keras-custom 0.1.1
Custom Layers in keras 3
Requires Python: >=3.9


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## 👋 Welcome to keras custom documentation!

**Keras Custom** is a Python library that extends Keras with custom, non-native classes and modules designed to enhance model manipulation. This library introduces powerful new features, including advanced model analysis tools and utilities, that are not available in Keras by default. It provides a clear, modular framework built on top of Keras, making it an invaluable tool for researchers, educators, and developers working in deep learning.

The new non-native classes in **Keras Custom** enable users to efficiently analyze, modify, and optimize Keras-based neural models for a variety of downstream tasks. These custom components open up new possibilities for customizing and extending Keras models beyond the built-in functionality.

Whether you're exploring new architectures, conducting research, or building complex deep learning workflows, **Keras Custom** offers the flexibility and power to streamline your work.

## 📚 Table of contents

- [📚 Table of contents](#-table-of-contents)
- [🔥 Tutorials](#-tutorials)
- [🚀 Quick Start](#-quick-start)
- [📦 What's Included](#-whats-included)
- [👍 Contributing](#-contributing)
- [🙏 Acknowledgments](#-acknowledgments)
- [📝 License](#-license)

## 🚀 Quick Start

You can install ``keras custom`` directly from pypi:

```python
pip install keras_custom
```

In order to use ``keras custom``, you also need a [valid Keras
installation](https://keras.io/getting_started/). ``keras custom``
supports Keras versions 3.x.

## 🔥 Tutorials

| **Tutorial Name**           | Notebook                                                                                                                                                           |
| :-------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Model splitting - Splitting an existing models into a sequence of nested models | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ducoffeM/keras_custom/blob/main/tutorials/ModelSplitting.ipynb)            |
| Model switching - Conversion to channel first to channel last and vice versa | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ducoffeM/keras_custom/blob/main/tutorials/SwitchingChannel.ipynb)            |
| Model fusion - Combining a sequence of models into a single model with only Layers | Stay tuned !


Documentation is available [**online**](https://ducoffeM.github.io/keras_custom/index.html).


## 👍 Contributing

#To contribute, you can open an
#[issue](https://github.com/ducoffeM/keras_custom/issues), or fork this
#repository and then submit changes through a
#[pull-request](https://github.com/ducoffeM/keras_custom/pulls).
We use [black](https://pypi.org/project/black/) to format the code and follow PEP-8 convention.
To check that your code will pass the lint-checks, you can run:

```python
tox -e py39-lint
```

You need [`tox`](https://tox.readthedocs.io/en/latest/) in order to
run this. You can install it via `pip`:

```python
pip install tox
```


## 🙏 Acknowledgments

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This project received funding from the French program within the <a href="https://aniti.univ-toulouse.fr/">Artificial and Natural Intelligence Toulouse Institute (ANITI)</a>. The authors gratefully acknowledge the support of the <a href="https://www.deel.ai/"> DEEL </a> project.



## 📝 License

The package is released under <a href="https://choosealicense.com/licenses/mit"> MIT license</a>.