Dense Variational Layer

This layer uses variational inference to fit a "surrogate" posterior to the distribution over both the kernel matrix and the bias terms which are otherwise used in a manner similar to layer_dense(). This layer fits the "weights posterior" according to the following generative process:

[K, b] ~ Prior()
M = matmul(X, K) + b
Y ~ Likelihood(M)

layer_dense_variational(
  object,
  units,
  make_posterior_fn,
  make_prior_fn,
  kl_weight = NULL,
  kl_use_exact = FALSE,
  activation = NULL,
  use_bias = TRUE,
  ...
)

Arguments

object	Model or layer object
units	Positive integer, dimensionality of the output space.
make_posterior_fn	function taking tf$size(kernel), tf$size(bias), dtype and returns another callable which takes an input and produces a tfd$Distribution instance.
make_prior_fn	function taking tf$size(kernel), tf$size(bias), dtype and returns another callable which takes an input and produces a tfd$Distribution instance.
kl_weight	Amount by which to scale the KL divergence loss between prior and posterior.
kl_use_exact	Logical indicating that the analytical KL divergence should be used rather than a Monte Carlo approximation.
activation	An activation function. See keras::layer_dense. Default: NULL.
use_bias	Whether or not the dense layers constructed in this layer should have a bias term. See keras::layer_dense. Default: TRUE.
...	Additional keyword arguments passed to the keras::layer_dense constructed by this layer.

Value

a Keras layer

See also

Other layers: layer_autoregressive(), layer_conv_1d_flipout(), layer_conv_1d_reparameterization(), layer_conv_2d_flipout(), layer_conv_2d_reparameterization(), layer_conv_3d_flipout(), layer_conv_3d_reparameterization(), layer_dense_flipout(), layer_dense_local_reparameterization(), layer_dense_reparameterization(), layer_variable()