arXiv:2105.14625 Abstract | arXiv Analytics

arXiv:2105.14625 [cs.LG]Abstract References Reviews Resources

Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT

Published 2021-05-30Version 1

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 % based on 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.

Categories: cs.LG

Subjects: 68T07, A.1, B.8.0, G.1.6, G.4, I.2.8

Keywords: surrogate model, elementary hyperparameter tuning task, mnist data, architecture-level parameters, deep learning models

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