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I can`t solve out this problem, there is always Output argument "varargout{2}" (and possibly others) not assigned a value in the execution with "dlarray/dlgradient" function.

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function [netG, stateG, lossG] = modelGStep(netG, wrappedImage, realImage, stateG, learningRate, beta1, beta2)
% insure GPU dlarray
if ~isa(wrappedImage, 'dlarray')
wrappedImage = dlarray(gpuArray(wrappedImage), 'SSCB');
elseif ~strcmp(underlyingType(wrappedImage), 'gpuArray')
wrappedImage = dlarray(gpuArray(extractdata(wrappedImage)), 'SSCB');
end
if ~isa(realImage, 'dlarray')
realImage = dlarray(gpuArray(realImage), 'SSCB');
elseif ~strcmp(underlyingType(realImage), 'gpuArray')
realImage = dlarray(gpuArray(extractdata(realImage)), 'SSCB');
end
wrappedImage = dlarray(gpuArray(wrappedImage), 'SSCB');
realImage = dlarray(gpuArray(realImage), 'SSCB');
% insure dlfeval use dlgradient
[gradG, lossG] = dlfeval(@dlgradient, lossG, netG.Learnables);
fakeImage = predict(netG, wrappedImage);
lossG = mean((fakeImage - realImage).^2, 'all');
[gradG, lossG] = dlgradient(lossG, netG.Learnables);
[netG, stateG] = adamupdate(netG, gradG, stateG, learningRate, beta1, beta2);
return
end
  • this is my function.
  • below is my code
for epoch = 1:epochs
for i = 1:size(unwrapImages, 4)
realImage = unwrapImages(:,:,:,i);
wrappedImage = wrappedImages(:,:,:,i);
[netG, stateG, lossG] = modelGStep(netG, wrappedImage, realImage, stateG, learningRate, beta1, beta2);
[lossD, gradD] = modelDStep(netD, realImage, wrappedImage, netG);
[netD, stateD] = adamupdate(netD, gradD, stateD, learningRate, beta1, beta2);
gLosses(epoch) = gLosses(epoch) + double(gather(extractdata(lossG)));
dLosses(epoch) = dLosses(epoch) + double(gather(extractdata(lossD)));
end
gLosses(epoch) = gLosses(epoch) / size(unwrapImages, 4);
dLosses(epoch) = dLosses(epoch) / size(unwrapImages, 4);
fprintf('Epoch %d, Generator Loss: %.4f, Discriminator Loss: %.4f\n', ...
epoch, gLosses(epoch), dLosses(epoch));
end
what should i do to solve this,thanks!

Accepted Answer

Joss Knight
Joss Knight on 18 Apr 2024

Your mistake is dlfeval(@dlgradient,...). You need to put your code that computes the loss and the loss gradients into a function and then pass that to dlfeval.

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R2023a

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