  With the recent rapid progress in the study of deep generative models (DGMs),
there is a need for a framework that can implement them in a simple and generic
way. In this research, we focus on two features of DGMs: (1) deep neural
networks are encapsulated by probability distributions, and (2) models are
designed and learned based on an objective function. Taking these features into
account, we propose a new Python library to implement DGMs called Pixyz. This
library adopts a step-by-step implementation method with three APIs, which
allows us to implement various DGMs more concisely and intuitively. In
addition, the library introduces memoization to reduce the cost of duplicate
computations in DGMs to speed up the computation. We demonstrate experimentally
that this library is faster than existing probabilistic programming languages
in training DGMs.
