Keras MAE和MSE source code

def mean_squared_error(y_true, y_pred):

    if not K.is_tensor(y_pred):

        y_pred = K.constant(y_pred)

    y_true = K.cast(y_true, y_pred.dtype)

    return K.mean(K.square(y_pred - y_true), axis=-1)

def mean_absolute_error(y_true, y_pred):

    if not K.is_tensor(y_pred):

        y_pred = K.constant(y_pred)

    y_true = K.cast(y_true, y_pred.dtype)

    return K.mean(K.abs(y_pred - y_true), axis=-1)

def mean_absolute_percentage_error(y_true, y_pred):

    if not K.is_tensor(y_pred):

        y_pred = K.constant(y_pred)

    y_true = K.cast(y_true, y_pred.dtype)

    diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true),

                                            K.epsilon(),

                                            None))

    return 100. * K.mean(diff, axis=-1)

def mean_squared_logarithmic_error(y_true, y_pred):

    if not K.is_tensor(y_pred):

        y_pred = K.constant(y_pred)

    y_true = K.cast(y_true, y_pred.dtype)

    first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)

    second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)

return K.mean(K.square(first_log - second_log), axis=-1)