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

tf.contrib.distributions.InverseGamma.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

class tf.python_io.TFRecordWriter A class to write records to a TFRecords file. This class implements __enter__ and __exit__, and can be used in with blocks like a normal file.

tf.contrib.distributions.DirichletMultinomial.n Parameter defining this distribution.

class tf.contrib.graph_editor.Transformer Transform a subgraph into another one. By default, the constructor create a transform which copy a subgraph and replaces inputs with placeholders. This behavior can be modified by changing the handlers.

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.input_dict

tf.contrib.distributions.StudentT.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.

tf.contrib.graph_editor.SubGraphView.inputs The input tensors of this subgraph view.

class tf.contrib.distributions.MultivariateNormalDiagPlusVDVT The multivariate normal distribution on R^k. Every batch member of this distribution is defined by a mean and a lightweight covariance matrix C.

class tf.contrib.distributions.Beta Beta distribution. This distribution is parameterized by a and b which are shape parameters.