Draws samples from a categorical distribution.

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).

Value

A 2-D tensor with (batch_size, num_samples).

Usage

Arguments

Value

See also