hi @jeremy,
If we come back to lesson3-rossman.ipynb I just wanted to confirm my understanding:
We create a custom loss function:
def inv_y(a): return np.exp(a)
def exp_rmspe(y_pred, targ):
targ = inv_y(targ)
pct_var = (targ - inv_y(y_pred))/targ
return math.sqrt((pct_var**2).mean())
We use it later in fit after we do m = md.get_learner(…):
m.fit(lr, 3, metrics=[exp_rmspe])
However, under the hood in column_data.py it defaults to F.mse_loss:
class StructuredLearner(Learner):
def init(self, data, models, **kwargs):
super().init(data, models, **kwargs)
self.crit = F.mse_loss
Since in the notebook we don’t override learner “crit” attribute, so my understanding we are using two loss functions F.mse_loss for training and custom exp_rmspe for accuracy metric and printing outputs.
Is that because exp_rmspe and mse_loss conceptually the same way of calculating the loss?
In general should we use same loss function algorithm for both metrics= and learner.crit=? Or I am missing something.