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additive_attention

[ allennlp.modules.attention.additive_attention ]

AdditiveAttention#

@Attention.register("additive")
class AdditiveAttention(Attention):
 | def __init__(
 |     self,
 |     vector_dim: int,
 |     matrix_dim: int,
 |     normalize: bool = True
 | ) -> None

Computes attention between a vector and a matrix using an additive attention function. This function has two matrices W, U and a vector V. The similarity between the vector x and the matrix y is computed as V tanh(Wx + Uy).

This attention is often referred as concat or additive attention. It was introduced in https://arxiv.org/abs/1409.0473 by Bahdanau et al.

Registered as an Attention with name "additive".

Parameters

  • vector_dim : int
    The dimension of the vector, x, described above. This is x.size()[-1] - the length of the vector that will go into the similarity computation. We need this so we can build the weight matrix correctly.
  • matrix_dim : int
    The dimension of the matrix, y, described above. This is y.size()[-1] - the length of the vector that will go into the similarity computation. We need this so we can build the weight matrix correctly.
  • normalize : bool, optional (default = True)
    If true, we normalize the computed similarities with a softmax, to return a probability distribution for your attention. If false, this is just computing a similarity score.

reset_parameters#

class AdditiveAttention(Attention):
 | ...
 | def reset_parameters(self)