February 2019
Intermediate to advanced
386 pages
9h 54m
English
Content preview from Hands-On Unsupervised Learning with Python
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Analyzing a GAN
Let's suppose that we have a GAN that has been properly trained by using a dataset X, drawn from pdata(x). Goodfellow, et al., proved that, given the generator distribution pg(x), the optimal discriminator is as follows:
The global objective can be rewritten by using the optimal discriminator:
We can now expand the previous expression:
Now, let's consider the Kullback-Leibler divergence between two distributions, a and b:
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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.