tf.contrib.distributions.Exponential.beta Inverse scale parameter.

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

tf.contrib.learn.LinearClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See evaluable.Evaluable.

tf.contrib.distributions.Distribution.name Name prepended to all ops created by this Distribution.

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

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.entropy(name='entropy')

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

tf.contrib.learn.LinearClassifier.export(export_dir, input_fn=None, input_feature_key=None, use_deprecated_input_fn=True, signature_fn=None, default_batch_size=1, exports_to_keep=None) See BaseEstimator.export.

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.value_type

tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.