after cross-validation, what measure of 'stopping' training should i use when training final model on whole dataset?
If you use the scikit-learn GridSearchCV class (from sklearn.model_selection) together with the scikit-learn wrapper in keras, you can get your final model refit on the whole training set directly via the best_estimator_ attribute (i.e. the model instanced with the best hyperparms found in the CV process) already refit with the whole training dataset.
I have seen some source which make a &quot;manual&quot; retrain on the whole training set, but it is not necessary as scikit-learn already let's you accessing the refit model on the whole set.
Interesting links:

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
for using the keras wrapper for scikit: https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/