def rmse(y_true, y_pred):
m_factor = rmse_factor
m = RootMeanSquaredError()
m.update_state(inverse_transform(y_true, mmn),
inverse_transform(y_pred, mmn))
# return denormalize(m.result().numpy(), mmn) * m_factor
return m.result().numpy() * m_factor
def measure_rmse_tf(y_true, y_pred):
"""Calculate the RMSE score between y_true and y_pred and return it.
Parameters
----------
y_true : np.ndarray
Array of true values
y_pred : np.ndarray
Array of predicted values
Returns
-------
rmse : float
The RMSE between the arrays y_true and y_pred
"""
m = RootMeanSquaredError()
m.update_state(y_true, y_pred)
rmse = m.result().numpy()
return rmse
def measure_rmse_tf(y_true, y_pred):
m = RootMeanSquaredError()
m.update_state(y_true, y_pred)
result = m.result().numpy()
return result
def rmse(y_true, y_pred):
m_factor = rmse_factor
m = RootMeanSquaredError()
m.update_state(y_true, y_pred)
return denormalize(m.result().numpy(), mmn) * m_factor
