Hello neural network programmers,
I am currently creating a neural network with keras, as I am not that familiar with tensorflow and it's a bit more difficult.
I want my optimization to optimize the validation loss and not the training loss/accuracy.
Ideally, I would want it to look something like this:
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['val_accuray'])
but val_accuracy is not something I can eneter into metrics.
Any ideas on how I can implement that?