tf.random.stateless_binomial
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Outputs deterministic pseudorandom values from a binomial distribution.
tf.random.stateless_binomial(
shape,
seed,
counts,
probs,
output_dtype=tf.dtypes.int32,
name=None
)
The generated values follow a binomial distribution with specified count and
probability of success parameters.
This is a stateless version of tf.random.Generator.binomial: if run twice
with the same seeds and shapes, it will produce the same pseudorandom numbers.
The output is consistent across multiple runs on the same hardware (and
between CPU and GPU), but may change between versions of TensorFlow or on
non-CPU/GPU hardware.
Example:
counts = [10., 20.]
# Probability of success.
probs = [0.8]
binomial_samples = tf.random.stateless_binomial(
shape=[2], seed=[123, 456], counts=counts, probs=probs)
counts = ... # Shape [3, 1, 2]
probs = ... # Shape [1, 4, 2]
shape = [3, 4, 3, 4, 2]
# Sample shape will be [3, 4, 3, 4, 2]
binomial_samples = tf.random.stateless_binomial(
shape=shape, seed=[123, 456], counts=counts, probs=probs)
Args |
|---|
shape
|
A 1-D integer Tensor or Python array. The shape of the output tensor.
|
seed
|
A shape [2] Tensor, the seed to the random number generator. Must have
dtype int32 or int64. (When using XLA, only int32 is allowed.)
|
counts
|
Tensor. The counts of the binomial distribution. Must be
broadcastable with probs, and broadcastable with the rightmost
dimensions of shape.
|
probs
|
Tensor. The probability of success for the binomial distribution.
Must be broadcastable with counts and broadcastable with the rightmost
dimensions of shape.
|
output_dtype
|
The type of the output. Default: tf.int32
|
name
|
A name for the operation (optional).
|
Returns |
|---|
samples
|
A Tensor of the specified shape filled with random binomial
values. For each i, each samples[..., i] is an independent draw from
the binomial distribution on counts[i] trials with probability of
success probs[i].
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.random.stateless_binomial\n\n\u003cbr /\u003e\n\n|----------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/stateless_random_ops.py#L529-L596) |\n\nOutputs deterministic pseudorandom values from a binomial distribution.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.random.stateless_binomial`](https://www.tensorflow.org/api_docs/python/tf/random/stateless_binomial)\n\n\u003cbr /\u003e\n\n tf.random.stateless_binomial(\n shape,\n seed,\n counts,\n probs,\n output_dtype=../../tf/dtypes#int32,\n name=None\n )\n\nThe generated values follow a binomial distribution with specified count and\nprobability of success parameters.\n\nThis is a stateless version of [`tf.random.Generator.binomial`](../../tf/random/Generator#binomial): if run twice\nwith the same seeds and shapes, it will produce the same pseudorandom numbers.\nThe output is consistent across multiple runs on the same hardware (and\nbetween CPU and GPU), but may change between versions of TensorFlow or on\nnon-CPU/GPU hardware.\n\n#### Example:\n\n counts = [10., 20.]\n # Probability of success.\n probs = [0.8]\n\n binomial_samples = tf.random.stateless_binomial(\n shape=[2], seed=[123, 456], counts=counts, probs=probs)\n\n counts = ... # Shape [3, 1, 2]\n probs = ... # Shape [1, 4, 2]\n shape = [3, 4, 3, 4, 2]\n # Sample shape will be [3, 4, 3, 4, 2]\n binomial_samples = tf.random.stateless_binomial(\n shape=shape, seed=[123, 456], counts=counts, probs=probs)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `shape` | A 1-D integer Tensor or Python array. The shape of the output tensor. |\n| `seed` | A shape \\[2\\] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) |\n| `counts` | Tensor. The counts of the binomial distribution. Must be broadcastable with `probs`, and broadcastable with the rightmost dimensions of `shape`. |\n| `probs` | Tensor. The probability of success for the binomial distribution. Must be broadcastable with `counts` and broadcastable with the rightmost dimensions of `shape`. |\n| `output_dtype` | The type of the output. Default: tf.int32 |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|-----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `samples` | A Tensor of the specified shape filled with random binomial values. For each i, each samples\\[..., i\\] is an independent draw from the binomial distribution on counts\\[i\\] trials with probability of success probs\\[i\\]. |\n\n\u003cbr /\u003e"]]
View source on GitHub