Advanced Deep Learning with Keras

Chapter 5
# save the model after training the generator
# the trained generator can be reloaded for future
# MNIST digit generation
generator.save(model_name + ".h5")
Figure 5.1.4: The sample outputs of WGAN vs. training steps.
WGAN does not suffer mode collapse in all the outputs during training and testing.
WGAN is stable even under network configuration changes. For example,
DCGAN is known to be unstable when batch normalization is inserted before
the ReLU in the discriminator network. The same configuration is stable in WGAN.
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