Output Function to Save Net on Every Validation
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I'm curious if it's possible to define an output function to spit out the current state of the network while training by using an output function to put that current net into a structure in the same way I have it defined to spit out [net,tr] = trainNetwork() when it finishes, but does so during training.
I can't use checkpoints because I am using an ADAM solver for my network.
1: Net,TR
2: Net, TR
3: Net, TR
4: Net, TR
etc.
1 Kommentar
Ameer Hamza
am 6 Mai 2020
It seems that the outputFcn cannot save the network itself after each iteration. Is saving just the state of network training enough?
Antworten (1)
Ameer Hamza
am 6 Mai 2020
Bearbeitet: Ameer Hamza
am 6 Mai 2020
If you just want to save the training states, then try the following example. It is adapted from this example: https://www.mathworks.com/help/releases/R2020a/deeplearning/ref/trainingoptions.html#bvniuj4
[XTrain,YTrain] = digitTrain4DArrayData;
idx = randperm(size(XTrain,4),1000);
XValidation = XTrain(:,:,:,idx);
XTrain(:,:,:,idx) = [];
YValidation = YTrain(idx);
YTrain(idx) = [];
layers = [
imageInputLayer([28 28 1])
convolution2dLayer(3,8,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,16,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2)
convolution2dLayer(3,32,'Padding','same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(10)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'MaxEpochs',8, ...
'ValidationData',{XValidation,YValidation}, ...
'ValidationFrequency',30, ...
'Verbose',false, ...
'Plots','training-progress', ...
'OutputFcn', @outFcn);
global training_state
training_state = [];
net = trainNetwork(XTrain,YTrain,layers,options);
function stop = outFcn(info)
global training_state
training_state = [training_state info];
stop = false;
end
Use of the global variable can be avoided if you define your own handle class and pass it to the outFcn. However, if you are fine with the use of global, then it shouldn't be an issue.
4 Kommentare
Ameer Hamza
am 12 Mai 2020
If you want to check the value of training_state in the base workspace after the execution of your function, then you should also run the following line in the command window before calling your function.
global training_state
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