Advanced Deep Learning with Keras

Chapter 6
z_dim])
z_dim])
real_z1 = np.random.normal(scale=0.5, size=[batch_size,
# real labels from dataset
real_labels = y_train[rand_indexes]
# generate fake feature1 using generator1 from
# real labels and 50-dim z1 latent code
fake_z1 = np.random.normal(scale=0.5, size=[batch_size,
fake_feature1 = gen1.predict([real_labels, fake_z1])
# real + fake data
feature1 = np.concatenate((real_feature1, fake_feature1))
z1 = np.concatenate((fake_z1, fake_z1))
# label 1st half as real and 2nd half as fake
y = np.ones([2 * batch_size, 1])
y[batch_size:, :] = 0
# train discriminator1 to classify feature1
# as real/fake and recover
# latent code (z1). real = from encoder1,
# fake = from genenerator1
# joint training using discriminator part of advserial1 loss
# and entropy1 loss
metrics = dis1.train_on_batch(feature1, [y, z1])
# log the overall loss only (fr dis1.metrics_names)
log = "%d: [dis1_loss: %f]" % (i, metrics[0])
z_dim])
# train the discriminator0 for 1 batch
# 1 batch of real (label=1.0) and fake images (label=0.0)
# generate random 50-dim z0 latent code
fake_z0 = np.random.normal(scale=0.5, size=[batch_size,
# generate fake images from real feature1 and fake z0
fake_images = gen0.predict([real_feature1, fake_z0])
# real + fake data
x = np.concatenate((real_images, fake_images))
z0 = np.concatenate((fake_z0, fake_z0))
# train discriminator0 to classify image as real/fake
and recover
# latent code (z0)
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