t-sne visualization of Vision transformer

How to visualize the features of the vision transfomer after training using t-sne visualizion?
I could not apply the same in vision transformer beacuse there are an error during using actication function as follows:
earlyLayerName = "'encoderblock1_mha1";
pool1Activations = activations(net,...
augimdsTrain,earlyLayerName,"OutputAs","rows");

Answers (1)

From what I gather, you are following the MATLAB examples “Train Vision Transformer Network for Image Classification” and “View Network Behavior Using tsne” to visualise the results of the network activations of a trained vision transformer. You are encountering an error in the process.
The line of code where you are encountering the error is trying to refer to a non-existent layer, assuming you are using the MATLAB default implementation of the “visionTransformer” network. By analysing the network, we can see the layer is named “encoderblock1_mha” and not “encoderblock1_mha1”. Unless you have renamed and replaced the layer, passing the correct layer name shall eliminate the result.
I hope you find the above explanation and suggestions useful!

5 Comments

Hi Garmit,
I think this is not the problem.
I ensured from the layer name . if you choose any layer for visulazion in the activation command :
pool1Activations = activations(net,...
augimdsTrain,earlyLayerName,"OutputAs","rows");
usually, I have this error "Incorrect number or types of inputs or outputs for function activations."
Hi Mahmoud
Are you using the same data as mentioned and included in the examples?
Hi Garmit
The problem is the same during using any dataset.
you can use the examples mentioned above and try to visualize the transformer after training using the same data of the example, and share the code for the visualiztion .
Hi Garmit
Have you solved the problem? I had the same problem when I was executing code.
Hi Mahmoud
Have you solved the problem? I had the same problem when I was executing code.

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R2023b

Asked:

on 28 Feb 2024

Commented:

on 15 May 2024

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