I've implemented an artificial recurrent neural network and want to start training it on a variety of tasks. I've extensive searching online and haven't found a satisfactory answer of how the algorithm can autonomously determine when to terminate training.
So far I'm doing a check if the past
n errors are below a certain hardcoded threshold, which is definitely not universal to all tasks. Perhaps there is some probabilistic interpretation of the problem so I can terminate on some universal probability of the derivative of the network decreasing by some order of magnitude?
I could throw together some statistical measure of this sort, but I'm not sure of its pitfalls and perhaps something better has been developed by researchers.