We introduce GPflux, a Python library for Bayesian deep learning with a strong emphasis on deep Gaussian processes (DGPs). Implementing DGPs is a challenging endeavour due to the various mathematical subtleties that arise when dealing with multivariate Gaussian distributions and the complex bookkeeping of indices. To date, there are no actively maintained, open-sourced and extendable libraries available that support research activities in this area. GPflux aims to fill this gap by providing a library with state-of-the-art DGP algorithms, as well as building blocks for implementing novel Bayesian and GP-based hierarchical models and inference schemes. GPflux is compatible with and built on top of the Keras deep learning eco-system. This enables practitioners to leverage tools from the deep learning community for building and training customised Bayesian models, and create hierarchical models that consist of Bayesian and standard neural network layers in a single coherent framework. GPflux relies on GPflow for most of its GP objects and operations, which makes it an efficient, modular and extensible library, while having a lean codebase.

翻译：我们引入了GPflus,这是巴耶斯深层学习的Python图书馆,重点是深高山进程。实施DGP是一项艰巨的工作,因为在处理多变高山分布和指数的复杂簿记时,出现了各种数学微妙之处。到目前为止,还没有积极维护、开放来源和可扩展的图书馆,支持这一领域的研究活动。GPflus的目的是填补这一空白,提供一个图书馆,提供最先进的DGP算法,以及实施新的Bayesian和GP的等级模型和推断方案。GPlus与Keras深层学习生态系统系统相容并建,使实践者能够利用深层学习社区的工具来建造和培训定制的Bayesian模型和标准神经网络层,并在一个统一的框架内创建由Bayesian和标准神经网络层组成的等级模型。GPflus主要依靠GP的流流源,使它成为一个高效的、模块化和可扩展的图书馆,同时有一个精密的代码基础。