Autoencoder_with_Three_Encoder_and_Decoder_Layers_(Unsupervised_Learning).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOYy4fBlq5aAmOwAgMcERTn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/naidu199/DeepLearning/blob/master/Autoencoder_with_Three_Encoder_and_Decoder_Layers_(Unsupervised_Learning).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "rhtZ_ROtnsqp"
      },
      "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\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Transformations for the dataset\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "\n",
        "])\n",
        "\n",
        "# Load the MNIST dataset\n",
        "train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
        "test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
        "\n",
        "# Data loaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=1000, shuffle=False)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the Neural Network Model\n",
        "class NeuralNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(NeuralNetwork, self).__init__()\n",
        "        self.flatten = nn.Flatten()\n",
        "        self.dense1 = nn.Linear(28*28, 256)\n",
        "        self.dense2 = nn.Linear(256, 128)\n",
        "        self.dense3 = nn.Linear(128, 56)\n",
        "        self.output = nn.Linear(56, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.flatten(x)\n",
        "        x = torch.relu(self.dense1(x))\n",
        "        x = torch.relu(self.dense2(x))\n",
        "        x = torch.relu(self.dense3(x))\n",
        "        x = torch.softmax(self.output(x), dim=1)\n",
        "        return x\n",
        "\n",
        "model = NeuralNetwork()\n",
        "print(model)\n",
        "\n",
        "# Loss function and optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "# Training the Model\n",
        "num_epochs = 10\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\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",
        "    print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}\")\n",
        "\n",
        "# Evaluating the Model\n",
        "model.eval()\n",
        "correct = 0\n",
        "total = 0\n",
        "with torch.no_grad():\n",
        "    for images, labels in test_loader:\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "test_acc = correct / total\n",
        "print(f\"Test accuracy: {test_acc:.4f}\")\n",
        "\n",
        "# Making Predictions\n",
        "model.eval()\n",
        "predicted_classes = []\n",
        "with torch.no_grad():\n",
        "    for images, labels in train_loader:\n",
        "        outputs = model(images)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        predicted_classes.extend(predicted.numpy())\n",
        "\n",
        "# Visualize Results (Optional)\n",
        "for i in range(5):\n",
        "    plt.subplot(1, 5, i + 1)\n",
        "    plt.imshow(train_dataset.data[i], cmap='gray')\n",
        "    plt.title(f'Pred: {predicted_classes[i]}')\n",
        "    plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "pVWOIzg1oWnU",
        "outputId": "5302a017-0836-4aae-bc59-d61c41422049"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NeuralNetwork(\n",
            "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            "  (dense1): Linear(in_features=784, out_features=256, bias=True)\n",
            "  (dense2): Linear(in_features=256, out_features=128, bias=True)\n",
            "  (dense3): Linear(in_features=128, out_features=56, bias=True)\n",
            "  (output): Linear(in_features=56, out_features=10, bias=True)\n",
            ")\n",
            "Epoch [1/10], Loss: 1.5273\n",
            "Epoch [2/10], Loss: 1.4892\n",
            "Epoch [3/10], Loss: 1.4633\n",
            "Epoch [4/10], Loss: 1.4926\n",
            "Epoch [5/10], Loss: 1.4612\n",
            "Epoch [6/10], Loss: 1.4868\n",
            "Epoch [7/10], Loss: 1.4695\n",
            "Epoch [8/10], Loss: 1.4697\n",
            "Epoch [9/10], Loss: 1.4612\n",
            "Epoch [10/10], Loss: 1.4612\n",
            "Test accuracy: 0.9696\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def visualize_predictions(model, data_loader, dataset, num_images=5, filename='predicted_images.png'):\n",
        "    # Set the model to evaluation mode\n",
        "    model.eval()\n",
        "    predicted_classes = []\n",
        "\n",
        "    # Make predictions\n",
        "    with torch.no_grad():\n",
        "        for images, _ in data_loader:\n",
        "            outputs = model(images)\n",
        "            _, predicted = torch.max(outputs, 1)\n",
        "            predicted_classes.extend(predicted.numpy())\n",
