Metadata-Version: 2.4
Name: nonlinear
Version: 0.0.11
Summary: A hand-curated collections of activations functions for deep learning research.
Author-email: Taha Shieenavaz <tahashieenavaz@gmail.com>
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Project-URL: Homepage, https://github.com/tahashieenavaz/nonlinear
Project-URL: Repository, https://github.com/tahashieenavaz/nonlinear
Project-URL: Documentation, https://github.com/tahashieenavaz/nonlinear#readme
Keywords: activation function,deep learning,machine learning,non-linearity
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Dynamic: license-file

# Non-Linear

A hand-curated collections of activations functions for deep learning research. 

## Channel-Based Activation Functions

<div align="center">

| Activation Function | Formula |
|---|---|
| [FPAF](nonlinear/channel_based/FPAF.py) | $f(x) = a \cdot \mu(x)$ if $x \geq 0$, else $b \cdot \nu(x)$ |
| [DPReLU](nonlinear/channel_based/DPReLU.py) | $f(x) = ax$ if $x \geq 0$, else $bx$ |
| [DualLine](nonlinear/channel_based/DualLine.py) | $f(x) = ax + m$ if $x \geq 0$, else $bx + m$ |
| [FReLU](nonlinear/channel_based/FReLU.py) | $f(x) = \text{ReLU}(x) + b$ |
| [LeLeLU](nonlinear/channel_based/LeLeLU.py) | $f(x) = ax$ if $x \geq 0$, else $0.01ax$ |
| [PREU](nonlinear/channel_based/PREU.py) | $f(x) = ax$ if $x \geq 0$, else $ax \exp(bx)$ |
| [ShiLU](nonlinear/channel_based/ShiLU.py) | $f(x) = a \cdot \text{ReLU}(x) + b$ |
| [StarReLU](nonlinear/channel_based/StarReLU.py) | $f(x) = a \cdot \text{ReLU}(x)^2 + b$ |
| [EPReLU](nonlinear/channel_based/EPReLU.py) | $f(x) = kx$ if $x \geq 0$, else $\frac{x}{a}$ (where $k$ updates dynamically) |
| [PairedReLU](nonlinear/channel_based/PairedReLU.py) | $f(x) = \left[\text{ReLU}(ax - b), \text{ReLU}(cx - d)\right]$ (concatenated along channel dimension) |
| [RMAF](nonlinear/channel_based/RMAF.py) | $f(x) = \frac{abx}{(0.25(1 + \exp(-x)) + 0.75)^c}$ |
| [PTELU](nonlinear/channel_based/PTELU.py) | $f(x) = x$ if $x \geq 0$, else $\vert a \vert \tanh(\vert b \vert x)$ |
| [TaLU](nonlinear/channel_based/TaLU.py) | $f(x) = x$ if $x \geq \vert b \vert$; $\tanh(x)$ if $x > \vert a \vert$; else $\tanh(\vert a \vert)$ |
| [TanhLU](nonlinear/channel_based/TanhLU.py) | $f(x) = a \tanh(cx) + bx$ |

</div>
