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

Recipe for CNN creation

The following points are based on our experience of training neural networks and of what is considered as current best practices from researchers in this field. Hopefully, they will help you if you ever need to design your own CNN architecture from scratch. But before trying out designing your own CNN, you should check out other off-the-shelf architectures to learn from them and also check if they already do the job for you.

  1. Use convolution layers with kernels of size 3x3. Larger kernels are more expensive in terms of both parameters and computation. On top of this, as we saw in the earlier chapters, you can stack conv layers to produce a bigger receptive field and with the benefit of more nonlinear activations.
  2. First layer convolutions should generally have at least 32 filters. This way, deeper layers are not restricted by the number of features that the first layer extracted.
  3. Try to avoid the use of pooling layers, if possible. Instead, use convolution layers with...