        "            break  # Only take the first batch for visualization\n",
        "\n",
        "    # Visualize Results\n",
        "    plt.figure(figsize=(10, 4))\n",
        "    for i in range(num_images):\n",
        "        ax = plt.subplot(2, num_images, i + 1)\n",
        "        plt.imshow(dataset.data[i], cmap='gray')\n",
        "        plt.title(\"Original\")\n",
        "        plt.axis('off')\n",
        "\n",
        "        ax = plt.subplot(2, num_images, i + 1 + num_images)\n",
        "        plt.imshow(images[i].view(28, 28).numpy(), cmap='gray')\n",
        "        plt.title(f'Pred: {predicted_classes[i]}')\n",
        "        plt.axis('off')\n",
        "\n",
        "    # Save the plot\n",
        "    plt.savefig(filename)\n",
        "    plt.show()\n",
        "\n",
        "# Example usage\n",
        "visualize_predictions(model, train_loader, train_dataset)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "AFIErC77vxwC",
        "outputId": "a3dff21a-65fd-4d9e-83da-d1746a391e9a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 10 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.parameters())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzUq4b9EocjO",
        "outputId": "e32fe36d-b633-44f0-e3bc-91132fab581c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<generator object Module.parameters at 0x78e4af593990>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Autoencoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Autoencoder, self).__init__()\n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Flatten(),\n",
        "            nn.Linear(28*28, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128, 64),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(64, 32),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.Linear(32, 64),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(64, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128, 28*28),\n",
        "            nn.Sigmoid(),\n",
        "            nn.Unflatten(1, (1, 28, 28))\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        encoded = self.encoder(x)\n",
        "        decoded = self.decoder(encoded)\n",
        "        return decoded\n",
        "\n",
        "model = Autoencoder()\n",
        "print(model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5kOck3wq0hj",
        "outputId": "9bf5ad43-988b-4faf-e9cf-e062d37d58bf"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Autoencoder(\n",
            "  (encoder): Sequential(\n",
            "    (0): Flatten(start_dim=1, end_dim=-1)\n",
            "    (1): Linear(in_features=784, out_features=128, bias=True)\n",
            "    (2): ReLU()\n",
            "    (3): Linear(in_features=128, out_features=64, bias=True)\n",
            "    (4): ReLU()\n",
            "    (5): Linear(in_features=64, out_features=32, bias=True)\n",
            "    (6): ReLU()\n",
            "  )\n",
            "  (decoder): Sequential(\n",
            "    (0): Linear(in_features=32, out_features=64, bias=True)\n",
            "    (1): ReLU()\n",
            "    (2): Linear(in_features=64, out_features=128, bias=True)\n",
            "    (3): ReLU()\n",
            "    (4): Linear(in_features=128, out_features=784, bias=True)\n",
            "    (5): Sigmoid()\n",
            "    (6): Unflatten(dim=1, unflattened_size=(1, 28, 28))\n",
            "  )\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loss function and optimizer\n",
        "criterion = nn.BCELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "# Training the model\n",
        "num_epochs = 10\n",
        "for epoch in range(num_epochs):\n",
        "    model.train()\n",
        "    running_loss = 0.0\n",
        "    for images, _ in train_loader:\n",
        "        # Forward pass\n",
        "        outputs = model(images)\n",
        "        loss = criterion(outputs, images)\n",
        "\n",
        "        # Backward pass\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        running_loss += loss.item()\n",
        "\n",
        "    print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47LmIcPTrbd_",
        "outputId": "5f0c6d42-53cc-4293-89b1-8ea8df91c51c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch [1/10], Loss: 0.2176\n",
            "Epoch [2/10], Loss: 0.1443\n",
            "Epoch [3/10], Loss: 0.1277\n",
            "Epoch [4/10], Loss: 0.1189\n",
            "Epoch [5/10], Loss: 0.1126\n",
            "Epoch [6/10], Loss: 0.1079\n",
            "Epoch [7/10], Loss: 0.1046\n",
            "Epoch [8/10], Loss: 0.1023\n",
            "Epoch [9/10], Loss: 0.1006\n",
            "Epoch [10/10], Loss: 0.0992\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    for images, _ in test_loader:\n",
