Validating Pre-Made Tensorflow Estimators Mid-Stream
In Francois Chollet’s book Deep Learning with Python , he stresses the importance of utilizing a separate validation set of data while training a machine learning model in order to test periodically (say after every epoch) that the accuracy on something else besides strictly the training data (e.g. this validation set) is in fact improving. Machine learning models are subject to learn relationships that have nothing to do with the problem at hand. For instance, a model tasked with trying to determine which way a military tank is facing might end up making assumptions based on whether it is day or night. This is often a result of trying to eke out the model’s maximum performance, say by optimizing for the smallest value of a loss function. However, what ends up happening is that the model overfits on the training data, which means it loses its generalization — its ability to predict the correct outcome of new samples or examples that we as humans would intend for it to cl