写一份基于pytorch深度学习框架LSTM交叉验证的代码

以下是一个基于pytorch深度学习框架的LSTM交叉验证代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import KFold

# 读入数据集
X = ...
y = ...

# 定义LSTM模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        lstm_out, _ = self.lstm(x.view(len(x), 1, -1))
        output = self.fc(lstm_out[-1])
        return output

# 定义交叉验证函数
def cross_validation(X, y, n_splits, hidden_size, learning_rate, num_epochs):

    kf = KFold(n_splits=n_splits, shuffle=True)

    for train_index, test_index in kf.split(X):

        # 划分训练集和测试集
        X_train, X_test = X[train_index], X[test_index]
        y_train, y_test = y[train_index], y[test_index]

        # 初始化模型
        model = LSTM(input_size=X_train.shape[1], hidden_size=hidden_size, output_size=y_train.shape[1])
        optimizer = optim.Adam(model.parameters(), lr=learning_rate)
        loss_fn = nn.MSELoss()

        # 训练模型
        for epoch in range(num_epochs):
            model.train()
            optimizer.zero_grad()
            output = model(torch.tensor(X_train, dtype=torch.float))
            loss = loss_fn(output, torch.tensor(y_train, dtype=torch.float))
            loss.backward()
            optimizer.step()

            # 打印训练进度
            if epoch % 10 == 0:
                print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

        # 测试模型
        model.eval()
        with torch.no_grad():
            test_output = model(torch.tensor(X_test, dtype=torch.float))
            test_loss = loss_fn(test_output, torch.tensor(y_test, dtype=torch.float))
            print('Test Loss: {:.4f}'.format(test_loss.item()))

# 运行交叉验证
cross_validation(X, y, n_splits=5, hidden_size=64, learning_rate=0.001, num_epochs=100)

该代码首先定义了一个LSTM模型，并在交叉验证函数中使用了该模型对数据进行训练和测试。其中，使用了KFold函数将数据集分为n_splits份，每次使用其中一份作为测试集，其余作为训练集，进行模型训练和测试。在训练过程中，使用了Adam优化器和均方误差损失函数。最后输出了测试集的损失值。