mushroom-x / K210_tutorial
Licence: mit
K210基础入门教程 edit by Kyle阿凯
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K210基础入门
作者: Kyle阿凯
关于课程
课程的第一部分是通过训练神经网络识别手写字符X跟O， 学会使用Tensorflow创建模型，并转换为K210的模型格式。
第二部分是教你用k210的SDK, 调用之前的模型，运算完成之后在LCD液晶屏上显示识别结果。
模型训练与转换
先尝试做一下分类，目标是通过神经网络分类，然后完成Tic-Tac-Toe的游戏．
数据集可以来自于手写字母，提取字母中的X跟O
使用Tensorflow构建一个神经网络模型，用于识别字符X跟字符O. 使用上节课处理过的训练数据, 对模型进行训练. 训练完成之后，冻结模型, 导出pb模型文件。
通过TensorBoard查看神经网络模型结构
Tensorflow模型的后缀是pb, TFLite模型的后缀是tflite.
从pb格式转换为tflite格式, 需要使用Maix_Toolbox 根目录下的pb2tflite.sh脚本。
使用nncase工具箱与MaixToolbox里面的工具完成K210模型转换。
K210 SDK编程指南
关于如何自己训练一个卷积神经网络，并在MAIX开发板上运行，Zepan已经在sipeed的Blog写了一篇比较完整的教程：30分钟训练，转换，运行MNIST于MAIX开发板
本教程主要是对mnist的K210例程做一个详细的解读, 对原来例程里面的main.c做了逐行注释.
需要注意的是Sipeed的Lichee K210 Examples 年久失修，已经跟最新版本的K210 ToolChain 还有 SDK不兼容了， 后续有时间重构一份源码。
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