tf.contrib.distributions.GammaWithSoftplusAlphaBeta.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.metrics.auc_using_histogram(boolean_labels, scores, score_range, nbins=100, collections=None, check_shape=True, name=None) AUC computed by maintaining histograms. Rather than computing AUC directly, this Op maintains Variables containing histograms of the scores associated with True and False labels. By comparing these the AUC is generated, with some discretization error. See: "Efficient AUC Learning Curve Calculation" by Bouckaert. This AUC Op updates in O(batch_size + nbins) time

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.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.

tf.contrib.distributions.Distribution.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.graph_editor.get_ops_ios(ops, control_inputs=False, control_outputs=None, control_ios=None) Return all the tf.Operation which are connected to an op in ops. Args: ops: an object convertible to a list of tf.Operation. control_inputs: A boolean indicating whether control inputs are enabled. control_outputs: An instance of util.ControlOutputs or None. If not None, control outputs are enabled. control_ios: An instance of util.ControlOutputs or None. If not None, both control inputs

tf.contrib.distributions.Multinomial.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 for df = 1

tf.contrib.distributions.Normal.pdf(value, name='pdf') Probability density function. 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. Raises: TypeError: if not is_continuous.

class tf.contrib.distributions.Chi2WithAbsDf Chi2 with parameter transform df = floor(abs(df)).

tf.matrix_diag_part(input, name=None) Returns the batched diagonal part of a batched tensor. This operation returns a tensor with the diagonal part of the batched input. The diagonal part is computed as follows: Assume input has k dimensions [I, J, K, ..., N, N], then the output is a tensor of rank k - 1 with dimensions [I, J, K, ..., N] where: diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]. The input must be at least a matrix. For example: # 'input' is [[[1, 0, 0, 0] [0,

tf.TFRecordReader.read(queue, name=None) Returns the next record (key, value pair) produced by a reader. Will dequeue a work unit from queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). Args: queue: A Queue or a mutable string Tensor representing a handle to a Queue, with string work items. name: A name for the operation (optional). Returns: A tuple of Tensors (key, value). key: A string scalar Tensor. value: A s