How do i increse the accuracy for my dataset beyond 78 %?
1 vue (au cours des 30 derniers jours)
Afficher commentaires plus anciens
imds = imageDatastore('ck_dataset', ...
'IncludeSubfolders',true,'LabelSource','foldernames')
[imdstrain, imdsvalid, imdstest]=splitEachLabel(imds,.8, 0.1);
aTest = augmentedImageDatastore([48 48], imdstest, 'ColorPreprocessing','gray2rgb')
CountLabel = imds.countEachLabel
aa=read(imds);
size(aa)
net = alexnet
layers = [
imageInputLayer([48 48 1])
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(5)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'InitialLearnRate',0.001, ...
'MaxEpochs',15, ...
'Shuffle','every-epoch', ...
'ValidationFrequency',50, ...
'MiniBatchSize',32,...
'Verbose',false, ...
'Plots','training-progress');
convnet = trainNetwork(imdstrain,layers,options);
YPred = classify(convnet,imdsvalid);
YValidation = imdsvalid.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)
plotconfusion(YValidation,YPred)
0 commentaires
Réponses (1)
Vidip
le 7 Mai 2024
Improving the accuracy of a model, especially one based on convolutional neural networks (CNNs) like in your case, can be approached from several angles like data augmentation, it artificially increases the size and variability of your training dataset by applying a series of transformations (e.g., rotations, translations, flipping, scaling, etc.), you can use ‘imageDataAugmenter’ as it configures a set of preprocessing options for image augmentation, such as resizing, rotation, and reflection. This can help the model generalize better. Also, consider adding or removing layers accordingly, hyperparameter tuning and transfer learning.
For more information, you can refer to the documentation links below –
0 commentaires
Voir également
Catégories
En savoir plus sur Image Data Workflows dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!