Can I have checkpoints in Bayesian optimization for tuning hyperparameters of a neural network?

herrysingh asked: 2021-04-20 deep learning , matlab , simulink

Explore Bayesian Optimization checkpoints for Neural Network hyperparameter tuning. Learn how to save & resume your progress, optimizing efficiently. Read more

Expert Answer

John Williams answered . 2025-07-30 06:33:06

Currently, there is no checkpointing argument. However, you can use the 'OutputFcn' argument along with the 'SaveFileName' argument to save to file, and the resume function to restart the process as follows,

x1 = optimizableVariable('x1',[-5,5]);
x2 = optimizableVariable('x2',[-5,5]);
fun = @rosenbrocks;

if exist('BayesoptResults.mat','file')
    load('BayesoptResults.mat');
    results = resume(BayesoptResults,...
    'SaveFileName', 'BayesoptResults.mat', ...
     'OutputFcn',{@saveToFile});
else
    results = bayesopt(fun, [x1, x2],'AcquisitionFunctionName',...
    'expected-improvement-plus', ...
    'SaveFileName', 'BayesoptResults.mat', ...
    'OutputFcn',{@saveToFile});
end


function f = rosenbrocks(x)
    f = 100*(x.x2 - x.x1^2)^2 + (1 - x.x1)^2;
end

Note that this saves to file on every iteration, so you might want to replace saveToFile with a custom function that saves occasionally, for performance reason.

The relevant docs are available here, resume and bayesopt.

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