tf.contrib.distributions.Chi2WithAbsDf.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.BernoulliWithSigmoidP.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.dtype

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.input_dict

tf.contrib.learn.monitors.LoggingTrainable.post_step(step, session)

tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.graph

tf.contrib.learn.monitors.EveryN.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

tf.contrib.distributions.Mixture.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.

tf.contrib.learn.monitors.ExportMonitor.signature_fn

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