1. First, we need to add an argument relating to what model_type we would like to use, as well as provide the ability for users to select either GAN or DCGAN, as follows:

class Discriminator(object):
    def __init__(self, width = 28, height= 28, channels = 1, latent_size=100,model_type = 'simple'):

2. Next, perform the following basic setup within the class, as follows:

self.W = width
self.H = height
self.C = channels
self.CAPACITY = width*height*channels
self.SHAPE = (width,height,channels)

3. The following is the same code used in Chapter 3, My First GAN in Under 100 Lines, except it is wrapped with an if statement to become selectable:

if model_type=='simple':
   self.Discriminator = self.model()
   self.OPTIMIZER = Adam(lr=0.0002, decay=8e-9)
   self.Discriminator.compile(loss='binary_crossentropy', 
        optimizer=self.OPTIMIZER, metrics=['accuracy'] )

4. The following else-if statement allows you to select the DCGAN architecture. Here, we use the Adam optimizer, but you can experiment with different types of optimizers:

elif model_type=='DCGAN':
     self.Discriminator = self.dc_model()
     self.OPTIMIZER = Adam(lr=1e-4, beta_1=0.2)
     self.Discriminator.compile(loss='binary_crossentropy', 
                 optimizer=self.OPTIMIZER, metrics=['accuracy'] )

5. Finally, save the model and provide a summary on the Terminal as follows:

self.save_model()
self.summary()

Remember—if the discriminator or generator loss falls to zero, the model has diverged!