Advanced Deep Learning with Keras

Disentangled Representation GANs
data = (x_train, y_train)
params = (batch_size, latent_size, train_steps, num_labels,
model_name)
train(models, data, params)
As far as the training is concerned, we can see that InfoGAN is similar to ACGAN
except that we need to supply c for the continuous code. c is drawn from normal
distribution with a standard deviation of 0.5 and mean of 0.0. We'll use randomly
sampled labels for the fake data and dataset class labels for the real data to represent
discrete latent code. Following listing highlights the changes made on the training
function. Similar to all previous GANs, the discriminator and generator (through
adversarial) are trained alternately. During adversarial training, the discriminator
weights are frozen. Sample generator output images are saved every 500 interval
steps by using the gan.py plot_images() function.
Listing 6.1.4, infogan-mnist-6.1.1.py shows us how the training function for
InfoGAN is similar to ACGAN. The only difference is that we supply continuous
codes sampled from a normal distribution:
def train(models, data, params):
"""Train the Discriminator and Adversarial networks
Alternately train discriminator and adversarial networks by batch.
Discriminator is trained first with real and fake images,
corresponding one-hot labels and continuous codes.
Adversarial is trained next with fake images pretending to be
real,
corresponding one-hot labels and continous codes.
Generate sample images per save_interval.
# Arguments
models (Models): Generator, Discriminator, Adversarial models
data (tuple): x_train, y_train data
params (tuple): Network parameters
"""
# the GAN models
generator, discriminator, adversarial = models
# images and their one-hot labels
x_train, y_train = data
# network parameters
batch_size, latent_size, train_steps, num_labels, model_name =
params
# the generator image is saved every 500 steps
save_interval = 500
# noise vector to see how the generator output evolves
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