tf.random.experimental.Generator

Class `Generator`

Random-number generator.

Inherits From: Checkpointable

Aliases:

It uses Variable to manage its internal state, and allows choosing an Random-Number-Generation (RNG) algorithm.

CPU, GPU and TPU with the same algorithm and seed will generate the same integer random numbers. Float-point results (such as the output of normal) may have small numerical discrepancies between CPU and GPU.

`init`

View source

__init__(
    copy_from=None,
    state=None,
    alg=None
)

Creates a generator.

The new generator will be initialized by one of the following ways, with decreasing precedence: (1) If copy_from is not None, the new generator is initialized by copying information from another generator. (3) If state and alg are not None (they must be set together), the new generator is initialized by a state.

Args:

copy_from: a generator to be copied from.
state: a vector of dtype STATE_TYPE representing the initial state of the RNG, whose length and semantics are algorithm-specific.
alg: the RNG algorithm. Possible values are RNG_ALG_PHILOX for the Philox algorithm and RNG_ALG_THREEFRY for the ThreeFry algorithm (see paper 'Parallel Random Numbers: As Easy as 1, 2, 3' [https://www.thesalmons.org/john/random123/papers/random123sc11.pdf]). Note RNG_ALG_PHILOX guarantees the same numbers are produced (given the same random state) across all architextures (CPU, GPU, XLA etc).

Properties

`algorithm`

The RNG algorithm.

`key`

The 'key' part of the state of a counter-based RNG.

For a counter-base RNG algorithm such as Philox and ThreeFry (as described in paper 'Parallel Random Numbers: As Easy as 1, 2, 3' [https://www.thesalmons.org/john/random123/papers/random123sc11.pdf]), the RNG state consists of two parts: counter and key. The output is generated via the formula: output=hash(key, counter), i.e. a hashing of the counter parametrized by the key. Two RNGs with two different keys can be thought as generating two independent random-number streams (a stream is formed by increasing the counter).

Returns:

A scalar which is the 'key' part of the state, if the RNG algorithm is counter-based; otherwise it raises a ValueError.

`state`

The internal state of the RNG.

Methods

`tf.random.experimental.Generator.binomial`

View source

binomial(
    shape,
    counts,
    probs,
    dtype=tf.dtypes.int32,
    name=None
)

Outputs random values from a binomial distribution.

The generated values follow a binomial distribution with specified count and probability of success parameters.

Example:

counts = [10., 20.]
# Probability of success.
probs = [0.8, 0.9]

rng = tf.random.experimental.Generator.from_seed(seed=234)
binomial_samples = rng.binomial(shape=[2], counts=counts, probs=probs)

Args:

shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
counts: A 0/1-D Tensor or Python value. The counts of the binomial distribution. Must be broadcastable with the leftmost dimension defined by shape.
probs: A 0/1-D Tensor or Python value. The probability of success for the binomial distribution. Must be broadcastable with the leftmost dimension defined by shape.
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].

`tf.random.experimental.Generator.from_key_counter`

View source

@classmethod
from_key_counter(
    cls,
    key,
    counter,
    alg
)

Creates a generator from a key and a counter.

This constructor only applies if the algorithm is a counter-based algorithm. See method key for the meaning of "key" and "counter".

Args:

key: the key for the RNG, a scalar of type STATE_TYPE.
counter: a vector of dtype STATE_TYPE representing the initial counter for the RNG, whose length is algorithm-specific.,
alg: the RNG algorithm. If None, it will be auto-selected. See __init__ for its possible values.

Returns:

The new generator.

`tf.random.experimental.Generator.from_non_deterministic_state`

View source

@classmethod
from_non_deterministic_state(
    cls,
    alg=None
)

Creates a generator by non-deterministically initializing its state.

The source of the non-determinism will be platform- and time-dependent.

Args:

alg: (optional) the RNG algorithm. If None, it will be auto-selected. See __init__ for its possible values.

Returns:

The new generator.

