README.md

LearnableSELUVariation Activation Function

The LearnableSELUVariation activation function is designed to dynamically adjust its behavior to better fit specific data characteristics by incorporating learnable parameters. This activation function combines the self-normalizing properties of SELU with additional flexibility, making it potentially more effective for complex patterns.

Mathematical Formula

The LearnableSELUVariation function is defined as follows:

$$f(x) = \lambda \cdot \left\{ \begin{array}{ll} x & \text{if } x > 0,\\ \alpha \cdot (e^{\beta \cdot x} - 1) + \gamma \cdot \sin(\omega \cdot x) & \text{if } x \leq 0. \end{array} \right.$$

Where ($\lambda$), ($\alpha$), ($\beta$), ($\gamma$), and ($\omega$) are learnable parameters, adjusting the function's behavior during training.

Code

LearnableSELUVariation.py

Installation

git clone https://github.com/ToyMath/LearnableSELUVariation.git
cd LearnableSELUVariation

Usage

TensorFlow Implementation

The TensorFlow implementation is provided above. Here's how to use it in a model:

import tensorflow as tf

class LearnableSELUVariation(tf.keras.layers.Layer):
    def __init__(self):
        super(LearnableSELUVariation, self).__init__()
        self.lambda_ = self.add_weight(name='lambda', shape=(), initializer=tf.constant_initializer(1.0507), trainable=True)
        self.alpha = self.add_weight(name='alpha', shape=(), initializer=tf.constant_initializer(1.67326), trainable=True)
        self.beta = self.add_weight(name='beta', shape=(), initializer=tf.constant_initializer(1.0), trainable=True)
        self.gamma = self.add_weight(name='gamma', shape=(), initializer=tf.constant_initializer(0.1), trainable=True)
        self.omega = self.add_weight(name='omega', shape=(), initializer=tf.constant_initializer(2.0), trainable=True)

    def call(self, inputs):
        return tf.where(inputs > 0, self.lambda_ * inputs,
                        self.lambda_ * (self.alpha * (tf.exp(self.beta * inputs) - 1) + self.gamma * tf.sin(self.omega * inputs)))

PyTorch Implementation

import torch
import torch.nn as nn

class LearnableSELUVariation(nn.Module):
    def __init__(self):
        super(LearnableSELUVariation, self).__init__()
        self.lambda_ = nn.Parameter(torch.tensor(1.0507))
        self.alpha = nn.Parameter(torch.tensor(1.67326))
        self.beta = nn.Parameter(torch.tensor(1.0))
        self.gamma = nn.Parameter(torch.tensor(0.1))
        self.omega = nn.Parameter(torch.tensor(2.0))

    def forward(self, inputs):
        return torch.where(inputs > 0, self.lambda_ * inputs,
                           self.lambda_ * (self.alpha * (torch.exp(self.beta * inputs) - 1) + self.gamma * torch.sin(self.omega * inputs)))

JAX Implementation

import jax.numpy as jnp
from jax import random, jit

class LearnableSELUVariation:
    def __init__(self, key):
        self.lambda_ = random.normal(key, ()) * 0.1 + 1.0507
        self.alpha = random.normal(key, ()) * 0.1 + 1.67326
        self.beta = random.normal(key, ()) * 0.1 + 1.0
        self.gamma = random.normal(key, ()) * 0.1 + 0.1
        self.omega = random.normal(key, ()) * 0.1 + 2.0
        # Initialize parameters here with JAX random if they are meant to be learnable

    @jit
    def __call__(self, inputs):
        return jnp.where(inputs > 0, self.lambda_ * inputs,
                         self.lambda_ * (self.alpha * (jnp.exp(self.beta * inputs) - 1) + self.gamma * jnp.sin(self.omega * inputs)))

Customization

The LearnableSELUVariation activation function is highly customizable through its learnable parameters. By training these parameters alongside the model's weights, LearnableSELUVariation can adapt its behavior to best suit the training data, potentially leading to

Citation

If you use LearnableSELUVariation in your research, please cite the following work:

@misc{LearnableSELUVariation-2024,
  author = {Aakash Apoorv},
  title = {LearnableSELUVariation},
  year = {2024},
  howpublished = {\url{https://github.com/ToyMath/LearnableSELUVariation}},
}