Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Towards Data Science.
discriminator.train_on_batch(genImages, np.zeros([halfSize, 1])) dLoss = np.add(dLossF, dLossR) * 0.5 discriminator.trainable = False noise = np.random.normal(0, 1, (batchSize, Noise_dim)) gLoss = gan.train_on_batch(noise, np.ones([batchSize, 1])) dLossReal.append([e, dLoss[0]])