我需要多输入变量多步预测的pytorch 程序

好的，以下是一个基本的多输入变量多步预测的 PyTorch 程序示例：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义模型类
class MultiInputMultiStepModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        # x 的形状为 (batch_size, sequence_length, input_size)
        batch_size = x.size(0)
        hidden = self.init_hidden(batch_size)
        out, hidden = self.rnn(x, hidden)
        out = self.fc(out)
        # out 的形状为 (batch_size, sequence_length, output_size)
        return out

    def init_hidden(self, batch_size):
        return torch.zeros(1, batch_size, self.hidden_size)

# 定义训练函数
def train(model, train_loader, criterion, optimizer):
    model.train()
    total_loss = 0.0
    for inputs, targets in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    return total_loss / len(train_loader)

# 定义测试函数
def test(model, test_loader, criterion):
    model.eval()
    total_loss = 0.0
    with torch.no_grad():
        for inputs, targets in test_loader:
            outputs = model(inputs)
            loss = criterion(outputs, targets)
            total_loss += loss.item()
    return total_loss / len(test_loader)

# 定义数据集类
class MultiInputMultiStepDataset(torch.utils.data.Dataset):
    def __init__(self, inputs, targets):
        self.inputs = inputs
        self.targets = targets

    def __len__(self):
        return len(self.inputs)

    def __getitem__(self, idx):
        return self.inputs[idx], self.targets[idx]

# 定义训练数据和测试数据
train_inputs = torch.randn(100, 10, 3)
train_targets = torch.randn(100, 5, 2)
test_inputs = torch.randn(20, 10, 3)
test_targets = torch.randn(20, 5, 2)

# 将数据封装成 DataLoader
train_dataset = MultiInputMultiStepDataset(train_inputs, train_targets)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=10, shuffle=True)
test_dataset = MultiInputMultiStepDataset(test_inputs, test_targets)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=10, shuffle=True)

# 初始化模型、损失函数和优化器
model = MultiInputMultiStepModel(input_size=3, hidden_size=10, output_size=2)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters())

# 训练模型
for epoch in range(10):
    train_loss = train(model, train_loader, criterion, optimizer)
    test_loss = test(model, test_loader, criterion)
    print(f"Epoch {epoch+1}: train loss={train_loss:.6f}, test loss={test_loss:.6f}")

这个示例程序实现了一个多输入变量多步预测的 RNN 模型，使用了 PyTorch 中的 Dataset 和 DataLoader 来加载数据，并使用了 Adam 优化器来训练模型。你可以根据自己的数据和需求来修改和扩展这个程序。