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Ptnn4both datatypes and alignment tests (#1827)

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* Init model for both dataset

* Remove some deprecated code

* Add model template;

* We must align with previous results

* We choose another mode as the initial version

* Almost success to run GRU

* Successfully run training

* Passed general_nn test

* gru test

* Alignment test passed

* comment

* fix readme & minor errors

* general nn updates & benchmarks

* Update examples/benchmarks/GeneralPtNN/workflow_config_gru2mlp.yaml

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Co-authored-by: Young <afe.young@gmail.com>
Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
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2024-07-11 17:59:18 +08:00
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# Introduction
What is GeneralPtNN
- Fix previous design that fail to support both Time-series and tabular data
- Now you can just replace the Pytorch model structure to run a NN model.
We provide an example to demonstrate the effectiveness of the current design.
- `workflow_config_gru.yaml` align with previous results [GRU(Kyunghyun Cho, et al.)](../README.md#Alpha158-dataset)
- `workflow_config_gru2mlp.yaml` to demonstrate we can convert config from time-series to tabular data with minimal changes
- You only have to change the net & dataset class to make the conversion.
- `workflow_config_mlp.yaml` achieved similar functionality with [MLP](../README.md#Alpha158-dataset)
# TODO
- We will align existing models to current design.
- The result of `workflow_config_mlp.yaml` is different with the result of [MLP](../README.md#Alpha158-dataset) since GeneralPtNN has a different stopping method compared to previous implementations. Specificly, GeneralPtNN controls training according to epoches, whereas previous methods controlled by max_steps.