pyabsa.networks.attention

Classes

Attention	Base class for all neural network modules.
NoQueryAttention	q is a parameter

Module Contents

class pyabsa.networks.attention.Attention(embed_dim, hidden_dim=None, out_dim=None, n_head=1, score_function='dot_product', dropout=0)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

embed_dim

hidden_dim = None

n_head = 1

score_function = 'dot_product'

w_k

w_q

proj

dropout

reset_parameters()

forward(k, q)

class pyabsa.networks.attention.NoQueryAttention(embed_dim, hidden_dim=None, out_dim=None, n_head=1, score_function='dot_product', q_len=1, dropout=0)

Bases: Attention

q is a parameter

q_len = 1

q

reset_q()

forward(k, **kwargs)