# improved_wgan_training

**Repository Path**: BolinLi-s/improved_wgan_training

## Basic Information

- **Project Name**: improved_wgan_training
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-16
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Improved Training of Wasserstein GANs
=====================================

Code for reproducing experiments in ["Improved Training of Wasserstein GANs"](https://arxiv.org/abs/1704.00028).


## Prerequisites

- Python, NumPy, TensorFlow, SciPy, Matplotlib
- A recent NVIDIA GPU

## Models

Configuration for all models is specified in a list of constants at the top of
the file. Two models should work "out of the box":

- `python gan_toy.py`: Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll). 
- `python gan_mnist.py`: MNIST

For the other models, edit the file to specify the path to the dataset in
`DATA_DIR` before running. Each model's dataset is publicly available; the
download URL is in the file.

- `python gan_64x64.py`: 64x64 architectures (this code trains on ImageNet instead of LSUN bedrooms in the paper)
- `python gan_language.py`: Character-level language model
- `python gan_cifar.py`: CIFAR-10