Python进行进行GRU和和LSTM 加载包加载包 import numpy as np import pandas as pd import math #Sequential多个网络层的线性堆叠；Dense隐含层 from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.layers import SimpleRNN from keras.layers import GRU from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error, mean_absolute_error 数据定义 数据定义 def create_dataset(dataset, look_back): dataX, dataY = [], [] for i in range(len(dataset)-look_back): a = dataset[i:(i+look_back), 0] #append向列表的尾部增加元素 dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) 数据分割 数据分割 # fix random seed for reproducibility #seed用于指定随机数生成时所用算法开始的整数值 np.random.seed(7) # load the dataset/下载数据集 #usecols获取数据的列，如果取前4列，则usecols=(0,1,2,3) #print(XXXX) dataframe = pd.read_csv(r'333.csv' \ , usecols=[0], engine='python') dataset = dataframe.values dataset = dataset.astype('float32') # normalize the dataset/标准化数据集 scaler = MinMaxScaler(feature_range=(0, 1)) dataset = scaler.fit_transform(dataset) # split into train and test sets/分割训练集与测试集 #train_size = int(len(dataset) * 0.84) vsize = 200; train_size = 800; #print(train_size ) # test_size = len(dataset) - train_size look_back =10 train, test = dataset[0:train_size-1,:], dataset[train_size-look_back-1:train_size+vsize-1,:] # dataset detail/具体分割后数据集 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) #predX, predY = create_dataset(pred, look_back) # reshape input to be [samples, feature_num, features] trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) 建模预测 建模预测 #predX = np.reshape(predX,(predX.shape[0], 1, predX.shape[1])) #print(trainX )) # start_cr_a_fit_net = time.time() # create and fit the LSTM network/创建并拟合LSTM网络 model = Sequential() model.add(GRU(15, input_shape=(None,look_back))) #model.add(SimpleRNN(18, input_shape=(None,look_back))) #model.add(GRU(XXXX[1], input_shape=(None,look_back))) model.add(Dense(1)) model.summary() #model.compile(loss='mean_squared_error', optimizer='adam') model.compile(loss='mean_squared_error', optimizer='sgd') model.fit(trainX, trainY, epochs=331, batch_size=1, verbose=0) # end_cr_a_fit_net = time.time()-start_cr_a_fit_net # print('Running time of creating and fitting the LSTM network: %.2f Seconds' % (end_cr_a_fit_net)) 

# make predictions/进行预测 trainPredict = model.predict(trainX) testPredict = model.predict(testX) #NpredY = model.predict(predX) ##### invert predictions start ##### / 将预测值转换为正常数值 # create empty table with 12 fields trainPredict=scaler.inverse_transform(trainPredict) trainY=scaler.inverse_transform([trainY]) testPredict=scaler.inverse_transform(testPredict) testY=scaler.inverse_transform([testY]) #NpredY = scaler.inverse_transform(NpredY) #predY=scaler.inverse_transform([predY]) # print(predY) # print(NpredY) # print('测试集预测值' ) # print(testPredict) ##### invert predictions end ##### # calculate root mean squared error/计算RMSE和误差率 # trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0])) # print('Train Score: %.8f RMSE' % (trainScore)) # testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0])) # print('Test Score: %.8f RMSE' % (testScore)) # df = pd.DataFrame(NpredY[:,0]) # aa=df.to_excel('12.xlsx',encoding='utf-8') #df = pd.DataFrame(NpredY[:,0]) #aa=df.to_excel('12.xlsx',encoding='utf-8') preRMSE = math.sqrt(mean_squared_error(testY[0], testPredict[:,0])) print('Test RMSE: %.8f RMSE' % (preRMSE)) preMSE = mean_squared_error(testY[0], testPredict[:,0]) print('Test MSE: %.8f MSE' % (preMSE)) trainMAE = mean_absolute_error(testY[0], testPredict[:,0]) print('Test MAE: %.8f MAE' % (trainMAE)) # testMAE = mean_absolute_error(testY[0], testPredict[:,0]) # print('Test MAE: %.8f MAE' % (testMAE)) #trainMAPE = np.mean(np.abs(trainY[0]-trainPredict[:,0])/trainY[0]) # print('Train MAPE: %.8f %% MAPE' % (trainMAPE*100)) #testMAPE = np.mean(np.abs(testY[0]-testPredict[:,0])/testY[0]) #print('Test MAPE: %.8f %% MAPE' % (testMAPE*100)) predMAPE = np.mean(np.abs(testY[0]-testPredict[:,0])/testY[0]) print('predY MAPE: %.8f %% MAPE' % (predMAPE*100)) 作者：dltan