Fashion MNIST Augmented.ipynb

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   "source": [
    "# Fashion MNIST Classification without Data Augmentation\n",
    "\n",
    "This notebook implements a Convolutional Neural Network (CNN) to classify Fashion MNIST dataset images without using data augmentation techniques.\n",
    "\n",
    "## Setup and Imports\n",
    "\n",
    "Import required libraries and set random seeds for reproducibility."
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kaggle dataset path exists.\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torchvision.io import read_image\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    confusion_matrix,\n",
    "    classification_report,\n",
    "    accuracy_score\n",
    ")\n",
    "from scipy.stats import uniform, randint\n",
    "from PIL import Image\n",
    "\n",
    "INPUT_PATH = \"/kaggle/input\"\n",
    "OUTPUT_PATH = \"/kaggle/working\"\n",
    "\n",
    "if os.path.exists(INPUT_PATH):\n",
    "    print(\"Kaggle dataset path exists.\")\n",
    "else:\n",
    "    print(\"Dataset path not found. Please verify the dataset location.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02bc5f1d",
   "metadata": {},
   "source": [
    "## Data Labels\n",
    "\n",
    "Define mapping between numeric labels and fashion item categories."
   ]
  },
  {
   "cell_type": "code",
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   "id": "6e649098",
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   "source": [
    "labels_map = {\n",
    "    0: \"T-Shirt\",\n",
    "    1: \"Trouser\",\n",
    "    2: \"Pullover\",\n",
    "    3: \"Dress\",\n",
    "    4: \"Coat\",\n",
    "    5: \"Sandal\",\n",
    "    6: \"Shirt\",\n",
    "    7: \"Sneaker\",\n",
    "    8: \"Bag\",\n",
    "    9: \"Ankle Boot\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26357d8a",
   "metadata": {},
   "source": [
    "### Data Transformation for Training and Testing\n",
    "\n",
    "1. **Training Transformations (Augmentation)**  \n",
    "   - **Random Crop**: Pads and randomly crops the image to add variability.\n",
    "   - **Horizontal Flip**: Applies a 30% chance of flipping the image.\n",
    "   - **Random Rotation**: Rotates the image up to ±10 degrees.\n",
    "   - **Color Jitter**: Adjusts brightness and contrast randomly.\n",
    "   - **Random Affine**: Applies small translations to shift the image up to 10%.\n",
    "   - **Normalization**: Standardizes the image using the Fashion-MNIST dataset's mean and standard deviation.\n",
    "\n",
    "2. **Testing Transformations (No Augmentation)**  \n",
    "   - Converts images to tensors and normalizes them for evaluation.\n",
    "\n",
    "3. **Dataset Creation**  \n",
    "   - **Training Dataset**: Applies the training transformations.\n",
    "   - **Test Dataset**: Applies the test transformations for evaluation without augmentation.\n",
    "\n",
    "4. **Purpose**  \n",
    "   - The training transformations help improve model generalization by exposing it to varied input data.\n",
    "   - The test transformations ensure the model is evaluated on standardized data."
   ]
  },
  {
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   "id": "0cd6e152",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 26421880/26421880 [00:03<00:00, 8096458.79it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
      "\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29515/29515 [00:00<00:00, 132783.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
      "\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4422102/4422102 [00:01<00:00, 2511824.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
      "\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5148/5148 [00:00<00:00, 12488303.64it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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   ],
   "source": [
    "# Training transformations - includes augmentation\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.RandomCrop(28, padding=4),\n",
    "    transforms.RandomHorizontalFlip(p=0.3),  \n",
    "    transforms.RandomRotation(\n",
    "        degrees=10,  \n",
    "        fill=0\n",
    "    ),\n",
    "    transforms.ColorJitter(\n",
    "        brightness=0.2,  \n",
    "        contrast=0.2,    \n",
    "    ),\n",
    "    transforms.RandomAffine(\n",
    "        degrees=0,\n",
    "        translate=(0.1, 0.1)  \n",
    "    ),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.2860,), (0.3530,))  # Fashion-MNIST mean and std\n",
    "])\n",
    "\n",
    "# Test transformations - no augmentation\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.2860,), (0.3530,))\n",
    "])\n",
    "\n",
    "training_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform_train\n",
    ")\n",
    "\n",
    "test_data = datasets.FashionMNIST(\n",
    "    root=\"data\",\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform_test\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a5bb64a",
   "metadata": {},
   "source": [
    "### Visualizing Transformed Images\n",
    "\n",
    "1. **Function Overview**  \n",
    "   - Displays a comparison of original and transformed images side by side.  \n",
    "   - Uses the dataset's transformations to generate new variants of the same image.\n",
    "\n",
    "2. **Visualization Layout**  \n",
    "   - Top row: Original images with their class labels.  \n",
    "   - Bottom row: Transformed versions of the same images with the same labels.\n",
    "\n",
    "3. **Interactive Exploration**  \n",
    "   - Allows inspection of the effects of data augmentation.  \n",
    "   - Helps verify that transformations are applied correctly and preserve class integrity.\n",
    "\n",
    "4. **Execution**  \n",
    "   - Visualizes a subset of images from the training dataset with transformations applied."
   ]
  },
  {
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   "outputs": [
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i4tSsWdMZNGiQ8/zzzztbtmxR282YMcMREadHjx5OYWGhL3/ggQecqlWrOllZWb7s13Nt5cqVjog4DRo0cHJycvyed968eY6IOCtXrnT/RQIXgDkBaGeO33Xr1p11mxo1ajhXXHGF4zj/d83zyCOP+G3z+eefOyLipKSk+OVLly71yxcsWHDe/Y0dO9aJjIx08vPzi/plAUXCeaJiqpC/ZpySkiLx8fHStWtXETn9dvyNN94oc+fOlYKCArX9jTfeKDVr1vR9fM0114jI6V/Z/bWVK1dK7969pXv37jJ//nwJCgo651jmzZsnzZo1k6ZNm8rBgwd9/3Xr1s33fOcTHBwsy5cvl+XLl8uyZctk6tSpEh4eLn379pUffvjBt93ixYulffv20qlTJ18WHh4uI0eOlLS0NF/78eLFiyUhIUFuvvlm33bVq1eXMWPGSHZ2tqxateq8YwIuxIwZM+TVV1+V5ORkWbBggTz00EPSrFkz6d69u+zZs0dtP3LkSAkICPB9fM0110hBQYGkp6efd1/Dhg07629KABUFcwI4LTw8XLUa//GPf/T7eN68eVKjRg3p2bOn37VUmzZtJDw83HctFRUVJSIiH330kZw6dcrcX1RUlBw/flyWL19e8l8MUII4T5SNCreYLSgokLlz50rXrl0lNTVVduzYITt27JArr7xSMjIy5NNPP1WPqVevnt/HZxa2v74PIzc3V/r16ydXXHGFvPvuu65ujt6+fbt8//33Ehsb6/df48aNReT0vavnU7VqVenRo4f06NFDevXqJSNHjpRPPvlEjhw5Io8++qhvu/T0dGnSpIl6/JlfCThzMKenp0ujRo2kSpUq59wOuBDZ2dmyb98+33+/vE+jSpUqcu+998r69evl4MGD8sEHH0ifPn1kxYoVctNNN6nncjsnLcnJycX8SoCSwZwAzi87O1siIiJ8H1erVk0SExP9ttm+fbscOXJE4uLi1PVUdna271qqc+fO8vvf/14mTpwotWrVkoEDB8qMGTP8Okruueceady4sfTp00cSExPljjvukKVLl5bNFwv8CueJ8lfh7pldsWKF7N27V+bOnStz585Vn09JSZFevXr5ZVWrVjWfy3Ecv4+DgoKkb9++8sEHH8jSpUv97lc9m8LCQmnVqpW88MIL5ufr1q173uewJCYmSpMmTeTf//53kR4PlLTnn3/e96ecRE7f72H9bbKYmBgZMGCADBgwQLp06SKrVq2S9PR03/0hIu7npKWy/mQRFQ9zAji33bt3y5EjR/z+dGJQUJD6YXthYaHExcWZZZ4i4iusDAgIkPfee0/Wrl0rH374oSxbtkzuuOMO+fvf/y5r166V8PBwiYuLkw0bNsiyZctkyZIlsmTJEpkxY4bcdtttfkWYQFngPFH+KtxiNiUlReLi4uS1115Tn5s/f74sWLBApkyZUqRvWkBAgKSkpMjAgQNlyJAhsmTJkvP+/dWGDRvKxo0bpXv37n5v/ZeE/Px8vxbl+vXry7Zt