rnn_lstm_2.py

# 2.构建网络
import torch
from torch import nn
import torch.nn.functional as F


class LSTMTagger(nn.Module):
    def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size):
        super(LSTMTagger, self).__init__()
        self.hidden_dim = hidden_dim

        self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)

        self.lstm = nn.LSTM(embedding_dim, hidden_dim)

        self.hidden2tag = nn.Linear(hidden_dim, tagset_size)
        self.hidden = self.init_hidden()

    # 初始化隐含状态State及C
    def init_hidden(self):
        return (torch.zeros(1, 1, self.hidden_dim),
                torch.zeros(1, 1, self.hidden_dim))

    def forward(self, sentence):
        # 获得词嵌入矩阵embeds
        embeds = self.word_embeddings(sentence)
        # 按lstm格式，修改embeds的形状
        lstm_out, self.hidden = self.lstm(embeds.view(len(sentence), 1, -1), self.hidden)
        # 修改隐含状态的形状，作为全连接层的输入
        tag_space = self.hidden2tag(lstm_out.view(len(sentence), -1))
        # 计算每个单词属于各词性的概率
        tag_scores = F.log_softmax(tag_space, dim=1)
        return tag_scores


def prepare_sequence(seq, to_ix):
    idxs = [to_ix[w] for w in seq]
    tensor = torch.LongTensor(idxs)
    return tensor