`tf.random.experimental.Generator.from_seed`

View source

@classmethod
from_seed(
    cls,
    seed,
    alg=None
)

Creates a generator from a seed.

A seed is a 1024-bit unsigned integer represented either as a Python integer or a vector of integers. Seeds shorter than 1024-bit will be padded. The padding, the internal structure of a seed and the way a seed is converted to a state are all opaque (unspecified). The only semantics specification of seeds is that two different seeds are likely to produce two independent generators (but no guarantee).

Args:

seed: the seed for the RNG.
alg: (optional) the RNG algorithm. If None, it will be auto-selected. See __init__ for its possible values.

Returns:

The new generator.

`tf.random.experimental.Generator.from_state`

View source

@classmethod
from_state(
    cls,
    state,
    alg
)

Creates a generator from a state.

See __init__ for description of state and alg.

Args:

state: the new state.
alg: the RNG algorithm.

Returns:

The new generator.

`tf.random.experimental.Generator.make_seeds`

View source

make_seeds(count=1)

Generates seeds for stateless random ops.

For example:

seeds = get_global_generator().make_seeds(count=10)
for i in range(10):
  seed = seeds[:, i]
  numbers = stateless_random_normal(shape=[2, 3], seed=seed)
  ...

Args:

count: the number of seed pairs (note that stateless random ops need a pair of seeds to invoke).

Returns:

A tensor of shape [2, count] and dtype int64.

`tf.random.experimental.Generator.normal`

View source

normal(
    shape,
    mean=0.0,
    stddev=1.0,
    dtype=tf.dtypes.float32,
    name=None
)

Outputs random values from a normal distribution.

Args:

shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A 0-D Tensor or Python value of type dtype. The mean of the normal distribution.
stddev: A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution.
dtype: The type of the output.
name: A name for the operation (optional).

Returns:

A tensor of the specified shape filled with random normal values.

`tf.random.experimental.Generator.reset`

View source

reset(state)

Resets the generator by a new state.

See __init__ for the meaning of "state".

Args:

state: the new state.

`tf.random.experimental.Generator.reset_from_key_counter`

View source

reset_from_key_counter(
    key,
    counter
)

Resets the generator by a new key-counter pair.

See from_key_counter for the meaning of "key" and "counter".

Args:

key: the new key.
counter: the new counter.

`tf.random.experimental.Generator.reset_from_seed`

View source

reset_from_seed(seed)

Resets the generator by a new seed.

See from_seed for the meaning of "seed".

Args:

seed: the new seed.

`tf.random.experimental.Generator.skip`

View source

skip(delta)

Advance the counter of a counter-based RNG.

Args:

delta: the amount of advancement. The state of the RNG after skip(n) will be the same as that after normal([n]) (or any other distribution). The actual increment added to the counter is an unspecified implementation detail.

`tf.random.experimental.Generator.split`

View source

split(count=1)

Returns a list of independent Generator objects.

Two generators are independent of each other in the sense that the random-number streams they generate don't have statistically detectable correlations. The new generators are also independent of the old one. The old generator's state will be changed (like other random-number generating methods), so two calls of split will return different new generators.

For example:

gens = get_global_generator().split(count=10)
for gen in gens:
  numbers = gen.normal(shape=[2, 3])
  # ...
gens2 = get_global_generator().split(count=10)
# gens2 will be different from gens

The new generators will be put on the current device (possible different from the old generator's), for example:

with tf.device("/device:CPU:0"):
  gen = Generator(seed=1234)  # gen is on CPU
with tf.device("/device:GPU:0"):
  gens = gen.split(count=10)  # gens are on GPU

Args:

count: the number of generators to return.

Returns:

A list (length count) of Generator objects independent of each other. The new generators have the same RNG algorithm as the old one.

`tf.random.experimental.Generator.truncated_normal`

View source

truncated_normal(
    shape,
    mean=0.0,
    stddev=1.0,
    dtype=tf.dtypes.float32,
    name=None
)

Outputs random values from a truncated normal distribution.

