Looking for an Auto Differentiation package can be easily used as a function
1 Ansicht (letzte 30 Tage)
Ältere Kommentare anzeigen
I construct a neural network in MATLAB using the basic array since I have no experience in the neural network toolbox. Now I need to take the derivative of all the parameters, like weight, bias, which are inside the activation function.
Is there any package (library) in MATLAB that can help me to do the auto differentiation without changing my basic data structure?
clc
clear
global stackw
stackw=1;
Ninput=2;
Noutput=2;
Nneuron=3; % each layer
Nlayers=3; % hidden layer
inputdata=ones(Ninput,1);
NNstruc=[];
NNstruc(1)=Ninput;
NNstruc(2:(Nlayers+1))=Nneuron;
NNstruc=[NNstruc,Noutput];
wsize=sum(NNstruc(1:(end-1)).*NNstruc(2:end));
bsize=Nlayers+1;
wset=rand(wsize,1);
bset=rand(bsize,1);
for i=1:(length(NNstruc)-1)
temp=NNtrack(inputdata, NNstruc, wset, i);
temp=logsig(temp+bset(i));
inputdata=temp;
end
function [output] = NNtrack(x, NNconfig, w, index)
global stackw;
x=x(:);
current=NNconfig(index);
next=NNconfig(index+1);
Nw=current*next;
Nb=next;
wtemp=reshape(w(stackw:(stackw+Nw-1)),[next current]);
stackw=stackw+Nw;
temp=wtemp*x;
output =wtemp*x;
end
0 Kommentare
Antworten (1)
Walter Roberson
am 3 Mai 2022
Bearbeitet: Walter Roberson
am 4 Mai 2022
No, the available package would require changes to your data structure.
2 Kommentare
Torsten
am 3 Mai 2022
So you don't lack theoretical knowledge about neural networks, but you want to gain knowledge on how to use the Neural Network Toolbox ? Then usually its documentation together with the examples provided is the best tutorial.
Siehe auch
Kategorien
Mehr zu Define Shallow Neural Network Architectures 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!