matchzoo.modules.attention

Attention module.

Module Contents

class matchzoo.modules.attention.Attention(input_size:int=100, mask:int=0)

Bases: torch.nn.Module

Attention module.

Parameters:

• input_size – Size of input.
• mask – An integer to mask the invalid values. Defaults to 0.

Examples

>>> import torch
>>> attention = Attention(input_size=10)
>>> x = torch.randn(4, 5, 10)
>>> x.shape
torch.Size([4, 5, 10])
>>> attention(x).shape
torch.Size([4, 5])

forward(self, x)

Perform attention on the input.

class matchzoo.modules.attention.BidirectionalAttention

Bases: torch.nn.Module

Computing the soft attention between two sequence.

forward(self, v1, v1_mask, v2, v2_mask)

Forward.

class matchzoo.modules.attention.MatchModule(hidden_size, dropout_rate=0)

Bases: torch.nn.Module

Computing the match representation for Match LSTM.

Parameters:

• hidden_size – Size of hidden vectors.
• dropout_rate – Dropout rate of the projection layer. Defaults to 0.

Examples

>>> import torch
>>> attention = MatchModule(hidden_size=10)
>>> v1 = torch.randn(4, 5, 10)
>>> v1.shape
torch.Size([4, 5, 10])
>>> v2 = torch.randn(4, 5, 10)
>>> v2_mask = torch.ones(4, 5).to(dtype=torch.uint8)
>>> attention(v1, v2, v2_mask).shape
torch.Size([4, 5, 20])

forward(self, v1, v2, v2_mask)

Computing attention vectors and projection vectors.