MLP实现多步预测pytorch代码

MLP（多层感知机）是一种常用的神经网络模型，可以用于多步预测任务。下面是一个使用PyTorch实现MLP进行多步预测的代码示例：

import torch
import torch.nn as nn

# 定义MLP模型
class MLP(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

# 定义训练函数
def train(model, train_data, train_labels, num_epochs, learning_rate):
    criterion = nn.MSELoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

    for epoch in range(num_epochs):
        inputs = torch.Tensor(train_data)
        labels = torch.Tensor(train_labels)

        # 前向传播
        outputs = model(inputs)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (epoch+1) % 100 == 0:
            print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 定义测试函数
def test(model, test_data):
    inputs = torch.Tensor(test_data)
    outputs = model(inputs)
    return outputs.detach().numpy()

# 示例数据
train_data = [[0.1], [0.2], [0.3], [0.4], [0.5]]
train_labels = [[0.2], [0.3], [0.4], [0.5], [0.6]]
test_data = [[0.6], [0.7], [0.8]]

# 定义模型参数
input_size = 1
hidden_size = 10
output_size = 1
num_epochs = 1000
learning_rate = 0.01

# 创建模型实例
model = MLP(input_size, hidden_size, output_size)

# 训练模型
train(model, train_data, train_labels, num_epochs, learning_rate)

# 测试模型
predictions = test(model, test_data)
print("预测结果：", predictions)

这段代码实现了一个简单的MLP模型，用于进行多步预测任务。其中，`MLP`类定义了MLP模型的结构，`train`函数用于训练模型，`test`函数用于测试模型。你可以根据自己的数据和需求进行相应的修改和调整。