Advanced Deep Learning with Keras

# 2) categorical cross entropy image label,
# 3) and 4) mutual information loss
loss = ['binary_crossentropy', 'categorical_crossentropy', mi_
loss, mi_loss]
# lamda or mi_loss weight is 0.5
loss_weights = [1.0, 1.0, 0.5, 0.5]
discriminator.compile(loss=loss,
loss_weights=loss_weights,
optimizer=optimizer,
metrics=['accuracy'])
discriminator.summary()
Chapter 6
# build generator model
input_shape = (latent_size, )
inputs = Input(shape=input_shape, name='z_input')
labels = Input(shape=label_shape, name='labels')
code1 = Input(shape=code_shape, name="code1")
code2 = Input(shape=code_shape, name="code2")
# call generator with inputs,
# labels and codes as total inputs to generator
generator = gan.generator(inputs,
image_size,
labels=labels,
codes=[code1, code2])
generator.summary()
# build adversarial model = generator + discriminator
optimizer = RMSprop(lr=lr*0.5, decay=decay*0.5)
discriminator.trainable = False
# total inputs = noise code, labels, and codes
inputs = [inputs, labels, code1, code2]
adversarial = Model(inputs,
discriminator(generator(inputs)),
name=model_name)
# same loss as discriminator
adversarial.compile(loss=loss,
loss_weights=loss_weights,
optimizer=optimizer,
metrics=['accuracy'])
adversarial.summary()
# train discriminator and adversarial networks
models = (generator, discriminator, adversarial)
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