tf.compat.v1.random.stateless_multinomial
Draws deterministic pseudorandom samples from a multinomial distribution. (deprecated)
tf.compat.v1.random.stateless_multinomial(
logits,
num_samples,
seed,
output_dtype=tf.dtypes.int64
,
name=None
)
This is a stateless version of tf.random.categorical
: 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:
# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.random.stateless_categorical(
tf.math.log([[0.5, 0.5]]), 5, seed=[7, 17])
Args |
logits
|
2-D Tensor with shape [batch_size, num_classes] . Each slice
[i, :] represents the unnormalized log-probabilities for all classes.
|
num_samples
|
0-D. Number of independent samples to draw for each row slice.
|
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.)
|
output_dtype
|
The integer type of the output: int32 or int64 . Defaults
to int64 .
|
name
|
Optional name for the operation.
|
Returns |
The drawn samples of shape [batch_size, num_samples] .
|
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Last updated 2023-10-06 UTC.
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