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")

Arguments

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.

Details

For each sample in the minibatch:

$l o s s (x, y) = - \frac{1}{C} * \sum_{i} y [i] * \log ((1 + \exp (- x [i]))^{- 1}) + (1 - y [i]) * \log (\frac{\exp (- x [i])}{(1 + \exp (- x [i]))})$

where $i \in {0, \dots, x.nElement () - 1}$ , $y [i] \in {0, 1}$ .

Shape

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 $(N)$ .