tf.contrib.distributions.Categorical.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.framework.is_tensor(x) Check for tensor types. Check whether an object is a tensor. Equivalent to isinstance(x, [tf.Tensor, tf.SparseTensor, tf.Variable]). Args: x: An python object to check. Returns: True if x is a tensor, False if not.

tf.contrib.learn.monitors.CaptureVariable.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

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

tf.ceil(x, name=None) Returns element-wise smallest integer in not less than x. 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.

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.graph

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.input_dict

tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See Trainable. Raises: ValueError: If x or y are not None while input_fn is not None. ValueError: If both steps and max_steps are not None.

tf.contrib.learn.monitors.PrintTensor.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

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