lili-activation 0.1.2

A custom non-monotonic activation function

lili_activation

Description

lili_activation is a custom activation function designed for TensorFlow and Keras frameworks. This package introduces fnm3, a sine-based transformation activation function that provides an alternative to traditional activation functions like ReLU or sigmoid. The fnm3 function is particularly useful in scenarios where traditional activation functions might not capture complex patterns effectively.

Installation

Install lili_activation directly from PyPI using pip:

pip install lili_activation

Ensure that you have pip updated and tensorflow installed in your environment, as lili_activation_keras depends on TensorFlow.

Usage

To use fnm3 in your Keras model, follow these steps:

import tensorflow as tf

from lili_activation import fnm3

Simple model example using fnm3 as the activation function

model = tf.keras.Sequentia([ tf.keras.layers.Dense(10, input_shape=(10,), activation=fnm3), tf.keras.layers.Dense(1, activation='sigmoid') ])

model.compile(optimizer='adam', loss='binary_crossentropy') model.summary()

Features

Non-monotonic: Introduces a controlled non-monotonic that may be more suitable in scenarios where the relationships among input data are complex.

Innovative: Explores new avenues in activation functions that could prove beneficial in certain types of neural networks.

Contributions

Contributions are always welcome. If you have ideas for improvements or extensions, please feel free to create an issue or pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Lili Chen - [e-mail]

Acknowledgements

Special thanks to the TensorFlow and Keras community for providing an excellent platform for the experimentation and development of new ideas in machine learning.