@fonnesbeck as i think he’ll be interested in batch processing bayesian models anyway I want to run lots of numpyro models in parallel I created a new post because This post uses numpyro instead of pyro i’m doing sampling instead of svi i’m using ray instead of dask that post was 2021 i’m running a simple neal’s funnel. Hello pyro community, i’m trying to build a bayesian cnn for mnist classification using pyro, but despite seeing the elbo loss decrease to around 10 during training, the model’s predictive accuracy remains at chance level (~10%) Could you help me understand why the loss improves while performance doesn’t, and suggest potential fixes
Import torch import pyro import pyro. This would appear to be a bug/unsupported feature If you like, you can make a feature request on github (please include a code snippet and stack trace) However, in the short term your best bet would be to try to do what you want in pyro, which should support this. I am running nuts/mcmc (on multiple cpu cores) for a quite large dataset (400k samples) for 4 chains x 2000 steps I assume upon trying to gather all results
Apologies for the rather long post This is the gmm code that works when i fit with both hmc and svi. Hi everyone, i am very new to numpyro and hierarchical modeling There is another prior (theta_part) which should be centered around theta_group I am trying to use lognormal as priors for both Hi, i’m new to pyro and trying to understand the basics of bayesian regression from a bayesian linear regression example
WATCH
