tensorflow / tensorflow/model-optimization
How to preform progressive quantization aware training using this framework?
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- Python
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Description
Hi,
I would like to know how can I perform a progressive quantization aware training using this framework?
For example, if I have to training my model in the below order
- Training the model by quantizing the activation outputs
- Then following that, I have to training weights with various bit widths
Can anyone please help me on how do I utilise this framework for that purpose?
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the framework's quantization-aware training entry points and documentation for activation and weight quantization. Determine whether the requested two-stage progressive sequence is supported, and document the supported procedure or its limitation with a concrete example.
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Assessment
- Tech stack
- keras, python, tensorflow
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100