def _find_variational_and_priors(model,
variational_with_prior,
require_prior=True):
"""Find upstream StochasticTensors and match with registered priors."""
if variational_with_prior is None:
# pylint: disable=protected-access
upstreams = sg._upstream_stochastic_nodes([model])
# pylint: enable=protected-access
upstreams = list(upstreams[model])
if not upstreams:
raise ValueError(
"No upstream stochastic nodes found for tensor: %s", model)
prior_map = dict(ops.get_collection(VI_PRIORS))
variational_with_prior = {}
for q in upstreams:
if require_prior and (q not in prior_map or prior_map[q] is None):
raise ValueError("No prior specified for StochasticTensor: %s",
q)
variational_with_prior[q] = prior_map.get(q)
if not all(
[isinstance(q, st.StochasticTensor) for q in variational_with_prior]):
raise TypeError("variationals must be StochasticTensors")
if not all([
p is None or isinstance(p, distributions.Distribution)
for p in variational_with_prior.values()
]):
raise TypeError("priors must be Distributions")
return variational_with_prior
def _find_variational_and_priors(model,
variational_with_prior,
require_prior=True):
"""Find upstream DistributionTensors and match with registered priors."""
if variational_with_prior is None:
# pylint: disable=protected-access
upstreams = sg._upstream_stochastic_nodes([model])
# pylint: enable=protected-access
upstreams = list(upstreams[model])
if not upstreams:
raise ValueError("No upstream stochastic nodes found for tensor: %s",
model)
prior_map = dict(ops.get_collection(VI_PRIORS))
variational_with_prior = {}
for q in upstreams:
if require_prior and (q not in prior_map or prior_map[q] is None):
raise ValueError("No prior specified for DistributionTensor: %s", q)
variational_with_prior[q] = prior_map.get(q)
if not all(
[isinstance(q, st.StochasticTensor) for q in variational_with_prior]):
raise TypeError("variationals must be DistributionTensors")
if not all([p is None or isinstance(p, distributions.Distribution)
for p in variational_with_prior.values()]):
raise TypeError("priors must be BaseDistributions")
return variational_with_prior