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A large database of artificial neural network statistics during training

lr-identify 최신버젼 다운로드

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// repository documentation

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# Identifying Learning Rules From Neural Network Observables

**Aran Nayebi\*, Sanjana Srivastava\*, Surya Ganguli, Daniel L. K. Yamins**

*34th Conference on Neural Information Processing Systems (NeurIPS 2020)*

[Preprint](https://arxiv.org/abs/2010.11765)

## Getting started

First clone this repo, then install dependencies
```
pip install -r requirements.txt
```
We recommend Python 3.6 if you run the above requirements file.

For users who use older versions of Python, we note that in the original paper:
- We used Python 2.7, so the code is backwards-compatible with this version of Python.
- We used TensorFlow 1.13.1 for all of our model training experiments on TPU and for generating these observable statistics on GPU.
- We used `numpy` 1.16.3, `scipy` 1.2.1, and `scikit-learn` 0.20.4 to train classifiers on these generated observable statistics.

## Tutorial

See this [Google Colab notebook](https://colab.research.google.com/drive/1Uvi0Q-JQ9VJsJiADMoL2eB_z65FzMNfm?usp=sharing) for a brief tutorial on the use of many parts of this codebase, including analyzing the dataset, visualizing saved classifier results, and training your own classifiers on the dataset.

## Downloading the dataset

To download the dataset of generated observable statistics, simply run

```
./get_dataset.sh
```
This will save `dataset.pkl` to the current directory.

## Downloading saved classifier results

To download results from pretrained classifiers (Random Forest, Linear SVM, and Conv1D MLP) when trained to separate all four learning rules on ten category-balanced 75%/25% train/test splits of the data using all of the observable statistics, simply run

```
./get_saved_results.sh
```
This will save the `.pkl` files to a new directory called `saved_classifier_results/`.

## TensorFlow implementations

The `tensorflow/` folder contains our implementations of models (`tensorflow/Models/`) and observable statistics (`tensorflow/Metrics/functions.py`).
It is mainly intended to be used for reference, as the code there is not meant to run.

## Cite

If you used this dataset or codebase for your research, please consider citing our paper:
```
@inproceedings{lridentify2020,
    title={Identifying Learning Rules From Neural Network Observables},
    author={Nayebi, Aran and Srivastava, Sanjana and Ganguli, Surya and Yamins, Daniel LK},
    booktitle={The 34th Conference on Neural Information Processing Systems (NeurIPS 2020)},
    url={https://arxiv.org/abs/2010.11765},
    year={2020}
}
```
## Contact

If you have any questions or encounter issues, either submit a Github issue here or email `anayebi@stanford.edu`.

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