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Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input \(x\) and target \(y\) of size \((N, C)\).


nn_multilabel_soft_margin_loss(weight = NULL, reduction = "mean")



(Tensor, optional): a manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is treated as if having all ones.


(string, 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.


For each sample in the minibatch:

$$ loss(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1}) + (1-y[i]) * \log\left(\frac{\exp(-x[i])}{(1 + \exp(-x[i]))}\right) $$

where \(i \in \left\{0, \; \cdots , \; \mbox{x.nElement}() - 1\right\}\), \(y[i] \in \left\{0, \; 1\right\}\).


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

  • Target: \((N, C)\), label targets padded by -1 ensuring same shape as the input.

  • Output: scalar. If reduction is 'none', then \((N)\).