FC_Implementation_25_Epochs.ipynb

{
  "cells": [
    {
      "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",
        "from torch.utils.data import DataLoader\n",
        "import matplotlib.pyplot as plt\n",
        "from tabulate import tabulate\n",
        "\n",
        "# Install required libraries\n",
        "!pip install -U portalocker>=2.0.0\n",
        "!pip install tabulate\n",
        "\n",
        "# Define data transformation\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5,), (0.5,))\n",
        "])\n",
        "\n",
        "# Load FashionMNIST dataset\n",
        "train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)\n",
        "test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)\n",
        "\n",
        "# Create data loaders\n",
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
        "\n",
        "# Labels mapping\n",
        "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",
        "# Display sample images\n",
        "def display_sample_images():\n",
        "    figure = plt.figure(figsize=(8, 8))\n",
        "    cols, rows = 3, 3\n",
        "\n",
        "    for i in range(1, cols * rows + 1):\n",
        "        sample_idx = torch.randint(len(train_dataset), size=(1,)).item()\n",
        "        img, label = train_dataset[sample_idx]\n",
        "        figure.add_subplot(rows, cols, i)\n",
        "        plt.title(labels_map[label])\n",
        "        plt.axis(\"off\")\n",
        "        plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "    plt.show()\n",
        "\n",
        "# Model definition\n",
        "class FashionMLP(nn.Module):\n",
        "    def __init__(self, num_hidden_layers, hidden_layer_sizes):\n",
        "        super(FashionMLP, self).__init__()\n",
        "        layers = []\n",
        "        layers.append(nn.Linear(28 * 28, hidden_layer_sizes[0]))  # Input layer\n",
        "        for i in range(num_hidden_layers - 1):  # Hidden layers\n",
        "            layers.append(nn.Linear(hidden_layer_sizes[i], hidden_layer_sizes[i + 1]))\n",
        "        layers.append(nn.Linear(hidden_layer_sizes[-1], 10))  # Output layer\n",
        "        self.layers = nn.ModuleList(layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 28 * 28)\n",
        "        for layer in self.layers[:-1]:\n",
        "            x = torch.sigmoid(layer(x))\n",
        "        x = self.layers[-1](x)\n",
        "        return x\n",
        "\n",
        "# Define configurations\n",
        "configurations = [\n",
        "    {\n",
        "        'name': \"Configuration-1\",\n",
        "        'num_hidden_layers': 3,\n",
        "        'hidden_layer_sizes': [32, 64, 128],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-2\",\n",
        "        'num_hidden_layers': 3,\n",
        "        'hidden_layer_sizes': [64, 128, 256],\n",
        "        'batch_size': 512,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.RMSprop\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-3\",\n",
        "        'num_hidden_layers': 3,\n",
        "        'hidden_layer_sizes': [128, 256, 512],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-4\",\n",
        "        'num_hidden_layers': 2,\n",
        "        'hidden_layer_sizes': [64, 128],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-5\",\n",
        "        'num_hidden_layers': 2,\n",
        "        'hidden_layer_sizes': [32, 64],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-6\",\n",
        "        'num_hidden_layers': 2,\n",
        "        'hidden_layer_sizes': [128, 256],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-7\",\n",
        "        'num_hidden_layers': 2,\n",
        "        'hidden_layer_sizes': [256, 512],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.RMSprop\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-8\",\n",
        "        'num_hidden_layers': 1,\n",
        "        'hidden_layer_sizes': [64],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-9\",\n",
        "        'num_hidden_layers': 1,\n",
        "        'hidden_layer_sizes': [128],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-10\",\n",
        "        'num_hidden_layers': 1,\n",
        "        'hidden_layer_sizes': [256],\n",
        "        'batch_size': 64,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-11\",\n",
        "        'num_hidden_layers': 3,\n",
        "        'hidden_layer_sizes': [32, 64, 128],\n",
        "        'batch_size': 128,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.RMSprop\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-12\",\n",
