76 lines
1.8 KiB
Mathematica
76 lines
1.8 KiB
Mathematica
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function final_result=objective_process(datapass,elechoose)
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traindata=datapass{1};
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trainclass=datapass{2};
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testdata=datapass{3};
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lengthdata=datapass{5};
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encode_word=datapass{6};
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flag=datapass{21};
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networkiter=datapass{22};
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datanum=find(elechoose==0);
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traindata_matrix=cell2mat(traindata);
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loc=find(ismember(traindata_matrix,datanum));
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traindata_matrix(loc)=0;
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[rr,cc]=size(traindata_matrix);
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for kr=1:rr
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traindata1{kr}=traindata_matrix(kr,:);
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end
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traindata=traindata1;
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datain_size=1;
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dim_data=50;
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hidden_len=80;
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total_word=encode_word.NumWords;
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no_of_class=3;
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network_layer_infor=[ ...
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sequenceInputLayer(datain_size)
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wordEmbeddingLayer(dim_data,total_word)
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lstmLayer(hidden_len,'OutputMode','last')
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fullyConnectedLayer(no_of_class)
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softmaxLayer
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classificationLayer];
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if(flag==1)
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train_opt=trainingOptions('adam', ...
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'MiniBatchSize',16, ...
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'GradientThreshold',2, ...
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'Shuffle','every-epoch', ...
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'Plots','training-progress', ...
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'Verbose',false);
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else
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train_opt=trainingOptions('adam', ...
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'MiniBatchSize',16, ...
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'GradientThreshold',2, ...
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'Shuffle','every-epoch', ...
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'Plots','none', ...
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'Verbose',false);
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end
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train_opt.MaxEpochs=networkiter;
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net=trainNetwork(traindata,trainclass,network_layer_infor,train_opt);
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resultout=predict(net,testdata);
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[maxval,maxlc]=max(((round(resultout.'))));
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ypred1=categorical(maxlc).';
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sin=double(trainclass);
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sout=double(ypred1);
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tardata=[];
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resdata=[];
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for km=1:length(sin)
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tardata=[tardata double(ismember([1;2;3],sin(km)))];
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resdata=[resdata double(ismember([1;2;3],sout(km)))];
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end
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[~,confu_result]=confusion(tardata,resdata);
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%% find accuracy
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accuracy=(sum(diag(confu_result))/sum(confu_result(:)))*100;
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final_result{1}=accuracy;
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final_result{2}=confu_result;
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final_result{3}=tardata;
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final_result{4}=resdata;
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