tf.contrib.distributions.Uniform.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.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.name Name prepended to all ops created by this Distribution.

tf.contrib.learn.DNNRegressor.bias_

tf.train.match_filenames_once(pattern, name=None) Save the list of files matching pattern, so it is only computed once. Args: pattern: A file pattern (glob). name: A name for the operations (optional). Returns: A variable that is initialized to the list of files matching pattern.

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

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.value_type

class tf.TensorArray Class wrapping dynamic-sized, per-time-step, write-once Tensor arrays. This class is meant to be used with dynamic iteration primitives such as while_loop and map_fn. It supports gradient back-propagation via special "flow" control flow dependencies.

tf.contrib.learn.LinearRegressor.dnn_bias_ Returns bias of deep neural network part.

tf.contrib.distributions.Multinomial.n Number of trials.

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