layer_dense_variational | R Documentation |
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, ... )
object |
What to compose the new
|
units |
Positive integer, dimensionality of the output space. |
make_posterior_fn |
function taking |
make_prior_fn |
function taking |
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 |
use_bias |
Whether or not the dense layers constructed in this layer
should have a bias term. See |
... |
Additional keyword arguments passed to the |
a Keras layer
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()
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