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BayesFlow Variational Inference (contrib)

[TOC]

Variational inference.

Ops

tf.contrib.bayesflow.variational_inference.elbo(log_likelihood, variational_with_prior=None, keep_batch_dim=True, form=None, name='ELBO') {#elbo}

Evidence Lower BOund. log p(x) >= ELBO.

Optimization objective for inference of hidden variables by variational inference.

This function is meant to be used in conjunction with StochasticTensor. The user should build out the inference network, using StochasticTensors as latent variables, and the generative network. elbo at minimum needs p(x|Z) and assumes that all StochasticTensors upstream of p(x|Z) are the variational distributions. Use register_prior to register Distribution priors for each StochasticTensor. Alternatively, pass in variational_with_prior specifying all variational distributions and their priors.

Mathematical details:

log p(x) =  log \int p(x, Z) dZ
         =  log \int \frac {q(Z)p(x, Z)}{q(Z)} dZ
         =  log E_q[\frac {p(x, Z)}{q(Z)}]
         >= E_q[log \frac {p(x, Z)}{q(Z)}] = L[q; p, x]  # ELBO

L[q; p, x] = E_q[log p(x|Z)p(Z)] - E_q[log q(Z)]
           = E_q[log p(x|Z)p(Z)] + H[q]           (1)
           = E_q[log p(x|Z)] - KL(q || p)         (2)

H - Entropy
KL - Kullback-Leibler divergence

See section 2.2 of Stochastic Variational Inference by Hoffman et al. for more, including the ELBO's equivalence to minimizing KL(q(Z)||p(Z|x)) in the fully Bayesian setting. https://arxiv.org/pdf/1206.7051.pdf.

form specifies which form of the ELBO is used. form=ELBOForms.default tries, in order of preference: analytic KL, analytic entropy, sampling.

Multiple entries in the variational_with_prior dict implies a factorization. e.g. q(Z) = q(z1)q(z2)q(z3).

Args:
  • log_likelihood: Tensor log p(x|Z).
  • variational_with_prior: dict from StochasticTensor q(Z) to Distribution p(Z). If None, defaults to all StochasticTensor objects upstream of log_likelihood with priors registered with register_prior.
  • keep_batch_dim: bool. Whether to keep the batch dimension when summing entropy/KL term. When the sample is per data point, this should be True; otherwise (e.g. in a Bayesian NN), this should be False.
  • form: ELBOForms constant. Controls how the ELBO is computed. Defaults to ELBOForms.default.
  • name: name to prefix ops with.
Returns:

Tensor ELBO of the same type and shape as log_likelihood.

Raises:
  • TypeError: if variationals in variational_with_prior are not StochasticTensors or if priors are not Distributions.
  • TypeError: if form is not a valid ELBOForms constant.
  • ValueError: if variational_with_prior is None and there are no StochasticTensors upstream of log_likelihood.
  • ValueError: if any variational does not have a prior passed or registered.

tf.contrib.bayesflow.variational_inference.elbo_with_log_joint(log_joint, variational=None, keep_batch_dim=True, form=None, name='ELBO') {#elbo_with_log_joint}

Evidence Lower BOund. log p(x) >= ELBO.

This method is for models that have computed p(x,Z) instead of p(x|Z). See elbo for further details.

Because only the joint is specified, analytic KL is not available.

Args:
  • log_joint: Tensor log p(x, Z).
  • variational: list of StochasticTensor q(Z). If None, defaults to all StochasticTensor objects upstream of log_joint.
  • keep_batch_dim: bool. Whether to keep the batch dimension when summing entropy term. When the sample is per data point, this should be True; otherwise (e.g. in a Bayesian NN), this should be False.
  • form: ELBOForms constant. Controls how the ELBO is computed. Defaults to ELBOForms.default.
  • name: name to prefix ops with.
Returns:

Tensor ELBO of the same type and shape as log_joint.

Raises:
  • TypeError: if variationals in variational are not StochasticTensors.
  • TypeError: if form is not a valid ELBOForms constant.
  • ValueError: if variational is None and there are no StochasticTensors upstream of log_joint.
  • ValueError: if form is ELBOForms.analytic_kl.

class tf.contrib.bayesflow.variational_inference.ELBOForms {#ELBOForms}

Constants to control the elbo calculation.

analytic_kl uses the analytic KL divergence between the variational distribution(s) and the prior(s).

analytic_entropy uses the analytic entropy of the variational distribution(s).

sample uses the sample KL or the sample entropy is the joint is provided.

See elbo for what is used with default.

tf.contrib.bayesflow.variational_inference.ELBOForms.check_form(form) {#ELBOForms.check_form}

tf.contrib.bayesflow.variational_inference.register_prior(variational, prior) {#register_prior}

Associate a variational StochasticTensor with a Distribution prior.

This is a helper function used in conjunction with elbo that allows users to specify the mapping between variational distributions and their priors without having to pass in variational_with_prior explicitly.

Args:
  • variational: StochasticTensor q(Z). Approximating distribution.
  • prior: Distribution p(Z). Prior distribution.
Returns:

None

Raises:
  • ValueError: if variational is not a StochasticTensor or prior is not a Distribution.