multiple input to a pre-trained model

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Rayan Matlob
Rayan Matlob 2022-7-5
编辑： Rayan Matlob 2022-7-6
I have three classes folders (Good, Moderate and Severe)
Each class folder of them has (5 subolders) which are (Original images, Red, Blue, Green, HUE, Value),
where (Red, Blue, Green, HUE,Value) are subolders contain images after applying filters on the (Original images folder).
I am using a pre-trained model (resnet50 or any other model you suggest), all images in all the folders are numbered in the same sequence (each subfolder contains images from 1 to 200).
How to train the model by taking each single image from subfolder(Original images),and to apply it in parralel with the images from the other subfoldere (Red, Blue, Green, HUe,Value) to the input of the model.
Note: for the validation, i need to use only the (original_images folder) and the model should fetch the other images from the other subfolders
Next is the matlab code, thanks:
  1 个评论
Rayan Matlob
Rayan Matlob 2022-7-6
编辑：Rayan Matlob 2022-7-6
imds = imageDatastore('C:\Users\Rayan\Desktop\9_8_balance_data\R_9_1_GSM_3', ...
'IncludeSubfolders',true, ...
'LabelSource','foldernames');
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.77,'randomized');
numTrainImages = numel(imdsTrain.Labels);
net = resnet50;
inputSize = net.Layers(1).InputSize;
lgraph = layerGraph(net);
edit(fullfile(matlabroot,'examples','nnet','main','findLayersToReplace.m'))
[learnableLayer,classLayer] = findLayersToReplace(lgraph);
numClasses = numel(categories(imdsTrain.Labels));
if isa(learnableLayer,'nnet.cnn.layer.FullyConnectedLayer')
newLearnableLayer = fullyConnectedLayer(numClasses, ...
'Name','new_fc', ...
'WeightLearnRateFactor',10, ...
'BiasLearnRateFactor',10);
elseif isa(learnableLayer,'nnet.cnn.layer.Convolution2DLayer')
newLearnableLayer = convolution2dLayer(1,numClasses, ...
'Name','new_conv', ...
'WeightLearnRateFactor',10, ...
'BiasLearnRateFactor',10);
end
lgraph = replaceLayer(lgraph,learnableLayer.Name,newLearnableLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,classLayer.Name,newClassLayer);
layers = lgraph.Layers;
connections = lgraph.Connections;
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain)
augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
miniBatchSize=10;
valFrequency = floor(numel(augimdsTrain.Files)/miniBatchSize);
options = trainingOptions('sgdm', ...
'MiniBatchSize',10, ...
'MaxEpochs',60, ...
'InitialLearnRate',0.00065, ...
'Shuffle','every-epoch', ...
'ValidationFrequency',valFrequency, ...
'ValidationData',augimdsValidation, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(augimdsTrain,lgraph,options);

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