Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Learning OpenCV 4 Computer Vision with Python 3

You're reading from   Learning OpenCV 4 Computer Vision with Python 3 Get to grips with tools, techniques, and algorithms for computer vision and machine learning

Arrow left icon
Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781789531619
Length 372 pages
Edition 3rd Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Joe Minichino Joe Minichino
Author Profile Icon Joe Minichino
Joe Minichino
Joseph Howse Joseph Howse
Author Profile Icon Joseph Howse
Joseph Howse
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Setting Up OpenCV 2. Handling Files, Cameras, and GUIs FREE CHAPTER 3. Processing Images with OpenCV 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. Other Book You May Enjoy Appendix A: Bending Color Space with the Curves Filter

Training an ANN classifier in multiple epochs

Let's create an ANN that attempts to classify animals based on three measurements: weight, length, and number of teeth. This is, of course, a mock scenario. Realistically, no one would describe an animal with just these three statistics. However, our intent is to improve our understanding of ANNs before we start applying them to image data.

Compared to the minimal example in the previous section, our animal classification mock-up will be more sophisticated in the following ways:

  • We will increase the number of neurons in the hidden layer.
  • We will use a larger training dataset. For convenience, we will generate this dataset pseudorandomly.
  • We will train the ANN in multiple epochs, meaning that we will train and retrain it multiple times with the same dataset each time.
The number of neurons in the hidden layer is an important...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image