SVI Parameters Not Converging

I am learning Pyro and am bumping up against the same problem as SVI doesn't change parameters in that parameters don’t change and the ELBO fluctuates randomly. However, I have actually declared params in my guide. My use case is involved enough that I do not want to use an AutoGuide (sp?), which is why I am trying to write one myself. Here is a small reprex:

import os
import torch
from torch.distributions import constraints
import numpy as np
import pandas as pd

import pyro
import pyro.distributions as dist
from pyro.infer import SVI, Trace_ELBO, Predictive

def model(predictors, pheno):
    n_predictors = predictors.shape[1]
    gamma_0 = pyro.sample("g0", dist.Normal(0., 1.))
    covs = pyro.sample("covs", dist.Normal(torch.zeros(n_predictors), torch.ones(n_predictors)).independent(1))
    l = (gamma_0 + torch.matmul(predictors, covs.squeeze())).squeeze(-1)
    with pyro.plate("pheno", len(pheno)):
        pyro.sample("obs", dist.Bernoulli(logits=l), obs=pheno)

def guide(predictors, pheno):
    n_predictors = predictors.shape[1]
    g0_loc = pyro.param("g0_loc", torch.tensor(0.))
    g0_scale = pyro.param("g0_scale", torch.tensor(1.), constraint=constraints.positive)
    gamma_0 = pyro.sample("g0", dist.Normal(g0_loc, g0_scale))
    covs_loc = pyro.param("covs_loc", torch.zeros(n_predictors))
    covs = pyro.sample("covs", dist.Normal(covs_loc, 1.).independent(1))

predictors = torch.rand([100, 3])
coefs = torch.tensor([1., -5., 0.2])
pheno = torch.bernoulli(torch.sigmoid(0.2 + torch.matmul(predictors, coefs)))

optim = pyro.optim.Adam({"lr": 0.001})
svi = SVI(model, guide, optim, loss=Trace_ELBO())

for i in range(1000):
    loss = svi.step(predictors, pheno)
    if i % 100 == 0:

predictive = Predictive(model, guide=guide, num_samples=100)
samples = predictive(predictors, pheno)

I feel that I am missing something important but simple - I should be able to use Pyro to fit this regression model, right?

Also, I parameterized covs as a matrix of independent parameters because in my application, this would be a set of nuisance covariates one has to include, but not sure if this is the best way to do this in Pyro.

Where are your feats and data defined? Can you provide a fully reproducible code?
You should also use to_event instead of deprecated independent.

@ordabayev I’m sorry, that was poor form on my part. I’ve updated the reprex with the correct variables - feats should have been predictors and data should have been pheno.

If you look at pheno they are all just ones, that should be the root of the problem since you can’t learn much from such data.

Good catch, that is certainly a problem for this toy example. I’ve updated it with parameters that will simulate both 1’s and 0’s. However, my problem persists: I am not really getting updated params. On a round of samples from the posterior, all params are coming up as ~0.

UPDATE: in my case the learning rate was too small and I did not run for enough samples. Setting LR to 0.05 and running for 5k iterations yields estimates pretty close to the simulation params.

I don’t see if there’s a way to mark as closed, but this is not an issue anymore.