VAE.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# VAE\n",
        "\n",
        "Variational Autoencoders (VAEs) are a type of generative model that learn to capture the underlying distribution of a dataset. They are particularly useful for modeling complex data such as images or text. VAEs are based on the concept of autoencoders, which are neural networks that learn to encode and decode data.\n",
        "\n",
        "An autoencoder consists of two main components: an encoder and a decoder. The encoder takes an input data point and maps it to a lower-dimensional representation called the latent space. The decoder, on the other hand, takes a point in the latent space and reconstructs the original input data. The encoder and decoder are trained together in an unsupervised manner to minimize the reconstruction error, aiming to reconstruct the input data as accurately as possible.\n",
        "\n",
        "What sets VAEs apart from traditional autoencoders is their probabilistic nature. VAEs model the latent space as a probability distribution, typically a multivariate Gaussian distribution. This probabilistic interpretation allows VAEs to learn more flexible and expressive latent representations.\n",
        "\n",
        "During training, VAEs not only aim to reconstruct the input data accurately but also to match the learned distribution of the latent space to a desired prior distribution, usually a standard Gaussian distribution. This is achieved by introducing a regularization term called the Kullback-Leibler (KL) divergence, which measures the difference between the learned distribution and the desired prior distribution.\n",
        "\n",
        "By optimizing the reconstruction error and the KL divergence simultaneously, VAEs learn to generate new data points by sampling from the learned latent space distribution. This enables the generation of new and realistic data samples that resemble the training data.\n",
        "\n",
        "One of the key advantages of VAEs is their ability to perform latent space interpolation. By linearly interpolating between two points in the latent space and decoding the interpolated points, we can generate new data samples that smoothly transition between the corresponding samples in the input space. This property allows VAEs to explore the underlying structure of the data and generate meaningful variations."
      ],
      "metadata": {
        "id": "6JOBBdfM29c1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pndEvwT1eUBA"
      },
      "outputs": [],
      "source": [
        "\n",
        "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\n",
        "import numpy as np\n",
        "\n",
        "# Set random seed for reproducibility\n",
        "torch.manual_seed(42)\n",
        "\n",
        "# Define the variational autoencoder architecture\n",
        "class VariationalAutoencoder(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(VariationalAutoencoder, self).__init__()\n",
        "\n",
        "        self.encoder = nn.Sequential(\n",
        "            nn.Linear(784, 256),\n",
        "            nn.ReLU(),\n",
        "        )\n",
        "\n",
        "        self.mean = nn.Linear(256, 2)\n",
        "        self.logvar = nn.Linear(256, 2)\n",
        "\n",
        "        self.decoder = nn.Sequential(\n",
        "            nn.Linear(2, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(256, 784),\n",
        "            nn.Sigmoid(),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.encoder(x)\n",
        "        mean = self.mean(x)\n",
        "        logvar = self.logvar(x)\n",
        "\n",
        "        # Reparameterization trick\n",
        "        std = torch.exp(0.5 * logvar)\n",
        "        eps = torch.randn_like(std)\n",
        "        z = mean + eps * std\n",
        "\n",
        "        decoded = self.decoder(z)\n",
        "        return decoded, mean, logvar\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Load the MNIST dataset\n",
        "transform = transforms.ToTensor()\n",
        "trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True)\n",
        "\n",
        "# Create an instance of the variational autoencoder model\n",
        "vae = VariationalAutoencoder()\n",
        "\n",
        "# Define the loss function and optimizer\n",
        "criterion = nn.BCELoss(reduction='sum')\n",
        "optimizer = optim.Adam(vae.parameters(), lr=0.001)\n",
        "\n",
        "# Train the variational autoencoder\n",
        "num_epochs = 10\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    running_loss = 0.0\n",
        "    for data in trainloader:\n",
        "        images, _ = data\n",
        "        images = images.view(images.size(0), -1)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        outputs, mean, logvar = vae(images)\n",
        "\n",
        "        # Compute reconstruction loss\n",
        "        reconstruction_loss = criterion(outputs, images)\n",
        "\n",
        "        # Compute KL divergence loss\n",
        "        kl_loss = -0.5 * torch.sum(1 + logvar - mean.pow(2) - logvar.exp())\n",
