May 2019
Intermediate to advanced
272 pages
7h 19m
English
Content preview from Hands-On Generative Adversarial Networks with Keras
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Loss functions
We learned about multiple loss functions, the problems they claim to solve, and how to implement them. We looked at the original GAN loss, the Wasserstein GAN (WGAN), the Wasserstein GAN with Gradient Penalty (WGAN-GP), the Least Squares GAN (LSGAN), and the Relativistic GAN (RGAN).
We also looked into adding terms to the GAN loss function, including feature matching loss, reconstruction loss, and VGG loss.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.