View source on GitHub |
Draws deterministic pseudorandom samples from a categorical distribution.
tf.random.stateless_categorical(
logits,
num_samples,
seed,
dtype=tf.dtypes.int64
,
name=None
)
This is a stateless version of tf.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])
Returns | |
---|---|
The drawn samples of shape [batch_size, num_samples] .
|