cnn-bilstm-attention模型的代码

以下是使用PyTorch实现的CNN-BiLSTM-Attention模型的代码：

import torch
import torch.nn as nn

class CNN_BiLSTM_Attention(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, dropout, filter_sizes, num_filters):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.convs = nn.ModuleList([
            nn.Conv2d(in_channels=1,
                      out_channels=num_filters,
                      kernel_size=(fs, embedding_dim))
            for fs in filter_sizes
        ])
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, bidirectional=True, batch_first=True)
        self.dropout = nn.Dropout(dropout)
        self.fc = nn.Linear(hidden_dim*2, output_dim)
        self.attention = nn.Linear(hidden_dim*2, 1)

    def forward(self, text):
        embedded = self.embedding(text) # [batch_size, seq_len, embedding_dim]
        embedded = embedded.unsqueeze(1) # [batch_size, 1, seq_len, embedding_dim]
        conved = [torch.relu(conv(embedded)).squeeze(3) for conv in self.convs] # [(batch_size, num_filters, seq_len - filter_size + 1), ...]
        pooled = [nn.functional.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved] # [(batch_size, num_filters), ...]
        cat = self.dropout(torch.cat(pooled, dim=1)) # [batch_size, num_filters * len(filter_sizes)]
        lstm_output, (hidden, cell) = self.lstm(embedded) # lstm_output: [batch_size, seq_len, hidden_dim*2], hidden: [2, batch_size, hidden_dim], cell: [2, batch_size, hidden_dim]
        lstm_output = self.dropout(lstm_output)
        attention_weights = torch.softmax(self.attention(lstm_output), dim=1) # [batch_size, seq_len, 1]
        attention_output = torch.sum(lstm_output * attention_weights, dim=1) # [batch_size, hidden_dim*2]
        output = self.fc(torch.cat((cat, attention_output), dim=1))
        return output

其中，`vocab_size`表示词汇表的大小，`embedding_dim`表示词向量的维度，`hidden_dim`表示LSTM的隐藏层维度，`output_dim`表示模型的输出维度，`dropout`表示dropout概率，`filter_sizes`表示卷积核的大小，`num_filters`表示卷积核的数量。

`forward()`方法中，首先通过`embedding`层将文本转换为词向量表示，然后使用`Conv2d`和`max_pool1d`对词向量进行卷积和池化操作，得到文本的卷积特征。接着，将词向量输入到LSTM中，得到LSTM的输出和隐藏状态。然后，通过`Linear`层和`softmax`计算注意力权重，将LSTM的输出和注意力权重相乘得到注意力输出。最后，将卷积特征和注意力输出拼接起来，通过`Linear`层得到模型的输出。