[ allennlp.modules.matrix_attention.matrix_attention ]
class MatrixAttention(torch.nn.Module, Registrable)
MatrixAttention takes two matrices as input and returns a matrix of attentions.
We compute the similarity between each row in each matrix and return unnormalized similarity scores. Because these scores are unnormalized, we don't take a mask as input; it's up to the caller to deal with masking properly when this output is used.
- matrix_1 :
(batch_size, num_rows_1, embedding_dim_1)
- matrix_2 :
(batch_size, num_rows_2, embedding_dim_2)
(batch_size, num_rows_1, num_rows_2)
class MatrixAttention(torch.nn.Module, Registrable): | ... | def forward( | self, | matrix_1: torch.Tensor, | matrix_2: torch.Tensor | ) -> torch.Tensor