tf.contrib.distributions.MultivariateNormalFull.sigma Dense (batch) covariance matrix, if available.

tf.igamma(a, x, name=None) Compute the lower regularized incomplete Gamma function Q(a, x). The lower regularized incomplete Gamma function is defined as: P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x) where gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt is the lower incomplete Gamma function. Note, above Q(a, x) (Igammac) is the upper regularized complete Gamma function. Args: a: A Tensor. Must be one of the following types: float32, float64. x: A Tensor. Must have the same type as a. name:

tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n') Generate n samples. Additional documentation from Gamma: See the documentation for tf.random_gamma for more details. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.distributions.Chi2WithAbsDf.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.distributions.Chi2WithAbsDf.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

string tensorflow::Tensor::SummarizeValue(int64 max_entries) const Render the first max_entries values in *this into a string.

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.atan(x, name=None) Computes atan of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, int32, int64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.graph_editor.swap_ts(ts0, ts1, can_modify=None, cannot_modify=None) For each tensor's pair, swap the end of (t0,t1). B0 B1 B0 B1 | | => X A0 A1 A0 A1 Args: ts0: an object convertible to a list of tf.Tensor. ts1: an object convertible to a list of tf.Tensor. can_modify: iterable of operations which can be modified. Any operation outside within_ops will be left untouched by this function. cannot_modify: iterable of operations which cannot be modified. Any operation within canno

tf.RandomShuffleQueue.__init__(capacity, min_after_dequeue, dtypes, shapes=None, names=None, seed=None, shared_name=None, name='random_shuffle_queue') Create a queue that dequeues elements in a random order. A RandomShuffleQueue has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A RandomShuffleQueue holds a list of up to capacity elements. Each element is a fixed-length tuple of tensors whose dtypes are described by dtypes, and whose