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deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | MANNINGMohamed Elgendy |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | Deep Learning for
Vision Systems
MOHAMED ELGENDY
MANNING
SHELTER ISLAND |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | For online information and ordering of this and other Manning books, please visit
www.manning.com . The publisher offers discounts on this book when ordered in quantity.
For more information, please contact
Special Sales Department
Manning Publications Co.
20 Baldwin Road
PO Box 761
Shelter Island, NY 11964
Email: ord... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | To my mom, Huda, who taught me perseverance and kindness
To my dad, Ali, who taught me patience and purpose
To my loving and supportive wife, Amanda, who always inspires me to keep climbing
To my two-year-old daughter, Emily, who teaches me every day that AI still has
a long way to go to catch up with even the tinies... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | null |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | vcontents
preface xiii
acknowledgments xv
about this book xvi
about the author xix
about the cover illustration xx
PART 1DEEP LEARNING FOUNDATION ............................. 1
1 Welcome to computer vision 3
1.1 Computer vision 4
What is visual perception? 5■Vision systems 5
Sensing devices 7■Interpreting devices 8
1... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | CONTENTS vi
1.5 Image preprocessing 23
Converting color images to grayscale to reduce computation
complexity 23
1.6 Feature extraction 27
What is a feature in computer vision? 27■What makes a good
(useful) feature? 28■Extracting features (handcrafted vs.
automatic extracting) 31
1.7 Classifier learning algorithm 33
... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | CONTENTS vii
3 Convolutional neural networks 92
3.1 Image classification using MLP 93
Input layer 94■Hidden layers 96■Output layer 96
Putting it all together 97■Drawbacks of MLPs for processing
images 99
3.2 CNN architecture 102
The big picture 102■A closer look at feature extraction 104
A closer look at classificatio... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | CONTENTS viii
4.5 Improving the network and tuning hyperparameters 162
Collecting more data vs. tuning hyperparameters 162
Parameters vs. hyperparameters 163■Neural network
hyperparameters 163■Network architecture 164
4.6 Learning and optimization 166
Learning rate and decay schedule 166■A systematic approach
to find... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | CONTENTS ix
5.5 Inception and GoogLeNet 217
Novel features of Inception 217■Inception module: Naive
version 218■Inception module with dimensionality
reduction 220■Inception architecture 223■GoogLeNet in
Keras 225■Learning hyperparameters 229■Inception
performance on the CIFAR dataset 229
5.6 ResNet 230
Novel featur... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | CONTENTS x
7.2 Region-based convolutional neural networks
(R-CNNs) 292
R-CNN 293■Fast R-CNN 297■Faster R-CNN 300
Recap of the R-CNN family 308
7.3 Single-shot detector (SSD) 310
High-level SSD architecture 311■Base network 313
Multi-scale feature layers 315■Non-maximum
suppression 319
7.4 You only look once (YOLO) 32... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | CONTENTS xi
9.2 DeepDream 384
How the DeepDream algorithm works 385■DeepDream
implementation in Keras 387
9.3 Neural style transfer 392
Content loss 393■Style loss 396■Total variance loss 397
Network training 397
10 Visual embeddings 400
10.1 Applications of visual embeddings 402
Face recognition 402■Image recommendat... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | null |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | xiiipreface
Two years ago, I decided to write a book to teach deep learning for computer vision
from an intuitive perspective. My goal was to develop a comprehensive resource
that takes learners from knowing only the basics of machine learning to building
advanced deep learning algorithms that they can apply to solve ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | PREFACE xiv
As a beginner, I searched but couldn’t find anything to meet these needs. So now
I’ve written it. My goal has been to write a book that not only teaches the content I
wanted when I was starting out, but also levels up your ability to learn on your own.
My solution is a comprehensive book that dives deep ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | xvacknowledgments
This book was a lot of work. No, make that really a lot of work! But I hope you will find it
valuable. There are quite a few people I’d like to thank for helping me along the way.
