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

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.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.

tf.contrib.learn.monitors.CaptureVariable.values Returns the values captured so far. Returns: dict mapping int step numbers to that values of the variable at the respective step.

tf.inv(x, name=None) Computes the reciprocal of x element-wise. I.e., \(y = 1 / x\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.value(name='value')