Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
losses.append((d_loss/2, g_loss)) print("Epoch: {:>3}/{} Discriminator Loss: {:>6.4f} Generator Loss: {:>6.4f}".format( e+1, epochs, d_loss, g_loss)) show_images(x_eval_fake[:10]) # show the result show_losses(losses) show_images(g.predict(np.random.normal(loc=0, scale=1, size=(15, sample_size))))