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.