dataloader = load_images(True)
netG = create_generator()
netD = create_discriminator()
optimizerD, optimizerG = create_optimizers(netD, netG)
criterion = nn.BCELoss()
fixed_noise = torch.randn(
64, nz, 1, 1, device=device
) # Latent vectors to visualize the progress of the generator
real_label = 1
fake_label = 0
D_losses, G_losses, img_list = Minimizer.train(dataloader, netD, netG,
optimizerD, optimizerG,
criterion, num_epochs,
device, nz, fixed_noise)
plt.figure(figsize=(10, 5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses, label="G")
plt.plot(D_losses, label="D")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()
# %%capture
fig = plt.figure(figsize=(8, 8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i, (1, 2, 0)), animated=True)]
