利用pytorch完成上述问题

好的，下面是使用PyTorch完成上述问题的示例代码：

import pandas as pd
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import KFold

# 定义一个自定义数据集类
class HousingDataset(Dataset):
    def __init__(self, X, y):
        self.X = torch.tensor(X.values, dtype=torch.float32)
        self.y = torch.tensor(y.values, dtype=torch.float32)
    
    def __len__(self):
        return len(self.X)
    
    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

# 定义一个神经网络模型类
class Net(nn.Module):
    def __init__(self, input_size):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(input_size, 10)
        self.fc2 = nn.Linear(10, 1)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 读取数据
data = pd.read_csv('housing_renamed.csv')
X = data.drop('value_per_sq_ft', axis=1)
y = data['value_per_sq_ft']

# 创建3折交叉验证器
kfold = KFold(n_splits=3, shuffle=True, random_state=42)

# 对两个模型进行3折交叉验证
mse_scores1 = []
mse_scores2 = []
for train_idx, test_idx in kfold.split(X):
    # 准备数据集
    train_dataset = HousingDataset(X.iloc[train_idx], y.iloc[train_idx])
    test_dataset = HousingDataset(X.iloc[test_idx], y.iloc[test_idx])
    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=32, shuffle=True)
    
    # 训练第一个模型
    net1 = Net(X.shape[1])
    optimizer1 = torch.optim.Adam(net1.parameters(), lr=0.01)
    criterion1 = nn.MSELoss()
    for epoch in range(10):
        for X_batch, y_batch in train_loader:
            optimizer1.zero_grad()
            y_pred = net1(X_batch)
            loss = criterion1(y_pred, y_batch.unsqueeze(1))
            loss.backward()
            optimizer1.step()
    mse_scores1.append(criterion1(net1(torch.tensor(X.values, dtype=torch.float32))).item())
    
    # 训练第二个模型
    net2 = Net(X.shape[1])
    optimizer2 = torch.optim.Adam(net2.parameters(), lr=0.01)
    criterion2 = nn.MSELoss()
    for epoch in range(20):
        for X_batch, y_batch in train_loader:
            optimizer2.zero_grad()
            y_pred = net2(X_batch)
            loss = criterion2(y_pred, y_batch.unsqueeze(1))
            loss.backward()
            optimizer2.step()
    mse_scores2.append(criterion2(net2(torch.tensor(X.values, dtype=torch.float32))).item())

# 计算MSE评分的平均值和标准差
print('Model 1 MSE: %.2f (%.2f) MSE' % (np.mean(mse_scores1), np.std(mse_scores1)))
print('Model 2 MSE: %.2f (%.2f) MSE' % (np.mean(mse_scores2), np.std(mse_scores2)))

这里使用了PyTorch中的Dataset和DataLoader类来准备数据集，并使用了自定义的神经网络模型和均方误差（MSE）损失函数。在每一个交叉验证的循环中，需要对每个模型进行训练，并计算其在整个数据集上的MSE评分。最后，计算MSE评分的平均值和标准差，并输出结果。