This layer implements the Bayesian variational inference analogue to a dense layer by assuming the kernel and/or the bias are drawn from distributions.

layer_dense_flipout(
object,
units,
activation = NULL,
activity_regularizer = NULL,
trainable = TRUE,
kernel_posterior_fn = tfp$layers$util$default_mean_field_normal_fn(), kernel_posterior_tensor_fn = function(d) d %>% tfd_sample(), kernel_prior_fn = tfp$layers$util$default_multivariate_normal_fn,
kernel_divergence_fn = function(q, p, ignore) tfd_kl_divergence(q, p),

a Keras layer

## Details

By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors,

kernel, bias ~ posterior
outputs = activation(matmul(inputs, kernel) + bias)


It uses the Flipout estimator (Wen et al., 2018), which performs a Monte Carlo approximation of the distribution integrating over the kernel and bias. Flipout uses roughly twice as many floating point operations as the reparameterization estimator but has the advantage of significantly lower variance.

The arguments permit separate specification of the surrogate posterior (q(W|x)), prior (p(W)), and divergence for both the kernel and bias distributions.

Upon being built, this layer adds losses (accessible via the losses property) representing the divergences of kernel and/or bias surrogate posteriors and their respective priors. When doing minibatch stochastic optimization, make sure to scale this loss such that it is applied just once per epoch (e.g. if kl is the sum of losses for each element of the batch, you should pass kl / num_examples_per_epoch to your optimizer).

## References

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_local_reparameterization(), layer_dense_reparameterization(), layer_dense_variational(), layer_variable()