How to Build DIY AI Projects Using Google TensorFlow and Raspberry Pi

Machine learning is a topic on everyone’s lips. It’s easy to see why. This is the future of data manipulation and is already being used in almost every business environment today. But can it be combined with Raspberry Pi? Is Pi the task of maintaining a running neural network? With Google TensorFlow it’s possible!

Here is how to install TensorFlow on Raspberry Pi, with some usage examples.

What is TensorFlow?

Before diving into TensorFlow usage examples, it’s worth knowing what it is.

In short, TensorFlow is Google’s trainable neural network that can perform many different tasks. By actively learning from a set of user data, TensorFlow neural networks make accurate predictions when new data is received.

Shortly speaking, think neural networks TensorFlow .

Check out our list of Tensorflow examples for more information.

How to install TensorFlow

While understanding the subject matter of machine learning requires some serious study, the basic usage of TensorFlow is easy to understand. Our Tutorial on Image Recognition with TensorFlow covers installing the library on your Pi. It also includes testing it and running the basic image classification program Inception.

In this case, TensorFlow provides an already trained neural network. All the user has to do is enter the correct data type and TensorFlow will guess what the image contains. Even the basic implementation of TensorFlow is capable of classifying images into 1000 classes. This is amazingly correct!

But what else can you do with TensorFlow on Raspberry Pi?

Portable image recognition

We looked at how to make a smart webcam for yourself. before, but this talking mobile image classifier takes it to the next level.

This detailed post describes the hardware setup and custom software integrated with the Inception image classifier. The code example shows how easy it is to integrate TensorFlow with a project (assuming you are familiar with the basics of the Python programming language). The article describes in detail the process of image recognition. All in all, this is a great resource for anyone with an interest in the field.

One distinct element of this setup may not be clear at first:

«An added bonus that many have commented is that no internet access is required after installation.»

Previous image recognition has always depended on huge processing time or internet connection. The Pi cannot always transmit information to the cloud and has limited processing power. This is the solution, a standalone standalone object recognizer that you can make at home. He’ll even tell you what he’s looking at. Isn’t the future amazing?

TensorFlow Magic Mirror

Homemade smart (or “magic”) mirrors are the coolest thing you can make. Requiring only a pi and an old laptop screen along with basic house supplies, this is a great project for beginners. Alasdair Allan decided not to settle for the average smart mirror and created a magic TensorFlow mirror with voice recognition.

Dissatisfied with the cost of speech recognition over the Internet, Alasdair chose TensorFlow as a standalone alternative. Integrating a pre-trained TensorFlow voice recognition model into an already used AIY kit code adds custom wakeup words to the project.

Google has put together a dataset with over 65,000 crowdsourced words. This open source dataset trained a neural network to understand some words.

In this case, he added several possible awakened words, but still runs into a well-known machine learning problem: it takes a lot of data to train a neural network.

Unless you want to create a unique dataset with tens of thousands of records, you are limited to what is freely available. This project shows the limitations of TensorFlow on the Pi in its current state. It’s fully functional but expands the Pi’s computing capabilities. As with all new technologies, this early implementation provides a glimpse into the future of smart home devices.

TensorFlow Autonomous RC Car

Considering Google’s history with self-driving cars. no wonder TensorFlow is well suited for autonomous driving.

DeepPiCar is a great example of such a neural network in action. Along with the standard remote control, this Raspberry Pi robot has something downright clever. Trained on the dataset provided on the GitHub project page, the network learns to stay on the given path.

This project is not for beginners. The necessary equipment can be found in almost any cheap robot kit. Software implementation requires more advanced knowledge. You should be well versed in machine learning before embarking on it.