The Discriminator class involves similar changes to those seen in Chapter 3, My First GAN in Under 100 Lines. However, there are two significant differences: the if statement in the initialization step and the method for the model instantiation.

Now, let's take a look at a simple discriminator pseudocode example by using the following steps:

1. As with the generator example, use python3 and perform the necessary imports, as follows:

#!/usr/bin/env python3
imports

2. Create a class called Discriminator that allows you to toggle the model_type, as follows:

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

3. Stub out the model type selector in the initialization step of the class, as follows:

if model_type=='simple':
   # Initialize Simple Generator
elif model_type=='DCGAN':
   # Initialize Convolutional Generator

4. Create a method that develops the dc_model, as follows:

def dc_model(self):
    # Convolutional Generator
    return model

5. Maintain the following original model from the previous chapter:

def model(self, block_starting_size=128,num_blocks=4):
    # Simple GAN
    return model

6. Finally, input the following helper functions for the Discriminator function:

def summary(self):
    # Summary Helper Function

def save_model(self):
    # Model Saver Helper Function