请帮我写一段bi-lstm二分类代码
时间: 2023-12-18 16:04:09 浏览: 28
当然可以帮你写一段Bi-LSTM二分类的代码。以下是一个简单的示例:
```python
import torch
import torch.nn as nn
class BiLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(BiLSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
self.fc = nn.Linear(hidden_size*2, num_classes)
def forward(self, x):
h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device) # 初始化隐藏状态
c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device) # 初始化cell状态
out, _ = self.lstm(x, (h0, c0)) # 前向传播
out = self.fc(out[:, -1, :]) # 取最后一个时间步的输出
return out
# 定义模型参数
input_size = 10 # 输入特征维度
hidden_size = 32 # LSTM隐藏层大小
num_layers = 2 # LSTM层数
num_classes = 2 # 分类类别数
# 实例化模型
model = BiLSTM(input_size, hidden_size, num_layers, num_classes)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 训练循环
for epoch in range(num_epochs):
# 前向传播
outputs = model(inputs)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 打印训练信息
if (epoch+1) % 10 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
```
这是一个简单的Bi-LSTM二分类模型,你可以根据自己的数据和需求进行修改和扩展。祝你编程愉快!