Advanced Deep Learning with Keras

Chapter 6
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 gan.py in the lib folder.
The complete code is available on GitHub:
https://github.com/PacktPublishing/Advanced-Deep-
Learning-with-Keras
Listing 6.1.1, infogan-mnist-6.1.1.py 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):
"""Build a Generator Model
Stack of BN-ReLU-Conv2DTranpose to generate fake images.
Output activation is sigmoid instead of tanh in [1].
Sigmoid converges easily.
# Arguments
inputs (Layer): Input layer of the generator (the z-vector)
image_size (int): Target size of one side (assuming square
image)
activation (string): Name of output activation layer
labels (tensor): Input labels
codes (list): 2-dim disentangled codes for InfoGAN
# Returns
Model: Generator Model
"""
image_resize = image_size // 4
# network parameters
kernel_size = 5
layer_filters = [128, 64, 32, 1]
if labels is not None:
if codes is None:
[ 167 ]