raspberry pi

Word of mouth now a day is machine learning. Well, it is easy to understand why. It is the future of data manipulation and is already being used in nearly every modern business environment. How would you pair it with a Raspberry Pi? Is the Pi up to the task of maintaining a working neural grid? It can do so with Google TensorFlow! A powerful combination of Tensorflow and Raspberry Pi can give you great results.

What is TensorFlow?



Before we discuss the example of TensorFlow, let’s know about TensorFlow.

TensorFlow is an open-source, computing software library for machine learning. It uses nodes to represent mathematical operations, and edges of graphs represent the multidimensional data arrays or tensors communicated in the data graph between them. Its architecture is versatile, allowing users to expand computing to one or more CPUs or GPUs with a single API in a laptop, server, or mobile device.

TensorFlow was developed by Google Brain team researchers and engineers inside Google’s science agency, Artificial Intelligence. The initial intent was to undertake research in machine learning and fundamental neural networks. It worked in a large variety of contexts. This was first launched by Google on November 9, 2015, and launched solidly on December 8, 2017.

In short, Google’s trainable neural network is TensorFlow, which can handle several different tasks. TensorFlow neural networks make accurate predictions when given new data by actively learning from a user-curated dataset.

For short, neural networks speak about TensorFlow. For more information, check our list of TensorFlow Examples.


Although it takes serious research to understand the topic of machine learning, the simple use of TensorFlow is easy to follow. Our TensorFlow Image Recognition tutorial includes downloading the library on your Pi. It also involves checking it and running the simple image recognition system Inception. TensorFlow can be installed in raspberry pi with these steps in the video or Click here.

Tensorflow and raspberry pi

In this case, TensorFlow provides a neural network which is already educated. Everything that the user needs to do is input the right form of data, and TensorFlow can infer what the image contains. Even the basic TensorFlow implementation can classify the images into 1000 classes. It gets the right shocking number!

But, what else can you do on the TensorFlow and Raspberry Pi?

Portable Image Recognition:

We’ve discussed here how to create a smart webcam before, but this smartphone picture recognition talker brings it to a new stage.

Tensorflow and raspberry pi applications

This whole post outlines the hardware configuration and the custom program that is implemented with the image classifier Inception. The example code shows how easy it is to integrate TensorFlow with a project (provided the basics of the Python programming language are comfortable with you). The article goes into great detail on the image recognition process. In fact, it is an outstanding tool for those interested in the field.

Initially, one distinctive element of this setup might not be transparent:

“A bonus, many pointed out, is that no internet access is required once installed.”

Previous identification of images has often relied on an immense amount of computing time or internet access. A Pi is not always able to pass information to the cloud and has minimal computing capacity. This is the workaround you can render at home, a self-contained offline object recognizer. It’ll also tell you what it’s looking at. Isn’t it a bright future?

TensorFlow Magic Mirror:

Homemade smart mirrors (or “magic”) are about the best thing you can make. It’s a perfect beginner idea, needing just one Pi and an old laptop screen along with simple DIY supplies. Alasdair Allan decided not to settle for the ordinary smart mirror and created a magic mirror with voice recognition from TensorFlow.

Unhappy with the web-based speech recognition costs, Alasdair decided to use TensorFlow as an offline alternative. Incorporating the pre-trained voice recognition model of TensorFlow into the already used AIY kit code adds custom wake words to the project. Google compiled over 65,000 crowdsourced terms into a dataset. This open-source dataset guided the neural net to learn specific terms. In this case, it has introduced some potential wake-up terms but also runs into a typical machine learning problem: a neural network takes a lot of data to practice.

Unless you’re prepared to create a unique dataset with tens of thousands of entries, you ‘re limited to what’s available freely. This project shows TensorFlow ‘s limitations on the Pi at its current state. It’s entirely usable but limits the computing capacities of the Pi. This early implementation, as with all new technologies, is a glimpse into the future of smart home devices.

TensorFlow Autonomous RC Car:

This idea is not meant for beginners. The required hardware can be found in nearly every cheap robot kit. Implementation of the app requires a little more in-depth experience. Before you take it on, you should have a good grasp of machine learning.

Cucumber Auto-Sorter:

One of TensorFlow’s best-known installations on the Pi, the cucumber sorter Makoto Koike is a hint of things to come.

Sorting fresh produce for multiple customers is a significant expense to smaller vendors. Sorting the cucumbers by scale and consistency is a task that could only be carried out by a human operator before recently. Sorting the computer was quite complicated and costly. TensorFlow solves this problem by categorizing the cucumbers via the camera in real-time.

Makoto learned a neural network to differentiate between various forms, using more than 7000 images of cucumbers. Webcams record images from three angles when in service.

Given the experience of Google with self-driving cars, it is no wonder that TensorFlow is well suited for autonomous driving.

The DeepPiCar is a prime example of such a neural network in action. This Raspberry Pi robot features something altogether more ingenious in addition to the standard remote control. Educated on a data set presented on the project page of GitHub, the network should learn to remain on a predetermined route.

The Pi classifies the files before forwarding them for further processing to a Linux server. The effect is a conveyor belt and a servo network where the cucumbers are sorted into boxes.

Hope you enjoyed this article on TensorFlow and Raspberry Pi. Stay tuned to Towardsrobotics for more articles.

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