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