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Yes, of course! However, you have to adjust some parameters for one dimensional data, because, in default, the parameters of ActivationMaximization is tuned for 2D-CNN model.
Modify input_range, input_modifiers and regularizers for your data properly.
Tune parameters (optimizer, input_modifiers, regularizers, or so on) to generagte the model inputs that maximize the output of the given score functions.
Is it possible to apply the method of activation maximization also on a 1D-CNN with one dimensional data?
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