www.gusucode.com > 有监督的 CNN 网络完成对MNIST 数字的识别 > 有监督的 CNN 网络完成对MNIST 数字的识别/CNN—卷积神经网络数字识别/@cnn/train.m
function [cnet] = train(cnet,Ip,labels,I_testp, labels_test)
%TRAIN train convolutional neural network using stochastic Levenberg-Marquardt
%
% Syntax
%
% [cnet, perf_plot] = train(cnet,Ip,labels,I_testp, labtst)
%
% Description
% Input:
% cnet - Convolutional neural network class object
% Ip - cell array, containing preprocessed images of handwriten digits
% labels - cell array of labels, corresponding to images
% I_testp - cell array, containing preprocessed images of handwriten
% digits of test set
% labtst - cell array of labels, corresponding to images of test set
% Output:
% cnet - trained convolutional neural network
% perf_plot - performance data
%Initialize GUI
h_gui = cnn_gui();
%Progress bars
h_HessPatch = findobj(h_gui,'Tag','HessPatch');
h_HessEdit = findobj(h_gui,'Tag','HessPrEdit');
h_TrainPatch = findobj(h_gui,'Tag','TrainPatch');
h_TrainEdit = findobj(h_gui,'Tag','TrainPrEdit');
%Axes
h_MCRaxes = findobj(h_gui,'Tag','MCRaxes');
h_RMSEaxes = findobj(h_gui,'Tag','RMSEaxes');
%Info textboxes
h_EpEdit = findobj(h_gui,'Tag','EpEdit');
h_ItEdit = findobj(h_gui,'Tag','ItEdit');
h_RMSEedit = findobj(h_gui,'Tag','RMSEedit');
h_MCRedit = findobj(h_gui,'Tag','MCRedit');
h_TetaEdit = findobj(h_gui,'Tag','TetaEdit');
%Buttons
h_AbortButton = findobj(h_gui,'Tag','AbortButton');
tic; %Fix the start time
perf_plot = []; %Array for storing performance data
%Coefficient, determining the running estimation of diagonal
%Hessian approximation leak
gamma = 0.1;
%Number of training patterns
numPats = length(Ip);
%Calculate the size of network
net_size = cnn_size(cnet);
ii = sparse(1:net_size,1:net_size,ones(1,net_size));
jj = sparse(0);
%Initial MCR calculation
mcr(1)=calcMCR(cnet,I_testp, labels_test, 1:100);
plot(h_MCRaxes,mcr);
SetText(h_MCRedit,mcr(end));
if(cnet.HcalcMode == 1)
for i=1:cnet.HrecalcSamplesNum
%Setting the right output to 1, others to -1
d = -ones(1,10);
d(labels(i)+1) = 1;
%Simulating
[out, cnet] = sim(cnet,Ip{i});
%Calculate the error
e = out-d;
%Calculate Jacobian times error, or in other words calculate
%gradient
[cnet,je] = calcje(cnet,e);
[cnet,hx] = calchx(cnet);
jj = jj+diag(sparse(hx));
SetHessianProgress(h_HessPatch,h_HessEdit,i/cnet.HrecalcSamplesNum);
end
%Averaging
jj = jj/cnet.HrecalcSamplesNum;
end
%For all epochs
for t=1:cnet.epochs
SetText(h_EpEdit,t);
SetTextHP(h_TetaEdit,cnet.teta);
%For all patterns
for n=1:numPats
%Setting the right output to 1, others to -1
d = -ones(1,10);
d(labels(n)+1) = 1;
%Simulating
[out, cnet] = sim(cnet,Ip{n});
%Calculate the error
e = out-d;
%Calculate Jacobian times error, or in other words calculate
%gradient
[cnet,je] = calcje(cnet,e);
%Calculate Hessian diagonal approximation
if(cnet.HcalcMode == 0)
[cnet,hx] = calchx(cnet);
%Calculate the running estimate of Hessian diagonal approximation
jj = gamma*diag(sparse(hx))+sparse((1-gamma)*jj);
end
if(cnet.HcalcMode == 1)
if(mod(t*numPats+n,cnet.Hrecalc)==0) %If it is time to recalculate Hessian
if(n+cnet.HrecalcSamplesNum>numPats)
stInd = numPats-cnet.HrecalcSamplesNum;
else
stInd = n;
end
for i=stInd:stInd+cnet.HrecalcSamplesNum
%Setting the right output to 1, others to -1
d = -ones(1,10);
d(labels(i)+1) = 1;
%Simulating
[out, cnet] = sim(cnet,Ip{i});
%Calculate the error
e = out-d;
%Calculate Jacobian times error, or in other words calculate
%gradient
[cnet,je] = calcje(cnet,e);
[cnet,hx] = calchx(cnet);
jj = jj+diag(sparse(hx));
SetHessianProgress(h_HessPatch,h_HessEdit,(i-stInd)/cnet.HrecalcSamplesNum);
end
%Averaging
jj = jj/cnet.HrecalcSamplesNum;
end
end
%The following is usefull for debugging.
%===========DEBUG
% tmp(1)=check_finit_dif(cnet,1,Ip{n},d,1);
%===========DEBUG
perf(n) = mse(e); %Store the error
if(cnet.HcalcMode == 2) %Gradient descent
dW = cnet.teta*je;
else
%Levenberg-Marquardt
dW = (jj+cnet.mu*ii)\(cnet.teta*je);
end
%Apply calculated weight updates
cnet = adapt_dw(cnet,dW);
%Plot mean of performance for every N patterns
if(n>1)
if(~mod(n-1,200))
mcr = [mcr calcMCR(cnet,I_testp, labels_test, 1:100)];
plot(h_MCRaxes,mcr);
SetText(h_MCRedit,mcr(end));
end
if(~mod(n-1,10))
perf_plot = [perf_plot,mean(sqrt(perf(n-10:n)))];
plot(h_RMSEaxes,perf_plot);
SetText(h_RMSEedit,perf_plot(end));
end
end
SetTrainingProgress(h_TrainPatch,h_TrainEdit,(n+(t-1)*numPats)/(numPats*cnet.epochs));
SetText(h_ItEdit,n);
drawnow;
if(~isempty(get(h_AbortButton,'UserData')))
fprintf('Training aborted \n');
return;
end
end
cnet.teta = cnet.teta*cnet.teta_dec;
end
toc
%Sets Hessian progress
%hp - handle of patch
%hs - handle of editbox
%pr - value from 0 to 1
function SetHessianProgress(hp,hs,pr)
xpatch = [0 pr*100 pr*100 0];
set(hp,'XData',xpatch);
set(hs,'String',[num2str(pr*100,'%5.2f'),'%']);
drawnow;
%Sets Training progress
%hp - handle of patch
%hs - handle of editbox
%pr - value from 0 to 1
function SetTrainingProgress(hp,hs,pr)
xpatch = [0 pr*100 pr*100 0];
set(hp,'XData',xpatch);
set(hs,'String',[num2str(pr*100,'%5.2f'),'%']);
%Set numeric text in the specified edit box
%hs - handle of textbox
%num - number to convert and set
function SetText(hs,num)
set(hs,'String',num2str(num,'%5.2f'));
%Set numeric text in the specified edit box with high preceition
%hs - handle of textbox
%num - number to convert and set
function SetTextHP(hs,num)
set(hs,'String',num2str(num,'%5.3e'));