lstm回归python

LSTM（Long Short-Term Memory）是一种循环神经网络结构，用于处理序列数据的回归问题。它能够记住长期的依赖关系，并且在处理长序列时表现出色。在Python中，你可以使用PyTorch库来实现LSTM回归模型。下面是一个基于PyTorch实现LSTM回归模型的示例代码：

import torch
import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMModel, 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(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# 定义模型参数
input_size = 1
hidden_size = 32
num_layers = 2
output_size = 1

# 创建模型实例
model = LSTMModel(input_size, hidden_size, num_layers, output_size)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
def train(model, train_loader, criterion, optimizer):
    model.train()
    for epoch in range(num_epochs):
        for i, (inputs, labels) in enumerate(train_loader):
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

# 使用示例数据进行训练
train(model, train_loader, criterion, optimizer)