Deep learning library made with numpy in the style of Keras API
Project description
nano-keras
Overview
nano-keras is a deep learning library written in Python using NumPy. It's designed to handle the creation and training process of most neural network types, allowing you for quick and easy prototyping and deployment.
The project is heavily inspired by Keras, the most popular deep learning API in the world, as I'm trying to implement my library in simmilar style and functionality to Keras
Key Features
- Simplicity: Built using Python and NumPy, making it easy to read and understand each part
- Educational: Intended as a learning tool to understand neural network components at a lower level
- Customization: Allows for tinkering and understanding the core mechanics of neural network operations
What you can find in nano-keras
Layers: Dense, Dropout, Reshaping layers, Convolutional layers, Pooling layers and Recurrental Layers
Optimizers: SGD, Adam, Adadelta, Adagrad, RMSProp, NAdam and much more
Activation functions: Sigmoid, Tanh, ReLU, ELU, LeakyReLU, Softmax
Loss functions: MAE, MSE, BCE, CCE, Hinge, Huber
Callbacks: EarlyStopping, LearningRateScheduler, CSVLogger
And much more, you can find all the implemented items in here
Instalation
nano-keras is available on PyPI so in order to download it open a terminal and paste:
pip install nano-keras
You now should have succesfully installed nano-keras so to use it in your python file you only need to import it like this:
import nano_keras
If you have an issue message me on github or send me an email
Documentation
Documentation is under development and should be finished in the next few days
You can access it here
License
This project is licensed under the MIT License - see the LICENSE file for details
Special thanks
I'd like to thank my teacher, Mateusz Kozlowski, who inspired me to start working on this project and kept me motivated to finish this
Everyone who showed support for me in real life and on LinkedIn
Without you this project would've never come to life
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