layer_one_hot_categorical {tfprobability} | R Documentation |
A d
-variate OneHotCategorical Keras layer from d
params.
Description
Typical choices for convert_to_tensor_fn
include:
-
tfp$distributions$Distribution$sample
-
tfp$distributions$Distribution$mean
-
tfp$distributions$Distribution$mode
-
tfp$distributions$OneHotCategorical$logits
Usage
layer_one_hot_categorical(
object,
event_size,
convert_to_tensor_fn = tfp$distributions$Distribution$sample,
sample_dtype = NULL,
validate_args = FALSE,
...
)
Arguments
object |
What to compose the new
|
event_size |
Scalar |
convert_to_tensor_fn |
A callable that takes a tfd$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 |
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()