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Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification
Michael Weiss; Paolo Tonella
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.
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Indexed in
- Publication date:
- June 1, 2021
- DOI:
-
- Grants:
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European Commission:
- PRECRIME - Self-assessment Oracles for Anticipatory Testing (787703)
- Related identifiers:
- Obsoleted by
- License (for files):
- Creative Commons Attribution 4.0 International