- The first criteria is if when the maximum number of epochs is reached then the training is stopped irrespective if the convergence is achieved or not.
- The second criteria is if the parameters changes are below a threshold then convergence is achieved.
- The third criteria is minimum gradient value is reached. You can adjust this by changing the hyper-parameter ‘net.trainlm.min_grad.’
- The fourth criteria is if the performance goal is reached (training loss value). You can adjust this by changing the hyper-parameter ‘net.trainlm.goal.’
How to make convergence criteria for Levenberg-Marquardt algorithm
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How to make convergence criteria for Levenberg-Marquardt algorithm, please give practical hint for matlab implementatio
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Krishna
on 25 Aug 2024
Edited: Krishna
on 25 Aug 2024
Hello,
From the question I understand you want to know the convergence criteria of Levenberg-Marquardt algorithm.
These are the 4 criterions by which you can check the convergence of trainlm algorithm.
Please go through the following documentation to learn more regarding the hyper-parameters of Levenberg-Marquardt algorithm and also the mathematical update function being used in the following algorithm,
Also please go through the following documentation to learn more about how to ask question on MATLAB answer and get a fast response,
Hope this helps.
2 Comments
Walter Roberson
on 25 Aug 2024
If the maximum number of epochs is reached, then you do not have convergence.
Krishna
on 25 Aug 2024
Yes, have corrected the answer, if the maximum epochs is reached then the training is stopped irrespective if the convergence is achieved or not.
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