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bilinear_attention

[ allennlp.modules.attention.bilinear_attention ]

BilinearAttention#

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

Computes attention between a vector and a matrix using a bilinear attention function. This function has a matrix of weights W and a bias b, and the similarity between the vector x and the matrix y is computed as x^T W y + b.

Registered as an Attention with name "bilinear".

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.
  • activation : Activation, optional (default = linear)
    An activation function applied after the x^T W y + b calculation. Default is linear, i.e. no activation.
  • 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 BilinearAttention(Attention):
 | ...
 | def reset_parameters(self)