Pradeepta Mishra
PyTorch RecipesA Problem-Solution Approach
Pradeepta Mishra
Bangalore, Karnataka, India
ISBN 978-1-4842-4257-5e-ISBN 978-1-4842-4258-2
https://doi.org/10.1007/978-1-4842-4258-2
Library of Congress Control Number: 2018968538
© Pradeepta Mishra 2019
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I would like to dedicate this book to my dear parents, my lovely wife, Prajna, and my daughter, Priyanshi (Aarya). This work would not have been possible without their inspiration, support, and encouragement.

Introduction

Development of artificial intelligent products and solutions has recently become a norm; hence, the demand for graph theory–based computational frameworks is on the rise. Making the deep learning models work in real-life applications is possible when the modeling framework is dynamic, flexible, and adaptable to other frameworks.

PyTorch is a recent entrant to the league of graph computation tools/programming languages. Addressing the limitations of previous frameworks, PyTorch promises a better user experience in the deployment of deep learning models, and the creation of advanced models using a combination of convolutional neural networks, recurrent neural networks, LSTMs, and deep neural networks.

PyTorch was created by Facebook’s Artificial Intelligence Research division, which seeks to make the model development process simple, straightforward, and dynamic, so that developers do not have to worry about declaring objects before compiling and executing the model. It is based on the Torch framework and is an extension of Python.

This book is intended for data scientists, natural language processing engineers, artificial intelligence solution developers, existing practitioners working on graph computation frameworks, and researchers of graph theory. This book will get you started with understanding tensor basics, computation, performing arithmetic-based operations, matrix algebra, and statistical distribution-based operations using the PyTorch framework.

Chapters 3 and 4 provide detailed descriptions on neural network basics. Advanced neural networks, such as convolutional neural networks, recurrent neural networks, and LSTMs are explored. Readers will be able to implement these models using PyTorch functions.

Chapters 5 and 6 discuss fine-tuning the models, hyper parameter tuning, and the refinement of existing PyTorch models in production. Readers learn how to choose the hyper parameters to fine-tune the model.

In Chapter 7 , natural language processing is explained. The deep learning models and their applications in natural language processing and artificial intelligence is one of the most demanding skill sets in the industry. Readers will be able to benchmark the execution and performance of PyTorch implementation in deep learning models to execute and process natural language. They will be able to compare PyTorch with other graph computation–based deep learning programming tools.

Acknowledgments

I would like to thank my wife, Prajna, for her continuous inspiration and support, and sacrificing her weekends just to sit alongside me to help me in completing the book; my daughter, Aarya, for being patient all through my writing time; my father, for his eagerness to know how many chapters I had completed.

A big thank you to Nikhil, Celestin, and Divya, for fast-tracking the whole process and helping me and guiding me in the right direction.

I would like to thank my bosses, Ashish and Saty, for always being supportive of my initiatives in the AI and ML journey, and their continuous motivation and inspiration in writing in the AI space.

