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112 lines
3.5 KiB
Markdown
112 lines
3.5 KiB
Markdown
# PyNet
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[](https://github.com/RichardLitt/standard-readme) [](https://conventionalcommits.org) [](http://commitizen.github.io/cz-cli/)
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[中文版本（Chinese version）](./STANDARD_README.zh-CN.md)
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> Numpy-based deep learning library
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Implementation of deep learning based on numpy, modular design guarantees easy implementation of the model, which is suitable for the introduction of junior researchers in deep learning.
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## Table of Contents
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- [Background](#background)
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- [Badge](#badge)
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- [Install](#install)
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- [Usage](#usage)
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- [CHANGELOG](#CHANGELOG)
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- [TODO](#todo)
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- [Maintainers](#maintainers)
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- [Thanks](#Thanks)
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- [Contributing](#contributing)
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- [License](#license)
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## Background
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Systematic learning convolution neural network has been nearly half a year.Using librarys such as pytorch can't understand the implementation in depth. So I plan to complete a deep learning framework from scratch.The initial implementation will refer to the operation of cs231n, and then we will implement it in the form of computational graphs. I hope this project can improve my programming ability and help others at the same time.
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## Badge
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If you use PyNet, add the following Badge
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[](https://github.com/zjZSTU/PyNet)
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To add in Markdown format, use this code:
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```
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[](https://github.com/zjZSTU/PyNet)
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```
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## Install
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PyNet need the following prerequisites
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* python3.x
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* numpy
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* opencv3.x
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## Usage
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Refer to the sample code under the [example](https://github.com/zjZSTU/PyNet/tree/master/examples) folder
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Full version reference [releases](https://github.com/zjZSTU/PyNet/releases)
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Realized Network Model（Located in [pynet/models](https://github.com/zjZSTU/PyNet/tree/master/pynet/models) folder）：
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* 2-Layer Neural Network
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* 3-Layer Neural Network
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* LeNet-5
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* AlexNet
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* NIN
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Realized Network Layer（Located in [pynet/nn](https://github.com/zjZSTU/PyNet/tree/master/pynet/nn) folder）：
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* Convolution Layer (Conv2d)
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* Fully-Connected Layer (FC)
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* Max-Pooling layer (MaxPool)
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* ReLU Layer (ReLU)
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* Random Dropout Layer (Dropout/Dropout2d)
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* Softmax
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* Cross Entropy Loss
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* Gloabl Average Pool (GAP)
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## CHANGELOG
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see the [CHANGELOG](./CHANGELOG) on this repository.
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## TODO
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* Realization of batch normalization
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* Realization of Computational Graph
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## Maintainers
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* zhujian - *Initial work* - [zjZSTU](https://github.com/zjZSTU)
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## Thanks
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Thank you for your participation.
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[](https://github.com/zjZSTU)
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Refer to the following Library
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* [cs231n](http://cs231n.github.io/)
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* [PyTorch](https://pytorch.org/)
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## Contributing
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Anyone's participation is welcome! Open an [issue](https://github.com/zjZSTU/PyNet/issues) or submit PRs.
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Small note:
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* Git submission specifications should be complied with [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0-beta.4/)
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* If versioned, please conform to the [Semantic Versioning 2.0.0](https://semver.org) specification
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* If editing the README, please conform to the [standard-readme](https://github.com/RichardLitt/standard-readme) specification.
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## License
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[Apache License 2.0](LICENSE) © 2019 zjZSTU
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