# torchact
<div align="center">
TorchAct, collection of activation function for PyTorch.
---
|  [](https://github.com/kaintels/torchact/actions/workflows/ci.yml) [](https://codecov.io/gh/kaintels/torchact) [](https://torchact.readthedocs.io/) |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|    [](https://badge.fury.io/py/torchact) [](https://pepy.tech/project/torchact)
|    [](https://github.com/psf/black)
</div>
## Quick Start
```python
import torch
import torch.nn as nn
import torchact.nn as actnn
model = nn.Sequential(
nn.Linear(5, 3),
actnn.ReLU(),
nn.Linear(3, 1),
nn.Sigmoid()
)
dummy = torch.rand(1, 5)
print(model(dummy))
```
## Installation
```shell
pip install torchact
```
## How to Contribute
Thanks for your contribution!
There are several steps for contributing.
0. Fork this repo (you can work dev branch.)
1. Install library using `requirements.txt`
2. Write your code in torchact folder.
3. Add your module in `__init__.py` (`__version__` cannot be changed. It will be decided later.)
For example.
```python
from .your_module import Your_Module
__all__ = ("ReLU", "SinLU", "Softmax", "Your_Module")
```
3. If you want to test case, Write test case.
For example.
```python
def test_has_attr():
for activation_name in __all__:
if activation_name == "Softmax":
assert hasattr(str_to_class(activation_name)(), "dim")
else:
pass
```
4. Run black style.`black .`
5. Send a PR. Code testing happens automatically. (PYPI is upgraded by the admin himself.)
## Citing TorchAct
To cite this repository:
```
@article{hantorchact,
title={TorchAct, collection of activation function for PyTorch.},
author={Seungwoo Han},
publisher={Engineering Archive},
doi={10.31224/2988},
url={https://engrxiv.org/preprint/view/2988}
year={2023}
}
```
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"description": "# torchact\r\n\r\n<div align=\"center\">\r\n\r\nTorchAct, collection of activation function for PyTorch.\r\n\r\n---\r\n\r\n|  [](https://github.com/kaintels/torchact/actions/workflows/ci.yml) [](https://codecov.io/gh/kaintels/torchact) [](https://torchact.readthedocs.io/) |\r\n|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\r\n|    [](https://badge.fury.io/py/torchact) [](https://pepy.tech/project/torchact) \r\n|    [](https://github.com/psf/black)\r\n\r\n</div>\r\n\r\n## Quick Start\r\n\r\n```python\r\nimport torch\r\nimport torch.nn as nn\r\nimport torchact.nn as actnn\r\n\r\nmodel = nn.Sequential(\r\n nn.Linear(5, 3),\r\n actnn.ReLU(),\r\n nn.Linear(3, 1),\r\n nn.Sigmoid()\r\n)\r\n\r\ndummy = torch.rand(1, 5)\r\nprint(model(dummy))\r\n```\r\n\r\n## Installation\r\n\r\n```shell\r\npip install torchact\r\n```\r\n\r\n## How to Contribute\r\n\r\nThanks for your contribution!\r\n\r\nThere are several steps for contributing.\r\n\r\n0. Fork this repo (you can work dev branch.)\r\n1. Install library using `requirements.txt`\r\n2. Write your code in torchact folder.\r\n3. Add your module in `__init__.py` (`__version__` cannot be changed. It will be decided later.)\r\n\r\nFor example.\r\n\r\n```python\r\nfrom .your_module import Your_Module\r\n__all__ = (\"ReLU\", \"SinLU\", \"Softmax\", \"Your_Module\")\r\n```\r\n\r\n3. If you want to test case, Write test case.\r\n\r\nFor example.\r\n\r\n```python\r\ndef test_has_attr():\r\n for activation_name in __all__:\r\n if activation_name == \"Softmax\":\r\n assert hasattr(str_to_class(activation_name)(), \"dim\")\r\n else:\r\n pass\r\n```\r\n\r\n4. Run black style.`black .`\r\n5. Send a PR. Code testing happens automatically. (PYPI is upgraded by the admin himself.)\r\n\r\n## Citing TorchAct\r\n\r\nTo cite this repository:\r\n\r\n```\r\n@article{hantorchact,\r\n title={TorchAct, collection of activation function for PyTorch.},\r\n author={Seungwoo Han},\r\n publisher={Engineering Archive},\r\n doi={10.31224/2988},\r\n url={https://engrxiv.org/preprint/view/2988}\r\n year={2023}\r\n}\r\n```\r\n",
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