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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