This implementation use the 'Hook' classes in Tensorflow 1.0.0 or newer, which is really convenient.
In carpedm20's implementation,unbalanced update of G and D is required to get good results. This is most probably due to the way of feeding input batch. In original paper, new z's (random vector) and new real images are sampled for each update of D or G. However, carpedm20 uses the same inputs for a whole update iteration of D and G, that is sampling only once before training D d_step times and then G g_step times.

I implement both training strategies. The original one is refered as strategy1, and carpedm20's is refered as strategy2. By defautl, I use strategy1, but you can explicitly assign it by setting --training_strategy=[1|2].

Python3
Tensorflow>=1.0.0
Scipy Test

This code was tested on celebA. Of course you can try on other datasets.

• Download celebA dataset (use the donwload.py script borrowed from carpedm20)

python download.py celebA

Downloads will put under ./data.

We offer two strategies to train the DCGAN model.

• Strategy 1 (Just use the default settings)

python main.py 

• Strategy 2

python main.py --training_strategy=2 --g_step=2 

Not implement yet.

Two strategies both produce similar results.

• Strategy1

  

• Strategy2

  

python make_gif.py /dir/to/images output_path.gif