torch：CrossEntropy是个构造器，所以loss = torch.nn.CrossEntropyLoss()(output, target)这么写就对了

criteria = nn.CrossEntropyLoss()
loss = criteria(output, target)

loss = torch.nn.functional.cross_entropy(output, target)

import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F

# input is of size N x C = 3 x 5
input = torch.randn(3, 5, requires_grad=True)
# each element in target has to have 0 <= value < C
target = torch.tensor([1, 0, 4])
output = F.nll_loss(F.log_softmax(input), target)
print(output)
output.backward()
print(output)

input = torch.tensor([[0.1939, 0.2019, 0.8598],
                      [0.4146, 0.1330, 0.9469],
                      [0.8549, 0.9154, 0.5434]])
# input = torch.rand(3, 3)

print(input)
sm = nn.Softmax(dim=1)
print(sm(input))
# tensor([[0.2529, 0.2549, 0.4922],
#         [0.2892, 0.2182, 0.4925],
#         [0.3578, 0.3801, 0.2620]])
print(torch.log(sm(input)))
# tensor([[-1.3748, -1.3668, -0.7089],
#         [-1.2406, -1.5221, -0.7082],
#         [-1.0277, -0.9672, -1.3392]])
# tar = torch.tensor([0,2,1])
tar = torch.tensor([0,2,1])
# targ = nn.NLLLoss(input,tar) #loss = torch.nn.functional.cross_entropy(output, target)
targ = F.nll_loss(input,tar)
print(targ)

D:ProgramDataMiniconda3python.exe E:/新脚本主文件夹/训练测试项目/test_torch/nll_loss.py
E:/新脚本主文件夹/训练测试项目/test_torch/nll_loss.py:10: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
  output = F.nll_loss(F.log_softmax(input), target)
tensor(3.2477, grad_fn=<NllLossBackward>)
tensor(3.2477, grad_fn=<NllLossBackward>)
tensor([[0.1939, 0.2019, 0.8598],
        [0.4146, 0.1330, 0.9469],
        [0.8549, 0.9154, 0.5434]])
tensor([[0.2529, 0.2549, 0.4922],
        [0.2892, 0.2182, 0.4925],
        [0.3578, 0.3801, 0.2620]])
tensor([[-1.3748, -1.3668, -0.7089],
        [-1.2405, -1.5221, -0.7082],
        [-1.0277, -0.9672, -1.3392]])
tensor(-0.6854)

Process finished with exit code 0