Regarding the gradient

[junkwhinger]

Hi, thanks for such a great blogpost. love it.

I'm trying to understand the paper's approach deeper with your code implementation.
As far as I know, the paper suggests getting the gradient of the Softmax input w.r.t the target conv layer.

In your code I think it's referring to the output of the softmax layer
loss = K.sum(model.layers[-1].output)
I was wondering if this should be corrected as
loss = K.sum(model.layers[-1].output.op.inputs[0])
and get the gradient with K.gradient function.

Please correct me if I've misunderstood the concept or your approach.

Thank you!