![]() ![]() But there are two parts to machine learning. There is a train/test part, where you use a lot of data to build a model. And there’s deployment, where you take a model and use it as part of a project. And that’s where the Raspberry Pi fits in.Īlthough 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. And 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. #How To Install Mplayer Raspberry Pi how to Hopefully, you now have TensorFlow up and running. Hello = tf.constant('Hello, TensorFlow!') Choose File > New File and enter the hello_tensorflow.py code: import tensorflow as tf Open Python 3 (IDLE) using Menu > Programming > Python 3 (IDLE). Save the code file as hello_tensorflow.py and Choose Run > Run Module. #How To Install Mplayer Raspberry Pi code You will get a warning because TensorFlow is compiled for Python 3.4 and we’re running Python 3.5. Google has a bunch of models developed for Raspberry Pi that you can test out. Start by cloning the TensorFlow repository: git clone įollow the instructions here to build the example models. Now head to the part of the TensorFlow repository to find Google example models and instructions. The default example is a picture of Grace Hopper. Run it and you will see that it identifies a ‘military uniform’, ‘suit’, and ‘academic gown’ (and then other items in order of decreasing probability). From here you can see how this model could be used to identify objects in your own images, and use that in your own code. There is also a link to an example that uses the Pi Camera Module directly. Now you have everything you need to start using TensorFlow. ![]() It’s a big subject and there’s far more to it than we could outline in this tutorial (or even this entire magazine). Learn by doing and follow some TensorFlow projects. Start with Sarthak Jain’s ‘How to easily detect objects with deep learning on Raspberry Pi’ or Alasdair Allan’s ‘Magic mirror with TensorFlow’. ![]() You can try to build TensorFlow using the wheel file. #How To Install Mplayer Raspberry Pi downloadĭownload the wheel file and run it, like this: sudo pip3 install -upgrade tensorflow-1.9.0rc0-cp34-none-linux_armv7l.whl In a Terminal, enter: sudo pip3 install -upgrade Īlternatively, you can use a nightly wheel built for Raspberry Pi, which is available from /xKLBzu.#How To Install Mplayer Raspberry Pi code.#How To Install Mplayer Raspberry Pi software. ![]() #How To Install Mplayer Raspberry Pi how to.Apt-get install python-rpi.gpio Now install MPlayer, which is what will be playing our audio. In a terminal, logged in as root, enter the following. Using Raspbian, and a Pi connected to the internet, open a terminal and switch to the root user: sudo su And update your list of packages, then upgrade your Pi to the latest software: apt-get update & apt-get upgrade -y Step 02 Install some extra packages We need to install the Python packages to access the GPIO. So let’s build our streaming radio using a Raspberry Pi, a speaker and a few odds and ends An example setup What you’ll need A wireless internet connection 2 x momentary switches 4 x female-to-male leads (to connect your Pi to a breadboard) 2 x 220-ohm resistors 4 x male-to-male leads Speakers connected to 3.5mm headphone jack Step-by-step Step 01 Let’s get set up Firstly, we need to prepare our Pi. There are thousands of free radio stations on the internet, and with this project you can listen to all of them from one tiny little box. ![]()
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