BLS宽度学习matlab代码.zip

%%%%%%%%%%%%%%%%%%%%%%%%This is the demo for the bls models including the %%%%%%%%%%%%%%%%%%%%%%%%proposed incremental learning algorithms when the %%%%%%%%%%%%%%%%%%%%%%%%memory of PC is not sufficient%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%load the dataset MNIST dataset%%%%%%%%%%%%%%%%%%%% clear; warning off all; format compact; load mnist; %%%%%%%%%%%%%%%the samples from the data are normalized and the lable data %%%%%%%%%%%%%%%train_y and test_y are reset as N*C matrices%%%%%%%%%%%%%% train_x = double(train_x/255); train_y = double(train_y); % test_x = double(train_x/255); % test_y = double(train_y); test_x = double(test_x/255); test_y = double(test_y); train_y=(train_y-1)*2+1; test_y=(test_y-1)*2+1; assert(isfloat(train_x), 'train_x must be a float'); assert(all(train_x(:)>=0) && all(train_x(:)<=1), 'all data in train_x must be in [0:1]'); assert(isfloat(test_x), 'test_x must be a float'); assert(all(test_x(:)>=0) && all(test_x(:)<=1), 'all data in test_x must be in [0:1]'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% disp('Press any key to run the one shot BLS demo'); pause %%%%%%%%%%%%%%%%%%%%This is the model of broad learning sytem with%%%%%% %%%%%%%%%%%%%%%%%%%%one shot structrue%%%%%%%%%%%%%%%%%%%%%%%% C = 2^-30; s = .8;%the l2 regularization parameter and the shrinkage scale of the enhancement nodes N11=10;%feature nodes per window N2=10;% number of windows of feature nodes N33=50;% number of enhancement nodes epochs=1;% number of epochs train_err=zeros(1,epochs);test_err=zeros(1,epochs); train_time=zeros(1,epochs);test_time=zeros(1,epochs); % rand('state',67797325) % 12000 %%%%% The random seed recommended by the % reference HELM [10]. N1=N11; N3=N33; for j=1:epochs [TrainingAccuracy,TestingAccuracy,Training_time,Testing_time] = bls_train(train_x,train_y,test_x,test_y,s,C,N1,N2,N3); train_err(j)=TrainingAccuracy * 100; test_err(j)=TestingAccuracy * 100; train_time(j)=Training_time; test_time(j)=Testing_time; end save ( ['mnist_result_oneshot_' num2str(N3)], 'train_err', 'test_err', 'train_time', 'test_time'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% disp('Press any key to run the one shot BLS demo with BP algorithm'); pause %%%%%%%%%%%%%%%%%%%This is the model of broad learning system for one%%%%%% %%%%%%%%%%%%%%%%%%%shot structrue with fine tuning under BP algorithm%%%%%%%%%%%%%%%%%%%%%%%% C = 2^-30; s = .8;%the l2 regularization parameter and the shrinkage scale of the enhancement nodes N11=10;%feature nodes per window N2=10;% number of windows of feature nodes N33=1000;% number of enhancement nodes epochs=1;% number of epochs train_err=zeros(1,epochs);test_err=zeros(1,epochs); train_time=zeros(1,epochs);test_time=zeros(1,epochs); % rand('state',67797325) % 12000 %%%%% The random seed recommended by the % reference HELM [10]. N1=N11; N3=N33; for j=1:epochs [TrainingAccuracy,TestingAccuracy,Training_time,Testing_time] = bls_train_bp(train_x,train_y,test_x,test_y,s,C,N1,N2,N3); train_err(j)=TrainingAccuracy * 100; test_err(j)=TestingAccuracy * 100; train_time(j)=Training_time; test_time(j)=Testing_time; end save ( ['mnist_result_bp_' num2str(N3)], 'train_err', 'test_err', 'train_time', 'test_time'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% disp('Press any key to run the BLS demo for increment of m enhancement nodes'); pause %%%%%%%%%%%%%%%%%%%%This is the model of broad learning system for%%%%%% %%%%%%%%%%%%%%%%%%%%increment of m enhancement nodes%%%%%%%%%%%%%%%%%%%%%%%% C = 2^-30; s = .8;%the l2 regularization parameter and the shrinkage scale of the enhancement nodes N11=10;%feature nodes per window N2=10;% number of windows of feature nodes N33=300;% number of enhancement nodes m=50;%number of