Advanced Deep Learning with Keras

Cross-Domain GANs
We introduce module mnist_svhn_utils.py to help us with this task. Listing 7.1.7
shows the initialization and training of the CycleGAN for cross-domain transfer.
The CycleGAN structure is same as in the previous section except that we use
a kernel size of 5 since the two domains are drastically different:
Figure 7.1.11: Two different domains with data that are not aligned. Original color photo can be
found on the book GitHub repository, https://github.com/PacktPublishing/Advanced-Deep-Learningwith-Keras/blob/master/chapter7-cross-domain-gan/README.md.
Remember to install keras-contrib before using instance
normalization:
$ sudo pip3 install git+https://www.github.com/kerasteam/keras-contrib.git
Listing 7.1.7, cyclegan-7.1.1.py shows us the CycleGAN for cross-domain style
transfer between MNIST and SVHN:
def mnist_cross_svhn(g_models=None):
"""Build and train a CycleGAN that can do mnist <--> svhn
"""
model_name = 'cyclegan_mnist_svhn'
batch_size = 32
train_steps = 100000
patchgan = True
kernel_size = 5
postfix = ('%dp' % kernel_size) if patchgan else ('%d' % kernel_
size)
data, shapes = mnist_svhn_utils.load_data()
source_data, _, test_source_data, test_target_data = data
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