Output size of GAN Example

Hello,

I am using the example of GAN to generate images, and I would like the output generated to be of a different size than 64x64x3, I've tried to change several parameters but I can't get it.

Any ideas?

Thanks in advance.

[Image Analyst]

Here's a suggestion: http://www.mathworks.com/matlabcentral/answers/6200-tutorial-how-to-ask-a-question-on-answers-and-get-a-fast-answer

It would help us answer if we could see a little code first.

[Sai Bhargav Avula]

Can you give the link which example you referred to? Also attach the code that you tried.

[Alvaro Huerta]

Hello,

thanks for your reply.

I have tested the code, the output are images with 64x64 pixels. The example is in the link below.

https://es.mathworks.com/help/deeplearning/ug/train-generative-adversarial-network.html

Thanks for all.

Answer 1

[Sai Bhargav Avula]

Hi,

The final size depends on the generator network architecture.

One way to achieve it is to change the filtersize of the generator may not be the ideal case for this example.

The ideal way is, based on your required output size you have to add transposedConv2dLayer to the architecture with proper filter size.

For example if you want the size to be 128*128 then simply add one more transposedConv2dLayer to the architecture

Remember you need to adjust the filtersize and channels accordingly

Hope this helps!

[Alvaro Huerta]

Hi,

thanks for your fast reply, I am going to try your solution.

Best regards.

[sara almheiri]

I keep getting a dlfeval error when I do the following adjustments. I would like to generate a 640x640 output size image but first I want to workout your solution. Would love to here back from you.

%Augment data

augmenter = imageDataAugmenter( ...

'RandXReflection',true, ...

'RandScale',[1 2]);

augimds = augmentedImageDatastore([128 128],imds,'DataAugmentation',augmenter);

%Define Generator Netwrok

filterSize = 5;

numFilters = 128;

numLatentInputs = 100;

projectionSize = [4 4 512];

layersGenerator = [

imageInputLayer([1 1 numLatentInputs],'Normalization','none','Name','in')

projectAndReshapeLayer(projectionSize,numLatentInputs,'proj');

transposedConv2dLayer(filterSize,8*numFilters,'Name','tconv1')

batchNormalizationLayer('Name','bnorm1')

reluLayer('Name','relu1')

transposedConv2dLayer(filterSize,4*numFilters,'Stride',2,'Cropping','same','Name','tconv2')

batchNormalizationLayer('Name','bnorm2')

reluLayer('Name','relu2')

transposedConv2dLayer(filterSize,2*numFilters,'Stride',2,'Cropping','same','Name','tconv3')

batchNormalizationLayer('Name','bnorm3')

reluLayer('Name','relu3')

transposedConv2dLayer(filterSize,3,'Stride',2,'Cropping','same','Name','tconv4')

tanhLayer('Name','tanh')];

lgraphGenerator = layerGraph(layersGenerator);

dlnetGenerator = dlnetwork(lgraphGenerator);

[Abdullah alsuhail]

Hi Sara,

Did you find the solution? I stuck with the same problem.

I will appreciate if you can share the code here.

Thank you so much