基于pytorch写一个多对多预测代码，数据集名称为data.csv，前四列为特征，其余列为标签，前800行为训练集其余为测试集，将其中一组预测值与真实值输出到excel工作表中

下面是一个基于 PyTorch 的多对多预测代码示例：

import torch
import pandas as pd
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

# 读取数据
data = pd.read_csv('data.csv')

# 划分训练集和测试集
train_data = data.iloc[:800, :-5]
train_label = data.iloc[:800, 4:]
test_data = data.iloc[800:, :-5]
test_label = data.iloc[800:, 4:]

# 将训练数据转换为 Tensor
train_dataset = TensorDataset(torch.tensor(train_data.values, dtype=torch.float32), torch.tensor(train_label.values, dtype=torch.float32))
test_dataset = TensorDataset(torch.tensor(test_data.values, dtype=torch.float32), torch.tensor(test_label.values, dtype=torch.float32))

# 设置 DataLoader
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# 定义网络模型
class Multi2Multi(nn.Module):
    def __init__(self):
        super(Multi2Multi, self).__init__()
        self.fc = nn.Linear(4, 5)
        
    def forward(self, x):
        x = self.fc(x)
        return x

# 初始化模型
model = Multi2Multi()

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# 训练模型
for epoch in range(100):
    for i, (data, label) in enumerate(train_loader):
        output = model(data)
        loss = criterion(output, label)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
    print(f'epoch {epoch+1}, loss: {loss.item()}')

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for data, label in test_loader:
        outputs = model(data)
        _, predicted = torch.max(outputs.data, 1)
        total += label.size(0)
        correct += (predicted == label).sum().item()