Split a dataset into training set and test set using the cross-validation principle

Hello everyone,

I have a dataset set of about 50 images, and I would like to split the dataset into training and test sets. I would like to do it in the way as cross-validation. That is, I would like to split the data into 5 equivalent subsets. Then, four of the subsets would be used as training data and the remaining one subset for testing. Finally, I would have five sets of experimental data comprising each a training set and a test set. I can perform this task online while training the network using some built-in functions. However, in this scenario, I would like to split the data offline (before the training) for conducting some experiments. Given my poor programming skills, I am unable to implement. Please, how can I achieve this? Any suggestions and comments would be highly appreciated.

Answer 1

[yanqi liu]

please use crossvalind，reference resources

such as

clc; clear all; close all;
% total 50
% split to 5
indices = crossvalind('Kfold', 50, 5);
for i=1:5
    fprintf('\ndemo %d\n', i);
    ind1=find(indices==i);
    ind2=find(indices~=i);
    fprintf('\nuse for train\n');
    disp(ind2(:)');
    fprintf('\nuse for test\n');
    disp(ind1(:)');
end

demo 1

use for train
  列 1 至 10

     1     2     3     4     5     6     7     8     9    10

  列 11 至 20

    11    15    17    18    19    21    22    23    24    25

  列 21 至 30

    26    28    29    31    32    33    36    37    38    39

  列 31 至 40

    40    41    42    43    44    45    46    47    48    49


use for test
    12    13    14    16    20    27    30    34    35    50