I would like to thank the people at Manning who made this book possible: pub-
lisher Marjan Bace and everyone on the edi... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | xviabout this book
Who should read this book
If you know the basic machine learning framework, can hack around in Python, and
want to learn how to build and train advanced, production-ready neural networks to
solve complex computer vision problems, I wrote this book for you. The book was
written for anyone with interme... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | ABOUT THIS BOOK xvii
doesn’t interrupt your flow of understanding the concepts without the math
part if you prefer.
How this book is organized: A roadmap
This book is structured into three parts. The first part explains deep leaning in detail
as a foundation for the remaining topics. I strongly recommend that you not ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | ABOUT THIS BOOK xviii
access the forum, go to https:/ /livebook.manning.com/#!/book/deep-learning-for-
vision-systems/discussion . You can also learn more about Manning’s forums and the
rules of conduct at https:/ /livebook.manning.com/#!/discussion .
Manning’s commitment to our readers is to provide a venue where a m... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | xixabout the author
Mohamed Elgendy is the vice president of engineering at Rakuten, where he is lead-
ing the development of its AI platform and products. Previously, he served as head of
engineering at Synapse Technology, building proprietary computer vision applica-
tions to detect threats at security checkpoints w... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | xxabout the cover illustration
The figure on the cover of Deep Learning for Vision Systems depicts Ibn al-Haytham, an
Arab mathematician, astronomer, and physicist who is often referred to as “the father
of modern optics” due to his significant contributions to the principles of optics and
visual perception. The illus... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | Part 1
Deep learning foundation
C omputer vision is a technological area that’s been advancing rapidly
thanks to the tremendous advances in artificial intelligence and deep learning
that have taken place in the past few years. Neural networks now help self-driving
cars to navigate around other cars, pedestrians, and ot... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 3Welcome to
computer vision
Hello! I’m very excited that you are here. You are making a great decision—to
grasp deep learning (DL) and computer vision (CV). The timing couldn’t be more
perfect. CV is an area that’s been advancing rapidly, thanks to the huge AI and DL
advances of recent years. Neural networks are now al... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 4 CHAPTER 1Welcome to computer vision
objects—DL has given computers the power to imagine and create new things like art-
work; new objects; and even unique, realistic human faces.
The main reason that I’m excited about deep learning for computer vision, and
w hat d re w m e t o thi s fi e ld, is ho w r apid ad... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 5 Computer vision
1.1.1 What is visual perception?
Visual perception , at its most basic, is the act of observing patterns and objects through
sight or visual input. With an autonomous vehicle, for example, visual perception means
understanding the surrounding objects and their specific details—such as pedestrians,
or ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 6 CHAPTER 1Welcome to computer vision
and detect objects in this image because we have been trained over the years to iden-
tify dogs.
Suppose someone shows you a picture of a dog for the first time—you definitely
don’t know what it is. Then they tell you that this is a dog. After a couple experiments
like this, you... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 7 Computer vision
1.1.3 Sensing devices
Vision systems are designed to fulfill a specific task. An important aspect of design is
selecting the best sensing device to capture the surroundings of a specific environ-
ment, whether that is a camera, radar, X-ray, CT scan, Lidar, or a combination of
devices to provide the f... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 8 CHAPTER 1Welcome to computer vision
1.1.4 Interpreting devices
Computer vision algorithms are typically employed as interpreting devices. The inter-
preter is the brain of the vision system. Its role is to take the output image from the
sensing device and learn features and patterns to identify objects. So we need t... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 9 Computer vision
DL methods learn representations through a sequence of transformations of data
through layers of neurons. In this book, we will explore different DL architectures,
such as ANNs and convolutional neural networks, and how they are used in CV
applications. Biological neuron Artificial neuron
Neuron
Flow o... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 10 CHAPTER 1Welcome to computer vision
CAN MACHINE LEARNING ACHIEVE BETTER PERFORMANCE THAN THE HUMAN BRAIN ?
