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

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

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

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

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.name

tf.contrib.distributions.ExponentialWithSoftplusLam.is_reparameterized

tf.contrib.learn.LinearRegressor.dnn_weights_ Returns weights of deep neural network part.

tf.contrib.distributions.ExponentialWithSoftplusLam.log_prob(value, name='log_prob') Log probability density/mass function (depending

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.validate_args Python boolean indicated possibly expensive checks are enabled

tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given