tf.contrib.distributions.Chi2WithAbsDf.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from _MultivariateNormalOperatorPD: x is a batch vector with compatible shape if x is a Tensor whose shape can be broadcast up to either: self.batch_shape + self.event_shape or [M1,...,Mm] + self.batch_shape + self.event_shape Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob

tf.contrib.distributions.Chi2WithAbsDf.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.learn.monitors.BaseMonitor.run_on_all_workers

tf.contrib.metrics.confusion_matrix(predictions, labels, num_classes=None, dtype=tf.int32, name=None, weights=None) Computes the confusion matrix from predictions and labels. Calculate the Confusion Matrix for a pair of prediction and label 1-D int arrays. Considering a prediction array such as: [1, 2, 3] And a label array such as: [2, 2, 3] The confusion matrix returned would be the following one: [[0, 0, 0] [0, 1, 0] [0, 1, 0] [0, 0, 1]] If weights is not None, then the confusion matrix

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from _MultivariateNormalOperatorPD: x is a batch vector with compatible shape if x is a Tensor whose shape can be broadcast up to either: self.batch_shape + self.event_shape or [M1,...,Mm] + self.batch_shape + self.event_shape Args: value: float or double Tensor. name: The name to give this op. Returns: log_

tf.contrib.layers.stack(inputs, layer, stack_args, **kwargs) Builds a stack of layers by applying layer repeatedly using stack_args. stack allows you to repeatedly apply the same operation with different arguments stack_args[i]. For each application of the layer, stack creates a new scope appended with an increasing number. For example: y = stack(x, fully_connected, [32, 64, 128], scope='fc') # It is equivalent to: x = fully_connected(x, 32, scope='fc/fc_1') x = fully_connected(x, 64, scope='

tf.contrib.graph_editor.get_forward_walk_ops(seed_ops, inclusive=True, within_ops=None, stop_at_ts=(), control_outputs=None) Do a forward graph walk and return all the visited ops. Args: seed_ops: an iterable of operations from which the forward graph walk starts. If a list of tensors is given instead, the seed_ops are set to be the consumers of those tensors. inclusive: if True the given seed_ops are also part of the resulting set. within_ops: an iterable of tf.Operation whithin which the

KL[q || p] If log_p(z) = Log[p(z)] for distribution p, this Op approximates the negative Kullback-Leibler divergence. elbo_ratio(log_p, q, n=100) = -1 * KL[q || p], KL[q || p] = E[ Log[q(Z)] - Log[p(Z)] ] Note that if p is a Distribution, then distributions.kl(q, p) may be defined and available as an exact result.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.