def __init__(self, variational, model, log_joint=None, latent_names=None,
latent_axis=None):
"""
Construct the :class:`VariationalChain`.
Args:
variational (BayesianNet): The variational net.
model (BayesianNet): The model net.
log_joint (tf.Tensor): The log-joint of the model net. If
:obj:`None`, the log-densities of all variables
within `model` net will be summed up as the log-joint.
(default :obj:`None`)
latent_names (Iterable[str]): Names of the latent variables in
variational inference. If :obj:`None`, all of the variables
within `variational` net will be collected. (default
:obj:`None`)
latent_axis: The axis or axes to be considered as the sampling
dimensions of latent variables. The specified axes will be
summed up in the variational lower-bounds or training
objectives. (default :obj:`None`)
"""
if latent_names is None:
latent_names = tuple(variational)
else:
latent_names = tuple(latent_names)
with tf.name_scope('VariationalChain'):
if log_joint is None:
with tf.name_scope('model_log_joint'):
log_joint = add_n_broadcast(
model.local_log_probs(iter(model)))
with tf.name_scope('latent_log_probs'):
latent_log_probs = variational.local_log_probs(latent_names)
self._variational = variational
self._model = model
self._log_joint = log_joint
self._latent_names = latent_names
self._latent_axis = latent_axis
self._vi = VariationalInference(
log_joint=self.log_joint,
latent_log_probs=latent_log_probs,
axis=latent_axis
)
def __init__(self, log_joint, latent_log_probs, axis=None):
"""
Construct the :class:`VariationalInference`.
Args:
log_joint (tf.Tensor): The log-joint of model.
latent_log_probs (Iterable[tf.Tensor]): The log-densities
of latent variables from the variational net.
axis: The axis or axes to be considered as the sampling dimensions
of latent variables. The specified axes will be summed up in
the variational lower-bounds or training objectives.
(default :obj:`None`)
"""
self._log_joint = tf.convert_to_tensor(log_joint)
self._latent_log_probs = tuple(
tf.convert_to_tensor(t) for t in latent_log_probs)
self._latent_log_prob = add_n_broadcast(self._latent_log_probs,
name='latent_log_prob')
self._axis = axis
self._lower_bound = VariationalLowerBounds(self)
self._training = VariationalTrainingObjectives(self)
self._evaluation = VariationalEvaluation(self)
def log_joint_func(observed):
net = model_func(observed)
return add_n_broadcast(net.local_log_prob(['z', 'x']))