multiple input to a pre-trained model

3 次查看（过去 30 天）
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);

请先登录，再进行评论。

请先登录，再回答此问题。

回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Image Data Workflows 的更多信息

标签

产品


版本

R2021b

Community Treasure Hunt

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

Start Hunting!

Translated by