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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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1
Section 1: Neural Network Fundamentals
4
Section 2: TensorFlow Fundamentals
8
Section 3: The Application of Neural Networks

Model training and evaluation

Although the network architecture is not that of an image classifier and the labels are not scalars, semantic segmentation can be seen as a traditional classification problem and therefore the training and evaluation processes can be the same.

For this reason, instead of writing a custom training loop, we can use the compile and fit Keras models to build the training loop and execute it respectively.

Data preparation

To use the Keras fit model, the tf.data.Dataset object should generate tuples in the (feature, label) format, where feature is the input image and label is the image label.

Therefore, it is worth defining some functions that can be applied to the elements produced by tf.data.Dataset...

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