tf.compat.v1.random

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`	integer type to use for the output. Defaults to int64.
`name`	Optional name for the operation.

Returns
The drawn samples of shape `[batch_size, num_samples]`.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.random.Overview

Example:

Args

Returns