GANs - Tensorflow 2

Tensorflow 2 implementations of DCGAN, LSGAN, WGAN-GP and DRAGAN.

Exemplar results

Fashion-MNIST

DCGAN	LSGAN	WGAN-GP	DRAGAN

CelebA

DCGAN	LSGAN

WGAN-GP	DRAGAN

Anime

WGAN-GP	DRAGAN

Usage

Environment

Python 3.6
TensorFlow 2.2, TensorFlow Addons 0.10.0
OpenCV, scikit-image, tqdm, oyaml

we recommend Anaconda or Miniconda, then you can create the TensorFlow 2.2 environment with commands below

conda create -n tensorflow-2.2 python=3.6

source activate tensorflow-2.2

conda install -c anaconda scikit-image

conda install -c anaconda tqdm

conda install -c conda-forge oyaml

conda install -c anaconda tensorflow-gpu=2.2

pip install tensorflow-addons==0.10.0

Datasets
- Fashion-MNIST will be automatically downloaded
- CelebA should be prepared by yourself in ./data/img_align_celeba/*.jpg
  - dataset link: Dropbox
  - the above link might be inaccessible, the alternatives are (find "img_align_celeba.zip")
    - Baidu Netdisk or
    - Google Drive
- the Anime dataset should be prepared by yourself in ./data/faces/*.jpg
  - dataset link: https://pan.baidu.com/s/1eSifHcA, password: g5qa
  - reference: https://zhuanlan.zhihu.com/p/24767059

Examples of training

Fashion-MNIST DCGAN

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=25 --adversarial_loss_mode=gan

CelebA DRAGAN

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=gan --gradient_penalty_mode=dragan

Anime WGAN-GP

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=200 --adversarial_loss_mode=wgan --gradient_penalty_mode=wgan-gp --n_d=5

see more training exampls in commands.sh

tensorboard for loss visualization