Semi-supervised learning GANs for classification

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Hello,

I have been reading the book to try to understand the GANS. I would like to try it (GANs) for classification.

I have been looking to this code :https://github.com/PacktPublishing/Deep-Learning-with-Keras/blob/master/Chapter04/example_gan_convolutional.py

To try to adapt it to a GAN with N+1 outputs being N the number of classes and +1 for the forge output.
Some things I dont understand on the example, like the output generation gan_targets(xtest.shape[0]). ... How exactly can the GAN architecture test if the output is fake or not ?

And to adapt this example, to make a Semi-supervised learning for classification, how should we incorporate the output layer of 10 with soft max and construct the outputs align with the inputs (and generated -fake- inputs with label ) ?

I would be grateful if there was some example, based on this one, were we could see GANs performing classification ?

Thanks in advance,
Rui