class tf.contrib.rnn.LayerNormBasicLSTMCell LSTM unit with layer normalization and recurrent dropout. This class adds layer normalization and recurrent dropout to a basic LSTM unit. Layer normalization implementation is based on: https://arxiv.org/abs/1607.06450. "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton and is applied before the internal nonlinearities. Recurrent dropout is base on: https://arxiv.org/abs/1603.05118 "Recurrent Dropout without Memory Loss" Stanisl

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

tf.contrib.learn.monitors.StepCounter.run_on_all_workers

tf.contrib.framework.with_shape(expected_shape, tensor) Asserts tensor has expected shape. If tensor shape and expected_shape, are fully defined, assert they match. Otherwise, add assert op that will validate the shape when tensor is evaluated, and set shape on tensor. Args: expected_shape: Expected shape to assert, as a 1D array of ints, or tensor of same. tensor: Tensor whose shape we're validating. Returns: tensor, perhaps with a dependent assert operation. Raises: ValueError: if tenso

tf.contrib.distributions.StudentT.std(name='std') Standard deviation.

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

tf.contrib.distributions.InverseGamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGamma') Construct InverseGamma distributions with parameters alpha and beta. The parameters alpha and beta must be shaped in a way that supports broadcasting (e.g. alpha + beta is a valid operation). Args: alpha: Floating point tensor, the shape params of the distribution(s). alpha must contain only positive values. beta: Floating point tensor, the scale params of the distribut

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.graph

tf.contrib.distributions.StudentT.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.bayesflow.stochastic_tensor.MultinomialTensor.entropy(name='entropy')