class_docstr: A Generative Adversarial Network with deep fully-connected neural nets as
  Generator and Discriminator.
  
  Training Data: MNIST Handwritten Digits (28x28 images)
class_docstr_tok: ["A","Generative","Adversarial","Network","with","deep","fully-connected","neural","nets","as","Generator","and","Discriminator",".","Training","Data",":","MNIST","Handwritten","Digits","(","28x28","images",")"]
code: def __init__(self):
      self.img_rows = 28
      self.img_cols = 28
      self.img_dim = self.img_rows * self.img_cols
      self.latent_dim = 100
  
      optimizer = Adam(learning_rate=0.0002, b1=0.5)
      loss_function = CrossEntropy
  
      # Build the discriminator
      self.discriminator = self.build_discriminator(
          optimizer, loss_function
      )
  
      # Build the generator
      self.generator = self.build_generator(optimizer, loss_function)
  
      # Build the combined model
      self.combined = NeuralNetwork(
          optimizer=optimizer, loss=loss_function
      )
      self.combined.layers.extend(self.generator.layers)
      self.combined.layers.extend(self.discriminator.layers)
  
      print()
      self.generator.summary(name="Generator")
      self.discriminator.summary(name="Discriminator")
  
file_path: ML-From-Scratch/mlfromscratch/unsupervised_learning/generative_adversarial_network.py
imports: from __future__ import print_function, division
  from sklearn import datasets
  import math
  import matplotlib.pyplot
  import numpy
  import progressbar
  from sklearn.datasets import fetch_mldata
  from mlfromscratch.deep_learning.optimizers import Adam
  from mlfromscratch.deep_learning.loss_functions import CrossEntropy
  from mlfromscratch.deep_learning.layers import Dense, Dropout, Flatten, Activation, Reshape, BatchNormalization
  from mlfromscratch.deep_learning import NeuralNetwork
label_desc: Use image node_id 1 to create a new GAN object  with example: obj = GAN()