Experimental setup

In this section, we are going to implement training functions that are compatible with the Progressive Growing of GANs methodology. These training and logging functions will be used to train the discriminator and the generator, which will also be implemented in this section.

We will also implement helper functions in order to make it easier to compute our loss function based on the Wasserstein GAN with Gradient Penalty.

Training

Our training method consists of the following two parts:

• Initializing the required variables and compiling models
• The training loop itself

We will start by looking at the training layout:

def train(n_channels=3, resolution=32, z_dim=128, n_labels=0, lr=1e-3,
          e_drift=1e...