medium10 pts · first answer countsdeep-learningpytorchautograd

backward() on a vector

What happens when this code runs?

x = torch.randn(3, requires_grad=True)
y = x * 2
y.backward()

A.x.grad becomes tensor([2., 2., 2.]) automaticallyB.RuntimeError: grad can only be implicitly created for scalarsC.x.grad becomes the single value tensor(6.)D.Nothing happens, because y is not called a loss

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backward() on a vector

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