LSTM如何增加网络层数代码示例
时间: 2023-07-20 09:37:30 浏览: 34
以下是一个示例代码,演示如何通过增加 LSTM 网络层数来创建一个更深的神经网络模型:
```
import torch.nn as nn
# 创建一个两层的 LSTM 神经网络
class TwoLayerLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(TwoLayerLSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm1 = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
out, (hn, cn) = self.lstm1(x, (h0, c0))
out, (hn, cn) = self.lstm2(out, (hn, cn))
out = self.fc(out[:, -1, :])
return out
# 创建一个更深的 LSTM 神经网络,添加一个额外的 LSTM 层
class ThreeLayerLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(ThreeLayerLSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm1 = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
self.lstm2 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.lstm3 = nn.LSTM(hidden_size, hidden_size, num_layers, batch_first=True)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
out, (hn, cn) = self.lstm1(x, (h0, c0))
out, (hn, cn) = self.lstm2(out, (hn, cn))
out, (hn, cn) = self.lstm3(out, (hn, cn))
out = self.fc(out[:, -1, :])
return out
```
在上面的示例中,我们首先定义了一个名为 TwoLayerLSTM 的两层 LSTM 神经网络。然后我们定义了一个名为 ThreeLayerLSTM 的更深的 LSTM 神经网络,它包含三个 LSTM 层。通过增加一个额外的 LSTM 层,我们可以使神经网络更深,从而更好地捕捉时间序列数据中的复杂关系。