写一个基于pytorch的LSTM模型代码 输入特征4 序列长度20 批次128
时间: 2023-06-18 14:08:56 浏览: 68
以下是一个基于PyTorch的LSTM模型的代码示例,输入特征为4,序列长度为20,批次为128:
```python
import torch
import torch.nn as nn
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super(LSTM, 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)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
# Initialize hidden state with zeros
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device=x.device)
# Initialize cell state with zeros
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device=x.device)
# Forward propagate LSTM
out, _ = self.lstm(x, (h0, c0)) # out: tensor of shape (batch_size, seq_length, hidden_size)
# Decode the hidden state of the last time step
out = self.fc(out[:, -1, :])
return out
# Define model hyperparameters
input_size = 4
hidden_size = 64
num_layers = 1
output_size = 1
# Instantiate LSTM model
model = LSTM(input_size, hidden_size, num_layers, output_size)
# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# Create dummy input tensor
x = torch.randn(128, 20, 4)
# Forward pass
output = model(x)
# Compute loss
loss = criterion(output, torch.randn(128, 1))
# Backward pass and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
在上述代码中,我们首先定义了一个LSTM类,该类继承自nn.Module,构造函数中定义了LSTM模型的各个层。在forward方法中,我们首先使用torch.zeros函数初始化了LSTM的隐藏状态和记忆状态。然后我们将输入张量x传递给lstm层进行前向传播。接着我们使用out[:, -1, :]选择最后一个时间步的输出,并将其传递给全连接层fc进行输出。
在实例化LSTM模型之后,我们定义了损失函数和优化器,并使用随机生成的输入张量x进行了一次前向传播和反向传播。
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