class tf.contrib.learn.monitors.CaptureVariable Captures a variable's values into a collection. This monitor is useful for unit testing. You should exercise caution when using this monitor in production, since it never discards values. This is an EveryN monitor and has consistent semantic for every_n and first_n.

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

Env* tensorflow::SessionOptions::env The environment to use.

tf.contrib.distributions.BetaWithSoftplusAB.mode(name='mode') Mode. Additional documentation from Beta: Note that the mode for the Beta distribution is only defined when a > 1, b > 1. This returns the mode when a > 1 and b > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.contrib.distributions.Chi2WithAbsDf.variance(name='variance') Variance.

tf.contrib.distributions.MultivariateNormalCholesky.mode(name='mode') Mode.

tf.FixedLenFeature.__repr__() Return a nicely formatted representation string

class tf.contrib.learn.RunConfig This class specifies the specific configurations for the run. If you're a Google-internal user using command line flags with learn_runner.py (for instance, to do distributed training or to use parameter servers), you probably want to use learn_runner.EstimatorConfig instead.

tf.contrib.distributions.Mixture.entropy(name='entropy') Shanon entropy in nats.

ConfigProto tensorflow::SessionOptions::config Configuration options.