if __name__ == '__main__':
#manualSeed = random.randint(1, 10000) # use if you want new results
print("Random Seed: ", manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
# Get data
dataloader, dataloader, device = data_loader()
# Generate Autoencoder
netAE = AutoEncoder(ngpu, activation).to(device)
# Apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.2.
netAE.apply(weights_init)
if plot_machines == True:
# Print the model
print(netAE)
# Create the Discriminators
netD = Discriminator(ngpu, activation).to(device)
netSD = Discriminator(ngpu, activation).to(device)
# Apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.2.
netD.apply(weights_init)
if plot_machines == True:
