Advanced Deep Learning with Keras

Cross-Domain GANs
# else use 1-dim output of probability
if patchgan:
x = LeakyReLU(alpha=0.2)(x)
outputs = Conv2D(1,
kernel_size=kernel_size,
strides=1,
padding='same')(x)
else:
x = Flatten()(x)
x = Dense(1)(x)
outputs = Activation('linear')(x)
discriminator = Model(inputs, outputs, name=name)
return discriminator
Using the generator and discriminator builders, we are now able to build the
CycleGAN. Listing 7.1.4 shows the builder function. In line with our discussion in
the previous section, two generators, g_source = F and g_target = G, and two
discriminators, d_source = D x
and d_target = D y
are instantiated. The forward
cycle is x ' = F(G(x)) = reco_source = g_source(g_target(source_input)).
The backward cycle is y ' = G(F(y)) = reco_target = g_target(g_source
(target_input)).
The inputs to the adversarial model are the source and target data while the outputs
are the outputs of D x
and D y
and the reconstructed inputs, x' and y.' The identity
network is not used in this example due to the difference between the number
of channels of the grayscale image and color image. We use the recommended
loss weights of λ
1
= 1.0 and λ
2
= 10.0 for the GAN and cyclic consistency losses
respectively. Similar to GANs in the previous chapters, we use RMSprop with
a learning rate of 2e-4 and decay rate of 6e-8 for the optimizer of the discriminators.
The learning and decay rate for the adversarial is half of the discriminator's.
Listing 7.1.4, cyclegan-7.1.1.py shows us the CycleGAN builder in Keras:
def build_cyclegan(shapes,
source_name='source',
target_name='target',
kernel_size=3,
patchgan=False,
identity=False
):
"""Build the CycleGAN
[ 218 ]