### entmax

#### by deep-spin

deep-spin /entmax

The entmax mapping and its loss, a family of sparse softmax alternatives.

164 Stars 9 Forks Last release: Not found MIT License 55 Commits 1 Releases

Available items

#### No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

# entmax

This package provides a pytorch implementation of entmax and entmax losses: a sparse family of probability mappings and corresponding loss functions, generalizing softmax / cross-entropy.

Features: - Exact partial-sort algorithms for 1.5-entmax and 2-entmax (sparsemax). - A bisection-based algorithm for generic alpha-entmax. - Gradients w.r.t. alpha for adaptive, learned sparsity!

Requirements: python 3, pytorch >= 1.0 (and pytest for unit tests)

## Example

In [1]: import torch

In [2]: from torch.nn.functional import softmax

In [2]: from entmax import sparsemax, entmax15, entmax_bisect

In [4]: x = torch.tensor([-2, 0, 0.5])

In [5]: softmax(x, dim=0) Out[5]: tensor([0.0486, 0.3592, 0.5922])

In [6]: sparsemax(x, dim=0) Out[6]: tensor([0.0000, 0.2500, 0.7500])

In [7]: entmax15(x, dim=0) Out[7]: tensor([0.0000, 0.3260, 0.6740])

Gradients w.r.t. alpha (continued):

In [1]: from torch.autograd import grad

In [2]: x = torch.tensor([[-1, 0, 0.5], [1, 2, 3.5]])

In [3]: alpha = torch.tensor(1.33, requires_grad=True)

In [4]: p = entmax_bisect(x, alpha)

In [5]: p Out[5]: tensor([[0.0460, 0.3276, 0.6264], [0.0026, 0.1012, 0.8963]], grad_fn=)

In [6]: grad(p[0, 0], alpha) Out[6]: (tensor(-0.2562),)

## Installation

pip install entmax

## Citations

Sparse Sequence-to-Sequence Models

@inproceedings{entmax,
author    = {Peters, Ben and Niculae, Vlad and Martins, Andr{\'e} FT},
title     = {Sparse Sequence-to-Sequence Models},
booktitle = {Proc. ACL},
year      = {2019},
url       = {https://www.aclweb.org/anthology/P19-1146}
}

Adaptively Sparse Transformers

@inproceedings{correia19adaptively,
author    = {Correia, Gon\c{c}alo M and Niculae, Vlad and Martins, Andr{\'e} FT},
title     = {Adaptively Sparse Transformers},
booktitle = {Proc. EMNLP-IJCNLP (to appear)},
year      = {2019},
}

Further reading:

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.