Advanced Deep Learning with Keras

Chapter 4
discriminator.trainable = False
# adversarial = generator + discriminator
adversarial = Model(inputs,
discriminator(generator(inputs)),
name=model_name)
adversarial.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
adversarial.summary()
# train discriminator and adversarial networks
models = (generator, discriminator, adversarial)
params = (batch_size, latent_size, train_steps, model_name)
train(models, x_train, params)
Listing 4.2.4 shows the function dedicated to training the discriminator and
adversarial networks. Due to custom training, the usual fit() function is not going
to be used. Instead, train_on_batch() is called up to run a single gradient update
for the given batch of data. The generator is then trained via an adversarial network.
The training first randomly picks a batch of real images from the dataset. This is
labeled as real (1.0). Then a batch of fake images will be generated by the generator.
This is labeled as fake (0.0). The two batches are concatenated and are used to train
the discriminator.
After this is completed, a new batch of fake images will be generated by the
generator and labeled as real (1.0). This batch will be used to train the adversarial
network. The two networks are trained alternately for about 40,000 steps. At regular
intervals, the generated MNIST digits based on a certain noise vector are saved on
the filesystem. At the last training step, the network has converged. The generator
model is also saved on a file so we can easily reuse the trained model for future
MNIST digits generation. However, only the generator model is saved since that
is the useful part of GANs in the generation of new MNIST digits. For example,
we can generate new and random MNIST digits by executing:
python3 dcgan-mnist-4.2.1.py --generator=dcgan_mnist.h5
Listing 4.2.4, dcgan-mnist-4.2.1.py shows us the function to train the
discriminator and adversarial networks:
def train(models, x_train, params):
"""Train the Discriminator and Adversarial Networks
Alternately train Discriminaor and Adversarial networks by batch.
Discriminator is trained first with properly real and fake images.
Adversarial is trained next with fake images pretending to be real
Generate sample images per save_interval.
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