nn_softmin: Softmin

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Softmin

Description

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).

Details

\mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}

Value

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

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)
}

torch documentation built on May 29, 2024, 9:54 a.m.