In previous sections it was shown that the depth of a network is a crucial factor that contributes in accuracy improvement (see VGG). It was also shown in Chapter 3, Image Classification in TensorFlow, that the problem of vanishing or exploding gradients in deep networks can be alleviated by correct weight initialization and batch normalization. Does this mean however, that the more layers we add the more accurate the system we get is? The authors in Deep Residual Learning for Image Recognition form Microsoft research Asia have found that accuracy gets saturated as soon as the network gets 30 layers deep. To solve this problem they introduced a new block of layers called the residual block, which adds the output of the previous layer to the output of the next layer (refer to the figure below). The Residual Net or ResNet has shown excellent results with very deep networks (greater than even 100 layers!), for example the 152-layer ResNet which won the 2015 LRVC image recognition...
Hands-On Convolutional Neural Networks with TensorFlow
By :
Hands-On Convolutional Neural Networks with TensorFlow
By:
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
Contributors
Preface
Free Chapter
Setup and Introduction to TensorFlow
Deep Learning and Convolutional Neural Networks
Image Classification in TensorFlow
Object Detection and Segmentation
VGG, Inception Modules, Residuals, and MobileNets
Autoencoders, Variational Autoencoders, and Generative Adversarial Networks
Transfer Learning
Machine Learning Best Practices and Troubleshooting
Training at Scale
Other Books You May Enjoy
Index
Customer Reviews