Using the callback system in fastai

Hi everyone,

I’ve put down a notebook to explore the system of callbacks in the fastai library, which I think is pretty amazing. It lets you customize your training completely as you want.

In particular, I show how to do three different kind of things:

  • record some data linked to the training (here validation loss at each epoch)
  • change the hyper-parameters when some condition is met (here dividing the learning rate by 10 each time the validation loss doesn’t get lower) I do not endorse that specific training policy, I just chose that example because it’s a very popular one.
  • make some parameters change as the training goes (here have the p of a dropout layer vary through training)

I hope you find it useful!


I’m going through this now… so that’s what callbacks are, this is great!

Great! Great! Great! Thank you!

It’s really great… Thanks Sylvian

Thanks for the knowledge, this is insightful. Now figuring out how to use this myself…

This is awesome. Callbacks were on my list of things to study. Looking forward to diving into the notebook.

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Hi @sgugger,

Is there a way to stop the learner if the learner’s metric (e.g. accuracy) is below or above a threshold value?

BR, Kimmo

Yes, if the on_epoch_end function of a Callback returns true, it will stop the training. So a very simple Callback with a test in this function should work just fine.

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I tried it and it works!