e_d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Define Transformations with Data Augmentation\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.Grayscale(num_output_channels=1),  # Convert to grayscale\n",
    "    transforms.Resize((48, 48)),  # Resize to 48x48 (FER2013 standard)\n",
    "    transforms.RandomHorizontalFlip(),  # Randomly flip the image horizontally\n",
    "    transforms.RandomRotation(10),  # Randomly rotate the image by up to 10 degrees\n",
    "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),  # Random color adjustments\n",
    "    transforms.ToTensor(),  # Convert to tensor\n",
    "    transforms.Normalize((0.5,), (0.5,))  # Normalize images\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_transform = transforms.Compose([\n",
    "    transforms.Grayscale(num_output_channels=1),  # Convert to grayscale\n",
    "    transforms.Resize((48, 48)),  # Resize to 48x48 (FER2013 standard)\n",
    "    transforms.ToTensor(),  # Convert to tensor\n",
    "    transforms.Normalize((0.5,), (0.5,))  # Normalize images\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Setting up Data Paths\n",
    "\n",
    "train_data_path = 'C:/Users/Priyanka Malavade/OneDrive/Desktop/emotion_detaction/DATA/train'\n",
    "test_data_path = 'C:/Users/Priyanka Malavade/OneDrive/Desktop/emotion_detaction/DATA/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = datasets.ImageFolder(root=train_data_path, transform=train_transform)\n",
    "test_dataset = datasets.ImageFolder(root=test_data_path, transform=test_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Define the CNN Model Architecture\n",
    "class EmotionCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(EmotionCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
    "        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)  # Additional layer\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.fc1 = nn.Linear(128 * 6 * 6, 256)  # Adjusted for the new input size\n",
    "        self.fc2 = nn.Linear(256, len(train_dataset.classes))  # Number of classes in the dataset\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(torch.relu(self.conv1(x)))\n",
    "        x = self.pool(torch.relu(self.conv2(x)))\n",
    "        x = self.pool(torch.relu(self.conv3(x)))  # Apply third convolution layer\n",
    "        x = x.view(-1, 128 * 6 * 6)  # Flatten for the fully connected layer\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Initialize Model, Loss Function, and Optimizer\n",
    "model = EmotionCNN()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. Early Stopping Parameters\n",
    "best_loss = float('inf')\n",
    "patience = 5  # Number of epochs to wait before stopping\n",
    "counter = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Training the Model with Tracking Loss and Accuracy\n",
    "train_losses = []\n",
    "train_accuracies = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/30], Loss: 1.6779, Accuracy: 0.3295\n",
      "Epoch [2/30], Loss: 1.4732, Accuracy: 0.4327\n",
      "Epoch [3/30], Loss: 1.3802, Accuracy: 0.4730\n",
      "Epoch [4/30], Loss: 1.3189, Accuracy: 0.4982\n",
      "Epoch [5/30], Loss: 1.2760, Accuracy: 0.5165\n",
      "Epoch [6/30], Loss: 1.2446, Accuracy: 0.5280\n",
      "Epoch [7/30], Loss: 1.2140, Accuracy: 0.5396\n",
      "Epoch [8/30], Loss: 1.1928, Accuracy: 0.5447\n",
      "Epoch [9/30], Loss: 1.1698, Accuracy: 0.5584\n",
      "Epoch [10/30], Loss: 1.1565, Accuracy: 0.5610\n",
      "Epoch [11/30], Loss: 1.1434, Accuracy: 0.5684\n",
      "Epoch [12/30], Loss: 1.1284, Accuracy: 0.5730\n",
      "Epoch [13/30], Loss: 1.1113, Accuracy: 0.5755\n",
      "Epoch [14/30], Loss: 1.1024, Accuracy: 0.5814\n",
      "Epoch [15/30], Loss: 1.0944, Accuracy: 0.5850\n",
      "Epoch [16/30], Loss: 1.0879, Accuracy: 0.5908\n",
      "Epoch [17/30], Loss: 1.0742, Accuracy: 0.5958\n",
      "Epoch [18/30], Loss: 1.0665, Accuracy: 0.5949\n",
      "Epoch [19/30], Loss: 1.0616, Accuracy: 0.5990\n",
      "Epoch [20/30], Loss: 1.0488, Accuracy: 0.5983\n",
      "Epoch [21/30], Loss: 1.0393, Accuracy: 0.6065\n",
      "Epoch [22/30], Loss: 1.0359, Accuracy: 0.6064\n",
      "Epoch [23/30], Loss: 1.0295, Accuracy: 0.6082\n",
      "Epoch [24/30], Loss: 1.0308, Accuracy: 0.6089\n",
      "Epoch [25/30], Loss: 1.0153, Accuracy: 0.6175\n",
      "Epoch [26/30], Loss: 1.0073, Accuracy: 0.6180\n",
      "Epoch [27/30], Loss: 1.0048, Accuracy: 0.6199\n",
      "Epoch [28/30], Loss: 0.9952, Accuracy: 0.6251\n",
      "Epoch [29/30], Loss: 0.9961, Accuracy: 0.6218\n",
      "Epoch [30/30], Loss: 0.9836, Accuracy: 0.6259\n"
     ]
    }
   ],
   "source": [
