用Pyorch写一段训练LSTM网络的代码

下面是一个使用 PyTorch 训练 LSTM 网络的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset

# 定义 LSTM 模型
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):
        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, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# 定义训练数据集
class MyDataset(Dataset):
    def __init__(self, data):
        self.data = data
    
    def __getitem__(self, index):
        x = torch.Tensor(self.data[index][0])
        y = torch.Tensor(self.data[index][1])
        return x, y
    
    def __len__(self):
        return len(self.data)

# 定义超参数
input_size = 1
hidden_size = 32
num_layers = 2
output_size = 1
learning_rate = 0.001
num_epochs = 100

# 准备数据
data = [(torch.randn(10, input_size), torch.randn(1)) for _ in range(100)]
train_dataset = MyDataset(data)
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

# 初始化模型和优化器
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = LSTM(input_size, hidden_size, num_layers, output_size).to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)
        
        # 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()
        
        if (i+1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 预测新数据
test_inputs = torch.randn(10, input_size).to(device)
test_outputs = model(test_inputs)
print('Test Input: {}'.format(test_inputs))
print('Test Output: {}'.format(test_outputs))

在这个示例代码中，我们定义了一个简单的 LSTM 模型，使用随机数据进行训练，并使用均方误差作为损失函数，使用 Adam 优化器进行优化。在训练过程中，我们使用 DataLoader 加载数据，并将数据传递给模型进行训练。最后，我们使用训练好的模型对新的数据进行预测。