self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
时间: 2023-03-15 07:13:16 浏览: 69
循环神经网络(RNN)中的长短期记忆(LSTM)是一种强大的模型,用于处理序列数据的学习和预测。它的基本结构包括一个输入层,一个隐藏层和一个输出层。通过将输入数据逐个传递到隐藏层,再将输出数据传递到输出层,LSTM可以在序列中学习长期依赖关系。
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def setup_layers(self): self.lstm = torch.nn.LSTM( input_size = self.lstm_inputsize, hidden_size = self.lstm_hiddensize, num_layers = self.lstm_layers, batch_first=True, dropout=(0 if self.lstm_layers == 1 else self.lstm_dropout), bidirectional=False )
这段代码使用PyTorch中的`nn.LSTM`构建了一个LSTM层,具体参数如下:
- `input_size`:输入特征的维度。
- `hidden_size`:隐藏状态的维度。
- `num_layers`:LSTM层数。
- `batch_first`:如果为True,则输入和输出张量的形状为(batch_size, seq_length, feature_dims);否则,形状为(seq_length, batch_size, feature_dims)。
- `dropout`:如果`num_layers=1`,则不使用dropout;否则,将应用dropout并指定丢弃概率。
- `bidirectional`:是否使用双向LSTM。
需要注意的是,这段代码构建的是单向LSTM,如果需要构建双向LSTM,需要将`bidirectional`参数设置为True,如下所示:
```python
self.lstm = torch.nn.LSTM(
input_size=self.lstm_inputsize,
hidden_size=self.lstm_hiddensize,
num_layers=self.lstm_layers,
batch_first=True,
dropout=(0 if self.lstm_layers == 1 else self.lstm_dropout),
bidirectional=True
)
```
class LSTM(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, num_layers): super(LSTM, self).__init__() self.hidden_dim = hidden_dim self.num_layers = num_layers self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True) self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, x): h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(device) c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(device) out, _ = self.lstm(x, (h0, c0)) out = self.fc(out) return out[:, -1, :]为我的这段代码添加一个dropout层
可以在LSTM类的__init__方法中添加一个nn.Dropout层,如下所示:
class LSTM(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super(LSTM, self).__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
self.dropout = nn.Dropout(p=.5) # 添加dropout层
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
h = torch.zeros(self.num_layers, x.size(), self.hidden_dim).to(device)
c = torch.zeros(self.num_layers, x.size(), self.hidden_dim).to(device)
out, _ = self.lstm(x, (h, c))
out = self.dropout(out) # 在LSTM输出后添加dropout层
out = self.fc(out)
return out[:, -1, :]