        "        'num_hidden_layers': 4,\n",
        "        'hidden_layer_sizes': [64, 128, 256, 512],\n",
        "        'batch_size': 128,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-13\",\n",
        "        'num_hidden_layers': 2,\n",
        "        'hidden_layer_sizes': [128, 256],\n",
        "        'batch_size': 256,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.RMSprop\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-14\",\n",
        "        'num_hidden_layers': 1,\n",
        "        'hidden_layer_sizes': [512],\n",
        "        'batch_size': 256,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.Adam\n",
        "    },\n",
        "    {\n",
        "        'name': \"Configuration-15\",\n",
        "        'num_hidden_layers': 4,\n",
        "        'hidden_layer_sizes': [64, 128, 256, 512],\n",
        "        'batch_size': 32,\n",
        "        'learning_rate': 0.001,\n",
        "        'optimizer': optim.RMSprop\n",
        "    }\n",
        "]\n",
        "\n",
        "\n",
        "# Running experiments\n",
        "FC_results = []\n",
        "\n",
        "for config in configurations:\n",
        "    name = config['name']\n",
        "    num_hidden_layers = config['num_hidden_layers']\n",
        "    hidden_layer_sizes = config['hidden_layer_sizes']\n",
        "    batch_size = config['batch_size']\n",
        "    learning_rate = config['learning_rate']\n",
        "    optimizer_func = config['optimizer']\n",
        "\n",
        "    # Initialize model, loss function, and optimizer\n",
        "    model = FashionMLP(num_hidden_layers, hidden_layer_sizes)\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = optimizer_func(model.parameters(), lr=learning_rate)\n",
        "\n",
        "    # Display configuration\n",
        "    print(f\"Configuration - {name}\")\n",
        "    print(\"Input data:\")\n",
        "    print(f\"- Num Hidden Layers: {num_hidden_layers}\")\n",
        "    print(f\"- Hidden Layer Sizes: {hidden_layer_sizes}\")\n",
        "    print(f\"- Batch Size: {batch_size}\")\n",
        "    print(f\"- Learning Rate: {learning_rate}\")\n",
        "\n",
        "    # Training\n",
        "    num_epochs = 25\n",
        "    epoch_losses = []\n",
        "    for epoch in range(num_epochs):\n",
        "        model.train()\n",
        "        running_loss = 0.0\n",
        "        for i, data in enumerate(train_loader, 0):\n",
        "            inputs, labels = data\n",
        "            optimizer.zero_grad()\n",
        "            outputs = model(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            running_loss += loss.item()\n",
        "\n",
        "            # Print epoch-wise batch loss\n",
        "            if i % 100 == 99:\n",
        "                print(f'Epoch [{epoch + 1}], Batch [{i + 1}/{len(train_loader)}], Loss: {running_loss / 100:.4f}')\n",
        "                running_loss = 0.0\n",
        "\n",
        "        epoch_losses.append(running_loss)\n",
        "\n",
        "    # Evaluating\n",
        "    model.eval()\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    with torch.no_grad():\n",
        "        for data in test_loader:\n",
        "            images, labels = data\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs.data, 1)\n",
        "            total += labels.size(0)\n",
        "            correct += (predicted == labels).sum().item()\n",
        "\n",
        "    accuracy = correct / total\n",
        "    print(f\"Accuracy for {name}: {accuracy * 100:.2f}%\\n\")\n",
        "\n",
        "    FC_results.append([name, num_hidden_layers, hidden_layer_sizes, batch_size, learning_rate, accuracy])\n",
        "\n",
        "    # Print dotted line break\n",
        "    print(\".\" * 50)\n",
        "    print()\n",
        "\n",
        "# Display FC_results\n",
        "print(tabulate(FC_results, headers=[\"Configuration\", \"Num Hidden Layers\", \"Hidden Layer Sizes\", \"Batch Size\", \"Learning Rate\", \"Accuracy\"],\n",
        "               floatfmt=\".2f\"))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GC7T-4h3mIhW",
        "outputId": "e7aa57da-870f-44e5-a55c-72a68bd4ea23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tabulate in /usr/local/lib/python3.10/dist-packages (0.9.0)\n",
            "Configuration - Configuration-1\n",
            "Input data:\n",
            "- Num Hidden Layers: 3\n",
            "- Hidden Layer Sizes: [32, 64, 128]\n",
            "- Batch Size: 64\n",
            "- Learning Rate: 0.001\n",
            "Epoch [1], Batch [100/938], Loss: 2.1994\n",
