Implementation of "From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification" André F. T. Martins, Ramón Fernandez Astudillo (http://arxiv.org/abs/1602.02068) in Torch

Applies the Sparsemax function to an n-dimensional input tensor, resulting in an n-dimensional output Tensor whose elements lie in the range (0,1) and sum to 1.

Compared to the Softmax function, Sparsemax is more likely to contain zero elements.

Sparsemax is defined as f(z) = argmin_p ||p - z||^2, where p lies on the n-1 dimensional probability simplex. In other words, Sparsemax is the Euclidean projection of a vector onto the probability simplex. If z corresponds to unnormalized event scores then it is likely for the projection to be on the boundary of the simplex, in which case f(z) is sparse.

Applies the SparseMaxLoss function to an n-dimensional tensor.

The SparseMaxLoss function defines a loss for Sparsemax that is similar to the LogSoftMax function for Softmax in that it defines a loss function whose gradient takes the same form as that of LogSoftMax. That is the gradient of SparseMaxLoss(z,y) is y - sparsemax(z) where y is a one-hot encoding of the correct target.

Because of its similar form, the SparseMaxLoss function can be combined with the ClassNLLCriterion criterion to train an nn model. Note however that the SparseMaxLoss is not the negative log-likelihood of the sparsemax distribution, which would be undefined on the zero elements, and does not have the same interpretation as LogSoftMax.

This criterion combines SparseMaxLoss and ClassNLLCriterion into one class, similar to CrossEntropyCriterion.

The loss can be described as loss(x, class) = -SparseMaxLoss(x)[class].

The losses are average across observations for each minibatch.

th> x = torch.randn(5)
th> y = torch.Tensor({3})

th> x
 0.8053
 0.4594
-0.6136
-0.9460
 1.0722
[torch.DoubleTensor of size 5]

th> nn.SoftMax():forward(x)
 0.2916
 0.2064
 0.0706
 0.0506
 0.3808
[torch.DoubleTensor of size 5]

th> nn.SparseMax():forward(x)
 0.3597
 0.0138
 0.0000
 0.0000
 0.6265
[torch.DoubleTensor of size 5]

...
======= SparseMaxLoss 1D Input Test =======
Input x:
-0.3218
 0.7395
-0.2319
 0.2312
 0.7185
[torch.DoubleTensor of size 5]

Target:
 3
[torch.DoubleTensor of size 1]

LogSoftMax x:
-2.2569
-1.1955
-2.1669
-1.7038
-1.2165
[torch.DoubleTensor of size 5]

SparseMaxLoss x:
-1.2482
-0.1869
-1.1582
-0.6951
-0.2078
[torch.DoubleTensor of size 5]

LogSoftMax+NLL, CrossEntropyCriterion:
2.1669343084448	2.1669343084448
SparseMaxLoss+NLL, SparseMaxCriterion:
1.1582469533367	1.1582469533367
NLL Gradient for Softmax:
 0
 0
-1
 0
 0
[torch.DoubleTensor of size 5]

NLL Gradient for Sparsemax:
 0
 0
-1
 0
 0
[torch.DoubleTensor of size 5]

LogSoftMax Gradient, CrossEntropyCriterion Gradient:
 0.1047
 0.3025
-0.8855
 0.1820
 0.2963
[torch.DoubleTensor of size 5]

 0.1047
 0.3025
-0.8855
 0.1820
 0.2963
[torch.DoubleTensor of size 5]

SparseMaxLoss Gradient, SparseMaxCriterion Gradient:
 0.0000
 0.5097
-1.0000
 0.0015
 0.4888
[torch.DoubleTensor of size 5]

 0.0000
 0.5097
-1.0000
 0.0015
 0.4888
[torch.DoubleTensor of size 5]

SoftMax x:
 0.1047
 0.3025
 0.1145
 0.1820
 0.2963
[torch.DoubleTensor of size 5]

SparseMax x (from loss backward):
 0.0000
 0.5097
 0.0000
 0.0015
 0.4888
[torch.DoubleTensor of size 5]

• SparseMax forward pass uses sort(input) to find threshold but this can be replaced with an expected linear time selection algorithm.
• Use sparse tensors/data structures to take advantage of sparsity for very high dimensional inputs.
• Optimized THNN implementation.