tf.raw_ops.StatelessMultinomial bookmark_border Stay organized with collections Save and categorize content based on your preferences.

Draws samples from a multinomial distribution.

Compat aliases for migration

tf.compat.v1.raw_ops.StatelessMultinomial

tf.raw_ops.StatelessMultinomial(
    logits,
    num_samples,
    seed,
    output_dtype=tf.dtypes.int64,
    name=None
)

Args
`logits`	A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` represents the unnormalized log probabilities for all classes.
`num_samples`	A `Tensor` of type `int32`. 0-D. Number of independent samples to draw for each row slice.
`seed`	A `Tensor`. Must be one of the following types: `int32`, `int64`. 2 seeds (shape [2]).
`output_dtype`	An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`.
`name`	A name for the operation (optional).

Returns
A `Tensor` of type `output_dtype`.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.

Last updated 2024-04-26 UTC.

tf.raw_ops.StatelessMultinomial bookmark_borderbookmark Stay organized with collections Save and categorize content based on your preferences.