A mixture distribution Keras layer, with independent logistic components.
layer_mixture_logistic(
object,
num_components,
event_shape = list(),
convert_to_tensor_fn = tfp$distributions$Distribution$sample,
validate_args = FALSE,
...
)a Keras layer
What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
Number of component distributions in the mixture distribution.
integer vector Tensor representing the shape of single
draw from this distribution.
A callable that takes a tfd$Distribution instance and returns a
tf$Tensor-like object. Default value: tfd$distributions$Distribution$sample.
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.
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_normal(),
layer_mixture_same_family(),
layer_multivariate_normal_tri_l(),
layer_one_hot_categorical()