83e80f82d7cf5aa0444c9bdc8709304f10b8d7fc,docs/_static/examples/gan.py,,,#,122

Before Change

// Configure data loader
os.makedirs("./data/mnist", exist_ok=True)
transform = transforms.Compose([
                        transforms.ToTensor(),
                        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                   ])

dataset = datasets.MNIST("./data/mnist", train=True, download=True, transform=transform)

dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, drop_last=True)


// Model and optimizer

After Change

    return state[G_LOSS] + state[D_LOSS]


batch = torch.randn(25, latent_dim).to(device)
@callbacks.on_step_training
def saver_callback(state):
    batches_done = state[tb.EPOCH] * len(state[tb.GENERATOR]) + state[tb.BATCH]

Instances