Symbolic DNN-Tuner is a system to drive the training of a Deep Neural Network, analysing the performance of each training experiment and automatizing the choice of HPs to obtain a network with better performance.
This analysis is performed by exploiting rule-based programming, particularly by using Probabilistic Logic Programming.
The code is available on GitHub.
The description of the system and its implementation are available here:
-  Michele Fraccaroli, Evelina Lamma & Fabrizio Riguzzi (2021): Symbolic DNN-Tuner. Machine Learning, pp. 1–26, doi:10.1007/s10994-021-06097-1.
-  Michele Fraccaroli, Evelina Lamma & Fabrizio Riguzzi (2022): Symbolic DNN-Tuner: A Python and ProbLog-based system for optimizing Deep Neural Networks hyperparameters. SoftwareX 17, p. 100957, doi:10.1016/j.softx.2021.100957.