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The training is unstable

Open shengkelong opened this issue 3 years ago • 2 comments

Thank you for your impressive work. But when I try to recurrent this network(I rewrite the code myself), sometimes the loss will suddenly increase by 10 times. The structure of the network is correct because I can load the pretrained network, so I think there may be some details I didn't notice. Could you tell me what methods you have taken in training to ensure stability?

shengkelong avatar Jun 24 '21 03:06 shengkelong

We restrict the range on exp() and apply gradient clipping. For reasons please refer to https://github.com/pkuxmq/Invertible-Image-Rescaling/issues/24.

pkuxmq avatar Jun 24 '21 09:06 pkuxmq

Thank you for your impressive work. But when I try to recurrent this network(I rewrite the code myself), sometimes the loss will suddenly increase by 10 times. The structure of the network is correct because I can load the pretrained network, so I think there may be some details I didn't notice. Could you tell me what methods you have taken in training to ensure stability?

I have also encountered this issue recently, despite using gradient clipping strategy. How did you solve it?

Feynman1999 avatar Sep 11 '23 02:09 Feynman1999