# import numpy as np
from keras import backend as K

def mean_squared_error(y_true, y_pred):
    return K.mean(K.square(y_pred - y_true))

def root_mean_square_error(y_true, y_pred):
    return mean_squared_error(y_true, y_pred) ** 0.5

def rmse(y_true, y_pred):
    return mean_squared_error(y_true, y_pred) ** 0.5

# aliases
mse = MSE = mean_squared_error
# rmse = RMSE = root_mean_square_error