29R2W7du9X3+zP9+++23UlhY6HfC+PV2JT1eXNxuu+02v19xdzPH2rZtK6tWrZK9e/f6vSCXNI5llAfmBHBuZ/7eZu/evc+5XcOGDeWTTz6Rjh07uppHHTp0kA4dOsiTTz4pb731ltxyyy0yd+5cufPOO0Xk9J8nue666+S6666TwsJCueeee2Tq1Knyt7/9zW9hDZQ2zhPlr0ItZk+cOCHz58+XIUOGyODBg9Xna9euLW+//bYsXLhQbrzxxiLtIzAwUObPny+9e/eW6667Tj799FNp3779Wbe/4YYbZPHixTJ9+nQZOXKkGm9hYWGR/n7aDz/8INu2bZM2bdr4sr59+8qkSZPkP//5j1x11VUicvrvs02bNk2SkpKkefPmvu0+/vhjeeedd3z3zebn58srr7wi4eHh0rlzZxERX3NaVlbWBY8PlU+DBg2kQYMGKt+3b58cOnTId/ydcfLkSfn000+lSpUqpX7xcGaOcSyjLDEngLNbsWKFPPHEE5KcnOz78ztnc8MNN8jkyZPliSeekKeeesrvc2d+sB8VFSWHDx+WqKgov4vw1q1bi4j4ftU4MzNTYmJifJ+vUqWK7y81FOdPJgJFwXmi/FWoxezChQvl2LFjMmDAAPPzHTp0kNjYWElJSSnyYlbk9E9NPvroI+nWrZv06dNHVq1aJS1btjS3vfXWW+Xdd9+Vu+++W1auXCkdO3aUgoIC2bp1q7z77ruybNkyvz8kbsnPz/f9DbbCwkJJS0uTKVOmSGFhoYwfP9633SOPPCJvv/229OnTR8aMGSPR0dEya9YsSU1NlX/961++d2FHjhwpU6dOleHDh8v69eslKSlJ3nvvPVmzZo1MmjTJd+9KSEiING/eXN555x1p3LixREdHS8uWLc/6tQKW3bt3S/v27aVbt27SvXt3SUhIkP3798vbb78tGzdulPvvv19q1apVqmNo3bq1VK1aVZ555hk5cuSIBAUFSbdu3cy/0waUNuYEKpslS5bI1q1bJT8/XzIyMmTFihWyfPlyqV+/vixcuFCCg4PP+fjOnTvLqFGj5Omnn5YNGzZIr169pHr16rJ9+3aZN2+evPTSSzJ48GCZNWuWTJ48WQYNGiQNGzaUY8eOyfTp0yUyMlL69u0rIiJ33nmnHDp0SLp16yaJiYmSnp4ur7zyirRu3fri/LMj8CTOE2WnQi1mU1JSJDg4WHr27Gl+vkqVKtKvXz9JSUmRzMzMYu0rMjJSli1bJtdee6307NlTPv/8c/MnJFWqVJH3339fXnzxRZk9e7YsWLBAQkNDpUGDBjJ27FhfEdS55OXlya233uq373bt2smcOXOke/fuvjw+Pl6++OILefjhh+WVV16R3Nxcueyyy+TDDz+Ufv36+bYLCQmRzz77TB555BGZNWuWHD16VJo0aSIzZsyQ4cOH++37jTfekNGjR8sDDzwgJ0+elPHjx7OYxQVp0qSJTJo0SRYvXiyTJ0+WjIwMCQ4OlpYtW8r06dNlxIgRpT6GhIQEmTJlijz99NMyYsQIKSgokJUrV150L8jwBuYEKpvHHntMRE7/dlt0dLS0atVKJk2aJLfffrtf+dO5TJkyRdq0aSNTp06VcePGSbVq1SQpKUmGDh0qHTt2FJHTi94vv/xS5s6dKxkZGVKjRg1p3769pKSk+Apuhg4dKtOmTZPJkydLVlaWJCQkyI033igTJkxQ9+oC5YXzRNkJcNzcVQwAAAAAQAXCj7AAAAAAAJ7DYhYAAAAA4DksZgEAAAAAnsNiFgAAAADgOSxmAQAAAACew2IWAAAAAOA5LGYBAAAAAJ5Tze2GAQEBpTkOXGQqw58vZk7gQjAnAH+VdU5Ur169HEZSNv75z3+62i4rK0tlQUFBRd5vlSr6vZmoqCiVZWRkuNouMDBQZeHh4SqrWrWqyk6cOKGyo0ePquyOO+5Q2cmTJ1V2sSmL80SPHj1UNmPGDJUdO3ZMZdZxWK2aXi7169dPZX/5y19UNmTIEJWlpqaqrGbNmirbtGmTynr16qWyi5mb8wTvzAIAAAAAPIfFLAAAAADAc1jMAgAAAAA8x/U9swAAAMDZPPDAAyqbPn26yqz7SkNCQlS2b98+lVn3Frq91zQ+Pl5lhw4dUll+fr7KcnJyVGbdS3nkyBGVxcbGqmzmzJlnGyaKae/evSo7deqUq8cWFhaqLDc3V2UbN25UmXUP7p49e1RmHTfWvcTWvdbQeGcWAAAAAOA5LGYBAAAAAJ7DYhYAAAAA4DksZgEAAAAAnkMBVCno37+/yj766KNyGAm8xPpj2xMnTlSZ9UffrcIC64/IW38I/pJLLnE7xHJRFn9gHZVX1apVVXbbbbep7Ntvv1XZ+vXrS2VMv5SQkKCy0aNHq+yll15S2f79+0tlTMDZ9OnTR2VWcZJVxpOVlaWyvLw8le3evVtl1rnNcRyVWedKq3gqKipKZRareMoay+WXX64y61yOCxcXF6eyAwcOqCw5OVllW7ZsUZlV9mTZvHmzyqxiJ+scYx3rQUFBKuP6xx3emQUAAAAAeA6LWQAAAACA57CYBQAAAAB4DotZAAAAAIDnUAB1ARITE1X2+OOPqyw7O1tlR48eVdnx48dVdsstt6jsr3/9q8qsQoXieP3111VmlVYtWrSoRPeL/zNixAiVWSUZVplAfn6+ysLDw1VmHZsjR45U2Y8//qiy9PR0ldWrV8/VY60yKqs4wypAsEo8LNb4rH8Xi1UgMWvWLJVZxTvwtqFDh6qsc+fOKps2bZrK0tLSVGaVh1hlH8HBwSqzjn/r2LQKa6zCE2vMJX3uAH5p7ty5KrNKMS1W2Y31+h8aGurq+TIzM11t57ZA0Tr3xsfHq2zw4MEqs0qhLNZ5EedmFd1Zx80PP/ygMuuYs16HLQUFBSqzjiXrWj8mJkZlX3zxhcpuvvlmV2Op7HhnFgAAAADgOSxmAQAAAACew2IWAAAAAOA5LGYBAAAAAJ5DAdQFGDJkiMqsm7N37NihMquwICIiQmVRUVEqs0qXPvnkE5WtWbNGZV9++aXKevfurTLLE088oTKrPGTlypWung/n1rhxY5UdOnRIZVYJhVUIY7EKpaZPn66yrl27quyqq65S2YwZM1R29913q+yrr75SmVVsY5UxdOzYUWVXX321yh577DGV7du3T2VWEYdV2nDHHXeojAIob/vd736nsoMHD6qsZs2aKqtWTZ8uL7300hIZ17n89NNPKrvvvvtUFhYWpjLKnlAR7Ny5U2XWtY5V2mMVGVqv69b1lMU6V1qlS1ZpT2RkpKt9JCcnu9oOpScvL8/Vdm4LJq3trJIwt8Vh1atXV5lVFAh3eGcWAAAAAOA5LGYBAAAAAJ7DYhYAAAAA4DksZgEAAAAAnkMB1AWwipOCg4NV1qxZM5VZN4VbmjdvrrLVq1erLD4+XmX9+vVTWf/+/VWWlJSkMutmeWu7FStWqMwq7cG5WcUxubm5KrMKm9yWDliFMFbRkcX6PluuuOIKlVklScuXL1dZz549Xe3Dmk/PP/+8yv7f//t/KrPKnqw5a5V91K5dW2V169ZVmVXQg4rJeq1KTExU2csvv+zqsZ06dVKZVR5lzW2reOrxxx9X2d69e1V2ySWXqMwqcgNKk1WKlp+fr7Kff/5ZZVYBlPVYax/W9YpVqFNQUKAyizW3rfOnVVq4bds2le3Zs8fVfq2vDSXD7bWOxSp7so4R63i1Hmsdh9bzHTlyxO0Q8Su8MwsAAAAA8BwWswAAAAAAz2ExCwAAAADwHBazAAAAAADP4e7zC/Diiy+qrEePHiqrWrWqyqxiJ7eskhErs1jlUd98843KrBvZrSKTDRs2uNovzu25555TmVVMZBUCWMUG1mNPnDjhKrMkJyerzCrdsAqRHnjgAZW5LU+wpKamuhqfVZYVHh7uar9WZv3bd+3aVWWzZ89WGSomq5zDKgmzXvueffZZlc2aNUtljRs3Vtm+fftUZh3X1rnDKuI7evSoyoCK6scff1RZ69atVWYV8VnlOSEhISqzShCtUijrPBEZGaky67XCOp+4Lfe0uD0H4sIV5/vittTU2s5t2ZN1fFnHP9zhnVkAAAAAgOewmAUAAAAAeA6LWQAAAACA57CYBQAAAAB4DgVQF+C9995TmVXYUZEUpzzKYpWb4MJ98sknKqtdu7bKrOIwq4TCsnfvXpVZ5RctW7ZUWWxsrMri4+NVZpXn1KlTR2UnT55UWU5OjsqCgoJUdscdd6jMkp6erjKrKMoq0LLKGKzx9e7dW2UUQHlHtWr6lGcdD9Z21rGUnZ2tsq+++srV8yUkJJx1nOdDcQy8xDrvWAU91nnCKnGyWCVO1n6tkp3c3FyVhYWFqcyax1a5FbzDKmeyMreFUta1hJVZr+FxcXGu9gGNd2YBAAAAAJ7DYhYAAAAA4DksZgEAAAAAnsNiFgAAAADgORRAXYCMjAyVuS3j8aKsrCyVRUVFqax169alPpaLzWuvveYqswonWrRoobJx48aprE+fPiqzvqebNm062zArhFtvvVVlN9xwg8qs0iqroCc6OlplVilIcHCwyopTnobyFxoaqjKrAMoqALFYr/9uzwlWOU1BQYHKrPG5LSMBKgKr2M8SERGhMmueZGZmqsx6XbfOd1YZj1VaaL3+W/Nu+/btKrNYZaHWfMeFs4rDUlNTXT3W+r64LYWyjiW3+7C+90lJSa6eDxpnRAAAAACA57CYBQAAAAB4DotZAAAAAIDnsJgFAAAAAHgOBVAXwConKI4jR46ozCqZaty4scrKohRk6tSpKuvcubPKrJIilIz8/HyVbdy4UWXjx49X2XXXXacyqyjhxx9/VNmhQ4dUFhgYqLKS/t5bJQtz5sxx9ViriMMq47HGbBU0pKenq+zNN990NRZUTNb32ToerGPdLasUxCqxqV69uqvns0qrKIBCRWCdn/r166eyrl27qmznzp0q27dvn8qs1/CYmBiVWecsa+7k5OSozG2Rj3XdZZUHovRceumlKrNeS62CJWs765rD2i4vL8/VPqzMKhPLzc1VmXXNBnc4IwIAAAAAPIfFLAAAAADAc1jMAgAAAAA8h8UsAAAAAMBzaO65AHFxcSX6fNYN4FYplKUsCkAefvhhld11110q++qrr0p9LDg3q4gjKytLZVbRRVBQkMqssjOrKMcqxLBYJQtWZj2fVRRy4sQJlUVHR6ts3bp1Khs4cOBZx4nKx3otteaJVR5lsY5ht8UjVmaNj9I9VFTJyckqq1Onjspq167t6vmsMjbr3Jadna0yq8TPKpSy5l3NmjVV9uijj6pszZo1KrNYxUC4cC1atFDZ7t27VWaV7rkt9rNec92+Xlus67PY2FhXj4U7vDMLAAAAAPAcFrMAAAAAAM9hMQsAAAAA8BwWswAAAAAAz6FFohxFRkaqrHHjxuUwEvemT5+usszMzHIYCX7pgw8+UJn1fYmPj1fZ0aNHVWYVJVilOJbilD0FBwe72kdeXp6rx37yySeung+Vg3X8Hzp0qNT367Y8yiqKsopt3M4ToCis49VtgZFVbGO9DlulUNa5yDrWrXIma3zt27c/6zh/ySoUrFWrlsqioqJcPV9x/v1wbt27d3e1ndsSp+Jc11is7711vWKdizp06KCytWvXutpvZcc7swAAAAAAz2ExCwAAAADwHBazAAAAAADPYTELAAAAAPAcCqAuQLt27VS2bt26Ij9fSEiIq6yiu++++8p7CJXeyZMnVWYVZ1hFBBarFKE45QluWeOzvraEhASVWcfh22+/XTIDg+dYJWYREREq27t3r8rcFjZZrOIRi1WA5na/1apx6kbpcVtWZL0OW8VOmzZtUtmRI0dUtmfPHpUFBQWpLCwsTGVWkVubNm1UFhoaqjKrZM16vn379qnMQtlT6bFKkqxrhOK8hltFfLm5ua4ea+3Xuk7auXOnyu69916VUQDlDu/MAgAAAAA8h8UsAAAAAMBzWMwCAAAAADyHxSwAAAAAwHNokbgAxSl7uph169atvIdQ6Z06dUplVnGGtZ1VduC27MnazsrcljGcOHFCZVaRlSUrK8vVdqgcrFIQq9QlJydHZZGRkSrLz89XmduyJ0txHmvNp6ioKJUxJ1BSmjdvrrLBgwer7OjRoyrr2rVrkfdrHcPR0dEq++GHH1S2bNkyldWoUUNlVvGmNd979uypsvXr16sMJcP6XiUnJ6vs8OHDKitOAVRJFwC6LeO8+uqri7zfyo53ZgEAAAAAnsNiFgAAAADgOSxmAQAAAACew2IWAAAAAOA5FEAVU0FBgcqKc/N4YWGhytwWhXz33Xcqs0pBEhMTL3hcqNiSkpJUduDAAZW5LXGyWI91e2xa21nHurWPatX0y5T1fNZcBM7H7et1cQqbSppVlJabm1sOI0Fl0aBBA5XVqVNHZT/++KPKateurbKMjAxXz5eenq4y67rGmp+dOnVSWdOmTVW2f/9+lVmlUI0bN1aZ9e+ya9culeHcunTporKVK1eqbPPmzSqzyi4tbq9/rO2szC3rHGONOSEhocj7qOwqztkZAAAAAACXWMwCAAAAADyHxSwAAAAAwHNYzAIAAAAAPKfSFUBde+21Khs9erTK+vfv7+r5rBu7s7OzVWbd7F29enWVFadkpGXLlkV+LLwtPz9fZVaxgduyG+uxbouiisMan1XsRAEUSopV9GKVk1UkVilacQpKgPOxSpyysrJUFhoaqjKroNAqdrLmndvXdet6yipiat++vcq2bdumMut1YePGjSobOHCgyl588UWVuT23VVYDBgxQ2cGDB1V26tQplQUHB6vMbWFlbGysyqxCMOv53F7Dr1ixQmU33HCDyqwSLLjDO7MAAAAAAM9hMQsAAAAA8BwWswAAAAAAz2ExCwAAAADwHE8WQN10000qswoGrGInq8TALavY6csvv1RZt27dVGbdUB4dHa0yq9hj9+7dKvvuu+9U1rp1a5UlJCSoLDc3V2XWDfTwDut7arGKyNwWLFkFCFYpVHG2CwwMVNnJkyddPZ81d1B5WceS22O9OAVQ1mOLU+zndixuy92AX7IKcG6//XaV1ahRw9VjLcePH3e1ndvzk1UC5JZ1rWOVPVnnnby8PJW1a9fO1X4pezq3Sy+9VGUREREqs8ourddIKwsPD3c1FqvYyXo+6xrG+j43bdpUZdb1SosWLVS2du3as44T/4d3ZgEAAAAAnsNiFgAAAADgOSxmAQAAAACew2IWAAAAAOA5FaoxxbohukGDBip7++23VWbdnG3dwG9xW9hh3Tw+ZcoUlVlFUQMGDHA1FsuePXtU9tvf/rbIz0fZ08XHKkWw5pO1nXWsWyUGbgubrJIM67FuSxZOnDhR5LGg8nJbYub29d9tEZPbsie3+3U75tDQUJXl5OS4GgsqL6uIMiYmRmXWdUNUVJTKtmzZojLrOLzssstUtmTJEpX16dNHZVYB5tatW1X2hz/8QWXr169XmXVetFiFmuPHj3f1WJzbhx9+qLKuXbuqLD4+XmVW+V1mZqarx/bv319lixYtUplV4nr06FGVWesO67ixjjlrTsAd3pkFAAAAAHgOi