Advanced Deep Learning with Keras

Disentangled Representation GANs
y[batch_size:, :] = 0
# train discriminator network, log the loss and label accuracy
outputs = [y, labels, codes1, codes2]
# metrics = ['loss', 'activation_1_loss', 'label_loss',
# 'code1_loss', 'code2_loss', 'activation_1_acc',
# 'label_acc', 'code1_acc', 'code2_acc']
# from discriminator.metrics_names
metrics = discriminator.train_on_batch(x, outputs)
fmt = "%d: [discriminator loss: %f, label_acc: %f]"
log = fmt % (i, metrics[0], metrics[6])
size])
# train the adversarial network for 1 batch
# 1 batch of fake images with label=1.0 and
# corresponding one-hot label or class + random codes
# since the discriminator weights are frozen in
# adversarial network only the generator is trained
# generate fake images, labels and codes
noise = np.random.uniform(-1.0, 1.0, size=[batch_size, latent_
fake_labels = np.eye(num_labels)[np.random.choice(num_labels,
batch_size)]
fake_code1 = np.random.normal(scale=0.5, size=[batch_size, 1])
fake_code2 = np.random.normal(scale=0.5, size=[batch_size, 1])
# label fake images as real
y = np.ones([batch_size, 1])
# note that unlike in discriminator training,
# we do not save the fake images in a variable
# the fake images go to the discriminator input of the
# adversarial for classification
# log the loss and label accuracy
inputs = [noise, fake_labels, fake_code1, fake_code2]
outputs = [y, fake_labels, fake_code1, fake_code2]
metrics = adversarial.train_on_batch(inputs, outputs)
fmt = "%s [adversarial loss: %f, label_acc: %f]"
log = fmt % (log, metrics[0], metrics[6])
print(log)
if (i + 1) % save_interval == 0:
if (i + 1) == train_steps:
show = True
else:
show = False
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