% Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying that the original programs are available from our
% web page.
% The programs and documents are distributed without any warranty, express or
% implied. As the programs were written for research purposes only, they have
% not been tested to the degree that would be advisable in any important
% application. All use of these programs is entirely at the user's own risk.
% This program fine-tunes an autoencoder with backpropagation.
% Weights of the autoencoder are going to be saved in mnist_weights.mat
% and trainig and test reconstruction errors in mnist_error.mat
% You can also set maxepoch, default value is 200 as in our paper.
maxepoch=200;
fprintf(1,'\nFine-tuning deep autoencoder by minimizing cross entropy error. \n');
fprintf(1,'60 batches of 1000 cases each. \n');
load mnistvh
load mnisthp
load mnisthp2
load mnistpo
makebatches;
[numcases numdims numbatches]=size(batchdata);
N=numcases;
%%%% PREINITIALIZE WEIGHTS OF THE AUTOENCODER %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
w1=[vishid; hidrecbiases];
w2=[hidpen; penrecbiases];
w3=[hidpen2; penrecbiases2];
w4=[hidtop; toprecbiases];
w5=[hidtop'; topgenbiases];
w6=[hidpen2'; hidgenbiases2];
w7=[hidpen'; hidgenbiases];
w8=[vishid'; visbiases];
%%%%%%%%%% END OF PREINITIALIZATIO OF WEIGHTS %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
l1=size(w1,1)-1;
l2=size(w2,1)-1;
l3=size(w3,1)-1;
l4=size(w4,1)-1;
l5=size(w5,1)-1;
l6=size(w6,1)-1;
l7=size(w7,1)-1;
l8=size(w8,1)-1;
l9=l1;
test_err=[];
train_err=[];
for epoch = 1:maxepoch
%%%%%%%%%%%%%%%%%%%% COMPUTE TRAINING RECONSTRUCTION ERROR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
err=0;
[numcases numdims numbatches]=size(batchdata);
N=numcases;
for batch = 1:numbatches
data = [batchdata(:,:,batch)];
data = [data ones(N,1)];
w1probs = 1./(1 + exp(-data*w1)); w1probs = [w1probs ones(N,1)];
w2probs = 1./(1 + exp(-w1probs*w2)); w2probs = [w2probs ones(N,1)];
w3probs = 1./(1 + exp(-w2probs*w3)); w3probs = [w3probs ones(N,1)];
w4probs = w3probs*w4; w4probs = [w4probs ones(N,1)];
w5probs = 1./(1 + exp(-w4probs*w5)); w5probs = [w5probs ones(N,1)];
w6probs = 1./(1 + exp(-w5probs*w6)); w6probs = [w6probs ones(N,1)];
w7probs = 1./(1 + exp(-w6probs*w7)); w7probs = [w7probs ones(N,1)];
dataout = 1./(1 + exp(-w7probs*w8));
err= err + 1/N*sum(sum( (data(:,1:end-1)-dataout).^2 ));
end
train_err(epoch)=err/numbatches;
%%%%%%%%%%%%%% END OF COMPUTING TRAINING RECONSTRUCTION ERROR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% DISPLAY FIGURE TOP ROW REAL DATA BOTTOM ROW RECONSTRUCTIONS %%%%%%%%%%%%%%%%%%%%%%%%%
fprintf(1,'Displaying in figure 1: Top row - real data, Bottom row -- reconstructions \n');
output=[];
for ii=1:15
output = [output data(ii,1:end-1)' dataout(ii,:)'];
end
if epoch==1
close all
figure('Position',[100,600,1000,200]);
else
figure(1)
end
mnistdisp(output);
drawnow;
%%%%%%%%%%%%%%%%%%%% COMPUTE TEST RECONSTRUCTION ERROR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[testnumcases testnumdims testnumbatches]=size(testbatchdata);
N=testnumcases;
err=0;
for batch = 1:testnumbatches
data = [testbatchdata(:,:,batch)];
data = [data ones(N,1)];
w1probs = 1./(1 + exp(-data*w1)); w1probs = [w1probs ones(N,1)];
w2probs = 1./(1 + exp(-w1probs*w2)); w2probs = [w2probs ones(N,1)];
w3probs = 1./(1 + exp(-w2probs*w3)); w3probs = [w3probs ones(N,1)];
w4probs = w3probs*w4; w4probs = [w4probs ones(N,1)];
w5probs = 1./(1 + exp(-w4probs*w5)); w5probs = [w5probs ones(N,1)];
w6probs = 1./(1 + exp(-w5probs*w6)); w6probs = [w6probs ones(N,1)];
w7probs = 1./(1 + exp(-w6probs*w7)); w7probs = [w7probs ones(N,1)];
dataout = 1./(1 + exp(-w7probs*w8));
err = err + 1/N*sum(sum( (data(:,1:end-1)-dataout).^2 ));
end
test_err(epoch)=err/testnumbatches;
fprintf(1,'Before epoch %d Train squared error: %6.3f Test squared error: %6.3f \t \t \n',epoch,train_err(epoch),test_err(epoch));
%%%%%%%%%%%%%% END OF COMPUTING TEST RECONSTRUCTION ERROR %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tt=0;
for batch = 1:numbatches/10
fprintf(1,'epoch %d batch %d\r',epoch,batch);
%%%%%%%%%%% COMBINE 10 MINIBATCHES INTO 1 LARGER MINIBATCH %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tt=tt+1;
data=[];
for kk=1:10
data=[data
batchdata(:,:,(tt-1)*10+kk)];
end
%%%%%%%%%%%%%%% PERFORM CONJUGATE GRADIENT WITH 3 LINESEARCHES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
max_iter=3;
VV = [w1(:)' w2(:)' w3(:)' w4(:)' w5(:)' w6(:)' w7(:)' w8(:)']';
Dim = [l1; l2; l3; l4; l5; l6; l7; l8; l9];
[X, fX] = minimize(VV,'CG_MNIST',max_iter,Dim,data);
w1 = reshape(X(1:(l1+1)*l2),l1+1,l2);
xxx = (l1+1)*l2;
w2 = reshape(X(xxx+1:xxx+(l2+1)*l3),l2+1,l3);
xxx = xxx+(l2+1)*l3;
w3 = reshape(X(xxx+1:xxx+(l3+1)*l4),l3+1,l4);
xxx = xxx+(l3+1)*l4;
w4 = reshape(X(xxx+1:xxx+(l4+1)*l5),l4+1,l5);
xxx = xxx+(l4+1)*l5;
w5 = reshape(X(xxx+1:xxx+(l5+1)*l6),l5+1,l6);
xxx = xxx+(l5+1)*l6;
w6 = reshape(X(xxx+1:xxx+(l6+1)*l7),l6+1,l7);
xxx = xxx+(l6+1)*l7;
w7 = reshape(X(xxx+1:xxx+(l7+1)*l8),l7+1,l8);
xxx = xxx+(l7+1)*l8;
w8 = reshape(X(xxx+1:xxx+(l8+1)*l9),l8+1,l9);
%%%%%%%%%%%%%%% END OF CONJUGATE GRADIENT WITH 3 LINESEARCHES %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end
save mnist_weights w1 w2 w3 w4 w5 w6 w7 w8
save mnist_error test_err train_err;
end