tf.contrib.distributions.MultivariateNormalCholesky.mode(name='mode') Mode.

tf.contrib.distributions.Chi2WithAbsDf.variance(name='variance') Variance.

tf.contrib.learn.BaseEstimator.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.graph

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.mean(name='mean')

tf.contrib.framework.reduce_sum_n(tensors, name=None) Reduce tensors to a scalar sum. This reduces each tensor in tensors to a scalar via tf.reduce_sum, then adds them via tf.add_n. Args: tensors: List of tensors, all of the same numeric type. name: Tensor name, and scope for all other ops. Returns: Total loss tensor, or None if no losses have been configured. Raises: ValueError: if losses is missing or empty.

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor MultivariateNormalDiagWithSoftplusStDevTensor is a StochasticTensor backed by the distribution MultivariateNormalDiagWithSoftplusStDev.

tf.contrib.distributions.WishartCholesky.is_continuous

tf.contrib.distributions.BetaWithSoftplusAB.mode(name='mode') Mode. Additional documentation from Beta: Note that the mode for the Beta distribution is only defined when a > 1, b > 1. This returns the mode when a > 1 and b > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.