https://github.com/shivendrra/axon
numpy written in python from scratch
https://github.com/shivendrra/axon
array-manipulations autograd-engine axon c micrograd python scalar-values tensor-library
Last synced: 2 months ago
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numpy written in python from scratch
- Host: GitHub
- URL: https://github.com/shivendrra/axon
- Owner: shivendrra
- License: mit
- Created: 2024-06-21T10:36:47.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T06:19:03.000Z (8 months ago)
- Last Synced: 2024-10-06T19:06:29.827Z (7 months ago)
- Topics: array-manipulations, autograd-engine, axon, c, micrograd, python, scalar-values, tensor-library
- Language: Python
- Homepage: https://pypi.org/project/axon-pypi/
- Size: 1.44 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Axon Library
**Axon:** is a lightweight Python library for creating and manipulating multi-dimensional arrays, inspired by libraries such as NumPy. It's written in python only, for now.
**Axon.micro:** You have seen [Micrograd](https://github.com/karpathy/micrograd) by Karpathy, this is the upgraded version of micrograd written in c/c++ & has more functions & operational support. A light weight scalar-level autograd engine written in c/c++ & python
## Features
- Element-wise operations (addition, multiplication, etc.)
- Matrix multiplication
- Broadcasting
- Activation functions (ReLU, tanh, sigmoid, GELU)
- Reshape, transpose, flatten
- Data type conversion
- Micrograd support(Scalar level autograd engine)
## Installation
Clone the repository:
```bash
git clone https://github.com/shivendrra/axon.git
cd axon
```
or
Install via pip:
```bash
pip install axon-pypi
```
## Usage
You can use this similar to micrograd to build a simple neural network or do scalar level backprop.
#### Axon.array
```python
import axon
from axon import array
# Create two 2D arrays
a = array([[1, 2], [3, 4]], dtype=axon.int32)
b = array([[5, 6], [7, 8]], dtype=axon.int32)
# Addition
c = a + b
print("Addition:\n", c)
# Multiplication
d = a * b
print("Multiplication:\n", d)
# Matrix Multiplication
e = a @ b
print("Matrix Multiplication:\n", e)
```
### Output:
```
Addition:
array([6, 8], [10, 12], dtype=int32)
Multiplication:
array([5, 12], [21, 32], dtype=int32)
Matrix Multiplication:
array([19, 22], [43, 50], dtype=int32)
```
anyway, prefer documentation for detailed usage guide:
1. [axon.md](https://github.com/shivendrra/axon/blob/main/docs/axon.md): for development purpose
2. [usage.md](https://github.com/shivendrra/axon/blob/main/docs/usage.md): for using it like numpy
3. [axon_micro.md]((https://github.com/shivendrra/axon/blob/main/docs/axon_micro.md)): for axon.micro i.e. scalar autograd engine
#### Axon.micro
```python
from axon.micro import scalar
a = scalar(2)
b = scalar(3)
c = a + b
d = a * b
e = c.relu()
f = d ** 2.0
f.backward()
print(a)
print(b)
print(c)
print(d)
print(e)
print(f)
```
you can even checkout [example](https://github.com/shivendrra/axon/tree/main/examples) neural networks to run them on your system, or build your own :-D.
## Forking the Repository
If you would like to contribute to this project, you can start by forking the repository:
1. Click the "Fork" button at the top right of this page.
2. Clone your forked repository to your local machine:
```bash
git clone https://github.com/shivendrra/axon.git
```
3. Create a new branch:
```bash
git checkout -b my-feature-branch
```
4. Make your changes.
5. Commit and push your changes:
```bash
git add .
git commit -m "Add my feature"
git push origin my-feature-branch
```
6. Create a pull request on the original repository.
## Testing
To run the unit tests you will have to install PyTorch & Numpy, which the tests use as a reference for verifying the correctness of the calculated gradients & calculated values. Then simply run each file according to your prefrence:
```shell
python -m tests/test_array.py # for testing the axon functions with numpy
python -m tests/test_micro.py # for testing the axon.micro functions with pytorch
```
## Contributing
We welcome contributions! Please follow these steps to contribute:
1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes.
4. Ensure all tests pass.
5. Submit a pull request with a clear description of your changes.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.