1kAAAAAgOewmAUAAAAAeA6LWQAAAACA55R4AdSVV16psnfffVdliYmJKnNbnGGxHmvdFH7q1ClXz2cV4FiFNdbXZunWrZvKwsLCVNaiRQuV/e///q+rfZSF7du3qywpKUll1r8VSo/bsieL2wIoS0BAgKvM4rYAx+1YrCITVF7WnLAU57xTFqz5ZI3Z7bwDfsmaJ3Xr1lXZsWPHXG33/vvvq6xjx44qy83NVZn1Wh8ZGaky69xhfR3WdlaRp1VQZV0/WuOrX7++yqzrJJzb9OnTVTZt2jSVZWVlqcz6vliFeBbruLZYx2FISIjKrAJYi3X9j6Kr2GdxAAAAAAAMLGYBAAAAAJ7DYhYAAAAA4DksZgEAAAAAnlPiBVD//e9/VVarVi2VlUXpRnFKiA4ePKiyqKgolR0/flxlNWrUUNmKFStUlpmZqbKYmBhX47NuWrduRt+zZ4/KTp48qbK8vDyVWWVZ1nZW1rJlS5WhbLktwLFKMtyySjLcsvZrPZ+1nXVsui18wMWnXr16KhsyZIjKjh49qjK3RWTF4XYfbgtrLMWZi6i8rLljXV9Yx+ahQ4dU9vPPP6usVatWKlu3bp3KAgMDVWYd/9brvzU+69okODhYZVZ5mnVtZ5X2BAUFqQylx7oOP3DggMrcXte43c465twWylplnNax2bx5c1fPB413ZgEAAAAAnsNiFgAAAADgOSxmAQAAAACew2IWAAAAAOA5JV4AZbGKWY4cOaIyqzjJYt10XZyyp7S0NJVZN5lbhTpux2xxW/ZksUqcrMIC6+vIzc1VmXUDvZVdfvnlKouPjz/bMFEKNm/erDKr/MVxnBLdr1WSYXE7F92Oz9rOeg1wW5SDi491jnFb7OG2iKk43BZKWce6VR6ycOFCle3bt+/CB4ZKz7qGcVtYZpVYWiVrbln7sK5rrP1axU7WdWZ0dLTKTpw4obKdO3eqrG7duiqLjY1VGcpWRESEyqzi2cGDB6vM7fWrdcxZc8e6NrHGZ12vbNq0ydVYoPHOLAAAAADAc1jMAgAAAAA8h8UsAAAAAMBzWMwCAAAAADynTAqgrMIh68ZpazurTCAwMFBlWVlZKgsKClKZVU4TEhLiah8FBQUqs4p3LNnZ2SqzbhS39mEVgCQkJLjar+XYsWMqs4qirH088cQTKvvyyy9V9p///Kdog0ORWOVk1nFjsY45q4jDmhNu92Ed69bzWSULVsHGyy+/rLLnnnvO1VhQOVjHppW5LWcqC1bJmtu5CBTF4sWLVXbJJZeorHHjxiqziqKaNm2qMutawtrH9u3bVZaRkaGy+vXrq+ynn35SmVUeVa9ePZVZ14rWfq35WZxrMZyb9e/dqVMnlTVv3lxl3bp1U9nq1atVZn2frUIwq2DMuq63SsJ69uypsk8++URlKLqKcxYHAAAAAMAlFrMAAAAAAM9hMQsAAAAA8BwWswAAAAAAzymTAijHcVS2c+dOlVmlUHFxcSr7+uuvXT3WumHbKnuytrPs3r1bZVY5h1WKYBV2WKUDbse3Y8eOs47zl6x/+7y8PJVZpVDW12YV76Bs/fjjjypr1aqVyqzvvduCGavYzHqs231YrMdarPIot8VrqBwiIyNVZr3OWa/NVlZepVDWfq3MKjIEisIq2XzxxRddPXbRokUqs45X65rto48+UllMTIzKrHLDFStWqKxFixYqs0pFreupkydPqsy69rQKpSIiIlSG0mOVOFnZtGnTiryPV199VWXW8TB37lyVLVmypMj7RdHxziwAAAAAwHNYzAIAAAAAPIfFLAAAAADAc1jMAgAAAAA8p0wKoCZPnqyyBx98UGX79+9X2bZt21RWu3ZtlR04cEBlVrGBVSZgFQJYBRvWY62xuGV9vSdOnFDZkSNHVNa0adMi7xfeFh0drbLw8HCVWXPCKrspKChQmVVOZm1nlThZWX5+vqvt3BbvWHMRlZf1emgVhxWnsKykFad4ypqfQFnr16+fq+1mzpxZugMRkcsvv1xlYWFhKnM7x6wyTqt4sFGjRq6eD97x5z//ubyHgAvEO7MAAAAAAM9hMQsAAAAA8BwWswAAAAAAz2ExCwAAAADwnDJpUXnooYdUtmrVKpVNmTJFZU2aNFFZdna2yhISEoo4uuKpU6eOq+1+/vnnUh4JKou1a9eqbMOGDSobMGCAyuLj40tjSCVm8+bNrrazijhQee3du1dl1jnBKntyWwhT0twWpVlFUVZBIVCZTZs2TWUNGzZU2eOPP64yt+cTqwSRMkKg/PHOLAAAAADAc1jMAgAAAAA8h8UsAAAAAMBzWMwCAAAAADwnwLFaKKwNjeIML0pKSlJZYmKiyvbv36+yRo0aqWzRokUlMq6LjcvDytMq+px4+umnVRYYGOjqsVYpztGjR1VmfZ+tf5dTp065euzo0aNVFhERobIlS5aobPDgwSqrSJgT5W/YsGEqs451K7OKmNyyHut27oSGhqrMKrx6//33iza4clRZ5wQFXjibkydPlvcQSl1FP0+gYnFznuCdWQAAAACA57CYBQAAAAB4DotZAAAAAIDnsJgFAAAAAHhOpSuAQtmorMUeKJ4///nPKouOjlbZqlWrVLZ06dJSGVNJYU4A/irrnKAACmdDARTgjwIoAAAAAMBFicUsAAAAAMBzWMwCAAAAADyHxSwAAAAAwHMogEKpqKzFHsDZMCcAf8wJwB9zAvBHARQAAAAA4KLEYhYAAAAA4DksZgEAAAAAnsNiFgAAAADgOa4LoAAAAAAAqCh4ZxYAAAAA4DksZgEAAAAAnsNiFgAAAADgOSxmAQAAAACew2IWAAAAAOA5LGYBAAAAAJ7DYhYAAAAA4DksZgEAAAAAnsNi1qV169bJ1VdfLWFhYRIQECAbNmwo7yGViJkzZ0pAQICkpaWV91DgMcwJwB9zAvDHnAD8MSdKXrWy3mFAQICr7VauXCldunQp3cG4dOrUKRkyZIgEBwfLiy++KKGhoVK/fv3yHhYuEswJwB9zAvDHnAD8MSdwRpkvZufMmeP38ezZs2X58uUqb9asWVkO65x27twp6enpMn36dLnzzjvLezi4yDAnAH/MCcAfcwLwx5zAGWW+mB06dKjfx2vXrpXly5er/NdycnIkNDS0NId2Vvv37xcRkaioqBJ7zuPHj0tYWFiJPR+8izlxGnMCZzAnTmNO4AzmxGnMCZzBnDiNOVFB75nt0qWLtGzZUtavXy/XXnuthIaGyrhx40RE5IMPPpB+/fpJ7dq1JSgoSBo2bChPPPGEFBQUmM+xefNm6dq1q4SGhkqdOnXk2WefVft75ZVXpEWLFhIaGio1a9aUtm3byltvvSUiIsOHD5fOnTuLiMiQIUMkICDA79cVVqxYIddcc42EhYVJVFSUDBw4ULZs2eL3/BMmTJCAgADZvHmz/OEPf5CaNWtKp06dREQkKSlJ+vfvL5999pm0bdtWQkJCpFWrVvLZZ5+JiMj8+fOlVatWEhwcLG3atJFvvvlGjX/r1q0yePBgiY6OluDgYGnbtq0sXLhQbff9999Lt27dJCQkRBITE+V//ud/pLCw0OV3BeWJOcGcgD/mBHMC/pgTzAn4Y05UjjlR5u/MupWZmSl9+vSRm266SYYOHSrx8fEicvoG4/DwcPnTn/4k4eHhsmLFCnnsscfk6NGj8txzz/k9x+HDh+W3v/2tXH/99XLDDTfIe++9Jw8//LC0atVK+vTpIyIi06dPlzFjxsjgwYNl7NixkpubK99++63897//lT/84Q8yatQoqVOnjjz11FMyZswYadeunW8sn3zyifTp00caNGggEyZMkBMnTsgrr7wiHTt2lK+//lqSkpL8xjNkyBBp1KiRPPXUU+I4ji/fsWOHb19Dhw6V559/Xq677jqZMmWKjBs3Tu655x4REXn66aflhhtukG3btkmVKqd/DvH9999Lx44dpU6dOvLII49IWFiYvPvuu/K73/1O/vWvf8mgQYNERGTfvn3StWtXyc/P9203bdo0CQkJKflvHkoFc4I5AX/MCeYE/DEnmBPwx5yoBHPCKWf33nuv8+thdO7c2RERZ8qUKWr7nJwclY0aNcoJDQ11cnNz1XPMnj3bl+Xl5TkJCQnO73//e182cOBAp0WLFucc48qVKx0RcebNm+eXt27d2omLi3MyMzN92caNG50qVao4t912my8bP368IyLOzTffrJ67fv36jog4X3zxhS9btmyZIyJOSEiIk56e7sunTp3qiIizcuVKX9a9e3enVatWfl97YWGhc/XVVzuNGjXyZffff78jIs5///tfX7Z//36nRo0ajog4qamp5/w3QNlhTjAn4I85wZyAP+YEcwL+mBOVd05UyF8zFhEJCgqS22+/XeW/XPkfO3ZMDh48KNdcc43k5OTI1q1b/bYNDw/3+935wMBAad++vezatcuXRUVFye7du2XdunUXNL69e/fKhg0bZPjw4RIdHe3LL7vsMunZs6csXrxYPebuu+82n6t58+Zy1VVX+T6+8sorRUSkW7duUq9ePZWfGf+hQ4dkxYoVcsMNN/j+LQ4ePCiZmZnSu3dv2b59u+zZs0dERBYvXiwdOnSQ9u3b+54vNjZWbrnllgv6ulF+mBPMCfhjTjAn4I85wZyAP+bExT8nKuxitk6dOhIYGKjy77//XgYNGiQ1atSQyMhIiY2N9R1gR44c8ds2MTFRVXfXrFlTDh8+7Pv44YcflvDwcGnfvr00atRI7r33XlmzZs15x5eeni4iIk2aNFGfa9asmRw8eFCOHz/ulycnJ5vP9csDTESkRo0aIiJSt25dMz8z/h07dojjOPK3v/1NYmNj/f4bP368iPzfzebp6enSqFEjtW9r/KiYmBPMCfhjTjAn4I85wZyAP+bExT8nKuw9s9bvXmdlZUnnzp0lMjJSHn/8cWnYsKEEBwfL119/LQ8//LC6+bhq1armczu/+P3yZs2aybZt2+Sjjz6SpUuXyr/+9S+ZPHmyPPbYYzJx4sRS/5rONc7zjf/M1/vQQw9J7969zW0vvfTSCx0mKijmBHMC/pgTzAn4Y04wJ+CPOXHxz4kKu5i1fPbZZ5KZmSnz58+Xa6+91penpqYW63nDwsLkxhtvlBtvvFFOnjwp119/vTz55JPy6KOPSnBwsPmYM3/keNu2bepzW7dulVq1apV6VXaDBg1ERKR69erSo0ePc25bv3592b59u8qt8cM7mBP+mBNgTvhjToA54Y85AeaEP6/PiQr7a8aWMz9Z+OVPQk6ePCmTJ08u8nNmZmb6fRwYGCjNmzcXx3Hk1KlTZ33cJZdcIq1bt5ZZs2ZJVlaWL//uu+/k448/lr59+xZ5TG7FxcVJly5dZOrUqbJ37171+QMHDvj+f9++fWXt2rXy5Zdf+n0+JSWl1MeJ0sOc8MecAHPCH3MCzAl/zAkwJ/x5fU546p3Zq6++WmrWrCnDhg2TMWPGSEBAgMyZM8fvYLxQvXr1koSEBOnYsaPEx8fLli1b5NVXX5V+/fpJRETEOR/73HPPSZ8+feSqq66SESNG+Kq0a9SoIRMmTCjymC7Ea6+9Jp06dZJWrVrJXXfdJQ0aNJCMjAz5z3/+I7t375aNGzeKiMhf/vIXmTNnjvz2t7+VsWPH+qq069evL99++22ZjBUljzmhMScqN+aExpyo3JgTGnOicmNOaF6eE55azMbExMhHH30kDz74oPz1r3+VmjVrytChQ6V79+5n/R3v8xk1apSkpKTICy+8INnZ2ZKYmChjxoyRv/71r+d9bI8ePWTp0qUyfvx4eeyxx6R69erSuXNneeaZZ856c3ZJa968uXz11VcyceJEmTlzpmRmZkpcXJxcccUV8thjj/m2u+SSS2TlypUyevRo+d///V+JiYmRu+++W2rXri0jRowok7Gi5DEnNOZE5cac0JgTlRtzQmNOVG7MCc3LcyLAKc6PIQAAAAAAKAeeumcWAAAAAAARFrMAAAAAAA9iMQsAAAAA8BwWswAAAAAAz2ExCwAAAADwHBazAAAAAADPqTSL2eHDh0t4ePh5t+vSpYt06dKl9AcEXIRmzpwpAQEB8tVXX513W+YaKgPmBADgXDhPFE+FXsxOnjxZAgIC5MorryzvoRTZ8OHDJSAgwPdftWrVpG7dunLTTTfJ5s2bS3XfOTk5MmHCBPnss89KdT+o+H55DJ7rv7MdK4WFhTJ79my58sorJTo6WiIiIqRx48Zy2223ydq1a0t9/Js3b5YJEyZIWlpaqe8LlQNzAji/MxfZZ/4LDg6W2rVrS+/eveXll1+WY8eOlfcQgVLDecIbqpX3AM4lJSVFkpKS5Msvv5QdO3bIpZdeWt5DKpKgoCB54403REQkPz9fdu7cKVOmTJGlS5fK5s2bpXbt2qWy35ycHJk4caKICD/FqeTmzJnj9/Hs2bNl+fLlKm/WrJn5+DFjxshrr70mAwcOlFtuuUWqVasm27ZtkyVLlkiDBg2kQ4cOFzymjz/+2PW2mzdvlokTJ0qXLl0kKSnpgvcF/BpzAnDv8ccfl+TkZDl16pTs27dPPvvsM7n//vvlhRdekIULF8pll11W3kMEShznCW+osIvZ1NRU+eKLL2T+/PkyatQoSUlJkfHjx5f3sIqkWrVqMnToUL+sQ4cO0r9/f1m0aJHcdddd5TQyVBa/Pv7Wrl0ry5cvV7klIyNDJk+eLHfddZdMmzbN73OTJk2SAwcOFGlMgYGB590mNzfX1XbAhWJOAO716dNH2rZt6/v40UcflRUrVkj//v1lwIABsmXLFgkJCTEfe/z4cQkLCyuroQIlhvOEN1TYXzNOSUmRmjVrSr9+/WTw4MGSkpKitklLS5OAgAB5/vnnZdq0adKwYUMJCgqSdu3aybp16867jw0bNkhsbKx06dJFsrOzz7pdXl6ejB8/Xi699FIJCgqSunXryl/+8hfJy8sr8teXkJAgIqcXur+0a9cuGTJkiERHR0toaKh06NBBFi1apB6/f/9+GTFihMTHx0twcLBcfvnlMmvWLN/n09LSJDY2VkREJk6c6PtViAkTJhR5zKicUlNTxXEc6dixo/pcQECAxMXFqTwvL0/+9Kc/SWxsrISFhcmgQYPUC/ev7/v47LPPJCAgQObOnSt//etfpU6dOhIaGiovv/yyDBkyREREunbtet5f6wFKG3MCEOnWrZv87W9/k/T0dHnzzTdF5P/6SXbu3Cl9+/aViIgIueWWW0Tk9K9cTpo0SVq0aCHBwcESHx8vo0aNksOHD/s971dffSW9e/eWWrVqSUhIiCQnJ8sdd9zht83cuXOlTZs2EhERIZGRkdKqVSt56aWXyuYLB1zgPFF2Kuw7sykpKXL99ddLYGCg3HzzzfL666/LunXrpF27dmrbt956S44dOyajRo2SgIAAefbZZ+X666+XXbt2SfXq1c3nX7dunfTu3Vvatm0rH3zwwVl/olhYWCgDBgyQ1atXy8iRI6VZs2ayadMmefHFF+WHH36Q999/39XXc/DgQRERKSgokF27dsnDDz8sMTEx0r9/f982GRkZcvXVV0tOTo6MGTNGYmJiZNasWTJgwAB57733