Here are my weights between VISIBLE and HIDDEN units
# h : hidden, v : visible ?
gemm!('N', 'T', lr, h_neg, v_neg, 0.0, rbm.dW)
gemm!('N', 'T', lr, h_pos, v_pos, -1.0, rbm.dW)
this is the code for standard weights updating .
now l want to modify this two functions to add L2 regularization . How can
l do that efficiently ? any ideas !!
the change to make and to add is between the two functions :
gemm!('N', 'T', lr, h_neg, v_neg, 0.0, rbm.dW)
*# l think it's here to do the regularization *
gemm!('N', 'T', lr, h_pos, v_pos, -1.0, rbm.dW)
thanks for help l'm dummy with this concepts
On Tue, Jul 19, 2016 at 8:42 AM, Andrei Zh <[email protected]>
wrote:
> 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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