Reparameterization of latent variables failed


Currently I am working on transforming the Sparse Gamma Deep Exponential Family example, see link. Instead of using Gamma distributions for the weights and latent variables, I’m trying to make a Sigmoid Belief Network (SBN) where the latent variables and weights follow Bernoulli and Normal distributions, respectively. My first attempt with an autoguide has a blog post here, but hasn’t solved my issues. I am now building a custom guide.

My current implementation includes, what I believe, a full reparameterization of all distributions included in the model, that is:

  • The top layer of weights, w_top, is resampled
  • The bottom layer of weights, w_bottom, is resampled
  • The top layer of latent variables, z_top, is resampled
  • The bottom layer of latent variables, z_bottom, is resampled

See also the code on the bottom of this post.

I am confused however that my code won’t run, and crashes because not all distributions in the guide are fully parameterized, or, the following error:

Traceback (most recent call last):
  File "C:/Users/posc8001/Documents/DEF/Scipio_DEF/", line 179, in <module>
    model = main(args)
  File "C:/Users/posc8001/Documents/DEF/Scipio_DEF/", line 158, in main
    loss = svi.step(data)
  File "C:\Users\posc8001\.virtualenvs\Scipio_DEF-F7b0vflQ\lib\site-packages\pyro\infer\", line 99, in step
    loss = self.loss_and_grads(self.model,, *args, **kwargs)
  File "C:\Users\posc8001\.virtualenvs\Scipio_DEF-F7b0vflQ\lib\site-packages\pyro\infer\", line 126, in loss_and_grads
    loss_particle, surrogate_loss_particle = self._differentiable_loss_particle(model_trace, guide_trace)
  File "C:\Users\posc8001\.virtualenvs\Scipio_DEF-F7b0vflQ\lib\site-packages\pyro\infer\", line 105, in _differentiable_loss_particle
  File "C:\Users\posc8001\.virtualenvs\Scipio_DEF-F7b0vflQ\lib\site-packages\pyro\infer\", line 39, in _check_fully_reparametrized
    raise NotImplementedError("All distributions in the guide must be fully reparameterized.")
NotImplementedError: All distributions in the guide must be fully reparameterized.

Process finished with exit code 1

Does anyone understand what this means and how I should proceed to solve this?

Here is the code that produced the error.

    import os
    import sys
    import argparse

    import numpy as np
    import torch
    from pathlib import Path

    import torch.optim as optim

    import pyro
    from pyro import poutine
    import pyro.optim as optim
    from pyro.distributions import Bernoulli, Normal, Beta
    from pyro.contrib.autoguide import AutoDiagonalNormal, AutoGuideList, AutoDiscreteParallel
    from pyro.infer import SVI, TraceMeanField_ELBO


    class SigmoidBeliefDEF(object):
        def __init__(self):
            # define the sizes of the layers in the deep exponential family
            self.top_width = 2
            self.bottom_width = 3
            self.data_size = 5
            # define hyperparameters that control the prior
            self.p_z = torch.tensor(0.5)
            self.mu_w = torch.tensor(0.0)
            self.sigma_w = torch.tensor(1.0)
            # define parameters used to initialize variational parameters
            self.z_mean_init = 0.0
            self.z_sigma_init = 3.0
            self.w_mean_init = 0.0
            self.w_sigma_init = 1.0
            self.softplus = torch.nn.Softplus()

        # 1
        # define the model
        def model(self, x):
            x_size = x.size(0)
            # 1.1
            # sample the global weights
            with pyro.plate("w_top_plate", self.top_width * self.bottom_width):
                w_top = pyro.sample("w_top", Normal(self.mu_w, self.sigma_w))
            with pyro.plate("w_bottom_plate", self.bottom_width * self.data_size):
                w_bottom = pyro.sample("w_bottom", Normal(self.mu_w, self.sigma_w))

