matrix_attention
[ allennlp.modules.matrix_attention.matrix_attention ]
MatrixAttention#
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.
Input:
- matrix_1 : (batch_size, num_rows_1, embedding_dim_1)
- matrix_2 : (batch_size, num_rows_2, embedding_dim_2)
Output:
- (batch_size, num_rows_1, num_rows_2)
forward#
class MatrixAttention(torch.nn.Module, Registrable):
| ...
| def forward(
| self,
| matrix_1: torch.Tensor,
| matrix_2: torch.Tensor
| ) -> torch.Tensor