(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.
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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);
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Thanks for reading my question.
4 个评论
Walter Roberson
2021-2-18
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
2024-5-29
编辑:Maksym Tymchenko
2024-5-29
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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