Notebook - EMNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "90fb60be-042a-4526-975f-fcd5adc3c7a6",
   "metadata": {},
   "source": [
    "# CNN Model for Handwritten Digit Recognition - EMNIST Dataset\n",
    "\n",
    "The EMNIST dataset is a set of handwritten character digits derived from the NIST Special Database 19  and converted to a 28x28 pixel image format and dataset structure that directly matches the MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ab02fcc3-faa7-4075-8aab-d92af04aa893",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from collections import Counter\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb1e288c-2f65-4250-b671-a093c81fce69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CNN Model\n",
    "class CNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)\n",
    "        self.fc1 = nn.Linear(64 * 5 * 5, 128)\n",
    "        self.fc2 = nn.Linear(128, 36)  # 36 classes (0-9, A-Z)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = nn.ReLU()(self.conv1(x))\n",
    "        x = nn.MaxPool2d(2)(x)\n",
    "        x = nn.ReLU()(self.conv2(x))\n",
    "        x = nn.MaxPool2d(2)(x)\n",
    "        x = x.view(-1, 64 * 5 * 5)\n",
    "        x = nn.ReLU()(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43908c10-b903-43bd-98d9-d6662329da42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://biometrics.nist.gov/cs_links/EMNIST/gzip.zip to ./data\\FilteredEMNISTDataset\\raw\\gzip.zip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████| 561753746/561753746 [19:49<00:00, 472141.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data\\FilteredEMNISTDataset\\raw\\gzip.zip to ./data\\FilteredEMNISTDataset\\raw\n"
     ]
    }
   ],
   "source": [
    "# Mapping for EMNIST Balanced labels (filter out lowercase)\n",
    "def map_emnist_labels(label):\n",
    "    if label < 10:  # Digits 0–9\n",
    "        return label\n",
    "    elif 10 <= label < 36:  # Uppercase letters A-Z\n",
    "        return label\n",
    "    else:  # Exclude lowercase letters\n",
    "        return None\n",
    "\n",
    "# Custom Dataset with Filtering\n",
    "class FilteredEMNISTDataset(datasets.EMNIST):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.data, self.targets = self._filter_invalid_labels()\n",
    "\n",
    "    def _filter_invalid_labels(self):\n",
    "        valid_indices = [i for i, label in enumerate(self.targets) if map_emnist_labels(label) is not None]\n",
    "        mapped_targets = [map_emnist_labels(label) for i, label in enumerate(self.targets) if map_emnist_labels(label) is not None]\n",
    "        return self.data[valid_indices], torch.tensor(mapped_targets)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        image, label = self.data[index], self.targets[index]\n",
    "        # No need to apply ToTensor since images are already in tensor form\n",
    "        image = image.unsqueeze(0).float()  # Adding channel dimension (1 channel for grayscale) and converting to float\n",
    "        return image, label\n",
    "\n",
    "# Transform for EMNIST images\n",
    "transform = transforms.Compose([\n",
    "    transforms.Normalize((0.5,), (0.5,))  # Normalize to mean=0.5, std=0.5\n",
    "])\n",
    "\n",
    "# Load and filter dataset\n",
    "emnist_data = FilteredEMNISTDataset(root='./data', split='balanced', train=True, download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "257ea31d-39d9-4fc9-b782-3225125ffbdd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86400"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(emnist_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4af727e9-f728-4a83-b106-55799d7e5fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into train and test sets\n",
    "train_size = int(0.8 * len(emnist_data))\n",
    "test_size = len(emnist_data) - train_size\n",
    "train_data, test_data = random_split(emnist_data, [train_size, test_size])\n",
    "\n",
    "# Data loaders\n",
    "batch_size = 64\n",
    "train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# Label map for visualization\n",
    "labels_map = {i: chr(48 + i) if i < 10 else chr(65 + i - 10) for i in range(36)}  # 0-9 and A-Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d9650535-23d3-4725-81fe-7d43a30c7d32",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of classes: 47\n",
      "Classes: ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'a', 'b', 'd', 'e', 'f', 'g', 'h', 'n', 'q', 'r', 't']\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Number of classes: {len(emnist_data.classes)}\")\n",
    "print(f\"Classes: {emnist_data.classes}\")\n",
    "\n",
    "# Count the number of images per class\n",
    "train_indices = train_data.indices\n",
    "test_indices = test_data.indices\n",
    "\n",
    "train_counts = Counter([emnist_data.targets[i].item() for i in train_indices])\n",
    "test_counts = Counter([emnist_data.targets[i].item() for i in test_indices])\n",
    "\n",
    "# Get the number of classes and labels\n",
    "classes = list(labels_map.values())\n",
    "\n",
    "# Plot the distributions\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
    "\n",
    "# Training dataset distribution\n",
    "ax[0].bar(classes, [train_counts.get(i, 0) for i in range(len(classes))])\n",
    "ax[0].set_title(\"Training Dataset Class Distribution\")\n",
    "ax[0].set_xticks(range(len(classes)))\n",
    "ax[0].set_xticklabels(classes, rotation=45)\n",
    "ax[0].set_ylabel(\"Number of Images\")\n",
    "\n",
    "# Testing dataset distribution\n",
    "ax[1].bar(classes, [test_counts.get(i, 0) for i in range(len(classes))])\n",
    "ax[1].set_title(\"Testing Dataset Class Distribution\")\n",
    "ax[1].set_xticks(range(len(classes)))\n",
    "ax[1].set_xticklabels(classes, rotation=45)\n",
    "ax[1].set_ylabel(\"Number of Images\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7fd4c9bf-85f7-4f13-bff4-63d36740993b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize random images\n",
    "def visualize_random_images(dataset, num_images=4):\n",
    "    fig, axes = plt.subplots(1, num_images, figsize=(10, 2.5))\n",
    "    for i in range(num_images):\n",
    "        random_idx = torch.randint(0, len(dataset), size=(1,)).item()\n",
    "        img, label = dataset[random_idx]\n",
    "        img = img.squeeze()  # Remove channel dimension\n",
    "        axes[i].imshow(img, cmap='gray')\n",
