There are a few helper methods that enable you to understand key information about the structures you are developing:

1. The first method called summary will print the summary that's available from Keras of the model you produced previously:

def summary(self):
    return self.Discriminator.summary()

The summary function should put out data just like this on the Terminal:

Layer (type)                 Output Shape              Param #   
=================================================================
flatten_1 (Flatten)          (None, 784)               0         
_________________________________________________________________
dense_6 (Dense)              (None, 784)               615440    
_________________________________________________________________
leaky_re_lu_5 (LeakyReLU)    (None, 784)               0         
_________________________________________________________________
dense_7 (Dense)              (None, 392)               307720    
_________________________________________________________________
leaky_re_lu_6 (LeakyReLU)    (None, 392)               0         
_________________________________________________________________
dense_8 (Dense)              (None, 1)                 393       
=================================================================
Total params: 923,553
Trainable params: 923,553
Non-trainable params: 0
_________________________________________________________________

2. Our next helper function, called save_model , produces the photographic version of the model structure:

def save_model(self):
        plot_model(self.Discriminator.model,  
        to_file='/data/Discriminator_Model.png') 

The output of the save model function will save an image just like this to the data folder:

Hence, you have now understood how to build your first GAN discriminator.