splitting dataset into training set and testing set

12 次查看（过去 30 天）
Munshida P
Munshida P 2020-1-14
评论： Ruaa waleed 2021-12-14
I have 400 images in my dataset(images).I want to split the dataset into 80% for training and 20% for testing.the below attached code works but , test_idx is empty?why?
train_idx contains 320 images.test_idx is empty.
clc
clear
% Load Image dataset
faceDatabase = imageSet('facedatabaseatt','recursive');
%splitting into training and testing sets
N = 400; % number of images
idx = 1:N ;
PD = 0.80 ;
train_idx = idx(1:round(PD*N)); % training indices
test_idx = idx(round(PD*N)+1:end,:) ; % test indices

请先登录，再回答此问题。

采纳的回答

Akira Agata
Akira Agata 2020-1-15
You can split your dataset by using partition function, like:
[setTrain, setTest] = partition(faceDatabase, [0.8, 0.2], 'randomized');
  6 个评论
显示 4更早的评论
Akira Agata
Akira Agata 2020-6-7
>the original dataset ORL Facedatabaseatt contains 40 folders s1,s2,.....,s40(10 images each of 40 persons).
> like that 10x40=400 images.i have to choose 8 images from each person for training and 2 images for testing.
OK. in that case, I would recommend using imageDataStore function, like:
dataFolder = pwd; % if your 40 folders are stored in different folder, please change.
imgSet = imageDatastore(dataFolder,...
'IncludeSubfolders', true,...
'LabelSource', 'foldernames');
% Choose first 8 images from each folder and set them to training dataset, and 2 images for test dataset
[imgSetTrain, imgSetTest] = splitEachLabel(imgSet,0.8);
% If you want to choose 8 and 2 images from each folder randomly, please set 'randomized' option
[imgSetTrain, imgSetTest] = splitEachLabel(imgSet,0.8,'randomized');
Ruaa waleed
Ruaa waleed 2021-12-14
please i split image data base but icant find [imgSetTrain, imgSetTest] how ican make output .txt
clc
F = fullfile('g:','iris-recognition---pm-diseased-human-driven-bsif-main','casia 2 device1');
imds = imageDatastore(F,'IncludeSubfolders',true,'LabelSource','foldernames');
labelCount = countEachLabel (imds)
% Choose first 8 images from each folder and set them to training dataset, and 2 images for test dataset
numTrainFiles= 0.8
% If you want to choose 8 and 2 images from each folder randomly, please set 'randomized' option
[imgSetTrain, imgSetTest] = splitEachLabel(imds,numTrainFiles);

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Biomedical Imaging 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by