Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
filename in test_path]) noise = np.random.normal(loc=0, scale=1, size=(eval_size, sample_size)) x_eval_fake = g.predict_on_batch(noise) d_loss = d.test_on_batch(x_eval_real, y_eval_real) d_loss += d.test_on_batch(x_eval_fake, y_eval_fake) g_loss = gan.test_on_batch(noise, y_eval_real)