Training GAN

(1) generate a batch of images with the generator. The initial image will not noise and easily distinguished by the discriminator. 

(2) grab a batch of real images. Marked the combined batch with label 0 or 1 for fake or real images. 

(3) feed the batch to the discriminator. Using back propagation to adjust the weighs and bias of the discriminator so that it can learn to recognise the real image  

(4) freeze the weigh and bias for the discriminator. Create a batch of fake image from the generator. Label these image as real and use the output from the discriminator to anjust the weigh and bias of the generator through back propagation and gradient descent for the complete model (discriminator toward generator)

(5) repeat the training until the discriminator cannot discern the fake picture from real (ie the out out is about 0.5.