Machine Learning, Data Science, Artificial Intelligence, Towards Data Science, Deep Learning.
np.random.randn(num_vis, LATENT_DIM) imgs_fake = generator.predict(noise) for obj_plot in [imgs_fake, imgs_real]: plt.figure(figsize=(num_vis * 3, 3)) for b in range(num_vis): disc_score = float(discriminator.predict(np.expand_dims(obj_plot[b], axis=0))[0]) plt.subplot(1, num_vis, b + 1)