tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sigma_det(name='sigma_det') Determinant of covariance matrix.

tf.is_numeric_tensor(tensor)

tf.contrib.learn.LinearRegressor.export(*args, **kwargs) Exports inference graph into given dir. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed altoge

tf.contrib.bayesflow.stochastic_tensor.SampleValue.stop_gradient

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

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

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

tf.contrib.distributions.Categorical.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.input_dict

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.mean(name='mean')