Well, if you had asked me this question 10 years ago, I would’ve probably said no,
machines cannot surpass the accuracy of a human. But let’s take a look at the follow-
ing two scenarios:
Suppose you w... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 11 Applications of computer vision
late stages. When diagnosing lung cancer, doctors typically use their eyes to
examine CT scan images, looking for small nodules in the lungs. In the early
stages, the nodules are usually very small and hard to spot. Several CV compa-
nies decided to tackle this challenge using DL tech... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 12 CHAPTER 1Welcome to computer vision
1.2.2 Object detection and localization
Image classification problems are the most basic applications for CNNs. In these prob-
lems, each image contains only one object, and our task is to identify it. But if we aim to
reach human levels of understanding, we have to add complexit... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 13 Applications of computer vision
1.2.4 Creating images
Although the earlier examples are truly impressive CV applications of AI, this is
where I see the real magic happening: the magic of creation. In 2014, Ian Good-
fellow invented a new DL model that can imagine new things called generative
adversarial networks (G... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 14 CHAPTER 1Welcome to computer vision
considered a major advancement in DL. So when you understand CNNs, GANs will
make a lot more sense to you.
GANs are sophisticated DL models that generate stunningly accurate synthesized
images of objects, people, and places, among other things. If you give them a set of
images,... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 15 Applications of computer vision
1.2.5 Face recognition
Face recognition (FR) allows us to exactly identify or tag an image of a person. Day-to-
day applications include searching for celebrities on the web and auto-tagging friends
and family in images. Face recognition is a form of fine-grained classification.
The... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 16 CHAPTER 1Welcome to computer vision
Face verification
Face
verification
systemPerson
1Person
1
Person
2
Not
person
1Face identification
Face
identification
system
Haven’t
seen her
before
Figure 1.10 Example of face verification (left) and face recognition (right)
Figure 1.11 Apparel search. The
leftmost image in each ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 17 Computer vision pipeline: The big picture
1.3 Computer vision pipeline: The big picture
Okay, now that I have your attention, let’s dig one level deeper into CV systems.
Remember that earlier in this chapter, we discussed how vision systems are composed
of two main components: sensing devices and interpreting device... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 18 CHAPTER 1Welcome to computer vision
DEFINITIONS An image classifier is an algorithm that takes in an image as input
and outputs a label or “class” that identifies that image. A class (also called a
category ) in machine learning is the output category of your data.
Here is how the image flows through the classific... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 19 Image input
dThe object has only two wheels; this is closer to a motorcycle.
eAnd you keep going through all the features like the body shape, pedal, and
so on, until you arrive at a best guess of the object in the image.
The output of this process is the probability of each class. As you can see in our exam-
ple, t... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 20 CHAPTER 1Welcome to computer vision
The image in figure 1.14 has a size of 32 × 16. This means the dimensions of the image
are 32 pixels wide and 16 pixels tall. The x-axis goes from 0 to 31, and the y-axis from
0 to 16. Overall, the image has 512 (32 × 16) pixels. In this grayscale image, each pixel
contains a val... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 21 Image input
1.4.2 How computers see images
When we look at an image, we see objects, landscape, colors, and so on. But that’s not
the case with computers. Consider figure 1.16. Your human brain can process it and
immediately know that it is a picture of a motorcycle. To a computer, the image looks
like a 2D matrix o... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 22 CHAPTER 1Welcome to computer vision
image has three numbers (0 to 255) associated with it. These numbers represent
intensity of red, green, and blue color in that particular pixel.
If we take the pixel (0,0) as an example, we will see that it represents the top-left
pixel of the image of green grass. When we view ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 23 Image preprocessing
1.5 Image preprocessing
In machine learning (ML) projects, you usually go through a data preprocessing or
cleaning step. As an ML engineer, you will spend a good amount of time cleaning up
and preparing the data before you build your learning model. The goal of this step is
to make your data read... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 24 CHAPTER 1Welcome to computer vision
necessary to recognize and interpret an image. Grayscale can be good enough for rec-
ognizing certain objects. Since color images contain more information than black-
and-white images, they can add unnecessary complexity and take up more space in
memory. Remember that color image... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 25 Image preprocessing
your images. This makes it more likely that your model will recognize objects
when they appear in any form and shape. Figure 1.21 shows an example of image
augmentation applied to a butterfly image.
