class torch.nn.CrossEntropyLoss(weight: Optional[torch.Tensor] = None, size_average=None, ignore_index: int = -100, reduce=None, reduction: str = 'mean')
[source]
This criterion combines nn.LogSoftmax()
and nn.NLLLoss()
in one single class.
It is useful when training a classification problem with C
classes. If provided, the optional argument weight
should be a 1D Tensor
assigning weight to each of the classes. This is particularly useful when you have an unbalanced training set.
The input
is expected to contain raw, unnormalized scores for each class.
input
has to be a Tensor of size either $(minibatch, C)$ or $(minibatch, C, d_1, d_2, ..., d_K)$ with $K \geq 1$ for the K
-dimensional case (described later).
This criterion expects a class index in the range $[0, C-1]$ as the target
for each value of a 1D tensor of size minibatch
; if ignore_index
is specified, this criterion also accepts this class index (this index may not necessarily be in the class range).
The loss can be described as:
or in the case of the weight
argument being specified:
The losses are averaged across observations for each minibatch. If the weight
argument is specified then this is a weighted average:
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 $K \geq 1$ , where $K$ is the number of dimensions, and a target of appropriate shape (see below).
C
reduction
). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field size_average
is set to False
, the losses are instead summed for each minibatch. Ignored when reduce is False
. Default: True
size_average
is True
, the loss is averaged over non-ignored targets.reduction
). By default, the losses are averaged or summed over observations for each minibatch depending on size_average
. When reduce
is False
, returns a loss per batch element instead and ignores size_average
. Default: True
'none'
| 'mean'
| 'sum'
. 'none'
: no reduction will be applied, 'mean'
: the weighted mean of the output is taken, 'sum'
: the output will be summed. Note: size_average
and reduce
are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction
. Default: 'mean'
C = number of classes
, or $(N, C, d_1, d_2, ..., d_K)$ with $K \geq 1$ in the case of K
-dimensional loss.reduction
is 'none'
, then the same size as the target: $(N)$ , or $(N, d_1, d_2, ..., d_K)$ with $K \geq 1$ in the case of K-dimensional loss.Examples:
>>> loss = nn.CrossEntropyLoss() >>> input = torch.randn(3, 5, requires_grad=True) >>> target = torch.empty(3, dtype=torch.long).random_(5) >>> output = loss(input, target) >>> output.backward()
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Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.CrossEntropyLoss.html