tf.contrib.framework.assign_from_values_fn(var_names_to_values) Returns a function that assigns specific variables from the given values. This function provides a mechanism for performing assignment of variables to values in a way that does not fill the graph with large assignment values. Args: var_names_to_values: A map from variable names to values. Returns: A function that takes a single argument, a tf.Session, that applies the assignment operation. Raises: ValueError: if any of the giv

tf.contrib.distributions.NormalWithSoftplusSigma.is_continuous

tf.WholeFileReader.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

int64 tensorflow::PartialTensorShape::dim_size(int d) const Returns the number of elements in dimension d. REQUIRES: 0 <= d < dims()

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.loss(final_loss, name='Loss')

class tf.FixedLenSequenceFeature Configuration for a dense input feature in a sequence item. To treat a sparse input as dense, provide allow_missing=True; otherwise, the parse functions will fail on any examples missing this feature. Fields: shape: Shape of input data. dtype: Data type of input. allow_missing: Whether to allow this feature to be missing from a feature list item.

tf.contrib.distributions.MultivariateNormalDiag.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.input_dict

tf.contrib.bayesflow.variational_inference.ELBOForms.check_form(form)