I have a very simple toy recurrent neural network implemented in keras which, given an input of N integers will return their mean value. I would like to be able to modify this to a bayesian neural network with either pymc3 or edward.lib so that I can get a posterior distribution on the output value
e.g. p(output | weights).
I have read through blog posts from autograd, pymc3 and edward [1,2,3] but all seem geared to classification problems.
Cheers
[1] https://github.com/HIPS/autograd/blob/master/examples/bayesian_neural_net.py
[2] http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/
[3] https://github.com/blei-lab/edward/blob/master/examples/getting_started_example.py
Edit
To clarify - I am asking if anyone can offer some experience/advice/references relevant to building a Bayesian RNN in anything other than a classification task.