Other techniques —Many more preprocessing techniques are available to get your
images ready for t... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 26 CHAPTER 1Welcome to computer vision
the appropriate processing techniques based on the dataset at hand and the
problem you are solving. You will see many image-processing techniques through-
out this book, helping you build your intuition of which ones you need when
working on your own projects.
No free lunch theor... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 27 Feature extraction
1.6 Feature extraction
Feature extraction is a core component of the CV pipeline. In fact, the entire DL model
works around the idea of extracting useful features that clearly define the objects in
the image. So we’ll spend a little more time here, because it is important that you
understand what... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 28 CHAPTER 1Welcome to computer vision
1.6.2 What makes a good (useful) feature?
Machine learning models are only as good as the features you provide. That means
coming up with good features is an important job in building ML models. But what
makes a good feature? And how can you tell? Feature generalizability
It is ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 29 Feature extraction
Let’s discuss this with an example. Suppose we want to build a classifier to tell the dif-
ference between two types of dogs: Greyhound and Labrador. Let’s take two features—
the dogs’ height and their eye color—and evaluate them (figure 1.23).
Let’s begin with height. How useful do you think thi... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 30 CHAPTER 1Welcome to computer vision
the dog is a Greyhound. Now, what about the data in the middle of the histogram
(heights from 20 to 30 inches)? We can see that the probability of each type of dog is
pretty close. The thought process in this case is as follows:
if height ≤ 20:
return higher probability t... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 31 Feature extraction
It is clear that for most values, the distribution is about 50/50 for both types. So practi-
cally, this feature tells us nothing, because it doesn’t correlate with the type of dog.
Hence, it doesn’t distinguish between Greyhounds and Labradors.
1.6.3 Extracting features (handcrafted vs. automatic... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 32 CHAPTER 1Welcome to computer vision
DEEP LEARNING USING AUTOMATICALLY EXTRACTED FEATURES
In DL, however, we do not need to manually extract features from the image. The net-
work extracts features automatically and learns their importance on the output by
applying weights to its connections. You just feed the r... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 33 Classifier learning algorithm
WHY USE FEATURES ?
The input image has too much extra information that is not necessary for classifica-
tion. Therefore, the first step after preprocessing the image is to simplify it by extract-
ing the important information and throwing away nonessential information. By
extracting imp... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 34 CHAPTER 1Welcome to computer vision
Now it is time to feed the extracted feature vector to the classifier to output a class
label for the images (for example, motorcycle or otherwise).
As we discussed in the previous section, the classification task is done one of these
ways: traditional ML algorithms like SVMs, ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 35 Summary
An image can be represented as a function of x and y. Computers see an image
as a matrix of pixel values: one channel for grayscale images and three channels
for color images.
Image-processing techniques vary for each problem and dataset. Some of these
techniques are converting images to grayscale to reduc... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 36Deep learning
and neural networks
In the last chapter, we discussed the computer vision (CV) pipeline components:
the input image, preprocessing, extracting features, and the learning algorithm
(classifier). We also discussed that in traditional ML algorithms, we manually
extract features that produce a vector of fea... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 37 Understanding perceptrons
back to CV applications with one of the most popular DL architectures: convolutional
neural networks.
The high-level layout of this chapter is as follows:
We will begin with the most basic component of the neural network: the perceptron ,
a neural network that contains only one neuron.
... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 38 CHAPTER 2Deep learning and neural networks
also called a multilayer perceptron , which is more intuitive because it implies that the net-
work consists of perceptrons structured in multiple layers. Both terms, MLP and ANN,
are used interchangeably to describe this neural network architecture.
