lstm - RNN.py

# -*- coding: utf-8 -*-


import numpy as np

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM


data = [[i for i in range(100)]]
data = np.array(data, dtype=float)
target = [[i for i in range(1,101)]]
target = np.array(target, dtype=float)

data = data.reshape((1, 1, 100)) 
target = target.reshape((1, 1, 100)) 
x_test=[i for i in range(100,200)]
x_test=np.array(x_test).reshape((1,1,100));
y_test=[i for i in range(101,201)]
y_test=np.array(y_test).reshape(1,1,100)


model = Sequential()  
model.add(LSTM(100, input_shape=(1, 100),return_sequences=True))
model.add(Dense(100))
model.compile(loss='mean_absolute_error', optimizer='adam',metrics=['accuracy'])
model.fit(data, target, nb_epoch=10000, batch_size=1, verbose=2,validation_data=(x_test, y_test))



predict = model.predict(data)