    "epochs = 30  # Number of epochs\n",
    "for epoch in range(epochs):\n",
    "    model.train()  # Set the model to training mode\n",
    "    running_loss = 0.0\n",
    "    correct_preds = 0\n",
    "    total_preds = 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",
    "        \n",
    "        running_loss += loss.item()\n",
    "        \n",
    "        # Calculate accuracy\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        correct_preds += (predicted == labels).sum().item()\n",
    "        total_preds += labels.size(0)\n",
    "    \n",
    "    avg_loss = running_loss / len(train_loader)\n",
    "    accuracy = correct_preds / total_preds\n",
    "    \n",
    "    train_losses.append(avg_loss)\n",
    "    train_accuracies.append(accuracy)\n",
    "    \n",
    "    print(f'Epoch [{epoch+1}/{epochs}], Loss: {avg_loss:.4f}, Accuracy: {accuracy:.4f}')\n",
    "\n",
    "    # Early stopping logic\n",
    "    if avg_loss < best_loss:\n",
    "        best_loss = avg_loss\n",
    "        counter = 0\n",
    "    else:\n",
    "        counter += 1\n",
    "\n",
    "    if counter >= patience:\n",
    "        print(f'Early stopping on epoch {epoch+1}')\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 7. Plot Training Loss and Accuracy\n",
    "plt.figure(figsize=(12, 6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(range(1, len(train_losses)+1), train_losses, label='Training Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training Loss Over Epochs')\n",
    "plt.grid(True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot accuracy\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(range(1, len(train_accuracies)+1), train_accuracies, label='Training Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Training Accuracy Over Epochs')\n",
    "plt.grid(True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8. Evaluate the Model on Test Set and Plot Confusion Matrix\n",
    "def evaluate_model(model, data_loader):\n",
    "    model.eval()  # Set the model to evaluation mode\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "    with torch.no_grad():  # No gradient calculation needed for inference\n",
    "        for images, labels in data_loader:\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs, 1)\n",
    "            all_preds.extend(predicted.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "    \n",
    "    # Calculate performance metrics\n",
    "    accuracy = accuracy_score(all_labels, all_preds)\n",
    "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
    "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
    "    f1 = f1_score(all_labels, all_preds, average='weighted')\n",
    "    \n",
    "    print(f'Accuracy: {accuracy:.4f}')\n",
    "    print(f'Precision: {precision:.4f}')\n",
    "    print(f'Recall: {recall:.4f}')\n",
    "    print(f'F1 Score: {f1:.4f}')\n",
    "    \n",
    "    \n",
    "    # Confusion Matrix\n",
    "    cm = confusion_matrix(all_labels, all_preds)\n",
    "    \n",
    "    # Plot confusion matrix\n",
    "    plt.figure(figsize=(10, 7))\n",
    "    sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=train_dataset.classes, yticklabels=train_dataset.classes)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.title('Confusion Matrix')\n",
    "    plt.show()\n",
    "\n",
    "    return accuracy, precision, recall, f1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6037\n",
      "Precision: 0.6082\n",
      "Recall: 0.6037\n",
      "F1 Score: 0.6032\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.6036500417943717, 0.6082268552382125, 0.6036500417943717, 0.603230247145791)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Call the evaluation function after training\n",
    "evaluate_model(model, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9. Save the Trained Model\n",
    "torch.save(model.state_dict(), 'emotion_cnn_model.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Priyanka Malavade\\AppData\\Local\\Temp\\ipykernel_9900\\2733676104.py:3: 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('emotion_cnn_model.pth'))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "EmotionCNN(\n",
       "  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=4608, out_features=256, bias=True)\n",
       "  (fc2): Linear(in_features=256, out_features=7, bias=True)\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10. Load the Model for Inference\n",
    "model = EmotionCNN()\n",
    "model.load_state_dict(torch.load('emotion_cnn_model.pth'))\n",
    "model.eval()  # Set to evaluation mode"
   ]
  }
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