TextRnnAtn.py

#!/usr/bin/env python
# _*_coding:utf-8 _*_
# @Time    :2022/7/22 21:37
# @Author  :Abner Wong
# @Software: PyCharm

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


'''Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification'''


class Model(nn.Module):
    def __init__(self, config):
        super(Model, self).__init__()
        if config.embedding_pretrained is not None:
            # 预训练词向量
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:
            self.embedding = nn.Embedding(num_embeddings=config.n_vocab, embedding_dim=config.embed, padding_idx=config.pad_idx)
        self.lstm = nn.LSTM(config.embed, config.hidden_size, config.num_layers,
                            bidirectional=True, batch_first=True, dropout=config.dropout)
        self.tanh1 = nn.Tanh()
        # self.u = nn.Parameter(torch.Tensor(config.hidden_size * 2, config.hidden_size * 2))
        self.w = nn.Parameter(torch.zeros(config.hidden_size * 2))
        self.tanh2 = nn.Tanh()
        self.fc1 = nn.Linear(config.hidden_size * 2, config.hidden_size2)
        self.fc = nn.Linear(config.hidden_size2, config.num_classes)

    def forward(self, x):
        emb = self.embedding(x)  # [batch_size, seq_len, embeding]=[128, 32, 300]
        H, _ = self.lstm(emb)  # [batch_size, seq_len, hidden_size * num_direction]=[128, 32, 256]
        M = self.tanh1(H)  # [128, 32, 256]
        # M = torch.tanh(torch.matmul(H, self.u))
        alpha = F.softmax(torch.matmul(M, self.w), dim=1).unsqueeze(-1)  # [128, 32, 1]
        out = H * alpha  # [128, 32, 256]
        out = torch.sum(out, 1)  # [128, 256]
        out = F.relu(out)
        out = self.fc1(out)
        out = self.fc(out)  # [128, 64]
        return out