Machine Learning, Data Science, Artificial Intelligence, Towards Data Science, Deep Learning.
range(NUM_SAMPLES): noise = np.random.randn(1, LATENT_DIM) pred_raw = generator.predict(noise)[0] pred = pred_raw * 0.5 + 0.5 plt.subplot(1, NUM_SAMPLES, i + 1) plt.imshow(pred) plt.show() Samples from trained GAN Voila! That is our first implementation of a basic GAN. This is probably the simplest