pytorch如何将csv数据输入cnn模型中，数据需要归一化且有14个特征

可以使用PyTorch的数据加载工具（DataLoader）和数据转换工具（Transform）来将CSV数据输入CNN模型中。

首先，将CSV数据读取为Pandas DataFrame对象，然后使用sklearn.preprocessing库中的MinMaxScaler对数据进行归一化处理。

接下来，使用PyTorch的Dataset和DataLoader将数据转换为可以输入模型的Tensor格式。在Dataset中，可以将特征和标签分开处理，其中特征需要通过Transform进行归一化处理。在DataLoader中，可以设置batch size、shuffle等参数。

最后，将数据加载到CNN模型中进行训练和测试。具体代码示例如下：

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms

# 读取CSV数据为Pandas DataFrame
df = pd.read_csv('data.csv')

# 分离特征和标签
X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

# 归一化处理特征
scaler = MinMaxScaler()
X = scaler.fit_transform(X)

# 定义Dataset和Transform
class MyDataset(Dataset):
    def __init__(self, X, y, transform=None):
        self.X = X
        self.y = y
        self.transform = transform
        
    def __len__(self):
        return len(self.X)
    
    def __getitem__(self, idx):
        sample = {'X': self.X[idx], 'y': self.y[idx]}
        if self.transform:
            sample = self.transform(sample)
        return sample

class MyTransform(object):
    def __init__(self):
        pass
    
    def __call__(self, sample):
        X, y = sample['X'], sample['y']
        X = torch.from_numpy(X).float()
        y = torch.tensor(y).long()
        return {'X': X, 'y': y}

# 定义Transform
transform = transforms.Compose([
    MyTransform()
])

# 定义Dataset和DataLoader
dataset = MyDataset(X, y, transform=transform)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# 定义CNN模型
class MyCNN(torch.nn.Module):
    def __init__(self):
        super(MyCNN, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
        self.conv2 = torch.nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc1 = torch.nn.Linear(32 * 7 * 7, 128)
        self.fc2 = torch.nn.Linear(128, 10)
        
    def forward(self, x):
        x = x.unsqueeze(1)
        x = torch.nn.functional.relu(self.conv1(x))
        x = torch.nn.functional.max_pool2d(x, kernel_size=2, stride=2)
        x = torch.nn.functional.relu(self.conv2(x))
        x = torch.nn.functional.max_pool2d(x, kernel_size=2, stride=2)
        x = x.view(-1, 32 * 7 * 7)
        x = torch.nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 训练CNN模型
cnn = MyCNN()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=0.001)

for epoch in range(10):
    for batch in dataloader:
        X_batch, y_batch = batch['X'], batch['y']
        optimizer.zero_grad()
        y_pred = cnn(X_batch)
        loss = criterion(y_pred, y_batch)
        loss.backward()
        optimizer.step()
        print('Epoch: {}, Loss: {:.4f}'.format(epoch+1, loss.item()))