Day_2_Lab_2_CNNs.ipynb

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      "gpuType": "T4",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DrKenReid/Introductory-Data-Science/blob/main/Day_2_Lab_2_CNNs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Problem/dataset Description:\n",
        "\n",
        "For this hands-on exercise, we will work with the CIFAR-10 dataset. We will use a CNN for this classification task.\n",
        "\n",
        "**Click runtime -> change runtime type -> GPU**"
      ],
      "metadata": {
        "id": "xbTmY2wPUMwz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "E1qx_QEjUdr7"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load and preprocess the CIFAR-10 dataset"
      ],
      "metadata": {
        "id": "nL7DmzqLVbwD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 testing images. The classes include everyday objects such as cars, birds, dogs, and ships.\n",
        "\n"
      ],
      "metadata": {
        "id": "fyu_QEbvVMND"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We first convert images to PyTorch tensors, and then normalize tensor images with mean and standard deviation.\n",
        "\n",
        "This standardization helps in speeding up the learning process."
      ],
      "metadata": {
        "id": "u8zg2-S3ZYtQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data augmentation and normalization for training\n",
        "transform_train = transforms.Compose([\n",
        "    transforms.RandomHorizontalFlip(),\n",
        "    transforms.RandomCrop(32, padding=4),\n",
        "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),\n",
        "    transforms.RandomRotation(10),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "])\n",
        "\n",
        "transform_test = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
        "])\n",
        "\n",
        "# Load and preprocess the CIFAR-10 dataset\n",
        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)\n",
        "\n",
        "testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPzGYgTyVZ2d",
        "outputId": "1e98f75c-9401-42b2-e634-b233bd1e69e7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 170498071/170498071 [00:03<00:00, 43023564.70it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Define the CNN architecture"
      ],
      "metadata": {
        "id": "P-aKkftBVD20"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n",
        "        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)\n",
        "        self.fc1 = nn.Linear(32 * 8 * 8, 128)\n",
        "        self.fc2 = nn.Linear(128, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.pool(x)\n",
        "        x = self.conv2(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.pool(x)\n",
        "        x = x.view(-1, 32 * 8 * 8)\n",
        "        x = self.fc1(x)\n",
        "        x = self.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n"
      ],
      "metadata": {
        "id": "xjyyy8HGUc1q"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The CNN we initialized has 3 convolutional layers (`conv1` `conv2` `conv3`), each followed by a ReLU activation and a max pooling layer. This helps capture hierarchical features from images.\n",
        "* the convolutional layers apply a number of filters to the input. For the first one, this layer outputs 16 feature maps by using 16 filters. Each filter is 3 pixels by 3 pixels (`kernel_size`), moves one pixel at a time (`stride`), and adds a one-pixel border around the input to allow the filter to cover the border of the image (`padding`).\n",
        "* a dropout layer (`nn.Dropout`) with a dropout probability of 0.25 to prevent overfitting by randomly setting input elements to zero during training.\n",
        "* the pooling layer reduces the spatial dimensions (width and height) of the input volume for the next convolutional layer. We set the kernel size 2 by 2. This reduces the feature map size by taking the maximum value in each 2x2 area of the feature map.\n",
        "\n",
        "`forward` defines how data flows: through three convolutional layers, ReLU activations, pooling, and fully connected layers ----  `fc1` and `fc2` map the learned features from the convolutional and pooling layers to the final output classes. The first one takes input 128 * 4 * 4 = 512 features, and transforms the input from feature extraction into a vector that can be used for classification. The second one outputs 10 classes, which is the number of classes in CIFAR-10, and provides a score for each class.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sn0tk-DWWKV5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Initialize the CNN model, loss function, and optimizer"
      ],
      "metadata": {
        "id": "RY_kI1aLVkKB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "net = Net()\n",
        "# sets up the device on which the network will be trained\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "net.to(device)\n",
        "# the Cross Entropy Loss for our classification task\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(net.parameters(), lr=0.001)"
      ],
      "metadata": {
        "id": "3m9TTD5aVitF"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We used an adaptive learning rate optimization algorithm (adam) for our optimizer, which automatically adjusts the learning rate during training.\n",
        "\n",
        "We set the learning rate to be 0.001."
