layer_mixture_normal {tfprobability}

A mixture distribution Keras layer, with independent normal components.

Description

A mixture distribution Keras layer, with independent normal components.

Usage

layer_mixture_normal(
  object,
  num_components,
  event_shape = list(),
  convert_to_tensor_fn = tfp$distributions$Distribution$sample,
  validate_args = FALSE,
  ...
)

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.

num_components

Number of component distributions in the mixture distribution.

event_shape

integer vector Tensor representing the shape 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.

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. @param ... Additional arguments passed to args of keras::create_layer.

...

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_same_family(), layer_multivariate_normal_tri_l(), layer_one_hot_categorical()