tf.contrib.bayesflow.stochastic_tensor.UniformTensor.graph

tf.contrib.distributions.Uniform.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 for df = 1 is u

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

class tf.FIFOQueue A queue implementation that dequeues elements in first-in first-out order. See tf.QueueBase for a description of the methods on this class.

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

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.mean(name='mean') Mean. Additional documentation from InverseGamma: The mean of an inverse gamma distribution is beta / (alpha - 1), when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN

tf.contrib.distributions.NormalWithSoftplusSigma.validate_args Python boolean indicated possibly expensive checks are enabled.

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

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.graph

tf.contrib.framework.assert_same_float_dtype(tensors=None, dtype=None) Validate and return float type based on tensors and dtype. For ops such as matrix multiplication, inputs and weights must be of the same float type. This function validates that all tensors are the same type, validates that type is dtype (if supplied), and returns the type. Type must be dtypes.float32 or dtypes.float64. If neither tensors nor dtype is supplied, default to dtypes.float32. Args: tensors: Tensors of input val