Output Function to Save Net on Every Validation
11 ビュー (過去 30 日間)
古いコメントを表示
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 件のコメント
Ameer Hamza
2020 年 5 月 6 日
It seems that the outputFcn cannot save the network itself after each iteration. Is saving just the state of network training enough?
回答 (1 件)
Ameer Hamza
2020 年 5 月 6 日
編集済み: Ameer Hamza
2020 年 5 月 6 日
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 件のコメント
Ameer Hamza
2020 年 5 月 12 日
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
参考
カテゴリ
Help Center および File Exchange で Sequence and Numeric Feature Data Workflows についてさらに検索
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!