Unable to save session in Classification Learner
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I have trained models in classification learner based on a dataset and am hoping to save the classificationLearner Session so that I do not have to rerun all the models again everytime, which takes quite a long time. I am currently using the R2022a version, and when I try to save the error, I get an error message that says "The file could not be closed, and might now be corrupt." Any advice on how I can get rid of this error and save the session?
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Image Analyst
2022 年 7 月 23 日
What do you mean by "rerun the models"? Why do you have to do that? Did your training data change? If not, why rerun?
What I do is to load by predictor table and ground truth responses and train the model. Then I click the Export->Compact Model to put the "trainedModel" variable into the workspace. Then I call save
save('myModel.mat', 'trainedModel');
to save the trained model to disk. Then to apply the model to predict (not train) new estimated response values, I call load
s = load('myModel.mat');
trainedModel = s.trainedModel;
then call predict() with a table of new data and the trainedModel variable. That will give me an estimated value for each new test value.
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その他の回答 (1 件)
Antonio
2024 年 1 月 19 日
In my case, this problem is related to MATLAB having problems saving files larger than 2GB in the default configuration of the "save" function. You can try two options:
- Export the model as a Compact Model (Export -> Export Compact Model) to the workspace and save from there as normally done with save(). If that compact model meets the 2GB limit, you will have no problem.
- Otherwise, export the complete or compact model from the workspace using the option '-v7.3' inside the save() function. It was this option that solved the problem in my case.
save(filename, 'modelTrained', '-v7.3')
I hope to be helpful! Sorry I'm late, but I respond for others who have the same problem.
Information about the '-v7.3' configuration: https://es.mathworks.com/matlabcentral/answers/15521-matlab-function-save-and-v7-3
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