Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
np.random.randint(0, X_train.shape[0], halfSize) images = X_train[index] noise = np.random.normal(0, 1, (halfSize, Noise_dim)) genImages = generator.predict(noise) # one-sided labels discriminator.trainable = True dLossR = discriminator.train_on_batch(images, np.ones([halfSize, 1])) dLossF =