Deep Learning from the Basics (2021) [Saitoh] [9781800206137]

317 Pages·2021·12.305 MB·English

by  Saitoh

Deep Learning from
the Basics
Python and Deep Learning:
Theory and Implementation
Koki Saitoh
Deep Learning from the Basics
Copyright © 2021 Packt Publishing
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Author: Koki Saitoh
Managing Editor: Ashish James
Acquisitions Editor: Bridget Neale
Production Editor: Salma Patel
Editorial Board: Megan Carlisle, Mahesh Dhyani, Heather Gopsill, Manasa Kumar, Alex
Mazonowicz, Monesh Mirpuri, Bridget Neale, Abhishek Rane, Brendan Rodrigues, Ankita
Thakur, Nitesh Thakur, and Jonathan Wray
First Published: March 2021
Production Reference: 1040321
ISBN: 978-1-80020-613-7
Published by Packt Publishing Ltd.
Livery Place, 35 Livery Street
Birmingham B3 2PB, UK
Table of Contents
Preface i
Introduction 1
Chapter 1: Introduction to Python 9
What is Python? .............................................................................................. 9
Installing Python .......................................................................................... 10
Python Versions ................................................................................................. 10
External Libraries That We Use ........................................................................ 10
Anaconda Distribution ...................................................................................... 11
Python Interpreter ....................................................................................... 11
Mathematical Operations ................................................................................. 12
Data Types .......................................................................................................... 12
Variables ............................................................................................................. 13
Lists ...................................................................................................................... 13
Dictionaries ......................................................................................................... 14
Boolean ............................................................................................................... 15
if Statements ...................................................................................................... 15
for Statements ................................................................................................... 16
Functions ............................................................................................................. 16
Python Script Files ....................................................................................... 16
Saving in a File .................................................................................................... 17
Classes ................................................................................................................. 17
NumPy ........................................................................................................... 18
Importing NumPy .............................................................................................. 18
Creating a NumPy Array ................................................................................... 19
Mathematical Operations in NumPy ............................................................... 19
N-Dimensional NumPy Arrays .......................................................................... 20
Broadcasting ....................................................................................................... 21
Accessing Elements ........................................................................................... 21
Matplotlib ..................................................................................................... 23
Drawing a Simple Graph ................................................................................... 23
Features of pyplot .............................................................................................. 24
Displaying Images .............................................................................................. 25
Summary ....................................................................................................... 26
Chapter 2: Perceptrons 29
What Is a Perceptron? ................................................................................. 29
Simple Logic Circuits .................................................................................... 31
AND Gate ............................................................................................................ 31
NAND and OR gates ........................................................................................... 32
Implementing Perceptrons ......................................................................... 33
Easy Implementation ........................................................................................ 33
Introducing Weights and Bias .......................................................................... 34
Implementation with Weights and Bias .......................................................... 35
Limitations of Perceptrons ......................................................................... 36
XOR Gate ............................................................................................................. 37
Linear and Nonlinear ........................................................................................ 39
Multilayer Perceptrons ............................................................................... 40
Combining the Existing Gates .......................................................................... 41
Implementing an XOR Gate .............................................................................. 43
From NAND to a Computer ........................................................................ 44
Summary ....................................................................................................... 46
Chapter 3: Neural Networks 49
From Perceptrons to Neural Networks ..................................................... 50
Neural Network Example .................................................................................. 50
Reviewing the Perceptron ................................................................................. 51
Introducing an Activation Function ................................................................. 53
Activation Function ...................................................................................... 54
Sigmoid Function ............................................................................................... 55
Implementing a Step Function ......................................................................... 55
Step Function Graph .......................................................................................... 57
Implementing a Sigmoid Function ................................................................... 58
Comparing the Sigmoid Function and the Step Function ............................. 59
Nonlinear Function ............................................................................................ 61
ReLU Function .................................................................................................... 62
Calculating Multidimensional Arrays ........................................................ 63
Multidimensional Arrays ................................................................................... 63
Matrix Multiplication ......................................................................................... 65
Matrix Multiplication in a Neural Network ..................................................... 68
Implementing a Three-Layer Neural Network ......................................... 69
Examining the Symbols ..................................................................................... 70
Implementing Signal Transmission in Each Layer ......................................... 71
Implementation Summary ............................................................................... 76
Designing the Output Layer ....................................................................... 77
Identity Function and Softmax Function .................................................. 78
Issues when Implementing the Softmax Function ........................................ 80
Characteristics of the Softmax Function ........................................................ 81
Number of Neurons in the Output Layer ....................................................... 83
Handwritten Digit Recognition ................................................................... 83
MNIST Dataset .................................................................................................... 84
Inference for Neural Network .......................................................................... 87
Batch Processing ................................................................................................ 89
Summary ....................................................................................................... 93
Chapter 4: Neural Network Training 95
Learning from Data ..................................................................................... 96
Data-Driven ......................................................................................................... 96
Training Data and Test Data ............................................................................. 99
Loss Function ................................................................................................ 99
Sum of Squared Errors ................................................................................... 100
Cross-Entropy Error ........................................................................................ 101
