Function closure

time_sequence_prediction/train.py:61–67

()

    for i in range(opt.steps):
        print('STEP: ', i)
        def closure():
            optimizer.zero_grad()
            out = seq(input)
            loss = criterion(out, target)
            print('loss:', loss.item())
            loss.backward()
            return loss
        optimizer.step(closure)
        # begin to predict, no need to track gradient here
        with torch.no_grad():