Hands-On Convolutional Neural Networks with TensorFlow

Generator loss

The generator wants to fool the discriminator, in other words, make the discriminator output 1 for a generated image G(z) . The generator loss is just the negative of the binomial cross entropy loss applied to the discriminator output of a result from the generator. Note that as the generator is always trying to generate "real" images, the cross entropy loss simplifies down to this:

Here, each term means as follows:

• m: Batch size
• D: Discriminator
• G: Generator
• z: Random noise vector

We want to maximize this loss function when training our GAN. When the loss is maximized, it means the generator is capable of generating images ...