Training The Fashion MNIST GAN

import torch
import matplotlib.pyplot as plt
from Dataset import FMnistDataset
from Discriminator import Discriminator
from Generator import Generator
# load data
fmnist_dataset = FMnistDataset('fashion-mnist_train.csv')
# functions to generate random data
def generate_random_image(size):
    random_data = torch.rand(size)
    return random_data
def generate_random_seed(size):
    random_data = torch.randn(size)
    return random_data
# create Discriminator and Generator
D = Discriminator()
G = Generator()
epochs = 4
for epoch in range(epochs):
  print ("epoch = ", epoch + 1)
  # train Discriminator and Generator
  for label, image_data_tensor, target_tensor in fmnist_dataset:
    # train discriminator on true
    D.train(image_data_tensor, torch.FloatTensor([1.0]))
    
    # train discriminator on false
    # use detach() so gradients in G are not calculated
    D.train(G.forward(generate_random_seed(100)).detach(), torch.FloatTensor([0.0]))
    
    # train generator
    G.train(D, generate_random_seed(100), torch.FloatTensor([1.0]))
    pass
    
  pass
# plot several outputs from the trained generator
# plot a 3 column, 2 row array of generated images
f, axarr = plt.subplots(2,3, figsize=(16,8))
for i in range(2):
    for j in range(3):
        output = G.forward(generate_random_seed(100))
        img = output.detach().numpy().reshape(28,28)
        axarr[i,j].imshow(img, interpolation='none', cmap='Blues')
        pass
    pass