Boltzmann.jl <https://github.com/dfdx/Boltzmann.jl> supports both - L1 and
L2 regularization (although it's not documented yet):
# install if needed
Pkg.add("Boltzmann")
using Boltzmann
# create dataset
X = randn(100, 2000)
X = (X + abs(minimum(X))) / (maximum(X) - minimum(X))
rbm = BernoulliRBM(100, 50)
# fit with L2 regularization (weight decay)
fit(rbm, X; weight_decay_kind=:l2, weight_decay_rate=0.9)
Note, that observations should be on columns, which goes along with many
other machine learning packages, but may be different from statistical
packages that often put observations on rows.
On Monday, July 18, 2016 at 6:22:19 PM UTC+3, Ahmed Mazari wrote:
>
> Hello;
>
> l'm looking for practical resources and code with julia for restricted
> boltzman machine with L2 regularization.
>
> Thanks for your helps
>
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