tf.contrib.learn.monitors.SummarySaver.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.

tf.contrib.distributions.Normal.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Normal.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.NormalWithSoftplusSigma.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.Laplace.is_continuous

tf.contrib.distributions.BernoulliWithSigmoidP.mode(name='mode') Mode. Additional documentation from Bernoulli: Returns 1 if p > 1-p and 0 otherwise.

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

tf.contrib.learn.monitors.CaptureVariable.post_step(step, session)

tf.contrib.learn.monitors.EveryN.run_on_all_workers

tf.SparseTensorValue.__new__(_cls, indices, values, shape) Create new instance of SparseTensorValue(indices, values, shape)