pytorch中如何只讓指定變量向后傳播梯度？

創(chuàng)新互聯(lián)長(zhǎng)期為上千家客戶提供的網(wǎng)站建設(shè)服務(wù)，團(tuán)隊(duì)從業(yè)經(jīng)驗(yàn)10年，關(guān)注不同地域、不同群體，并針對(duì)不同對(duì)象提供差異化的產(chǎn)品和服務(wù)；打造開放共贏平臺(tái)，與合作伙伴共同營造健康的互聯(lián)網(wǎng)生態(tài)環(huán)境。為響水企業(yè)提供專業(yè)的成都做網(wǎng)站、網(wǎng)站建設(shè)，響水網(wǎng)站改版等技術(shù)服務(wù)。擁有10多年豐富建站經(jīng)驗(yàn)和眾多成功案例,為您定制開發(fā)。

（或者說如何讓指定變量不參與后向傳播？）

有以下公式，假如要讓L對(duì)xvar求導(dǎo)：

（1）中，L對(duì)xvar的求導(dǎo)將同時(shí)計(jì)算out1部分和out2部分；

（2）中，L對(duì)xvar的求導(dǎo)只計(jì)算out2部分，因?yàn)閛ut1的requires_grad=False；

（3）中，L對(duì)xvar的求導(dǎo)只計(jì)算out1部分，因?yàn)閛ut2的requires_grad=False；

驗(yàn)證如下：

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed May 23 10:02:04 2018
@author: hy
"""
 
import torch
from torch.autograd import Variable
print("Pytorch version: {}".format(torch.__version__))
x=torch.Tensor([1])
xvar=Variable(x,requires_grad=True)
y1=torch.Tensor([2])
y2=torch.Tensor([7])
y1var=Variable(y1)
y2var=Variable(y2)
#(1)
print("For (1)")
print("xvar requres_grad: {}".format(xvar.requires_grad))
print("y1var requres_grad: {}".format(y1var.requires_grad))
print("y2var requres_grad: {}".format(y2var.requires_grad))
out1 = xvar*y1var
print("out1 requres_grad: {}".format(out1.requires_grad))
out2 = xvar*y2var
print("out2 requres_grad: {}".format(out2.requires_grad))
L=torch.pow(out1-out2,2)
L.backward()
print("xvar.grad: {}".format(xvar.grad))
xvar.grad.data.zero_()
#(2)
print("For (2)")
print("xvar requres_grad: {}".format(xvar.requires_grad))
print("y1var requres_grad: {}".format(y1var.requires_grad))
print("y2var requres_grad: {}".format(y2var.requires_grad))
out1 = xvar*y1var
print("out1 requres_grad: {}".format(out1.requires_grad))
out2 = xvar*y2var
print("out2 requres_grad: {}".format(out2.requires_grad))
out1 = out1.detach()
print("after out1.detach(), out1 requres_grad: {}".format(out1.requires_grad))
L=torch.pow(out1-out2,2)
L.backward()
print("xvar.grad: {}".format(xvar.grad))
xvar.grad.data.zero_()
#(3)
print("For (3)")
print("xvar requres_grad: {}".format(xvar.requires_grad))
print("y1var requres_grad: {}".format(y1var.requires_grad))
print("y2var requres_grad: {}".format(y2var.requires_grad))
out1 = xvar*y1var
print("out1 requres_grad: {}".format(out1.requires_grad))
out2 = xvar*y2var
print("out2 requres_grad: {}".format(out2.requires_grad))
#out1 = out1.detach()
out2 = out2.detach()
print("after out2.detach(), out2 requres_grad: {}".format(out1.requires_grad))
L=torch.pow(out1-out2,2)
L.backward()
print("xvar.grad: {}".format(xvar.grad))
xvar.grad.data.zero_()

網(wǎng)站題目：在pytorch中實(shí)現(xiàn)只讓指定變量向后傳播梯度-創(chuàng)新互聯(lián)
本文URL：http://weahome.cn/article/cedgjs.html