如何写基于lstm神经网络实现对特定数据集的回归预测的python代码

下面是一个基于 LSTM 神经网络实现回归预测的 Python 代码示例：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Dense

# 读取数据集
data = pd.read_csv('your_data.csv')

# 预处理数据
data = data.values
data = data.astype('float32')

# 划分训练集和测试集
train_size = int(len(data) * 0.67)
test_size = len(data) - train_size
train_data = data[0:train_size,:]
test_data = data[train_size:len(data),:]

# 数据归一化
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
test_data -= mean
test_data /= std

# 生成 LSTM 输入数据
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), 0]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 1
trainX, trainY = create_dataset(train_data, look_back)
testX, testY = create_dataset(test_data, look_back)

# 将输入转换为 [样本, 时间步骤, 特征] 的张量形状
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

# 创建 LSTM 模型
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

# 评估模型
train_score = model.evaluate(trainX, trainY, verbose=0)
print('Train Score: %.2f MSE