      ],
      "metadata": {
        "id": "nNgZ1micugTi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train the CNN model"
      ],
      "metadata": {
        "id": "YWFC-BaFVll6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model learns by adjusting weights to minimize the loss computed from the outputs and true labels.\n"
      ],
      "metadata": {
        "id": "pdds_iQZZ1nz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This process takes ~15 mins"
      ],
      "metadata": {
        "id": "JNWdqH3mvW1F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 15\n",
        "train_losses = []\n",
        "val_losses = []\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    running_loss = 0.0\n",
        "    net.train()\n",
        "\n",
        "    # Training loop\n",
        "    for i, data in enumerate(trainloader, 0):\n",
        "        inputs, labels = data\n",
        "        inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    # Store the average training loss for this epoch\n",
        "    avg_train_loss = running_loss / len(trainloader)\n",
        "    train_losses.append(avg_train_loss)\n",
        "\n",
        "    # Validation loop\n",
        "    running_val_loss = 0.0\n",
        "    net.eval()\n",
        "    with torch.no_grad():\n",
        "        for data in testloader:\n",
        "            inputs, labels = data\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "            outputs = net(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            running_val_loss += loss.item()\n",
        "\n",
        "    avg_val_loss = running_val_loss / len(testloader)\n",
        "    val_losses.append(avg_val_loss)\n",
        "\n",
        "    print(f'Epoch {epoch + 1}, Training Loss: {avg_train_loss}, Validation Loss: {avg_val_loss}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhoHJJnTvyoo",
        "outputId": "f8ba95ef-46f8-4b01-9252-220e1fa2cd7f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Training Loss: 1.7389244499718746, Validation Loss: 1.4263083889514585\n",
            "Epoch 2, Training Loss: 1.4780328548167978, Validation Loss: 1.2906058196780048\n",
            "Epoch 3, Training Loss: 1.3543002126771775, Validation Loss: 1.198362133170985\n",
            "Epoch 4, Training Loss: 1.2697967406733872, Validation Loss: 1.119468339636356\n",
            "Epoch 5, Training Loss: 1.1977893211652555, Validation Loss: 1.0757740117326569\n",
            "Epoch 6, Training Loss: 1.149902096184928, Validation Loss: 1.0216606840302673\n",
            "Epoch 7, Training Loss: 1.1160499965748214, Validation Loss: 0.9763127754006204\n",
            "Epoch 8, Training Loss: 1.0787361430390108, Validation Loss: 0.9361725434472289\n",
            "Epoch 9, Training Loss: 1.0522030739833022, Validation Loss: 0.9276537072809437\n",
            "Epoch 10, Training Loss: 1.0321754890939463, Validation Loss: 0.9021536074107206\n",
            "Epoch 11, Training Loss: 1.016137340794439, Validation Loss: 0.9171890193902994\n",
            "Epoch 12, Training Loss: 1.0024969268332966, Validation Loss: 0.8753161158742784\n",
            "Epoch 13, Training Loss: 0.9783983774807142, Validation Loss: 0.8703726937499228\n",
            "Epoch 14, Training Loss: 0.9701926933834925, Validation Loss: 0.8621150564543808\n",
            "Epoch 15, Training Loss: 0.9615844169541088, Validation Loss: 0.8387452356423004\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model evaluation"
      ],
      "metadata": {
        "id": "zMsRnzx1Vv73"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the trained model on the test set\n",
        "correct = 0\n",
        "total = 0\n",
        "net.eval()\n",
        "with torch.no_grad():\n",
        "    for data in testloader:\n",
        "        images, labels = data\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "accuracy = 100 * correct / total\n",
        "print(f'Accuracy on the test set: {accuracy}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "awthvWFKO0lg",
        "outputId": "aeb80923-5485-43f7-fda8-1a719fbee1ff"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on the test set: 70.47%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy on the test set: 73.5%"
      ],
      "metadata": {
        "id": "iczQQMQt1num"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import numpy as np\n",
        "\n",
        "net.eval()\n",
        "all_labels = []\n",
        "all_predictions = []\n",
