How to pass images to a cnn for analysis?

Question

Teshan Rezel 2020-1-31

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/503034-how-to-pass-images-to-a-cnn-for-analysis

评论： Teshan Rezel 2020-2-6

Hi folks,

I'm not very familiar with matlab so apologies for the obvious question, but how can I pass an image to my cnn to be analysed?

My cnn's code is as follows:

AnisotropyDatasetPath = fullfile(matlabroot,'Training', 'Anisotropy');
IsotropyDatasetPath = fullfile(matlabroot,'Training', 'Isotropy');
FillerDatasetPath = fullfile(matlabroot,'Training', 'Filler');
TrainingDatasetPath = fullfile(matlabroot,'Training');
imds = imageDatastore(TrainingDatasetPath, 'IncludeSubfolders',true,...
    'LabelSource','foldernames');
labelCount = countEachLabel(imds)
numTrainFiles = 999;
[imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');
layers = [
    imageInputLayer([227 227 3])
    
    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(3)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'InitialLearnRate',0.01, ...
    'MaxEpochs',4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);
YPred = classify(net,imdsValidation);
YValidation = imdsValidation.Labels;
accuracy = sum(YPred == YValidation)/numel(YValidation)

thanks!

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Answer 1

Vinai Datta Thatiparthi 2020-2-3

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/503034-how-to-pass-images-to-a-cnn-for-analysis#answer_413432

Hey Teshan,

After you train a neural network for deep learning using trainNetwork into net, follow this procedure:

Use the function augmentedImageDatastore for effective preprocessing of the images
Use the activations function to compute the DL Network layer activations i.e. the image features using the CNN net
Use the predict function to finally make a prediction using the classifier