用python写一个lstm神经网络预测代码

好的，下面是一个简单的LSTM神经网络预测代码，使用Python和Keras库实现：

# 导入必要的库
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM

# 读取数据
data = pd.read_csv('data.csv')

# 数据预处理
data = data.dropna()  # 删除缺失值
dataset = data.values  # 转化为numpy数组
dataset = dataset.astype('float32')  # 转化为float类型

# 将数据集分成训练集和测试集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

# 准备训练数据和标签
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 = 3
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# 将输入数据重塑为LSTM所需的格式
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))

# 创建模型
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))

# 编译模型
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
trainScore = model.evaluate(trainX, trainY, verbose=0)
print('Train Score: %.2f MSE (%.2f RMSE)' % (trainScore, np.sqrt(trainScore)))
testScore = model.evaluate(testX, testY, verbose=0)
print('Test Score: %.2f MSE (%.2f RMSE)' % (testScore, np.sqrt(testScore)))

# 预测结果
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# 反归一化预测结果
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])

以上代码是一个简单的LSTM神经网络预测模型，你需要将数据集替换为自己的数据集，并根据需要调整模型参数。