        "        outputs = model(images)\n",
        "        break\n",
        "\n",
        "# Visualize some results\n",
        "n = 5\n",
        "plt.figure(figsize=(10, 4))\n",
        "for i in range(n):\n",
        "    # Original images\n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.imshow(images[i].view(28, 28).numpy(), cmap='gray')\n",
        "    plt.title(\"Original\")\n",
        "    plt.axis('off')\n",
        "\n",
        "    # Reconstructed images\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\n",
        "    plt.imshow(outputs[i].view(28, 28).numpy(), cmap='gray')\n",
        "    plt.title(\"Reconstructed\")\n",
        "    plt.axis('off')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "RXOqOo_ssTs-",
        "outputId": "7ee065e8-30e1-4b64-b0ec-561fd0d002f8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "def compute_mse(original, reconstructed):\n",
        "    return F.mse_loss(reconstructed, original).item()\n"
      ],
      "metadata": {
        "id": "zNpMLLEKt5nY"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "total_mse = 0.0\n",
        "num_batches = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for images, _ in test_loader:\n",
        "        outputs = model(images)\n",
        "        mse = compute_mse(images, outputs)\n",
        "        total_mse += mse\n",
        "        num_batches += 1\n",
        "\n",
        "average_mse = total_mse / num_batches\n",
        "print(f\"Average MSE on test set: {average_mse:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StOURMFAuObt",
        "outputId": "6aa036a4-a99f-4ce3-e349-72a6f233a3be"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average MSE on test set: 0.0119\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_accuracy(original, reconstructed, threshold=0.1):\n",
        "    diff = torch.abs(original - reconstructed)\n",
        "    correct = torch.sum(diff < threshold).item()\n",
        "    total = torch.numel(diff)\n",
        "    return correct / total\n",
        "\n",
        "model.eval()\n",
        "total_correct = 0\n",
        "total_pixels = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for images, _ in test_loader:\n",
        "        outputs = model(images)\n",
        "        correct = compute_accuracy(images, outputs)\n",
        "        total_correct += correct * images.size(0) * images.size(2) * images.size(3)\n",
        "        total_pixels += images.size(0) * images.size(2) * images.size(3)\n",
        "\n",
        "accuracy = total_correct / total_pixels\n",
        "print(f\"Reconstruction accuracy on test set: {accuracy:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJ5_NmnruRCv",
        "outputId": "a410b755-ceda-4568-f2b9-5ccf3b6281e0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reconstruction accuracy on test set: 0.8849\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate MSE\n",
        "model.eval()\n",
        "total_mse = 0.0\n",
        "num_batches = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for images, _ in test_loader:\n",
        "        outputs = model(images)\n",
        "        mse = compute_mse(images, outputs)\n",
        "        total_mse += mse\n",
        "        num_batches += 1\n",
        "\n",
        "average_mse = total_mse / num_batches\n",
        "print(f\"Average MSE on test set: {average_mse:.4f}\")\n",
        "\n",
        "# Compute accuracy based on a threshold\n",
        "def compute_accuracy(original, reconstructed, threshold=0.1):\n",
        "    diff = torch.abs(original - reconstructed)\n",
        "    correct = torch.sum(diff < threshold).item()\n",
        "    total = torch.numel(diff)\n",
        "    return correct / total\n",
        "\n",
        "# Evaluate accuracy\n",
        "model.eval()\n",
        "total_correct = 0\n",
        "total_pixels = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for images, _ in test_loader:\n",
        "        outputs = model(images)\n",
        "        correct = compute_accuracy(images, outputs)\n",
        "        total_correct += correct * images.size(0) * images.size(2) * images.size(3)\n",
        "        total_pixels += images.size(0) * images.size(2) * images.size(3)\n",
        "\n",
        "accuracy = total_correct / total_pixels\n",
        "print(f\"Reconstruction accuracy on test set: {accuracy:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-J1G4bFKuUeX",
        "outputId": "4986b9f2-f5ec-4a2a-ffff-05bec3553ae7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average MSE on test set: 0.0119\n",
            "Reconstruction accuracy on test set: 0.8849\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "96eWVDLZu_sj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}