Fashion MNIST without Data Augmentation.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "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.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Labels\n",
    "\n",
    "Define mapping between numeric labels and fashion item categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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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",
   "metadata": {},
   "source": [
    "## Data Loading and Preprocessing\n",
    "\n",
    "Load the Fashion MNIST dataset and apply basic transformations:\n",
    "- Convert to tensor\n",
    "- Normalize using dataset mean and std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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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 [00:02<00:00, 9791616.40it/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, 210152.02it/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, 3918312.15it/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, 9549879.25it/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"
     ]
    }
   ],
   "source": [
    "# Training transformations - includes augmentation\n",
    "transform_train = transforms.Compose([\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",
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Displaying transformed Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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    "execution": {
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   "outputs": [
    {
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",
      "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",
    "    for i in range(num_images):\n",
    "        img, label = dataset[i]\n",
    "        \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",
    "        img, label = dataset[i] \n",
    "        \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",
    "show_transformed_images(training_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "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,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T15:55:34.216506Z",
     "iopub.status.busy": "2024-12-01T15:55:34.216127Z",
     "iopub.status.idle": "2024-12-01T15:55:47.421595Z",
     "shell.execute_reply": "2024-12-01T15:55:47.420643Z",
     "shell.execute_reply.started": "2024-12-01T15:55:34.216467Z"
    },
    "trusted": true
   },
   "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",
    "    labels = [label for _, label in dataset]\n",
    "    \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",
    "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",
    "    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",
   "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,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T15:55:47.423201Z",
     "iopub.status.busy": "2024-12-01T15:55:47.422886Z",
     "iopub.status.idle": "2024-12-01T15:55:47.442697Z",
     "shell.execute_reply": "2024-12-01T15:55:47.441720Z",
     "shell.execute_reply.started": "2024-12-01T15:55:47.423172Z"
    },
    "trusted": true
   },
   "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",
   "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."
   ]
  },
  {
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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) \n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x) \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",
   "metadata": {},
   "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",
   "metadata": {},
   "source": [
    "## Training Functions\n",
    "\n",
    "Helper functions for:\n",
    "- Training loop\n",
    "- Validation loop\n",
    "- Computing accuracy\n",
    "- Saving model checkpoints"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "Train Loss: 0.5585, Train Acc: 79.46%                                   \n",
      "Val Loss: 0.3472, Val Acc: 86.83%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 2/50\n",
      "Train Loss: 0.3775, Train Acc: 86.29%                                   \n",
      "Val Loss: 0.2965, Val Acc: 89.04%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 3/50\n",
      "Train Loss: 0.3302, Train Acc: 88.12%                                   \n",
      "Val Loss: 0.2941, Val Acc: 88.83%\n",
      "--------------------------------------------------\n",
      "Epoch 4/50\n",
      "Train Loss: 0.3054, Train Acc: 88.86%                                   \n",
      "Val Loss: 0.2671, Val Acc: 90.22%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 5/50\n",
      "Train Loss: 0.2818, Train Acc: 89.67%                                   \n",
      "Val Loss: 0.2400, Val Acc: 91.23%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 6/50\n",
      "Train Loss: 0.2715, Train Acc: 90.08%                                    \n",
      "Val Loss: 0.2371, Val Acc: 91.11%\n",
      "--------------------------------------------------\n",
      "Epoch 7/50\n",
      "Train Loss: 0.2563, Train Acc: 90.53%                                    \n",
      "Val Loss: 0.2239, Val Acc: 91.83%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 8/50\n",
      "Train Loss: 0.2502, Train Acc: 90.75%                                    \n",
      "Val Loss: 0.2210, Val Acc: 91.84%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 9/50\n",
      "Train Loss: 0.2415, Train Acc: 91.11%                                    \n",
      "Val Loss: 0.2254, Val Acc: 91.92%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 10/50\n",
      "Train Loss: 0.2302, Train Acc: 91.36%                                    \n",
      "Val Loss: 0.2179, Val Acc: 91.83%\n",
      "--------------------------------------------------\n",
