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]#

Compute the cross entropy loss between input logits and target.

SeeCrossEntropyLoss 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 eachclass. If given, has to be a Tensor of sizeC

• size_average (bool,optional) – Deprecated (seereduction).

• ignore_index (int,optional) – Specifies a target value that is ignoredand does not contribute to the input gradient. Whensize_average isTrue, the loss is averaged over non-ignored targets. Note thatignore_index is only applicable when the target contains class indices.Default: -100

• reduce (bool,optional) – Deprecated (seereduction).

• 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 ofelements in the output,'sum': the output will be summed. Note:size_averageandreduce 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 amountof smoothing when computing the loss, where 0.0 means no smoothing. The targetsbecome a mixture of the original ground truth and a uniform distribution as described inRethinking the Inception Architecture for Computer Vision. Default:$0.0$0.0.

Return type

Tensor

Shape:

• Input: Shape$(C)$(C),$(N,C)$(N,C) or$(N,C,d_1,d_2,\mathrm{.}\mathrm{.}\mathrm{.},d_K)$(N,C,d1​,d2​,...,dK​) with$K\ge 1$K≥1in the case ofK-dimensional loss.

• Target: If containing class indices, shape$()$(),$(N)$(N) or$(N,d_1,d_2,\mathrm{.}\mathrm{.}\mathrm{.},d_K)$(N,d1​,d2​,...,dK​) with$K\ge 1$K≥1 in the case of K-dimensional loss where each value should be between$[0,C)$[0,C).If containing class probabilities, same shape as the input and each value should be between$[0,1]$[0,1].

where:

C=N=​number of classesbatch size​

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()