In the MLP diagram in... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 39 Understanding perceptrons
In the perceptron diagram in figure 2.4, you can see the following:
Input vector —The feature vector that is fed to the neuron. It is usually denoted
with an uppercase X to represent a vector of inputs ( x1, x2, . . ., xn).
Weights vector —Each x1 is assigned a weight value w1 that repres... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 40 CHAPTER 2Deep learning and neural networks
Neuron functions —The calculations performed within the neuron to modulate
the input signals: the weighted sum and step activation function.
Output —Controlled by the type of activation function you choose for your net-
work. There are different activation functions, as ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 41 Understanding perceptrons
What is a bias in the perceptron, and why do we add it?
Let’s brush up our memory on some linear algebra concepts to help understand
what’s happening under the hood. Here is the function of the straight line:
The function of a straight line is represented by the equation ( y = mx + b), whe... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 42 CHAPTER 2Deep learning and neural networks
STEP ACTIVATION FUNCTION
In both artificial and biological neural networks, a neuron does not just output the
bare input it receives. Instead, there is one more step, called an activation function ; this
is the decision-making unit of the brain. In ANNs, the activation fu... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 43 Understanding perceptrons
2.1.2 How does the perceptron learn?
The perceptron uses trial and error to learn from its mistakes. It uses the weights as
knobs by tuning their values up and down until the network is trained (figure 2.6).
The perceptron’s learning logic goes like this:
1The neuron calculates the weighted... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 44 CHAPTER 2Deep learning and neural networks
perceptron to predict whether players will be accepted based on only two features
(height and weight). The trained perceptron will find the best weights and bias values
to produce the straight line that best separates the accepted from non-accepted (best
fit). The line ha... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 45 Multilayer perceptrons
training data. In fact, if we add too many neurons, this will make the network overfit
the training data (not good). But we will talk about this later. The general rule here is
that the more complex our network is, the better it learns the features of our data.
2.2 Multilayer perceptrons
We ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 46 CHAPTER 2Deep learning and neural networks
To split a nonlinear dataset, we need more than one line. This means we need to
come up with an architecture to use tens and hundreds of neurons in our neural net-
work. Let’s look at the example in figure 2.9. Remember that a perceptron is a linear
function that produces ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 47 Multilayer perceptrons
Output layer —We get the answer or prediction from our model from the output
layer. Depending on the setup of the neural network, the final output may be a
real-valued output (regression problem) or a set of probabilities (classification
problem). This is determined by the type of activation ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 48 CHAPTER 2Deep learning and neural networks
well as recommend some starting points. The number of layers and the number of
neurons in each layer are among the important hyperparameters you will be design-
ing when working with neural networks.
A network can have one or more hidden layers (technically, as many as yo... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 49 Multilayer perceptrons
In later chapters, we will discuss other variations of neural network architecture (like
convolutional and recurrent networks). For now, know that this is the most basic neu-
ral network architecture, and it can be referred to by any of these names: ANN, MLP,
fully connected network, or feedfo... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 50 CHAPTER 2Deep learning and neural networks
2.2.4 Some takeaways from this section
Let’s recap what we’ve discussed so far:
We talked about the analogy between biological and artificial neurons: both
have inputs and a neuron that does some calculations to modulate the input
signals and create output.
We zoomed in... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 51 Activation functions
mini-batch gradient descent. Adam and RMSprop are two other popular opti-
mizers that we don’t discuss.
Batch size —Mini-batch size is the number of sub-samples given to the network,
after which parameter update happens. Bigger batch sizes learn faster but
require more memory space. A good def... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 52 CHAPTER 2Deep learning and neural networks
Why use activation functions at all? Why not just calculate the weighted sum of our
network and propagate that through the hidden layers to produce an output?