            # 1.2
            # sample the local latent random variables
            # (the plate encodes the fact that the z's for different datapoints are conditionally independent)
            with pyro.plate("data", x_size):
                z_top = pyro.sample("z_top", Bernoulli(self.p_z).expand([self.top_width]).to_event(1))
                # note that we need to use matmul (batch matrix multiplication) as well as appropriate reshaping
                # to make sure our code is fully vectorized
                w_top = w_top.reshape(self.top_width, self.bottom_width) if w_top.dim() == 1 else \
                    w_top.reshape(-1, self.top_width, self.bottom_width)
                mean_bottom = torch.sigmoid(torch.matmul(z_top, w_top))
                z_bottom = pyro.sample("z_bottom", Bernoulli(mean_bottom).to_event(1))

                w_bottom = w_bottom.reshape(self.bottom_width, self.data_size) if w_bottom.dim() == 1 else \
                    w_bottom.reshape(-1, self.bottom_width, self.data_size)
                mean_obs = torch.sigmoid(torch.matmul(z_bottom, w_bottom))

                # observe the data using a Bernoulli likelihood
                pyro.sample('obs', Bernoulli(mean_obs).to_event(1), obs=x)

        # 2
        # define our custom guide a.k.a. variational distribution.
        # (note the guide is mean field)
        def guide(self, x):
            x_size = x.size(0)

            # helper for initializing variational parameters
            def rand_tensor(shape, mean, sigma):
                return mean * torch.ones(shape) + sigma * torch.randn(shape)

            # 2.1
            # define a helper function to sample z's for a single layer
            def sample_zs(name, width):
                p_z_q = pyro.param("p_z_q_%s" % name,
                                   lambda: rand_tensor((x_size, width), self.z_mean_init, self.z_sigma_init))
                p_z_q = torch.sigmoid(p_z_q)
                poutine.block(pyro.sample("z_%s" % name, Bernoulli(p_z_q).to_event(1)))

            # define a helper function to sample w's for a single layer
            def sample_ws(name, width):
                mean_w_q = pyro.param("mean_w_q_%s" % name, lambda: rand_tensor(width, self.w_mean_init, self.w_sigma_init))
                sigma_w_q = pyro.param("sigma_w_q_%s" % name, lambda: rand_tensor(width, self.w_mean_init, self.w_sigma_init))
                sigma_w_q = self.softplus(sigma_w_q)
                pyro.sample("w_%s" % name, Normal(mean_w_q, sigma_w_q))

            # sample the global weights
            with pyro.plate("w_top_plate", self.top_width * self.bottom_width):
                sample_ws("top", self.top_width * self.bottom_width)
            with pyro.plate("w_bottom_plate", self.bottom_width * self.data_size):
                sample_ws("bottom", self.bottom_width * self.data_size)

            # sample the local latent random variables
            with pyro.plate("data", x_size):
                sample_zs("top", self.top_width)
                sample_zs("bottom", self.bottom_width)

    def main(args):
        dataset_path = Path(r"C:\Users\posc8001\Documents\DEF\Data\Simulation_1")
        file_to_open = dataset_path / "small_data.csv"
        f = open(file_to_open)
        data = torch.tensor(np.loadtxt(f, delimiter=',')).float()
        sigmoid_belief_def = SigmoidBeliefDEF()

        # Specify hyperparameters of optimization
        learning_rate = 0.2
        momentum = 0.05
        opt = optim.AdagradRMSProp({"eta": learning_rate, "t": momentum})

        # Specify the guide
        guide =

        # Specify Stochastic Variational Inference
        svi = SVI(sigmoid_belief_def.model, guide, opt, loss=TraceMeanField_ELBO())

        # we use svi_eval during evaluation; since we took care to write down our model in
        # a fully vectorized way, this computation can be done efficiently with large tensor ops
        svi_eval = SVI(sigmoid_belief_def.model, guide, opt,
                       loss=TraceMeanField_ELBO(num_particles=args.eval_particles,     vectorize_particles=True))

