TensorFlow is a powerful framework of open-source software that is used to power Al projects around the world. It gives an understanding of how Al works and the method to put Al into practical use in your project.
Machine learning and the creation of neural networks are done by the use of Google Tensor Flow. It can make computers capable of performing complex tasks like text analysis and image recognition.
How to Install TensorFlow on Raspberry Pi?
Machine learning has two parts; one is a train part where data build a model and the second is the deployment part where you use the model as part of a project.
The second part is the part where Raspberry Pi fits in and many community projects put it to good use.
Installing TensorFlow on Raspberry Pi
To get started, we need to install TensorFlow on our Raspberry Pi.
In order for this process:
- Go as smoothly and quickly as possible.
- It’s important that you have the following items:
- A computer with internet access (laptop or desktop), an SD card reader/writer pair, etc.
- Alongside any other hardware needed such software. You will also use.
It is a very frustrating and hectic thing to install TensorFlow on Raspberry Pi. However, with the use of Google TensorFlow’s new versions is supported on the Raspberry Pi, you simply need to follow some simple steps to install it.
First of all, you have to make sure that you are using the up-to-date version of Raspberry Pi. You can get the latest versions on your Raspberry Pi by following some commands.
sudo apt-get update sudo apt-get upgrade sudo apt clean
If you add these codes you can get the latest version of Raspberry Pi on your TensorFlow.
After this, you can install and create an effective environment in which you will install TensorFlow.
Python virtual environments can isolate package installation from the system. Now, you have to upgrade pip, setup tools, and start your TensorFlow by the following code.
python3 –m pip install virtualenv cd Desktop python3-m venv tf2.0 cs tf2.0 source bin/activate.
Installing Dependencies for Installing TensorFlow:
TensorFlow requires some dependencies so we have to install them on Raspberry Pi.
sudo apt install –y libatlas-dey-dev libhdf5-dev
Now, we will have the latest version of TensorFlow that is compatible with Raspberry Pi and you have to install it.
python3 –m pip install tensorflow-2.0.0-cp37-none- linux_armv71.whl
Testing the Version of TensorFlow:
Once we install the TensorFlow, we can confirm it by double-checking it by trying to import TensorFlow by typing “python3”. After that, import TensorFlow as ‘tf’ which may cause an error.
You can simply ignore this error because it is not much bothering you. You can also check the version of TensorFlow by typing the command “tf.__version__”.
$ python3 import tensorflow tensorflow.__version__ “ ” exit ()
There are three common TensorFlow pip packages that you can install.
They are named as,
tensorflow==1.15 ( the final version )
Start the TensorFlow:
Install TensorFlow to run it and then check it by using the Hello example.
- Open Python by using Menu > Programming > Python 3.
- Then you have to choose a file open a new file and enter the code “wholeblogs_tensorflow.py” in the window.
- After saving this code Choose Run and Run Module.
- By following these steps you will get ‘Hello. TensorFlow!’ on the display.
import tensorflow as tf msg = tf.constant('Hello, TensorFlow!') tf.print(msg)
In this, you may get some errors in the end but you can ignore them.
So, TensorFlow is a large software library that is specially developed for deep learning projects. It consumes a large number of resources and you can execute TensorFlow on a Raspberry Pi. 4.
It will help you in running your models but it cannot train new models. In addition to running your pre-built deep learning models, you can also use the TensorFlow library to transform TensorFlow models to TensorFlow Lite models.
Reasons to Install TensorFlow
There are many reasons to install TensorFlow. Here are some of the most popular ones:
- TensorFlow is an open source library for machine learning, developed by Google Brain Team.
- It has a Python API and it is easy to learn and use.
- TensorFlow can be used for Hardware Acceleration with either CPUs or GPUs.
- This one provides robust tools for training and deploying machine learning models in production environments.
- TensorFlow integrates seamlessly with other popular software frameworks, such as Keras, Theano, andMXNet.
- It supports many different types of architectures, including convolutional neural networks.