tf.contrib.learn.monitors.ValidationMonitor.every_n_step_begin(step) Callback before every n'th step begins. Args: step: int, the current value of the global step. Returns: A list of tensors that will be evaluated at this step.

tf.contrib.distributions.InverseGamma.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.input_dict

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy') Shanon entropy in nats.

tf.lgamma(x, name=None) Computes the log of the absolute value of Gamma(x) element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

TTypes< T, NDIMS >::Tensor tensorflow::Tensor::bit_casted_tensor() Return the tensor data to an Eigen::Tensor with the same size but a bitwise cast to the specified dtype T. Using a bitcast is useful for move and copy operations. NOTE: this is the same as tensor() except a bitcast is allowed.

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

tf.contrib.distributions.Laplace.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.test.assert_equal_graph_def(actual, expected) Asserts that two GraphDefs are (mostly) the same. Compares two GraphDef protos for equality, ignoring versions and ordering of nodes, attrs, and control inputs. Node names are used to match up nodes between the graphs, so the naming of nodes must be consistent. Args: actual: The GraphDef we have. expected: The GraphDef we expected. Raises: AssertionError: If the GraphDefs do not match. TypeError: If either argument is not a GraphDef.