torch.nn.functional.cross_entropy¶
- torch.nn.functional.cross_entropy(input, target, weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean', label_smoothing=0.0)[source]¶
This criterion computes the cross entropy loss between input logits and target.
See
CrossEntropyLoss
for details.- Parameters:
input (Tensor) – Predicted unnormalized logits; see Shape section below for supported shapes.
target (Tensor) – Ground truth class indices or class probabilities; see Shape section below for supported shapes.
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 multiple elements per sample. If the fieldsize_average
is set toFalse
, the losses are instead summed for each minibatch. Ignored when reduce 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 non-ignored targets. Note thatignore_index
is only applicable when the target contains class indices. Default: -100reduce (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 (str, optional) – Specifies the reduction to apply to the output:
'none'
|'mean'
|'sum'
.'none'
: no reduction will be applied,'mean'
: the sum of the output will be divided by the number of elements in the output,'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'
label_smoothing (float, optional) – A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets become a mixture of the original ground truth and a uniform distribution as described in Rethinking the Inception Architecture for Computer Vision. Default: $0.0$.
- Return type:
- Shape:
Input: Shape $(C)$, $(N, C)$ or $(N, C, d_1, d_2, ..., d_K)$ with $K \geq 1$ in the case of K-dimensional loss.
Target: If containing class indices, shape $()$, $(N)$ or $(N, d_1, d_2, ..., d_K)$ with $K \geq 1$ in the case of K-dimensional loss where each value should be between $[0, C)$. If containing class probabilities, same shape as the input and each value should be between $[0, 1]$.
where:
$\begin{aligned} C ={} & \text{number of classes} \\ N ={} & \text{batch size} \\ \end{aligned}$
Examples:
>>> # Example of target with class indices >>> input = torch.randn(3, 5, requires_grad=True) >>> target = torch.randint(5, (3,), dtype=torch.int64) >>> loss = F.cross_entropy(input, target) >>> loss.backward() >>> >>> # Example of target with class probabilities >>> input = torch.randn(3, 5, requires_grad=True) >>> target = torch.randn(3, 5).softmax(dim=1) >>> loss = F.cross_entropy(input, target) >>> loss.backward()