tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.mean(name='mean')

tf.contrib.distributions.Multinomial.std(name='std') Standard deviation.

tf.contrib.distributions.BetaWithSoftplusAB.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

tf.assert_less_equal(x, y, data=None, summarize=None, message=None, name=None) Assert the condition x <= y holds element-wise. Example of adding a dependency to an operation: with tf.control_dependencies([tf.assert_less_equal(x, y)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: x = tf.with_dependencies([tf.assert_less_equal(x, y)], x) This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] <= y[i]. If bo

tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_shape(x) + self.b

tf.contrib.distributions.DirichletMultinomial.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.Session.reset(target, containers=None, config=None) Resets resource containers on target, and close all connected sessions. A resource container is distributed across all workers in the same cluster as target. When a resource container on target is reset, resources associated with that container will be cleared. In particular, all Variables in the container will become undefined: they lose their values and shapes. NOTE: (i) reset() is currently only implemented for distributed sessions. (ii

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

tf.contrib.distributions.Multinomial.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.graph_editor.remove_control_inputs(op, cops) Remove the control inputs cops from co. Warning: this function is directly manipulating the internals of the tf.Graph. Args: op: a tf.Operation from which to remove the control inputs. cops: an object convertible to a list of tf.Operation. Raises: TypeError: if op is not a tf.Operation ValueError: if any cop in cops is not a control input of op.