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The point of EarlyStopping is to stop training at a point where validation loss (or some other metric) does not improve.

If I have set EarlyStopping(patience=10, restore_best_weights=False), Keras will return the model trained for 10 extra epochs after val_loss reached a minimum. Why would I ever want this? Has this model not just trained for 10 unnecessary epochs? Wouldn't it make more sense to give me back the model that was trained at the lowest validation loss i.e. with restore_best_weights=True?

Would love to hear situations where doing those extra 10 epochs of training is better than not doing them.

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    $\begingroup$ Try here github.com/keras-team/keras/issues/11371 $\endgroup$
    – WBM
    Apr 12, 2021 at 13:29
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    $\begingroup$ Great thanks! It looks like this is the default behavior to save memory. Though, there seem to be no arguments for restore_best_weights=False other than that. $\endgroup$
    – codeananda
    Apr 13, 2021 at 10:25

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