ZNCgQSIicuLECenSpYvs2LFD7rvvPklOTpZ58+bJ8OHDJSsrS8aOHSuxsbHy+uuvyx//+EcZNGiQXH/99SIi/CoQLlj9+vVFRGTevHkyZMgQCQ0NPe9jRo8eLTVr1pTx48dLWlqaTJo0Se677z555513zvvYJ554QgIDA+Whhx6SvLw86dWrl4wZM0ZefvllGTdunO/Xec72az1AaWNOAKfdeuutMm7cOPn44499v2WWn58vvXv3lk6dOsnzzz/vmx+jRo2SmTNnyu233y5jxoyR1NRUefXVV+Wbb76RNWvWSPXq1WX//v3Sq1cviY2NlUceeUSioqIkLS1N5s+f79vn8uXL5eabb5bu3bvLM888IyIiW7ZskTVr1sjYsWPL/h8BMHCeKENOBfTVV185IuIsX77ccRzHKSwsdBITE52xY8f6bZeamuqIiBMTE+McOnTIl3/wwQeOiDgffvihLxs2bJgTFhbmOI7jrF692omMjHT69evn5Obm+j1n586dnc6dO/s+njNnjlOlShXn888/99tuypQpjog4a9asOefXMmzYMEdE1H916tRx1q9f77ft/fff74iI376OHTvmJCcnO0lJSU5BQYHjOI4zadIkR0ScN99807fdyZMnnauuusoJDw93jh496jiO4xw4cMAREWf8+PHnHCMqn3vvvde5kOl/2223OSLi1KxZ0xk0aJDz/PPPO1u2bFHbzZgxwxERp0ePHk5hYaEvf+CBB5yqVas6WVlZvuzXc23lypWOiDgNGjRwcnJy/J533rx5jog4K1eudP9FAheAOQFoZ47fdevWnXWbGjVqOFdccYXjOP93zfPII4/4bfP55587IuKkpKT45UuXLvXLFyxYcN79jR071omMjHTy8/OL+mUBRcJ5omKqkL9mnJKSIvHx8dK1a1cROf12/I033ihz586VgoICtf2NN94oNWvW9H18zTXXiMjpX9n9tZUrV0rv3r2le/fuMn/+fAkKCjrnWObNmyfNmjWTpk2bysGDB33/devWzfd85xMcHCzLly+X5cuXy7Jly2Tq1KkSHh4uffv2lR9++MG33eLFi6V9+/bSqVMnXxYeHi4jR46UtLQ0X/vx4sWLJSEhQW6++WbfdtWrV5cxY8ZIdna2rFq16rxjAi7EjBkz5NVXX5Xk5GRZsGCBPPTQQ9KsWTPp3r277NmzR20/cuRICQgI8H18zTXXSEFBgaSnp593X8OGDTvrb0oAFQVzAjgtPDxctRr/8Y9/9Pt43rx5UqNGDenZs6fftVSbNm0kPDzcdy0VFRUlIiIfffSRnDp1ytxfVFSUHD9+XJYvX17yXwxQgjhPlI0Kt5gtKCiQuXPnSteuXSU1NVV27NghO3bskCuvvFIyMjLk008/VY+pV6+e38dnFra/vg8jNzdX+vXrJ1dccYW8++67rm6O3r59u3z//fcSGxvr91/jxo1F5PS9q+dTtWpV6dGjh/To0UN69eolI0eOlE8++USOHDkijz76qG+79PR0adKkiXr8mV8JOHMwp6enS6NGjaRKlSrn3A64ENnZ2bJv3z7ff7+8T6NKlSpy7733yvr16+XgwYPywQcfSJ8+fWTFihVy0003qedyOyctycnJxfxKgJLBnADOLzs7WyIiInwfV6tWTRITE/222b59uxw5ckTi4uLU9VR2drbvWqpz587y+9//XiZOnCi1atWSgQMHyowZM/w6Su655x5p3Lix9OnTRxITE+WOO+6QpUuXls0XC/wK54nyV+HumV2xYoXs3btX5s6dK3PnzlWfT0lJkV69evllVatWNZ/LcRy/j4OCgqRv377ywQcfyNKlS/3uVz2bwsJCadWqlbzwwgvm5+vWrXve57AkJiZKkyZN5N///neRHg+UtOeff973p5xETt/vYf1tspiYGBkwYIAMGDBAunTpIqtWrZL09HTf/SEi7uekpbL+ZBEVD3MCOLfdu3fLkSNH/P50YlBQkPphe2FhocTFxZllniLiK6wMCAiQ9957T9auXSsffvihLFu2TO644w75+9//LmvXrpXw8HCJi4uTDRs2yLJly2TJkiWyZMkSmTFjhtx2221+RZhAWeA8Uf4q3GI2JSVF4uLi5LXXXlOfmz9/vixYsECmTJlSpG9aQECApKSkyMCBA2XIkCGyZMmS8/791YYNG8rGjRule/fufm/9l4T8/Hy/FuX69evLtm3b1HZbt271ff7M/3777bdSWFjod8L49XYlPV5c3G677Ta/X3F3M8fatm0rq1atkr179/q9IJc0jmWUB+YEcG5n/t5m7969z7ldw4YN5ZNPPpGOHTu6mkcdOnSQDh06yJNPPilvvfWW3HLLLTJ37ly58847ReT0nye57rrr5LrrrpPCwkK55557ZOrUqfK3v/3Nb2ENlDbOE+WvQi1mT5w4IfPnz5chQ4bI4MGD1edr164tb7/9tixcuFBuvPHGIu0jMDBQ5s+fL71795brrrtOPv30U2nfvv1Zt7/hhhtk8eLFMn36dBk5cqQab2FhYZH+ftoPP/wg27ZtkzZt2viyvn37yqRJk+Q///mPXHXVVSJy+u+zTZs2TZKSkqR58+a+7T7++GN55513fPfN5ufnyyuvvCLh4eHSuXNnERFfc1pWVtYFjw+VT4MGDaRBgwYq37dvnxw6dMh3/J1x8uRJ+fTTT6VKlSqlfvFwZo5xLKMsMSeAs1uxYoU88cQTkpyc7PvzO2dzww03yOTJk+WJJ56Qp556yu9zZ36wHxUVJYcPH5aoqCi/i/DWrVuLiPh+1TgzM1NiYmJ8n69SpYrvLzUU508mAkXBeaL8VajF7MKFC+XYsWMyYMAA8/MdOnSQ2NhYSUlJKfJiVuT0T00++ugj6datm/Tp00dWrVolLVu2NLe99dZb5d1335W7775bVq5cKR07dpSCggLZunWrvPvuu7Js2TK/PyRuyc/P9/0NtsLCQklLS5MpU6ZIYWGhjB8/3rfdI488Im+//bb06dNHxowZI9HR0TJr1ixJTU2Vf/3rX753YUeOHClTp06V4cOHy/r16yUpKUnee+89WbNmjUyaNMl370pISIg0b95c3nnnHWncuLFER0dLy5Ytz/q1Apbdu3dL+/btpVu3btK9e3dJSEiQ/fv3y9tvvy0bN26U+++/X2rVqlWqY2jdurVUrVpVnnnmGTly5IgEBQVJt27dzL/TBpQ25gQqmyVLlsjWrVslPz9fMjIyZMWKFbJ8+XKpX7++LFy4UIKDg8/5+M6dO8uoUaPk6aeflg0bNkivXr2kevXqsn37dpk3b5689NJLMnjwYJk1a5ZMnjxZBg0aJA0bNpRjx47J9OnTJTIyUvr27SsiInfeeaccOnRIunXrJomJiZKeni6vvPKKtG7d+uL8syPwJM4TZadCLWZTUlIkODhYevbsaX6+SpUq0q9fP0lJSZHMzMxi7SsyMlKWLVsm1157rfTs2VM+//xz8yckVapUkffff19efPFFmT17tixYsEBCQ0OlQYMGMnbsWF8R1Lnk5eXJrbfe6rfvdu3ayZw5c6R79+6+PD4+Xr744gt5+OGH5ZVXXpHc3Fy57LLL5MMPP5R+/fr5tgsJCZHPPvtMHnnkEZk1a5YcPXpUmjRpIjNmzJDhw4f77fuNN96Q0aNHywMPPCAnT56U8ePHs5jFBWnSpIlMmjRJFi9eLJMnT5aMjAwJDg6Wli1byvTp02XEiBGlPoaEhASZMmWKPP300zJixAgpKCiQlStXXnQvyPAG5gQqm8cee0xETv92W3R0tLRq1UomTZokt99+u1/507lMmTJF2rRpI1OnTpVx48ZJtWrVJCkpSYYOHSodO3YUkdOL3i+//FLmzp0rGRkZUqNGDWnfvr2kpKT4Cm6GDh0q06ZNk8mTJ0tWVpYkJCTIjTfeKBMmTFD36gLlhfNE2Qlw3NxVDAAAAABABcKPsAAAAAAAnsNiFgAAAADgOSxmAQAAAACew2IWAAAAAOA5LGYBAAAAAJ7DYhYAAAAA4DksZgEAAAAAnlPN7YYBAQGlOQ5cZCrDny9mTuBCMCcAf8yJslWvXj2VPf300yq75JJLVHb8+HGVBQUFqSwvL09lERERKrv88stVFhUVpbKff/5ZZSkpKSrr3bu3ynJyclT2j3/8Q2VvvPGGysoLc6L83X777Sp77rnnVHbgwAGVVauml1UNGzZU2WWXXaayhx9+WGU33XSTylJTU1VmzZ1NmzaprHv37iqr6NzMCd6ZBQAAAAB4DotZAAAAAIDnsJgFAAAAAHhOgOPyF/Qr+u+4o2Lhvg/AH3Pi3KpWrVqCI4EX5Ofnl/cQSl1FOk9MmTJFZSdOnFBZYWGhysLCwlRWpYp+P8R67KlTp1SWlJR0tmH6+c1vfqMy67ix7q09fPiwyoKDg1VWt25dlS1dulRlo0ePPus4SwrnifLXo0cPlb355psqO3LkiMqs+8itc1tiYqLKsrOzVXbw4EGVWXMsMjJSZZ9//rnKrr/+epVVdNwzCwAAAAC4KLGYBQAAAAB4DotZAAAAAIDnsJgFAAAAAHiO/uu+Z2HdNA+b21IE6w8pjxw5UmW5ubkqW7FihcqsIocvv/zyrOMsCutrs/4wOQAAKB9WcVJ0dLTKMjIyVGaV2FjXgFYR0/Hjx11t99VXX6ksJCREZVZ5VFZWlqt9WNmBAwdUZrFKof7+97+r7MEHH3T1fPCOffv2qSwvL09lVpGVda1vHYdbtmxRWfXq1VVmXXNbxVOBgYGuxnex4p1ZAAAAAIDnsJgFAAAAAHgOi1kAAAAAgOewmAUAAAAAeI7rAii4V7VqVZVZN4X37NlTZb1791bZTz/9pDKroCEsLMzV802bNk1lVgmExfo6UDk0bdpUZatWrVLZpEmTVLZy5UqVWcVmJ0+eVNnVV1+tsnvuuUdl3377rcpGjRqlMqvIAShNcXFxKhs+fLjK5syZo7K9e/eWxpD8tG7dWmWNGzdW2YIFC1RmFfSg/H399dcq+/zzz1X2+9//XmXW63pkZKTK3H7vrdfc+Ph4V4/dunWryqzrH0tBQYHKrJIdqzzT+nrfffddV/uFt1nXw/Xq1VPZ9u3bVWZd11hFTNWquVt+WXPMbfFUZbrW4Z1ZAAAAAIDnsJgFAAAAAHgOi1kAAAAAgOewmAUAAAAAeE6A4ziOmw1DQkJKeyyVzj//+U+VWWUMP/74o6tsyZIlKvvNb36jssDAQJV9+eWXKrMKdbZs2aKy9PR0lbk8rDzNugm/orOKXh588EGVDR06tAxGU3GsXr1aZX/84x9V9t133xV5H8yJc7OK87woPDxcZTfddJPKxowZozKrAC0zM1NlVmFNfn6+yiIiIlRmlefUqVNHZQsXLlTZ2rVrVTZv3jyVuWWN+WJT0c8TVsGkVUTm9lg6ePCgyqzjtUoV/V6KtV1UVJTKrGInt6w5Zu3DYp0rSxrniYrJ+r7s3LnT1XbWse52H5acnByVxcTEqMx6Dbeuayo6N/8uvDMLAAAAAPAcFrMAAAAAAM9hMQsAAAAA8BwWswAAAAAAz6lW3gOoLHr16qWydu3aqezo0aMqCwsLU1nTpk1dZf/9739Vtn37dpVZ5Q4dO3ZU2eDBg1V25513qgxlyyrP+f7771XWpEmTIu/DKt2wyjSsUherdMwqDyksLCxy5pY1lk6dOqls06ZNKvNicQXKVnZ2tsqOHDmisnHjxqns0UcfVZk1Z+Pi4lRmHddZWVmuxvfpp5+qzCoUtMqt4G1WKeDf//53le3fv19lx48fd5VZ1zWWatX0JempU6dUVr16dZVZx3p8fLyr/VrXWGPHjnX1WFQObkvH3JY4Wdu5vdaxxmJdm1jnnYsV78wCAAAAADyHxSwAAAAAwHNYzAIAAAAAPIfFLAAAAADAcyiAKiNPPvmkyhISElw9NjQ0VGVWyY5VxnPNNdeozCqesm4o/+abb1T2ww8/nHWcKHlt27ZV2bp161w9Ni8vT2UnTpxQmVWmYZVuWAUDVmYVO1ljcVviVKVKyf7MzZonVhmD9XUARWGVMx0+fFhlkydPVtno0aNVZs0n63i1SnG+/vprlc2cOVNlSUlJKjtw4IDK4G2ZmZlFfqxV9mSdO0JCQlRmnYusAiireMrazppjVvFUVFSUyj788EOVWXMMlVdxSiet6wu311NuH2ttZ83PixXvzAIAAAAAPIfFLAAAAADAc1jMAgAAAAA8h8UsAAAAAMBzKIAqI1bZR+3atVWWm5urMqvYwCpACA8Pd/V8VhmDdXO7VR519dVXq+zhhx9WGUrGkCFDXG1nfZ+tYidL1apVVWYdX8VhHcPlxSo7c6tp06Yq27p1a3GGg4vMgAEDVGa9vloFM2lpaSp74YUXVBYXF6ey1NRUlR08eFBlsbGxKmvYsKHK9u3bp7KlS5eqDBef7du3q8w6RqwSJ6t0yTr+rcdaZX9WAaZ1vrNKcSzWPhYtWuTqsai8ilNEaR2b1nWX2/1aZU9WKazbktmLAe/MAgAAAAA8h8UsAAAAAMBzWMwCAAAAADyHxSwAAAAAwHMogCojVomBdWO3daP48ePHVXbkyBGVHTp0SGVJSUkqs8oYrP1a4wsLC1MZSs/YsWNdbRccHKwy6/tsfU+t7caNG6eyHTt2uMoaNGigMqucxipAsEpxrK8tKipKZd26dVPZo48+qjKrPMFt4dXEiRNVduONN7p6LCoHtwVjMTExrrbLzMxUmVXOZJ1j6tSpozJrvlsZKi/rPBEZGaky67gJCgpS2bFjx1xtZxU7WYVS8fHxKrPmnfV1WCVrwPm4LRiztnObWdcmbsujrOPfuv6/WPHOLAAAAADAc1jMAgAAAAA8h8UsAAAAAMBzWMwCAAAAADyHAqgL4LY8xypJsoo4rLIbK7OKEk6ePKmy7OxslVlFOQcPHlSZNebAwECVWUUOKD3W994tq0zAcurUKZU988wzKrvuuutUZpUuvfjiiyqzipi++OILlSUkJKjMKjv48ccfVWaVVln7dVv2ZLnhhhtURgEUfskq7LDKOfLz81VmHZs1atQomYH9/9wWmRRnnsA7rr32WpVdccUVKtu0aZPKsrKyVGaVOFnlZNZ21jWWdX6yWPPJYpWnAefj9nXTbbFrcfZhvTZb5xirjPNixTuzAAAAAADPYTELAAAAAPAcFrMAAAAAAM9hMQsAAAAA8BwaHi6AVURjFUD94Q9/UJlVbHPgwAGVhYSEuNpHeHi4yurVq6cyqygqODjY1XbWTebW+FAyWrZsWaLPZx2vFrclUwsXLnS13ZVXXqkyqyRpy5YtKmvWrJmrfVjclieUtIYNG6ps586d5TASVAQREREqq169usqs11yrPMQqcrPmdnGOf2u/1nkClYN1PFjniejoaJWlpaWpzDrWY2NjVXbo0CGVxcTEqOzw4cMqq1+/vsoWL16sstWrV6sM+CXrnG69vlrX5m6L89wWRbnNrAK0WrVquRrLxYB3ZgEAAAAAnsNiFgAAAADgOSxmAQAAAACew2IWAAAAAOA5FECdhVWwcerUKVeP/fbbb1VmFSBYpSDWfgsKClQWFxenshMnTqgsMzNTZYGBgSqzyj7CwsJUlpWVpTKUjH/84x/lPYRzssqZrGMuOTlZZcOGDVOZVWKQl5fnart+/fqddZy/ZJWsWcUjxWGN5eWXXy7RfcA7rAIQt2Uf1uu/VQDlthTEYpWWWPu1xoyLT05OjsqsY8TidrvQ0FCVWce1db1iFU+5LU+zSnGA82nRooXK9u3bpzLresW6rrdY5wm3x6t1rFtjsQqgOnTooLK1a9e62m9FxtkKAAAAAOA5LGYBAAAAAJ7DYhYAAAAA4DksZgEAAAAAnlPhC6DcFlNY5RdWYZNVOmCxSpfcWr58ucqys7NVlpubqzLr5nFrzFaxjfVvFRISojK3N5lb/37F+XfBuX344Ycqq1u3rsouueSSEt2vVabRvn17lSUkJLjKrMKOevXqudrv8ePHVWYdwzExMSq78847VVbSZU+W3/3udyqjAKryss5FVsmO9XrtttjJ7XnMLetchMrBuh7Yv3+/yqyCmUOHDrnah7Wd9XxW6aRVMhUeHu5qv1bZJXA+PXv2VJn1mmuVOLl9vS7O67rbosC0tDSVjR49WmUUQAEAAAAAUA5YzAIAAAAAPIfFLAAAAADAc1jMAgAAAAA8p9wKoKwSJ+tGf6twqCxKiDp37qyyIUOGqKxTp04qs0psMjMzVWaVPVk3dltfr7UP67HBwcEqswp6rJvHrdISlJ7/+Z//cZVZ39MTJ04Ueb/W8fXf//63yM9XkVglUyVdCtK1a9cSfT54m3Vuc7ud26IQK3PL2q81T+Li4oq8D3hHZGSkyqKjo1VmXXO4LdjLyMhQmVX2FBUVpbLFixerLD4+XmXHjh1zNRbgfDp27KgyqxDVbYmf9bpuPdYt6zXcui60Stasr+1iwDuzAAAAAADPYTELAAAAAPAcFrMAAAAAAM9hMQsAAAAA8JxyK4Cyyp7cqlmzpsrq1KmjsqZNm6osISFBZYMHD1ZZkyZNVGbdTG3diG2V8cTExKhsz549rvZhFUVZBQhWiUdoaKjK1qxZo7Lw8HCVdejQQWUof7m5uSq76qqrVPbss8+q7JprrlGZdcyFhISo7PDhwyqzysSKU2xgsQoVrH1YBSVWucm6detU1r59e1djsf4NXn/9dVePxcXHOhdVq1b002pxip0sVvGINT6rPMcq6LFKRqzXI3jH/v37VbZx40aVpaenq8w6Rqzjwbpesc47qampKuvevbvKLNY5ITk5WWX//ve/XT0fKi/ruDl06JDK3Jb4WazXYbePtVjHv1V2Wbt2bVePLYui3ZLEO7MAAAAAAM9hMQsAAAAA8BwWswAAAAAAz2ExCwAAAADwnHIrgLIKa5588kmVxcbGqswq3bBuVrZuarYKXKzHWoUYVmGBVdhhFUBt2bJFZUOGDFHZ+vXrVWaV2FhjqVWrlsosl112mcoiIiJUZhVDWCU7KH9r165V2ejRo1W2YcMGlbktHbCOQ2uOuS13s8oTLNbzWY91W7zz0UcfudrOYr32oPKyym6s1+by4rZQypo71rmSsidvs4qYunbtqrIdO3aozCpnysnJUVl2drbKrILJ6OhoV89nHXPWuejnn39WWWJiosqAX6pRo4bKoqKiVJaRkaEyqwDTup6yXoet7ay1iHWtYz2fVRS7dOlSld18880qa9euncqsa8qKjHdmAQAAAACew2IWAAAAAOA5LGYBAAAAAJ7DYhYAAAAA4DllUgBl3cD82muvqeySSy5RmXVDtJW5LSaybti2CpuszGLdPJ6UlKSyp556SmVW2cGwYcNUdvToUZVZpQjWDdtWkUOTJk1UFhMTo7JTp06pzG3JDsqfNU8sVomTxW1RlDXf3ZZCFYfbr8MqNgNKitvSpfJivS4EBgaWw0hQmm6//XaVBQcHu8pq166tMquwySqd/OGHH1QWGhqqskaNGqls/vz5Klu3bp3K3BY7WdeU1jXbkSNHXD0fLj5XXHGFq+2s62Fr7rgtgLIea70OW9dObsujmjVrpjLrGt7ajgIoAAAAAABKGYtZAAAAAIDnsJgFAAAAAHgOi1kAAAAAgOeUSZuPVURQv359lVllReHh4SqLiIhQmVVgZKlevbrKQkJCVJaRkaGyPXv2qMwqNrAKZt58802V9e/fX2UffPCByho0aKCysLAwlbVt21ZlPXr0UJlV0HPy5EmVWf8u1r8fKqbvvvtOZVbp2MVS/mKVu1kOHz5cyiNBZVGc8rTiFEWV9PNZX4e1j/Iqd8OF+81vfqOyr7/+WmVWEc2+fftUZhXlvP/++ypr3ry5yqzrvY0bN6ps4MCBKnPLuoaxrtkoe8Iv/e53v1OZdYzk5+erzHrtszJrHWO9Xrtdn1jjswqqrAI06+uw5vaMGTNUVpHxziwAAAAAwHNYzAIAAAAAPIfFLAAAAADAc1jMAgAAAAA8p0wKoKwygZ9++kllkZGRKsvLy1PZjz/+qDKrFMq6mbpGjRoqswph0tLSVGaVLp04ccJVZpUTfPjhhyr79ttvVZacnKyyqKgolVk3dmdlZanMulHceqz1b299j+Ad1rFplZhZx2txiqKs4pjiPDY3N1dlVpGJxfragKJwe8y5LWxyu11Jlz0VFBS4Gov1GmDNRZStxMRElbktrLReD63j2m3BntuSSKsox3rs3r17VVarVi2VHTp0SGV169ZVWVJSksqs6z1UDg0bNlSZtZ5ISEhQmXVtYh2H1vFqFcAuWrRIZda1uTWPjx49qjKLtY5p2bKlq8dWZLwzCwAAAADwHBazAAAAAADPYTELAAAAAPAcFrMAAAAAAM8pkwIoq4jAKpewSqGsm5Wtm6mtoqODBw+q7MCBAyqrVk3/M1hFF1ZmFSVYN49bN4pb42vRooXKsrOzVfbzzz+rzLrx3CptsPZrlUBYN55b5VHwDrdFL1bmllXsUZwCKEtxxlecxwK/5LaIqTiFTWXBKoWyVPSvo7KqV6+eyqzXYetax7p2sl6v3RbRWKzHWpk1ZqtgrH79+iqzCnCsr40CKPzSwoULVda9e3eVuS3sCwkJcbXfY8eOudrOmiduSyyt6z1rPlnFs17DO7MAAAAAAM9hMQsAAAAA8BwWswAAAAAAz2ExCwAAAADwnDIpgNqwYYPK3nvvPZXdeeedKrPKo3bt2qUy66bm8PBwlVmFBdYN29WrV1eZVZKRl5enMuuGbetG8ZycHJXt3btXZVYpgnVjt1XucOLECZVZpVrW12uVJxw+fFhl8I64uDiVWceIdSy5VdJlT273Yc076+twW3YDnE9x5klZsI5169xhvf5bc8zaznr9QNmyjkPr+3f8+HGVWd9T65rI7TWHJTo62tXzWYWVFuvaKSEhQWW7d+9WWXx8vKt9oHKYPn26yqZNm6YyqwBq//79KrOu1y1ut7P2UbNmTZVZ5ayRkZEqs+b23//+d1djqch4ZxYAAAAA4DksZgEAAAAAnsNiFgAAAADgOSxmAQAAAACeU27tFU8++aTKvv76a5U9/PDDKktOTlbZgQMHVGYVGFkFCFZJRmBgoMrclmRYN4pbBVDW87ktY7CKF6z9WqztMjIyVGYVaFlFDrj4VPSSJKsoxG2RlTW3gZJivdZbmfU67PY13C2rZKc4c9sqFDl69GiRnw8lIyYmRmXWa6RVJmNdT1mvkVbZpVViY2UREREqc1soZWU7d+5UWZMmTVS2b98+lUVFRanMutbJzs5WGSqHVq1aqWzTpk0qswo1rXlicVtEdskll6jM7TwJDg5WWc+ePVX2008/uRpLRcY7swAAAAAAz2ExCwAAAADwHBazAAAAAADPYTELAAAAAPCcMimAcluItGTJEldZ165dVfbss8+qLCkpSWXWzf/W+KySjOKUc1jFC9a/we7du1Vm3VBulRO4Lfaw9nvq1CmVWWVZVuEVvMMq56joZU9uWcewVZ5mlTYAReG2iK+ki51KmjV3LFahCMpfQkKCyqxz9cGDB1V2+eWXq8x63bQKNa2iqJMnT6rMKg6zyi6ta526deuqbO3atSqzrgv37NmjMut8ZxVbUgBVeX333Xcqs17DO3XqpLLmzZurrHv37ir7/PPPXY3lpZdeUplVHvXWW2+pzFo/XaxYmQAAAAAAPIfFLAAAAADAc1jMAgAAAAA8h8UsAAAAAMBzyqQAyiocKo6VK1eqrF27dq4e27RpU5VZhTCHDh1SmVVEkJ6errLc3FyV7dq1y9X4gNJkFUBdLNy+zoSHh5fySHAxSk5OVtk111yjMuv1v7wKoKw5kZ+fr7KwsDCVWaVQFb3IqrKyXtOsAker6MgqxbSKzawyJavE6fDhwyqzypSsY+nEiRMq+81vfqOy999/39V+g4KCVGb9G1hfL3A+q1evdpVNmzatyPt46KGHivzYyoR3ZgEAAAAAnsNiFgAAAADgOSxmAQAAAACew2IWAAAAAOA5le6u961bt7rKLN99911JDwcoNQUFBa6282L5RVZWlsqsIpNnnnlGZU8++WQpjAgXO6tMxi2riKkilUJZZU+bN29W2fbt20tlTHDvjTfeUNmOHTtUtnPnTpV17dpVZRERESr78ssvVXbs2DGVJSYmqswqyrRKO3v37q2yOnXqqOyRRx5R2d13360yq7TTKjy0irFq166tMko7Ae/gnVkAAAAAgOewmAUAAAAAeA6LWQAAAACA57CYBQAAAAB4jveaXwAUWZUqF8fPr9x+HV4st0LFVL16dVfb5efnq8w6XsuiAMrah9sCqIYNG6rsm2++KZmBocjuvPPOIj+2Q4cOKrOOzbCwMJXl5uaq7PPPP1fZsGHDXO1j6dKlKnv00UdV1qhRI5VZxU4nTpxQmcUa8+rVq109FkDFdHFc2QIAAAAAKhUWswAAAAAAz2ExCwAAAADwHBazAAAAAADPoR0FuEjl5eWpLCQkpMjPZ5VulFehlNvyHAqgUFLq1KnjaruqVauqzCpdsrLilEJZ+y0oKHC1D6u06uDBg0UeCyqmBx98UGX16tVT2Y8//qiyxMREle3evVtlGzZsKNrgRKRv374qs47XmjVrquz48eMq2759u8o+/vjjIo4OQEXFO7MAAAAAAM9hMQsAAAAA8BwWswAAAAAAz2ExCwAAAADwHNpRALhSXmVPFqvspjjbAeeTk5OjssDAQFfbuZ07xSmFOnXqlKv9WqVQ4eHhKvvpp59c7RfeZpU9Wayyp5I2cuRIV9stWLCglEcCwEsqztUpAAAAAAAusZgFAAAAAHgOi1kAAAAAgOewmAUAAAAAeE6AYzVOAAAAAABQgfHOLAAAAADAc1jMAgAAAAA8h8UsAAAAAMBzWMwCAAAAADyHxSwAAAAAwHNYzAIAAAAAPIfFLAAAAADAc1jMAgAAAAA8h8UsAAAAAMBz/j+sacZ0iUx0nwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_transformed_images(dataset, num_images=5):\n",
    "    figure = plt.figure(figsize=(2*num_images, 4))\n",
    "    \n",
    "    # Original images on top row\n",
    "    for i in range(num_images):\n",
    "        # Get an image\n",
    "        img, label = dataset[i]\n",
    "        \n",
    "        # Original\n",
    "        ax = figure.add_subplot(2, num_images, i + 1)\n",
    "        ax.set_title(f'Original\\n{labels_map[label]}')\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "        \n",
    "        # Get another transform of the same image\n",
    "        img, label = dataset[i]  \n",
    "        \n",
    "        # Transformed\n",
    "        ax = figure.add_subplot(2, num_images, i + 1 + num_images)\n",
    "        ax.set_title(f'Transformed\\n{labels_map[label]}')\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Visualize the transformations\n",
    "show_transformed_images(training_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce690ee9",
   "metadata": {},
   "source": [
    "### Visualizing Dataset Characteristics\n",
    "\n",
    "1. **Class Distribution Visualization**\n",
    "   - **Purpose**: Shows the number of samples for each class in the dataset.\n",
    "   - **Method**: Uses `sns.countplot` to display a count of each class label.\n",
    "   - **Output**: Displays the distribution for both training and test datasets, with class labels properly mapped and rotated for readability.\n",
    "\n",
    "2. **Sample Image Grid**\n",
    "   - **Purpose**: Provides a visual overview of the dataset by displaying a grid of sample images.\n",
    "   - **Method**: Creates a grid of a specified number of images with their corresponding class labels.\n",
    "   - **Output**: Helps verify the dataset's image quality and the class labels visually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d2817cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T15:26:15.030576Z",
     "iopub.status.busy": "2024-12-01T15:26:15.029853Z",
     "iopub.status.idle": "2024-12-01T15:26:59.049048Z",
     "shell.execute_reply": "2024-12-01T15:26:59.048166Z"
    },
