GMMN-PyTorch

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This is a simple implementation of the ICML2015 paper ‘Generative Moment Matching Networks’ in pytorch. See

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# Generative Moment Matching Networks(GMMN)
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## Introduction
 * The code is widely adapted from two nice repositories: 
<https://github.com/Abhipanda4/GMMN-Pytorch> 
<https://github.com/siddharth-agrawal/Generative-Moment-Matching-Networks>. 
 * I failed to implement the greedy layer-wise pretraining and fine-tuning scheme mentioned in the paper as I have not fully understood it.

## Environment & Requirements
* CentOS Linux release 7.2.1511 (Core) 
* python 3.6.5 
* pytorch 1.0.0 
* torchvision 
* argparse 
* pickle

## Architecture
![](https://github.com/gxwangupc/GMMN-PyTorch/blob/master/architecture.png)
## Usage
### 1. Train the networks using MNIST: 
 python train.py
Two folders will be created, i.e., `./data` & `./models`. As their names imply, they are used to store data and trained models, repectively. 
### 2. Visualize the outputs of the gmmn: 
 python Visualize.py --visualize gmmn
### 3. Visualize the outputs of the autoencoder: 
 python Visualize.py --visualize autoencoder

## References 
<https://github.com/Abhipanda4/GMMN-Pytorch> 
<https://github.com/siddharth-agrawal/Generative-Moment-Matching-Networks>

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