DemonRangerOptimizer
Gradient-Centralization-TensorFlow
| DemonRangerOptimizer | Gradient-Centralization-TensorFlow | |
|---|---|---|
| 1 | 5 | |
| 23 | 105 | |
| - | - | |
| 0.0 | 0.0 | |
| over 3 years ago | about 2 years ago | |
| Python | Python | |
| - | Apache License 2.0 |
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DemonRangerOptimizer
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[R] AdasOptimizer Update: Cifar-100+MobileNetV2 Adas generalizes with Adas 15% better and 9x faster than Adam
The results are interesting, but in terms of novelty of the main theory - isn't it almost identical to Baydin et al.? https://arxiv.org/pdf/1703.04782.pdf It seems the difference may be in some implementation details, like using a running average for the past gradient. If it's useful, I implemented a bunch of optimizers with options to synergize different techniques (https://github.com/JRC1995/DemonRangerOptimizer) including hypergradient updates for stuffs (and taking into account decorrelated weight decay and per-parameter lrs for hypergradient lr) when I was bored before practically abandoning it all together. I didn't really run any experiments with it though, but some people tried although they may not have got any particularly striking results.
Gradient-Centralization-TensorFlow
- How many epochs of constant loss (e.g. 0.6933) will cause you to try something else?
- A Python package to boost your ML training performance
- A Python package to sizably boost your ML performance
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[P] I made gctf: a package to sizeably improve your performance (link in comments)
GitHub repo: https://github.com/Rishit-dagli/Gradient-Centralization-TensorFlow
- A Python package to sizably increase your ML Training performance
What are some alternatives?
pytorch-optimizer - torch-optimizer -- collection of optimizers for Pytorch
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AdasOptimizer - ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance
Im2txt - Image captioning ready-to-go inference: show and tell model compatible with Tensorflow r1.9
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RAdam - On the Variance of the Adaptive Learning Rate and Beyond
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
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