A d-variate OneHotCategorical Keras layer from d params.

Typical choices for convert_to_tensor_fn include:

• tfp$distributions$Distribution$sample

• tfp$distributions$Distribution$mean

• tfp$distributions$Distribution$mode

• tfp$distributions$OneHotCategorical$logits

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

Arguments

object	Model or layer object
event_size	Scalar integer representing the size of single draw from this distribution.
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.

Value

a Keras layer

See also

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

Other distribution_layers: layer_categorical_mixture_of_one_hot_categorical(), 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()