Metadata-Version: 2.3
Name: light-recurrent-unit-pytorch
Version: 0.0.8
Summary: Light Recurrent Unit
Project-URL: Homepage, https://pypi.org/project/light-recurrent-unit-pytorch/
Project-URL: Repository, https://github.com/lucidrains/light-recurrent-unit-pytorch
Author-email: Phil Wang <lucidrains@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Phil Wang
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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License-File: LICENSE
Keywords: artificial intelligence,deep learning,recurrent neural networks
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Requires-Dist: torch>=2.0
Provides-Extra: examples
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

<img src="./lru.png" width="400px"></img>

## Light Recurrent Unit - Pytorch

Implementation of the <a href="https://www.mdpi.com/2079-9292/13/16/3204">Light Recurrent Unit</a> in Pytorch

## Install

```bash
$ pip install light-recurrent-unit-pytorch
```

## Usage

```python
import torch
from light_recurrent_unit_pytorch import LightRecurrentUnitCell

lru_cell = LightRecurrentUnitCell(256)

x = torch.randn(2, 256)
hidden = torch.randn(2, 256)

next_hidden = lru_cell(x, hidden) # (2, 256)
```

Single layer

```python
import torch
from light_recurrent_unit_pytorch import LightRecurrentUnitLayer

lru_layer = LightRecurrentUnitLayer(256)

x = torch.randn(2, 1024, 256)

output = lru_layer(x) # (2, 1024, 256)
assert x.shape == output.shape
```

Stacked

```python
import torch
from light_recurrent_unit_pytorch import LightRecurrentUnit

lru = LightRecurrentUnit(256, depth = 4)

x = torch.randn(2, 1024, 256)

out = lru(x)

assert out.shape == x.shape
```

## Citations

```bibtex
@Article{electronics13163204,
    AUTHOR = {Ye, Hong and Zhang, Yibing and Liu, Huizhou and Li, Xuannong and Chang, Jiaming and Zheng, Hui},
    TITLE = {Light Recurrent Unit: Towards an Interpretable Recurrent Neural Network for Modeling Long-Range Dependency},
    JOURNAL = {Electronics},
    VOLUME = {13},
    YEAR = {2024},
    NUMBER = {16},
    ARTICLE-NUMBER = {3204},
    URL = {https://www.mdpi.com/2079-9292/13/16/3204},
    ISSN = {2079-9292},
    DOI = {10.3390/electronics13163204}
}
```
