用torch解决问题：使用housing_renamed.csv中的数据构建两个不同的神经网络模型预测value_per_sq_ft：使用3折交叉验证来评价这两个不同的模型的好坏。

好的，我可以帮助你回答这个问题。

首先，我们需要导入必要的库和数据：

import torch
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from torch import nn, optim

# 读取数据
df = pd.read_csv('housing_renamed.csv')

# 特征和目标值
features = df.drop(['value_per_sq_ft'], axis=1).values
target = df['value_per_sq_ft'].values

然后，我们需要定义两个不同的神经网络模型。这里，我们可以使用两个不同的层数、神经元数量和激活函数：

class Model1(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = nn.Linear(13, 64)
        self.linear2 = nn.Linear(64, 1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        return x


class Model2(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear1 = nn.Linear(13, 128)
        self.linear2 = nn.Linear(128, 64)
        self.linear3 = nn.Linear(64, 1)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        x = self.relu(x)
        x = self.linear3(x)
        return x

接下来，我们需要定义评价函数。这里，我们可以使用均方根误差（RMSE）：

def rmse(y_true, y_pred):
    return torch.sqrt(torch.mean((y_true - y_pred)**2))

然后，我们需要准备数据并进行标准化：

# 准备数据
kf = KFold(n_splits=3, shuffle=True, random_state=42)

# 标准化
scaler = StandardScaler()
features = scaler.fit_transform(features)

现在，我们可以进行交叉验证了。对于每个模型和每个折叠，我们将使用均方根误差作为评价指标：

# 交叉验证
for i, (train_index, test_index) in enumerate(kf.split(features)):
    print(f'Fold {i+1}')
    train_features, train_target = features[train_index], target[train_index]
    test_features, test_target = features[test_index], target[test_index]

    # 训练模型1
    model1 = Model1()
    optimizer1 = optim.Adam(model1.parameters(), lr=0.001)
    criterion1 = nn.MSELoss()
    for epoch in range(100):
        optimizer1.zero_grad()
        outputs = model1(torch.Tensor(train_features))
        loss = criterion1(outputs.squeeze(), torch.Tensor(train_target))
        loss.backward()
        optimizer1.step()
    y_pred1 = model1(torch.Tensor(test_features))
    print(f'Model 1 RMSE: {rmse(torch.Tensor(test_target), y_pred1.squeeze())}')

    # 训练模型2
    model2 = Model2()
    optimizer2 = optim.Adam(model2.parameters(), lr=0.001)
    criterion2 = nn.MSELoss()
    for epoch in range(100):
        optimizer2.zero_grad()
        outputs = model2(torch.Tensor(train_features))
        loss = criterion2(outputs.squeeze(), torch.Tensor(train_target))
        loss.backward()
        optimizer2.step()
    y_pred2 = model2(torch.Tensor(test_features))
    print(f'Model 2 RMSE: {rmse(torch.Tensor(test_target), y_pred2.squeeze())}')

最后，我们可以比较两个模型的RMSE，并确定哪个模型更好：

# 比较模型
if rmse(torch.Tensor(target), model1(torch.Tensor(features)).squeeze()) < rmse(torch.Tensor(target), model2(torch.Tensor(features)).squeeze()):
    print('Model 1 is better than Model 2')
else:
    print('Model 2 is better than Model 1')

希望这可以帮助你解决问题！