I understand that you would like to set up your system for cross validation when input is given as a minibatch queue.
A simple way you could achieve this is by manipulating the data you feed to the network within a loop. At every iteration you can create a folder structure which contains the "train folder" of the 9 chosen data points and the "test folder" containing the last data point. Here is conceptual working of the same:
mainFolder = 'master_folder_path'; % Replace with the path to your main folder
subfolders = dir(mainFolder);
subfolders = subfolders([subfolders.isdir]);
subfolders = subfolders(~ismember({subfolders.name}, {'.', '..'}));
numSubfolders = numel(subfolders);
combinations = nchoosek(1:numSubfolders, numSubfolders-1); % As we need 1 data for test set, can be modified
tDir = tempdir;
for i = 1:size(combinations, 1)
combinationFolder = fullfile(tDir, sprintf('Batch_%d', i));
if ~exist(combinationFolder, 'dir')
mkdir(combinationFolder);
end
trainFolders = combinations(i, :);
testFolder = setdiff(1:numSubfolders, trainFolders);
for j = 1:numSubfolders
if ismember(j, trainFolders)
folderName = 'train';
else
folderName = 'test';
end
subfolderPath = fullfile(combinationFolder, folderName, subfolders(j).name);
if ~exist(subfolderPath, 'dir')
mkdir(subfolderPath);
end
end
trainFol = combinationFolder+"\train";
testFol = combinationFolder+"\test";
% -----------------------------------
% Train using the newly created folder
% -----------------------------------
rmdir(combinationFolder,'s') % Delete the folder once used
end
Further, you may modify the function "nchoosek()" to use training data of other sizes.
Hope this helps!
