bilinear_matrix_attention

allennlp.modules.matrix_attention.bilinear_matrix_attention

BilinearMatrixAttention¶

@MatrixAttention.register("bilinear")
class BilinearMatrixAttention(MatrixAttention):
 | def __init__(
 |     self,
 |     matrix_1_dim: int,
 |     matrix_2_dim: int,
 |     activation: Activation = None,
 |     use_input_biases: bool = False,
 |     label_dim: int = 1
 | ) -> None

Computes attention between two matrices using a bilinear attention function. This function has a matrix of weights W and a bias b, and the similarity between the two matrices X and Y is computed as X W Y^T + b.

Registered as a MatrixAttention with name "bilinear".

Parameters¶

matrix_1_dim : int
The dimension of the matrix 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_2_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 W Y^T + b calculation. Default is linear, i.e. no activation.
use_input_biases : bool, optional (default = False)
If True, we add biases to the inputs such that the final computation is equivalent to the original bilinear matrix multiplication plus a projection of both inputs.
label_dim : int, optional (default = 1)
The number of output classes. Typically in an attention setting this will be one, but this parameter allows this class to function as an equivalent to torch.nn.Bilinear for matrices, rather than vectors.

reset_parameters¶

class BilinearMatrixAttention(MatrixAttention):
 | ...
 | def reset_parameters(self)

forward¶

class BilinearMatrixAttention(MatrixAttention):
 | ...
 | def forward(
 |     self,
 |     matrix_1: torch.Tensor,
 |     matrix_2: torch.Tensor
 | ) -> torch.Tensor