Accessing gradient values of keras model outputs with respect to inputs

Question

I made a pretty simple NN model to do some non-linear regressions for me in Keras, as an introduction exercise. I uploaded my jupyter notebookit as a gist here (renders prop

Answer 1

As you mention, Theano and TF are symbolic, so doing a derivative should be quite easy:

import theano
import theano.tensor as T
import keras.backend as K
J = T.grad(model.output[0, 0], model.input)
jacobian = K.function([model.input, K.learning_phase()], [J])

First you compute the symbolic gradient (T.grad) of the output given the input, then you build a function that you can call and does the computation. Note that sometimes this is not that trivial due to shape problems, as you get one derivative for each element in the input.