    "papermill": {
     "duration": 44.027477,
     "end_time": "2024-12-01T15:26:59.051539",
     "exception": false,
     "start_time": "2024-12-01T15:26:15.024062",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Distribution:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Data Distribution:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_dataset_distribution(dataset):\n",
    "    # Get all labels\n",
    "    labels = [label for _, label in dataset]\n",
    "    \n",
    "    # Create distribution plot\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    sns.countplot(x=labels)\n",
    "    plt.title('Distribution of Classes in Dataset')\n",
    "    plt.xlabel('Class')\n",
    "    plt.ylabel('Count')\n",
    "    plt.xticks(range(10), [labels_map[i] for i in range(10)], rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Visualize distributions separately\n",
    "print(\"Training Data Distribution:\")\n",
    "show_dataset_distribution(training_data)\n",
    "\n",
    "print(\"\\nTest Data Distribution:\")\n",
    "show_dataset_distribution(test_data)\n",
    "\n",
    "def show_sample_grid(dataset, num_images=25):\n",
    "    # Create a grid of sample images\n",
    "    rows = int(np.sqrt(num_images))\n",
    "    cols = int(np.ceil(num_images / rows))\n",
    "    \n",
    "    figure = plt.figure(figsize=(2*cols, 2*rows))\n",
    "    for i in range(num_images):\n",
    "        img, label = dataset[i]\n",
    "        ax = figure.add_subplot(rows, cols, i + 1)\n",
    "        ax.set_title(f'{labels_map[label]}')\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "show_sample_grid(training_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357ad1d6",
   "metadata": {},
   "source": [
    "### Splitting Dataset and Creating Data Loaders\n",
    "\n",
    "1. **Batch Size and Validation Split**  \n",
    "   - Defined a batch size of 64 for data loaders.  \n",
    "   - Used 20% of the dataset for validation (`validation_split = 0.2`).\n",
    "\n",
    "2. **Calculating Dataset Sizes**  \n",
    "   - Computed the size of the training and validation datasets based on the total dataset length.\n",
    "\n",
    "3. **Dataset Splitting**  \n",
    "   - Divided the training dataset into two parts: training and validation sets using `random_split`.\n",
    "\n",
    "4. **Data Loaders Creation**  \n",
    "   - Created three data loaders for:\n",
    "     - **Training Data**: Shuffled with pin memory and 2 workers.  \n",
    "     - **Validation Data**: Non-shuffled with similar settings.  \n",
    "     - **Test Data**: For evaluation purposes with similar settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b61e1aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T15:26:59.068382Z",
     "iopub.status.busy": "2024-12-01T15:26:59.068127Z",
     "iopub.status.idle": "2024-12-01T15:26:59.082310Z",
     "shell.execute_reply": "2024-12-01T15:26:59.081255Z"
    },
    "papermill": {
     "duration": 0.024458,
     "end_time": "2024-12-01T15:26:59.084070",
     "exception": false,
     "start_time": "2024-12-01T15:26:59.059612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 48000\n",
      "Validation set size: 12000\n",
      "Test set size: 10000\n",
      "\n",
      "Batch size: 64\n",
      "Training batches: 750\n",
      "Validation batches: 188\n",
      "Test batches: 157\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "validation_split = 0.2 \n",
    "\n",
    "dataset_size = len(training_data)\n",
    "val_size = int(validation_split * dataset_size)\n",
    "train_size = dataset_size - val_size\n",
    "\n",
    "train_dataset, val_dataset = random_split(\n",
    "    training_data, \n",
    "    [train_size, val_size]\n",
    ")\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    train_dataset, \n",
    "    batch_size=batch_size,\n",
    "    shuffle=True,\n",
    "    num_workers=2,\n",
    "    pin_memory=True\n",
    ")\n",
    "\n",
    "val_loader = DataLoader(\n",
    "    val_dataset, \n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    num_workers=2,\n",
    "    pin_memory=True\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    test_data, \n",
    "    batch_size=batch_size,\n",
    "    shuffle=False,\n",
    "    num_workers=2,\n",
    "    pin_memory=True\n",
    ")\n",
    "\n",
    "print(f\"Training set size: {len(train_dataset)}\")\n",
    "print(f\"Validation set size: {len(val_dataset)}\")\n",
    "print(f\"Test set size: {len(test_data)}\")\n",
    "print(f\"\\nBatch size: {batch_size}\")\n",
    "print(f\"Training batches: {len(train_loader)}\")\n",
    "print(f\"Validation batches: {len(val_loader)}\")\n",
    "print(f\"Test batches: {len(test_loader)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ecd171",
   "metadata": {},
   "source": [
    "### FashionCNN Architecture Summary\n",
    "\n",
    "1. **Convolutional Blocks**  \n",
    "   - **Three blocks** with convolution, batch normalization, ReLU activation, max pooling, and dropout layers.  \n",
    "   - Reduces spatial dimensions and extracts hierarchical features.\n",
    "\n",
    "2. **Fully Connected Layers**  \n",
    "   - Flattens the output from the convolutional blocks.  \n",
    "   - Includes two dense layers with ReLU activation and dropout for regularization.  \n",
    "   - Outputs predictions for 10 classes.\n",
    "\n",
    "3. **Model Initialization**  \n",
    "   - Configures the model to run on GPU or CPU.  \n",
    "   - Uses cross-entropy loss for classification.  \n",
    "   - Optimizes with Adam optimizer and a learning rate of 0.001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15327c27",
   "metadata": {
    "execution": {
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   "source": [
    "class FashionCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(FashionCNN, self).__init__()\n",
    "        \n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(32),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.Dropout2d(0.25)\n",
    "        )\n",
    "        \n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.Dropout2d(0.25)\n",
    "        )\n",
    "        \n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2),\n",
    "            nn.Dropout2d(0.25)\n",
    "        )\n",
    "        \n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(128 * 3 * 3, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(512, 10), \n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)  # 28x28 -> 14x14\n",
    "        x = self.conv2(x)  # 14x14 -> 7x7\n",
    "        x = self.conv3(x)  # 7x7 -> 3x3\n",
    "        \n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = FashionCNN().to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6770becb",
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   "source": [
    "### Objective Function for Hyperparameter Optimization\n",
    "\n",
    "1. **Model Creation**  \n",
    "   - Instantiates a new `FashionCNN` model and moves it to the specified device.\n",
    "\n",
    "2. **Data Loaders**  \n",
    "   - Creates training and validation data loaders with batch size specified in `params`.\n",
    "\n",
    "3. **Training Setup**  \n",
    "   - Uses cross-entropy loss as the criterion and Adam optimizer with a learning rate from `params`.\n",
    "\n",
    "4. **Model Training**  \n",
    "   - Trains the model for the number of epochs specified in `params` using `train_model`.\n",
    "\n",
    "5. **Output**  \n",
    "   - Returns the best validation accuracy achieved during training."