      "Epoch 11/50\n",
      "Train Loss: 0.2238, Train Acc: 91.61%                                    \n",
      "Val Loss: 0.2171, Val Acc: 92.18%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 12/50\n",
      "Train Loss: 0.2194, Train Acc: 91.95%                                    \n",
      "Val Loss: 0.2213, Val Acc: 91.98%\n",
      "--------------------------------------------------\n",
      "Epoch 13/50\n",
      "Train Loss: 0.2160, Train Acc: 91.91%                                    \n",
      "Val Loss: 0.2139, Val Acc: 92.05%\n",
      "--------------------------------------------------\n",
      "Epoch 14/50\n",
      "Train Loss: 0.2100, Train Acc: 92.08%                                    \n",
      "Val Loss: 0.2163, Val Acc: 92.10%\n",
      "--------------------------------------------------\n",
      "Epoch 15/50\n",
      "Train Loss: 0.2044, Train Acc: 92.33%                                    \n",
      "Val Loss: 0.2027, Val Acc: 92.57%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 16/50\n",
      "Train Loss: 0.1982, Train Acc: 92.57%                                    \n",
      "Val Loss: 0.2068, Val Acc: 92.42%\n",
      "--------------------------------------------------\n",
      "Epoch 17/50\n",
      "Train Loss: 0.1957, Train Acc: 92.60%                                    \n",
      "Val Loss: 0.2075, Val Acc: 92.53%\n",
      "--------------------------------------------------\n",
      "Epoch 18/50\n",
      "Train Loss: 0.1948, Train Acc: 92.79%                                    \n",
      "Val Loss: 0.2128, Val Acc: 92.41%\n",
      "--------------------------------------------------\n",
      "Epoch 19/50\n",
      "Train Loss: 0.1877, Train Acc: 92.87%                                    \n",
      "Val Loss: 0.2041, Val Acc: 92.71%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 20/50\n",
      "Train Loss: 0.1832, Train Acc: 93.26%                                    \n",
      "Val Loss: 0.2053, Val Acc: 92.59%\n",
      "--------------------------------------------------\n",
      "Epoch 21/50\n",
      "Train Loss: 0.1823, Train Acc: 93.08%                                    \n",
      "Val Loss: 0.2081, Val Acc: 92.49%\n",
      "--------------------------------------------------\n",
      "Epoch 22/50\n",
      "Train Loss: 0.1816, Train Acc: 93.18%                                    \n",
      "Val Loss: 0.1991, Val Acc: 92.91%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 23/50\n",
      "Train Loss: 0.1761, Train Acc: 93.25%                                    \n",
      "Val Loss: 0.2094, Val Acc: 92.45%\n",
      "--------------------------------------------------\n",
      "Epoch 24/50\n",
      "Train Loss: 0.1707, Train Acc: 93.46%                                    \n",
      "Val Loss: 0.1962, Val Acc: 92.94%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 25/50\n",
      "Train Loss: 0.1701, Train Acc: 93.47%                                   \n",
      "Val Loss: 0.2005, Val Acc: 92.64%\n",
      "--------------------------------------------------\n",
      "Epoch 26/50\n",
      "Train Loss: 0.1686, Train Acc: 93.64%                                    \n",
      "Val Loss: 0.1971, Val Acc: 93.14%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 27/50\n",
      "Train Loss: 0.1637, Train Acc: 93.85%                                    \n",
      "Val Loss: 0.2027, Val Acc: 92.95%\n",
      "--------------------------------------------------\n",
      "Epoch 28/50\n",
      "Train Loss: 0.1635, Train Acc: 93.80%                                    \n",
      "Val Loss: 0.2041, Val Acc: 93.22%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 29/50\n",
      "Train Loss: 0.1592, Train Acc: 93.91%                                    \n",
      "Val Loss: 0.2041, Val Acc: 93.03%\n",
      "--------------------------------------------------\n",
      "Epoch 30/50\n",
      "Train Loss: 0.1585, Train Acc: 94.01%                                    \n",
      "Val Loss: 0.2053, Val Acc: 93.11%\n",
      "--------------------------------------------------\n",
      "Epoch 31/50\n",
      "Train Loss: 0.1562, Train Acc: 94.06%                                    \n",
      "Val Loss: 0.2054, Val Acc: 92.97%\n",
      "--------------------------------------------------\n",
      "Epoch 32/50\n",
      "Train Loss: 0.1508, Train Acc: 94.23%                                    \n",
      "Val Loss: 0.2145, Val Acc: 92.84%\n",
      "--------------------------------------------------\n",
      "Epoch 33/50\n",
      "Train Loss: 0.1530, Train Acc: 94.22%                                    \n",
      "Val Loss: 0.2063, Val Acc: 93.20%\n",
      "--------------------------------------------------\n",
      "Epoch 34/50\n",
      "Train Loss: 0.1492, Train Acc: 94.33%                                    \n",
      "Val Loss: 0.2096, Val Acc: 92.95%\n",
      "--------------------------------------------------\n",
      "Epoch 35/50\n",
      "Train Loss: 0.1540, Train Acc: 94.05%                                    \n",
      "Val Loss: 0.2072, Val Acc: 92.98%\n",
      "--------------------------------------------------\n",
      "Epoch 36/50\n",
      "Train Loss: 0.1474, Train Acc: 94.44%                                    \n",
      "Val Loss: 0.2109, Val Acc: 93.09%\n",
      "--------------------------------------------------\n",
      "Epoch 37/50\n",
      "Train Loss: 0.1434, Train Acc: 94.59%                                    \n",
      "Val Loss: 0.2072, Val Acc: 93.22%\n",
      "--------------------------------------------------\n",
      "Epoch 38/50\n",
      "Train Loss: 0.1435, Train Acc: 94.57%                                    \n",
      "Val Loss: 0.2111, Val Acc: 93.09%\n",
      "--------------------------------------------------\n",
