I am currently training a neural network and I cannot decide which to use to implement my Early Stopping criteria: validation loss or a metrics like accuracy/f1score/auc/whatever calculated on the validation set.

In my research, I came upon articles defending both standpoints. Keras seems to default to the validation loss but I have also come across convincing answers for the opposite approach (e.g. [here](https://github.com/keras-team/keras/issues/5359)).

Anyone has directions on when to use preferably the validation loss and when to use a specific metric?