The training function

Next comes the training function. Yes, it is a big one. Yet, as you will soon see, it is quite intuitive, and basically combines everything we have implemented so far:

def train(
    g_learning_rate,   # learning rate for the generator
    g_beta_1,          # the exponential decay rate for the 1st moment estimates in Adam optimizer
    d_learning_rate,   # learning rate for the discriminator
    d_beta_1,          # the exponential decay rate for the 1st moment estimates in Adam optimizer
    leaky_alpha,
    init_std,
    smooth=0.1,        # label smoothing
    sample_size=100,   # latent sample size (i.e. 100 random numbers)
    epochs=200,
    batch_size=128,    # train batch size
    eval_size=16):      # evaluate size
    
    # 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...