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

Chapter 3. Image Classification in TensorFlow

Image classification refers to the problem of classifying images into categories according to their contents. Let's start with an example task of classifying, where a picture may be an image of a dog, or not. A naive approach that someone might take to accomplish this task is to take an input image, reshape it into a vector, and then train a linear classifier (or some other kind of classifier), like we did in Chapter 1, Setup and Introduction to TensorFlow. However, you would very quickly discover that this idea is bad for several reasons. Besides not scaling well to the size of your input image, your linear classifier will simply have a hard time being able to separate one image from another.

In contrast to humans, who can see meaningful patterns and content in an image, the computer only sees an array of numbers from 0 to 255. The wide fluctuation of these numbers at the same locations for different images of the same class prohibits using them...