A minimal implementation of an Variational Auto-Encoder
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Minimal Variational Auto-Encoder

This is a minimal implementation of an Variational Auto-Encoder in Tensorflow applied to MNIST.

Some example generated numbers:

VAE results

How to run

Simply clone the directory and run the file vae_mnist.py. Results will be displayed in real time, while full training takes a few minutes.

Implementation details

The implementation follows Auto-Encoding Variational Bayes. Both the generator and discriminator uses 3 convolutional layers with 5x5 convolutions, with obvious room for improvements.