Advanced Deep Learning with Keras

Improved GANs
The functions include:
• generator(): A generator model builder
• discriminator(): Discriminator model builder
• train(): DCGAN trainer
• plot_images(): Generic generator outputs plotter
• test_generator(): Generic generator test utility
As shown in Listing 5.1.1, we can build a discriminator by simply calling:
discriminator = gan.discriminator(inputs, activation='linear')
WGAN uses linear output activation. For the generator, we execute:
generator = gan.generator(inputs, image_size)
The overall network model in Keras is similar to the one seen in Figure 4.2.1
for DCGAN.
Listing 5.1.1 highlights the use of the RMSprop optimizer and Wasserstein loss
function. The hyper-parameters in Algorithm 5.1.1 are used during training. Listing
5.1.2 is the training function that closely follows the algorithm. However, there
is a minor tweak in the training of the discriminator. Instead of training the weights
in a single combined batch of both real and fake data, we'll train with one batch
of real data first and then a batch of fake data. This tweak will prevent the gradient
from vanishing because of the opposite sign in the label of real and fake data and
the small magnitude of weights due to clipping.
The complete code is available on GitHub:
https://github.com/PacktPublishing/Advanced-Deep-
Learning-with-Keras
Figure 5.1.4 shows the evolution of the WGAN outputs on MNIST dataset.
Listing 5.1.1, wgan-mnist-5.1.2.py. The WGAN model instantiation and training.
Both discriminator and generator use Wassertein 1 loss, wasserstein_loss():
def build_and_train_models():
# load MNIST dataset
(x_train, _), (_, _) = mnist.load_data()
# reshape data for CNN as (28, 28, 1) and normalize
image_size = x_train.shape[1]
x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
[ 136 ]