# tf.multinomial

tf.multinomial(
logits,
num_samples,
seed=None,
name=None,
output_dtype=None
)


See the guide: Constants, Sequences, and Random Values > Random Tensors

Draws samples from a multinomial distribution.

Example:

# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.multinomial(tf.log([[10., 10.]]), 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.
• seed: A Python integer. Used to create a random seed for the distribution. See tf.set_random_seed for behavior.
• name: Optional name for the operation.
• output_dtype: integer type to use for the output. Defaults to int64.

#### Returns:

The drawn samples of shape [batch_size, num_samples].