Applies the Softmax 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.
Softmax is defined as:
Arguments
- dim
(int): A dimension along which Softmax will be computed (so every slice along dim will sum to 1).
Details
$$ \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)} $$
When the input Tensor is a sparse tensor then the unspecifed
values are treated as -Inf.
Note
This module doesn't work directly with NLLLoss,
which expects the Log to be computed between the Softmax and itself.
Use LogSoftmax instead (it's faster and has better numerical properties).
Shape
Input: \((*)\) where
*means, any number of additional dimensionsOutput: \((*)\), same shape as the input
Examples
if (torch_is_installed()) {
m <- nn_softmax(1)
input <- torch_randn(2, 3)
output <- m(input)
}