UNCERTAINTY-WIZARD: Fast and User-Friendly Neural Network Uncertainty Quantification

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.