数据为一个文件夹数据，文件夹里面有100excle数据 excle里面有行车的速度和油耗数据，基于pytroch写一个LSTM预测油耗代码

好的，以下是基于PyTorch实现的LSTM油耗预测代码：

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# 读取数据
data = pd.read_excel('/path/to/data/folder/*.xlsx', sheet_name=None)
df = pd.concat(data.values())

# 数据处理
data = df['油耗'].values.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(-1, 1))
data = scaler.fit_transform(data)

# 划分训练集和测试集
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train_data, test_data = data[:train_size, :], data[train_size:, :]

# 创建数据集
def create_dataset(dataset, look_back=1):
    data_X, data_Y = [], []
    for i in range(len(dataset) - look_back):
        data_X.append(dataset[i:(i + look_back), 0])
        data_Y.append(dataset[i + look_back, 0])
    return np.array(data_X), np.array(data_Y)

look_back = 10
train_X, train_Y = create_dataset(train_data, look_back=look_back)
test_X, test_Y = create_dataset(test_data, look_back=look_back)

# 转换为PyTorch张量
train_X = torch.from_numpy(train_X).float()
train_Y = torch.from_numpy(train_Y).float()
test_X = torch.from_numpy(test_X).float()

# 定义LSTM模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTM, self).__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, x):
        h0 = torch.zeros(1, x.size(1), self.hidden_size)
        c0 = torch.zeros(1, x.size(1), self.hidden_size)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[-1, :, :])
        return out

input_size = 1
hidden_size = 32
output_size = 1
model = LSTM(input_size, hidden_size, output_size)

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
    outputs = model(train_X)
    loss = criterion(outputs, train_Y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if (epoch + 1) % 10 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
    test_predict = model(test_X)
    test_predict = scaler.inverse_transform(test_predict.numpy())
    test_Y = scaler.inverse_transform(test_Y.numpy())
    rmse = np.sqrt(np.mean((test_predict - test_Y) ** 2))
    print('RMSE: {:.2f}'.format(rmse))

以上代码将数据文件夹中的所有Excel文件合并，然后进行数据预处理和LSTM模型的训练和测试。请将`/path/to/data/folder/*.xlsx`替换为你的数据文件夹路径。