I'm struggling to understand the GAN Loss Function as provided in this blog post.In the standard cross-entropy loss we have an output that has been run through a sigmoid function (squashed between 0 and 1) and a resulting class (in binary the classes are 0 and 1).For each data point we get a:y * ln(a) + (1-y)*ln(1-a)which is just the log of the expectation which makes sense.However, I fail to understand how in the GAN loss function we can the data from the true distribution and the data from the generative model in the same iteration?
Understanding GAN Loss function
neural networks;deep learning