TensorFlow is a multipurpose machine learning framework. TensorFlow can be used anywhere from training huge models across clusters in the cloud, to running models locally on an embedded system like your phone. This codelab uses TensorFlow Lite to run an image recognition model on an iOS device. What you'll Learn. Jul 29, 2020.
TensorFlow is still one of the popular Deep learning frameworks. It has been used in many different fields of applications including handwritten digit classification, image recognition, object detection, word embeddings, and natural language processing (NLP).
TensorFlow is a Python library for high-performance numerical calculations that allows users to create sophisticated deep learning and machine learning applications. Released as open source software in 2015, TensorFlow has seen tremendous growth and popularity in the data science community. Dec 18, 2018.
In September last year, 2019, Google finally announced the availability of the final release of TensorFlow 2.0. With eager execution by default and tight integration with Keras, now TensorFlow 2.0 makes the development of machine learning applications much easier than before.
We can now easily debug TensorFlow’s variables and print their values just like in the standard Python. That’s way, TensorFlow 2.0 is more friendly than the older version 1.x.
For those of you who don’t have prior experience with this topic, this post is special for you. Here, I’m going to show you how to install TensorFlow 2.0 in Anaconda.
What is Anaconda and why I recommend it?
Anaconda is a Python-based data processing built for data science. It comes with many useful built-in third-party libraries. Installing Anaconda meaning installing Python with some commonly used libraries such as Numpy, Pandas, Scrip, and Matplotlib.
For a Python developer or a data science researcher, using Anaconda has a lot of advantages, such as independently installing/updating packages without ruining the system. So, we no need to worry about the system library or anything like that. This can save time and energy for other things.
Anaconda can be used across different platforms, Windows, macOS, and Linux. If we want to use a different Python version or package libraries, just create a different environment and play around without any risk of crashing the system library.
Now, let’s install Anaconda first.
Installing Anaconda
Anaconda is available for Windows, Mac OS X, and Linux, you can find the installation file in the anaconda official site. I suggest you choose the Python version 3.7 64-bit installer if you have a 64-bit machine, otherwise choose the 32-bit installer, instead. If you need, you can easily install Python 2.7 versions later.
In case you have already installed Python on your computer, don’t worry, it won’t ruin anything. Instead, the default Python used by your programs will be the one that comes with Anaconda. Go ahead and choose the appropriate version, follow the instructions and install it. Cubase free download mac.
I will let you explore it, but anyhow, if you have any problem, you can simply post a comment in the comment section and I will try to do my best for you.
(Note: For more details on how to use Anaconda, you can visit the Anaconda user guide here).
(Note: For more details on how to use Anaconda, you can visit the Anaconda user guide here).
Now, we’re going to create our first environment, but be sure that you’ve installed Anaconda on your computer.
Creating an Environment
Open Anaconda prompt, and create a new environment called yolov3_tf2 ( I gave this name because it relates to my next article about the implementation of YOLOv3 in TensorFlow 2.0). You can name it whatever you want. Just type or copy the following command to your Anaconda prompt and hit Enter.
After that, you will be prompted something like this, just type ‘y‘ and then hit the Enter.
Note: you might be prompted a bit different to this, it doesn’t matter just hit Enter, Anaconda will do the best for you.
Note: you might be prompted a bit different to this, it doesn’t matter just hit Enter, Anaconda will do the best for you.
Wait until all packages installed successfully, and then you can activate your new Anaconda environment.
Copy and paste this command to your Anaconda prompt and hit Enter.
Copy and paste this command to your Anaconda prompt and hit Enter.
Now, your Conda’s environment is ready to use. Let’s install TensorFlow 2.0.
Installing TensorFlow 2.0
When you are in the yolov3_tf2 environment, now you can install any package you want. To install TensorFlow 2.0, type this command and hit Enter.
GPU:
CPU:
Verify the Cuda toolkit and
cudnn
that will be installed, it must come with Cudatoolkit 10 and cudnn 7.6
. If everything goes right, just type ‘y’ and hit Enter. Basically, your TensorFlow has been installed now. Let’s check whether it’s installed correctly or not.
Type
Type
python
in Anaconda command prompt and hit Enter, your Python must be version 3.7, then type import tensorflow as tf
and hit Enter, followed by typing tf.__version__
and hit Enter. If you have TensorFlow installed on your environment, you’ll get no errors, otherwise, you’ll need to re-install it. If everything has been installed correctly, you’ll get the result as shown in the figure below. Your TF version must be ‘2.0.0’.
