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  • Hands-On Neural Networks with TensorFlow 2.0
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Hands-On Neural Networks with TensorFlow 2.0

Hands-On Neural Networks with TensorFlow 2.0

By : Galeone
3.7 (7)
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Hands-On Neural Networks with TensorFlow 2.0

Hands-On Neural Networks with TensorFlow 2.0

3.7 (7)
By: Galeone

Overview of this book

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
Table of Contents (15 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: Neural Network Fundamentals
Section 1: Neural Network Fundamentals
2
What is Machine Learning?
What is Machine Learning?
The importance of the dataset
Supervised learning
Unsupervised learning
Semi-supervised learning
Summary
Exercises
3
Neural Networks and Deep Learning
Neural Networks and Deep Learning
Neural networks
Optimization
Convolutional neural networks
Regularization
Summary
Exercises
4
Section 2: TensorFlow Fundamentals
Section 2: TensorFlow Fundamentals
5
TensorFlow Graph Architecture
TensorFlow Graph Architecture
Environment setup
Dataflow graphs
Model definition and training
Interacting with the graph using Python
Summary
Exercises
6
TensorFlow 2.0 Architecture
TensorFlow 2.0 Architecture
Relearning the framework
The Keras framework and its models
Eager execution and new features
Codebase migration
Summary
Exercises
7
Efficient Data Input Pipelines and Estimator API
Efficient Data Input Pipelines and Estimator API
Efficient data input pipelines
Estimator API
Summary
Exercises
8
Section 3: The Application of Neural Networks
Section 3: The Application of Neural Networks
9
Image Classification Using TensorFlow Hub
Image Classification Using TensorFlow Hub
Getting the data
Transfer learning
Fine-tuning
Summary
Exercises
10
Introduction to Object Detection
Introduction to Object Detection
Getting the data
Object localization
Classification and localization
Summary
Exercises
11
Semantic Segmentation and Custom Dataset Builder
Semantic Segmentation and Custom Dataset Builder
Semantic segmentation
Create a TensorFlow DatasetBuilder
Model training and evaluation
Summary
Exercises
12
Generative Adversarial Networks
Generative Adversarial Networks
Understanding GANs and their applications
Unconditional GANs
Conditional GANs
Summary
Exercises
13
Bringing a Model to Production
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Bringing a Model to Production
The SavedModel serialization format
Python deployment
Supported deployment platforms
Summary
Exercises
14
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The SavedModel serialization format

As we explained in Chapter 3, TensorFlow Graph Architecture, representing computations using DataFlow graphs has several advantages in terms of model portability since a graph is a language-agnostic representation of the computation.

SavedModel is a universal serialization format for TensorFlow models that extends the TensorFlow standard graph representation by creating a language-agnostic representation for the computation that is recoverable and hermetic. This representation has been designed not only to carry the graph description and values (like the standard graph) but also to offer additional features that were designed to simplify the usage of the trained models in heterogeneous production environments.

TensorFlow 2.0 has been designed with simplicity in mind. This design choice is visible in the following diagram, where it is possible...

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