Optimization, Pytorch, Machine Learning, Deep Learning, Tensorboard.
experiments: for step, data in enumerate(train_loader): inputs = data[0].to(device=device, non_blocking=True) labels = data[1].to(device=device, non_blocking=True) inputs = (inputs.to(torch.float32) / 255. - 0.5) / 0.5 if step >= (1 + 4 + 3) * 1: break outputs = model(inputs) loss =