tf.contrib.distributions.BetaWithSoftplusAB.batch_shape(name='batch_shape') Shape of a single sample from a single event index

tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape') Shape of a single sample from a single batch

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

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

tf.ReaderBase.read_up_to(queue, num_records, name=None) Returns up to num_records (key, value pairs) produced by a reader.

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.dtype

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

tf.contrib.distributions.Mixture.sample_n(n, seed=None, name='sample_n') Generate n samples.

tf.contrib.distributions.WishartFull.get_event_shape() Shape of a single sample from a single batch as a TensorShape

tf.contrib.distributions.Gamma.pmf(value, name='pmf') Probability mass function. Args: