tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.loss(final_loss, name='Loss')

tf.contrib.distributions.Gamma.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.MultivariateNormalFull.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or double

virtual Status tensorflow::WritableFile::Sync()=0

tf.contrib.distributions.InverseGamma.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.metrics.streaming_mean_relative_error(predictions, labels, normalizer, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the mean relative error by normalizing with the given values. The streaming_mean_relative_error function creates two local variables, total and count that are used to compute the mean relative absolute error. This average is weighted by weights, and it is ultimately returned as mean_relative_error: an idempotent operation that s

class tf.contrib.distributions.Dirichlet Dirichlet distribution. This distribution is parameterized by a vector alpha of concentration parameters for k classes.

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.distribution