Python深度学习教程:LSTM时间序列预测小练习—国航乘客数量预测 参考数据: 数据一共两列,左边是日期,右边是乘客数量 对数据做可视化: import math import numpy as np import pandas as pd import matplotlib.pyplot as plt from pandas import read_csv from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error
#load dataset dataframe = read_csv('./international-airline-passengers.csv',usecols =[1],header = None,engine = 'python',skipfooter = 3) dataset = dataframe.values #将整型变为float dataset = dataset.astype('float32') plt.plot(dataset) plt.show()
可视化结果: 下面开始进行建模: 完整代码: import math import numpy import pandas as pd import matplotlib.pyplot as plt from pandas import read_csv from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error
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] b = dataset[i+look_back,0] dataX.append(a) dataY.append(b) return numpy.array(dataX),numpy.array(dataY)
numpy.random.seed(7)
dataframe = read_csv('./international-airline-passengers.csv',usecols = [1],header = None,engine = 'python') dataset = dataframe.values dataset = dataset.astype('float32')
scaler = MinMaxScaler(feature_range = (0,1)) dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset) * 0.67) test_size = len(dataset) - train_size train,test = dataset[0:train_size,:],dataset[train_size:len(dataset),:]
look_back = 1 trainX,trainY = create_dataset(train,look_back) testX,testY = create_dataset(test,look_back) #reshape input to be [samples, time steps, features] trainX = numpy.reshape(trainX,(trainX.shape[0],look_back,trainX.shape[1])) testX = numpy.reshape(testX,(testX.shape[0],look_back,testX.shape[1]))
#create and fit the LSTM network model = Sequential() model.add(LSTM(4,input_shape = (1,look_back))) model.add(Dense(1)) model.compile(loss = 'mean_squared_error',optimizer = 'adam') model.fit(trainX,trainY,epochs = 100,batch_size = 1,verbose = 2) # make predictions trainPredict = model.predict(trainX) testPredict = model.predict(testX) # invert predictions trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY]) # calculate root mean squared error trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0])) print('Train Score: %.2f RMSE' % (trainScore)) testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0])) print('Test Score: %.2f RMSE' % (testScore)) # shift train predictions for plotting trainPredictPlot = numpy.empty_like(dataset) trainPredictPlot[:, :] = numpy.nan trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict # shift test predictions for plotting testPredictPlot = numpy.empty_like(dataset) testPredictPlot[:, :] = numpy.nan testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict # plot baseline and predictions plt.plot(scaler.inverse_transform(dataset)) plt.plot(trainPredictPlot) plt.plot(testPredictPlot) plt.show()
运行结果: 本次的Python学习教程涉及到深度学习内容,不清楚的地方可以留言,需要Python深度学习视频教程的伙伴可以后台回复:Python深度学习,领取相关Python学习教程!
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