mnistdeepauto analysis 3

% makebatches.m
% 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.

digitdata=[];   %  concats all digit images in digitdata array
targets=[]; % and adds 10 bits constant label info for each digit to the targets array.
load digit0; digitdata = [digitdata; D]; targets = [targets; repmat([1 0 0 0 0 0 0 0 0 0], size(D,1), 1)];  %targets(5923x10) digitdata(5923x784) D(5923x784)
load digit1; digitdata = [digitdata; D]; targets = [targets; repmat([0 1 0 0 0 0 0 0 0 0], size(D,1), 1)];  %targets(12665x10) digitdata(12665x784) D(6742x784)
load digit2; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 1 0 0 0 0 0 0 0], size(D,1), 1)];
load digit3; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 1 0 0 0 0 0 0], size(D,1), 1)];
load digit4; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 1 0 0 0 0 0], size(D,1), 1)];
load digit5; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 1 0 0 0 0], size(D,1), 1)];
load digit6; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 0 1 0 0 0], size(D,1), 1)];
load digit7; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 0 0 1 0 0], size(D,1), 1)];
load digit8; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 0 0 0 1 0], size(D,1), 1)];
load digit9; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 0 0 0 0 1], size(D,1), 1)];
digitdata = digitdata/255;

totnum=size(digitdata,1); %60000 x 784
fprintf(1, 'Size of the training dataset= %5d \n', totnum);

rand('state',0); %so we know the permutation of the training data
randomorder=randperm(totnum); %random order sequence holder of 60000 digits

numbatches=totnum/100;
numdims  =  size(digitdata,2);  % 784
batchsize = 100;
batchdata = zeros(batchsize, numdims, numbatches); %100x784x600
batchtargets = zeros(batchsize, 10, numbatches); %100x10x600

for b=1:numbatches %600
  batchdata(:,:,b) = digitdata(randomorder(1+(b-1)*batchsize:b*batchsize), :); %100x784x600 batchdata is created according to random order indexes
  batchtargets(:,:,b) = targets(randomorder(1+(b-1)*batchsize:b*batchsize), :); %100x10x600 similar to batchdata
end;

clear digitdata targets;

digitdata=[];
targets=[];

load test0; digitdata = [digitdata; D]; targets = [targets; repmat([1 0 0 0 0 0 0 0 0 0], size(D,1), 1)];
load test1; digitdata = [digitdata; D]; targets = [targets; repmat([0 1 0 0 0 0 0 0 0 0], size(D,1), 1)];
load test2; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 1 0 0 0 0 0 0 0], size(D,1), 1)];
load test3; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 1 0 0 0 0 0 0], size(D,1), 1)];
load test4; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 1 0 0 0 0 0], size(D,1), 1)];
load test5; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 1 0 0 0 0], size(D,1), 1)];
load test6; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 0 1 0 0 0], size(D,1), 1)];
load test7; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 0 0 1 0 0], size(D,1), 1)];
load test8; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 0 0 0 1 0], size(D,1), 1)];
load test9; digitdata = [digitdata; D]; targets = [targets; repmat([0 0 0 0 0 0 0 0 0 1], size(D,1), 1)];
%}
digitdata = digitdata/255;  % 10000,784

totnum=size(digitdata,1); % 10000
%totnum=10000;
fprintf(1, 'Size of the test dataset= %5d \n', totnum);

rand('state',0); %so we know the permutation of the training data
randomorder=randperm(totnum);

numbatches=totnum/100; % 100
%numbatches=100;
numdims  =  size(digitdata,2);  % 784
%mumdims=784;
batchsize = 100;
testbatchdata = zeros(batchsize, numdims, numbatches); % 100, 784, 100
testbatchtargets = zeros(batchsize, 10, numbatches); % 100, 10, 100

for b=1:numbatches
  testbatchdata(:,:,b) = digitdata(randomorder(1+(b-1)*batchsize:b*batchsize), :);
  testbatchtargets(:,:,b) = targets(randomorder(1+(b-1)*batchsize:b*batchsize), :);
end;


clear digitdata targets;


%%% Reset random seeds
rand('state',sum(100*clock));
randn('state',sum(100*clock));