In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesian inference, thus supporting various kinds of probabilistic models, including both the traditional hierarchical Bayesian models and recent deep generative models. We use running examples to illustrate the probabilistic programming on ZhuSuan, including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural networks.

翻译：在本文中,我们介绍ZhuSuan,这是巴耶斯人深层学习的比武概率规划图书馆,它结合了巴耶斯人方法和深层学习的互补优势。ZhuSuan建在Tensorflow上。与现有的主要为确定性神经网络和受监督的任务设计的深层学习图书馆不同,ZhuSuan以其深深植根于巴耶斯人的推理而著称,从而支持各种概率模型,包括传统的巴耶斯人等级模型和最近的深层基因模型。我们用实例来说明ZhuSuan的概率规划,包括巴耶斯人的后勤回归、变式自动进入器、深层次的血型信仰网络和巴耶斯人的经常性神经网络。