帮我写一个文本分类的Bilstm代码 pytorch
时间: 2023-03-27 12:01:08 浏览: 73
好的,以下是一个简单的文本分类的Bilstm代码,使用Pytorch实现:
```python
import torch
import torch.nn as nn
import torch.optim as optim
class BiLSTM(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, num_layers, bidirectional, dropout):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, bidirectional=bidirectional, dropout=dropout)
self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, text):
embedded = self.dropout(self.embedding(text))
output, (hidden, cell) = self.lstm(embedded)
hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1))
return self.fc(hidden)
# 定义模型参数
vocab_size = 10000
embedding_dim = 100
hidden_dim = 256
output_dim = 2
num_layers = 2
bidirectional = True
dropout = .5
# 初始化模型
model = BiLSTM(vocab_size, embedding_dim, hidden_dim, output_dim, num_layers, bidirectional, dropout)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())
# 训练模型
for epoch in range(num_epochs):
for batch in train_iterator:
optimizer.zero_grad()
text, labels = batch.text, batch.label
predictions = model(text).squeeze(1)
loss = criterion(predictions, labels)
loss.backward()
optimizer.step()
```
这个代码实现了一个双向LSTM模型,用于文本分类任务。具体来说,它使用了一个Embedding层将输入的文本转换为向量表示,然后通过LSTM层进行序列建模,最后通过全连接层输出分类结果。在训练过程中,我们使用交叉熵损失函数和Adam优化器进行模型优化。
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