Multi label soft margin loss — nn_multilabel_soft_margin

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

Usage

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

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