(How) Higher derivative by 'dlgradient' or Higher derivative in matlab
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Hi
I am currently coding custom deep learning, but the process stopped at the higher derivative.
I am curious about how to make a higher derivative through dlgradient.
Alternatively, you are welcome to suggest a way to do higher derivatives in matlab.
For example, how to get ddydxx in the following example.
-----------------------------------------------------------------------------------------------------------------------
Code
clc,clear,close all
x=3;
x0=dlarray(x);
[fval,gradval,ggradval] = dlfeval(@Myfunc,x0);
function [fval,gradval,ggradval] = Myfunc(x)
y = 100*(3*x - 7*x.^2).^2;
dydx=dlgradient(y,x,'RetainData',true);
ddydxx=dlgradient(dydx,x);
end
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Error
Error using dlfeval (line 43)
Value to differentiate must be a traced dlarray scalar.
Error in gradtest (line 7)
[fval,gradval,dd] = dlfeval(@Myfunc,x0,y);
-----------------------------------------------------------------------------------------------------------------------
Thanks for reading my question.
4 commentaires
Walter Roberson
le 18 Fév 2021
To use automatic differentiation, you must call dlgradient inside a function and evaluate the function using dlfeval. Represent the point where you take a derivative as a dlarray object, which manages the data structures and enables tracing of evaluation
Maksym Tymchenko
le 29 Mai 2024
Modifié(e) : Maksym Tymchenko
le 29 Mai 2024
To make the code above work, you need to set the name value argument "EnableHigherDerivatives" to true.
Here's the modified version of your code that works as expected:
clc,clear,close all
x=3;
x0=dlarray(x);
[fval,gradval,ggradval] = dlfeval(@Myfunc,x0);
function [y,dydx,ddydxx] = Myfunc(x)
y = 100*(3*x - 7*x.^2).^2;
dydx=dlgradient(y,x,EnableHigherDerivatives=true);
ddydxx=dlgradient(dydx,x);
end
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