        "\n",
        "        loss = reconstruction_loss + kl_loss\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(trainloader)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bv0llLsdegl8",
        "outputId": "085382ae-c869-4623-f322-7b0688f9ab3b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 119711922.45it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 8381932.87it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 28689026.14it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4542/4542 [00:00<00:00, 13997449.50it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
            "\n",
            "Epoch 1/10, Loss: 25059.37525819563\n",
            "Epoch 2/10, Loss: 21855.93986748734\n",
            "Epoch 3/10, Loss: 21171.954707489338\n",
            "Epoch 4/10, Loss: 20814.680934751465\n",
            "Epoch 5/10, Loss: 20576.895501565832\n",
            "Epoch 6/10, Loss: 20398.878770905518\n",
            "Epoch 7/10, Loss: 20252.7305416278\n",
            "Epoch 8/10, Loss: 20133.942851312633\n",
            "Epoch 9/10, Loss: 20023.200626332622\n",
            "Epoch 10/10, Loss: 19935.12286155717\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate images in the 2D latent space\n",
        "with torch.no_grad():\n",
        "    # Define the grid in the 2D latent space\n",
        "    n_samples_per_class = 12\n",
        "    x_values = torch.linspace(-3, 3, n_samples_per_class)\n",
        "    y_values = torch.linspace(-3, 3, n_samples_per_class)\n",
        "    grid_x, grid_y = torch.meshgrid(x_values, y_values)\n",
        "    z_grid = torch.stack([grid_x.flatten(), grid_y.flatten()], dim=1)\n",
        "\n",
        "    # Decode the grid points to generate images\n",
        "    generated_images = vae.decoder(z_grid).detach().numpy()\n",
        "\n",
        "# Visualize the generated images in the 2D latent space\n",
        "plt.figure(figsize=(15, 15))\n",
        "\n",
        "image_size = 28\n",
        "for i in range(generated_images.shape[0]):\n",
        "    image = generated_images[i].reshape(image_size, image_size)\n",
        "\n",
        "    ax = plt.subplot(n_samples_per_class, n_samples_per_class, i+1)\n",
        "    ax.imshow(image, cmap='gray')\n",
        "    ax.axis('off')\n",
        "\n",
        "plt.suptitle(\"Variational Autoencoder - Generated Images in 2D Latent Space\", fontsize=16)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7TG4DIUB0aoe",
        "outputId": "7025ed26-8ab2-41ba-b805-f1fc1d9176e7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 144 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The loss function\n",
        "\n",
        "The loss function for a Variational Autoencoder (VAE) consists of two main components: the reconstruction loss and the Kullback-Leibler (KL) divergence. These components work together to train the VAE to reconstruct the input data accurately while also regularizing the learned latent space distribution to match a prior distribution.\n",
        "\n",
        "Reconstruction Loss:\n",
        "The reconstruction loss measures the dissimilarity between the original input data and the reconstructed data produced by the VAE's decoder. It encourages the VAE to learn a meaningful latent representation that can faithfully reconstruct the input data. Typically, the reconstruction loss is computed using a pixel-wise error metric such as mean squared error (MSE) or binary cross-entropy (BCE), depending on the type of data.\n",
        "\n",
        "Kullback-Leibler (KL) Divergence:\n",
        "The KL divergence quantifies the difference between two probability distributions. In the case of VAEs, it measures the discrepancy between the learned latent space distribution and a prior distribution, usually a multivariate Gaussian distribution. The KL divergence encourages the VAE to shape the latent space distribution to match the desired prior distribution. By doing so, it helps regularize the learned representation and supports the generation of new data points from the latent space.\n",
        "\n",
        "The total loss function for the VAE combines these two components:\n",
        "\n",
        "\n",
        "**total_loss = reconstruction_loss + kl_loss**\n",
        "\n",
        "The reconstruction loss encourages the VAE to produce accurate reconstructions, while the KL loss encourages the VAE to learn a latent space distribution that aligns with the prior distribution. The relative weighting of these two components can be adjusted using a hyperparameter to balance the reconstruction fidelity and regularization strength.\n",
        "\n",
        "During training, the VAE aims to minimize this total loss function using gradient-based optimization techniques such as stochastic gradient descent (SGD) or Adam. By optimizing the loss function, the VAE learns to encode the input data into a meaningful latent space representation and generate new data samples by sampling from the latent space distribution.\n",
        "\n",
