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View Code? Open in Web Editor NEWimplement infoGAN using pytorch
License: MIT License
implement infoGAN using pytorch
License: MIT License
class log_gaussian:
def call(self, x, mu, var):
logli = -0.5*(var.mul(2*np.pi)+1e-6).log() -
(x-mu).pow(2).div(var.mul(2.0)+1e-6)
return logli.sum(1).mean().mul(-1)
1、What do mu and var represent?
2、I'm doubt about the log_gaussion's theory.
thank you!
Thanks for this awesome simple to understand repo.
I'm trying to use this for data augmentation, for a new dataset with size 128x128, and a latent variable which is a vector of size 37.
I tried to play a bit with the Conv2d
parameters, but unfortunately no configuration I tried made it run without errors. I was wondering if you can please add a size-dependent convolution to the source code, so in the future anyone with any size can just input their size and get this working correctly.
Thanks
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