TensorFlow Pi

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Teacher Overview 

Raspbian is a Debian-based computer operating system for the Raspberry Pi computer. There are several versions of Raspbian including: Raspbian Stretch and Raspbian Jessie. Raspbian was created by Mike Thompson and Peter Green as an independent project, with an initial build completed in June 2012. Since 2015, Raspbian has been officially provided by the Raspberry Pi Foundation as its primary operating system for the family of Raspberry Pi single-board computers and is highly optimized for the Raspberry Pi line's low-performance ARM CPUs. 

The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. 

IoT devices could be a person with a heart monitor implant, a farm animal with a biochip transponder, an automobile that has built-in sensors to alert the driver when tire pressure is low or any other natural or man-made object that can be assigned an IP address and is able to transfer data over a network. Increasingly, organisations in a variety of industries are using IoT to operate more efficiently, better understand customers to deliver enhanced customer service, improve decision-making and increase the value of the business. 

Google TensorFlow is a powerful open-source software framework used to power AI projects around the globe and is used for machine learning and the creation of neural networks. These capabilities make it possible for computers to perform increasingly complex tasks, such as image recognition and text analysis. When it comes to AI, most people think of powerful supercomputers crunching billions of numbers in giant databanks. But there are two parts to machine learning: First, the there is a train/test part, where you use significant amounts of data to build a model. Secondly, there is deployment, where you take a model and use it as part of a project, and that’s where the Raspberry Pi fits in. Although Raspberry Pi isn’t officially supported by Google, there are example models included for the Raspberry Pi and it can be fun to get TensorFlow up and running on a Pi. Additionally, there are lots of interesting community projects around that put TensorFlow to good use. Using TensorFlow can give you a good understanding of how AI works, and how to put AI to practical use in your projects. 

Suggested Year Group  

The suggested age group is 16+, due to the nature of this project and the skills it is developing.

 

Learning Outcomes 

  • Learn the basics of IoT; 

  • Understand the concepts of TensorFlow; 

  • Learn how TensorFlow can be used in different projects.  

 

Hardware Required

Component Number (Peli Case) 

Slot 4 + 5 

Slot 5 

Slot 9 

Slot 3 

Slot 3 

Slot 5 

Slot 1 

Component Number (Box Case) 

Slot 1 

Slot 10 

Slot 2 

Slot 11 

Slot 11 

Slot 12 

Slot 13 

Component 

Raspberry Pi + Case 

Raspbian SD Card 

TFT Screen 

HDMI 

Wireless Keyboard 

Wireless Mouse 

Power Supply Unit 

 

Video Guide

 

conclusion

This project is designed to introduce you to TensorFlow and the applications it has within IoT. You will have run multiple commands and compiled a small python script that will configure the Raspberry Pi to be able to run TensorFlow to recognise different images. It will take a guess at what it could be and put percentages next to the guesses of the chance of it being that certain guess. TensorFlow is an extremely powerful tool, and this project is just scratching the surface of what can be achieved with it, there are so many different projects that can be built around this.