camir-aes2014: toolboxes/FullBNT-1.0.7/bnt/CPDs/@mlp_CPD/maximize

first hg version after svn

rev	line source
wolffd@0	1 function CPD = maximize_params(CPD, temp)
wolffd@0	2 % MAXIMIZE_PARAMS Find ML params of an MLP using Scaled Conjugated Gradient (SCG)
wolffd@0	3 % CPD = maximize_params(CPD, temperature)
wolffd@0	4 % temperature parameter is ignored
wolffd@0	5
wolffd@0	6 if ~adjustable_CPD(CPD), return; end
wolffd@0	7 options = foptions;
wolffd@0	8
wolffd@0	9 % options(1) >= 0 means print an annoying message when the max. num. iter. is reached
wolffd@0	10 if CPD.verbose
wolffd@0	11 options(1) = 1;
wolffd@0	12 else
wolffd@0	13 options(1) = -1;
wolffd@0	14 end
wolffd@0	15 %options(1) = CPD.verbose;
wolffd@0	16
wolffd@0	17 options(2) = CPD.wthresh;
wolffd@0	18 options(3) = CPD.llthresh;
wolffd@0	19 options(14) = CPD.max_iter;
wolffd@0	20
wolffd@0	21 dpsz=length(CPD.mlp);
wolffd@0	22
wolffd@0	23 for i=1:dpsz
wolffd@0	24 mask=[];
wolffd@0	25 mask=find(CPD.eso_weights(:,:,i)>0); % for adapting the parameters we use only positive weighted example
wolffd@0	26 if ~isempty(mask),
wolffd@0	27 CPD.mlp{i} = netopt_weighted(CPD.mlp{i}, options, CPD.parent_vals(mask',:), CPD.self_vals(mask',:,i), CPD.eso_weights(mask',:,i), 'scg');
wolffd@0	28
wolffd@0	29 CPD.W1(:,:,i)=CPD.mlp{i}.w1; % update the parameters matrix
wolffd@0	30 CPD.b1(i,:)=CPD.mlp{i}.b1; %
wolffd@0	31 CPD.W2(:,:,i)=CPD.mlp{i}.w2; % update the parameters matrix
wolffd@0	32 CPD.b2(i,:)=CPD.mlp{i}.b2; %
wolffd@0	33 end
wolffd@0	34 end

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