PyTorch Variational Autoencoder

Introduction

PyTorch variational autoencoder (VAE) example for MNIST dataset. The modeled posterior distribution follows a Gaussian distribution with a full covariance matrix.

Usages

Build Docker Image

$ docker build -f docker/pytorch.Dockerfile --no-cache --tag=pytorch:2.2.0 .

Run Docker Container

$ docker run -it --rm --gpus device=0 --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -v $(pwd):/mnt pytorch:2.2.0

Run Variational Autoencoder MNIST Training

$ python train.py

Examine Results

The results will be saved to the results directory.

References

• Variational Autoencoder
• PyTorch Variational Autoencoder