# 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
#nb_epoch = 350
model.fit(x_train, y_train, batch_size=3028, nb_epoch=30, validation_split=0.1)
# In[ ]:
# save the model because the training is long (1h30) and we don't want to do it every time
"""
# serialize model to JSON
model_json = model.to_json()
