CrossEntropyLoss¶

class
torch.nn.
CrossEntropyLoss
(weight=None, size_average=None, ignore_index=100, reduce=None, reduction='mean')[source]¶ This criterion combines
LogSoftmax
andNLLLoss
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 Kdimensional case (described later).
This criterion expects a class index in the range $[0, C1]$ 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:
$\text{loss}(x, class) = \log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right) = x[class] + \log\left(\sum_j \exp(x[j])\right)$or in the case of the
weight
argument being specified:$\text{loss}(x, class) = weight[class] \left(x[class] + \log\left(\sum_j \exp(x[j])\right)\right)$The losses are averaged across observations for each minibatch. If the
weight
argument is specified then this is a weighted average:$\text{loss} = \frac{\sum^{N}_{i=1} loss(i, class[i])}{\sum^{N}_{i=1} weight[class[i]]}$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).
 Parameters
weight (Tensor, optional) – a manual rescaling weight given to each class. If given, has to be a Tensor of size C
size_average (bool, optional) – Deprecated (see
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 fieldsize_average
is set toFalse
, the losses are instead summed for each minibatch. Ignored whenreduce
isFalse
. Default:True
ignore_index (int, optional) – Specifies a target value that is ignored and does not contribute to the input gradient. When
size_average
isTrue
, the loss is averaged over nonignored targets.reduce (bool, optional) – Deprecated (see
reduction
). By default, the losses are averaged or summed over observations for each minibatch depending onsize_average
. Whenreduce
isFalse
, returns a loss per batch element instead and ignoressize_average
. Default:True
reduction (string, optional) – Specifies the reduction to apply to the output:
'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
andreduce
are in the process of being deprecated, and in the meantime, specifying either of those two args will overridereduction
. Default:'mean'
 Shape:
Input: $(N, C)$ where C = number of classes, or $(N, C, d_1, d_2, ..., d_K)$ with $K \geq 1$ in the case of Kdimensional loss.
Target: $(N)$ where each value is $0 \leq \text{targets}[i] \leq C1$ , or $(N, d_1, d_2, ..., d_K)$ with $K \geq 1$ in the case of Kdimensional loss.
Output: scalar. If
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 Kdimensional 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()