| Name | light-recurrent-unit-pytorch JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | Light Recurrent Unit |
| upload_time | 2024-08-31 12:31:08 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| 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 to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
artificial intelligence
deep learning
recurrent neural networks
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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No Travis.
|
| coveralls test coverage |
No coveralls.
|
<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
cell = LightRecurrentUnitCell(256)
x = torch.randn(2, 256)
hidden = torch.randn(2, 256)
next_hidden = cell(x, hidden) # (2, 256)
```
Single layer
```python
import torch
from light_recurrent_unit_pytorch import LightRecurrentUnitLayer
layer = LightRecurrentUnitLayer(256)
x = torch.randn(2, 1024, 256)
out = layer(x) # (2, 1024, 256)
assert out.shape == x.shape
```
Stacked
```python
import torch
from light_recurrent_unit_pytorch import LightRecurrentUnit
lru = LightRecurrentUnit(256, depth = 4)
x = torch.randn(2, 1024, 256)
out, layer_hiddens = lru(x) # (2, 1024, 256), List[(2, 256)]
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}
}
```
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"description": "<img src=\"./lru.png\" width=\"400px\"></img>\n\n## Light Recurrent Unit - Pytorch\n\nImplementation of the <a href=\"https://www.mdpi.com/2079-9292/13/16/3204\">Light Recurrent Unit</a> in Pytorch\n\n## Install\n\n```bash\n$ pip install light-recurrent-unit-pytorch\n```\n\n## Usage\n\n```python\nimport torch\nfrom light_recurrent_unit_pytorch import LightRecurrentUnitCell\n\ncell = LightRecurrentUnitCell(256)\n\nx = torch.randn(2, 256)\nhidden = torch.randn(2, 256)\n\nnext_hidden = cell(x, hidden) # (2, 256)\n```\n\nSingle layer\n\n```python\nimport torch\nfrom light_recurrent_unit_pytorch import LightRecurrentUnitLayer\n\nlayer = LightRecurrentUnitLayer(256)\n\nx = torch.randn(2, 1024, 256)\n\nout = layer(x) # (2, 1024, 256)\n\nassert out.shape == x.shape\n```\n\nStacked\n\n```python\nimport torch\nfrom light_recurrent_unit_pytorch import LightRecurrentUnit\n\nlru = LightRecurrentUnit(256, depth = 4)\n\nx = torch.randn(2, 1024, 256)\n\nout, layer_hiddens = lru(x) # (2, 1024, 256), List[(2, 256)]\n\nassert out.shape == x.shape\n```\n\n## Citations\n\n```bibtex\n@Article{electronics13163204,\n AUTHOR = {Ye, Hong and Zhang, Yibing and Liu, Huizhou and Li, Xuannong and Chang, Jiaming and Zheng, Hui},\n TITLE = {Light Recurrent Unit: Towards an Interpretable Recurrent Neural Network for Modeling Long-Range Dependency},\n JOURNAL = {Electronics},\n VOLUME = {13},\n YEAR = {2024},\n NUMBER = {16},\n ARTICLE-NUMBER = {3204},\n URL = {https://www.mdpi.com/2079-9292/13/16/3204},\n ISSN = {2079-9292},\n DOI = {10.3390/electronics13163204}\n}\n```\n",
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