    "        axes[i].set_title(f\"Label: {labels_map[label.item()]}\")\n",
    "        axes[i].axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "de814b48-9ecf-4597-b460-c51cc7656ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_random_images(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1527c131-56a8-4cd9-b826-c0c7ea2c7ea6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_random_images(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ddacb8a-9835-4a3d-b7ac-6bec4815bc85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10], Loss: 0.7299\n",
      "Epoch [2/10], Loss: 0.3386\n",
      "Epoch [3/10], Loss: 0.2915\n",
      "Epoch [4/10], Loss: 0.2572\n",
      "Epoch [5/10], Loss: 0.2348\n",
      "Epoch [6/10], Loss: 0.2207\n",
      "Epoch [7/10], Loss: 0.2031\n",
      "Epoch [8/10], Loss: 0.1902\n",
      "Epoch [9/10], Loss: 0.1798\n",
      "Epoch [10/10], Loss: 0.1694\n"
     ]
    }
   ],
   "source": [
    "# Initialize model, loss, and optimizer\n",
    "model = CNN()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# Training loop\n",
    "epochs = 10\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    for images, labels in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(images)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        total_loss += loss.item()\n",
    "    print(f\"Epoch [{epoch + 1}/{epochs}], Loss: {total_loss / len(train_loader):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b3945708-b8f7-41f8-8de9-3045365ab5cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 0.8914\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.57      0.61       472\n",
      "           1       0.54      0.75      0.63       480\n",
      "           2       0.88      0.90      0.89       498\n",
      "           3       0.96      0.97      0.96       482\n",
      "           4       0.89      0.91      0.90       489\n",
      "           5       0.92      0.89      0.90       496\n",
      "           6       0.94      0.95      0.95       465\n",
      "           7       0.93      0.97      0.95       468\n",
      "           8       0.96      0.91      0.94       478\n",
      "           9       0.98      0.97      0.97       459\n",
      "           A       0.95      0.95      0.95       492\n",
      "           B       0.92      0.96      0.94       477\n",
      "           C       0.97      0.94      0.95       504\n",
      "           D       0.90      0.91      0.91       431\n",
      "           E       0.96      0.97      0.97       473\n",
      "           F       0.95      0.96      0.95       469\n",
      "           G       0.95      0.91      0.93       469\n",
      "           H       0.98      0.93      0.96       476\n",
      "           I       0.63      0.67      0.65       469\n",
      "           J       0.93      0.89      0.91       506\n",
      "           K       0.94      0.96      0.95       471\n",
      "           L       0.72      0.41      0.53       515\n",
      "           M       0.97      0.98      0.97       484\n",
      "           N       0.95      0.96      0.95       507\n",
      "           O       0.63      0.74      0.68       465\n",
      "           P       0.94      0.98      0.96       461\n",
      "           Q       0.97      0.95      0.96       472\n",
      "           R       0.96      0.95      0.95       509\n",
      "           S       0.86      0.92      0.89       462\n",
      "           T       0.97      0.96      0.96       497\n",
      "           U       0.92      0.90      0.91       496\n",
      "           V       0.89      0.92      0.90       472\n",
      "           W       0.94      0.97      0.96       485\n",
      "           X       0.95      0.94      0.95       446\n",
      "           Y       0.91      0.84      0.87       518\n",
      "           Z       0.89      0.87      0.88       467\n",
      "\n",
      "    accuracy                           0.89     17280\n",
      "   macro avg       0.89      0.89      0.89     17280\n",
      "weighted avg       0.89      0.89      0.89     17280\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluation\n",
    "model.eval()\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for images, labels in test_loader:\n",
    "        outputs = model(images)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        all_preds.extend(preds.numpy())\n",
    "        all_labels.extend(labels.numpy())\n",
    "\n",
    "class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']        \n",
    "\n",
    "# Metrics using Sklearn\n",
    "all_preds = np.array(all_preds)\n",
    "all_labels = np.array(all_labels)\n",
    "\n",
    "accuracy = accuracy_score(all_labels, all_preds)\n",
    "print(f\"\\nAccuracy: {accuracy:.4f}\")\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(all_labels, all_preds, target_names=class_names))\n",
    "\n",
    "# ----------------------\n",
    "conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('True Class')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf52bcd3-1d33-4604-a289-6a5c710e340d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved as emnist_cnn.pth\n"
     ]
    }
   ],
   "source": [
    "# Save the trained model\n",
    "torch.save(model.state_dict(), \"emnist_cnn.pth\")\n",
    "print(\"Model saved as emnist_cnn.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dcabf286-93fc-421a-aabf-b94b75734211",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\AppData\\Local\\Temp\\ipykernel_7420\\3973581801.py:2: 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(\"emnist_cnn.pth\"))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy: 0.8914\n"
     ]
    }
   ],
   "source": [
    "model = CNN()\n",
    "model.load_state_dict(torch.load(\"emnist_cnn.pth\"))\n",
    "model.eval()\n",
    "\n",
    "all_preds = []\n",
    "all_labels = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for images, labels in test_loader:\n",
    "        outputs = model(images)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        all_preds.extend(preds.numpy())\n",
    "        all_labels.extend(labels.numpy())\n",
    "\n",
    "all_preds = np.array(all_preds)\n",
    "all_labels = np.array(all_labels)\n",
    "\n",
    "accuracy = accuracy_score(all_labels, all_preds)\n",
    "print(f\"\\nAccuracy: {accuracy:.4f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}