tf.keras.random.categorical

Draws samples from a categorical distribution.

tf.keras.random.categorical(
    logits, num_samples, dtype='int32', seed=None
)

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.

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 Python integer or instance of keras.random.SeedGenerator. By default, the seed argument is None, and an internal global keras.random.SeedGenerator 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 keras.random.SeedGenerator must be provided as the seed value. Remark concerning the JAX backend: When tracing functions with the JAX backend the global keras.random.SeedGenerator is not supported. Therefore, during tracing the default value seed=None will produce an error, and a seed argument must be provided.

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