darkon icon indicating copy to clipboard operation
darkon copied to clipboard

Returning the Hessian of the empirical risk

Open tranlm opened this issue 7 years ago • 3 comments

Hi,

I came across your implementation of the Influence Function and am interested in separately grabbing the Hessian that is needed for the calculations. Is there a way for me to grab it without having to break apart the code?

thank you very much.

tranlm avatar Dec 08 '17 04:12 tranlm

Hi tranlm,

Did you mean Hessian vector product like here? If you are using python tensorflow, you have to import python module and just call as below.

from tensorflow.python.ops.gradients_impl import _hessian_vector_product

_hessian_vector_product(ys, xs, v)

In our code in here for Hessian vector product.

Thank you.

zironycho avatar Dec 08 '17 06:12 zironycho

Thanks!

I noticed while looking into this that the original authors have also released their code implementation of the paper. Have you noticed any implementation differences?

tranlm avatar Dec 08 '17 21:12 tranlm

Our implementation is basically not much different. The original one only considered output layer as trainable parameters to calculate influence and it only applied to their experimental networks, but ours use all layers and is more general applicable to any networks, and more optimized and easy to use.

teradepth avatar Dec 09 '17 14:12 teradepth