Softmin — nn_softmin • torch

Applies the Softmin function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0, 1] and sum to 1. Softmin is defined as:

Usage

nn_softmin(dim)

Arguments

dim: (int): A dimension along which Softmin will be computed (so every slice along dim will sum to 1).

Value

a Tensor of the same dimension and shape as the input, with values in the range [0, 1].

Details

$Softmin (x_{i}) = \frac{\exp (- x_{i})}{\sum_{j} \exp (- x_{j})}$

Shape

Input: $(*)$ where * means, any number of additional dimensions
Output: $(*)$ , same shape as the input

Examples

if (torch_is_installed()) {
m <- nn_softmin(dim = 1)
input <- torch_randn(2, 2)
output <- m(input)
}