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TensorFlow Deep Learning Projects

By Abhishek Thakur , Alberto Boschetti , Luca Massaron and 2 more
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  1. Free Chapter
    Recognizing traffic signs using Convnets
About this book
TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.
Publication date:
March 2018
Publisher
Packt
Pages
320
ISBN
9781788398060

 

Recognizing traffic signs using Convnets

As the first project of the book, we'll try to work on a simple model where deep learning performs very well: traffic sign recognition. Briefly, given a color image of a traffic sign, the model should recognize which signal it is. We will explore the following areas:

  • How the dataset is composed
  • Which deep network to use
  • How to pre-process the images in the dataset
  • How to train and make predictions with an eye on performance
 

The dataset

Since we'll try to predict some traffic signs using their images, we will use a dataset built for the same purpose. Fortunately, researchers of Institute für Neuroinformatik, Germany, created a dataset containing almost 40,000 images, all different and related to 43 traffic signs. The dataset we will use is part of a competition named German Traffic Sign Recognition Benchmark (GTSRB), which attempted to score the performance of multiple models for the same goal. The dataset is pretty old—2011! But it looks like a nice and well-organized dataset to start our project from.

The dataset used in this project is freely available at http://benchmark.ini.rub.de/Dataset/GTSRB_Final_Training_Images.zip.

Before you start running the code, please download the file and unpack it in the same directory as the code. After decompressing the archive, you'll have a new folder, named GTSRB, containing the dataset.

The authors of the book would like to thank those who worked on the dataset and made it open source.
Also, refer http://cs231n.github.io/convolutional-networks/ to learn more about CNN.

Let's now see some examples:

"Speed limit 20 km/h":

"go straight or turn right":

"roundabout":

As you can see, the signals don't have a uniform brightness (some are very dark and some others are very bright), they're different in size, the perspective is different, they have different backgrounds, and they may contain pieces of other traffic signs.

The dataset is organized in this way: all the images of the same label are inside the same folder. For example, inside the path GTSRB/Final_Training/Images/00040/, all the images have the same label, 40. For the images with another label, 5, open the folder GTSRB/Final_Training/Images/00005/. Note also that all the images are in PPM format, a lossless compression format for images with many open source decoders/encoders.

 

The CNN network

For our project, we will use a pretty simple network with the following architecture:

In this architecture, we still have the choice of:

  • The number of filters and kernel size in the 2D convolution
  • The kernel size in the Max pool
  • The number of units in the Fully Connected layer
  • The batch size, optimization algorithm, learning step (eventually, its decay rate), activation function of each layer, and number of epochs
       
About the Authors
  • Abhishek Thakur

    Abhishek Thakur is a data scientist. His focus is mainly on applied machine learning and deep learning, rather than theoretical aspects. He completed his master's in computer science at the University of Bonn in early 2014. Since then, he has worked in various industries, with a research focus on automatic machine learning. He likes taking part in machine learning competitions and has attained a third place in the worldwide rankings on the popular website Kaggle.

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  • Alberto Boschetti

    Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

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  • Luca Massaron

    Luca Massaron is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of bestselling books on AI, machine learning, and algorithms. Luca is also a Kaggle Grandmaster who reached no. 7 in the worldwide user rankings for his performance in data science competitions, and a Google Developer Expert (GDE) in machine learning. He was part of books like The Kaggle Book, Machine Learning For Dummies, Algorithms for Dummies, Artificial Intelligence For Dummies. My warmest thanks go to my family, Yukiko and Amelia, for their support and loving patience.

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  • Alexey Grigorev

    Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 8 years of professional experience. He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science. Right now, Alexey works as a data scientist at Simplaex, where, in his day-to-day job, he actively uses Java and Python for data cleaning, data analysis, and modeling. His areas of expertise are machine learning and text mining.

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  • Rajalingappaa Shanmugamani

    Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

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Latest Reviews (2 reviews total)
It was a great value and I look forward to learning all of the techniques in the books.
Anlatım ve örnekler çok iyi. Kodların hepsi çalıştırılabiliyor.
TensorFlow Deep Learning Projects
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