Table of Contents

Chapter 1:​ Introduction to PyTorch, Tensors, and Tensor Operations 1
What Is PyTorch?​ 6
PyTorch Installation 7
Recipe 1-1.​ Using Tensors 9
Problem 9
Solution 10
How It Works 10
Conclusion 27
Chapter 2:​ Probability Distributions Using PyTorch 29
Recipe 2-1.​ Sampling Tensors 30
Problem 30
Solution 30
How It Works 30
Recipe 2-2.​ Variable Tensors 33
Problem 33
Solution 34
How It Works 35
Recipe 2-3.​ Basic Statistics 36
Problem 36
Solution 36
How It Works 36
Recipe 2-4.​ Gradient Computation 38
Problem 38
Solution 38
How It Works 39
Recipe 2-5.​ Tensor Operations 41
Problem 41
Solution 41
How It Works 41
Recipe 2-6.​ Tensor Operations 42
Problem 42
Solution 42
How It Works 43
Recipe 2-7.​ Distributions 45
Problem 45
Solution 45
How It Works 45
Conclusion 48
Chapter 3:​ CNN and RNN Using PyTorch 49
Recipe 3-1.​ Setting Up a Loss Function 49
Problem 49
Solution 50
How It Works 50
Recipe 3-2.​ Estimating the Derivative of the Loss Function 53
Problem 53
Solution 53
How It Works 53
Recipe 3-3.​ Fine-Tuning a Model 59
Problem 59
Solution 59
How It Works 60
Recipe 3-4.​ Selecting an Optimization Function 62
Problem 62
Solution 62
How It Works 62
Recipe 3-5.​ Further Optimizing the Function 67
Problem 67
Solution 67
How It Works 67
Recipe 3-6.​ Implementing a Convolutional Neural Network (CNN) 71
Problem 71
Solution 71
How It Works 71
Recipe 3-7.​ Reloading a Model 77
Problem 77
Solution 77
How It Works 77
Recipe 3-8.​ Implementing a Recurrent Neural Network (RNN) 80
Problem 80
Solution 80
How It Works 80
Recipe 3-9.​ Implementing a RNN for Regression Problems 85
Problem 85
Solution 86
How It Works 86
Recipe 3-10.​ Using PyTorch Built-in Functions 87
Problem 87
Solution 87
How It Works 88
Recipe 3-11.​ Working with Autoencoders 91
Problem 91
Solution 91
How It Works 91
Recipe 3-12.​ Fine-Tuning Results Using Autoencoder 95
Problem 95
Solution 95
How It Works 95
Recipe 3-13.​ Visualizing the Encoded Data in a 3D Plot 98
Problem 98
Solution 98
How It Works 98
Recipe 3-14.​ Restricting Model Overfitting 99
Problem 99
Solution 99
How It Works 100
Recipe 3-15.​ Visualizing the Model Overfit 102
Problem 102
Solution 102
How It Works 102
Recipe 3-16.​ Initializing Weights in the Dropout Rate 104
Problem 104
Solution 104
How It Works 105
Recipe 3-17.​ Adding Math Operations 106
Problem 106
Solution 106
How It Works 106
Recipe 3-18.​ Embedding Layers in RNN 108
Problem 108
Solution 108
How It Works 108
Conclusion 109
Chapter 4:​ Introduction to Neural Networks Using PyTorch 111
Recipe 4-1.​ Working with Activation Functions 112
Problem 112
Solution 112
How It Works 112
Recipe 4-2.​ Visualizing the Shape of Activation Functions 119
Problem 119
Solution 119
How It Works 119
Recipe 4-3.​ Basic Neural Network Model 122
Problem 122
Solution 122
How It Works 122
Recipe 4-4.​ Tensor Differentiation 125
Problem 125
Solution 125
How It Works 125
Conclusion 126
Chapter 5:​ Supervised Learning Using PyTorch 127
Introduction to Linear Regression 129
Recipe 5-1.​ Data Preparation for the Supervised Model 133
Problem 133
Solution 133
How It Works 133
Recipe 5-2.​ Forward and Backward Propagation 135
Problem 135
Solution 135
How It Works 136
Recipe 5-3.​ Optimization and Gradient Computation 139
Problem 139
Solution 139
How It Works 140
Recipe 5-4.​ Viewing Predictions 141
Problem 141
Solution 141
How It Works 141
Recipe 5-5.​ Supervised Model Logistic Regression 145
Problem 145
Solution 145
How It Works 145
Conclusion 149
Chapter 6:​ Fine-Tuning Deep Learning Models Using PyTorch 151
Recipe 6-1.​ Building Sequential Neural Networks 153
Problem 153
Solution 153
How It Works 153
Recipe 6-2.​ Deciding the Batch Size 155
Problem 155
Solution 155
How It Works 155
Recipe 6-3.​ Deciding the Learning Rate 158
Problem 158
Solution 158
How It Works 158
Recipe 6-4.​ Performing Parallel Training 162
Problem 162
Solution 163
How It Works 163
Conclusion 164
Chapter 7:​ Natural Language Processing Using PyTorch 165
Recipe 7-1.​ Word Embedding 168
Problem 168
Solution 169
How It Works 169
Recipe 7-2.​ CBOW Model in PyTorch 172
Problem 172
Solution 173
How It Works 173
Recipe 7-3.​ LSTM Model 175
Problem 175
Solution 175
How It Works 175
Index 179

About the Author and About the Technical Reviewer

About the Author

Pradeepta Mishra
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is a data scientist and artificial intelligence architect. He currently heads NLP, ML, and AI initiatives at Lymbyc, a leading-edge innovator in AI and machine learning based out of Bangalore, India. He has expertise in designing artificial intelligence systems for performing tasks such as understanding natural language and recommendations based on natural language processing. He has filed three patents as an inventor and has authored and co-authored two books: R Data Mining Blueprints (Packt Publishing, 2016) and R: Mining Spatial, Text, Web, and Social Media Data (Packt Publishing, 2017). There are two courses available on Udemy based on these books.

Pradeepta presented a keynote talk on the application of bidirectional LSTM for time series forecasting at the 2018 Global Data Science Conference 2018. He delivered a TEDx talk titled “Can Machines Think?”, a session on the power of artificial intelligence in transforming industries and changing job roles across industries. He has also delivered more than 150 tech talks on data science, machine learning, and artificial intelligence at various meetups, technical institutions, universities, and community forums.

He is on LinkedIn at www.linkedin.com/in/pradeepta/ .

 

About the Technical Reviewer

Shivendra Upadhyay
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has more than eight years of experience working for consulting and software firms. He has worked in data science with KPMG for more than three years, and has a firm grasp of machine learning and data science tools and technologies.

 
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