class torch.nn.SoftMarginLoss(size_average=None, reduce=None, reduction: str = 'mean')
Creates a criterion that optimizes a two-class classification logistic loss between input tensor and target tensor (containing 1 or -1).
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
reduction). By default, the losses are averaged or summed over observations for each minibatch depending on
False, returns a loss per batch element instead and ignores
'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:
reduceare in the process of being deprecated, and in the meantime, specifying either of those two args will override
'none', then same shape as the input
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