python astype()_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...

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 mathimport 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floatdataset = 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 mathimport 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.valuesdataset = 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_sizetrain,test = dataset[0:train_size,:],dataset[train_size:len(dataset),:]look_back = 1trainX,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 predictionstrainPredict = model.predict(trainX)testPredict = model.predict(testX)# invert predictionstrainPredict = scaler.inverse_transform(trainPredict)trainY = scaler.inverse_transform([trainY])testPredict = scaler.inverse_transform(testPredict)testY = scaler.inverse_transform([testY])# calculate root mean squared errortrainScore = 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 plottingtrainPredictPlot = numpy.empty_like(dataset)trainPredictPlot[:, :] = numpy.nantrainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict# shift test predictions for plottingtestPredictPlot = numpy.empty_like(dataset)testPredictPlot[:, :] = numpy.nantestPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict# plot baseline and predictionsplt.plot(scaler.inverse_transform(dataset))plt.plot(trainPredictPlot)plt.plot(testPredictPlot)plt.show()

运行结果：

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