tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.variance(name='variance') Variance. Additional documentation from StudentT: The variance for Student's T equals df / (df - 2), when df > 2 infinity, when 1 < df <= 2 NaN, when df <= 1

tf.contrib.distributions.Dirichlet.is_reparameterized

tf.contrib.distributions.LaplaceWithSoftplusScale.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.einsum(axes, *inputs) A generalized contraction between tensors of arbitrary dimension. Like numpy.einsum.

tf.contrib.distributions.Exponential.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.dtype

tf.contrib.graph_editor.matcher.control_input_ops(*args) Add input matches.

tf.contrib.distributions.Categorical.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

bool tensorflow::PartialTensorShape::IsCompatibleWith(const PartialTensorShape &shape) const Return true iff the ranks match, and if the dimensions all either match or one is unknown.

tf.contrib.distributions.Categorical.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.