Comments (3)
juntang-zhuang
commented on September 25, 2024
Please write model in TensorFlow not Keras, as example in the jupyter notebook.
Currently I don't have a version compatible with Keras, actually I'm totally confused by the way Keras implements Adam and AdamW, so I don't have a good implementation for tensorflow.
Also note that decoupled weight decay is not available in adablief-tf. Apologize for this, but I recommend using adabelief-pytorch, as stated it's the only version I have extensively tested for the paper.
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ben-arnao
commented on September 25, 2024
Please write model in TensorFlow not Keras, as example in the jupyter notebook.
Currently I don't have a version compatible with Keras, actually I'm totally confused by the way Keras implements Adam and AdamW, so I don't have a good implementation for tensorflow.Also note that decoupled weight decay is not available in adablief-tf. Apologize for this, but I recommend using adabelief-pytorch, as stated it's the only version I have extensively tested for the paper.
Ok gotcha, thanks for the response
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juntang-zhuang
commented on September 25, 2024
@ben-arnao Hi, we have released "adabelief-tf==0.1.0", which is available on pip. It supports Tensorflow>=2.0 and Keras, and supports decoupled weight and rectification as the PyTorch implementation.
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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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