Auto-Encoding-Variational-Bayes (VAE)

This project implements the models proposed in Auto-Encoding Variational Bayes (VAE) paper written by Diederik P Kingma and Max Welling.

The models were ran over the following datasets:

• MNIST
• MNIST Fashion

The results of the models can be found in results.pdf.

Below are samples generated by the trained VAE model on MNIST and Fashion-MNIST datasets:

• Fashion-MNIST Samples: Images generated after training a VAE on the Fashion-MNIST dataset. The model has learned to capture and reproduce the structure of clothing items like shoes, shirts, and bags.

  

• MNIST Samples: Digits generated by a VAE trained on the MNIST dataset.

  

Here’s a README.md-style section in the format you asked for, tailored to the VAE training project:

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Setup

Prerequisites

• Python 3.x
• NumPy
• torch
• torchvision
• matplotlib

You can install the dependencies using:

pip install -r requirements.txt

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Running the Code

To train the VAE model on either MNIST or Fashion-MNIST:

python main.py

You can customize the training with the following command-line arguments:

Argument	Description	Default
--dataset	Dataset to train on: mnist or fashion-mnist	mnist
--batch_size	Number of images per mini-batch	128
--epochs	Number of training epochs	50
--sample_size	Number of images to generate during sampling	64
--latent-dim	Dimensionality of the latent space (z vector size)	100
--lr	Initial learning rate for the optimizer	1e-3

Example with Custom Parameters:

python main.py --dataset fashion-mnist --batch_size 64 --epochs 40 --latent-dim 50 --lr 0.0005

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Output Files

• Generated Samples: After training, 100 samples are generated and saved in the ./samples/ folder as:

  • fashion-mnist_batch128_mid100_.png
  • mnist_batch128_mid100_.png

• Loss Tracking: The training and testing loss for each epoch is saved in a pickle file:

  • loss_batches_mnist.pkl or loss_batches_fashion-mnist.pkl