See you and check this out, my tutorial about YOLOv3 object detection.
This is the first of a 4 articles series on how to get you started with Deep Learning in Python.
In this guide I'll show you how to:
- download and install Anaconda Python on your laptop
- create a conda environment for your deep learning development
- install the required packages in that environment
Anaconda is the leading open data science platform powered by Python. The open source version of Anaconda is a high performance distribution of Python and R and includes over 100 of the most popular Python, R and Scala packages for data science.
It can be downloaded here. Python comes in two major versions: 2.7 and 3.x. Python 2.7 is considered legacy, while 3.x is the present and future of Python. Despite this, I often recommend to install Python 2.7, because of the larger library support. If you are a complete beginner, you may want to start directly with Python 3.x. A detailed explanation of the differences between 2.7 and 3.x can be found here, and here you can find a discussion about what version to choose.
In Dataweekends workshops we use Python 2.7, because that's what most of our users are already familiar with.
Once you've downloaded Anaconda, you should install it on your Mac following the instructions provided by the Graphical installer.
Here are a couple of screenshots of key steps:
Unless you have very specific reasons to do so, we recommend to install anaconda for the local user only.
The guys at Continuum have developed an extremely versatile package manager called
conda
. It is a package manager that quickly installs, runs, and updates packages and their dependencies. It can also query and search the package index and current installation, create new environments, and install and update packages into existing conda environments.Conda environments are coherent collections of packages with specific versions that can be used to ensure portability of your python code. For example, imagine you developed a short python program that uses version 1.2 of a certain package. Let's say you want to share your code with a friend, but are not sure if she has the same package on her laptop. You ask your friend, and she is currently using version 1.0 of that package, because she uses it as part of another project. So, your friend doesn't want to update the library to 1.2 and would also like to test your script, which requires the upgrade. An environment solves this problem by allowing your friend to have both versions of the library, 1.0 and 1.2, in two separate environments, so that they do not interfere with one another.
Let's create an environment for our data science development, we'll call this environment
dataweekends
, but you can call it with any name you want.In a terminal window type:
hit
Enter
and answer y
when prompted to proceed. If all goes to plan at the end you should see this message:You can then go ahead and activate the environment typing:
This will prepend
(dataweekends)
to your terminal prompt. You can verify that you are in the correct active environment by typing:which should return:
Great! We have created an environment and successfully activated it. Now let's install
keras
.Tensorflow is an Open Source Software Library for Machine Intelligence originally developed by researchers and engineers working on the Google Brain Team. Version 1.0 has been announced in February, so that's the version we will install. There are several ways to install it, we'll use the
pip
method for this tutorial. In your active dataweekends
environment terminal type:Keras is a high-level neural networks api specification, implemented in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation and it allows to go from idea to result with the least possible delay.
Although Keras is also provided by community channel of Anaconda packages (conda-forge), it's most recent version is best installed with
pip
, so we'll go ahead and use that version. In your active dataweekends
environment terminal type:At the time of writing this installs
keras
version 1.2.2. Also, at the Tensorflow Dev Summit it was announced that keras
will become part of Tensorflow
from version 1.1
, so in the future it will be already installed whith Tensorflow.Voilà! You are done installing Anaconda, Tensorflow and Keras.
To test your installation you can type:
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which will start the ipython console:
You can then type:
to which it should reply: Using TensorFlow backend.
Congratulations!!! You have successfully set up your Mac for development with Python, Keras and Tensorflow!
To stop the ipython console just type
CTRL+D
twice.To exit the
dataweekends
environment, type:Check the version of installed package
If you are not sure about the version of a
Tensorflow
(or any other package), it's easy to check that. In the ipython console (make sure you started it from within your environment)If you are not at the current version, you can always upgrade it using pip as explained earlier.
Switching Keras backend
Keras' backend is set in a hidden file stored in your home path. You can find it at
$/.keras/keras.json
. You can open it with a text editor and you should see something like this:![Tensorflow Download Ans Setip Mac Tensorflow Download Ans Setip Mac](/uploads/1/2/6/8/126860106/141887105.png)
Tensorflow Download Ans Setup Macro
Switching backend is as easy as replacing
tensorflow
with theano
in the last key. Then save the file and close it. If you then open ipython
and import keras
you should see:Tensorflow Download Ans Setup Mac Os
You are now ready to step to the second part of this tutorial.