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SVI loss looks good, model not converging - Pyro Discussion Forum Sorry about the multiple posts - Thanks for the response So I agree that the issue is with the likelihood I’ve got: # pyro sample("obs", dist Bernoulli(loc_img) to_event(1), obs=x reshape(-1, self data_dim)) pyro deterministic("obs", loc_img) Bernoulli is supported on [0,1] but my data is [-1,1] My pytorch implementation works directly with the decoded latents, to calculate loss - no
Bad performance Bayesian Convolutional Neural Network - Misc. - Pyro . . . 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? Code Overview: import torch import pyro import pyro
Batch processing numpyro models using Ray - forum. pyro. ai Hello again, Related post: 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
Adam optimizer before NUTS? - Pyro Discussion Forum I’m trying to infer the parameters of a non-linear ODE system Would using a gradient descent optimizer like Adam (eg from optax) to initialize the guess starting point for NUTS be useful? Is something like this already implemented in numpyro? I’m finding that the time to convergence for my NUTS inference is very sensitive to how small my uncertainties are that go into my Gaussian
Help post，shape problem - Pyro Discussion Forum with pyro plate("data", x size(0)): pyro sample("obs", dist Normal(mu, sigma), obs=y squeeze(-1),infer={"scale": annealing_factor}) #obs，真实数据+噪声
Custom distribution for mixture model - Pyro Discussion Forum I think I am doing the log_prob calculation correctly as the two methods produce the same values for the same data, but when I try and fit the model using MCMC I don’t get anything like sensible results The traces of cluster_proba jump back and forth between 0 and 1 during sampling when the clusters are about equally probable This works with minor modifications (priors from a Beta
Denoising VAE - Tutorials - Pyro Discussion Forum Hi, I’m using the latest pyro and tutorials In another place I have a BVAE pytorch implementation that trains on audio waveforms and denoises them by losing information during reconstruction The training step is as f hellip;
AutoGuide For a deep learning MLP Model - Pyro Discussion Forum Hello, I am not sure that I understand well how the AutoDiagonalNormal AutoGuide works I have a MLP model and I want to do an SVI on this model Given that my model uses PyroSample statements (that trigger pyro sample statements), I guess that my guide has the same structure (except that I can’t add a name to these PyroSample statements as I usually do with pyro sample) But my real
How to speed up Predictive - numpyro - Pyro Discussion Forum Hi! I am using Predictive to predict Y for a given set of parameters predictive = numpyro infer Predictive(model, samples, parallel=True) pred = predictive(rng_key, X=X, D_Y=D_Y, Y=None, D_H=D_H, prior_std=prior_std) Here samples are a single set of sampled parameters and X has many samples (and I need to make sequential predictions so I will loop over timesteps for the same set of parameters