tensorflow::ops::RandomPoissonV2

Outputs random values from the Poisson distribution(s) described by rate.

Summary

This op uses two algorithms, depending on rate. If rate >= 10, then the algorithm by Hormann is used to acquire samples via transformation-rejection. See http://www.sciencedirect.com/science/article/pii/0167668793909974.

Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform random variables. See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Addison Wesley

Args:

• scope: A Scope object
• shape: 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate.
• rate: A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution.

Optional attributes (see Attrs):

• seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
• seed2: A second seed to avoid seed collision.

Returns:

• Output: A tensor with shape shape + shape(rate). Each slice [:, ..., :, i0, i1, ...iN] contains the samples drawn for rate[i0, i1, ...iN].

Public attributes

Operation operation

::tensorflow::Output output

Public functions

 RandomPoissonV2(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input shape,
  ::tensorflow::Input rate
)

 RandomPoissonV2(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input shape,
  ::tensorflow::Input rate,
  const RandomPoissonV2::Attrs & attrs
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Public static functions

Attrs Dtype(
  DataType x
)

Attrs Seed(
  int64 x
)

Attrs Seed2(
  int64 x
)