Comments (4)
Yes, that’s what I had in mind and the it is reason why I wanted to log the values for the losses and take a look at the development of the values over time to see how they behave
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Losses in GANs fluctuate all the time you have to look at the long duration as over a short period they don't tell you anything, So if the magnitude in general does decline slowly if it instead goes up and up over time then you have a problem.
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I'm quite a newbie at GitHub so I'm not sure if doing this is right, but I would like to poke this issue, since this is a problem I'm having right now. I need to be able to log the progress of the GAN so that I can at least plot it.
Any idea on how to get this done?
from lightweight-gan.
I'm quite a newbie at GitHub so I'm not sure if doing this is right, but I would like to poke this issue, since this is a problem I'm having right now. I need to be able to log the progress of the GAN so that I can at least plot it.
Any idea on how to get this done?
Probably best to use tensorboard:
- Clone repo, make sure you can run the code from the source
- install tensorboard with pip or conda
- import tensorboard at the top of the lighweight_gan.py file
- Add your logging code here (i think):
- Start tensorboard from terminal and open it in the browser (you can find plenty of info online)
All the best in your voyages!
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Related Issues (20)
- Error(s) in loading state_dict for LightweightGAN HOT 2
- Batchnorm layer missing in the discriminator?
- showing results while training ? HOT 2
- G is showing up a negative number. What does that mean?
- Multi GPU utilization problem.
- TypeError: __init__() got an unexpected keyword argument 'hparams' HOT 1
- unable to load save model. please try downgrading the package to the version specified by the saved model HOT 7
- --load_strict=False failing to load model HOT 1
- Can't find "__main__" module (sorry if noob question) HOT 4
- loss implementation differs from paper HOT 1
- Doesn't detect CUDA from Nvidia GPU HOT 1
- Torchvision Assertion error while importing custom data
- Aim installation error HOT 4
- Discriminator Loss converges to 0 while Generator loss pretty high HOT 3
- Dont detects cuda HOT 1
- Using Lightweight GAN as a library
- Projecting generated Images to Latent Space HOT 1
- Executing with a trailing \ in the arguments sets the variable new to the truthy value '\\' and deletes all progress
- CUDA out of memory error while generating interpolations
- Minimum number data training
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