Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervision.We present uncertainty-wizard, a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and easy to understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.
@article{arxiv.2101.00982,
title = {Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification},
author = {Michael Weiss and Paolo Tonella},
journal= {arXiv preprint arXiv:2101.00982},
year = {2021}
}
Comments
Accepted for publication at the IEEE International Conference on Software Testing, Verification and Validation 2021