        "\n",
        "with torch.no_grad():\n",
        "    for images, labels in testloader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        all_labels.append(labels.cpu().numpy())\n",
        "        all_predictions.append(predicted.cpu().numpy())\n",
        "\n",
        "all_labels = np.concatenate(all_labels)\n",
        "all_predictions = np.concatenate(all_predictions)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = np.mean(all_labels == all_predictions) * 100\n",
        "print(f'Overall Accuracy: {accuracy:.2f}%')\n",
        "\n",
        "# Generate and print classification report\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(all_labels, all_predictions, target_names=testset.classes))\n",
        "\n",
        "# Generate and print the confusion matrix\n",
        "print(\"Confusion Matrix:\")\n",
        "conf_matrix = confusion_matrix(all_labels, all_predictions)\n",
        "print(conf_matrix)\n"
      ],
      "metadata": {
        "id": "cwuLkzCQuZJ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28cff3dd-5df9-46e8-dc67-95e304ef813b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall Accuracy: 70.47%\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.71      0.78      0.74      1000\n",
            "  automobile       0.80      0.84      0.82      1000\n",
            "        bird       0.65      0.54      0.59      1000\n",
            "         cat       0.57      0.44      0.49      1000\n",
            "        deer       0.70      0.64      0.67      1000\n",
            "         dog       0.52      0.71      0.60      1000\n",
            "        frog       0.74      0.80      0.77      1000\n",
            "       horse       0.78      0.71      0.75      1000\n",
            "        ship       0.85      0.78      0.81      1000\n",
            "       truck       0.77      0.80      0.78      1000\n",
            "\n",
            "    accuracy                           0.70     10000\n",
            "   macro avg       0.71      0.70      0.70     10000\n",
            "weighted avg       0.71      0.70      0.70     10000\n",
            "\n",
            "Confusion Matrix:\n",
            "[[783  26  33  13  12  10   9  19  54  41]\n",
            " [ 25 845   1  11   0   5   7   3  12  91]\n",
            " [ 85   8 542  41  76 108  81  35   8  16]\n",
            " [ 26   8  59 438  41 299  67  25  16  21]\n",
            " [ 28   7  66  54 638  61  71  62   7   6]\n",
            " [ 17   3  47 116  40 709  20  31   7  10]\n",
            " [  7   7  35  54  38  38 800   7   8   6]\n",
            " [ 14   2  33  22  58 123  13 715   2  18]\n",
            " [ 98  47  12  17   7   5   2   3 775  34]\n",
            " [ 22 109   7   7   4  11   6  14  18 802]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "report = classification_report(all_labels, all_predictions, target_names=testset.classes, output_dict=True)\n",
        "\n",
        "classes = list(report.keys())[:-3]  # exclude 'accuracy', 'macro avg', 'weighted avg'\n",
        "precision = [report[cls]['precision'] for cls in classes]\n",
        "recall = [report[cls]['recall'] for cls in classes]\n",
        "f1_score = [report[cls]['f1-score'] for cls in classes]\n",
        "\n",
        "df = pd.DataFrame({\n",
        "    'Precision': precision,\n",
        "    'Recall': recall,\n",
        "    'F1-Score': f1_score\n",
        "}, index=classes)\n",
        "\n",
        "# Plotting side-by-side bar plot\n",
        "ax = df.plot(kind='bar', figsize=(12, 8), width=0.8, colormap='viridis')\n",
        "plt.title('Classification Metrics by Class')\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Scores')\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(title='Metrics')\n",
        "\n",
        "for p in ax.patches:  # loop to display the data\n",
        "    ax.annotate(f'{p.get_height():.2f}', (p.get_x() + p.get_width() / 2., p.get_height()), ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "NQUnDAC121lx",
        "outputId": "265ee41e-66aa-4ad1-dc57-599f04d410e0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualize the confusion matrix:"
      ],
      "metadata": {
        "id": "vcZD-1n6u5-a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap='Blues', xticklabels=testset.classes, yticklabels=testset.classes)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "z5eDDRpcu7oG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "outputId": "37c22c6f-2591-4889-d57e-e23ffd1cbfab"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizations"
