Negative TrainedVariance error when running matlab example of image GAN(i change the code enlarge the image size from 64x64 to 128x128)

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Ziqi Sun
Ziqi Sun 2022-10-13
回答： Ayush Modi 2024-1-12
I follow this: https://www.mathworks.com/matlabcentral/answers/585146-how-to-change-gan-example-to-generate-images-with-a-larger-size to change the image size processed in GAN from 64x64 to 128x128. A guy said he can make it at the end of post but I got a negative TrainedVariance error when running it.
Here is the system error warning:
Error using nnet.internal.cnn.dlnetwork/set.State
Layer 'batchnorm_1': Invalid State. Expected TrainedVariance to be positive.
Error in dlnetwork/set.State (line 551)
net.PrivateNetworkStorage.State = values;
Error in trainGAN (line 70)
netG.State = stateG;
The error pumped out when it runs for 1 to 2 minutes.
I Googled it and tried to add those codes to replace the line netG.State = stateG;:
idx = netG.State.Parameter == "TrainedVariance";
boundAwayFromZero = @(X) max(X, eps('single'));
netG.State(idx,:) = dlupdate(boundAwayFromZero, netG.State(idx,:));
But cant work since netG.State isnt get value from stateG
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Ayush Modi
Ayush Modi 2024-1-12
Hi Ziqi,
As per my understanding, you are trying to process 128x128 images in GAN but you are getting negative TrainedVariance error while running it.
I was able to achieve this by following the answer provided by @Fred Liu in the Matlab community question you are following (https://www.mathworks.com/matlabcentral/answers/585146-how-to-change-gan-example-to-generate-images-with-a-larger-size).
Below is the code for your reference:
filterSize = 5;
numFilters = 128;
numLatentInputs = 100;
projectionSize = [4 4 512];
layersGenerator = [
featureInputLayer(numLatentInputs)
projectAndReshapeLayer(projectionSize)
transposedConv2dLayer(filterSize,8*numFilters)
batchNormalizationLayer
reluLayer
transposedConv2dLayer(filterSize,4*numFilters,Stride=2,Cropping="same")
batchNormalizationLayer
reluLayer
transposedConv2dLayer(filterSize,2*numFilters,Stride=2,Cropping="same")
batchNormalizationLayer
reluLayer
transposedConv2dLayer(filterSize,numFilters,Stride=2,Cropping="same")
batchNormalizationLayer
reluLayer
transposedConv2dLayer(filterSize,3,Stride=2,Cropping="same")
tanhLayer];
netG = dlnetwork(layersGenerator);
dropoutProb = 0.5;
numFilters = 128;
scale = 0.2;
inputSize = [128 128 3];
filterSize = 5;
layersDiscriminator = [
imageInputLayer(inputSize,Normalization="none")
dropoutLayer(dropoutProb)
convolution2dLayer(filterSize,numFilters,Stride=2,Padding="same")
leakyReluLayer(scale)
dropoutLayer(dropoutProb)
convolution2dLayer(filterSize,2*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
dropoutLayer(dropoutProb)
convolution2dLayer(filterSize,4*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
dropoutLayer(dropoutProb)
convolution2dLayer(filterSize,8*numFilters,Stride=2,Padding="same")
batchNormalizationLayer
leakyReluLayer(scale)
convolution2dLayer(8,1)
sigmoidLayer];
netD = dlnetwork(layersDiscriminator);
I hope this helps!

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