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templates/LICENSE

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templates/MINIMAL_README.md

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+# PyNet
+
+[中文版本（Chinese version）](./MINIMAL_README.zh-CN.md)
+
+> Numpy-based deep learning library
+
+## Table of Contents
+
+- [Install](#install)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Install
+
+```
+```
+
+## Usage
+
+```
+```
+
+## Contributing
+
+PRs accepted.
+
+Small note:
+
+* Git submission specifications should be complied with [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0-beta.4/)
+* If versioned, please conform to the [Semantic Versioning 2.0.0](https://semver.org) specification
+* If editing the README, please conform to the [standard-readme](https://github.com/RichardLitt/standard-readme) specification.
+
+## License
+
+[Apache License 2.0](LICENSE) © 2019 zjZSTU

templates/MINIMAL_README.zh-CN.md

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+# PyNet
+
+[英文版本（English version）](./MINIMAL_README.md)
+
+> 基于numpy的深度学习库
+
+## 内容列表
+
+- [安装](#安装)
+- [用法](#用法)
+- [参与贡献方式](#参与贡献方式)
+- [许可证](#许可证)
+
+## 安装
+
+```
+```
+
+## 用法
+
+```
+```
+
+## 参与贡献方式
+
+接受合并请求
+
+请注意:
+
+* git提交请遵守[Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0-beta.4/)
+* 如果进行版本化，请遵守[Semantic Versioning 2.0.0](https://semver.org)规范
+* 如果修改README，请遵守[standard-readme](https://github.com/RichardLitt/standard-readme)规范
+
+## 许可证
+
+[Apache License 2.0](LICENSE) © 2019 zjZSTU

templates/STANDARD_README.md

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+# PyNet
+
+![](logo.png)
+
+[![standard-readme compliant](https://img.shields.io/badge/standard--readme-OK-green.svg?style=flat-square)](https://github.com/RichardLitt/standard-readme) [![Conventional Commits](https://img.shields.io/badge/Conventional%20Commits-1.0.0-yellow.svg)](https://conventionalcommits.org) [![Commitizen friendly](https://img.shields.io/badge/commitizen-friendly-brightgreen.svg)](http://commitizen.github.io/cz-cli/)
+
+[中文版本（Chinese version）](./STANDARD_README.zh-CN.md)
+
+> Numpy-based deep learning library
+
+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.
+
+## Table of Contents
+
+- [Background](#background)
+- [Badge](#badge)
+- [Install](#install)
+- [Usage](#usage)
+- [CHANGELOG](#CHANGELOG)
+- [TODO](#todo)
+- [Maintainers](#maintainers)
+- [Thanks](#Thanks)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Background
+
+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. 
+
+## Badge
+
+If you use PyNet, add the following Badge 
+
+[![pynet](https://img.shields.io/badge/pynet-ok-brightgreen)](https://github.com/zjZSTU/PyNet)
+
+To add in Markdown format, use this code:
+
+```
+[![pynet](https://img.shields.io/badge/pynet-ok-brightgreen)](https://github.com/zjZSTU/PyNet)
+```
+
+## Install
+
+PyNet need the following prerequisites
+
+* python3.x
+* numpy
+* opencv3.x
+
+## Usage
+
+Refer to the sample code under the [example](https://github.com/zjZSTU/PyNet/tree/master/examples) folder
+
+Full version reference [releases](https://github.com/zjZSTU/PyNet/releases)
+
+Realized Network Model（Located in [pynet/models](https://github.com/zjZSTU/PyNet/tree/master/pynet/models) folder）：
+
+* 2-Layer Neural Network
+* 3-Layer Neural Network
+* LeNet-5
+* AlexNet
+* NIN
+
+Realized Network Layer（Located in [pynet/nn](https://github.com/zjZSTU/PyNet/tree/master/pynet/nn) folder）：
+
+* Convolution Layer (Conv2d)
+* Fully-Connected Layer (FC)
+* Max-Pooling layer (MaxPool)
+* ReLU Layer (ReLU)
+* Random Dropout Layer (Dropout/Dropout2d)
+* Softmax
+* Cross Entropy Loss
+* Gloabl Average Pool (GAP)
+
+## CHANGELOG
+
+see the [CHANGELOG](./CHANGELOG) on this repository.
+
+## TODO
+
+* Realization of batch normalization
+* Realization of Computational Graph
+
+## Maintainers
+
+* zhujian - *Initial work* - [zjZSTU](https://github.com/zjZSTU)
+
+## Thanks
+
+Thank you for your participation.
+
+[![](https://avatars3.githubusercontent.com/u/13742735?s=460&v=4)](https://github.com/zjZSTU)
+
+Refer to the following Library
+
+* [cs231n](http://cs231n.github.io/)
+* [PyTorch](https://pytorch.org/)
+
+## Contributing
+
+Anyone's participation is welcome! Open an [issue](https://github.com/zjZSTU/PyNet/issues) or submit PRs.
+
+Small note:
+
+* Git submission specifications should be complied with [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0-beta.4/)
+* If versioned, please conform to the [Semantic Versioning 2.0.0](https://semver.org) specification
+* If editing the README, please conform to the [standard-readme](https://github.com/RichardLitt/standard-readme) specification.
+
+## License
+
+[Apache License 2.0](LICENSE) © 2019 zjZSTU

