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