DCGAN

CNNs for a GAN had been unsuccessful for some time until authors of the paper() came up with the following approach.

Here are the architecture guidelines for stable deep convolutional GANs:

• Replace any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator)
• Use batch norm in both the generator and the discriminator
• Remove fully connected hidden layers for deeper architectures
• Use ReLU activation in the generator for all layers except for the output, which uses tanh
• Use LeakyReLU activation in the discriminator for all layers

To build this architecture, we are going to use the same Fashion-MNIST dataset. 

Getting ready

Make relevant imports and initialize the DCGAN...