SoftMarginLoss — PyTorch 2.7 documentation (original) (raw)

class torch.nn.SoftMarginLoss(size_average=None, reduce=None, reduction='mean')[source][source]¶

Creates a criterion that optimizes a two-class classification logistic loss between input tensor xx and target tensor yy(containing 1 or -1).

loss(x,y)=∑ilog⁡(1+exp⁡(−y[i]∗x[i]))x.nelement()\text{loss}(x, y) = \sum_i \frac{\log(1 + \exp(-y[i]*x[i]))}{\text{x.nelement}()}

Parameters

• 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 field size_averageis set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
• reduce (bool, optional) – Deprecated (see 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
• 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_averageand reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'

Shape:

• Input: (∗)(*), where ∗* means any number of dimensions.
• Target: (∗)(*), same shape as the input.
• Output: scalar. If reduction is 'none', then (∗)(*), same shape as input.