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cnn_classifier.m
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function layers = cnn_classifier(patch_size)
layers = [
imageInputLayer(patch_size, 'Normalization', 'none');
convolution2dLayer([3,3], 20);
batchNormalizationLayer();
reluLayer();
dropoutLayer(0.05); %http://mipal.snu.ac.kr/images/1/16/Dropout_ACCV2016.pdf
convolution2dLayer([3,3], 40);
batchNormalizationLayer();
convolution2dLayer([3,3], 80);
maxPooling2dLayer([2,2], 'Stride', 2, 'Padding',[1 0 1 0] );
dropoutLayer(0.1);
convolution2dLayer([3,3], 160);
batchNormalizationLayer();
reluLayer();
dropoutLayer(0.05);
convolution2dLayer([3,3], 320);
batchNormalizationLayer();
reluLayer();
dropoutLayer(0.1);
convolution2dLayer([3,3], 640);
batchNormalizationLayer();
reluLayer();
dropoutLayer(0.05);
convolution2dLayer([3,3], 1280);
batchNormalizationLayer();
reluLayer();
dropoutLayer(0.05);
convolution2dLayer([2,2], 2);
batchNormalizationLayer();
softmaxLayer();
pixelClassificationLayer()];
end
% layers = [
% imageInputLayer(patch_size, 'Normalization', 'none');
% convolution2dLayer([3,3], 10);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.05); %http://mipal.snu.ac.kr/images/1/16/Dropout_ACCV2016.pdf
% convolution2dLayer([3,3], 20);
% batchNormalizationLayer();
% %maxPooling2dLayer([2,2], 'Stride', 2, 'Padding',[1 0 1 0] );
% convolution2dLayer([3,3], 20);
% batchNormalizationLayer();
% reluLayer();
% %maxPooling2dLayer([2,2], 'Stride', 2);
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 40);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 40);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 80);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 80);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 80);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 80);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 160);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 160);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 320);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 320);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 160);
% batchNormalizationLayer();
% convolution2dLayer([3,3], 2);
% batchNormalizationLayer();
%
% softmaxLayer();
% pixelClassificationLayer()];
%
% layers = [
% imageInputLayer(patch_size, 'Normalization', 'none');
% convolution2dLayer([3,3], 10);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.05); %http://mipal.snu.ac.kr/images/1/16/Dropout_ACCV2016.pdf
% convolution2dLayer([3,3], 20);
% batchNormalizationLayer();
% maxPooling2dLayer([2,2], 'Stride', 2, 'Padding',[1 0 1 0] );
% convolution2dLayer([3,3], 40);
% batchNormalizationLayer();
% reluLayer();
% %maxPooling2dLayer([2,2], 'Stride', 2);
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 80);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 160);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 320);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 160);
% batchNormalizationLayer();
% convolution2dLayer([4,4], 2);
% softmaxLayer();
% pixelClassificationLayer()];
% layers = [
% imageInputLayer(patch_size, 'Normalization', 'none');
% convolution2dLayer([3,3], 10);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.05); %http://mipal.snu.ac.kr/images/1/16/Dropout_ACCV2016.pdf
% convolution2dLayer([3,3], 20);
% batchNormalizationLayer();
% %maxPooling2dLayer([2,2], 'Stride', 2, 'Padding',[1 0 1 0] );
% convolution2dLayer([3,3], 40);
% batchNormalizationLayer();
% reluLayer();
% %maxPooling2dLayer([2,2], 'Stride', 2);
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 80);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 160);
% batchNormalizationLayer();
% reluLayer();
% dropoutLayer(0.1);
% convolution2dLayer([3,3], 320);
% batchNormalizationLayer();
% %reluLayer();
% %dropoutLayer(0.1);
% convolution2dLayer([3,3], 2);
% %batchNormalizationLayer();
% %convolution2dLayer([4,4], 2);
% softmaxLayer();
% pixelClassificationLayer()];