sparsemax: Sparsemax

In mlverse/tabnet: Fit 'TabNet' Models for Classification and Regression

Sparsemax

Description

Normalizing sparse transform (a la softmax).

Usage

sparsemax(dim = -1L)

sparsemax15(dim = -1L, k = NULL)

Arguments

dim	The dimension along which to apply sparsemax.
k	The number of largest elements to partial-sort input over. For optimal performance, k should be slightly bigger than the expected number of non-zeros in the solution. If the solution is more than k-sparse, this function is recursively called with a 2*k schedule. If NULL, full sorting is performed from the beginning.

Details

Solves the projection:

\min_P ||input - P||_2 \text{ s.t. } P \geq0, \sum(P) ==1

Value

The projection result, such that \sum_{dim} P = 1 \forall dim elementwise.

Examples

input <- torch::torch_randn(10, 5, requires_grad = TRUE)
# create a top3 alpha=1.5 sparsemax on last input dimension
nn_sparsemax <- sparsemax15(dim=1, k=3)
result <- nn_sparsemax(input)
print(result)

mlverse/tabnet documentation built on July 17, 2025, 4:15 a.m.