opt.Descent: Standard gradient descent

In enweg/BFluxR: Implementation of Bayesian Neural Networks

Standard gradient descent

Description

Standard gradient descent

Usage

opt.Descent(eta = 0.1)

Arguments

eta

stepsize

Value

list containing

• 'julivar' - julia variable holding the optimiser

• 'juliacode' - string representation

Examples

## 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)
  find_mode(bnn, opt.Descent(1e-5), 10, 100)

## End(Not run)

enweg/BFluxR documentation built on Jan. 27, 2024, 6:43 p.m.