Machine Learning, Data Science, Artificial Intelligence, Towards Data Science, Deep Learning.
np.random.randn(BATCH_SIZE, LATENT_DIM) loss += gan.train_on_batch(noise, y)[1] loss_discriminator.append((d_loss_real + d_loss_fake) / 2.) loss_generator.append(loss / GEN_UPDATES) total_it += 1 # Progress visualization. clear_output(True) print('Epoch', epoch)