This example shows how to train a generative adversarial network (GAN) to generate images. A generative adversarial network (GAN) is a type of deep learning network that can generate data with similar characteristics as the input training data. The generator - Given a vector or random values as input, this network generates data with the same structure as the training data. The discriminator - Given batches of data containing observations from both the training data, and generated data from the generator, this network attempts to classify the observations as "real" or "generated". Train the generator to generate data that "fools" the discriminator.