The purpose of the activation function is to introduce nonlinearity into the net-
work. Without it, a multilayer... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 53 Activation functions
2.3.1 Linear transfer function
A linear transfer function , also called an identity function , indicates that the function
passes a signal through unchanged. In practical terms, the output will be equal to the
input, which means we don’t actually have an activation function. So no matter how
ma... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 54 CHAPTER 2Deep learning and neural networks
2.3.2 Heaviside step function (binary classifier)
The step function produces a binary output. It basically says that if the input x > 0, it
fires (output y = 1); else (input < 0), it doesn’t fire (output y = 0). It is mainly used in
binary classification problems like tru... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 55 Activation functions
2.3.3 Sigmoid/logistic function
This is one of the most common activation functions. It is often used in binary classifi-
ers to predict the probability of a class when you have two classes. The sigmoid squishes
all the values to a probability between 0 and 1, which reduces extreme values or ou... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 56 CHAPTER 2Deep learning and neural networks
Just-in-time linear algebra (optional)
Let’s take a deeper dive into the math side of the sigmoid function to understand the
problem it helps solve and how the sigmoid function equation is driven. Suppose that
we are trying to predict whether patients have diabetes based o... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 57 Activation functions
2.3.4 Softmax function
The softmax function is a generalization of the sigmoid function. It is used to obtain
classification probabilities when we have more than two classes. It forces the outputs
of a neural network to sum to 1 (for example, 0 < output < 1). A very common use
case in deep learn... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 58 CHAPTER 2Deep learning and neural networks
TIP Softmax is the go-to function that you will often use at the output layer of
a classifier when you are working on a problem where you need to predict a
class between more than two classes. Softmax works fine if you are classifying
two classes, as well. It will basicall... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 59 Activation functions
At the time of writing, ReLU is considered the state-of-the-art activation function
because it works well in many different situations, and it tends to train better than sig-
moid and tanh in hidden layers (figure 2.18).
Here is how ReLU is implemented in Python:
def relu(x):
if x < 0:... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 60 CHAPTER 2Deep learning and neural networks
Here is how Leaky ReLU is implemented in Python:
def leaky_relu(x):
if x < 0:
return x * 0.01
else:
return x
Table 2.1 summarizes the various activation functions we’ve discussed in this section.
Table 2.1 A cheat sheet of the most common activation fu... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 61 Activation functions
Sigmoid/
logistic
functionSquishes all the
values to a probabil-
ity between 0 and 1,
which reduces
extreme values
or outliers in the
data. Usually
used to classify
two classes.σ(z) =
Softmax
functionA generalization of
the sigmoid func-
tion. Used to obtain
classification proba-
bil... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 62 CHAPTER 2Deep learning and neural networks
2.4 The feedforward process
Now that you understand how to stack perceptrons in layers, connect them with
weights/edges, perform a weighted sum function, and apply activation functions, let’s
implement the complete forward-pass calculations to produce a prediction output.
... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 63 The feedforward process
Weights and biases (w, b) —The edges between nodes are assigned random
weights denoted as Wab(n), where ( n) indicates the layer number and ( ab) indi-
cates the weighted edge connecting the ath neuron in layer ( n) to the bth neu-
ron in the previous layer ( n – 1). For example, W23(2) is t... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 64 CHAPTER 2Deep learning and neural networks
2.4.1 Feedforward calculations
We have all we need to start the feedforward calculations:
a1(1) = σ(w11(1)x1 + w21(1)x2 + w31(1)x3)
a2(1) = σ(w12(1)x1 + w22(1)x2 + w32(1)x3)
a3(1) = σ(w13(1)x1 + w23(1)x2 + w33(1)x3)
Then we do the same calculations for layer 2
, and a4(2... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 65 The feedforward process
2.4.2 Feature learning
The nodes in the hidden layers ( ai) are the new features that are learned after each
layer. For example, if you look at figure 2.20, you see that we have three feature
inputs ( x1, x2, and x3). After computing the forward pass in the first layer, the net-
work learns p... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 66 CHAPTER 2Deep learning and neural networks
That is how a neural network learns new features: via the network’s hidden layers.