        # the training loop
        for k in range(args.num_epochs):
            loss = svi.step(data)

            if k % args.eval_frequency == 0 and k > 0 or k == args.num_epochs - 1:
                loss = svi_eval.evaluate_loss(data)
                print("[epoch %04d] training elbo: %.4g" % (k, -loss))

    if __name__ == '__main__':
        assert pyro.__version__.startswith('0.3.0')
        # parse command line arguments
        parser = argparse.ArgumentParser(description="parse args")
        parser.add_argument('-n', '--num-epochs', default=1000, type=int, help='number of training epochs')
        parser.add_argument('-ef', '--eval-frequency', default=25, type=int,
                            help='how often to evaluate elbo (number of epochs)')
        parser.add_argument('-ep', '--eval-particles', default=20, type=int,
                            help='number of samples/particles to use during evaluation')
        parser.add_argument('--auto-guide', action='store_true', help='whether to use an automatically constructed guide')
        args = parser.parse_args()
        model = main(args)


you can’t use TraceMeanField_ELBO with discrete latent variables. for the kind of model you’ve written down, you probably want to use TraceGraph_ELBO.

however, be advised that blackbox variational inference for a sigmoid belief network may not work very well (especially if the dimension of the latent space is large). this is because the gradient variance is likely to be large (precisely because the bernoulli distributions cannot be reparameterized). to train SBNs with large latent spaces you probably need custom inference algorithms to get robust model training.

another option is to try using the RelaxedBernoulliStraightThrough distribution in your guide program. roughly speaking, this distribution relaxes the bernoulli distribution to have a continuous support (so that it can be reparameterized). however, this distribution hasn’t seen much use and so it might possibly have numerical stability issues.


Thank you so much, @martinjankowiak, for your reply. Changing TraceMeanField_ELBO to TraceGraph_ELBO did the trick!

I’m look into your other advices, do you know if there are any examples of custom inference algorithm implementations? I can’t seem to locate any in the examples.

When I’ve got it implemented I’ll update on the performance of using RelaxedBernoulliStraightThrough distributions instead of Bernoulli.


As promised, an update on the use of RelaxedBernoulliStraightThrough rather than Bernoulli.

The value for the ELBO is a lot lower when using the Bernoulli distribution compared to the RelaxedBernoulliStraightThrough distribution. Do you think this goes to show the better performance of the latter method? Or are the two statistics not comparable due to the nature of the ELBO statistic?


i think you’re plotting the loss (negative elbo), right?

the elbo’s for both methods are directly comparable. for small models i might expect Bernoulli to give better results since it is unbiased but might expect RelaxedBernoulliStraightThrough to work better in the opposite regime (it yields biased gradients but the variance will be much lower, thus making learning easier).


Are you still focus on the topic? I am interested in the prediction performance instead of only loss performance here. From the example for Semi-supervised VAE ss-vae, the variance will lead the prediction performance (like accuracy rate, or other measurement) quite different.

In the meanwhile, svi_iii introduces several ways to reduce the variance, maybe you can directly use it in your Bernoulii model, such as adding even learnable baselines.


@martinjankowiak Thanks, that’s very well possible. I’ll keep you updated on what results I get. This was a network with 5 latent, and 5 observed variables.


@beyondpie Yes I’m still working on it, thanks for the guidance. I have some topics that require my attention first but I will look into predictive performance and get back at you about it.

However, be advised that a variational autoencoder in general is more suited for looking at predictive performance than the model I am building. My ‘predictions’ will consist of performing conditional expectations of some variables (X_unobserved) conditional on other variables (X_observed). It is a somewhat subjective statistic because one has to make a choice in terms of what is selected as evidence.

Basically the difference between a VAE model and my model is that of supervised vs unsupervised learning. If you have any thoughts on how to construct a more objective measure of predictive power, please share them.

Best regards,