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

CNN model architecture

The crucial part of an image classification model is its CNN layers. These layers will be responsible for extracting features from image data. The output of these CNN layers will be a feature vector, which like before, we can use as input for the classifier of our choice. For many CNN models, the classifier will be just a fully connected layer attached to the output of our CNN. As shown in Chapter 1Setup and Introduction to TensorFlow, our linear classifier is just a fully connected layer; this is exactly the case here, except that the size and input to the layer will be different.


It is important to note that at its core, the CNN architecture used in classification or a regression problem such as localization (or any other problems that use images for that matter) would be the same. The only real difference will be what happens after the CNN layers have done their feature extraction. For example, one difference could be the loss function used for different tasks...