TensorFlow Lite for Microcontrollers的Micro speech example實作

接續前面文章與環境設定,進行TensorFlow Lite for Microcontrollers的Micro speech example實作。這個實作對應SparkFun Edge、STM32F7 discovery kit與Arduino Nano 33 BLE Sense三塊開發板,SparkFun Edge與STM32F7 discovery kit開發板的實作環境在Ubuntu 16.04 64bits Linux,Arduino Nano 33 BLE Sense開發板的實作環境在Windows 7 64bits上。另外,請參照閱讀TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers一書的第7章與第8章,以瞭解程式碼的細節與範例運作方式。首先,進行SparkFun Edge開發板的實作;在tensorflow原始碼的目錄下,先進入SparkFun編譯的Python虛擬環境:

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TensorFlow Lite for Microcontrollers的Person detection example實作

接續前面文章與環境設定,進行TensorFlow Lite for Microcontrollers的Person detection example實作。這個實作只對應SparkFun Edge與Arduino Nano 33 BLE Sense兩塊開發板,SparkFun Edge開發板的實作環境在Ubuntu 16.04 64bits Linux,Arduino Nano 33 BLE Sense開發板的實作環境在Windows 7 64bits上。另外,請參照閱讀TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers一書的第9章與第10章,以瞭解程式碼的細節與範例運作方式。首先,進行SparkFun Edge開發板的實作;在tensorflow原始碼的目錄下,先進入SparkFun編譯的Python虛擬環境:

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TensorFlow Lite for Microcontrollers的Magic wand example實作

接續前面文章與環境設定,進行TensorFlow Lite for Microcontrollers的Magic wand example實作。這個實作只對應SparkFun Edge與Arduino Nano 33 BLE Sense兩塊開發板,SparkFun Edge開發板的實作環境在Ubuntu 16.04 64bits Linux,Arduino Nano 33 BLE Sense開發板的實作環境在Windows 7 64bits上。另外,請參照閱讀TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers一書的第11章與第12章,以瞭解程式碼的細節。首先,進行SparkFun Edge開發板的實作;在tensorflow原始碼的目錄下,先進入SparkFun編譯的Python虛擬環境:

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在Hatch One與Linkit 7697上執行TensorFlow Lite for Microcontrollers的Hello World example

本次說明”非官方”的Arduino開發板如何執行TensorFlow Lite for Microcontrollers的Hello World example。一樣是在Windows7 64Bits上進行驗證。程式庫安裝與使用請參閱前面的文章,注意需選擇” precompiled”的版本。首先介紹Linkit 7697開發板的Arduino部份,先將相關軟體元件安裝起來:

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於Arduino上執行TensorFlow Lite for Microcontrollers的Hello World example

接續前文,本文說明在Arduino上執行TensorFlow Lite for MicrocontrollersHello World example;舊的版本與相關設定請參考之前文章。本次使用的開發板為Arduino MKRZERO與Arduino Nano 33 BLE Sense,開發環境則是Windows 7 64Bits。首先下載並安裝Arduino IDE,安裝完後先點選選單上的”管理程式庫”:

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TensorFlow Lite for Microcontrollers的Hello World example編譯與執行

延續前面的文章,接下來都是以” Moved TensorFlow Lite Micro out of experimental folder”的原始碼架構來說明如何編譯TensorFlow Lite for MicrocontrollersHello World example。本次說明並不包含Arduino的部份,因其是使用官方的Arduino_TensorFlowLite程式庫,會用另外一篇文章進行說明。本文都是在Ubuntu 16.04.6 LTS (Xenial Xerus) 64-bit PC (AMD64) desktop系統上進行,並使用Anaconda Distributionx的虛擬環境來管理不同Microcontroller之編譯環境(除了ESP-IDF必需安裝到Ubuntu原生環境外)。首先,先安裝SparkFun Serial Basic Breakout所需使用的USB Serial Port驅動程式,修改/etc/modprobe.d/blacklist.conf加入這一行:

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TensorFlow Lite for Microcontrollers專案移出experimental資料夾

先前提到,TensorFlow Lite for Microcontrollers專案進展的相當快速;在發文(2019年12月11日)的昨日GitHub上出現更新訊息” Moved TensorFlow Lite Micro out of experimental folder”,代表其已不是TensorFlow Lite的實驗性的專案。文件的部份目前只有TensorFlow官網有做調整,TinyML一書的預覽版在發文的當下並未做修改:

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