Comments (5)
juntang-zhuang
commented on September 25, 2024
thanks for feedback, could you provide more details, such as which version? If possible please copy the error message here. will look into it later.
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juntang-zhuang
commented on September 25, 2024
I tested with PyTorch 16 and adabelief-pytorch 0.0.5, seems works fine. Did you use some older version?
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mcmingchang
commented on September 25, 2024
train centernet and use resume
…---Original---
From: "Juntang Zhuang"<[email protected]>
Date: Thu, Oct 22, 2020 00:27 AM
To: "juntang-zhuang/Adabelief-Optimizer"<[email protected]>;
Cc: "mcmingchang"<[email protected]>;"Author"<[email protected]>;
Subject: Re: [juntang-zhuang/Adabelief-Optimizer] keyerror exp_avg_var (#7)
I tested with PyTorch 16 and adabelief-pytorch 0.0.5, seems works fine. Did you use some older version?
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mcmingchang
commented on September 25, 2024
在训练centernet的时候，之前是用adam，更新优化器后再resume出现上述问题
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juntang-zhuang
commented on September 25, 2024
You can not simply resume adabelief from an adam, though you can resume model weights. Model keys in the states of optimizers are different. PS, epsilon for adabelief needs to bet set roughly as the square of eps in Adam if you want them to behave similar.
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Related Issues (20)
- Please add a license HOT 1
- Upgrade with Adas optimizer HOT 3
- MSVAG HOT 1
- Why does g_t substract m_t, instead of m_{t-1} ? HOT 1
- On imagenet accuracy result 70.08 HOT 1
- Documentation (at least for TF) and weight_decouple is not an option HOT 2
- FileNotFoundError for ImageNet HOT 1
- Changing init learning rate HOT 2
- Question about SGD optimizer in LSTM experiments HOT 1
- Compatibility with warmup HOT 2
- Inconsistent computation of weight_decay and grad_residual among pytorch versions HOT 5
- Your method is just equivalent to SGD with a changable global learning rate. HOT 3
- Some questions related to import adabelief HOT 2
- Tensorflow restoration issue HOT 1
- weight_decouple in adabelief tf HOT 1
- Inconsistent use of epsilon HOT 4
- Suppressing weight decoupling and rectification messages HOT 1
- The problem of reproducing the result of ImageNet HOT 4
- AttributeError: 'AdaBeliefOptimizer' object has no attribute '_set_hyper' HOT 4
- loss become nan when beta1=0
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