tf.parse_tensor(serialized, out_type, name=None) Transforms a serialized tensorflow.TensorProto proto into a Tensor. Args: serialized: A Tensor of type string. A scalar string containing a serialized TensorProto proto. out_type: A tf.DType. The type of the serialized tensor. The provided type must match the type of the serialized tensor and no implicit conversion will take place. name: A name for the operation (optional). Returns: A Tensor of type out_type. A Tensor of type out_type.

Static helper routines for PartialTensorShape. Includes a few common predicates on a partially known tensor shape. Member Details string tensorflow::PartialTensorShapeUtils::PartialShapeListString(const gtl::ArraySlice< PartialTensorShape > &shapes) bool tensorflow::PartialTensorShapeUtils::AreCompatible(const gtl::ArraySlice< PartialTensorShape > &shapes0, const gtl::ArraySlice< PartialTensorShape > &shapes1)

class tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor GammaWithSoftplusAlphaBetaTensor is a StochasticTensor backed by the distribution GammaWithSoftplusAlphaBeta.

tf.contrib.distributions.MultivariateNormalFull.sigma_det(name='sigma_det') Determinant of covariance matrix.

tf.contrib.distributions.kl(dist_a, dist_b, allow_nan=False, name=None) Get the KL-divergence KL(dist_a || dist_b). Args: dist_a: The first distribution. dist_b: The second distribution. allow_nan: If False (default), a runtime error is raised if the KL returns NaN values for any batch entry of the given distributions. If True, the KL may return a NaN for the given entry. name: (optional) Name scope to use for created operations. Returns: A Tensor with the batchwise KL-divergence between

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

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

void tensorflow::TensorShape::set_dim(int d, int64 size) Modifies the size of the dimension d to be size REQUIRES: 0 <= d < dims() REQUIRES: size >= 0

class tf.ReaderBase Base class for different Reader types, that produce a record every step. Conceptually, Readers convert string 'work units' into records (key, value pairs). Typically the 'work units' are filenames and the records are extracted from the contents of those files. We want a single record produced per step, but a work unit can correspond to many records. Therefore we introduce some decoupling using a queue. The queue contains the work units and the Reader dequeues from the queue

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value_type