tf.contrib.distributions.Laplace.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.distributions.Binomial.parameters Dictionary of parameters used by this Distribution.

tf.contrib.training.batch_sequences_with_states(input_key, input_sequences, input_context, input_length, initial_states, num_unroll, batch_size, num_threads=3, capacity=1000, allow_small_batch=True, pad=True, name=None) Creates batches of segments of sequential input. This method creates a SequenceQueueingStateSaver (SQSS) and adds it to the queuerunners. It returns a NextQueuedSequenceBatch. It accepts one example at a time identified by a unique input_key. input_sequence is a dict with value

tf.contrib.distributions.Categorical.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.

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

tf.contrib.distributions.QuantizedDistribution.name Name prepended to all ops created by this Distribution.

tf.self_adjoint_eig(tensor, name=None) Computes the eigen decomposition of a batch of self-adjoint matrices. Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices in tensor such that tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i], for i=0...N-1. Args: tensor: Tensor of shape [..., N, N]. Only the lower triangular part of each inner inner matrix is referenced. name: string, optional name of the operation. Returns: e: Eigenvalues. Shape is [..., N]. v: Eigenvec

tf.contrib.distributions.LaplaceWithSoftplusScale.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.StudentT.mean(name='mean') Mean. Additional documentation from StudentT: The mean of Student's T equals mu if df > 1, otherwise it is NaN. If self.allow_nan_stats=True, then an exception will be raised rather than returning NaN.

tf.contrib.distributions.MultivariateNormalCholesky.sigma Dense (batch) covariance matrix, if available.