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torch CrossEntropyLoss nan
自己研究出来了，分子分母同时为0的时候，会报异常NaN
import torch
a=torch.zeros(1)
b=torch.zeros(1,requires_grad=True)
print(b/0)
During the training, the loss is Nan. The loss function is Torch.nn.CrossEntropyLoss.
Solution:
The NaNs appear, because softmax + log separately can be a numerically unstable operation.
If you’re using CrossEntropyLoss for training, you could use the F.log_softmax function at the end of your model and use NLLLoss. The loss will be equivalent, but much more stable.
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链接地址：
c=torch.isnan()