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I started with hyperparameters similar to the papers/tutorials I referenced at the bottom of the notebook. Then I wrote a little parameter squashing function for the learning rates for the generator and discriminator. I believe I first wrote it to work with smaller images (16/16 or 32/32?) and then scaled up, changing hyperparameters as needed. But mostly just a few days of playing around with numbers and seeing what worked best.
Hey!
I have basically implemented a version of your model myself. I got it to work on MNIST but I also want to make it work on other datasets.
My question is how did you find the optimal hyperparameters on the celebA dataset, did you do a grid search?
regards,
Riaan
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