Tensorflow implemetation of various GANs on MNIST.

Download mnist.npz from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz .

Put it into data/mnist.

Train a wgan with gradient clipping to -0.05 to 0.05, with D and G using CNN structure, D:G=5:1.

python main.py --gan_type wgan --net_type cnn --clip 0.05 --D_iter 5 --epoch 10 

Execute run.sh to train all models and generate images.

./run.sh

Clean figs, checkpoints and logs of one model.

python main.py --todo clear --gan_type gan --net_type cnn  

Name	Epoch 0	Epoch 5	Epoch 9
GAN
WGAN
WGAN-GP
LSGAN
DRAGAN
CGAN

Name	Epoch 0	Epoch 10	Epoch 19
GAN
WGAN
WGAN-GP
LSGAN
DRAGAN
CGAN

• tensorflow-generative-model-collections
• sngan_projection
• Spectral_Normalization-Tensorflow
• WassersteinGAN
• gan_practice

G and D in tensorflow-generative-model-collection

G and D in Simple-GAN-MNIST