"splitEachLabel" built-in function does not really randomize the picture distribution?
5 visualizzazioni (ultimi 30 giorni)
Mostra commenti meno recenti
cui,xingxing
il 26 Feb 2018
Commentato: cui,xingxing
il 24 Ott 2023
When I use R2017b to do deep learning classification, the imageDatasotre object is divided into training and test set,whether or not to specify the number or proportion, 'splitEachLabel' optional parameters specified as 'randomized', the training set inside the picture is not randomly arranged, and why?
as the document said: https://cn.mathworks.com/help/nnet/examples/create-simple-deep-learning-network-for-classification.html
digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
'nndatasets','DigitDataset');
digitData = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');
trainingNumFiles = 750;
rng(1) % For reproducibility
[trainDigitData,testDigitData] = splitEachLabel(digitData, ...
trainingNumFiles,'randomize');
When you open "trainDigitData.Files" and "trainDigitData.Labels" in a workspace, they do not disrupt the order?
0 Commenti
Risposta accettata
Wentao Du
il 1 Mar 2018
Here the order you see will not be completely different because the labels of "digitData" are in order (from 0 to 9). To observe the effect of "randomize" parameter, you can run
[trainDigitData,valDigitData] = splitEachLabel(digitData,trainNumFiles,'randomize');
multiple times and will find the distribution of actual image files keeps changing.
0 Commenti
Più risposte (1)
cui,xingxing
il 1 Mar 2018
2 Commenti
debojit sharma
il 8 Lug 2023
Since,it may be risky to do a standard random train/test split when having strong class imbalance.Because very small number of positive cases, we might end up with a train and test set that have very different class distributions. We may even end up with close to zero positive cases in our test set. So, is there anyfunction to do stratified sampling during train/test split that avoids disturbing class balance in our samples in MatLab @cui @Wentao Du . Like the following code in python:
from sklearn.model_selection import train_test_split
train, test = train_test_split(data, test_size = 0.3, stratify=data.buy)
Vedere anche
Categorie
Scopri di più su 图像深度学习 in Help Center e File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!