tf.contrib.learn.LinearRegressor.__repr__()

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

tf.contrib.distributions.Distribution.__init__(dtype, parameters, is_continuous, is_reparameterized, validate_args, allow_nan_stats, name=None)

tf.contrib.distributions.DirichletMultinomial.alpha Parameter defining this distribution.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.survival_function(value, name='survival_function') Survival function.

tf.contrib.distributions.Dirichlet.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes.

tf.contrib.distributions.QuantizedDistribution.get_event_shape() Shape of a single sample from a single batch as a TensorShape

tf.contrib.distributions.ExponentialWithSoftplusLam.sample_n(n, seed=None, name='sample_n') Generate n samples.

tf.contrib.distributions.Chi2.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats

tf.contrib.distributions.Categorical.num_classes Scalar int32 tensor: the number of classes.