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.

tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.nn.rnn_cell.LSTMStateTuple.h Alias for field number 1