Pytorch：如何访问 CrossEntropyLoss() 梯度？

Question

I want to modify the tensor that stores the CrossEntropyLoss() gradient, that is, P(i)-T(i). Where is it stored and how do I access it?

code:

input = torch.randn(3, 5, requires_grad=True)
input.register_hook(lambda x: print(" \n input hook: ",x))
print(input)
target = torch.empty(3, dtype=torch.long).random_(5)
print(target)

criterion = nn.CrossEntropyLoss()
criterion.requires_grad = True
loss0 = criterion(input,target)
loss0.register_hook(lambda x: print(" \n loss0 hook: ",x))
print("before backward loss0.grad :",loss0.grad)
print("loss0 :",loss0)
loss0.backward()
print("after backward loss0.grad :",loss0.grad)

output:

tensor([[-0.6149, -0.8179,  0.6084, -0.2837, -0.5316],
        [ 1.7246,  0.5348,  1.3646, -0.7148, -0.3421],
        [-0.3478, -0.6732, -0.7610, -1.0381, -0.5570]], requires_grad=True)
tensor([4, 1, 0])
before backward loss0.grad : None
loss0 : tensor(1.7500, grad_fn=<NllLossBackward>)

 loss0 hook:  tensor(1.)

 input hook:  tensor([[ 0.0433,  0.0354,  0.1472,  0.0603, -0.2862],
        [ 0.1504, -0.2876,  0.1050,  0.0131,  0.0190],
        [-0.2432,  0.0651,  0.0597,  0.0452,  0.0732]])
after backward loss0.grad : None

Answer 1

Given your specification in the comments you want the gradient with respect to the input (output of the model), in your code you look at the gradient of the loss which does not exist. So you could so something like:

import torch
input = torch.tensor([1,0,1,0], dtype=float, requires_grad=True)
target = torch.tensor([1,2,3,4], dtype=float)
loss = (input - target).abs().mean()
loss.backward()

Here loss.grad gives you None , but input.grad returns:

tensor([ 0.0000, -0.2500, -0.2500, -0.2500], dtype=torch.float64)

Which should be the gradient you are interested in.