PyTorch Note -- 自动求导

自动求导有两种方式 torch.autograd.grad(loss_func, [parameter, ...])  或者是 loss_func.backward() ; parameter.grad

x = torch.ones(1)
# print(x)
w = torch.full([1], 2, requires_grad=True)
# print(w)  # tensor([2.], requires_grad=True)
mse = torch.nn.functional.mse_loss(x*w, torch.ones(1))
# print(mse)  # tensor(1., grad_fn=<MseLossBackward>)
print(torch.autograd.grad(mse, [w]))  # 对[w] 参数求梯度  # (tensor([2.]),)  与下面两行等价
mse2 = torch.nn.functional.mse_loss(x * w, torch.ones(1))
mse2.backward()
print(w.grad)  # tensor([2.])