tf.contrib.distributions.ExponentialWithSoftplusLam.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of

tf.contrib.framework.convert_to_tensor_or_sparse_tensor(value, dtype=None, name=None, as_ref=False) Converts value to a

tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance') Computes the Levenshtein distance between sequences

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

tf.contrib.distributions.WishartCholesky.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.Multinomial.log_survival_function(value, name='log_survival_function') Log survival function.

tf.contrib.distributions.Laplace.allow_nan_stats Python boolean describing behavior when a stat is undefined.

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

tf.contrib.distributions.Chi2.mean(name='mean') Mean.

tf.contrib.learn.TensorFlowEstimator.get_params(deep=True) Get parameters for this estimator. Args: