Comments (3)
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with some combination of output neurons.
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Nice running into you at ISMB yesterday! To recap, all the backpropagation-based interpretation methods rely on a single output at which to start the backprop. My recommendation is that for interpretation, you could add a fully-connected neuron that encodes the combination of maxpooling neurons you are interested in, and then do interpretation with respect to that fully-connected neuron. If you run into issues of particular architectures not being supported, you could also use DeepSHAP (a combination of DeepLIFT + the SHAP method): https://github.com/kundajelab/deeplift#my-model-architecture-is-not-supported-by-this-deeplift-implementation-what-should-i-do
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Related Issues (20)
- categorial variables HOT 2
- adjustment for softmax HOT 2
- KeyError: 'batch_input_shape' while using deeplift on keras
- KeyError: 'zeropadding2d' HOT 1
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