Saya baru saja membangun jaringan saraf LSTM ini dengan Keras
import numpy as np
import pandas as pd
from sklearn import preprocessing
from keras.layers.core import Dense, Dropout, Activation
from keras.activations import linear
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from matplotlib import pyplot
#read and prepare data from datafile
data_file_name = "DailyDemand.csv"
data_csv = pd.read_csv(data_file_name, delimiter = ';',header=None, usecols=[1,2,3,4,5])
yt = data_csv[1:]
data = yt
data.columns = ['MoyenneTransactHier', 'MaxTransaction', 'MinTransaction','CountTransaction','Demand']
# print (data.head(10))
pd.options.display.float_format = '{:,.0f}'.format
data = data.dropna ()
y=data['Demand'].astype(int)
cols=['MoyenneTransactHier', 'MaxTransaction', 'MinTransaction','CountTransaction']
x=data[cols].astype(int)
#scaling data
scaler_x = preprocessing.MinMaxScaler(feature_range =(-1, 1))
x = np.array(x).reshape ((len(x),4 ))
x = scaler_x.fit_transform(x)
scaler_y = preprocessing.MinMaxScaler(feature_range =(-1, 1))
y = np.array(y).reshape ((len(y), 1))
y = scaler_y.fit_transform(y)
print("longeur de y",len(y))
# Split train and test data
train_end = 80
x_train=x[0: train_end ,]
x_test=x[train_end +1: ,]
y_train=y[0: train_end]
y_test=y[train_end +1:]
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
print("Data well prepared")
print ('x_train shape ', x_train.shape)
print ('y_train', y_train.shape)
#Design the model - LSTM Network
seed = 2016
np.random.seed(seed)
fit1 = Sequential ()
fit1.add(LSTM(
output_dim = 4,
activation='tanh',
input_shape =(4, 1)))
fit1.add(Dense(output_dim =1))
fit1.add(Activation(linear))
#rmsprop or sgd
batchsize = 1
fit1.compile(loss="mean_squared_error",optimizer="rmsprop")
#train the model
fit1.fit(x_train , y_train , batch_size = batchsize, nb_epoch =20, shuffle=True)
print(fit1.summary ())
#Model error
score_train = fit1.evaluate(x_train ,y_train ,batch_size =batchsize)
score_test = fit1.evaluate(x_test , y_test ,batch_size =batchsize)
print("in train MSE = ",round(score_train,4))
print("in test MSE = ",round(score_test ,4))
#Make prediction
pred1=fit1.predict(x_test)
pred1 = scaler_y.inverse_transform(np.array(pred1).reshape ((len(pred1), 1)))
real_test = scaler_y.inverse_transform(np.array(y_test).reshape ((len(y_test), 1))).astype(int)
#save prediction
testData = pd.DataFrame(real_test)
preddData = pd.DataFrame(pred1)
dataF = pd.concat([testData,preddData], axis=1)
dataF.columns =['Real demand','Predicted Demand']
dataF.to_csv('Demandprediction.csv')
pyplot.plot(pred1, label='Forecast')
pyplot.plot(real_test,label='Actual')
pyplot.legend()
pyplot.show()
kemudian menghasilkan hasil ini:
Setelah membangun dan melatih model yang baik tentang data historis, saya tidak tahu bagaimana saya bisa menghasilkan prediksi untuk nilai-nilai masa depan? Misalnya permintaan 10 hari ke depan. Data setiap hari.
NB: ini adalah contoh bagaimana data dibentuk, hijau adalah label dan yang kuning adalah fitur.
setelah dropna()
(hapus nilai nol) tetap 100 baris data, saya telah menggunakan 80 dalam pelatihan dan 20 dalam tes.