Defining the discriminator labels

Next, we define the label arrays to be used for the training and evaluation images, by calling the make_labels() function, and using the appropriate batch dimension. This will return us arrays with the labels 1 and 0 for each instance of the training and evaluation image:

# labels for the batch size and the test size
    y_train_real, y_train_fake = make_labels(batch_size)
    y_eval_real,  y_eval_fake  = make_labels(eval_size)

Initializing the GAN

Following this, we initialize the GAN network by calling the make_DCGAN() function we defined earlier and providing it with the appropriate arguments:

# create a GAN, a generator and a discriminator
    generator, discriminator, gan = make_DCGAN...