# specific libraries for RNN
# keras is a high layer build on Tensorflow layer to stay in high level/easy implementation
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
import time #helper libraries
from keras.models import model_from_json
import sys
# In[ ]:
# Build the model
model = Sequential()
model.add(
LSTM(input_dim=x_train.shape[-1], output_dim=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(100, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(units=1))
model.add(Activation('linear'))
start = time.time()
model.compile(loss='mse', optimizer='rmsprop')
print('compilation time : {}'.format(time.time() - start))
# In[ ]:
# Train the model
