Advanced Deep Learning with Keras

Chapter 5
# labels for real data
real_labels = np.ones((batch_size, 1))
for i in range(train_steps):
# train discriminator n_critic times
loss = 0
acc = 0
for _ in range(n_critic):
# train the discriminator for 1 batch
# 1 batch of real (label=1.0) and
# fake images (label=-1.0)
# randomly pick real images from dataset
rand_indexes = np.random.randint(0,
train_size,
size=batch_size)
real_images = x_train[rand_indexes]
# generate fake images from noise using generator
# generate noise using uniform distribution
noise = np.random.uniform(-1.0,
1.0,
size=[batch_size,
latent_size])
fake_images = generator.predict(noise)
# train the discriminator network
# real data label=1, fake data label=-1
# instead of 1 combined batch of real and fake images,
# train with 1 batch of real data first, then 1 batch
# of fake images.
# this tweak prevents the gradient from vanishing
# due to opposite signs of real and
# fake data labels (i.e. +1 and -1) and
# small magnitude of weights due to clipping.
real_loss, real_acc =
discriminator.train_on_batch(real_images,
real_labels)
fake_loss, fake_acc =
discriminator.train_on_batch(fake_images,
real_labels)
# accumulate average loss and accuracy
loss += 0.5 * (real_loss + fake_loss)
acc += 0.5 * (real_acc + fake_acc)
# clip discriminator weights to satisfy
# Lipschitz constraint
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