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Hi
Can you explain why do we need the random seed? I noticed the random seed for ImageNet classification is set to 34. I also trained a model for face verification, without random seed, it sometimes doesn't converge. But when I set the random seed as 1000, it always converges. Can you explain how to determine random seed when faced with different training tasks?
The text was updated successfully, but these errors were encountered:
If you don't set a seed for the random number generator, it will have a new seed every time you train. If you want numerically repeatable results (useful for debugging), then it is necessary to select a seed.
DNNs tend to be pretty sensitive to their parameter initialization, and some seeds just end up not converging for some models and datasets. It's a common issue in machine learning in general.
In practice, people often train a few models with different seeds and find one that converges well.
Hi
Can you explain why do we need the random seed? I noticed the random seed for ImageNet classification is set to 34. I also trained a model for face verification, without random seed, it sometimes doesn't converge. But when I set the random seed as 1000, it always converges. Can you explain how to determine random seed when faced with different training tasks?
The text was updated successfully, but these errors were encountered: