tfprobability (version 0.15.1)

layer_categorical_mixture_of_one_hot_categorical: A OneHotCategorical mixture Keras layer from k * (1 + d) params.

Description

k (i.e., num_components) represents the number of component OneHotCategorical distributions and d (i.e., event_size) represents the number of categories within each OneHotCategorical distribution.

Usage

layer_categorical_mixture_of_one_hot_categorical(
  object,
  event_size,
  num_components,
  convert_to_tensor_fn = tfp$distributions$Distribution$sample,
  sample_dtype = NULL,
  validate_args = FALSE,
  ...
)

Value

a Keras layer

Arguments

object

What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:

• missing or NULL, the Layer instance is returned.

• a Sequential model, the model with an additional layer is returned.

• a Tensor, the output tensor from layer_instance(object) is returned.

event_size

Scalar integer representing the size of single draw from this distribution.

num_components

Scalar integer representing the number of mixture components. Must be at least 1. (If num_components=1, it's more efficient to use the OneHotCategorical layer.)

convert_to_tensor_fn

A callable that takes a tfd$Distribution instance and returns a tf$Tensor-like object. Default value: tfd$distributions$Distribution$sample.

sample_dtype

dtype of samples produced by this distribution. Default value: NULL (i.e., previous layer's dtype).

validate_args

Logical, default FALSE. When TRUE distribution parameters are checked for validity despite possibly degrading runtime performance. When FALSE invalid inputs may silently render incorrect outputs. Default value: FALSE.

...

Additional arguments passed to args of keras::create_layer.

Details

Typical choices for convert_to_tensor_fn include:

• tfp$distributions$Distribution$sample

• tfp$distributions$Distribution$mean

• tfp$distributions$Distribution$mode

See Also

For an example how to use in a Keras model, see layer_independent_normal().

Other distribution_layers: layer_distribution_lambda(), layer_independent_bernoulli(), layer_independent_logistic(), layer_independent_normal(), layer_independent_poisson(), layer_kl_divergence_add_loss(), layer_kl_divergence_regularizer(), layer_mixture_logistic(), layer_mixture_normal(), layer_mixture_same_family(), layer_multivariate_normal_tri_l(), layer_one_hot_categorical()