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Early Stopping in Practice: an example with Keras and TensorFlow 2.0
A step to step tutorial to add and customize Early Stopping
In this article, we will focus on adding and customizing Early Stopping in our machine learning model and look at an example of how we do this in practice with Keras and TensorFlow 2.0.
Introduction to Early Stopping
In machine learning, early stopping is one of the most widely used regularization techniques to combat the overfitting issue.
Early Stopping monitors the performance of the model for every epoch on a held-out validation set during the training, and terminate the training conditional on the validation performance.
From Hands-on ML [1]
Early Stopping is a very different way to regularize the machine learning model. The way it does is to stop training as soon as the validation error reaches a minimum. The figure below shows a model being trained.