tf.segment_prod(data, segment_ids, name=None) Computes the product along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \prod_j data_j\) where the product is over j such that segment_ids[j] == i. Args: data: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. segment_ids: A Tensor. Must be one of the follo

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.Poisson.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.

tf.contrib.bayesflow.variational_inference.elbo_with_log_joint(log_joint, variational=None, keep_batch_dim=True, form=None, name='ELBO') Evidence Lower BOund. log p(x) >= ELBO. This method is for models that have computed p(x,Z) instead of p(x|Z). See elbo for further details. Because only the joint is specified, analytic KL is not available. Args: log_joint: Tensor log p(x, Z). variational: list of DistributionTensor q(Z). If None, defaults to all DistributionTensor objects upstream of l

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.distributions.Poisson.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.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

tf.QueueBase.enqueue_many(vals, name=None) Enqueues zero or more elements to this queue. This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tensors in vals must have the same size in the 0th dimension. If the queue is full when this operation executes, it will block until all of the elements have been enqueued. At runtime, this operation may raise an error if the queue is closed before or during its execution. If the queue is closed

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