enhancement nodes in each incremental learning l=5;% steps of incremental learning epochs=1;% number of epochs % rand('state',67797325) % 12000 %%%%% The random seed recommended by the % reference HELM [10]. N1=N11; N3=N33; [train_err,test_err,train_time,test_time,Testing_time,Training_time] = bls_train_enhance(train_x,train_y,test_x,test_y,s,C,N1,N2,N3,epochs,m,l); save ( ['mnist_result_enhance'], 'train_err', 'test_err', 'train_time', 'test_time','Testing_time','Training_time'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% disp('Press any key to run the BLS demo for increment of m2+m3 enhancement nodes and m1 feature nodes'); pause %%%%%%%%%%%%%%%%%%%%This is the model of broad learning system for%%%%%% %%%%%%%%%%%%%%%%%%%%increment of m2+m3 enhancement nodes and m1 feature nodes %%%%%%%%%%%%%%%%%%%%%%%% C = 2^-30;%the regularization parameter for sparse regualarization s = .8;%the shrinkage parameter for enhancement nodes N11=10;%feature nodes per window N2=6;% number of windows of feature nodes N33=300;% number of enhancement nodes epochs=1;% number of epochs m1=10;%number of feature nodes per increment step m2=15;%number of enhancement nodes related to the incremental feature nodes per increment step m3=35;%number of enhancement nodes in each incremental learning l=5;% steps of incremental learning train_err_t=zeros(epochs,l);test_err_t=zeros(epochs,l);train_time_t=zeros(epochs,l);test_time_t=zeros(epochs,l); Testing_time_t=zeros(epochs,1);Training_time_t=zeros(epochs,1); % rand('state',67797325) % 12000 %%%%% The random seed recommended by the % reference HELM [10]. N1=N11; N3=N33; for i=1:epochs [train_err,test_err,train_time,test_time,Testing_time,Training_time] = bls_train_enhancefeature(train_x,train_y,test_x,test_y,s,C,N1,N2,N3,m1,m2,m3,l); train_err_t(i,:)=train_err;test_err_t(i,:)=test_err;train_time_t(i,:)=train_time;test_time_t(i,:)=test_time; Testing_time_t(i)=Testing_time;Training_time_t(i)=Training_time; end save ( [ 'mnist_result_enhancefeature'], 'train_err_t', 'test_err_t', 'train_time_t', 'test_time_t','Testing_time_t','Training_time_t'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% disp('Press any key to run the BLS demo for increment of m input patterns'); pause %%%%%%%%%%%%%%%%%%%%This is the model of broad learning system for%%%%%% %%%%%%%%%%%%%%%%%%%% increment of m input patterns %%%%%%%%%%%%%%%%%%%%%%%% train_xf=train_x;train_yf=train_y; train_x=train_xf(1:10000,:);train_y=train_yf(1:10000,:); % the selected input patterns of int incremental learning C = 2^-30;%the regularization parameter for sparse regualarization s = .8;%the shrinkage parameter for enhancement nodes N11=10;%feature nodes per window N2=10;% number of windows of feature nodes N33=500;% number of enhancement nodes epochs=1;% number of epochs m=10000;%number of added input patterns per increment step l=6;% steps of incremental learning train_err_t=zeros(epochs,l);test_err_t=zeros(epochs,l);train_time_t=zeros(epochs,l);test_time_t=zeros(epochs,l); Testing_time_t=zeros(epochs,1);Training_time_t=zeros(epochs,1); % rand('state',67797325) % 12000 %%%%% The random seed recommended by the % reference HELM [10]. N1=N11; N3=N33; for i=1:epochs [train_err,test_err,train_time,test_time,Testing_time,Training_time] = bls_train_input(train_x,train_y,train_xf,train_yf,test_x,test_y,s,C,N1,N2,N3,m,l); train_err_t(i,:)=train_err;test_err_t(i,:)=test_err;train_time_t(i,:)=train_time;test_time_t(i,:)=test_time; Testing_time_t(i)=Testing_time;Training_time_t(i)=Training_time; end save ( [ 'mnist_result_input'], 'train_err_t', 'test_err_t', 'train_time_t', 'test_time_t','Testing_time_t','Training_time_t'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% disp('Press any key to run the BLS