Advanced Deep Learning with Keras

Cross-Domain GANs
kernel_size=kernel_size,
name=dt_name)
d_source = build_discriminator(source_shape,
patchgan=patchgan,
kernel_size=kernel_size,
name=ds_name)
print('---- TARGET DISCRIMINATOR ----')
d_target.summary()
print('---- SOURCE DISCRIMINATOR ----')
d_source.summary()
optimizer = RMSprop(lr=lr, decay=decay)
d_target.compile(loss='mse',
optimizer=optimizer,
metrics=['accuracy'])
d_source.compile(loss='mse',
optimizer=optimizer,
metrics=['accuracy'])
# freeze the discriminator weights in the adversarial model
d_target.trainable = False
d_source.trainable = False
# build the computational graph for the adversarial model
# forward cycle network and target discriminator
source_input = Input(shape=source_shape)
fake_target = g_target(source_input)
preal_target = d_target(fake_target)
reco_source = g_source(fake_target)
# backward cycle network and source discriminator
target_input = Input(shape=target_shape)
fake_source = g_source(target_input)
preal_source = d_source(fake_source)
reco_target = g_target(fake_source)
# if we use identity loss, add 2 extra loss terms
# and outputs
if identity:
iden_source = g_source(source_input)
iden_target = g_target(target_input)
loss = ['mse', 'mse', 'mae', 'mae', 'mae', 'mae']
loss_weights = [1., 1., 10., 10., 0.5, 0.5]
inputs = [source_input, target_input]
outputs = [preal_source,
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