为什么 autograd 不为中间变量产生梯度? [英] Why does autograd not produce gradient for intermediate variables?

问题描述

试图围绕渐变的表示方式以及 autograd 的工作原理:

trying to wrap my head around how gradients are represented and how autograd works:

import torch
from torch.autograd import Variable

x = Variable(torch.Tensor([2]), requires_grad=True)
y = x * x
z = y * y

z.backward()

print(x.grad)
#Variable containing:
#32
#[torch.FloatTensor of size 1]

print(y.grad)
#None

为什么它不会为 y 产生渐变?如果y.grad = dz/dy，那么它不应该至少产生一个像y.grad = 2*y这样的变量吗?

Why does it not produce a gradient for y? If y.grad = dz/dy, then shouldn't it at least produce a variable like y.grad = 2*y?

推荐答案

默认情况下，梯度仅保留叶变量.不保留非叶变量的梯度以供以后检查.这是按设计完成，以节省内存.

-soumith chintala

-soumith chintala

参见:https://discuss.pytorch.org/t/why-cant-i-see-grad-of-an-intermediate-variable/94

调用y.retain_grad()

x = Variable(torch.Tensor([2]), requires_grad=True)
y = x * x
z = y * y

y.retain_grad()

z.backward()

print(y.grad)
#Variable containing:
# 8
#[torch.FloatTensor of size 1]

来源:https://discuss.pytorch.org/t/why-cant-i-see-grad-of-an-intermediate-variable/94/16

注册一个hook，它基本上是一个在计算梯度时调用的函数.然后你可以保存它，分配它，打印它，不管......

Register a hook, which is basically a function called when that gradient is calculated. Then you can save it, assign it, print it, whatever...

from __future__ import print_function
import torch
from torch.autograd import Variable

x = Variable(torch.Tensor([2]), requires_grad=True)
y = x * x
z = y * y

y.register_hook(print) ## this can be anything you need it to be

z.backward()

输出:

Variable containing:  8 [torch.FloatTensor of size 1

来源:https://discuss.pytorch.org/t/why-cant-i-see-grad-of-an-intermediate-variable/94/2

另见:https://discuss.pytorch.org/t/why-cant-i-see-grad-of-an-intermediate-variable/94/7

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