tfprobability (version 0.15.2)

layer_independent_normal: An independent Normal Keras layer.

Description

An independent Normal Keras layer.

Usage

layer_independent_normal(
  object,
  event_shape,
  convert_to_tensor_fn = tfp$distributions$Distribution$sample,
  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_shape

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.

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.

See Also

Other distribution_layers: layer_categorical_mixture_of_one_hot_categorical(), layer_distribution_lambda(), layer_independent_bernoulli(), layer_independent_logistic(), 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()

Examples

if (FALSE) {
library(keras)
py_require_legacy_keras()
input_shape <- c(28, 28, 1)
encoded_shape <- 2
n <- 2
model <- keras_model_sequential(
  list(
    layer_input(shape = input_shape),
    layer_flatten(),
    layer_dense(units = n),
    layer_dense(units = params_size_independent_normal(encoded_shape)),
    layer_independent_normal(event_shape = encoded_shape)
    )
  )
}