import torch import torch.nn as nn import pandas as pd from sklearn.model_selection import train_test_split # 加载数据集 data = pd.read_csv('../dataset/train_10000.csv') # 数据预处理 X = data.drop('target', axis=1).values y = data['target'].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X_train = torch.from_numpy(X_train).float() X_test = torch.from_numpy(X_test).float() y_train = torch.from_numpy(y_train).float() y_test = torch.from_numpy(y_test).float() # 定义LSTM模型 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 # 初始化模型和定义超参数 input_size = X_train.shape[1] hidden_size = 64 num_layers = 2 output_size = 1 model = LSTMModel(input_size, hidden_size, num_layers, output_size) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # 训练模型 num_epochs = 100 for epoch in range(num_epochs): model.train() outputs = model(X_train) loss = criterion(outputs, y_train) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch+1) % 10 == 0: print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}') # 在测试集上评估模型 model.eval() with torch.no_grad(): outputs = model(X_test) loss = criterion(outputs, y_test) print(f'Test Loss: {loss.item():.4f}') 我有额外的数据集CSV，请帮我数据集和测试集分离

以下是将数据集和测试集分离的代码示例：

import torch
import torch.nn as nn
import pandas as pd
from sklearn.model_selection import train_test_split

# 加载数据集
data = pd.read_csv('../dataset/train_10000.csv')

# 数据预处理
X = data.drop('target', axis=1).values
y = data['target'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

X_train = torch.from_numpy(X_train).float()
X_test = torch.from_numpy(X_test).float()
y_train = torch.from_numpy(y_train).float()
y_test = torch.from_numpy(y_test).float()

以上代码中，我们使用了`sklearn`库的`train_test_split`函数将数据集分割成训练集和测试集。你可以根据需要调整`test_size`参数来确定测试集的比例。然后，我们将数据转换为`torch`张量以供模型使用。

希望这可以帮助到你！如果有任何其他问题，请随时问我。

相关问题

修改一下这段代码在pycharm中的实现，import pandas as pd import numpy as np from sklearn.model_selection import train_test_split import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim #from torchvision import datasets,transforms import torch.utils.data as data #from torch .nn:utils import weight_norm import matplotlib.pyplot as plt from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics import f1_score from sklearn.metrics import cohen_kappa_score data_ = pd.read_csv(open(r"C:\Users\zhangjinyue\Desktop\rice.csv"),header=None) data_ = np.array(data_).astype('float64') train_data =data_[:,:520] train_Data =np.array(train_data).astype('float64') train_labels=data_[:,520] train_labels=np.array(train_data).astype('float64') train_data,train_data,train_labels,train_labels=train_test_split(train_data,train_labels,test_size=0.33333) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) train_data=torch.Tensor(train_data) train_data=torch.LongTensor(train_labels) train_data=train_data.reshape(-1,1,20,26) start_epoch=1 num_epoch=1 BATCH_SIZE=70 Ir=0.001 classes=('0','1','2','3','4','5') device=torch.device("cuda"if torch.cuda.is_available()else"cpu") torch.backends.cudnn.benchmark=True best_acc=0.0 train_dataset=data.TensorDataset(train_data,train_labels) test_dataset=data.TensorDataset(train_data,train_labels) train_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True) test_loader=torch.utills.data.DataLoader(dtaset=train_dataset,batch_size=BATCH_SIZE,shuffle=True)

修改后的代码如下所示：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import matplotlib.pyplot as plt
from sklearn.metrics import precision_score, recall_score, f1_score, cohen_kappa_score

data_ = pd.read_csv(r"C:\Users\zhangjinyue\Desktop\rice.csv", header=None)
data_ = np.array(data_).astype('float64')

train_data = data_[:, :520]
train_labels = data_[:, 520]
train_data, _, train_labels, _ = train_test_split(train_data, train_labels, test_size=0.33333)

train_data = torch.Tensor(train_data)
train_labels = torch.LongTensor(train_labels)
train_data = train_data.reshape(-1, 1, 20, 26)

start_epoch = 1
num_epoch = 1
BATCH_SIZE = 70
Ir = 0.001
classes = ('0', '1', '2', '3', '4', '5')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cudnn.benchmark = True
best_acc = 0.0

train_dataset = data.TensorDataset(train_data, train_labels)
test_dataset = data.TensorDataset(train_data, train_labels)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=BATCH_SIZE, shuffle=True)

