The BayesianDense layer is a Dense layer parameterized by a weight distribution, instead of a point estimate. Each BayesianDense layer learns a Gaussian distribution over weights and biases that can be regularized.
VariationalRegularizer is an exemplary regularizer calculating -0.5 * mean(1 + p - K.exp(p)) where p is log of sigma squared. This is just a simple regularizer. Please experiment and let me know any interesting variations.
My implementation of the following paper:
Blundell et. al., Weight Uncertainty in Neural Networks, https://arxiv.org/pdf/1505.05424.pdf
