bayes_by_backprop {BayesFluxR} | R Documentation |
This was proposed in Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015, June). Weight uncertainty in neural network. In International conference on machine learning (pp. 1613-1622). PMLR.
bayes_by_backprop(
bnn,
batchsize,
epochs,
mc_samples = 1,
opt = opt.ADAM(),
n_samples_convergence = 10
)
bnn |
a BNN obtained using |
batchsize |
batch size |
epochs |
number of epochs to run for |
mc_samples |
samples to use in each iteration for the MC approximation usually one is enough. |
opt |
An optimiser. These all start with 'opt.'. See for example |
n_samples_convergence |
At the end of each iteration convergence is checked using this many MC samples. |
a list containing
'juliavar' - julia variable storing VI
'juliacode' - julia representation of function call
'params' - variational family parameters for each iteration
'losses' - BBB loss in each iteration
## 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(RNN(5, 1))
like <- likelihood.seqtoone_normal(net, Gamma(2.0, 0.5))
prior <- prior.gaussian(net, 0.5)
init <- initialise.allsame(Normal(0, 0.5), like, prior)
data <- matrix(rnorm(10*1000), ncol = 10)
# Choosing sequences of length 10 and predicting one period ahead
tensor <- tensor_embed_mat(data, 10+1)
x <- tensor[1:10, , , drop = FALSE]
# Last value in each sequence is the target value
y <- tensor[11,,]
bnn <- BNN(x, y, like, prior, init)
vi <- bayes_by_backprop(bnn, 100, 100)
vi_samples <- vi.get_samples(vi, n = 1000)
## End(Not run)