Optimization, Pytorch, Machine Learning, Deep Learning, Tensorboard.
= (inputs.to(torch.float32) / 255. - 0.5) / 0.5 if step >= (1 + 4 + 3) * 1: break outputs = model(inputs) loss = criterion(outputs, labels) optimizer.zero_grad(set_to_none=True) loss.backward() optimizer.step() prof.step() An experienced PyTorch developer may have already noticed that our example