How to pass images to a cnn for analysis?

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Teshan Rezel
Teshan Rezel 2020-1-31
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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采纳的回答

Vinai Datta Thatiparthi
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
The overall workflow that I mentioned is available in this example as well.
Hope this helps!

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