Book Image

Applied Deep Learning and Computer Vision for Self-Driving Cars

By : Sumit Ranjan, Dr. S. Senthamilarasu
Book Image

Applied Deep Learning and Computer Vision for Self-Driving Cars

By: Sumit Ranjan, Dr. S. Senthamilarasu

Overview of this book

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.
Table of Contents (18 chapters)
1
Section 1: Deep Learning Foundation and SDC Basics
5
Section 2: Deep Learning and Computer Vision Techniques for SDC
10
Section 3: Semantic Segmentation for Self-Driving Cars
13
Section 4: Advanced Implementations

One-hot encoding the output

In this section, we're going to one-hot encode the output data. By using one-hot encoding we can convert a categorical variable, and the variable with a new format helps to do a better machine learning prediction. It is easier for the computer as well to interpret the inputs in the form of one-hot encoding.

An example of one-hot encoding can be seen in the following screenshot:

Fig 6.19: One-hot encoding

In the preceding screenshot, we have three products, and their categorical values are 1, 2, and 3. We can see how products are represented by one-hot encoding: for Product A, it is (1, 0, 0) and for Product B, it is (0, 1, 0). Similarly, if we want to do the same for our data, we will get (0, 0, 0, 0, 1, 0, 0, 0, 0) for 5

The following code will help us to one-hot encode the output:

from keras.utils import np_utils

y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)

print ("Number of classes: &quot...