tf.contrib.framework.assert_or_get_global_step(graph=None, global_step_tensor=None) Verifies that a global step tensor is valid or gets one if None is given. If global_step_tensor is not None, check that it is a valid global step tensor (using assert_global_step). Otherwise find a global step tensor using get_global_step and return it. Args: graph: The graph to find the global step tensor for. global_step_tensor: The tensor to check for suitability as a global step. If None is given (the def

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.distribution

tf.contrib.distributions.Multinomial.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.VarLenFeature.__repr__() Return a nicely formatted representation string

tf.contrib.bayesflow.stochastic_tensor.StudentTTensor.entropy(name='entropy')

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.entropy(name='entropy')

tf.contrib.graph_editor.SubGraphView.find_op_by_name(op_name) Return the op named op_name. Args: op_name: the name to search for Returns: The op named op_name. Raises: ValueError: if the op_name could not be found. AssertionError: if the name was found multiple time.

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.name

tf.contrib.distributions.BaseDistribution.sample_n(n, seed=None, name='sample') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

class tf.contrib.learn.monitors.StopAtStep Monitor to request stop at a specified step.