tf.TensorArray.concat(name=None) Return the values in the TensorArray as a concatenated Tensor. All of the values must have been written, their ranks must match, and and their shapes must all match for all dimensions except the first. Args: name: A name for the operation (optional). Returns: All the tensors in the TensorArray concatenated into one tensor.

tf.zeros(shape, dtype=tf.float32, name=None) Creates a tensor with all elements set to zero. This operation returns a tensor of type dtype with shape shape and all elements set to zero. For example: tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]] Args: shape: Either a list of integers, or a 1-D Tensor of type int32. dtype: The type of an element in the resulting Tensor. name: A name for the operation (optional). Returns: A Tensor with all elements set to zero.

tf.TensorArray.__init__(dtype, size=None, dynamic_size=None, clear_after_read=None, tensor_array_name=None, handle=None, flow=None, infer_shape=True, name=None) Construct a new TensorArray or wrap an existing TensorArray handle. A note about the parameter name: The name of the TensorArray (even if passed in) is uniquified: each time a new TensorArray is created at runtime it is assigned its own name for the duration of the run. This avoids name collisions if a TensorArray is created within a w

tf.contrib.distributions.WishartFull.cholesky_input_output_matrices Boolean indicating if Tensor input/outputs are Cholesky factorized.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.value(name='value')

tf.contrib.distributions.InverseGamma.variance(name='variance') Variance. Additional documentation from InverseGamma: Variance for inverse gamma is defined only for alpha > 2. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from Dirichlet: Note that the input must be a non-negative tensor with dtype dtype and whose shape can be broadcast with self.alpha. For fixed leading dimensions, the last dimension represents counts for the corresponding Dirichlet distribution in self.alpha. x is only legal if it sums up to one. Args: value: float or double Tensor.

tf.contrib.distributions.MultivariateNormalCholesky.mu

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.mean(name='mean')