random_categorical: Draws samples from a categorical distribution.
In keras3: R Interface to 'Keras'

random_categorical

R Documentation

Draws samples from a categorical distribution.

Description

This function takes as input logits, a 2-D input tensor with shape (batch_size, num_classes). Each row of the input represents a categorical distribution, with each column index containing the log-probability for a given class.

The function will output a 2-D tensor with shape (batch_size, num_samples), where each row contains samples from the corresponding row in logits. Each column index contains an independent samples drawn from the input distribution.

x <- matrix(c(100, .1, 99), nrow = 1)
random_categorical(x, num_samples = 5, seed = 1234)

## tf.Tensor([[3 1 1 3 3]], shape=(1, 5), dtype=int32)

random_categorical(x, num_samples = 5, seed = 1234,
                   zero_indexed = TRUE)

## tf.Tensor([[2 0 0 2 2]], shape=(1, 5), dtype=int32)

op_take(x, random_categorical(x, num_samples = 5, seed = 1234))

## tf.Tensor([[ 99. 100. 100.  99.  99.]], shape=(1, 5), dtype=float64)

op_take(x, random_categorical(x, num_samples = 5, seed = 1234,
                              zero_indexed = TRUE),
        zero_indexed = TRUE)

## tf.Tensor([[ 99. 100. 100.  99.  99.]], shape=(1, 5), dtype=float64)

Usage

random_categorical(
  logits,
  num_samples,
  dtype = "int32",
  seed = NULL,
  zero_indexed = FALSE
)

Arguments

`logits`	2-D Tensor with shape (batch_size, num_classes). Each row should define a categorical distribution with the unnormalized log-probabilities for all classes.
`num_samples`	Int, the number of independent samples to draw for each row of the input. This will be the second dimension of the output tensor's shape.
`dtype`	Optional dtype of the output tensor.
`seed`	Optional R integer or instance of `random_seed_generator()`. By default, the `seed` argument is `NULL`, and an internal global `random_seed_generator()` is used. The `seed` argument can be used to ensure deterministic (repeatable) random number generation. Note that passing an integer as the `seed` value will produce the same random values for each call. To generate different random values for repeated calls, an instance of `random_seed_generator()` must be provided as the `seed` value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global `random_seed_generator()` is not supported. Therefore, during tracing the default value `seed=NULL` will produce an error, and a `seed` argument must be provided.
`zero_indexed`	If `TRUE`, the returned indices are zero-based (`0` encodes to first position); if `FALSE` (default), the returned indices are one-based (`1` encodes to first position).