用pytorch写一个用卷积神经网络分类的python代码，数据集有337183组1*33维的数据，其中前32列为数据，最后1列为对应的标签，总共5类标签，即分成5类。

好的，这是一个关于卷积神经网络分类的 Python 代码，它使用 PyTorch 框架：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

class MyDataset(Dataset):
    def __init__(self, data):
        self.data = data[:, :-1]
        self.targets = data[:, -1]
    
    def __len__(self):
        return len(self.targets)
    
    def __getitem__(self, idx):
        sample = self.data[idx]
        target = self.targets[idx]
        return sample, target

class MyCNN(nn.Module):
    def __init__(self):
        super(MyCNN, self).__init__()
        self.conv1 = nn.Conv1d(1, 10, kernel_size=3)
        self.conv2 = nn.Conv1d(10, 20, kernel_size=3)
        self.conv2_drop = nn.Dropout()
        self.fc1 = nn.Linear(580, 50)
        self.fc2 = nn.Linear(50, 5)

    def forward(self, x):
        x = x.unsqueeze(1)
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool1d(x, 2)
        x = nn.functional.relu(self.conv2_drop(self.conv2(x)))
        x = nn.functional.max_pool1d(x, 2)
        x = x.view(-1, 580)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.dropout(x, training=self.training)
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

model = MyCNN()
optimizer = optim.SGD(model.parameters(), lr=0.01)
loss_fn = nn.CrossEntropyLoss()

data = torch.load("my_data.pth")
dataset = MyDataset(data)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)

for epoch in range(10):
    for batch_idx, (data, target) in enumerate(dataloader):
        optimizer.zero_grad()
        output = model(data.float())
        loss = loss_fn(output, target.long())
        loss.backward()
        optimizer.step()

print("Training done!")

请注意，此代码需要将数据集保存为 PyTorch 张量，方法如下：

import numpy as np
import torch

# Load data from file
data = np.loadtxt("my_data.csv", delimiter=",", dtype=np.float32)

# Save data as PyTorch tensor
torch.save(torch.from_numpy(data), "my_data.pth")

这里假设数据文件名为 my_data.csv，格式与您的描述相符。