请注意，我做了以下修改：

1. 移除了不需要的导入语句。
2. 修复了变量名拼写错误。
3. 移除了重复的代码行。
4. 修正了 `torch.utils.data.DataLoader` 的拼写错误。
5. 修正了数据集分割时的变量名错误。

请根据你的实际需求进一步调整代码。

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,要求使用torch

好的，我明白了。为了回答你的问题，我将分为以下几个步骤：

1. 加载数据集
2. 数据集预处理
3. 构建两个不同的神经网络模型
4. 定义损失函数和优化器
5. 利用3折交叉验证来评价模型的好坏

以下是详细的步骤：

1. 加载数据集

首先，我们需要使用 pandas 库来加载数据集：

import pandas as pd

df = pd.read_csv('housing_renamed.csv')

2. 数据集预处理

接下来，我们需要对数据集进行预处理，包括将数据集分为训练集和测试集，并将数据进行归一化处理：

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# 将数据集分为训练集和测试集
train_data, test_data = train_test_split(df, test_size=0.2)

# 对训练集和测试集进行归一化处理
scaler = StandardScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

3. 构建两个不同的神经网络模型

我们将构建两个简单的神经网络模型，一个包含一个隐藏层，另一个包含两个隐藏层。

import torch
import torch.nn as nn

class Net1(nn.Module):
    def __init__(self):
        super(Net1, self).__init__()
        self.fc1 = nn.Linear(13, 8)  # 输入层到隐藏层
        self.fc2 = nn.Linear(8, 1)  # 隐藏层到输出层

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

class Net2(nn.Module):
    def __init__(self):
        super(Net2, self).__init__()
        self.fc1 = nn.Linear(13, 8)  # 输入层到第一个隐藏层
        self.fc2 = nn.Linear(8, 4)  # 第一个隐藏层到第二个隐藏层
        self.fc3 = nn.Linear(4, 1)  # 第二个隐藏层到输出层

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

4. 定义损失函数和优化器

我们使用均方误差（MSE）作为损失函数，并使用 Adam 优化器进行优化：

criterion = nn.MSELoss()
optimizer1 = torch.optim.Adam(net1.parameters(), lr=0.01)
optimizer2 = torch.optim.Adam(net2.parameters(), lr=0.01)

5. 利用3折交叉验证来评价模型的好坏

最后，我们将使用 3 折交叉验证来评价模型的好坏：

from sklearn.model_selection import KFold

# 设置 k 折交叉验证
k = 3
kf = KFold(n_splits=k)

# 初始化损失
loss1_list = []
loss2_list = []

# 进行 k 折交叉验证
for train_index, test_index in kf.split(train_data):
    # 分离训练集和验证集
    x_train, x_val = train_data[train_index], train_data[test_index]
    y_train, y_val = train_data[train_index], train_data[test_index]

    # 将数据转换为张量
    x_train = torch.tensor(x_train, dtype=torch.float32)
    x_val = torch.tensor(x_val, dtype=torch.float32)
    y_train = torch.tensor(y_train, dtype=torch.float32)
    y_val = torch.tensor(y_val, dtype=torch.float32)

    # 训练模型 1
    net1 = Net1()
    optimizer1 = torch.optim.Adam(net1.parameters(), lr=0.01)
    for epoch in range(100):
        y_pred = net1(x_train)
        loss = criterion(y_pred, y_train)
        optimizer1.zero_grad()
        loss.backward()
        optimizer1.step()
    loss1_list.append(criterion(net1(x_val), y_val).item())

    # 训练模型 2
    net2 = Net2()
    optimizer2 = torch.optim.Adam(net2.parameters(), lr=0.01)
    for epoch in range(100):
        y_pred = net2(x_train)
        loss = criterion(y_pred, y_train)
        optimizer2.zero_grad()
        loss.backward()
        optimizer2.step()
    loss2_list.append(criterion(net2(x_val), y_val).item())

# 输出结果
print('Model 1 average validation loss:', sum(loss1_list) / len(loss1_list))
print('Model 2 average validation loss:', sum(loss2_list) / len(loss2_list))

这里我们使用了 100 个 epoch 进行训练，你可以根据你的需求进行修改。最后，输出结果将给出每个模型的平均验证损失。