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jchen7 avatar jchen7 commented on August 25, 2024

Couple questions, divamgupta. Which version of keras did you try it on? Did you try changing anything to get it to work, or did you run the model as is? I am having some trouble training as well.

Thanks

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kastnerkyle avatar kastnerkyle commented on August 25, 2024

You might need to closer match DCGAN's original setup - feeding the samples and data as separate minibatches to the discriminator is important. See the line here and just before https://github.com/Newmu/dcgan_code/blob/master/faces/train_uncond_dcgan.py#L138

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MinaRe avatar MinaRe commented on August 25, 2024

Dear @jacobgil

Thanks for sharing and nice implementation,
I want to generate 5 different type gray scale images (on my dataset) the image input should be hdf5 format?

Thanks in advance!

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jacobgil avatar jacobgil commented on August 25, 2024

@MinaRe The images should be in any format that can be read with opencv - i.e .jpg, .png, .pgm etc.

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jacobgil avatar jacobgil commented on August 25, 2024

@MinaRe
I'm not sure what is the question.
One way to load images would to be just use opencv, and load them into numpy arrays.
import cv2
img = cv2.imread("/home/MinaRe/img.png", 0)
img = cv2.imresize(img, (32, 32))

And then set X_train to be these images.
Since you have many images and the RAM space might be a bottleneck, you will probably want to load a next batch of images from disk in the training loop.
Hopefully that helps.

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