Pytorch implementation of a Variational Autoencoder with Gumbel-Softmax Distribution. Refer to the following paper:
- Categorical Reparametrization with Gumbel-Softmax by Jang, Gu and Poole
The program requires the following dependencies (easy to install using pip or Ananconda):
- python 2.7/3.5
- pytorch (version 0.3.1)
- numpy
Train VAE-Gumbel-Softmax model on the local machine using MNIST dataset:
python vae_gumbel_softmax.py
Batch Size: 128
Learning Rate: 0.0001
Initial Temperature: 1.0
Minimum Temperature: 0.5
Anneal Rate: 0.00003
Learnable Temperature: False
Ground Truth/Reconstructions | Generated Samples |
---|---|