        "It's important to note that the choice of loss function may vary depending on the specific application and type of data. Additionally, other regularization techniques or modifications to the loss function, such as incorporating additional terms for specific objectives, can be introduced to further enhance the VAE's performance.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_E7klbNW9ZWC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The reparameterization Trick\n",
        "\n",
        "The reparameterization trick is a technique used in Variational Autoencoders (VAEs) to enable efficient gradient-based optimization during training. It is particularly useful when dealing with stochastic operations in the VAE's latent space.\n",
        "\n",
        "In VAEs, the latent space is modeled as a probability distribution, typically a multivariate Gaussian distribution. During training, we need to sample latent space points to generate new data samples and compute the reconstruction loss and KL divergence. However, directly backpropagating through these stochastic samples is not feasible because the gradients cannot flow through random variables.\n",
        "\n",
        "The reparameterization trick circumvents this issue by decoupling the randomness from the parameterization of the distribution. Instead of directly sampling from the distribution, we introduce a deterministic transformation that incorporates both the distribution parameters (mean and variance) and a noise term. By sampling the noise term from a fixed distribution, such as a standard Gaussian, we can obtain a stochastic sample that is differentiable with respect to the distribution parameters.\n",
        "\n",
        "Specifically, let's consider a latent space point z that follows a Gaussian distribution with mean μ and standard deviation σ. The reparameterization trick rewrites z as:\n",
        "\n",
        "\n",
        "\n",
        "**z = μ + σ * ε**\n",
        "\n",
        "where ε is the noise term sampled from a fixed distribution (e.g., a standard Gaussian).\n",
        "\n",
        "By reformulating the sampling process in this way, we can treat z as a deterministic function of the noise term ε and the distribution parameters μ and σ. Consequently, the gradients can flow through the deterministic path, allowing efficient backpropagation and gradient-based optimization.\n",
        "\n",
        "During training, the VAE learns to optimize the distribution parameters μ and σ to shape the latent space distribution. The KL divergence term in the loss function encourages the learned distribution to align with a prior distribution, such as a standard Gaussian. The reparameterization trick enables us to compute gradients for these distribution parameters and update them using standard backpropagation algorithms.\n",
        "\n"
      ],
      "metadata": {
        "id": "9hGsuoH191bY"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gdt6ZR4R1abM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# The manifold hypotheis again and VAE\n",
        "\n",
        "Variational Autoencoders (VAEs) offer a framework that supports the manifold hypothesis by learning a lower-dimensional latent space representation that captures the essential structure of the data. VAEs consist of an encoder network, a decoder network, and a probabilistic modeling approach.\n",
        "\n",
        "The encoder network maps the high-dimensional input data onto a lower-dimensional latent space. This latent space acts as a bottleneck where the data is compressed into a lower-dimensional representation. Despite this compression, the decoder network is designed to reconstruct the original input data from the latent space representation.\n",
        "\n",
        "The ability of VAEs to accurately reconstruct the entire information from the compressed latent space is a crucial aspect. It demonstrates that the high-dimensional data can be effectively represented in a lower-dimensional subspace without significant loss of information. This observation supports the idea that the real data lies in a low-dimensional subspace or manifold embedded within the high-dimensional input space.\n",
        "\n",
        "By imposing a prior distribution on the latent space, typically a multivariate Gaussian, VAEs encourage the learned distribution to follow a smooth and continuous structure. The smoothness of the latent space corresponds to the notion that similar data points in the input space are mapped to nearby points in the latent space. As a result, VAEs can effectively capture the underlying manifold structure of the data.\n",
        "\n",
        "Through the latent space representation, VAEs enable smooth transitions and interpolation between different data points. By sampling from the latent space, new data points can be generated that respect the learned manifold structure. This capability further supports the idea that the data distribution is concentrated in a lower-dimensional subspace rather than being uniformly spread throughout the high-dimensional space."
      ],
      "metadata": {
        "id": "Ko3XuyUK2s7G"
      }
    }
  ]
}