SoftMarginLoss
class torch.nn.SoftMarginLoss(size_average=None, reduce=None, reduction='mean')
Creates a criterion that optimizes a two-class classification
   logistic loss between input tensor x and target tensor y
   (containing 1 or -1).
  \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_average"
        is 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
