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TensorFlow 2.0 Quick Start Guide
TensorFlow 2.0 Quick Start Guide

TensorFlow 2.0 Quick Start Guide: Get up to speed with the newly introduced features of TensorFlow 2.0

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TensorFlow 2.0 Quick Start Guide

Introducing TensorFlow 2

TensorFlow began its life in 2011 as DisBelief, an internal, closed source project at Google. DisBelief was a machine learning system that employed deep learning neural networks. This system morphed into TensorFlow, which was released to the developer community under an Apache 2.0 open source license, on November 9, 2015. Version 1.0.0 made its appearance on February 11, 2017. There have been a number of point releases since then that have incorporated a wealth of new features.

At the time of writing this book, the most recent version is TensorFlow 2.0.0 alpha release, which was announced at the TensorFlow Dev Summit on March 6, 2019.

TensorFlow takes its name from, well, tensors. A tensor is a generalization of vectors and matrices to possibly higher dimensions. The rank of a tensor is the number of indices it takes to uniquely specify each element of...

Looking at the modern TensorFlow ecosystem

Let's discuss eager execution. The first incarnation of TensorFlow involved constructing a computational graph made up of operations and tensors, which had to be subsequently evaluated in what Google termed as session (this is known as declarative programming). This is still a common way to write TensorFlow programs. However, eager execution, available from release 1.5 onward in research form and baked into TensorFlow proper from release 1.7, involves the immediate evaluation of operations, with the consequence that tensors can be treated like NumPy arrays (this is known as imperative programming).

Google says that eager execution is the preferred method for research and development but that computational graphs are to be preferred for serving TensorFlow production applications.

tf.data is an API that allows you to build complicated...

Installing TensorFlow

The best programming support for TensorFlow is provided for Python (although libraries do exist for Java, C, and Go, while those for other languages are under active development).

There is a wealth of information on the web for installing TensorFlow for Python.

It is standard practice, also recommended by Google, to install TensorFlow in a virtual environment, that is, an environment that isolates a set of APIs and code from other APIs and code and from the system-wide environment.

There are two distinct versions of TensorFlow—one for execution on a CPU and another for execution on a GPU. This last requires that the numerical libraries CUDA and CuDNN are installed. Tensorflow will default to GPU execution where possible. See https://www.tensorflow.org/alpha/guide/using_gpu.

Rather than attempt to reinvent the wheel here, there follow resources for...

Housekeeping and eager operations

We will first look at how to import TensorFlow, then TensorFlow coding style, and how to do some basic housekeeping. After this, we will look at some basic TensorFlow operations. You can either create a Jupyter Notebook for these snippets or use your favorite IDE to create your source code. The code is all available in the GitHub repository.

Importing TensorFlow

Importing TensorFlow is straightforward. Note a couple of system checks:

import tensorflow as tf
print("TensorFlow version: {}".format(tf.__version__))
print("Eager execution is: {}".format(tf.executing_eagerly()))
print("Keras version: {}".format(tf.keras.__version__))
...

Providing useful TensorFlow operations

Finding the squared difference between two tensors

Later in this book, we will need to find the square of the difference between two tensors. The method is as follows:

tf.math.squared.difference( x,  y, name=None)

Take the following example:

x = [1,3,5,7,11]
y = 5
s = tf.math.squared_difference(x,y)
s

The output will be as follows:

<tf...

Summary

In this chapter, we started to become familiar with TensorFlow by looking at a number of snippets of code illustrating some basic operations. We had a look at an overview of the modern TensorFlow ecosystem and how to install TensorFlow. We also examined some housekeeping operations, some eager operations, and a variety of TensorFlow operations that will be useful in the rest of this book. There is an excellent introduction to TensorFlow 2 at www.youtube.com/watch?v=k5c-vg4rjBw.

Also check out Appendix A for details of a tf1.12 to tf2 conversion tool. In the next chapter, we will take a look at Keras, which is a high-level API for TensorFlow 2.

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Key benefits

  • Train your own models for effective prediction, using high-level Keras API
  • Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks
  • Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha

Description

TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.

Who is this book for?

Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.

What you will learn

  • Use tf.Keras for fast prototyping, building, and training deep learning neural network models
  • Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files
  • Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications
  • Understand image recognition techniques using TensorFlow
  • Perform neural style transfer for image hybridization using a neural network
  • Code a recurrent neural network in TensorFlow to perform text-style generation
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Publication date : Mar 29, 2019
Length: 196 pages
Edition : 1st
Language : English
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Length: 196 pages
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Table of Contents

14 Chapters
Section 1: Introduction to TensorFlow 2.00 Alpha Chevron down icon Chevron up icon
Introducing TensorFlow 2 Chevron down icon Chevron up icon
Keras, a High-Level API for TensorFlow 2 Chevron down icon Chevron up icon
ANN Technologies Using TensorFlow 2 Chevron down icon Chevron up icon
Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha Chevron down icon Chevron up icon
Supervised Machine Learning Using TensorFlow 2 Chevron down icon Chevron up icon
Unsupervised Learning Using TensorFlow 2 Chevron down icon Chevron up icon
Section 3: Neural Network Applications of TensorFlow 2.00 Alpha Chevron down icon Chevron up icon
Recognizing Images with TensorFlow 2 Chevron down icon Chevron up icon
Neural Style Transfer Using TensorFlow 2 Chevron down icon Chevron up icon
Recurrent Neural Networks Using TensorFlow 2 Chevron down icon Chevron up icon
TensorFlow Estimators and TensorFlow Hub Chevron down icon Chevron up icon
Converting from tf1.12 to tf2 Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 0%
2 star 0%
1 star 66.7%
Amazon Kunde Aug 18, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's a great book with a lot of well explained examples. It starts with small simple examples and got more complex in the end. So you will get a good feeling how things work out in TensorFlow 2.0 .
Amazon Verified review Amazon
Yuhong Wang Jun 06, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I got the kindle version, read the first a few chapters, and returned. As my title said, the tutorials from tensorflow.org and other web sites are more informational.
Amazon Verified review Amazon
Amazon Customer Apr 26, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
If you're familiar with TensorFlow already,and this book might be a guide to do a quick review on TF 2.0. Strongly not recommend to the user who is also new to TensorFlow... too many errors and also some code cannot run at all... I just follow the content of the book and still rely on TensorFlow official documents for coding part.... so not recommend it at all....
Amazon Verified review Amazon
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