Machine Learning, Data Science, Artificial Intelligence, Towards Data Science, Deep Learning.
entries, too). imgs_real = X[np.random.randint(0, X.shape[0], size=BATCH_SIZE)] # Generate fake examples. noise = np.random.randn(BATCH_SIZE, LATENT_DIM) imgs_fake = generator.predict(noise) d_loss_real = discriminator.train_on_batch(imgs_real, np.ones([BATCH_SIZE]))[1] d_loss_fake =