tf.FixedLengthRecordReader.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) Outputs random values from a truncated normal distribution. The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. mean: A 0-D Tensor or Python value of type dtype. The mean

tf.contrib.distributions.BetaWithSoftplusAB.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.Categorical.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.name

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.graph

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

tf.sparse_tensor_dense_matmul(sp_a, b, adjoint_a=False, adjoint_b=False, name=None) Multiply SparseTensor (of rank 2) "A" by dense matrix "B". No validity checking is performed on the indices of A. However, the following input format is recommended for optimal behavior: if adjoint_a == false: A should be sorted in lexicographically increasing order. Use sparse_reorder if you're not sure. if adjoint_a == true: A should be sorted in order of increasing dimension 1 (i.e., "column major" order ins

tf.PriorityQueue.__init__(capacity, types, shapes=None, names=None, shared_name=None, name='priority_queue') Creates a queue that dequeues elements in a first-in first-out order. A PriorityQueue has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A PriorityQueue holds a list of up to capacity elements. Each element is a fixed-length tuple of tensors whose dtypes are described by types, and whose shapes are optionally described by the