tf.contrib.distributions.MultivariateNormalCholesky.is_reparameterized

tf.contrib.distributions.Binomial.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.clone(name=None, **dist_args)

tf.errors.NotFoundError.__init__(node_def, op, message) Creates a NotFoundError.

tf.mod(x, y, name=None) Returns element-wise remainder of division. NOTE: Mod supports broadcasting. More about broadcasting here Args: x: A Tensor. Must be one of the following types: int32, int64, float32, float64. y: A Tensor. Must have the same type as x. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.OpError.op The operation that failed, if known. N.B. If the failed op was synthesized at runtime, e.g. a Send or Recv op, there will be no corresponding Operation object. In that case, this will return None, and you should instead use the OpError.node_def to discover information about the op. Returns: The Operation that failed, or None.

tf.contrib.distributions.BetaWithSoftplusAB.mean(name='mean') Mean.

tf.igammac(a, x, name=None) Compute the upper regularized incomplete Gamma function Q(a, x). The upper regularized incomplete Gamma function is defined as: Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x) where Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt is the upper incomplete Gama function. Note, above P(a, x) (Igamma) is the lower regularized complete Gamma function. Args: a: A Tensor. Must be one of the following types: float32, float64. x: A Tensor. Must have the same type as a. n

tf.contrib.learn.monitors.NanLoss.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

tf.contrib.training.NextQueuedSequenceBatch.insertion_index The insertion indices of the examples (when they were first added). These indices start with the value -2**63 and increase with every call to the prefetch op. Each whole example gets its own insertion index, and this is used to prioritize the example so that its truncated segments appear in adjacent iterations, even if new examples are inserted by the prefetch op between iterations. Returns: An int64 vector of length batch_size, the i