MultiMarginLoss#

classtorch.nn.modules.loss.MultiMarginLoss(p=1,margin=1.0,weight=None,size_average=None,reduce=None,reduction='mean')[source]#

Creates a criterion that optimizes a multi-class classification hingeloss (margin-based loss) between input$x$x (a 2D mini-batchTensor) andoutput$y$y (which is a 1D tensor of target class indices,$0\le y\le \text{x.size}(1)-1$0≤y≤x.size(1)−1):

For each mini-batch sample, the loss in terms of the 1D input$x$x and scalaroutput$y$y is:

$$\text{loss}(x,y)=\frac{\sum _i\max (0,\text{margin}-x[y]+x[i]{)}^p}{\text{x.size}(0)}$$loss(x,y)=x.size(0)∑i​max(0,margin−x[y]+x[i])p​

where$i\in \{0,\cdots ,\text{x.size}(0)-1\}$i∈{0,⋯,x.size(0)−1}and$i\mathrm{\ne }y$i=y.

Optionally, you can give non-equal weighting on the classes by passinga 1Dweight tensor into the constructor.

The loss function then becomes:

$$\text{loss}(x,y)=\frac{\sum _{i}w[y]\ast \max (0,\text{margin}-x[y]+x[i]{)}^p}{\text{x.size}(0)}$$loss(x,y)=x.size(0)∑i​w[y]∗max(0,margin−x[y]+x[i])p​

Parameters

• p (int,optional) – Has a default value of$1$1.$1$1 and$2$2are the only supported values.

• margin (float,optional) – Has a default value of$1$1.

• weight (Tensor,optional) – a manual rescaling weight given to eachclass. If given, it has to be a Tensor of sizeC. Otherwise, it istreated as if having all ones.

• size_average (bool,optional) – Deprecated (seereduction). By default,the losses are averaged over each loss element in the batch. Note that forsome losses, there are multiple elements per sample. If the fieldsize_averageis set toFalse, the losses are instead summed for each minibatch. Ignoredwhenreduce isFalse. Default:True

• reduce (bool,optional) – Deprecated (seereduction). By default, thelosses are averaged or summed over observations for each minibatch dependingonsize_average. Whenreduce isFalse, returns a loss perbatch 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 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'

Shape:

• Input:$(N,C)$(N,C) or$(C)$(C), where$N$N is the batch size and$C$C is the number of classes.

• Target:$(N)$(N) or$()$(), where each value is$0\le \text{targets}[i]\le C-1$0≤targets[i]≤C−1.

• Output: scalar. Ifreduction is'none', then same shape as the target.

Examples

>>>loss=nn.MultiMarginLoss()>>>x=torch.tensor([[0.1,0.2,0.4,0.8]])>>>y=torch.tensor([3])>>># 0.25 * ((1-(0.8-0.1)) + (1-(0.8-0.2)) + (1-(0.8-0.4)))>>>loss(x,y)tensor(0.32...)

forward(input,target)[source]#

Runs the forward pass.

Return type

Tensor