输入数据类型为tensor型输入数据为440000行256列每一行为一个数据样本输入标签为440000行1列的矩阵如何用python实现使用K折交叉验证对数据进行分类分为训练数据训练样本测试数据测试样本优化器为SGD损失函数为MSE学习率为00001每次循环进行30轮训练统计每一轮测试集上的累计损失以及累计NRMSE

下面是使用Python实现使用K折交叉验证对数据进行分类的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
import numpy as np

# 定义模型
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc = nn.Linear(256, 1)

    def forward(self, x):
        x = self.fc(x)
        return x

# 定义损失函数
criterion = nn.MSELoss()

# 定义优化器
optimizer = optim.SGD(model.parameters(), lr=0.0001)

# 定义K折交叉验证
kfold = KFold(n_splits=K)

# 定义累计损失和累计NRMSE
total_loss = 0
total_nrmse = 0

# 进行K折交叉验证
for train_index, test_index in kfold.split(data):
    # 划分训练数据和测试数据
    train_data, test_data = data[train_index], data[test_index]
    train_labels, test_labels = labels[train_index], labels[test_index]

    # 初始化模型
    model = Model()

    # 训练模型
    for epoch in range(30):
        # 清零梯度
        optimizer.zero_grad()

        # 前向传播
        train_output = model(train_data)
        train_loss = criterion(train_output, train_labels)

        # 反向传播
        train_loss.backward()

        # 更新参数
        optimizer.step()

        # 统计累计损失
        total_loss += train_loss.item()

    # 在测试集上计算NRMSE
    test_output = model(test_data)
    test_loss = criterion(test_output, test_labels)
    nrmse = np.sqrt(mean_squared_error(test_labels.numpy(), test_output.detach().numpy())) / (test_labels.max() - test_labels.min())

    # 统计累计NRMSE
    total_nrmse += nrmse

# 计算平均损失和平均NRMSE
avg_loss = total_loss / K
avg_nrmse = total_nrmse / K
``