Tensorflow is a library for high-scale numerical computing and Machine Learning. Google created Tensorflow and opened to the public with an open source license. Tensorflow can be used for train models and running deep learning with a neural network. It’s like the nerves of humans who can learn objects quickly and deeply.
Tensorflow can be used to object recognition, natural language processing recurrent neural networks, handwriting recognition, and others. This is Tensorflow, a library for deep learning using artificial neural networks. It will work and study objects just as humans learn.
To learn more about Tensorflow, please visit the https://www.tensorflow.org/ page.
Same with Keras. Keras is a library that is built with python to build and train deep learning. Keras can be used for research, prototype, or production. To learn Keras, please visit the official website at https://keras.io/.
In this article, we will try Tensorflow and Keras installations on a Raspberry Pi device.
You must have a Raspberry Pi 3 Model B+ (Buy Here) that has OpenCV installed. Please read the previous article entitled Install OpenCV 4 on the Raspberry Pi. Suggested SSH or VNC access has been installed before.
In this tutorial, we will install using the cv Virtual Environment that was previously created.
For Tensorflow and Keras, the Raspberry Pi currently provides a wheel for python. You can directly install via PIP. However, when we tried, we got some errors. It might be fixed soon in another version. We will still write the method, but we will write how to install different versions.
Install in easily way
Before installing tensorflow and Keras, install some of the libraries that are needed.
sudo apt-get install python3-numpy sudo apt-get install libblas-dev sudo apt-get install liblapack-dev sudo apt-get install python3-dev sudo apt-get install libatlas-base-dev sudo apt-get install gfortran sudo apt-get install python3-setuptools sudo apt-get install python3-scipy sudo apt-get update sudo apt-get install python3-h5py
When done, go to Virtual Environment cv, and install it with PIP.
workon cv pip install --upgrade scipy pip install --upgrade cython pip install tensorflow pip install keras
If there is no error, then you can successfully install Tensorflow and Keras in an easy way. You can go directly to the installation Test section. However, if you don’t succeed, try the installation in another way below.
Install in other ways
Before continuing, make sure the install dependencies needed in the easy way above are already running. When this article was written, the latest version of tensorflow is v1.13.1. You can try checking and installing the latest version on the page https://github.com/lhelontra/tensorflow-on-arm/releases.
workon cv wget https://github.com/lhelontra/tensorflow-on-arm/releases/download/v1.13.1/tensorflow-1.13.1-cp35-none-linux_armv7l.whl pip install tensorflow-1.13.1-cp35-none-linux_armv7l.whl pip install tensorflow
To install it Keras, we get an error when installing Scipy. Therefore, we try to install the manual for the Scipy.
wget https://www.piwheels.org/simple/scipy/scipy-1.2.1-cp35-cp35m-linux_armv7l.whl pip install scipy-1.2.1-cp35-cp35m-linux_armv7l.whl pip install scipy
Then install Keras.
pip install keras
If it’s ok, you can test the installation.
Type the following command to test the Tensorflow and Keras installation.
python -c 'import tensorflow as tf; print(tf.__version__)'
If the output is a version, for example, 1.13.1, then your tensorflow installation process is successful.
python -c 'import keras; print(keras.__version__)'
If the output is a version like 2.2.4, that means your Keras install is successful.
Tensorflow and Keras are essential libraries for those of you who are studying deep learning and neural networks. Also, the good thing is, Tensorflow and Keras can be installed on Raspberry Pi quickly. This will make our Raspberry Pi even smarter. The development can be even wider.
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