Next, we weave together the two modules using this function shown here. As arguments, it takes the size of the latent samples for the generator, which will be transformed by the generator network to produce synthetic images. It also accepts a learning rate and a decay rate for both the generator and discriminator networks. Finally, the last two arguments denote the alpha value for the LeakyReLU activation function used, as well as a standard deviation value for the random initialization of network weights:
def make_DCGAN(sample_size, g_learning_rate, g_beta_1, d_learning_rate, d_beta_1, leaky_alpha, init_std): # clear first K.clear_session() # generator generator = gen(sample_size, leaky_alpha, init_std) # discriminator discriminator...
