Regularizer that adds a KL divergence penalty to the model loss
When using Monte Carlo approximation (e.g., use_exact = FALSE), it is presumed that the input
distribution's concretization (i.e., tf$convert_to_tensor(distribution)) corresponds to a random
sample. To override this behavior, set test_points_fn.
layer_kl_divergence_regularizer( object, distribution_b, use_exact_kl = FALSE, test_points_reduce_axis = NULL, test_points_fn = tf$convert_to_tensor, weight = NULL, ... )
object |
Model or layer object |
distribution_b |
Distribution instance corresponding to b as in |
use_exact_kl |
Logical indicating if KL divergence should be
calculated exactly via |
test_points_reduce_axis |
Integer vector or scalar representing dimensions over which to reduce_mean while calculating the Monte Carlo approximation of the KL divergence. As is with all tf$reduce_* ops, NULL means reduce over all dimensions; () means reduce over none of them. Default value: () (i.e., no reduction). |
test_points_fn |
A callable taking a |
weight |
Multiplier applied to the calculated KL divergence for each Keras batch member. Default value: NULL (i.e., do not weight each batch member). |
... |
Additional arguments passed to |
a Keras 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_mixture_logistic(),
layer_mixture_normal(),
layer_mixture_same_family(),
layer_multivariate_normal_tri_l(),
layer_one_hot_categorical()
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