Machine Learning, Data Science, Artificial Intelligence, Towards Data Science, Deep Learning.
discriminator.train_on_batch(imgs_fake, np.zeros([BATCH_SIZE]))[1] # Progress visualizations. if total_it % PROGRESS_INTERVAL == 0: plt.figure(figsize=(5,2)) # We sample separate images. num_vis = min(BATCH_SIZE, 8) imgs_real = X[np.random.randint(0, X.shape[0], size=num_vis)] noise =