tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.entropy(name='entropy')

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

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='GammaWithSoftplusAlphaBeta')

class tf.errors.NotFoundError Raised when a requested entity (e.g., a file or directory) was not found. For example, running the tf.WholeFileReader.read() operation could raise NotFoundError if it receives the name of a file that does not exist.

tf.contrib.layers.summarize_activation(op) Summarize an activation. This applies the given activation and adds useful summaries specific to the activation. Args: op: The tensor to summarize (assumed to be a layer activation). Returns: The summary op created to summarize op.

tf.contrib.distributions.Categorical.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.SparseTensor.__str__()

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

tf.contrib.distributions.Poisson.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Poisson: Note thet the input value must be a non-negative floating point tensor with dtype dtype and whose shape can be broadcast with self.lam. x is only legal if it is non-negative and its components are equal to integer values. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_s

tf.contrib.learn.monitors.SummarySaver.every_n_step_begin(step)