tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.graph

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.parameters Dictionary of parameters used by this Distribution.

class tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor InverseGammaWithSoftplusAlphaBetaTensor is a StochasticTensor backed by the distribution InverseGammaWithSoftplusAlphaBeta.

class tf.WholeFileReader A Reader that outputs the entire contents of a file as a value. To use, enqueue filenames in a Queue. The output of Read will be a filename (key) and the contents of that file (value). See ReaderBase for supported methods.

tf.contrib.distributions.BernoulliWithSigmoidP.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T f

tf.contrib.distributions.MultivariateNormalDiag.log_sigma_det(name='log_sigma_det') Log of determinant of covariance matrix.

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

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.dtype

tf.contrib.distributions.Normal.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.