GANs tend to be sensitive to the network architecture, I implemented a DCGAN architecture in this example, because it is relatively stable during training while being simple to implement. We use a constant number of filters throughout the network, use a sigmoid instead of tanh in the last layer of the generator, and use default initialization instead of random normal as further simplifications.