templates/STANDARD_README.zh-CN.md

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+
+# PyNet
+
+![](logo.png)
+
+[![standard-readme compliant](https://img.shields.io/badge/standard--readme-OK-green.svg?style=flat-square)](https://github.com/RichardLitt/standard-readme) [![Conventional Commits](https://img.shields.io/badge/Conventional%20Commits-1.0.0-yellow.svg)](https://conventionalcommits.org) [![Commitizen friendly](https://img.shields.io/badge/commitizen-friendly-brightgreen.svg)](http://commitizen.github.io/cz-cli/)
+
+[英文版本（English version）](./STANDARD_README.md)
+
+> 基于Numpy的深度学习库
+
+基于`Numpy`的深度学习实现，模块化设计保证模型的轻松实现，适用于深度学习初级研究人员的入门
+
+## 内容列表
+
+- [背景](#背景)
+- [徽章](#徽章)
+- [安装](#安装)
+- [用法](#用法)
+- [版本更新日志](#版本更新日志)
+- [待办事项](#待办事项)
+- [主要维护人员](#主要维护人员)
+- [致谢](#致谢)
+- [参与贡献方式](#参与贡献方式)
+- [许可证](#许可证)
+
+## 背景
+
+系统性的学习卷积神经网络也快半年了，使用`pytorch`等库不能很好的深入理解实现，所以打算从头完成一个深度学习框架。最开始的实现会参考`cs231n`的作业，之后会以计算图的方式实现。希望这个项目能够切实提高自己的编程能力，同时也能够帮助到其他人
+
+## 徽章
+
+如果你使用了`PyNet`，请添加以下徽章
+
+[![pynet](https://img.shields.io/badge/pynet-ok-brightgreen)](https://github.com/zjZSTU/PyNet)
+
+Markdown格式代码如下：
+
+```
+[![pynet](https://img.shields.io/badge/pynet-ok-brightgreen)](https://github.com/zjZSTU/PyNet)
+```
+
+## 安装
+
+`PyNet`需要以下必备条件
+
+* `python3.x`
+* `numpy`
+* `opencv3.x`
+
+## 用法
+
+参考[example](https://github.com/zjZSTU/PyNet/tree/master/examples)文件夹下的示例代码
+
+完整版本参考[releases](https://github.com/zjZSTU/PyNet/releases)
+
+已实现网络模型（位于[pynet/models](https://github.com/zjZSTU/PyNet/tree/master/pynet/models)文件夹）：
+
+* `2`层神经网络
+* `3`层神经网络
+* `LeNet-5`
+* `AlexNet`
+* `NIN`
+
+已实现网络层（位于[pynet/nn](https://github.com/zjZSTU/PyNet/tree/master/pynet/nn)文件夹）：
+
+* 卷积层
+* 全连接层
+* 最大池化层
+* `ReLU`
+* 随机失活
+* `Softmax`
+* 交叉熵损失
+* 全局平均池化层
+
+## 版本更新日志
+
+请参阅仓库中的[CHANGELOG](./CHANGELOG)
+
+## 待办事项
+
+* 批量归一化实现
+* 计算图实现
+
+## 主要维护人员
+
+* zhujian - *Initial work* - [zjZSTU](https://github.com/zjZSTU)
+
+## 致谢
+
+感谢以下人员的参与
+
+[![](https://avatars3.githubusercontent.com/u/13742735?s=460&v=4)](https://github.com/zjZSTU)
+
+参考以下库
+
+* [cs231n](http://cs231n.github.io/)
+* [PyTorch](https://pytorch.org/)
+
+## 参与贡献方式
+
+欢迎任何人的参与！打开[issue](https://github.com/zjZSTU/PyNet/issues)或提交合并请求。
+
+注意:
+
+* `git`提交请遵守[Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0-beta.4/)
+* 如果进行版本化，请遵守[Semantic Versioning 2.0.0](https://semver.org)规范
+* 如果修改README，请遵守[standard-readme](https://github.com/RichardLitt/standard-readme)规范
+
+## 许可证
+
+[Apache License 2.0](LICENSE) © 2019 zjZSTU