from numpy import array

from keras.models import Sequential

from keras.layers import Dense, LSTM, Dropout, BatchNormalization

# 13 * 3

x = array([[1,2,3],[2,3,4],[3,4,5],[4,5,6],

[5,6,7],[6,7,8],[7,8,9],[8,9,10],

[9,10,11],[10,11,12],

[20,30,40],[30,40,50],[40,50,60]])

y = array([4,5,6,7,8,9,10,11,12,13,50,60,70])

x = x.reshape(x.shape[0],x.shape[1],1)

model = Sequential()

model.add(LSTM(20,activation='relu',input_shape=(3,1)))

model.add(Dense(100))

model.add(Dropout(0.2))

model.add(BatchNormalization())

model.add(Dense(100))

model.add(Dense(100))

model.add(Dense(100))

model.add(Dense(100))

model.add(Dense(100))

model.add(Dense(5))

model.add(Dense(1))

model.summary()

#3.훈련

model.compile(loss ='mse', optimizer ='adam', metrics=['mse'])

from keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='loss', patience=20, mode='auto')

model.fit(x,y,epochs=1000,batch_size=1,verbose=1,callbacks=[early_stopping])

loss, mae = model.evaluate(x,y,batch_size=1)

print(loss,mae)

x_input = array([[6.5,7.5,8.5],[50,60,70],[70,80,90],[100,110,120]])

x_input = x_input.reshape(x_input.shape[0],x_input.shape[1],1)

y_predict = model.predict(x_input)

print(y_predict)