To debug high-loss values during GAN training, you can:
- Check Learning Rates: Ensure learning rates for the generator and discriminator are balanced.
- Loss Clipping: Clip gradients to prevent exploding gradients.
- Monitor Mode Collapse: Ensure the generator isn’t producing repetitive outputs.
- Discriminator Overpowering: Prevent the discriminator from becoming too strong by limiting its training steps.
Here is the code snippet you can refer to:
You can follow these debugging steps:
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Inspect Loss Trends:
- Sudden spikes: Check for exploding gradients.
- Discriminator loss near zero: It’s too strong.
- Generator loss constant: It’s not learning.
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Balance Learning Rates:
- Use similar learning rates for the generator and discriminator.
- Adjust dynamically if one model dominates.
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Monitor Outputs:
- Save generated samples regularly.
- Ensure diversity in outputs to catch mode collapse.
Hence, this systematic approach helps identify and resolve issues causing high loss values during GAN training.