I have a neural network that starts to overfit in that the validation loss begins to increase while the training loss stays ~ flat with epochs.

Is there a generic algorithm - obvious or otherwise, well-known or not - to stop the training early if overfitting is somehow detected?

I note that catboost implements such an algorithm but I have found it nowhere else.


Is this all simply a matter of rolling my own callback function and stopping when the training and validation losses start to diverge..?

Preference for TF, Keras, python3, ...

Thanks as ever


1 Answer 1


Sounds like you're just looking for EarlyStopping, which will stop training when validation loss does not improve for N epochs. It's the same as Iter in catboost.

  • $\begingroup$ Perfect, thank you so much! $\endgroup$
    – jtlz2
    May 18, 2020 at 8:48

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