class tf.contrib.bayesflow.stochastic_tensor.DirichletTensor DirichletTensor is a StochasticTensor backed by the distribution Dirichlet.

tf.sqrt(x, name=None) Computes square root of x element-wise. I.e., (y = \sqrt{x} = x^{1/2}). Args: x: A Tensor or SparseTensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor or SparseTensor, respectively. Has the same type as x.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.name

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

tf.contrib.learn.monitors.NanLoss.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.name

tf.contrib.distributions.DirichletMultinomial.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.learn.monitors.GraphDump.step_end(step, output)

tf.contrib.distributions.BetaWithSoftplusAB.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.

static bool tensorflow::TensorShapeUtils::IsVectorOrHigher(const TensorShape &shape)