tf.contrib.distributions.Exponential.__init__(lam, validate_args=False, allow_nan_stats=True, name='Exponential') Construct Exponential

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

tf.string_join(inputs, separator=None, name=None) Joins the strings in the given list of string tensors into one tensor;

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate

class tf.errors.InvalidArgumentError Raised when an operation receives an invalid argument. This

tf.segment_mean(data, segment_ids, name=None) Computes the mean along segments of a tensor. Read

tf.contrib.graph_editor.bypass(sgv) Bypass the given subgraph by connecting its inputs to its outputs.

tf.sparse_segment_sqrt_n(data, indices, segment_ids, name=None) Computes the sum along sparse segments of a tensor divided by

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