A surrogate model based hyperparameter tuning approach for deep learning is presented. This article demonstrates how the architecture-level parameters (hyperparameters) of deep learning models that were implemented in Keras/tensorflow can be optimized. The implementation of the tuning procedure is 100% accessible from R, the software environment for statistical computing. With a few lines of code, existing R packages (tfruns and SPOT) can be combined to perform hyperparameter tuning. An elementary hyperparameter tuning task (neural network and the MNIST data) is used to exemplify this approach
@article{arxiv.2105.14625,
title = {Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT},
author = {Thomas Bartz-Beielstein and Frederik Rehbach and Amrita Sen and Martin Zaefferer},
journal= {arXiv preprint arXiv:2105.14625},
year = {2021}
}