Is it possible to use pretrained network like alexnet with a new dataset that has different category?

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Suheer Ali
Suheer Ali 2018-12-13
回答： Mahmoud Afifi 2020-1-6
I used alexnet with a dataset which it is categores {1-50,51-100, 101- ...}. is this possible?
if yes, why I get this error?
Error using trainNetwork (line 150)
Training and validation data must have the same labels.
Error in AlexNet_ex_density (line 114)
netTransfer = trainNetwork(augimdsTrain,layers,options);
Caused by:
Error using trainNetwork>iAssertClassNamesAreTheSame (line 376)
Training and validation data must have the same labels.
I used
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
% Create augmentedImageDatastore from training and test sets to resize
% images in imds to the size required by the network.
imageSize = net.Layers(1).InputSize;
augmentedTrainingSet = augmentedImageDatastore(imageSize, imdsTrain, 'ColorPreprocessing',...
'gray2rgb','DataAugmentation',imageAugmenter);
augmentedTestSet = augmentedImageDatastore(imageSize, imdsTest, 'ColorPreprocessing', 'gray2rgb',...
'DataAugmentation',imageAugmenter);
augimdsValidation = augmentedImageDatastore(imageSize, imdsValidation, 'ColorPreprocessing',...
'gray2rgb','DataAugmentation',imageAugmenter);
and in options of the network I used
'ValidationData',augimdsValidation, ...
'ValidationFrequency',3, ...
then I uesd
netTransfer = trainNetwork(augimdsTrain,layers,options);
I got the error in above sentince.

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回答（1 个）

Mahmoud Afifi
Mahmoud Afifi 2020-1-6
You need to change the last fully connected layer of Alexnet with a new one with the same number of expected output (either for regression or number of classes for classification).
https://www.mathworks.com/help/deeplearning/examples/transfer-learning-using-alexnet.html;jsessionid=82bda7d7b633139e3e0a8f6e685d

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