Keras wasserstein loss. In a standard GAN, the discriminator has a sigmoid output, representing the probability that samples are real or generated. The score is maximizing for real examples and minimizing for fake examples. 0 GAN? Wasserstein GAN? Code! Loss function for Discriminator Creating Discriminator Creating Generator Training Results Links Code for the article While reading the Wasserstein GAN paper I decided that the best way to understand it is to code it. Jul 23, 2025 · Our goal is to minimize the Wasserstein distance between distribution of generated samples and distribution of real samples. Jul 14, 2019 · In the Keras deep learning library (and some others), we cannot implement the Wasserstein loss function directly as described in the paper and as implemented in PyTorch and TensorFlow. However i don't understand your given loss as well, even it is found through implementations. Jul 19, 2019 · That means there exist a Lipschitz function that gives a positive value which is larger than that Lipschitz function that gives negative value. The network learns to minimize the amount of discrepancy between generated and real values. 1. Jul 14, 2019 · The Wasserstein Generative Adversarial Network, or Wasserstein GAN, is an extension to the generative adversarial network that both improves the stability when training the model and provides a loss function that correlates with the quality of generated images. iim wqhn sinsxo heyo beydgvc rehe phme ell rmj qgywwuz