Should We Retrain the Model after getting the Best Threshold for MultiCategory?

Question Regarding 06_mutlicat.ipynb

In the lesson, there’s a part we are trying to set the best thresh to use with accuracy_multi

The initial learner was created by:

learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.2))

and it gave us a final validation multi accuracy of 0.95

First we try:

learn.metrics = partial(accuracy_multi, thresh=0.1)

which gives us 0.93


learn.metrics = partial(accuracy_multi, thresh=0.99)

gives us 0.94

Instead of trial and error, Jeremy suggests we use:

preds,targs = learn.get_preds()

xs = torch.linspace(0.05,0.95,29)
accs = [accuracy_multi(preds, targs, thresh=i, sigmoid=False) for i in xs]

which gives us this


So the best thresh would be around 0.5 - 0.6

Here’s my question, after we get this thresh should we create a new learner and re-run the training of the model using this syntax:

learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.5))
learn.fine_tune(3, base_lr=3e-3, freeze_epochs=4)

Or does this method that we were using earlier change the threshold of the learner and that new threshold will be used when I export the model?

learn.metrics = partial(accuracy_multi, thresh=0.5)

In short, does changing the metrics of a Learner warrant a retrain?

Yes but something very important here. You need to set the loss functions threshold too, otherwise it will be training with .5

So loss_func = BCELossLogitsFlat(thresh=thresh)

And then pass that loss function into cnn_learner along with the rest of it

Thanks! @muellerzr. I seriously didn’t know the loss functions use thresholds too. I will update accordingly

Is this provided by fastai?
Because the one from PyTorch nn.BCEWithLogitsLoss() doesn’t seem to have a parameter called thresh?

Im getting this output:

TypeError: init() got an unexpected keyword argument ‘thresh’

and running


results in:

NameError: name ‘BCELossLogitsFlat’ is not defined

Couldn’t quite remember what fastai’s loss function was :slight_smile: try it with BCEWithLogitsLossFlat

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