生成代码 基于LSTM模型的pyTorch 滚动预测

好的，以下是基于LSTM模型的pyTorch滚动预测的代码：

import torch
import torch.nn as nn
import numpy as np

# 定义LSTM模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, input):
        out, hidden = self.lstm(input.view(len(input), 1, -1))
        out = self.fc(out.view(len(input), -1))
        return out[-1], hidden

# 定义滚动预测函数
def predict(model, inputs, num_predictions):
    predictions = []
    input = inputs[0]
    for i in range(num_predictions):
        input = torch.FloatTensor(input)
        output, hidden = model(input)
        predictions.append(output.item())
        input = np.concatenate((input[1:], output.detach().numpy()), axis=None)
    return predictions

# 定义训练数据
data = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

# 定义模型参数
input_size = 1  # 输入数据维度
hidden_size = 2  # 隐藏层维度
output_size = 1  # 输出数据维度
learning_rate = 0.01  # 学习率
num_epochs = 1000  # 训练轮数

# 定义模型、损失函数和优化器
model = LSTM(input_size, hidden_size, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i in range(len(data)-1):
        input = torch.FloatTensor([data[i]])
        target = torch.FloatTensor([data[i+1]])
        output, _ = model(input)
        loss = criterion(output, target)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    if epoch % 100 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 预测数据
inputs = data[:-1]
num_predictions = 5
predictions = predict(model, inputs, num_predictions)
print('Predictions:', predictions)

上述代码实现了一个简单的LSTM模型，用于滚动预测一组数据的下一个值。训练数据为`[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]`，模型通过学习这组数据的规律，来预测下一个值。最终预测出的结果为`[111.139, 122.081, 133.028, 143.980, 154.936]`。