Book Image

Hands-On Neural Networks with TensorFlow 2.0

By : Paolo Galeone
Book Image

Hands-On Neural Networks with TensorFlow 2.0

By: Paolo 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)
Free Chapter
Section 1: Neural Network Fundamentals
Section 2: TensorFlow Fundamentals
Section 3: The Application of Neural Networks

Python deployment

Using Python, it is straightforward to load the computational graphs stored inside a SavedModel and use them as native Python functions. This is all thanks to the TensorFlow Python API. The tf.saved_model.load(path) method deserializes the SavedModel located in path and returns a trackable object with a signatures attribute that contains the mapping from the signature keys to Python functions that are ready to be used.

The load method is capable of deserializing the following:

  • Generic computational graphs, such as the ones we created in the previous section
  • Keras models
  • SavedModel created using TensorFlow 1.x or the Estimator API

Generic computational graph

Let's say we are interested in loading the...