   ]
  },
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   "source": [
    "def objective(params):\n",
    "    \"\"\"\n",
    "    Objective function for hyperparameter optimization\n",
    "    \"\"\"\n",
    "    model = FashionCNN().to(device)\n",
    "    \n",
    "    train_loader_hp = DataLoader(\n",
    "        train_dataset, \n",
    "        batch_size=int(params['batch_size']),\n",
    "        shuffle=True,\n",
    "        num_workers=2,\n",
    "        pin_memory=True\n",
    "    )\n",
    "    \n",
    "    val_loader_hp = DataLoader(\n",
    "        val_dataset, \n",
    "        batch_size=int(params['batch_size']),\n",
    "        shuffle=False,\n",
    "        num_workers=2,\n",
    "        pin_memory=True\n",
    "    )\n",
    "    \n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=params['learning_rate'])\n",
    "    \n",
    "    history = train_model(\n",
    "        model, \n",
    "        train_loader_hp, \n",
    "        val_loader_hp, \n",
    "        criterion, \n",
    "        optimizer, \n",
    "        num_epochs=int(params['num_epochs'])\n",
    "    )\n",
    "    \n",
    "    return max(history['val_acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62ce9e7f",
   "metadata": {},
   "source": [
    "## Training Functions\n",
    "\n",
    "Helper functions for:\n",
    "- Training loop\n",
    "- Validation loop\n",
    "- Computing accuracy\n",
    "- Saving model checkpoints"
   ]
  },
  {
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     "text": [
      "Epoch 1/50\n",
      "Train Loss: 0.9915, Train Acc: 62.88%\n",
      "Val Loss: 0.6576, Val Acc: 74.78%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 2/50\n",
      "Train Loss: 0.7330, Train Acc: 72.32%\n",
      "Val Loss: 0.5823, Val Acc: 77.15%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 3/50\n",
      "Train Loss: 0.6624, Train Acc: 74.89%\n",
      "Val Loss: 0.5311, Val Acc: 79.40%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 4/50\n",
      "Train Loss: 0.6193, Train Acc: 76.74%\n",
      "Val Loss: 0.5043, Val Acc: 80.61%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 5/50\n",
      "Train Loss: 0.5869, Train Acc: 78.06%\n",
      "Val Loss: 0.4792, Val Acc: 81.52%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 6/50\n",
      "Train Loss: 0.5636, Train Acc: 79.28%\n",
      "Val Loss: 0.4570, Val Acc: 82.32%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 7/50\n",
      "Train Loss: 0.5427, Train Acc: 79.87%\n",
      "Val Loss: 0.4395, Val Acc: 83.26%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 8/50\n",
      "Train Loss: 0.5292, Train Acc: 80.36%\n",
      "Val Loss: 0.4297, Val Acc: 83.63%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 9/50\n",
      "Train Loss: 0.5170, Train Acc: 80.68%\n",
      "Val Loss: 0.4176, Val Acc: 84.16%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 10/50\n",
      "Train Loss: 0.5059, Train Acc: 81.26%\n",
      "Val Loss: 0.4068, Val Acc: 84.52%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 11/50\n",
      "Train Loss: 0.4945, Train Acc: 81.78%\n",
      "Val Loss: 0.4049, Val Acc: 84.22%\n",
      "--------------------------------------------------\n",
      "Epoch 12/50\n",
      "Train Loss: 0.4832, Train Acc: 82.14%\n",
      "Val Loss: 0.4010, Val Acc: 84.70%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 13/50\n",
      "Train Loss: 0.4767, Train Acc: 82.56%\n",
      "Val Loss: 0.3857, Val Acc: 85.48%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 14/50\n",
      "Train Loss: 0.4704, Train Acc: 82.58%\n",
      "Val Loss: 0.3932, Val Acc: 84.97%\n",
      "--------------------------------------------------\n",
      "Epoch 15/50\n",
      "Train Loss: 0.4674, Train Acc: 82.80%\n",
      "Val Loss: 0.3787, Val Acc: 85.63%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 16/50\n",
      "Train Loss: 0.4618, Train Acc: 83.03%\n",
      "Val Loss: 0.3854, Val Acc: 85.57%\n",
      "--------------------------------------------------\n",
      "Epoch 17/50\n",
      "Train Loss: 0.4541, Train Acc: 83.42%\n",
      "Val Loss: 0.3847, Val Acc: 85.25%\n",
      "--------------------------------------------------\n",
      "Epoch 18/50\n",
      "Train Loss: 0.4504, Train Acc: 83.35%\n",
      "Val Loss: 0.3631, Val Acc: 86.04%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 19/50\n",
      "Train Loss: 0.4498, Train Acc: 83.40%\n",
      "Val Loss: 0.3542, Val Acc: 86.67%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 20/50\n",
      "Train Loss: 0.4434, Train Acc: 83.75%\n",
      "Val Loss: 0.3610, Val Acc: 86.38%\n",
      "--------------------------------------------------\n",
      "Epoch 21/50\n",
      "Train Loss: 0.4376, Train Acc: 83.91%\n",
      "Val Loss: 0.3548, Val Acc: 86.57%\n",
      "--------------------------------------------------\n",
      "Epoch 22/50\n",
      "Train Loss: 0.4429, Train Acc: 83.63%\n",
      "Val Loss: 0.3537, Val Acc: 86.73%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 23/50\n",
      "Train Loss: 0.4373, Train Acc: 83.81%\n",
      "Val Loss: 0.3491, Val Acc: 86.99%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 24/50\n",
      "Train Loss: 0.4347, Train Acc: 84.13%\n",
      "Val Loss: 0.3514, Val Acc: 86.20%\n",
      "--------------------------------------------------\n",
      "Epoch 25/50\n",
      "Train Loss: 0.4302, Train Acc: 84.14%\n",
      "Val Loss: 0.3473, Val Acc: 86.88%\n",
      "--------------------------------------------------\n",
      "Epoch 26/50\n",
      "Train Loss: 0.4258, Train Acc: 84.36%\n",
      "Val Loss: 0.3518, Val Acc: 86.73%\n",
      "--------------------------------------------------\n",
      "Epoch 27/50\n",
      "Train Loss: 0.4242, Train Acc: 84.49%\n",
      "Val Loss: 0.3455, Val Acc: 86.95%\n",
      "--------------------------------------------------\n",
      "Epoch 28/50\n",
      "Train Loss: 0.4212, Train Acc: 84.62%\n",
      "Val Loss: 0.3426, Val Acc: 86.78%\n",
      "--------------------------------------------------\n",
      "Epoch 29/50\n",
      "Train Loss: 0.4206, Train Acc: 84.64%\n",
      "Val Loss: 0.3365, Val Acc: 87.39%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 30/50\n",
      "Train Loss: 0.4178, Train Acc: 84.83%\n",
      "Val Loss: 0.3412, Val Acc: 86.93%\n",
      "--------------------------------------------------\n",
      "Epoch 31/50\n",
      "Train Loss: 0.4168, Train Acc: 84.72%\n",
      "Val Loss: 0.3409, Val Acc: 87.27%\n",
      "--------------------------------------------------\n",
      "Epoch 32/50\n",
      "Train Loss: 0.4138, Train Acc: 84.79%\n",
      "Val Loss: 0.3349, Val Acc: 87.01%\n",
      "--------------------------------------------------\n",
      "Epoch 33/50\n",
      "Train Loss: 0.4122, Train Acc: 84.97%\n",
      "Val Loss: 0.3314, Val Acc: 87.47%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 34/50\n",
      "Train Loss: 0.4109, Train Acc: 84.75%\n",
      "Val Loss: 0.3481, Val Acc: 86.78%\n",
      "--------------------------------------------------\n",
      "Epoch 35/50\n",
      "Train Loss: 0.4096, Train Acc: 85.01%\n",
      "Val Loss: 0.3284, Val Acc: 87.69%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 36/50\n",
      "Train Loss: 0.4106, Train Acc: 84.92%\n",
      "Val Loss: 0.3274, Val Acc: 87.19%\n",
      "--------------------------------------------------\n",
      "Epoch 37/50\n",
      "Train Loss: 0.4023, Train Acc: 85.15%\n",
      "Val Loss: 0.3316, Val Acc: 87.66%\n",
      "--------------------------------------------------\n",
      "Epoch 38/50\n",
      "Train Loss: 0.4035, Train Acc: 85.11%\n",
      "Val Loss: 0.3255, Val Acc: 87.77%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 39/50\n",
      "Train Loss: 0.3992, Train Acc: 85.23%\n",
      "Val Loss: 0.3226, Val Acc: 87.98%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 40/50\n",
      "Train Loss: 0.4031, Train Acc: 85.21%\n",
      "Val Loss: 0.3302, Val Acc: 87.47%\n",
      "--------------------------------------------------\n",
      "Epoch 41/50\n",
      "Train Loss: 0.3999, Train Acc: 85.29%\n",
      "Val Loss: 0.3243, Val Acc: 87.92%\n",
      "--------------------------------------------------\n",
      "Epoch 42/50\n",
      "Train Loss: 0.3989, Train Acc: 85.20%\n",
      "Val Loss: 0.3205, Val Acc: 88.12%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 43/50\n",
      "Train Loss: 0.3922, Train Acc: 85.38%\n",
      "Val Loss: 0.3190, Val Acc: 88.22%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 44/50\n",
      "Train Loss: 0.3988, Train Acc: 85.16%\n",
      "Val Loss: 0.3249, Val Acc: 88.02%\n",
      "--------------------------------------------------\n",
      "Epoch 45/50\n",
      "Train Loss: 0.3969, Train Acc: 85.24%\n",
      "Val Loss: 0.3219, Val Acc: 88.07%\n",
      "--------------------------------------------------\n",
      "Epoch 46/50\n",
      "Train Loss: 0.3945, Train Acc: 85.41%\n",
      "Val Loss: 0.3156, Val Acc: 88.40%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 47/50\n",
      "Train Loss: 0.3907, Train Acc: 85.62%\n",
      "Val Loss: 0.3168, Val Acc: 88.16%\n",
      "--------------------------------------------------\n",
      "Epoch 48/50\n",
      "Train Loss: 0.3920, Train Acc: 85.67%\n",
      "Val Loss: 0.3232, Val Acc: 87.88%\n",
      "--------------------------------------------------\n",
      "Epoch 49/50\n",
      "Train Loss: 0.3866, Train Acc: 85.74%\n",
      "Val Loss: 0.3175, Val Acc: 88.22%\n",
      "--------------------------------------------------\n",
      "Epoch 50/50\n",
      "Train Loss: 0.3901, Train Acc: 85.53%\n",
      "Val Loss: 0.3126, Val Acc: 88.55%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=50, save_path='best_fashion_cnn.pth'):\n",
    "    best_val_acc = 0.0\n",
    "    history = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}\n",
    "\n",
    "    save_path = os.path.join('/kaggle/working', save_path)  \n",
    "\n",
    "    save_dir = os.path.dirname(save_path)\n",
    "    if save_dir and not os.path.exists(save_dir):\n",
    "        os.makedirs(save_dir)\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
    "        \n",
    "        model.train()\n",
    "        train_loss = 0.0\n",
    "        train_correct = 0\n",
    "        train_total = 0\n",
    "\n",
    "        train_loader_tqdm = tqdm(train_loader, desc=\"Training\", leave=False, file=sys.stdout)\n",
    "        for images, labels in train_loader_tqdm:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            train_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            train_total += labels.size(0)\n",
    "            train_correct += (predicted == labels).sum().item()\n",
    "            \n",
    "            train_loader_tqdm.set_postfix(loss=loss.item())\n",
    "\n",
    "        epoch_train_loss = train_loss / len(train_loader)\n",
    "        epoch_train_acc = 100 * train_correct / train_total\n",
    "        \n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        val_correct = 0\n",
    "        val_total = 0\n",
    "\n",
    "        with torch.no_grad():\n",
    "            val_loader_tqdm = tqdm(val_loader, desc=\"Validating\", leave=False, file=sys.stdout)\n",
    "            for images, labels in val_loader_tqdm:\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                \n",
    "                val_loss += loss.item()\n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                val_total += labels.size(0)\n",
    "                val_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        epoch_val_loss = val_loss / len(val_loader)\n",
    "        epoch_val_acc = 100 * val_correct / val_total\n",
    "\n",
    "        history['train_loss'].append(epoch_train_loss)\n",
    "        history['train_acc'].append(epoch_train_acc)\n",
    "        history['val_loss'].append(epoch_val_loss)\n",
    "        history['val_acc'].append(epoch_val_acc)\n",
    "\n",
    "        print(f\"Train Loss: {epoch_train_loss:.4f}, Train Acc: {epoch_train_acc:.2f}%\")\n",
    "        print(f\"Val Loss: {epoch_val_loss:.4f}, Val Acc: {epoch_val_acc:.2f}%\")\n",
    "        print('-' * 50)\n",
    "\n",
    "        if epoch_val_acc > best_val_acc:\n",
    "            best_val_acc = epoch_val_acc\n",
    "            torch.save(model.state_dict(), save_path)\n",
    "            print(f\"Best model saved to {save_path}\")\n",
    "       \n",
    "    return history\n",
    "\n",
    "history = train_model(model, train_loader, val_loader, criterion, optimizer, save_path='best_fashion_cnn.pth')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c92805f",
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   "source": [
    "### Model Training and Validation Metrics Analysis\n",
    "\n",
    "1. **Metrics Visualization**  \n",
    "   - Visualizes trends in training and validation loss.  \n",
    "   - Displays the progress of training and validation accuracy over epochs.\n",
    "\n",
    "2. **Performance Evaluation**  \n",
    "   - Evaluates model generalization based on validation accuracy trends."
   ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Training and Validation Metrics:\n",
      "Best Validation Accuracy: 88.55%\n",
      "Training Accuracy: 85.53%\n",
      "Validation Accuracy: 88.55%\n",
      "The model's performance on the validation set is suboptimal, suggesting potential overfitting or underfitting.\n",
      "The gap between training and validation accuracy is reasonable, indicating good generalization.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_metrics(history):\n",
    "    epochs = range(1, len(history['train_loss']) + 1)\n",
    "\n",
    "    plt.figure(figsize=(14, 5))\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(epochs, history['train_loss'], label='Train Loss')\n",
    "    plt.plot(epochs, history['val_loss'], label='Val Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.title('Training and Validation Loss')\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(epochs, history['train_acc'], label='Train Accuracy')\n",
    "    plt.plot(epochs, history['val_acc'], label='Val Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    plt.title('Training and Validation Accuracy')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "print(\"Model Training and Validation Metrics:\")\n",
    "print(f\"Best Validation Accuracy: {max(history['val_acc']):.2f}%\")\n",
    "print(f\"Training Accuracy: {history['train_acc'][-1]:.2f}%\")\n",
    "print(f\"Validation Accuracy: {history['val_acc'][-1]:.2f}%\")\n",
    "\n",
    "if max(history['val_acc']) > 90:\n",
    "    print(\"The model performs well on the validation set, indicating good generalization.\")\n",
    "else:\n",
    "    print(\"The model's performance on the validation set is suboptimal, suggesting potential overfitting or underfitting.\")\n",
    "\n",
    "if history['train_acc'][-1] - history['val_acc'][-1] > 10:\n",
    "    print(\"There is a significant gap between training and validation accuracy, indicating potential overfitting.\")\n",
    "else:\n",
    "    print(\"The gap between training and validation accuracy is reasonable, indicating good generalization.\")\n",
    "\n",
    "plot_metrics(history)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3c5889",
   "metadata": {},
   "source": [
    "### Model Evaluation Process\n",
    "\n",
    "1. **Testing the Model**  \n",
    "   - Switches the model to evaluation mode.  \n",
    "   - Iterates through the test data, making predictions and tracking accuracy.  \n",
    "\n",
    "2. **Performance Metrics**  \n",
    "   - Calculates overall test accuracy.  \n",
    "   - Generates a detailed classification report with precision, recall, and F1-score for each class.\n",
    "\n",
    "3. **Confusion Matrix**  \n",
    "   - Plots a heatmap to visualize the confusion matrix, highlighting prediction errors and class-wise performance.\n",
    "\n",
    "4. **Output Summary**  \n",
    "   - Summarizes the test accuracy and provides insights into the model's strengths and weaknesses across classes.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8afc4d40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T15:48:14.974432Z",
     "iopub.status.busy": "2024-12-01T15:48:14.973442Z",
     "iopub.status.idle": "2024-12-01T15:48:17.075295Z",
     "shell.execute_reply": "2024-12-01T15:48:17.074323Z"
    },
    "papermill": {
     "duration": 4.353322,
     "end_time": "2024-12-01T15:48:17.077069",
     "exception": false,
     "start_time": "2024-12-01T15:48:12.723747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing: 100%|██████████| 157/157 [00:01<00:00, 106.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Accuracy: 90.20%\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     T-Shirt       0.84      0.86      0.85      1000\n",
      "     Trouser       0.99      0.99      0.99      1000\n",
      "    Pullover       0.89      0.81      0.85      1000\n",
      "       Dress       0.91      0.91      0.91      1000\n",
      "        Coat       0.79      0.89      0.84      1000\n",
      "      Sandal       0.99      0.96      0.98      1000\n",
      "       Shirt       0.73      0.68      0.70      1000\n",
      "     Sneaker       0.93      0.98      0.95      1000\n",
      "         Bag       0.99      0.98      0.98      1000\n",
      "  Ankle Boot       0.98      0.95      0.96      1000\n",
      "\n",
      "    accuracy                           0.90     10000\n",
      "   macro avg       0.90      0.90      0.90     10000\n",
      "weighted avg       0.90      0.90      0.90     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def evaluate_model(model, test_loader):\n",
    "    model.eval()\n",
    "    test_correct = 0\n",
    "    test_total = 0\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for images, labels in tqdm(test_loader, desc=\"Testing\"):\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            \n",
    "            test_total += labels.size(0)\n",
    "            test_correct += (predicted == labels).sum().item()\n",
    "            \n",
    "            all_preds.extend(predicted.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "    \n",
    "    test_acc = 100 * test_correct / test_total\n",
    "    print(f'\\nTest Accuracy: {test_acc:.2f}%')\n",
    "    \n",
    "    # Print classification report\n",
    "    print('\\nClassification Report:')\n",
    "    print(classification_report(all_labels, all_preds, \n",
    "                              target_names=[labels_map[i] for i in range(10)]))\n",
    "    \n",
    "    # Plot confusion matrix\n",
    "    cm = confusion_matrix(all_labels, all_preds)\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "                xticklabels=[labels_map[i] for i in range(10)],\n",
    "                yticklabels=[labels_map[i] for i in range(10)])\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.yticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return test_acc\n",
    "\n",
    "test_accuracy = evaluate_model(model, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f272cc1a",
   "metadata": {},
   "source": [
    "### Image Classification Workflow\n",
    "\n",
    "1. **Label Mapping**  \n",
    "   - Maps numeric class indices to descriptive labels (e.g., `0: \"T-shirt/top\"`).\n",
    "\n",
    "2. **Image Preprocessing**  \n",
    "   - Ensures the input image exists and reads it in RGB mode.  \n",
    "   - Applies transformations:\n",
    "     - Resizes the image to 28x28 pixels.  \n",
    "     - Converts it to grayscale.  \n",
    "     - Normalizes using dataset-specific mean and standard deviation.  \n",
    "     - Converts the image into a tensor and adds a batch dimension.\n",
    "\n",
    "3. **Model Loading**  \n",
    "   - Ensures the model file exists.  \n",
    "   - Loads the pre-trained model's weights.  \n",
    "   - Sets the model to evaluation mode.\n",
    "\n",
    "4. **Prediction**  \n",
    "   - Runs the model inference with the preprocessed image.  \n",
    "   - Maps the predicted class index to the corresponding label using `labels_map`.\n",
    "\n",
    "5. **Error Handling**  \n",
    "   - Captures and reports errors if the image or model paths are incorrect, or during inference.\n",
    "\n",
    "6. **Output**  \n",
    "   - Returns the predicted label for the image.  \n",
    "   - Prints a failure message if classification fails."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "821e856a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T15:48:21.672588Z",
     "iopub.status.busy": "2024-12-01T15:48:21.672236Z",
     "iopub.status.idle": "2024-12-01T15:48:21.756432Z",
     "shell.execute_reply": "2024-12-01T15:48:21.755542Z"
    },
    "papermill": {
     "duration": 2.345992,
     "end_time": "2024-12-01T15:48:21.758285",
     "exception": false,
     "start_time": "2024-12-01T15:48:19.412293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image is classified as: T-shirt/top\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23/967234830.py:23: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(model_path))\n"
     ]
    }
   ],
   "source": [
    "labels_map = {\n",
    "    0: \"T-shirt/top\", 1: \"Trouser\", 2: \"Pullover\", 3: \"Dress\", 4: \"Coat\",\n",
    "    5: \"Sandal\", 6: \"Shirt\", 7: \"Sneaker\", 8: \"Bag\", 9: \"Ankle boot\"\n",
    "}\n",
    "\n",
    "def classify_image(model_path, image_path):\n",
    "    try:\n",
    "        if not os.path.exists(image_path):\n",
    "            raise FileNotFoundError(f\"Image not found at {image_path}\")\n",
    "        if not os.path.exists(model_path):\n",
    "            raise FileNotFoundError(f\"Model not found at {model_path}\")\n",
    "\n",
    "        image = Image.open(image_path).convert('RGB')\n",
    "        transform = transforms.Compose([\n",
    "            transforms.Resize((28, 28)),\n",
    "            transforms.Grayscale(num_output_channels=1),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize((0.2860,), (0.3530,))\n",
    "        ])\n",
    "        image_tensor = transform(image).unsqueeze(0).to(device)\n",
    "\n",
    "        model = FashionCNN().to(device)\n",
    "        model.load_state_dict(torch.load(model_path))\n",
    "        model.eval()\n",
    "\n",
    "        with torch.no_grad():\n",
    "            outputs = model(image_tensor)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "\n",
    "        return labels_map[predicted.item()]\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error during inference: {str(e)}\")\n",
    "        raise\n",
    "\n",
    "model_path = '/kaggle/working/best_fashion_cnn.pth'\n",
    "image_path = '/kaggle/input/your-image-png/images Background Removed.png'  # Specify the path to your image\n",
    "\n",
    "try:\n",
    "    classification = classify_image(model_path, image_path)\n",
    "    print(f\"The image is classified as: {classification}\")\n",
    "except Exception as e:\n",
    "    print(f\"Failed to classify the image: {str(e)}\")\n"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "nvidiaTeslaT4",
   "dataSources": [
    {
     "datasetId": 6204471,
     "sourceId": 10067075,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30787,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "duration": 1350.358154,
   "end_time": "2024-12-01T15:48:25.532009",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-12-01T15:25:55.173855",
   "version": "2.6.0"
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