f36ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_name': 'FashionMNISTModelV2',\n",
       " 'model_loss': 0.3364392817020416,\n",
       " 'model_acc': 87.94928115015975}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate loaded model\n",
    "torch.manual_seed(42)\n",
    "\n",
    "loaded_model_2_results = eval_model(\n",
    "    model=loaded_model_2,\n",
    "    data_loader=test_dataloader,\n",
    "    loss_fn=loss_fn, \n",
    "    accuracy_fn=accuracy_fn\n",
    ")\n",
    "\n",
    "loaded_model_2_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eb8fff07-4be8-44f8-8a8b-381d890ede16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_name': 'FashionMNISTModelV2',\n",
       " 'model_loss': 0.3364392817020416,\n",
       " 'model_acc': 87.94928115015975}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ddfa42f6-d0e3-45c0-9130-b9e30c6ff671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(True)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to see if results are close to each other (if they are very far away, there may be an error)\n",
    "torch.isclose(torch.tensor(model_2_results[\"model_loss\"]), \n",
    "              torch.tensor(loaded_model_2_results[\"model_loss\"]),\n",
    "              atol=1e-08, # absolute tolerance\n",
    "              rtol=0.0001) # relative tolerance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ed740b5-757b-4196-b712-2cff028cd3af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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