View source: R/distribution-layers.R
layer_categorical_mixture_of_one_hot_categorical | R Documentation |
k * (1 + d)
params.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.
layer_categorical_mixture_of_one_hot_categorical( object, event_size, num_components, convert_to_tensor_fn = tfp$distributions$Distribution$sample, sample_dtype = NULL, validate_args = FALSE, ... )
object |
What to compose the new
|
event_size |
Scalar |
num_components |
Scalar |
convert_to_tensor_fn |
A callable that takes a tfd$Distribution instance and returns a
tf$Tensor-like object. Default value: |
sample_dtype |
|
validate_args |
Logical, default FALSE. When TRUE distribution parameters are checked for validity despite possibly degrading runtime performance. When FALSE invalid inputs may silently render incorrect outputs. Default value: FALSE. |
... |
Additional arguments passed to |
Typical choices for convert_to_tensor_fn
include:
tfp$distributions$Distribution$sample
tfp$distributions$Distribution$mean
tfp$distributions$Distribution$mode
a Keras layer
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()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.