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.
翻译:事实证明,不确定性和信心是建议进行深层学习测试的各种技术的有用衡量尺度,包括测试数据选择和系统监督。 我们提出了不确定性和不确定性,这是一个可以量化这种不确定性和人工神经网络信任度的工具,它建在行业领先的深层学习 API tf.keras 的顶端,提供了一个接近透明且易于理解的界面。 同时,它还包括我们根据两种不同的机器和不同的配置设定基准的主要性能优化。