Get input/output gradient of neural network
9 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Matlab's built in functions in the NN toolbox seem to provide a good set of options for getting the gradient of the network performance wrt the network parameters. Is there a way to get the gradient of the network output with respect to the network input?
0 Kommentare
Antworten (1)
Mahesh Taparia
am 14 Sep. 2020
Hi
In general, in any neural network, the network tries to learn the weights which can reduce the cost/ loss function. The gradients are updated iteratively by using the derivative of loss function with respect to weights.
Usually for a fix input, calculating gradients of loss with respect to input is not meaningful because if input is fix, then d(loss)/d(Input) is not defined. If the network is feed with 2 different input sequence, in this case you can find the gradient by calculating (Loss2-Loss1)/(X2-X1), where Loss is the value of network loss with respect to input X. There is no use of this while training the network.
Hope it will helps!
6 Kommentare
David Leather
am 24 Nov. 2020
Bearbeitet: David Leather
am 24 Nov. 2020
This seems like an oversight. When applying the trained neural network to other applications, it is essential to be able to evaluate the gradient wrt to the output of the neural network, and not the loss function....
Ruyue Yang
am 22 Jul. 2021
Get the gradient dy/dx can be really trick for the trained multi-layer neural network (not that deep, maybe 3 or 4 layer). Such function can help a lot for the network's various application.
Siehe auch
Kategorien
Mehr zu Sequence and Numeric Feature Data Workflows finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!