# Develop WGAN-GP for 3-D image

조회 수: 5 (최근 30일)
Shuaibin WAN 2022년 3월 23일
댓글: Shuaibin WAN 2022년 4월 3일
Hi, I am beginner in using MATLAB to develop generative adversarial networks (GANs). Based on the MATLAB WGAN-GP tutorial, I have developed a WGAN-GP model for 3-D images (H x W x D x C). The modified 'modelGradientsD' function is shown as below.
function [gradientsD, lossD, lossDUnregularized, D_X, D_G_Z1] = modelGradientsD(dlnetG, dlnetD, dlZ, dlX, lambda)
% Calculate the prediction for training images with D
dlYPred = forward(dlnetD, dlX);
% Calculate the prediction for G-generated images with D
dlXGenerated = forward(dlnetG, dlZ);
dlYPredGenerated = forward(dlnetD, dlXGenerated);
% Calculate D(X) and D(G(Z))
D_X = mean(dlYPred);
D_G_Z1 = mean(dlYPredGenerated);
% Calculate the unregularized loss
lossDUnregularized = D_G_Z1 - D_X;
% Get the interpolated image from the training and generated images
epsilon = rand([1 1 1 1 size(dlX,5)], 'like', dlX);
dlXInterpolated = epsilon.*dlX + (1-epsilon).*dlXGenerated;
dlYPredInterpolated = forward(dlnetD, dlXInterpolated);
% Calculate the loss with gradient penalty
% Calculate the gradients of the loss with respect to learnable parameters
end
When running the program, however, an error pops up (as shown below). It seems that there is something wrong in calculating 'gradientsD'. After many debug attempts, I find that removing 'EnableHigherDerivatives' from calculating 'gradientsInterpolated' can make it. But the WGAN-GP perform not well, and I have several questions: (1) Does removing 'EnableHigherDerivatives' affect the model training significantly? (2) Is there robustness issue in the 'dlgradient' function? (3) Are there other solutions to this error?
I really appreciate it if you could offer any idea or suggestion. Thanks a lot!
Error using +
Arrays have incompatible sizes for this operation.
Error in gpuArray/internal_dlconv (line 57)
stride, dilation, numGroups) + bias;
Error in deep.internal.recording.operations.DlconvBackwardOp/backward (line 88)
ddZ2 = internal_dlconv(ddX,weights,zeroBias,op.Args{:});
Error in deep.internal.recording.RecordingArray/backwardPass (line 89)
Error in deep.internal.dlfeval (line 17)
[varargout{1:nargout}] = fun(x{:});
Error in dlfeval (line 40)
[varargout{1:nargout}] = deep.internal.dlfeval(fun,varargin{:});
Error in WGANGP_V1 (line 226)
[gradientsD, lossD, lossDUnregularized, D_X, D_G_Z1] = dlfeval(@modelGradientsD, dlnetG, dlnetD, dlZ, dlX, lambda);

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### 채택된 답변

Joss Knight 2022년 3월 24일
You definitely need to use EnableHigherOrderDerivatives here because you are including computed gradients in the loss term. Without it your training will not work correctly.
It looks like this is a bug with higher order derivatives and 3-D data, which is fixed in R2022a. Can you get the latest version of MATLAB?
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Joss Knight 2022년 4월 2일
I have requested for this to be fixed in a future update of R2021b.
Shuaibin WAN 2022년 4월 3일
That sounds GREAT! Thank you so much, sir.

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