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wolffd@0
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1 function [cr, cr_] = rbm_fig3(preloaded)
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wolffd@0
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2
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wolffd@0
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3 % cr_: training correct
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wolffd@0
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4 % cr : testing correct
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5 feature_file = 'rel_music_raw_features+simdata_ISMIR12';
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6 vars = whos('-file', feature_file);
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7 A = load(feature_file,vars(1).name,vars(2).name,vars(3).name,vars(4).name);
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8 raw_features = A.(vars(1).name);
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9 indices = A.(vars(2).name);
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10 tst_inx = A.(vars(3).name);
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11 trn_inx = A.(vars(4).name);
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wolffd@0
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12 %
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13 %figure(1); imagesc(raw_features);colorbar;
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14
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15 % load the RBM, or compute a new one
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16 if ischar(preloaded)
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17 mod = load(preloaded);
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18 else
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19 if isstruct(preloaded)
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20 mod = preloaded;
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21 else
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22 % create new RBM
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wolffd@0
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23 mod = new_rbm(preloaded,'grad');
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wolffd@0
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24 end
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25 end
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26 features = logistic(raw_features*mod.W_max{1} + repmat(mod.hB_max{1},size(raw_features,1),1));
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wolffd@0
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27 %figure(2); imagesc(features);colorbar;
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28
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29 num_case = size(trn_inx,1);
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30 [trnd_12 trnd_13] = subspace_distances(trn_inx,features,indices,1,1);
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31 [tstd_12 tstd_13] = subspace_distances(tst_inx,features,indices,1,1);
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32 cr_ = 0; % correct rate for training
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33 cr = 0; % correct rate for testing
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34 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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35 %% CODE HERE %%
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36 %[Ws cr_] = gradient_ascent(trnd_12,trnd_13,0.1,0.1,0.00002); %eNum = 10
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wolffd@0
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37 [Ws cr_] = gradient_ascent(trnd_12,trnd_13,0.05,0.01,0.00002);
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wolffd@0
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38
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wolffd@0
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39 for i = 1:num_case
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wolffd@0
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40 cr = cr + sum((tstd_13{i}-tstd_12{i})*Ws{i}' > 0, 1)/size(tstd_12{i},1);
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wolffd@0
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41 end
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wolffd@0
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42 cr = cr/num_case;
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wolffd@0
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43 %% Check the result
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wolffd@0
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44 fprintf('Gradient RBM Test / Train Result=%f / %f\n',cr*100,cr_*100);
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wolffd@0
|
45 %fprintf('Training=%f Testing=%f\n',cr_,cr);
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wolffd@0
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46
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wolffd@0
|
47 end |
