madapter.RMSProp {BayesFluxR} | R Documentation |
Use RMSProp as a preconditions/mass matrix adapter. This was proposed in Li, C., Chen, C., Carlson, D., & Carin, L. (2016, February). Preconditioned stochastic gradient Langevin dynamics for deep neural networks. In Thirtieth AAAI Conference on Artificial Intelligence for the use in SGLD and related methods.
madapter.RMSProp(adapt_steps, lambda = 1e-05, alpha = 0.99)
adapt_steps |
number of adaptation steps |
lambda |
see above paper |
alpha |
see above paper |
list with 'juliavar' and 'juliacode' and all given arguments
## Not run:
## Needs previous call to `BayesFluxR_setup` which is time
## consuming and requires Julia and BayesFlux.jl
BayesFluxR_setup(installJulia=TRUE, seed=123)
net <- Chain(Dense(5, 1))
like <- likelihood.feedforward_normal(net, Gamma(2.0, 0.5))
prior <- prior.gaussian(net, 0.5)
init <- initialise.allsame(Normal(0, 0.5), like, prior)
x <- matrix(rnorm(5*100), nrow = 5)
y <- rnorm(100)
bnn <- BNN(x, y, like, prior, init)
madapter <- madapter.RMSProp(100)
sampler <- sampler.GGMC(madapter = madapter)
ch <- mcmc(bnn, 10, 1000, sampler)
## End(Not run)