Draws samples from a categorical distribution.
tf.random.categorical(
logits, num_samples, dtype=None, seed=None, name=None
)
Example:
# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.random.categorical(tf.math.log([[0.5, 0.5]]), 5)
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.
|
dtype
|
integer type to use for the output. Defaults to int64.
|
seed
|
A Python integer. Used to create a random seed for the distribution.
See tf.compat.v1.set_random_seed for behavior.
|
name
|
Optional name for the operation.
|
Returns |
The drawn samples of shape [batch_size, num_samples] .
|