训练数据和测试数据的标签值怎么没有读取进去？

对不起，我忘记在代码中添加读取标签的部分。以下是后的代码，包读取训练数据和测试数据的标值：

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
import matplotlib.pyplot as plt

# 定义CNN模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc = nn.Linear(32 * 45 * 235, 150 * 675)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 读取训练数据及标签值
train_data = pd.read_excel('data.xlsx', sheet_name='Sheet1', usecols=[0], nrows=700)
train_labels = pd.read_excel('data.xlsx', sheet_name='Sheet1', usecols=[1], nrows=700)
train_data = torch.tensor(train_data.values.reshape(-1, 1, 45, 235), dtype=torch.float32)
train_labels = torch.tensor(train_labels.values, dtype=torch.float32)

# 读取测试数据及标签值
test_data = pd.read_excel('data.xlsx', sheet_name='Sheet1', usecols=[0], skiprows=700)
test_labels = pd.read_excel('data.xlsx', sheet_name='Sheet1', usecols=[1], skiprows=700)
test_data = torch.tensor(test_data.values.reshape(-1, 1, 45, 235), dtype=torch.float32)
test_labels = torch.tensor(test_labels.values, dtype=torch.float32)

# 初始化模型、损失函数和优化器
model = CNN()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 10
train_losses = []
test_losses = []

for epoch in range(num_epochs):
    model.train()
    train_loss = 0.0
    
    for i, data in enumerate(train_data):
        optimizer.zero_grad()
        outputs = model(data.unsqueeze(0))
        loss = criterion(outputs, train_labels[i])
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
    
    train_losses.append(train_loss / len(train_data))
    
    model.eval()
    test_loss = 0.0
    
    with torch.no_grad():
        for i, data in enumerate(test_data):
            outputs = model(data.unsqueeze(0))
            loss = criterion(outputs, test_labels[i])
            test_loss += loss.item()
            
    test_losses.append(test_loss / len(test_data))

# 绘制训练误差和测试误差的图表
plt.plot(range(1, num_epochs+1), train_losses, label='Train Loss')
plt.plot(range(1, num_epochs+1), test_losses, label='Test Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()

再次对之前的遗漏表示抱歉。更新后的代码会读取训练数据和测试数据的标签值，并在训练过程中使用它们计算损失。希望这次能满足你的需求。