python多层lstm模型 lstm python

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技术博主 2023-11-29 21:29:48

文章标签 python多层lstm模型 tensorflow 3c 数据集 文章分类 Python 后端开发

程序简介
程序/数据集下载
代码分析

程序简介

程序调用tensorflow.keras搭建了一个简单长短记忆型网络(LSTM)，以上证指数为例，对数据进行标准化处理，输入5天的'收盘价', '最高价', '最低价','开盘价'，输出1天的'收盘价'，利用训练集训练网络后，输出测试集的MAE

长短记忆型网络(LSTM)：是一种改进之后的循环神经网络，可以解决RNN无法处理长距离的依赖的问题。

python多层lstm模型 lstm python_3c

程序/数据集下载

代码分析

导入模块、路径

# -*- coding: utf-8 -*-
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.layers import Input,Dense,LSTM,GRU,BatchNormalization
from tensorflow.keras.layers import PReLU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import mean_absolute_error as MAE
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import pandas as pd
import numpy as np
import os

#用来正常显示中文标签
plt.rcParams['font.sans-serif']=['SimHei'] 
#用来正常显示负号
plt.rcParams['axes.unicode_minus']=False
#路径目录
baseDir = ''#当前目录
staticDir = os.path.join(baseDir,'Static')#静态文件目录
resultDir = os.path.join(baseDir,'Result')#结果文件目录

读取数据，查看5行

#读取数据
data = pd.read_csv(staticDir+'/000001.csv',encoding='gbk').iloc[-100:,:]
data = data.set_index(['日期'])
data.head()

	股票代码	名称	收盘价	最高价	最低价	开盘价	前收盘	涨跌额	涨跌幅	成交量	成交金额
日期
2019/9/16	'000001	上证指数	3030.7544	3042.9284	3020.0495	3041.9220	3031.2351	-0.4807	-0.0159	221878959	2.37E+11
2019/9/17	'000001	上证指数	2978.1178	3023.7109	2970.5704	3023.7109	3030.7544	-52.6366	-1.7367	223338061	2.38E+11
2019/9/18	'000001	上证指数	2985.6586	2996.4022	2982.4003	2984.0837	2978.1178	7.5408	0.2532	168046699	2.00E+11
2019/9/19	'000001	上证指数	2999.2789	2999.2789	2975.3978	2992.9222	2985.6586	13.6203	0.4562	162690615	1.93E+11
2019/9/20	'000001	上证指数	3006.4467	3011.3400	2996.1929	3004.8142	2999.2789	7.1678	0.239	182145302	2.18E+11

对输入输出进行标准化，查看5行

#标准化数据集
outputCol = ['收盘价']#输出列
inputCol = ['收盘价', '最高价','最低价','开盘价']#输入列
X = data[inputCol]
Y = data[outputCol]
xScaler = StandardScaler()
yScaler = StandardScaler()
X = xScaler.fit_transform(X)
Y = yScaler.fit_transform(Y)
X[:5,:]

array([[0.94704786, 0.91606531, 0.98497021, 1.04253169],
       [0.21175964, 0.65151178, 0.33108448, 0.80913257],
       [0.31709816, 0.2755725 , 0.48742125, 0.30125807],
       [0.50736208, 0.31517397, 0.39488046, 0.41453503],
       [0.60749011, 0.48121048, 0.66969587, 0.5669466 ]])

将数据按时间步进行整理，时间步这里设置为5天，输入为1天

#按时间步组成输入输出集
timeStep = 5#输入天数
outStep = 1#输出天数
xAll = list()
yAll = list()
#按时间步整理数据 输入数据尺寸是(timeStep,5) 输出尺寸是(outSize)
for row in range(data.shape[0]-timeStep-outStep+1):
    x = X[row:row+timeStep]
    y = Y[row+timeStep:row+timeStep+outStep]
    xAll.append(x)
    yAll.append(y)
xAll = np.array(xAll).reshape(-1,timeStep,len(inputCol))
yAll = np.array(yAll).reshape(-1,outStep)
print('输入集尺寸',xAll.shape)
print('输出集尺寸',yAll.shape)

输入集尺寸 (95, 5, 4)
输出集尺寸 (95, 1)

数据集分割为训练集和测试集

#分成测试集，训练集
testRate = 0.2#测试比例
splitIndex = int(xAll.shape[0]*(1-testRate))
xTrain = xAll[:splitIndex]
xTest = xAll[splitIndex:]
yTrain = yAll[:splitIndex]
yTest = yAll[splitIndex:]

搭建一个简单的LSTM网络，结构下文会打印出来

def buildLSTM(timeStep,inputColNum,outStep,learnRate=1e-4):
    '''
    搭建LSTM网络，激活函数为tanh
    timeStep：输入时间步
    inputColNum：输入列数
    outStep：输出时间步
    learnRate：学习率    
    '''
    #输入层
    inputLayer = Input(shape=(timeStep,inputColNum))

    #中间层
    middle = LSTM(100,activation='tanh')(inputLayer)
    middle = Dense(100,activation='tanh')(middle)

    #输出层 全连接
    outputLayer = Dense(outStep)(middle)
    
    #建模
    model = Model(inputs=inputLayer,outputs=outputLayer)
    optimizer = Adam(lr=learnRate)
    model.compile(optimizer=optimizer,loss='mse') 
    model.summary()
    return model

#搭建LSTM
lstm = buildLSTM(timeStep=timeStep,inputColNum=len(inputCol),outStep=outStep,learnRate=1e-4)

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 5, 4)              0         
_________________________________________________________________
lstm (LSTM)                  (None, 100)               42000     
_________________________________________________________________
dense (Dense)                (None, 100)               10100     
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 101       
=================================================================
Total params: 52,201
Trainable params: 52,201
Non-trainable params: 0
_________________________________________________________________

利用训练集对网络进行训练

#训练网络
epochs = 1000#迭代次数
batchSize = 500#批处理量
lstm.fit(xTrain,yTrain,epochs=epochs,verbose=0,batch_size=batchSize)

对测试集进行预测，保存预测结果，查看5行

#预测 测试集对比
yPredict = lstm.predict(xTest)
yPredict = yScaler.inverse_transform(yPredict)[:,0]
yTest = yScaler.inverse_transform(yTest)[:,0]
result = {'观测值':yTest,'预测值':yPredict}
result = pd.DataFrame(result)
result.index = data.index[timeStep+xTrain.shape[0]:result.shape[0]+timeStep+xTrain.shape[0]]
result.to_excel(resultDir+'/预测结果.xlsx')
result.head()

	观测值	预测值
日期
2020/1/15	3090.0379	3119.753662
2020/1/16	3074.0814	3103.595947
2020/1/17	3075.4955	3085.278809
2020/1/20	3095.7873	3079.762451
2020/1/21	3052.1419	3094.907471

计算测试集MAE，进行可视化

mae = MAE(result['观测值'],result['预测值'])
print('模型测试集MAE',mae)
#可视化
fig,ax = plt.subplots(1,1)
ax.plot(result.index,result['预测值'],label='预测值')
ax.plot(result.index,result['观测值'],label='观测值')
ax.set_title('LSTM预测效果，MAE：%2f'%mae)
ax.legend()
ax.xaxis.set_major_locator(ticker.MultipleLocator(5))
fig.savefig(resultDir+'/预测折线图.png',dpi=500)