CRLB in Mean Square Error

9 Ansichten (letzte 30 Tage)
Bhavana
Bhavana am 26 Jan. 2025
Kommentiert: Bhavana am 28 Jan. 2025
in my code i am having FIM Function[ function [J_11, J_12, J_22] = FIM(para, Rx, beta, scale)]. i want to use this function in my dnn for MSE to calculate traces of CRLB. how i can do
  2 Kommentare
Torsten
Torsten am 26 Jan. 2025
Could you give a meaning to your abbreviations dnn, MSE CRLB FIM ? Maybe insiders know them, but I don't.
Bhavana
Bhavana am 26 Jan. 2025
Deep Neural Network, Mean Square Error,Cramer Rao lower bound, Fisher Information Matrix

Melden Sie sich an, um zu kommentieren.

Antworten (1)

Sourabh
Sourabh am 27 Jan. 2025
You need to create a custom function to use the FIM function in your dnn to calculate traces of CRLB. You need to start by calculating the CRLB trace followed by calculating the loss and then integrating it into the loss function. Then, proceed with training your model. Kindly follow the below steps:
1. Calculate CRLB trace.
The CRLB is derived as the inverse of the FIM:
CRLB=inv(FIM);
And the trace is the sum of its diagonal elements.
traceCRLB = trace(CRLB);
2. Calculate the loss
Assuming you have MSE defined, compute the loss by taking the sum of MSE and CRLB Trace
loss = mse + traceCRLB;
3. Integrate into Loss Function
Define the custom loss function to include the CRLB trace. For example:
function loss = customLoss(predicted, trueValues, para, Rx, beta, scale)
% Compute the Fisher Information Matrix
% Compute the CRLB
CRLB = inv(FIM);
% Compute the trace of the CRLB
traceCRLB = trace(CRLB);
% Calculate Mean Squared Error (MSE)
% Combine MSE with the trace of CRLB
loss = mse + traceCRLB;
end
4. Proceed with training the DNN
Use MATLAB's trainNetwork or a custom training loop. Pass the customLoss function as part of your training configuration.
For more information, kindly refer to the following MATLAB documentation:
  1 Kommentar
Bhavana
Bhavana am 28 Jan. 2025
Thank you so much sir for your kind response. It will be very helpful for me.
Regards
Bhavana Agrawal

Melden Sie sich an, um zu kommentieren.

Kategorien

Mehr zu Deep Learning Toolbox finden Sie in Help Center und File Exchange

Tags

Produkte


Version

R2021b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by