Comments (2)
Thanks for the feedback!
I'll update readme file to clarify the configurations for tf version.
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It's also in the log for version 2.0:
�[31mPlease check your arguments if you have upgraded adabelief-tf from version 0.0.1.
�[31mModifications to default arguments:
�[31m eps weight_decouple rectify
----------------------- ----- ----------------- -------------
adabelief-tf=0.0.1 1e-08 Not supported Not supported
>=0.1.0 (Current 0.2.0) 1e-14 supported default: True
�[34mSGD better than Adam (e.g. CNN for Image Classification) Adam better than SGD (e.g. Transformer, GAN)
---------------------------------------------------------- ----------------------------------------------
Recommended epsilon = 1e-7 Recommended epsilon = 1e-14
�[34mFor a complete table of recommended hyperparameters, see
�[34mhttps://github.com/juntang-zhuang/Adabelief-Optimizer
�[32mYou can disable the log message by setting "print_change_log = False", though it is recommended to keep as a reminder.
�[0m
But was I supposed to use version 2.1? The readme said 2.0 was the current version so that's what I went with.
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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
- 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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