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

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.loss(final_loss, name='Loss')

tf.contrib.rnn.GRUBlockCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args:

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor MultivariateNormalDiagPlusVDVTTensor

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.value(name='value')

tf.contrib.distributions.Binomial.prob(value, name='prob') Probability density/mass function (depending on is_continuous)

tf.contrib.losses.sparse_softmax_cross_entropy(logits, labels, weight=1.0, scope=None) Cross-entropy loss using tf.nn.sparse_

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.pdf(value, name='pdf') Probability density function.

tf.contrib.distributions.Multinomial.get_batch_shape() Shape of a single sample from a single event index as a TensorShape

tf.contrib.distributions.TransformedDistribution.inverse Inverse function of transform, y => x.