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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%) I want to generalize this into a chinese restaurant process involving an “infinite” number of states Could you help me understand why the loss improves while performance doesn’t, and suggest potential fixes
Import torch import pyro import pyro. This is my first time using pyro so i am very excited to see what i can built with it.🙂 specifically, i am trying to do finite dirichlet process clustering with variational inference Batch processing pyro models so cc
@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, first off, amazing job on pyro
At the moment, i sample a guide trace for each desired posterior predictive sample, replay the model with the guide trace, and sample once from it, like this Ppc = [] dummy_obs = torch.zeros((1,self.d)) for sample in range(n_samples) 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.
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 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 (there might be some unnecessary memory duplication going on in this step?) are there any “quick fixes” to reduce the memory footprint of mcmc
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