Mini-Batch Learning ....................................................................................... 103
Implementing Cross-Entropy Error (Using Batches) ................................... 105
Why Do We Configure a Loss Function? ....................................................... 106
Numerical Differentiation ........................................................................ 108
Derivative ......................................................................................................... 108
Examples of Numerical Differentiation ....................................................... 111
Partial Derivative ............................................................................................ 113
Gradient ...................................................................................................... 115
Gradient Method ............................................................................................. 117
Gradients for a Neural Network ................................................................... 121
Implementing a Training Algorithm ........................................................ 124
A Two-Layer Neural Network as a Class ...................................................... 125
Implementing Mini-Batch Training ............................................................... 129
Using Test Data for Evaluation ...................................................................... 131
Summary ..................................................................................................... 134
Chapter 5: Backpropagation 137
Computational Graphs .............................................................................. 138
Using Computational Graphs to Solve Problems ........................................ 138
Local Calculation ............................................................................................. 140
Why Do We Use Computational Graphs? ..................................................... 141
Chain Rule ................................................................................................... 142
Backward Propagation in a Computational Graph ..................................... 143
What Is the Chain Rule? ................................................................................. 143
The Chain Rule and Computational Graphs ................................................ 144
Backward Propagation .............................................................................. 145
Backward Propagation in an Addition Node ............................................... 146
Backward Propagation in a Multiplication Node ........................................ 148
Apples Example ............................................................................................... 149
Implementing a Simple Layer .................................................................. 150
Implementing a Multiplication Layer ........................................................... 151
Implementing an Addition Layer .................................................................. 153
Implementing the Activation Function Layer ......................................... 155
ReLU Layer ....................................................................................................... 155
Sigmoid Layer .................................................................................................. 156
Implementing the Affine and Softmax Layers ....................................... 161
Affine Layer ...................................................................................................... 161
Batch-Based Affine Layer ............................................................................... 164
Softmax-with-Loss Layer ................................................................................ 166
Implementing Backpropagation .............................................................. 170
Overall View of Neural Network Training .................................................... 170
Presupposition ................................................................................................ 170
Implementing a Neural Network That Supports Backpropagation ......... 171
Gradient Check ................................................................................................ 175
Training Using Backpropagation ................................................................... 176
Summary ..................................................................................................... 178
Chapter 6: Training Techniques 181
Updating Parameters ................................................................................ 182
Story of an Adventurer ................................................................................... 182
SGD ................................................................................................................... 183
Disadvantage of SGD ...................................................................................... 184
Momentum ...................................................................................................... 186
AdaGrad ........................................................................................................... 188
Adam ................................................................................................................ 191
Which Update Technique Should We Use? .................................................. 192
Using the MNIST Dataset to Compare the Update Techniques ................ 193
Initial Weight Values .................................................................................. 194
How About Setting the Initial Weight Values to 0? ..................................... 194
Distribution of Activations in the Hidden Layers ........................................ 195
Initial Weight Values for ReLU ....................................................................... 199
Using the MNIST Dataset to Compare the Weight Initializers .................. 200
Batch Normalization ....................................................................................... 202
Batch Normalization Algorithm .................................................................... 203
Evaluating Batch Normalization ................................................................... 205
Regularization ............................................................................................ 206
Overfitting ........................................................................................................ 206
Weight Decay ................................................................................................... 210
Dropout ............................................................................................................ 211
Validating Hyperparameters .................................................................... 215
Validation Data ................................................................................................ 215
Optimizing Hyperparameters ....................................................................... 216
Implementing Hyperparameter Optimization ............................................ 217
Summary ..................................................................................................... 219
Chapter 7: Convolutional Neural Networks 221
Overall Architecture .................................................................................. 221
The Convolution Layer .............................................................................. 223
Issues with the Fully Connected Layer ......................................................... 223
Convolution Operations ................................................................................. 224
Padding ............................................................................................................ 226
Stride ................................................................................................................ 227
Performing a Convolution Operation on Three-Dimensional Data .......... 229
Thinking in Blocks ........................................................................................... 231
Batch Processing ............................................................................................. 233
The Pooling Layer ...................................................................................... 234
Characteristics of a Pooling Layer ................................................................ 234
Implementing the Convolution and Pooling Layers .............................. 235
Four-Dimensional Arrays ............................................................................... 236
Expansion by im2col ....................................................................................... 236
Implementing a Convolution Layer .............................................................. 238
Implementing a Pooling Layer ...................................................................... 241
Implementing a CNN ................................................................................. 244
Visualizing a CNN ....................................................................................... 248
Visualizing the Weight of the First Layer ..................................................... 248
Using a Hierarchical Structure to Extract Information .............................. 249
Typical CNNs ............................................................................................... 250
LeNet ................................................................................................................ 250
AlexNet ............................................................................................................. 250
Summary ..................................................................................................... 251