First, they recognize patterns in the data. Then, they recognize patterns within patterns;
then patterns within patterns within patterns, and so on. The deeper the network is,
the more it le... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 67 The feedforward process
Vectors and matrices refresher
If you understood the matrix calculations we just did in the feedforward discussion, feel
free to skip this sidebar. If you are still not convinced, hang tight: this sidebar is for you.
The feedforward calculations are a set of matrix multiplications. While you ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 68 CHAPTER 2Deep learning and neural networks
2.5 Error functions
So far, you have learned how to implement the forward pass in neural networks to
produce a prediction that consists of the weighted sum plus activation operations.
Now, how do we evaluate the prediction that the network just produced? More
importantly,... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 69 Error functions
2.5.1 What is the error function?
The error function is a measure of how “wrong” the neural network prediction is with
respect to the expected output (the label). It quantifies how far we are from the cor-
rect solution. For example, if we have a high loss, then our model is not doing a good
job. T... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 70 CHAPTER 2Deep learning and neural networks
2.5.4 Mean square error
Mean squared error (MSE) is commonly used in regression problems that require the
output to be a real value (like house pricing). Instead of just comparing the predic-
tion output with the label ( yˆi – yi), the error is squared and averaged over t... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 71 Error functions
mean absolute error (MAE) was developed just for this purpose. It averages the absolute
error over the entire dataset without taking the square of the error:
E(W, b) = | yˆi – yi|
2.5.5 Cross-entropy
Cross-entropy is commonly used in classification problems because it quantifies the
difference betw... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 72 CHAPTER 2Deep learning and neural networks
where ( y) is the target probability, ( p) is the predicted probability, and ( m) is the num-
ber of classes. The sum is over the three classes: cat, dog, and fish. In this case, the loss
is 1.2:
E = - (0.0 * log(0.2) + 1.0 * log(0.3) + 0.0 * log(0.5)) = 1.2
So that is how... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 73 Error functions
Suppose the input x = 0.3, and its label (goal prediction) y = 0.8. The prediction out-
put ( yˆ) of this perception is calculated as follows:
yˆi = w · x = w · 0.3
And the error, in its simplest form, is calculated by comparing the prediction yˆ and
the label y:
error = | yˆ – y|
... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 74 CHAPTER 2Deep learning and neural networks
2.6 Optimization algorithms
Training a neural network involves showing the network many examples (a training
dataset); the network makes predictions through feedforward calculations and com-
pares them with the correct labels to calculate the error. Finally, the neural net... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 75 Optimization algorithms
Since we humans are only equipped to understand a maximum of 3 dimensions, it is
impossible for us to visualize error graphs when we have 10 weights, not to mention
hundreds or thousands of weight parameters. So, from this point on, we will study the
error function using the 2D or 3D plane of... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 76 CHAPTER 2Deep learning and neural networks
have very few inputs and only one or two neurons in our network. Let me try to con-
vince you that this approach wouldn’t scale. Let’s take a look at a scenario where we
have a very simple neural network. Suppose we want to predict house prices based on
only four features ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 77 Optimization algorithms
operations per second (FLOPs). In the best-case scenario, this supercomputer would
need
= 1.08 × 1058 seconds = 3.42 × 1050 years
That is a huge number: it’s longer than the universe has existed. Who has that kind of
time to wait for the network to train? Remember that this is a very simple ... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 78 CHAPTER 2Deep learning and neural networks
HOW DOES GRADIENT DESCENT WORK ?
To visualize how gradient descent works, let’s plot the error function in a 3D graph
(figure 2.31) and go through the process step by step. The random initial weight
(starting weight) is at point A, and our goal is to descend this error m... |
deep-learning-for-vision-systems-1nbsped-1617296198-9781617296192.pdf | 79 Optimization algorithms
THE DIRECTION (GRADIENT )
Suppose you are standing on the top of the error mountain at point A. To get to the
bottom, you need to determine the step direction that results in the deepest descent
(has the steepest slope). And what is the slope, again? It is the derivative of the curve.
So if ... |
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