      "Epoch 39/50\n",
      "Train Loss: 0.1410, Train Acc: 94.47%                                    \n",
      "Val Loss: 0.2163, Val Acc: 92.73%\n",
      "--------------------------------------------------\n",
      "Epoch 40/50\n",
      "Train Loss: 0.1434, Train Acc: 94.55%                                    \n",
      "Val Loss: 0.2057, Val Acc: 93.26%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 41/50\n",
      "Train Loss: 0.1418, Train Acc: 94.69%                                    \n",
      "Val Loss: 0.2163, Val Acc: 93.17%\n",
      "--------------------------------------------------\n",
      "Epoch 42/50\n",
      "Train Loss: 0.1369, Train Acc: 94.73%                                    \n",
      "Val Loss: 0.2103, Val Acc: 93.12%\n",
      "--------------------------------------------------\n",
      "Epoch 43/50\n",
      "Train Loss: 0.1368, Train Acc: 94.73%                                    \n",
      "Val Loss: 0.2102, Val Acc: 93.16%\n",
      "--------------------------------------------------\n",
      "Epoch 44/50\n",
      "Train Loss: 0.1325, Train Acc: 94.92%                                    \n",
      "Val Loss: 0.2114, Val Acc: 93.12%\n",
      "--------------------------------------------------\n",
      "Epoch 45/50\n",
      "Train Loss: 0.1291, Train Acc: 94.99%                                     \n",
      "Val Loss: 0.2162, Val Acc: 93.16%\n",
      "--------------------------------------------------\n",
      "Epoch 46/50\n",
      "Train Loss: 0.1320, Train Acc: 95.00%                                    \n",
      "Val Loss: 0.2079, Val Acc: 93.39%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 47/50\n",
      "Train Loss: 0.1299, Train Acc: 95.06%                                    \n",
      "Val Loss: 0.2163, Val Acc: 93.38%\n",
      "--------------------------------------------------\n",
      "Epoch 48/50\n",
      "Train Loss: 0.1307, Train Acc: 95.10%                                    \n",
      "Val Loss: 0.2082, Val Acc: 93.50%\n",
      "--------------------------------------------------\n",
      "Best model saved to /kaggle/working/best_fashion_cnn.pth\n",
      "Epoch 49/50\n",
      "Train Loss: 0.1281, Train Acc: 95.02%                                    \n",
      "Val Loss: 0.2147, Val Acc: 93.22%\n",
      "--------------------------------------------------\n",
      "Epoch 50/50\n",
      "Train Loss: 0.1259, Train Acc: 95.29%                                    \n",
      "Val Loss: 0.2161, Val Acc: 93.28%\n",
      "--------------------------------------------------\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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T16:03:47.719059Z",
     "iopub.status.busy": "2024-12-01T16:03:47.718746Z",
     "iopub.status.idle": "2024-12-01T16:03:48.371661Z",
     "shell.execute_reply": "2024-12-01T16:03:48.370821Z",
     "shell.execute_reply.started": "2024-12-01T16:03:47.719027Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Training and Validation Metrics:\n",
      "Best Validation Accuracy: 93.50%\n",
      "Training Accuracy: 95.29%\n",
      "Validation Accuracy: 93.28%\n",
      "The model performs well on the validation set, indicating good generalization.\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",
   "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,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T16:03:48.373629Z",
     "iopub.status.busy": "2024-12-01T16:03:48.373236Z",
     "iopub.status.idle": "2024-12-01T16:03:50.571330Z",
     "shell.execute_reply": "2024-12-01T16:03:50.570290Z",
     "shell.execute_reply.started": "2024-12-01T16:03:48.373571Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Testing: 100%|██████████| 157/157 [00:01<00:00, 99.97it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Accuracy: 92.55%\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     T-Shirt       0.87      0.88      0.87      1000\n",
      "     Trouser       1.00      0.99      0.99      1000\n",
      "    Pullover       0.89      0.89      0.89      1000\n",
      "       Dress       0.93      0.92      0.93      1000\n",
      "        Coat       0.85      0.91      0.88      1000\n",
      "      Sandal       0.99      0.98      0.99      1000\n",
      "       Shirt       0.79      0.77      0.78      1000\n",
      "     Sneaker       0.96      0.98      0.97      1000\n",
      "         Bag       0.99      0.98      0.99      1000\n",
      "  Ankle Boot       0.97      0.97      0.97      1000\n",
      "\n",
      "    accuracy                           0.93     10000\n",
      "   macro avg       0.93      0.93      0.93     10000\n",
      "weighted avg       0.93      0.93      0.93     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('\\nClassification Report:')\n",
    "    print(classification_report(all_labels, all_preds, \n",
    "                              target_names=[labels_map[i] for i in range(10)]))\n",
    "    \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",
   "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": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-12-01T16:06:40.116073Z",
     "iopub.status.busy": "2024-12-01T16:06:40.115746Z",
     "iopub.status.idle": "2024-12-01T16:06:40.164811Z",
     "shell.execute_reply": "2024-12-01T16:06:40.163951Z",
     "shell.execute_reply.started": "2024-12-01T16:06:40.116047Z"
    },
    "trusted": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image is classified as: T-shirt/top\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_30/2614254154.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-pg/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)}\")"
   ]
  }
 ],
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