Generative Models#
A generative adversarial network trains two models together. A generator turns random noise into images, while a discriminator tries to distinguish generated images from real training images.
These notebooks preserve the source DCGAN structure for MNIST, including the latent dimension, batch size, optimizer choices, and fixed-noise image grids used to follow training progress.