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GANs - Tensorflow 2
Tensorflow 2 implementations of DCGAN, LSGAN, WGAN-GP and DRAGAN.
DCGAN | LSGAN | WGAN-GP | DRAGAN |
---|---|---|---|
DCGAN | LSGAN |
---|---|
WGAN-GP | DRAGAN |
WGAN-GP | DRAGAN |
---|---|
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 scikit-image tqdm tensorflow-gpu=2.2
conda install -c conda-forge oyaml
pip install tensorflow-addons==0.10.0
NOTICE: if you create a new conda environment, remember to activate it before any other command
source activate tensorflow-2.2
Datasets
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
tensorboard --logdir ./output/fashion_mnist_gan/summaries --port 6006