This blog covers a wide range of TensorFlow Raspberry Pi examples. You’ll learn how to set up TensorFlow on your Raspberry Pi, and how to use it to perform image classification, object detection, and more.
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This directory contains basic examples of using TensorFlow on a Raspberry Pi.
All the examples are executed on a Raspberry Pi 3 Model B. They should also work on a Raspberry Pi 2 Model B, with some minor modifications.
The examples are grouped into the following directories:
* **Classification** – Classification example using Inception v3. This example shows how to use the TensorFlow Lite library to run classification on an image embedded in the browser. It also includes an example of using the TensorBoard visualizer for debugging and optimization.
* **Object detection** – Object detection example using SSD MobileNet v2. This example shows how to use the TensorFlow Lite library to run object detection on an image embedded in the browser. It also includes an example of using the TensorBoard visualizer for debugging and optimization.
What is TensorFlow?
TensorFlow is an open source machine learning platform for doing numerical computation using data flow graphs. It enables efficient numerical computation and machine learning by using a similar approach to how we represent numbers and operations on them algebraically. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization to conduct machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
What is a Raspberry Pi?
A Raspberry Pi is a credit card-sized computer that can be used for many different projects. TensorFlow is an open source software library for numerical computation using data flow graphs. The Raspberry Pi has the ability to interface with sensors, motors, and other devices. This makes it a perfect platform for running TensorFlow applications.
TensorFlow on a Raspberry Pi
TensorFlow is an open source software library for numerical computation using data flow graphs. In other words, the best way to understand TensorFlow is to think about computational graphs. Nodes in the graph represent mathematical operations, while the edges represent the data that flows between them. You can use TensorFlow to build neural network models to recognize patterns in images, identify objects, or even predict future events.
The Raspberry Pi is a credit-card sized computer that costs around $35. It’s a great platform for learning about computer science and electronics, and it’s also a great platform for running TensorFlow. In this article, we’ll show you how to get started with TensorFlow on a Raspberry Pi.
First, you’ll need to install Raspbian on your Raspberry Pi. Raspbian is a Debian-based Linux operating system for the Raspberry Pi. You can download it from the official Raspberry Pi website.
Once you have Raspbian installed, you can install TensorFlow using pip, a package manager for Python. To install TensorFlow, open a terminal and type:
$ sudo pip install tensorflow
Installing TensorFlow on a Raspberry Pi
TensorFlow is an open source software library for numerical computation using data flow graphs. In other words, the basic idea is to represent computations as graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
This tutorial shows you how to install TensorFlow on a Raspberry Pi. We will build a simple convolutional neural network (CNN) that can classify images of hand-written digits with 96% accuracy. The CNN was built using the TensorFlow high-level API, and trained on the MNIST dataset consisting of 70,000 images of hand-written digits.
Running TensorFlow on a Raspberry Pi
This section shows how to run TensorFlow on a Raspberry Pi.
You will need the following:
-A Raspberry Pi 3 Model B or Model B+
-An 8GB or larger microSD card
-A compatible power supply
-A monitor with an HDMI cable, or a TV with an HDMI input
-A USB keyboard and mouse
TensorFlow Raspberry Pi Examples
This directory contains examples using TensorFlow on a Raspberry Pi.
To get started, you can either follow the instructions in the official TensorFlow documentation for installing TensorFlow on a Raspberry Pi, or you can install pre-built binaries.
If you want to install TensorFlow from source, follow the instructions in the official TensorFlow documentation. You will need a Raspberry Pi 3 with amicroSD card (4 GB or larger).
If you want to install pre-built binaries, you can use the following command:
For Python 2.7:
$ sudo pip install https://github.com/samjabrahams/tensorflow-on-raspberry-pi/releases/download/v0.1.0/tensorflow-1.0.1-cp27-none-linux_armv7l.whl
In this guide, we’ve looked at how to install and use TensorFlow on a Raspberry Pi. We’ve also seen how to run some simple example programs.
Hopefully, this has given you a good foundation for using TensorFlow on the Raspberry Pi. If you have any questions or comments, please feel free to post them in the comments section below.
– [official TensorFlow Lite reference](https://www.tensorflow.org/lite/guide/python)
Keyword: TensorFlow Raspberry Pi Examples