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When to stop training your neural network model?

Neural networks are tools in wide domains in academia and industries.


The major objective of neural networks is to learn the objective function from a set of data that is set aside for training.


Minimizing the error according to the objectives is the main property when training networks there will be a point when the network will start memorizing the data used for training and will perform worse elsewhere resulting in worse generalization.



The most popular methods for early stopping methods are:


1. No change in value accuracy over a given number of epochs


2. Decrease in highest test accuracy observed over a given number of epochs.


3. Average change in test accuracy observed over a given number of epochs.


When training it is normal that the testing accuracy is increasing and after a certain stage when it stagnates the curve of the validation metric looks like noise and there are scenarios where central limit theorem can be put to test in such cases methods like


  1. Shapiro Wilk test

  2. Single sample T-Test

  3. Clipped exponential smoothing can be used







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