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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from  Applied Deep Learning and Computer Vision for Self-Driving Cars

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Pages 332 pages
Edition 1st Edition
Languages
Authors (2):
Sumit Ranjan Sumit Ranjan
Profile icon Sumit Ranjan
Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Profile icon Dr. S. Senthamilarasu
View More author details

Table of Contents (18) Chapters

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

Building and compiling our model

We have already read about building and compiling models in Chapter 3, Implementing a Deep Learning Model Using Keras. Let's build a simple neural network, and then we will start building the model. In this section, we will add the layers to be used in our deep learning model:

  1. We will first import the important libraries from Keras:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
  1. Let's design the CNN with the following code. We add our first layer as the convolution layer with a filter of 32, kernal_size set to (3,3), and ReLU as our activation function. Then, we add a convolution layer with a filter value of 64 with ReLU as our activation function. Then, we added a maxpooling layer. Finally, we drop out and flatten a dense layer with a filter size of 128 and our ReLU activation function. Finally, we add one more dropout layer:
model = tf.keras.Sequential()

model.add(tf.keras.layers.Conv2D(10, kernel_size...
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