How can I make Grad-cam of CNN net
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I made CNN net based on 1-D data (1*204)
layers=[
imageInputLayer([1 204]);
convolution2dLayer([1 3],64,'Stride',1,'Padding',[0 1]);
convolution2dLayer([1 3],64,'Stride',1,'Padding',[0 1]);
batchNormalizationLayer
reluLayer();
maxPooling2dLayer([1 2],'Stride',[1 2]);
convolution2dLayer([1 3],128,'Stride',1,'Padding',[0 1]);
convolution2dLayer([1 3],128,'Stride',1,'Padding',[0 1]);
batchNormalizationLayer
reluLayer();
maxPooling2dLayer([1 2],'Stride',[1 2]);
convolution2dLayer([1 3],256,'Stride',1,'Padding',[0 1]);
convolution2dLayer([1 3],256,'Stride',1,'Padding',[0 1]);
batchNormalizationLayer
reluLayer();
maxPooling2dLayer([1 2],'Stride',[1 2]);
fullyConnectedLayer(400);
fullyConnectedLayer(400);
fullyConnectedLayer(4);
softmaxLayer();
classificationLayer();
];
I get a good CNN net, But I would like to which part of data is important to decide result.
I think It will be good way to use Grad-cam.
Many examples of Grad-cam are focused on Image. But my case is 1-D data not Image file..
Can you tell me which method will be good in my case?
Can I just use Grad-cam?,or more easy way to check which part is critical on result?
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Kenta
il 11 Lug 2020
Grad-cam is mainly used for images. In your case, LIME might be helpful.
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