[dustintran]

Hello. I'm the person that was linked to in that GitHub issue!

I sympathize with the post's frustration. The TF tutorials on the official website are well-written. But they mostly cover basic features, and as a recent Reddit thread described (https://old.reddit.com/r/MachineLearning/comments/e4pxqp/d_i...), the support ecosystem is lacking as StackOverflow and blog posts are out-of-date due to all the software churning. I'm not a TF engineer, but as someone with experience designing libraries on top of TF, even I find myself sifting through Stack Overflow/blog post code to find the new best practices..

Regarding Bayesian layers, it's actually a NeurIPS paper this year (https://papers.nips.cc/paper/9607-bayesian-layers-a-module-f...). I worked on an early prototype in TensorFlow Probability but ended up abandoning the design as I found it inflexible in practice. The solution is the NeurIPS paper, and it's experimental: there are no promises of stability (in fact, we even moved the code from Tensor2Tensor to another repository (https://github.com/google/edward2/), of which has yet to have an official package release!).

Software for uncertainty models is more on the research fringe, and this should be made clearer in official TensorFlow solutions building on these designs.