def root_mean_squared_error(y_true, y_pred):
"""
Root Mean Squared Error
"""
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
return R.pow(mean_squared_error(y_true, y_pred), R.Scalar(0.5))
def mean_squared_log_error(y_true, y_pred):
"""
Mean Squared Log Error
"""
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
return R.mean(R.pow(R.sub(R.natlog(R.add(y_true, R.one())), R.natlog(R.add(y_pred, R.one()))), R.Scalar(2)))
def mean_squared_error(y_true, y_pred):
"""
Mean Squared Error
"""
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
return R.mean(R.pow(R.sub(y_true, y_pred), R.Scalar(2)))