The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

Args:

shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A 0-D Tensor or Python value of type dtype. The mean of the truncated normal distribution.
stddev: A 0-D Tensor or Python value of type dtype. The standard deviation of the normal distribution, before truncation.
dtype: The type of the output.
name: A name for the operation (optional).

Returns:

A tensor of the specified shape filled with random truncated normal values.

`tf.random.experimental.Generator.uniform`

View source

uniform(
    shape,
    minval=0,
    maxval=None,
    dtype=tf.dtypes.float32,
    name=None
)

Outputs random values from a uniform distribution.

The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded. (For float numbers especially low-precision types like bfloat16, because of rounding, the result may sometimes include maxval.)

For floats, the default range is [0, 1). For ints, at least maxval must be specified explicitly.

In the integer case, the random integers are slightly biased unless maxval - minval is an exact power of two. The bias is small for values of maxval - minval significantly smaller than the range of the output (either 2**32 or 2**64).

Args:

shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
minval: A 0-D Tensor or Python value of type dtype. The lower bound on the range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type dtype. The upper bound on the range of random values to generate. Defaults to 1 if dtype is floating point.
dtype: The type of the output.
name: A name for the operation (optional).

Returns:

A tensor of the specified shape filled with random uniform values.

Raises:

ValueError: If dtype is integral and maxval is not specified.

`tf.random.experimental.Generator.uniform_full_int`

View source

uniform_full_int(
    shape,
    dtype=tf.dtypes.uint64,
    name=None
)

Uniform distribution on an integer type's entire range.

The other method uniform only covers the range [minval, maxval), which cannot be dtype's full range because maxval is of type dtype.

Args:

shape: the shape of the output.
dtype: (optional) the integer type, default to uint64.
name: (optional) the name of the node.

Returns:

A tensor of random numbers of the required shape.

tf.random.experimental.Generator

Class Generator

Aliases:

__init__

Args:

Properties

algorithm

key

Returns:

state

Methods

tf.random.experimental.Generator.binomial

Example:

Args:

Returns:

tf.random.experimental.Generator.from_key_counter

Args:

Returns:

tf.random.experimental.Generator.from_non_deterministic_state

Args:

Returns:

tf.random.experimental.Generator.from_seed

Args:

Returns:

tf.random.experimental.Generator.from_state

Args:

Returns:

tf.random.experimental.Generator.make_seeds

For example:

Args:

Returns:

tf.random.experimental.Generator.normal

Args:

Returns:

tf.random.experimental.Generator.reset

Args:

tf.random.experimental.Generator.reset_from_key_counter

Args:

tf.random.experimental.Generator.reset_from_seed

Args:

tf.random.experimental.Generator.skip

Args:

tf.random.experimental.Generator.split

For example:

Args:

Returns:

tf.random.experimental.Generator.truncated_normal

Args:

Returns:

tf.random.experimental.Generator.uniform

Args:

Returns:

Raises:

tf.random.experimental.Generator.uniform_full_int

Args:

Returns:

Class `Generator`

`init`

`algorithm`

`key`

`state`

`tf.random.experimental.Generator.binomial`

`tf.random.experimental.Generator.from_key_counter`

`tf.random.experimental.Generator.from_non_deterministic_state`

`tf.random.experimental.Generator.from_seed`

`tf.random.experimental.Generator.from_state`

`tf.random.experimental.Generator.make_seeds`

`tf.random.experimental.Generator.normal`

`tf.random.experimental.Generator.reset`

`tf.random.experimental.Generator.reset_from_key_counter`

`tf.random.experimental.Generator.reset_from_seed`

`tf.random.experimental.Generator.skip`

`tf.random.experimental.Generator.split`

`tf.random.experimental.Generator.truncated_normal`

`tf.random.experimental.Generator.uniform`

`tf.random.experimental.Generator.uniform_full_int`