      ],
      "metadata": {
        "id": "zkZQVQduVscq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualize the training and validation loss curves:"
      ],
      "metadata": {
        "id": "eBwEnakhu-FC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "plt.plot(range(1, num_epochs + 1), train_losses, marker='o', label='Training Loss', color='blue')\n",
        "plt.plot(range(1, num_epochs + 1), val_losses, marker='x', label='Validation Loss', color='red')\n",
        "\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Training and Validation Loss Curve')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "Z65rDaKsbj8y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "8dc30c5c-2575-4605-d2f6-2f3bbbc6e8c9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualize sample predictions:"
      ],
      "metadata": {
        "id": "AluYoEyEu_4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "\n",
        "# Function to unnormalize for visualization\n",
        "def unnormalize(image):\n",
        "    image = image * 0.5 + 0.5\n",
        "    image = image.clamp(0, 1)\n",
        "\n",
        "    if image.is_cuda:  # Check if the tensor is on CUDA\n",
        "        image = image.cpu()  # Move to CPU\n",
        "\n",
        "    return image.numpy()\n",
        "\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(12, 12))\n",
        "axes = axes.flatten()\n",
        "\n",
        "# Loop to plot the data\n",
        "for i in range(16):\n",
        "    img = unnormalize(images[i]).transpose(1, 2, 0)\n",
        "    axes[i].imshow(img)\n",
        "    correct = labels[i] == predicted[i]\n",
        "    color = 'green' if correct else 'red'\n",
        "    axes[i].set_title(f\"True: {classes[labels[i]]}\\nPred: {classes[predicted[i]]}\", color=color)\n",
        "    axes[i].axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "-d2NSGUAbe1H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "42045d99-c504-438c-b248-ae5b387410e1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 16 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion\n",
        "\n",
        "Congratulations on completing this lab on Convolutional Neural Networks (CNNs) using the CIFAR-10 dataset! Throughout this exercise, you've successfully:\n",
        "\n",
        "1. Loaded and preprocessed the CIFAR-10 dataset using PyTorch and torchvision\n",
        "2. Implemented data augmentation techniques to improve model generalization\n",
        "3. Designed and implemented a CNN architecture using PyTorch's nn.Module\n",
        "4. Set up the training process with CrossEntropyLoss and Adam optimizer\n",
        "5. Trained the CNN model on the CIFAR-10 dataset\n",
        "6. Evaluated the model's performance using various metrics:\n",
        "   - Accuracy\n",
        "   - Precision\n",
        "   - Recall\n",
        "   - F1 Score\n",
        "7. Visualized the results using:\n",
        "   - Confusion Matrix\n",
        "   - Classification metrics by class (bar plot)\n",
        "   - Training and validation loss curves\n",
        "   - Sample predictions with true and predicted labels\n",
        "\n",
        "Key takeaways from this lab:\n",
        "\n",
        "- Understanding the structure and implementation of CNNs in PyTorch\n",
        "- The importance of data augmentation in improving model performance\n",
        "- The process of designing a CNN architecture with convolutional, pooling, and fully connected layers\n",
        "- The significance of various evaluation metrics in assessing model performance on a multi-class problem\n",
        "- The value of visualizations in interpreting model results, tracking training progress, and identifying areas for improvement\n",
        "\n",
        "This lab has provided you with hands-on experience in building, training, and evaluating a CNN for image classification. You've gained insights into the entire deep learning pipeline, from data preparation and augmentation to model architecture design and result interpretation. These skills form a solid foundation for tackling more complex computer vision tasks and understanding advanced CNN architectures."
      ],
      "metadata": {
        "id": "4pu5FwGSPvfV"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mmw5seB9Pwjx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}