Implementation of InfoGAN in Keras

To implement InfoGAN on MNIST dataset, there are some changes that need to be made in the base code of ACGAN. As highlighted in following listing, the generator concatenates both entangled (z noise code) and disentangled codes (one-hot label and continuous codes) to serve as input. The builder functions for the generator and discriminator are also implemented in in the lib folder.


The complete code is available on GitHub:

Listing 6.1.1, shows us how the InfoGAN generator concatenates both entangled and disentangled codes to serve as input:

def generator(inputs, image_size, activation='sigmoid', labels=None, codes=None): ...

Get Advanced Deep Learning with Keras now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.