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- python - `CrossEntropyLoss ()` in PyTorch - Stack Overflow
The combination of nn LogSoftmax and nn NLLLoss is equivalent to using nn CrossEntropyLoss This terminology is a particularity of PyTorch, as the nn NLLoss [sic] computes, in fact, the cross entropy but with log probability predictions as inputs where nn CrossEntropyLoss takes scores (sometimes called logits)
- Pytorch inputs for nn. CrossEntropyLoss () - Stack Overflow
Since the question is specifically about nn CrossEntropyLoss(), and not nn BCELoss(), it's slightly off, even though it correctly solves the problem of LogisticRegression, as you mention Furthermore, though, `nn Sigmoid() wil apply element-wise , i e the output is the same shape as the input; you have to make sure that there is only one
- Pytorch的nn. CrossEntropyLoss()的weight怎么使用？ - 知乎
那我们来解释一下，nn CrossEntropyLoss()的weight如何解决样本不平衡问题的。当类别中的样本数量不均衡的时候，对于训练图像数量较少的类，你给它更多的权重，这样如果网络在预测这些类的标签时出错，就会受到更多的惩罚。
- Using weights in CrossEntropyLoss and BCELoss (PyTorch)
from torch import nn weights = torch FloatTensor([2 0, 1 2]) loss = nn BCELoss(weights=weights) You can find a more concrete example here or another helpful PT forum discussion here Share
- Cross entropy loss in pytorch nn. CrossEntropyLoss ()
maybe someone is able to help me here I am trying to compute the cross entropy loss of a given output of my network print output Variable containing: 1 00000e-02 * -2 2739 2 9964 -7 8353 7 4667
- python - Should I use softmax as output when using cross entropy loss . . .
For the loss, I am choosing nn CrossEntropyLoss() in PyTorch, which (as I have found out) does not want to take one-hot encoded labels as true labels, but takes LongTensor of classes instead My model is nn Sequential() and when I am using softmax in the end, it gives me worse results in terms of accuracy on testing data
- python - Pytorch nn. CrossEntropyLoss giving, ValueError: Expected . . .
This is what the documentation says about K-dimensional loss: Can also be used for higher dimension inputs, such as 2D images, by providing an input of size (minibatch, C, d_1, d_2, , d_K) with K ≥ 1 , where K is the number of dimensions, and a target of appropriate shape (see below)
- pytorch - comparing torch. nn. CrossEntropyLoss with label as int and . . .
nn CrossEntropyLoss expects its label input to be of type torch Long and not torch Float Note that this behavior is opposite to nn BCELoss where target is expected to be of the same type as the input If you simply remove the from your label: label = torch tensor([0]) # no after 0 - now it is an integer
- 如何理解loss = nn. CrossEntropyLoss(reduction=none)？ - 知乎
reduction参数指定在计算损失是如何处理样本级别的损失值： ①"none' 不进行任何降维， 每个样本都会产生一个损失值，如果选择了reduction='none',那么将得到一个与输出张量的形状相同的张量，其中每个元素对应一个样本的损失值。
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