tf.contrib.distributions.LaplaceWithSoftplusScale.mean(name='mean') Mean.

tf.contrib.learn.monitors.EveryN.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.IdentityReader.__init__(name=None) Create a IdentityReader. Args: name: A name for the operation (optional).

tf.IdentityReader.read(queue, name=None) Returns the next record (key, value pair) produced by a reader. Will dequeue a work unit from queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. name: A name for the operation (optional). Returns: A tuple of Tensors (key, value). key: A string scalar Tensor. value: A s

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.dtype

tf.contrib.learn.BaseEstimator.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.contrib.learn.LinearRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See Evaluable. Raises: ValueError: If at least one of x or y is provided, and at least one of input_fn or feed_fn is provided. Or if metrics is not None or dict.

TensorShapeIter tensorflow::TensorShape::begin() const For iterating through the dimensions.

tf.contrib.learn.monitors.EveryN.end(session=None)

tf.contrib.learn.LinearRegressor.weights_