Advanced Deep Learning with Keras

Cross-Domain GANs
Finally, before we can use the CycleGAN to build and train functions, we have
to perform some data preparation. The modules cifar10_utils.py and other_
utils.py load the CIFAR10 train and test data. Please refer to the source code
for details of these two files. After loading, the train and test images are converted
to grayscale to generate the source data and test source data.
Following listing shows how the CycleGAN is used to build and train a generator
network (g_target) for colorization of grayscale images. Since CycleGAN is
symmetric, we also build and train a second generator network (g_source) that
converts from color to grayscale. Two CycleGAN colorization networks were
trained. The first use discriminators with a scalar output similar to vanilla GAN.
The second uses a 2 × 2 PatchGAN.
Listing 7.1.6, cyclegan-7.1.1.py shows us the CycleGAN for colorization problem:
def graycifar10_cross_colorcifar10(g_models=None):
"""Build and train a CycleGAN that can do grayscale <--> color
cifar10 images
"""
model_name = 'cyclegan_cifar10'
batch_size = 32
train_steps = 100000
patchgan = True
kernel_size = 3
postfix = ('%dp' % kernel_size) if patchgan else
('%d' % kernel_size)
data, shapes = cifar10_utils.load_data()
source_data, _, test_source_data, test_target_data = data
titles = ('CIFAR10 predicted source images.',
'CIFAR10 predicted target images.',
'CIFAR10 reconstructed source images.',
'CIFAR10 reconstructed target images.')
dirs = ('cifar10_source-%s' % postfix, 'cifar10_target-%s'
% postfix)
# generate predicted target(color) and source(gray) images
if g_models is not None:
g_source, g_target = g_models
other_utils.test_generator((g_source, g_target),
(test_source_data, test_target_
data),
step=0,
titles=titles,
[ 224 ]