model = Sequential()
model.add(Dense(SEGMENT_LEN, input_dim=SEGMENT_LEN, init='normal', activation='relu'))
model.add(Dense(SEGMENT_LEN, init='normal', activation='relu'))
model.add(Dense(1, init='normal'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
history = model.fit(X, Y, validation_split=0.33, nb_epoch=100, batch_size=100, verbose=0)