Multilabel margin loss — nn_multilabel_margin

Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input $x$ (a 2D mini-batch Tensor) and output $y$ (which is a 2D Tensor of target class indices). For each sample in the mini-batch:

Usage

nn_multilabel_margin_loss(reduction = "mean")

Arguments

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

$loss (x, y) = \sum_{i j} \frac{max (0, 1 - (x [y [j]] - x [i]))}{x.size (0)}$

where $x \in {0, \dots, x.size (0) - 1}$ , \ $y \in {0, \dots, y.size (0) - 1}$ , \ $0 \leq y [j] \leq x.size (0) - 1$ , \ and $i \neq y [j]$ for all $i$ and $j$ . $y$ and $x$ must have the same size.

The criterion only considers a contiguous block of non-negative targets that starts at the front. This allows for different samples to have variable amounts of target classes.

Shape

Input: $(C)$ or $(N, C)$ where N is the batch size and C is the number of classes.
Target: $(C)$ or $(N, C)$ , label targets padded by -1 ensuring same shape as the input.
Output: scalar. If reduction is 'none', then $(N)$ .

Examples

if (torch_is_installed()) {
loss <- nn_multilabel_margin_loss()
x <- torch_tensor(c(0.1, 0.2, 0.4, 0.8))$view(c(1, 4))
# for target y, only consider labels 4 and 1, not after label -1
y <- torch_tensor(c(4, 1, -1, 2), dtype = torch_long())$view(c(1, 4))
loss(x, y)
}
#> torch_tensor
#> 0.85
#> [ CPUFloatType{} ]