Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
np.array([preprocess(load_image(filename)) for filename in path_batch]) noise = np.random.normal(0, 1, size=(batch_size, noise_dim)) generated_images = g.predict_on_batch(noise) # Train discriminator on generated images d.trainable = True d.train_on_batch(image_batch, y_train_real*(1-smooth))