Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By : Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By: Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo

Overview of this book

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Semantic segmentation

In semantic segmentation, the goal is to label each individual pixel of an image according to what object class that pixel belongs to. The final result is a bitmap where each pixel will belong to a certain class:

There are several popular CNN architectures that have been shown to do well at the segmentation task. Most of them are variants of a class of model called an autoencoder, which we will look at in detail in Chapter 6, Autoencoders, Variational Autoencoders, and Generative Models. For now, their basic idea is to first spatially reduce the input volume to some compressed representation and then recover the original spatial size:

In order to increase the spatial size, there are some common operations that are used, which include the following:

  • Max Unpooling
  • Deconvolution/Transposed Convolution
  • Dilated/Atrous Convolution

There's also a new variant of softmax that is used in the semantic segmentation task that we will learn about, which is called spatial softmax.

In this...