一维张量的Pytorch CrossEntropyLoss

debugcn 发表于 Dev

斯蒂芬·博斯

我在这里缺少一些东西：为什么CrossEntropyLoss不能使用一维张量？

from  torch  import Tensor                                                                                                                                     
X =Tensor([1.0,2.0,3.0])                                                                                                                                       
labs = Tensor([2,2,3])                                                                                                                                         
loss = nn.CrossEntropyLoss().forward(X,labs)

_stacklevel, dtype)
   1315         dim = _get_softmax_dim('log_softmax', input.dim(), _stacklevel)
   1316     if dtype is None:
-> 1317         ret = input.log_softmax(dim)
   1318     else:
   1319         ret = input.log_softmax(dim, dtype=dtype)

IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

为什么会失败，应该进行哪些更改才能获得理想的结果？

Prajot Kuvalekar

如果您在这里看到文档！

Input: (N,C) where C = number of classes
Target: (N) where each value is 0 <= targets[i] <= C-1
Output: scalar. If reduce is False, then (N) instead.

因此，它期望输入为2D张量，目标为1D

import torch
from  torch  import Tensor                                                                                                                                 
X =Tensor([[1.0,2.0,3.0]])   #2D                                                                                                                                   
labs = torch.LongTensor([2]) # 0 <= targets[i] <= C-1                                                                                                                          
loss = nn.CrossEntropyLoss().forward(X,labs)

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