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proto_model={'T_h1_m1';'S_h1_m1';'T_h1_m2';'S_h1_m2';'T_h2_m1';'S_h2_m1'}; |
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nmb_of_proto_models=length(proto_model); |
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featured_model={'model_1';'model_2';'model_3';'model_4';'model_5';'model_6'}; |
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[nmb_of_ft_models,~]=size(featured_model); |
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for ii=1:nmb_of_proto_models |
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md=char(proto_model(ii)); |
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nmb_of_image_set=zeros(1,10); |
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pr_set=zeros(1,10); |
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top_1_set=zeros(1,10); |
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model_accuracy_comparison=zeros(2,nmb_of_ft_models); |
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for fm=1:nmb_of_ft_models |
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for imgnt1kdataset=1:10 |
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reportname1 = sprintf('Model_%s/Evaluation_Data/Model_Accuracy/training_data_batch_%d_feature_module_performance_%s_var.mat',... |
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md,imgnt1kdataset, md); |
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aa=sprintf('classification_data_%d',fm); |
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bb=load(reportname1,aa); |
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c_data=bb.(aa); |
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true_lab=c_data(:,1); |
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pred_lab=c_data(:,2); |
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likelyhood=c_data(:,3); |
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top_1_majority=c_data(1,3); |
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nmb_of_images=length(true_lab); |
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nmb_of_image_set(imgnt1kdataset)=nmb_of_images; |
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idx=(abs(true_lab-pred_lab)==0); |
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aa=sum(1*idx); |
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pr=aa/nmb_of_images*100; |
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pr_set(imgnt1kdataset)=pr; |
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bb=likelyhood(idx); |
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cc=length(bb); |
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idx1=(bb==top_1_majority); |
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aa=sum(idx1*1); |
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top_1=aa/cc*100; |
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top_1_set(imgnt1kdataset)=top_1; |
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end |
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model_accuracy_comparison(1,fm)=nmb_of_image_set*pr_set'/sum(nmb_of_image_set); |
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model_accuracy_comparison(2,fm)=nmb_of_image_set*top_1_set'/sum(nmb_of_image_set); |
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end |
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assignin('base',md, model_accuracy_comparison') |
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end |
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%% |
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tb1=table(featured_model,T_h1_m1,S_h1_m1,T_h1_m2,S_h1_m2,T_h2_m1,S_h2_m1) |
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%% |
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table(featured_model,T_h1_m1,T_h1_m2,T_h2_m1) |
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table(featured_model,S_h1_m1,S_h1_m2,S_h2_m1) |
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%% |
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for fm=1:nmb_of_ft_models |
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%%%%%%% |
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nmb_of_image_set=zeros(1,10); |
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pr_set=zeros(1,10); |
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top_1_set=zeros(1,10); |
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model_accuracy_comparison_2=zeros(2,nmb_of_ft_models); |
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for ii=1:nmb_of_proto_models |
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md=char(proto_model(ii)); |
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for imgnt1kdataset=1:10 |
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reportname1 = sprintf('Model_ |
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md,imgnt1kdataset, md); |
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aa=sprintf('classification_data_%d',fm); |
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bb=load(reportname1,aa); |
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c_data=bb.(aa); |
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true_lab=c_data(:,1); |
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pred_lab=c_data(:,2); |
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likelyhood=c_data(:,3); |
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top_1_majority=c_data(1,3); |
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nmb_of_images=length(true_lab); |
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nmb_of_image_set(imgnt1kdataset)=nmb_of_images; |
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idx=(abs(true_lab-pred_lab)==0); |
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aa=sum(1*idx); |
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pr=aa/nmb_of_images*100; |
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pr_set(imgnt1kdataset)=pr; |
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bb=likelyhood(idx); |
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cc=length(bb); |
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idx1=(bb==top_1_majority); |
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aa=sum(idx1*1); |
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top_1=aa/cc*100; |
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top_1_set(imgnt1kdataset)=top_1; |
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end |
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model_accuracy_comparison_2(1,ii)=nmb_of_image_set*pr_set'/sum(nmb_of_image_set); |
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model_accuracy_comparison_2(2,ii)=nmb_of_image_set*top_1_set'/sum(nmb_of_image_set); |
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end |
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assignin('base',char(featured_model(fm)), round(model_accuracy_comparison_2,3)') |
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end |
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tb2=table(proto_model,model_1,model_2,model_3,model_4,model_5,model_6) |
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