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

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.value(name='value')

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.graph

tf.contrib.learn.TensorFlowRNNClassifier.weights_ Returns weights of the rnn layer.

class tf.contrib.learn.TensorFlowRNNRegressor TensorFlow RNN Regressor model.

Rep64* tensorflow::TensorShape::unused_aligner

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

tf.contrib.distributions.WishartFull.log_normalizing_constant(name='log_normalizing_constant') Computes the log normalizing constant, log(Z).

tf.errors.UnauthenticatedError.__init__(node_def, op, message) Creates an UnauthenticatedError.

tf.contrib.layers.batch_norm(*args, **kwargs) Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167. "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" Sergey Ioffe, Christian Szegedy Can be used as a normalizer function for conv2d and fully_connected. Note: When is_training is True the moving_mean and moving_variance need to be updated, by default the update_ops are placed in tf.GraphKeys.UPDATE_OPS so they need to be added as a depe