How to change training and plotting options when training a deep network?

I have Matlab R2021a and I need to modify the option ‘OutputNetwork’ from the default ('last-iteration') into 'best-validation-loss' in trainingOptions by using this command:
options = trainingOptions('OutputNetwork', 'best-validation-loss');
but this option is not found in matlab R2021a. Could you please help me?

Respuestas (1)

From matlab documentation, you can only use this option if you specified validation data as well.
  • 'best-validation-loss' – Return the network corresponding to the training iteration with the lowest validation loss. To use this option, you must specify 'ValidationData'.

2 comentarios

Fatma Diab
Fatma Diab el 12 de Nov. de 2021
Editada: Fatma Diab el 12 de Nov. de 2021
I have validation data and my options are:
options = trainingOptions('adam', ...
'MiniBatchSize',20, ...
'MaxEpochs',10, ...
'InitialLearnRate',0.0001, ...
'Shuffle','every-epoch', ...
'ValidationData', imdsValidation, ...
'ValidationFrequency',300, ...
'OutputNetwork', 'best-validation-loss',...
'Verbose',true, ...
'Plots','training-progress');
but this error appears:
Error using nnet.cnn.TrainingOptionsADAM (line 132)
'OutputNetwork' is not an option for solver 'adam'.
Error in trainingOptions (line 317)
opts =nnet.cnn.TrainingOptionsADAM(varargin{:});
This functionality is recently introduced in Matlab R2021b.
https://www.mathworks.com/help/deeplearning/release-notes.html
Network Training: Return network with lowest validation loss
When training a neural network using the trainNetwork function, output the network with the lowest validation loss by setting the OutputNetwork name-value argument of the trainingOptions function to "best-validation-loss".
You may try using checkpoint path for this perhaps. See the available options in R2020a here
CheckpointPathPath for saving checkpoint networks
character vector
Path where checkpoint networks are saved, specified as a character vector.
Data Types: char
For more on checkpoint see here

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R2020a

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Preguntada:

el 11 de Nov. de 2021

Comentada:

el 22 de Nov. de 2021

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