WGAN-TensorFlow

This repository is a Tensorflow implementation of Martin Arjovsky's Wasserstein GAN, arXiv:1701.07875v3.

Requirements

tensorflow 1.9.0
python 3.5.3
numpy 1.14.2
pillow 5.0.0
scipy 0.19.0
matplotlib 2.2.2

Applied GAN Structure

Generator (DCGAN)

Critic (DCGAN)

Generated Images

MNIST

CelebA

**Note:** The results are not good as paper mentioned. We found that the Wasserstein distance can't converge well in the CelebA dataset, but it decreased in MNIST dataset.

Documentation

Download Dataset

MNIST dataset will be downloaded automatically if in a specific folder there are no dataset. Use the following command to download CelebA dataset and copy the `CelebA' dataset on the corresponding file as introduced in Directory Hierarchy information.

python download2.py celebA

Directory Hierarchy

.
│   WGAN
│   ├── src
│   │   ├── dataset.py
│   │   ├── download2.py
│   │   ├── main.py
│   │   ├── solver.py
│   │   ├── tensorflow_utils.py
│   │   ├── utils.py
│   │   └── wgan.py
│   Data
│   ├── celebA
│   └── mnist

src: source codes of the WGAN

Implementation Details

Implementation uses TensorFlow to train the WGAN. Same generator and critic networks are used as described in Alec Radford's paper. WGAN does not use a sigmoid function in the last layer of the critic, a log-likelihood in the cost function. Optimizer is used RMSProp instead of Adam.

Training WGAN

Use main.py to train a WGAN network. Example usage:

python main.py --is_train=true --dataset=[celebA|mnist]

gpu_index: gpu index, default: 0
batch_size: batch size for one feed forward, default: 64
dataset: dataset name for choice [celebA|mnist], default: celebA
is_train: training or inference mode, default: False
learning_rate: initial learning rate, default: 0.00005
num_critic: the number of iterations of the critic per generator iteration, default: 5
z_dim: dimension of z vector, default: 100
iters: number of interations, default: 100000
print_freq: print frequency for loss, default: 50
save_freq: save frequency for model, default: 10000
sample_freq: sample frequency for saving image, default: 200
sample_size: sample size for check generated image quality, default: 64
load_model: folder of save model that you wish to test, (e.g. 20180704-1736). default: None

Wasserstein Distance During Training

MNIST

CelebA

Evaluate WGAN

Use main.py to evaluate a WGAN network. Example usage:

python main.py --is_train=false --load_model=folder/you/wish/to/test/e.g./20180704-1746

Please refer to the above arguments.

Citation

  @misc{chengbinjin2018wgan,
    author = {Cheng-Bin Jin},
    title = {WGAN-tensorflow},
    year = {2018},
    howpublished = {\url{https://github.com/ChengBinJin/WGAN-TensorFlow}},
    note = {commit xxxxxxx}
  }

Attributions/Thanks

This project borrowed some code from wiseodd
Some readme formatting was borrowed from Logan Engstrom

License

Copyright (c) 2018 Cheng-Bin Jin. Contact me for commercial use (or rather any use that is not academic research) (email: sbkim0407@gmail.com). Free for research use, as long as proper attribution is given and this copyright notice is retained.

liuguiyangnwpu / WGAN-TensorFlow