            "Epoch [1], Batch [200/938], Loss: 1.6571\n",
            "Epoch [1], Batch [300/938], Loss: 1.2642\n",
            "Epoch [1], Batch [400/938], Loss: 1.0673\n",
            "Epoch [1], Batch [500/938], Loss: 0.9412\n",
            "Epoch [1], Batch [600/938], Loss: 0.8457\n",
            "Epoch [1], Batch [700/938], Loss: 0.7610\n",
            "Epoch [1], Batch [800/938], Loss: 0.7278\n",
            "Epoch [1], Batch [900/938], Loss: 0.6943\n",
            "Epoch [2], Batch [100/938], Loss: 0.6452\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract configurations and accuracies for plotting\n",
        "configs = [result[0] for result in FC_results]\n",
        "accuracies = [result[5] * 100 for result in FC_results]\n",
        "\n",
        "# Plotting accuracy vs configurations\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.bar(range(1, len(configs) + 1), accuracies, color='skyblue')\n",
        "plt.xlabel('Configuration')\n",
        "plt.ylabel('Accuracy (%)')  # Updated ylabel to show percentage\n",
        "plt.title('Accuracy vs Configurations')\n",
        "plt.xticks(range(1, len(configs) + 1), range(1, len(configs) + 1), rotation=45, ha='right')\n",
        "plt.ylim(0, 100)  # Setting y-axis limit from 0 to 100 for percentage\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "# Adding text inside the bars\n",
        "for bar, accuracy in zip(bars, accuracies):\n",
        "    plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height() - 5, f'{accuracy:.2f}%',\n",
        "             ha='center', va='bottom', color='black', fontsize=7)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "inCFtkJnoCJn",
        "outputId": "402172e4-b770-431c-8ef1-b2560f60d439"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_results = sorted(FC_results, key=lambda x: x[5], reverse=True)\n",
        "top_5_results = sorted_results[:5]\n",
        "\n",
        "# Extract configurations and accuracies for plotting\n",
        "top_5_configs = [result[0] for result in top_5_results]\n",
        "top_5_accuracies = [result[5] * 100 for result in top_5_results]\n",
        "\n",
        "# Plotting top 5 accuracies\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.bar(top_5_configs, top_5_accuracies, color='skyblue')\n",
        "plt.xlabel('Configuration')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Top 5 Configurations with Highest Accuracy')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.ylim(0, 100)  # Setting y-axis limit from 0 to 100 for percentage\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "# Adding text inside the bars\n",
        "for bar, accuracy in zip(bars, top_5_accuracies):\n",
        "    plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height() - 5, f'{accuracy:.2f}%',\n",
        "             ha='center', va='bottom', color='black', fontsize=10)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "5hSWWWNApMbe",
        "outputId": "6720f9f7-6c07-4f0b-e128-e7ac97374b8a"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Extract the configuration with the highest accuracy\n",
        "best_config = max(FC_results, key=lambda x: x[5])\n",
        "\n",
        "# Extract configuration parameters\n",
        "config_name = best_config[0]\n",
        "num_layers = best_config[1]\n",
        "layer_sizes = sum(best_config[2])\n",
        "batch_size = best_config[3]\n",
        "learning_rate = best_config[4]\n",
        "accuracy = best_config[5] * 100\n",
        "\n",
        "# Create a bar plot for the configuration parameters\n",
        "labels = ['Num Hidden Layers', 'Hidden Layer Sizes', 'Batch Size', 'Learning Rate']\n",
        "values = [num_layers, layer_sizes, batch_size, learning_rate]\n",
        "\n",
        "# Plot each parameter separately\n",
        "fig, axs = plt.subplots(2, 2, figsize=(12, 12))\n",
        "fig.suptitle(f\"{config_name} (Accuracy: {accuracy:.2f}%)\", fontsize=16)\n",
        "\n",
        "for i, label in enumerate(labels):\n",
        "    ax = axs[i//2][i%2]\n",
        "    ax.bar(label, values[i], color='skyblue')\n",
        "    ax.set_title(label)\n",
        "    ax.set_ylabel('Values')\n",
        "\n",
        "    # Annotate the bars with their corresponding values inside the bars\n",
        "    ax.text(0.5, values[i], str(values[i]), color='black', va='center', ha='center')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "c9TKxwLDprHQ",
        "outputId": "7f378785-a8f5-4ccf-c5ed-186d677c12c7"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Welcome to Colaboratory",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}