how can I replace the softmax layer with another classifier as svm in convolution network

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I made deep learning application that using softmax
layers = [ imageInputLayer(varSize); conv1; reluLayer;
convolution2dLayer(5,32,'Padding',2,'BiasLearnRateFactor',2);
reluLayer()
maxPooling2dLayer(4,'Stride',2);
convolution2dLayer(5,32,'Padding',2,'BiasLearnRateFactor',2);
reluLayer()
maxPooling2dLayer(2,'Stride',2);
convolution2dLayer(5,64,'Padding',2,'BiasLearnRateFactor',2);
reluLayer();
maxPooling2dLayer(4,'Stride',2)
fc1;
reluLayer();
fc2;
reluLayer();
%returns a softmax layer for classification problems. The softmax layer uses the softmax activation function.
softmaxLayer()
classificationLayer()];
I want to use SVM and random forest classifiers instead of softmax. and use a deep learning for feature extraction. I hope I can get a link for a tutorial.

Answers (4)

Johannes Bergstrom
Johannes Bergstrom on 17 Apr 2018
Here is an example: https://www.mathworks.com/help/nnet/examples/feature-extraction-using-alexnet.html
  1 Comment
Suheer Ali
Suheer Ali on 17 Apr 2018
Thanks for your answer but I don't want to use pre-trained models. I want to design mine and use it as a feature extraction.

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Nagwa megahed
Nagwa megahed on 21 Apr 2022
the only possible solution is to save the extracted features by the deep model , then use this features as an input to the SVM or any other wanted classifier.

Saifullah Razali
Saifullah Razali on 19 Feb 2019
hello.. just wondering.. have u got the answer yet? i have the same exact problem

Mahzad Pirghayesh
Mahzad Pirghayesh on 28 Jan 2021
I have the same problem too,can any body help us

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