The following diagram illustrates the basic architecture of GANs:
A random input is used to generate a sample of data. For example, a generator, G(z), uses a prior distribution, p(z), to achieve an input, z. Using z, it then generates some data. This output is fed as input to the discriminator neural network, D(x). It takes an input x from 
, where 
is our real data distribution. D(x) then solves a binary classification problem using ...
Get TensorFlow Machine Learning Projects now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
Get Mark Richards’s Software Architecture Patterns ebook to better understand how to design components—and how they should interact.
Dive in for free with a 10-day trial of the O’Reilly learning platform—then explore all the other resources our members count on to build skills and solve problems every day.
Start your free trial Become a member now