How to Stop Training Deep Neural Networks At the Right Time Using Early Stopping

A problem with training neural networks is in the choice of the number of training epochs to use. Too many epochs can lead to overfitting of the training dataset, whereas too few may result in an underfit model. Early stopping is a method that allows you to specify an arbitrary large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset. In this tutorial, you will discover the Keras API for adding early stopping to overfit deep learning neural network models. How to Stop Training Deep Neural Networks At the Right Time With Using Early Stopping Photo by Ian D. Keating, some rights reserved. Callbacks provide a way to execute code and interact with the training model process automatically. Callbacks can be provided to the fit() function via the "callbacks" argument. First, callbacks must be instantiated.