Sh-31 / VAE-MNIST

Implementation of Variational Autoencoder (VAE) for the MNIST dataset, supporting both fully connected (FNN) and convolutional (CNN) architectures. It includes training, evaluation, and empirical analysis.

VAE-MNIST

Educational implementation of Variational Autoencoder (VAE) for the MNIST dataset, supporting both fully connected (FNN) and convolutional (CNN) architectures. It includes training, evaluation, and empirical analysis.

Features

• Train VAE models (FNN/CNN) on MNIST
• Configurable loss functions (BCE, MSE)
• Checkpointing and logging
• Empirical evaluation notebook
• Utilities for loading checkpoints and configuration

Project Structure

• main.py: Entry point for training
• model_fnn.py, model_cnn.py: Model definitions
• trainer.py: Training loop and logic
• utils/: Utility scripts (checkpoint loading, YAML config)
• empirical_eval.ipynb: Notebook for empirical evaluation
• data/: MNIST data
• outputs/: Model checkpoints, logs, generated samples

Installation

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt

Usage

• Training:

  python main.py --config config.yaml

• Empirical Evaluation: Open empirical_eval.ipynb in Jupyter and run the cells.

Configuration

Edit config.yaml to change model, training parameters.

Requirements

See requirements.txt for Python dependencies.

Empirical analysis

• FNN-VAEs and CNN-VAEs were trained with both MSE and BCE losses.
• For each model, we visualize:
  • Reconstruction of real images (original vs. generated)
  • Generation of new images by sampling from the latent space

Example Results

Model	Loss	Reconstruction Example	Generation Example
FNN	BCE
FNN	MSE
CNN	BCE
CNN	MSE

Observations

• CNN-VAEs outperform FNN-VAEs, likely due to better preservation of image structure.
• BCE loss yields better reconstructions than MSE loss in this experiment.

For detailed code and more visualizations, see the empirical_eval.ipynb notebook.

License

MIT License